= 2.0 * tab_dens_cm[num_tab_dens]:\n raise Exception(\n 'Your input density is higher than the largest tabulated value %f' % tab_dens_cm[num_tab_dens])\n\n if dens_cm <= tab_dens_cm[0]:\n raise Exception('Your input density is smaller than the smallest tabulated value %f' % tab_dens_cm[0])\n\n if temp_k >= tab_temp_k[9]:\n raise Exception('Your input temperature is higher than the largest tabulated value %f' % tab_temp_k[9])\n\n if temp_k <= tab_temp_k[0]:\n raise Exception('Your input temperature is lower than the smallest tabulated value %f' % tab_temp_k[0])\n\n normal_holtsmark_field = 1.25e-9 * (dens_cm ** (2. / 3.)) # normal field value in ues\n\n # calculate line centre wavelength and frequency using Rydberg formula\n # JSA: I have made this step clearer and corrected for deuteron mass in the Rydberg constant (though the effect is small)\n # TODO make sure this matches olam0 parameter above -- why were there two variables in the first place?!\n # rydberg_m = Rydberg / (1. + (electron_mass / physical_constants['deuteron mass'][0]))\n # wl_0_angst = 1e10 * (rydberg_m * (1 / n_lower ** 2 - 1 / n_upper ** 2)) ** -1\n\n wl_centre_angst = self.wl_centre * 1e10\n\n c_angst = c * 1e10 # velocity of light in Ansgtroms / s\n angular_freq_0 = 2 * np.pi * c_angst / wl_centre_angst # rad / s\n\n otrans = -2 * np.pi * c_angst / wl_centre_angst ** 2\n\n olines = o1lines / np.abs(otrans)\n oline = o1line / np.abs(otrans)\n\n # Limit analysis_tools to uncorrelated plasmas.\n # check that mean interelectronic distance is smaller than the electronic Debye length (equ. 10)\n PR0_exp = 0.0898 * (dens_cm ** (1. / 6.)) / np.sqrt(temp_k) # = (r0 / debye)\n if PR0_exp > 1.:\n raise Exception('The plasma is too strongly correlated\\ni.e. r0/debye=0.1\\nthe line cannot be computed.')\n\n # fainom_exp=fainom*(F00_exp**1.5)\n # fainum_exp=fainom_exp/( (OPI*2.)**1.5)\n\n # ========================\n # TABULATION Format CDS\n # si on veut ecrire\n # n -np lambda0 kalpha Ne E0 T R0/Debye Dalpha iDoppler iStark\n\n # IN_cds= N+0.01\n # INP_cds = NP+0.01\n\n # ***********************************************************\n # Don't edit the CDS format...\n # ***********************************************************\n\n # Skipped the code in the IF statement starting at line 470, since it\n # isn't used, if (.FALSE.) ...\n\n # ==============================================\n # define an unique detunings grid - domm - for the tabulated\n # profiles ( various temperatures , densities)\n # calculate all the line shapes for this common grid\n # units used at this points are Domega_new= Delta(omega)/F00\n # in rd/(s-1 ues)\n\n max_num_dens = 30 # Maximum number of densities\n max_num_tab_temp = 10\n max_num_detunings = 60 # Maximum number of detunings\n jtot = jtot.astype(np.int)\n domm = np.zeros(100000)\n dom0 = np.zeros(10000)\n tprof = np.zeros([max_num_dens, max_num_tab_temp, 10000])\n tprofs = np.zeros([max_num_dens, max_num_tab_temp, 10000])\n uprof = np.zeros([max_num_dens, 10000])\n uprofs = np.zeros([max_num_dens, 10000])\n\n inc = 0\n domm[inc] = 0.0\n # ---- Look to replace this loop\n for id in np.arange(num_tab_dens + 1): # loop over tab densities\n for j in np.arange(max_num_tab_temp): # loop over tab temperatures (?)\n for i in np.arange(1, jtot[id, j]):\n inc += 1\n dom0[inc] = dom[id, j, i]\n\n inc = np.count_nonzero(dom)\n npik = inc + 1\n # nut=10000\n\n # Calling numpy sort instead of piksrt\n tmp = np.sort(dom0[0:npik])\n dom0[0:npik] = tmp[0:npik]\n # dom0 seems to agree with the FORTRAN version\n\n inc = 0\n domm[0] = 0.0\n # print 'npik',npik\n # ---- Look to replace this loop\n for i in np.arange(1, npik):\n dif = (dom0[i] - dom0[i - 1])\n if dif <= 1.0E-6:\n continue\n if dif / np.abs(dom0[i]) <= 0.1:\n continue\n inc = inc + 1\n domm[inc] = dom0[i]\n\n jdom = inc + 1 # One line after marker 35\n\n for id in np.arange(num_tab_dens):\n for j in np.arange(10):\n if pr0[id, j] > 1.0:\n continue\n\n tprof[id, j, 0] = oline[id, j, 0]\n tprofs[id, j, 0] = olines[id, j, 0]\n\n if jtot[id, j] == 0:\n continue\n\n for i in np.arange(1, jdom + 1):\n skip1 = False\n skip2 = False\n # print 'i',i\n domeg = domm[i]\n ij_max = jtot[id, j]\n # print 'domeg,ij_max',domeg,ij_max\n for ij in np.arange(1, ij_max - 1):\n # print 'ij',ij\n test = (domeg - dom[id, j, ij]) * (domeg - dom[id, j, ij - 1])\n # print 'test1:',test\n if test <= 0.0:\n # print 'triggered test1'\n x1 = dom[id, j, ij - 1]\n x2 = dom[id, j, ij]\n x3 = dom[id, j, ij + 1]\n y1 = oline[id, j, ij - 1]\n y2 = oline[id, j, ij]\n y3 = oline[id, j, ij + 1]\n # print 'x1,x2,x3',x1,x2,x3\n # print 'y1,y2,y3',y1,y2,y3\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n y1 = olines[id, j, ij - 1]\n y2 = olines[id, j, ij]\n y3 = olines[id, j, ij + 1]\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n # print 'tprof[id,j,i]',tprof[id,j,i]\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\n skip1 = True\n skip2 = True\n break\n\n if skip1 is False:\n test = (domeg - dom[id, j, ij_max - 2]) * (domeg - dom[id, j, ij_max - 1])\n # print 'test2:',test\n # print 'domeg',domeg\n # print 'dom[id,j,ij_max-1]',dom[id,j,ij_max-2]\n # print 'dom[id,j,ij_max]',dom[id,j,ij_max-1]\n if test <= 0.0:\n # print 'triggered test2'\n x1 = dom[id, j, ij_max - 3]\n x2 = dom[id, j, ij_max - 2]\n x3 = dom[id, j, ij_max - 1]\n y1 = oline[id, j, ij_max - 3]\n y2 = oline[id, j, ij_max - 2]\n y3 = oline[id, j, ij_max - 1]\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n y1 = olines[id, j, ij_max - 3]\n y2 = olines[id, j, ij_max - 2]\n y3 = olines[id, j, ij_max - 1]\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n skip2 = True\n # print 'x1,x2,x3',x1,x2,x3\n # print 'y1,y2,y3',y1,y2,y3\n # print 'tprof[id,j,i]',tprof[id,j,i]\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\n continue\n\n if skip2 is False:\n if domeg > dom[id, j, ij_max]:\n # print 'triggered test3'\n tprof[id, j, i] = fainom / (domeg ** 2.5)\n tprofs[id, j, i] = tprof[id, j, i]\n continue\n\n # We can skip writing the intermediate file\n\n\n for id in np.arange(num_tab_dens):\n otest_dens = (dens_cm - tab_dens_cm[id]) * (dens_cm - tab_dens_cm[id + 1])\n if otest_dens <= 0.0:\n dense1 = tab_dens_cm[id]\n dense2 = tab_dens_cm[id + 1]\n id1 = id\n id2 = id + 1\n break\n\n if dens_cm >= tab_dens_cm[num_tab_dens]:\n dense1 = tab_dens_cm[num_tab_dens - 1]\n dense2 = tab_dens_cm[num_tab_dens]\n id1 = num_tab_dens - 1\n id2 = num_tab_dens\n\n for it in np.arange(10):\n otest = (temp_k - tab_temp_k[it]) * (temp_k - tab_temp_k[it + 1])\n if otest <= 0.0:\n it1 = it\n it2 = it + 1\n # pr01 = pr0[id2,it1] # max value of pr0 for T1,T2,dense1,dense2\n tempe1 = tab_temp_k[it]\n tempe2 = tab_temp_k[it + 1]\n break\n\n # interpolation in temperature\n for id in np.arange(id1, id2 + 1):\n for i in np.arange(jdom):\n uprof[id, i] = tprof[id, it1, i] + (temp_k - tempe1) * (tprof[id, it2, i] - tprof[id, it1, i]) / (\n tempe2 - tempe1)\n uprofs[id, i] = tprofs[id, it1, i] + (temp_k - tempe1) * (tprofs[id, it2, i] - tprofs[id, it1, i]) / (\n tempe2 - tempe1)\n\n delta_lambda = np.zeros(jdom)\n delta_nu = np.zeros(jdom)\n wprof_nu = np.zeros(jdom)\n wprofs_nu = np.zeros(jdom)\n\n for i in np.arange(jdom):\n wprof = uprof[id1, i] + (dens_cm - dense1) * (uprof[id2, i] - uprof[id1, i]) / (dense2 - dense1)\n wprofs = uprofs[id1, i] + (dens_cm - dense1) * (uprofs[id2, i] - uprofs[id1, i]) / (dense2 - dense1)\n delta_omega = domm[i] * normal_holtsmark_field\n delta_nu[i] = delta_omega / (2 * np.pi)\n delta_lambda[i] = wl_centre_angst * delta_omega / (angular_freq_0 + delta_omega)\n # print(delta_lambda[i])\n wprof_nu[i] = (wprof / normal_holtsmark_field) * (2. * np.pi)\n wprofs_nu[i] = (wprofs / normal_holtsmark_field) * (2. * np.pi)\n # print '%e %e %e %e' %(delta_lambda[i],delta_nu[i],wprof_nu[i],wprofs_nu[i])\n\n delta_lambda2 = np.concatenate((-delta_lambda[::-1], delta_lambda)) + wl_centre_angst # + olam0\n delta_nu2 = np.concatenate((-delta_nu[::-1], delta_nu))\n wprof_nu2 = np.concatenate((wprof_nu[::-1], wprof_nu))\n wprofs_nu2 = np.concatenate((wprofs_nu[::-1], wprofs_nu))\n\n # for some reason, i only get a good agreement with the other models if i take the pure Stark broadened Stehle\n # output and manually convolve it with the Doppler profile -- not sure why...\n ls_sd = wprofs_nu2\n\n # interpolate onto frequency axis\n ls_sd = np.interp(self.freq_axis, delta_nu2 + self.freq_centre, ls_sd)\n\n return ls_sd","def get_hbls_hbbl(self):\n [Ly,N] = self.b.shape\n z_u_w = self.grid_dict['z_u_w']\n z_u_r = self.grid_dict['z_u_r']\n u = self.u\n v = self.v\n \n v_upts = TTTW_func.v2u(v)\n Hz = z_u_w[:,1:] - z_u_w[:,:-1]\n\n\n\n # CALCULATE swr_frac\n self.swr_frac = TTTW_func.lmd_swr_frac(self.grid_dict)\n\n\n # WHOLE THING HAPPENS IN j loop through y-indices\n \n # INITIALIZE ARRAYS\n self.kmo = np.zeros([Ly])\n self.Cr = np.zeros([Ly])\n self.kbl = np.empty([Ly],dtype='int')\n self.C_h_MO = np.zeros([Ly])\n self.Cr = np.zeros([Ly,N+1]) # sum term\n self.FC = np.zeros([Ly,N+1])\n self.swdk_r = np.zeros([Ly,N+1])\n \n self.zscale = np.zeros([Ly,N])\n self.Kern = np.zeros([Ly,N])\n\n \n # --> LOOP THROUGH Y-INDICES\n for j in range(Ly):\n if self.LIMIT_MO_DEPTH:\n self.kmo[j] = 0\n self.C_h_MO[j] = self.C_MO *self.ustar[j]**3/self.vonKar\n \n self.kbl[j] = 0\n self.Cr[j,-1] = 0 # set top Cr\n self.Cr[j,0] = 0 # set bottom Cr\n \n # SEARCH FOR MIXED LAYER DEPTH\n self.FC[j,-1] = 0.\n\n\n # ---> LOOP TOP TO BOTTOM (FORTRAN ==> k=N-1,1,-1)\n for k in range(N-1,0,-1):\n # INDEX MAP\n k_r = k-1\n k_w = k\n\n \n zscale = z_u_w[j,N] - z_u_r[j,k_r]\n self.zscale[j,k_w] = zscale\n if self.LMD_KPP:\n if self.LMD_BKPP:\n zscaleb = z_u_r[j,k_r] - z_u_w[j,0]\n Kern = zscale * zscaleb**2 / ( (zscale + self.epssfcs*self.hbls_old[j]) * (zscaleb**2+(self.epssfcb**2*self.hbbl_old[j]**2)))\n else:\n Kern = zscale / (zscale + (self.epssfcs*self.hbls_old[j]))\n else:\n Kern = 1.\n \n\n\n self.Kern[j,k_w] = Kern\n self.FC[j,k_w] = self.FC[j,k_w+1] + Kern * (\\\n ( ( u[j,k_r+1] - u[j,k_r] )**2 + ( v_upts[j,k_r+1] - v_upts[j,k_r])**2 ) \\\n / (Hz[j,k_r] + Hz[j,k_r+1]) \\\n - 0.5 * ( Hz[j,k_r] + Hz[j,k_r+1]) * (self.Ri_inv * self.bvf[j,k_w] + self.C_Ek*self.f[j]*self.f[j]))\n\n\n #\t\tLOOP THAT FINDS BL DEPTH ##\n #----> LOOP TOP TO BOTTOM (start at free surface, w-level surface) \n \n if self.LMD_KPP:\n #swdk_r only used in this function so don't need to be class attribute\n # but for testing make it an attribute to see what it is\n \n # fortran equivlanet ===> k=N,1,-1 \n for k in range(N,0,-1):\n # INDEX MAP\n k_r = k-1\n k_w = k\n\n ###################################################################### \n self.swdk_r[j,k_w] = np.sqrt( self.swr_frac[j,k_w] * self.swr_frac[j,k_w-1])\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\n Bfsfc = self.Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])\n \n self.bvf_max = np.sqrt(np.max([0,self.bvf[j,k_w-1]]))\n \n # CALCULATE TURBULENT VELOCITY SCALE FOR TRACERS\n \t\t\t self.ws = self.lmd_wscale_ws_only(Bfsfc, zscale,self.hbls_old[j],self.ustar[j])\n \n self.Vtsq = self.Vtc * self.ws* self.bvf_max + self.V0\n \n\n self.Cr[j,k_w] = self.FC[j,k_w] + self.Vtsq\n \n\n #######################################################################\n \n # SEARCH FOR hbls vertical level #\n '''\n kbl is specified at vertical w-level (via Cr which is at\n vertical w-levels)\n '''\n if self.kbl[j] == 0 and self.Cr[j,k_w] < 0:\n self.kbl[j] = k_w\n if self.LIMIT_MO_DEPTH:\n if self.kmo[j] == 0 and Bfsfc*(z_u_w[j,N] - z_u_r[j,k_r]) > self.C_h_MO[j]:\n self.kmo[j] = k_w\n\n \n #--> still in j-loop\n #######################################################\n \n # \t\tGET SURFACE BOUNDARY LAYER DEPTH # \n self.hbls[j] = z_u_w[j,N] - z_u_w[j,0] + self.eps # set hbls as depth of entire water column\n if self.kbl[j] > 0:\n k_w = self.kbl[j]\n k_r = k_w - 1 \n if k_w == N: # set hbls at the surface btwn w- and rho-levels at surface\n self.hbls[j] = z_u_w[j,N] - z_u_r[j,N-1]\n \n else:\n self.hbls[j] = z_u_w[j,N] - ( z_u_r[j,k_r] * self.Cr[j,k_w+1] - z_u_r[j,k_r+1] * self.Cr[j,k_w]) / \\\n (self.Cr[j,k_w+1] - self.Cr[j,k_w])\n \n if self.LIMIT_MO_DEPTH:\n if self.kmo[j] > 0:\n k_w = self.kmo[j]\n k_r = k_w-1\n if k_w == N:\n z_up = z_u_w[j,N]\n cff_up = np.max([0,Bo[j]])\n else:\n z_up = z_r[j,k_w+1]\n cff_up = np.max([0, Bo[j] + self.Bosol[j]*(1-self.swdk_r[j,(k_w-1)+1])])\n \n cff_dn = np.max([0,Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])]) \n h_MO = z_u_w[j,N] + self.C_h_MO[j] * ( cff_up*z_up - cff_dn * z_u_r[j,k_r] ) \\\n / ( cff_up * cff_dn * (z_up - z_u_r[j,k_r]) ) \\\n + self.C_h_MO[j] * (cff_dn - cff_up)\n\n self.hbls[j] = np.min([self.hbls[j],np.max([h_MO,0])])\n\n\n\n #### GET BOTTOM BOUNDARY LAYER DEPTH #######\n if self.LMD_BKPP:\n self.kbl[j] = 0 # reset Cr at bottom and kbl for BKPP\n self.Cr[j,0] = 0.\n self.FC[j,0] = 1.5 * self.FC[j,1] - 0.5 * self.FC[j,2] # linear extrapolation\n \n #---> LOOP BOTTOM TO TOP\n # FIND kbl for BBL\n for k in range(1,N+1):\n k_r = k-1\n k_w = k \n self.Cr[j,k_w] = self.FC[j,k_w] - self.FC[j,0]\n \n # LOOK FOR FIRST ZERO CROSSING FROM BOTTOM UP\n if self.kbl[j] == 0 and self.Cr[j,k_w] > 0:\n self.kbl[j] = k_w \n \n\n self.hbbl[j] = z_u_w[j,N] - z_u_w[j,0] # total depth\n if self.kbl[j] > 0 :\n k_w = self.kbl[j] \n k_r = k_w -1\n if k_w == 1: # NO BBL CASE\n self.hbbl[j] = z_u_r[j,0] - z_u_w[j,0] #in between bottom rho and w-level\n else:\n self.hbbl[j] = ( z_u_r[j,k_r-1] * self.Cr[j,k_w] - z_u_r[j,k_r] * self.Cr[j,k_w-1]) / \\\n (self.Cr[j,k_w] - self.Cr[j,k_w-1]) - z_u_w[j,0]","def power_output_hydro_rule(_m, g, y, s, t):\r\n\r\n if y != m.Y.last() and t != m.T.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_8[g, y, s, t]\r\n + m.sigma_20[g, y, s, t] - m.sigma_20[g, y, s, t + 1]\r\n - m.sigma_23[g, y, s, t] + m.sigma_23[g, y, s, t + 1]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + (m.DELTA[y] * m.RHO[y, s] * m.C_MC[g, y])\r\n == 0)\r\n\r\n elif y != m.Y.last() and t == m.T.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_8[g, y, s, t]\r\n + m.sigma_20[g, y, s, t]\r\n - m.sigma_23[g, y, s, t]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + (m.DELTA[y] * m.RHO[y, s] * m.C_MC[g, y])\r\n == 0)\r\n\r\n elif y == m.Y.last() and t != m.T.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_8[g, y, s, t]\r\n + m.sigma_20[g, y, s, t] - m.sigma_20[g, y, s, t + 1]\r\n - m.sigma_23[g, y, s, t] + m.sigma_23[g, y, s, t + 1]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + (m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE)) * m.C_MC[g, y])\r\n == 0)\r\n\r\n elif y == m.Y.last() and t == m.T.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_8[g, y, s, t]\r\n + m.sigma_20[g, y, s, t]\r\n - m.sigma_23[g, y, s, t]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + (m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE)) * m.C_MC[g, y])\r\n == 0)\r\n\r\n else:\r\n raise Exception(f'Unexpected case: {g, y, s, t}')","def pyridine_mH():\n coords = [\n [1.3603, 0.0256, 0.0],\n [0.6971, -1.202, 0.0],\n [-0.6944, -1.2184, 0.0],\n [-1.3895, -0.0129, 0.0],\n [-0.6712, 1.1834, 0.0],\n [0.6816, 1.196, 0.0],\n [1.2665, -2.1365, 0.0],\n [-1.2365, -2.1696, 0.0],\n [-2.4837, 0.0011, 0.0],\n [-1.1569, 2.1657, 0.0],\n ]\n coords = [[float(j) / Bohr for j in i] for i in coords]\n\n symbols = [\n \"C\",\n \"C\",\n \"C\",\n \"C\",\n \"C\",\n \"N\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n ]\n\n atoms = []\n for i, _ in enumerate(coords):\n atoms.append(Atom(symbols[i], position=coords[i]))\n return Molecule(symbols=atoms)","def power_output_existing_thermal_rule(_m, g, y, s, t):\r\n\r\n if y != m.Y.last() and t != m.T.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_2[g, y, s, t]\r\n + m.sigma_20[g, y, s, t] - m.sigma_20[g, y, s, t + 1]\r\n - m.sigma_23[g, y, s, t] + m.sigma_23[g, y, s, t + 1]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + ((m.DELTA[y] * m.RHO[y, s]) * (\r\n m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])))\r\n == 0)\r\n\r\n elif y != m.Y.last() and t == m.T.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_2[g, y, s, t]\r\n + m.sigma_20[g, y, s, t]\r\n - m.sigma_23[g, y, s, t]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + ((m.DELTA[y] * m.RHO[y, s]) * (\r\n m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])))\r\n == 0)\r\n\r\n elif y == m.Y.last() and t != m.T.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_2[g, y, s, t]\r\n + m.sigma_20[g, y, s, t] - m.sigma_20[g, y, s, t + 1]\r\n - m.sigma_23[g, y, s, t] + m.sigma_23[g, y, s, t + 1]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + ((m.DELTA[y] * m.RHO[y, s]) * (1 + (1 / m.INTEREST_RATE)) * (\r\n m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])))\r\n == 0)\r\n\r\n elif y == m.Y.last() and t == m.T.last():\r\n return (- m.sigma_1[g, y, s, t] + m.sigma_2[g, y, s, t]\r\n + m.sigma_20[g, y, s, t]\r\n - m.sigma_23[g, y, s, t]\r\n - m.lamb[self.k(m, g), y, s, t]\r\n + ((m.DELTA[y] * m.RHO[y, s]) * (1 + (1 / m.INTEREST_RATE)) * (\r\n m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])))\r\n == 0)\r\n\r\n else:\r\n raise Exception(f'Unhandled case: {g, y, s, t}')","def _get_properties_building_systems(self):\n\n # Refactored from CalcThermalLoads\n\n # geometry properties.\n\n Ll = self.geometry['Blength']\n Lw = self.geometry['Bwidth']\n nf_ag = self.geometry['floors_ag']\n nf_bg = self.geometry['floors_bg']\n phi_pipes = self._calculate_pipe_transmittance_values()\n\n # nominal temperatures\n Ths_sup_ahu_0 = float(self.hvac['Tshs0_ahu_C'])\n Ths_re_ahu_0 = float(Ths_sup_ahu_0 - self.hvac['dThs0_ahu_C'])\n Ths_sup_aru_0 = float(self.hvac['Tshs0_aru_C'])\n Ths_re_aru_0 = float(Ths_sup_aru_0 - self.hvac['dThs0_aru_C'])\n Ths_sup_shu_0 = float(self.hvac['Tshs0_shu_C'])\n Ths_re_shu_0 = float(Ths_sup_shu_0 - self.hvac['dThs0_shu_C'])\n Tcs_sup_ahu_0 = self.hvac['Tscs0_ahu_C']\n Tcs_re_ahu_0 = Tcs_sup_ahu_0 + self.hvac['dTcs0_ahu_C']\n Tcs_sup_aru_0 = self.hvac['Tscs0_aru_C']\n Tcs_re_aru_0 = Tcs_sup_aru_0 + self.hvac['dTcs0_aru_C']\n Tcs_sup_scu_0 = self.hvac['Tscs0_scu_C']\n Tcs_re_scu_0 = Tcs_sup_scu_0 + self.hvac['dTcs0_scu_C']\n\n Tww_sup_0 = self.hvac['Tsww0_C']\n # Identification of equivalent lengths\n fforma = self._calc_form() # factor form comparison real surface and rectangular\n Lv = (2 * Ll + 0.0325 * Ll * Lw + 6) * fforma # length vertical lines\n if nf_ag < 2 and nf_bg < 2: # it is assumed that building with less than a floor and less than 2 floors udnerground do not have\n Lcww_dis = 0\n Lvww_c = 0\n else:\n Lcww_dis = 2 * (Ll + 2.5 + nf_ag * self.geometry['floor_height']) * fforma # length hot water piping circulation circuit\n Lvww_c = (2 * Ll + 0.0125 * Ll * Lw) * fforma # length piping heating system circulation circuit\n\n Lsww_dis = 0.038 * Ll * Lw * nf_ag * self.geometry['floor_height'] * fforma # length hot water piping distribution circuit\n Lvww_dis = (Ll + 0.0625 * Ll * Lw) * fforma # length piping heating system distribution circuit\n\n building_systems = pd.Series({'Lcww_dis': Lcww_dis,\n 'Lsww_dis': Lsww_dis,\n 'Lv': Lv,\n 'Lvww_c': Lvww_c,\n 'Lvww_dis': Lvww_dis,\n 'Ths_sup_ahu_0': Ths_sup_ahu_0,\n 'Ths_re_ahu_0': Ths_re_ahu_0,\n 'Ths_sup_aru_0': Ths_sup_aru_0,\n 'Ths_re_aru_0': Ths_re_aru_0,\n 'Ths_sup_shu_0': Ths_sup_shu_0,\n 'Ths_re_shu_0': Ths_re_shu_0,\n 'Tcs_sup_ahu_0': Tcs_sup_ahu_0,\n 'Tcs_re_ahu_0': Tcs_re_ahu_0,\n 'Tcs_sup_aru_0': Tcs_sup_aru_0,\n 'Tcs_re_aru_0': Tcs_re_aru_0,\n 'Tcs_sup_scu_0': Tcs_sup_scu_0,\n 'Tcs_re_scu_0': Tcs_re_scu_0,\n 'Tww_sup_0': Tww_sup_0,\n 'Y': phi_pipes,\n 'fforma': fforma})\n return building_systems","def calculate_sh(self):\n if self.data.get('Specific_Humidity') is None:\n if self.data.get('Mixing_Ratio') is None:\n raise KeyError('Calculate mixing ratio first!')\n else:\n w_kg = self.data['Mixing_Ratio'] / 1000\n self.data['Specific_Humidity'] = (w_kg / (w_kg + 1)) * 1000"],"string":"[\n \"def manipulate_heat_data(self): \\n self.exh.T_array = ( 0.5 * (self.exh.T_inlet_array +\\n self.exh.T_outlet_array) + 273.15)\\n self.exh.delta_T_array = ( self.exh.T_inlet_array -\\n self.exh.T_outlet_array )\\n \\n self.cool.delta_T_array = ( self.cool.T_inlet_array -\\n self.cool.T_outlet_array )\\n self.cool.C = self.cool.mdot * self.cool.c_p\",\n \"def shear(self):\\n \\n ux,_ = np.gradient(self._obj['u'],self._obj['x'],self._obj['y'],axis=(0,1))\\n _,vy = np.gradient(self._obj['v'],self._obj['x'],self._obj['y'],axis=(0,1))\\n # self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y'])\\n self._obj['w'] = xr.DataArray(vy + ux, dims=['x', 'y','t'])\\n if len(self._obj.attrs['units']) == 4:\\n vel_units = self._obj.attrs['units'][-1]\\n self._obj.attrs['units'].append('1/dt')\\n else:\\n vel_units = self._obj.attrs['units'][-2]\\n self._obj.attrs['units'][-1] = ('1/dt') \\n \\n return self._obj\",\n \"def get_specific_heat() -> float:\\n return 1006.0\",\n \"def flatNoiseCGH():\\n #Get data\\n wdir = '/home/rallured/Dropbox/AXRO/Metrology/NoiseStudy/FlatMeasurements/'\\n d1,dx1 = met.read4DFits(wdir+'161205_RefFlat_Avg8_Meas1.fits')\\n d2,dx2 = met.read4DFits(wdir+'161205_RefFlat_Avg8_Meas2.fits')\\n p1,px1 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestPitch_Meas1.fits')\\n p2,px2 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestPitch_Meas2.fits')\\n p3,px3 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestRoll_Meas1.fits')\\n p4,px4 = met.read4DFits(wdir+'161205_RefFlat_ParrotingTestRoll_Meas2.fits')\\n\\n #Construct baseline power spectra\\n f1,pow1 = fourier.meanPSD(d1-d2,win=np.hanning,dx=dx1)\\n f2,pow2 = fourier.meanPSD(d1-d2,win=np.hanning,dx=dx1,axis=1)\\n \\n #Construct parroted power spectra\\n f3,pow3 = fourier.meanPSD(p1-p2,win=np.hanning,dx=dx1)\\n f4,pow4 = fourier.meanPSD(p1-p2,win=np.hanning,dx=dx2,axis=1)\\n f5,pow5 = fourier.meanPSD(p3-p4,win=np.hanning,dx=dx1)\\n f6,pow6 = fourier.meanPSD(p3-p4,win=np.hanning,dx=dx2,axis=1)\\n\\n #Plot\\n plt.loglog(f1,pow1/f1[0],label='Axial Baseline')\\n plt.loglog(f2,pow2/f2[0],label='Azimuthal Baseline')\\n plt.loglog(f3,pow3/f3[0],label='Pitch Axial')\\n plt.loglog(f4,pow4/f4[0],label='Pitch Azimuthal')\\n plt.loglog(f5,pow5/f5[0],label='Roll Axial')\\n plt.loglog(f6,pow6/f6[0],label='Roll Azimuthal')\\n plt.title('Residual Fringe Repeatability Impact')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n plt.grid()\\n plt.legend(loc='lower left')\\n\\n return f1,pow1\",\n \"def get_evaporation_latent_heat() -> float:\\n theta = 28.0\\n return 2500.8 - 2.3668 * theta\",\n \"def calc_enginprops(self):\\n # Let's assemble the ABD matrix even if it is not required\\n ABD = np.bmat([[self.A, self.B], [self.B, self.D]])\\n ABD_inv = np.linalg.inv(ABD)\\n # We would use the whole matrix. This gives results similar to elamX and considers poisson effects\\n A_inv = ABD_inv[0:3, 0:3]\\n self.Ex = 1 / (self.total_t * A_inv[0, 0]) # It is 2 * t because we need total thickness\\n self.Ey = 1 / (self.total_t * A_inv[1, 1])\\n self.Gxy = 1 / (self.total_t * A_inv[2, 2])\\n self.poissonxy = - A_inv[0,1] / A_inv[0, 0]\\n # Flexural stiffness properties\\n self.zx = 0.0\\n self.zy = 0.0\\n zx_dem = 0.0\\n zy_dem = 0.0\\n self.EIx = 0.0\\n self.EIy = 0.0\\n z = 0.0\\n # Calculate neutral axis in direction x and y\\n for S_bar, t in zip(self.S_bars, self.ts):\\n Ex = 1 / S_bar[0, 0]\\n Ey = 1 / S_bar[1, 1]\\n z += t / 2.0\\n self.zx += Ex * t * z\\n zx_dem += Ex * t\\n self.zy += Ey * t * z\\n zy_dem += Ey * t\\n z += t / 2.0\\n self.zx = self.zx / zx_dem\\n self.zy = self.zy / zy_dem\\n # Calculate EI in direction x and y\\n z = 0.0\\n for S_bar, t in zip(self.S_bars, self.ts):\\n Ex = 1 / S_bar[0, 0]\\n Ey = 1 / S_bar[1, 1]\\n Gxy = 1 / S_bar[2, 2]\\n z += t / 2.0\\n self.EIx += Ex * (t**3 / 12 + t * (z - self.zx)**2)\\n self.EIy += Ey * (t**3 / 12 + t * (z - self.zy)**2)\\n self.GA += Gxy * t\\n z += t / 2.0\\n return self.Ex, self.Ey, self.Gxy, self.poissonxy\",\n \"def get_properties(self):\\n assert self.kekulize, '#ERROR: u need to get explicit BOs for amon generation'\\n self.vs = np.array([ ai.GetTotalValence() for ai in self.m0.GetAtoms() ], np.int)\\n #self.update_bom()\\n self.ias_heav = self.ias[ self.zs > 1 ]\\n bom_heav = self.bom[ self.ias_heav, : ][ :, self.ias_heav ]\\n self.vs_heav = bom_heav.sum(axis=0)\\n self.cns_heav = ( bom_heav > 0 ).sum(axis=0)\\n self.nhs = self.vs[:self.nheav] - self.vs_heav - self.chgs[:self.nheav]\\n self.dvs = self.vs_heav - self.cns_heav\\n self.hybs = np.array([ _hyb[ai.GetHybridization()] for ai in self.m.GetAtoms() ])\",\n \"def intensity(self, crystal):\\n fp = os.path.join(os.path.dirname(__file__), \\\"database/atomic_scattering_params.json\\\")\\n with open(fp, 'r') as f:\\n ATOMIC_SCATTERING_PARAMS = json.load(f)\\n\\n d0 = (1/2/self.d_hkl)**2\\n\\n # obtiain scattering parameters, atomic numbers, and occus (need to look into occus)\\n coeffs = []\\n zs = []\\n\\n for elem in crystal.get_chemical_symbols():\\n if elem == 'D':\\n elem = 'H'\\n c = ATOMIC_SCATTERING_PARAMS[elem]\\n z = Element(elem).z\\n coeffs.append(c)\\n zs.append(z) \\n\\n coeffs = np.array(coeffs)\\n self.peaks = {}\\n two_thetas = []\\n\\n # self.march_parameter = 1\\n TWO_THETA_TOL = 1e-5 # tolerance to find repeating angles\\n SCALED_INTENSITY_TOL = 1e-5 # threshold for intensities\\n \\n\\n for hkl, s2, theta, d_hkl in zip(self.hkl_list, d0, self.theta, self.d_hkl):\\n \\n # calculate the scattering factor sf\\n g_dot_r = np.dot(crystal.get_scaled_positions(), np.transpose([hkl])).T[0]\\n sf = zs - 41.78214 * s2 * np.sum(coeffs[:, :, 0] * np.exp(-coeffs[:, :, 1] * s2), axis=1)\\n \\n # calculate the structure factor f\\n f = np.sum(sf * np.exp(2j * np.pi * g_dot_r))\\n \\n # calculate the lorentz polarization factor lf\\n lf = (1 + np.cos(2 * theta) ** 2) / (np.sin(theta) ** 2 * np.cos(theta))\\n\\n # calculate the preferred orientation factor\\n if self.preferred_orientation != False:\\n G = self.march_parameter\\n po = ((G * np.cos(theta))**2 + 1/G * np.sin(theta)**2)**(-3/2) \\n else:\\n po = 1\\n \\n # calculate the intensity I\\n I = (f * f.conjugate()).real\\n \\n # calculate 2*theta\\n two_theta = np.degrees(2 * theta)\\n \\n # find where the scattered angles are equal\\n ind = np.where(np.abs(np.subtract(two_thetas, two_theta)) < TWO_THETA_TOL)\\n\\n # append intensity, hkl plane, and thetas to lists\\n if len(ind[0]) > 0:\\n self.peaks[two_thetas[ind[0][0]]][0] += I * lf * po\\n self.peaks[two_thetas[ind[0][0]]][1].append(tuple(hkl))\\n else:\\n self.peaks[two_theta] = [I * lf * po, [tuple(hkl)],d_hkl]\\n two_thetas.append(two_theta)\\n\\n # obtain important intensities (defined by SCALED_INTENSITY_TOL)\\n # and corresponding 2*theta, hkl plane + multiplicity, and d_hkl\\n # print(peaks.keys())\\n max_intensity = max([v[0] for v in self.peaks.values()])\\n x = []\\n y = []\\n hkls = []\\n d_hkls = []\\n count = 0 \\n for k in sorted(self.peaks.keys()):\\n count +=1\\n v = self.peaks[k]\\n fam = self.get_unique_families(v[1])\\n if v[0] / max_intensity * 100 > SCALED_INTENSITY_TOL:\\n x.append(k)\\n y.append(v[0])\\n \\n hkls.append([{\\\"hkl\\\": hkl, \\\"multiplicity\\\": mult}\\n for hkl, mult in fam.items()])\\n d_hkls.append(v[2])\\n\\n self.theta2 = x\\n self.xrd_intensity = y\\n self.hkl_labels = hkls\\n self.d_hkls = d_hkls\",\n \"def statsi(h):\\n\\n # Define constants\\n zsa = np.array([0.0, 11000.0, 20000.0, 32000.0, 47000.0, 52000.0, 61000.0, 79000.0, 9.9e20])\\n Tsa = np.array([288.15, 216.65, 216.65, 228.65, 270.65, 270.65,252.65, 180.65, 180.65])\\n g = 9.80665\\n R = 287.0528\\n Re = 6346766.0\\n Psa = 101325.0\\n\\n # Calculate geopotential altitude\\n z = Re*h/(Re+h)\\n\\n # Loop through atmosphere layers\\n for i in range(8):\\n \\n # Calculate layer temperature gradient\\n Lt = -(Tsa[i+1]-Tsa[i])/(zsa[i+1]-zsa[i])\\n\\n # If no temperature gradient\\n if Lt == 0.0:\\n\\n # Are we in this layer of the atmosphere?\\n if z <= zsa[i+1]:\\n t = Tsa[i] # Temp isn't changing\\n p = Psa*np.exp(-g*(z-zsa[i])/R/Tsa[i])\\n d = p/R/t\\n break\\n\\n # We need to go higher\\n else:\\n Psa *= np.exp(-g*(zsa[i+1]-zsa[i])/R/Tsa[i])\\n\\n # Temperature gradient\\n else:\\n ex = g/R/Lt\\n if z <= zsa[i+1]:\\n t = Tsa[i]-Lt*(z-zsa[i])\\n p = Psa*(t/Tsa[i])**ex\\n d = p/R/t\\n break\\n else:\\n Psa *= (Tsa[i+1]/Tsa[i])**ex\\n\\n # We have left the atmosphere...\\n else:\\n t = Tsa[-1]\\n p = 0.0\\n d = 0.0\\n\\n return z, t, p, d\",\n \"def calc(self,index,counter_values):\\n gr = self.grSign * self.grPitch['Value'].value\\n m = self.mSign * self.mPitch['Value'].value\\n \\n offsetG,offsetM = self.checkOffset()\\n beta = self.toRadians(gr) - (math.pi/2.0) - offsetG\\n theta = (math.pi/2.0) - (self.toRadians(m)) - offsetM\\n alpha = (2.0*theta) + beta\\n numerator = (math.sin(alpha) + math.sin(beta))\\n denominator = (self.DiffrOrder * self.look_at_grx())\\n wavelength = numerator / denominator\\n \\n if wavelength == 0.0:\\n energy_physicalmot = 0.0\\n else:\\n energy_physicalmot = self.hc / wavelength\\n #if self.FixedM2Pit: \\n Cff = math.cos(beta)/math.cos(alpha)\\n if energy_physicalmot < 0 :\\n #warning: wavelength se vuelve negativo ... ??????\\n energy_physicalmot = energy_physicalmot *(-1) \\n \\n # Real Energy is equal to the energy calculated by the encoders\\n # minus an offset that depends on the same energy calculated by the \\n # encoders:\\n # E_physicalmot = Ereal + offset\\n # with offset = a*Ereal + b\\n # This implies that: Ereal = (Ephysicalmot - b)/(1+a) \\n a_coeff = self.EnergyDP.a_offset_coeff\\n b_coeff = self.EnergyDP.b_offset_coeff\\n numerator = energy_physicalmot - b_coeff\\n denominator = 1 + a_coeff\\n energy = numerator / denominator\\n \\n if index == 1:\\n return energy\\n elif index == 2:\\n return Cff\",\n \"def Make_pixel_metric(data, folder, cadence, argsMV={'cmap':'cool', 'cbar':False}, nside=64, recov=7, nb_day=30, fps=20, SUB=320, BAND=['gri', 'griz', 'g', 'r', 'i', 'z'], FMT=['.:k', '.-k', '.:r', '.:g', '.:b', '.:y'], figsize=(16, 10)):\\n\\n argsMV['nest'] = True\\n \\n mjd_i = int(data['mjd'][0])\\n mjd_f = int(data['mjd'][-1])\\n\\n t = np.arange(mjd_i, mjd_f)\\n floor = np.floor(data['mjd']+5./24.)\\n\\n hpx0 = np.zeros(hp.nside2npix(nside)).astype(float) + recov\\n tot_pxl = np.size(hpx0)\\n f = FocPS()\\n p = f.pixellize()\\n\\n HPX = []\\n HPXs = []\\n ADDpix = []\\n t = t[:nb_day]\\n\\n MET = np.zeros((np.size(BAND), np.size(t)))\\n\\n for band in BAND:\\n HPX.append(np.copy(hpx0))\\n HPXs.append(np.copy(hpx0 * 0))\\n ADDpix.append([])\\n\\n for k, ti in enumerate(t):\\n I = np.where(floor == ti)\\n d = data[I]\\n\\n plt.figure(figsize=figsize).suptitle('{} - [{}]'.format(cadence, ti))\\n\\n for i, band in enumerate(BAND):\\n\\n hpxN = make_hpmoll(d, HPX[i], recov, band, f, p, SUB + 1 + i, nside, argsMV)\\n HPX[i] = np.copy(hpxN)\\n\\n MET[i, k] = np.size(np.where(HPX[i] != hp.UNSEEN)[0])\\n\\n fini = HPXs[i][hpxN == hp.UNSEEN]\\n ADDpix[i] += list(fini[fini != 0])\\n \\n HPXs[i][hpxN == hp.UNSEEN] = 0\\n HPXs[i][hpxN != hp.UNSEEN] += 1\\n\\n plt.savefig('Save_Metric/' + folder + cadence + '/fig/fig' + str(k) + '.png')\\n plt.close()\\n\\n\\n #Make the film with the figs\\n path_folder = 'Save_Metric/' + folder + cadence + '/'\\n create_film(nb_day, fps, path_folder+'fig/', prefixe='fig', extension='png')\\n\\n #Make duration activ pixel metric hsito\\n plt.figure(figsize=figsize).suptitle('Count of duration of activ pixel')\\n for i, band in enumerate(BAND):\\n plt.subplot(SUB + 1 + i)\\n plt.hist(ADDpix[i], 200, color='r')\\n plt.yscale('log')\\n plt.title(band)\\n\\n plt.savefig('Save_Metric/' + folder + cadence + '/Metric_duration_activ_pixel.png')\\n\\n #Make the %sky activ pixel metric\\n plt.figure(figsize=figsize)\\n\\n for fmt, band, met in zip(FMT, BAND, MET):\\n\\n plt.plot(t, met/tot_pxl*100, fmt, label=band)\\n\\n plt.xlabel('Day')\\n plt.ylabel('% of activ pixel in sky')\\n plt.title('Metric of Activ pixel of ' + cadence)\\n plt.legend()\\n\\n plt.savefig(path_folder + 'pc_activ_sky.png')\\n\\n #Save data for cadence set metric\\n moy_pcs = np.zeros(np.size(BAND))\\n moy_act = np.zeros(np.size(BAND))\\n\\n for i, met in enumerate(MET):\\n moy_pcs[i] = np.mean(met)/tot_pxl*100\\n moy_act[i] = np.mean(ADDpix[i])\\n\\n with open(path_folder + 'save_data.dat', 'rb') as f:\\n DATA = pUk(f).load()\\n\\n DATA['moy_pcs'] = moy_pcs\\n DATA['moy_act'] = moy_act\\n\\n with open(path_folder + 'save_data.dat', 'wb') as f:\\n pPk(f).dump(DATA)\",\n \"def computeProp(self):\\n self.chem = {}\\n for key in self.config.C:\\n if key in ['P', 'T', 'Z', 'DZ']:\\n continue\\n self.chem[key] = chemistry.ConstituentProperties(key)\\n\\n # nAtm = len(self.gas[self.config.C['P']])\\n self.property = []\\n for op in self.config.LP:\\n self.property.append([])\\n zOffset = 0.0\\n iOffset = 0\\n psep = 1.0E6\\n for i, zv in enumerate(self.gas[self.config.C['Z']]): # find the nearest z value at p_ref\\n P = self.gas[self.config.C['P']][i]\\n if abs(P - self.config.p_ref) < psep:\\n psep = abs(P - self.config.p_ref)\\n iOffset = i\\n zOffset = self.gas[self.config.C['Z']][iOffset]\\n z_at_p_ref = self.config.Req\\n\\n for i, zv in enumerate(self.gas[self.config.C['Z']]):\\n T = self.gas[self.config.C['T']][i]\\n P = self.gas[self.config.C['P']][i]\\n self.property[self.config.LP['P']].append(P)\\n self.property[self.config.LP['Z']].append(zv)\\n rr = z_at_p_ref + zv - zOffset\\n # note that this is the \\\"actual\\\"z along equator referenced to planet center (aka radius)\\n self.property[self.config.LP['R']].append(rr)\\n # ##set mean amu\\n amulyr = 0.0\\n for key in self.chem:\\n amulyr += self.chem[key].amu * self.gas[self.config.C[key]][i]\\n self.property[self.config.LP['AMU']].append(amulyr)\\n # ##set GM pre-calc (normalized further down) and get lapse rate\\n if not i:\\n self.property[self.config.LP['GM']].append(0.0)\\n self.property[self.config.LP['LAPSE']].append(0.0)\\n self.property[self.config.LP['LAPSEP']].append(0.0)\\n else:\\n rho = (amulyr * P) / (chemistry.R * T)\\n dr = abs(zv - self.gas[self.config.C['Z']][i - 1])\\n dV = 4.0 * np.pi * (rr**2) * dr\\n dM = 1.0e11 * rho * dV\\n GdM = self.property[self.config.LP['GM']][i - 1] + chemistry.GravConst * dM\\n # in km3/s2\\n # mass added as you make way into atmosphere by radius r (times G)\\n self.property[self.config.LP['GM']].append(GdM)\\n dT = abs(T - self.gas[self.config.C['T']][i - 1])\\n dP = abs(P - self.gas[self.config.C['P']][i - 1])\\n self.property[self.config.LP['LAPSE']].append(dT / dr)\\n self.property[self.config.LP['LAPSEP']].append(dT / dP)\\n # ##set refractivity and index of refraction\\n refrlyr = 0.0\\n for key in self.chem:\\n refrlyr += self.chem[key].refractivity(T=T) * self.gas[self.config.C[key]][i]\\n refrlyr = refrlyr * P * (293.0 / T)\\n self.property[self.config.LP['REFR']].append(refrlyr)\\n nlyr = refrlyr / 1.0E6 + 1.0\\n self.property[self.config.LP['N']].append(nlyr)\\n\\n # ##Now need to normalize GM to planet and calculate scale height (H)\\n GMnorm = self.property[self.config.LP['GM']][iOffset] # G*(Mass added by p_ref)\\n for i, mv in enumerate(self.property[self.config.LP['GM']]):\\n gm = self.config.GM_ref - (mv - GMnorm)\\n self.property[self.config.LP['GM']][i] = gm\\n little_g = gm / self.property[self.config.LP['R']][i]**2\\n m_bar = self.property[self.config.LP['AMU']][i]\\n T = self.gas[self.config.C['T']][i]\\n self.property[self.config.LP['H']].append((chemistry.R * T) /\\n (little_g * m_bar) / 1000.0)\\n self.property[self.config.LP['g']].append(little_g)\\n self.property = np.array(self.property)\",\n \"def _calculate_monomer(self, raw=False):\\n ta = self.TimeAxis\\n # transition frequency\\n om = self.system.elenergies[1]-self.system.elenergies[0]\\n # transition dipole moment\\n dm = self.system.dmoments[0,1,:]\\n # dipole^2\\n dd = numpy.dot(dm,dm)\\n # natural life-time from the dipole moment\\n gama = [0.0] #[-1.0/self.system.get_electronic_natural_lifetime(1)]\\n sbi = self.system.get_SystemBathInteraction(self.TimeAxis)\\n reorg = sbi.CC.get_reorganization_energy(0,0)\\n \\n if self.system._has_system_bath_coupling:\\n # correlation function\\n ct = self.system.get_egcf((0,1)) \\n gt = self._c2g(ta,ct.data)\\n tr = {\\\"ta\\\":ta,\\\"dd\\\":dd,\\\"om\\\":om-self.rwa,\\\"ct\\\":ct,\\\"gt\\\":gt,\\\"gg\\\":gama,\\\"fwhm\\\":0.0}\\n else:\\n tr = {\\\"ta\\\":ta,\\\"dd\\\":dd,\\\"om\\\":om-self.rwa,\\\"gg\\\":gama,\\\"fwhm\\\":0.0}\\n \\n if self._gauss_broad:\\n tr[\\\"fwhm\\\"] = self.gauss\\n\\n tr[\\\"re\\\"] = reorg\\n\\n if self._gauss_broad:\\n tr[\\\"fwhm\\\"] = self.gauss\\n\\n # calculates the one transition of the monomer \\n data = numpy.real(self.one_transition_spectrum_abs(tr))\\n data_fl = numpy.real(self.one_transition_spectrum_fluor(tr))\\n\\n \\n for ii in range(2,self.system.Nb[1]+1):\\n \\n # transition frequency\\n om = self.system.elenergies[ii]-self.system.elenergies[0]\\n # transition dipole moment\\n dm = self.system.dmoments[0,ii,:]\\n # dipole^2\\n dd = numpy.dot(dm,dm)\\n # natural life-time from the dipole moment\\n gama = [0.0] #[-1.0/self.system.get_electronic_natural_lifetime(ii)]\\n \\n if self.system._has_system_bath_coupling:\\n # correlation function\\n ct = self.system.get_egcf((0,ii)) \\n gt = self._c2g(ta,ct.data)\\n tr = {\\\"ta\\\":ta,\\\"dd\\\":dd,\\\"om\\\":om-self.rwa,\\\"ct\\\":ct,\\\"gt\\\":gt,\\\"gg\\\":gama,\\\"fwhm\\\":0.0}\\n else:\\n tr = {\\\"ta\\\":ta,\\\"dd\\\":dd,\\\"om\\\":om-self.rwa,\\\"gg\\\":gama,\\\"fwhm\\\":0.0}\\n\\n if self._gauss_broad: \\n tr[\\\"fwhm\\\"] = self.gauss\\n \\n if self._gauss_broad:\\n tr[\\\"fwhm\\\"] = self.gauss\\n \\n data += numpy.real(self.one_transition_spectrum_abs(tr))\\n\\n # we only want to retain the upper half of the spectrum\\n Nt = len(self.frequencyAxis.data)//2 \\n do = self.frequencyAxis.data[1]-self.frequencyAxis.data[0]\\n st = self.frequencyAxis.data[Nt//2]\\n # we represent the Frequency axis anew\\n axis = FrequencyAxis(st,Nt,do)\\n\\n # multiply the spectrum by frequency (compulsory prefactor)\\n if not raw:\\n data = axis.data*data\\n data_fl = (axis.data**3)*data_fl\\n\\n \\n spect_abs = LinSpectrum(axis=axis, data=data)\\n fluor_spect = LinSpectrum(axis=axis, data=data_fl)\\n \\n return {\\\"abs\\\": spect_abs, \\\"fluor\\\": fluor_spect}\",\n \"def molar_mass_dry_air():\\n return 28.9647\",\n \"def _calc(self):\\r\\n tot_sum: float = 0 # ? total sum of the noise values\\r\\n max_amp: float = 0 # ? keep the sum in [0,1]\\r\\n amp: float = 1.0 # ? amplitude of each noise value\\r\\n freq: float = 1.0 # ? frequency for getting the detailed noise\\r\\n\\r\\n # for each octave we twice the frequency and multiply the amplitude \\r\\n # by persistance to get the detailed noise value\\r\\n # to keep the final sum value in the range [0, 1] we keep track of the \\r\\n # max amplitude (sum of all the amplitudes)\\r\\n for octave in range(self.octaves):\\r\\n noise_obj = PerlinNoise(self.inp_x*freq, self.inp_y*freq, self.inp_z*freq)\\r\\n # ? multiply the noise value by the amplitude\\r\\n tot_sum += noise_obj.val() * amp\\r\\n max_amp += amp\\r\\n\\r\\n amp *= self.persist\\r\\n freq *= 2.0 # double the freq each iteration\\r\\n\\r\\n # value is in the range [0,1]\\r\\n self.value = tot_sum / max_amp\",\n \"def fluxes_to_heating_rate(example_dict):\\n\\n down_flux_matrix_w_m02 = get_field_from_dict(\\n example_dict=example_dict, field_name=SHORTWAVE_DOWN_FLUX_NAME\\n )\\n up_flux_matrix_w_m02 = get_field_from_dict(\\n example_dict=example_dict, field_name=SHORTWAVE_UP_FLUX_NAME\\n )\\n pressure_matrix_pascals = get_field_from_dict(\\n example_dict=example_dict, field_name=PRESSURE_NAME\\n ) + 0.\\n\\n dummy_pressure_matrix_pascals = (\\n pressure_matrix_pascals[:, [-1]] +\\n (pressure_matrix_pascals[:, [-1]] - pressure_matrix_pascals[:, [-2]])\\n )\\n pressure_matrix_pascals = numpy.concatenate(\\n (pressure_matrix_pascals, dummy_pressure_matrix_pascals), axis=1\\n )\\n\\n net_flux_matrix_w_m02 = down_flux_matrix_w_m02 - up_flux_matrix_w_m02\\n dummy_net_flux_matrix_w_m02 = (\\n net_flux_matrix_w_m02[:, [-1]] +\\n (net_flux_matrix_w_m02[:, [-1]] - net_flux_matrix_w_m02[:, [-2]])\\n )\\n net_flux_matrix_w_m02 = numpy.concatenate(\\n (net_flux_matrix_w_m02, dummy_net_flux_matrix_w_m02), axis=1\\n )\\n\\n coefficient = GRAVITY_CONSTANT_M_S02 / DRY_AIR_SPECIFIC_HEAT_J_KG01_K01\\n\\n # heating_rate_matrix_k_day01 = DAYS_TO_SECONDS * coefficient * (\\n # numpy.gradient(net_flux_matrix_w_m02, axis=1) /\\n # numpy.absolute(numpy.gradient(pressure_matrix_pascals, axis=1))\\n # )\\n\\n heating_rate_matrix_k_day01 = DAYS_TO_SECONDS * coefficient * (\\n numpy.diff(net_flux_matrix_w_m02, axis=1) /\\n numpy.absolute(numpy.diff(pressure_matrix_pascals, axis=1))\\n )\\n\\n error_checking.assert_is_numpy_array_without_nan(net_flux_matrix_w_m02)\\n error_checking.assert_is_numpy_array_without_nan(pressure_matrix_pascals)\\n heating_rate_matrix_k_day01[numpy.isnan(heating_rate_matrix_k_day01)] = 0.\\n\\n vector_target_names = example_dict[VECTOR_TARGET_NAMES_KEY]\\n found_heating_rate = SHORTWAVE_HEATING_RATE_NAME in vector_target_names\\n if not found_heating_rate:\\n vector_target_names.append(SHORTWAVE_HEATING_RATE_NAME)\\n\\n heating_rate_index = vector_target_names.index(SHORTWAVE_HEATING_RATE_NAME)\\n example_dict[VECTOR_TARGET_NAMES_KEY] = vector_target_names\\n\\n if found_heating_rate:\\n example_dict[VECTOR_TARGET_VALS_KEY][..., heating_rate_index] = (\\n heating_rate_matrix_k_day01\\n )\\n else:\\n example_dict[VECTOR_TARGET_VALS_KEY] = numpy.insert(\\n example_dict[VECTOR_TARGET_VALS_KEY],\\n obj=heating_rate_index, values=heating_rate_matrix_k_day01, axis=-1\\n )\\n\\n return example_dict\",\n \"def get_shadow(self):\\n grad_H = torch.autograd.grad(self.hamiltonian_,self.parameters,create_graph = True)[0]\\n grad_H_copy = grad_H.clone().detach()\\n velocity = self.inverse_ @ self.momentum.t()\\n velocity_copy = velocity.clone().detach()\\n prehessian = velocity_copy @ grad_H.t()\\n hessian_velocity = torch.autograd.grad(prehessian,self.parameters,retain_graph = True)[0]\\n shadow1 = hessian_velocity @ velocity.t()\\n premix = velocity @ grad_H_copy.t()\\n mixed_term = torch.autograd.grad(premix,self.parameters,retain_graph = True)[0]\\n shadow2 = -0.5 * grad_H @ self.inverse_ @ grad_H.t()\\n shadow3 = mixed_term @ velocity.t()\\n shadow = self.stepsize**2 * (shadow1 + shadow2 + shadow3)/12\\n return shadow\",\n \"def calculate_haze_reduction(dark, band, model, mult=1.0):\\n # wavelengths, micrometers\\n colors = ['red', 'green', 'blue']\\n wavelengths = {'blue': 0.48, 'green': 0.56, 'red': 0.655}\\n haze = [dark * ((wavelengths[color] ** model) / \\\\\\n (wavelengths[band] ** model)) * mult for color in colors]\\n return haze\",\n \"def waveparameterh(L):\\r\\n return 8.13 - ((250 - 0.7 * L) / 125) ** 3\",\n \"def _calculate_strehl(self):\\n\\n self.strehl = np.exp(-1*((2*np.pi/self.science_wavelength)*self.high_order_wfe)**2)\",\n \"def radiance_map(file, config, vmax=4200, levels=20, typ=''):\\n \\n # Select data from configuration \\n azimuths = config['skymap'][:, 0] # +180 # azimuths\\n zeniths = config['skymap'][:, 1] # zeniths\\n\\n if typ == 'sim':\\n # look for wavelength index in array\\n waves_sim = dataset.attrs['simulated_Columns'].split('nm')[0].split('[')[1].split(\\n ']')[0].split(',')\\n waves = np.asarray(list(map(int, waves_sim)))\\n wave_indx = np.where(waves == wave)\\n try:\\n wave_indx = np.int(wave_indx[0][0])\\n except:\\n print(\\\"Wavelength is not in dataset\\\")\\n z = dataset.simulated[:, wave_indx, time_indx]\\n\\n elif typ == 'meas':\\n wave_indx = int((config['wavelength'] - 250 - config['wave_correction']) / 0.446)\\n with h5py.File(file, 'r') as data:\\n z = data['data'][:, wave_indx]\\n else:\\n print('Select a input data type(sim or meas)')\\n\\n # Add values in the origin to close the surface interpolation\\n azimuths = np.append(azimuths, [270, 0, 0, 0, 0, 0, 0, 0])\\n zeniths = np.append(zeniths, [0, 12, 24, 36, 48, 60, 72, 84])\\n z = np.append(z, [z[0], z[3], z[9], z[19], z[33], z[51], z[73], z[99]])\\n\\n # Convert x to radians\\n azimuths = np.radians(azimuths)\\n zeniths = np.radians(zeniths)\\n\\n # Remove dead channels of the dataset\\n azimuths = np.delete(azimuths, config['dead_fibre'])\\n zeniths = np.delete(zeniths, config['dead_fibre'])\\n z = np.delete(z, config['dead_fibre'])\\n\\n # Set up a regular grid of interpolation point\\n thetai, ri = np.linspace(azimuths.min(), azimuths.max(),\\n num=len(azimuths)), \\\\\\n np.linspace(zeniths.min(), zeniths.max(), num=len(zeniths))\\n\\n ri, thetai = np.meshgrid(ri, thetai, indexing='ij')\\n\\n # zi = scipy.interpolate.griddata((azimuths, zeniths), z, (thetai, ri),\\n # method='linear')\\n\\n rbf = scipy.interpolate.Rbf(azimuths, zeniths, z, fucntion='gaussian',\\n epsilon=0.05)\\n\\n ZI = rbf(thetai, ri)\\n\\n if typ == 'sim':\\n name = str(dataset.time[time_indx].values) # ''\\n else:\\n name = 'testing' #str(dataset.time[time_indx].values)\\n\\n # Create the directory to save the results\\n # os.makedirs(os.path.dirname(config['path_note'] + '/figures/'),\\n # exist_ok=True)\\n if vmax == 'default':\\n vmax = 4200\\n else:\\n vmax = vmax\\n\\n # Plot the dataset\\n fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))\\n cmap = 'Spectral_r' # 'rainbow'\\n a = plt.contourf(thetai, ri, ZI, levels, cmap=cmap, vmin=0,\\n vmax=vmax) # , vmax=4932)\\n plt.title('{} UTC {}nm'.format(name, config['wavelength']))\\n plt.axis([0, 2*np.pi, 0, 1.48])\\n\\n plt.scatter(azimuths, zeniths, cmap=cmap, s=1)\\n ax.grid(False)\\n ax.set_theta_zero_location(\\\"N\\\") # Set the direction of polar plot\\n ax.set_theta_direction(1) # Set the increase direction on azimuth angles\\n # (-1 to clockwise, 1 counterclockwise)\\n cbar = plt.colorbar(a)\\n cbar.set_label(\\\"counts\\\", rotation=90)\\n\\n # if typ == 'sim':\\n # plt.savefig(\\n # 'figures/skymap/simulated/skymap{}nm_{}UTC_sim.jpeg'.format(wave,\\n # name),\\n # dpi=300)\\n # plt.show();\\n # else:\\n # plt.savefig(\\n # 'figures/skymap/measured/skymap{}nm_{}UTC_meas.jpeg'.format(wave,\\n # name),\\n # dpi=300)\",\n \"def sectional_moment(self,strain,na_z):\\r\\n\\t\\treturn self.steel_total_moment(strain,na_z) + \\\\\\r\\n\\t\\t\\tself.concrete_total_moment(strain,na_z)\",\n \"def width_h_invis(self):\\n if m_higgs > 2.0 * self.mx:\\n coupling = self.gsxx * self.stheta / np.sqrt(1 - self.stheta**2)\\n\\n val = (\\n (coupling**2 * (m_higgs**2 - 4 * self.mx**2) ** 1.5)\\n / (8.0 * m_higgs**2 * np.pi)\\n ).real\\n\\n assert val >= 0\\n\\n return val\\n else:\\n return 0.0\",\n \"def calc_atmos_disp(self, site):\\n bar_press_mbar = site.pressure\\n temp_degc = site.temperature\\n rh_pct = self.humidity\\n wl = self.wavelength\\n zd_rad = math.pi / 2. - self.alt\\n tzd = math.tan(zd_rad)\\n if wl is None:\\n raise ValueError('Wavelength is None')\\n else:\\n if isinstance(wl, dict):\\n atmos_disp_rad = {}\\n for k, w in wl.items():\\n wl_mm = w * self.angstrom_to_mm\\n refa, refb = self._calc_atmos_refco(bar_press_mbar, temp_degc, rh_pct, wl_mm)\\n atmos_disp_rad[k] = (refa + refb * tzd * tzd) * tzd\\n elif isinstance(wl, list):\\n atmos_disp_rad = []\\n for w in wl:\\n wl_mm = w * self.angstrom_to_mm\\n refa, refb = self._calc_atmos_refco(bar_press_mbar, temp_degc, rh_pct, wl_mm)\\n atmos_disp_rad.append((refa + refb * tzd * tzd) * tzd)\\n else:\\n wl_mm = wl * self.angstrom_to_mm\\n refa, refb = self._calc_atmos_refco(bar_press_mbar, temp_degc, rh_pct, wl_mm)\\n atmos_disp_rad = (refa + refb * tzd * tzd) * tzd\\n return atmos_disp_rad\",\n \"def MH(self):\\n\\n #return math.log10(self.glb[user_params_index[\\\"Zs\\\"]]*constants.solar_x/(self.glb[user_params_index[\\\"Xs\\\"]]*constants.solar_z))\\n return math.log10(self.glb[iz0]*constants.solar_x/(self.glb[ix0]*constants.solar_z))\",\n \"def get_metrics(H):\\n theta = np.arctan2(H[0,1], H[0,0])\\n scale = H[0,0] / np.cos(theta)\\n tx = H[0,2]\\n ty = H[1,2]\\t\\n return tx,ty,theta\",\n \"def flatNoisePellicle():\\n #Get data\\n wdir = '/home/rallured/Dropbox/AXRO/Metrology/' \\\\\\n 'NoiseStudy/SolarBwPellicle/'\\n d1,dx1 = met.read4DFits(wdir+'161209_Avg8_Meas1.fits')\\n d2,dx2 = met.read4DFits(wdir+'161209_Avg8_Meas2.fits')\\n d3,dx3 = met.read4DFits(wdir+'161209_Avg8_Meas3.fits')\\n d4,dx4 = met.read4DFits(wdir+'161209_Avg8_Meas4.fits')\\n\\n #Construct power spectra\\n f12,pow12 = fourier.meanPSD((d1-d2)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n f23,pow23 = fourier.meanPSD((d2-d3)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n f34,pow34 = fourier.meanPSD((d3-d4)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n f14,pow14 = fourier.meanPSD((d1-d4)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n\\n #Mid frequency\\n midfreq = [1000*np.sqrt(np.sum(p[np.logical_and(f>.1,f<1.)])) \\\\\\n for f,p in zip([f12,f23,f34,f14],[pow12,pow23,pow34,pow14])]\\n\\n #Plot\\n plt.loglog(f12,pow12/f12[0],label='1-2: %.2f' % midfreq[0])\\n plt.loglog(f23,pow23/f23[0],label='2-3: %.2f' % midfreq[1])\\n plt.loglog(f34,pow34/f34[0],label='3-4: %.2f' % midfreq[2])\\n plt.loglog(f14,pow14/f14[0],label='1-4: %.2f' % midfreq[3])\\n plt.legend(loc='lower left')\\n plt.grid()\\n plt.title('4D Repeatability: SolarB Flat+Pellicle')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n\\n print midfreq\\n\\n return f12,pow12\",\n \"def calcEnergy(self):\\n speed_light = constants.physical_constants[\\\"speed of light in vacuum\\\"][0]#m/sec by default\\n if self.mass is None:\\n raise CoordinateVector(\\\"The particle mass needs to be specified to calculate the energy.\\\")\\n return speed_light*math.sqrt(self.p*self.p + (self.mass*speed_light)**2)\",\n \"def moments(self):\",\n \"def calculate_surface_heatflux(self, weather, spaces_dict, surface, temp_record, Coeff, space, h_surface, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, Aflag, terrain, areaDict, areaWinDict, shadowRatios, shadowRatioIndex):\\r\\n #print \\\"Reaching Surface function...\\\"\\r\\n\\r\\n # First get the As\\r\\n A_total = self.get_A(surface, areaDict, areaWinDict)\\r\\n if Aflag == 0:\\r\\n # If it is the first surface of the space, label the space ID in the log file:\\r\\n la = str(surface.obj_id)\\r\\n lb = str(surface.obj_type)\\r\\n #TM_user.info(\\\"%s,surface area,%s,%s\\\" % (la, A_total, lb))\\r\\n A_noWin = self.get_A_noWin(surface, areaDict, areaWinDict)\\r\\n A_noOp = self.get_A_noOp(surface, areaDict, areaWinDict)\\r\\n T_space = spaces_dict[space.obj_id][1]\\r\\n T1 = weather[\\\"t_outside\\\"]\\r\\n hc_external = float(self.get_hc_external(weather, surface, h_surface, terrain))\\r\\n transmitted_win = 0\\r\\n Q_flux = 0\\r\\n\\r\\n # need the surface related information, T_space, U, R3\\r\\n U = self.get_U_surface_e(A_total, A_noOp, surface, areaWinDict) # U = Infor_surface{11,i_surface}; Defined Below\\r\\n #print U\\r\\n R3 = 1/U\\r\\n # Using calculations from: self.surface.constr.layer.C # Infor_surface{10, i_surface} ; from gbXML\\r\\n C = self.get_C_surface(A_total, A_noOp, surface, Coeff, areaWinDict) # need to pass surface and opening ids\\r\\n #print C\\r\\n\\r\\n temperature = Temperature()\\r\\n\\r\\n #Sub-routines for each wall type based on the returned hc_external\\r\\n # This hc is different for each surface type so moved under this sub-routine area\\r\\n #hc = 3.076 sent this to the Temperature Object\\r\\n if surface.obj_type == \\\"ExteriorWall\\\":\\r\\n transmitted_win, Q_flux = temperature.exterior_wall(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\\r\\n #print Q_flux\\r\\n if surface.obj_type == \\\"Roof\\\":\\r\\n transmitted_win, Q_flux = temperature.roof(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\\r\\n #print Q_flux # Matches for Four Room\\r\\n if surface.obj_type == \\\"InteriorWall\\\":\\r\\n transmitted_win, Q_flux = temperature.interior_wall(surface, A_total, R3, C, spaces_dict, T_space, temp_record)\\r\\n #print Q_flux # Matches for Four Room\\r\\n if surface.obj_type == \\\"UndergroundWall\\\":\\r\\n transmitted_win, Q_flux = temperature.underground_wall(surface, A_total, R3, C, T_space, temp_record) # No instance of yet to test\\r\\n if surface.obj_type == \\\"RaisedFloor\\\":\\r\\n # This will eventually need some values when we start using raised floors\\r\\n transmitted_win, Q_flux = temperature.raised_floor(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record) # Not instance of yet to test\\r\\n\\r\\n return transmitted_win, Q_flux\",\n \"def sound_horizon_EH(self):\\n om_m = self.omega_cb\\n om_b = self.omega_b\\n om_n = np.sum(self.omega_nu)\\n h = self.h \\n if self.M_nu_tot == 0.: rs = 44.5*np.log(9.83/om_m)/np.sqrt(1+10*om_b**0.75)*h\\n else: rs = 55.154*np.exp(-72.3*(om_n+0.0006)**2.)/(om_m**0.25351*om_b**0.12807)*h\\n return rs\",\n \"def latent_heat_vapourisation(self, tair):\\n return (2.501 - 0.00237 * tair) * 1E06\",\n \"def __init__(self, temperatures, daytypes, consumptions, nb_days, nb_particles, sigma2, kappa, u_heat):\\n self.temperatures = temperatures\\n self.daytypes = daytypes\\n self.consumptions = consumptions\\n self.nb_days = nb_days\\n self.nb_particles = nb_particles\\n self.sigma2 = sigma2\\n self.kappa = kappa\\n self.u_heat = u_heat\\n #Var init\\n self.s = np.zeros((nb_days, nb_particles)) \\n self.g_heat = np.zeros((nb_days, nb_particles))\\n #sigma_s and sigma_g are fixed\\n self.sigma_s_star_2 = np.zeros((1, nb_particles)) \\n self.sigma_g_star_2 = np.zeros((1, nb_particles))\\n self.x_season = np.zeros((1, nb_particles))\\n self.x_heat = np.zeros((1, nb_particles))\\n self.x = np.zeros((1, nb_particles))\\n self.w = np.zeros((1, nb_particles))\",\n \"def light_measure_Z0_weit(data, weit_data, pix_size, cx, cy, R_bins):\\n\\tNx = data.shape[1]\\n\\tNy = data.shape[0]\\n\\tx0 = np.linspace(0, Nx-1, Nx)\\n\\ty0 = np.linspace(0, Ny-1, Ny)\\n\\tpix_id = np.array(np.meshgrid(x0,y0))\\n\\n\\t#..center pixel point\\n\\tdev_05_x = cx - np.int( cx )\\n\\tdev_05_y = cy - np.int( cy )\\n\\n\\tif dev_05_x > 0.5:\\n\\t\\txn = np.int( cx ) + 1\\n\\telse:\\n\\t\\txn = np.int( cx )\\n\\n\\tif dev_05_y > 0.5:\\n\\t\\tyn = np.int( cy ) + 1\\n\\telse:\\n\\t\\tyn = np.int( cy )\\n\\n\\ttheta = np.arctan2((pix_id[1,:] - yn), (pix_id[0,:] - xn))\\n\\tchi = theta * 180 / np.pi\\n\\t# radius in unit of pixel number\\n\\trbin = R_bins + 0.\\n\\n\\tN_bins = len( rbin )\\n\\n\\tintens = np.zeros(N_bins, dtype = np.float)\\n\\tintens_err = np.zeros(N_bins, dtype = np.float)\\n\\tAngl_r = np.zeros(N_bins, dtype = np.float)\\n\\tN_pix = np.zeros(N_bins, dtype = np.float)\\n\\tnsum_ratio = np.zeros(N_bins, dtype = np.float)\\n\\n\\tdr = np.sqrt(((2*pix_id[0] + 1) / 2 - (2*xn + 1) / 2)**2 + ((2*pix_id[1] + 1) / 2 - (2*yn + 1) / 2)**2)\\n\\n\\tfor k in range(N_bins - 1):\\n\\t\\tcdr = rbin[k + 1] - rbin[k]\\n\\t\\td_phi = (cdr / ( 0.5 * (rbin[k] + rbin[k + 1]) ) ) * 180 / np.pi\\n\\t\\tN_phi = np.int(360 / d_phi) + 1\\n\\t\\tphi = np.linspace(0, 360, N_phi)\\n\\t\\tphi = phi - 180\\n\\n\\t\\tir = (dr >= rbin[k]) * (dr < rbin[k + 1])\\n\\n\\t\\tbool_sum = np.sum(ir)\\n\\n\\t\\tr_iner = rbin[k]\\n\\t\\tr_out = rbin[k + 1]\\n\\n\\t\\tif bool_sum == 0:\\n\\t\\t\\tAngl_r[k] = 0.5 * (r_iner + r_out) * pix_size\\n\\t\\telse:\\n\\t\\t\\tweit_arr = weit_data[ir]\\n\\t\\t\\tsamp_flux = data[ir]\\n\\t\\t\\tsamp_chi = chi[ir]\\n\\n\\t\\t\\ttot_flux = np.nansum(samp_flux * weit_arr) / np.nansum(weit_arr)\\n\\t\\t\\tidnn = np.isnan( samp_flux )\\n\\t\\t\\tN_pix[k] = np.sum( idnn == False )\\n\\t\\t\\tnsum_ratio[k] = np.nansum(weit_arr) / np.sum( idnn == False )\\n\\n\\t\\t\\tintens[k] = tot_flux\\n\\t\\t\\t#Angl_r[k] = 0.5 * (r_iner + r_out) * pix_size\\n\\t\\t\\tAngl_r[k] = np.nansum( dr[ir] * weit_arr ) / np.nansum( weit_arr ) * pix_size\\n\\n\\t\\t\\ttmpf = []\\n\\t\\t\\tfor tt in range(len(phi) - 1):\\n\\t\\t\\t\\tiv = (samp_chi >= phi[tt]) & (samp_chi <= phi[tt+1])\\n\\n\\t\\t\\t\\tset_samp = samp_flux[iv]\\n\\t\\t\\t\\tset_weit = weit_arr[iv]\\n\\n\\t\\t\\t\\tttf = np.nansum(set_samp * set_weit) / np.nansum(set_weit)\\n\\t\\t\\t\\ttmpf.append(ttf)\\n\\n\\t\\t\\t# rms of flux\\n\\t\\t\\ttmpf = np.array(tmpf)\\n\\t\\t\\tid_inf = np.isnan(tmpf)\\n\\t\\t\\ttmpf[id_inf] = np.nan\\n\\t\\t\\tid_zero = tmpf == 0\\n\\t\\t\\ttmpf[id_zero] = np.nan\\n\\t\\t\\tid_nan = np.isnan(tmpf)\\n\\t\\t\\tid_fals = id_nan == False\\n\\t\\t\\tTmpf = tmpf[id_fals]\\n\\n\\t\\t\\t#RMS = np.sqrt( np.sum(Tmpf**2) / len(Tmpf) )\\n\\t\\t\\tRMS = np.std(Tmpf)\\n\\t\\t\\tif len(Tmpf) > 1:\\n\\t\\t\\t\\tintens_err[k] = RMS / np.sqrt(len(Tmpf) - 1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tintens_err[k] = RMS\\n\\n\\tidzo = N_pix < 1\\n\\n\\tIntns = intens.copy()\\n\\tIntns[idzo] = 0.\\n\\tIntns_err = intens_err.copy()\\n\\tIntns_err[idzo] = 0.\\n\\tnsum_ratio[idzo] = 0.\\n\\n\\tIntns, Intns_err = Intns / pix_size**2, Intns_err / pix_size**2\\n\\n\\treturn Intns, Angl_r, Intns_err, N_pix, nsum_ratio\",\n \"def shadowingfunction_wallheight_23(a, vegdem, vegdem2, azimuth, altitude, scale, amaxvalue, bush, walls, aspect):\\n\\n if not walls.size:\\n \\\"\\\"\\\" needs to be checked\\n walls=ordfilt2(a,4,[0 1 0; 1 0 1; 0 1 0]);\\n walls=walls-a;\\n walls(walls<3)=0;\\n sizex=size(a,1);%might be wrong\\n sizey=size(a,2);\\n dirwalls = filter1Goodwin_as_aspect_v3(walls,sizex,sizey,scale,a);\\n aspect=dirwalls*pi/180;\\n \\\"\\\"\\\"\\n\\n # conversion\\n degrees = np.pi/180\\n azimuth *= degrees\\n altitude *= degrees\\n \\n # measure the size of the image\\n\\n sizex = np.shape(a)[0]\\n sizey = np.shape(a)[1]\\n \\n # initialise parameters\\n dx = 0\\n dy = 0\\n dz = 0\\n \\n sh = np.zeros((sizex, sizey))\\n vbshvegsh = np.copy(sh)\\n vegsh = np.copy(sh)\\n f = np.copy(a)\\n shvoveg = np.copy(vegdem) # for vegetation shadowvolume\\n g = np.copy(sh)\\n bushplant = bush > 1\\n #wallbol = np.array([np.float(boolean) for row in walls > 0 for boolean in row])\\n # wallbol = np.copy(sh)\\n # wallbol[walls > 0] = 1.\\n wallbol = (walls > 0).astype(float)\\n wallbol[wallbol == 0] = np.nan\\n\\n pibyfour = np.pi/4\\n threetimespibyfour = 3*pibyfour\\n fivetimespibyfour = 5*pibyfour\\n seventimespibyfour = 7*pibyfour\\n sinazimuth = np.sin(azimuth)\\n cosazimuth = np.cos(azimuth)\\n tanazimuth = np.tan(azimuth)\\n signsinazimuth = np.sign(sinazimuth)\\n signcosazimuth = np.sign(cosazimuth)\\n dssin = np.abs(1/sinazimuth)\\n dscos = np.abs(1/cosazimuth)\\n tanaltitudebyscale = np.tan(altitude)/scale\\n\\n tempvegdem = np.zeros((sizex, sizey))\\n tempvegdem2 = np.zeros((sizex, sizey))\\n temp = np.zeros((sizex, sizey))\\n\\n index = 0\\n\\n # main loop\\n while (amaxvalue>=dz) and (np.abs(dx)<sizex) and (np.abs(dy)<sizey):\\n if ((pibyfour <= azimuth) and (azimuth < threetimespibyfour)) or \\\\\\n ((fivetimespibyfour <= azimuth) and (azimuth < seventimespibyfour)):\\n dy = signsinazimuth*(index+1)\\n dx = -1*signcosazimuth*np.abs(np.round((index+1)/tanazimuth))\\n ds = dssin\\n else:\\n dy = signsinazimuth*np.abs(np.round((index+1)*tanazimuth))\\n dx = -1*signcosazimuth*(index+1)\\n ds = dscos\\n\\n # note: dx and dy represent absolute values while ds is an incremental value\\n dz = ds*(index+1)*tanaltitudebyscale\\n tempvegdem[0:sizex, 0:sizey] = 0\\n tempvegdem2[0:sizex, 0:sizey] = 0\\n temp[0:sizex, 0:sizey] = 0\\n \\n absdx = np.abs(dx)\\n absdy = np.abs(dy)\\n\\n xc1 = int((dx+absdx)/2)\\n xc2 = int(sizex+(dx-absdx)/2)\\n yc1 = int((dy+absdy)/2)\\n yc2 = int(sizey+(dy-absdy)/2)\\n\\n xp1 = -int((dx-absdx)/2)\\n xp2 = int(sizex-(dx+absdx)/2)\\n yp1 = -int((dy-absdy)/2)\\n yp2 = int(sizey-(dy+absdy)/2)\\n\\n tempvegdem[int(xp1):int(xp2), int(yp1):int(yp2)] = vegdem[int(xc1):int(xc2), int(yc1):int(yc2)] - dz\\n tempvegdem2[int(xp1):int(xp2), int(yp1):int(yp2)] = vegdem2[int(xc1):int(xc2), int(yc1):int(yc2)] - dz\\n temp[int(xp1):int(xp2), int(yp1):int(yp2)] = a[int(xc1):int(xc2), int(yc1):int(yc2)] - dz\\n\\n f = np.max([f, temp], axis=0)\\n #f = np.array([np.max(val) for val in zip(f, temp)])\\n shvoveg = np.max([shvoveg, tempvegdem], axis=0)\\n sh[f > a] = 1\\n sh[f <= a] = 0 #Moving building shadow\\n fabovea = (tempvegdem > a).astype(int) #vegdem above DEM\\n gabovea = (tempvegdem2 > a).astype(int) #vegdem2 above DEM\\n vegsh2 = fabovea - gabovea\\n vegsh = np.max([vegsh, vegsh2], axis=0)\\n vegsh[vegsh*sh > 0] = 0 # removing shadows 'behind' buildings\\n vbshvegsh = np.copy(vegsh) + vbshvegsh\\n\\n # vegsh at high sun altitudes\\n if index == 0:\\n firstvegdem = np.copy(tempvegdem) - np.copy(temp)\\n firstvegdem[firstvegdem <= 0] = 1000\\n vegsh[firstvegdem < dz] = 1\\n vegsh *= (vegdem2 > a)\\n vbshvegsh = np.zeros((sizex, sizey))\\n\\n # Bush shadow on bush plant\\n if np.max(bush) > 0 and np.max(fabovea*bush) > 0:\\n tempbush = np.zeros((sizex, sizey))\\n tempbush[int(xp1):int(xp2), int(yp1):int(yp2)] = bush[int(xc1):int(xc2), int(yc1):int(yc2)] - dz\\n g = np.max([g, tempbush], axis=0)\\n g = bushplant * g\\n \\n # if index<3 #removing shadowed walls 1\\n # tempfirst(1:sizex,1:sizey)=0;\\n # tempfirst(xp1:xp2,yp1:yp2)= a(xc1:xc2,yc1:yc2);\\n # if index==1 # removing shadowed walls 2\\n # tempwalls(1:sizex,1:sizey)=0;\\n # tempwalls(xp1:xp2,yp1:yp2)= wallbol(xc1:xc2,yc1:yc2);\\n # wallfirst=((tempwalls+wallbol).*wallbol)==2;\\n # wallfirstaspect=aspect.*wallbol.*wallfirst;\\n # wallfirstaspect(wallfirstaspect==0)=NaN;\\n # wallfirstsun=(wallfirstaspect>azimuth-pi/2 & wallfirstaspect<azimuth+pi/2);\\n # wallfirstshade=wallfirst-wallfirstsun;\\n # end\\n # end\\n \\n index += 1\\n # imagesc(h),axis image,colorbar\\n # Stopping loop if all shadows reached the ground\\n # stopbuild=stopbuild==f;\\n # imagesc(stopbuild),axis image,pause(0.3)\\n # fin=find(stopbuild==0, 1);\\n # stopbuild=f;\\n # stopveg=stopveg==vegsh;\\n # finveg=find(stopveg==0, 1);\\n # stopveg=vegsh;\\n # if isempty(fin) && isempty(finveg)\\n # dz=amaxvalue+9999;\\n # end\\n\\n # Removing walls in shadow due to selfshadowing\\n azilow = azimuth - np.pi/2\\n azihigh = azimuth + np.pi/2\\n if azilow >= 0 and azihigh < 2*np.pi: # 90 to 270 (SHADOW)\\n facesh = np.logical_or(aspect < azilow, aspect >= azihigh).astype(float) - wallbol + 1 # TODO check\\n elif azilow < 0 and azihigh <= 2*np.pi: # 0 to 90\\n azilow = azilow + 2*np.pi\\n facesh = np.logical_or(aspect > azilow, aspect <= azihigh) * -1 + 1 # (SHADOW)\\n elif azilow > 0 and azihigh >= 2*np.pi: # 270 to 360\\n azihigh -= 2 * np.pi\\n facesh = np.logical_or(aspect > azilow, aspect <= azihigh)*-1 + 1 # (SHADOW)\\n\\n sh = 1-sh\\n vbshvegsh[vbshvegsh > 0] = 1\\n vbshvegsh = vbshvegsh-vegsh\\n \\n if np.max(bush) > 0:\\n g = g-bush\\n g[g > 0] = 1\\n g[g < 0] = 0\\n vegsh = vegsh-bushplant+g\\n vegsh[vegsh < 0] = 0\\n\\n vegsh[vegsh > 0] = 1\\n shvoveg = (shvoveg-a) * vegsh #Vegetation shadow volume\\n vegsh = 1-vegsh\\n vbshvegsh = 1-vbshvegsh\\n\\n #removing walls in shadow\\n # tempfirst=tempfirst-a;\\n # tempfirst(tempfirst<2)=1;\\n # tempfirst(tempfirst>=2)=0;\\n \\n shvo = f - a # building shadow volume\\n\\n facesun = np.logical_and(facesh + (walls > 0).astype(float) == 1, walls > 0).astype(float)\\n #facesun = np.reshape(np.array([np.float(boolean) for row in facesun for boolean in row]), facesun.shape)\\n\\n wallsun = np.copy(walls-shvo)\\n wallsun[wallsun < 0] = 0\\n wallsun[facesh == 1] = 0 # Removing walls in \\\"self\\\"-shadow\\n # wallsun(tempfirst = = 0) = 0# Removing walls in shadow 1\\n # wallsun(wallfirstshade = = 1) = 0# Removing walls in shadow 2\\n wallsh = np.copy(walls-wallsun)\\n # wallsh(wallfirstshade = = 1) = 0\\n # wallsh = wallsh+(wallfirstshade.*walls)\\n #wallbol = np.reshape(np.array([np.float(boolean) for row in walls > 0 for boolean in row]), walls.shape)\\n wallbol = (walls > 0).astype(float)\\n\\n wallshve = shvoveg * wallbol\\n wallshve = wallshve - wallsh\\n wallshve[wallshve < 0] = 0\\n id = np.where(wallshve > walls)\\n wallshve[id] = walls[id]\\n wallsun = wallsun-wallshve # problem with wallshve only\\n id = np.where(wallsun < 0)\\n wallshve[id] = 0\\n wallsun[id] = 0\\n \\n # subplot(2,2,1),imagesc(facesh),axis image ,colorbar,title('facesh')#\\n # subplot(2,2,2),imagesc(wallsun,[0 20]),axis image, colorbar,title('Wallsun')#\\n # subplot(2,2,3),imagesc(sh-vegsh*0.8), colorbar,axis image,title('Groundsh')#\\n # subplot(2, 2, 4), imagesc(wallshve, [0 20]), axis image, colorbar, title('Wallshve')#\\n return vegsh, sh, vbshvegsh, wallsh, wallsun, wallshve, facesh, facesun\",\n \"def __init__(self):\\n # Set constants\\n self.fromHztoeV = 6.58e-16\\n self.gramstoeV = 1 / ( 1.78 * 1e-33)\\n self.mtoev = 1/(1.97 * 1e-7) \\n self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value) #expressed in 1/s\\n self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV /(1e-2)**3 # eV/m**3\\n self.Om0 = cosmo.Om0 #total matter \\n self.OLambda0 = cosmo.Ode0 # cosmological constant\\n self.DM0 = self.Om0 - cosmo.Ob0 # dark matter\\n self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ\\n self.evtoJoule = 1.60218 * 1e-19 # from eV to J\\n PSgal1h = np.loadtxt(\\\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_1h.dat\\\")\\n PSgal2h = np.loadtxt(\\\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_2h.dat\\\")\\n self.Mpc = 1e3 * const.kpc.value\\n self.zmin = 0.001\\n self.zmax = 30.001\\n self.zbins = 301\\n self.h = cosmo.h\\n self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)\\n self.k_vect = PSgal1h[:,0]* self.h\\n self.Power1h = PSgal1h[:,1:]/(self.h**3)\\n self.Power2h = PSgal2h[:,1:]/(self.h**3)\\n self.Power = self.Power1h + self.Power2h\\n self.Praw_prova1h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power1h))\\n self.Praw_prova2h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power2h))\\n self.Praw_prova = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power))\",\n \"def calculateTemperature(self):\\n \\n # CIE XYZ space\\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\\n\\n # CIE Chromaticities xy\\n self.x = self.X/(self.X + self.Y + self.Z)\\n self.y = self.Y/(self.X + self.Y + self.Z)\\n \\n # CIE Chromaticities uv\\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n \\n # constant for McCamy's/Hernandez-Andrés formula\\n n = (self.x - self.x_e)/(self.y - self.y_e)\\n \\n # Correlated color temperature according to Hernández-Andrés (1999)\\n self.color_temp = ( self.A_0 + \\n self.A_1*np.exp(-n/self.t_1) + \\n self.A_2*np.exp(-n/self.t_2) + \\n self.A_3*np.exp(-n/self.t_3) )\\n \\n # Delete too high values\\n self.color_temp[self.color_temp > 30000] = 0\\n \\n # Affichage de la CCT\\n self.mean_temp = int(round(self.color_temp.mean()))\\n self.mean_temp_label.setText(\\\"Temperature moyenne = \\\"+str(self.mean_temp))\\n self.mean_temp_label.adjustSize()\\n \\t\\n # Affichage de l'illuminance (Y)\\n self.mean_illu = int(round((self.Y.mean())))\\n self.illuminance_label.setText(\\\"Illuminance moyenne = \\\"+str(self.mean_illu))\\n self.illuminance_label.adjustSize()\",\n \"def compute_surface_temperature(heat_flux):\\n\\n return 1.1e-4*heat_flux + 323\",\n \"def do_hc_wavesol(p, loc):\\n\\n # ----------------------------------------------------------------------\\n # Read UNe solution\\n # ----------------------------------------------------------------------\\n wave_u_ne, amp_u_ne = spirouImage.ReadLineList(p)\\n loc['LL_LINE'], loc['AMPL_LINE'] = wave_u_ne, amp_u_ne\\n source = __NAME__ + '.main() + spirouImage.ReadLineList()'\\n loc.set_sources(['ll_line', 'ampl_line'], source)\\n\\n # ----------------------------------------------------------------------\\n # Generate wave map from wave solution\\n # ----------------------------------------------------------------------\\n loc = generate_wave_map(p, loc)\\n\\n # ----------------------------------------------------------------------\\n # Create new wavelength solution (method 0, old cal_HC_E2DS_EA)\\n # ----------------------------------------------------------------------\\n if p['WAVE_MODE_HC'] == 0:\\n\\n # ---------------------------------------------------------------------\\n # Find Gaussian Peaks in HC spectrum\\n # ---------------------------------------------------------------------\\n loc = find_hc_gauss_peaks(p, loc)\\n\\n # ---------------------------------------------------------------------\\n # Start plotting session\\n # ---------------------------------------------------------------------\\n if p['DRS_PLOT'] > 0:\\n # start interactive plot\\n sPlt.start_interactive_session(p)\\n\\n # ---------------------------------------------------------------------\\n # Fit Gaussian peaks (in triplets) to\\n # ---------------------------------------------------------------------\\n loc = fit_gaussian_triplets(p, loc)\\n\\n # ---------------------------------------------------------------------\\n # Generate Resolution map and line profiles\\n # ---------------------------------------------------------------------\\n # log progress\\n wmsg = 'Generating resolution map and calculating line spread function'\\n WLOG(p, '', wmsg)\\n # generate resolution map\\n loc = generate_resolution_map(p, loc)\\n # map line profile map\\n if p['DRS_PLOT'] > 0:\\n sPlt.wave_ea_plot_line_profiles(p, loc)\\n\\n # ---------------------------------------------------------------------\\n # End plotting session\\n # ---------------------------------------------------------------------\\n # end interactive session\\n if p['DRS_PLOT'] > 0:\\n sPlt.end_interactive_session(p)\\n\\n # ----------------------------------------------------------------------\\n # Set up all_lines storage\\n # ----------------------------------------------------------------------\\n\\n # initialise up all_lines storage\\n all_lines_1 = []\\n\\n # get parameters from p\\n n_ord_start = p['WAVE_N_ORD_START']\\n n_ord_final = p['WAVE_N_ORD_FINAL']\\n\\n # get values from loc:\\n # line centers in pixels\\n xgau = np.array(loc['XGAU_T'])\\n # distance from catalogue in km/s - used for sanity checks\\n dv = np.array(loc['DV_T'])\\n # fitted polynomials per order\\n fit_per_order = np.array(loc['POLY_WAVE_SOL'])\\n # equivalent width of fitted gaussians to each line (in pixels)\\n ew = np.array(loc['EW_T'])\\n # amplitude of fitted gaussians to each line\\n peak = np.array(loc['PEAK_T'])\\n # catalogue line amplitude\\n amp_catalog = np.array(loc['AMP_CATALOG'])\\n # catalogue line wavelength\\n wave_catalog = np.array(loc['WAVE_CATALOG'])\\n # spectral order for each line\\n ord_t = np.array(loc['ORD_T'])\\n\\n # loop through orders\\n for iord in range(n_ord_start, n_ord_final):\\n # keep relevant lines\\n # -> right order\\n # -> finite dv\\n gg = (ord_t == iord) & (np.isfinite(dv))\\n # put lines into ALL_LINES structure\\n # reminder:\\n # gparams[0] = output wavelengths\\n # gparams[1] = output sigma(gauss fit width)\\n # gparams[2] = output amplitude(gauss fit)\\n # gparams[3] = difference in input / output wavelength\\n # gparams[4] = input amplitudes\\n # gparams[5] = output pixel positions\\n # gparams[6] = output pixel sigma width (gauss fit width in pixels)\\n # gparams[7] = output weights for the pixel position\\n\\n # dummy array for weights\\n test = np.ones(np.shape(xgau[gg]), 'd') * 1e4\\n # get the final wavelength value for each peak in the order\\n output_wave_1 = np.polyval(fit_per_order[iord][::-1], xgau[gg])\\n # convert the pixel equivalent width to wavelength units\\n xgau_ew_ini = xgau[gg] - ew[gg] / 2\\n xgau_ew_fin = xgau[gg] + ew[gg] / 2\\n ew_ll_ini = np.polyval(fit_per_order[iord, :], xgau_ew_ini)\\n ew_ll_fin = np.polyval(fit_per_order[iord, :], xgau_ew_fin)\\n ew_ll = ew_ll_fin - ew_ll_ini\\n # put all lines in the order into single array\\n gau_params = np.column_stack((output_wave_1, ew_ll, peak[gg],\\n wave_catalog[gg] - output_wave_1,\\n amp_catalog[gg],\\n xgau[gg], ew[gg], test))\\n # append the array for the order into a list\\n all_lines_1.append(gau_params)\\n\\n # add to loc\\n loc['ALL_LINES_1'] = all_lines_1\\n loc['LL_PARAM_1'] = np.array(fit_per_order)\\n loc['LL_OUT_1'] = np.array(loc['WAVE_MAP2'])\\n loc.set_sources(['ALL_LINES_1', 'LL_PARAM_1'], __NAME__ + '/main()')\\n\\n # For compatibility w/already defined functions, I need to save\\n # here all_lines_2\\n all_lines_2 = list(all_lines_1)\\n loc['ALL_LINES_2'] = all_lines_2\\n\\n # ------------------------------------------------------------------\\n # Littrow test\\n # ------------------------------------------------------------------\\n\\n start = p['IC_LITTROW_ORDER_INIT_1']\\n end = p['IC_LITTROW_ORDER_FINAL_1']\\n\\n # calculate echelle orders\\n o_orders = np.arange(start, end)\\n echelle_order = p['IC_HC_T_ORDER_START'] - o_orders\\n loc['ECHELLE_ORDERS'] = echelle_order\\n loc.set_source('ECHELLE_ORDERS', __NAME__ + '/main()')\\n\\n # Do Littrow check\\n ckwargs = dict(ll=loc['LL_OUT_1'][start:end, :], iteration=1, log=True)\\n loc = calculate_littrow_sol(p, loc, **ckwargs)\\n\\n # Plot wave solution littrow check\\n if p['DRS_PLOT'] > 0:\\n # plot littrow x pixels against fitted wavelength solution\\n sPlt.wave_littrow_check_plot(p, loc, iteration=1)\\n\\n # ------------------------------------------------------------------\\n # extrapolate Littrow solution\\n # ------------------------------------------------------------------\\n ekwargs = dict(ll=loc['LL_OUT_1'], iteration=1)\\n loc = extrapolate_littrow_sol(p, loc, **ekwargs)\\n\\n # ------------------------------------------------------------------\\n # Plot littrow solution\\n # ------------------------------------------------------------------\\n if p['DRS_PLOT'] > 0:\\n # plot littrow x pixels against fitted wavelength solution\\n sPlt.wave_littrow_extrap_plot(p, loc, iteration=1)\\n\\n return loc\",\n \"def SC_generation(hourly_radiation, prop_observers, number_groups, weather_data, g, Sz, Az, ha, Tin_C, height,\\n panel_properties, latitude):\\n\\n\\n n0 = panel_properties['n0']\\n c1 = panel_properties['c1']\\n c2 = panel_properties['c2']\\n mB0_r = panel_properties['mB0_r']\\n mB_max_r = panel_properties['mB_max_r']\\n mB_min_r = panel_properties['mB_min_r']\\n C_eff = panel_properties['C_eff']\\n t_max = panel_properties['t_max']\\n IAM_d = panel_properties['IAM_d']\\n Aratio = panel_properties['aperture_area_ratio']\\n Apanel = panel_properties['module_area']\\n dP1 = panel_properties['dP1']\\n dP2 = panel_properties['dP2']\\n dP3 = panel_properties['dP3']\\n dP4 = panel_properties['dP4']\\n Cp_fluid_JperkgK = panel_properties['Cp_fluid'] # J/kgK\\n\\n # create lists to store results\\n list_results = [None] * number_groups\\n list_areas_groups = [None] * number_groups\\n Sum_mcp_kWperC = np.zeros(8760)\\n Sum_qout_kWh = np.zeros(8760)\\n Sum_Eaux_kWh = np.zeros(8760)\\n Sum_qloss = np.zeros(8760)\\n Sum_radiation_kWh = np.zeros(8760)\\n\\n Tin_array_C = np.zeros(8760) + Tin_C\\n aperature_area_per_module = Aratio * Apanel\\n total_area_module = prop_observers['total_area_module'].sum() # total area for panel installation\\n\\n # calculate equivalent length of pipes\\n lv = panel_properties['module_length'] # module length\\n number_modules = round(total_area_module/Apanel) # this is an estimation\\n l_ext_mperm2 = (2 * lv * number_modules/ (total_area_module * Aratio)) # pipe length within the collectors\\n l_int_mperm2 = 2 * height / (total_area_module * Aratio) # pipe length from building substation to roof top collectors\\n Leq_mperm2 = l_int_mperm2 + l_ext_mperm2 # in m/m2 aperture\\n\\n if panel_properties['type'] == 'ET': # for evacuated tubes\\n Nseg = 100 # default number of subsdivisions for the calculation\\n else:\\n Nseg = 10 # default number of subsdivisions for the calculation\\n\\n for group in range(number_groups):\\n # load panel angles from group\\n teta_z = prop_observers.loc[group, 'surface_azimuth'] # azimuth of panels of group\\n area_per_group = prop_observers.loc[group, 'total_area_module']\\n tilt_angle_deg = prop_observers.loc[group, 'tilt'] # tilt angle of panels\\n\\n # create dataframe with irradiation from group\\n\\n radiation_Wh = pd.DataFrame({'I_sol': hourly_radiation[group]})\\n radiation_Wh['I_diffuse'] = weather_data.ratio_diffhout * radiation_Wh.I_sol # calculate diffuse radiation\\n radiation_Wh['I_direct'] = radiation_Wh['I_sol'] - radiation_Wh['I_diffuse'] # calculate direct radiation\\n radiation_Wh.fillna(0, inplace=True) # set nan to zero\\n\\n # calculate incidence angle modifier for beam radiation\\n IAM_b = calc_IAM_beam_SC(Az, g, ha, teta_z, tilt_angle_deg, panel_properties['type'], Sz, latitude)\\n\\n # calculate heat production from a solar collector of each group\\n list_results[group] = calc_SC_module(tilt_angle_deg, IAM_b, IAM_d, radiation_Wh.I_direct,\\n radiation_Wh.I_diffuse, weather_data.drybulb_C, n0,\\n c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max,\\n aperature_area_per_module, dP1, dP2, dP3, dP4,\\n Cp_fluid_JperkgK, Tin_C, Leq_mperm2, l_ext_mperm2,\\n l_int_mperm2, Nseg)\\n\\n\\n # multiplying the results with the number of panels in each group and write to list\\n number_modules_per_group = area_per_group / Apanel\\n list_areas_groups[group] = area_per_group\\n radiation_array = hourly_radiation[group] * list_areas_groups[group] / 1000 # kWh\\n Sum_qout_kWh = Sum_qout_kWh + list_results[group][1] * number_modules_per_group\\n Sum_Eaux_kWh = Sum_Eaux_kWh + list_results[group][2] * number_modules_per_group\\n Sum_qloss = Sum_qloss + list_results[group][0] * number_modules_per_group\\n Sum_mcp_kWperC = Sum_mcp_kWperC + list_results[group][5] * number_modules_per_group\\n Sum_radiation_kWh = Sum_radiation_kWh + radiation_Wh['I_sol']*area_per_group/1000\\n\\n Tout_group_C = (Sum_qout_kWh / Sum_mcp_kWperC) + Tin_C # in C assuming all collectors are connected in parallel\\n\\n Final = pd.DataFrame(\\n {'Q_SC_gen_kWh': Sum_qout_kWh, 'T_SC_sup_C': Tin_array_C, 'T_SC_re_C': Tout_group_C, 'mcp_SC_kWperC': Sum_mcp_kWperC, 'Eaux_SC_kWh': Sum_Eaux_kWh,\\n 'Q_SC_l_kWh': Sum_qloss, 'Area_SC_m2': sum(list_areas_groups), 'radiation_kWh': Sum_radiation_kWh}, index=range(8760))\\n\\n return list_results, Final\",\n \"def plot_intensity_prop(stack, wavelengths_arr, colors_arr):\\n\\n # for the electric fields profile\\n for i, wl in enumerate(wavelengths_arr):\\n electric_tot_te, electric_tot_tm, reflectivity_te, reflectivity_tm, transmission_te, transmission_tm, index_tot, L_tot, theta_tot = transfer_matrix_method(\\n stack, 1, 0, wl, 0)\\n intensity = np.abs(electric_tot_te[::-1]) ** 2\\n plt.plot(L_tot * 1e6, intensity / max(intensity) * 2, color=colors_arr[i])\\n # for the indexes profile\\n ax.plot(L_tot * 1e6, index_tot[::-1], color='black')\\n ax.fill_between(L_tot * 1e6, index_tot[::-1], color='azure')\",\n \"def fluxes(wavelength, s, line, lowlow= 14, lowhigh=6, highlow=6, highhigh = 14, lmin=0, lmax=0, fmin=0, fmax=0, \\n broad=2.355, plot=True, verbose=True, plot_sus = False, fcal = True, fit_continuum = True, median_kernel=35, warnings = True ): # Broad is FWHM for Gaussian sigma= 1,\\n # s must be an array, no a list\\n try: \\n index_maximo_del_rango = s.tolist().index(np.nanmax(s))\\n #print \\\" is AN ARRAY\\\"\\n except Exception:\\n #print \\\" s is A LIST -> must be converted into an ARRAY\\\" \\n s = np.array(s)\\n \\n # Setup wavelength limits\\n if lmin == 0 :\\n lmin = line-65. # By default, +-65 A with respect to line\\n if lmax == 0 :\\n lmax = line+65.\\n \\n # Extract subrange to fit\\n w_spec = []\\n f_spec = []\\n w_spec.extend((wavelength[i]) for i in range(len(wavelength)) if (wavelength[i] > lmin and wavelength[i] < lmax) ) \\n f_spec.extend((s[i]) for i in range(len(wavelength)) if (wavelength[i] > lmin and wavelength[i] < lmax) ) \\n \\n if np.isnan(np.nanmedian(f_spec)): \\n # The data are NAN!! Nothing to do\\n if verbose or warnings: print(\\\" There is no valid data in the wavelength range [{},{}] !!\\\".format(lmin,lmax))\\n \\n resultado = [0, line, 0, 0, 0, 0, 0, 0, 0, 0, 0, s ] \\n\\n return resultado\\n \\n else: \\n \\n ## 20 Sep 2020\\n f_spec_m=signal.medfilt(f_spec,median_kernel) # median_kernel = 35 default\\n \\n \\n # Remove nans\\n median_value = np.nanmedian(f_spec)\\n f_spec = [median_value if np.isnan(x) else x for x in f_spec] \\n \\n \\n # Setup min and max flux values in subrange to fit\\n if fmin == 0 :\\n fmin = np.nanmin(f_spec) \\n if fmax == 0 :\\n fmax = np.nanmax(f_spec) \\n \\n # We have to find some \\\"guess numbers\\\" for the Gaussian. Now guess_centre is line\\n guess_centre = line\\n \\n # Define continuum regions: [-lowlow, -lowhigh] and [highlow,highhigh] in Angstroms with respect to guess_centre\\n \\n w_cont=[]\\n f_cont=[]\\n w_cont.extend((w_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre-lowlow and w_spec[i] < guess_centre-lowhigh) or (w_spec[i] > guess_centre+highlow and w_spec[i] < guess_centre+highhigh) ) \\n f_cont.extend((f_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre-lowlow and w_spec[i] < guess_centre-lowhigh) or (w_spec[i] > guess_centre+highlow and w_spec[i] < guess_centre+highhigh) ) \\n \\n if fit_continuum:\\n # Linear Fit to continuum \\n f_cont_filtered=sig.medfilt(f_cont,np.int(median_kernel))\\n #print line #f_cont\\n # if line == 8465.0:\\n # print w_cont\\n # print f_cont_filtered\\n # plt.plot(w_cont,f_cont_filtered)\\n # plt.show()\\n # plt.close()\\n # warnings=True\\n try: \\n mm,bb = np.polyfit(w_cont, f_cont_filtered, 1)\\n except Exception:\\n bb = np.nanmedian(f_cont_filtered)\\n mm = 0.\\n if warnings: \\n print(\\\" WARNING: Impossible to get the continuum!\\\")\\n print(\\\" Scaling the continuum to the median value b = \\\",bb,\\\": cont = 0 * w_spec + \\\", bb)\\n continuum = mm*np.array(w_spec)+bb \\n c_cont = mm*np.array(w_cont)+bb \\n \\n else: \\n # Median value in each continuum range # NEW 15 Sep 2019\\n w_cont_low = []\\n f_cont_low = []\\n w_cont_low.extend((w_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre-lowlow and w_spec[i] < guess_centre-lowhigh) ) \\n f_cont_low.extend((f_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre-lowlow and w_spec[i] < guess_centre-lowhigh) ) \\n median_w_cont_low = np.nanmedian(w_cont_low)\\n median_f_cont_low = np.nanmedian(f_cont_low)\\n w_cont_high = []\\n f_cont_high = []\\n w_cont_high.extend((w_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre+highlow and w_spec[i] < guess_centre+highhigh) ) \\n f_cont_high.extend((f_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre+highlow and w_spec[i] < guess_centre+highhigh) ) \\n median_w_cont_high = np.nanmedian(w_cont_high)\\n median_f_cont_high = np.nanmedian(f_cont_high) \\n \\n b = (median_f_cont_low-median_f_cont_high)/(median_w_cont_low-median_w_cont_high)\\n a = median_f_cont_low- b * median_w_cont_low\\n \\n continuum = a + b*np.array(w_spec)\\n c_cont = a + b*np.array(w_cont) \\n \\n \\n # rms continuum\\n rms_cont = np.nansum([ np.abs(f_cont[i] - c_cont[i]) for i in range(len(w_cont)) ]) / len(c_cont)\\n \\n # Search for index here w_spec(index) closest to line\\n min_w = np.abs(np.array(w_spec)-line)\\n mini = np.nanmin(min_w)\\n # guess_peak = f_spec[min_w.tolist().index(mini)] # WE HAVE TO SUSTRACT CONTINUUM!!!\\n guess_peak = f_spec[min_w.tolist().index(mini)] - continuum[min_w.tolist().index(mini)]\\n \\n # LOW limit\\n low_limit=0\\n w_fit = []\\n f_fit = []\\n w_fit.extend((w_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre-15 and w_spec[i] < guess_centre)) \\n f_fit.extend((f_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre-15 and w_spec[i] < guess_centre)) \\n if fit_continuum: \\n c_fit=mm*np.array(w_fit)+bb \\n else: \\n c_fit=b*np.array(w_fit)+a \\n \\n fs=[]\\n ws=[]\\n for ii in range(len(w_fit)-1,1,-1):\\n if f_fit[ii]/c_fit[ii] < 1.05 and f_fit[ii-1]/c_fit[ii-1] < 1.05 and low_limit == 0: low_limit = w_fit[ii]\\n # if f_fit[ii]/c_fit[ii] < 1.05 and low_limit == 0: low_limit = w_fit[ii]\\n fs.append(f_fit[ii]/c_fit[ii])\\n ws.append(w_fit[ii])\\n if low_limit == 0: \\n sorted_by_flux=np.argsort(fs)\\n try:\\n low_limit = ws[sorted_by_flux[0]]\\n except Exception:\\n plot=True\\n low_limit = 0\\n \\n # HIGH LIMIT \\n high_limit=0\\n w_fit = []\\n f_fit = []\\n w_fit.extend((w_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre and w_spec[i] < guess_centre+15)) \\n f_fit.extend((f_spec[i]) for i in range(len(w_spec)) if (w_spec[i] > guess_centre and w_spec[i] < guess_centre+15)) \\n if fit_continuum: \\n c_fit=mm*np.array(w_fit)+bb \\n else: \\n c_fit=b*np.array(w_fit)+a\\n \\n fs=[]\\n ws=[]\\n for ii in range(len(w_fit)-1):\\n if f_fit[ii]/c_fit[ii] < 1.05 and f_fit[ii+1]/c_fit[ii+1] < 1.05 and high_limit == 0: high_limit = w_fit[ii]\\n # if f_fit[ii]/c_fit[ii] < 1.05 and high_limit == 0: high_limit = w_fit[ii]\\n fs.append(f_fit[ii]/c_fit[ii])\\n ws.append(w_fit[ii])\\n if high_limit == 0: \\n sorted_by_flux=np.argsort(fs)\\n try:\\n high_limit = ws[sorted_by_flux[0]] \\n except Exception:\\n plot=True\\n high_limit = 0 \\n \\n # Guess centre will be the highest value in the range defined by [low_limit,high_limit]\\n \\n try: \\n rango = np.where((high_limit >= wavelength ) & (low_limit <= wavelength)) \\n index_maximo_del_rango = s.tolist().index(np.nanmax(s[rango]))\\n guess_centre = wavelength[index_maximo_del_rango]\\n except Exception:\\n guess_centre = line #### It was 0 before\\n \\n \\n # Fit a Gaussian to data - continuum \\n p0 = [guess_centre, guess_peak, broad/2.355] # broad is the Gaussian sigma, 1.0 for emission lines\\n try:\\n fit, pcov = curve_fit(gauss, w_spec, f_spec-continuum, p0=p0, maxfev=10000) # If this fails, increase maxfev...\\n fit_error = np.sqrt(np.diag(pcov))\\n \\n # New 28th Feb 2019: Check central value between low_limit and high_limit\\n # Better: between guess_centre - broad, guess_centre + broad\\n # If not, redo fit fixing central value to the peak (it does not work... just fix FWHM= (high_limit-low_limit)/2.5 )\\n \\n if verbose != False: print(\\\" ----------------------------------------------------------------------------------------\\\")\\n # if low_limit < fit[0] < high_limit:\\n if fit[0] < guess_centre - broad or fit[0] > guess_centre + broad:\\n # if verbose: print \\\" Fitted center wavelength\\\", fit[0],\\\"is NOT in the range [\\\",low_limit,\\\",\\\",high_limit,\\\"]\\\"\\n if verbose: print(\\\" Fitted center wavelength\\\", fit[0],\\\"is NOT in the expected range [\\\",guess_centre - broad,\\\",\\\",guess_centre + broad,\\\"]\\\")\\n \\n # print \\\"Re-do fitting fixing center wavelength\\\"\\n # p01 = [guess_peak, broad]\\n # fit1, pcov1 = curve_fit(gauss_fix_x0, w_spec, f_spec-continuum, p0=p01, maxfev=100000) # If this fails, increase maxfev...\\n # fit_error1 = np.sqrt(np.diag(pcov1))\\n # fit[0]=guess_centre\\n # fit_error[0] = 0.\\n # fit[1] = fit1[0]\\n # fit_error[1] = fit_error1[0]\\n # fit[2] = fit1[1]\\n # fit_error[2] = fit_error1[1] \\n \\n fit[0]=guess_centre\\n fit_error[0] = 0.000001\\n fit[1]=guess_peak\\n fit_error[1] = 0.000001\\n fit[2] = broad/2.355\\n fit_error[2] = 0.000001 \\n else:\\n if verbose: print(\\\" Fitted center wavelength\\\", fit[0],\\\"IS in the expected range [\\\",guess_centre - broad,\\\",\\\",guess_centre + broad,\\\"]\\\")\\n \\n \\n if verbose: print(\\\" Fit parameters = \\\", fit[0], fit[1], fit[2])\\n if fit[2] == broad and warnings == True : \\n print(\\\" WARNING: Fit in\\\",fit[0],\\\"failed! Using given centre wavelength (cw), peak at (cv) & sigma = broad/2.355 given.\\\") \\n gaussian_fit = gauss(w_spec, fit[0], fit[1], fit[2])\\n \\n \\n # Estimate rms of the Gaussian fit in range [low_limit, high_limit]\\n residuals = f_spec-gaussian_fit-continuum\\n rms_fit = np.nansum([ ((residuals[i]**2)/(len(residuals)-2))**0.5 for i in range(len(w_spec)) if (w_spec[i] >= low_limit and w_spec[i] <= high_limit) ]) \\n \\n # Fluxes, FWHM and Eq. Width calculations\\n gaussian_flux = gauss_flux(fit[1],fit[2])\\n error1 = np.abs(gauss_flux(fit[1]+fit_error[1],fit[2]) - gaussian_flux)\\n error2 = np.abs(gauss_flux(fit[1],fit[2]+fit_error[2]) - gaussian_flux)\\n gaussian_flux_error = 1 / ( 1/error1**2 + 1/error2**2 )**0.5\\n \\n \\n fwhm=fit[2]*2.355\\n fwhm_error = fit_error[2] *2.355\\n fwhm_vel = fwhm / fit[0] * C \\n fwhm_vel_error = fwhm_error / fit[0] * C \\n \\n gaussian_ew = gaussian_flux/np.nanmedian(f_cont)\\n gaussian_ew_error = gaussian_ew * gaussian_flux_error/gaussian_flux \\n \\n # Integrated flux\\n # IRAF: flux = sum ((I(i)-C(i)) * (w(i2) - w(i1)) / (i2 - i2) \\n flux = np.nansum([ (f_spec[i]-continuum[i])*(w_spec[i+1]-w_spec[i]) for i in range(len(w_spec)) if (w_spec[i] >= low_limit and w_spec[i] <= high_limit) ]) \\n flux_error = rms_cont * (high_limit - low_limit)\\n wave_resolution = (wavelength[-1]-wavelength[0])/len(wavelength)\\n ew = wave_resolution * np.nansum ([ (1 - f_spec[i]/continuum[i]) for i in range(len(w_spec)) if (w_spec[i] >= low_limit and w_spec[i] <= high_limit) ]) \\n ew_error = np.abs(ew*flux_error/flux) \\n gauss_to_integrated = gaussian_flux/flux * 100.\\n \\n index=0\\n s_s=np.zeros_like(s)\\n for wave in range(len(wavelength)):\\n s_s[wave]=s[wave]\\n if wavelength[wave] == w_spec[0] : \\n s_s[wave] = f_spec[0]-gaussian_fit[0]\\n index=1\\n if wavelength[wave] > w_spec[0] and wavelength[wave] <= w_spec[-1]:\\n s_s[wave] = f_spec[index]-gaussian_fit[index]\\n index=index+1\\n \\n # Plotting \\n ptitle = 'Fit: x0=%.2f y0=%.2e sigma=%.2f flux=%.2e rms=%.3e' % (fit[0], fit[1], fit[2], gaussian_flux, rms_fit)\\n if plot :\\n plt.figure(figsize=(10, 4))\\n # Plot input spectrum\\n plt.plot(np.array(w_spec),np.array(f_spec), \\\"b\\\", lw=3, alpha = 0.8)\\n # Plot median input spectrum\\n plt.plot(np.array(w_spec),np.array(f_spec_m), \\\"orange\\\", lw=3, alpha = 0.5) # 2021: era \\\"g\\\"\\n # Plot spectrum - gauss subtracted\\n plt.plot(wavelength,s_s,\\\"g\\\",lw=3, alpha = 0.6)\\n \\n plt.minorticks_on() \\n plt.xlabel(\\\"Wavelength [$\\\\mathrm{\\\\AA}$ ]\\\")\\n if fcal:\\n plt.ylabel(\\\"Flux [ erg cm$^{-2}$ s$^{-1}$ $\\\\mathrm{\\\\AA}^{-1}$ ]\\\")\\n else:\\n plt.ylabel(\\\"Flux [ counts ]\\\")\\n plt.xlim(lmin,lmax)\\n plt.ylim(fmin,fmax)\\n \\n # Vertical line at guess_centre\\n plt.axvline(x=guess_centre, color='r', linestyle='-', alpha=0.3)\\n # Horizontal line at y = 0\\n plt.axhline(y=0, color='k', linestyle=':', alpha=0.5) \\n # Dashed green regions for continuum, defined by [lowlow, lowhigh] and [highlow,highhigh]\\n plt.axvspan(guess_centre+highlow, guess_centre+highhigh, facecolor='g', alpha=0.15,zorder=3)\\n plt.axvspan(guess_centre-lowlow, guess_centre-lowhigh, facecolor='g', alpha=0.15,zorder=3)\\n # Plot linear fit for continuum\\n plt.plot(w_spec, continuum,\\\"g--\\\")\\n # Plot Gaussian fit \\n plt.plot(w_spec, gaussian_fit+continuum, 'r-', alpha=0.8) \\n # Vertical line at Gaussian center\\n plt.axvline(x=fit[0], color='k', linestyle='-', alpha=0.5)\\n # Vertical lines to emission line\\n plt.axvline(x= low_limit, color='k', linestyle=':', alpha=0.5)\\n plt.axvline(x= high_limit, color='k', linestyle=':', alpha=0.5) \\n # Plot residuals\\n plt.plot(w_spec, residuals, 'k')\\n plt.title(ptitle)\\n plt.show()\\n \\n # Printing results\\n if verbose :\\n print(\\\"\\\\n - Gauss and continuum fitting + integrated flux calculations:\\\\n\\\")\\n print(\\\" rms continuum = %.3e erg/cm/s/A \\\" % (rms_cont)) \\n print(\\\" Gaussian Fit parameters: x0 = ( %.2f +- %.2f ) A \\\" % (fit[0], fit_error[0]))\\n print(\\\" y0 = ( %.3f +- %.3f ) 1E-16 erg/cm2/s/A\\\" % (fit[1]/1E-16, fit_error[1]/1E-16 ))\\n print(\\\" sigma = ( %.3f +- %.3f ) A\\\" % (fit[2], fit_error[2])) \\n print(\\\" rms fit = %.3e erg/cm2/s/A\\\" % (rms_fit))\\n print(\\\" Gaussian Flux = ( %.2f +- %.2f ) 1E-16 erg/s/cm2 (error = %.1f per cent)\\\" % (gaussian_flux/1E-16, gaussian_flux_error/1E-16, gaussian_flux_error/gaussian_flux*100))\\n print(\\\" FWHM = ( %.3f +- %.3f ) A = ( %.1f +- %.1f ) km/s \\\" % (fwhm, fwhm_error, fwhm_vel, fwhm_vel_error))\\n print(\\\" Eq. Width = ( %.1f +- %.1f ) A\\\" % (-gaussian_ew, gaussian_ew_error)) \\n print(\\\"\\\\n Integrated flux = ( %.2f +- %.2f ) 1E-16 erg/s/cm2 (error = %.1f per cent) \\\" % ( flux/1E-16, flux_error/1E-16, flux_error/flux *100)) \\n print(\\\" Eq. Width = ( %.1f +- %.1f ) A\\\" % (ew, ew_error))\\n print(\\\" Gauss/Integrated = %.2f per cent \\\" % gauss_to_integrated)\\n \\n \\n # Plot independent figure with substraction if requested \\n if plot_sus: plot_plot(wavelength,[s,s_s], xmin=lmin, xmax=lmax, ymin=fmin, ymax=fmax, fcal=fcal, frameon=True, ptitle=ptitle)\\n \\n # 0 1 2 3 4 5 6 7 8 9 10 11\\n resultado = [rms_cont, fit[0], fit_error[0], gaussian_flux, gaussian_flux_error, fwhm, fwhm_error, flux, flux_error, ew, ew_error, s_s ]\\n return resultado \\n except Exception:\\n if verbose: \\n print(\\\" - Gaussian fit failed!\\\")\\n print(\\\" However, we can compute the integrated flux and the equivalent width:\\\")\\n \\n flux = np.nansum([ (f_spec[i]-continuum[i])*(w_spec[i+1]-w_spec[i]) for i in range(len(w_spec)) if (w_spec[i] >= low_limit and w_spec[i] <= high_limit) ]) \\n flux_error = rms_cont * (high_limit - low_limit)\\n wave_resolution = (wavelength[-1]-wavelength[0])/len(wavelength)\\n ew = wave_resolution * np.nansum ([ (1 - f_spec[i]/continuum[i]) for i in range(len(w_spec)) if (w_spec[i] >= low_limit and w_spec[i] <= high_limit) ]) \\n ew_error = np.abs(ew*flux_error/flux) \\n \\n if verbose:\\n print(\\\" Integrated flux = ( %.2f +- %.2f ) 1E-16 erg/s/cm2 (error = %.1f per cent) \\\" % ( flux/1E-16, flux_error/1E-16, flux_error/flux *100)) \\n print(\\\" Eq. Width = ( %.1f +- %.1f ) A\\\" % (ew, ew_error))\\n \\n resultado = [0, guess_centre, 0, 0, 0, 0, 0, flux, flux_error, ew, ew_error, s ] # guess_centre was identified at maximum value in the [low_limit,high_limit] range but Gaussian fit failed\\n \\n \\n # Plotting \\n if plot :\\n plt.figure(figsize=(10, 4))\\n plt.plot(np.array(w_spec),np.array(f_spec), \\\"b\\\", lw=3, alpha = 0.5)\\n plt.minorticks_on() \\n plt.xlabel(\\\"Wavelength [$\\\\mathrm{\\\\AA}$]\\\")\\n if fcal:\\n plt.ylabel(\\\"Flux [ erg cm$^{-2}$ s$^{-1}$ $\\\\mathrm{\\\\AA}^{-1}$ ]\\\")\\n else:\\n plt.ylabel(\\\"Flux [ counts ]\\\") \\n plt.xlim(lmin,lmax)\\n plt.ylim(fmin,fmax)\\n \\n # Vertical line at guess_centre\\n plt.axvline(x=guess_centre, color='r', linestyle='-', alpha=0.5)\\n # Horizontal line at y = 0\\n plt.axhline(y=0, color='k', linestyle=':', alpha=0.5) \\n # Dashed green regions for continuum, defined by [lowlow, lowhigh] and [highlow,highhigh]\\n plt.axvspan(guess_centre+highlow, guess_centre+highhigh, facecolor='g', alpha=0.15,zorder=3)\\n plt.axvspan(guess_centre-lowlow, guess_centre-lowhigh, facecolor='g', alpha=0.15,zorder=3)\\n # Plot linear fit for continuum\\n plt.plot(w_spec, continuum,\\\"g--\\\")\\n # Plot Gaussian fit \\n # plt.plot(w_spec, gaussian_fit+continuum, 'r-', alpha=0.8) \\n # Vertical line at Gaussian center\\n # plt.axvline(x=fit[0], color='k', linestyle='-', alpha=0.5)\\n # Vertical lines to emission line\\n plt.axvline(x= low_limit, color='k', linestyle=':', alpha=0.5)\\n plt.axvline(x= high_limit, color='k', linestyle=':', alpha=0.5) \\n # Plot residuals\\n # plt.plot(w_spec, residuals, 'k')\\n plt.title(\\\"No Gaussian fit obtained...\\\")\\n plt.show()\\n \\n \\n return resultado\",\n \"def test_cube_attributes(self):\\n t_min, t_max, t_increment = 200.15, 220.15, 10.0\\n result = SaturatedVapourPressureTable(\\n t_min=t_min, t_max=t_max, t_increment=t_increment\\n ).process()\\n self.assertEqual(result.attributes[\\\"minimum_temperature\\\"], t_min)\\n self.assertEqual(result.attributes[\\\"maximum_temperature\\\"], t_max)\\n self.assertEqual(result.attributes[\\\"temperature_increment\\\"], t_increment)\\n self.assertEqual(result.units, Unit(\\\"Pa\\\"))\",\n \"def aluminum_hexathiohypodiphosphate():\\n\\n positions = [[0.000000, 0.000000, 0.000000],\\n [0.500000, 0.000000, 0.500000],\\n [0.000000, 0.500000, 0.000000],\\n [0.000000, 0.000000, 0.500000],\\n [0.197847, 0.276435, 0.101916],\\n [0.197847, 0.723565, 0.898084],\\n [0.802153, 0.276435, 0.898084],\\n [0.802153, 0.723565, 0.101916],\\n [0.776404, 0.800507, 0.601208],\\n [0.776404, 0.199493, 0.398792],\\n [0.223596, 0.800507, 0.398792],\\n [0.223596, 0.199493, 0.601208]]\\n\\n species = ['Al','Al','P','P','S','S','S','S','S','S','S','S']\\n\\n bravais = 'orthorhombic'\\n\\n space_group = 16\\n lattice_parameters = {'a': Set(5.71230345, 'angstrom'),\\n 'b': Set(5.71644625, 'angstrom'),\\n 'c': Set(11.46678755,'angstrom')}\\n data = {'fractional': positions,\\n 'species': species,\\n 'lattice_parameters': lattice_parameters,\\n 'space_group': ('', space_group),\\n 'n_atoms': len(species)}\\n\\n return data\",\n \"def warm_region_cal(audio_samples, fs):\\n #window the audio\\n windowed_samples = timbral_util.window_audio(audio_samples)\\n\\n # need to define a function for the roughness stimuli, emphasising the 20 - 40 region (of the bark scale)\\n min_bark_band = 10\\n max_bark_band = 40\\n mean_bark_band = (min_bark_band + max_bark_band) / 2.0\\n array = np.arange(min_bark_band, max_bark_band)\\n x = timbral_util.normal_dist(array, theta=0.01, mean=mean_bark_band)\\n x -= np.min(x)\\n x /= np.max(x)\\n\\n wr_array = np.zeros(240)\\n wr_array[min_bark_band:max_bark_band] = x\\n\\n # need to define a second array emphasising the 20 - 40 region (of the bark scale)\\n min_bark_band = 80\\n max_bark_band = 240\\n mean_bark_band = (min_bark_band + max_bark_band) / 2.0\\n array = np.arange(min_bark_band, max_bark_band)\\n x = timbral_util.normal_dist(array, theta=0.01, mean=mean_bark_band)\\n x -= np.min(x)\\n x /= np.max(x)\\n\\n hf_array = np.zeros(240)\\n hf_array[min_bark_band:max_bark_band] = x\\n\\n windowed_loud_spec = []\\n windowed_rms = []\\n\\n wr_vals = []\\n hf_vals = []\\n\\n for i in range(windowed_samples.shape[0]):\\n samples = windowed_samples[i, :]\\n N_entire, N_single = timbral_util.specific_loudness(samples, Pref=100.0, fs=fs, Mod=0)\\n\\n # append the loudness spec\\n windowed_loud_spec.append(N_single)\\n windowed_rms.append(np.sqrt(np.mean(samples * samples)))\\n\\n wr_vals.append(np.sum(wr_array * N_single))\\n hf_vals.append(np.sum(hf_array * N_single))\\n\\n mean_wr = np.mean(wr_vals)\\n mean_hf = np.mean(hf_vals)\\n weighted_wr = np.average(wr_vals, weights=windowed_rms)\\n weighted_hf = np.average(hf_vals, weights=windowed_rms)\\n\\n return mean_wr, weighted_wr, mean_hf, weighted_hf\",\n \"def dynamic(self):\\n # FrostIndexChangeRate=-(1-Afrost)*FrostIndex - Tavg*exp(-0.04*Kfrost*SnowCover/SnowWaterEquivalent);\\n\\n FrostIndexChangeRate = -(1 - self.var.Afrost) * self.var.FrostIndex - self.var.Tavg * \\\\\\n np.exp(-0.04 * self.var.Kfrost * self.var.SnowCover / self.var.SnowWaterEquivalent)\\n # FrostIndexChangeRate=self.var.AfrostIndex - self.var.Tavg* pcraster.exp(self.var.Kfrost*self.var.SnowCover*self.var.InvSnowWaterEquivalent)\\n # Rate of change of frost index (expressed as rate, [degree days/day])\\n # CHANGED 9 September 2004:\\n # - first term should be negative\\n # - second term should be subtracted, not added!!\\n\\n self.var.FrostIndex = np.maximum(self.var.FrostIndex + FrostIndexChangeRate * self.var.DtDay, 0)\\n # frost index in soil [degree days]\\n # based on Molnau and Bissel (1983, A Continuous Frozen Ground Index for Flood\\n # Forecasting. In: Maidment, Handbook of Hydrology, p. 7.28, 7.55)\\n # if Tavg is above zero, FrostIndex will stay 0\\n # if Tavg is negative, FrostIndex will increase with 1 per degree C per day\\n # Exponent of 0.04 (instead of 0.4 in HoH): conversion [cm] to [mm]!\\n # Division by SnowDensity because SnowDepth is expressed as equivalent water\\n # depth(always less than depth of snow pack)\\n # SnowWaterEquivalent taken as 0.100 (based on density of 100 kg/m3) (Handbook of Hydrology, p. 7.5)\\n # Afrost, (daily decay coefficient) is taken as 0.97 (Handbook of Hydrology,\\n # p. 7.28)\\n # Kfrost, (snow depth reduction coefficient) is taken as 0.57 [1/cm],\\n # (HH, p. 7.28)\",\n \"def _calc(self):\\r\\n u = self._fadefunc(self.xf)\\r\\n v = self._fadefunc(self.yf)\\r\\n w = self._fadefunc(self.zf)\\r\\n\\r\\n # populate the hashes dict\\r\\n self._hash()\\r\\n \\r\\n # once the hash dict is populated, start calculating the dot product between \\r\\n # the gradient vector and the distance vectors, which is done in the _grad method.\\r\\n # finally linearly interpolate the values to get the avg value\\r\\n # first interpolate in the x-dir, then in y-dir\\r\\n x1: float = self._lerp(self._grad(self.hashes[\\\"aaa\\\"], self.xf, self.yf, self.zf),\\r\\n self._grad(self.hashes[\\\"baa\\\"], self.xf - 1, self.yf, self.zf), u)\\r\\n\\r\\n x2: float = self._lerp(self._grad(self.hashes[\\\"aba\\\"], self.xf, self.yf - 1, self.zf),\\r\\n self._grad(self.hashes[\\\"bba\\\"], self.xf - 1, self.yf - 1, self.zf), u)\\r\\n\\r\\n # the first y-dir lerp\\r\\n y1: float = self._lerp(x1, x2, v)\\r\\n\\r\\n x1: float = self._lerp(self._grad(self.hashes[\\\"aab\\\"], self.xf, self.yf, self.zf - 1),\\r\\n self._grad(self.hashes[\\\"bab\\\"], self.xf - 1, self.yf, self.zf - 1), u)\\r\\n\\r\\n x2: float = self._lerp(self._grad(self.hashes[\\\"abb\\\"], self.xf, self.yf - 1, self.zf - 1),\\r\\n self._grad(self.hashes[\\\"bbb\\\"], self.xf-1, self.yf-1, self.zf-1), u)\\r\\n\\r\\n # the second y-dir lerp\\r\\n y2: float = self._lerp(x1, x2, v)\\r\\n\\r\\n # the final noise value, which will be in the range [0, 1]\\r\\n self.value = (self._lerp(y1, y2, w) + 1)/2\\r\\n return self.value\",\n \"def atmosphereVariation(img, header, chanInfo, airmass=1.5, pwv=-1, removeSlope=True):\\n freqs, values = CalcAtmTransmissionForImage(img, header, chanInfo, airmass=airmass, pwv=pwv, value='transmission')\\n if removeSlope:\\n slope, intercept = linfit(freqs, values, values*0.001)\\n casalogPost(\\\"Computed atmospheric variation and determined slope: %f per GHz (%.0f,%.2f)\\\" % (slope,freqs[0],values[0]))\\n values = values - (freqs*slope + intercept) + np.mean(values)\\n maxMinusMin = np.max(values)-np.min(values)\\n percentage = maxMinusMin/np.mean(values)\\n freqs, values = CalcAtmTransmissionForImage(img, header, chanInfo, airmass=airmass, pwv=pwv, value='tsky')\\n if removeSlope:\\n slope, intercept = linfit(freqs, values, values*0.001)\\n values = values - (freqs*slope + intercept) + np.mean(values)\\n TmaxMinusMin = np.max(values)-np.min(values)\\n Tpercentage = TmaxMinusMin*100/np.mean(values)\\n stdValues = np.std(values)\\n return(maxMinusMin, percentage, TmaxMinusMin, Tpercentage, stdValues)\",\n \"def heat(self, delta_temp):\\n return self.heat_capacity * self.mass * delta_temp\",\n \"def calculate_averaged_properties_cpp(poly_data):\\n\\n gt_filter = vtkParticlesPython.vtkGranularTemperature()\\n if vtk.vtkVersion.GetVTKMajorVersion() < 6:\\n gt_filter.SetInput(poly_data)\\n else:\\n gt_filter.SetInputData(poly_data)\\n gt_filter.Update()\\n poly_data.DeepCopy(gt_filter.GetOutput())\\n\\n return numpy_support.vtk_to_numpy(poly_data.GetPointData().GetVectors(\\\"SolidPressureGradient\\\"))\",\n \"def wavelength(energy):\\r\\n return 2 * np.pi * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy\",\n \"def sky_observed(seed=425, th=150, old=False):\\n \\n # impact parameters\\n M = 3e7*u.Msun\\n #M = 6e7*u.Msun\\n B = 19.95*u.kpc\\n V = 190*u.km/u.s\\n phi = coord.Angle(0*u.deg)\\n theta = coord.Angle(th*u.deg)\\n Tenc = 0.01*u.Gyr\\n T = 0.5*u.Gyr\\n dt = 0.05*u.Myr\\n rs = 0*u.pc\\n \\n old_label = ''\\n \\n if old:\\n old_label = '_old'\\n \\n # impact parameters\\n M = 5e7*u.Msun\\n B = 19.8*u.kpc\\n V = 210*u.km/u.s\\n phi = coord.Angle(0*u.deg)\\n th = 150\\n theta = coord.Angle(th*u.deg)\\n Tenc = 0.05*u.Gyr\\n T = 2*u.Gyr\\n dt = 0.1*u.Myr\\n #dt = 1*u.Myr\\n rs = 0*u.pc\\n \\n # potential parameters\\n potential = 3\\n Vh = 220*u.km/u.s\\n q = 1*u.Unit(1)\\n rhalo = 20*u.pc\\n par_pot = np.array([Vh.si.value, q.value, rhalo.si.value])\\n \\n # setup tube\\n Nstar = 1400\\n wx = 30*u.kpc\\n wy = 0*u.pc\\n wz = 0*u.pc\\n sx = 0*u.km/u.s\\n \\n np.random.seed(seed)\\n observer = {'z_sun': 27.*u.pc, 'galcen_distance': 8.3*u.kpc, 'roll': 60*u.deg, 'galcen_coord': coord.SkyCoord(ra=300*u.deg, dec=-90*u.deg, frame='icrs')}\\n alt_observer = {'z_sun': 27.*u.pc, 'galcen_distance': 8.3*u.kpc, 'roll': -45*u.deg, 'galcen_coord': coord.SkyCoord(ra=300*u.deg, dec=-90*u.deg, frame='icrs')}\\n vobs = {'vcirc': 220*u.km/u.s, 'vlsr': [0, 0, 0]*u.km/u.s}\\n wangle = 180*u.deg\\n \\n xphi = np.linspace(-0.3*np.pi,0.3*np.pi, Nstar)\\n xphi0 = np.linspace(-0.1*np.pi, 0.1*np.pi, 1000)\\n xphi1 = np.linspace(-0.28*np.pi, -0.1*np.pi, 200)\\n xphi2 = np.linspace(0.1*np.pi, 0.32*np.pi, 200)\\n xphi = np.concatenate([xphi1, xphi0, xphi2])\\n \\n xr = 20*u.kpc + np.random.randn(Nstar)*0.0*u.kpc\\n x = np.sin(xphi) * xr\\n y = np.cos(xphi) * xr\\n z = x * 0\\n vx = -np.cos(xphi) * Vh\\n vy = np.sin(xphi) * Vh\\n vz = vx * 0\\n # closest to impact\\n ienc = np.argmin(np.abs(x))\\n \\n # generate stream model\\n potential_perturb = 1\\n par_perturb = np.array([M.si.value, 0., 0., 0.])\\n x1, x2, x3, v1, v2, v3 = interact.interact(par_perturb, B.si.value, phi.rad, V.si.value, theta.rad, Tenc.si.value, T.si.value, dt.si.value, par_pot, potential, potential_perturb, x.si.value, y.si.value, z.si.value, vx.si.value, vy.si.value, vz.si.value)\\n stream = {}\\n stream['x'] = (np.array([x1, x2, x3])*u.m).to(u.pc)\\n stream['v'] = (np.array([v1, v2, v3])*u.m/u.s).to(u.km/u.s)\\n \\n # sky coordinates\\n xgal = coord.Galactocentric(stream['x'], **observer)\\n xeq = xgal.transform_to(coord.ICRS)\\n veq_ = gc.vgal_to_hel(xeq, stream['v'], **vobs)\\n veq = [None] * 3\\n veq[0] = veq_[0].to(u.mas/u.yr)\\n veq[1] = veq_[1].to(u.mas/u.yr)\\n veq[2] = veq_[2].to(u.km/u.s)\\n \\n # alternative sky coordinates\\n xgal_alt = coord.Galactocentric(stream['x'], **alt_observer)\\n xeq_alt = xgal_alt.transform_to(coord.ICRS)\\n veq_alt_ = gc.vgal_to_hel(xeq_alt, stream['v'], **vobs)\\n veq_alt = [None] * 3\\n veq_alt[0] = veq_alt_[0].to(u.mas/u.yr)\\n veq_alt[1] = veq_alt_[1].to(u.mas/u.yr)\\n veq_alt[2] = veq_alt_[2].to(u.km/u.s)\\n \\n # unperturbed stream\\n par_perturb = np.array([0*M.si.value, 0., 0., 0.])\\n x1, x2, x3, v1, v2, v3 = interact.interact(par_perturb, B.si.value, phi.rad, V.si.value, theta.rad, Tenc.si.value, T.si.value, dt.si.value, par_pot, potential, potential_perturb, x.si.value, y.si.value, z.si.value, vx.si.value, vy.si.value, vz.si.value)\\n stream0 = {}\\n stream0['x'] = (np.array([x1, x2, x3])*u.m).to(u.pc)\\n stream0['v'] = (np.array([v1, v2, v3])*u.m/u.s).to(u.km/u.s)\\n \\n # sky coordinates\\n xgal0 = coord.Galactocentric(stream0['x'], **observer)\\n xeq0 = xgal0.transform_to(coord.ICRS)\\n veq0_ = gc.vgal_to_hel(xeq0, stream0['v'], **vobs)\\n veq0 = [None] * 3\\n veq0[0] = veq0_[0].to(u.mas/u.yr)\\n veq0[1] = veq0_[1].to(u.mas/u.yr)\\n veq0[2] = veq0_[2].to(u.km/u.s)\\n \\n # alternative sky coordinates\\n xgal0_alt = coord.Galactocentric(stream0['x'], **alt_observer)\\n xeq0_alt = xgal0_alt.transform_to(coord.ICRS)\\n veq0_alt_ = gc.vgal_to_hel(xeq0_alt, stream0['v'], **vobs)\\n veq0_alt = [None] * 3\\n veq0_alt[0] = veq0_alt_[0].to(u.mas/u.yr)\\n veq0_alt[1] = veq0_alt_[1].to(u.mas/u.yr)\\n veq0_alt[2] = veq0_alt_[2].to(u.km/u.s)\\n \\n # rotate to native coordinate system\\n R = find_greatcircle(xeq0.ra.deg[::10], xeq0.dec.deg[::10])\\n xi0, eta0 = myutils.rotate_angles(xeq0.ra, xeq0.dec, R)\\n xi0 = coord.Angle(xi0*u.deg)\\n \\n # place gap at xi~0\\n xioff = xi0[ienc]\\n xi0 -= xioff\\n \\n xi, eta = myutils.rotate_angles(xeq.ra, xeq.dec, R)\\n xi = coord.Angle(xi*u.deg)\\n xi -= xioff\\n \\n # alternative observer\\n R_alt = find_greatcircle(xeq0_alt.ra.deg[::10], xeq0_alt.dec.deg[::10])\\n xi0_alt, eta0_alt = myutils.rotate_angles(xeq0_alt.ra, xeq0_alt.dec, R_alt)\\n xi0_alt = coord.Angle(xi0_alt*u.deg)\\n \\n # place gap at xi~0\\n xioff_alt = xi0_alt[ienc]\\n xi0_alt -= xioff_alt\\n \\n xi_alt, eta_alt = myutils.rotate_angles(xeq_alt.ra, xeq_alt.dec, R_alt)\\n xi_alt = coord.Angle(xi_alt*u.deg)\\n xi_alt -= xioff_alt\\n \\n\\n # observed gd1\\n g = Table(fits.getdata('/home/ana/projects/GD1-DR2/output/gd1_members.fits'))\\n \\n vlabel = ['$\\\\mu_{\\\\\\\\alpha_\\\\star}$ [mas yr$^{-1}$]','$\\\\mu_{\\\\delta}$ [mas yr$^{-1}$]', '$V_r$ [km s$^{-1}$]']\\n ylims = [[-1.5, 1.5], [-1.5, 1.5], [-30,30]]\\n color = '0.35'\\n ms = 4\\n alpha = 0.7\\n \\n # plotting\\n plt.close()\\n fig, ax = plt.subplots(2,4,figsize=(17,8), sharex=True, sharey='col')\\n \\n plt.sca(ax[0][0])\\n plt.plot(xi.wrap_at(wangle), eta, '.', mec='none', color=color, ms=ms, label='Simulated GD-1')\\n \\n #plt.xlabel('$\\\\phi_1$ [deg]')\\n plt.ylabel('$\\\\phi_2$ [deg]')\\n plt.xlim(-20,20)\\n plt.ylim(-5,5)\\n \\n plt.sca(ax[1][0])\\n plt.plot(xi_alt.wrap_at(wangle), eta_alt, '.', mec='none', color=color, ms=ms, label='Simulated GD-1')\\n \\n plt.xlabel('$\\\\phi_1$ [deg]')\\n plt.ylabel('$\\\\phi_2$ [deg]')\\n plt.xlim(-20,20)\\n plt.ylim(-5,5)\\n \\n xeqs = [xeq.ra, xeq.dec, xeq.distance.to(u.kpc)]\\n dv = []\\n dv_alt = []\\n for i in range(3):\\n plt.sca(ax[0][i+1])\\n \\n # interpolate expected kinematics from an unperturbed stream\\n vexp = np.interp(xi.wrap_at(wangle), xi0.wrap_at(wangle), veq0[i].value) * veq0[i].unit\\n dv += [veq[i]-vexp]\\n plt.plot(xi.wrap_at(wangle), dv[i], '.', mec='none', color=color, ms=ms)\\n \\n plt.ylabel('$\\\\Delta$ {}'.format(vlabel[i]))\\n plt.ylim(*ylims[i])\\n \\n plt.sca(ax[1][i+1])\\n # interpolate expected kinematics from an unperturbed stream\\n vexp_alt = np.interp(xi_alt.wrap_at(wangle), xi0_alt.wrap_at(wangle), veq0_alt[i].value) * veq0_alt[i].unit\\n dv_alt += [veq_alt[i]-vexp_alt]\\n plt.plot(xi_alt.wrap_at(wangle), dv_alt[i], '.', mec='none', color=color, ms=ms)\\n \\n plt.ylabel('$\\\\Delta$ {}'.format(vlabel[i]))\\n plt.ylim(*ylims[i])\\n plt.xlabel('$\\\\phi_1$ [deg]')\\n \\n # find closest model star to the gd-1 stars\\n Ngd1 = len(g)\\n p = np.array([g['phi1']+40, g['phi2']])\\n q = np.array([xi.wrap_at(wangle).to(u.deg).value, eta])\\n idmin = np.empty(Ngd1, dtype='int')\\n \\n for i in range(Ngd1):\\n dist = np.sqrt((p[0,i]-q[0])**2 + (p[1,i]-q[1])**2)\\n idmin[i] = np.argmin(dist)\\n\\n # mask stream, mask spur\\n onstream_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>-0.2) & (g['phi2']<0.2))\\n spur_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>1) & (g['phi2']<1.4))\\n all_mask = np.ones(Ngd1, dtype='bool')\\n \\n # plot scaled data uncertainties on model pm drawn from a corresponding obs uncertainty\\n np.random.seed(seed+1)\\n fgaia = np.sqrt(2/5)\\n print(2/5, fgaia)\\n phi1 = xi[idmin].wrap_at(wangle).to(u.deg).value\\n phi2 = eta[idmin]\\n pmra = dv[0][idmin] + g['pmra_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\\n pmdec = dv[1][idmin] + g['pmdec_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\\n \\n colors = ['tab:red', 'tab:blue', '0.4']\\n labels = ['Stream', 'Spur']\\n labels = ['Gaia DR4', '']\\n \\n for e, mask in enumerate([onstream_mask, spur_mask]):\\n plt.sca(ax[0][0])\\n plt.plot(phi1[mask], phi2[mask], 'o', color=colors[e], mec='none', alpha=alpha, label=labels[e])\\n \\n plt.sca(ax[0][1])\\n plt.errorbar(phi1[mask], pmra[mask].value, yerr=g['pmra_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\\n \\n plt.sca(ax[0][2])\\n plt.errorbar(phi1[mask], pmdec[mask].value, yerr=g['pmdec_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\\n \\n print(np.sqrt(np.sum(g['pmra_error'][mask]**2))/np.sum(mask))\\n print(np.sqrt(np.sum(g['pmdec_error'][mask]**2))/np.sum(mask))\\n\\n Nfield = 2\\n p2 = np.array([np.array([-32.77,-32.77])+40, [1.167,0]])\\n q = np.array([xi.wrap_at(wangle).to(u.deg).value, eta])\\n idmin2 = np.empty(Nfield, dtype='int')\\n \\n for i in range(Nfield):\\n dist = np.sqrt((p2[0,i]-q[0])**2 + (p2[1,i]-q[1])**2)\\n idmin2[i] = np.argmin(dist)\\n \\n pmerr = np.array([0.0848, 0.0685])\\n \\n np.random.seed(seed+2)\\n phi1 = xi[idmin2].wrap_at(wangle).to(u.deg).value\\n phi2 = eta[idmin2]\\n pmra = dv[0][idmin2].value + pmerr*np.random.randn(Nfield)\\n pmdec = dv[1][idmin2].value + pmerr*np.random.randn(Nfield)\\n \\n plt.sca(ax[0][0])\\n plt.errorbar(phi1, phi2, color='k', fmt='o', label='HST')\\n \\n plt.sca(ax[0][1])\\n plt.errorbar(phi1, pmra, yerr=pmerr, color='k', fmt='o')\\n \\n plt.sca(ax[0][2])\\n plt.errorbar(phi1, pmdec, yerr=pmerr, color='k', fmt='o')\\n \\n \\n ##############\\n # alt observer\\n \\n # find closest model star to the gd-1 stars\\n Ngd1 = len(g)\\n p = np.array([g['phi1']+40, g['phi2']])\\n q = np.array([xi_alt.wrap_at(wangle).to(u.deg).value, eta_alt])\\n idmin = np.empty(Ngd1, dtype='int')\\n \\n for i in range(Ngd1):\\n dist = np.sqrt((p[0,i]-q[0])**2 + (p[1,i]-q[1])**2)\\n idmin[i] = np.argmin(dist)\\n\\n # mask stream, mask spur\\n onstream_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>-0.2) & (g['phi2']<0.2))\\n spur_mask = ((g['phi1']<-30.5) & (g['phi1']>-35.5) & (g['phi2']>1) & (g['phi2']<1.4))\\n all_mask = np.ones(Ngd1, dtype='bool')\\n \\n # plot scaled data uncertainties on model pm drawn from a corresponding obs uncertainty\\n #np.random.seed(seed+3)\\n phi1 = xi_alt[idmin].wrap_at(wangle).to(u.deg).value\\n phi2 = eta_alt[idmin]\\n pmra = dv_alt[0][idmin] + g['pmra_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\\n pmdec = dv_alt[1][idmin] + g['pmdec_error']*u.mas/u.yr*np.random.randn(Ngd1) * fgaia\\n \\n colors = ['tab:red', 'tab:blue', '0.4']\\n labels = ['Gaia DR4', '']\\n \\n for e, mask in enumerate([onstream_mask, spur_mask]):\\n plt.sca(ax[1][0])\\n plt.plot(phi1[mask], phi2[mask], 'o', color=colors[e], mec='none', alpha=alpha, label=labels[e])\\n \\n plt.sca(ax[1][1])\\n plt.errorbar(phi1[mask], pmra[mask].value, yerr=g['pmra_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\\n \\n plt.sca(ax[1][2])\\n plt.errorbar(phi1[mask], pmdec[mask].value, yerr=g['pmdec_error'][mask]*fgaia, fmt='o', color=colors[e], mec='none', alpha=alpha)\\n \\n Nfield = 2\\n p2 = np.array([np.array([-32.77,-32.77])+40, [1.167,0]])\\n q = np.array([xi_alt.wrap_at(wangle).to(u.deg).value, eta_alt])\\n idmin2 = np.empty(Nfield, dtype='int')\\n \\n for i in range(Nfield):\\n dist = np.sqrt((p2[0,i]-q[0])**2 + (p2[1,i]-q[1])**2)\\n idmin2[i] = np.argmin(dist)\\n \\n pmerr = np.array([0.11, 0.08])\\n \\n np.random.seed(seed+6)\\n phi1 = xi_alt[idmin2].wrap_at(wangle).to(u.deg).value\\n phi2 = eta_alt[idmin2]\\n pmra = dv_alt[0][idmin2].value + pmerr*np.random.randn(Nfield)\\n pmdec = dv_alt[1][idmin2].value + pmerr*np.random.randn(Nfield)\\n \\n plt.sca(ax[1][0])\\n plt.errorbar(phi1, phi2, color='k', fmt='o', label='HST')\\n \\n plt.sca(ax[1][1])\\n plt.errorbar(phi1, pmra, yerr=pmerr, color='k', fmt='o')\\n \\n plt.sca(ax[1][2])\\n plt.errorbar(phi1, pmdec, yerr=pmerr, color='k', fmt='o')\\n \\n \\n plt.sca(ax[0][0])\\n plt.text(0.1,0.85, '$\\\\\\\\theta_{roll}$ = 60$^\\\\circ$', fontsize='small', transform=plt.gca().transAxes)\\n\\n plt.sca(ax[1][0])\\n plt.text(0.1,0.85, '$\\\\\\\\theta_{roll}$ = -45$^\\\\circ$', fontsize='small', transform=plt.gca().transAxes)\\n plt.legend(fontsize='small', loc=3, handlelength=0.2)\\n \\n plt.suptitle('Expected astrometric performance', fontsize='medium')\\n plt.tight_layout(rect=[0,0,1,0.94])\\n plt.savefig('../plots/astrometric_performance.png')\",\n \"def get_effective_mass():\\n\\n H_BAR = 6.582119514e-16 # eV*s\\n M_0 = 9.10938356e-31 # kg\\n N_KPTS = 6 # Number of k-points included in the parabola.\\n\\n spin_up = Spin(1)\\n\\n band_structure = Vasprun('vasprun.xml').get_band_structure()\\n\\n # Locations of CBM and VBM in band_structure.bands\\n cbm_band_index = band_structure.get_cbm()['band_index'][spin_up][0]\\n cbm_kpoint_index = band_structure.get_cbm()['kpoint_index'][0]\\n\\n vbm_band_index = band_structure.get_vbm()['band_index'][spin_up][0]\\n vbm_kpoint_index = band_structure.get_vbm()['kpoint_index'][0]\\n\\n k = {'electron': {'left': [], 'right': []},\\n 'hole': {'left': [], 'right': []}}\\n E = {'electron': {'left': [], 'right': []},\\n 'hole': {'left': [], 'right': []}}\\n\\n e_ref_coords = band_structure.kpoints[cbm_kpoint_index]._ccoords\\n h_ref_coords = band_structure.kpoints[vbm_kpoint_index]._ccoords\\n\\n for n in range(-N_KPTS, 1):\\n e_coords = band_structure.kpoints[cbm_kpoint_index + n]._ccoords\\n h_coords = band_structure.kpoints[vbm_kpoint_index + n]._ccoords\\n\\n k['electron']['left'].append(\\n ((e_coords[0] - e_ref_coords[0])**2 +\\n (e_coords[1] - e_ref_coords[1])**2 +\\n (e_coords[2] - e_ref_coords[2])**2)**0.5\\n )\\n k['hole']['left'].append(\\n ((h_coords[0] - h_ref_coords[0])**2 +\\n (h_coords[1] - h_ref_coords[1])**2 +\\n (h_coords[2] - h_ref_coords[2])**2)**0.5\\n )\\n\\n e_energy = band_structure.bands[\\n spin_up][cbm_band_index][cbm_kpoint_index + n]\\n h_energy = band_structure.bands[\\n spin_up][vbm_band_index][vbm_kpoint_index + n]\\n\\n E['electron']['left'].append(e_energy)\\n E['hole']['left'].append(h_energy)\\n\\n for n in range(1, 1 + N_KPTS):\\n e_coords = band_structure.kpoints[cbm_kpoint_index + n]._ccoords\\n h_coords = band_structure.kpoints[vbm_kpoint_index + n]._ccoords\\n\\n k['electron']['right'].append(\\n ((e_coords[0] - e_ref_coords[0])**2 +\\n (e_coords[1] - e_ref_coords[1])**2 +\\n (e_coords[2] - e_ref_coords[2])**2)**0.5\\n )\\n k['hole']['right'].append(\\n ((h_coords[0] - h_ref_coords[0])**2 +\\n (h_coords[1] - h_ref_coords[1])**2 +\\n (h_coords[2] - h_ref_coords[2])**2)**0.5\\n )\\n\\n e_energy = band_structure.bands[\\n spin_up][cbm_band_index][cbm_kpoint_index + n]\\n h_energy = band_structure.bands[\\n spin_up][vbm_band_index][vbm_kpoint_index + n]\\n\\n E['electron']['right'].append(e_energy)\\n E['hole']['right'].append(h_energy)\\n\\n # 2nd order fits\\n e_l_fit = np.poly1d(\\n np.polyfit(k['electron']['left'], E['electron']['left'], 2))\\n e_r_fit = np.poly1d(\\n np.polyfit(k['electron']['right'], E['electron']['right'], 2))\\n h_l_fit = np.poly1d(\\n np.polyfit(k['hole']['left'], E['hole']['left'], 2))\\n h_r_fit = np.poly1d(\\n np.polyfit(k['hole']['right'], E['hole']['right'], 2))\\n\\n # Curvatures\\n e_l_curvature = e_l_fit.deriv().deriv()[0]\\n e_r_curvature = e_r_fit.deriv().deriv()[0]\\n h_l_curvature = h_l_fit.deriv().deriv()[0]\\n h_r_curvature = h_r_fit.deriv().deriv()[0]\\n\\n # Unit conversion\\n e_m_eff_l = 10 * ((H_BAR ** 2) / e_l_curvature) / M_0\\n e_m_eff_r = 10 * ((H_BAR ** 2) / e_r_curvature) / M_0\\n h_m_eff_l = -10 * ((H_BAR ** 2) / h_l_curvature) / M_0\\n h_m_eff_r = -10 * ((H_BAR ** 2) / h_r_curvature) / M_0\\n\\n return {'electron': {'left': e_m_eff_l, 'right': e_m_eff_r},\\n 'hole': {'left': h_m_eff_l, 'right': h_m_eff_r}}\",\n \"def calcMag(self):\\n M = np.sum(self.config)\\n return M\",\n \"def calculate_averaged_properties(poly_data, bucket):\\n\\n locator = vtk.vtkPointLocator()\\n locator.SetDataSet(poly_data)\\n locator.BuildLocator()\\n\\n LENGTH = 0.03\\n MODIFIER = 3e3\\n\\n volume = numpy.zeros(poly_data.GetNumberOfPoints())\\n temperature = numpy.zeros(poly_data.GetNumberOfPoints())\\n solid_pressure = numpy.zeros(poly_data.GetNumberOfPoints())\\n velocity = numpy.zeros((poly_data.GetNumberOfPoints(), 3))\\n solid_pressure_gradient = numpy.zeros((poly_data.GetNumberOfPoints(), 3))\\n\\n for particle in bucket:\\n point_list = vtk.vtkIdList()\\n locator.FindPointsWithinRadius(LENGTH, particle.pos, point_list)\\n\\n beta = 1.0/6.0*numpy.pi*particle.parameters.diameter**3\\n\\n for _ in range(point_list.GetNumberOfIds()):\\n point_index = point_list.GetId(_)\\n\\n particle2 = bucket.particles[point_index]\\n\\n rad2 = distance2(particle2.pos, particle.pos)\\n rad2 /= LENGTH**2\\n\\n gamma = beta*numpy.exp(-rad2)*MODIFIER\\n\\n volume[point_index] += gamma\\n\\n velocity[point_index, :] += particle.vel*gamma\\n\\n volume /= 0.5*LENGTH**2*(1.0-numpy.exp(-1.0**2))\\n velocity /= 0.5*LENGTH**2*(1.0-numpy.exp(-1.0**2))\\n\\n for i in range(3):\\n velocity[:, i] /= volume\\n\\n for k, particle in enumerate(bucket):\\n point_list = vtk.vtkIdList()\\n locator.FindPointsWithinRadius(LENGTH, particle.pos, point_list)\\n\\n beta = 1.0/6.0*numpy.pi*particle.parameters.diameter**3\\n\\n for _ in range(point_list.GetNumberOfIds()):\\n point_index = point_list.GetId(_)\\n\\n rad2 = distance2(poly_data.GetPoints().GetPoint(point_index), particle.pos)\\n rad2 /= LENGTH**2\\n\\n gamma = beta*numpy.exp(-rad2)*MODIFIER\\n\\n c = distance2(particle.vel, velocity[k, :])\\n\\n temperature[point_index] += c*gamma\\n\\n\\n for particle in bucket:\\n point_list = vtk.vtkIdList()\\n locator.FindPointsWithinRadius(LENGTH, particle.pos, point_list)\\n\\n beta = 1.0/6.0*numpy.pi*particle.parameters.diameter**3\\n\\n for _ in range(point_list.GetNumberOfIds()):\\n point_index = point_list.GetId(_)\\n\\n rad2 = distance2(poly_data.GetPoints().GetPoint(point_index), particle.pos)\\n rad2 /= LENGTH **2\\n\\n gamma = beta*numpy.exp(-rad2)*MODIFIER\\n\\n c = distance2(particle.vel, velocity[point_index, :])\\n\\n val = (bucket.particles[point_index].pos-particle.pos)/LENGTH**2\\n\\n spg = ((radial_distribution_function(volume[point_index])\\n +volume[point_index]*rdf_deriv(volume[point_index]))*temperature[point_index]\\n +c*volume[point_index]*radial_distribution_function(volume[point_index]))\\n\\n solid_pressure_gradient[point_index, :] += (val*spg*gamma)\\n\\n for _ in range(poly_data.GetNumberOfPoints()):\\n\\n solid_pressure[_] = (bucket.particles[0].parameters.rho*volume[_]\\n *radial_distribution_function(volume[_])*temperature[_])\\n\\n data = [vtk.vtkDoubleArray()]\\n data[0].SetName('SolidVolumeFraction')\\n data.append(vtk.vtkDoubleArray())\\n data[1].SetName('SolidVolumeVelocity')\\n data[1].SetNumberOfComponents(3)\\n data.append(vtk.vtkDoubleArray())\\n data[2].SetName('GranularTemperature')\\n data.append(vtk.vtkDoubleArray())\\n data[3].SetName('SolidPressure')\\n data.append(vtk.vtkDoubleArray())\\n data[4].SetName('SolidPressureGradient')\\n data[4].SetNumberOfComponents(3)\\n\\n for _ in range(poly_data.GetNumberOfPoints()):\\n data[0].InsertNextValue(volume[_])\\n data[1].InsertNextTuple3(*velocity[_])\\n data[2].InsertNextValue(temperature[_])\\n data[3].InsertNextValue(solid_pressure[_])\\n data[4].InsertNextTuple3(*solid_pressure_gradient[_])\\n\\n for _ in data:\\n poly_data.GetPointData().AddArray(_)\\n\\n return data[4]\",\n \"def heat_vaporization_func(ts):\\n heat_vaporization = np.copy(ts).astype(np.float64)\\n heat_vaporization -= 273.15\\n heat_vaporization *= -0.00236\\n heat_vaporization += 2.501\\n heat_vaporization *= 1E6\\n return heat_vaporization.astype(np.float32)\",\n \"def swe(lon, lat, month):\\n\\n im = month - 1\\n\\n h0 = np.array( [ 8.37, 9.43, 10.74, 11.67, 11.80, 12.48,\\n 4.01, 1.08, 3.84, 6.24, 7.54, 8.00 ] )\\n a = np.array( [ -0.0270, 0.0058, 0.1618, 0.0841, -0.0043, 0.2084,\\n 0.0970, 0.0712, 0.0393, 0.1158, 0.0567, -0.0540 ] )\\n b = np.array( [ -0.3400, -0.1309, 0.0276, -0.1328, -0.4284, -0.5739,\\n -0.4930, -0.1450, -0.2107, -0.2803, -0.3201, -0.3650 ] )\\n c = np.array( [ -0.0319, 0.0017, 0.0213, 0.0081, -0.0380, -0.0468,\\n -0.0333, -0.0155, -0.0182, -0.0215, -0.0284, -0.0362 ] )\\n d = np.array( [ -0.0056, -0.0021, 0.0076, -0.0003, -0.0071, -0.0023,\\n -0.0026, 0.0014, -0.0053, 0.0015, -0.0032, -0.0112 ] )\\n e = np.array( [ -0.0005, -0.0072, -0.0125, -0.0301, -0.0063, -0.0253,\\n -0.0343, -0.0000, -0.0190, -0.0176, -0.0129, -0.0035 ] )\\n\\n x = (90. - lat) * np.cos( np.radians(lon) )\\n y = (90. - lat) * np.sin( np.radians(lon) )\\n\\n h = ( h0[im] + ( a[im] * x ) + ( b[im] * y ) + ( c[im] * x * y ) +\\n ( d[im] * x * x ) + ( e[im] * y * y ) )\\n\\n# h = np.where( h < 0., 0., h)\\n \\n return h\",\n \"def muscovite():\\n\\n rho = 2834.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 181.; C[0,1] = 48.8; C[0,2] = 25.6; C[0,3] = 0.; C[0,4] = -14.2; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 178.4; C[1,2] = 21.2; C[1,3] = 0.; C[1,4] = 1.1; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 58.6; C[2,3] = 0.; C[2,4] = 1.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 16.5; C[3,4] = 0.; C[3,5] = -5.2\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 19.5; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 72.\\n\\n return C, rho\",\n \"def bndy_plasma(self):\\n self.ne[0], self.ne[-1] = 1e11, 1e11\\n self.ni[0], self.ni[-1] = 1e11, 1e11\\n self.nn[0], self.nn[-1] = 1e11, 1e11\\n self.Te[0], self.Te[-1] = 0.1, 0.1\\n self.Ti[0], self.Ti[-1] = 0.01, 0.01\\n # self.coll_em[0], self.coll_em[-1] = 1e5, 1e5\\n # self.coll_im[0], self.coll_im[-1] = 1e5, 1e5\",\n \"def _microstrip_Z(wire_width, dielectric_thickness, eps_r):\\n u = wire_width / dielectric_thickness\\n eta = 376.73 # Vacuum impedance\\n\\n a = 1 + log((u**4 + (u/52)**2) / (u**4 + 0.432)) / 49 \\\\\\n + log(1 + (u/18.1)**3) / 18.7\\n b = 0.564*((eps_r-0.9) / (eps_r+3))**0.053\\n F = 6 + (2*pi - 6) * exp(-(30.666/u)**0.7528)\\n eps_eff = 0.5*(eps_r+1) + 0.5*(eps_r-1)*(1 + 10/u)**(-a*b)\\n Z = eta/(2*pi) * log(F/u + sqrt(1 + (2/u)**2)) / sqrt(eps_eff)\\n return Z, eps_eff\",\n \"def sh( values ):\\n # ECMWF normalizes the spherical harmonic coeffs differently than NCEP.\\n # (m=0,n=0 is global mean, instead of sqrt(2)/2 times global mean)\\n fld = 2.*values/np.sqrt(2.)\\n \\n #------SPLITTING IT UP IN AN IMAGARY AND REAL PART--------\\n fldr = fld[ 0::2 ] #annenhver verdi fra 0\\n fldi = fld[ 1::2 ] #annenhver verdi fra 1\\n fldn = np.zeros( fldr.shape, 'F' ) #blir halvparten så stor som orginale fld\\n fldn.real = fldr #legges da til i fldn vectoren\\n fldn.imag = fldi\\n #----------------------------------------------------------\\n \\n nlons = 360 #Have a feeling it probably is number of values like grid val\\n nlats = 1280 #web sais it shourld be 180.. wellwell, seems to work\\n s = spharm.Spharmt( nlons, nlats ) \\n \\n data = s.spectogrd( fldn ) #Hvis nlats = 180, så feiler denne delen pga hvordan formelen fungerer..\\n \\n lons = ( 360./nlons ) * np.arange( nlons )\\n lats = 90.-( 180./( nlats - 1 ) ) * np.arange( nlats )\\n lons, lats = np.meshgrid( lons, lats )\\n \\n #stack grids side-by-side (in longitiudinal direction), so\\n # any range of longitudes (between -360 and 360) may be plotted on a world map.\\n lons = np.concatenate(( lons - 360, lons ), 1 )\\n lats = np.concatenate(( lats, lats ), 1 )\\n data = np.concatenate(( data, data ), 1 )\\n \\n return lats, lons, data\",\n \"def amet_memoryWise(self):\\r\\n # set up logging files to monitor the calculation\\r\\n logging.basicConfig(filename = os.path.join(self.path,'history_amet_python.log'),\\r\\n filemode = 'w+', level = logging.DEBUG,\\r\\n format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\\r\\n # initialize the time span\\r\\n # define sigma level\\r\\n A, B = self.defineSigmaLevels()\\r\\n # use example input file to load the basic dimensions information\\r\\n datapath_var = os.path.join(self.path, 'MERRA2_400.inst3_3d_asm_Nv.20160101.nc4.nc')\\r\\n var_key = Dataset(datapath_var)\\r\\n lat = var_key.variables['lat'][:]\\r\\n lon = var_key.variables['lon'][:]\\r\\n # calculate the reference levels based on A & B and standard surface pressure\\r\\n half_level = A + B * 101325\\r\\n level = (half_level[1:] + half_level[:-1]) / 2\\r\\n # create space for the output\\r\\n # AMET in the entire column\\r\\n E = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n cpT = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n Lvq = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n gz = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n uv2 = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n logging.info(\\\"Start retrieving variables T,q,u,v,sp\\\")\\r\\n # The shape of each variable is (8,72,361,576)\\r\\n T = var_key.variables['T'][:]\\r\\n q = var_key.variables['QV'][:]\\r\\n sp = var_key.variables['PS'][:] #(8,361,576)\\r\\n u = var_key.variables['U'][:]\\r\\n v = var_key.variables['V'][:]\\r\\n logging.info(\\\"Extracting variables successfully!\\\") \\r\\n # compute gz\\r\\n z_model = self.calc_gz(var_key)\\r\\n # get the basic shape\\r\\n tt, hh, yy, xx = q.shape\\r\\n AMET = amet.met()\\r\\n E, cpT, Lvq, gz, uv2 = AMET.calc_met(T, q, sp, u, v, z_model, A, B,\\r\\n tt, hh, len(lat), len(lon), lat, self.lat_unit)\\r\\n\\r\\n return np.mean(E)\",\n \"def test_calculate_specific_heat(self):\\n expected = np.array([1089.5, 1174.0, 1258.5], dtype=np.float32)\\n result = WetBulbTemperature()._calculate_specific_heat(self.mixing_ratio)\\n self.assertArrayAlmostEqual(result, expected, decimal=2)\",\n \"def lcfig( z=7 ):\\n\\n for modelname in ['z15G','z25G','z40G'] :\\n\\n # initialize a supernova model :\\n snmodel = sncosmo.Model(source=modelname)\\n\\n # Fix the redshift for this instantiation of the model\\n # (NOTE: this does not apply any cosmological dimming. It only\\n # shifts the wavelengths)\\n snmodel.set( z = z )\\n\\n # generate the H band light curve\\n tobs = np.arange( 0, 1000, 10 )\\n M160 = snmodel.bandmag( 'f160w', 'ab', tobs ) # Absolute Magnitude\\n m160 = M160 + cosmo.distmod( z ).value # apparent magnitude\\n\\n pl.plot( tobs, m160 )\\n ax = pl.gca()\\n ax.invert_yaxis()\\n ax.set_xlabel('Time (observer-frame days)')\\n ax.set_ylabel('Apparent Magnitude in F160W')\\n ax.set_xlim( 0, 1000 )\\n ax.set_ylim( 36, 28 )\\n\\n pl.draw()\",\n \"def getMeasures():\",\n \"def flat_to_wm2(sky_transparency,\\n obs_wavelength,\\n pixel_deltalambda,\\n sky_temp=270*units.K,\\n cabin_temp=288*units.K,\\n beam_size=((6*units.arcsec/2)**2*np.pi/np.log(2)).to(units.steradian)\\n ):\\n frequency = (const.c/obs_wavelength).to(units.Hz)\\n bt = units.brightness_temperature(frequency)\\n opacity = 1-sky_transparency\\n temp_delta = cabin_temp-(sky_temp*opacity)\\n brightness_temp = (temp_delta).to(\\\"Jy/steradian\\\", equivalencies=bt)\\n spectral_stuff = units.spectral_density(obs_wavelength) \\n calib_flux_density = (brightness_temp*beam_size).to(\\\"W / (m^2 Hz)\\\",equivalencies=spectral_stuff)\\n bin_width = (const.c/obs_wavelength - const.c/(obs_wavelength+pixel_deltalambda)).to(\\\"Hz\\\")\\n return calib_flux_density*bin_width\",\n \"def intensity(self) -> int:\",\n \"def compute_pulse_properties(w, initial_baseline_samples):\\n\\n # First compute baseline\\n baseline = 0.0\\n initial_baseline_samples = min(initial_baseline_samples, len(w))\\n for x in w[:initial_baseline_samples]:\\n baseline += x\\n baseline /= initial_baseline_samples\\n\\n # Now compute mean, noise, and min\\n n = 0 # Running count of samples included in noise sample\\n m2 = 0 # Running sum of squares of differences from the baseline\\n max_a = -1.0e6 # Running max amplitude\\n min_a = 1.0e6 # Running min amplitude\\n\\n for x in w:\\n if x > max_a:\\n max_a = x\\n if x < min_a:\\n min_a = x\\n if x < baseline:\\n delta = x - baseline\\n n += 1\\n m2 += delta*(x-baseline)\\n\\n if n == 0:\\n # Should only happen if w = baseline everywhere\\n noise = 0\\n else:\\n noise = (m2/n)**0.5\\n\\n return baseline, noise, min_a - baseline, max_a - baseline\",\n \"def calculateUSky(self):\\n skyline = []\\n for p in self.pruned:\\n pastart = [self.drange[0] for i in range(self.dim)]\\n pamax = p.getLocationMax()\\n pdom = list(self.index.intersection(tuple(pastart+pamax),objects=True))\\n if len(pdom) == 1 and pdom[0].object == p:\\n skyline.append([p, 1.0])\\n else:\\n finalp = 0.0\\n for i in range(p.getPCount()):\\n base = p.getProb(i)\\n loc = p.getLocation(i)\\n intersec = list(self.index.intersection(tuple(pastart+loc),objects=True))\\n for d in intersec:\\n dobj = d.object\\n if dobj != p:\\n tprob = 0.0\\n for idx in range(dobj.getPCount()):\\n if dominateStat(dobj.getLocation(idx),loc) == True:\\n tprob += dobj.getProb(idx)\\n tprob = 1.0 - tprob\\n base *= tprob\\n finalp += base\\n skyline.append([p, finalp])\\n for p in skyline:\\n print(p[0])\\n print(p[1])\\n print(\\\"\\\")\\n # print(skyline)\",\n \"def get_mechanical_ventilation(a_hcz_r: np.ndarray, a_hcz: np.ndarray) -> np.ndarray:\\n\\n # referenced mechanical ventilation volume, m3/h\\n v_vent_r = np.array([60.0, 20.0, 40.0, 20.0, 20.0])\\n\\n # referenced floor area of the occupant room(sliced 0 to 5)\\n# a_hcz_r = envelope.get_referenced_floor_area()[0:5]\\n a_hcz_r = a_hcz_r[0:5]\\n\\n # floor area of the occupant room(sliced 0 to 5)\\n# a_hcz = envelope.get_hc_floor_areas(zone_floor_area)[0:5]\\n a_hcz = a_hcz[0:5]\\n\\n return v_vent_r * a_hcz / a_hcz_r\",\n \"def SFR_Custom(self, z):\\n a = self.customSFR[0] \\n b = self.customSFR[1] \\n c = self.customSFR[2] \\n d = self.customSFR[3] \\n SFR = a * ((1+z)**b) / (1 + ((1+z)/c)**d) * 1e9 #[1e9 for GPc-3]\\n return SFR # [Msun yr-1 Gpc-3] in comoving volume\",\n \"def zen_init(data, pixels):\\n\\t# Set number of frames and image dimensions\\n\\tnframes, ny, nx, nsets = np.shape(data)\\n\\n\\t# Set number of pixels\\n\\tnpix = len(pixels)\\n\\t\\n\\t# Initialize array for flux values of pixels\\n\\tp = np.zeros((nframes, npix))\\n\\n\\t# Extract flux values from images\\n\\tfor i in range(npix):\\n\\t\\tfor j in range(nframes):\\n\\t\\t\\tp[j,i] = data[j, pixels[i][0], pixels[i][1],0]\\n\\n\\t# Initialize array for normalized flux of pixels\\n\\tphat = np.zeros(p.shape)\\n\\t\\n\\t# Remove astrophysics by normalizing\\n\\tfor t in range(nframes):\\n\\t\\tphat[t]\\t= p[t]/np.sum(p[t])\\n\\n\\t# Calculate mean flux through all frames at each pixel\\n\\tpbar\\t= np.mean(phat, axis=0)\\n\\n\\t# Difference from the mean flux\\n\\tdP\\t= phat - pbar\\n\\n\\treturn(phat, dP)\",\n \"def calculate(self, waste_heat=0):\\n radiator_type = self.radiators_type.get()\\n data = self.radiators[radiator_type]\\n if not waste_heat:\\n for _, subsystem in self.data.wasteheat.items():\\n waste_heat += subsystem\\n self.waste_heat.set(waste_heat)\\n area = waste_heat / (data[\\\"Specific area heat\\\"] * 1000)\\n mass = (area * 1000) * data[\\\"Specific area mass\\\"]\\n self.data.masses[\\\"Lifesupport Radiators\\\"] = mass\\n self.area.set(area)\\n self.mass.set(mass)\\n self.radiator_temperature.set(self.radiators[radiator_type][\\\"Radiator Temperature\\\"])\",\n \"def radiation_measurement_analysis():\\n import pint\\n ureg = pint.UnitRegistry()\\n\\n mrem_h = ureg.parse_units('mrem') / ureg.hour\\n m = ureg.parse_units('meters')\\n s = ureg.parse_units('seconds')\\n\\n # Measurements of background radiation\\n bg_dist = ureg.parse_expression('10 m') # estimate of how far away we are wrt background\\n background_rows = [\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=0.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.022 * mrem_h, capture_time=0.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=4.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.021 * mrem_h, capture_time=5.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=11.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=16.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.024 * mrem_h, capture_time=20.0 * s),\\n ]\\n\\n # Measurements of sample radiation\\n esp_dist = ureg.parse_expression('1 inch').to(m) / 2 # estimate of how far we are from the sample when very close\\n dist0_rows = [\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=0.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=3.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=5.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=9.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=10.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=11.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.057 * mrem_h, capture_time=12.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.058 * mrem_h, capture_time=13.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=14.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=15.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=16.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=20.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=22.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.066 * mrem_h, capture_time=23.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=24.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=25.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=26.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=28.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=30.0 * s),\\n ]\\n\\n dist0_v2_rows = [\\n dict(vid=3, distance=esp_dist, rad=0.012 * mrem_h, capture_time=0.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.011 * mrem_h, capture_time=1.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=8.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=9.0 * s),\\n ]\\n\\n close_rows = [\\n dict(vid=4, distance=0.5 * m, rad=0.013 * mrem_h, capture_time=0.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.014 * mrem_h, capture_time=5.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=7.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.011 * mrem_h, capture_time=15.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=16.0 * s),\\n ]\\n\\n mid_rows = [\\n dict(vid=5, distance=1.0 * m, rad=0.014 * mrem_h, capture_time=0.0 * s),\\n dict(vid=5, distance=1.0 * m, rad=0.015 * mrem_h, capture_time=5.0 * s),\\n dict(vid=5, distance=1.0 * m, rad=0.013 * mrem_h, capture_time=10.0 * s),\\n ]\\n\\n far_rows = [\\n dict(vid=6, distance=2.0 * m, rad=0.023 * mrem_h, capture_time=0.0 * s),\\n dict(vid=6, distance=2.0 * m, rad=0.025 * mrem_h, capture_time=0.1 * s),\\n ]\\n\\n # guess_dist = ureg.parse_expression('0.3 m') # estimate of how far away we are wrt background\\n # guess_rows = [\\n # dict(vid=9, distance=guess_dist, rad=0.030 * mrem_h, capture_time=0.0 * s),\\n # dict(vid=9, distance=guess_dist, rad=0.041 * mrem_h, capture_time=2.0 * s),\\n # dict(vid=9, distance=guess_dist, rad=0.051 * mrem_h, capture_time=3.0 * s),\\n # ]\\n\\n rows = dist0_rows + background_rows + dist0_v2_rows + close_rows + mid_rows + far_rows\\n # rows += guess_rows\\n\\n import pandas as pd\\n import numpy as np\\n table = pd.DataFrame(rows)\\n\\n # Ensure comparable units\\n units = {\\n 'rad': mrem_h,\\n 'distance': m,\\n 'capture_time': s,\\n }\\n for key, unit in units.items():\\n table[key] = table[key].apply(lambda c: c.to(unit).m)\\n table['rad'] = table['rad'].astype(float)\\n table['distance'] = table['distance'].astype(float)\\n\\n # Weight each measurement based on the amount of time the measurement was\\n # sustained in the video.\\n average_rad_rows = []\\n for vid, group in table.groupby('vid'):\\n from statsmodels.stats.weightstats import DescrStatsW\\n weights = (-1 * group['capture_time'].diff(periods=-1).fillna(0)) / group['capture_time'].iloc[-1]\\n table.loc[group.index, 'weight'] = weights\\n values = group['rad']\\n weighted_stats = DescrStatsW(values, weights=weights, ddof=0)\\n dists = group['distance'].unique()\\n assert len(dists) == 1\\n average_rad_rows.append({\\n 'vid': vid,\\n 'distance': dists[0],\\n 'rad_mean': weighted_stats.mean,\\n 'rad_std': weighted_stats.std,\\n })\\n stats_table = pd.DataFrame(average_rad_rows)\\n\\n bg_row = stats_table.loc[stats_table['distance'].argmax()]\\n fg_row = stats_table.loc[stats_table['distance'].argmin()]\\n\\n # -------------------\\n ADD_DUMMY_VALUES = 0\\n if ADD_DUMMY_VALUES:\\n # Hack: because we don't have enough samples we can fudge the value\\n # knowning that the value should be the background radiation in the\\n # limit.\\n\\n dummy_measurements = []\\n extra_support = 1\\n for idx in range(3, 3 + extra_support):\\n dummy_row = {\\n 'vid': -idx,\\n 'distance': bg_row['distance'] + idx,\\n 'rad_mean': bg_row['rad_mean'],\\n 'rad_std': 0.01,\\n }\\n dummy_measurements.append(dummy_row)\\n\\n # also add an extra value close to the sample\\n rad_bg = bg_row['rad_mean']\\n rad_above_bg = fg_row['rad_mean'] - rad_bg\\n dummy_row = {\\n 'vid': -1,\\n 'distance': fg_row['distance'] / 2,\\n 'rad_mean': rad_bg + (rad_above_bg * 4),\\n 'rad_std': 0.5,\\n }\\n dummy_measurements.append(dummy_row)\\n\\n # dummy_row = {\\n # 'vid': -2,\\n # 'distance': fg_row['distance'] / 4,\\n # 'rad_mean': rad_bg + (rad_above_bg * 16),\\n # }\\n # dummy_measurements.append(dummy_row)\\n\\n dummy_stats = pd.DataFrame(dummy_measurements)\\n dummy_stats['weight'] = 0.5\\n stats_table['weight'] = 1.0\\n stats_table2 = pd.concat([stats_table, dummy_stats]).reset_index(drop=True).sort_values('distance')\\n else:\\n stats_table2 = stats_table\\n # -------------------\\n\\n import scipy\\n scipy.optimize.curve_fit\\n\\n # Because we know the radiation should follow an inverse square law wrt to\\n # distance, we can fit a polynomial of degree 2 (parabola) to interpolate /\\n # extrapolate the **inverse** values.\\n x = stats_table2['distance'].values\\n y = stats_table2['rad_mean'].values\\n s = stats_table2['rad_std'].values\\n\\n # Model the squared falloff directly\\n def invsquare(x, a, b):\\n return a * (1 / (0.01 + x ** 2)) + b\\n # bg_row['rad_mean']\\n # Use curve_fit to constrain the first coefficient to be zero\\n try:\\n coef = scipy.optimize.curve_fit(invsquare, x, y, sigma=s, method='trf')[0]\\n except Exception as ex:\\n coef = None\\n print(f'ex={ex}')\\n\\n # Also fit one to the raw weighted points as a sanity check\\n # inv_poly2 = Polynomial.fit(table['distance'], 1 / table['rad'], w=table['weight'], deg=2)\\n\\n import kwplot\\n sns = kwplot.autosns()\\n plt = kwplot.autoplt()\\n # ax = sns.boxplot(data=table, x='distance', y='rad', width=0.1)\\n\\n # Add in points to show each observation\\n ax = sns.relplot(x=\\\"distance\\\", y=\\\"rad\\\", data=table, size=4, color=\\\".3\\\",\\n linewidth=0, alpha=0.5, palette='deep')\\n\\n ax = plt.gca()\\n ax.set_xlabel('distance from sample ({})'.format(str(units['distance'])))\\n ax.set_ylabel('radiation dosage ({})'.format(str(units['rad'])))\\n\\n max_meters = 10\\n\\n extrap_x = np.linspace(0, max_meters, 1000)\\n if coef is not None:\\n extrap_y1 = invsquare(extrap_x, *coef)\\n # extrap_y2 = 1 / inv_poly2(extrap_x)\\n ax.plot(stats_table2['distance'].values, stats_table2['rad_mean'].values, 'rx')\\n ax.plot(stats_table['distance'].values, stats_table['rad_mean'].values, 'bo')\\n ax.plot(extrap_x, extrap_y1, '--')\\n ax.set_ylim(0.001, 0.1)\\n ax.set_yscale('log')\\n # ax.plot(extrap_x, extrap_y2, '--')\",\n \"def blackbody( wave, T, waveunit='Angstrom' ):\\n \\n if waveunit=='Angstrom':\\n # convert wavelength from angstroms to cm\\n wave = wave / 1e10 * 100.\\n elif waveunit=='nm':\\n # convert wavelength from angstroms to cm\\n wave = wave / 1e9 * 100.\\n\\n return( ((2 * h * c* c)/wave**5 ) / (exp(h*c/(wave*k*T))-1) )\",\n \"def create_parametric_fin_heat_sink(\\n self,\\n hs_height=100,\\n hs_width=100,\\n hs_basethick=10,\\n pitch=20,\\n thick=10,\\n length=40,\\n height=40,\\n draftangle=0,\\n patternangle=10,\\n separation=5,\\n symmetric=True,\\n symmetric_separation=20,\\n numcolumn_perside=2,\\n vertical_separation=10,\\n matname=\\\"Al-Extruded\\\",\\n center=[0, 0, 0],\\n plane_enum=0,\\n rotation=0,\\n tolerance=1e-3,\\n ):\\n all_objs = self.modeler.primitives.object_names\\n self[\\\"FinPitch\\\"] = self.modeler.primitives._arg_with_dim(pitch)\\n self[\\\"FinThickness\\\"] = self.modeler.primitives._arg_with_dim(thick)\\n self[\\\"FinLength\\\"] = self.modeler.primitives._arg_with_dim(length)\\n self[\\\"FinHeight\\\"] = self.modeler.primitives._arg_with_dim(height)\\n self[\\\"DraftAngle\\\"] = draftangle\\n self[\\\"PatternAngle\\\"] = patternangle\\n self[\\\"FinSeparation\\\"] = self.modeler.primitives._arg_with_dim(separation)\\n self[\\\"VerticalSeparation\\\"] = self.modeler.primitives._arg_with_dim(vertical_separation)\\n self[\\\"HSHeight\\\"] = self.modeler.primitives._arg_with_dim(hs_height)\\n self[\\\"HSWidth\\\"] = self.modeler.primitives._arg_with_dim(hs_width)\\n self[\\\"HSBaseThick\\\"] = self.modeler.primitives._arg_with_dim(hs_basethick)\\n if numcolumn_perside > 1:\\n self[\\\"NumColumnsPerSide\\\"] = numcolumn_perside\\n if symmetric:\\n self[\\\"SymSeparation\\\"] = self.modeler.primitives._arg_with_dim(symmetric_separation)\\n # ipk['PatternDirection'] = 'Y'\\n # ipk['LengthDirection'] = 'X'\\n # ipk['HeightDirection'] = 'Z'\\n self[\\\"Tolerance\\\"] = self.modeler.primitives._arg_with_dim(tolerance)\\n\\n self.modeler.primitives.create_box(\\n [\\\"-HSWidth/200\\\", \\\"-HSHeight/200\\\", \\\"-HSBaseThick\\\"],\\n [\\\"HSWidth*1.01\\\", \\\"HSHeight*1.01\\\", \\\"HSBaseThick+Tolerance\\\"],\\n \\\"HSBase\\\",\\n matname,\\n )\\n Fin_Line = []\\n Fin_Line.append(self.Position(0, 0, 0))\\n Fin_Line.append(self.Position(0, \\\"FinThickness\\\", 0))\\n Fin_Line.append(self.Position(\\\"FinLength\\\", \\\"FinThickness + FinLength*sin(PatternAngle*3.14/180)\\\", 0))\\n Fin_Line.append(self.Position(\\\"FinLength\\\", \\\"FinLength*sin(PatternAngle*3.14/180)\\\", 0))\\n Fin_Line.append(self.Position(0, 0, 0))\\n self.modeler.primitives.create_polyline(Fin_Line, cover_surface=True, name=\\\"Fin\\\")\\n Fin_Line2 = []\\n Fin_Line2.append(self.Position(0, \\\"sin(DraftAngle*3.14/180)*FinThickness\\\", \\\"FinHeight\\\"))\\n Fin_Line2.append(self.Position(0, \\\"FinThickness-sin(DraftAngle*3.14/180)*FinThickness\\\", \\\"FinHeight\\\"))\\n Fin_Line2.append(\\n self.Position(\\n \\\"FinLength\\\",\\n \\\"FinThickness + FinLength*sin(PatternAngle*3.14/180)-sin(DraftAngle*3.14/180)*FinThickness\\\",\\n \\\"FinHeight\\\",\\n )\\n )\\n Fin_Line2.append(\\n self.Position(\\n \\\"FinLength\\\", \\\"FinLength*sin(PatternAngle*3.14/180)+sin(DraftAngle*3.14/180)*FinThickness\\\", \\\"FinHeight\\\"\\n )\\n )\\n Fin_Line2.append(self.Position(0, \\\"sin(DraftAngle*3.14/180)*FinThickness\\\", \\\"FinHeight\\\"))\\n self.modeler.primitives.create_polyline(Fin_Line2, cover_surface=True, name=\\\"Fin_top\\\")\\n self.modeler.connect([\\\"Fin\\\", \\\"Fin_top\\\"])\\n self.modeler.primitives[\\\"Fin\\\"].material_name = matname\\n # self.modeler.thicken_sheet(\\\"Fin\\\",'-FinHeight')\\n num = int((hs_width / (separation + thick)) / (max(1 - math.sin(patternangle * 3.14 / 180), 0.1)))\\n self.modeler.duplicate_along_line(\\\"Fin\\\", self.Position(0, \\\"FinSeparation+FinThickness\\\", 0), num, True)\\n self.modeler.duplicate_along_line(\\\"Fin\\\", self.Position(0, \\\"-FinSeparation-FinThickness\\\", 0), num / 4, True)\\n\\n all_names = self.modeler.primitives.object_names\\n list = [i for i in all_names if \\\"Fin\\\" in i]\\n if numcolumn_perside > 0:\\n self.modeler.duplicate_along_line(\\n list,\\n self.Position(\\\"FinLength+VerticalSeparation\\\", \\\"FinLength*sin(PatternAngle*3.14/180)\\\", 0),\\n \\\"NumColumnsPerSide\\\",\\n True,\\n )\\n\\n all_names = self.modeler.primitives.object_names\\n list = [i for i in all_names if \\\"Fin\\\" in i]\\n self.modeler.split(list, self.CoordinateSystemPlane.ZXPlane, \\\"PositiveOnly\\\")\\n all_names = self.modeler.primitives.object_names\\n list = [i for i in all_names if \\\"Fin\\\" in i]\\n self.modeler.create_coordinate_system(self.Position(0, \\\"HSHeight\\\", 0), mode=\\\"view\\\", view=\\\"XY\\\", name=\\\"TopRight\\\")\\n\\n self.modeler.split(list, self.CoordinateSystemPlane.ZXPlane, \\\"NegativeOnly\\\")\\n\\n if symmetric:\\n\\n self.modeler.create_coordinate_system(\\n self.Position(\\\"(HSWidth-SymSeparation)/2\\\", 0, 0),\\n mode=\\\"view\\\",\\n view=\\\"XY\\\",\\n name=\\\"CenterRightSep\\\",\\n reference_cs=\\\"TopRight\\\",\\n )\\n\\n self.modeler.split(list, self.CoordinateSystemPlane.YZPlane, \\\"NegativeOnly\\\")\\n self.modeler.create_coordinate_system(\\n self.Position(\\\"SymSeparation/2\\\", 0, 0),\\n mode=\\\"view\\\",\\n view=\\\"XY\\\",\\n name=\\\"CenterRight\\\",\\n reference_cs=\\\"CenterRightSep\\\",\\n )\\n self.modeler.duplicate_and_mirror(list, self.Position(0, 0, 0), self.Position(1, 0, 0))\\n Center_Line = []\\n Center_Line.append(self.Position(\\\"-SymSeparation\\\", \\\"Tolerance\\\", \\\"-Tolerance\\\"))\\n Center_Line.append(self.Position(\\\"SymSeparation\\\", \\\"Tolerance\\\", \\\"-Tolerance\\\"))\\n Center_Line.append(self.Position(\\\"VerticalSeparation\\\", \\\"-HSHeight-Tolerance\\\", \\\"-Tolerance\\\"))\\n Center_Line.append(self.Position(\\\"-VerticalSeparation\\\", \\\"-HSHeight-Tolerance\\\", \\\"-Tolerance\\\"))\\n Center_Line.append(self.Position(\\\"-SymSeparation\\\", \\\"Tolerance\\\", \\\"-Tolerance\\\"))\\n self.modeler.primitives.create_polyline(Center_Line, cover_surface=True, name=\\\"Center\\\")\\n self.modeler.thicken_sheet(\\\"Center\\\", \\\"-FinHeight-2*Tolerance\\\")\\n all_names = self.modeler.primitives.object_names\\n list = [i for i in all_names if \\\"Fin\\\" in i]\\n self.modeler.subtract(list, \\\"Center\\\", False)\\n else:\\n self.modeler.create_coordinate_system(\\n self.Position(\\\"HSWidth\\\", 0, 0), mode=\\\"view\\\", view=\\\"XY\\\", name=\\\"BottomRight\\\", reference_cs=\\\"TopRight\\\"\\n )\\n self.modeler.split(list, self.CoordinateSystemPlane.YZPlane, \\\"NegativeOnly\\\")\\n all_objs2 = self.modeler.primitives.object_names\\n list_to_move = [i for i in all_objs2 if i not in all_objs]\\n center[0] -= hs_width / 2\\n center[1] -= hs_height / 2\\n center[2] += hs_basethick\\n self.modeler.set_working_coordinate_system(\\\"Global\\\")\\n self.modeler.translate(list_to_move, center)\\n if plane_enum == self.CoordinateSystemPlane.XYPlane:\\n self.modeler.rotate(list_to_move, self.CoordinateSystemAxis.XAxis, rotation)\\n elif plane_enum == self.CoordinateSystemPlane.ZXPlane:\\n self.modeler.rotate(list_to_move, self.CoordinateSystemAxis.XAxis, 90)\\n self.modeler.rotate(list_to_move, self.CoordinateSystemAxis.YAxis, rotation)\\n elif plane_enum == self.CoordinateSystemPlane.YZPlane:\\n self.modeler.rotate(list_to_move, self.CoordinateSystemAxis.YAxis, 90)\\n self.modeler.rotate(list_to_move, self.CoordinateSystemAxis.ZAxis, rotation)\\n self.modeler.unite(list_to_move)\\n self.modeler.primitives[list_to_move[0]].name = \\\"HeatSink1\\\"\\n return True\",\n \"def map_cell_property(**kwargs):\\n\\n GR = glo.global_results()\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n counter = 0\\n fignum = 1\\n if p.gal_index == 'all':\\n for gal_index in range(GR.N_gal):\\n\\n if counter == 0:\\n fig, axes = plt.subplots(3, 3, figsize=(20,15))\\n axs = [axes[0,0],axes[0,1],axes[0,2],axes[1,0],axes[1,1],axes[1,2],axes[2,0],axes[2,1],axes[2,2]]\\n counter = 9\\n\\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\\n print('Now mapping %s' % gal_ob.name)\\n isrf_ob = gal.isrf(gal_index)\\n\\n # Load SKIRT output\\n wavelengths,bin_width = aux.read_probe_wavelengths(isrf_ob.name)\\n N_start,N_stop = aux.FUV_index(wavelengths)\\n image_data,units = isrf_ob._get_cut_probe(orientation=p.orientation)\\n\\n # Plot\\n ax1 = axs[9 - counter]\\n if p.prop == 'FUV':\\n # FUV_xy_image = np.array([np.trapz(image_data[N_start:N_stop,:,:],x=wavelengths[N_start:N_stop]) \\\\\\n # for i in range(len(df))])\\n FUV_xy_image = image_data[N_start:N_stop,:,:].sum(axis=0) * 4 * np.pi\\n FUV_xy_image = ndimage.rotate(FUV_xy_image, 0, reshape=True)\\n # FUV_xy_image = np.fliplr(FUV_xy_image)\\n FUV_xy_image[FUV_xy_image <= 0] = np.min(FUV_xy_image[FUV_xy_image > 0])\\n im = ax1.imshow(np.log10(FUV_xy_image),\\\\\\n extent=[-isrf_ob.radius,isrf_ob.radius,-isrf_ob.radius,isrf_ob.radius],\\\\\\n vmin=p.vmin,\\\\\\n cmap='twilight')\\n lab = 'FUV flux [W/m$^2$/micron]'\\n\\n # pdb.set_trace()\\n\\n ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\\n # Limit axes limits a bit to avoid area with no particles...\\n ax1.set_xlim([-0.8*gal_ob.radius,0.8*gal_ob.radius])\\n ax1.set_ylim([-0.8*gal_ob.radius,0.8*gal_ob.radius])\\n if p.prop == 'm':\\n ax1.text(0.05,0.85,'M$_{gas}$=%.2eM$_{\\\\odot}$' % np.sum(simgas.m),\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n\\n counter -= 1\\n\\n\\n if counter == 0:\\n cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95, label=lab)\\n # fig.colorbar(im,shrink=0.8,label=lab)\\n\\n if counter == 0 or gal_index == GR.N_gal-1:\\n figname = p.d_plot + 'cell_data/map_%s_%s_gals_%s_%i.png' % (p.prop,p.z1,p.orientation,fignum)\\n print('Saving in ' + figname)\\n # plt.tight_layout()\\n plt.savefig(figname, format='png', dpi=250, facecolor='w')\\n fignum += 1\\n pdb.set_trace()\\n else:\\n fig, ax1 = plt.subplots(figsize=(10,10))\\n gal_ob = gal.galaxy(GR=GR, gal_index=p.gal_index)\\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type='cell_data')\\n print(simgas.keys())\\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\\n\\n # Plot\\n Rmax = max_scale/2\\n if p.log:\\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\\n map2D = np.log10(map2D)\\n if not p.log: map2D[map2D < p.vmin] = p.vmin/2 #np.min(map2D[map2D > 0])\\n im = ax1.imshow(map2D,\\\\\\n extent=[-Rmax,Rmax,-Rmax,Rmax],vmin=p.vmin,cmap=p.cmap)\\n # Limit axes limits a bit to avoid area with no particles...\\n ax1.set_xlim([-2/3*gal_ob.radius,2/3*gal_ob.radius])\\n ax1.set_ylim([-2/3*gal_ob.radius,2/3*gal_ob.radius])\\n fig.colorbar(im,shrink=0.8,ax=ax1,label=lab)\\n ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\\n\\n print('Saving in ' + p.d_plot + 'sim_data/map_%s_G%i.png' % (p.prop,p.gal_index))\\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/')\\n plt.savefig(p.d_plot + 'cell_data/map_%s_G%i.png' % (p.prop,p.gal_index), format='png', dpi=250, facecolor='w')\",\n \"def ohms(self):\\n # Rwb = Rwiper + Rtotal * (counts / 256)\\n # Rwa = Rwiper + Rtotal * ((256 - counts) / 256)\\n g = 0\\n rtotal=0.0\\n reach=[]\\n for chan in self.get_channel_list(self.nchans):\\n self.rwa[chan] = float( 256 - self.vals[chan] ) / 256.0\\n self.rwb[chan] = float( self.vals[chan] ) / 256.0\\n self.rwa[chan] *= self.Rtotal\\n self.rwb[chan] *= self.Rtotal \\n self.rwa[chan] += self.Rwiper\\n self.rwb[chan] += self.Rwiper\",\n \"def mhu(self, sign, spin):\\n index = self.Frontier_MO_index(sign=sign, spin=spin)\\n if spin == \\\"alpha\\\":\\n return self._molecule.mo.energies[index[0]]\\n else:\\n return self._molecule.mo.energies[index[1]]\",\n \"def prada(self):\\n scale_factor = 1.0 / (1.0 + self.snapshot.header.redshift)\\n r200c_physical = self.r200c * scale_factor / 1000.0 # units Mpc\\n\\n v200 = (\\n (self.snapshot.const.G * self.m200c)\\n / r200c_physical\\n * self.snapshot.const.Mpc ** 2\\n / 1000.0 ** 2\\n ) ** 0.5 # units km/s\\n\\n def y(x, vmax, v200):\\n func = np.log(1 + x) - (x / (1 + x))\\n return ((0.216 * x) / func) ** 0.5 - (vmax / v200)\\n\\n concentration = np.zeros((len(self.vmax)))\\n for halo in range(self.N_halos):\\n if v200[halo] > self.vmax[halo]:\\n concentration[halo] = -9999.0\\n else:\\n try:\\n concentration[halo] = newton(\\n y, x0=5.0, args=(self.vmax[halo], v200[halo])\\n )\\n except:\\n concentration[halo] = -9999.0\\n\\n return concentration\",\n \"def spectra_analysis(file_name, sky_file_name): \\n\\n # read file name and select out the id that we are dealing with\\n curr_file_name = file_name.split('.')\\n curr_file_name = curr_file_name[0].split('/')\\n stk_f_n = curr_file_name[-1]\\n cube_id = int(re.search(r'\\\\d+', stk_f_n).group())\\n\\n # read catalogue and obtain the HST redshift estimate\\n #catalogue = np.load(\\\"data/matched_catalogue.npy\\\")\\n catalogue = np.load(\\\"data/low_redshift_catalogue.npy\\\")\\n cat_loc = np.where(catalogue[:,0] == cube_id)[0]\\n cube_info = catalogue[cat_loc][0]\\n \\n hst_redshift = cube_info[7]\\n\\n # spectra and sky noise data\\n spectra_data = spectrum_creator(file_name)\\n wl_soln = wavelength_solution(file_name)\\n sn_data = sky_noise(sky_file_name)\\n\\n galaxy_data = spectra_data['galaxy']\\n\\n # removing baseline from data\\n base = peakutils.baseline(galaxy_data, 3)\\n gd_mc = galaxy_data - base\\n\\n # scaling sky-noise to be similar to spectra data\\n gd_max = np.amax(galaxy_data)\\n sn_data_max = np.amax(sn_data)\\n sn_scale = gd_max / sn_data_max\\n\\n sn_data = sn_data * sn_scale\\n\\n # spectra lines\\n sl = {\\n 'emis': {\\n '[OII]': '3727',\\n 'CaK': '3933',\\n 'CaH': '3968',\\n 'Hdelta': '4101', \\n }, \\n 'abs': {'K': '3934.777',\\n }\\n } \\n\\n # we can use the redshift from the HST catalogue to define the region to search for\\n # the doublet in\\n\\n # lower and upper bound on wavelength range\\n lower_lambda = (1+hst_redshift)*3600\\n upper_lambda = (1+hst_redshift)*3850\\n\\n # x-axis data\\n data_h_range = np.linspace(wl_soln['begin'], wl_soln['end'], wl_soln['steps'])\\n mask = (lower_lambda < data_h_range) & (data_h_range < upper_lambda) \\n\\n lambda_data = data_h_range[mask]\\n flux_data = gd_mc[mask] \\n \\n # Finding peaks with PeakUtils\\n pu_peaks = peakutils.indexes(flux_data, thres=600, thres_abs=True)\\n pu_peaks_x = peakutils.interpolate(lambda_data, flux_data, pu_peaks)\\n\\n pu_peaks_x = np.sort(pu_peaks_x)\\n pu_peaks_x = pu_peaks_x[lower_lambda < pu_peaks_x]\\n pu_peaks_x = pu_peaks_x[pu_peaks_x < upper_lambda]\\n \\n data_dir = 'cube_results/' + stk_f_n\\n if not os.path.exists(data_dir):\\n os.mkdir(data_dir)\\n\\n peaks_file = open(data_dir + '/' + stk_f_n + '_peaks.txt', 'w')\\n peaks_file.write(\\\"Peaks found on \\\" + str(datetime.datetime.now()) + \\\"\\\\n\\\\n\\\")\\n\\n peaks_file.write(\\\"Number Wavelength \\\\n\\\")\\n for i_peak in range(len(pu_peaks_x)):\\n curr_peak = pu_peaks_x[i_peak]\\n peaks_file.write(str(i_peak) + \\\" \\\" + str(curr_peak) + \\\"\\\\n\\\")\\n\\n # manually selecting which peak is the [OII] peak - given in wavelength\\n if (pu_peaks_x.size != 0):\\n otwo_wav = float(pu_peaks_x[0]) \\n otwo_acc = float(sl['emis']['[OII]'])\\n\\n redshift = (otwo_wav / otwo_acc) - 1\\n else:\\n # accepting HST redshift if cannot find peak\\n redshift = hst_redshift\\n\\n return {'gd_shifted': gd_mc, 'sky_noise': sn_data, 'spectra': sl, 'redshift': \\n redshift, 'pu_peaks': pu_peaks_x}\",\n \"def angular_memory_effect_analysis(tilt_coef_range, input_field, TM, data_shape, sample_pitch, wavelength, plot_std = True):\\n zernike_rho = np.linspace(-0.5, 0.5, data_shape[1])\\n\\n\\n centered_outputs = np.zeros([tilt_coef_range.size, data_shape[1]]) # For storage of outputs that where tilted and then artificially shifted back to the center\\n shift_idxs = np.zeros(tilt_coef_range.size) # Will store information on how much outputs needed to be shifted to be centered\\n for idx, coef in enumerate(tilt_coef_range):\\n output_intensity = np.abs( TM @ ( input_field * np.exp(2j * np.pi * coef * zernike_rho) ).ravel() )**2 # propagation via TM, only saving intensity\\n centered_outputs[idx, :], shift_idxs[idx] = calc.center_tilted_output(output_intensity, coef, data_shape, sample_pitch, wavelength, return_shift_idx = True)\\n\\n centered_outputs = centered_outputs / np.linalg.norm(centered_outputs, 1) # Normalising all outputs\\n\\n\\n input_field = input_field.ravel()\\n reference_output = np.abs( TM @ input_field )**2 # untilted output field, which we will use for std\\n reference_output = reference_output / np.linalg.norm(reference_output)\\n\\n # fig, axs = plt.subplots()\\n # axs.plot(centered_outputs[-1,:])\\n # plt.show()\\n\\n #Comparing the outputs with a standard deviation method\\n subtracted_outputs = np.zeros(centered_outputs.shape)\\n for idx in range(centered_outputs[:,0].size):\\n subtracted_outputs[idx, :] = centered_outputs[idx, :] - centered_outputs[0, :] # subtracting the outputs with the reference\\n\\n std_of_outputs = np.std(subtracted_outputs, axis = 1) # Computing the standard deviation of subtracted outputs\\n if plot_std:\\n fig, axs = plt.subplots()\\n img_1, = axs.plot(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, color = 'black')\\n img_2 = axs.scatter(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, s = 40, color = 'crimson', edgecolors = 'black')\\n axs.set_title('Angular memory effect decay')\\n axs.set(xlabel = '$\\\\delta x, \\\\ \\\\mu m$', ylabel = 'Standard deviation, p.d.u.')\\n\\n\\n #Fitting a logistic curve onto the results\\n popt, pcov = curve_fit(logistic_curve, (shift_idxs * sample_pitch[1] * 1e6), std_of_outputs)\\n smooth_range = np.linspace(-0.1, shift_idxs[-1] * sample_pitch[1] * 1e6, 1000)\\n fitted_std = logistic_curve(smooth_range, *popt)\\n img_fit, = axs.plot(smooth_range, fitted_std, color = 'blue')\\n\\n axs.text(0.65 * (shift_idxs[-1] * sample_pitch[1] * 1e6), 0.2 * std_of_outputs.max(), 'Fitting parameters: \\\\nL = {:.3g} \\\\nx_0 = {:.3g} \\\\nk = {:.3g}'.format(*popt))\\n\\n # img_3 = axs.scatter((shift_idxs * sample_pitch[1] * 1e6)[-8:], std_of_outputs[-8:], s = 70, marker = 'x', color = 'green')#excluded point\\n plt.legend([(img_2, img_1), img_fit], ['STD($\\\\delta x $)', '$L / [1 + \\\\exp(-k (\\\\delta x - x_0)) ]$'])\\n plt.plot(block = False)\\n\\n return centered_outputs\",\n \"def beam(xb,yb,zb,wx,wy,wavelen):\\n\\n zRx = np.pi * wx**2 / wavelen\\n zRy = np.pi * wy**2 / wavelen \\n \\n sqrtX = np.sqrt( 1 + np.power(zb/zRx,2) ) \\n sqrtY = np.sqrt( 1 + np.power(zb/zRy,2) ) \\n intensity = np.exp( -2.*( np.power(xb/(wx*sqrtX ),2) \\\\\\n + np.power(yb/(wy*sqrtY),2) )) / sqrtX / sqrtY\\n return intensity\",\n \"def getCalibration(self):\\n self.a0 = float(self.getParameter(index=1))\\n self.a1 = float(self.getParameter(index=2))\\n self.a2 = float(self.getParameter(index=3))\\n self.a3 = float(self.getParameter(index=4))\\n status = self.getStatus()\\n self.wavelength = [ self.a0 + self.a1*x + self.a2*x*x + self.a3*x*x*x \\n for x in range(status.pixels)]\\n if self.discardTrailingSamples > 0:\\n self.wavelength = self.wavelength[:-self.discardTrailingSamples]\\n if self.discardLeadingSamples > 0:\\n self.wavelength = self.wavelength[self.discardLeadingSamples:]\",\n \"def artificial_data():\\n \\n num_voxels = 10\\n c = (5.0, 5.0, 5.0)\\n\\n data_x = []\\n data_y = []\\n data_z = []\\n data_intensity = []\\n\\n volume = numpy.zeros((num_voxels, num_voxels, num_voxels))\\n\\n for x in range(num_voxels):\\n for y in range(num_voxels):\\n for z in range(num_voxels):\\n\\n if numpy.abs(numpy.sqrt((x-c[0])**2 + (y-c[1])**2 + (z-c[2])**2) - 5) < 1.5:\\n data_x.append(x)\\n data_y.append(y)\\n data_z.append(z)\\n data_intensity.append(200.0)\\n\\n volume[x,y,z] = 200.0\\n\\n\\n return data_x, data_y, data_z, data_intensity, volume\",\n \"def mann_whitney_plus_means(turnstile_weather):\\n with_rain = turnstile_weather[turnstile_weather.rain == 1]\\n without_rain = turnstile_weather[turnstile_weather.rain == 0]\\n\\n with_rain_mean = with_rain['ENTRIESn_hourly'].mean()\\n without_rain_mean = without_rain['ENTRIESn_hourly'].mean()\\n U, p = scipy.stats.mannwhitneyu(with_rain['ENTRIESn_hourly'], without_rain['ENTRIESn_hourly'])\\n\\n return with_rain_mean, without_rain_mean, U, p\",\n \"def _calculate_magnification(self, times):\\n if self._model.n_lenses == 2:\\n factor = 10.\\n params = self._model.parameters\\n t_1 = params.t_0 - factor * params.t_E\\n t_2 = params.t_0 + factor * params.t_E\\n self._model.set_magnification_methods([t_1, 'VBBL', t_2])\\n self._model.set_default_magnification_method(\\n 'point_source_point_lens')\\n\\n magnification = self._model.magnification(times)\\n return magnification\",\n \"def PCO1S12Noise():\\n #Get data\\n wdir = '/home/rallured/Dropbox/AXRO/Metrology/' \\\\\\n 'NoiseStudy/TestOptics_PCO1S12/'\\n d1,dx1 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas1.fits')\\n d2,dx2 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas2.fits')\\n d3,dx3 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas3.fits')\\n\\n #Construct power spectra\\n f12,pow12 = fourier.meanPSD((d1-d2)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n f23,pow23 = fourier.meanPSD((d2-d3)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n f13,pow13 = fourier.meanPSD((d1-d3)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n\\n #Mid frequency\\n midfreq = [1000*np.sqrt(np.sum(p[np.logical_and(f>.1,f<1.)])) \\\\\\n for f,p in zip([f12,f23,f13],[pow12,pow23,pow13])]\\n\\n #Plot\\n plt.loglog(f12,pow12/f12[0],label='1-2: %.2f' % midfreq[0])\\n plt.loglog(f23,pow23/f23[0],label='2-3: %.2f' % midfreq[1])\\n plt.loglog(f13,pow13/f13[0],label='1-3: %.2f' % midfreq[2])\\n plt.legend(loc='lower left')\\n plt.grid()\\n plt.title('4D Repeatability: PCO1S12')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n\\n print midfreq\\n\\n return f12,pow12\",\n \"def compute_mixing_coefficients_surf(self):\\n [Ly,N] = self.b.shape\\n z_u_w = self.grid_dict['z_u_w']\\n\\n # SET UP NEW MIXING COEFFICIENT ARRAYS\\n self.Kv_surf = np.zeros([Ly,N+1])\\n self.Kt_surf = np.zeros([Ly,N+1])\\n \\n self.ghat = np.zeros([Ly,N+1])\\n \\n\\n #################################\\n # \\tSURFACE KPP\\n ################################\\n #---> j-loop\\n \\n self.wm2 = []\\n self.ws2 = []\\n self.sigma_y = []\\n for j in range(Ly):\\n #--> k-loop (top to kbl[j])\\n # in fortran k=N-1,kbl(j),-1\\n for k in range(N-1,self.kbl[j]-1,-1):\\n k_w = k\\n k_r = k-1\\n\\n Bfsfc = self.Bfsfc_bl[j]\\n zscale = z_u_w[j,N] - z_u_w[j,k_w]\\n \\n # CALCULATE TURBULENT VELOCITY SCALES\\n wm,ws = self.lmd_wscale_wm_and_ws(Bfsfc,zscale,self.ustar[j],self.hbls[j])\\n self.wm2.append(wm)\\n self.ws2.append(ws)\\n # COMPUTE VERTICAL MIXING COEFFICIENTS\\n sigma = (z_u_w[j,N] - z_u_w[j,k_w]) / np.max([self.hbls[j],self.eps])\\n self.sigma1 = sigma #for debugging\\n if j == 25: \\n self.sigma_y.append(sigma)\\n a1 = sigma - 2.\\n a2 = 3.-2.*sigma\\n a3 = sigma - 1.\\n\\n if sigma < 0.07:\\n cff = 0.5 * (sigma-0.07)**2/0.07\\n else:\\n cff = 0\\n \\n \\n if k == N-1: \\n self.wm_debug = wm\\n self.hbls_debug = self.hbls[j]\\n self.cff_debug = cff\\n self.sigma_debug = sigma\\n self.a1_debug = a1\\n self.a2_debug = a2\\n self.a3_debug = a3\\n\\n self.Kv_surf[j,k_w] = wm * self.hbls[j] * ( cff + sigma * (1. + sigma * (\\\\\\n a1 + a2*self.Gm1[j]+a3*self.dGm1_dS[j])))\\n\\n if k == N-1:\\n self.ws_debug = ws\\n self.hbls_debug = self.hbls[j]\\n self.cff_debug = cff\\n self.sigma_debug = sigma\\n self.a1_debug = a1\\n self.a2_debug = a2\\n self.a3_debug = a3\\n \\n self.Kt_surf[j,k_w] = ws * self.hbls[j] * ( cff + sigma * (1. + sigma * (\\\\\\n a1 + a2*self.Gt1[j]+a3*self.dGt1_dS[j])))\\n #---> end k-loop \\n if self.LMD_NONLOCAL:\\n if Bfsfc < 0:\\n self.ghat[j,k_w] = 0\\n self.ghat[j,k_w] = self.Cg * sigma * (1.-sigma)**2\\n else:\\n self.ghat[j,k_w] = 0.\\n\\n # ADD CONVECTIVE ADJUSTMENT IN SURFACE MIXED LAYER \\n if self.LMD_CONVEC and self.MLCONVEC: \\n for k in range(N-1,int(self.kbl[j]-1),-1):\\n k_w = k\\n k_r = k -1\\n\\n if self.bvf[j,k_w] < 0:\\n self.Kt_surf[j,k_w] = self.Kt_surf[j,k_w] + self.ffac*self.nu0c\\n\\n # ADD CONVECTIVE ADJUSTMENT BELOW SURFACE MIXED LAYER\\n # IF BKPP IS SWITCHED OFF!!\\n for k in range(int(self.kbl[j]-1),-1,-1):\\n k_w = k\\n k_r = k -1\\n if self.LMD_NONLOCAL:\\n self.ghat[j,k_w] = 0\\n if self.LMD_CONVEC and self.LMD_BKPP == False:\\n if self.bvf[j,k_w] < 0:\\n self.Kv_surf[j,k_w] = self.Kv_surf[j,k_w] + self.nu0c\\n self.Kt_surf[j,k_w] = self.Kt_surf[j,k_w] + self.nu0c\\n \\n\\n #---> end j-loop\",\n \"def _electron_multiplier(self, hdr):\\n d = {}\\n d['em yield'], d['em background'], d['em deadtime'] = \\\\\\n unpack(self._bo + 'd 2i', hdr.read(16))\\n return d\",\n \"def calc_gravitational_energy(self):\\n\\n star = self.star\\n\\n M, K, N = star.mesh_size\\n ph = star.phi_coords\\n mu = star.mu_coords\\n r = star.r_coords\\n\\n def S1(j, k):\\n sum = 0\\n\\n for i in range(0, M - 2, 2):\\n sum += (1 / 6) * (ph[i + 2] - ph[i]) * (star.rho[i, j, k] * star.Phi[i, j, k] + 4 *\\n star.rho[i + 1, j, k] * star.Phi[i + 1, j, k] +\\n star.rho[i + 2, j, k] * star.Phi[i + 2, j, k])\\n return 2 * sum\\n\\n def S2(k):\\n sum = 0\\n\\n for j in range(0, K - 2, 2):\\n sum += (1 / 6) * (mu[j + 2] - mu[j]) * \\\\\\n (S1(j, k) + 4 * S1(j + 1, k) + S1(j + 2, k))\\n\\n return 2 * sum\\n\\n W = 0\\n\\n for k in range(0, N - 2, 2):\\n W -= 0.5 * (1 / 6) * (r[k + 2] - r[k]) * (r[k]**2 * S2(k) +\\n 4 * r[k + 1]**2 * S2(k + 1) +\\n r[k + 2]**2 * S2(k + 2))\\n\\n return W\",\n \"def metro_ising(L, T, h):\\n\\n lattice = transform_lattice(np.random.rand(N + 2, N + 2)) # +2 because of periodic bounds\\n ising_chain = [lattice]\\n m = 0\\n\\n for i in range(L):\\n rand_row = np.random.choice(index)\\n rand_col = np.random.choice(index)\\n\\n if H(ising_chain[i], rand_row, rand_col, h, T):\\n new_lattice = ising_chain[i].copy()\\n new_lattice[rand_row][rand_col] *= -1\\n ising_chain.append(transform_lattice(new_lattice))\\n else:\\n ising_chain.append(ising_chain[i])\\n\\n m += np.sum(ising_chain[i][1:N + 1, 1:N + 1]) # magnetization\\n\\n return m\",\n \"def get_model_with_properties():\\n \\n m = ConcreteModel()\\n\\n # ------------------------------------------------------------------\\n # Data\\n # ------------------------------------------------------------------\\n\\n m.np = 25 # Number of possible tays\\n m.c = 4 # Number of components\\n m.lc = 1 # Light component\\n m.hc = 4 # Heavy component\\n\\n #### Constant parameters\\n m.Rgas = 8.314 # Ideal gas constant in J/mol K\\n m.Tref = 298.15 # Reference temperature in K\\n\\n #### Product specifications\\n m.xspec_lc = 0.99 # Final liquid composition for methanol (1)\\n m.xspec_hc = 0.99 # Fnal liquid composition for butanol (4)\\n m.xspec_inter2 = 0.99 # Final liquid composition for ethanol (2)\\n m.xspec_inter3 = 0.99 # Final liquid composition for propanol (3)\\n m.Ddes = 50 # Final flowrate in distillate in mol/s\\n m.Bdes = 50 # Final flowrate in bottoms in mol/s\\n m.Sdes = 50 # Final flowrate in side product streams in mol/s\\n\\n # #### Known initial values\\n m.Fi = m.Ddes + m.Bdes + 2 * m.Sdes # Side feed flowrate in mol/s\\n m.Vi = 400 # Initial value for vapor flowrate in mol/s\\n m.Li = 400 # Initial value for liquid flowrate in mol/s\\n\\n m.Tf = 358 # Side feed temperature in K\\n\\n m.Preb = 1.2 # Reboiler pressure in bar\\n m.Pbot = 1.12 # Bottom-most tray pressure in bar\\n m.Ptop = 1.08 # Top-most tray pressure in bar\\n m.Pcon = 1.05 # Condenser pressure in bar\\n m.Pf = 1.02\\n\\n m.rr0 = 0.893 # Internal reflux ratio initial value\\n m.bu0 = 0.871 # Internal reflux ratio initial value\\n\\n\\n #### Scaling factors\\n m.Hscale = 1e3 \\n m.Qscale = 1e-3 \\n\\n \\n #### Constants for the calculation of liquid heat capacity\\n m.cpc = {} # Constant 1 for liquid heat capacity \\n m.cpc2 = {} # Constant 2 for liquid heat capacity \\n m.cpc[1] = m.Rgas \\n m.cpc[2] = 1\\n m.cpc2['A', 1] = 1 / 100\\n m.cpc2['B', 1] = 1 / 1e4\\n m.cpc2['A', 2] = 1\\n m.cpc2['B', 2] = 1\\n\\n\\n # ------------------------------------------------------------------\\n # Physical Properties\\n #\\n # Notation:\\n # MW ........................ molecular weight in g/gmol\\n # TB ........................ boiling point temperature in K\\n # TC ........................ critical temperature in K\\n # PC ........................ critical pressure in bar\\n # w ........................ acentric factor\\n # lden ...................... liquid density g/m3,\\n # dHvap ..................... heat of vaporization in J/mol.\\n # vpA, vpB, vpC, and vpD .... vapor pressure constants\\n # cpA, cpB, cpC, and cpD .... heat capacity constants J/mol:\\n # 1 for liq and 2 for vapor phase\\n #\\n # Reference A: R.C. Reid, J.M. Prausnitz and B.E. Poling,\\n # \\\"The Properties of gases and liquids\\\", 1987 and 2004 Eds.\\n #\\n # ------------------------------------------------------------------\\n\\n m.prop = {} # Properties of components:\\n cpL = {} # Ruczika-D method for liquid heat capacity calculation\\n # (Reference A, page 6.20)\\n sumA = {}\\n sumB = {}\\n sumC = {}\\n cpL['a', 'C(H3)(C)'] = 4.19845\\n cpL['b', 'C(H3)(C)'] = -0.312709\\n cpL['c', 'C(H3)(C)'] = 0.178609\\n cpL['a', 'C(H2)(C2)'] = 2.7345\\n cpL['b', 'C(H2)(C2)'] = 0.122732\\n cpL['c', 'C(H2)(C2)'] = -0.123482\\n cpL['a', 'C(H2)(C)(O)'] = 0.517007\\n cpL['b', 'C(H2)(C)(O)'] = 1.26631\\n cpL['c', 'C(H2)(C)(O)'] = -0.0939713\\n cpL['a', 'O(H)(C)'] = 16.1555\\n cpL['b', 'O(H)(C)'] = -11.938\\n cpL['c', 'O(H)(C)'] = 2.85117\\n cpL['a', 'C(H3)(O)'] = 3.70344\\n cpL['b', 'C(H3)(O)'] = -1.12884\\n cpL['c', 'C(H3)(O)'] = 0.51239\\n sumA[1] = (cpL['a', 'C(H3)(O)']\\n + cpL['a', 'O(H)(C)']) \\n sumB[1] = (cpL['b', 'C(H3)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[1] = (cpL['c', 'C(H3)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[2] = (cpL['a', 'C(H3)(C)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[2] = (cpL['b', 'C(H3)(C)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[2] = (cpL['c', 'C(H3)(C)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[3] = (cpL['a', 'C(H3)(C)']\\n + cpL['a', 'C(H2)(C2)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[3] = (cpL['b', 'C(H3)(C)']\\n + cpL['b', 'C(H2)(C2)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[3] = (cpL['c', 'C(H3)(C)']\\n + cpL['c', 'C(H2)(C2)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[4] = (cpL['a', 'C(H3)(C)']\\n + 2 * cpL['a', 'C(H2)(C2)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[4] = (cpL['b', 'C(H3)(C)']\\n + 2 * cpL['b', 'C(H2)(C2)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[4] = (cpL['c', 'C(H3)(C)']\\n + 2 * cpL['c', 'C(H2)(C2)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n\\n ## Methanol: component 1\\n m.prop[1, 'MW'] = 32.042\\n m.prop[1, 'TB'] = 337.7\\n m.prop[1, 'TC'] = 512.6\\n m.prop[1, 'PC'] = 80.9\\n m.prop[1, 'w'] = 0.556\\n m.prop[1, 'lden'] = 792e3\\n m.prop[1, 'dHvap'] = 38.376e3\\n m.prop[1, 'vpA'] = -8.54796\\n m.prop[1, 'vpB'] = 0.76982\\n m.prop[1, 'vpC'] = -3.10850\\n m.prop[1, 'vpD'] = 1.54481\\n m.prop[1, 'cpA', 1] = sumA[1]\\n m.prop[1, 'cpB', 1] = sumB[1]\\n m.prop[1, 'cpC', 1] = sumC[1]\\n m.prop[1, 'cpD', 1] = 0\\n m.prop[1, 'cpA', 2] = 2.115e1\\n m.prop[1, 'cpB', 2] = 7.092e-2\\n m.prop[1, 'cpC', 2] = 2.587e-5\\n m.prop[1, 'cpD', 2] = -2.852e-8\\n\\n\\n ## Ethanol: component 2\\n m.prop[2, 'MW'] = 46.069\\n m.prop[2, 'TB'] = 351.4\\n m.prop[2, 'TC'] = 513.9\\n m.prop[2, 'PC'] = 61.4\\n m.prop[2, 'w'] = 0.644\\n m.prop[2, 'lden'] = 789.3e3\\n m.prop[2, 'dHvap'] = 42.698e3\\n m.prop[2, 'vpA'] = -8.51838\\n m.prop[2, 'vpB'] = 0.34163\\n m.prop[2, 'vpC'] = -5.73683\\n m.prop[2, 'vpD'] = 8.32581\\n m.prop[2, 'cpA', 1] = sumA[2]\\n m.prop[2, 'cpB', 1] = sumB[2]\\n m.prop[2, 'cpC', 1] = sumC[2]\\n m.prop[2, 'cpD', 1] = 0\\n m.prop[2, 'cpA', 2] = 9.014\\n m.prop[2, 'cpB', 2] = 2.141e-1\\n m.prop[2, 'cpC', 2] = -8.390e-5\\n m.prop[2, 'cpD', 2] = 1.373e-9\\n\\n\\n ## Propanol: component 3\\n m.prop[3, 'MW'] = 60.096\\n m.prop[3, 'TB'] = 370.3\\n m.prop[3, 'TC'] = 536.8\\n m.prop[3, 'PC'] = 51.7\\n m.prop[3, 'w'] = 0.623\\n m.prop[3, 'lden'] = 804e3\\n m.prop[3, 'dHvap'] = 47.763e3\\n m.prop[3, 'vpA'] = -8.05594\\n m.prop[3, 'vpB'] = 4.25183e-2\\n m.prop[3, 'vpC'] = -7.51296\\n m.prop[3, 'vpD'] = 6.89004\\n m.prop[3, 'cpA', 1] = sumA[3]\\n m.prop[3, 'cpB', 1] = sumB[3]\\n m.prop[3, 'cpC', 1] = sumC[3]\\n m.prop[3, 'cpD', 1] = 0\\n m.prop[3, 'cpA', 2] = 2.47\\n m.prop[3, 'cpB', 2] = 3.325e-1\\n m.prop[3, 'cpC', 2] = -1.855e-4\\n m.prop[3, 'cpD', 2] = 4.296e-8\\n\\n\\n ## Butanol: component 4\\n m.prop[4, 'MW'] = 74.123\\n m.prop[4, 'TB'] = 390.9\\n m.prop[4, 'TC'] = 563.1\\n m.prop[4, 'PC'] = 44.2\\n m.prop[4, 'w'] = 0.593\\n m.prop[4, 'lden'] = 810e3\\n m.prop[4, 'dHvap'] = 52.607e3\\n m.prop[4, 'vpA'] = -8.00756\\n m.prop[4, 'vpB'] = 0.53783\\n m.prop[4, 'vpC'] = -9.34240\\n m.prop[4, 'vpD'] = 6.68692\\n m.prop[4, 'cpA', 1] = sumA[4]\\n m.prop[4, 'cpB', 1] = sumB[4]\\n m.prop[4, 'cpC', 1] = sumC[4]\\n m.prop[4, 'cpD', 1] = 0\\n m.prop[4, 'cpA', 2] = 3.266\\n m.prop[4, 'cpB', 2] = 4.18e-1\\n m.prop[4, 'cpC', 2] = -2.242e-4\\n m.prop[4, 'cpD', 2] = 4.685e-8\\n\\n\\n return m\",\n \"def getFWHM(antenna, freq):\\n diameter = getDiameter(antenna)\\n lam = 299792458.0 / (freq * 1e9)\\n fwhmo = lam / math.pi * 180.0 * 60.0\\n fwhm = 1.22 * fwhmo / diameter\\n return fwhm\",\n \"def make_stehle(self):\\n\\n temp_k = self.temp * e / k # temperature in K\\n dens_cm = self.e_dens * 1.e-6 # electronic density in cm-3\\n prefix = 'n_' + str(self.n_upper) + '_' + str(self.n_lower) + '_'\\n\\n # extract raw tabulated tabulated_data\\n tab_temp_k = np.array(pystark.nc.variables[prefix + 'tempe'].data) # tabulated electron temperatures (K)\\n olam0 = pystark.nc.variables[prefix + 'olam0'].data # line centre wavelength (A)\\n num_tab_dens = pystark.nc.variables[prefix + 'id_max'].data\\n fainom = pystark.nc.variables[prefix + 'fainom'].data\\n tab_dens_cm = np.array(pystark.nc.variables[prefix + 'dense'].data) # tabulated electron densities (cm ** -3)\\n f00 = np.array(pystark.nc.variables[prefix + 'f00'].data) # normal Holtsmark field strength (30 kV / m)\\n dl12 = np.array(pystark.nc.variables[prefix + 'dl12'].data)\\n dl12s = np.array(pystark.nc.variables[prefix + 'dl12s'].data)\\n fainu = pystark.nc.variables[\\n prefix + 'fainu'].data # Asymptotic value of iStark * (alpha ** 2.5) (\\\"wings factor in alfa units\\\")\\n pr0 = np.array(pystark.nc.variables[\\n prefix + 'pr0'].data) # Ratio of the mean interelectronic distance to the electronic Debye length\\n jtot = np.array(pystark.nc.variables[prefix + 'jtot'].data,\\n dtype=np.int) # \\\"number of wave lengths for the couple (T,Ne)\\\"\\n dom = np.array(pystark.nc.variables[prefix + 'dom'].data) # frequency detunings in units (rad / (s*ues)\\n d1om = np.array(pystark.nc.variables[prefix + 'd1om'].data)\\n o1line = np.array(pystark.nc.variables[prefix + 'o1line'].data)\\n o1lines = np.array(pystark.nc.variables[prefix + 'o1lines'].data)\\n\\n # ensure given temperature + density falls within tabulated values\\n # change sligtly the value of the input density\\n # dens_cm in order to remain , as far as possible, inside the tabulation\\n # JSA: this first step seems bogus!\\n\\n if np.abs(dens_cm - tab_dens_cm[0]) / dens_cm <= 1.0E-3:\\n dens_cm = tab_dens_cm[0] * 1.001\\n\\n for id in np.arange(1, num_tab_dens + 1):\\n if np.abs(dens_cm - tab_dens_cm[id]) / dens_cm <= 1.0E-3:\\n dens_cm = tab_dens_cm[id] * 0.999\\n\\n if dens_cm >= 2.0 * tab_dens_cm[num_tab_dens]:\\n raise Exception(\\n 'Your input density is higher than the largest tabulated value %f' % tab_dens_cm[num_tab_dens])\\n\\n if dens_cm <= tab_dens_cm[0]:\\n raise Exception('Your input density is smaller than the smallest tabulated value %f' % tab_dens_cm[0])\\n\\n if temp_k >= tab_temp_k[9]:\\n raise Exception('Your input temperature is higher than the largest tabulated value %f' % tab_temp_k[9])\\n\\n if temp_k <= tab_temp_k[0]:\\n raise Exception('Your input temperature is lower than the smallest tabulated value %f' % tab_temp_k[0])\\n\\n normal_holtsmark_field = 1.25e-9 * (dens_cm ** (2. / 3.)) # normal field value in ues\\n\\n # calculate line centre wavelength and frequency using Rydberg formula\\n # JSA: I have made this step clearer and corrected for deuteron mass in the Rydberg constant (though the effect is small)\\n # TODO make sure this matches olam0 parameter above -- why were there two variables in the first place?!\\n # rydberg_m = Rydberg / (1. + (electron_mass / physical_constants['deuteron mass'][0]))\\n # wl_0_angst = 1e10 * (rydberg_m * (1 / n_lower ** 2 - 1 / n_upper ** 2)) ** -1\\n\\n wl_centre_angst = self.wl_centre * 1e10\\n\\n c_angst = c * 1e10 # velocity of light in Ansgtroms / s\\n angular_freq_0 = 2 * np.pi * c_angst / wl_centre_angst # rad / s\\n\\n otrans = -2 * np.pi * c_angst / wl_centre_angst ** 2\\n\\n olines = o1lines / np.abs(otrans)\\n oline = o1line / np.abs(otrans)\\n\\n # Limit analysis_tools to uncorrelated plasmas.\\n # check that mean interelectronic distance is smaller than the electronic Debye length (equ. 10)\\n PR0_exp = 0.0898 * (dens_cm ** (1. / 6.)) / np.sqrt(temp_k) # = (r0 / debye)\\n if PR0_exp > 1.:\\n raise Exception('The plasma is too strongly correlated\\\\ni.e. r0/debye=0.1\\\\nthe line cannot be computed.')\\n\\n # fainom_exp=fainom*(F00_exp**1.5)\\n # fainum_exp=fainom_exp/( (OPI*2.)**1.5)\\n\\n # ========================\\n # TABULATION Format CDS\\n # si on veut ecrire\\n # n -np lambda0 kalpha Ne E0 T R0/Debye Dalpha iDoppler iStark\\n\\n # IN_cds= N+0.01\\n # INP_cds = NP+0.01\\n\\n # ***********************************************************\\n # Don't edit the CDS format...\\n # ***********************************************************\\n\\n # Skipped the code in the IF statement starting at line 470, since it\\n # isn't used, if (.FALSE.) ...\\n\\n # ==============================================\\n # define an unique detunings grid - domm - for the tabulated\\n # profiles ( various temperatures , densities)\\n # calculate all the line shapes for this common grid\\n # units used at this points are Domega_new= Delta(omega)/F00\\n # in rd/(s-1 ues)\\n\\n max_num_dens = 30 # Maximum number of densities\\n max_num_tab_temp = 10\\n max_num_detunings = 60 # Maximum number of detunings\\n jtot = jtot.astype(np.int)\\n domm = np.zeros(100000)\\n dom0 = np.zeros(10000)\\n tprof = np.zeros([max_num_dens, max_num_tab_temp, 10000])\\n tprofs = np.zeros([max_num_dens, max_num_tab_temp, 10000])\\n uprof = np.zeros([max_num_dens, 10000])\\n uprofs = np.zeros([max_num_dens, 10000])\\n\\n inc = 0\\n domm[inc] = 0.0\\n # ---- Look to replace this loop\\n for id in np.arange(num_tab_dens + 1): # loop over tab densities\\n for j in np.arange(max_num_tab_temp): # loop over tab temperatures (?)\\n for i in np.arange(1, jtot[id, j]):\\n inc += 1\\n dom0[inc] = dom[id, j, i]\\n\\n inc = np.count_nonzero(dom)\\n npik = inc + 1\\n # nut=10000\\n\\n # Calling numpy sort instead of piksrt\\n tmp = np.sort(dom0[0:npik])\\n dom0[0:npik] = tmp[0:npik]\\n # dom0 seems to agree with the FORTRAN version\\n\\n inc = 0\\n domm[0] = 0.0\\n # print 'npik',npik\\n # ---- Look to replace this loop\\n for i in np.arange(1, npik):\\n dif = (dom0[i] - dom0[i - 1])\\n if dif <= 1.0E-6:\\n continue\\n if dif / np.abs(dom0[i]) <= 0.1:\\n continue\\n inc = inc + 1\\n domm[inc] = dom0[i]\\n\\n jdom = inc + 1 # One line after marker 35\\n\\n for id in np.arange(num_tab_dens):\\n for j in np.arange(10):\\n if pr0[id, j] > 1.0:\\n continue\\n\\n tprof[id, j, 0] = oline[id, j, 0]\\n tprofs[id, j, 0] = olines[id, j, 0]\\n\\n if jtot[id, j] == 0:\\n continue\\n\\n for i in np.arange(1, jdom + 1):\\n skip1 = False\\n skip2 = False\\n # print 'i',i\\n domeg = domm[i]\\n ij_max = jtot[id, j]\\n # print 'domeg,ij_max',domeg,ij_max\\n for ij in np.arange(1, ij_max - 1):\\n # print 'ij',ij\\n test = (domeg - dom[id, j, ij]) * (domeg - dom[id, j, ij - 1])\\n # print 'test1:',test\\n if test <= 0.0:\\n # print 'triggered test1'\\n x1 = dom[id, j, ij - 1]\\n x2 = dom[id, j, ij]\\n x3 = dom[id, j, ij + 1]\\n y1 = oline[id, j, ij - 1]\\n y2 = oline[id, j, ij]\\n y3 = oline[id, j, ij + 1]\\n # print 'x1,x2,x3',x1,x2,x3\\n # print 'y1,y2,y3',y1,y2,y3\\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n y1 = olines[id, j, ij - 1]\\n y2 = olines[id, j, ij]\\n y3 = olines[id, j, ij + 1]\\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n # print 'tprof[id,j,i]',tprof[id,j,i]\\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\\n skip1 = True\\n skip2 = True\\n break\\n\\n if skip1 is False:\\n test = (domeg - dom[id, j, ij_max - 2]) * (domeg - dom[id, j, ij_max - 1])\\n # print 'test2:',test\\n # print 'domeg',domeg\\n # print 'dom[id,j,ij_max-1]',dom[id,j,ij_max-2]\\n # print 'dom[id,j,ij_max]',dom[id,j,ij_max-1]\\n if test <= 0.0:\\n # print 'triggered test2'\\n x1 = dom[id, j, ij_max - 3]\\n x2 = dom[id, j, ij_max - 2]\\n x3 = dom[id, j, ij_max - 1]\\n y1 = oline[id, j, ij_max - 3]\\n y2 = oline[id, j, ij_max - 2]\\n y3 = oline[id, j, ij_max - 1]\\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n y1 = olines[id, j, ij_max - 3]\\n y2 = olines[id, j, ij_max - 2]\\n y3 = olines[id, j, ij_max - 1]\\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n skip2 = True\\n # print 'x1,x2,x3',x1,x2,x3\\n # print 'y1,y2,y3',y1,y2,y3\\n # print 'tprof[id,j,i]',tprof[id,j,i]\\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\\n continue\\n\\n if skip2 is False:\\n if domeg > dom[id, j, ij_max]:\\n # print 'triggered test3'\\n tprof[id, j, i] = fainom / (domeg ** 2.5)\\n tprofs[id, j, i] = tprof[id, j, i]\\n continue\\n\\n # We can skip writing the intermediate file\\n\\n\\n for id in np.arange(num_tab_dens):\\n otest_dens = (dens_cm - tab_dens_cm[id]) * (dens_cm - tab_dens_cm[id + 1])\\n if otest_dens <= 0.0:\\n dense1 = tab_dens_cm[id]\\n dense2 = tab_dens_cm[id + 1]\\n id1 = id\\n id2 = id + 1\\n break\\n\\n if dens_cm >= tab_dens_cm[num_tab_dens]:\\n dense1 = tab_dens_cm[num_tab_dens - 1]\\n dense2 = tab_dens_cm[num_tab_dens]\\n id1 = num_tab_dens - 1\\n id2 = num_tab_dens\\n\\n for it in np.arange(10):\\n otest = (temp_k - tab_temp_k[it]) * (temp_k - tab_temp_k[it + 1])\\n if otest <= 0.0:\\n it1 = it\\n it2 = it + 1\\n # pr01 = pr0[id2,it1] # max value of pr0 for T1,T2,dense1,dense2\\n tempe1 = tab_temp_k[it]\\n tempe2 = tab_temp_k[it + 1]\\n break\\n\\n # interpolation in temperature\\n for id in np.arange(id1, id2 + 1):\\n for i in np.arange(jdom):\\n uprof[id, i] = tprof[id, it1, i] + (temp_k - tempe1) * (tprof[id, it2, i] - tprof[id, it1, i]) / (\\n tempe2 - tempe1)\\n uprofs[id, i] = tprofs[id, it1, i] + (temp_k - tempe1) * (tprofs[id, it2, i] - tprofs[id, it1, i]) / (\\n tempe2 - tempe1)\\n\\n delta_lambda = np.zeros(jdom)\\n delta_nu = np.zeros(jdom)\\n wprof_nu = np.zeros(jdom)\\n wprofs_nu = np.zeros(jdom)\\n\\n for i in np.arange(jdom):\\n wprof = uprof[id1, i] + (dens_cm - dense1) * (uprof[id2, i] - uprof[id1, i]) / (dense2 - dense1)\\n wprofs = uprofs[id1, i] + (dens_cm - dense1) * (uprofs[id2, i] - uprofs[id1, i]) / (dense2 - dense1)\\n delta_omega = domm[i] * normal_holtsmark_field\\n delta_nu[i] = delta_omega / (2 * np.pi)\\n delta_lambda[i] = wl_centre_angst * delta_omega / (angular_freq_0 + delta_omega)\\n # print(delta_lambda[i])\\n wprof_nu[i] = (wprof / normal_holtsmark_field) * (2. * np.pi)\\n wprofs_nu[i] = (wprofs / normal_holtsmark_field) * (2. * np.pi)\\n # print '%e %e %e %e' %(delta_lambda[i],delta_nu[i],wprof_nu[i],wprofs_nu[i])\\n\\n delta_lambda2 = np.concatenate((-delta_lambda[::-1], delta_lambda)) + wl_centre_angst # + olam0\\n delta_nu2 = np.concatenate((-delta_nu[::-1], delta_nu))\\n wprof_nu2 = np.concatenate((wprof_nu[::-1], wprof_nu))\\n wprofs_nu2 = np.concatenate((wprofs_nu[::-1], wprofs_nu))\\n\\n # for some reason, i only get a good agreement with the other models if i take the pure Stark broadened Stehle\\n # output and manually convolve it with the Doppler profile -- not sure why...\\n ls_sd = wprofs_nu2\\n\\n # interpolate onto frequency axis\\n ls_sd = np.interp(self.freq_axis, delta_nu2 + self.freq_centre, ls_sd)\\n\\n return ls_sd\",\n \"def get_hbls_hbbl(self):\\n [Ly,N] = self.b.shape\\n z_u_w = self.grid_dict['z_u_w']\\n z_u_r = self.grid_dict['z_u_r']\\n u = self.u\\n v = self.v\\n \\n v_upts = TTTW_func.v2u(v)\\n Hz = z_u_w[:,1:] - z_u_w[:,:-1]\\n\\n\\n\\n # CALCULATE swr_frac\\n self.swr_frac = TTTW_func.lmd_swr_frac(self.grid_dict)\\n\\n\\n # WHOLE THING HAPPENS IN j loop through y-indices\\n \\n # INITIALIZE ARRAYS\\n self.kmo = np.zeros([Ly])\\n self.Cr = np.zeros([Ly])\\n self.kbl = np.empty([Ly],dtype='int')\\n self.C_h_MO = np.zeros([Ly])\\n self.Cr = np.zeros([Ly,N+1]) # sum term\\n self.FC = np.zeros([Ly,N+1])\\n self.swdk_r = np.zeros([Ly,N+1])\\n \\n self.zscale = np.zeros([Ly,N])\\n self.Kern = np.zeros([Ly,N])\\n\\n \\n # --> LOOP THROUGH Y-INDICES\\n for j in range(Ly):\\n if self.LIMIT_MO_DEPTH:\\n self.kmo[j] = 0\\n self.C_h_MO[j] = self.C_MO *self.ustar[j]**3/self.vonKar\\n \\n self.kbl[j] = 0\\n self.Cr[j,-1] = 0 # set top Cr\\n self.Cr[j,0] = 0 # set bottom Cr\\n \\n # SEARCH FOR MIXED LAYER DEPTH\\n self.FC[j,-1] = 0.\\n\\n\\n # ---> LOOP TOP TO BOTTOM (FORTRAN ==> k=N-1,1,-1)\\n for k in range(N-1,0,-1):\\n # INDEX MAP\\n k_r = k-1\\n k_w = k\\n\\n \\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\\n self.zscale[j,k_w] = zscale\\n if self.LMD_KPP:\\n if self.LMD_BKPP:\\n zscaleb = z_u_r[j,k_r] - z_u_w[j,0]\\n Kern = zscale * zscaleb**2 / ( (zscale + self.epssfcs*self.hbls_old[j]) * (zscaleb**2+(self.epssfcb**2*self.hbbl_old[j]**2)))\\n else:\\n Kern = zscale / (zscale + (self.epssfcs*self.hbls_old[j]))\\n else:\\n Kern = 1.\\n \\n\\n\\n self.Kern[j,k_w] = Kern\\n self.FC[j,k_w] = self.FC[j,k_w+1] + Kern * (\\\\\\n ( ( u[j,k_r+1] - u[j,k_r] )**2 + ( v_upts[j,k_r+1] - v_upts[j,k_r])**2 ) \\\\\\n / (Hz[j,k_r] + Hz[j,k_r+1]) \\\\\\n - 0.5 * ( Hz[j,k_r] + Hz[j,k_r+1]) * (self.Ri_inv * self.bvf[j,k_w] + self.C_Ek*self.f[j]*self.f[j]))\\n\\n\\n #\\t\\tLOOP THAT FINDS BL DEPTH ##\\n #----> LOOP TOP TO BOTTOM (start at free surface, w-level surface) \\n \\n if self.LMD_KPP:\\n #swdk_r only used in this function so don't need to be class attribute\\n # but for testing make it an attribute to see what it is\\n \\n # fortran equivlanet ===> k=N,1,-1 \\n for k in range(N,0,-1):\\n # INDEX MAP\\n k_r = k-1\\n k_w = k\\n\\n ###################################################################### \\n self.swdk_r[j,k_w] = np.sqrt( self.swr_frac[j,k_w] * self.swr_frac[j,k_w-1])\\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\\n Bfsfc = self.Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])\\n \\n self.bvf_max = np.sqrt(np.max([0,self.bvf[j,k_w-1]]))\\n \\n # CALCULATE TURBULENT VELOCITY SCALE FOR TRACERS\\n \\t\\t\\t self.ws = self.lmd_wscale_ws_only(Bfsfc, zscale,self.hbls_old[j],self.ustar[j])\\n \\n self.Vtsq = self.Vtc * self.ws* self.bvf_max + self.V0\\n \\n\\n self.Cr[j,k_w] = self.FC[j,k_w] + self.Vtsq\\n \\n\\n #######################################################################\\n \\n # SEARCH FOR hbls vertical level #\\n '''\\n kbl is specified at vertical w-level (via Cr which is at\\n vertical w-levels)\\n '''\\n if self.kbl[j] == 0 and self.Cr[j,k_w] < 0:\\n self.kbl[j] = k_w\\n if self.LIMIT_MO_DEPTH:\\n if self.kmo[j] == 0 and Bfsfc*(z_u_w[j,N] - z_u_r[j,k_r]) > self.C_h_MO[j]:\\n self.kmo[j] = k_w\\n\\n \\n #--> still in j-loop\\n #######################################################\\n \\n # \\t\\tGET SURFACE BOUNDARY LAYER DEPTH # \\n self.hbls[j] = z_u_w[j,N] - z_u_w[j,0] + self.eps # set hbls as depth of entire water column\\n if self.kbl[j] > 0:\\n k_w = self.kbl[j]\\n k_r = k_w - 1 \\n if k_w == N: # set hbls at the surface btwn w- and rho-levels at surface\\n self.hbls[j] = z_u_w[j,N] - z_u_r[j,N-1]\\n \\n else:\\n self.hbls[j] = z_u_w[j,N] - ( z_u_r[j,k_r] * self.Cr[j,k_w+1] - z_u_r[j,k_r+1] * self.Cr[j,k_w]) / \\\\\\n (self.Cr[j,k_w+1] - self.Cr[j,k_w])\\n \\n if self.LIMIT_MO_DEPTH:\\n if self.kmo[j] > 0:\\n k_w = self.kmo[j]\\n k_r = k_w-1\\n if k_w == N:\\n z_up = z_u_w[j,N]\\n cff_up = np.max([0,Bo[j]])\\n else:\\n z_up = z_r[j,k_w+1]\\n cff_up = np.max([0, Bo[j] + self.Bosol[j]*(1-self.swdk_r[j,(k_w-1)+1])])\\n \\n cff_dn = np.max([0,Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])]) \\n h_MO = z_u_w[j,N] + self.C_h_MO[j] * ( cff_up*z_up - cff_dn * z_u_r[j,k_r] ) \\\\\\n / ( cff_up * cff_dn * (z_up - z_u_r[j,k_r]) ) \\\\\\n + self.C_h_MO[j] * (cff_dn - cff_up)\\n\\n self.hbls[j] = np.min([self.hbls[j],np.max([h_MO,0])])\\n\\n\\n\\n #### GET BOTTOM BOUNDARY LAYER DEPTH #######\\n if self.LMD_BKPP:\\n self.kbl[j] = 0 # reset Cr at bottom and kbl for BKPP\\n self.Cr[j,0] = 0.\\n self.FC[j,0] = 1.5 * self.FC[j,1] - 0.5 * self.FC[j,2] # linear extrapolation\\n \\n #---> LOOP BOTTOM TO TOP\\n # FIND kbl for BBL\\n for k in range(1,N+1):\\n k_r = k-1\\n k_w = k \\n self.Cr[j,k_w] = self.FC[j,k_w] - self.FC[j,0]\\n \\n # LOOK FOR FIRST ZERO CROSSING FROM BOTTOM UP\\n if self.kbl[j] == 0 and self.Cr[j,k_w] > 0:\\n self.kbl[j] = k_w \\n \\n\\n self.hbbl[j] = z_u_w[j,N] - z_u_w[j,0] # total depth\\n if self.kbl[j] > 0 :\\n k_w = self.kbl[j] \\n k_r = k_w -1\\n if k_w == 1: # NO BBL CASE\\n self.hbbl[j] = z_u_r[j,0] - z_u_w[j,0] #in between bottom rho and w-level\\n else:\\n self.hbbl[j] = ( z_u_r[j,k_r-1] * self.Cr[j,k_w] - z_u_r[j,k_r] * self.Cr[j,k_w-1]) / \\\\\\n (self.Cr[j,k_w] - self.Cr[j,k_w-1]) - z_u_w[j,0]\",\n \"def power_output_hydro_rule(_m, g, y, s, t):\\r\\n\\r\\n if y != m.Y.last() and t != m.T.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_8[g, y, s, t]\\r\\n + m.sigma_20[g, y, s, t] - m.sigma_20[g, y, s, t + 1]\\r\\n - m.sigma_23[g, y, s, t] + m.sigma_23[g, y, s, t + 1]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + (m.DELTA[y] * m.RHO[y, s] * m.C_MC[g, y])\\r\\n == 0)\\r\\n\\r\\n elif y != m.Y.last() and t == m.T.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_8[g, y, s, t]\\r\\n + m.sigma_20[g, y, s, t]\\r\\n - m.sigma_23[g, y, s, t]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + (m.DELTA[y] * m.RHO[y, s] * m.C_MC[g, y])\\r\\n == 0)\\r\\n\\r\\n elif y == m.Y.last() and t != m.T.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_8[g, y, s, t]\\r\\n + m.sigma_20[g, y, s, t] - m.sigma_20[g, y, s, t + 1]\\r\\n - m.sigma_23[g, y, s, t] + m.sigma_23[g, y, s, t + 1]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + (m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE)) * m.C_MC[g, y])\\r\\n == 0)\\r\\n\\r\\n elif y == m.Y.last() and t == m.T.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_8[g, y, s, t]\\r\\n + m.sigma_20[g, y, s, t]\\r\\n - m.sigma_23[g, y, s, t]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + (m.DELTA[y] * m.RHO[y, s] * (1 + (1 / m.INTEREST_RATE)) * m.C_MC[g, y])\\r\\n == 0)\\r\\n\\r\\n else:\\r\\n raise Exception(f'Unexpected case: {g, y, s, t}')\",\n \"def pyridine_mH():\\n coords = [\\n [1.3603, 0.0256, 0.0],\\n [0.6971, -1.202, 0.0],\\n [-0.6944, -1.2184, 0.0],\\n [-1.3895, -0.0129, 0.0],\\n [-0.6712, 1.1834, 0.0],\\n [0.6816, 1.196, 0.0],\\n [1.2665, -2.1365, 0.0],\\n [-1.2365, -2.1696, 0.0],\\n [-2.4837, 0.0011, 0.0],\\n [-1.1569, 2.1657, 0.0],\\n ]\\n coords = [[float(j) / Bohr for j in i] for i in coords]\\n\\n symbols = [\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"N\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n ]\\n\\n atoms = []\\n for i, _ in enumerate(coords):\\n atoms.append(Atom(symbols[i], position=coords[i]))\\n return Molecule(symbols=atoms)\",\n \"def power_output_existing_thermal_rule(_m, g, y, s, t):\\r\\n\\r\\n if y != m.Y.last() and t != m.T.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_2[g, y, s, t]\\r\\n + m.sigma_20[g, y, s, t] - m.sigma_20[g, y, s, t + 1]\\r\\n - m.sigma_23[g, y, s, t] + m.sigma_23[g, y, s, t + 1]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + ((m.DELTA[y] * m.RHO[y, s]) * (\\r\\n m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])))\\r\\n == 0)\\r\\n\\r\\n elif y != m.Y.last() and t == m.T.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_2[g, y, s, t]\\r\\n + m.sigma_20[g, y, s, t]\\r\\n - m.sigma_23[g, y, s, t]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + ((m.DELTA[y] * m.RHO[y, s]) * (\\r\\n m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])))\\r\\n == 0)\\r\\n\\r\\n elif y == m.Y.last() and t != m.T.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_2[g, y, s, t]\\r\\n + m.sigma_20[g, y, s, t] - m.sigma_20[g, y, s, t + 1]\\r\\n - m.sigma_23[g, y, s, t] + m.sigma_23[g, y, s, t + 1]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + ((m.DELTA[y] * m.RHO[y, s]) * (1 + (1 / m.INTEREST_RATE)) * (\\r\\n m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])))\\r\\n == 0)\\r\\n\\r\\n elif y == m.Y.last() and t == m.T.last():\\r\\n return (- m.sigma_1[g, y, s, t] + m.sigma_2[g, y, s, t]\\r\\n + m.sigma_20[g, y, s, t]\\r\\n - m.sigma_23[g, y, s, t]\\r\\n - m.lamb[self.k(m, g), y, s, t]\\r\\n + ((m.DELTA[y] * m.RHO[y, s]) * (1 + (1 / m.INTEREST_RATE)) * (\\r\\n m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])))\\r\\n == 0)\\r\\n\\r\\n else:\\r\\n raise Exception(f'Unhandled case: {g, y, s, t}')\",\n \"def _get_properties_building_systems(self):\\n\\n # Refactored from CalcThermalLoads\\n\\n # geometry properties.\\n\\n Ll = self.geometry['Blength']\\n Lw = self.geometry['Bwidth']\\n nf_ag = self.geometry['floors_ag']\\n nf_bg = self.geometry['floors_bg']\\n phi_pipes = self._calculate_pipe_transmittance_values()\\n\\n # nominal temperatures\\n Ths_sup_ahu_0 = float(self.hvac['Tshs0_ahu_C'])\\n Ths_re_ahu_0 = float(Ths_sup_ahu_0 - self.hvac['dThs0_ahu_C'])\\n Ths_sup_aru_0 = float(self.hvac['Tshs0_aru_C'])\\n Ths_re_aru_0 = float(Ths_sup_aru_0 - self.hvac['dThs0_aru_C'])\\n Ths_sup_shu_0 = float(self.hvac['Tshs0_shu_C'])\\n Ths_re_shu_0 = float(Ths_sup_shu_0 - self.hvac['dThs0_shu_C'])\\n Tcs_sup_ahu_0 = self.hvac['Tscs0_ahu_C']\\n Tcs_re_ahu_0 = Tcs_sup_ahu_0 + self.hvac['dTcs0_ahu_C']\\n Tcs_sup_aru_0 = self.hvac['Tscs0_aru_C']\\n Tcs_re_aru_0 = Tcs_sup_aru_0 + self.hvac['dTcs0_aru_C']\\n Tcs_sup_scu_0 = self.hvac['Tscs0_scu_C']\\n Tcs_re_scu_0 = Tcs_sup_scu_0 + self.hvac['dTcs0_scu_C']\\n\\n Tww_sup_0 = self.hvac['Tsww0_C']\\n # Identification of equivalent lengths\\n fforma = self._calc_form() # factor form comparison real surface and rectangular\\n Lv = (2 * Ll + 0.0325 * Ll * Lw + 6) * fforma # length vertical lines\\n if nf_ag < 2 and nf_bg < 2: # it is assumed that building with less than a floor and less than 2 floors udnerground do not have\\n Lcww_dis = 0\\n Lvww_c = 0\\n else:\\n Lcww_dis = 2 * (Ll + 2.5 + nf_ag * self.geometry['floor_height']) * fforma # length hot water piping circulation circuit\\n Lvww_c = (2 * Ll + 0.0125 * Ll * Lw) * fforma # length piping heating system circulation circuit\\n\\n Lsww_dis = 0.038 * Ll * Lw * nf_ag * self.geometry['floor_height'] * fforma # length hot water piping distribution circuit\\n Lvww_dis = (Ll + 0.0625 * Ll * Lw) * fforma # length piping heating system distribution circuit\\n\\n building_systems = pd.Series({'Lcww_dis': Lcww_dis,\\n 'Lsww_dis': Lsww_dis,\\n 'Lv': Lv,\\n 'Lvww_c': Lvww_c,\\n 'Lvww_dis': Lvww_dis,\\n 'Ths_sup_ahu_0': Ths_sup_ahu_0,\\n 'Ths_re_ahu_0': Ths_re_ahu_0,\\n 'Ths_sup_aru_0': Ths_sup_aru_0,\\n 'Ths_re_aru_0': Ths_re_aru_0,\\n 'Ths_sup_shu_0': Ths_sup_shu_0,\\n 'Ths_re_shu_0': Ths_re_shu_0,\\n 'Tcs_sup_ahu_0': Tcs_sup_ahu_0,\\n 'Tcs_re_ahu_0': Tcs_re_ahu_0,\\n 'Tcs_sup_aru_0': Tcs_sup_aru_0,\\n 'Tcs_re_aru_0': Tcs_re_aru_0,\\n 'Tcs_sup_scu_0': Tcs_sup_scu_0,\\n 'Tcs_re_scu_0': Tcs_re_scu_0,\\n 'Tww_sup_0': Tww_sup_0,\\n 'Y': phi_pipes,\\n 'fforma': fforma})\\n return building_systems\",\n \"def calculate_sh(self):\\n if self.data.get('Specific_Humidity') is None:\\n if self.data.get('Mixing_Ratio') is None:\\n raise KeyError('Calculate mixing ratio first!')\\n else:\\n w_kg = self.data['Mixing_Ratio'] / 1000\\n self.data['Specific_Humidity'] = (w_kg / (w_kg + 1)) * 1000\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63388246","0.59296554","0.58371013","0.5741513","0.5706787","0.5706253","0.5702298","0.559826","0.5589624","0.5559046","0.55509424","0.5541427","0.5477874","0.54478437","0.54435796","0.54355675","0.5431773","0.54197073","0.54046607","0.5366216","0.5342906","0.5332282","0.5297081","0.5296378","0.5293722","0.5293243","0.5289924","0.5285701","0.52830154","0.5265153","0.5252846","0.5240023","0.5232306","0.52303547","0.52186745","0.5215392","0.52062607","0.5205631","0.5191218","0.5183793","0.5178077","0.5175086","0.5174846","0.51748145","0.5174548","0.5173657","0.5173308","0.5159481","0.5157932","0.51502347","0.5144459","0.51438767","0.51435935","0.51435876","0.51418734","0.5130194","0.5124589","0.5121607","0.5114437","0.51135933","0.51107615","0.51062477","0.5106064","0.51018065","0.5100941","0.50968075","0.50930965","0.5090978","0.508625","0.5084683","0.5079283","0.50767165","0.5072533","0.50634205","0.50610393","0.5057483","0.5056152","0.50500214","0.50484574","0.5042405","0.5041083","0.50390947","0.5035177","0.50351423","0.5030713","0.5028759","0.50259537","0.5021321","0.501806","0.5012193","0.5011524","0.50098175","0.5007625","0.49991122","0.49936348","0.49916163","0.49907434","0.49878383","0.4986744","0.4985991","0.49848711"],"string":"[\n \"0.63388246\",\n \"0.59296554\",\n \"0.58371013\",\n \"0.5741513\",\n \"0.5706787\",\n \"0.5706253\",\n \"0.5702298\",\n \"0.559826\",\n \"0.5589624\",\n \"0.5559046\",\n \"0.55509424\",\n \"0.5541427\",\n \"0.5477874\",\n \"0.54478437\",\n \"0.54435796\",\n \"0.54355675\",\n \"0.5431773\",\n \"0.54197073\",\n \"0.54046607\",\n \"0.5366216\",\n \"0.5342906\",\n \"0.5332282\",\n \"0.5297081\",\n \"0.5296378\",\n \"0.5293722\",\n \"0.5293243\",\n \"0.5289924\",\n \"0.5285701\",\n \"0.52830154\",\n \"0.5265153\",\n \"0.5252846\",\n \"0.5240023\",\n \"0.5232306\",\n \"0.52303547\",\n \"0.52186745\",\n \"0.5215392\",\n \"0.52062607\",\n \"0.5205631\",\n \"0.5191218\",\n \"0.5183793\",\n \"0.5178077\",\n \"0.5175086\",\n \"0.5174846\",\n \"0.51748145\",\n \"0.5174548\",\n \"0.5173657\",\n \"0.5173308\",\n \"0.5159481\",\n \"0.5157932\",\n \"0.51502347\",\n \"0.5144459\",\n \"0.51438767\",\n \"0.51435935\",\n \"0.51435876\",\n \"0.51418734\",\n \"0.5130194\",\n \"0.5124589\",\n \"0.5121607\",\n \"0.5114437\",\n \"0.51135933\",\n \"0.51107615\",\n \"0.51062477\",\n \"0.5106064\",\n \"0.51018065\",\n \"0.5100941\",\n \"0.50968075\",\n \"0.50930965\",\n \"0.5090978\",\n \"0.508625\",\n \"0.5084683\",\n \"0.5079283\",\n \"0.50767165\",\n \"0.5072533\",\n \"0.50634205\",\n \"0.50610393\",\n \"0.5057483\",\n \"0.5056152\",\n \"0.50500214\",\n \"0.50484574\",\n \"0.5042405\",\n \"0.5041083\",\n \"0.50390947\",\n \"0.5035177\",\n \"0.50351423\",\n \"0.5030713\",\n \"0.5028759\",\n \"0.50259537\",\n \"0.5021321\",\n \"0.501806\",\n \"0.5012193\",\n \"0.5011524\",\n \"0.50098175\",\n \"0.5007625\",\n \"0.49991122\",\n \"0.49936348\",\n \"0.49916163\",\n \"0.49907434\",\n \"0.49878383\",\n \"0.4986744\",\n \"0.4985991\",\n \"0.49848711\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77465,"cells":{"query":{"kind":"string","value":"find the peak time for that event (INTENSITY)"},"document":{"kind":"string","value":"def find_peak(mhw, mhw_relSeas, ev, tt_start):\n tt_peak = np.argmax(mhw_relSeas)\n mhw[\"time_peak\"].append(mhw[\"time_start\"][ev] + tt_peak)\n mhw[\"date_peak\"].append(date.fromordinal(mhw[\"time_start\"][ev] + tt_peak))\n mhw[\"index_peak\"].append(tt_start + tt_peak)\n\n return mhw, tt_peak"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def peak_time(self):\n return np.array([self.wftime[ch][self.waveform[ch].argmax()] for ch in range(self.nchannels)])","def peak(self):\n pass","def find_alert_time(self) -> None:\n \n # Also not clear from the paper how to doe this,\n # use the first 10 data points in the light curve to determine the magnitude\n # baseline\n\n mean_mag = np.mean(self.mags[:10])\n std_mag = np.std(self.mags[:10])\n\n num_above = 0 \n i = 9\n\n while num_above < 3 and i < len(self.times)-1:\n \n i += 1 \n\n if self.mags[i] < mean_mag - std_mag:\n num_above += 1\n else:\n num_above = 0.0\n\n if len(self.times) - 1 == i:\n print(\"Give me more training data, not alerted yet, this is probably going to fail\")\n \n return self.times[i-1]","def max_intensity(self, time):\n ti = np.where(time == self.times)[0][0]\n return self.timesteps[ti].max()","def pull_peak_times(data):\n bin_centers = np.arange(0.,1.501,0.002)\n data = np.asarray(data)\n maxs = np.argmax(data, axis=1)\n return bin_centers[maxs]","def get_peak_Vm_time(self, cellname, tstart, tend):\n istart = int(tstart/self.plotdt+0.5)\n iend = int(tend/self.plotdt+0.5)\n try:\n data = self.datafile['/Vm/'+cellname][istart:iend]\n except KeyError:\n print 'get_peak_Vm_time: no Vm entry for', cellname\n return -1\n peakindex = data.argmax()\n peaktime = tstart + peakindex * self.plotdt\n print 'peak time:', cellname, ':', peaktime\n return peaktime","def peak_uncertainty(t, S, P):\n t = t.jyear\n N = S.size\n T = t.max() - t.min()\n amp, phase, offset = find_sine_params(t, S - S.mean(), P)\n sine = sinefunc(t, P, amp, phase, offset)\n residual = (S - S.mean()) - sine\n sigma = residual.std()\n #print \"P=%0.3f N=%i T=%0.1f amp=%0.3g sigma=%0.3g\" % (P, N, T, amp, sigma)\n return 3. * sigma * P**2 / (4. * T * amp * np.sqrt(N))","def peak_PSF(self):\n im, strehl = self.compute_PSF(np.zeros(self.N_act))\n return strehl","def getPeakValue( self ):\n nCurrentMax = max( self.data.max(), -self.data.min() )\n return float(nCurrentMax) / self.getSampleMaxValue()","def peak(self) -> Tuple[MeasureInput, MeasureResult]:\n assert self._data\n return self._data[0][2]","def peak(self):\n return self.peak","def max_time(self):\n return self.time[np.argmax(self.flux)]","def peak(self) -> Point:\n return self.most_intense_point()","def _peaktimes(x, prc=95, t_buffer=.01, fs=1000):\n if np.logical_or(prc < 0, prc >= 100):\n raise ValueError('Percentile threshold must be between 0 and 100.')\n\n samp_buffer = np.int(np.round(t_buffer * fs))\n hi = x > np.percentile(x, prc)\n event_intervals = _chunk_time(hi, samp_buffer=samp_buffer)\n E = np.int(np.size(event_intervals) / 2)\n events = np.zeros(E, dtype=object)\n\n for e in range(E):\n temp = x[np.arange(event_intervals[e][0], event_intervals[e][1] + 1)]\n events[e] = event_intervals[e][0] + np.argmax(temp)\n\n return events","def peak_finder(thresh=0):\n last = 0 # Track last input value\n ascent_dist = 0 # Distance from last trough.\n ascent_start = None # Last trough height\n\n def detect_peak(data):\n \"\"\" Returns initialized function to detect peaks on live streaming data.\n\n Args:\n data (numeric value): Input data point.\n\n Returns:\n If peak is detected return peak value, else return None\n \"\"\"\n nonlocal last, ascent_dist, ascent_start\n if data > last:\n if ascent_start is None:\n ascent_start = last\n ascent_dist += 1\n else:\n if ascent_dist:\n peak = last\n ascent_dist = 0\n if (peak - ascent_start) > thresh:\n last = data\n ascent_start = None\n return peak\n ascent_start = None\n last = data\n return None\n\n return detect_peak","def get_peakiness(spot_data):\n return spot_data[3] / np.mean((spot_data[5], spot_data[6]))","def peak_height(self):\n return np.array([max(self.waveform[ch]) for ch in range(self.nchannels)])","def peak(self):\n\n return self._data[0]","def calculate_peak_prominence(data, index):\n current_peak = data[index]\n\n # ignore values at either end of the dataset or values that are not local maxima\n if (\n index == 0\n or index == len(data) - 1\n or data[index - 1] > current_peak\n or data[index + 1] > current_peak\n or (data[index - 1] == current_peak and data[index + 1] == current_peak)\n ):\n return 0\n\n # by definition, the prominence of the highest value in a dataset is equal to the value itself\n if current_peak == max(data):\n return np.log(current_peak)\n\n # find index of nearest maxima which is higher than the current peak\n higher_peaks_inds = [i for i, x in enumerate(data) if x > current_peak]\n\n right_peaks = [x for x in higher_peaks_inds if x > index]\n if right_peaks:\n closest_right_ind = min(right_peaks)\n else:\n closest_right_ind = np.inf\n\n left_peaks = [x for x in higher_peaks_inds if x < index]\n if left_peaks:\n closest_left_ind = max(left_peaks)\n else:\n closest_left_ind = -np.inf\n\n right_distance = closest_right_ind - index\n left_distance = index - closest_left_ind\n\n if (right_distance) > (left_distance):\n closest = closest_left_ind\n else:\n closest = closest_right_ind\n\n # find the value at the lowest point between the nearest higher peak (the key col)\n lo = min(closest, index)\n hi = max(closest, index)\n between_slice = data[lo:hi]\n key_col = min(between_slice)\n\n prominence = np.log(data[index] - key_col + 1)\n\n return prominence","def detect_peak(data):\n nonlocal last, ascent_dist, ascent_start\n if data > last:\n if ascent_start is None:\n ascent_start = last\n ascent_dist += 1\n else:\n if ascent_dist:\n peak = last\n ascent_dist = 0\n if (peak - ascent_start) > thresh:\n last = data\n ascent_start = None\n return peak\n ascent_start = None\n last = data\n return None","def normalizedpeakerror():\r\n peak_simulation = max(hydrograph)\r\n peak_observation = max(obs_data)\r\n peak_error = abs(peak_simulation - peak_observation)/(peak_observation + peak_simulation)\r\n return peak_error","def get_peak(self):\r\n \r\n sensor_1_list = []\r\n\r\n for i in self.return_data:\r\n sensor_1_list.append(i[0])\r\n\r\n sensor_peak = max(sensor_1_list)\r\n \r\n return(sensor_peak)","def get_peak_day(fuel_yh):\n if np.sum(fuel_yh) == 0:\n #logging.info(\"No peak can be found because no fuel assigned\")\n # Return first entry of element (which is zero)\n return 0\n else:\n # Sum fuel within every hour for every day and get day with maximum fuel\n peak_day_nr = np.argmax(fuel_yh)\n\n return int(peak_day_nr)","def peak(data, fft_data=None):\n return np.max(np.abs(data))","def average_energy(power,events,borders,eventName):\n event_consider = events[events['eventName']==eventName].reset_index(drop=True)\n average = 0\n i = 0\n count = 0\n minValue = 10000\n maxValue = 0\n minAverage = 10000\n maxAverage = 0 \n while(i<len(event_consider)):\n date = time.mktime(datetime.strptime(event_consider['time'][i], \"%Y-%m-%d %H:%M:%S\").timetuple())\n start = str(datetime.fromtimestamp(date+borders[0]))\n end = str(datetime.fromtimestamp(date+borders[1]))\n values = power[(power['time']>=start)&(power['time']<=end)]['value']\n sum_values = sum(values)\n tot_values = len(values)\n if tot_values>0:\n if values.max() > maxValue:\n maxValue = values.max()\n if values.min() < minValue:\n minValue = values.min()\n if sum_values/tot_values > maxAverage:\n maxAverage = sum_values/tot_values\n if sum_values/tot_values < minAverage:\n minAverage = sum_values/tot_values\n average = average + sum_values/tot_values\n count += 1\n i += 1\n if count>0:\n average = average / count\n print(\"number of\", eventName ,\"in groudtruth and power=\",count)\n print(\"minValue=\",minValue,\"maxValue=\",maxValue)\n print(\"minAverage=\",minAverage,\"maxAverage=\",maxAverage)\n print(\"Final Av=\",average)\n return average\n else:\n print(\"Not values found in the range\")","def calc_time_to_peak(pgm, trace, IMarray, origintime, hypdist):\n \n import numpy as np\n from obspy.core import UTCDateTime\n import datetime\n \n # Calculate time from origin \n pgm_index = np.where(IMarray==pgm)\n tPGM_orig = trace.times(reftime=UTCDateTime(origintime))[pgm_index]\n \n # Calculate time from p-arrival\n p_time = hypdist/6.5\n dp = datetime.timedelta(seconds=p_time)\n p_arrival = origintime+dp\n tPGM_parriv = trace.times(reftime=UTCDateTime(p_arrival))[pgm_index]\n \n return(tPGM_orig, tPGM_parriv)","def max_spike_frequency(t, V):\n intervals = interspike_intervals(t, V)\n raise_if_not_multiple_spikes(intervals)\n min_int = np.amin(intervals)\n return 1/min_int","def last_peak_delta(self):\n return self._p1_time - self._p2_time","def max(ev):\n profData = getProfilingData(ev)\n if profData is not None:\n return profData.Tmax()\n return \"\"","def _psp_max_time(rise, decay, rise_power):\n return rise * np.log(1 + (decay * rise_power / rise))","def calc_peakt(self, trial_dur):\n if trial_dur <= 11.0:\n peakt = 0.5375*trial_dur + 6.09625\n else:\n peakt = 11.75\n return peakt","def averageTime(self):\n \n pass","def peak_index(A):\n peak = 0\n for idx in range(1, len(A)-1):\n if A[idx] > A[idx-1]:\n peak = idx\n return peak","def find_chimes(self):\n while True:\n self.recursion += 1\n if self.recursion > 10:\n self.exit_status = \"Recursion limit reached\"\n return None\n\n peaks, peaks_meta_data = find_peaks(self.amplitude, height=self.height,\n distance=int(self.fs / 2),\n prominence=[self.prominence_min, self.prominence_max])\n # If correct number of peaks are present, go with that.\n correct_profile_peaks = self.search_range_for_fit(peaks, mean_peak_distance=1.5)\n if len(correct_profile_peaks) == 1:\n return correct_profile_peaks[0] # Correct peaks in there.\n\n elif len(correct_profile_peaks) == 0:\n # not captured any peaks so broaden the search, drop height, drop prom min and increase prom max\n # Height is too high\n self.max_height = self.height\n self.height = (self.max_height - self.min_height) // 2\n\n # Prominence\n self.prominence_min -= self.prominence_min // 4\n self.prominence_max += self.prominence_max // 4\n\n elif len(correct_profile_peaks) > 1:\n # Captured too many peaks so narrow the criteria.\n self.min_height = self.height\n self.height = (self.max_height - self.min_height) * 2\n\n self.prominence_min += self.prominence_min // 4\n self.prominence_max -= self.prominence_max // 4","def avg_extend_time(self):\r\n if self.total_extended:\r\n return self.total_extend_time/self.total_extended\r\n else: return 0","def max(self):\n\n return time_stat(self, stat=\"max\")","def estimate_pulse_time(self, f=0.75):\n idxs = np.abs(self.flux) > f * self.max_flux\n return np.mean(self.time[idxs])","def peak_PSF(self):\n return self.compute_PSF(np.zeros(self.N_zern))","def testUpperBound(self,time,accel):\n\t\tif (time - self.timestamp) > ParserSettings.TIME_DELTA:#tests lockout threshold of a flick event\n\t\t\tif accel < self.upper:#tests if flick maximum is found, relative to previous magnitude\n\t\t\t\tself.timestamp\t= time#once peak found, set appropriate data and return a flick\n\t\t\t\ttoReturn\t\t= self.upper\n\t\t\t\tself.upper\t\t= 0\n\t\t\t\treturn toReturn\n\t\t\telse:\n\t\t\t\tself.upper = accel#if no flick yet, update most recent flick to test\n\t\t\t\treturn 0\n\t\telse:\n\t\t\treturn 0","def peak_enhance(signal, peaks, window: int = 0.08, fs: int = processing.FS):\n window = int(fs * window)\n if not window % 2 == 0:\n window += 1\n enhanced_peaks = np.zeros(len(peaks), dtype=int)\n signal = np.abs(signal)\n for i, peak in enumerate(peaks):\n if peak < window // 2:\n enhanced_peaks[i] = np.argmax(signal[0:peak + window // 2 + 1])\n elif peak + window // 2 + 1 > signal.shape[0]:\n enhanced_peaks[i] = np.argmax(signal[peak - window // 2:]) + peak - window // 2\n else:\n # Because of one-side lag -> window: p - w * 0.25% : p + w * 75%\n enhanced_peaks[i] = np.argmax(signal[peak - window // 4:peak + 3 * window // 4]) + peak - window // 4\n\n return enhanced_peaks","def __get_max_peak(x_value, raw_values):\n\n\n raw_values_index = raw_values[raw_values[\"m/z\"] == x_value].index[0]\n\n value, index = float(raw_values.loc[raw_values_index - 5, \"intensity_normalized\"]), raw_values_index - 5\n\n for z in range(-5, 15):\n if float(raw_values.loc[raw_values_index + z, \"intensity_normalized\"]) > value:\n value, index = float(raw_values.loc[raw_values_index + z, \"intensity_normalized\"]), raw_values_index + z\n return value","def _baseline_value(self):\n t = self['primary']\n return np.median(t.data[:int(10e-3/t.dt)])","def calculate_demand_peak_hour(self, building_location, year):\r\n peak_timestep = helpers.get_max_index(building_location[year])\r\n\r\n month = self.inputs.timesteps[peak_timestep][year]['month']\r\n dayofmonth = self.inputs.timesteps[peak_timestep][year]['dayofmonth']\r\n\r\n peak_timesteps = sorted([i for i in list(self.inputs.timesteps.keys()) if self.inputs.timesteps[i][year]['month'] == month\r\n and self.inputs.timesteps[i][year]['dayofmonth'] == dayofmonth])\r\n max_load = {}\r\n peak_isweekday = {}\r\n max_load[year] = {i: building_location[year][peak_timesteps[i]] for i in range(24)}\r\n peak_isweekday[year] = self.inputs.timesteps[peak_timesteps[0]][year]['is_weekday']\r\n\r\n peak_shape_kw = {i: max_load[year][i] * 1000 for i in range(24)}\r\n\r\n return helpers.get_max_index(peak_shape_kw)","def eeg_peaks(array,tim,onset,plot='false'):\n\tp1_i,n1_i,p2_i = onset+56,onset+104,onset+176\n\twin_p1,win_n1,win_p2 = 15,20,40\n\t# determine P1,N1 and P2 values on the basis of the maximum in GFP in a window around the expected values\n\tidx_p1 = np.logical_and(tim>p1_i-win_p1, tim<p1_i+win_p1)\n\tidx_n1 = np.logical_and(tim>n1_i-win_n1, tim<n1_i+win_n1)\n\tidx_p2 = np.logical_and(tim>p2_i-win_p2, tim<p2_i+win_p2)\n\tp1 = np.max(array[idx_p1])\n\ttp1 = tim[idx_p1][array[idx_p1].argmax()]\n\tn1 = np.min(array[idx_n1])\n\ttn1 = tim[idx_n1][array[idx_n1].argmin()]\n\tp2 = np.max(array[idx_p2])\n\ttp2 = tim[idx_p2][array[idx_p2].argmax()]\n\n\tlineax = dict(linewidth=1, color='black', linestyle='--')\n\tlinep1 = dict(linewidth=1, color='red', linestyle='--')\n\tlinen1 = dict(linewidth=1, color='green', linestyle='--')\n\tlinep2 = dict(linewidth=1, color='blue', linestyle='--')\n\n\tif plot == 'true':\t\t\n\t\tfig = plt.figure(19,figsize=[7,5])\n\t\tax = fig.add_subplot(111, autoscale_on=False, xlim=[onset-100,tp2+200], ylim=[1.25*np.min([p1,n1,p2]),1.25*np.max([p1,n1,p2])])\n\t\tplt.plot(tim,array,'k-',lw=3)\n\t\tplt.plot(tp1,p1,'ro')\n\t\tplt.plot(tn1,n1,'go')\n\t\tplt.plot(tp2,p2,'bo')\n\t\tax.axvline(p1_i-win_p1,**linep1)\n\t\tax.axvline(p1_i+win_p1,**linep1)\n\t\tax.axvline(n1_i-win_n1,**linen1)\n\t\tax.axvline(n1_i+win_n1,**linen1)\n\t\tax.axvline(p2_i-win_p2,**linep2)\n\t\tax.axvline(p2_i+win_p2,**linep2)\n\t\tax.axhline(**lineax)\n\t\tplt.text(tp1-120,1.25*p1,'P1 = %.2f muV at %.0f ms' %(p1,tp1),fontsize=10)\n\t\tplt.text(tn1-40,1.1*n1,'N1 = %.2f muV at %.0f ms' %(n1,tn1),fontsize=10)\n\t\tplt.text(tn1+40,1.1*p2,'P2 = %.2f muV at %.0f ms' %(p2,tp2),fontsize=10)\n\t\tplt.xlabel('time (ms)',fontsize = 13)\n\t\tplt.ylabel('Amplitude',fontsize = 13)\n\treturn [p1,n1,p2,tp1,tn1,tp2]","def _peakdet(ts, threshold_ratio=.1):\n THRESH = threshold_ratio * (max(ts)-min(ts))\n maxima = []\n minima = []\n extrema = []\n looking_for_maximum = True\n last = 0\n for i in range(1, len(ts)):\n if looking_for_maximum:\n if ts[i] > ts[last]:\n last = i\n elif ts[i] + THRESH < ts[last]:\n maxima.append(last)\n extrema.append(last)\n looking_for_maximum = False\n else: #looking for minimum\n if ts[i] < ts[last]:\n last = i\n elif ts[i] - THRESH > ts[last]:\n minima.append(last)\n extrema.append(last)\n looking_for_maximum = True\n \n return extrema","def get_peak_day_single_fueltype(fuel_yh):\n fuel_yh_8760 = fuel_yh.reshape(8760)\n\n if np.sum(fuel_yh_8760) == 0:\n #logging.info(\"No peak can be found because no fuel assigned\")\n # Return first entry of element (which is zero)\n return 0, 0\n else:\n # Sum fuel within every hour for every day and get day with maximum fuel\n peak_day_nr = int(basic_functions.round_down(np.argmax(fuel_yh_8760) / 24, 1))\n\n peak_h = np.max(fuel_yh_8760)\n\n return int(peak_day_nr), peak_h","def get_peak_mem():\n if not os.path.exists(\"/proc/self/status\"):\n return 0.0\n for line in open(\"/proc/self/status\", \"r\"):\n if \"VmPeak\" in line:\n mem = re.findall(r\"\\d+\", line)[0]\n return int(mem) / 1024 / 1024\n return 0.0","def event(self,evt,evn):\n #import pdb; pdb.set_trace()\n if (evt.get(\"skip_event\")):\n return\n # check if FEE data is one or two dimensional\n data = evt.get(Camera.FrameV1, self.src)\n if data is None:\n one_D = True\n data = evt.get(Bld.BldDataSpectrometerV1, self.src)\n else:\n one_D = False\n # get event timestamp\n timestamp = cspad_tbx.evt_timestamp(cspad_tbx.evt_time(evt)) # human readable format\n\n if data is None:\n self.nnodata +=1\n #self.logger.warning(\"event(): No spectrum data\")\n evt.put(skip_event_flag(),\"skip_event\")\n\n if timestamp is None:\n evt.put(skip_event_flag(),\"skip_event\")\n #self.logger.warning(\"event(): No TIMESTAMP, skipping shot\")\n\n elif data is not None:\n self.nshots +=1\n # get data as array and split into two half to find each peak\n if one_D:\n # filtering out outlier spikes in FEE data\n data = np.array(data.hproj().astype(np.float64))\n for i in range(len(data)):\n if data[i]>1000000000:\n data[i]=data[i]-(2**32)\n if self.dark is not None:\n data = data - self.dark\n spectrum = data\n spectrum1 = data[:data.shape[0]//2]\n spectrum2 = data[data.shape[0]//2:]\n else:\n data = np.array(data.data16().astype(np.int32))\n if self.dark is not None:\n data = data - self.dark\n data = np.double(data)\n data_split1 = data[:,:data.shape[1]//2]\n data_split2 = data[:,data.shape[1]//2:]\n # make a 1D trace of entire spectrum and each half to find peaks\n spectrum = np.sum(data,0)/data.shape[0]\n spectrum1 = np.sum(data_split1,0)/data_split1.shape[0]\n spectrum2 = np.sum(data_split2,0)/data_split2.shape[0]\n if not one_D:\n # the x-coordinate of the weighted center of peak region\n weighted_peak_one_positions = []\n for i in range(self.peak_one_range_min,self.peak_one_range_max):\n weighted_peak_one_positions.append(spectrum[i]*i)\n weighted_sum_peak_one = np.sum(weighted_peak_one_positions)\n weighted_peak_one_center_position = weighted_sum_peak_one/np.sum(spectrum[self.peak_one_range_min:self.peak_one_range_max])\n\n weighted_peak_two_positions = []\n for i in range(self.peak_two_range_min,self.peak_two_range_max):\n weighted_peak_two_positions.append(spectrum[i]*i)\n weighted_sum_peak_two = np.sum(weighted_peak_two_positions)\n weighted_peak_two_center_position = weighted_sum_peak_two/np.sum(spectrum[self.peak_two_range_min:self.peak_two_range_max])\n\n # normalized integrated regions between the peaks\n #int_left_region = np.sum(spectrum[weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2:(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])\n int_left_region = np.sum(spectrum[:weighted_peak_two_center_position/2])\n\n #int_left_region_norm = np.sum(spectrum[weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2:(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])/len(spectrum[weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2:(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])\n int_left_region_norm = np.sum(spectrum[:weighted_peak_two_center_position/2])/len(spectrum[:weighted_peak_two_center_position/2])\n\n int_right_region = np.sum(spectrum[self.peak_two_range_max:])\n\n int_right_region_norm = np.sum(spectrum[self.peak_two_range_max:])/len(spectrum[self.peak_two_range_max:])\n\n # normalized integrated peaks\n int_peak_one = np.sum(spectrum[(weighted_peak_one_center_position-len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2):(weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2)])\n\n int_peak_one_norm = np.sum(spectrum[(weighted_peak_one_center_position-len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2):(weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2)])/len(spectrum[(weighted_peak_one_center_position-len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2):(weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2)])\n\n int_peak_two = np.sum(spectrum[(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2):(weighted_peak_two_center_position+len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])\n\n int_peak_two_norm = np.sum(spectrum[(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2):(weighted_peak_two_center_position+len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])/len(spectrum[(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2):(weighted_peak_two_center_position+len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])\n\n if not one_D:\n if int_peak_one_norm/int_peak_two_norm > self.peak_ratio:\n print(\"event(): inflection peak too high\")\n evt.put(skip_event_flag(), \"skip_event\")\n return\n if int_left_region_norm > self.normalized_peak_to_noise_ratio*int_peak_two_norm:\n print(\"event(): noisy left of low energy peak\")\n evt.put(skip_event_flag(), \"skip_event\")\n return\n if int_right_region_norm > self.normalized_peak_to_noise_ratio*int_peak_two_norm:\n print(\"event(): noisy right of high energy peak\")\n evt.put(skip_event_flag(), \"skip_event\")\n return\n #self.logger.info(\"TIMESTAMP %s accepted\" %timestamp)\n self.naccepted += 1\n self.ntwo_color += 1\n print(\"%d Remote shot\" %self.ntwo_color)\n print(\"%s Remote timestamp\" %timestamp)","def get_number_of_peak_log_entries(self):\n return self.n_peak_log_entries","def eeg_bootptp(bootsample,tim,window):\n\t[ntpts,nboot] = bootsample.shape\n\tptpvals = np.zeros((nboot))\n\tfor i in range(0,nboot):\n\t\tptpval, minval, t_min, maxval, t_max = eeg_ptp(bootsample[:,i],tim,win)\n\t\tptpvals[i]=ptpval\n\t\tcnt = 100.0*i/nboot\n\t\tsys.stdout.write(\"progress: \\r%d%%\" %cnt)\n\treturn ptpvals","def find_peaks_(image):\n\n height, width = image.shape[:2]\n img_matrix = [sum(i)/len(i) for i in image]\n x=[i for i in range(height)]\n y = [255-i for i in img_matrix]\n y = gaussian_filter(y, sigma=20)\n maxs, _ = find_peaks(y)\n maxs = maxs.tolist()\n\n return maxs","def memory_get_peak_usage():\n raise NotImplementedError()","def getFirstVallay(hist):\n hist = smooth(hist)\n safe_range = (len(hist)//100, len(hist)//4)\n start = max(int(round(np.argmax(hist[:int(len(hist)/4)]))), 10) # Start searching from maximum peak\n start = min(start, safe_range[1]) # start point should be smaller than 50 pixel\n start = max(safe_range[0], start) # start point should be bigger than 20\n limit = min(int(start * 1.5), 100) # Set limit of the first valley as 150% of maximum peak location\n max_val = 0\n for i in range(start, limit):\n if hist[i] > max_val:\n max_val = hist[i]\n if hist[i] <= max_val*0.5:\n return i\n return limit","def whichPeaks(trace):\n peaks = []\n df = np.diff(trace)\n for t in range(len(df)-4):\n if df[t] > 0 and df[t+1] > 0:\n if df[t+2] < 0 and df[t+3] < 0: # Potential peak\n if trace[t+2] > np.mean(trace):\n peaks.append([t+2, trace[t+2]])\n return peaks","def spike_amplitude(V, t_spike):\n # handle no spike found\n if t_spike is None:\n return None\n Vmax = V[t_spike]\n Vmin = np.min(V[t_spike+1:t_spike+500])\n\n return Vmax - Vmin","def center_peaktime(self):\n ind = np.argmax(abs(self.Et))\n shift = (self.Et.shape[0] / 2 - ind).astype(np.int)\n self.Et = np.roll(self.Et, shift)","def narrowIncandPeakInfo(self):\r\n\t\tself.narrowIncandBaseline = (np.mean(self.narrowBandIncandData[0:10]))\r\n\t\t\t\t\r\n\t\traw_narrowIncand_max = np.amax(self.narrowBandIncandData)\r\n\t\tnarrowIncand_max = raw_narrowIncand_max - self.narrowIncandBaseline\t\t\r\n\t\tnarrowIncand_max_index = np.argmax(self.narrowBandIncandData)\r\n\t\t\r\n\t\tself.narrowIncandMax =narrowIncand_max\r\n\t\tself.narrowIncandMaxPos = narrowIncand_max_index","def first_peak_detect(beam, start_point):\n logging.debug('running first_peak_detect function')\n for i in range(start_point, len(beam)):\n logging.debug('current value of i is %d', i)\n if beam[i-1] < beam[i] > beam[i+1]:\n logging.debug('value determined to be the center of the values %d, %d, %d', beam[i-1], beam[i], beam[i+1])\n return i\n\n logging.error(\"no peak was found. will try working with the length of the beam\")\n return len(beam)","def get_max_draw_down(ts_vals):\r\n MDD = 0\r\n DD = 0\r\n peak = -99999\r\n for value in ts_vals:\r\n if (value > peak):\r\n peak = value\r\n else:\r\n DD = (peak - value) / peak\r\n if (DD > MDD):\r\n MDD = DD\r\n return MDD","def get_steps_between_peaks(self):\n max_x, max_y = self.get_local_maxes()\n full_steps = np.ediff1d(max_x)\n # _full_mean, _full_std = np.mean(full_steps), np.std(full_steps)\n _full_count = len(full_steps)\n\n unique_steps_between_peaks, unique_steps_counts = np.unique(full_steps, return_counts=True)\n\n _filter = np.logical_and(full_steps < unique_steps_between_peaks[np.argmax(unique_steps_counts)] * 1.7,\n full_steps > unique_steps_between_peaks[np.argmax(unique_steps_counts)] * 0.3)\n # 1.7 chosen as filter, as there seems to be another peak ~2* (probably due to single missed peaks)\n # 1.7 avoids the start of the gaussian at 2*\n\n if not _filter.all():\n steps = full_steps[_filter]\n # print(unique_steps_between_peaks[np.argmax(unique_steps_counts)])\n _filtered_count = len(steps)\n _counts = (_full_count, _filtered_count, _full_count - _filtered_count)\n # print('Original Count: %s, Filtered Count: %s, Excluded Count: %s' % _counts)\n # print('Filtered:', full_steps[np.invert(_filter)])\n unique_steps_between_peaks, unique_steps_counts = np.unique(steps, return_counts=True)\n else:\n steps = full_steps\n\n return steps, unique_steps_between_peaks, unique_steps_counts","def reduce_peaks(self,peaks,odf_min):\n if len(peaks)==0:\n return -1 \n if odf_min<self.iso_thr*peaks[0]:\n #remove small peaks\n ismallp=np.where(peaks<self.peak_thr*peaks[0])\n if len(ismallp[0])>0:\n l=ismallp[0][0]\n else:\n l=len(peaks)\n else:\n return -1\n return l","def maxpeaks(sig):\n diff_sig = np.diff(sig)\n\n return np.sum([1 for nd in range(len(diff_sig[:-1])) if (diff_sig[nd+1]<0 and diff_sig[nd]>0)])","def bus_interruptions_avg_duration(self) -> float:\n return self.dss_obj.BUSF(8, 0)","def fit_average_event(self, tb, average_event, debug=False, label='', inittaus=[0.001, 0.005], initdelay=None):\n #tsel = np.argwhere(self.avgeventtb > self.tpre)[0] # only fit data in event, not baseline\n tsel = 0 # use whole averaged trace\n self.tsel = tsel\n self.tau1 = inittaus[0]\n self.tau2 = inittaus[1]\n self.tau2_range = 10.\n self.tau1_minimum_factor = 5.\n time_past_peak = 2.5e-4\n self.fitted_tau1 = np.nan\n self.fitted_tau2 = np.nan\n self.Amplitude = np.nan\n # peak_pos = np.argmax(self.sign*self.avgevent[self.tsel:])\n # decay_fit_start = peak_pos + int(time_past_peak/self.dt)\n # init_vals = [self.sign*10., 1.0, 4., 0.]\n # init_vals_exp = [20., 5.0]\n # bounds_exp = [(0., 0.5), (10000., 50.)]\n \n res, rdelay = self.event_fitter(tb, average_event, time_past_peak=time_past_peak, initdelay=initdelay, debug=debug, label=label)\n # print('rdelay: ', rdelay)\n self.fitresult = res.x\n self.Amplitude = self.fitresult[0]\n self.fitted_tau1 = self.fitresult[1]\n self.fitted_tau2 = self.fitresult[2]\n self.bfdelay = rdelay\n self.DC = 0. # best_vals[3]\n self.avg_best_fit = self.doubleexp(self.fitresult, tb[self.tsel:],\n np.zeros_like(tb[self.tsel:]), risepower=self.risepower, mode=0, fixed_delay=self.bfdelay)\n self.avg_best_fit = self.sign*self.avg_best_fit\n fiterr = np.linalg.norm(self.avg_best_fit-average_event[self.tsel:])\n self.avg_fiterr = fiterr\n ave = self.sign*average_event\n ipk = np.argmax(ave)\n pk = ave[ipk]\n p10 = 0.1*pk\n p90 = 0.9*pk\n p37 = 0.37*pk\n try:\n i10 = np.argmin(np.fabs(ave[:ipk]-p10))\n except:\n self.fitted = False\n return\n i90 = np.argmin(np.fabs(ave[:ipk]-p90))\n i37 = np.argmin(np.fabs(ave[ipk:]-p37))\n self.risetenninety = self.dt*(i90-i10)\n self.decaythirtyseven = self.dt*(i37-ipk)\n self.Qtotal = self.dt*np.sum(average_event[self.tsel:])\n self.fitted = True","def eeg_det_ptp_avg(array,win_p1,win_n1,win_p2,tim):\t\n\tnsub,npts = array.shape\n\tidx_p1 = np.squeeze(np.logical_and(tim>=win_p1[0],tim<=win_p1[1]))\n\tidx_n1 = np.squeeze(np.logical_and(tim>=win_n1[0],tim<=win_n1[1]))\n\tidx_p2 = np.squeeze(np.logical_and(tim>=win_p2[0],tim<=win_p2[1]))\n\t\n\tptp_val = np.zeros(nsub)\n\tfor i in range(nsub):\t\n\t\tp1 = np.mean(array[i,idx_p1],axis=0)\n\t\tn1 = np.mean(array[i,idx_n1],axis=0)\t\n\t\tp2 = np.mean(array[i,idx_p2],axis=0)\n\t\tptp_val[i] = (p1+p2)/2 - n1\n\t\t\n\treturn ptp_val","def get_HRF(duration,TR,peak):\n n = int(np.ceil(duration/TR))\n x = np.linspace(0,duration,n)\n h = np.zeros(n)\n h[x<peak] = x[x<peak]/peak\n h[x>=peak] = (x[x>=peak]-duration)/(peak-duration)\n h = h/np.sum(h)\n return h","def avg_inference_time(self):\n return self._avg_inference_time","def avgtime(self):\n return (self._total_time['value'] / 1000) / self._total_time['count'] if self._total_time['count'] else 0","def incandPeakInfo(self):\t\r\n\t\tself.incandBaseline = (np.mean(self.wideBandIncandData[0:10]))\r\n\t\t\t\t\r\n\t\traw_incand_max = np.amax(self.wideBandIncandData)\r\n\t\tincand_max = raw_incand_max - self.incandBaseline\r\n\t\tincand_max_index = np.argmax(self.wideBandIncandData)\r\n\t\t\r\n\t\tself.incandMax =incand_max\r\n\t\tself.incandMaxPos = incand_max_index","def measure_peak(sig, use_inflection=True, return_allinfo=False):\n sig = np.array(sig)\n cr = locate_peak(sig)\n cr_crosszero = np.zeros_like(cr)\n cr_inflection = np.zeros_like(cr)\n\n # cross zero points\n cr_cr1 = -int_sign(sig[1:] * sig[:-1])\n cr_cr2 = -int_sign(sig[:-1] * sig[1:])\n cr_cr1[cr_cr1<0] = 0\n cr_cr2[cr_cr2<0] = 0\n cr_crosszero[1:] = cr_cr1\n cr_crosszero[:-1] += cr_cr2\n cr_crosszero = int_sign(cr_crosszero * sig) * 4\n\n # inflection points\n d2 = second_derivate(sig)\n d2p = locate_peak(d2)\n d2p[np.where( np.abs(d2p) != 1 )] = 0\n d2p[np.where( ((d2p==1) & (sig<0)) | ((d2p==-1) & (sig>0)) )] = 0\n cr_inflection[np.where(d2p==-1)] = 8\n cr_inflection[np.where(d2p==1)] = -8\n \n if use_inflection:\n cr_combine = cr + cr_inflection + cr_crosszero \n else:\n cr_combine = cr + cr_crosszero\n\n oned = False\n if len(np.shape(sig)) == 1:\n oned = True\n sig = sig[:, np.newaxis]\n \n peaks_list = []\n for i in range(np.shape(sig)[1]):\n pvs = np.where(np.abs(cr[:,i]) == 1)[0]\n lims = np.where(np.abs(cr_combine[:,i]) >= 2)[0]\n if len(pvs) == 0 :\n peaks_list.append([])\n continue\n if np.shape(lims)[0] == 0:\n lower_pos = pvs\n upper_pos = pvs\n else:\n lower_arr = (pvs > lims[:, np.newaxis])\n upper_arr = (pvs < lims[:, np.newaxis])\n lower_arr_r = np.flipud(lower_arr)\n upper_pos_i = np.argmax(upper_arr, axis=0)\n upper_pos = lims[(upper_pos_i, )]\n w_upper_none = np.where(upper_arr[-1,:] == False)\n upper_pos[w_upper_none] = pvs[w_upper_none]\n lower_pos_r_i = np.argmax(lower_arr_r, axis=0)\n lower_pos_i = len(lims) - 1 - lower_pos_r_i\n lower_pos = lims[(lower_pos_i, )]\n w_lower_none = np.where(lower_arr[0, :] == False)\n lower_pos[w_lower_none] = 0\n\n peaks = []\n for center, lower, upper in zip(pvs, lower_pos, upper_pos):\n depth = sig[center, i]\n sig_range = sig[lower:upper+1, i]\n sig_range[np.where(int_sign(sig_range) != int_sign(depth))] = 0.0\n volume = np.sum(sig_range)\n peaks.append(Peak(center=center, lower=lower, upper=upper, depth=depth, volume=volume))\n peaks_list.append(peaks)\n if oned:\n peaks_list = peaks_list[0]\n \n if return_allinfo:\n return peaks_list, cr, cr_crosszero, cr_inflection \n else:\n return peaks_list","def max_time(self):\n #{{{ function to return time of last sample\n\n if self.maxtime == -1:\n return stock.now()\n\n return self.maxtime","def peak(net_electricity_consumption: List[float], window: int = None) -> List[float]:\n\n window = 24 if window is None else window\n data = pd.DataFrame({'net_electricity_consumption':net_electricity_consumption})\n data['group'] = (data.index/window).astype(int)\n data = data.groupby(['group'])[['net_electricity_consumption']].max()\n data['net_electricity_consumption'] = data['net_electricity_consumption'].rolling(window=data.shape[0],min_periods=1).mean()\n \n return data['net_electricity_consumption'].tolist()","def avg(ev):\n profData = getProfilingData(ev)\n if profData is not None:\n return profData.Tavg()\n return \"\"","def getElapseTimes( self ):\n\n pars\t= ( _EVENT_ELAPSED_TIME,0, 0, 0 )\n values = self.adbGetEvent( pars )\n return values[2]","def find_peaks(self, t_measure):\n self._check_time(t_measure)\n #widths = np.arange(2,7) # range of widths to check by find_peaks_cwt\n #peak_nodes = find_peaks_cwt(self.get_velocities(t_measure), widths, min_snr=2.0,noise_perc=30.0)\n peak_beads = peakutils.peak.indexes(self.get_velocities(t_measure), thres=0.75, min_dist=7)\n return peak_beads","def n_max_nexposure(self):\n n = ct.c_int()\n self.lib.GetMaximumNumberRingExposureTimes(ct.pointer(n))\n return n.value","def findPeakAndValley(np):\n peakValleyArray = []\n for i in range (1, len(np) - 1):\n if (np[i][STOCK_VALUE_INDEX] / np[i - 1][STOCK_VALUE_INDEX] > 1 and np[i + 1][STOCK_VALUE_INDEX] / np[i][STOCK_VALUE_INDEX] < 1):\n peakValleyArray.append(i)\n if (np[i][STOCK_VALUE_INDEX] / np[i - 1][STOCK_VALUE_INDEX] < 1 and np[i + 1][STOCK_VALUE_INDEX] / np[i][STOCK_VALUE_INDEX] > 1):\n peakValleyArray.append(i)\n return peakValleyArray","def pc_work_time_avg(self) -> \"float\":\n return _beamforming_swig.phasedarray_sptr_pc_work_time_avg(self)","def peak_idx(self) -> Optional[int]:\n if any(item.energy is None for item in self):\n logger.warning(\"An energy was None - cannot locate peak\")\n return None\n\n peaks = [i for i in range(1, len(self) - 1) if self.is_saddle(i)]\n\n if len(peaks) > 0:\n peak_rel_es = self.rel_energies[np.array(peaks, dtype=int)]\n logger.info(\n f\"Found peaks at {peaks} with relative energies \"\n f\"∆E = {np.round(peak_rel_es, 1)} kcal mol-1\"\n )\n\n # Return the highest energy peak i.e. sorted high -> low\n for peak_idx in sorted(peaks, key=lambda i: -self.energies[i]):\n return peak_idx\n\n return None","def hr_report():\n\n # Load the peak data.\n db = Persistence()\n if not (activities := db.load_all()):\n print(\"No data to report on\")\n return\n\n # Find the maximum for each value.\n max = _load_max_values(activities)\n\n # Totals for the current week\n week_distance_total = 0\n week_elevation_total = 0\n week_duration_total = timedelta()\n week_work_days = 0\n week_5sec_average = []\n week_30sec_average = []\n week_60sec_average = []\n week_5min_average = []\n week_10min_average = []\n week_20min_average = []\n week_30min_average = []\n week_60min_average = []\n week_90min_average = []\n week_120min_average = []\n\n # Print the peak data for each week.\n current_weekday = None\n for activity in activities:\n\n # Time to break to a new week?\n if current_weekday is None or current_weekday > activity.start_time.weekday():\n if current_weekday:\n _print_footer(\n week_distance_total=week_distance_total,\n week_elevation_total=week_elevation_total,\n week_duration_total=week_duration_total,\n week_work_days=week_work_days,\n week_5sec_average=week_5sec_average,\n week_30sec_average=week_30sec_average,\n week_60sec_average=week_60sec_average,\n week_5min_average=week_5min_average,\n week_10min_average=week_10min_average,\n week_20min_average=week_20min_average,\n week_30min_average=week_30min_average,\n week_60min_average=week_60min_average,\n week_90min_average=week_90min_average,\n week_120min_average=week_120min_average,\n )\n week_distance_total = 0\n week_elevation_total = 0\n week_duration_total = timedelta(0)\n week_work_days = 0\n week_5sec_average = []\n week_30sec_average = []\n week_60sec_average = []\n week_5min_average = []\n week_10min_average = []\n week_20min_average = []\n week_30min_average = []\n week_60min_average = []\n week_90min_average = []\n week_120min_average = []\n\n _print_header()\n\n # Capture the weekday.\n if current_weekday is None or current_weekday != activity.start_time.weekday():\n week_work_days = week_work_days + 1\n\n current_weekday = activity.start_time.weekday()\n\n # Print the detail.\n _print_detail(activity, max)\n\n # Find the duration.\n duration = activity.end_time - activity.start_time\n\n # Accumulate for this week\n week_distance_total = week_distance_total + activity.distance\n if activity.elevation:\n week_elevation_total = week_elevation_total + activity.elevation\n week_duration_total = week_duration_total + duration\n week_5sec_average.append(activity.peak_5sec_hr)\n week_30sec_average.append(activity.peak_30sec_hr)\n week_60sec_average.append(activity.peak_60sec_hr)\n if activity.peak_5min_hr:\n week_5min_average.append(activity.peak_5min_hr)\n if activity.peak_10min_hr:\n week_10min_average.append(activity.peak_10min_hr)\n if activity.peak_20min_hr:\n week_20min_average.append(activity.peak_20min_hr)\n if activity.peak_30min_hr:\n week_30min_average.append(activity.peak_30min_hr)\n if activity.peak_60min_hr:\n week_60min_average.append(activity.peak_60min_hr)\n if activity.peak_90min_hr:\n week_90min_average.append(activity.peak_90min_hr)\n if activity.peak_120min_hr:\n week_120min_average.append(activity.peak_120min_hr)\n\n # Final footer.\n _print_footer(\n week_distance_total=week_distance_total,\n week_elevation_total=week_elevation_total,\n week_duration_total=week_duration_total,\n week_work_days=week_work_days,\n week_5sec_average=week_5sec_average,\n week_30sec_average=week_30sec_average,\n week_60sec_average=week_60sec_average,\n week_5min_average=week_5min_average,\n week_10min_average=week_10min_average,\n week_20min_average=week_20min_average,\n week_30min_average=week_30min_average,\n week_60min_average=week_60min_average,\n week_90min_average=week_90min_average,\n week_120min_average=week_120min_average,\n )\n\n # Print the summary.\n _print_summary(max)","def getLatestSpectrumMeasurements(self): \n return self.spectrum[len(self.spectrum)-1]","def peak_height(self, logM, k = [], pk = []):\n # Checks\n pk=np.atleast_2d(pk)\n assert len(pk[0])==len(k), \"Length of scales is different from power spectra\"\n sigma2 = self.mass_variance(logM,k,pk)\n nu = self.delta_sc/sigma2**.5\n return nu","def get_peak_frequency(ut_average: np.ndarray, \n omega: np.ndarray) -> Tuple[float, float, float]:\n\n # We get the indices of the peak of the average fft.\n n = len(omega)\n argmax = np.argmax(np.abs(ut_average))\n [index_x, index_y, index_z] = np.unravel_index(argmax, (n, n, n))\n\n # We then use those indices to get the peak frequency.\n return (omega[index_x], omega[index_y], omega[index_z])","def max_frequency(sig, FS):\n\n f, fs = plotfft(sig, FS)\n t = np.cumsum(fs)\n\n try:\n ind_mag = np.where(t > t[-1]*0.95)[0][0]\n except:\n ind_mag = np.argmax(t)\n f_max = f[ind_mag]\n\n return f_max","def pc_work_time_avg(self) -> \"float\":\n return _beamforming_swig.beamformer_sptr_pc_work_time_avg(self)","def spectral_maxpeaks(sign, FS):\n f, ff = plotfft(sign, FS)\n diff_sig = np.diff(ff)\n\n return np.sum([1 for nd in range(len(diff_sig[:-1])) if (diff_sig[nd+1]<0 and diff_sig[nd]>0)])","def get_peak_in_aperture(self,data,mask=None, method='exact',subpixels=5,unit=None):\n data_cutouts = self.get_data_cutouts(data,mask=None,method='exact',subpixels=5,unit=None)\n \n aperture_peaks = []\n for data_cutout in data_cutouts:\n aperture_peaks.append(np.max(data_cutout))\n \n aperture_peaks = self._prepare_photometry_output(aperture_peaks,\n unit=unit)\n return aperture_peaks","def calc_escape_peak_ratios(lineE,detectorelement='Si'):\n \n if(detectorelement=='Si'):\n #\n # For Si the K peak is 95% of the transition\n # and the photoionization to total cross section is ~ 95% \n # Si escape peak is typically only 0.2-1% (bigger at lower energies) \n #\n jump = xl.JumpFactor(14,xl.K_SHELL)\n fluy = xl.FluorYield(14,xl.K_SHELL)\n corr = fluy*(jump-1.0)/jump\n corr_photo = xl.CS_Photo(14,lineE)/xl.CS_Total(14,lineE)\n corr_trans = xl.RadRate(14,xl.KA_LINE)+xl.RadRate(14,xl.KB_LINE)\n mu_si= xl.CS_Total(14,lineE)\n mu_internal = xl.CS_Total(14,1.73998)\n r = mu_internal/mu_si\n eta = corr_trans*corr_photo*corr*0.5*(1.0-r*log(1.0+1.0/r))\n ratio = eta/(1.0-eta)\n #\n # escape peak sigma should be narrower than the main peak.\n #\n return ratio\n else:\n # \n # Ge detector...\n # Ge has a large escape peak ratio ~ 5-15% and a Ka and kb component\n #\n if(lineE < 11.5):\n return 0.0,0.0\n jump = xl.JumpFactor(32,xl.K_SHELL)\n fluy = xl.FluorYield(32,xl.K_SHELL)\n corr = fluy*(jump-1.0)/jump\n corr_photo = xl.CS_Photo(32,lineE)/xl.CS_Total(32,lineE)\n corr_trans_ka = xl.RadRate(32,xl.KA_LINE)\n corr_trans_kb =xl.RadRate(32,xl.KB_LINE)\n mu_ge= xl.CS_Total(32,lineE)#\n # one for the Ka and one for the Kb peak...\n mu_internal_ka = xl.CS_Total(32,xl.LineEnergy(32,xl.KA_LINE))\n r_ka = mu_internal_ka/mu_ge\n eta_ka = corr_trans_ka*corr_photo*corr*0.5*(1.0-r_ka*log(1.0+1.0/r_ka))\n ratio_ka = eta_ka/(1.0-eta_ka)\n\n mu_internal_kb = xl.CS_Total(32,xl.LineEnergy(32,xl.KB_LINE))\n r_kb = mu_internal_kb/mu_ge\n eta_kb = corr_trans_kb*corr_photo*corr*0.5*(1.0-r_kb*log(1.0+1.0/r_kb))\n ratio_kb = eta_kb/(1.0-eta_kb)\n\n return ratio_ka,ratio_kb","def analyze_ev_wf_compact(self, event, n_bsl, pic_name, peak_height=0.001, peak_prominences=0.0001):\n\n fig, ax = plt.subplots(nrows=3, ncols=3)\n peaks_temp = pd.DataFrame()\n\n for i in range(0, 9):\n if event < len(self.table_sipm_time):\n # Creo un np.array con gli indici della singola waveform..\n wf_idx = [event*self.points_per_wf, event *\n self.points_per_wf+self.points_per_wf]\n # ..i tempi di ciascun punto..\n wf_time = self.table_sipm_time['t'].iloc[event] + \\\n self.table_sipm_wf['TIME'][int(wf_idx[0]):int(wf_idx[1])]\n # ..e i valori del segnale di ciascun ppunto\n wf_ch = - \\\n self.table_sipm_wf['CH1'][int(wf_idx[0]):int(wf_idx[1])]\n\n # Per trovare la baseline, faccio un fit polinomiale di grado 0..\n # ..su un numero finito di punti iniziali, specificato dall'utente..\n # ..poi la salvo internamente alla classe\n self.baseline = np.polyfit(\n wf_time[0:n_bsl], wf_ch[0:n_bsl], 0)[0]\n # Voglio anche disegnarla sui plot, quindi mi creo una lista di x e di y..\n # ..nello spazio della waveform\n bsl_time = wf_time[0:n_bsl]\n bsl_ch = [self.baseline] * n_bsl\n\n # Per trovre i picchi, uso la funzione find_peaks di scipy.signal\n # I valori di height e prominence sono specificati dall'utente..\n # ..e scalti per selezionare tutti i picchi senza prendere rumore\n peaks, _ = sp.find_peaks(\n wf_ch, height=peak_height, prominence=peak_prominences)\n\n # Ora posso plottare tutto:\n plt.ticklabel_format(axis='x', style='sci', scilimits=(0, 0))\n # la waveform..\n ax[int(i / 3)][i % 3].plot(wf_time,\n wf_ch, linestyle='-', linewidth=1)\n # ..la baseline..\n ax[int(i / 3)][i % 3].plot(bsl_time, bsl_ch, linestyle='-',\n linewidth=1, c='darkgreen')\n # ..e i picchi (se ci sono)\n if len(peaks) > 0:\n ax[int(i / 3)][i % 3].scatter(wf_time.iloc[peaks],\n wf_ch.iloc[peaks], c='darkred')\n\n # Set common labels\n fig.text(0.5, 0.01, 'Time (s)', ha='center', va='center')\n fig.text(0.02, 0.5, 'Amplitude (V)', ha='center', va='center', rotation='vertical')\n \n \n # plt.show()\n peaks_temp = pd.concat([peaks_temp, pd.DataFrame(\n {'t': wf_time.iloc[peaks], 'A': wf_ch.iloc[peaks]-self.baseline})], ignore_index=True)\n event += 1\n\n # ..e salvo il plot in una cartella a parte\n folder_name = 'plot'\n plot_name = '{0}/{1}_ev{2}.png'.format(\n folder_name, pic_name, event)\n fig.savefig(plot_name)\n plt.close(fig)\n\n # La funzione restituisce i valori di tempo e ampiezza (ottenuta come Ch1-baseline)..\n # ..agli indici dei massimi trovati da find_peaks\n return peaks_temp","def myfitpeak(v,a):\n x = np.array(v) # voltage\n y = np.array(a) # current\n\n y = smooth(y)\n # limit peak width to 1/50 of the totoal scan length to entire scan.\n # limit minimum peak height to be over 0.2 percentile of all neighbors\n heightlimit = np.quantile(np.absolute(y[0:-1] - y[1:]), 0.8) * 3\n # heightlimit = np.absolute(y[0:-1] - y[1:]).mean() * 3\n # set height limit so that props return limits\n peaks, props = signal.find_peaks(\n y, height=heightlimit, prominence=heightlimit, width=len(y) / 30, rel_height=0.5)\n\n # return if no peaks found.\n if len(peaks) == 0:\n return {'fx': [v[0],v[1]], 'fy': [0,0], 'pc': 0, 'pv': 0, 'err': 1}\n\n peak = pickpeaks(peaks, props, len(y))\n\n # find tagent to 3X peak width window\n x1, x2 = find_tangent(y, peak)\n\n y1 = y[x1]\n y2 = y[x2]\n k = (y2-y1)/(x2-x1)\n b = -k*x2 + y2\n\n peakcurrent = y[peak] - (k*peak + b)\n peakvoltage = x[peak]\n\n twopointx = np.array([x[x1], x[x2]]).tolist()\n twopointy = np.array([y[x1], y[x2]]).tolist()\n\n # for compatibility return the same length tuple of results.\n # currently, no error is calculated.\n return {'fx': twopointx, 'fy': twopointy, 'pc': float(peakcurrent), 'pv': float(peakvoltage), 'err': 0}","def test_peak_statistics(RE):\n x = 'motor'\n y = 'det'\n ps = PeakStats(x, y)\n RE.subscribe(ps)\n RE(scan([det], motor, -5, 5, 100))\n\n fields = [\"x\", \"y\", \"min\", \"max\", \"com\", \"cen\", \"crossings\", \"fwhm\", \"lin_bkg\"]\n for field in fields:\n assert hasattr(ps, field), f\"{field} is not an attribute of ps\"\n\n np.allclose(ps.cen, 0, atol=1e-6)\n np.allclose(ps.com, 0, atol=1e-6)\n fwhm_gauss = 2 * np.sqrt(2 * np.log(2)) # theoretical value with std=1\n assert np.allclose(ps.fwhm, fwhm_gauss, atol=1e-2)","def time_window_frequency(self, t0, tend):\n eigs = self.time_window_eigs(t0, tend)\n return np.log(eigs).imag/(2*np.pi*self.original_time['dt'])","def get_peak_day_all_fueltypes(fuel_yh):\n fuel_8760 = fuel_yh.reshape(fuel_yh.shape[0], 8760)\n\n # Sum all fuel across all fueltypes for every hour in a year\n all_fueltypes_tot_h = np.sum(fuel_8760, axis=0)\n\n if np.sum(all_fueltypes_tot_h) == 0:\n logging.warning(\"No peak can be found because no fuel assigned\")\n return 0\n else:\n\n # Get day with maximum hour\n peak_day_nr = basic_functions.round_down(np.argmax(all_fueltypes_tot_h) / 24, 1)\n\n return int(peak_day_nr)","def loudest_time(self, start=0, duration=0):\n if duration == 0:\n duration = self.sound.nframes\n self.current_frame = start\n arr = self.read_frames(duration)\n # get the frame of the maximum amplitude\n # different names for the same thing...\n # max_amp_sample = a.argmax(axis=0)[a.max(axis=0).argmax()]\n max_amp_sample = int(np.floor(arr.argmax()/2)) + start\n return max_amp_sample","def scatteringPeakInfoLG(self):\t\t\r\n\t\tself.scatteringBaselineLG = (np.mean(self.lowGainScatData[0:10]))\r\n\t\tself.scatteringBaselineNoiseThreshLG = 3*np.std(self.lowGainScatData[0:10])\r\n\r\n\t\traw_max = np.amax(self.lowGainScatData)\r\n\t\tmax = raw_max - self.scatteringBaselineLG\r\n\t\t\r\n\t\tself.scatteringMaxPos_LG = np.argmax(self.lowGainScatData)\r\n\t\tself.scatteringMax_LG = max","def fitness(self, analysis):\n signal = analysis.get_signal()\n frequency = signal.spike_frequency()\n return float((self.frequency - frequency) ** 2)","def find_optimal_threshold(self, hist):\n\n\n threshold = int((len(hist)-1)/2)\n ct = len(hist) - 1\n\n while True:\n if(ct < 1):\n break\n threshold1 = self.evalue(hist,0,threshold)\n threshold2 = self.evalue(hist,threshold,len(hist) - 2)\n nt = int((threshold1+threshold2)/2)\n ct = nt - threshold\n threshold = nt\n\n return threshold","def check_peak_win(self):\n if self.peak_win[0] < 0.0:\n self.peak_win[0] = 0.0\n if self.logger is not None:\n self.logger.warning(('Start of peak window < 0 sec for cond: {}. ' +\n 'Setting to 0.').format(self.cond))\n if self.peak_win[1] > self.psc_dur:\n self.peak_win[1] = self.psc_dur\n if self.logger is not None:\n logger.warning(('End of peak window is longer than trial HRF ' +\n 'for cond: {}. Truncating.').format(self.cond))\n return","def prediction_error_absolute(event_times, intensity, window_size, obs_time, pred_time, dt):\n events_time_pred = event_times[event_times >= obs_time]\n win_int = int(window_size / dt)\n tp = np.arange(obs_time, pred_time, window_size)\n err = 0\n for i, t_cur in enumerate(tp):\n t_end = t_cur + window_size\n if t_end > pred_time:\n break\n count_current = get_event_count(events_time_pred, t_cur, t_end)\n pred_count = dt * intensity[(i * win_int):((i + 1) * win_int)].sum()\n err += abs(count_current - pred_count)\n\n return err","def globalMaximum(self):\n # The global maximum is at one peak's position\n potential_max = list()\n for func, pos, height, width in zip(self.peaks_function,\n self.peaks_position,\n self.peaks_height,\n self.peaks_width):\n potential_max.append((func(pos, pos, height, width), pos))\n return max(potential_max)"],"string":"[\n \"def peak_time(self):\\n return np.array([self.wftime[ch][self.waveform[ch].argmax()] for ch in range(self.nchannels)])\",\n \"def peak(self):\\n pass\",\n \"def find_alert_time(self) -> None:\\n \\n # Also not clear from the paper how to doe this,\\n # use the first 10 data points in the light curve to determine the magnitude\\n # baseline\\n\\n mean_mag = np.mean(self.mags[:10])\\n std_mag = np.std(self.mags[:10])\\n\\n num_above = 0 \\n i = 9\\n\\n while num_above < 3 and i < len(self.times)-1:\\n \\n i += 1 \\n\\n if self.mags[i] < mean_mag - std_mag:\\n num_above += 1\\n else:\\n num_above = 0.0\\n\\n if len(self.times) - 1 == i:\\n print(\\\"Give me more training data, not alerted yet, this is probably going to fail\\\")\\n \\n return self.times[i-1]\",\n \"def max_intensity(self, time):\\n ti = np.where(time == self.times)[0][0]\\n return self.timesteps[ti].max()\",\n \"def pull_peak_times(data):\\n bin_centers = np.arange(0.,1.501,0.002)\\n data = np.asarray(data)\\n maxs = np.argmax(data, axis=1)\\n return bin_centers[maxs]\",\n \"def get_peak_Vm_time(self, cellname, tstart, tend):\\n istart = int(tstart/self.plotdt+0.5)\\n iend = int(tend/self.plotdt+0.5)\\n try:\\n data = self.datafile['/Vm/'+cellname][istart:iend]\\n except KeyError:\\n print 'get_peak_Vm_time: no Vm entry for', cellname\\n return -1\\n peakindex = data.argmax()\\n peaktime = tstart + peakindex * self.plotdt\\n print 'peak time:', cellname, ':', peaktime\\n return peaktime\",\n \"def peak_uncertainty(t, S, P):\\n t = t.jyear\\n N = S.size\\n T = t.max() - t.min()\\n amp, phase, offset = find_sine_params(t, S - S.mean(), P)\\n sine = sinefunc(t, P, amp, phase, offset)\\n residual = (S - S.mean()) - sine\\n sigma = residual.std()\\n #print \\\"P=%0.3f N=%i T=%0.1f amp=%0.3g sigma=%0.3g\\\" % (P, N, T, amp, sigma)\\n return 3. * sigma * P**2 / (4. * T * amp * np.sqrt(N))\",\n \"def peak_PSF(self):\\n im, strehl = self.compute_PSF(np.zeros(self.N_act))\\n return strehl\",\n \"def getPeakValue( self ):\\n nCurrentMax = max( self.data.max(), -self.data.min() )\\n return float(nCurrentMax) / self.getSampleMaxValue()\",\n \"def peak(self) -> Tuple[MeasureInput, MeasureResult]:\\n assert self._data\\n return self._data[0][2]\",\n \"def peak(self):\\n return self.peak\",\n \"def max_time(self):\\n return self.time[np.argmax(self.flux)]\",\n \"def peak(self) -> Point:\\n return self.most_intense_point()\",\n \"def _peaktimes(x, prc=95, t_buffer=.01, fs=1000):\\n if np.logical_or(prc < 0, prc >= 100):\\n raise ValueError('Percentile threshold must be between 0 and 100.')\\n\\n samp_buffer = np.int(np.round(t_buffer * fs))\\n hi = x > np.percentile(x, prc)\\n event_intervals = _chunk_time(hi, samp_buffer=samp_buffer)\\n E = np.int(np.size(event_intervals) / 2)\\n events = np.zeros(E, dtype=object)\\n\\n for e in range(E):\\n temp = x[np.arange(event_intervals[e][0], event_intervals[e][1] + 1)]\\n events[e] = event_intervals[e][0] + np.argmax(temp)\\n\\n return events\",\n \"def peak_finder(thresh=0):\\n last = 0 # Track last input value\\n ascent_dist = 0 # Distance from last trough.\\n ascent_start = None # Last trough height\\n\\n def detect_peak(data):\\n \\\"\\\"\\\" Returns initialized function to detect peaks on live streaming data.\\n\\n Args:\\n data (numeric value): Input data point.\\n\\n Returns:\\n If peak is detected return peak value, else return None\\n \\\"\\\"\\\"\\n nonlocal last, ascent_dist, ascent_start\\n if data > last:\\n if ascent_start is None:\\n ascent_start = last\\n ascent_dist += 1\\n else:\\n if ascent_dist:\\n peak = last\\n ascent_dist = 0\\n if (peak - ascent_start) > thresh:\\n last = data\\n ascent_start = None\\n return peak\\n ascent_start = None\\n last = data\\n return None\\n\\n return detect_peak\",\n \"def get_peakiness(spot_data):\\n return spot_data[3] / np.mean((spot_data[5], spot_data[6]))\",\n \"def peak_height(self):\\n return np.array([max(self.waveform[ch]) for ch in range(self.nchannels)])\",\n \"def peak(self):\\n\\n return self._data[0]\",\n \"def calculate_peak_prominence(data, index):\\n current_peak = data[index]\\n\\n # ignore values at either end of the dataset or values that are not local maxima\\n if (\\n index == 0\\n or index == len(data) - 1\\n or data[index - 1] > current_peak\\n or data[index + 1] > current_peak\\n or (data[index - 1] == current_peak and data[index + 1] == current_peak)\\n ):\\n return 0\\n\\n # by definition, the prominence of the highest value in a dataset is equal to the value itself\\n if current_peak == max(data):\\n return np.log(current_peak)\\n\\n # find index of nearest maxima which is higher than the current peak\\n higher_peaks_inds = [i for i, x in enumerate(data) if x > current_peak]\\n\\n right_peaks = [x for x in higher_peaks_inds if x > index]\\n if right_peaks:\\n closest_right_ind = min(right_peaks)\\n else:\\n closest_right_ind = np.inf\\n\\n left_peaks = [x for x in higher_peaks_inds if x < index]\\n if left_peaks:\\n closest_left_ind = max(left_peaks)\\n else:\\n closest_left_ind = -np.inf\\n\\n right_distance = closest_right_ind - index\\n left_distance = index - closest_left_ind\\n\\n if (right_distance) > (left_distance):\\n closest = closest_left_ind\\n else:\\n closest = closest_right_ind\\n\\n # find the value at the lowest point between the nearest higher peak (the key col)\\n lo = min(closest, index)\\n hi = max(closest, index)\\n between_slice = data[lo:hi]\\n key_col = min(between_slice)\\n\\n prominence = np.log(data[index] - key_col + 1)\\n\\n return prominence\",\n \"def detect_peak(data):\\n nonlocal last, ascent_dist, ascent_start\\n if data > last:\\n if ascent_start is None:\\n ascent_start = last\\n ascent_dist += 1\\n else:\\n if ascent_dist:\\n peak = last\\n ascent_dist = 0\\n if (peak - ascent_start) > thresh:\\n last = data\\n ascent_start = None\\n return peak\\n ascent_start = None\\n last = data\\n return None\",\n \"def normalizedpeakerror():\\r\\n peak_simulation = max(hydrograph)\\r\\n peak_observation = max(obs_data)\\r\\n peak_error = abs(peak_simulation - peak_observation)/(peak_observation + peak_simulation)\\r\\n return peak_error\",\n \"def get_peak(self):\\r\\n \\r\\n sensor_1_list = []\\r\\n\\r\\n for i in self.return_data:\\r\\n sensor_1_list.append(i[0])\\r\\n\\r\\n sensor_peak = max(sensor_1_list)\\r\\n \\r\\n return(sensor_peak)\",\n \"def get_peak_day(fuel_yh):\\n if np.sum(fuel_yh) == 0:\\n #logging.info(\\\"No peak can be found because no fuel assigned\\\")\\n # Return first entry of element (which is zero)\\n return 0\\n else:\\n # Sum fuel within every hour for every day and get day with maximum fuel\\n peak_day_nr = np.argmax(fuel_yh)\\n\\n return int(peak_day_nr)\",\n \"def peak(data, fft_data=None):\\n return np.max(np.abs(data))\",\n \"def average_energy(power,events,borders,eventName):\\n event_consider = events[events['eventName']==eventName].reset_index(drop=True)\\n average = 0\\n i = 0\\n count = 0\\n minValue = 10000\\n maxValue = 0\\n minAverage = 10000\\n maxAverage = 0 \\n while(i<len(event_consider)):\\n date = time.mktime(datetime.strptime(event_consider['time'][i], \\\"%Y-%m-%d %H:%M:%S\\\").timetuple())\\n start = str(datetime.fromtimestamp(date+borders[0]))\\n end = str(datetime.fromtimestamp(date+borders[1]))\\n values = power[(power['time']>=start)&(power['time']<=end)]['value']\\n sum_values = sum(values)\\n tot_values = len(values)\\n if tot_values>0:\\n if values.max() > maxValue:\\n maxValue = values.max()\\n if values.min() < minValue:\\n minValue = values.min()\\n if sum_values/tot_values > maxAverage:\\n maxAverage = sum_values/tot_values\\n if sum_values/tot_values < minAverage:\\n minAverage = sum_values/tot_values\\n average = average + sum_values/tot_values\\n count += 1\\n i += 1\\n if count>0:\\n average = average / count\\n print(\\\"number of\\\", eventName ,\\\"in groudtruth and power=\\\",count)\\n print(\\\"minValue=\\\",minValue,\\\"maxValue=\\\",maxValue)\\n print(\\\"minAverage=\\\",minAverage,\\\"maxAverage=\\\",maxAverage)\\n print(\\\"Final Av=\\\",average)\\n return average\\n else:\\n print(\\\"Not values found in the range\\\")\",\n \"def calc_time_to_peak(pgm, trace, IMarray, origintime, hypdist):\\n \\n import numpy as np\\n from obspy.core import UTCDateTime\\n import datetime\\n \\n # Calculate time from origin \\n pgm_index = np.where(IMarray==pgm)\\n tPGM_orig = trace.times(reftime=UTCDateTime(origintime))[pgm_index]\\n \\n # Calculate time from p-arrival\\n p_time = hypdist/6.5\\n dp = datetime.timedelta(seconds=p_time)\\n p_arrival = origintime+dp\\n tPGM_parriv = trace.times(reftime=UTCDateTime(p_arrival))[pgm_index]\\n \\n return(tPGM_orig, tPGM_parriv)\",\n \"def max_spike_frequency(t, V):\\n intervals = interspike_intervals(t, V)\\n raise_if_not_multiple_spikes(intervals)\\n min_int = np.amin(intervals)\\n return 1/min_int\",\n \"def last_peak_delta(self):\\n return self._p1_time - self._p2_time\",\n \"def max(ev):\\n profData = getProfilingData(ev)\\n if profData is not None:\\n return profData.Tmax()\\n return \\\"\\\"\",\n \"def _psp_max_time(rise, decay, rise_power):\\n return rise * np.log(1 + (decay * rise_power / rise))\",\n \"def calc_peakt(self, trial_dur):\\n if trial_dur <= 11.0:\\n peakt = 0.5375*trial_dur + 6.09625\\n else:\\n peakt = 11.75\\n return peakt\",\n \"def averageTime(self):\\n \\n pass\",\n \"def peak_index(A):\\n peak = 0\\n for idx in range(1, len(A)-1):\\n if A[idx] > A[idx-1]:\\n peak = idx\\n return peak\",\n \"def find_chimes(self):\\n while True:\\n self.recursion += 1\\n if self.recursion > 10:\\n self.exit_status = \\\"Recursion limit reached\\\"\\n return None\\n\\n peaks, peaks_meta_data = find_peaks(self.amplitude, height=self.height,\\n distance=int(self.fs / 2),\\n prominence=[self.prominence_min, self.prominence_max])\\n # If correct number of peaks are present, go with that.\\n correct_profile_peaks = self.search_range_for_fit(peaks, mean_peak_distance=1.5)\\n if len(correct_profile_peaks) == 1:\\n return correct_profile_peaks[0] # Correct peaks in there.\\n\\n elif len(correct_profile_peaks) == 0:\\n # not captured any peaks so broaden the search, drop height, drop prom min and increase prom max\\n # Height is too high\\n self.max_height = self.height\\n self.height = (self.max_height - self.min_height) // 2\\n\\n # Prominence\\n self.prominence_min -= self.prominence_min // 4\\n self.prominence_max += self.prominence_max // 4\\n\\n elif len(correct_profile_peaks) > 1:\\n # Captured too many peaks so narrow the criteria.\\n self.min_height = self.height\\n self.height = (self.max_height - self.min_height) * 2\\n\\n self.prominence_min += self.prominence_min // 4\\n self.prominence_max -= self.prominence_max // 4\",\n \"def avg_extend_time(self):\\r\\n if self.total_extended:\\r\\n return self.total_extend_time/self.total_extended\\r\\n else: return 0\",\n \"def max(self):\\n\\n return time_stat(self, stat=\\\"max\\\")\",\n \"def estimate_pulse_time(self, f=0.75):\\n idxs = np.abs(self.flux) > f * self.max_flux\\n return np.mean(self.time[idxs])\",\n \"def peak_PSF(self):\\n return self.compute_PSF(np.zeros(self.N_zern))\",\n \"def testUpperBound(self,time,accel):\\n\\t\\tif (time - self.timestamp) > ParserSettings.TIME_DELTA:#tests lockout threshold of a flick event\\n\\t\\t\\tif accel < self.upper:#tests if flick maximum is found, relative to previous magnitude\\n\\t\\t\\t\\tself.timestamp\\t= time#once peak found, set appropriate data and return a flick\\n\\t\\t\\t\\ttoReturn\\t\\t= self.upper\\n\\t\\t\\t\\tself.upper\\t\\t= 0\\n\\t\\t\\t\\treturn toReturn\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.upper = accel#if no flick yet, update most recent flick to test\\n\\t\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\treturn 0\",\n \"def peak_enhance(signal, peaks, window: int = 0.08, fs: int = processing.FS):\\n window = int(fs * window)\\n if not window % 2 == 0:\\n window += 1\\n enhanced_peaks = np.zeros(len(peaks), dtype=int)\\n signal = np.abs(signal)\\n for i, peak in enumerate(peaks):\\n if peak < window // 2:\\n enhanced_peaks[i] = np.argmax(signal[0:peak + window // 2 + 1])\\n elif peak + window // 2 + 1 > signal.shape[0]:\\n enhanced_peaks[i] = np.argmax(signal[peak - window // 2:]) + peak - window // 2\\n else:\\n # Because of one-side lag -> window: p - w * 0.25% : p + w * 75%\\n enhanced_peaks[i] = np.argmax(signal[peak - window // 4:peak + 3 * window // 4]) + peak - window // 4\\n\\n return enhanced_peaks\",\n \"def __get_max_peak(x_value, raw_values):\\n\\n\\n raw_values_index = raw_values[raw_values[\\\"m/z\\\"] == x_value].index[0]\\n\\n value, index = float(raw_values.loc[raw_values_index - 5, \\\"intensity_normalized\\\"]), raw_values_index - 5\\n\\n for z in range(-5, 15):\\n if float(raw_values.loc[raw_values_index + z, \\\"intensity_normalized\\\"]) > value:\\n value, index = float(raw_values.loc[raw_values_index + z, \\\"intensity_normalized\\\"]), raw_values_index + z\\n return value\",\n \"def _baseline_value(self):\\n t = self['primary']\\n return np.median(t.data[:int(10e-3/t.dt)])\",\n \"def calculate_demand_peak_hour(self, building_location, year):\\r\\n peak_timestep = helpers.get_max_index(building_location[year])\\r\\n\\r\\n month = self.inputs.timesteps[peak_timestep][year]['month']\\r\\n dayofmonth = self.inputs.timesteps[peak_timestep][year]['dayofmonth']\\r\\n\\r\\n peak_timesteps = sorted([i for i in list(self.inputs.timesteps.keys()) if self.inputs.timesteps[i][year]['month'] == month\\r\\n and self.inputs.timesteps[i][year]['dayofmonth'] == dayofmonth])\\r\\n max_load = {}\\r\\n peak_isweekday = {}\\r\\n max_load[year] = {i: building_location[year][peak_timesteps[i]] for i in range(24)}\\r\\n peak_isweekday[year] = self.inputs.timesteps[peak_timesteps[0]][year]['is_weekday']\\r\\n\\r\\n peak_shape_kw = {i: max_load[year][i] * 1000 for i in range(24)}\\r\\n\\r\\n return helpers.get_max_index(peak_shape_kw)\",\n \"def eeg_peaks(array,tim,onset,plot='false'):\\n\\tp1_i,n1_i,p2_i = onset+56,onset+104,onset+176\\n\\twin_p1,win_n1,win_p2 = 15,20,40\\n\\t# determine P1,N1 and P2 values on the basis of the maximum in GFP in a window around the expected values\\n\\tidx_p1 = np.logical_and(tim>p1_i-win_p1, tim<p1_i+win_p1)\\n\\tidx_n1 = np.logical_and(tim>n1_i-win_n1, tim<n1_i+win_n1)\\n\\tidx_p2 = np.logical_and(tim>p2_i-win_p2, tim<p2_i+win_p2)\\n\\tp1 = np.max(array[idx_p1])\\n\\ttp1 = tim[idx_p1][array[idx_p1].argmax()]\\n\\tn1 = np.min(array[idx_n1])\\n\\ttn1 = tim[idx_n1][array[idx_n1].argmin()]\\n\\tp2 = np.max(array[idx_p2])\\n\\ttp2 = tim[idx_p2][array[idx_p2].argmax()]\\n\\n\\tlineax = dict(linewidth=1, color='black', linestyle='--')\\n\\tlinep1 = dict(linewidth=1, color='red', linestyle='--')\\n\\tlinen1 = dict(linewidth=1, color='green', linestyle='--')\\n\\tlinep2 = dict(linewidth=1, color='blue', linestyle='--')\\n\\n\\tif plot == 'true':\\t\\t\\n\\t\\tfig = plt.figure(19,figsize=[7,5])\\n\\t\\tax = fig.add_subplot(111, autoscale_on=False, xlim=[onset-100,tp2+200], ylim=[1.25*np.min([p1,n1,p2]),1.25*np.max([p1,n1,p2])])\\n\\t\\tplt.plot(tim,array,'k-',lw=3)\\n\\t\\tplt.plot(tp1,p1,'ro')\\n\\t\\tplt.plot(tn1,n1,'go')\\n\\t\\tplt.plot(tp2,p2,'bo')\\n\\t\\tax.axvline(p1_i-win_p1,**linep1)\\n\\t\\tax.axvline(p1_i+win_p1,**linep1)\\n\\t\\tax.axvline(n1_i-win_n1,**linen1)\\n\\t\\tax.axvline(n1_i+win_n1,**linen1)\\n\\t\\tax.axvline(p2_i-win_p2,**linep2)\\n\\t\\tax.axvline(p2_i+win_p2,**linep2)\\n\\t\\tax.axhline(**lineax)\\n\\t\\tplt.text(tp1-120,1.25*p1,'P1 = %.2f muV at %.0f ms' %(p1,tp1),fontsize=10)\\n\\t\\tplt.text(tn1-40,1.1*n1,'N1 = %.2f muV at %.0f ms' %(n1,tn1),fontsize=10)\\n\\t\\tplt.text(tn1+40,1.1*p2,'P2 = %.2f muV at %.0f ms' %(p2,tp2),fontsize=10)\\n\\t\\tplt.xlabel('time (ms)',fontsize = 13)\\n\\t\\tplt.ylabel('Amplitude',fontsize = 13)\\n\\treturn [p1,n1,p2,tp1,tn1,tp2]\",\n \"def _peakdet(ts, threshold_ratio=.1):\\n THRESH = threshold_ratio * (max(ts)-min(ts))\\n maxima = []\\n minima = []\\n extrema = []\\n looking_for_maximum = True\\n last = 0\\n for i in range(1, len(ts)):\\n if looking_for_maximum:\\n if ts[i] > ts[last]:\\n last = i\\n elif ts[i] + THRESH < ts[last]:\\n maxima.append(last)\\n extrema.append(last)\\n looking_for_maximum = False\\n else: #looking for minimum\\n if ts[i] < ts[last]:\\n last = i\\n elif ts[i] - THRESH > ts[last]:\\n minima.append(last)\\n extrema.append(last)\\n looking_for_maximum = True\\n \\n return extrema\",\n \"def get_peak_day_single_fueltype(fuel_yh):\\n fuel_yh_8760 = fuel_yh.reshape(8760)\\n\\n if np.sum(fuel_yh_8760) == 0:\\n #logging.info(\\\"No peak can be found because no fuel assigned\\\")\\n # Return first entry of element (which is zero)\\n return 0, 0\\n else:\\n # Sum fuel within every hour for every day and get day with maximum fuel\\n peak_day_nr = int(basic_functions.round_down(np.argmax(fuel_yh_8760) / 24, 1))\\n\\n peak_h = np.max(fuel_yh_8760)\\n\\n return int(peak_day_nr), peak_h\",\n \"def get_peak_mem():\\n if not os.path.exists(\\\"/proc/self/status\\\"):\\n return 0.0\\n for line in open(\\\"/proc/self/status\\\", \\\"r\\\"):\\n if \\\"VmPeak\\\" in line:\\n mem = re.findall(r\\\"\\\\d+\\\", line)[0]\\n return int(mem) / 1024 / 1024\\n return 0.0\",\n \"def event(self,evt,evn):\\n #import pdb; pdb.set_trace()\\n if (evt.get(\\\"skip_event\\\")):\\n return\\n # check if FEE data is one or two dimensional\\n data = evt.get(Camera.FrameV1, self.src)\\n if data is None:\\n one_D = True\\n data = evt.get(Bld.BldDataSpectrometerV1, self.src)\\n else:\\n one_D = False\\n # get event timestamp\\n timestamp = cspad_tbx.evt_timestamp(cspad_tbx.evt_time(evt)) # human readable format\\n\\n if data is None:\\n self.nnodata +=1\\n #self.logger.warning(\\\"event(): No spectrum data\\\")\\n evt.put(skip_event_flag(),\\\"skip_event\\\")\\n\\n if timestamp is None:\\n evt.put(skip_event_flag(),\\\"skip_event\\\")\\n #self.logger.warning(\\\"event(): No TIMESTAMP, skipping shot\\\")\\n\\n elif data is not None:\\n self.nshots +=1\\n # get data as array and split into two half to find each peak\\n if one_D:\\n # filtering out outlier spikes in FEE data\\n data = np.array(data.hproj().astype(np.float64))\\n for i in range(len(data)):\\n if data[i]>1000000000:\\n data[i]=data[i]-(2**32)\\n if self.dark is not None:\\n data = data - self.dark\\n spectrum = data\\n spectrum1 = data[:data.shape[0]//2]\\n spectrum2 = data[data.shape[0]//2:]\\n else:\\n data = np.array(data.data16().astype(np.int32))\\n if self.dark is not None:\\n data = data - self.dark\\n data = np.double(data)\\n data_split1 = data[:,:data.shape[1]//2]\\n data_split2 = data[:,data.shape[1]//2:]\\n # make a 1D trace of entire spectrum and each half to find peaks\\n spectrum = np.sum(data,0)/data.shape[0]\\n spectrum1 = np.sum(data_split1,0)/data_split1.shape[0]\\n spectrum2 = np.sum(data_split2,0)/data_split2.shape[0]\\n if not one_D:\\n # the x-coordinate of the weighted center of peak region\\n weighted_peak_one_positions = []\\n for i in range(self.peak_one_range_min,self.peak_one_range_max):\\n weighted_peak_one_positions.append(spectrum[i]*i)\\n weighted_sum_peak_one = np.sum(weighted_peak_one_positions)\\n weighted_peak_one_center_position = weighted_sum_peak_one/np.sum(spectrum[self.peak_one_range_min:self.peak_one_range_max])\\n\\n weighted_peak_two_positions = []\\n for i in range(self.peak_two_range_min,self.peak_two_range_max):\\n weighted_peak_two_positions.append(spectrum[i]*i)\\n weighted_sum_peak_two = np.sum(weighted_peak_two_positions)\\n weighted_peak_two_center_position = weighted_sum_peak_two/np.sum(spectrum[self.peak_two_range_min:self.peak_two_range_max])\\n\\n # normalized integrated regions between the peaks\\n #int_left_region = np.sum(spectrum[weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2:(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])\\n int_left_region = np.sum(spectrum[:weighted_peak_two_center_position/2])\\n\\n #int_left_region_norm = np.sum(spectrum[weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2:(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])/len(spectrum[weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2:(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])\\n int_left_region_norm = np.sum(spectrum[:weighted_peak_two_center_position/2])/len(spectrum[:weighted_peak_two_center_position/2])\\n\\n int_right_region = np.sum(spectrum[self.peak_two_range_max:])\\n\\n int_right_region_norm = np.sum(spectrum[self.peak_two_range_max:])/len(spectrum[self.peak_two_range_max:])\\n\\n # normalized integrated peaks\\n int_peak_one = np.sum(spectrum[(weighted_peak_one_center_position-len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2):(weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2)])\\n\\n int_peak_one_norm = np.sum(spectrum[(weighted_peak_one_center_position-len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2):(weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2)])/len(spectrum[(weighted_peak_one_center_position-len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2):(weighted_peak_one_center_position+len(spectrum[self.peak_one_range_min:self.peak_one_range_max])/2)])\\n\\n int_peak_two = np.sum(spectrum[(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2):(weighted_peak_two_center_position+len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])\\n\\n int_peak_two_norm = np.sum(spectrum[(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2):(weighted_peak_two_center_position+len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])/len(spectrum[(weighted_peak_two_center_position-len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2):(weighted_peak_two_center_position+len(spectrum[self.peak_two_range_min:self.peak_two_range_max])/2)])\\n\\n if not one_D:\\n if int_peak_one_norm/int_peak_two_norm > self.peak_ratio:\\n print(\\\"event(): inflection peak too high\\\")\\n evt.put(skip_event_flag(), \\\"skip_event\\\")\\n return\\n if int_left_region_norm > self.normalized_peak_to_noise_ratio*int_peak_two_norm:\\n print(\\\"event(): noisy left of low energy peak\\\")\\n evt.put(skip_event_flag(), \\\"skip_event\\\")\\n return\\n if int_right_region_norm > self.normalized_peak_to_noise_ratio*int_peak_two_norm:\\n print(\\\"event(): noisy right of high energy peak\\\")\\n evt.put(skip_event_flag(), \\\"skip_event\\\")\\n return\\n #self.logger.info(\\\"TIMESTAMP %s accepted\\\" %timestamp)\\n self.naccepted += 1\\n self.ntwo_color += 1\\n print(\\\"%d Remote shot\\\" %self.ntwo_color)\\n print(\\\"%s Remote timestamp\\\" %timestamp)\",\n \"def get_number_of_peak_log_entries(self):\\n return self.n_peak_log_entries\",\n \"def eeg_bootptp(bootsample,tim,window):\\n\\t[ntpts,nboot] = bootsample.shape\\n\\tptpvals = np.zeros((nboot))\\n\\tfor i in range(0,nboot):\\n\\t\\tptpval, minval, t_min, maxval, t_max = eeg_ptp(bootsample[:,i],tim,win)\\n\\t\\tptpvals[i]=ptpval\\n\\t\\tcnt = 100.0*i/nboot\\n\\t\\tsys.stdout.write(\\\"progress: \\\\r%d%%\\\" %cnt)\\n\\treturn ptpvals\",\n \"def find_peaks_(image):\\n\\n height, width = image.shape[:2]\\n img_matrix = [sum(i)/len(i) for i in image]\\n x=[i for i in range(height)]\\n y = [255-i for i in img_matrix]\\n y = gaussian_filter(y, sigma=20)\\n maxs, _ = find_peaks(y)\\n maxs = maxs.tolist()\\n\\n return maxs\",\n \"def memory_get_peak_usage():\\n raise NotImplementedError()\",\n \"def getFirstVallay(hist):\\n hist = smooth(hist)\\n safe_range = (len(hist)//100, len(hist)//4)\\n start = max(int(round(np.argmax(hist[:int(len(hist)/4)]))), 10) # Start searching from maximum peak\\n start = min(start, safe_range[1]) # start point should be smaller than 50 pixel\\n start = max(safe_range[0], start) # start point should be bigger than 20\\n limit = min(int(start * 1.5), 100) # Set limit of the first valley as 150% of maximum peak location\\n max_val = 0\\n for i in range(start, limit):\\n if hist[i] > max_val:\\n max_val = hist[i]\\n if hist[i] <= max_val*0.5:\\n return i\\n return limit\",\n \"def whichPeaks(trace):\\n peaks = []\\n df = np.diff(trace)\\n for t in range(len(df)-4):\\n if df[t] > 0 and df[t+1] > 0:\\n if df[t+2] < 0 and df[t+3] < 0: # Potential peak\\n if trace[t+2] > np.mean(trace):\\n peaks.append([t+2, trace[t+2]])\\n return peaks\",\n \"def spike_amplitude(V, t_spike):\\n # handle no spike found\\n if t_spike is None:\\n return None\\n Vmax = V[t_spike]\\n Vmin = np.min(V[t_spike+1:t_spike+500])\\n\\n return Vmax - Vmin\",\n \"def center_peaktime(self):\\n ind = np.argmax(abs(self.Et))\\n shift = (self.Et.shape[0] / 2 - ind).astype(np.int)\\n self.Et = np.roll(self.Et, shift)\",\n \"def narrowIncandPeakInfo(self):\\r\\n\\t\\tself.narrowIncandBaseline = (np.mean(self.narrowBandIncandData[0:10]))\\r\\n\\t\\t\\t\\t\\r\\n\\t\\traw_narrowIncand_max = np.amax(self.narrowBandIncandData)\\r\\n\\t\\tnarrowIncand_max = raw_narrowIncand_max - self.narrowIncandBaseline\\t\\t\\r\\n\\t\\tnarrowIncand_max_index = np.argmax(self.narrowBandIncandData)\\r\\n\\t\\t\\r\\n\\t\\tself.narrowIncandMax =narrowIncand_max\\r\\n\\t\\tself.narrowIncandMaxPos = narrowIncand_max_index\",\n \"def first_peak_detect(beam, start_point):\\n logging.debug('running first_peak_detect function')\\n for i in range(start_point, len(beam)):\\n logging.debug('current value of i is %d', i)\\n if beam[i-1] < beam[i] > beam[i+1]:\\n logging.debug('value determined to be the center of the values %d, %d, %d', beam[i-1], beam[i], beam[i+1])\\n return i\\n\\n logging.error(\\\"no peak was found. will try working with the length of the beam\\\")\\n return len(beam)\",\n \"def get_max_draw_down(ts_vals):\\r\\n MDD = 0\\r\\n DD = 0\\r\\n peak = -99999\\r\\n for value in ts_vals:\\r\\n if (value > peak):\\r\\n peak = value\\r\\n else:\\r\\n DD = (peak - value) / peak\\r\\n if (DD > MDD):\\r\\n MDD = DD\\r\\n return MDD\",\n \"def get_steps_between_peaks(self):\\n max_x, max_y = self.get_local_maxes()\\n full_steps = np.ediff1d(max_x)\\n # _full_mean, _full_std = np.mean(full_steps), np.std(full_steps)\\n _full_count = len(full_steps)\\n\\n unique_steps_between_peaks, unique_steps_counts = np.unique(full_steps, return_counts=True)\\n\\n _filter = np.logical_and(full_steps < unique_steps_between_peaks[np.argmax(unique_steps_counts)] * 1.7,\\n full_steps > unique_steps_between_peaks[np.argmax(unique_steps_counts)] * 0.3)\\n # 1.7 chosen as filter, as there seems to be another peak ~2* (probably due to single missed peaks)\\n # 1.7 avoids the start of the gaussian at 2*\\n\\n if not _filter.all():\\n steps = full_steps[_filter]\\n # print(unique_steps_between_peaks[np.argmax(unique_steps_counts)])\\n _filtered_count = len(steps)\\n _counts = (_full_count, _filtered_count, _full_count - _filtered_count)\\n # print('Original Count: %s, Filtered Count: %s, Excluded Count: %s' % _counts)\\n # print('Filtered:', full_steps[np.invert(_filter)])\\n unique_steps_between_peaks, unique_steps_counts = np.unique(steps, return_counts=True)\\n else:\\n steps = full_steps\\n\\n return steps, unique_steps_between_peaks, unique_steps_counts\",\n \"def reduce_peaks(self,peaks,odf_min):\\n if len(peaks)==0:\\n return -1 \\n if odf_min<self.iso_thr*peaks[0]:\\n #remove small peaks\\n ismallp=np.where(peaks<self.peak_thr*peaks[0])\\n if len(ismallp[0])>0:\\n l=ismallp[0][0]\\n else:\\n l=len(peaks)\\n else:\\n return -1\\n return l\",\n \"def maxpeaks(sig):\\n diff_sig = np.diff(sig)\\n\\n return np.sum([1 for nd in range(len(diff_sig[:-1])) if (diff_sig[nd+1]<0 and diff_sig[nd]>0)])\",\n \"def bus_interruptions_avg_duration(self) -> float:\\n return self.dss_obj.BUSF(8, 0)\",\n \"def fit_average_event(self, tb, average_event, debug=False, label='', inittaus=[0.001, 0.005], initdelay=None):\\n #tsel = np.argwhere(self.avgeventtb > self.tpre)[0] # only fit data in event, not baseline\\n tsel = 0 # use whole averaged trace\\n self.tsel = tsel\\n self.tau1 = inittaus[0]\\n self.tau2 = inittaus[1]\\n self.tau2_range = 10.\\n self.tau1_minimum_factor = 5.\\n time_past_peak = 2.5e-4\\n self.fitted_tau1 = np.nan\\n self.fitted_tau2 = np.nan\\n self.Amplitude = np.nan\\n # peak_pos = np.argmax(self.sign*self.avgevent[self.tsel:])\\n # decay_fit_start = peak_pos + int(time_past_peak/self.dt)\\n # init_vals = [self.sign*10., 1.0, 4., 0.]\\n # init_vals_exp = [20., 5.0]\\n # bounds_exp = [(0., 0.5), (10000., 50.)]\\n \\n res, rdelay = self.event_fitter(tb, average_event, time_past_peak=time_past_peak, initdelay=initdelay, debug=debug, label=label)\\n # print('rdelay: ', rdelay)\\n self.fitresult = res.x\\n self.Amplitude = self.fitresult[0]\\n self.fitted_tau1 = self.fitresult[1]\\n self.fitted_tau2 = self.fitresult[2]\\n self.bfdelay = rdelay\\n self.DC = 0. # best_vals[3]\\n self.avg_best_fit = self.doubleexp(self.fitresult, tb[self.tsel:],\\n np.zeros_like(tb[self.tsel:]), risepower=self.risepower, mode=0, fixed_delay=self.bfdelay)\\n self.avg_best_fit = self.sign*self.avg_best_fit\\n fiterr = np.linalg.norm(self.avg_best_fit-average_event[self.tsel:])\\n self.avg_fiterr = fiterr\\n ave = self.sign*average_event\\n ipk = np.argmax(ave)\\n pk = ave[ipk]\\n p10 = 0.1*pk\\n p90 = 0.9*pk\\n p37 = 0.37*pk\\n try:\\n i10 = np.argmin(np.fabs(ave[:ipk]-p10))\\n except:\\n self.fitted = False\\n return\\n i90 = np.argmin(np.fabs(ave[:ipk]-p90))\\n i37 = np.argmin(np.fabs(ave[ipk:]-p37))\\n self.risetenninety = self.dt*(i90-i10)\\n self.decaythirtyseven = self.dt*(i37-ipk)\\n self.Qtotal = self.dt*np.sum(average_event[self.tsel:])\\n self.fitted = True\",\n \"def eeg_det_ptp_avg(array,win_p1,win_n1,win_p2,tim):\\t\\n\\tnsub,npts = array.shape\\n\\tidx_p1 = np.squeeze(np.logical_and(tim>=win_p1[0],tim<=win_p1[1]))\\n\\tidx_n1 = np.squeeze(np.logical_and(tim>=win_n1[0],tim<=win_n1[1]))\\n\\tidx_p2 = np.squeeze(np.logical_and(tim>=win_p2[0],tim<=win_p2[1]))\\n\\t\\n\\tptp_val = np.zeros(nsub)\\n\\tfor i in range(nsub):\\t\\n\\t\\tp1 = np.mean(array[i,idx_p1],axis=0)\\n\\t\\tn1 = np.mean(array[i,idx_n1],axis=0)\\t\\n\\t\\tp2 = np.mean(array[i,idx_p2],axis=0)\\n\\t\\tptp_val[i] = (p1+p2)/2 - n1\\n\\t\\t\\n\\treturn ptp_val\",\n \"def get_HRF(duration,TR,peak):\\n n = int(np.ceil(duration/TR))\\n x = np.linspace(0,duration,n)\\n h = np.zeros(n)\\n h[x<peak] = x[x<peak]/peak\\n h[x>=peak] = (x[x>=peak]-duration)/(peak-duration)\\n h = h/np.sum(h)\\n return h\",\n \"def avg_inference_time(self):\\n return self._avg_inference_time\",\n \"def avgtime(self):\\n return (self._total_time['value'] / 1000) / self._total_time['count'] if self._total_time['count'] else 0\",\n \"def incandPeakInfo(self):\\t\\r\\n\\t\\tself.incandBaseline = (np.mean(self.wideBandIncandData[0:10]))\\r\\n\\t\\t\\t\\t\\r\\n\\t\\traw_incand_max = np.amax(self.wideBandIncandData)\\r\\n\\t\\tincand_max = raw_incand_max - self.incandBaseline\\r\\n\\t\\tincand_max_index = np.argmax(self.wideBandIncandData)\\r\\n\\t\\t\\r\\n\\t\\tself.incandMax =incand_max\\r\\n\\t\\tself.incandMaxPos = incand_max_index\",\n \"def measure_peak(sig, use_inflection=True, return_allinfo=False):\\n sig = np.array(sig)\\n cr = locate_peak(sig)\\n cr_crosszero = np.zeros_like(cr)\\n cr_inflection = np.zeros_like(cr)\\n\\n # cross zero points\\n cr_cr1 = -int_sign(sig[1:] * sig[:-1])\\n cr_cr2 = -int_sign(sig[:-1] * sig[1:])\\n cr_cr1[cr_cr1<0] = 0\\n cr_cr2[cr_cr2<0] = 0\\n cr_crosszero[1:] = cr_cr1\\n cr_crosszero[:-1] += cr_cr2\\n cr_crosszero = int_sign(cr_crosszero * sig) * 4\\n\\n # inflection points\\n d2 = second_derivate(sig)\\n d2p = locate_peak(d2)\\n d2p[np.where( np.abs(d2p) != 1 )] = 0\\n d2p[np.where( ((d2p==1) & (sig<0)) | ((d2p==-1) & (sig>0)) )] = 0\\n cr_inflection[np.where(d2p==-1)] = 8\\n cr_inflection[np.where(d2p==1)] = -8\\n \\n if use_inflection:\\n cr_combine = cr + cr_inflection + cr_crosszero \\n else:\\n cr_combine = cr + cr_crosszero\\n\\n oned = False\\n if len(np.shape(sig)) == 1:\\n oned = True\\n sig = sig[:, np.newaxis]\\n \\n peaks_list = []\\n for i in range(np.shape(sig)[1]):\\n pvs = np.where(np.abs(cr[:,i]) == 1)[0]\\n lims = np.where(np.abs(cr_combine[:,i]) >= 2)[0]\\n if len(pvs) == 0 :\\n peaks_list.append([])\\n continue\\n if np.shape(lims)[0] == 0:\\n lower_pos = pvs\\n upper_pos = pvs\\n else:\\n lower_arr = (pvs > lims[:, np.newaxis])\\n upper_arr = (pvs < lims[:, np.newaxis])\\n lower_arr_r = np.flipud(lower_arr)\\n upper_pos_i = np.argmax(upper_arr, axis=0)\\n upper_pos = lims[(upper_pos_i, )]\\n w_upper_none = np.where(upper_arr[-1,:] == False)\\n upper_pos[w_upper_none] = pvs[w_upper_none]\\n lower_pos_r_i = np.argmax(lower_arr_r, axis=0)\\n lower_pos_i = len(lims) - 1 - lower_pos_r_i\\n lower_pos = lims[(lower_pos_i, )]\\n w_lower_none = np.where(lower_arr[0, :] == False)\\n lower_pos[w_lower_none] = 0\\n\\n peaks = []\\n for center, lower, upper in zip(pvs, lower_pos, upper_pos):\\n depth = sig[center, i]\\n sig_range = sig[lower:upper+1, i]\\n sig_range[np.where(int_sign(sig_range) != int_sign(depth))] = 0.0\\n volume = np.sum(sig_range)\\n peaks.append(Peak(center=center, lower=lower, upper=upper, depth=depth, volume=volume))\\n peaks_list.append(peaks)\\n if oned:\\n peaks_list = peaks_list[0]\\n \\n if return_allinfo:\\n return peaks_list, cr, cr_crosszero, cr_inflection \\n else:\\n return peaks_list\",\n \"def max_time(self):\\n #{{{ function to return time of last sample\\n\\n if self.maxtime == -1:\\n return stock.now()\\n\\n return self.maxtime\",\n \"def peak(net_electricity_consumption: List[float], window: int = None) -> List[float]:\\n\\n window = 24 if window is None else window\\n data = pd.DataFrame({'net_electricity_consumption':net_electricity_consumption})\\n data['group'] = (data.index/window).astype(int)\\n data = data.groupby(['group'])[['net_electricity_consumption']].max()\\n data['net_electricity_consumption'] = data['net_electricity_consumption'].rolling(window=data.shape[0],min_periods=1).mean()\\n \\n return data['net_electricity_consumption'].tolist()\",\n \"def avg(ev):\\n profData = getProfilingData(ev)\\n if profData is not None:\\n return profData.Tavg()\\n return \\\"\\\"\",\n \"def getElapseTimes( self ):\\n\\n pars\\t= ( _EVENT_ELAPSED_TIME,0, 0, 0 )\\n values = self.adbGetEvent( pars )\\n return values[2]\",\n \"def find_peaks(self, t_measure):\\n self._check_time(t_measure)\\n #widths = np.arange(2,7) # range of widths to check by find_peaks_cwt\\n #peak_nodes = find_peaks_cwt(self.get_velocities(t_measure), widths, min_snr=2.0,noise_perc=30.0)\\n peak_beads = peakutils.peak.indexes(self.get_velocities(t_measure), thres=0.75, min_dist=7)\\n return peak_beads\",\n \"def n_max_nexposure(self):\\n n = ct.c_int()\\n self.lib.GetMaximumNumberRingExposureTimes(ct.pointer(n))\\n return n.value\",\n \"def findPeakAndValley(np):\\n peakValleyArray = []\\n for i in range (1, len(np) - 1):\\n if (np[i][STOCK_VALUE_INDEX] / np[i - 1][STOCK_VALUE_INDEX] > 1 and np[i + 1][STOCK_VALUE_INDEX] / np[i][STOCK_VALUE_INDEX] < 1):\\n peakValleyArray.append(i)\\n if (np[i][STOCK_VALUE_INDEX] / np[i - 1][STOCK_VALUE_INDEX] < 1 and np[i + 1][STOCK_VALUE_INDEX] / np[i][STOCK_VALUE_INDEX] > 1):\\n peakValleyArray.append(i)\\n return peakValleyArray\",\n \"def pc_work_time_avg(self) -> \\\"float\\\":\\n return _beamforming_swig.phasedarray_sptr_pc_work_time_avg(self)\",\n \"def peak_idx(self) -> Optional[int]:\\n if any(item.energy is None for item in self):\\n logger.warning(\\\"An energy was None - cannot locate peak\\\")\\n return None\\n\\n peaks = [i for i in range(1, len(self) - 1) if self.is_saddle(i)]\\n\\n if len(peaks) > 0:\\n peak_rel_es = self.rel_energies[np.array(peaks, dtype=int)]\\n logger.info(\\n f\\\"Found peaks at {peaks} with relative energies \\\"\\n f\\\"∆E = {np.round(peak_rel_es, 1)} kcal mol-1\\\"\\n )\\n\\n # Return the highest energy peak i.e. sorted high -> low\\n for peak_idx in sorted(peaks, key=lambda i: -self.energies[i]):\\n return peak_idx\\n\\n return None\",\n \"def hr_report():\\n\\n # Load the peak data.\\n db = Persistence()\\n if not (activities := db.load_all()):\\n print(\\\"No data to report on\\\")\\n return\\n\\n # Find the maximum for each value.\\n max = _load_max_values(activities)\\n\\n # Totals for the current week\\n week_distance_total = 0\\n week_elevation_total = 0\\n week_duration_total = timedelta()\\n week_work_days = 0\\n week_5sec_average = []\\n week_30sec_average = []\\n week_60sec_average = []\\n week_5min_average = []\\n week_10min_average = []\\n week_20min_average = []\\n week_30min_average = []\\n week_60min_average = []\\n week_90min_average = []\\n week_120min_average = []\\n\\n # Print the peak data for each week.\\n current_weekday = None\\n for activity in activities:\\n\\n # Time to break to a new week?\\n if current_weekday is None or current_weekday > activity.start_time.weekday():\\n if current_weekday:\\n _print_footer(\\n week_distance_total=week_distance_total,\\n week_elevation_total=week_elevation_total,\\n week_duration_total=week_duration_total,\\n week_work_days=week_work_days,\\n week_5sec_average=week_5sec_average,\\n week_30sec_average=week_30sec_average,\\n week_60sec_average=week_60sec_average,\\n week_5min_average=week_5min_average,\\n week_10min_average=week_10min_average,\\n week_20min_average=week_20min_average,\\n week_30min_average=week_30min_average,\\n week_60min_average=week_60min_average,\\n week_90min_average=week_90min_average,\\n week_120min_average=week_120min_average,\\n )\\n week_distance_total = 0\\n week_elevation_total = 0\\n week_duration_total = timedelta(0)\\n week_work_days = 0\\n week_5sec_average = []\\n week_30sec_average = []\\n week_60sec_average = []\\n week_5min_average = []\\n week_10min_average = []\\n week_20min_average = []\\n week_30min_average = []\\n week_60min_average = []\\n week_90min_average = []\\n week_120min_average = []\\n\\n _print_header()\\n\\n # Capture the weekday.\\n if current_weekday is None or current_weekday != activity.start_time.weekday():\\n week_work_days = week_work_days + 1\\n\\n current_weekday = activity.start_time.weekday()\\n\\n # Print the detail.\\n _print_detail(activity, max)\\n\\n # Find the duration.\\n duration = activity.end_time - activity.start_time\\n\\n # Accumulate for this week\\n week_distance_total = week_distance_total + activity.distance\\n if activity.elevation:\\n week_elevation_total = week_elevation_total + activity.elevation\\n week_duration_total = week_duration_total + duration\\n week_5sec_average.append(activity.peak_5sec_hr)\\n week_30sec_average.append(activity.peak_30sec_hr)\\n week_60sec_average.append(activity.peak_60sec_hr)\\n if activity.peak_5min_hr:\\n week_5min_average.append(activity.peak_5min_hr)\\n if activity.peak_10min_hr:\\n week_10min_average.append(activity.peak_10min_hr)\\n if activity.peak_20min_hr:\\n week_20min_average.append(activity.peak_20min_hr)\\n if activity.peak_30min_hr:\\n week_30min_average.append(activity.peak_30min_hr)\\n if activity.peak_60min_hr:\\n week_60min_average.append(activity.peak_60min_hr)\\n if activity.peak_90min_hr:\\n week_90min_average.append(activity.peak_90min_hr)\\n if activity.peak_120min_hr:\\n week_120min_average.append(activity.peak_120min_hr)\\n\\n # Final footer.\\n _print_footer(\\n week_distance_total=week_distance_total,\\n week_elevation_total=week_elevation_total,\\n week_duration_total=week_duration_total,\\n week_work_days=week_work_days,\\n week_5sec_average=week_5sec_average,\\n week_30sec_average=week_30sec_average,\\n week_60sec_average=week_60sec_average,\\n week_5min_average=week_5min_average,\\n week_10min_average=week_10min_average,\\n week_20min_average=week_20min_average,\\n week_30min_average=week_30min_average,\\n week_60min_average=week_60min_average,\\n week_90min_average=week_90min_average,\\n week_120min_average=week_120min_average,\\n )\\n\\n # Print the summary.\\n _print_summary(max)\",\n \"def getLatestSpectrumMeasurements(self): \\n return self.spectrum[len(self.spectrum)-1]\",\n \"def peak_height(self, logM, k = [], pk = []):\\n # Checks\\n pk=np.atleast_2d(pk)\\n assert len(pk[0])==len(k), \\\"Length of scales is different from power spectra\\\"\\n sigma2 = self.mass_variance(logM,k,pk)\\n nu = self.delta_sc/sigma2**.5\\n return nu\",\n \"def get_peak_frequency(ut_average: np.ndarray, \\n omega: np.ndarray) -> Tuple[float, float, float]:\\n\\n # We get the indices of the peak of the average fft.\\n n = len(omega)\\n argmax = np.argmax(np.abs(ut_average))\\n [index_x, index_y, index_z] = np.unravel_index(argmax, (n, n, n))\\n\\n # We then use those indices to get the peak frequency.\\n return (omega[index_x], omega[index_y], omega[index_z])\",\n \"def max_frequency(sig, FS):\\n\\n f, fs = plotfft(sig, FS)\\n t = np.cumsum(fs)\\n\\n try:\\n ind_mag = np.where(t > t[-1]*0.95)[0][0]\\n except:\\n ind_mag = np.argmax(t)\\n f_max = f[ind_mag]\\n\\n return f_max\",\n \"def pc_work_time_avg(self) -> \\\"float\\\":\\n return _beamforming_swig.beamformer_sptr_pc_work_time_avg(self)\",\n \"def spectral_maxpeaks(sign, FS):\\n f, ff = plotfft(sign, FS)\\n diff_sig = np.diff(ff)\\n\\n return np.sum([1 for nd in range(len(diff_sig[:-1])) if (diff_sig[nd+1]<0 and diff_sig[nd]>0)])\",\n \"def get_peak_in_aperture(self,data,mask=None, method='exact',subpixels=5,unit=None):\\n data_cutouts = self.get_data_cutouts(data,mask=None,method='exact',subpixels=5,unit=None)\\n \\n aperture_peaks = []\\n for data_cutout in data_cutouts:\\n aperture_peaks.append(np.max(data_cutout))\\n \\n aperture_peaks = self._prepare_photometry_output(aperture_peaks,\\n unit=unit)\\n return aperture_peaks\",\n \"def calc_escape_peak_ratios(lineE,detectorelement='Si'):\\n \\n if(detectorelement=='Si'):\\n #\\n # For Si the K peak is 95% of the transition\\n # and the photoionization to total cross section is ~ 95% \\n # Si escape peak is typically only 0.2-1% (bigger at lower energies) \\n #\\n jump = xl.JumpFactor(14,xl.K_SHELL)\\n fluy = xl.FluorYield(14,xl.K_SHELL)\\n corr = fluy*(jump-1.0)/jump\\n corr_photo = xl.CS_Photo(14,lineE)/xl.CS_Total(14,lineE)\\n corr_trans = xl.RadRate(14,xl.KA_LINE)+xl.RadRate(14,xl.KB_LINE)\\n mu_si= xl.CS_Total(14,lineE)\\n mu_internal = xl.CS_Total(14,1.73998)\\n r = mu_internal/mu_si\\n eta = corr_trans*corr_photo*corr*0.5*(1.0-r*log(1.0+1.0/r))\\n ratio = eta/(1.0-eta)\\n #\\n # escape peak sigma should be narrower than the main peak.\\n #\\n return ratio\\n else:\\n # \\n # Ge detector...\\n # Ge has a large escape peak ratio ~ 5-15% and a Ka and kb component\\n #\\n if(lineE < 11.5):\\n return 0.0,0.0\\n jump = xl.JumpFactor(32,xl.K_SHELL)\\n fluy = xl.FluorYield(32,xl.K_SHELL)\\n corr = fluy*(jump-1.0)/jump\\n corr_photo = xl.CS_Photo(32,lineE)/xl.CS_Total(32,lineE)\\n corr_trans_ka = xl.RadRate(32,xl.KA_LINE)\\n corr_trans_kb =xl.RadRate(32,xl.KB_LINE)\\n mu_ge= xl.CS_Total(32,lineE)#\\n # one for the Ka and one for the Kb peak...\\n mu_internal_ka = xl.CS_Total(32,xl.LineEnergy(32,xl.KA_LINE))\\n r_ka = mu_internal_ka/mu_ge\\n eta_ka = corr_trans_ka*corr_photo*corr*0.5*(1.0-r_ka*log(1.0+1.0/r_ka))\\n ratio_ka = eta_ka/(1.0-eta_ka)\\n\\n mu_internal_kb = xl.CS_Total(32,xl.LineEnergy(32,xl.KB_LINE))\\n r_kb = mu_internal_kb/mu_ge\\n eta_kb = corr_trans_kb*corr_photo*corr*0.5*(1.0-r_kb*log(1.0+1.0/r_kb))\\n ratio_kb = eta_kb/(1.0-eta_kb)\\n\\n return ratio_ka,ratio_kb\",\n \"def analyze_ev_wf_compact(self, event, n_bsl, pic_name, peak_height=0.001, peak_prominences=0.0001):\\n\\n fig, ax = plt.subplots(nrows=3, ncols=3)\\n peaks_temp = pd.DataFrame()\\n\\n for i in range(0, 9):\\n if event < len(self.table_sipm_time):\\n # Creo un np.array con gli indici della singola waveform..\\n wf_idx = [event*self.points_per_wf, event *\\n self.points_per_wf+self.points_per_wf]\\n # ..i tempi di ciascun punto..\\n wf_time = self.table_sipm_time['t'].iloc[event] + \\\\\\n self.table_sipm_wf['TIME'][int(wf_idx[0]):int(wf_idx[1])]\\n # ..e i valori del segnale di ciascun ppunto\\n wf_ch = - \\\\\\n self.table_sipm_wf['CH1'][int(wf_idx[0]):int(wf_idx[1])]\\n\\n # Per trovare la baseline, faccio un fit polinomiale di grado 0..\\n # ..su un numero finito di punti iniziali, specificato dall'utente..\\n # ..poi la salvo internamente alla classe\\n self.baseline = np.polyfit(\\n wf_time[0:n_bsl], wf_ch[0:n_bsl], 0)[0]\\n # Voglio anche disegnarla sui plot, quindi mi creo una lista di x e di y..\\n # ..nello spazio della waveform\\n bsl_time = wf_time[0:n_bsl]\\n bsl_ch = [self.baseline] * n_bsl\\n\\n # Per trovre i picchi, uso la funzione find_peaks di scipy.signal\\n # I valori di height e prominence sono specificati dall'utente..\\n # ..e scalti per selezionare tutti i picchi senza prendere rumore\\n peaks, _ = sp.find_peaks(\\n wf_ch, height=peak_height, prominence=peak_prominences)\\n\\n # Ora posso plottare tutto:\\n plt.ticklabel_format(axis='x', style='sci', scilimits=(0, 0))\\n # la waveform..\\n ax[int(i / 3)][i % 3].plot(wf_time,\\n wf_ch, linestyle='-', linewidth=1)\\n # ..la baseline..\\n ax[int(i / 3)][i % 3].plot(bsl_time, bsl_ch, linestyle='-',\\n linewidth=1, c='darkgreen')\\n # ..e i picchi (se ci sono)\\n if len(peaks) > 0:\\n ax[int(i / 3)][i % 3].scatter(wf_time.iloc[peaks],\\n wf_ch.iloc[peaks], c='darkred')\\n\\n # Set common labels\\n fig.text(0.5, 0.01, 'Time (s)', ha='center', va='center')\\n fig.text(0.02, 0.5, 'Amplitude (V)', ha='center', va='center', rotation='vertical')\\n \\n \\n # plt.show()\\n peaks_temp = pd.concat([peaks_temp, pd.DataFrame(\\n {'t': wf_time.iloc[peaks], 'A': wf_ch.iloc[peaks]-self.baseline})], ignore_index=True)\\n event += 1\\n\\n # ..e salvo il plot in una cartella a parte\\n folder_name = 'plot'\\n plot_name = '{0}/{1}_ev{2}.png'.format(\\n folder_name, pic_name, event)\\n fig.savefig(plot_name)\\n plt.close(fig)\\n\\n # La funzione restituisce i valori di tempo e ampiezza (ottenuta come Ch1-baseline)..\\n # ..agli indici dei massimi trovati da find_peaks\\n return peaks_temp\",\n \"def myfitpeak(v,a):\\n x = np.array(v) # voltage\\n y = np.array(a) # current\\n\\n y = smooth(y)\\n # limit peak width to 1/50 of the totoal scan length to entire scan.\\n # limit minimum peak height to be over 0.2 percentile of all neighbors\\n heightlimit = np.quantile(np.absolute(y[0:-1] - y[1:]), 0.8) * 3\\n # heightlimit = np.absolute(y[0:-1] - y[1:]).mean() * 3\\n # set height limit so that props return limits\\n peaks, props = signal.find_peaks(\\n y, height=heightlimit, prominence=heightlimit, width=len(y) / 30, rel_height=0.5)\\n\\n # return if no peaks found.\\n if len(peaks) == 0:\\n return {'fx': [v[0],v[1]], 'fy': [0,0], 'pc': 0, 'pv': 0, 'err': 1}\\n\\n peak = pickpeaks(peaks, props, len(y))\\n\\n # find tagent to 3X peak width window\\n x1, x2 = find_tangent(y, peak)\\n\\n y1 = y[x1]\\n y2 = y[x2]\\n k = (y2-y1)/(x2-x1)\\n b = -k*x2 + y2\\n\\n peakcurrent = y[peak] - (k*peak + b)\\n peakvoltage = x[peak]\\n\\n twopointx = np.array([x[x1], x[x2]]).tolist()\\n twopointy = np.array([y[x1], y[x2]]).tolist()\\n\\n # for compatibility return the same length tuple of results.\\n # currently, no error is calculated.\\n return {'fx': twopointx, 'fy': twopointy, 'pc': float(peakcurrent), 'pv': float(peakvoltage), 'err': 0}\",\n \"def test_peak_statistics(RE):\\n x = 'motor'\\n y = 'det'\\n ps = PeakStats(x, y)\\n RE.subscribe(ps)\\n RE(scan([det], motor, -5, 5, 100))\\n\\n fields = [\\\"x\\\", \\\"y\\\", \\\"min\\\", \\\"max\\\", \\\"com\\\", \\\"cen\\\", \\\"crossings\\\", \\\"fwhm\\\", \\\"lin_bkg\\\"]\\n for field in fields:\\n assert hasattr(ps, field), f\\\"{field} is not an attribute of ps\\\"\\n\\n np.allclose(ps.cen, 0, atol=1e-6)\\n np.allclose(ps.com, 0, atol=1e-6)\\n fwhm_gauss = 2 * np.sqrt(2 * np.log(2)) # theoretical value with std=1\\n assert np.allclose(ps.fwhm, fwhm_gauss, atol=1e-2)\",\n \"def time_window_frequency(self, t0, tend):\\n eigs = self.time_window_eigs(t0, tend)\\n return np.log(eigs).imag/(2*np.pi*self.original_time['dt'])\",\n \"def get_peak_day_all_fueltypes(fuel_yh):\\n fuel_8760 = fuel_yh.reshape(fuel_yh.shape[0], 8760)\\n\\n # Sum all fuel across all fueltypes for every hour in a year\\n all_fueltypes_tot_h = np.sum(fuel_8760, axis=0)\\n\\n if np.sum(all_fueltypes_tot_h) == 0:\\n logging.warning(\\\"No peak can be found because no fuel assigned\\\")\\n return 0\\n else:\\n\\n # Get day with maximum hour\\n peak_day_nr = basic_functions.round_down(np.argmax(all_fueltypes_tot_h) / 24, 1)\\n\\n return int(peak_day_nr)\",\n \"def loudest_time(self, start=0, duration=0):\\n if duration == 0:\\n duration = self.sound.nframes\\n self.current_frame = start\\n arr = self.read_frames(duration)\\n # get the frame of the maximum amplitude\\n # different names for the same thing...\\n # max_amp_sample = a.argmax(axis=0)[a.max(axis=0).argmax()]\\n max_amp_sample = int(np.floor(arr.argmax()/2)) + start\\n return max_amp_sample\",\n \"def scatteringPeakInfoLG(self):\\t\\t\\r\\n\\t\\tself.scatteringBaselineLG = (np.mean(self.lowGainScatData[0:10]))\\r\\n\\t\\tself.scatteringBaselineNoiseThreshLG = 3*np.std(self.lowGainScatData[0:10])\\r\\n\\r\\n\\t\\traw_max = np.amax(self.lowGainScatData)\\r\\n\\t\\tmax = raw_max - self.scatteringBaselineLG\\r\\n\\t\\t\\r\\n\\t\\tself.scatteringMaxPos_LG = np.argmax(self.lowGainScatData)\\r\\n\\t\\tself.scatteringMax_LG = max\",\n \"def fitness(self, analysis):\\n signal = analysis.get_signal()\\n frequency = signal.spike_frequency()\\n return float((self.frequency - frequency) ** 2)\",\n \"def find_optimal_threshold(self, hist):\\n\\n\\n threshold = int((len(hist)-1)/2)\\n ct = len(hist) - 1\\n\\n while True:\\n if(ct < 1):\\n break\\n threshold1 = self.evalue(hist,0,threshold)\\n threshold2 = self.evalue(hist,threshold,len(hist) - 2)\\n nt = int((threshold1+threshold2)/2)\\n ct = nt - threshold\\n threshold = nt\\n\\n return threshold\",\n \"def check_peak_win(self):\\n if self.peak_win[0] < 0.0:\\n self.peak_win[0] = 0.0\\n if self.logger is not None:\\n self.logger.warning(('Start of peak window < 0 sec for cond: {}. ' +\\n 'Setting to 0.').format(self.cond))\\n if self.peak_win[1] > self.psc_dur:\\n self.peak_win[1] = self.psc_dur\\n if self.logger is not None:\\n logger.warning(('End of peak window is longer than trial HRF ' +\\n 'for cond: {}. Truncating.').format(self.cond))\\n return\",\n \"def prediction_error_absolute(event_times, intensity, window_size, obs_time, pred_time, dt):\\n events_time_pred = event_times[event_times >= obs_time]\\n win_int = int(window_size / dt)\\n tp = np.arange(obs_time, pred_time, window_size)\\n err = 0\\n for i, t_cur in enumerate(tp):\\n t_end = t_cur + window_size\\n if t_end > pred_time:\\n break\\n count_current = get_event_count(events_time_pred, t_cur, t_end)\\n pred_count = dt * intensity[(i * win_int):((i + 1) * win_int)].sum()\\n err += abs(count_current - pred_count)\\n\\n return err\",\n \"def globalMaximum(self):\\n # The global maximum is at one peak's position\\n potential_max = list()\\n for func, pos, height, width in zip(self.peaks_function,\\n self.peaks_position,\\n self.peaks_height,\\n self.peaks_width):\\n potential_max.append((func(pos, pos, height, width), pos))\\n return max(potential_max)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7247735","0.6565663","0.64705825","0.64565206","0.6371662","0.62771463","0.62676597","0.61803085","0.61606777","0.61517483","0.6071804","0.6058603","0.60551894","0.6047215","0.60207856","0.5992068","0.5982672","0.5955427","0.5916465","0.59072816","0.5906133","0.5904086","0.5880243","0.5862169","0.58558446","0.579958","0.5780116","0.5770375","0.5730833","0.5727898","0.5700366","0.5669361","0.56593555","0.561199","0.55989844","0.55947316","0.55942","0.5593758","0.5568033","0.555567","0.5552354","0.5542753","0.5532246","0.5526671","0.55232704","0.5520822","0.5518968","0.5489338","0.5488895","0.5488641","0.54679763","0.5461291","0.5454695","0.5454068","0.543713","0.54164356","0.5407873","0.54014915","0.53997225","0.5398242","0.53930193","0.53847003","0.53794914","0.53613883","0.535532","0.53408945","0.534033","0.5337823","0.53281444","0.5324222","0.5321595","0.53162414","0.531071","0.5278126","0.52760834","0.5267603","0.52606845","0.5254966","0.52503836","0.5250141","0.52295506","0.52288806","0.5225623","0.52206665","0.5216183","0.5204411","0.5199928","0.5195846","0.51939654","0.5180702","0.5173873","0.5172112","0.5170674","0.5169357","0.5168303","0.5161385","0.5150744","0.51472676","0.5144469","0.5137896"],"string":"[\n \"0.7247735\",\n \"0.6565663\",\n \"0.64705825\",\n \"0.64565206\",\n \"0.6371662\",\n \"0.62771463\",\n \"0.62676597\",\n \"0.61803085\",\n \"0.61606777\",\n \"0.61517483\",\n \"0.6071804\",\n \"0.6058603\",\n \"0.60551894\",\n \"0.6047215\",\n \"0.60207856\",\n \"0.5992068\",\n \"0.5982672\",\n \"0.5955427\",\n \"0.5916465\",\n \"0.59072816\",\n \"0.5906133\",\n \"0.5904086\",\n \"0.5880243\",\n \"0.5862169\",\n \"0.58558446\",\n \"0.579958\",\n \"0.5780116\",\n \"0.5770375\",\n \"0.5730833\",\n \"0.5727898\",\n \"0.5700366\",\n \"0.5669361\",\n \"0.56593555\",\n \"0.561199\",\n \"0.55989844\",\n \"0.55947316\",\n \"0.55942\",\n \"0.5593758\",\n \"0.5568033\",\n \"0.555567\",\n \"0.5552354\",\n \"0.5542753\",\n \"0.5532246\",\n \"0.5526671\",\n \"0.55232704\",\n \"0.5520822\",\n \"0.5518968\",\n \"0.5489338\",\n \"0.5488895\",\n \"0.5488641\",\n \"0.54679763\",\n \"0.5461291\",\n \"0.5454695\",\n \"0.5454068\",\n \"0.543713\",\n \"0.54164356\",\n \"0.5407873\",\n \"0.54014915\",\n \"0.53997225\",\n \"0.5398242\",\n \"0.53930193\",\n \"0.53847003\",\n \"0.53794914\",\n \"0.53613883\",\n \"0.535532\",\n \"0.53408945\",\n \"0.534033\",\n \"0.5337823\",\n \"0.53281444\",\n \"0.5324222\",\n \"0.5321595\",\n \"0.53162414\",\n \"0.531071\",\n \"0.5278126\",\n \"0.52760834\",\n \"0.5267603\",\n \"0.52606845\",\n \"0.5254966\",\n \"0.52503836\",\n \"0.5250141\",\n \"0.52295506\",\n \"0.52288806\",\n \"0.5225623\",\n \"0.52206665\",\n \"0.5216183\",\n \"0.5204411\",\n \"0.5199928\",\n \"0.5195846\",\n \"0.51939654\",\n \"0.5180702\",\n \"0.5173873\",\n \"0.5172112\",\n \"0.5170674\",\n \"0.5169357\",\n \"0.5168303\",\n \"0.5161385\",\n \"0.5150744\",\n \"0.51472676\",\n \"0.5144469\",\n \"0.5137896\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6051851"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":77466,"cells":{"query":{"kind":"string","value":"Requires getting MHW strength at \"start\" and \"end\""},"document":{"kind":"string","value":"def rates_of_onset_and_decline(mhw, tt_start, tt_end, tt_peak, mhw_relSeas, temp, clim):\n ## For START of event\n if tt_start > 0:\n mhw_relSeas_start = 0.5 * (\n mhw_relSeas[0] + temp[tt_start - 1] - clim[\"seas\"][tt_start - 1]\n )\n mhw[\"rate_onset\"].append(\n (mhw_relSeas[tt_peak] - mhw_relSeas_start) / (tt_peak + 0.5)\n )\n else: # MHW starts at beginning of time series\n if (\n tt_peak == 0\n ): # Peak is also at begining of time series, assume onset time = 1 day\n mhw[\"rate_onset\"].append((mhw_relSeas[tt_peak] - mhw_relSeas[0]) / 1.0)\n else:\n mhw[\"rate_onset\"].append((mhw_relSeas[tt_peak] - mhw_relSeas[0]) / tt_peak)\n\n ## For END of event\n if tt_end < T - 1:\n mhw_relSeas_end = 0.5 * (\n mhw_relSeas[-1] + temp[tt_end + 1] - clim[\"seas\"][tt_end + 1]\n )\n mhw[\"rate_decline\"].append(\n (mhw_relSeas[tt_peak] - mhw_relSeas_end)\n / (tt_end - tt_start - tt_peak + 0.5)\n )\n else: # MHW finishes at end of time series\n if (\n tt_peak == T - 1\n ): # Peak is also at end of time series, assume decline time = 1 day\n mhw[\"rate_decline\"].append((mhw_relSeas[tt_peak] - mhw_relSeas[-1]) / 1.0)\n else:\n mhw[\"rate_decline\"].append(\n (mhw_relSeas[tt_peak] - mhw_relSeas[-1]) / (tt_end - tt_start - tt_peak)\n )\n\n return mhw"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def strength(self) -> float:\n ...","def test_W_start(self):\t\t\n self.assertAlmostEqual(attempt.W[0], 12)","def test_sw2():\n B1 = 100\n B2 = 200\n h = 18\n t = 1\n H = h + 2 * t\n E1 = 20000\n E2 = 10000\n sections = ((B1, t, 0, E1), (B2, t, h + t, E2))\n EI, top, bot = bm.EI(sections, E1)\n EIc = E1 * B1 * (H ** 3 - h ** 3) / 12\n assert 0.99 < EI / EIc < 1.01","def max_linear_acceleration():","def get_gains(self):\n return tuple([lib.is_SetHWGainFactor(self.hcam,0x8000+i,0)/100 for i in range(4)])","def test_W_end(self):\t\t\n self.assertAlmostEqual(attempt.W[-1], 9.494852380803035)","def YoungModulus(material):\n if material == \"mild\":\n return 200e9\n else:\n if material == \"al\":\n return 69e9\n else:\n raise ValueError(\"Invalid material `\"+material+\"'\")","def weight_mm(self,m1,m2):\n lw = 1.\n\n # particle id and isolation\n lw *= self._muIDISOWeight.value(m1.pt(),m1.eta(),'0')\n lw *= self._muIDISOWeight.value(m2.pt(),m2.eta(),'0')\n\n # Trigger\n hlt_sf_run2012_a = (self._muTRIGGERWeight_leg8_A.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg17_A.value(m2.pt(),m2.eta(),'0') +\\\n self._muTRIGGERWeight_leg17_A.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg8_A.value(m2.pt(),m2.eta(),'0') -\\\n self._muTRIGGERWeight_leg17_A.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg17_A.value(m2.pt(),m2.eta(),'0'))\n\n hlt_sf_run2012_b = (self._muTRIGGERWeight_leg8_B.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg17_B.value(m2.pt(),m2.eta(),'0') +\\\n self._muTRIGGERWeight_leg17_B.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg8_B.value(m2.pt(),m2.eta(),'0') -\\\n self._muTRIGGERWeight_leg17_B.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg17_B.value(m2.pt(),m2.eta(),'0'))\n \n lw *= (0.5*hlt_sf_run2012_a + 0.5*hlt_sf_run2012_b) ##percentage according to the lumi in which they were not prescaled (apparently same efficinecy for AB)\n #lw *= 0.966 ## temporary solution!\n\n if abs(configuration.LeptonTnPfactor)<0.01 :\n return lw\n else:\n return lw + configuration.LeptonTnPfactor*self.uncertainty_mm(m1,m2)","def get_strength_text(currentstrength):\n for i in range(0, 5): \n strengthrange = (79, 59, 39, 19, 0)\n if currentstrength in range(strengthrange[i], strengthrange[i] + 20):\n strength = STRENGTH_TEXT[i]\n if currentstrength > 99:\n strength = STRENGTH_TEXT[0]\n\n return strength","def get_power_management() -> int:","def _check_hardware_control(self):\n if self._under_hardware_control:\n v_input = self._ai_client.get_ai_voltage(self._hwc_ai_channel, max_range=10) #CHeck status of hwc voltage input\n v_input = v_input[-1]\n if self._is_stabilizing:\n if v_input < self._hwc_thresh:\n self.stop()\n else:\n if v_input > self._hwc_thresh:\n self.start()","def idealOpAmp():","def get_max_gains(self):\n return tuple([lib.is_SetHWGainFactor(self.hcam,0x800c+i,100)/100 for i in range(4)])","def getRangeMM(self) -> float:\n ...","def expose(self, w):\n # Compute the weighted sum of the firing inputs\n s = self.strength[list(w.offset)].sum()\n if self.training:\n return s >= self.H\n else:\n return s >= self.H*self.G","def get_hardwired_speed_weights(self):\n \n phase_shift=self.speed_phase_shift\n \n # row 1 has the weights of speed cells to grid cell 1\n self.W_speed_east=np.zeros_like(self.W_ee) \n self.W_speed_west=np.zeros_like(self.W_ee) \n self.W_speed_north=np.zeros_like(self.W_ee) \n self.W_speed_south=np.zeros_like(self.W_ee) \n\n if self.use_eight_directions is True:\n self.W_speed_north_east=np.zeros_like(self.W_ee) \n self.W_speed_north_west=np.zeros_like(self.W_ee) \n self.W_speed_south_east=np.zeros_like(self.W_ee) \n self.W_speed_south_west=np.zeros_like(self.W_ee) \n\n\n for phase_idx,phase in enumerate(self.gp.phases):\n shifted_north_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi/2.),self.gp.phases)\n shifted_south_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi/2.),self.gp.phases)\n shifted_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(0),self.gp.phases)\n shifted_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi),self.gp.phases)\n\n self.W_speed_north[phase_idx,:]=self.W_ee[shifted_north_phase_idx,:]\n self.W_speed_south[phase_idx,:]=self.W_ee[shifted_south_phase_idx,:]\n self.W_speed_east[phase_idx,:]=self.W_ee[shifted_east_phase_idx,:]\n self.W_speed_west[phase_idx,:]=self.W_ee[shifted_west_phase_idx,:] \n \n if self.use_eight_directions is True:\n shifted_north_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi/4),self.gp.phases)\n shifted_north_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi*3/4),self.gp.phases)\n shifted_south_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi/4),self.gp.phases)\n shifted_south_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi*3/4),self.gp.phases)\n \n self.W_speed_north_east[phase_idx,:]=self.W_ee[shifted_north_east_phase_idx,:]\n self.W_speed_north_west[phase_idx,:]=self.W_ee[shifted_north_west_phase_idx,:]\n self.W_speed_south_east[phase_idx,:]=self.W_ee[shifted_south_east_phase_idx,:]\n self.W_speed_south_west[phase_idx,:]=self.W_ee[shifted_south_west_phase_idx,:]","def cMACW(self):\n return 3.5","def max_angular_acceleration():","def chk_hamming(data):\n pass","def test_optimal_thickness():\n structure = Material(input)\n assert (structure.calc_optimal_thickness() == 1.9552936422413782)","def get_m1_m4_SEN_info(tx, ty, m1_info, y_k):\n \"\"\"\n 1 Get information from m1_info\n \"\"\"\n x_m1 = m1_info[0]\n y_m1 = m1_info[1]\n z_m = m1_info[2]\n t_m = z_m / 3\n\n t_sen = m1_info[4]\n\n m1_points = m1_info[3]\n\n m1_p0 = m1_points[0]\n m1_p1 = m1_points[1]\n m1_p2 = m1_points[2]\n m1_p3 = m1_points[3]\n\n b_p = (tx, ty)\n\n u_distance = rs.Distance(m1_p3, b_p) - y_k / 2\n l_distance = rs.Distance(m1_p0, b_p) - y_k / 2\n\n \"\"\"\n 2 Get SEN information\n \"\"\"\n # Automatically fixed---------------------------------------------------\n # t_sen = rs.GetReal(\"Put Int(mm): Thickness of material to cut SEN.\", t_m / 2, None, None)\n w_sen = t_sen\n n_w_sen = w_sen / 2\n h_sen = z_m\n\n u_max_n = u_distance / (2 * w_sen - n_w_sen) # NOTE: divide max_n by 2 to controll \"n\"\n u_max_n = int(u_max_n)\n\n u_n = u_max_n / 4\n u_n = int(u_n)\n\n l_max_n = l_distance / (2 * w_sen - n_w_sen) # NOTE: divide max_n by 2 to controll \"n\"\n l_max_n = int(l_max_n)\n\n l_n = l_max_n / 4\n l_n = int(l_n)\n\n\n set = 20\n u_offset = (u_distance - 2 * set) / (u_n - 1)\n l_offset = (l_distance - 2 * set) / (l_n - 1)\n\n SEN_info = [w_sen, n_w_sen, h_sen, t_sen, u_n, l_n, set, u_offset, l_offset]\n\n return SEN_info","def test_get_team_strength(self):\n pass","def test_offsets():\n B = 100\n H = 20\n E = 210000\n sections = ((B, H, 0, E),)\n sections2 = ((B, H, 12.435, E),)\n EI, top, bot = bm.EI(sections, E)\n EI2, top2, bot2 = bm.EI(sections2, E)\n assert 0.99 < EI / EI2 < 1.01\n assert 0.99 < top / top2 < 1.01\n assert 0.99 < bot / bot2 < 1.01","def hw_func(self):\n i, o = self.inl[0].to_flow(), self.outl[0].to_flow()\n\n if abs(i[0]) < 1e-4:\n return i[1] - o[1]\n\n v_i = v_mix_ph(i, T0=self.inl[0].T.val_SI)\n v_o = v_mix_ph(o, T0=self.outl[0].T.val_SI)\n flow_dir = np.sign(i[0])\n\n return ((i[1] - o[1]) * flow_dir -\n (10.67 * abs(i[0]) ** 1.852 * self.L.val /\n (self.ks.val ** 1.852 * self.D.val ** 4.871)) *\n (9.81 * ((v_i + v_o) / 2) ** 0.852))","def check(self):\n\n Rbo = self.get_Rbo()\n alpha = self.comp_alpha()\n\n if self.W0 < (self.W2 + self.W3):\n raise S51_WCheckError(\"You must have W2+W3 < W0\")\n\n if Rbo < self.H0 + self.H2:\n raise S51_RHCheckError(\"You must have H0+H2 < Rbo\")\n\n if alpha > pi / 2:\n raise S51_AlphaCheckError(\"You must have alpha < pi/2\")","def print_if_armstrong(start, end):\n for i in range(start, end):\n h, t, u = get_digits(i)\n if i == h**3 + t**3 + u**3 :\n print(i)","def _test_out_of_range(self):\n self.cdbconf.setup('KKG')\n self.cdbconf.setConfiguration('CUSTOM_OPT')\n az, el, latitude = [radians(50)] * 3\n site_info = {'latitude': latitude}\n self.p.setup(site_info, self.source, self.device)\n self.p.setRewindingMode('AUTO')\n offset = 20\n max_limit = self.device.getMaxLimit() \n min_limit = self.device.getMinLimit()\n Pis = max_limit - offset/2\n time.sleep(0.2) if self.using_mock else time.sleep(3)\n self.p.setPosition(Pis)\n time.sleep(0.2) # Wait a bit for the setup\n max_rewinding_steps = (max_limit - min_limit) // self.device.getStep()\n expected = Pis - max_rewinding_steps*self.device.getStep() + offset\n self.source.setAzimuth(az)\n self.source.setElevation(el)\n self.p.startUpdating('MNG_TRACK', 'ANT_NORTH', az, el, None, None)\n time.sleep(0.2) if self.using_mock else time.sleep(3)\n self.p.setOffset(offset)\n time.sleep(0.2) if self.using_mock else time.sleep(3)\n self.assertEqual(self.device.getActPosition(), expected)","def regenShields(self, kW, kWtoSPfactor):\n remainder = kW \n if self.currentSP < self.maxSP:\n # how many kW required to fully charge?\n # desiredSP = kWneeded * fact\n # kWneeded = desiredSP/fact\n desiredSP = self.maxSP - self.currentSP\n kWneeded = desiredSP / kWtoSPfactor\n if kW > kWneeded:\n remainder = kW - kWneeded\n #print \"more kW than need for sheilds; remainder\",remainder\n else:\n remainder = 0\n \n self.currentSP += (kW * kWtoSPfactor)\n if self.currentSP > self.maxSP:\n self.currentSP = self.maxSP\n self.myParent.updateMyGUIValue(self.position+'Shields', self.currentSP)\n return remainder","def get_strength(self):\n return 10 - self.get_agility()","def test_mw(self):\n self.assertEqual(self.PROT(\"\").mw(), 0)\n self.assertEqual(self.RNA(\"\").mw(), 0)\n self.assertFloatEqual(self.PROT(\"A\").mw(), 89.09)\n self.assertFloatEqual(self.RNA(\"A\").mw(), 375.17)\n self.assertFloatEqual(self.PROT(\"AAA\").mw(), 231.27)\n self.assertFloatEqual(self.RNA(\"AAA\").mw(), 1001.59)\n self.assertFloatEqual(self.RNA(\"AAACCCA\").mw(), 2182.37)","def get_total_shield(self,obs):","def low(self):","def learning_rate_range():\n # Lower and upper bounds\n #######\n lower_bound = 0.1 \n upper_bound = 1e-6\n #######\n return lower_bound, upper_bound","def total_sdram_requirements(self):","def calc_wmr(BMR, PAL):\n return BMR * (PAL - 1)","def test_single_caps(self):\n result_h = mmc.find_molar_mass('H')\n actual_h = 1.007940\n self.assertTrue(abs(percent_error(result_h, actual_h)) < self.ERR_THRESHOLD)","def _get_sight_range(self):\n raise NotImplementedError","def get_sumw(self):\n sumw_l = self.read_register(4109, 0, 3) \n sumw_h = self.read_register(4110, 0, 3) \n return (sumw_h * 255) + sumw_l \n\n# def get_braketype(self):\n# \"\"\"Return the brake type of wind turbine.\"\"\"\n# return self.read_register(7, 0, 3) \n\n# def set_modbus_ad(self,modbus_ad):\n\t\"\"\"Set the modbus address of the Aeolos controller\"\"\"\n\t\"\"\"Register address: 0x1999 \"\"\"\n\tself.write_registers(6553, modbus_ad, 0)","def vga_gain_range(self):\n return {'start': 8., 'stop': 15., 'step': 1.}","def __init__(self, Strength:int, Defense:int):\n if (Strength + Defense) <= 7:\n if Strength <= 5:\n if Defense <= 5:\n self.Strength = Strength\n self.Defense = Defense\n else:\n raise ValueError(\"Total Power is over 7\")","def get_source_power_offset(self) -> Tuple[float, bool]:\n (power, rel) = self.SOURcePOWer.LEVel.IMMediate.OFFSet.q().split_comma()\n rel = rel.upper()\n assert rel in (\"CPAD\", \"ONLY\")\n return float(power), rel == \"CPAD\"","def weight(self):","def mortality(self):\n pass","def arduPusherWPM(self):\r\n tmp = (self.wpm / self.maxWPM) * self.available_degrees \\\r\n if self.wpm < self.maxWPM else self.available_degrees\r\n # send number as char \r\n self.ardu.write(chr(int(tmp)))","def getCharacteristics(self) -> int:\n ...","def getSpeed(start, end, time):\n\t# Warning wrap up counter\n\tif start > (MAX_VALUE_SNMP_COUNTER32/2) and end < (MAX_VALUE_SNMP_COUNTER32/2):\n\t\ttotal = (MAX_VALUE_SNMP_COUNTER32 - start) + end\n\t\tspeed = float(total)/time.total_seconds()\n\telse:\n\t\tspeed = float(end - start)/time.total_seconds()\n\n\t# In Mbites\n\treturn (speed/1048576)*8","def checkHSPS(hsp1, hsp2, HSPMIN=100):\n dict_update = {18: 0}\n\n # Check if the hsp2 is in a different orientation than hsp1: 'sens': 14\n if hsp1[14] != hsp2[14]:\n # print(f'orientation wrong: {hsp1[14]} != {hsp2[14]}')\n return dict_update\n\n # Check is hsp2 inside hsp1 for the query sequence: 'qstart': 6, 'qend': 7,\n if hsp1[7] >= hsp2[7] and hsp1[8] <= hsp2[8]:\n # print(f'hsp2 inside hsp1 for query: {hsp1[6]} >= {hsp2[6]} and {hsp1[7]} <= {hsp2[7]}')\n return dict_update\n\n # Check is hsp1 inside hsp2 for the query sequence: 'qstart': 6, 'qend': 7,\n elif hsp1[7] <= hsp2[7] and hsp1[8] >= hsp2[8]:\n # print(f'hsp1 inside hsp2 for query: {hsp1[6]} <= {hsp2[6]} and {hsp1[7]} >= {hsp2[7]}')\n return dict_update\n\n # Check is hsp1 inside hsp2 for the subject sequence: 'sstart': 8, 'send': 9,\n elif hsp1[9] >= hsp2[9] and hsp1[10] <= hsp2[10]:\n # print(f'hsp2 inside hsp1 for subject: {hsp1[8]} >= {hsp2[8]} and {hsp1[9]} <= {hsp2[9]}')\n return dict_update\n\n # Check is hsp2 inside hsp1 for the subject sequence: 'sstart': 8, 'send': 9,\n elif hsp1[9] <= hsp2[9] and hsp1[10] >= hsp2[10]:\n # print(f'hsp1 inside hsp2 for subject: {hsp1[8]} <= {hsp2[8]} and {hsp1[9]} >= {hsp2[9]}')\n return dict_update\n\n # reject HSPs that are in different orientation: 'qstart': 6, 'qend': 7, 'sstart': 8, 'send': 9,\n # Query: ---- A ---- B ----- A = HSP1\n # Sbjct: ---- B ---- A ----- B = HSP2\n\n if (hsp1[8] - hsp2[8]) * (hsp1[10] - hsp2[10]) < 0:\n # print(f'HSPs are in different orientation 1: ({hsp1[7]} - {hsp2[7]}) * ({hsp1[9]} - {hsp2[9]}) ===> {(hsp1[7] - hsp2[7]) * (hsp1[9] - hsp2[9])} < 0')\n return dict_update\n elif (hsp1[7] - hsp2[7]) * (hsp1[9] - hsp2[9]) < 0:\n # print(f'HSPs are in different orientation 2: ({hsp1[6]} - {hsp2[6]}) * ({hsp1[8]} - {hsp2[8]}) ===> {(hsp1[6] - hsp2[6]) * (hsp1[8] - hsp2[8])} < 0')\n return dict_update\n\n overlap_q = (hsp2[7] - hsp1[8]) * (hsp2[8] - hsp1[7])\n overlap_s = (hsp2[9] - hsp1[10]) * (hsp2[10] - hsp1[9])\n\n # Accept non-overlapping HSPs in correct orientation\n if overlap_q > 0 and overlap_s > 0:\n # print(f'No overlap in query and subject: {overlap_q} > 0 and {overlap_s} > 0')\n dict_update[18] = 1\n return dict_update\n\n # Test if the query is overlaping\n elif overlap_q < 0:\n # print(f'Overlap in query: {overlap_q} > 0')\n dict_update = remove_overlap_query(hsp1=hsp1, hsp2=hsp2)\n\n # update the hsp2 array with the new values\n for index_key in dict_update:\n hsp2[index_key] = dict_update[index_key]\n\n overlap_s = (hsp2[9] - hsp1[10]) * (hsp2[10] - hsp1[9])\n\n # Test if the subject is overlaping after possible update of an overlaping query\n if overlap_s < 0 and hsp2[17] > 0:\n # print(f'Overlap in subject: {overlap_s} > 0')\n dict_update = remove_overlap_subject(hsp1=hsp1, hsp2=hsp2)\n\n # update the hsp2 array with the new values\n for index_key in dict_update:\n hsp2[index_key] = dict_update[index_key]\n\n # Filter out HSPs that are too short\n if hsp2[17] < HSPMIN:\n # print(f'HSP too short: {hsp2[17]} < {HSPMIN}')\n dict_update[18] = 0\n return dict_update\n\n # Set status to 1 for consistent HSPs\n dict_update[18] = 1\n return dict_update","def getHadler(event):","def __init__(self, denoiser_strength): \n self._strength = denoiser_strength","def weapon_strength(weapon):\n weapon_strength_int = WEAPON_STRENGTHS[weapon]\n #print weapon_strength_int\n return weapon_strength_int","def get_resistance(self):\n\t\tif self.channel == 0 :\n\t\t\tdata = bus.read_byte_data(AD5252_DEFAULT_ADDRESS, AD5252_WORD_RDAC1_EEMEM1)\n\t\telif self.channel == 1 :\n\t\t\tdata = bus.read_byte_data(AD5252_DEFAULT_ADDRESS, AD5252_WORD_RDAC3_EEMEM3)\n\t\t\n\t\t# Convert the data\n\t\tresistance_wb = (data / 256.0) * 1.0 + 0.075\n\t\tresistance_wa = 1.0 - resistance_wb\n\t\t\n\t\treturn {'a' : resistance_wa, 'b' : resistance_wb}","def sendTwoM(self):\n startM = self.countMissionaryOnStart()\n if startM < 2:\n return None\n else:\n newStart = str(startM-2) + self.start[1:]\n newEnd = str(5-startM) + self.end[1:]\n return MissionaryState(newStart,newEnd,\"sendTwoM\")","def pre_flop_strength(hand):\n highs = {}\n highs[4] = [\n \"AA\", \"AKs\", \"AQs\", \"AJs\", \"ATs\", \"AKo\", \"KK\", \"KQs\", \"KJs\", \"AQo\",\n \"QQ\", \"QJs\", \"JJ\", \"TT\"\n ]\n highs[3] = [\n \"A5s\", \"A4s\", \"A3s\", \"KTs\", \"KQo\", \"QTs\", \"AJo\", \"JTs\", \"T9s\", \"99\",\n \"98s\", \"88\", \"87s\", \"77\", \"66\"\n ]\n highs[2] = [\n \"A9s\", \"A8s\", \"A7s\", \"A6s\", \"A2s\", \"K9s\", \"K8s\", \"Q9s\", \"KJo\", \"QJo\",\n \"J9s\", \"ATo\", \"KTo\", \"QTo\", \"JTo\", \"T8s\", \"A9o\", \"J9o\", \"T9o\", \"97s\",\n \"98o\", \"86s\", \"76s\", \"75s\", \"65s\", \"55\", \"44\", \"33\", \"22\"\n ]\n highs[1] = [\n \"K7s\", \"K6s\", \"K5s\", \"K4s\", \"K3s\", \"Q8s\", \"Q7s\", \"Q6s\", \"Q5s\", \"Q4s\",\n \"J8s\", \"J7s\", \"J6s\", \"J5s\", \"T7s\", \"T6s\", \"K9o\", \"Q9o\", \"96s\", \"A8o\",\n \"K8o\", \"Q8o\", \"J8o\", \"T8o\", \"85s\", \"A7o\", \"K7o\", \"Q7o\", \"T7o\", \"97o\",\n \"87o\", \"74s\", \"A6o\", \"K6o\", \"86o\", \"76o\", \"64s\", \"63s\", \"A5o\", \"75o\",\n \"65o\", \"54s\", \"53s\", \"A4o\", \"43s\", \"A3o\"\n ]\n card0, card1 = hand\n if card0[0] == card1[0]:\n pair = \"\".join([card0[0], card1[0]])\n elif card0[1] == card1[1]:\n pair = \"\".join([card0[0], card1[0], \"s\"])\n else:\n pair = \"\".join([card0[0], card1[0], \"o\"])\n for strenght in highs:\n if pair in highs[strenght]:\n return strenght\n return 0","def test_uneven_sw():\n B = 100\n t = 1\n H = 30\n E = 20000\n sections = ((2 * B, t, 0, E), (B, t, H - t, E))\n EI, top, bot = bm.EI(sections, E)\n assert 1.95 < abs(bot) / top < 1.96","def test_simple():\n B = 100\n H = 20\n E = 210000\n sections = ((B, H, 0, E),)\n EI, top, bot = bm.EI(sections, E)\n EIc = E * B * (H ** 3) / 12\n assert 0.99 < EI / EIc < 1.01\n assert top == H / 2\n assert bot == -H / 2","def high(self):","def err_est_vv_hwmf_pos(self, Y, m, Cp0, Cp1):\n # if(self._weight>=1.5):\n # raise ValueError,\" Error bounds only accurate for k<1.5! got k=%s\" % weight\n\n twopiY = mpmath.pi() * mp2 * Y\n fourpiY = mp2 * twopiY\n tmp = mpmath.mpf(len(self.multiplier().weil_module().D()))\n etmp1 = mpmath.sqrt(mpmath.mpf(m)) - Cp1 / fourpiY\n etmp2 = mpmath.exp(-twopiY * etmp1**2)\n etmp3 = mp2 + mpsqrtpi * Cp1 / mp2 / mpmath.sqrt(twopiY)\n etmp4 = Cp0 / twopiY\n err_pos = tmp * etmp4 * etmp3 * etmp2\n return err_pos","def tx_power(self) -> int:\n # Follow table 10 truth table from the datasheet for determining power\n # level from the individual PA level bits and output power register.\n pa0 = self.pa_0_on\n pa1 = self.pa_1_on\n pa2 = self.pa_2_on\n current_output_power = self.output_power\n if pa0 and not pa1 and not pa2:\n # -18 to 13 dBm range\n return -18 + current_output_power\n if not pa0 and pa1 and not pa2:\n # -2 to 13 dBm range\n return -18 + current_output_power\n if not pa0 and pa1 and pa2 and not self.high_power:\n # 2 to 17 dBm range\n return -14 + current_output_power\n if not pa0 and pa1 and pa2 and self.high_power:\n # 5 to 20 dBm range\n return -11 + current_output_power\n raise RuntimeError(\"Power amps state unknown!\")","def return_weight(self, startVertex: np.int, endVertex:np.int):\n return self.__mat[startVertex][endVertex]","def test_wl_metric():\n z1 = np.random.normal(size=int(1e5)) + 1\n z2 = np.random.normal(size=int(1e5)) + 2\n res = pval.wl_metric(z1, z2)\n np.testing.assert_almost_equal(res, 1, 2)","def bm_and_dvr_supported(self):","def get_field_strength(self):\n \n valid_characters = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\",\n \"6\", \"7\", \"8\", \"9\", \".\", \"-\"]\n\n try:\n \n # Reset serial buffer to pervent it from growing too large.\n self.interface.reset_input_buffer()\n time.sleep(0.001)\n \n # Read in data\n field_string = self.interface.readline().decode(\"utf-8\")\n x_string, y_string, z_string = \"\", \"\", \"\"\n currently_reading = None\n \n # Record raw output\n #with open(\"temp_mag_field_raw.txt\", \"a\") as f:\n #f.write(field_string + \"\\n\")\n #print(\"read in field data\")\n\n # If currently reading for float, add character if it's valid\n for character in field_string:\n if (currently_reading == \"X\") and (character in valid_characters):\n x_string += character\n if (currently_reading == \"Y\") and (character in valid_characters):\n y_string += character\n if (currently_reading == \"Z\") and (character in valid_characters):\n z_string += character\n\n # See if it's time to change what is being read\n if character == \"X\":\n currently_reading = \"X\"\n elif character == \"Y\":\n currently_reading = \"Y\"\n elif character == \"Z\":\n currently_reading = \"Z\"\n\n # Convert the string chunks to floats\n try:\n x_field = float(x_string)\n except Exception as err:\n x_field = None\n print(\"Could not determine x field from string {} | {}\"\n .format(x_string, err))\n try:\n y_field = float(y_string)\n except Exception as err:\n y_field = None\n print(\"Could not determine y field from string {} | {}\"\n .format(y_string, err))\n try:\n z_field = float(z_string)\n except Exception as err:\n z_field = None\n print(\"Could not determine z field from string {} | {}\"\n .format(z_string, err))\n \n return (x_field, y_field, z_field)\n \n # Print 999 so that it is not confused with 0.0\n except AttributeError:\n return (999, 999, 999)","def digital_gain():\n def r(x):\n return x/512.\n\n def w(x):\n return int(x*512)\n return r, w","def test_password_strength_validator_missing_letter(self):\n with self.assertRaises(ValidationError):\n validate_password_strength('1234567')","def test_password_strength_validator(self):\n self.assertIsNone(validate_password_strength('abcd123'))","def scan(self, wl1, wl2):\r\n # Wavelength position can be sent in either um, nm or Angstrom, as 2-byte form.\r\n # Since an int value must be sent, this limits both the resolution and max value that can be set\r\n if max(wl1, wl2) > (2**16 / 10):\r\n # Value too high to send in Angstrom units, so send in nm (setting resolution = 1 nm)\r\n if self._current_units != 'nm':\r\n self.set_units('nm')\r\n # Combine the one-byte goto command with two-byte wl position\r\n cmd = struct.pack('>B', 12) + struct.pack('>H', wl1) + struct.pack('>H', wl2)\r\n else:\r\n # Value low enough to use Angstrom units (setting resolution = 1A = 0.1 nm)\r\n if self._current_units != 'A':\r\n self.set_units('A')\r\n wl1_angstrom = int(wl1 * 10)\r\n wl2_angstrom = int(wl2 * 10)\r\n cmd = struct.pack('>B', 12) + struct.pack('>H', wl1_angstrom) + struct.pack('>H', wl2_angstrom)\r\n\r\n self.send(cmd)","def get_h_score(start, end):\n #uses a heuristic function\n #return 0 #used if you want Djikstras algorithm\n return (abs(end[0]-start[0])+abs(end[1]-start[1])) * 10","def event_m20_11_4000000():\n \"\"\"State 0,2: [Lib] Character: Petrified: Key Guide_SubState\"\"\"\n assert event_m20_11_x37(z94=5300, z95=0, z96=15, z97=211000030, z98=0, z99=1600, z100=6, z101=4000010)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def efficiency_vs_w(wmax, wmin=10, Lmax=1000, Lmin=102.4, p=75, fmax=5.7e9,\r\n p1=database['K+'], p2=database['pi+'], p3=database['p+'],\r\n E=1e6, delta_p=1.6e-2, nw=10, nL=10, ng=50, nl=50, nf=20,\r\n resolution=0.01, w_resolution=0.01, plot=True,\r\n set_freq=5.7e9, count_L=False, count_w=True):\r\n if resolution == None:\r\n if set_freq == None:\r\n est = timing(nw*nL*ng*nl*nf*4e-05)\r\n else:\r\n est = timing(nw*nL*ng*nl*4e-05)\r\n print('Estimated total time: {0}'.format(est))\r\n else:\r\n num = int(round(np.log10(Lmax-Lmin))-1)-np.log10(resolution)+1\r\n if set_freq == None:\r\n est = timing(nw*10*num*ng*nl*nf*4e-05)\r\n else:\r\n est = timing(nw*10*num*ng*nl*4e-05)\r\n print('Estimated maximum total time: {0}'.format(est))\r\n start = time.time()\r\n if w_resolution == None:\r\n w_range = np.linspace(wmin, wmax, int(nw))\r\n plot_w, length, intensity = [], [], []\r\n for w in w_range:\r\n if count_w == True:\r\n if count_L == True:\r\n print(f'w = {w} mm')\r\n else:\r\n print(w)\r\n eff = efficiency_vs_length(w, Lmax, Lmin, p, fmax,p1, p2, p3, E,\r\n delta_p, nL, ng, nl, nf, resolution,\r\n False, set_freq, False, count_L)\r\n if eff != None:\r\n plot_w.append(w)\r\n length.append(eff[0])\r\n intensity.append(eff[2])\r\n file = open(f'Save_Data_efficiency_vs_w_{wmin}_{wmax}.txt','a')\r\n file.write(f'{[plot_w[-1], length[-1], intensity[-1]]}\\n')\r\n file.close()\r\n length, intensity = np.array(length), np.array(intensity)\r\n ratio = 1/(length*intensity)\r\n ratio *= (np.max(intensity)-np.min(intensity))/(np.max(ratio)-np.min(ratio))\r\n ratio += ((np.min(intensity))-np.min(ratio))\r\n output = [plot_w[np.argmax(ratio)], plot_w[np.argmin(length)], plot_w[np.argmin(intensity)]]\r\n else:\r\n wmax = 10*(int(wmax/10)+2)\r\n wmin = 10*int(wmin/10)\r\n n = 10\r\n while n >= w_resolution:\r\n if n == 10 and plot == True:\r\n w_range = np.arange(wmin, wmax, n)\r\n graph_w, graph_length, graph_intensity, graph_ratio = [], [], [], []\r\n for w in w_range:\r\n if count_w == True:\r\n if count_L == True:\r\n print(f'w = {w} mm')\r\n else:\r\n print(w)\r\n eff = efficiency_vs_length(w, Lmax, Lmin, p, fmax,p1, p2, p3, E,\r\n delta_p, nL, ng, nl, nf, resolution,\r\n False, set_freq, False, count_L)\r\n if eff != None:\r\n graph_w.append(w)\r\n graph_length.append(eff[0])\r\n graph_intensity.append(eff[2])\r\n graph_ratio.append(1/(eff[0]*eff[2]))\r\n i, d_ratio = 1, 0\r\n while d_ratio >= 0 and i < np.size(graph_ratio):\r\n d_ratio = graph_ratio[i] - graph_ratio[i-1]\r\n i += 1\r\n if i >= np.size(graph_ratio):\r\n output = None\r\n break\r\n graph_ratio = np.array(graph_ratio)\r\n graph_ratio *= (np.max(graph_intensity)-np.min(graph_intensity))/(np.max(graph_ratio)-np.min(graph_ratio))\r\n graph_ratio += ((np.min(graph_intensity))-np.min(graph_ratio))\r\n d = -int(np.log10(n))\r\n output = [round(graph_w[np.argmax(graph_ratio)], d),\r\n graph_length[np.argmax(graph_ratio)],\r\n graph_intensity[np.argmax(graph_ratio)]]\r\n wmax = graph_w[i-1]\r\n wmin = graph_w[i-3]\r\n n /= 10\r\n else:\r\n w_range = np.arange(wmin, wmax+n, n)\r\n plot_w, length, intensity, ratio = [], [], [], []\r\n i, d_ratio = 0, 0\r\n d = -int(np.log10(n))\r\n while d_ratio >= 0 and i < np.size(w_range):\r\n if count_w == True:\r\n if count_L == True:\r\n print(f'w = {round(w_range[i], d)} mm')\r\n else:\r\n print(round(w_range[i], d))\r\n eff = efficiency_vs_length(w_range[i], Lmax, Lmin, p, fmax,p1,\r\n p2, p3, E, delta_p, nL, ng, nl, nf,\r\n resolution, False, set_freq, False,\r\n count_L)\r\n if eff != None:\r\n plot_w.append(w_range[i])\r\n length.append(eff[0])\r\n intensity.append(eff[2])\r\n ratio.append(1/(eff[0]*eff[2]))\r\n if np.size(ratio) == 1:\r\n d_ratio = ratio[-1]\r\n else:\r\n d_ratio = ratio[-1] - ratio[-2]\r\n i += 1\r\n if i >= np.size(w_range):\r\n output = None\r\n break\r\n output = [round(plot_w[np.argmax(ratio)], d),\r\n length[np.argmax(ratio)],\r\n intensity[np.argmax(ratio)]]\r\n wmax = w_range[i-1]\r\n wmin = w_range[i-3]\r\n n /= 10\r\n if plot == True:\r\n if w_resolution != None:\r\n plot_w, length, intensity, ratio = graph_w, graph_length, graph_intensity, graph_ratio\r\n fig = plt.figure(figsize=[12, 4])\r\n ax = fig.add_subplot(1, 1, 1)\r\n line1, = ax.plot(plot_w, length, 'r', lw=2, alpha=0.6, label='Target Distance')\r\n ax2 = ax.twinx()\r\n line2, = ax2.plot(plot_w, intensity, 'g', lw=2, alpha=0.6, label='Intensity Required')\r\n line3, = ax2.plot(plot_w, ratio, 'b', lw=2, alpha=0.6, label='Reciprocal Product')\r\n ax.set_xlabel('Collimator Width / mm', fontsize=20)\r\n ax.set_ylabel('Target Distance / m', fontsize=20, color='r')\r\n ax2.set_ylabel(r'Intensity / I$_0$', fontsize=20, color='g')\r\n ax.set_xlim(np.min(plot_w), np.max(plot_w))\r\n ax.tick_params(axis='y', colors=line1.get_color())\r\n ax2.tick_params(axis='y', colors=line2.get_color())\r\n# ax.set_ylim(0)\r\n lines = [line1, line2, line3]\r\n ax.legend(lines, [l.get_label() for l in lines], loc=[0.70, 0.12], fontsize=15)\r\n ax.minorticks_on()\r\n ax2.minorticks_on()\r\n ax.grid()\r\n ax.set_title(' Minimum required target distance and proton intensity as a function of beam stopper inner radius', fontsize=16)\r\n plt.show()\r\n print(f'Total time: {timing(time.time()-start)}')\r\n return output","def getFWHM(antenna, freq):\n diameter = getDiameter(antenna)\n lam = 299792458.0 / (freq * 1e9)\n fwhmo = lam / math.pi * 180.0 * 60.0\n fwhm = 1.22 * fwhmo / diameter\n return fwhm","def from_dbm_to_watt(dbm: Optional[float]) -> float:\n return 0 if dbm is None else from_db_to_natural(dbm - 30)","def ohms(self):\n # Rwb = Rwiper + Rtotal * (counts / 256)\n # Rwa = Rwiper + Rtotal * ((256 - counts) / 256)\n g = 0\n rtotal=0.0\n reach=[]\n for chan in self.get_channel_list(self.nchans):\n self.rwa[chan] = float( 256 - self.vals[chan] ) / 256.0\n self.rwb[chan] = float( self.vals[chan] ) / 256.0\n self.rwa[chan] *= self.Rtotal\n self.rwb[chan] *= self.Rtotal \n self.rwa[chan] += self.Rwiper\n self.rwb[chan] += self.Rwiper","def cal_consistency_weight(epoch, init_ep=0, end_ep=150, init_w=0.0, end_w=20.0):\n if epoch > end_ep:\n weight_cl = end_w\n elif epoch < init_ep:\n weight_cl = init_w\n else:\n T = float(epoch - init_ep)/float(end_ep - init_ep)\n #weight_mse = T * (end_w - init_w) + init_w #linear\n weight_cl = (math.exp(-5.0 * (1.0 - T) * (1.0 - T))) * (end_w - init_w) + init_w #exp\n #print('Consistency weight: %f'%weight_cl)\n return weight_cl","def cal_consistency_weight(epoch, init_ep=0, end_ep=150, init_w=0.0, end_w=20.0):\n if epoch > end_ep:\n weight_cl = end_w\n elif epoch < init_ep:\n weight_cl = init_w\n else:\n T = float(epoch - init_ep)/float(end_ep - init_ep)\n #weight_mse = T * (end_w - init_w) + init_w #linear\n weight_cl = (math.exp(-5.0 * (1.0 - T) * (1.0 - T))) * (end_w - init_w) + init_w #exp\n #print('Consistency weight: %f'%weight_cl)\n return weight_cl","def test_add_shorts_and_srational_le(self):\n self.image_le.contrast = 1\n self.image_le.light_source = 24\n self.image_le.brightness_value = 5.4321\n\n assert self.image_le.light_source == 24\n assert self.image_le.contrast == 1\n assert self.image_le.brightness_value == 5.4321\n\n # Verify pre-existing attributes can still be read as expected.\n for attribute, func, value in read_attributes_little_endian:\n assert func(getattr(self.image_le, attribute)) in value\n\n segment_hex = (\n binascii.hexlify(self.image_le._segments[\"APP1\"].get_segment_bytes())\n .decode(\"utf-8\")\n .upper()\n )\n self.assertEqual(\n \"\\n\".join(textwrap.wrap(segment_hex, 90)), ADD_SHORT_LE_BASELINE\n )","def test_get_hyperflex_feature_limit_external_by_moid(self):\n pass","def test_get_hyperflex_feature_limit_internal_by_moid(self):\n pass","def _update_strength(self, event=None):\n password = self.password.GetValue()\n nd = calc_password_strength(password, WEB_SPEED)\n self.strength.UpdateStrength(nd)","def part2(input):\n sys = AmpSystem(input)\n return sys.max_thruster_signal([i for i in range(5, 10)])","def penaltyFactor(m):\n \"\"\" m in GeV \"\"\"\n if m*m>0.71:\n return math.pow(m*m/0.71,-4)\n else:\n return 1","def align(): # open EH and fast shutter\n\t#marAuxiliary.closeMarShield()\n\td2in()\n\td3in()\n\tsh('o')","def EM_gain_range(self):\n mini, maxi = ct.c_int(), ct.c_int()\n self.lib.GetEMGainRange(ct.pointer(mini), ct.pointer(maxi))\n\n return (mini.value, maxi.value)","def generate_power_law(alpha, wmin=40., wmax=300.):\n c = 2.99792e14 # cm/s\n # cm2mum = 1e4 # Convert cm to microns\n wref = 24.0\n fref = 1.0\n # nuref = c * cm2mum / wref\n nuref = c / wref\n dw = 0.005\n nw = int((wmax - wmin) / dw) + 1\n wstar = np.arange(nw) * dw + wmin\n nuarr = c / wstar\n fstar = fref * (nuarr / nuref) ** alpha\n\n return wstar, fstar","def test_parameters(self):\n assert self.hll.p == 8\n assert self.hll.m == 256\n assert round(self.hll.alpha - 0.7182725932495458, 5) == 0\n assert round(self.hll.error - 0.065, 5) == 0\n assert self.hll64.treshold == 120000","def set_physical_minmax(self, min, max):\n # This allows you to set the min and the max of the quantity that you want the MLP to measure. \n # Once you set this, you can pass in a physical number to get_mlp_value() and it will be mapped to an MLP value and returned\n pass\n \n # Maybe we should let the holder of the MLP determine these values and do the mapping? ","def switchAndCheck(sw, name, loLim, hiLim, unit = 'V', delay = 0):\n\n v = DMM.readSwitch(sw, delay)\n #Log.logValue(name, v, unit)\n chkLimits(name, v, loLim, hiLim)\n return v","def gain_opt(machine, T):\n res = (np.arange(T)+1)\n return res * np.amax(machine)","def input_resistance(self):\n return None","def get_w(self):\n raise NotImplementedError","def getStrength(self):\n return self.st","def validate_strength(cls, value: str) -> (bool, dict):\n if value is None:\n return False, {}\n\n length = cls._validate_length(value)\n digit = cls._validate_digit(value)\n uppercase = cls._validate_uppercase(value)\n lowercase = cls._validate_lowercase(value)\n symbol = cls._validate_symbol(value)\n\n valid = all([length, digit, uppercase, lowercase, symbol])\n error_dict = {\n 'length': length,\n 'digit': digit,\n 'uppercase': uppercase,\n 'lowercase': lowercase,\n 'symbol': symbol,\n }\n\n return valid, error_dict","def _convert_energy(self, mwh, kwh, active, position):\n if active & position:\n kwh = self._calculate_value(kwh, 0.1, INT16_POSITIVE_MASK)\n return mwh * 1000 + kwh\n else:\n return None","def _get_observation_lower_bound(self):\n lower_bound = -self._get_observation_upper_bound()\n lower_bound[-7] = 0.0\n lower_bound[-2:] = [self.min_speed, self.min_side_speed]\n return lower_bound","def get_resistance(self):\n\t\tdata = bus.read_byte_data(AD5259_DEFAULT_ADDRESS, AD5259_WORD_ADDR_RDAC)\n\t\t\n\t\t# Convert the data\n\t\tresistance_wb = (data / 256.0) * 5.0\n\t\tresistance_wa = 5 - resistance_wb\n\t\t\n\t\treturn {'a' : resistance_wa, 'b' : resistance_wb}","def __init__(self, NW, SE):\n self.type = \"HOLD AREA\"\n self.NW = NW\n self.SE = SE\n self.ctr = [(self.NW[0] + self.SE[0]) / 2, (self.NW[1] + self.SE[1]) / 2]","def hanging_man(self):\n self.data['hanging_man'] = (((self.data['High'] - self.data['Low']) > ((self.data['Open'] - self.data['Close'])*4)) & \\\n ((self.data['Close'] - self.data['Low']) / ((.001 + self.data['High'] - self.data['Low']) >= 0.75)) &\\\n ((self.data['Open'] - self.data['Low']) / ((.001 + self.data['High'] - self.data['Low']) >= .075)))","def check(self):\n self.lower_bound(5e-4)\n self.upper_bound(5e2)","def Wang_SSM(data, do_padding = False, endianness='little'):\n \n if do_padding:\n \n M = MD4_get_words(MD4_pad_data(data), endianness)\n \n else:\n \n if len(data) < 64:\n data += b'\\x00'*(64-len(data))\n \n M = MD4_get_words(data, endianness) \n \n A, B, C, D = MD4_get_IVs(M) \n # Modify the message to meet several of the constraints in Table 6\n # All bit #ing has been converted from base 1 to base 0\n \n A[1] = bitset(A[1], 6, bitget(B[0], 6))\n \n D[1] = bitset(D[1], 6, 0)\n D[1] = bitset(D[1], 7, bitget(A[1], 7))\n D[1] = bitset(D[1], 10, bitget(A[1], 10))\n C[1] = bitset(C[1], 6, 1)\n C[1] = bitset(C[1], 7, 1)\n C[1] = bitset(C[1], 10, 0)\n C[1] = bitset(C[1], 25, bitget(D[1], 25))\n B[1] = bitset(B[1], 6, 1)\n B[1] = bitset(B[1], 7, 0)\n B[1] = bitset(B[1], 10, 0)\n B[1] = bitset(B[1], 25, 0)\n A[2] = bitset(A[2], 7, 1)\n A[2] = bitset(A[2], 10, 1)\n A[2] = bitset(A[2], 13, bitget(B[1], 13))\n A[2] = bitset(A[2], 25, 0) \n D[2] = bitset(D[2], 13, 0)\n D[2] = bitset(D[2], 18, bitget(A[2], 18))\n D[2] = bitset(D[2], 19, bitget(A[2], 19))\n D[2] = bitset(D[2], 20, bitget(A[2], 20))\n D[2] = bitset(D[2], 21, bitget(A[2], 21))\n D[2] = bitset(D[2], 25, 1) \n C[2] = bitset(C[2], 12, bitget(D[2], 12))\n C[2] = bitset(C[2], 13, 0)\n C[2] = bitset(C[2], 14, bitget(D[2], 14))\n C[2] = bitset(C[2], 18, 0)\n C[2] = bitset(C[2], 19, 0)\n C[2] = bitset(C[2], 20, 1)\n C[2] = bitset(C[2], 21, 0)\n B[2] = bitset(B[2], 12, 1)\n B[2] = bitset(B[2], 13, 1)\n B[2] = bitset(B[2], 14, 0)\n B[2] = bitset(B[2], 16, bitget(C[2], 16))\n B[2] = bitset(B[2], 18, 0)\n B[2] = bitset(B[2], 19, 0)\n B[2] = bitset(B[2], 20, 0)\n B[2] = bitset(B[2], 21, 0)\n A[3] = bitset(A[3], 12, 1)\n A[3] = bitset(A[3], 13, 1)\n A[3] = bitset(A[3], 14, 1)\n A[3] = bitset(A[3], 16, 0)\n A[3] = bitset(A[3], 18, 0)\n A[3] = bitset(A[3], 19, 0)\n A[3] = bitset(A[3], 20, 0)\n A[3] = bitset(A[3], 21, 1)\n A[3] = bitset(A[3], 22, bitget(B[2], 22))\n A[3] = bitset(A[3], 25, bitget(B[2], 25)) \n D[3] = bitset(D[3], 12, 1)\n D[3] = bitset(D[3], 13, 1)\n D[3] = bitset(D[3], 14, 1)\n D[3] = bitset(D[3], 16, 0)\n D[3] = bitset(D[3], 19, 0)\n D[3] = bitset(D[3], 20, 1)\n D[3] = bitset(D[3], 21, 1)\n D[3] = bitset(D[3], 22, 0)\n D[3] = bitset(D[3], 25, 1)\n D[3] = bitset(D[3], 29, bitget(A[3], 29)) \n C[3] = bitset(C[3], 16, 1)\n C[3] = bitset(C[3], 19, 0)\n C[3] = bitset(C[3], 20, 0)\n C[3] = bitset(C[3], 21, 0)\n C[3] = bitset(C[3], 22, 0)\n C[3] = bitset(C[3], 25, 0)\n C[3] = bitset(C[3], 29, 1)\n C[3] = bitset(C[3], 31, bitget(D[3], 31))\n B[3] = bitset(B[3], 19, 0)\n B[3] = bitset(B[3], 20, 1)\n B[3] = bitset(B[3], 21, 1)\n B[3] = bitset(B[3], 22, bitget(C[3], 22))\n B[3] = bitset(B[3], 25, 1)\n B[3] = bitset(B[3], 29, 0)\n B[3] = bitset(B[3], 31, 0) \n A[4] = bitset(A[4], 22, 0)\n A[4] = bitset(A[4], 25, 0)\n A[4] = bitset(A[4], 26, bitget(B[3], 26))\n A[4] = bitset(A[4], 28, bitget(B[3], 28))\n A[4] = bitset(A[4], 29, 1)\n A[4] = bitset(A[4], 31, 0) \n D[4] = bitset(D[4], 22, 0)\n D[4] = bitset(D[4], 25, 0)\n D[4] = bitset(D[4], 26, 1)\n D[4] = bitset(D[4], 28, 1)\n D[4] = bitset(D[4], 29, 0)\n D[4] = bitset(D[4], 31, 1) \n C[4] = bitset(C[4], 18, bitget(D[4], 18))\n C[4] = bitset(C[4], 22, 1)\n C[4] = bitset(C[4], 25, 1)\n C[4] = bitset(C[4], 26, 0)\n C[4] = bitset(C[4], 28, 0)\n C[4] = bitset(C[4], 29, 0) \n B[4] = bitset(B[4], 18, 0)\n B[4] = bitset(B[4], 25, 1)\n B[4] = bitset(B[4], 26, 1)\n B[4] = bitset(B[4], 28, 1)\n B[4] = bitset(B[4], 29, 0)\n # Extra constraint\n B[4] = bitset(B[4], 31, bitget(C[4], 31))\n \n M = ABCD_to_M(A,B,C,D)\n new_data = MD4_get_data(M, endianness)\n M = MD4_get_words(new_data)\n A, B, C, D = MD4_get_IVs(M) \n check_conditions(A, B, C, D)\n \n return(new_data)","def front_column_model_p_gain():","def max_front_wheel_speed():","def test_get_node_hardware_fast(self):\n pass"],"string":"[\n \"def strength(self) -> float:\\n ...\",\n \"def test_W_start(self):\\t\\t\\n self.assertAlmostEqual(attempt.W[0], 12)\",\n \"def test_sw2():\\n B1 = 100\\n B2 = 200\\n h = 18\\n t = 1\\n H = h + 2 * t\\n E1 = 20000\\n E2 = 10000\\n sections = ((B1, t, 0, E1), (B2, t, h + t, E2))\\n EI, top, bot = bm.EI(sections, E1)\\n EIc = E1 * B1 * (H ** 3 - h ** 3) / 12\\n assert 0.99 < EI / EIc < 1.01\",\n \"def max_linear_acceleration():\",\n \"def get_gains(self):\\n return tuple([lib.is_SetHWGainFactor(self.hcam,0x8000+i,0)/100 for i in range(4)])\",\n \"def test_W_end(self):\\t\\t\\n self.assertAlmostEqual(attempt.W[-1], 9.494852380803035)\",\n \"def YoungModulus(material):\\n if material == \\\"mild\\\":\\n return 200e9\\n else:\\n if material == \\\"al\\\":\\n return 69e9\\n else:\\n raise ValueError(\\\"Invalid material `\\\"+material+\\\"'\\\")\",\n \"def weight_mm(self,m1,m2):\\n lw = 1.\\n\\n # particle id and isolation\\n lw *= self._muIDISOWeight.value(m1.pt(),m1.eta(),'0')\\n lw *= self._muIDISOWeight.value(m2.pt(),m2.eta(),'0')\\n\\n # Trigger\\n hlt_sf_run2012_a = (self._muTRIGGERWeight_leg8_A.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg17_A.value(m2.pt(),m2.eta(),'0') +\\\\\\n self._muTRIGGERWeight_leg17_A.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg8_A.value(m2.pt(),m2.eta(),'0') -\\\\\\n self._muTRIGGERWeight_leg17_A.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg17_A.value(m2.pt(),m2.eta(),'0'))\\n\\n hlt_sf_run2012_b = (self._muTRIGGERWeight_leg8_B.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg17_B.value(m2.pt(),m2.eta(),'0') +\\\\\\n self._muTRIGGERWeight_leg17_B.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg8_B.value(m2.pt(),m2.eta(),'0') -\\\\\\n self._muTRIGGERWeight_leg17_B.value(m1.pt(),m1.eta(),'0')*self._muTRIGGERWeight_leg17_B.value(m2.pt(),m2.eta(),'0'))\\n \\n lw *= (0.5*hlt_sf_run2012_a + 0.5*hlt_sf_run2012_b) ##percentage according to the lumi in which they were not prescaled (apparently same efficinecy for AB)\\n #lw *= 0.966 ## temporary solution!\\n\\n if abs(configuration.LeptonTnPfactor)<0.01 :\\n return lw\\n else:\\n return lw + configuration.LeptonTnPfactor*self.uncertainty_mm(m1,m2)\",\n \"def get_strength_text(currentstrength):\\n for i in range(0, 5): \\n strengthrange = (79, 59, 39, 19, 0)\\n if currentstrength in range(strengthrange[i], strengthrange[i] + 20):\\n strength = STRENGTH_TEXT[i]\\n if currentstrength > 99:\\n strength = STRENGTH_TEXT[0]\\n\\n return strength\",\n \"def get_power_management() -> int:\",\n \"def _check_hardware_control(self):\\n if self._under_hardware_control:\\n v_input = self._ai_client.get_ai_voltage(self._hwc_ai_channel, max_range=10) #CHeck status of hwc voltage input\\n v_input = v_input[-1]\\n if self._is_stabilizing:\\n if v_input < self._hwc_thresh:\\n self.stop()\\n else:\\n if v_input > self._hwc_thresh:\\n self.start()\",\n \"def idealOpAmp():\",\n \"def get_max_gains(self):\\n return tuple([lib.is_SetHWGainFactor(self.hcam,0x800c+i,100)/100 for i in range(4)])\",\n \"def getRangeMM(self) -> float:\\n ...\",\n \"def expose(self, w):\\n # Compute the weighted sum of the firing inputs\\n s = self.strength[list(w.offset)].sum()\\n if self.training:\\n return s >= self.H\\n else:\\n return s >= self.H*self.G\",\n \"def get_hardwired_speed_weights(self):\\n \\n phase_shift=self.speed_phase_shift\\n \\n # row 1 has the weights of speed cells to grid cell 1\\n self.W_speed_east=np.zeros_like(self.W_ee) \\n self.W_speed_west=np.zeros_like(self.W_ee) \\n self.W_speed_north=np.zeros_like(self.W_ee) \\n self.W_speed_south=np.zeros_like(self.W_ee) \\n\\n if self.use_eight_directions is True:\\n self.W_speed_north_east=np.zeros_like(self.W_ee) \\n self.W_speed_north_west=np.zeros_like(self.W_ee) \\n self.W_speed_south_east=np.zeros_like(self.W_ee) \\n self.W_speed_south_west=np.zeros_like(self.W_ee) \\n\\n\\n for phase_idx,phase in enumerate(self.gp.phases):\\n shifted_north_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi/2.),self.gp.phases)\\n shifted_south_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi/2.),self.gp.phases)\\n shifted_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(0),self.gp.phases)\\n shifted_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi),self.gp.phases)\\n\\n self.W_speed_north[phase_idx,:]=self.W_ee[shifted_north_phase_idx,:]\\n self.W_speed_south[phase_idx,:]=self.W_ee[shifted_south_phase_idx,:]\\n self.W_speed_east[phase_idx,:]=self.W_ee[shifted_east_phase_idx,:]\\n self.W_speed_west[phase_idx,:]=self.W_ee[shifted_west_phase_idx,:] \\n \\n if self.use_eight_directions is True:\\n shifted_north_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi/4),self.gp.phases)\\n shifted_north_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi*3/4),self.gp.phases)\\n shifted_south_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi/4),self.gp.phases)\\n shifted_south_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi*3/4),self.gp.phases)\\n \\n self.W_speed_north_east[phase_idx,:]=self.W_ee[shifted_north_east_phase_idx,:]\\n self.W_speed_north_west[phase_idx,:]=self.W_ee[shifted_north_west_phase_idx,:]\\n self.W_speed_south_east[phase_idx,:]=self.W_ee[shifted_south_east_phase_idx,:]\\n self.W_speed_south_west[phase_idx,:]=self.W_ee[shifted_south_west_phase_idx,:]\",\n \"def cMACW(self):\\n return 3.5\",\n \"def max_angular_acceleration():\",\n \"def chk_hamming(data):\\n pass\",\n \"def test_optimal_thickness():\\n structure = Material(input)\\n assert (structure.calc_optimal_thickness() == 1.9552936422413782)\",\n \"def get_m1_m4_SEN_info(tx, ty, m1_info, y_k):\\n \\\"\\\"\\\"\\n 1 Get information from m1_info\\n \\\"\\\"\\\"\\n x_m1 = m1_info[0]\\n y_m1 = m1_info[1]\\n z_m = m1_info[2]\\n t_m = z_m / 3\\n\\n t_sen = m1_info[4]\\n\\n m1_points = m1_info[3]\\n\\n m1_p0 = m1_points[0]\\n m1_p1 = m1_points[1]\\n m1_p2 = m1_points[2]\\n m1_p3 = m1_points[3]\\n\\n b_p = (tx, ty)\\n\\n u_distance = rs.Distance(m1_p3, b_p) - y_k / 2\\n l_distance = rs.Distance(m1_p0, b_p) - y_k / 2\\n\\n \\\"\\\"\\\"\\n 2 Get SEN information\\n \\\"\\\"\\\"\\n # Automatically fixed---------------------------------------------------\\n # t_sen = rs.GetReal(\\\"Put Int(mm): Thickness of material to cut SEN.\\\", t_m / 2, None, None)\\n w_sen = t_sen\\n n_w_sen = w_sen / 2\\n h_sen = z_m\\n\\n u_max_n = u_distance / (2 * w_sen - n_w_sen) # NOTE: divide max_n by 2 to controll \\\"n\\\"\\n u_max_n = int(u_max_n)\\n\\n u_n = u_max_n / 4\\n u_n = int(u_n)\\n\\n l_max_n = l_distance / (2 * w_sen - n_w_sen) # NOTE: divide max_n by 2 to controll \\\"n\\\"\\n l_max_n = int(l_max_n)\\n\\n l_n = l_max_n / 4\\n l_n = int(l_n)\\n\\n\\n set = 20\\n u_offset = (u_distance - 2 * set) / (u_n - 1)\\n l_offset = (l_distance - 2 * set) / (l_n - 1)\\n\\n SEN_info = [w_sen, n_w_sen, h_sen, t_sen, u_n, l_n, set, u_offset, l_offset]\\n\\n return SEN_info\",\n \"def test_get_team_strength(self):\\n pass\",\n \"def test_offsets():\\n B = 100\\n H = 20\\n E = 210000\\n sections = ((B, H, 0, E),)\\n sections2 = ((B, H, 12.435, E),)\\n EI, top, bot = bm.EI(sections, E)\\n EI2, top2, bot2 = bm.EI(sections2, E)\\n assert 0.99 < EI / EI2 < 1.01\\n assert 0.99 < top / top2 < 1.01\\n assert 0.99 < bot / bot2 < 1.01\",\n \"def hw_func(self):\\n i, o = self.inl[0].to_flow(), self.outl[0].to_flow()\\n\\n if abs(i[0]) < 1e-4:\\n return i[1] - o[1]\\n\\n v_i = v_mix_ph(i, T0=self.inl[0].T.val_SI)\\n v_o = v_mix_ph(o, T0=self.outl[0].T.val_SI)\\n flow_dir = np.sign(i[0])\\n\\n return ((i[1] - o[1]) * flow_dir -\\n (10.67 * abs(i[0]) ** 1.852 * self.L.val /\\n (self.ks.val ** 1.852 * self.D.val ** 4.871)) *\\n (9.81 * ((v_i + v_o) / 2) ** 0.852))\",\n \"def check(self):\\n\\n Rbo = self.get_Rbo()\\n alpha = self.comp_alpha()\\n\\n if self.W0 < (self.W2 + self.W3):\\n raise S51_WCheckError(\\\"You must have W2+W3 < W0\\\")\\n\\n if Rbo < self.H0 + self.H2:\\n raise S51_RHCheckError(\\\"You must have H0+H2 < Rbo\\\")\\n\\n if alpha > pi / 2:\\n raise S51_AlphaCheckError(\\\"You must have alpha < pi/2\\\")\",\n \"def print_if_armstrong(start, end):\\n for i in range(start, end):\\n h, t, u = get_digits(i)\\n if i == h**3 + t**3 + u**3 :\\n print(i)\",\n \"def _test_out_of_range(self):\\n self.cdbconf.setup('KKG')\\n self.cdbconf.setConfiguration('CUSTOM_OPT')\\n az, el, latitude = [radians(50)] * 3\\n site_info = {'latitude': latitude}\\n self.p.setup(site_info, self.source, self.device)\\n self.p.setRewindingMode('AUTO')\\n offset = 20\\n max_limit = self.device.getMaxLimit() \\n min_limit = self.device.getMinLimit()\\n Pis = max_limit - offset/2\\n time.sleep(0.2) if self.using_mock else time.sleep(3)\\n self.p.setPosition(Pis)\\n time.sleep(0.2) # Wait a bit for the setup\\n max_rewinding_steps = (max_limit - min_limit) // self.device.getStep()\\n expected = Pis - max_rewinding_steps*self.device.getStep() + offset\\n self.source.setAzimuth(az)\\n self.source.setElevation(el)\\n self.p.startUpdating('MNG_TRACK', 'ANT_NORTH', az, el, None, None)\\n time.sleep(0.2) if self.using_mock else time.sleep(3)\\n self.p.setOffset(offset)\\n time.sleep(0.2) if self.using_mock else time.sleep(3)\\n self.assertEqual(self.device.getActPosition(), expected)\",\n \"def regenShields(self, kW, kWtoSPfactor):\\n remainder = kW \\n if self.currentSP < self.maxSP:\\n # how many kW required to fully charge?\\n # desiredSP = kWneeded * fact\\n # kWneeded = desiredSP/fact\\n desiredSP = self.maxSP - self.currentSP\\n kWneeded = desiredSP / kWtoSPfactor\\n if kW > kWneeded:\\n remainder = kW - kWneeded\\n #print \\\"more kW than need for sheilds; remainder\\\",remainder\\n else:\\n remainder = 0\\n \\n self.currentSP += (kW * kWtoSPfactor)\\n if self.currentSP > self.maxSP:\\n self.currentSP = self.maxSP\\n self.myParent.updateMyGUIValue(self.position+'Shields', self.currentSP)\\n return remainder\",\n \"def get_strength(self):\\n return 10 - self.get_agility()\",\n \"def test_mw(self):\\n self.assertEqual(self.PROT(\\\"\\\").mw(), 0)\\n self.assertEqual(self.RNA(\\\"\\\").mw(), 0)\\n self.assertFloatEqual(self.PROT(\\\"A\\\").mw(), 89.09)\\n self.assertFloatEqual(self.RNA(\\\"A\\\").mw(), 375.17)\\n self.assertFloatEqual(self.PROT(\\\"AAA\\\").mw(), 231.27)\\n self.assertFloatEqual(self.RNA(\\\"AAA\\\").mw(), 1001.59)\\n self.assertFloatEqual(self.RNA(\\\"AAACCCA\\\").mw(), 2182.37)\",\n \"def get_total_shield(self,obs):\",\n \"def low(self):\",\n \"def learning_rate_range():\\n # Lower and upper bounds\\n #######\\n lower_bound = 0.1 \\n upper_bound = 1e-6\\n #######\\n return lower_bound, upper_bound\",\n \"def total_sdram_requirements(self):\",\n \"def calc_wmr(BMR, PAL):\\n return BMR * (PAL - 1)\",\n \"def test_single_caps(self):\\n result_h = mmc.find_molar_mass('H')\\n actual_h = 1.007940\\n self.assertTrue(abs(percent_error(result_h, actual_h)) < self.ERR_THRESHOLD)\",\n \"def _get_sight_range(self):\\n raise NotImplementedError\",\n \"def get_sumw(self):\\n sumw_l = self.read_register(4109, 0, 3) \\n sumw_h = self.read_register(4110, 0, 3) \\n return (sumw_h * 255) + sumw_l \\n\\n# def get_braketype(self):\\n# \\\"\\\"\\\"Return the brake type of wind turbine.\\\"\\\"\\\"\\n# return self.read_register(7, 0, 3) \\n\\n# def set_modbus_ad(self,modbus_ad):\\n\\t\\\"\\\"\\\"Set the modbus address of the Aeolos controller\\\"\\\"\\\"\\n\\t\\\"\\\"\\\"Register address: 0x1999 \\\"\\\"\\\"\\n\\tself.write_registers(6553, modbus_ad, 0)\",\n \"def vga_gain_range(self):\\n return {'start': 8., 'stop': 15., 'step': 1.}\",\n \"def __init__(self, Strength:int, Defense:int):\\n if (Strength + Defense) <= 7:\\n if Strength <= 5:\\n if Defense <= 5:\\n self.Strength = Strength\\n self.Defense = Defense\\n else:\\n raise ValueError(\\\"Total Power is over 7\\\")\",\n \"def get_source_power_offset(self) -> Tuple[float, bool]:\\n (power, rel) = self.SOURcePOWer.LEVel.IMMediate.OFFSet.q().split_comma()\\n rel = rel.upper()\\n assert rel in (\\\"CPAD\\\", \\\"ONLY\\\")\\n return float(power), rel == \\\"CPAD\\\"\",\n \"def weight(self):\",\n \"def mortality(self):\\n pass\",\n \"def arduPusherWPM(self):\\r\\n tmp = (self.wpm / self.maxWPM) * self.available_degrees \\\\\\r\\n if self.wpm < self.maxWPM else self.available_degrees\\r\\n # send number as char \\r\\n self.ardu.write(chr(int(tmp)))\",\n \"def getCharacteristics(self) -> int:\\n ...\",\n \"def getSpeed(start, end, time):\\n\\t# Warning wrap up counter\\n\\tif start > (MAX_VALUE_SNMP_COUNTER32/2) and end < (MAX_VALUE_SNMP_COUNTER32/2):\\n\\t\\ttotal = (MAX_VALUE_SNMP_COUNTER32 - start) + end\\n\\t\\tspeed = float(total)/time.total_seconds()\\n\\telse:\\n\\t\\tspeed = float(end - start)/time.total_seconds()\\n\\n\\t# In Mbites\\n\\treturn (speed/1048576)*8\",\n \"def checkHSPS(hsp1, hsp2, HSPMIN=100):\\n dict_update = {18: 0}\\n\\n # Check if the hsp2 is in a different orientation than hsp1: 'sens': 14\\n if hsp1[14] != hsp2[14]:\\n # print(f'orientation wrong: {hsp1[14]} != {hsp2[14]}')\\n return dict_update\\n\\n # Check is hsp2 inside hsp1 for the query sequence: 'qstart': 6, 'qend': 7,\\n if hsp1[7] >= hsp2[7] and hsp1[8] <= hsp2[8]:\\n # print(f'hsp2 inside hsp1 for query: {hsp1[6]} >= {hsp2[6]} and {hsp1[7]} <= {hsp2[7]}')\\n return dict_update\\n\\n # Check is hsp1 inside hsp2 for the query sequence: 'qstart': 6, 'qend': 7,\\n elif hsp1[7] <= hsp2[7] and hsp1[8] >= hsp2[8]:\\n # print(f'hsp1 inside hsp2 for query: {hsp1[6]} <= {hsp2[6]} and {hsp1[7]} >= {hsp2[7]}')\\n return dict_update\\n\\n # Check is hsp1 inside hsp2 for the subject sequence: 'sstart': 8, 'send': 9,\\n elif hsp1[9] >= hsp2[9] and hsp1[10] <= hsp2[10]:\\n # print(f'hsp2 inside hsp1 for subject: {hsp1[8]} >= {hsp2[8]} and {hsp1[9]} <= {hsp2[9]}')\\n return dict_update\\n\\n # Check is hsp2 inside hsp1 for the subject sequence: 'sstart': 8, 'send': 9,\\n elif hsp1[9] <= hsp2[9] and hsp1[10] >= hsp2[10]:\\n # print(f'hsp1 inside hsp2 for subject: {hsp1[8]} <= {hsp2[8]} and {hsp1[9]} >= {hsp2[9]}')\\n return dict_update\\n\\n # reject HSPs that are in different orientation: 'qstart': 6, 'qend': 7, 'sstart': 8, 'send': 9,\\n # Query: ---- A ---- B ----- A = HSP1\\n # Sbjct: ---- B ---- A ----- B = HSP2\\n\\n if (hsp1[8] - hsp2[8]) * (hsp1[10] - hsp2[10]) < 0:\\n # print(f'HSPs are in different orientation 1: ({hsp1[7]} - {hsp2[7]}) * ({hsp1[9]} - {hsp2[9]}) ===> {(hsp1[7] - hsp2[7]) * (hsp1[9] - hsp2[9])} < 0')\\n return dict_update\\n elif (hsp1[7] - hsp2[7]) * (hsp1[9] - hsp2[9]) < 0:\\n # print(f'HSPs are in different orientation 2: ({hsp1[6]} - {hsp2[6]}) * ({hsp1[8]} - {hsp2[8]}) ===> {(hsp1[6] - hsp2[6]) * (hsp1[8] - hsp2[8])} < 0')\\n return dict_update\\n\\n overlap_q = (hsp2[7] - hsp1[8]) * (hsp2[8] - hsp1[7])\\n overlap_s = (hsp2[9] - hsp1[10]) * (hsp2[10] - hsp1[9])\\n\\n # Accept non-overlapping HSPs in correct orientation\\n if overlap_q > 0 and overlap_s > 0:\\n # print(f'No overlap in query and subject: {overlap_q} > 0 and {overlap_s} > 0')\\n dict_update[18] = 1\\n return dict_update\\n\\n # Test if the query is overlaping\\n elif overlap_q < 0:\\n # print(f'Overlap in query: {overlap_q} > 0')\\n dict_update = remove_overlap_query(hsp1=hsp1, hsp2=hsp2)\\n\\n # update the hsp2 array with the new values\\n for index_key in dict_update:\\n hsp2[index_key] = dict_update[index_key]\\n\\n overlap_s = (hsp2[9] - hsp1[10]) * (hsp2[10] - hsp1[9])\\n\\n # Test if the subject is overlaping after possible update of an overlaping query\\n if overlap_s < 0 and hsp2[17] > 0:\\n # print(f'Overlap in subject: {overlap_s} > 0')\\n dict_update = remove_overlap_subject(hsp1=hsp1, hsp2=hsp2)\\n\\n # update the hsp2 array with the new values\\n for index_key in dict_update:\\n hsp2[index_key] = dict_update[index_key]\\n\\n # Filter out HSPs that are too short\\n if hsp2[17] < HSPMIN:\\n # print(f'HSP too short: {hsp2[17]} < {HSPMIN}')\\n dict_update[18] = 0\\n return dict_update\\n\\n # Set status to 1 for consistent HSPs\\n dict_update[18] = 1\\n return dict_update\",\n \"def getHadler(event):\",\n \"def __init__(self, denoiser_strength): \\n self._strength = denoiser_strength\",\n \"def weapon_strength(weapon):\\n weapon_strength_int = WEAPON_STRENGTHS[weapon]\\n #print weapon_strength_int\\n return weapon_strength_int\",\n \"def get_resistance(self):\\n\\t\\tif self.channel == 0 :\\n\\t\\t\\tdata = bus.read_byte_data(AD5252_DEFAULT_ADDRESS, AD5252_WORD_RDAC1_EEMEM1)\\n\\t\\telif self.channel == 1 :\\n\\t\\t\\tdata = bus.read_byte_data(AD5252_DEFAULT_ADDRESS, AD5252_WORD_RDAC3_EEMEM3)\\n\\t\\t\\n\\t\\t# Convert the data\\n\\t\\tresistance_wb = (data / 256.0) * 1.0 + 0.075\\n\\t\\tresistance_wa = 1.0 - resistance_wb\\n\\t\\t\\n\\t\\treturn {'a' : resistance_wa, 'b' : resistance_wb}\",\n \"def sendTwoM(self):\\n startM = self.countMissionaryOnStart()\\n if startM < 2:\\n return None\\n else:\\n newStart = str(startM-2) + self.start[1:]\\n newEnd = str(5-startM) + self.end[1:]\\n return MissionaryState(newStart,newEnd,\\\"sendTwoM\\\")\",\n \"def pre_flop_strength(hand):\\n highs = {}\\n highs[4] = [\\n \\\"AA\\\", \\\"AKs\\\", \\\"AQs\\\", \\\"AJs\\\", \\\"ATs\\\", \\\"AKo\\\", \\\"KK\\\", \\\"KQs\\\", \\\"KJs\\\", \\\"AQo\\\",\\n \\\"QQ\\\", \\\"QJs\\\", \\\"JJ\\\", \\\"TT\\\"\\n ]\\n highs[3] = [\\n \\\"A5s\\\", \\\"A4s\\\", \\\"A3s\\\", \\\"KTs\\\", \\\"KQo\\\", \\\"QTs\\\", \\\"AJo\\\", \\\"JTs\\\", \\\"T9s\\\", \\\"99\\\",\\n \\\"98s\\\", \\\"88\\\", \\\"87s\\\", \\\"77\\\", \\\"66\\\"\\n ]\\n highs[2] = [\\n \\\"A9s\\\", \\\"A8s\\\", \\\"A7s\\\", \\\"A6s\\\", \\\"A2s\\\", \\\"K9s\\\", \\\"K8s\\\", \\\"Q9s\\\", \\\"KJo\\\", \\\"QJo\\\",\\n \\\"J9s\\\", \\\"ATo\\\", \\\"KTo\\\", \\\"QTo\\\", \\\"JTo\\\", \\\"T8s\\\", \\\"A9o\\\", \\\"J9o\\\", \\\"T9o\\\", \\\"97s\\\",\\n \\\"98o\\\", \\\"86s\\\", \\\"76s\\\", \\\"75s\\\", \\\"65s\\\", \\\"55\\\", \\\"44\\\", \\\"33\\\", \\\"22\\\"\\n ]\\n highs[1] = [\\n \\\"K7s\\\", \\\"K6s\\\", \\\"K5s\\\", \\\"K4s\\\", \\\"K3s\\\", \\\"Q8s\\\", \\\"Q7s\\\", \\\"Q6s\\\", \\\"Q5s\\\", \\\"Q4s\\\",\\n \\\"J8s\\\", \\\"J7s\\\", \\\"J6s\\\", \\\"J5s\\\", \\\"T7s\\\", \\\"T6s\\\", \\\"K9o\\\", \\\"Q9o\\\", \\\"96s\\\", \\\"A8o\\\",\\n \\\"K8o\\\", \\\"Q8o\\\", \\\"J8o\\\", \\\"T8o\\\", \\\"85s\\\", \\\"A7o\\\", \\\"K7o\\\", \\\"Q7o\\\", \\\"T7o\\\", \\\"97o\\\",\\n \\\"87o\\\", \\\"74s\\\", \\\"A6o\\\", \\\"K6o\\\", \\\"86o\\\", \\\"76o\\\", \\\"64s\\\", \\\"63s\\\", \\\"A5o\\\", \\\"75o\\\",\\n \\\"65o\\\", \\\"54s\\\", \\\"53s\\\", \\\"A4o\\\", \\\"43s\\\", \\\"A3o\\\"\\n ]\\n card0, card1 = hand\\n if card0[0] == card1[0]:\\n pair = \\\"\\\".join([card0[0], card1[0]])\\n elif card0[1] == card1[1]:\\n pair = \\\"\\\".join([card0[0], card1[0], \\\"s\\\"])\\n else:\\n pair = \\\"\\\".join([card0[0], card1[0], \\\"o\\\"])\\n for strenght in highs:\\n if pair in highs[strenght]:\\n return strenght\\n return 0\",\n \"def test_uneven_sw():\\n B = 100\\n t = 1\\n H = 30\\n E = 20000\\n sections = ((2 * B, t, 0, E), (B, t, H - t, E))\\n EI, top, bot = bm.EI(sections, E)\\n assert 1.95 < abs(bot) / top < 1.96\",\n \"def test_simple():\\n B = 100\\n H = 20\\n E = 210000\\n sections = ((B, H, 0, E),)\\n EI, top, bot = bm.EI(sections, E)\\n EIc = E * B * (H ** 3) / 12\\n assert 0.99 < EI / EIc < 1.01\\n assert top == H / 2\\n assert bot == -H / 2\",\n \"def high(self):\",\n \"def err_est_vv_hwmf_pos(self, Y, m, Cp0, Cp1):\\n # if(self._weight>=1.5):\\n # raise ValueError,\\\" Error bounds only accurate for k<1.5! got k=%s\\\" % weight\\n\\n twopiY = mpmath.pi() * mp2 * Y\\n fourpiY = mp2 * twopiY\\n tmp = mpmath.mpf(len(self.multiplier().weil_module().D()))\\n etmp1 = mpmath.sqrt(mpmath.mpf(m)) - Cp1 / fourpiY\\n etmp2 = mpmath.exp(-twopiY * etmp1**2)\\n etmp3 = mp2 + mpsqrtpi * Cp1 / mp2 / mpmath.sqrt(twopiY)\\n etmp4 = Cp0 / twopiY\\n err_pos = tmp * etmp4 * etmp3 * etmp2\\n return err_pos\",\n \"def tx_power(self) -> int:\\n # Follow table 10 truth table from the datasheet for determining power\\n # level from the individual PA level bits and output power register.\\n pa0 = self.pa_0_on\\n pa1 = self.pa_1_on\\n pa2 = self.pa_2_on\\n current_output_power = self.output_power\\n if pa0 and not pa1 and not pa2:\\n # -18 to 13 dBm range\\n return -18 + current_output_power\\n if not pa0 and pa1 and not pa2:\\n # -2 to 13 dBm range\\n return -18 + current_output_power\\n if not pa0 and pa1 and pa2 and not self.high_power:\\n # 2 to 17 dBm range\\n return -14 + current_output_power\\n if not pa0 and pa1 and pa2 and self.high_power:\\n # 5 to 20 dBm range\\n return -11 + current_output_power\\n raise RuntimeError(\\\"Power amps state unknown!\\\")\",\n \"def return_weight(self, startVertex: np.int, endVertex:np.int):\\n return self.__mat[startVertex][endVertex]\",\n \"def test_wl_metric():\\n z1 = np.random.normal(size=int(1e5)) + 1\\n z2 = np.random.normal(size=int(1e5)) + 2\\n res = pval.wl_metric(z1, z2)\\n np.testing.assert_almost_equal(res, 1, 2)\",\n \"def bm_and_dvr_supported(self):\",\n \"def get_field_strength(self):\\n \\n valid_characters = [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\", \\\"5\\\",\\n \\\"6\\\", \\\"7\\\", \\\"8\\\", \\\"9\\\", \\\".\\\", \\\"-\\\"]\\n\\n try:\\n \\n # Reset serial buffer to pervent it from growing too large.\\n self.interface.reset_input_buffer()\\n time.sleep(0.001)\\n \\n # Read in data\\n field_string = self.interface.readline().decode(\\\"utf-8\\\")\\n x_string, y_string, z_string = \\\"\\\", \\\"\\\", \\\"\\\"\\n currently_reading = None\\n \\n # Record raw output\\n #with open(\\\"temp_mag_field_raw.txt\\\", \\\"a\\\") as f:\\n #f.write(field_string + \\\"\\\\n\\\")\\n #print(\\\"read in field data\\\")\\n\\n # If currently reading for float, add character if it's valid\\n for character in field_string:\\n if (currently_reading == \\\"X\\\") and (character in valid_characters):\\n x_string += character\\n if (currently_reading == \\\"Y\\\") and (character in valid_characters):\\n y_string += character\\n if (currently_reading == \\\"Z\\\") and (character in valid_characters):\\n z_string += character\\n\\n # See if it's time to change what is being read\\n if character == \\\"X\\\":\\n currently_reading = \\\"X\\\"\\n elif character == \\\"Y\\\":\\n currently_reading = \\\"Y\\\"\\n elif character == \\\"Z\\\":\\n currently_reading = \\\"Z\\\"\\n\\n # Convert the string chunks to floats\\n try:\\n x_field = float(x_string)\\n except Exception as err:\\n x_field = None\\n print(\\\"Could not determine x field from string {} | {}\\\"\\n .format(x_string, err))\\n try:\\n y_field = float(y_string)\\n except Exception as err:\\n y_field = None\\n print(\\\"Could not determine y field from string {} | {}\\\"\\n .format(y_string, err))\\n try:\\n z_field = float(z_string)\\n except Exception as err:\\n z_field = None\\n print(\\\"Could not determine z field from string {} | {}\\\"\\n .format(z_string, err))\\n \\n return (x_field, y_field, z_field)\\n \\n # Print 999 so that it is not confused with 0.0\\n except AttributeError:\\n return (999, 999, 999)\",\n \"def digital_gain():\\n def r(x):\\n return x/512.\\n\\n def w(x):\\n return int(x*512)\\n return r, w\",\n \"def test_password_strength_validator_missing_letter(self):\\n with self.assertRaises(ValidationError):\\n validate_password_strength('1234567')\",\n \"def test_password_strength_validator(self):\\n self.assertIsNone(validate_password_strength('abcd123'))\",\n \"def scan(self, wl1, wl2):\\r\\n # Wavelength position can be sent in either um, nm or Angstrom, as 2-byte form.\\r\\n # Since an int value must be sent, this limits both the resolution and max value that can be set\\r\\n if max(wl1, wl2) > (2**16 / 10):\\r\\n # Value too high to send in Angstrom units, so send in nm (setting resolution = 1 nm)\\r\\n if self._current_units != 'nm':\\r\\n self.set_units('nm')\\r\\n # Combine the one-byte goto command with two-byte wl position\\r\\n cmd = struct.pack('>B', 12) + struct.pack('>H', wl1) + struct.pack('>H', wl2)\\r\\n else:\\r\\n # Value low enough to use Angstrom units (setting resolution = 1A = 0.1 nm)\\r\\n if self._current_units != 'A':\\r\\n self.set_units('A')\\r\\n wl1_angstrom = int(wl1 * 10)\\r\\n wl2_angstrom = int(wl2 * 10)\\r\\n cmd = struct.pack('>B', 12) + struct.pack('>H', wl1_angstrom) + struct.pack('>H', wl2_angstrom)\\r\\n\\r\\n self.send(cmd)\",\n \"def get_h_score(start, end):\\n #uses a heuristic function\\n #return 0 #used if you want Djikstras algorithm\\n return (abs(end[0]-start[0])+abs(end[1]-start[1])) * 10\",\n \"def event_m20_11_4000000():\\n \\\"\\\"\\\"State 0,2: [Lib] Character: Petrified: Key Guide_SubState\\\"\\\"\\\"\\n assert event_m20_11_x37(z94=5300, z95=0, z96=15, z97=211000030, z98=0, z99=1600, z100=6, z101=4000010)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def efficiency_vs_w(wmax, wmin=10, Lmax=1000, Lmin=102.4, p=75, fmax=5.7e9,\\r\\n p1=database['K+'], p2=database['pi+'], p3=database['p+'],\\r\\n E=1e6, delta_p=1.6e-2, nw=10, nL=10, ng=50, nl=50, nf=20,\\r\\n resolution=0.01, w_resolution=0.01, plot=True,\\r\\n set_freq=5.7e9, count_L=False, count_w=True):\\r\\n if resolution == None:\\r\\n if set_freq == None:\\r\\n est = timing(nw*nL*ng*nl*nf*4e-05)\\r\\n else:\\r\\n est = timing(nw*nL*ng*nl*4e-05)\\r\\n print('Estimated total time: {0}'.format(est))\\r\\n else:\\r\\n num = int(round(np.log10(Lmax-Lmin))-1)-np.log10(resolution)+1\\r\\n if set_freq == None:\\r\\n est = timing(nw*10*num*ng*nl*nf*4e-05)\\r\\n else:\\r\\n est = timing(nw*10*num*ng*nl*4e-05)\\r\\n print('Estimated maximum total time: {0}'.format(est))\\r\\n start = time.time()\\r\\n if w_resolution == None:\\r\\n w_range = np.linspace(wmin, wmax, int(nw))\\r\\n plot_w, length, intensity = [], [], []\\r\\n for w in w_range:\\r\\n if count_w == True:\\r\\n if count_L == True:\\r\\n print(f'w = {w} mm')\\r\\n else:\\r\\n print(w)\\r\\n eff = efficiency_vs_length(w, Lmax, Lmin, p, fmax,p1, p2, p3, E,\\r\\n delta_p, nL, ng, nl, nf, resolution,\\r\\n False, set_freq, False, count_L)\\r\\n if eff != None:\\r\\n plot_w.append(w)\\r\\n length.append(eff[0])\\r\\n intensity.append(eff[2])\\r\\n file = open(f'Save_Data_efficiency_vs_w_{wmin}_{wmax}.txt','a')\\r\\n file.write(f'{[plot_w[-1], length[-1], intensity[-1]]}\\\\n')\\r\\n file.close()\\r\\n length, intensity = np.array(length), np.array(intensity)\\r\\n ratio = 1/(length*intensity)\\r\\n ratio *= (np.max(intensity)-np.min(intensity))/(np.max(ratio)-np.min(ratio))\\r\\n ratio += ((np.min(intensity))-np.min(ratio))\\r\\n output = [plot_w[np.argmax(ratio)], plot_w[np.argmin(length)], plot_w[np.argmin(intensity)]]\\r\\n else:\\r\\n wmax = 10*(int(wmax/10)+2)\\r\\n wmin = 10*int(wmin/10)\\r\\n n = 10\\r\\n while n >= w_resolution:\\r\\n if n == 10 and plot == True:\\r\\n w_range = np.arange(wmin, wmax, n)\\r\\n graph_w, graph_length, graph_intensity, graph_ratio = [], [], [], []\\r\\n for w in w_range:\\r\\n if count_w == True:\\r\\n if count_L == True:\\r\\n print(f'w = {w} mm')\\r\\n else:\\r\\n print(w)\\r\\n eff = efficiency_vs_length(w, Lmax, Lmin, p, fmax,p1, p2, p3, E,\\r\\n delta_p, nL, ng, nl, nf, resolution,\\r\\n False, set_freq, False, count_L)\\r\\n if eff != None:\\r\\n graph_w.append(w)\\r\\n graph_length.append(eff[0])\\r\\n graph_intensity.append(eff[2])\\r\\n graph_ratio.append(1/(eff[0]*eff[2]))\\r\\n i, d_ratio = 1, 0\\r\\n while d_ratio >= 0 and i < np.size(graph_ratio):\\r\\n d_ratio = graph_ratio[i] - graph_ratio[i-1]\\r\\n i += 1\\r\\n if i >= np.size(graph_ratio):\\r\\n output = None\\r\\n break\\r\\n graph_ratio = np.array(graph_ratio)\\r\\n graph_ratio *= (np.max(graph_intensity)-np.min(graph_intensity))/(np.max(graph_ratio)-np.min(graph_ratio))\\r\\n graph_ratio += ((np.min(graph_intensity))-np.min(graph_ratio))\\r\\n d = -int(np.log10(n))\\r\\n output = [round(graph_w[np.argmax(graph_ratio)], d),\\r\\n graph_length[np.argmax(graph_ratio)],\\r\\n graph_intensity[np.argmax(graph_ratio)]]\\r\\n wmax = graph_w[i-1]\\r\\n wmin = graph_w[i-3]\\r\\n n /= 10\\r\\n else:\\r\\n w_range = np.arange(wmin, wmax+n, n)\\r\\n plot_w, length, intensity, ratio = [], [], [], []\\r\\n i, d_ratio = 0, 0\\r\\n d = -int(np.log10(n))\\r\\n while d_ratio >= 0 and i < np.size(w_range):\\r\\n if count_w == True:\\r\\n if count_L == True:\\r\\n print(f'w = {round(w_range[i], d)} mm')\\r\\n else:\\r\\n print(round(w_range[i], d))\\r\\n eff = efficiency_vs_length(w_range[i], Lmax, Lmin, p, fmax,p1,\\r\\n p2, p3, E, delta_p, nL, ng, nl, nf,\\r\\n resolution, False, set_freq, False,\\r\\n count_L)\\r\\n if eff != None:\\r\\n plot_w.append(w_range[i])\\r\\n length.append(eff[0])\\r\\n intensity.append(eff[2])\\r\\n ratio.append(1/(eff[0]*eff[2]))\\r\\n if np.size(ratio) == 1:\\r\\n d_ratio = ratio[-1]\\r\\n else:\\r\\n d_ratio = ratio[-1] - ratio[-2]\\r\\n i += 1\\r\\n if i >= np.size(w_range):\\r\\n output = None\\r\\n break\\r\\n output = [round(plot_w[np.argmax(ratio)], d),\\r\\n length[np.argmax(ratio)],\\r\\n intensity[np.argmax(ratio)]]\\r\\n wmax = w_range[i-1]\\r\\n wmin = w_range[i-3]\\r\\n n /= 10\\r\\n if plot == True:\\r\\n if w_resolution != None:\\r\\n plot_w, length, intensity, ratio = graph_w, graph_length, graph_intensity, graph_ratio\\r\\n fig = plt.figure(figsize=[12, 4])\\r\\n ax = fig.add_subplot(1, 1, 1)\\r\\n line1, = ax.plot(plot_w, length, 'r', lw=2, alpha=0.6, label='Target Distance')\\r\\n ax2 = ax.twinx()\\r\\n line2, = ax2.plot(plot_w, intensity, 'g', lw=2, alpha=0.6, label='Intensity Required')\\r\\n line3, = ax2.plot(plot_w, ratio, 'b', lw=2, alpha=0.6, label='Reciprocal Product')\\r\\n ax.set_xlabel('Collimator Width / mm', fontsize=20)\\r\\n ax.set_ylabel('Target Distance / m', fontsize=20, color='r')\\r\\n ax2.set_ylabel(r'Intensity / I$_0$', fontsize=20, color='g')\\r\\n ax.set_xlim(np.min(plot_w), np.max(plot_w))\\r\\n ax.tick_params(axis='y', colors=line1.get_color())\\r\\n ax2.tick_params(axis='y', colors=line2.get_color())\\r\\n# ax.set_ylim(0)\\r\\n lines = [line1, line2, line3]\\r\\n ax.legend(lines, [l.get_label() for l in lines], loc=[0.70, 0.12], fontsize=15)\\r\\n ax.minorticks_on()\\r\\n ax2.minorticks_on()\\r\\n ax.grid()\\r\\n ax.set_title(' Minimum required target distance and proton intensity as a function of beam stopper inner radius', fontsize=16)\\r\\n plt.show()\\r\\n print(f'Total time: {timing(time.time()-start)}')\\r\\n return output\",\n \"def getFWHM(antenna, freq):\\n diameter = getDiameter(antenna)\\n lam = 299792458.0 / (freq * 1e9)\\n fwhmo = lam / math.pi * 180.0 * 60.0\\n fwhm = 1.22 * fwhmo / diameter\\n return fwhm\",\n \"def from_dbm_to_watt(dbm: Optional[float]) -> float:\\n return 0 if dbm is None else from_db_to_natural(dbm - 30)\",\n \"def ohms(self):\\n # Rwb = Rwiper + Rtotal * (counts / 256)\\n # Rwa = Rwiper + Rtotal * ((256 - counts) / 256)\\n g = 0\\n rtotal=0.0\\n reach=[]\\n for chan in self.get_channel_list(self.nchans):\\n self.rwa[chan] = float( 256 - self.vals[chan] ) / 256.0\\n self.rwb[chan] = float( self.vals[chan] ) / 256.0\\n self.rwa[chan] *= self.Rtotal\\n self.rwb[chan] *= self.Rtotal \\n self.rwa[chan] += self.Rwiper\\n self.rwb[chan] += self.Rwiper\",\n \"def cal_consistency_weight(epoch, init_ep=0, end_ep=150, init_w=0.0, end_w=20.0):\\n if epoch > end_ep:\\n weight_cl = end_w\\n elif epoch < init_ep:\\n weight_cl = init_w\\n else:\\n T = float(epoch - init_ep)/float(end_ep - init_ep)\\n #weight_mse = T * (end_w - init_w) + init_w #linear\\n weight_cl = (math.exp(-5.0 * (1.0 - T) * (1.0 - T))) * (end_w - init_w) + init_w #exp\\n #print('Consistency weight: %f'%weight_cl)\\n return weight_cl\",\n \"def cal_consistency_weight(epoch, init_ep=0, end_ep=150, init_w=0.0, end_w=20.0):\\n if epoch > end_ep:\\n weight_cl = end_w\\n elif epoch < init_ep:\\n weight_cl = init_w\\n else:\\n T = float(epoch - init_ep)/float(end_ep - init_ep)\\n #weight_mse = T * (end_w - init_w) + init_w #linear\\n weight_cl = (math.exp(-5.0 * (1.0 - T) * (1.0 - T))) * (end_w - init_w) + init_w #exp\\n #print('Consistency weight: %f'%weight_cl)\\n return weight_cl\",\n \"def test_add_shorts_and_srational_le(self):\\n self.image_le.contrast = 1\\n self.image_le.light_source = 24\\n self.image_le.brightness_value = 5.4321\\n\\n assert self.image_le.light_source == 24\\n assert self.image_le.contrast == 1\\n assert self.image_le.brightness_value == 5.4321\\n\\n # Verify pre-existing attributes can still be read as expected.\\n for attribute, func, value in read_attributes_little_endian:\\n assert func(getattr(self.image_le, attribute)) in value\\n\\n segment_hex = (\\n binascii.hexlify(self.image_le._segments[\\\"APP1\\\"].get_segment_bytes())\\n .decode(\\\"utf-8\\\")\\n .upper()\\n )\\n self.assertEqual(\\n \\\"\\\\n\\\".join(textwrap.wrap(segment_hex, 90)), ADD_SHORT_LE_BASELINE\\n )\",\n \"def test_get_hyperflex_feature_limit_external_by_moid(self):\\n pass\",\n \"def test_get_hyperflex_feature_limit_internal_by_moid(self):\\n pass\",\n \"def _update_strength(self, event=None):\\n password = self.password.GetValue()\\n nd = calc_password_strength(password, WEB_SPEED)\\n self.strength.UpdateStrength(nd)\",\n \"def part2(input):\\n sys = AmpSystem(input)\\n return sys.max_thruster_signal([i for i in range(5, 10)])\",\n \"def penaltyFactor(m):\\n \\\"\\\"\\\" m in GeV \\\"\\\"\\\"\\n if m*m>0.71:\\n return math.pow(m*m/0.71,-4)\\n else:\\n return 1\",\n \"def align(): # open EH and fast shutter\\n\\t#marAuxiliary.closeMarShield()\\n\\td2in()\\n\\td3in()\\n\\tsh('o')\",\n \"def EM_gain_range(self):\\n mini, maxi = ct.c_int(), ct.c_int()\\n self.lib.GetEMGainRange(ct.pointer(mini), ct.pointer(maxi))\\n\\n return (mini.value, maxi.value)\",\n \"def generate_power_law(alpha, wmin=40., wmax=300.):\\n c = 2.99792e14 # cm/s\\n # cm2mum = 1e4 # Convert cm to microns\\n wref = 24.0\\n fref = 1.0\\n # nuref = c * cm2mum / wref\\n nuref = c / wref\\n dw = 0.005\\n nw = int((wmax - wmin) / dw) + 1\\n wstar = np.arange(nw) * dw + wmin\\n nuarr = c / wstar\\n fstar = fref * (nuarr / nuref) ** alpha\\n\\n return wstar, fstar\",\n \"def test_parameters(self):\\n assert self.hll.p == 8\\n assert self.hll.m == 256\\n assert round(self.hll.alpha - 0.7182725932495458, 5) == 0\\n assert round(self.hll.error - 0.065, 5) == 0\\n assert self.hll64.treshold == 120000\",\n \"def set_physical_minmax(self, min, max):\\n # This allows you to set the min and the max of the quantity that you want the MLP to measure. \\n # Once you set this, you can pass in a physical number to get_mlp_value() and it will be mapped to an MLP value and returned\\n pass\\n \\n # Maybe we should let the holder of the MLP determine these values and do the mapping? \",\n \"def switchAndCheck(sw, name, loLim, hiLim, unit = 'V', delay = 0):\\n\\n v = DMM.readSwitch(sw, delay)\\n #Log.logValue(name, v, unit)\\n chkLimits(name, v, loLim, hiLim)\\n return v\",\n \"def gain_opt(machine, T):\\n res = (np.arange(T)+1)\\n return res * np.amax(machine)\",\n \"def input_resistance(self):\\n return None\",\n \"def get_w(self):\\n raise NotImplementedError\",\n \"def getStrength(self):\\n return self.st\",\n \"def validate_strength(cls, value: str) -> (bool, dict):\\n if value is None:\\n return False, {}\\n\\n length = cls._validate_length(value)\\n digit = cls._validate_digit(value)\\n uppercase = cls._validate_uppercase(value)\\n lowercase = cls._validate_lowercase(value)\\n symbol = cls._validate_symbol(value)\\n\\n valid = all([length, digit, uppercase, lowercase, symbol])\\n error_dict = {\\n 'length': length,\\n 'digit': digit,\\n 'uppercase': uppercase,\\n 'lowercase': lowercase,\\n 'symbol': symbol,\\n }\\n\\n return valid, error_dict\",\n \"def _convert_energy(self, mwh, kwh, active, position):\\n if active & position:\\n kwh = self._calculate_value(kwh, 0.1, INT16_POSITIVE_MASK)\\n return mwh * 1000 + kwh\\n else:\\n return None\",\n \"def _get_observation_lower_bound(self):\\n lower_bound = -self._get_observation_upper_bound()\\n lower_bound[-7] = 0.0\\n lower_bound[-2:] = [self.min_speed, self.min_side_speed]\\n return lower_bound\",\n \"def get_resistance(self):\\n\\t\\tdata = bus.read_byte_data(AD5259_DEFAULT_ADDRESS, AD5259_WORD_ADDR_RDAC)\\n\\t\\t\\n\\t\\t# Convert the data\\n\\t\\tresistance_wb = (data / 256.0) * 5.0\\n\\t\\tresistance_wa = 5 - resistance_wb\\n\\t\\t\\n\\t\\treturn {'a' : resistance_wa, 'b' : resistance_wb}\",\n \"def __init__(self, NW, SE):\\n self.type = \\\"HOLD AREA\\\"\\n self.NW = NW\\n self.SE = SE\\n self.ctr = [(self.NW[0] + self.SE[0]) / 2, (self.NW[1] + self.SE[1]) / 2]\",\n \"def hanging_man(self):\\n self.data['hanging_man'] = (((self.data['High'] - self.data['Low']) > ((self.data['Open'] - self.data['Close'])*4)) & \\\\\\n ((self.data['Close'] - self.data['Low']) / ((.001 + self.data['High'] - self.data['Low']) >= 0.75)) &\\\\\\n ((self.data['Open'] - self.data['Low']) / ((.001 + self.data['High'] - self.data['Low']) >= .075)))\",\n \"def check(self):\\n self.lower_bound(5e-4)\\n self.upper_bound(5e2)\",\n \"def Wang_SSM(data, do_padding = False, endianness='little'):\\n \\n if do_padding:\\n \\n M = MD4_get_words(MD4_pad_data(data), endianness)\\n \\n else:\\n \\n if len(data) < 64:\\n data += b'\\\\x00'*(64-len(data))\\n \\n M = MD4_get_words(data, endianness) \\n \\n A, B, C, D = MD4_get_IVs(M) \\n # Modify the message to meet several of the constraints in Table 6\\n # All bit #ing has been converted from base 1 to base 0\\n \\n A[1] = bitset(A[1], 6, bitget(B[0], 6))\\n \\n D[1] = bitset(D[1], 6, 0)\\n D[1] = bitset(D[1], 7, bitget(A[1], 7))\\n D[1] = bitset(D[1], 10, bitget(A[1], 10))\\n C[1] = bitset(C[1], 6, 1)\\n C[1] = bitset(C[1], 7, 1)\\n C[1] = bitset(C[1], 10, 0)\\n C[1] = bitset(C[1], 25, bitget(D[1], 25))\\n B[1] = bitset(B[1], 6, 1)\\n B[1] = bitset(B[1], 7, 0)\\n B[1] = bitset(B[1], 10, 0)\\n B[1] = bitset(B[1], 25, 0)\\n A[2] = bitset(A[2], 7, 1)\\n A[2] = bitset(A[2], 10, 1)\\n A[2] = bitset(A[2], 13, bitget(B[1], 13))\\n A[2] = bitset(A[2], 25, 0) \\n D[2] = bitset(D[2], 13, 0)\\n D[2] = bitset(D[2], 18, bitget(A[2], 18))\\n D[2] = bitset(D[2], 19, bitget(A[2], 19))\\n D[2] = bitset(D[2], 20, bitget(A[2], 20))\\n D[2] = bitset(D[2], 21, bitget(A[2], 21))\\n D[2] = bitset(D[2], 25, 1) \\n C[2] = bitset(C[2], 12, bitget(D[2], 12))\\n C[2] = bitset(C[2], 13, 0)\\n C[2] = bitset(C[2], 14, bitget(D[2], 14))\\n C[2] = bitset(C[2], 18, 0)\\n C[2] = bitset(C[2], 19, 0)\\n C[2] = bitset(C[2], 20, 1)\\n C[2] = bitset(C[2], 21, 0)\\n B[2] = bitset(B[2], 12, 1)\\n B[2] = bitset(B[2], 13, 1)\\n B[2] = bitset(B[2], 14, 0)\\n B[2] = bitset(B[2], 16, bitget(C[2], 16))\\n B[2] = bitset(B[2], 18, 0)\\n B[2] = bitset(B[2], 19, 0)\\n B[2] = bitset(B[2], 20, 0)\\n B[2] = bitset(B[2], 21, 0)\\n A[3] = bitset(A[3], 12, 1)\\n A[3] = bitset(A[3], 13, 1)\\n A[3] = bitset(A[3], 14, 1)\\n A[3] = bitset(A[3], 16, 0)\\n A[3] = bitset(A[3], 18, 0)\\n A[3] = bitset(A[3], 19, 0)\\n A[3] = bitset(A[3], 20, 0)\\n A[3] = bitset(A[3], 21, 1)\\n A[3] = bitset(A[3], 22, bitget(B[2], 22))\\n A[3] = bitset(A[3], 25, bitget(B[2], 25)) \\n D[3] = bitset(D[3], 12, 1)\\n D[3] = bitset(D[3], 13, 1)\\n D[3] = bitset(D[3], 14, 1)\\n D[3] = bitset(D[3], 16, 0)\\n D[3] = bitset(D[3], 19, 0)\\n D[3] = bitset(D[3], 20, 1)\\n D[3] = bitset(D[3], 21, 1)\\n D[3] = bitset(D[3], 22, 0)\\n D[3] = bitset(D[3], 25, 1)\\n D[3] = bitset(D[3], 29, bitget(A[3], 29)) \\n C[3] = bitset(C[3], 16, 1)\\n C[3] = bitset(C[3], 19, 0)\\n C[3] = bitset(C[3], 20, 0)\\n C[3] = bitset(C[3], 21, 0)\\n C[3] = bitset(C[3], 22, 0)\\n C[3] = bitset(C[3], 25, 0)\\n C[3] = bitset(C[3], 29, 1)\\n C[3] = bitset(C[3], 31, bitget(D[3], 31))\\n B[3] = bitset(B[3], 19, 0)\\n B[3] = bitset(B[3], 20, 1)\\n B[3] = bitset(B[3], 21, 1)\\n B[3] = bitset(B[3], 22, bitget(C[3], 22))\\n B[3] = bitset(B[3], 25, 1)\\n B[3] = bitset(B[3], 29, 0)\\n B[3] = bitset(B[3], 31, 0) \\n A[4] = bitset(A[4], 22, 0)\\n A[4] = bitset(A[4], 25, 0)\\n A[4] = bitset(A[4], 26, bitget(B[3], 26))\\n A[4] = bitset(A[4], 28, bitget(B[3], 28))\\n A[4] = bitset(A[4], 29, 1)\\n A[4] = bitset(A[4], 31, 0) \\n D[4] = bitset(D[4], 22, 0)\\n D[4] = bitset(D[4], 25, 0)\\n D[4] = bitset(D[4], 26, 1)\\n D[4] = bitset(D[4], 28, 1)\\n D[4] = bitset(D[4], 29, 0)\\n D[4] = bitset(D[4], 31, 1) \\n C[4] = bitset(C[4], 18, bitget(D[4], 18))\\n C[4] = bitset(C[4], 22, 1)\\n C[4] = bitset(C[4], 25, 1)\\n C[4] = bitset(C[4], 26, 0)\\n C[4] = bitset(C[4], 28, 0)\\n C[4] = bitset(C[4], 29, 0) \\n B[4] = bitset(B[4], 18, 0)\\n B[4] = bitset(B[4], 25, 1)\\n B[4] = bitset(B[4], 26, 1)\\n B[4] = bitset(B[4], 28, 1)\\n B[4] = bitset(B[4], 29, 0)\\n # Extra constraint\\n B[4] = bitset(B[4], 31, bitget(C[4], 31))\\n \\n M = ABCD_to_M(A,B,C,D)\\n new_data = MD4_get_data(M, endianness)\\n M = MD4_get_words(new_data)\\n A, B, C, D = MD4_get_IVs(M) \\n check_conditions(A, B, C, D)\\n \\n return(new_data)\",\n \"def front_column_model_p_gain():\",\n \"def max_front_wheel_speed():\",\n \"def test_get_node_hardware_fast(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.611811","0.58543104","0.54258955","0.53168124","0.527644","0.525759","0.52391386","0.5229558","0.5170525","0.5161343","0.5128514","0.511606","0.5106863","0.50794834","0.50664425","0.50602925","0.5058106","0.5052371","0.50257456","0.49980128","0.49905187","0.49799287","0.49786142","0.4960222","0.4940439","0.49384373","0.49328968","0.49120203","0.49114376","0.49059558","0.48994127","0.4895769","0.48857853","0.48693082","0.48636162","0.48632005","0.48613915","0.4841269","0.48357767","0.48280922","0.48246798","0.4808608","0.48079422","0.48072618","0.48023042","0.48016196","0.47998756","0.47976843","0.47959855","0.47895223","0.4781173","0.47777587","0.477044","0.47684503","0.47565266","0.4745894","0.47454137","0.47442743","0.47340807","0.47314665","0.4725949","0.47206986","0.47140285","0.47135285","0.47128397","0.47103322","0.46991244","0.46973133","0.46963802","0.4694765","0.46919605","0.4690229","0.46821553","0.46821553","0.4678789","0.46778202","0.46757314","0.46734127","0.4661277","0.4659719","0.46579376","0.46541962","0.46505222","0.46449938","0.46406034","0.464027","0.46398938","0.4638868","0.46354297","0.46325642","0.4622239","0.46174383","0.46169117","0.46154556","0.46132594","0.46100253","0.46095577","0.46066976","0.45992112","0.45922026","0.45912582"],"string":"[\n \"0.611811\",\n \"0.58543104\",\n \"0.54258955\",\n \"0.53168124\",\n \"0.527644\",\n \"0.525759\",\n \"0.52391386\",\n \"0.5229558\",\n \"0.5170525\",\n \"0.5161343\",\n \"0.5128514\",\n \"0.511606\",\n \"0.5106863\",\n \"0.50794834\",\n \"0.50664425\",\n \"0.50602925\",\n \"0.5058106\",\n \"0.5052371\",\n \"0.50257456\",\n \"0.49980128\",\n \"0.49905187\",\n \"0.49799287\",\n \"0.49786142\",\n \"0.4960222\",\n \"0.4940439\",\n \"0.49384373\",\n \"0.49328968\",\n \"0.49120203\",\n \"0.49114376\",\n \"0.49059558\",\n \"0.48994127\",\n \"0.4895769\",\n \"0.48857853\",\n \"0.48693082\",\n \"0.48636162\",\n \"0.48632005\",\n \"0.48613915\",\n \"0.4841269\",\n \"0.48357767\",\n \"0.48280922\",\n \"0.48246798\",\n \"0.4808608\",\n \"0.48079422\",\n \"0.48072618\",\n \"0.48023042\",\n \"0.48016196\",\n \"0.47998756\",\n \"0.47976843\",\n \"0.47959855\",\n \"0.47895223\",\n \"0.4781173\",\n \"0.47777587\",\n \"0.477044\",\n \"0.47684503\",\n \"0.47565266\",\n \"0.4745894\",\n \"0.47454137\",\n \"0.47442743\",\n \"0.47340807\",\n \"0.47314665\",\n \"0.4725949\",\n \"0.47206986\",\n \"0.47140285\",\n \"0.47135285\",\n \"0.47128397\",\n \"0.47103322\",\n \"0.46991244\",\n \"0.46973133\",\n \"0.46963802\",\n \"0.4694765\",\n \"0.46919605\",\n \"0.4690229\",\n \"0.46821553\",\n \"0.46821553\",\n \"0.4678789\",\n \"0.46778202\",\n \"0.46757314\",\n \"0.46734127\",\n \"0.4661277\",\n \"0.4659719\",\n \"0.46579376\",\n \"0.46541962\",\n \"0.46505222\",\n \"0.46449938\",\n \"0.46406034\",\n \"0.464027\",\n \"0.46398938\",\n \"0.4638868\",\n \"0.46354297\",\n \"0.46325642\",\n \"0.4622239\",\n \"0.46174383\",\n \"0.46169117\",\n \"0.46154556\",\n \"0.46132594\",\n \"0.46100253\",\n \"0.46095577\",\n \"0.46066976\",\n \"0.45992112\",\n \"0.45922026\",\n \"0.45912582\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77467,"cells":{"query":{"kind":"string","value":"Return input array [1D numpy array] with all nan values removed"},"document":{"kind":"string","value":"def nonans(array):\n return array[~np.isnan(array)]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def remove_nans(arr, nan_rows=None):\n # Remove NaNs\n nconfigs, nt = arr.shape\n if nan_rows is None:\n mask = np.isfinite(arr)\n else:\n mask = np.array([n for n in np.arange(nconfigs) if n not in nan_rows])\n return arr[mask].reshape(-1, nt)","def removeNans(data):\n for i in data[:]:\n ind = data.index(i)\n for j in i:\n if np.isnan(j):\n data.remove(i)\n break\n return data","def remove_nans(arr):\n not_nan = [i for i in range(len(arr)) if not np.isnan(arr[i])]\n\n return not_nan, arr[not_nan]","def filter_nans(seq):\n return np.array([x for x in seq if not isinstance(x, float)])","def removeNan(values):\n\treturn list(filter(lambda v: not math.isnan(v), values))","def remove_nans(coords):\n s = np.apply_along_axis(sum,1,np.isnan(coords[1])) == 0\n coords[0] = (np.asarray(coords[0])[s]).tolist()\n coords[1] = coords[1][s,:]","def remove_none_from_arrays(self):\r\n\r\n is_nan = numpy.isnan(self.y_values) # array of booleans, element is True if the corresponding element in\r\n # self.y_values is None\r\n\r\n self.x_values = self.x_values[numpy.logical_not(is_nan)]\r\n self.y_values = self.y_values[numpy.logical_not(is_nan)] # replace all None elements\r","def remove_nan(X, y):\n newX = []\n newY = []\n for i in range(0, len(X)):\n lst = X[i]\n lbl = y[i]\n flag = True\n for i in lst:\n if np.isnan(i):\n flag = False\n break\n if flag:\n newX.append(lst)\n newY.append(lbl)\n return np.array(newX), np.array(newY)","def sanitize_array(array):\n a = np.ravel(array)\n maxi = np.nanmax(a[np.isfinite(a)])\n mini = np.nanmin(a[np.isfinite(a)])\n array[array == float('inf')] = maxi\n array[array == float('-inf')] = mini\n mid = (maxi + mini) / 2\n array[np.isnan(array)] = mid\n return array","def remove_nan(self, dataframe):\n return dataframe.dropna()","def dropna(self) -> \"Dataset\":\n if not self[0]._has_time_axis: # type: ignore\n raise ValueError(\"Not available if no time axis!\")\n\n all_index: List[int] = []\n for i in range(self.n_items):\n x = self[i].to_numpy()\n\n # this seems overly complicated...\n axes = tuple(range(1, x.ndim))\n idx = list(np.where(~np.isnan(x).all(axis=axes))[0])\n if i == 0:\n all_index = idx\n else:\n all_index = list(np.intersect1d(all_index, idx))\n\n return self.isel(all_index, axis=0)","def convert_non_monotonic_to_nan(array):\n keep = np.arange(0, len(array))\n is_monotonic = False\n while not is_monotonic:\n is_monotonic_array = np.hstack(\n (array[keep][1:] >= array[keep][:-1], np.array(True))\n )\n is_monotonic = is_monotonic_array.all()\n keep = keep[is_monotonic_array]\n out_array = np.full_like(array.astype(np.float), np.nan)\n out_array[keep] = array[keep]\n return out_array","def filter_nan(s,o):\n data = np.array([s.flatten(),o.flatten()])\n data = np.transpose(data)\n data = data[~np.isnan(data).any(1)]\n #data = data[~np.isnan(data)]\n return data[:,0],data[:,1]","def _strip_nan(val):\n if isinstance(val, float) and np.isnan(val):\n return '__NaN__'\n elif isinstance(val, dict):\n return {key: Database._strip_nan(item) for key, item in list(val.items())}\n elif isinstance(val, list) or isinstance(val, tuple):\n return [Database._strip_nan(item) for item in val]\n elif isinstance(val, set):\n raise NotImplementedError\n return val","def removeNull(self):\n self.data.dropna(axis=0, how='any')","def drop_nan_columns(arrays):\n\n # Keep all column indices\n not_nan_filter = ones(len(arrays[0]), dtype=bool)\n\n # Currently keeping all columns!\n\n # Keep column indices without missing value in all arrays\n # for a in arrays:\n # not_nan_filter &= ~isnan(a)\n\n return [a[not_nan_filter] for a in arrays]","def nans(shape, dtype=float):\n a = np.empty(shape, dtype)\n a.fill(np.nan)\n return a","def clean_list(l):\n return [x for x in l if str(x) != \"nan\"]","def remove_blanks(mat):\n ids = []\n for idx, row in enumerate(mat):\n if not 1 in row:\n ids.append(idx)\n mat = np.delete(mat, ids, 0)\n mat = np.delete(mat, ids, 1)\n return mat","def clean_data(x, null, drop_thresh):\n # Do not modify the original dataset\n x_clean = np.copy(x)\n \n # Vector holding, for each feature, the fraction of datapoints with a null value\n null_frac = (1/x_clean.shape[0]) * np.sum(x_clean==null, axis=0)\n # Boolean vector holding, for each feature, whether or not it needs to be kept\n column_to_keep = null_frac <= drop_thresh\n \n # Drop bad columns\n x_clean = x_clean[:, column_to_keep]\n \n # Vector of (list of) indices of columns where there are still null values\n columns_to_interpolate = np.argwhere(np.any(x_clean==null, axis=0))\n \n # For each of those columns, find the mean of non-null values, and substitute it to null values\n for col_list in columns_to_interpolate:\n # Extrapolate only entry of col_list\n col = col_list[0]\n \n # Boolean vector holding, for each row, whether or not it has a \"null\" entry at position \"col\"\n row_non_null = x_clean[:, col] != null\n # Find mean\n interpolation = np.mean(x_clean[row_non_null, col])\n # Substitute it to null values\n row_null = np.logical_not(row_non_null)\n x_clean[row_null, col] = interpolation\n \n return x_clean","def remove_nans(a, b):\n a = np.asarray(a)\n b = np.asarray(b)\n\n mask = ~np.isnan(a) & ~np.isnan(b)\n a = a[mask]\n b = b[mask]\n\n return a, b","def replace_nans(lst):\r\n return [None if np.isnan(v) else v for v in lst]","def trim_zeros(x):\n assert len(x.shape) == 2\n return x[~np.all(x == 0, axis=1)]","def ffill_1d_nb(a):\n out = np.empty_like(a, dtype=np.float_)\n lastval = a[0]\n for i in range(a.shape[0]):\n if np.isnan(a[i]):\n out[i] = lastval\n else:\n lastval = out[i] = a[i]\n return out","def trim_zeros(array):\n multislice = []\n for i in range(array.ndim):\n sum_axes = tuple(j for j in range(array.ndim) if j is not i)\n edges = np.where(np.sum(array, axis=sum_axes) > 0)\n if edges[0].size == 0:\n return np.array([], dtype=array.dtype)\n low = edges[0][0]\n high = edges[0][-1]\n multislice.append(slice(low, high+1, 1))\n return array[tuple(multislice)]","def clean_data(array):\n ret = np.zeros(len(array))\n for i in range(len(array)):\n drop_id = len(str(i+1)) + 1\n array[i, 0] = array[i, 0][int(drop_id):]\n return array","def remove_na(self, data, remove_na_how):\n data = data.dropna(how=remove_na_how) \n return data","def remove_nans4(a, b, c, d):\n a = np.asarray(a)\n b = np.asarray(b)\n c = np.asarray(c)\n d = np.asarray(d)\n\n mask = ~np.isnan(a) & ~np.isnan(b) & ~np.isnan(c) & ~np.isnan(d)\n a = a[mask]\n b = b[mask]\n c = c[mask]\n d = d[mask]\n\n return a, b, c, d","def _remove_nan(parsed_dictionary):\n for key, value in parsed_dictionary.items():\n if isinstance(value, np.ndarray):\n non_nan_value = np.nan_to_num(value, nan=123456789, posinf=2e308, neginf=-2e308)\n parsed_dictionary.update({key: non_nan_value})\n\n return parsed_dictionary","def replace_nan(arr, value):\n arr[np.isnan(arr)] = value\n return arr","def removeEmptyData(self, data):\n \n data = data.dropna()\n data = data.reset_index(drop =True)\n return data","def unmasked_data(self):\n return numpy.ma.filled(self.data.astype(numpy.float_),\n fill_value=numpy.nan)","def nanmedian(arr, **kwargs):\r\n return ma.median( ma.masked_where(arr!=arr, arr), **kwargs )","def dropna(*args):\n # (True|False) mask for each row based on NaN values present in the first dataframe\n mask = ~pd.isnull(args[0])\n if mask.ndim == 2:\n mask = mask.all(1) # in 2d array, keep row only if all values are not NaN\n\n return [x.loc[mask] if isinstance(x, pd.DataFrame) else x[mask] for x in args]","def drop_nans(target_df):\n target_df.dropna(axis=0, inplace=True)\n return target_df","def remove_halo(field):\n if not isinstance(field, np.ndarray):\n field = np.asarray(field)\n if len(field) <= 2:\n return np.array([])\n return field[1:-1, :-1]","def purgeNanEveryWhere(df):\n #Row-wise dropping\n toDrop = np.array([])\n for i in range(df.shape[0]):\n if( np.sum ( pd.isnull(df.iloc[i]) ) == df.shape[1]-1 ):\n toDrop= np.append(toDrop,i)\n df.drop(df.index[toDrop.astype(int)],inplace=True) \n #Column-wise dropping\n for col in df.columns:\n arr = pd.notnull(df[col])\n nnan = np.sum(arr) \n if (nnan == df.shape[1]):\n df.drop(col,inplace=True,axis=1)\n return df","def asarray_chkfinite(a):\n a = asarray(a)\n if (a.dtype.char in typecodes['AllFloat']) \\\n and (_nx.isnan(a).any() or _nx.isinf(a).any()):\n raise ValueError, \"array must not contain infs or NaNs\"\n return a","def remove_nans3(a, b, c):\n a = np.asarray(a)\n b = np.asarray(b)\n c = np.asarray(c)\n\n mask = ~np.isnan(a) & ~np.isnan(b) & ~np.isnan(c)\n a = a[mask]\n b = b[mask]\n c = c[mask]\n\n return a, b, c","def _nanmedian(arr1d, preop=None): # This only works on 1d arrays\n x = arr1d.copy()\n c = np.isnan(x)\n s = np.where(c)[0]\n if s.size == x.size:\n warnings.warn(\"All-NaN slice encountered\", RuntimeWarning)\n return np.nan\n elif s.size != 0:\n # select non-nans at end of array\n enonan = x[-s.size:][~c[-s.size:]]\n # fill nans in beginning of array with non-nans of end\n x[s[:enonan.size]] = enonan\n # slice nans away\n x = x[:-s.size]\n if preop:\n x = preop(x)\n return np.median(x, overwrite_input=True)","def demean_normalize(one_d_array):\n\n temp_arr = one_d_array - np.nanmean(one_d_array)\n\n return temp_arr/np.nanstd(temp_arr)","def omit_nans(self, data, label):\n maskarray=np.full(data.shape[0], True)\n masker=np.unique(np.argwhere(np.isnan(data))[:,0])\n maskarray[masker]=False\n traindata=data[maskarray,:,:,:]\n trainlabel=label[maskarray]\n return traindata, trainlabel","def set_nan(x):\n x[x == -999] = np.nan\n return x","def data_cleaning(df):\n df = df.dropna(how='all')\n \n return df","def fill_nan(array):\n idx = np.arange(array.shape[0])\n good = np.where(np.isfinite(array))\n interp = interpolate.interp1d(idx[good], array[good], bounds_error=False)\n return np.where(np.isfinite(array), array, interp(idx))","def _nanmedian(arr1d): # This only works on 1d arrays\n cond = 1-np.isnan(arr1d)\n x = np.sort(np.compress(cond,arr1d,axis=-1))\n if x.size == 0:\n return np.nan\n return np.median(x)","def strip_zeros(a):\n\n return np.trim_zeros(a, trim='b')","def _pure_data(data, missing_mask):\n missing_rows = np.where(missing_mask)[0]\n pure_data = np.delete(data, missing_rows, axis=0)\n\n return pure_data","def cloud_filter(array, bqa):\n array_dest = array.copy()\n array_dest[np.where((bqa != 2720) & (bqa != 2724) & (bqa != 2728) & (bqa != 2732)) ] = 'nan'\n return array_dest","def removeNaNs(a, replaceWithMin=False, verbose=False, nanBufferChannels=0, \n replaceWithZero=False):\n a = np.array(a) \n if (len(a) < 1): return(a)\n startLength = len(a)\n if (replaceWithMin or replaceWithZero):\n idx = np.isnan(a)\n print \"Found %d nan channels: %s\" % (len(np.where(idx==True)[0]), np.where(idx==True)[0])\n idx2 = np.isinf(a)\n print \"Found %d inf channels\" % (len(np.where(idx2==True)[0]))\n a_nanmin = np.nanmin(a)\n if (nanBufferChannels > 0):\n idxlist = splitListIntoHomogeneousLists(idx)\n idx = []\n for i,mylist in enumerate(idxlist):\n if (mylist[0]):\n idx += mylist\n else:\n newSubString = nanBufferChannels*[True] \n if (i < len(idxlist)-1):\n newSubString += mylist[nanBufferChannels:-nanBufferChannels] + nanBufferChannels*[True]\n else:\n newSubString += mylist[-nanBufferChannels:]\n # If the channel block was less than 2*nanBufferChannels wide, then only insert up to its width\n idx += newSubString[:len(mylist)]\n idx = np.array(idx)\n if (verbose):\n print \"Replaced %d NaNs\" % (len(idx))\n print \"Replaced %d infs\" % (len(idx2))\n if (replaceWithMin):\n a[idx] = a_nanmin\n a[idx2] = a_nanmin\n return(a, a_nanmin)\n elif (replaceWithZero):\n a[idx] = 0\n a[idx2] = 0\n return(a, 0)\n else:\n a = a[np.where(np.isnan(a) == False)]\n if (verbose):\n print \"Removed %d NaNs\" % (startLength-len(a))\n startLength = len(a)\n a = a[np.where(np.isinf(a) == False)]\n if (verbose):\n print \"Removed %d infs\" % (startLength-len(a))\n return(a)","def exclude_empty_feats(self):\n for dataset in self:\n dataset.dropna(axis=1, how=\"all\", inplace=True)","def _dropna_after_fe(X, y):\n X = X.dropna()\n y = y[len(y)-X.shape[0]:len(y)]\n return X, y","def nanmin_nb(a):\n out = np.empty(a.shape[1], dtype=np.float_)\n for col in range(a.shape[1]):\n out[col] = np.nanmin(a[:, col])\n return out","def dropNaN(featZ):\n \n n_cols = len(featZ.columns)\n featZ.dropna(axis=1, inplace=True)\n n_dropped = n_cols - len(featZ.columns)\n \n if n_dropped > 0:\n print(\"Dropped %d features after normalisation (NaN)\" % n_dropped)\n \n return featZ","def test_remove_nan_observations(test_coords):\n x, y = test_coords[0], test_coords[1]\n\n z = np.array([np.nan, np.nan, np.nan, 1, 1, 1, 1, 1, 1, 1])\n\n x_, y_, z_ = remove_nan_observations(x, y, z)\n\n truthx = np.array([10, 52, 53, 98, 34, 15, 58])\n truthy = np.array([94, 98, 66, 14, 24, 60, 16])\n truthz = np.array([1, 1, 1, 1, 1, 1, 1])\n\n assert_array_almost_equal(truthx, x_)\n assert_array_almost_equal(truthy, y_)\n assert_array_almost_equal(truthz, z_)","def check_categories(a: np.ndarray):\n return list(np.unique(a[~np.isnan(a)]))","def transform(self, X):\n\n X = X.copy()\n\n X[pd.isnull(X)] = self.fill\n\n return np.asarray(X)","def filter_nb(a, filter_func_nb, *args):\n out = a.astype(np.float_)\n\n for col in range(out.shape[1]):\n idxs = np.flatnonzero(~np.isnan(a[:, col]))\n for i in idxs:\n if not filter_func_nb(i, col, a[i, col], *args):\n out[i, col] = np.nan\n return out","def isna(self):\n # type: () -> np.ndarray\n return extract_isnull_bytemap(self.data)","def isna(self):\n # type: () -> np.ndarray\n return extract_isnull_bytemap(self.data)","def relu(x: jnp.DeviceArray) -> jnp.DeviceArray:\n return jnp.clip(x, a_min=0)","def filter_nan(y, x, ids, remove=True, replace_val=0.0):\n mask = np.isnan(x)\n \n if remove:\n # Remove the rows containing any NaN\n row_mask = ~mask.any(axis=1) # Sets to False any rows containing NaN\n x_copy, y_copy, ids_copy = x[row_mask], y[row_mask], ids[row_mask]\n # Remove 0 filled columns\n col_mask = x_copy.sum(axis=0) != 0 # True if the columns is filled with 0\n x_copy = x_copy[:, col_mask] \n return y_copy, x_copy, ids_copy, col_mask\n else:\n # Replace NaN values by replace_val\n x[mask] = replace_val\n return y, x, ids","def get_fixed_array():\n return np.array([[[np.nan, np.nan],\n [np.nan, np.nan],\n [np.nan, np.nan]],\n\n [[3., 7.],\n [2., 4.],\n [1., 16.]],\n\n [[17., 5.],\n [10., 0.],\n [14., 12.]],\n\n [[8., 6.],\n [13., 11.],\n [9., 15.]]])","def fill_nan(A):\n\tinds = np.arange(A.shape[0])\n\tgood = np.where(np.isfinite(A))\n\tA[np.isnan(A)] = np.interp(inds[np.isnan(A)], inds[good], A[good])\n\treturn A","def filter_zeros(X):\n\tnoNonzeros = np.count_nonzero(X, axis=1)\n\tmask = np.where(noNonzeros > 0)\n\treturn X[mask[0], :]","def test_csum_ignore_nans():\n source = [np.zeros((16,), dtype=float) for _ in range(10)]\n source.append(np.full((16,), fill_value=np.nan))\n summed = csum(source, ignore_nan=True)\n assert np.allclose(summed, np.zeros_like(summed))","def filter_nan_samples(self, train_x, train_y):\n\n n_samples = train_x.shape[0]\n if n_samples != train_y.shape[0]:\n raise ValueError(\"x and y sample lengths don't match\")\n\n validity_array = np.zeros(n_samples)\n for i in range(n_samples):\n x_sample = train_x[i, :]\n y_sample = train_y[i, :]\n validity_array[i] = np.isfinite(x_sample).all() and np.isfinite(y_sample).all()\n\n mask = np.where(validity_array)[0]\n\n return train_x[mask, :], train_y[mask, :]","def get_signal_with_nans(self):\n pairs = zip(self.time_scale, self.signal)\n nan_pairs = zip(self.nan_times,[float('nan')]*len(self.nan_times))\n pairs.extend(nan_pairs)\n pairs.sort()\n time_scale, signal = zip(*pairs)\n return np.array(time_scale), np.array(signal)","def finite(x):\n return numpy.array(x)[numpy.isfinite(x)]","def remove_duplicates_array(self, array: List) -> List:\n seen = set()\n seen_add = seen.add\n return [x for x in array if not (x in seen or seen_add(x))]","def nanmasked(x):\n mask = ~np.isnan(x)\n maskA = x[mask]\n return (maskA,mask)","def remove_filler(dgm, val=np.inf):\r\n inds = (dgm[:,0] != val)\r\n return dgm[inds,:]","def diff_1d_nb(a):\n out = np.empty_like(a, dtype=np.float_)\n out[0] = np.nan\n out[1:] = a[1:] - a[:-1]\n return out","def xnd_xnd(arr, nan_to_null=False):\n import numpy as np\n import xnd\n xd = xnd.xnd.from_buffer(arr)\n if nan_to_null and issubclass(arr.dtype.type,\n (np.floating, np.complexfloating)):\n isnan = np.isnan(arr)\n if isnan.any():\n raise NotImplementedError('xnd view of numpy ndarray with nans')\n return xd","def normalise(a):\n return (a - np.nanmin(a)) / np.nanmax(a - np.nanmin(a))","def product_1d_nb(a):\n out = a[0]\n for i in range(1, a.shape[0]):\n if ~np.isnan(a[i]):\n if np.isnan(out):\n out = a[i]\n else:\n out *= a[i]\n return out","def nan(self, check_inf = True):\n return self.foreach(\n lambda k,v: (k, numpy.isnan(v) + (check_inf == True) * numpy.isinf(v)),\n dimensions = self.dims,\n shape = self.shape,\n )","def getMask(self):\r\n mask = np.array(self.array, dtype=np.float32)\r\n mask[mask == 0] = np.nan\r\n return mask","def nanmedian_nb(a):\n out = np.empty(a.shape[1], dtype=np.float_)\n for col in range(a.shape[1]):\n out[col] = np.nanmedian(a[:, col])\n return out","def removeInfinities(mat):\n isFinite = np.all(np.isfinite(mat))\n \n if not isFinite:\n nrow, ncol = mat.shape\n matCopy = mat.copy()\n matReshaped = matCopy.reshape(-1)\n minVal = np.nanmin(matReshaped[matReshaped != -np.inf])\n maxVal = max(matReshaped)\n \n #count infinities and get their indicies\n #countInfinites(matReshaped) \n \n #replace all infinities with nan\n for i in range(0,len(matReshaped)):\n if matReshaped[i] in [-np.inf, np.inf]:\n matReshaped[i] = minVal\n '''\n #get average of matrix values and replace the nan with averages\n def averageVal(array):\n total = 0\n count = 0\n for elmt in array:\n if elmt != -90001:\n total+=elmt\n count+=1\n return total/count\n\n average = averageVal(matReshaped)\n\n #replace nan with average\n for i in range(0,len(matReshaped)):\n if matReshaped[i] == -90001:\n matReshaped[i] = average\n '''\n #catch any potential errors \n if np.any(np.isinf(matReshaped)):\n raise ValueError\n\n return matReshaped.reshape((nrow, ncol))\n else:\n return mat","def removed(bin_arr, extent, intent):\n result = np.copy(bin_arr)\n ree = result[extent]\n ree[:, intent] = 0\n result[extent] = ree\n return result","def isnan(self):\n return self.isAny( (lambda x: np.isnan(x)) )","def replace_value(cls, data, nodata):\n data = data.astype('float64')\n mask = data != nodata\n if hasattr(data, 'where'):\n return data.where(mask, np.NaN)\n return np.where(mask, data, np.NaN)","def _clean_data(self, dataset):\n dataset.dropna(inplace=True)\n # Problem: handle missing data (in a different way), noisy data, inconsistent data","def no_reg(w):\n return np.zeros_like(w)","def strip_array_wrappers(arry):\n curr = arry\n if curr.ndim == 0:\n if isinstance(curr[...], np.ndarray):\n return strip_array_wrappers(curr[...])\n return curr\n\n # there is a possibility for infinite looping\n # e.g. [np.ndarray, str, dict] would stay object array\n # impossible if homogeneous (implied by 1-element wrappers)\n while isinstance(curr[0], np.ndarray):\n if curr.shape[0] == 1:\n curr = curr[0]\n else:\n curr = np.array(tuple(curr))\n\n return curr","def isnan(*obj):\n\n out = [flatten(o) if istensor(o) else o for o in obj]\n return any([any(o != o) if istensor(o) else o != o for o in out])","def nanmean_nb(a):\n out = np.empty(a.shape[1], dtype=np.float_)\n for col in range(a.shape[1]):\n out[col] = np.nanmean(a[:, col])\n return out","def filter_isolated_pixels(array):\n filtered_array = np.copy(array)\n id_regions, num_ids = ndimage.label(filtered_array,\n structure=np.ones((3, 3)))\n id_sizes = np.array(ndimage.sum(array, id_regions, range(num_ids+1)))\n area_mask = (id_sizes == 1)\n filtered_array[area_mask[id_regions]] = 0\n return filtered_array","def isnan(data):\n return _make.isnan(data)","def filter(self):\n self.data = self.data.loc[~self.data.isnull().any(1),:]","def fillna_1d_nb(a, value):\n return set_by_mask_1d_nb(a, np.isnan(a), value)","def clean_coordinates(coords, silent=False):\n l1 = len(coords)\n coords = coords[~N.isnan(coords).any(axis=1)] # remove NaN values\n l2 = len(coords)\n if not silent:\n msg = \"{0} coordinates\".format(l2)\n if l2 < l1:\n msg += \" ({0} removed as invalid)\".format(l1-l2)\n print(msg)\n return coords","def arraycleaner(array):\r\n lengths = [len(x) for x in array]\r\n #shortindex = lengths.index(min(lengths))\r\n shortest = min(lengths)\r\n for n in range(len(array)):\r\n line = array[n]\r\n if len(array[n]) != shortest:\r\n this = len(line)\r\n cut = np.ceil(1/((this/shortest) - 1))\r\n #import pdb; pdb.set_trace()\r\n for m in range(len(array[n])):\r\n if m % cut == 0 and m != 0:\r\n line[m] = 'del'\r\n for m in range(len(array[n])):\r\n try:\r\n if line[m] == 'del':\r\n del line[m]\r\n except IndexError:\r\n continue\r\n array[n] = line[0:shortest]\r\n return array","def nansum(array_data, axis=0):\n\n mdat = np.ma.masked_array(array_data, np.isnan(array_data));\n retval = np.sum(mdat, axis=axis);\n \n return retval;","def scalararray(inp) -> np.ndarray:\n return np.array([None, inp], dtype=object)[[1]].reshape([])","def nanvar(array_data, axis=0):\n\n mdat = np.ma.masked_array(array_data, np.isnan(array_data));\n retval = np.var(mdat, axis=axis);\n \n return retval;","def noiseRemoval(array, minSize, classes):\n img=array.astype('int')\n for i in range(classes):\n B=(img!=i) # return a bool array\n B = morphology.remove_small_objects(B, min_size=minSize, connectivity=1) \n img[B==False]=i\n \n return img","def _remove_outliers(self, data, nsigma):\n while (np.abs(np.ma.fix_invalid(data)-np.nanmean(data)) > nsigma *\n np.nanstd(data)).any():\n\n data[np.where(np.abs(np.ma.fix_invalid(data)-np.nanmean(data)) >\n nsigma * np.nanstd(data))] = np.nan\n\n return data","def _mask_array(mask, *args):\n invalid = ~mask # True if invalid\n args_masked = []\n for arg in args:\n if arg.size > 1 and arg.shape != invalid.shape:\n raise ValueError('Shape mismatch between mask and array.')\n arg_masked = arg.astype(np.float64)\n if arg.size == 1:\n pass\n elif invalid.size == 1:\n arg_masked = np.nan if invalid.item() else arg_masked\n elif arg.size > 1:\n arg_masked[invalid] = np.nan\n args_masked.append(arg_masked)\n return args_masked[0] if len(args_masked) == 1 else args_masked"],"string":"[\n \"def remove_nans(arr, nan_rows=None):\\n # Remove NaNs\\n nconfigs, nt = arr.shape\\n if nan_rows is None:\\n mask = np.isfinite(arr)\\n else:\\n mask = np.array([n for n in np.arange(nconfigs) if n not in nan_rows])\\n return arr[mask].reshape(-1, nt)\",\n \"def removeNans(data):\\n for i in data[:]:\\n ind = data.index(i)\\n for j in i:\\n if np.isnan(j):\\n data.remove(i)\\n break\\n return data\",\n \"def remove_nans(arr):\\n not_nan = [i for i in range(len(arr)) if not np.isnan(arr[i])]\\n\\n return not_nan, arr[not_nan]\",\n \"def filter_nans(seq):\\n return np.array([x for x in seq if not isinstance(x, float)])\",\n \"def removeNan(values):\\n\\treturn list(filter(lambda v: not math.isnan(v), values))\",\n \"def remove_nans(coords):\\n s = np.apply_along_axis(sum,1,np.isnan(coords[1])) == 0\\n coords[0] = (np.asarray(coords[0])[s]).tolist()\\n coords[1] = coords[1][s,:]\",\n \"def remove_none_from_arrays(self):\\r\\n\\r\\n is_nan = numpy.isnan(self.y_values) # array of booleans, element is True if the corresponding element in\\r\\n # self.y_values is None\\r\\n\\r\\n self.x_values = self.x_values[numpy.logical_not(is_nan)]\\r\\n self.y_values = self.y_values[numpy.logical_not(is_nan)] # replace all None elements\\r\",\n \"def remove_nan(X, y):\\n newX = []\\n newY = []\\n for i in range(0, len(X)):\\n lst = X[i]\\n lbl = y[i]\\n flag = True\\n for i in lst:\\n if np.isnan(i):\\n flag = False\\n break\\n if flag:\\n newX.append(lst)\\n newY.append(lbl)\\n return np.array(newX), np.array(newY)\",\n \"def sanitize_array(array):\\n a = np.ravel(array)\\n maxi = np.nanmax(a[np.isfinite(a)])\\n mini = np.nanmin(a[np.isfinite(a)])\\n array[array == float('inf')] = maxi\\n array[array == float('-inf')] = mini\\n mid = (maxi + mini) / 2\\n array[np.isnan(array)] = mid\\n return array\",\n \"def remove_nan(self, dataframe):\\n return dataframe.dropna()\",\n \"def dropna(self) -> \\\"Dataset\\\":\\n if not self[0]._has_time_axis: # type: ignore\\n raise ValueError(\\\"Not available if no time axis!\\\")\\n\\n all_index: List[int] = []\\n for i in range(self.n_items):\\n x = self[i].to_numpy()\\n\\n # this seems overly complicated...\\n axes = tuple(range(1, x.ndim))\\n idx = list(np.where(~np.isnan(x).all(axis=axes))[0])\\n if i == 0:\\n all_index = idx\\n else:\\n all_index = list(np.intersect1d(all_index, idx))\\n\\n return self.isel(all_index, axis=0)\",\n \"def convert_non_monotonic_to_nan(array):\\n keep = np.arange(0, len(array))\\n is_monotonic = False\\n while not is_monotonic:\\n is_monotonic_array = np.hstack(\\n (array[keep][1:] >= array[keep][:-1], np.array(True))\\n )\\n is_monotonic = is_monotonic_array.all()\\n keep = keep[is_monotonic_array]\\n out_array = np.full_like(array.astype(np.float), np.nan)\\n out_array[keep] = array[keep]\\n return out_array\",\n \"def filter_nan(s,o):\\n data = np.array([s.flatten(),o.flatten()])\\n data = np.transpose(data)\\n data = data[~np.isnan(data).any(1)]\\n #data = data[~np.isnan(data)]\\n return data[:,0],data[:,1]\",\n \"def _strip_nan(val):\\n if isinstance(val, float) and np.isnan(val):\\n return '__NaN__'\\n elif isinstance(val, dict):\\n return {key: Database._strip_nan(item) for key, item in list(val.items())}\\n elif isinstance(val, list) or isinstance(val, tuple):\\n return [Database._strip_nan(item) for item in val]\\n elif isinstance(val, set):\\n raise NotImplementedError\\n return val\",\n \"def removeNull(self):\\n self.data.dropna(axis=0, how='any')\",\n \"def drop_nan_columns(arrays):\\n\\n # Keep all column indices\\n not_nan_filter = ones(len(arrays[0]), dtype=bool)\\n\\n # Currently keeping all columns!\\n\\n # Keep column indices without missing value in all arrays\\n # for a in arrays:\\n # not_nan_filter &= ~isnan(a)\\n\\n return [a[not_nan_filter] for a in arrays]\",\n \"def nans(shape, dtype=float):\\n a = np.empty(shape, dtype)\\n a.fill(np.nan)\\n return a\",\n \"def clean_list(l):\\n return [x for x in l if str(x) != \\\"nan\\\"]\",\n \"def remove_blanks(mat):\\n ids = []\\n for idx, row in enumerate(mat):\\n if not 1 in row:\\n ids.append(idx)\\n mat = np.delete(mat, ids, 0)\\n mat = np.delete(mat, ids, 1)\\n return mat\",\n \"def clean_data(x, null, drop_thresh):\\n # Do not modify the original dataset\\n x_clean = np.copy(x)\\n \\n # Vector holding, for each feature, the fraction of datapoints with a null value\\n null_frac = (1/x_clean.shape[0]) * np.sum(x_clean==null, axis=0)\\n # Boolean vector holding, for each feature, whether or not it needs to be kept\\n column_to_keep = null_frac <= drop_thresh\\n \\n # Drop bad columns\\n x_clean = x_clean[:, column_to_keep]\\n \\n # Vector of (list of) indices of columns where there are still null values\\n columns_to_interpolate = np.argwhere(np.any(x_clean==null, axis=0))\\n \\n # For each of those columns, find the mean of non-null values, and substitute it to null values\\n for col_list in columns_to_interpolate:\\n # Extrapolate only entry of col_list\\n col = col_list[0]\\n \\n # Boolean vector holding, for each row, whether or not it has a \\\"null\\\" entry at position \\\"col\\\"\\n row_non_null = x_clean[:, col] != null\\n # Find mean\\n interpolation = np.mean(x_clean[row_non_null, col])\\n # Substitute it to null values\\n row_null = np.logical_not(row_non_null)\\n x_clean[row_null, col] = interpolation\\n \\n return x_clean\",\n \"def remove_nans(a, b):\\n a = np.asarray(a)\\n b = np.asarray(b)\\n\\n mask = ~np.isnan(a) & ~np.isnan(b)\\n a = a[mask]\\n b = b[mask]\\n\\n return a, b\",\n \"def replace_nans(lst):\\r\\n return [None if np.isnan(v) else v for v in lst]\",\n \"def trim_zeros(x):\\n assert len(x.shape) == 2\\n return x[~np.all(x == 0, axis=1)]\",\n \"def ffill_1d_nb(a):\\n out = np.empty_like(a, dtype=np.float_)\\n lastval = a[0]\\n for i in range(a.shape[0]):\\n if np.isnan(a[i]):\\n out[i] = lastval\\n else:\\n lastval = out[i] = a[i]\\n return out\",\n \"def trim_zeros(array):\\n multislice = []\\n for i in range(array.ndim):\\n sum_axes = tuple(j for j in range(array.ndim) if j is not i)\\n edges = np.where(np.sum(array, axis=sum_axes) > 0)\\n if edges[0].size == 0:\\n return np.array([], dtype=array.dtype)\\n low = edges[0][0]\\n high = edges[0][-1]\\n multislice.append(slice(low, high+1, 1))\\n return array[tuple(multislice)]\",\n \"def clean_data(array):\\n ret = np.zeros(len(array))\\n for i in range(len(array)):\\n drop_id = len(str(i+1)) + 1\\n array[i, 0] = array[i, 0][int(drop_id):]\\n return array\",\n \"def remove_na(self, data, remove_na_how):\\n data = data.dropna(how=remove_na_how) \\n return data\",\n \"def remove_nans4(a, b, c, d):\\n a = np.asarray(a)\\n b = np.asarray(b)\\n c = np.asarray(c)\\n d = np.asarray(d)\\n\\n mask = ~np.isnan(a) & ~np.isnan(b) & ~np.isnan(c) & ~np.isnan(d)\\n a = a[mask]\\n b = b[mask]\\n c = c[mask]\\n d = d[mask]\\n\\n return a, b, c, d\",\n \"def _remove_nan(parsed_dictionary):\\n for key, value in parsed_dictionary.items():\\n if isinstance(value, np.ndarray):\\n non_nan_value = np.nan_to_num(value, nan=123456789, posinf=2e308, neginf=-2e308)\\n parsed_dictionary.update({key: non_nan_value})\\n\\n return parsed_dictionary\",\n \"def replace_nan(arr, value):\\n arr[np.isnan(arr)] = value\\n return arr\",\n \"def removeEmptyData(self, data):\\n \\n data = data.dropna()\\n data = data.reset_index(drop =True)\\n return data\",\n \"def unmasked_data(self):\\n return numpy.ma.filled(self.data.astype(numpy.float_),\\n fill_value=numpy.nan)\",\n \"def nanmedian(arr, **kwargs):\\r\\n return ma.median( ma.masked_where(arr!=arr, arr), **kwargs )\",\n \"def dropna(*args):\\n # (True|False) mask for each row based on NaN values present in the first dataframe\\n mask = ~pd.isnull(args[0])\\n if mask.ndim == 2:\\n mask = mask.all(1) # in 2d array, keep row only if all values are not NaN\\n\\n return [x.loc[mask] if isinstance(x, pd.DataFrame) else x[mask] for x in args]\",\n \"def drop_nans(target_df):\\n target_df.dropna(axis=0, inplace=True)\\n return target_df\",\n \"def remove_halo(field):\\n if not isinstance(field, np.ndarray):\\n field = np.asarray(field)\\n if len(field) <= 2:\\n return np.array([])\\n return field[1:-1, :-1]\",\n \"def purgeNanEveryWhere(df):\\n #Row-wise dropping\\n toDrop = np.array([])\\n for i in range(df.shape[0]):\\n if( np.sum ( pd.isnull(df.iloc[i]) ) == df.shape[1]-1 ):\\n toDrop= np.append(toDrop,i)\\n df.drop(df.index[toDrop.astype(int)],inplace=True) \\n #Column-wise dropping\\n for col in df.columns:\\n arr = pd.notnull(df[col])\\n nnan = np.sum(arr) \\n if (nnan == df.shape[1]):\\n df.drop(col,inplace=True,axis=1)\\n return df\",\n \"def asarray_chkfinite(a):\\n a = asarray(a)\\n if (a.dtype.char in typecodes['AllFloat']) \\\\\\n and (_nx.isnan(a).any() or _nx.isinf(a).any()):\\n raise ValueError, \\\"array must not contain infs or NaNs\\\"\\n return a\",\n \"def remove_nans3(a, b, c):\\n a = np.asarray(a)\\n b = np.asarray(b)\\n c = np.asarray(c)\\n\\n mask = ~np.isnan(a) & ~np.isnan(b) & ~np.isnan(c)\\n a = a[mask]\\n b = b[mask]\\n c = c[mask]\\n\\n return a, b, c\",\n \"def _nanmedian(arr1d, preop=None): # This only works on 1d arrays\\n x = arr1d.copy()\\n c = np.isnan(x)\\n s = np.where(c)[0]\\n if s.size == x.size:\\n warnings.warn(\\\"All-NaN slice encountered\\\", RuntimeWarning)\\n return np.nan\\n elif s.size != 0:\\n # select non-nans at end of array\\n enonan = x[-s.size:][~c[-s.size:]]\\n # fill nans in beginning of array with non-nans of end\\n x[s[:enonan.size]] = enonan\\n # slice nans away\\n x = x[:-s.size]\\n if preop:\\n x = preop(x)\\n return np.median(x, overwrite_input=True)\",\n \"def demean_normalize(one_d_array):\\n\\n temp_arr = one_d_array - np.nanmean(one_d_array)\\n\\n return temp_arr/np.nanstd(temp_arr)\",\n \"def omit_nans(self, data, label):\\n maskarray=np.full(data.shape[0], True)\\n masker=np.unique(np.argwhere(np.isnan(data))[:,0])\\n maskarray[masker]=False\\n traindata=data[maskarray,:,:,:]\\n trainlabel=label[maskarray]\\n return traindata, trainlabel\",\n \"def set_nan(x):\\n x[x == -999] = np.nan\\n return x\",\n \"def data_cleaning(df):\\n df = df.dropna(how='all')\\n \\n return df\",\n \"def fill_nan(array):\\n idx = np.arange(array.shape[0])\\n good = np.where(np.isfinite(array))\\n interp = interpolate.interp1d(idx[good], array[good], bounds_error=False)\\n return np.where(np.isfinite(array), array, interp(idx))\",\n \"def _nanmedian(arr1d): # This only works on 1d arrays\\n cond = 1-np.isnan(arr1d)\\n x = np.sort(np.compress(cond,arr1d,axis=-1))\\n if x.size == 0:\\n return np.nan\\n return np.median(x)\",\n \"def strip_zeros(a):\\n\\n return np.trim_zeros(a, trim='b')\",\n \"def _pure_data(data, missing_mask):\\n missing_rows = np.where(missing_mask)[0]\\n pure_data = np.delete(data, missing_rows, axis=0)\\n\\n return pure_data\",\n \"def cloud_filter(array, bqa):\\n array_dest = array.copy()\\n array_dest[np.where((bqa != 2720) & (bqa != 2724) & (bqa != 2728) & (bqa != 2732)) ] = 'nan'\\n return array_dest\",\n \"def removeNaNs(a, replaceWithMin=False, verbose=False, nanBufferChannels=0, \\n replaceWithZero=False):\\n a = np.array(a) \\n if (len(a) < 1): return(a)\\n startLength = len(a)\\n if (replaceWithMin or replaceWithZero):\\n idx = np.isnan(a)\\n print \\\"Found %d nan channels: %s\\\" % (len(np.where(idx==True)[0]), np.where(idx==True)[0])\\n idx2 = np.isinf(a)\\n print \\\"Found %d inf channels\\\" % (len(np.where(idx2==True)[0]))\\n a_nanmin = np.nanmin(a)\\n if (nanBufferChannels > 0):\\n idxlist = splitListIntoHomogeneousLists(idx)\\n idx = []\\n for i,mylist in enumerate(idxlist):\\n if (mylist[0]):\\n idx += mylist\\n else:\\n newSubString = nanBufferChannels*[True] \\n if (i < len(idxlist)-1):\\n newSubString += mylist[nanBufferChannels:-nanBufferChannels] + nanBufferChannels*[True]\\n else:\\n newSubString += mylist[-nanBufferChannels:]\\n # If the channel block was less than 2*nanBufferChannels wide, then only insert up to its width\\n idx += newSubString[:len(mylist)]\\n idx = np.array(idx)\\n if (verbose):\\n print \\\"Replaced %d NaNs\\\" % (len(idx))\\n print \\\"Replaced %d infs\\\" % (len(idx2))\\n if (replaceWithMin):\\n a[idx] = a_nanmin\\n a[idx2] = a_nanmin\\n return(a, a_nanmin)\\n elif (replaceWithZero):\\n a[idx] = 0\\n a[idx2] = 0\\n return(a, 0)\\n else:\\n a = a[np.where(np.isnan(a) == False)]\\n if (verbose):\\n print \\\"Removed %d NaNs\\\" % (startLength-len(a))\\n startLength = len(a)\\n a = a[np.where(np.isinf(a) == False)]\\n if (verbose):\\n print \\\"Removed %d infs\\\" % (startLength-len(a))\\n return(a)\",\n \"def exclude_empty_feats(self):\\n for dataset in self:\\n dataset.dropna(axis=1, how=\\\"all\\\", inplace=True)\",\n \"def _dropna_after_fe(X, y):\\n X = X.dropna()\\n y = y[len(y)-X.shape[0]:len(y)]\\n return X, y\",\n \"def nanmin_nb(a):\\n out = np.empty(a.shape[1], dtype=np.float_)\\n for col in range(a.shape[1]):\\n out[col] = np.nanmin(a[:, col])\\n return out\",\n \"def dropNaN(featZ):\\n \\n n_cols = len(featZ.columns)\\n featZ.dropna(axis=1, inplace=True)\\n n_dropped = n_cols - len(featZ.columns)\\n \\n if n_dropped > 0:\\n print(\\\"Dropped %d features after normalisation (NaN)\\\" % n_dropped)\\n \\n return featZ\",\n \"def test_remove_nan_observations(test_coords):\\n x, y = test_coords[0], test_coords[1]\\n\\n z = np.array([np.nan, np.nan, np.nan, 1, 1, 1, 1, 1, 1, 1])\\n\\n x_, y_, z_ = remove_nan_observations(x, y, z)\\n\\n truthx = np.array([10, 52, 53, 98, 34, 15, 58])\\n truthy = np.array([94, 98, 66, 14, 24, 60, 16])\\n truthz = np.array([1, 1, 1, 1, 1, 1, 1])\\n\\n assert_array_almost_equal(truthx, x_)\\n assert_array_almost_equal(truthy, y_)\\n assert_array_almost_equal(truthz, z_)\",\n \"def check_categories(a: np.ndarray):\\n return list(np.unique(a[~np.isnan(a)]))\",\n \"def transform(self, X):\\n\\n X = X.copy()\\n\\n X[pd.isnull(X)] = self.fill\\n\\n return np.asarray(X)\",\n \"def filter_nb(a, filter_func_nb, *args):\\n out = a.astype(np.float_)\\n\\n for col in range(out.shape[1]):\\n idxs = np.flatnonzero(~np.isnan(a[:, col]))\\n for i in idxs:\\n if not filter_func_nb(i, col, a[i, col], *args):\\n out[i, col] = np.nan\\n return out\",\n \"def isna(self):\\n # type: () -> np.ndarray\\n return extract_isnull_bytemap(self.data)\",\n \"def isna(self):\\n # type: () -> np.ndarray\\n return extract_isnull_bytemap(self.data)\",\n \"def relu(x: jnp.DeviceArray) -> jnp.DeviceArray:\\n return jnp.clip(x, a_min=0)\",\n \"def filter_nan(y, x, ids, remove=True, replace_val=0.0):\\n mask = np.isnan(x)\\n \\n if remove:\\n # Remove the rows containing any NaN\\n row_mask = ~mask.any(axis=1) # Sets to False any rows containing NaN\\n x_copy, y_copy, ids_copy = x[row_mask], y[row_mask], ids[row_mask]\\n # Remove 0 filled columns\\n col_mask = x_copy.sum(axis=0) != 0 # True if the columns is filled with 0\\n x_copy = x_copy[:, col_mask] \\n return y_copy, x_copy, ids_copy, col_mask\\n else:\\n # Replace NaN values by replace_val\\n x[mask] = replace_val\\n return y, x, ids\",\n \"def get_fixed_array():\\n return np.array([[[np.nan, np.nan],\\n [np.nan, np.nan],\\n [np.nan, np.nan]],\\n\\n [[3., 7.],\\n [2., 4.],\\n [1., 16.]],\\n\\n [[17., 5.],\\n [10., 0.],\\n [14., 12.]],\\n\\n [[8., 6.],\\n [13., 11.],\\n [9., 15.]]])\",\n \"def fill_nan(A):\\n\\tinds = np.arange(A.shape[0])\\n\\tgood = np.where(np.isfinite(A))\\n\\tA[np.isnan(A)] = np.interp(inds[np.isnan(A)], inds[good], A[good])\\n\\treturn A\",\n \"def filter_zeros(X):\\n\\tnoNonzeros = np.count_nonzero(X, axis=1)\\n\\tmask = np.where(noNonzeros > 0)\\n\\treturn X[mask[0], :]\",\n \"def test_csum_ignore_nans():\\n source = [np.zeros((16,), dtype=float) for _ in range(10)]\\n source.append(np.full((16,), fill_value=np.nan))\\n summed = csum(source, ignore_nan=True)\\n assert np.allclose(summed, np.zeros_like(summed))\",\n \"def filter_nan_samples(self, train_x, train_y):\\n\\n n_samples = train_x.shape[0]\\n if n_samples != train_y.shape[0]:\\n raise ValueError(\\\"x and y sample lengths don't match\\\")\\n\\n validity_array = np.zeros(n_samples)\\n for i in range(n_samples):\\n x_sample = train_x[i, :]\\n y_sample = train_y[i, :]\\n validity_array[i] = np.isfinite(x_sample).all() and np.isfinite(y_sample).all()\\n\\n mask = np.where(validity_array)[0]\\n\\n return train_x[mask, :], train_y[mask, :]\",\n \"def get_signal_with_nans(self):\\n pairs = zip(self.time_scale, self.signal)\\n nan_pairs = zip(self.nan_times,[float('nan')]*len(self.nan_times))\\n pairs.extend(nan_pairs)\\n pairs.sort()\\n time_scale, signal = zip(*pairs)\\n return np.array(time_scale), np.array(signal)\",\n \"def finite(x):\\n return numpy.array(x)[numpy.isfinite(x)]\",\n \"def remove_duplicates_array(self, array: List) -> List:\\n seen = set()\\n seen_add = seen.add\\n return [x for x in array if not (x in seen or seen_add(x))]\",\n \"def nanmasked(x):\\n mask = ~np.isnan(x)\\n maskA = x[mask]\\n return (maskA,mask)\",\n \"def remove_filler(dgm, val=np.inf):\\r\\n inds = (dgm[:,0] != val)\\r\\n return dgm[inds,:]\",\n \"def diff_1d_nb(a):\\n out = np.empty_like(a, dtype=np.float_)\\n out[0] = np.nan\\n out[1:] = a[1:] - a[:-1]\\n return out\",\n \"def xnd_xnd(arr, nan_to_null=False):\\n import numpy as np\\n import xnd\\n xd = xnd.xnd.from_buffer(arr)\\n if nan_to_null and issubclass(arr.dtype.type,\\n (np.floating, np.complexfloating)):\\n isnan = np.isnan(arr)\\n if isnan.any():\\n raise NotImplementedError('xnd view of numpy ndarray with nans')\\n return xd\",\n \"def normalise(a):\\n return (a - np.nanmin(a)) / np.nanmax(a - np.nanmin(a))\",\n \"def product_1d_nb(a):\\n out = a[0]\\n for i in range(1, a.shape[0]):\\n if ~np.isnan(a[i]):\\n if np.isnan(out):\\n out = a[i]\\n else:\\n out *= a[i]\\n return out\",\n \"def nan(self, check_inf = True):\\n return self.foreach(\\n lambda k,v: (k, numpy.isnan(v) + (check_inf == True) * numpy.isinf(v)),\\n dimensions = self.dims,\\n shape = self.shape,\\n )\",\n \"def getMask(self):\\r\\n mask = np.array(self.array, dtype=np.float32)\\r\\n mask[mask == 0] = np.nan\\r\\n return mask\",\n \"def nanmedian_nb(a):\\n out = np.empty(a.shape[1], dtype=np.float_)\\n for col in range(a.shape[1]):\\n out[col] = np.nanmedian(a[:, col])\\n return out\",\n \"def removeInfinities(mat):\\n isFinite = np.all(np.isfinite(mat))\\n \\n if not isFinite:\\n nrow, ncol = mat.shape\\n matCopy = mat.copy()\\n matReshaped = matCopy.reshape(-1)\\n minVal = np.nanmin(matReshaped[matReshaped != -np.inf])\\n maxVal = max(matReshaped)\\n \\n #count infinities and get their indicies\\n #countInfinites(matReshaped) \\n \\n #replace all infinities with nan\\n for i in range(0,len(matReshaped)):\\n if matReshaped[i] in [-np.inf, np.inf]:\\n matReshaped[i] = minVal\\n '''\\n #get average of matrix values and replace the nan with averages\\n def averageVal(array):\\n total = 0\\n count = 0\\n for elmt in array:\\n if elmt != -90001:\\n total+=elmt\\n count+=1\\n return total/count\\n\\n average = averageVal(matReshaped)\\n\\n #replace nan with average\\n for i in range(0,len(matReshaped)):\\n if matReshaped[i] == -90001:\\n matReshaped[i] = average\\n '''\\n #catch any potential errors \\n if np.any(np.isinf(matReshaped)):\\n raise ValueError\\n\\n return matReshaped.reshape((nrow, ncol))\\n else:\\n return mat\",\n \"def removed(bin_arr, extent, intent):\\n result = np.copy(bin_arr)\\n ree = result[extent]\\n ree[:, intent] = 0\\n result[extent] = ree\\n return result\",\n \"def isnan(self):\\n return self.isAny( (lambda x: np.isnan(x)) )\",\n \"def replace_value(cls, data, nodata):\\n data = data.astype('float64')\\n mask = data != nodata\\n if hasattr(data, 'where'):\\n return data.where(mask, np.NaN)\\n return np.where(mask, data, np.NaN)\",\n \"def _clean_data(self, dataset):\\n dataset.dropna(inplace=True)\\n # Problem: handle missing data (in a different way), noisy data, inconsistent data\",\n \"def no_reg(w):\\n return np.zeros_like(w)\",\n \"def strip_array_wrappers(arry):\\n curr = arry\\n if curr.ndim == 0:\\n if isinstance(curr[...], np.ndarray):\\n return strip_array_wrappers(curr[...])\\n return curr\\n\\n # there is a possibility for infinite looping\\n # e.g. [np.ndarray, str, dict] would stay object array\\n # impossible if homogeneous (implied by 1-element wrappers)\\n while isinstance(curr[0], np.ndarray):\\n if curr.shape[0] == 1:\\n curr = curr[0]\\n else:\\n curr = np.array(tuple(curr))\\n\\n return curr\",\n \"def isnan(*obj):\\n\\n out = [flatten(o) if istensor(o) else o for o in obj]\\n return any([any(o != o) if istensor(o) else o != o for o in out])\",\n \"def nanmean_nb(a):\\n out = np.empty(a.shape[1], dtype=np.float_)\\n for col in range(a.shape[1]):\\n out[col] = np.nanmean(a[:, col])\\n return out\",\n \"def filter_isolated_pixels(array):\\n filtered_array = np.copy(array)\\n id_regions, num_ids = ndimage.label(filtered_array,\\n structure=np.ones((3, 3)))\\n id_sizes = np.array(ndimage.sum(array, id_regions, range(num_ids+1)))\\n area_mask = (id_sizes == 1)\\n filtered_array[area_mask[id_regions]] = 0\\n return filtered_array\",\n \"def isnan(data):\\n return _make.isnan(data)\",\n \"def filter(self):\\n self.data = self.data.loc[~self.data.isnull().any(1),:]\",\n \"def fillna_1d_nb(a, value):\\n return set_by_mask_1d_nb(a, np.isnan(a), value)\",\n \"def clean_coordinates(coords, silent=False):\\n l1 = len(coords)\\n coords = coords[~N.isnan(coords).any(axis=1)] # remove NaN values\\n l2 = len(coords)\\n if not silent:\\n msg = \\\"{0} coordinates\\\".format(l2)\\n if l2 < l1:\\n msg += \\\" ({0} removed as invalid)\\\".format(l1-l2)\\n print(msg)\\n return coords\",\n \"def arraycleaner(array):\\r\\n lengths = [len(x) for x in array]\\r\\n #shortindex = lengths.index(min(lengths))\\r\\n shortest = min(lengths)\\r\\n for n in range(len(array)):\\r\\n line = array[n]\\r\\n if len(array[n]) != shortest:\\r\\n this = len(line)\\r\\n cut = np.ceil(1/((this/shortest) - 1))\\r\\n #import pdb; pdb.set_trace()\\r\\n for m in range(len(array[n])):\\r\\n if m % cut == 0 and m != 0:\\r\\n line[m] = 'del'\\r\\n for m in range(len(array[n])):\\r\\n try:\\r\\n if line[m] == 'del':\\r\\n del line[m]\\r\\n except IndexError:\\r\\n continue\\r\\n array[n] = line[0:shortest]\\r\\n return array\",\n \"def nansum(array_data, axis=0):\\n\\n mdat = np.ma.masked_array(array_data, np.isnan(array_data));\\n retval = np.sum(mdat, axis=axis);\\n \\n return retval;\",\n \"def scalararray(inp) -> np.ndarray:\\n return np.array([None, inp], dtype=object)[[1]].reshape([])\",\n \"def nanvar(array_data, axis=0):\\n\\n mdat = np.ma.masked_array(array_data, np.isnan(array_data));\\n retval = np.var(mdat, axis=axis);\\n \\n return retval;\",\n \"def noiseRemoval(array, minSize, classes):\\n img=array.astype('int')\\n for i in range(classes):\\n B=(img!=i) # return a bool array\\n B = morphology.remove_small_objects(B, min_size=minSize, connectivity=1) \\n img[B==False]=i\\n \\n return img\",\n \"def _remove_outliers(self, data, nsigma):\\n while (np.abs(np.ma.fix_invalid(data)-np.nanmean(data)) > nsigma *\\n np.nanstd(data)).any():\\n\\n data[np.where(np.abs(np.ma.fix_invalid(data)-np.nanmean(data)) >\\n nsigma * np.nanstd(data))] = np.nan\\n\\n return data\",\n \"def _mask_array(mask, *args):\\n invalid = ~mask # True if invalid\\n args_masked = []\\n for arg in args:\\n if arg.size > 1 and arg.shape != invalid.shape:\\n raise ValueError('Shape mismatch between mask and array.')\\n arg_masked = arg.astype(np.float64)\\n if arg.size == 1:\\n pass\\n elif invalid.size == 1:\\n arg_masked = np.nan if invalid.item() else arg_masked\\n elif arg.size > 1:\\n arg_masked[invalid] = np.nan\\n args_masked.append(arg_masked)\\n return args_masked[0] if len(args_masked) == 1 else args_masked\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77405065","0.7408961","0.7318458","0.72726125","0.70151496","0.6928957","0.6729866","0.6716962","0.65393853","0.65328234","0.64841986","0.6428685","0.6409222","0.6259641","0.6246225","0.62417316","0.62114847","0.62003404","0.61770093","0.6146045","0.6140856","0.6138152","0.613758","0.6133004","0.61210954","0.6084048","0.60804665","0.6049211","0.60205173","0.60204434","0.59803283","0.59614223","0.5950598","0.59118223","0.58746177","0.5862925","0.58506536","0.5823901","0.5806769","0.5798864","0.5798456","0.5777712","0.5774643","0.57615584","0.57607996","0.57581294","0.5757498","0.5738537","0.57279325","0.5719687","0.5717279","0.5704401","0.57022136","0.568867","0.5683825","0.5679859","0.5679654","0.56758344","0.5674777","0.5674777","0.56640565","0.56475186","0.5643772","0.56242925","0.5623685","0.56206435","0.56197023","0.56123835","0.5609359","0.55902267","0.55867743","0.55813617","0.5577912","0.55757827","0.5573859","0.5536476","0.55166626","0.5510827","0.54969656","0.54885924","0.5486388","0.5483376","0.5479","0.54764813","0.5474243","0.547196","0.5463953","0.54537416","0.54443574","0.5442147","0.54419595","0.5426826","0.54154557","0.540156","0.5384906","0.5382143","0.53722","0.536655","0.5362641","0.535661"],"string":"[\n \"0.77405065\",\n \"0.7408961\",\n \"0.7318458\",\n \"0.72726125\",\n \"0.70151496\",\n \"0.6928957\",\n \"0.6729866\",\n \"0.6716962\",\n \"0.65393853\",\n \"0.65328234\",\n \"0.64841986\",\n \"0.6428685\",\n \"0.6409222\",\n \"0.6259641\",\n \"0.6246225\",\n \"0.62417316\",\n \"0.62114847\",\n \"0.62003404\",\n \"0.61770093\",\n \"0.6146045\",\n \"0.6140856\",\n \"0.6138152\",\n \"0.613758\",\n \"0.6133004\",\n \"0.61210954\",\n \"0.6084048\",\n \"0.60804665\",\n \"0.6049211\",\n \"0.60205173\",\n \"0.60204434\",\n \"0.59803283\",\n \"0.59614223\",\n \"0.5950598\",\n \"0.59118223\",\n \"0.58746177\",\n \"0.5862925\",\n \"0.58506536\",\n \"0.5823901\",\n \"0.5806769\",\n \"0.5798864\",\n \"0.5798456\",\n \"0.5777712\",\n \"0.5774643\",\n \"0.57615584\",\n \"0.57607996\",\n \"0.57581294\",\n \"0.5757498\",\n \"0.5738537\",\n \"0.57279325\",\n \"0.5719687\",\n \"0.5717279\",\n \"0.5704401\",\n \"0.57022136\",\n \"0.568867\",\n \"0.5683825\",\n \"0.5679859\",\n \"0.5679654\",\n \"0.56758344\",\n \"0.5674777\",\n \"0.5674777\",\n \"0.56640565\",\n \"0.56475186\",\n \"0.5643772\",\n \"0.56242925\",\n \"0.5623685\",\n \"0.56206435\",\n \"0.56197023\",\n \"0.56123835\",\n \"0.5609359\",\n \"0.55902267\",\n \"0.55867743\",\n \"0.55813617\",\n \"0.5577912\",\n \"0.55757827\",\n \"0.5573859\",\n \"0.5536476\",\n \"0.55166626\",\n \"0.5510827\",\n \"0.54969656\",\n \"0.54885924\",\n \"0.5486388\",\n \"0.5483376\",\n \"0.5479\",\n \"0.54764813\",\n \"0.5474243\",\n \"0.547196\",\n \"0.5463953\",\n \"0.54537416\",\n \"0.54443574\",\n \"0.5442147\",\n \"0.54419595\",\n \"0.5426826\",\n \"0.54154557\",\n \"0.540156\",\n \"0.5384906\",\n \"0.5382143\",\n \"0.53722\",\n \"0.536655\",\n \"0.5362641\",\n \"0.535661\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7642145"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":77468,"cells":{"query":{"kind":"string","value":"Linearly interpolate over missing data (NaNs) in a time series."},"document":{"kind":"string","value":"def pad(data, maxPadLength=False):\n data_padded = data.copy()\n bad_indexes = np.isnan(data)\n good_indexes = np.logical_not(bad_indexes)\n good_data = data[good_indexes]\n interpolated = np.interp(\n bad_indexes.nonzero()[0], good_indexes.nonzero()[0], good_data\n )\n data_padded[bad_indexes] = interpolated\n if maxPadLength:\n blocks, n_blocks = ndimage.label(np.isnan(data))\n for bl in range(1, n_blocks + 1):\n # if greater than max pad length then keep as nan\n # i.e. don't interpolate over too large a range\n if (blocks == bl).sum() > maxPadLength:\n data_padded[blocks == bl] = np.nan\n\n return data_padded"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def interpolate_missing(y):\n if y.isna().any():\n y = y.interpolate(method='linear', limit_direction='both')\n return y","def fill_missing_data_points(data):\n return data.interpolate()","def check_and_interpolate_nans(df):\n nan_count = df.isna().sum().sum()\n if nan_count > 0:\n df.interpolate(method='linear', inplace=True)\n return df","def auto_fillna(ts: TimeSeries,\n **interpolate_kwargs) -> TimeSeries:\n\n ts_temp = ts.pd_dataframe()\n\n # pandas interpolate wrapper, with chosen `method`\n if 'limit_direction' not in interpolate_kwargs:\n interpolate_kwargs['limit_direction'] = 'both'\n interpolate_kwargs['inplace'] = True\n ts_temp.interpolate(**interpolate_kwargs)\n\n return TimeSeries.from_times_and_values(ts.time_index(), ts_temp.values)","def fill_nan(A):\n\tinds = np.arange(A.shape[0])\n\tgood = np.where(np.isfinite(A))\n\tA[np.isnan(A)] = np.interp(inds[np.isnan(A)], inds[good], A[good])\n\treturn A","def fill_nan(array):\n idx = np.arange(array.shape[0])\n good = np.where(np.isfinite(array))\n interp = interpolate.interp1d(idx[good], array[good], bounds_error=False)\n return np.where(np.isfinite(array), array, interp(idx))","def interpolate_timeseries(self, x, t, **kw):\n v, t_v = self.timeseries(x, rmnans=True)\n kw.update(dict(bounds_error=False))\n interpolant = sp.interpolate.interp1d(t_v, v, **kw)\n return interpolant(t)","def test_linear_interpolation_nan_points(self):\n\n # Define pixel centers along each direction\n x = [1.0, 2.0, 4.0]\n y = [5.0, 9.0]\n\n # Define ny by nx array with corresponding values\n A = numpy.zeros((len(x), len(y)))\n\n # Define values for each x, y pair as a linear function\n for i in range(len(x)):\n for j in range(len(y)):\n A[i, j] = linear_function(x[i], y[j])\n\n # Then test that interpolated points can contain NaN\n xis = numpy.linspace(x[0], x[-1], 10)\n etas = numpy.linspace(y[0], y[-1], 10)\n xis[6:7] = numpy.nan\n etas[3] = numpy.nan\n points = combine_coordinates(xis, etas)\n\n vals = interpolate2d(x, y, A, points, mode='linear')\n refs = linear_function(points[:, 0], points[:, 1])\n assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)","def interpolate_nans(self):\n\n signal = self.signal\n\n # check for more than one nan in row\n for i in range(len(signal)-1) :\n if np.isnan(signal[i]) and np.isnan(signal[i+1]) :\n raise Exception('There are two nans in a row ask moritz what to do !')\n\n if np.isnan(signal[0]) :\n np.signal[0] = signal[1]\n if np.isnan(signal[-1]) :\n signal[-1] = signal[-2]\n\n for i in range(1,len(signal)-1) :\n if np.isnan(signal[i]):\n signal[i] = (signal[i-1] + signal[i+1])/2","def interpolate1d(X):\n ind = np.arange(X.shape[0])\n ind_not_nans = np.where(~np.isnan(X)) #fill last values by last non nan values\n last_non_nan = X[ind_not_nans[0][-1]]\n f = interpolate.interp1d(ind[ind_not_nans], X[ind_not_nans], bounds_error=False, fill_value=last_non_nan)\n X_int = np.where(np.isfinite(X), X, f(ind))\n \n return X_int","def test_linear_interpolation_nan_array(self):\n\n # Define pixel centers along each direction\n x = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]\n y = [4.0, 5.0, 7.0, 9.0, 11.0, 13.0]\n\n # Define ny by nx array with corresponding values\n A = numpy.zeros((len(x), len(y)))\n\n # Define values for each x, y pair as a linear function\n for i in range(len(x)):\n for j in range(len(y)):\n A[i, j] = linear_function(x[i], y[j])\n A[2, 3] = numpy.nan # (x=2.0, y=9.0): NaN\n\n # Then test that interpolated points can contain NaN\n xis = numpy.linspace(x[0], x[-1], 12)\n etas = numpy.linspace(y[0], y[-1], 10)\n points = combine_coordinates(xis, etas)\n\n vals = interpolate2d(x, y, A, points, mode='linear')\n refs = linear_function(points[:, 0], points[:, 1])\n\n # Set reference result with expected NaNs and compare\n for i, (xi, eta) in enumerate(points):\n if (1.0 < xi <= 3.0) and (7.0 < eta <= 11.0):\n refs[i] = numpy.nan\n\n assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)","def pad(input_data):\n # source : https://stackoverflow.com/questions/6518811/interpolate-nan-values-in-a-numpy-array \n data = input_data.copy()\n bad_indexes = np.isnan(data)\n good_indexes = np.logical_not(bad_indexes)\n good_data = data[good_indexes]\n interpolated = np.interp(bad_indexes.nonzero()[0], good_indexes.nonzero()[0], good_data)\n data[bad_indexes] = interpolated\n return data","def interpolate(df):\n for x in df.columns:\n if x == \"date\":\n continue\n df[x] = df[x].interpolate(method='linear', axis=0).ffill().bfill()\n return df","def fill_nan(x):\n (n_rows, wdw) = x.shape\n new_x = np.zeros((n_rows,wdw)); new_x[:] = np.nan\n for i in range(n_rows):\n indMissing = np.where(np.isnan(x[i,:]))[0]\n l = len(x[i,indMissing]) #number of MVs\n if l < 4*wdw/5: #20% available values otherwise discarded\n new_x[i,:] = x[i,:]\n if l > 0 and indMissing[0] == 0: #missing value at index 0 \n c = 0\n while c + 1 < len(indMissing) and indMissing[c+1] == indMissing[c] + 1:\n c += 1\n new_x[i,:c+1] = x[i,c+1] #first nans replaced by first non nan value\n indMissing = np.where(np.isnan(new_x[i,:]))[0]\n l = len(new_x[i,indMissing])\n if l > 0 and indMissing[0] > 0:\n new_x[i,:] = interpolate1d(new_x[i,:]) #interpolate intermediate nans\n ind = np.where(~np.isnan(new_x).all(axis=1))[0]\n new_x = new_x[ind] #remove NaNs \n \n return new_x, ind","def check_nan(wseries: pd.Series) -> pd.Series:\n\n if len(wseries[pd.Series([\n (type(val) == str or isnan(val)) for val in wseries\n ], index=wseries.index)]) == 0:\n return wseries # nothing to change\n\n # ensure that all are either float or nan\n def _float_or_nan(ent):\n \"\"\"\n Force values to be either a float or nan first\n \"\"\"\n try:\n return float(ent)\n except ValueError:\n return float('nan')\n\n wseries = pd.Series(\n [_float_or_nan(val) for val in wseries], index=wseries.index,\n name=wseries.name\n )\n\n # continue with interpolation or extrapolation if needed\n inds = where(\n pd.Series([\n (isinstance(val, str) or isnan(val)) for val in wseries\n ], index=wseries.index)\n )[0] # locate the position of the problematic readings\n for ind in inds:\n try:\n wseries[ind] = interpolate_with_s(\n wseries.index[ind], wseries.index[ind-1],\n wseries.index[ind+1],\n wseries[ind-1], wseries[ind+1]\n )\n if isnan(wseries[ind]): # interpolation does not work\n wseries[ind] = interpolate_with_s(\n wseries.index[ind], wseries.index[ind-2],\n wseries.index[ind-1],\n wseries[ind-2], wseries[ind-1]\n )\n except IndexError: # extrapolation\n try:\n wseries[ind] = interpolate_with_s(\n wseries.index[ind], wseries.index[ind-2],\n wseries.index[ind-1],\n wseries[ind-2], wseries[ind-1]\n )\n except IndexError:\n wseries[ind] = interpolate_with_s(\n wseries.index[ind], wseries.index[ind+2],\n wseries.index[ind+1],\n wseries[ind+2], wseries[ind+1]\n )\n return wseries\n\n return wseries","def interp1d_stair_aver_withnan(x, y): #TODO: deal with the case x not sorted\n def f(xp):\n xmod=x[~(np.isnan(x)+np.isnan(y))]\n ymod=y[~(np.isnan(x)+np.isnan(y))]\n yp=np.empty(np.size(xp)-1)\n yint=np.cumsum(np.concatenate((np.array([0]),ymod[:-1]*(xmod[1:]-xmod[:-1]))))\n g=interp1d(xmod,yint, bounds_error=False, fill_value=np.nan)\n# yp=np.where((xp[:-1]>min(xmod))*(xp[1:]<max(xmod)),(g(xp[1:])-g(xp[:-1]))/(xp[1:]-xp[:-1]),np.nan) #Maybe this is suboptimal since we compute twice g(xp[i])\n yp=np.where((xp[:-1]>min(xmod))*(xp[1:]<max(xmod)),(g(xp[1:])-g(xp[:-1]))/(xp[1:]-xp[:-1]),np.nan) #Maybe this is suboptimal since we compute twice g(xp[i])\n for i in range(np.size(xp)-1):\n if np.isnan(y[np.where((x>=xp[i])*(x<xp[i+1]))]).all():\n yp[i]=np.nan\n return yp\n\n return f","def interp1d_lin_aver_withoutnan(x,y): #FIXME: there is a problem in this routine when the x are in decreasing order.\n def f(xp):\n yp=np.empty(np.size(xp)-1)\n for i in range(np.size(xp)-1):\n# print i, xp[i], xp[i+1]\n xmod=x[~(np.isnan(x)+np.isnan(y))]\n ymod=y[~(np.isnan(x)+np.isnan(y))]\n xmod2=xmod[np.where((xmod>xp[i])*(xmod<xp[i+1]))]\n ymod2=ymod[np.where((xmod>xp[i])*(xmod<xp[i+1]))]\n xmod3=np.concatenate((np.array([xp[i]]),xmod2,np.array([xp[i+1]])))\n g=interp1d(xmod,ymod, bounds_error=False, fill_value=np.nan)\n ymod3=np.concatenate((np.array([g(xp[i])]),ymod2,np.array([g(xp[i+1])])))\n# print xmod3\n# print ymod3\n if np.isnan(ymod3).all():\n yp[i]=np.nan\n else:\n xmod4=xmod3[np.where(~(np.isnan(ymod3)+np.isnan(xmod3)))]\n ymod4=ymod3[np.where(~(np.isnan(ymod3)+np.isnan(xmod3)))]\n# if i==9:\n# print xmod4,ymod4\n yp[i]=np.sum((ymod4[1:]+ymod4[:-1])/2*(xmod4[1:]-xmod4[:-1]))\n yp[i]=yp[i]/(xmod4[-1]-xmod4[0])\n# print yp[i]\n return yp\n return f","def add_missing_weather_data(df):\n\n full_date_range = pd.date_range(start=min(df.timestamp), end=max(df.timestamp), freq='H')\n sites = list(set(df.site_id))\n full_data_site_range = pd.DataFrame(itertools.product(sites, full_date_range),\n columns=['site_id', 'timestamp'])\n df_all_dates = full_data_site_range.merge(df, on=['site_id', 'timestamp'], how='left')\n df_all_dates = df_all_dates.groupby('site_id').apply(lambda group: group.interpolate(limit_direction='both'))\n\n return df_all_dates","def interpolate(series, **options):\n if has_nan(series.index):\n msg = \"\"\"The Series you passed to interpolate contains\n NaN values in the index, which would result in\n undefined behavior. So I'm putting a stop to that.\"\"\"\n raise ValueError(msg)\n\n if not is_strictly_increasing(series.index):\n msg = \"\"\"The Series you passed to interpolate has an index\n that is not strictly increasing, which would result in\n undefined behavior. So I'm putting a stop to that.\"\"\"\n raise ValueError(msg)\n\n # make the interpolate function extrapolate past the ends of\n # the range, unless `options` already specifies a value for `fill_value`\n underride(options, fill_value=\"extrapolate\")\n\n # call interp1d, which returns a new function object\n x = series.index\n y = series.values\n interp_func = interp1d(x, y, **options)\n return interp_func","def interpolate_data(data: pd.DataFrame) -> pd.DataFrame:\n if data.empty:\n return data\n data_arg_sort = np.argsort(data.values, axis=0)\n data_sorted = np.take_along_axis(data.values, data_arg_sort, axis=0)\n data_index = data.index.values[data_arg_sort]\n\n rows, cols = data_sorted.shape\n\n num_na = data.isna().sum(axis=0)\n not_na = rows - num_na\n\n # create a linspace for each column, each with #rows entries that span from 0 to #non-missing values in that column\n float_index = (np.tile(np.linspace(0, 1, rows), (cols, 1)) * (not_na.values[:, np.newaxis] - 1)).T\n index = np.floor(float_index).astype(int)\n decimal = float_index % 1\n\n index_min = np.minimum(index + 1, not_na.values[np.newaxis, :] - 1)\n # take the weighted average of the two neighboring values, based on the decimal\n # slightly simplified expression (1 - dec) * sorted_data[index] + decimal * sorted_data[index + 1]\n new_values = (1 - decimal) * np.take_along_axis(data_sorted, index, axis=0) +\\\n decimal * np.take_along_axis(data_sorted, index_min, axis=0)\n\n # now the index of the new values needs to be reconstructed, since each column was sorted differently\n result = []\n # reconstruct one column at a time\n for column_index, column_name in enumerate(data.columns):\n series_index = np.empty((rows,))\n series_index[:] = np.nan\n\n index_mapping = ddict(lambda: (np.nan, np.inf))\n for i, (index_val, float_val) in enumerate(zip(index[:, column_index], float_index[:, column_index])):\n decimal = float_val - index_val\n if index_mapping[index_val][1] > decimal:\n index_mapping[index_val] = i, decimal\n\n if index_mapping[index_val + 1][1] > 1 - decimal:\n index_mapping[index_val + 1] = i, 1 - decimal\n\n # the index that was added last needs to be removed\n index_mapping.pop(index_val + 1)\n\n for key, (idx, _) in index_mapping.items():\n series_index[idx] = key\n\n series_index[np.isnan(series_index)] = np.arange(np.nanmax(series_index) + 1, rows, 1)\n s_ind = data_index[:, column_index][series_index.astype(int)]\n result.append(pd.Series(new_values[:, column_index], index=s_ind, name=column_name))\n\n return pd.concat(result, axis=1, sort=False)","def _auto_fill(series: TimeSeries, **interpolate_kwargs) -> TimeSeries:\n\n series_temp = series.pd_dataframe()\n\n # pandas interpolate wrapper, with chosen `method`\n if \"limit_direction\" not in interpolate_kwargs:\n interpolate_kwargs[\"limit_direction\"] = \"both\"\n interpolate_kwargs[\"inplace\"] = True\n series_temp.interpolate(**interpolate_kwargs)\n return TimeSeries.from_dataframe(\n series_temp,\n freq=series.freq,\n static_covariates=series.static_covariates,\n hierarchy=series.hierarchy,\n )","def extrapolate_nans(x, y, v):\n if numpy.ma.is_masked(v):\n nans = v.mask\n else:\n nans = numpy.isnan(v)\n notnans = numpy.logical_not(nans)\n v[nans] = scipy.interpolate.griddata((x[notnans], y[notnans]), v[notnans],\n (x[nans], y[nans]),\n method='nearest').ravel()\n return v","def linear_interpolate_value_at_time(t0, v0, t1, v1, t):\n return v0 + linear_interpolate_value_change(t0, v0, t1, v1, t - t0)","def interpolate_series(t,y,start,stop):\n \n # daily timestamsp\n numdays = (stop-start).days\n D = [start + datetime.timedelta(days=x) for x in range(0,numdays)]\n T = [time.mktime(date.timetuple()) for date in D]\n \n # interpolated variable\n Y = np.interp(T, t, y)\n \n return (D, Y)","def _interpolation(matrix):\n try:\n\tok = ~np.isnan(matrix)\n \txp = ok.ravel().nonzero()[0]\n \tfp = matrix[~np.isnan(matrix)]\n \tx = np.isnan(matrix).ravel().nonzero()[0]\n \tmatrix[np.isnan(matrix)] = np.interp(x, xp, fp)\n \treturn matrix\n except:\n return matrix","def interpolate_na(\n self, method: str = \"nearest\", extrapolate: bool = False, **kwargs\n ):\n dim0 = self.dim0\n kwargs.update(dict(method=method, extrapolate=extrapolate))\n if dim0:\n interp_data = np.empty(self._obj.shape, dtype=self._obj.dtype)\n for i, (_, sub_xds) in enumerate(self._obj.groupby(dim0)):\n interp_data[i, ...] = self._interpolate_na(\n sub_xds.load().data, **kwargs\n )\n else:\n interp_data = self._interpolate_na(self._obj.load().data, **kwargs)\n interp_array = xr.DataArray(\n name=self._obj.name,\n dims=self._obj.dims,\n coords=self._obj.coords,\n data=interp_data,\n attrs=self._obj.attrs,\n )\n interp_array.raster.set_nodata(self.nodata)\n interp_array.raster.set_crs(self.crs)\n return interp_array","def interpolate(series, indices):\r\n not_included = [t for t in indices if t not in series.index.values]\r\n new_indices_series = pd.Series(index=not_included, data=[np.nan] * len(not_included))\r\n with_new_indices = series.append(new_indices_series).sort_index()\r\n interpolated = with_new_indices.interpolate(method=\"linear\")\r\n\r\n # We want the values on the indices before the (originally) first index to be equal to the first value. Similarly,\r\n # we want the values after the (originally) last index to be equal to the last value.\r\n min_index = min(series.index.values)\r\n max_index = max(series.index.values)\r\n start_value = series.values[0]\r\n end_value = series.values[-1]\r\n\r\n new_values = []\r\n for t, p in interpolated.items():\r\n if t < min_index:\r\n new_values.append(start_value)\r\n elif t > max_index:\r\n new_values.append(end_value)\r\n else:\r\n new_values.append(p)\r\n\r\n new_series = pd.Series(data=new_values, index=interpolated.index)\r\n\r\n return new_series","def zero_end_interpolation(df: pd.DataFrame):\n end = df.index[-1]\n empty_df = pd.DataFrame(index=np.arange(0, end + 1, 1))\n res = pd.concat([df, empty_df], axis=1)\n res = res.fillna(method='ffill')\n res = res.fillna(method='bfill')\n return res","def interpolate(f):\n @funnel\n def fill_missing(data, return_model=False, **kwargs):\n impute_kwargs = kwargs.pop('impute_kwargs', {})\n\n if impute_kwargs:\n model = impute_kwargs.pop('model', eval(defaults['impute']['model']))\n imputed_data, model = apply_sklearn_model(model, data, return_model=True, **impute_kwargs)\n data = pd.DataFrame(data=imputed_data, index=data.index, columns=data.columns)\n else:\n model = None\n\n if kwargs:\n kwargs = update_dict(defaults['interpolate'], kwargs, from_config=True)\n data = data.interpolate(**kwargs)\n\n if return_model:\n return data, {'model': model, 'args': [], 'kwargs': kwargs}\n else:\n return data\n\n @functools.wraps(f)\n def wrapped(data, *args, **kwargs):\n interp_kwargs = kwargs.pop('interp_kwargs', {})\n return f(fill_missing(data, *args, **interp_kwargs), **kwargs)\n\n return wrapped","def fillna(ts: TimeSeries, fill: float = 0) -> TimeSeries:\n\n return TimeSeries.from_times_and_values(ts.time_index(), ts.pd_dataframe().fillna(value=fill))","def fill_missing_values(\n series: TimeSeries, fill: Union[str, float] = \"auto\", **interpolate_kwargs\n) -> TimeSeries:\n raise_if_not(\n isinstance(fill, str) or isinstance(fill, float),\n \"`fill` should either be a string or a float\",\n logger,\n )\n raise_if(\n isinstance(fill, str) and fill != \"auto\",\n \"invalid string for `fill`: can only be set to 'auto'\",\n logger,\n )\n\n if fill == \"auto\":\n return _auto_fill(series, **interpolate_kwargs)\n return _const_fill(series, fill)","def interpolate_na(self, method: str = \"nearest\", **kwargs):\n ds_out = xr.Dataset(attrs=self._obj.attrs)\n for var in self.vars:\n ds_out[var] = self._obj[var].raster.interpolate_na(method=method, **kwargs)\n return ds_out","def interp1d_lin_aver(x,y): #FIXME: there is a problem in this routine when the x are in decreasing order.\n def f(xp):\n yp=np.empty(np.size(xp)-1)\n for i in range(np.size(xp)-1):\n# print i, xp[i], xp[i+1]\n xmod=x[~(np.isnan(x)+np.isnan(y))]\n ymod=y[~(np.isnan(x)+np.isnan(y))]\n xmod2=xmod[np.where((xmod>xp[i])*(xmod<xp[i+1]))]\n ymod2=ymod[np.where((xmod>xp[i])*(xmod<xp[i+1]))]\n xmod3=np.concatenate((np.array([xp[i]]),xmod2,np.array([xp[i+1]])))\n g=interp1d(x,y, bounds_error=False, fill_value=np.nan)\n ymod3=np.concatenate((np.array([g(xp[i])]),ymod2,np.array([g(xp[i+1])])))\n# print xmod3\n# print ymod3\n if np.isnan(ymod3).all():\n yp[i]=np.nan\n else:\n xmod4=xmod3[np.where(~(np.isnan(ymod3)+np.isnan(xmod3)))]\n ymod4=ymod3[np.where(~(np.isnan(ymod3)+np.isnan(xmod3)))]\n# if i==9:\n# print xmod4,ymod4\n yp[i]=np.sum((ymod4[1:]+ymod4[:-1])/2*(xmod4[1:]-xmod4[:-1]))\n yp[i]=yp[i]/(xmod4[-1]-xmod4[0])\n# print yp[i]\n return yp\n return f","def interpolate(self, freq, data):\n func = scipy.interpolate.interp1d(\n freq, data,\n kind='nearest',\n copy=False,\n assume_sorted=True,\n bounds_error=False,\n fill_value=np.nan,\n )\n return func(self.freq)","def test_interpolation_random_array_and_nan(self):\n\n # Define pixel centers along each direction\n x = numpy.arange(20) * 1.0\n y = numpy.arange(25) * 1.0\n\n # Define ny by nx array with corresponding values\n A = numpy.zeros((len(x), len(y)))\n\n # Define arbitrary values for each x, y pair\n numpy.random.seed(17)\n A = numpy.random.random((len(x), len(y))) * 10\n\n # Create islands of NaN\n A[5, 13] = numpy.nan\n A[6, 14] = A[6, 18] = numpy.nan\n A[7, 14:18] = numpy.nan\n A[8, 13:18] = numpy.nan\n A[9, 12:19] = numpy.nan\n A[10, 14:17] = numpy.nan\n A[11, 15] = numpy.nan\n\n A[15, 5:6] = numpy.nan\n\n # Creat interpolation points\n xis = numpy.linspace(x[0], x[-1], 39) # Hit all mid points\n etas = numpy.linspace(y[0], y[-1], 73) # Hit thirds\n points = combine_coordinates(xis, etas)\n\n for mode in ['linear', 'constant']:\n vals = interpolate2d(x, y, A, points, mode=mode)\n\n # Calculate reference result with expected NaNs and compare\n i = j = 0\n for k, (xi, eta) in enumerate(points):\n\n # Find indices of nearest higher value in x and y\n i = numpy.searchsorted(x, xi)\n j = numpy.searchsorted(y, eta)\n\n if i > 0 and j > 0:\n\n # Get four neigbours\n A00 = A[i - 1, j - 1]\n A01 = A[i - 1, j]\n A10 = A[i, j - 1]\n A11 = A[i, j]\n\n if numpy.allclose(xi, x[i]):\n alpha = 1.0\n else:\n alpha = 0.5\n\n if numpy.allclose(eta, y[j]):\n beta = 1.0\n else:\n beta = eta - y[j - 1]\n\n if mode == 'linear':\n if numpy.any(numpy.isnan([A00, A01, A10, A11])):\n ref = numpy.nan\n else:\n ref = (A00 * (1 - alpha) * (1 - beta) +\n A01 * (1 - alpha) * beta +\n A10 * alpha * (1 - beta) +\n A11 * alpha * beta)\n elif mode == 'constant':\n assert alpha >= 0.5 # Only case in this test\n\n if beta < 0.5:\n ref = A10\n else:\n ref = A11\n else:\n msg = 'Unknown mode: %s' % mode\n raise Exception(msg)\n\n #print i, j, xi, eta, alpha, beta, vals[k], ref\n assert nanallclose(vals[k], ref, rtol=1e-12, atol=1e-12)","def interp1d_stair_aver(x, y): #TODO: deal with the case x not sorted\n def f(xp):\n yp=np.empty(np.size(xp)-1)\n xmod=x[~(np.isnan(x)+np.isnan(y))]\n ymod=y[~(np.isnan(x)+np.isnan(y))]\n yint=np.cumsum(np.concatenate((np.array([0]),ymod[:-1]*(xmod[1:]-xmod[:-1]))))\n g=interp1d(xmod,yint, bounds_error=False, fill_value=np.nan)\n# yp=np.where((xp[:-1]>min(xmod))*(xp[1:]<max(xmod)),(g(xp[1:])-g(xp[:-1]))/(xp[1:]-xp[:-1]),np.nan) #Maybe this is suboptimal since we compute twice g(xp[i])\n yp=np.where((xp[:-1]>min(xmod))*(xp[1:]<max(xmod)),(g(xp[1:])-g(xp[:-1]))/(xp[1:]-xp[:-1]),np.nan) #Maybe this is suboptimal since we compute twice g(xp[i])\n return yp\n\n return f","def test_nans_in_x_data(self):\n alpha = 0.9\n num_x = 150\n num_y = 100\n x_vals = np.linspace(0, 2 * np.pi, num_x)\n x_vals[45] = np.nan\n y_vals = (np.sin(x_vals[:num_y]) + 0.3 * np.random.randn(num_y) + 0.5)\n\n with pytest.raises(RuntimeError):\n extrapolated_lowess(x_vals, y_vals, alpha=alpha)","def linear_interpolate(x, x0, y0, x1, y1):\n try:\n return (y0 * (x1 - x) + y1 * (x - x0)) / (x1 - x0)\n except ZeroDivisionError:\n return 0.0","def linear_interpolate_value_change(t0, v0, t1, v1, dt):\n return (v1 - v0)/float(t1-t0) * dt","def interpolateTime(ser, time):\n def findTime(a_list, func):\n if len(a_list) == 0:\n return np.nan\n else:\n return func(a_list)\n def findValue(time):\n if np.isnan(time):\n return np.nan\n else:\n return ser[time]\n #\n time_lb = findTime([t for t in ser.index if t <= time], max)\n time_ub = findTime([t for t in ser.index if t >= time], min)\n value_lb = findValue(time_lb)\n value_ub = findValue(time_ub)\n if np.isnan(value_lb):\n return value_ub\n if np.isnan(value_ub):\n return value_lb\n if time_ub == time_lb:\n return value_ub\n frac = (time - time_lb)/(time_ub - time_lb)\n return (1 - frac)*value_lb + frac*value_ub","def replaces_nans_ma(series):\n series = series.replace([np.inf, -np.inf], np.nan)\n result = series.fillna(series.rolling(window=len(series), min_periods=0).mean())\n return result","def image_naninterp(data):\n if not isinstance(data, np.ndarray) or len(data.shape) != 2:\n log.error(\"data must be a 2D %s\" % np.ndarray)\n return\n mask = np.isnan(data)\n if not mask.any():\n return data\n if mask.all():\n log.error(\"data are all NaN\")\n return\n\n yy, xx = np.mgrid[:data.shape[0], :data.shape[1]]\n points = np.array([yy[~mask], xx[~mask]]).T\n interp = interpolate.CloughTocher2DInterpolator(points, data[~mask])\n result = data.copy()\n result[mask] = interp(np.array([yy[mask], xx[mask]]).T)\n return result","def interpolate(self, **kwargs): # noqa: PR01\n return DataFrameDefault.register(pandas.DataFrame.interpolate)(self, **kwargs)","def interpolate_none(self):\n\n # Reset processed data\n self.u_processed_mps = np.copy(self.u_mps)\n self.v_processed_mps = np.copy(self.v_mps)\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan","def ts_resample(self):\n try:\n ts_freq = pd.DataFrame(\n index=pd.date_range(self.ts_df.index[0], self.ts_df.index[len(self.ts_df) - 1], freq=self.freq),\n columns=['dummy'])\n except ValueError:\n self._uvts_cls_logger.exception(\"Exception occurred, possibly incompatible frequency!\")\n sys.exit(\"STOP\")\n\n if self.fill_method == 'ffill':\n self.ts_df = ts_freq.join(self.ts_df).drop(['dummy'], axis=1)\n self.ts_df.y = self.ts_df.y.fillna(method='ffill')\n # if np.isnan ( self.ts_df.y ).any ():\n # self.ts_df.y = self.ts_df.y.fillna ( method='bfill' )\n else: # interp\n xp = np.linspace(0, self.ts_df.size, self.ts_df.size, endpoint=False)\n fp = self.ts_df['y']\n # join\n self.ts_df = ts_freq.join(self.ts_df).drop(['dummy'], axis=1)\n # pick new points\n x = np.linspace(0, ts_freq.size, ts_freq.size, endpoint=False)\n x = x[self.ts_df['y'].isna()]\n print(x.size)\n print(x)\n\n # put the values\n self.ts_df.y[self.ts_df['y'].isna()] = np.interp(x, xp, fp)\n\n if np.isnan(self.ts_df.y).any():\n self._uvts_cls_logger.warning(\"Some NaN found, something went wrong, check the data!\")\n sys.exit(\"STOP\")\n\n self._uvts_cls_logger.info(\"Time series resampled at frequency: \" + str(self.ts_df.index.freq) +\n \". New shape of the data: \" + str(self.ts_df.shape))\n self._uvts_cls_logger.info(\"Using time series data of range: \" + str(min(self.ts_df.index)) + ' - ' + str(\n max(self.ts_df.index)) + \" and shape: \" + str(self.ts_df.shape))\n\n return self","def interpolate_missing_data(times, fluxes, cadences=None):\n first_time = times[0]\n\n if cadences is not None:\n # Median time between cadences\n dt = np.median(np.diff(times) / np.diff(cadences))\n cadence_indices = cadences - cadences[0]\n else:\n # Find typical time between cadences:\n dt = np.median(np.diff(times))\n # Approximate the patchy grid of integer cadence indices,\n # i.e.: (0, 1, 3, 4, 5, 8, ...)\n cadence_indices = np.rint((times - first_time)/dt)\n\n # Find missing cadence indices if that grid were complete\n expected_cadence_indices = set(np.arange(cadence_indices.min(),\n cadence_indices.max()))\n missing_cadence_indices = expected_cadence_indices.difference(set(cadence_indices))\n # Convert the missing cadences to times\n missing_times = first_time + np.array(list(missing_cadence_indices))*dt\n\n # Interpolate to find fluxes at missing times\n interp_fluxes = np.interp(missing_times, times, fluxes)\n\n # Combine the interpolated and input times, fluxes\n interpolated_fluxes = np.concatenate([fluxes, interp_fluxes])\n interpolated_times = np.concatenate([times, missing_times])\n\n # Sort the times, fluxes, so that you can compute the ACF on them:\n sort_by_time = np.argsort(interpolated_times)\n interpolated_fluxes = interpolated_fluxes[sort_by_time]\n interpolated_times = interpolated_times[sort_by_time]\n return interpolated_times, interpolated_fluxes","def ffill_nans(df):\n return df.fillna(method='ffill', axis=0)","def interpolate_linear(self, known_coords, known_values, interp_coords, groupname):\n\t\t#First need to reshape known_coords and known_values\n\t\tn_params = self.signal[groupname]['dimension']\n\t\tknown_coords = np.reshape( known_coords, (-1,n_params) )\n\t\tknown_values = np.reshape( known_values, (-1) )\t\t\n\t\treturn griddata(known_coords, known_values, interp_coords, method='linear')","def ts_transform(data, ts_col, date_col=None, fill_zeros=False, fill_nan=False, fix_trend=False, lag_differentiation=1, log_transform=False, ma_transform=False, ma_periods=2,\n accumulate_transform=False, accumulation_periods=None):\n df = data.copy(deep=True)\n\n if fill_zeros is True:\n df[df[ts_col] == 0] = np.nan\n if fill_nan is True:\n if df[ts_col].isna().sum() != 0:\n print(df[ts_col].isna().sum(), ' values will be interpolated \\n', 'Of a total of ', df[ts_col].shape[0])\n df[ts_col] = df[ts_col].interpolate()\n else:\n print('No NaN Values Found')\n\n if log_transform is True:\n if any(df[ts_col] == 0):\n print('Warning, ', ts_col, ' contains ', (df[ts_col] == 0).sum(), ' Zeros, Transformation will be done with log(1+ Value)')\n df[ts_col] = np.log(df[ts_col] + 1)\n else:\n df[ts_col] = np.log(df[ts_col])\n\n if fix_trend is True:\n df[ts_col] = df[ts_col] - df[ts_col].shift(lag_differentiation)\n\n if ma_transform is True:\n df[ts_col] = df[ts_col].rolling(ma_periods).mean()\n if fill_nan is True:\n df = df.dropna(subset=[ts_col])\n\n if accumulate_transform is True:\n if accumulation_periods is None:\n print('Accumulation periods are not defined!')\n df[ts_col] = df[ts_col].rolling(accumulation_periods).sum()\n\n if date_col is not None:\n df.set_index([date_col], inplace=True)\n print('Index set as ', type(df.index))\n\n return df[ts_col]","def interpolate_linear(self, transect):\n\n u = np.copy(self.u_mps)\n v = np.copy(self.v_mps)\n\n valid = np.isnan(u) == False\n\n # Check for valid data\n if sum(valid) > 1 and sum(self.valid_data[0, :]) > 1:\n\n # Compute ens_time\n ens_time = np.nancumsum(transect.date_time.ens_duration_sec)\n\n # Apply linear interpolation\n self.u_processed_mps = np.interp(x=ens_time,\n xp=ens_time[self.valid_data[0, :]],\n fp=u[self.valid_data[0, :]],\n left=np.nan,\n right=np.nan)\n # Apply linear interpolation\n self.v_processed_mps = np.interp(x=ens_time,\n xp=ens_time[self.valid_data[0, :]],\n fp=v[self.valid_data[0, :]],\n left=np.nan,\n right=np.nan)","def interpolate_arrays(t, idx, x_array, time_array):\n x1 = np.array([x_array[i, idx[i] - 1] for i in range(len(idx))])\n x2 = np.array([x_array[i, idx[i]] for i in range(len(idx))])\n t1 = np.array([time_array[i, idx[i] - 1] for i in range(len(idx))])\n t2 = np.array([time_array[i, idx[i]] for i in range(len(idx))])\n ta = t * np.ones(time_array.shape[0])\n return x1 + np.multiply(np.divide(x2-x1, t2-t1), ta - t1)","def test_interpolate_values_1_hour_gap(self, forcing_processor):\n forcing_processor.data = {}\n forcing_processor.data['air_temperature'] = [\n (datetime.datetime(2011, 9, 25, 9, 0, 0), 215.0),\n (datetime.datetime(2011, 9, 25, 10, 0, 0), None),\n (datetime.datetime(2011, 9, 25, 11, 0, 0), 235.0),\n ]\n forcing_processor.interpolate_values('air_temperature', 1, 1)\n expected = (datetime.datetime(2011, 9, 25, 10, 0, 0), 225.0)\n assert forcing_processor.data['air_temperature'][1] == expected","def interpolate(timepoint_defined, signal, interp_type, TR):\n\n timepoint_defined = np.array(timepoint_defined)\n\n true_inds = np.where(timepoint_defined == True)[0]\n false_inds = np.where(timepoint_defined == False)[0]\n\n\n signal_copy = np.array(signal)\n\n if interp_type == 'linear':\n\n #Still need to handle beginning/end cases\n\n for temp_timepoint in false_inds:\n\n\n #past_timepoint = true_inds[np.sort(np.where(true_inds < temp_timepoint)[0])[-1]]\n #future_timepoint = true_inds[np.sort(np.where(true_inds > temp_timepoint)[0])[0]]\n\n\n #Be sure there is at least one future timepoint and one past timepoint.\n #If there isn't, then grab either two past or two future timepoints and use those\n #for interpolation. If there aren't even two total past + future timepoints, then\n #just set the output to 0. Could also set the output to be unadjusted, but this\n #is a way to make the issue more obvious.\n temp_past_timepoint = np.sort(np.where(true_inds < temp_timepoint)[0])\n temp_future_timepoint = np.sort(np.where(true_inds > temp_timepoint)[0])\n\n #If we don't have enough data to interpolate/extrapolate\n if len(temp_past_timepoint) + len(temp_future_timepoint) < 2:\n\n signal_copy[temp_timepoint] = 0\n\n #If we do have enough data to interpolate/extrapolate\n else:\n\n if len(temp_past_timepoint) == 0:\n past_timepoint = true_inds[temp_future_timepoint[1]]\n else:\n past_timepoint = true_inds[temp_past_timepoint[-1]]\n\n if len(temp_future_timepoint) == 0:\n future_timepoint = true_inds[temp_past_timepoint[-2]]\n else:\n future_timepoint = true_inds[temp_future_timepoint[0]]\n\n #Find the appopriate past/future values\n past_value = signal_copy[int(past_timepoint)]\n future_value = signal_copy[int(future_timepoint)]\n\n #Use the interp1d function for interpolation\n interp_object = interp.interp1d([past_timepoint, future_timepoint], [past_value, future_value], bounds_error=False, fill_value='extrapolate')\n signal_copy[temp_timepoint] = interp_object(temp_timepoint).item(0)\n\n return signal_copy\n\n\n #For cubic spline interpolation, instead of taking the past/future timepoint\n #we will just take the closest 5 timepoints. If there aren't 5 timepoints, we will\n #set the output to 0\n if interp_type == 'cubic_spline':\n\n sorted_good = np.sort(signal_copy[true_inds])\n min_bound = sorted_good[0]\n max_bound = sorted_good[-1]\n\n #Continue if there are at least 5 good inds\n true_inds_needed = 5\n if len(true_inds) >= true_inds_needed:\n\n for temp_timepoint in false_inds:\n\n closest_inds = true_inds[np.argsort(np.absolute(true_inds - temp_timepoint))]\n closest_vals = signal_copy[closest_inds.astype(int)]\n interp_object = interp.interp1d(closest_inds, closest_vals, kind = 'cubic', bounds_error=False, fill_value='extrapolate')\n signal_copy[temp_timepoint.astype(int)] = interp_object(temp_timepoint).item(0)\n\n min_bound_exceded = np.where(signal_copy < min_bound)[0]\n if len(min_bound_exceded) > 0:\n\n signal_copy[min_bound_exceded] = min_bound\n\n max_bound_exceded = np.where(signal_copy > max_bound)[0]\n if len(max_bound_exceded) > 0:\n\n signal_copy[max_bound_exceded] = max_bound\n\n #If there aren't enough good timepoints, then set the bad timepoints = 0\n else:\n\n signal_copy[false_inds.astype(int)] = 0\n\n\n return signal_copy\n\n\n if interp_type == 'spectral':\n\n signal_copy = spectral_interpolation(timepoint_defined, signal_copy, TR)\n\n return signal_copy","def log_interpolate(array):\n idx = np.arange(0, len(array))\n log_array = np.log(array.astype(np.float32) + 1e-1)\n interp_array = np.interp(\n x=idx, xp=idx[~np.isnan(array)], fp=log_array[~np.isnan(array)]\n )\n return np.round(np.exp(interp_array)).astype(np.int32)","def linear_interp(x,y,xi) :\n \n f = interp1d(x,y,kind='linear')\n yi = f(xi)\n \n return yi","def test_1d_linear_interpolation_basic(self):\n\n # Define pixel centers along each direction\n x = [1.0, 2.0, 4.0]\n\n # Define array with corresponding values\n A = numpy.zeros((len(x)))\n\n # Define values for each xas a linear function\n for i in range(len(x)):\n A[i] = linear_function(x[i], 0)\n\n # Test first that original points are reproduced correctly\n for i, xi in enumerate(x):\n val = interpolate1d(x, A, [xi], mode='linear')[0]\n ref = linear_function(xi, 0)\n assert numpy.allclose(val, ref, rtol=1e-12, atol=1e-12)\n\n # Then test that genuinly interpolated points are correct\n xis = numpy.linspace(x[0], x[-1], 10)\n points = xis\n\n vals = interpolate1d(x, A, points, mode='linear')\n refs = linear_function(points, 0)\n assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)\n\n # Exercise bounds_error flag\n vals = interpolate1d(x, A, points, mode='linear',\n bounds_error=True)\n refs = linear_function(points, 0)\n assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)","def _const_fill(series: TimeSeries, fill: float = 0) -> TimeSeries:\n\n return TimeSeries.from_times_and_values(\n series.time_index,\n series.pd_dataframe().fillna(value=fill),\n freq=series.freq,\n columns=series.columns,\n static_covariates=series.static_covariates,\n hierarchy=series.hierarchy,\n )","def interpolate(i0, d0, i1, d1):\n if i0 == i1:\n return [d0]\n values = []\n a = (d1 - d0) / (i1 - i0)\n d = d0\n for i in range(i0,i1+1):\n values.append(d)\n d = d + a\n return values","def _handle_missing_vars(netcdf_start_date, netcdf_end_date, tmp_dir):\n hrs_range = arrow.Arrow.range(\n \"hour\", netcdf_start_date.shift(days=-1), netcdf_end_date.shift(hours=+23)\n )\n missing_var_hrs = {}\n for netcdf_hr in hrs_range:\n nemo_date = f\"y{netcdf_hr.year}m{netcdf_hr.month:02d}d{netcdf_hr.day:02d}\"\n nemo_hr_ds_path = tmp_dir / f\"gemlam_{nemo_date}_{netcdf_hr.hour:03d}.nc\"\n with xarray.open_dataset(nemo_hr_ds_path) as ds:\n missing_vars = ds.attrs.get(\"missing_variables\")\n if missing_vars is None:\n for var, missing_hrs in missing_var_hrs.copy().items():\n if len(missing_hrs) <= 4:\n _interpolate_intra_day_missing_var_hrs(var, missing_hrs)\n del missing_var_hrs[var]\n else:\n _interpolate_inter_day_missing_var_hrs(var, missing_hrs)\n del missing_var_hrs[var]\n else:\n for var in missing_vars.split(\", \"):\n try:\n missing_var_hrs[var].append(\n {\"hr\": netcdf_hr, \"ds_path\": nemo_hr_ds_path}\n )\n except KeyError:\n missing_var_hrs[var] = [\n {\"hr\": netcdf_hr, \"ds_path\": nemo_hr_ds_path}\n ]\n for missing_hr in missing_var_hrs.get(\"solar\", []).copy():\n # Special handling for 1feb07 to 23feb07 period in which there are no solar radiation values\n in_feb07_solar_gap = missing_hr[\"hr\"].is_between(\n arrow.get(\"2007-02-01\"), arrow.get(\"2007-02-24\"), bounds=\"[)\"\n )\n if not in_feb07_solar_gap:\n break\n # Calculate solar radiation from cloud fraction and time of day sun angle\n solar = _calc_solar_from_clouds_and_angle(\n missing_hr[\"hr\"], missing_hr[\"ds_path\"]\n )\n solar = solar.astype(\"float32\", casting=\"same_kind\")\n solar.name = \"solar\"\n solar.attrs[\"level\"] = \"surface\"\n solar.attrs[\"long_name\"] = \"Downward Short-Wave Radiation Flux\"\n solar.attrs[\"standard_name\"] = \"net_downward_shortwave_flux_in_air\"\n solar.attrs[\"units\"] = \"W/m^2\"\n with xarray.open_dataset(missing_hr[\"ds_path\"]) as ds:\n ds_w_solar = ds.copy(deep=True).assign(solar=solar)\n missing_vars = ds_w_solar.attrs[\"missing_variables\"].split(\", \")\n missing_vars.remove(\"solar\")\n if missing_vars:\n ds_w_solar.attrs[\"missing_variables\"] = \", \".join(missing_vars)\n else:\n del ds_w_solar.attrs[\"missing_variables\"]\n _write_netcdf_file(ds_w_solar, missing_hr[\"ds_path\"])\n missing_var_hrs[\"solar\"].remove(missing_hr)\n else:\n if \"solar\" in missing_var_hrs and not missing_var_hrs[\"solar\"]:\n del missing_var_hrs[\"solar\"]\n if missing_var_hrs:\n raise ValueError(f\"missing variables at end of date range: {missing_var_hrs}\")","def fix_missing_data(contour_data_list):\n contour_data = np.array(contour_data_list)\n if contour_data.any() == \"\":\n logger.warning(\" Missing values detected.\")\n missing_values = np.where(contour_data == \"\")[0]\n if missing_values.shape[0] > 1:\n logger.warning(\" More than one value missing, fixing this isn't implemented yet...\")\n else:\n logger.warning(\" Only one value missing.\")\n missing_index = missing_values[0]\n missing_axis = missing_index % 3\n if missing_axis == 0:\n logger.warning(\" Missing value in x axis: interpolating.\")\n if missing_index > len(contour_data) - 3:\n lower_val = contour_data[missing_index - 3]\n upper_val = contour_data[0]\n elif missing_index == 0:\n lower_val = contour_data[-3]\n upper_val = contour_data[3]\n else:\n lower_val = contour_data[missing_index - 3]\n upper_val = contour_data[missing_index + 3]\n contour_data[missing_index] = 0.5 * (lower_val + upper_val)\n elif missing_axis == 1:\n logger.warning(\" Missing value in y axis: interpolating.\")\n if missing_index > len(contour_data) - 2:\n lower_val = contour_data[missing_index - 3]\n upper_val = contour_data[1]\n elif missing_index == 0:\n lower_val = contour_data[-2]\n upper_val = contour_data[4]\n else:\n lower_val = contour_data[missing_index - 3]\n upper_val = contour_data[missing_index + 3]\n contour_data[missing_index] = 0.5 * (lower_val + upper_val)\n else:\n logger.warning(\" Missing value in z axis: taking slice value\")\n temp = contour_data[2::3].tolist()\n temp.remove(\"\")\n contour_data[missing_index] = np.min(np.array(temp, dtype=np.double))\n return contour_data","def interpolate(self, step=1, kind=1):\n vt = Reference.interpolate(self, step)\n if vt is False:\n return False\n # Value sequence\n # starting from the oldest time minus step\n oldi = self.get_time(vt[0] - step)\n # Check if we have enough points to interpolate\n if len(self) - oldi < 5:\n return False\n # If possible, go back one more point, for interpolation safety\n if oldi > 1:\n oldi -= 1\n # Decode values and separate time and value vectors\n dat = self[oldi:]\n# print 'Getting data',self.path,dat,vt\n dat = np.array(dat)\n dat = dat.transpose()\n # Build a linear spline using vt points as knots\n #f=LSQUnivariateSpline(dat[0],dat[1],vt, k=kind)\n # Do a linear fitting\n (slope, const), res, rank, sing, rcond = np.polyfit(\n dat[0], dat[1], kind, full=True)\n # Build a vectorized evaluator\n f = np.vectorize(lambda x: slope * x + const)\n while vt[0] < dat[0][0] and len(vt) > 1:\n vt = vt[1:]\n while vt[-1] > dat[0][-1] and len(vt) > 1:\n vt = vt[:-1]\n if len(vt) <= 1:\n return False\n try:\n # Interpret time series\n out = f(vt)\n except:\n print 'Array.interpolate', self.path, vt, dat\n raise\n # Encode in (t,v) append-able list\n out = np.array([vt, out]).transpose()\n self.summary.commit(out)\n return True","def interp1d(f0, kind=\"slinear\"):\n ndim = f0.ndim\n if len(f0) != f0.size:\n raise RuntimeError(\"1d array is only supported\")\n continuous_f0 = f0.flatten()\n nonzero_indices = np.where(continuous_f0 > 0)[0]\n\n # Nothing to do\n if len(nonzero_indices) <= 0:\n return f0\n\n # Need this to insert continuous values for the first/end silence segments\n continuous_f0[0] = continuous_f0[nonzero_indices[0]]\n continuous_f0[-1] = continuous_f0[nonzero_indices[-1]]\n\n # Build interpolation function\n nonzero_indices = np.where(continuous_f0 > 0)[0]\n interp_func = interpolate.interp1d(\n nonzero_indices, continuous_f0[continuous_f0 > 0], kind=kind\n )\n\n # Fill silence segments with interpolated values\n zero_indices = np.where(continuous_f0 <= 0)[0]\n continuous_f0[zero_indices] = interp_func(zero_indices)\n\n if ndim == 2:\n return continuous_f0[:, None]\n return continuous_f0","def dask_gd2_nanfill(xx, yy, z_array, algorithm='cubic', **kwargs):\n n_jobs = kwargs.pop(\"n_jobs\", 4)\n chunk_size = kwargs.get(\"chunk_size\", int(xx.size / (n_jobs - 1)))\n # make dask arrays\n dask_xyz = da.from_array((xx, yy, z_array), chunks=(3, chunk_size, \"auto\"), name=\"dask_all\")\n dask_xx = dask_xyz[0,:,:]\n dask_yy = dask_xyz[1,:,:]\n dask_zz = dask_xyz[2,:,:]\n\n # select only valid values\n dask_valid_x1 = dask_xx[~da.isnan(dask_zz)]\n dask_valid_y1 = dask_yy[~da.isnan(dask_zz)]\n dask_valid_z1 = dask_zz[~da.isnan(dask_zz)]\n\n # interpolate for missing values\n return dask_interpolate(dask_valid_x1, dask_valid_y1, dask_valid_z1, dask_xx, dask_yy, algorithm=algorithm, **kwargs)","def replace_nan(data):\r\n lst_ind = np.array(['valence_intensity', 'anger_intensity',\r\n 'fear_intensity', 'sadness_intensity', 'joy_intensity'])\r\n for i in lst_ind:\r\n native = data[:][i]\r\n avg = np.nanmean(native)\r\n data[:][i] = np.where(np.isnan(native), avg, native)\r\n return data","def get_signal_with_nans(self):\n pairs = zip(self.time_scale, self.signal)\n nan_pairs = zip(self.nan_times,[float('nan')]*len(self.nan_times))\n pairs.extend(nan_pairs)\n pairs.sort()\n time_scale, signal = zip(*pairs)\n return np.array(time_scale), np.array(signal)","def ffill_1d_nb(a):\n out = np.empty_like(a, dtype=np.float_)\n lastval = a[0]\n for i in range(a.shape[0]):\n if np.isnan(a[i]):\n out[i] = lastval\n else:\n lastval = out[i] = a[i]\n return out","def interpolate_eleMean(model):\n # Get mean of columns (data at the same elevation) without taking int account NaNs\n el_mean = nanmean(model,axis=0)\n #print(el_mean) \n # Find indices for NaNs, and replace them by the column mean\n ind_nan = np.where(np.isnan(model))\n model[ind_nan] = np.take(el_mean,ind_nan[1])\n\n return model","def impute_cumulative_array(array):\n array = np.array(array).copy()\n array = convert_non_monotonic_to_nan(array)\n array = log_interpolate(array)\n return array","def fill_nans(data):\n for col in data.columns:\n data[col].fillna(-999, inplace=True)","def missing_values_ratio(series: TimeSeries) -> float:\n\n return series.pd_dataframe().isnull().sum().mean() / len(series)","def _index_to_nan_fast(data, existing_nans, to_nan):\n index_nan = []\n randgen = (np.random.choice(len(data)) for _ in cnt(start=1))\n for i in range(to_nan):\n ix = next(filter(lambda x: x not in existing_nans and x not in index_nan, randgen))\n index_nan.append(ix)\n data_imp = data.copy()\n data_imp[index_nan] = np.nan\n return data_imp, index_nan","def interpolate_time(x, xp, yp, clamp=True):\n x = np.asarray(x)\n \n # Specific case for empty domain\n if xp[0] == xp[-1] or len(xp)<2:\n if len(x.shape) == 0:\n return yp[0]\n else:\n return [yp[0],]*len(x)\n\n delta = np.cumsum(yp[1:] - yp[:-1])\n delta = np.insert(delta, 0, 0)\n dtype = delta.dtype\n\n interpolated = np.array(interpolate_number(x, xp, delta.astype(int), clamp))\n return yp[0] + interpolated.astype(delta.dtype)","def fix_null_vals(dataset):\n\tprint(\"\\tFixing null values\")\n\n\tif not dataset.isnull().any().any():\n\t\treturn dataset\n\telse:\n\t\treturn dataset.fillna(method=\"ffill\")","def interpolate(self, column_name):\n self.check_for_column(column_name)\n\n start_date = min(self.data.index)\n end_date = max(self.data.index)\n date_range = pd.date_range(start_date, end_date, freq='H')\n self.data = self.data.reindex(date_range)\n column = self.data[column_name]\n column = column.interpolate()\n self.data[column_name] = column","def interpolate(data, x):\n n = len(data)\n\n if isinstance(data, dict):\n if x in data:\n return S(data[x])\n X, Y = list(zip(*data.items()))\n else:\n if isinstance(data[0], tuple):\n X, Y = list(zip(*data))\n if x in X:\n return S(Y[X.index(x)])\n else:\n if x in range(1, n + 1):\n return S(data[x - 1])\n Y = list(data)\n X = list(range(1, n + 1))\n\n try:\n return interpolating_poly(n, x, X, Y).expand()\n except ValueError:\n d = Dummy()\n return interpolating_poly(n, d, X, Y).expand().subs(d, x)","def linear_slope(self, dim=\"time\", nan_policy=\"none\"):\n return linear_slope(self._obj, dim=dim, nan_policy=nan_policy)","def test_linear_interpolation_range(self):\n\n for x in [[1.0, 2.0, 4.0], [-20, -19, 0], numpy.arange(200) + 1000]:\n for y in [[5.0, 9.0], [100, 200, 10000]]:\n\n # Define ny by nx array with corresponding values\n A = numpy.zeros((len(x), len(y)))\n\n # Define values for each x, y pair as a linear function\n for i in range(len(x)):\n for j in range(len(y)):\n A[i, j] = linear_function(x[i], y[j])\n\n # Test that linearly interpolated points are correct\n xis = numpy.linspace(x[0], x[-1], 100)\n etas = numpy.linspace(y[0], y[-1], 100)\n points = combine_coordinates(xis, etas)\n\n vals = interpolate2d(x, y, A, points, mode='linear')\n refs = linear_function(points[:, 0], points[:, 1])\n assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)","def interp_ts(cls, df_in, ts_col, interp_ts):\n\n interp=interp1d(df_in['timestamp'],\n df_in[ts_col],\n kind='previous',\n fill_value='extrapolate')\n df_interp = pd.DataFrame({\n 'date' : [datetime.utcfromtimestamp(t) for t in interp_ts],\n 'timestamp' : interp_ts,\n ts_col : interp(interp_ts)\n })\n\n return df_interp","def impute(sd, imputation_values, method='ffill', mask=True):\n ts = pd.read_csv(os.path.join(sd, 'timeseries.csv'))\n ts = ts.set_index('CHARTTIME')\n\n variables = list(imputation_values.keys())\n\n if mask:\n # Create an imputation mask\n ts_mask = ts.mask(ts.notna(), 1)\n ts_mask = ts_mask.mask(ts.isna(), 0)\n ts_mask = ts_mask.drop(['LOS_HOURS', 'TARGET_COARSE', 'TARGET_FINE'],\n axis=1)\n ts_mask = ts_mask.add_prefix('mask_')\n\n # Make sure that the first row contains values such that we can\n # do a forward fill impute\n for var in variables:\n if math.isnan(ts[var].iloc[0]):\n if var == 'WEIGHT' or var == 'HEIGHT':\n ts[var].iloc[0] = imputation_values[var] \\\n [str(int(round(ts['GESTATIONAL_AGE_DAYS'].iloc[0] / 7)\n ))]\n else:\n ts[var].iloc[0] = imputation_values[var]\n\n\n if method == 'ffill':\n # Impute through forward filling\n ts = ts.fillna(method='ffill')\n elif method == 'zeros':\n # Impute through filling with zeros\n ts = ts.fillna(value=0)\n else:\n raise ValueError(f'{method} must be one of \"ffill\" or \"zeros\"')\n\n if mask:\n # Concatenate the timeseries with the imputation mask\n ts = pd.concat([ts, ts_mask], axis=1)\n\n ts.to_csv(os.path.join(sd, 'timeseries_imputed.csv'))","def interpolate(self, N):\n self.upsampling_rate *= N\n self.data = np.array([interpolate_spline(x,N) for x in self.data])","def interpolate(self, t):\n try:\n n = len(self.t)\n except TypeError:\n # self.t is not a sequence. Re-raise the exception\n # with an appropriate error message.\n raise TypeError(\"Please run the simulation first\")\n else:\n if (n < 2):\n raise ValueError(\"Not enough simulation steps\")\n tmin = self.t[0]\n tmax = self.t[n-1]\n if t < tmin or t > tmax:\n raise ValueError(\"Requested time is outside the simulated interval\")\n if self.adaptiveStepSize:\n nbelow = bisect_right(self.t, t) - 1 \n else:\n dt = (tmax - tmin)*1.0/(n - 1)\n nbelow = int(math.floor((t - tmin)/dt))\n nabove = nbelow + 1\n if nabove >= n:\n nabove = n - 1\n nbelow = nabove - 1\n x = interpolate_Hermite(t, self.t[nbelow], self.x[nbelow], self.v[nbelow],\n self.t[nabove], self.x[nabove], self.v[nabove])\n v = interpolate_linear(t, self.t[nbelow], self.v[nbelow],\n self.t[nabove], self.v[nabove])\n return x, v","def test_interpolate_values_2_hour_gap(self, forcing_processor):\n forcing_processor.data = {}\n forcing_processor.data['air_temperature'] = [\n (datetime.datetime(2011, 9, 25, 9, 0, 0), 215.0),\n (datetime.datetime(2011, 9, 25, 10, 0, 0), None),\n (datetime.datetime(2011, 9, 25, 11, 0, 0), None),\n (datetime.datetime(2011, 9, 25, 12, 0, 0), 230.0),\n ]\n forcing_processor.interpolate_values('air_temperature', 1, 2)\n expected = (datetime.datetime(2011, 9, 25, 10, 0, 0), 220.0)\n assert forcing_processor.data['air_temperature'][1] == expected\n expected = (datetime.datetime(2011, 9, 25, 11, 0, 0), 225.0)\n assert forcing_processor.data['air_temperature'][2] == expected","def missing_data(self, plot=True):\n # Set some of the observations to missing\n self.remove_data()\n # Create empty arrays to store values\n F = np.zeros(len(self.y))\n a = np.zeros(len(self.y))\n v = np.zeros(len(self.y))\n P = np.zeros(len(self.y))\n # Initialize at the initial values parsed to the class\n P[0] = self.pardict['P1']\n sigma_eps2 = self.pardict['sigma_eps2']\n sigma_eta2 = self.pardict['sigma_eta2']\n # initialise a for estimated model\n if self.var_name != 'Volume of Nile':\n a[0] = self.y[0]\n # Iterate\n for t in range(0, len(self.y) - 1):\n v[t] = np.nan_to_num(self.y[t] - a[t])\n F[t] = P[t] + sigma_eps2 if np.isfinite(self.y[t]) else np.inf\n # K is defined as ratio of P and F\n Kt = P[t] / F[t] if np.isfinite(self.y[t]) else 0\n a[t + 1] = a[t] + Kt * v[t]\n P[t + 1] = P[t] * (1 - Kt) + sigma_eta2\n v[-1] = self.y[-1] - a[-1]\n F[-1] = P[-1] + sigma_eps2\n # Obtain smoothed state\n # Obtain all time values for L\n L = self.pardict['sigma_eps2'] / F\n index_missing = np.argwhere(np.isnan(self.y))\n L[index_missing] = 1\n\n # Do the recursion for r\n r = np.zeros(len(self.y))\n N = np.zeros(len(self.y))\n V = np.zeros(len(self.y))\n\n for t in np.arange(len(self.y) - 1, 0, -1):\n r[t - 1] = v[t] / F[t] + L[t] * r[t]\n for t in np.arange(len(self.y) - 1, 0, -1):\n N[t - 1] = 1 / F[t] + L[t] ** 2 * N[t]\n for t in np.arange(len(self.y) - 1, 0, -1):\n V[t] = P[t] - P[t] ** 2 * N[t - 1]\n\n # Do the recursion for alpha\n alphas = np.zeros(len(self.y))\n alphas[0] = a[t]\n for t in range(1, len(self.y) - 1):\n alphas[t] = a[t] + P[t] * r[t - 1]\n alphas[-1] = np.nan\n\n if plot:\n fig_name = self.var_name + 'Fig25.pdf'\n plot_fig2_5(self.times, self.y, a, P, alphas, V, fname=fig_name, var_name=self.var_name)\n # Restore data\n self.reset_data()","def fill_lower_part(data, index, aver_num=3):\n if type(data) is LidarDataset:\n d = data['data']\n else:\n d = data\n to_fill = np.ma.masked_invalid(d[..., index:index+aver_num]).mean(axis=-1)[..., np.newaxis]\n d[..., :index] = to_fill","def replace_nan(data_jets):\n \n data_mean = np.empty_like(data_jets)\n data_median = np.empty_like(data_jets)\n data_null = np.empty_like(data_jets)\n \n for jet in range(4):\n # Replace Remaining undefined values by Mean, median or zero\n data_mean[jet] = np.where(np.isnan(data_jets[jet]), np.nanmean(data_jets[jet], axis=0), data_jets[jet])\n data_median[jet] = np.where(np.isnan(data_jets[jet]), np.nanmedian(data_jets[jet], axis=0), data_jets[jet])\n data_null[jet] = np.where(np.isnan(data_jets[jet]), np.float64(0), data_jets[jet])\n \n return data_mean, data_median, data_null","def interpolate_inverse(series, **options):\n inverse = pd.Series(series.index, index=series.values)\n interp_func = interpolate(inverse, **options)\n return interp_func","def FillNA(df, method: str = 'ffill', window: int = 10):\n method = str(method).replace(\" \", \"_\")\n\n if method == 'zero':\n return fill_zero(df)\n\n elif method == 'ffill':\n return fill_forward(df)\n\n elif method == 'mean':\n return fill_mean(df)\n\n elif method == 'median':\n return fill_median(df)\n\n elif method == 'rolling_mean':\n return rolling_mean(df, window=window)\n\n elif method == 'rolling_mean_24':\n return rolling_mean(df, window=24)\n\n elif method == 'ffill_mean_biased':\n return biased_ffill(df)\n\n elif method == 'fake_date':\n return fake_date_fill(df, back_method='slice')\n\n elif method in df_interpolate_full:\n df = df.interpolate(method=method, order=5).fillna(method='bfill')\n if df.isnull().values.any():\n df = fill_forward(df)\n return df\n\n elif method == 'IterativeImputer':\n cols = df.columns\n indx = df.index\n try:\n from sklearn.experimental import enable_iterative_imputer # noqa\n except Exception:\n pass\n from sklearn.impute import IterativeImputer\n\n df = IterativeImputer(random_state=0, max_iter=100).fit_transform(df)\n if not isinstance(df, pd.DataFrame):\n df = pd.DataFrame(df)\n df.index = indx\n df.columns = cols\n return df\n\n elif method == 'IterativeImputerExtraTrees':\n cols = df.columns\n indx = df.index\n try:\n from sklearn.experimental import enable_iterative_imputer # noqa\n except Exception:\n pass\n from sklearn.ensemble import ExtraTreesRegressor\n from sklearn.impute import IterativeImputer\n\n df = IterativeImputer(\n ExtraTreesRegressor(n_estimators=10, random_state=0),\n random_state=0,\n max_iter=100,\n ).fit_transform(df)\n if not isinstance(df, pd.DataFrame):\n df = pd.DataFrame(df)\n df.index = indx\n df.columns = cols\n return df\n\n elif method == 'KNNImputer':\n cols = df.columns\n indx = df.index\n from sklearn.impute import KNNImputer\n\n df = KNNImputer(n_neighbors=5).fit_transform(df)\n if not isinstance(df, pd.DataFrame):\n df = pd.DataFrame(df)\n df.index = indx\n df.columns = cols\n return df\n\n elif method is None or method == 'None':\n return df\n\n else:\n print(f\"FillNA method `{str(method)}` not known, returning original\")\n return df","def interp_spectrum(spectrum, energies, new_energies):\n I = interp1d(energies, spectrum, bounds_error=False)\n new_spec = I(new_energies)\n if np.any(np.isnan( new_spec)):\n print(\"There are Nan in your spectrum...\")\n new_spec[ new_spec < 0] = 0\n return new_spec","def loginterpolate(x_vector, y_vector, xout, replace_nan=np.nan):\n interpolant = interpolate.interp1d(np.log10(x_vector), np.log10(y_vector),\n bounds_error=False,\n fill_value=np.nan)\n yout = 10. ** interpolant(np.log10(xout))\n if replace_nan != np.nan:\n yout[np.isnan(yout)] = replace_nan\n return yout","def stupidImputer(vals, ti, ws):\n imputed = vals.copy()\n\n for s in range(0, vals.shape[1]):\n for t in range(ti, ti + ws):\n if np.isnan(imputed[t, s]):\n lastObsT = t-1\n while np.isnan(imputed[lastObsT, s]):\n lastObsT = lastObsT - 1\n\n imputed[t, s] = imputed[lastObsT, s]\n\n return imputed","def nan_interp(A):\n\tni,nj = np.shape(A)\n\t# extend edges of A by one\n\tA = np.concatenate((np.array([A[:,0]]).transpose(),A,np.array([A[:,-1]]).transpose()),axis=1)\n\tA = np.concatenate((np.array([A[0,:]]),A,np.array([A[-1,:]])),axis=0)\n\t\n\t#nit = 0\n\t#while np.sum(np.isnan(A)) != 0:\n\t#nit+=1\n\tnanp = np.isnan(A)\n\tfor i in range(1,ni+1):\n\t\tfor j in range(1,nj+1):\n\t\t\tif nanp[i,j]:\n\t\t\t\t#\t# edges\n\t\t\t\t#\tif (i==0) & (j!=0)& (j!=nj-1):\n\t\t\t\t#\t\tb = np.array([A[i+1,j],A[i,j-1],A[i,j+1]])\n\t\t\t\t#\tif (i==ni-1) & (j!=0)& (j!=nj-1):\n\t\t\t\t#\t\tb = np.array([A[i-1,j],A[i,j-1],A[i,j+1]])\n\t\t\t\t#\tif (j==0) & (i!=0)& (i!=ni-1):\n\t\t\t\t#\t\tb = np.array([A[i-1,j],A[i+1,j],A[i,j+1]])\n\t\t\t\t#\tif (j==nj-1) & (i!=0)& (i!=ni-1):\n\t\t\t\t#\t\tb = np.array([A[i-1,j],A[i+1,j],A[i,j-1]])\n\t\t\t\t#\t# corners\n\t\t\t\t#\tif (i==0) & (j==0):\n\t\t\t\t#\t\tb = np.array([A[i+1,j],A[i,j+1]])\n\t\t\t\t#\tif (i==ni-1) & (j==0):\n\t\t\t\t#\t\tb = np.array([A[i-1,j],A[i,j+1]])\n\t\t\t\t#\tif (i==0) & (j==nj-1):\n\t\t\t\t#\t\tb = np.array([A[i+1,j],A[i,j-1]])\n\t\t\t\t#\tif (i==ni-1) & (j==nj-1):\n\t\t\t\t#\t\tb = np.array([A[i-1,j],A[i,j-1]])\n\t\t\t\t#\t# core\n\t\t\t\t#\telse:\n\t\t\t\tb = np.array([A[i-1,j],A[i,j-1],A[i+1,j],A[i,j+1]])\n\t\t\t\tsnan = np.sum(np.isnan(b))\n\t\t\t\tsb = np.nansum(b)\n\t\t\t\tA[i,j] = sb/(len(b)-snan)\n\t\t\t\t#print(i,j)\n\t# only the core matters\n\tA = A[1:ni+1,1:nj+1]\n\treturn A","def fill_forward(df):\n df = df.fillna(method='ffill')\n df = df.fillna(method='bfill').fillna(0)\n return df","def data_missing() -> ExtensionArray:\n data_matrix = np.arange(\n 2 * 10 * 10 * 3,\n dtype=np.float_,\n ).reshape(2, 10, 10, 3)\n data_matrix[0, ...] = np.NaN\n grid_points = [\n np.arange(10),\n np.arange(10) / 10,\n ]\n\n return skfda.FDataGrid(data_matrix, grid_points=grid_points)","def missing_values(self, layout={}, **kwargs):\n df = self._data.isna().astype(int)\n kwargs.update(\n {'zmin': 0, 'zmax': 1,\n 'colors': 'reds', 'ncolors': 9,\n 'xgap': 3, 'ygap': 3,\n 'showscale': False, }\n )\n\n layout = recursive_update(\n layout, updater={\n 'xaxis': {'showgrid': False, 'zeroline': False},\n 'yaxis': {'showgrid': False, 'zeroline': False},\n })\n return df.iplot.heatmap(layout=layout, **kwargs)","def interpolation(x, y, x_new, model=\"InterpolatedUnivariateSpline\", plot=False, title=\"\"):\n\n num_col = y.shape[1]\n\n if isinstance(x_new, float):\n num_row = 1\n else:\n num_row = len(x_new)\n\n y_new = np.zeros((num_row, num_col))\n y_new_dot = np.zeros((num_row, num_col))\n\n if model == \"InterpolatedUnivariateSpline\":\n\n for idx in range(0, num_col):\n\n # TODO: InterpolatedUnivariateSpline seems to have problems with multidimensional arrays\n interpolate_ = interpolate.InterpolatedUnivariateSpline(x, y[:, idx], k=3)\n y_new[:, idx] = interpolate_(x_new)\n # y_new_dot[:,idx] = spline.derivative()(x_new) #TODO: Does this work too?\n y_new_dot[:, idx] = _determine_time_derivate(interpolate_, x_new, 1)\n\n if plot == True:\n _plot_interpolation(x, y, x_new, y_new, title=title)\n\n elif model == \"interp1d\":\n\n # TODO: Extrapolation has to be handled (e.g. with fill_value=(0.0,0.0) argument)\n interpolate_ = interpolate.interp1d(x, y, kind=\"cubic\", axis=0, bounds_error=False)\n y_new = interpolate_(x_new)\n y_new_dot = _determine_time_derivate(interpolate_, x_new, num_col)\n\n if plot == True:\n _plot_interpolation(x, y, x_new, y_new, title=title)\n\n elif model == \"lagrange\":\n\n for idx in range(0, num_col):\n\n # Rescaling of data necessary, because Lagrange interpolation is numerical unstable\n xm = np.mean(x)\n ym = np.mean(y[:, idx])\n xs = np.std(x)\n ys = np.std(y[:, idx])\n xscaled = (x - xm) / xs\n yscaled = (y[:, idx] - ym) / ys\n\n # interpolate_ = interpolate.lagrange(xscaled, yscaled) # worst performance\n # interpolate_ = _lagrange2(xscaled, yscaled) # improved performance\n interpolate_ = _lagrange(xscaled, yscaled) # fastest performance\n y_new[:, idx] = interpolate_((x_new - xm) / xs) * ys + ym\n\n # Determine derivate of 'y_new'\n t_offset = 0.5\n y1 = interpolate_((x_new - t_offset - xm) / xs) * ys + ym\n y2 = interpolate_((x_new + t_offset - xm) / xs) * ys + ym\n\n y_new_dot[:, idx] = (y2 - y1) / (t_offset * 2)\n\n if plot == True:\n _plot_interpolation(x, y, x_new, y_new, title=title)\n\n elif model == \"BarycentricInterpolator\":\n\n for idx in range(0, num_col):\n\n # TODO: Is the rescaling of data necessary here?\n xm = np.mean(x)\n ym = np.mean(y[:, idx])\n xs = np.std(x)\n ys = np.std(y[:, idx])\n xscaled = (x - xm) / xs\n yscaled = (y[:, idx] - ym) / ys\n\n interpolate_ = interpolate.BarycentricInterpolator(xscaled, yscaled)\n y_new[:, idx] = interpolate_((x_new - xm) / xs) * ys + ym\n\n # Determine derivate of 'y_new'\n t_offset = 0.5\n y1 = interpolate_((x_new - t_offset - xm) / xs) * ys + ym\n y2 = interpolate_((x_new + t_offset - xm) / xs) * ys + ym\n\n y_new_dot[:, idx] = (y2 - y1) / (t_offset * 2)\n\n if plot == True:\n _plot_interpolation(x, y, x_new, y_new, title=title)\n\n # elif model == 'polyfit':\n\n # #y_new_dot = np.zeros((len(x_new), num_col))\n # degree = 2\n # poly_dot_coeff = np.zeros((degree, num_col))\n #\n\n # #TODO: Polynomial fit over the complete time period of 3 days are not convenient.\n # # Difference of up to 1000 km between original SP3 orbit data and polynomial\n # # fit.\n\n # x_ref = np.amin(x) #TODO: orb_ref_time should be part of 'orb' Dataset\n # x = (x - x_ref) * 86400\n # poly_coeff = poly.polyfit(x, y, degree)\n # y_new = poly.polyval(x_new,poly_coeff)\n\n # for idx in range(0,num_col):\n # poly_dot_coeff[:,idx] = np.polyder(poly_coeff[:,idx])\n # polynomial_dot = np.poly1d(poly_dot_coeff[:,idx])\n # y_new_dot[:,idx] = polynomial_dot((x_new - x_ref) * 86400)\n\n # if plot == True:\n # _plot_orbit_polynomial(sat, poly_coeff, x, y)\n\n return y_new, y_new_dot","def plot_tseries(*args, **kwargs) :\n data = kwargs.pop('data')\n return data.dropna().plot(x=args[0], y=args[1], **kwargs)","def _index_to_nan(data, existing_nans, to_nan):\n index_nan = np.random.choice([i for i in range(len(data)) if i not in existing_nans],\n size=to_nan, replace=False)\n data_imp = data.copy()\n data_imp[index_nan] = np.nan\n return data_imp, index_nan","def fill_gaps(\n t, order=1, extrapolate=0, frame_max=None, hdim_1_max=None, hdim_2_max=None\n):\n\n from scipy.interpolate import InterpolatedUnivariateSpline\n\n logging.debug(\"start filling gaps\")\n\n t_list = [] # empty list to store interpolated DataFrames\n\n # group by cell number and perform process for each cell individually:\n t_grouped = t.groupby(\"cell\")\n for cell, track in t_grouped:\n # Setup interpolator from existing points (of order given as keyword)\n frame_in = track[\"frame\"].values\n hdim_1_in = track[\"hdim_1\"].values\n hdim_2_in = track[\"hdim_2\"].values\n s_x = InterpolatedUnivariateSpline(frame_in, hdim_1_in, k=order)\n s_y = InterpolatedUnivariateSpline(frame_in, hdim_2_in, k=order)\n\n # Create new index filling in gaps and possibly extrapolating:\n index_min = min(frame_in) - extrapolate\n index_min = max(index_min, 0)\n index_max = max(frame_in) + extrapolate\n index_max = min(index_max, frame_max)\n new_index = range(index_min, index_max + 1) # +1 here to include last value\n track = track.reindex(new_index)\n\n # Interpolate to extended index:\n frame_out = new_index\n hdim_1_out = s_x(frame_out)\n hdim_2_out = s_y(frame_out)\n\n # Replace fields in data frame with\n track[\"frame\"] = new_index\n track[\"hdim_1\"] = hdim_1_out\n track[\"hdim_2\"] = hdim_2_out\n track[\"cell\"] = cell\n\n # Append DataFrame to list of DataFrames\n t_list.append(track)\n # Concatenate interpolated trajectories into one DataFrame:\n t_out = pd.concat(t_list)\n # Restrict output trajectories to input data in time and space:\n t_out = t_out.loc[\n (t_out[\"hdim_1\"] < hdim_1_max)\n & (t_out[\"hdim_2\"] < hdim_2_max)\n & (t_out[\"hdim_1\"] > 0)\n & (t_out[\"hdim_2\"] > 0)\n ]\n t_out = t_out.reset_index(drop=True)\n return t_out","def data_missing(data):\n return type(data)._from_sequence([None, data[0]])","def ts_argmin(x: pd.Series, d: int or float) -> pd.Series:\n if isinstance(d, float):\n d = math.floor(d)\n if isinstance(x.index, pd.MultiIndex):\n return x.groupby(level=1).rolling(d).apply(lambda r: d - np.nanargmin(r)).droplevel(0).sort_index()\n else:\n return x.rolling(d).apply(lambda r: d - np.nanargmin(r))","def ts_fit(series: TimeSeries) -> TimeSeries:\n pass"],"string":"[\n \"def interpolate_missing(y):\\n if y.isna().any():\\n y = y.interpolate(method='linear', limit_direction='both')\\n return y\",\n \"def fill_missing_data_points(data):\\n return data.interpolate()\",\n \"def check_and_interpolate_nans(df):\\n nan_count = df.isna().sum().sum()\\n if nan_count > 0:\\n df.interpolate(method='linear', inplace=True)\\n return df\",\n \"def auto_fillna(ts: TimeSeries,\\n **interpolate_kwargs) -> TimeSeries:\\n\\n ts_temp = ts.pd_dataframe()\\n\\n # pandas interpolate wrapper, with chosen `method`\\n if 'limit_direction' not in interpolate_kwargs:\\n interpolate_kwargs['limit_direction'] = 'both'\\n interpolate_kwargs['inplace'] = True\\n ts_temp.interpolate(**interpolate_kwargs)\\n\\n return TimeSeries.from_times_and_values(ts.time_index(), ts_temp.values)\",\n \"def fill_nan(A):\\n\\tinds = np.arange(A.shape[0])\\n\\tgood = np.where(np.isfinite(A))\\n\\tA[np.isnan(A)] = np.interp(inds[np.isnan(A)], inds[good], A[good])\\n\\treturn A\",\n \"def fill_nan(array):\\n idx = np.arange(array.shape[0])\\n good = np.where(np.isfinite(array))\\n interp = interpolate.interp1d(idx[good], array[good], bounds_error=False)\\n return np.where(np.isfinite(array), array, interp(idx))\",\n \"def interpolate_timeseries(self, x, t, **kw):\\n v, t_v = self.timeseries(x, rmnans=True)\\n kw.update(dict(bounds_error=False))\\n interpolant = sp.interpolate.interp1d(t_v, v, **kw)\\n return interpolant(t)\",\n \"def test_linear_interpolation_nan_points(self):\\n\\n # Define pixel centers along each direction\\n x = [1.0, 2.0, 4.0]\\n y = [5.0, 9.0]\\n\\n # Define ny by nx array with corresponding values\\n A = numpy.zeros((len(x), len(y)))\\n\\n # Define values for each x, y pair as a linear function\\n for i in range(len(x)):\\n for j in range(len(y)):\\n A[i, j] = linear_function(x[i], y[j])\\n\\n # Then test that interpolated points can contain NaN\\n xis = numpy.linspace(x[0], x[-1], 10)\\n etas = numpy.linspace(y[0], y[-1], 10)\\n xis[6:7] = numpy.nan\\n etas[3] = numpy.nan\\n points = combine_coordinates(xis, etas)\\n\\n vals = interpolate2d(x, y, A, points, mode='linear')\\n refs = linear_function(points[:, 0], points[:, 1])\\n assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)\",\n \"def interpolate_nans(self):\\n\\n signal = self.signal\\n\\n # check for more than one nan in row\\n for i in range(len(signal)-1) :\\n if np.isnan(signal[i]) and np.isnan(signal[i+1]) :\\n raise Exception('There are two nans in a row ask moritz what to do !')\\n\\n if np.isnan(signal[0]) :\\n np.signal[0] = signal[1]\\n if np.isnan(signal[-1]) :\\n signal[-1] = signal[-2]\\n\\n for i in range(1,len(signal)-1) :\\n if np.isnan(signal[i]):\\n signal[i] = (signal[i-1] + signal[i+1])/2\",\n \"def interpolate1d(X):\\n ind = np.arange(X.shape[0])\\n ind_not_nans = np.where(~np.isnan(X)) #fill last values by last non nan values\\n last_non_nan = X[ind_not_nans[0][-1]]\\n f = interpolate.interp1d(ind[ind_not_nans], X[ind_not_nans], bounds_error=False, fill_value=last_non_nan)\\n X_int = np.where(np.isfinite(X), X, f(ind))\\n \\n return X_int\",\n \"def test_linear_interpolation_nan_array(self):\\n\\n # Define pixel centers along each direction\\n x = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]\\n y = [4.0, 5.0, 7.0, 9.0, 11.0, 13.0]\\n\\n # Define ny by nx array with corresponding values\\n A = numpy.zeros((len(x), len(y)))\\n\\n # Define values for each x, y pair as a linear function\\n for i in range(len(x)):\\n for j in range(len(y)):\\n A[i, j] = linear_function(x[i], y[j])\\n A[2, 3] = numpy.nan # (x=2.0, y=9.0): NaN\\n\\n # Then test that interpolated points can contain NaN\\n xis = numpy.linspace(x[0], x[-1], 12)\\n etas = numpy.linspace(y[0], y[-1], 10)\\n points = combine_coordinates(xis, etas)\\n\\n vals = interpolate2d(x, y, A, points, mode='linear')\\n refs = linear_function(points[:, 0], points[:, 1])\\n\\n # Set reference result with expected NaNs and compare\\n for i, (xi, eta) in enumerate(points):\\n if (1.0 < xi <= 3.0) and (7.0 < eta <= 11.0):\\n refs[i] = numpy.nan\\n\\n assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)\",\n \"def pad(input_data):\\n # source : https://stackoverflow.com/questions/6518811/interpolate-nan-values-in-a-numpy-array \\n data = input_data.copy()\\n bad_indexes = np.isnan(data)\\n good_indexes = np.logical_not(bad_indexes)\\n good_data = data[good_indexes]\\n interpolated = np.interp(bad_indexes.nonzero()[0], good_indexes.nonzero()[0], good_data)\\n data[bad_indexes] = interpolated\\n return data\",\n \"def interpolate(df):\\n for x in df.columns:\\n if x == \\\"date\\\":\\n continue\\n df[x] = df[x].interpolate(method='linear', axis=0).ffill().bfill()\\n return df\",\n \"def fill_nan(x):\\n (n_rows, wdw) = x.shape\\n new_x = np.zeros((n_rows,wdw)); new_x[:] = np.nan\\n for i in range(n_rows):\\n indMissing = np.where(np.isnan(x[i,:]))[0]\\n l = len(x[i,indMissing]) #number of MVs\\n if l < 4*wdw/5: #20% available values otherwise discarded\\n new_x[i,:] = x[i,:]\\n if l > 0 and indMissing[0] == 0: #missing value at index 0 \\n c = 0\\n while c + 1 < len(indMissing) and indMissing[c+1] == indMissing[c] + 1:\\n c += 1\\n new_x[i,:c+1] = x[i,c+1] #first nans replaced by first non nan value\\n indMissing = np.where(np.isnan(new_x[i,:]))[0]\\n l = len(new_x[i,indMissing])\\n if l > 0 and indMissing[0] > 0:\\n new_x[i,:] = interpolate1d(new_x[i,:]) #interpolate intermediate nans\\n ind = np.where(~np.isnan(new_x).all(axis=1))[0]\\n new_x = new_x[ind] #remove NaNs \\n \\n return new_x, ind\",\n \"def check_nan(wseries: pd.Series) -> pd.Series:\\n\\n if len(wseries[pd.Series([\\n (type(val) == str or isnan(val)) for val in wseries\\n ], index=wseries.index)]) == 0:\\n return wseries # nothing to change\\n\\n # ensure that all are either float or nan\\n def _float_or_nan(ent):\\n \\\"\\\"\\\"\\n Force values to be either a float or nan first\\n \\\"\\\"\\\"\\n try:\\n return float(ent)\\n except ValueError:\\n return float('nan')\\n\\n wseries = pd.Series(\\n [_float_or_nan(val) for val in wseries], index=wseries.index,\\n name=wseries.name\\n )\\n\\n # continue with interpolation or extrapolation if needed\\n inds = where(\\n pd.Series([\\n (isinstance(val, str) or isnan(val)) for val in wseries\\n ], index=wseries.index)\\n )[0] # locate the position of the problematic readings\\n for ind in inds:\\n try:\\n wseries[ind] = interpolate_with_s(\\n wseries.index[ind], wseries.index[ind-1],\\n wseries.index[ind+1],\\n wseries[ind-1], wseries[ind+1]\\n )\\n if isnan(wseries[ind]): # interpolation does not work\\n wseries[ind] = interpolate_with_s(\\n wseries.index[ind], wseries.index[ind-2],\\n wseries.index[ind-1],\\n wseries[ind-2], wseries[ind-1]\\n )\\n except IndexError: # extrapolation\\n try:\\n wseries[ind] = interpolate_with_s(\\n wseries.index[ind], wseries.index[ind-2],\\n wseries.index[ind-1],\\n wseries[ind-2], wseries[ind-1]\\n )\\n except IndexError:\\n wseries[ind] = interpolate_with_s(\\n wseries.index[ind], wseries.index[ind+2],\\n wseries.index[ind+1],\\n wseries[ind+2], wseries[ind+1]\\n )\\n return wseries\\n\\n return wseries\",\n \"def interp1d_stair_aver_withnan(x, y): #TODO: deal with the case x not sorted\\n def f(xp):\\n xmod=x[~(np.isnan(x)+np.isnan(y))]\\n ymod=y[~(np.isnan(x)+np.isnan(y))]\\n yp=np.empty(np.size(xp)-1)\\n yint=np.cumsum(np.concatenate((np.array([0]),ymod[:-1]*(xmod[1:]-xmod[:-1]))))\\n g=interp1d(xmod,yint, bounds_error=False, fill_value=np.nan)\\n# yp=np.where((xp[:-1]>min(xmod))*(xp[1:]<max(xmod)),(g(xp[1:])-g(xp[:-1]))/(xp[1:]-xp[:-1]),np.nan) #Maybe this is suboptimal since we compute twice g(xp[i])\\n yp=np.where((xp[:-1]>min(xmod))*(xp[1:]<max(xmod)),(g(xp[1:])-g(xp[:-1]))/(xp[1:]-xp[:-1]),np.nan) #Maybe this is suboptimal since we compute twice g(xp[i])\\n for i in range(np.size(xp)-1):\\n if np.isnan(y[np.where((x>=xp[i])*(x<xp[i+1]))]).all():\\n yp[i]=np.nan\\n return yp\\n\\n return f\",\n \"def interp1d_lin_aver_withoutnan(x,y): #FIXME: there is a problem in this routine when the x are in decreasing order.\\n def f(xp):\\n yp=np.empty(np.size(xp)-1)\\n for i in range(np.size(xp)-1):\\n# print i, xp[i], xp[i+1]\\n xmod=x[~(np.isnan(x)+np.isnan(y))]\\n ymod=y[~(np.isnan(x)+np.isnan(y))]\\n xmod2=xmod[np.where((xmod>xp[i])*(xmod<xp[i+1]))]\\n ymod2=ymod[np.where((xmod>xp[i])*(xmod<xp[i+1]))]\\n xmod3=np.concatenate((np.array([xp[i]]),xmod2,np.array([xp[i+1]])))\\n g=interp1d(xmod,ymod, bounds_error=False, fill_value=np.nan)\\n ymod3=np.concatenate((np.array([g(xp[i])]),ymod2,np.array([g(xp[i+1])])))\\n# print xmod3\\n# print ymod3\\n if np.isnan(ymod3).all():\\n yp[i]=np.nan\\n else:\\n xmod4=xmod3[np.where(~(np.isnan(ymod3)+np.isnan(xmod3)))]\\n ymod4=ymod3[np.where(~(np.isnan(ymod3)+np.isnan(xmod3)))]\\n# if i==9:\\n# print xmod4,ymod4\\n yp[i]=np.sum((ymod4[1:]+ymod4[:-1])/2*(xmod4[1:]-xmod4[:-1]))\\n yp[i]=yp[i]/(xmod4[-1]-xmod4[0])\\n# print yp[i]\\n return yp\\n return f\",\n \"def add_missing_weather_data(df):\\n\\n full_date_range = pd.date_range(start=min(df.timestamp), end=max(df.timestamp), freq='H')\\n sites = list(set(df.site_id))\\n full_data_site_range = pd.DataFrame(itertools.product(sites, full_date_range),\\n columns=['site_id', 'timestamp'])\\n df_all_dates = full_data_site_range.merge(df, on=['site_id', 'timestamp'], how='left')\\n df_all_dates = df_all_dates.groupby('site_id').apply(lambda group: group.interpolate(limit_direction='both'))\\n\\n return df_all_dates\",\n \"def interpolate(series, **options):\\n if has_nan(series.index):\\n msg = \\\"\\\"\\\"The Series you passed to interpolate contains\\n NaN values in the index, which would result in\\n undefined behavior. So I'm putting a stop to that.\\\"\\\"\\\"\\n raise ValueError(msg)\\n\\n if not is_strictly_increasing(series.index):\\n msg = \\\"\\\"\\\"The Series you passed to interpolate has an index\\n that is not strictly increasing, which would result in\\n undefined behavior. So I'm putting a stop to that.\\\"\\\"\\\"\\n raise ValueError(msg)\\n\\n # make the interpolate function extrapolate past the ends of\\n # the range, unless `options` already specifies a value for `fill_value`\\n underride(options, fill_value=\\\"extrapolate\\\")\\n\\n # call interp1d, which returns a new function object\\n x = series.index\\n y = series.values\\n interp_func = interp1d(x, y, **options)\\n return interp_func\",\n \"def interpolate_data(data: pd.DataFrame) -> pd.DataFrame:\\n if data.empty:\\n return data\\n data_arg_sort = np.argsort(data.values, axis=0)\\n data_sorted = np.take_along_axis(data.values, data_arg_sort, axis=0)\\n data_index = data.index.values[data_arg_sort]\\n\\n rows, cols = data_sorted.shape\\n\\n num_na = data.isna().sum(axis=0)\\n not_na = rows - num_na\\n\\n # create a linspace for each column, each with #rows entries that span from 0 to #non-missing values in that column\\n float_index = (np.tile(np.linspace(0, 1, rows), (cols, 1)) * (not_na.values[:, np.newaxis] - 1)).T\\n index = np.floor(float_index).astype(int)\\n decimal = float_index % 1\\n\\n index_min = np.minimum(index + 1, not_na.values[np.newaxis, :] - 1)\\n # take the weighted average of the two neighboring values, based on the decimal\\n # slightly simplified expression (1 - dec) * sorted_data[index] + decimal * sorted_data[index + 1]\\n new_values = (1 - decimal) * np.take_along_axis(data_sorted, index, axis=0) +\\\\\\n decimal * np.take_along_axis(data_sorted, index_min, axis=0)\\n\\n # now the index of the new values needs to be reconstructed, since each column was sorted differently\\n result = []\\n # reconstruct one column at a time\\n for column_index, column_name in enumerate(data.columns):\\n series_index = np.empty((rows,))\\n series_index[:] = np.nan\\n\\n index_mapping = ddict(lambda: (np.nan, np.inf))\\n for i, (index_val, float_val) in enumerate(zip(index[:, column_index], float_index[:, column_index])):\\n decimal = float_val - index_val\\n if index_mapping[index_val][1] > decimal:\\n index_mapping[index_val] = i, decimal\\n\\n if index_mapping[index_val + 1][1] > 1 - decimal:\\n index_mapping[index_val + 1] = i, 1 - decimal\\n\\n # the index that was added last needs to be removed\\n index_mapping.pop(index_val + 1)\\n\\n for key, (idx, _) in index_mapping.items():\\n series_index[idx] = key\\n\\n series_index[np.isnan(series_index)] = np.arange(np.nanmax(series_index) + 1, rows, 1)\\n s_ind = data_index[:, column_index][series_index.astype(int)]\\n result.append(pd.Series(new_values[:, column_index], index=s_ind, name=column_name))\\n\\n return pd.concat(result, axis=1, sort=False)\",\n \"def _auto_fill(series: TimeSeries, **interpolate_kwargs) -> TimeSeries:\\n\\n series_temp = series.pd_dataframe()\\n\\n # pandas interpolate wrapper, with chosen `method`\\n if \\\"limit_direction\\\" not in interpolate_kwargs:\\n interpolate_kwargs[\\\"limit_direction\\\"] = \\\"both\\\"\\n interpolate_kwargs[\\\"inplace\\\"] = True\\n series_temp.interpolate(**interpolate_kwargs)\\n return TimeSeries.from_dataframe(\\n series_temp,\\n freq=series.freq,\\n static_covariates=series.static_covariates,\\n hierarchy=series.hierarchy,\\n )\",\n \"def extrapolate_nans(x, y, v):\\n if numpy.ma.is_masked(v):\\n nans = v.mask\\n else:\\n nans = numpy.isnan(v)\\n notnans = numpy.logical_not(nans)\\n v[nans] = scipy.interpolate.griddata((x[notnans], y[notnans]), v[notnans],\\n (x[nans], y[nans]),\\n method='nearest').ravel()\\n return v\",\n \"def linear_interpolate_value_at_time(t0, v0, t1, v1, t):\\n return v0 + linear_interpolate_value_change(t0, v0, t1, v1, t - t0)\",\n \"def interpolate_series(t,y,start,stop):\\n \\n # daily timestamsp\\n numdays = (stop-start).days\\n D = [start + datetime.timedelta(days=x) for x in range(0,numdays)]\\n T = [time.mktime(date.timetuple()) for date in D]\\n \\n # interpolated variable\\n Y = np.interp(T, t, y)\\n \\n return (D, Y)\",\n \"def _interpolation(matrix):\\n try:\\n\\tok = ~np.isnan(matrix)\\n \\txp = ok.ravel().nonzero()[0]\\n \\tfp = matrix[~np.isnan(matrix)]\\n \\tx = np.isnan(matrix).ravel().nonzero()[0]\\n \\tmatrix[np.isnan(matrix)] = np.interp(x, xp, fp)\\n \\treturn matrix\\n except:\\n return matrix\",\n \"def interpolate_na(\\n self, method: str = \\\"nearest\\\", extrapolate: bool = False, **kwargs\\n ):\\n dim0 = self.dim0\\n kwargs.update(dict(method=method, extrapolate=extrapolate))\\n if dim0:\\n interp_data = np.empty(self._obj.shape, dtype=self._obj.dtype)\\n for i, (_, sub_xds) in enumerate(self._obj.groupby(dim0)):\\n interp_data[i, ...] = self._interpolate_na(\\n sub_xds.load().data, **kwargs\\n )\\n else:\\n interp_data = self._interpolate_na(self._obj.load().data, **kwargs)\\n interp_array = xr.DataArray(\\n name=self._obj.name,\\n dims=self._obj.dims,\\n coords=self._obj.coords,\\n data=interp_data,\\n attrs=self._obj.attrs,\\n )\\n interp_array.raster.set_nodata(self.nodata)\\n interp_array.raster.set_crs(self.crs)\\n return interp_array\",\n \"def interpolate(series, indices):\\r\\n not_included = [t for t in indices if t not in series.index.values]\\r\\n new_indices_series = pd.Series(index=not_included, data=[np.nan] * len(not_included))\\r\\n with_new_indices = series.append(new_indices_series).sort_index()\\r\\n interpolated = with_new_indices.interpolate(method=\\\"linear\\\")\\r\\n\\r\\n # We want the values on the indices before the (originally) first index to be equal to the first value. Similarly,\\r\\n # we want the values after the (originally) last index to be equal to the last value.\\r\\n min_index = min(series.index.values)\\r\\n max_index = max(series.index.values)\\r\\n start_value = series.values[0]\\r\\n end_value = series.values[-1]\\r\\n\\r\\n new_values = []\\r\\n for t, p in interpolated.items():\\r\\n if t < min_index:\\r\\n new_values.append(start_value)\\r\\n elif t > max_index:\\r\\n new_values.append(end_value)\\r\\n else:\\r\\n new_values.append(p)\\r\\n\\r\\n new_series = pd.Series(data=new_values, index=interpolated.index)\\r\\n\\r\\n return new_series\",\n \"def zero_end_interpolation(df: pd.DataFrame):\\n end = df.index[-1]\\n empty_df = pd.DataFrame(index=np.arange(0, end + 1, 1))\\n res = pd.concat([df, empty_df], axis=1)\\n res = res.fillna(method='ffill')\\n res = res.fillna(method='bfill')\\n return res\",\n \"def interpolate(f):\\n @funnel\\n def fill_missing(data, return_model=False, **kwargs):\\n impute_kwargs = kwargs.pop('impute_kwargs', {})\\n\\n if impute_kwargs:\\n model = impute_kwargs.pop('model', eval(defaults['impute']['model']))\\n imputed_data, model = apply_sklearn_model(model, data, return_model=True, **impute_kwargs)\\n data = pd.DataFrame(data=imputed_data, index=data.index, columns=data.columns)\\n else:\\n model = None\\n\\n if kwargs:\\n kwargs = update_dict(defaults['interpolate'], kwargs, from_config=True)\\n data = data.interpolate(**kwargs)\\n\\n if return_model:\\n return data, {'model': model, 'args': [], 'kwargs': kwargs}\\n else:\\n return data\\n\\n @functools.wraps(f)\\n def wrapped(data, *args, **kwargs):\\n interp_kwargs = kwargs.pop('interp_kwargs', {})\\n return f(fill_missing(data, *args, **interp_kwargs), **kwargs)\\n\\n return wrapped\",\n \"def fillna(ts: TimeSeries, fill: float = 0) -> TimeSeries:\\n\\n return TimeSeries.from_times_and_values(ts.time_index(), ts.pd_dataframe().fillna(value=fill))\",\n \"def fill_missing_values(\\n series: TimeSeries, fill: Union[str, float] = \\\"auto\\\", **interpolate_kwargs\\n) -> TimeSeries:\\n raise_if_not(\\n isinstance(fill, str) or isinstance(fill, float),\\n \\\"`fill` should either be a string or a float\\\",\\n logger,\\n )\\n raise_if(\\n isinstance(fill, str) and fill != \\\"auto\\\",\\n \\\"invalid string for `fill`: can only be set to 'auto'\\\",\\n logger,\\n )\\n\\n if fill == \\\"auto\\\":\\n return _auto_fill(series, **interpolate_kwargs)\\n return _const_fill(series, fill)\",\n \"def interpolate_na(self, method: str = \\\"nearest\\\", **kwargs):\\n ds_out = xr.Dataset(attrs=self._obj.attrs)\\n for var in self.vars:\\n ds_out[var] = self._obj[var].raster.interpolate_na(method=method, **kwargs)\\n return ds_out\",\n \"def interp1d_lin_aver(x,y): #FIXME: there is a problem in this routine when the x are in decreasing order.\\n def f(xp):\\n yp=np.empty(np.size(xp)-1)\\n for i in range(np.size(xp)-1):\\n# print i, xp[i], xp[i+1]\\n xmod=x[~(np.isnan(x)+np.isnan(y))]\\n ymod=y[~(np.isnan(x)+np.isnan(y))]\\n xmod2=xmod[np.where((xmod>xp[i])*(xmod<xp[i+1]))]\\n ymod2=ymod[np.where((xmod>xp[i])*(xmod<xp[i+1]))]\\n xmod3=np.concatenate((np.array([xp[i]]),xmod2,np.array([xp[i+1]])))\\n g=interp1d(x,y, bounds_error=False, fill_value=np.nan)\\n ymod3=np.concatenate((np.array([g(xp[i])]),ymod2,np.array([g(xp[i+1])])))\\n# print xmod3\\n# print ymod3\\n if np.isnan(ymod3).all():\\n yp[i]=np.nan\\n else:\\n xmod4=xmod3[np.where(~(np.isnan(ymod3)+np.isnan(xmod3)))]\\n ymod4=ymod3[np.where(~(np.isnan(ymod3)+np.isnan(xmod3)))]\\n# if i==9:\\n# print xmod4,ymod4\\n yp[i]=np.sum((ymod4[1:]+ymod4[:-1])/2*(xmod4[1:]-xmod4[:-1]))\\n yp[i]=yp[i]/(xmod4[-1]-xmod4[0])\\n# print yp[i]\\n return yp\\n return f\",\n \"def interpolate(self, freq, data):\\n func = scipy.interpolate.interp1d(\\n freq, data,\\n kind='nearest',\\n copy=False,\\n assume_sorted=True,\\n bounds_error=False,\\n fill_value=np.nan,\\n )\\n return func(self.freq)\",\n \"def test_interpolation_random_array_and_nan(self):\\n\\n # Define pixel centers along each direction\\n x = numpy.arange(20) * 1.0\\n y = numpy.arange(25) * 1.0\\n\\n # Define ny by nx array with corresponding values\\n A = numpy.zeros((len(x), len(y)))\\n\\n # Define arbitrary values for each x, y pair\\n numpy.random.seed(17)\\n A = numpy.random.random((len(x), len(y))) * 10\\n\\n # Create islands of NaN\\n A[5, 13] = numpy.nan\\n A[6, 14] = A[6, 18] = numpy.nan\\n A[7, 14:18] = numpy.nan\\n A[8, 13:18] = numpy.nan\\n A[9, 12:19] = numpy.nan\\n A[10, 14:17] = numpy.nan\\n A[11, 15] = numpy.nan\\n\\n A[15, 5:6] = numpy.nan\\n\\n # Creat interpolation points\\n xis = numpy.linspace(x[0], x[-1], 39) # Hit all mid points\\n etas = numpy.linspace(y[0], y[-1], 73) # Hit thirds\\n points = combine_coordinates(xis, etas)\\n\\n for mode in ['linear', 'constant']:\\n vals = interpolate2d(x, y, A, points, mode=mode)\\n\\n # Calculate reference result with expected NaNs and compare\\n i = j = 0\\n for k, (xi, eta) in enumerate(points):\\n\\n # Find indices of nearest higher value in x and y\\n i = numpy.searchsorted(x, xi)\\n j = numpy.searchsorted(y, eta)\\n\\n if i > 0 and j > 0:\\n\\n # Get four neigbours\\n A00 = A[i - 1, j - 1]\\n A01 = A[i - 1, j]\\n A10 = A[i, j - 1]\\n A11 = A[i, j]\\n\\n if numpy.allclose(xi, x[i]):\\n alpha = 1.0\\n else:\\n alpha = 0.5\\n\\n if numpy.allclose(eta, y[j]):\\n beta = 1.0\\n else:\\n beta = eta - y[j - 1]\\n\\n if mode == 'linear':\\n if numpy.any(numpy.isnan([A00, A01, A10, A11])):\\n ref = numpy.nan\\n else:\\n ref = (A00 * (1 - alpha) * (1 - beta) +\\n A01 * (1 - alpha) * beta +\\n A10 * alpha * (1 - beta) +\\n A11 * alpha * beta)\\n elif mode == 'constant':\\n assert alpha >= 0.5 # Only case in this test\\n\\n if beta < 0.5:\\n ref = A10\\n else:\\n ref = A11\\n else:\\n msg = 'Unknown mode: %s' % mode\\n raise Exception(msg)\\n\\n #print i, j, xi, eta, alpha, beta, vals[k], ref\\n assert nanallclose(vals[k], ref, rtol=1e-12, atol=1e-12)\",\n \"def interp1d_stair_aver(x, y): #TODO: deal with the case x not sorted\\n def f(xp):\\n yp=np.empty(np.size(xp)-1)\\n xmod=x[~(np.isnan(x)+np.isnan(y))]\\n ymod=y[~(np.isnan(x)+np.isnan(y))]\\n yint=np.cumsum(np.concatenate((np.array([0]),ymod[:-1]*(xmod[1:]-xmod[:-1]))))\\n g=interp1d(xmod,yint, bounds_error=False, fill_value=np.nan)\\n# yp=np.where((xp[:-1]>min(xmod))*(xp[1:]<max(xmod)),(g(xp[1:])-g(xp[:-1]))/(xp[1:]-xp[:-1]),np.nan) #Maybe this is suboptimal since we compute twice g(xp[i])\\n yp=np.where((xp[:-1]>min(xmod))*(xp[1:]<max(xmod)),(g(xp[1:])-g(xp[:-1]))/(xp[1:]-xp[:-1]),np.nan) #Maybe this is suboptimal since we compute twice g(xp[i])\\n return yp\\n\\n return f\",\n \"def test_nans_in_x_data(self):\\n alpha = 0.9\\n num_x = 150\\n num_y = 100\\n x_vals = np.linspace(0, 2 * np.pi, num_x)\\n x_vals[45] = np.nan\\n y_vals = (np.sin(x_vals[:num_y]) + 0.3 * np.random.randn(num_y) + 0.5)\\n\\n with pytest.raises(RuntimeError):\\n extrapolated_lowess(x_vals, y_vals, alpha=alpha)\",\n \"def linear_interpolate(x, x0, y0, x1, y1):\\n try:\\n return (y0 * (x1 - x) + y1 * (x - x0)) / (x1 - x0)\\n except ZeroDivisionError:\\n return 0.0\",\n \"def linear_interpolate_value_change(t0, v0, t1, v1, dt):\\n return (v1 - v0)/float(t1-t0) * dt\",\n \"def interpolateTime(ser, time):\\n def findTime(a_list, func):\\n if len(a_list) == 0:\\n return np.nan\\n else:\\n return func(a_list)\\n def findValue(time):\\n if np.isnan(time):\\n return np.nan\\n else:\\n return ser[time]\\n #\\n time_lb = findTime([t for t in ser.index if t <= time], max)\\n time_ub = findTime([t for t in ser.index if t >= time], min)\\n value_lb = findValue(time_lb)\\n value_ub = findValue(time_ub)\\n if np.isnan(value_lb):\\n return value_ub\\n if np.isnan(value_ub):\\n return value_lb\\n if time_ub == time_lb:\\n return value_ub\\n frac = (time - time_lb)/(time_ub - time_lb)\\n return (1 - frac)*value_lb + frac*value_ub\",\n \"def replaces_nans_ma(series):\\n series = series.replace([np.inf, -np.inf], np.nan)\\n result = series.fillna(series.rolling(window=len(series), min_periods=0).mean())\\n return result\",\n \"def image_naninterp(data):\\n if not isinstance(data, np.ndarray) or len(data.shape) != 2:\\n log.error(\\\"data must be a 2D %s\\\" % np.ndarray)\\n return\\n mask = np.isnan(data)\\n if not mask.any():\\n return data\\n if mask.all():\\n log.error(\\\"data are all NaN\\\")\\n return\\n\\n yy, xx = np.mgrid[:data.shape[0], :data.shape[1]]\\n points = np.array([yy[~mask], xx[~mask]]).T\\n interp = interpolate.CloughTocher2DInterpolator(points, data[~mask])\\n result = data.copy()\\n result[mask] = interp(np.array([yy[mask], xx[mask]]).T)\\n return result\",\n \"def interpolate(self, **kwargs): # noqa: PR01\\n return DataFrameDefault.register(pandas.DataFrame.interpolate)(self, **kwargs)\",\n \"def interpolate_none(self):\\n\\n # Reset processed data\\n self.u_processed_mps = np.copy(self.u_mps)\\n self.v_processed_mps = np.copy(self.v_mps)\\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan\",\n \"def ts_resample(self):\\n try:\\n ts_freq = pd.DataFrame(\\n index=pd.date_range(self.ts_df.index[0], self.ts_df.index[len(self.ts_df) - 1], freq=self.freq),\\n columns=['dummy'])\\n except ValueError:\\n self._uvts_cls_logger.exception(\\\"Exception occurred, possibly incompatible frequency!\\\")\\n sys.exit(\\\"STOP\\\")\\n\\n if self.fill_method == 'ffill':\\n self.ts_df = ts_freq.join(self.ts_df).drop(['dummy'], axis=1)\\n self.ts_df.y = self.ts_df.y.fillna(method='ffill')\\n # if np.isnan ( self.ts_df.y ).any ():\\n # self.ts_df.y = self.ts_df.y.fillna ( method='bfill' )\\n else: # interp\\n xp = np.linspace(0, self.ts_df.size, self.ts_df.size, endpoint=False)\\n fp = self.ts_df['y']\\n # join\\n self.ts_df = ts_freq.join(self.ts_df).drop(['dummy'], axis=1)\\n # pick new points\\n x = np.linspace(0, ts_freq.size, ts_freq.size, endpoint=False)\\n x = x[self.ts_df['y'].isna()]\\n print(x.size)\\n print(x)\\n\\n # put the values\\n self.ts_df.y[self.ts_df['y'].isna()] = np.interp(x, xp, fp)\\n\\n if np.isnan(self.ts_df.y).any():\\n self._uvts_cls_logger.warning(\\\"Some NaN found, something went wrong, check the data!\\\")\\n sys.exit(\\\"STOP\\\")\\n\\n self._uvts_cls_logger.info(\\\"Time series resampled at frequency: \\\" + str(self.ts_df.index.freq) +\\n \\\". New shape of the data: \\\" + str(self.ts_df.shape))\\n self._uvts_cls_logger.info(\\\"Using time series data of range: \\\" + str(min(self.ts_df.index)) + ' - ' + str(\\n max(self.ts_df.index)) + \\\" and shape: \\\" + str(self.ts_df.shape))\\n\\n return self\",\n \"def interpolate_missing_data(times, fluxes, cadences=None):\\n first_time = times[0]\\n\\n if cadences is not None:\\n # Median time between cadences\\n dt = np.median(np.diff(times) / np.diff(cadences))\\n cadence_indices = cadences - cadences[0]\\n else:\\n # Find typical time between cadences:\\n dt = np.median(np.diff(times))\\n # Approximate the patchy grid of integer cadence indices,\\n # i.e.: (0, 1, 3, 4, 5, 8, ...)\\n cadence_indices = np.rint((times - first_time)/dt)\\n\\n # Find missing cadence indices if that grid were complete\\n expected_cadence_indices = set(np.arange(cadence_indices.min(),\\n cadence_indices.max()))\\n missing_cadence_indices = expected_cadence_indices.difference(set(cadence_indices))\\n # Convert the missing cadences to times\\n missing_times = first_time + np.array(list(missing_cadence_indices))*dt\\n\\n # Interpolate to find fluxes at missing times\\n interp_fluxes = np.interp(missing_times, times, fluxes)\\n\\n # Combine the interpolated and input times, fluxes\\n interpolated_fluxes = np.concatenate([fluxes, interp_fluxes])\\n interpolated_times = np.concatenate([times, missing_times])\\n\\n # Sort the times, fluxes, so that you can compute the ACF on them:\\n sort_by_time = np.argsort(interpolated_times)\\n interpolated_fluxes = interpolated_fluxes[sort_by_time]\\n interpolated_times = interpolated_times[sort_by_time]\\n return interpolated_times, interpolated_fluxes\",\n \"def ffill_nans(df):\\n return df.fillna(method='ffill', axis=0)\",\n \"def interpolate_linear(self, known_coords, known_values, interp_coords, groupname):\\n\\t\\t#First need to reshape known_coords and known_values\\n\\t\\tn_params = self.signal[groupname]['dimension']\\n\\t\\tknown_coords = np.reshape( known_coords, (-1,n_params) )\\n\\t\\tknown_values = np.reshape( known_values, (-1) )\\t\\t\\n\\t\\treturn griddata(known_coords, known_values, interp_coords, method='linear')\",\n \"def ts_transform(data, ts_col, date_col=None, fill_zeros=False, fill_nan=False, fix_trend=False, lag_differentiation=1, log_transform=False, ma_transform=False, ma_periods=2,\\n accumulate_transform=False, accumulation_periods=None):\\n df = data.copy(deep=True)\\n\\n if fill_zeros is True:\\n df[df[ts_col] == 0] = np.nan\\n if fill_nan is True:\\n if df[ts_col].isna().sum() != 0:\\n print(df[ts_col].isna().sum(), ' values will be interpolated \\\\n', 'Of a total of ', df[ts_col].shape[0])\\n df[ts_col] = df[ts_col].interpolate()\\n else:\\n print('No NaN Values Found')\\n\\n if log_transform is True:\\n if any(df[ts_col] == 0):\\n print('Warning, ', ts_col, ' contains ', (df[ts_col] == 0).sum(), ' Zeros, Transformation will be done with log(1+ Value)')\\n df[ts_col] = np.log(df[ts_col] + 1)\\n else:\\n df[ts_col] = np.log(df[ts_col])\\n\\n if fix_trend is True:\\n df[ts_col] = df[ts_col] - df[ts_col].shift(lag_differentiation)\\n\\n if ma_transform is True:\\n df[ts_col] = df[ts_col].rolling(ma_periods).mean()\\n if fill_nan is True:\\n df = df.dropna(subset=[ts_col])\\n\\n if accumulate_transform is True:\\n if accumulation_periods is None:\\n print('Accumulation periods are not defined!')\\n df[ts_col] = df[ts_col].rolling(accumulation_periods).sum()\\n\\n if date_col is not None:\\n df.set_index([date_col], inplace=True)\\n print('Index set as ', type(df.index))\\n\\n return df[ts_col]\",\n \"def interpolate_linear(self, transect):\\n\\n u = np.copy(self.u_mps)\\n v = np.copy(self.v_mps)\\n\\n valid = np.isnan(u) == False\\n\\n # Check for valid data\\n if sum(valid) > 1 and sum(self.valid_data[0, :]) > 1:\\n\\n # Compute ens_time\\n ens_time = np.nancumsum(transect.date_time.ens_duration_sec)\\n\\n # Apply linear interpolation\\n self.u_processed_mps = np.interp(x=ens_time,\\n xp=ens_time[self.valid_data[0, :]],\\n fp=u[self.valid_data[0, :]],\\n left=np.nan,\\n right=np.nan)\\n # Apply linear interpolation\\n self.v_processed_mps = np.interp(x=ens_time,\\n xp=ens_time[self.valid_data[0, :]],\\n fp=v[self.valid_data[0, :]],\\n left=np.nan,\\n right=np.nan)\",\n \"def interpolate_arrays(t, idx, x_array, time_array):\\n x1 = np.array([x_array[i, idx[i] - 1] for i in range(len(idx))])\\n x2 = np.array([x_array[i, idx[i]] for i in range(len(idx))])\\n t1 = np.array([time_array[i, idx[i] - 1] for i in range(len(idx))])\\n t2 = np.array([time_array[i, idx[i]] for i in range(len(idx))])\\n ta = t * np.ones(time_array.shape[0])\\n return x1 + np.multiply(np.divide(x2-x1, t2-t1), ta - t1)\",\n \"def test_interpolate_values_1_hour_gap(self, forcing_processor):\\n forcing_processor.data = {}\\n forcing_processor.data['air_temperature'] = [\\n (datetime.datetime(2011, 9, 25, 9, 0, 0), 215.0),\\n (datetime.datetime(2011, 9, 25, 10, 0, 0), None),\\n (datetime.datetime(2011, 9, 25, 11, 0, 0), 235.0),\\n ]\\n forcing_processor.interpolate_values('air_temperature', 1, 1)\\n expected = (datetime.datetime(2011, 9, 25, 10, 0, 0), 225.0)\\n assert forcing_processor.data['air_temperature'][1] == expected\",\n \"def interpolate(timepoint_defined, signal, interp_type, TR):\\n\\n timepoint_defined = np.array(timepoint_defined)\\n\\n true_inds = np.where(timepoint_defined == True)[0]\\n false_inds = np.where(timepoint_defined == False)[0]\\n\\n\\n signal_copy = np.array(signal)\\n\\n if interp_type == 'linear':\\n\\n #Still need to handle beginning/end cases\\n\\n for temp_timepoint in false_inds:\\n\\n\\n #past_timepoint = true_inds[np.sort(np.where(true_inds < temp_timepoint)[0])[-1]]\\n #future_timepoint = true_inds[np.sort(np.where(true_inds > temp_timepoint)[0])[0]]\\n\\n\\n #Be sure there is at least one future timepoint and one past timepoint.\\n #If there isn't, then grab either two past or two future timepoints and use those\\n #for interpolation. If there aren't even two total past + future timepoints, then\\n #just set the output to 0. Could also set the output to be unadjusted, but this\\n #is a way to make the issue more obvious.\\n temp_past_timepoint = np.sort(np.where(true_inds < temp_timepoint)[0])\\n temp_future_timepoint = np.sort(np.where(true_inds > temp_timepoint)[0])\\n\\n #If we don't have enough data to interpolate/extrapolate\\n if len(temp_past_timepoint) + len(temp_future_timepoint) < 2:\\n\\n signal_copy[temp_timepoint] = 0\\n\\n #If we do have enough data to interpolate/extrapolate\\n else:\\n\\n if len(temp_past_timepoint) == 0:\\n past_timepoint = true_inds[temp_future_timepoint[1]]\\n else:\\n past_timepoint = true_inds[temp_past_timepoint[-1]]\\n\\n if len(temp_future_timepoint) == 0:\\n future_timepoint = true_inds[temp_past_timepoint[-2]]\\n else:\\n future_timepoint = true_inds[temp_future_timepoint[0]]\\n\\n #Find the appopriate past/future values\\n past_value = signal_copy[int(past_timepoint)]\\n future_value = signal_copy[int(future_timepoint)]\\n\\n #Use the interp1d function for interpolation\\n interp_object = interp.interp1d([past_timepoint, future_timepoint], [past_value, future_value], bounds_error=False, fill_value='extrapolate')\\n signal_copy[temp_timepoint] = interp_object(temp_timepoint).item(0)\\n\\n return signal_copy\\n\\n\\n #For cubic spline interpolation, instead of taking the past/future timepoint\\n #we will just take the closest 5 timepoints. If there aren't 5 timepoints, we will\\n #set the output to 0\\n if interp_type == 'cubic_spline':\\n\\n sorted_good = np.sort(signal_copy[true_inds])\\n min_bound = sorted_good[0]\\n max_bound = sorted_good[-1]\\n\\n #Continue if there are at least 5 good inds\\n true_inds_needed = 5\\n if len(true_inds) >= true_inds_needed:\\n\\n for temp_timepoint in false_inds:\\n\\n closest_inds = true_inds[np.argsort(np.absolute(true_inds - temp_timepoint))]\\n closest_vals = signal_copy[closest_inds.astype(int)]\\n interp_object = interp.interp1d(closest_inds, closest_vals, kind = 'cubic', bounds_error=False, fill_value='extrapolate')\\n signal_copy[temp_timepoint.astype(int)] = interp_object(temp_timepoint).item(0)\\n\\n min_bound_exceded = np.where(signal_copy < min_bound)[0]\\n if len(min_bound_exceded) > 0:\\n\\n signal_copy[min_bound_exceded] = min_bound\\n\\n max_bound_exceded = np.where(signal_copy > max_bound)[0]\\n if len(max_bound_exceded) > 0:\\n\\n signal_copy[max_bound_exceded] = max_bound\\n\\n #If there aren't enough good timepoints, then set the bad timepoints = 0\\n else:\\n\\n signal_copy[false_inds.astype(int)] = 0\\n\\n\\n return signal_copy\\n\\n\\n if interp_type == 'spectral':\\n\\n signal_copy = spectral_interpolation(timepoint_defined, signal_copy, TR)\\n\\n return signal_copy\",\n \"def log_interpolate(array):\\n idx = np.arange(0, len(array))\\n log_array = np.log(array.astype(np.float32) + 1e-1)\\n interp_array = np.interp(\\n x=idx, xp=idx[~np.isnan(array)], fp=log_array[~np.isnan(array)]\\n )\\n return np.round(np.exp(interp_array)).astype(np.int32)\",\n \"def linear_interp(x,y,xi) :\\n \\n f = interp1d(x,y,kind='linear')\\n yi = f(xi)\\n \\n return yi\",\n \"def test_1d_linear_interpolation_basic(self):\\n\\n # Define pixel centers along each direction\\n x = [1.0, 2.0, 4.0]\\n\\n # Define array with corresponding values\\n A = numpy.zeros((len(x)))\\n\\n # Define values for each xas a linear function\\n for i in range(len(x)):\\n A[i] = linear_function(x[i], 0)\\n\\n # Test first that original points are reproduced correctly\\n for i, xi in enumerate(x):\\n val = interpolate1d(x, A, [xi], mode='linear')[0]\\n ref = linear_function(xi, 0)\\n assert numpy.allclose(val, ref, rtol=1e-12, atol=1e-12)\\n\\n # Then test that genuinly interpolated points are correct\\n xis = numpy.linspace(x[0], x[-1], 10)\\n points = xis\\n\\n vals = interpolate1d(x, A, points, mode='linear')\\n refs = linear_function(points, 0)\\n assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)\\n\\n # Exercise bounds_error flag\\n vals = interpolate1d(x, A, points, mode='linear',\\n bounds_error=True)\\n refs = linear_function(points, 0)\\n assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)\",\n \"def _const_fill(series: TimeSeries, fill: float = 0) -> TimeSeries:\\n\\n return TimeSeries.from_times_and_values(\\n series.time_index,\\n series.pd_dataframe().fillna(value=fill),\\n freq=series.freq,\\n columns=series.columns,\\n static_covariates=series.static_covariates,\\n hierarchy=series.hierarchy,\\n )\",\n \"def interpolate(i0, d0, i1, d1):\\n if i0 == i1:\\n return [d0]\\n values = []\\n a = (d1 - d0) / (i1 - i0)\\n d = d0\\n for i in range(i0,i1+1):\\n values.append(d)\\n d = d + a\\n return values\",\n \"def _handle_missing_vars(netcdf_start_date, netcdf_end_date, tmp_dir):\\n hrs_range = arrow.Arrow.range(\\n \\\"hour\\\", netcdf_start_date.shift(days=-1), netcdf_end_date.shift(hours=+23)\\n )\\n missing_var_hrs = {}\\n for netcdf_hr in hrs_range:\\n nemo_date = f\\\"y{netcdf_hr.year}m{netcdf_hr.month:02d}d{netcdf_hr.day:02d}\\\"\\n nemo_hr_ds_path = tmp_dir / f\\\"gemlam_{nemo_date}_{netcdf_hr.hour:03d}.nc\\\"\\n with xarray.open_dataset(nemo_hr_ds_path) as ds:\\n missing_vars = ds.attrs.get(\\\"missing_variables\\\")\\n if missing_vars is None:\\n for var, missing_hrs in missing_var_hrs.copy().items():\\n if len(missing_hrs) <= 4:\\n _interpolate_intra_day_missing_var_hrs(var, missing_hrs)\\n del missing_var_hrs[var]\\n else:\\n _interpolate_inter_day_missing_var_hrs(var, missing_hrs)\\n del missing_var_hrs[var]\\n else:\\n for var in missing_vars.split(\\\", \\\"):\\n try:\\n missing_var_hrs[var].append(\\n {\\\"hr\\\": netcdf_hr, \\\"ds_path\\\": nemo_hr_ds_path}\\n )\\n except KeyError:\\n missing_var_hrs[var] = [\\n {\\\"hr\\\": netcdf_hr, \\\"ds_path\\\": nemo_hr_ds_path}\\n ]\\n for missing_hr in missing_var_hrs.get(\\\"solar\\\", []).copy():\\n # Special handling for 1feb07 to 23feb07 period in which there are no solar radiation values\\n in_feb07_solar_gap = missing_hr[\\\"hr\\\"].is_between(\\n arrow.get(\\\"2007-02-01\\\"), arrow.get(\\\"2007-02-24\\\"), bounds=\\\"[)\\\"\\n )\\n if not in_feb07_solar_gap:\\n break\\n # Calculate solar radiation from cloud fraction and time of day sun angle\\n solar = _calc_solar_from_clouds_and_angle(\\n missing_hr[\\\"hr\\\"], missing_hr[\\\"ds_path\\\"]\\n )\\n solar = solar.astype(\\\"float32\\\", casting=\\\"same_kind\\\")\\n solar.name = \\\"solar\\\"\\n solar.attrs[\\\"level\\\"] = \\\"surface\\\"\\n solar.attrs[\\\"long_name\\\"] = \\\"Downward Short-Wave Radiation Flux\\\"\\n solar.attrs[\\\"standard_name\\\"] = \\\"net_downward_shortwave_flux_in_air\\\"\\n solar.attrs[\\\"units\\\"] = \\\"W/m^2\\\"\\n with xarray.open_dataset(missing_hr[\\\"ds_path\\\"]) as ds:\\n ds_w_solar = ds.copy(deep=True).assign(solar=solar)\\n missing_vars = ds_w_solar.attrs[\\\"missing_variables\\\"].split(\\\", \\\")\\n missing_vars.remove(\\\"solar\\\")\\n if missing_vars:\\n ds_w_solar.attrs[\\\"missing_variables\\\"] = \\\", \\\".join(missing_vars)\\n else:\\n del ds_w_solar.attrs[\\\"missing_variables\\\"]\\n _write_netcdf_file(ds_w_solar, missing_hr[\\\"ds_path\\\"])\\n missing_var_hrs[\\\"solar\\\"].remove(missing_hr)\\n else:\\n if \\\"solar\\\" in missing_var_hrs and not missing_var_hrs[\\\"solar\\\"]:\\n del missing_var_hrs[\\\"solar\\\"]\\n if missing_var_hrs:\\n raise ValueError(f\\\"missing variables at end of date range: {missing_var_hrs}\\\")\",\n \"def fix_missing_data(contour_data_list):\\n contour_data = np.array(contour_data_list)\\n if contour_data.any() == \\\"\\\":\\n logger.warning(\\\" Missing values detected.\\\")\\n missing_values = np.where(contour_data == \\\"\\\")[0]\\n if missing_values.shape[0] > 1:\\n logger.warning(\\\" More than one value missing, fixing this isn't implemented yet...\\\")\\n else:\\n logger.warning(\\\" Only one value missing.\\\")\\n missing_index = missing_values[0]\\n missing_axis = missing_index % 3\\n if missing_axis == 0:\\n logger.warning(\\\" Missing value in x axis: interpolating.\\\")\\n if missing_index > len(contour_data) - 3:\\n lower_val = contour_data[missing_index - 3]\\n upper_val = contour_data[0]\\n elif missing_index == 0:\\n lower_val = contour_data[-3]\\n upper_val = contour_data[3]\\n else:\\n lower_val = contour_data[missing_index - 3]\\n upper_val = contour_data[missing_index + 3]\\n contour_data[missing_index] = 0.5 * (lower_val + upper_val)\\n elif missing_axis == 1:\\n logger.warning(\\\" Missing value in y axis: interpolating.\\\")\\n if missing_index > len(contour_data) - 2:\\n lower_val = contour_data[missing_index - 3]\\n upper_val = contour_data[1]\\n elif missing_index == 0:\\n lower_val = contour_data[-2]\\n upper_val = contour_data[4]\\n else:\\n lower_val = contour_data[missing_index - 3]\\n upper_val = contour_data[missing_index + 3]\\n contour_data[missing_index] = 0.5 * (lower_val + upper_val)\\n else:\\n logger.warning(\\\" Missing value in z axis: taking slice value\\\")\\n temp = contour_data[2::3].tolist()\\n temp.remove(\\\"\\\")\\n contour_data[missing_index] = np.min(np.array(temp, dtype=np.double))\\n return contour_data\",\n \"def interpolate(self, step=1, kind=1):\\n vt = Reference.interpolate(self, step)\\n if vt is False:\\n return False\\n # Value sequence\\n # starting from the oldest time minus step\\n oldi = self.get_time(vt[0] - step)\\n # Check if we have enough points to interpolate\\n if len(self) - oldi < 5:\\n return False\\n # If possible, go back one more point, for interpolation safety\\n if oldi > 1:\\n oldi -= 1\\n # Decode values and separate time and value vectors\\n dat = self[oldi:]\\n# print 'Getting data',self.path,dat,vt\\n dat = np.array(dat)\\n dat = dat.transpose()\\n # Build a linear spline using vt points as knots\\n #f=LSQUnivariateSpline(dat[0],dat[1],vt, k=kind)\\n # Do a linear fitting\\n (slope, const), res, rank, sing, rcond = np.polyfit(\\n dat[0], dat[1], kind, full=True)\\n # Build a vectorized evaluator\\n f = np.vectorize(lambda x: slope * x + const)\\n while vt[0] < dat[0][0] and len(vt) > 1:\\n vt = vt[1:]\\n while vt[-1] > dat[0][-1] and len(vt) > 1:\\n vt = vt[:-1]\\n if len(vt) <= 1:\\n return False\\n try:\\n # Interpret time series\\n out = f(vt)\\n except:\\n print 'Array.interpolate', self.path, vt, dat\\n raise\\n # Encode in (t,v) append-able list\\n out = np.array([vt, out]).transpose()\\n self.summary.commit(out)\\n return True\",\n \"def interp1d(f0, kind=\\\"slinear\\\"):\\n ndim = f0.ndim\\n if len(f0) != f0.size:\\n raise RuntimeError(\\\"1d array is only supported\\\")\\n continuous_f0 = f0.flatten()\\n nonzero_indices = np.where(continuous_f0 > 0)[0]\\n\\n # Nothing to do\\n if len(nonzero_indices) <= 0:\\n return f0\\n\\n # Need this to insert continuous values for the first/end silence segments\\n continuous_f0[0] = continuous_f0[nonzero_indices[0]]\\n continuous_f0[-1] = continuous_f0[nonzero_indices[-1]]\\n\\n # Build interpolation function\\n nonzero_indices = np.where(continuous_f0 > 0)[0]\\n interp_func = interpolate.interp1d(\\n nonzero_indices, continuous_f0[continuous_f0 > 0], kind=kind\\n )\\n\\n # Fill silence segments with interpolated values\\n zero_indices = np.where(continuous_f0 <= 0)[0]\\n continuous_f0[zero_indices] = interp_func(zero_indices)\\n\\n if ndim == 2:\\n return continuous_f0[:, None]\\n return continuous_f0\",\n \"def dask_gd2_nanfill(xx, yy, z_array, algorithm='cubic', **kwargs):\\n n_jobs = kwargs.pop(\\\"n_jobs\\\", 4)\\n chunk_size = kwargs.get(\\\"chunk_size\\\", int(xx.size / (n_jobs - 1)))\\n # make dask arrays\\n dask_xyz = da.from_array((xx, yy, z_array), chunks=(3, chunk_size, \\\"auto\\\"), name=\\\"dask_all\\\")\\n dask_xx = dask_xyz[0,:,:]\\n dask_yy = dask_xyz[1,:,:]\\n dask_zz = dask_xyz[2,:,:]\\n\\n # select only valid values\\n dask_valid_x1 = dask_xx[~da.isnan(dask_zz)]\\n dask_valid_y1 = dask_yy[~da.isnan(dask_zz)]\\n dask_valid_z1 = dask_zz[~da.isnan(dask_zz)]\\n\\n # interpolate for missing values\\n return dask_interpolate(dask_valid_x1, dask_valid_y1, dask_valid_z1, dask_xx, dask_yy, algorithm=algorithm, **kwargs)\",\n \"def replace_nan(data):\\r\\n lst_ind = np.array(['valence_intensity', 'anger_intensity',\\r\\n 'fear_intensity', 'sadness_intensity', 'joy_intensity'])\\r\\n for i in lst_ind:\\r\\n native = data[:][i]\\r\\n avg = np.nanmean(native)\\r\\n data[:][i] = np.where(np.isnan(native), avg, native)\\r\\n return data\",\n \"def get_signal_with_nans(self):\\n pairs = zip(self.time_scale, self.signal)\\n nan_pairs = zip(self.nan_times,[float('nan')]*len(self.nan_times))\\n pairs.extend(nan_pairs)\\n pairs.sort()\\n time_scale, signal = zip(*pairs)\\n return np.array(time_scale), np.array(signal)\",\n \"def ffill_1d_nb(a):\\n out = np.empty_like(a, dtype=np.float_)\\n lastval = a[0]\\n for i in range(a.shape[0]):\\n if np.isnan(a[i]):\\n out[i] = lastval\\n else:\\n lastval = out[i] = a[i]\\n return out\",\n \"def interpolate_eleMean(model):\\n # Get mean of columns (data at the same elevation) without taking int account NaNs\\n el_mean = nanmean(model,axis=0)\\n #print(el_mean) \\n # Find indices for NaNs, and replace them by the column mean\\n ind_nan = np.where(np.isnan(model))\\n model[ind_nan] = np.take(el_mean,ind_nan[1])\\n\\n return model\",\n \"def impute_cumulative_array(array):\\n array = np.array(array).copy()\\n array = convert_non_monotonic_to_nan(array)\\n array = log_interpolate(array)\\n return array\",\n \"def fill_nans(data):\\n for col in data.columns:\\n data[col].fillna(-999, inplace=True)\",\n \"def missing_values_ratio(series: TimeSeries) -> float:\\n\\n return series.pd_dataframe().isnull().sum().mean() / len(series)\",\n \"def _index_to_nan_fast(data, existing_nans, to_nan):\\n index_nan = []\\n randgen = (np.random.choice(len(data)) for _ in cnt(start=1))\\n for i in range(to_nan):\\n ix = next(filter(lambda x: x not in existing_nans and x not in index_nan, randgen))\\n index_nan.append(ix)\\n data_imp = data.copy()\\n data_imp[index_nan] = np.nan\\n return data_imp, index_nan\",\n \"def interpolate_time(x, xp, yp, clamp=True):\\n x = np.asarray(x)\\n \\n # Specific case for empty domain\\n if xp[0] == xp[-1] or len(xp)<2:\\n if len(x.shape) == 0:\\n return yp[0]\\n else:\\n return [yp[0],]*len(x)\\n\\n delta = np.cumsum(yp[1:] - yp[:-1])\\n delta = np.insert(delta, 0, 0)\\n dtype = delta.dtype\\n\\n interpolated = np.array(interpolate_number(x, xp, delta.astype(int), clamp))\\n return yp[0] + interpolated.astype(delta.dtype)\",\n \"def fix_null_vals(dataset):\\n\\tprint(\\\"\\\\tFixing null values\\\")\\n\\n\\tif not dataset.isnull().any().any():\\n\\t\\treturn dataset\\n\\telse:\\n\\t\\treturn dataset.fillna(method=\\\"ffill\\\")\",\n \"def interpolate(self, column_name):\\n self.check_for_column(column_name)\\n\\n start_date = min(self.data.index)\\n end_date = max(self.data.index)\\n date_range = pd.date_range(start_date, end_date, freq='H')\\n self.data = self.data.reindex(date_range)\\n column = self.data[column_name]\\n column = column.interpolate()\\n self.data[column_name] = column\",\n \"def interpolate(data, x):\\n n = len(data)\\n\\n if isinstance(data, dict):\\n if x in data:\\n return S(data[x])\\n X, Y = list(zip(*data.items()))\\n else:\\n if isinstance(data[0], tuple):\\n X, Y = list(zip(*data))\\n if x in X:\\n return S(Y[X.index(x)])\\n else:\\n if x in range(1, n + 1):\\n return S(data[x - 1])\\n Y = list(data)\\n X = list(range(1, n + 1))\\n\\n try:\\n return interpolating_poly(n, x, X, Y).expand()\\n except ValueError:\\n d = Dummy()\\n return interpolating_poly(n, d, X, Y).expand().subs(d, x)\",\n \"def linear_slope(self, dim=\\\"time\\\", nan_policy=\\\"none\\\"):\\n return linear_slope(self._obj, dim=dim, nan_policy=nan_policy)\",\n \"def test_linear_interpolation_range(self):\\n\\n for x in [[1.0, 2.0, 4.0], [-20, -19, 0], numpy.arange(200) + 1000]:\\n for y in [[5.0, 9.0], [100, 200, 10000]]:\\n\\n # Define ny by nx array with corresponding values\\n A = numpy.zeros((len(x), len(y)))\\n\\n # Define values for each x, y pair as a linear function\\n for i in range(len(x)):\\n for j in range(len(y)):\\n A[i, j] = linear_function(x[i], y[j])\\n\\n # Test that linearly interpolated points are correct\\n xis = numpy.linspace(x[0], x[-1], 100)\\n etas = numpy.linspace(y[0], y[-1], 100)\\n points = combine_coordinates(xis, etas)\\n\\n vals = interpolate2d(x, y, A, points, mode='linear')\\n refs = linear_function(points[:, 0], points[:, 1])\\n assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)\",\n \"def interp_ts(cls, df_in, ts_col, interp_ts):\\n\\n interp=interp1d(df_in['timestamp'],\\n df_in[ts_col],\\n kind='previous',\\n fill_value='extrapolate')\\n df_interp = pd.DataFrame({\\n 'date' : [datetime.utcfromtimestamp(t) for t in interp_ts],\\n 'timestamp' : interp_ts,\\n ts_col : interp(interp_ts)\\n })\\n\\n return df_interp\",\n \"def impute(sd, imputation_values, method='ffill', mask=True):\\n ts = pd.read_csv(os.path.join(sd, 'timeseries.csv'))\\n ts = ts.set_index('CHARTTIME')\\n\\n variables = list(imputation_values.keys())\\n\\n if mask:\\n # Create an imputation mask\\n ts_mask = ts.mask(ts.notna(), 1)\\n ts_mask = ts_mask.mask(ts.isna(), 0)\\n ts_mask = ts_mask.drop(['LOS_HOURS', 'TARGET_COARSE', 'TARGET_FINE'],\\n axis=1)\\n ts_mask = ts_mask.add_prefix('mask_')\\n\\n # Make sure that the first row contains values such that we can\\n # do a forward fill impute\\n for var in variables:\\n if math.isnan(ts[var].iloc[0]):\\n if var == 'WEIGHT' or var == 'HEIGHT':\\n ts[var].iloc[0] = imputation_values[var] \\\\\\n [str(int(round(ts['GESTATIONAL_AGE_DAYS'].iloc[0] / 7)\\n ))]\\n else:\\n ts[var].iloc[0] = imputation_values[var]\\n\\n\\n if method == 'ffill':\\n # Impute through forward filling\\n ts = ts.fillna(method='ffill')\\n elif method == 'zeros':\\n # Impute through filling with zeros\\n ts = ts.fillna(value=0)\\n else:\\n raise ValueError(f'{method} must be one of \\\"ffill\\\" or \\\"zeros\\\"')\\n\\n if mask:\\n # Concatenate the timeseries with the imputation mask\\n ts = pd.concat([ts, ts_mask], axis=1)\\n\\n ts.to_csv(os.path.join(sd, 'timeseries_imputed.csv'))\",\n \"def interpolate(self, N):\\n self.upsampling_rate *= N\\n self.data = np.array([interpolate_spline(x,N) for x in self.data])\",\n \"def interpolate(self, t):\\n try:\\n n = len(self.t)\\n except TypeError:\\n # self.t is not a sequence. Re-raise the exception\\n # with an appropriate error message.\\n raise TypeError(\\\"Please run the simulation first\\\")\\n else:\\n if (n < 2):\\n raise ValueError(\\\"Not enough simulation steps\\\")\\n tmin = self.t[0]\\n tmax = self.t[n-1]\\n if t < tmin or t > tmax:\\n raise ValueError(\\\"Requested time is outside the simulated interval\\\")\\n if self.adaptiveStepSize:\\n nbelow = bisect_right(self.t, t) - 1 \\n else:\\n dt = (tmax - tmin)*1.0/(n - 1)\\n nbelow = int(math.floor((t - tmin)/dt))\\n nabove = nbelow + 1\\n if nabove >= n:\\n nabove = n - 1\\n nbelow = nabove - 1\\n x = interpolate_Hermite(t, self.t[nbelow], self.x[nbelow], self.v[nbelow],\\n self.t[nabove], self.x[nabove], self.v[nabove])\\n v = interpolate_linear(t, self.t[nbelow], self.v[nbelow],\\n self.t[nabove], self.v[nabove])\\n return x, v\",\n \"def test_interpolate_values_2_hour_gap(self, forcing_processor):\\n forcing_processor.data = {}\\n forcing_processor.data['air_temperature'] = [\\n (datetime.datetime(2011, 9, 25, 9, 0, 0), 215.0),\\n (datetime.datetime(2011, 9, 25, 10, 0, 0), None),\\n (datetime.datetime(2011, 9, 25, 11, 0, 0), None),\\n (datetime.datetime(2011, 9, 25, 12, 0, 0), 230.0),\\n ]\\n forcing_processor.interpolate_values('air_temperature', 1, 2)\\n expected = (datetime.datetime(2011, 9, 25, 10, 0, 0), 220.0)\\n assert forcing_processor.data['air_temperature'][1] == expected\\n expected = (datetime.datetime(2011, 9, 25, 11, 0, 0), 225.0)\\n assert forcing_processor.data['air_temperature'][2] == expected\",\n \"def missing_data(self, plot=True):\\n # Set some of the observations to missing\\n self.remove_data()\\n # Create empty arrays to store values\\n F = np.zeros(len(self.y))\\n a = np.zeros(len(self.y))\\n v = np.zeros(len(self.y))\\n P = np.zeros(len(self.y))\\n # Initialize at the initial values parsed to the class\\n P[0] = self.pardict['P1']\\n sigma_eps2 = self.pardict['sigma_eps2']\\n sigma_eta2 = self.pardict['sigma_eta2']\\n # initialise a for estimated model\\n if self.var_name != 'Volume of Nile':\\n a[0] = self.y[0]\\n # Iterate\\n for t in range(0, len(self.y) - 1):\\n v[t] = np.nan_to_num(self.y[t] - a[t])\\n F[t] = P[t] + sigma_eps2 if np.isfinite(self.y[t]) else np.inf\\n # K is defined as ratio of P and F\\n Kt = P[t] / F[t] if np.isfinite(self.y[t]) else 0\\n a[t + 1] = a[t] + Kt * v[t]\\n P[t + 1] = P[t] * (1 - Kt) + sigma_eta2\\n v[-1] = self.y[-1] - a[-1]\\n F[-1] = P[-1] + sigma_eps2\\n # Obtain smoothed state\\n # Obtain all time values for L\\n L = self.pardict['sigma_eps2'] / F\\n index_missing = np.argwhere(np.isnan(self.y))\\n L[index_missing] = 1\\n\\n # Do the recursion for r\\n r = np.zeros(len(self.y))\\n N = np.zeros(len(self.y))\\n V = np.zeros(len(self.y))\\n\\n for t in np.arange(len(self.y) - 1, 0, -1):\\n r[t - 1] = v[t] / F[t] + L[t] * r[t]\\n for t in np.arange(len(self.y) - 1, 0, -1):\\n N[t - 1] = 1 / F[t] + L[t] ** 2 * N[t]\\n for t in np.arange(len(self.y) - 1, 0, -1):\\n V[t] = P[t] - P[t] ** 2 * N[t - 1]\\n\\n # Do the recursion for alpha\\n alphas = np.zeros(len(self.y))\\n alphas[0] = a[t]\\n for t in range(1, len(self.y) - 1):\\n alphas[t] = a[t] + P[t] * r[t - 1]\\n alphas[-1] = np.nan\\n\\n if plot:\\n fig_name = self.var_name + 'Fig25.pdf'\\n plot_fig2_5(self.times, self.y, a, P, alphas, V, fname=fig_name, var_name=self.var_name)\\n # Restore data\\n self.reset_data()\",\n \"def fill_lower_part(data, index, aver_num=3):\\n if type(data) is LidarDataset:\\n d = data['data']\\n else:\\n d = data\\n to_fill = np.ma.masked_invalid(d[..., index:index+aver_num]).mean(axis=-1)[..., np.newaxis]\\n d[..., :index] = to_fill\",\n \"def replace_nan(data_jets):\\n \\n data_mean = np.empty_like(data_jets)\\n data_median = np.empty_like(data_jets)\\n data_null = np.empty_like(data_jets)\\n \\n for jet in range(4):\\n # Replace Remaining undefined values by Mean, median or zero\\n data_mean[jet] = np.where(np.isnan(data_jets[jet]), np.nanmean(data_jets[jet], axis=0), data_jets[jet])\\n data_median[jet] = np.where(np.isnan(data_jets[jet]), np.nanmedian(data_jets[jet], axis=0), data_jets[jet])\\n data_null[jet] = np.where(np.isnan(data_jets[jet]), np.float64(0), data_jets[jet])\\n \\n return data_mean, data_median, data_null\",\n \"def interpolate_inverse(series, **options):\\n inverse = pd.Series(series.index, index=series.values)\\n interp_func = interpolate(inverse, **options)\\n return interp_func\",\n \"def FillNA(df, method: str = 'ffill', window: int = 10):\\n method = str(method).replace(\\\" \\\", \\\"_\\\")\\n\\n if method == 'zero':\\n return fill_zero(df)\\n\\n elif method == 'ffill':\\n return fill_forward(df)\\n\\n elif method == 'mean':\\n return fill_mean(df)\\n\\n elif method == 'median':\\n return fill_median(df)\\n\\n elif method == 'rolling_mean':\\n return rolling_mean(df, window=window)\\n\\n elif method == 'rolling_mean_24':\\n return rolling_mean(df, window=24)\\n\\n elif method == 'ffill_mean_biased':\\n return biased_ffill(df)\\n\\n elif method == 'fake_date':\\n return fake_date_fill(df, back_method='slice')\\n\\n elif method in df_interpolate_full:\\n df = df.interpolate(method=method, order=5).fillna(method='bfill')\\n if df.isnull().values.any():\\n df = fill_forward(df)\\n return df\\n\\n elif method == 'IterativeImputer':\\n cols = df.columns\\n indx = df.index\\n try:\\n from sklearn.experimental import enable_iterative_imputer # noqa\\n except Exception:\\n pass\\n from sklearn.impute import IterativeImputer\\n\\n df = IterativeImputer(random_state=0, max_iter=100).fit_transform(df)\\n if not isinstance(df, pd.DataFrame):\\n df = pd.DataFrame(df)\\n df.index = indx\\n df.columns = cols\\n return df\\n\\n elif method == 'IterativeImputerExtraTrees':\\n cols = df.columns\\n indx = df.index\\n try:\\n from sklearn.experimental import enable_iterative_imputer # noqa\\n except Exception:\\n pass\\n from sklearn.ensemble import ExtraTreesRegressor\\n from sklearn.impute import IterativeImputer\\n\\n df = IterativeImputer(\\n ExtraTreesRegressor(n_estimators=10, random_state=0),\\n random_state=0,\\n max_iter=100,\\n ).fit_transform(df)\\n if not isinstance(df, pd.DataFrame):\\n df = pd.DataFrame(df)\\n df.index = indx\\n df.columns = cols\\n return df\\n\\n elif method == 'KNNImputer':\\n cols = df.columns\\n indx = df.index\\n from sklearn.impute import KNNImputer\\n\\n df = KNNImputer(n_neighbors=5).fit_transform(df)\\n if not isinstance(df, pd.DataFrame):\\n df = pd.DataFrame(df)\\n df.index = indx\\n df.columns = cols\\n return df\\n\\n elif method is None or method == 'None':\\n return df\\n\\n else:\\n print(f\\\"FillNA method `{str(method)}` not known, returning original\\\")\\n return df\",\n \"def interp_spectrum(spectrum, energies, new_energies):\\n I = interp1d(energies, spectrum, bounds_error=False)\\n new_spec = I(new_energies)\\n if np.any(np.isnan( new_spec)):\\n print(\\\"There are Nan in your spectrum...\\\")\\n new_spec[ new_spec < 0] = 0\\n return new_spec\",\n \"def loginterpolate(x_vector, y_vector, xout, replace_nan=np.nan):\\n interpolant = interpolate.interp1d(np.log10(x_vector), np.log10(y_vector),\\n bounds_error=False,\\n fill_value=np.nan)\\n yout = 10. ** interpolant(np.log10(xout))\\n if replace_nan != np.nan:\\n yout[np.isnan(yout)] = replace_nan\\n return yout\",\n \"def stupidImputer(vals, ti, ws):\\n imputed = vals.copy()\\n\\n for s in range(0, vals.shape[1]):\\n for t in range(ti, ti + ws):\\n if np.isnan(imputed[t, s]):\\n lastObsT = t-1\\n while np.isnan(imputed[lastObsT, s]):\\n lastObsT = lastObsT - 1\\n\\n imputed[t, s] = imputed[lastObsT, s]\\n\\n return imputed\",\n \"def nan_interp(A):\\n\\tni,nj = np.shape(A)\\n\\t# extend edges of A by one\\n\\tA = np.concatenate((np.array([A[:,0]]).transpose(),A,np.array([A[:,-1]]).transpose()),axis=1)\\n\\tA = np.concatenate((np.array([A[0,:]]),A,np.array([A[-1,:]])),axis=0)\\n\\t\\n\\t#nit = 0\\n\\t#while np.sum(np.isnan(A)) != 0:\\n\\t#nit+=1\\n\\tnanp = np.isnan(A)\\n\\tfor i in range(1,ni+1):\\n\\t\\tfor j in range(1,nj+1):\\n\\t\\t\\tif nanp[i,j]:\\n\\t\\t\\t\\t#\\t# edges\\n\\t\\t\\t\\t#\\tif (i==0) & (j!=0)& (j!=nj-1):\\n\\t\\t\\t\\t#\\t\\tb = np.array([A[i+1,j],A[i,j-1],A[i,j+1]])\\n\\t\\t\\t\\t#\\tif (i==ni-1) & (j!=0)& (j!=nj-1):\\n\\t\\t\\t\\t#\\t\\tb = np.array([A[i-1,j],A[i,j-1],A[i,j+1]])\\n\\t\\t\\t\\t#\\tif (j==0) & (i!=0)& (i!=ni-1):\\n\\t\\t\\t\\t#\\t\\tb = np.array([A[i-1,j],A[i+1,j],A[i,j+1]])\\n\\t\\t\\t\\t#\\tif (j==nj-1) & (i!=0)& (i!=ni-1):\\n\\t\\t\\t\\t#\\t\\tb = np.array([A[i-1,j],A[i+1,j],A[i,j-1]])\\n\\t\\t\\t\\t#\\t# corners\\n\\t\\t\\t\\t#\\tif (i==0) & (j==0):\\n\\t\\t\\t\\t#\\t\\tb = np.array([A[i+1,j],A[i,j+1]])\\n\\t\\t\\t\\t#\\tif (i==ni-1) & (j==0):\\n\\t\\t\\t\\t#\\t\\tb = np.array([A[i-1,j],A[i,j+1]])\\n\\t\\t\\t\\t#\\tif (i==0) & (j==nj-1):\\n\\t\\t\\t\\t#\\t\\tb = np.array([A[i+1,j],A[i,j-1]])\\n\\t\\t\\t\\t#\\tif (i==ni-1) & (j==nj-1):\\n\\t\\t\\t\\t#\\t\\tb = np.array([A[i-1,j],A[i,j-1]])\\n\\t\\t\\t\\t#\\t# core\\n\\t\\t\\t\\t#\\telse:\\n\\t\\t\\t\\tb = np.array([A[i-1,j],A[i,j-1],A[i+1,j],A[i,j+1]])\\n\\t\\t\\t\\tsnan = np.sum(np.isnan(b))\\n\\t\\t\\t\\tsb = np.nansum(b)\\n\\t\\t\\t\\tA[i,j] = sb/(len(b)-snan)\\n\\t\\t\\t\\t#print(i,j)\\n\\t# only the core matters\\n\\tA = A[1:ni+1,1:nj+1]\\n\\treturn A\",\n \"def fill_forward(df):\\n df = df.fillna(method='ffill')\\n df = df.fillna(method='bfill').fillna(0)\\n return df\",\n \"def data_missing() -> ExtensionArray:\\n data_matrix = np.arange(\\n 2 * 10 * 10 * 3,\\n dtype=np.float_,\\n ).reshape(2, 10, 10, 3)\\n data_matrix[0, ...] = np.NaN\\n grid_points = [\\n np.arange(10),\\n np.arange(10) / 10,\\n ]\\n\\n return skfda.FDataGrid(data_matrix, grid_points=grid_points)\",\n \"def missing_values(self, layout={}, **kwargs):\\n df = self._data.isna().astype(int)\\n kwargs.update(\\n {'zmin': 0, 'zmax': 1,\\n 'colors': 'reds', 'ncolors': 9,\\n 'xgap': 3, 'ygap': 3,\\n 'showscale': False, }\\n )\\n\\n layout = recursive_update(\\n layout, updater={\\n 'xaxis': {'showgrid': False, 'zeroline': False},\\n 'yaxis': {'showgrid': False, 'zeroline': False},\\n })\\n return df.iplot.heatmap(layout=layout, **kwargs)\",\n \"def interpolation(x, y, x_new, model=\\\"InterpolatedUnivariateSpline\\\", plot=False, title=\\\"\\\"):\\n\\n num_col = y.shape[1]\\n\\n if isinstance(x_new, float):\\n num_row = 1\\n else:\\n num_row = len(x_new)\\n\\n y_new = np.zeros((num_row, num_col))\\n y_new_dot = np.zeros((num_row, num_col))\\n\\n if model == \\\"InterpolatedUnivariateSpline\\\":\\n\\n for idx in range(0, num_col):\\n\\n # TODO: InterpolatedUnivariateSpline seems to have problems with multidimensional arrays\\n interpolate_ = interpolate.InterpolatedUnivariateSpline(x, y[:, idx], k=3)\\n y_new[:, idx] = interpolate_(x_new)\\n # y_new_dot[:,idx] = spline.derivative()(x_new) #TODO: Does this work too?\\n y_new_dot[:, idx] = _determine_time_derivate(interpolate_, x_new, 1)\\n\\n if plot == True:\\n _plot_interpolation(x, y, x_new, y_new, title=title)\\n\\n elif model == \\\"interp1d\\\":\\n\\n # TODO: Extrapolation has to be handled (e.g. with fill_value=(0.0,0.0) argument)\\n interpolate_ = interpolate.interp1d(x, y, kind=\\\"cubic\\\", axis=0, bounds_error=False)\\n y_new = interpolate_(x_new)\\n y_new_dot = _determine_time_derivate(interpolate_, x_new, num_col)\\n\\n if plot == True:\\n _plot_interpolation(x, y, x_new, y_new, title=title)\\n\\n elif model == \\\"lagrange\\\":\\n\\n for idx in range(0, num_col):\\n\\n # Rescaling of data necessary, because Lagrange interpolation is numerical unstable\\n xm = np.mean(x)\\n ym = np.mean(y[:, idx])\\n xs = np.std(x)\\n ys = np.std(y[:, idx])\\n xscaled = (x - xm) / xs\\n yscaled = (y[:, idx] - ym) / ys\\n\\n # interpolate_ = interpolate.lagrange(xscaled, yscaled) # worst performance\\n # interpolate_ = _lagrange2(xscaled, yscaled) # improved performance\\n interpolate_ = _lagrange(xscaled, yscaled) # fastest performance\\n y_new[:, idx] = interpolate_((x_new - xm) / xs) * ys + ym\\n\\n # Determine derivate of 'y_new'\\n t_offset = 0.5\\n y1 = interpolate_((x_new - t_offset - xm) / xs) * ys + ym\\n y2 = interpolate_((x_new + t_offset - xm) / xs) * ys + ym\\n\\n y_new_dot[:, idx] = (y2 - y1) / (t_offset * 2)\\n\\n if plot == True:\\n _plot_interpolation(x, y, x_new, y_new, title=title)\\n\\n elif model == \\\"BarycentricInterpolator\\\":\\n\\n for idx in range(0, num_col):\\n\\n # TODO: Is the rescaling of data necessary here?\\n xm = np.mean(x)\\n ym = np.mean(y[:, idx])\\n xs = np.std(x)\\n ys = np.std(y[:, idx])\\n xscaled = (x - xm) / xs\\n yscaled = (y[:, idx] - ym) / ys\\n\\n interpolate_ = interpolate.BarycentricInterpolator(xscaled, yscaled)\\n y_new[:, idx] = interpolate_((x_new - xm) / xs) * ys + ym\\n\\n # Determine derivate of 'y_new'\\n t_offset = 0.5\\n y1 = interpolate_((x_new - t_offset - xm) / xs) * ys + ym\\n y2 = interpolate_((x_new + t_offset - xm) / xs) * ys + ym\\n\\n y_new_dot[:, idx] = (y2 - y1) / (t_offset * 2)\\n\\n if plot == True:\\n _plot_interpolation(x, y, x_new, y_new, title=title)\\n\\n # elif model == 'polyfit':\\n\\n # #y_new_dot = np.zeros((len(x_new), num_col))\\n # degree = 2\\n # poly_dot_coeff = np.zeros((degree, num_col))\\n #\\n\\n # #TODO: Polynomial fit over the complete time period of 3 days are not convenient.\\n # # Difference of up to 1000 km between original SP3 orbit data and polynomial\\n # # fit.\\n\\n # x_ref = np.amin(x) #TODO: orb_ref_time should be part of 'orb' Dataset\\n # x = (x - x_ref) * 86400\\n # poly_coeff = poly.polyfit(x, y, degree)\\n # y_new = poly.polyval(x_new,poly_coeff)\\n\\n # for idx in range(0,num_col):\\n # poly_dot_coeff[:,idx] = np.polyder(poly_coeff[:,idx])\\n # polynomial_dot = np.poly1d(poly_dot_coeff[:,idx])\\n # y_new_dot[:,idx] = polynomial_dot((x_new - x_ref) * 86400)\\n\\n # if plot == True:\\n # _plot_orbit_polynomial(sat, poly_coeff, x, y)\\n\\n return y_new, y_new_dot\",\n \"def plot_tseries(*args, **kwargs) :\\n data = kwargs.pop('data')\\n return data.dropna().plot(x=args[0], y=args[1], **kwargs)\",\n \"def _index_to_nan(data, existing_nans, to_nan):\\n index_nan = np.random.choice([i for i in range(len(data)) if i not in existing_nans],\\n size=to_nan, replace=False)\\n data_imp = data.copy()\\n data_imp[index_nan] = np.nan\\n return data_imp, index_nan\",\n \"def fill_gaps(\\n t, order=1, extrapolate=0, frame_max=None, hdim_1_max=None, hdim_2_max=None\\n):\\n\\n from scipy.interpolate import InterpolatedUnivariateSpline\\n\\n logging.debug(\\\"start filling gaps\\\")\\n\\n t_list = [] # empty list to store interpolated DataFrames\\n\\n # group by cell number and perform process for each cell individually:\\n t_grouped = t.groupby(\\\"cell\\\")\\n for cell, track in t_grouped:\\n # Setup interpolator from existing points (of order given as keyword)\\n frame_in = track[\\\"frame\\\"].values\\n hdim_1_in = track[\\\"hdim_1\\\"].values\\n hdim_2_in = track[\\\"hdim_2\\\"].values\\n s_x = InterpolatedUnivariateSpline(frame_in, hdim_1_in, k=order)\\n s_y = InterpolatedUnivariateSpline(frame_in, hdim_2_in, k=order)\\n\\n # Create new index filling in gaps and possibly extrapolating:\\n index_min = min(frame_in) - extrapolate\\n index_min = max(index_min, 0)\\n index_max = max(frame_in) + extrapolate\\n index_max = min(index_max, frame_max)\\n new_index = range(index_min, index_max + 1) # +1 here to include last value\\n track = track.reindex(new_index)\\n\\n # Interpolate to extended index:\\n frame_out = new_index\\n hdim_1_out = s_x(frame_out)\\n hdim_2_out = s_y(frame_out)\\n\\n # Replace fields in data frame with\\n track[\\\"frame\\\"] = new_index\\n track[\\\"hdim_1\\\"] = hdim_1_out\\n track[\\\"hdim_2\\\"] = hdim_2_out\\n track[\\\"cell\\\"] = cell\\n\\n # Append DataFrame to list of DataFrames\\n t_list.append(track)\\n # Concatenate interpolated trajectories into one DataFrame:\\n t_out = pd.concat(t_list)\\n # Restrict output trajectories to input data in time and space:\\n t_out = t_out.loc[\\n (t_out[\\\"hdim_1\\\"] < hdim_1_max)\\n & (t_out[\\\"hdim_2\\\"] < hdim_2_max)\\n & (t_out[\\\"hdim_1\\\"] > 0)\\n & (t_out[\\\"hdim_2\\\"] > 0)\\n ]\\n t_out = t_out.reset_index(drop=True)\\n return t_out\",\n \"def data_missing(data):\\n return type(data)._from_sequence([None, data[0]])\",\n \"def ts_argmin(x: pd.Series, d: int or float) -> pd.Series:\\n if isinstance(d, float):\\n d = math.floor(d)\\n if isinstance(x.index, pd.MultiIndex):\\n return x.groupby(level=1).rolling(d).apply(lambda r: d - np.nanargmin(r)).droplevel(0).sort_index()\\n else:\\n return x.rolling(d).apply(lambda r: d - np.nanargmin(r))\",\n \"def ts_fit(series: TimeSeries) -> TimeSeries:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7629114","0.7248559","0.71787924","0.7115637","0.70973015","0.7010133","0.69124943","0.68163675","0.68025076","0.67993385","0.6695243","0.6660976","0.66580003","0.6648088","0.6600599","0.6532182","0.6478971","0.6434493","0.63816845","0.6318016","0.62516487","0.6093491","0.6064243","0.6048406","0.6040174","0.5955138","0.5944428","0.590514","0.5899695","0.58976364","0.58882236","0.58839464","0.5811134","0.5802838","0.5776383","0.57570624","0.57340175","0.57333547","0.5725579","0.5719774","0.57011914","0.56550974","0.56373537","0.5618691","0.5607732","0.5575338","0.5563171","0.5539942","0.5502402","0.5477878","0.5437451","0.54141223","0.5408161","0.5395678","0.5333488","0.5327655","0.5318879","0.53152794","0.5308393","0.5302605","0.5302174","0.52945495","0.5262936","0.5253622","0.5245622","0.52195066","0.52128345","0.52074736","0.51869524","0.51826876","0.5175513","0.51752675","0.5175013","0.51656526","0.5146438","0.51463807","0.51425546","0.5130391","0.5129941","0.51275027","0.5109617","0.5074785","0.5069916","0.50699085","0.50683755","0.50644803","0.5049768","0.503547","0.50316554","0.5026985","0.5019734","0.49818975","0.49536362","0.49535367","0.49485162","0.49306837","0.49218085","0.49193206","0.49108854","0.49075648","0.48961523"],"string":"[\n \"0.7629114\",\n \"0.7248559\",\n \"0.71787924\",\n \"0.7115637\",\n \"0.70973015\",\n \"0.7010133\",\n \"0.69124943\",\n \"0.68163675\",\n \"0.68025076\",\n \"0.67993385\",\n \"0.6695243\",\n \"0.6660976\",\n \"0.66580003\",\n \"0.6648088\",\n \"0.6600599\",\n \"0.6532182\",\n \"0.6478971\",\n \"0.6434493\",\n \"0.63816845\",\n \"0.6318016\",\n \"0.62516487\",\n \"0.6093491\",\n \"0.6064243\",\n \"0.6048406\",\n \"0.6040174\",\n \"0.5955138\",\n \"0.5944428\",\n \"0.590514\",\n \"0.5899695\",\n \"0.58976364\",\n \"0.58882236\",\n \"0.58839464\",\n \"0.5811134\",\n \"0.5802838\",\n \"0.5776383\",\n \"0.57570624\",\n \"0.57340175\",\n \"0.57333547\",\n \"0.5725579\",\n \"0.5719774\",\n \"0.57011914\",\n \"0.56550974\",\n \"0.56373537\",\n \"0.5618691\",\n \"0.5607732\",\n \"0.5575338\",\n \"0.5563171\",\n \"0.5539942\",\n \"0.5502402\",\n \"0.5477878\",\n \"0.5437451\",\n \"0.54141223\",\n \"0.5408161\",\n \"0.5395678\",\n \"0.5333488\",\n \"0.5327655\",\n \"0.5318879\",\n \"0.53152794\",\n \"0.5308393\",\n \"0.5302605\",\n \"0.5302174\",\n \"0.52945495\",\n \"0.5262936\",\n \"0.5253622\",\n \"0.5245622\",\n \"0.52195066\",\n \"0.52128345\",\n \"0.52074736\",\n \"0.51869524\",\n \"0.51826876\",\n \"0.5175513\",\n \"0.51752675\",\n \"0.5175013\",\n \"0.51656526\",\n \"0.5146438\",\n \"0.51463807\",\n \"0.51425546\",\n \"0.5130391\",\n \"0.5129941\",\n \"0.51275027\",\n \"0.5109617\",\n \"0.5074785\",\n \"0.5069916\",\n \"0.50699085\",\n \"0.50683755\",\n \"0.50644803\",\n \"0.5049768\",\n \"0.503547\",\n \"0.50316554\",\n \"0.5026985\",\n \"0.5019734\",\n \"0.49818975\",\n \"0.49536362\",\n \"0.49535367\",\n \"0.49485162\",\n \"0.49306837\",\n \"0.49218085\",\n \"0.49193206\",\n \"0.49108854\",\n \"0.49075648\",\n \"0.48961523\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77469,"cells":{"query":{"kind":"string","value":"Performs a running average of an input time series using uniform window of width w. This function assumes that the input time series is periodic."},"document":{"kind":"string","value":"def runavg(ts, w):\n # Original length of ts\n N = len(ts)\n # make ts three-fold periodic\n ts = np.append(ts, np.append(ts, ts))\n # smooth by convolution with a window of equal weights\n ts_smooth = np.convolve(ts, np.ones(w) / w, mode=\"same\")\n # Only output central section, of length equal to the original length of ts\n ts = ts_smooth[N : 2 * N]\n\n return ts"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def moving_average(data, temporal_window=100):\n window = np.ones(temporal_window) / temporal_window\n return np.convolve(data, window, 'valid')","def moving_average(data, window_size):\n window= np.ones(int(window_size))/float(window_size)\n return np.convolve(data, window, 'same')","def running_mean(data, n):\n\treturn np.convolve(data, np.ones((n, ))/n)[(n-1):-(n-1)]","def moving_average(x, window_len = 11):\n\n one_over_size = 1./float(window_len)\n window = np.ones(window_len) * one_over_size\n result = np.convolve(x, window, 'valid')\n # Cut off the clutter at the boundaries\n #return result[ (window_len-1)/2 : -(window_len-1)/2]\n return result","def get_running_mean(data,time_window):\n \n print('--> Starting to calculate running mean') \n timer_start = dt.now()\n filt = [1./float(time_window)]*int(time_window)\n running_mean = np.apply_along_axis(lambda m: np.convolve(m, filt, mode='valid'), axis=0, arr=data)\n running_mean = np.append(np.ones([len(data)-len(running_mean),*data.shape[1:]])*np.nan,running_mean,axis=0)\n print('--> Completed calculating running mean (%.1f seconds)' \\\n % (dt.now()-timer_start).total_seconds())\n return running_mean","def running_mean(x, N):\n return np.convolve(x, np.ones((N,))/N, mode='valid')","def running_mean(x, N):\n cumsum = np.cumsum(np.insert(x, 0, 0)) \n return (cumsum[N:] - cumsum[:-N]) / float(N)","def moving_average(time_series, window_len):\n # Re-index the time series with duration in seconds from the first value\n time_series.index = (\n (time_series.index\n - time_series.index[0]) / np.timedelta64(1, 's')).astype('int')\n\n window_area = time_series.iloc[0] * window_len\n\n # It may not always be possible to construct a window of length exactly equal\n # to window_len using timestamps present in the data. To handle this, the left\n # side of the window is allowed to fall between timestamps (the right side is\n # always fixed to a timestamp in the data). Therefore we need to separately\n # compute the area of the inter-timestamp region on the left side of the\n # window so that it can be added to the window area. left_area is that value.\n left_area = window_area\n\n out = np.zeros(len(time_series))\n out[0] = time_series.iloc[0]\n\n # i is the left side of the window and j is the right\n i = 0\n for j in xrange(1, len(time_series)):\n # Remove the last iteration's left_area as a new right window bound may\n # change the left_area required in this iteration\n window_area -= left_area\n\n # Expand window to the right\n window_area += time_series.iloc[j-1] * (time_series.index[j]\n - time_series.index[j-1])\n\n # Shrink window from the left if expanding to the right has created too\n # large a window. new_left_time may fall between timestamps present in the\n # data, which is fine, since that's handled by left_area.\n new_left_time = time_series.index[j] - window_len\n while time_series.index[i] < new_left_time:\n window_area -= time_series.iloc[i] * (time_series.index[i+1]\n - time_series.index[i])\n i += 1\n\n # Add left side inter-timestamp area to window\n left_area = time_series.iloc[max(0, i - 1)] * (time_series.index[i]\n - new_left_time)\n window_area += left_area\n\n out[j] = window_area / window_len\n\n return out","def moving_average(data, width):\n return np.convolve(data, np.ones(width), 'same') / width","def running_mean(x, N):\n cumsum = np.cumsum(np.insert(x, 0, 0))\n return (cumsum[N:] - cumsum[:-N]) / float(N)","def sliding_window_average(data, weights=None, window_size=500, shift_size=1):\n num_chunks = len(_sliding_window_chunkoffsets(data, window_size, shift_size))\n if num_chunks == 0:\n return np.asarray([])\n if weights is None:\n return _numba_sliding_window_average(data, num_chunks, window_size, shift_size)\n else:\n return _numba_sliding_window_average_with_weights(data, num_chunks, weights, window_size, shift_size)","def runmean(y, n, /, wintype='boxcar', axis=-1, pad=np.nan):\n # * For 1-D data numpy `convolve` would be appropriate, problem is `convolve`\n # doesn't take multidimensional input!\n # * If `y` has odd number of obs along axis, result will have last element\n # trimmed. Just like `filter`.\n # * Strides are apparently the 'number of bytes' one has to skip in memory\n # to move to next position *on the given axis*. For example, a 5 by 5\n # array of 64bit (8byte) values will have array.strides == (40, 8).\n # Roll axis, reshape, and get generate running dimension\n if axis < 0:\n axis += y.ndim\n y = np.moveaxis(y, axis, -1)\n w = window(n, wintype)\n\n # Manipulate array\n shape = y.shape[:-1] + (y.shape[-1] - (n - 1), n)\n strides = (*y.strides, y.strides[-1]) # repeat striding on end\n yr = np.lib.stride_tricks.as_strided(y, shape=shape, strides=strides)\n yr = (yr * w).mean(axis=-1) # broadcasts to right\n\n # Optionally fill the rows taken up\n # NOTE: Data might be shifted left by one if had even numbered window\n if pad is not None:\n n1 = (y.shape[-1] - yr.shape[-1]) // 2\n n2 = (y.shape[-1] - yr.shape[-1]) - n1\n y1 = pad * np.ones((*yr.shape[:-1], n1))\n y2 = pad * np.ones((*yr.shape[:-1], n2))\n yr = np.concatenate((y1, yr, y2), axis=-1)\n\n return np.moveaxis(yr, -1, axis)","def windowMean(data, time=[], winsize=0, overlap=0, st_time=None, op=np.mean):\n #check inputs and initialize\n #Set resolution to 1 if no times supplied\n if len(time) == 0:\n startpt, res = 0, 1\n time = list(range(len(data)))\n pts = True\n else:\n if len(data) != len(time):\n raise ValueError('windowmean error: data and time must have same length')\n #First check if datetime objects\n try:\n assert type(winsize) == datetime.timedelta\n assert type(overlap) == datetime.timedelta\n except AssertionError:\n raise TypeError('windowmean error: winsize/overlap must be timedeltas')\n pts = False #force time-based averaging\n if (type(time[0]) != datetime.datetime):\n startpt = time[0]\n\n #now actually do windowing mean\n outdata, outtime = [], []\n data = np.array(data)\n if pts:\n #loop for fixed number of points in window\n try:\n inttypes = (int, long)\n except NameError:\n inttypes = (int,)\n if not isinstance(winsize, inttypes):\n winsize = int(round(winsize))\n warnings.warn('windowmean: non-integer windowsize, rounding to %d' \\\n % winsize)\n if winsize < 1:\n winsize = 1\n warnings.warn('windowmean: window length < 1, defaulting to 1')\n if overlap >= winsize:\n overlap = winsize - 1\n warnings.warn('''windowmean: overlap longer than window, truncated to\n %d''' % overlap)\n lastpt = winsize-1 #set last point to end of window size\n while lastpt < len(data):\n datwin = np.ma.masked_where(np.isnan(data[startpt:startpt+winsize]), \\\n data[startpt:startpt+winsize])\n getmean = op(datwin.compressed()) #mean of window, excl. NaNs\n gettime = (time[startpt+winsize] - time[startpt])/2. \\\n + time[startpt]#new timestamp\n startpt = startpt+winsize-overlap\n lastpt = startpt+winsize\n outdata.append(getmean) #construct output arrays\n outtime.append(gettime)\n else:\n #loop with time-based window\n lastpt = time[0] + winsize\n delta = datetime.timedelta(microseconds=1) #TODO: replace this with an explicit check for times on the boundary?\n if st_time:\n startpt = st_time\n else:\n startpt = time[0]\n if overlap >= winsize:\n raise ValueError('Overlap requested greater than size of window')\n while startpt < time[-1]:\n getinds = tOverlapHalf([startpt,startpt+winsize-delta], time, presort=True)\n if getinds: #if not None\n getdata = np.ma.masked_where(np.isnan(data[getinds[0]:getinds[-1]+1]), data[getinds[0]:getinds[-1]+1])\n getmean = op(getdata.compressed()) #find mean excluding NaNs\n else:\n getmean = np.nan\n gettime = startpt + winsize//2 #new timestamp -floordiv req'd with future division\n startpt = startpt + winsize - overlap #advance window start\n lastpt = startpt + winsize\n outdata.append(getmean) #construct output arrays\n outtime.append(gettime)\n\n return outdata, outtime","def moving_avg(a, halfwindow, mask=None):\r\n\r\n\r\n if mask is None:\r\n mask = np.ones_like(a, dtype='bool')\r\n\r\n zeros = np.zeros(a.shape[:-1] + (halfwindow,))\r\n falses = zeros.astype('bool')\r\n\r\n a_padded = np.concatenate((zeros, np.where(mask, a, 0), zeros), axis=-1)\r\n mask_padded = np.concatenate((falses, mask, falses), axis=-1)\r\n\r\n\r\n npt = 2 * halfwindow + 1 # total size of the averaging window\r\n rolling_a = as_strided(a_padded,\r\n shape=a.shape + (npt,),\r\n strides=a_padded.strides + (a.strides[-1],))\r\n rolling_mask = as_strided(mask_padded,\r\n shape=mask.shape + (npt,),\r\n strides=mask_padded.strides + (mask.strides[-1],))\r\n\r\n # moving average\r\n n = rolling_mask.sum(axis=-1)\r\n return np.where(n > 0, rolling_a.sum(axis=-1).astype('float') / n, np.nan)","def rolling_mean(\n self,\n window: tuple[int | int] = (0, 0),\n where: tuple[float | float] = (-inf, inf),\n ) -> pd.Series:\n where = _replace_none_with_infs(where)\n assert len(window) == 2, \"Window should be a listlike object of length 2.\"\n left_delta, right_delta = window\n lower, upper = where\n clipped = self.clip(lower, upper)\n if clipped._data is None:\n return pd.Series([clipped.initial_value] * 2, index=where)\n step_points = clipped._data.index\n sample_points = pd.Index.union(\n step_points - left_delta,\n step_points - right_delta,\n )\n ii = pd.IntervalIndex.from_arrays(\n sample_points + left_delta, sample_points + right_delta\n )\n s = pd.Series(\n clipped.slice(ii).mean().values,\n index=sample_points,\n )\n if lower != -inf:\n s = s.loc[s.index >= lower - left_delta]\n if upper != inf:\n s = s.loc[s.index <= upper - right_delta]\n return s","def ExpMovingAverage(values, window):\n weights = np.exp(np.linspace(-1.0, 0.0, window))\n weights /= weights.sum()\n a = np.convolve(values, weights, mode=\"full\")[: len(values)]\n a[:window] = a[window]\n return a","def running_avg (mylist, N):\n import numpy as np\n \n cumsum = np.cumsum(np.insert(mylist, 0, 0))\n return (cumsum[N:] - cumsum[:-N]) / float(N)","def daymean(x):\n return nstepf(x, 24, func='mean')","def moving_average(x, n, type='simple'):\n x = np.asarray(x)\n if type == 'simple':\n weights = np.ones(n)\n else:\n weights = np.exp(np.linspace(-1., 0., n))\n\n weights /= weights.sum()\n\n a = np.convolve(x, weights, mode='full')[:len(x)]\n a[:n] = a[n]\n return a","def smooth_avg(arr, winlen):\n\n window = np.ones(winlen) / (1.0 * winlen)\n return fftconvolve(arr, window, 'same')","def moving_average_forecast(series, window_size):\n\tforecast= []\n\tfor time in range(len(series)- window_size):\n\t\tforecast.append(series[time:time + window_size].mean())\n\treturn np.array(forecast)","def moving_rms(x, window_len = 11, smp=False, nthreads=4):\n #if ( np.size(running_mean) != np.size(x)):\n # raise ValueError, \"array and running mean of the array must be the same size.\"\n # Assure that window_len is even. If not, decrease window_len by one\n if window_len % 2 == 0:\n window_len = window_len-1\n i0 = (window_len - 1) / 2\n result = np.zeros_like(x)\n if smp:\n print 'smp Keyword does not do anything yet'\n\n# if smp:\n# p = Pool(nthreads)\n# f = lambda x, i, i0: x[i-i0:i+i0].std()\n#\n# else:\n for i in np.arange(i0, x.size - i0):\n #result[i] = x[i-i0:i+i0].std()\n result[i] = np.sqrt((x[i - i0:i + i0 + 1] * x[i - i0 : i + i0 + 1]).sum() / (window_len))\n\n return result[i0:-i0]","def agc(data,dt,wagc=1.0):\n nt = data.shape[0] # number of time samples\n iwagc = int(wagc/dt/2) # half window size in samples\n data_orig = np.copy(data) # copy of input data\n d = data_orig # copy of input data\n nwin = iwagc # nwin is #samples in RMS computation\n sum = 0\n\n # compute initial window for first datum\n sum = np.apply_along_axis(lambda m: np.sum(m[0:iwagc]**2.0), axis=0, arr=d)\n with np.errstate(divide='ignore', invalid='ignore'):\n d[0,:] = np.true_divide(data_orig[0,:],np.sqrt(sum/nwin))\n d[0,d[0,:] == np.inf] = 0\n d[0,:] = np.nan_to_num(d[0,:])\n\n # The value tmp gets subtracted each time the moving window moves 1 sample\n # forward\n tmp = data_orig[0]**2.0\n\n # ramping on\n # Add a squared sample and increase window length nwin each iteration\n for t in range(0,iwagc+1):\n sum = sum + data_orig[t+iwagc,:]**2.0\n nwin += 1\n with np.errstate(divide='ignore', invalid='ignore'):\n d[t,:] = np.true_divide(data_orig[t,:],np.sqrt(sum/nwin))\n d[t,d[t,:] == np.inf] = 0\n d[t,:] = np.nan_to_num(d[t,:])\n\n\t# middle range -- full rms window\n # Add and subtract a squared sample\n for t in range(iwagc+1,nt-iwagc):\n sum = sum + data_orig[t+iwagc,:]**2.0 - tmp\n tmp = data_orig[t-iwagc,:]**2.0\n with np.errstate(divide='ignore', invalid='ignore'):\n d[t,:] = np.true_divide(data_orig[t,:],np.sqrt(sum/nwin))\n d[t,d[t,:] == np.inf] = 0\n d[t,:] = np.nan_to_num(d[t,:])\n\n # ramping off\n # Subtract a squared sample and decrease window length nwin each iteration\n for t in range(nt-iwagc,nt):\n sum = sum - tmp\n tmp = data_orig[t-iwagc,:]**2.0\n nwin -= 1\n with np.errstate(divide='ignore', invalid='ignore'):\n d[t,:] = np.true_divide(data_orig[t,:],np.sqrt(sum/nwin))\n d[t,d[t,:] == np.inf] = 0\n d[t,:] = np.nan_to_num(d[t,:])\n\n return d","def moving_averages(ts_init, window):\n ts_init = pd.Series(ts_init)\n if len(ts_init) % 2 == 0:\n ts_ma = ts_init.rolling(window, center=True).mean()\n ts_ma = ts_ma.rolling(window=2, center=True).mean()\n ts_ma = np.roll(ts_ma, -1)\n else:\n ts_ma = ts_init.rolling(window, center=True).mean()\n return ts_ma","def get_rolling_mean(values, window):\r\n return pd.rolling_mean(values, window=window)","def rolling_mean_nb(a, window, minp=None):\n out = np.empty_like(a, dtype=np.float_)\n for col in range(a.shape[1]):\n out[:, col] = rolling_mean_1d_nb(a[:, col], window, minp=minp)\n return out","def runningMeanFast(x, N=N):\n x1 = np.pad(x,N,mode='reflect')\n output =np.convolve(x1, np.ones((N,))/N)[(N-1):]\n return output[N:-N]","def windowfunction(time, freq):\n \n Ntime = len(time)\n Nfreq = len(freq)\n winkernel = np.empty_like(freq)\n\n for i in range(Nfreq):\n winkernel[i] = np.sum(np.cos(2.0*pi*freq[i]*time))**2 \\\n + np.sum(np.sin(2.0*pi*freq[i]*time))**2\n\n # Normalise such that winkernel(nu = 0.0) = 1.0 \n\n return winkernel/Ntime**2","def running_mean(self,windowlength=3*86400.0,overlap=12*3600.0, plot=True):\r\n \r\n # Make sure that omega is a list\r\n\r\n pt1,pt2 = window_index_time(self.t,windowlength,overlap)\r\n npt = len(pt1)\r\n tmid = []\r\n ymean = np.zeros((npt,))\r\n yrms = np.zeros((npt,))\r\n\r\n ii=-1\r\n for t1,t2 in zip(pt1,pt2):\r\n ii+=1\r\n\r\n ymean[ii] = self.y[t1:t2].mean()\r\n yrms[ii] = crms(self.tsec[t1:t2],self.y[t1:t2]-ymean[ii])\r\n \r\n # Return the mid time point\r\n ind = np.floor(t1 + (t2-t1)/2)\r\n tmid.append(self.t[ind])\r\n \r\n tmid = np.asarray(tmid)\r\n \r\n if plot:\r\n plt.subplot(211)\r\n self.plot()\r\n plt.plot(tmid,ymean,'r')\r\n plt.fill_between(tmid,ymean-yrms,y2=ymean+yrms,color=[0.5,0.5,0.5],alpha=0.5)\r\n plt.legend(('Original Signal','Mean','$\\pm$ RMS'))\r\n \r\n ax=plt.subplot(212)\r\n plt.fill_between(tmid,yrms,alpha=0.7)\r\n plt.xticks(rotation=17)\r\n plt.ylabel('RMS')\r\n ax.set_xlim([self.t[0],self.t[-1]])\r\n \r\n \r\n return tmid, ymean, yrms","def moving_average(a, n=3) :\n ret = np.cumsum(a, dtype=float)\n ret[n:] = ret[n:] - ret[:-n]\n return ret[n - 1:] / n","def moving_average(data, period, type='simple'):\n\ttry:\n\t\tx = np.asarray(data['Adj Close'])\n\texcept:\n\t\tx = np.asarray(data)\n\n\tif type == 'simple':\n\t\tweights = np.ones(period)\n\telse:\n\t\tweights = np.exp(np.linspace(-1., 0., period))\n\n\tweights /= weights.sum()\n\n\ta = np.convolve(x, weights, mode='full')[:len(x)]\n\ta[:period] = a[period]\n\treturn a","def moving_average(a, n=3) :\r\n a = a.ravel()\r\n a = np.concatenate(([a[0]]*(n-1),a)) # repeating first values\r\n ret = np.cumsum(a, dtype = float)\r\n ret[n:] = ret[n:] - ret[:-n]\r\n ret=ret[n - 1:] / n\r\n return ret","def average(self, window_size):\n\n # numpy.datetime64 objects cannot be averaged directly\n if self.dtype.type == np.datetime64:\n data = self.astype(\"M8[ns]\").astype(\"int\")\n else:\n data = self\n\n # This code is taken from https://stackoverflow.com/a/15956341\n padded = np.pad(\n data, (0, window_size - self.size % window_size),\n mode='constant', constant_values=np.NaN\n )\n\n if self.dtype.type == np.datetime64:\n return np.nanmean(\n padded.reshape(-1, window_size), axis=1\n ).astype(\"M8[ns]\")\n else:\n return np.nanmean(padded.reshape(-1, window_size), axis=1)","def rolling_mean_1d_nb(a, window, minp=None):\n if minp is None:\n minp = window\n if minp > window:\n raise ValueError(\"minp must be <= window\")\n out = np.empty_like(a, dtype=np.float_)\n cumsum_arr = np.zeros_like(a)\n cumsum = 0\n nancnt_arr = np.zeros_like(a)\n nancnt = 0\n for i in range(a.shape[0]):\n if np.isnan(a[i]):\n nancnt = nancnt + 1\n else:\n cumsum = cumsum + a[i]\n nancnt_arr[i] = nancnt\n cumsum_arr[i] = cumsum\n if i < window:\n window_len = i + 1 - nancnt\n window_cumsum = cumsum\n else:\n window_len = window - (nancnt - nancnt_arr[i - window])\n window_cumsum = cumsum - cumsum_arr[i - window]\n if window_len < minp:\n out[i] = np.nan\n else:\n out[i] = window_cumsum / window_len\n return out","def get_rolling_mean(values, window = 20):\n\treturn values.rolling(window, center=False).mean()","def moving_average(a, n=5):\n ret = np.cumsum(a, dtype=float)\n ret[n:] = ret[n:] - ret[:-n]\n ret[n-1:] *= 1 / n\n ret[:n-1] *= 1 / np.arange(1, n)\n return ret","def moving_average(data, size):\n out = np.ma.zeros(data.shape) # Create the output array\n assert type(data) == np.ma.masked_array, 'Input data is not a masked array, use an alternative function or ' \\\n 'convert input to masked array before use'\n for ran in range(data.shape[1]): # for each range gate\n # Normal condition\n if ran >= size:\n window = data[:, ran - size:ran + size]\n \t out[:, ran] = np.ma.average(window,\n axis=1)\n out[:, ran] = np.where(out[:, ran].mask == True,\n np.nan,\n out[:, ran])\n\n # Shortened window at start of the array\n else:\n out[:,ran] = np.nan\n return out","def rolling_mean(df, window: int = 10):\n df = fill_forward(df.fillna(df.rolling(window=window, min_periods=1).mean()))\n return df","def kday_moving_average(x, k):\n if not isinstance(k, int):\n raise ValueError('k must be int.')\n # temp = np.append(np.zeros(k - 1), x)\n temp = np.append(np.nan*np.ones(k-1), x)\n y = np.convolve(temp, np.ones(k, dtype=int), 'valid') / k\n return y","def runningAvg(f,nDays):\n disi,disb = _runningAvgWgts(nDays)\n ka = nDays // 2\n npts = f.shape[0]\n favg = np.empty_like(f)\n # interior\n for i in range(ka,favg.shape[0]-ka):\n favg[i] = np.sum(disi*f[i-ka:i+ka+1])\n # boundaries\n for i in range(ka):\n fwidth = len(disb[i])\n favg[i] = np.sum(disb[i]*f[0:fwidth])\n favg[npts-1-i] = np.sum(disb[i]*f[npts-1:npts-fwidth-1:-1])\n return favg","def dFF_runningmean(data, rm_window, signal='Rcamp', freq=12,\n sample_rate=1220, filter_order=2):\n # filter signals using Butteworth High Pass filter\n data['filt_%s' % signal] = fp.butter_lowpass_filter(df.Gcamp, freq,\n sample_rate,\n order=filter_order)\n\n # calculate running mean to use as reference\n data['rm_%s' % signal] = running_mean(data['filt_%s' % signal], rm_window)\n\n # caluclates dF/F as (filter_signal-scaled_reference)/scaled_reference\n data['dFF_%s' % signal] = ((data['filt_%s' % signal]\n - data['rm_%s' % signal])\n / data['rm_%s' % signal])\n\n # returns dataframe with calculations in new columns\n return data","def __ExpMovingAverage(self, values, window):\n weights = np.exp(np.linspace(-1., 0., window))\n weights /= weights.sum()\n a = np.convolve(values, weights, mode='full')[:len(values)]\n a[:window] = a[window]\n return a","def movingAverage(requestContext, seriesList, windowSize):\n if not seriesList:\n return []\n windowInterval = None\n if isinstance(windowSize, basestring):\n delta = parseTimeOffset(windowSize)\n windowInterval = abs(delta.seconds + (delta.days * 86400))\n\n if windowInterval:\n bootstrapSeconds = windowInterval\n else:\n bootstrapSeconds = max([s.step for s in seriesList]) * int(windowSize)\n\n bootstrapList = _fetchWithBootstrap(requestContext, seriesList, seconds=bootstrapSeconds)\n result = []\n\n for bootstrap, series in zip(bootstrapList, seriesList):\n if windowInterval:\n windowPoints = windowInterval / series.step\n else:\n windowPoints = int(windowSize)\n\n if isinstance(windowSize, basestring):\n newName = 'movingAverage(%s,\"%s\")' % (series.name, windowSize)\n else:\n newName = \"movingAverage(%s,%s)\" % (series.name, windowSize)\n newSeries = TimeSeries(newName, series.start, series.end, series.step, [])\n newSeries.pathExpression = newName\n\n offset = len(bootstrap) - len(series)\n for i in range(len(series)):\n window = bootstrap[i + offset - windowPoints:i + offset]\n newSeries.append(safeAvg(window))\n\n result.append(newSeries)\n\n return result","def moving_average(a, n: int = 3) -> np.array:\n ret = np.cumsum(a, dtype=float)\n ret[n:] = ret[n:] - ret[:-n]\n return ret[n - 1:] / n","def get_rolling_mean(values, window):\n return pd.rolling_mean(values, window=window)","def simple_moving_average(n, data):\n result = []\n for m in range(n-1, len(data)):\n total = sum([data[m-i] for i in range(n)])\n result.append(total/n)\n return result","def smooth(x, window_len=11, window='hanning'):\n\n# if x.ndim != 1:\n# raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n# if x.size < window_len:\n# raise ValueError, \"Input vector needs to be bigger than window size.\"\n\n if window_len<3:\n return x\n\n# if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n# raise ValueError, \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n\n\n s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]\n# print(len(s))\n if window == 'flat': # moving average\n w = np.ones(window_len, 'd')\n else:\n w = eval('np.' + window + '(window_len)')\n\n y = np.convolve(w / w.sum(), s, mode='valid')\n return y","def moving_average(sig, n=100):\n window = deque(maxlen=n) # last n scores\n sig_ma = []\n for i in range(len(sig)):\n window.append(sig[i])\n sig_ma.append(np.mean(window))\n return sig_ma","def _moving_average(self, values, window=1, stride=1, p=None):\n # Cache results for convenience.\n key = (id(values), window, stride, p)\n if key in self._moving_average_cache:\n return self._moving_average_cache[key]\n # Compute rolling windows efficiently.\n def _rolling_window(a, window):\n shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)\n strides = a.strides + (a.strides[-1],)\n return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)\n x = np.mean(\n _rolling_window(np.arange(len(values)), window), axis=-1)[::stride]\n y = _rolling_window(values, window)\n y_mean = np.mean(y, axis=-1)[::stride]\n y_lower, y_upper = _errorbars(y, axis=-1, p=p)\n ret = (x, y_mean, (y_lower, y_upper))\n self._moving_average_cache[key] = ret\n return ret","def time_mean(self, width):\n import math\n\n for i in range(len(self.data)):\n for j in range(len(self.chans)):\n self.data[i,:,j,:] = self.data[i - width[j]/2 : i + int(math.ceil(width[j]/2.)), :, j, :].mean(axis=0)","def _moving_average(self, series=None, window_length=None, train_subwindow_len=None):\n import numpy as np\n\n moving_averages = []\n iter_length = len(series) - window_length\n for i in range(0, iter_length):\n ma_current = np.mean(series[i:i + window_length])\n moving_averages.append(ma_current)\n\n # Moving average shrinkes w.r.t the actual series based on the moving average window. Hence, to keep the\n # length of the moving average equal to the series, we append proxy padding which are the moving averages\n # from the closest representative training sub-window.\n moving_averages_padded = moving_averages[(train_subwindow_len - (\n window_length // 2)):train_subwindow_len] + moving_averages + moving_averages[-train_subwindow_len:-(\n train_subwindow_len - (window_length // 2))]\n\n return moving_averages_padded","def rolling_std_1d_nb(a, window, minp=None, ddof=0):\n if minp is None:\n minp = window\n if minp > window:\n raise ValueError(\"minp must be <= window\")\n out = np.empty_like(a, dtype=np.float_)\n cumsum_arr = np.zeros_like(a)\n cumsum = 0\n cumsum_sq_arr = np.zeros_like(a)\n cumsum_sq = 0\n nancnt_arr = np.zeros_like(a)\n nancnt = 0\n mean = 0\n for i in range(a.shape[0]):\n if np.isnan(a[i]):\n nancnt = nancnt + 1\n else:\n cumsum = cumsum + a[i]\n cumsum_sq = cumsum_sq + a[i] ** 2\n nancnt_arr[i] = nancnt\n cumsum_arr[i] = cumsum\n cumsum_sq_arr[i] = cumsum_sq\n if i < window:\n window_len = i + 1 - nancnt\n window_cumsum = cumsum\n window_cumsum_sq = cumsum_sq\n else:\n window_len = window - (nancnt - nancnt_arr[i - window])\n window_cumsum = cumsum - cumsum_arr[i - window]\n window_cumsum_sq = cumsum_sq - cumsum_sq_arr[i - window]\n if window_len < minp or window_len == ddof:\n out[i] = np.nan\n else:\n mean = window_cumsum / window_len\n out[i] = np.sqrt(np.abs(window_cumsum_sq - 2 * window_cumsum *\n mean + window_len * mean ** 2) / (window_len - ddof))\n return out","def average_over_interval(raw_rate, weight_function, intervals):\n\n def averaging_function(t):\n return raw_rate(t) * weight_function(t)\n\n results = np.zeros(len(intervals), dtype=np.float)\n\n for interval_idx in range(len(intervals)):\n start = intervals.start[interval_idx]\n finish = intervals.finish[interval_idx]\n results[interval_idx] = quad(averaging_function, start, finish)[0]\n\n return results","def smooth_1D(data, window_len=50, window='hanning'):\n # TODO: add comnparison\n window_len += (window_len + 1) % 2\n s = np.r_['-1', data[window_len - 1:0:-1], data, data[-2:-window_len - 1:-1]]\n\n if window == 'flat': # moving average\n w = np.ones(window_len, 'd')\n else:\n w = eval('np.' + window + '(window_len)')\n\n # y=np.convolve(w/w.sum(),s,mode='valid')\n # return y[(window_len-1):-(window_len)]\n surplus_data = int((window_len - 1) * 0.5)\n data = np.apply_along_axis(lambda m: np.convolve(m, w / w.sum(), mode='valid'), axis=0, arr=s)[\n surplus_data:-surplus_data]\n return data","def update_running_average_weights(self, t): \n \n self.w_bar = self.w_bar * (float(t)/(t+1)) + self.w / float(t+1) \n #print('Running average particle weigths =', self.w_bar)\n return None","def smooth(x,window_len=11,window='hanning'):\r\n\r\n if window_len<3:\r\n return x\r\n\r\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\r\n #print(len(s))\r\n if window == 'flat': #moving average\r\n w=np.ones(window_len,'d')\r\n else:\r\n w=eval('np.'+window+'(window_len)')\r\n\r\n y=np.convolve(w/w.sum(),s,mode='valid')\r\n return y[0:256]","def approximate(inp,w_len):\n\t\t\n\t\top = []\n\t\t\n\t\tfor i in range(0,len(inp),w_len):\n\t\t\n\t\t\top.append(np.mean(inp[i:i+w_len]))\n\t\t\t\n\t\treturn np.array(op)","def moving_linear_masked_average(data, size, weights=False, weight_data=None):\n out = np.ma.zeros(data.shape) # Create the output array\n assert type(data) == np.ma.masked_array, 'Input data is not a masked array, use an alternative function or ' \\\n 'convert input to masked array before use'\n for ran in range(data.shape[1]): # for each range gate\n # Normal condition\n if ran >= size:\n window = data[:, ran - size:ran + size + 1]\n\n if weights:\n weight_window = weight_data[:, ran - size:ran + size + 1]\n # Shortened window at start of the array\n else:\n window = data[:, :ran + size + 1]\n if weights:\n weight_window = weight_data[:, :ran + size + 1]\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n if weights:\n out[:, ran] = np.ma.average(window,\n weights=weight_window,\n axis=1)\n out[:, ran] = np.where(out[:, ran].mask == True,\n np.nan,\n out[:, ran])\n else:\n out[:, ran] = np.ma.average(window,\n axis=1)\n out[:, ran] = np.where(out[:, ran].mask == True,\n np.nan,\n out[:, ran])\n return out","def windowed_means(out_features, param):\n sampling_fq = param.t_max * 1000 + 1\n temp_wnd = np.linspace(param.min_latency, param.max_latency, param.steps + 1)\n intervals = np.zeros((param.steps, 2))\n for i in range(0, temp_wnd.shape[0] - 1):\n intervals[i, 0] = temp_wnd[i]\n intervals[i, 1] = temp_wnd[i + 1]\n intervals = intervals - param.t_min\n output_features = []\n for i in range(out_features.shape[0]):\n feature = []\n for j in range(out_features.shape[1]):\n time_course = out_features[i][j]\n for k in range(intervals.shape[0]):\n borders = intervals[k] * sampling_fq\n feature.append(np.average(time_course[int(borders[0] - 1):int(borders[1] - 1)]))\n output_features.append(feature)\n out = preprocessing.scale(np.array(output_features), axis=1)\n return out","def smooth(data, window_len=10, window='hanning', keep_original=False):\n # TODO: add comnparison\n window_len += (window_len + 1) % 2\n s = np.r_['-1', data[:, window_len - 1:0:-1], data, data[:, -2:-window_len - 1:-1]]\n\n if window == 'flat': # moving average\n w = np.ones(window_len, 'd')\n else:\n w = eval('np.' + window + '(window_len)')\n\n # y=np.convolve(w/w.sum(),s,mode='valid')\n surplus_data = int((window_len - 1) * 0.5)\n return np.apply_along_axis(lambda m: np.convolve(m, w / w.sum(), mode='valid'), axis=1, arr=s)[:,\n surplus_data:-surplus_data]","def smooth(x,window_len=11,window='hanning'):\n\n if x.ndim != 1:\n raise(ValueError, \"smooth only accepts 1 dimension arrays.\")\n\n if x.size < window_len:\n raise(ValueError, \"Input vector needs to be bigger than window size.\")\n\n\n if window_len<3:\n return x\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise (ValueError,\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n\n\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w=np.ones(window_len,'d')\n else:\n w=eval('np.'+window+'(window_len)')\n\n y=np.convolve(w/w.sum(),s,mode='valid')\n if len(y) is not len(x):\n y = y[window_len/2-1:-(window_len/2)]\n return y","def _smooth(x, window_len=11, window='hanning'):\n if x.ndim != 1:\n raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n \n if x.size < window_len:\n raise ValueError, \"Input vector needs to be bigger than window size.\"\n \n if window_len<3:\n return x\n \n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise(ValueError,\n \"Window is not one of '{0}', '{1}', '{2}', '{3}', '{4}'\".format(\n *('flat', 'hanning', 'hamming', 'bartlett', 'blackman')))\n \n s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w = np.ones(window_len,'d')\n else:\n w = eval('np.' + window + '(window_len)')\n \n y = np.convolve(w / w.sum(), s, mode = 'valid')\n return y","def smooth(x,window_len=11,window='hanning'):\n\n if x.ndim != 1:\n raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n\n if x.size < window_len:\n raise ValueError, \"Input vector needs to be bigger than window size.\"\n\n\n if window_len<3:\n return x\n\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError, \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n\n\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w=np.ones(window_len,'d')\n else:\n w=eval('np.'+window+'(window_len)')\n\n y=np.convolve(w/w.sum(),s,mode='valid')\n #return y\n return y[(window_len/2):-(window_len/2)]","def fastsmooth(a, win=11):\n # check to see if 'window' is odd (even does not work)\n if win % 2 == 0:\n win -= 1 # subtract 1 from window if it is even.\n # trick for efficient 'rolling' computation in numpy\n # shape = a.shape[:-1] + (a.shape[-1] - win + 1, win)\n # strides = a.strides + (a.strides[-1], )\n # wins = np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)\n wins = rolling_window(a, win)\n # apply rolling gradient to data\n a = map(np.nanmean, wins)\n\n return np.concatenate([np.zeros(int(win / 2)), list(a),\n np.zeros(int(win / 2))])","def rolling_mean_dispersion(\n data: np.ndarray, time_windows: np.ndarray, fps: int\n ) -> np.ndarray:\n\n results = np.full((data.shape[0], time_windows.shape[0]), -1)\n for time_window in prange(time_windows.shape[0]):\n jump_frms = int(time_windows[time_window] * fps)\n for current_frm in prange(jump_frms, results.shape[0] + 1):\n data_window = np.deg2rad(data[current_frm - jump_frms : current_frm])\n results[current_frm - 1][time_window] = np.rad2deg(\n stats.circmean(data_window)\n ).astype(int)\n return results","def mean_wave_period(F, f, df):\n return np.sum(F * df) / np.sum(F * f * df)","def moving_average(self, a, n=3):\n ret = np.nancumsum(a, dtype=float)\n ret[n:] = ret[n:] - ret[:-n]\n return ret[n - 1:] / n","def moving_avg(df, key, lag):\n\n def _sma(key, window):\n values = list(map(lambda w: w[key], window))\n avg = round(reduce(lambda a, b: a + b, values) / len(values), 2)\n return avg\n\n return [_sma(key, df[i : i + lag + 1]) for i in range(len(df) - lag)]","def smooth(x,window_len=11,window='hanning'): \n \n if x.ndim != 1:\n raise ValueError(\"smooth only accepts 1 dimension arrays.\")\n\n if x.size < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n \n\n if window_len<3:\n return x\n \n \n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError(\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n \n\n s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w=np.ones(window_len,'d')\n else:\n w=eval('np.'+window+'(window_len)')\n \n y=np.convolve(w/w.sum(),s,mode='valid')\n return y","def moving_average(iterable, n):\n it = iter(iterable)\n d = collections.deque(itertools.islice(it, n-1))\n d.appendleft(0)\n s = sum(d)\n for elem in it:\n s += elem - d.popleft()\n d.append(elem)\n yield s / float(n)","def smooth(x,window_len=11,window='hanning'):\n\n if x.ndim != 1:\n raise ValueError(\"smooth only accepts 1 dimension arrays.\")\n\n if x.size < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n\n\n if window_len<3:\n return x\n\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError(\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n\n\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w=np.ones(window_len,'d')\n else:\n w=eval('np.'+window+'(window_len)')\n\n y=np.convolve(w/w.sum(),s,mode='valid')\n return y","def add_simple_moving_average(smas, n, data):\n total = sum([data[-1-i] for i in range(n)])\n smas.append(total/n)","def get_rolling_mean(self, metric: str = 'cfs', min: Union[int, float] = None, max: Union[int, float] = None,\n period_count: int = 5, period: str = 'year', start_date: datetime = None,\n end_date: datetime = None, rolling_window: str = '28D',\n apply_smoothing: bool = True) -> pd.DataFrame:\n\n # retrive observations\n obs = self.get_observations(period=period, period_count=period_count, metrics=metric, start_date=start_date,\n end_date=end_date)\n\n # standardize the dataframe\n obs.rename(columns={metric: 'flow'}, inplace=True)\n\n\n # calculate the mean and standard deviation over a rolling window\n mean_df = obs['flow'].rolling(rolling_window).mean()\n std_df = obs['flow'].rolling(rolling_window).std()\n\n # combine the means and standard deviations\n obs_join = obs.join(mean_df, rsuffix='_mean').join(std_df, rsuffix='_std')\n\n # calculate a curve one standard deviation above and below the mean\n obs_join[f'flow_plus_std'] = obs_join[f'flow_mean'] + obs_join[f'flow_std']\n obs_join[f'flow_less_std'] = obs_join[f'flow_mean'] - obs_join[f'flow_std']\n\n # drop out leap year days\n obs_nrml = obs_join[~((obs_join.index.month == 2) & (obs_join.index.day == 29))].copy()\n obs_nrml['timestamp'] = [val.replace(year=1973) for val in obs_nrml.index]\n\n # tack on values for the preceeding and trailing months to ensure the curve covers an entire year\n lead_obs = obs_nrml[obs_nrml.index.month == 12].copy()\n lead_obs['timestamp'] = [val.replace(year=1972) for val in lead_obs.index]\n lag_obs = obs_nrml[obs_nrml.index.month == 1].copy()\n lag_obs['timestamp'] = [val.replace(year=1973) for val in lag_obs.index]\n obs_nrml = pd.concat([obs_nrml, lead_obs, lag_obs])\n\n # create an average curve table for one year\n mean_cols = [f'flow_mean', f'flow_plus_std', f'flow_less_std', 'timestamp']\n mean_df = obs_nrml[mean_cols].groupby('timestamp').mean()\n\n # if min and max range flows are provided, add them\n if min is not None:\n mean_df[f'flow_bott'] = min\n if max is not None:\n mean_df[f'flow_top'] = max\n\n # consolidate into days...because nobody wants needs every 15 minutes for an entire year\n mean_df = mean_df.groupby(pd.Grouper(freq='D')).mean()\n\n # if smoothing the curve (a VERY good idea), do it\n if apply_smoothing:\n mean_df = mean_df.rolling(window=5).mean()\n\n mean_df = mean_df[mean_df.index.year == 1973].copy()\n\n return mean_df","def smooth(x, window_len=11, window='hanning'):\n\n if x.ndim != 1:\n raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n\n if x.size < window_len:\n raise ValueError, \"Input vector needs to be bigger than window size.\"\n\n if window_len < 3:\n return x\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError, \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n\n s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w = np.ones(window_len, 'd')\n else:\n w = eval('np.' + window + '(window_len)')\n\n y = np.convolve(w / w.sum(), s, mode='valid')\n return y","def window_average(\n self, key: str, window_start_timestamp_s: float, do_compact: bool = True\n ) -> Optional[float]:\n points_after_idx = self._get_datapoints(key, window_start_timestamp_s)\n\n if do_compact:\n self.data[key] = points_after_idx\n\n if len(points_after_idx) == 0:\n return\n return sum(point.value for point in points_after_idx) / len(points_after_idx)","def moving_averages(df, start_step, window_size=None):\n if window_size == None: # Use a large window to compute average over all historical data\n window_size = df.shape[0]\n fea = df.shift(start_step).rolling(min_periods=1, center=False, window=window_size).mean()\n fea.columns = fea.columns + \"_mean\"\n return fea","def smooth(x,window_len=11,window='hanning'):\n\n if x.ndim != 1:\n raise ValueError(\"smooth only accepts 1 dimension arrays.\")\n\n if x.size < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n\n\n if window_len<3:\n return x\n\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError(\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n\n\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w=np.ones(window_len,'d')\n else:\n w=eval('np.'+window+'(window_len)')\n\n y=np.convolve(w/w.sum(),s,mode='same')\n return y","def smooth(x,window_len=10,window='hanning'):\n #\n if x.ndim != 1:\n raise ValueError(\"smooth only accepts 1 dimension arrays.\")\n #\n if x.size < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n #\n if window_len<3:\n return x\n #\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError(\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n #\n s=r_[2*x[0]-x[window_len:1:-1],x,2*x[-1]-x[-1:-window_len:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w=ones(window_len,'d')\n else:\n w=eval(window+'(window_len)')\n #\n y=convolve(w/w.sum(),s,mode='same')\n return y[window_len-1:-window_len+1]","def ewm_mean_1d_nb(a, span, minp=None, adjust=False):\n if minp is None:\n minp = span\n if minp > span:\n raise ValueError(\"minp must be <= span\")\n N = len(a)\n out = np.empty(N, dtype=np.float_)\n if N == 0:\n return out\n com = (span - 1) / 2.0\n alpha = 1. / (1. + com)\n old_wt_factor = 1. - alpha\n new_wt = 1. if adjust else alpha\n weighted_avg = a[0]\n is_observation = (weighted_avg == weighted_avg)\n nobs = int(is_observation)\n out[0] = weighted_avg if (nobs >= minp) else np.nan\n old_wt = 1.\n\n for i in range(1, N):\n cur = a[i]\n is_observation = (cur == cur)\n nobs += is_observation\n if weighted_avg == weighted_avg:\n old_wt *= old_wt_factor\n if is_observation:\n # avoid numerical errors on constant series\n if weighted_avg != cur:\n weighted_avg = ((old_wt * weighted_avg) + (new_wt * cur)) / (old_wt + new_wt)\n if adjust:\n old_wt += new_wt\n else:\n old_wt = 1.\n elif is_observation:\n weighted_avg = cur\n out[i] = weighted_avg if (nobs >= minp) else np.nan\n return out","def smooth(x, window_len=11, window=\"hanning\"):\n\n if x.ndim != 1:\n raise ValueError(\"smooth only accepts 1 dimension arrays.\")\n\n if x.size < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n\n if window_len < 3:\n return x\n\n if not window in [\"flat\", \"hanning\", \"hamming\", \"bartlett\", \"blackman\"]:\n raise ValueError(\n \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n )\n\n s = np.r_[x[window_len - 1 : 0 : -1], x, x[-2 : -window_len - 1 : -1]]\n # print(len(s))\n if window == \"flat\": # moving average\n w = np.ones(window_len, \"d\")\n else:\n w = eval(\"np.\" + window + \"(window_len)\")\n\n y = np.convolve(w / w.sum(), s, mode=\"valid\")\n return y[(window_len // 2 - 1) : -(window_len // 2 + 1)]","def smooth( x, window_len = 5, window = 'hanning' ):\n\n if x.ndim != 1:\n raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n\n if x.size < window_len:\n raise ValueError, \"Input vector needs to be bigger than window size.\"\n\n if window_len<3:\n return x\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError, \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n\n s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]\n\n if window == 'flat': #moving average\n w=np.ones(window_len,'d')\n else:\n w=eval('np.'+window+'(window_len)')\n\n y=np.convolve(w/w.sum(),s,mode='valid')\n return y","def get_rolling_mean(df, window):\n # return pd.rolling_mean(values, window=window)\n rolling_df=pd.DataFrame(index=df.index)\n for sym in df.columns:\n df_tmp= pd.Series.rolling(df[sym],window=window).mean().to_frame() # to_frame converts series to a data frame\n rolling_df=rolling_df.join(df_tmp)\n \n # Update the NaN values which are initial values for the \n \n rolling_df.ix[:window, :] = 0\n \n return rolling_df\n #return pd.Series.rolling(values,window=window).mean()","def smooth(x,window_len=11,window='hanning'):\n\n if x.ndim != 1:\n raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n\n if x.size < window_len:\n raise ValueError, \"Input vector needs to be bigger than window size.\"\n\n\n if window_len<3:\n return x\n\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError, \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n\n\n s=numpy.r_[2*x[0]-x[window_len:1:-1],x,2*x[-1]-x[-1:-window_len:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w=ones(window_len,'d')\n else:\n w=eval('numpy.'+window+'(window_len)')\n\n y=numpy.convolve(w/w.sum(),s,mode='same')\n return y[window_len-1:-window_len+1]","def smooth(x, window_len=11, window='hanning'):\n window_len = min(window_len, len(x) - 1)\n if x.ndim != 1:\n raise ValueError(\"smooth only accepts 1 dimension arrays.\")\n\n if x.size < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n\n if window_len < 3:\n return x\n\n if window not in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError(\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n\n s = np.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]\n # print(len(s))\n if window == 'flat': # moving average\n w = np.ones(window_len, 'd')\n else:\n w = eval('np.' + window + '(window_len)')\n\n y = np.convolve(w / w.sum(), s, mode='valid')\n return y","def _dba_iteration(tseries, ftvector, avg, weights, fs):\r\n # the number of time series in the set\r\n n = len(tseries)\r\n # length of the time series \r\n ntime = avg.shape[0]\r\n # features of avg\r\n avg_ft = ExtraFeatures(avg,fs)\r\n scaler.fit(ftvector) \r\n # number of dimensions (useful for MTS)\r\n # array containing the new weighted average sequence \r\n new_avg = np.zeros((ntime),dtype=np.float64) \r\n # array of sum of weights \r\n sum_weights = np.zeros((ntime),dtype=np.float64)\r\n # loop the time series \r\n for s in range(n): \r\n series = tseries[s]\r\n ft = ftvector[s]\r\n dist = np.linalg.norm(scaler.transform(avg_ft.reshape(1,-1))-scaler.transform(ft.reshape(1,-1)))\r\n new_avg = new_avg + dist*weights[s]*series\r\n sum_weights = sum_weights + dist*weights[s]\r\n \r\n new_avg = new_avg/sum_weights\r\n \r\n return new_avg","def smooth(x, window,nan=True,old=False,fill='mean'):\n if nan:\n from Sp_parameters import nanmasked\n from scipy import interpolate\n ix = np.arange(len(x))\n xmasked, mask = nanmasked(x)\n if fill == 'mean':\n fv = np.mean(xmasked)\n elif fill == 'median':\n fv = np.median(xmasked)\n elif fill == 'zero':\n fv = 0.0\n else:\n raise ValueError('the fill keyword doesnt match possible values, try mean, median, or zero')\n fx = interpolate.interp1d(ix[mask],xmasked,bounds_error=False,fill_value=fv)\n xinterp = fx(ix)\n if old:\n xout = np.convolve(xinterp, np.ones(window)/window, 'same')\n else:\n s = np.r_[xinterp[window-1:0:-1],xinterp,xinterp[-1:-window:-1]]\n w = np.ones(window,'d')\n xout = np.convolve(w/w.sum(),s,mode='valid')\n istart = window/2\n iend = -window/2+1\n if iend==0:\n iend = len(xout)\n xout = xout[istart:iend]\n else:\n if old:\n xout = np.convolve(x, np.ones(window)/window, 'same')\n else:\n s = np.r_[x[window-1:0:-1],x,x[-1:-window:-1]]\n w = np.ones(window,'d')\n xout = np.convolve(w/w.sum(),s,mode='valid')\n xout = xout[window/2:-window/2+1]\n return xout","def lin_trim_mean(a: np.ndarray, start: float = 0.5, end: float = 0.1,\n start_v: float = 0, end_v: float = 0.5) -> float:\n start_w = np.linspace(start_v, 1, start * len(a), endpoint=False)\n end_w = np.linspace(end_v, 1, end * len(a), endpoint=False)[::-1]\n mid_w = np.ones(len(a) - len(start_w) - len(end_w))\n weights = np.concatenate((start_w, mid_w, end_w))\n return ((a * weights).sum() / weights.sum()).item()","def running_mean(sequence: list):\n if not sequence:\n return []\n\n mean = []\n \"\"\"\n [1] = 1 / 1\n [1,2] = 3 / 2 \n [1,2,3] = 6 / 3\n \"\"\"\n for idx, num in enumerate(sequence):\n\n sum_total = sum(sequence[:(idx + 1)])\n result = sum_total / (idx + 1)\n\n mean.append(round(result, 2))\n\n return mean","def exponential_running_mean(data, factor_new, init_block_size=None,\n start_mean=None, axis=None):\n assert not (start_mean is None and init_block_size is None), (\n \"Need either an init block or a start mean\")\n assert start_mean is None or init_block_size is None, (\n \"Can only use start mean \"\n \"or init block size\")\n assert factor_new <= 1.0\n assert factor_new >= 0.0\n if isinstance(axis, int):\n axis = (axis,)\n factor_old = 1 - factor_new\n\n # first preallocate the shape for the running means\n # shape depends on which axes will be removed\n running_mean_shape = list(data.shape)\n if axis is not None:\n for ax in axis:\n # keep dim as empty dim\n running_mean_shape[ax] = 1\n\n running_means = (np.ones(running_mean_shape) * np.nan).astype(np.float32)\n\n if start_mean is None:\n start_data = data[0:init_block_size]\n if axis is not None:\n axes_for_start_mean = (0,) + axis # also average across init trials\n else:\n axes_for_start_mean = 0\n # possibly temporarily upcast to float32 to avoid overflows in sum\n # that is computed to compute mean\n current_mean = np.mean(start_data.astype(np.float32),\n keepdims=True,\n axis=axes_for_start_mean).astype(\n start_data.dtype)\n # repeat mean for running means\n running_means[:init_block_size] = current_mean\n i_start = init_block_size\n else:\n current_mean = start_mean\n i_start = 0\n\n for i in range(i_start, len(data)):\n if axis is not None:\n datapoint_mean = np.mean(data[i:i + 1], axis=axis, keepdims=True)\n else:\n datapoint_mean = data[i:i + 1]\n next_mean = factor_new * datapoint_mean + factor_old * current_mean\n running_means[i] = next_mean\n current_mean = next_mean\n\n assert not np.any(np.isnan(running_means)), (\n \"RUnning mean has NaNs :\\n{:s}\".format(str(running_means)))\n assert not np.any(np.isinf(running_means)), (\n \"RUnning mean has Infs :\\n{:s}\".format(str(running_means)))\n return running_means","def smooth(x,window_len=11,window='hanning'):\n\n\t# if x.ndim != 1:\n\t# raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n\n\t# if x.size < window_len:\n\t# raise ValueError, \"Input vector needs to be bigger than window size.\"\n\n\tassert x.ndim==1\n\tassert x.size==window_len\n\n\tif window_len<3:\n\t\treturn x\n\n\tflag = (window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman'])\n\tassert flag==1\n\n\ts=numpy.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\n\tif window == 'flat': #moving average\n\t\tw=numpy.ones(window_len,'d')\n\telse:\n\t\tw=eval('numpy.'+window+'(window_len)')\n\n\ty=numpy.convolve(w/w.sum(),s,mode='valid')\n\treturn y","def normalizeValuesByWindows(data, window_size=None):\n\n if not options.window_size:\n window_size = min(map(lambda x: x[1] - x[0], data))\n\n new_values = []\n\n for start, end, value in data:\n if end - start < window_size:\n continue\n start = start - start % window_size\n for z in range(start, end, window_size):\n new_values.append((z, value))\n\n new_values.sort()\n\n # interpolate values for the same windows with average\n xvals = []\n yvals = []\n last_x = None\n values = []\n for x, value in new_values:\n\n if last_x != x:\n if last_x is not None:\n xvals.append(last_x)\n yvals.append(numpy.mean(values))\n values = []\n\n last_x = x\n values.append(value)\n\n return xvals, yvals","def sliding_window_integral(data, window=None, window_size=500, shift_size=1):\n num_chunks = len(_sliding_window_chunkoffsets(data, window_size, shift_size))\n if num_chunks == 0:\n return np.asarray([])\n if window is None:\n return _numba_sliding_window_integral(data, num_chunks, window_size, shift_size)\n else:\n return _numba_sliding_window_integral_with_window(data, num_chunks, window, window_size, shift_size)","def compute_means(window_means):\n return np.mean(window_means, axis=0)","def rolling_std_nb(a, window, minp=None, ddof=0):\n out = np.empty_like(a, dtype=np.float_)\n for col in range(a.shape[1]):\n out[:, col] = rolling_std_1d_nb(a[:, col], window, minp=minp, ddof=ddof)\n return out","def smooth(x,window_len=11,window='bartlett',data = None):\n\n x = array(x)\n\n # use externally derieved window evaluation\n if data is not None:\n window_len = len(data)\n window = 'extern'\n\n if x.size < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n\n\n if window_len<3:\n raise ValueError(\"window must not be shorter than 3\")\n\n if window_len%2 is 0:\n raise ValueError(\"window_len should be odd\")\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman','triang','extern']:\n raise ValueError(\"Window is none of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman','triang','extern'\")\n\n \n s=r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]\n #print(len(s))\n if window == 'flat': #moving average\n w=ones(window_len,'d')\n\n elif window == 'triang':\n w = triang(window_len)\n\n elif window == 'extern':\n w = data\n \n else:\n w=eval(window+'(window_len)')\n\n y=convolve(w/w.sum(),s,mode='valid')\n \n return y[int((window_len-1)/2):len(y)-int((window_len-1)/2)]","def rolling_window(series, window):\n shape = series.shape[:-1] + (series.shape[-1] - window + 1, window)\n strides = series.strides + (series.strides[-1],)\n return np.lib.stride_tricks.as_strided(series, shape=shape, strides=strides)","def smooth(x, window_len=3, window='hanning'):\n s = np.r_[2*x[0]-x[window_len:1:-1], x, 2*x[-1]-x[-1:-window_len:-1]]\n w = getattr(np, window)(window_len)\n y = np.convolve(w/w.sum(), s, mode='same') \n return y[window_len-1:-window_len+1]","def get_SMA(values, window=20):\n\treturn values.rolling(window, center=False).mean()","def smooth(x, window_len=11, window='hanning', mode='same'):\n import numpy\n if x.ndim != 1:\n raise ValueError(\"smooth only accepts 1 dimension arrays.\")\n\n if x.size < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n\n if window_len < 3:\n return x\n\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError(\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n\n s = numpy.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]\n # print(len(s))\n if window == 'flat': # moving average\n w = numpy.ones(window_len, 'd')\n else:\n w = eval('numpy.' + window + '(window_len)')\n\n y = numpy.convolve(w / w.sum(), s, mode=mode)\n if mode == 'same':\n return y[np.int_(window_len) - 1:-np.int_(window_len) + 1]\n else:\n return y[np.int_(window_len / 2 - 1):-np.int_(window_len / 2)]","def avg_pool(\n inputs, window_shape, strides=None, padding=\"VALID\", count_include_pad=True\n):\n y = pool(inputs, 0.0, lax.add, window_shape, strides, padding)\n if count_include_pad:\n y = y / np.prod(window_shape)\n else:\n div_shape = inputs.shape[:-1] + (1,)\n if len(div_shape) - 2 == len(window_shape):\n div_shape = (1,) + div_shape[1:]\n y = y / pool(\n jnp.ones(div_shape), 0.0, lax.add, window_shape, strides, padding\n )\n return y"],"string":"[\n \"def moving_average(data, temporal_window=100):\\n window = np.ones(temporal_window) / temporal_window\\n return np.convolve(data, window, 'valid')\",\n \"def moving_average(data, window_size):\\n window= np.ones(int(window_size))/float(window_size)\\n return np.convolve(data, window, 'same')\",\n \"def running_mean(data, n):\\n\\treturn np.convolve(data, np.ones((n, ))/n)[(n-1):-(n-1)]\",\n \"def moving_average(x, window_len = 11):\\n\\n one_over_size = 1./float(window_len)\\n window = np.ones(window_len) * one_over_size\\n result = np.convolve(x, window, 'valid')\\n # Cut off the clutter at the boundaries\\n #return result[ (window_len-1)/2 : -(window_len-1)/2]\\n return result\",\n \"def get_running_mean(data,time_window):\\n \\n print('--> Starting to calculate running mean') \\n timer_start = dt.now()\\n filt = [1./float(time_window)]*int(time_window)\\n running_mean = np.apply_along_axis(lambda m: np.convolve(m, filt, mode='valid'), axis=0, arr=data)\\n running_mean = np.append(np.ones([len(data)-len(running_mean),*data.shape[1:]])*np.nan,running_mean,axis=0)\\n print('--> Completed calculating running mean (%.1f seconds)' \\\\\\n % (dt.now()-timer_start).total_seconds())\\n return running_mean\",\n \"def running_mean(x, N):\\n return np.convolve(x, np.ones((N,))/N, mode='valid')\",\n \"def running_mean(x, N):\\n cumsum = np.cumsum(np.insert(x, 0, 0)) \\n return (cumsum[N:] - cumsum[:-N]) / float(N)\",\n \"def moving_average(time_series, window_len):\\n # Re-index the time series with duration in seconds from the first value\\n time_series.index = (\\n (time_series.index\\n - time_series.index[0]) / np.timedelta64(1, 's')).astype('int')\\n\\n window_area = time_series.iloc[0] * window_len\\n\\n # It may not always be possible to construct a window of length exactly equal\\n # to window_len using timestamps present in the data. To handle this, the left\\n # side of the window is allowed to fall between timestamps (the right side is\\n # always fixed to a timestamp in the data). Therefore we need to separately\\n # compute the area of the inter-timestamp region on the left side of the\\n # window so that it can be added to the window area. left_area is that value.\\n left_area = window_area\\n\\n out = np.zeros(len(time_series))\\n out[0] = time_series.iloc[0]\\n\\n # i is the left side of the window and j is the right\\n i = 0\\n for j in xrange(1, len(time_series)):\\n # Remove the last iteration's left_area as a new right window bound may\\n # change the left_area required in this iteration\\n window_area -= left_area\\n\\n # Expand window to the right\\n window_area += time_series.iloc[j-1] * (time_series.index[j]\\n - time_series.index[j-1])\\n\\n # Shrink window from the left if expanding to the right has created too\\n # large a window. new_left_time may fall between timestamps present in the\\n # data, which is fine, since that's handled by left_area.\\n new_left_time = time_series.index[j] - window_len\\n while time_series.index[i] < new_left_time:\\n window_area -= time_series.iloc[i] * (time_series.index[i+1]\\n - time_series.index[i])\\n i += 1\\n\\n # Add left side inter-timestamp area to window\\n left_area = time_series.iloc[max(0, i - 1)] * (time_series.index[i]\\n - new_left_time)\\n window_area += left_area\\n\\n out[j] = window_area / window_len\\n\\n return out\",\n \"def moving_average(data, width):\\n return np.convolve(data, np.ones(width), 'same') / width\",\n \"def running_mean(x, N):\\n cumsum = np.cumsum(np.insert(x, 0, 0))\\n return (cumsum[N:] - cumsum[:-N]) / float(N)\",\n \"def sliding_window_average(data, weights=None, window_size=500, shift_size=1):\\n num_chunks = len(_sliding_window_chunkoffsets(data, window_size, shift_size))\\n if num_chunks == 0:\\n return np.asarray([])\\n if weights is None:\\n return _numba_sliding_window_average(data, num_chunks, window_size, shift_size)\\n else:\\n return _numba_sliding_window_average_with_weights(data, num_chunks, weights, window_size, shift_size)\",\n \"def runmean(y, n, /, wintype='boxcar', axis=-1, pad=np.nan):\\n # * For 1-D data numpy `convolve` would be appropriate, problem is `convolve`\\n # doesn't take multidimensional input!\\n # * If `y` has odd number of obs along axis, result will have last element\\n # trimmed. Just like `filter`.\\n # * Strides are apparently the 'number of bytes' one has to skip in memory\\n # to move to next position *on the given axis*. For example, a 5 by 5\\n # array of 64bit (8byte) values will have array.strides == (40, 8).\\n # Roll axis, reshape, and get generate running dimension\\n if axis < 0:\\n axis += y.ndim\\n y = np.moveaxis(y, axis, -1)\\n w = window(n, wintype)\\n\\n # Manipulate array\\n shape = y.shape[:-1] + (y.shape[-1] - (n - 1), n)\\n strides = (*y.strides, y.strides[-1]) # repeat striding on end\\n yr = np.lib.stride_tricks.as_strided(y, shape=shape, strides=strides)\\n yr = (yr * w).mean(axis=-1) # broadcasts to right\\n\\n # Optionally fill the rows taken up\\n # NOTE: Data might be shifted left by one if had even numbered window\\n if pad is not None:\\n n1 = (y.shape[-1] - yr.shape[-1]) // 2\\n n2 = (y.shape[-1] - yr.shape[-1]) - n1\\n y1 = pad * np.ones((*yr.shape[:-1], n1))\\n y2 = pad * np.ones((*yr.shape[:-1], n2))\\n yr = np.concatenate((y1, yr, y2), axis=-1)\\n\\n return np.moveaxis(yr, -1, axis)\",\n \"def windowMean(data, time=[], winsize=0, overlap=0, st_time=None, op=np.mean):\\n #check inputs and initialize\\n #Set resolution to 1 if no times supplied\\n if len(time) == 0:\\n startpt, res = 0, 1\\n time = list(range(len(data)))\\n pts = True\\n else:\\n if len(data) != len(time):\\n raise ValueError('windowmean error: data and time must have same length')\\n #First check if datetime objects\\n try:\\n assert type(winsize) == datetime.timedelta\\n assert type(overlap) == datetime.timedelta\\n except AssertionError:\\n raise TypeError('windowmean error: winsize/overlap must be timedeltas')\\n pts = False #force time-based averaging\\n if (type(time[0]) != datetime.datetime):\\n startpt = time[0]\\n\\n #now actually do windowing mean\\n outdata, outtime = [], []\\n data = np.array(data)\\n if pts:\\n #loop for fixed number of points in window\\n try:\\n inttypes = (int, long)\\n except NameError:\\n inttypes = (int,)\\n if not isinstance(winsize, inttypes):\\n winsize = int(round(winsize))\\n warnings.warn('windowmean: non-integer windowsize, rounding to %d' \\\\\\n % winsize)\\n if winsize < 1:\\n winsize = 1\\n warnings.warn('windowmean: window length < 1, defaulting to 1')\\n if overlap >= winsize:\\n overlap = winsize - 1\\n warnings.warn('''windowmean: overlap longer than window, truncated to\\n %d''' % overlap)\\n lastpt = winsize-1 #set last point to end of window size\\n while lastpt < len(data):\\n datwin = np.ma.masked_where(np.isnan(data[startpt:startpt+winsize]), \\\\\\n data[startpt:startpt+winsize])\\n getmean = op(datwin.compressed()) #mean of window, excl. NaNs\\n gettime = (time[startpt+winsize] - time[startpt])/2. \\\\\\n + time[startpt]#new timestamp\\n startpt = startpt+winsize-overlap\\n lastpt = startpt+winsize\\n outdata.append(getmean) #construct output arrays\\n outtime.append(gettime)\\n else:\\n #loop with time-based window\\n lastpt = time[0] + winsize\\n delta = datetime.timedelta(microseconds=1) #TODO: replace this with an explicit check for times on the boundary?\\n if st_time:\\n startpt = st_time\\n else:\\n startpt = time[0]\\n if overlap >= winsize:\\n raise ValueError('Overlap requested greater than size of window')\\n while startpt < time[-1]:\\n getinds = tOverlapHalf([startpt,startpt+winsize-delta], time, presort=True)\\n if getinds: #if not None\\n getdata = np.ma.masked_where(np.isnan(data[getinds[0]:getinds[-1]+1]), data[getinds[0]:getinds[-1]+1])\\n getmean = op(getdata.compressed()) #find mean excluding NaNs\\n else:\\n getmean = np.nan\\n gettime = startpt + winsize//2 #new timestamp -floordiv req'd with future division\\n startpt = startpt + winsize - overlap #advance window start\\n lastpt = startpt + winsize\\n outdata.append(getmean) #construct output arrays\\n outtime.append(gettime)\\n\\n return outdata, outtime\",\n \"def moving_avg(a, halfwindow, mask=None):\\r\\n\\r\\n\\r\\n if mask is None:\\r\\n mask = np.ones_like(a, dtype='bool')\\r\\n\\r\\n zeros = np.zeros(a.shape[:-1] + (halfwindow,))\\r\\n falses = zeros.astype('bool')\\r\\n\\r\\n a_padded = np.concatenate((zeros, np.where(mask, a, 0), zeros), axis=-1)\\r\\n mask_padded = np.concatenate((falses, mask, falses), axis=-1)\\r\\n\\r\\n\\r\\n npt = 2 * halfwindow + 1 # total size of the averaging window\\r\\n rolling_a = as_strided(a_padded,\\r\\n shape=a.shape + (npt,),\\r\\n strides=a_padded.strides + (a.strides[-1],))\\r\\n rolling_mask = as_strided(mask_padded,\\r\\n shape=mask.shape + (npt,),\\r\\n strides=mask_padded.strides + (mask.strides[-1],))\\r\\n\\r\\n # moving average\\r\\n n = rolling_mask.sum(axis=-1)\\r\\n return np.where(n > 0, rolling_a.sum(axis=-1).astype('float') / n, np.nan)\",\n \"def rolling_mean(\\n self,\\n window: tuple[int | int] = (0, 0),\\n where: tuple[float | float] = (-inf, inf),\\n ) -> pd.Series:\\n where = _replace_none_with_infs(where)\\n assert len(window) == 2, \\\"Window should be a listlike object of length 2.\\\"\\n left_delta, right_delta = window\\n lower, upper = where\\n clipped = self.clip(lower, upper)\\n if clipped._data is None:\\n return pd.Series([clipped.initial_value] * 2, index=where)\\n step_points = clipped._data.index\\n sample_points = pd.Index.union(\\n step_points - left_delta,\\n step_points - right_delta,\\n )\\n ii = pd.IntervalIndex.from_arrays(\\n sample_points + left_delta, sample_points + right_delta\\n )\\n s = pd.Series(\\n clipped.slice(ii).mean().values,\\n index=sample_points,\\n )\\n if lower != -inf:\\n s = s.loc[s.index >= lower - left_delta]\\n if upper != inf:\\n s = s.loc[s.index <= upper - right_delta]\\n return s\",\n \"def ExpMovingAverage(values, window):\\n weights = np.exp(np.linspace(-1.0, 0.0, window))\\n weights /= weights.sum()\\n a = np.convolve(values, weights, mode=\\\"full\\\")[: len(values)]\\n a[:window] = a[window]\\n return a\",\n \"def running_avg (mylist, N):\\n import numpy as np\\n \\n cumsum = np.cumsum(np.insert(mylist, 0, 0))\\n return (cumsum[N:] - cumsum[:-N]) / float(N)\",\n \"def daymean(x):\\n return nstepf(x, 24, func='mean')\",\n \"def moving_average(x, n, type='simple'):\\n x = np.asarray(x)\\n if type == 'simple':\\n weights = np.ones(n)\\n else:\\n weights = np.exp(np.linspace(-1., 0., n))\\n\\n weights /= weights.sum()\\n\\n a = np.convolve(x, weights, mode='full')[:len(x)]\\n a[:n] = a[n]\\n return a\",\n \"def smooth_avg(arr, winlen):\\n\\n window = np.ones(winlen) / (1.0 * winlen)\\n return fftconvolve(arr, window, 'same')\",\n \"def moving_average_forecast(series, window_size):\\n\\tforecast= []\\n\\tfor time in range(len(series)- window_size):\\n\\t\\tforecast.append(series[time:time + window_size].mean())\\n\\treturn np.array(forecast)\",\n \"def moving_rms(x, window_len = 11, smp=False, nthreads=4):\\n #if ( np.size(running_mean) != np.size(x)):\\n # raise ValueError, \\\"array and running mean of the array must be the same size.\\\"\\n # Assure that window_len is even. If not, decrease window_len by one\\n if window_len % 2 == 0:\\n window_len = window_len-1\\n i0 = (window_len - 1) / 2\\n result = np.zeros_like(x)\\n if smp:\\n print 'smp Keyword does not do anything yet'\\n\\n# if smp:\\n# p = Pool(nthreads)\\n# f = lambda x, i, i0: x[i-i0:i+i0].std()\\n#\\n# else:\\n for i in np.arange(i0, x.size - i0):\\n #result[i] = x[i-i0:i+i0].std()\\n result[i] = np.sqrt((x[i - i0:i + i0 + 1] * x[i - i0 : i + i0 + 1]).sum() / (window_len))\\n\\n return result[i0:-i0]\",\n \"def agc(data,dt,wagc=1.0):\\n nt = data.shape[0] # number of time samples\\n iwagc = int(wagc/dt/2) # half window size in samples\\n data_orig = np.copy(data) # copy of input data\\n d = data_orig # copy of input data\\n nwin = iwagc # nwin is #samples in RMS computation\\n sum = 0\\n\\n # compute initial window for first datum\\n sum = np.apply_along_axis(lambda m: np.sum(m[0:iwagc]**2.0), axis=0, arr=d)\\n with np.errstate(divide='ignore', invalid='ignore'):\\n d[0,:] = np.true_divide(data_orig[0,:],np.sqrt(sum/nwin))\\n d[0,d[0,:] == np.inf] = 0\\n d[0,:] = np.nan_to_num(d[0,:])\\n\\n # The value tmp gets subtracted each time the moving window moves 1 sample\\n # forward\\n tmp = data_orig[0]**2.0\\n\\n # ramping on\\n # Add a squared sample and increase window length nwin each iteration\\n for t in range(0,iwagc+1):\\n sum = sum + data_orig[t+iwagc,:]**2.0\\n nwin += 1\\n with np.errstate(divide='ignore', invalid='ignore'):\\n d[t,:] = np.true_divide(data_orig[t,:],np.sqrt(sum/nwin))\\n d[t,d[t,:] == np.inf] = 0\\n d[t,:] = np.nan_to_num(d[t,:])\\n\\n\\t# middle range -- full rms window\\n # Add and subtract a squared sample\\n for t in range(iwagc+1,nt-iwagc):\\n sum = sum + data_orig[t+iwagc,:]**2.0 - tmp\\n tmp = data_orig[t-iwagc,:]**2.0\\n with np.errstate(divide='ignore', invalid='ignore'):\\n d[t,:] = np.true_divide(data_orig[t,:],np.sqrt(sum/nwin))\\n d[t,d[t,:] == np.inf] = 0\\n d[t,:] = np.nan_to_num(d[t,:])\\n\\n # ramping off\\n # Subtract a squared sample and decrease window length nwin each iteration\\n for t in range(nt-iwagc,nt):\\n sum = sum - tmp\\n tmp = data_orig[t-iwagc,:]**2.0\\n nwin -= 1\\n with np.errstate(divide='ignore', invalid='ignore'):\\n d[t,:] = np.true_divide(data_orig[t,:],np.sqrt(sum/nwin))\\n d[t,d[t,:] == np.inf] = 0\\n d[t,:] = np.nan_to_num(d[t,:])\\n\\n return d\",\n \"def moving_averages(ts_init, window):\\n ts_init = pd.Series(ts_init)\\n if len(ts_init) % 2 == 0:\\n ts_ma = ts_init.rolling(window, center=True).mean()\\n ts_ma = ts_ma.rolling(window=2, center=True).mean()\\n ts_ma = np.roll(ts_ma, -1)\\n else:\\n ts_ma = ts_init.rolling(window, center=True).mean()\\n return ts_ma\",\n \"def get_rolling_mean(values, window):\\r\\n return pd.rolling_mean(values, window=window)\",\n \"def rolling_mean_nb(a, window, minp=None):\\n out = np.empty_like(a, dtype=np.float_)\\n for col in range(a.shape[1]):\\n out[:, col] = rolling_mean_1d_nb(a[:, col], window, minp=minp)\\n return out\",\n \"def runningMeanFast(x, N=N):\\n x1 = np.pad(x,N,mode='reflect')\\n output =np.convolve(x1, np.ones((N,))/N)[(N-1):]\\n return output[N:-N]\",\n \"def windowfunction(time, freq):\\n \\n Ntime = len(time)\\n Nfreq = len(freq)\\n winkernel = np.empty_like(freq)\\n\\n for i in range(Nfreq):\\n winkernel[i] = np.sum(np.cos(2.0*pi*freq[i]*time))**2 \\\\\\n + np.sum(np.sin(2.0*pi*freq[i]*time))**2\\n\\n # Normalise such that winkernel(nu = 0.0) = 1.0 \\n\\n return winkernel/Ntime**2\",\n \"def running_mean(self,windowlength=3*86400.0,overlap=12*3600.0, plot=True):\\r\\n \\r\\n # Make sure that omega is a list\\r\\n\\r\\n pt1,pt2 = window_index_time(self.t,windowlength,overlap)\\r\\n npt = len(pt1)\\r\\n tmid = []\\r\\n ymean = np.zeros((npt,))\\r\\n yrms = np.zeros((npt,))\\r\\n\\r\\n ii=-1\\r\\n for t1,t2 in zip(pt1,pt2):\\r\\n ii+=1\\r\\n\\r\\n ymean[ii] = self.y[t1:t2].mean()\\r\\n yrms[ii] = crms(self.tsec[t1:t2],self.y[t1:t2]-ymean[ii])\\r\\n \\r\\n # Return the mid time point\\r\\n ind = np.floor(t1 + (t2-t1)/2)\\r\\n tmid.append(self.t[ind])\\r\\n \\r\\n tmid = np.asarray(tmid)\\r\\n \\r\\n if plot:\\r\\n plt.subplot(211)\\r\\n self.plot()\\r\\n plt.plot(tmid,ymean,'r')\\r\\n plt.fill_between(tmid,ymean-yrms,y2=ymean+yrms,color=[0.5,0.5,0.5],alpha=0.5)\\r\\n plt.legend(('Original Signal','Mean','$\\\\pm$ RMS'))\\r\\n \\r\\n ax=plt.subplot(212)\\r\\n plt.fill_between(tmid,yrms,alpha=0.7)\\r\\n plt.xticks(rotation=17)\\r\\n plt.ylabel('RMS')\\r\\n ax.set_xlim([self.t[0],self.t[-1]])\\r\\n \\r\\n \\r\\n return tmid, ymean, yrms\",\n \"def moving_average(a, n=3) :\\n ret = np.cumsum(a, dtype=float)\\n ret[n:] = ret[n:] - ret[:-n]\\n return ret[n - 1:] / n\",\n \"def moving_average(data, period, type='simple'):\\n\\ttry:\\n\\t\\tx = np.asarray(data['Adj Close'])\\n\\texcept:\\n\\t\\tx = np.asarray(data)\\n\\n\\tif type == 'simple':\\n\\t\\tweights = np.ones(period)\\n\\telse:\\n\\t\\tweights = np.exp(np.linspace(-1., 0., period))\\n\\n\\tweights /= weights.sum()\\n\\n\\ta = np.convolve(x, weights, mode='full')[:len(x)]\\n\\ta[:period] = a[period]\\n\\treturn a\",\n \"def moving_average(a, n=3) :\\r\\n a = a.ravel()\\r\\n a = np.concatenate(([a[0]]*(n-1),a)) # repeating first values\\r\\n ret = np.cumsum(a, dtype = float)\\r\\n ret[n:] = ret[n:] - ret[:-n]\\r\\n ret=ret[n - 1:] / n\\r\\n return ret\",\n \"def average(self, window_size):\\n\\n # numpy.datetime64 objects cannot be averaged directly\\n if self.dtype.type == np.datetime64:\\n data = self.astype(\\\"M8[ns]\\\").astype(\\\"int\\\")\\n else:\\n data = self\\n\\n # This code is taken from https://stackoverflow.com/a/15956341\\n padded = np.pad(\\n data, (0, window_size - self.size % window_size),\\n mode='constant', constant_values=np.NaN\\n )\\n\\n if self.dtype.type == np.datetime64:\\n return np.nanmean(\\n padded.reshape(-1, window_size), axis=1\\n ).astype(\\\"M8[ns]\\\")\\n else:\\n return np.nanmean(padded.reshape(-1, window_size), axis=1)\",\n \"def rolling_mean_1d_nb(a, window, minp=None):\\n if minp is None:\\n minp = window\\n if minp > window:\\n raise ValueError(\\\"minp must be <= window\\\")\\n out = np.empty_like(a, dtype=np.float_)\\n cumsum_arr = np.zeros_like(a)\\n cumsum = 0\\n nancnt_arr = np.zeros_like(a)\\n nancnt = 0\\n for i in range(a.shape[0]):\\n if np.isnan(a[i]):\\n nancnt = nancnt + 1\\n else:\\n cumsum = cumsum + a[i]\\n nancnt_arr[i] = nancnt\\n cumsum_arr[i] = cumsum\\n if i < window:\\n window_len = i + 1 - nancnt\\n window_cumsum = cumsum\\n else:\\n window_len = window - (nancnt - nancnt_arr[i - window])\\n window_cumsum = cumsum - cumsum_arr[i - window]\\n if window_len < minp:\\n out[i] = np.nan\\n else:\\n out[i] = window_cumsum / window_len\\n return out\",\n \"def get_rolling_mean(values, window = 20):\\n\\treturn values.rolling(window, center=False).mean()\",\n \"def moving_average(a, n=5):\\n ret = np.cumsum(a, dtype=float)\\n ret[n:] = ret[n:] - ret[:-n]\\n ret[n-1:] *= 1 / n\\n ret[:n-1] *= 1 / np.arange(1, n)\\n return ret\",\n \"def moving_average(data, size):\\n out = np.ma.zeros(data.shape) # Create the output array\\n assert type(data) == np.ma.masked_array, 'Input data is not a masked array, use an alternative function or ' \\\\\\n 'convert input to masked array before use'\\n for ran in range(data.shape[1]): # for each range gate\\n # Normal condition\\n if ran >= size:\\n window = data[:, ran - size:ran + size]\\n \\t out[:, ran] = np.ma.average(window,\\n axis=1)\\n out[:, ran] = np.where(out[:, ran].mask == True,\\n np.nan,\\n out[:, ran])\\n\\n # Shortened window at start of the array\\n else:\\n out[:,ran] = np.nan\\n return out\",\n \"def rolling_mean(df, window: int = 10):\\n df = fill_forward(df.fillna(df.rolling(window=window, min_periods=1).mean()))\\n return df\",\n \"def kday_moving_average(x, k):\\n if not isinstance(k, int):\\n raise ValueError('k must be int.')\\n # temp = np.append(np.zeros(k - 1), x)\\n temp = np.append(np.nan*np.ones(k-1), x)\\n y = np.convolve(temp, np.ones(k, dtype=int), 'valid') / k\\n return y\",\n \"def runningAvg(f,nDays):\\n disi,disb = _runningAvgWgts(nDays)\\n ka = nDays // 2\\n npts = f.shape[0]\\n favg = np.empty_like(f)\\n # interior\\n for i in range(ka,favg.shape[0]-ka):\\n favg[i] = np.sum(disi*f[i-ka:i+ka+1])\\n # boundaries\\n for i in range(ka):\\n fwidth = len(disb[i])\\n favg[i] = np.sum(disb[i]*f[0:fwidth])\\n favg[npts-1-i] = np.sum(disb[i]*f[npts-1:npts-fwidth-1:-1])\\n return favg\",\n \"def dFF_runningmean(data, rm_window, signal='Rcamp', freq=12,\\n sample_rate=1220, filter_order=2):\\n # filter signals using Butteworth High Pass filter\\n data['filt_%s' % signal] = fp.butter_lowpass_filter(df.Gcamp, freq,\\n sample_rate,\\n order=filter_order)\\n\\n # calculate running mean to use as reference\\n data['rm_%s' % signal] = running_mean(data['filt_%s' % signal], rm_window)\\n\\n # caluclates dF/F as (filter_signal-scaled_reference)/scaled_reference\\n data['dFF_%s' % signal] = ((data['filt_%s' % signal]\\n - data['rm_%s' % signal])\\n / data['rm_%s' % signal])\\n\\n # returns dataframe with calculations in new columns\\n return data\",\n \"def __ExpMovingAverage(self, values, window):\\n weights = np.exp(np.linspace(-1., 0., window))\\n weights /= weights.sum()\\n a = np.convolve(values, weights, mode='full')[:len(values)]\\n a[:window] = a[window]\\n return a\",\n \"def movingAverage(requestContext, seriesList, windowSize):\\n if not seriesList:\\n return []\\n windowInterval = None\\n if isinstance(windowSize, basestring):\\n delta = parseTimeOffset(windowSize)\\n windowInterval = abs(delta.seconds + (delta.days * 86400))\\n\\n if windowInterval:\\n bootstrapSeconds = windowInterval\\n else:\\n bootstrapSeconds = max([s.step for s in seriesList]) * int(windowSize)\\n\\n bootstrapList = _fetchWithBootstrap(requestContext, seriesList, seconds=bootstrapSeconds)\\n result = []\\n\\n for bootstrap, series in zip(bootstrapList, seriesList):\\n if windowInterval:\\n windowPoints = windowInterval / series.step\\n else:\\n windowPoints = int(windowSize)\\n\\n if isinstance(windowSize, basestring):\\n newName = 'movingAverage(%s,\\\"%s\\\")' % (series.name, windowSize)\\n else:\\n newName = \\\"movingAverage(%s,%s)\\\" % (series.name, windowSize)\\n newSeries = TimeSeries(newName, series.start, series.end, series.step, [])\\n newSeries.pathExpression = newName\\n\\n offset = len(bootstrap) - len(series)\\n for i in range(len(series)):\\n window = bootstrap[i + offset - windowPoints:i + offset]\\n newSeries.append(safeAvg(window))\\n\\n result.append(newSeries)\\n\\n return result\",\n \"def moving_average(a, n: int = 3) -> np.array:\\n ret = np.cumsum(a, dtype=float)\\n ret[n:] = ret[n:] - ret[:-n]\\n return ret[n - 1:] / n\",\n \"def get_rolling_mean(values, window):\\n return pd.rolling_mean(values, window=window)\",\n \"def simple_moving_average(n, data):\\n result = []\\n for m in range(n-1, len(data)):\\n total = sum([data[m-i] for i in range(n)])\\n result.append(total/n)\\n return result\",\n \"def smooth(x, window_len=11, window='hanning'):\\n\\n# if x.ndim != 1:\\n# raise ValueError, \\\"smooth only accepts 1 dimension arrays.\\\"\\n# if x.size < window_len:\\n# raise ValueError, \\\"Input vector needs to be bigger than window size.\\\"\\n\\n if window_len<3:\\n return x\\n\\n# if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n# raise ValueError, \\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\"\\n\\n\\n s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]\\n# print(len(s))\\n if window == 'flat': # moving average\\n w = np.ones(window_len, 'd')\\n else:\\n w = eval('np.' + window + '(window_len)')\\n\\n y = np.convolve(w / w.sum(), s, mode='valid')\\n return y\",\n \"def moving_average(sig, n=100):\\n window = deque(maxlen=n) # last n scores\\n sig_ma = []\\n for i in range(len(sig)):\\n window.append(sig[i])\\n sig_ma.append(np.mean(window))\\n return sig_ma\",\n \"def _moving_average(self, values, window=1, stride=1, p=None):\\n # Cache results for convenience.\\n key = (id(values), window, stride, p)\\n if key in self._moving_average_cache:\\n return self._moving_average_cache[key]\\n # Compute rolling windows efficiently.\\n def _rolling_window(a, window):\\n shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)\\n strides = a.strides + (a.strides[-1],)\\n return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)\\n x = np.mean(\\n _rolling_window(np.arange(len(values)), window), axis=-1)[::stride]\\n y = _rolling_window(values, window)\\n y_mean = np.mean(y, axis=-1)[::stride]\\n y_lower, y_upper = _errorbars(y, axis=-1, p=p)\\n ret = (x, y_mean, (y_lower, y_upper))\\n self._moving_average_cache[key] = ret\\n return ret\",\n \"def time_mean(self, width):\\n import math\\n\\n for i in range(len(self.data)):\\n for j in range(len(self.chans)):\\n self.data[i,:,j,:] = self.data[i - width[j]/2 : i + int(math.ceil(width[j]/2.)), :, j, :].mean(axis=0)\",\n \"def _moving_average(self, series=None, window_length=None, train_subwindow_len=None):\\n import numpy as np\\n\\n moving_averages = []\\n iter_length = len(series) - window_length\\n for i in range(0, iter_length):\\n ma_current = np.mean(series[i:i + window_length])\\n moving_averages.append(ma_current)\\n\\n # Moving average shrinkes w.r.t the actual series based on the moving average window. Hence, to keep the\\n # length of the moving average equal to the series, we append proxy padding which are the moving averages\\n # from the closest representative training sub-window.\\n moving_averages_padded = moving_averages[(train_subwindow_len - (\\n window_length // 2)):train_subwindow_len] + moving_averages + moving_averages[-train_subwindow_len:-(\\n train_subwindow_len - (window_length // 2))]\\n\\n return moving_averages_padded\",\n \"def rolling_std_1d_nb(a, window, minp=None, ddof=0):\\n if minp is None:\\n minp = window\\n if minp > window:\\n raise ValueError(\\\"minp must be <= window\\\")\\n out = np.empty_like(a, dtype=np.float_)\\n cumsum_arr = np.zeros_like(a)\\n cumsum = 0\\n cumsum_sq_arr = np.zeros_like(a)\\n cumsum_sq = 0\\n nancnt_arr = np.zeros_like(a)\\n nancnt = 0\\n mean = 0\\n for i in range(a.shape[0]):\\n if np.isnan(a[i]):\\n nancnt = nancnt + 1\\n else:\\n cumsum = cumsum + a[i]\\n cumsum_sq = cumsum_sq + a[i] ** 2\\n nancnt_arr[i] = nancnt\\n cumsum_arr[i] = cumsum\\n cumsum_sq_arr[i] = cumsum_sq\\n if i < window:\\n window_len = i + 1 - nancnt\\n window_cumsum = cumsum\\n window_cumsum_sq = cumsum_sq\\n else:\\n window_len = window - (nancnt - nancnt_arr[i - window])\\n window_cumsum = cumsum - cumsum_arr[i - window]\\n window_cumsum_sq = cumsum_sq - cumsum_sq_arr[i - window]\\n if window_len < minp or window_len == ddof:\\n out[i] = np.nan\\n else:\\n mean = window_cumsum / window_len\\n out[i] = np.sqrt(np.abs(window_cumsum_sq - 2 * window_cumsum *\\n mean + window_len * mean ** 2) / (window_len - ddof))\\n return out\",\n \"def average_over_interval(raw_rate, weight_function, intervals):\\n\\n def averaging_function(t):\\n return raw_rate(t) * weight_function(t)\\n\\n results = np.zeros(len(intervals), dtype=np.float)\\n\\n for interval_idx in range(len(intervals)):\\n start = intervals.start[interval_idx]\\n finish = intervals.finish[interval_idx]\\n results[interval_idx] = quad(averaging_function, start, finish)[0]\\n\\n return results\",\n \"def smooth_1D(data, window_len=50, window='hanning'):\\n # TODO: add comnparison\\n window_len += (window_len + 1) % 2\\n s = np.r_['-1', data[window_len - 1:0:-1], data, data[-2:-window_len - 1:-1]]\\n\\n if window == 'flat': # moving average\\n w = np.ones(window_len, 'd')\\n else:\\n w = eval('np.' + window + '(window_len)')\\n\\n # y=np.convolve(w/w.sum(),s,mode='valid')\\n # return y[(window_len-1):-(window_len)]\\n surplus_data = int((window_len - 1) * 0.5)\\n data = np.apply_along_axis(lambda m: np.convolve(m, w / w.sum(), mode='valid'), axis=0, arr=s)[\\n surplus_data:-surplus_data]\\n return data\",\n \"def update_running_average_weights(self, t): \\n \\n self.w_bar = self.w_bar * (float(t)/(t+1)) + self.w / float(t+1) \\n #print('Running average particle weigths =', self.w_bar)\\n return None\",\n \"def smooth(x,window_len=11,window='hanning'):\\r\\n\\r\\n if window_len<3:\\r\\n return x\\r\\n\\r\\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\\r\\n #print(len(s))\\r\\n if window == 'flat': #moving average\\r\\n w=np.ones(window_len,'d')\\r\\n else:\\r\\n w=eval('np.'+window+'(window_len)')\\r\\n\\r\\n y=np.convolve(w/w.sum(),s,mode='valid')\\r\\n return y[0:256]\",\n \"def approximate(inp,w_len):\\n\\t\\t\\n\\t\\top = []\\n\\t\\t\\n\\t\\tfor i in range(0,len(inp),w_len):\\n\\t\\t\\n\\t\\t\\top.append(np.mean(inp[i:i+w_len]))\\n\\t\\t\\t\\n\\t\\treturn np.array(op)\",\n \"def moving_linear_masked_average(data, size, weights=False, weight_data=None):\\n out = np.ma.zeros(data.shape) # Create the output array\\n assert type(data) == np.ma.masked_array, 'Input data is not a masked array, use an alternative function or ' \\\\\\n 'convert input to masked array before use'\\n for ran in range(data.shape[1]): # for each range gate\\n # Normal condition\\n if ran >= size:\\n window = data[:, ran - size:ran + size + 1]\\n\\n if weights:\\n weight_window = weight_data[:, ran - size:ran + size + 1]\\n # Shortened window at start of the array\\n else:\\n window = data[:, :ran + size + 1]\\n if weights:\\n weight_window = weight_data[:, :ran + size + 1]\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\", category=RuntimeWarning)\\n if weights:\\n out[:, ran] = np.ma.average(window,\\n weights=weight_window,\\n axis=1)\\n out[:, ran] = np.where(out[:, ran].mask == True,\\n np.nan,\\n out[:, ran])\\n else:\\n out[:, ran] = np.ma.average(window,\\n axis=1)\\n out[:, ran] = np.where(out[:, ran].mask == True,\\n np.nan,\\n out[:, ran])\\n return out\",\n \"def windowed_means(out_features, param):\\n sampling_fq = param.t_max * 1000 + 1\\n temp_wnd = np.linspace(param.min_latency, param.max_latency, param.steps + 1)\\n intervals = np.zeros((param.steps, 2))\\n for i in range(0, temp_wnd.shape[0] - 1):\\n intervals[i, 0] = temp_wnd[i]\\n intervals[i, 1] = temp_wnd[i + 1]\\n intervals = intervals - param.t_min\\n output_features = []\\n for i in range(out_features.shape[0]):\\n feature = []\\n for j in range(out_features.shape[1]):\\n time_course = out_features[i][j]\\n for k in range(intervals.shape[0]):\\n borders = intervals[k] * sampling_fq\\n feature.append(np.average(time_course[int(borders[0] - 1):int(borders[1] - 1)]))\\n output_features.append(feature)\\n out = preprocessing.scale(np.array(output_features), axis=1)\\n return out\",\n \"def smooth(data, window_len=10, window='hanning', keep_original=False):\\n # TODO: add comnparison\\n window_len += (window_len + 1) % 2\\n s = np.r_['-1', data[:, window_len - 1:0:-1], data, data[:, -2:-window_len - 1:-1]]\\n\\n if window == 'flat': # moving average\\n w = np.ones(window_len, 'd')\\n else:\\n w = eval('np.' + window + '(window_len)')\\n\\n # y=np.convolve(w/w.sum(),s,mode='valid')\\n surplus_data = int((window_len - 1) * 0.5)\\n return np.apply_along_axis(lambda m: np.convolve(m, w / w.sum(), mode='valid'), axis=1, arr=s)[:,\\n surplus_data:-surplus_data]\",\n \"def smooth(x,window_len=11,window='hanning'):\\n\\n if x.ndim != 1:\\n raise(ValueError, \\\"smooth only accepts 1 dimension arrays.\\\")\\n\\n if x.size < window_len:\\n raise(ValueError, \\\"Input vector needs to be bigger than window size.\\\")\\n\\n\\n if window_len<3:\\n return x\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise (ValueError,\\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\")\\n\\n\\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w=np.ones(window_len,'d')\\n else:\\n w=eval('np.'+window+'(window_len)')\\n\\n y=np.convolve(w/w.sum(),s,mode='valid')\\n if len(y) is not len(x):\\n y = y[window_len/2-1:-(window_len/2)]\\n return y\",\n \"def _smooth(x, window_len=11, window='hanning'):\\n if x.ndim != 1:\\n raise ValueError, \\\"smooth only accepts 1 dimension arrays.\\\"\\n \\n if x.size < window_len:\\n raise ValueError, \\\"Input vector needs to be bigger than window size.\\\"\\n \\n if window_len<3:\\n return x\\n \\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise(ValueError,\\n \\\"Window is not one of '{0}', '{1}', '{2}', '{3}', '{4}'\\\".format(\\n *('flat', 'hanning', 'hamming', 'bartlett', 'blackman')))\\n \\n s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w = np.ones(window_len,'d')\\n else:\\n w = eval('np.' + window + '(window_len)')\\n \\n y = np.convolve(w / w.sum(), s, mode = 'valid')\\n return y\",\n \"def smooth(x,window_len=11,window='hanning'):\\n\\n if x.ndim != 1:\\n raise ValueError, \\\"smooth only accepts 1 dimension arrays.\\\"\\n\\n if x.size < window_len:\\n raise ValueError, \\\"Input vector needs to be bigger than window size.\\\"\\n\\n\\n if window_len<3:\\n return x\\n\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError, \\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\"\\n\\n\\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w=np.ones(window_len,'d')\\n else:\\n w=eval('np.'+window+'(window_len)')\\n\\n y=np.convolve(w/w.sum(),s,mode='valid')\\n #return y\\n return y[(window_len/2):-(window_len/2)]\",\n \"def fastsmooth(a, win=11):\\n # check to see if 'window' is odd (even does not work)\\n if win % 2 == 0:\\n win -= 1 # subtract 1 from window if it is even.\\n # trick for efficient 'rolling' computation in numpy\\n # shape = a.shape[:-1] + (a.shape[-1] - win + 1, win)\\n # strides = a.strides + (a.strides[-1], )\\n # wins = np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)\\n wins = rolling_window(a, win)\\n # apply rolling gradient to data\\n a = map(np.nanmean, wins)\\n\\n return np.concatenate([np.zeros(int(win / 2)), list(a),\\n np.zeros(int(win / 2))])\",\n \"def rolling_mean_dispersion(\\n data: np.ndarray, time_windows: np.ndarray, fps: int\\n ) -> np.ndarray:\\n\\n results = np.full((data.shape[0], time_windows.shape[0]), -1)\\n for time_window in prange(time_windows.shape[0]):\\n jump_frms = int(time_windows[time_window] * fps)\\n for current_frm in prange(jump_frms, results.shape[0] + 1):\\n data_window = np.deg2rad(data[current_frm - jump_frms : current_frm])\\n results[current_frm - 1][time_window] = np.rad2deg(\\n stats.circmean(data_window)\\n ).astype(int)\\n return results\",\n \"def mean_wave_period(F, f, df):\\n return np.sum(F * df) / np.sum(F * f * df)\",\n \"def moving_average(self, a, n=3):\\n ret = np.nancumsum(a, dtype=float)\\n ret[n:] = ret[n:] - ret[:-n]\\n return ret[n - 1:] / n\",\n \"def moving_avg(df, key, lag):\\n\\n def _sma(key, window):\\n values = list(map(lambda w: w[key], window))\\n avg = round(reduce(lambda a, b: a + b, values) / len(values), 2)\\n return avg\\n\\n return [_sma(key, df[i : i + lag + 1]) for i in range(len(df) - lag)]\",\n \"def smooth(x,window_len=11,window='hanning'): \\n \\n if x.ndim != 1:\\n raise ValueError(\\\"smooth only accepts 1 dimension arrays.\\\")\\n\\n if x.size < window_len:\\n raise ValueError(\\\"Input vector needs to be bigger than window size.\\\")\\n \\n\\n if window_len<3:\\n return x\\n \\n \\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError(\\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\")\\n \\n\\n s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w=np.ones(window_len,'d')\\n else:\\n w=eval('np.'+window+'(window_len)')\\n \\n y=np.convolve(w/w.sum(),s,mode='valid')\\n return y\",\n \"def moving_average(iterable, n):\\n it = iter(iterable)\\n d = collections.deque(itertools.islice(it, n-1))\\n d.appendleft(0)\\n s = sum(d)\\n for elem in it:\\n s += elem - d.popleft()\\n d.append(elem)\\n yield s / float(n)\",\n \"def smooth(x,window_len=11,window='hanning'):\\n\\n if x.ndim != 1:\\n raise ValueError(\\\"smooth only accepts 1 dimension arrays.\\\")\\n\\n if x.size < window_len:\\n raise ValueError(\\\"Input vector needs to be bigger than window size.\\\")\\n\\n\\n if window_len<3:\\n return x\\n\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError(\\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\")\\n\\n\\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w=np.ones(window_len,'d')\\n else:\\n w=eval('np.'+window+'(window_len)')\\n\\n y=np.convolve(w/w.sum(),s,mode='valid')\\n return y\",\n \"def add_simple_moving_average(smas, n, data):\\n total = sum([data[-1-i] for i in range(n)])\\n smas.append(total/n)\",\n \"def get_rolling_mean(self, metric: str = 'cfs', min: Union[int, float] = None, max: Union[int, float] = None,\\n period_count: int = 5, period: str = 'year', start_date: datetime = None,\\n end_date: datetime = None, rolling_window: str = '28D',\\n apply_smoothing: bool = True) -> pd.DataFrame:\\n\\n # retrive observations\\n obs = self.get_observations(period=period, period_count=period_count, metrics=metric, start_date=start_date,\\n end_date=end_date)\\n\\n # standardize the dataframe\\n obs.rename(columns={metric: 'flow'}, inplace=True)\\n\\n\\n # calculate the mean and standard deviation over a rolling window\\n mean_df = obs['flow'].rolling(rolling_window).mean()\\n std_df = obs['flow'].rolling(rolling_window).std()\\n\\n # combine the means and standard deviations\\n obs_join = obs.join(mean_df, rsuffix='_mean').join(std_df, rsuffix='_std')\\n\\n # calculate a curve one standard deviation above and below the mean\\n obs_join[f'flow_plus_std'] = obs_join[f'flow_mean'] + obs_join[f'flow_std']\\n obs_join[f'flow_less_std'] = obs_join[f'flow_mean'] - obs_join[f'flow_std']\\n\\n # drop out leap year days\\n obs_nrml = obs_join[~((obs_join.index.month == 2) & (obs_join.index.day == 29))].copy()\\n obs_nrml['timestamp'] = [val.replace(year=1973) for val in obs_nrml.index]\\n\\n # tack on values for the preceeding and trailing months to ensure the curve covers an entire year\\n lead_obs = obs_nrml[obs_nrml.index.month == 12].copy()\\n lead_obs['timestamp'] = [val.replace(year=1972) for val in lead_obs.index]\\n lag_obs = obs_nrml[obs_nrml.index.month == 1].copy()\\n lag_obs['timestamp'] = [val.replace(year=1973) for val in lag_obs.index]\\n obs_nrml = pd.concat([obs_nrml, lead_obs, lag_obs])\\n\\n # create an average curve table for one year\\n mean_cols = [f'flow_mean', f'flow_plus_std', f'flow_less_std', 'timestamp']\\n mean_df = obs_nrml[mean_cols].groupby('timestamp').mean()\\n\\n # if min and max range flows are provided, add them\\n if min is not None:\\n mean_df[f'flow_bott'] = min\\n if max is not None:\\n mean_df[f'flow_top'] = max\\n\\n # consolidate into days...because nobody wants needs every 15 minutes for an entire year\\n mean_df = mean_df.groupby(pd.Grouper(freq='D')).mean()\\n\\n # if smoothing the curve (a VERY good idea), do it\\n if apply_smoothing:\\n mean_df = mean_df.rolling(window=5).mean()\\n\\n mean_df = mean_df[mean_df.index.year == 1973].copy()\\n\\n return mean_df\",\n \"def smooth(x, window_len=11, window='hanning'):\\n\\n if x.ndim != 1:\\n raise ValueError, \\\"smooth only accepts 1 dimension arrays.\\\"\\n\\n if x.size < window_len:\\n raise ValueError, \\\"Input vector needs to be bigger than window size.\\\"\\n\\n if window_len < 3:\\n return x\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError, \\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\"\\n\\n s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w = np.ones(window_len, 'd')\\n else:\\n w = eval('np.' + window + '(window_len)')\\n\\n y = np.convolve(w / w.sum(), s, mode='valid')\\n return y\",\n \"def window_average(\\n self, key: str, window_start_timestamp_s: float, do_compact: bool = True\\n ) -> Optional[float]:\\n points_after_idx = self._get_datapoints(key, window_start_timestamp_s)\\n\\n if do_compact:\\n self.data[key] = points_after_idx\\n\\n if len(points_after_idx) == 0:\\n return\\n return sum(point.value for point in points_after_idx) / len(points_after_idx)\",\n \"def moving_averages(df, start_step, window_size=None):\\n if window_size == None: # Use a large window to compute average over all historical data\\n window_size = df.shape[0]\\n fea = df.shift(start_step).rolling(min_periods=1, center=False, window=window_size).mean()\\n fea.columns = fea.columns + \\\"_mean\\\"\\n return fea\",\n \"def smooth(x,window_len=11,window='hanning'):\\n\\n if x.ndim != 1:\\n raise ValueError(\\\"smooth only accepts 1 dimension arrays.\\\")\\n\\n if x.size < window_len:\\n raise ValueError(\\\"Input vector needs to be bigger than window size.\\\")\\n\\n\\n if window_len<3:\\n return x\\n\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError(\\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\")\\n\\n\\n s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w=np.ones(window_len,'d')\\n else:\\n w=eval('np.'+window+'(window_len)')\\n\\n y=np.convolve(w/w.sum(),s,mode='same')\\n return y\",\n \"def smooth(x,window_len=10,window='hanning'):\\n #\\n if x.ndim != 1:\\n raise ValueError(\\\"smooth only accepts 1 dimension arrays.\\\")\\n #\\n if x.size < window_len:\\n raise ValueError(\\\"Input vector needs to be bigger than window size.\\\")\\n #\\n if window_len<3:\\n return x\\n #\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError(\\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\")\\n #\\n s=r_[2*x[0]-x[window_len:1:-1],x,2*x[-1]-x[-1:-window_len:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w=ones(window_len,'d')\\n else:\\n w=eval(window+'(window_len)')\\n #\\n y=convolve(w/w.sum(),s,mode='same')\\n return y[window_len-1:-window_len+1]\",\n \"def ewm_mean_1d_nb(a, span, minp=None, adjust=False):\\n if minp is None:\\n minp = span\\n if minp > span:\\n raise ValueError(\\\"minp must be <= span\\\")\\n N = len(a)\\n out = np.empty(N, dtype=np.float_)\\n if N == 0:\\n return out\\n com = (span - 1) / 2.0\\n alpha = 1. / (1. + com)\\n old_wt_factor = 1. - alpha\\n new_wt = 1. if adjust else alpha\\n weighted_avg = a[0]\\n is_observation = (weighted_avg == weighted_avg)\\n nobs = int(is_observation)\\n out[0] = weighted_avg if (nobs >= minp) else np.nan\\n old_wt = 1.\\n\\n for i in range(1, N):\\n cur = a[i]\\n is_observation = (cur == cur)\\n nobs += is_observation\\n if weighted_avg == weighted_avg:\\n old_wt *= old_wt_factor\\n if is_observation:\\n # avoid numerical errors on constant series\\n if weighted_avg != cur:\\n weighted_avg = ((old_wt * weighted_avg) + (new_wt * cur)) / (old_wt + new_wt)\\n if adjust:\\n old_wt += new_wt\\n else:\\n old_wt = 1.\\n elif is_observation:\\n weighted_avg = cur\\n out[i] = weighted_avg if (nobs >= minp) else np.nan\\n return out\",\n \"def smooth(x, window_len=11, window=\\\"hanning\\\"):\\n\\n if x.ndim != 1:\\n raise ValueError(\\\"smooth only accepts 1 dimension arrays.\\\")\\n\\n if x.size < window_len:\\n raise ValueError(\\\"Input vector needs to be bigger than window size.\\\")\\n\\n if window_len < 3:\\n return x\\n\\n if not window in [\\\"flat\\\", \\\"hanning\\\", \\\"hamming\\\", \\\"bartlett\\\", \\\"blackman\\\"]:\\n raise ValueError(\\n \\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\"\\n )\\n\\n s = np.r_[x[window_len - 1 : 0 : -1], x, x[-2 : -window_len - 1 : -1]]\\n # print(len(s))\\n if window == \\\"flat\\\": # moving average\\n w = np.ones(window_len, \\\"d\\\")\\n else:\\n w = eval(\\\"np.\\\" + window + \\\"(window_len)\\\")\\n\\n y = np.convolve(w / w.sum(), s, mode=\\\"valid\\\")\\n return y[(window_len // 2 - 1) : -(window_len // 2 + 1)]\",\n \"def smooth( x, window_len = 5, window = 'hanning' ):\\n\\n if x.ndim != 1:\\n raise ValueError, \\\"smooth only accepts 1 dimension arrays.\\\"\\n\\n if x.size < window_len:\\n raise ValueError, \\\"Input vector needs to be bigger than window size.\\\"\\n\\n if window_len<3:\\n return x\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError, \\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\"\\n\\n s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]\\n\\n if window == 'flat': #moving average\\n w=np.ones(window_len,'d')\\n else:\\n w=eval('np.'+window+'(window_len)')\\n\\n y=np.convolve(w/w.sum(),s,mode='valid')\\n return y\",\n \"def get_rolling_mean(df, window):\\n # return pd.rolling_mean(values, window=window)\\n rolling_df=pd.DataFrame(index=df.index)\\n for sym in df.columns:\\n df_tmp= pd.Series.rolling(df[sym],window=window).mean().to_frame() # to_frame converts series to a data frame\\n rolling_df=rolling_df.join(df_tmp)\\n \\n # Update the NaN values which are initial values for the \\n \\n rolling_df.ix[:window, :] = 0\\n \\n return rolling_df\\n #return pd.Series.rolling(values,window=window).mean()\",\n \"def smooth(x,window_len=11,window='hanning'):\\n\\n if x.ndim != 1:\\n raise ValueError, \\\"smooth only accepts 1 dimension arrays.\\\"\\n\\n if x.size < window_len:\\n raise ValueError, \\\"Input vector needs to be bigger than window size.\\\"\\n\\n\\n if window_len<3:\\n return x\\n\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError, \\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\"\\n\\n\\n s=numpy.r_[2*x[0]-x[window_len:1:-1],x,2*x[-1]-x[-1:-window_len:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w=ones(window_len,'d')\\n else:\\n w=eval('numpy.'+window+'(window_len)')\\n\\n y=numpy.convolve(w/w.sum(),s,mode='same')\\n return y[window_len-1:-window_len+1]\",\n \"def smooth(x, window_len=11, window='hanning'):\\n window_len = min(window_len, len(x) - 1)\\n if x.ndim != 1:\\n raise ValueError(\\\"smooth only accepts 1 dimension arrays.\\\")\\n\\n if x.size < window_len:\\n raise ValueError(\\\"Input vector needs to be bigger than window size.\\\")\\n\\n if window_len < 3:\\n return x\\n\\n if window not in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError(\\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\")\\n\\n s = np.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]\\n # print(len(s))\\n if window == 'flat': # moving average\\n w = np.ones(window_len, 'd')\\n else:\\n w = eval('np.' + window + '(window_len)')\\n\\n y = np.convolve(w / w.sum(), s, mode='valid')\\n return y\",\n \"def _dba_iteration(tseries, ftvector, avg, weights, fs):\\r\\n # the number of time series in the set\\r\\n n = len(tseries)\\r\\n # length of the time series \\r\\n ntime = avg.shape[0]\\r\\n # features of avg\\r\\n avg_ft = ExtraFeatures(avg,fs)\\r\\n scaler.fit(ftvector) \\r\\n # number of dimensions (useful for MTS)\\r\\n # array containing the new weighted average sequence \\r\\n new_avg = np.zeros((ntime),dtype=np.float64) \\r\\n # array of sum of weights \\r\\n sum_weights = np.zeros((ntime),dtype=np.float64)\\r\\n # loop the time series \\r\\n for s in range(n): \\r\\n series = tseries[s]\\r\\n ft = ftvector[s]\\r\\n dist = np.linalg.norm(scaler.transform(avg_ft.reshape(1,-1))-scaler.transform(ft.reshape(1,-1)))\\r\\n new_avg = new_avg + dist*weights[s]*series\\r\\n sum_weights = sum_weights + dist*weights[s]\\r\\n \\r\\n new_avg = new_avg/sum_weights\\r\\n \\r\\n return new_avg\",\n \"def smooth(x, window,nan=True,old=False,fill='mean'):\\n if nan:\\n from Sp_parameters import nanmasked\\n from scipy import interpolate\\n ix = np.arange(len(x))\\n xmasked, mask = nanmasked(x)\\n if fill == 'mean':\\n fv = np.mean(xmasked)\\n elif fill == 'median':\\n fv = np.median(xmasked)\\n elif fill == 'zero':\\n fv = 0.0\\n else:\\n raise ValueError('the fill keyword doesnt match possible values, try mean, median, or zero')\\n fx = interpolate.interp1d(ix[mask],xmasked,bounds_error=False,fill_value=fv)\\n xinterp = fx(ix)\\n if old:\\n xout = np.convolve(xinterp, np.ones(window)/window, 'same')\\n else:\\n s = np.r_[xinterp[window-1:0:-1],xinterp,xinterp[-1:-window:-1]]\\n w = np.ones(window,'d')\\n xout = np.convolve(w/w.sum(),s,mode='valid')\\n istart = window/2\\n iend = -window/2+1\\n if iend==0:\\n iend = len(xout)\\n xout = xout[istart:iend]\\n else:\\n if old:\\n xout = np.convolve(x, np.ones(window)/window, 'same')\\n else:\\n s = np.r_[x[window-1:0:-1],x,x[-1:-window:-1]]\\n w = np.ones(window,'d')\\n xout = np.convolve(w/w.sum(),s,mode='valid')\\n xout = xout[window/2:-window/2+1]\\n return xout\",\n \"def lin_trim_mean(a: np.ndarray, start: float = 0.5, end: float = 0.1,\\n start_v: float = 0, end_v: float = 0.5) -> float:\\n start_w = np.linspace(start_v, 1, start * len(a), endpoint=False)\\n end_w = np.linspace(end_v, 1, end * len(a), endpoint=False)[::-1]\\n mid_w = np.ones(len(a) - len(start_w) - len(end_w))\\n weights = np.concatenate((start_w, mid_w, end_w))\\n return ((a * weights).sum() / weights.sum()).item()\",\n \"def running_mean(sequence: list):\\n if not sequence:\\n return []\\n\\n mean = []\\n \\\"\\\"\\\"\\n [1] = 1 / 1\\n [1,2] = 3 / 2 \\n [1,2,3] = 6 / 3\\n \\\"\\\"\\\"\\n for idx, num in enumerate(sequence):\\n\\n sum_total = sum(sequence[:(idx + 1)])\\n result = sum_total / (idx + 1)\\n\\n mean.append(round(result, 2))\\n\\n return mean\",\n \"def exponential_running_mean(data, factor_new, init_block_size=None,\\n start_mean=None, axis=None):\\n assert not (start_mean is None and init_block_size is None), (\\n \\\"Need either an init block or a start mean\\\")\\n assert start_mean is None or init_block_size is None, (\\n \\\"Can only use start mean \\\"\\n \\\"or init block size\\\")\\n assert factor_new <= 1.0\\n assert factor_new >= 0.0\\n if isinstance(axis, int):\\n axis = (axis,)\\n factor_old = 1 - factor_new\\n\\n # first preallocate the shape for the running means\\n # shape depends on which axes will be removed\\n running_mean_shape = list(data.shape)\\n if axis is not None:\\n for ax in axis:\\n # keep dim as empty dim\\n running_mean_shape[ax] = 1\\n\\n running_means = (np.ones(running_mean_shape) * np.nan).astype(np.float32)\\n\\n if start_mean is None:\\n start_data = data[0:init_block_size]\\n if axis is not None:\\n axes_for_start_mean = (0,) + axis # also average across init trials\\n else:\\n axes_for_start_mean = 0\\n # possibly temporarily upcast to float32 to avoid overflows in sum\\n # that is computed to compute mean\\n current_mean = np.mean(start_data.astype(np.float32),\\n keepdims=True,\\n axis=axes_for_start_mean).astype(\\n start_data.dtype)\\n # repeat mean for running means\\n running_means[:init_block_size] = current_mean\\n i_start = init_block_size\\n else:\\n current_mean = start_mean\\n i_start = 0\\n\\n for i in range(i_start, len(data)):\\n if axis is not None:\\n datapoint_mean = np.mean(data[i:i + 1], axis=axis, keepdims=True)\\n else:\\n datapoint_mean = data[i:i + 1]\\n next_mean = factor_new * datapoint_mean + factor_old * current_mean\\n running_means[i] = next_mean\\n current_mean = next_mean\\n\\n assert not np.any(np.isnan(running_means)), (\\n \\\"RUnning mean has NaNs :\\\\n{:s}\\\".format(str(running_means)))\\n assert not np.any(np.isinf(running_means)), (\\n \\\"RUnning mean has Infs :\\\\n{:s}\\\".format(str(running_means)))\\n return running_means\",\n \"def smooth(x,window_len=11,window='hanning'):\\n\\n\\t# if x.ndim != 1:\\n\\t# raise ValueError, \\\"smooth only accepts 1 dimension arrays.\\\"\\n\\n\\t# if x.size < window_len:\\n\\t# raise ValueError, \\\"Input vector needs to be bigger than window size.\\\"\\n\\n\\tassert x.ndim==1\\n\\tassert x.size==window_len\\n\\n\\tif window_len<3:\\n\\t\\treturn x\\n\\n\\tflag = (window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman'])\\n\\tassert flag==1\\n\\n\\ts=numpy.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\\n\\tif window == 'flat': #moving average\\n\\t\\tw=numpy.ones(window_len,'d')\\n\\telse:\\n\\t\\tw=eval('numpy.'+window+'(window_len)')\\n\\n\\ty=numpy.convolve(w/w.sum(),s,mode='valid')\\n\\treturn y\",\n \"def normalizeValuesByWindows(data, window_size=None):\\n\\n if not options.window_size:\\n window_size = min(map(lambda x: x[1] - x[0], data))\\n\\n new_values = []\\n\\n for start, end, value in data:\\n if end - start < window_size:\\n continue\\n start = start - start % window_size\\n for z in range(start, end, window_size):\\n new_values.append((z, value))\\n\\n new_values.sort()\\n\\n # interpolate values for the same windows with average\\n xvals = []\\n yvals = []\\n last_x = None\\n values = []\\n for x, value in new_values:\\n\\n if last_x != x:\\n if last_x is not None:\\n xvals.append(last_x)\\n yvals.append(numpy.mean(values))\\n values = []\\n\\n last_x = x\\n values.append(value)\\n\\n return xvals, yvals\",\n \"def sliding_window_integral(data, window=None, window_size=500, shift_size=1):\\n num_chunks = len(_sliding_window_chunkoffsets(data, window_size, shift_size))\\n if num_chunks == 0:\\n return np.asarray([])\\n if window is None:\\n return _numba_sliding_window_integral(data, num_chunks, window_size, shift_size)\\n else:\\n return _numba_sliding_window_integral_with_window(data, num_chunks, window, window_size, shift_size)\",\n \"def compute_means(window_means):\\n return np.mean(window_means, axis=0)\",\n \"def rolling_std_nb(a, window, minp=None, ddof=0):\\n out = np.empty_like(a, dtype=np.float_)\\n for col in range(a.shape[1]):\\n out[:, col] = rolling_std_1d_nb(a[:, col], window, minp=minp, ddof=ddof)\\n return out\",\n \"def smooth(x,window_len=11,window='bartlett',data = None):\\n\\n x = array(x)\\n\\n # use externally derieved window evaluation\\n if data is not None:\\n window_len = len(data)\\n window = 'extern'\\n\\n if x.size < window_len:\\n raise ValueError(\\\"Input vector needs to be bigger than window size.\\\")\\n\\n\\n if window_len<3:\\n raise ValueError(\\\"window must not be shorter than 3\\\")\\n\\n if window_len%2 is 0:\\n raise ValueError(\\\"window_len should be odd\\\")\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman','triang','extern']:\\n raise ValueError(\\\"Window is none of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman','triang','extern'\\\")\\n\\n \\n s=r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]\\n #print(len(s))\\n if window == 'flat': #moving average\\n w=ones(window_len,'d')\\n\\n elif window == 'triang':\\n w = triang(window_len)\\n\\n elif window == 'extern':\\n w = data\\n \\n else:\\n w=eval(window+'(window_len)')\\n\\n y=convolve(w/w.sum(),s,mode='valid')\\n \\n return y[int((window_len-1)/2):len(y)-int((window_len-1)/2)]\",\n \"def rolling_window(series, window):\\n shape = series.shape[:-1] + (series.shape[-1] - window + 1, window)\\n strides = series.strides + (series.strides[-1],)\\n return np.lib.stride_tricks.as_strided(series, shape=shape, strides=strides)\",\n \"def smooth(x, window_len=3, window='hanning'):\\n s = np.r_[2*x[0]-x[window_len:1:-1], x, 2*x[-1]-x[-1:-window_len:-1]]\\n w = getattr(np, window)(window_len)\\n y = np.convolve(w/w.sum(), s, mode='same') \\n return y[window_len-1:-window_len+1]\",\n \"def get_SMA(values, window=20):\\n\\treturn values.rolling(window, center=False).mean()\",\n \"def smooth(x, window_len=11, window='hanning', mode='same'):\\n import numpy\\n if x.ndim != 1:\\n raise ValueError(\\\"smooth only accepts 1 dimension arrays.\\\")\\n\\n if x.size < window_len:\\n raise ValueError(\\\"Input vector needs to be bigger than window size.\\\")\\n\\n if window_len < 3:\\n return x\\n\\n if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\\n raise ValueError(\\\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\\\")\\n\\n s = numpy.r_[x[window_len - 1:0:-1], x, x[-2:-window_len - 1:-1]]\\n # print(len(s))\\n if window == 'flat': # moving average\\n w = numpy.ones(window_len, 'd')\\n else:\\n w = eval('numpy.' + window + '(window_len)')\\n\\n y = numpy.convolve(w / w.sum(), s, mode=mode)\\n if mode == 'same':\\n return y[np.int_(window_len) - 1:-np.int_(window_len) + 1]\\n else:\\n return y[np.int_(window_len / 2 - 1):-np.int_(window_len / 2)]\",\n \"def avg_pool(\\n inputs, window_shape, strides=None, padding=\\\"VALID\\\", count_include_pad=True\\n):\\n y = pool(inputs, 0.0, lax.add, window_shape, strides, padding)\\n if count_include_pad:\\n y = y / np.prod(window_shape)\\n else:\\n div_shape = inputs.shape[:-1] + (1,)\\n if len(div_shape) - 2 == len(window_shape):\\n div_shape = (1,) + div_shape[1:]\\n y = y / pool(\\n jnp.ones(div_shape), 0.0, lax.add, window_shape, strides, padding\\n )\\n return y\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72755015","0.7019223","0.6980032","0.69147635","0.6886311","0.6849971","0.67949075","0.67820406","0.66995203","0.66644543","0.6640706","0.6443174","0.6416299","0.64145225","0.64019024","0.6376318","0.63155216","0.6246977","0.6240341","0.62263185","0.6224209","0.62219054","0.6220054","0.6191532","0.61786985","0.6175559","0.6141754","0.6129736","0.60862297","0.6062484","0.6057803","0.6021036","0.599902","0.5998237","0.59869486","0.5984099","0.5974826","0.59703755","0.5963283","0.5947873","0.5925202","0.59013915","0.58932376","0.5890593","0.58852226","0.5882079","0.58659387","0.5861695","0.5855752","0.58489865","0.58414054","0.5833594","0.5829045","0.5821435","0.5807524","0.57943827","0.57536876","0.5745894","0.5742943","0.5732481","0.5725219","0.57228553","0.57133615","0.57112855","0.5710513","0.57049054","0.56999457","0.5698606","0.5697206","0.56919074","0.5688443","0.5672889","0.5672613","0.5652042","0.5651519","0.563859","0.56249464","0.56175935","0.56148577","0.5600222","0.5594597","0.5582","0.55509317","0.55482614","0.5505924","0.55021924","0.5499572","0.5496683","0.54954255","0.5491362","0.54879695","0.54845184","0.54809564","0.5441337","0.5434811","0.54172266","0.5411248","0.54033697","0.5401713","0.53821486"],"string":"[\n \"0.72755015\",\n \"0.7019223\",\n \"0.6980032\",\n \"0.69147635\",\n \"0.6886311\",\n \"0.6849971\",\n \"0.67949075\",\n \"0.67820406\",\n \"0.66995203\",\n \"0.66644543\",\n \"0.6640706\",\n \"0.6443174\",\n \"0.6416299\",\n \"0.64145225\",\n \"0.64019024\",\n \"0.6376318\",\n \"0.63155216\",\n \"0.6246977\",\n \"0.6240341\",\n \"0.62263185\",\n \"0.6224209\",\n \"0.62219054\",\n \"0.6220054\",\n \"0.6191532\",\n \"0.61786985\",\n \"0.6175559\",\n \"0.6141754\",\n \"0.6129736\",\n \"0.60862297\",\n \"0.6062484\",\n \"0.6057803\",\n \"0.6021036\",\n \"0.599902\",\n \"0.5998237\",\n \"0.59869486\",\n \"0.5984099\",\n \"0.5974826\",\n \"0.59703755\",\n \"0.5963283\",\n \"0.5947873\",\n \"0.5925202\",\n \"0.59013915\",\n \"0.58932376\",\n \"0.5890593\",\n \"0.58852226\",\n \"0.5882079\",\n \"0.58659387\",\n \"0.5861695\",\n \"0.5855752\",\n \"0.58489865\",\n \"0.58414054\",\n \"0.5833594\",\n \"0.5829045\",\n \"0.5821435\",\n \"0.5807524\",\n \"0.57943827\",\n \"0.57536876\",\n \"0.5745894\",\n \"0.5742943\",\n \"0.5732481\",\n \"0.5725219\",\n \"0.57228553\",\n \"0.57133615\",\n \"0.57112855\",\n \"0.5710513\",\n \"0.57049054\",\n \"0.56999457\",\n \"0.5698606\",\n \"0.5697206\",\n \"0.56919074\",\n \"0.5688443\",\n \"0.5672889\",\n \"0.5672613\",\n \"0.5652042\",\n \"0.5651519\",\n \"0.563859\",\n \"0.56249464\",\n \"0.56175935\",\n \"0.56148577\",\n \"0.5600222\",\n \"0.5594597\",\n \"0.5582\",\n \"0.55509317\",\n \"0.55482614\",\n \"0.5505924\",\n \"0.55021924\",\n \"0.5499572\",\n \"0.5496683\",\n \"0.54954255\",\n \"0.5491362\",\n \"0.54879695\",\n \"0.54845184\",\n \"0.54809564\",\n \"0.5441337\",\n \"0.5434811\",\n \"0.54172266\",\n \"0.5411248\",\n \"0.54033697\",\n \"0.5401713\",\n \"0.53821486\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7964289"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77470,"cells":{"query":{"kind":"string","value":"Get all cover devices and setup them via config entry."},"document":{"kind":"string","value":"async def async_setup_entry(\n hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\n) -> None:\n entities = []\n\n for gateway in hass.data[DOMAIN][entry.entry_id][\"gateways\"]:\n for device in gateway.multi_level_switch_devices:\n for multi_level_switch in device.multi_level_switch_property:\n if multi_level_switch.startswith(\"devolo.Blinds\"):\n entities.append(\n DevoloCoverDeviceEntity(\n homecontrol=gateway,\n device_instance=device,\n element_uid=multi_level_switch,\n )\n )\n\n async_add_entities(entities)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_devices():","async def async_setup_entry(hass, config_entry, async_add_entities):\n entities = []\n gateway = hass.data[DOMAIN][GATEWAYS_KEY][config_entry.entry_id]\n gateway.discover()\n for device in list(gateway.devices.values()): #TODO filter by type cover\n device_type = SELVE_CLASSTYPES.get(device.device_type.value)\n if device_type is None:\n _LOGGER.warning('Unsupported type %s for Selve device %s',\n device.device_type, device.name)\n continue\n entities.append( SelveCover(device, device_type))\n\n async_add_entities(entities)","def initialize_devices(self):\n for k in self.devices:\n dev = self.devices[k]\n print('Starting %s' % dev.properties['name'])\n dev.initialize_driver()\n # print('Error initializing %s' % dev.properties['name'])\n if 'defaults' in dev.properties:\n defaults_file = dev.properties['defaults']\n defaults = from_yaml_to_dict(defaults_file)[dev.properties['name']]\n dev.apply_values(defaults)\n if dev.properties['type'] == 'daq':\n self.daqs[dev.properties['name']] = {'input': [],\n 'output': [],\n 'monitor': [], } # Creates an entry for every different DAQ.","def setup_platform(hass, config, add_devices, discovery_info=None):\n\n lights = []\n\n from nmap import PortScanner, PortScannerError\n scanner = PortScanner()\n\n _LOGGER.error(scanner)\n\n ip = ''\n\n for i in range(255):\n try:\n result = scanner.scan(hosts='192.168.1.'+str(i), arguments=\" --privileged -sP \") # hosts=config[CONF_HOSTS]\n except PortScannerError:\n return _LOGGER.error(\"PortScannerError\")\n\n _LOGGER.error(result)\n\n for ipv4, info in result['scan'].items():\n mac = info['addresses'].get('mac') or _arp(ipv4)\n _LOGGER.error(mac)\n _LOGGER.error(ipv4)\n if mac == '5C:CF:7F:E2:40:49': #put in config file\n ip = ipv4\n _LOGGER.error(ip)\n break\n if ip is not '':\n break\n\n\n lights.append(DaliLight('http://'+ip))\n add_devices(lights)","def device_config(i):\n apipath = \"/targets/devices\"\n url = SERVER + apipath\n params = {\n 'q': '(deviceType:ASA)',\n 'resolve': '[targets/devices.{name,deviceConfig}]',\n 'sort': 'name:desc',\n 'limit': NUM_DEVICES_TO_RETRIEVE_PER_QUERY,\n 'offset': i}\n headers = {\n 'Accept': \"application/json\",\n 'Content-Type': \"application/json\",\n 'Authorization': \"bearer {}\".format(token)}\n response = requests.get(url, verify=False, stream=True, headers=headers, params=params)\n getstatuscode = response.status_code\n getresponse = response.json()\n if getstatuscode == 200:\n return getresponse\n else:\n response.raise_for_status()","async def async_load_config(\n path: str, hass: HomeAssistant, consider_home: timedelta\n) -> list[Device]:\n dev_schema = vol.Schema(\n {\n vol.Required(CONF_NAME): cv.string,\n vol.Optional(CONF_ICON, default=None): vol.Any(None, cv.icon),\n vol.Optional(\"track\", default=False): cv.boolean,\n vol.Optional(CONF_MAC, default=None): vol.Any(\n None, vol.All(cv.string, vol.Upper)\n ),\n vol.Optional(\"gravatar\", default=None): vol.Any(None, cv.string),\n vol.Optional(\"picture\", default=None): vol.Any(None, cv.string),\n vol.Optional(CONF_CONSIDER_HOME, default=consider_home): vol.All(\n cv.time_period, cv.positive_timedelta\n ),\n }\n )\n result: list[Device] = []\n try:\n devices = await hass.async_add_executor_job(load_yaml_config_file, path)\n except HomeAssistantError as err:\n LOGGER.error(\"Unable to load %s: %s\", path, str(err))\n return []\n except FileNotFoundError:\n return []\n\n for dev_id, device in devices.items():\n # Deprecated option. We just ignore it to avoid breaking change\n device.pop(\"vendor\", None)\n device.pop(\"hide_if_away\", None)\n try:\n device = dev_schema(device)\n device[\"dev_id\"] = cv.slugify(dev_id)\n except vol.Invalid as exp:\n async_log_exception(exp, dev_id, devices, hass)\n else:\n result.append(Device(hass, **device))\n return result","def test_setup_adds_proper_devices(self, mock_light):\n good_config = {\n \"mochad\": {},\n \"light\": {\n \"platform\": \"mochad\",\n \"devices\": [{\"name\": \"Light1\", \"address\": \"a1\"}],\n },\n }\n assert setup_component(self.hass, light.DOMAIN, good_config)","def setup(hass, config):\n\n _LOGGER.info(\"Connected Devices loading.\")\n \n component = EntityComponent(_LOGGER, DOMAIN, hass, SCAN_INTERVAL)\n\n component.setup(config)\n\n descriptions = load_yaml_config_file(\n os.path.join(os.path.dirname(__file__), 'services.yaml'))\n\n def update_connected_device(service):\n \"\"\"Update a connected device.\"\"\"\n\n update_obj = service.data.get('value')\n\n connected_devices = hass.states.get('connected_devices.connected_devices').as_dict()\n \n attributes = connected_devices[\"attributes\"]\n\n for obj in update_obj:\n # _LOGGER.info(\"update value: %s\", obj[\"value\"])\n # _LOGGER.info(\"target: %s\", obj[\"target\"])\n # _LOGGER.info(\"previous value: %s\", attributes[\"devices\"][obj[\"device\"]][obj[\"target\"]])\n\n attributes[\"devices\"][obj[\"device\"]][obj[\"target\"]] = obj[\"value\"]\n # _LOGGER.info(\"after update: %s\", attributes[\"devices\"][obj[\"device\"]][obj[\"target\"]])\n \n connected_devices[\"attributes\"] = attributes\n\n hass.states.set('connected_devices.connected_devices', 'On', attributes, True)\n\n hass.services.register(\n DOMAIN,\n 'update_connected_device',\n update_connected_device,\n descriptions['update_connected_device'])\n\n return True","def main():\n for dev in Discover.discover().values():\n print(dev)","def initialize_devices(self):\n if not self.loaded_devices:\n raise Exception('Devices have to be loaded before being initialized.')\n\n for dev in self.devices:\n d = self.devices[dev]['dev'] # This is the Device instance\n d.initialize_driver()","def async_add_devices_discovery(hass, discovery_info, async_add_devices):\n items = discovery_info[CONF_ITEMS]\n for item in items:\n async_add_devices([AmpioSwitch(hass, item)])","def discover(self):\n\n # Get the Huge Page configuration\n self.get_hugepages()\n\n # Get the device configuration\n self.get_devices_per_node()\n\n # Get the CPU configuration\n self.get_cpu()\n\n # Get the current grub cmdline\n self.get_grub()","def load_devices(self, source=None):\n if source is not None:\n return\n init = self.measure['init']\n devices_file = init['devices']\n devices_list = from_yaml_to_devices(devices_file)\n for dev in devices_list:\n self.devices[dev.properties['name']] = dev\n if 'outputs' in dev.properties:\n self.output_devices.append(dev)\n print('Added %s to the experiment' % dev)\n if dev.properties['type'] == \"Rotation Stage\":\n self.rotation_stages.append(dev.properties['name'])","def test_get_devices(self):\n pass","def test_get_devices(self):\n pass","async def async_setup_platforms(hass, config):\n tasks = [\n hass.helpers.discovery.async_load_platform(component, DOMAIN, {}, config)\n for component in INSTEON_COMPONENTS\n ]\n await asyncio.gather(*tasks)\n\n for address in devices:\n device = devices[address]\n platforms = get_device_platforms(device)\n if ON_OFF_EVENTS in platforms:\n add_on_off_event_device(hass, device)\n\n _LOGGER.debug(\"Insteon device count: %s\", len(devices))\n register_new_device_callback(hass, config)\n async_register_services(hass)\n\n # Cannot be done concurrently due to issues with the underlying protocol.\n for address in devices:\n await devices[address].async_status()\n await async_id_unknown_devices(hass.config.config_dir)","def setup(hass, base_config):\n from pyhusmow import API as HUSMOW_API\n\n config = base_config.get(DOMAIN)\n\n if hass.data.get(DOMAIN) is None:\n hass.data[DOMAIN] = { 'devices': [] }\n\n api = HUSMOW_API()\n api.login(config.get(CONF_USERNAME), config.get(CONF_PASSWORD))\n\n robots = api.list_robots()\n\n if not robots:\n return False\n\n for robot in robots:\n hass.data[DOMAIN]['devices'].append(AutomowerDevice(robot, api))\n\n for component in AUTOMOWER_COMPONENTS:\n discovery.load_platform(hass, component, DOMAIN, {}, base_config)\n\n return True","async def async_setup_entry(hass, config_entry, async_add_entities):\n\n device = []\n session: SHCSession = hass.data[DOMAIN][config_entry.entry_id]\n\n for cover in session.device_helper.shutter_controls:\n _LOGGER.debug(f\"Found shutter control: {cover.name} ({cover.id})\")\n device.append(\n ShutterControlCover(\n device=cover,\n room_name=session.room(cover.room_id).name,\n controller_ip=config_entry.data[CONF_IP_ADDRESS],\n )\n )\n\n if device:\n async_add_entities(device)","def setup(hass: HomeAssistant, base_config: ConfigType) -> bool: # noqa: C901\n\n hass.data[DOMAIN] = {}\n\n # Parse configuration into a dict of device name to physical address\n # represented as a list of four elements.\n device_aliases = {}\n devices = base_config[DOMAIN].get(CONF_DEVICES, {})\n _LOGGER.debug(\"Parsing config %s\", devices)\n device_aliases.update(parse_mapping(devices))\n _LOGGER.debug(\"Parsed devices: %s\", device_aliases)\n\n platform = base_config[DOMAIN].get(CONF_PLATFORM, SWITCH)\n\n loop = (\n # Create own thread if more than 1 CPU\n hass.loop\n if multiprocessing.cpu_count() < 2\n else None\n )\n host = base_config[DOMAIN].get(CONF_HOST)\n display_name = base_config[DOMAIN].get(CONF_DISPLAY_NAME, DEFAULT_DISPLAY_NAME)\n if host:\n adapter = TcpAdapter(host, name=display_name, activate_source=False)\n else:\n adapter = CecAdapter(name=display_name[:12], activate_source=False)\n hdmi_network = HDMINetwork(adapter, loop=loop)\n\n def _adapter_watchdog(now=None):\n _LOGGER.debug(\"Reached _adapter_watchdog\")\n event.call_later(hass, WATCHDOG_INTERVAL, _adapter_watchdog_job)\n if not adapter.initialized:\n _LOGGER.info(\"Adapter not initialized; Trying to restart\")\n hass.bus.fire(EVENT_HDMI_CEC_UNAVAILABLE)\n adapter.init()\n\n _adapter_watchdog_job = HassJob(_adapter_watchdog, cancel_on_shutdown=True)\n\n @callback\n def _async_initialized_callback(*_: Any):\n \"\"\"Add watchdog on initialization.\"\"\"\n return event.async_call_later(hass, WATCHDOG_INTERVAL, _adapter_watchdog_job)\n\n hdmi_network.set_initialized_callback(_async_initialized_callback)\n\n def _volume(call: ServiceCall) -> None:\n \"\"\"Increase/decrease volume and mute/unmute system.\"\"\"\n mute_key_mapping = {\n ATTR_TOGGLE: KEY_MUTE_TOGGLE,\n ATTR_ON: KEY_MUTE_ON,\n ATTR_OFF: KEY_MUTE_OFF,\n }\n for cmd, att in call.data.items():\n if cmd == CMD_UP:\n _process_volume(KEY_VOLUME_UP, att)\n elif cmd == CMD_DOWN:\n _process_volume(KEY_VOLUME_DOWN, att)\n elif cmd == CMD_MUTE:\n hdmi_network.send_command(\n KeyPressCommand(mute_key_mapping[att], dst=ADDR_AUDIOSYSTEM)\n )\n hdmi_network.send_command(KeyReleaseCommand(dst=ADDR_AUDIOSYSTEM))\n _LOGGER.info(\"Audio muted\")\n else:\n _LOGGER.warning(\"Unknown command %s\", cmd)\n\n def _process_volume(cmd, att):\n if isinstance(att, (str,)):\n att = att.strip()\n if att == CMD_PRESS:\n hdmi_network.send_command(KeyPressCommand(cmd, dst=ADDR_AUDIOSYSTEM))\n elif att == CMD_RELEASE:\n hdmi_network.send_command(KeyReleaseCommand(dst=ADDR_AUDIOSYSTEM))\n else:\n att = 1 if att == \"\" else int(att)\n for _ in range(0, att):\n hdmi_network.send_command(KeyPressCommand(cmd, dst=ADDR_AUDIOSYSTEM))\n hdmi_network.send_command(KeyReleaseCommand(dst=ADDR_AUDIOSYSTEM))\n\n def _tx(call: ServiceCall) -> None:\n \"\"\"Send CEC command.\"\"\"\n data = call.data\n if ATTR_RAW in data:\n command = CecCommand(data[ATTR_RAW])\n else:\n src = data.get(ATTR_SRC, ADDR_UNREGISTERED)\n dst = data.get(ATTR_DST, ADDR_BROADCAST)\n if ATTR_CMD in data:\n cmd = data[ATTR_CMD]\n else:\n _LOGGER.error(\"Attribute 'cmd' is missing\")\n return\n if ATTR_ATT in data:\n if isinstance(data[ATTR_ATT], (list,)):\n att = data[ATTR_ATT]\n else:\n att = reduce(lambda x, y: f\"{x}:{y:x}\", data[ATTR_ATT])\n else:\n att = \"\"\n command = CecCommand(cmd, dst, src, att)\n hdmi_network.send_command(command)\n\n def _standby(call: ServiceCall) -> None:\n hdmi_network.standby()\n\n def _power_on(call: ServiceCall) -> None:\n hdmi_network.power_on()\n\n def _select_device(call: ServiceCall) -> None:\n \"\"\"Select the active device.\"\"\"\n if not (addr := call.data[ATTR_DEVICE]):\n _LOGGER.error(\"Device not found: %s\", call.data[ATTR_DEVICE])\n return\n if addr in device_aliases:\n addr = device_aliases[addr]\n else:\n entity = hass.states.get(addr)\n _LOGGER.debug(\"Selecting entity %s\", entity)\n if entity is not None:\n addr = entity.attributes[\"physical_address\"]\n _LOGGER.debug(\"Address acquired: %s\", addr)\n if addr is None:\n _LOGGER.error(\n \"Device %s has not physical address\", call.data[ATTR_DEVICE]\n )\n return\n if not isinstance(addr, (PhysicalAddress,)):\n addr = PhysicalAddress(addr)\n hdmi_network.active_source(addr)\n _LOGGER.info(\"Selected %s (%s)\", call.data[ATTR_DEVICE], addr)\n\n def _update(call: ServiceCall) -> None:\n \"\"\"Update if device update is needed.\n\n Called by service, requests CEC network to update data.\n \"\"\"\n hdmi_network.scan()\n\n def _new_device(device):\n \"\"\"Handle new devices which are detected by HDMI network.\"\"\"\n key = f\"{DOMAIN}.{device.name}\"\n hass.data[DOMAIN][key] = device\n ent_platform = base_config[DOMAIN][CONF_TYPES].get(key, platform)\n discovery.load_platform(\n hass,\n ent_platform,\n DOMAIN,\n discovered={ATTR_NEW: [key]},\n hass_config=base_config,\n )\n\n def _shutdown(call):\n hdmi_network.stop()\n\n def _start_cec(callback_event):\n \"\"\"Register services and start HDMI network to watch for devices.\"\"\"\n hass.services.register(\n DOMAIN, SERVICE_SEND_COMMAND, _tx, SERVICE_SEND_COMMAND_SCHEMA\n )\n hass.services.register(\n DOMAIN, SERVICE_VOLUME, _volume, schema=SERVICE_VOLUME_SCHEMA\n )\n hass.services.register(\n DOMAIN,\n SERVICE_UPDATE_DEVICES,\n _update,\n schema=SERVICE_UPDATE_DEVICES_SCHEMA,\n )\n hass.services.register(DOMAIN, SERVICE_POWER_ON, _power_on)\n hass.services.register(DOMAIN, SERVICE_STANDBY, _standby)\n hass.services.register(DOMAIN, SERVICE_SELECT_DEVICE, _select_device)\n\n hdmi_network.set_new_device_callback(_new_device)\n hdmi_network.start()\n\n hass.bus.listen_once(EVENT_HOMEASSISTANT_START, _start_cec)\n hass.bus.listen_once(EVENT_HOMEASSISTANT_STOP, _shutdown)\n return True","def setup_platform(hass, config: ConfigType,\n add_devices: Callable[[list], None], discovery_info=None):\n devices = []\n\n for node in hass.data[ISY994_NODES][DOMAIN]:\n _LOGGER.debug(\"Loading %s\", node.name)\n devices.append(ISYSensorDevice(node))\n\n for node in hass.data[ISY994_WEATHER]:\n devices.append(ISYWeatherDevice(node))\n\n add_devices(devices)","def get_devices(self):\n devices = self.get(\"event/device\")","def setup_platform(hass, config, add_devices, discovery_info=None):\n \n devices = []\n resource = requests.get('{host}/rest/items?type=json'.format(\n host=config.get('host')))\n\n for item in resource.json()['item']:\n if item['type'] in ['DimmerItem', ]:\n devices.append(OpenhabLight(\n hass,\n item,\n config.get('brightness', DEFAULT_BRIGHTNESS)))\n\n add_devices(devices)","def set_devices(args):\n global devices\n if args is not None:\n devices = [torch.device(i) for i in ast.literal_eval('[' + args + ']')]\n torch.cuda.set_device(devices[0])\n else:\n devices = [torch.device('cpu')]","def discover_devices(self):\n # First let the STLink firmware discover devices\n self._stlink_probe()\n\n if self.stlink_devices:\n print(\"Discovered %d STLink device(s).\" % len(self.stlink_devices))\n\n # Grab lower level information about the USB devices (port, dev-id, etc)\n self._get_usb_devices()\n\n # Use the information from STLink probe and USB to build a more complete picture\n # of which device is on which port\n self._assign_port_to_device()\n\n else:\n print(\"No STLink devices were discovered.\")","def get_configuration(self):\n\t\tdevice = DeviceBase(self.name)\n\n\t\tif len(self.master_url) > 0:\n\t\t\tdevice.master_url = self.master_url\n\t\t\tr = requests.get(self.master_url + '/configuration/' + self.name)\n\n\t\t\tif r.status_code == 200:\n\t\t\t\ttry:\n\t\t\t\t\t#Request success\n\t\t\t\t\tconfig = json.loads(r.text)\n\t\t\t\t\tif config['deviceType'] == 1:\n\t\t\t\t\t\t\"\"\" HID Reader \"\"\"\n\t\t\t\t\t\tdevice = HIDReader(self.name)\n\t\t\t\t\tif config['deviceType'] == 2:\n\t\t\t\t\t\t\"\"\" ZK45Reader \"\"\"\n\t\t\t\t\t\tdevice = ZK45Reader(self.name)\n\t\t\t\t\tif config['deviceType'] == 4:\n\t\t\t\t\t\t\"\"\" ZFM20Reader \"\"\"\n\t\t\t\t\t\tdevice = ZFM20Reader(self.name)\n\t\t\t\t\tif config['deviceType'] == 5:\n\t\t\t\t\t\t\"\"\" IOEcho \"\"\"\n\t\t\t\t\t\tdevice = IOEcho(name=self.name, pin_and_label_matrix='')\n\t\t\t\t\telif config['deviceType'] == 0:\n\t\t\t\t\t\t\"\"\" None \"\"\"\n\t\t\t\t\t\tdevice = DeviceBase(name=self.name)\n\t\t\t\t\telse:\n\t\t\t\t\t\t\"\"\" Disable \"\"\"\n\t\t\t\t\t\tdevice = DeviceBase(self.name)\n\n\t\t\t\t\tdevice.zone_id = config['zone']\n\n\t\t\t\t\tdevice.is_zone_enabled = config['enabled']\n\t\t\t\t\tdevice.is_zone_day_time_only = config['dayTimeOnly']\n\t\t\t\t\tdevice.is_configuration_loaded = True\n\n\t\t\t\t\tdevice.master_secret = config['secret']\n\t\t\t\t\tdevice.master_url = self.master_url\n\n\t\t\t\t\tdevice.is_in_error = False\n\t\t\t\t\tdevice.error_status = \"OK\"\n\t\t\t\t\tdevice.type = config['deviceType']\n\n\t\t\t\t\tprint(\"Configuration loaded.\")\n\t\t\t\texcept Exception as e:\n\t\t\t\t\terror_message = \"Device type not supported by current platform. Configuration aborted. (\" + str(e) + \")\"\n\t\t\t\t\tprint(error_message)\n\t\t\t\t\tdevice.zone_id = 1\n\n\t\t\t\t\tdevice.is_zone_enabled = False\n\t\t\t\t\tdevice.is_zone_day_time_only = False\n\t\t\t\t\tdevice.is_in_error = True\n\t\t\t\t\tdevice.error_status = error_message\n\t\t\telse:\n\t\t\t\tprint(\"Configuration loading failed. (Server response : \" + str(r.status_code) + \")\")\n\t\t\t\tdevice.zone_id = 1\n\t\t\t\tdevice.is_zone_enabled = False\n\t\t\t\tdevice.is_zone_day_time_only = False\n\t\t\t\tdevice.is_in_error = True\n\t\t\t\tdevice.error_status = \"Configuration loading failed. (Server response : \" + str(r.status_code) + \")\"\n\t\telse:\n\t\t\tself.zone_id = 1\n\t\t\tself.is_zone_enabled = True\n\t\t\tself.is_zone_day_time_only = True\n\t\t\tdevice.is_in_error = True\n\t\t\tdevice.error_status = \"No master URL defined\"\n\n\t\tdevice.report_state()\n\t\treturn device","def setup_platform(hass, config, add_devices, discovery_info=None):\n switches = []\n for coil in config.get(\"coils\"):\n switches.append(ModbusCoilSwitch(\n coil.get(CONF_NAME),\n coil.get(CONF_SLAVE),\n coil.get(CONF_COIL)))\n add_devices(switches)","def collectNet(self):\n network = self.options.net\n # net option from the config file is a string\n if isinstance(network, basestring):\n network = [network]\n # in case someone uses 10.0.0.0,192.168.0.1 instead of\n # --net 10.0.0.0 --net 192.168.0.1\n if isinstance(network, (list, tuple)) and \",\" in network[0]:\n network = [n.strip() for n in network[0].split(\",\")]\n count = 0\n devices = []\n if not network:\n network = yield self.config().callRemote(\"getDefaultNetworks\")\n\n if not network:\n self.log.warning(\"No networks configured\")\n defer.returnValue(None)\n\n for net in network:\n try:\n nets = yield self.config().callRemote(\n \"getNetworks\", net, self.options.subnets\n )\n if not nets:\n self.log.warning(\"No networks found for %s\", net)\n continue\n ips = yield self.discoverIps(nets)\n devices += ips\n count += len(ips)\n except Exception as ex:\n self.log.exception(\n \"Error performing net discovery on %s: %s\", net, ex\n )\n self.log.info(\"Working on devices: %s\", devices)\n\n foundDevices = []\n for device in devices:\n result = yield self.discoverDevice(\n device, self.options.deviceclass, self.options.productionState\n )\n if result is not None:\n foundDevices.append(result)\n defer.returnValue(foundDevices)","async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:\n conf = config.get(DOMAIN, {})\n hass.data[DOMAIN] = {\n DATA_CUSTOM_EFFECTS: conf.get(CONF_CUSTOM_EFFECTS, {}),\n DATA_CONFIG_ENTRIES: {},\n }\n # Make sure the scanner is always started in case we are\n # going to retry via ConfigEntryNotReady and the bulb has changed\n # ip\n scanner = YeelightScanner.async_get(hass)\n await scanner.async_setup()\n\n # Import manually configured devices\n for host, device_config in config.get(DOMAIN, {}).get(CONF_DEVICES, {}).items():\n _LOGGER.debug(\"Importing configured %s\", host)\n entry_config = {CONF_HOST: host, **device_config}\n hass.async_create_task(\n hass.config_entries.flow.async_init(\n DOMAIN, context={\"source\": SOURCE_IMPORT}, data=entry_config\n )\n )\n\n return True","async def async_id_unknown_devices(config_dir):\n await devices.async_load(id_devices=1)\n for addr in devices:\n device = devices[addr]\n flags = True\n for name in device.operating_flags:\n if not device.operating_flags[name].is_loaded:\n flags = False\n break\n if flags:\n for name in device.properties:\n if not device.properties[name].is_loaded:\n flags = False\n break\n\n # Cannot be done concurrently due to issues with the underlying protocol.\n if not device.aldb.is_loaded or not flags:\n await device.async_read_config()\n\n await devices.async_save(workdir=config_dir)","async def async_setup_platform(hass, config, add_entities, discovery_info=None):\n # Assign configuration variables.\n # The configuration check takes care they are present.\n # host = config[CONF_HOST]\n\n # Setup connection with devices/cloud\n things = hass.data[\"things\"]\n # Verify that passed in configuration works\n # if not hub.is_valid_login():\n # _LOGGER.error(\"Could not connect to AwesomeLight hub\")\n # return\n\n # Add devices\n for thing in things:\n if \"Cover\" in thing[\"@type\"]:\n add_entities([WebthingCover(thing)])","def _internal_get_device_configs(self) -> List[dict]:\n\n device_config_list = []\n\n self.landscape_lock.acquire()\n try:\n pod_info = self._landscape_info[\"pod\"]\n for dev_config_info in pod_info[\"devices\"]:\n if \"skip\" in dev_config_info and dev_config_info[\"skip\"]:\n continue\n device_config_list.append(dev_config_info)\n finally:\n self.landscape_lock.release()\n\n return device_config_list","def setup_platform(\n hass: HomeAssistant,\n config: ConfigType,\n add_entities: AddEntitiesCallback,\n discovery_info: DiscoveryInfoType | None = None,\n) -> None:\n bt_device_id: int = config[CONF_BT_DEVICE_ID]\n\n beacons: dict[str, dict[str, str]] = config[CONF_BEACONS]\n devices: list[EddystoneTemp] = []\n\n for dev_name, properties in beacons.items():\n namespace = get_from_conf(properties, CONF_NAMESPACE, 20)\n instance = get_from_conf(properties, CONF_INSTANCE, 12)\n name = properties.get(CONF_NAME, dev_name)\n\n if instance is None or namespace is None:\n _LOGGER.error(\"Skipping %s\", dev_name)\n continue\n\n devices.append(EddystoneTemp(name, namespace, instance))\n\n if devices:\n mon = Monitor(hass, devices, bt_device_id)\n\n def monitor_stop(event: Event) -> None:\n \"\"\"Stop the monitor thread.\"\"\"\n _LOGGER.info(\"Stopping scanner for Eddystone beacons\")\n mon.stop()\n\n def monitor_start(event: Event) -> None:\n \"\"\"Start the monitor thread.\"\"\"\n _LOGGER.info(\"Starting scanner for Eddystone beacons\")\n mon.start()\n\n add_entities(devices)\n mon.start()\n hass.bus.listen_once(EVENT_HOMEASSISTANT_STOP, monitor_stop)\n hass.bus.listen_once(EVENT_HOMEASSISTANT_START, monitor_start)\n else:\n _LOGGER.warning(\"No devices were added\")","def test_get_devices1(self):\n pass","async def async_setup_entry(\n hass: HomeAssistant,\n config_entry: ConfigEntry,\n async_add_entities: AddEntitiesCallback,\n) -> None:\n data: LutronCasetaData = hass.data[CASETA_DOMAIN][config_entry.entry_id]\n bridge = data.bridge\n cover_devices = bridge.get_devices_by_domain(DOMAIN)\n async_add_entities(\n LutronCasetaCover(cover_device, data) for cover_device in cover_devices\n )","def get_devices(self):\n\n md_configstore = os.path.join(\n os.environ['VOLTTRON_HOME'],\n \"configuration_store/platform.driver.store\"\n )\n\n if not os.path.exists(md_configstore):\n _log.debug(\"No master driver currently on this platform.\")\n return {}\n\n statinfo = os.stat(md_configstore)\n\n if self._master_driver_stat_time is None or \\\n self._master_driver_stat_time != statinfo.st_mtime:\n self._master_driver_stat_time = statinfo.st_mtime\n\n # else no change in the md file and we have the same stat time.\n else:\n keys = list(self._devices.keys())\n\n for k in keys:\n new_key = self.get_renamed_topic(k)\n if new_key != k:\n self._devices[new_key] = self._devices[k]\n del self._devices[k]\n\n return self._devices\n\n _log.debug('Getting devices')\n config_list = self.vip.rpc.call(CONFIGURATION_STORE,\n 'manage_list_configs',\n 'platform.driver').get(timeout=5)\n\n _log.debug('Config list is: {}'.format(config_list))\n devices = defaultdict(dict)\n\n for cfg_name in config_list:\n # Skip as we are only looking to do devices in this call.\n if not cfg_name.startswith('devices/'):\n continue\n\n device_config = self.vip.rpc.call('config.store', 'manage_get',\n 'platform.driver',\n cfg_name,\n raw=False).get(timeout=5)\n _log.debug('DEVICE CONFIG IS: {}'.format(device_config))\n\n reg_cfg_name = device_config.get(\n 'registry_config')[len('config://'):]\n _log.debug('Reading registry_config file {}'.format(\n reg_cfg_name\n ))\n registry_config = self.vip.rpc.call('config.store',\n 'manage_get', 'platform.driver',\n reg_cfg_name,\n raw=False).get(timeout=5)\n _log.debug('Registry Config: {}'.format(registry_config))\n\n points = []\n for pnt in registry_config:\n points.append(pnt['Volttron Point Name'])\n\n devices[cfg_name]['points'] = points\n\n return devices","async def async_setup_entry(\n hass: HomeAssistant,\n config_entry: ConfigEntry,\n async_add_entities: AddEntitiesCallback,\n) -> None:\n # Priority 1: manual config\n if hass.data[DOMAIN].get(CONF_IP_ADDRESS):\n devices = hass.data[DOMAIN][CONF_IP_ADDRESS]\n else:\n # Priority 2: scanned interfaces\n devices = await AehW4a1().discovery()\n\n entities = [_build_entity(device) for device in devices]\n async_add_entities(entities, True)","async def find_devices() -> List[DeviceInfo]:\n return await Discovery.search_devices()","def setup_platform(hass, config, add_entities, discovery_info=None):\n import jsonpath\n jsonpath = jsonpath.jsonpath\n global HEAT_PUMPS\n hub.update_overview()\n if int(hub.config.get(CONF_CLIMATE, 1)):\n HEAT_PUMPS = hub.get('$.heatPumps')\n if HEAT_PUMPS:\n for heat_pump in HEAT_PUMPS[0]:\n device_label = jsonpath(heat_pump, '$.deviceLabel')[0]\n add_entities([\n VerisureHeatPump(device_label)\n ])","async def async_setup_entry(hass, config_entry, async_add_devices):\n loxconfig = hass.data[DOMAIN]['loxconfig']\n identify = loxconfig['msInfo']['serialNr']\n\n devices = []\n all_dimmers = []\n all_light_controller_dimmers = []\n all_color_picker = []\n all_switches = []\n all_dimmers = get_all_dimmer(loxconfig)\n\n for light_controller in get_all_light_controller(loxconfig):\n light_controller.update({'room': get_room_name_from_room_uuid(loxconfig, light_controller.get('room', '')),\n 'cat': get_cat_name_from_cat_uuid(loxconfig, light_controller.get('cat', '')),\n 'async_add_devices': async_add_devices\n })\n new_light_controller = LoxonelightcontrollerV2(**light_controller)\n\n if 'subControls' in light_controller:\n if len(light_controller['subControls']) > 0:\n for sub_controll in light_controller['subControls']:\n if light_controller['subControls'][sub_controll]['type'] == \"Dimmer\":\n light_controller['subControls'][sub_controll]['room'] = light_controller.get('room', '')\n light_controller['subControls'][sub_controll]['cat'] = light_controller.get('cat', '')\n light_controller['subControls'][sub_controll][\n 'lightcontroller_id'] = new_light_controller.unique_id\n all_light_controller_dimmers.append(light_controller['subControls'][sub_controll])\n\n elif light_controller['subControls'][sub_controll]['type'] == \"Switch\":\n light_controller['subControls'][sub_controll]['room'] = light_controller.get('room', '')\n light_controller['subControls'][sub_controll]['cat'] = light_controller.get('cat', '')\n light_controller['subControls'][sub_controll][\n 'lightcontroller_id'] = new_light_controller.unique_id\n all_switches.append(light_controller['subControls'][sub_controll])\n\n elif light_controller['subControls'][sub_controll]['type'] == \"ColorPickerV2\":\n light_controller['subControls'][sub_controll]['room'] = light_controller.get('room', '')\n light_controller['subControls'][sub_controll]['cat'] = light_controller.get('cat', '')\n light_controller['subControls'][sub_controll][\n 'lightcontroller_id'] = new_light_controller.unique_id\n all_color_picker.append(light_controller['subControls'][sub_controll])\n\n hass.bus.async_listen(EVENT, new_light_controller.event_handler)\n devices.append(new_light_controller)\n\n _ = all_dimmers + all_light_controller_dimmers\n\n for dimmer in _:\n if dimmer in all_light_controller_dimmers:\n dimmer.update({'room': get_room_name_from_room_uuid(loxconfig, light_controller.get('room', '')),\n 'cat': get_cat_name_from_cat_uuid(loxconfig, light_controller.get('cat', '')),\n 'async_add_devices': async_add_devices\n })\n else:\n dimmer.update({'room': get_room_name_from_room_uuid(loxconfig, dimmer.get('room', '')),\n 'cat': get_cat_name_from_cat_uuid(loxconfig, dimmer.get('cat', '')),\n 'async_add_devices': async_add_devices\n })\n\n new_dimmer = LoxoneDimmer(**dimmer)\n hass.bus.async_listen(EVENT, new_dimmer.event_handler)\n devices.append(new_dimmer)\n\n for switch in all_switches:\n switch.update({'room': get_room_name_from_room_uuid(loxconfig, light_controller.get('room', '')),\n 'cat': get_cat_name_from_cat_uuid(loxconfig, light_controller.get('cat', '')),\n 'async_add_devices': async_add_devices\n })\n new_switch = LoxoneLight(**switch)\n hass.bus.async_listen(EVENT, new_switch.event_handler)\n devices.append(new_switch)\n\n for color_picker in all_color_picker:\n color_picker.update({'room': get_room_name_from_room_uuid(loxconfig, light_controller.get('room', '')),\n 'cat': get_cat_name_from_cat_uuid(loxconfig, light_controller.get('cat', '')),\n 'async_add_devices': async_add_devices\n })\n new_color_picker = LoxoneColorPickerV2(**color_picker)\n hass.bus.async_listen(EVENT, new_color_picker.event_handler)\n devices.append(new_color_picker)\n\n async_add_devices(devices, True)\n return True","async def async_setup(self) -> None:\n await self.hass.async_add_executor_job(self._setup)\n\n # set already known devices to away instead of unavailable\n device_registry = dr.async_get(self.hass)\n devices = dr.async_entries_for_config_entry(device_registry, self.entry_id)\n for device_entry in devices:\n if device_entry.via_device_id is None:\n continue # do not add the router itself\n\n device_mac = dict(device_entry.connections).get(dr.CONNECTION_NETWORK_MAC)\n self.devices[device_mac] = {\n \"mac\": device_mac,\n \"name\": device_entry.name,\n \"active\": False,\n \"last_seen\": dt_util.utcnow() - timedelta(days=365),\n \"device_model\": None,\n \"device_type\": None,\n \"type\": None,\n \"link_rate\": None,\n \"signal\": None,\n \"ip\": None,\n }\n\n await self.async_update_device_trackers()\n self.entry.async_on_unload(\n async_track_time_interval(\n self.hass, self.async_update_device_trackers, SCAN_INTERVAL\n )\n )\n\n async_dispatcher_send(self.hass, self.signal_device_new)","def _import_devices(self) -> None:\n self._devices.clear()\n\n # Exctract all devices\n for device in self._udev.list_devices():\n # Skip devices without mapping\n if not device.device_node or self.helper.hide_virtual_device(device):\n continue\n self._devices[device.sys_name] = Device.import_udev(device)","def config_device(self, cfg):\n\n\t\tif self.host is not None:\n\t\t\tself.tell(\"Configuring device\")\n\n\t\tself._dev = cfg\n\n\t\tself.do_checklist([])","def setup_usb(self):\n global DEVICE\n global epBulkWriter\n global epBulkReader\n global VID\n global PID\n\n DEVICE = usb.core.find(idVendor=0x2AB9,idProduct=0xFFFF)\n if DEVICE is None:#If not a LVPM, look for an HVPM.\n DEVICE = usb.core.find(idVendor=0x04d8,idProduct=0x000b)\n VID = '0x4d8'\n PID = '0xb'\n if \"Linux\" == platform.system():\n try:\n DEVICE.detach_kernel_driver(0)\n except:\n pass # already unregistered\n DEVICE.set_configuration()\n\n cfg = DEVICE.get_active_configuration()\n intf = cfg[(0,0)]\n\n epBulkWriter = usb.util.find_descriptor(\n intf,\n custom_match = \\\n lambda e: \\\n usb.util.endpoint_direction(e.bEndpointAddress) == \\\n usb.util.ENDPOINT_OUT)\n epBulkReader = usb.util.find_descriptor(\n intf,\n custom_match = \\\n lambda e: \\\n usb.util.endpoint_direction(e.bEndpointAddress) == \\\n usb.util.ENDPOINT_IN)","def _configure_device():\n vendor_id = 0x04D8 # These ids are microchip's libusb based device\n product_id = 0x0204 # ids\n dev = usb.core.find(idVendor=vendor_id, idProduct = product_id)\n try:\n dev.set_configuration()\n return dev\n except:\n return None","def multiple_device_details(\n self, hosts, config_data, pre_name, action, post_name):\n res_obj = []\n self.host_list = []\n host_dict={}\n\n first_entry = hosts[0]\n if 'include' in first_entry:\n devices_file_name = first_entry['include']\n if os.path.isfile(devices_file_name):\n lfile = devices_file_name\n else:\n lfile = os.path.join(\n expanduser(get_path(\n 'DEFAULT',\n 'test_file_path')),\n devices_file_name)\n login_file = open(lfile, 'r')\n dev_file = yaml.load(login_file)\n gp = first_entry.get('group', 'all')\n\n dgroup = [i.strip().lower() for i in gp.split(',')]\n for dgp in dev_file:\n if dgroup[0].lower() == 'all' or dgp.lower() in dgroup:\n for val in dev_file[dgp]:\n hostname = list(val)[0]\n self.log_detail = {'hostname': hostname}\n if val.get(hostname) is not None and hostname not in host_dict:\n host_dict[hostname] = deepcopy(val.get(hostname))\n self.host_list.append(hostname)\n else:\n for host in hosts:\n try:\n hostname = host['device']\n self.log_detail = {'hostname': hostname}\n except KeyError as ex:\n self.logger.error(\n colorama.Fore.RED +\n \"ERROR!! KeyError 'device' key not found\",\n extra=self.log_detail)\n except Exception as ex:\n self.logger.error(\n colorama.Fore.RED +\n \"ERROR!! %s\" %\n ex,\n extra=self.log_detail)\n else:\n if hostname not in host_dict:\n self.host_list.append(hostname)\n host_dict[hostname] = deepcopy(host)\n\n for (hostname, key_value) in iteritems(host_dict):\n username = key_value.get('username')\n password = key_value.get('passwd')\n key_value = self.get_values(key_value)\n t = Thread(\n target=self.connect,\n args=(\n hostname,\n username,\n password,\n pre_name,\n config_data,\n action,\n post_name),\n kwargs= key_value\n )\n t.start()\n t.join()\n if action == \"snap\":\n if not self.snap_q.empty():\n res_obj.append(self.snap_q.get())\n elif action in [\"snapcheck\", \"check\"]:\n if not self.q.empty():\n res_obj.append(self.q.get())\n else:\n res_obj.append(None)\n\n return res_obj","def init_devices(self):\n self.hp_nb = int(self.rs_nb* self.hp_proportion/(1- self.hp_proportion))\n self.defense_cost = self.hp_nb * self.hp_unit_cost\n rs_devices = [True for i in range(self.rs_nb)] #rs --> True\n hp_devices = [False for i in range(self.hp_nb)] #hp --> False\n self.devices = rs_devices + hp_devices\n shuffle(self.devices)","async def async_setup(hass, config):\n\n conf = config[DOMAIN]\n port = conf.get(CONF_PORT)\n host = conf.get(CONF_HOST)\n ip_port = conf.get(CONF_IP_PORT)\n username = conf.get(CONF_HUB_USERNAME)\n password = conf.get(CONF_HUB_PASSWORD)\n hub_version = conf.get(CONF_HUB_VERSION)\n\n if host:\n _LOGGER.info(\"Connecting to Insteon Hub on %s:%d\", host, ip_port)\n else:\n _LOGGER.info(\"Connecting to Insteon PLM on %s\", port)\n\n try:\n await async_connect(\n device=port,\n host=host,\n port=ip_port,\n username=username,\n password=password,\n hub_version=hub_version,\n )\n except ConnectionError:\n _LOGGER.error(\"Could not connect to Insteon modem\")\n return False\n _LOGGER.info(\"Connection to Insteon modem successful\")\n\n hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, close_insteon_connection)\n conf = config[DOMAIN]\n overrides = conf.get(CONF_OVERRIDE, [])\n x10_devices = conf.get(CONF_X10, [])\n\n await devices.async_load(\n workdir=hass.config.config_dir, id_devices=0, load_modem_aldb=0\n )\n\n for device_override in overrides:\n # Override the device default capabilities for a specific address\n address = device_override.get(\"address\")\n if not devices.get(address):\n cat = device_override[CONF_CAT]\n subcat = device_override[CONF_SUBCAT]\n firmware = device_override.get(CONF_FIRMWARE)\n if firmware is None:\n firmware = device_override.get(CONF_PRODUCT_KEY, 0)\n devices.set_id(address, cat, subcat, firmware)\n\n for device in x10_devices:\n housecode = device.get(CONF_HOUSECODE)\n unitcode = device.get(CONF_UNITCODE)\n x10_type = \"on_off\"\n steps = device.get(CONF_DIM_STEPS, 22)\n if device.get(CONF_PLATFORM) == \"light\":\n x10_type = \"dimmable\"\n elif device.get(CONF_PLATFORM) == \"binary_sensor\":\n x10_type = \"sensor\"\n _LOGGER.debug(\n \"Adding X10 device to Insteon: %s %d %s\", housecode, unitcode, x10_type\n )\n device = devices.add_x10_device(housecode, unitcode, x10_type, steps)\n\n asyncio.create_task(async_setup_platforms(hass, config))\n return True","async def async_setup_platform(hass, config, async_add_devices, _discovery_info=None):\n pass","def setup_platform(hass, config, add_entities, discovery_info=None):\n\n for fan in pywink.get_fans():\n if fan.object_id() + fan.name() not in hass.data[DOMAIN][\"unique_ids\"]:\n add_entities([WinkFanDevice(fan, hass)])","def setup_platform(\n hass: HomeAssistant,\n config: Dict,\n add_devices: Callable,\n discovery_info: Optional[Dict] = None,\n) -> None:\n havdalah = config[HAVDALAH_MINUTES]\n candle_light = config[CANDLE_LIGHT_MINUTES]\n cities = config[GEONAMES]\n cities_list = cities.split(\",\")\n\n add_devices(\n [\n ShabbatTimes(\n hass,\n city,\n \"Shabbat Times {}\".format(city.replace(\"-\", \"_\")),\n havdalah,\n candle_light,\n )\n for city in cities_list\n ]\n )","def _async_get_nest_devices(\n hass: HomeAssistant, config_entry: ConfigEntry\n) -> dict[str, Device]:\n if DATA_SDM not in config_entry.data:\n return {}\n\n if (\n config_entry.entry_id not in hass.data[DOMAIN]\n or DATA_DEVICE_MANAGER not in hass.data[DOMAIN][config_entry.entry_id]\n ):\n return {}\n\n device_manager: DeviceManager = hass.data[DOMAIN][config_entry.entry_id][\n DATA_DEVICE_MANAGER\n ]\n return device_manager.devices","def setup(hass, config):\n component = DeviceComponent(\n logging.getLogger(__name__), DOMAIN, hass, SCAN_INTERVAL,\n DISCOVERY_PLATFORMS)\n\n component.setup(config)\n\n def media_player_service_handler(service):\n \"\"\" Maps services to methods on MediaPlayerDevice. \"\"\"\n target_players = component.extract_from_service(service)\n\n method = SERVICE_TO_METHOD[service.service]\n\n for player in target_players:\n getattr(player, method)()\n\n if player.should_poll:\n player.update_ha_state(True)\n\n for service in SERVICE_TO_METHOD:\n hass.services.register(DOMAIN, service, media_player_service_handler)\n\n def play_youtube_video_service(service, media_id):\n \"\"\" Plays specified media_id on the media player. \"\"\"\n target_players = component.extract_from_service(service)\n\n if media_id:\n for player in target_players:\n player.play_youtube(media_id)\n\n hass.services.register(DOMAIN, \"start_fireplace\",\n lambda service:\n play_youtube_video_service(service, \"eyU3bRy2x44\"))\n\n hass.services.register(DOMAIN, \"start_epic_sax\",\n lambda service:\n play_youtube_video_service(service, \"kxopViU98Xo\"))\n\n hass.services.register(DOMAIN, SERVICE_YOUTUBE_VIDEO,\n lambda service:\n play_youtube_video_service(\n service, service.data.get('video')))\n\n return True","def setup_platform(hass, config, add_devices, discovery_info=None) -> None:\n friendly_name = config.get(CONF_FRIENDLY_NAME)\n mac_addr = config.get(CONF_MAC)\n add_devices([Switchmate(mac_addr, friendly_name)], True)","def devices(self, devices):\n\n self._devices = devices","def devices(self, devices):\n\n self._devices = devices","def get_devices(self):\n\t\tself.ise.headers.update({'Accept': 'application/vnd.com.cisco.ise.network.networkdevice.1.0+xml'})\n\n\t\tresp = self.ise.get('{0}/config/networkdevice'.format(self.url_base))\n\n\t\tresult = {\n\t\t\t'success': False,\n\t\t\t'response': '',\n\t\t\t'error': '',\n\t\t}\n\n\t\tjson_res = ERS._to_json(resp.text)['ns3:searchResult']\n\n\t\tif resp.status_code == 200 and int(json_res['@total']) > 1:\n\t\t\tresult['success'] = True\n\t\t\tresult['response'] = [(i['@name'], i['@id'])\n\t\t\t\t\t\t\t\t for i in json_res['ns3:resources']['ns5:resource']]\n\t\t\treturn result\n\n\t\telif resp.status_code == 200 and int(json_res['@total']) == 1:\n\t\t\tresult['success'] = True\n\t\t\tresult['response'] = [(json_res['ns3:resources']['ns5:resource']['@name'],\n\t\t\t\t\t\t\t\t json_res['ns3:resources']['ns5:resource']['@id'])]\n\t\t\treturn result\n\n\t\telif resp.status_code == 200 and int(json_res['@total']) == 0:\n\t\t\tresult['success'] = True\n\t\t\tresult['response'] = []\n\t\t\treturn result\n\n\t\telse:\n\t\t\tresult['response'] = ERS._to_json(resp.text)['ns3:ersResponse']['messages']['message']['title']\n\t\t\tresult['error'] = resp.status_code\n\t\t\treturn result","async def subdevice_fixture(hass: HomeAssistant, ufp: MockUFPFixture, light: Light):\n\n await init_entry(hass, ufp, [light])\n\n device_registry = dr.async_get(hass)\n\n return [d for d in device_registry.devices.values() if d.name != \"UnifiProtect\"][0]","def device_discovery(endless):\r\n click.echo(\"start device discovery ...\")\r\n _device_discovery(endless)","async def async_setup_entry(\n hass: HomeAssistantType, entry: ConfigEntry, async_add_entities\n) -> None:\n entities = []\n\n for device in hass.data[DOMAIN][\"homecontrol\"].multi_level_sensor_devices:\n for multi_level_sensor in device.multi_level_sensor_property:\n entities.append(\n DevoloMultiLevelDeviceEntity(\n homecontrol=hass.data[DOMAIN][\"homecontrol\"],\n device_instance=device,\n element_uid=multi_level_sensor,\n )\n )\n async_add_entities(entities, False)","def pop_adv_devices(self):\r\n if self.localSDK.devList:\r\n del self.localSDK.devList[:]\r\n try:\r\n self.localSDK.get_devices() # Get list of boards from KSDK manifest file\r\n except IOError:\r\n self.localSDK.devList = ['None']\r\n return","def launch_devices(self):\n self.data[0], temp = alghoritm.temperature(self.data[0], self.set_thermostat, 0) # get value\n HC35_3S.launch(self.data_path, self.samples_size, temp) # set it via device\n\n self.data[1], humidi = alghoritm.humidity(self.data[1], self.set_humidifier, 0)\n humidifier.launch(self.data_path, self.samples_size, humidi)\n\n self.data[2], moistu = alghoritm.moisture(self.data[2], self.set_sprinklers, 0)\n HUNTER.launch(self.data_path, self.samples_size, moistu)\n\n self.data[3], o2 = alghoritm.o2(self.data[3], self.set_ventilation, 0)\n ventilation.launch_o2(self.data_path, self.samples_size, o2)\n\n self.data[4], co2 = alghoritm.co2(self.data[4], self.set_ventilation, 0)\n ventilation.launch_co2(self.data_path, self.samples_size, co2)","def setup_scanner(hass, config, see, discovery_info=None):\n accounts = hass.data[BMW_DOMAIN]\n _LOGGER.debug(\"Found BMW accounts: %s\", \", \".join([a.name for a in accounts]))\n for account in accounts:\n for vehicle in account.account.vehicles:\n tracker = BMWDeviceTracker(see, vehicle)\n account.add_update_listener(tracker.update)\n tracker.update()\n return True","def get_devices():\n global managed_objects\n global devices_by_adr\n \n devices_by_adr = {}\n \n r = re.compile(\"\\/org\\/bluez\\/hci\\d*\\/dev\\_(.*)\")\n # e.g., match a string like this:\n # /org/bluez/hci0/dev_58_C9_35_2F_A1_EF\n \n for key, value in managed_objects.items():\n # print(\"key=\", key)\n m = r.match(key)\n if m is not None:\n dev_str = m.group(1) # we have a device string!\n # print(\"dev_str=\", dev_str)\n # let's flatten that dict a bit\n devices_by_adr[dev_str] = value[\"org.bluez.Device1\"]","async def get_all_units():\n all_devices = []\n if network is None:\n import netifaces\n\n gateway = netifaces.gateways().get(\"default\", {})\n subnet = gateway.get(netifaces.AF_INET, ())[0][:-1] + \"0/24\"\n else:\n subnet = network\n async with gdh_session() as session:\n googledevices = NetworkScan(loop, session)\n result = await googledevices.scan_for_units(subnet)\n if feature:\n for unit in result:\n if unit[feature]:\n all_devices.append(unit)\n else:\n all_devices = result\n print(format_json(all_devices))","async def get_all_units():\n all_devices = []\n if network is None:\n import netifaces\n\n gateway = netifaces.gateways().get(\"default\", {})\n subnet = gateway.get(netifaces.AF_INET, ())[0][:-1] + \"0/24\"\n else:\n subnet = network\n async with gdh_session() as session:\n googledevices = NetworkScan(loop, session)\n result = await googledevices.scan_for_units(subnet)\n if feature:\n for unit in result:\n if unit[feature]:\n all_devices.append(unit)\n else:\n all_devices = result\n print(format_json(all_devices))","async def test_get_device_config(hass, hass_client):\n with patch.object(config, \"SECTIONS\", [\"automation\"]):\n await async_setup_component(hass, \"config\", {})\n\n client = await hass_client()\n\n def mock_read(path):\n \"\"\"Mock reading data.\"\"\"\n return [{\"id\": \"sun\"}, {\"id\": \"moon\"}]\n\n with patch(\"homeassistant.components.config._read\", mock_read):\n resp = await client.get(\"/api/config/automation/config/moon\")\n\n assert resp.status == 200\n result = await resp.json()\n\n assert result == {\"id\": \"moon\"}","def configure(args):\n\n emu = Emulator(args,\n cpu='68030',\n frequency=24 * 1000 * 1000)\n # initially only the EEPROM exists; aliased at 0 all the way up to 0xfe000000\n # we only map the low and high aliases, as the intermediates aren't interesting\n emu.add_memory(base=0, size=512 * 1024, writable=False, from_file=args.rom)\n emu.add_memory(base=0xfe000000, size=512 * 1024, writable=False, from_file=args.rom)\n\n emu.add_device(args,\n MC68681,\n address=0xfffff000,\n interrupt=m68k.IRQ_2,\n register_arrangement='16-bit-doubled')\n emu.add_device(args,\n CompactFlash,\n address=0xffffe000,\n register_arrangement='8-bit' if args.cf_width == 8 else '16-bit')\n emu.add_device(args,\n CB030Remap,\n address=0xffff8000)\n emu.add_device(args,\n CB030Ticker,\n address=0xffff9000,\n interrupt=m68k.IRQ_6)\n return emu","async def async_setup(hass: HomeAssistant, config):\n if DOMAIN not in config:\n return True\n\n conf_adapters = config[DOMAIN].get(CONF_ADAPTERS)\n if not conf_adapters:\n return False\n\n hass.data.setdefault(DOMAIN, {}).update({CONF_INDOOR_UNITS: {}})\n\n for conf_adapter in conf_adapters:\n conf_adapter_name = conf_adapter[CONF_ADAPTER_NAME]\n conf_adapter_host = conf_adapter[CONF_ADAPTER_HOST]\n conf_adapter_port = conf_adapter[CONF_ADAPTER_PORT]\n conf_adapter_slave = conf_adapter[CONF_ADAPTER_SLAVE]\n adapter : DaikinAPI = await hass.async_add_executor_job(create_adapter, conf_adapter_host, conf_adapter_port, conf_adapter_slave)\n\n for indoor_unit in adapter.indoor_units.values():\n indoor_unit_global_name = \"daikin_dta116a621_\" + conf_adapter_name + \"_\" + str(indoor_unit.indoor_unit_id).replace(\"-\",\"_\")\n hass.data[DOMAIN][CONF_INDOOR_UNITS][indoor_unit_global_name] = indoor_unit\n hass.helpers.discovery.load_platform('climate', DOMAIN, {}, config)\n #discovery.async_load_platform(hass, DOMAIN, 'climate', {}, config)\n return True","def setup_platform(hass, config, add_entities, discovery_info=None):\n devices = []\n dev = discovery_info.get(\"dev\")\n param = discovery_info.get(\"param\")\n devices = []\n for idx in dev['data']:\n if dev['devtype'] in OT_SENSOR_TYPES and idx in [\"Z\",\"V\",\"P3\",\"P4\"]:\n devices.append(LifeSmartSensor(dev,idx,dev['data'][idx],param))\n else:\n devices.append(LifeSmartSensor(dev,idx,dev['data'][idx],param))\n add_entities(devices)","def get_devices(self):\n return self.api_request('GET', self.url + '/device', {})","def setup_platform(hass, config, add_devices, discovery_info=None):\n name = config.get(CONF_NAME)\n code = config.get(CONF_CODE)\n username = config.get(CONF_USERNAME)\n password = config.get(CONF_PASSWORD)\n\n add_devices([SimpliSafeAlarm(name, username, password, code)])","async def load_devices(self, force_refresh=False) -> None:\n if force_refresh or len(self._devices) == 0:\n devices: List[Dict[str, Any]] = await self._api_request(\"devices\")\n _LOGGER.debug(\"Loaded device list\")\n\n # load devices sequentially to avoid overloading the hub\n for dev in devices:\n await self._load_device(dev[\"id\"], force_refresh)","def setup_platform(hass, config: ConfigType,\n add_devices: Callable[[list], None], discovery_info=[]):\n elk = hass.data['PyElk']['connection']\n elk_config = hass.data['PyElk']['config']\n discovered_devices = hass.data['PyElk']['discovered_devices']\n if elk is None:\n _LOGGER.error('Elk is None')\n return False\n if not elk.connected:\n _LOGGER.error('A connection has not been made to the Elk panel.')\n return False\n devices = []\n from PyElk.Thermostat import Thermostat as ElkThermostat\n # If no discovery info was passed in, discover automatically\n if len(discovery_info) == 0:\n # Gather areas\n for node in elk.THERMOSTATS:\n if node:\n if node.included is True and node.enabled is True:\n discovery_info.append(node)\n # If discovery info was passed in, check if we want to include it\n else:\n for node in discovery_info:\n if node.included is True and node.enabled is True:\n continue\n else:\n discovery_info.remove(node)\n # Add discovered devices\n for node in discovery_info:\n if isinstance(node, ElkThermostat):\n node_name = 'climate.' + 'elk_thermostat_' + format(node.number, '02')\n else:\n continue\n if node_name not in discovered_devices:\n _LOGGER.debug('Loading Elk %s: %s', node.classname, node.description_pretty())\n device = ElkClimateDevice(node)\n discovered_devices[node_name] = device\n devices.append(device)\n else:\n _LOGGER.debug('Skipping already loaded Elk %s: %s', node.classname, node.description_pretty())\n\n add_devices(devices, True)\n return True","def YieldAllDevices(self):\n\n for deviceManagedObject in self.managedObject.config.hardware.device:\n yield Device(vm=self, managedObject=deviceManagedObject)","async def async_setup_platform(\n hass, config, async_add_entities, discovery_info=None):\n\n for device_config in config[CONF_DEVICES]:\n host = device_config[CONF_HOST]\n username = device_config[CONF_USERNAME]\n password = device_config[CONF_PASSWORD]\n interval = device_config.get(CONF_SCAN_INTERVAL, SCAN_INTERVAL)\n\n session = async_create_clientsession(hass, cookie_jar=aiohttp.CookieJar(unsafe=True))\n\n poe_data = ZyxelPoeData(host, username, password, interval, session)\n\n await poe_data.async_update()\n\n switches = list()\n for port, data in poe_data.ports.items():\n switches.append(ZyxelPoeSwitch(poe_data, host, port))\n\n async_add_entities(switches, False)","def setup_platform(hass, config, add_entities, discovery_info=None):\n\n host = config.get(CONF_HOST)\n if host:\n devices = [ThinkingCleaner(host, \"unknown\")]\n else:\n discovery = Discovery()\n devices = discovery.discover()\n\n @util.Throttle(MIN_TIME_BETWEEN_SCANS, MIN_TIME_BETWEEN_FORCED_SCANS)\n def update_devices():\n \"\"\"Update all devices.\"\"\"\n for device_object in devices:\n device_object.update()\n\n dev = []\n for device in devices:\n for type_name in SENSOR_TYPES:\n dev.append(ThinkingCleanerSensor(device, type_name, update_devices))\n\n add_entities(dev)","def setUp(self):\n super().setUp()\n self.devices = _DEVICE_STRATEGY()\n command_line = [\"pool\", \"create\", self._POOLNAME] + self.devices\n RUNNER(command_line)","def setup_platform(\n hass: HomeAssistant,\n config: ConfigType,\n add_entities: AddEntitiesCallback,\n discovery_info: DiscoveryInfoType | None = None,\n) -> None:\n lights = []\n for channel, device_config in config[CONF_DEVICES].items():\n device = {}\n device[\"name\"] = device_config[CONF_NAME]\n device[\"dimmable\"] = device_config[\"dimmable\"]\n device[\"channel\"] = channel\n device[\"driver\"] = config[CONF_DRIVER]\n device[\"host\"] = config[CONF_HOST]\n device[\"port\"] = config[CONF_PORT]\n lights.append(FutureNowLight(device))\n\n add_entities(lights, True)","def setup_platform(hass, config, add_devices, discovery_info=None):\n name = config.get(CONF_NAME)\n ip_addr = config.get(CONF_HOST)\n mac_addr = binascii.unhexlify(config.get(CONF_MAC).encode().replace(b':', b''))\n target_temp_default = config.get(CONF_TARGET_TEMP)\n target_temp_step = config.get(CONF_TARGET_TEMP_STEP)\n operation_list = DEFAULT_OPERATION_LIST\n \n import broadlink\n \n broadlink_device = broadlink.hysen((ip_addr, 80), mac_addr, None)\n broadlink_device.timeout = config.get(CONF_TIMEOUT)\n\n try:\n broadlink_device.auth()\n add_devices([\n BroadlinkHysenClimate(hass, name, broadlink_device, target_temp_default, target_temp_step, operation_list)\n ])\n except socket.timeout:\n _LOGGER.error(\"Failed to connect to Broadlink Hysen Device IP:%s\",ip_addr)","def get_devices(self):\n return get_devices(self.api_key)","def setup_devices(self, devices):\n \n self.devices = devices\n \n barrier = ReusableBarrier(len(devices))\n lock = Lock()\n aux_dict = {}\n\n for device in devices:\n device.barrier = barrier\n device.global_lock = lock\n for location in device.sensor_data: \n if location not in aux_dict:\n aux_dict[location] = Semaphore() \n \n for device in devices:\n device.device_semaphores = aux_dict\n\n self.setup_master_thread()","def addExtraDevices(self):\n \n # These tables were extracted from\n # pirates/src/piratesgui/GameOptions.py.\n \n ati_device_list = [ \n [\"ATI MOBILITY/RADEON X700\", 0x5653],\n [1, \"Radeon X1950 XTX Uber - Limited Edition\", 0x7248],\n [1, \"Radeon X1950 XTX Uber - Limited Edition Secondary\", 0x7268],\n [1, \"Radeon X800 CrossFire Edition\", 0x554D],\n [1, \"Radeon X800 CrossFire Edition Secondary\", 0x556D],\n [1, \"Radeon X850 CrossFire Edition\", 0x5D52],\n [1, \"Radeon X850 CrossFire Edition Secondary\", 0x5D72],\n [\"Radeon X550/X700 Series\", 0x564F],\n [\"ATI FireGL T2\", 0x4154],\n [\"ATI FireGL T2 Secondary\", 0x4174],\n [\"ATI FireGL V3100\", 0x5B64],\n [\"ATI FireGL V3100 Secondary\", 0x5B74],\n [\"ATI FireGL V3200\", 0x3E54],\n [\"ATI FireGL V3200 Secondary\", 0x3E74],\n [\"ATI FireGL V3300\", 0x7152],\n [\"ATI FireGL V3300 Secondary\", 0x7172],\n [\"ATI FireGL V3350\", 0x7153],\n [\"ATI FireGL V3350 Secondary\", 0x7173],\n [\"ATI FireGL V3400\", 0x71D2],\n [\"ATI FireGL V3400 Secondary\", 0x71F2],\n [\"ATI FireGL V5000\", 0x5E48],\n [\"ATI FireGL V5000 Secondary\", 0x5E68],\n [\"ATI FireGL V5100\", 0x5551],\n [\"ATI FireGL V5100 Secondary\", 0x5571],\n [\"ATI FireGL V5200\", 0x71DA],\n [\"ATI FireGL V5200 Secondary\", 0x71FA],\n [\"ATI FireGL V5300\", 0x7105],\n [\"ATI FireGL V5300 Secondary\", 0x7125],\n [\"ATI FireGL V7100\", 0x5550],\n [\"ATI FireGL V7100 Secondary\", 0x5570],\n [\"ATI FireGL V7200\", 0x5D50],\n [\"ATI FireGL V7200 \", 0x7104],\n [\"ATI FireGL V7200 Secondary\", 0x5D70],\n [\"ATI FireGL V7200 Secondary \", 0x7124],\n [\"ATI FireGL V7300\", 0x710E],\n [\"ATI FireGL V7300 Secondary\", 0x712E],\n [\"ATI FireGL V7350\", 0x710F],\n [\"ATI FireGL V7350 Secondary\", 0x712F],\n [\"ATI FireGL X1\", 0x4E47],\n [\"ATI FireGL X1 Secondary\", 0x4E67],\n [\"ATI FireGL X2-256/X2-256t\", 0x4E4B],\n [\"ATI FireGL X2-256/X2-256t Secondary\", 0x4E6B],\n [\"ATI FireGL X3-256\", 0x4A4D],\n [\"ATI FireGL X3-256 Secondary\", 0x4A6D],\n [\"ATI FireGL Z1\", 0x4147],\n [\"ATI FireGL Z1 Secondary\", 0x4167],\n [\"ATI FireMV 2200\", 0x5B65],\n [\"ATI FireMV 2200 Secondary\", 0x5B75],\n [\"ATI FireMV 2250\", 0x719B],\n [\"ATI FireMV 2250 Secondary\", 0x71BB],\n [\"ATI FireMV 2400\", 0x3151],\n [\"ATI FireMV 2400 Secondary\", 0x3171],\n [\"ATI FireStream 2U\", 0x724E],\n [\"ATI FireStream 2U Secondary\", 0x726E],\n [\"ATI MOBILITY FIRE GL 7800\", 0x4C58],\n [\"ATI MOBILITY FIRE GL T2/T2e\", 0x4E54],\n [\"ATI MOBILITY FireGL V3100\", 0x5464],\n [\"ATI MOBILITY FireGL V3200\", 0x3154],\n [\"ATI MOBILITY FireGL V5000\", 0x564A],\n [\"ATI MOBILITY FireGL V5000 \", 0x564B],\n [\"ATI MOBILITY FireGL V5100\", 0x5D49],\n [\"ATI MOBILITY FireGL V5200\", 0x71C4],\n [\"ATI MOBILITY FireGL V5250\", 0x71D4],\n [\"ATI MOBILITY FireGL V7100\", 0x7106],\n [\"ATI MOBILITY FireGL V7200\", 0x7103],\n [\"ATI MOBILITY RADEON\", 0x4C59],\n [\"ATI MOBILITY RADEON 7500\", 0x4C57],\n [\"ATI MOBILITY RADEON 9500\", 0x4E52],\n [\"ATI MOBILITY RADEON 9550\", 0x4E56],\n [\"ATI MOBILITY RADEON 9600/9700 Series\", 0x4E50],\n [\"ATI MOBILITY RADEON 9800\", 0x4A4E],\n [\"ATI Mobility Radeon HD 2300\", 0x7210],\n [\"ATI Mobility Radeon HD 2300 \", 0x7211],\n [\"ATI Mobility Radeon HD 2400\", 0x94C9],\n [\"ATI Mobility Radeon HD 2400 XT\", 0x94C8],\n [1, \"ATI Mobility Radeon HD 2600\", 0x9581],\n [1, \"ATI Mobility Radeon HD 2600 XT\", 0x9583],\n [\"ATI Mobility Radeon X1300\", 0x714A],\n [\"ATI Mobility Radeon X1300 \", 0x7149],\n [\"ATI Mobility Radeon X1300 \", 0x714B],\n [\"ATI Mobility Radeon X1300 \", 0x714C],\n [\"ATI Mobility Radeon X1350\", 0x718B],\n [\"ATI Mobility Radeon X1350 \", 0x718C],\n [\"ATI Mobility Radeon X1350 \", 0x7196],\n [\"ATI Mobility Radeon X1400\", 0x7145],\n [\"ATI Mobility Radeon X1450\", 0x7186],\n [\"ATI Mobility Radeon X1450 \", 0x718D],\n [\"ATI Mobility Radeon X1600\", 0x71C5],\n [\"ATI Mobility Radeon X1700\", 0x71D5],\n [\"ATI Mobility Radeon X1700 \", 0x71DE],\n [\"ATI Mobility Radeon X1700 XT\", 0x71D6],\n [1, \"ATI Mobility Radeon X1800\", 0x7102],\n [1, \"ATI Mobility Radeon X1800 XT\", 0x7101],\n [1, \"ATI Mobility Radeon X1900\", 0x7284],\n [1, \"ATI Mobility Radeon X2300\", 0x718A],\n [1, \"ATI Mobility Radeon X2300 \", 0x7188],\n [\"ATI MOBILITY RADEON X300\", 0x5461],\n [\"ATI MOBILITY RADEON X300 \", 0x5460],\n [\"ATI MOBILITY RADEON X300 \", 0x3152],\n [\"ATI MOBILITY RADEON X600\", 0x3150],\n [\"ATI MOBILITY RADEON X600 SE\", 0x5462],\n [\"ATI MOBILITY RADEON X700\", 0x5652],\n [\"ATI MOBILITY RADEON X700 \", 0x5653],\n [\"ATI MOBILITY RADEON X700 Secondary\", 0x5673],\n [1, \"ATI MOBILITY RADEON X800\", 0x5D4A],\n [1, \"ATI MOBILITY RADEON X800 XT\", 0x5D48],\n [\"ATI Radeon 9550/X1050 Series\", 0x4153],\n [\"ATI Radeon 9550/X1050 Series Secondary\", 0x4173],\n [\"ATI RADEON 9600 Series\", 0x4150],\n [\"ATI RADEON 9600 Series \", 0x4E51],\n [\"ATI RADEON 9600 Series \", 0x4151],\n [\"ATI RADEON 9600 Series \", 0x4155],\n [\"ATI RADEON 9600 Series \", 0x4152],\n [\"ATI RADEON 9600 Series Secondary\", 0x4E71],\n [\"ATI RADEON 9600 Series Secondary \", 0x4171],\n [\"ATI RADEON 9600 Series Secondary \", 0x4170],\n [\"ATI RADEON 9600 Series Secondary \", 0x4175],\n [\"ATI RADEON 9600 Series Secondary \", 0x4172],\n [1, \"ATI Radeon HD 2900 XT\", 0x9402],\n [1, \"ATI Radeon HD 2900 XT \", 0x9403],\n [1, \"ATI Radeon HD 2900 XT \", 0x9400],\n [1, \"ATI Radeon HD 2900 XT \", 0x9401],\n [\"ATI Radeon X1200 Series\", 0x791E],\n [\"ATI Radeon X1200 Series \", 0x791F],\n [1, \"ATI Radeon X1950 GT\", 0x7288],\n [1, \"ATI Radeon X1950 GT Secondary\", 0x72A8],\n [1, \"ATI RADEON X800 GT\", 0x554E],\n [1, \"ATI RADEON X800 GT Secondary\", 0x556E],\n [1, \"ATI RADEON X800 XL\", 0x554D],\n [1, \"ATI RADEON X800 XL Secondary\", 0x556D],\n [1, \"ATI RADEON X850 PRO\", 0x4B4B],\n [1, \"ATI RADEON X850 PRO Secondary\", 0x4B6B],\n [1, \"ATI RADEON X850 SE\", 0x4B4A],\n [1, \"ATI RADEON X850 SE Secondary\", 0x4B6A],\n [1, \"ATI RADEON X850 XT\", 0x4B49],\n [1, \"ATI RADEON X850 XT Platinum Edition\", 0x4B4C],\n [1, \"ATI RADEON X850 XT Platinum Edition Secondary\", 0x4B6C],\n [1, \"ATI RADEON X850 XT Secondary\", 0x4B69],\n [\"ATI Radeon Xpress 1200 Series\", 0x793F],\n [\"ATI Radeon Xpress 1200 Series \", 0x7941],\n [\"ATI Radeon Xpress 1200 Series \", 0x7942],\n [\"ATI Radeon Xpress Series\", 0x5A61],\n [\"ATI Radeon Xpress Series \", 0x5A63],\n [\"ATI Radeon Xpress Series \", 0x5A62],\n [\"ATI Radeon Xpress Series \", 0x5A41],\n [\"ATI Radeon Xpress Series \", 0x5A43],\n [\"ATI Radeon Xpress Series \", 0x5A42],\n [\"ATI Radeon Xpress Series \", 0x5954],\n [\"ATI Radeon Xpress Series \", 0x5854],\n [\"ATI Radeon Xpress Series \", 0x5955],\n [\"ATI Radeon Xpress Series \", 0x5974],\n [\"ATI Radeon Xpress Series \", 0x5874],\n [\"ATI Radeon Xpress Series \", 0x5975],\n [\"Radeon 9500\", 0x4144],\n [\"Radeon 9500 \", 0x4149],\n [\"Radeon 9500 PRO / 9700\", 0x4E45],\n [\"Radeon 9500 PRO / 9700 Secondary\", 0x4E65],\n [\"Radeon 9500 Secondary\", 0x4164],\n [\"Radeon 9500 Secondary \", 0x4169],\n [\"Radeon 9600 TX\", 0x4E46],\n [\"Radeon 9600 TX Secondary\", 0x4E66],\n [\"Radeon 9600TX\", 0x4146],\n [\"Radeon 9600TX Secondary\", 0x4166],\n [\"Radeon 9700 PRO\", 0x4E44],\n [\"Radeon 9700 PRO Secondary\", 0x4E64],\n [\"Radeon 9800\", 0x4E49],\n [\"Radeon 9800 PRO\", 0x4E48],\n [\"Radeon 9800 PRO Secondary\", 0x4E68],\n [\"Radeon 9800 SE\", 0x4148],\n [\"Radeon 9800 SE Secondary\", 0x4168],\n [\"Radeon 9800 Secondary\", 0x4E69],\n [\"Radeon 9800 XT\", 0x4E4A],\n [\"Radeon 9800 XT Secondary\", 0x4E6A],\n [\"Radeon X1300 / X1550 Series\", 0x7146],\n [\"Radeon X1300 / X1550 Series Secondary\", 0x7166],\n [\"Radeon X1300 Series\", 0x714E],\n [\"Radeon X1300 Series \", 0x715E],\n [\"Radeon X1300 Series \", 0x714D],\n [\"Radeon X1300 Series \", 0x71C3],\n [\"Radeon X1300 Series \", 0x718F],\n [\"Radeon X1300 Series Secondary\", 0x716E],\n [\"Radeon X1300 Series Secondary \", 0x717E],\n [\"Radeon X1300 Series Secondary \", 0x716D],\n [\"Radeon X1300 Series Secondary \", 0x71E3],\n [\"Radeon X1300 Series Secondary \", 0x71AF],\n [\"Radeon X1300/X1550 Series\", 0x7142],\n [\"Radeon X1300/X1550 Series \", 0x7180],\n [\"Radeon X1300/X1550 Series \", 0x7183],\n [\"Radeon X1300/X1550 Series \", 0x7187],\n [\"Radeon X1300/X1550 Series Secondary\", 0x7162],\n [\"Radeon X1300/X1550 Series Secondary \", 0x71A0],\n [\"Radeon X1300/X1550 Series Secondary \", 0x71A3],\n [\"Radeon X1300/X1550 Series Secondary \", 0x71A7],\n [\"Radeon X1550 64-bit\", 0x7147],\n [\"Radeon X1550 64-bit \", 0x715F],\n [\"Radeon X1550 64-bit \", 0x719F],\n [\"Radeon X1550 64-bit Secondary\", 0x7167],\n [\"Radeon X1550 64-bit Secondary \", 0x717F],\n [\"Radeon X1550 Series\", 0x7143],\n [\"Radeon X1550 Series \", 0x7193],\n [\"Radeon X1550 Series Secondary\", 0x7163],\n [\"Radeon X1550 Series Secondary \", 0x71B3],\n [\"Radeon X1600 Pro / Radeon X1300 XT\", 0x71CE],\n [\"Radeon X1600 Pro / Radeon X1300 XT Secondary\", 0x71EE],\n [\"Radeon X1600 Series\", 0x7140],\n [\"Radeon X1600 Series \", 0x71C0],\n [\"Radeon X1600 Series \", 0x71C2],\n [\"Radeon X1600 Series \", 0x71C6],\n [\"Radeon X1600 Series \", 0x7181],\n [\"Radeon X1600 Series \", 0x71CD],\n [\"Radeon X1600 Series Secondary\", 0x7160],\n [\"Radeon X1600 Series Secondary \", 0x71E2],\n [\"Radeon X1600 Series Secondary \", 0x71E6],\n [\"Radeon X1600 Series Secondary \", 0x71A1],\n [\"Radeon X1600 Series Secondary \", 0x71ED],\n [\"Radeon X1600 Series Secondary \", 0x71E0],\n [\"Radeon X1650 Series\", 0x71C1],\n [\"Radeon X1650 Series \", 0x7293],\n [\"Radeon X1650 Series \", 0x7291],\n [\"Radeon X1650 Series \", 0x71C7],\n [\"Radeon X1650 Series Secondary\", 0x71E1],\n [\"Radeon X1650 Series Secondary \", 0x72B3],\n [\"Radeon X1650 Series Secondary \", 0x72B1],\n [\"Radeon X1650 Series Secondary \", 0x71E7],\n [1, \"Radeon X1800 Series\", 0x7100],\n [1, \"Radeon X1800 Series \", 0x7108],\n [1, \"Radeon X1800 Series \", 0x7109],\n [1, \"Radeon X1800 Series \", 0x710A],\n [1, \"Radeon X1800 Series \", 0x710B],\n [1, \"Radeon X1800 Series \", 0x710C],\n [1, \"Radeon X1800 Series Secondary\", 0x7120],\n [1, \"Radeon X1800 Series Secondary \", 0x7128],\n [1, \"Radeon X1800 Series Secondary \", 0x7129],\n [1, \"Radeon X1800 Series Secondary \", 0x712A],\n [1, \"Radeon X1800 Series Secondary \", 0x712B],\n [1, \"Radeon X1800 Series Secondary \", 0x712C],\n [1, \"Radeon X1900 Series\", 0x7243],\n [1, \"Radeon X1900 Series \", 0x7245],\n [1, \"Radeon X1900 Series \", 0x7246],\n [1, \"Radeon X1900 Series \", 0x7247],\n [1, \"Radeon X1900 Series \", 0x7248],\n [1, \"Radeon X1900 Series \", 0x7249],\n [1, \"Radeon X1900 Series \", 0x724A],\n [1, \"Radeon X1900 Series \", 0x724B],\n [1, \"Radeon X1900 Series \", 0x724C],\n [1, \"Radeon X1900 Series \", 0x724D],\n [1, \"Radeon X1900 Series \", 0x724F],\n [1, \"Radeon X1900 Series Secondary\", 0x7263],\n [1, \"Radeon X1900 Series Secondary \", 0x7265],\n [1, \"Radeon X1900 Series Secondary \", 0x7266],\n [1, \"Radeon X1900 Series Secondary \", 0x7267],\n [1, \"Radeon X1900 Series Secondary \", 0x7268],\n [1, \"Radeon X1900 Series Secondary \", 0x7269],\n [1, \"Radeon X1900 Series Secondary \", 0x726A],\n [1, \"Radeon X1900 Series Secondary \", 0x726B],\n [1, \"Radeon X1900 Series Secondary \", 0x726C],\n [1, \"Radeon X1900 Series Secondary \", 0x726D],\n [1, \"Radeon X1900 Series Secondary \", 0x726F],\n [1, \"Radeon X1950 Series\", 0x7280],\n [1, \"Radeon X1950 Series \", 0x7240],\n [1, \"Radeon X1950 Series \", 0x7244],\n [1, \"Radeon X1950 Series Secondary\", 0x72A0],\n [1, \"Radeon X1950 Series Secondary \", 0x7260],\n [1, \"Radeon X1950 Series Secondary \", 0x7264],\n [\"Radeon X300/X550/X1050 Series\", 0x5B60],\n [\"Radeon X300/X550/X1050 Series \", 0x5B63],\n [\"Radeon X300/X550/X1050 Series Secondary\", 0x5B73],\n [\"Radeon X300/X550/X1050 Series Secondary \", 0x5B70],\n [\"Radeon X550/X700 Series \", 0x5657],\n [\"Radeon X550/X700 Series Secondary\", 0x5677],\n [\"Radeon X600 Series\", 0x5B62],\n [\"Radeon X600 Series Secondary\", 0x5B72],\n [\"Radeon X600/X550 Series\", 0x3E50],\n [\"Radeon X600/X550 Series Secondary\", 0x3E70],\n [\"Radeon X700\", 0x5E4D],\n [\"Radeon X700 PRO\", 0x5E4B],\n [\"Radeon X700 PRO Secondary\", 0x5E6B],\n [\"Radeon X700 SE\", 0x5E4C],\n [\"Radeon X700 SE Secondary\", 0x5E6C],\n [\"Radeon X700 Secondary\", 0x5E6D],\n [\"Radeon X700 XT\", 0x5E4A],\n [\"Radeon X700 XT Secondary\", 0x5E6A],\n [\"Radeon X700/X550 Series\", 0x5E4F],\n [\"Radeon X700/X550 Series Secondary\", 0x5E6F],\n [1, \"Radeon X800 GT\", 0x554B],\n [1, \"Radeon X800 GT Secondary\", 0x556B],\n [1, \"Radeon X800 GTO\", 0x5549],\n [1, \"Radeon X800 GTO \", 0x554F],\n [1, \"Radeon X800 GTO \", 0x5D4F],\n [1, \"Radeon X800 GTO Secondary\", 0x5569],\n [1, \"Radeon X800 GTO Secondary \", 0x556F],\n [1, \"Radeon X800 GTO Secondary \", 0x5D6F],\n [1, \"Radeon X800 PRO\", 0x4A49],\n [1, \"Radeon X800 PRO Secondary\", 0x4A69],\n [1, \"Radeon X800 SE\", 0x4A4F],\n [1, \"Radeon X800 SE Secondary\", 0x4A6F],\n [1, \"Radeon X800 Series\", 0x4A48],\n [1, \"Radeon X800 Series \", 0x4A4A],\n [1, \"Radeon X800 Series \", 0x4A4C],\n [1, \"Radeon X800 Series \", 0x5548],\n [1, \"Radeon X800 Series Secondary\", 0x4A68],\n [1, \"Radeon X800 Series Secondary \", 0x4A6A],\n [1, \"Radeon X800 Series Secondary \", 0x4A6C],\n [1, \"Radeon X800 Series Secondary \", 0x5568],\n [1, \"Radeon X800 VE\", 0x4A54],\n [1, \"Radeon X800 VE Secondary\", 0x4A74],\n [1, \"Radeon X800 XT\", 0x4A4B],\n [1, \"Radeon X800 XT \", 0x5D57],\n [1, \"Radeon X800 XT Platinum Edition\", 0x4A50],\n [1, \"Radeon X800 XT Platinum Edition \", 0x554A],\n [1, \"Radeon X800 XT Platinum Edition Secondary\", 0x4A70],\n [1, \"Radeon X800 XT Platinum Edition Secondary \", 0x556A],\n [1, \"Radeon X800 XT Secondary\", 0x4A6B],\n [1, \"Radeon X800 XT Secondary \", 0x5D77],\n [1, \"Radeon X850 XT\", 0x5D52],\n [1, \"Radeon X850 XT Platinum Edition\", 0x5D4D],\n [1, \"Radeon X850 XT Platinum Edition Secondary\", 0x5D6D],\n [1, \"Radeon X850 XT Secondary\", 0x5D72],\n ]\n vendorId = 0x1002\n for entry in ati_device_list:\n if len(entry) == 3:\n flag, deviceName, deviceId = entry\n else:\n deviceName, deviceId = entry\n self.devices[(vendorId, deviceId)] = deviceName.strip()\n \n nvidia_device_list = [\n [0x014F, \"GeForce 6200\"],\n [0x00F3, \"GeForce 6200\"],\n [0x0221, \"GeForce 6200\"],\n [0x0163, \"GeForce 6200 LE\"],\n [0x0162, \"GeForce 6200SE TurboCache(TM)\"],\n [0x0161, \"GeForce 6200 TurboCache(TM)\"],\n [0x0162, \"GeForce 6200SE TurboCache(TM)\"],\n [0x0160, \"GeForce 6500\"],\n [1, 0x0141, \"GeForce 6600\"],\n [1, 0x00F2, \"GeForce 6600\"],\n [1, 0x0140, \"GeForce 6600 GT\"],\n [1, 0x00F1, \"GeForce 6600 GT\"],\n [1, 0x0142, \"GeForce 6600 LE\"],\n [1, 0x00F4, \"GeForce 6600 LE\"],\n [1, 0x0143, \"GeForce 6600 VE\"],\n [1, 0x0147, \"GeForce 6700 XL\"],\n [1, 0x0041, \"GeForce 6800\"],\n [1, 0x00C1, \"GeForce 6800\"],\n [1, 0x0047, \"GeForce 6800 GS\"],\n [1, 0x00F6, \"GeForce 6800 GS\"],\n [1, 0x00C0, \"GeForce 6800 GS\"],\n [1, 0x0045, \"GeForce 6800 GT\"],\n [1, 0x00F9, \"GeForce 6800 Series GPU\"],\n [1, 0x00C2, \"GeForce 6800 LE\"],\n [1, 0x0040, \"GeForce 6800 Ultra\"],\n [1, 0x00F9, \"GeForce 6800 Series GPU\"],\n [1, 0x0043, \"GeForce 6800 XE\"],\n [1, 0x0048, \"GeForce 6800 XT\"],\n [1, 0x0218, \"GeForce 6800 XT\"],\n [1, 0x00C3, \"GeForce 6800 XT\"],\n [0x01DF, \"GeForce 7300 GS\"],\n [0x0393, \"GeForce 7300 GT\"],\n [0x01D1, \"GeForce 7300 LE\"],\n [0x01D3, \"GeForce 7300 SE\"],\n [0x01DD, \"GeForce 7500 LE\"],\n [1, 0x0392, \"GeForce 7600 GS\"],\n [1, 0x0392, \"GeForce 7600 GS\"],\n [1, 0x02E1, \"GeForce 7600 GS\"],\n [1, 0x0391, \"GeForce 7600 GT\"],\n [1, 0x0394, \"GeForce 7600 LE\"],\n [1, 0x00F5, \"GeForce 7800 GS\"],\n [1, 0x0092, \"GeForce 7800 GT\"],\n [1, 0x0091, \"GeForce 7800 GTX\"],\n [1, 0x0291, \"GeForce 7900 GT/GTO\"],\n [1, 0x0290, \"GeForce 7900 GTX\"],\n [1, 0x0293, \"GeForce 7900 GX2\"],\n [1, 0x0294, \"GeForce 7950 GX2\"],\n [0x0322, \"GeForce FX 5200\"],\n [0x0321, \"GeForce FX 5200 Ultra\"],\n [0x0323, \"GeForce FX 5200LE\"],\n [0x0326, \"GeForce FX 5500\"],\n [0x0326, \"GeForce FX 5500\"],\n [0x0312, \"GeForce FX 5600\"],\n [0x0311, \"GeForce FX 5600 Ultra\"],\n [0x0314, \"GeForce FX 5600XT\"],\n [0x0342, \"GeForce FX 5700\"],\n [0x0341, \"GeForce FX 5700 Ultra\"],\n [0x0343, \"GeForce FX 5700LE\"],\n [0x0344, \"GeForce FX 5700VE\"],\n [0x0302, \"GeForce FX 5800\"],\n [0x0301, \"GeForce FX 5800 Ultra\"],\n [0x0331, \"GeForce FX 5900\"],\n [0x0330, \"GeForce FX 5900 Ultra\"],\n [0x0333, \"GeForce FX 5950 Ultra\"],\n [0x0324, \"GeForce FX Go5200 64M\"],\n [0x031A, \"GeForce FX Go5600\"],\n [0x0347, \"GeForce FX Go5700\"],\n [0x0167, \"GeForce Go 6200/6400\"],\n [0x0168, \"GeForce Go 6200/6400\"],\n [1, 0x0148, \"GeForce Go 6600\"],\n [1, 0x00c8, \"GeForce Go 6800\"],\n [1, 0x00c9, \"GeForce Go 6800 Ultra\"],\n [1, 0x0098, \"GeForce Go 7800\"],\n [1, 0x0099, \"GeForce Go 7800 GTX\"],\n [1, 0x0298, \"GeForce Go 7900 GS\"],\n [1, 0x0299, \"GeForce Go 7900 GTX\"],\n [0x0185, \"GeForce MX 4000\"],\n [0x00FA, \"GeForce PCX 5750\"],\n [0x00FB, \"GeForce PCX 5900\"],\n [0x0110, \"GeForce2 MX/MX 400\"],\n [0x0111, \"GeForce2 MX200\"],\n [0x0110, \"GeForce2 MX/MX 400\"],\n [0x0200, \"GeForce3\"],\n [0x0201, \"GeForce3 Ti200\"],\n [0x0202, \"GeForce3 Ti500\"],\n [0x0172, \"GeForce4 MX 420\"],\n [0x0171, \"GeForce4 MX 440\"],\n [0x0181, \"GeForce4 MX 440 with AGP8X\"],\n [0x0173, \"GeForce4 MX 440-SE\"],\n [0x0170, \"GeForce4 MX 460\"],\n [0x0253, \"GeForce4 Ti 4200\"],\n [0x0281, \"GeForce4 Ti 4200 with AGP8X\"],\n [0x0251, \"GeForce4 Ti 4400\"],\n [0x0250, \"GeForce4 Ti 4600\"],\n [0x0280, \"GeForce4 Ti 4800\"],\n [0x0282, \"GeForce4 Ti 4800SE\"],\n [0x0203, \"Quadro DCC\"],\n [0x0309, \"Quadro FX 1000\"],\n [0x034E, \"Quadro FX 1100\"],\n [0x00FE, \"Quadro FX 1300\"],\n [0x00CE, \"Quadro FX 1400\"],\n [0x0308, \"Quadro FX 2000\"],\n [0x0338, \"Quadro FX 3000\"],\n [0x00FD, \"Quadro PCI-E Series\"],\n [1, 0x00F8, \"Quadro FX 3400/4400\"],\n [1, 0x00CD, \"Quadro FX 3450/4000 SDI\"],\n [1, 0x004E, \"Quadro FX 4000\"],\n [1, 0x00CD, \"Quadro FX 3450/4000 SDI\"],\n [1, 0x00F8, \"Quadro FX 3400/4400\"],\n [1, 0x009D, \"Quadro FX 4500\"],\n [1, 0x029F, \"Quadro FX 4500 X2\"],\n [0x032B, \"Quadro FX 500/FX 600\"],\n [0x014E, \"Quadro FX 540\"],\n [0x014C, \"Quadro FX 540 MXM\"],\n [0x032B, \"Quadro FX 500/FX 600\"],\n [0X033F, \"Quadro FX 700\"],\n [0x034C, \"Quadro FX Go1000\"],\n [0x00CC, \"Quadro FX Go1400\"],\n [0x031C, \"Quadro FX Go700\"],\n [0x018A, \"Quadro NVS with AGP8X\"],\n [0x032A, \"Quadro NVS 280 PCI\"],\n [0x00FD, \"Quadro PCI-E Series\"],\n [0x0165, \"Quadro NVS 285\"],\n [0x017A, \"Quadro NVS\"],\n [0x018A, \"Quadro NVS with AGP8X\"],\n [0x0113, \"Quadro2 MXR/EX\"],\n [0x017A, \"Quadro NVS\"],\n [0x018B, \"Quadro4 380 XGL\"],\n [0x0178, \"Quadro4 550 XGL\"],\n [0x0188, \"Quadro4 580 XGL\"],\n [0x025B, \"Quadro4 700 XGL\"],\n [0x0259, \"Quadro4 750 XGL\"],\n [0x0258, \"Quadro4 900 XGL\"],\n [0x0288, \"Quadro4 980 XGL\"],\n [0x028C, \"Quadro4 Go700\"],\n [1, 0x0295, \"NVIDIA GeForce 7950 GT\"],\n [0x03D0, \"NVIDIA GeForce 6100 nForce 430\"],\n [0x03D1, \"NVIDIA GeForce 6100 nForce 405\"],\n [0x03D2, \"NVIDIA GeForce 6100 nForce 400\"],\n [0x0241, \"NVIDIA GeForce 6150 LE\"],\n [0x0242, \"NVIDIA GeForce 6100\"],\n [0x0245, \"NVIDIA Quadro NVS 210S / NVIDIA GeForce 6150LE\"],\n [1, 0x029C, \"NVIDIA Quadro FX 5500\"],\n [1, 0x0191, \"NVIDIA GeForce 8800 GTX\"],\n [1, 0x0193, \"NVIDIA GeForce 8800 GTS\"],\n [1, 0x0400, \"NVIDIA GeForce 8600 GTS\"],\n [1, 0x0402, \"NVIDIA GeForce 8600 GT\"],\n [0x0421, \"NVIDIA GeForce 8500 GT\"],\n [0x0422, \"NVIDIA GeForce 8400 GS\"],\n [0x0423, \"NVIDIA GeForce 8300 GS\"],\n ]\n vendorId = 0x10de\n for entry in nvidia_device_list:\n if len(entry) == 3:\n flag, deviceId, deviceName = entry\n else:\n deviceId, deviceName = entry\n self.devices[(vendorId, deviceId)] = deviceName.strip()","def setup_platform(hass, config, add_devices, discovery_info=None):\n resource = config.get(CONF_RESOURCE)\n pin = config.get(CONF_PIN)\n\n if None in (resource, pin):\n _LOGGER.error('Not all required config keys present: %s',\n ', '.join((CONF_RESOURCE, CONF_PIN)))\n return False\n\n try:\n response = requests.get(resource, timeout=10).json()\n except requests.exceptions.MissingSchema:\n _LOGGER.error('Missing resource or schema in configuration. '\n 'Add http:// to your URL.')\n return False\n except requests.exceptions.ConnectionError:\n _LOGGER.error('No route to device at %s. '\n 'Please check the IP address in the configuration file.',\n resource)\n return False\n\n arest = ArestData(resource, pin)\n\n add_devices([ArestBinarySensor(arest,\n resource,\n config.get('name', response['name']),\n pin)])","async def async_setup_entry(\n hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\n):\n data = hass.data[DOMAIN][entry.entry_id]\n coordinator = data[\"coordinator\"]\n\n entities = [\n Awning(device.deviceurl, coordinator)\n for device in data[\"platforms\"].get(COVER)\n if \"Awning\" in device.definition.qualified_name\n ]\n\n entities += [\n VerticalCover(device.deviceurl, coordinator)\n for device in data[\"platforms\"].get(COVER)\n if \"Awning\" not in device.definition.qualified_name\n ]\n\n async_add_entities(entities)\n\n platform = entity_platform.current_platform.get()\n platform.async_register_entity_service(\n SERVICE_COVER_MY_POSITION, {}, \"async_my\", [SUPPORT_MY]\n )\n\n platform.async_register_entity_service(\n SERVICE_COVER_POSITION_LOW_SPEED,\n {\n vol.Required(ATTR_POSITION): vol.All(\n vol.Coerce(int), vol.Range(min=0, max=100)\n )\n },\n \"async_set_cover_position_low_speed\",\n [SUPPORT_COVER_POSITION_LOW_SPEED],\n )","def get_list_devices(self, verbose=False):\n # TODO: refresh region_names if more regions get devices available\n self.backends = {}\n region_names = ['us-west-1', 'us-east-1']\n for region in region_names:\n client = boto3.client(\n 'braket',\n region_name=region,\n aws_access_key_id=self._credentials['AWS_ACCESS_KEY_ID'],\n aws_secret_access_key=self._credentials['AWS_SECRET_KEY'],\n )\n filters = []\n devicelist = client.search_devices(filters=filters)\n for result in devicelist['devices']:\n if result['deviceType'] not in ['QPU', 'SIMULATOR']:\n continue\n if result['deviceType'] == 'QPU':\n device_capabilities = json.loads(\n client.get_device(deviceArn=result['deviceArn'])['deviceCapabilities']\n )\n self.backends[result['deviceName']] = {\n 'nq': device_capabilities['paradigm']['qubitCount'],\n 'coupling_map': device_capabilities['paradigm']['connectivity']['connectivityGraph'],\n 'version': device_capabilities['braketSchemaHeader']['version'],\n 'location': region, # deviceCapabilities['service']['deviceLocation'],\n 'deviceArn': result['deviceArn'],\n 'deviceParameters': device_capabilities['deviceParameters']['properties']['braketSchemaHeader'][\n 'const'\n ],\n 'deviceModelParameters': device_capabilities['deviceParameters']['definitions'][\n 'GateModelParameters'\n ]['properties']['braketSchemaHeader']['const'],\n }\n # Unfortunately the Capabilities schemas are not homogeneus for real devices and simulators\n elif result['deviceType'] == 'SIMULATOR':\n device_capabilities = json.loads(\n client.get_device(deviceArn=result['deviceArn'])['deviceCapabilities']\n )\n self.backends[result['deviceName']] = {\n 'nq': device_capabilities['paradigm']['qubitCount'],\n 'coupling_map': {},\n 'version': device_capabilities['braketSchemaHeader']['version'],\n 'location': 'us-east-1',\n 'deviceArn': result['deviceArn'],\n 'deviceParameters': device_capabilities['deviceParameters']['properties']['braketSchemaHeader'][\n 'const'\n ],\n 'deviceModelParameters': device_capabilities['deviceParameters']['definitions'][\n 'GateModelParameters'\n ]['properties']['braketSchemaHeader']['const'],\n }\n\n if verbose:\n print('- List of AWSBraket devices available:')\n print(list(self.backends))\n\n return self.backends","async def device_fixture(hass: HomeAssistant, ufp: MockUFPFixture):\n\n await init_entry(hass, ufp, [])\n\n device_registry = dr.async_get(hass)\n\n return list(device_registry.devices.values())[0]","async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):\n pockets = config.get(CONF_POCKETS)\n\n switch = pocketSwitch(pockets)\n\n async_add_entities([switch], True)\n\n for poc in pockets:\n id = poc[CONF_POCKET_ID]\n name = poc[CONF_POCKET_NAME]\n path = poc[CONF_FILE_PATH]\n\n api = pocketAPI(path)\n\n if hass.config.is_allowed_path(path):\n async_add_entities([FileSensor(id, name, path, api, switch)], True)\n else:\n _LOGGER.error(\"'%s' is not awhitelisted directory\", path)","def _get_data(self):\n devices = []\n try:\n if not self.router_client.login():\n self.hass.states.set(f\"{DOMAIN}.statusmsg\", self.router_client.statusmsg)\n _LOGGER.warning(\"Login failed: {0}:{1}@{2}\".format(self.router_client.username, self.router_client.password,self.router_client.host))\n self.router_client.logout()\n return devices\n\n devices_json = self.router_client.get_devices_response()\n finally:\n self.router_client.logout()\n\n self.hass.states.set(f\"{DOMAIN}.scanning\", devices_json != False)\n\n if devices_json != False:\n for device in devices_json:\n # _LOGGER.debug(\"Device: {0}\".format(device))\n dev = Device(\n device['HostName'].replace('未知设备', 'Unknown'),\n device['IPAddress'],\n device['MACAddress'],\n device['Active'],\n ICONS.get(device['IconType'])\n )\n # _LOGGER.debug(\"Device: {0}\".format(dev))\n devices.append(dev)\n return devices\n else:\n return []","async def async_setup_entry(hass, config_entry, async_add_entities):\n\n data = hass.data[DOMAIN]\n\n if not data.client.cars:\n LOGGER.info(\"No Cars found.\")\n return\n\n sensor_list = []\n\n\n for car in data.client.cars:\n for key, value in sorted(DEVICE_TRACKER.items()):\n# if value[5] is None or getattr(car.features, value[5]) is True:\n device = MercedesMEDeviceTracker(\n hass,\n data,\n key,\n value[0],\n car.finorvin,\n value[1],\n car.licenseplate,\n value[2],\n value[3],\n value[4],\n value[6])\n if device.device_retrieval_status() in [\"VALID\", \"NOT_RECEIVED\"] :\n sensor_list.append(device)\n\n\n async_add_entities(sensor_list, True)","def setup_platform(hass, config, add_devices, discovery_info=None):\n station_id = config.get(CONF_STATION_ID)\n name = config.get(CONF_NAME)\n\n logger = logging.getLogger(__name__)\n probe = ZamgData(station_id=station_id, logger=logger)\n\n sensors = [ZamgSensor(probe, variable, name)\n for variable in config[CONF_MONITORED_CONDITIONS]]\n\n add_devices(sensors, True)","def device_get_config(self, filters={}):\n return {}","def get_devices():\n devices, errors = [], []\n\n for path in hookenv.action_get('devices').split(' '):\n path = path.strip()\n if not os.path.isabs(path):\n errors.append('{}: Not absolute path.'.format(path))\n elif not os.path.exists(path):\n errors.append('{}: Device does not exist.'.format(path))\n else:\n devices.append(path)\n\n if errors:\n raise ZapDiskError(\", \".join(errors))\n\n return devices","def find_hardware(self, device_info=None):\n if os.name is not 'nt': # If not on a Windows system, just set up soundcard\n self.setup_soundcard()\n self.hardware.append('Soundcard')\n self.out_samplefreq = 44100\n else:\n if 'NIDAQ' in self.required_hardware and self.setup_nidaq(device_info):\n self.hardware.append('NIDAQ')\n if 'RP21' in self.required_hardware and self.setup_RP21('c:\\pystartle\\startle.rco'):\n self.hardware.append('RP21')\n if 'PA5' in self.required_hardware and self.setup_PA5():\n self.hardware.append('PA5')\n if 'RZ5D' in self.required_hardware and self.setup_RZ5D():\n self.hardware.append('RZ5D')","def async_setup_scanner_platform(\n hass: HomeAssistant,\n config: ConfigType,\n scanner: DeviceScanner,\n async_see_device: Callable[..., Coroutine[None, None, None]],\n platform: str,\n) -> None:\n interval = config.get(CONF_SCAN_INTERVAL, SCAN_INTERVAL)\n update_lock = asyncio.Lock()\n scanner.hass = hass\n\n # Initial scan of each mac we also tell about host name for config\n seen: set[str] = set()\n\n async def async_device_tracker_scan(now: datetime | None) -> None:\n \"\"\"Handle interval matches.\"\"\"\n if update_lock.locked():\n LOGGER.warning(\n (\n \"Updating device list from %s took longer than the scheduled \"\n \"scan interval %s\"\n ),\n platform,\n interval,\n )\n return\n\n async with update_lock:\n found_devices = await scanner.async_scan_devices()\n\n device_name_uses_executor = (\n scanner.async_get_device_name.__func__ # type: ignore[attr-defined]\n is DeviceScanner.async_get_device_name\n )\n extra_attributes_uses_executor = (\n scanner.async_get_extra_attributes.__func__ # type: ignore[attr-defined]\n is DeviceScanner.async_get_extra_attributes\n )\n host_name_by_mac: dict[str, str | None] = {}\n extra_attributes_by_mac: dict[str, dict[str, Any]] = {}\n if device_name_uses_executor or extra_attributes_uses_executor:\n (\n host_name_by_mac,\n extra_attributes_by_mac,\n ) = await hass.async_add_executor_job(\n _load_device_names_and_attributes,\n scanner,\n device_name_uses_executor,\n extra_attributes_uses_executor,\n seen,\n found_devices,\n )\n\n for mac in found_devices:\n if mac in seen:\n host_name = None\n else:\n host_name = host_name_by_mac.get(\n mac, await scanner.async_get_device_name(mac)\n )\n seen.add(mac)\n\n try:\n extra_attributes = extra_attributes_by_mac.get(\n mac, await scanner.async_get_extra_attributes(mac)\n )\n except NotImplementedError:\n extra_attributes = {}\n\n kwargs: dict[str, Any] = {\n \"mac\": mac,\n \"host_name\": host_name,\n \"source_type\": SourceType.ROUTER,\n \"attributes\": {\n \"scanner\": scanner.__class__.__name__,\n **extra_attributes,\n },\n }\n\n zone_home = hass.states.get(hass.components.zone.ENTITY_ID_HOME)\n if zone_home is not None:\n kwargs[\"gps\"] = [\n zone_home.attributes[ATTR_LATITUDE],\n zone_home.attributes[ATTR_LONGITUDE],\n ]\n kwargs[\"gps_accuracy\"] = 0\n\n hass.async_create_task(async_see_device(**kwargs))\n\n cancel_legacy_scan = async_track_time_interval(\n hass,\n async_device_tracker_scan,\n interval,\n name=f\"device_tracker {platform} legacy scan\",\n )\n hass.async_create_task(async_device_tracker_scan(None))\n\n @callback\n def _on_hass_stop(_: Event) -> None:\n \"\"\"Cleanup when Home Assistant stops.\n\n Cancel the legacy scan.\n \"\"\"\n cancel_legacy_scan()\n\n hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, _on_hass_stop)","def test_gwservice_listdevices(self, setup_controller):\n resp = setup_controller.request(\"gw\", \"devices\", \"GET\", None, None)\n body = resp.url + \",\" + str(resp.status_code) + ',' + resp.text\n allure.attach(name=\"gw list devices\", body=body)\n if resp.status_code != 200:\n assert False\n devices = json.loads(resp.text)\n print (devices)","def main():\n configs = [\"show configuration sessions\"]\n with EOSDriver(**MY_DEVICE) as conn:\n conn.register_configuration_session(session_name=\"my-config-session\")\n # for configuration sessions we have to first \"register\" the session with scrapli:\n result = conn.send_configs(configs=configs, privilege_level=\"my-config-session\")\n\n # we should see our session name with an \"*\" indicating that is the active config session\n print(result[0].result)","def get_devices():\n try:\n with open(DEVICES, 'r') as f:\n data = json.load(f)['devices']\n except (IOError, ValueError) as err:\n raise SwiftlmCheckFailure('Failure opening %s: %s' % (DEVICES, err))\n\n devices = []\n for d in data:\n l = d.get('label', LABEL_CHECK_DISABLED)\n devices.append(Device(\n device=d['name'],\n mount=MOUNT_PATH+d['swift_drive_name'],\n label=l\n ))\n\n return devices","def setup_platform(\n hass: HomeAssistant,\n config: ConfigType,\n add_entities: AddEntitiesCallback,\n discovery_info: DiscoveryInfoType | None = None,\n) -> None:\n name = config.get(CONF_NAME)\n mac = config.get(CONF_MAC)\n _LOGGER.debug(\"Setting up\")\n\n mon = Monitor(hass, mac, name)\n add_entities([SkybeaconTemp(name, mon)])\n add_entities([SkybeaconHumid(name, mon)])\n\n def monitor_stop(_service_or_event):\n \"\"\"Stop the monitor thread.\"\"\"\n _LOGGER.info(\"Stopping monitor for %s\", name)\n mon.terminate()\n\n hass.bus.listen_once(EVENT_HOMEASSISTANT_STOP, monitor_stop)\n mon.start()","def load_device():","async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):\n data = hass.data[DOMAIN]\n \n devices = []\n for device_info in data.neviweb_client.gateway_data:\n if \"signature\" in device_info and \\\n \"type\" in device_info[\"signature\"] and \\\n device_info[\"signature\"][\"type\"] in IMPLEMENTED_DEVICE_TYPES:\n device_name = '{} {} {}'.format(DEFAULT_NAME, \n \"dimmer\" if device_info[\"signature\"][\"type\"] in DEVICE_TYPE_DIMMER \n else \"light\", device_info[\"name\"])\n devices.append(NeviwebLight(data, device_info, device_name))\n for device_info in data.neviweb_client.gateway_data2:\n if \"signature\" in device_info and \\\n \"type\" in device_info[\"signature\"] and \\\n device_info[\"signature\"][\"type\"] in IMPLEMENTED_DEVICE_TYPES:\n device_name = '{} {} {}'.format(DEFAULT_NAME, \n \"dimmer\" if device_info[\"signature\"][\"type\"] in DEVICE_TYPE_DIMMER \n else \"light\", device_info[\"name\"])\n devices.append(NeviwebLight(data, device_info, device_name))\n \n async_add_entities(devices, True)"],"string":"[\n \"def load_devices():\",\n \"async def async_setup_entry(hass, config_entry, async_add_entities):\\n entities = []\\n gateway = hass.data[DOMAIN][GATEWAYS_KEY][config_entry.entry_id]\\n gateway.discover()\\n for device in list(gateway.devices.values()): #TODO filter by type cover\\n device_type = SELVE_CLASSTYPES.get(device.device_type.value)\\n if device_type is None:\\n _LOGGER.warning('Unsupported type %s for Selve device %s',\\n device.device_type, device.name)\\n continue\\n entities.append( SelveCover(device, device_type))\\n\\n async_add_entities(entities)\",\n \"def initialize_devices(self):\\n for k in self.devices:\\n dev = self.devices[k]\\n print('Starting %s' % dev.properties['name'])\\n dev.initialize_driver()\\n # print('Error initializing %s' % dev.properties['name'])\\n if 'defaults' in dev.properties:\\n defaults_file = dev.properties['defaults']\\n defaults = from_yaml_to_dict(defaults_file)[dev.properties['name']]\\n dev.apply_values(defaults)\\n if dev.properties['type'] == 'daq':\\n self.daqs[dev.properties['name']] = {'input': [],\\n 'output': [],\\n 'monitor': [], } # Creates an entry for every different DAQ.\",\n \"def setup_platform(hass, config, add_devices, discovery_info=None):\\n\\n lights = []\\n\\n from nmap import PortScanner, PortScannerError\\n scanner = PortScanner()\\n\\n _LOGGER.error(scanner)\\n\\n ip = ''\\n\\n for i in range(255):\\n try:\\n result = scanner.scan(hosts='192.168.1.'+str(i), arguments=\\\" --privileged -sP \\\") # hosts=config[CONF_HOSTS]\\n except PortScannerError:\\n return _LOGGER.error(\\\"PortScannerError\\\")\\n\\n _LOGGER.error(result)\\n\\n for ipv4, info in result['scan'].items():\\n mac = info['addresses'].get('mac') or _arp(ipv4)\\n _LOGGER.error(mac)\\n _LOGGER.error(ipv4)\\n if mac == '5C:CF:7F:E2:40:49': #put in config file\\n ip = ipv4\\n _LOGGER.error(ip)\\n break\\n if ip is not '':\\n break\\n\\n\\n lights.append(DaliLight('http://'+ip))\\n add_devices(lights)\",\n \"def device_config(i):\\n apipath = \\\"/targets/devices\\\"\\n url = SERVER + apipath\\n params = {\\n 'q': '(deviceType:ASA)',\\n 'resolve': '[targets/devices.{name,deviceConfig}]',\\n 'sort': 'name:desc',\\n 'limit': NUM_DEVICES_TO_RETRIEVE_PER_QUERY,\\n 'offset': i}\\n headers = {\\n 'Accept': \\\"application/json\\\",\\n 'Content-Type': \\\"application/json\\\",\\n 'Authorization': \\\"bearer {}\\\".format(token)}\\n response = requests.get(url, verify=False, stream=True, headers=headers, params=params)\\n getstatuscode = response.status_code\\n getresponse = response.json()\\n if getstatuscode == 200:\\n return getresponse\\n else:\\n response.raise_for_status()\",\n \"async def async_load_config(\\n path: str, hass: HomeAssistant, consider_home: timedelta\\n) -> list[Device]:\\n dev_schema = vol.Schema(\\n {\\n vol.Required(CONF_NAME): cv.string,\\n vol.Optional(CONF_ICON, default=None): vol.Any(None, cv.icon),\\n vol.Optional(\\\"track\\\", default=False): cv.boolean,\\n vol.Optional(CONF_MAC, default=None): vol.Any(\\n None, vol.All(cv.string, vol.Upper)\\n ),\\n vol.Optional(\\\"gravatar\\\", default=None): vol.Any(None, cv.string),\\n vol.Optional(\\\"picture\\\", default=None): vol.Any(None, cv.string),\\n vol.Optional(CONF_CONSIDER_HOME, default=consider_home): vol.All(\\n cv.time_period, cv.positive_timedelta\\n ),\\n }\\n )\\n result: list[Device] = []\\n try:\\n devices = await hass.async_add_executor_job(load_yaml_config_file, path)\\n except HomeAssistantError as err:\\n LOGGER.error(\\\"Unable to load %s: %s\\\", path, str(err))\\n return []\\n except FileNotFoundError:\\n return []\\n\\n for dev_id, device in devices.items():\\n # Deprecated option. We just ignore it to avoid breaking change\\n device.pop(\\\"vendor\\\", None)\\n device.pop(\\\"hide_if_away\\\", None)\\n try:\\n device = dev_schema(device)\\n device[\\\"dev_id\\\"] = cv.slugify(dev_id)\\n except vol.Invalid as exp:\\n async_log_exception(exp, dev_id, devices, hass)\\n else:\\n result.append(Device(hass, **device))\\n return result\",\n \"def test_setup_adds_proper_devices(self, mock_light):\\n good_config = {\\n \\\"mochad\\\": {},\\n \\\"light\\\": {\\n \\\"platform\\\": \\\"mochad\\\",\\n \\\"devices\\\": [{\\\"name\\\": \\\"Light1\\\", \\\"address\\\": \\\"a1\\\"}],\\n },\\n }\\n assert setup_component(self.hass, light.DOMAIN, good_config)\",\n \"def setup(hass, config):\\n\\n _LOGGER.info(\\\"Connected Devices loading.\\\")\\n \\n component = EntityComponent(_LOGGER, DOMAIN, hass, SCAN_INTERVAL)\\n\\n component.setup(config)\\n\\n descriptions = load_yaml_config_file(\\n os.path.join(os.path.dirname(__file__), 'services.yaml'))\\n\\n def update_connected_device(service):\\n \\\"\\\"\\\"Update a connected device.\\\"\\\"\\\"\\n\\n update_obj = service.data.get('value')\\n\\n connected_devices = hass.states.get('connected_devices.connected_devices').as_dict()\\n \\n attributes = connected_devices[\\\"attributes\\\"]\\n\\n for obj in update_obj:\\n # _LOGGER.info(\\\"update value: %s\\\", obj[\\\"value\\\"])\\n # _LOGGER.info(\\\"target: %s\\\", obj[\\\"target\\\"])\\n # _LOGGER.info(\\\"previous value: %s\\\", attributes[\\\"devices\\\"][obj[\\\"device\\\"]][obj[\\\"target\\\"]])\\n\\n attributes[\\\"devices\\\"][obj[\\\"device\\\"]][obj[\\\"target\\\"]] = obj[\\\"value\\\"]\\n # _LOGGER.info(\\\"after update: %s\\\", attributes[\\\"devices\\\"][obj[\\\"device\\\"]][obj[\\\"target\\\"]])\\n \\n connected_devices[\\\"attributes\\\"] = attributes\\n\\n hass.states.set('connected_devices.connected_devices', 'On', attributes, True)\\n\\n hass.services.register(\\n DOMAIN,\\n 'update_connected_device',\\n update_connected_device,\\n descriptions['update_connected_device'])\\n\\n return True\",\n \"def main():\\n for dev in Discover.discover().values():\\n print(dev)\",\n \"def initialize_devices(self):\\n if not self.loaded_devices:\\n raise Exception('Devices have to be loaded before being initialized.')\\n\\n for dev in self.devices:\\n d = self.devices[dev]['dev'] # This is the Device instance\\n d.initialize_driver()\",\n \"def async_add_devices_discovery(hass, discovery_info, async_add_devices):\\n items = discovery_info[CONF_ITEMS]\\n for item in items:\\n async_add_devices([AmpioSwitch(hass, item)])\",\n \"def discover(self):\\n\\n # Get the Huge Page configuration\\n self.get_hugepages()\\n\\n # Get the device configuration\\n self.get_devices_per_node()\\n\\n # Get the CPU configuration\\n self.get_cpu()\\n\\n # Get the current grub cmdline\\n self.get_grub()\",\n \"def load_devices(self, source=None):\\n if source is not None:\\n return\\n init = self.measure['init']\\n devices_file = init['devices']\\n devices_list = from_yaml_to_devices(devices_file)\\n for dev in devices_list:\\n self.devices[dev.properties['name']] = dev\\n if 'outputs' in dev.properties:\\n self.output_devices.append(dev)\\n print('Added %s to the experiment' % dev)\\n if dev.properties['type'] == \\\"Rotation Stage\\\":\\n self.rotation_stages.append(dev.properties['name'])\",\n \"def test_get_devices(self):\\n pass\",\n \"def test_get_devices(self):\\n pass\",\n \"async def async_setup_platforms(hass, config):\\n tasks = [\\n hass.helpers.discovery.async_load_platform(component, DOMAIN, {}, config)\\n for component in INSTEON_COMPONENTS\\n ]\\n await asyncio.gather(*tasks)\\n\\n for address in devices:\\n device = devices[address]\\n platforms = get_device_platforms(device)\\n if ON_OFF_EVENTS in platforms:\\n add_on_off_event_device(hass, device)\\n\\n _LOGGER.debug(\\\"Insteon device count: %s\\\", len(devices))\\n register_new_device_callback(hass, config)\\n async_register_services(hass)\\n\\n # Cannot be done concurrently due to issues with the underlying protocol.\\n for address in devices:\\n await devices[address].async_status()\\n await async_id_unknown_devices(hass.config.config_dir)\",\n \"def setup(hass, base_config):\\n from pyhusmow import API as HUSMOW_API\\n\\n config = base_config.get(DOMAIN)\\n\\n if hass.data.get(DOMAIN) is None:\\n hass.data[DOMAIN] = { 'devices': [] }\\n\\n api = HUSMOW_API()\\n api.login(config.get(CONF_USERNAME), config.get(CONF_PASSWORD))\\n\\n robots = api.list_robots()\\n\\n if not robots:\\n return False\\n\\n for robot in robots:\\n hass.data[DOMAIN]['devices'].append(AutomowerDevice(robot, api))\\n\\n for component in AUTOMOWER_COMPONENTS:\\n discovery.load_platform(hass, component, DOMAIN, {}, base_config)\\n\\n return True\",\n \"async def async_setup_entry(hass, config_entry, async_add_entities):\\n\\n device = []\\n session: SHCSession = hass.data[DOMAIN][config_entry.entry_id]\\n\\n for cover in session.device_helper.shutter_controls:\\n _LOGGER.debug(f\\\"Found shutter control: {cover.name} ({cover.id})\\\")\\n device.append(\\n ShutterControlCover(\\n device=cover,\\n room_name=session.room(cover.room_id).name,\\n controller_ip=config_entry.data[CONF_IP_ADDRESS],\\n )\\n )\\n\\n if device:\\n async_add_entities(device)\",\n \"def setup(hass: HomeAssistant, base_config: ConfigType) -> bool: # noqa: C901\\n\\n hass.data[DOMAIN] = {}\\n\\n # Parse configuration into a dict of device name to physical address\\n # represented as a list of four elements.\\n device_aliases = {}\\n devices = base_config[DOMAIN].get(CONF_DEVICES, {})\\n _LOGGER.debug(\\\"Parsing config %s\\\", devices)\\n device_aliases.update(parse_mapping(devices))\\n _LOGGER.debug(\\\"Parsed devices: %s\\\", device_aliases)\\n\\n platform = base_config[DOMAIN].get(CONF_PLATFORM, SWITCH)\\n\\n loop = (\\n # Create own thread if more than 1 CPU\\n hass.loop\\n if multiprocessing.cpu_count() < 2\\n else None\\n )\\n host = base_config[DOMAIN].get(CONF_HOST)\\n display_name = base_config[DOMAIN].get(CONF_DISPLAY_NAME, DEFAULT_DISPLAY_NAME)\\n if host:\\n adapter = TcpAdapter(host, name=display_name, activate_source=False)\\n else:\\n adapter = CecAdapter(name=display_name[:12], activate_source=False)\\n hdmi_network = HDMINetwork(adapter, loop=loop)\\n\\n def _adapter_watchdog(now=None):\\n _LOGGER.debug(\\\"Reached _adapter_watchdog\\\")\\n event.call_later(hass, WATCHDOG_INTERVAL, _adapter_watchdog_job)\\n if not adapter.initialized:\\n _LOGGER.info(\\\"Adapter not initialized; Trying to restart\\\")\\n hass.bus.fire(EVENT_HDMI_CEC_UNAVAILABLE)\\n adapter.init()\\n\\n _adapter_watchdog_job = HassJob(_adapter_watchdog, cancel_on_shutdown=True)\\n\\n @callback\\n def _async_initialized_callback(*_: Any):\\n \\\"\\\"\\\"Add watchdog on initialization.\\\"\\\"\\\"\\n return event.async_call_later(hass, WATCHDOG_INTERVAL, _adapter_watchdog_job)\\n\\n hdmi_network.set_initialized_callback(_async_initialized_callback)\\n\\n def _volume(call: ServiceCall) -> None:\\n \\\"\\\"\\\"Increase/decrease volume and mute/unmute system.\\\"\\\"\\\"\\n mute_key_mapping = {\\n ATTR_TOGGLE: KEY_MUTE_TOGGLE,\\n ATTR_ON: KEY_MUTE_ON,\\n ATTR_OFF: KEY_MUTE_OFF,\\n }\\n for cmd, att in call.data.items():\\n if cmd == CMD_UP:\\n _process_volume(KEY_VOLUME_UP, att)\\n elif cmd == CMD_DOWN:\\n _process_volume(KEY_VOLUME_DOWN, att)\\n elif cmd == CMD_MUTE:\\n hdmi_network.send_command(\\n KeyPressCommand(mute_key_mapping[att], dst=ADDR_AUDIOSYSTEM)\\n )\\n hdmi_network.send_command(KeyReleaseCommand(dst=ADDR_AUDIOSYSTEM))\\n _LOGGER.info(\\\"Audio muted\\\")\\n else:\\n _LOGGER.warning(\\\"Unknown command %s\\\", cmd)\\n\\n def _process_volume(cmd, att):\\n if isinstance(att, (str,)):\\n att = att.strip()\\n if att == CMD_PRESS:\\n hdmi_network.send_command(KeyPressCommand(cmd, dst=ADDR_AUDIOSYSTEM))\\n elif att == CMD_RELEASE:\\n hdmi_network.send_command(KeyReleaseCommand(dst=ADDR_AUDIOSYSTEM))\\n else:\\n att = 1 if att == \\\"\\\" else int(att)\\n for _ in range(0, att):\\n hdmi_network.send_command(KeyPressCommand(cmd, dst=ADDR_AUDIOSYSTEM))\\n hdmi_network.send_command(KeyReleaseCommand(dst=ADDR_AUDIOSYSTEM))\\n\\n def _tx(call: ServiceCall) -> None:\\n \\\"\\\"\\\"Send CEC command.\\\"\\\"\\\"\\n data = call.data\\n if ATTR_RAW in data:\\n command = CecCommand(data[ATTR_RAW])\\n else:\\n src = data.get(ATTR_SRC, ADDR_UNREGISTERED)\\n dst = data.get(ATTR_DST, ADDR_BROADCAST)\\n if ATTR_CMD in data:\\n cmd = data[ATTR_CMD]\\n else:\\n _LOGGER.error(\\\"Attribute 'cmd' is missing\\\")\\n return\\n if ATTR_ATT in data:\\n if isinstance(data[ATTR_ATT], (list,)):\\n att = data[ATTR_ATT]\\n else:\\n att = reduce(lambda x, y: f\\\"{x}:{y:x}\\\", data[ATTR_ATT])\\n else:\\n att = \\\"\\\"\\n command = CecCommand(cmd, dst, src, att)\\n hdmi_network.send_command(command)\\n\\n def _standby(call: ServiceCall) -> None:\\n hdmi_network.standby()\\n\\n def _power_on(call: ServiceCall) -> None:\\n hdmi_network.power_on()\\n\\n def _select_device(call: ServiceCall) -> None:\\n \\\"\\\"\\\"Select the active device.\\\"\\\"\\\"\\n if not (addr := call.data[ATTR_DEVICE]):\\n _LOGGER.error(\\\"Device not found: %s\\\", call.data[ATTR_DEVICE])\\n return\\n if addr in device_aliases:\\n addr = device_aliases[addr]\\n else:\\n entity = hass.states.get(addr)\\n _LOGGER.debug(\\\"Selecting entity %s\\\", entity)\\n if entity is not None:\\n addr = entity.attributes[\\\"physical_address\\\"]\\n _LOGGER.debug(\\\"Address acquired: %s\\\", addr)\\n if addr is None:\\n _LOGGER.error(\\n \\\"Device %s has not physical address\\\", call.data[ATTR_DEVICE]\\n )\\n return\\n if not isinstance(addr, (PhysicalAddress,)):\\n addr = PhysicalAddress(addr)\\n hdmi_network.active_source(addr)\\n _LOGGER.info(\\\"Selected %s (%s)\\\", call.data[ATTR_DEVICE], addr)\\n\\n def _update(call: ServiceCall) -> None:\\n \\\"\\\"\\\"Update if device update is needed.\\n\\n Called by service, requests CEC network to update data.\\n \\\"\\\"\\\"\\n hdmi_network.scan()\\n\\n def _new_device(device):\\n \\\"\\\"\\\"Handle new devices which are detected by HDMI network.\\\"\\\"\\\"\\n key = f\\\"{DOMAIN}.{device.name}\\\"\\n hass.data[DOMAIN][key] = device\\n ent_platform = base_config[DOMAIN][CONF_TYPES].get(key, platform)\\n discovery.load_platform(\\n hass,\\n ent_platform,\\n DOMAIN,\\n discovered={ATTR_NEW: [key]},\\n hass_config=base_config,\\n )\\n\\n def _shutdown(call):\\n hdmi_network.stop()\\n\\n def _start_cec(callback_event):\\n \\\"\\\"\\\"Register services and start HDMI network to watch for devices.\\\"\\\"\\\"\\n hass.services.register(\\n DOMAIN, SERVICE_SEND_COMMAND, _tx, SERVICE_SEND_COMMAND_SCHEMA\\n )\\n hass.services.register(\\n DOMAIN, SERVICE_VOLUME, _volume, schema=SERVICE_VOLUME_SCHEMA\\n )\\n hass.services.register(\\n DOMAIN,\\n SERVICE_UPDATE_DEVICES,\\n _update,\\n schema=SERVICE_UPDATE_DEVICES_SCHEMA,\\n )\\n hass.services.register(DOMAIN, SERVICE_POWER_ON, _power_on)\\n hass.services.register(DOMAIN, SERVICE_STANDBY, _standby)\\n hass.services.register(DOMAIN, SERVICE_SELECT_DEVICE, _select_device)\\n\\n hdmi_network.set_new_device_callback(_new_device)\\n hdmi_network.start()\\n\\n hass.bus.listen_once(EVENT_HOMEASSISTANT_START, _start_cec)\\n hass.bus.listen_once(EVENT_HOMEASSISTANT_STOP, _shutdown)\\n return True\",\n \"def setup_platform(hass, config: ConfigType,\\n add_devices: Callable[[list], None], discovery_info=None):\\n devices = []\\n\\n for node in hass.data[ISY994_NODES][DOMAIN]:\\n _LOGGER.debug(\\\"Loading %s\\\", node.name)\\n devices.append(ISYSensorDevice(node))\\n\\n for node in hass.data[ISY994_WEATHER]:\\n devices.append(ISYWeatherDevice(node))\\n\\n add_devices(devices)\",\n \"def get_devices(self):\\n devices = self.get(\\\"event/device\\\")\",\n \"def setup_platform(hass, config, add_devices, discovery_info=None):\\n \\n devices = []\\n resource = requests.get('{host}/rest/items?type=json'.format(\\n host=config.get('host')))\\n\\n for item in resource.json()['item']:\\n if item['type'] in ['DimmerItem', ]:\\n devices.append(OpenhabLight(\\n hass,\\n item,\\n config.get('brightness', DEFAULT_BRIGHTNESS)))\\n\\n add_devices(devices)\",\n \"def set_devices(args):\\n global devices\\n if args is not None:\\n devices = [torch.device(i) for i in ast.literal_eval('[' + args + ']')]\\n torch.cuda.set_device(devices[0])\\n else:\\n devices = [torch.device('cpu')]\",\n \"def discover_devices(self):\\n # First let the STLink firmware discover devices\\n self._stlink_probe()\\n\\n if self.stlink_devices:\\n print(\\\"Discovered %d STLink device(s).\\\" % len(self.stlink_devices))\\n\\n # Grab lower level information about the USB devices (port, dev-id, etc)\\n self._get_usb_devices()\\n\\n # Use the information from STLink probe and USB to build a more complete picture\\n # of which device is on which port\\n self._assign_port_to_device()\\n\\n else:\\n print(\\\"No STLink devices were discovered.\\\")\",\n \"def get_configuration(self):\\n\\t\\tdevice = DeviceBase(self.name)\\n\\n\\t\\tif len(self.master_url) > 0:\\n\\t\\t\\tdevice.master_url = self.master_url\\n\\t\\t\\tr = requests.get(self.master_url + '/configuration/' + self.name)\\n\\n\\t\\t\\tif r.status_code == 200:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t#Request success\\n\\t\\t\\t\\t\\tconfig = json.loads(r.text)\\n\\t\\t\\t\\t\\tif config['deviceType'] == 1:\\n\\t\\t\\t\\t\\t\\t\\\"\\\"\\\" HID Reader \\\"\\\"\\\"\\n\\t\\t\\t\\t\\t\\tdevice = HIDReader(self.name)\\n\\t\\t\\t\\t\\tif config['deviceType'] == 2:\\n\\t\\t\\t\\t\\t\\t\\\"\\\"\\\" ZK45Reader \\\"\\\"\\\"\\n\\t\\t\\t\\t\\t\\tdevice = ZK45Reader(self.name)\\n\\t\\t\\t\\t\\tif config['deviceType'] == 4:\\n\\t\\t\\t\\t\\t\\t\\\"\\\"\\\" ZFM20Reader \\\"\\\"\\\"\\n\\t\\t\\t\\t\\t\\tdevice = ZFM20Reader(self.name)\\n\\t\\t\\t\\t\\tif config['deviceType'] == 5:\\n\\t\\t\\t\\t\\t\\t\\\"\\\"\\\" IOEcho \\\"\\\"\\\"\\n\\t\\t\\t\\t\\t\\tdevice = IOEcho(name=self.name, pin_and_label_matrix='')\\n\\t\\t\\t\\t\\telif config['deviceType'] == 0:\\n\\t\\t\\t\\t\\t\\t\\\"\\\"\\\" None \\\"\\\"\\\"\\n\\t\\t\\t\\t\\t\\tdevice = DeviceBase(name=self.name)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\\"\\\"\\\" Disable \\\"\\\"\\\"\\n\\t\\t\\t\\t\\t\\tdevice = DeviceBase(self.name)\\n\\n\\t\\t\\t\\t\\tdevice.zone_id = config['zone']\\n\\n\\t\\t\\t\\t\\tdevice.is_zone_enabled = config['enabled']\\n\\t\\t\\t\\t\\tdevice.is_zone_day_time_only = config['dayTimeOnly']\\n\\t\\t\\t\\t\\tdevice.is_configuration_loaded = True\\n\\n\\t\\t\\t\\t\\tdevice.master_secret = config['secret']\\n\\t\\t\\t\\t\\tdevice.master_url = self.master_url\\n\\n\\t\\t\\t\\t\\tdevice.is_in_error = False\\n\\t\\t\\t\\t\\tdevice.error_status = \\\"OK\\\"\\n\\t\\t\\t\\t\\tdevice.type = config['deviceType']\\n\\n\\t\\t\\t\\t\\tprint(\\\"Configuration loaded.\\\")\\n\\t\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\t\\terror_message = \\\"Device type not supported by current platform. Configuration aborted. (\\\" + str(e) + \\\")\\\"\\n\\t\\t\\t\\t\\tprint(error_message)\\n\\t\\t\\t\\t\\tdevice.zone_id = 1\\n\\n\\t\\t\\t\\t\\tdevice.is_zone_enabled = False\\n\\t\\t\\t\\t\\tdevice.is_zone_day_time_only = False\\n\\t\\t\\t\\t\\tdevice.is_in_error = True\\n\\t\\t\\t\\t\\tdevice.error_status = error_message\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"Configuration loading failed. (Server response : \\\" + str(r.status_code) + \\\")\\\")\\n\\t\\t\\t\\tdevice.zone_id = 1\\n\\t\\t\\t\\tdevice.is_zone_enabled = False\\n\\t\\t\\t\\tdevice.is_zone_day_time_only = False\\n\\t\\t\\t\\tdevice.is_in_error = True\\n\\t\\t\\t\\tdevice.error_status = \\\"Configuration loading failed. (Server response : \\\" + str(r.status_code) + \\\")\\\"\\n\\t\\telse:\\n\\t\\t\\tself.zone_id = 1\\n\\t\\t\\tself.is_zone_enabled = True\\n\\t\\t\\tself.is_zone_day_time_only = True\\n\\t\\t\\tdevice.is_in_error = True\\n\\t\\t\\tdevice.error_status = \\\"No master URL defined\\\"\\n\\n\\t\\tdevice.report_state()\\n\\t\\treturn device\",\n \"def setup_platform(hass, config, add_devices, discovery_info=None):\\n switches = []\\n for coil in config.get(\\\"coils\\\"):\\n switches.append(ModbusCoilSwitch(\\n coil.get(CONF_NAME),\\n coil.get(CONF_SLAVE),\\n coil.get(CONF_COIL)))\\n add_devices(switches)\",\n \"def collectNet(self):\\n network = self.options.net\\n # net option from the config file is a string\\n if isinstance(network, basestring):\\n network = [network]\\n # in case someone uses 10.0.0.0,192.168.0.1 instead of\\n # --net 10.0.0.0 --net 192.168.0.1\\n if isinstance(network, (list, tuple)) and \\\",\\\" in network[0]:\\n network = [n.strip() for n in network[0].split(\\\",\\\")]\\n count = 0\\n devices = []\\n if not network:\\n network = yield self.config().callRemote(\\\"getDefaultNetworks\\\")\\n\\n if not network:\\n self.log.warning(\\\"No networks configured\\\")\\n defer.returnValue(None)\\n\\n for net in network:\\n try:\\n nets = yield self.config().callRemote(\\n \\\"getNetworks\\\", net, self.options.subnets\\n )\\n if not nets:\\n self.log.warning(\\\"No networks found for %s\\\", net)\\n continue\\n ips = yield self.discoverIps(nets)\\n devices += ips\\n count += len(ips)\\n except Exception as ex:\\n self.log.exception(\\n \\\"Error performing net discovery on %s: %s\\\", net, ex\\n )\\n self.log.info(\\\"Working on devices: %s\\\", devices)\\n\\n foundDevices = []\\n for device in devices:\\n result = yield self.discoverDevice(\\n device, self.options.deviceclass, self.options.productionState\\n )\\n if result is not None:\\n foundDevices.append(result)\\n defer.returnValue(foundDevices)\",\n \"async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:\\n conf = config.get(DOMAIN, {})\\n hass.data[DOMAIN] = {\\n DATA_CUSTOM_EFFECTS: conf.get(CONF_CUSTOM_EFFECTS, {}),\\n DATA_CONFIG_ENTRIES: {},\\n }\\n # Make sure the scanner is always started in case we are\\n # going to retry via ConfigEntryNotReady and the bulb has changed\\n # ip\\n scanner = YeelightScanner.async_get(hass)\\n await scanner.async_setup()\\n\\n # Import manually configured devices\\n for host, device_config in config.get(DOMAIN, {}).get(CONF_DEVICES, {}).items():\\n _LOGGER.debug(\\\"Importing configured %s\\\", host)\\n entry_config = {CONF_HOST: host, **device_config}\\n hass.async_create_task(\\n hass.config_entries.flow.async_init(\\n DOMAIN, context={\\\"source\\\": SOURCE_IMPORT}, data=entry_config\\n )\\n )\\n\\n return True\",\n \"async def async_id_unknown_devices(config_dir):\\n await devices.async_load(id_devices=1)\\n for addr in devices:\\n device = devices[addr]\\n flags = True\\n for name in device.operating_flags:\\n if not device.operating_flags[name].is_loaded:\\n flags = False\\n break\\n if flags:\\n for name in device.properties:\\n if not device.properties[name].is_loaded:\\n flags = False\\n break\\n\\n # Cannot be done concurrently due to issues with the underlying protocol.\\n if not device.aldb.is_loaded or not flags:\\n await device.async_read_config()\\n\\n await devices.async_save(workdir=config_dir)\",\n \"async def async_setup_platform(hass, config, add_entities, discovery_info=None):\\n # Assign configuration variables.\\n # The configuration check takes care they are present.\\n # host = config[CONF_HOST]\\n\\n # Setup connection with devices/cloud\\n things = hass.data[\\\"things\\\"]\\n # Verify that passed in configuration works\\n # if not hub.is_valid_login():\\n # _LOGGER.error(\\\"Could not connect to AwesomeLight hub\\\")\\n # return\\n\\n # Add devices\\n for thing in things:\\n if \\\"Cover\\\" in thing[\\\"@type\\\"]:\\n add_entities([WebthingCover(thing)])\",\n \"def _internal_get_device_configs(self) -> List[dict]:\\n\\n device_config_list = []\\n\\n self.landscape_lock.acquire()\\n try:\\n pod_info = self._landscape_info[\\\"pod\\\"]\\n for dev_config_info in pod_info[\\\"devices\\\"]:\\n if \\\"skip\\\" in dev_config_info and dev_config_info[\\\"skip\\\"]:\\n continue\\n device_config_list.append(dev_config_info)\\n finally:\\n self.landscape_lock.release()\\n\\n return device_config_list\",\n \"def setup_platform(\\n hass: HomeAssistant,\\n config: ConfigType,\\n add_entities: AddEntitiesCallback,\\n discovery_info: DiscoveryInfoType | None = None,\\n) -> None:\\n bt_device_id: int = config[CONF_BT_DEVICE_ID]\\n\\n beacons: dict[str, dict[str, str]] = config[CONF_BEACONS]\\n devices: list[EddystoneTemp] = []\\n\\n for dev_name, properties in beacons.items():\\n namespace = get_from_conf(properties, CONF_NAMESPACE, 20)\\n instance = get_from_conf(properties, CONF_INSTANCE, 12)\\n name = properties.get(CONF_NAME, dev_name)\\n\\n if instance is None or namespace is None:\\n _LOGGER.error(\\\"Skipping %s\\\", dev_name)\\n continue\\n\\n devices.append(EddystoneTemp(name, namespace, instance))\\n\\n if devices:\\n mon = Monitor(hass, devices, bt_device_id)\\n\\n def monitor_stop(event: Event) -> None:\\n \\\"\\\"\\\"Stop the monitor thread.\\\"\\\"\\\"\\n _LOGGER.info(\\\"Stopping scanner for Eddystone beacons\\\")\\n mon.stop()\\n\\n def monitor_start(event: Event) -> None:\\n \\\"\\\"\\\"Start the monitor thread.\\\"\\\"\\\"\\n _LOGGER.info(\\\"Starting scanner for Eddystone beacons\\\")\\n mon.start()\\n\\n add_entities(devices)\\n mon.start()\\n hass.bus.listen_once(EVENT_HOMEASSISTANT_STOP, monitor_stop)\\n hass.bus.listen_once(EVENT_HOMEASSISTANT_START, monitor_start)\\n else:\\n _LOGGER.warning(\\\"No devices were added\\\")\",\n \"def test_get_devices1(self):\\n pass\",\n \"async def async_setup_entry(\\n hass: HomeAssistant,\\n config_entry: ConfigEntry,\\n async_add_entities: AddEntitiesCallback,\\n) -> None:\\n data: LutronCasetaData = hass.data[CASETA_DOMAIN][config_entry.entry_id]\\n bridge = data.bridge\\n cover_devices = bridge.get_devices_by_domain(DOMAIN)\\n async_add_entities(\\n LutronCasetaCover(cover_device, data) for cover_device in cover_devices\\n )\",\n \"def get_devices(self):\\n\\n md_configstore = os.path.join(\\n os.environ['VOLTTRON_HOME'],\\n \\\"configuration_store/platform.driver.store\\\"\\n )\\n\\n if not os.path.exists(md_configstore):\\n _log.debug(\\\"No master driver currently on this platform.\\\")\\n return {}\\n\\n statinfo = os.stat(md_configstore)\\n\\n if self._master_driver_stat_time is None or \\\\\\n self._master_driver_stat_time != statinfo.st_mtime:\\n self._master_driver_stat_time = statinfo.st_mtime\\n\\n # else no change in the md file and we have the same stat time.\\n else:\\n keys = list(self._devices.keys())\\n\\n for k in keys:\\n new_key = self.get_renamed_topic(k)\\n if new_key != k:\\n self._devices[new_key] = self._devices[k]\\n del self._devices[k]\\n\\n return self._devices\\n\\n _log.debug('Getting devices')\\n config_list = self.vip.rpc.call(CONFIGURATION_STORE,\\n 'manage_list_configs',\\n 'platform.driver').get(timeout=5)\\n\\n _log.debug('Config list is: {}'.format(config_list))\\n devices = defaultdict(dict)\\n\\n for cfg_name in config_list:\\n # Skip as we are only looking to do devices in this call.\\n if not cfg_name.startswith('devices/'):\\n continue\\n\\n device_config = self.vip.rpc.call('config.store', 'manage_get',\\n 'platform.driver',\\n cfg_name,\\n raw=False).get(timeout=5)\\n _log.debug('DEVICE CONFIG IS: {}'.format(device_config))\\n\\n reg_cfg_name = device_config.get(\\n 'registry_config')[len('config://'):]\\n _log.debug('Reading registry_config file {}'.format(\\n reg_cfg_name\\n ))\\n registry_config = self.vip.rpc.call('config.store',\\n 'manage_get', 'platform.driver',\\n reg_cfg_name,\\n raw=False).get(timeout=5)\\n _log.debug('Registry Config: {}'.format(registry_config))\\n\\n points = []\\n for pnt in registry_config:\\n points.append(pnt['Volttron Point Name'])\\n\\n devices[cfg_name]['points'] = points\\n\\n return devices\",\n \"async def async_setup_entry(\\n hass: HomeAssistant,\\n config_entry: ConfigEntry,\\n async_add_entities: AddEntitiesCallback,\\n) -> None:\\n # Priority 1: manual config\\n if hass.data[DOMAIN].get(CONF_IP_ADDRESS):\\n devices = hass.data[DOMAIN][CONF_IP_ADDRESS]\\n else:\\n # Priority 2: scanned interfaces\\n devices = await AehW4a1().discovery()\\n\\n entities = [_build_entity(device) for device in devices]\\n async_add_entities(entities, True)\",\n \"async def find_devices() -> List[DeviceInfo]:\\n return await Discovery.search_devices()\",\n \"def setup_platform(hass, config, add_entities, discovery_info=None):\\n import jsonpath\\n jsonpath = jsonpath.jsonpath\\n global HEAT_PUMPS\\n hub.update_overview()\\n if int(hub.config.get(CONF_CLIMATE, 1)):\\n HEAT_PUMPS = hub.get('$.heatPumps')\\n if HEAT_PUMPS:\\n for heat_pump in HEAT_PUMPS[0]:\\n device_label = jsonpath(heat_pump, '$.deviceLabel')[0]\\n add_entities([\\n VerisureHeatPump(device_label)\\n ])\",\n \"async def async_setup_entry(hass, config_entry, async_add_devices):\\n loxconfig = hass.data[DOMAIN]['loxconfig']\\n identify = loxconfig['msInfo']['serialNr']\\n\\n devices = []\\n all_dimmers = []\\n all_light_controller_dimmers = []\\n all_color_picker = []\\n all_switches = []\\n all_dimmers = get_all_dimmer(loxconfig)\\n\\n for light_controller in get_all_light_controller(loxconfig):\\n light_controller.update({'room': get_room_name_from_room_uuid(loxconfig, light_controller.get('room', '')),\\n 'cat': get_cat_name_from_cat_uuid(loxconfig, light_controller.get('cat', '')),\\n 'async_add_devices': async_add_devices\\n })\\n new_light_controller = LoxonelightcontrollerV2(**light_controller)\\n\\n if 'subControls' in light_controller:\\n if len(light_controller['subControls']) > 0:\\n for sub_controll in light_controller['subControls']:\\n if light_controller['subControls'][sub_controll]['type'] == \\\"Dimmer\\\":\\n light_controller['subControls'][sub_controll]['room'] = light_controller.get('room', '')\\n light_controller['subControls'][sub_controll]['cat'] = light_controller.get('cat', '')\\n light_controller['subControls'][sub_controll][\\n 'lightcontroller_id'] = new_light_controller.unique_id\\n all_light_controller_dimmers.append(light_controller['subControls'][sub_controll])\\n\\n elif light_controller['subControls'][sub_controll]['type'] == \\\"Switch\\\":\\n light_controller['subControls'][sub_controll]['room'] = light_controller.get('room', '')\\n light_controller['subControls'][sub_controll]['cat'] = light_controller.get('cat', '')\\n light_controller['subControls'][sub_controll][\\n 'lightcontroller_id'] = new_light_controller.unique_id\\n all_switches.append(light_controller['subControls'][sub_controll])\\n\\n elif light_controller['subControls'][sub_controll]['type'] == \\\"ColorPickerV2\\\":\\n light_controller['subControls'][sub_controll]['room'] = light_controller.get('room', '')\\n light_controller['subControls'][sub_controll]['cat'] = light_controller.get('cat', '')\\n light_controller['subControls'][sub_controll][\\n 'lightcontroller_id'] = new_light_controller.unique_id\\n all_color_picker.append(light_controller['subControls'][sub_controll])\\n\\n hass.bus.async_listen(EVENT, new_light_controller.event_handler)\\n devices.append(new_light_controller)\\n\\n _ = all_dimmers + all_light_controller_dimmers\\n\\n for dimmer in _:\\n if dimmer in all_light_controller_dimmers:\\n dimmer.update({'room': get_room_name_from_room_uuid(loxconfig, light_controller.get('room', '')),\\n 'cat': get_cat_name_from_cat_uuid(loxconfig, light_controller.get('cat', '')),\\n 'async_add_devices': async_add_devices\\n })\\n else:\\n dimmer.update({'room': get_room_name_from_room_uuid(loxconfig, dimmer.get('room', '')),\\n 'cat': get_cat_name_from_cat_uuid(loxconfig, dimmer.get('cat', '')),\\n 'async_add_devices': async_add_devices\\n })\\n\\n new_dimmer = LoxoneDimmer(**dimmer)\\n hass.bus.async_listen(EVENT, new_dimmer.event_handler)\\n devices.append(new_dimmer)\\n\\n for switch in all_switches:\\n switch.update({'room': get_room_name_from_room_uuid(loxconfig, light_controller.get('room', '')),\\n 'cat': get_cat_name_from_cat_uuid(loxconfig, light_controller.get('cat', '')),\\n 'async_add_devices': async_add_devices\\n })\\n new_switch = LoxoneLight(**switch)\\n hass.bus.async_listen(EVENT, new_switch.event_handler)\\n devices.append(new_switch)\\n\\n for color_picker in all_color_picker:\\n color_picker.update({'room': get_room_name_from_room_uuid(loxconfig, light_controller.get('room', '')),\\n 'cat': get_cat_name_from_cat_uuid(loxconfig, light_controller.get('cat', '')),\\n 'async_add_devices': async_add_devices\\n })\\n new_color_picker = LoxoneColorPickerV2(**color_picker)\\n hass.bus.async_listen(EVENT, new_color_picker.event_handler)\\n devices.append(new_color_picker)\\n\\n async_add_devices(devices, True)\\n return True\",\n \"async def async_setup(self) -> None:\\n await self.hass.async_add_executor_job(self._setup)\\n\\n # set already known devices to away instead of unavailable\\n device_registry = dr.async_get(self.hass)\\n devices = dr.async_entries_for_config_entry(device_registry, self.entry_id)\\n for device_entry in devices:\\n if device_entry.via_device_id is None:\\n continue # do not add the router itself\\n\\n device_mac = dict(device_entry.connections).get(dr.CONNECTION_NETWORK_MAC)\\n self.devices[device_mac] = {\\n \\\"mac\\\": device_mac,\\n \\\"name\\\": device_entry.name,\\n \\\"active\\\": False,\\n \\\"last_seen\\\": dt_util.utcnow() - timedelta(days=365),\\n \\\"device_model\\\": None,\\n \\\"device_type\\\": None,\\n \\\"type\\\": None,\\n \\\"link_rate\\\": None,\\n \\\"signal\\\": None,\\n \\\"ip\\\": None,\\n }\\n\\n await self.async_update_device_trackers()\\n self.entry.async_on_unload(\\n async_track_time_interval(\\n self.hass, self.async_update_device_trackers, SCAN_INTERVAL\\n )\\n )\\n\\n async_dispatcher_send(self.hass, self.signal_device_new)\",\n \"def _import_devices(self) -> None:\\n self._devices.clear()\\n\\n # Exctract all devices\\n for device in self._udev.list_devices():\\n # Skip devices without mapping\\n if not device.device_node or self.helper.hide_virtual_device(device):\\n continue\\n self._devices[device.sys_name] = Device.import_udev(device)\",\n \"def config_device(self, cfg):\\n\\n\\t\\tif self.host is not None:\\n\\t\\t\\tself.tell(\\\"Configuring device\\\")\\n\\n\\t\\tself._dev = cfg\\n\\n\\t\\tself.do_checklist([])\",\n \"def setup_usb(self):\\n global DEVICE\\n global epBulkWriter\\n global epBulkReader\\n global VID\\n global PID\\n\\n DEVICE = usb.core.find(idVendor=0x2AB9,idProduct=0xFFFF)\\n if DEVICE is None:#If not a LVPM, look for an HVPM.\\n DEVICE = usb.core.find(idVendor=0x04d8,idProduct=0x000b)\\n VID = '0x4d8'\\n PID = '0xb'\\n if \\\"Linux\\\" == platform.system():\\n try:\\n DEVICE.detach_kernel_driver(0)\\n except:\\n pass # already unregistered\\n DEVICE.set_configuration()\\n\\n cfg = DEVICE.get_active_configuration()\\n intf = cfg[(0,0)]\\n\\n epBulkWriter = usb.util.find_descriptor(\\n intf,\\n custom_match = \\\\\\n lambda e: \\\\\\n usb.util.endpoint_direction(e.bEndpointAddress) == \\\\\\n usb.util.ENDPOINT_OUT)\\n epBulkReader = usb.util.find_descriptor(\\n intf,\\n custom_match = \\\\\\n lambda e: \\\\\\n usb.util.endpoint_direction(e.bEndpointAddress) == \\\\\\n usb.util.ENDPOINT_IN)\",\n \"def _configure_device():\\n vendor_id = 0x04D8 # These ids are microchip's libusb based device\\n product_id = 0x0204 # ids\\n dev = usb.core.find(idVendor=vendor_id, idProduct = product_id)\\n try:\\n dev.set_configuration()\\n return dev\\n except:\\n return None\",\n \"def multiple_device_details(\\n self, hosts, config_data, pre_name, action, post_name):\\n res_obj = []\\n self.host_list = []\\n host_dict={}\\n\\n first_entry = hosts[0]\\n if 'include' in first_entry:\\n devices_file_name = first_entry['include']\\n if os.path.isfile(devices_file_name):\\n lfile = devices_file_name\\n else:\\n lfile = os.path.join(\\n expanduser(get_path(\\n 'DEFAULT',\\n 'test_file_path')),\\n devices_file_name)\\n login_file = open(lfile, 'r')\\n dev_file = yaml.load(login_file)\\n gp = first_entry.get('group', 'all')\\n\\n dgroup = [i.strip().lower() for i in gp.split(',')]\\n for dgp in dev_file:\\n if dgroup[0].lower() == 'all' or dgp.lower() in dgroup:\\n for val in dev_file[dgp]:\\n hostname = list(val)[0]\\n self.log_detail = {'hostname': hostname}\\n if val.get(hostname) is not None and hostname not in host_dict:\\n host_dict[hostname] = deepcopy(val.get(hostname))\\n self.host_list.append(hostname)\\n else:\\n for host in hosts:\\n try:\\n hostname = host['device']\\n self.log_detail = {'hostname': hostname}\\n except KeyError as ex:\\n self.logger.error(\\n colorama.Fore.RED +\\n \\\"ERROR!! KeyError 'device' key not found\\\",\\n extra=self.log_detail)\\n except Exception as ex:\\n self.logger.error(\\n colorama.Fore.RED +\\n \\\"ERROR!! %s\\\" %\\n ex,\\n extra=self.log_detail)\\n else:\\n if hostname not in host_dict:\\n self.host_list.append(hostname)\\n host_dict[hostname] = deepcopy(host)\\n\\n for (hostname, key_value) in iteritems(host_dict):\\n username = key_value.get('username')\\n password = key_value.get('passwd')\\n key_value = self.get_values(key_value)\\n t = Thread(\\n target=self.connect,\\n args=(\\n hostname,\\n username,\\n password,\\n pre_name,\\n config_data,\\n action,\\n post_name),\\n kwargs= key_value\\n )\\n t.start()\\n t.join()\\n if action == \\\"snap\\\":\\n if not self.snap_q.empty():\\n res_obj.append(self.snap_q.get())\\n elif action in [\\\"snapcheck\\\", \\\"check\\\"]:\\n if not self.q.empty():\\n res_obj.append(self.q.get())\\n else:\\n res_obj.append(None)\\n\\n return res_obj\",\n \"def init_devices(self):\\n self.hp_nb = int(self.rs_nb* self.hp_proportion/(1- self.hp_proportion))\\n self.defense_cost = self.hp_nb * self.hp_unit_cost\\n rs_devices = [True for i in range(self.rs_nb)] #rs --> True\\n hp_devices = [False for i in range(self.hp_nb)] #hp --> False\\n self.devices = rs_devices + hp_devices\\n shuffle(self.devices)\",\n \"async def async_setup(hass, config):\\n\\n conf = config[DOMAIN]\\n port = conf.get(CONF_PORT)\\n host = conf.get(CONF_HOST)\\n ip_port = conf.get(CONF_IP_PORT)\\n username = conf.get(CONF_HUB_USERNAME)\\n password = conf.get(CONF_HUB_PASSWORD)\\n hub_version = conf.get(CONF_HUB_VERSION)\\n\\n if host:\\n _LOGGER.info(\\\"Connecting to Insteon Hub on %s:%d\\\", host, ip_port)\\n else:\\n _LOGGER.info(\\\"Connecting to Insteon PLM on %s\\\", port)\\n\\n try:\\n await async_connect(\\n device=port,\\n host=host,\\n port=ip_port,\\n username=username,\\n password=password,\\n hub_version=hub_version,\\n )\\n except ConnectionError:\\n _LOGGER.error(\\\"Could not connect to Insteon modem\\\")\\n return False\\n _LOGGER.info(\\\"Connection to Insteon modem successful\\\")\\n\\n hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, close_insteon_connection)\\n conf = config[DOMAIN]\\n overrides = conf.get(CONF_OVERRIDE, [])\\n x10_devices = conf.get(CONF_X10, [])\\n\\n await devices.async_load(\\n workdir=hass.config.config_dir, id_devices=0, load_modem_aldb=0\\n )\\n\\n for device_override in overrides:\\n # Override the device default capabilities for a specific address\\n address = device_override.get(\\\"address\\\")\\n if not devices.get(address):\\n cat = device_override[CONF_CAT]\\n subcat = device_override[CONF_SUBCAT]\\n firmware = device_override.get(CONF_FIRMWARE)\\n if firmware is None:\\n firmware = device_override.get(CONF_PRODUCT_KEY, 0)\\n devices.set_id(address, cat, subcat, firmware)\\n\\n for device in x10_devices:\\n housecode = device.get(CONF_HOUSECODE)\\n unitcode = device.get(CONF_UNITCODE)\\n x10_type = \\\"on_off\\\"\\n steps = device.get(CONF_DIM_STEPS, 22)\\n if device.get(CONF_PLATFORM) == \\\"light\\\":\\n x10_type = \\\"dimmable\\\"\\n elif device.get(CONF_PLATFORM) == \\\"binary_sensor\\\":\\n x10_type = \\\"sensor\\\"\\n _LOGGER.debug(\\n \\\"Adding X10 device to Insteon: %s %d %s\\\", housecode, unitcode, x10_type\\n )\\n device = devices.add_x10_device(housecode, unitcode, x10_type, steps)\\n\\n asyncio.create_task(async_setup_platforms(hass, config))\\n return True\",\n \"async def async_setup_platform(hass, config, async_add_devices, _discovery_info=None):\\n pass\",\n \"def setup_platform(hass, config, add_entities, discovery_info=None):\\n\\n for fan in pywink.get_fans():\\n if fan.object_id() + fan.name() not in hass.data[DOMAIN][\\\"unique_ids\\\"]:\\n add_entities([WinkFanDevice(fan, hass)])\",\n \"def setup_platform(\\n hass: HomeAssistant,\\n config: Dict,\\n add_devices: Callable,\\n discovery_info: Optional[Dict] = None,\\n) -> None:\\n havdalah = config[HAVDALAH_MINUTES]\\n candle_light = config[CANDLE_LIGHT_MINUTES]\\n cities = config[GEONAMES]\\n cities_list = cities.split(\\\",\\\")\\n\\n add_devices(\\n [\\n ShabbatTimes(\\n hass,\\n city,\\n \\\"Shabbat Times {}\\\".format(city.replace(\\\"-\\\", \\\"_\\\")),\\n havdalah,\\n candle_light,\\n )\\n for city in cities_list\\n ]\\n )\",\n \"def _async_get_nest_devices(\\n hass: HomeAssistant, config_entry: ConfigEntry\\n) -> dict[str, Device]:\\n if DATA_SDM not in config_entry.data:\\n return {}\\n\\n if (\\n config_entry.entry_id not in hass.data[DOMAIN]\\n or DATA_DEVICE_MANAGER not in hass.data[DOMAIN][config_entry.entry_id]\\n ):\\n return {}\\n\\n device_manager: DeviceManager = hass.data[DOMAIN][config_entry.entry_id][\\n DATA_DEVICE_MANAGER\\n ]\\n return device_manager.devices\",\n \"def setup(hass, config):\\n component = DeviceComponent(\\n logging.getLogger(__name__), DOMAIN, hass, SCAN_INTERVAL,\\n DISCOVERY_PLATFORMS)\\n\\n component.setup(config)\\n\\n def media_player_service_handler(service):\\n \\\"\\\"\\\" Maps services to methods on MediaPlayerDevice. \\\"\\\"\\\"\\n target_players = component.extract_from_service(service)\\n\\n method = SERVICE_TO_METHOD[service.service]\\n\\n for player in target_players:\\n getattr(player, method)()\\n\\n if player.should_poll:\\n player.update_ha_state(True)\\n\\n for service in SERVICE_TO_METHOD:\\n hass.services.register(DOMAIN, service, media_player_service_handler)\\n\\n def play_youtube_video_service(service, media_id):\\n \\\"\\\"\\\" Plays specified media_id on the media player. \\\"\\\"\\\"\\n target_players = component.extract_from_service(service)\\n\\n if media_id:\\n for player in target_players:\\n player.play_youtube(media_id)\\n\\n hass.services.register(DOMAIN, \\\"start_fireplace\\\",\\n lambda service:\\n play_youtube_video_service(service, \\\"eyU3bRy2x44\\\"))\\n\\n hass.services.register(DOMAIN, \\\"start_epic_sax\\\",\\n lambda service:\\n play_youtube_video_service(service, \\\"kxopViU98Xo\\\"))\\n\\n hass.services.register(DOMAIN, SERVICE_YOUTUBE_VIDEO,\\n lambda service:\\n play_youtube_video_service(\\n service, service.data.get('video')))\\n\\n return True\",\n \"def setup_platform(hass, config, add_devices, discovery_info=None) -> None:\\n friendly_name = config.get(CONF_FRIENDLY_NAME)\\n mac_addr = config.get(CONF_MAC)\\n add_devices([Switchmate(mac_addr, friendly_name)], True)\",\n \"def devices(self, devices):\\n\\n self._devices = devices\",\n \"def devices(self, devices):\\n\\n self._devices = devices\",\n \"def get_devices(self):\\n\\t\\tself.ise.headers.update({'Accept': 'application/vnd.com.cisco.ise.network.networkdevice.1.0+xml'})\\n\\n\\t\\tresp = self.ise.get('{0}/config/networkdevice'.format(self.url_base))\\n\\n\\t\\tresult = {\\n\\t\\t\\t'success': False,\\n\\t\\t\\t'response': '',\\n\\t\\t\\t'error': '',\\n\\t\\t}\\n\\n\\t\\tjson_res = ERS._to_json(resp.text)['ns3:searchResult']\\n\\n\\t\\tif resp.status_code == 200 and int(json_res['@total']) > 1:\\n\\t\\t\\tresult['success'] = True\\n\\t\\t\\tresult['response'] = [(i['@name'], i['@id'])\\n\\t\\t\\t\\t\\t\\t\\t\\t for i in json_res['ns3:resources']['ns5:resource']]\\n\\t\\t\\treturn result\\n\\n\\t\\telif resp.status_code == 200 and int(json_res['@total']) == 1:\\n\\t\\t\\tresult['success'] = True\\n\\t\\t\\tresult['response'] = [(json_res['ns3:resources']['ns5:resource']['@name'],\\n\\t\\t\\t\\t\\t\\t\\t\\t json_res['ns3:resources']['ns5:resource']['@id'])]\\n\\t\\t\\treturn result\\n\\n\\t\\telif resp.status_code == 200 and int(json_res['@total']) == 0:\\n\\t\\t\\tresult['success'] = True\\n\\t\\t\\tresult['response'] = []\\n\\t\\t\\treturn result\\n\\n\\t\\telse:\\n\\t\\t\\tresult['response'] = ERS._to_json(resp.text)['ns3:ersResponse']['messages']['message']['title']\\n\\t\\t\\tresult['error'] = resp.status_code\\n\\t\\t\\treturn result\",\n \"async def subdevice_fixture(hass: HomeAssistant, ufp: MockUFPFixture, light: Light):\\n\\n await init_entry(hass, ufp, [light])\\n\\n device_registry = dr.async_get(hass)\\n\\n return [d for d in device_registry.devices.values() if d.name != \\\"UnifiProtect\\\"][0]\",\n \"def device_discovery(endless):\\r\\n click.echo(\\\"start device discovery ...\\\")\\r\\n _device_discovery(endless)\",\n \"async def async_setup_entry(\\n hass: HomeAssistantType, entry: ConfigEntry, async_add_entities\\n) -> None:\\n entities = []\\n\\n for device in hass.data[DOMAIN][\\\"homecontrol\\\"].multi_level_sensor_devices:\\n for multi_level_sensor in device.multi_level_sensor_property:\\n entities.append(\\n DevoloMultiLevelDeviceEntity(\\n homecontrol=hass.data[DOMAIN][\\\"homecontrol\\\"],\\n device_instance=device,\\n element_uid=multi_level_sensor,\\n )\\n )\\n async_add_entities(entities, False)\",\n \"def pop_adv_devices(self):\\r\\n if self.localSDK.devList:\\r\\n del self.localSDK.devList[:]\\r\\n try:\\r\\n self.localSDK.get_devices() # Get list of boards from KSDK manifest file\\r\\n except IOError:\\r\\n self.localSDK.devList = ['None']\\r\\n return\",\n \"def launch_devices(self):\\n self.data[0], temp = alghoritm.temperature(self.data[0], self.set_thermostat, 0) # get value\\n HC35_3S.launch(self.data_path, self.samples_size, temp) # set it via device\\n\\n self.data[1], humidi = alghoritm.humidity(self.data[1], self.set_humidifier, 0)\\n humidifier.launch(self.data_path, self.samples_size, humidi)\\n\\n self.data[2], moistu = alghoritm.moisture(self.data[2], self.set_sprinklers, 0)\\n HUNTER.launch(self.data_path, self.samples_size, moistu)\\n\\n self.data[3], o2 = alghoritm.o2(self.data[3], self.set_ventilation, 0)\\n ventilation.launch_o2(self.data_path, self.samples_size, o2)\\n\\n self.data[4], co2 = alghoritm.co2(self.data[4], self.set_ventilation, 0)\\n ventilation.launch_co2(self.data_path, self.samples_size, co2)\",\n \"def setup_scanner(hass, config, see, discovery_info=None):\\n accounts = hass.data[BMW_DOMAIN]\\n _LOGGER.debug(\\\"Found BMW accounts: %s\\\", \\\", \\\".join([a.name for a in accounts]))\\n for account in accounts:\\n for vehicle in account.account.vehicles:\\n tracker = BMWDeviceTracker(see, vehicle)\\n account.add_update_listener(tracker.update)\\n tracker.update()\\n return True\",\n \"def get_devices():\\n global managed_objects\\n global devices_by_adr\\n \\n devices_by_adr = {}\\n \\n r = re.compile(\\\"\\\\/org\\\\/bluez\\\\/hci\\\\d*\\\\/dev\\\\_(.*)\\\")\\n # e.g., match a string like this:\\n # /org/bluez/hci0/dev_58_C9_35_2F_A1_EF\\n \\n for key, value in managed_objects.items():\\n # print(\\\"key=\\\", key)\\n m = r.match(key)\\n if m is not None:\\n dev_str = m.group(1) # we have a device string!\\n # print(\\\"dev_str=\\\", dev_str)\\n # let's flatten that dict a bit\\n devices_by_adr[dev_str] = value[\\\"org.bluez.Device1\\\"]\",\n \"async def get_all_units():\\n all_devices = []\\n if network is None:\\n import netifaces\\n\\n gateway = netifaces.gateways().get(\\\"default\\\", {})\\n subnet = gateway.get(netifaces.AF_INET, ())[0][:-1] + \\\"0/24\\\"\\n else:\\n subnet = network\\n async with gdh_session() as session:\\n googledevices = NetworkScan(loop, session)\\n result = await googledevices.scan_for_units(subnet)\\n if feature:\\n for unit in result:\\n if unit[feature]:\\n all_devices.append(unit)\\n else:\\n all_devices = result\\n print(format_json(all_devices))\",\n \"async def get_all_units():\\n all_devices = []\\n if network is None:\\n import netifaces\\n\\n gateway = netifaces.gateways().get(\\\"default\\\", {})\\n subnet = gateway.get(netifaces.AF_INET, ())[0][:-1] + \\\"0/24\\\"\\n else:\\n subnet = network\\n async with gdh_session() as session:\\n googledevices = NetworkScan(loop, session)\\n result = await googledevices.scan_for_units(subnet)\\n if feature:\\n for unit in result:\\n if unit[feature]:\\n all_devices.append(unit)\\n else:\\n all_devices = result\\n print(format_json(all_devices))\",\n \"async def test_get_device_config(hass, hass_client):\\n with patch.object(config, \\\"SECTIONS\\\", [\\\"automation\\\"]):\\n await async_setup_component(hass, \\\"config\\\", {})\\n\\n client = await hass_client()\\n\\n def mock_read(path):\\n \\\"\\\"\\\"Mock reading data.\\\"\\\"\\\"\\n return [{\\\"id\\\": \\\"sun\\\"}, {\\\"id\\\": \\\"moon\\\"}]\\n\\n with patch(\\\"homeassistant.components.config._read\\\", mock_read):\\n resp = await client.get(\\\"/api/config/automation/config/moon\\\")\\n\\n assert resp.status == 200\\n result = await resp.json()\\n\\n assert result == {\\\"id\\\": \\\"moon\\\"}\",\n \"def configure(args):\\n\\n emu = Emulator(args,\\n cpu='68030',\\n frequency=24 * 1000 * 1000)\\n # initially only the EEPROM exists; aliased at 0 all the way up to 0xfe000000\\n # we only map the low and high aliases, as the intermediates aren't interesting\\n emu.add_memory(base=0, size=512 * 1024, writable=False, from_file=args.rom)\\n emu.add_memory(base=0xfe000000, size=512 * 1024, writable=False, from_file=args.rom)\\n\\n emu.add_device(args,\\n MC68681,\\n address=0xfffff000,\\n interrupt=m68k.IRQ_2,\\n register_arrangement='16-bit-doubled')\\n emu.add_device(args,\\n CompactFlash,\\n address=0xffffe000,\\n register_arrangement='8-bit' if args.cf_width == 8 else '16-bit')\\n emu.add_device(args,\\n CB030Remap,\\n address=0xffff8000)\\n emu.add_device(args,\\n CB030Ticker,\\n address=0xffff9000,\\n interrupt=m68k.IRQ_6)\\n return emu\",\n \"async def async_setup(hass: HomeAssistant, config):\\n if DOMAIN not in config:\\n return True\\n\\n conf_adapters = config[DOMAIN].get(CONF_ADAPTERS)\\n if not conf_adapters:\\n return False\\n\\n hass.data.setdefault(DOMAIN, {}).update({CONF_INDOOR_UNITS: {}})\\n\\n for conf_adapter in conf_adapters:\\n conf_adapter_name = conf_adapter[CONF_ADAPTER_NAME]\\n conf_adapter_host = conf_adapter[CONF_ADAPTER_HOST]\\n conf_adapter_port = conf_adapter[CONF_ADAPTER_PORT]\\n conf_adapter_slave = conf_adapter[CONF_ADAPTER_SLAVE]\\n adapter : DaikinAPI = await hass.async_add_executor_job(create_adapter, conf_adapter_host, conf_adapter_port, conf_adapter_slave)\\n\\n for indoor_unit in adapter.indoor_units.values():\\n indoor_unit_global_name = \\\"daikin_dta116a621_\\\" + conf_adapter_name + \\\"_\\\" + str(indoor_unit.indoor_unit_id).replace(\\\"-\\\",\\\"_\\\")\\n hass.data[DOMAIN][CONF_INDOOR_UNITS][indoor_unit_global_name] = indoor_unit\\n hass.helpers.discovery.load_platform('climate', DOMAIN, {}, config)\\n #discovery.async_load_platform(hass, DOMAIN, 'climate', {}, config)\\n return True\",\n \"def setup_platform(hass, config, add_entities, discovery_info=None):\\n devices = []\\n dev = discovery_info.get(\\\"dev\\\")\\n param = discovery_info.get(\\\"param\\\")\\n devices = []\\n for idx in dev['data']:\\n if dev['devtype'] in OT_SENSOR_TYPES and idx in [\\\"Z\\\",\\\"V\\\",\\\"P3\\\",\\\"P4\\\"]:\\n devices.append(LifeSmartSensor(dev,idx,dev['data'][idx],param))\\n else:\\n devices.append(LifeSmartSensor(dev,idx,dev['data'][idx],param))\\n add_entities(devices)\",\n \"def get_devices(self):\\n return self.api_request('GET', self.url + '/device', {})\",\n \"def setup_platform(hass, config, add_devices, discovery_info=None):\\n name = config.get(CONF_NAME)\\n code = config.get(CONF_CODE)\\n username = config.get(CONF_USERNAME)\\n password = config.get(CONF_PASSWORD)\\n\\n add_devices([SimpliSafeAlarm(name, username, password, code)])\",\n \"async def load_devices(self, force_refresh=False) -> None:\\n if force_refresh or len(self._devices) == 0:\\n devices: List[Dict[str, Any]] = await self._api_request(\\\"devices\\\")\\n _LOGGER.debug(\\\"Loaded device list\\\")\\n\\n # load devices sequentially to avoid overloading the hub\\n for dev in devices:\\n await self._load_device(dev[\\\"id\\\"], force_refresh)\",\n \"def setup_platform(hass, config: ConfigType,\\n add_devices: Callable[[list], None], discovery_info=[]):\\n elk = hass.data['PyElk']['connection']\\n elk_config = hass.data['PyElk']['config']\\n discovered_devices = hass.data['PyElk']['discovered_devices']\\n if elk is None:\\n _LOGGER.error('Elk is None')\\n return False\\n if not elk.connected:\\n _LOGGER.error('A connection has not been made to the Elk panel.')\\n return False\\n devices = []\\n from PyElk.Thermostat import Thermostat as ElkThermostat\\n # If no discovery info was passed in, discover automatically\\n if len(discovery_info) == 0:\\n # Gather areas\\n for node in elk.THERMOSTATS:\\n if node:\\n if node.included is True and node.enabled is True:\\n discovery_info.append(node)\\n # If discovery info was passed in, check if we want to include it\\n else:\\n for node in discovery_info:\\n if node.included is True and node.enabled is True:\\n continue\\n else:\\n discovery_info.remove(node)\\n # Add discovered devices\\n for node in discovery_info:\\n if isinstance(node, ElkThermostat):\\n node_name = 'climate.' + 'elk_thermostat_' + format(node.number, '02')\\n else:\\n continue\\n if node_name not in discovered_devices:\\n _LOGGER.debug('Loading Elk %s: %s', node.classname, node.description_pretty())\\n device = ElkClimateDevice(node)\\n discovered_devices[node_name] = device\\n devices.append(device)\\n else:\\n _LOGGER.debug('Skipping already loaded Elk %s: %s', node.classname, node.description_pretty())\\n\\n add_devices(devices, True)\\n return True\",\n \"def YieldAllDevices(self):\\n\\n for deviceManagedObject in self.managedObject.config.hardware.device:\\n yield Device(vm=self, managedObject=deviceManagedObject)\",\n \"async def async_setup_platform(\\n hass, config, async_add_entities, discovery_info=None):\\n\\n for device_config in config[CONF_DEVICES]:\\n host = device_config[CONF_HOST]\\n username = device_config[CONF_USERNAME]\\n password = device_config[CONF_PASSWORD]\\n interval = device_config.get(CONF_SCAN_INTERVAL, SCAN_INTERVAL)\\n\\n session = async_create_clientsession(hass, cookie_jar=aiohttp.CookieJar(unsafe=True))\\n\\n poe_data = ZyxelPoeData(host, username, password, interval, session)\\n\\n await poe_data.async_update()\\n\\n switches = list()\\n for port, data in poe_data.ports.items():\\n switches.append(ZyxelPoeSwitch(poe_data, host, port))\\n\\n async_add_entities(switches, False)\",\n \"def setup_platform(hass, config, add_entities, discovery_info=None):\\n\\n host = config.get(CONF_HOST)\\n if host:\\n devices = [ThinkingCleaner(host, \\\"unknown\\\")]\\n else:\\n discovery = Discovery()\\n devices = discovery.discover()\\n\\n @util.Throttle(MIN_TIME_BETWEEN_SCANS, MIN_TIME_BETWEEN_FORCED_SCANS)\\n def update_devices():\\n \\\"\\\"\\\"Update all devices.\\\"\\\"\\\"\\n for device_object in devices:\\n device_object.update()\\n\\n dev = []\\n for device in devices:\\n for type_name in SENSOR_TYPES:\\n dev.append(ThinkingCleanerSensor(device, type_name, update_devices))\\n\\n add_entities(dev)\",\n \"def setUp(self):\\n super().setUp()\\n self.devices = _DEVICE_STRATEGY()\\n command_line = [\\\"pool\\\", \\\"create\\\", self._POOLNAME] + self.devices\\n RUNNER(command_line)\",\n \"def setup_platform(\\n hass: HomeAssistant,\\n config: ConfigType,\\n add_entities: AddEntitiesCallback,\\n discovery_info: DiscoveryInfoType | None = None,\\n) -> None:\\n lights = []\\n for channel, device_config in config[CONF_DEVICES].items():\\n device = {}\\n device[\\\"name\\\"] = device_config[CONF_NAME]\\n device[\\\"dimmable\\\"] = device_config[\\\"dimmable\\\"]\\n device[\\\"channel\\\"] = channel\\n device[\\\"driver\\\"] = config[CONF_DRIVER]\\n device[\\\"host\\\"] = config[CONF_HOST]\\n device[\\\"port\\\"] = config[CONF_PORT]\\n lights.append(FutureNowLight(device))\\n\\n add_entities(lights, True)\",\n \"def setup_platform(hass, config, add_devices, discovery_info=None):\\n name = config.get(CONF_NAME)\\n ip_addr = config.get(CONF_HOST)\\n mac_addr = binascii.unhexlify(config.get(CONF_MAC).encode().replace(b':', b''))\\n target_temp_default = config.get(CONF_TARGET_TEMP)\\n target_temp_step = config.get(CONF_TARGET_TEMP_STEP)\\n operation_list = DEFAULT_OPERATION_LIST\\n \\n import broadlink\\n \\n broadlink_device = broadlink.hysen((ip_addr, 80), mac_addr, None)\\n broadlink_device.timeout = config.get(CONF_TIMEOUT)\\n\\n try:\\n broadlink_device.auth()\\n add_devices([\\n BroadlinkHysenClimate(hass, name, broadlink_device, target_temp_default, target_temp_step, operation_list)\\n ])\\n except socket.timeout:\\n _LOGGER.error(\\\"Failed to connect to Broadlink Hysen Device IP:%s\\\",ip_addr)\",\n \"def get_devices(self):\\n return get_devices(self.api_key)\",\n \"def setup_devices(self, devices):\\n \\n self.devices = devices\\n \\n barrier = ReusableBarrier(len(devices))\\n lock = Lock()\\n aux_dict = {}\\n\\n for device in devices:\\n device.barrier = barrier\\n device.global_lock = lock\\n for location in device.sensor_data: \\n if location not in aux_dict:\\n aux_dict[location] = Semaphore() \\n \\n for device in devices:\\n device.device_semaphores = aux_dict\\n\\n self.setup_master_thread()\",\n \"def addExtraDevices(self):\\n \\n # These tables were extracted from\\n # pirates/src/piratesgui/GameOptions.py.\\n \\n ati_device_list = [ \\n [\\\"ATI MOBILITY/RADEON X700\\\", 0x5653],\\n [1, \\\"Radeon X1950 XTX Uber - Limited Edition\\\", 0x7248],\\n [1, \\\"Radeon X1950 XTX Uber - Limited Edition Secondary\\\", 0x7268],\\n [1, \\\"Radeon X800 CrossFire Edition\\\", 0x554D],\\n [1, \\\"Radeon X800 CrossFire Edition Secondary\\\", 0x556D],\\n [1, \\\"Radeon X850 CrossFire Edition\\\", 0x5D52],\\n [1, \\\"Radeon X850 CrossFire Edition Secondary\\\", 0x5D72],\\n [\\\"Radeon X550/X700 Series\\\", 0x564F],\\n [\\\"ATI FireGL T2\\\", 0x4154],\\n [\\\"ATI FireGL T2 Secondary\\\", 0x4174],\\n [\\\"ATI FireGL V3100\\\", 0x5B64],\\n [\\\"ATI FireGL V3100 Secondary\\\", 0x5B74],\\n [\\\"ATI FireGL V3200\\\", 0x3E54],\\n [\\\"ATI FireGL V3200 Secondary\\\", 0x3E74],\\n [\\\"ATI FireGL V3300\\\", 0x7152],\\n [\\\"ATI FireGL V3300 Secondary\\\", 0x7172],\\n [\\\"ATI FireGL V3350\\\", 0x7153],\\n [\\\"ATI FireGL V3350 Secondary\\\", 0x7173],\\n [\\\"ATI FireGL V3400\\\", 0x71D2],\\n [\\\"ATI FireGL V3400 Secondary\\\", 0x71F2],\\n [\\\"ATI FireGL V5000\\\", 0x5E48],\\n [\\\"ATI FireGL V5000 Secondary\\\", 0x5E68],\\n [\\\"ATI FireGL V5100\\\", 0x5551],\\n [\\\"ATI FireGL V5100 Secondary\\\", 0x5571],\\n [\\\"ATI FireGL V5200\\\", 0x71DA],\\n [\\\"ATI FireGL V5200 Secondary\\\", 0x71FA],\\n [\\\"ATI FireGL V5300\\\", 0x7105],\\n [\\\"ATI FireGL V5300 Secondary\\\", 0x7125],\\n [\\\"ATI FireGL V7100\\\", 0x5550],\\n [\\\"ATI FireGL V7100 Secondary\\\", 0x5570],\\n [\\\"ATI FireGL V7200\\\", 0x5D50],\\n [\\\"ATI FireGL V7200 \\\", 0x7104],\\n [\\\"ATI FireGL V7200 Secondary\\\", 0x5D70],\\n [\\\"ATI FireGL V7200 Secondary \\\", 0x7124],\\n [\\\"ATI FireGL V7300\\\", 0x710E],\\n [\\\"ATI FireGL V7300 Secondary\\\", 0x712E],\\n [\\\"ATI FireGL V7350\\\", 0x710F],\\n [\\\"ATI FireGL V7350 Secondary\\\", 0x712F],\\n [\\\"ATI FireGL X1\\\", 0x4E47],\\n [\\\"ATI FireGL X1 Secondary\\\", 0x4E67],\\n [\\\"ATI FireGL X2-256/X2-256t\\\", 0x4E4B],\\n [\\\"ATI FireGL X2-256/X2-256t Secondary\\\", 0x4E6B],\\n [\\\"ATI FireGL X3-256\\\", 0x4A4D],\\n [\\\"ATI FireGL X3-256 Secondary\\\", 0x4A6D],\\n [\\\"ATI FireGL Z1\\\", 0x4147],\\n [\\\"ATI FireGL Z1 Secondary\\\", 0x4167],\\n [\\\"ATI FireMV 2200\\\", 0x5B65],\\n [\\\"ATI FireMV 2200 Secondary\\\", 0x5B75],\\n [\\\"ATI FireMV 2250\\\", 0x719B],\\n [\\\"ATI FireMV 2250 Secondary\\\", 0x71BB],\\n [\\\"ATI FireMV 2400\\\", 0x3151],\\n [\\\"ATI FireMV 2400 Secondary\\\", 0x3171],\\n [\\\"ATI FireStream 2U\\\", 0x724E],\\n [\\\"ATI FireStream 2U Secondary\\\", 0x726E],\\n [\\\"ATI MOBILITY FIRE GL 7800\\\", 0x4C58],\\n [\\\"ATI MOBILITY FIRE GL T2/T2e\\\", 0x4E54],\\n [\\\"ATI MOBILITY FireGL V3100\\\", 0x5464],\\n [\\\"ATI MOBILITY FireGL V3200\\\", 0x3154],\\n [\\\"ATI MOBILITY FireGL V5000\\\", 0x564A],\\n [\\\"ATI MOBILITY FireGL V5000 \\\", 0x564B],\\n [\\\"ATI MOBILITY FireGL V5100\\\", 0x5D49],\\n [\\\"ATI MOBILITY FireGL V5200\\\", 0x71C4],\\n [\\\"ATI MOBILITY FireGL V5250\\\", 0x71D4],\\n [\\\"ATI MOBILITY FireGL V7100\\\", 0x7106],\\n [\\\"ATI MOBILITY FireGL V7200\\\", 0x7103],\\n [\\\"ATI MOBILITY RADEON\\\", 0x4C59],\\n [\\\"ATI MOBILITY RADEON 7500\\\", 0x4C57],\\n [\\\"ATI MOBILITY RADEON 9500\\\", 0x4E52],\\n [\\\"ATI MOBILITY RADEON 9550\\\", 0x4E56],\\n [\\\"ATI MOBILITY RADEON 9600/9700 Series\\\", 0x4E50],\\n [\\\"ATI MOBILITY RADEON 9800\\\", 0x4A4E],\\n [\\\"ATI Mobility Radeon HD 2300\\\", 0x7210],\\n [\\\"ATI Mobility Radeon HD 2300 \\\", 0x7211],\\n [\\\"ATI Mobility Radeon HD 2400\\\", 0x94C9],\\n [\\\"ATI Mobility Radeon HD 2400 XT\\\", 0x94C8],\\n [1, \\\"ATI Mobility Radeon HD 2600\\\", 0x9581],\\n [1, \\\"ATI Mobility Radeon HD 2600 XT\\\", 0x9583],\\n [\\\"ATI Mobility Radeon X1300\\\", 0x714A],\\n [\\\"ATI Mobility Radeon X1300 \\\", 0x7149],\\n [\\\"ATI Mobility Radeon X1300 \\\", 0x714B],\\n [\\\"ATI Mobility Radeon X1300 \\\", 0x714C],\\n [\\\"ATI Mobility Radeon X1350\\\", 0x718B],\\n [\\\"ATI Mobility Radeon X1350 \\\", 0x718C],\\n [\\\"ATI Mobility Radeon X1350 \\\", 0x7196],\\n [\\\"ATI Mobility Radeon X1400\\\", 0x7145],\\n [\\\"ATI Mobility Radeon X1450\\\", 0x7186],\\n [\\\"ATI Mobility Radeon X1450 \\\", 0x718D],\\n [\\\"ATI Mobility Radeon X1600\\\", 0x71C5],\\n [\\\"ATI Mobility Radeon X1700\\\", 0x71D5],\\n [\\\"ATI Mobility Radeon X1700 \\\", 0x71DE],\\n [\\\"ATI Mobility Radeon X1700 XT\\\", 0x71D6],\\n [1, \\\"ATI Mobility Radeon X1800\\\", 0x7102],\\n [1, \\\"ATI Mobility Radeon X1800 XT\\\", 0x7101],\\n [1, \\\"ATI Mobility Radeon X1900\\\", 0x7284],\\n [1, \\\"ATI Mobility Radeon X2300\\\", 0x718A],\\n [1, \\\"ATI Mobility Radeon X2300 \\\", 0x7188],\\n [\\\"ATI MOBILITY RADEON X300\\\", 0x5461],\\n [\\\"ATI MOBILITY RADEON X300 \\\", 0x5460],\\n [\\\"ATI MOBILITY RADEON X300 \\\", 0x3152],\\n [\\\"ATI MOBILITY RADEON X600\\\", 0x3150],\\n [\\\"ATI MOBILITY RADEON X600 SE\\\", 0x5462],\\n [\\\"ATI MOBILITY RADEON X700\\\", 0x5652],\\n [\\\"ATI MOBILITY RADEON X700 \\\", 0x5653],\\n [\\\"ATI MOBILITY RADEON X700 Secondary\\\", 0x5673],\\n [1, \\\"ATI MOBILITY RADEON X800\\\", 0x5D4A],\\n [1, \\\"ATI MOBILITY RADEON X800 XT\\\", 0x5D48],\\n [\\\"ATI Radeon 9550/X1050 Series\\\", 0x4153],\\n [\\\"ATI Radeon 9550/X1050 Series Secondary\\\", 0x4173],\\n [\\\"ATI RADEON 9600 Series\\\", 0x4150],\\n [\\\"ATI RADEON 9600 Series \\\", 0x4E51],\\n [\\\"ATI RADEON 9600 Series \\\", 0x4151],\\n [\\\"ATI RADEON 9600 Series \\\", 0x4155],\\n [\\\"ATI RADEON 9600 Series \\\", 0x4152],\\n [\\\"ATI RADEON 9600 Series Secondary\\\", 0x4E71],\\n [\\\"ATI RADEON 9600 Series Secondary \\\", 0x4171],\\n [\\\"ATI RADEON 9600 Series Secondary \\\", 0x4170],\\n [\\\"ATI RADEON 9600 Series Secondary \\\", 0x4175],\\n [\\\"ATI RADEON 9600 Series Secondary \\\", 0x4172],\\n [1, \\\"ATI Radeon HD 2900 XT\\\", 0x9402],\\n [1, \\\"ATI Radeon HD 2900 XT \\\", 0x9403],\\n [1, \\\"ATI Radeon HD 2900 XT \\\", 0x9400],\\n [1, \\\"ATI Radeon HD 2900 XT \\\", 0x9401],\\n [\\\"ATI Radeon X1200 Series\\\", 0x791E],\\n [\\\"ATI Radeon X1200 Series \\\", 0x791F],\\n [1, \\\"ATI Radeon X1950 GT\\\", 0x7288],\\n [1, \\\"ATI Radeon X1950 GT Secondary\\\", 0x72A8],\\n [1, \\\"ATI RADEON X800 GT\\\", 0x554E],\\n [1, \\\"ATI RADEON X800 GT Secondary\\\", 0x556E],\\n [1, \\\"ATI RADEON X800 XL\\\", 0x554D],\\n [1, \\\"ATI RADEON X800 XL Secondary\\\", 0x556D],\\n [1, \\\"ATI RADEON X850 PRO\\\", 0x4B4B],\\n [1, \\\"ATI RADEON X850 PRO Secondary\\\", 0x4B6B],\\n [1, \\\"ATI RADEON X850 SE\\\", 0x4B4A],\\n [1, \\\"ATI RADEON X850 SE Secondary\\\", 0x4B6A],\\n [1, \\\"ATI RADEON X850 XT\\\", 0x4B49],\\n [1, \\\"ATI RADEON X850 XT Platinum Edition\\\", 0x4B4C],\\n [1, \\\"ATI RADEON X850 XT Platinum Edition Secondary\\\", 0x4B6C],\\n [1, \\\"ATI RADEON X850 XT Secondary\\\", 0x4B69],\\n [\\\"ATI Radeon Xpress 1200 Series\\\", 0x793F],\\n [\\\"ATI Radeon Xpress 1200 Series \\\", 0x7941],\\n [\\\"ATI Radeon Xpress 1200 Series \\\", 0x7942],\\n [\\\"ATI Radeon Xpress Series\\\", 0x5A61],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5A63],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5A62],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5A41],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5A43],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5A42],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5954],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5854],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5955],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5974],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5874],\\n [\\\"ATI Radeon Xpress Series \\\", 0x5975],\\n [\\\"Radeon 9500\\\", 0x4144],\\n [\\\"Radeon 9500 \\\", 0x4149],\\n [\\\"Radeon 9500 PRO / 9700\\\", 0x4E45],\\n [\\\"Radeon 9500 PRO / 9700 Secondary\\\", 0x4E65],\\n [\\\"Radeon 9500 Secondary\\\", 0x4164],\\n [\\\"Radeon 9500 Secondary \\\", 0x4169],\\n [\\\"Radeon 9600 TX\\\", 0x4E46],\\n [\\\"Radeon 9600 TX Secondary\\\", 0x4E66],\\n [\\\"Radeon 9600TX\\\", 0x4146],\\n [\\\"Radeon 9600TX Secondary\\\", 0x4166],\\n [\\\"Radeon 9700 PRO\\\", 0x4E44],\\n [\\\"Radeon 9700 PRO Secondary\\\", 0x4E64],\\n [\\\"Radeon 9800\\\", 0x4E49],\\n [\\\"Radeon 9800 PRO\\\", 0x4E48],\\n [\\\"Radeon 9800 PRO Secondary\\\", 0x4E68],\\n [\\\"Radeon 9800 SE\\\", 0x4148],\\n [\\\"Radeon 9800 SE Secondary\\\", 0x4168],\\n [\\\"Radeon 9800 Secondary\\\", 0x4E69],\\n [\\\"Radeon 9800 XT\\\", 0x4E4A],\\n [\\\"Radeon 9800 XT Secondary\\\", 0x4E6A],\\n [\\\"Radeon X1300 / X1550 Series\\\", 0x7146],\\n [\\\"Radeon X1300 / X1550 Series Secondary\\\", 0x7166],\\n [\\\"Radeon X1300 Series\\\", 0x714E],\\n [\\\"Radeon X1300 Series \\\", 0x715E],\\n [\\\"Radeon X1300 Series \\\", 0x714D],\\n [\\\"Radeon X1300 Series \\\", 0x71C3],\\n [\\\"Radeon X1300 Series \\\", 0x718F],\\n [\\\"Radeon X1300 Series Secondary\\\", 0x716E],\\n [\\\"Radeon X1300 Series Secondary \\\", 0x717E],\\n [\\\"Radeon X1300 Series Secondary \\\", 0x716D],\\n [\\\"Radeon X1300 Series Secondary \\\", 0x71E3],\\n [\\\"Radeon X1300 Series Secondary \\\", 0x71AF],\\n [\\\"Radeon X1300/X1550 Series\\\", 0x7142],\\n [\\\"Radeon X1300/X1550 Series \\\", 0x7180],\\n [\\\"Radeon X1300/X1550 Series \\\", 0x7183],\\n [\\\"Radeon X1300/X1550 Series \\\", 0x7187],\\n [\\\"Radeon X1300/X1550 Series Secondary\\\", 0x7162],\\n [\\\"Radeon X1300/X1550 Series Secondary \\\", 0x71A0],\\n [\\\"Radeon X1300/X1550 Series Secondary \\\", 0x71A3],\\n [\\\"Radeon X1300/X1550 Series Secondary \\\", 0x71A7],\\n [\\\"Radeon X1550 64-bit\\\", 0x7147],\\n [\\\"Radeon X1550 64-bit \\\", 0x715F],\\n [\\\"Radeon X1550 64-bit \\\", 0x719F],\\n [\\\"Radeon X1550 64-bit Secondary\\\", 0x7167],\\n [\\\"Radeon X1550 64-bit Secondary \\\", 0x717F],\\n [\\\"Radeon X1550 Series\\\", 0x7143],\\n [\\\"Radeon X1550 Series \\\", 0x7193],\\n [\\\"Radeon X1550 Series Secondary\\\", 0x7163],\\n [\\\"Radeon X1550 Series Secondary \\\", 0x71B3],\\n [\\\"Radeon X1600 Pro / Radeon X1300 XT\\\", 0x71CE],\\n [\\\"Radeon X1600 Pro / Radeon X1300 XT Secondary\\\", 0x71EE],\\n [\\\"Radeon X1600 Series\\\", 0x7140],\\n [\\\"Radeon X1600 Series \\\", 0x71C0],\\n [\\\"Radeon X1600 Series \\\", 0x71C2],\\n [\\\"Radeon X1600 Series \\\", 0x71C6],\\n [\\\"Radeon X1600 Series \\\", 0x7181],\\n [\\\"Radeon X1600 Series \\\", 0x71CD],\\n [\\\"Radeon X1600 Series Secondary\\\", 0x7160],\\n [\\\"Radeon X1600 Series Secondary \\\", 0x71E2],\\n [\\\"Radeon X1600 Series Secondary \\\", 0x71E6],\\n [\\\"Radeon X1600 Series Secondary \\\", 0x71A1],\\n [\\\"Radeon X1600 Series Secondary \\\", 0x71ED],\\n [\\\"Radeon X1600 Series Secondary \\\", 0x71E0],\\n [\\\"Radeon X1650 Series\\\", 0x71C1],\\n [\\\"Radeon X1650 Series \\\", 0x7293],\\n [\\\"Radeon X1650 Series \\\", 0x7291],\\n [\\\"Radeon X1650 Series \\\", 0x71C7],\\n [\\\"Radeon X1650 Series Secondary\\\", 0x71E1],\\n [\\\"Radeon X1650 Series Secondary \\\", 0x72B3],\\n [\\\"Radeon X1650 Series Secondary \\\", 0x72B1],\\n [\\\"Radeon X1650 Series Secondary \\\", 0x71E7],\\n [1, \\\"Radeon X1800 Series\\\", 0x7100],\\n [1, \\\"Radeon X1800 Series \\\", 0x7108],\\n [1, \\\"Radeon X1800 Series \\\", 0x7109],\\n [1, \\\"Radeon X1800 Series \\\", 0x710A],\\n [1, \\\"Radeon X1800 Series \\\", 0x710B],\\n [1, \\\"Radeon X1800 Series \\\", 0x710C],\\n [1, \\\"Radeon X1800 Series Secondary\\\", 0x7120],\\n [1, \\\"Radeon X1800 Series Secondary \\\", 0x7128],\\n [1, \\\"Radeon X1800 Series Secondary \\\", 0x7129],\\n [1, \\\"Radeon X1800 Series Secondary \\\", 0x712A],\\n [1, \\\"Radeon X1800 Series Secondary \\\", 0x712B],\\n [1, \\\"Radeon X1800 Series Secondary \\\", 0x712C],\\n [1, \\\"Radeon X1900 Series\\\", 0x7243],\\n [1, \\\"Radeon X1900 Series \\\", 0x7245],\\n [1, \\\"Radeon X1900 Series \\\", 0x7246],\\n [1, \\\"Radeon X1900 Series \\\", 0x7247],\\n [1, \\\"Radeon X1900 Series \\\", 0x7248],\\n [1, \\\"Radeon X1900 Series \\\", 0x7249],\\n [1, \\\"Radeon X1900 Series \\\", 0x724A],\\n [1, \\\"Radeon X1900 Series \\\", 0x724B],\\n [1, \\\"Radeon X1900 Series \\\", 0x724C],\\n [1, \\\"Radeon X1900 Series \\\", 0x724D],\\n [1, \\\"Radeon X1900 Series \\\", 0x724F],\\n [1, \\\"Radeon X1900 Series Secondary\\\", 0x7263],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x7265],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x7266],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x7267],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x7268],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x7269],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x726A],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x726B],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x726C],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x726D],\\n [1, \\\"Radeon X1900 Series Secondary \\\", 0x726F],\\n [1, \\\"Radeon X1950 Series\\\", 0x7280],\\n [1, \\\"Radeon X1950 Series \\\", 0x7240],\\n [1, \\\"Radeon X1950 Series \\\", 0x7244],\\n [1, \\\"Radeon X1950 Series Secondary\\\", 0x72A0],\\n [1, \\\"Radeon X1950 Series Secondary \\\", 0x7260],\\n [1, \\\"Radeon X1950 Series Secondary \\\", 0x7264],\\n [\\\"Radeon X300/X550/X1050 Series\\\", 0x5B60],\\n [\\\"Radeon X300/X550/X1050 Series \\\", 0x5B63],\\n [\\\"Radeon X300/X550/X1050 Series Secondary\\\", 0x5B73],\\n [\\\"Radeon X300/X550/X1050 Series Secondary \\\", 0x5B70],\\n [\\\"Radeon X550/X700 Series \\\", 0x5657],\\n [\\\"Radeon X550/X700 Series Secondary\\\", 0x5677],\\n [\\\"Radeon X600 Series\\\", 0x5B62],\\n [\\\"Radeon X600 Series Secondary\\\", 0x5B72],\\n [\\\"Radeon X600/X550 Series\\\", 0x3E50],\\n [\\\"Radeon X600/X550 Series Secondary\\\", 0x3E70],\\n [\\\"Radeon X700\\\", 0x5E4D],\\n [\\\"Radeon X700 PRO\\\", 0x5E4B],\\n [\\\"Radeon X700 PRO Secondary\\\", 0x5E6B],\\n [\\\"Radeon X700 SE\\\", 0x5E4C],\\n [\\\"Radeon X700 SE Secondary\\\", 0x5E6C],\\n [\\\"Radeon X700 Secondary\\\", 0x5E6D],\\n [\\\"Radeon X700 XT\\\", 0x5E4A],\\n [\\\"Radeon X700 XT Secondary\\\", 0x5E6A],\\n [\\\"Radeon X700/X550 Series\\\", 0x5E4F],\\n [\\\"Radeon X700/X550 Series Secondary\\\", 0x5E6F],\\n [1, \\\"Radeon X800 GT\\\", 0x554B],\\n [1, \\\"Radeon X800 GT Secondary\\\", 0x556B],\\n [1, \\\"Radeon X800 GTO\\\", 0x5549],\\n [1, \\\"Radeon X800 GTO \\\", 0x554F],\\n [1, \\\"Radeon X800 GTO \\\", 0x5D4F],\\n [1, \\\"Radeon X800 GTO Secondary\\\", 0x5569],\\n [1, \\\"Radeon X800 GTO Secondary \\\", 0x556F],\\n [1, \\\"Radeon X800 GTO Secondary \\\", 0x5D6F],\\n [1, \\\"Radeon X800 PRO\\\", 0x4A49],\\n [1, \\\"Radeon X800 PRO Secondary\\\", 0x4A69],\\n [1, \\\"Radeon X800 SE\\\", 0x4A4F],\\n [1, \\\"Radeon X800 SE Secondary\\\", 0x4A6F],\\n [1, \\\"Radeon X800 Series\\\", 0x4A48],\\n [1, \\\"Radeon X800 Series \\\", 0x4A4A],\\n [1, \\\"Radeon X800 Series \\\", 0x4A4C],\\n [1, \\\"Radeon X800 Series \\\", 0x5548],\\n [1, \\\"Radeon X800 Series Secondary\\\", 0x4A68],\\n [1, \\\"Radeon X800 Series Secondary \\\", 0x4A6A],\\n [1, \\\"Radeon X800 Series Secondary \\\", 0x4A6C],\\n [1, \\\"Radeon X800 Series Secondary \\\", 0x5568],\\n [1, \\\"Radeon X800 VE\\\", 0x4A54],\\n [1, \\\"Radeon X800 VE Secondary\\\", 0x4A74],\\n [1, \\\"Radeon X800 XT\\\", 0x4A4B],\\n [1, \\\"Radeon X800 XT \\\", 0x5D57],\\n [1, \\\"Radeon X800 XT Platinum Edition\\\", 0x4A50],\\n [1, \\\"Radeon X800 XT Platinum Edition \\\", 0x554A],\\n [1, \\\"Radeon X800 XT Platinum Edition Secondary\\\", 0x4A70],\\n [1, \\\"Radeon X800 XT Platinum Edition Secondary \\\", 0x556A],\\n [1, \\\"Radeon X800 XT Secondary\\\", 0x4A6B],\\n [1, \\\"Radeon X800 XT Secondary \\\", 0x5D77],\\n [1, \\\"Radeon X850 XT\\\", 0x5D52],\\n [1, \\\"Radeon X850 XT Platinum Edition\\\", 0x5D4D],\\n [1, \\\"Radeon X850 XT Platinum Edition Secondary\\\", 0x5D6D],\\n [1, \\\"Radeon X850 XT Secondary\\\", 0x5D72],\\n ]\\n vendorId = 0x1002\\n for entry in ati_device_list:\\n if len(entry) == 3:\\n flag, deviceName, deviceId = entry\\n else:\\n deviceName, deviceId = entry\\n self.devices[(vendorId, deviceId)] = deviceName.strip()\\n \\n nvidia_device_list = [\\n [0x014F, \\\"GeForce 6200\\\"],\\n [0x00F3, \\\"GeForce 6200\\\"],\\n [0x0221, \\\"GeForce 6200\\\"],\\n [0x0163, \\\"GeForce 6200 LE\\\"],\\n [0x0162, \\\"GeForce 6200SE TurboCache(TM)\\\"],\\n [0x0161, \\\"GeForce 6200 TurboCache(TM)\\\"],\\n [0x0162, \\\"GeForce 6200SE TurboCache(TM)\\\"],\\n [0x0160, \\\"GeForce 6500\\\"],\\n [1, 0x0141, \\\"GeForce 6600\\\"],\\n [1, 0x00F2, \\\"GeForce 6600\\\"],\\n [1, 0x0140, \\\"GeForce 6600 GT\\\"],\\n [1, 0x00F1, \\\"GeForce 6600 GT\\\"],\\n [1, 0x0142, \\\"GeForce 6600 LE\\\"],\\n [1, 0x00F4, \\\"GeForce 6600 LE\\\"],\\n [1, 0x0143, \\\"GeForce 6600 VE\\\"],\\n [1, 0x0147, \\\"GeForce 6700 XL\\\"],\\n [1, 0x0041, \\\"GeForce 6800\\\"],\\n [1, 0x00C1, \\\"GeForce 6800\\\"],\\n [1, 0x0047, \\\"GeForce 6800 GS\\\"],\\n [1, 0x00F6, \\\"GeForce 6800 GS\\\"],\\n [1, 0x00C0, \\\"GeForce 6800 GS\\\"],\\n [1, 0x0045, \\\"GeForce 6800 GT\\\"],\\n [1, 0x00F9, \\\"GeForce 6800 Series GPU\\\"],\\n [1, 0x00C2, \\\"GeForce 6800 LE\\\"],\\n [1, 0x0040, \\\"GeForce 6800 Ultra\\\"],\\n [1, 0x00F9, \\\"GeForce 6800 Series GPU\\\"],\\n [1, 0x0043, \\\"GeForce 6800 XE\\\"],\\n [1, 0x0048, \\\"GeForce 6800 XT\\\"],\\n [1, 0x0218, \\\"GeForce 6800 XT\\\"],\\n [1, 0x00C3, \\\"GeForce 6800 XT\\\"],\\n [0x01DF, \\\"GeForce 7300 GS\\\"],\\n [0x0393, \\\"GeForce 7300 GT\\\"],\\n [0x01D1, \\\"GeForce 7300 LE\\\"],\\n [0x01D3, \\\"GeForce 7300 SE\\\"],\\n [0x01DD, \\\"GeForce 7500 LE\\\"],\\n [1, 0x0392, \\\"GeForce 7600 GS\\\"],\\n [1, 0x0392, \\\"GeForce 7600 GS\\\"],\\n [1, 0x02E1, \\\"GeForce 7600 GS\\\"],\\n [1, 0x0391, \\\"GeForce 7600 GT\\\"],\\n [1, 0x0394, \\\"GeForce 7600 LE\\\"],\\n [1, 0x00F5, \\\"GeForce 7800 GS\\\"],\\n [1, 0x0092, \\\"GeForce 7800 GT\\\"],\\n [1, 0x0091, \\\"GeForce 7800 GTX\\\"],\\n [1, 0x0291, \\\"GeForce 7900 GT/GTO\\\"],\\n [1, 0x0290, \\\"GeForce 7900 GTX\\\"],\\n [1, 0x0293, \\\"GeForce 7900 GX2\\\"],\\n [1, 0x0294, \\\"GeForce 7950 GX2\\\"],\\n [0x0322, \\\"GeForce FX 5200\\\"],\\n [0x0321, \\\"GeForce FX 5200 Ultra\\\"],\\n [0x0323, \\\"GeForce FX 5200LE\\\"],\\n [0x0326, \\\"GeForce FX 5500\\\"],\\n [0x0326, \\\"GeForce FX 5500\\\"],\\n [0x0312, \\\"GeForce FX 5600\\\"],\\n [0x0311, \\\"GeForce FX 5600 Ultra\\\"],\\n [0x0314, \\\"GeForce FX 5600XT\\\"],\\n [0x0342, \\\"GeForce FX 5700\\\"],\\n [0x0341, \\\"GeForce FX 5700 Ultra\\\"],\\n [0x0343, \\\"GeForce FX 5700LE\\\"],\\n [0x0344, \\\"GeForce FX 5700VE\\\"],\\n [0x0302, \\\"GeForce FX 5800\\\"],\\n [0x0301, \\\"GeForce FX 5800 Ultra\\\"],\\n [0x0331, \\\"GeForce FX 5900\\\"],\\n [0x0330, \\\"GeForce FX 5900 Ultra\\\"],\\n [0x0333, \\\"GeForce FX 5950 Ultra\\\"],\\n [0x0324, \\\"GeForce FX Go5200 64M\\\"],\\n [0x031A, \\\"GeForce FX Go5600\\\"],\\n [0x0347, \\\"GeForce FX Go5700\\\"],\\n [0x0167, \\\"GeForce Go 6200/6400\\\"],\\n [0x0168, \\\"GeForce Go 6200/6400\\\"],\\n [1, 0x0148, \\\"GeForce Go 6600\\\"],\\n [1, 0x00c8, \\\"GeForce Go 6800\\\"],\\n [1, 0x00c9, \\\"GeForce Go 6800 Ultra\\\"],\\n [1, 0x0098, \\\"GeForce Go 7800\\\"],\\n [1, 0x0099, \\\"GeForce Go 7800 GTX\\\"],\\n [1, 0x0298, \\\"GeForce Go 7900 GS\\\"],\\n [1, 0x0299, \\\"GeForce Go 7900 GTX\\\"],\\n [0x0185, \\\"GeForce MX 4000\\\"],\\n [0x00FA, \\\"GeForce PCX 5750\\\"],\\n [0x00FB, \\\"GeForce PCX 5900\\\"],\\n [0x0110, \\\"GeForce2 MX/MX 400\\\"],\\n [0x0111, \\\"GeForce2 MX200\\\"],\\n [0x0110, \\\"GeForce2 MX/MX 400\\\"],\\n [0x0200, \\\"GeForce3\\\"],\\n [0x0201, \\\"GeForce3 Ti200\\\"],\\n [0x0202, \\\"GeForce3 Ti500\\\"],\\n [0x0172, \\\"GeForce4 MX 420\\\"],\\n [0x0171, \\\"GeForce4 MX 440\\\"],\\n [0x0181, \\\"GeForce4 MX 440 with AGP8X\\\"],\\n [0x0173, \\\"GeForce4 MX 440-SE\\\"],\\n [0x0170, \\\"GeForce4 MX 460\\\"],\\n [0x0253, \\\"GeForce4 Ti 4200\\\"],\\n [0x0281, \\\"GeForce4 Ti 4200 with AGP8X\\\"],\\n [0x0251, \\\"GeForce4 Ti 4400\\\"],\\n [0x0250, \\\"GeForce4 Ti 4600\\\"],\\n [0x0280, \\\"GeForce4 Ti 4800\\\"],\\n [0x0282, \\\"GeForce4 Ti 4800SE\\\"],\\n [0x0203, \\\"Quadro DCC\\\"],\\n [0x0309, \\\"Quadro FX 1000\\\"],\\n [0x034E, \\\"Quadro FX 1100\\\"],\\n [0x00FE, \\\"Quadro FX 1300\\\"],\\n [0x00CE, \\\"Quadro FX 1400\\\"],\\n [0x0308, \\\"Quadro FX 2000\\\"],\\n [0x0338, \\\"Quadro FX 3000\\\"],\\n [0x00FD, \\\"Quadro PCI-E Series\\\"],\\n [1, 0x00F8, \\\"Quadro FX 3400/4400\\\"],\\n [1, 0x00CD, \\\"Quadro FX 3450/4000 SDI\\\"],\\n [1, 0x004E, \\\"Quadro FX 4000\\\"],\\n [1, 0x00CD, \\\"Quadro FX 3450/4000 SDI\\\"],\\n [1, 0x00F8, \\\"Quadro FX 3400/4400\\\"],\\n [1, 0x009D, \\\"Quadro FX 4500\\\"],\\n [1, 0x029F, \\\"Quadro FX 4500 X2\\\"],\\n [0x032B, \\\"Quadro FX 500/FX 600\\\"],\\n [0x014E, \\\"Quadro FX 540\\\"],\\n [0x014C, \\\"Quadro FX 540 MXM\\\"],\\n [0x032B, \\\"Quadro FX 500/FX 600\\\"],\\n [0X033F, \\\"Quadro FX 700\\\"],\\n [0x034C, \\\"Quadro FX Go1000\\\"],\\n [0x00CC, \\\"Quadro FX Go1400\\\"],\\n [0x031C, \\\"Quadro FX Go700\\\"],\\n [0x018A, \\\"Quadro NVS with AGP8X\\\"],\\n [0x032A, \\\"Quadro NVS 280 PCI\\\"],\\n [0x00FD, \\\"Quadro PCI-E Series\\\"],\\n [0x0165, \\\"Quadro NVS 285\\\"],\\n [0x017A, \\\"Quadro NVS\\\"],\\n [0x018A, \\\"Quadro NVS with AGP8X\\\"],\\n [0x0113, \\\"Quadro2 MXR/EX\\\"],\\n [0x017A, \\\"Quadro NVS\\\"],\\n [0x018B, \\\"Quadro4 380 XGL\\\"],\\n [0x0178, \\\"Quadro4 550 XGL\\\"],\\n [0x0188, \\\"Quadro4 580 XGL\\\"],\\n [0x025B, \\\"Quadro4 700 XGL\\\"],\\n [0x0259, \\\"Quadro4 750 XGL\\\"],\\n [0x0258, \\\"Quadro4 900 XGL\\\"],\\n [0x0288, \\\"Quadro4 980 XGL\\\"],\\n [0x028C, \\\"Quadro4 Go700\\\"],\\n [1, 0x0295, \\\"NVIDIA GeForce 7950 GT\\\"],\\n [0x03D0, \\\"NVIDIA GeForce 6100 nForce 430\\\"],\\n [0x03D1, \\\"NVIDIA GeForce 6100 nForce 405\\\"],\\n [0x03D2, \\\"NVIDIA GeForce 6100 nForce 400\\\"],\\n [0x0241, \\\"NVIDIA GeForce 6150 LE\\\"],\\n [0x0242, \\\"NVIDIA GeForce 6100\\\"],\\n [0x0245, \\\"NVIDIA Quadro NVS 210S / NVIDIA GeForce 6150LE\\\"],\\n [1, 0x029C, \\\"NVIDIA Quadro FX 5500\\\"],\\n [1, 0x0191, \\\"NVIDIA GeForce 8800 GTX\\\"],\\n [1, 0x0193, \\\"NVIDIA GeForce 8800 GTS\\\"],\\n [1, 0x0400, \\\"NVIDIA GeForce 8600 GTS\\\"],\\n [1, 0x0402, \\\"NVIDIA GeForce 8600 GT\\\"],\\n [0x0421, \\\"NVIDIA GeForce 8500 GT\\\"],\\n [0x0422, \\\"NVIDIA GeForce 8400 GS\\\"],\\n [0x0423, \\\"NVIDIA GeForce 8300 GS\\\"],\\n ]\\n vendorId = 0x10de\\n for entry in nvidia_device_list:\\n if len(entry) == 3:\\n flag, deviceId, deviceName = entry\\n else:\\n deviceId, deviceName = entry\\n self.devices[(vendorId, deviceId)] = deviceName.strip()\",\n \"def setup_platform(hass, config, add_devices, discovery_info=None):\\n resource = config.get(CONF_RESOURCE)\\n pin = config.get(CONF_PIN)\\n\\n if None in (resource, pin):\\n _LOGGER.error('Not all required config keys present: %s',\\n ', '.join((CONF_RESOURCE, CONF_PIN)))\\n return False\\n\\n try:\\n response = requests.get(resource, timeout=10).json()\\n except requests.exceptions.MissingSchema:\\n _LOGGER.error('Missing resource or schema in configuration. '\\n 'Add http:// to your URL.')\\n return False\\n except requests.exceptions.ConnectionError:\\n _LOGGER.error('No route to device at %s. '\\n 'Please check the IP address in the configuration file.',\\n resource)\\n return False\\n\\n arest = ArestData(resource, pin)\\n\\n add_devices([ArestBinarySensor(arest,\\n resource,\\n config.get('name', response['name']),\\n pin)])\",\n \"async def async_setup_entry(\\n hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\\n):\\n data = hass.data[DOMAIN][entry.entry_id]\\n coordinator = data[\\\"coordinator\\\"]\\n\\n entities = [\\n Awning(device.deviceurl, coordinator)\\n for device in data[\\\"platforms\\\"].get(COVER)\\n if \\\"Awning\\\" in device.definition.qualified_name\\n ]\\n\\n entities += [\\n VerticalCover(device.deviceurl, coordinator)\\n for device in data[\\\"platforms\\\"].get(COVER)\\n if \\\"Awning\\\" not in device.definition.qualified_name\\n ]\\n\\n async_add_entities(entities)\\n\\n platform = entity_platform.current_platform.get()\\n platform.async_register_entity_service(\\n SERVICE_COVER_MY_POSITION, {}, \\\"async_my\\\", [SUPPORT_MY]\\n )\\n\\n platform.async_register_entity_service(\\n SERVICE_COVER_POSITION_LOW_SPEED,\\n {\\n vol.Required(ATTR_POSITION): vol.All(\\n vol.Coerce(int), vol.Range(min=0, max=100)\\n )\\n },\\n \\\"async_set_cover_position_low_speed\\\",\\n [SUPPORT_COVER_POSITION_LOW_SPEED],\\n )\",\n \"def get_list_devices(self, verbose=False):\\n # TODO: refresh region_names if more regions get devices available\\n self.backends = {}\\n region_names = ['us-west-1', 'us-east-1']\\n for region in region_names:\\n client = boto3.client(\\n 'braket',\\n region_name=region,\\n aws_access_key_id=self._credentials['AWS_ACCESS_KEY_ID'],\\n aws_secret_access_key=self._credentials['AWS_SECRET_KEY'],\\n )\\n filters = []\\n devicelist = client.search_devices(filters=filters)\\n for result in devicelist['devices']:\\n if result['deviceType'] not in ['QPU', 'SIMULATOR']:\\n continue\\n if result['deviceType'] == 'QPU':\\n device_capabilities = json.loads(\\n client.get_device(deviceArn=result['deviceArn'])['deviceCapabilities']\\n )\\n self.backends[result['deviceName']] = {\\n 'nq': device_capabilities['paradigm']['qubitCount'],\\n 'coupling_map': device_capabilities['paradigm']['connectivity']['connectivityGraph'],\\n 'version': device_capabilities['braketSchemaHeader']['version'],\\n 'location': region, # deviceCapabilities['service']['deviceLocation'],\\n 'deviceArn': result['deviceArn'],\\n 'deviceParameters': device_capabilities['deviceParameters']['properties']['braketSchemaHeader'][\\n 'const'\\n ],\\n 'deviceModelParameters': device_capabilities['deviceParameters']['definitions'][\\n 'GateModelParameters'\\n ]['properties']['braketSchemaHeader']['const'],\\n }\\n # Unfortunately the Capabilities schemas are not homogeneus for real devices and simulators\\n elif result['deviceType'] == 'SIMULATOR':\\n device_capabilities = json.loads(\\n client.get_device(deviceArn=result['deviceArn'])['deviceCapabilities']\\n )\\n self.backends[result['deviceName']] = {\\n 'nq': device_capabilities['paradigm']['qubitCount'],\\n 'coupling_map': {},\\n 'version': device_capabilities['braketSchemaHeader']['version'],\\n 'location': 'us-east-1',\\n 'deviceArn': result['deviceArn'],\\n 'deviceParameters': device_capabilities['deviceParameters']['properties']['braketSchemaHeader'][\\n 'const'\\n ],\\n 'deviceModelParameters': device_capabilities['deviceParameters']['definitions'][\\n 'GateModelParameters'\\n ]['properties']['braketSchemaHeader']['const'],\\n }\\n\\n if verbose:\\n print('- List of AWSBraket devices available:')\\n print(list(self.backends))\\n\\n return self.backends\",\n \"async def device_fixture(hass: HomeAssistant, ufp: MockUFPFixture):\\n\\n await init_entry(hass, ufp, [])\\n\\n device_registry = dr.async_get(hass)\\n\\n return list(device_registry.devices.values())[0]\",\n \"async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):\\n pockets = config.get(CONF_POCKETS)\\n\\n switch = pocketSwitch(pockets)\\n\\n async_add_entities([switch], True)\\n\\n for poc in pockets:\\n id = poc[CONF_POCKET_ID]\\n name = poc[CONF_POCKET_NAME]\\n path = poc[CONF_FILE_PATH]\\n\\n api = pocketAPI(path)\\n\\n if hass.config.is_allowed_path(path):\\n async_add_entities([FileSensor(id, name, path, api, switch)], True)\\n else:\\n _LOGGER.error(\\\"'%s' is not awhitelisted directory\\\", path)\",\n \"def _get_data(self):\\n devices = []\\n try:\\n if not self.router_client.login():\\n self.hass.states.set(f\\\"{DOMAIN}.statusmsg\\\", self.router_client.statusmsg)\\n _LOGGER.warning(\\\"Login failed: {0}:{1}@{2}\\\".format(self.router_client.username, self.router_client.password,self.router_client.host))\\n self.router_client.logout()\\n return devices\\n\\n devices_json = self.router_client.get_devices_response()\\n finally:\\n self.router_client.logout()\\n\\n self.hass.states.set(f\\\"{DOMAIN}.scanning\\\", devices_json != False)\\n\\n if devices_json != False:\\n for device in devices_json:\\n # _LOGGER.debug(\\\"Device: {0}\\\".format(device))\\n dev = Device(\\n device['HostName'].replace('未知设备', 'Unknown'),\\n device['IPAddress'],\\n device['MACAddress'],\\n device['Active'],\\n ICONS.get(device['IconType'])\\n )\\n # _LOGGER.debug(\\\"Device: {0}\\\".format(dev))\\n devices.append(dev)\\n return devices\\n else:\\n return []\",\n \"async def async_setup_entry(hass, config_entry, async_add_entities):\\n\\n data = hass.data[DOMAIN]\\n\\n if not data.client.cars:\\n LOGGER.info(\\\"No Cars found.\\\")\\n return\\n\\n sensor_list = []\\n\\n\\n for car in data.client.cars:\\n for key, value in sorted(DEVICE_TRACKER.items()):\\n# if value[5] is None or getattr(car.features, value[5]) is True:\\n device = MercedesMEDeviceTracker(\\n hass,\\n data,\\n key,\\n value[0],\\n car.finorvin,\\n value[1],\\n car.licenseplate,\\n value[2],\\n value[3],\\n value[4],\\n value[6])\\n if device.device_retrieval_status() in [\\\"VALID\\\", \\\"NOT_RECEIVED\\\"] :\\n sensor_list.append(device)\\n\\n\\n async_add_entities(sensor_list, True)\",\n \"def setup_platform(hass, config, add_devices, discovery_info=None):\\n station_id = config.get(CONF_STATION_ID)\\n name = config.get(CONF_NAME)\\n\\n logger = logging.getLogger(__name__)\\n probe = ZamgData(station_id=station_id, logger=logger)\\n\\n sensors = [ZamgSensor(probe, variable, name)\\n for variable in config[CONF_MONITORED_CONDITIONS]]\\n\\n add_devices(sensors, True)\",\n \"def device_get_config(self, filters={}):\\n return {}\",\n \"def get_devices():\\n devices, errors = [], []\\n\\n for path in hookenv.action_get('devices').split(' '):\\n path = path.strip()\\n if not os.path.isabs(path):\\n errors.append('{}: Not absolute path.'.format(path))\\n elif not os.path.exists(path):\\n errors.append('{}: Device does not exist.'.format(path))\\n else:\\n devices.append(path)\\n\\n if errors:\\n raise ZapDiskError(\\\", \\\".join(errors))\\n\\n return devices\",\n \"def find_hardware(self, device_info=None):\\n if os.name is not 'nt': # If not on a Windows system, just set up soundcard\\n self.setup_soundcard()\\n self.hardware.append('Soundcard')\\n self.out_samplefreq = 44100\\n else:\\n if 'NIDAQ' in self.required_hardware and self.setup_nidaq(device_info):\\n self.hardware.append('NIDAQ')\\n if 'RP21' in self.required_hardware and self.setup_RP21('c:\\\\pystartle\\\\startle.rco'):\\n self.hardware.append('RP21')\\n if 'PA5' in self.required_hardware and self.setup_PA5():\\n self.hardware.append('PA5')\\n if 'RZ5D' in self.required_hardware and self.setup_RZ5D():\\n self.hardware.append('RZ5D')\",\n \"def async_setup_scanner_platform(\\n hass: HomeAssistant,\\n config: ConfigType,\\n scanner: DeviceScanner,\\n async_see_device: Callable[..., Coroutine[None, None, None]],\\n platform: str,\\n) -> None:\\n interval = config.get(CONF_SCAN_INTERVAL, SCAN_INTERVAL)\\n update_lock = asyncio.Lock()\\n scanner.hass = hass\\n\\n # Initial scan of each mac we also tell about host name for config\\n seen: set[str] = set()\\n\\n async def async_device_tracker_scan(now: datetime | None) -> None:\\n \\\"\\\"\\\"Handle interval matches.\\\"\\\"\\\"\\n if update_lock.locked():\\n LOGGER.warning(\\n (\\n \\\"Updating device list from %s took longer than the scheduled \\\"\\n \\\"scan interval %s\\\"\\n ),\\n platform,\\n interval,\\n )\\n return\\n\\n async with update_lock:\\n found_devices = await scanner.async_scan_devices()\\n\\n device_name_uses_executor = (\\n scanner.async_get_device_name.__func__ # type: ignore[attr-defined]\\n is DeviceScanner.async_get_device_name\\n )\\n extra_attributes_uses_executor = (\\n scanner.async_get_extra_attributes.__func__ # type: ignore[attr-defined]\\n is DeviceScanner.async_get_extra_attributes\\n )\\n host_name_by_mac: dict[str, str | None] = {}\\n extra_attributes_by_mac: dict[str, dict[str, Any]] = {}\\n if device_name_uses_executor or extra_attributes_uses_executor:\\n (\\n host_name_by_mac,\\n extra_attributes_by_mac,\\n ) = await hass.async_add_executor_job(\\n _load_device_names_and_attributes,\\n scanner,\\n device_name_uses_executor,\\n extra_attributes_uses_executor,\\n seen,\\n found_devices,\\n )\\n\\n for mac in found_devices:\\n if mac in seen:\\n host_name = None\\n else:\\n host_name = host_name_by_mac.get(\\n mac, await scanner.async_get_device_name(mac)\\n )\\n seen.add(mac)\\n\\n try:\\n extra_attributes = extra_attributes_by_mac.get(\\n mac, await scanner.async_get_extra_attributes(mac)\\n )\\n except NotImplementedError:\\n extra_attributes = {}\\n\\n kwargs: dict[str, Any] = {\\n \\\"mac\\\": mac,\\n \\\"host_name\\\": host_name,\\n \\\"source_type\\\": SourceType.ROUTER,\\n \\\"attributes\\\": {\\n \\\"scanner\\\": scanner.__class__.__name__,\\n **extra_attributes,\\n },\\n }\\n\\n zone_home = hass.states.get(hass.components.zone.ENTITY_ID_HOME)\\n if zone_home is not None:\\n kwargs[\\\"gps\\\"] = [\\n zone_home.attributes[ATTR_LATITUDE],\\n zone_home.attributes[ATTR_LONGITUDE],\\n ]\\n kwargs[\\\"gps_accuracy\\\"] = 0\\n\\n hass.async_create_task(async_see_device(**kwargs))\\n\\n cancel_legacy_scan = async_track_time_interval(\\n hass,\\n async_device_tracker_scan,\\n interval,\\n name=f\\\"device_tracker {platform} legacy scan\\\",\\n )\\n hass.async_create_task(async_device_tracker_scan(None))\\n\\n @callback\\n def _on_hass_stop(_: Event) -> None:\\n \\\"\\\"\\\"Cleanup when Home Assistant stops.\\n\\n Cancel the legacy scan.\\n \\\"\\\"\\\"\\n cancel_legacy_scan()\\n\\n hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, _on_hass_stop)\",\n \"def test_gwservice_listdevices(self, setup_controller):\\n resp = setup_controller.request(\\\"gw\\\", \\\"devices\\\", \\\"GET\\\", None, None)\\n body = resp.url + \\\",\\\" + str(resp.status_code) + ',' + resp.text\\n allure.attach(name=\\\"gw list devices\\\", body=body)\\n if resp.status_code != 200:\\n assert False\\n devices = json.loads(resp.text)\\n print (devices)\",\n \"def main():\\n configs = [\\\"show configuration sessions\\\"]\\n with EOSDriver(**MY_DEVICE) as conn:\\n conn.register_configuration_session(session_name=\\\"my-config-session\\\")\\n # for configuration sessions we have to first \\\"register\\\" the session with scrapli:\\n result = conn.send_configs(configs=configs, privilege_level=\\\"my-config-session\\\")\\n\\n # we should see our session name with an \\\"*\\\" indicating that is the active config session\\n print(result[0].result)\",\n \"def get_devices():\\n try:\\n with open(DEVICES, 'r') as f:\\n data = json.load(f)['devices']\\n except (IOError, ValueError) as err:\\n raise SwiftlmCheckFailure('Failure opening %s: %s' % (DEVICES, err))\\n\\n devices = []\\n for d in data:\\n l = d.get('label', LABEL_CHECK_DISABLED)\\n devices.append(Device(\\n device=d['name'],\\n mount=MOUNT_PATH+d['swift_drive_name'],\\n label=l\\n ))\\n\\n return devices\",\n \"def setup_platform(\\n hass: HomeAssistant,\\n config: ConfigType,\\n add_entities: AddEntitiesCallback,\\n discovery_info: DiscoveryInfoType | None = None,\\n) -> None:\\n name = config.get(CONF_NAME)\\n mac = config.get(CONF_MAC)\\n _LOGGER.debug(\\\"Setting up\\\")\\n\\n mon = Monitor(hass, mac, name)\\n add_entities([SkybeaconTemp(name, mon)])\\n add_entities([SkybeaconHumid(name, mon)])\\n\\n def monitor_stop(_service_or_event):\\n \\\"\\\"\\\"Stop the monitor thread.\\\"\\\"\\\"\\n _LOGGER.info(\\\"Stopping monitor for %s\\\", name)\\n mon.terminate()\\n\\n hass.bus.listen_once(EVENT_HOMEASSISTANT_STOP, monitor_stop)\\n mon.start()\",\n \"def load_device():\",\n \"async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):\\n data = hass.data[DOMAIN]\\n \\n devices = []\\n for device_info in data.neviweb_client.gateway_data:\\n if \\\"signature\\\" in device_info and \\\\\\n \\\"type\\\" in device_info[\\\"signature\\\"] and \\\\\\n device_info[\\\"signature\\\"][\\\"type\\\"] in IMPLEMENTED_DEVICE_TYPES:\\n device_name = '{} {} {}'.format(DEFAULT_NAME, \\n \\\"dimmer\\\" if device_info[\\\"signature\\\"][\\\"type\\\"] in DEVICE_TYPE_DIMMER \\n else \\\"light\\\", device_info[\\\"name\\\"])\\n devices.append(NeviwebLight(data, device_info, device_name))\\n for device_info in data.neviweb_client.gateway_data2:\\n if \\\"signature\\\" in device_info and \\\\\\n \\\"type\\\" in device_info[\\\"signature\\\"] and \\\\\\n device_info[\\\"signature\\\"][\\\"type\\\"] in IMPLEMENTED_DEVICE_TYPES:\\n device_name = '{} {} {}'.format(DEFAULT_NAME, \\n \\\"dimmer\\\" if device_info[\\\"signature\\\"][\\\"type\\\"] in DEVICE_TYPE_DIMMER \\n else \\\"light\\\", device_info[\\\"name\\\"])\\n devices.append(NeviwebLight(data, device_info, device_name))\\n \\n async_add_entities(devices, True)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.65474904\",\n \"0.6335435\",\n \"0.6185986\",\n \"0.60845584\",\n \"0.59925437\",\n \"0.5884936\",\n \"0.5849123\",\n \"0.58073187\",\n \"0.58024555\",\n \"0.5795098\",\n \"0.579066\",\n \"0.57888335\",\n \"0.57872635\",\n \"0.578623\",\n \"0.578623\",\n \"0.57815707\",\n \"0.5763278\",\n \"0.57449365\",\n \"0.57423085\",\n \"0.5685192\",\n \"0.5684512\",\n \"0.5676689\",\n \"0.56408614\",\n \"0.5626854\",\n \"0.56247145\",\n \"0.5611814\",\n \"0.55916554\",\n \"0.55882376\",\n \"0.5569677\",\n \"0.5568587\",\n \"0.5567084\",\n \"0.55489266\",\n \"0.55488056\",\n \"0.55439496\",\n \"0.55101633\",\n \"0.5508721\",\n \"0.54985917\",\n \"0.5493355\",\n \"0.54928917\",\n \"0.5473293\",\n \"0.54728127\",\n \"0.54532266\",\n \"0.54446954\",\n \"0.54438055\",\n \"0.5405314\",\n \"0.54016024\",\n \"0.53926474\",\n \"0.5391529\",\n \"0.5387261\",\n \"0.53827167\",\n \"0.53773534\",\n \"0.53716296\",\n \"0.5354217\",\n \"0.53524405\",\n \"0.53524405\",\n \"0.5336781\",\n \"0.53354067\",\n \"0.53333706\",\n \"0.53256583\",\n \"0.53095245\",\n \"0.53011394\",\n \"0.529534\",\n \"0.52933\",\n \"0.5291131\",\n \"0.5291131\",\n \"0.5287456\",\n \"0.52789146\",\n \"0.5274747\",\n \"0.52625316\",\n \"0.52574587\",\n \"0.525464\",\n \"0.52466005\",\n \"0.5246574\",\n \"0.5245371\",\n \"0.52426577\",\n \"0.5238046\",\n \"0.5232212\",\n \"0.5204637\",\n \"0.5204126\",\n \"0.52034813\",\n \"0.5199906\",\n \"0.5198765\",\n \"0.5194276\",\n \"0.51930344\",\n \"0.51898956\",\n \"0.5189393\",\n \"0.51887566\",\n \"0.5175397\",\n \"0.5171066\",\n \"0.51634586\",\n \"0.5162894\",\n \"0.51621884\",\n \"0.5155393\",\n \"0.515267\",\n \"0.51523614\",\n \"0.5149066\",\n \"0.51408195\",\n \"0.5140777\",\n \"0.51250964\",\n \"0.51238686\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58917755"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":77471,"cells":{"query":{"kind":"string","value":"Initialize a climate entity within devolo Home Control."},"document":{"kind":"string","value":"def __init__(\n self, homecontrol: HomeControl, device_instance: Zwave, element_uid: str\n ) -> None:\n super().__init__(\n homecontrol=homecontrol,\n device_instance=device_instance,\n element_uid=element_uid,\n )\n\n self._attr_device_class = CoverDeviceClass.BLIND\n self._attr_supported_features = (\n CoverEntityFeature.OPEN\n | CoverEntityFeature.CLOSE\n | CoverEntityFeature.SET_POSITION\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n \n self._citydb = CityDB()\n self._depression = 6 # Set default depression in degrees","def test_init(self, fixture_environment):\n\n # Generate city object\n city_object = cit.City(environment=fixture_environment)\n\n # Check inheritance from citydistrict object of pycity\n assert city_object._kind == 'citydistrict'","def __init__(self):\n\n self.Cp_air0 = config_earth.earth_properties['Cp_air0']\n self.Rsp_air = config_earth.earth_properties['Rsp_air']\n\n self.d = config_earth.balloon_properties['d']\n self.vol = math.pi*4/3*pow((self.d/2),3) #volume m^3\n self.surfArea = math.pi*self.d*self.d #m^2\n self.cs_area = math.pi*self.d*self.d/4.0 #m^2\n\n #self.emissEnv = config_earth.balloon_properties['emissEnv']\n self.areaDensityEnv = config_earth.balloon_properties['areaDensityEnv']\n self.mp = config_earth.balloon_properties['mp']\n self.mdot = 0\n self.massEnv = config_earth.balloon_properties['mEnv']\n self.Upsilon = config_earth.balloon_properties['Upsilon']\n\n self.vent = config_earth.simulation['vent']\n self.coord = config_earth.simulation['start_coord']\n self.t = config_earth.simulation['start_time']\n self.lat = math.radians(self.coord['lat'])\n self.Ls = self.t.timetuple().tm_yday\n self.min_alt = config_earth.simulation['min_alt']\n\n self.vm_coeff = .1 #virtual mass coefficient\n self.k = self.massEnv*config_earth.balloon_properties['cp'] #thermal mass coefficient\n\n self.dt = config_earth.dt","async def _async_setup_entity(\n hass: HomeAssistant,\n async_add_entities: AddEntitiesCallback,\n config: ConfigType,\n config_entry: ConfigEntry,\n discovery_data: DiscoveryInfoType | None = None,\n) -> None:\n async_add_entities([MqttClimate(hass, config, config_entry, discovery_data)])","def __init__(self, world, location, elevation):\n LandCell.__init__(self, world, location, elevation)\n self.plant = 0\n self.reset_food_level()","def __init__(self):\n\n super().__init__(\n dynamics_model=DoorDynamicsModel(),\n virtual_sensor_model=UnimodalVirtualSensorModel(\n virtual_sensor_model=[\n DoorVirtualSensorModel(modalities={\"image\"}),\n DoorVirtualSensorModel(modalities={\"pos\", \"sensors\"}),\n ],\n state_dim=3,\n ),\n )","def __init__(self, alt=0, temp_offset=0):\n\t\tWorkingAtmosphere.__init__(self, alt)\n\t\t#self.temperature_offset = tOffset\n\t\tself.Temperature_offset = temp_offset\n\t\tself.make_environment()","def __init__(self):\n \n self.mineLatLong = np.array([0.0,0.0]) \n self.theOreBody = OreBodyDataManager()\n self.theMiningSystem = MiningSystemDataManager()\n self.theProcessingSystem = ProcessingSystemDataManager()\n self.theEconomicDataManager = EconomicDataManager()\n self.theInfrastructureManager = InfrastructureDataManager()","def initialize_carla_client(self) -> None:\n\n self.carla_client = carla.Client(CARLA_SERVER_HOST, CARLA_SERVER_PORT)\n self.carla_client.load_world(\"Town05\")\n self.carla_world = self.carla_client.get_world()\n self.carla_map = self.carla_world.get_map()\n\n self.settings = self.carla_world.get_settings()\n self.settings.synchronous_mode = True\n self.settings.fixed_delta_seconds = 1 / CARLA_SERVER_FPS\n\n self.carla_world.apply_settings(self.settings)","def __init__(self, world, location, elevation):\n LandCell.__init__(self, world, location, elevation)","def __init__(\n self,\n hass: HomeAssistant,\n config: ConfigType,\n config_entry: ConfigEntry,\n discovery_data: DiscoveryInfoType | None,\n ) -> None:\n self._attr_fan_mode = None\n self._attr_hvac_action = None\n self._attr_hvac_mode = None\n self._attr_is_aux_heat = None\n self._attr_swing_mode = None\n MqttTemperatureControlEntity.__init__(\n self, hass, config, config_entry, discovery_data\n )","def horde_init(self, horde_info= {}):","def init_ocean(web3, account=None):\n ocn = Ocean()\n token_contract = deploy_contract(web3, account, 'OceanToken')\n ContractHandler.set('OceanToken', token_contract)\n ContractHandler.set('DIDRegistry', deploy_contract(web3, account, 'DIDRegistry'))\n ContractHandler.set('OceanMarket',\n deploy_contract(web3, account, 'OceanMarket', token_contract.address))\n service_agreement_contract = deploy_contract(web3, account, 'ServiceExecutionAgreement')\n ContractHandler.set('ServiceExecutionAgreement', service_agreement_contract)\n ContractHandler.set('PaymentConditions', deploy_contract(web3, account, 'PaymentConditions',\n service_agreement_contract.address,\n token_contract.address))\n ContractHandler.set('AccessConditions', deploy_contract(web3, account, 'AccessConditions',\n service_agreement_contract.address))\n return ocn","def __defaults__(self): \n self.tag = 'Constant-property atmosphere'\n self.composition = Data()\n self.composition.gas = 1.0","def __init__(self):\r\n\r\n super(Metallized, self).__init__()\r\n\r\n # Initialize public scalar attributes.\r\n self.spec_sheet = 0\r\n if self.hazard_rate_type < 3: # MIL-HDBK-217\r\n self.reference_temperature = 358.0","def _initialize_dipole_model(self, model):\n for key, hyst in model['Hysteresis'].items():\n direction = np.array([float(x) for x in hyst['dir'].split(\" \")])\n self.dipoleM.addHysteresis(direction, hyst['vol'], hyst['Hc'], hyst['Bs'], hyst['Br'])\n\n # initialize values for Hysteresis (need B-field @ initial position)\n spacecraft_state = self.state_observer.spacecraftState\n self.inertial2Sat = spacecraft_state.getAttitude().getRotation()\n self.satPos_i = spacecraft_state.getPVCoordinates().getPosition()\n\n gP = self.earth.transform(self.satPos_i, self.in_frame, self.in_date)\n\n topoframe = TopocentricFrame(self.earth, gP, 'ENU')\n topo2inertial = topoframe.getTransformTo(self.in_frame, self.in_date)\n\n lat = gP.getLatitude()\n lon = gP.getLongitude()\n alt = gP.getAltitude() / 1e3 # Mag. Field needs degrees and [km]\n\n # get B-field in geodetic system (X:East, Y:North, Z:Nadir)\n B_geo = FileDataHandler.mag_field_model.calculateField(\n degrees(lat), degrees(lon), alt).getFieldVector()\n\n # convert geodetic frame to inertial and from [nT] to [T]\n B_i = topo2inertial.transformVector(Vector3D(1e-9, B_geo))\n\n B_b = self.inertial2Sat.applyTo(B_i)\n B_field = np.array([B_b.x, B_b.y, B_b.z])\n\n self.dipoleM.initializeHysteresisModel(B_field)\n\n # add bar magnets to satellite\n for key, bar in model['BarMagnet'].items():\n direction = np.array([float(x) for x in bar['dir'].split(\" \")])\n self.dipoleM.addBarMagnet(direction, bar['m'])","def _initialize_dipole_model(self, model):\n for key, hyst in model['Hysteresis'].items():\n direction = np.array([float(x) for x in hyst['dir'].split(\" \")])\n self.dipoleM.addHysteresis(direction, hyst['vol'], hyst['Hc'], hyst['Bs'], hyst['Br'])\n\n # initialize values for Hysteresis (need B-field @ initial position)\n spacecraft_state = self.state_observer.spacecraftState\n self.inertial2Sat = spacecraft_state.getAttitude().getRotation()\n self.satPos_i = spacecraft_state.getPVCoordinates().getPosition()\n\n gP = self.earth.transform(self.satPos_i, self.in_frame, self.in_date)\n\n topoframe = TopocentricFrame(self.earth, gP, 'ENU')\n topo2inertial = topoframe.getTransformTo(self.in_frame, self.in_date)\n\n lat = gP.getLatitude()\n lon = gP.getLongitude()\n alt = gP.getAltitude() / 1e3 # Mag. Field needs degrees and [km]\n\n # get B-field in geodetic system (X:East, Y:North, Z:Nadir)\n B_geo = FileDataHandler.mag_field_model.calculateField(\n degrees(lat), degrees(lon), alt).getFieldVector()\n\n # convert geodetic frame to inertial and from [nT] to [T]\n B_i = topo2inertial.transformVector(Vector3D(1e-9, B_geo))\n\n B_b = self.inertial2Sat.applyTo(B_i)\n B_field = np.array([B_b.x, B_b.y, B_b.z])\n\n self.dipoleM.initializeHysteresisModel(B_field)\n\n # add bar magnets to satellite\n for key, bar in model['BarMagnet'].items():\n direction = np.array([float(x) for x in bar['dir'].split(\" \")])\n self.dipoleM.addBarMagnet(direction, bar['m'])","def init_geofence(self):\n\t\tself.create_ring()\n\t\tself.create_geofence()","async def async_setup_entry(\n hass: HomeAssistant,\n config_entry: ConfigEntry,\n async_add_entities: AddEntitiesCallback,\n) -> None:\n async_add_entities(\n [\n DemoClimate(\n unique_id=\"climate_1\",\n device_name=\"HeatPump\",\n target_temperature=68,\n unit_of_measurement=UnitOfTemperature.FAHRENHEIT,\n preset=None,\n current_temperature=77,\n fan_mode=None,\n target_humidity=None,\n current_humidity=None,\n swing_mode=None,\n hvac_mode=HVACMode.HEAT,\n hvac_action=HVACAction.HEATING,\n aux=None,\n target_temp_high=None,\n target_temp_low=None,\n hvac_modes=[HVACMode.HEAT, HVACMode.OFF],\n ),\n DemoClimate(\n unique_id=\"climate_2\",\n device_name=\"Hvac\",\n target_temperature=21,\n unit_of_measurement=UnitOfTemperature.CELSIUS,\n preset=None,\n current_temperature=22,\n fan_mode=\"On High\",\n target_humidity=67,\n current_humidity=54,\n swing_mode=\"Off\",\n hvac_mode=HVACMode.COOL,\n hvac_action=HVACAction.COOLING,\n aux=False,\n target_temp_high=None,\n target_temp_low=None,\n hvac_modes=[cls for cls in HVACMode if cls != HVACMode.HEAT_COOL],\n ),\n DemoClimate(\n unique_id=\"climate_3\",\n device_name=\"Ecobee\",\n target_temperature=None,\n unit_of_measurement=UnitOfTemperature.CELSIUS,\n preset=\"home\",\n preset_modes=[\"home\", \"eco\"],\n current_temperature=23,\n fan_mode=\"Auto Low\",\n target_humidity=None,\n current_humidity=None,\n swing_mode=\"Auto\",\n hvac_mode=HVACMode.HEAT_COOL,\n hvac_action=None,\n aux=None,\n target_temp_high=24,\n target_temp_low=21,\n hvac_modes=[cls for cls in HVACMode if cls != HVACMode.HEAT],\n ),\n ]\n )","def __init__(self, hass: HomeAssistant, client: VisonicClient, visonic_device: PySensorDevice):\n SelectEntity.__init__(self)\n #_LOGGER.debug(\"Creating select entity for %s\",visonic_device.getDeviceName())\n self.hass = hass\n self._client = client\n self._visonic_device = visonic_device\n self._panel = client.getPanelID()\n if self._panel > 0:\n self._name = \"visonic_p\" + str(self._panel) + \"_\" + visonic_device.getDeviceName().lower()\n else:\n self._name = \"visonic_\" + visonic_device.getDeviceName().lower()\n self._is_available = self._visonic_device.isEnrolled()\n self._is_armed = not self._visonic_device.isBypass()\n self._pending_state_is_armed = None\n self._dispatcher = client.getDispatcher()","def initialize(self, topo, data, date_time=None):\n\n self._logger.debug('Initializing distribute.thermal')\n self.date_time = date_time\n self._initialize(topo, data.metadata)\n\n self.veg_height = topo.veg_height\n self.veg_tau = topo.veg_tau\n self.veg_k = topo.veg_k\n self.sky_view_factor = topo.sky_view_factor\n if not self.correct_terrain:\n self.sky_view_factor = None\n self.dem = topo.dem\n\n if self.correct_cloud:\n self.add_thread_variables('thermal_cloud')\n\n if self.correct_veg:\n self.add_thread_variables('thermal_veg')","def initialize(self):\n\n # --------- BEGIN YOUR CODE ----------\n\n # This is exactly the same as Human.initialize, just copy the code over\n\n # --------- END YOUR CODE ----------\n pass","def __init__(self, cpara, dz_soil):\n\n # --- grid ---\n self.z = np.linspace(0, cpara['grid']['zmax'], cpara['grid']['Nlayers']) # grid [m] above ground\n self.dz = self.z[1] - self.z[0] # gridsize [m]\n self.ones = np.ones(len(self.z)) # dummy\n\n # --- switches ---\n self.Switch_Eflow = cpara['ctr']['Eflow'] # True assumes constant U/ustar at upper boundary\n self.Switch_WMA = cpara['ctr']['WMA'] # True solves scalar profiles\n self.Switch_Ebal = cpara['ctr']['Ebal'] # True solves leaf energy balance\n\n logger.info('Eflow: %s, WMA: %s, Ebal: %s',\n self.Switch_Eflow,\n self.Switch_WMA,\n self.Switch_Ebal)\n\n # --- PlantTypes ---\n ptypes = []\n ptnames = list(cpara['planttypes'].keys())\n ptnames.sort()\n for pt in ptnames:\n ptypes.append(PlantType(self.z, cpara['planttypes'][pt], dz_soil, ctr=cpara['ctr'], loc=cpara['loc']))\n self.planttypes = ptypes\n \n # --- stand characteristics: sum over planttypes---\n\n # total leaf area index [m2 m-2]\n self.LAI = sum([pt.LAI for pt in self.planttypes])\n # total leaf area density [m2 m-3]\n self.lad = sum([pt.lad for pt in self.planttypes])\n\n # layerwise mean leaf characteristic dimension [m] for interception model\n self.leaf_length = sum([pt.leafp['lt'] * pt.lad for pt in self.planttypes]) / (self.lad + EPS)\n\n # root area density [m2 m-3]\n rad = np.zeros(np.shape(dz_soil))\n imax = 1\n for pt in self.planttypes:\n rad[:len(pt.Roots.rad)] += pt.Roots.rad\n imax = max(imax, len(pt.Roots.rad))\n \n self.ix_roots = np.array(range(imax)) # soil model layers corresponding to root zone\n self.rad = rad[self.ix_roots]\n \n # total root area index [m2 m-2]\n self.RAI = sum([pt.Roots.RAI for pt in self.planttypes])\n # distribution of roots across soil model layers [-]\n self.root_distr = self.rad * dz_soil[self.ix_roots] / (self.RAI + EPS)\n\n # canopy height [m]\n if len(np.where(self.lad > 0)[0]) > 0:\n f = np.where(self.lad > 0)[0][-1]\n self.hc = self.z[f].copy()\n else:\n self.hc = 0.0\n\n # --- create radiation, micromet, interception, and forestfloor model instances\n self.radiation = Radiation(cpara['radiation'], self.Switch_Ebal)\n\n self.micromet = Micromet(self.z, self.lad, self.hc, cpara['micromet'])\n\n self.interception = Interception(cpara['interception'], self.lad * self.dz)\n\n self.forestfloor = ForestFloor(cpara['forestfloor'])","def setUp(self):\n commandutils.CommandTestCaseMixin.setUp(self)\n self.lighting = objects.LocationLighting.createFor(\n self.location, candelas=0)","def initial(self):\n\n self.var.Kfrost = loadmap('Kfrost')\n self.var.Afrost = loadmap('Afrost')\n self.var.FrostIndexThreshold = loadmap('FrostIndexThreshold')\n self.var.SnowWaterEquivalent = loadmap('SnowWaterEquivalent')\n\n # FrostIndexInit=ifthen(defined(self.var.MaskMap),scalar(loadmap('FrostIndexInitValue')))\n # self.var.FrostIndex=FrostIndexInit\n self.var.FrostIndex = loadmap('FrostIndexInitValue')\n # self.var.AfrostIndex=-(1-self.var.Afrost)*self.var.FrostIndex\n # initial Frost Index value","def on_init(self):\n self.model.maze.initialize(os.path.join(\n config.value['src']['data'], 'maze.csv'))","def __init__(\n self,\n unique_id: str,\n device_name: str,\n target_temperature: float | None,\n unit_of_measurement: str,\n preset: str | None,\n current_temperature: float,\n fan_mode: str | None,\n target_humidity: int | None,\n current_humidity: int | None,\n swing_mode: str | None,\n hvac_mode: HVACMode,\n hvac_action: HVACAction | None,\n aux: bool | None,\n target_temp_high: float | None,\n target_temp_low: float | None,\n hvac_modes: list[HVACMode],\n preset_modes: list[str] | None = None,\n ) -> None:\n self._unique_id = unique_id\n self._attr_supported_features = SUPPORT_FLAGS\n if target_temperature is not None:\n self._attr_supported_features |= ClimateEntityFeature.TARGET_TEMPERATURE\n if preset is not None:\n self._attr_supported_features |= ClimateEntityFeature.PRESET_MODE\n if fan_mode is not None:\n self._attr_supported_features |= ClimateEntityFeature.FAN_MODE\n if target_humidity is not None:\n self._attr_supported_features |= ClimateEntityFeature.TARGET_HUMIDITY\n if swing_mode is not None:\n self._attr_supported_features |= ClimateEntityFeature.SWING_MODE\n if aux is not None:\n self._attr_supported_features |= ClimateEntityFeature.AUX_HEAT\n if HVACMode.HEAT_COOL in hvac_modes or HVACMode.AUTO in hvac_modes:\n self._attr_supported_features |= (\n ClimateEntityFeature.TARGET_TEMPERATURE_RANGE\n )\n self._target_temperature = target_temperature\n self._target_humidity = target_humidity\n self._unit_of_measurement = unit_of_measurement\n self._preset = preset\n self._preset_modes = preset_modes\n self._current_temperature = current_temperature\n self._current_humidity = current_humidity\n self._current_fan_mode = fan_mode\n self._hvac_action = hvac_action\n self._hvac_mode = hvac_mode\n self._aux = aux\n self._current_swing_mode = swing_mode\n self._fan_modes = [\"on_low\", \"on_high\", \"auto_low\", \"auto_high\", \"off\"]\n self._hvac_modes = hvac_modes\n self._swing_modes = [\"auto\", \"1\", \"2\", \"3\", \"off\"]\n self._target_temperature_high = target_temp_high\n self._target_temperature_low = target_temp_low\n self._attr_device_info = DeviceInfo(\n identifiers={(DOMAIN, unique_id)},\n name=device_name,\n )","def __init__(self, island_map):\n self.map = island_map\n self.cells = None\n self.array_to_island()\n self.herbivores_on_island = None\n self.carnivores_on_island = None","def __init__(self, hass, name, latitude, longitude, radius, include, exclude):\n self._hass = hass\n self._name = name\n self._include = include\n self._exclude = exclude\n radius_kilometers = convert(radius, LENGTH_METERS, LENGTH_KILOMETERS)\n self._crimereports = crimereports.CrimeReports(\n (latitude, longitude), radius_kilometers\n )\n self._attributes = None\n self._state = None\n self._previous_incidents = set()","def __init__(self, topology, dofs_per_codim_entity):\n self.topology = topology\n self.datasets = dict((eset, DataSet(eset, dofs_per_codim_entity.get(eset.codimension, 0)))\n for eset in topology.entity_variants())","def __init__(self, hass: HomeAssistant) -> None:\n super().__init__(\n hass,\n LOGGER,\n name=DOMAIN,\n update_interval=SCAN_INTERVAL,\n )\n\n self.easyenergy = EasyEnergy(session=async_get_clientsession(hass))","def __init__(self, *args):\n super(DefaultSystem, self).__init__(*args)\n self.use_strong_form = True\n self.main = self.cavity_grid.main\n self.cavities = self.cavity_grid.cavities","def __init__(self, coor):\n self._coordinates = coor","def initialise(self):\n self.set_up()","def __init__(self, num_of_cheeses, num_of_stools):\n\n self.toah_model = TOAHModel(num_of_stools)\n self.toah_model.fill_first_stool(num_of_cheeses)","def _construct_climate(self, timestep):\n cl = {}\n now, end = self._get_now_and_end(timestep)\n if now==-1:\n return -1\n if self.simulation:\n if self.md.duration_unit == 'month':\n cl['duration'] = self.timestep_length / 12.\n elif self.md.duration_unit == 'year':\n cl['duration'] = self.timestep_length\n else:\n cl['duration'] = STEADY_STATE_TIMESTEP\n if self.md.climate_mode == 'constant yearly':\n cl['rain'] = self.md.constant_climate.annual_rainfall\n cl['temp'] = self.md.constant_climate.mean_temperature\n cl['amplitude'] = self.md.constant_climate.variation_amplitude\n elif self.md.climate_mode == 'monthly':\n cl = self._construct_monthly_climate(cl, now, end)\n elif self.md.climate_mode == 'yearly':\n cl = self._construct_yearly_climate(cl, now, end)\n return cl","def __init__(self, year=None, month=None, day=None, map_type='Cases'):\n self.covid_df = pd.DataFrame([])\n self.geo_data = pd.DataFrame([])\n self.name_iso2_mapping = {}\n self.countries_centroids = pd.DataFrame([])\n \n try:\n self.date = datetime(year=year, month=month, day=day)\n except:\n print('Invalid/empty date entry (year, month, day take valid int inputs)! Date defaulted to today.')\n self.date = datetime.today()\n \n if self.date > datetime.today():\n print('Can\\'t input future date! Date defaulted to today.')\n self.date = datetime.today()\n \n if map_type not in ['Cases', 'Deaths']:\n sys.exit('Please specify either \"Cases\" or \"Deaths\" as map type!')\n else:\n self.map_type = map_type","def init_physical(\n ombh2=0.022161, omch2=0.11889, H0=67.77, omkh2=0.0, t0=2.726, nnu=3.046\n ):\n h = H0 / 100.0\n\n c = Cosmology()\n\n c.omega_b = ombh2 / h ** 2\n c.omega_c = omch2 / h ** 2\n c.H0 = H0\n\n rhoc = 3.0 * c.H() ** 2 * c_sl ** 2 / (8.0 * math.pi * G_n)\n rhorad = a_rad * t0 ** 4\n c.omega_g = rhorad / rhoc\n\n rhonu = nnu * rhorad * 7.0 / 8.0 * (4.0 / 11.0) ** (4.0 / 3.0)\n c.omega_n = rhonu / rhoc\n\n c.omega_l = 1.0 - (omkh2 + ombh2 + omch2) / h ** 2 - (c.omega_g + c.omega_n)\n\n return c","def __init__(\n self,\n dem_path: str,\n model_name: Optional[str] = None,\n ela: int = 2850,\n m: float = 0.006,\n plot: bool = True,\n ) -> None:\n\n # Load DEM ------------------------------------------------------------\n dem = open(dem_path)\n ele = dem.read(1).astype(np.float32)\n\n # Instance variables --------------------------------------------------\n self.model_name = Path(dem_path).stem if model_name is None else model_name\n self.dem_path = dem_path\n\n # Mass balance parameters\n self.m = m # Mass balance gradient\n self.ela_start = ela # Equilibrium line altitude\n self._setup_params() # Variable parameters (i, ela, steady_state)\n\n # 2D arrays\n self.ele_orig = np.copy(ele) # Original topography\n self._setup_ndarrays() # Variable arrays (ele, h, u ,hs)\n\n # Coordinate reference system and dem resolution\n self._dem_meta = dem.meta\n self.res = dem.res[0]\n\n # Geographical extent of the dem\n x0, y0, x1, y1 = dem.bounds\n self.extent = (x0, x1, y1, y0)\n\n # Setup statistics\n self._setup_stats()\n\n # Setup plot\n self.plot = plot","def __init__(self, melon_type, shape, color_rating, harvest_from, harvest_by):\n self.melon_type = melon_type\n self.shape = shape\n self.color_rating = color_rating\n self.harvest_from = harvest_from\n self.harvest_by = harvest_by","def __init__(self,\n launchSiteLat,\n launchSiteLon,\n launchSiteElev,\n dateAndTime,\n UTC_offset=0,\n inflationTemperature=0.0,\n forceNonHD=False,\n forecastDuration=4,\n use_async=True,\n requestSimultaneous=True,\n debugging=False,\n progressHandler=None,\n load_on_init=False):\n # Initialize extra forecast-specific variables\n self.forceNonHD = forceNonHD\n self.forecastDuration = forecastDuration\n self.use_async = use_async\n self.requestSimultaneous = requestSimultaneous\n\n self._GFSmodule = None\n\n # This should be the last thing that is called on init, since the base\n # (environment) class init calls self.load (if load_on_init is True)\n super(forecastEnvironment, self).__init__(\n inflationTemperature=inflationTemperature,\n launchSiteLat=launchSiteLat,\n launchSiteLon=launchSiteLon,\n launchSiteElev=launchSiteElev,\n dateAndTime=dateAndTime,\n UTC_offset=UTC_offset,\n debugging=debugging,\n load_on_init=load_on_init)","def __init__(self, number_of_cheeses, number_of_stools):\n self.model = TOAHModel(number_of_stools)\n self.model.fill_first_stool(number_of_cheeses)","def __init__(self):\n self._read_calibration_data()\n self.set_oversamplings_and_mode(\n HumidityOversampling.x08,\n TemperatureOversampling.x08,\n PressureOversampling.x16,\n SensorMode.Normal)\n self.set_config(\n InactiveDuration.ms1000,\n FilterCoefficient.fc04)","def __init__(self, device: SensemeDevice) -> None:\n super().__init__(device, f\"{device.name} Light\")\n self._attr_supported_color_modes = {ColorMode.COLOR_TEMP}\n self._attr_color_mode = ColorMode.COLOR_TEMP\n self._attr_min_mireds = color_temperature_kelvin_to_mired(\n device.light_color_temp_max\n )\n self._attr_max_mireds = color_temperature_kelvin_to_mired(\n device.light_color_temp_min\n )","def __init__(\n self,\n hass: HomeAssistant,\n config: ConfigType,\n config_entry: ConfigEntry,\n discovery_data: DiscoveryInfoType | None,\n ) -> None:\n self._attr_target_temperature_low = None\n self._attr_target_temperature_high = None\n self._feature_preset_mode = False\n MqttEntity.__init__(self, hass, config, config_entry, discovery_data)","def __init__(self):\n self._read_calibration_data()\n self.configure_sensor(\n TemperatureOversamplings.x08,\n PressureOversamplings.x16,\n HumidityOversamplings.x08,\n IIRFilterCoefficients.FC_003,\n 250,\n 250)","def __init__(self):\n\n self.name = \"\"\n self.address = \"\"\n self.coordinates = []\n self.weatherHours = []\n self.currentHour = 0","def __init__(\n self,\n xknx: XKNX,\n group_address: GroupAddressesType | None = None,\n group_address_state: GroupAddressesType | None = None,\n sync_state: bool | int | float | str = True,\n device_name: str | None = None,\n feature_name: str = \"Climate mode\",\n climate_mode_type: ClimateModeType | None = None,\n after_update_cb: AsyncCallbackType | None = None,\n ):\n super().__init__(\n xknx,\n group_address=group_address,\n group_address_state=group_address_state,\n sync_state=sync_state,\n device_name=device_name,\n feature_name=feature_name,\n after_update_cb=after_update_cb,\n )\n if not isinstance(climate_mode_type, self.ClimateModeType):\n raise ConversionError(\n \"invalid climate mode type\",\n climate_mode_type=str(climate_mode_type),\n device_name=str(device_name),\n feature_name=feature_name,\n )\n self._climate_mode_transcoder: (\n DPTControllerStatus | DPTHVACMode\n ) = climate_mode_type.value","def setUp(self):\n self.m = m = ConcreteModel()\n m.t = ContinuousSet(bounds=(0, 10))\n m.v1 = Var(m.t)\n m.dv1 = DerivativeVar(m.v1)\n m.s = Set(initialize=[1, 2, 3], ordered=True)","def __init__(self, home=False, away=False, night=False, vacation=False):\n self.home, self.away, self.night = home, away, night\n self.vacation = vacation","def setup(self):\n print(\"INIT DATA\")\n\n self.nutella = Product.objects.create(name=\"nutella\", nutriscore=\"e\")","def test_init():\n cd = Delta(MAIN_JSON, 'civic')\n assert cd._main_json == MAIN_JSON\n assert cd._updated_json is None\n\n cd = Delta(MAIN_JSON, 'civic', _updated_json=UPDATED_JSON)\n assert cd._main_json == MAIN_JSON\n assert cd._updated_json == UPDATED_JSON","def setUp(self):\n self.my_city = City()","def __init__(\n self,\n weather,\n us_holidays,\n islamic_holidays,\n jewish_holidays,\n events,\n seahawks,\n huskies,\n sounders,\n ):\n self.weather = weather\n self.us_holidays = us_holidays\n self.islamic_holidays = islamic_holidays\n self.jewish_holidays = jewish_holidays\n self.events = events\n self.seahawks = seahawks\n self.huskies = huskies\n self.sounders = sounders\n self.X = None\n self.y = None","def initialize(self):\n\t\ttable = NetworkTables.getTable('SmartDashboard')\n\t\tinitial_pos = table.getData('initial_pos')\n\t\t#XXX Doing this will start the path following immediately. May want this\n\t\t# in the execute or something\n\t\tpath = self.dt.which_path(initial_pos)","def setUpClass(self):\n self.coal = Simulation(logging_level=30)\n self.tree = CoalescenceTree()\n self.coal.set_simulation_parameters(\n 1,\n 28,\n \"output\",\n 0.1,\n 4,\n 4,\n deme=1,\n sample_size=1.0,\n max_time=2,\n dispersal_relative_cost=1,\n min_num_species=1,\n dispersal_method=\"normal\",\n )\n self.coal.set_map_parameters(\"null\", 10, 10, \"null\", 10, 10, 0, 0, \"null\", 20, 20, 0, 0, 1, \"null\", \"null\")\n self.coal.set_speciation_rates([0.1, 0.2])\n self.coal.add_sample_time(100000000.0)\n self.coal.run()\n self.tree.set_database(self.coal)","def __init__(self, *args):\n super().__init__(*args, category=CATEGORY_GARAGE_DOOR_OPENER)\n self._flag_state = False\n\n serv_garage_door = self.add_preload_service(SERV_GARAGE_DOOR_OPENER)\n self.char_current_state = serv_garage_door.configure_char(\n CHAR_CURRENT_DOOR_STATE, value=0\n )\n self.char_target_state = serv_garage_door.configure_char(\n CHAR_TARGET_DOOR_STATE, value=0, setter_callback=self.set_state\n )","def __init__(self, type, high_elevation, low_elevation):\n\t\tself.type = type\n\t\tself.high_elevation = high_elevation\n\t\tself.low_elevation = low_elevation\n\t\tself.occupant = 0 # use 0 to initilize tile which \n\t\t\t\t\t\t\t# means it has not been explored.\n\t\tif type == \"plains\": # initialize the shade\n\t\t\tself.terrain = \" \"\n\t\telse:\n\t\t\tself.terrain = \"#\"","def init(self):\n\n\t\tstatus, param = self.execute(self.mission, 'on_init', self.kingdom)\n\n\t\treturn status","def __init__(self):\n self.mu = None\n self.type = None\n self.direction = None\n self.eq_points = None\n self.init_conds = None\n self.time = None\n self.trajectory = None\n self.initial_point = None\n self.contour_levels = []","def __init__(self, device: Thermostat, gateway: DeconzGateway) -> None:\n super().__init__(device, gateway)\n\n self._attr_hvac_modes = [\n HVACMode.HEAT,\n HVACMode.OFF,\n ]\n if device.mode:\n self._attr_hvac_modes.append(HVACMode.AUTO)\n\n if \"coolsetpoint\" in device.raw[\"config\"]:\n self._attr_hvac_modes.append(HVACMode.COOL)\n\n self._deconz_to_hvac_mode = {\n HVAC_MODE_TO_DECONZ[item]: item for item in self._attr_hvac_modes\n }\n\n self._attr_supported_features = ClimateEntityFeature.TARGET_TEMPERATURE\n\n if device.fan_mode:\n self._attr_supported_features |= ClimateEntityFeature.FAN_MODE\n self._attr_fan_modes = list(FAN_MODE_TO_DECONZ)\n\n if device.preset:\n self._attr_supported_features |= ClimateEntityFeature.PRESET_MODE\n self._attr_preset_modes = list(PRESET_MODE_TO_DECONZ)","def create_climatology(self):\n # Because data from all years are merged, the time coordinate must be \n # made consistent with basic integer values. Monthly dates values are \n # added to the attributes.\n time_points = self.cubelist[0].coord(self.time_coord).points\n new_time_points = range(1, len(time_points) + 1)\n new_time_atts = {'dates' : self.cube_dates}\n new_time_coord = iris.coords.DimCoord(new_time_points,\n standard_name=self.time_coord,\n attributes=new_time_atts)\n \n new_cubelist = []\n realization_num = 1\n for cube in self.cubelist:\n if len(cube.coord(self.realization).points) > 1:\n cube = self.cube_ensemble_mean(cube)\n # Make sure all realization points are unique.\n cube.coord(self.realization).points = [realization_num]\n # Replace time dimension.\n time_dim = cube.coord_dims(cube.coord(self.time_coord))\n cube.remove_coord(self.time_coord)\n if time_dim:\n cube.add_dim_coord(new_time_coord, time_dim)\n else:\n # If no time_dim, coordinate is auxiliary or scalar.\n cube.add_aux_coord(new_time_coord)\n \n new_cubelist.append(cube)\n realization_num += 1\n \n new_cube = iris.cube.CubeList(new_cubelist).merge_cube()\n clim_cube = self.cube_ensemble_mean(new_cube)\n \n # The initialisation data is now a mean of all years, so like with time\n # replace the coordinate with a single point and monthly initialisation\n # dates added to the attributes.\n init_points = clim_cube.coord(self.forecast_ref_time).points\n new_init_points = range(1, len(init_points) + 1)\n new_init_atts = {'dates' : self.cube_init_dates}\n new_init_coord = iris.coords.AuxCoord(new_init_points,\n standard_name=self.forecast_ref_time,\n attributes=new_init_atts)\n clim_cube.remove_coord(self.forecast_ref_time)\n clim_cube.add_aux_coord(new_init_coord)\n \n self.clim_cube = clim_cube\n return self.clim_cube","def __init__(self, world, location, elevation):\n self.world = world\n self.location = location # (row, col)\n self.elevation = elevation\n if elevation < 0:\n self.water_level = -elevation\n else:\n self.water_level = 0\n self.pollution = 0\n self.crab = None","def __init__(self):\n self.model = None\n self.joined_datasets = None\n self.id_col = None\n self.val_col = None\n self.pop_col = None\n self.total_population_per_unit = None\n self.centroids_of_areal_data = None\n self.prepared_data = None\n self.unknown_area_id = None\n\n # Parameters\n self.lags = None\n self.step = None\n self.min_no_of_observations = None\n self.max_search_radius = None","def __init__(self):\n self.TECRDB_compounds_data_dict = {}\n self.TECRDB_compounds_pH7_species_id_dict = {}\n self.TECRDB_compounds_least_H_sid_dict = {}\n self.get_TECRDB_compounds_data()","def __init__(self, c_config, client):\n self.id = c_config['id']\n self.nodes = []\n self.client = client\n self.period = 20\n self.__init_load(c_config)","def setUpClass(cls):\n cls.coal = Simulation()\n cls.tree = CoalescenceTree()\n cls.coal.set_simulation_parameters(\n 1,\n 1,\n \"output\",\n 0.1,\n 4,\n 4,\n 1,\n 1.0,\n 2,\n dispersal_relative_cost=1,\n min_num_species=1,\n dispersal_method=\"fat-tail\",\n landscape_type=True,\n )\n cls.coal.set_map_parameters(\"null\", 10, 10, \"null\", 10, 10, 0, 0, \"null\", 20, 20, 0, 0, 1, \"none\", \"none\")\n cls.coal.set_speciation_rates([0.1, 0.2])\n cls.coal.run()\n cls.tree.set_database(\"output/data_1_1.db\")\n cls.tree.calculate_octaves()\n cls.tree.calculate_richness()","def __init__(self):\n self.temperature = 70.0\n self.status = \"sunny\"","def __init__(self):\n self.temperature = 70.0\n self.status = \"sunny\"","def __init__(self):\n self.temperature = 70.0\n self.status = \"sunny\"","def __init__(self, guid=str(uuid.uuid4()), speciesId=Species, caughtLat=0, caughtLong=0, consumption=Consumption):\n self.guid = guid\n self.speciesId = speciesId\n self.caughtLat = caughtLat\n self.caughtLong = caughtLong\n self.consumption = consumption","def __init__(self, parent, connection):\n ConnectionScript.__init__(self, parent, connection)\n self.entity = Entity()","def __init__(self, coordinator, sensor):\n self._sensor = sensor\n self.coordinator = coordinator\n self._name = DOMAIN.title()\n\n self._state = None","def __init__(self, param_dictionary):\n\n BaseModel.__init__(self)\n\n # set starting compartment values\n self.set_compartment(\"susceptible\",\n param_dictionary[\"population\"] - param_dictionary[\"start_infectious\"])\n self.set_compartment(\"infectious\", param_dictionary[\"start_infectious\"])\n self.set_compartment(\"immune\", 0.)\n\n # set model parameters\n self.set_param(\"infection_beta\",\n param_dictionary[\"r0\"]\n / (param_dictionary[\"duration_infectious\"] * param_dictionary[\"population\"]))\n self.set_param(\"infection_rate_recover\", 1. / param_dictionary[\"duration_infectious\"])","def __init__(self, hass, device, endpoint):\n self._device = device\n self._endpoint = endpoint\n self._is_on = False\n a = self._device.get_attribute(endpoint, 6, 0)\n if a:\n self._is_on = a.get('value', False)\n ieee = device.ieee or device.addr # compatibility\n entity_id = 'zigate_{}_{}'.format(ieee,\n endpoint)\n self.entity_id = ENTITY_ID_FORMAT.format(entity_id)\n hass.bus.listen('zigate.attribute_updated', self._handle_event)","def __init__(self, eqn_set=0, atmosphere=0, ra_steps=(1, 1e3, 40, True),\n kx_steps=(0.01, 1, 40, True), ky_steps=None, threeD=False, atmo_kwargs={}, eqn_args=[],\n eqn_kwargs={}, bc_kwargs={}):\n self._eqn_set = eqn_set\n self._atmosphere = atmosphere\n self._ra_steps = ra_steps\n self._kx_steps = kx_steps\n self._ky_steps = ky_steps\n self.threeD = threeD\n\n self._atmo_kwargs = atmo_kwargs\n self._eqn_args = eqn_args\n self._eqn_kwargs = eqn_kwargs\n self._bc_kwargs = bc_kwargs\n self.cf = CriticalFinder(self.solve_problem, CW)","def __init__(self, manager: db.Manager):\n super().__init__(manager)\n self._calculations = manager.get_collection(\"calculations\")\n self._forward_flux_label = \"_forward_edge_flux\"\n self._backward_flux_label = \"_backward_edge_flux\"\n self._zero_break_scale = 1e-4\n self._kinetic_modeling_job_order = \"kinetx_kinetic_modeling\"\n self._reaction_id_key = \"reaction_ids\"","async def async_setup_entry(\n hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\n) -> None:\n await platform_async_setup_entry(\n hass,\n entry,\n async_add_entities,\n info_type=ClimateInfo,\n entity_type=EsphomeClimateEntity,\n state_type=ClimateState,\n )","def Init(self):\n RobotMap.Init()\n from commands import *\n from subsystems import *\n#@autogenerated_code(\"constructors\", \" \")\n#parse(\"${exporter-path}core/robot-constructors.py\")\n#end\n # This MUST be here. If the OI creates Commands (which it very likely\n # will), constructing it during the construction of CommandBase (from\n # which commands extend), subsystems are not guaranteed to be\n # yet. Thus, their requires() statements may grab null pointers. Bad\n # news. Don't move it.\n self.oi = OI()\n\n # instantiate the command used for the autonomous period","def __init__(self, heatpumpid):\n import jsonpath\n self.jsonpath = jsonpath.jsonpath\n self._target_temperature = None\n self._current_operation = None\n self._current_fan_mode = None\n self._current_swing_mode = None\n self._on = None\n self.heatpump_id = heatpumpid\n self._support_flags = SUPPORT_TARGET_TEMPERATURE | SUPPORT_FAN_MODE |\\\n SUPPORT_OPERATION_MODE | SUPPORT_ON_OFF | SUPPORT_SWING_MODE\n self._unit_of_measurement = TEMP_CELSIUS\n self._fan_list = ['Auto', 'Low', 'Medium_Low', 'Medium',\n 'Medium_High', 'High']\n self._operation_list = ['heat', 'cool', 'auto', 'dry', 'fan_only']\n self._swing_list = ['Auto', '0_Degrees', '30_Degrees', '60_Degrees',\n '90_Degrees']\n self._config_date = None\n self.sync_data()","def __init__(self, body, observer, date):\n self.site = observer.site\n self.body = body\n self.date = observer.date_to_local(date)\n self.date_utc = observer.date_to_utc(self.date)\n\n self.humidity = observer.humidity\n self.wavelength = observer.wavelength\n\n # Can/should this calculation be postponed?\n self.site.date = ephem.Date(self.date_utc)\n self.body.compute(self.site)\n\n self.lt = self.date\n self.ra = self.body.ra\n self.dec = self.body.dec\n self.alt = float(self.body.alt)\n self.az = float(self.body.az)\n # TODO: deprecate\n self.alt_deg = math.degrees(self.alt)\n self.az_deg = math.degrees(self.az)\n self.will_be_visible = not self.body.neverup\n\n # properties\n self._ut = None\n self._gmst = None\n self._lmst = None\n self._ha = None\n self._pang = None\n self._am = None\n self._moon_alt = None\n self._moon_pct = None\n self._moon_sep = None\n self._atmos_disp = None\n\n # Conversion factor for wavelengths (Angstrom -> micrometer)\n self.angstrom_to_mm = 1. / 10000.","def make_environment(self):\n\t\tbase_layer = 0\n\t\tself.Gravity = 9.81\n\n\t\t#Private data for to define model\n\t\t__model_max_altitude = 87000\n\t\t__atmosphere_layers = {0:0, 11000:1, 20000:2, 32000:3, 47000:4, 51000:5, 71000:6}\n\t\t__layer_base_data = {\n\t\t\t0:{'temp':288.15, 'lapse':-0.0065, 'press':101325},\n\t\t\t1:{'temp':216.65, 'lapse':0, 'press':22632.1},\n\t\t\t2:{'temp':216.65, 'lapse':0.001, 'press':5474.89},\n\t\t\t3:{'temp':228.65, 'lapse':0.0028, 'press':868.019},\n\t\t\t4:{'temp':270.65, 'lapse':0, 'press':110.906},\n\t\t\t5:{'temp':270.65, 'lapse':-0.0028, 'press':66.9389},\n\t\t\t6:{'temp':214.65, 'lapse':-0.002, 'press':3.95642},\n\t\t\t}\n\t\t__gas_constant = 8.31432#e3\n\t\t__air_molar_mass = 0.0289644\n\t\t__specific_heat_ratio = 1.4\n\t\t__visc_lambda = 1.51204129e-6\n\t\t__visc_sutherland_const = 120.0\n\n\t\tif self.Altitude > __model_max_altitude:\n\t\t\traise helpers.extra_exceptions.ModelExtrapolationException(\n\t\t\t'Exceeded model maximum altitude')\n\n\t\tlayerKeys = __atmosphere_layers.keys()\n\t\tlayerKeys = list(layerKeys)\n\t\tlayerKeys.sort()\n\t\tfor layer in layerKeys:\n\t\t\tif self.Altitude >= layer:\n\t\t\t\tbase_layer = __atmosphere_layers[layer]\n\t\t\t\tbase_alt = layer\n\t\tbase_temp = __layer_base_data[base_layer]['temp']\n\t\tbase_lapse = __layer_base_data[base_layer]['lapse']\n\t\tbase_press = __layer_base_data[base_layer]['press']\n\n\t\tself.Temperature = base_temp + base_lapse * (self.Altitude - base_alt)\n\t\t+ self.Temperature_offset\n\n\t\tif base_lapse == 0:\n\t\t\tself.Pressure = base_press * \\\n\t\t\t\tnp.exp( (-self.Gravity*__air_molar_mass*(self.Altitude-base_alt)) \\\n\t\t\t\t/(__gas_constant*base_temp))\n\t\telse:\n\t\t\tself.Pressure = base_press * \\\n\t\t\t\t(base_temp/self.Temperature) ** \\\n\t\t\t\t(self.Gravity*__air_molar_mass/__gas_constant/base_lapse)\n\n\t\tself.Density = __air_molar_mass*self.Pressure / \\\n\t\t\t__gas_constant/self.Temperature\n\t\tself.Speed_of_sound = np.sqrt(__specific_heat_ratio*__gas_constant* \\\n\t\t\tself.Temperature/__air_molar_mass)\n\t\tself.Dynamic_viscosity = __visc_lambda*self.Temperature**(3.0/2.0)/ \\\n\t\t\t(self.Temperature+__visc_sutherland_const)","def Initialize(self):\n return _gmat_py.EphemManager_Initialize(self)","def setUpClass(self):\n self.coal = Simulation()\n self.tree = CoalescenceTree()\n self.coal.set_simulation_parameters(\n 4,\n 4,\n \"output\",\n 0.1,\n 4,\n 4,\n 1,\n 0.01,\n 2,\n dispersal_relative_cost=1,\n min_num_species=1,\n dispersal_method=\"fat-tail\",\n )\n self.coal.set_map_files(\n \"null\", fine_file=\"sample/SA_sample_fine.tif\", coarse_file=\"sample/SA_sample_coarse.tif\"\n )\n self.coal.detect_map_dimensions()\n self.coal.set_speciation_rates([0.1, 0.2])\n self.coal.run()\n self.tree.set_database(self.coal)\n self.tree.calculate_octaves()\n self.tree.calculate_richness()","def initialize(self):\n\n myg = grid_setup(self.rp, ng=4)\n\n bc_dens, bc_xodd, bc_yodd = bc_setup(self.rp)\n\n my_data = patch.CellCenterData2d(myg)\n\n my_data.register_var(\"density\", bc_dens)\n my_data.register_var(\"x-velocity\", bc_xodd)\n my_data.register_var(\"y-velocity\", bc_yodd)\n\n # we'll keep the internal energy around just as a diagnostic\n my_data.register_var(\"eint\", bc_dens)\n\n # phi -- used for the projections. The boundary conditions\n # here depend on velocity. At a wall or inflow, we already\n # have the velocity we want on the boundary, so we want\n # Neumann (dphi/dn = 0). For outflow, we want Dirichlet (phi\n # = 0) -- this ensures that we do not introduce any tangental\n # acceleration.\n bcs = []\n for bc in [self.rp.get_param(\"mesh.xlboundary\"),\n self.rp.get_param(\"mesh.xrboundary\"),\n self.rp.get_param(\"mesh.ylboundary\"),\n self.rp.get_param(\"mesh.yrboundary\")]:\n if bc == \"periodic\":\n bctype = \"periodic\"\n elif bc in [\"reflect\", \"slipwall\"]:\n bctype = \"neumann\"\n elif bc in [\"outflow\"]:\n bctype = \"dirichlet\"\n bcs.append(bctype)\n\n bc_phi = bnd.BC(xlb=bcs[0], xrb=bcs[1], ylb=bcs[2], yrb=bcs[3])\n\n my_data.register_var(\"phi-MAC\", bc_phi)\n my_data.register_var(\"phi\", bc_phi)\n\n # gradp -- used in the projection and interface states. We'll do the\n # same BCs as density\n my_data.register_var(\"gradp_x\", bc_dens)\n my_data.register_var(\"gradp_y\", bc_dens)\n\n my_data.create()\n\n self.cc_data = my_data\n\n # some auxiliary data that we'll need to fill GC in, but isn't\n # really part of the main solution\n aux_data = patch.CellCenterData2d(myg)\n\n aux_data.register_var(\"coeff\", bc_dens)\n aux_data.register_var(\"source_y\", bc_yodd)\n\n aux_data.create()\n self.aux_data = aux_data\n\n # we also need storage for the 1-d base state -- we'll store this\n # in the main class directly.\n self.base[\"rho0\"] = Basestate(myg.ny, ng=myg.ng)\n self.base[\"p0\"] = Basestate(myg.ny, ng=myg.ng)\n\n # now set the initial conditions for the problem\n problem = importlib.import_module(f\"pyro.lm_atm.problems.{self.problem_name}\")\n problem.init_data(self.cc_data, self.base, self.rp)\n\n # Construct beta_0\n gamma = self.rp.get_param(\"eos.gamma\")\n self.base[\"beta0\"] = Basestate(myg.ny, ng=myg.ng)\n self.base[\"beta0\"].d[:] = self.base[\"p0\"].d**(1.0/gamma)\n\n # we'll also need beta_0 on vertical edges -- on the domain edges,\n # just do piecewise constant\n self.base[\"beta0-edges\"] = Basestate(myg.ny, ng=myg.ng)\n self.base[\"beta0-edges\"].jp(1)[:] = \\\n 0.5*(self.base[\"beta0\"].v() + self.base[\"beta0\"].jp(1))\n self.base[\"beta0-edges\"].d[myg.jlo] = self.base[\"beta0\"].d[myg.jlo]\n self.base[\"beta0-edges\"].d[myg.jhi+1] = self.base[\"beta0\"].d[myg.jhi]","def __init__(self, effective_from=None, effective_at=None, tenor=None, roll_convention=None, holiday_calendars=None, valuation_date_times=None, local_vars_configuration=None): # noqa: E501 # noqa: E501\n if local_vars_configuration is None:\n local_vars_configuration = Configuration.get_default_copy()\n self.local_vars_configuration = local_vars_configuration\n\n self._effective_from = None\n self._effective_at = None\n self._tenor = None\n self._roll_convention = None\n self._holiday_calendars = None\n self._valuation_date_times = None\n self.discriminator = None\n\n self.effective_from = effective_from\n self.effective_at = effective_at\n self.tenor = tenor\n self.roll_convention = roll_convention\n self.holiday_calendars = holiday_calendars\n self.valuation_date_times = valuation_date_times","def __init__(self) -> None:\n self.homee_host: str = None\n self.homee_id: str = None\n self.homee: Homee = None","def init_base_stations(mec_set: Dict[str,'Mec']) -> None:\n \n mec_keys = []\n mec_names = []\n \n for key, value in mec_set.items():\n mec_keys.append(key)\n mec_names.append(value.name)\n \n \n base_station_set = {\n \"base_station_set\": {}\n }\n \n bs_topology = onos_controller.OnosController.get_topology()\n mec_index = 0\n \n for base_station in bs_topology: \n bs_links = {}\n \n for link in base_station.links:\n dst_id = link.dst.device\n dst_port = link.dst.port\n dst_latency = None\n bs_links[dst_id] = {\n 'port': dst_port,\n 'latency': dst_latency\n } \n \n mec_name = 'MEC' + base_station.name[2:]\n mec_index = mec_names.index(mec_name)\n \n new_base_station_id = base_station.id\n new_base_station = BaseStation(\n name = base_station.name, \n mec_id = mec_keys[mec_index], \n wireless_latency = round(random.uniform(0.1, 0.3), 2),\n links = DefaultMunch.fromDict(bs_links)\n )\n base_station_set['base_station_set'][new_base_station_id] = new_base_station\n \n \n BaseStationController.set_bs_net_latency(base_station_set['base_station_set'])\n json_controller.EncoderController.encoding_to_json(base_station_set)\n return","def setUpClass(self):\n self.coal = Simulation()\n self.tree = CoalescenceTree()\n self.coal.set_simulation_parameters(\n 2,\n 2,\n \"output\",\n 0.1,\n 4,\n 4,\n 1,\n 1.0,\n 2,\n dispersal_relative_cost=1,\n min_num_species=1,\n dispersal_method=\"normal\",\n m_prob=1,\n landscape_type=True,\n )\n self.coal.set_map_parameters(\"null\", 10, 10, \"null\", 10, 10, 0, 0, \"null\", 20, 20, 0, 0, 1, \"null\", \"null\")\n self.coal.set_speciation_rates([0.1, 0.2])\n self.coal.run()\n self.tree.set_database(\"output/data_2_2.db\")\n self.tree.calculate_octaves()\n self.tree.calculate_richness()","def __init__(self, x, y, z): \n\t\tself.x = x # x coordinate (EW distance from observatory center)\n\t\tself.y = y # y coordinate (NS distance from observatory center)\n\t\tself.z = z # z coordinate (altitude rel. to observatory center)","def setUpClass(self):\n self.coal = Simulation()\n self.tree = CoalescenceTree()\n self.coal.set_simulation_parameters(\n 3, 3, \"output\", 0.1, 4, 4, 1, 0.1, 2, dispersal_relative_cost=1, min_num_species=1, cutoff=0.0\n )\n self.coal.set_map_files(\"null\", fine_file=\"sample/SA_sample_fine.tif\")\n self.coal.detect_map_dimensions()\n self.coal.set_speciation_rates([0.1, 0.2])\n self.coal.run()\n self.tree.set_database(self.coal)\n self.tree.calculate_octaves()\n self.tree.calculate_richness()","def __init__(self, name, node, temperature_driven=False, repr_days=None):\n Submodel.__init__(self, name=name,\n temperature_driven=temperature_driven)\n\n self.logger = logging.getLogger('modesto.Node')\n self.logger.info('Initializing Node {}'.format(name))\n\n self.node = node\n self.loc = self.get_loc()\n\n self.components = {}\n self.pipes = {}\n\n self.compiled = False\n self.repr_days = repr_days\n\n self.build()","def __init__(self):\n\n super(aero_csm_component,self).__init__()","def test_init(self):\r\n c = AlphaDiversityCalc(observed_otus)\r\n self.assertEqual(c.Metric, observed_otus)\r\n self.assertEqual(c.Params, {})","def initialize(self):\r\n state_name = self.state\r\n\r\n state_name = state_name.lower()\r\n\r\n response = requests.get(\"https://cdn-api.co-vin.in/api/v2/admin/location/states\") \r\n\r\n if response.ok:\r\n\r\n df = pd.DataFrame(json.loads(response.text)[\"states\"]) \r\n\r\n state = process.extractOne(state_name, df[\"state_name\"].tolist()) # fuzzy match to get best state match \r\n\r\n self.state_id = df.loc[df.state_name == state[0],[\"state_id\"]].values[0][0] \r\n self.load_districts()","def __init__(self):\r\n\r\n super(Feedthrough, self).__init__()\r\n\r\n # Initialize public scalar attributes.\r\n self.spec_sheet = 0 # Characteristic E, K, P, or W.\r\n if self.hazard_rate_type < 3: # MIL-HDBK-217\r\n self.reference_temperature = 358.0","def __init__(self, date_debut, date_fin, lieu_depart, lieu_arrivee, distance, voiture, client, cout):\r\n\r\n self._date_debut = date_debut\r\n self._date_fin = date_fin\r\n self._lieu_depart = lieu_depart\r\n self._lieu_arrivee = lieu_arrivee\r\n self._distance = distance\r\n self._voiture = voiture\r\n self._client = client","def initialize_electronics(self):\n\n self.electronics = ArduinoModel(**self.config['electronics']['arduino'])\n self.logger.info('Initializing electronics arduino')\n self.electronics.initialize()","def readFromMetadata(cls, context, fqId):\n newInstance = ClimatePointStation()\n (newInstance.type, newInstance.id) = fqId.split(GenericMetadata.COMPOUND_KEY_SEP)\n\n climate = GenericMetadata.readClimatePointEntries(context)\n \n keyProto = 'station' + GenericMetadata.COMPOUND_KEY_SEP + fqId + GenericMetadata.COMPOUND_KEY_SEP\n longitude = keyProto + 'longitude'\n newInstance.longitude = float(climate[longitude])\n latitude = keyProto + 'latitude'\n newInstance.latitude = float(climate[latitude])\n elevation = keyProto + 'elevation'\n newInstance.elevation = float(climate[elevation])\n name = keyProto + 'name'\n newInstance.name = climate[name] \n startDate = keyProto + 'startdate'\n try:\n newInstance.startDate = datetime.strptime(climate[startDate], ClimatePointStation.FMT_DATE)\n except KeyError:\n pass \n endDate = keyProto + 'enddate'\n try:\n newInstance.endDate = datetime.strptime(climate[endDate], ClimatePointStation.FMT_DATE)\n except KeyError:\n pass\n variablesKey = keyProto + 'variables'\n try:\n newInstance.variables = climate[variablesKey].split(GenericMetadata.VALUE_DELIM)\n except KeyError:\n pass\n data = keyProto + 'data'\n try:\n newInstance.data = climate[data]\n except KeyError:\n pass\n try:\n for var in newInstance.variables:\n varKey = keyProto + var + GenericMetadata.COMPOUND_KEY_SEP + 'data'\n newInstance.variablesData[var] = climate[varKey]\n except KeyError:\n pass\n \n return newInstance","def __init__(self, reset_pycity_default_values=True,\n chp_tax_return=0.0055, eeg_pay=0.0688,\n eex_baseload=[0.03309, 0.03309, 0.03309, 0.03309],\n grid_av_fee=0.0055, hp_day_tarif=0.22, hp_night_tarif=0.2):\n\n super(GermanMarket, self).__init__(reset_pycity_default_values=\n reset_pycity_default_values)\n\n # List of CHP subsidies for fed-in electric energy\n self._sub_chp = [0.08, 0.06, 0.05, 0.044, 0.031]\n\n # List of CHP subsidies for self-consumed electric energy\n self._sub_chp_self = [0.04, 0.03, 0]\n\n # List of PV subsidies (<= 10 kW, <= 40 kW, <= 100 kW, commercial\n self._sub_pv = [0.1183, 0.115, 0.1028, 0.0818] # Since 01.10.2018\n # E.g. https://www.photovoltaik4all.de/aktuelle-eeg-verguetungssaetze-fuer-photovoltaikanlagen-2017\n\n # CHP tax return on gas\n self.chp_tax_return = chp_tax_return\n\n # HP tarifs\n self.hp_day_tarif = hp_day_tarif\n self.hp_night_tarif = hp_night_tarif\n\n # EEX baseload prices\n self.eex_baseload = eex_baseload\n\n # Grid usage avoidance fee\n self.grid_av_fee = grid_av_fee\n\n # EEG payment\n self.eeg_pay = eeg_pay\n\n # Dict with EEG payment on self consumed energy (status quo: 2017)\n self._dict_eeg_self = {'pv': 0.4 * eeg_pay, 'chp': 0.4 * eeg_pay}","def __init__(self, param_dictionary):\n\n BaseModel.__init__(self)\n\n # set starting compartment values\n self.set_compartment(\"susceptible\",\n param_dictionary[\"population\"] - param_dictionary[\"start_infectious\"])\n self.set_compartment(\"preinfectious\", 0.)\n self.set_compartment(\"infectious\", param_dictionary[\"start_infectious\"])\n self.set_compartment(\"immune\", 0.)\n\n # set model parameters\n self.set_param(\"infection_beta\",\n param_dictionary[\"r0\"]\n / (param_dictionary[\"duration_infectious\"] * param_dictionary[\"population\"]))\n self.set_param(\"infection_rate_progress\", 1. / param_dictionary[\"duration_preinfectious\"])\n self.set_param(\"infection_rate_recover\", 1. / param_dictionary[\"duration_infectious\"])"],"string":"[\n \"def __init__(self):\\n \\n self._citydb = CityDB()\\n self._depression = 6 # Set default depression in degrees\",\n \"def test_init(self, fixture_environment):\\n\\n # Generate city object\\n city_object = cit.City(environment=fixture_environment)\\n\\n # Check inheritance from citydistrict object of pycity\\n assert city_object._kind == 'citydistrict'\",\n \"def __init__(self):\\n\\n self.Cp_air0 = config_earth.earth_properties['Cp_air0']\\n self.Rsp_air = config_earth.earth_properties['Rsp_air']\\n\\n self.d = config_earth.balloon_properties['d']\\n self.vol = math.pi*4/3*pow((self.d/2),3) #volume m^3\\n self.surfArea = math.pi*self.d*self.d #m^2\\n self.cs_area = math.pi*self.d*self.d/4.0 #m^2\\n\\n #self.emissEnv = config_earth.balloon_properties['emissEnv']\\n self.areaDensityEnv = config_earth.balloon_properties['areaDensityEnv']\\n self.mp = config_earth.balloon_properties['mp']\\n self.mdot = 0\\n self.massEnv = config_earth.balloon_properties['mEnv']\\n self.Upsilon = config_earth.balloon_properties['Upsilon']\\n\\n self.vent = config_earth.simulation['vent']\\n self.coord = config_earth.simulation['start_coord']\\n self.t = config_earth.simulation['start_time']\\n self.lat = math.radians(self.coord['lat'])\\n self.Ls = self.t.timetuple().tm_yday\\n self.min_alt = config_earth.simulation['min_alt']\\n\\n self.vm_coeff = .1 #virtual mass coefficient\\n self.k = self.massEnv*config_earth.balloon_properties['cp'] #thermal mass coefficient\\n\\n self.dt = config_earth.dt\",\n \"async def _async_setup_entity(\\n hass: HomeAssistant,\\n async_add_entities: AddEntitiesCallback,\\n config: ConfigType,\\n config_entry: ConfigEntry,\\n discovery_data: DiscoveryInfoType | None = None,\\n) -> None:\\n async_add_entities([MqttClimate(hass, config, config_entry, discovery_data)])\",\n \"def __init__(self, world, location, elevation):\\n LandCell.__init__(self, world, location, elevation)\\n self.plant = 0\\n self.reset_food_level()\",\n \"def __init__(self):\\n\\n super().__init__(\\n dynamics_model=DoorDynamicsModel(),\\n virtual_sensor_model=UnimodalVirtualSensorModel(\\n virtual_sensor_model=[\\n DoorVirtualSensorModel(modalities={\\\"image\\\"}),\\n DoorVirtualSensorModel(modalities={\\\"pos\\\", \\\"sensors\\\"}),\\n ],\\n state_dim=3,\\n ),\\n )\",\n \"def __init__(self, alt=0, temp_offset=0):\\n\\t\\tWorkingAtmosphere.__init__(self, alt)\\n\\t\\t#self.temperature_offset = tOffset\\n\\t\\tself.Temperature_offset = temp_offset\\n\\t\\tself.make_environment()\",\n \"def __init__(self):\\n \\n self.mineLatLong = np.array([0.0,0.0]) \\n self.theOreBody = OreBodyDataManager()\\n self.theMiningSystem = MiningSystemDataManager()\\n self.theProcessingSystem = ProcessingSystemDataManager()\\n self.theEconomicDataManager = EconomicDataManager()\\n self.theInfrastructureManager = InfrastructureDataManager()\",\n \"def initialize_carla_client(self) -> None:\\n\\n self.carla_client = carla.Client(CARLA_SERVER_HOST, CARLA_SERVER_PORT)\\n self.carla_client.load_world(\\\"Town05\\\")\\n self.carla_world = self.carla_client.get_world()\\n self.carla_map = self.carla_world.get_map()\\n\\n self.settings = self.carla_world.get_settings()\\n self.settings.synchronous_mode = True\\n self.settings.fixed_delta_seconds = 1 / CARLA_SERVER_FPS\\n\\n self.carla_world.apply_settings(self.settings)\",\n \"def __init__(self, world, location, elevation):\\n LandCell.__init__(self, world, location, elevation)\",\n \"def __init__(\\n self,\\n hass: HomeAssistant,\\n config: ConfigType,\\n config_entry: ConfigEntry,\\n discovery_data: DiscoveryInfoType | None,\\n ) -> None:\\n self._attr_fan_mode = None\\n self._attr_hvac_action = None\\n self._attr_hvac_mode = None\\n self._attr_is_aux_heat = None\\n self._attr_swing_mode = None\\n MqttTemperatureControlEntity.__init__(\\n self, hass, config, config_entry, discovery_data\\n )\",\n \"def horde_init(self, horde_info= {}):\",\n \"def init_ocean(web3, account=None):\\n ocn = Ocean()\\n token_contract = deploy_contract(web3, account, 'OceanToken')\\n ContractHandler.set('OceanToken', token_contract)\\n ContractHandler.set('DIDRegistry', deploy_contract(web3, account, 'DIDRegistry'))\\n ContractHandler.set('OceanMarket',\\n deploy_contract(web3, account, 'OceanMarket', token_contract.address))\\n service_agreement_contract = deploy_contract(web3, account, 'ServiceExecutionAgreement')\\n ContractHandler.set('ServiceExecutionAgreement', service_agreement_contract)\\n ContractHandler.set('PaymentConditions', deploy_contract(web3, account, 'PaymentConditions',\\n service_agreement_contract.address,\\n token_contract.address))\\n ContractHandler.set('AccessConditions', deploy_contract(web3, account, 'AccessConditions',\\n service_agreement_contract.address))\\n return ocn\",\n \"def __defaults__(self): \\n self.tag = 'Constant-property atmosphere'\\n self.composition = Data()\\n self.composition.gas = 1.0\",\n \"def __init__(self):\\r\\n\\r\\n super(Metallized, self).__init__()\\r\\n\\r\\n # Initialize public scalar attributes.\\r\\n self.spec_sheet = 0\\r\\n if self.hazard_rate_type < 3: # MIL-HDBK-217\\r\\n self.reference_temperature = 358.0\",\n \"def _initialize_dipole_model(self, model):\\n for key, hyst in model['Hysteresis'].items():\\n direction = np.array([float(x) for x in hyst['dir'].split(\\\" \\\")])\\n self.dipoleM.addHysteresis(direction, hyst['vol'], hyst['Hc'], hyst['Bs'], hyst['Br'])\\n\\n # initialize values for Hysteresis (need B-field @ initial position)\\n spacecraft_state = self.state_observer.spacecraftState\\n self.inertial2Sat = spacecraft_state.getAttitude().getRotation()\\n self.satPos_i = spacecraft_state.getPVCoordinates().getPosition()\\n\\n gP = self.earth.transform(self.satPos_i, self.in_frame, self.in_date)\\n\\n topoframe = TopocentricFrame(self.earth, gP, 'ENU')\\n topo2inertial = topoframe.getTransformTo(self.in_frame, self.in_date)\\n\\n lat = gP.getLatitude()\\n lon = gP.getLongitude()\\n alt = gP.getAltitude() / 1e3 # Mag. Field needs degrees and [km]\\n\\n # get B-field in geodetic system (X:East, Y:North, Z:Nadir)\\n B_geo = FileDataHandler.mag_field_model.calculateField(\\n degrees(lat), degrees(lon), alt).getFieldVector()\\n\\n # convert geodetic frame to inertial and from [nT] to [T]\\n B_i = topo2inertial.transformVector(Vector3D(1e-9, B_geo))\\n\\n B_b = self.inertial2Sat.applyTo(B_i)\\n B_field = np.array([B_b.x, B_b.y, B_b.z])\\n\\n self.dipoleM.initializeHysteresisModel(B_field)\\n\\n # add bar magnets to satellite\\n for key, bar in model['BarMagnet'].items():\\n direction = np.array([float(x) for x in bar['dir'].split(\\\" \\\")])\\n self.dipoleM.addBarMagnet(direction, bar['m'])\",\n \"def _initialize_dipole_model(self, model):\\n for key, hyst in model['Hysteresis'].items():\\n direction = np.array([float(x) for x in hyst['dir'].split(\\\" \\\")])\\n self.dipoleM.addHysteresis(direction, hyst['vol'], hyst['Hc'], hyst['Bs'], hyst['Br'])\\n\\n # initialize values for Hysteresis (need B-field @ initial position)\\n spacecraft_state = self.state_observer.spacecraftState\\n self.inertial2Sat = spacecraft_state.getAttitude().getRotation()\\n self.satPos_i = spacecraft_state.getPVCoordinates().getPosition()\\n\\n gP = self.earth.transform(self.satPos_i, self.in_frame, self.in_date)\\n\\n topoframe = TopocentricFrame(self.earth, gP, 'ENU')\\n topo2inertial = topoframe.getTransformTo(self.in_frame, self.in_date)\\n\\n lat = gP.getLatitude()\\n lon = gP.getLongitude()\\n alt = gP.getAltitude() / 1e3 # Mag. Field needs degrees and [km]\\n\\n # get B-field in geodetic system (X:East, Y:North, Z:Nadir)\\n B_geo = FileDataHandler.mag_field_model.calculateField(\\n degrees(lat), degrees(lon), alt).getFieldVector()\\n\\n # convert geodetic frame to inertial and from [nT] to [T]\\n B_i = topo2inertial.transformVector(Vector3D(1e-9, B_geo))\\n\\n B_b = self.inertial2Sat.applyTo(B_i)\\n B_field = np.array([B_b.x, B_b.y, B_b.z])\\n\\n self.dipoleM.initializeHysteresisModel(B_field)\\n\\n # add bar magnets to satellite\\n for key, bar in model['BarMagnet'].items():\\n direction = np.array([float(x) for x in bar['dir'].split(\\\" \\\")])\\n self.dipoleM.addBarMagnet(direction, bar['m'])\",\n \"def init_geofence(self):\\n\\t\\tself.create_ring()\\n\\t\\tself.create_geofence()\",\n \"async def async_setup_entry(\\n hass: HomeAssistant,\\n config_entry: ConfigEntry,\\n async_add_entities: AddEntitiesCallback,\\n) -> None:\\n async_add_entities(\\n [\\n DemoClimate(\\n unique_id=\\\"climate_1\\\",\\n device_name=\\\"HeatPump\\\",\\n target_temperature=68,\\n unit_of_measurement=UnitOfTemperature.FAHRENHEIT,\\n preset=None,\\n current_temperature=77,\\n fan_mode=None,\\n target_humidity=None,\\n current_humidity=None,\\n swing_mode=None,\\n hvac_mode=HVACMode.HEAT,\\n hvac_action=HVACAction.HEATING,\\n aux=None,\\n target_temp_high=None,\\n target_temp_low=None,\\n hvac_modes=[HVACMode.HEAT, HVACMode.OFF],\\n ),\\n DemoClimate(\\n unique_id=\\\"climate_2\\\",\\n device_name=\\\"Hvac\\\",\\n target_temperature=21,\\n unit_of_measurement=UnitOfTemperature.CELSIUS,\\n preset=None,\\n current_temperature=22,\\n fan_mode=\\\"On High\\\",\\n target_humidity=67,\\n current_humidity=54,\\n swing_mode=\\\"Off\\\",\\n hvac_mode=HVACMode.COOL,\\n hvac_action=HVACAction.COOLING,\\n aux=False,\\n target_temp_high=None,\\n target_temp_low=None,\\n hvac_modes=[cls for cls in HVACMode if cls != HVACMode.HEAT_COOL],\\n ),\\n DemoClimate(\\n unique_id=\\\"climate_3\\\",\\n device_name=\\\"Ecobee\\\",\\n target_temperature=None,\\n unit_of_measurement=UnitOfTemperature.CELSIUS,\\n preset=\\\"home\\\",\\n preset_modes=[\\\"home\\\", \\\"eco\\\"],\\n current_temperature=23,\\n fan_mode=\\\"Auto Low\\\",\\n target_humidity=None,\\n current_humidity=None,\\n swing_mode=\\\"Auto\\\",\\n hvac_mode=HVACMode.HEAT_COOL,\\n hvac_action=None,\\n aux=None,\\n target_temp_high=24,\\n target_temp_low=21,\\n hvac_modes=[cls for cls in HVACMode if cls != HVACMode.HEAT],\\n ),\\n ]\\n )\",\n \"def __init__(self, hass: HomeAssistant, client: VisonicClient, visonic_device: PySensorDevice):\\n SelectEntity.__init__(self)\\n #_LOGGER.debug(\\\"Creating select entity for %s\\\",visonic_device.getDeviceName())\\n self.hass = hass\\n self._client = client\\n self._visonic_device = visonic_device\\n self._panel = client.getPanelID()\\n if self._panel > 0:\\n self._name = \\\"visonic_p\\\" + str(self._panel) + \\\"_\\\" + visonic_device.getDeviceName().lower()\\n else:\\n self._name = \\\"visonic_\\\" + visonic_device.getDeviceName().lower()\\n self._is_available = self._visonic_device.isEnrolled()\\n self._is_armed = not self._visonic_device.isBypass()\\n self._pending_state_is_armed = None\\n self._dispatcher = client.getDispatcher()\",\n \"def initialize(self, topo, data, date_time=None):\\n\\n self._logger.debug('Initializing distribute.thermal')\\n self.date_time = date_time\\n self._initialize(topo, data.metadata)\\n\\n self.veg_height = topo.veg_height\\n self.veg_tau = topo.veg_tau\\n self.veg_k = topo.veg_k\\n self.sky_view_factor = topo.sky_view_factor\\n if not self.correct_terrain:\\n self.sky_view_factor = None\\n self.dem = topo.dem\\n\\n if self.correct_cloud:\\n self.add_thread_variables('thermal_cloud')\\n\\n if self.correct_veg:\\n self.add_thread_variables('thermal_veg')\",\n \"def initialize(self):\\n\\n # --------- BEGIN YOUR CODE ----------\\n\\n # This is exactly the same as Human.initialize, just copy the code over\\n\\n # --------- END YOUR CODE ----------\\n pass\",\n \"def __init__(self, cpara, dz_soil):\\n\\n # --- grid ---\\n self.z = np.linspace(0, cpara['grid']['zmax'], cpara['grid']['Nlayers']) # grid [m] above ground\\n self.dz = self.z[1] - self.z[0] # gridsize [m]\\n self.ones = np.ones(len(self.z)) # dummy\\n\\n # --- switches ---\\n self.Switch_Eflow = cpara['ctr']['Eflow'] # True assumes constant U/ustar at upper boundary\\n self.Switch_WMA = cpara['ctr']['WMA'] # True solves scalar profiles\\n self.Switch_Ebal = cpara['ctr']['Ebal'] # True solves leaf energy balance\\n\\n logger.info('Eflow: %s, WMA: %s, Ebal: %s',\\n self.Switch_Eflow,\\n self.Switch_WMA,\\n self.Switch_Ebal)\\n\\n # --- PlantTypes ---\\n ptypes = []\\n ptnames = list(cpara['planttypes'].keys())\\n ptnames.sort()\\n for pt in ptnames:\\n ptypes.append(PlantType(self.z, cpara['planttypes'][pt], dz_soil, ctr=cpara['ctr'], loc=cpara['loc']))\\n self.planttypes = ptypes\\n \\n # --- stand characteristics: sum over planttypes---\\n\\n # total leaf area index [m2 m-2]\\n self.LAI = sum([pt.LAI for pt in self.planttypes])\\n # total leaf area density [m2 m-3]\\n self.lad = sum([pt.lad for pt in self.planttypes])\\n\\n # layerwise mean leaf characteristic dimension [m] for interception model\\n self.leaf_length = sum([pt.leafp['lt'] * pt.lad for pt in self.planttypes]) / (self.lad + EPS)\\n\\n # root area density [m2 m-3]\\n rad = np.zeros(np.shape(dz_soil))\\n imax = 1\\n for pt in self.planttypes:\\n rad[:len(pt.Roots.rad)] += pt.Roots.rad\\n imax = max(imax, len(pt.Roots.rad))\\n \\n self.ix_roots = np.array(range(imax)) # soil model layers corresponding to root zone\\n self.rad = rad[self.ix_roots]\\n \\n # total root area index [m2 m-2]\\n self.RAI = sum([pt.Roots.RAI for pt in self.planttypes])\\n # distribution of roots across soil model layers [-]\\n self.root_distr = self.rad * dz_soil[self.ix_roots] / (self.RAI + EPS)\\n\\n # canopy height [m]\\n if len(np.where(self.lad > 0)[0]) > 0:\\n f = np.where(self.lad > 0)[0][-1]\\n self.hc = self.z[f].copy()\\n else:\\n self.hc = 0.0\\n\\n # --- create radiation, micromet, interception, and forestfloor model instances\\n self.radiation = Radiation(cpara['radiation'], self.Switch_Ebal)\\n\\n self.micromet = Micromet(self.z, self.lad, self.hc, cpara['micromet'])\\n\\n self.interception = Interception(cpara['interception'], self.lad * self.dz)\\n\\n self.forestfloor = ForestFloor(cpara['forestfloor'])\",\n \"def setUp(self):\\n commandutils.CommandTestCaseMixin.setUp(self)\\n self.lighting = objects.LocationLighting.createFor(\\n self.location, candelas=0)\",\n \"def initial(self):\\n\\n self.var.Kfrost = loadmap('Kfrost')\\n self.var.Afrost = loadmap('Afrost')\\n self.var.FrostIndexThreshold = loadmap('FrostIndexThreshold')\\n self.var.SnowWaterEquivalent = loadmap('SnowWaterEquivalent')\\n\\n # FrostIndexInit=ifthen(defined(self.var.MaskMap),scalar(loadmap('FrostIndexInitValue')))\\n # self.var.FrostIndex=FrostIndexInit\\n self.var.FrostIndex = loadmap('FrostIndexInitValue')\\n # self.var.AfrostIndex=-(1-self.var.Afrost)*self.var.FrostIndex\\n # initial Frost Index value\",\n \"def on_init(self):\\n self.model.maze.initialize(os.path.join(\\n config.value['src']['data'], 'maze.csv'))\",\n \"def __init__(\\n self,\\n unique_id: str,\\n device_name: str,\\n target_temperature: float | None,\\n unit_of_measurement: str,\\n preset: str | None,\\n current_temperature: float,\\n fan_mode: str | None,\\n target_humidity: int | None,\\n current_humidity: int | None,\\n swing_mode: str | None,\\n hvac_mode: HVACMode,\\n hvac_action: HVACAction | None,\\n aux: bool | None,\\n target_temp_high: float | None,\\n target_temp_low: float | None,\\n hvac_modes: list[HVACMode],\\n preset_modes: list[str] | None = None,\\n ) -> None:\\n self._unique_id = unique_id\\n self._attr_supported_features = SUPPORT_FLAGS\\n if target_temperature is not None:\\n self._attr_supported_features |= ClimateEntityFeature.TARGET_TEMPERATURE\\n if preset is not None:\\n self._attr_supported_features |= ClimateEntityFeature.PRESET_MODE\\n if fan_mode is not None:\\n self._attr_supported_features |= ClimateEntityFeature.FAN_MODE\\n if target_humidity is not None:\\n self._attr_supported_features |= ClimateEntityFeature.TARGET_HUMIDITY\\n if swing_mode is not None:\\n self._attr_supported_features |= ClimateEntityFeature.SWING_MODE\\n if aux is not None:\\n self._attr_supported_features |= ClimateEntityFeature.AUX_HEAT\\n if HVACMode.HEAT_COOL in hvac_modes or HVACMode.AUTO in hvac_modes:\\n self._attr_supported_features |= (\\n ClimateEntityFeature.TARGET_TEMPERATURE_RANGE\\n )\\n self._target_temperature = target_temperature\\n self._target_humidity = target_humidity\\n self._unit_of_measurement = unit_of_measurement\\n self._preset = preset\\n self._preset_modes = preset_modes\\n self._current_temperature = current_temperature\\n self._current_humidity = current_humidity\\n self._current_fan_mode = fan_mode\\n self._hvac_action = hvac_action\\n self._hvac_mode = hvac_mode\\n self._aux = aux\\n self._current_swing_mode = swing_mode\\n self._fan_modes = [\\\"on_low\\\", \\\"on_high\\\", \\\"auto_low\\\", \\\"auto_high\\\", \\\"off\\\"]\\n self._hvac_modes = hvac_modes\\n self._swing_modes = [\\\"auto\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"off\\\"]\\n self._target_temperature_high = target_temp_high\\n self._target_temperature_low = target_temp_low\\n self._attr_device_info = DeviceInfo(\\n identifiers={(DOMAIN, unique_id)},\\n name=device_name,\\n )\",\n \"def __init__(self, island_map):\\n self.map = island_map\\n self.cells = None\\n self.array_to_island()\\n self.herbivores_on_island = None\\n self.carnivores_on_island = None\",\n \"def __init__(self, hass, name, latitude, longitude, radius, include, exclude):\\n self._hass = hass\\n self._name = name\\n self._include = include\\n self._exclude = exclude\\n radius_kilometers = convert(radius, LENGTH_METERS, LENGTH_KILOMETERS)\\n self._crimereports = crimereports.CrimeReports(\\n (latitude, longitude), radius_kilometers\\n )\\n self._attributes = None\\n self._state = None\\n self._previous_incidents = set()\",\n \"def __init__(self, topology, dofs_per_codim_entity):\\n self.topology = topology\\n self.datasets = dict((eset, DataSet(eset, dofs_per_codim_entity.get(eset.codimension, 0)))\\n for eset in topology.entity_variants())\",\n \"def __init__(self, hass: HomeAssistant) -> None:\\n super().__init__(\\n hass,\\n LOGGER,\\n name=DOMAIN,\\n update_interval=SCAN_INTERVAL,\\n )\\n\\n self.easyenergy = EasyEnergy(session=async_get_clientsession(hass))\",\n \"def __init__(self, *args):\\n super(DefaultSystem, self).__init__(*args)\\n self.use_strong_form = True\\n self.main = self.cavity_grid.main\\n self.cavities = self.cavity_grid.cavities\",\n \"def __init__(self, coor):\\n self._coordinates = coor\",\n \"def initialise(self):\\n self.set_up()\",\n \"def __init__(self, num_of_cheeses, num_of_stools):\\n\\n self.toah_model = TOAHModel(num_of_stools)\\n self.toah_model.fill_first_stool(num_of_cheeses)\",\n \"def _construct_climate(self, timestep):\\n cl = {}\\n now, end = self._get_now_and_end(timestep)\\n if now==-1:\\n return -1\\n if self.simulation:\\n if self.md.duration_unit == 'month':\\n cl['duration'] = self.timestep_length / 12.\\n elif self.md.duration_unit == 'year':\\n cl['duration'] = self.timestep_length\\n else:\\n cl['duration'] = STEADY_STATE_TIMESTEP\\n if self.md.climate_mode == 'constant yearly':\\n cl['rain'] = self.md.constant_climate.annual_rainfall\\n cl['temp'] = self.md.constant_climate.mean_temperature\\n cl['amplitude'] = self.md.constant_climate.variation_amplitude\\n elif self.md.climate_mode == 'monthly':\\n cl = self._construct_monthly_climate(cl, now, end)\\n elif self.md.climate_mode == 'yearly':\\n cl = self._construct_yearly_climate(cl, now, end)\\n return cl\",\n \"def __init__(self, year=None, month=None, day=None, map_type='Cases'):\\n self.covid_df = pd.DataFrame([])\\n self.geo_data = pd.DataFrame([])\\n self.name_iso2_mapping = {}\\n self.countries_centroids = pd.DataFrame([])\\n \\n try:\\n self.date = datetime(year=year, month=month, day=day)\\n except:\\n print('Invalid/empty date entry (year, month, day take valid int inputs)! Date defaulted to today.')\\n self.date = datetime.today()\\n \\n if self.date > datetime.today():\\n print('Can\\\\'t input future date! Date defaulted to today.')\\n self.date = datetime.today()\\n \\n if map_type not in ['Cases', 'Deaths']:\\n sys.exit('Please specify either \\\"Cases\\\" or \\\"Deaths\\\" as map type!')\\n else:\\n self.map_type = map_type\",\n \"def init_physical(\\n ombh2=0.022161, omch2=0.11889, H0=67.77, omkh2=0.0, t0=2.726, nnu=3.046\\n ):\\n h = H0 / 100.0\\n\\n c = Cosmology()\\n\\n c.omega_b = ombh2 / h ** 2\\n c.omega_c = omch2 / h ** 2\\n c.H0 = H0\\n\\n rhoc = 3.0 * c.H() ** 2 * c_sl ** 2 / (8.0 * math.pi * G_n)\\n rhorad = a_rad * t0 ** 4\\n c.omega_g = rhorad / rhoc\\n\\n rhonu = nnu * rhorad * 7.0 / 8.0 * (4.0 / 11.0) ** (4.0 / 3.0)\\n c.omega_n = rhonu / rhoc\\n\\n c.omega_l = 1.0 - (omkh2 + ombh2 + omch2) / h ** 2 - (c.omega_g + c.omega_n)\\n\\n return c\",\n \"def __init__(\\n self,\\n dem_path: str,\\n model_name: Optional[str] = None,\\n ela: int = 2850,\\n m: float = 0.006,\\n plot: bool = True,\\n ) -> None:\\n\\n # Load DEM ------------------------------------------------------------\\n dem = open(dem_path)\\n ele = dem.read(1).astype(np.float32)\\n\\n # Instance variables --------------------------------------------------\\n self.model_name = Path(dem_path).stem if model_name is None else model_name\\n self.dem_path = dem_path\\n\\n # Mass balance parameters\\n self.m = m # Mass balance gradient\\n self.ela_start = ela # Equilibrium line altitude\\n self._setup_params() # Variable parameters (i, ela, steady_state)\\n\\n # 2D arrays\\n self.ele_orig = np.copy(ele) # Original topography\\n self._setup_ndarrays() # Variable arrays (ele, h, u ,hs)\\n\\n # Coordinate reference system and dem resolution\\n self._dem_meta = dem.meta\\n self.res = dem.res[0]\\n\\n # Geographical extent of the dem\\n x0, y0, x1, y1 = dem.bounds\\n self.extent = (x0, x1, y1, y0)\\n\\n # Setup statistics\\n self._setup_stats()\\n\\n # Setup plot\\n self.plot = plot\",\n \"def __init__(self, melon_type, shape, color_rating, harvest_from, harvest_by):\\n self.melon_type = melon_type\\n self.shape = shape\\n self.color_rating = color_rating\\n self.harvest_from = harvest_from\\n self.harvest_by = harvest_by\",\n \"def __init__(self,\\n launchSiteLat,\\n launchSiteLon,\\n launchSiteElev,\\n dateAndTime,\\n UTC_offset=0,\\n inflationTemperature=0.0,\\n forceNonHD=False,\\n forecastDuration=4,\\n use_async=True,\\n requestSimultaneous=True,\\n debugging=False,\\n progressHandler=None,\\n load_on_init=False):\\n # Initialize extra forecast-specific variables\\n self.forceNonHD = forceNonHD\\n self.forecastDuration = forecastDuration\\n self.use_async = use_async\\n self.requestSimultaneous = requestSimultaneous\\n\\n self._GFSmodule = None\\n\\n # This should be the last thing that is called on init, since the base\\n # (environment) class init calls self.load (if load_on_init is True)\\n super(forecastEnvironment, self).__init__(\\n inflationTemperature=inflationTemperature,\\n launchSiteLat=launchSiteLat,\\n launchSiteLon=launchSiteLon,\\n launchSiteElev=launchSiteElev,\\n dateAndTime=dateAndTime,\\n UTC_offset=UTC_offset,\\n debugging=debugging,\\n load_on_init=load_on_init)\",\n \"def __init__(self, number_of_cheeses, number_of_stools):\\n self.model = TOAHModel(number_of_stools)\\n self.model.fill_first_stool(number_of_cheeses)\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.set_oversamplings_and_mode(\\n HumidityOversampling.x08,\\n TemperatureOversampling.x08,\\n PressureOversampling.x16,\\n SensorMode.Normal)\\n self.set_config(\\n InactiveDuration.ms1000,\\n FilterCoefficient.fc04)\",\n \"def __init__(self, device: SensemeDevice) -> None:\\n super().__init__(device, f\\\"{device.name} Light\\\")\\n self._attr_supported_color_modes = {ColorMode.COLOR_TEMP}\\n self._attr_color_mode = ColorMode.COLOR_TEMP\\n self._attr_min_mireds = color_temperature_kelvin_to_mired(\\n device.light_color_temp_max\\n )\\n self._attr_max_mireds = color_temperature_kelvin_to_mired(\\n device.light_color_temp_min\\n )\",\n \"def __init__(\\n self,\\n hass: HomeAssistant,\\n config: ConfigType,\\n config_entry: ConfigEntry,\\n discovery_data: DiscoveryInfoType | None,\\n ) -> None:\\n self._attr_target_temperature_low = None\\n self._attr_target_temperature_high = None\\n self._feature_preset_mode = False\\n MqttEntity.__init__(self, hass, config, config_entry, discovery_data)\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.configure_sensor(\\n TemperatureOversamplings.x08,\\n PressureOversamplings.x16,\\n HumidityOversamplings.x08,\\n IIRFilterCoefficients.FC_003,\\n 250,\\n 250)\",\n \"def __init__(self):\\n\\n self.name = \\\"\\\"\\n self.address = \\\"\\\"\\n self.coordinates = []\\n self.weatherHours = []\\n self.currentHour = 0\",\n \"def __init__(\\n self,\\n xknx: XKNX,\\n group_address: GroupAddressesType | None = None,\\n group_address_state: GroupAddressesType | None = None,\\n sync_state: bool | int | float | str = True,\\n device_name: str | None = None,\\n feature_name: str = \\\"Climate mode\\\",\\n climate_mode_type: ClimateModeType | None = None,\\n after_update_cb: AsyncCallbackType | None = None,\\n ):\\n super().__init__(\\n xknx,\\n group_address=group_address,\\n group_address_state=group_address_state,\\n sync_state=sync_state,\\n device_name=device_name,\\n feature_name=feature_name,\\n after_update_cb=after_update_cb,\\n )\\n if not isinstance(climate_mode_type, self.ClimateModeType):\\n raise ConversionError(\\n \\\"invalid climate mode type\\\",\\n climate_mode_type=str(climate_mode_type),\\n device_name=str(device_name),\\n feature_name=feature_name,\\n )\\n self._climate_mode_transcoder: (\\n DPTControllerStatus | DPTHVACMode\\n ) = climate_mode_type.value\",\n \"def setUp(self):\\n self.m = m = ConcreteModel()\\n m.t = ContinuousSet(bounds=(0, 10))\\n m.v1 = Var(m.t)\\n m.dv1 = DerivativeVar(m.v1)\\n m.s = Set(initialize=[1, 2, 3], ordered=True)\",\n \"def __init__(self, home=False, away=False, night=False, vacation=False):\\n self.home, self.away, self.night = home, away, night\\n self.vacation = vacation\",\n \"def setup(self):\\n print(\\\"INIT DATA\\\")\\n\\n self.nutella = Product.objects.create(name=\\\"nutella\\\", nutriscore=\\\"e\\\")\",\n \"def test_init():\\n cd = Delta(MAIN_JSON, 'civic')\\n assert cd._main_json == MAIN_JSON\\n assert cd._updated_json is None\\n\\n cd = Delta(MAIN_JSON, 'civic', _updated_json=UPDATED_JSON)\\n assert cd._main_json == MAIN_JSON\\n assert cd._updated_json == UPDATED_JSON\",\n \"def setUp(self):\\n self.my_city = City()\",\n \"def __init__(\\n self,\\n weather,\\n us_holidays,\\n islamic_holidays,\\n jewish_holidays,\\n events,\\n seahawks,\\n huskies,\\n sounders,\\n ):\\n self.weather = weather\\n self.us_holidays = us_holidays\\n self.islamic_holidays = islamic_holidays\\n self.jewish_holidays = jewish_holidays\\n self.events = events\\n self.seahawks = seahawks\\n self.huskies = huskies\\n self.sounders = sounders\\n self.X = None\\n self.y = None\",\n \"def initialize(self):\\n\\t\\ttable = NetworkTables.getTable('SmartDashboard')\\n\\t\\tinitial_pos = table.getData('initial_pos')\\n\\t\\t#XXX Doing this will start the path following immediately. May want this\\n\\t\\t# in the execute or something\\n\\t\\tpath = self.dt.which_path(initial_pos)\",\n \"def setUpClass(self):\\n self.coal = Simulation(logging_level=30)\\n self.tree = CoalescenceTree()\\n self.coal.set_simulation_parameters(\\n 1,\\n 28,\\n \\\"output\\\",\\n 0.1,\\n 4,\\n 4,\\n deme=1,\\n sample_size=1.0,\\n max_time=2,\\n dispersal_relative_cost=1,\\n min_num_species=1,\\n dispersal_method=\\\"normal\\\",\\n )\\n self.coal.set_map_parameters(\\\"null\\\", 10, 10, \\\"null\\\", 10, 10, 0, 0, \\\"null\\\", 20, 20, 0, 0, 1, \\\"null\\\", \\\"null\\\")\\n self.coal.set_speciation_rates([0.1, 0.2])\\n self.coal.add_sample_time(100000000.0)\\n self.coal.run()\\n self.tree.set_database(self.coal)\",\n \"def __init__(self, *args):\\n super().__init__(*args, category=CATEGORY_GARAGE_DOOR_OPENER)\\n self._flag_state = False\\n\\n serv_garage_door = self.add_preload_service(SERV_GARAGE_DOOR_OPENER)\\n self.char_current_state = serv_garage_door.configure_char(\\n CHAR_CURRENT_DOOR_STATE, value=0\\n )\\n self.char_target_state = serv_garage_door.configure_char(\\n CHAR_TARGET_DOOR_STATE, value=0, setter_callback=self.set_state\\n )\",\n \"def __init__(self, type, high_elevation, low_elevation):\\n\\t\\tself.type = type\\n\\t\\tself.high_elevation = high_elevation\\n\\t\\tself.low_elevation = low_elevation\\n\\t\\tself.occupant = 0 # use 0 to initilize tile which \\n\\t\\t\\t\\t\\t\\t\\t# means it has not been explored.\\n\\t\\tif type == \\\"plains\\\": # initialize the shade\\n\\t\\t\\tself.terrain = \\\" \\\"\\n\\t\\telse:\\n\\t\\t\\tself.terrain = \\\"#\\\"\",\n \"def init(self):\\n\\n\\t\\tstatus, param = self.execute(self.mission, 'on_init', self.kingdom)\\n\\n\\t\\treturn status\",\n \"def __init__(self):\\n self.mu = None\\n self.type = None\\n self.direction = None\\n self.eq_points = None\\n self.init_conds = None\\n self.time = None\\n self.trajectory = None\\n self.initial_point = None\\n self.contour_levels = []\",\n \"def __init__(self, device: Thermostat, gateway: DeconzGateway) -> None:\\n super().__init__(device, gateway)\\n\\n self._attr_hvac_modes = [\\n HVACMode.HEAT,\\n HVACMode.OFF,\\n ]\\n if device.mode:\\n self._attr_hvac_modes.append(HVACMode.AUTO)\\n\\n if \\\"coolsetpoint\\\" in device.raw[\\\"config\\\"]:\\n self._attr_hvac_modes.append(HVACMode.COOL)\\n\\n self._deconz_to_hvac_mode = {\\n HVAC_MODE_TO_DECONZ[item]: item for item in self._attr_hvac_modes\\n }\\n\\n self._attr_supported_features = ClimateEntityFeature.TARGET_TEMPERATURE\\n\\n if device.fan_mode:\\n self._attr_supported_features |= ClimateEntityFeature.FAN_MODE\\n self._attr_fan_modes = list(FAN_MODE_TO_DECONZ)\\n\\n if device.preset:\\n self._attr_supported_features |= ClimateEntityFeature.PRESET_MODE\\n self._attr_preset_modes = list(PRESET_MODE_TO_DECONZ)\",\n \"def create_climatology(self):\\n # Because data from all years are merged, the time coordinate must be \\n # made consistent with basic integer values. Monthly dates values are \\n # added to the attributes.\\n time_points = self.cubelist[0].coord(self.time_coord).points\\n new_time_points = range(1, len(time_points) + 1)\\n new_time_atts = {'dates' : self.cube_dates}\\n new_time_coord = iris.coords.DimCoord(new_time_points,\\n standard_name=self.time_coord,\\n attributes=new_time_atts)\\n \\n new_cubelist = []\\n realization_num = 1\\n for cube in self.cubelist:\\n if len(cube.coord(self.realization).points) > 1:\\n cube = self.cube_ensemble_mean(cube)\\n # Make sure all realization points are unique.\\n cube.coord(self.realization).points = [realization_num]\\n # Replace time dimension.\\n time_dim = cube.coord_dims(cube.coord(self.time_coord))\\n cube.remove_coord(self.time_coord)\\n if time_dim:\\n cube.add_dim_coord(new_time_coord, time_dim)\\n else:\\n # If no time_dim, coordinate is auxiliary or scalar.\\n cube.add_aux_coord(new_time_coord)\\n \\n new_cubelist.append(cube)\\n realization_num += 1\\n \\n new_cube = iris.cube.CubeList(new_cubelist).merge_cube()\\n clim_cube = self.cube_ensemble_mean(new_cube)\\n \\n # The initialisation data is now a mean of all years, so like with time\\n # replace the coordinate with a single point and monthly initialisation\\n # dates added to the attributes.\\n init_points = clim_cube.coord(self.forecast_ref_time).points\\n new_init_points = range(1, len(init_points) + 1)\\n new_init_atts = {'dates' : self.cube_init_dates}\\n new_init_coord = iris.coords.AuxCoord(new_init_points,\\n standard_name=self.forecast_ref_time,\\n attributes=new_init_atts)\\n clim_cube.remove_coord(self.forecast_ref_time)\\n clim_cube.add_aux_coord(new_init_coord)\\n \\n self.clim_cube = clim_cube\\n return self.clim_cube\",\n \"def __init__(self, world, location, elevation):\\n self.world = world\\n self.location = location # (row, col)\\n self.elevation = elevation\\n if elevation < 0:\\n self.water_level = -elevation\\n else:\\n self.water_level = 0\\n self.pollution = 0\\n self.crab = None\",\n \"def __init__(self):\\n self.model = None\\n self.joined_datasets = None\\n self.id_col = None\\n self.val_col = None\\n self.pop_col = None\\n self.total_population_per_unit = None\\n self.centroids_of_areal_data = None\\n self.prepared_data = None\\n self.unknown_area_id = None\\n\\n # Parameters\\n self.lags = None\\n self.step = None\\n self.min_no_of_observations = None\\n self.max_search_radius = None\",\n \"def __init__(self):\\n self.TECRDB_compounds_data_dict = {}\\n self.TECRDB_compounds_pH7_species_id_dict = {}\\n self.TECRDB_compounds_least_H_sid_dict = {}\\n self.get_TECRDB_compounds_data()\",\n \"def __init__(self, c_config, client):\\n self.id = c_config['id']\\n self.nodes = []\\n self.client = client\\n self.period = 20\\n self.__init_load(c_config)\",\n \"def setUpClass(cls):\\n cls.coal = Simulation()\\n cls.tree = CoalescenceTree()\\n cls.coal.set_simulation_parameters(\\n 1,\\n 1,\\n \\\"output\\\",\\n 0.1,\\n 4,\\n 4,\\n 1,\\n 1.0,\\n 2,\\n dispersal_relative_cost=1,\\n min_num_species=1,\\n dispersal_method=\\\"fat-tail\\\",\\n landscape_type=True,\\n )\\n cls.coal.set_map_parameters(\\\"null\\\", 10, 10, \\\"null\\\", 10, 10, 0, 0, \\\"null\\\", 20, 20, 0, 0, 1, \\\"none\\\", \\\"none\\\")\\n cls.coal.set_speciation_rates([0.1, 0.2])\\n cls.coal.run()\\n cls.tree.set_database(\\\"output/data_1_1.db\\\")\\n cls.tree.calculate_octaves()\\n cls.tree.calculate_richness()\",\n \"def __init__(self):\\n self.temperature = 70.0\\n self.status = \\\"sunny\\\"\",\n \"def __init__(self):\\n self.temperature = 70.0\\n self.status = \\\"sunny\\\"\",\n \"def __init__(self):\\n self.temperature = 70.0\\n self.status = \\\"sunny\\\"\",\n \"def __init__(self, guid=str(uuid.uuid4()), speciesId=Species, caughtLat=0, caughtLong=0, consumption=Consumption):\\n self.guid = guid\\n self.speciesId = speciesId\\n self.caughtLat = caughtLat\\n self.caughtLong = caughtLong\\n self.consumption = consumption\",\n \"def __init__(self, parent, connection):\\n ConnectionScript.__init__(self, parent, connection)\\n self.entity = Entity()\",\n \"def __init__(self, coordinator, sensor):\\n self._sensor = sensor\\n self.coordinator = coordinator\\n self._name = DOMAIN.title()\\n\\n self._state = None\",\n \"def __init__(self, param_dictionary):\\n\\n BaseModel.__init__(self)\\n\\n # set starting compartment values\\n self.set_compartment(\\\"susceptible\\\",\\n param_dictionary[\\\"population\\\"] - param_dictionary[\\\"start_infectious\\\"])\\n self.set_compartment(\\\"infectious\\\", param_dictionary[\\\"start_infectious\\\"])\\n self.set_compartment(\\\"immune\\\", 0.)\\n\\n # set model parameters\\n self.set_param(\\\"infection_beta\\\",\\n param_dictionary[\\\"r0\\\"]\\n / (param_dictionary[\\\"duration_infectious\\\"] * param_dictionary[\\\"population\\\"]))\\n self.set_param(\\\"infection_rate_recover\\\", 1. / param_dictionary[\\\"duration_infectious\\\"])\",\n \"def __init__(self, hass, device, endpoint):\\n self._device = device\\n self._endpoint = endpoint\\n self._is_on = False\\n a = self._device.get_attribute(endpoint, 6, 0)\\n if a:\\n self._is_on = a.get('value', False)\\n ieee = device.ieee or device.addr # compatibility\\n entity_id = 'zigate_{}_{}'.format(ieee,\\n endpoint)\\n self.entity_id = ENTITY_ID_FORMAT.format(entity_id)\\n hass.bus.listen('zigate.attribute_updated', self._handle_event)\",\n \"def __init__(self, eqn_set=0, atmosphere=0, ra_steps=(1, 1e3, 40, True),\\n kx_steps=(0.01, 1, 40, True), ky_steps=None, threeD=False, atmo_kwargs={}, eqn_args=[],\\n eqn_kwargs={}, bc_kwargs={}):\\n self._eqn_set = eqn_set\\n self._atmosphere = atmosphere\\n self._ra_steps = ra_steps\\n self._kx_steps = kx_steps\\n self._ky_steps = ky_steps\\n self.threeD = threeD\\n\\n self._atmo_kwargs = atmo_kwargs\\n self._eqn_args = eqn_args\\n self._eqn_kwargs = eqn_kwargs\\n self._bc_kwargs = bc_kwargs\\n self.cf = CriticalFinder(self.solve_problem, CW)\",\n \"def __init__(self, manager: db.Manager):\\n super().__init__(manager)\\n self._calculations = manager.get_collection(\\\"calculations\\\")\\n self._forward_flux_label = \\\"_forward_edge_flux\\\"\\n self._backward_flux_label = \\\"_backward_edge_flux\\\"\\n self._zero_break_scale = 1e-4\\n self._kinetic_modeling_job_order = \\\"kinetx_kinetic_modeling\\\"\\n self._reaction_id_key = \\\"reaction_ids\\\"\",\n \"async def async_setup_entry(\\n hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\\n) -> None:\\n await platform_async_setup_entry(\\n hass,\\n entry,\\n async_add_entities,\\n info_type=ClimateInfo,\\n entity_type=EsphomeClimateEntity,\\n state_type=ClimateState,\\n )\",\n \"def Init(self):\\n RobotMap.Init()\\n from commands import *\\n from subsystems import *\\n#@autogenerated_code(\\\"constructors\\\", \\\" \\\")\\n#parse(\\\"${exporter-path}core/robot-constructors.py\\\")\\n#end\\n # This MUST be here. If the OI creates Commands (which it very likely\\n # will), constructing it during the construction of CommandBase (from\\n # which commands extend), subsystems are not guaranteed to be\\n # yet. Thus, their requires() statements may grab null pointers. Bad\\n # news. Don't move it.\\n self.oi = OI()\\n\\n # instantiate the command used for the autonomous period\",\n \"def __init__(self, heatpumpid):\\n import jsonpath\\n self.jsonpath = jsonpath.jsonpath\\n self._target_temperature = None\\n self._current_operation = None\\n self._current_fan_mode = None\\n self._current_swing_mode = None\\n self._on = None\\n self.heatpump_id = heatpumpid\\n self._support_flags = SUPPORT_TARGET_TEMPERATURE | SUPPORT_FAN_MODE |\\\\\\n SUPPORT_OPERATION_MODE | SUPPORT_ON_OFF | SUPPORT_SWING_MODE\\n self._unit_of_measurement = TEMP_CELSIUS\\n self._fan_list = ['Auto', 'Low', 'Medium_Low', 'Medium',\\n 'Medium_High', 'High']\\n self._operation_list = ['heat', 'cool', 'auto', 'dry', 'fan_only']\\n self._swing_list = ['Auto', '0_Degrees', '30_Degrees', '60_Degrees',\\n '90_Degrees']\\n self._config_date = None\\n self.sync_data()\",\n \"def __init__(self, body, observer, date):\\n self.site = observer.site\\n self.body = body\\n self.date = observer.date_to_local(date)\\n self.date_utc = observer.date_to_utc(self.date)\\n\\n self.humidity = observer.humidity\\n self.wavelength = observer.wavelength\\n\\n # Can/should this calculation be postponed?\\n self.site.date = ephem.Date(self.date_utc)\\n self.body.compute(self.site)\\n\\n self.lt = self.date\\n self.ra = self.body.ra\\n self.dec = self.body.dec\\n self.alt = float(self.body.alt)\\n self.az = float(self.body.az)\\n # TODO: deprecate\\n self.alt_deg = math.degrees(self.alt)\\n self.az_deg = math.degrees(self.az)\\n self.will_be_visible = not self.body.neverup\\n\\n # properties\\n self._ut = None\\n self._gmst = None\\n self._lmst = None\\n self._ha = None\\n self._pang = None\\n self._am = None\\n self._moon_alt = None\\n self._moon_pct = None\\n self._moon_sep = None\\n self._atmos_disp = None\\n\\n # Conversion factor for wavelengths (Angstrom -> micrometer)\\n self.angstrom_to_mm = 1. / 10000.\",\n \"def make_environment(self):\\n\\t\\tbase_layer = 0\\n\\t\\tself.Gravity = 9.81\\n\\n\\t\\t#Private data for to define model\\n\\t\\t__model_max_altitude = 87000\\n\\t\\t__atmosphere_layers = {0:0, 11000:1, 20000:2, 32000:3, 47000:4, 51000:5, 71000:6}\\n\\t\\t__layer_base_data = {\\n\\t\\t\\t0:{'temp':288.15, 'lapse':-0.0065, 'press':101325},\\n\\t\\t\\t1:{'temp':216.65, 'lapse':0, 'press':22632.1},\\n\\t\\t\\t2:{'temp':216.65, 'lapse':0.001, 'press':5474.89},\\n\\t\\t\\t3:{'temp':228.65, 'lapse':0.0028, 'press':868.019},\\n\\t\\t\\t4:{'temp':270.65, 'lapse':0, 'press':110.906},\\n\\t\\t\\t5:{'temp':270.65, 'lapse':-0.0028, 'press':66.9389},\\n\\t\\t\\t6:{'temp':214.65, 'lapse':-0.002, 'press':3.95642},\\n\\t\\t\\t}\\n\\t\\t__gas_constant = 8.31432#e3\\n\\t\\t__air_molar_mass = 0.0289644\\n\\t\\t__specific_heat_ratio = 1.4\\n\\t\\t__visc_lambda = 1.51204129e-6\\n\\t\\t__visc_sutherland_const = 120.0\\n\\n\\t\\tif self.Altitude > __model_max_altitude:\\n\\t\\t\\traise helpers.extra_exceptions.ModelExtrapolationException(\\n\\t\\t\\t'Exceeded model maximum altitude')\\n\\n\\t\\tlayerKeys = __atmosphere_layers.keys()\\n\\t\\tlayerKeys = list(layerKeys)\\n\\t\\tlayerKeys.sort()\\n\\t\\tfor layer in layerKeys:\\n\\t\\t\\tif self.Altitude >= layer:\\n\\t\\t\\t\\tbase_layer = __atmosphere_layers[layer]\\n\\t\\t\\t\\tbase_alt = layer\\n\\t\\tbase_temp = __layer_base_data[base_layer]['temp']\\n\\t\\tbase_lapse = __layer_base_data[base_layer]['lapse']\\n\\t\\tbase_press = __layer_base_data[base_layer]['press']\\n\\n\\t\\tself.Temperature = base_temp + base_lapse * (self.Altitude - base_alt)\\n\\t\\t+ self.Temperature_offset\\n\\n\\t\\tif base_lapse == 0:\\n\\t\\t\\tself.Pressure = base_press * \\\\\\n\\t\\t\\t\\tnp.exp( (-self.Gravity*__air_molar_mass*(self.Altitude-base_alt)) \\\\\\n\\t\\t\\t\\t/(__gas_constant*base_temp))\\n\\t\\telse:\\n\\t\\t\\tself.Pressure = base_press * \\\\\\n\\t\\t\\t\\t(base_temp/self.Temperature) ** \\\\\\n\\t\\t\\t\\t(self.Gravity*__air_molar_mass/__gas_constant/base_lapse)\\n\\n\\t\\tself.Density = __air_molar_mass*self.Pressure / \\\\\\n\\t\\t\\t__gas_constant/self.Temperature\\n\\t\\tself.Speed_of_sound = np.sqrt(__specific_heat_ratio*__gas_constant* \\\\\\n\\t\\t\\tself.Temperature/__air_molar_mass)\\n\\t\\tself.Dynamic_viscosity = __visc_lambda*self.Temperature**(3.0/2.0)/ \\\\\\n\\t\\t\\t(self.Temperature+__visc_sutherland_const)\",\n \"def Initialize(self):\\n return _gmat_py.EphemManager_Initialize(self)\",\n \"def setUpClass(self):\\n self.coal = Simulation()\\n self.tree = CoalescenceTree()\\n self.coal.set_simulation_parameters(\\n 4,\\n 4,\\n \\\"output\\\",\\n 0.1,\\n 4,\\n 4,\\n 1,\\n 0.01,\\n 2,\\n dispersal_relative_cost=1,\\n min_num_species=1,\\n dispersal_method=\\\"fat-tail\\\",\\n )\\n self.coal.set_map_files(\\n \\\"null\\\", fine_file=\\\"sample/SA_sample_fine.tif\\\", coarse_file=\\\"sample/SA_sample_coarse.tif\\\"\\n )\\n self.coal.detect_map_dimensions()\\n self.coal.set_speciation_rates([0.1, 0.2])\\n self.coal.run()\\n self.tree.set_database(self.coal)\\n self.tree.calculate_octaves()\\n self.tree.calculate_richness()\",\n \"def initialize(self):\\n\\n myg = grid_setup(self.rp, ng=4)\\n\\n bc_dens, bc_xodd, bc_yodd = bc_setup(self.rp)\\n\\n my_data = patch.CellCenterData2d(myg)\\n\\n my_data.register_var(\\\"density\\\", bc_dens)\\n my_data.register_var(\\\"x-velocity\\\", bc_xodd)\\n my_data.register_var(\\\"y-velocity\\\", bc_yodd)\\n\\n # we'll keep the internal energy around just as a diagnostic\\n my_data.register_var(\\\"eint\\\", bc_dens)\\n\\n # phi -- used for the projections. The boundary conditions\\n # here depend on velocity. At a wall or inflow, we already\\n # have the velocity we want on the boundary, so we want\\n # Neumann (dphi/dn = 0). For outflow, we want Dirichlet (phi\\n # = 0) -- this ensures that we do not introduce any tangental\\n # acceleration.\\n bcs = []\\n for bc in [self.rp.get_param(\\\"mesh.xlboundary\\\"),\\n self.rp.get_param(\\\"mesh.xrboundary\\\"),\\n self.rp.get_param(\\\"mesh.ylboundary\\\"),\\n self.rp.get_param(\\\"mesh.yrboundary\\\")]:\\n if bc == \\\"periodic\\\":\\n bctype = \\\"periodic\\\"\\n elif bc in [\\\"reflect\\\", \\\"slipwall\\\"]:\\n bctype = \\\"neumann\\\"\\n elif bc in [\\\"outflow\\\"]:\\n bctype = \\\"dirichlet\\\"\\n bcs.append(bctype)\\n\\n bc_phi = bnd.BC(xlb=bcs[0], xrb=bcs[1], ylb=bcs[2], yrb=bcs[3])\\n\\n my_data.register_var(\\\"phi-MAC\\\", bc_phi)\\n my_data.register_var(\\\"phi\\\", bc_phi)\\n\\n # gradp -- used in the projection and interface states. We'll do the\\n # same BCs as density\\n my_data.register_var(\\\"gradp_x\\\", bc_dens)\\n my_data.register_var(\\\"gradp_y\\\", bc_dens)\\n\\n my_data.create()\\n\\n self.cc_data = my_data\\n\\n # some auxiliary data that we'll need to fill GC in, but isn't\\n # really part of the main solution\\n aux_data = patch.CellCenterData2d(myg)\\n\\n aux_data.register_var(\\\"coeff\\\", bc_dens)\\n aux_data.register_var(\\\"source_y\\\", bc_yodd)\\n\\n aux_data.create()\\n self.aux_data = aux_data\\n\\n # we also need storage for the 1-d base state -- we'll store this\\n # in the main class directly.\\n self.base[\\\"rho0\\\"] = Basestate(myg.ny, ng=myg.ng)\\n self.base[\\\"p0\\\"] = Basestate(myg.ny, ng=myg.ng)\\n\\n # now set the initial conditions for the problem\\n problem = importlib.import_module(f\\\"pyro.lm_atm.problems.{self.problem_name}\\\")\\n problem.init_data(self.cc_data, self.base, self.rp)\\n\\n # Construct beta_0\\n gamma = self.rp.get_param(\\\"eos.gamma\\\")\\n self.base[\\\"beta0\\\"] = Basestate(myg.ny, ng=myg.ng)\\n self.base[\\\"beta0\\\"].d[:] = self.base[\\\"p0\\\"].d**(1.0/gamma)\\n\\n # we'll also need beta_0 on vertical edges -- on the domain edges,\\n # just do piecewise constant\\n self.base[\\\"beta0-edges\\\"] = Basestate(myg.ny, ng=myg.ng)\\n self.base[\\\"beta0-edges\\\"].jp(1)[:] = \\\\\\n 0.5*(self.base[\\\"beta0\\\"].v() + self.base[\\\"beta0\\\"].jp(1))\\n self.base[\\\"beta0-edges\\\"].d[myg.jlo] = self.base[\\\"beta0\\\"].d[myg.jlo]\\n self.base[\\\"beta0-edges\\\"].d[myg.jhi+1] = self.base[\\\"beta0\\\"].d[myg.jhi]\",\n \"def __init__(self, effective_from=None, effective_at=None, tenor=None, roll_convention=None, holiday_calendars=None, valuation_date_times=None, local_vars_configuration=None): # noqa: E501 # noqa: E501\\n if local_vars_configuration is None:\\n local_vars_configuration = Configuration.get_default_copy()\\n self.local_vars_configuration = local_vars_configuration\\n\\n self._effective_from = None\\n self._effective_at = None\\n self._tenor = None\\n self._roll_convention = None\\n self._holiday_calendars = None\\n self._valuation_date_times = None\\n self.discriminator = None\\n\\n self.effective_from = effective_from\\n self.effective_at = effective_at\\n self.tenor = tenor\\n self.roll_convention = roll_convention\\n self.holiday_calendars = holiday_calendars\\n self.valuation_date_times = valuation_date_times\",\n \"def __init__(self) -> None:\\n self.homee_host: str = None\\n self.homee_id: str = None\\n self.homee: Homee = None\",\n \"def init_base_stations(mec_set: Dict[str,'Mec']) -> None:\\n \\n mec_keys = []\\n mec_names = []\\n \\n for key, value in mec_set.items():\\n mec_keys.append(key)\\n mec_names.append(value.name)\\n \\n \\n base_station_set = {\\n \\\"base_station_set\\\": {}\\n }\\n \\n bs_topology = onos_controller.OnosController.get_topology()\\n mec_index = 0\\n \\n for base_station in bs_topology: \\n bs_links = {}\\n \\n for link in base_station.links:\\n dst_id = link.dst.device\\n dst_port = link.dst.port\\n dst_latency = None\\n bs_links[dst_id] = {\\n 'port': dst_port,\\n 'latency': dst_latency\\n } \\n \\n mec_name = 'MEC' + base_station.name[2:]\\n mec_index = mec_names.index(mec_name)\\n \\n new_base_station_id = base_station.id\\n new_base_station = BaseStation(\\n name = base_station.name, \\n mec_id = mec_keys[mec_index], \\n wireless_latency = round(random.uniform(0.1, 0.3), 2),\\n links = DefaultMunch.fromDict(bs_links)\\n )\\n base_station_set['base_station_set'][new_base_station_id] = new_base_station\\n \\n \\n BaseStationController.set_bs_net_latency(base_station_set['base_station_set'])\\n json_controller.EncoderController.encoding_to_json(base_station_set)\\n return\",\n \"def setUpClass(self):\\n self.coal = Simulation()\\n self.tree = CoalescenceTree()\\n self.coal.set_simulation_parameters(\\n 2,\\n 2,\\n \\\"output\\\",\\n 0.1,\\n 4,\\n 4,\\n 1,\\n 1.0,\\n 2,\\n dispersal_relative_cost=1,\\n min_num_species=1,\\n dispersal_method=\\\"normal\\\",\\n m_prob=1,\\n landscape_type=True,\\n )\\n self.coal.set_map_parameters(\\\"null\\\", 10, 10, \\\"null\\\", 10, 10, 0, 0, \\\"null\\\", 20, 20, 0, 0, 1, \\\"null\\\", \\\"null\\\")\\n self.coal.set_speciation_rates([0.1, 0.2])\\n self.coal.run()\\n self.tree.set_database(\\\"output/data_2_2.db\\\")\\n self.tree.calculate_octaves()\\n self.tree.calculate_richness()\",\n \"def __init__(self, x, y, z): \\n\\t\\tself.x = x # x coordinate (EW distance from observatory center)\\n\\t\\tself.y = y # y coordinate (NS distance from observatory center)\\n\\t\\tself.z = z # z coordinate (altitude rel. to observatory center)\",\n \"def setUpClass(self):\\n self.coal = Simulation()\\n self.tree = CoalescenceTree()\\n self.coal.set_simulation_parameters(\\n 3, 3, \\\"output\\\", 0.1, 4, 4, 1, 0.1, 2, dispersal_relative_cost=1, min_num_species=1, cutoff=0.0\\n )\\n self.coal.set_map_files(\\\"null\\\", fine_file=\\\"sample/SA_sample_fine.tif\\\")\\n self.coal.detect_map_dimensions()\\n self.coal.set_speciation_rates([0.1, 0.2])\\n self.coal.run()\\n self.tree.set_database(self.coal)\\n self.tree.calculate_octaves()\\n self.tree.calculate_richness()\",\n \"def __init__(self, name, node, temperature_driven=False, repr_days=None):\\n Submodel.__init__(self, name=name,\\n temperature_driven=temperature_driven)\\n\\n self.logger = logging.getLogger('modesto.Node')\\n self.logger.info('Initializing Node {}'.format(name))\\n\\n self.node = node\\n self.loc = self.get_loc()\\n\\n self.components = {}\\n self.pipes = {}\\n\\n self.compiled = False\\n self.repr_days = repr_days\\n\\n self.build()\",\n \"def __init__(self):\\n\\n super(aero_csm_component,self).__init__()\",\n \"def test_init(self):\\r\\n c = AlphaDiversityCalc(observed_otus)\\r\\n self.assertEqual(c.Metric, observed_otus)\\r\\n self.assertEqual(c.Params, {})\",\n \"def initialize(self):\\r\\n state_name = self.state\\r\\n\\r\\n state_name = state_name.lower()\\r\\n\\r\\n response = requests.get(\\\"https://cdn-api.co-vin.in/api/v2/admin/location/states\\\") \\r\\n\\r\\n if response.ok:\\r\\n\\r\\n df = pd.DataFrame(json.loads(response.text)[\\\"states\\\"]) \\r\\n\\r\\n state = process.extractOne(state_name, df[\\\"state_name\\\"].tolist()) # fuzzy match to get best state match \\r\\n\\r\\n self.state_id = df.loc[df.state_name == state[0],[\\\"state_id\\\"]].values[0][0] \\r\\n self.load_districts()\",\n \"def __init__(self):\\r\\n\\r\\n super(Feedthrough, self).__init__()\\r\\n\\r\\n # Initialize public scalar attributes.\\r\\n self.spec_sheet = 0 # Characteristic E, K, P, or W.\\r\\n if self.hazard_rate_type < 3: # MIL-HDBK-217\\r\\n self.reference_temperature = 358.0\",\n \"def __init__(self, date_debut, date_fin, lieu_depart, lieu_arrivee, distance, voiture, client, cout):\\r\\n\\r\\n self._date_debut = date_debut\\r\\n self._date_fin = date_fin\\r\\n self._lieu_depart = lieu_depart\\r\\n self._lieu_arrivee = lieu_arrivee\\r\\n self._distance = distance\\r\\n self._voiture = voiture\\r\\n self._client = client\",\n \"def initialize_electronics(self):\\n\\n self.electronics = ArduinoModel(**self.config['electronics']['arduino'])\\n self.logger.info('Initializing electronics arduino')\\n self.electronics.initialize()\",\n \"def readFromMetadata(cls, context, fqId):\\n newInstance = ClimatePointStation()\\n (newInstance.type, newInstance.id) = fqId.split(GenericMetadata.COMPOUND_KEY_SEP)\\n\\n climate = GenericMetadata.readClimatePointEntries(context)\\n \\n keyProto = 'station' + GenericMetadata.COMPOUND_KEY_SEP + fqId + GenericMetadata.COMPOUND_KEY_SEP\\n longitude = keyProto + 'longitude'\\n newInstance.longitude = float(climate[longitude])\\n latitude = keyProto + 'latitude'\\n newInstance.latitude = float(climate[latitude])\\n elevation = keyProto + 'elevation'\\n newInstance.elevation = float(climate[elevation])\\n name = keyProto + 'name'\\n newInstance.name = climate[name] \\n startDate = keyProto + 'startdate'\\n try:\\n newInstance.startDate = datetime.strptime(climate[startDate], ClimatePointStation.FMT_DATE)\\n except KeyError:\\n pass \\n endDate = keyProto + 'enddate'\\n try:\\n newInstance.endDate = datetime.strptime(climate[endDate], ClimatePointStation.FMT_DATE)\\n except KeyError:\\n pass\\n variablesKey = keyProto + 'variables'\\n try:\\n newInstance.variables = climate[variablesKey].split(GenericMetadata.VALUE_DELIM)\\n except KeyError:\\n pass\\n data = keyProto + 'data'\\n try:\\n newInstance.data = climate[data]\\n except KeyError:\\n pass\\n try:\\n for var in newInstance.variables:\\n varKey = keyProto + var + GenericMetadata.COMPOUND_KEY_SEP + 'data'\\n newInstance.variablesData[var] = climate[varKey]\\n except KeyError:\\n pass\\n \\n return newInstance\",\n \"def __init__(self, reset_pycity_default_values=True,\\n chp_tax_return=0.0055, eeg_pay=0.0688,\\n eex_baseload=[0.03309, 0.03309, 0.03309, 0.03309],\\n grid_av_fee=0.0055, hp_day_tarif=0.22, hp_night_tarif=0.2):\\n\\n super(GermanMarket, self).__init__(reset_pycity_default_values=\\n reset_pycity_default_values)\\n\\n # List of CHP subsidies for fed-in electric energy\\n self._sub_chp = [0.08, 0.06, 0.05, 0.044, 0.031]\\n\\n # List of CHP subsidies for self-consumed electric energy\\n self._sub_chp_self = [0.04, 0.03, 0]\\n\\n # List of PV subsidies (<= 10 kW, <= 40 kW, <= 100 kW, commercial\\n self._sub_pv = [0.1183, 0.115, 0.1028, 0.0818] # Since 01.10.2018\\n # E.g. https://www.photovoltaik4all.de/aktuelle-eeg-verguetungssaetze-fuer-photovoltaikanlagen-2017\\n\\n # CHP tax return on gas\\n self.chp_tax_return = chp_tax_return\\n\\n # HP tarifs\\n self.hp_day_tarif = hp_day_tarif\\n self.hp_night_tarif = hp_night_tarif\\n\\n # EEX baseload prices\\n self.eex_baseload = eex_baseload\\n\\n # Grid usage avoidance fee\\n self.grid_av_fee = grid_av_fee\\n\\n # EEG payment\\n self.eeg_pay = eeg_pay\\n\\n # Dict with EEG payment on self consumed energy (status quo: 2017)\\n self._dict_eeg_self = {'pv': 0.4 * eeg_pay, 'chp': 0.4 * eeg_pay}\",\n \"def __init__(self, param_dictionary):\\n\\n BaseModel.__init__(self)\\n\\n # set starting compartment values\\n self.set_compartment(\\\"susceptible\\\",\\n param_dictionary[\\\"population\\\"] - param_dictionary[\\\"start_infectious\\\"])\\n self.set_compartment(\\\"preinfectious\\\", 0.)\\n self.set_compartment(\\\"infectious\\\", param_dictionary[\\\"start_infectious\\\"])\\n self.set_compartment(\\\"immune\\\", 0.)\\n\\n # set model parameters\\n self.set_param(\\\"infection_beta\\\",\\n param_dictionary[\\\"r0\\\"]\\n / (param_dictionary[\\\"duration_infectious\\\"] * param_dictionary[\\\"population\\\"]))\\n self.set_param(\\\"infection_rate_progress\\\", 1. / param_dictionary[\\\"duration_preinfectious\\\"])\\n self.set_param(\\\"infection_rate_recover\\\", 1. / param_dictionary[\\\"duration_infectious\\\"])\"\n]","isConversational":false},"negative_scores":{"kind":"list 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open."},"document":{"kind":"string","value":"def current_cover_position(self) -> int:\n return int(self._value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getPos(self):\n return self.__current_pos","def _get_pos(self):\n return self._pos","def get_pos(self):\r\n return self.pos","def tell(self):\n _complain_ifclosed(self._closed)\n return self._position","def get_pos(self):\n return self.pos","def getPosition(self):\n if self.connection is not None:\n c = self.connection.getChannel(self.chanNamePrefix % 'position')\n\n return c.read()","def position(self):\n return self._position","def getPosition(self):\n c = self.connection.getChannel(self.chanNamePrefix % 'position')\n\n return c.read()","def position(self):\n if self.p:\n if self._finished:\n return None\n return self.p.get_position()*10","def pos(self):\n return self._pos","def pos(self):\n return self._pos","def current_position(self):\n\n # It is an error to call playlist_current_pos when there are\n # no entries in the playlist.\n r = self.x.playlist_current_pos()\n r.wait()\n if r.iserror():\n print r.get_error()\n return None\n else:\n return r.get_dict()['position']","def get_pos(self):\n self.__param_lock.acquire()\n pos = self.__pos\n self.__param_lock.release()\n return pos","def get_position(self) -> Tuple[int]:\n return self.position.copy()","def get_pos(self):\n\n return self._pos","def position(self) -> int:\n return self.__pos","def getPosition(self):\n\t\treturn self._position","def get_pos(self) -> tuple:\n return self.pos","def get_current_position(self) -> Tuple[int, int]:\n return self.__row_position, self.__col_position","def pos(self):\n return self._position","def get_position(self):\n return self._pos","def get_position(self):\n return self._pos","def getPosition(self):\n return self.x","def __getpos__(self, num):\n return self.num_to_pos[num]","def get_position(self, position):","def current_cover_position(self):\n state = self.channel_data.get(\"state\")\n if state:\n return 100 - state[\"shut\"]\n return None","def get_position(self):\n return self.position","def get_position(self):\n return self.position","def position(self):\n return self.__position","def getPosition(self):\n return self.position","def get_position(self):\n return self.bot_client.send_command(_Command.GetPosition)","def position(self):\n return (self.__position)","def position(self):\r\n return self._position","def position(self):\n return self.__position","def position(self):\n return self.__position","def position(self):\n return self.__position","def get_position(self):\n return self.__position","def get_next_position(self):","def get_curpos(self):\n for i in range(len(self.tree)):\n if self.path == self.tree[i][2]:\n return i\n else:\n return -1","def position(self) -> int:\n return self._position","def position(self) -> int:\n return self._position","def get_position(self):\n return self._position","def getPosition(self):\n return self.mPosition","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def position(self):\n return self._position","def getPosition(self):\n\tleft = self.getLeft()\n\ttop = self.getTop()\n\treturn (left,top)","def pos(self) -> str:\n return self._pos","def cursor_pos(self):\n return self._cursor_pos","def position(self):\n\n\t\treturn self._interface.position","def get_current(self):\n return self.x","def get_position(self) -> float:\n return self._select_interface(self._rc_get_position,\n self._http_get_position)","def position(self) -> int:\n\n return self.active_session.position if self.active_session is not None else 0","def get_current(self) -> int:\n return self._current","def pos(self):\n self._ser.reset_input_buffer()\n self._ser.write(b\"Z\\r\\n\")\n resp = self._ser.read(100).decode(\"utf-8\")\n match = self._pos_regex.search(resp)\n try:\n text_pos = match[1]\n except IndexError:\n click.echo(f\"Couldn't parse monochromator position from response: {resp}\", err=True)\n sys.exit(-1)\n current_pos = int(text_pos)\n return current_pos","def GetPosition(self):\n return self._pos","def current_cover_position(self) -> int | None:\n return self._current_position","def _get_cursor_index_position(self) :\n return self._cursor_index_position","def pos(self):\n return self.info['value']","def get_position(self):\n raise NotImplementedError()","def _cur_close(self):\n open = self._data['open'][self._offset]\n rel_close = self._data['close'][self._offset]\n return open * (1.0 + rel_close)","def current_cover_position(self) -> int | None:\n return self._position","def current_cover_position(self):\n return self._pos","def current_pos(self):\n new_ptr = _decode(self._read_fixed_block(0x0020),\n lo_fix_format['current_pos'])\n if new_ptr is None:\n raise ObservationError('current_pos is None')\n if new_ptr == self._current_ptr:\n return self._current_ptr\n if self._current_ptr and new_ptr != self.inc_ptr(self._current_ptr):\n for k in reading_len:\n if (new_ptr - self._current_ptr) == reading_len[k]:\n log.error('changing data format from %s to %s' % (self.data_format, k))\n self.data_format = k\n break\n self._current_ptr = new_ptr\n return self._current_ptr","def current_cover_position(self) -> int | None:\n position = None\n if self.data.levelpercentage is not None:\n position = 100 - self.data.levelpercentage\n return position","def getDialPosition(self):\n c = self.connection.getChannel(self.chanNamePrefix % 'dial_position')\n\n return c.read()","def position(self):\n\n return self.scanner.position()","def curpos(self, *args):\n return _ida_hexrays.Hexrays_Hooks_curpos(self, *args)","def tell(self):\n return self._pos","def get_position(self): # maybe encoded in filepath at some point\n result = (self.iter * self.row_step)% self.row_size, self.iter // (self.row_size * self.row_step)* self.col_step\n self.iter += 1\n return result","def getPlungerPos(self):\n cmd_string = '?'\n data = self.sendRcv(cmd_string)\n self.state['plunger_pos'] = int(data)\n return self.state['plunger_pos']","def current_cover_position(self) -> int:\n return self._device[\"current_state\"]","def get_position(self):\n return self.gripper_io.get_signal_value(\"position_response_m\")","def pos(self):\n return (self.raw - self.raw_zero) / self.ratio","def position(self):\n return self._position.copy()","def get_position(self) -> typing.Tuple[int, int]:\n raise NotImplementedError","def current(self) -> str:\n return self.s[self.pos]","def open(self) -> float:\n return self.current_candle[1]","def grab_current_point(self):\n self.open_gripper(80)\n time.sleep(2.5)\n self.execute_action((0, 0, -10), self.GRAB_ORIENTATION)\n self.open_gripper(-30)\n time.sleep(2.5)\n self.execute_action((0, 0, 10), self.GRAB_ORIENTATION)\n time.sleep(2.5)\n self.initial_position = np.array(self.get_current_cartesian_position().position)\n print self.initial_position","def position(self):\r\n pass","def position(self):\n return self.base_platform.position","def get_pos(term):\n # pylint: disable=invalid-name\n # Invalid variable name \"Position\"\n Position = collections.namedtuple('Position', ('row', 'column'))\n\n pos = Position(*term.get_location(timeout=5.0))\n\n if -1 in pos:\n print('stdin: not a human', file=sys.stderr)\n exit(2)\n\n return pos","def get_pos(self):\n return self.rect.midtop","def get_current_move(self):\n x_count = self.game_board.count('X')\n o_count = self.game_board.count('O')\n if x_count <= o_count:\n return 'X'\n return 'O'","def position(self):\n return self.stack.position()","def get_start_loc(self) -> Tuple[int, int]:\n assert self.pos_marker\n return self.pos_marker.working_loc","def current_close(self):\n open = self._prices.open[self._offset]\n relativ_close = self._prices.close[self._offset]\n return open * (1.0 + relativ_close)","def GetPosition(self):\n ...","def get_cursor_pos(self):\n return (self.text_maker.pos[0] + 9, self.text_maker.pos[1] + 120 + 8)","def position(self) -> Direction:\n return self._position","def get_position():\n return win32api.GetCursorPos()"],"string":"[\n \"def getPos(self):\\n return self.__current_pos\",\n \"def _get_pos(self):\\n return self._pos\",\n \"def get_pos(self):\\r\\n return self.pos\",\n \"def tell(self):\\n _complain_ifclosed(self._closed)\\n return self._position\",\n \"def get_pos(self):\\n return self.pos\",\n \"def getPosition(self):\\n if self.connection is not None:\\n c = self.connection.getChannel(self.chanNamePrefix % 'position')\\n\\n return c.read()\",\n \"def position(self):\\n return self._position\",\n \"def getPosition(self):\\n c = self.connection.getChannel(self.chanNamePrefix % 'position')\\n\\n return c.read()\",\n \"def position(self):\\n if self.p:\\n if self._finished:\\n return None\\n return self.p.get_position()*10\",\n \"def pos(self):\\n return self._pos\",\n \"def pos(self):\\n return self._pos\",\n \"def current_position(self):\\n\\n # It is an error to call playlist_current_pos when there are\\n # no entries in the playlist.\\n r = self.x.playlist_current_pos()\\n r.wait()\\n if r.iserror():\\n print r.get_error()\\n return None\\n else:\\n return r.get_dict()['position']\",\n \"def get_pos(self):\\n self.__param_lock.acquire()\\n pos = self.__pos\\n self.__param_lock.release()\\n return pos\",\n \"def get_position(self) -> Tuple[int]:\\n return self.position.copy()\",\n \"def get_pos(self):\\n\\n return self._pos\",\n \"def position(self) -> int:\\n return self.__pos\",\n \"def getPosition(self):\\n\\t\\treturn self._position\",\n \"def get_pos(self) -> tuple:\\n return self.pos\",\n \"def get_current_position(self) -> Tuple[int, int]:\\n return self.__row_position, self.__col_position\",\n \"def pos(self):\\n return self._position\",\n \"def get_position(self):\\n return self._pos\",\n \"def get_position(self):\\n return self._pos\",\n \"def getPosition(self):\\n return self.x\",\n \"def __getpos__(self, num):\\n return self.num_to_pos[num]\",\n \"def get_position(self, position):\",\n \"def current_cover_position(self):\\n state = self.channel_data.get(\\\"state\\\")\\n if state:\\n return 100 - state[\\\"shut\\\"]\\n return None\",\n \"def get_position(self):\\n return self.position\",\n \"def get_position(self):\\n return self.position\",\n \"def position(self):\\n return self.__position\",\n \"def getPosition(self):\\n return self.position\",\n \"def get_position(self):\\n return self.bot_client.send_command(_Command.GetPosition)\",\n \"def position(self):\\n return (self.__position)\",\n \"def position(self):\\r\\n return self._position\",\n \"def position(self):\\n return self.__position\",\n \"def position(self):\\n return self.__position\",\n \"def position(self):\\n return self.__position\",\n \"def get_position(self):\\n return self.__position\",\n \"def get_next_position(self):\",\n \"def get_curpos(self):\\n for i in range(len(self.tree)):\\n if self.path == self.tree[i][2]:\\n return i\\n else:\\n return -1\",\n \"def position(self) -> int:\\n return self._position\",\n \"def position(self) -> int:\\n return self._position\",\n \"def get_position(self):\\n return self._position\",\n \"def getPosition(self):\\n return self.mPosition\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def position(self):\\n return self._position\",\n \"def getPosition(self):\\n\\tleft = self.getLeft()\\n\\ttop = self.getTop()\\n\\treturn (left,top)\",\n \"def pos(self) -> str:\\n return self._pos\",\n \"def cursor_pos(self):\\n return self._cursor_pos\",\n \"def position(self):\\n\\n\\t\\treturn self._interface.position\",\n \"def get_current(self):\\n return self.x\",\n \"def get_position(self) -> float:\\n return self._select_interface(self._rc_get_position,\\n self._http_get_position)\",\n \"def position(self) -> int:\\n\\n return self.active_session.position if self.active_session is not None else 0\",\n \"def get_current(self) -> int:\\n return self._current\",\n \"def pos(self):\\n self._ser.reset_input_buffer()\\n self._ser.write(b\\\"Z\\\\r\\\\n\\\")\\n resp = self._ser.read(100).decode(\\\"utf-8\\\")\\n match = self._pos_regex.search(resp)\\n try:\\n text_pos = match[1]\\n except IndexError:\\n click.echo(f\\\"Couldn't parse monochromator position from response: {resp}\\\", err=True)\\n sys.exit(-1)\\n current_pos = int(text_pos)\\n return current_pos\",\n \"def GetPosition(self):\\n return self._pos\",\n \"def current_cover_position(self) -> int | None:\\n return self._current_position\",\n \"def _get_cursor_index_position(self) :\\n return self._cursor_index_position\",\n \"def pos(self):\\n return self.info['value']\",\n \"def get_position(self):\\n raise NotImplementedError()\",\n \"def _cur_close(self):\\n open = self._data['open'][self._offset]\\n rel_close = self._data['close'][self._offset]\\n return open * (1.0 + rel_close)\",\n \"def current_cover_position(self) -> int | None:\\n return self._position\",\n \"def current_cover_position(self):\\n return self._pos\",\n \"def current_pos(self):\\n new_ptr = _decode(self._read_fixed_block(0x0020),\\n lo_fix_format['current_pos'])\\n if new_ptr is None:\\n raise ObservationError('current_pos is None')\\n if new_ptr == self._current_ptr:\\n return self._current_ptr\\n if self._current_ptr and new_ptr != self.inc_ptr(self._current_ptr):\\n for k in reading_len:\\n if (new_ptr - self._current_ptr) == reading_len[k]:\\n log.error('changing data format from %s to %s' % (self.data_format, k))\\n self.data_format = k\\n break\\n self._current_ptr = new_ptr\\n return self._current_ptr\",\n \"def current_cover_position(self) -> int | None:\\n position = None\\n if self.data.levelpercentage is not None:\\n position = 100 - self.data.levelpercentage\\n return position\",\n \"def getDialPosition(self):\\n c = self.connection.getChannel(self.chanNamePrefix % 'dial_position')\\n\\n return c.read()\",\n \"def position(self):\\n\\n return self.scanner.position()\",\n \"def curpos(self, *args):\\n return _ida_hexrays.Hexrays_Hooks_curpos(self, *args)\",\n \"def tell(self):\\n return self._pos\",\n \"def get_position(self): # maybe encoded in filepath at some point\\n result = (self.iter * self.row_step)% self.row_size, self.iter // (self.row_size * self.row_step)* self.col_step\\n self.iter += 1\\n return result\",\n \"def getPlungerPos(self):\\n cmd_string = '?'\\n data = self.sendRcv(cmd_string)\\n self.state['plunger_pos'] = int(data)\\n return self.state['plunger_pos']\",\n \"def current_cover_position(self) -> int:\\n return self._device[\\\"current_state\\\"]\",\n \"def get_position(self):\\n return self.gripper_io.get_signal_value(\\\"position_response_m\\\")\",\n \"def pos(self):\\n return (self.raw - self.raw_zero) / self.ratio\",\n \"def position(self):\\n return self._position.copy()\",\n \"def get_position(self) -> typing.Tuple[int, int]:\\n raise NotImplementedError\",\n \"def current(self) -> str:\\n return self.s[self.pos]\",\n \"def open(self) -> float:\\n return self.current_candle[1]\",\n \"def grab_current_point(self):\\n self.open_gripper(80)\\n time.sleep(2.5)\\n self.execute_action((0, 0, -10), self.GRAB_ORIENTATION)\\n self.open_gripper(-30)\\n time.sleep(2.5)\\n self.execute_action((0, 0, 10), self.GRAB_ORIENTATION)\\n time.sleep(2.5)\\n self.initial_position = np.array(self.get_current_cartesian_position().position)\\n print self.initial_position\",\n \"def position(self):\\r\\n pass\",\n \"def position(self):\\n return self.base_platform.position\",\n \"def get_pos(term):\\n # pylint: disable=invalid-name\\n # Invalid variable name \\\"Position\\\"\\n Position = collections.namedtuple('Position', ('row', 'column'))\\n\\n pos = Position(*term.get_location(timeout=5.0))\\n\\n if -1 in pos:\\n print('stdin: not a human', file=sys.stderr)\\n exit(2)\\n\\n return pos\",\n \"def get_pos(self):\\n return self.rect.midtop\",\n \"def get_current_move(self):\\n x_count = self.game_board.count('X')\\n o_count = self.game_board.count('O')\\n if x_count <= o_count:\\n return 'X'\\n return 'O'\",\n \"def position(self):\\n return self.stack.position()\",\n \"def get_start_loc(self) -> Tuple[int, int]:\\n assert self.pos_marker\\n return self.pos_marker.working_loc\",\n \"def current_close(self):\\n open = self._prices.open[self._offset]\\n relativ_close = self._prices.close[self._offset]\\n return open * (1.0 + relativ_close)\",\n \"def GetPosition(self):\\n ...\",\n \"def get_cursor_pos(self):\\n return (self.text_maker.pos[0] + 9, self.text_maker.pos[1] + 120 + 8)\",\n \"def position(self) -> Direction:\\n return self._position\",\n \"def get_position():\\n return win32api.GetCursorPos()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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is_closed(self) -> bool:\n return not bool(self._value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _isclose(self):\n return self.dp.state()==PyTango.DevState.CLOSE","def is_closed(self) -> bool:","def is_closed(self):\r\n return self.position == SinglePhaseBreaker.CLOSED","def isClosed(self):\n pass","def is_closed(self) -> bool:\n lopen = self.left_boundary['open']\n ropen = self.right_boundary['open']\n return not lopen and not ropen","def is_close(self) -> bool:\n return not self.open","def isClosed(self) -> bool:\r\n\r\n return self.__is_closed","def closed(self):\n return self.state == \"CLOSED\"","def _is_closed(self) -> bool:\n return self._status == Status.CLOSED","def is_closed(self):\n return self.state == CLOSED or self.state == CLOSING","def is_closed(self): # -> bool | Any:\n ...","def is_closed(self) -> bool:\n return self._device[\"current_state\"] < 1","def closed(self):\n return self._close_state.is_set()","def closed(self):\n\n return not self.open","def is_closed(self) -> bool:\n return self._closed or super().is_closed","def is_closed(self) -> bool:\n return self._closed","def is_closed(self) -> bool:\n return self._closed","def is_closed(self) -> bool | None:\n if self._is_open is None:\n return None\n return not self._is_open","def is_closed(self):\n return self.current_cover_position <= 0","def is_closed(self) -> bool:\n raise NotImplementedError() # pragma: nocover","def is_closed(self):\n # if self.current_cover_position is not None:\n # return self.current_cover_position == 0\n return None","def is_open(self):\n return not self.end","def is_closed(self) -> bool | None:\n if self.current_cover_position is None:\n return None\n return self.current_cover_position == 0","def is_closed(self):\n return None","def is_closing(self) -> bool:\n return self._state == STATE_CLOSING","def closed(self):\n return self.__closeEvent.is_set()","def _isopen(self):\n return self.dp.state()==PyTango.DevState.OPEN","def is_closed(self):\n if self.current_cover_position == None:\n return None\n elif self.current_cover_position == 0.0:\n return True\n return False","def is_closed(self):\n if self.current_cover_position is None:\n return None\n return self.current_cover_position == 0","def closed(self) -> bool:\n return self._closed","def closed(self) -> bool:\n return self._closed","def is_closed(self) -> bool | None:\n if self.data.levelpercentage is None:\n return None\n return self.data.levelpercentage == 100 # type: ignore [no-any-return]","def is_closing(self):\n return self._is_closing","def hasClosed() -> bool:\n\n return block.timestamp > self.closingTime","def is_closed(self):\n return self._current_cover_position == MIN_POSITION","def is_closed(self):\n return self.boundary.is_subset(self)","def is_closed(self):\n raise NotImplementedError","def 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ordered_edges\n return len(missing_edges) == 0","def closed(self):\n return not self._file.is_open()","def is_open(self):\n return Intersection(self, self.boundary).is_empty","def isClosed(self) -> bool:\n return self._connection is None","def __bool__(self):\n return not bool(self.db.closed)","def isClosed(self) -> bool:\n return self.host is None","def is_closed(self):\n return self.__com.has_quit()","def is_open(self) -> bool:\n pass","def closed(self):\r\n return self._closed","def isOpen(self):\n return self._isOpen","def get_closed(self, ):\n return self.get_parameter('close')","def is_open(self):\n return self._open","def is_closed(self) -> bool:\n return self._http_connection is None","def IsOpen(self):\n return self._is_open","def closed(self):\n return self._stream is None","def open(self):\n\n return self._state == states['open']","def isopen(self):\n return _image.image_isopen(self)","def get_closed_state(self):\n return 1 if self.pull_up_down == GPIO.PUD_DOWN else 0","def closed(self) -> bool:\n return self.pgconn.status == ConnStatus.BAD","def is_opened(self):\n return self.hcam is not None","def is_opened(self):\n return len(Container._construction_stack) > 0 and self == Container._construction_stack[-1]","def closed_v(self):\n sa = ShapeAnalysis_Surface(self.surface())\n return sa.IsVClosed()","def close(self) -> bool:\n return True","def isOpened(self) -> bool:\n return True if self.data_file else False","def is_open(self):\n return self.__port.isOpen()","def is_opened(self):\n return True","def is_opened(self):\n return True","def is_opening(self) -> bool:\n return self._state == STATE_OPENING","def is_opened(self):\n return self._is_opened","def HasCloseButton(self):\r\n\r\n return self.HasFlag(self.buttonClose)","def isDown ( self ) :\n return self._ilhcbmagnet.isDown()","def is_open(self) -> bool:\n return self.__interval is not None","def is_pressed(self):\n return GPIO.input(self.pin) == self.closed_status","def is_closed(self, channel=None):\n return not self.get_state(channel)","def is_opening(self):\n return self._is_opening","def is_closed(self, channel=None):\n return self.get_state(channel) == 0","def is_open(self) -> bool:\n return self._connection is not None","def __bool__(self) -> bool:\n return not self._disconnected","def is_window_closed():\n return _lib.TCOD_console_is_window_closed()","def is_open(self):\n\t\treturn self._session is not None","def closed(self):\n return self._resource.closed","def closed(self):\n return self._resource.closed","def connection_closed(self):\n return self.conn_status == self.CONN_CLOSED","def is_closed(geometry, **kwargs):\n return lib.is_closed(geometry, **kwargs)","def _is_closing_shape(self, x, y, new_obstacle):\n first_obstacle_edge = new_obstacle[0]\n\n close_x = abs(first_obstacle_edge[0] - x) < self.CLOSE_ENOUGH_POINTS_TOLERANCE\n close_y = abs(first_obstacle_edge[1] - y) < self.CLOSE_ENOUGH_POINTS_TOLERANCE\n\n return close_x and close_y","def 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not lopen and not ropen\",\n \"def is_close(self) -> bool:\\n return not self.open\",\n \"def isClosed(self) -> bool:\\r\\n\\r\\n return self.__is_closed\",\n \"def closed(self):\\n return self.state == \\\"CLOSED\\\"\",\n \"def _is_closed(self) -> bool:\\n return self._status == Status.CLOSED\",\n \"def is_closed(self):\\n return self.state == CLOSED or self.state == CLOSING\",\n \"def is_closed(self): # -> bool | Any:\\n ...\",\n \"def is_closed(self) -> bool:\\n return self._device[\\\"current_state\\\"] < 1\",\n \"def closed(self):\\n return self._close_state.is_set()\",\n \"def closed(self):\\n\\n return not self.open\",\n \"def is_closed(self) -> bool:\\n return self._closed or super().is_closed\",\n \"def is_closed(self) -> bool:\\n return self._closed\",\n \"def is_closed(self) -> bool:\\n return self._closed\",\n \"def is_closed(self) -> bool | None:\\n if self._is_open is None:\\n return None\\n return not self._is_open\",\n \"def is_closed(self):\\n return self.current_cover_position <= 0\",\n \"def is_closed(self) -> bool:\\n raise NotImplementedError() # pragma: nocover\",\n \"def is_closed(self):\\n # if self.current_cover_position is not None:\\n # return self.current_cover_position == 0\\n return None\",\n \"def is_open(self):\\n return not self.end\",\n \"def is_closed(self) -> bool | None:\\n if self.current_cover_position is None:\\n return None\\n return self.current_cover_position == 0\",\n \"def is_closed(self):\\n return None\",\n \"def is_closing(self) -> bool:\\n return self._state == STATE_CLOSING\",\n \"def closed(self):\\n return self.__closeEvent.is_set()\",\n \"def _isopen(self):\\n return self.dp.state()==PyTango.DevState.OPEN\",\n \"def is_closed(self):\\n if self.current_cover_position == None:\\n return None\\n elif self.current_cover_position == 0.0:\\n return True\\n return False\",\n \"def is_closed(self):\\n if self.current_cover_position is None:\\n return None\\n return self.current_cover_position == 0\",\n \"def closed(self) -> bool:\\n return self._closed\",\n \"def closed(self) -> bool:\\n return self._closed\",\n \"def is_closed(self) -> bool | None:\\n if self.data.levelpercentage is None:\\n return None\\n return self.data.levelpercentage == 100 # type: ignore [no-any-return]\",\n \"def is_closing(self):\\n return self._is_closing\",\n \"def hasClosed() -> bool:\\n\\n return block.timestamp > self.closingTime\",\n \"def is_closed(self):\\n return self._current_cover_position == MIN_POSITION\",\n \"def is_closed(self):\\n return self.boundary.is_subset(self)\",\n \"def is_closed(self):\\n raise NotImplementedError\",\n \"def isOpen(self):\\n\\t\\treturn not self.endgame\",\n \"def is_open_for_betting(self):\\n return self.is_open\",\n \"def is_open(self):\\n return self.get_data(\\\"state\\\") == self.STATE_OPEN\",\n \"def is_open(self):\\n return (not self.interface.is_open)\",\n \"def closed(self) -> bool:\\n return self._out_of_scope or self._consumed\",\n \"def is_open(self):\\n 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\"0.64167905\",\n \"0.6407018\",\n \"0.63979685\",\n \"0.63641876\",\n \"0.6360128\",\n \"0.63254994\",\n \"0.63017106\",\n \"0.6278936\",\n \"0.62701374\",\n \"0.62512064\",\n \"0.62254006\",\n \"0.6220989\",\n \"0.6202036\",\n \"0.61982155\",\n \"0.61982155\",\n \"0.61975235\",\n \"0.6195285\",\n \"0.61949104\",\n \"0.6183846\",\n \"0.6173752\",\n \"0.6114974\",\n \"0.61141187\",\n \"0.60993654\",\n \"0.6096629\",\n \"0.60939956\",\n \"0.6075332\",\n \"0.60731804\",\n \"0.6054412\",\n \"0.6050672\",\n \"0.6050672\",\n \"0.6029717\",\n \"0.6019617\",\n \"0.6012409\",\n \"0.60088176\",\n \"0.6003785\",\n \"0.6003249\",\n \"0.6003082\",\n \"0.60014135\",\n \"0.59765875\",\n \"0.59765875\",\n \"0.59724957\",\n \"0.5938806\",\n \"0.5934429\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71877086"},"document_rank":{"kind":"string","value":"18"}}},{"rowIdx":77474,"cells":{"query":{"kind":"string","value":"Set the blind to the given position."},"document":{"kind":"string","value":"def set_cover_position(self, **kwargs: Any) -> None:\n self._multi_level_switch_property.set(kwargs[\"position\"])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setPosition(position):","def setPosition(self, *args):\n return _libsbml.BoundingBox_setPosition(self, *args)","def bias(self, value):\n self.mbmod.bias = value","def set_position(self, x, y):\n self.tx = -x\n self.ty = -y","def SetBlindMode(self, agwFlags):\r\n\r\n self._blindMode = (agwFlags & AUI_MGR_VENETIAN_BLINDS_HINT) != 0\r\n\r\n if self._blindMode or not self.CanSetTransparent():\r\n self.MakeVenetianBlinds()\r\n self.SetTransparent(255)\r\n \r\n else:\r\n self.SetShape(wx.Region())\r\n if agwFlags & AUI_MGR_HINT_FADE == 0: \r\n self.SetTransparent(80)\r\n else:\r\n self.SetTransparent(0)","def update(self):\n self.bpos_x += 3","def set_general_position_blue(self, position):\n\n self._general_position_blue = position","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def setPosition(*args):","def set_bias(self, bias):\n assert 0x10 <= bias <= 0x17, \"Bias must be one of BIAS_1_4..BIAS_1_11.\"\n assert self.instr == self.INSTR_EXT, \"Please switch to extended instruction set first.\"\n self.bias = bias\n self.command([bias])","def set_position(self, position):\n raise NotImplementedError()","def set_position(self, updated):\n self.buff_x = updated[0]\n self.buff_y = updated[1]","def set_position(self, pos):\n self.ref_pos = pos","def _set_block(self, pos, block_):\n _get_mc().setBlock(pos, block_)","def setBallPos(self, x: int, y: int):\n self.x = x\n self.y = y","def apply_changes(self):\n self.x = self.buff_x\n self.y = self.buff_y\n self.buff_x = None\n self.buff_y = None","def _set_block(self, pos, block_):\n self._changes[deepcopy(pos)] = block","def set_position(self, position):\n self.position = tuple(position)","def translate(self, vector):\n if self.blender_object:\n self.blender_object.location = vector","def set_position(self, position):\n self.position = position","def set_position(self, position):\n self.set_current_position(position)","def setRoboPos(self,x,y):\r\n self.RoboPosX=x\r\n self.RoboPosY=y","def force_set(self, pos):\n self.rect.center = pos","def setRobotPosition(self, position):\n self.position = position","def setRobotPosition(self, position):\n self.position = position","def take_blinds(self, sb_ind):\n bb_ind = (sb_ind + 1) % len(self.in_game_players)\n\n self.in_game_players[sb_ind].move_to_pot(self.get_sb())\n self.in_game_players[bb_ind].move_to_pot(self.get_bb())","def SetCurrentPosition(self,pos):\n\n if self.Reverse: pos*=-1\n self.Bus.Transaction(chr(self.Address)+chr(0x40)+struct.pack('@l',pos))","def reset_position(self):\n self.set_position(copy.deepcopy(self.ab_pos))","def set_position(self, position):\r\n\r\n self.position = position\r\n if (self.rect):\r\n self.rect.x = position[0]\r\n self.rect.y = position[1]","def update_bias(self):\n self._bias = self._bias + self.update_bias_value\n self.bias_clipping()","def set_offset(self, pos):\n self.offset = self.bbox().offset(pos)","def set_coord(self, l, sign, b):\n if l is not None:\n self.l = float(l)\n if b is not None:\n self.b = float(b)\n if sign == '-':\n self.b *= -1.","def position(self, position):\n self.move_to(position)","def set_position(self, position: Union[pygame.math.Vector2,\n Tuple[int, int],\n Tuple[float, float]]):\n super().set_position(position)\n self.text_block.set_position(position)","def setPixel(self, value, position):\n (x,y,z) = position\n if z<0 or z>=self.length:\n mamba.raiseExceptionOnError(mambaCore.ERR_BAD_SIZE)\n err = mambaCore.MB_PutPixel(self.seq[z].mbIm, value, position[0], position[1])\n mamba.raiseExceptionOnError(err)","def position(self, position):\n\n self._position = position","def position(self, position):\n\n self._position = position","def position(self, position):\n\n self._position = position","def position(self, position):\n\n self._position = position","def position(self, position):\n\n self._position = position","def position(self, position):\n\n self._position = position","def position(self, position):\n\n self._position = position","def position(self, position):\n\n self._position = position","def setLightPos( self, lightPos ):\r\n self.cameraPath.setPos( lightPos )\r\n self.cameraPath.lookAt( self.lightPath, 0,0,0 )","def set_bbox(self, bbox):\n self.bbox = bbox","def set_postition(self, position):\n self._border.set_position(position)","def set_pos(self, x):\n self._pos = x","def set_position(self, position):\n self.gripper_io.set_signal_value(\"position_m\", position)","def _setPosLock( self, bSeed, bPosLock ):\n\t\t\n\t\tif bPosLock > 255: bPosLock = 255\n\t\tif bPosLock < 0: bPosLock = 0\n\t\tbSeed &= 0xFFFFFF00\n\t\tbSeed |= bPosLock\n\t\treturn bSeed","def set_block(self, chunk, coords, value):\n\n chunk.set_block(coords, value)","def setRobotPosition(self, position):\n posx = position.getX()\n posy = position.getY()\n self.position = Position(posx, posy)\n #raise NotImplementedError","def set_wavelength_absolute(self, pos):\n # don't complain if the user asked for non reachable wl: he couldn't know\n minp, maxp = self.get_wavelength_limits(self.get_grating())\n pos = min(max(minp, pos), maxp)\n self.set_wavelength(pos)","def set_offset( self, offset ):\n assert offset in range( len( self.buffer ) )\n self.pos = offset\n self._fill_buffer()","def _set_lb(o, d):\n o.setlb(d)","def set_pos(self, p: tuple) -> None:\n self.pos = p","def subdToBlind(*args, absolutePosition: bool=True, includeCreases: bool=False,\n includeZeroOffsets: bool=False, **kwargs)->bool:\n pass","def setDesiredPosition(self, x, y):\n (self.setX, self.setY) = (x , y)","def set_position(self, x, y):\n self.position.x = x\n self.position.y = y\n self.rect.topleft = x, y","def set_position(self,position,midpoint = False):\n\n # Find the image size and midpoint of the image\n imagesize = self.image.get_size()\n imagemidp = (int(imagesize[0] * 0.5),int(imagesize[1] * 0.5))\n\n # if a midpoint arguement is passed, set the pos to the top left pixel\n # such that the position passed in is in the middle of the button\n if midpoint:\n self.pos = (position[0] - imagemidp[0], position[1] - imagemidp[1])\n else:\n self.pos = position\n\n # set the rectangle to be used for collision detection\n self.rect = pygame.Rect(self.pos,self.image.get_size())\n\n # Set up the information that is needed to blit the image to the surface\n self.blitinfo = (self.image, self.pos)","def _onSetParameterBIgnoreBounds(self, value):\n self._parameters['b'] = value\n self._logger.info(\"Parameter 'b' of function '{}' changed to {}\".format(self._function, value))\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())","def set_new_location(self, xPos, yPos):","def __call__(self, brd):\n brdcpy = brd.copy() \n return brdcpy[self.y][self.x].makemove(self.direction)","def _set_block(self, pos, block_):\n raise NotImplementedError","def update_ballpos(self,pos):\n if self.options.visualize_switch_xy:\n self.col.set_offsets(pos[:,::-1]) # reverse x-y direction\n else:\n self.col.set_offsets(pos)","def setRobotPosition(self, position):\n self.position = position\n #raise NotImplementedError","def set_offset(self, xpos, ypos):\n self.offset = [xpos, ypos]","def set_offset(self, xpos, ypos):\n self.offset = [xpos, ypos]","def set_offset(self, xpos, ypos):\n self.offset = [xpos, ypos]","def set_offset(self, xpos, ypos):\n self.offset = [xpos, ypos]","def set_boresight(self, session, params):\n with self.boresight_lock.acquire_timeout(0, job='set_boresight') as acquired:\n if not acquired:\n return False, f\"Operation failed: {self.boresight_lock.job} is running.\"\n\n ok, msg = yield self._check_ready_motion(session)\n if not ok:\n return False, msg\n\n target = params['target']\n\n for axis, target in {'boresight': target}.items():\n limit_func, limits = self._get_limit_func(axis)\n if target != limit_func(target):\n raise ocs_agent.ParamError(\n f'{axis}={target} not in accepted range, '\n f'[{limits[0]}, {limits[1]}].')\n\n self.log.info(f'Commanded position: boresight={target}')\n session.set_status('running')\n\n ok, msg = yield self._go_to_axis(session, 'Boresight', target)\n\n if ok and params['end_stop']:\n yield self.acu_control.http.Command('DataSets.CmdModeTransfer',\n 'Set3rdAxisMode', 'Stop')\n\n return ok, msg","def set_position(self, pos, debug=False):\n pos = max(pos, 0)\n pos = min(pos, 1)\n posrange = pos * self.range\n pos = posrange + self.min\n if debug:\n print('Setting Dynamixel {} with posrange {} to position {}'.format(self.id, posrange, pos))\n self.motor.set_position(int(pos))","def setPosition(self, position, view) -> None:\n ...","def set_position( self, posx, posy ):\n\n self.__foodx = posx\n self.__foody = posy","def set_target_pos(self, pos: Vec2d):\n\t\tself.__target.set(pos)","def setPos(self,pos):\n self.Xpos,self.Ypos=pos","def setPoint(self, point):\n self.position = point.position","def position(self, pos: int):\n self.__pos = pos","def setPos(self, x, y, anchor='ll'):\n self.transform.setPos(glm.vec3(x, y, 0))\n if anchor == 'ul':\n offx = 0\n offy = - self.font.table['ascent']\n elif anchor == 'uc':\n offx = - self._labelWidth / 2\n offy = - self.font.table['ascent']\n elif anchor == 'ur':\n offx = - self._labelWidth\n offy = - self.font.table['ascent']\n elif anchor == 'cl':\n offx = 0\n offy = self._labelHeight / 2 - self.font.table['ascent']\n elif anchor == 'cc':\n offx = - self._labelWidth / 2\n offy = self._labelHeight / 2 - self.font.table['ascent']\n elif anchor == 'cr':\n offx = - self._labelWidth\n offy = self._labelHeight / 2 - self.font.table['ascent']\n elif anchor == 'll':\n offx = 0\n offy = self._labelHeight - self.font.table['ascent']\n elif anchor == 'lc':\n offx = - self._labelWidth / 2\n offy = self._labelHeight - self.font.table['ascent']\n elif anchor == 'lr':\n offx = - self._labelWidth\n offy = self._labelHeight - self.font.table['ascent']\n else:\n raise SystemExit(f\"Unimplemented anchor '{anchor}'\")\n self.model.setPos(glm.vec3(offx, offy, 0))","def set_position(self, x: float, y: float):\n self._shape.body.position.x = x\n self._shape.body.position.y = y","def __set_ball_position(self):\n self.__window.remove(self.__ball)\n self.__window.add(self.__ball, (self.__window.width - self.__ball.width) / 2,\n (self.__window.height - self.__ball.height) / 2)","def set_position(self, x, y):\n self.pos = pygame.Rect(x, y, 0, 0)","def set_offset(self, offset):\r\n for b in self.buf:\r\n b.set_offset(offset)","def set_position(self):\n raise RuntimeError(\"the 'set_position' method must be overriden\")","def bx(self, bx):\n\n self._bx = bx","def set(self, x, y, color):\n if x < 0 or x >= self.width or y < 0 or y >= self.height:\n return\n i = self.map[y][x]\n super().set(i, color)","def SetPosition(self, pos):\n self._pos = pos","def __setitem__(self, pos, value):\n\t\t#pprint(pos)\n\t\t#pprint(self.leds.__getitem__(pos))\n\t\t# Handle if a slice of positions are passed in by setting the appropriate\n\t\t# LED data values to the provided values.\n\t\tself.setPixelColor(pos, value)","def set_position(self, new_pos):\n self._position = new_pos","def setPixel(self, x, y, val):\r\n self.__buffer[y][x].setValue(val)","def set_brightness(self, brightness, bulb_index=-1):\n\n # TODO: Throw an error if value not in range\n\n bulb_index = int(bulb_index) if bulb_index is not None else -1\n brightness = int(brightness)\n if bulb_index == -1:\n for bulb in self.bulbs:\n bulb.set_brightness(brightness, effect=\"smooth\", duration=1000)\n elif bulb_index <= len(self.bulbs):\n self.bulbs[bulb_index].set_brightness(brightness, effect=\"smooth\", duration=1000)","def set_biases(self, biases):\n\n if not np.any(biases):\n return\n\n v_rest = self.config.getfloat('cell', 'v_rest')\n v_thresh = self.config.getfloat('cell', 'v_thresh')\n cm = self.config.getfloat('cell', 'cm')\n\n i_offset = biases * cm * (v_thresh - v_rest) / self._duration\n\n self.layers[-1].set(i_offset=i_offset)","def setPosition(self,newPos):\n self._position = newPos"],"string":"[\n \"def setPosition(position):\",\n \"def setPosition(self, *args):\\n return _libsbml.BoundingBox_setPosition(self, *args)\",\n \"def bias(self, value):\\n self.mbmod.bias = value\",\n \"def set_position(self, x, y):\\n self.tx = -x\\n self.ty = -y\",\n \"def SetBlindMode(self, agwFlags):\\r\\n\\r\\n self._blindMode = (agwFlags & AUI_MGR_VENETIAN_BLINDS_HINT) != 0\\r\\n\\r\\n if self._blindMode or not self.CanSetTransparent():\\r\\n self.MakeVenetianBlinds()\\r\\n self.SetTransparent(255)\\r\\n \\r\\n else:\\r\\n self.SetShape(wx.Region())\\r\\n if agwFlags & AUI_MGR_HINT_FADE == 0: \\r\\n self.SetTransparent(80)\\r\\n else:\\r\\n self.SetTransparent(0)\",\n \"def update(self):\\n self.bpos_x += 3\",\n \"def set_general_position_blue(self, position):\\n\\n self._general_position_blue = position\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def setPosition(*args):\",\n \"def set_bias(self, bias):\\n assert 0x10 <= bias <= 0x17, \\\"Bias must be one of BIAS_1_4..BIAS_1_11.\\\"\\n assert self.instr == self.INSTR_EXT, \\\"Please switch to extended instruction set first.\\\"\\n self.bias = bias\\n self.command([bias])\",\n \"def set_position(self, position):\\n raise NotImplementedError()\",\n \"def set_position(self, updated):\\n self.buff_x = updated[0]\\n self.buff_y = updated[1]\",\n \"def set_position(self, pos):\\n self.ref_pos = pos\",\n \"def _set_block(self, pos, block_):\\n _get_mc().setBlock(pos, block_)\",\n \"def setBallPos(self, x: int, y: int):\\n self.x = x\\n self.y = y\",\n \"def apply_changes(self):\\n self.x = self.buff_x\\n self.y = self.buff_y\\n self.buff_x = None\\n self.buff_y = None\",\n \"def _set_block(self, pos, block_):\\n self._changes[deepcopy(pos)] = block\",\n \"def set_position(self, position):\\n self.position = tuple(position)\",\n \"def translate(self, vector):\\n if self.blender_object:\\n self.blender_object.location = vector\",\n \"def set_position(self, position):\\n self.position = position\",\n \"def set_position(self, position):\\n self.set_current_position(position)\",\n \"def setRoboPos(self,x,y):\\r\\n self.RoboPosX=x\\r\\n self.RoboPosY=y\",\n \"def force_set(self, pos):\\n self.rect.center = pos\",\n \"def setRobotPosition(self, position):\\n self.position = position\",\n \"def setRobotPosition(self, position):\\n self.position = position\",\n \"def take_blinds(self, sb_ind):\\n bb_ind = (sb_ind + 1) % len(self.in_game_players)\\n\\n self.in_game_players[sb_ind].move_to_pot(self.get_sb())\\n self.in_game_players[bb_ind].move_to_pot(self.get_bb())\",\n \"def SetCurrentPosition(self,pos):\\n\\n if self.Reverse: pos*=-1\\n self.Bus.Transaction(chr(self.Address)+chr(0x40)+struct.pack('@l',pos))\",\n \"def reset_position(self):\\n self.set_position(copy.deepcopy(self.ab_pos))\",\n \"def set_position(self, position):\\r\\n\\r\\n self.position = position\\r\\n if (self.rect):\\r\\n self.rect.x = position[0]\\r\\n self.rect.y = position[1]\",\n \"def update_bias(self):\\n self._bias = self._bias + self.update_bias_value\\n self.bias_clipping()\",\n \"def set_offset(self, pos):\\n self.offset = self.bbox().offset(pos)\",\n \"def set_coord(self, l, sign, b):\\n if l is not None:\\n self.l = float(l)\\n if b is not None:\\n self.b = float(b)\\n if sign == '-':\\n self.b *= -1.\",\n \"def position(self, position):\\n self.move_to(position)\",\n \"def set_position(self, position: Union[pygame.math.Vector2,\\n Tuple[int, int],\\n Tuple[float, float]]):\\n super().set_position(position)\\n self.text_block.set_position(position)\",\n \"def setPixel(self, value, position):\\n (x,y,z) = position\\n if z<0 or z>=self.length:\\n mamba.raiseExceptionOnError(mambaCore.ERR_BAD_SIZE)\\n err = mambaCore.MB_PutPixel(self.seq[z].mbIm, value, position[0], position[1])\\n mamba.raiseExceptionOnError(err)\",\n \"def position(self, position):\\n\\n self._position = position\",\n \"def position(self, position):\\n\\n self._position = position\",\n \"def position(self, position):\\n\\n self._position = position\",\n \"def position(self, position):\\n\\n self._position = position\",\n \"def position(self, position):\\n\\n self._position = position\",\n \"def position(self, position):\\n\\n self._position = position\",\n \"def position(self, position):\\n\\n self._position = position\",\n \"def position(self, position):\\n\\n self._position = position\",\n \"def setLightPos( self, lightPos ):\\r\\n self.cameraPath.setPos( lightPos )\\r\\n self.cameraPath.lookAt( self.lightPath, 0,0,0 )\",\n \"def set_bbox(self, bbox):\\n self.bbox = bbox\",\n \"def set_postition(self, position):\\n self._border.set_position(position)\",\n \"def set_pos(self, x):\\n self._pos = x\",\n \"def set_position(self, position):\\n self.gripper_io.set_signal_value(\\\"position_m\\\", position)\",\n \"def _setPosLock( self, bSeed, bPosLock ):\\n\\t\\t\\n\\t\\tif bPosLock > 255: bPosLock = 255\\n\\t\\tif bPosLock < 0: bPosLock = 0\\n\\t\\tbSeed &= 0xFFFFFF00\\n\\t\\tbSeed |= bPosLock\\n\\t\\treturn bSeed\",\n \"def set_block(self, chunk, coords, value):\\n\\n chunk.set_block(coords, value)\",\n \"def setRobotPosition(self, position):\\n posx = position.getX()\\n posy = position.getY()\\n self.position = Position(posx, posy)\\n #raise NotImplementedError\",\n \"def set_wavelength_absolute(self, pos):\\n # don't complain if the user asked for non reachable wl: he couldn't know\\n minp, maxp = self.get_wavelength_limits(self.get_grating())\\n pos = min(max(minp, pos), maxp)\\n self.set_wavelength(pos)\",\n \"def set_offset( self, offset ):\\n assert offset in range( len( self.buffer ) )\\n self.pos = offset\\n self._fill_buffer()\",\n \"def _set_lb(o, d):\\n o.setlb(d)\",\n \"def set_pos(self, p: tuple) -> None:\\n self.pos = p\",\n \"def subdToBlind(*args, absolutePosition: bool=True, includeCreases: bool=False,\\n includeZeroOffsets: bool=False, **kwargs)->bool:\\n pass\",\n \"def setDesiredPosition(self, x, y):\\n (self.setX, self.setY) = (x , y)\",\n \"def set_position(self, x, y):\\n self.position.x = x\\n self.position.y = y\\n self.rect.topleft = x, y\",\n \"def set_position(self,position,midpoint = False):\\n\\n # Find the image size and midpoint of the image\\n imagesize = self.image.get_size()\\n imagemidp = (int(imagesize[0] * 0.5),int(imagesize[1] * 0.5))\\n\\n # if a midpoint arguement is passed, set the pos to the top left pixel\\n # such that the position passed in is in the middle of the button\\n if midpoint:\\n self.pos = (position[0] - imagemidp[0], position[1] - imagemidp[1])\\n else:\\n self.pos = position\\n\\n # set the rectangle to be used for collision detection\\n self.rect = pygame.Rect(self.pos,self.image.get_size())\\n\\n # Set up the information that is needed to blit the image to the surface\\n self.blitinfo = (self.image, self.pos)\",\n \"def _onSetParameterBIgnoreBounds(self, value):\\n self._parameters['b'] = value\\n self._logger.info(\\\"Parameter 'b' of function '{}' changed to {}\\\".format(self._function, value))\\n self.functionChanged.emit(self._dim, self._function, self._parameters.copy())\",\n \"def set_new_location(self, xPos, yPos):\",\n \"def __call__(self, brd):\\n brdcpy = brd.copy() \\n return brdcpy[self.y][self.x].makemove(self.direction)\",\n \"def _set_block(self, pos, block_):\\n raise NotImplementedError\",\n \"def update_ballpos(self,pos):\\n if self.options.visualize_switch_xy:\\n self.col.set_offsets(pos[:,::-1]) # reverse x-y direction\\n else:\\n self.col.set_offsets(pos)\",\n \"def setRobotPosition(self, position):\\n self.position = position\\n #raise NotImplementedError\",\n \"def set_offset(self, xpos, ypos):\\n self.offset = [xpos, ypos]\",\n \"def set_offset(self, xpos, ypos):\\n self.offset = [xpos, ypos]\",\n \"def set_offset(self, xpos, ypos):\\n self.offset = [xpos, ypos]\",\n \"def set_offset(self, xpos, ypos):\\n self.offset = [xpos, ypos]\",\n \"def set_boresight(self, session, params):\\n with self.boresight_lock.acquire_timeout(0, job='set_boresight') as acquired:\\n if not acquired:\\n return False, f\\\"Operation failed: {self.boresight_lock.job} is running.\\\"\\n\\n ok, msg = yield self._check_ready_motion(session)\\n if not ok:\\n return False, msg\\n\\n target = params['target']\\n\\n for axis, target in {'boresight': target}.items():\\n limit_func, limits = self._get_limit_func(axis)\\n if target != limit_func(target):\\n raise ocs_agent.ParamError(\\n f'{axis}={target} not in accepted range, '\\n f'[{limits[0]}, {limits[1]}].')\\n\\n self.log.info(f'Commanded position: boresight={target}')\\n session.set_status('running')\\n\\n ok, msg = yield self._go_to_axis(session, 'Boresight', target)\\n\\n if ok and params['end_stop']:\\n yield self.acu_control.http.Command('DataSets.CmdModeTransfer',\\n 'Set3rdAxisMode', 'Stop')\\n\\n return ok, msg\",\n \"def set_position(self, pos, debug=False):\\n pos = max(pos, 0)\\n pos = min(pos, 1)\\n posrange = pos * self.range\\n pos = posrange + self.min\\n if debug:\\n print('Setting Dynamixel {} with posrange {} to position {}'.format(self.id, posrange, pos))\\n self.motor.set_position(int(pos))\",\n \"def setPosition(self, position, view) -> None:\\n ...\",\n \"def set_position( self, posx, posy ):\\n\\n self.__foodx = posx\\n self.__foody = posy\",\n \"def set_target_pos(self, pos: Vec2d):\\n\\t\\tself.__target.set(pos)\",\n \"def setPos(self,pos):\\n self.Xpos,self.Ypos=pos\",\n \"def setPoint(self, point):\\n self.position = point.position\",\n \"def position(self, pos: int):\\n self.__pos = pos\",\n \"def setPos(self, x, y, anchor='ll'):\\n self.transform.setPos(glm.vec3(x, y, 0))\\n if anchor == 'ul':\\n offx = 0\\n offy = - self.font.table['ascent']\\n elif anchor == 'uc':\\n offx = - self._labelWidth / 2\\n offy = - self.font.table['ascent']\\n elif anchor == 'ur':\\n offx = - self._labelWidth\\n offy = - self.font.table['ascent']\\n elif anchor == 'cl':\\n offx = 0\\n offy = self._labelHeight / 2 - self.font.table['ascent']\\n elif anchor == 'cc':\\n offx = - self._labelWidth / 2\\n offy = self._labelHeight / 2 - self.font.table['ascent']\\n elif anchor == 'cr':\\n offx = - self._labelWidth\\n offy = self._labelHeight / 2 - self.font.table['ascent']\\n elif anchor == 'll':\\n offx = 0\\n offy = self._labelHeight - self.font.table['ascent']\\n elif anchor == 'lc':\\n offx = - self._labelWidth / 2\\n offy = self._labelHeight - self.font.table['ascent']\\n elif anchor == 'lr':\\n offx = - self._labelWidth\\n offy = self._labelHeight - self.font.table['ascent']\\n else:\\n raise SystemExit(f\\\"Unimplemented anchor '{anchor}'\\\")\\n self.model.setPos(glm.vec3(offx, offy, 0))\",\n \"def set_position(self, x: float, y: float):\\n self._shape.body.position.x = x\\n self._shape.body.position.y = y\",\n \"def __set_ball_position(self):\\n self.__window.remove(self.__ball)\\n self.__window.add(self.__ball, (self.__window.width - self.__ball.width) / 2,\\n (self.__window.height - self.__ball.height) / 2)\",\n \"def set_position(self, x, y):\\n self.pos = pygame.Rect(x, y, 0, 0)\",\n \"def set_offset(self, offset):\\r\\n for b in self.buf:\\r\\n b.set_offset(offset)\",\n \"def set_position(self):\\n raise RuntimeError(\\\"the 'set_position' method must be overriden\\\")\",\n \"def bx(self, bx):\\n\\n self._bx = bx\",\n \"def set(self, x, y, color):\\n if x < 0 or x >= self.width or y < 0 or y >= self.height:\\n return\\n i = self.map[y][x]\\n super().set(i, color)\",\n \"def SetPosition(self, pos):\\n self._pos = pos\",\n \"def __setitem__(self, pos, value):\\n\\t\\t#pprint(pos)\\n\\t\\t#pprint(self.leds.__getitem__(pos))\\n\\t\\t# Handle if a slice of positions are passed in by setting the appropriate\\n\\t\\t# LED data values to the provided values.\\n\\t\\tself.setPixelColor(pos, value)\",\n \"def set_position(self, new_pos):\\n self._position = new_pos\",\n \"def setPixel(self, x, y, val):\\r\\n self.__buffer[y][x].setValue(val)\",\n \"def set_brightness(self, brightness, bulb_index=-1):\\n\\n # TODO: Throw an error if value not in range\\n\\n bulb_index = int(bulb_index) if bulb_index is not None else -1\\n brightness = int(brightness)\\n if bulb_index == -1:\\n for bulb in self.bulbs:\\n bulb.set_brightness(brightness, effect=\\\"smooth\\\", duration=1000)\\n elif bulb_index <= len(self.bulbs):\\n self.bulbs[bulb_index].set_brightness(brightness, effect=\\\"smooth\\\", duration=1000)\",\n \"def set_biases(self, biases):\\n\\n if not np.any(biases):\\n return\\n\\n v_rest = self.config.getfloat('cell', 'v_rest')\\n v_thresh = self.config.getfloat('cell', 'v_thresh')\\n cm = self.config.getfloat('cell', 'cm')\\n\\n i_offset = biases * cm * (v_thresh - v_rest) / self._duration\\n\\n self.layers[-1].set(i_offset=i_offset)\",\n \"def setPosition(self,newPos):\\n self._position = newPos\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6343822","0.60181844","0.5908633","0.58790344","0.5868035","0.58086485","0.5772332","0.5769417","0.5769417","0.5769417","0.5769417","0.5769417","0.5769417","0.5769417","0.5769417","0.5769417","0.5769417","0.5769417","0.57621205","0.57330173","0.5698484","0.5697799","0.5679826","0.5671287","0.56578356","0.56481975","0.56459445","0.56351274","0.56304455","0.56249356","0.56222886","0.5596241","0.55961186","0.55961186","0.5595103","0.5579039","0.5551219","0.5549309","0.5543381","0.5532058","0.5531471","0.5504733","0.54877263","0.54528487","0.54321307","0.54321307","0.54321307","0.54321307","0.54321307","0.54321307","0.54321307","0.54321307","0.54046035","0.54026854","0.5395539","0.539492","0.53910184","0.53791815","0.53749985","0.53675216","0.5360583","0.53600526","0.5356758","0.5356548","0.5355305","0.5350528","0.5350512","0.533855","0.5335871","0.5334485","0.5331898","0.53297794","0.532392","0.53189754","0.5317525","0.5317525","0.5317525","0.5317525","0.53128064","0.53081024","0.53059125","0.5297087","0.5295587","0.5295097","0.52874625","0.52703303","0.52614224","0.5257743","0.52566034","0.5256142","0.5255484","0.52500343","0.52485347","0.5246674","0.52458704","0.52457744","0.5241861","0.5238846","0.52350825","0.523099","0.5230341"],"string":"[\n \"0.6343822\",\n \"0.60181844\",\n \"0.5908633\",\n \"0.58790344\",\n \"0.5868035\",\n \"0.58086485\",\n \"0.5772332\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.5769417\",\n \"0.57621205\",\n \"0.57330173\",\n \"0.5698484\",\n \"0.5697799\",\n \"0.5679826\",\n \"0.5671287\",\n \"0.56578356\",\n \"0.56481975\",\n \"0.56459445\",\n \"0.56351274\",\n \"0.56304455\",\n \"0.56249356\",\n \"0.56222886\",\n \"0.5596241\",\n \"0.55961186\",\n \"0.55961186\",\n \"0.5595103\",\n \"0.5579039\",\n \"0.5551219\",\n \"0.5549309\",\n \"0.5543381\",\n \"0.5532058\",\n \"0.5531471\",\n \"0.5504733\",\n \"0.54877263\",\n \"0.54528487\",\n \"0.54321307\",\n \"0.54321307\",\n \"0.54321307\",\n \"0.54321307\",\n \"0.54321307\",\n \"0.54321307\",\n \"0.54321307\",\n \"0.54321307\",\n \"0.54046035\",\n \"0.54026854\",\n \"0.5395539\",\n \"0.539492\",\n \"0.53910184\",\n \"0.53791815\",\n \"0.53749985\",\n \"0.53675216\",\n \"0.5360583\",\n \"0.53600526\",\n \"0.5356758\",\n \"0.5356548\",\n \"0.5355305\",\n \"0.5350528\",\n \"0.5350512\",\n \"0.533855\",\n \"0.5335871\",\n \"0.5334485\",\n \"0.5331898\",\n \"0.53297794\",\n \"0.532392\",\n \"0.53189754\",\n \"0.5317525\",\n \"0.5317525\",\n \"0.5317525\",\n \"0.5317525\",\n \"0.53128064\",\n \"0.53081024\",\n \"0.53059125\",\n \"0.5297087\",\n \"0.5295587\",\n \"0.5295097\",\n \"0.52874625\",\n \"0.52703303\",\n \"0.52614224\",\n \"0.5257743\",\n \"0.52566034\",\n \"0.5256142\",\n \"0.5255484\",\n \"0.52500343\",\n \"0.52485347\",\n \"0.5246674\",\n \"0.52458704\",\n \"0.52457744\",\n \"0.5241861\",\n \"0.5238846\",\n \"0.52350825\",\n \"0.523099\",\n \"0.5230341\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77475,"cells":{"query":{"kind":"string","value":"Convert something in [1,1] to [0,1]"},"document":{"kind":"string","value":"def normalize_to_prob(inp):\n return (inp + 1)/2"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def zero_to_ones(L):\n return [-1 if val == 0 else 1 for val in L]","def invert_zero_one(sequence):\n return [1 - code for code in sequence]","def intconv(arg):\n return([arg[0], int(arg[1])] + arg[2:])","def invert_v(val):\n return [val[0],1-val[1]]","def binary_converter(probs):\n return np.array([[1 - p, p] for p in probs])","def __convert_boolean_list(boolean_list):\n return ['0' if b else '1' for b in boolean_list]","def scaleto01(val,check=True):\n if type(val) is not list and type(val) is not np.ndarray:\n val = [val]\n if type(val) is list:\n val = np.array(val)\n assert type(val) is np.ndarray\n assert not check or np.all((val==-1) + (val==1))\n return (val+1)/2.0","def dummies2int(cBin):\n c = []\n for i in cBin.tolist():\n c.append(i.index(1) + 1)\n return c","def one_hot_to_flat(val):\n idxs = np.array(np.where(val == 1.0))[-1]\n if len(val.shape) == 1:\n return int(idxs)\n return idxs","def convertToIntArray(booleanArray: typing.List[bool]) -> typing.List[int]:\n ...","def onehot(isTrue):\n if isTrue:\n return [1, 0]\n else:\n return [0, 1]","def toIntList(values):\n\treturn list(map(lambda va: int(va), values))","def transform(self, x):\n res = [x[i] for i in range(len(x))\n if i not in self.index_value_pairs]\n return res if isinstance(x, list) else np.asarray(res)","def flipAndInvertImage(self, A: List[List[int]]) -> List[List[int]]:\n return [list(map(lambda x: 0 if x else 1, row[::-1])) for row in A]","def convert_vector_in_sign_vector(vec):\n ret = []\n for x in vec:\n if x<0:\n ret += [m]\n elif x>0:\n ret += [p]\n else:\n ret += [n]\n return ret","def comp1(numb):\n\n\tbi= binario(numb)\n\tc11 = []\n\tif int(numb) < 0:\n\t\tfor x in bi:\n\t\t\tc11.append(int( not x))\n\n\t\tc11.append(1)\n\t\tc11.reverse()\n\telse:\n\t\tc11 = bi\n\t\tc11.append(0)\n\t\tc11.reverse()\n\treturn c11","def set24_to_list(v):\n return [x for x in range(24) if v & (1 << x)]","def binary_ones(lst_sorted):\n i = 0\n res = []\n while lst_sorted[i][1] == 1:\n res.append(lst_sorted[i])\n i += 1\n return res","def decode_int_list(L):\n return [] if L == '[]' else [int(a) for a in L[1:-1].split(\",\")]","def convert_one_hot_encoded_cards_to_int_encoded_list(cards: np.ndarray) -> Iterable[int]:\n return np.flatnonzero(cards).tolist()","def operator_1_to_01(operator1):\n N = len(operator1)\n operator01 = np.zeros((N + 1, N + 1))\n operator01[1:, 1:] = operator1\n return operator01","def ints(xs: Iterable) -> list[int]:\n return lmap(int, xs)","def list2int(lst):\n def foo(x, y):\n return (x << 1) + y\n return reduce(foo, reversed(lst), 0)","def single_number(nums):\n tmp = 0\n for num in nums:\n tmp ^= num\n marker = 1\n while marker & tmp != marker:\n marker <<= 1\n a = 0\n for num in nums:\n if marker & num:\n a ^= num\n b = tmp ^ a\n return [a, b]","def _one_pass(nums):\n pattern = [0, 1, 0, -1]\n return [\n int(str(sum(\n v * pattern[(i // n) % len(pattern)]\n for i, v in enumerate(nums, start=1)\n ))[-1])\n for n in range(1, len(nums) + 1)\n ]","def transform_fields(categorical_fields):\n total = [4, 13, 12]\n converted = []\n for key, field in enumerate(categorical_fields):\n value = int(field)\n temp = list(np.zeros(total[key]))\n if value is not 0:\n temp[value - 1] = 1\n converted = converted + temp\n return converted","def _BoolsToInts(arg_list):\n result = []\n for arg in arg_list:\n if isinstance(arg, (list, tuple)):\n result.append(_BoolsToInts(arg))\n elif arg is True:\n result.append(1)\n elif arg is False:\n result.append(0)\n else:\n result.append(arg)\n\n return result","def scaleto11(val,check=True):\n if type(val) is not list and type(val) is not np.ndarray:\n val = [val]\n if type(val) is list:\n val = np.array(val)\n assert type(val) is np.ndarray\n assert not check or np.all((val==0) + (val==1))\n return val*2-1","def convert(self, input):\n if not isinstance(input, list):\n input = [input]\n converted = []\n for item in input:\n try:\n if \".\" in item:\n converted.append(float(item))\n else:\n converted.append(int(item))\n except ValueError:\n converted.append(item)\n if len(converted) == 1:\n converted = converted[0]\n return converted","def xrange1(value):\n try:\n i = int(value)\n return [x+1 for x in xrange(i)]\n except:\n return []","def dec2BinList(self, value=0, bits=8):\r\n\t\tbinList = [int(x) for x in bin(value)[2:]]\r\n\t\tpadLength = bits - len(binList)\r\n\t\tbinList.reverse()\r\n\t\tfor x in range(padLength):\r\n\t\t\tbinList.append(0)\r\n\t\treturn binList","def game_to_bin(game : List[int]) -> List[List[bool]]:\n return list(map(int_to_bin_three_bit, game))\n # equivalent to\n # converted_game = []\n # for line in game:\n # converted_game.append(int_to_bin_three_bit(line))\n # return converted_game","def list_to_set24(l):\n res = 0\n for x in l: res ^= 1 << x\n return res & 0xffffff","def convert_to_bits(n):\n result = []\n if n == 0:\n return [0]\n while n > 0:\n result = [int(n % 2)] + result\n n = n / 2\n return result","def slide(line):\n\n result = [ 0 for dummy_i in range(len(line)) ]\n index = 0\n for num in line:\n if num != 0:\n # Add this num, since it's a non-zero, to result\n result[index] = num\n index += 1\n return result","def test_cast_list(self):\n dim = Integer(\"yolo\", \"uniform\", -3, 5)\n assert dim.cast([\"1\", \"2\"]) == [1, 2]","def normalize(self,rgb):\n\n rgb = Numeric.array(rgb)\n return Numeric.clip(rgb,0,1).tolist()","def convert2int(self,seq_pep):\n\t\treturn [self.aminoacids.index(pep) for pep in seq_pep]","def to_bit_list(val, width=16):\n return [(1 if val & (1<<n) else 0) for n in range(width)]","def binList2Dec(self, list=[]):\r\n\t\tvalue = 0\r\n\t\tfor x in range(len(list)):\r\n\t\t\tvalue = value + list[x]*(2**x)\r\n\t\treturn value","def tolist(x):\n return x if isinstance(x, list) else [x]","def flipAndInvertImage(self, A: List[List[int]]) -> List[List[int]]:\n return [[1- x for x in reversed(row)] for row in A]","def binconv(fp, fp_len):\n vec = [1] * fp_len\n #print(fp, len(fp))\n for indx, b in enumerate(fp):\n if indx == 224:\n break\n if b == '0':\n vec[indx] = -1\n return vec","def encode(self, value):\n\n encoded = []\n if self.is_binary:\n encoded.append(0 if value == self.unique_values[0] else 1)\n elif not self.is_categorical:\n lower_bound = self.unique_values[0]\n upper_bound = self.unique_values[1]\n encoded.append(1 if lower_bound < value < upper_bound else 0)\n else: \n for index in range(len(self.unique_values)):\n unique = self.unique_values[index]\n encoded.append(1 if value == unique else 0)\n\n return encoded","def to_unary(data, C):\r\n coordinates = []\r\n\r\n for i in range(len(data)):\r\n t = np.zeros(C, dtype=np.int64)\r\n t[0:data[i]] = 1\r\n t[data[i]:C] = 0\r\n coordinates.append(t)\r\n\r\n return np.concatenate(coordinates)","def encode(self, value):\n\n encoded = []\n if self.is_categorical:\n if self.is_binary:\n encoded.append(0 if value == self.unique_values[0] else 1)\n else: \n for index in range(len(self.unique_values)):\n unique = self.unique_values[index]\n encoded.append(1 if value == unique else 0)\n else: # continuous data\n normalized = self.__normalize(value, -1, 1)\n encoded.append(normalized)\n \n\n return encoded","def cat2int(x):\n s = set(x)\n v2int = {}\n for i, v in enumerate(unique):\n v2int[v] = i\n assert len(s) >= 2, 'Kind of values is smaller than 2'\n return np.array([v2int[e] for e in x], dtype=np.int32), v2int","def invert_y(val):\n return [val[0],-val[1],val[2]]","def _to_list( self, input ):\n import numpy\n listtypes = (list, tuple, numpy.ndarray)\n if input == None:\n return None\n elif type(input) in listtypes:\n return list(input)\n else:\n return [input]","def to_list(x):\n if isinstance(x, list):\n return x\n return [x]","def binary_encode(x: int) -> List[int]:\n return [x >> i & 1 for i in range(10)]","def binary_encode(x: int) -> List[int]:\n return [x >> i & 1 for i in range(10)]","def convertToShortArray(booleanArray: typing.List[bool]) -> typing.List[int]:\n ...","def normalize(column):\n value_set = set(column)\n unique_count = len(value_set)\n if unique_count == 1:\n # skip everything in this column. \n return []\n elif unique_count == 2:\n zero = list(value_set)[0]\n one = list(value_set)[1]\n normalized_column = []\n for value in column:\n normalized_column.append(1 if value == one else 0)\n return [normalized_column]\n else: \n all_values = list(value_set)\n normalized_column = []\n\n # expand into multiple columns \n for index in range(len(all_values)):\n normalized_column.append([])\n\n for value in column:\n for index in range(len(all_values)):\n normalized_column[index].append(1 if value == all_values[index] else 0)\n \n return normalized_column","def filtreNormales(liste):\r\n l=[]\r\n for i in range(len(liste)):\r\n if liste[i][0]!=0: l.append(i)\r\n return l","def liste_inverse(liste):\n liste1=[]\n a=0\n for i in range (len(liste)):\n a=liste.pop()\n liste1.append(a)\n return liste1","def makelist(input):\n if isinstance(input, list) or isinstance(input, np.array):\n output = input\n else:\n output = [input]\n return output","def _make_zero(p):\n\n return [pi == 0 for pi in p]","def to_binary(y):\n y_ = np.zeros(y.shape[0])\n y_[y>0] = 1\n return(y_)","def numberToArray(number):\n mantisse = getMantisse(number)\n exponent = getExponent(number)\n number = [mantisse, exponent]\n return(number)","def right_cones(array: List[int]) -> List[int]:\n return []","def encode_int_list(L):\n return str(L).replace(\" \", \"\")","def prepare_numbers(numbers):\n \n numb = []\n for item in numbers:\n numb.append(int(item))\n return numb","def to_binary(x,L, sort='revsort'):\n x = SORT_CONVENTIONS[sort](x, 2**L)\n b=np.zeros(L,dtype=int)\n for i in range(L):\n x,r=divmod(x,2)\n b[L-i-1]=r\n return b","def gray_code(number: int) -> list:\n if number <= 0:\n return -1\n\n result: list = []\n result.append(\"0\")\n result.append(\"1\")\n num1 = 2\n\n while True:\n if num1 >= 1 << number:\n break\n\n for j in range (num1 - 1, -1, -1):\n result.append(result[j])\n\n for j in range (num1):\n result[j] = \"0\" + result[j]\n\n for j in range (num1, 2 * num1):\n result[j] = \"1\" + result[j]\n\n num1 = num1 << 1\n\n return result","def test_to_int_list(self):\n self.assertEqual(to_int_list([u\"3\", None, \"asdf\", u\"42\"]), [3, 0, 0, 42])","def convert_labels_to_int(labels): \n if not len(labels):\n raise ValueError(\"Nothing to convert!\")\n\n if isinstance(labels[0], basestring) or isinstance(labels[0], int):\n label_legend = list(set(labels))\n converted_labels = [ label_legend.index(l) for l in labels ]\n elif is_iterable(labels[0]): # Multiple labels, handle each one individually\n num_labels = len(labels[0])\n label_legend = []\n converted_labels = []\n \n # Get the unique sets of labels for each index\n for i in xrange(num_labels):\n temp_labels = [l[i] for l in labels]\n label_legend.append(list(set(temp_labels)))\n\n # Apply mapping to each label\n for label in labels:\n converted_label = [ leg.index(l) for leg, l in \n zip(label_legend, label) ]\n converted_labels.append(converted_label)\n else: # Not a string/int, not a list... hmmm\n raise ValueError('Unexpected label type!')\n\n return label_legend, array(converted_labels)","def _convert_to_list(self, input_argument):\n if type(input_argument) is not list:\n input_argument = [input_argument]\n return input_argument","def array_to_list(llist, source):\r\n while source: #a list is considered True as long as it is not empty\r\n llist.append(source.pop(0))\r\n \r\n return","def list2int(thelist,base=2):\n return reduce(lambda x,y:base*x+y,reversed(thelist),0","def intToVec(n):\r\n return [n >> 12, (n >> 4) & 0xf, (n >> 8) & 0xf, n & 0xf]","def binary_to_one_hot(arr: np.ndarray) -> np.ndarray:\n res = np.zeros((arr.shape[0], 2))\n res[np.where(arr == 1)[0], 0] = 1\n res[np.where(arr == 0)[0], 1] = 1\n return res","def strToListInt(x):\n if type(x)==str:\n return [int(i) for i in x[1:-1].split(\", \")]","def value_to_two_class(lim,val_arr,cla_arr=string.ascii_lowercase):\n\n return [cla_arr[i] for i in (np.array(val_arr)>lim).astype(int)]","def get_list_of_int2(self):\n pass","def value_to_two_class(lim,val_arr,cla_arr=string.ascii_lowercase):\n \n return [cla_arr[i] for i in (np.array(val_arr)>lim).astype(int)]","def bin_bil_to_unil(a):\n return (a + 1)/2","def map_minus_one_to_one(x, a, b):\n assert b > a\n s = 2./(b - a)\n t = (a+b)/(a-b)\n y = s*x + t\n y[y>1] = 1\n y[y<-1] = -1\n return y","def to_sparse(a):\n flat = a.flatten()\n indices = np.nonzero(flat)\n values = flat[indices]\n return indices[0], values","def _to_vectors(labels):\n res = [[int(s) for s in label] for label in labels]\n max_len = 0\n for ele in res:\n if len(ele) > max_len:\n max_len = len(ele)\n return [ele + [0] * (max_len - len(ele)) for ele in res]","def predicted_results_to_binary(predicted_results):\n return [int(round(answer)) for answer in predicted_results]","def ones(num):\n if num < 1:\n raise IndexError('num must be >= 1.')\n return Vector.fromSequence([1] * num)","def clean_int_list(s):\n if isinstance(s, basestring):\n return [int(s)]\n elif isinstance(s, int):\n return [s]\n return [int(x) for x in s]","def int2list(num):\n return [int(d) for d in str(num)]","def array2list(input_array):\r\n return [element for element in input_array]","def task5_push_zeros(input_list):\n for elem in input_list:\n if elem == 0:\n input_list.remove(elem)\n input_list.append(0)\n return input_list","def f1to2(x):\n assert_equal(x.ndim, 1)\n res = x[::-1] * x[1:,None]\n return np.ma.masked_where(res%5==0, res)","def convert_selection(selection):\n\n\n bits = selection.split(\",\")\n all_bits = []\n for bit in bits:\n small_bits = bit.split(\"-\")\n if len(small_bits) == 1:\n all_bits.append(int(small_bits[0]))\n else:\n range_of_bits = range(int(small_bits[0]), int(small_bits[1])+1)\n for tiny_bit in range_of_bits:\n all_bits.append(tiny_bit)\n\n return all_bits","def make_binary(y, positive):\n pos = np.in1d(y, positive).reshape(y.shape)\n new_y = np.zeros(y.shape, dtype=np.int)\n new_y[pos] = 1\n return new_y","def __init__(self, list):\n self.array = [0] + list\n for idx in range(1, len(self.array)):\n idx2 = idx + (idx & -idx)\n if idx2 < len(self.array):\n self.array[idx2] += self.array[idx]","def scale_1d(x):\n return (min(x), max(x), len(x))","def _idx_to_bits(self, i):\n bits = bin(i)[2:].zfill(self.nb_hyperplanes) # Pad the bits str with 0\n return [-1.0 if b == \"0\" else 1.0 for b in bits]","def map_zero_one(x, a, b):\n assert b > a\n s = 1./(b - a)\n t = a/(a-b)\n y = s*x + t\n y[y>1] = 1\n y[y<0] = 0\n return y","def convert_to_one_hot(a):\n a = a[:, 0]\n a = a.astype(int)\n A = np.zeros((len(a), config.num_classes))\n A[np.arange(len(a)), a] = 1\n return A","def vec_to_bytes(val):\n return [int(x*255) for x in val]","def to_zero_one(thing):\n\n if is_iterable(thing):\n zero_or_one = np.zeros(len(thing))\n zero_or_one[np.array(thing) >= 0.5] = 1\n else:\n if thing >= 0.5:\n zero_or_one = 1\n else:\n zero_or_one = 0\n return zero_or_one","def int_repr(arr):\n return list(map(list2int, arr))","def turn_to_one_hot(self,length,ind):\n one_hot = [0 for _ in range(length)]\n if (ind >= len(one_hot)):\n one_hot[-1] = 1\n else:\n one_hot[ind] = 1\n return one_hot","def fluxes_to_labels(fluxes: np.ndarray) -> np.ndarray:\n return ((1 - fluxes) // 2).astype(np.int8)","def grayCode(self, n):\n res = [0]\n for i in range(0, n):\n res += [(1 << i) + x for x in reversed(res)]\n return res","def one_hot_from_int(int_or_list, batch_size=1):\n if isinstance(int_or_list, int):\n int_or_list = [int_or_list]\n\n if len(int_or_list) == 1 and batch_size > 1:\n int_or_list = [int_or_list[0]] * batch_size\n\n assert batch_size == len(int_or_list)\n\n array = np.zeros((batch_size, 1000), dtype=np.float32)\n for i, j in enumerate(int_or_list):\n array[i, j] = 1.0\n return array"],"string":"[\n \"def zero_to_ones(L):\\n return [-1 if val == 0 else 1 for val in L]\",\n \"def invert_zero_one(sequence):\\n return [1 - code for code in sequence]\",\n \"def intconv(arg):\\n return([arg[0], int(arg[1])] + arg[2:])\",\n \"def invert_v(val):\\n return [val[0],1-val[1]]\",\n \"def binary_converter(probs):\\n return np.array([[1 - p, p] for p in probs])\",\n \"def __convert_boolean_list(boolean_list):\\n return ['0' if b else '1' for b in boolean_list]\",\n \"def scaleto01(val,check=True):\\n if type(val) is not list and type(val) is not np.ndarray:\\n val = [val]\\n if type(val) is list:\\n val = np.array(val)\\n assert type(val) is np.ndarray\\n assert not check or np.all((val==-1) + (val==1))\\n return (val+1)/2.0\",\n \"def dummies2int(cBin):\\n c = []\\n for i in cBin.tolist():\\n c.append(i.index(1) + 1)\\n return c\",\n \"def one_hot_to_flat(val):\\n idxs = np.array(np.where(val == 1.0))[-1]\\n if len(val.shape) == 1:\\n return int(idxs)\\n return idxs\",\n \"def convertToIntArray(booleanArray: typing.List[bool]) -> typing.List[int]:\\n ...\",\n \"def onehot(isTrue):\\n if isTrue:\\n return [1, 0]\\n else:\\n return [0, 1]\",\n \"def toIntList(values):\\n\\treturn list(map(lambda va: int(va), values))\",\n \"def transform(self, x):\\n res = [x[i] for i in range(len(x))\\n if i not in self.index_value_pairs]\\n return res if isinstance(x, list) else np.asarray(res)\",\n \"def flipAndInvertImage(self, A: List[List[int]]) -> List[List[int]]:\\n return [list(map(lambda x: 0 if x else 1, row[::-1])) for row in A]\",\n \"def convert_vector_in_sign_vector(vec):\\n ret = []\\n for x in vec:\\n if x<0:\\n ret += [m]\\n elif x>0:\\n ret += [p]\\n else:\\n ret += [n]\\n return ret\",\n \"def comp1(numb):\\n\\n\\tbi= binario(numb)\\n\\tc11 = []\\n\\tif int(numb) < 0:\\n\\t\\tfor x in bi:\\n\\t\\t\\tc11.append(int( not x))\\n\\n\\t\\tc11.append(1)\\n\\t\\tc11.reverse()\\n\\telse:\\n\\t\\tc11 = bi\\n\\t\\tc11.append(0)\\n\\t\\tc11.reverse()\\n\\treturn c11\",\n \"def set24_to_list(v):\\n return [x for x in range(24) if v & (1 << x)]\",\n \"def binary_ones(lst_sorted):\\n i = 0\\n res = []\\n while lst_sorted[i][1] == 1:\\n res.append(lst_sorted[i])\\n i += 1\\n return res\",\n \"def decode_int_list(L):\\n return [] if L == '[]' else [int(a) for a in L[1:-1].split(\\\",\\\")]\",\n \"def convert_one_hot_encoded_cards_to_int_encoded_list(cards: np.ndarray) -> Iterable[int]:\\n return np.flatnonzero(cards).tolist()\",\n \"def operator_1_to_01(operator1):\\n N = len(operator1)\\n operator01 = np.zeros((N + 1, N + 1))\\n operator01[1:, 1:] = operator1\\n return operator01\",\n \"def ints(xs: Iterable) -> list[int]:\\n return lmap(int, xs)\",\n \"def list2int(lst):\\n def foo(x, y):\\n return (x << 1) + y\\n return reduce(foo, reversed(lst), 0)\",\n \"def single_number(nums):\\n tmp = 0\\n for num in nums:\\n tmp ^= num\\n marker = 1\\n while marker & tmp != marker:\\n marker <<= 1\\n a = 0\\n for num in nums:\\n if marker & num:\\n a ^= num\\n b = tmp ^ a\\n return [a, b]\",\n \"def _one_pass(nums):\\n pattern = [0, 1, 0, -1]\\n return [\\n int(str(sum(\\n v * pattern[(i // n) % len(pattern)]\\n for i, v in enumerate(nums, start=1)\\n ))[-1])\\n for n in range(1, len(nums) + 1)\\n ]\",\n \"def transform_fields(categorical_fields):\\n total = [4, 13, 12]\\n converted = []\\n for key, field in enumerate(categorical_fields):\\n value = int(field)\\n temp = list(np.zeros(total[key]))\\n if value is not 0:\\n temp[value - 1] = 1\\n converted = converted + temp\\n return converted\",\n \"def _BoolsToInts(arg_list):\\n result = []\\n for arg in arg_list:\\n if isinstance(arg, (list, tuple)):\\n result.append(_BoolsToInts(arg))\\n elif arg is True:\\n result.append(1)\\n elif arg is False:\\n result.append(0)\\n else:\\n result.append(arg)\\n\\n return result\",\n \"def scaleto11(val,check=True):\\n if type(val) is not list and type(val) is not np.ndarray:\\n val = [val]\\n if type(val) is list:\\n val = np.array(val)\\n assert type(val) is np.ndarray\\n assert not check or np.all((val==0) + (val==1))\\n return val*2-1\",\n \"def convert(self, input):\\n if not isinstance(input, list):\\n input = [input]\\n converted = []\\n for item in input:\\n try:\\n if \\\".\\\" in item:\\n converted.append(float(item))\\n else:\\n converted.append(int(item))\\n except ValueError:\\n converted.append(item)\\n if len(converted) == 1:\\n converted = converted[0]\\n return converted\",\n \"def xrange1(value):\\n try:\\n i = int(value)\\n return [x+1 for x in xrange(i)]\\n except:\\n return []\",\n \"def dec2BinList(self, value=0, bits=8):\\r\\n\\t\\tbinList = [int(x) for x in bin(value)[2:]]\\r\\n\\t\\tpadLength = bits - len(binList)\\r\\n\\t\\tbinList.reverse()\\r\\n\\t\\tfor x in range(padLength):\\r\\n\\t\\t\\tbinList.append(0)\\r\\n\\t\\treturn binList\",\n \"def game_to_bin(game : List[int]) -> List[List[bool]]:\\n return list(map(int_to_bin_three_bit, game))\\n # equivalent to\\n # converted_game = []\\n # for line in game:\\n # converted_game.append(int_to_bin_three_bit(line))\\n # return converted_game\",\n \"def list_to_set24(l):\\n res = 0\\n for x in l: res ^= 1 << x\\n return res & 0xffffff\",\n \"def convert_to_bits(n):\\n result = []\\n if n == 0:\\n return [0]\\n while n > 0:\\n result = [int(n % 2)] + result\\n n = n / 2\\n return result\",\n \"def slide(line):\\n\\n result = [ 0 for dummy_i in range(len(line)) ]\\n index = 0\\n for num in line:\\n if num != 0:\\n # Add this num, since it's a non-zero, to result\\n result[index] = num\\n index += 1\\n return result\",\n \"def test_cast_list(self):\\n dim = Integer(\\\"yolo\\\", \\\"uniform\\\", -3, 5)\\n assert dim.cast([\\\"1\\\", \\\"2\\\"]) == [1, 2]\",\n \"def normalize(self,rgb):\\n\\n rgb = Numeric.array(rgb)\\n return Numeric.clip(rgb,0,1).tolist()\",\n \"def convert2int(self,seq_pep):\\n\\t\\treturn [self.aminoacids.index(pep) for pep in seq_pep]\",\n \"def to_bit_list(val, width=16):\\n return [(1 if val & (1<<n) else 0) for n in range(width)]\",\n \"def binList2Dec(self, list=[]):\\r\\n\\t\\tvalue = 0\\r\\n\\t\\tfor x in range(len(list)):\\r\\n\\t\\t\\tvalue = value + list[x]*(2**x)\\r\\n\\t\\treturn value\",\n \"def tolist(x):\\n return x if isinstance(x, list) else [x]\",\n \"def flipAndInvertImage(self, A: List[List[int]]) -> List[List[int]]:\\n return [[1- x for x in reversed(row)] for row in A]\",\n \"def binconv(fp, fp_len):\\n vec = [1] * fp_len\\n #print(fp, len(fp))\\n for indx, b in enumerate(fp):\\n if indx == 224:\\n break\\n if b == '0':\\n vec[indx] = -1\\n return vec\",\n \"def encode(self, value):\\n\\n encoded = []\\n if self.is_binary:\\n encoded.append(0 if value == self.unique_values[0] else 1)\\n elif not self.is_categorical:\\n lower_bound = self.unique_values[0]\\n upper_bound = self.unique_values[1]\\n encoded.append(1 if lower_bound < value < upper_bound else 0)\\n else: \\n for index in range(len(self.unique_values)):\\n unique = self.unique_values[index]\\n encoded.append(1 if value == unique else 0)\\n\\n return encoded\",\n \"def to_unary(data, C):\\r\\n coordinates = []\\r\\n\\r\\n for i in range(len(data)):\\r\\n t = np.zeros(C, dtype=np.int64)\\r\\n t[0:data[i]] = 1\\r\\n t[data[i]:C] = 0\\r\\n coordinates.append(t)\\r\\n\\r\\n return np.concatenate(coordinates)\",\n \"def encode(self, value):\\n\\n encoded = []\\n if self.is_categorical:\\n if self.is_binary:\\n encoded.append(0 if value == self.unique_values[0] else 1)\\n else: \\n for index in range(len(self.unique_values)):\\n unique = self.unique_values[index]\\n encoded.append(1 if value == unique else 0)\\n else: # continuous data\\n normalized = self.__normalize(value, -1, 1)\\n encoded.append(normalized)\\n \\n\\n return encoded\",\n \"def cat2int(x):\\n s = set(x)\\n v2int = {}\\n for i, v in enumerate(unique):\\n v2int[v] = i\\n assert len(s) >= 2, 'Kind of values is smaller than 2'\\n return np.array([v2int[e] for e in x], dtype=np.int32), v2int\",\n \"def invert_y(val):\\n return [val[0],-val[1],val[2]]\",\n \"def _to_list( self, input ):\\n import numpy\\n listtypes = (list, tuple, numpy.ndarray)\\n if input == None:\\n return None\\n elif type(input) in listtypes:\\n return list(input)\\n else:\\n return [input]\",\n \"def to_list(x):\\n if isinstance(x, list):\\n return x\\n return [x]\",\n \"def binary_encode(x: int) -> List[int]:\\n return [x >> i & 1 for i in range(10)]\",\n \"def binary_encode(x: int) -> List[int]:\\n return [x >> i & 1 for i in range(10)]\",\n \"def convertToShortArray(booleanArray: typing.List[bool]) -> typing.List[int]:\\n ...\",\n \"def normalize(column):\\n value_set = set(column)\\n unique_count = len(value_set)\\n if unique_count == 1:\\n # skip everything in this column. \\n return []\\n elif unique_count == 2:\\n zero = list(value_set)[0]\\n one = list(value_set)[1]\\n normalized_column = []\\n for value in column:\\n normalized_column.append(1 if value == one else 0)\\n return [normalized_column]\\n else: \\n all_values = list(value_set)\\n normalized_column = []\\n\\n # expand into multiple columns \\n for index in range(len(all_values)):\\n normalized_column.append([])\\n\\n for value in column:\\n for index in range(len(all_values)):\\n normalized_column[index].append(1 if value == all_values[index] else 0)\\n \\n return normalized_column\",\n \"def filtreNormales(liste):\\r\\n l=[]\\r\\n for i in range(len(liste)):\\r\\n if liste[i][0]!=0: l.append(i)\\r\\n return l\",\n \"def liste_inverse(liste):\\n liste1=[]\\n a=0\\n for i in range (len(liste)):\\n a=liste.pop()\\n liste1.append(a)\\n return liste1\",\n \"def makelist(input):\\n if isinstance(input, list) or isinstance(input, np.array):\\n output = input\\n else:\\n output = [input]\\n return output\",\n \"def _make_zero(p):\\n\\n return [pi == 0 for pi in p]\",\n \"def to_binary(y):\\n y_ = np.zeros(y.shape[0])\\n y_[y>0] = 1\\n return(y_)\",\n \"def numberToArray(number):\\n mantisse = getMantisse(number)\\n exponent = getExponent(number)\\n number = [mantisse, exponent]\\n return(number)\",\n \"def right_cones(array: List[int]) -> List[int]:\\n return []\",\n \"def encode_int_list(L):\\n return str(L).replace(\\\" \\\", \\\"\\\")\",\n \"def prepare_numbers(numbers):\\n \\n numb = []\\n for item in numbers:\\n numb.append(int(item))\\n return numb\",\n \"def to_binary(x,L, sort='revsort'):\\n x = SORT_CONVENTIONS[sort](x, 2**L)\\n b=np.zeros(L,dtype=int)\\n for i in range(L):\\n x,r=divmod(x,2)\\n b[L-i-1]=r\\n return b\",\n \"def gray_code(number: int) -> list:\\n if number <= 0:\\n return -1\\n\\n result: list = []\\n result.append(\\\"0\\\")\\n result.append(\\\"1\\\")\\n num1 = 2\\n\\n while True:\\n if num1 >= 1 << number:\\n break\\n\\n for j in range (num1 - 1, -1, -1):\\n result.append(result[j])\\n\\n for j in range (num1):\\n result[j] = \\\"0\\\" + result[j]\\n\\n for j in range (num1, 2 * num1):\\n result[j] = \\\"1\\\" + result[j]\\n\\n num1 = num1 << 1\\n\\n return result\",\n \"def test_to_int_list(self):\\n self.assertEqual(to_int_list([u\\\"3\\\", None, \\\"asdf\\\", u\\\"42\\\"]), [3, 0, 0, 42])\",\n \"def convert_labels_to_int(labels): \\n if not len(labels):\\n raise ValueError(\\\"Nothing to convert!\\\")\\n\\n if isinstance(labels[0], basestring) or isinstance(labels[0], int):\\n label_legend = list(set(labels))\\n converted_labels = [ label_legend.index(l) for l in labels ]\\n elif is_iterable(labels[0]): # Multiple labels, handle each one individually\\n num_labels = len(labels[0])\\n label_legend = []\\n converted_labels = []\\n \\n # Get the unique sets of labels for each index\\n for i in xrange(num_labels):\\n temp_labels = [l[i] for l in labels]\\n label_legend.append(list(set(temp_labels)))\\n\\n # Apply mapping to each label\\n for label in labels:\\n converted_label = [ leg.index(l) for leg, l in \\n zip(label_legend, label) ]\\n converted_labels.append(converted_label)\\n else: # Not a string/int, not a list... hmmm\\n raise ValueError('Unexpected label type!')\\n\\n return label_legend, array(converted_labels)\",\n \"def _convert_to_list(self, input_argument):\\n if type(input_argument) is not list:\\n input_argument = [input_argument]\\n return input_argument\",\n \"def array_to_list(llist, source):\\r\\n while source: #a list is considered True as long as it is not empty\\r\\n llist.append(source.pop(0))\\r\\n \\r\\n return\",\n \"def list2int(thelist,base=2):\\n return reduce(lambda x,y:base*x+y,reversed(thelist),0\",\n \"def intToVec(n):\\r\\n return [n >> 12, (n >> 4) & 0xf, (n >> 8) & 0xf, n & 0xf]\",\n \"def binary_to_one_hot(arr: np.ndarray) -> np.ndarray:\\n res = np.zeros((arr.shape[0], 2))\\n res[np.where(arr == 1)[0], 0] = 1\\n res[np.where(arr == 0)[0], 1] = 1\\n return res\",\n \"def strToListInt(x):\\n if type(x)==str:\\n return [int(i) for i in x[1:-1].split(\\\", \\\")]\",\n \"def value_to_two_class(lim,val_arr,cla_arr=string.ascii_lowercase):\\n\\n return [cla_arr[i] for i in (np.array(val_arr)>lim).astype(int)]\",\n \"def get_list_of_int2(self):\\n pass\",\n \"def value_to_two_class(lim,val_arr,cla_arr=string.ascii_lowercase):\\n \\n return [cla_arr[i] for i in (np.array(val_arr)>lim).astype(int)]\",\n \"def bin_bil_to_unil(a):\\n return (a + 1)/2\",\n \"def map_minus_one_to_one(x, a, b):\\n assert b > a\\n s = 2./(b - a)\\n t = (a+b)/(a-b)\\n y = s*x + t\\n y[y>1] = 1\\n y[y<-1] = -1\\n return y\",\n \"def to_sparse(a):\\n flat = a.flatten()\\n indices = np.nonzero(flat)\\n values = flat[indices]\\n return indices[0], values\",\n \"def _to_vectors(labels):\\n res = [[int(s) for s in label] for label in labels]\\n max_len = 0\\n for ele in res:\\n if len(ele) > max_len:\\n max_len = len(ele)\\n return [ele + [0] * (max_len - len(ele)) for ele in res]\",\n \"def predicted_results_to_binary(predicted_results):\\n return [int(round(answer)) for answer in predicted_results]\",\n \"def ones(num):\\n if num < 1:\\n raise IndexError('num must be >= 1.')\\n return Vector.fromSequence([1] * num)\",\n \"def clean_int_list(s):\\n if isinstance(s, basestring):\\n return [int(s)]\\n elif isinstance(s, int):\\n return [s]\\n return [int(x) for x in s]\",\n \"def int2list(num):\\n return [int(d) for d in str(num)]\",\n \"def array2list(input_array):\\r\\n return [element for element in input_array]\",\n \"def task5_push_zeros(input_list):\\n for elem in input_list:\\n if elem == 0:\\n input_list.remove(elem)\\n input_list.append(0)\\n return input_list\",\n \"def f1to2(x):\\n assert_equal(x.ndim, 1)\\n res = x[::-1] * x[1:,None]\\n return np.ma.masked_where(res%5==0, res)\",\n \"def convert_selection(selection):\\n\\n\\n bits = selection.split(\\\",\\\")\\n all_bits = []\\n for bit in bits:\\n small_bits = bit.split(\\\"-\\\")\\n if len(small_bits) == 1:\\n all_bits.append(int(small_bits[0]))\\n else:\\n range_of_bits = range(int(small_bits[0]), int(small_bits[1])+1)\\n for tiny_bit in range_of_bits:\\n all_bits.append(tiny_bit)\\n\\n return all_bits\",\n \"def make_binary(y, positive):\\n pos = np.in1d(y, positive).reshape(y.shape)\\n new_y = np.zeros(y.shape, dtype=np.int)\\n new_y[pos] = 1\\n return new_y\",\n \"def __init__(self, list):\\n self.array = [0] + list\\n for idx in range(1, len(self.array)):\\n idx2 = idx + (idx & -idx)\\n if idx2 < len(self.array):\\n self.array[idx2] += self.array[idx]\",\n \"def scale_1d(x):\\n return (min(x), max(x), len(x))\",\n \"def _idx_to_bits(self, i):\\n bits = bin(i)[2:].zfill(self.nb_hyperplanes) # Pad the bits str with 0\\n return [-1.0 if b == \\\"0\\\" else 1.0 for b in bits]\",\n \"def map_zero_one(x, a, b):\\n assert b > a\\n s = 1./(b - a)\\n t = a/(a-b)\\n y = s*x + t\\n y[y>1] = 1\\n y[y<0] = 0\\n return y\",\n \"def convert_to_one_hot(a):\\n a = a[:, 0]\\n a = a.astype(int)\\n A = np.zeros((len(a), config.num_classes))\\n A[np.arange(len(a)), a] = 1\\n return A\",\n \"def vec_to_bytes(val):\\n return [int(x*255) for x in val]\",\n \"def to_zero_one(thing):\\n\\n if is_iterable(thing):\\n zero_or_one = np.zeros(len(thing))\\n zero_or_one[np.array(thing) >= 0.5] = 1\\n else:\\n if thing >= 0.5:\\n zero_or_one = 1\\n else:\\n zero_or_one = 0\\n return zero_or_one\",\n \"def int_repr(arr):\\n return list(map(list2int, arr))\",\n \"def turn_to_one_hot(self,length,ind):\\n one_hot = [0 for _ in range(length)]\\n if (ind >= len(one_hot)):\\n one_hot[-1] = 1\\n else:\\n one_hot[ind] = 1\\n return one_hot\",\n \"def fluxes_to_labels(fluxes: np.ndarray) -> np.ndarray:\\n return ((1 - fluxes) // 2).astype(np.int8)\",\n \"def grayCode(self, n):\\n res = [0]\\n for i in range(0, n):\\n res += [(1 << i) + x for x in reversed(res)]\\n return res\",\n \"def one_hot_from_int(int_or_list, batch_size=1):\\n if isinstance(int_or_list, int):\\n int_or_list = [int_or_list]\\n\\n if len(int_or_list) == 1 and batch_size > 1:\\n int_or_list = [int_or_list[0]] * batch_size\\n\\n assert batch_size == len(int_or_list)\\n\\n array = np.zeros((batch_size, 1000), dtype=np.float32)\\n for i, j in enumerate(int_or_list):\\n array[i, j] = 1.0\\n return array\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6727341","0.66507214","0.6430734","0.6361047","0.6328756","0.6125416","0.6044102","0.59924","0.5975442","0.5952622","0.5947834","0.59292996","0.58282095","0.58242655","0.5808415","0.5786242","0.5779058","0.575488","0.5746223","0.57387036","0.5726756","0.57009995","0.56932795","0.5685848","0.56579","0.56374747","0.56355333","0.55795854","0.5573783","0.55712336","0.55676544","0.55656403","0.55550367","0.5546105","0.5513494","0.55050236","0.5501761","0.5474527","0.5465937","0.5465571","0.5461302","0.54422104","0.54339457","0.5431374","0.54212207","0.54111975","0.54092443","0.54091054","0.5407443","0.54027414","0.5390611","0.5390611","0.53790295","0.53723454","0.5368406","0.5365057","0.53587097","0.53570604","0.5349992","0.53496915","0.53368104","0.5324765","0.5323573","0.5321498","0.5316621","0.53155684","0.531229","0.5307581","0.5302374","0.5299042","0.5298409","0.5292411","0.5291786","0.5287732","0.528076","0.5275599","0.52716774","0.5262253","0.5252997","0.52357787","0.52328223","0.522685","0.5223764","0.5217721","0.5215873","0.52131337","0.5212979","0.5212556","0.51991683","0.51985484","0.5198035","0.5195939","0.519415","0.5193235","0.5189434","0.51870936","0.51788247","0.5176762","0.5176329","0.51762795","0.51755804"],"string":"[\n \"0.6727341\",\n \"0.66507214\",\n \"0.6430734\",\n \"0.6361047\",\n \"0.6328756\",\n \"0.6125416\",\n \"0.6044102\",\n \"0.59924\",\n \"0.5975442\",\n \"0.5952622\",\n \"0.5947834\",\n \"0.59292996\",\n \"0.58282095\",\n \"0.58242655\",\n \"0.5808415\",\n \"0.5786242\",\n \"0.5779058\",\n \"0.575488\",\n \"0.5746223\",\n \"0.57387036\",\n \"0.5726756\",\n \"0.57009995\",\n \"0.56932795\",\n \"0.5685848\",\n \"0.56579\",\n \"0.56374747\",\n \"0.56355333\",\n \"0.55795854\",\n \"0.5573783\",\n \"0.55712336\",\n \"0.55676544\",\n \"0.55656403\",\n \"0.55550367\",\n \"0.5546105\",\n \"0.5513494\",\n \"0.55050236\",\n \"0.5501761\",\n \"0.5474527\",\n \"0.5465937\",\n \"0.5465571\",\n \"0.5461302\",\n \"0.54422104\",\n \"0.54339457\",\n \"0.5431374\",\n \"0.54212207\",\n \"0.54111975\",\n \"0.54092443\",\n \"0.54091054\",\n \"0.5407443\",\n \"0.54027414\",\n \"0.5390611\",\n \"0.5390611\",\n \"0.53790295\",\n \"0.53723454\",\n \"0.5368406\",\n \"0.5365057\",\n \"0.53587097\",\n \"0.53570604\",\n \"0.5349992\",\n \"0.53496915\",\n \"0.53368104\",\n \"0.5324765\",\n \"0.5323573\",\n \"0.5321498\",\n \"0.5316621\",\n \"0.53155684\",\n \"0.531229\",\n \"0.5307581\",\n \"0.5302374\",\n \"0.5299042\",\n \"0.5298409\",\n \"0.5292411\",\n \"0.5291786\",\n \"0.5287732\",\n \"0.528076\",\n \"0.5275599\",\n \"0.52716774\",\n \"0.5262253\",\n \"0.5252997\",\n \"0.52357787\",\n \"0.52328223\",\n \"0.522685\",\n \"0.5223764\",\n \"0.5217721\",\n \"0.5215873\",\n \"0.52131337\",\n \"0.5212979\",\n \"0.5212556\",\n \"0.51991683\",\n \"0.51985484\",\n \"0.5198035\",\n \"0.5195939\",\n \"0.519415\",\n \"0.5193235\",\n \"0.5189434\",\n \"0.51870936\",\n \"0.51788247\",\n \"0.5176762\",\n \"0.5176329\",\n \"0.51762795\",\n \"0.51755804\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77476,"cells":{"query":{"kind":"string","value":"Assigns a score to the coursePlan based on the rarity of the course, the number of courses this one makes available in the future, and the users need and preference for a particular type of elective"},"document":{"kind":"string","value":"def heuristics(course, suggestedPlan, user):\n score = course.score\n bonus = 0\n return score + bonus"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _score_to_decision(self, score):","def scoring(self):\n pass","def score(self):\n raise NotImplementedError()","def _cost_caught_by_police(self):\n if self.fine_frequency != 0:\n if self.number_of_courses % self.fine_frequency == 0 and self.number_of_courses != 0:\n if self.number_of_courses % self.fine_frequency_paid_by_driver == 0 and self.number_of_courses != 0:\n self.fine_paid_number_of_courses += 1\n fine_value = np.random.choice([100, 200, 500], p=[0.25, 0.4, 0.35])\n self.total_penalty_points += self._add_penalty_points() # adding penalty points\n return fine_value\n else:\n return 0\n else:\n return 0\n else:\n return 0","def update_score():\n pass","def score(self):\n\n self.link()\n roc, _ = self.aggregate()\n\n return roc","def __init__(self, plan, score):\n self.id = plan.id\n self.score = score","def score(self,*val):\n if len(val):\n self._score = val[0]\n self.evaluated = 1\n else: self.evaluate()\n return self._score","def define_score(self, votes_string):\n\t\t#2*REW + colleagues + post-doctorate associate + 2* JBW\n\t\tvotes = [int(x) for x in votes_string] \n\t\tweights = [2,1,1,2]\n\t\tscore = 0\n\t\tfor i in range(0, 4):\n\t\t\tif votes[i] >= 0 and votes[i] <= 2:\n\t\t\t\tscore += votes[i]*weights[i]\n\t\treturn score","def score(self, model, context):\n pass","def score(self):","def score(self):\n # loop over aminoacids in protein and calculate how often H and C are surrounded by H and C\n for aminoacid in self.aminoacids:\n if aminoacid.aminoacid_type == \"H\":\n self.stability = self.stability + (-1 * self.surrounded_by(aminoacid.location, \"H\", aminoacid.aminoacid_number)) + (-1 * self.surrounded_by(aminoacid.location, \"C\", aminoacid.aminoacid_number))\n elif aminoacid.aminoacid_type == \"C\":\n self.stability = self.stability + (-5 * self.surrounded_by(aminoacid.location, \"C\", aminoacid.aminoacid_number)) + (-1 * self.surrounded_by(aminoacid.location, \"H\", aminoacid.aminoacid_number))\n self.stability = self.stability/2\n return int(self.stability)","def _adjust_score(self, my_choice, their_choice):\n self._score += p.params['score_matrix'][my_choice][their_choice]\n self._score -= p.params['loss_per_tick']","def administer(self):\n\n score = 0.0\n for question in self.questions:\n if question.ask_and_evaluate() is True:\n score += 1\n return (score / len(self.questions)) * 100","def score(self, test_data):\n\n\t\tpass","def calculScore(self):\n for cell in self.notComputeRouter:\n if(cell.isCovered==True):\n self.score += 1000\n self.score += self.budget","def review(self, performance_rating):\n self.correct = performance_rating >= 0.6\n now = datetime.datetime.now()\n if self.date_last_reviewed is None:\n self.date_last_reviewed = now\n percent_overdue = self.percent_overdue\n self.difficulty += percent_overdue / 17 * (8 - 9 * performance_rating)\n self.difficulty = max(0, min(self.difficulty, 1)) # clamp difficulty to [0, 1]\n difficulty_weight = 3 - 1.7 * self.difficulty\n if self.correct:\n self.days_between = 1 + (difficulty_weight - 1) * percent_overdue\n else:\n self.days_between = max(1, 1 / (difficulty_weight ** 2))\n self.date_last_reviewed = now","def supply_score(pickups, pickupsfromcarepackages):\n\n\t# get the total number for each supply category\n\tAttachment = pickups[\"Attachment\"] if \"Attachment\" in pickups else 0\n\tUse = pickups[\"Use\"] if \"Use\" in pickups else 0\n\tAmmunition = pickups[\"Ammunition\"] if \"Ammunition\" in pickups else 0\n\tEquipment = pickups[\"Equipment\"] if \"Equipment\" in pickups else 0\n\tWeapon = pickups[\"Weapon\"] if \"Weapon\" in pickups else 0\n\n\t# calculate care package score\n\tif pickupsfromcarepackages > 0:\n\t\tcare_package_score = 100\n\telse:\n\t\tcare_package_score = 0\n\n\t# calculate attachment score\n\tif Attachment <= 5:\n\t\tattachment_score = 50\n\telif Attachment <= 9:\n\t\tattachment_score = 75\n\telse:\n\t\tattachment_score = 100\n\n\t# calculate use score\n\tif Use <= 5:\n\t\tuse_score = 70\n\telif Use <= 10:\n\t\tuse_score = 85\n\telse:\n\t\tuse_score = 100\n\n\t# calculate equipment score\n\tif Equipment <= 5:\n\t\tequipment_score = 75\n\telif Equipment <= 10:\n\t\tequipment_score = 90\n\telse:\n\t\tequipment_score = 100\n\n\t# calculate weapon score\n\tif Weapon <= 1:\n\t\tweapon_score = 75\n\telif Weapon == 2:\n\t\tweapon_score = 90\n\telse:\n\t\tweapon_score = 100\n\n\t# calculate ammunition score\n\tif Ammunition <= 5:\n\t\tammunition_score = 50\n\telif Ammunition <= 10:\n\t\tammunition_score = 75\n\telif Ammunition <= 14:\n\t\tammunition_score = 90\n\telse:\n\t\tammunition_score = 100\n\n\tsupplies_score = (equipment_score + use_score + weapon_score + ammunition_score) * 0.225 + attachment_score * 0.1\n\tsupply_score = int(supplies_score * 0.8 + care_package_score * 0.2)\n\n\treturn supply_score","def score_for_attempt(self, index):\r\n attempt = self.child_history[index]\r\n score = attempt.get('score')\r\n post_assessment_data = self._parse_score_msg(attempt.get('post_assessment', \"{}\"), self.system)\r\n grader_types = post_assessment_data.get('grader_types')\r\n\r\n # According to _parse_score_msg in ML grading there should be only one grader type.\r\n if len(grader_types) == 1 and grader_types[0] == 'ML':\r\n rubric_scores = post_assessment_data.get(\"rubric_scores\")\r\n\r\n # Similarly there should be only one list of rubric scores.\r\n if len(rubric_scores) == 1:\r\n rubric_scores_sum = sum(rubric_scores[0])\r\n log.debug(\"\"\"Score normalized for location={loc}, old_score={old_score},\r\n new_score={new_score}, rubric_score={rubric_score}\"\"\".format(\r\n loc=self.location_string,\r\n old_score=score,\r\n new_score=rubric_scores_sum,\r\n rubric_score=rubric_scores\r\n ))\r\n return rubric_scores_sum\r\n return score","def update_score(score, role):\n if role == 'winner':\n score = score + 1\n if role == 'loser':\n score = score - 1\n return score","def get_score(self):\r\n return self.lcp.get_score()","def set_score(self, a, b, score):\n ### FILL IN ###","def calc_score(allcourses, student_list, chambers):\n\n\t# start-score of a valid schedule\n\tpoints = 1000\n\tallcoursespoints = []\n\troomlocks_per_day = 28\n\n\t#* create groups and dict to keep up the days per activity *#\n\n\t# iterate over courses\n\tfor course in allcourses:\n\n\t\tcoursepoints = 0\n\n\t\t# if seminar(s) exist\n\t\tif course.seminars > 0:\n\n\t\t\t# create list with amount of seminar-groups\n\t\t\tgroups = list(range(1, int(course.seminars) + 1))\n\n\t\t# if practical exist\n\t\telif course.practicals > 0:\n\n\t\t\t# create list with amount of practical-groups\n\t\t\tgroups = list(range(1, int(course.practicals) + 1))\n\n\t\t# otherwise only one group (lecture)\n\t\telse:\n\t\t\tgroups = [1]\n\n\t\t# create dict with groupnumber as key, [days] as values\n\t\tdayActivity = {k: [] for k in groups}\n\n\t\t#* subtract points based on activities per day *#\n\n\t\t# iterate over activities per course\n\t\tfor activity in course.activities:\n\n\t\t\t# determine day\n\t\t\tday = int(activity[0] / roomlocks_per_day)\n\n\t\t\t# if activity = lecture\n\t\t\tif activity[2] == 0:\n\n\t\t\t\t# add day to list linked to lecture\n\t\t\t\tfor group in groups:\n\t\t\t\t\tdayActivity[group].append(day)\n\n\t\t\t# for seminars and practicals\n\t\t\telse:\n\n\t\t\t\t# add day to list linked to group\n\t\t\t\tdayActivity[activity[2]].append(day)\n\n\t\t# for each group\n\t\tfor group in groups:\n\n\t\t\t# for each working day\n\t\t\tfor day in range(0, 5):\n\n\t\t\t\t# count how many of the same activities a day\n\t\t\t\tday_occurance = dayActivity[group].count(day)\n\n\t\t\t\t# if more often than 1\n\t\t\t\tif day_occurance > 1:\n\n\t\t\t\t\t# substract points\n\t\t\t\t\tpoints -= ((day_occurance - 1) * 10)\n\t\t\t\t\tcoursepoints -= ((day_occurance - 1) * 10)\n\n\t\t#* add points for spreading of activities over week *#\n\n\t\t# iterate over groups\n\t\tfor group in groups:\n\n\t\t\t# when 2 activities in week\n\t\t\tif (len(dayActivity[group]) == 2):\n\n\t\t\t\t# add points if they are spread (mo- thu, mo-fri)\n\t\t\t\tif (abs(dayActivity[group][0] - dayActivity[group][1]) >= 3):\n\t\t\t\t\tpoints += 20\n\t\t\t\t\tcoursepoints += 20\n\n\t\t\t# when 3 activities in the week\n\t\t\tif (len(dayActivity[group]) == 3):\n\n\t\t\t\t# add points if spread (mo-we-fr)\n\t\t\t\tif 0 in dayActivity[group] and 2 in dayActivity[group] and 4 in dayActivity[group]:\n\t\t\t\t\tpoints += 20\n\t\t\t\t\tcoursepoints += 20\n\n\t\t\t# if 4 activities in the week\n\t\t\tif (len(dayActivity[group]) == 4):\n\n\t\t\t\t# add points if spread (mo-tu-th-fr)\n\t\t\t\tif 0 in dayActivity[group] and 1 in dayActivity[group] and 3 in dayActivity[group] and 4 in dayActivity[group]:\n\t\t\t\t\tpoints += 20\n\t\t\t\t\tcoursepoints += 20\n\n\t\t# for each activity of a course\n\t\tfor activity in course.activities:\n\n\t\t\t# substract room and timelock\n\t\t\troom, timelock = translate_roomlock(activity[0])\n\n\t\t\t# for lectures\n\t\t\tif activity[2] == 0:\n\n\t\t\t\t# if too many students for room\n\t\t\t\tif int(chambers[room].capacity) < course.students:\n\n\t\t\t\t\t# substract points\n\t\t\t\t\tmaluspoints = course.students - int(chambers[room].capacity)\n\t\t\t\t\tpoints -= maluspoints\n\t\t\t\t\tcoursepoints -= maluspoints\n\n\t\t\t# for practical/seminars\n\t\t\telse:\n\n\t\t\t\t# if seminar\n\t\t\t\tif course.seminars > 0:\n\n\t\t\t\t\t# and too many students fro room, substract points\n\t\t\t\t\tif int(chambers[room].capacity) < course.maxstudentssem:\n\n\t\t\t\t\t\t# substract points\n\t\t\t\t\t\tmaluspoints = course.maxstudentssem - int(chambers[room].capacity)\n\t\t\t\t\t\tpoints -= maluspoints\n\t\t\t\t\t\tcoursepoints -= maluspoints\n\n\t\t\t\t# if practical\n\t\t\t\telse:\n\n\t\t\t\t\t# and too many students from room, substract points\n\t\t\t\t\tif int(chambers[room].capacity) < course.maxstudentsprac:\n\t\t\t\t\t\tmaluspoints = course.maxstudentsprac - int(chambers[room].capacity)\n\t\t\t\t\t\tpoints -= maluspoints\n\t\t\t\t\t\tcoursepoints -= maluspoints\n\n\t\t# add points of allcourses\n\t\tallcoursespoints.append([course.name, coursepoints])\n\n\t#* substract points when 2 courses on same time *#\n\n\t# for all students\n\tfor student in student_list:\n\n\t\t# create empty list\n\t\ttimelocks_student = []\n\n\t\t# for each activity in schedule\n\t\tfor activity in student.schedule:\n\n\t\t\t# if roomlock is occupied substract points\n\t\t\tif activity[0] in timelocks_student:\n\t\t\t\tpoints -= 1\n\t\t\t\tcoursepoints -= 1\n\n\t\t\t# add roomlock to list\n\t\t\ttimelocks_student.append(activity[0])\n\n\treturn points","def set_offensive_ratio(self):\n bx = self.get_standard_stats()\n team = self.get_team_stats()\n opp_team = self.get_opp_team_stats()\n if bx[\"minutes\"] > 0 and (bx[\"t2p_int\"] + bx[\"t3p_int\"]) > 0:\n fgm = bx[\"t2p_conv\"] + bx[\"t3p_conv\"]\n fga = bx[\"t2p_int\"] + bx[\"t3p_int\"]\n team_fgm = team[\"t2p_conv\"] + team[\"t3p_conv\"]\n team_fga = team[\"t2p_int\"] + team[\"t3p_int\"]\n team_points = team[\"t2p_conv\"]*2 + team[\"t3p_conv\"]*3 + team[\"tl_conv\"]\n points = bx[\"t2p_conv\"]*2 + bx[\"t3p_conv\"]*3 + bx[\"tl_conv\"]\n\n try:\n qAST = (Decimal(bx[\"minutes\"] / (team[\"minutes\"] / 5)) * (Decimal('1.14') * Decimal((team[\"assists\"] - bx[\"assists\"]) / team_fgm))) + \\\n Decimal((((team[\"assists\"] / team[\"minutes\"]) * bx[\"minutes\"] * 5 - bx[\"assists\"]) / ((team_fgm / team[\"minutes\"]) * bx[\"minutes\"] * 5 - fgm)) * (1 - (bx[\"minutes\"] / (team[\"minutes\"] / 5))))\n except ZeroDivisionError:\n print(BCOLORS.WARNING + \"Error: División por cero\" + BCOLORS.ENDC)\n qAST = 1\n except InvalidOperation:\n print(BCOLORS.WARNING + \"Error: Invalid Operation\" + BCOLORS.ENDC)\n qAST = 1\n\n fg_part = fgm * (1 - Decimal('0.5') * Decimal((points - bx[\"tl_conv\"]) / (2 * fga)) * qAST)\n\n try:\n ast_part = Decimal('0.5') * Decimal(((team_points - team[\"tl_conv\"]) - (points - bx[\"tl_conv\"])) / (2*(team_fga - fga))) * bx[\"assists\"]\n except ZeroDivisionError:\n print(BCOLORS.WARNING + \"Error: División por cero\" + BCOLORS.ENDC)\n ast_part = 0\n\n if bx[\"tl_int\"] > 0:\n ft_part = Decimal(1 - (1 - (bx[\"tl_conv\"] / bx[\"tl_int\"]))**2) * Decimal('0.4') * bx[\"tl_int\"]\n else:\n ft_part = 0\n team_scoring_poss = Decimal(team_fgm + Decimal(1 - (1 - (team[\"tl_conv\"] / team[\"tl_int\"]))**2) * team[\"tl_int\"] * Decimal('0.4'))\n try:\n team_orb_percentage = Decimal(team[\"reb_of\"] / (team[\"reb_of\"] + ((opp_team[\"reb_def\"] + opp_team[\"reb_of\"]) - opp_team[\"reb_of\"])))\n except ZeroDivisionError:\n print(BCOLORS.FAIL + \"Error: División por cero\" + BCOLORS.ENDC)\n team_orb_percentage = 0\n except InvalidOperation:\n print(BCOLORS.FAIL + \"Error: Invalid Operation\" + BCOLORS.ENDC)\n team_orb_percentage = 0\n\n team_play_percentage = Decimal(team_scoring_poss / (team_fga + team[\"tl_int\"] * Decimal('0.4') + team[\"turnovers\"]))\n try:\n team_orb_weight = ((1 - team_orb_percentage) * team_play_percentage) / ((1 - team_orb_percentage) * team_play_percentage + team_orb_percentage * (1 - team_play_percentage))\n except InvalidOperation:\n print(BCOLORS.FAIL + \"Error: Invalid Operation\" + BCOLORS.ENDC)\n team_orb_weight = 0\n\n orb_part = bx[\"reb_of\"] * team_orb_weight * team_play_percentage\n\n fg_x_poss = (fga - fgm) * (1 - Decimal('1.07') * team_orb_percentage)\n if bx[\"tl_conv\"] > 0:\n ft_x_poss = Decimal((1 - (bx[\"tl_conv\"] / bx[\"tl_int\"]))**2) * Decimal('0.4') * bx[\"tl_int\"]\n else:\n ft_x_poss = Decimal(1 - (bx[\"tl_conv\"] / 1)**2) * Decimal('0.4') * bx[\"tl_int\"]\n try:\n sc_poss = (fg_part + ast_part + ft_part) * (1 - (team[\"reb_of\"] / team_scoring_poss) * team_orb_weight * team_play_percentage) + orb_part\n except InvalidOperation:\n print(BCOLORS.FAIL + \"Error: Invalid Operation\" + BCOLORS.ENDC)\n sc_poss =0\n\n tot_poss = sc_poss + fg_x_poss + ft_x_poss + bx[\"turnovers\"]\n\n pprod_fg_part = 2 * (fgm + Decimal('0.5') * bx[\"t3p_conv\"]) * (1 - Decimal('0.5') * Decimal((points - bx[\"tl_conv\"]) / (2 * fga)) * qAST)\n\n try:\n pprod_ast_part = 2 * ((team_fgm - fgm + Decimal('0.5') * (team[\"t3p_conv\"] - bx[\"t3p_conv\"])) / (team_fgm - fgm)) * Decimal('0.5') * Decimal(((team_points - team[\"tl_conv\"]) - (points - bx[\"tl_conv\"])) / (2 * (team_fga - fga))) * bx[\"assists\"]\n except:\n pprod_ast_part = 0\n\n pprod_orb_part = bx[\"reb_of\"] * team_orb_weight * team_play_percentage * (team_points / (team_fgm + Decimal(1 - (team[\"tl_conv\"] / team[\"tl_int\"])**2) * Decimal('0.4') * team[\"tl_int\"]))\n try:\n pprod = (pprod_fg_part + pprod_ast_part + bx[\"tl_conv\"]) * (1 - (team[\"reb_of\"] / team_scoring_poss) * team_orb_weight * team_play_percentage) + pprod_orb_part\n except InvalidOperation:\n print(BCOLORS.FAIL + \"Error: Invalid Operation\" + BCOLORS.ENDC)\n pprod = 0\n\n try:\n result = 100 * (pprod / tot_poss)\n except InvalidOperation:\n print(BCOLORS.FAIL + \"Error: Invalid Operation\" + BCOLORS.ENDC)\n result = 0\n\n # print(\"fgm: \" + str(fgm))\n # print(\"fga: \" + str(fga))\n # print(\"team_fgm: \" + str(team_fgm))\n # print(\"team_fga: \" + str(team_fga))\n # print(\"team_points: \" + str(team_points))\n # print(\"points: \" + str(points))\n # print(\"qAST: \" + str(qAST))\n # print(\"fg_part: \" + str(fg_part))\n # print(\"ast_part: \" + str(ast_part))\n # print(\"ft_part: \" + str(ft_part))\n # print(\"team_scoring_poss: \" + str(team_scoring_poss))\n # print(\"team_orb_percentage: \" + str(team_orb_percentage))\n # print(\"team_play_percentage: \" + str(team_play_percentage))\n # print(\"team_orb_weight: \" + str(team_orb_weight))\n # print(\"orb_part: \" + str(orb_part))\n # print(\"fg_x_poss: \" + str(fg_x_poss))\n # print(\"ft_x_poss: \" + str(ft_x_poss))\n # print(\"sc_poss: \" + str(sc_poss))\n # print(\"tot_poss: \" + str(tot_poss))\n # print(\"pprod_fg_part: \" + str(pprod_fg_part))\n # print(\"pprod_ast_part: \" + str(pprod_ast_part))\n # print(\"pprod_orb_part: \" + str(pprod_orb_part))\n # print(\"pprod: \" + str(pprod))\n # print(\"result: \" + str(result) + \"\\n\")\n else:\n result = 0.00\n\n self.ortg = \"%.2f\" % round(result, 2)\n if Decimal(self.ortg) < 0 or Decimal(self.ortg) >= 1000:\n \"\"\"For one game, maybe we've got a negative result or one so big, so, for just only a game, we get the ORTG \n using team's formula\"\"\"\n print(BCOLORS.OKBLUE + \"ORTG negativo o superior a 1000 para jugadora => recalculamos a través de la fórmula de equipo\" + BCOLORS.ENDC)\n bx = self.get_standard_stats()\n result = round((bx[\"t2p_conv\"]*2 + bx[\"t3p_conv\"]*3 + bx[\"tl_conv\"])/self.get_team_possessions(), 2)\n self.ortg = \"%.2f\" % result","def update_score(self):\n self.score = TurboMQ.calculate_fitness(self.result, self.graph)","def factor_in_multiple_professors(self):\n professors = [professor for professor in self.course.professors if professor.lower() != \"none\"]\n number_professors = len(set(professors))\n if number_professors > 1:\n self.score = self.score + number_professors","def call(self, model):\n raise NotImplementedError('Define your score here')","def planning_score(score_type,\n bendable_hard_levels_size=None,\n bendable_soft_levels_size=None,\n score_definition_class=None):\n def planning_score_function_wrapper(getter_function):\n ensure_init()\n from org.optaplanner.core.api.domain.solution import PlanningScore as JavaPlanningScore\n getter_function.__optaplannerPlanningScore = {\n 'annotationType': JavaPlanningScore,\n 'bendableHardLevelsSize': bendable_hard_levels_size,\n 'bendableSoftLevelsSize': bendable_soft_levels_size,\n 'scoreDefinitionClass': score_definition_class\n }\n getter_function.__return = score_type\n return getter_function\n return planning_score_function_wrapper","def test_set_score_publish(self, credit_dict, result):\n self.xblock.credit_dict = credit_dict\n self.xblock.instructor_answer = 10\n self.xblock.set_score()\n self.xblock.runtime.publish.assert_called_with(\n self.xblock,\n 'grade',\n {\n 'value': result,\n 'max_value': 1,\n },\n )","def _return_cost(self, child_confidence, informant_reputation, child_action, informant_action, value=\"3yo\"):\n if value == '3yo':\n if child_confidence == 1 and informant_reputation == 1 and child_action == 1 and informant_action == 1:\n cost = -1.0 # (knowledge, knowledge, accept, accept) = reinforce\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 0 and informant_action == 1):\n cost = +0.5 # (knowledge, knowledge, reject, accept) = slightly punish\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 1 and informant_action == 0):\n cost = +0.5 # (knowledge, knowledge, accept, reject) = slightly punish\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 0 and informant_action == 0):\n cost = -1.0 # (knowledge, knowledge, reject, reject) = reinforce\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 1 and informant_action == 1):\n cost = -1.0 # (non-knowledge, knowledge, accept, accept) = reinforce\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 0 and informant_action == 0):\n cost = -1.0 # (non-knowledge, knowledge, reject, reject) = reinforce\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 0 and informant_action == 1):\n cost = +1.0 # (non-knowledge, knowledge, reject, accept) = reinforce\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 1 and informant_action == 0):\n cost = +1.0 # (non-knowledge, knowledge, accept, reject) = punish\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 1 and informant_action == 1):\n cost = -1.0 # (knowledge, non-knowledge, accept, accept) = reinforce\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 0 and informant_action == 1):\n cost = +0.5 # (knowledge, non-knowledge, reject, accept) = slightly punish\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 1 and informant_action == 0):\n cost = +0.5 # (knowledge, non-knowledge, accept, reject) = slightly punish\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 0 and informant_action == 0):\n cost = -1.0 # (knowledge, non-knowledge, reject, reject) = reinforce\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 1 and informant_action == 1):\n cost = -1.0 # (non-knowledge, non-knowledge, accept, accept) = reinforce\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 0 and informant_action == 1):\n cost = +1.0 # (non-knowledge, non-knowledge, reject, accept) = punish\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 1 and informant_action == 0):\n cost = +1.0 # (non-knowledge, non-knowledge, accept, reject) = punish\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 0 and informant_action == 0):\n cost = -1.0 # (non-knowledge, non-knowledge, reject, reject) = reinforce\n else:\n raise ValueError(\"ERROR: the '3yo' Bayesian Networks input values are out of range\")\n return cost\n\n elif value == '4yo':\n if (child_confidence == 1 and informant_reputation == 1 and child_action == 1 and informant_action == 1):\n cost = -1.0 # (knowledge, knowledge, accept, accept) = reinforce\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 0 and informant_action == 1):\n cost = +0.5 # (knowledge, knowledge, reject, accept) = slight punish\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 1 and informant_action == 0):\n cost = +0.5 # (knowledge, knowledge, accept, reject) = slight punish\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 0 and informant_action == 0):\n cost = -1.0 # (knowledge, knowledge, reject, reject) = reinforce\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 1 and informant_action == 1):\n cost = -1.0 # (non-knowledge, knowledge, accept, accept) = reinforce\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 0 and informant_action == 0):\n cost = -1.0 # (non-knowledge, knowledge, reject, reject) = reinforce\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 0 and informant_action == 1):\n cost = +1.0 # (non-knowledge, knowledge, reject, accept) = punish\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 1 and informant_action == 0):\n cost = +1.0 # (non-knowledge, knowledge, accept, reject) = punish\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 1 and informant_action == 1):\n cost = 0.0 # (knowledge, non-knowledge, accept, accept) =\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 0 and informant_action == 1):\n cost = 0.0 # (knowledge, non-knowledge, reject, accept) =\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 1 and informant_action == 0):\n cost = 0.0 # (knowledge, non-knowledge, accept, reject) =\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 0 and informant_action == 0):\n cost = 0.0 # (knowledge, non-knowledge, reject, reject) =\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 1 and informant_action == 1):\n cost = 0.0 # (non-knowledge, non-knowledge, accept, accept) = zero_cost\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 0 and informant_action == 1):\n cost = 0.0 # (non-knowledge, non-knowledge, reject, accept) = zero_cost\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 1 and informant_action == 0):\n cost = 0.0 # (non-knowledge, non-knowledge, accept, reject) = zero_cost\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 0 and informant_action == 0):\n cost = 0.0 # (non-knowledge, non-knowledge, reject, reject) = zero_cost\n else:\n raise ValueError(\"ERROR: the '4yo' Bayesian Networks input values are out of range\")\n return cost\n else:\n raise ValueError(\"ERROR: input value not recognised, correct values are '3yo' and '4yo'\")","def automated(user):\n\n # Start should be a Plan() and will include any classes the user has already taken to this point. It should have\n # accurate counts of its course typesTaken before starting the search. Goal is the end state desired by the user.\n # Usually this will be the gradReqs for the users Curriculum object, but this could also be used to create\n # limited searches. number_of_classes_per_quarter is the max number of courses a user is willing to take in a\n # given quarter.\n\n # Cost should represent how many quarters are needed to graduate. They should however be multiplied by some factor\n # so that they are larger than our heuristic values. Our heuristics should produce values for rarity that are 8 - #\n # of times offered in 8 quarters. So the values will range between 0-8. Unlocks could be anything between 0 and\n # the # of total classes opened up by taking it. Unlocks seems to range between 0-281. We opted to cap this number\n # to 50. We decided to cap the unlocks score to 50 and in order to have rarity weigh as much as unlocks we opted to\n # multiply its results by 6. The result is that rarity ranges between 0-42 and unlocks ranges from 0-50. We never\n # want to over-estimate the cost of a path to graduation. 50 is chosen because it is the maximum value of h(n) seen\n # in our data.\n # Should equal max(rarity) + max(unlocks) + bonus\n # g(n) = (quarters x stdCost) or cost so far\n # h(n) = stdCost - (rarity + unlocks + bonus)\n # f(n) = g(n) + h(n)\n # Record actual cost of a path as g(n) / stdCost = number of quarters to graduate.\n # A course that is offered every quarter and unlocks nothing and does not match a preferred elective type will cost\n # stdCost which is exactly what adding a class costs. Selecting something more rare, and/or unlocks more classes\n # will appear to cost less than a normal quarter. So the path will be an under-estimate of cost and therefore it\n # will be admissible. As long as stdCost is >= h(n) we will never have negative costs therefore h(n) will be\n # considered consistent.\n # stdCost must = max(rarity) + max(unlocks) + max(bonus)\n\n # Should we disallow online courses?\n removeOnline = user.disallowOnline\n\n # Get students undergrad degree type\n undergrad = user.undergraduate_degree\n\n # Setup Curriculum\n curriculum = user.curriculum\n\n # Setup concentration\n if curriculum is CS:\n userPref = int(user.getCSFocus)\n else:\n userPref = 1\n\n\n # Create null node\n start = Plan(\n selectionOrder = list(),\n coursesTaken = user.getCoursesTaken,\n termNum = user.getTerm,\n currTermIdx = 0,\n daysFilled = [],\n maxCourses = user.max_courses,\n typesTaken = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n selectionsWithDay = list()\n )\n\n # If the users undergrad type matches their graduate type waive introductory courses\n start = waiveCourses(start, undergrad, curriculum)\n\n # Initialize variables\n frontier = PriorityQueue()\n frontier.put(start, 0)\n costSoFar = {}\n stdCost = setStdCost(curriculum)\n courseLimit = 20 # max number of courses in a solution\n\n # Store results of the query once for reuse later in search\n terms = ['0975', '0980', '0985', '0990', '0995', '1000', '1005']\n queryResults = dict((term, TermCourses.getAvailableCourses(term)) for term in terms)\n\n # Modify the heuristic score of classes to emphasize certain course types and the students focus in particular\n queryResults = modifyHeuristics(userPref, queryResults, terms, curriculum)\n\n plansPopped = 0\n while not frontier.empty():\n # Select current plan\n curr_plan = frontier.get()\n plansPopped += 1\n\n # If the search has gone on too long return an empty list so the user can restart the search\n if timedOut(plansPopped):\n return list(('Please see advisor', 'mon'))\n\n # Goal Checking\n if isGoal(curr_plan, curriculum, courseLimit, userPref):\n break\n\n # Count up non-capstone courses in plan\n cur = curr_plan.typesTaken\n tot = cur[0] + cur[1] + cur[2] + cur[13]\n\n # Filter the query removing courses that the student cannot take\n subsetResults = queryResults[TermCourses.convert_stream(curr_plan.termNum)]\n filteredResults = filter(subsetResults, curr_plan, curr_plan.daysFilled, curriculum, tot, removeOnline)\n\n # Loop through the top 8 filtered results and try each suggested plan\n for suggestedCourseInfo in filteredResults[:8]:\n suggestedPlan = Plan(\n selectionOrder = copy.deepcopy(curr_plan.selectionOrder),\n coursesTaken = copy.deepcopy(curr_plan.coursesTaken),\n termNum = copy.deepcopy(curr_plan.termNum),\n currTermIdx = copy.deepcopy(curr_plan.currTermIdx),\n daysFilled = copy.deepcopy(curr_plan.daysFilled),\n maxCourses = user.max_courses,\n typesTaken = copy.deepcopy(curr_plan.typesTaken),\n selectionsWithDay = copy.deepcopy(curr_plan.selectionsWithDay))\n\n # Add suggested course to current plan\n addUpdateCourse(suggestedPlan, suggestedCourseInfo, curriculum)\n\n # Calculate the true cost of the current plan (non heuristic)\n new_cost = costSoFar.get(str(curr_plan.coursesTaken), costSoFar.get(str(curr_plan.coursesTaken), 0))+stdCost\n\n # Do not explore plans with excessive numbers of courses\n taken = suggestedPlan.typesTaken\n totCourses = taken[0] + taken[1] + taken[2] + taken [3] + taken[4] + taken[13]\n if curriculum == CS:\n if totCourses >= courseLimit or suggestedPlan.typesTaken[2] > 8:\n continue\n else:\n if totCourses >= courseLimit or suggestedPlan.typesTaken[2] > 3:\n continue\n\n # Only explore a plan if it has not been seen or it is a better plan than a previously seen version\n if str(suggestedPlan.coursesTaken) not in costSoFar or new_cost < costSoFar[str(suggestedPlan.coursesTaken)]:\n costSoFar[str(suggestedPlan.coursesTaken)] = new_cost\n priority = -new_cost + heuristics(suggestedCourseInfo, suggestedPlan, user)\n frontier.put(suggestedPlan, priority)\n\n return curr_plan.selectionsWithDay","def score( self ):\r\n result = 0.0\r\n for rr in self.ee.getRsrcs( ):\r\n value = self.scoreRsrc( rr )\r\n result += value\r\n print( \"INFO: Value for the schedule is %s \" % ( rr, result ) )\r\n return( result )","def get_score(self, student_answers):\r\n pass","def evaluate(self, mode=0):\r\n winner = self.determine_winner()\r\n if winner:\r\n return winner * self.WIN_SCORE\r\n\r\n if mode == 1:\r\n return self.centre_priority_evaluate()\r\n elif mode == 2:\r\n return 0.5 * (self.centre_priority_evaluate() + self.piece_evaluate())\r\n else:\r\n return self.piece_evaluate()","def set_user_module_score(self, user, score, max_score, comment=u\"\"):\r\n if score is not None and max_score is not None:\r\n scaled_score = score * max_score\r\n else:\r\n scaled_score = None\r\n\r\n self.system.rebind_noauth_module_to_user(self, user)\r\n\r\n # have to publish for the progress page...\r\n self.system.publish(\r\n self,\r\n 'grade',\r\n {\r\n 'value': scaled_score,\r\n 'max_value': max_score,\r\n 'user_id': user.id,\r\n },\r\n )\r\n self.module_score = scaled_score\r\n self.score_comment = comment","def score(self):\n return None","def generate_score(self, answered_question, date, time_value):\n correct_answer_objects = map(lambda correct_key: Answer.get_by_id(correct_key.id_or_name()),\n answered_question.question.correctAnswers)\n invite = answered_question.invite\n earned_knowledge_area_score = {}\n max_knowledge_area_score = {}\n total_earned_value = 0.0\n\n for correct_answer in correct_answer_objects:\n for knowledge_area in correct_answer.knowledgeAreas:\n #We don't want negative values to influence the max score; skip those\n if correct_answer.value > float(0):\n earned_knowledge_area_score[knowledge_area] = 0.0\n if knowledge_area in max_knowledge_area_score.keys():\n max_knowledge_area_score[knowledge_area] += correct_answer.value\n else:\n max_knowledge_area_score[knowledge_area] = correct_answer.value\n round(max_knowledge_area_score[knowledge_area], 2)\n\n for given_answer_key in answered_question.givenAnswers:\n given_answer_object = Answer.get_by_id(given_answer_key.id_or_name())\n earned_value = float(given_answer_object.value)\n total_earned_value = float(round(total_earned_value + earned_value, 2))\n for knowledge_area in given_answer_object.knowledgeAreas:\n if knowledge_area in earned_knowledge_area_score.keys():\n earned_knowledge_area_score[knowledge_area] += earned_value\n else:\n earned_knowledge_area_score[knowledge_area] = earned_value\n round(earned_knowledge_area_score[knowledge_area], 2)\n\n multiple_choice_score_object = MultipleChoiceScore()\n multiple_choice_score_object.parse(correct_answer_objects, total_earned_value, earned_knowledge_area_score,\n max_knowledge_area_score, invite, date, time_value)\n multiple_choice_score_object.put()\n return multiple_choice_score_object","def get_score(self):\r\n if self.is_complete():\r\n score = 1\r\n elif self.is_half_complete():\r\n score = 0.5\r\n else:\r\n score = 0\r\n return {'score': score,\r\n 'total': self.max_score()}","def _update_score(self) -> None:\n\n # setting new score by iterating over players\n self.score_play[self.n_play_turns, ] = [\n self._score_table[(\n self.contract.level,\n self.contract.suit,\n self.tricks[i],\n self.contract.player_vulnerability[i],\n int(self.contract.double + self.contract.redouble)\n )]\n for i in range(NUM_PLAYERS)\n ]","def base_contribute_score():\n return 1","def take_test(exam, student):\n\n student.score = exam.administer()\n return student.score","def eval_evalassignment(request, pk, pts):\n student = request.user.student\n evalassignment = Evalassignment.objects.\\\n filter(pk=pk, assignment__student=student).first()\n if evalassignment:\n evalassignment.grade_evaluation = pts\n evalassignment.save()\n redirect_item = '#assignment%s' % evalassignment.assignment.id\n else:\n redirect_item = ''\n return redirect('/dashboard_student/' + redirect_item)","def judge(name):\n score = 0\n for scoreID, scorer, weight in weights:\n subscore = scorer(name)\n score += subscore * weight\n name.scores[scoreID] = subscore\n name.score = score\n return score","def take_test(exam, student):\n\n student.score = exam.administer()","def disp_score():","def evaluation(self):\n rows_list = []\n for name in self.single_classifier_best.keys():\n row = {}\n row['algorithm'] = name \n row[self.scoring_metric] = self.single_classifier_best[name].best_score_\n rows_list.append(row)\n \n scoring_df = pd.DataFrame(rows_list)\n scoring_sorted = scoring_df.sort_values(self.scoring_metric, ascending=False)\n print()\n print('*'*shutil.get_terminal_size().columns)\n print(scoring_sorted)\n print('*'*shutil.get_terminal_size().columns)\n self.evaluation_scores = scoring_sorted","def to_score(self):\n self._bottom_tab(2)\n self._goto(\"score\")","def calculate(self):\n\n rating = 0\n\n props = ['aroma', 'appearance', 'taste', 'palate', 'bottle_style']\n for item in props:\n rating += getattr(self, item, 0)\n\n self.overall = (rating / self.total) / .2","def set_score(self, points):\n self.score += points","def score(self):\n result = 1\n one_node = self.cups.locate_node(1)\n a = one_node.next()\n b = a.next()\n\n result = a.value * b.value\n\n return result","def perform_set_score(responder, options):\n match = options['<match-id>']\n tla = options['<tla>']\n score = options['<score>']\n scores.set_match_score(match, tla, score)\n responder('Scored {0} points for {1} in match {2}'.format(score, tla, match))","def _calculate_score(self):\n mul = self._check_board()\n if mul > 0:\n inc = 100 * mul + ((mul - 1) * 25)\n self.score += inc","def rating(grade_count):\r\n if grade_count == 0 :\r\n grade = 7\r\n else:\r\n grade = 2\r\n \r\n return grade","def vanilaScore(self,attended,state,W):","def update_score(best_score: int, new_score: int) -> int:\n if new_score > best_score:\n return new_score\n else:\n return best_score","def scoreEvaluationFunction(currentGameState):\r\n return currentGameState.getScore()","def adjust_score(self):\n self.score += game.temporary_score","def user_interaction_score(uv, recommended_News, ranked=True):\n\n iv = recommended_News[\"topical_vector\"]\n\n product = simple_doct_product(uv, iv)\n\n epsilon = 10e-5\n\n if (product + epsilon) > 1.0:\n vui = 0.99\n else:\n vui = beta_distribution(product)\n\n # Awared preference\n ita = beta_distribution(0.98)\n pui = vui * ita\n\n return pui","def score(self, params):\n\n if self.use_sqrt:\n return self.score_sqrt(params)\n else:\n return self.score_full(params)","def scores_(self, value):\n self.predictor.scores_ = value\n if self.n_good_features_ <= 1:\n sys.stderr.write(\"cutoff: %.4f\\n\" % self.score_cutoff_)\n UserWarning(\"These classifier settings produced <= 1 important \"\n \"feature, consider reducing score_coefficient. \"\n \"DataFramePCA will fail with this error: \"\n \"\\\"ValueError: failed to create intent(\"\n \"cache|hide)|optional array-- must have defined \"\n \"dimensions but got (0,)\\\"\\n\")","def grade(self, request, collection_id=None):\n collection = self.get_object()\n # get learner\n serializer = LearnerSerializer(data=request.data['learner'])\n if not serializer.is_valid():\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n # get or create learner\n learner, created = Learner.objects.get_or_create(**serializer.data)\n\n grade = get_engine().grade(learner, collection)\n return Response({'learner': serializer.data, 'grade': grade})","def testDriver():\n exam1=90\n exam2=85\n assignmentScores = [50, 60, 70, 80, ]\n computeGrades(exam1, exam2, assignmentScores)","def score_update(scoreboard, compare):\r\n if compare == 'Victory':\r\n scoreboard['W'] += 1\r\n elif compare == 'Defeat':\r\n scoreboard['L'] += 1\r\n elif compare == 'Tie':\r\n scoreboard['T'] += 1","def get_local_score(self):\n for candidate in self.candidate_list:\n self.score += candidate.get_score()","def score(self, board: Block) -> int:\r\n raise NotImplementedError","def score(self, board: Block) -> int:\r\n raise NotImplementedError","def score(self, board: Block) -> int:\r\n raise NotImplementedError","def rock_p_scissor(user_input, comp_choice):\r\n if user_input.lower() == comp_choice:\r\n compare = results[2]\r\n score_update(scoreboard, compare)\r\n banner_text(\"*\")\r\n banner_text(\"Tie! We both chose {0}\".format(user_input))\r\n banner_text(\"*\")\r\n print(scoreboard)\r\n banner_text(\"*\")\r\n elif user_input.lower() == 'rock' and comp_choice == 'paper':\r\n compare = results[1]\r\n score_update(scoreboard, compare)\r\n print(\"You lost! you chose {0} and I chose {1}\".format(user_input, comp_choice))\r\n banner_text(\"*\")\r\n print(scoreboard)\r\n banner_text(\"*\")\r\n elif user_input.lower() == 'rock' and comp_choice == 'scissor':\r\n compare = results[0]\r\n score_update(scoreboard, compare)\r\n print(\"You won! you chose {0} and I chose {1}\".format(user_input, comp_choice))\r\n banner_text(\"*\")\r\n print(scoreboard)\r\n banner_text(\"*\")\r\n elif user_input.lower() == 'paper' and comp_choice == 'scissor':\r\n compare = results[1]\r\n score_update(scoreboard, compare)\r\n print(\"You lost! you chose {0} and I chose {1}\".format(user_input, comp_choice))\r\n banner_text(\"*\")\r\n print(scoreboard)\r\n banner_text(\"*\")\r\n elif user_input.lower() == 'paper' and comp_choice == 'rock':\r\n compare = results[0]\r\n score_update(scoreboard, compare)\r\n print(\"You won! you chose {0} and I chose {1}\".format(user_input, comp_choice))\r\n banner_text(\"*\")\r\n print(scoreboard)\r\n banner_text(\"*\")\r\n elif user_input.lower() == 'scissor' or user_input.lower() == 'scissors' and comp_choice == 'rock':\r\n compare = results[1]\r\n score_update(scoreboard, compare)\r\n print(\"You lost! you chose {0} and I chose {1}\".format(user_input, comp_choice))\r\n banner_text(\"*\")\r\n print(scoreboard)\r\n banner_text(\"*\")\r\n elif user_input.lower() == 'scissor' or user_input.lower() == 'scissors' and comp_choice == 'paper':\r\n compare = results[0]\r\n score_update(scoreboard, compare)\r\n print(\"You won! you chose {0} and I chose {1}\".format(user_input, comp_choice))\r\n banner_text(\"*\")\r\n print(scoreboard)\r\n banner_text(\"*\")\r\n else:\r\n print(\"Your input was not valid. Please try again\")","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def __score_t(self, *args, **kwargs):\n pass","def test_instructor_assessment(self):\r\n\r\n # Navigate to the AI-assessment problem and submit an essay\r\n # We have configured the stub to simulate that this essay will be staff-graded\r\n self.course_nav.go_to_sequential('AI-Assessed')\r\n self.submit_essay('ai', 'Censorship in the Libraries')\r\n\r\n # Refresh the page to get the updated feedback\r\n # then verify that we get the feedback sent by our stub XQueue implementation\r\n self.assertEqual(self.get_asynch_feedback('ai'), ['incorrect', 'correct'])\r\n\r\n # Verify the progress page\r\n self.progress_page.visit()\r\n scores = self.progress_page.scores('Test Section', 'Test Subsection')\r\n\r\n # First score is the self-assessment score, which we haven't answered, so it's 0/2\r\n # Second score is the AI-assessment score, which we have answered, so it's 1/2\r\n # Third score is peer-assessment, which we haven't answered, so it's 0/2\r\n self.assertEqual(scores, [(0, 2), (1, 2), (0, 2)])","def course_heuristic(schedule, course):\n if course in schedule.heuristic_dictionary:\n return schedule.heuristic_dictionary[course] + (0 if schedule.is_program_in_goal(course[0]) else 10)\n return 0","def rules(self):\n self.rule1 = min(self.location_is_lessDemand, self.area_is_small, self.unfunishing)\n self.rule2 = min(self.location_is_lessDemand, max(self.area_is_small, self.area_is_average), self.access_is_good)\n self.rule3 = min(self.location_is_veryHighDemand, self.area_is_average, self.fac_is_low, self.access_is_average)\n self.rule4 = min(self.location_is_veryLessDemand, self.area_is_verysmall, self.fully_funishing)\n self.rule5 = min(self.location_is_lessDemand, self.fac_is_average, max(self.area_is_small, self.area_is_average))\n self.rule6 = min(max(self.location_is_lessDemand, self.location_is_averageDemand), self.access_is_good)\n self.rule7 = min(self.location_is_lessDemand, self.access_is_good, self.area_is_large, self.partially_funishing)\n self.rule8 = min(self.location_is_highDemand, self.access_is_good, max(self.bed_is_less, self.bath_is_average))\n self.rule9 = min(self.location_is_veryHighDemand, self.area_is_large, self.unfunishing)\n self.rule10 = min(self.access_is_good, self.area_is_average, (1 - self.unfunishing))\n self.rule11 = min(self.access_is_good, self.area_is_large, self.partially_funishing, self.bed_is_more, self.bath_is_more)","def lookup_relevant(score):\n category = \"\"\n if score > 2.0:\n category = \"RELEVANT\"\n elif score > 0.0:\n category = \"PARTIALLY RELEVANT\"\n else:\n category = \"NOT RELEVANT\"\n return category","def assignment(kind, grade, weight=1):\n global running_total\n global total_weight\n global total_grade\n\n if kind not in running_total:\n running_total[kind] = grade\n total_grade[kind] = 0\n total_weight[kind] = 0\n if weight > 1:\n grade *= weight\n total_grade[kind] += grade\n total_weight[kind] += weight\n running_total[kind] = total_grade[kind] / total_weight[kind]","def score(self, score):\n\n self._score = score","def score(self, score):\n\n self._score = score","def score(self, score):\n\n self._score = score","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()","def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()"],"string":"[\n \"def _score_to_decision(self, score):\",\n \"def scoring(self):\\n pass\",\n \"def score(self):\\n raise NotImplementedError()\",\n \"def _cost_caught_by_police(self):\\n if self.fine_frequency != 0:\\n if self.number_of_courses % self.fine_frequency == 0 and self.number_of_courses != 0:\\n if self.number_of_courses % self.fine_frequency_paid_by_driver == 0 and self.number_of_courses != 0:\\n self.fine_paid_number_of_courses += 1\\n fine_value = np.random.choice([100, 200, 500], p=[0.25, 0.4, 0.35])\\n self.total_penalty_points += self._add_penalty_points() # adding penalty points\\n return fine_value\\n else:\\n return 0\\n else:\\n return 0\\n else:\\n return 0\",\n \"def update_score():\\n pass\",\n \"def score(self):\\n\\n self.link()\\n roc, _ = self.aggregate()\\n\\n return roc\",\n \"def __init__(self, plan, score):\\n self.id = plan.id\\n self.score = score\",\n \"def score(self,*val):\\n if len(val):\\n self._score = val[0]\\n self.evaluated = 1\\n else: self.evaluate()\\n return self._score\",\n \"def define_score(self, votes_string):\\n\\t\\t#2*REW + colleagues + post-doctorate associate + 2* JBW\\n\\t\\tvotes = [int(x) for x in votes_string] \\n\\t\\tweights = [2,1,1,2]\\n\\t\\tscore = 0\\n\\t\\tfor i in range(0, 4):\\n\\t\\t\\tif votes[i] >= 0 and votes[i] <= 2:\\n\\t\\t\\t\\tscore += votes[i]*weights[i]\\n\\t\\treturn score\",\n \"def score(self, model, context):\\n pass\",\n \"def score(self):\",\n \"def score(self):\\n # loop over aminoacids in protein and calculate how often H and C are surrounded by H and C\\n for aminoacid in self.aminoacids:\\n if aminoacid.aminoacid_type == \\\"H\\\":\\n self.stability = self.stability + (-1 * self.surrounded_by(aminoacid.location, \\\"H\\\", aminoacid.aminoacid_number)) + (-1 * self.surrounded_by(aminoacid.location, \\\"C\\\", aminoacid.aminoacid_number))\\n elif aminoacid.aminoacid_type == \\\"C\\\":\\n self.stability = self.stability + (-5 * self.surrounded_by(aminoacid.location, \\\"C\\\", aminoacid.aminoacid_number)) + (-1 * self.surrounded_by(aminoacid.location, \\\"H\\\", aminoacid.aminoacid_number))\\n self.stability = self.stability/2\\n return int(self.stability)\",\n \"def _adjust_score(self, my_choice, their_choice):\\n self._score += p.params['score_matrix'][my_choice][their_choice]\\n self._score -= p.params['loss_per_tick']\",\n \"def administer(self):\\n\\n score = 0.0\\n for question in self.questions:\\n if question.ask_and_evaluate() is True:\\n score += 1\\n return (score / len(self.questions)) * 100\",\n \"def score(self, test_data):\\n\\n\\t\\tpass\",\n \"def calculScore(self):\\n for cell in self.notComputeRouter:\\n if(cell.isCovered==True):\\n self.score += 1000\\n self.score += self.budget\",\n \"def review(self, performance_rating):\\n self.correct = performance_rating >= 0.6\\n now = datetime.datetime.now()\\n if self.date_last_reviewed is None:\\n self.date_last_reviewed = now\\n percent_overdue = self.percent_overdue\\n self.difficulty += percent_overdue / 17 * (8 - 9 * performance_rating)\\n self.difficulty = max(0, min(self.difficulty, 1)) # clamp difficulty to [0, 1]\\n difficulty_weight = 3 - 1.7 * self.difficulty\\n if self.correct:\\n self.days_between = 1 + (difficulty_weight - 1) * percent_overdue\\n else:\\n self.days_between = max(1, 1 / (difficulty_weight ** 2))\\n self.date_last_reviewed = now\",\n \"def supply_score(pickups, pickupsfromcarepackages):\\n\\n\\t# get the total number for each supply category\\n\\tAttachment = pickups[\\\"Attachment\\\"] if \\\"Attachment\\\" in pickups else 0\\n\\tUse = pickups[\\\"Use\\\"] if \\\"Use\\\" in pickups else 0\\n\\tAmmunition = pickups[\\\"Ammunition\\\"] if \\\"Ammunition\\\" in pickups else 0\\n\\tEquipment = pickups[\\\"Equipment\\\"] if \\\"Equipment\\\" in pickups else 0\\n\\tWeapon = pickups[\\\"Weapon\\\"] if \\\"Weapon\\\" in pickups else 0\\n\\n\\t# calculate care package score\\n\\tif pickupsfromcarepackages > 0:\\n\\t\\tcare_package_score = 100\\n\\telse:\\n\\t\\tcare_package_score = 0\\n\\n\\t# calculate attachment score\\n\\tif Attachment <= 5:\\n\\t\\tattachment_score = 50\\n\\telif Attachment <= 9:\\n\\t\\tattachment_score = 75\\n\\telse:\\n\\t\\tattachment_score = 100\\n\\n\\t# calculate use score\\n\\tif Use <= 5:\\n\\t\\tuse_score = 70\\n\\telif Use <= 10:\\n\\t\\tuse_score = 85\\n\\telse:\\n\\t\\tuse_score = 100\\n\\n\\t# calculate equipment score\\n\\tif Equipment <= 5:\\n\\t\\tequipment_score = 75\\n\\telif Equipment <= 10:\\n\\t\\tequipment_score = 90\\n\\telse:\\n\\t\\tequipment_score = 100\\n\\n\\t# calculate weapon score\\n\\tif Weapon <= 1:\\n\\t\\tweapon_score = 75\\n\\telif Weapon == 2:\\n\\t\\tweapon_score = 90\\n\\telse:\\n\\t\\tweapon_score = 100\\n\\n\\t# calculate ammunition score\\n\\tif Ammunition <= 5:\\n\\t\\tammunition_score = 50\\n\\telif Ammunition <= 10:\\n\\t\\tammunition_score = 75\\n\\telif Ammunition <= 14:\\n\\t\\tammunition_score = 90\\n\\telse:\\n\\t\\tammunition_score = 100\\n\\n\\tsupplies_score = (equipment_score + use_score + weapon_score + ammunition_score) * 0.225 + attachment_score * 0.1\\n\\tsupply_score = int(supplies_score * 0.8 + care_package_score * 0.2)\\n\\n\\treturn supply_score\",\n \"def score_for_attempt(self, index):\\r\\n attempt = self.child_history[index]\\r\\n score = attempt.get('score')\\r\\n post_assessment_data = self._parse_score_msg(attempt.get('post_assessment', \\\"{}\\\"), self.system)\\r\\n grader_types = post_assessment_data.get('grader_types')\\r\\n\\r\\n # According to _parse_score_msg in ML grading there should be only one grader type.\\r\\n if len(grader_types) == 1 and grader_types[0] == 'ML':\\r\\n rubric_scores = post_assessment_data.get(\\\"rubric_scores\\\")\\r\\n\\r\\n # Similarly there should be only one list of rubric scores.\\r\\n if len(rubric_scores) == 1:\\r\\n rubric_scores_sum = sum(rubric_scores[0])\\r\\n log.debug(\\\"\\\"\\\"Score normalized for location={loc}, old_score={old_score},\\r\\n new_score={new_score}, rubric_score={rubric_score}\\\"\\\"\\\".format(\\r\\n loc=self.location_string,\\r\\n old_score=score,\\r\\n new_score=rubric_scores_sum,\\r\\n rubric_score=rubric_scores\\r\\n ))\\r\\n return rubric_scores_sum\\r\\n return score\",\n \"def update_score(score, role):\\n if role == 'winner':\\n score = score + 1\\n if role == 'loser':\\n score = score - 1\\n return score\",\n \"def get_score(self):\\r\\n return self.lcp.get_score()\",\n \"def set_score(self, a, b, score):\\n ### FILL IN ###\",\n \"def calc_score(allcourses, student_list, chambers):\\n\\n\\t# start-score of a valid schedule\\n\\tpoints = 1000\\n\\tallcoursespoints = []\\n\\troomlocks_per_day = 28\\n\\n\\t#* create groups and dict to keep up the days per activity *#\\n\\n\\t# iterate over courses\\n\\tfor course in allcourses:\\n\\n\\t\\tcoursepoints = 0\\n\\n\\t\\t# if seminar(s) exist\\n\\t\\tif course.seminars > 0:\\n\\n\\t\\t\\t# create list with amount of seminar-groups\\n\\t\\t\\tgroups = list(range(1, int(course.seminars) + 1))\\n\\n\\t\\t# if practical exist\\n\\t\\telif course.practicals > 0:\\n\\n\\t\\t\\t# create list with amount of practical-groups\\n\\t\\t\\tgroups = list(range(1, int(course.practicals) + 1))\\n\\n\\t\\t# otherwise only one group (lecture)\\n\\t\\telse:\\n\\t\\t\\tgroups = [1]\\n\\n\\t\\t# create dict with groupnumber as key, [days] as values\\n\\t\\tdayActivity = {k: [] for k in groups}\\n\\n\\t\\t#* subtract points based on activities per day *#\\n\\n\\t\\t# iterate over activities per course\\n\\t\\tfor activity in course.activities:\\n\\n\\t\\t\\t# determine day\\n\\t\\t\\tday = int(activity[0] / roomlocks_per_day)\\n\\n\\t\\t\\t# if activity = lecture\\n\\t\\t\\tif activity[2] == 0:\\n\\n\\t\\t\\t\\t# add day to list linked to lecture\\n\\t\\t\\t\\tfor group in groups:\\n\\t\\t\\t\\t\\tdayActivity[group].append(day)\\n\\n\\t\\t\\t# for seminars and practicals\\n\\t\\t\\telse:\\n\\n\\t\\t\\t\\t# add day to list linked to group\\n\\t\\t\\t\\tdayActivity[activity[2]].append(day)\\n\\n\\t\\t# for each group\\n\\t\\tfor group in groups:\\n\\n\\t\\t\\t# for each working day\\n\\t\\t\\tfor day in range(0, 5):\\n\\n\\t\\t\\t\\t# count how many of the same activities a day\\n\\t\\t\\t\\tday_occurance = dayActivity[group].count(day)\\n\\n\\t\\t\\t\\t# if more often than 1\\n\\t\\t\\t\\tif day_occurance > 1:\\n\\n\\t\\t\\t\\t\\t# substract points\\n\\t\\t\\t\\t\\tpoints -= ((day_occurance - 1) * 10)\\n\\t\\t\\t\\t\\tcoursepoints -= ((day_occurance - 1) * 10)\\n\\n\\t\\t#* add points for spreading of activities over week *#\\n\\n\\t\\t# iterate over groups\\n\\t\\tfor group in groups:\\n\\n\\t\\t\\t# when 2 activities in week\\n\\t\\t\\tif (len(dayActivity[group]) == 2):\\n\\n\\t\\t\\t\\t# add points if they are spread (mo- thu, mo-fri)\\n\\t\\t\\t\\tif (abs(dayActivity[group][0] - dayActivity[group][1]) >= 3):\\n\\t\\t\\t\\t\\tpoints += 20\\n\\t\\t\\t\\t\\tcoursepoints += 20\\n\\n\\t\\t\\t# when 3 activities in the week\\n\\t\\t\\tif (len(dayActivity[group]) == 3):\\n\\n\\t\\t\\t\\t# add points if spread (mo-we-fr)\\n\\t\\t\\t\\tif 0 in dayActivity[group] and 2 in dayActivity[group] and 4 in dayActivity[group]:\\n\\t\\t\\t\\t\\tpoints += 20\\n\\t\\t\\t\\t\\tcoursepoints += 20\\n\\n\\t\\t\\t# if 4 activities in the week\\n\\t\\t\\tif (len(dayActivity[group]) == 4):\\n\\n\\t\\t\\t\\t# add points if spread (mo-tu-th-fr)\\n\\t\\t\\t\\tif 0 in dayActivity[group] and 1 in dayActivity[group] and 3 in dayActivity[group] and 4 in dayActivity[group]:\\n\\t\\t\\t\\t\\tpoints += 20\\n\\t\\t\\t\\t\\tcoursepoints += 20\\n\\n\\t\\t# for each activity of a course\\n\\t\\tfor activity in course.activities:\\n\\n\\t\\t\\t# substract room and timelock\\n\\t\\t\\troom, timelock = translate_roomlock(activity[0])\\n\\n\\t\\t\\t# for lectures\\n\\t\\t\\tif activity[2] == 0:\\n\\n\\t\\t\\t\\t# if too many students for room\\n\\t\\t\\t\\tif int(chambers[room].capacity) < course.students:\\n\\n\\t\\t\\t\\t\\t# substract points\\n\\t\\t\\t\\t\\tmaluspoints = course.students - int(chambers[room].capacity)\\n\\t\\t\\t\\t\\tpoints -= maluspoints\\n\\t\\t\\t\\t\\tcoursepoints -= maluspoints\\n\\n\\t\\t\\t# for practical/seminars\\n\\t\\t\\telse:\\n\\n\\t\\t\\t\\t# if seminar\\n\\t\\t\\t\\tif course.seminars > 0:\\n\\n\\t\\t\\t\\t\\t# and too many students fro room, substract points\\n\\t\\t\\t\\t\\tif int(chambers[room].capacity) < course.maxstudentssem:\\n\\n\\t\\t\\t\\t\\t\\t# substract points\\n\\t\\t\\t\\t\\t\\tmaluspoints = course.maxstudentssem - int(chambers[room].capacity)\\n\\t\\t\\t\\t\\t\\tpoints -= maluspoints\\n\\t\\t\\t\\t\\t\\tcoursepoints -= maluspoints\\n\\n\\t\\t\\t\\t# if practical\\n\\t\\t\\t\\telse:\\n\\n\\t\\t\\t\\t\\t# and too many students from room, substract points\\n\\t\\t\\t\\t\\tif int(chambers[room].capacity) < course.maxstudentsprac:\\n\\t\\t\\t\\t\\t\\tmaluspoints = course.maxstudentsprac - int(chambers[room].capacity)\\n\\t\\t\\t\\t\\t\\tpoints -= maluspoints\\n\\t\\t\\t\\t\\t\\tcoursepoints -= maluspoints\\n\\n\\t\\t# add points of allcourses\\n\\t\\tallcoursespoints.append([course.name, coursepoints])\\n\\n\\t#* substract points when 2 courses on same time *#\\n\\n\\t# for all students\\n\\tfor student in student_list:\\n\\n\\t\\t# create empty list\\n\\t\\ttimelocks_student = []\\n\\n\\t\\t# for each activity in schedule\\n\\t\\tfor activity in student.schedule:\\n\\n\\t\\t\\t# if roomlock is occupied substract points\\n\\t\\t\\tif activity[0] in timelocks_student:\\n\\t\\t\\t\\tpoints -= 1\\n\\t\\t\\t\\tcoursepoints -= 1\\n\\n\\t\\t\\t# add roomlock to list\\n\\t\\t\\ttimelocks_student.append(activity[0])\\n\\n\\treturn points\",\n \"def set_offensive_ratio(self):\\n bx = self.get_standard_stats()\\n team = self.get_team_stats()\\n opp_team = self.get_opp_team_stats()\\n if bx[\\\"minutes\\\"] > 0 and (bx[\\\"t2p_int\\\"] + bx[\\\"t3p_int\\\"]) > 0:\\n fgm = bx[\\\"t2p_conv\\\"] + bx[\\\"t3p_conv\\\"]\\n fga = bx[\\\"t2p_int\\\"] + bx[\\\"t3p_int\\\"]\\n team_fgm = team[\\\"t2p_conv\\\"] + team[\\\"t3p_conv\\\"]\\n team_fga = team[\\\"t2p_int\\\"] + team[\\\"t3p_int\\\"]\\n team_points = team[\\\"t2p_conv\\\"]*2 + team[\\\"t3p_conv\\\"]*3 + team[\\\"tl_conv\\\"]\\n points = bx[\\\"t2p_conv\\\"]*2 + bx[\\\"t3p_conv\\\"]*3 + bx[\\\"tl_conv\\\"]\\n\\n try:\\n qAST = (Decimal(bx[\\\"minutes\\\"] / (team[\\\"minutes\\\"] / 5)) * (Decimal('1.14') * Decimal((team[\\\"assists\\\"] - bx[\\\"assists\\\"]) / team_fgm))) + \\\\\\n Decimal((((team[\\\"assists\\\"] / team[\\\"minutes\\\"]) * bx[\\\"minutes\\\"] * 5 - bx[\\\"assists\\\"]) / ((team_fgm / team[\\\"minutes\\\"]) * bx[\\\"minutes\\\"] * 5 - fgm)) * (1 - (bx[\\\"minutes\\\"] / (team[\\\"minutes\\\"] / 5))))\\n except ZeroDivisionError:\\n print(BCOLORS.WARNING + \\\"Error: División por cero\\\" + BCOLORS.ENDC)\\n qAST = 1\\n except InvalidOperation:\\n print(BCOLORS.WARNING + \\\"Error: Invalid Operation\\\" + BCOLORS.ENDC)\\n qAST = 1\\n\\n fg_part = fgm * (1 - Decimal('0.5') * Decimal((points - bx[\\\"tl_conv\\\"]) / (2 * fga)) * qAST)\\n\\n try:\\n ast_part = Decimal('0.5') * Decimal(((team_points - team[\\\"tl_conv\\\"]) - (points - bx[\\\"tl_conv\\\"])) / (2*(team_fga - fga))) * bx[\\\"assists\\\"]\\n except ZeroDivisionError:\\n print(BCOLORS.WARNING + \\\"Error: División por cero\\\" + BCOLORS.ENDC)\\n ast_part = 0\\n\\n if bx[\\\"tl_int\\\"] > 0:\\n ft_part = Decimal(1 - (1 - (bx[\\\"tl_conv\\\"] / bx[\\\"tl_int\\\"]))**2) * Decimal('0.4') * bx[\\\"tl_int\\\"]\\n else:\\n ft_part = 0\\n team_scoring_poss = Decimal(team_fgm + Decimal(1 - (1 - (team[\\\"tl_conv\\\"] / team[\\\"tl_int\\\"]))**2) * team[\\\"tl_int\\\"] * Decimal('0.4'))\\n try:\\n team_orb_percentage = Decimal(team[\\\"reb_of\\\"] / (team[\\\"reb_of\\\"] + ((opp_team[\\\"reb_def\\\"] + opp_team[\\\"reb_of\\\"]) - opp_team[\\\"reb_of\\\"])))\\n except ZeroDivisionError:\\n print(BCOLORS.FAIL + \\\"Error: División por cero\\\" + BCOLORS.ENDC)\\n team_orb_percentage = 0\\n except InvalidOperation:\\n print(BCOLORS.FAIL + \\\"Error: Invalid Operation\\\" + BCOLORS.ENDC)\\n team_orb_percentage = 0\\n\\n team_play_percentage = Decimal(team_scoring_poss / (team_fga + team[\\\"tl_int\\\"] * Decimal('0.4') + team[\\\"turnovers\\\"]))\\n try:\\n team_orb_weight = ((1 - team_orb_percentage) * team_play_percentage) / ((1 - team_orb_percentage) * team_play_percentage + team_orb_percentage * (1 - team_play_percentage))\\n except InvalidOperation:\\n print(BCOLORS.FAIL + \\\"Error: Invalid Operation\\\" + BCOLORS.ENDC)\\n team_orb_weight = 0\\n\\n orb_part = bx[\\\"reb_of\\\"] * team_orb_weight * team_play_percentage\\n\\n fg_x_poss = (fga - fgm) * (1 - Decimal('1.07') * team_orb_percentage)\\n if bx[\\\"tl_conv\\\"] > 0:\\n ft_x_poss = Decimal((1 - (bx[\\\"tl_conv\\\"] / bx[\\\"tl_int\\\"]))**2) * Decimal('0.4') * bx[\\\"tl_int\\\"]\\n else:\\n ft_x_poss = Decimal(1 - (bx[\\\"tl_conv\\\"] / 1)**2) * Decimal('0.4') * bx[\\\"tl_int\\\"]\\n try:\\n sc_poss = (fg_part + ast_part + ft_part) * (1 - (team[\\\"reb_of\\\"] / team_scoring_poss) * team_orb_weight * team_play_percentage) + orb_part\\n except InvalidOperation:\\n print(BCOLORS.FAIL + \\\"Error: Invalid Operation\\\" + BCOLORS.ENDC)\\n sc_poss =0\\n\\n tot_poss = sc_poss + fg_x_poss + ft_x_poss + bx[\\\"turnovers\\\"]\\n\\n pprod_fg_part = 2 * (fgm + Decimal('0.5') * bx[\\\"t3p_conv\\\"]) * (1 - Decimal('0.5') * Decimal((points - bx[\\\"tl_conv\\\"]) / (2 * fga)) * qAST)\\n\\n try:\\n pprod_ast_part = 2 * ((team_fgm - fgm + Decimal('0.5') * (team[\\\"t3p_conv\\\"] - bx[\\\"t3p_conv\\\"])) / (team_fgm - fgm)) * Decimal('0.5') * Decimal(((team_points - team[\\\"tl_conv\\\"]) - (points - bx[\\\"tl_conv\\\"])) / (2 * (team_fga - fga))) * bx[\\\"assists\\\"]\\n except:\\n pprod_ast_part = 0\\n\\n pprod_orb_part = bx[\\\"reb_of\\\"] * team_orb_weight * team_play_percentage * (team_points / (team_fgm + Decimal(1 - (team[\\\"tl_conv\\\"] / team[\\\"tl_int\\\"])**2) * Decimal('0.4') * team[\\\"tl_int\\\"]))\\n try:\\n pprod = (pprod_fg_part + pprod_ast_part + bx[\\\"tl_conv\\\"]) * (1 - (team[\\\"reb_of\\\"] / team_scoring_poss) * team_orb_weight * team_play_percentage) + pprod_orb_part\\n except InvalidOperation:\\n print(BCOLORS.FAIL + \\\"Error: Invalid Operation\\\" + BCOLORS.ENDC)\\n pprod = 0\\n\\n try:\\n result = 100 * (pprod / tot_poss)\\n except InvalidOperation:\\n print(BCOLORS.FAIL + \\\"Error: Invalid Operation\\\" + BCOLORS.ENDC)\\n result = 0\\n\\n # print(\\\"fgm: \\\" + str(fgm))\\n # print(\\\"fga: \\\" + str(fga))\\n # print(\\\"team_fgm: \\\" + str(team_fgm))\\n # print(\\\"team_fga: \\\" + str(team_fga))\\n # print(\\\"team_points: \\\" + str(team_points))\\n # print(\\\"points: \\\" + str(points))\\n # print(\\\"qAST: \\\" + str(qAST))\\n # print(\\\"fg_part: \\\" + str(fg_part))\\n # print(\\\"ast_part: \\\" + str(ast_part))\\n # print(\\\"ft_part: \\\" + str(ft_part))\\n # print(\\\"team_scoring_poss: \\\" + str(team_scoring_poss))\\n # print(\\\"team_orb_percentage: \\\" + str(team_orb_percentage))\\n # print(\\\"team_play_percentage: \\\" + str(team_play_percentage))\\n # print(\\\"team_orb_weight: \\\" + str(team_orb_weight))\\n # print(\\\"orb_part: \\\" + str(orb_part))\\n # print(\\\"fg_x_poss: \\\" + str(fg_x_poss))\\n # print(\\\"ft_x_poss: \\\" + str(ft_x_poss))\\n # print(\\\"sc_poss: \\\" + str(sc_poss))\\n # print(\\\"tot_poss: \\\" + str(tot_poss))\\n # print(\\\"pprod_fg_part: \\\" + str(pprod_fg_part))\\n # print(\\\"pprod_ast_part: \\\" + str(pprod_ast_part))\\n # print(\\\"pprod_orb_part: \\\" + str(pprod_orb_part))\\n # print(\\\"pprod: \\\" + str(pprod))\\n # print(\\\"result: \\\" + str(result) + \\\"\\\\n\\\")\\n else:\\n result = 0.00\\n\\n self.ortg = \\\"%.2f\\\" % round(result, 2)\\n if Decimal(self.ortg) < 0 or Decimal(self.ortg) >= 1000:\\n \\\"\\\"\\\"For one game, maybe we've got a negative result or one so big, so, for just only a game, we get the ORTG \\n using team's formula\\\"\\\"\\\"\\n print(BCOLORS.OKBLUE + \\\"ORTG negativo o superior a 1000 para jugadora => recalculamos a través de la fórmula de equipo\\\" + BCOLORS.ENDC)\\n bx = self.get_standard_stats()\\n result = round((bx[\\\"t2p_conv\\\"]*2 + bx[\\\"t3p_conv\\\"]*3 + bx[\\\"tl_conv\\\"])/self.get_team_possessions(), 2)\\n self.ortg = \\\"%.2f\\\" % result\",\n \"def update_score(self):\\n self.score = TurboMQ.calculate_fitness(self.result, self.graph)\",\n \"def factor_in_multiple_professors(self):\\n professors = [professor for professor in self.course.professors if professor.lower() != \\\"none\\\"]\\n number_professors = len(set(professors))\\n if number_professors > 1:\\n self.score = self.score + number_professors\",\n \"def call(self, model):\\n raise NotImplementedError('Define your score here')\",\n \"def planning_score(score_type,\\n bendable_hard_levels_size=None,\\n bendable_soft_levels_size=None,\\n score_definition_class=None):\\n def planning_score_function_wrapper(getter_function):\\n ensure_init()\\n from org.optaplanner.core.api.domain.solution import PlanningScore as JavaPlanningScore\\n getter_function.__optaplannerPlanningScore = {\\n 'annotationType': JavaPlanningScore,\\n 'bendableHardLevelsSize': bendable_hard_levels_size,\\n 'bendableSoftLevelsSize': bendable_soft_levels_size,\\n 'scoreDefinitionClass': score_definition_class\\n }\\n getter_function.__return = score_type\\n return getter_function\\n return planning_score_function_wrapper\",\n \"def test_set_score_publish(self, credit_dict, result):\\n self.xblock.credit_dict = credit_dict\\n self.xblock.instructor_answer = 10\\n self.xblock.set_score()\\n self.xblock.runtime.publish.assert_called_with(\\n self.xblock,\\n 'grade',\\n {\\n 'value': result,\\n 'max_value': 1,\\n },\\n )\",\n \"def _return_cost(self, child_confidence, informant_reputation, child_action, informant_action, value=\\\"3yo\\\"):\\n if value == '3yo':\\n if child_confidence == 1 and informant_reputation == 1 and child_action == 1 and informant_action == 1:\\n cost = -1.0 # (knowledge, knowledge, accept, accept) = reinforce\\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 0 and informant_action == 1):\\n cost = +0.5 # (knowledge, knowledge, reject, accept) = slightly punish\\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 1 and informant_action == 0):\\n cost = +0.5 # (knowledge, knowledge, accept, reject) = slightly punish\\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 0 and informant_action == 0):\\n cost = -1.0 # (knowledge, knowledge, reject, reject) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 1 and informant_action == 1):\\n cost = -1.0 # (non-knowledge, knowledge, accept, accept) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 0 and informant_action == 0):\\n cost = -1.0 # (non-knowledge, knowledge, reject, reject) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 0 and informant_action == 1):\\n cost = +1.0 # (non-knowledge, knowledge, reject, accept) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 1 and informant_action == 0):\\n cost = +1.0 # (non-knowledge, knowledge, accept, reject) = punish\\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 1 and informant_action == 1):\\n cost = -1.0 # (knowledge, non-knowledge, accept, accept) = reinforce\\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 0 and informant_action == 1):\\n cost = +0.5 # (knowledge, non-knowledge, reject, accept) = slightly punish\\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 1 and informant_action == 0):\\n cost = +0.5 # (knowledge, non-knowledge, accept, reject) = slightly punish\\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 0 and informant_action == 0):\\n cost = -1.0 # (knowledge, non-knowledge, reject, reject) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 1 and informant_action == 1):\\n cost = -1.0 # (non-knowledge, non-knowledge, accept, accept) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 0 and informant_action == 1):\\n cost = +1.0 # (non-knowledge, non-knowledge, reject, accept) = punish\\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 1 and informant_action == 0):\\n cost = +1.0 # (non-knowledge, non-knowledge, accept, reject) = punish\\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 0 and informant_action == 0):\\n cost = -1.0 # (non-knowledge, non-knowledge, reject, reject) = reinforce\\n else:\\n raise ValueError(\\\"ERROR: the '3yo' Bayesian Networks input values are out of range\\\")\\n return cost\\n\\n elif value == '4yo':\\n if (child_confidence == 1 and informant_reputation == 1 and child_action == 1 and informant_action == 1):\\n cost = -1.0 # (knowledge, knowledge, accept, accept) = reinforce\\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 0 and informant_action == 1):\\n cost = +0.5 # (knowledge, knowledge, reject, accept) = slight punish\\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 1 and informant_action == 0):\\n cost = +0.5 # (knowledge, knowledge, accept, reject) = slight punish\\n elif (child_confidence == 1 and informant_reputation == 1 and child_action == 0 and informant_action == 0):\\n cost = -1.0 # (knowledge, knowledge, reject, reject) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 1 and informant_action == 1):\\n cost = -1.0 # (non-knowledge, knowledge, accept, accept) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 0 and informant_action == 0):\\n cost = -1.0 # (non-knowledge, knowledge, reject, reject) = reinforce\\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 0 and informant_action == 1):\\n cost = +1.0 # (non-knowledge, knowledge, reject, accept) = punish\\n elif (child_confidence == 0 and informant_reputation == 1 and child_action == 1 and informant_action == 0):\\n cost = +1.0 # (non-knowledge, knowledge, accept, reject) = punish\\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 1 and informant_action == 1):\\n cost = 0.0 # (knowledge, non-knowledge, accept, accept) =\\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 0 and informant_action == 1):\\n cost = 0.0 # (knowledge, non-knowledge, reject, accept) =\\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 1 and informant_action == 0):\\n cost = 0.0 # (knowledge, non-knowledge, accept, reject) =\\n elif (child_confidence == 1 and informant_reputation == 0 and child_action == 0 and informant_action == 0):\\n cost = 0.0 # (knowledge, non-knowledge, reject, reject) =\\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 1 and informant_action == 1):\\n cost = 0.0 # (non-knowledge, non-knowledge, accept, accept) = zero_cost\\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 0 and informant_action == 1):\\n cost = 0.0 # (non-knowledge, non-knowledge, reject, accept) = zero_cost\\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 1 and informant_action == 0):\\n cost = 0.0 # (non-knowledge, non-knowledge, accept, reject) = zero_cost\\n elif (child_confidence == 0 and informant_reputation == 0 and child_action == 0 and informant_action == 0):\\n cost = 0.0 # (non-knowledge, non-knowledge, reject, reject) = zero_cost\\n else:\\n raise ValueError(\\\"ERROR: the '4yo' Bayesian Networks input values are out of range\\\")\\n return cost\\n else:\\n raise ValueError(\\\"ERROR: input value not recognised, correct values are '3yo' and '4yo'\\\")\",\n \"def automated(user):\\n\\n # Start should be a Plan() and will include any classes the user has already taken to this point. It should have\\n # accurate counts of its course typesTaken before starting the search. Goal is the end state desired by the user.\\n # Usually this will be the gradReqs for the users Curriculum object, but this could also be used to create\\n # limited searches. number_of_classes_per_quarter is the max number of courses a user is willing to take in a\\n # given quarter.\\n\\n # Cost should represent how many quarters are needed to graduate. They should however be multiplied by some factor\\n # so that they are larger than our heuristic values. Our heuristics should produce values for rarity that are 8 - #\\n # of times offered in 8 quarters. So the values will range between 0-8. Unlocks could be anything between 0 and\\n # the # of total classes opened up by taking it. Unlocks seems to range between 0-281. We opted to cap this number\\n # to 50. We decided to cap the unlocks score to 50 and in order to have rarity weigh as much as unlocks we opted to\\n # multiply its results by 6. The result is that rarity ranges between 0-42 and unlocks ranges from 0-50. We never\\n # want to over-estimate the cost of a path to graduation. 50 is chosen because it is the maximum value of h(n) seen\\n # in our data.\\n # Should equal max(rarity) + max(unlocks) + bonus\\n # g(n) = (quarters x stdCost) or cost so far\\n # h(n) = stdCost - (rarity + unlocks + bonus)\\n # f(n) = g(n) + h(n)\\n # Record actual cost of a path as g(n) / stdCost = number of quarters to graduate.\\n # A course that is offered every quarter and unlocks nothing and does not match a preferred elective type will cost\\n # stdCost which is exactly what adding a class costs. Selecting something more rare, and/or unlocks more classes\\n # will appear to cost less than a normal quarter. So the path will be an under-estimate of cost and therefore it\\n # will be admissible. As long as stdCost is >= h(n) we will never have negative costs therefore h(n) will be\\n # considered consistent.\\n # stdCost must = max(rarity) + max(unlocks) + max(bonus)\\n\\n # Should we disallow online courses?\\n removeOnline = user.disallowOnline\\n\\n # Get students undergrad degree type\\n undergrad = user.undergraduate_degree\\n\\n # Setup Curriculum\\n curriculum = user.curriculum\\n\\n # Setup concentration\\n if curriculum is CS:\\n userPref = int(user.getCSFocus)\\n else:\\n userPref = 1\\n\\n\\n # Create null node\\n start = Plan(\\n selectionOrder = list(),\\n coursesTaken = user.getCoursesTaken,\\n termNum = user.getTerm,\\n currTermIdx = 0,\\n daysFilled = [],\\n maxCourses = user.max_courses,\\n typesTaken = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n selectionsWithDay = list()\\n )\\n\\n # If the users undergrad type matches their graduate type waive introductory courses\\n start = waiveCourses(start, undergrad, curriculum)\\n\\n # Initialize variables\\n frontier = PriorityQueue()\\n frontier.put(start, 0)\\n costSoFar = {}\\n stdCost = setStdCost(curriculum)\\n courseLimit = 20 # max number of courses in a solution\\n\\n # Store results of the query once for reuse later in search\\n terms = ['0975', '0980', '0985', '0990', '0995', '1000', '1005']\\n queryResults = dict((term, TermCourses.getAvailableCourses(term)) for term in terms)\\n\\n # Modify the heuristic score of classes to emphasize certain course types and the students focus in particular\\n queryResults = modifyHeuristics(userPref, queryResults, terms, curriculum)\\n\\n plansPopped = 0\\n while not frontier.empty():\\n # Select current plan\\n curr_plan = frontier.get()\\n plansPopped += 1\\n\\n # If the search has gone on too long return an empty list so the user can restart the search\\n if timedOut(plansPopped):\\n return list(('Please see advisor', 'mon'))\\n\\n # Goal Checking\\n if isGoal(curr_plan, curriculum, courseLimit, userPref):\\n break\\n\\n # Count up non-capstone courses in plan\\n cur = curr_plan.typesTaken\\n tot = cur[0] + cur[1] + cur[2] + cur[13]\\n\\n # Filter the query removing courses that the student cannot take\\n subsetResults = queryResults[TermCourses.convert_stream(curr_plan.termNum)]\\n filteredResults = filter(subsetResults, curr_plan, curr_plan.daysFilled, curriculum, tot, removeOnline)\\n\\n # Loop through the top 8 filtered results and try each suggested plan\\n for suggestedCourseInfo in filteredResults[:8]:\\n suggestedPlan = Plan(\\n selectionOrder = copy.deepcopy(curr_plan.selectionOrder),\\n coursesTaken = copy.deepcopy(curr_plan.coursesTaken),\\n termNum = copy.deepcopy(curr_plan.termNum),\\n currTermIdx = copy.deepcopy(curr_plan.currTermIdx),\\n daysFilled = copy.deepcopy(curr_plan.daysFilled),\\n maxCourses = user.max_courses,\\n typesTaken = copy.deepcopy(curr_plan.typesTaken),\\n selectionsWithDay = copy.deepcopy(curr_plan.selectionsWithDay))\\n\\n # Add suggested course to current plan\\n addUpdateCourse(suggestedPlan, suggestedCourseInfo, curriculum)\\n\\n # Calculate the true cost of the current plan (non heuristic)\\n new_cost = costSoFar.get(str(curr_plan.coursesTaken), costSoFar.get(str(curr_plan.coursesTaken), 0))+stdCost\\n\\n # Do not explore plans with excessive numbers of courses\\n taken = suggestedPlan.typesTaken\\n totCourses = taken[0] + taken[1] + taken[2] + taken [3] + taken[4] + taken[13]\\n if curriculum == CS:\\n if totCourses >= courseLimit or suggestedPlan.typesTaken[2] > 8:\\n continue\\n else:\\n if totCourses >= courseLimit or suggestedPlan.typesTaken[2] > 3:\\n continue\\n\\n # Only explore a plan if it has not been seen or it is a better plan than a previously seen version\\n if str(suggestedPlan.coursesTaken) not in costSoFar or new_cost < costSoFar[str(suggestedPlan.coursesTaken)]:\\n costSoFar[str(suggestedPlan.coursesTaken)] = new_cost\\n priority = -new_cost + heuristics(suggestedCourseInfo, suggestedPlan, user)\\n frontier.put(suggestedPlan, priority)\\n\\n return curr_plan.selectionsWithDay\",\n \"def score( self ):\\r\\n result = 0.0\\r\\n for rr in self.ee.getRsrcs( ):\\r\\n value = self.scoreRsrc( rr )\\r\\n result += value\\r\\n print( \\\"INFO: Value for the schedule is %s \\\" % ( rr, result ) )\\r\\n return( result )\",\n \"def get_score(self, student_answers):\\r\\n pass\",\n \"def evaluate(self, mode=0):\\r\\n winner = self.determine_winner()\\r\\n if winner:\\r\\n return winner * self.WIN_SCORE\\r\\n\\r\\n if mode == 1:\\r\\n return self.centre_priority_evaluate()\\r\\n elif mode == 2:\\r\\n return 0.5 * (self.centre_priority_evaluate() + self.piece_evaluate())\\r\\n else:\\r\\n return self.piece_evaluate()\",\n \"def set_user_module_score(self, user, score, max_score, comment=u\\\"\\\"):\\r\\n if score is not None and max_score is not None:\\r\\n scaled_score = score * max_score\\r\\n else:\\r\\n scaled_score = None\\r\\n\\r\\n self.system.rebind_noauth_module_to_user(self, user)\\r\\n\\r\\n # have to publish for the progress page...\\r\\n self.system.publish(\\r\\n self,\\r\\n 'grade',\\r\\n {\\r\\n 'value': scaled_score,\\r\\n 'max_value': max_score,\\r\\n 'user_id': user.id,\\r\\n },\\r\\n )\\r\\n self.module_score = scaled_score\\r\\n self.score_comment = comment\",\n \"def score(self):\\n return None\",\n \"def generate_score(self, answered_question, date, time_value):\\n correct_answer_objects = map(lambda correct_key: Answer.get_by_id(correct_key.id_or_name()),\\n answered_question.question.correctAnswers)\\n invite = answered_question.invite\\n earned_knowledge_area_score = {}\\n max_knowledge_area_score = {}\\n total_earned_value = 0.0\\n\\n for correct_answer in correct_answer_objects:\\n for knowledge_area in correct_answer.knowledgeAreas:\\n #We don't want negative values to influence the max score; skip those\\n if correct_answer.value > float(0):\\n earned_knowledge_area_score[knowledge_area] = 0.0\\n if knowledge_area in max_knowledge_area_score.keys():\\n max_knowledge_area_score[knowledge_area] += correct_answer.value\\n else:\\n max_knowledge_area_score[knowledge_area] = correct_answer.value\\n round(max_knowledge_area_score[knowledge_area], 2)\\n\\n for given_answer_key in answered_question.givenAnswers:\\n given_answer_object = Answer.get_by_id(given_answer_key.id_or_name())\\n earned_value = float(given_answer_object.value)\\n total_earned_value = float(round(total_earned_value + earned_value, 2))\\n for knowledge_area in given_answer_object.knowledgeAreas:\\n if knowledge_area in earned_knowledge_area_score.keys():\\n earned_knowledge_area_score[knowledge_area] += earned_value\\n else:\\n earned_knowledge_area_score[knowledge_area] = earned_value\\n round(earned_knowledge_area_score[knowledge_area], 2)\\n\\n multiple_choice_score_object = MultipleChoiceScore()\\n multiple_choice_score_object.parse(correct_answer_objects, total_earned_value, earned_knowledge_area_score,\\n max_knowledge_area_score, invite, date, time_value)\\n multiple_choice_score_object.put()\\n return multiple_choice_score_object\",\n \"def get_score(self):\\r\\n if self.is_complete():\\r\\n score = 1\\r\\n elif self.is_half_complete():\\r\\n score = 0.5\\r\\n else:\\r\\n score = 0\\r\\n return {'score': score,\\r\\n 'total': self.max_score()}\",\n \"def _update_score(self) -> None:\\n\\n # setting new score by iterating over players\\n self.score_play[self.n_play_turns, ] = [\\n self._score_table[(\\n self.contract.level,\\n self.contract.suit,\\n self.tricks[i],\\n self.contract.player_vulnerability[i],\\n int(self.contract.double + self.contract.redouble)\\n )]\\n for i in range(NUM_PLAYERS)\\n ]\",\n \"def base_contribute_score():\\n return 1\",\n \"def take_test(exam, student):\\n\\n student.score = exam.administer()\\n return student.score\",\n \"def eval_evalassignment(request, pk, pts):\\n student = request.user.student\\n evalassignment = Evalassignment.objects.\\\\\\n filter(pk=pk, assignment__student=student).first()\\n if evalassignment:\\n evalassignment.grade_evaluation = pts\\n evalassignment.save()\\n redirect_item = '#assignment%s' % evalassignment.assignment.id\\n else:\\n redirect_item = ''\\n return redirect('/dashboard_student/' + redirect_item)\",\n \"def judge(name):\\n score = 0\\n for scoreID, scorer, weight in weights:\\n subscore = scorer(name)\\n score += subscore * weight\\n name.scores[scoreID] = subscore\\n name.score = score\\n return score\",\n \"def take_test(exam, student):\\n\\n student.score = exam.administer()\",\n \"def disp_score():\",\n \"def evaluation(self):\\n rows_list = []\\n for name in self.single_classifier_best.keys():\\n row = {}\\n row['algorithm'] = name \\n row[self.scoring_metric] = self.single_classifier_best[name].best_score_\\n rows_list.append(row)\\n \\n scoring_df = pd.DataFrame(rows_list)\\n scoring_sorted = scoring_df.sort_values(self.scoring_metric, ascending=False)\\n print()\\n print('*'*shutil.get_terminal_size().columns)\\n print(scoring_sorted)\\n print('*'*shutil.get_terminal_size().columns)\\n self.evaluation_scores = scoring_sorted\",\n \"def to_score(self):\\n self._bottom_tab(2)\\n self._goto(\\\"score\\\")\",\n \"def calculate(self):\\n\\n rating = 0\\n\\n props = ['aroma', 'appearance', 'taste', 'palate', 'bottle_style']\\n for item in props:\\n rating += getattr(self, item, 0)\\n\\n self.overall = (rating / self.total) / .2\",\n \"def set_score(self, points):\\n self.score += points\",\n \"def score(self):\\n result = 1\\n one_node = self.cups.locate_node(1)\\n a = one_node.next()\\n b = a.next()\\n\\n result = a.value * b.value\\n\\n return result\",\n \"def perform_set_score(responder, options):\\n match = options['<match-id>']\\n tla = options['<tla>']\\n score = options['<score>']\\n scores.set_match_score(match, tla, score)\\n responder('Scored {0} points for {1} in match {2}'.format(score, tla, match))\",\n \"def _calculate_score(self):\\n mul = self._check_board()\\n if mul > 0:\\n inc = 100 * mul + ((mul - 1) * 25)\\n self.score += inc\",\n \"def rating(grade_count):\\r\\n if grade_count == 0 :\\r\\n grade = 7\\r\\n else:\\r\\n grade = 2\\r\\n \\r\\n return grade\",\n \"def vanilaScore(self,attended,state,W):\",\n \"def update_score(best_score: int, new_score: int) -> int:\\n if new_score > best_score:\\n return new_score\\n else:\\n return best_score\",\n \"def scoreEvaluationFunction(currentGameState):\\r\\n return currentGameState.getScore()\",\n \"def adjust_score(self):\\n self.score += game.temporary_score\",\n \"def user_interaction_score(uv, recommended_News, ranked=True):\\n\\n iv = recommended_News[\\\"topical_vector\\\"]\\n\\n product = simple_doct_product(uv, iv)\\n\\n epsilon = 10e-5\\n\\n if (product + epsilon) > 1.0:\\n vui = 0.99\\n else:\\n vui = beta_distribution(product)\\n\\n # Awared preference\\n ita = beta_distribution(0.98)\\n pui = vui * ita\\n\\n return pui\",\n \"def score(self, params):\\n\\n if self.use_sqrt:\\n return self.score_sqrt(params)\\n else:\\n return self.score_full(params)\",\n \"def scores_(self, value):\\n self.predictor.scores_ = value\\n if self.n_good_features_ <= 1:\\n sys.stderr.write(\\\"cutoff: %.4f\\\\n\\\" % self.score_cutoff_)\\n UserWarning(\\\"These classifier settings produced <= 1 important \\\"\\n \\\"feature, consider reducing score_coefficient. \\\"\\n \\\"DataFramePCA will fail with this error: \\\"\\n \\\"\\\\\\\"ValueError: failed to create intent(\\\"\\n \\\"cache|hide)|optional array-- must have defined \\\"\\n \\\"dimensions but got (0,)\\\\\\\"\\\\n\\\")\",\n \"def grade(self, request, collection_id=None):\\n collection = self.get_object()\\n # get learner\\n serializer = LearnerSerializer(data=request.data['learner'])\\n if not serializer.is_valid():\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\\n\\n # get or create learner\\n learner, created = Learner.objects.get_or_create(**serializer.data)\\n\\n grade = get_engine().grade(learner, collection)\\n return Response({'learner': serializer.data, 'grade': grade})\",\n \"def testDriver():\\n exam1=90\\n exam2=85\\n assignmentScores = [50, 60, 70, 80, ]\\n computeGrades(exam1, exam2, assignmentScores)\",\n \"def score_update(scoreboard, compare):\\r\\n if compare == 'Victory':\\r\\n scoreboard['W'] += 1\\r\\n elif compare == 'Defeat':\\r\\n scoreboard['L'] += 1\\r\\n elif compare == 'Tie':\\r\\n scoreboard['T'] += 1\",\n \"def get_local_score(self):\\n for candidate in self.candidate_list:\\n self.score += candidate.get_score()\",\n \"def score(self, board: Block) -> int:\\r\\n raise NotImplementedError\",\n \"def score(self, board: Block) -> int:\\r\\n raise NotImplementedError\",\n \"def score(self, board: Block) -> int:\\r\\n raise NotImplementedError\",\n \"def rock_p_scissor(user_input, comp_choice):\\r\\n if user_input.lower() == comp_choice:\\r\\n compare = results[2]\\r\\n score_update(scoreboard, compare)\\r\\n banner_text(\\\"*\\\")\\r\\n banner_text(\\\"Tie! We both chose {0}\\\".format(user_input))\\r\\n banner_text(\\\"*\\\")\\r\\n print(scoreboard)\\r\\n banner_text(\\\"*\\\")\\r\\n elif user_input.lower() == 'rock' and comp_choice == 'paper':\\r\\n compare = results[1]\\r\\n score_update(scoreboard, compare)\\r\\n print(\\\"You lost! you chose {0} and I chose {1}\\\".format(user_input, comp_choice))\\r\\n banner_text(\\\"*\\\")\\r\\n print(scoreboard)\\r\\n banner_text(\\\"*\\\")\\r\\n elif user_input.lower() == 'rock' and comp_choice == 'scissor':\\r\\n compare = results[0]\\r\\n score_update(scoreboard, compare)\\r\\n print(\\\"You won! you chose {0} and I chose {1}\\\".format(user_input, comp_choice))\\r\\n banner_text(\\\"*\\\")\\r\\n print(scoreboard)\\r\\n banner_text(\\\"*\\\")\\r\\n elif user_input.lower() == 'paper' and comp_choice == 'scissor':\\r\\n compare = results[1]\\r\\n score_update(scoreboard, compare)\\r\\n print(\\\"You lost! you chose {0} and I chose {1}\\\".format(user_input, comp_choice))\\r\\n banner_text(\\\"*\\\")\\r\\n print(scoreboard)\\r\\n banner_text(\\\"*\\\")\\r\\n elif user_input.lower() == 'paper' and comp_choice == 'rock':\\r\\n compare = results[0]\\r\\n score_update(scoreboard, compare)\\r\\n print(\\\"You won! you chose {0} and I chose {1}\\\".format(user_input, comp_choice))\\r\\n banner_text(\\\"*\\\")\\r\\n print(scoreboard)\\r\\n banner_text(\\\"*\\\")\\r\\n elif user_input.lower() == 'scissor' or user_input.lower() == 'scissors' and comp_choice == 'rock':\\r\\n compare = results[1]\\r\\n score_update(scoreboard, compare)\\r\\n print(\\\"You lost! you chose {0} and I chose {1}\\\".format(user_input, comp_choice))\\r\\n banner_text(\\\"*\\\")\\r\\n print(scoreboard)\\r\\n banner_text(\\\"*\\\")\\r\\n elif user_input.lower() == 'scissor' or user_input.lower() == 'scissors' and comp_choice == 'paper':\\r\\n compare = results[0]\\r\\n score_update(scoreboard, compare)\\r\\n print(\\\"You won! you chose {0} and I chose {1}\\\".format(user_input, comp_choice))\\r\\n banner_text(\\\"*\\\")\\r\\n print(scoreboard)\\r\\n banner_text(\\\"*\\\")\\r\\n else:\\r\\n print(\\\"Your input was not valid. Please try again\\\")\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def __score_t(self, *args, **kwargs):\\n pass\",\n \"def test_instructor_assessment(self):\\r\\n\\r\\n # Navigate to the AI-assessment problem and submit an essay\\r\\n # We have configured the stub to simulate that this essay will be staff-graded\\r\\n self.course_nav.go_to_sequential('AI-Assessed')\\r\\n self.submit_essay('ai', 'Censorship in the Libraries')\\r\\n\\r\\n # Refresh the page to get the updated feedback\\r\\n # then verify that we get the feedback sent by our stub XQueue implementation\\r\\n self.assertEqual(self.get_asynch_feedback('ai'), ['incorrect', 'correct'])\\r\\n\\r\\n # Verify the progress page\\r\\n self.progress_page.visit()\\r\\n scores = self.progress_page.scores('Test Section', 'Test Subsection')\\r\\n\\r\\n # First score is the self-assessment score, which we haven't answered, so it's 0/2\\r\\n # Second score is the AI-assessment score, which we have answered, so it's 1/2\\r\\n # Third score is peer-assessment, which we haven't answered, so it's 0/2\\r\\n self.assertEqual(scores, [(0, 2), (1, 2), (0, 2)])\",\n \"def course_heuristic(schedule, course):\\n if course in schedule.heuristic_dictionary:\\n return schedule.heuristic_dictionary[course] + (0 if schedule.is_program_in_goal(course[0]) else 10)\\n return 0\",\n \"def rules(self):\\n self.rule1 = min(self.location_is_lessDemand, self.area_is_small, self.unfunishing)\\n self.rule2 = min(self.location_is_lessDemand, max(self.area_is_small, self.area_is_average), self.access_is_good)\\n self.rule3 = min(self.location_is_veryHighDemand, self.area_is_average, self.fac_is_low, self.access_is_average)\\n self.rule4 = min(self.location_is_veryLessDemand, self.area_is_verysmall, self.fully_funishing)\\n self.rule5 = min(self.location_is_lessDemand, self.fac_is_average, max(self.area_is_small, self.area_is_average))\\n self.rule6 = min(max(self.location_is_lessDemand, self.location_is_averageDemand), self.access_is_good)\\n self.rule7 = min(self.location_is_lessDemand, self.access_is_good, self.area_is_large, self.partially_funishing)\\n self.rule8 = min(self.location_is_highDemand, self.access_is_good, max(self.bed_is_less, self.bath_is_average))\\n self.rule9 = min(self.location_is_veryHighDemand, self.area_is_large, self.unfunishing)\\n self.rule10 = min(self.access_is_good, self.area_is_average, (1 - self.unfunishing))\\n self.rule11 = min(self.access_is_good, self.area_is_large, self.partially_funishing, self.bed_is_more, self.bath_is_more)\",\n \"def lookup_relevant(score):\\n category = \\\"\\\"\\n if score > 2.0:\\n category = \\\"RELEVANT\\\"\\n elif score > 0.0:\\n category = \\\"PARTIALLY RELEVANT\\\"\\n else:\\n category = \\\"NOT RELEVANT\\\"\\n return category\",\n \"def assignment(kind, grade, weight=1):\\n global running_total\\n global total_weight\\n global total_grade\\n\\n if kind not in running_total:\\n running_total[kind] = grade\\n total_grade[kind] = 0\\n total_weight[kind] = 0\\n if weight > 1:\\n grade *= weight\\n total_grade[kind] += grade\\n total_weight[kind] += weight\\n running_total[kind] = total_grade[kind] / total_weight[kind]\",\n \"def score(self, score):\\n\\n self._score = score\",\n \"def score(self, score):\\n\\n self._score = score\",\n \"def score(self, score):\\n\\n self._score = score\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\",\n \"def scoreEvaluationFunction(currentGameState):\\n return currentGameState.getScore()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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graduation."},"document":{"kind":"string","value":"def automated(user):\n\n # Start should be a Plan() and will include any classes the user has already taken to this point. It should have\n # accurate counts of its course typesTaken before starting the search. Goal is the end state desired by the user.\n # Usually this will be the gradReqs for the users Curriculum object, but this could also be used to create\n # limited searches. number_of_classes_per_quarter is the max number of courses a user is willing to take in a\n # given quarter.\n\n # Cost should represent how many quarters are needed to graduate. They should however be multiplied by some factor\n # so that they are larger than our heuristic values. Our heuristics should produce values for rarity that are 8 - #\n # of times offered in 8 quarters. So the values will range between 0-8. Unlocks could be anything between 0 and\n # the # of total classes opened up by taking it. Unlocks seems to range between 0-281. We opted to cap this number\n # to 50. We decided to cap the unlocks score to 50 and in order to have rarity weigh as much as unlocks we opted to\n # multiply its results by 6. The result is that rarity ranges between 0-42 and unlocks ranges from 0-50. We never\n # want to over-estimate the cost of a path to graduation. 50 is chosen because it is the maximum value of h(n) seen\n # in our data.\n # Should equal max(rarity) + max(unlocks) + bonus\n # g(n) = (quarters x stdCost) or cost so far\n # h(n) = stdCost - (rarity + unlocks + bonus)\n # f(n) = g(n) + h(n)\n # Record actual cost of a path as g(n) / stdCost = number of quarters to graduate.\n # A course that is offered every quarter and unlocks nothing and does not match a preferred elective type will cost\n # stdCost which is exactly what adding a class costs. Selecting something more rare, and/or unlocks more classes\n # will appear to cost less than a normal quarter. So the path will be an under-estimate of cost and therefore it\n # will be admissible. As long as stdCost is >= h(n) we will never have negative costs therefore h(n) will be\n # considered consistent.\n # stdCost must = max(rarity) + max(unlocks) + max(bonus)\n\n # Should we disallow online courses?\n removeOnline = user.disallowOnline\n\n # Get students undergrad degree type\n undergrad = user.undergraduate_degree\n\n # Setup Curriculum\n curriculum = user.curriculum\n\n # Setup concentration\n if curriculum is CS:\n userPref = int(user.getCSFocus)\n else:\n userPref = 1\n\n\n # Create null node\n start = Plan(\n selectionOrder = list(),\n coursesTaken = user.getCoursesTaken,\n termNum = user.getTerm,\n currTermIdx = 0,\n daysFilled = [],\n maxCourses = user.max_courses,\n typesTaken = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n selectionsWithDay = list()\n )\n\n # If the users undergrad type matches their graduate type waive introductory courses\n start = waiveCourses(start, undergrad, curriculum)\n\n # Initialize variables\n frontier = PriorityQueue()\n frontier.put(start, 0)\n costSoFar = {}\n stdCost = setStdCost(curriculum)\n courseLimit = 20 # max number of courses in a solution\n\n # Store results of the query once for reuse later in search\n terms = ['0975', '0980', '0985', '0990', '0995', '1000', '1005']\n queryResults = dict((term, TermCourses.getAvailableCourses(term)) for term in terms)\n\n # Modify the heuristic score of classes to emphasize certain course types and the students focus in particular\n queryResults = modifyHeuristics(userPref, queryResults, terms, curriculum)\n\n plansPopped = 0\n while not frontier.empty():\n # Select current plan\n curr_plan = frontier.get()\n plansPopped += 1\n\n # If the search has gone on too long return an empty list so the user can restart the search\n if timedOut(plansPopped):\n return list(('Please see advisor', 'mon'))\n\n # Goal Checking\n if isGoal(curr_plan, curriculum, courseLimit, userPref):\n break\n\n # Count up non-capstone courses in plan\n cur = curr_plan.typesTaken\n tot = cur[0] + cur[1] + cur[2] + cur[13]\n\n # Filter the query removing courses that the student cannot take\n subsetResults = queryResults[TermCourses.convert_stream(curr_plan.termNum)]\n filteredResults = filter(subsetResults, curr_plan, curr_plan.daysFilled, curriculum, tot, removeOnline)\n\n # Loop through the top 8 filtered results and try each suggested plan\n for suggestedCourseInfo in filteredResults[:8]:\n suggestedPlan = Plan(\n selectionOrder = copy.deepcopy(curr_plan.selectionOrder),\n coursesTaken = copy.deepcopy(curr_plan.coursesTaken),\n termNum = copy.deepcopy(curr_plan.termNum),\n currTermIdx = copy.deepcopy(curr_plan.currTermIdx),\n daysFilled = copy.deepcopy(curr_plan.daysFilled),\n maxCourses = user.max_courses,\n typesTaken = copy.deepcopy(curr_plan.typesTaken),\n selectionsWithDay = copy.deepcopy(curr_plan.selectionsWithDay))\n\n # Add suggested course to current plan\n addUpdateCourse(suggestedPlan, suggestedCourseInfo, curriculum)\n\n # Calculate the true cost of the current plan (non heuristic)\n new_cost = costSoFar.get(str(curr_plan.coursesTaken), costSoFar.get(str(curr_plan.coursesTaken), 0))+stdCost\n\n # Do not explore plans with excessive numbers of courses\n taken = suggestedPlan.typesTaken\n totCourses = taken[0] + taken[1] + taken[2] + taken [3] + taken[4] + taken[13]\n if curriculum == CS:\n if totCourses >= courseLimit or suggestedPlan.typesTaken[2] > 8:\n continue\n else:\n if totCourses >= courseLimit or suggestedPlan.typesTaken[2] > 3:\n continue\n\n # Only explore a plan if it has not been seen or it is a better plan than a previously seen version\n if str(suggestedPlan.coursesTaken) not in costSoFar or new_cost < costSoFar[str(suggestedPlan.coursesTaken)]:\n costSoFar[str(suggestedPlan.coursesTaken)] = new_cost\n priority = -new_cost + heuristics(suggestedCourseInfo, suggestedPlan, user)\n frontier.put(suggestedPlan, priority)\n\n return curr_plan.selectionsWithDay"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compute_pathways(users, job_graph, debug, min_likelihood_thr=0.2):\r\n start_time = time.time()\r\n __print_msg('Computing career pathways...', debug)\r\n user_pathways = {}\r\n tot_users = len(users)\r\n i = 0\r\n for user, user_jobs in users.items():\r\n user_pathway = compute_user_pathway(user_jobs, job_graph)\r\n recommended_pathway = recommend_pathway(user_jobs, job_graph, user_pathway[-1], min_likelihood_thr)\r\n user_pathways[user] = (user_pathway, recommended_pathway)\r\n i += 1\r\n if i % 1000 == 0:\r\n __print_msg('Num users processed: {}/{}'.format(i, tot_users), debug)\r\n end_time = time.time()\r\n __print_msg('Execution time: {} seconds'.format(end_time - start_time), debug)\r\n return user_pathways","def go_to_x_y(user_x, user_y, deltaball=1.0):\n global regions\n goal = Point()\n goal.x = user_x\n goal.y = user_y\n inc_x = user_x - x\n inc_y = user_y - y\n th = math.atan2(inc_y, inc_x)\n if th > 3.14:\n th = -(6.28 - th)\n elif th < -3.14:\n th = 6.28 + th\n go_to_angle(th)\n while math.sqrt(((user_x - x) * (user_x - x)) + ((user_y - y) * (user_y - y))) > deltaball:\n if regions['front'] > 2:\n if math.sqrt(((goal.x - x) * (goal.x - x)) + ((goal.y - y) * (goal.y - y))) > deltaball:\n speed.linear.x = 0.8\n speed.angular.z = 0\n pub.publish(speed)\n rate.sleep()\n else:\n speed.linear.x = 0\n speed.angular.z = 0\n pub.publish(speed)\n rate.sleep()\n # Let's find possible path\n if regions['right'] > 3:\n # Right path available\n obs_avoid(-0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n elif regions['left'] > 3:\n # front left path available\n obs_avoid(0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n elif regions['fright'] > 3:\n # Right path available\n obs_avoid(-0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n elif regions['fleft'] > 3:\n # front left path available\n obs_avoid(0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n else:\n obs_avoid(-0.5)\n inc_x = goal.x - x\n inc_y = goal.y - y\n th = math.atan2(inc_y, inc_x)\n go_to_angle(th)\n rate.sleep()\n speed.linear.x = 0\n speed.angular.z = 0\n pub.publish(speed)","def shortest_path(source, target):\n #although lecture checks for goal when a node is popped off the frontier, efficiency of search can be improved\n #by checking for a goal as nodes are ADDED. If goal detected, don't add it to frontier, just return the solution\n #immediately\n\n #create start point\n start = Node(state = source, parent = None, action = None)\n frontier = QueueFrontier()\n frontier.add(start)\n\n #create explored set\n explored = set()\n\n while True:\n #if nothing left in frontier, no path exists\n if frontier.empty():\n return None\n\n #choose a node from the frontier\n node = frontier.remove()\n #if node is goal, we have solution\n\n #add neighbors 2 frontier using function THATS ALR THERE DUMMY\n for (movie, star) in neighbors_for_person(node.state):\n newNode = Node(state = star, parent = node, action=movie)\n if not frontier.contains_state(newNode) and newNode.state not in explored:\n if newNode.state == target:\n #reverse the solution\n solution = []\n while newNode.parent is not None:\n actionTuple = (newNode.action, newNode.state)\n solution.append(actionTuple)\n newNode = newNode.parent\n solution.reverse()\n return solution\n else: frontier.add(newNode)\n\n #mark state as explored\n explored.add(node.state)","def recommend_pathway(user_jobs, job_graph, goal_state, min_likelihood_thr):\r\n user_jobs_for_mdp = [user_jobs[0]]\r\n mdp = MDP(job_graph, user_jobs_for_mdp, goal_state, min_likelihood_thr=min_likelihood_thr)\r\n return mdp.solve_mdp()","def get_all_social_paths(self, user_id):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n # BFTs starting at user_id, return first path to every reachable person\n q = [[user_id]]\n while q:\n path = q.pop(0)\n person = path[-1]\n # add the person and the path to the person\n for friend in self.friendships[person]:\n if friend not in visited and friend != user_id:\n q.append(path + [friend])\n visited[friend] = path + [friend]\n\n return visited","def compute_user_pathway(user_jobs, job_graph, debug=False):\r\n pathway = []\r\n for i, job in enumerate(user_jobs):\r\n if i == 0:\r\n continue\r\n cluster, _ = job_graph.assign_job_to_jobgraph_state(job)\r\n pathway.append(cluster)\r\n return pathway","def dijkstra_shortest_path(graph_object: Graph, start_node: int) -> List[float]:\n\n def init_distance(g: Graph, s: int) -> List[float]:\n d = [inf] * len(g) # type: List[float]\n d[s] = 0.0\n return d\n\n # Assign a key value to all vertices in the input graph.\n # Initial value is inf for all but the first one\n distance = init_distance(graph_object, start_node)\n priority_queue = MinHeap.build([(i, priority) for i, priority in enumerate(distance)])\n\n while priority_queue: # Priority queue has O(V) elements\n min_index = priority_queue.pop() # O(log(V))\n for edge in graph_object.graph[min_index]: # A graph has at most 2E Edges in adjacency list, so O(E)\n source, destination, edge_weight = edge\n if priority_queue.contains_item(destination) and distance[source] + edge_weight < distance[destination]:\n distance[destination] = distance[source] + edge_weight\n priority_queue.push(item=destination, priority=edge_weight) # O(log(V))\n\n return distance","def get_all_social_paths(self, user_id):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n # graphs=Graph()\n # for i in self.users:\n # graphs.add_vertex(i)\n \n # for i in self.users:\n # for x in self.friendships[i]:\n # graphs.add_edge(i,x)\n\n # for i in graphs.vertices:\n # if graphs.bfs(i,user_id):\n # visited[i]=graphs.bfs(i,user_id)\n queue=Queue()\n queue.enqueue([user_id])\n while queue.size()>0:\n path=queue.dequeue()\n current_user = path[-1]\n if current_user not in visited:\n visited[current_user]=path\n for ID in self.friendships[current_user]:\n new_path=list(path)\n new_path.append(ID)\n queue.enqueue(new_path)\n return visited","def generate_graph(\n locs_user, sp_user, study, trips_user=None, gap_threshold=None\n):\n # print(sp_user[\"location_id\"])\n # print(locs_user)\n AG = ActivityGraph(\n sp_user,\n locs_user,\n trips=trips_user,\n gap_threshold=gap_threshold,\n )\n # Add purpose feature\n if study == \"geolife\" or study == \"yumuv_graph_rep\":\n AG.add_node_features_from_staypoints(\n sp_user, agg_dict={\"started_at\": list, \"finished_at\": list}\n )\n else:\n AG.add_node_features_from_staypoints(\n sp_user,\n agg_dict={\n \"started_at\": list,\n \"finished_at\": list,\n \"purpose\": list,\n },\n )\n return AG","def shortestpath(graph,start,end,visited=[],distances={},predecessors={}):\r\n # detect if first time through, set current distance to zero\r\n try:\r\n if not visited: \r\n distances[start]=0\r\n # if we've found our end node, find the path to it, and return\r\n if start==end:\r\n path=[]\r\n while end != None:\r\n path.append(end)\r\n end=predecessors.get(end,None)\r\n return distances[start], path[::-1]\r\n # process neighbors as per algorithm, keep track of predecessors\r\n for neighbor in graph[start]:\r\n if neighbor not in visited:\r\n neighbordist = distances.get(neighbor,float(math.inf))\r\n tentativedist = distances[start] + graph[start][neighbor]\r\n if tentativedist < neighbordist:\r\n distances[neighbor] = tentativedist\r\n predecessors[neighbor]=start\r\n # neighbors processed, now mark the current node as visited \r\n visited.append(start)\r\n # finds the closest unvisited node to the start \r\n unvisiteds = dict((k, distances.get(k,float(math.inf))) for k in graph if k not in visited)\r\n closestnode = min(unvisiteds, key=unvisiteds.get)\r\n # now take the closest node and recurse, making it current\r\n except:\r\n pygame.time.delay(700)\r\n SCREEN.fill((0,0,0))\r\n displayText(\"Path Not Found\",400,200)\r\n displayText(\"Try Again....!!\",400,240)\r\n pygame.display.update()\r\n pygame.time.delay(1500)\r\n pygame.quit()\r\n sys.exit() \r\n return shortestpath(graph,closestnode,end,visited,distances,predecessors)","def constructShortestPath(self):","def user_path(a, b):\n tx = cypher_transaction()\n\n # Limit the number of relationships in the path?\n # p = shortestPath((a)-[*..15]-(b))\n query = \"\"\"\n MATCH\n (a:user {username:{username_a}}),\n (b:user {username:{username_b}}),\n p = shortestPath((a)-[]->(b))\n RETURN LENGTH(p), p\n \"\"\"\n params = {\n 'username_a': a['username'],\n 'username_b': b['username']\n }\n tx.append(query, parameters=params)\n results = _first(tx.commit())\n paths = []\n for record in results:\n length, path = record.values\n m = \"There are {0} hops from {1} to {2}:\\n\"\n print(m.format(length, a['name'], b['name']))\n for rel in path.relationships:\n print(\" ({0})-[:{1}]->({2})\".format(\n rel.start_node['name'],\n rel.type,\n rel.end_node['name']\n ))\n paths.append(path)\n return paths","def getPath(\n self,\n source,\n dest,\n as_nodes=False,\n ):\n\n self.dist = {} # A map from nodes to their labels (float)\n self.predecessor = {} # A map from a node to a node\n\n # Initialize the distance labels to \"infinity\"\n\n vertices = self.g.nodes()\n for vertex in vertices:\n self.dist[vertex] = self.inf\n self.predecessor[vertex] = source\n\n # Further set up the distance from the source to itself and\n # to all one hops away.\n\n self.dist[source] = 0.0\n if self.g.is_directed():\n outEdges = self.g.out_edges([source])\n else:\n outEdges = self.g.edges([source])\n for edge in outEdges:\n self.dist[edge[1]] = self.g[edge[0]][edge[1]][self.wt]\n\n s = set(vertices)\n s.remove(source)\n currentMin = self._findMinNode(s)\n if currentMin == None:\n return None\n s.remove(currentMin)\n while currentMin != dest and len(s) != 0 and currentMin != None:\n if self.g.is_directed():\n outEdges = self.g.out_edges([currentMin])\n else:\n outEdges = self.g.edges([currentMin])\n for edge in outEdges:\n opposite = edge[1]\n if self.dist[currentMin] + self.g[edge[0]][edge[1]][self.wt] \\\n < self.dist[opposite]:\n self.dist[opposite] = self.dist[currentMin] \\\n + self.g[edge[0]][edge[1]][self.wt]\n self.predecessor[opposite] = currentMin\n s.add(opposite)\n\n currentMin = self._findMinNode(s)\n\n # print \"Current min node {}, s = {}\".format(currentMin, s)\n\n if currentMin == None:\n return None\n s.remove(currentMin)\n\n # Compute the path as a list of edges\n\n currentNode = dest\n predNode = self.predecessor.get(dest)\n node_list = [dest]\n done = False\n path = []\n while not done:\n path.append((predNode, currentNode))\n currentNode = predNode\n predNode = self.predecessor[predNode]\n node_list.append(currentNode)\n done = currentNode == source\n node_list.reverse()\n if as_nodes:\n return node_list\n else:\n return path","def get_all_social_paths(self, user_id): #each user v v each path V V\n # output example- {1: [1], 8: [1, 8], 10: [1, 10], 5: [1, 5], 2: [1, 10, 2], 6: [1, 10, 6], 7: [1, 10, 2, 7]}\n visited = {} # Note that this is a dictionary, not a set\n # Need to do a bfs using the user id\n # first step is to traverse the graph and record all the vertices as keys in visited using bft\n # then take those keys and use bfs on each, using user_id as the starting node and and the key as\n # the destination node\n\n # Modification of BFT\n # create an empty dict\n # q = Queue()\n q = []\n\n # init enqueue the starting node\n q.append(user_id)\n\n while len(q) > 0:\n # Dequeue the first item\n v = q.pop(0)\n # If it's not been visited:\n if v not in visited:\n # Mark as visited (i.e. add to the visited set)\n visited[v] = []\n\n # Do something with the node\n print(f\"Visited {v}\")\n\n # Add all neighbors to the queue\n for next_vert in self.friendships[v]:\n q.append(next_vert)\n\n # once visited is filled, then we start the bfs\n #print('vv',visited)\n possible_paths = {}\n #run a bfs for each key in visited\n for v in visited:\n possible_paths[v] = []\n \n if v == user_id:\n visited[v] = [user_id]\n\n path = []\n while len(path) < len(visited):\n\n # Add all neighbors to the queue\n for next_vert in self.friendships[v]:\n print(possible_paths[v])\n # copy the path\n # temp_path = list(path)\n # temp_path.append(next_vert)\n # add path to possible_paths\n path.append(next_vert)\n\n possible_paths[v].append(path) # HAVE TO USE QUEUE OR STACK, THEY ENSURE THE NEIGHBORS\n # FOLLOW THE CORRECT ORDER WHEN LOOPING \n\n if v == path[-1]:\n \n # IF SO, RETURN PATH\n visited[v] = path\n break\n\n # for x in visited:\n # bfs(user_id, x)\n # visited[x].add(path)\n \n print('pct of total users in network', len(visited[1])/len(visited))\n print('degrees of separation', len(visited[1]) - 1)\n return visited","def shortestPath(self, source, target):\n dist = {}\n prev = {}\n q = []\n for y,a in enumerate(self.sm):\n for x,b in enumerate(self.sm[y]):\n dist[(x,y)] = sys.maxint\n prev[(x,y)] = None\n q.append((x,y))\n dist[source] = 0\n\n while len(q) is not 0:\n # find the node with minimum value (u)\n d = deepcopy(dist)\n while True:\n b = dict(map(lambda item: (item[1],item[0]), d.items()))\n u = b[min(b.keys())]\n if u not in q:\n d.pop(u)\n else:\n break\n\n if dist[u] == sys.maxint: # remaining nodes are inaccessible\n break\n\n q.remove(u)\n\n\n if u == target: # target found\n break\n\n for v in self.getNeighbors(u):\n alt = dist[u] + 1\n if alt < dist[v]:\n dist[v] = alt\n prev[v] = u\n\n s = []\n u = target\n while prev[u] is not None:\n s.append(u)\n u = prev[u]\n s.reverse()\n\n return s","def create_path(network, user_A, user_B, path=[]):\n path = path + [user_A] # all paths include starting node\n if user_A == user_B: # id the last node is user_B a valid path exists\n return path # base case\n for node in network[user_A][0]:\n if node not in path: # otherwise path is an infinite loop\n path = create_path(network, node, user_B, path)\n if path: # after the recursion hits the base case\n return path\n return None","def shortestPath(graph, start, end, maxOutdistance, toPrint = False):\r\n return DFS(graph, start, end, [], None, sys.maxsize, sys.maxsize, 0, maxOutdistance, toPrint)","def shortest_path(self, from_node):\n\n\t\t# add the final nodes when algorithm goes past the target prediction time \n\t\tif self.get_real_time(from_node.time) >= self.target:\n\t\t\tself.g.add_node(from_node, usage_cost=0, best_action=None, max_demand=self.starting_max_demand)\n\t\t\treturn\n\n\t\t#create the action set (0 is for do nothing, 1 is for cooling, 2 is for heating)\n\t\taction_set = self.safety.safety_actions(from_node.temps)\n\t\t\n\t\t#iterate for each available action\n\t\tfor action in action_set:\n\t\t\t\n\t\t\t# predict temperature and energy consumption of action\n\t\t\tnew_temperature = []\n\t\t\tconsumption = []\n\t\t\tfor i in range(self.noZones):\n\t\t\t\tnew_temperature.append(self.th.next_temperature(from_node.temps[i], action[i], zone=i))\n\t\t\t\tconsumption.append(self.energy.calc_cost(action[i], from_node.time/self.interval))\n\t\t\t\t\n\t\t\tif self.safety.safety_check(new_temperature) and len(action_set) > 1:\n\t\t\t\tcontinue\n\n\t\t\t# create the node that describes the predicted data\n\t\t\tnew_node = Node(\n\t\t\t\ttemps=new_temperature,\n\t\t\t\ttime=from_node.time + self.interval\n\t\t\t)\n\n\t\t\t# calculate interval costs\n\t\t\tdemand_balancer = (((self.target - self.get_real_time(from_node.time/self.interval)).total_seconds() / 60) / self.billing_period)\n\t\t\tinterval_usage_cost = sum(consumption) + self.interval_usage[int(from_node.time/self.interval)]\n\t\t\tinterval_demand = self.interval_demand[int(from_node.time/self.interval)]\n\n\t\t\t#calculate interval discomfort\n\t\t\tdiscomfort = [0] * self.noZones\n\n\t\t\tfor i in range(self.noZones):\n\t\t\t\tdiscomfort[i] = self.disc.disc(new_temperature[i], self.occ.occ(from_node.time/self.interval), from_node.time)\n\n\t\t\t# create node if the new node is not allready in graph\n\t\t\t# recursively run shortest path for the new node\n\t\t\tif new_node not in self.g:\n\t\t\t\tself.g.add_node(new_node, usage_cost=np.inf, best_action=None, max_demand=np.inf)\n\t\t\t\tself.shortest_path(new_node)\n\n\t\t\t# calculate path costs\n\t\t\tthis_path_demand = max(self.g.node[new_node]['max_demand'], interval_demand)\n\t\t\tthis_path_demand_cost = demand_balancer * this_path_demand\n\n\t\t\tinterval_overall_cost = ((1 - self.l) * (interval_usage_cost + this_path_demand_cost)) + (self.l * (sum(discomfort)))\n\n\t\t\tthis_path_cost = self.g.node[new_node]['usage_cost'] + interval_overall_cost\n\n\t\t\t# add the edge connecting this state to the previous\n\t\t\tself.g.add_edge(from_node, new_node, action=action)\n\n\t\t\t# choose the shortest path\n\t\t\tif this_path_cost <= self.g.node[from_node]['usage_cost']:\n\t\t\t\tif this_path_cost == self.g.node[from_node]['usage_cost'] and self.g.node[from_node]['best_action'] == '0':\n\t\t\t\t\tcontinue\n\t\t\t\tself.g.add_node(from_node, best_action=new_node, usage_cost=this_path_cost, max_demand=this_path_demand)","def __dikjstra(self, start_node):\n visited = []\n unvisited = [x for x in self.__node]\n shortest_dist_from_start_node = 0\n current_node = start_node\n\n current_node.setShortestDist(shortest_dist_from_start_node)\n\n while current_node:\n #check unvisited neighbor\n for neighbor_node, distance in current_node.getNeighbors().items():\n #print(neighbor_node.getId(), distance) troubleshoot je ni\n if neighbor_node in visited:\n continue\n\n #add up shortest_dist_from_start_node with distance from neighbor distance\n calc_dist = shortest_dist_from_start_node + distance\n\n if calc_dist < neighbor_node.getShortestDist():\n neighbor_node.setShortestDist(calc_dist)\n neighbor_node.setPrevNode(current_node)\n\n # add current node to visited array\n visited.append(current_node)\n unvisited.remove(current_node)\n \n #update next node and next shortest distance\n next_shortest_dist_from_start_node = inf\n next_node = None\n\n for unvisited_node in unvisited:\n if unvisited_node.getShortestDist() < next_shortest_dist_from_start_node:\n next_shortest_dist_from_start_node = unvisited_node.getShortestDist()\n next_node = unvisited_node\n\n # update current node and shortest distance from start vertex\n if next_node:\n current_node = next_node\n shortest_dist_from_start_node = next_shortest_dist_from_start_node\n #if there are left over unvisited node\n else: \n if unvisited:\n current_node = unvisited[0]\n else:\n current_node = None","def shortest_route(self, start, finish):\n distances = dict()\n previous = dict()\n nodes = list()\n result = dict()\n best_price = 0\n\n for vertex in self.vertices:\n if vertex == start:\n distances[vertex] = 0\n heapq.heappush(nodes, [0, vertex])\n else:\n distances[vertex] = sys.maxsize\n heapq.heappush(nodes, [sys.maxsize, vertex])\n previous[vertex] = None\n\n while nodes:\n smallest = heapq.heappop(nodes)[1]\n if smallest == finish:\n path = []\n while previous[smallest]:\n path.append(smallest)\n smallest = previous[smallest]\n if len(path) == 1:\n result[\"Cost\"] = distances[finish]\n else:\n result[\"Cost\"] = best_price\n\n if len(path) > 0:\n path.append(start)\n result[\"Path\"] = path[::-1]\n else:\n result = dict()\n\n self.spa_result = result\n return result\n\n if distances[smallest] == sys.maxsize:\n break\n\n for neighbor in self.vertices[smallest]:\n cost = distances[smallest] + self.vertices[smallest][neighbor]\n if cost < distances[neighbor]:\n distances[neighbor] = cost\n previous[neighbor] = smallest\n for n in nodes:\n if n[1] == neighbor:\n n[0] = cost\n best_price = cost\n break\n heapq.heapify(nodes)\n\n return result","def constructShortestPath(self):\r\n sp = []\r\n v = self.t\r\n while self.preds[v]: # is not None\r\n sp.append(v)\r\n v = self.preds[v]\r\n sp.append(self.s) # source\r\n sp.reverse() # to have the path from source to dest and not t to s\r\n return sp, self.graph.getCoords(sp)","def find_path(g: Graph, start: str) -> None:\n g.dijkstra(start)","def plan_path(self, start_point, end_point, map_obj):\n # STUFF FOR TESTING \n if self.enable_vis:\n marker = Marker()\n marker.header.frame_id = \"/map\"\n marker.type = marker.POINTS\n marker.action = marker.ADD\n \n marker.scale.x = 0.1\n marker.scale.y = 0.1\n self.vis_pub.publish(marker)\n \n exploration_bias = 1.0 - self.goal_bias\n final_node = None\n num_existing_path_points_added = 0\n \n self.rrt_star = RRTStar(Node(start_point))\n self.max_iterations = self.rrt_star.max_size\n while self.rrt_star.size <= self.max_iterations:\n p = np.random.uniform()\n if p < exploration_bias:\n \n x_rand = self.map.sample_free_space()\n else:\n if final_node is None:\n x_rand = end_point\n else:\n x_rand = self.branched_from_existing_path(\n final_node,\n depth_underestimate=num_existing_path_points_added\n )\n num_existing_path_points_added += 1\n\n x_nearest = self.rrt_star.nearest(x_rand) # Find the nearest node to x_rand\n\n path = self.map.generate_line_path(x_nearest.value, x_rand, eta=self.eta)\n if path is not None: # no obstacles between x_nearest and x_rand\n x_new = path[-1]\n X_nearby_connectable = self.find_nearby_connectable(x_nearest, x_new)\n\n cost_min, node_min = self.find_best_parent(X_nearby_connectable, x_new)\n\n X_nearby_connectable.remove(node_min) # Remove x_new's parent node from the list of nearby nodes so it is not considered for rewiring\n \n # Create the new node at x_new!\n node_new = self.rrt_star.add_config(node_min, x_new)\n \n if self.enable_vis:\n # FOR TESTING ONLY #\n # Code to publish marker for new node\n ###########################################################################################\n TEMP = Point()\n TEMP.x = x_new[0]\n TEMP.y = x_new[1]\n TEMP.z = .05\n marker.points.append(TEMP)\n \n TEMP = ColorRGBA()\n TEMP.r = 1\n TEMP.g = 0\n TEMP.b = 0\n TEMP.a = 1\n \n marker.colors.append(TEMP)\n \n self.vis_pub.publish(marker)\n ###########################################################################################\n\n self.rewire(cost_min, node_new, X_nearby_connectable)\n \n if np.allclose(node_new.value, end_point, .05, 0) and (final_node is None):#np.array_equal(node_new.value, end_point):\n final_node = node_new\n # reduce exploration bias so that we reinforce the existing path\n exploration_bias = .5\n if VERBOSE:\n print(\"Path found!!!!\")\n print(final_node.cost)\n if rospy.get_time() - self.start_time > self.time_thresh:\n if VERBOSE:\n print(self.rrt_star.size)\n break\n\n \n if final_node is not None:\n if self.enable_vis:\n marker = Marker()\n marker.header.frame_id = \"/map\"\n marker.type = marker.POINTS\n marker.action = marker.ADD\n \n marker.scale.x = 0.1\n marker.scale.y = 0.1\n marker.points = []\n marker.colors = []\n def recur(node):\n if self.enable_vis:\n TEMP = Point()\n TEMP.x = node.value[0]\n TEMP.y = node.value[1]\n TEMP.z = .05\n marker.points.append(TEMP)\n \n TEMP = ColorRGBA()\n TEMP.r = 1\n TEMP.g = 0\n TEMP.b = 0\n TEMP.a = 1\n \n marker.colors.append(TEMP)\n \n \n self.trajectory.points.append([node.value[0], node.value[1]])\n parent = node.parent\n if parent is not None:\n recur(parent)\n recur(final_node)\n self.trajectory.points.reverse()\n if self.enable_vis:\n self.vis_pub.publish(marker)\n if VERBOSE:\n print (final_node.depth)\n else:\n if VERBOSE:\n print(\"No path found! Please try again.\")\n \n \n \n # publish trajectory\n self.traj_pub.publish(self.trajectory.toPoseArray())\n\n # visualize trajectory Markers\n self.trajectory.publish_viz()","def bfs_shortest_path(graph: dict=g2, start: str = \"1\", goal: str = \"4\") -> list:\n visited = []\n queue = [[start]]\n\n while queue:\n path = queue.pop(0)\n node = path[-1]\n if node not in visited:\n neighbours = graph[node]\n for neighbour in neighbours:\n new_path = path[:]\n new_path.append(neighbour)\n queue.append(new_path)\n if neighbour == goal:\n return new_path\n visited.append(node)\n # No path\n return [\"No Path\"]","def shortestPath(self, G, start, end):\n def flatten(L): # Flatten linked list of form [0,[1,[2,[]]]]\n while len(L) > 0:\n yield L[0]\n L = L[1]\n\n q = [(0, start, ())] # Heap of (cost, path_head, path_rest).\n visited = set() # Visited vertices.\n while True:\n (cost, v1, path) = heapq.heappop(q)\n if v1 not in visited:\n visited.add(v1)\n if v1 == end:\n return list(flatten(path))[::-1] + [v1]\n path = (v1, path)\n for (v2, cost2) in G[v1].items():\n if v2 not in visited:\n heapq.heappush(q, (cost + cost2, v2, path))","def shortest_path(self, start_node_id, end_node_id):\n # convert to lowercase \n start_node_id = start_node_id.lower()\n end_node_id = end_node_id.lower()\n\n # validate whether both node id exist \n start_node = None\n end_node = None\n \n # for loop to convert id to obj --> can be used for passing param in dijkstra method\n for x in self.__node:\n if x.getId() == start_node_id:\n start_node = x\n if x.getId() == end_node_id:\n end_node = x\n \n #validate if the id exists\n if start_node == None or end_node == None:\n print(\"The ID does not exist\")\n return\n\n # execute dikstra method\n self.__dikjstra(start_node)\n\n output_node = [end_node.getId()]\n temp_end_node = end_node # to be used to display shortest distance from start node\n\n while end_node.getId() != start_node.getId():\n end_node = end_node.getPrevNode()\n if end_node == None:\n break\n output_node.insert(0,end_node.getId())\n\n #if first element of otput_node is not the start node id, display error, other wise, display the \n if output_node[0] != start_node_id:\n print(\"Error. No path between vertex \"+start_node_id+\" and vertex \"+end_node_id)\n return \n \n # shortest path \n print(\"Shortest Path: \")\n \n for node in output_node:\n if node == end_node_id:\n print(node, end=\"\")\n break \n\n print(node+' -> ', end=\"\")\n \n # shortest distance\n print ('\\nShortest distance to '+ temp_end_node.getId() + ' = ' + str(temp_end_node.getShortestDist()) )\n\n #display table\n self.__display_table(start_node)","def _bidirectional_shortest_path(G, source, target, exclude):\n # call helper to do the real work\n results = _bidirectional_pred_succ(G, source, target, exclude)\n pred, succ, w = results\n\n # build path from pred+w+succ\n path = []\n # from source to w\n while w is not None:\n path.append(w)\n w = pred[w]\n path.reverse()\n # from w to target\n w = succ[path[-1]]\n while w is not None:\n path.append(w)\n w = succ[w]\n\n return path","def get_all_social_paths(self, user_id):\n if len(self.friendships) > 0:\n visited = {}\n q = Queue()\n q.enqueue([user_id])\n\n while q.size() > 0:\n curr_path = q.dequeue()\n curr_vertex = curr_path[-1]\n\n if curr_vertex not in visited:\n visited[curr_vertex] = curr_path\n\n for friend in self.friendships[curr_vertex]:\n path_copy = curr_path[:]\n path_copy.append(friend)\n q.enqueue(path_copy)\n\n return visited\n\n else:\n print(\"There are currently no friendship paths in the network\")","def path(most_important_up, most_important_down, total_distance, to_source2, to_source1):\n\n if total_distance == min(total_distance, to_source2[0], to_source1[0]):\n return source_to_source(most_important_up, most_important_down), total_distance\n elif to_source2[0] == min(total_distance, to_source2[0], to_source1[0]):\n return most_important_to_source(to_source2[1]), to_source2[0]\n else:\n return most_important_to_source(to_source1[1], up=False), to_source1[0]","def get_distance(restuarant, user):\n lat1 = math.radians(restuarant[1])\n lon1 = math.radians(restuarant[0])\n lat2 = math.radians(user[1])\n lon2 = math.radians(user[0])\n dlon = lon1 - lon2\n dlat = lat1 - lat2\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * \\\n math.cos(lat2) * math.sin(dlon / 2) ** 2\n dist = 2 * math.asin(math.sqrt(a))*6371\n return dist","def shortJourney(Alist,s,d):\n \"\"\"Find shortest distances to s in weighted graph, G\"\"\"\n \n #Initialize dictionaries\n dinit = 10**6\n Edict = {} #Explored nodes\n Udict = {} #Unexplored nodes\n path = [[] for l in Alist]\n\n Alen = len(Alist) #length of Alist\n dinits = [dinit]*Alen #list of airport indexes\n Udict = dict(zip(list(range(Alen)),dinits)) #zip into dictionary\n Udict[s] = 0\n path[s] = [s]\n \n #Main search\n while len(Udict)>0:\n #Find node with min d in Udict and move to Edict\n dmin = dinit\n for n,w in Udict.items():\n if w<dmin:\n dmin=w\n nmin=n\n Edict[nmin] = Udict.pop(nmin)\n print(\"moved node\", nmin)\n\n #Update provisional distances for unexplored neighbors of nmin \n for item in Alist[nmin]: #nminth element is a list of two element tuples (node, weight)\n n = item[0] #first elt of tuple is node/neighbour\n w = item[1] #2nd elt is density/weigh\n #for n,w in etc_______________________-\n \n if n in Edict:\n pass\n elif n in Udict:\n #key difference below\n dcomp = (w+dmin) #take sum as you go along\n if dcomp<Udict[n]:\n print(Udict)\n Udict[n]=dcomp\n path[n] = path[nmin] + [n]\n print(path) \n if nmin == d: #if current node is destination\n return [path[d],Edict[d]]\n return [] #no path","def creat_graph_and_calc_dist_verb(A):\n return gp.graph_shortest_path(A, method='auto', directed=False)","def dijkstra(graph,start,goal):\n shortest_distance = {}\n predecessor = {}\n unseenNodes = graph\n path = []\n for node in unseenNodes:\n shortest_distance[node] = infinity\n shortest_distance[start] = 0\n \n while unseenNodes:\n minNode = None\n for node in unseenNodes:\n if minNode is None:\n minNode = node\n elif shortest_distance[node] < shortest_distance[minNode]:\n minNode = node\n \n for childNode, weight in graph[minNode].items():\n \n if weight + shortest_distance[minNode] < shortest_distance[childNode]:\n shortest_distance[childNode] = weight + shortest_distance[minNode]\n predecessor[childNode] = minNode\n unseenNodes.pop(minNode)\n \n currentNode = goal\n while currentNode != start:\n try:\n path.insert(0,currentNode)\n currentNode = predecessor[currentNode]\n except KeyError:\n print('Path not reachable')\n break\n path.insert(0,start)\n if shortest_distance[goal] != infinity:\n return path","def DFS1(graph, start, end, path=[], shortest=None):\n path = path + [start]\n print 'Current DFS path:', printPath(path)\n if start == end:\n return path\n for node in graph.childrenOf(start):\n if node not in path: #avoid cycles\n if shortest == None or len(path) < len(shortest):\n newPath = DFS1(graph, node, end, path, shortest)\n if newPath != None:\n shortest = newPath\n return shortest","def get_path(prevs, goal, start):\n path = OD({goal: 0})\n cur = goal\n while cur != start:\n (cost, node) = prevs.get(cur)\n if node == None or node in path:\n print(\"ERROR: No path found from %s -> %s\" % (start, goal))\n return (0, None)\n path[node] = path[cur] + cost\n cur = node\n return (path[start], path.keys()[::-1])","def tot_grad_obj(self,x0,gradient):\n tmp = self.tot_objective(self.rebuild_param(x0,**self.fixed))\n obj = tmp[0]\n if gradient:\n grad =self.fix_par(tmp[1], **self.fixed)[0]\n return obj,grad\n else:\n return obj","def DFS(graph, start, end, path, shortest,shortest_path_length,shortest_out_dist,\\\r\n pathlength,maxOutdistance,toPrint = False):\r\n \r\n \r\n path = path + [start]\r\n totalpathlength=getTotalPathLength(path)\r\n outerpathlength=getOutPathLength(path)\r\n \r\n \r\n if toPrint:\r\n pass \r\n \r\n if start == end and outerpathlength<=maxOutdistance and (totalpathlength <= shortest_path_length):\r\n print('A Shorter Path found',path,[totalpathlength],[outerpathlength])\r\n return path\r\n for node in graph.childrenOf(start):\r\n if node not in path: #avoid cycles\r\n \r\n \r\n \r\n if shortest== None or totalpathlength <= shortest_path_length:\r\n if totalpathlength <shortest_path_length and outerpathlength<=maxOutdistance :\r\n newPath = DFS(graph, node, end, path, shortest, shortest_path_length,\\\r\n shortest_out_dist,pathlength, maxOutdistance, toPrint)\r\n \r\n if newPath != None:\r\n \r\n shortest = newPath\r\n shortest_path_length=getTotalPathLength(shortest)\r\n shortest_out_dist=getOutPathLength(shortest)\r\n \r\n\r\n \r\n return shortest","def shortestPath(self, origin):\n backlink = dict()\n cost = dict()\n \n for i in self.node:\n backlink[i] = utils.NO_PATH_EXISTS\n cost[i] = utils.INFINITY\n cost[origin] = 0\n \n scanList = [self.link[ij].head for ij in self.node[origin].forwardStar]\n \n while len(scanList) > 0:\n i = scanList[0]\n scanList.remove(i)\n labelChanged = False\n for hi in self.node[i].reverseStar:\n h = self.link[hi].tail\n if h < self.firstThroughNode and h != origin:\n continue\n tempCost = cost[h] + self.link[hi].cost\n if tempCost < cost[i]:\n cost[i] = tempCost\n backlink[i] = hi\n labelChanged = True\n if labelChanged == True:\n scanList.extend([self.link[ij].head for ij in self.node[i].forwardStar\n if self.link[ij].head not in scanList])\n \n return (backlink, cost)","def shortest_path(self):\n\t\t#dict that will hold the cost of traveling to each station\n\t\t#add the initial cost of the starting station, which is 0\n\t\tD = {0:0}\n\n\t\t#add all of our dict keys (stations) to our queue\n\t\tstation_queue = self.station_graph.keys()\n\n\t\t#sort the keys! since the graph is directed and acyclic, the stations\n\t\t#can be explored one at a time, in order, without having to adjust\n\t\t#for the lowest distance value via priority queue.\n\t\t#\n\t\t#sort them with reverse=True so that they can be popped from the\n\t\t#end of the list instead of from the beginning. This should save\n\t\t#some cpu time.\n\t\tstation_queue.sort(reverse=True)\n\t\twhile len(station_queue) > 0:\n\n\t\t\tstation = station_queue.pop() #grab the next node in the queue\n\n\t\t\tfor next_st, next_cost in self.station_graph[station].iteritems():\n\t\t\t\t#loops through the current station's neighbors, and calculates\n\t\t\t\t#their costs from the starting node, making sure to store\n\t\t\t\t#the lowest cost in our D dict\n\t\t\t\talt = D[station] + next_cost #sum the costs\n\t\t\t\tif not D.has_key(next_st) or alt < D[next_st]:\n\t\t\t\t\t#if there is no cost on record, or if the newly calculated\n\t\t\t\t\t#cost is lower than the currently recorded one, then\n\t\t\t\t\t#record the newly calculated cost as the lowest\n\t\t\t\t\tD[next_st] = alt #set the cost to get to next_st\n\n\t\treturn D[self.final_stop]","def dfs(self, starting_vertex, destination_vertex):\n pass # TODO","def shortest_path(self, source, destination, parameter=None):\n paths = []\n for path in self.graph.shortest_paths(source, destination, parameter):\n paths.append({'hops': path})\n return jsonify({'paths': paths})","def shortestPath(G, start, end):\n\n D, P = Dijkstra(G, start)\n print(D)\n print(P)\n Path = []\n while 1:\n Path.append(end)\n if end == start: break\n end = P[end]\n Path.reverse()\n return Path","def shortestPath( self, source, target, weight = None ):\n if weight == None:\n return nx.shortest_path(self._G, source, target)\n else:\n return nx.shortest_path(self._G, source, target, weight = weight)","def reconstruct_path(goal: Vector2D, prev_node: dict) -> list:\n path = []\n prev = prev_node[goal] # remove 'goal' from path\n \n while prev != None:\n path.append(prev)\n prev = prev_node[prev]\n \n path = path[:-1] # remove 'start' from path\n path.reverse()\n return path","def shortest_path(graph, source, target):\n return shortest_path_recursive(graph, source, target, set())","def DFS(graph, start, end, path, shortest, toPrint=False):\n path = path + [start]\n if toPrint:\n print('Current DFS path:', printPath(path))\n if start == end:\n return path\n for node in graph.childrenOf(start):\n if node not in path: # avoid cycles\n if shortest == None or len(path) < len(shortest):\n newPath = DFS(graph, node, end, path, shortest, toPrint)\n if newPath != None:\n shortest = newPath\n elif toPrint:\n print('Already visited', node)\n return shortest","def getAllSocialPaths(self, userID):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n q = Queue()\n q.put([userID])\n\n while not q.empty():\n user_path = q.get()\n user = user_path[-1]\n\n if user not in visited.keys():\n visited[user] = user_path\n for friend in self.friendships[user]:\n new_path = user_path[::]\n new_path.append(friend)\n q.put(new_path)\n\n # get average degree of separation per user\n degrees = 0\n for key, item in visited.items():\n degrees += len(item)\n \n print('average degree of separation', degrees/len(self.users))\n\n return visited","def directedDFS(digraph, start, end, maxTotalDist, maxDistOutdoors, path=[],shortest = None):\n #TODO\n #assumes graph is a Digraph\n #assumes start and end are nodes in graph\n def Dist(path):\n result = 0\n if path == None:\n return result\n if len(path) == 0:\n return result\n for i in range(len(path)-1):\n src = path[i]\n dest = path[i+1]\n for item in digraph.edges[src]:\n if item[0] == dest:\n result += item[1]\n return result \n \n # Helper function to calculate Total Outdoor Distance in a path\n def Out(path):\n result = 0\n if path == None:\n return result \n if len(path) == 0:\n return result\n for i in range(len(path)-1):\n src = path[i]\n dest = path[i+1]\n for item in digraph.edges[src]:\n if item[0] == dest:\n result += item[2]\n return result\n \n# Helper function using DFS method\n def DFS(graph, start, end, maxD, maxO, path = []):\n path = path + [start]\n if start == end:\n return path\n shortest = None\n distShort = maxD\n outShort = maxO\n for node in graph.childrenOf(start):\n if node not in path: #avoid cycles\n newPath = DFS(graph, node, end, maxD, maxO, path)\n distNew = Dist(newPath)\n outNew = Out(newPath)\n if newPath!= None and distNew <= maxD and outNew <= maxO:\n if not shortest or distNew < distShort: #check if shorter than shortest\n shortest = newPath\n distShort = distNew\n outShort = outNew\n return shortest\n\n result = DFS(digraph, start, end, maxTotalDist, maxDistOutdoors)\n if result == None:\n raise ValueError\n else:\n return result","def shortestPath(G,start,end):\n\n D,P = Dijkstra(G,start)\n Path = []\n while 1:\n Path.append(end)\n if end == start: break`\u001b`\n end = P[end]\n Path.reverse()\n return Path","def dfs(self, starting_vertex, destination_vertex):\n # TODO","def random_path_forward(g, source, path_len, start_time=0, cost=1, max_duration=sys.maxint):\n\n # TODO: maybe we can do a v2 that tries to generate another path if the current path is not valid\n # (although we have to take into account that such path may not exist)\n\n current_time = start_time\n path = [source]\n for _ in range(path_len):\n e = list(g.edges(path[-1], data=True))\n if len(e):\n n = choice(e)\n if n[1] in path:\n # print \"Node already visited\"\n return None\n if n[2][\"start\"] + n[2][\"duration\"] >= current_time + cost:\n current_time = max(current_time + cost, n[2][\"start\"] + cost)\n if current_time - start_time < max_duration:\n path.append(n[1])\n else:\n # print \"Too long path\"\n return None\n else:\n # print \"Invalid time path\"\n return None\n else:\n # print \"No neighbors\"\n return None\n\n # print path\n return path[-1]","def shortest_path(self, source, target, via=None, weight='length', bbox=None):\n\n if self._graph_backend == 'networkx':\n return networkx.shortest_path(self._graph, source, target, weight=weight)\n else:\n if isinstance(via, list):\n return self._pgr.get_route(source, target, via_nodes=via, bbox_nodes=bbox)\n else:\n return self._pgr.get_route(source, target)","def dft(self, starting_vertex):\n # Create a s and push starting vertex\n ss = Stack()\n ss.push([starting_vertex])\n # Create a set of traversed vertices\n visited = []\n eldest = [] \n # While stack is not empty:\n while ss.size() > 0:\n # dequeue/pop the first vertex\n path = ss.pop()\n if path[-1] not in visited:\n # DO THE THING!!!!!!!\n # print(path[-1])\n # mark as visited\n visited.append(path[-1])\n print(visited)\n # enqueue all neightbors\n if not self.get_neighbors(path[-1]):\n if starting_vertex == path[-1]:\n return -1\n else:\n # print(\"eldest ancestor:\",path[-1])\n eldest.append(path[-1])\n\n for next_vert in self.get_neighbors(path[-1]):\n new_path = list(path)\n # print(new_path)\n new_path.append(next_vert)\n ss.push(new_path)\n \n return min(eldest)","def shortest_path(self, source, target):\r\n key = self.d.keys()\r\n #check that endpoints are in graph\r\n if source not in key or target not in key:\r\n raise KeyError(str(source) + \" and \" + str(target) + \" must be in graph\")\r\n #initialize V,Q and M\r\n V = []\r\n vis = dict()\r\n Q = deque()\r\n Q.append(source)\r\n M = set(source)\r\n #while target has not been visited\r\n while target not in M:\r\n #take first element of Q\r\n current = Q.popleft()\r\n #add element to visited\r\n V.append(current)\r\n neighbors = self.d[current]\r\n #for each neighbor of element\r\n for n in neighbors:\r\n #if element has not been checked, add it to queue\r\n #also save traveled edge in visited\r\n if n not in M:\r\n Q.append(n)\r\n vis.update({n:current})\r\n M.add(n)\r\n L = [target]\r\n #reverse the order of the traveled edges\r\n while L[-1] in vis.keys():\r\n L.append(vis[L[-1]])\r\n return L[::-1]","def dfs_recursive(self, starting_vertex, destination_vertex):\n pass # TODO","def dfs_recursive(self, starting_vertex, destination_vertex):\n pass # TODO","def dykstra(self,origin):\n G = nx.Graph()\n G.add_node(origin,shortest_path = 0)\n active_nodes = [origin]\n\n while len(active_nodes) > 0:\n a_n = active_nodes.pop() # active node\n\n for n in self.neighbors.get(a_n,[]): #catch no neighbor exception\n d = self.difference(n, origin)**4 # increase cost of extra distance\n this_path = G.node[a_n]['shortest_path'] + d\n\n if this_path < self.epsilon:\n if n in G.node: # seen this node before\n if this_path < G.node[n]['shortest_path']: # if this path is better than previous best path\n G.node[n]['shortest_path'] = this_path\n G.add_edge(a_n,n) # add edge just because\n continue\n\n G.add_node(n,shortest_path = this_path) # if this is new, add it to graph\n G.add_edge(a_n,n) # add edge just because\n active_nodes.append(n) # add new active node\n return G","def bfsShortestPath(graph, start, goal):\n\n # set up a path list\n path = [start]\n\n # return a simple path if start is the goal\n if start == goal:\n return path\n\n # list to keep track of all visited nodes\n explored = []\n\n # the FIFO queue\n queue = []\n\n # add the first path to the queue\n queue.append(path)\n\n # keep looping until there are no nodes still to be checked\n while len(queue) > 0:\n\n # pop first item from queue (FIFO)\n path = queue.pop(0)\n\n # retrieve the last node from the path list\n node = path[-1]\n\n # check if the node has already been explored\n if node not in explored:\n\n # add node to list of checked nodes\n explored.append(node)\n\n # get neighbours if node is present, otherwise default to empty list\n neighbours = graph.get(node, [])\n\n # go through all neighbour nodes\n for neighbour in neighbours:\n # make a copy of the current path\n path1 = path[:]\n\n # add this neighbour to the path\n path1.append(neighbour)\n\n # return path if neighbour is goal\n if neighbour == goal:\n return path1\n\n # push it onto the queue for further exploration\n queue.append(path1)\n\n # we couldn't find the goal... :(\n return None","def shortest_path(M, start, goal):\n\n print(\"shortest path called\")\n\n came_from = {}\n g_score = {}\n came_from[start] = None\n g_score[start] = 0\n open_heap = []\n heappush(open_heap, (0, start))\n\n while open_heap:\n current = heappop(open_heap)[1]\n\n if current == goal:\n break\n\n for neighbor in M.roads[current]:\n new_g_score = g_score[current] + heuristic(M.intersections[current], M.intersections[neighbor])\n\n if neighbor not in g_score or new_g_score < g_score[neighbor]:\n came_from[neighbor] = current\n g_score[neighbor] = new_g_score\n heappush(open_heap, (new_g_score, neighbor))\n\n optimal_path = []\n node = goal\n\n while came_from[node]:\n optimal_path.append(node)\n node = came_from[node]\n else:\n optimal_path.append(node)\n\n optimal_path.reverse()\n\n return optimal_path","def least_cost_path(G, start, dest, cost):\n\n # Create a priority queue\n todo = pqueue.PQueue()\n todo.update(start, 0);\n\n # v in visited when the vertex v's least cost from start has been determined\n visited = set()\n\n # parent[v] is the vertex that just precedes v in the path from start to v\n parent = {}\n\n while todo and (dest not in visited):\n\n # priority queue operation\n # remove smallest estimated cost vertex from todo list\n (cur, c) = todo.pop_smallest()\n\n # it is now visited, and will never have a smaller cost\n visited.add(cur)\n\n for n in G.adj_to(cur):\n if n in visited: continue\n if todo.update(n, c+cost((cur,n))):\n parent[n] = cur\n\n # now, if there is a path, extract it. The graph may be disconnected\n # so in that case return None\n if dest not in visited:\n return None\n\n path = [dest]\n cur = dest\n while start not in path:\n cur = parent[cur]\n path.append(cur)\n\n path.reverse()\n return path","def fastest_path_estimation(sol):\n\n class Path:\n def __init__(self, places, graph):\n self.g = 0 # current cost\n self.graph = graph\n self.visited = [places[0]] # list of already visited attractions\n self.not_visited = copy.deepcopy(places[1:]) # list of attractions not yet visited\n\n def __lt__(self, other):\n return self.g < other.g\n\n def add(self, idx):\n # add the cost\n self.g += self.graph[self.visited[-1], idx]\n # add the to the visited place and remove from the unvisited places\n self.visited.append(idx)\n self.not_visited.remove(idx)\n\n def add_to_heap_queue(path):\n # custom function to add to heap queue sorted by the solution's cost\n heappush(h_queue, path)\n\n if len(sol.not_visited) == 0:\n return 0\n elif len(sol.not_visited) == 1:\n return sol.graph[sol.visited[-1], sol.not_visited[0]]\n\n c = sol.visited[-1]\n pm = sol.not_visited[-1]\n # the heap queue of solution sorted by their cost - change all to tuples with g for dijkstra\n h_queue = []\n\n # the places to use for the graph\n sub_search_places = [c]\n sub_search_places.extend(sol.not_visited)\n\n # push the first \"node\" in the queue\n add_to_heap_queue(Path(sub_search_places, sol.graph))\n while True:\n # take the next solution with the shortest cost\n path = heappop(h_queue)\n # if it contains destination, stop and return that solution\n if pm in path.visited:\n return path.g\n # create a new solution for each neighbor of the current vertex and add it to heap queue\n for place in path.not_visited:\n new_path = copy.deepcopy(path)\n new_path.add(place)\n add_to_heap_queue(new_path)","def find_min_hamiltonian_path(G,weights,probs_instead_of_weights=False):\n\n # Create a new model\n m = Model(\"hamiltonian_cycle\")\n \n # Create variables\n x_vars = {}\n u_vars = {}\n for var1 in permute(G.vertices()):\n for var2 in permute(G.vertices()):\n if var1 != var2:\n x_vars[(var1,var2)] = m.addVar(vtype='B', name=\"x_\"+str(var1)+'_'+str(var2))\n u_vars[var1] = m.addVar(vtype=GRB.INTEGER, name=\"u_\"+str(var1))\n m.update()\n \n for var in G.vertices():\n if var != START_NODE:\n cur_incoming = LinExpr([(1.0,v) for k,v in x_vars.items() if (k[1] == var)])\n #print(cur_incoming)\n m.addConstr(cur_incoming,GRB.EQUAL,1.0)\n \n if var != END_NODE:\n cur_outgoing = LinExpr([(1.0,v) for k,v in x_vars.items() if (k[0] == var)])\n #print(cur_outgoing)\n m.addConstr(cur_outgoing,GRB.EQUAL,1.0)\n \n for var1 in G.vertices():\n for var2 in G.vertices():\n if var1 != var2:\n c = LinExpr([(1.0,u_vars[var1]),(-1.0,u_vars[var2]),(G.num_vertices(),x_vars[(var1,var2)])])\n #print(c)\n m.addConstr(c,GRB.LESS_EQUAL,G.num_vertices()-1)\n \n # Set objective\n #try:\n edge_weights = permute(G.get_edge_weights(weights))\n if probs_instead_of_weights:\n all_probs = []\n for v in G.vertices():\n if v != END_NODE:\n batch_scores = [(e,w) for e,w in edge_weights if e[0] == v]\n S = logsumexp([x[1] for x in batch_scores])\n batch_scores = [(e,np.exp(w-S)) for e,w in batch_scores]\n all_probs.extend(batch_scores)\n edge_weights = all_probs\n objective = LinExpr([(weight,x_vars[edge]) for edge,weight in edge_weights])\n #except TypeError:\n # return None\n \n m.setObjective(objective,GRB.MINIMIZE)\n m.update()\n code = m.optimize()\n \n try:\n return [k for k,v in x_vars.items() if v.x > 0.98]\n except GurobiError:\n return None","def dfs_recursive(self, starting_vertex, destination_vertex, visited=None, path=None):\n if visited is None: # if visited is not empty\n visited = set() # create an empty 'set' to store visisted vertex, set sorts \n\n if path is None: # if the path is empty \n path = [] # create an empty list \n visited.add(starting_vertex) # add the starting_vertex to the set \n path = path + [starting_vertex] # set the path \n\n if starting_vertex == destination_vertex: # if the starting_vertex is equal to the destination_vertex\n return path # return the path \n\n for neighbor in self.vertices[starting_vertex]: # loop through neighbors \n if neighbor not in visited: # if the neighbor has not been visited \n new_path = self.dfs_recursive(neighbor, destination_vertex, visited, path) # create a new path using the dfs_recursive method\n\n if new_path: # if there is a new_path \n return new_path # return the new path \n\n return None # return None ","def path_to(start, end):\n sol = djikstra(start, end)\n if sol is None:\n return None\n (distances, cost) = sol\n return compute_path(distances, cost, start, end)","def calculateShortestPath(self, startVertex: str, vertices: Tuple) -> dict:\n minDistance, shortestPath, visited = {}, {}, {}\n\n for v in vertices:\n minDistance[v] = sys.maxsize\n shortestPath[v] = \"\"\n visited[v] = False\n\n minDistance[startVertex] = 0\n\n n = len(vertices) - 1\n while n > 0:\n fromVertex = min(minDistance, key=lambda x: minDistance[x] if not visited[x] else sys.maxsize)\n visited[fromVertex] = True\n\n for toVertex, edgeWeight in self.adjacencyList[fromVertex]:\n if not visited[toVertex] and minDistance[fromVertex] + edgeWeight < minDistance[toVertex]:\n minDistance[toVertex] = minDistance[fromVertex] + edgeWeight\n shortestPath[toVertex] = shortestPath[fromVertex][:-1] + fromVertex + toVertex\n n -= 1\n\n shortestPathWithDistance = {\n v: dict(\n distance=minDistance[v],\n path=shortestPath[v]\n )\n for v in vertices\n }\n return shortestPathWithDistance","def find_shortest_path(self, start, end):\n\n if start==None:\n return\n\n visited = {}\n\n distance = {start:0}\n parent = {start:None}\n\n queue = deque()\n queue.append(start)\n\n while queue:\n\n cn = queue.popleft()\n\n for n in self.adjacencylist[cn]:\n if n not in visited:\n queue.append(n)\n parent[n] = cn\n if n not in distance:\n distance[n] = 1\n else:\n distance[n] += 1\n\n visited[cn] = True\n\n if all(visited.values()) == True:\n print('BFS done')\n\n print(\"Finding shortest path\")\n\n path = []\n cn = end\n path.append(cn)\n\n while cn != start:\n cn = parent[cn]\n path.append(cn)\n\n print (path[::-1])","def allPairsShortestPath(g):\r\n #dist+pred will be dicts (key:vtx, val:dict->KEY:toVtx, VAL:dist/pred) \r\n dist = {}\r\n pred = {}\r\n #for all vertices, create dict values with vertex as key\r\n for u in g: \r\n dist[u] = {} \r\n pred[u] = {}\r\n #for all vertices, set init. dist+pred to all vertices, including self\r\n for v in g: \r\n dist[u][v] = sys.maxsize\r\n pred[u][v] = None\r\n\r\n #finish off init with obvious dist and pred to self as 0 and None\r\n dist[u][u] = 0\r\n pred[u][u] = None\r\n\r\n #for all vertices in graph for current vertex u, set obvious..\r\n #..neighboring direct dist and pred\r\n for v in g[u]:\r\n dist[u][v] = g[u][v]\r\n pred[u][v] = u\r\n\r\n #this is the heavy lifting to figure out...\r\n for mid in g:\r\n for u in g:\r\n for v in g:\r\n newlen = dist[u][mid] + dist[mid][v]\r\n if newlen < dist[u][v]:\r\n dist[u][v] = newlen\r\n pred[u][v] = pred[mid][v]\r\n\r\n return (dist,pred)","def return_path(self, start, target):\n\n # return early if we don't need to go anywhere\n if start == target: return []\n\n # otherwise look for a path\n visited = set()\n path = {}\n costs = {}\n pq = [(0, start)]\n\n # Look until we find the target, dijkstra's guarantees\n # minimum cost path according to cost function.\n #\n # Cost function is: Cost to get to current worker + workload at worker\n while target not in visited:\n cost, curr = heapq.heappop(pq)\n if curr in visited: continue\n visited.add(curr)\n for neighbor in self.neighbors[curr]:\n if neighbor in visited: continue\n cost_new = cost + self.workload[neighbor] \n if neighbor not in costs or cost_new < costs[neighbor]:\n costs[neighbor] = cost_new\n path[neighbor] = curr\n heapq.heappush(pq, (cost_new, neighbor))\n\n ret_path = [0]\n while ret_path[-1] != start:\n ret_path.append(path[ret_path[-1]])\n\n return ret_path[::-1]","def uniformCostSearch(problem):\n \"*** YOUR CODE HERE ***\"\n path_to_point = {}\n cost_to_point = {}\n\n # Get the start node\n start_node = problem.getStartState()\n fringe_node = [start_node]\n path_to_point[start_node] = []\n cost_to_point[start_node] = problem.getCostOfActions(path_to_point[start_node])\n\n goal_found = False\n\n while(not goal_found):\n #for i in range(100): \n nodes_to_expand = set()\n # get max value node in the fringe node\n min_val = float(\"inf\")\n for one_node in fringe_node:\n # Compute the cost to reach a node\n if cost_to_point[one_node] < min_val:\n min_val = cost_to_point[one_node]\n \n for one_node in fringe_node:\n # Compute the cost to reach a node\n if cost_to_point[one_node] == min_val:\n nodes_to_expand.add(one_node)\n fringe_node.remove(one_node)\n\n # Expand the fringe node \n for one_node in nodes_to_expand:\n path_to_parent = path_to_point[one_node]\n for nxt_node in problem.getSuccessors(one_node):\n pos = nxt_node[0]\n mv = nxt_node[1]\n # check if point already present in path to point\n prev_cost = float(\"inf\")\n if pos in cost_to_point:\n prev_cost = cost_to_point[pos]\n new_path = path_to_parent + [mv]\n if prev_cost > problem.getCostOfActions(new_path):\n path_to_point[pos] = new_path\n cost_to_point[pos] = problem.getCostOfActions(new_path)\n fringe_node.append(pos)\n\n # Check if destination is reached in the fringe node\n for one_node in fringe_node:\n if problem.isGoalState(one_node):\n final_node = one_node\n goal_found = True\n break\n \n #print(len(fringe_node))\n print(final_node)\n print(path_to_point[final_node])\n return path_to_point[final_node] \n\n util.raiseNotDefined()","def calculateNewPath(self):\r\n\r\n\t\tnodeDict = self.simulationHandle.getMap().getNodeDict()\r\n\t\tdistDict = self.simulationHandle.getMap().getDistDict()\r\n\r\n\t\tself.pathToGoal = pathfinder.findPath(self.currentNode, self.goalNode, nodeDict, distDict)","def path_correction(data, user_coords):\n # Return list if it only has the destination\n if len(data) == 1:\n return data\n\n # Calculate distance from user to second waypoint\n second_coords = (data[1][\"lat\"], data[1][\"lon\"])\n user_second_dist = geopy.distance.distance(user_coords, second_coords).miles\n\n # Calculate distance from user to first waypoint\n first_coords = (data[0][\"lat\"], data[0][\"lon\"])\n user_first_dist = geopy.distance.distance(user_coords, first_coords).km\n\n # Calculate distance from first waypoint to second waypoint\n first_second_dist = geopy.distance.distance(first_coords, second_coords).miles\n\n # Determine if path correction is applicable\n if user_second_dist < first_second_dist or user_first_dist < 0.01:\n # Delete first element of list so that user doesn't backtrack\n return data[1:]\n else:\n # No path correction needed\n return data","def dfs_recursive(self, starting_vertex, target_vertex, visited=None, path=None):\n # if starting_vertex in visited:\n # return None\n # if starting_vertex == destination_vertex:\n # return [starting_vertex]\n # visited.add(starting_vertex)\n # for vertex in self.get_neighbors(starting_vertex):\n # path = self.dfs_recursive(vertex, destination_vertex, visited)\n # if path is not None: \n # return [starting_vertex] + path\n if visited == None:\n visited = set()\n if path == None:\n path = []\n visited.add(starting_vertex)\n path = path + [starting_vertex]\n if starting_vertex == target_vertex:\n return path\n for neighbor in self.get_neighbors(starting_vertex):\n if neighbor not in visited:\n new_path = self.dfs_recursive(neighbor, target_vertex, visited, path)\n if new_path is not None: \n return new_path\n return None","def dfs(self, starting_vertex, destination_vertex):\n # This solution takes a slightly different approach as to how we are storing the path\n # Now, we always queue up the next vertex we want to see, and a list of all the vertices we looked at to get here\n # so if we are queueing up vertex 3 from our example, the tuple we create will be (3, [1,2])\n # because we had to go through 1 and 2 to get here\n neighbors_to_visit = Stack()\n visited = set()\n # add the first vertex, and an empty list indicating that we have not been to any other vertices yet\n neighbors_to_visit.push([starting_vertex])\n # loop through the stack\n while neighbors_to_visit.size() > 0:\n path = neighbors_to_visit.pop()\n # pull out the current vertex so its easier to read\n vertex = path[-1] # last one in the path is our current vertex\n # if the vertex is the destination return it plus the path we took to get here\n if vertex == destination_vertex:\n return path\n # make sure the vertex isnt something we have seen already\n if vertex not in visited:\n # mark the vertex as visited\n visited.add(vertex)\n # add neighbors to the stack\n for neighbor in self.get_neighbors(vertex):\n new_path = path\n new_path.append(neighbor)\n neighbors_to_visit.push(new_path)","def acyclicShortestPath(self, origin):\n backlink = dict()\n cost = dict()\n \n for i in self.node:\n backlink[i] = utils.NO_PATH_EXISTS\n cost[i] = utils.INFINITY\n cost[origin] = 0\n \n for topoNode in range(self.node[origin].order + 1, self.numNodes + 1):\n i = self.topologicalList[topoNode]\n for hi in self.node[i].reverseStar:\n h = self.link[hi].tail\n if h < self.firstThroughNode and h != origin:\n continue\n tempCost = cost[h] + self.link[hi].cost\n if tempCost < cost[i]:\n cost[i] = tempCost\n backlink[i] = hi\n \n return (backlink, cost)","def djikstra(self, source, target):\r\n dist = {}\r\n prev = {}\r\n set_q = {}\r\n for vertex in self.vertices.keys():\r\n dist[vertex] = sys.maxsize\r\n prev[vertex] = None\r\n set_q[vertex] = dist[vertex]\r\n dist[source] = 0\r\n set_q[source] = 0\r\n while set_q:\r\n vertex_u = min(set_q, key=set_q.get)\r\n if vertex_u == target:\r\n break\r\n set_q.pop(vertex_u)\r\n for edge in self.edges[vertex_u]:\r\n alt = dist[vertex_u] + edge.distance\r\n if alt < dist[edge.destination]:\r\n dist[edge.destination] = alt\r\n set_q[edge.destination] = dist[edge.destination]\r\n prev[edge.destination] = vertex_u\r\n path = []\r\n vertex_u = target\r\n while prev[vertex_u]:\r\n path.insert(0, vertex_u)\r\n vertex_u = prev[vertex_u]\r\n path.insert(0, vertex_u)\r\n return path","def shortest_path_search(start, successors, is_goal):\n if is_goal(start):\n return [start]\n explored = set()\n frontier = [ [start] ] \n while frontier:\n path = frontier.pop(0)\n s = path[-1]\n for (state, action) in successors(s).items():\n if state not in explored:\n explored.add(state)\n path2 = path + [action, state]\n if is_goal(state):\n return path2\n else:\n frontier.append(path2)\n return Fail","def updatePath(self):\n logging.debug(\"ShortestPathUI.updatePath function started\")\n error = False\n fromVertex = self.fromLineEdit.text()\n toVertex = self.toLineEdit.text()\n graphVertices = self.controller.getVertices()\n if fromVertex not in graphVertices:\n self.__drawErrorInfo(self.fromLineEdit)\n error = True\n if toVertex not in graphVertices:\n self.__drawErrorInfo(self.toLineEdit)\n error = True\n if not error:\n self.pathLineEdit.setText(\"\")\n self.lengthLabel.setText(\"\")\n path, length = self.controller.calculateShortestPath(fromVertex, toVertex)\n path = \" -- \".join(path)\n self.pathLineEdit.setText(f\"{path}\")\n self.lengthLabel.setText(f\"{length}\")\n\n logging.debug(\"ShortestPathUI.updatePath function ended\\n\")","def calculate_path(self):\n #Se repite el ciclo para el número especificado de veces\n for i in range(self.iterations):\n for ant in self.ants:\n ant.setup_ant()\n while not ant.final_node_reached:\n #Seleccion aleatoria del nodo a visitar\n node_to_vist = self.select_next_node(self.map.nodes_array[int(ant.actual_node[0])][int(ant.actual_node[1])])\n #Mover la hormiga al siguiente nodo seleccionado al azar\n ant.move_ant(node_to_visit)\n #Compruebe si se ha alcanzado la solución\n ant.is_final_node_reached()\n #Agregar la ruta resultante a la lista de rutas\n self.add_to_path_results(self.delete_loops(ant.get_visited_nodes()))\n # Habilitar a la hormiga para otra busqueda\n ant.enable_start_new_path()\n \n # Actualizar el nivel global de feromonas\n self.pheromone_update()\n self.best_result = self.paths[0]\n\n #Vaciar la lista de rutas\n self.empty_paths()\n print('Iteration: ', i, 'lenght of the path: ', len(self.best_result))\n return self.best_result","def generate_path(goal_node, visited):\n goal_state = goal_node['state']\n path = [goal_state]\n while goal_node['parent']:\n path.append(goal_node['state'])\n goal_node = visited[goal_node['parent']]\n return path","def dfs(self, starting_vertex, destination_vertex):\n visited = set()\n paths = [[starting_vertex]]\n \"\"\"\n While the length of possible paths is not zero. \n Store the current path and remove it from possible \n paths. Return the last path if it's the destination. \n If the path hasn't been visited yet add it to the \n visited list and loop over it's edges creating paths \n to check later. \n \"\"\"\n while len(paths) > 0:\n path = paths.pop(-1)\n vertex = path[-1]\n if vertex == destination_vertex:\n return path\n if vertex not in visited:\n visited.add(vertex)\n for key in self.get_neighbors(vertex):\n newPath = path + [key]\n paths.append(newPath)","def getShortestPath(self, src, dest):\n vertices = self.floorGraph.getVertList()\n unvisitedQueue = []\n srcPath = Path()\n srcPath.addNode(src)\n srcPath.pathValue = 0\n unvisitedQueue.append(srcPath)\n connections = self.floorGraph.getVertex(src).getConnections()\n #initialisez distances\n for vertex in vertices:\n newPath = Path()\n newPath.nodeList = list(srcPath.nodeList)\n newPath.addNode(vertex)\n if self.floorGraph.getVertex(vertex) in connections:\n newPath.pathValue = self.floorGraph.getVertex(src).getWeight(self.floorGraph.getVertex(vertex))\n unvisitedQueue.append(newPath)\n else:\n newPath.pathValue = math.inf\n self.shortestDistanceMap[src+vertex] = newPath\n # updates distances as per shorter routes\n while len(unvisitedQueue) is not 0:\n unvisitedQueue = sorted(unvisitedQueue, key=functools.cmp_to_key(compareNodes))\n chkPath = unvisitedQueue.pop(0)\n chkNode = chkPath.nodeList[len(chkPath.nodeList)-1]\n for vertex in vertices:\n if(self.floorGraph.getVertex(vertex) in self.floorGraph.getVertex(chkNode).getConnections()):\n newWeight = chkPath.pathValue + self.floorGraph.getVertex(chkNode).getWeight(self.floorGraph.getVertex(vertex))\n if(newWeight < self.shortestDistanceMap[src+vertex].pathValue):\n self.shortestDistanceMap[src+vertex].pathValue = newWeight\n self.shortestDistanceMap[src+vertex].nodeList = list(chkPath.nodeList)\n self.shortestDistanceMap[src+vertex].nodeList.append(vertex)\n newPath = Path()\n newPath.nodeList = list(self.shortestDistanceMap[src+vertex].nodeList)\n newPath.pathValue = newWeight\n unvisitedQueue.append(newPath)\n print(self.shortestDistanceMap[src+dest].nodeList)\n print(self.shortestDistanceMap[src+dest].pathValue)","def get_best_path(digraph, start, end, path, max_dist_outdoors, best_dist,\n best_path):\n path[0] = path[0] + [start]\n \n if digraph.has_node(Node(start)) == False or digraph.has_node(Node(end)) == False:\n raise ValueError('Start or end node are not valid')\n elif start == end :\n return path\n else:\n for edge in digraph.get_edges_for_node(Node(start)):\n \n node = str(edge.get_destination())\n tot_distance = edge.get_total_distance() + path[1]\n tot_outdoors = edge.get_outdoor_distance() + path[2]\n\n if node not in path[0] and tot_distance <= best_dist and tot_outdoors <= max_dist_outdoors :\n updated_path = [path[0], tot_distance, tot_outdoors]\n new_path = get_best_path(digraph, node, end, updated_path, max_dist_outdoors, best_dist, best_path)\n \n if new_path:\n if not best_dist or new_path[1] < best_dist: \n best_path = new_path[0]\n best_dist = new_path[1]\n\n return best_path, best_dist","def dft_recursive(self, starting_vertex):\n # TODO\n # creating a function inside that includes a list\n # of previously traversed vertices\n def recursive(graph, traversed, vertex):\n # if the vertex is in traversed already, return none\n if vertex in traversed:\n return \n # otherwise we print it out\n print(vertex)\n # append the vertex to our traversed list\n traversed.add(vertex)\n # running the function on the neighbors of the vertex\n for val in graph[vertex]:\n recursive(graph, traversed, val)\n\n recursive(self.vertices, set(), starting_vertex)","def dfs(self, starting_vertex, destination_vertex):\n \"\"\" LIFO\n Create a stack\n Create a set to store visited\n PUSH starting vertex into an array (STACK)\n While the STACK is NOT empty \n get((pop) first PATH vertex\n get Vertex from END of PATH\n check if NOT visited\n mark as visited\n check if vertex is destination_vertex\n If TRUE, return path \n PUSH path to ALL of neighbors\n make copy of current path\n add neighbor to path copy\n PUSH path copy\n \"\"\" \n s = Stack() # Create a stack\n s.push([starting_vertex]) # PUSH starting vertex into an array (STACK)\n visited = set() # Create a set to store visited\n\n while s.size() > 0: # While the STACK is NOT empty\n path = s.pop() # get(pop) first PATH vertex)\n v = path[-1] # get Vertex from END of PATH \n\n while v not in visited: # check if NOT visited\n visited.add(v) # mark as visited\n\n if v == destination_vertex: # check if vertex is destination_vertex\n return path # If TRUE, return path \n\n for n in self.get_neighbors(v): # PUSH path to ALL of neighbors\n path_c = path[:] # make copy of current path\n # path_c.extend([n]) # add neighbor to path copy\n path_c.append(n) # add neighbor to path copy\n s.push(path_c) # PUSH path copy","def shortest_path(self, start: str, goal: str) -> Path:\n return next(self.bfs_paths(start, goal), [])","def path_planning_with_cost(x_x, x_y, s, T, epochs, stepsize, cost_f, ax=None, ax_lims=None, debug=False):\n ax = ax or plt.gca()\n plt.plot(0, 0, 'gx', markersize=20, markeredgewidth=5)\n plt.plot(x_x, x_y, 'rx', markersize=20, markeredgewidth=5)\n u = nn.Parameter(torch.zeros(T, 2))\n optimizer = SGD((u,), lr=stepsize)\n dt = 1 # s\n costs = []\n for epoch in range(epochs):\n x = [torch.tensor((0, 0, 0, s),dtype=torch.float32)]\n for t in range(1, T+1):\n x.append(x[-1] + f(x[-1], u[t-1]) * dt)\n x_t = torch.stack(x)\n τ = torch.stack(x).detach()\n cost = cost_f(x_t, (x_x, x_y))\n costs.append(cost.item())\n optimizer.zero_grad()\n cost.backward()\n optimizer.step()\n if debug: \n print(u.data)\n # Only plot the first and last trajectories\n if epoch == 0: \n plot_τ(ax, τ, ax_lims=ax_lims)\n if epoch == epochs-1:\n plot_τ(ax, τ, car=True, ax_lims=ax_lims)","def shortest_path_search(start, successors, is_goal):\r\n if is_goal(start):\r\n return [start]\r\n\r\n explored = set() # set of states we have visited\r\n frontier = [ [start] ] # ordered list of paths we have blazed\r\n while frontier:\r\n path = frontier.pop(0)\r\n s = path[-1]\r\n for (state, action) in successors(s).items():\r\n if state not in explored:\r\n explored.add(state)\r\n path2 = path + [action, state]\r\n if is_goal(state):\r\n return path2\r\n else:\r\n frontier.append(path2)\r\n return Fail","def shortest_path(graph, start, end):\n nodes_to_visit = {start}\n visited_nodes = set()\n # Distance from start to start is 0\n distance_from_start = {start: 0}\n predecessors = {} # Store previous node for shortest route for each node\n\n while nodes_to_visit:\n # Get node with smallest weight\n current = min(\n [(distance_from_start[node], node) for node in nodes_to_visit]\n )[1]\n\n # If the end is reached, quit\n if current == end:\n break\n\n nodes_to_visit.discard(current)\n visited_nodes.add(current)\n\n edges = graph[current]\n unvisited_neighbours = set(edges).difference(visited_nodes)\n for neighbour in unvisited_neighbours:\n neighbour_distance = distance_from_start[current] + \\\n edges[neighbour]\n if neighbour_distance < distance_from_start.get(neighbour,\n float('inf')):\n distance_from_start[neighbour] = neighbour_distance\n predecessors[neighbour] = current\n nodes_to_visit.add(neighbour)\n\n return _deconstruct_path(predecessors, end)","def FindShortestPath(graph, start, end, path=[]):\n path = path + [start]\n if start == end:\n return path\n if start not in graph:\n return None\n shortest = None\n for node in graph[start]:\n if node not in path:\n newpath = FindShortestPath(graph, node, end, path)\n if newpath:\n if not shortest or len(newpath) < len(shortest):\n shortest = newpath\n return shortest","def generate_possible_paths(self, obstacle):\n if self.does_uav_intersect_obstacle_vertically(obstacle, self.drone.get_point(), self.drone.get_waypoint_holder().get_current_waypoint()):\n if self.does_path_intersect_obstacle_2d(obstacle, self.drone.get_point(), self.drone.get_waypoint_holder().get_current_waypoint()):\n new_attempt_pos_points = [\n [obstacle.get_point()[0] + obstacle.get_radius(), obstacle.get_point()[1] + obstacle.get_radius(), self.drone.get_point()[2]],\n [obstacle.get_point()[0] - obstacle.get_radius(), obstacle.get_point()[1] - obstacle.get_radius(), self.drone.get_point()[2]],\n [obstacle.get_point()[0] + obstacle.get_radius(), obstacle.get_point()[1] - obstacle.get_radius(), self.drone.get_point()[2]],\n [obstacle.get_point()[0] - obstacle.get_radius(), obstacle.get_point()[1] + obstacle.get_radius(), self.drone.get_point()[2]],\n [obstacle.get_point()[0], obstacle.get_point()[1] + obstacle.get_radius(), obstacle.get_height() + (Constants.STATIONARY_OBSTACLE_SAFETY_RADIUS * 2)],\n [obstacle.get_point()[0], obstacle.get_point()[1] - obstacle.get_radius(), obstacle.get_height() + (Constants.STATIONARY_OBSTACLE_SAFETY_RADIUS * 2)],\n [obstacle.get_point()[0] + obstacle.get_radius(), obstacle.get_point()[1], obstacle.get_height() + (Constants.STATIONARY_OBSTACLE_SAFETY_RADIUS * 2)],\n [obstacle.get_point()[0] - obstacle.get_radius(), obstacle.get_point()[1], obstacle.get_height() + (Constants.STATIONARY_OBSTACLE_SAFETY_RADIUS * 2)]\n ]\n\n new_paths = []\n for new_pos_point in new_attempt_pos_points:\n if not self.does_path_intersect_obstacle_3d(obstacle, self.drone.get_point(), new_pos_point) and self.flight_boundary.is_point_in_bounds(new_pos_point):\n for recursive_new_pos_point in new_attempt_pos_points:\n if self.flight_boundary.is_point_in_bounds(recursive_new_pos_point) and abs(recursive_new_pos_point[2] - new_pos_point[2]) < 5:\n if recursive_new_pos_point[0] != new_pos_point[0] or recursive_new_pos_point[1] != new_pos_point[1]:\n if not self.does_path_intersect_obstacle_3d(obstacle, new_pos_point, recursive_new_pos_point) and not self.does_path_intersect_obstacle_3d(obstacle, recursive_new_pos_point, self.drone.get_waypoint_holder().get_current_waypoint()):\n new_paths.append([new_pos_point, recursive_new_pos_point])\n\n # Uncomment for DEBUGGING ONLY\n for path in new_paths:\n print(\"Point:\", str(path))\n\n return new_paths\n\n return []","def calculateFirstPath(self):\n rd.shuffle(self.goals)\n self.path = self.goals","def shortest_path(theta, adj=adjusted_adj_list_4):\n dists = 50*np.ones((4604))\n visited = np.zeros((4604), dtype='b')\n #\n current_node = theta\n# visited[theta] = True\n count = 0\n dists[theta] = 0\n while visited[current_node] == False:\n cur = dists[current_node]\n for other_node in adj[current_node]:\n if dists[other_node] > cur + 1:\n dists[other_node] = cur + 1\n visited[current_node] = True\n x1 = set(np.where(visited == False)[0])\n x2 = set(adj.keys())\n x = x1.intersection(x2)\n# x = [x for x in np.where(visited == False)[0] if x in adj.keys()]\n if len(x) == 0:\n np.save('./short_paths/' + str(theta) + '.npy', dists) \n return dists\n else:\n current_node = x.pop()\n count += 1","def _find_path_start(G):\r\n is_directed = G.is_directed()\r\n\r\n # list to check the odd degree condition\r\n check_odd = []\r\n\r\n if is_directed:\r\n degree = G.in_degree\r\n out_degree = G.out_degree\r\n else:\r\n degree = G.degree\r\n\r\n # Verify if an Euler path can be found. Complexity O(|V|)\r\n for v in G:\r\n deg = degree(v)\r\n # directed case\r\n if is_directed:\r\n outdeg = out_degree(v)\r\n if deg != outdeg:\r\n # if we have more than 2 odd nodes no Euler path exists\r\n if len(check_odd) > 2:\r\n return False\r\n # is odd and we append it.\r\n check_odd.append(v)\r\n # undirected case\r\n else:\r\n if deg % 2 != 0:\r\n # if we have more than 2 odd nodes no Euler path exists\r\n if len(check_odd) > 2:\r\n return False\r\n # is odd and we append it.\r\n check_odd.append(v)\r\n start = None\r\n if is_directed:\r\n\r\n first = check_odd[0]\r\n second = check_odd[1]\r\n if G.out_degree(first) == G.in_degree(first) + 1 and \\\r\n G.in_degree(second) == G.out_degree(second) + 1:\r\n start = second\r\n elif G.out_degree(second) == G.in_degree(second) + 1 and \\\r\n G.in_degree(first) == G.out_degree(first) + 1:\r\n start = first\r\n else:\r\n start = None\r\n\r\n else:\r\n if len(check_odd) > 0:\r\n start = check_odd[0]\r\n\r\n return start","def build(self, source, target):\n return self.shortestPath(self.graph, source, target)","def create_user_path_assoc():\n if not check_content_type():\n return jsonify(status=CONTENT_TYPE_ERROR)\n reqdata = request.json\n if not check_token(reqdata[\"token\"]):\n return jsonify(status=TOKEN_ERROR)\n #TODO check that request body contain needed data\n #if [\"user_id\", \"path_id\", \"ready\", \"coordinate\", \"first_name\", \"second_name\", \"phone\"].sort() != (data.keys()).sort():\n # return jsonify(status=\"err\")\n user_id = reqdata[\"user_id\"]\n path_id = reqdata[\"path_id\"]\n ready = True\n coordinate = reqdata[\"coordinate\"]\n first_name = reqdata[\"first_name\"]\n second_name = reqdata[\"second_name\"]\n phone = reqdata[\"phone\"]\n #TODO data validation\n user = db.session.query(User).filter(User.id==user_id).scalar() is not None\n path = db.session.query(Path).filter(Path.id==path_id).scalar() is not None\n if user and path:\n db.session.add(UserPathAssociation(user_id=user_id, path_id=path_id, ready=ready,coordinate=coordinate, first_name=first_name, second_name=second_name,phone=phone))\n try:\n db.session.commit()\n return jsonify(status=OK_STATUS)\n except:\n db.session.rollback()\n return jsonify(status=DATABASE_INTEGRITY_ERROR)\n else:\n return jsonify(status=\"err\")","def dijkstra_shortest_path(graph, search):\n distances_from_start = [None] * len(graph)\n\n visited_vertexes = []\n\n current_vertex = 0\n\n distances_from_start[current_vertex] = [0, 0] # [distance from start, via vertex]\n\n for row in range(len(graph)):\n\n current_vertex = row\n\n #print(\"Current vertex: \", current_vertex)\n\n # Iterate through each column in the current row in the adjacency matrix\n for col in range(len(graph[current_vertex])):\n\n if graph[current_vertex][col] is not None and distances_from_start[col] is None:\n distances_from_start[col] = [distances_from_start[current_vertex][0] + graph[current_vertex][col], current_vertex]\n\n elif graph[current_vertex][col] is not None and (graph[current_vertex][col] + distances_from_start[current_vertex][0]) < distances_from_start[col][0]:\n distances_from_start[col] = [(graph[current_vertex][col] + distances_from_start[current_vertex][0]), current_vertex]\n\n print(\"Distances from start: \", distances_from_start) # show updated distances_from_start array\n\n # Add current_vertex to visited list so that its distance from the start is calculated again in future\n if current_vertex not in visited_vertexes:\n visited_vertexes.append(current_vertex)\n\n # print(\"Visited vertexes: \", visited_vertexes)\n\n # Print the shortest path in a friendly format\n print(\"Shortest path:\")\n current_vertex = search #len(graph) - 1\n path_string = \"\"\n orderlist = []\n while current_vertex > 0:\n\n # Add the distance for the current vertex from the start in brackets after the letter of the vertex.\n path_string = \"{0}({1}) \".format(chr(current_vertex + 65), distances_from_start[current_vertex][0]) + path_string\n\n temp = [chr(current_vertex + 65), distances_from_start[current_vertex][0]]\n\n orderlist.append(temp)\n\n # Update the current vertex to be the one that the current one goes via on its way back to the start\n current_vertex = distances_from_start[current_vertex][1] # distances_from_start[vertex number, via vertex]\n\n\n # Add the start vertex to the output string as the while loop will stop before we add its details to the string\n path_string = \"{0}({1}) \".format(chr(current_vertex + 65), distances_from_start[current_vertex][0]) + path_string\n\n temp = [chr(current_vertex + 65), distances_from_start[current_vertex][0]]\n orderlist.append(temp)\n\n print(path_string)\n\n return orderlist[::-1]","def djikstra(nodes,links,source,dest):\n route = []\n vertexes = []\n for v in nodes:\n v.set_dist(float(\"inf\"))\n v.set_prev(None)\n heappush(vertexes, v)\n source.set_dist(0)\n heapify(vertexes)\n while vertexes:\n unsorted = False\n u = heappop(vertexes)\n if u == dest:\n break #because we found the destination no need to look further\n for v in u.get_links():\n if v.get_enabled():\n alt = u.get_dist() + 1\n target = v.get_target()\n if alt < target.get_dist():\n target.set_dist(alt)\n target.set_prev(u)\n unsorted = True #just a variable that help check if changes were made to the objects inside the heap\n if unsorted: #because i updated the variables but the heap wasn't maintained, i just heapify it again\n heapify(vertexes) \n #this is the part that saves the distance and route \n if dest.get_dist() == float(\"inf\"): #if there is no route then we just return None\n return None\n u = dest\n while u.get_prev() != None:\n v = u.get_prev()\n route.insert(0, v.get_specific_link(u)) \n u = v\n return route","def dijsktra(graph, initial, end):\n shortest_paths = {initial: (None, 0)}\n current_node = initial\n visited = set()\n\n while current_node != end:\n visited.add(current_node)\n destinations = graph.edges[current_node]\n weight_to_current_node = shortest_paths[current_node][1]\n logger.debug(\"shortest_paths = {}\".format(shortest_paths))\n\n for next_node in destinations:\n weight = graph.weights[(current_node, next_node)] + weight_to_current_node\n logger.debug(\"next_node = {}\".format(next_node))\n logger.debug(\"graph.weights[(current_node, next_node)] = {}\".format(graph.weights[(current_node, next_node)]))\n logger.debug(\"weight_to_current_node = {}\".format(weight_to_current_node))\n logger.debug(\"weight = {}\".format(weight))\n if next_node not in shortest_paths:\n shortest_paths[next_node] = (current_node, weight)\n else:\n current_shortest_weight = shortest_paths[next_node][1]\n if current_shortest_weight > weight:\n shortest_paths[next_node] = (current_node, weight)\n\n next_destinations = {node: shortest_paths[node] for node in shortest_paths if node not in visited}\n logger.debug(\"next_destinations = {}\".format(next_destinations))\n\n if not next_destinations:\n return \"Route not possible\"\n\n current_node = min(next_destinations, key=lambda k: next_destinations[k][1])\n logger.debug(\"current_node = {}\".format(current_node))\n\n path = []\n while current_node is not None:\n path.append(current_node)\n next_node = shortest_paths[current_node][0]\n current_node = next_node\n\n # Reverse the path\n path = path[::-1]\n return path","def shortestpath(graph, current, end, visited=[], distances={}, predecessors={}):\n\t# we've found our end node, now find the path to it, and return\n\tif current == end:\n\t\tpathShortest = []\n\t\twhile end != None:\n\t\t\tpathShortest.append(end)\n\t\t\tend = predecessors.get(end, None)\n\t\tpass\n\t\treturn distances[current], pathShortest[::-1]\n\tpass\n\t\n\t# detect if it's the first time through, set current distance to zero\n\tif not visited: distances[current] = 0\n\t# process neighbors as per algorithm, keep track of predecessors\n\tfor adjacent in graph[current]:\n\t\tif adjacent not in visited:\n\t\t\tadjDist = distances.get(adjacent, sys.maxint)\n\t\t\tcurDist = distances[current] + graph[current][adjacent]\n\t\t\tif curDist < adjDist:\n\t\t\t\tdistances[adjacent] = curDist\n\t\t\t\tpredecessors[adjacent] = current\n\t\t\tpass\n\t\tpass\n\tpass\n\t# neighbors processed, now mark the current node as visited\n\tvisited.append(current)\n\t# finds the closest unvisited node to the start\n\tnotVisited = dict((k, distances.get(k, sys.maxint)) for k in graph if k not in visited)\n\tclosest = min(notVisited, key=notVisited.get)\n\t# now we can take the closest node and recurse, making it current\n\treturn shortestpath(graph, closest, end, visited, distances, predecessors)","def shortest_path(self, id1: int, id2: int) -> (float, list):\n\n if id1 == id2:\n return 0, [id1]\n if id1 not in self.dw_graph.nodes or id2 not in self.dw_graph.nodes:\n return math.inf, []\n\n for n in self.dw_graph.get_all_v().values(): # Set all distance to be max value.\n if n.node_id != id1:\n n.distance = sys.maxsize\n n.visited = 0\n path = []\n min_heap=[]\n self.dw_graph.nodes[id1].distance = 0\n heapq.heappush(min_heap,(self.dw_graph.nodes[id1].distance,self.dw_graph.nodes[id1]))\n\n while len(min_heap):\n node = heapq.heappop(min_heap) # pop the smallest item\n current = node[1] # Get node from tuples\n current.visited = 1 # Set the node to visited\n\n for neighbour in self.dw_graph.all_out_edges_of_node(current.node_id).values(): # Get neighbours\n if self.dw_graph.nodes[neighbour.dest].visited == 0: # if we didn't visit this neighbour\n new_dist = current.distance + neighbour.weight # Set new distance\n\n if self.dw_graph.nodes[neighbour.dest].distance > new_dist: # If new distance is smaller , update it.\n self.dw_graph.nodes[neighbour.dest].distance = new_dist\n\n heapq.heappush(min_heap,(self.dw_graph.nodes[neighbour.dest].distance,\n self.dw_graph.nodes[neighbour.dest])) # add to priority queue\n self.dw_graph.nodes[neighbour.dest].parent = current.node_id # Update parent\n\n if self.dw_graph.nodes[id2].distance == sys.maxsize: # if the distance is still max value , can't reach\n return math.inf, []\n\n\n path.append(id2)\n current = self.dw_graph.nodes[id2].parent\n self.dw_graph.nodes[id1].parent=-1\n while current != -1: # Traverse backwards until parent is -1\n path.append(current)\n current = self.dw_graph.nodes[current].parent\n path.reverse()\n return self.dw_graph.nodes[id2].distance, path"],"string":"[\n \"def compute_pathways(users, job_graph, debug, min_likelihood_thr=0.2):\\r\\n start_time = time.time()\\r\\n __print_msg('Computing career pathways...', debug)\\r\\n user_pathways = {}\\r\\n tot_users = len(users)\\r\\n i = 0\\r\\n for user, user_jobs in users.items():\\r\\n user_pathway = compute_user_pathway(user_jobs, job_graph)\\r\\n recommended_pathway = recommend_pathway(user_jobs, job_graph, user_pathway[-1], min_likelihood_thr)\\r\\n user_pathways[user] = (user_pathway, recommended_pathway)\\r\\n i += 1\\r\\n if i % 1000 == 0:\\r\\n __print_msg('Num users processed: {}/{}'.format(i, tot_users), debug)\\r\\n end_time = time.time()\\r\\n __print_msg('Execution time: {} seconds'.format(end_time - start_time), debug)\\r\\n return user_pathways\",\n \"def go_to_x_y(user_x, user_y, deltaball=1.0):\\n global regions\\n goal = Point()\\n goal.x = user_x\\n goal.y = user_y\\n inc_x = user_x - x\\n inc_y = user_y - y\\n th = math.atan2(inc_y, inc_x)\\n if th > 3.14:\\n th = -(6.28 - th)\\n elif th < -3.14:\\n th = 6.28 + th\\n go_to_angle(th)\\n while math.sqrt(((user_x - x) * (user_x - x)) + ((user_y - y) * (user_y - y))) > deltaball:\\n if regions['front'] > 2:\\n if math.sqrt(((goal.x - x) * (goal.x - x)) + ((goal.y - y) * (goal.y - y))) > deltaball:\\n speed.linear.x = 0.8\\n speed.angular.z = 0\\n pub.publish(speed)\\n rate.sleep()\\n else:\\n speed.linear.x = 0\\n speed.angular.z = 0\\n pub.publish(speed)\\n rate.sleep()\\n # Let's find possible path\\n if regions['right'] > 3:\\n # Right path available\\n obs_avoid(-0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n elif regions['left'] > 3:\\n # front left path available\\n obs_avoid(0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n elif regions['fright'] > 3:\\n # Right path available\\n obs_avoid(-0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n elif regions['fleft'] > 3:\\n # front left path available\\n obs_avoid(0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n else:\\n obs_avoid(-0.5)\\n inc_x = goal.x - x\\n inc_y = goal.y - y\\n th = math.atan2(inc_y, inc_x)\\n go_to_angle(th)\\n rate.sleep()\\n speed.linear.x = 0\\n speed.angular.z = 0\\n pub.publish(speed)\",\n \"def shortest_path(source, target):\\n #although lecture checks for goal when a node is popped off the frontier, efficiency of search can be improved\\n #by checking for a goal as nodes are ADDED. If goal detected, don't add it to frontier, just return the solution\\n #immediately\\n\\n #create start point\\n start = Node(state = source, parent = None, action = None)\\n frontier = QueueFrontier()\\n frontier.add(start)\\n\\n #create explored set\\n explored = set()\\n\\n while True:\\n #if nothing left in frontier, no path exists\\n if frontier.empty():\\n return None\\n\\n #choose a node from the frontier\\n node = frontier.remove()\\n #if node is goal, we have solution\\n\\n #add neighbors 2 frontier using function THATS ALR THERE DUMMY\\n for (movie, star) in neighbors_for_person(node.state):\\n newNode = Node(state = star, parent = node, action=movie)\\n if not frontier.contains_state(newNode) and newNode.state not in explored:\\n if newNode.state == target:\\n #reverse the solution\\n solution = []\\n while newNode.parent is not None:\\n actionTuple = (newNode.action, newNode.state)\\n solution.append(actionTuple)\\n newNode = newNode.parent\\n solution.reverse()\\n return solution\\n else: frontier.add(newNode)\\n\\n #mark state as explored\\n explored.add(node.state)\",\n \"def recommend_pathway(user_jobs, job_graph, goal_state, min_likelihood_thr):\\r\\n user_jobs_for_mdp = [user_jobs[0]]\\r\\n mdp = MDP(job_graph, user_jobs_for_mdp, goal_state, min_likelihood_thr=min_likelihood_thr)\\r\\n return mdp.solve_mdp()\",\n \"def get_all_social_paths(self, user_id):\\n visited = {} # Note that this is a dictionary, not a set\\n # !!!! IMPLEMENT ME\\n # BFTs starting at user_id, return first path to every reachable person\\n q = [[user_id]]\\n while q:\\n path = q.pop(0)\\n person = path[-1]\\n # add the person and the path to the person\\n for friend in self.friendships[person]:\\n if friend not in visited and friend != user_id:\\n q.append(path + [friend])\\n visited[friend] = path + [friend]\\n\\n return visited\",\n \"def compute_user_pathway(user_jobs, job_graph, debug=False):\\r\\n pathway = []\\r\\n for i, job in enumerate(user_jobs):\\r\\n if i == 0:\\r\\n continue\\r\\n cluster, _ = job_graph.assign_job_to_jobgraph_state(job)\\r\\n pathway.append(cluster)\\r\\n return pathway\",\n \"def dijkstra_shortest_path(graph_object: Graph, start_node: int) -> List[float]:\\n\\n def init_distance(g: Graph, s: int) -> List[float]:\\n d = [inf] * len(g) # type: List[float]\\n d[s] = 0.0\\n return d\\n\\n # Assign a key value to all vertices in the input graph.\\n # Initial value is inf for all but the first one\\n distance = init_distance(graph_object, start_node)\\n priority_queue = MinHeap.build([(i, priority) for i, priority in enumerate(distance)])\\n\\n while priority_queue: # Priority queue has O(V) elements\\n min_index = priority_queue.pop() # O(log(V))\\n for edge in graph_object.graph[min_index]: # A graph has at most 2E Edges in adjacency list, so O(E)\\n source, destination, edge_weight = edge\\n if priority_queue.contains_item(destination) and distance[source] + edge_weight < distance[destination]:\\n distance[destination] = distance[source] + edge_weight\\n priority_queue.push(item=destination, priority=edge_weight) # O(log(V))\\n\\n return distance\",\n \"def get_all_social_paths(self, user_id):\\n visited = {} # Note that this is a dictionary, not a set\\n # !!!! IMPLEMENT ME\\n # graphs=Graph()\\n # for i in self.users:\\n # graphs.add_vertex(i)\\n \\n # for i in self.users:\\n # for x in self.friendships[i]:\\n # graphs.add_edge(i,x)\\n\\n # for i in graphs.vertices:\\n # if graphs.bfs(i,user_id):\\n # visited[i]=graphs.bfs(i,user_id)\\n queue=Queue()\\n queue.enqueue([user_id])\\n while queue.size()>0:\\n path=queue.dequeue()\\n current_user = path[-1]\\n if current_user not in visited:\\n visited[current_user]=path\\n for ID in self.friendships[current_user]:\\n new_path=list(path)\\n new_path.append(ID)\\n queue.enqueue(new_path)\\n return visited\",\n \"def generate_graph(\\n locs_user, sp_user, study, trips_user=None, gap_threshold=None\\n):\\n # print(sp_user[\\\"location_id\\\"])\\n # print(locs_user)\\n AG = ActivityGraph(\\n sp_user,\\n locs_user,\\n trips=trips_user,\\n gap_threshold=gap_threshold,\\n )\\n # Add purpose feature\\n if study == \\\"geolife\\\" or study == \\\"yumuv_graph_rep\\\":\\n AG.add_node_features_from_staypoints(\\n sp_user, agg_dict={\\\"started_at\\\": list, \\\"finished_at\\\": list}\\n )\\n else:\\n AG.add_node_features_from_staypoints(\\n sp_user,\\n agg_dict={\\n \\\"started_at\\\": list,\\n \\\"finished_at\\\": list,\\n \\\"purpose\\\": list,\\n },\\n )\\n return AG\",\n \"def shortestpath(graph,start,end,visited=[],distances={},predecessors={}):\\r\\n # detect if first time through, set current distance to zero\\r\\n try:\\r\\n if not visited: \\r\\n distances[start]=0\\r\\n # if we've found our end node, find the path to it, and return\\r\\n if start==end:\\r\\n path=[]\\r\\n while end != None:\\r\\n path.append(end)\\r\\n end=predecessors.get(end,None)\\r\\n return distances[start], path[::-1]\\r\\n # process neighbors as per algorithm, keep track of predecessors\\r\\n for neighbor in graph[start]:\\r\\n if neighbor not in visited:\\r\\n neighbordist = distances.get(neighbor,float(math.inf))\\r\\n tentativedist = distances[start] + graph[start][neighbor]\\r\\n if tentativedist < neighbordist:\\r\\n distances[neighbor] = tentativedist\\r\\n predecessors[neighbor]=start\\r\\n # neighbors processed, now mark the current node as visited \\r\\n visited.append(start)\\r\\n # finds the closest unvisited node to the start \\r\\n unvisiteds = dict((k, distances.get(k,float(math.inf))) for k in graph if k not in visited)\\r\\n closestnode = min(unvisiteds, key=unvisiteds.get)\\r\\n # now take the closest node and recurse, making it current\\r\\n except:\\r\\n pygame.time.delay(700)\\r\\n SCREEN.fill((0,0,0))\\r\\n displayText(\\\"Path Not Found\\\",400,200)\\r\\n displayText(\\\"Try Again....!!\\\",400,240)\\r\\n pygame.display.update()\\r\\n pygame.time.delay(1500)\\r\\n pygame.quit()\\r\\n sys.exit() \\r\\n return shortestpath(graph,closestnode,end,visited,distances,predecessors)\",\n \"def constructShortestPath(self):\",\n \"def user_path(a, b):\\n tx = cypher_transaction()\\n\\n # Limit the number of relationships in the path?\\n # p = shortestPath((a)-[*..15]-(b))\\n query = \\\"\\\"\\\"\\n MATCH\\n (a:user {username:{username_a}}),\\n (b:user {username:{username_b}}),\\n p = shortestPath((a)-[]->(b))\\n RETURN LENGTH(p), p\\n \\\"\\\"\\\"\\n params = {\\n 'username_a': a['username'],\\n 'username_b': b['username']\\n }\\n tx.append(query, parameters=params)\\n results = _first(tx.commit())\\n paths = []\\n for record in results:\\n length, path = record.values\\n m = \\\"There are {0} hops from {1} to {2}:\\\\n\\\"\\n print(m.format(length, a['name'], b['name']))\\n for rel in path.relationships:\\n print(\\\" ({0})-[:{1}]->({2})\\\".format(\\n rel.start_node['name'],\\n rel.type,\\n rel.end_node['name']\\n ))\\n paths.append(path)\\n return paths\",\n \"def getPath(\\n self,\\n source,\\n dest,\\n as_nodes=False,\\n ):\\n\\n self.dist = {} # A map from nodes to their labels (float)\\n self.predecessor = {} # A map from a node to a node\\n\\n # Initialize the distance labels to \\\"infinity\\\"\\n\\n vertices = self.g.nodes()\\n for vertex in vertices:\\n self.dist[vertex] = self.inf\\n self.predecessor[vertex] = source\\n\\n # Further set up the distance from the source to itself and\\n # to all one hops away.\\n\\n self.dist[source] = 0.0\\n if self.g.is_directed():\\n outEdges = self.g.out_edges([source])\\n else:\\n outEdges = self.g.edges([source])\\n for edge in outEdges:\\n self.dist[edge[1]] = self.g[edge[0]][edge[1]][self.wt]\\n\\n s = set(vertices)\\n s.remove(source)\\n currentMin = self._findMinNode(s)\\n if currentMin == None:\\n return None\\n s.remove(currentMin)\\n while currentMin != dest and len(s) != 0 and currentMin != None:\\n if self.g.is_directed():\\n outEdges = self.g.out_edges([currentMin])\\n else:\\n outEdges = self.g.edges([currentMin])\\n for edge in outEdges:\\n opposite = edge[1]\\n if self.dist[currentMin] + self.g[edge[0]][edge[1]][self.wt] \\\\\\n < self.dist[opposite]:\\n self.dist[opposite] = self.dist[currentMin] \\\\\\n + self.g[edge[0]][edge[1]][self.wt]\\n self.predecessor[opposite] = currentMin\\n s.add(opposite)\\n\\n currentMin = self._findMinNode(s)\\n\\n # print \\\"Current min node {}, s = {}\\\".format(currentMin, s)\\n\\n if currentMin == None:\\n return None\\n s.remove(currentMin)\\n\\n # Compute the path as a list of edges\\n\\n currentNode = dest\\n predNode = self.predecessor.get(dest)\\n node_list = [dest]\\n done = False\\n path = []\\n while not done:\\n path.append((predNode, currentNode))\\n currentNode = predNode\\n predNode = self.predecessor[predNode]\\n node_list.append(currentNode)\\n done = currentNode == source\\n node_list.reverse()\\n if as_nodes:\\n return node_list\\n else:\\n return path\",\n \"def get_all_social_paths(self, user_id): #each user v v each path V V\\n # output example- {1: [1], 8: [1, 8], 10: [1, 10], 5: [1, 5], 2: [1, 10, 2], 6: [1, 10, 6], 7: [1, 10, 2, 7]}\\n visited = {} # Note that this is a dictionary, not a set\\n # Need to do a bfs using the user id\\n # first step is to traverse the graph and record all the vertices as keys in visited using bft\\n # then take those keys and use bfs on each, using user_id as the starting node and and the key as\\n # the destination node\\n\\n # Modification of BFT\\n # create an empty dict\\n # q = Queue()\\n q = []\\n\\n # init enqueue the starting node\\n q.append(user_id)\\n\\n while len(q) > 0:\\n # Dequeue the first item\\n v = q.pop(0)\\n # If it's not been visited:\\n if v not in visited:\\n # Mark as visited (i.e. add to the visited set)\\n visited[v] = []\\n\\n # Do something with the node\\n print(f\\\"Visited {v}\\\")\\n\\n # Add all neighbors to the queue\\n for next_vert in self.friendships[v]:\\n q.append(next_vert)\\n\\n # once visited is filled, then we start the bfs\\n #print('vv',visited)\\n possible_paths = {}\\n #run a bfs for each key in visited\\n for v in visited:\\n possible_paths[v] = []\\n \\n if v == user_id:\\n visited[v] = [user_id]\\n\\n path = []\\n while len(path) < len(visited):\\n\\n # Add all neighbors to the queue\\n for next_vert in self.friendships[v]:\\n print(possible_paths[v])\\n # copy the path\\n # temp_path = list(path)\\n # temp_path.append(next_vert)\\n # add path to possible_paths\\n path.append(next_vert)\\n\\n possible_paths[v].append(path) # HAVE TO USE QUEUE OR STACK, THEY ENSURE THE NEIGHBORS\\n # FOLLOW THE CORRECT ORDER WHEN LOOPING \\n\\n if v == path[-1]:\\n \\n # IF SO, RETURN PATH\\n visited[v] = path\\n break\\n\\n # for x in visited:\\n # bfs(user_id, x)\\n # visited[x].add(path)\\n \\n print('pct of total users in network', len(visited[1])/len(visited))\\n print('degrees of separation', len(visited[1]) - 1)\\n return visited\",\n \"def shortestPath(self, source, target):\\n dist = {}\\n prev = {}\\n q = []\\n for y,a in enumerate(self.sm):\\n for x,b in enumerate(self.sm[y]):\\n dist[(x,y)] = sys.maxint\\n prev[(x,y)] = None\\n q.append((x,y))\\n dist[source] = 0\\n\\n while len(q) is not 0:\\n # find the node with minimum value (u)\\n d = deepcopy(dist)\\n while True:\\n b = dict(map(lambda item: (item[1],item[0]), d.items()))\\n u = b[min(b.keys())]\\n if u not in q:\\n d.pop(u)\\n else:\\n break\\n\\n if dist[u] == sys.maxint: # remaining nodes are inaccessible\\n break\\n\\n q.remove(u)\\n\\n\\n if u == target: # target found\\n break\\n\\n for v in self.getNeighbors(u):\\n alt = dist[u] + 1\\n if alt < dist[v]:\\n dist[v] = alt\\n prev[v] = u\\n\\n s = []\\n u = target\\n while prev[u] is not None:\\n s.append(u)\\n u = prev[u]\\n s.reverse()\\n\\n return s\",\n \"def create_path(network, user_A, user_B, path=[]):\\n path = path + [user_A] # all paths include starting node\\n if user_A == user_B: # id the last node is user_B a valid path exists\\n return path # base case\\n for node in network[user_A][0]:\\n if node not in path: # otherwise path is an infinite loop\\n path = create_path(network, node, user_B, path)\\n if path: # after the recursion hits the base case\\n return path\\n return None\",\n \"def shortestPath(graph, start, end, maxOutdistance, toPrint = False):\\r\\n return DFS(graph, start, end, [], None, sys.maxsize, sys.maxsize, 0, maxOutdistance, toPrint)\",\n \"def shortest_path(self, from_node):\\n\\n\\t\\t# add the final nodes when algorithm goes past the target prediction time \\n\\t\\tif self.get_real_time(from_node.time) >= self.target:\\n\\t\\t\\tself.g.add_node(from_node, usage_cost=0, best_action=None, max_demand=self.starting_max_demand)\\n\\t\\t\\treturn\\n\\n\\t\\t#create the action set (0 is for do nothing, 1 is for cooling, 2 is for heating)\\n\\t\\taction_set = self.safety.safety_actions(from_node.temps)\\n\\t\\t\\n\\t\\t#iterate for each available action\\n\\t\\tfor action in action_set:\\n\\t\\t\\t\\n\\t\\t\\t# predict temperature and energy consumption of action\\n\\t\\t\\tnew_temperature = []\\n\\t\\t\\tconsumption = []\\n\\t\\t\\tfor i in range(self.noZones):\\n\\t\\t\\t\\tnew_temperature.append(self.th.next_temperature(from_node.temps[i], action[i], zone=i))\\n\\t\\t\\t\\tconsumption.append(self.energy.calc_cost(action[i], from_node.time/self.interval))\\n\\t\\t\\t\\t\\n\\t\\t\\tif self.safety.safety_check(new_temperature) and len(action_set) > 1:\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t# create the node that describes the predicted data\\n\\t\\t\\tnew_node = Node(\\n\\t\\t\\t\\ttemps=new_temperature,\\n\\t\\t\\t\\ttime=from_node.time + self.interval\\n\\t\\t\\t)\\n\\n\\t\\t\\t# calculate interval costs\\n\\t\\t\\tdemand_balancer = (((self.target - self.get_real_time(from_node.time/self.interval)).total_seconds() / 60) / self.billing_period)\\n\\t\\t\\tinterval_usage_cost = sum(consumption) + self.interval_usage[int(from_node.time/self.interval)]\\n\\t\\t\\tinterval_demand = self.interval_demand[int(from_node.time/self.interval)]\\n\\n\\t\\t\\t#calculate interval discomfort\\n\\t\\t\\tdiscomfort = [0] * self.noZones\\n\\n\\t\\t\\tfor i in range(self.noZones):\\n\\t\\t\\t\\tdiscomfort[i] = self.disc.disc(new_temperature[i], self.occ.occ(from_node.time/self.interval), from_node.time)\\n\\n\\t\\t\\t# create node if the new node is not allready in graph\\n\\t\\t\\t# recursively run shortest path for the new node\\n\\t\\t\\tif new_node not in self.g:\\n\\t\\t\\t\\tself.g.add_node(new_node, usage_cost=np.inf, best_action=None, max_demand=np.inf)\\n\\t\\t\\t\\tself.shortest_path(new_node)\\n\\n\\t\\t\\t# calculate path costs\\n\\t\\t\\tthis_path_demand = max(self.g.node[new_node]['max_demand'], interval_demand)\\n\\t\\t\\tthis_path_demand_cost = demand_balancer * this_path_demand\\n\\n\\t\\t\\tinterval_overall_cost = ((1 - self.l) * (interval_usage_cost + this_path_demand_cost)) + (self.l * (sum(discomfort)))\\n\\n\\t\\t\\tthis_path_cost = self.g.node[new_node]['usage_cost'] + interval_overall_cost\\n\\n\\t\\t\\t# add the edge connecting this state to the previous\\n\\t\\t\\tself.g.add_edge(from_node, new_node, action=action)\\n\\n\\t\\t\\t# choose the shortest path\\n\\t\\t\\tif this_path_cost <= self.g.node[from_node]['usage_cost']:\\n\\t\\t\\t\\tif this_path_cost == self.g.node[from_node]['usage_cost'] and self.g.node[from_node]['best_action'] == '0':\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tself.g.add_node(from_node, best_action=new_node, usage_cost=this_path_cost, max_demand=this_path_demand)\",\n \"def __dikjstra(self, start_node):\\n visited = []\\n unvisited = [x for x in self.__node]\\n shortest_dist_from_start_node = 0\\n current_node = start_node\\n\\n current_node.setShortestDist(shortest_dist_from_start_node)\\n\\n while current_node:\\n #check unvisited neighbor\\n for neighbor_node, distance in current_node.getNeighbors().items():\\n #print(neighbor_node.getId(), distance) troubleshoot je ni\\n if neighbor_node in visited:\\n continue\\n\\n #add up shortest_dist_from_start_node with distance from neighbor distance\\n calc_dist = shortest_dist_from_start_node + distance\\n\\n if calc_dist < neighbor_node.getShortestDist():\\n neighbor_node.setShortestDist(calc_dist)\\n neighbor_node.setPrevNode(current_node)\\n\\n # add current node to visited array\\n visited.append(current_node)\\n unvisited.remove(current_node)\\n \\n #update next node and next shortest distance\\n next_shortest_dist_from_start_node = inf\\n next_node = None\\n\\n for unvisited_node in unvisited:\\n if unvisited_node.getShortestDist() < next_shortest_dist_from_start_node:\\n next_shortest_dist_from_start_node = unvisited_node.getShortestDist()\\n next_node = unvisited_node\\n\\n # update current node and shortest distance from start vertex\\n if next_node:\\n current_node = next_node\\n shortest_dist_from_start_node = next_shortest_dist_from_start_node\\n #if there are left over unvisited node\\n else: \\n if unvisited:\\n current_node = unvisited[0]\\n else:\\n current_node = None\",\n \"def shortest_route(self, start, finish):\\n distances = dict()\\n previous = dict()\\n nodes = list()\\n result = dict()\\n best_price = 0\\n\\n for vertex in self.vertices:\\n if vertex == start:\\n distances[vertex] = 0\\n heapq.heappush(nodes, [0, vertex])\\n else:\\n distances[vertex] = sys.maxsize\\n heapq.heappush(nodes, [sys.maxsize, vertex])\\n previous[vertex] = None\\n\\n while nodes:\\n smallest = heapq.heappop(nodes)[1]\\n if smallest == finish:\\n path = []\\n while previous[smallest]:\\n path.append(smallest)\\n smallest = previous[smallest]\\n if len(path) == 1:\\n result[\\\"Cost\\\"] = distances[finish]\\n else:\\n result[\\\"Cost\\\"] = best_price\\n\\n if len(path) > 0:\\n path.append(start)\\n result[\\\"Path\\\"] = path[::-1]\\n else:\\n result = dict()\\n\\n self.spa_result = result\\n return result\\n\\n if distances[smallest] == sys.maxsize:\\n break\\n\\n for neighbor in self.vertices[smallest]:\\n cost = distances[smallest] + self.vertices[smallest][neighbor]\\n if cost < distances[neighbor]:\\n distances[neighbor] = cost\\n previous[neighbor] = smallest\\n for n in nodes:\\n if n[1] == neighbor:\\n n[0] = cost\\n best_price = cost\\n break\\n heapq.heapify(nodes)\\n\\n return result\",\n \"def constructShortestPath(self):\\r\\n sp = []\\r\\n v = self.t\\r\\n while self.preds[v]: # is not None\\r\\n sp.append(v)\\r\\n v = self.preds[v]\\r\\n sp.append(self.s) # source\\r\\n sp.reverse() # to have the path from source to dest and not t to s\\r\\n return sp, self.graph.getCoords(sp)\",\n \"def find_path(g: Graph, start: str) -> None:\\n g.dijkstra(start)\",\n \"def plan_path(self, start_point, end_point, map_obj):\\n # STUFF FOR TESTING \\n if self.enable_vis:\\n marker = Marker()\\n marker.header.frame_id = \\\"/map\\\"\\n marker.type = marker.POINTS\\n marker.action = marker.ADD\\n \\n marker.scale.x = 0.1\\n marker.scale.y = 0.1\\n self.vis_pub.publish(marker)\\n \\n exploration_bias = 1.0 - self.goal_bias\\n final_node = None\\n num_existing_path_points_added = 0\\n \\n self.rrt_star = RRTStar(Node(start_point))\\n self.max_iterations = self.rrt_star.max_size\\n while self.rrt_star.size <= self.max_iterations:\\n p = np.random.uniform()\\n if p < exploration_bias:\\n \\n x_rand = self.map.sample_free_space()\\n else:\\n if final_node is None:\\n x_rand = end_point\\n else:\\n x_rand = self.branched_from_existing_path(\\n final_node,\\n depth_underestimate=num_existing_path_points_added\\n )\\n num_existing_path_points_added += 1\\n\\n x_nearest = self.rrt_star.nearest(x_rand) # Find the nearest node to x_rand\\n\\n path = self.map.generate_line_path(x_nearest.value, x_rand, eta=self.eta)\\n if path is not None: # no obstacles between x_nearest and x_rand\\n x_new = path[-1]\\n X_nearby_connectable = self.find_nearby_connectable(x_nearest, x_new)\\n\\n cost_min, node_min = self.find_best_parent(X_nearby_connectable, x_new)\\n\\n X_nearby_connectable.remove(node_min) # Remove x_new's parent node from the list of nearby nodes so it is not considered for rewiring\\n \\n # Create the new node at x_new!\\n node_new = self.rrt_star.add_config(node_min, x_new)\\n \\n if self.enable_vis:\\n # FOR TESTING ONLY #\\n # Code to publish marker for new node\\n ###########################################################################################\\n TEMP = Point()\\n TEMP.x = x_new[0]\\n TEMP.y = x_new[1]\\n TEMP.z = .05\\n marker.points.append(TEMP)\\n \\n TEMP = ColorRGBA()\\n TEMP.r = 1\\n TEMP.g = 0\\n TEMP.b = 0\\n TEMP.a = 1\\n \\n marker.colors.append(TEMP)\\n \\n self.vis_pub.publish(marker)\\n ###########################################################################################\\n\\n self.rewire(cost_min, node_new, X_nearby_connectable)\\n \\n if np.allclose(node_new.value, end_point, .05, 0) and (final_node is None):#np.array_equal(node_new.value, end_point):\\n final_node = node_new\\n # reduce exploration bias so that we reinforce the existing path\\n exploration_bias = .5\\n if VERBOSE:\\n print(\\\"Path found!!!!\\\")\\n print(final_node.cost)\\n if rospy.get_time() - self.start_time > self.time_thresh:\\n if VERBOSE:\\n print(self.rrt_star.size)\\n break\\n\\n \\n if final_node is not None:\\n if self.enable_vis:\\n marker = Marker()\\n marker.header.frame_id = \\\"/map\\\"\\n marker.type = marker.POINTS\\n marker.action = marker.ADD\\n \\n marker.scale.x = 0.1\\n marker.scale.y = 0.1\\n marker.points = []\\n marker.colors = []\\n def recur(node):\\n if self.enable_vis:\\n TEMP = Point()\\n TEMP.x = node.value[0]\\n TEMP.y = node.value[1]\\n TEMP.z = .05\\n marker.points.append(TEMP)\\n \\n TEMP = ColorRGBA()\\n TEMP.r = 1\\n TEMP.g = 0\\n TEMP.b = 0\\n TEMP.a = 1\\n \\n marker.colors.append(TEMP)\\n \\n \\n self.trajectory.points.append([node.value[0], node.value[1]])\\n parent = node.parent\\n if parent is not None:\\n recur(parent)\\n recur(final_node)\\n self.trajectory.points.reverse()\\n if self.enable_vis:\\n self.vis_pub.publish(marker)\\n if VERBOSE:\\n print (final_node.depth)\\n else:\\n if VERBOSE:\\n print(\\\"No path found! Please try again.\\\")\\n \\n \\n \\n # publish trajectory\\n self.traj_pub.publish(self.trajectory.toPoseArray())\\n\\n # visualize trajectory Markers\\n self.trajectory.publish_viz()\",\n \"def bfs_shortest_path(graph: dict=g2, start: str = \\\"1\\\", goal: str = \\\"4\\\") -> list:\\n visited = []\\n queue = [[start]]\\n\\n while queue:\\n path = queue.pop(0)\\n node = path[-1]\\n if node not in visited:\\n neighbours = graph[node]\\n for neighbour in neighbours:\\n new_path = path[:]\\n new_path.append(neighbour)\\n queue.append(new_path)\\n if neighbour == goal:\\n return new_path\\n visited.append(node)\\n # No path\\n return [\\\"No Path\\\"]\",\n \"def shortestPath(self, G, start, end):\\n def flatten(L): # Flatten linked list of form [0,[1,[2,[]]]]\\n while len(L) > 0:\\n yield L[0]\\n L = L[1]\\n\\n q = [(0, start, ())] # Heap of (cost, path_head, path_rest).\\n visited = set() # Visited vertices.\\n while True:\\n (cost, v1, path) = heapq.heappop(q)\\n if v1 not in visited:\\n visited.add(v1)\\n if v1 == end:\\n return list(flatten(path))[::-1] + [v1]\\n path = (v1, path)\\n for (v2, cost2) in G[v1].items():\\n if v2 not in visited:\\n heapq.heappush(q, (cost + cost2, v2, path))\",\n \"def shortest_path(self, start_node_id, end_node_id):\\n # convert to lowercase \\n start_node_id = start_node_id.lower()\\n end_node_id = end_node_id.lower()\\n\\n # validate whether both node id exist \\n start_node = None\\n end_node = None\\n \\n # for loop to convert id to obj --> can be used for passing param in dijkstra method\\n for x in self.__node:\\n if x.getId() == start_node_id:\\n start_node = x\\n if x.getId() == end_node_id:\\n end_node = x\\n \\n #validate if the id exists\\n if start_node == None or end_node == None:\\n print(\\\"The ID does not exist\\\")\\n return\\n\\n # execute dikstra method\\n self.__dikjstra(start_node)\\n\\n output_node = [end_node.getId()]\\n temp_end_node = end_node # to be used to display shortest distance from start node\\n\\n while end_node.getId() != start_node.getId():\\n end_node = end_node.getPrevNode()\\n if end_node == None:\\n break\\n output_node.insert(0,end_node.getId())\\n\\n #if first element of otput_node is not the start node id, display error, other wise, display the \\n if output_node[0] != start_node_id:\\n print(\\\"Error. No path between vertex \\\"+start_node_id+\\\" and vertex \\\"+end_node_id)\\n return \\n \\n # shortest path \\n print(\\\"Shortest Path: \\\")\\n \\n for node in output_node:\\n if node == end_node_id:\\n print(node, end=\\\"\\\")\\n break \\n\\n print(node+' -> ', end=\\\"\\\")\\n \\n # shortest distance\\n print ('\\\\nShortest distance to '+ temp_end_node.getId() + ' = ' + str(temp_end_node.getShortestDist()) )\\n\\n #display table\\n self.__display_table(start_node)\",\n \"def _bidirectional_shortest_path(G, source, target, exclude):\\n # call helper to do the real work\\n results = _bidirectional_pred_succ(G, source, target, exclude)\\n pred, succ, w = results\\n\\n # build path from pred+w+succ\\n path = []\\n # from source to w\\n while w is not None:\\n path.append(w)\\n w = pred[w]\\n path.reverse()\\n # from w to target\\n w = succ[path[-1]]\\n while w is not None:\\n path.append(w)\\n w = succ[w]\\n\\n return path\",\n \"def get_all_social_paths(self, user_id):\\n if len(self.friendships) > 0:\\n visited = {}\\n q = Queue()\\n q.enqueue([user_id])\\n\\n while q.size() > 0:\\n curr_path = q.dequeue()\\n curr_vertex = curr_path[-1]\\n\\n if curr_vertex not in visited:\\n visited[curr_vertex] = curr_path\\n\\n for friend in self.friendships[curr_vertex]:\\n path_copy = curr_path[:]\\n path_copy.append(friend)\\n q.enqueue(path_copy)\\n\\n return visited\\n\\n else:\\n print(\\\"There are currently no friendship paths in the network\\\")\",\n \"def path(most_important_up, most_important_down, total_distance, to_source2, to_source1):\\n\\n if total_distance == min(total_distance, to_source2[0], to_source1[0]):\\n return source_to_source(most_important_up, most_important_down), total_distance\\n elif to_source2[0] == min(total_distance, to_source2[0], to_source1[0]):\\n return most_important_to_source(to_source2[1]), to_source2[0]\\n else:\\n return most_important_to_source(to_source1[1], up=False), to_source1[0]\",\n \"def get_distance(restuarant, user):\\n lat1 = math.radians(restuarant[1])\\n lon1 = math.radians(restuarant[0])\\n lat2 = math.radians(user[1])\\n lon2 = math.radians(user[0])\\n dlon = lon1 - lon2\\n dlat = lat1 - lat2\\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * \\\\\\n math.cos(lat2) * math.sin(dlon / 2) ** 2\\n dist = 2 * math.asin(math.sqrt(a))*6371\\n return dist\",\n \"def shortJourney(Alist,s,d):\\n \\\"\\\"\\\"Find shortest distances to s in weighted graph, G\\\"\\\"\\\"\\n \\n #Initialize dictionaries\\n dinit = 10**6\\n Edict = {} #Explored nodes\\n Udict = {} #Unexplored nodes\\n path = [[] for l in Alist]\\n\\n Alen = len(Alist) #length of Alist\\n dinits = [dinit]*Alen #list of airport indexes\\n Udict = dict(zip(list(range(Alen)),dinits)) #zip into dictionary\\n Udict[s] = 0\\n path[s] = [s]\\n \\n #Main search\\n while len(Udict)>0:\\n #Find node with min d in Udict and move to Edict\\n dmin = dinit\\n for n,w in Udict.items():\\n if w<dmin:\\n dmin=w\\n nmin=n\\n Edict[nmin] = Udict.pop(nmin)\\n print(\\\"moved node\\\", nmin)\\n\\n #Update provisional distances for unexplored neighbors of nmin \\n for item in Alist[nmin]: #nminth element is a list of two element tuples (node, weight)\\n n = item[0] #first elt of tuple is node/neighbour\\n w = item[1] #2nd elt is density/weigh\\n #for n,w in etc_______________________-\\n \\n if n in Edict:\\n pass\\n elif n in Udict:\\n #key difference below\\n dcomp = (w+dmin) #take sum as you go along\\n if dcomp<Udict[n]:\\n print(Udict)\\n Udict[n]=dcomp\\n path[n] = path[nmin] + [n]\\n print(path) \\n if nmin == d: #if current node is destination\\n return [path[d],Edict[d]]\\n return [] #no path\",\n \"def creat_graph_and_calc_dist_verb(A):\\n return gp.graph_shortest_path(A, method='auto', directed=False)\",\n \"def dijkstra(graph,start,goal):\\n shortest_distance = {}\\n predecessor = {}\\n unseenNodes = graph\\n path = []\\n for node in unseenNodes:\\n shortest_distance[node] = infinity\\n shortest_distance[start] = 0\\n \\n while unseenNodes:\\n minNode = None\\n for node in unseenNodes:\\n if minNode is None:\\n minNode = node\\n elif shortest_distance[node] < shortest_distance[minNode]:\\n minNode = node\\n \\n for childNode, weight in graph[minNode].items():\\n \\n if weight + shortest_distance[minNode] < shortest_distance[childNode]:\\n shortest_distance[childNode] = weight + shortest_distance[minNode]\\n predecessor[childNode] = minNode\\n unseenNodes.pop(minNode)\\n \\n currentNode = goal\\n while currentNode != start:\\n try:\\n path.insert(0,currentNode)\\n currentNode = predecessor[currentNode]\\n except KeyError:\\n print('Path not reachable')\\n break\\n path.insert(0,start)\\n if shortest_distance[goal] != infinity:\\n return path\",\n \"def DFS1(graph, start, end, path=[], shortest=None):\\n path = path + [start]\\n print 'Current DFS path:', printPath(path)\\n if start == end:\\n return path\\n for node in graph.childrenOf(start):\\n if node not in path: #avoid cycles\\n if shortest == None or len(path) < len(shortest):\\n newPath = DFS1(graph, node, end, path, shortest)\\n if newPath != None:\\n shortest = newPath\\n return shortest\",\n \"def get_path(prevs, goal, start):\\n path = OD({goal: 0})\\n cur = goal\\n while cur != start:\\n (cost, node) = prevs.get(cur)\\n if node == None or node in path:\\n print(\\\"ERROR: No path found from %s -> %s\\\" % (start, goal))\\n return (0, None)\\n path[node] = path[cur] + cost\\n cur = node\\n return (path[start], path.keys()[::-1])\",\n \"def tot_grad_obj(self,x0,gradient):\\n tmp = self.tot_objective(self.rebuild_param(x0,**self.fixed))\\n obj = tmp[0]\\n if gradient:\\n grad =self.fix_par(tmp[1], **self.fixed)[0]\\n return obj,grad\\n else:\\n return obj\",\n \"def DFS(graph, start, end, path, shortest,shortest_path_length,shortest_out_dist,\\\\\\r\\n pathlength,maxOutdistance,toPrint = False):\\r\\n \\r\\n \\r\\n path = path + [start]\\r\\n totalpathlength=getTotalPathLength(path)\\r\\n outerpathlength=getOutPathLength(path)\\r\\n \\r\\n \\r\\n if toPrint:\\r\\n pass \\r\\n \\r\\n if start == end and outerpathlength<=maxOutdistance and (totalpathlength <= shortest_path_length):\\r\\n print('A Shorter Path found',path,[totalpathlength],[outerpathlength])\\r\\n return path\\r\\n for node in graph.childrenOf(start):\\r\\n if node not in path: #avoid cycles\\r\\n \\r\\n \\r\\n \\r\\n if shortest== None or totalpathlength <= shortest_path_length:\\r\\n if totalpathlength <shortest_path_length and outerpathlength<=maxOutdistance :\\r\\n newPath = DFS(graph, node, end, path, shortest, shortest_path_length,\\\\\\r\\n shortest_out_dist,pathlength, maxOutdistance, toPrint)\\r\\n \\r\\n if newPath != None:\\r\\n \\r\\n shortest = newPath\\r\\n shortest_path_length=getTotalPathLength(shortest)\\r\\n shortest_out_dist=getOutPathLength(shortest)\\r\\n \\r\\n\\r\\n \\r\\n return shortest\",\n \"def shortestPath(self, origin):\\n backlink = dict()\\n cost = dict()\\n \\n for i in self.node:\\n backlink[i] = utils.NO_PATH_EXISTS\\n cost[i] = utils.INFINITY\\n cost[origin] = 0\\n \\n scanList = [self.link[ij].head for ij in self.node[origin].forwardStar]\\n \\n while len(scanList) > 0:\\n i = scanList[0]\\n scanList.remove(i)\\n labelChanged = False\\n for hi in self.node[i].reverseStar:\\n h = self.link[hi].tail\\n if h < self.firstThroughNode and h != origin:\\n continue\\n tempCost = cost[h] + self.link[hi].cost\\n if tempCost < cost[i]:\\n cost[i] = tempCost\\n backlink[i] = hi\\n labelChanged = True\\n if labelChanged == True:\\n scanList.extend([self.link[ij].head for ij in self.node[i].forwardStar\\n if self.link[ij].head not in scanList])\\n \\n return (backlink, cost)\",\n \"def shortest_path(self):\\n\\t\\t#dict that will hold the cost of traveling to each station\\n\\t\\t#add the initial cost of the starting station, which is 0\\n\\t\\tD = {0:0}\\n\\n\\t\\t#add all of our dict keys (stations) to our queue\\n\\t\\tstation_queue = self.station_graph.keys()\\n\\n\\t\\t#sort the keys! since the graph is directed and acyclic, the stations\\n\\t\\t#can be explored one at a time, in order, without having to adjust\\n\\t\\t#for the lowest distance value via priority queue.\\n\\t\\t#\\n\\t\\t#sort them with reverse=True so that they can be popped from the\\n\\t\\t#end of the list instead of from the beginning. This should save\\n\\t\\t#some cpu time.\\n\\t\\tstation_queue.sort(reverse=True)\\n\\t\\twhile len(station_queue) > 0:\\n\\n\\t\\t\\tstation = station_queue.pop() #grab the next node in the queue\\n\\n\\t\\t\\tfor next_st, next_cost in self.station_graph[station].iteritems():\\n\\t\\t\\t\\t#loops through the current station's neighbors, and calculates\\n\\t\\t\\t\\t#their costs from the starting node, making sure to store\\n\\t\\t\\t\\t#the lowest cost in our D dict\\n\\t\\t\\t\\talt = D[station] + next_cost #sum the costs\\n\\t\\t\\t\\tif not D.has_key(next_st) or alt < D[next_st]:\\n\\t\\t\\t\\t\\t#if there is no cost on record, or if the newly calculated\\n\\t\\t\\t\\t\\t#cost is lower than the currently recorded one, then\\n\\t\\t\\t\\t\\t#record the newly calculated cost as the lowest\\n\\t\\t\\t\\t\\tD[next_st] = alt #set the cost to get to next_st\\n\\n\\t\\treturn D[self.final_stop]\",\n \"def dfs(self, starting_vertex, destination_vertex):\\n pass # TODO\",\n \"def shortest_path(self, source, destination, parameter=None):\\n paths = []\\n for path in self.graph.shortest_paths(source, destination, parameter):\\n paths.append({'hops': path})\\n return jsonify({'paths': paths})\",\n \"def shortestPath(G, start, end):\\n\\n D, P = Dijkstra(G, start)\\n print(D)\\n print(P)\\n Path = []\\n while 1:\\n Path.append(end)\\n if end == start: break\\n end = P[end]\\n Path.reverse()\\n return Path\",\n \"def shortestPath( self, source, target, weight = None ):\\n if weight == None:\\n return nx.shortest_path(self._G, source, target)\\n else:\\n return nx.shortest_path(self._G, source, target, weight = weight)\",\n \"def reconstruct_path(goal: Vector2D, prev_node: dict) -> list:\\n path = []\\n prev = prev_node[goal] # remove 'goal' from path\\n \\n while prev != None:\\n path.append(prev)\\n prev = prev_node[prev]\\n \\n path = path[:-1] # remove 'start' from path\\n path.reverse()\\n return path\",\n \"def shortest_path(graph, source, target):\\n return shortest_path_recursive(graph, source, target, set())\",\n \"def DFS(graph, start, end, path, shortest, toPrint=False):\\n path = path + [start]\\n if toPrint:\\n print('Current DFS path:', printPath(path))\\n if start == end:\\n return path\\n for node in graph.childrenOf(start):\\n if node not in path: # avoid cycles\\n if shortest == None or len(path) < len(shortest):\\n newPath = DFS(graph, node, end, path, shortest, toPrint)\\n if newPath != None:\\n shortest = newPath\\n elif toPrint:\\n print('Already visited', node)\\n return shortest\",\n \"def getAllSocialPaths(self, userID):\\n visited = {} # Note that this is a dictionary, not a set\\n # !!!! IMPLEMENT ME\\n q = Queue()\\n q.put([userID])\\n\\n while not q.empty():\\n user_path = q.get()\\n user = user_path[-1]\\n\\n if user not in visited.keys():\\n visited[user] = user_path\\n for friend in self.friendships[user]:\\n new_path = user_path[::]\\n new_path.append(friend)\\n q.put(new_path)\\n\\n # get average degree of separation per user\\n degrees = 0\\n for key, item in visited.items():\\n degrees += len(item)\\n \\n print('average degree of separation', degrees/len(self.users))\\n\\n return visited\",\n \"def directedDFS(digraph, start, end, maxTotalDist, maxDistOutdoors, path=[],shortest = None):\\n #TODO\\n #assumes graph is a Digraph\\n #assumes start and end are nodes in graph\\n def Dist(path):\\n result = 0\\n if path == None:\\n return result\\n if len(path) == 0:\\n return result\\n for i in range(len(path)-1):\\n src = path[i]\\n dest = path[i+1]\\n for item in digraph.edges[src]:\\n if item[0] == dest:\\n result += item[1]\\n return result \\n \\n # Helper function to calculate Total Outdoor Distance in a path\\n def Out(path):\\n result = 0\\n if path == None:\\n return result \\n if len(path) == 0:\\n return result\\n for i in range(len(path)-1):\\n src = path[i]\\n dest = path[i+1]\\n for item in digraph.edges[src]:\\n if item[0] == dest:\\n result += item[2]\\n return result\\n \\n# Helper function using DFS method\\n def DFS(graph, start, end, maxD, maxO, path = []):\\n path = path + [start]\\n if start == end:\\n return path\\n shortest = None\\n distShort = maxD\\n outShort = maxO\\n for node in graph.childrenOf(start):\\n if node not in path: #avoid cycles\\n newPath = DFS(graph, node, end, maxD, maxO, path)\\n distNew = Dist(newPath)\\n outNew = Out(newPath)\\n if newPath!= None and distNew <= maxD and outNew <= maxO:\\n if not shortest or distNew < distShort: #check if shorter than shortest\\n shortest = newPath\\n distShort = distNew\\n outShort = outNew\\n return shortest\\n\\n result = DFS(digraph, start, end, maxTotalDist, maxDistOutdoors)\\n if result == None:\\n raise ValueError\\n else:\\n return result\",\n \"def shortestPath(G,start,end):\\n\\n D,P = Dijkstra(G,start)\\n Path = []\\n while 1:\\n Path.append(end)\\n if end == start: break`\\u001b`\\n end = P[end]\\n Path.reverse()\\n return Path\",\n \"def dfs(self, starting_vertex, destination_vertex):\\n # TODO\",\n \"def random_path_forward(g, source, path_len, start_time=0, cost=1, max_duration=sys.maxint):\\n\\n # TODO: maybe we can do a v2 that tries to generate another path if the current path is not valid\\n # (although we have to take into account that such path may not exist)\\n\\n current_time = start_time\\n path = [source]\\n for _ in range(path_len):\\n e = list(g.edges(path[-1], data=True))\\n if len(e):\\n n = choice(e)\\n if n[1] in path:\\n # print \\\"Node already visited\\\"\\n return None\\n if n[2][\\\"start\\\"] + n[2][\\\"duration\\\"] >= current_time + cost:\\n current_time = max(current_time + cost, n[2][\\\"start\\\"] + cost)\\n if current_time - start_time < max_duration:\\n path.append(n[1])\\n else:\\n # print \\\"Too long path\\\"\\n return None\\n else:\\n # print \\\"Invalid time path\\\"\\n return None\\n else:\\n # print \\\"No neighbors\\\"\\n return None\\n\\n # print path\\n return path[-1]\",\n \"def shortest_path(self, source, target, via=None, weight='length', bbox=None):\\n\\n if self._graph_backend == 'networkx':\\n return networkx.shortest_path(self._graph, source, target, weight=weight)\\n else:\\n if isinstance(via, list):\\n return self._pgr.get_route(source, target, via_nodes=via, bbox_nodes=bbox)\\n else:\\n return self._pgr.get_route(source, target)\",\n \"def dft(self, starting_vertex):\\n # Create a s and push starting vertex\\n ss = Stack()\\n ss.push([starting_vertex])\\n # Create a set of traversed vertices\\n visited = []\\n eldest = [] \\n # While stack is not empty:\\n while ss.size() > 0:\\n # dequeue/pop the first vertex\\n path = ss.pop()\\n if path[-1] not in visited:\\n # DO THE THING!!!!!!!\\n # print(path[-1])\\n # mark as visited\\n visited.append(path[-1])\\n print(visited)\\n # enqueue all neightbors\\n if not self.get_neighbors(path[-1]):\\n if starting_vertex == path[-1]:\\n return -1\\n else:\\n # print(\\\"eldest ancestor:\\\",path[-1])\\n eldest.append(path[-1])\\n\\n for next_vert in self.get_neighbors(path[-1]):\\n new_path = list(path)\\n # print(new_path)\\n new_path.append(next_vert)\\n ss.push(new_path)\\n \\n return min(eldest)\",\n \"def shortest_path(self, source, target):\\r\\n key = self.d.keys()\\r\\n #check that endpoints are in graph\\r\\n if source not in key or target not in key:\\r\\n raise KeyError(str(source) + \\\" and \\\" + str(target) + \\\" must be in graph\\\")\\r\\n #initialize V,Q and M\\r\\n V = []\\r\\n vis = dict()\\r\\n Q = deque()\\r\\n Q.append(source)\\r\\n M = set(source)\\r\\n #while target has not been visited\\r\\n while target not in M:\\r\\n #take first element of Q\\r\\n current = Q.popleft()\\r\\n #add element to visited\\r\\n V.append(current)\\r\\n neighbors = self.d[current]\\r\\n #for each neighbor of element\\r\\n for n in neighbors:\\r\\n #if element has not been checked, add it to queue\\r\\n #also save traveled edge in visited\\r\\n if n not in M:\\r\\n Q.append(n)\\r\\n vis.update({n:current})\\r\\n M.add(n)\\r\\n L = [target]\\r\\n #reverse the order of the traveled edges\\r\\n while L[-1] in vis.keys():\\r\\n L.append(vis[L[-1]])\\r\\n return L[::-1]\",\n \"def dfs_recursive(self, starting_vertex, destination_vertex):\\n pass # TODO\",\n \"def dfs_recursive(self, starting_vertex, destination_vertex):\\n pass # TODO\",\n \"def dykstra(self,origin):\\n G = nx.Graph()\\n G.add_node(origin,shortest_path = 0)\\n active_nodes = [origin]\\n\\n while len(active_nodes) > 0:\\n a_n = active_nodes.pop() # active node\\n\\n for n in self.neighbors.get(a_n,[]): #catch no neighbor exception\\n d = self.difference(n, origin)**4 # increase cost of extra distance\\n this_path = G.node[a_n]['shortest_path'] + d\\n\\n if this_path < self.epsilon:\\n if n in G.node: # seen this node before\\n if this_path < G.node[n]['shortest_path']: # if this path is better than previous best path\\n G.node[n]['shortest_path'] = this_path\\n G.add_edge(a_n,n) # add edge just because\\n continue\\n\\n G.add_node(n,shortest_path = this_path) # if this is new, add it to graph\\n G.add_edge(a_n,n) # add edge just because\\n active_nodes.append(n) # add new active node\\n return G\",\n \"def bfsShortestPath(graph, start, goal):\\n\\n # set up a path list\\n path = [start]\\n\\n # return a simple path if start is the goal\\n if start == goal:\\n return path\\n\\n # list to keep track of all visited nodes\\n explored = []\\n\\n # the FIFO queue\\n queue = []\\n\\n # add the first path to the queue\\n queue.append(path)\\n\\n # keep looping until there are no nodes still to be checked\\n while len(queue) > 0:\\n\\n # pop first item from queue (FIFO)\\n path = queue.pop(0)\\n\\n # retrieve the last node from the path list\\n node = path[-1]\\n\\n # check if the node has already been explored\\n if node not in explored:\\n\\n # add node to list of checked nodes\\n explored.append(node)\\n\\n # get neighbours if node is present, otherwise default to empty list\\n neighbours = graph.get(node, [])\\n\\n # go through all neighbour nodes\\n for neighbour in neighbours:\\n # make a copy of the current path\\n path1 = path[:]\\n\\n # add this neighbour to the path\\n path1.append(neighbour)\\n\\n # return path if neighbour is goal\\n if neighbour == goal:\\n return path1\\n\\n # push it onto the queue for further exploration\\n queue.append(path1)\\n\\n # we couldn't find the goal... :(\\n return None\",\n \"def shortest_path(M, start, goal):\\n\\n print(\\\"shortest path called\\\")\\n\\n came_from = {}\\n g_score = {}\\n came_from[start] = None\\n g_score[start] = 0\\n open_heap = []\\n heappush(open_heap, (0, start))\\n\\n while open_heap:\\n current = heappop(open_heap)[1]\\n\\n if current == goal:\\n break\\n\\n for neighbor in M.roads[current]:\\n new_g_score = g_score[current] + heuristic(M.intersections[current], M.intersections[neighbor])\\n\\n if neighbor not in g_score or new_g_score < g_score[neighbor]:\\n came_from[neighbor] = current\\n g_score[neighbor] = new_g_score\\n heappush(open_heap, (new_g_score, neighbor))\\n\\n optimal_path = []\\n node = goal\\n\\n while came_from[node]:\\n optimal_path.append(node)\\n node = came_from[node]\\n else:\\n optimal_path.append(node)\\n\\n optimal_path.reverse()\\n\\n return optimal_path\",\n \"def least_cost_path(G, start, dest, cost):\\n\\n # Create a priority queue\\n todo = pqueue.PQueue()\\n todo.update(start, 0);\\n\\n # v in visited when the vertex v's least cost from start has been determined\\n visited = set()\\n\\n # parent[v] is the vertex that just precedes v in the path from start to v\\n parent = {}\\n\\n while todo and (dest not in visited):\\n\\n # priority queue operation\\n # remove smallest estimated cost vertex from todo list\\n (cur, c) = todo.pop_smallest()\\n\\n # it is now visited, and will never have a smaller cost\\n visited.add(cur)\\n\\n for n in G.adj_to(cur):\\n if n in visited: continue\\n if todo.update(n, c+cost((cur,n))):\\n parent[n] = cur\\n\\n # now, if there is a path, extract it. The graph may be disconnected\\n # so in that case return None\\n if dest not in visited:\\n return None\\n\\n path = [dest]\\n cur = dest\\n while start not in path:\\n cur = parent[cur]\\n path.append(cur)\\n\\n path.reverse()\\n return path\",\n \"def fastest_path_estimation(sol):\\n\\n class Path:\\n def __init__(self, places, graph):\\n self.g = 0 # current cost\\n self.graph = graph\\n self.visited = [places[0]] # list of already visited attractions\\n self.not_visited = copy.deepcopy(places[1:]) # list of attractions not yet visited\\n\\n def __lt__(self, other):\\n return self.g < other.g\\n\\n def add(self, idx):\\n # add the cost\\n self.g += self.graph[self.visited[-1], idx]\\n # add the to the visited place and remove from the unvisited places\\n self.visited.append(idx)\\n self.not_visited.remove(idx)\\n\\n def add_to_heap_queue(path):\\n # custom function to add to heap queue sorted by the solution's cost\\n heappush(h_queue, path)\\n\\n if len(sol.not_visited) == 0:\\n return 0\\n elif len(sol.not_visited) == 1:\\n return sol.graph[sol.visited[-1], sol.not_visited[0]]\\n\\n c = sol.visited[-1]\\n pm = sol.not_visited[-1]\\n # the heap queue of solution sorted by their cost - change all to tuples with g for dijkstra\\n h_queue = []\\n\\n # the places to use for the graph\\n sub_search_places = [c]\\n sub_search_places.extend(sol.not_visited)\\n\\n # push the first \\\"node\\\" in the queue\\n add_to_heap_queue(Path(sub_search_places, sol.graph))\\n while True:\\n # take the next solution with the shortest cost\\n path = heappop(h_queue)\\n # if it contains destination, stop and return that solution\\n if pm in path.visited:\\n return path.g\\n # create a new solution for each neighbor of the current vertex and add it to heap queue\\n for place in path.not_visited:\\n new_path = copy.deepcopy(path)\\n new_path.add(place)\\n add_to_heap_queue(new_path)\",\n \"def find_min_hamiltonian_path(G,weights,probs_instead_of_weights=False):\\n\\n # Create a new model\\n m = Model(\\\"hamiltonian_cycle\\\")\\n \\n # Create variables\\n x_vars = {}\\n u_vars = {}\\n for var1 in permute(G.vertices()):\\n for var2 in permute(G.vertices()):\\n if var1 != var2:\\n x_vars[(var1,var2)] = m.addVar(vtype='B', name=\\\"x_\\\"+str(var1)+'_'+str(var2))\\n u_vars[var1] = m.addVar(vtype=GRB.INTEGER, name=\\\"u_\\\"+str(var1))\\n m.update()\\n \\n for var in G.vertices():\\n if var != START_NODE:\\n cur_incoming = LinExpr([(1.0,v) for k,v in x_vars.items() if (k[1] == var)])\\n #print(cur_incoming)\\n m.addConstr(cur_incoming,GRB.EQUAL,1.0)\\n \\n if var != END_NODE:\\n cur_outgoing = LinExpr([(1.0,v) for k,v in x_vars.items() if (k[0] == var)])\\n #print(cur_outgoing)\\n m.addConstr(cur_outgoing,GRB.EQUAL,1.0)\\n \\n for var1 in G.vertices():\\n for var2 in G.vertices():\\n if var1 != var2:\\n c = LinExpr([(1.0,u_vars[var1]),(-1.0,u_vars[var2]),(G.num_vertices(),x_vars[(var1,var2)])])\\n #print(c)\\n m.addConstr(c,GRB.LESS_EQUAL,G.num_vertices()-1)\\n \\n # Set objective\\n #try:\\n edge_weights = permute(G.get_edge_weights(weights))\\n if probs_instead_of_weights:\\n all_probs = []\\n for v in G.vertices():\\n if v != END_NODE:\\n batch_scores = [(e,w) for e,w in edge_weights if e[0] == v]\\n S = logsumexp([x[1] for x in batch_scores])\\n batch_scores = [(e,np.exp(w-S)) for e,w in batch_scores]\\n all_probs.extend(batch_scores)\\n edge_weights = all_probs\\n objective = LinExpr([(weight,x_vars[edge]) for edge,weight in edge_weights])\\n #except TypeError:\\n # return None\\n \\n m.setObjective(objective,GRB.MINIMIZE)\\n m.update()\\n code = m.optimize()\\n \\n try:\\n return [k for k,v in x_vars.items() if v.x > 0.98]\\n except GurobiError:\\n return None\",\n \"def dfs_recursive(self, starting_vertex, destination_vertex, visited=None, path=None):\\n if visited is None: # if visited is not empty\\n visited = set() # create an empty 'set' to store visisted vertex, set sorts \\n\\n if path is None: # if the path is empty \\n path = [] # create an empty list \\n visited.add(starting_vertex) # add the starting_vertex to the set \\n path = path + [starting_vertex] # set the path \\n\\n if starting_vertex == destination_vertex: # if the starting_vertex is equal to the destination_vertex\\n return path # return the path \\n\\n for neighbor in self.vertices[starting_vertex]: # loop through neighbors \\n if neighbor not in visited: # if the neighbor has not been visited \\n new_path = self.dfs_recursive(neighbor, destination_vertex, visited, path) # create a new path using the dfs_recursive method\\n\\n if new_path: # if there is a new_path \\n return new_path # return the new path \\n\\n return None # return None \",\n \"def path_to(start, end):\\n sol = djikstra(start, end)\\n if sol is None:\\n return None\\n (distances, cost) = sol\\n return compute_path(distances, cost, start, end)\",\n \"def calculateShortestPath(self, startVertex: str, vertices: Tuple) -> dict:\\n minDistance, shortestPath, visited = {}, {}, {}\\n\\n for v in vertices:\\n minDistance[v] = sys.maxsize\\n shortestPath[v] = \\\"\\\"\\n visited[v] = False\\n\\n minDistance[startVertex] = 0\\n\\n n = len(vertices) - 1\\n while n > 0:\\n fromVertex = min(minDistance, key=lambda x: minDistance[x] if not visited[x] else sys.maxsize)\\n visited[fromVertex] = True\\n\\n for toVertex, edgeWeight in self.adjacencyList[fromVertex]:\\n if not visited[toVertex] and minDistance[fromVertex] + edgeWeight < minDistance[toVertex]:\\n minDistance[toVertex] = minDistance[fromVertex] + edgeWeight\\n shortestPath[toVertex] = shortestPath[fromVertex][:-1] + fromVertex + toVertex\\n n -= 1\\n\\n shortestPathWithDistance = {\\n v: dict(\\n distance=minDistance[v],\\n path=shortestPath[v]\\n )\\n for v in vertices\\n }\\n return shortestPathWithDistance\",\n \"def find_shortest_path(self, start, end):\\n\\n if start==None:\\n return\\n\\n visited = {}\\n\\n distance = {start:0}\\n parent = {start:None}\\n\\n queue = deque()\\n queue.append(start)\\n\\n while queue:\\n\\n cn = queue.popleft()\\n\\n for n in self.adjacencylist[cn]:\\n if n not in visited:\\n queue.append(n)\\n parent[n] = cn\\n if n not in distance:\\n distance[n] = 1\\n else:\\n distance[n] += 1\\n\\n visited[cn] = True\\n\\n if all(visited.values()) == True:\\n print('BFS done')\\n\\n print(\\\"Finding shortest path\\\")\\n\\n path = []\\n cn = end\\n path.append(cn)\\n\\n while cn != start:\\n cn = parent[cn]\\n path.append(cn)\\n\\n print (path[::-1])\",\n \"def allPairsShortestPath(g):\\r\\n #dist+pred will be dicts (key:vtx, val:dict->KEY:toVtx, VAL:dist/pred) \\r\\n dist = {}\\r\\n pred = {}\\r\\n #for all vertices, create dict values with vertex as key\\r\\n for u in g: \\r\\n dist[u] = {} \\r\\n pred[u] = {}\\r\\n #for all vertices, set init. dist+pred to all vertices, including self\\r\\n for v in g: \\r\\n dist[u][v] = sys.maxsize\\r\\n pred[u][v] = None\\r\\n\\r\\n #finish off init with obvious dist and pred to self as 0 and None\\r\\n dist[u][u] = 0\\r\\n pred[u][u] = None\\r\\n\\r\\n #for all vertices in graph for current vertex u, set obvious..\\r\\n #..neighboring direct dist and pred\\r\\n for v in g[u]:\\r\\n dist[u][v] = g[u][v]\\r\\n pred[u][v] = u\\r\\n\\r\\n #this is the heavy lifting to figure out...\\r\\n for mid in g:\\r\\n for u in g:\\r\\n for v in g:\\r\\n newlen = dist[u][mid] + dist[mid][v]\\r\\n if newlen < dist[u][v]:\\r\\n dist[u][v] = newlen\\r\\n pred[u][v] = pred[mid][v]\\r\\n\\r\\n return (dist,pred)\",\n \"def return_path(self, start, target):\\n\\n # return early if we don't need to go anywhere\\n if start == target: return []\\n\\n # otherwise look for a path\\n visited = set()\\n path = {}\\n costs = {}\\n pq = [(0, start)]\\n\\n # Look until we find the target, dijkstra's guarantees\\n # minimum cost path according to cost function.\\n #\\n # Cost function is: Cost to get to current worker + workload at worker\\n while target not in visited:\\n cost, curr = heapq.heappop(pq)\\n if curr in visited: continue\\n visited.add(curr)\\n for neighbor in self.neighbors[curr]:\\n if neighbor in visited: continue\\n cost_new = cost + self.workload[neighbor] \\n if neighbor not in costs or cost_new < costs[neighbor]:\\n costs[neighbor] = cost_new\\n path[neighbor] = curr\\n heapq.heappush(pq, (cost_new, neighbor))\\n\\n ret_path = [0]\\n while ret_path[-1] != start:\\n ret_path.append(path[ret_path[-1]])\\n\\n return ret_path[::-1]\",\n \"def uniformCostSearch(problem):\\n \\\"*** YOUR CODE HERE ***\\\"\\n path_to_point = {}\\n cost_to_point = {}\\n\\n # Get the start node\\n start_node = problem.getStartState()\\n fringe_node = [start_node]\\n path_to_point[start_node] = []\\n cost_to_point[start_node] = problem.getCostOfActions(path_to_point[start_node])\\n\\n goal_found = False\\n\\n while(not goal_found):\\n #for i in range(100): \\n nodes_to_expand = set()\\n # get max value node in the fringe node\\n min_val = float(\\\"inf\\\")\\n for one_node in fringe_node:\\n # Compute the cost to reach a node\\n if cost_to_point[one_node] < min_val:\\n min_val = cost_to_point[one_node]\\n \\n for one_node in fringe_node:\\n # Compute the cost to reach a node\\n if cost_to_point[one_node] == min_val:\\n nodes_to_expand.add(one_node)\\n fringe_node.remove(one_node)\\n\\n # Expand the fringe node \\n for one_node in nodes_to_expand:\\n path_to_parent = path_to_point[one_node]\\n for nxt_node in problem.getSuccessors(one_node):\\n pos = nxt_node[0]\\n mv = nxt_node[1]\\n # check if point already present in path to point\\n prev_cost = float(\\\"inf\\\")\\n if pos in cost_to_point:\\n prev_cost = cost_to_point[pos]\\n new_path = path_to_parent + [mv]\\n if prev_cost > problem.getCostOfActions(new_path):\\n path_to_point[pos] = new_path\\n cost_to_point[pos] = problem.getCostOfActions(new_path)\\n fringe_node.append(pos)\\n\\n # Check if destination is reached in the fringe node\\n for one_node in fringe_node:\\n if problem.isGoalState(one_node):\\n final_node = one_node\\n goal_found = True\\n break\\n \\n #print(len(fringe_node))\\n print(final_node)\\n print(path_to_point[final_node])\\n return path_to_point[final_node] \\n\\n util.raiseNotDefined()\",\n \"def calculateNewPath(self):\\r\\n\\r\\n\\t\\tnodeDict = self.simulationHandle.getMap().getNodeDict()\\r\\n\\t\\tdistDict = self.simulationHandle.getMap().getDistDict()\\r\\n\\r\\n\\t\\tself.pathToGoal = pathfinder.findPath(self.currentNode, self.goalNode, nodeDict, distDict)\",\n \"def path_correction(data, user_coords):\\n # Return list if it only has the destination\\n if len(data) == 1:\\n return data\\n\\n # Calculate distance from user to second waypoint\\n second_coords = (data[1][\\\"lat\\\"], data[1][\\\"lon\\\"])\\n user_second_dist = geopy.distance.distance(user_coords, second_coords).miles\\n\\n # Calculate distance from user to first waypoint\\n first_coords = (data[0][\\\"lat\\\"], data[0][\\\"lon\\\"])\\n user_first_dist = geopy.distance.distance(user_coords, first_coords).km\\n\\n # Calculate distance from first waypoint to second waypoint\\n first_second_dist = geopy.distance.distance(first_coords, second_coords).miles\\n\\n # Determine if path correction is applicable\\n if user_second_dist < first_second_dist or user_first_dist < 0.01:\\n # Delete first element of list so that user doesn't backtrack\\n return data[1:]\\n else:\\n # No path correction needed\\n return data\",\n \"def dfs_recursive(self, starting_vertex, target_vertex, visited=None, path=None):\\n # if starting_vertex in visited:\\n # return None\\n # if starting_vertex == destination_vertex:\\n # return [starting_vertex]\\n # visited.add(starting_vertex)\\n # for vertex in self.get_neighbors(starting_vertex):\\n # path = self.dfs_recursive(vertex, destination_vertex, visited)\\n # if path is not None: \\n # return [starting_vertex] + path\\n if visited == None:\\n visited = set()\\n if path == None:\\n path = []\\n visited.add(starting_vertex)\\n path = path + [starting_vertex]\\n if starting_vertex == target_vertex:\\n return path\\n for neighbor in self.get_neighbors(starting_vertex):\\n if neighbor not in visited:\\n new_path = self.dfs_recursive(neighbor, target_vertex, visited, path)\\n if new_path is not None: \\n return new_path\\n return None\",\n \"def dfs(self, starting_vertex, destination_vertex):\\n # This solution takes a slightly different approach as to how we are storing the path\\n # Now, we always queue up the next vertex we want to see, and a list of all the vertices we looked at to get here\\n # so if we are queueing up vertex 3 from our example, the tuple we create will be (3, [1,2])\\n # because we had to go through 1 and 2 to get here\\n neighbors_to_visit = Stack()\\n visited = set()\\n # add the first vertex, and an empty list indicating that we have not been to any other vertices yet\\n neighbors_to_visit.push([starting_vertex])\\n # loop through the stack\\n while neighbors_to_visit.size() > 0:\\n path = neighbors_to_visit.pop()\\n # pull out the current vertex so its easier to read\\n vertex = path[-1] # last one in the path is our current vertex\\n # if the vertex is the destination return it plus the path we took to get here\\n if vertex == destination_vertex:\\n return path\\n # make sure the vertex isnt something we have seen already\\n if vertex not in visited:\\n # mark the vertex as visited\\n visited.add(vertex)\\n # add neighbors to the stack\\n for neighbor in self.get_neighbors(vertex):\\n new_path = path\\n new_path.append(neighbor)\\n neighbors_to_visit.push(new_path)\",\n \"def acyclicShortestPath(self, origin):\\n backlink = dict()\\n cost = dict()\\n \\n for i in self.node:\\n backlink[i] = utils.NO_PATH_EXISTS\\n cost[i] = utils.INFINITY\\n cost[origin] = 0\\n \\n for topoNode in range(self.node[origin].order + 1, self.numNodes + 1):\\n i = self.topologicalList[topoNode]\\n for hi in self.node[i].reverseStar:\\n h = self.link[hi].tail\\n if h < self.firstThroughNode and h != origin:\\n continue\\n tempCost = cost[h] + self.link[hi].cost\\n if tempCost < cost[i]:\\n cost[i] = tempCost\\n backlink[i] = hi\\n \\n return (backlink, cost)\",\n \"def djikstra(self, source, target):\\r\\n dist = {}\\r\\n prev = {}\\r\\n set_q = {}\\r\\n for vertex in self.vertices.keys():\\r\\n dist[vertex] = sys.maxsize\\r\\n prev[vertex] = None\\r\\n set_q[vertex] = dist[vertex]\\r\\n dist[source] = 0\\r\\n set_q[source] = 0\\r\\n while set_q:\\r\\n vertex_u = min(set_q, key=set_q.get)\\r\\n if vertex_u == target:\\r\\n break\\r\\n set_q.pop(vertex_u)\\r\\n for edge in self.edges[vertex_u]:\\r\\n alt = dist[vertex_u] + edge.distance\\r\\n if alt < dist[edge.destination]:\\r\\n dist[edge.destination] = alt\\r\\n set_q[edge.destination] = dist[edge.destination]\\r\\n prev[edge.destination] = vertex_u\\r\\n path = []\\r\\n vertex_u = target\\r\\n while prev[vertex_u]:\\r\\n path.insert(0, vertex_u)\\r\\n vertex_u = prev[vertex_u]\\r\\n path.insert(0, vertex_u)\\r\\n return path\",\n \"def shortest_path_search(start, successors, is_goal):\\n if is_goal(start):\\n return [start]\\n explored = set()\\n frontier = [ [start] ] \\n while frontier:\\n path = frontier.pop(0)\\n s = path[-1]\\n for (state, action) in successors(s).items():\\n if state not in explored:\\n explored.add(state)\\n path2 = path + [action, state]\\n if is_goal(state):\\n return path2\\n else:\\n frontier.append(path2)\\n return Fail\",\n \"def updatePath(self):\\n logging.debug(\\\"ShortestPathUI.updatePath function started\\\")\\n error = False\\n fromVertex = self.fromLineEdit.text()\\n toVertex = self.toLineEdit.text()\\n graphVertices = self.controller.getVertices()\\n if fromVertex not in graphVertices:\\n self.__drawErrorInfo(self.fromLineEdit)\\n error = True\\n if toVertex not in graphVertices:\\n self.__drawErrorInfo(self.toLineEdit)\\n error = True\\n if not error:\\n self.pathLineEdit.setText(\\\"\\\")\\n self.lengthLabel.setText(\\\"\\\")\\n path, length = self.controller.calculateShortestPath(fromVertex, toVertex)\\n path = \\\" -- \\\".join(path)\\n self.pathLineEdit.setText(f\\\"{path}\\\")\\n self.lengthLabel.setText(f\\\"{length}\\\")\\n\\n logging.debug(\\\"ShortestPathUI.updatePath function ended\\\\n\\\")\",\n \"def calculate_path(self):\\n #Se repite el ciclo para el número especificado de veces\\n for i in range(self.iterations):\\n for ant in self.ants:\\n ant.setup_ant()\\n while not ant.final_node_reached:\\n #Seleccion aleatoria del nodo a visitar\\n node_to_vist = self.select_next_node(self.map.nodes_array[int(ant.actual_node[0])][int(ant.actual_node[1])])\\n #Mover la hormiga al siguiente nodo seleccionado al azar\\n ant.move_ant(node_to_visit)\\n #Compruebe si se ha alcanzado la solución\\n ant.is_final_node_reached()\\n #Agregar la ruta resultante a la lista de rutas\\n self.add_to_path_results(self.delete_loops(ant.get_visited_nodes()))\\n # Habilitar a la hormiga para otra busqueda\\n ant.enable_start_new_path()\\n \\n # Actualizar el nivel global de feromonas\\n self.pheromone_update()\\n self.best_result = self.paths[0]\\n\\n #Vaciar la lista de rutas\\n self.empty_paths()\\n print('Iteration: ', i, 'lenght of the path: ', len(self.best_result))\\n return self.best_result\",\n \"def generate_path(goal_node, visited):\\n goal_state = goal_node['state']\\n path = [goal_state]\\n while goal_node['parent']:\\n path.append(goal_node['state'])\\n goal_node = visited[goal_node['parent']]\\n return path\",\n \"def dfs(self, starting_vertex, destination_vertex):\\n visited = set()\\n paths = [[starting_vertex]]\\n \\\"\\\"\\\"\\n While the length of possible paths is not zero. \\n Store the current path and remove it from possible \\n paths. Return the last path if it's the destination. \\n If the path hasn't been visited yet add it to the \\n visited list and loop over it's edges creating paths \\n to check later. \\n \\\"\\\"\\\"\\n while len(paths) > 0:\\n path = paths.pop(-1)\\n vertex = path[-1]\\n if vertex == destination_vertex:\\n return path\\n if vertex not in visited:\\n visited.add(vertex)\\n for key in self.get_neighbors(vertex):\\n newPath = path + [key]\\n paths.append(newPath)\",\n \"def getShortestPath(self, src, dest):\\n vertices = self.floorGraph.getVertList()\\n unvisitedQueue = []\\n srcPath = Path()\\n srcPath.addNode(src)\\n srcPath.pathValue = 0\\n unvisitedQueue.append(srcPath)\\n connections = self.floorGraph.getVertex(src).getConnections()\\n #initialisez distances\\n for vertex in vertices:\\n newPath = Path()\\n newPath.nodeList = list(srcPath.nodeList)\\n newPath.addNode(vertex)\\n if self.floorGraph.getVertex(vertex) in connections:\\n newPath.pathValue = self.floorGraph.getVertex(src).getWeight(self.floorGraph.getVertex(vertex))\\n unvisitedQueue.append(newPath)\\n else:\\n newPath.pathValue = math.inf\\n self.shortestDistanceMap[src+vertex] = newPath\\n # updates distances as per shorter routes\\n while len(unvisitedQueue) is not 0:\\n unvisitedQueue = sorted(unvisitedQueue, key=functools.cmp_to_key(compareNodes))\\n chkPath = unvisitedQueue.pop(0)\\n chkNode = chkPath.nodeList[len(chkPath.nodeList)-1]\\n for vertex in vertices:\\n if(self.floorGraph.getVertex(vertex) in self.floorGraph.getVertex(chkNode).getConnections()):\\n newWeight = chkPath.pathValue + self.floorGraph.getVertex(chkNode).getWeight(self.floorGraph.getVertex(vertex))\\n if(newWeight < self.shortestDistanceMap[src+vertex].pathValue):\\n self.shortestDistanceMap[src+vertex].pathValue = newWeight\\n self.shortestDistanceMap[src+vertex].nodeList = list(chkPath.nodeList)\\n self.shortestDistanceMap[src+vertex].nodeList.append(vertex)\\n newPath = Path()\\n newPath.nodeList = list(self.shortestDistanceMap[src+vertex].nodeList)\\n newPath.pathValue = newWeight\\n unvisitedQueue.append(newPath)\\n print(self.shortestDistanceMap[src+dest].nodeList)\\n print(self.shortestDistanceMap[src+dest].pathValue)\",\n \"def get_best_path(digraph, start, end, path, max_dist_outdoors, best_dist,\\n best_path):\\n path[0] = path[0] + [start]\\n \\n if digraph.has_node(Node(start)) == False or digraph.has_node(Node(end)) == False:\\n raise ValueError('Start or end node are not valid')\\n elif start == end :\\n return path\\n else:\\n for edge in digraph.get_edges_for_node(Node(start)):\\n \\n node = str(edge.get_destination())\\n tot_distance = edge.get_total_distance() + path[1]\\n tot_outdoors = edge.get_outdoor_distance() + path[2]\\n\\n if node not in path[0] and tot_distance <= best_dist and tot_outdoors <= max_dist_outdoors :\\n updated_path = [path[0], tot_distance, tot_outdoors]\\n new_path = get_best_path(digraph, node, end, updated_path, max_dist_outdoors, best_dist, best_path)\\n \\n if new_path:\\n if not best_dist or new_path[1] < best_dist: \\n best_path = new_path[0]\\n best_dist = new_path[1]\\n\\n return best_path, best_dist\",\n \"def dft_recursive(self, starting_vertex):\\n # TODO\\n # creating a function inside that includes a list\\n # of previously traversed vertices\\n def recursive(graph, traversed, vertex):\\n # if the vertex is in traversed already, return none\\n if vertex in traversed:\\n return \\n # otherwise we print it out\\n print(vertex)\\n # append the vertex to our traversed list\\n traversed.add(vertex)\\n # running the function on the neighbors of the vertex\\n for val in graph[vertex]:\\n recursive(graph, traversed, val)\\n\\n recursive(self.vertices, set(), starting_vertex)\",\n \"def dfs(self, starting_vertex, destination_vertex):\\n \\\"\\\"\\\" LIFO\\n Create a stack\\n Create a set to store visited\\n PUSH starting vertex into an array (STACK)\\n While the STACK is NOT empty \\n get((pop) first PATH vertex\\n get Vertex from END of PATH\\n check if NOT visited\\n mark as visited\\n check if vertex is destination_vertex\\n If TRUE, return path \\n PUSH path to ALL of neighbors\\n make copy of current path\\n add neighbor to path copy\\n PUSH path copy\\n \\\"\\\"\\\" \\n s = Stack() # Create a stack\\n s.push([starting_vertex]) # PUSH starting vertex into an array (STACK)\\n visited = set() # Create a set to store visited\\n\\n while s.size() > 0: # While the STACK is NOT empty\\n path = s.pop() # get(pop) first PATH vertex)\\n v = path[-1] # get Vertex from END of PATH \\n\\n while v not in visited: # check if NOT visited\\n visited.add(v) # mark as visited\\n\\n if v == destination_vertex: # check if vertex is destination_vertex\\n return path # If TRUE, return path \\n\\n for n in self.get_neighbors(v): # PUSH path to ALL of neighbors\\n path_c = path[:] # make copy of current path\\n # path_c.extend([n]) # add neighbor to path copy\\n path_c.append(n) # add neighbor to path copy\\n s.push(path_c) # PUSH path copy\",\n \"def shortest_path(self, start: str, goal: str) -> Path:\\n return next(self.bfs_paths(start, goal), [])\",\n \"def path_planning_with_cost(x_x, x_y, s, T, epochs, stepsize, cost_f, ax=None, ax_lims=None, debug=False):\\n ax = ax or plt.gca()\\n plt.plot(0, 0, 'gx', markersize=20, markeredgewidth=5)\\n plt.plot(x_x, x_y, 'rx', markersize=20, markeredgewidth=5)\\n u = nn.Parameter(torch.zeros(T, 2))\\n optimizer = SGD((u,), lr=stepsize)\\n dt = 1 # s\\n costs = []\\n for epoch in range(epochs):\\n x = [torch.tensor((0, 0, 0, s),dtype=torch.float32)]\\n for t in range(1, T+1):\\n x.append(x[-1] + f(x[-1], u[t-1]) * dt)\\n x_t = torch.stack(x)\\n τ = torch.stack(x).detach()\\n cost = cost_f(x_t, (x_x, x_y))\\n costs.append(cost.item())\\n optimizer.zero_grad()\\n cost.backward()\\n optimizer.step()\\n if debug: \\n print(u.data)\\n # Only plot the first and last trajectories\\n if epoch == 0: \\n plot_τ(ax, τ, ax_lims=ax_lims)\\n if epoch == epochs-1:\\n plot_τ(ax, τ, car=True, ax_lims=ax_lims)\",\n \"def shortest_path_search(start, successors, is_goal):\\r\\n if is_goal(start):\\r\\n return [start]\\r\\n\\r\\n explored = set() # set of states we have visited\\r\\n frontier = [ [start] ] # ordered list of paths we have blazed\\r\\n while frontier:\\r\\n path = frontier.pop(0)\\r\\n s = path[-1]\\r\\n for (state, action) in successors(s).items():\\r\\n if state not in explored:\\r\\n explored.add(state)\\r\\n path2 = path + [action, state]\\r\\n if is_goal(state):\\r\\n return path2\\r\\n else:\\r\\n frontier.append(path2)\\r\\n return Fail\",\n \"def shortest_path(graph, start, end):\\n nodes_to_visit = {start}\\n visited_nodes = set()\\n # Distance from start to start is 0\\n distance_from_start = {start: 0}\\n predecessors = {} # Store previous node for shortest route for each node\\n\\n while nodes_to_visit:\\n # Get node with smallest weight\\n current = min(\\n [(distance_from_start[node], node) for node in nodes_to_visit]\\n )[1]\\n\\n # If the end is reached, quit\\n if current == end:\\n break\\n\\n nodes_to_visit.discard(current)\\n visited_nodes.add(current)\\n\\n edges = graph[current]\\n unvisited_neighbours = set(edges).difference(visited_nodes)\\n for neighbour in unvisited_neighbours:\\n neighbour_distance = distance_from_start[current] + \\\\\\n edges[neighbour]\\n if neighbour_distance < distance_from_start.get(neighbour,\\n float('inf')):\\n distance_from_start[neighbour] = neighbour_distance\\n predecessors[neighbour] = current\\n nodes_to_visit.add(neighbour)\\n\\n return _deconstruct_path(predecessors, end)\",\n \"def FindShortestPath(graph, start, end, path=[]):\\n path = path + [start]\\n if start == end:\\n return path\\n if start not in graph:\\n return None\\n shortest = None\\n for node in graph[start]:\\n if node not in path:\\n newpath = FindShortestPath(graph, node, end, path)\\n if newpath:\\n if not shortest or len(newpath) < len(shortest):\\n shortest = newpath\\n return shortest\",\n \"def generate_possible_paths(self, obstacle):\\n if self.does_uav_intersect_obstacle_vertically(obstacle, self.drone.get_point(), self.drone.get_waypoint_holder().get_current_waypoint()):\\n if self.does_path_intersect_obstacle_2d(obstacle, self.drone.get_point(), self.drone.get_waypoint_holder().get_current_waypoint()):\\n new_attempt_pos_points = [\\n [obstacle.get_point()[0] + obstacle.get_radius(), obstacle.get_point()[1] + obstacle.get_radius(), self.drone.get_point()[2]],\\n [obstacle.get_point()[0] - obstacle.get_radius(), obstacle.get_point()[1] - obstacle.get_radius(), self.drone.get_point()[2]],\\n [obstacle.get_point()[0] + obstacle.get_radius(), obstacle.get_point()[1] - obstacle.get_radius(), self.drone.get_point()[2]],\\n [obstacle.get_point()[0] - obstacle.get_radius(), obstacle.get_point()[1] + obstacle.get_radius(), self.drone.get_point()[2]],\\n [obstacle.get_point()[0], obstacle.get_point()[1] + obstacle.get_radius(), obstacle.get_height() + (Constants.STATIONARY_OBSTACLE_SAFETY_RADIUS * 2)],\\n [obstacle.get_point()[0], obstacle.get_point()[1] - obstacle.get_radius(), obstacle.get_height() + (Constants.STATIONARY_OBSTACLE_SAFETY_RADIUS * 2)],\\n [obstacle.get_point()[0] + obstacle.get_radius(), obstacle.get_point()[1], obstacle.get_height() + (Constants.STATIONARY_OBSTACLE_SAFETY_RADIUS * 2)],\\n [obstacle.get_point()[0] - obstacle.get_radius(), obstacle.get_point()[1], obstacle.get_height() + (Constants.STATIONARY_OBSTACLE_SAFETY_RADIUS * 2)]\\n ]\\n\\n new_paths = []\\n for new_pos_point in new_attempt_pos_points:\\n if not self.does_path_intersect_obstacle_3d(obstacle, self.drone.get_point(), new_pos_point) and self.flight_boundary.is_point_in_bounds(new_pos_point):\\n for recursive_new_pos_point in new_attempt_pos_points:\\n if self.flight_boundary.is_point_in_bounds(recursive_new_pos_point) and abs(recursive_new_pos_point[2] - new_pos_point[2]) < 5:\\n if recursive_new_pos_point[0] != new_pos_point[0] or recursive_new_pos_point[1] != new_pos_point[1]:\\n if not self.does_path_intersect_obstacle_3d(obstacle, new_pos_point, recursive_new_pos_point) and not self.does_path_intersect_obstacle_3d(obstacle, recursive_new_pos_point, self.drone.get_waypoint_holder().get_current_waypoint()):\\n new_paths.append([new_pos_point, recursive_new_pos_point])\\n\\n # Uncomment for DEBUGGING ONLY\\n for path in new_paths:\\n print(\\\"Point:\\\", str(path))\\n\\n return new_paths\\n\\n return []\",\n \"def calculateFirstPath(self):\\n rd.shuffle(self.goals)\\n self.path = self.goals\",\n \"def shortest_path(theta, adj=adjusted_adj_list_4):\\n dists = 50*np.ones((4604))\\n visited = np.zeros((4604), dtype='b')\\n #\\n current_node = theta\\n# visited[theta] = True\\n count = 0\\n dists[theta] = 0\\n while visited[current_node] == False:\\n cur = dists[current_node]\\n for other_node in adj[current_node]:\\n if dists[other_node] > cur + 1:\\n dists[other_node] = cur + 1\\n visited[current_node] = True\\n x1 = set(np.where(visited == False)[0])\\n x2 = set(adj.keys())\\n x = x1.intersection(x2)\\n# x = [x for x in np.where(visited == False)[0] if x in adj.keys()]\\n if len(x) == 0:\\n np.save('./short_paths/' + str(theta) + '.npy', dists) \\n return dists\\n else:\\n current_node = x.pop()\\n count += 1\",\n \"def _find_path_start(G):\\r\\n is_directed = G.is_directed()\\r\\n\\r\\n # list to check the odd degree condition\\r\\n check_odd = []\\r\\n\\r\\n if is_directed:\\r\\n degree = G.in_degree\\r\\n out_degree = G.out_degree\\r\\n else:\\r\\n degree = G.degree\\r\\n\\r\\n # Verify if an Euler path can be found. Complexity O(|V|)\\r\\n for v in G:\\r\\n deg = degree(v)\\r\\n # directed case\\r\\n if is_directed:\\r\\n outdeg = out_degree(v)\\r\\n if deg != outdeg:\\r\\n # if we have more than 2 odd nodes no Euler path exists\\r\\n if len(check_odd) > 2:\\r\\n return False\\r\\n # is odd and we append it.\\r\\n check_odd.append(v)\\r\\n # undirected case\\r\\n else:\\r\\n if deg % 2 != 0:\\r\\n # if we have more than 2 odd nodes no Euler path exists\\r\\n if len(check_odd) > 2:\\r\\n return False\\r\\n # is odd and we append it.\\r\\n check_odd.append(v)\\r\\n start = None\\r\\n if is_directed:\\r\\n\\r\\n first = check_odd[0]\\r\\n second = check_odd[1]\\r\\n if G.out_degree(first) == G.in_degree(first) + 1 and \\\\\\r\\n G.in_degree(second) == G.out_degree(second) + 1:\\r\\n start = second\\r\\n elif G.out_degree(second) == G.in_degree(second) + 1 and \\\\\\r\\n G.in_degree(first) == G.out_degree(first) + 1:\\r\\n start = first\\r\\n else:\\r\\n start = None\\r\\n\\r\\n else:\\r\\n if len(check_odd) > 0:\\r\\n start = check_odd[0]\\r\\n\\r\\n return start\",\n \"def build(self, source, target):\\n return self.shortestPath(self.graph, source, target)\",\n \"def create_user_path_assoc():\\n if not check_content_type():\\n return jsonify(status=CONTENT_TYPE_ERROR)\\n reqdata = request.json\\n if not check_token(reqdata[\\\"token\\\"]):\\n return jsonify(status=TOKEN_ERROR)\\n #TODO check that request body contain needed data\\n #if [\\\"user_id\\\", \\\"path_id\\\", \\\"ready\\\", \\\"coordinate\\\", \\\"first_name\\\", \\\"second_name\\\", \\\"phone\\\"].sort() != (data.keys()).sort():\\n # return jsonify(status=\\\"err\\\")\\n user_id = reqdata[\\\"user_id\\\"]\\n path_id = reqdata[\\\"path_id\\\"]\\n ready = True\\n coordinate = reqdata[\\\"coordinate\\\"]\\n first_name = reqdata[\\\"first_name\\\"]\\n second_name = reqdata[\\\"second_name\\\"]\\n phone = reqdata[\\\"phone\\\"]\\n #TODO data validation\\n user = db.session.query(User).filter(User.id==user_id).scalar() is not None\\n path = db.session.query(Path).filter(Path.id==path_id).scalar() is not None\\n if user and path:\\n db.session.add(UserPathAssociation(user_id=user_id, path_id=path_id, ready=ready,coordinate=coordinate, first_name=first_name, second_name=second_name,phone=phone))\\n try:\\n db.session.commit()\\n return jsonify(status=OK_STATUS)\\n except:\\n db.session.rollback()\\n return jsonify(status=DATABASE_INTEGRITY_ERROR)\\n else:\\n return jsonify(status=\\\"err\\\")\",\n \"def dijkstra_shortest_path(graph, search):\\n distances_from_start = [None] * len(graph)\\n\\n visited_vertexes = []\\n\\n current_vertex = 0\\n\\n distances_from_start[current_vertex] = [0, 0] # [distance from start, via vertex]\\n\\n for row in range(len(graph)):\\n\\n current_vertex = row\\n\\n #print(\\\"Current vertex: \\\", current_vertex)\\n\\n # Iterate through each column in the current row in the adjacency matrix\\n for col in range(len(graph[current_vertex])):\\n\\n if graph[current_vertex][col] is not None and distances_from_start[col] is None:\\n distances_from_start[col] = [distances_from_start[current_vertex][0] + graph[current_vertex][col], current_vertex]\\n\\n elif graph[current_vertex][col] is not None and (graph[current_vertex][col] + distances_from_start[current_vertex][0]) < distances_from_start[col][0]:\\n distances_from_start[col] = [(graph[current_vertex][col] + distances_from_start[current_vertex][0]), current_vertex]\\n\\n print(\\\"Distances from start: \\\", distances_from_start) # show updated distances_from_start array\\n\\n # Add current_vertex to visited list so that its distance from the start is calculated again in future\\n if current_vertex not in visited_vertexes:\\n visited_vertexes.append(current_vertex)\\n\\n # print(\\\"Visited vertexes: \\\", visited_vertexes)\\n\\n # Print the shortest path in a friendly format\\n print(\\\"Shortest path:\\\")\\n current_vertex = search #len(graph) - 1\\n path_string = \\\"\\\"\\n orderlist = []\\n while current_vertex > 0:\\n\\n # Add the distance for the current vertex from the start in brackets after the letter of the vertex.\\n path_string = \\\"{0}({1}) \\\".format(chr(current_vertex + 65), distances_from_start[current_vertex][0]) + path_string\\n\\n temp = [chr(current_vertex + 65), distances_from_start[current_vertex][0]]\\n\\n orderlist.append(temp)\\n\\n # Update the current vertex to be the one that the current one goes via on its way back to the start\\n current_vertex = distances_from_start[current_vertex][1] # distances_from_start[vertex number, via vertex]\\n\\n\\n # Add the start vertex to the output string as the while loop will stop before we add its details to the string\\n path_string = \\\"{0}({1}) \\\".format(chr(current_vertex + 65), distances_from_start[current_vertex][0]) + path_string\\n\\n temp = [chr(current_vertex + 65), distances_from_start[current_vertex][0]]\\n orderlist.append(temp)\\n\\n print(path_string)\\n\\n return orderlist[::-1]\",\n \"def djikstra(nodes,links,source,dest):\\n route = []\\n vertexes = []\\n for v in nodes:\\n v.set_dist(float(\\\"inf\\\"))\\n v.set_prev(None)\\n heappush(vertexes, v)\\n source.set_dist(0)\\n heapify(vertexes)\\n while vertexes:\\n unsorted = False\\n u = heappop(vertexes)\\n if u == dest:\\n break #because we found the destination no need to look further\\n for v in u.get_links():\\n if v.get_enabled():\\n alt = u.get_dist() + 1\\n target = v.get_target()\\n if alt < target.get_dist():\\n target.set_dist(alt)\\n target.set_prev(u)\\n unsorted = True #just a variable that help check if changes were made to the objects inside the heap\\n if unsorted: #because i updated the variables but the heap wasn't maintained, i just heapify it again\\n heapify(vertexes) \\n #this is the part that saves the distance and route \\n if dest.get_dist() == float(\\\"inf\\\"): #if there is no route then we just return None\\n return None\\n u = dest\\n while u.get_prev() != None:\\n v = u.get_prev()\\n route.insert(0, v.get_specific_link(u)) \\n u = v\\n return route\",\n \"def dijsktra(graph, initial, end):\\n shortest_paths = {initial: (None, 0)}\\n current_node = initial\\n visited = set()\\n\\n while current_node != end:\\n visited.add(current_node)\\n destinations = graph.edges[current_node]\\n weight_to_current_node = shortest_paths[current_node][1]\\n logger.debug(\\\"shortest_paths = {}\\\".format(shortest_paths))\\n\\n for next_node in destinations:\\n weight = graph.weights[(current_node, next_node)] + weight_to_current_node\\n logger.debug(\\\"next_node = {}\\\".format(next_node))\\n logger.debug(\\\"graph.weights[(current_node, next_node)] = {}\\\".format(graph.weights[(current_node, next_node)]))\\n logger.debug(\\\"weight_to_current_node = {}\\\".format(weight_to_current_node))\\n logger.debug(\\\"weight = {}\\\".format(weight))\\n if next_node not in shortest_paths:\\n shortest_paths[next_node] = (current_node, weight)\\n else:\\n current_shortest_weight = shortest_paths[next_node][1]\\n if current_shortest_weight > weight:\\n shortest_paths[next_node] = (current_node, weight)\\n\\n next_destinations = {node: shortest_paths[node] for node in shortest_paths if node not in visited}\\n logger.debug(\\\"next_destinations = {}\\\".format(next_destinations))\\n\\n if not next_destinations:\\n return \\\"Route not possible\\\"\\n\\n current_node = min(next_destinations, key=lambda k: next_destinations[k][1])\\n logger.debug(\\\"current_node = {}\\\".format(current_node))\\n\\n path = []\\n while current_node is not None:\\n path.append(current_node)\\n next_node = shortest_paths[current_node][0]\\n current_node = next_node\\n\\n # Reverse the path\\n path = path[::-1]\\n return path\",\n \"def shortestpath(graph, current, end, visited=[], distances={}, predecessors={}):\\n\\t# we've found our end node, now find the path to it, and return\\n\\tif current == end:\\n\\t\\tpathShortest = []\\n\\t\\twhile end != None:\\n\\t\\t\\tpathShortest.append(end)\\n\\t\\t\\tend = predecessors.get(end, None)\\n\\t\\tpass\\n\\t\\treturn distances[current], pathShortest[::-1]\\n\\tpass\\n\\t\\n\\t# detect if it's the first time through, set current distance to zero\\n\\tif not visited: distances[current] = 0\\n\\t# process neighbors as per algorithm, keep track of predecessors\\n\\tfor adjacent in graph[current]:\\n\\t\\tif adjacent not in visited:\\n\\t\\t\\tadjDist = distances.get(adjacent, sys.maxint)\\n\\t\\t\\tcurDist = distances[current] + graph[current][adjacent]\\n\\t\\t\\tif curDist < adjDist:\\n\\t\\t\\t\\tdistances[adjacent] = curDist\\n\\t\\t\\t\\tpredecessors[adjacent] = current\\n\\t\\t\\tpass\\n\\t\\tpass\\n\\tpass\\n\\t# neighbors processed, now mark the current node as visited\\n\\tvisited.append(current)\\n\\t# finds the closest unvisited node to the start\\n\\tnotVisited = dict((k, distances.get(k, sys.maxint)) for k in graph if k not in visited)\\n\\tclosest = min(notVisited, key=notVisited.get)\\n\\t# now we can take the closest node and recurse, making it current\\n\\treturn shortestpath(graph, closest, end, visited, distances, predecessors)\",\n \"def shortest_path(self, id1: int, id2: int) -> (float, list):\\n\\n if id1 == id2:\\n return 0, [id1]\\n if id1 not in self.dw_graph.nodes or id2 not in self.dw_graph.nodes:\\n return math.inf, []\\n\\n for n in self.dw_graph.get_all_v().values(): # Set all distance to be max value.\\n if n.node_id != id1:\\n n.distance = sys.maxsize\\n n.visited = 0\\n path = []\\n min_heap=[]\\n self.dw_graph.nodes[id1].distance = 0\\n heapq.heappush(min_heap,(self.dw_graph.nodes[id1].distance,self.dw_graph.nodes[id1]))\\n\\n while len(min_heap):\\n node = heapq.heappop(min_heap) # pop the smallest item\\n current = node[1] # Get node from tuples\\n current.visited = 1 # Set the node to visited\\n\\n for neighbour in self.dw_graph.all_out_edges_of_node(current.node_id).values(): # Get neighbours\\n if self.dw_graph.nodes[neighbour.dest].visited == 0: # if we didn't visit this neighbour\\n new_dist = current.distance + neighbour.weight # Set new distance\\n\\n if self.dw_graph.nodes[neighbour.dest].distance > new_dist: # If new distance is smaller , update it.\\n self.dw_graph.nodes[neighbour.dest].distance = new_dist\\n\\n heapq.heappush(min_heap,(self.dw_graph.nodes[neighbour.dest].distance,\\n self.dw_graph.nodes[neighbour.dest])) # add to priority queue\\n self.dw_graph.nodes[neighbour.dest].parent = current.node_id # Update parent\\n\\n if self.dw_graph.nodes[id2].distance == sys.maxsize: # if the distance is still max value , can't reach\\n return math.inf, []\\n\\n\\n path.append(id2)\\n current = self.dw_graph.nodes[id2].parent\\n self.dw_graph.nodes[id1].parent=-1\\n while current != -1: # Traverse backwards until parent is -1\\n path.append(current)\\n current = self.dw_graph.nodes[current].parent\\n path.reverse()\\n return self.dw_graph.nodes[id2].distance, path\"\n]","isConversational":false},"negative_scores":{"kind":"list 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password combination is valid."},"document":{"kind":"string","value":"def check_auth(username, password):\n return username == 'jeffkoons' and password == 'likesweirdbaloons'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def check_auth(username, password, expected_user, expected_pw):\n return username == expected_user and password == expected_pw","def validate_authentication(self, username, password):\n return self.user_table[username]['pwd'] == password","def check_auth(username, password):\n return username == USERNAME and password == PASSWORD","def check_valid(self, username, password):\n con = self.connect()\n cursor = con.cursor()\n cursor.execute(\"SELECT username,password \\\n FROM users WHERE username = %s\", (username,))\n credentials = cursor.fetchone()\n cursor.close()\n con.commit()\n con.close()\n if credentials is None:\n return False\n if username != credentials[0]:\n return False\n if sha256_crypt.verify(password, credentials[1]):\n return True\n return False","def check_auth(username, password):\n return username == 'aweber' and password == 'aweber1100'","def check_auth(username, password):\n return username == 'nicholas' and password == ADMIN_PASS","def check_auth(username, password):\n return username == 'admin' and password == 'worcester'","def validate_login(self, username, password):\n user = User(self).get(username)\n return user and user['Password'] == password","def check_auth(username, password):\n return username == 'admin' and password == 'pebble'","def check_auth(username, password):\n return username == 'admin' and password == 'Passw0rd'","def check_auth(username, password):\n return username == 'asimov' and password == 'tagada72'","def check_auth(username, password):\n return username == 'sammy' and password == 'BasicPassword!'","def is_valid_password(password, username):\n import string\n if len(password) < 4 or ' ' in password:\n return False\n if username:\n if string.lower(username) in string.lower(password):\n return False\n return True","def check_auth_password(self, username, password):\n return AUTH_FAILED","def check_auth(username, password):\n return username == app.config['USERNAME'] and (\n password == app.config['PASSWORD'])","def check_auth(username, password):\n return username == 'admin' and password == 'password'","def _credentials_are_valid(self, username, password):\n LDAP_SERVER = 'ldap://xxx.xxx.xxx' # EDIT THIS\n LDAP_USERNAME = '%s@xxx.com' % username # EDIT THIS\n LDAP_PASSWORD = password\n\n try:\n # build a client\n ldap_client = ldap.initialize(LDAP_SERVER)\n # perform a synchronous bind\n ldap_client.set_option(ldap.OPT_REFERRALS, 0)\n ldap_client.simple_bind_s(LDAP_USERNAME, LDAP_PASSWORD)\n except ldap.INVALID_CREDENTIALS:\n ldap_client.unbind()\n # Wrong username or password\n return False\n except ldap.SERVER_DOWN:\n # AD server not available\n return False\n # all is well\n ldap_client.unbind()\n # Login successful\n return True","def username_is_valid(username):\n\n if len(username) < MINIMUM_PASSWORD_LENGTH:\n return False\n else:\n return True","def check_auth(username, password):\n return (username == app.config['USERNAME'] and\n password == app.config['PASSWORD'])","def check_auth(username, password):\n return username == c.id and password == c.pw","def check_auth(username, password):\n return username == 'admin' and password == 'root'","def check_auth(username, password):\n return username == 'admin' and password == 'secret'","def check_auth(username, password):\n return username == 'admin' and password == 'secret'","def password_validator(username, password):\n digits = re.search(r'\\d+', password)\n capital_letters = re.search(r'[A-Z]+', password)\n lenght = len(password) > PASSWORD_MIN_LENGTH\n special_symbol = re.search(r'[\\-\\/\\@\\?\\!\\,\\.\\#\\&\\*]+', password)\n\n statement = digits and capital_letters and lenght and special_symbol\n\n if statement:\n return True\n return False","def check_auth(username, password):\n return username == 'admin' and password == 'admin'","def check_auth(username, password):\n ADMIN_USER = config.CONFIG_VARS['ADMIN_USER']\n ADMIN_PASS = config.CONFIG_VARS['ADMIN_PASS']\n return username == ADMIN_USER and password == ADMIN_PASS","def check_auth(username, password):\n # return username == app.config['USER'] and password == app.config['PASS']\n\n return username == app.config['USER'] and password == app.config['PASS']","def check_auth(username, password):\r\n return username == current_app.config['ADMIN_USERNAME'] \\\r\n and password == current_app.config['ADMIN_PASSWORD']","def check_credentials(username, password):\n if not validate_username(username) or not validate_password(password):\n return False\n sql = \"SELECT password \" \\\n \"FROM users \" \\\n \"WHERE username=:username AND is_active=TRUE\"\n result = db.session.execute(sql, {\"username\": username})\n user = result.fetchone()\n if user is None:\n return False\n password_hash = user[0]\n if check_password_hash(password_hash, password):\n return True\n return False","def is_logged_in_user_valid(user_name, password):\n if user_name.upper() == \"HELLO\" and password == \"World\":\n return True # User input matches user name and password.\n else:\n return False # User input does not match user name and password.s","def check_auth(username, password):\n\n config = get_app_configurations()\n\n with open(config[\"credentials\"], \"r\") as fh:\n u, p = fh.readline().rstrip().split(\",\")\n\n return username == u and password == p","def verify_pw(username, password):\n credentials = HtpasswdFile(app.config[\"CREDENTIAL_FILE\"])\n if not credentials.check_password(username, password):\n logging.warning(\"%s tried to login with wrong password\", username)\n return False\n return True","def check_auth(username, password):\n return password == os.getenv('PASSWORD')","def validate_user(self, username, password, client, request, *args, **kwargs):\n log.debug('Validating username %r and its password', username)\n if self._usergetter is not None:\n user = self._usergetter(username, password, client, request, *args, **kwargs)\n if user:\n log.debug('Successfully validated username %r', username)\n request.user = user\n return True\n return False\n log.debug('Password credential authorization is disabled.')\n return False","def _validate_password_works_with_username(password, username=None):\n if password == username:\n raise errors.AccountPasswordInvalid(accounts.PASSWORD_CANT_EQUAL_USERNAME_MSG) # lint-amnesty, pylint: disable=no-member","def check_auth(username, password):\n return username == os.environ['USERNAME'] and password == os.environ['PASSWORD']","def is_valid_login(self, username, password):\n\t\treturn self._send_command_to_entity_server(us.SERVER_COMMAND_IS_LOGIN_INFORMATION_VALID, username + '|' + password)","def authenticate(self):\n rv = Form.validate(self)\n if not rv:\n return False\n\n user = self.username.data\n\n cur = get_cursor()\n if email_exists(cur, user):\n user = get_username(cur, user)\n\n if username_exists(cur, user):\n pw_hash = get_pw_hash(cur, user)\n\n if check_password(self.password.data, pw_hash):\n self.username.data = user\n return True\n\n return False","def is_valid_user(self, username, password): # WORKS\n done1 = self.cur.execute(\"SELECT password FROM users WHERE username=\\\"{}\\\"\".format(username))\n done2 = self.cur.execute(\"SELECT username FROM admins WHERE username=\\\"{}\\\"\".format(username))\n if done1 == 0 and done2 == 0: # If both queries are unsuccessful, username doesn't exist in both tables.\n return False\n else:\n if done1 == 1: # If username exists in USERS table.\n self.cur.execute(\"SELECT password FROM users WHERE username=\\\"{}\\\"\".format(username))\n stored_password = self.cur.fetchone()[0]\n return check_password_hash(stored_password, password) # Returns True if the hashes match.\n else: # If username exists in ADMINS table.\n self.cur.execute(\"SELECT password FROM admins WHERE username=\\\"{}\\\"\".format(username))\n stored_password = self.cur.fetchone()[0]\n return check_password_hash(stored_password, password) # Returns True if the hashes match.","def check_user(self,username, password):\n safe_input = (username, password)\n vals = self.cur.execute(\"SELECT Username, Password FROM Users WHERE Username=? AND Password=?\",safe_input).fetchone()\n if vals:\n logging.info('%s was authenticated', username)\n return True\n else:\n logging.info('Failed login for %s', username)\n return False","def valid_login(username, password):\n db = get_db()\n db.ping(True)\n cur = db.cursor()\n\n try:\n sql = \"SELECT password FROM users WHERE user_name = '{}';\".format(username)\n cur.execute(sql)\n for i in cur:\n return check_password_hash(i[0], password)\n return False\n except mysql.connector.Error as err:\n flash(err, \"set\")\n return False\n finally:\n cur.close()\n db.close()","def check_credentials(self, username, password):\n user = None\n if username != \"\":\n # Calling DB and fetching userdetails\n user = userdetails_API_query(username)\n print \"id \", user['_id']\n if user != None:\n #u = app.config['BASIC_AUTH_USERNAME'] = user['username']\n #pwd = app.config['BASIC_AUTH_PASSWORD'] = user['pw_hash']\n # print \" u & pwd\",username\n if user['username'] == username and check_password_hash(user['pw_hash'], password):\n g.user = user['_id'], username, user['email']\n return True\n print \"g.user\", g.user\n return False","def test_credentials(self):\r\n data = self._deep_clean('zekebarge@gmail.com')\r\n error = data.get(ERROR_CODE, None)\r\n if error in (1,2):\r\n raise InvalidCredentialsError(\"Credentials are invalid for user '{}'\".format(self._username))\r\n return True","def check_auth(username, password):\n return get_ct_object(username, password) is not None","def check_user(self):\n try:\n if (self.get_user()[0][0] == self.username) and (self.check_password(self.password)):\n return True\n else:\n return False\n except:\n return False","def check_auth(username, password):\n return username == 'daniel' and password == config['redis_auth_key']","def validate_authentication(self, username, password, handler):\n hash = md5(password).hexdigest()\n msg = \"Authentication failed.\"\n if not self.has_user(username):\n if username == 'anonymous':\n msg = \"Anonymous access not allowed.\"\n raise AuthenticationFailed(msg)\n if username != 'anonymous':\n if self.user_table[username]['pwd'] != hash:\n raise AuthenticationFailed(msg)","def is_correct_user(self, login, password):\n pass","def check_auth(username, password):\n return username == get_env('UPLOAD_USER') and password == get_env('UPLOAD_PASSWORD')","def verify_password(self, username, password):\n\n try:\n self.c.execute('SELECT password FROM profiles WHERE name=(?)', (username,))\n\n db_pw = self.c.fetchone()[0]\n print(password)\n\n return db_pw == password\n\n except TypeError:\n return False","def check_credentials(username, password):\n\n return db.auth_user(username, password)","def check_auth(_, http_password):\n return (password is not None) and (password == http_password)","def check_user(self, username, password):\n user = [user for user in self.db if user['username'] == username]\n if user:\n if check_password_hash(user[0][\"password\"], password):\n return True\n return False\n return False","def validate(self) -> bool:\n if not super().validate():\n return False\n\n # Does the user exist\n user = User.query.filter_by(username=self.username.data).first()\n if not user:\n self.username.errors.append('Invalid username or password')\n return False\n\n # Does given password match user's password\n if not user.check_password(self.password.data):\n self.username.errors.append('Invalid username or password')\n return False\n\n return True","def check_credentials(username, password):\n\t\n\tconn = sqlite3.connect('db/user_task.db')\n\tcursor = conn.execute(\"SELECT password from user WHERE username == \\'%s\\'\" % (username))\n\tdata = cursor.fetchall()\n\tconn.close()\n\n\tif len(data) == 0:\n\t\treturn u\"Incorrect username\"\n\n\tfor row in data:\n\t\tencoded_password = hashlib.sha1(password.encode('utf-8')).hexdigest()\n\t\tif row[0] == encoded_password:\n\t\t\treturn None\n\n\treturn u\"Incorrect password\"\n\t\n\t# An example implementation which uses an ORM could be:\n\t# u = User.get(username)\n\t# if u is None:\n\t# return u\"Username %s is unknown to me.\" % username\n\t# if u.password != md5.new(password).hexdigest():\n\t# return u\"Incorrect password\"","def check_auth(username, password):\n try:\n locust_username = os.environ['LOCUST_USER_NAME']\n locust_password = os.environ['LOCUST_PASSWORD']\n return username == locust_username and password == locust_password\n except:\n return True","def validate_password(data):\n\n if \"password\" not in data:\n return False\n password = data[\"password\"]\n if len(password) < 4 or len(password) > 16:\n return False\n\n return True","def _check_user_pass(self):\n if not self.username:\n self.username = input(' 请输入手机号：')\n if self.username.isdigit() and '+86' not in self.username:\n self.username = '+86' + self.username\n\n if not self.password:\n self.password = input(' 请输入密码：')","def check_password(self, username, password): # tested\r\n conn = self.get_db()\r\n with conn:\r\n c = conn.cursor()\r\n sql = ('select password from gameuser where username=%s')\r\n c.execute(sql,(username,))\r\n hashedpass = md5.new(password).hexdigest()\r\n u = c.fetchone()\r\n if u == None:\r\n raise NoUserExistsException(username)\r\n # print 'database contains {}, entered password was {}'.format(u[0],hashedpass)\r\n return u[0] == hashedpass","def valid_login_password(username, password, pw_hash):\n salt = pw_hash.split(',')[1]\n return pw_hash == make_pw_hash(username, password, salt)","def check_auth(username, password):\n return username == 'admin' and password in app.config[\"CLAIM_SECRETS\"]","def validate_password(self, field):\n username = self.username.data\n if platform.system().lower() == 'linux':\n g.logger.debug('use pam for authenticate.')\n from pam import authenticate\n if authenticate(username, field.data):\n g.logger.info('session opened for user %s.' % username)\n return username\n else:\n raise ValueError('Authentication failure.')\n return username","def check_credentials_typo(credentials):\n regex_username = r'^[\\w\\.\\-]{2,}$'\n regex_password = r'[^.]{4,10}$'\n\n if not match(regex_username, credentials['username']):\n raise ValueError('invalid username typo')\n\n if not match(regex_password, credentials['password']):\n raise ValueError('invalid password typo')","def username_and_password_check(username: str, password: str) -> bool:\n table = metadata.tables['users']\n s = select(\n [\n func.count(table.c.id)\n ]).where(tuple_(table.c['username'], table.c['password'])\n .in_([(username, password)]))\n result = conn.execute(s).scalar()\n return bool(result)","def check_auth(username, password):\n user = User.query.filter(User.username == username).first()\n\n if user:\n return user.password == password\n else:\n return False","def verify_password(username, password):\n if username in user_auth and check_password_hash(user_auth.get(username), password):\n return username","def authentication_validation(username, password, access_token):\n if bool(username) is not bool(password):\n raise Exception(\"Basic authentication requires a username AND\" \" password.\")\n if (username and access_token) or (password and access_token):\n raise Exception(\n \"Cannot use both Basic Authentication and\"\n \" OAuth2.0. Please use only one authentication\"\n \" method.\"\n )","def invalid_credentials( form , field ): \n\tusername_entered = form.username.data\n\tpassword_entered = field.data \n\tuser_object = User.query.filter_by(username = username_entered).first()\n\tif user_object is None : \n\t\traise ValidationError(\"Username or Password is incorrect !\")\n\telif not pbkdf2_sha256.verify(password_entered , user_object.password) : \n\t\traise ValidationError(\"Username or Password is incorrect !\")","def is_password_valid(password):\n #TODO : This should also be handled by the front_end\n pass","def check_password(username, password, htpasswd_fn):\n entries = parse_htpasswd(htpasswd_fn, username)\n if not entries.has_key(username):\n raise NoSuchUser('No user: %r' % username)\n return check_entry_password(\n username, password, entries[username])","def auth(username, password):\n return username == password","def _check_password(self, password):\n if self.password_regex.search(password) is not None:\n print(\"Correct password\")\n return True\n else:\n print(\"Wrong password\")\n return False","def check_auth(username, password):\n return username == current_app.config['DOC_USERNAME'] and password == current_app.config['DOC_PASSWORD']","def _check_username(self, username):\n if self.username_regex.search(username) is not None:\n print(\"Correct username\")\n return True\n else: \n print(\"Wrong username\")\n return False","def check_auth_password(self, username, password):\n if username == self.username and password == self.password:\n return paramiko.AUTH_SUCCESSFUL\n return paramiko.AUTH_FAILED","def check_credentials(input_password, real_password):\n return pwd_context.verify(input_password, real_password)","def test_valid_username_invalid_password(self):\n response = self.client.post(reverse('users:login'), {'username': self.user['username'], 'password': '1sfsdf'})\n self.assertEqual(response.status_code, 200)\n self.assertFormError(response, 'form', None, ERROR_MSG)","def authorise_login(self, username, password):\n con = self.connect()\n cursor = con.cursor()\n cursor.execute(\"SELECT username,password \\\n FROM users WHERE password = %s\", (password,))\n credentials = cursor.fetchone()\n cursor.close()\n con.commit()\n con.close()\n if credentials is None:\n return False\n if username != credentials[0]:\n return False\n if password != credentials[1]:\n return False\n return True","def clean(self):\n cleaned_data = super().clean()\n username = cleaned_data['username']\n password = cleaned_data['password']\n\n if authenticate(username=username, password=password) is None:\n raise ValidationError('Your username or password is incorrect.')","def _check_password(self, password):\n rule = re.compile(constant.password_regex)\n if not rule.match(password):\n return False\n # disallow password from azure guide, yes, it's hard code.\n disallowed = constant.password_disallowed\n return password not in disallowed","def validate(username: str, password: str) -> dict:\n validation_check = {}\n \n # Check to see if the username was left blank\n if username.strip() == \"\":\n validation_check[\"success\"] = False\n validation_check[\"username\"] = \"Username cannot be left blank.\"\n\n # Check to see if the username is taken\n elif not sql.is_username_taken(username):\n validation_check[\"success\"] = False\n validation_check[\"username\"] = \"Username is incorrect\"\n\n # Check to see if the password was left blank\n if password.strip() == \"\":\n validation_check[\"success\"] = False\n validation_check[\"password\"] = \"Password cannot be left blank.\"\n\n\n if not validation_check.get(\"success\", True):\n return validation_check\n\n else:\n return sql.verify_credentials(username, password)","def check_friend(self, username):\n if (self.isBlank(username) or self.isValidLen(username)):\n return False\n\n safe_input = (username,)\n \n vals = self.cur.execute(\"SELECT Username, Password FROM Users WHERE Username=?\" ,safe_input).fetchone()\n if vals:\n return True\n else:\n return False","def test_check_password():\n assert check_password('Longpassword') == False\n assert check_password('123456') == False\n assert check_password('short') == False\n assert check_password('C0rect') == False\n assert check_password('Correct8') == True","def check_password(self, password):\n return self.password == password","def test_validate_credentials(self):\n pass","def check_my_users(user):\n user_data = my_users.get(user['username'])\n if not user_data:\n return False # <--- invalid credentials\n elif user_data.get('password') == user['password']:\n return True # <--- user is logged in!\n\n return False # <--- invalid credentials","def valid_login(username: str, password: str):\n\n try:\n LoginHandler.login(username, password)\n global validLogin\n validLogin = True\n except:\n validLogin = False","def checkLogin(username, password):\n\n # Query database for username\n cur = initialise(2)\n cur.execute(\"SELECT * FROM users WHERE username = ?\", [username])\n users = cur.fetchall()\n\n # Ensure username exists and password is correct\n if len(users) != 1 or not check_password_hash(users[0][\"hash\"], password):\n return False, username\n else:\n return True, users[0][\"id\"]","def validate_login(name,password):\n\t\n\t#Read the attendance excelsheet check if username and password matched\n\tdf_atten=pd.read_csv(\"datasrc/People.csv\")\n\t# 10006 ultbjxu\n\t\n\tif (df_atten.Username.astype(str).str.contains(name).any() and df_atten.Password.astype(str).str.contains(password).any()):\t\t\n\t\treturn True\n\telse: \n\t\treturn False","def isUSER(username, password):\n usr = UserData[\"username\"].to_dict()\n listofUsers = list(UserData[\"username\"])\n if username in listofUsers:\n # print('Is a User')\n getindex = listofUsers.index(username)\n listofPasswords = list(UserData[\"password\"])\n # print(getindex)\n if listofPasswords[getindex] == password:\n # print('Password Correct')\n return True, True\n return True, False\n return False, False","def _input(self):\n\t\tself.username = input(\"Username:\")\n\t\tself.password = input(\"password:\")\n\t\tret = re.match(r\"^\\w{1,20}$\", self.username)\n\t\tprint(\"------>input string\", self.username)\n\t\tprint(\"------>matched string\",ret)\t\n\t\ttime.sleep(1)\n\t\tif ret == None:\n\t\t\treturn True\n\t\telse:\n\t\t\tpass\n\n\t\tsql = f\"\"\"select name, password from users where name='{self.username}';\"\"\"\n\t\tprint(\"----------->\", sql)\n\t\ttime.sleep(1)\n\t\tcount = self.cs.execute(sql)\n\t\tprint(count)\n\t\tif (self.username, self.password) == self.cs.fetchone():\n\t\t\tprint(\"Login Successfully\")\n\t\t\ttime.sleep(1)\n\t\t\treturn False\n\t\telse:\n\t\t\tprint(\"Wrong username or password\")\n\t\t\ttime.sleep(1)\n\t\t\treturn True","def is_valid_login(username, password):\n with open(PASSFILE, \"r\") as passfile:\n for record in passfile:\n try:\n valid_user, valid_password = False, False\n r_username, r_salt_hash = record.split()\n if username == r_username:\n valid_user = True\n if sha256_crypt.verify(password, r_salt_hash):\n valid_password = True\n if valid_user and valid_password:\n return True\n except ValueError:\n pass\n return False","def check_auth(username, password):\n session.pop('username', None)\n session.pop('password', None)\n session['username'] = username\n session['password'] = password\n # Test if we can connect to a region\n connect_to_region()\n return True","def check_credentials_validation(credentials):\n spec = {'_id': credentials['username'], 'password': credentials['password']}\n if not current_app.mongo.observer.users.find_one(spec):\n raise Unauthorized('invalid credentials')","def username_check(username):\n\n try: \n pwd.getpwnam(username)\n print(\"User %s DOES EXIST. Try a different username.\" % (username)) \n return False\n\n except KeyError: \n print(\"User %s DOES NOT exist. Continuing...\" % (username)) \n return True","def test_invalid_username_valid_password(self):\n response = self.client.post(reverse('users:login'), {'username': 'xyzabe', 'password': self.user['password1']})\n self.assertEqual(response.status_code, 200)\n self.assertFormError(response, 'form', None, ERROR_MSG)","def test_valid_password(self):\n pass_field = Field(\"\".join(['x' * (int(self.app.config['MAX_PWD_LEN']))]))\n\n valid_password(None, pass_field)","def authenticate(username, password):\n test = User.load(username)\n test_password = test.password\n input_password = md5.new(\n password + config.get('security', 'salt')).digest()\n if input_password == test_password:\n return True\n else:\n return False","def test_authenticate_invalid_username(self):\r\n print(\"Authenticate user invalid username\")\r\n username = \"test9999user\"\r\n password = \"password\"\r\n\r\n self.assertEqual(self.system.authenticate(username, password), False)","def verify_pw(username, password):\n global password_store\n logger = logging.getLogger('verify_pw')\n if not password_store:\n logger.error(\"No password store specified\")\n return False\n logger.debug(\"Verifying password for %s\" % username)\n return password_store.verify(username, password)"],"string":"[\n \"def check_auth(username, password, expected_user, expected_pw):\\n return username == expected_user and password == expected_pw\",\n \"def validate_authentication(self, username, password):\\n return self.user_table[username]['pwd'] == password\",\n \"def check_auth(username, password):\\n return username == USERNAME and password == PASSWORD\",\n \"def check_valid(self, username, password):\\n con = self.connect()\\n cursor = con.cursor()\\n cursor.execute(\\\"SELECT username,password \\\\\\n FROM users WHERE username = %s\\\", (username,))\\n credentials = cursor.fetchone()\\n cursor.close()\\n con.commit()\\n con.close()\\n if credentials is None:\\n return False\\n if username != credentials[0]:\\n return False\\n if sha256_crypt.verify(password, credentials[1]):\\n return True\\n return False\",\n \"def check_auth(username, password):\\n return username == 'aweber' and password == 'aweber1100'\",\n \"def check_auth(username, password):\\n return username == 'nicholas' and password == ADMIN_PASS\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'worcester'\",\n \"def validate_login(self, username, password):\\n user = User(self).get(username)\\n return user and user['Password'] == password\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'pebble'\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'Passw0rd'\",\n \"def check_auth(username, password):\\n return username == 'asimov' and password == 'tagada72'\",\n \"def check_auth(username, password):\\n return username == 'sammy' and password == 'BasicPassword!'\",\n \"def is_valid_password(password, username):\\n import string\\n if len(password) < 4 or ' ' in password:\\n return False\\n if username:\\n if string.lower(username) in string.lower(password):\\n return False\\n return True\",\n \"def check_auth_password(self, username, password):\\n return AUTH_FAILED\",\n \"def check_auth(username, password):\\n return username == app.config['USERNAME'] and (\\n password == app.config['PASSWORD'])\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'password'\",\n \"def _credentials_are_valid(self, username, password):\\n LDAP_SERVER = 'ldap://xxx.xxx.xxx' # EDIT THIS\\n LDAP_USERNAME = '%s@xxx.com' % username # EDIT THIS\\n LDAP_PASSWORD = password\\n\\n try:\\n # build a client\\n ldap_client = ldap.initialize(LDAP_SERVER)\\n # perform a synchronous bind\\n ldap_client.set_option(ldap.OPT_REFERRALS, 0)\\n ldap_client.simple_bind_s(LDAP_USERNAME, LDAP_PASSWORD)\\n except ldap.INVALID_CREDENTIALS:\\n ldap_client.unbind()\\n # Wrong username or password\\n return False\\n except ldap.SERVER_DOWN:\\n # AD server not available\\n return False\\n # all is well\\n ldap_client.unbind()\\n # Login successful\\n return True\",\n \"def username_is_valid(username):\\n\\n if len(username) < MINIMUM_PASSWORD_LENGTH:\\n return False\\n else:\\n return True\",\n \"def check_auth(username, password):\\n return (username == app.config['USERNAME'] and\\n password == app.config['PASSWORD'])\",\n \"def check_auth(username, password):\\n return username == c.id and password == c.pw\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'root'\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'secret'\",\n \"def password_validator(username, password):\\n digits = re.search(r'\\\\d+', password)\\n capital_letters = re.search(r'[A-Z]+', password)\\n lenght = len(password) > PASSWORD_MIN_LENGTH\\n special_symbol = re.search(r'[\\\\-\\\\/\\\\@\\\\?\\\\!\\\\,\\\\.\\\\#\\\\&\\\\*]+', password)\\n\\n statement = digits and capital_letters and lenght and special_symbol\\n\\n if statement:\\n return True\\n return False\",\n \"def check_auth(username, password):\\n return username == 'admin' and password == 'admin'\",\n \"def check_auth(username, password):\\n ADMIN_USER = config.CONFIG_VARS['ADMIN_USER']\\n ADMIN_PASS = config.CONFIG_VARS['ADMIN_PASS']\\n return username == ADMIN_USER and password == ADMIN_PASS\",\n \"def check_auth(username, password):\\n # return username == app.config['USER'] and password == app.config['PASS']\\n\\n return username == app.config['USER'] and password == app.config['PASS']\",\n \"def check_auth(username, password):\\r\\n return username == current_app.config['ADMIN_USERNAME'] \\\\\\r\\n and password == current_app.config['ADMIN_PASSWORD']\",\n \"def check_credentials(username, password):\\n if not validate_username(username) or not validate_password(password):\\n return False\\n sql = \\\"SELECT password \\\" \\\\\\n \\\"FROM users \\\" \\\\\\n \\\"WHERE username=:username AND is_active=TRUE\\\"\\n result = db.session.execute(sql, {\\\"username\\\": username})\\n user = result.fetchone()\\n if user is None:\\n return False\\n password_hash = user[0]\\n if check_password_hash(password_hash, password):\\n return True\\n return False\",\n \"def is_logged_in_user_valid(user_name, password):\\n if user_name.upper() == \\\"HELLO\\\" and password == \\\"World\\\":\\n return True # User input matches user name and password.\\n else:\\n return False # User input does not match user name and password.s\",\n \"def check_auth(username, password):\\n\\n config = get_app_configurations()\\n\\n with open(config[\\\"credentials\\\"], \\\"r\\\") as fh:\\n u, p = fh.readline().rstrip().split(\\\",\\\")\\n\\n return username == u and password == p\",\n \"def verify_pw(username, password):\\n credentials = HtpasswdFile(app.config[\\\"CREDENTIAL_FILE\\\"])\\n if not credentials.check_password(username, password):\\n logging.warning(\\\"%s tried to login with wrong password\\\", username)\\n return False\\n return True\",\n \"def check_auth(username, password):\\n return password == os.getenv('PASSWORD')\",\n \"def validate_user(self, username, password, client, request, *args, **kwargs):\\n log.debug('Validating username %r and its password', username)\\n if self._usergetter is not None:\\n user = self._usergetter(username, password, client, request, *args, **kwargs)\\n if user:\\n log.debug('Successfully validated username %r', username)\\n request.user = user\\n return True\\n return False\\n log.debug('Password credential authorization is disabled.')\\n return False\",\n \"def _validate_password_works_with_username(password, username=None):\\n if password == username:\\n raise errors.AccountPasswordInvalid(accounts.PASSWORD_CANT_EQUAL_USERNAME_MSG) # lint-amnesty, pylint: disable=no-member\",\n \"def check_auth(username, password):\\n return username == os.environ['USERNAME'] and password == os.environ['PASSWORD']\",\n \"def is_valid_login(self, username, password):\\n\\t\\treturn self._send_command_to_entity_server(us.SERVER_COMMAND_IS_LOGIN_INFORMATION_VALID, username + '|' + password)\",\n \"def authenticate(self):\\n rv = Form.validate(self)\\n if not rv:\\n return False\\n\\n user = self.username.data\\n\\n cur = get_cursor()\\n if email_exists(cur, user):\\n user = get_username(cur, user)\\n\\n if username_exists(cur, user):\\n pw_hash = get_pw_hash(cur, user)\\n\\n if check_password(self.password.data, pw_hash):\\n self.username.data = user\\n return True\\n\\n return False\",\n \"def is_valid_user(self, username, password): # WORKS\\n done1 = self.cur.execute(\\\"SELECT password FROM users WHERE username=\\\\\\\"{}\\\\\\\"\\\".format(username))\\n done2 = self.cur.execute(\\\"SELECT username FROM admins WHERE username=\\\\\\\"{}\\\\\\\"\\\".format(username))\\n if done1 == 0 and done2 == 0: # If both queries are unsuccessful, username doesn't exist in both tables.\\n return False\\n else:\\n if done1 == 1: # If username exists in USERS table.\\n self.cur.execute(\\\"SELECT password FROM users WHERE username=\\\\\\\"{}\\\\\\\"\\\".format(username))\\n stored_password = self.cur.fetchone()[0]\\n return check_password_hash(stored_password, password) # Returns True if the hashes match.\\n else: # If username exists in ADMINS table.\\n self.cur.execute(\\\"SELECT password FROM admins WHERE username=\\\\\\\"{}\\\\\\\"\\\".format(username))\\n stored_password = self.cur.fetchone()[0]\\n return check_password_hash(stored_password, password) # Returns True if the hashes match.\",\n \"def check_user(self,username, password):\\n safe_input = (username, password)\\n vals = self.cur.execute(\\\"SELECT Username, Password FROM Users WHERE Username=? AND Password=?\\\",safe_input).fetchone()\\n if vals:\\n logging.info('%s was authenticated', username)\\n return True\\n else:\\n logging.info('Failed login for %s', username)\\n return False\",\n \"def valid_login(username, password):\\n db = get_db()\\n db.ping(True)\\n cur = db.cursor()\\n\\n try:\\n sql = \\\"SELECT password FROM users WHERE user_name = '{}';\\\".format(username)\\n cur.execute(sql)\\n for i in cur:\\n return check_password_hash(i[0], password)\\n return False\\n except mysql.connector.Error as err:\\n flash(err, \\\"set\\\")\\n return False\\n finally:\\n cur.close()\\n db.close()\",\n \"def check_credentials(self, username, password):\\n user = None\\n if username != \\\"\\\":\\n # Calling DB and fetching userdetails\\n user = userdetails_API_query(username)\\n print \\\"id \\\", user['_id']\\n if user != None:\\n #u = app.config['BASIC_AUTH_USERNAME'] = user['username']\\n #pwd = app.config['BASIC_AUTH_PASSWORD'] = user['pw_hash']\\n # print \\\" u & pwd\\\",username\\n if user['username'] == username and check_password_hash(user['pw_hash'], password):\\n g.user = user['_id'], username, user['email']\\n return True\\n print \\\"g.user\\\", g.user\\n return False\",\n \"def test_credentials(self):\\r\\n data = self._deep_clean('zekebarge@gmail.com')\\r\\n error = data.get(ERROR_CODE, None)\\r\\n if error in (1,2):\\r\\n raise InvalidCredentialsError(\\\"Credentials are invalid for user '{}'\\\".format(self._username))\\r\\n return True\",\n \"def check_auth(username, password):\\n return get_ct_object(username, password) is not None\",\n \"def check_user(self):\\n try:\\n if (self.get_user()[0][0] == self.username) and (self.check_password(self.password)):\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"def check_auth(username, password):\\n return username == 'daniel' and password == config['redis_auth_key']\",\n \"def validate_authentication(self, username, password, handler):\\n hash = md5(password).hexdigest()\\n msg = \\\"Authentication failed.\\\"\\n if not self.has_user(username):\\n if username == 'anonymous':\\n msg = \\\"Anonymous access not allowed.\\\"\\n raise AuthenticationFailed(msg)\\n if username != 'anonymous':\\n if self.user_table[username]['pwd'] != hash:\\n raise AuthenticationFailed(msg)\",\n \"def is_correct_user(self, login, password):\\n pass\",\n \"def check_auth(username, password):\\n return username == get_env('UPLOAD_USER') and password == get_env('UPLOAD_PASSWORD')\",\n \"def verify_password(self, username, password):\\n\\n try:\\n self.c.execute('SELECT password FROM profiles WHERE name=(?)', (username,))\\n\\n db_pw = self.c.fetchone()[0]\\n print(password)\\n\\n return db_pw == password\\n\\n except TypeError:\\n return False\",\n \"def check_credentials(username, password):\\n\\n return db.auth_user(username, password)\",\n \"def check_auth(_, http_password):\\n return (password is not None) and (password == http_password)\",\n \"def check_user(self, username, password):\\n user = [user for user in self.db if user['username'] == username]\\n if user:\\n if check_password_hash(user[0][\\\"password\\\"], password):\\n return True\\n return False\\n return False\",\n \"def validate(self) -> bool:\\n if not super().validate():\\n return False\\n\\n # Does the user exist\\n user = User.query.filter_by(username=self.username.data).first()\\n if not user:\\n self.username.errors.append('Invalid username or password')\\n return False\\n\\n # Does given password match user's password\\n if not user.check_password(self.password.data):\\n self.username.errors.append('Invalid username or password')\\n return False\\n\\n return True\",\n \"def check_credentials(username, password):\\n\\t\\n\\tconn = sqlite3.connect('db/user_task.db')\\n\\tcursor = conn.execute(\\\"SELECT password from user WHERE username == \\\\'%s\\\\'\\\" % (username))\\n\\tdata = cursor.fetchall()\\n\\tconn.close()\\n\\n\\tif len(data) == 0:\\n\\t\\treturn u\\\"Incorrect username\\\"\\n\\n\\tfor row in data:\\n\\t\\tencoded_password = hashlib.sha1(password.encode('utf-8')).hexdigest()\\n\\t\\tif row[0] == encoded_password:\\n\\t\\t\\treturn None\\n\\n\\treturn u\\\"Incorrect password\\\"\\n\\t\\n\\t# An example implementation which uses an ORM could be:\\n\\t# u = User.get(username)\\n\\t# if u is None:\\n\\t# return u\\\"Username %s is unknown to me.\\\" % username\\n\\t# if u.password != md5.new(password).hexdigest():\\n\\t# return u\\\"Incorrect password\\\"\",\n \"def check_auth(username, password):\\n try:\\n locust_username = os.environ['LOCUST_USER_NAME']\\n locust_password = os.environ['LOCUST_PASSWORD']\\n return username == locust_username and password == locust_password\\n except:\\n return True\",\n \"def validate_password(data):\\n\\n if \\\"password\\\" not in data:\\n return False\\n password = data[\\\"password\\\"]\\n if len(password) < 4 or len(password) > 16:\\n return False\\n\\n return True\",\n \"def _check_user_pass(self):\\n if not self.username:\\n self.username = input(' 请输入手机号：')\\n if self.username.isdigit() and '+86' not in self.username:\\n self.username = '+86' + self.username\\n\\n if not self.password:\\n self.password = input(' 请输入密码：')\",\n \"def check_password(self, username, password): # tested\\r\\n conn = self.get_db()\\r\\n with conn:\\r\\n c = conn.cursor()\\r\\n sql = ('select password from gameuser where username=%s')\\r\\n c.execute(sql,(username,))\\r\\n hashedpass = md5.new(password).hexdigest()\\r\\n u = c.fetchone()\\r\\n if u == None:\\r\\n raise NoUserExistsException(username)\\r\\n # print 'database contains {}, entered password was {}'.format(u[0],hashedpass)\\r\\n return u[0] == hashedpass\",\n \"def valid_login_password(username, password, pw_hash):\\n salt = pw_hash.split(',')[1]\\n return pw_hash == make_pw_hash(username, password, salt)\",\n \"def check_auth(username, password):\\n return username == 'admin' and password in app.config[\\\"CLAIM_SECRETS\\\"]\",\n \"def validate_password(self, field):\\n username = self.username.data\\n if platform.system().lower() == 'linux':\\n g.logger.debug('use pam for authenticate.')\\n from pam import authenticate\\n if authenticate(username, field.data):\\n g.logger.info('session opened for user %s.' % username)\\n return username\\n else:\\n raise ValueError('Authentication failure.')\\n return username\",\n \"def check_credentials_typo(credentials):\\n regex_username = r'^[\\\\w\\\\.\\\\-]{2,}$'\\n regex_password = r'[^.]{4,10}$'\\n\\n if not match(regex_username, credentials['username']):\\n raise ValueError('invalid username typo')\\n\\n if not match(regex_password, credentials['password']):\\n raise ValueError('invalid password typo')\",\n \"def username_and_password_check(username: str, password: str) -> bool:\\n table = metadata.tables['users']\\n s = select(\\n [\\n func.count(table.c.id)\\n ]).where(tuple_(table.c['username'], table.c['password'])\\n .in_([(username, password)]))\\n result = conn.execute(s).scalar()\\n return bool(result)\",\n \"def check_auth(username, password):\\n user = User.query.filter(User.username == username).first()\\n\\n if user:\\n return user.password == password\\n else:\\n return False\",\n \"def verify_password(username, password):\\n if username in user_auth and check_password_hash(user_auth.get(username), password):\\n return username\",\n \"def authentication_validation(username, password, access_token):\\n if bool(username) is not bool(password):\\n raise Exception(\\\"Basic authentication requires a username AND\\\" \\\" password.\\\")\\n if (username and access_token) or (password and access_token):\\n raise Exception(\\n \\\"Cannot use both Basic Authentication and\\\"\\n \\\" OAuth2.0. Please use only one authentication\\\"\\n \\\" method.\\\"\\n )\",\n \"def invalid_credentials( form , field ): \\n\\tusername_entered = form.username.data\\n\\tpassword_entered = field.data \\n\\tuser_object = User.query.filter_by(username = username_entered).first()\\n\\tif user_object is None : \\n\\t\\traise ValidationError(\\\"Username or Password is incorrect !\\\")\\n\\telif not pbkdf2_sha256.verify(password_entered , user_object.password) : \\n\\t\\traise ValidationError(\\\"Username or Password is incorrect !\\\")\",\n \"def is_password_valid(password):\\n #TODO : This should also be handled by the front_end\\n pass\",\n \"def check_password(username, password, htpasswd_fn):\\n entries = parse_htpasswd(htpasswd_fn, username)\\n if not entries.has_key(username):\\n raise NoSuchUser('No user: %r' % username)\\n return check_entry_password(\\n username, password, entries[username])\",\n \"def auth(username, password):\\n return username == password\",\n \"def _check_password(self, password):\\n if self.password_regex.search(password) is not None:\\n print(\\\"Correct password\\\")\\n return True\\n else:\\n print(\\\"Wrong password\\\")\\n return False\",\n \"def check_auth(username, password):\\n return username == current_app.config['DOC_USERNAME'] and password == current_app.config['DOC_PASSWORD']\",\n \"def _check_username(self, username):\\n if self.username_regex.search(username) is not None:\\n print(\\\"Correct username\\\")\\n return True\\n else: \\n print(\\\"Wrong username\\\")\\n return False\",\n \"def check_auth_password(self, username, password):\\n if username == self.username and password == self.password:\\n return paramiko.AUTH_SUCCESSFUL\\n return paramiko.AUTH_FAILED\",\n \"def check_credentials(input_password, real_password):\\n return pwd_context.verify(input_password, real_password)\",\n \"def test_valid_username_invalid_password(self):\\n response = self.client.post(reverse('users:login'), {'username': self.user['username'], 'password': '1sfsdf'})\\n self.assertEqual(response.status_code, 200)\\n self.assertFormError(response, 'form', None, ERROR_MSG)\",\n \"def authorise_login(self, username, password):\\n con = self.connect()\\n cursor = con.cursor()\\n cursor.execute(\\\"SELECT username,password \\\\\\n FROM users WHERE password = %s\\\", (password,))\\n credentials = cursor.fetchone()\\n cursor.close()\\n con.commit()\\n con.close()\\n if credentials is None:\\n return False\\n if username != credentials[0]:\\n return False\\n if password != credentials[1]:\\n return False\\n return True\",\n \"def clean(self):\\n cleaned_data = super().clean()\\n username = cleaned_data['username']\\n password = cleaned_data['password']\\n\\n if authenticate(username=username, password=password) is None:\\n raise ValidationError('Your username or password is incorrect.')\",\n \"def _check_password(self, password):\\n rule = re.compile(constant.password_regex)\\n if not rule.match(password):\\n return False\\n # disallow password from azure guide, yes, it's hard code.\\n disallowed = constant.password_disallowed\\n return password not in disallowed\",\n \"def validate(username: str, password: str) -> dict:\\n validation_check = {}\\n \\n # Check to see if the username was left blank\\n if username.strip() == \\\"\\\":\\n validation_check[\\\"success\\\"] = False\\n validation_check[\\\"username\\\"] = \\\"Username cannot be left blank.\\\"\\n\\n # Check to see if the username is taken\\n elif not sql.is_username_taken(username):\\n validation_check[\\\"success\\\"] = False\\n validation_check[\\\"username\\\"] = \\\"Username is incorrect\\\"\\n\\n # Check to see if the password was left blank\\n if password.strip() == \\\"\\\":\\n validation_check[\\\"success\\\"] = False\\n validation_check[\\\"password\\\"] = \\\"Password cannot be left blank.\\\"\\n\\n\\n if not validation_check.get(\\\"success\\\", True):\\n return validation_check\\n\\n else:\\n return sql.verify_credentials(username, password)\",\n \"def check_friend(self, username):\\n if (self.isBlank(username) or self.isValidLen(username)):\\n return False\\n\\n safe_input = (username,)\\n \\n vals = self.cur.execute(\\\"SELECT Username, Password FROM Users WHERE Username=?\\\" ,safe_input).fetchone()\\n if vals:\\n return True\\n else:\\n return False\",\n \"def test_check_password():\\n assert check_password('Longpassword') == False\\n assert check_password('123456') == False\\n assert check_password('short') == False\\n assert check_password('C0rect') == False\\n assert check_password('Correct8') == True\",\n \"def check_password(self, password):\\n return self.password == password\",\n \"def test_validate_credentials(self):\\n pass\",\n \"def check_my_users(user):\\n user_data = my_users.get(user['username'])\\n if not user_data:\\n return False # <--- invalid credentials\\n elif user_data.get('password') == user['password']:\\n return True # <--- user is logged in!\\n\\n return False # <--- invalid credentials\",\n \"def valid_login(username: str, password: str):\\n\\n try:\\n LoginHandler.login(username, password)\\n global validLogin\\n validLogin = True\\n except:\\n validLogin = False\",\n \"def checkLogin(username, password):\\n\\n # Query database for username\\n cur = initialise(2)\\n cur.execute(\\\"SELECT * FROM users WHERE username = ?\\\", [username])\\n users = cur.fetchall()\\n\\n # Ensure username exists and password is correct\\n if len(users) != 1 or not check_password_hash(users[0][\\\"hash\\\"], password):\\n return False, username\\n else:\\n return True, users[0][\\\"id\\\"]\",\n \"def validate_login(name,password):\\n\\t\\n\\t#Read the attendance excelsheet check if username and password matched\\n\\tdf_atten=pd.read_csv(\\\"datasrc/People.csv\\\")\\n\\t# 10006 ultbjxu\\n\\t\\n\\tif (df_atten.Username.astype(str).str.contains(name).any() and df_atten.Password.astype(str).str.contains(password).any()):\\t\\t\\n\\t\\treturn True\\n\\telse: \\n\\t\\treturn False\",\n \"def isUSER(username, password):\\n usr = UserData[\\\"username\\\"].to_dict()\\n listofUsers = list(UserData[\\\"username\\\"])\\n if username in listofUsers:\\n # print('Is a User')\\n getindex = listofUsers.index(username)\\n listofPasswords = list(UserData[\\\"password\\\"])\\n # print(getindex)\\n if listofPasswords[getindex] == password:\\n # print('Password Correct')\\n return True, True\\n return True, False\\n return False, False\",\n \"def _input(self):\\n\\t\\tself.username = input(\\\"Username:\\\")\\n\\t\\tself.password = input(\\\"password:\\\")\\n\\t\\tret = re.match(r\\\"^\\\\w{1,20}$\\\", self.username)\\n\\t\\tprint(\\\"------>input string\\\", self.username)\\n\\t\\tprint(\\\"------>matched string\\\",ret)\\t\\n\\t\\ttime.sleep(1)\\n\\t\\tif ret == None:\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\tpass\\n\\n\\t\\tsql = f\\\"\\\"\\\"select name, password from users where name='{self.username}';\\\"\\\"\\\"\\n\\t\\tprint(\\\"----------->\\\", sql)\\n\\t\\ttime.sleep(1)\\n\\t\\tcount = self.cs.execute(sql)\\n\\t\\tprint(count)\\n\\t\\tif (self.username, self.password) == self.cs.fetchone():\\n\\t\\t\\tprint(\\\"Login Successfully\\\")\\n\\t\\t\\ttime.sleep(1)\\n\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"Wrong username or password\\\")\\n\\t\\t\\ttime.sleep(1)\\n\\t\\t\\treturn True\",\n \"def is_valid_login(username, password):\\n with open(PASSFILE, \\\"r\\\") as passfile:\\n for record in passfile:\\n try:\\n valid_user, valid_password = False, False\\n r_username, r_salt_hash = record.split()\\n if username == r_username:\\n valid_user = True\\n if sha256_crypt.verify(password, r_salt_hash):\\n valid_password = True\\n if valid_user and valid_password:\\n return True\\n except ValueError:\\n pass\\n return False\",\n \"def check_auth(username, password):\\n session.pop('username', None)\\n session.pop('password', None)\\n session['username'] = username\\n session['password'] = password\\n # Test if we can connect to a region\\n connect_to_region()\\n return True\",\n \"def check_credentials_validation(credentials):\\n spec = {'_id': credentials['username'], 'password': credentials['password']}\\n if not current_app.mongo.observer.users.find_one(spec):\\n raise Unauthorized('invalid credentials')\",\n \"def username_check(username):\\n\\n try: \\n pwd.getpwnam(username)\\n print(\\\"User %s DOES EXIST. Try a different username.\\\" % (username)) \\n return False\\n\\n except KeyError: \\n print(\\\"User %s DOES NOT exist. Continuing...\\\" % (username)) \\n return True\",\n \"def test_invalid_username_valid_password(self):\\n response = self.client.post(reverse('users:login'), {'username': 'xyzabe', 'password': self.user['password1']})\\n self.assertEqual(response.status_code, 200)\\n self.assertFormError(response, 'form', None, ERROR_MSG)\",\n \"def test_valid_password(self):\\n pass_field = Field(\\\"\\\".join(['x' * (int(self.app.config['MAX_PWD_LEN']))]))\\n\\n valid_password(None, pass_field)\",\n \"def authenticate(username, password):\\n test = User.load(username)\\n test_password = test.password\\n input_password = md5.new(\\n password + config.get('security', 'salt')).digest()\\n if input_password == test_password:\\n return True\\n else:\\n return False\",\n \"def test_authenticate_invalid_username(self):\\r\\n print(\\\"Authenticate user invalid username\\\")\\r\\n username = \\\"test9999user\\\"\\r\\n password = \\\"password\\\"\\r\\n\\r\\n self.assertEqual(self.system.authenticate(username, password), False)\",\n \"def verify_pw(username, password):\\n global password_store\\n logger = logging.getLogger('verify_pw')\\n if not password_store:\\n logger.error(\\\"No password store specified\\\")\\n return False\\n logger.debug(\\\"Verifying password for %s\\\" % username)\\n return password_store.verify(username, password)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82211864","0.8146436","0.7975914","0.7962432","0.7885527","0.78588223","0.78382313","0.78288996","0.7815222","0.77856815","0.77700555","0.7760794","0.7754975","0.77548647","0.7750886","0.7749223","0.77322245","0.7715806","0.7687129","0.7685904","0.7673684","0.766307","0.766307","0.76620096","0.76567376","0.7652723","0.765255","0.7611541","0.75952524","0.7564781","0.7557824","0.7547461","0.75408834","0.75314397","0.7506924","0.75042623","0.74691737","0.7442317","0.741009","0.740618","0.73972464","0.7394501","0.7389306","0.73495054","0.7336364","0.7299509","0.7276375","0.7247304","0.7230579","0.7205749","0.7199119","0.71977973","0.7193516","0.71885014","0.71854734","0.71695465","0.7169254","0.7159132","0.7140503","0.7135009","0.71116257","0.7110569","0.7108786","0.709969","0.7093436","0.7031712","0.7030077","0.7026029","0.7025823","0.69905216","0.6989299","0.69882464","0.69840413","0.696945","0.69658744","0.69613117","0.6940676","0.6931497","0.6914118","0.6901826","0.68954366","0.68901163","0.68864787","0.6883074","0.68827283","0.68799317","0.68769723","0.68669415","0.6864661","0.68634623","0.68553925","0.6848534","0.6846545","0.6840525","0.6836461","0.6829205","0.6827091","0.682658","0.68223697","0.6813599"],"string":"[\n \"0.82211864\",\n \"0.8146436\",\n \"0.7975914\",\n \"0.7962432\",\n \"0.7885527\",\n \"0.78588223\",\n \"0.78382313\",\n \"0.78288996\",\n \"0.7815222\",\n \"0.77856815\",\n \"0.77700555\",\n \"0.7760794\",\n \"0.7754975\",\n \"0.77548647\",\n \"0.7750886\",\n \"0.7749223\",\n \"0.77322245\",\n \"0.7715806\",\n \"0.7687129\",\n \"0.7685904\",\n \"0.7673684\",\n \"0.766307\",\n \"0.766307\",\n \"0.76620096\",\n \"0.76567376\",\n \"0.7652723\",\n \"0.765255\",\n \"0.7611541\",\n \"0.75952524\",\n \"0.7564781\",\n \"0.7557824\",\n \"0.7547461\",\n \"0.75408834\",\n \"0.75314397\",\n \"0.7506924\",\n \"0.75042623\",\n \"0.74691737\",\n \"0.7442317\",\n \"0.741009\",\n \"0.740618\",\n \"0.73972464\",\n \"0.7394501\",\n \"0.7389306\",\n \"0.73495054\",\n \"0.7336364\",\n \"0.7299509\",\n \"0.7276375\",\n \"0.7247304\",\n \"0.7230579\",\n \"0.7205749\",\n \"0.7199119\",\n \"0.71977973\",\n \"0.7193516\",\n \"0.71885014\",\n \"0.71854734\",\n \"0.71695465\",\n \"0.7169254\",\n \"0.7159132\",\n \"0.7140503\",\n \"0.7135009\",\n \"0.71116257\",\n \"0.7110569\",\n \"0.7108786\",\n \"0.709969\",\n \"0.7093436\",\n \"0.7031712\",\n \"0.7030077\",\n \"0.7026029\",\n \"0.7025823\",\n \"0.69905216\",\n \"0.6989299\",\n \"0.69882464\",\n \"0.69840413\",\n \"0.696945\",\n \"0.69658744\",\n \"0.69613117\",\n \"0.6940676\",\n \"0.6931497\",\n \"0.6914118\",\n \"0.6901826\",\n \"0.68954366\",\n \"0.68901163\",\n \"0.68864787\",\n \"0.6883074\",\n \"0.68827283\",\n \"0.68799317\",\n \"0.68769723\",\n \"0.68669415\",\n \"0.6864661\",\n \"0.68634623\",\n \"0.68553925\",\n \"0.6848534\",\n \"0.6846545\",\n \"0.6840525\",\n \"0.6836461\",\n \"0.6829205\",\n \"0.6827091\",\n \"0.682658\",\n \"0.68223697\",\n \"0.6813599\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7798579"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":77479,"cells":{"query":{"kind":"string","value":"Sends a 401 response that enables basic auth"},"document":{"kind":"string","value":"def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def authenticate():\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\"api\"'})","def authenticate():\n return Response(\n '', 401, {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","def authenticate():\n return Response(\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate(self):\n abort(\n 401,\n description=self.exception,\n www_authenticate=(\"WWW-Authenticate\", 'Basic realm=\"%s\"' % __package__),\n )","def authenticate():\n return Response(\n 'Could not verify your credentials for that url', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\r\n return Response(\r\n 'Could not verify your access level for that URL.\\n'\r\n 'You have to login with proper credentials', 401,\r\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n\treturn Response(\n\t'Could not verify your access level for that URL.\\n'\n\t'You have to login with proper credentials', 401,\n\t{'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials.', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response('Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials',\n 401,\n {\n 'WWW-Authenticate': 'Basic realm=\"Login Required\"'\n }\n )","def authenticate():\n return send_msg(\n 401,\n 'Must be connected',\n headers={'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(render_template('index.html', auth=False), 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return flask.Response('Login required.', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL, need token.\\n', 403,\n {'WWW-Authenticate': 'Basic realm=\"token Required\"'})","def authenticate(self):\n return Response(\n 'Could not verify your access level for that URL.\\nYou have to login with proper credentials',\n 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","def user_must_authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with Web Manager credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def unauthorized():\n return HttpError(401)","def basic_auth_error():\n logger.debug(\"Basic authentication failed.\")\n return unauthorized(\"Invalid credentials.\")","def response_unauthorised():\n\n response = {\n 'status': 'failed',\n 'error': 'Not Authorised'\n }\n\n return response_json(response, status=401)","def challenge(self, environ, status, app_headers=(), forget_headers=()):\n resp = Response()\n resp.status = 401\n resp.headers = self.forget(environ, {})\n for headers in (app_headers, forget_headers):\n for name, value in headers:\n resp.headers[name] = value\n resp.content_type = \"text/plain\"\n resp.body = \"Unauthorized\"\n return resp","def authenticate(self):\n resp = Response(None, 401)\n abort(401, description='Please provide proper credentials', response=resp)","def _login(self, environ, start_response):\n response = HTTPUnauthorized()\n response.www_authenticate = ('Basic', {'realm': self._realm})\n return response(environ, start_response)","def add_basic_auth(blueprint: Blueprint, username, password, realm='api'):\n\n @blueprint.before_request\n def basic_http_auth(*args, **kwargs):\n auth = request.authorization\n if auth is None or auth.password != password or auth.username != username:\n return Response('Please login', 401, {'WWW-Authenticate': f'Basic realm=\"{realm}\"'})","def authenticate():\n return abort(401)","def forget(self, request):\n return [('WWW-Authenticate', 'Basic realm=\"%s\"' % self.realm)]","def unauthorized():\n return {'errors': ['Unauthorized']}, 401","def _respond_unauthorized(self, request, message=\"Unauthorized\"):\n resp = Response()\n resp.status = 401\n resp.headers = self.forget(request.environ, {})\n resp.content_type = \"text/plain\"\n resp.body = message\n request.environ[\"repoze.who.application\"] = resp\n return None","def authenticate():\n resp = {\"status\": 401, \"message\": \"Could not verify your access level for that URL\"}\n return Response(dumps(resp), status=404, mimetype='application/json')","def hidden_basic_auth(user=\"user\", passwd=\"passwd\"):\n\n if not check_basic_auth(user, passwd):\n return status_code(404)\n return jsonify(authenticated=True, user=user)","def CR_authentication():\n \n # create a random 10 character string\n choices = string.letters + string.digits + string.punctuation;\n randomString = ''.join(random.choice(choices) for i in range(10))\n session['challenge'] = randomString\n \n return Response('Access failed.', 401, {'WWW-Authenticate': str.format('Basic realm=\\\"Protected iStreet event data; Challenge: {0}\\\"', randomString)})","def authenticate():\n return Response(\n '''Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter. Please do so from a corporate email or\n with some kind of proof that you are a security engineer, academic, or need this data for research purposes (e.g. email from a corporate or .edu email or provide\n a linkedin or twitter account showing as much)\n You have to login with proper credentials''', 401, {'WWW-Authenticate': 'Basic realm=\"Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter. Please do so from a corporate email or with some kind of proof that you are a security engineer, academic, or need this data for research purposes (e.g. email from a corporate or .edu email or provide a linkedin or twitter account showing as much)\"'})","def auth_failure():\n return \"Request denied due to failed authorization\", 201, {'Content-Type': 'text/html'}","def unauthorized(self, error):\n return jsonify({'error': \"NOT AUTHORIZED\"}), 401","def _handle_authentication_error(self):\n response = make_response('Access Denied')\n response.headers['WWW-Authenticate'] = self.auth.get_authenticate_header()\n response.status_code = 401\n return response","def authenticate():\n\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n get_auth_headers())","def test_unauthorized_exception(exception_app):\n request, response = exception_app.test_client.get('/401')\n assert response.status == 401\n\n request, response = exception_app.test_client.get('/401/basic')\n assert response.status == 401\n assert response.headers.get('WWW-Authenticate') is not None\n assert response.headers.get('WWW-Authenticate') == \"Basic realm='Sanic'\"\n\n request, response = exception_app.test_client.get('/401/digest')\n assert response.status == 401\n\n auth_header = response.headers.get('WWW-Authenticate')\n assert auth_header is not None\n assert auth_header.startswith('Digest')\n assert \"qop='auth, auth-int'\" in auth_header\n assert \"algorithm='MD5'\" in auth_header\n assert \"nonce='abcdef'\" in auth_header\n assert \"opaque='zyxwvu'\" in auth_header\n\n request, response = exception_app.test_client.get('/401/bearer')\n assert response.status == 401\n assert response.headers.get('WWW-Authenticate') == \"Bearer\"","def display_401(error):\n return render_template('/error401.html'), 401","def test_unauthorized(self):\n self._error_test(fitbit_exceptions.HTTPUnauthorized)","def basic_auth(user=\"user\", passwd=\"passwd\"):\n\n if not check_basic_auth(user, passwd):\n return status_code(401)\n\n return jsonify(authenticated=True, user=user)","def test_unauthenticated_request(self):\n url = self.get_url(self.active_user.id)\n response = self.client.get(url)\n\n expected_status_code = 401\n self.assertEqual(response.status_code, expected_status_code)","def basic_http_auth(f):\n def wrap(request, *args, **kwargs):\n if request.META.get('HTTP_AUTHORIZATION', False):\n authtype, auth = request.META['HTTP_AUTHORIZATION'].split(' ')\n auth = base64.b64decode(auth)\n username, password = auth.split(':')\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n return f(request, *args, **kwargs)\n else:\n r = HttpResponse(\"Auth Required\", status = 401)\n r['WWW-Authenticate'] = 'Basic realm=\"ThatPanda DDNS\"'\n return r\n r = HttpResponse(\"Auth Required\", status = 401)\n r['WWW-Authenticate'] = 'Basic realm=\"ThatPanda DDNS\"'\n return r\n \n return wrap","def auth_error():\n return unauthorized('Invalid credentials')","def require_http_auth(request):\n\n if http_auth_allowed(request) and not request.user.is_authenticated:\n site = get_current_site(request)\n response = HttpResponse(status=401)\n response['WWW-Authenticate'] = (\n 'Basic realm=\"{}\", charset=\"UTF-8\"'.format(site.name)\n )\n # Check whether the client supports cookies.\n response.set_cookie('testcookie', '1', secure=(not settings.DEBUG),\n httponly=True, samesite='Lax')\n return response\n else:\n raise PermissionDenied()","def response_401(description=None):\n resp_def = dict(util.RESPONSE_404)\n if description is not None:\n resp_def['description'] = description\n\n return response(401, resp_def)","def http_basic_auth():\n users = ['administrator', 'admin']\n passwords = ['administrator', 'admin']\n protectedResource = 'http://localhost/secured_path'\n foundPass = False\n for user in users:\n if foundPass:\n break\n for passwd in passwords:\n encoded = base64.encodestring(user + ':' + passwd)\n response = requests.get(protectedResource, auth=(user, passwd))\n if response.status_code != 401:\n print('User Found!')\n print('User: %s, Pass: %s' % (user, passwd))\n foundPass = True\n break","def assertHttpUnauthorized(self, resp):\r\n return self.assertEqual(resp.status_code, 401)","def basic_auth_required(view_func):\n # http://djangosnippets.org/snippets/448/\n def _auth(request, *args, **kwargs):\n if 'HTTP_AUTHORIZATION' in request.META:\n auth = request.META['HTTP_AUTHORIZATION'].split()\n if len(auth) == 2:\n if auth[0].lower() == \"basic\":\n uname, passwd = base64.b64decode(auth[1]).split(':')\n user = authenticate(username=uname, password=passwd)\n if user is not None:\n if user.is_active:\n return view_func(request, *args, **kwargs)\n response = HttpResponse(\"Authorization Required\", status=401)\n response['WWW-Authenticate'] = 'Basic realm=\"Secure Area\"'\n return response\n return _auth","def http_basic_auth(func):\r\n\t@wraps(func)\r\n\tdef _decorator(request, *args, **kwargs):\r\n\r\n\t\tif request.META.has_key('HTTP_AUTHORIZATION'):\r\n\t\t\ttry:\r\n\t\t\t\tauthmeth, auth = request.META['HTTP_AUTHORIZATION'].split(' ', 1)\r\n\t\t\t\tif authmeth.lower() == 'basic':\r\n\t\t\t\t\tauth = auth.strip().decode('base64')\r\n\t\t\t\t\tusername, password = auth.split(':', 1)\r\n\t\t\t\t\tuser = authenticate(username=username, password=password)\r\n\r\n\t\t\t\t\tif user:\r\n\r\n\t\t\t\t\t\tlogin(request, user)\r\n\t\t\t\t\t\r\n\t\t\t\t\telse:\r\n\r\n\t\t\t\t\t\treturn HttpResponseForbidden()\r\n\r\n\t\t\texcept ValueError:\r\n\t\t\t\t# Bad HTTP_AUTHORIZATION header\r\n\t\t\t\treturn HttpResponseForbidden()\r\n\t\t\t\t\r\n\t\treturn func(request, *args, **kwargs)\r\n\treturn _decorator","def unauthorized(self, message=None):\n return self.send_message(message, status=401)","def basic_auth_required(fn):\n @wraps(fn)\n def _wrapper(request, *args, **kwargs):\n authentication = request.headers.get('Authentication', None)\n\n if authentication:\n if not authentication.startswith(\"Basic \"):\n request.response.status = 401\n\n return {\n 'error': \"Authentication failed!\"\n }\n\n auth_data = authentication[6:]\n\n try:\n username, password = base64.urlsafe_b64decode(auth_data).decode(\"UTF8\").split(\":\")\n\n user = request.dbsession.query(User).filter(\n User.email == username\n ).one()\n\n if user.is_password(password.encode(\"UTF8\")):\n return fn(request, *args, **kwargs)\n except (ValueError, NoResultFound):\n pass\n\n request.response.status = 401\n\n return {\n 'error': 'Authentication failed!'\n }\n\n return _wrapper","def test_user_get_failure_using_basic_auth(self):\n # setup\n user = self.generate_username_password()\n resp = self.create_user(user)\n resp_body = resp.json()\n try:\n assert resp.status_code == 201\n assert resp.headers[\"Content-Type\"] == \"application/json; charset=utf-8\"\n assert resp_body[\"username\"] == user[\"userName\"]\n assert resp_body[\"userID\"] != \"\"\n except AssertionError:\n raise\n finally:\n self.pprint_request(resp.request)\n self.pprint_response(resp)\n uuid_ = str(uuid.uuid4())\n\n # test\n resp2 = self.get_user_basic_auth(uuid_, user)\n resp_body2 = resp2.json()\n assert resp2.status_code == 401\n assert resp2.headers[\"Content-Type\"] == \"application/json; charset=utf-8\"\n assert resp_body2[\"code\"] == \"1207\"\n assert resp_body2[\"message\"] == \"User not found!\"\n\n # teardown:\n resp3 = self.delete_user_basic_auth(resp_body[\"userID\"], user)\n try:\n assert resp3.status_code == 204\n except AssertionError:\n raise\n finally:\n self.pprint_request(resp3.request)\n self.pprint_response(resp3)","def test_no_auth(self):\n url = 'https://domain.com/project/objects.inv'\n expected = 'https://domain.com/project/objects.inv'\n actual = _strip_basic_auth(url)\n assert expected == actual","def test_unhappy_path_unauthorized(self):\n\n response = self.client.get(self.url)\n expected_data = {\"detail\": \"Authentication credentials were not provided.\"}\n\n self.assertDictEqual(response.data, expected_data)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def test_unauthorized_access(flask_test_client, http_method, endpoint):\n response = flask_test_client.open(\n method=http_method, path=endpoint, headers=get_headers()\n )\n assert response.status == \"401 UNAUTHORIZED\"\n assert response.content_type == \"application/json\"\n assert response.json[\"message\"] == \"Access token is invalid or expired.\"","def test_get_unauthenticated(self):\n del self.client.request_kwargs['auth']\n self.verify_get_response(self.client.get(STATUS_PATH))","def get_authenticate_header(self):\n return f'Basic realm=\"{self.www_authenticate_realm}\"'","def test_retrieve_user_unauthorized(self):\r\n res = self.client.get(ME_URL)\r\n\r\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)","def basic_header(self):\n self.auth = base64.encodestring('%s:%s' % (self.username, self.password)).replace('\\n', '')\n return { \n #\"Authorization\" : \"Basic %s\" % self.auth, \n \"Content-type\": \"text/plain\" }","def test_retrieve_unauthorized_user(self):\n\n response = self.client.get(URL_ME)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def test_retrieve_user_unauthorized(self):\n res = self.client.get(ME_URL)\n\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)","def test_retrieve_user_unauthorized(self):\n res = self.client.get(ME_URL)\n\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)","def challenge_view(self, request):\n headerlist = [(\"Content-Type\", \"text/plain\")]\n headerlist.extend(self._get_challenge_headers(request))\n return Response(\"Unauthorized\", status=\"401 Unauthorized\",\n headerlist=headerlist)","def denied_response(self, req):\n if req.remote_user:\n return HTTPForbidden(request=req)\n else:\n return HTTPUnauthorized(request=req)","def test_401_unauthorized(app, client):\n\n @app.route(\"/401\")\n def unauthorized():\n abort(401)\n\n response = client.get(\"/401\")\n assert response.status_code == 401\n assert \"401 Unauthorized\" in str(response.data)","def test_status_unauthenticated(self):\n rv = self.client.post('/statusize/', data={'message': 'foo'},\n follow_redirects=True)\n eq_(rv.status_code, 403)","def test_call_unauthenticated(self):\n\n with Client('username', 'password') as client:\n self.setSessionResponse(401)\n with self.assertRaises(APIError):\n data = client.call(**self.build_parameters)","def test_unhappy_path_unauthorized(self):\n\n response = self.client.post(self.url)\n expected_data = {\"detail\": \"Authentication credentials were not provided.\"}\n\n self.assertDictEqual(response.data, expected_data)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)","def test_retrieve_users_unauthorized(setup_client):\n client = setup_client\n res = client.get(ME_URL)\n assert res.status_code == status.HTTP_401_UNAUTHORIZED","def __call__(self, resp):\r\n if not self.auth_token:\r\n self.auth()\r\n resp.register_hook('response', self.handle_error)\r\n resp.headers['X-Auth-Token'] = self.auth_token\r\n return resp","def get_auth_headers():\n\n auth_type = \"Basic\"\n if request.headers.get('UseXBasic'):\n auth_type = \"XBasic\"\n\n return {\n 'WWW-Authenticate': '%s realm=\"Login Required\"' % auth_type\n }"],"string":"[\n \"def authenticate():\\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\\\"api\\\"'})\",\n \"def authenticate():\\n return Response(\\n '', 401, {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"def authenticate():\\n return Response(\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate(self):\\n abort(\\n 401,\\n description=self.exception,\\n www_authenticate=(\\\"WWW-Authenticate\\\", 'Basic realm=\\\"%s\\\"' % __package__),\\n )\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your credentials for that url', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\r\\n return Response(\\r\\n 'Could not verify your access level for that URL.\\\\n'\\r\\n 'You have to login with proper credentials', 401,\\r\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n\\treturn Response(\\n\\t'Could not verify your access level for that URL.\\\\n'\\n\\t'You have to login with proper credentials', 401,\\n\\t{'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials.', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response('Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials',\\n 401,\\n {\\n 'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'\\n }\\n )\",\n \"def authenticate():\\n return send_msg(\\n 401,\\n 'Must be connected',\\n headers={'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(render_template('index.html', auth=False), 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return flask.Response('Login required.', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL, need token.\\\\n', 403,\\n {'WWW-Authenticate': 'Basic realm=\\\"token Required\\\"'})\",\n \"def authenticate(self):\\n return Response(\\n 'Could not verify your access level for that URL.\\\\nYou have to login with proper credentials',\\n 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"def user_must_authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with Web Manager credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def unauthorized():\\n return HttpError(401)\",\n \"def basic_auth_error():\\n logger.debug(\\\"Basic authentication failed.\\\")\\n return unauthorized(\\\"Invalid credentials.\\\")\",\n \"def response_unauthorised():\\n\\n response = {\\n 'status': 'failed',\\n 'error': 'Not Authorised'\\n }\\n\\n return response_json(response, status=401)\",\n \"def challenge(self, environ, status, app_headers=(), forget_headers=()):\\n resp = Response()\\n resp.status = 401\\n resp.headers = self.forget(environ, {})\\n for headers in (app_headers, forget_headers):\\n for name, value in headers:\\n resp.headers[name] = value\\n resp.content_type = \\\"text/plain\\\"\\n resp.body = \\\"Unauthorized\\\"\\n return resp\",\n \"def authenticate(self):\\n resp = Response(None, 401)\\n abort(401, description='Please provide proper credentials', response=resp)\",\n \"def _login(self, environ, start_response):\\n response = HTTPUnauthorized()\\n response.www_authenticate = ('Basic', {'realm': self._realm})\\n return response(environ, start_response)\",\n \"def add_basic_auth(blueprint: Blueprint, username, password, realm='api'):\\n\\n @blueprint.before_request\\n def basic_http_auth(*args, **kwargs):\\n auth = request.authorization\\n if auth is None or auth.password != password or auth.username != username:\\n return Response('Please login', 401, {'WWW-Authenticate': f'Basic realm=\\\"{realm}\\\"'})\",\n \"def authenticate():\\n return abort(401)\",\n \"def forget(self, request):\\n return [('WWW-Authenticate', 'Basic realm=\\\"%s\\\"' % self.realm)]\",\n \"def unauthorized():\\n return {'errors': ['Unauthorized']}, 401\",\n \"def _respond_unauthorized(self, request, message=\\\"Unauthorized\\\"):\\n resp = Response()\\n resp.status = 401\\n resp.headers = self.forget(request.environ, {})\\n resp.content_type = \\\"text/plain\\\"\\n resp.body = message\\n request.environ[\\\"repoze.who.application\\\"] = resp\\n return None\",\n \"def authenticate():\\n resp = {\\\"status\\\": 401, \\\"message\\\": \\\"Could not verify your access level for that URL\\\"}\\n return Response(dumps(resp), status=404, mimetype='application/json')\",\n \"def hidden_basic_auth(user=\\\"user\\\", passwd=\\\"passwd\\\"):\\n\\n if not check_basic_auth(user, passwd):\\n return status_code(404)\\n return jsonify(authenticated=True, user=user)\",\n \"def CR_authentication():\\n \\n # create a random 10 character string\\n choices = string.letters + string.digits + string.punctuation;\\n randomString = ''.join(random.choice(choices) for i in range(10))\\n session['challenge'] = randomString\\n \\n return Response('Access failed.', 401, {'WWW-Authenticate': str.format('Basic realm=\\\\\\\"Protected iStreet event data; Challenge: {0}\\\\\\\"', randomString)})\",\n \"def authenticate():\\n return Response(\\n '''Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter. Please do so from a corporate email or\\n with some kind of proof that you are a security engineer, academic, or need this data for research purposes (e.g. email from a corporate or .edu email or provide\\n a linkedin or twitter account showing as much)\\n You have to login with proper credentials''', 401, {'WWW-Authenticate': 'Basic realm=\\\"Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter. Please do so from a corporate email or with some kind of proof that you are a security engineer, academic, or need this data for research purposes (e.g. email from a corporate or .edu email or provide a linkedin or twitter account showing as much)\\\"'})\",\n \"def auth_failure():\\n return \\\"Request denied due to failed authorization\\\", 201, {'Content-Type': 'text/html'}\",\n \"def unauthorized(self, error):\\n return jsonify({'error': \\\"NOT AUTHORIZED\\\"}), 401\",\n \"def _handle_authentication_error(self):\\n response = make_response('Access Denied')\\n response.headers['WWW-Authenticate'] = self.auth.get_authenticate_header()\\n response.status_code = 401\\n return response\",\n \"def authenticate():\\n\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n get_auth_headers())\",\n \"def test_unauthorized_exception(exception_app):\\n request, response = exception_app.test_client.get('/401')\\n assert response.status == 401\\n\\n request, response = exception_app.test_client.get('/401/basic')\\n assert response.status == 401\\n assert response.headers.get('WWW-Authenticate') is not None\\n assert response.headers.get('WWW-Authenticate') == \\\"Basic realm='Sanic'\\\"\\n\\n request, response = exception_app.test_client.get('/401/digest')\\n assert response.status == 401\\n\\n auth_header = response.headers.get('WWW-Authenticate')\\n assert auth_header is not None\\n assert auth_header.startswith('Digest')\\n assert \\\"qop='auth, auth-int'\\\" in auth_header\\n assert \\\"algorithm='MD5'\\\" in auth_header\\n assert \\\"nonce='abcdef'\\\" in auth_header\\n assert \\\"opaque='zyxwvu'\\\" in auth_header\\n\\n request, response = exception_app.test_client.get('/401/bearer')\\n assert response.status == 401\\n assert response.headers.get('WWW-Authenticate') == \\\"Bearer\\\"\",\n \"def display_401(error):\\n return render_template('/error401.html'), 401\",\n \"def test_unauthorized(self):\\n self._error_test(fitbit_exceptions.HTTPUnauthorized)\",\n \"def basic_auth(user=\\\"user\\\", passwd=\\\"passwd\\\"):\\n\\n if not check_basic_auth(user, passwd):\\n return status_code(401)\\n\\n return jsonify(authenticated=True, user=user)\",\n \"def test_unauthenticated_request(self):\\n url = self.get_url(self.active_user.id)\\n response = self.client.get(url)\\n\\n expected_status_code = 401\\n self.assertEqual(response.status_code, expected_status_code)\",\n \"def basic_http_auth(f):\\n def wrap(request, *args, **kwargs):\\n if request.META.get('HTTP_AUTHORIZATION', False):\\n authtype, auth = request.META['HTTP_AUTHORIZATION'].split(' ')\\n auth = base64.b64decode(auth)\\n username, password = auth.split(':')\\n user = authenticate(username=username, password=password)\\n if user is not None:\\n if user.is_active:\\n login(request, user)\\n return f(request, *args, **kwargs)\\n else:\\n r = HttpResponse(\\\"Auth Required\\\", status = 401)\\n r['WWW-Authenticate'] = 'Basic realm=\\\"ThatPanda DDNS\\\"'\\n return r\\n r = HttpResponse(\\\"Auth Required\\\", status = 401)\\n r['WWW-Authenticate'] = 'Basic realm=\\\"ThatPanda DDNS\\\"'\\n return r\\n \\n return wrap\",\n \"def auth_error():\\n return unauthorized('Invalid credentials')\",\n \"def require_http_auth(request):\\n\\n if http_auth_allowed(request) and not request.user.is_authenticated:\\n site = get_current_site(request)\\n response = HttpResponse(status=401)\\n response['WWW-Authenticate'] = (\\n 'Basic realm=\\\"{}\\\", charset=\\\"UTF-8\\\"'.format(site.name)\\n )\\n # Check whether the client supports cookies.\\n response.set_cookie('testcookie', '1', secure=(not settings.DEBUG),\\n httponly=True, samesite='Lax')\\n return response\\n else:\\n raise PermissionDenied()\",\n \"def response_401(description=None):\\n resp_def = dict(util.RESPONSE_404)\\n if description is not None:\\n resp_def['description'] = description\\n\\n return response(401, resp_def)\",\n \"def http_basic_auth():\\n users = ['administrator', 'admin']\\n passwords = ['administrator', 'admin']\\n protectedResource = 'http://localhost/secured_path'\\n foundPass = False\\n for user in users:\\n if foundPass:\\n break\\n for passwd in passwords:\\n encoded = base64.encodestring(user + ':' + passwd)\\n response = requests.get(protectedResource, auth=(user, passwd))\\n if response.status_code != 401:\\n print('User Found!')\\n print('User: %s, Pass: %s' % (user, passwd))\\n foundPass = True\\n break\",\n \"def assertHttpUnauthorized(self, resp):\\r\\n return self.assertEqual(resp.status_code, 401)\",\n \"def basic_auth_required(view_func):\\n # http://djangosnippets.org/snippets/448/\\n def _auth(request, *args, **kwargs):\\n if 'HTTP_AUTHORIZATION' in request.META:\\n auth = request.META['HTTP_AUTHORIZATION'].split()\\n if len(auth) == 2:\\n if auth[0].lower() == \\\"basic\\\":\\n uname, passwd = base64.b64decode(auth[1]).split(':')\\n user = authenticate(username=uname, password=passwd)\\n if user is not None:\\n if user.is_active:\\n return view_func(request, *args, **kwargs)\\n response = HttpResponse(\\\"Authorization Required\\\", status=401)\\n response['WWW-Authenticate'] = 'Basic realm=\\\"Secure Area\\\"'\\n return response\\n return _auth\",\n \"def http_basic_auth(func):\\r\\n\\t@wraps(func)\\r\\n\\tdef _decorator(request, *args, **kwargs):\\r\\n\\r\\n\\t\\tif request.META.has_key('HTTP_AUTHORIZATION'):\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tauthmeth, auth = request.META['HTTP_AUTHORIZATION'].split(' ', 1)\\r\\n\\t\\t\\t\\tif authmeth.lower() == 'basic':\\r\\n\\t\\t\\t\\t\\tauth = auth.strip().decode('base64')\\r\\n\\t\\t\\t\\t\\tusername, password = auth.split(':', 1)\\r\\n\\t\\t\\t\\t\\tuser = authenticate(username=username, password=password)\\r\\n\\r\\n\\t\\t\\t\\t\\tif user:\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tlogin(request, user)\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\telse:\\r\\n\\r\\n\\t\\t\\t\\t\\t\\treturn HttpResponseForbidden()\\r\\n\\r\\n\\t\\t\\texcept ValueError:\\r\\n\\t\\t\\t\\t# Bad HTTP_AUTHORIZATION header\\r\\n\\t\\t\\t\\treturn HttpResponseForbidden()\\r\\n\\t\\t\\t\\t\\r\\n\\t\\treturn func(request, *args, **kwargs)\\r\\n\\treturn _decorator\",\n \"def unauthorized(self, message=None):\\n return self.send_message(message, status=401)\",\n \"def basic_auth_required(fn):\\n @wraps(fn)\\n def _wrapper(request, *args, **kwargs):\\n authentication = request.headers.get('Authentication', None)\\n\\n if authentication:\\n if not authentication.startswith(\\\"Basic \\\"):\\n request.response.status = 401\\n\\n return {\\n 'error': \\\"Authentication failed!\\\"\\n }\\n\\n auth_data = authentication[6:]\\n\\n try:\\n username, password = base64.urlsafe_b64decode(auth_data).decode(\\\"UTF8\\\").split(\\\":\\\")\\n\\n user = request.dbsession.query(User).filter(\\n User.email == username\\n ).one()\\n\\n if user.is_password(password.encode(\\\"UTF8\\\")):\\n return fn(request, *args, **kwargs)\\n except (ValueError, NoResultFound):\\n pass\\n\\n request.response.status = 401\\n\\n return {\\n 'error': 'Authentication failed!'\\n }\\n\\n return _wrapper\",\n \"def test_user_get_failure_using_basic_auth(self):\\n # setup\\n user = self.generate_username_password()\\n resp = self.create_user(user)\\n resp_body = resp.json()\\n try:\\n assert resp.status_code == 201\\n assert resp.headers[\\\"Content-Type\\\"] == \\\"application/json; charset=utf-8\\\"\\n assert resp_body[\\\"username\\\"] == user[\\\"userName\\\"]\\n assert resp_body[\\\"userID\\\"] != \\\"\\\"\\n except AssertionError:\\n raise\\n finally:\\n self.pprint_request(resp.request)\\n self.pprint_response(resp)\\n uuid_ = str(uuid.uuid4())\\n\\n # test\\n resp2 = self.get_user_basic_auth(uuid_, user)\\n resp_body2 = resp2.json()\\n assert resp2.status_code == 401\\n assert resp2.headers[\\\"Content-Type\\\"] == \\\"application/json; charset=utf-8\\\"\\n assert resp_body2[\\\"code\\\"] == \\\"1207\\\"\\n assert resp_body2[\\\"message\\\"] == \\\"User not found!\\\"\\n\\n # teardown:\\n resp3 = self.delete_user_basic_auth(resp_body[\\\"userID\\\"], user)\\n try:\\n assert resp3.status_code == 204\\n except AssertionError:\\n raise\\n finally:\\n self.pprint_request(resp3.request)\\n self.pprint_response(resp3)\",\n \"def test_no_auth(self):\\n url = 'https://domain.com/project/objects.inv'\\n expected = 'https://domain.com/project/objects.inv'\\n actual = _strip_basic_auth(url)\\n assert expected == actual\",\n \"def test_unhappy_path_unauthorized(self):\\n\\n response = self.client.get(self.url)\\n expected_data = {\\\"detail\\\": \\\"Authentication credentials were not provided.\\\"}\\n\\n self.assertDictEqual(response.data, expected_data)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def test_unauthorized_access(flask_test_client, http_method, endpoint):\\n response = flask_test_client.open(\\n method=http_method, path=endpoint, headers=get_headers()\\n )\\n assert response.status == \\\"401 UNAUTHORIZED\\\"\\n assert response.content_type == \\\"application/json\\\"\\n assert response.json[\\\"message\\\"] == \\\"Access token is invalid or expired.\\\"\",\n \"def test_get_unauthenticated(self):\\n del self.client.request_kwargs['auth']\\n self.verify_get_response(self.client.get(STATUS_PATH))\",\n \"def get_authenticate_header(self):\\n return f'Basic realm=\\\"{self.www_authenticate_realm}\\\"'\",\n \"def test_retrieve_user_unauthorized(self):\\r\\n res = self.client.get(ME_URL)\\r\\n\\r\\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def basic_header(self):\\n self.auth = base64.encodestring('%s:%s' % (self.username, self.password)).replace('\\\\n', '')\\n return { \\n #\\\"Authorization\\\" : \\\"Basic %s\\\" % self.auth, \\n \\\"Content-type\\\": \\\"text/plain\\\" }\",\n \"def test_retrieve_unauthorized_user(self):\\n\\n response = self.client.get(URL_ME)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def test_retrieve_user_unauthorized(self):\\n res = self.client.get(ME_URL)\\n\\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def test_retrieve_user_unauthorized(self):\\n res = self.client.get(ME_URL)\\n\\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def challenge_view(self, request):\\n headerlist = [(\\\"Content-Type\\\", \\\"text/plain\\\")]\\n headerlist.extend(self._get_challenge_headers(request))\\n return Response(\\\"Unauthorized\\\", status=\\\"401 Unauthorized\\\",\\n headerlist=headerlist)\",\n \"def denied_response(self, req):\\n if req.remote_user:\\n return HTTPForbidden(request=req)\\n else:\\n return HTTPUnauthorized(request=req)\",\n \"def test_401_unauthorized(app, client):\\n\\n @app.route(\\\"/401\\\")\\n def unauthorized():\\n abort(401)\\n\\n response = client.get(\\\"/401\\\")\\n assert response.status_code == 401\\n assert \\\"401 Unauthorized\\\" in str(response.data)\",\n \"def test_status_unauthenticated(self):\\n rv = self.client.post('/statusize/', data={'message': 'foo'},\\n follow_redirects=True)\\n eq_(rv.status_code, 403)\",\n \"def test_call_unauthenticated(self):\\n\\n with Client('username', 'password') as client:\\n self.setSessionResponse(401)\\n with self.assertRaises(APIError):\\n data = client.call(**self.build_parameters)\",\n \"def test_unhappy_path_unauthorized(self):\\n\\n response = self.client.post(self.url)\\n expected_data = {\\\"detail\\\": \\\"Authentication credentials were not provided.\\\"}\\n\\n self.assertDictEqual(response.data, expected_data)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def test_retrieve_users_unauthorized(setup_client):\\n client = setup_client\\n res = client.get(ME_URL)\\n assert res.status_code == status.HTTP_401_UNAUTHORIZED\",\n \"def __call__(self, resp):\\r\\n if not self.auth_token:\\r\\n self.auth()\\r\\n resp.register_hook('response', self.handle_error)\\r\\n resp.headers['X-Auth-Token'] = self.auth_token\\r\\n return resp\",\n \"def get_auth_headers():\\n\\n auth_type = \\\"Basic\\\"\\n if request.headers.get('UseXBasic'):\\n auth_type = \\\"XBasic\\\"\\n\\n return {\\n 'WWW-Authenticate': '%s realm=\\\"Login Required\\\"' % auth_type\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8095378","0.80943215","0.7960329","0.785324","0.7844362","0.78259075","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.7812201","0.77677304","0.77562714","0.77206504","0.7669128","0.7665531","0.7650957","0.7616053","0.7615072","0.75932217","0.74509746","0.7416319","0.73916256","0.73083735","0.72990364","0.700498","0.69885224","0.6985766","0.6949786","0.69435173","0.69050115","0.68424904","0.6841659","0.68142194","0.67640007","0.67031765","0.6702555","0.6668073","0.66589564","0.6640943","0.6613479","0.66094875","0.65494776","0.6547005","0.64198357","0.6413675","0.63915294","0.63841695","0.632656","0.6300798","0.6284987","0.6246946","0.6239865","0.62332","0.62162846","0.61675954","0.61424595","0.6089737","0.6042366","0.6040813","0.60276806","0.60193795","0.6017502","0.59903026","0.597031","0.5944663","0.5927156","0.5927156","0.5922799","0.5900396","0.588356","0.58686936","0.58670604","0.58657974","0.5846941","0.58444345","0.5828828"],"string":"[\n \"0.8095378\",\n \"0.80943215\",\n \"0.7960329\",\n \"0.785324\",\n \"0.7844362\",\n \"0.78259075\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.7812201\",\n \"0.77677304\",\n \"0.77562714\",\n \"0.77206504\",\n \"0.7669128\",\n \"0.7665531\",\n \"0.7650957\",\n \"0.7616053\",\n \"0.7615072\",\n \"0.75932217\",\n \"0.74509746\",\n \"0.7416319\",\n \"0.73916256\",\n \"0.73083735\",\n \"0.72990364\",\n \"0.700498\",\n \"0.69885224\",\n \"0.6985766\",\n \"0.6949786\",\n \"0.69435173\",\n \"0.69050115\",\n \"0.68424904\",\n \"0.6841659\",\n \"0.68142194\",\n \"0.67640007\",\n \"0.67031765\",\n \"0.6702555\",\n \"0.6668073\",\n \"0.66589564\",\n \"0.6640943\",\n \"0.6613479\",\n \"0.66094875\",\n \"0.65494776\",\n \"0.6547005\",\n \"0.64198357\",\n \"0.6413675\",\n \"0.63915294\",\n \"0.63841695\",\n \"0.632656\",\n \"0.6300798\",\n \"0.6284987\",\n \"0.6246946\",\n \"0.6239865\",\n \"0.62332\",\n \"0.62162846\",\n \"0.61675954\",\n \"0.61424595\",\n \"0.6089737\",\n \"0.6042366\",\n \"0.6040813\",\n \"0.60276806\",\n \"0.60193795\",\n \"0.6017502\",\n \"0.59903026\",\n \"0.597031\",\n \"0.5944663\",\n \"0.5927156\",\n \"0.5927156\",\n \"0.5922799\",\n \"0.5900396\",\n \"0.588356\",\n \"0.58686936\",\n \"0.58670604\",\n \"0.58657974\",\n \"0.5846941\",\n \"0.58444345\",\n \"0.5828828\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7749071"},"document_rank":{"kind":"string","value":"35"}}},{"rowIdx":77480,"cells":{"query":{"kind":"string","value":"graph from the task json with TaskResource named tuples living in nodes"},"document":{"kind":"string","value":"def build_taskgraph(tasks):\n global taskgraph\n for task in tasks:\n if not task['id'] in taskgraph:\n taskgraph.add_node(task[\"id\"])\n data = str(task[\"DATA\"])\n tr = TaskResources(ram=int(task['RAM']), cpu=int(\n task['CPU']), hdd=int(task['HDD']))\n taskgraph.node[task[\"id\"]] = [tr, data]\n for dest in task[\"dest\"]:\n taskgraph.add_edge(task[\"id\"], dest[\"id\"])\n\n return taskgraph"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def task_relation_json(self) -> List[Dict]:\n if not self.tasks:\n return [self.tasks]\n else:\n self._handle_root_relation()\n return [tr.get_define() for tr in self._task_relations]","def _subgraph(self):\n return {\n 'type': 'SubgraphTask',\n 'id': 0,\n 'dependencies': [],\n 'parameters': {\n 'info': {},\n 'current_retries': 0,\n 'send_task_events': False,\n 'containing_subgraph': None,\n 'task_kwargs': {}\n }\n }","def json_node_list(graph, source, target):\n output = []\n idx = 0\n for node in graph.nodes:\n node_dict = {}\n node_dict[\"name\"] = node\n node_dict[\"index\"] = idx\n node_dict[\"title\"] = graph.nodes[node][\"title\"]\n node_dict[\"url\"] = graph.nodes[node][\"url\"]\n node_dict[\"steps\"] = n_shortest_paths(graph, source, node)[1] - 1\n output.append(node_dict)\n idx += 1\n return output","def collect_tasks_fn(\n tn: TraversalNode, data: Dict[CollectionAddress, GraphTask]\n ) -> None:\n if not tn.is_root_node():\n data[tn.address] = GraphTask(tn, resources)","def collect_tasks_fn(\n tn: TraversalNode, data: Dict[CollectionAddress, GraphTask]\n ) -> None:\n if not tn.is_root_node():\n data[tn.address] = GraphTask(tn, resources)","def build_graph():\n file = open(\"../data/data.json\", \"r\")\n data = json.load(file)\n node_dict = {}\n for id in data:\n node_dict[id] = Node(data[id][\"name\"], data[id][\"product\"], data[id][\"production_volume\"])\n for id in data:\n current_node = node_dict[id]\n for costumer_id in data[id][\"costumers\"]:\n current_node.costumers.append(node_dict[str(costumer_id)])\n current_node.out_edge_capacity_drop[node_dict[str(costumer_id)].name] = 0\n for supplier_id in data[id][\"suppliers\"]:\n current_node.suppliers.append(node_dict[str(supplier_id)])\n current_node.in_edge_capacity_drop[node_dict[str(supplier_id)].name] = 0\n return node_dict","def connect(tree, taskinfo):\n delinfo = []\n for node in tree.keys():\n for task, val in taskinfo.iteritems():\n if val['parent_task_id'] == node:\n tree[node][val['id']] = {}\n delinfo.append(task)\n for task in delinfo:\n del taskinfo[task]\n delinfo = []\n connect(tree[node], taskinfo)","def gen_graph(self):","def task_dependencies_from_dotfile(stream):\n\n decorator_regex = re.compile(r\"([a-z_]+) *(\\(.*)\")\n attributes_regex = re.compile(r\"\\[.*\\]\")\n\n which_task_follows = defaultdict(list)\n task_decorators = defaultdict(list)\n task_descriptions = dict()\n\n #\n # remember node\n #\n\n all_tasks = dict()\n io_tasks = set()\n for linenum, line in enumerate(stream):\n # remove heading and trailing spaces\n line = line.strip()\n\n if \"digraph\" in line:\n continue;\n if not len(line):\n continue\n\n #\n # decorators\n #\n if line[0:2] == '#@':\n fields = line[2:].split('::', 2)\n if len(fields) != 3:\n raise Exception(\"Unexpected task specification on line# %d\\n(%s)\" %\n (linenum, line))\n task_name, decorators, description = fields\n if decorators[0] != '@':\n raise Exception(\"Task decorator missing starting ampersand '@' on line# %d\\n(%s)\" %\n (linenum, line))\n for d in decorators[1:].split(\"@\"):\n m = decorator_regex.match(d)\n if not m:\n raise Exception(\"Task decorator (%s) missing parentheses on line# %d\\n(%s)\" %\n (d, linenum, line))\n task_decorators[task_name].append((m.group(1), m.group(2)))\n if m.group(1)[0:5] == \"files\" or m.group(1) == \"parallel\":\n io_tasks.add(task_name)\n\n task_descriptions[task_name] = description\n continue\n\n #\n # other comments\n #\n if line[0] in '#{}/':\n continue;\n line = line.strip(';')\n line = attributes_regex.sub(\"\", line)\n\n #\n # ignore assignments\n #\n if \"=\" in line:\n continue;\n nodes = [x.strip() for x in line.split('->')]\n for name1, name2 in adjacent_pairs_iterate(nodes):\n which_task_follows[name2].append(name1)\n all_tasks[name1] = 1\n all_tasks[name2] = 1\n\n for task in task_decorators:\n if task not in all_tasks:\n raise Exception(\"Decorated task %s not in dependencies\")\n\n\n # order tasks by precedence the dump way: iterating until true\n disordered = True\n while (disordered):\n disordered = False\n for to_task, from_tasks in which_task_follows.items():\n for f in from_tasks:\n if all_tasks[to_task] <= all_tasks[f]:\n all_tasks[to_task] += all_tasks[f]\n disordered = True\n\n sorted_task_names = list(sorted(list(all_tasks.keys()), key=lambda x:all_tasks[x]))\n return which_task_follows, sorted_task_names, task_decorators, io_tasks, task_descriptions","def populate_graph(self):","def __get_task_dependencies(self):\n task_dependencies = {}\n\n with open(self.task_id_path) as tasks:\n for task in tasks.read().split(','):\n task_dependencies[task] = set()\n\n with open(self.relation_path) as relations:\n for relation in relations.readlines():\n [dependency, task] = relation.rstrip('\\n').split('->')\n task_dependencies[task].add(dependency)\n\n return task_dependencies","def generate_tasks(self, task):","def get_tasks(self):\n return [getattr(self, k).value() for k in self._node_dict.values()]","def build_nodes(self, tasks, clean_list):\n for name in clean_list:\n # add node itself if not in list of nodes\n self.nodes.setdefault(name, [])\n task = tasks[name]\n # reversing not required\n for dep_name in reversed(task.task_dep):\n if tasks[dep_name].subtask_of == name:\n rev_dep = self.nodes.setdefault(dep_name, [])\n rev_dep.append(name)","def by_nodes(item):\n return Line['nodes', item]","def graph(self):\n ...","def graph():\n return jsonify(app.config[\"jsonified\"])","def parse_task(k):\r\n return [stringify_children(xml_object.xpath(k)[i]) for i in xrange(0, len(xml_object.xpath(k)))]","def build_nodes_with_deps(self, tasks, task_name):\n if task_name in self._processed:\n return\n else:\n self._processed.add(task_name)\n\n # add node itself if not in list of nodes\n self.nodes.setdefault(task_name, [])\n task = tasks[task_name]\n # reversing not required\n for dep_name in reversed(task.setup_tasks + task.task_dep):\n rev_dep = self.nodes.setdefault(dep_name, [])\n rev_dep.append(task_name)\n self.build_nodes_with_deps(tasks, dep_name)","def task_definition_json(self) -> List[Dict]:\n if not self.tasks:\n return [self.tasks]\n else:\n return [task.get_define() for task in self.tasks.values()]","def _build(self, replace=None, profile=False):\n self.__node_dict.clear()\n replace = dict() if replace is None else replace\n\n # check if there are item in the replace that is not in the graph\n task_ids = set([task[TaskSpecSchema.task_id] for task in self])\n for rkey in replace.keys():\n if rkey not in task_ids:\n warnings.warn(\n 'Replace task-id {} not found in task-graph'.format(rkey),\n RuntimeWarning)\n\n # instantiate node objects\n for task in self:\n task_id = task[TaskSpecSchema.task_id]\n nodetype = task[TaskSpecSchema.node_type]\n nodetype = nodetype if isinstance(nodetype, str) else \\\n nodetype.__name__\n if nodetype == OUTPUT_TYPE:\n output_task = task\n node = get_node_obj(output_task, tgraph_mixin=True)\n else:\n node = get_node_obj(task, replace.get(task_id), profile,\n tgraph_mixin=True)\n self.__node_dict[task_id] = node\n\n # build the graph\n for task_id in self.__node_dict:\n node = self.__node_dict[task_id]\n task_inputs = node._task_obj[TaskSpecSchema.inputs]\n for iport in task_inputs:\n # node_inputs should be a dict with entries:\n # {iport: taskid.oport}\n input_task = task_inputs[iport].split('.')\n dst_port = iport\n\n input_id = input_task[0]\n # src_port = input_task[1] if len(input_task) > 1 else None\n src_port = input_task[1]\n\n try:\n input_node = self.__node_dict[input_id]\n except KeyError:\n raise LookupError(\n 'Missing task \"{}\". Add task spec to TaskGraph.'\n .format(input_id))\n\n node.inputs.append({\n 'from_node': input_node,\n 'from_port': src_port,\n 'to_port': dst_port\n })\n # input_node.outputs.append(node)\n input_node.outputs.append({\n 'to_node': node,\n 'to_port': dst_port,\n 'from_port': src_port\n })","def pipeline_dependencies_tasks(g):\n deps = dict()\n for step_name in nx.topological_sort(g):\n deps[step_name] = list(g.predecessors(step_name)) # copy list\n return deps","def previous_tasks(self,task):\n #if task.name==\"target\":\n # return self.taskGraph.all_nodes()\n return self.taskGraph.list_children(task)","def tasks_recv():\n recv_json = request.get_json()\n build_taskgraph(recv_json)\n return 'ok'","def nodes_from_dict(nd=None,**kwargs):\n\n if not nd:\n err_msg = \"ERROR: No nodes data provided\"\n print(err_msg)\n return 1\n \n nodes = []\n\n ####################\n #Create BUS objects#\n ####################\n busd = {}\n for i, row in nd[\"buses\"].iterrows():\n if row[\"active\"] and not pd.isnull(row[\"active\"]):\n logger.info(\"bus {} will be created\".format(row[\"label\"]))\n bus = solph.Bus(label=row[\"label\"])\n nodes.append(bus)\n busd[row[\"label\"]] = bus\n \n if row[\"excess\"] and not pd.isnull(row[\"excess\"]):\n # Automatically add Sink for curtailment (excess)\n # Add variable cost for excess cost --> minimise curtailment\n nodes.append(\n solph.Sink(\n label=row[\"label\"] + \"_excess\",\n inputs={\n busd[row[\"label\"]]:solph.Flow(\n variable_costs = row[\"excess costs\"]\n )\n },\n )\n )\n # Automatically add Source for shortage\n # Add variable cost for shortage --> minimize shortage\n if row[\"shortage\"] and not pd.isnull(row[\"shortage\"]):\n nodes.append(\n solph.Source(\n label = row[\"label\"] + \"_shortage\",\n outputs={\n busd[row[\"label\"]]:solph.Flow(\n variable_costs=row[\"shortage costs\"]\n )\n },\n )\n )\n ########################\n # Create Source objects#\n ########################\n for i, row in nd[\"commodity_sources\"].iterrows():\n if row[\"active\"] and not pd.isnull(row[\"active\"]):\n nodes.append(\n solph.Source(\n label=row[\"label\"],\n outputs={\n busd[row[\"to\"]]: solph.Flow(\n variable_costs = row[\"variable costs\"]\n )\n },\n )\n )\n ########################\n # Create Source objects with fixed time series from 'renewables' table\n ########################\n \"\"\"\n A source can represent a pv-system, a wind power plant, an import of natural gas or a slack variable to avoid creating an in-feasible model.\n While a wind power plant will have an hourly feed-in depending on the weather conditions the natural_gas import might be restricted by \n maximum value (nominal_value) and an annual limit (summed_max). As we do have to pay for imported gas we should set variable costs. \n Comparable to the demand series an fix is used to define a fixed the normalised output of a wind power plant. \n Alternatively, you might use max to allow for easy curtailment. The nominal_value sets the installed capacity.\n \"\"\"\n for i, row in nd[\"renewables\"].iterrows():\n if row[\"active\"] and not pd.isnull(row[\"active\"]):\n # set static outflow values\n outflow_args = {}\n\n # get time series for node and parameter\n for col in nd[\"timeseries\"].columns.values:\n if col.split(\".\")[0] == row[\"label\"]:\n outflow_args[col.split(\".\")[1]]=nd[\"timeseries\"][col]\n # outflow_args[\"fix\"]=nd[\"timeseries\"][col]\n \n # TODO add NON-CONVEX to outflow_args\n if row[\"capex\"] and not pd.isnull(row[\"capex\"]):\n # with investment mode, nominal_value must be None\n logger.info(\"Invest {} capacity\".format(row[\"label\"]))\n invest_args = {}\n if not row[\"epc_invest\"] or pd.isnull(row[\"epc_invest\"]):\n epc_invest = economics.annuity(row[\"capex\"],20,0.08)\n else:\n epc_invest=row[\"epc_invest\"]\n invest_args[\"ep_costs\"] = epc_invest\n\n if row[\"max\"] and not pd.isnull(row[\"max\"]):\n invest_args[\"maximum\"] = row[\"max\"]\n\n if row[\"min\"] and not pd.isnull(row[\"min\"]):\n invest_args[\"minimum\"]=row[\"min\"]\n\n if row[\"existing\"] and not pd.isnull(row[\"existing\"]):\n invest_args[\"existing\"]=row[\"existing\"]\n \n outflow_args[\"investment\"] = solph.Investment(**invest_args) \n else: \n outflow_args[\"nominal_value\"] = row[\"capacity\"]\n \n # create\n nodes.append(\n solph.Source(\n label=row[\"label\"],\n outputs = {\n busd[row[\"to\"]]:solph.Flow(**outflow_args)\n }\n )\n )\n #######################\n # Create Sink objects # \n #######################\n \"\"\"\n A sink is normally used to define the demand within an energy model but it can also be used to detect excesses.\n\n The example shows the electricity demand of the electricity_bus defined above.\n - 'nd['timeseries'][col]' should be sequence of normalised values\n - 'nominal_value' is the maximum demand the normalised sequence is multiplied with.\n - Giving 'nd['timeseries'][col]' as parameter 'fix' means that the demand cannot be changed by the solver. \n \n In contrast to the 'demand sink' the 'excess sink' has normally less restrictions but is open to take the whole excess.\n \"\"\"\n for i, de in nd[\"demand\"].iterrows():\n if de[\"active\"] and not pd.isnull(de[\"active\"]):\n # set static inflow values\n inflow_args = {\n \"nominal_value\":de[\"nominal value\"]\n }\n # get time series for node and parameter\n for col in nd[\"timeseries\"].columns.values:\n if col.split(\".\")[0]==de[\"label\"]:\n # inflow_args[col.split(\".\")[1]]=nd[\"timeseries\"][col]\n # TODO: veriry other key than 'fix'?????\n inflow_args[\"fix\"]=nd[\"timeseries\"][col] \n \n # Create Sink object and append to nodes\n nodes.append(\n solph.Sink(\n label=de[\"label\"],\n inputs={\n busd[de[\"from\"]]:solph.Flow(**inflow_args)\n }\n )\n )\n #############################\n # Create Transformer object #\n #############################\n \"\"\"\n An instance of the Transformer class can represent a node with multiple input and output flows such as:\n - a power plant\n - a transport line \n - or any kind of a transforming process as electrolysis, a cooling device or a heat pump. \n The efficiency has to be constant within one time step to get a linear transformation.\n You can define a different efficiency for every time step (e.g. the thermal powerplant efficiency according \n to the ambient temperature) but this series has to be predefined and cannot be changed within the optimisation.\n\n A condensing power plant can be defined by a transformer with one input (fuel) and one output (electricity)\n ```\n b_gas = solph.Bus(label='natural_gas')\n b_el = solph.Bus(label='electricity')\n solph.Transformer(\n label=\"pp_gas\",\n inputs={bgas: solph.Flow()},\n outputs={b_el: solph.Flow(nominal_value=10e10)},\n conversion_factors={electricity_bus: 0.58})\n ```\n\n A CHP power plant would be defined in the same manner but with two outputs:\n ```\n b_gas = solph.Bus(label='natural_gas')\n b_el = solph.Bus(label='electricity')\n b_th = solph.Bus(label='heat')\n\n solph.Transformer(\n label='pp_chp',\n inputs={b_gas: Flow()},\n outputs={b_el: Flow(nominal_value=30),\n b_th: Flow(nominal_value=40)},\n conversion_factors={b_el: 0.3, b_th: 0.4})\n ```\n A CHP power plant with 70% coal and 30% natural gas can be defined with two inputs and two outputs:\n ```\n b_gas = solph.Bus(label='natural_gas')\n b_coal = solph.Bus(label='hard_coal')\n b_el = solph.Bus(label='electricity')\n b_th = solph.Bus(label='heat')\n\n solph.Transformer(\n label='pp_chp',\n inputs={b_gas: Flow(), b_coal: Flow()},\n outputs={b_el: Flow(nominal_value=30),\n b_th: Flow(nominal_value=40)},\n conversion_factors={b_el: 0.3, b_th: 0.4,\n b_coal: 0.7, b_gas: 0.3})\n ```\n \"\"\"\n for i, row in nd[\"transformers\"].iterrows():\n if row[\"active\"] and not pd.isnull(row[\"active\"]):\n # set static inflow values\n inflow_args = {\n \"variable_costs\":row[\"variable input costs\"]\n }\n # inflow_args = {}\n outflow_args = {}\n # get time series for inflow transformer\n for col in nd[\"timeseries\"].columns.values:\n if col.split(\".\")[0]==row[\"label\"]:\n # inflow_args[col.split(\".\")[1]] = nd[\"timeseries\"][col]\n inflow_args[\"fix\"] = nd[\"timeseries\"][col]\n \n #TODO: multi inputs/outputs and add investment\n\n if row[\"capex inflow\"] and not pd.isnull(row[\"capex inflow\"]):\n logger.info(\"Invest {} inflow capacity\".format(row[\"label\"])) \n invest_args = {}\n invest_args[\"ep_costs\"] = economics.annuity(row[\"capex inflow\"],20,0.08)\n\n if row[\"max inflow\"] and not pd.isnull(row[\"max inflow\"]):\n invest_args[\"maximum\"] = row[\"max inflow\"]\n\n if row[\"min inflow\"] and not pd.isnull(row[\"min inflow\"]):\n invest_args[\"minimum\"] = row[\"min inflow\"]\n\n if row[\"existing inflow\"] and not pd.isnull(row[\"existing inflow\"]):\n invest_args[\"existing\"] = row[\"existing inflow\"]\n\n inflow_args[\"investment\"] = solph.Investment(**invest_args)\n else: \n outflow_args[\"nominal_value\"] = row[\"capacity\"] # should be specify capacity inflow or outflow\n\n # create\n nodes.append(\n solph.Transformer(\n label=row[\"label\"],\n inputs = {\n busd[row[\"from\"]]:solph.Flow(**inflow_args)\n },\n outputs={\n busd[row[\"to\"]]:solph.Flow(**outflow_args)\n },\n conversion_factors = {\n busd[row[\"to\"]]:row[\"efficiency\"]\n }\n )\n )\n ##################################\n # Create Transformer CHP objects #\n ##################################\n for i, row in nd[\"transformers_chp\"].iterrows():\n if row[\"active\"] and not pd.isnull(row[\"active\"]):\n\n inflow_args = {}\n outflow_elec_args = {}\n outflow_heat_args = {}\n\n inflow_args[\"variable_costs\"] = row[\"variable input costs\"]\n\n if row[\"capex elec\"] and not pd.isnull(row[\"capex elec\"]):\n logger.info(\"Invest {} inflow capacity\".format(row[\"label\"])) \n invest_args = {}\n invest_args[\"ep_costs\"] = economics.annuity(row[\"capex elec\"],20,0.08)\n if row[\"max elec\"] and not pd.isnull(row[\"max elec\"]):\n invest_args[\"maximum\"] = row[\"max elec\"]\n if row[\"min elec\"] and not pd.isnull(row[\"min elec\"]):\n invest_args[\"minimum\"] = row[\"min elec\"]\n if row[\"existing elec\"] and not pd.isnull(row[\"existing elec\"]):\n invest_args[\"existing\"] = row[\"existing elec\"]\n \n outflow_elec_args[\"investment\"] = solph.Investment(**invest_args)\n investment = solph.Investment(**invest_args)\n else:\n # inflow_args[\"nominal_value\"] = row[\"capacity_el\"]\n outflow_elec_args[\"nominal_value\"] = row[\"capacity_el\"]\n outflow_heat_args[\"nominal_value\"] = row[\"capacity_heat\"]\n\n # Create\n nodes.append(\n solph.Transformer(\n label = row[\"label\"],\n inputs ={\n busd[row[\"from\"]]:solph.Flow(**inflow_args)\n },\n outputs={\n busd[row[\"to_el\"]]:solph.Flow(**outflow_elec_args),\n busd[row[\"to_heat\"]]:solph.Flow(**outflow_heat_args)\n },\n conversion_factors={\n busd[row[\"to_el\"]]:row[\"efficiency_el\"],\n busd[row[\"to_heat\"]]:row[\"efficiency_heat\"]\n }\n )\n )\n\n ##########################\n # Create Storage objects #\n ##########################\n for i, row in nd[\"storages\"].iterrows():\n if row[\"active\"] and not pd.isnull(row[\"active\"]):\n\n inflow_args = {}\n outflow_args = {}\n\n if row[\"capex\"] and not pd.isnull(row[\"capex\"]):\n logger.info(\"Invest {} storage capacity\".format(row[\"label\"]))\n\n invest_args = {}\n invest_args[\"ep_costs\"] = economics.annuity(row[\"capex\"],20,0.08)\n if row[\"max\"] and not pd.isnull(row[\"max\"]):\n invest_args[\"maximum\"] = row[\"max\"]\n if row[\"min\"] and not pd.isnull(row[\"min\"]):\n invest_args[\"minimum\"] = row[\"min\"]\n if row[\"existing\"] and not pd.isnull(row[\"existing\"]):\n invest_args[\"existing\"] = row[\"existing\"]\n\n investment=solph.Investment(\n **invest_args\n )\n nominal_capacity=None\n \n #TODO add if row[\"capex inflow\"] and if row[\"capex outflow\"]\n #TODO read relation_capacity_inflow/outflow from excel\n \n else:\n investment = None\n nominal_capacity = row[\"nominal capacity\"] \n \n if row[\"capacity inflow\"] and row[\"capacity inflow ratio\"]:\n logger.error(\"{} is overdetermined, only capacity inflow or capacity inflow ratio shoul be set\".format(row[\"label\"]))\n return 1\n if row[\"capacity inflow\"]:\n inflow_args[\"nominal_value\"] = row[\"capacity inflow\"]\n if row[\"capacity inflow ratio\"]:\n capacity_inflow_ratio = row[\"capacity inflow ratio\"]\n else:\n capacity_inflow_ratio = None\n inflow_args[\"variable_costs\"] = row[\"variable input costs\"]\n\n \n if row[\"capacity outflow\"] and row[\"capacity outflow ratio\"]:\n logger.error(\"{} is overdetermined, only capacity outflow or capacity outflow ratio shoul be set\".format(row[\"label\"]))\n return 1\n if row[\"capacity outflow\"]:\n outflow_args[\"nominal_value\"] = row[\"capacity outflow\"]\n if row[\"capacity outflow ratio\"]:\n capacity_outflow_ratio = row[\"capacity outflow ratio\"]\n else:\n capacity_outflow_ratio = None\n\n outflow_args[\"variable_costs\"] = row[\"variable output costs\"]\n\n nodes.append(\n solph.components.GenericStorage(\n label=row[\"label\"],\n inputs = {\n busd[row[\"bus\"]]:solph.Flow(**inflow_args)\n },\n outputs = {\n busd[row[\"bus\"]]:solph.Flow(**outflow_args)\n },\n investment=investment,\n nominal_storage_capacity=nominal_capacity,\n loss_rate = row[\"capacity loss\"],\n initial_storage_level = row[\"initial capacity\"],\n max_storage_level=row[\"capacity max\"],\n min_storage_level=row[\"capacity min\"],\n invest_relation_input_capacity = capacity_inflow_ratio,\n invest_relation_output_capacity = capacity_outflow_ratio,\n inflow_conversion_factor = row[\"efficiency inflow\"],\n outflow_conversion_factor = row[\"efficiency outflow\"]\n )\n )\n #######################\n # Create Link objects #\n #######################\n \"\"\"\n A Link object with 1...2 inputs and 1...2 outputs\n Note: This component is experimental. Use it with care\n \"\"\"\n for i, p in nd[\"powerlines\"].iterrows():\n if p[\"active\"] and not pd.isnull(p[\"active\"]):\n bus1 = busd[p[\"bus_1\"]]\n bus2 = busd[p[\"bus_2\"]]\n nodes.append(\n solph.custom.Link(\n label = \"powerline\" + \"_\" + p[\"bus_1\"] + \"_\" + p[\"bus_2\"],\n inputs = {\n bus1:solph.Flow(),\n bus2:solph.Flow()\n },\n outputs = {\n bus1: solph.Flow(nominal_value = p[\"capacity\"]),\n bus2: solph.Flow(nominal_value=p[\"capacity\"]),\n },\n conversion_factors={\n (bus1,bus2):p[\"efficiency\"],\n (bus2,bus1):p[\"efficiency\"]\n }\n )\n ) \n return nodes","def make_tree(taskinfo):\n tree = {}\n tasks = deepcopy(taskinfo)\n for task, val in tasks.iteritems():\n if val['parent_task_id'] is None:\n rootid = val['id']\n del tasks[task]\n break\n tree[rootid] = {}\n connect(tree, tasks)\n return tree","def load_and_process_json(self, format_fn: Formatter):\n required_keys = [\n \"canary\",\n \"name\",\n \"description\",\n \"keywords\",\n ]\n subtask_keys = [\n \"metrics\",\n \"examples\",\n \"preferred_score\",\n ]\n\n if isinstance(self.task_data, str):\n json_file = self.task_data\n json_dir = os.path.split(os.path.abspath(os.path.expanduser(json_file)))[0]\n\n with tf.io.gfile.GFile(self.task_data, \"rb\") as file:\n self.task_data = json.load(file)\n\n if \"examples\" not in self.task_data:\n subtasks = tf.io.gfile.glob(os.path.join(json_dir, \"*\", \"task.json\"))\n if not subtasks:\n raise ValueError(\n f\"task {json_file} must have either examples or subtasks\"\n )\n\n self.task_data[\"subtasks\"] = []\n for t in subtasks:\n with tf.io.gfile.GFile(t, \"r\") as f:\n self.task_data[\"subtasks\"].append(json.load(f))\n\n elif isinstance(self.task_data, dict):\n self.task_data = _sanitize_task_data(self.task_data)\n else:\n raise ValueError(\"Either json path or dict has to be provided.\")\n\n if \"name\" not in self.task_data:\n raise ValueError(\"task must have a name\")\n\n self.absolute_normalization = self.task_data.get(\n \"absolute_normalization\", False\n )\n\n # unicode causing errors in some environments\n task_name = self.task_data[\"name\"].encode(\"ascii\", errors=\"replace\").decode()\n\n if self.parent_task:\n self.name = f\"{self.parent_task.name}:{task_name}\"\n else:\n self.name = task_name\n\n for key in required_keys:\n if key not in self.task_data.keys():\n raise ValueError(f\"{self.name}: Task needs a \" + key + \" field.\")\n\n subtask_max_examples = None\n\n if self.max_examples:\n num_subtasks = len(self.task_data.get(\"subtasks\", []))\n if num_subtasks:\n subtask_max_examples = self.max_examples // num_subtasks\n if subtask_max_examples < 1:\n raise ValueError(\n f\"for task {self.name}: \"\n f\"max_examples ({self.max_examples}) must be >= number \"\n f\"of subtasks ({num_subtasks})\"\n )\n\n self.subtasks = [\n JsonTask(\n task_data=t,\n shot_list=self.shot_list,\n verbose=self.verbose,\n format_fn=self.format_fn,\n parent_task=self,\n max_examples=subtask_max_examples,\n )\n for t in self.task_data.get(\"subtasks\", [])\n ]\n\n subtask_names = set()\n for t in self.subtasks:\n if t.name in subtask_names:\n raise ValueError(f\"in {self.name} subtask name {t.name} is duplicated\")\n subtask_names.add(t.name)\n\n if self.subtasks: # this is a container task\n subtask_details = [t.get_task_details() for t in self.subtasks]\n self.task_data[\"max_input_length\"] = max(\n [d.max_input_length_per_query for d in subtask_details]\n )\n self.task_data[\"max_queries\"] = sum(\n [d.max_queries for d in subtask_details]\n )\n return\n\n for key in subtask_keys:\n if key not in self.task_data.keys():\n raise ValueError(f\"{self.name}: Task needs a \" + key + \" field.\")\n\n self.metrics = self.task_data[\"metrics\"]\n self.generative_metrics = list(set(self.metrics) & set(GENERATIVE_METRICS))\n self.multiple_choice_metrics = list(\n set(self.metrics) & set(MULTIPLE_CHOICE_METRICS)\n )\n\n input_prefix = self.task_data.get(\"example_input_prefix\", \"\\nQ: \")\n output_prefix = self.task_data.get(\"example_output_prefix\", \"\\nA: \")\n choice_prefix = self.task_data.get(\"choice_prefix\", \"\\n choice: \")\n append_choices_to_input = self.task_data.get(\"append_choices_to_input\", True)\n self.few_shot_example_separator = self.task_data.get(\n \"few_shot_example_separator\", \"\\n\"\n )\n\n for metric in self.metrics:\n\n if metric not in GENERATIVE_METRICS + MULTIPLE_CHOICE_METRICS:\n raise ValueError(f\"Metric {metric} not supported.\")\n\n # remove duplicates, keeping example order the same\n example_set = set()\n distinct_examples = []\n for x in self.task_data[\"examples\"]:\n try:\n example_string = json.dumps(x)\n except TypeError as e:\n print(f\"example_string failure: {x}\")\n raise e\n\n if example_string in example_set:\n continue\n else:\n distinct_examples.append(x)\n example_set.add(example_string)\n\n num_examples = len(self.task_data[\"examples\"])\n num_distinct = len(distinct_examples)\n num_duplicates = num_examples - num_distinct\n if num_duplicates:\n print(\n f\"warning: {self.name} has {num_duplicates} duplicate examples \"\n f\"out of {num_examples}\"\n )\n\n max_shots = max(self.shot_list)\n if num_distinct < (max_shots + 1):\n raise ValueError(\n f\"insufficient distinct examples ({num_distinct}) for {max_shots} shots\"\n )\n\n self.task_data[\"examples\"] = distinct_examples\n\n for sample in self.task_data[\"examples\"]:\n example_keys = sample.keys()\n if \"input\" not in example_keys or not (\n \"target\" in example_keys or \"target_scores\" in example_keys\n ):\n raise ValueError(\"Examples missing a target or input field.\")\n if self.multiple_choice_metrics and \"target_scores\" not in example_keys:\n raise ValueError(\n \"Some sample does not have a target_scores field, required for multiple choice metric.\"\n )\n if self.generative_metrics and \"target\" not in example_keys:\n raise ValueError(\n \"Some sample does not have a target field, required for text-to-text metric.\"\n )\n\n self._ds = [\n format_fn(\n sample,\n input_prefix=input_prefix,\n output_prefix=output_prefix,\n choice_prefix=choice_prefix,\n rng=self.rng,\n append_choices_to_input=append_choices_to_input,\n )\n for sample in self.task_data[\"examples\"]\n ]\n\n self.task_data[\"max_input_length\"] = max(\n [len(re.findall(r\"\\w+\", sample[\"input\"])) for sample in self._ds]\n ) * max(self.shot_list)\n\n self.preferred_score = self.task_data[\"preferred_score\"]\n self.low_score = SCORE_RANGES[self.preferred_score][0]\n\n if self.preferred_score == \"multiple_choice_grade\":\n num_examples, random_score = 0, 0\n for sample in self.task_data[\"examples\"]:\n target_scores = sample[\"target_scores\"].values()\n random_score += sum(target_scores) / len(target_scores)\n num_examples += 1\n self.low_score = random_score / num_examples\n\n self.high_score = SCORE_RANGES[self.preferred_score][1]\n self.task_prefix = str(self.task_data.get(\"task_prefix\", \"\"))\n self.output_regex, self.stop_string = None, None\n if \"output_regex\" in self.task_data:\n self.output_regex = self.task_data[\"output_regex\"]\n if \"stop_string\" in self.task_data:\n self.stop_string = self.task_data[\"stop_string\"]\n\n if self.output_regex is None and self.stop_string is None:\n self.output_regex = _DEFAULT_REGEX\n\n for metric in self.metrics:\n if metric not in GENERATIVE_METRICS + MULTIPLE_CHOICE_METRICS:\n tf.logging.info(f\"Metric {metric} not supported. Will be ignored.\")\n\n is_present = lambda val: 1 if val else 0\n self.task_data[\"max_queries\"] = len(self.task_data[\"examples\"]) * (\n is_present(self.generative_metrics)\n + is_present(self.multiple_choice_metrics)\n )\n\n return","def get_all_tasks(rows):\n lista = list()\n for row in rows:\n arr = dict()\n for k,v in row.items():\n arr[k] = str(v)\n lista.append(arr)\n return dict(task_list=gluon.contrib.simplejson.dumps(lista))","def parse_graph_nodes(graph_def):\n name_to_node = {}\n for node_def in graph_def.node:\n name_to_node[node_def.name] = node_def\n return name_to_node","def query_tasks(dbh):\n # assumes attempt task_ids are in gtt_id table\n\n curs = dbh.cursor()\n\n # query the task table\n sql = \"select t.label,t.exec_host,t.name,t.status,t.id,t.parent_task_id,t.start_time,t.root_task_id, SYSTIMESTAMP-t.start_time as length from task t, gtt_id g where t.root_task_id=g.id and (end_time is null or status!=0)\"\n curs.execute(sql)\n desc = [d[0].lower() for d in curs.description]\n\n results = {}\n for row in curs:\n dat = dict(zip(desc, row))\n if dat['root_task_id'] not in results:\n results[dat['root_task_id']] = {}\n results[dat['root_task_id']][dat['id']] = dat\n\n sql = \"select s.request_time,s.grant_time,s.release_time, t.exec_host,t.name,t.id,t.root_task_id,t.parent_task_id,t.start_time from seminfo s, task t, gtt_id g where t.root_task_id=g.id and t.name like 'trans%' and t.id=s.task_id and t.end_time is NULL\"\n curs.execute(sql)\n desc = [dd[0].lower() for dd in curs.description]\n\n results_trans = {}\n for row in curs:\n dat = dict(zip(desc, row))\n if dat['root_task_id'] not in results_trans:\n results_trans[dat['root_task_id']] = {}\n results_trans[dat['root_task_id']][dat['id']] = dat\n\n return results, results_trans","def viz_graph(self, show_ports=False, pydot_options=None):\n import networkx as nx\n G = nx.DiGraph()\n if pydot_options:\n G.graph['graph'] = pydot_options\n # instantiate objects\n for itask in self:\n task_inputs = itask[TaskSpecSchema.inputs]\n to_task = itask[TaskSpecSchema.task_id]\n to_type = itask[TaskSpecSchema.node_type]\n if to_task == \"\":\n to_task = OUTPUT_TYPE\n for iport_or_tid in task_inputs:\n # iport_or_tid: it is either to_port or task id (tid) b/c\n # if using ports API task_inputs is a dictionary otherwise\n # task_inputs is a list.\n taskin_and_oport = task_inputs[iport_or_tid] \\\n if isinstance(task_inputs, dict) else iport_or_tid\n isplit = taskin_and_oport.split('.')\n from_task = isplit[0]\n from_port = isplit[1] if len(isplit) > 1 else None\n if show_ports and from_port is not None:\n to_port = iport_or_tid\n common_tip = taskin_and_oport\n G.add_edge(from_task, common_tip, label=from_port)\n G.add_edge(common_tip, to_task, label=to_port)\n tnode = G.nodes[common_tip]\n tnode.update({\n # 'label': '',\n 'shape': 'point'})\n else:\n G.add_edge(from_task, to_task)\n\n # draw output ports\n if show_ports:\n\n if (to_type == OUTPUT_TYPE):\n continue\n task_node = get_node_obj(itask, tgraph_mixin=True)\n # task_outputs = itask.get(TaskSpecSchema.outputs, [])\n for pout in task_node._get_output_ports():\n out_tip = '{}.{}'.format(\n itask[TaskSpecSchema.task_id], pout)\n G.add_edge(to_task, out_tip, label=pout)\n tnode = G.nodes[out_tip]\n tnode.update({\n # 'label': '',\n 'shape': 'point'})\n return G","def load(toks):\r\n graph = nx.Graph()\r\n\r\n for tok in toks:\r\n graph.add_node(tok['id'])\r\n\r\n for tok in toks:\r\n if 'governor' in tok:\r\n governor = tok['governor']\r\n dependant = tok['id']\r\n graph.add_edge(governor, dependant)\r\n return graph","def linearly_connect_tasks(*tasks: Union[EOTask, Tuple[EOTask, str]]) -> List[EONode]:\n nodes = []\n endpoint: Sequence[EONode] = tuple()\n for task_spec in tasks:\n if isinstance(task_spec, EOTask):\n node = EONode(task_spec, inputs=endpoint)\n else:\n task, name = task_spec\n node = EONode(task, inputs=endpoint, name=name)\n nodes.append(node)\n endpoint = [node]\n\n return nodes","def scheduled_task_labels(self):\n tasks = self._get_decision_artifact('task-graph.json').values()\n return {t['label'] for t in tasks}","def create_graph(events):\n events.sort()\n base_str = events[0].time_start\n ticks = create_time_tree(base_str, events[-1].time_end)\n base = str_to_timedelta(base_str)\n for e in events:\n start = str_to_timedelta(e.starts())\n end = str_to_timedelta(e.ends())\n start_tick = timedelta_to_ticks(start - base)\n end_tick = timedelta_to_ticks(end - base)\n for tick in xrange(start_tick, end_tick):\n _log.debug(\"Node %s has neighbour node %s.\" % (e, ticks[tick]))\n e.add_neighbour(ticks[tick])\n ticks[tick].add_neighbour(e)\n return events","def example_tasks():\n tasks = set()\n for i in range(3):\n task = BaseTask(\n name=f'task{i}',\n ancestors=[\n BaseTask(name=f'task{i}-ancestor{j}') for j in range(3)\n ],\n )\n for task0 in task.flatten():\n task0.priority = i\n tasks.add(task)\n\n return tasks","def set_task(self, task: dict):\n self.graph = task['graph']\n self.game_length = task['game_length']\n\n # Setup task parameters.\n self.num_subtasks = len(self.graph.subtask_id_list)\n self.subtask_reward = self.graph.subtask_reward\n self.subtask_id_list = self.graph.subtask_id_list # task-specific subtask ids\n\n # Setup map parameters.\n self.map.set_map(\n subtask_id_list=self.subtask_id_list,\n map_parameters=task['map_parameters']\n )","def get_nodes(self):\n self.map_graph_id()\n self.nodes_list = [\n self.NX_GRAPHS[self.graph_id].nodes[idx]['label'] \n for idx in range(len(self.NX_GRAPHS[self.graph_id].nodes))]","def parse_dependency_graph(fn):\n nodes = {}\n edges = []\n\n with open(fn) as f:\n for line in f:\n if line.startswith(\"digraph {\") or line.startswith(\"}\"):\n continue\n if line.startswith(\"\\t\\t\"):\n # edge\n n1, n2 = map(int, line.split(\"->\"))\n edges.append((n1,n2))\n if n1 not in nodes:\n nodes[n1] = None\n if n2 not in nodes:\n nodes[n2] = None\n elif line.startswith(\"\\t\"):\n # node\n node, rest = line.split(None, 1)\n node = int(node)\n _, jobname = rest.split(\"\\\"\", 1)\n jobname = jobname.strip()\n nodes[node] = jobname\n\n return nodes, edges","def build_graph(nodes):\n\n job_instances_map = {}\n\n # first create node structure\n nodes_map = {}\n root_nodes = []\n for node in nodes:\n new_node = JobGraphNode(node, job_instances_map)\n nodes_map[node.id] = new_node\n # check if it is root node\n try:\n node.relationships.next()\n except StopIteration:\n root_nodes.append(new_node)\n\n # then set relationships\n for _, child in nodes_map.iteritems():\n for relationship in child.cfy_node.relationships:\n parent = nodes_map[relationship.target_node.id]\n parent.add_child(child)\n child.add_parent(parent)\n\n return root_nodes, job_instances_map","def __visit_task_nodes(self, curr_node, start_nodes):\n if curr_node in start_nodes:\n return [curr_node]\n\n visited_nodes = [curr_node]\n for dependency in self.task_dependencies[curr_node]:\n visited_nodes += self.__visit_task_nodes(dependency, start_nodes)\n\n return visited_nodes","def _get_full_graph(self):","def getNodeFinishedTasks(self,node):\n data = self.connect('get','nodes/%s/tasks' % (node),None)\n return data","def _convert_hashring_nodes(self, hashring_nodes):\n results = []\n for node in hashring_nodes or []:\n node_data = json.loads(node.data)\n service_info = ServiceInfo.from_json(node_data[\"service_info\"])\n service_node = ServiceHashringNode(\n token=node.token,\n service_info = service_info, \n data=node_data[\"data\"])\n results.append(service_node)\n return results","def _construct_graph(self):\n raise NotImplementedError","def __post_init__(self) -> None:\n for node in self.task_graph:\n self.task_graph.nodes[node][\"info\"] = TaskInfo(TaskStatus.NOT_STARTED)","def __post_init__(self) -> None:\n for node in self.task_graph:\n self.task_graph.nodes[node][\"info\"] = TaskInfo(TaskStatus.NOT_STARTED)","def _build_graph(self):\n pass","def data():\n result = {}\n for thread in DATA.threads:\n result[thread] = [formatNode(node) for node in DATA.threads[thread].tree]\n return json.dumps({\n 'checkpoints': DATA.checkpoints,\n 'threads': result\n })","def build_graph(self):\n pass","def multi_edge():\n from networkx.readwrite import json_graph\n import networkx as nx\n import autonetkit\n # returns a house graph\n data = {'directed': False,\n 'graph': [],\n 'links': [{'_ports': {'r4': 2, 'r5': 1},\n 'raw_interfaces': {},\n 'source': 0,\n 'target': 1},\n {'_ports': {'r2': 3, 'r4': 1},\n 'raw_interfaces': {},\n 'source': 0,\n 'target': 3},\n {'_ports': {'r2': 4, 'r4': 3},\n 'raw_interfaces': {},\n 'source': 0,\n 'target': 3},\n {'_ports': {'r3': 3, 'r5': 2},\n 'raw_interfaces': {},\n 'source': 1,\n 'target': 4},\n {'_ports': {'r1': 1, 'r2': 1},\n 'raw_interfaces': {},\n 'source': 2,\n 'target': 3},\n {'_ports': {'r1': 3, 'r2': 5},\n 'raw_interfaces': {},\n 'source': 2,\n 'target': 3},\n {'_ports': {'r1': 2, 'r3': 1},\n 'raw_interfaces': {},\n 'source': 2,\n 'target': 4},\n {'_ports': {'r1': 4, 'r3': 4},\n 'raw_interfaces': {},\n 'source': 2,\n 'target': 4},\n {'_ports': {'r1': 5, 'r3': 5},\n 'raw_interfaces': {},\n 'source': 2,\n 'target': 4},\n {'_ports': {'r2': 2, 'r3': 2},\n 'raw_interfaces': {},\n 'source': 3,\n 'target': 4}],\n 'multigraph': True,\n 'nodes': [{'_ports': {0: {'category': 'physical', 'description': None},\n 1: {'category': 'physical', 'description': 'r4 to r2', 'id': 'eth0'},\n 2: {'category': 'physical', 'description': 'r4 to r5', 'id': 'eth1'},\n 3: {'category': 'physical', 'description': 'r4 to r2', 'id': 'eth2'}},\n 'asn': 2,\n 'device_type': 'router',\n 'id': 'r4',\n 'label': 'r4',\n 'x': 675,\n 'y': 300},\n {'_ports': {0: {'category': 'physical', 'description': None},\n 1: {'category': 'physical', 'description': 'r5 to r4', 'id': 'eth0'},\n 2: {'category': 'physical', 'description': 'r5 to r3', 'id': 'eth1'}},\n 'asn': 2,\n 'device_type': 'router',\n 'id': 'r5',\n 'label': 'r5',\n 'x': 675,\n 'y': 500},\n {'_ports': {0: {'category': 'physical', 'description': None},\n 1: {'category': 'physical', 'description': 'r1 to r2', 'id': 'eth0'},\n 2: {'category': 'physical', 'description': 'r1 to r3', 'id': 'eth1'},\n 3: {'category': 'physical', 'description': 'r1 to r2', 'id': 'eth2'},\n 4: {'category': 'physical', 'description': 'r1 to r3', 'id': 'eth3'},\n 5: {'category': 'physical', 'description': 'r1 to r3', 'id': 'eth4'}},\n 'asn': 1,\n 'device_type': 'router',\n 'id': 'r1',\n 'label': 'r1',\n 'x': 350,\n 'y': 400},\n {'_ports': {0: {'category': 'physical', 'description': None},\n 1: {'category': 'physical', 'description': 'r2 to r1', 'id': 'eth0'},\n 2: {'category': 'physical', 'description': 'r2 to r3', 'id': 'eth1'},\n 3: {'category': 'physical', 'description': 'r2 to r4', 'id': 'eth2'},\n 4: {'category': 'physical', 'description': 'r2 to r4', 'id': 'eth3'},\n 5: {'category': 'physical', 'description': 'r2 to r1', 'id': 'eth4'}},\n 'asn': 1,\n 'device_type': 'router',\n 'id': 'r2',\n 'label': 'r2',\n 'x': 500,\n 'y': 300},\n {'_ports': {0: {'category': 'physical', 'description': None},\n 1: {'category': 'physical', 'description': 'r3 to r1', 'id': 'eth0'},\n 2: {'category': 'physical', 'description': 'r3 to r2', 'id': 'eth1'},\n 3: {'category': 'physical', 'description': 'r3 to r5', 'id': 'eth2'},\n 4: {'category': 'physical', 'description': 'r3 to r1', 'id': 'eth3'},\n 5: {'category': 'physical', 'description': 'r3 to r1', 'id': 'eth4'}},\n 'asn': 1,\n 'device_type': 'router',\n 'id': 'r3',\n 'label': 'r3',\n 'x': 500,\n 'y': 500}]}\n graph = json_graph.node_link_graph(data)\n anm = autonetkit.anm.NetworkModel()\n g_in = anm.add_overlay(\"input\")\n g_in._replace_graph(nx.MultiGraph(graph))\n # TODO: check if should build overlays here rather than clone in?\n g_phy = anm[\"phy\"]\n g_phy._replace_graph(graph)\n return anm","def test_read_json1():\n s = JsonSource()\n g = s.parse(os.path.join(RESOURCE_DIR, 'valid.json'))\n nodes = {}\n edges = {}\n for rec in g:\n if rec:\n if len(rec) == 4:\n edges[(rec[0], rec[1])] = rec[3]\n else:\n nodes[rec[0]] = rec[1]\n\n assert len(nodes.keys()) == 6\n assert len(edges.keys()) == 5\n\n n = nodes['MONDO:0017148']\n assert 'id' in n and n['id'] == 'MONDO:0017148'\n assert n['name'] == 'heritable pulmonary arterial hypertension'\n assert n['category'][0] == 'biolink:Disease'\n\n e = edges[('HGNC:11603', 'MONDO:0017148')]\n assert e['subject'] == 'HGNC:11603'\n assert e['object'] == 'MONDO:0017148'\n assert e['predicate'] == 'biolink:related_to'\n assert e['relation'] == 'RO:0004013'","def _build_nodes_dict(self, graph):\n nodes_dict = {}\n for node, data in graph.nodes_iter(data=True):\n nodes_dict.update({node: data['label']})\n return nodes_dict","def build_curriculum(self,target_task,groups,setOfTasks):\n connections = [] #list of graph connections\n graph = Graph([],directed = True)\n #Intra-group transfer\n for g in groups:\n group = groups[g]\n #For all tasks inside each group\n for i in range(len(group)):\n currentTask = group[i][0]\n maxTransferPot = -float('inf')\n argMaxTask = None\n # get task with maximum potential\n for j in range(i+1,len(group)):\n testTask = group[j][0]\n if testTask != currentTask:\n pot = self.transfer_potential(testTask,currentTask,graph.list_children(currentTask))\n if pot > maxTransferPot:\n maxTransferPot = pot\n argMaxTask = testTask\n if argMaxTask != None and maxTransferPot > self.thresholdTask:\n # Add connection\n connections.append((argMaxTask,currentTask))\n graph.add(argMaxTask,currentTask)\n \n #Creating graph structure\n graph = Graph(connections,directed = True)\n \n groupKeys = list(groups.keys())\n #Inter-group transfer\n for i in range(len(groupKeys)):\n #Get task features of the first task in the group \n #(this is equal for all tasks inside it)\n g = groups[groupKeys[i]]\n gFeatures = g[0][0].task_features()\n #For all groups that have task features contained in g:\n for j in range(i+1,len(groupKeys)):\n gPrime = groups[groupKeys[j]]\n gPrimeFeatures = gPrime[0][0].task_features()\n \n if task.is_contained(gPrimeFeatures,gFeatures):\n #For all tasks in g, get the argmax task in gPrime and\n #Possibly creates a link\n for gTaskAndPot in g:\n gTask = gTaskAndPot[0]\n maxTransferPot = -float('inf')\n argMaxTask = None\n for gPrimeTaskAndPot in gPrime:\n gPrimeTask = gPrimeTaskAndPot[0]\n pot = self.transfer_potential(gPrimeTask,gTask,graph.list_children(currentTask))\n if pot > maxTransferPot:\n maxTransferPot = pot\n argMaxTask = gPrimeTask\n if argMaxTask != None and maxTransferPot > self.thresholdTask:\n # Add connection\n graph.add(argMaxTask,gTask)\n connections.append((argMaxTask,gTask))\n \n #For all tasks with zero degree, a connection to the target-task is added\n for currentTask in setOfTasks:\n if graph.out_degree(currentTask) == 0:\n graph.add(currentTask,target_task)\n #The task graph is completed\n self.taskGraph = graph","def gen_task_item(self) -> Dict[str, Any]:\n return {}","def export_nodes(self):\n return ['lon', 'lat', 'speed', 'heading'], \\\n [{'speed': self.node_speed_limit[v],\n 'lon': self.node_locations[v][0],\n 'lat': self.node_locations[v][1],\n 'heading': self.node_heading[v]} for v in self.graph.vertices()]","def load_taskgraph(filename):\n\n with open(filename) as f:\n yaml = ruamel.yaml.YAML(typ='safe')\n yaml.constructor.yaml_constructors[\n u'tag:yaml.org,2002:timestamp'] = \\\n yaml.constructor.yaml_constructors[u'tag:yaml.org,2002:str']\n obj = yaml.load(f)\n t = TaskGraph(obj)\n return t","def tasks_by_status(self) -> Dict[TaskStatus, Sequence[str]]:\n out = {}\n for key in self.task_graph.keys():\n info = self.task_graph.get_info(key)\n out.setdefault(info.status, []).append(key)\n return out","def translate_graph(lines: List[str]) -> Graph:\n nodes = []\n edges = []\n is_node = True\n\n # first_value used to offset node/edge ids to start from 0\n first = True\n first_value = 0\n\n for line in lines:\n a = line.split(\",\")\n\n if \"edge\" in line:\n is_node = False\n elif is_node:\n if first:\n first = False\n first_value = int(a[0])\n\n # Create node with Node(id, label, size)\n pos_x = float(a[4])\n pos_y = float(a[5])\n n = Node(int(a[0]),\n str(a[1]).replace(\"\\\"\", \"\"),\n int(float(a[2])),\n kp=KeyPoint(pos_x, pos_y, float(a[2])),\n pos=(float(a[4]),float(a[5])))\n\n if (a[1] == \"\"):\n print(\"Node \" + a[0] + \" missing label.\")\n exit(1)\n nodes.append(n)\n else:\n n1 = None\n n2 = None\n for node in nodes:\n if node.node_id == int(a[0]):\n n1 = node\n for node in nodes:\n if node.node_id == int(a[1]):\n n2 = node\n e = Edge(n1, n2, distance(n1.pos,n2.pos))\n edges.append(e)\n #all_edges.append(e)\n #edges.append((int(a[0]) - first_value, int(a[1]) - first_value))\n\n\n return Graph(nodes, edges, 1)","def add_datapoints(self, tasks):\n\n counters = {\n 'SUCCESS': 0,\n 'PENDING': 0,\n 'STARTED': 0,\n 'FAILURE': 0,\n 'RETRY' : 0,\n 'REVOKED': 0,\n 'RECEIVED': 0,\n };\n\n for task in tasks:\n counters[task[1]['state']] += 1\n\n for counter in counters:\n self.add_gauge_value('Tasks/' + counter.capitalize(), 'tasks', counters.get(counter, 0))","def _process_nodes(self):\n # Sort the nodes by metanode type, then by id\n self.node_df = self.node_df.sort_values(['label', 'id']).reset_index(drop=True)\n # Get all the ids\n self.nodes = self.node_df['id']\n # Get mapping from the index to the node ID (one to many so need different one for each node type)\n self.index_to_nid = dict()\n for group_name, group in self.node_df.groupby('label'):\n self.index_to_nid[group_name] = group['id'].reset_index(drop=True).to_dict()\n # Get the reverse mapping (many to one so don't need to separate based on type).\n self.nid_to_index = dict()\n for mapper in self.index_to_nid.values():\n for index, nid in mapper.items():\n self.nid_to_index[nid] = index\n # Finally, we need a mapper from id to node type\n self.id_to_metanode = self.node_df.set_index('id')['label'].to_dict()\n # And from node type to a list of ids\n self.metanode_to_ids = dict()\n for group_name, group in self.node_df.groupby('label'):\n self.metanode_to_ids[group_name] = group['id'].tolist()\n # One more mapper of id to name\n self.nid_to_name = self.node_df.set_index('id')['name'].to_dict()","def graph(self):\n\n def start_graph():\n return \"digraph services {\\n\\n\"\n\n def end_graph(graph_string):\n graph_string += \"\\n}\\n\"\n return graph_string\n\n def start_cluster(graph_string, cluster_id, cluster_name):\n graph_string += \"subgraph cluster_%s {\\n\" % cluster_id\n graph_string += \" label = \\\"%s\\\";\\n\" % cluster_name\n return graph_string\n\n def end_cluster(graph_string):\n graph_string += \"\\n}\\n\"\n return graph_string\n\n def add_path(graph_string, from_node, to_node, protocol, port):\n if not from_node.name:\n cidr_blocks = [subnetwork.cidr_block for subnetwork in from_node.subnetworks]\n from_name = \",\".join(cidr_blocks)\n from_network_name = \"external\"\n else:\n from_name = from_node.name\n from_network_name = from_node.network.name\n path_template = \"\\\"%s (%s)\\\" -> \\\"%s (%s)\\\" [ label=\\\"(%s:%s)\\\" ];\\n\"\n graph_string += path_template % (from_name, from_network_name, to_node.name,\n to_node.network.name, protocol, port)\n return graph_string\n\n def add_node(graph_string, node_name, network_name):\n graph_string += \" \\\"%s (%s)\\\";\\n\" % (node_name, network_name)\n return graph_string\n\n def group_paths_by_network(paths_info):\n net_to_path = {}\n for path in paths_info:\n if path.network.name not in net_to_path:\n net_to_path[path.network.name] = []\n net_to_path[path.network.name].append(path)\n return net_to_path\n\n def group_services_by_network(services_info):\n net_to_service = {}\n for service_info in services_info:\n if service_info.network.name not in net_to_service:\n net_to_service[service_info.network.name] = []\n net_to_service[service_info.network.name].append(service_info)\n return net_to_service\n\n # First group paths and services by network\n paths_info = self.paths.list()\n net_to_path = group_paths_by_network(paths_info)\n services_info = self.service.list()\n net_to_service = group_services_by_network(services_info)\n networks_info = self.network.list()\n\n graph_string = start_graph()\n cluster_id = 0\n for network_info in networks_info:\n\n # Skip networks with no name for now\n if not network_info.name:\n continue\n\n # Each network is a \"cluster\" in graphviz terms\n graph_string = start_cluster(graph_string, cluster_id, network_info.name)\n cluster_id += 1\n\n # If the network is empty just make a placeholder node\n if network_info.name not in net_to_service and network_info.name not in net_to_path:\n graph_string = add_node(graph_string, \"Empty Network\", network_info.name)\n graph_string = end_cluster(graph_string)\n continue\n\n # Otherwise, add all the services and path in this network\n if network_info.name in net_to_service:\n for service_info in net_to_service[network_info.name]:\n graph_string = add_node(graph_string, service_info.name,\n service_info.network.name)\n graph_string = end_cluster(graph_string)\n\n # We do all paths outside the cluster so that public CIDRs will show up outside the\n # networks.\n if network_info.name in net_to_path:\n for path_info in net_to_path[network_info.name]:\n graph_string = add_path(graph_string, path_info.source, path_info.destination,\n path_info.protocol, path_info.port)\n\n graph_string = end_graph(graph_string)\n return graph_string","def task_relabel_nodes(self, req, resp, json_data):\n action = json_data.get('action', None)\n\n if action != 'relabel_nodes':\n self.error(\n req.context,\n \"Task body ended up in wrong handler: action %s in task_relabel_nodes\"\n % action)\n self.return_error(resp,\n falcon.HTTP_500,\n message=\"Error\",\n retry=False)\n\n try:\n task = self.create_task(json_data, req.context)\n resp.text = json.dumps(task.to_dict())\n resp.append_header('Location',\n \"/api/v1.0/tasks/%s\" % str(task.task_id))\n resp.status = falcon.HTTP_201\n except errors.InvalidFormat as ex:\n self.error(req.context, ex.msg)\n self.return_error(resp,\n falcon.HTTP_400,\n message=ex.msg,\n retry=False)","def getNodes(self):\n nodes = [{\"address\": \"http://0.0.0.0:100\"}\n ,{\"address\": \"http://0.0.0.0:200\"}\n ,{\"address\": \"http://0.0.0.0:300\"}\n ,{\"address\": \"http://0.0.0.0:400\"}\n ,{\"address\": \"http://0.0.0.0:500\"}]\n return nodes","def load_task(self, task):\n with self._driver.session() as session:\n session.write_transaction(tx.create_task, task=task)\n session.write_transaction(tx.create_task_hint_nodes, task=task)\n session.write_transaction(tx.create_task_requirement_nodes, task=task)\n session.write_transaction(tx.create_task_input_nodes, task=task)\n session.write_transaction(tx.create_task_output_nodes, task=task)\n session.write_transaction(tx.create_task_metadata_node, task=task)\n session.write_transaction(tx.add_dependencies, task=task)","def get_dependent_tasks(self, task):\n task_records = self._read_transaction(tx.get_dependent_tasks, task=task)\n tuples = self._get_task_data_tuples(task_records)\n return [_reconstruct_task(tup[0], tup[1], tup[2], tup[3], tup[4]) for tup in tuples]","def get_task_info(self):\n\n print()\n employee_name = self.task.get_employee_name()\n task_name = self.task.get_task_name()\n mins = self.task.get_time_spent()\n notes = self.task.get_notes()\n date = self.task.get_date()\n\n task = {\n 'employee_name': employee_name,\n 'task_name': task_name,\n 'mins': mins,\n 'notes': notes,\n 'date': date\n }\n\n return task","def get_info(self, key: str) -> TaskInfo:\n return self.task_graph.nodes[key][\"info\"]","def create_execution_graph(self, env_config: EnvironmentConfig):\n\n # create a map of Nodes, mapping their order (basically their ID) to a Node for that resource\n res_list = sorted(env_config.har_resources, key=lambda r: r.order)\n self.node_map = {\n res.order: Node(resource=res, priority=res.order, children=[]) for res in res_list if res.order == 0\n }\n\n # The root is the node corresponding to the 0th order\n self.root = self.node_map.get(0)\n\n # for each resource, unless it's the start resource, add it as a child of its initiator\n for res in res_list:\n if res != self.root.resource:\n self.node_map[res.order] = Node(\n resource=res, priority=res.order, children=[], parent=self.node_map.get(res.initiator, None)\n )\n self.node_map[res.initiator].children.append(self.node_map[res.order])\n\n # sort each child list by its order\n self.url_to_node_map = {node.resource.url: node for node in self.node_map.values()}\n for node in self.node_map.values():\n node.children.sort(key=lambda n: n.resource.order)","def nodes(self):\n return self.sort_dict(self.trajectory_data)","def read_graph_nodes(dict1,node_list):\n\t#print fname\n\t#dict1={}\n\t#with open(fname,'r') as fp:\n\t#\tfor line in fp:\n\t#\t\tline=line.rstrip('\\n')\n\t#\t\tn_id,name=line.split('\\t')\n\t#\t\tdict1[int(n_id)]=name\n\thuman_readable_node_list=[]\n\tfor node in node_list:\n\t\tif int(node) in dict1:\n\t\t\t#print 'found'\n\t\t\thuman_readable_node_list.append(dict1[node])\n\t\telse:\n\t\t\thuman_readable_node_list.append('-1')\n\treturn human_readable_node_list","def get_tasks_summary(self):\n columns = [\"id\", \"name\", \"state\", \"warning\", \"warning_message\", \"parent_job\", \"tags\"]\n \n cur = self.conn.cursor()\n cur.execute(\"SELECT \" + \", \".join(columns) + \" FROM tangerine;\")\n self.conn.commit()\n \n return [Task([(column,) for column in columns], task, interpolate=False) for task in cur.fetchall()]","def _update_tasks_from_jsons(self):\n\n tasks = {}\n\n for fn in listdir(SIMPLI_JSON_DIR):\n fp = join(SIMPLI_JSON_DIR, fn)\n\n self._print('Loading task-specifying JSON {} ...'.format(fp))\n\n with open(fp) as f:\n tasks_json = loads(reset_encoding(f.read()))\n\n # Load library path, which is common for all tasks in this JSON\n library_path = tasks_json['library_path']\n\n # Load each task\n for t in tasks_json['tasks']:\n\n # Split function path into library_name and function_name\n function_path = t['function_path']\n if '.' in function_path:\n split = function_path.split('.')\n library_name = '.'.join(split[:-1])\n function_name = split[-1]\n else:\n raise ValueError(\n 'function_path must be like: \\'file.function\\'.')\n\n # Task label is this task's UID; so no duplicates are allowed\n label = t.get('label',\n '{} (no task label)'.format(function_name))\n\n tasks[label] = {\n 'library_path':\n library_path,\n 'library_name':\n library_name,\n 'function_name':\n function_name,\n 'description':\n t.get('description', 'No description.'),\n 'required_args':\n self._process_args(t.get('required_args', [])),\n 'default_args':\n self._process_args(t.get('default_args', [])),\n 'optional_args':\n self._process_args(t.get('optional_args', [])),\n 'returns':\n self._process_returns(t.get('returns', [])),\n }\n\n self._print('\\tLoaded {}: {}.'.format(label, tasks[label]))\n\n self._update_tasks(tasks)\n return tasks","def get_graph_and_nodes(path):\n graph = hgraph.read_graph(path)\n resource_nodes = hgraph.resource_nodes_no_data(graph)\n blocked_nodes = hgraph.blocked_resources_no_data(graph, resource_nodes)\n unblocked_nodes = list(set(resource_nodes) - set(blocked_nodes))\n \n return graph, resource_nodes, blocked_nodes, unblocked_nodes","def get_links(self, node):\n\n # TODO: send help\n\n if not node.label:\n return []\n cls = node.__class__\n\n return [\n (\n e.__src_dst_assoc__,\n psqlgraph.Node.get_subclass_named(e.__dst_class__).label,\n )\n for e in psqlgraph.Edge._get_edges_with_src(cls.__name__)\n if hasattr(psqlgraph.Node.get_subclass_named(e.__dst_class__), \"project_id\")\n ] + [\n (\n e.__dst_src_assoc__,\n psqlgraph.Node.get_subclass_named(e.__src_class__).label,\n )\n for e in psqlgraph.Edge._get_edges_with_dst(cls.__name__)\n if hasattr(psqlgraph.Node.get_subclass_named(e.__src_class__), \"project_id\")\n ]","def assemble_graph():\n response = request.body.read().decode('utf-8')\n body = json.loads(response)\n stmts_json = body.get('statements')\n stmts = stmts_from_json(stmts_json)\n ga = GraphAssembler(stmts)\n model_str = ga.make_model()\n res = {'model': model_str}\n return res","def _jsonify_graph(nodes_list, relations_list,\n n_elements=settings.PREVIEW_NODES, full=False):\n nodes = {}\n edges = []\n partial_edges = []\n partial_nodes = {}\n partial_nodes_ids = []\n nodes_display = {}\n for node in nodes_list:\n json_node = node.to_json()\n nodes[node.display] = json_node\n nodes_display[node.id] = node.display\n for rel in relations_list:\n source_id = rel.source.id\n target_id = rel.target.id\n if (source_id in nodes_display\n and target_id in nodes_display):\n edge = {\n 'id': rel.id,\n 'source': nodes_display[source_id],\n 'type': rel.label_display,\n 'target': nodes_display[target_id],\n 'properties': rel.properties\n }\n edges.append(edge)\n if len(partial_nodes_ids) <= n_elements:\n if source_id not in partial_nodes_ids:\n node_display = nodes_display[source_id]\n partial_nodes[node_display] = nodes[node_display]\n partial_nodes_ids.append(source_id)\n if target_id not in partial_nodes_ids:\n node_display = nodes_display[target_id]\n partial_nodes[node_display] = nodes[node_display]\n partial_nodes_ids.append(target_id)\n if (source_id in partial_nodes_ids\n and target_id in partial_nodes_ids):\n partial_edges.append(edge)\n return (nodes, edges, partial_nodes, partial_edges)","def from_dict(cls, dikt) -> 'Tasks':\n return util.deserialize_model(dikt, cls)","def _get_task_data_tuples(self, task_records):\n with self._driver.session() as session:\n trecords = list(task_records)\n hint_records = [session.read_transaction(tx.get_task_hints,\n task_id=rec[\"t\"][\"id\"]) for rec in trecords]\n req_records = [session.read_transaction(tx.get_task_requirements,\n task_id=rec[\"t\"][\"id\"]) for rec in trecords]\n input_records = [session.read_transaction(tx.get_task_inputs,\n task_id=rec[\"t\"][\"id\"]) for rec in trecords]\n output_records = [session.read_transaction(tx.get_task_outputs,\n task_id=rec[\"t\"][\"id\"]) for rec in trecords]\n\n hints = [_reconstruct_hints(hint_record) for hint_record in hint_records]\n reqs = [_reconstruct_requirements(req_record) for req_record in req_records]\n inputs = [_reconstruct_task_inputs(input_record) for input_record in input_records]\n outputs = [_reconstruct_task_outputs(output_record) for output_record in output_records]\n\n return list(zip(trecords, hints, reqs, inputs, outputs))","def get_graph(self):\n return json.dumps(self.graph.get_edgelist(), separators=(',',':'))","def test_restore_with_dependencies(self):\n task1 = self._remote_task()\n task1['id'] = 1\n task2 = self._remote_task()\n task2['id'] = 2\n task2['dependencies'] = [1]\n\n graph = self._restore_graph([task1, task2])\n assert len(graph.tasks) == 2\n assert graph._dependencies[2] == set([1])","def json_to_graph(json_str: str) -> (dict, list, dict):\n json_graph = GraphMessage.parse_raw(json_str)\n node_dict = {}\n inputs_to_resolve = []\n\n for node_message in json_graph.nodes:\n name, node, inputs = get_node(node_message)\n node = Node(name=name, node_type=node)\n inputs_to_resolve.append((name, inputs))\n node_dict[name] = node\n\n for name, inputs in inputs_to_resolve:\n for variable, node_input in inputs.items():\n try:\n keys = node_input.split(\".\")\n node_id, node_output = keys[0], keys[1]\n node_dict[name].inputs[variable] = (node_id, node_output)\n except (\n KeyError,\n IndexError,\n AttributeError,\n ): # If it can't resolve the node_input to some output, treat it as a default value\n node_dict[name].items[variable] = node_input\n\n presets = {}\n preset_list = []\n for name, preset in json_graph.presets.items():\n toggle = isinstance(preset, TogglePresetMessage)\n preset_list.append((name, toggle))\n presets[name] = preset\n return node_dict, preset_list, presets","def read_toml(filename: str) -> Graph:\n\n file_load = toml.load(filename)\n contents = file_load[\"routers\"]\n address = dict()\n for item in contents:\n address[item['address']] = item['name']\n neighbors = contents[0]['neighbors']\n address_copy = address.copy()\n\n for item in neighbors:\n new_dict = item['address']\n name = address_copy[new_dict]\n item['name'] = name\n for item in contents:\n n = item['neighbors']\n \n\n data = Graph() #Trying to add dictionary values to data so it can be called in find path function.\n data.set_vertex(item['name'])\n for i in n:\n i['name'] = address[i['address']]\n data.add_edge(item['name'], i['name'], i['cost'])\n return data","def get_graph(nodes, edges, deps, sec, containers):\n root = RootResource()\n nodes.append(root)\n # connect accessible nodes to the web\n for node in nodes:\n if node.get_original_type() is not None and \\\n node.get_original_type().is_accessible():\n root.add_child(node)\n\n # Add children for each edge we collected before\n for edge in edges:\n parents = get_resource(nodes, edge['from'])\n children = get_resource(nodes, edge['to'])\n for node in parents:\n for child in children:\n node.add_child(child)\n\n # Same with dependencies\n for dep in deps:\n parents = get_resource(nodes, dep['from'])\n children = get_resource(nodes, dep['to'])\n for node in parents:\n for child in children:\n if node.get_origin() == TARGET and child.get_origin() == TARGET:\n node.add_dependency(child)\n\n # Ensure containers only has Resource as keys and in values by creating more\n # entries if necessary.\n expanded_containers = {}\n for container in containers:\n for c in get_resource(nodes, container):\n if not c in expanded_containers:\n expanded_containers[c] = []\n for x in containers[container]:\n expanded_containers[c].extend(get_resource(nodes, x))\n\n # Same with sec\n expanded_sec = {}\n for security in sec:\n for s in get_resource(nodes, security):\n if not s in expanded_sec:\n expanded_sec[s] = {'from': [], 'to': []}\n for x in sec[security]['from']:\n expanded_sec[s]['from'].extend(get_resource(nodes, x))\n for x in sec[security]['to']:\n expanded_sec[s]['to'].extend(get_resource(nodes, x))\n\n nodes = move_security_nodes(nodes, expanded_sec, expanded_containers)\n nodes = move_container_nodes(nodes, expanded_containers)\n return (nodes, root)","def _CreateGraph(self):\n self.nodes = []\n self.edges = []\n for i, r in self.airports.set_index('airport_id').iterrows():\n self.nodes.append((i,r.to_dict()))\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\n self.edges.append((i[0],i[1],r.to_dict()))\n # print('node ex: {}'.format(self.nodes[0]))\n # print('edge ex: {}'.format(self.edges[0]))\n\n self.graph = self._CreateAdjacencyListGraph()","def graph (self, next_nodes, query):\n programs = self.type_graph.get_transitions (query)\n result = []\n for program in programs:\n result += self.graph_inner (next_nodes, program)\n return result","def convert(data):\n NODES, EDGES = {}, {}\n\n for duid, entity in data['entity'].iteritems():\n if entity['entityType'] == 'Trigger':\n continue\n if Node.add(duid, NODES):\n NODES[duid].Label = entity['entityText']\n NODES[duid].source = entity['source']\n NODES[duid].json = json.dumps(entity)\n\n for duid, relation in data['relation'].iteritems():\n duid = relation['duid']\n sn, tn = None, None\n if relation['source'] == 'miRTex':\n sn, tn = tm.mirtex.cytoscape_relation_args(relation)\n # elif relation['source'] == 'RLIMS-P':\n # sn, tn = tm.rlims.cytoscape_relation_args(relation)\n if sn not in NODES or tn not in NODES:\n continue\n if Edge.add(duid, sn, tn, relation['relationType'], EDGES):\n EDGES[duid].json = json.dumps(relation)\n\n return dump_network(NODES, EDGES)","def create_node2edges_on2freq_grid(self):\n trip_id2model = pickle.load(open('pickles/trip_id2model.pickle','rb'))\n old_trip_id = -1\n model = trip_id2model[1]\n sub_x = 5\n sub_y = 5\n node2edges_on2sub_grid2points = {}\n for line in self.lines:\n trip_id,lat,lon = normalize_simple(line)\n if trip_id != old_trip_id:\n #print trip_id\n model = trip_id2model[trip_id]\n old_trip_id = trip_id\n node = self.gps_to_node(lat,lon)\n if node == -1:\n continue\n #print \"pushed through\"\n incident_edges = self.incident_edges(node)\n edges_on = []\n for edge in incident_edges:\n if model[edge] == 1:\n edges_on.append(edge)\n edges_on.sort()\n edges_on = tuple(edges_on)\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon(self.node_to_coords(node))\n\n sub_row,sub_col = gen_gps_to_coords(lat,lon,sub_x,sub_y,min_lat,max_lat,min_lon,max_lon)\n sub_tuple = (sub_row,sub_col)\n if node not in node2edges_on2sub_grid2points:\n node2edges_on2sub_grid2points[node] = {}\n edges_on2sub_grid2points = node2edges_on2sub_grid2points[node]\n if edges_on not in edges_on2sub_grid2points:\n edges_on2sub_grid2points[edges_on] = defaultdict(list)\n sub_grid2points = edges_on2sub_grid2points[edges_on]\n points = sub_grid2points[sub_tuple]\n node2edges_on2sub_grid2points[node][edges_on][sub_tuple].append([lat,lon])\n #points.append([lat,lon])\n\n print node2edges_on2sub_grid2points.keys()\n print node2edges_on2sub_grid2points[2].keys()\n print node2edges_on2sub_grid2points[2][(2,3)].keys()\n \n node2edges_on2median = {}\n for node in node2edges_on2sub_grid2points:\n print node\n edges_on2sub_grid2points = node2edges_on2sub_grid2points[node]\n node2edges_on2median[node] = {}\n for edges_on in edges_on2sub_grid2points:\n sub_grid2points = edges_on2sub_grid2points[edges_on]\n best_spot = (-1,-1)\n best_score = 0\n for spot in sub_grid2points:\n score = len(sub_grid2points[spot])\n if score > best_score:\n best_score = score\n best_spot = spot\n node2edges_on2median[node][edges_on] = list_median(sub_grid2points[spot])\n \n with open('pickles/node2edges_on2median-%d-%d.pickle' % (self.rows,self.cols),'wb') as output:\n pickle.dump(node2edges_on2median,output)","def get_running_task_dicts(tasks):\n running_task_dicts = []\n with database.engine.begin() as connection:\n for task in tasks:\n print(json.loads(task.meta))\n job = Job.fetch(task.id, connection=redis_conn)\n project = connection.execute(select([sqlalchemy.text(\n '*')]).select_from(models.projects).where(models.projects.c.project_id == task.project_id)).first()\n task_dict = dict(id=task.id, name=task.name, description=task.description,\n complete=task.complete, result=task.result, progress=task.get_progress(), project_id=task.project_id)\n task_dict['meta'] = json.loads(\n task.meta) if task.meta is not None else {}\n\n if job:\n task_dict['status'] = job.get_status()\n # task_dict['started_at'] = datetime.datetime.fromtimestamp(\n # task_dict['meta']['scheduled_at'])\n # print('scheduled_at: {}'.format(task_dict['started_at']))\n if project:\n task_dict['project_name'] = project['name']\n running_task_dicts.append(task_dict)\n return running_task_dicts","def prepare_graph(label, nodes, edges, graphID):\n features = {'label': label}\n\n G = nx.DiGraph()\n nodes[\"id\"] = nodes[\"id\"].apply(lambda x : str(x))\n features['num_nodes'] = nodes.shape[0]\n op_node = None\n times = []\n friends = []\n followers = []\n for index, row in nodes.iterrows():\n G.add_node(row['id'], time=row['time'], friends=row['friends'], followers = row['followers'])\n times.append(row['time'])\n friends.append(2**row['friends'])\n followers.append(2**row['followers'])\n if row['time'] == 0:\n features['poster_friend_cnt'] = 2**row['friends']\n features['poster_follower_cnt'] = 2**row['followers']\n tweeter_id = row['id']\n op_node = row['id']\n features['avg_time'] = np.mean(times)\n features['avg_friends'] = np.mean(friends)\n features['avg_followers'] = np.mean(followers)\n features['max_followers'] = max(followers)\n features['max_friends'] = max(friends)\n features['friends_25th_percentile'] = np.percentile(friends, 25)\n features['friends_75th_percentile'] = np.percentile(friends, 75)\n features['followers_25th_percentile'] = np.percentile(followers, 25)\n features['followers_75th_percentile'] = np.percentile(followers, 75)\n node_list = []\n edge_count = 0\n for pair in edges:\n node1, node2 = pair.split()[0], pair.split()[1]\n node_list.append(node1)\n node_list.append(node2)\n G.add_edge(node1, node2)\n edge_count += 1\n features['num_edges'] = edge_count\n sum_users_without_followers = sum([1 for (node, val) in G.in_degree() if val==0])\n features['ratio_users_w/out_followers'] = sum_users_without_followers / len(G.nodes)\n features['num_connected_components'] = nx.number_strongly_connected_components(G)\n features['number_of_OPs_followers_who_retweeted'] = G.in_degree(tweeter_id)\n features['percentage_of_OPs_followers_who_retweeted'] = G.in_degree(tweeter_id) / features['poster_follower_cnt']\n features['avg_clustering'] = nx.average_clustering(G)\n features['op_clustering'] = nx.clustering(G,op_node)\n features['transitivity'] = nx.transitivity(G)\n node_list = list(set(node_list))\n features['nodeID_list'] = np.array(node_list)\n features['graph_id'] = graphID\n return features, G","def generate_dagre_data(g: Graph) -> Dict:\n\n add_benchmark = graph_has_benchmark(g)\n\n def _node_to_dict(node: Node) -> Dict:\n if node in g.inputs:\n node_type = \"input\"\n elif node in g.outputs:\n node_type = \"output\"\n else:\n node_type = None\n\n return {\n **node.to_dict(),\n \"name\": node.name_str(),\n \"type\": node_type,\n \"times\": node.benchmark.get(\"self_eval_times\") if add_benchmark else None\n }\n\n graph = {\n \"nodes\": [],\n \"edges\": []\n }\n\n visited = []\n todo = g.inputs + g.outputs\n\n while len(todo) > 0:\n node = todo.pop()\n graph[\"nodes\"].append(node)\n visited.append(node)\n succ = [n for n in node.successors if n not in visited]\n graph[\"edges\"] += [(node, s) for s in succ]\n pred = [n for n in node.predecessors if n not in visited]\n graph[\"edges\"] += [(p, node) for p in pred]\n todo += (succ + pred)\n\n graph[\"nodes\"] = [_node_to_dict(n) for n in graph[\"nodes\"]]\n graph[\"edges\"] = [(x.name_str(), y.name_str()) for x, y in graph[\"edges\"]]\n\n return graph","def create_tasks_set(self): # noqa\n task0 = self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 0', 'status': 'active',\n 'priority': 'high', 'parent_id': None})\n task1 = self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 1', 'status': 'active',\n 'priority': 'medium', 'parent_id': task0.json['task_id']})\n task2 = self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 2', 'status': 'done',\n 'priority': 'veryhigh', 'parent_id': None})\n task3 = self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 3', 'status': 'done',\n 'priority': 'high', 'parent_id': task1.json['task_id']})\n task4 = self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 4', 'status': 'active',\n 'priority': 'medium', 'parent_id': task1.json['task_id']})\n self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 5', 'status': 'active',\n 'priority': 'medium', 'parent_id': task1.json['task_id']})\n self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 6', 'status': 'active',\n 'priority': 'high', 'parent_id': task0.json['task_id']})\n self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 7', 'status': 'done',\n 'priority': 'medium', 'parent_id': task2.json['task_id']})\n self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 8', 'status': 'done',\n 'priority': 'medium', 'parent_id': task3.json['task_id']})\n self.client.post(f\"/api/v1/tasks/{self.todolist.todolist_id}\",\n json={'label': 'Task 9', 'status': 'active',\n 'priority': 'medium', 'parent_id': task4.json['task_id']})\n return [task0.json, task1.json, task2.json]","def create_nodes(self):","def add_task(bpmn, counts, label):\r\n from pm4py.objects.bpmn.bpmn_graph import BPMN\r\n task = BPMN.Task(name=label)\r\n bpmn.add_node(task)\r\n return bpmn, task, counts","def map_tree(a_task: Task, tree: Any) -> Any:\n\n def map_node(node):\n if isinstance(node, list):\n return [map_node(child) for child in node]\n else:\n return a_task(node)\n\n return map_node(tree)","def build_nodes(graph):\n\n acc = {}\n for k, v in graph.items():\n if callable(v):\n acc[k] = util.fninfo(v)\n else:\n acc[k] = v\n\n return acc","def details(thread, path):\n parts = [int(x) for x in path.split('.')]\n node = DATA.threads[thread].tree[parts[0]]\n for part in parts[1:]:\n node = node.children[int(part)]\n result = formatNode(node)\n result['children'] = [formatNode(child) for child in node.children]\n return json.dumps(result)","def get_nodes():\n\n host = str(request.args['host'])\n days = float(request.args['days'])\n\n to_time = int(time.time())\n to_day = int(time.strftime('%Y%m%d', time.gmtime(float(to_time))))\n from_time = to_time-int(days*24*60*60)\n from_day = int(time.strftime('%Y%m%d', time.gmtime(float(from_time))))\n day_in=''\n for x in range(from_day, to_day+1):\n day_in = day_in + ',' + str(x)\n day_in=re.sub(r\"^,\", \"\", day_in)\n day_in=re.sub(r\",$\", \"\", day_in)\n query = \"SELECT * FROM metrics WHERE host='\" + str(host) + \"' and date IN (\"\n query = query + str(day_in) + \") and time>=\" + str(int(int(from_time)*1000)) + \" and time<=\"\n query = query + str(int(int(to_time)*1000)) + \" ALLOW FILTERING\"\n rows = session.execute(query);\n reply={}\n last_value={}\n for r in rows:\n if str(r.host) not in reply:\n reply[r.host]={}\n last_value[r.host]={}\n if str(r.metric) not in reply[r.host]:\n reply[r.host][r.metric]=[]\n last_value[r.host][r.metric]=int(r.value)\n continue\n real_value = (r.value-last_value[r.host][r.metric])/60\n\tlast_value[r.host][r.metric]=int(r.value)\n reply[str(r.host)][r.metric].append({ 'value': int(real_value),\n 'time': str(r.time) })\n return json.dumps(reply)","def insert_tasks(note, exer_tasks):\n for i in range(len(exer_tasks)):\n if 'graphml' in exer_tasks[i]:\n insert_single_graph_task(note,exer_tasks[i],i)\n else:\n insert_single_task(note,exer_tasks[i],i)\n return","def json(cls):\n\n nodes = []\n for node in cls._nodes:\n nodes.append(node.json())\n return nodes"],"string":"[\n \"def task_relation_json(self) -> List[Dict]:\\n if not self.tasks:\\n return [self.tasks]\\n else:\\n self._handle_root_relation()\\n return [tr.get_define() for tr in self._task_relations]\",\n \"def _subgraph(self):\\n return {\\n 'type': 'SubgraphTask',\\n 'id': 0,\\n 'dependencies': [],\\n 'parameters': {\\n 'info': {},\\n 'current_retries': 0,\\n 'send_task_events': False,\\n 'containing_subgraph': None,\\n 'task_kwargs': {}\\n }\\n }\",\n \"def json_node_list(graph, source, target):\\n output = []\\n idx = 0\\n for node in graph.nodes:\\n node_dict = {}\\n node_dict[\\\"name\\\"] = node\\n node_dict[\\\"index\\\"] = idx\\n node_dict[\\\"title\\\"] = graph.nodes[node][\\\"title\\\"]\\n node_dict[\\\"url\\\"] = graph.nodes[node][\\\"url\\\"]\\n node_dict[\\\"steps\\\"] = n_shortest_paths(graph, source, node)[1] - 1\\n output.append(node_dict)\\n idx += 1\\n return output\",\n \"def collect_tasks_fn(\\n tn: TraversalNode, data: Dict[CollectionAddress, GraphTask]\\n ) -> None:\\n if not tn.is_root_node():\\n data[tn.address] = GraphTask(tn, resources)\",\n \"def collect_tasks_fn(\\n tn: TraversalNode, data: Dict[CollectionAddress, GraphTask]\\n ) -> None:\\n if not tn.is_root_node():\\n data[tn.address] = GraphTask(tn, resources)\",\n \"def build_graph():\\n file = open(\\\"../data/data.json\\\", \\\"r\\\")\\n data = json.load(file)\\n node_dict = {}\\n for id in data:\\n node_dict[id] = Node(data[id][\\\"name\\\"], data[id][\\\"product\\\"], data[id][\\\"production_volume\\\"])\\n for id in data:\\n current_node = node_dict[id]\\n for costumer_id in data[id][\\\"costumers\\\"]:\\n current_node.costumers.append(node_dict[str(costumer_id)])\\n current_node.out_edge_capacity_drop[node_dict[str(costumer_id)].name] = 0\\n for supplier_id in data[id][\\\"suppliers\\\"]:\\n current_node.suppliers.append(node_dict[str(supplier_id)])\\n current_node.in_edge_capacity_drop[node_dict[str(supplier_id)].name] = 0\\n return node_dict\",\n \"def connect(tree, taskinfo):\\n delinfo = []\\n for node in tree.keys():\\n for task, val in taskinfo.iteritems():\\n if val['parent_task_id'] == node:\\n tree[node][val['id']] = {}\\n delinfo.append(task)\\n for task in delinfo:\\n del taskinfo[task]\\n delinfo = []\\n connect(tree[node], taskinfo)\",\n \"def gen_graph(self):\",\n \"def task_dependencies_from_dotfile(stream):\\n\\n decorator_regex = re.compile(r\\\"([a-z_]+) *(\\\\(.*)\\\")\\n attributes_regex = re.compile(r\\\"\\\\[.*\\\\]\\\")\\n\\n which_task_follows = defaultdict(list)\\n task_decorators = defaultdict(list)\\n task_descriptions = dict()\\n\\n #\\n # remember node\\n #\\n\\n all_tasks = dict()\\n io_tasks = set()\\n for linenum, line in enumerate(stream):\\n # remove heading and trailing spaces\\n line = line.strip()\\n\\n if \\\"digraph\\\" in line:\\n continue;\\n if not len(line):\\n continue\\n\\n #\\n # decorators\\n #\\n if line[0:2] == '#@':\\n fields = line[2:].split('::', 2)\\n if len(fields) != 3:\\n raise Exception(\\\"Unexpected task specification on line# %d\\\\n(%s)\\\" %\\n (linenum, line))\\n task_name, decorators, description = fields\\n if decorators[0] != '@':\\n raise Exception(\\\"Task decorator missing starting ampersand '@' on line# %d\\\\n(%s)\\\" %\\n (linenum, line))\\n for d in decorators[1:].split(\\\"@\\\"):\\n m = decorator_regex.match(d)\\n if not m:\\n raise Exception(\\\"Task decorator (%s) missing parentheses on line# %d\\\\n(%s)\\\" %\\n (d, linenum, line))\\n task_decorators[task_name].append((m.group(1), m.group(2)))\\n if m.group(1)[0:5] == \\\"files\\\" or m.group(1) == \\\"parallel\\\":\\n io_tasks.add(task_name)\\n\\n task_descriptions[task_name] = description\\n continue\\n\\n #\\n # other comments\\n #\\n if line[0] in '#{}/':\\n continue;\\n line = line.strip(';')\\n line = attributes_regex.sub(\\\"\\\", line)\\n\\n #\\n # ignore assignments\\n #\\n if \\\"=\\\" in line:\\n continue;\\n nodes = [x.strip() for x in line.split('->')]\\n for name1, name2 in adjacent_pairs_iterate(nodes):\\n which_task_follows[name2].append(name1)\\n all_tasks[name1] = 1\\n all_tasks[name2] = 1\\n\\n for task in task_decorators:\\n if task not in all_tasks:\\n raise Exception(\\\"Decorated task %s not in dependencies\\\")\\n\\n\\n # order tasks by precedence the dump way: iterating until true\\n disordered = True\\n while (disordered):\\n disordered = False\\n for to_task, from_tasks in which_task_follows.items():\\n for f in from_tasks:\\n if all_tasks[to_task] <= all_tasks[f]:\\n all_tasks[to_task] += all_tasks[f]\\n disordered = True\\n\\n sorted_task_names = list(sorted(list(all_tasks.keys()), key=lambda x:all_tasks[x]))\\n return which_task_follows, sorted_task_names, task_decorators, io_tasks, task_descriptions\",\n \"def populate_graph(self):\",\n \"def __get_task_dependencies(self):\\n task_dependencies = {}\\n\\n with open(self.task_id_path) as tasks:\\n for task in tasks.read().split(','):\\n task_dependencies[task] = set()\\n\\n with open(self.relation_path) as relations:\\n for relation in relations.readlines():\\n [dependency, task] = relation.rstrip('\\\\n').split('->')\\n task_dependencies[task].add(dependency)\\n\\n return task_dependencies\",\n \"def generate_tasks(self, task):\",\n \"def get_tasks(self):\\n return [getattr(self, k).value() for k in self._node_dict.values()]\",\n \"def build_nodes(self, tasks, clean_list):\\n for name in clean_list:\\n # add node itself if not in list of nodes\\n self.nodes.setdefault(name, [])\\n task = tasks[name]\\n # reversing not required\\n for dep_name in reversed(task.task_dep):\\n if tasks[dep_name].subtask_of == name:\\n rev_dep = self.nodes.setdefault(dep_name, [])\\n rev_dep.append(name)\",\n \"def by_nodes(item):\\n return Line['nodes', item]\",\n \"def graph(self):\\n ...\",\n \"def graph():\\n return jsonify(app.config[\\\"jsonified\\\"])\",\n \"def parse_task(k):\\r\\n return [stringify_children(xml_object.xpath(k)[i]) for i in xrange(0, len(xml_object.xpath(k)))]\",\n \"def build_nodes_with_deps(self, tasks, task_name):\\n if task_name in self._processed:\\n return\\n else:\\n self._processed.add(task_name)\\n\\n # add node itself if not in list of nodes\\n self.nodes.setdefault(task_name, [])\\n task = tasks[task_name]\\n # reversing not required\\n for dep_name in reversed(task.setup_tasks + task.task_dep):\\n rev_dep = self.nodes.setdefault(dep_name, [])\\n rev_dep.append(task_name)\\n self.build_nodes_with_deps(tasks, dep_name)\",\n \"def task_definition_json(self) -> List[Dict]:\\n if not self.tasks:\\n return [self.tasks]\\n else:\\n return [task.get_define() for task in self.tasks.values()]\",\n \"def _build(self, replace=None, profile=False):\\n self.__node_dict.clear()\\n replace = dict() if replace is None else replace\\n\\n # check if there are item in the replace that is not in the graph\\n task_ids = set([task[TaskSpecSchema.task_id] for task in self])\\n for rkey in replace.keys():\\n if rkey not in task_ids:\\n warnings.warn(\\n 'Replace task-id {} not found in task-graph'.format(rkey),\\n RuntimeWarning)\\n\\n # instantiate node objects\\n for task in self:\\n task_id = task[TaskSpecSchema.task_id]\\n nodetype = task[TaskSpecSchema.node_type]\\n nodetype = nodetype if isinstance(nodetype, str) else \\\\\\n nodetype.__name__\\n if nodetype == OUTPUT_TYPE:\\n output_task = task\\n node = get_node_obj(output_task, tgraph_mixin=True)\\n else:\\n node = get_node_obj(task, replace.get(task_id), profile,\\n tgraph_mixin=True)\\n self.__node_dict[task_id] = node\\n\\n # build the graph\\n for task_id in self.__node_dict:\\n node = self.__node_dict[task_id]\\n task_inputs = node._task_obj[TaskSpecSchema.inputs]\\n for iport in task_inputs:\\n # node_inputs should be a dict with entries:\\n # {iport: taskid.oport}\\n input_task = task_inputs[iport].split('.')\\n dst_port = iport\\n\\n input_id = input_task[0]\\n # src_port = input_task[1] if len(input_task) > 1 else None\\n src_port = input_task[1]\\n\\n try:\\n input_node = self.__node_dict[input_id]\\n except KeyError:\\n raise LookupError(\\n 'Missing task \\\"{}\\\". Add task spec to TaskGraph.'\\n .format(input_id))\\n\\n node.inputs.append({\\n 'from_node': input_node,\\n 'from_port': src_port,\\n 'to_port': dst_port\\n })\\n # input_node.outputs.append(node)\\n input_node.outputs.append({\\n 'to_node': node,\\n 'to_port': dst_port,\\n 'from_port': src_port\\n })\",\n \"def pipeline_dependencies_tasks(g):\\n deps = dict()\\n for step_name in nx.topological_sort(g):\\n deps[step_name] = list(g.predecessors(step_name)) # copy list\\n return deps\",\n \"def previous_tasks(self,task):\\n #if task.name==\\\"target\\\":\\n # return self.taskGraph.all_nodes()\\n return self.taskGraph.list_children(task)\",\n \"def tasks_recv():\\n recv_json = request.get_json()\\n build_taskgraph(recv_json)\\n return 'ok'\",\n \"def nodes_from_dict(nd=None,**kwargs):\\n\\n if not nd:\\n err_msg = \\\"ERROR: No nodes data provided\\\"\\n print(err_msg)\\n return 1\\n \\n nodes = []\\n\\n ####################\\n #Create BUS objects#\\n ####################\\n busd = {}\\n for i, row in nd[\\\"buses\\\"].iterrows():\\n if row[\\\"active\\\"] and not pd.isnull(row[\\\"active\\\"]):\\n logger.info(\\\"bus {} will be created\\\".format(row[\\\"label\\\"]))\\n bus = solph.Bus(label=row[\\\"label\\\"])\\n nodes.append(bus)\\n busd[row[\\\"label\\\"]] = bus\\n \\n if row[\\\"excess\\\"] and not pd.isnull(row[\\\"excess\\\"]):\\n # Automatically add Sink for curtailment (excess)\\n # Add variable cost for excess cost --> minimise curtailment\\n nodes.append(\\n solph.Sink(\\n label=row[\\\"label\\\"] + \\\"_excess\\\",\\n inputs={\\n busd[row[\\\"label\\\"]]:solph.Flow(\\n variable_costs = row[\\\"excess costs\\\"]\\n )\\n },\\n )\\n )\\n # Automatically add Source for shortage\\n # Add variable cost for shortage --> minimize shortage\\n if row[\\\"shortage\\\"] and not pd.isnull(row[\\\"shortage\\\"]):\\n nodes.append(\\n solph.Source(\\n label = row[\\\"label\\\"] + \\\"_shortage\\\",\\n outputs={\\n busd[row[\\\"label\\\"]]:solph.Flow(\\n variable_costs=row[\\\"shortage costs\\\"]\\n )\\n },\\n )\\n )\\n ########################\\n # Create Source objects#\\n ########################\\n for i, row in nd[\\\"commodity_sources\\\"].iterrows():\\n if row[\\\"active\\\"] and not pd.isnull(row[\\\"active\\\"]):\\n nodes.append(\\n solph.Source(\\n label=row[\\\"label\\\"],\\n outputs={\\n busd[row[\\\"to\\\"]]: solph.Flow(\\n variable_costs = row[\\\"variable costs\\\"]\\n )\\n },\\n )\\n )\\n ########################\\n # Create Source objects with fixed time series from 'renewables' table\\n ########################\\n \\\"\\\"\\\"\\n A source can represent a pv-system, a wind power plant, an import of natural gas or a slack variable to avoid creating an in-feasible model.\\n While a wind power plant will have an hourly feed-in depending on the weather conditions the natural_gas import might be restricted by \\n maximum value (nominal_value) and an annual limit (summed_max). As we do have to pay for imported gas we should set variable costs. \\n Comparable to the demand series an fix is used to define a fixed the normalised output of a wind power plant. \\n Alternatively, you might use max to allow for easy curtailment. The nominal_value sets the installed capacity.\\n \\\"\\\"\\\"\\n for i, row in nd[\\\"renewables\\\"].iterrows():\\n if row[\\\"active\\\"] and not pd.isnull(row[\\\"active\\\"]):\\n # set static outflow values\\n outflow_args = {}\\n\\n # get time series for node and parameter\\n for col in nd[\\\"timeseries\\\"].columns.values:\\n if col.split(\\\".\\\")[0] == row[\\\"label\\\"]:\\n outflow_args[col.split(\\\".\\\")[1]]=nd[\\\"timeseries\\\"][col]\\n # outflow_args[\\\"fix\\\"]=nd[\\\"timeseries\\\"][col]\\n \\n # TODO add NON-CONVEX to outflow_args\\n if row[\\\"capex\\\"] and not pd.isnull(row[\\\"capex\\\"]):\\n # with investment mode, nominal_value must be None\\n logger.info(\\\"Invest {} capacity\\\".format(row[\\\"label\\\"]))\\n invest_args = {}\\n if not row[\\\"epc_invest\\\"] or pd.isnull(row[\\\"epc_invest\\\"]):\\n epc_invest = economics.annuity(row[\\\"capex\\\"],20,0.08)\\n else:\\n epc_invest=row[\\\"epc_invest\\\"]\\n invest_args[\\\"ep_costs\\\"] = epc_invest\\n\\n if row[\\\"max\\\"] and not pd.isnull(row[\\\"max\\\"]):\\n invest_args[\\\"maximum\\\"] = row[\\\"max\\\"]\\n\\n if row[\\\"min\\\"] and not pd.isnull(row[\\\"min\\\"]):\\n invest_args[\\\"minimum\\\"]=row[\\\"min\\\"]\\n\\n if row[\\\"existing\\\"] and not pd.isnull(row[\\\"existing\\\"]):\\n invest_args[\\\"existing\\\"]=row[\\\"existing\\\"]\\n \\n outflow_args[\\\"investment\\\"] = solph.Investment(**invest_args) \\n else: \\n outflow_args[\\\"nominal_value\\\"] = row[\\\"capacity\\\"]\\n \\n # create\\n nodes.append(\\n solph.Source(\\n label=row[\\\"label\\\"],\\n outputs = {\\n busd[row[\\\"to\\\"]]:solph.Flow(**outflow_args)\\n }\\n )\\n )\\n #######################\\n # Create Sink objects # \\n #######################\\n \\\"\\\"\\\"\\n A sink is normally used to define the demand within an energy model but it can also be used to detect excesses.\\n\\n The example shows the electricity demand of the electricity_bus defined above.\\n - 'nd['timeseries'][col]' should be sequence of normalised values\\n - 'nominal_value' is the maximum demand the normalised sequence is multiplied with.\\n - Giving 'nd['timeseries'][col]' as parameter 'fix' means that the demand cannot be changed by the solver. \\n \\n In contrast to the 'demand sink' the 'excess sink' has normally less restrictions but is open to take the whole excess.\\n \\\"\\\"\\\"\\n for i, de in nd[\\\"demand\\\"].iterrows():\\n if de[\\\"active\\\"] and not pd.isnull(de[\\\"active\\\"]):\\n # set static inflow values\\n inflow_args = {\\n \\\"nominal_value\\\":de[\\\"nominal value\\\"]\\n }\\n # get time series for node and parameter\\n for col in nd[\\\"timeseries\\\"].columns.values:\\n if col.split(\\\".\\\")[0]==de[\\\"label\\\"]:\\n # inflow_args[col.split(\\\".\\\")[1]]=nd[\\\"timeseries\\\"][col]\\n # TODO: veriry other key than 'fix'?????\\n inflow_args[\\\"fix\\\"]=nd[\\\"timeseries\\\"][col] \\n \\n # Create Sink object and append to nodes\\n nodes.append(\\n solph.Sink(\\n label=de[\\\"label\\\"],\\n inputs={\\n busd[de[\\\"from\\\"]]:solph.Flow(**inflow_args)\\n }\\n )\\n )\\n #############################\\n # Create Transformer object #\\n #############################\\n \\\"\\\"\\\"\\n An instance of the Transformer class can represent a node with multiple input and output flows such as:\\n - a power plant\\n - a transport line \\n - or any kind of a transforming process as electrolysis, a cooling device or a heat pump. \\n The efficiency has to be constant within one time step to get a linear transformation.\\n You can define a different efficiency for every time step (e.g. the thermal powerplant efficiency according \\n to the ambient temperature) but this series has to be predefined and cannot be changed within the optimisation.\\n\\n A condensing power plant can be defined by a transformer with one input (fuel) and one output (electricity)\\n ```\\n b_gas = solph.Bus(label='natural_gas')\\n b_el = solph.Bus(label='electricity')\\n solph.Transformer(\\n label=\\\"pp_gas\\\",\\n inputs={bgas: solph.Flow()},\\n outputs={b_el: solph.Flow(nominal_value=10e10)},\\n conversion_factors={electricity_bus: 0.58})\\n ```\\n\\n A CHP power plant would be defined in the same manner but with two outputs:\\n ```\\n b_gas = solph.Bus(label='natural_gas')\\n b_el = solph.Bus(label='electricity')\\n b_th = solph.Bus(label='heat')\\n\\n solph.Transformer(\\n label='pp_chp',\\n inputs={b_gas: Flow()},\\n outputs={b_el: Flow(nominal_value=30),\\n b_th: Flow(nominal_value=40)},\\n conversion_factors={b_el: 0.3, b_th: 0.4})\\n ```\\n A CHP power plant with 70% coal and 30% natural gas can be defined with two inputs and two outputs:\\n ```\\n b_gas = solph.Bus(label='natural_gas')\\n b_coal = solph.Bus(label='hard_coal')\\n b_el = solph.Bus(label='electricity')\\n b_th = solph.Bus(label='heat')\\n\\n solph.Transformer(\\n label='pp_chp',\\n inputs={b_gas: Flow(), b_coal: Flow()},\\n outputs={b_el: Flow(nominal_value=30),\\n b_th: Flow(nominal_value=40)},\\n conversion_factors={b_el: 0.3, b_th: 0.4,\\n b_coal: 0.7, b_gas: 0.3})\\n ```\\n \\\"\\\"\\\"\\n for i, row in nd[\\\"transformers\\\"].iterrows():\\n if row[\\\"active\\\"] and not pd.isnull(row[\\\"active\\\"]):\\n # set static inflow values\\n inflow_args = {\\n \\\"variable_costs\\\":row[\\\"variable input costs\\\"]\\n }\\n # inflow_args = {}\\n outflow_args = {}\\n # get time series for inflow transformer\\n for col in nd[\\\"timeseries\\\"].columns.values:\\n if col.split(\\\".\\\")[0]==row[\\\"label\\\"]:\\n # inflow_args[col.split(\\\".\\\")[1]] = nd[\\\"timeseries\\\"][col]\\n inflow_args[\\\"fix\\\"] = nd[\\\"timeseries\\\"][col]\\n \\n #TODO: multi inputs/outputs and add investment\\n\\n if row[\\\"capex inflow\\\"] and not pd.isnull(row[\\\"capex inflow\\\"]):\\n logger.info(\\\"Invest {} inflow capacity\\\".format(row[\\\"label\\\"])) \\n invest_args = {}\\n invest_args[\\\"ep_costs\\\"] = economics.annuity(row[\\\"capex inflow\\\"],20,0.08)\\n\\n if row[\\\"max inflow\\\"] and not pd.isnull(row[\\\"max inflow\\\"]):\\n invest_args[\\\"maximum\\\"] = row[\\\"max inflow\\\"]\\n\\n if row[\\\"min inflow\\\"] and not pd.isnull(row[\\\"min inflow\\\"]):\\n invest_args[\\\"minimum\\\"] = row[\\\"min inflow\\\"]\\n\\n if row[\\\"existing inflow\\\"] and not pd.isnull(row[\\\"existing inflow\\\"]):\\n invest_args[\\\"existing\\\"] = row[\\\"existing inflow\\\"]\\n\\n inflow_args[\\\"investment\\\"] = solph.Investment(**invest_args)\\n else: \\n outflow_args[\\\"nominal_value\\\"] = row[\\\"capacity\\\"] # should be specify capacity inflow or outflow\\n\\n # create\\n nodes.append(\\n solph.Transformer(\\n label=row[\\\"label\\\"],\\n inputs = {\\n busd[row[\\\"from\\\"]]:solph.Flow(**inflow_args)\\n },\\n outputs={\\n busd[row[\\\"to\\\"]]:solph.Flow(**outflow_args)\\n },\\n conversion_factors = {\\n busd[row[\\\"to\\\"]]:row[\\\"efficiency\\\"]\\n }\\n )\\n )\\n ##################################\\n # Create Transformer CHP objects #\\n ##################################\\n for i, row in nd[\\\"transformers_chp\\\"].iterrows():\\n if row[\\\"active\\\"] and not pd.isnull(row[\\\"active\\\"]):\\n\\n inflow_args = {}\\n outflow_elec_args = {}\\n outflow_heat_args = {}\\n\\n inflow_args[\\\"variable_costs\\\"] = row[\\\"variable input costs\\\"]\\n\\n if row[\\\"capex elec\\\"] and not pd.isnull(row[\\\"capex elec\\\"]):\\n logger.info(\\\"Invest {} inflow capacity\\\".format(row[\\\"label\\\"])) \\n invest_args = {}\\n invest_args[\\\"ep_costs\\\"] = economics.annuity(row[\\\"capex elec\\\"],20,0.08)\\n if row[\\\"max elec\\\"] and not pd.isnull(row[\\\"max elec\\\"]):\\n invest_args[\\\"maximum\\\"] = row[\\\"max elec\\\"]\\n if row[\\\"min elec\\\"] and not pd.isnull(row[\\\"min elec\\\"]):\\n invest_args[\\\"minimum\\\"] = row[\\\"min elec\\\"]\\n if row[\\\"existing elec\\\"] and not pd.isnull(row[\\\"existing elec\\\"]):\\n invest_args[\\\"existing\\\"] = row[\\\"existing elec\\\"]\\n \\n outflow_elec_args[\\\"investment\\\"] = solph.Investment(**invest_args)\\n investment = solph.Investment(**invest_args)\\n else:\\n # inflow_args[\\\"nominal_value\\\"] = row[\\\"capacity_el\\\"]\\n outflow_elec_args[\\\"nominal_value\\\"] = row[\\\"capacity_el\\\"]\\n outflow_heat_args[\\\"nominal_value\\\"] = row[\\\"capacity_heat\\\"]\\n\\n # Create\\n nodes.append(\\n solph.Transformer(\\n label = row[\\\"label\\\"],\\n inputs ={\\n busd[row[\\\"from\\\"]]:solph.Flow(**inflow_args)\\n },\\n outputs={\\n busd[row[\\\"to_el\\\"]]:solph.Flow(**outflow_elec_args),\\n busd[row[\\\"to_heat\\\"]]:solph.Flow(**outflow_heat_args)\\n },\\n conversion_factors={\\n busd[row[\\\"to_el\\\"]]:row[\\\"efficiency_el\\\"],\\n busd[row[\\\"to_heat\\\"]]:row[\\\"efficiency_heat\\\"]\\n }\\n )\\n )\\n\\n ##########################\\n # Create Storage objects #\\n ##########################\\n for i, row in nd[\\\"storages\\\"].iterrows():\\n if row[\\\"active\\\"] and not pd.isnull(row[\\\"active\\\"]):\\n\\n inflow_args = {}\\n outflow_args = {}\\n\\n if row[\\\"capex\\\"] and not pd.isnull(row[\\\"capex\\\"]):\\n logger.info(\\\"Invest {} storage capacity\\\".format(row[\\\"label\\\"]))\\n\\n invest_args = {}\\n invest_args[\\\"ep_costs\\\"] = economics.annuity(row[\\\"capex\\\"],20,0.08)\\n if row[\\\"max\\\"] and not pd.isnull(row[\\\"max\\\"]):\\n invest_args[\\\"maximum\\\"] = row[\\\"max\\\"]\\n if row[\\\"min\\\"] and not pd.isnull(row[\\\"min\\\"]):\\n invest_args[\\\"minimum\\\"] = row[\\\"min\\\"]\\n if row[\\\"existing\\\"] and not pd.isnull(row[\\\"existing\\\"]):\\n invest_args[\\\"existing\\\"] = row[\\\"existing\\\"]\\n\\n investment=solph.Investment(\\n **invest_args\\n )\\n nominal_capacity=None\\n \\n #TODO add if row[\\\"capex inflow\\\"] and if row[\\\"capex outflow\\\"]\\n #TODO read relation_capacity_inflow/outflow from excel\\n \\n else:\\n investment = None\\n nominal_capacity = row[\\\"nominal capacity\\\"] \\n \\n if row[\\\"capacity inflow\\\"] and row[\\\"capacity inflow ratio\\\"]:\\n logger.error(\\\"{} is overdetermined, only capacity inflow or capacity inflow ratio shoul be set\\\".format(row[\\\"label\\\"]))\\n return 1\\n if row[\\\"capacity inflow\\\"]:\\n inflow_args[\\\"nominal_value\\\"] = row[\\\"capacity inflow\\\"]\\n if row[\\\"capacity inflow ratio\\\"]:\\n capacity_inflow_ratio = row[\\\"capacity inflow ratio\\\"]\\n else:\\n capacity_inflow_ratio = None\\n inflow_args[\\\"variable_costs\\\"] = row[\\\"variable input costs\\\"]\\n\\n \\n if row[\\\"capacity outflow\\\"] and row[\\\"capacity outflow ratio\\\"]:\\n logger.error(\\\"{} is overdetermined, only capacity outflow or capacity outflow ratio shoul be set\\\".format(row[\\\"label\\\"]))\\n return 1\\n if row[\\\"capacity outflow\\\"]:\\n outflow_args[\\\"nominal_value\\\"] = row[\\\"capacity outflow\\\"]\\n if row[\\\"capacity outflow ratio\\\"]:\\n capacity_outflow_ratio = row[\\\"capacity outflow ratio\\\"]\\n else:\\n capacity_outflow_ratio = None\\n\\n outflow_args[\\\"variable_costs\\\"] = row[\\\"variable output costs\\\"]\\n\\n nodes.append(\\n solph.components.GenericStorage(\\n label=row[\\\"label\\\"],\\n inputs = {\\n busd[row[\\\"bus\\\"]]:solph.Flow(**inflow_args)\\n },\\n outputs = {\\n busd[row[\\\"bus\\\"]]:solph.Flow(**outflow_args)\\n },\\n investment=investment,\\n nominal_storage_capacity=nominal_capacity,\\n loss_rate = row[\\\"capacity loss\\\"],\\n initial_storage_level = row[\\\"initial capacity\\\"],\\n max_storage_level=row[\\\"capacity max\\\"],\\n min_storage_level=row[\\\"capacity min\\\"],\\n invest_relation_input_capacity = capacity_inflow_ratio,\\n invest_relation_output_capacity = capacity_outflow_ratio,\\n inflow_conversion_factor = row[\\\"efficiency inflow\\\"],\\n outflow_conversion_factor = row[\\\"efficiency outflow\\\"]\\n )\\n )\\n #######################\\n # Create Link objects #\\n #######################\\n \\\"\\\"\\\"\\n A Link object with 1...2 inputs and 1...2 outputs\\n Note: This component is experimental. Use it with care\\n \\\"\\\"\\\"\\n for i, p in nd[\\\"powerlines\\\"].iterrows():\\n if p[\\\"active\\\"] and not pd.isnull(p[\\\"active\\\"]):\\n bus1 = busd[p[\\\"bus_1\\\"]]\\n bus2 = busd[p[\\\"bus_2\\\"]]\\n nodes.append(\\n solph.custom.Link(\\n label = \\\"powerline\\\" + \\\"_\\\" + p[\\\"bus_1\\\"] + \\\"_\\\" + p[\\\"bus_2\\\"],\\n inputs = {\\n bus1:solph.Flow(),\\n bus2:solph.Flow()\\n },\\n outputs = {\\n bus1: solph.Flow(nominal_value = p[\\\"capacity\\\"]),\\n bus2: solph.Flow(nominal_value=p[\\\"capacity\\\"]),\\n },\\n conversion_factors={\\n (bus1,bus2):p[\\\"efficiency\\\"],\\n (bus2,bus1):p[\\\"efficiency\\\"]\\n }\\n )\\n ) \\n return nodes\",\n \"def make_tree(taskinfo):\\n tree = {}\\n tasks = deepcopy(taskinfo)\\n for task, val in tasks.iteritems():\\n if val['parent_task_id'] is None:\\n rootid = val['id']\\n del tasks[task]\\n break\\n tree[rootid] = {}\\n connect(tree, tasks)\\n return tree\",\n \"def load_and_process_json(self, format_fn: Formatter):\\n required_keys = [\\n \\\"canary\\\",\\n \\\"name\\\",\\n \\\"description\\\",\\n \\\"keywords\\\",\\n ]\\n subtask_keys = [\\n \\\"metrics\\\",\\n \\\"examples\\\",\\n \\\"preferred_score\\\",\\n ]\\n\\n if isinstance(self.task_data, str):\\n json_file = self.task_data\\n json_dir = os.path.split(os.path.abspath(os.path.expanduser(json_file)))[0]\\n\\n with tf.io.gfile.GFile(self.task_data, \\\"rb\\\") as file:\\n self.task_data = json.load(file)\\n\\n if \\\"examples\\\" not in self.task_data:\\n subtasks = tf.io.gfile.glob(os.path.join(json_dir, \\\"*\\\", \\\"task.json\\\"))\\n if not subtasks:\\n raise ValueError(\\n f\\\"task {json_file} must have either examples or subtasks\\\"\\n )\\n\\n self.task_data[\\\"subtasks\\\"] = []\\n for t in subtasks:\\n with tf.io.gfile.GFile(t, \\\"r\\\") as f:\\n self.task_data[\\\"subtasks\\\"].append(json.load(f))\\n\\n elif isinstance(self.task_data, dict):\\n self.task_data = _sanitize_task_data(self.task_data)\\n else:\\n raise ValueError(\\\"Either json path or dict has to be provided.\\\")\\n\\n if \\\"name\\\" not in self.task_data:\\n raise ValueError(\\\"task must have a name\\\")\\n\\n self.absolute_normalization = self.task_data.get(\\n \\\"absolute_normalization\\\", False\\n )\\n\\n # unicode causing errors in some environments\\n task_name = self.task_data[\\\"name\\\"].encode(\\\"ascii\\\", errors=\\\"replace\\\").decode()\\n\\n if self.parent_task:\\n self.name = f\\\"{self.parent_task.name}:{task_name}\\\"\\n else:\\n self.name = task_name\\n\\n for key in required_keys:\\n if key not in self.task_data.keys():\\n raise ValueError(f\\\"{self.name}: Task needs a \\\" + key + \\\" field.\\\")\\n\\n subtask_max_examples = None\\n\\n if self.max_examples:\\n num_subtasks = len(self.task_data.get(\\\"subtasks\\\", []))\\n if num_subtasks:\\n subtask_max_examples = self.max_examples // num_subtasks\\n if subtask_max_examples < 1:\\n raise ValueError(\\n f\\\"for task {self.name}: \\\"\\n f\\\"max_examples ({self.max_examples}) must be >= number \\\"\\n f\\\"of subtasks ({num_subtasks})\\\"\\n )\\n\\n self.subtasks = [\\n JsonTask(\\n task_data=t,\\n shot_list=self.shot_list,\\n verbose=self.verbose,\\n format_fn=self.format_fn,\\n parent_task=self,\\n max_examples=subtask_max_examples,\\n )\\n for t in self.task_data.get(\\\"subtasks\\\", [])\\n ]\\n\\n subtask_names = set()\\n for t in self.subtasks:\\n if t.name in subtask_names:\\n raise ValueError(f\\\"in {self.name} subtask name {t.name} is duplicated\\\")\\n subtask_names.add(t.name)\\n\\n if self.subtasks: # this is a container task\\n subtask_details = [t.get_task_details() for t in self.subtasks]\\n self.task_data[\\\"max_input_length\\\"] = max(\\n [d.max_input_length_per_query for d in subtask_details]\\n )\\n self.task_data[\\\"max_queries\\\"] = sum(\\n [d.max_queries for d in subtask_details]\\n )\\n return\\n\\n for key in subtask_keys:\\n if key not in self.task_data.keys():\\n raise ValueError(f\\\"{self.name}: Task needs a \\\" + key + \\\" field.\\\")\\n\\n self.metrics = self.task_data[\\\"metrics\\\"]\\n self.generative_metrics = list(set(self.metrics) & set(GENERATIVE_METRICS))\\n self.multiple_choice_metrics = list(\\n set(self.metrics) & set(MULTIPLE_CHOICE_METRICS)\\n )\\n\\n input_prefix = self.task_data.get(\\\"example_input_prefix\\\", \\\"\\\\nQ: \\\")\\n output_prefix = self.task_data.get(\\\"example_output_prefix\\\", \\\"\\\\nA: \\\")\\n choice_prefix = self.task_data.get(\\\"choice_prefix\\\", \\\"\\\\n choice: \\\")\\n append_choices_to_input = self.task_data.get(\\\"append_choices_to_input\\\", True)\\n self.few_shot_example_separator = self.task_data.get(\\n \\\"few_shot_example_separator\\\", \\\"\\\\n\\\"\\n )\\n\\n for metric in self.metrics:\\n\\n if metric not in GENERATIVE_METRICS + MULTIPLE_CHOICE_METRICS:\\n raise ValueError(f\\\"Metric {metric} not supported.\\\")\\n\\n # remove duplicates, keeping example order the same\\n example_set = set()\\n distinct_examples = []\\n for x in self.task_data[\\\"examples\\\"]:\\n try:\\n example_string = json.dumps(x)\\n except TypeError as e:\\n print(f\\\"example_string failure: {x}\\\")\\n raise e\\n\\n if example_string in example_set:\\n continue\\n else:\\n distinct_examples.append(x)\\n example_set.add(example_string)\\n\\n num_examples = len(self.task_data[\\\"examples\\\"])\\n num_distinct = len(distinct_examples)\\n num_duplicates = num_examples - num_distinct\\n if num_duplicates:\\n print(\\n f\\\"warning: {self.name} has {num_duplicates} duplicate examples \\\"\\n f\\\"out of {num_examples}\\\"\\n )\\n\\n max_shots = max(self.shot_list)\\n if num_distinct < (max_shots + 1):\\n raise ValueError(\\n f\\\"insufficient distinct examples ({num_distinct}) for {max_shots} shots\\\"\\n )\\n\\n self.task_data[\\\"examples\\\"] = distinct_examples\\n\\n for sample in self.task_data[\\\"examples\\\"]:\\n example_keys = sample.keys()\\n if \\\"input\\\" not in example_keys or not (\\n \\\"target\\\" in example_keys or \\\"target_scores\\\" in example_keys\\n ):\\n raise ValueError(\\\"Examples missing a target or input field.\\\")\\n if self.multiple_choice_metrics and \\\"target_scores\\\" not in example_keys:\\n raise ValueError(\\n \\\"Some sample does not have a target_scores field, required for multiple choice metric.\\\"\\n )\\n if self.generative_metrics and \\\"target\\\" not in example_keys:\\n raise ValueError(\\n \\\"Some sample does not have a target field, required for text-to-text metric.\\\"\\n )\\n\\n self._ds = [\\n format_fn(\\n sample,\\n input_prefix=input_prefix,\\n output_prefix=output_prefix,\\n choice_prefix=choice_prefix,\\n rng=self.rng,\\n append_choices_to_input=append_choices_to_input,\\n )\\n for sample in self.task_data[\\\"examples\\\"]\\n ]\\n\\n self.task_data[\\\"max_input_length\\\"] = max(\\n [len(re.findall(r\\\"\\\\w+\\\", sample[\\\"input\\\"])) for sample in self._ds]\\n ) * max(self.shot_list)\\n\\n self.preferred_score = self.task_data[\\\"preferred_score\\\"]\\n self.low_score = SCORE_RANGES[self.preferred_score][0]\\n\\n if self.preferred_score == \\\"multiple_choice_grade\\\":\\n num_examples, random_score = 0, 0\\n for sample in self.task_data[\\\"examples\\\"]:\\n target_scores = sample[\\\"target_scores\\\"].values()\\n random_score += sum(target_scores) / len(target_scores)\\n num_examples += 1\\n self.low_score = random_score / num_examples\\n\\n self.high_score = SCORE_RANGES[self.preferred_score][1]\\n self.task_prefix = str(self.task_data.get(\\\"task_prefix\\\", \\\"\\\"))\\n self.output_regex, self.stop_string = None, None\\n if \\\"output_regex\\\" in self.task_data:\\n self.output_regex = self.task_data[\\\"output_regex\\\"]\\n if \\\"stop_string\\\" in self.task_data:\\n self.stop_string = self.task_data[\\\"stop_string\\\"]\\n\\n if self.output_regex is None and self.stop_string is None:\\n self.output_regex = _DEFAULT_REGEX\\n\\n for metric in self.metrics:\\n if metric not in GENERATIVE_METRICS + MULTIPLE_CHOICE_METRICS:\\n tf.logging.info(f\\\"Metric {metric} not supported. Will be ignored.\\\")\\n\\n is_present = lambda val: 1 if val else 0\\n self.task_data[\\\"max_queries\\\"] = len(self.task_data[\\\"examples\\\"]) * (\\n is_present(self.generative_metrics)\\n + is_present(self.multiple_choice_metrics)\\n )\\n\\n return\",\n \"def get_all_tasks(rows):\\n lista = list()\\n for row in rows:\\n arr = dict()\\n for k,v in row.items():\\n arr[k] = str(v)\\n lista.append(arr)\\n return dict(task_list=gluon.contrib.simplejson.dumps(lista))\",\n \"def parse_graph_nodes(graph_def):\\n name_to_node = {}\\n for node_def in graph_def.node:\\n name_to_node[node_def.name] = node_def\\n return name_to_node\",\n \"def query_tasks(dbh):\\n # assumes attempt task_ids are in gtt_id table\\n\\n curs = dbh.cursor()\\n\\n # query the task table\\n sql = \\\"select t.label,t.exec_host,t.name,t.status,t.id,t.parent_task_id,t.start_time,t.root_task_id, SYSTIMESTAMP-t.start_time as length from task t, gtt_id g where t.root_task_id=g.id and (end_time is null or status!=0)\\\"\\n curs.execute(sql)\\n desc = [d[0].lower() for d in curs.description]\\n\\n results = {}\\n for row in curs:\\n dat = dict(zip(desc, row))\\n if dat['root_task_id'] not in results:\\n results[dat['root_task_id']] = {}\\n results[dat['root_task_id']][dat['id']] = dat\\n\\n sql = \\\"select s.request_time,s.grant_time,s.release_time, t.exec_host,t.name,t.id,t.root_task_id,t.parent_task_id,t.start_time from seminfo s, task t, gtt_id g where t.root_task_id=g.id and t.name like 'trans%' and t.id=s.task_id and t.end_time is NULL\\\"\\n curs.execute(sql)\\n desc = [dd[0].lower() for dd in curs.description]\\n\\n results_trans = {}\\n for row in curs:\\n dat = dict(zip(desc, row))\\n if dat['root_task_id'] not in results_trans:\\n results_trans[dat['root_task_id']] = {}\\n results_trans[dat['root_task_id']][dat['id']] = dat\\n\\n return results, results_trans\",\n \"def viz_graph(self, show_ports=False, pydot_options=None):\\n import networkx as nx\\n G = nx.DiGraph()\\n if pydot_options:\\n G.graph['graph'] = pydot_options\\n # instantiate objects\\n for itask in self:\\n task_inputs = itask[TaskSpecSchema.inputs]\\n to_task = itask[TaskSpecSchema.task_id]\\n to_type = itask[TaskSpecSchema.node_type]\\n if to_task == \\\"\\\":\\n to_task = OUTPUT_TYPE\\n for iport_or_tid in task_inputs:\\n # iport_or_tid: it is either to_port or task id (tid) b/c\\n # if using ports API task_inputs is a dictionary otherwise\\n # task_inputs is a list.\\n taskin_and_oport = task_inputs[iport_or_tid] \\\\\\n if isinstance(task_inputs, dict) else iport_or_tid\\n isplit = taskin_and_oport.split('.')\\n from_task = isplit[0]\\n from_port = isplit[1] if len(isplit) > 1 else None\\n if show_ports and from_port is not None:\\n to_port = iport_or_tid\\n common_tip = taskin_and_oport\\n G.add_edge(from_task, common_tip, label=from_port)\\n G.add_edge(common_tip, to_task, label=to_port)\\n tnode = G.nodes[common_tip]\\n tnode.update({\\n # 'label': '',\\n 'shape': 'point'})\\n else:\\n G.add_edge(from_task, to_task)\\n\\n # draw output ports\\n if show_ports:\\n\\n if (to_type == OUTPUT_TYPE):\\n continue\\n task_node = get_node_obj(itask, tgraph_mixin=True)\\n # task_outputs = itask.get(TaskSpecSchema.outputs, [])\\n for pout in task_node._get_output_ports():\\n out_tip = '{}.{}'.format(\\n itask[TaskSpecSchema.task_id], pout)\\n G.add_edge(to_task, out_tip, label=pout)\\n tnode = G.nodes[out_tip]\\n tnode.update({\\n # 'label': '',\\n 'shape': 'point'})\\n return G\",\n \"def load(toks):\\r\\n graph = nx.Graph()\\r\\n\\r\\n for tok in toks:\\r\\n graph.add_node(tok['id'])\\r\\n\\r\\n for tok in toks:\\r\\n if 'governor' in tok:\\r\\n governor = tok['governor']\\r\\n dependant = tok['id']\\r\\n graph.add_edge(governor, dependant)\\r\\n return graph\",\n \"def linearly_connect_tasks(*tasks: Union[EOTask, Tuple[EOTask, str]]) -> List[EONode]:\\n nodes = []\\n endpoint: Sequence[EONode] = tuple()\\n for task_spec in tasks:\\n if isinstance(task_spec, EOTask):\\n node = EONode(task_spec, inputs=endpoint)\\n else:\\n task, name = task_spec\\n node = EONode(task, inputs=endpoint, name=name)\\n nodes.append(node)\\n endpoint = [node]\\n\\n return nodes\",\n \"def scheduled_task_labels(self):\\n tasks = self._get_decision_artifact('task-graph.json').values()\\n return {t['label'] for t in tasks}\",\n \"def create_graph(events):\\n events.sort()\\n base_str = events[0].time_start\\n ticks = create_time_tree(base_str, events[-1].time_end)\\n base = str_to_timedelta(base_str)\\n for e in events:\\n start = str_to_timedelta(e.starts())\\n end = str_to_timedelta(e.ends())\\n start_tick = timedelta_to_ticks(start - base)\\n end_tick = timedelta_to_ticks(end - base)\\n for tick in xrange(start_tick, end_tick):\\n _log.debug(\\\"Node %s has neighbour node %s.\\\" % (e, ticks[tick]))\\n e.add_neighbour(ticks[tick])\\n ticks[tick].add_neighbour(e)\\n return events\",\n \"def example_tasks():\\n tasks = set()\\n for i in range(3):\\n task = BaseTask(\\n name=f'task{i}',\\n ancestors=[\\n BaseTask(name=f'task{i}-ancestor{j}') for j in range(3)\\n ],\\n )\\n for task0 in task.flatten():\\n task0.priority = i\\n tasks.add(task)\\n\\n return tasks\",\n \"def set_task(self, task: dict):\\n self.graph = task['graph']\\n self.game_length = task['game_length']\\n\\n # Setup task parameters.\\n self.num_subtasks = len(self.graph.subtask_id_list)\\n self.subtask_reward = self.graph.subtask_reward\\n self.subtask_id_list = self.graph.subtask_id_list # task-specific subtask ids\\n\\n # Setup map parameters.\\n self.map.set_map(\\n subtask_id_list=self.subtask_id_list,\\n map_parameters=task['map_parameters']\\n )\",\n \"def get_nodes(self):\\n self.map_graph_id()\\n self.nodes_list = [\\n self.NX_GRAPHS[self.graph_id].nodes[idx]['label'] \\n for idx in range(len(self.NX_GRAPHS[self.graph_id].nodes))]\",\n \"def parse_dependency_graph(fn):\\n nodes = {}\\n edges = []\\n\\n with open(fn) as f:\\n for line in f:\\n if line.startswith(\\\"digraph {\\\") or line.startswith(\\\"}\\\"):\\n continue\\n if line.startswith(\\\"\\\\t\\\\t\\\"):\\n # edge\\n n1, n2 = map(int, line.split(\\\"->\\\"))\\n edges.append((n1,n2))\\n if n1 not in nodes:\\n nodes[n1] = None\\n if n2 not in nodes:\\n nodes[n2] = None\\n elif line.startswith(\\\"\\\\t\\\"):\\n # node\\n node, rest = line.split(None, 1)\\n node = int(node)\\n _, jobname = rest.split(\\\"\\\\\\\"\\\", 1)\\n jobname = jobname.strip()\\n nodes[node] = jobname\\n\\n return nodes, edges\",\n \"def build_graph(nodes):\\n\\n job_instances_map = {}\\n\\n # first create node structure\\n nodes_map = {}\\n root_nodes = []\\n for node in nodes:\\n new_node = JobGraphNode(node, job_instances_map)\\n nodes_map[node.id] = new_node\\n # check if it is root node\\n try:\\n node.relationships.next()\\n except StopIteration:\\n root_nodes.append(new_node)\\n\\n # then set relationships\\n for _, child in nodes_map.iteritems():\\n for relationship in child.cfy_node.relationships:\\n parent = nodes_map[relationship.target_node.id]\\n parent.add_child(child)\\n child.add_parent(parent)\\n\\n return root_nodes, job_instances_map\",\n \"def __visit_task_nodes(self, curr_node, start_nodes):\\n if curr_node in start_nodes:\\n return [curr_node]\\n\\n visited_nodes = [curr_node]\\n for dependency in self.task_dependencies[curr_node]:\\n visited_nodes += self.__visit_task_nodes(dependency, start_nodes)\\n\\n return visited_nodes\",\n \"def _get_full_graph(self):\",\n \"def getNodeFinishedTasks(self,node):\\n data = self.connect('get','nodes/%s/tasks' % (node),None)\\n return data\",\n \"def _convert_hashring_nodes(self, hashring_nodes):\\n results = []\\n for node in hashring_nodes or []:\\n node_data = json.loads(node.data)\\n service_info = ServiceInfo.from_json(node_data[\\\"service_info\\\"])\\n service_node = ServiceHashringNode(\\n token=node.token,\\n service_info = service_info, \\n data=node_data[\\\"data\\\"])\\n results.append(service_node)\\n return results\",\n \"def _construct_graph(self):\\n raise NotImplementedError\",\n \"def __post_init__(self) -> None:\\n for node in self.task_graph:\\n self.task_graph.nodes[node][\\\"info\\\"] = TaskInfo(TaskStatus.NOT_STARTED)\",\n \"def __post_init__(self) -> None:\\n for node in self.task_graph:\\n self.task_graph.nodes[node][\\\"info\\\"] = TaskInfo(TaskStatus.NOT_STARTED)\",\n \"def _build_graph(self):\\n pass\",\n \"def data():\\n result = {}\\n for thread in DATA.threads:\\n result[thread] = [formatNode(node) for node in DATA.threads[thread].tree]\\n return json.dumps({\\n 'checkpoints': DATA.checkpoints,\\n 'threads': result\\n })\",\n \"def build_graph(self):\\n pass\",\n \"def multi_edge():\\n from networkx.readwrite import json_graph\\n import networkx as nx\\n import autonetkit\\n # returns a house graph\\n data = {'directed': False,\\n 'graph': [],\\n 'links': [{'_ports': {'r4': 2, 'r5': 1},\\n 'raw_interfaces': {},\\n 'source': 0,\\n 'target': 1},\\n {'_ports': {'r2': 3, 'r4': 1},\\n 'raw_interfaces': {},\\n 'source': 0,\\n 'target': 3},\\n {'_ports': {'r2': 4, 'r4': 3},\\n 'raw_interfaces': {},\\n 'source': 0,\\n 'target': 3},\\n {'_ports': {'r3': 3, 'r5': 2},\\n 'raw_interfaces': {},\\n 'source': 1,\\n 'target': 4},\\n {'_ports': {'r1': 1, 'r2': 1},\\n 'raw_interfaces': {},\\n 'source': 2,\\n 'target': 3},\\n {'_ports': {'r1': 3, 'r2': 5},\\n 'raw_interfaces': {},\\n 'source': 2,\\n 'target': 3},\\n {'_ports': {'r1': 2, 'r3': 1},\\n 'raw_interfaces': {},\\n 'source': 2,\\n 'target': 4},\\n {'_ports': {'r1': 4, 'r3': 4},\\n 'raw_interfaces': {},\\n 'source': 2,\\n 'target': 4},\\n {'_ports': {'r1': 5, 'r3': 5},\\n 'raw_interfaces': {},\\n 'source': 2,\\n 'target': 4},\\n {'_ports': {'r2': 2, 'r3': 2},\\n 'raw_interfaces': {},\\n 'source': 3,\\n 'target': 4}],\\n 'multigraph': True,\\n 'nodes': [{'_ports': {0: {'category': 'physical', 'description': None},\\n 1: {'category': 'physical', 'description': 'r4 to r2', 'id': 'eth0'},\\n 2: {'category': 'physical', 'description': 'r4 to r5', 'id': 'eth1'},\\n 3: {'category': 'physical', 'description': 'r4 to r2', 'id': 'eth2'}},\\n 'asn': 2,\\n 'device_type': 'router',\\n 'id': 'r4',\\n 'label': 'r4',\\n 'x': 675,\\n 'y': 300},\\n {'_ports': {0: {'category': 'physical', 'description': None},\\n 1: {'category': 'physical', 'description': 'r5 to r4', 'id': 'eth0'},\\n 2: {'category': 'physical', 'description': 'r5 to r3', 'id': 'eth1'}},\\n 'asn': 2,\\n 'device_type': 'router',\\n 'id': 'r5',\\n 'label': 'r5',\\n 'x': 675,\\n 'y': 500},\\n {'_ports': {0: {'category': 'physical', 'description': None},\\n 1: {'category': 'physical', 'description': 'r1 to r2', 'id': 'eth0'},\\n 2: {'category': 'physical', 'description': 'r1 to r3', 'id': 'eth1'},\\n 3: {'category': 'physical', 'description': 'r1 to r2', 'id': 'eth2'},\\n 4: {'category': 'physical', 'description': 'r1 to r3', 'id': 'eth3'},\\n 5: {'category': 'physical', 'description': 'r1 to r3', 'id': 'eth4'}},\\n 'asn': 1,\\n 'device_type': 'router',\\n 'id': 'r1',\\n 'label': 'r1',\\n 'x': 350,\\n 'y': 400},\\n {'_ports': {0: {'category': 'physical', 'description': None},\\n 1: {'category': 'physical', 'description': 'r2 to r1', 'id': 'eth0'},\\n 2: {'category': 'physical', 'description': 'r2 to r3', 'id': 'eth1'},\\n 3: {'category': 'physical', 'description': 'r2 to r4', 'id': 'eth2'},\\n 4: {'category': 'physical', 'description': 'r2 to r4', 'id': 'eth3'},\\n 5: {'category': 'physical', 'description': 'r2 to r1', 'id': 'eth4'}},\\n 'asn': 1,\\n 'device_type': 'router',\\n 'id': 'r2',\\n 'label': 'r2',\\n 'x': 500,\\n 'y': 300},\\n {'_ports': {0: {'category': 'physical', 'description': None},\\n 1: {'category': 'physical', 'description': 'r3 to r1', 'id': 'eth0'},\\n 2: {'category': 'physical', 'description': 'r3 to r2', 'id': 'eth1'},\\n 3: {'category': 'physical', 'description': 'r3 to r5', 'id': 'eth2'},\\n 4: {'category': 'physical', 'description': 'r3 to r1', 'id': 'eth3'},\\n 5: {'category': 'physical', 'description': 'r3 to r1', 'id': 'eth4'}},\\n 'asn': 1,\\n 'device_type': 'router',\\n 'id': 'r3',\\n 'label': 'r3',\\n 'x': 500,\\n 'y': 500}]}\\n graph = json_graph.node_link_graph(data)\\n anm = autonetkit.anm.NetworkModel()\\n g_in = anm.add_overlay(\\\"input\\\")\\n g_in._replace_graph(nx.MultiGraph(graph))\\n # TODO: check if should build overlays here rather than clone in?\\n g_phy = anm[\\\"phy\\\"]\\n g_phy._replace_graph(graph)\\n return anm\",\n \"def test_read_json1():\\n s = JsonSource()\\n g = s.parse(os.path.join(RESOURCE_DIR, 'valid.json'))\\n nodes = {}\\n edges = {}\\n for rec in g:\\n if rec:\\n if len(rec) == 4:\\n edges[(rec[0], rec[1])] = rec[3]\\n else:\\n nodes[rec[0]] = rec[1]\\n\\n assert len(nodes.keys()) == 6\\n assert len(edges.keys()) == 5\\n\\n n = nodes['MONDO:0017148']\\n assert 'id' in n and n['id'] == 'MONDO:0017148'\\n assert n['name'] == 'heritable pulmonary arterial hypertension'\\n assert n['category'][0] == 'biolink:Disease'\\n\\n e = edges[('HGNC:11603', 'MONDO:0017148')]\\n assert e['subject'] == 'HGNC:11603'\\n assert e['object'] == 'MONDO:0017148'\\n assert e['predicate'] == 'biolink:related_to'\\n assert e['relation'] == 'RO:0004013'\",\n \"def _build_nodes_dict(self, graph):\\n nodes_dict = {}\\n for node, data in graph.nodes_iter(data=True):\\n nodes_dict.update({node: data['label']})\\n return nodes_dict\",\n \"def build_curriculum(self,target_task,groups,setOfTasks):\\n connections = [] #list of graph connections\\n graph = Graph([],directed = True)\\n #Intra-group transfer\\n for g in groups:\\n group = groups[g]\\n #For all tasks inside each group\\n for i in range(len(group)):\\n currentTask = group[i][0]\\n maxTransferPot = -float('inf')\\n argMaxTask = None\\n # get task with maximum potential\\n for j in range(i+1,len(group)):\\n testTask = group[j][0]\\n if testTask != currentTask:\\n pot = self.transfer_potential(testTask,currentTask,graph.list_children(currentTask))\\n if pot > maxTransferPot:\\n maxTransferPot = pot\\n argMaxTask = testTask\\n if argMaxTask != None and maxTransferPot > self.thresholdTask:\\n # Add connection\\n connections.append((argMaxTask,currentTask))\\n graph.add(argMaxTask,currentTask)\\n \\n #Creating graph structure\\n graph = Graph(connections,directed = True)\\n \\n groupKeys = list(groups.keys())\\n #Inter-group transfer\\n for i in range(len(groupKeys)):\\n #Get task features of the first task in the group \\n #(this is equal for all tasks inside it)\\n g = groups[groupKeys[i]]\\n gFeatures = g[0][0].task_features()\\n #For all groups that have task features contained in g:\\n for j in range(i+1,len(groupKeys)):\\n gPrime = groups[groupKeys[j]]\\n gPrimeFeatures = gPrime[0][0].task_features()\\n \\n if task.is_contained(gPrimeFeatures,gFeatures):\\n #For all tasks in g, get the argmax task in gPrime and\\n #Possibly creates a link\\n for gTaskAndPot in g:\\n gTask = gTaskAndPot[0]\\n maxTransferPot = -float('inf')\\n argMaxTask = None\\n for gPrimeTaskAndPot in gPrime:\\n gPrimeTask = gPrimeTaskAndPot[0]\\n pot = self.transfer_potential(gPrimeTask,gTask,graph.list_children(currentTask))\\n if pot > maxTransferPot:\\n maxTransferPot = pot\\n argMaxTask = gPrimeTask\\n if argMaxTask != None and maxTransferPot > self.thresholdTask:\\n # Add connection\\n graph.add(argMaxTask,gTask)\\n connections.append((argMaxTask,gTask))\\n \\n #For all tasks with zero degree, a connection to the target-task is added\\n for currentTask in setOfTasks:\\n if graph.out_degree(currentTask) == 0:\\n graph.add(currentTask,target_task)\\n #The task graph is completed\\n self.taskGraph = graph\",\n \"def gen_task_item(self) -> Dict[str, Any]:\\n return {}\",\n \"def export_nodes(self):\\n return ['lon', 'lat', 'speed', 'heading'], \\\\\\n [{'speed': self.node_speed_limit[v],\\n 'lon': self.node_locations[v][0],\\n 'lat': self.node_locations[v][1],\\n 'heading': self.node_heading[v]} for v in self.graph.vertices()]\",\n \"def load_taskgraph(filename):\\n\\n with open(filename) as f:\\n yaml = ruamel.yaml.YAML(typ='safe')\\n yaml.constructor.yaml_constructors[\\n u'tag:yaml.org,2002:timestamp'] = \\\\\\n yaml.constructor.yaml_constructors[u'tag:yaml.org,2002:str']\\n obj = yaml.load(f)\\n t = TaskGraph(obj)\\n return t\",\n \"def tasks_by_status(self) -> Dict[TaskStatus, Sequence[str]]:\\n out = {}\\n for key in self.task_graph.keys():\\n info = self.task_graph.get_info(key)\\n out.setdefault(info.status, []).append(key)\\n return out\",\n \"def translate_graph(lines: List[str]) -> Graph:\\n nodes = []\\n edges = []\\n is_node = True\\n\\n # first_value used to offset node/edge ids to start from 0\\n first = True\\n first_value = 0\\n\\n for line in lines:\\n a = line.split(\\\",\\\")\\n\\n if \\\"edge\\\" in line:\\n is_node = False\\n elif is_node:\\n if first:\\n first = False\\n first_value = int(a[0])\\n\\n # Create node with Node(id, label, size)\\n pos_x = float(a[4])\\n pos_y = float(a[5])\\n n = Node(int(a[0]),\\n str(a[1]).replace(\\\"\\\\\\\"\\\", \\\"\\\"),\\n int(float(a[2])),\\n kp=KeyPoint(pos_x, pos_y, float(a[2])),\\n pos=(float(a[4]),float(a[5])))\\n\\n if (a[1] == \\\"\\\"):\\n print(\\\"Node \\\" + a[0] + \\\" missing label.\\\")\\n exit(1)\\n nodes.append(n)\\n else:\\n n1 = None\\n n2 = None\\n for node in nodes:\\n if node.node_id == int(a[0]):\\n n1 = node\\n for node in nodes:\\n if node.node_id == int(a[1]):\\n n2 = node\\n e = Edge(n1, n2, distance(n1.pos,n2.pos))\\n edges.append(e)\\n #all_edges.append(e)\\n #edges.append((int(a[0]) - first_value, int(a[1]) - first_value))\\n\\n\\n return Graph(nodes, edges, 1)\",\n \"def add_datapoints(self, tasks):\\n\\n counters = {\\n 'SUCCESS': 0,\\n 'PENDING': 0,\\n 'STARTED': 0,\\n 'FAILURE': 0,\\n 'RETRY' : 0,\\n 'REVOKED': 0,\\n 'RECEIVED': 0,\\n };\\n\\n for task in tasks:\\n counters[task[1]['state']] += 1\\n\\n for counter in counters:\\n self.add_gauge_value('Tasks/' + counter.capitalize(), 'tasks', counters.get(counter, 0))\",\n \"def _process_nodes(self):\\n # Sort the nodes by metanode type, then by id\\n self.node_df = self.node_df.sort_values(['label', 'id']).reset_index(drop=True)\\n # Get all the ids\\n self.nodes = self.node_df['id']\\n # Get mapping from the index to the node ID (one to many so need different one for each node type)\\n self.index_to_nid = dict()\\n for group_name, group in self.node_df.groupby('label'):\\n self.index_to_nid[group_name] = group['id'].reset_index(drop=True).to_dict()\\n # Get the reverse mapping (many to one so don't need to separate based on type).\\n self.nid_to_index = dict()\\n for mapper in self.index_to_nid.values():\\n for index, nid in mapper.items():\\n self.nid_to_index[nid] = index\\n # Finally, we need a mapper from id to node type\\n self.id_to_metanode = self.node_df.set_index('id')['label'].to_dict()\\n # And from node type to a list of ids\\n self.metanode_to_ids = dict()\\n for group_name, group in self.node_df.groupby('label'):\\n self.metanode_to_ids[group_name] = group['id'].tolist()\\n # One more mapper of id to name\\n self.nid_to_name = self.node_df.set_index('id')['name'].to_dict()\",\n \"def graph(self):\\n\\n def start_graph():\\n return \\\"digraph services {\\\\n\\\\n\\\"\\n\\n def end_graph(graph_string):\\n graph_string += \\\"\\\\n}\\\\n\\\"\\n return graph_string\\n\\n def start_cluster(graph_string, cluster_id, cluster_name):\\n graph_string += \\\"subgraph cluster_%s {\\\\n\\\" % cluster_id\\n graph_string += \\\" label = \\\\\\\"%s\\\\\\\";\\\\n\\\" % cluster_name\\n return graph_string\\n\\n def end_cluster(graph_string):\\n graph_string += \\\"\\\\n}\\\\n\\\"\\n return graph_string\\n\\n def add_path(graph_string, from_node, to_node, protocol, port):\\n if not from_node.name:\\n cidr_blocks = [subnetwork.cidr_block for subnetwork in from_node.subnetworks]\\n from_name = \\\",\\\".join(cidr_blocks)\\n from_network_name = \\\"external\\\"\\n else:\\n from_name = from_node.name\\n from_network_name = from_node.network.name\\n path_template = \\\"\\\\\\\"%s (%s)\\\\\\\" -> \\\\\\\"%s (%s)\\\\\\\" [ label=\\\\\\\"(%s:%s)\\\\\\\" ];\\\\n\\\"\\n graph_string += path_template % (from_name, from_network_name, to_node.name,\\n to_node.network.name, protocol, port)\\n return graph_string\\n\\n def add_node(graph_string, node_name, network_name):\\n graph_string += \\\" \\\\\\\"%s (%s)\\\\\\\";\\\\n\\\" % (node_name, network_name)\\n return graph_string\\n\\n def group_paths_by_network(paths_info):\\n net_to_path = {}\\n for path in paths_info:\\n if path.network.name not in net_to_path:\\n net_to_path[path.network.name] = []\\n net_to_path[path.network.name].append(path)\\n return net_to_path\\n\\n def group_services_by_network(services_info):\\n net_to_service = {}\\n for service_info in services_info:\\n if service_info.network.name not in net_to_service:\\n net_to_service[service_info.network.name] = []\\n net_to_service[service_info.network.name].append(service_info)\\n return net_to_service\\n\\n # First group paths and services by network\\n paths_info = self.paths.list()\\n net_to_path = group_paths_by_network(paths_info)\\n services_info = self.service.list()\\n net_to_service = group_services_by_network(services_info)\\n networks_info = self.network.list()\\n\\n graph_string = start_graph()\\n cluster_id = 0\\n for network_info in networks_info:\\n\\n # Skip networks with no name for now\\n if not network_info.name:\\n continue\\n\\n # Each network is a \\\"cluster\\\" in graphviz terms\\n graph_string = start_cluster(graph_string, cluster_id, network_info.name)\\n cluster_id += 1\\n\\n # If the network is empty just make a placeholder node\\n if network_info.name not in net_to_service and network_info.name not in net_to_path:\\n graph_string = add_node(graph_string, \\\"Empty Network\\\", network_info.name)\\n graph_string = end_cluster(graph_string)\\n continue\\n\\n # Otherwise, add all the services and path in this network\\n if network_info.name in net_to_service:\\n for service_info in net_to_service[network_info.name]:\\n graph_string = add_node(graph_string, service_info.name,\\n service_info.network.name)\\n graph_string = end_cluster(graph_string)\\n\\n # We do all paths outside the cluster so that public CIDRs will show up outside the\\n # networks.\\n if network_info.name in net_to_path:\\n for path_info in net_to_path[network_info.name]:\\n graph_string = add_path(graph_string, path_info.source, path_info.destination,\\n path_info.protocol, path_info.port)\\n\\n graph_string = end_graph(graph_string)\\n return graph_string\",\n \"def task_relabel_nodes(self, req, resp, json_data):\\n action = json_data.get('action', None)\\n\\n if action != 'relabel_nodes':\\n self.error(\\n req.context,\\n \\\"Task body ended up in wrong handler: action %s in task_relabel_nodes\\\"\\n % action)\\n self.return_error(resp,\\n falcon.HTTP_500,\\n message=\\\"Error\\\",\\n retry=False)\\n\\n try:\\n task = self.create_task(json_data, req.context)\\n resp.text = json.dumps(task.to_dict())\\n resp.append_header('Location',\\n \\\"/api/v1.0/tasks/%s\\\" % str(task.task_id))\\n resp.status = falcon.HTTP_201\\n except errors.InvalidFormat as ex:\\n self.error(req.context, ex.msg)\\n self.return_error(resp,\\n falcon.HTTP_400,\\n message=ex.msg,\\n retry=False)\",\n \"def getNodes(self):\\n nodes = [{\\\"address\\\": \\\"http://0.0.0.0:100\\\"}\\n ,{\\\"address\\\": \\\"http://0.0.0.0:200\\\"}\\n ,{\\\"address\\\": \\\"http://0.0.0.0:300\\\"}\\n ,{\\\"address\\\": \\\"http://0.0.0.0:400\\\"}\\n ,{\\\"address\\\": \\\"http://0.0.0.0:500\\\"}]\\n return nodes\",\n \"def load_task(self, task):\\n with self._driver.session() as session:\\n session.write_transaction(tx.create_task, task=task)\\n session.write_transaction(tx.create_task_hint_nodes, task=task)\\n session.write_transaction(tx.create_task_requirement_nodes, task=task)\\n session.write_transaction(tx.create_task_input_nodes, task=task)\\n session.write_transaction(tx.create_task_output_nodes, task=task)\\n session.write_transaction(tx.create_task_metadata_node, task=task)\\n session.write_transaction(tx.add_dependencies, task=task)\",\n \"def get_dependent_tasks(self, task):\\n task_records = self._read_transaction(tx.get_dependent_tasks, task=task)\\n tuples = self._get_task_data_tuples(task_records)\\n return [_reconstruct_task(tup[0], tup[1], tup[2], tup[3], tup[4]) for tup in tuples]\",\n \"def get_task_info(self):\\n\\n print()\\n employee_name = self.task.get_employee_name()\\n task_name = self.task.get_task_name()\\n mins = self.task.get_time_spent()\\n notes = self.task.get_notes()\\n date = self.task.get_date()\\n\\n task = {\\n 'employee_name': employee_name,\\n 'task_name': task_name,\\n 'mins': mins,\\n 'notes': notes,\\n 'date': date\\n }\\n\\n return task\",\n \"def get_info(self, key: str) -> TaskInfo:\\n return self.task_graph.nodes[key][\\\"info\\\"]\",\n \"def create_execution_graph(self, env_config: EnvironmentConfig):\\n\\n # create a map of Nodes, mapping their order (basically their ID) to a Node for that resource\\n res_list = sorted(env_config.har_resources, key=lambda r: r.order)\\n self.node_map = {\\n res.order: Node(resource=res, priority=res.order, children=[]) for res in res_list if res.order == 0\\n }\\n\\n # The root is the node corresponding to the 0th order\\n self.root = self.node_map.get(0)\\n\\n # for each resource, unless it's the start resource, add it as a child of its initiator\\n for res in res_list:\\n if res != self.root.resource:\\n self.node_map[res.order] = Node(\\n resource=res, priority=res.order, children=[], parent=self.node_map.get(res.initiator, None)\\n )\\n self.node_map[res.initiator].children.append(self.node_map[res.order])\\n\\n # sort each child list by its order\\n self.url_to_node_map = {node.resource.url: node for node in self.node_map.values()}\\n for node in self.node_map.values():\\n node.children.sort(key=lambda n: n.resource.order)\",\n \"def nodes(self):\\n return self.sort_dict(self.trajectory_data)\",\n \"def read_graph_nodes(dict1,node_list):\\n\\t#print fname\\n\\t#dict1={}\\n\\t#with open(fname,'r') as fp:\\n\\t#\\tfor line in fp:\\n\\t#\\t\\tline=line.rstrip('\\\\n')\\n\\t#\\t\\tn_id,name=line.split('\\\\t')\\n\\t#\\t\\tdict1[int(n_id)]=name\\n\\thuman_readable_node_list=[]\\n\\tfor node in node_list:\\n\\t\\tif int(node) in dict1:\\n\\t\\t\\t#print 'found'\\n\\t\\t\\thuman_readable_node_list.append(dict1[node])\\n\\t\\telse:\\n\\t\\t\\thuman_readable_node_list.append('-1')\\n\\treturn human_readable_node_list\",\n \"def get_tasks_summary(self):\\n columns = [\\\"id\\\", \\\"name\\\", \\\"state\\\", \\\"warning\\\", \\\"warning_message\\\", \\\"parent_job\\\", \\\"tags\\\"]\\n \\n cur = self.conn.cursor()\\n cur.execute(\\\"SELECT \\\" + \\\", \\\".join(columns) + \\\" FROM tangerine;\\\")\\n self.conn.commit()\\n \\n return [Task([(column,) for column in columns], task, interpolate=False) for task in cur.fetchall()]\",\n \"def _update_tasks_from_jsons(self):\\n\\n tasks = {}\\n\\n for fn in listdir(SIMPLI_JSON_DIR):\\n fp = join(SIMPLI_JSON_DIR, fn)\\n\\n self._print('Loading task-specifying JSON {} ...'.format(fp))\\n\\n with open(fp) as f:\\n tasks_json = loads(reset_encoding(f.read()))\\n\\n # Load library path, which is common for all tasks in this JSON\\n library_path = tasks_json['library_path']\\n\\n # Load each task\\n for t in tasks_json['tasks']:\\n\\n # Split function path into library_name and function_name\\n function_path = t['function_path']\\n if '.' in function_path:\\n split = function_path.split('.')\\n library_name = '.'.join(split[:-1])\\n function_name = split[-1]\\n else:\\n raise ValueError(\\n 'function_path must be like: \\\\'file.function\\\\'.')\\n\\n # Task label is this task's UID; so no duplicates are allowed\\n label = t.get('label',\\n '{} (no task label)'.format(function_name))\\n\\n tasks[label] = {\\n 'library_path':\\n library_path,\\n 'library_name':\\n library_name,\\n 'function_name':\\n function_name,\\n 'description':\\n t.get('description', 'No description.'),\\n 'required_args':\\n self._process_args(t.get('required_args', [])),\\n 'default_args':\\n self._process_args(t.get('default_args', [])),\\n 'optional_args':\\n self._process_args(t.get('optional_args', [])),\\n 'returns':\\n self._process_returns(t.get('returns', [])),\\n }\\n\\n self._print('\\\\tLoaded {}: {}.'.format(label, tasks[label]))\\n\\n self._update_tasks(tasks)\\n return tasks\",\n \"def get_graph_and_nodes(path):\\n graph = hgraph.read_graph(path)\\n resource_nodes = hgraph.resource_nodes_no_data(graph)\\n blocked_nodes = hgraph.blocked_resources_no_data(graph, resource_nodes)\\n unblocked_nodes = list(set(resource_nodes) - set(blocked_nodes))\\n \\n return graph, resource_nodes, blocked_nodes, unblocked_nodes\",\n \"def get_links(self, node):\\n\\n # TODO: send help\\n\\n if not node.label:\\n return []\\n cls = node.__class__\\n\\n return [\\n (\\n e.__src_dst_assoc__,\\n psqlgraph.Node.get_subclass_named(e.__dst_class__).label,\\n )\\n for e in psqlgraph.Edge._get_edges_with_src(cls.__name__)\\n if hasattr(psqlgraph.Node.get_subclass_named(e.__dst_class__), \\\"project_id\\\")\\n ] + [\\n (\\n e.__dst_src_assoc__,\\n psqlgraph.Node.get_subclass_named(e.__src_class__).label,\\n )\\n for e in psqlgraph.Edge._get_edges_with_dst(cls.__name__)\\n if hasattr(psqlgraph.Node.get_subclass_named(e.__src_class__), \\\"project_id\\\")\\n ]\",\n \"def assemble_graph():\\n response = request.body.read().decode('utf-8')\\n body = json.loads(response)\\n stmts_json = body.get('statements')\\n stmts = stmts_from_json(stmts_json)\\n ga = GraphAssembler(stmts)\\n model_str = ga.make_model()\\n res = {'model': model_str}\\n return res\",\n \"def _jsonify_graph(nodes_list, relations_list,\\n n_elements=settings.PREVIEW_NODES, full=False):\\n nodes = {}\\n edges = []\\n partial_edges = []\\n partial_nodes = {}\\n partial_nodes_ids = []\\n nodes_display = {}\\n for node in nodes_list:\\n json_node = node.to_json()\\n nodes[node.display] = json_node\\n nodes_display[node.id] = node.display\\n for rel in relations_list:\\n source_id = rel.source.id\\n target_id = rel.target.id\\n if (source_id in nodes_display\\n and target_id in nodes_display):\\n edge = {\\n 'id': rel.id,\\n 'source': nodes_display[source_id],\\n 'type': rel.label_display,\\n 'target': nodes_display[target_id],\\n 'properties': rel.properties\\n }\\n edges.append(edge)\\n if len(partial_nodes_ids) <= n_elements:\\n if source_id not in partial_nodes_ids:\\n node_display = nodes_display[source_id]\\n partial_nodes[node_display] = nodes[node_display]\\n partial_nodes_ids.append(source_id)\\n if target_id not in partial_nodes_ids:\\n node_display = nodes_display[target_id]\\n partial_nodes[node_display] = nodes[node_display]\\n partial_nodes_ids.append(target_id)\\n if (source_id in partial_nodes_ids\\n and target_id in partial_nodes_ids):\\n partial_edges.append(edge)\\n return (nodes, edges, partial_nodes, partial_edges)\",\n \"def from_dict(cls, dikt) -> 'Tasks':\\n return util.deserialize_model(dikt, cls)\",\n \"def _get_task_data_tuples(self, task_records):\\n with self._driver.session() as session:\\n trecords = list(task_records)\\n hint_records = [session.read_transaction(tx.get_task_hints,\\n task_id=rec[\\\"t\\\"][\\\"id\\\"]) for rec in trecords]\\n req_records = [session.read_transaction(tx.get_task_requirements,\\n task_id=rec[\\\"t\\\"][\\\"id\\\"]) for rec in trecords]\\n input_records = [session.read_transaction(tx.get_task_inputs,\\n task_id=rec[\\\"t\\\"][\\\"id\\\"]) for rec in trecords]\\n output_records = [session.read_transaction(tx.get_task_outputs,\\n task_id=rec[\\\"t\\\"][\\\"id\\\"]) for rec in trecords]\\n\\n hints = [_reconstruct_hints(hint_record) for hint_record in hint_records]\\n reqs = [_reconstruct_requirements(req_record) for req_record in req_records]\\n inputs = [_reconstruct_task_inputs(input_record) for input_record in input_records]\\n outputs = [_reconstruct_task_outputs(output_record) for output_record in output_records]\\n\\n return list(zip(trecords, hints, reqs, inputs, outputs))\",\n \"def get_graph(self):\\n return json.dumps(self.graph.get_edgelist(), separators=(',',':'))\",\n \"def test_restore_with_dependencies(self):\\n task1 = self._remote_task()\\n task1['id'] = 1\\n task2 = self._remote_task()\\n task2['id'] = 2\\n task2['dependencies'] = [1]\\n\\n graph = self._restore_graph([task1, task2])\\n assert len(graph.tasks) == 2\\n assert graph._dependencies[2] == set([1])\",\n \"def json_to_graph(json_str: str) -> (dict, list, dict):\\n json_graph = GraphMessage.parse_raw(json_str)\\n node_dict = {}\\n inputs_to_resolve = []\\n\\n for node_message in json_graph.nodes:\\n name, node, inputs = get_node(node_message)\\n node = Node(name=name, node_type=node)\\n inputs_to_resolve.append((name, inputs))\\n node_dict[name] = node\\n\\n for name, inputs in inputs_to_resolve:\\n for variable, node_input in inputs.items():\\n try:\\n keys = node_input.split(\\\".\\\")\\n node_id, node_output = keys[0], keys[1]\\n node_dict[name].inputs[variable] = (node_id, node_output)\\n except (\\n KeyError,\\n IndexError,\\n AttributeError,\\n ): # If it can't resolve the node_input to some output, treat it as a default value\\n node_dict[name].items[variable] = node_input\\n\\n presets = {}\\n preset_list = []\\n for name, preset in json_graph.presets.items():\\n toggle = isinstance(preset, TogglePresetMessage)\\n preset_list.append((name, toggle))\\n presets[name] = preset\\n return node_dict, preset_list, presets\",\n \"def read_toml(filename: str) -> Graph:\\n\\n file_load = toml.load(filename)\\n contents = file_load[\\\"routers\\\"]\\n address = dict()\\n for item in contents:\\n address[item['address']] = item['name']\\n neighbors = contents[0]['neighbors']\\n address_copy = address.copy()\\n\\n for item in neighbors:\\n new_dict = item['address']\\n name = address_copy[new_dict]\\n item['name'] = name\\n for item in contents:\\n n = item['neighbors']\\n \\n\\n data = Graph() #Trying to add dictionary values to data so it can be called in find path function.\\n data.set_vertex(item['name'])\\n for i in n:\\n i['name'] = address[i['address']]\\n data.add_edge(item['name'], i['name'], i['cost'])\\n return data\",\n \"def get_graph(nodes, edges, deps, sec, containers):\\n root = RootResource()\\n nodes.append(root)\\n # connect accessible nodes to the web\\n for node in nodes:\\n if node.get_original_type() is not None and \\\\\\n node.get_original_type().is_accessible():\\n root.add_child(node)\\n\\n # Add children for each edge we collected before\\n for edge in edges:\\n parents = get_resource(nodes, edge['from'])\\n children = get_resource(nodes, edge['to'])\\n for node in parents:\\n for child in children:\\n node.add_child(child)\\n\\n # Same with dependencies\\n for dep in deps:\\n parents = get_resource(nodes, dep['from'])\\n children = get_resource(nodes, dep['to'])\\n for node in parents:\\n for child in children:\\n if node.get_origin() == TARGET and child.get_origin() == TARGET:\\n node.add_dependency(child)\\n\\n # Ensure containers only has Resource as keys and in values by creating more\\n # entries if necessary.\\n expanded_containers = {}\\n for container in containers:\\n for c in get_resource(nodes, container):\\n if not c in expanded_containers:\\n expanded_containers[c] = []\\n for x in containers[container]:\\n expanded_containers[c].extend(get_resource(nodes, x))\\n\\n # Same with sec\\n expanded_sec = {}\\n for security in sec:\\n for s in get_resource(nodes, security):\\n if not s in expanded_sec:\\n expanded_sec[s] = {'from': [], 'to': []}\\n for x in sec[security]['from']:\\n expanded_sec[s]['from'].extend(get_resource(nodes, x))\\n for x in sec[security]['to']:\\n expanded_sec[s]['to'].extend(get_resource(nodes, x))\\n\\n nodes = move_security_nodes(nodes, expanded_sec, expanded_containers)\\n nodes = move_container_nodes(nodes, expanded_containers)\\n return (nodes, root)\",\n \"def _CreateGraph(self):\\n self.nodes = []\\n self.edges = []\\n for i, r in self.airports.set_index('airport_id').iterrows():\\n self.nodes.append((i,r.to_dict()))\\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\\n self.edges.append((i[0],i[1],r.to_dict()))\\n # print('node ex: {}'.format(self.nodes[0]))\\n # print('edge ex: {}'.format(self.edges[0]))\\n\\n self.graph = self._CreateAdjacencyListGraph()\",\n \"def graph (self, next_nodes, query):\\n programs = self.type_graph.get_transitions (query)\\n result = []\\n for program in programs:\\n result += self.graph_inner (next_nodes, program)\\n return result\",\n \"def convert(data):\\n NODES, EDGES = {}, {}\\n\\n for duid, entity in data['entity'].iteritems():\\n if entity['entityType'] == 'Trigger':\\n continue\\n if Node.add(duid, NODES):\\n NODES[duid].Label = entity['entityText']\\n NODES[duid].source = entity['source']\\n NODES[duid].json = json.dumps(entity)\\n\\n for duid, relation in data['relation'].iteritems():\\n duid = relation['duid']\\n sn, tn = None, None\\n if relation['source'] == 'miRTex':\\n sn, tn = tm.mirtex.cytoscape_relation_args(relation)\\n # elif relation['source'] == 'RLIMS-P':\\n # sn, tn = tm.rlims.cytoscape_relation_args(relation)\\n if sn not in NODES or tn not in NODES:\\n continue\\n if Edge.add(duid, sn, tn, relation['relationType'], EDGES):\\n EDGES[duid].json = json.dumps(relation)\\n\\n return dump_network(NODES, EDGES)\",\n \"def create_node2edges_on2freq_grid(self):\\n trip_id2model = pickle.load(open('pickles/trip_id2model.pickle','rb'))\\n old_trip_id = -1\\n model = trip_id2model[1]\\n sub_x = 5\\n sub_y = 5\\n node2edges_on2sub_grid2points = {}\\n for line in self.lines:\\n trip_id,lat,lon = normalize_simple(line)\\n if trip_id != old_trip_id:\\n #print trip_id\\n model = trip_id2model[trip_id]\\n old_trip_id = trip_id\\n node = self.gps_to_node(lat,lon)\\n if node == -1:\\n continue\\n #print \\\"pushed through\\\"\\n incident_edges = self.incident_edges(node)\\n edges_on = []\\n for edge in incident_edges:\\n if model[edge] == 1:\\n edges_on.append(edge)\\n edges_on.sort()\\n edges_on = tuple(edges_on)\\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon(self.node_to_coords(node))\\n\\n sub_row,sub_col = gen_gps_to_coords(lat,lon,sub_x,sub_y,min_lat,max_lat,min_lon,max_lon)\\n sub_tuple = (sub_row,sub_col)\\n if node not in node2edges_on2sub_grid2points:\\n node2edges_on2sub_grid2points[node] = {}\\n edges_on2sub_grid2points = node2edges_on2sub_grid2points[node]\\n if edges_on not in edges_on2sub_grid2points:\\n edges_on2sub_grid2points[edges_on] = defaultdict(list)\\n sub_grid2points = edges_on2sub_grid2points[edges_on]\\n points = sub_grid2points[sub_tuple]\\n node2edges_on2sub_grid2points[node][edges_on][sub_tuple].append([lat,lon])\\n #points.append([lat,lon])\\n\\n print node2edges_on2sub_grid2points.keys()\\n print node2edges_on2sub_grid2points[2].keys()\\n print node2edges_on2sub_grid2points[2][(2,3)].keys()\\n \\n node2edges_on2median = {}\\n for node in node2edges_on2sub_grid2points:\\n print node\\n edges_on2sub_grid2points = node2edges_on2sub_grid2points[node]\\n node2edges_on2median[node] = {}\\n for edges_on in edges_on2sub_grid2points:\\n sub_grid2points = edges_on2sub_grid2points[edges_on]\\n best_spot = (-1,-1)\\n best_score = 0\\n for spot in sub_grid2points:\\n score = len(sub_grid2points[spot])\\n if score > best_score:\\n best_score = score\\n best_spot = spot\\n node2edges_on2median[node][edges_on] = list_median(sub_grid2points[spot])\\n \\n with open('pickles/node2edges_on2median-%d-%d.pickle' % (self.rows,self.cols),'wb') as output:\\n pickle.dump(node2edges_on2median,output)\",\n \"def get_running_task_dicts(tasks):\\n running_task_dicts = []\\n with database.engine.begin() as connection:\\n for task in tasks:\\n print(json.loads(task.meta))\\n job = Job.fetch(task.id, connection=redis_conn)\\n project = connection.execute(select([sqlalchemy.text(\\n '*')]).select_from(models.projects).where(models.projects.c.project_id == task.project_id)).first()\\n task_dict = dict(id=task.id, name=task.name, description=task.description,\\n complete=task.complete, result=task.result, progress=task.get_progress(), project_id=task.project_id)\\n task_dict['meta'] = json.loads(\\n task.meta) if task.meta is not None else {}\\n\\n if job:\\n task_dict['status'] = job.get_status()\\n # task_dict['started_at'] = datetime.datetime.fromtimestamp(\\n # task_dict['meta']['scheduled_at'])\\n # print('scheduled_at: {}'.format(task_dict['started_at']))\\n if project:\\n task_dict['project_name'] = project['name']\\n running_task_dicts.append(task_dict)\\n return running_task_dicts\",\n \"def prepare_graph(label, nodes, edges, graphID):\\n features = {'label': label}\\n\\n G = nx.DiGraph()\\n nodes[\\\"id\\\"] = nodes[\\\"id\\\"].apply(lambda x : str(x))\\n features['num_nodes'] = nodes.shape[0]\\n op_node = None\\n times = []\\n friends = []\\n followers = []\\n for index, row in nodes.iterrows():\\n G.add_node(row['id'], time=row['time'], friends=row['friends'], followers = row['followers'])\\n times.append(row['time'])\\n friends.append(2**row['friends'])\\n followers.append(2**row['followers'])\\n if row['time'] == 0:\\n features['poster_friend_cnt'] = 2**row['friends']\\n features['poster_follower_cnt'] = 2**row['followers']\\n tweeter_id = row['id']\\n op_node = row['id']\\n features['avg_time'] = np.mean(times)\\n features['avg_friends'] = np.mean(friends)\\n features['avg_followers'] = np.mean(followers)\\n features['max_followers'] = max(followers)\\n features['max_friends'] = max(friends)\\n features['friends_25th_percentile'] = np.percentile(friends, 25)\\n features['friends_75th_percentile'] = np.percentile(friends, 75)\\n features['followers_25th_percentile'] = np.percentile(followers, 25)\\n features['followers_75th_percentile'] = np.percentile(followers, 75)\\n node_list = []\\n edge_count = 0\\n for pair in edges:\\n node1, node2 = pair.split()[0], pair.split()[1]\\n node_list.append(node1)\\n node_list.append(node2)\\n G.add_edge(node1, node2)\\n edge_count += 1\\n features['num_edges'] = edge_count\\n sum_users_without_followers = sum([1 for (node, val) in G.in_degree() if val==0])\\n features['ratio_users_w/out_followers'] = sum_users_without_followers / len(G.nodes)\\n features['num_connected_components'] = nx.number_strongly_connected_components(G)\\n features['number_of_OPs_followers_who_retweeted'] = G.in_degree(tweeter_id)\\n features['percentage_of_OPs_followers_who_retweeted'] = G.in_degree(tweeter_id) / features['poster_follower_cnt']\\n features['avg_clustering'] = nx.average_clustering(G)\\n features['op_clustering'] = nx.clustering(G,op_node)\\n features['transitivity'] = nx.transitivity(G)\\n node_list = list(set(node_list))\\n features['nodeID_list'] = np.array(node_list)\\n features['graph_id'] = graphID\\n return features, G\",\n \"def generate_dagre_data(g: Graph) -> Dict:\\n\\n add_benchmark = graph_has_benchmark(g)\\n\\n def _node_to_dict(node: Node) -> Dict:\\n if node in g.inputs:\\n node_type = \\\"input\\\"\\n elif node in g.outputs:\\n node_type = \\\"output\\\"\\n else:\\n node_type = None\\n\\n return {\\n **node.to_dict(),\\n \\\"name\\\": node.name_str(),\\n \\\"type\\\": node_type,\\n \\\"times\\\": node.benchmark.get(\\\"self_eval_times\\\") if add_benchmark else None\\n }\\n\\n graph = {\\n \\\"nodes\\\": [],\\n \\\"edges\\\": []\\n }\\n\\n visited = []\\n todo = g.inputs + g.outputs\\n\\n while len(todo) > 0:\\n node = todo.pop()\\n graph[\\\"nodes\\\"].append(node)\\n visited.append(node)\\n succ = [n for n in node.successors if n not in visited]\\n graph[\\\"edges\\\"] += [(node, s) for s in succ]\\n pred = [n for n in node.predecessors if n not in visited]\\n graph[\\\"edges\\\"] += [(p, node) for p in pred]\\n todo += (succ + pred)\\n\\n graph[\\\"nodes\\\"] = [_node_to_dict(n) for n in graph[\\\"nodes\\\"]]\\n graph[\\\"edges\\\"] = [(x.name_str(), y.name_str()) for x, y in graph[\\\"edges\\\"]]\\n\\n return graph\",\n \"def create_tasks_set(self): # noqa\\n task0 = self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 0', 'status': 'active',\\n 'priority': 'high', 'parent_id': None})\\n task1 = self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 1', 'status': 'active',\\n 'priority': 'medium', 'parent_id': task0.json['task_id']})\\n task2 = self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 2', 'status': 'done',\\n 'priority': 'veryhigh', 'parent_id': None})\\n task3 = self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 3', 'status': 'done',\\n 'priority': 'high', 'parent_id': task1.json['task_id']})\\n task4 = self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 4', 'status': 'active',\\n 'priority': 'medium', 'parent_id': task1.json['task_id']})\\n self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 5', 'status': 'active',\\n 'priority': 'medium', 'parent_id': task1.json['task_id']})\\n self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 6', 'status': 'active',\\n 'priority': 'high', 'parent_id': task0.json['task_id']})\\n self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 7', 'status': 'done',\\n 'priority': 'medium', 'parent_id': task2.json['task_id']})\\n self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 8', 'status': 'done',\\n 'priority': 'medium', 'parent_id': task3.json['task_id']})\\n self.client.post(f\\\"/api/v1/tasks/{self.todolist.todolist_id}\\\",\\n json={'label': 'Task 9', 'status': 'active',\\n 'priority': 'medium', 'parent_id': task4.json['task_id']})\\n return [task0.json, task1.json, task2.json]\",\n \"def create_nodes(self):\",\n \"def add_task(bpmn, counts, label):\\r\\n from pm4py.objects.bpmn.bpmn_graph import BPMN\\r\\n task = BPMN.Task(name=label)\\r\\n bpmn.add_node(task)\\r\\n return bpmn, task, counts\",\n \"def map_tree(a_task: Task, tree: Any) -> Any:\\n\\n def map_node(node):\\n if isinstance(node, list):\\n return [map_node(child) for child in node]\\n else:\\n return a_task(node)\\n\\n return map_node(tree)\",\n \"def build_nodes(graph):\\n\\n acc = {}\\n for k, v in graph.items():\\n if callable(v):\\n acc[k] = util.fninfo(v)\\n else:\\n acc[k] = v\\n\\n return acc\",\n \"def details(thread, path):\\n parts = [int(x) for x in path.split('.')]\\n node = DATA.threads[thread].tree[parts[0]]\\n for part in parts[1:]:\\n node = node.children[int(part)]\\n result = formatNode(node)\\n result['children'] = [formatNode(child) for child in node.children]\\n return json.dumps(result)\",\n \"def get_nodes():\\n\\n host = str(request.args['host'])\\n days = float(request.args['days'])\\n\\n to_time = int(time.time())\\n to_day = int(time.strftime('%Y%m%d', time.gmtime(float(to_time))))\\n from_time = to_time-int(days*24*60*60)\\n from_day = int(time.strftime('%Y%m%d', time.gmtime(float(from_time))))\\n day_in=''\\n for x in range(from_day, to_day+1):\\n day_in = day_in + ',' + str(x)\\n day_in=re.sub(r\\\"^,\\\", \\\"\\\", day_in)\\n day_in=re.sub(r\\\",$\\\", \\\"\\\", day_in)\\n query = \\\"SELECT * FROM metrics WHERE host='\\\" + str(host) + \\\"' and date IN (\\\"\\n query = query + str(day_in) + \\\") and time>=\\\" + str(int(int(from_time)*1000)) + \\\" and time<=\\\"\\n query = query + str(int(int(to_time)*1000)) + \\\" ALLOW FILTERING\\\"\\n rows = session.execute(query);\\n reply={}\\n last_value={}\\n for r in rows:\\n if str(r.host) not in reply:\\n reply[r.host]={}\\n last_value[r.host]={}\\n if str(r.metric) not in reply[r.host]:\\n reply[r.host][r.metric]=[]\\n last_value[r.host][r.metric]=int(r.value)\\n continue\\n real_value = (r.value-last_value[r.host][r.metric])/60\\n\\tlast_value[r.host][r.metric]=int(r.value)\\n reply[str(r.host)][r.metric].append({ 'value': int(real_value),\\n 'time': str(r.time) })\\n return json.dumps(reply)\",\n \"def insert_tasks(note, exer_tasks):\\n for i in range(len(exer_tasks)):\\n if 'graphml' in exer_tasks[i]:\\n insert_single_graph_task(note,exer_tasks[i],i)\\n else:\\n insert_single_task(note,exer_tasks[i],i)\\n return\",\n \"def json(cls):\\n\\n nodes = []\\n for node in cls._nodes:\\n nodes.append(node.json())\\n return nodes\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6246238","0.6081832","0.6065744","0.6018206","0.6018206","0.58417726","0.5779049","0.5658437","0.5583271","0.55294436","0.55002636","0.5468481","0.54389864","0.53757447","0.5373088","0.53719306","0.53705764","0.5357756","0.53515995","0.5348182","0.53369325","0.5334147","0.52959776","0.52913433","0.528274","0.5280223","0.52750325","0.5270374","0.5257569","0.5239859","0.5208319","0.52078116","0.5189643","0.5178198","0.5167628","0.5167387","0.5151315","0.51511115","0.5145502","0.51435953","0.5136953","0.5127948","0.51241124","0.5118109","0.5117338","0.5115179","0.5115179","0.5102179","0.50936097","0.5090208","0.5088249","0.50746495","0.50675994","0.5066169","0.5056744","0.5042978","0.50381744","0.5033902","0.5033873","0.50317144","0.502833","0.50272775","0.50267774","0.5018332","0.5006327","0.49752268","0.49737445","0.4964643","0.4964188","0.49624938","0.49623123","0.4961709","0.49584162","0.49480295","0.4932669","0.49322236","0.49298033","0.49160913","0.4910687","0.4910656","0.49048573","0.4904156","0.49008307","0.48971587","0.48970821","0.48951364","0.4894822","0.48927975","0.48923424","0.48885363","0.48865116","0.48834312","0.4878847","0.48705173","0.48690173","0.4862953","0.48626146","0.48600507","0.48596045","0.4855556"],"string":"[\n \"0.6246238\",\n \"0.6081832\",\n \"0.6065744\",\n \"0.6018206\",\n \"0.6018206\",\n \"0.58417726\",\n \"0.5779049\",\n \"0.5658437\",\n \"0.5583271\",\n \"0.55294436\",\n \"0.55002636\",\n \"0.5468481\",\n \"0.54389864\",\n \"0.53757447\",\n \"0.5373088\",\n \"0.53719306\",\n \"0.53705764\",\n \"0.5357756\",\n \"0.53515995\",\n \"0.5348182\",\n \"0.53369325\",\n \"0.5334147\",\n \"0.52959776\",\n \"0.52913433\",\n \"0.528274\",\n \"0.5280223\",\n \"0.52750325\",\n \"0.5270374\",\n \"0.5257569\",\n \"0.5239859\",\n \"0.5208319\",\n \"0.52078116\",\n \"0.5189643\",\n \"0.5178198\",\n \"0.5167628\",\n \"0.5167387\",\n \"0.5151315\",\n \"0.51511115\",\n \"0.5145502\",\n \"0.51435953\",\n \"0.5136953\",\n \"0.5127948\",\n \"0.51241124\",\n \"0.5118109\",\n \"0.5117338\",\n \"0.5115179\",\n \"0.5115179\",\n \"0.5102179\",\n \"0.50936097\",\n \"0.5090208\",\n \"0.5088249\",\n \"0.50746495\",\n \"0.50675994\",\n \"0.5066169\",\n \"0.5056744\",\n \"0.5042978\",\n \"0.50381744\",\n \"0.5033902\",\n \"0.5033873\",\n \"0.50317144\",\n \"0.502833\",\n \"0.50272775\",\n \"0.50267774\",\n \"0.5018332\",\n \"0.5006327\",\n \"0.49752268\",\n \"0.49737445\",\n \"0.4964643\",\n \"0.4964188\",\n \"0.49624938\",\n \"0.49623123\",\n \"0.4961709\",\n \"0.49584162\",\n \"0.49480295\",\n \"0.4932669\",\n \"0.49322236\",\n \"0.49298033\",\n \"0.49160913\",\n \"0.4910687\",\n \"0.4910656\",\n \"0.49048573\",\n \"0.4904156\",\n \"0.49008307\",\n \"0.48971587\",\n \"0.48970821\",\n \"0.48951364\",\n \"0.4894822\",\n \"0.48927975\",\n \"0.48923424\",\n \"0.48885363\",\n \"0.48865116\",\n \"0.48834312\",\n \"0.4878847\",\n \"0.48705173\",\n \"0.48690173\",\n \"0.4862953\",\n \"0.48626146\",\n \"0.48600507\",\n \"0.48596045\",\n \"0.4855556\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71598744"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77481,"cells":{"query":{"kind":"string","value":"receive and build taskgraph"},"document":{"kind":"string","value":"def tasks_recv():\n recv_json = request.get_json()\n build_taskgraph(recv_json)\n return 'ok'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def build_taskgraph(tasks):\n global taskgraph\n for task in tasks:\n if not task['id'] in taskgraph:\n taskgraph.add_node(task[\"id\"])\n data = str(task[\"DATA\"])\n tr = TaskResources(ram=int(task['RAM']), cpu=int(\n task['CPU']), hdd=int(task['HDD']))\n taskgraph.node[task[\"id\"]] = [tr, data]\n for dest in task[\"dest\"]:\n taskgraph.add_edge(task[\"id\"], dest[\"id\"])\n\n return taskgraph","def task():","def tasks():","def generate_tasks(self, task):","def build() -> List[asyncio.Task]:","def create_task():","def task(self):","def task(self):","def build_curriculum(self,target_task,groups,setOfTasks):\n connections = [] #list of graph connections\n graph = Graph([],directed = True)\n #Intra-group transfer\n for g in groups:\n group = groups[g]\n #For all tasks inside each group\n for i in range(len(group)):\n currentTask = group[i][0]\n maxTransferPot = -float('inf')\n argMaxTask = None\n # get task with maximum potential\n for j in range(i+1,len(group)):\n testTask = group[j][0]\n if testTask != currentTask:\n pot = self.transfer_potential(testTask,currentTask,graph.list_children(currentTask))\n if pot > maxTransferPot:\n maxTransferPot = pot\n argMaxTask = testTask\n if argMaxTask != None and maxTransferPot > self.thresholdTask:\n # Add connection\n connections.append((argMaxTask,currentTask))\n graph.add(argMaxTask,currentTask)\n \n #Creating graph structure\n graph = Graph(connections,directed = True)\n \n groupKeys = list(groups.keys())\n #Inter-group transfer\n for i in range(len(groupKeys)):\n #Get task features of the first task in the group \n #(this is equal for all tasks inside it)\n g = groups[groupKeys[i]]\n gFeatures = g[0][0].task_features()\n #For all groups that have task features contained in g:\n for j in range(i+1,len(groupKeys)):\n gPrime = groups[groupKeys[j]]\n gPrimeFeatures = gPrime[0][0].task_features()\n \n if task.is_contained(gPrimeFeatures,gFeatures):\n #For all tasks in g, get the argmax task in gPrime and\n #Possibly creates a link\n for gTaskAndPot in g:\n gTask = gTaskAndPot[0]\n maxTransferPot = -float('inf')\n argMaxTask = None\n for gPrimeTaskAndPot in gPrime:\n gPrimeTask = gPrimeTaskAndPot[0]\n pot = self.transfer_potential(gPrimeTask,gTask,graph.list_children(currentTask))\n if pot > maxTransferPot:\n maxTransferPot = pot\n argMaxTask = gPrimeTask\n if argMaxTask != None and maxTransferPot > self.thresholdTask:\n # Add connection\n graph.add(argMaxTask,gTask)\n connections.append((argMaxTask,gTask))\n \n #For all tasks with zero degree, a connection to the target-task is added\n for currentTask in setOfTasks:\n if graph.out_degree(currentTask) == 0:\n graph.add(currentTask,target_task)\n #The task graph is completed\n self.taskGraph = graph","def _build_graph(self):\n pass","def build_graph(self):\n pass","def task_gen(self):\n pass","def run(self):\n\n self._build_dependency_graph()","def task_stagnant(task):","def defineTasks(self,partition):\n recv_slots = partition.recvSlices()\n strm_slots = partition.streamSlices()\n recvNodes = partition.recvNodesFromSlots()\n strmNodes = partition.streamNodesFromSlots()\n opt = '/'+self.manager.hostName()+'/'+partition.manager.name()+'/'+partition.name+'/'\n cl0 = '/Class0'+opt\n cl1 = '/Class1'+opt\n\n partition.setDataSources([])\n tasks = []\n pn = self.partitionName()\n print '---------------------- Partition name is:',pn\n for i in xrange(len(recv_slots)):\n slot = recv_slots[i]\n node = slot[:slot.find(':')]\n sub_farm = 'SF%02d'%(i,)\n short_name = sub_farm+'_SND' # Keep this name to ensure storageMon is working!\n task = pn+'_'+node+'_'+short_name\n tasks.append(node+'/'+task+'/'+short_name+'/RecStorageSend'+cl1+'(\"'+sub_farm+'\",'+str(i)+',)')\n partition.setRecvSenders(tasks)\n tasks = []\n for i in xrange(len(strm_slots)):\n slot = strm_slots[i]\n node = slot[:slot.find(':')]\n sub_farm = 'SF%02d'%(i,)\n short_name = sub_farm+'_HLT' # Keep this name to ensure storageMon is working!\n task = pn+'_'+node+'_'+short_name\n tasks.append(node+'/'+task+'/'+short_name+'/RecStorageRecv'+cl1+'(\"'+sub_farm+'\",'+str(i)+',)')\n partition.setStreamReceivers(tasks)\n cnt = 0\n tasks = []\n infra = []\n for j in recvNodes:\n for itm in self.rcvInfra.data:\n i,cl=itm.split('/')\n infra.append(j+'/'+pn+'_'+j+'_'+i+'/'+i+'/'+i+'/'+cl+opt+'(\"'+str(cnt)+'\",)')\n cnt = cnt + 1\n partition.setRecvInfrastructure(infra)\n partition.setRecvReceivers(tasks)\n cnt = 0\n tasks = []\n infra = []\n for j in strmNodes:\n for itm in self.strInfra.data:\n i,cl=itm.split('/')\n infra.append(j+'/'+pn+'_'+j+'_'+i+'/'+i+'/'+i+'/'+cl+opt+'(\"'+str(cnt)+'\",)')\n cnt = cnt + 1\n partition.setStreamInfrastructure(infra)\n partition.setStreamSenders(tasks)\n if partition.saveTasks():\n tasks = partition.collectTasks(tasks={},with_data_sources=0)\n return tasks\n return None","def _build(self, replace=None, profile=False):\n self.__node_dict.clear()\n replace = dict() if replace is None else replace\n\n # check if there are item in the replace that is not in the graph\n task_ids = set([task[TaskSpecSchema.task_id] for task in self])\n for rkey in replace.keys():\n if rkey not in task_ids:\n warnings.warn(\n 'Replace task-id {} not found in task-graph'.format(rkey),\n RuntimeWarning)\n\n # instantiate node objects\n for task in self:\n task_id = task[TaskSpecSchema.task_id]\n nodetype = task[TaskSpecSchema.node_type]\n nodetype = nodetype if isinstance(nodetype, str) else \\\n nodetype.__name__\n if nodetype == OUTPUT_TYPE:\n output_task = task\n node = get_node_obj(output_task, tgraph_mixin=True)\n else:\n node = get_node_obj(task, replace.get(task_id), profile,\n tgraph_mixin=True)\n self.__node_dict[task_id] = node\n\n # build the graph\n for task_id in self.__node_dict:\n node = self.__node_dict[task_id]\n task_inputs = node._task_obj[TaskSpecSchema.inputs]\n for iport in task_inputs:\n # node_inputs should be a dict with entries:\n # {iport: taskid.oport}\n input_task = task_inputs[iport].split('.')\n dst_port = iport\n\n input_id = input_task[0]\n # src_port = input_task[1] if len(input_task) > 1 else None\n src_port = input_task[1]\n\n try:\n input_node = self.__node_dict[input_id]\n except KeyError:\n raise LookupError(\n 'Missing task \"{}\". Add task spec to TaskGraph.'\n .format(input_id))\n\n node.inputs.append({\n 'from_node': input_node,\n 'from_port': src_port,\n 'to_port': dst_port\n })\n # input_node.outputs.append(node)\n input_node.outputs.append({\n 'to_node': node,\n 'to_port': dst_port,\n 'from_port': src_port\n })","def invoke(self, msg, req):\n node = Node.create()\n node.acquire_lock()\n\n if msg.name == 'forward':\n try:\n with node.graph.as_default():\n if node.num_devices == 5:\n output, name = Model_5.forward(req['input'], req['next'], node)\n Thread(target=self.send, args=(output, name, req['tag'])).start()\n elif node.num_devices == 6:\n output, name = Model_6.forward(req['input'], req['next'], node)\n Thread(target=self.send, args=(output, name, req['tag'])).start()\n elif node.num_devices == 7:\n output, name = Model_7.forward(req['input'], req['next'], node)\n Thread(target=self.send, args=(output, name, req['tag'])).start()\n elif node.num_devices == 8:\n output, name = Model_8.forward(req['input'], req['next'], node)\n Thread(target=self.send, args=(output, name, req['tag'])).start()\n\n node.release_lock()\n return\n\n except Exception, e:\n node.log('Error', e.message)\n elif msg.name == 'update':\n \"\"\"update this node's task configuration,based on the received massage \"\"\"\n try:\n node.num_devices = req['num_devices']\n available_ip = req['available_ip']\n\n update_ip(get_file(node.num_devices), available_ip)\n load_ip(node)\n\n node.release_lock()\n return\n\n except Exception, e:\n node.log('Error', e.message)\n\n else:\n raise schema.AvroException('unexpected message:', msg.getname())","def get_graph(self, with_fix=False):\n nodes = self.get_tasks()\n if with_fix:\n for n in nodes:\n n.fix_arguments()\n deps = self.get_dependencies()\n graph = nx.DiGraph()\n graph.add_nodes_from(nodes)\n graph.add_edges_from( [d.edge() for d in deps] )\n return graph","def get_initial_graph(n_in, code_spec, n_out):\n G = nx.MultiDiGraph()\n\n G.add_node(\"source\", label=\"SOURCE\", shape=\"cylinder\", color=\"gold\")\n G.add_node(\"sink\", label=\"SINK\", shape=\"cylinder\", color=\"gold\")\n\n for task in tasks:\n task_source_key = f\"{task.name}-source\"\n G.add_node(task_source_key, label=task_source_key, shape=\"cylinder\", color=\"gold\")\n G.add_edge(\"source\", task_source_key)\n task_sink_key = f\"{task.name}-sink\"\n G.add_node(task_sink_key, label=task_sink_key, shape=\"cylinder\", color=\"gold\")\n G.add_edge(task_sink_key, \"sink\")\n for i, input in enumerate(task.inputs):\n input_key = f\"{task.name}-input-{i}\"\n G.add_node(input_key, label=input_key, shape=\"circle\", color=\"blue\",\n node_type=\"input\", input=input)\n G.add_edge(task_source_key, input_key)\n encoder_key = f\"{input_key}-encoder\"\n G.add_node(encoder_key, label=encoder_key, shape=\"diamond\", color=\"black\",\n node_type=\"encoder\", input=input, output=CODE)\n G.add_edge(input_key, encoder_key)\n for box_number in range(initial_boxes):\n if box_number not in G.nodes():\n G.add_node(box_number, label=box_number, shape=\"square\", color=\"black\")\n G.add_edge(encoder_key, box_number)\n for o, output in enumerate(task.outputs):\n output_key = f\"{task.name}-output-{o}\"\n G.add_node(output_key, label=output_key, shape=\"triangle\", color=\"red\",\n node_type=\"output\", output=output)\n G.add_edge(output_key, task_sink_key)\n decoder_key = f\"{output_key}-decoder\"\n G.add_node(decoder_key, label=decoder_key, shape=\"diamond\", color=\"black\",\n node_type=\"decoder\", input=CODE, output=output)\n for box_number in range(initial_boxes):\n G.add_edge(box_number, decoder_key)\n G.add_edge(decoder_key, output_key)\n return G","def collect(self):\n while True:\n if not self._queue.empty():\n message = self._queue.get()\n self.working_on = message['job_type']\n else:\n break\n logging.info(\"Popped off message: {}\\n\".format(str(message)))\n\n if message['job_type'] == 'STOP':\n break\n\n if message['job_type'] != 'MAINTAIN' and message['job_type'] != 'UPDATE':\n raise ValueError('{} is not a recognized task type'.format(message['job_type']))\n pass\n\n # Query all repos with repo url of given task\n repoUrlSQL = s.sql.text(\"\"\"\n SELECT min(repo_id) as repo_id FROM repo WHERE repo_git = '{}'\n \"\"\".format(message['given']['github_url']))\n repo_id = int(pd.read_sql(repoUrlSQL, self.db, params={}).iloc[0]['repo_id'])\n\n try:\n if message['models'][0] == 'pull_requests':\n self.pull_requests_model(message, repo_id)\n elif message['models'][0] == 'pull_request_commits':\n self.pull_request_commits_model(message, repo_id)\n elif message['models'][0] == 'pull_request_files':\n self.pull_requests_graphql(message, repo_id)\n except Exception as e:\n register_task_failure(self, message, repo_id, e)\n pass","def execute(self, g, inputs):\n \n if self._started:\n raise ValueError(\"Traverser can only be started once.\")\n self._started = True\n \n self._graph = graph.Graph()\n self._graph = graphdecorator.DataGraphDecorator(self._graph)\n self._graph = graphdecorator.RefineDecorator(self._graph)\n self._graph = graphdecorator.ReadyDecorator(self._graph)\n \n # copy the graph into g\n for tick in g.get_all_ticks():\n self._graph.add_task(tick, g.get_task(tick), g.get_task_properties(tick))\n for tick in g.get_all_ticks() + [graph.FINAL_TICK]:\n for source, dest in g.get_in_connections(tick):\n self._graph.connect(source, dest)\n\n # ingest graph inputs\n self._graph.set_output_data(graph.START_TICK, inputs)\n \n self._iterate()\n \n return self._result","def build_graph(self, graph, inst_name, port_nets):\n return","def collect(self):\n while True:\n if not self._queue.empty():\n message = self._queue.get()\n self.working_on = message['job_type']\n else:\n break\n logging.info(\"Popped off message: {}\\n\".format(str(message)))\n\n if message['job_type'] == 'STOP':\n break\n\n if message['job_type'] != 'MAINTAIN' and message['job_type'] != 'UPDATE':\n raise ValueError('{} is not a recognized task type'.format(message['job_type']))\n pass\n\n \"\"\" Query all repos with repo url of given task \"\"\"\n repoUrlSQL = s.sql.text(\"\"\"\n SELECT min(repo_id) as repo_id FROM repo WHERE repo_git = '{}'\n \"\"\".format(message['given']['git_url']))\n repo_id = int(pd.read_sql(repoUrlSQL, self.db, params={}).iloc[0]['repo_id'])\n\n try:\n if message['models'][0] == 'badges':\n self.badges_model(message, repo_id)\n except Exception as e:\n register_task_failure(self, logging, message, repo_id, e)\n pass","def build_graph(self):\n raise NotImplementedError","def _build_network(self):\n pass","def main():\n \n desc = \"\"\"TODO: ... ask me later! I'm on a deadline! ...\"\"\"\n \n parser = argparse.ArgumentParser(description=desc)\n \n parser.add_argument('config_file', type=str,\n help=\"\"\"Path to a yaml formatted config file containing setup options for the graph.\"\"\")\n \n args = parser.parse_args()\n \n yopts = bunchify(yaml.load(open(args.config_file,'rU')))\n \n parser_list = setup_parsers(yopts)\n \n gBuilder = GraphBuilder(parser_list)\n gBuilder.populate_registries()\n gHandler = gBuilder.map_registries_to_graph()\n\n # init metric objs, RelHandler and Vote Handler\n metrics = init_metrics()\n relHandler = RelationsHandler(metrics)\n voteHandler = VoteHandler(gHandler.graph)\n voteHandler.set_vote_types(relHandler.get_vote_types(),weight_by='branch_length')\n \n gHandler.install_metric_handlers(rel_hndler=relHandler,vote_hndlr=voteHandler)\n gHandler.measure_relations()\n \n # dump built construct as pickle\n cPickle.dump(gHandler,open(yopts.outputs.graph_pickle,'w'))","def octopus_tasks(self, msg, args):\r\n self.tasks.send_tasks(msg, args)","def task(ctx, config):\n pass","def task1(self):\n \n pass","def task_generate_tasks():\n \n yield {\n 'basename': 'generate_tasks',\n 'name': None,\n # 'doc': 'docs for X',\n 'watch': ['trains/'],\n 'task_dep': ['create_folders'],\n }\n \n for root, dirs, files in os.walk('trains/',topdown=False):\n for f in files:\n #print(f)\n yield template_train_model(os.path.join(root,f))","def task():\n pass","def task():\n pass","def build_graph(self):\n self._build_model()\n if self.mode == 'train':\n self._build_train_op()","def task2(self):\n\n pass","def collect_tasks_fn(\n tn: TraversalNode, data: Dict[CollectionAddress, GraphTask]\n ) -> None:\n if not tn.is_root_node():\n data[tn.address] = GraphTask(tn, resources)","def collect_tasks_fn(\n tn: TraversalNode, data: Dict[CollectionAddress, GraphTask]\n ) -> None:\n if not tn.is_root_node():\n data[tn.address] = GraphTask(tn, resources)","def gen_graph(self):","def build(self, graph):\n self.results = None\n\n self.graph = graph\n\n self.__build_nodes()\n self.__build_edges()","def _build_computation_graph(self):\n raise NotImplementedError","def add_statistics_build_tasks(self, task_graph: TaskGraph,\n **kwargs: Any) -> list[Task]:\n raise NotImplementedError()","def create_inference_tasks(task_queue, image_layer_path, convnet_path, \n mask_layer_path, output_layer_path, output_block_start, output_block_size, \n grid_size, patch_size, patch_overlap, cropping_margin_size,\n output_key='output', num_output_channels=3, \n image_mip=1, output_mip=1, mask_mip=3):\n for z in tqdm(range(grid_size[0]), desc='z loop'):\n for y in range(grid_size[1]):\n for x in range(grid_size[2]):\n output_offset = tuple(s+x*b for (s, x, b) in \n zip(output_block_start, (z, y, x), \n output_block_size))\n task = InferenceTask(\n image_layer_path=image_layer_path,\n convnet_path=convnet_path,\n mask_layer_path=mask_layer_path,\n output_layer_path=output_layer_path,\n output_offset=output_offset,\n output_shape=output_block_size,\n patch_size=patch_size, \n patch_overlap=patch_overlap,\n cropping_margin_size=cropping_margin_size,\n output_key=output_key,\n num_output_channels=num_output_channels,\n image_mip=image_mip,\n output_mip=output_mip,\n mask_mip=mask_mip\n )\n task_queue.insert(task)\n task_queue.wait('Uploading InferenceTasks')\n\n vol = CloudVolume(output_layer_path, mip=output_mip)\n vol.provenance.processing.append({\n 'method': {\n 'task': 'InferenceTask',\n 'image_layer_path': image_layer_path,\n 'convnet_path': convnet_path,\n 'mask_layer_path': mask_layer_path,\n 'output_layer_path': output_layer_path,\n 'output_offset': output_offset,\n 'output_shape': output_block_size,\n 'patch_size': patch_size,\n 'patch_overlap': patch_overlap,\n 'cropping_margin_size': cropping_margin_size,\n 'output_key': output_key,\n 'num_output_channels': num_output_channels,\n 'image_mip': image_mip,\n 'output_mip': output_mip,\n 'mask_mip': mask_mip,\n },\n 'by': OPERATOR_CONTACT,\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\n })\n vol.commit_provenance()","def make_graph(self):\n\n # the root node\n self.graph.node(self.playbook_filename, style=\"dotted\", id=\"root_node\")\n\n # loop through the plays\n for play_counter, play in enumerate(self.playbook.get_plays(), 1):\n\n # the load basedir is relative to the playbook path\n if play._included_path is not None:\n self.data_loader.set_basedir(play._included_path)\n else:\n self.data_loader.set_basedir(self.playbook._basedir)\n self.display.vvv(\"Loader basedir set to {}\".format(self.data_loader.get_basedir()))\n\n play_vars = self.variable_manager.get_vars(play)\n play_hosts = [h.get_name() for h in self.inventory_manager.get_hosts(self.template(play.hosts, play_vars))]\n play_name = \"Play #{}: {} ({})\".format(play_counter, clean_name(play.get_name()), len(play_hosts))\n play_name = self.template(play_name, play_vars)\n\n self.display.banner(\"Graphing \" + play_name)\n\n play_id = \"play_\" + str(uuid.uuid4())\n\n self.graph_representation.add_node(play_id)\n\n with self.graph.subgraph(name=play_name) as play_subgraph:\n color, play_font_color = get_play_colors(play)\n # play node\n play_subgraph.node(play_name, id=play_id, style=\"filled\", shape=\"box\", color=color,\n fontcolor=play_font_color, tooltip=\" \".join(play_hosts))\n\n # edge from root node to plays\n play_edge_id = \"edge_\" + str(uuid.uuid4())\n play_subgraph.edge(self.playbook_filename, play_name, id=play_edge_id, style=\"bold\",\n label=str(play_counter), color=color, fontcolor=color)\n\n # loop through the pre_tasks\n self.display.v(\"Graphing pre_tasks...\")\n nb_pre_tasks = 0\n for pre_task_block in play.pre_tasks:\n nb_pre_tasks = self._include_tasks_in_blocks(current_play=play, graph=play_subgraph,\n parent_node_name=play_name, parent_node_id=play_id,\n block=pre_task_block, color=color,\n current_counter=nb_pre_tasks, play_vars=play_vars,\n node_name_prefix=\"[pre_task] \")\n\n # loop through the roles\n self.display.v(\"Graphing roles...\")\n role_number = 0\n for role in play.get_roles():\n # Don't insert tasks from ``import/include_role``, preventing duplicate graphing\n if role.from_include:\n continue\n\n # the role object doesn't inherit the tags from the play. So we add it manually.\n role.tags = role.tags + play.tags\n if not role.evaluate_tags(only_tags=self.options.tags, skip_tags=self.options.skip_tags,\n all_vars=play_vars):\n self.display.vv(\"The role '{}' is skipped due to the tags.\".format(role.get_name()))\n # Go to the next role\n continue\n\n role_number += 1\n role_name = \"[role] \" + clean_name(role.get_name())\n\n with self.graph.subgraph(name=role_name, node_attr={}) as role_subgraph:\n current_counter = role_number + nb_pre_tasks\n role_id = \"role_\" + str(uuid.uuid4())\n edge_id = \"edge_\" + str(uuid.uuid4())\n\n role_subgraph.node(role_name, id=role_id)\n # edge from play to role\n role_subgraph.edge(play_name, role_name, label=str(current_counter), color=color,\n fontcolor=color, id=edge_id)\n\n self.graph_representation.add_link(play_id, edge_id)\n self.graph_representation.add_link(edge_id, role_id)\n\n # loop through the tasks of the roles\n if self.options.include_role_tasks:\n role_tasks_counter = 0\n for block in role.compile(play):\n role_tasks_counter = self._include_tasks_in_blocks(current_play=play,\n graph=role_subgraph,\n parent_node_name=role_name,\n parent_node_id=role_id, block=block,\n color=color, play_vars=play_vars,\n current_counter=role_tasks_counter,\n node_name_prefix=\"[task] \")\n role_tasks_counter += 1\n self.display.v(\"{} roles added to the graph\".format(role_number))\n\n # loop through the tasks\n self.display.v(\"Graphing tasks...\")\n nb_tasks = 0\n for task_block in play.tasks:\n nb_tasks = self._include_tasks_in_blocks(current_play=play, graph=play_subgraph,\n parent_node_name=play_name, parent_node_id=play_id,\n block=task_block, color=color,\n current_counter=role_number + nb_pre_tasks,\n play_vars=play_vars, node_name_prefix=\"[task] \")\n\n # loop through the post_tasks\n self.display.v(\"Graphing post_tasks...\")\n for post_task_block in play.post_tasks:\n self._include_tasks_in_blocks(current_play=play, graph=play_subgraph, parent_node_name=play_name,\n parent_node_id=play_id, block=post_task_block, color=color,\n current_counter=nb_tasks, play_vars=play_vars,\n node_name_prefix=\"[post_task] \")\n\n self.display.banner(\"Done graphing {}\".format(play_name))\n self.display.display(\"\") # just an empty line\n # moving to the next play","def build_graph(self):\n tf.logging.info('Building graph...')\n t0 = time.time()\n\n self._add_placeholders()\n\n with tf.device(\"/gpu:%d\"%(config.gpu_selection)):\n self._add_seq2seq()\n\n self.global_step = tf.Variable(0, name='global_step', trainable=False)\n\n\n if self._mode == 'train':\n self._add_train_op()\n\n self._summaries = tf.summary.merge_all()\n\n t1 = time.time()\n tf.logging.info('Time to build graph: %i seconds', t1 - t0)","def build():","def dump_subgraph_for_debug(self):\n\n import pypipegraph2 as ppg\n\n nodes = []\n seen = set()\n edges = []\n counter = [0]\n node_to_counters = {}\n\n def descend(node):\n if node in seen:\n return\n seen.add(node)\n j = self.runner.jobs[node]\n if isinstance(j, ppg.FileInvariant):\n nodes.append(f\"Path('{counter[0]}').write_text('A')\")\n nodes.append(f\"job_{counter[0]} = ppg.FileInvariant('{counter[0]}')\")\n elif isinstance(j, ppg.ParameterInvariant):\n nodes.append(\n f\"job_{counter[0]} = ppg.ParameterInvariant('{counter[0]}', 55)\"\n )\n elif isinstance(j, ppg.FunctionInvariant):\n nodes.append(\n f\"job_{counter[0]} = ppg.FunctionInvariant('{counter[0]}', lambda: 55)\"\n )\n elif isinstance(j, ppg.SharedMultiFileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.SharedMultiFileGeneratingJob('{counter[0]}', {[x.name for x in j.files]!r}, dummy_smfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.TempFileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.TempFileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.FileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.FileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.MultiTempFileGeneratingJob):\n files = [counter[0] + \"/\" + x.name for x in j.files]\n nodes.append(\n f\"job_{counter[0]} = ppg.MultiTempFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.MultiFileGeneratingJob):\n files = [str(counter[0]) + \"/\" + x.name for x in j.files]\n nodes.append(\n f\"job_{counter[0]} = ppg.MultiFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.DataLoadingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.DataLoadingJob('{counter[0]}', lambda: None, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.AttributeLoadingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.AttributeLoadingJob('{counter[0]}', DummyObject(), 'attr_{counter[0]}', lambda: None, depend_on_function=False)\"\n )\n else:\n raise ValueError(j)\n node_to_counters[node] = counter[0]\n counter[0] += 1\n for parent in self.runner.dag.predecessors(node):\n descend(parent)\n\n def build_edges(node):\n for parent in self.runner.dag.predecessors(node):\n edges.append(\n f\"edges.append(('{node_to_counters[node]}', '{node_to_counters[parent]}'))\"\n )\n build_edges(parent)\n\n descend(self.job_id)\n edges.append(\"edges = []\")\n build_edges(self.job_id)\n edges.extend(\n [\n \"for (a,b) in edges:\",\n \" if a in ppg.global_pipegraph.jobs and b in ppg.global_pipegraph.jobs:\",\n \" ppg.global_pipegraph.jobs[a].depends_on(ppg.global_pipegraph.jobs[b])\",\n ]\n )\n with open(\"subgraph_debug.py\", \"w\") as op:\n lines = \"\"\"\nclass DummyObject:\n pass\n\ndef dummy_smfg(files, prefix):\n Path(prefix).mkdir(exist_ok=True, parents=True)\n for f in files:\n f.write_text(\"hello\")\n\n\ndef dummy_mfg(files):\n for f in files:\n f.parent.mkdir(exist_ok=True, parents=True)\n f.write_text(\"hello\")\n\ndef dummy_fg(of):\n of.parent.mkdir(exist_ok=True, parents=True)\n of.write_text(\"fg\")\n\n\"\"\".split(\n \"\\n\"\n )\n lines += nodes\n lines += edges\n lines += [\"\", \"ppg.run()\", \"ppg.run\"]\n\n op.write(\"\\n\".join(\" \" + l for l in lines))","def build_nodes_with_deps(self, tasks, task_name):\n if task_name in self._processed:\n return\n else:\n self._processed.add(task_name)\n\n # add node itself if not in list of nodes\n self.nodes.setdefault(task_name, [])\n task = tasks[task_name]\n # reversing not required\n for dep_name in reversed(task.setup_tasks + task.task_dep):\n rev_dep = self.nodes.setdefault(dep_name, [])\n rev_dep.append(task_name)\n self.build_nodes_with_deps(tasks, dep_name)","def build_graph(self):\n self.import_tree(ZOO_PATH, self.import_zoo, self.verify_zoos)\n self.import_tree(WILD_PATH, self.import_wild, self.verify_wilds)\n self.import_tree(PANDA_PATH, self.import_redpanda, self.verify_pandas)\n self.import_tree(MEDIA_PATH, self.import_media, self.verify_media)","def build_tf_graph(self):\n raise NotImplementedError","def build_graph():\n os.environ['CUDA_VISIBLE_DEVICES']= '0'\n\n # frozen_model = '/home/kevin/Codes/DeepNet/log/20180419_221132/frozen_model.pb'\n # frozen_model = '/home/kevin/Downloads/deeplabv3_cityscapes_train/frozen_inference_graph.pb'\n # frozen_model = '/home/kevin/Codes/EnvNet/RUNS/used3/frozen_model.pb'\n frozen_model = '/home/kevin/Codes/DeepNet/log/20180716_212035/frozen_model1.pb'\n graph = load_graph(frozen_model)\n\n for op in graph.get_operations():\n print(op.name)\n\n ## model_envnet/frozen_model.pb\n image_pl = graph.get_tensor_by_name('ImagePlaceholder:0')\n pred_seg = graph.get_tensor_by_name('SemanticPredictions:0')\n\n ## model_deeplab/frozen_inference_graph.pb\n # image_pl = graph.get_tensor_by_name('ImageTensor:0')\n # pred_seg = graph.get_tensor_by_name('SemanticPredictions:0')\n\n # ## model_deepnet/frozen_model.pb\n # image_pl = graph.get_tensor_by_name('ImagePlaceholder:0')\n # pred_seg = graph.get_tensor_by_name('SemanticPredictions:0')\n\n config = tf.ConfigProto() \n config.gpu_options.per_process_gpu_memory_fraction = 0.5\n sess = tf.Session(graph=graph,config=config)\n\n return image_pl, pred_seg, sess","def evaluate(self, g, tick, task, inputs):\n # create the output file locally - relative output path is given by the model path of the given node. \n context=inputs[CONTEXT]\n transporter=context[TRANSPORTER]\n parentdir=_dataflow_path(g, tick)\n localpath=os.path.join(context[LOCALWORKDIR], parentdir, self.outputlistname)\n if not os.path.exists(os.path.join(str(context[LOCALWORKDIR]), parentdir)):\n os.makedirs(os.path.join(str(context[LOCALWORKDIR]), parentdir))\n\n outputlist=[] \n for i in range(self.num_splits):\n tuple_from_split=tuple([ inputs[\"%s_%i\" % (name,i+1)] for name in self.portnames ])\n outputlist.append(tuple_from_split)\n with open(localpath, 'w') as localfile:\n pickle.dump(outputlist, localfile)\n \n # now compose the remote path where this file needs to be copied to\n # use the transporter obtained the context has been initialized with\n remotepath=os.path.join(str(context[WSROOT]), str(context[WORKDIR]), parentdir, self.outputlistname)\n relativepath = os.path.join(parentdir, self.outputlistname)\n d = transporter.upload_file(localpath, remotepath)\n d.addCallback(lambda _: relativepath)\n\n def on_success(_path):\n return EvalResult({self.outputlistname:_path})\n\n def on_failure(reason):\n return Failure(ValueError((\"Exception while uploading file to %s. \\n\"\n + \"Reason:%s.\")%(remotepath, reason.getTraceback())))\n\n d.addCallback(on_success)\n d.addErrback(on_failure)\n return d","def parse(self, graph):\n\n # This function is very similar to its counterpart in the regular\n # (non-tree-decomposing) parser. Read the comments there to understand how it\n # works.\n\n start_time = time.clock()\n log.chatter('parse...')\n\n # ensure that the input graph has its shortest-path table precomputed\n graph.compute_fw_table()\n\n chart = ddict(set)\n # TODO command line option to switch rule filtering on/off\n pgrammar = [self.grammar[r] for r in self.grammar.reachable_rules(graph, None)] \n queue = deque()\n pending = set()\n attempted = set()\n visited = set()\n terminal_lookup = ddict(set)\n passive_item_lookup = ddict(set)\n tree_node_lookup = ddict(set)\n passive_item_rev_lookup = ddict(set)\n tree_node_rev_lookup = ddict(set)\n\n for edge in graph.triples(nodelabels = self.nodelabels):\n terminal_lookup[edge[1]].add(edge)\n\n for rule in pgrammar:\n for leaf in rule.tree_leaves:\n axiom = self.item_class(rule, leaf, graph, nodelabels = self.nodelabels)\n queue.append(axiom)\n pending.add(axiom)\n assert leaf not in rule.tree_to_edge\n\n success = False\n\n while queue:\n item = queue.popleft()\n pending.remove(item)\n visited.add(item)\n log.debug('handling', item, item.subgraph)\n\n if item.target == Item.NONE:\n log.debug(' none')\n tree_node_lookup[item.self_key].add(item)\n for ritem in tree_node_rev_lookup[item.self_key]:\n if ritem not in pending:\n queue.append(ritem)\n pending.add(ritem)\n\n elif item.target == Item.ROOT:\n log.debug(' root')\n if self.is_goal(item):\n chart['START'].add((item,))\n success = True\n log.debug(\"success!\")\n\n passive_item_lookup[item.self_key].add(item)\n for ritem in passive_item_rev_lookup[item.self_key]:\n if ritem not in pending:\n log.debug(' retrieving', ritem)\n queue.append(ritem)\n pending.add(ritem)\n\n elif item.target == Item.TERMINAL:\n log.debug(' terminal')\n new_items = [item.terminal(edge) for edge in terminal_lookup[item.next_key]]\n new_items = [i for i in new_items if i]\n for nitem in new_items:\n chart[nitem].add((item,))\n if nitem not in pending and nitem not in visited:\n log.debug(' new item!', nitem)\n queue.append(nitem)\n pending.add(nitem)\n\n else:\n if item.target == Item.BINARY:\n log.debug(' binary')\n rev_lookup = tree_node_rev_lookup\n lookup = tree_node_lookup\n action = self.item_class.binary\n elif item.target == Item.NONTERMINAL:\n log.debug(' nonterminal')\n rev_lookup = passive_item_rev_lookup\n lookup = passive_item_lookup\n action = self.item_class.nonterminal\n else:\n assert False\n\n rev_lookup[item.next_key].add(item)\n for oitem in lookup[item.next_key]:\n if (item, oitem) in attempted:\n continue\n attempted.add((item, oitem))\n log.debug(' try', oitem, oitem.subgraph)\n nitem = action(item, oitem)\n if not nitem:\n continue\n log.debug(' new item!', nitem)\n chart[nitem].add((item, oitem))\n if nitem not in pending and nitem not in visited:\n queue.append(nitem)\n pending.add(nitem)\n\n if success:\n log.chatter(' success!')\n\n etime = time.clock() - start_time\n log.chatter('done in %.2fs' % etime)\n return chart","def build_graph(self):\n if self.model == 'dense':\n # ForecastNet with two densely connected hidden layers in a cell and Mixture Density Network outputs\n self.outputs, self.mu, self.sigma, self.cost = forecastnet_graph(self.X,\n self.Y,\n self.hidden_dim,\n self.out_seq_length,\n self.is_training)\n elif self.model == 'conv':\n # ForecastNet with a convlutional neural network in a cell and Mixture Density Network outputs\n self.outputs, self.mu, self.sigma, self.cost = forecastnet_conv_graph(self.X,\n self.Y,\n self.hidden_dim,\n self.out_seq_length,\n self.is_training)\n elif self.model == 'dense2':\n # ForecastNet with two densely connected hidden layers in a cell and linear outputs\n self.outputs, self.cost = forecastnet_graph2(self.X,\n self.Y,\n self.hidden_dim,\n self.out_seq_length,\n self.is_training)\n elif self.model == 'conv2':\n # ForecastNet with a convolutional neural network in a cell and linear outputs\n self.outputs, self.cost = forecastnet_conv_graph2(self.X,\n self.Y,\n self.hidden_dim,\n self.out_seq_length,\n self.is_training)","def build(self):\n self.logger.info('Rebuilding adjacency information')\n self.edges = collections.defaultdict(list)\n\n topic_to_publisher = collections.defaultdict(list)\n topic_to_subscribers = collections.defaultdict(list)\n node_to_missing_deps = collections.defaultdict(list)\n\n result = True\n\n for node in self.nodes.values():\n for topic in node.provided_topics.keys():\n topic_to_publisher[topic].append(node)\n\n for topic in node.required_topics:\n topic_to_subscribers[topic].append(node)\n\n for dep in node.additional_dependencies:\n if dep not in self.nodes:\n node_to_missing_deps[node].append(dep)\n\n if len(node_to_missing_deps) > 0:\n result = False\n msg = io.StringIO()\n print('Found [{}] managed processes with missing dependencies'.format(len(node_to_missing_deps)), file=msg)\n fmt = ' Managed process [{}] is missing [{}]'\n\n for (node, missing) in node_to_missing_deps.items():\n print(fmt.format(node.name, ', '.join(missing)), file=msg)\n self.logger.error(msg.getvalue())\n\n missing_publishers = []\n for topic in topic_to_subscribers.keys():\n if topic not in topic_to_publisher:\n missing_publishers.append(topic)\n\n if len(missing_publishers) > 0:\n result = False\n msg = io.StringIO()\n print('Found [{}] topics that do not have publishers'.format(len(missing_publishers)), file=msg)\n fmt = ' Topic [{}] with subscribers [{}]'\n\n for topic in missing_publishers:\n print(fmt.format(topic, ', '.join([x.name for x in topic_to_subscribers[topic]])), file=msg)\n self.logger.error(msg.getvalue())\n\n if not result:\n self.logger.error('Found errors when building adjacency information')\n raise GraphBuildError(\n 'Found errors when building adjacency information / graph edges. Check log for details')\n\n # Now we have enough information to build our edges. Phase 1: pub/sub stuff\n for (topic, subscribers) in topic_to_subscribers.items():\n publishers = topic_to_publisher[topic]\n\n for p in publishers:\n for s in subscribers:\n self.edges[p].append(s)\n\n # Phase 2: additional dependencies\n for node in self.nodes.values():\n for dep in node.additional_dependencies:\n src = self.nodes[dep]\n self.edges[src].append(node)","def update_dependencies():\r\n\r\n if par['task'] == 'mnist':\r\n par['n_tasks'] = 100\r\n par['input_shape'] = [28, 28]\r\n par['n_input'] = np.product(par['input_shape'])\r\n par['n_output'] = 10\r\n elif par['task'] == 'omniglot':\r\n par['input_shape'] = [26, 26]\r\n par['n_input'] = 256 if par['conv_input'] else np.product(par['input_shape'])\r\n par['n_output'] = par['n_ways'] #par['n_meta_tasks'] + par['n_test_tasks']\r\n\r\n par['layer_dims'] = [par['n_input']] + par['hidden_layers'] + [par['n_output']]\r\n\r\n\r\n par['n_layers'] = len(par['layer_dims'])\r\n if par['task'] == 'mnist' or par['task'] == 'imagenet':\r\n par['labels_per_task'] = 10\r\n elif par['task'] == 'cifar':\r\n par['labels_per_task'] = 5","def build_graph(self, graph, inst_name, port_nets):\n self.add_graph_edges(graph, port_nets)","def process_task(params):\n params['task'](params)","def build_inference_graph(self):\n self.build_train_graph()","def task_start_parsing():\n add_task(url_for(\"task_queue_users\"))\n add_task(url_for(\"task_clean_tmp_files\"))\n return OK_RESPONSE","def build_graph(self):\n start_time = time.time()\n\n # init temp node\n for value in self.domain:\n node = TempDepNode(value)\n self.nodes[value] = node\n\n attr_data = self.graph_data[self.attr_name]\n print(f'{len(attr_data)} records in data')\n\n # init temp edge\n for source_ix, value_i in tqdm(attr_data.items()):\n visited = set()\n for target_ix, value_j in attr_data[source_ix+1:].items():\n if value_j in visited:\n continue\n else:\n visited.add(value_j)\n time_diff = self.graph_data[self.time][target_ix] - \\\n self.graph_data[self.time][source_ix]\n if time_diff > self.time_diff_threshold:\n break\n if (value_i, value_j) not in self.edges or (value_j, value_i) not in self.edges:\n self.edges[(value_i, value_j)] = TempDepEdge(value_i, value_j)\n self.edges[(value_j, value_i)] = TempDepEdge(value_j, value_i)\n self.edges[(value_i, value_j)].add_event(time_diff)\n if value_i != value_j:\n self.edges[(value_j, value_i)].add_event(time_diff)\n end_time = time.time()\n print(f'{end_time-start_time} seconds for graph building')","def __init__(self):\n Task.__init__(self)","def Run(self):\n # print \"len of tostartFlows \", len(self.sched.toStartFlows)\n # start all the flows along with updating related flow transfer time\n while self.sched.toStartFlows:\n # the first flow is with earliest startTime\n curStartFlow = self.sched.toStartFlows[0]\n # update flows if there are flows has already finished\n while self.sched.runningFlows:\n # the first flow is with earliest finishTime\n toFinishFlow = self.sched.runningFlows[0]\n if toFinishFlow.finishTime <= curStartFlow.startTime:\n # remove this flow from running flows\n self.sched.runningFlows.remove(toFinishFlow)\n # add this flow to finished flows\n self.sched.finishedFlows.append(toFinishFlow)\n # Update related flow's transfer time in removing a flow\n self.sched.UpdateFlow(toFinishFlow, \"remove\")\n # Resort runningFlows by endTime\n self.sched.runningFlows.sort(key=lambda x: x.finishTime)\n \n if self.Qlearning_enable == 1:\n self.action = toFinishFlow.pathNodeIds\n if len(self.action) == 5:\n self.pre_state = self.state[:]\n self.Update(toFinishFlow)\n self.printQlearningLog()\n #self.Update(self.pre_state, self.action, self.state, self.reward)\n\n else:\n break\n # insert current start flow to running list\n if self.Qlearning_enable == 1:\n self.routing.BuildPath(curStartFlow.startId, curStartFlow.endId, curStartFlow, self.state)\n else:\n self.routing.BuildPath(curStartFlow.startId, curStartFlow.endId, curStartFlow)\n pathNodeIds = self.routing.GetPath(curStartFlow.startId, curStartFlow.endId)\n curStartFlow.BuildPath(pathNodeIds)\n self.sched.runningFlows.append(curStartFlow)\n # Update related flow's transfer time in removing a flow\n # self.lb(curStartFlow)\n # self.topo.GetLinkOfLeastFlow()\n # Step 1 find out which spine is less loaded\n\n # Hedera load balancing for spine leaf\n # print self.topo.GetCoreLeastFlow()\n # if self.topo.name == \"spineleaf\":\n # if self.topo.GetCoreLeastFlow() not in curStartFlow.pathNodeIds:\n # if len(curStartFlow.pathNodeIds) == 5:\n # if curStartFlow.coflowId == 0:\n # self.changeSpine(curStartFlow, self.topo.GetCoreLeastFlow())\n # # print \"general flow reroute to spine {}\".format(self.topo.GetCoreLeastFlow().nodeId)\n # else:\n # self.changeSpine(curStartFlow,\n # self.topo.GetCoreNode((curStartFlow.coflowId % self.topo.numOfCores)+1))\n # print \"coflow reroute to spine {}\".format(self.topo.GetCoreNode((curStartFlow.coflowId % self.topo.numOfCores)+1).nodeId)\n #print curStartFlow.pathNodeIds\n # Less loaded in terms of more flows\n\n # update state and reward for Qlearning algorithm\n self.sched.UpdateFlow(curStartFlow, \"insert\")\n #self.updatenum += 1\n #print \"updatenum= \",self.updatenum\n if self.Qlearning_enable == 1:\n self.action = curStartFlow.pathNodeIds\n if len(self.action) == 5:\n self.pre_state = self.state[:]\n self.Update(curStartFlow)\n self.printQlearningLog()\n #self.Update(self.pre_state, self.action, self.state, self.reward)\n\n # Resort runningFlows by endTime\n self.sched.runningFlows.sort(key=lambda x: x.finishTime)\n # remove this flow from start list\n self.sched.toStartFlows.remove(curStartFlow)\n #print \"finished\"\n #print len(self.sched.toStartFlows)\n\n # Now, all the flows are started\n # Iteratively update flow's transfer time in running list until all the flows are finished\n while self.sched.runningFlows:\n # the first flow is always with earliest finish Time\n curFinishFlow = self.sched.runningFlows[0]\n # remove it from running list\n self.sched.runningFlows.remove(curFinishFlow)\n # insert it to finished flows\n self.sched.finishedFlows.append(curFinishFlow)\n # Update related flow's transfer time in removing a flow\n self.sched.UpdateFlow(curFinishFlow, \"remove\")\n # Resort runningFlows by endTime\n self.sched.runningFlows.sort(key=lambda x: x.finishTime)\n\n # update state and reward for Qlearning algorithm\n if self.Qlearning_enable == 1:\n self.action = curFinishFlow.pathNodeIds\n if len(self.action) == 5:\n self.pre_state = self.state[:]\n self.Update(curFinishFlow)\n self.printQlearningLog()\n #self.Update(self.pre_state, self.action, self.state, self.reward)\n\n\n # Finally, all the flows are finished\n self.sched.PrintFlows()\n if self.Qlearning_enable == 1:\n self.logf.close()\n # print \"final stateId= \", self.stateId","def buildGraph(self):\n return None","def _build_forward_graph(self):\n\n print('[*] Building a Neural Turing Machine.')\n\n self._initalize_state()\n\n # present start token\n controller_out = self.controller.emit_feature_vector(self.start_token, self.r_t[0], reuse=None)\n self._read_write(controller_out, reuse=None)\n\n # present inputs\n print('Input chain: ')\n for t in range(0, self.sequence_length):\n print_progress(float(t + 1) / self.sequence_length)\n\n controller_out = self.controller.emit_feature_vector(self.inputs[t], self.r_t[-1], reuse=True)\n self._read_write(controller_out, reuse=True)\n\n # present end token\n controller_out = self.controller.emit_feature_vector(self.end_token, self.r_t[-1], reuse=True)\n self._read_write(controller_out, reuse=True)\n\n # present outputs\n print('Output chain: ')\n for t in range(0, self.sequence_length):\n print_progress(float(t + 1) / self.sequence_length)\n\n controller_out = self.controller.emit_feature_vector(self.zeros, self.r_t[-1], reuse=True)\n self._read_write(controller_out, reuse=True)\n\n reuse = None if t == 0 else True\n self.outputs.append(self._decode_read_vector(self.r_t[-1], reuse=reuse))\n print('Done.')","def new_tasks(self, extra):\n\n tasks = []\n\n try:\n fd = open(self.params.command_file)\n \n self.result_dir = os.path.dirname(self.params.output)\n \n for line in fd:\n command = line.strip()\n\n if not command:\n # ignore black lines\n continue\n\n cmd_args = _parse_command(command)\n \n # setting jobname\n jobname = \"gc_gps-%s%s%s%s%s\" % (cmd_args['pos'],\n cmd_args['realizations'],\n cmd_args['snr'],\n cmd_args['mast.h'],\n cmd_args['sd.mast.o'])\n\n extra_args = extra.copy()\n extra_args['jobname'] = jobname\n # FIXME: ignore SessionBasedScript feature of customizing \n # output folder\n extra_args['output_dir'] = self.params.output\n\n extra_args['output_dir'] = extra_args['output_dir'].replace('NAME', os.path.join('.computation',jobname))\n extra_args['output_dir'] = extra_args['output_dir'].replace('SESSION', os.path.join('.computation',jobname))\n extra_args['output_dir'] = extra_args['output_dir'].replace('DATE', os.path.join('.computation',jobname))\n extra_args['output_dir'] = extra_args['output_dir'].replace('TIME', os.path.join('.computation',jobname))\n\n self.log.debug(\"Creating Task for command: %s\" % command)\n\n tasks.append(GcgpsTask(\n command,\n self.params.R_source_folder,\n self.result_dir,\n self.params.input_dir,\n **extra_args))\n\n except IOError, ioe:\n self.log.error(\"Error while reading command file \" +\n \"%s.\" % self.params.command_file +\n \"Message: %s\" % ioe.message)\n except Exception, ex:\n self.log.error(\"Unexpected error. Error type: %s, Message: %s\" % (type(ex),str(ex)))\n\n finally:\n fd.close()\n\n return tasks","def _build_graph(self):\n self._setup_placeholders()\n self._embed()\n self.p_emb = tf.concat([self.p_emb, tf.expand_dims(self.em, -1)], -1)\n self._encode()\n self._match()\n self._fuse()\n\n with tf.variable_scope('boundary'):\n self._decode()\n with tf.variable_scope('content'):\n self._content()\n with tf.variable_scope('verif'):\n self._verify()\n\n self._compute_loss()","def task2_3():","def build_graph(self):\n\t\tself._create_placeholders()\n\t\tself._create_embedding()\n\t\tself._create_recurrent_layers()\n\t\tself._create_de_embedding()\n\t\tself._create_loss()\n\t\tself._create_optimizer()\n\t\tself._create_summaries()","def _dispatcher_generator(self, selected_tasks):\n # each selected task will create a tree (from dependencies) of\n # tasks to be processed\n tasks_to_run = list(reversed(selected_tasks))\n node = None # current active ExecNode\n\n while True:\n # get current node\n if not node:\n node = self._get_next_node(self.ready, tasks_to_run)\n if not node:\n if self.waiting:\n # all tasks are waiting, hold on\n processed = (yield \"hold on\")\n self._update_waiting(processed)\n continue\n # we are done!\n return\n\n # get next step from current node\n next_step = node.step()\n\n # got None, nothing left for this generator\n if next_step is None:\n node = None\n continue\n\n # got a task, send ExecNode to runner\n if isinstance(next_step, Task):\n processed = (yield self.nodes[next_step.name])\n self._update_waiting(processed)\n\n # got new ExecNode, add to ready_queue\n elif isinstance(next_step, ExecNode):\n self.ready.append(next_step)\n\n # node just performed a delayed creation of tasks, restart\n elif next_step == \"reset generator\":\n node.reset_task(self.tasks[node.task.name],\n self._add_task(node))\n\n # got 'wait', add ExecNode to waiting queue\n else:\n assert next_step == \"wait\"\n self.waiting.add(node)\n node = None","def run(self):\n with hp.a_temp_file() as fle:\n fle.write(dedent(\"\"\"\n ---\n environments: { dev: {account_id: \"123\"} }\n stacks: { app: {} }\n \"\"\").encode('utf-8'))\n fle.seek(0)\n collector = Collector()\n collector.prepare(fle.name, {'bespin': {'extra': \"\"}, \"command\": None, \"bash\": None})\n\n section = nodes.section()\n section['ids'].append(\"available-tasks\")\n\n title = nodes.title()\n title += nodes.Text(\"Default tasks\")\n section += title\n\n for name, task in sorted(collector.configuration['task_finder'].tasks.items(), key=lambda x: len(x[0])):\n\n lines = [name] + [\" {0}\".format(line.strip()) for line in task.description.split('\\n')]\n viewlist = ViewList()\n for line in lines:\n viewlist.append(line, name)\n self.state.nested_parse(viewlist, self.content_offset, section)\n\n return [section]","def task_parse_results():\n pass","def build_graph(self):\n self.__create_placeholders()\n self.__create_encoder()\n self.__create_latent()\n self.__create_decoder()\n self.__create_loss()\n self.__create_generate()\n self.__create_reconstruct()\n self.__create_optimizer()\n self.__create_summary()","def run():\n ftpd_thread = mini_buildd.misc.run_as_thread(\n mini_buildd.ftpd.run,\n name=\"ftpd\",\n bind=get().model.ftpd_bind,\n queue=get().incoming_queue)\n\n builder_thread = mini_buildd.misc.run_as_thread(\n mini_buildd.builder.run,\n name=\"builder\",\n daemon_=get())\n\n while True:\n event = get().incoming_queue.get()\n if event == \"SHUTDOWN\":\n break\n\n try:\n LOG.info(\"Status: {0} active packages, {1} changes waiting in incoming.\".\n format(len(get().packages), get().incoming_queue.qsize()))\n\n changes = None\n changes = mini_buildd.changes.Changes(event)\n\n if changes.type == changes.TYPE_BREQ:\n # Build request: builder\n\n def queue_buildrequest(event):\n \"\"\"Queue in extra thread so we don't block here in case builder is busy.\"\"\"\n get().build_queue.put(event)\n mini_buildd.misc.run_as_thread(queue_buildrequest, name=\"build queuer\", daemon=True, event=event)\n\n else:\n # User upload or build result: packager\n mini_buildd.packager.run(\n daemon=get(),\n changes=changes)\n\n except BaseException as e:\n mini_buildd.config.log_exception(LOG, \"Invalid changes file\", e)\n\n # Try to notify\n try:\n with mini_buildd.misc.open_utf8(event, \"r\") as body:\n subject = \"INVALID CHANGES: {c}: {e}\".format(c=event, e=e)\n get().model.mbd_notify(subject, body.read())\n except BaseException as e:\n mini_buildd.config.log_exception(LOG, \"Invalid changes notify failed\", e)\n\n # Try to clean up\n try:\n if changes:\n changes.remove()\n else:\n os.remove(event)\n except BaseException as e:\n mini_buildd.config.log_exception(LOG, \"Invalid changes cleanup failed\", e)\n\n finally:\n get().incoming_queue.task_done()\n\n get().build_queue.put(\"SHUTDOWN\")\n mini_buildd.ftpd.shutdown()\n builder_thread.join()\n ftpd_thread.join()\n\n # keyrings.close() is not called implicitly; this leaves tmp files around.\n # There should be a nicer way, really...\n try:\n get().keyrings.close()\n except BaseException:\n pass","def task_process(args):\n if args.mode == 'change model':\n for i in range(config.center_len):\n h, w = config.center_list[i][0], config.center_list[i][1]\n os.system('rm -rf ctpn_change_{}x{}.onnx'.format(h, w))\n for i in range(config.center_len):\n h, w = config.center_list[i][0], config.center_list[i][1]\n os.system('{} change_model.py --input_path={}/ctpn_{}x{}.onnx --output_path={}/ctpn_change_{}x{}.onnx' \\\n .format(args.interpreter, args.src_dir, h, w,args.res_dir, h, w)) \n if args.mode == 'preprocess':\n for i in range(config.center_len):\n os.system('mkdir -p {}_{}x{}'.format(args.res_dir, config.center_list[i][0], config.center_list[i][1]))\n os.system('{} ctpn_preprocess.py --src_dir={} --save_path={}' \\\n .format(args.interpreter, args.src_dir, args.res_dir))\n if args.mode == 'ais_infer':\n fps_all = 0\n os.system('mkdir -p {}/inf_output'.format(args.res_dir))\n for i in range(config.center_len):\n h, w = config.center_list[i][0], config.center_list[i][1]\n\n os.system('{} --model={} --input={}_{}x{} --dymHW {},{} --device {} --batchsize={} --output={}/inf_output' \\\n .format(args.interpreter, args.om_path, args.src_dir ,h , w, h, w,args.device, args.batch_size, args.res_dir))\n\n sumary_path = glob.glob('{}/inf_output/*ary.json'.format(args.res_dir))[0]\n with open(sumary_path, 'r') as f:\n output = json.load(f)\n throughput = output['throughput'] \n fps_all = fps_all + throughput * config.center_count[i]\n os.system('rm -f {}'.format(sumary_path))\n os.system('mv {}/inf_output/*/*.bin {}'.format(args.res_dir, args.res_dir))\n os.system('rm {}/inf_output -rf'.format(args.res_dir))\n fps_all = fps_all / config.imgs_len\n print(\"====performance data====\")\n print('CTPN bs{} models fps:{}'.format(args.batch_size, fps_all))","def execute(self):\n # Ensure a true no-op (in particular, we don't want to GET the feed) if\n # there are no Subtasks\n if not any([self._tx_by_uuid, self._common_tx.subtasks,\n self._post_exec]):\n LOG.info(_(\"FeedTask %s has no Subtasks; no-op execution.\"),\n self.name)\n return\n rets = {'wrapper_task_rets': {}}\n try:\n # Calling .wrapper_tasks will cause the feed to be fetched and\n # WrapperTasks to be replicated, if not already done. Only do this\n # if there exists at least one WrapperTask with Subtasks.\n # (NB: It is legal to have a FeedTask that *only* has post-execs.)\n if self._tx_by_uuid or self._common_tx.subtasks:\n pflow = tf_uf.Flow(\"%s_parallel_flow\" % self.name)\n pflow.add(*self.wrapper_tasks.values())\n # Execute the parallel flow now so the results can be provided\n # to any post-execs.\n rets['wrapper_task_rets'] = self._process_subtask_rets(\n tf_eng.run(\n pflow, engine='parallel',\n executor=ContextThreadPoolExecutor(self.max_workers)))\n if self._post_exec:\n flow = tf_lf.Flow('%s_post_execs' % self.name)\n flow.add(*self._post_exec)\n eng = tf_eng.load(flow, store=rets)\n eng.run()\n rets = eng.storage.fetch_all()\n except tf_ex.WrappedFailure as wfail:\n LOG.error(_(\"FeedTask %s experienced multiple exceptions. They \"\n \"are logged individually below.\"), self.name)\n for fail in wfail:\n LOG.exception(fail.pformat(fail.traceback_str))\n raise ex.MultipleExceptionsInFeedTask(self.name, wfail)\n\n # Let a non-wrapped exception (which happens if there's only one\n # element in the feed) bubble up as-is.\n\n return rets","def __init__(self, graph_a, graph_b):\n\n self.can_be_reprocessed = True\n self.reasons = []\n self._graph_a = graph_a\n self._graph_b = graph_b\n self._matched_recipe_inputs = {} # {Recipe Input B: Recipe Input A}\n self._identical_nodes = {} # {Job Name B: Job Name A}\n self._changed_nodes = {} # {Job Name B: Job Name A}\n self._new_nodes = set() # {Job Name B}\n self._deleted_nodes = set() # {Job Name A}\n self._force_reprocess = set()\n self._changes = {} # {Job Name B: [Change]}\n self._topo_a_nodes = self._graph_a.get_topological_order() # Job names from A in topological order\n self._topo_b_nodes = self._graph_b.get_topological_order() # Job names from B in topological order\n self._unresolved_a_nodes = set(self._topo_a_nodes) # {Job Name A}\n self._unresolved_b_nodes = set(self._topo_b_nodes) # {Job Name B}\n\n self._match_recipe_inputs()\n self._match_identical_nodes()\n self._match_changed_nodes()\n self._new_nodes = self._unresolved_b_nodes\n self._deleted_nodes = self._unresolved_a_nodes\n self._unresolved_a_nodes = set()\n self._unresolved_b_nodes = set()","def viz_graph(self, show_ports=False, pydot_options=None):\n import networkx as nx\n G = nx.DiGraph()\n if pydot_options:\n G.graph['graph'] = pydot_options\n # instantiate objects\n for itask in self:\n task_inputs = itask[TaskSpecSchema.inputs]\n to_task = itask[TaskSpecSchema.task_id]\n to_type = itask[TaskSpecSchema.node_type]\n if to_task == \"\":\n to_task = OUTPUT_TYPE\n for iport_or_tid in task_inputs:\n # iport_or_tid: it is either to_port or task id (tid) b/c\n # if using ports API task_inputs is a dictionary otherwise\n # task_inputs is a list.\n taskin_and_oport = task_inputs[iport_or_tid] \\\n if isinstance(task_inputs, dict) else iport_or_tid\n isplit = taskin_and_oport.split('.')\n from_task = isplit[0]\n from_port = isplit[1] if len(isplit) > 1 else None\n if show_ports and from_port is not None:\n to_port = iport_or_tid\n common_tip = taskin_and_oport\n G.add_edge(from_task, common_tip, label=from_port)\n G.add_edge(common_tip, to_task, label=to_port)\n tnode = G.nodes[common_tip]\n tnode.update({\n # 'label': '',\n 'shape': 'point'})\n else:\n G.add_edge(from_task, to_task)\n\n # draw output ports\n if show_ports:\n\n if (to_type == OUTPUT_TYPE):\n continue\n task_node = get_node_obj(itask, tgraph_mixin=True)\n # task_outputs = itask.get(TaskSpecSchema.outputs, [])\n for pout in task_node._get_output_ports():\n out_tip = '{}.{}'.format(\n itask[TaskSpecSchema.task_id], pout)\n G.add_edge(to_task, out_tip, label=pout)\n tnode = G.nodes[out_tip]\n tnode.update({\n # 'label': '',\n 'shape': 'point'})\n return G","def build_graph(self):\n\n\n\n self.inputs.append( #uint8\n tf.placeholder(tf.float32, shape=[None, None, None, self.channel],\n name='input/lr')) \n\n self.label.append(\n tf.placeholder(tf.float32, shape=[None, None, None, self.channel],\n name='label/hr'))","def build_dag(self, user=None):\n cwd = os.getcwd()\n #os.chdir(self.production.event.meta['working directory'])\n #os.chdir(os.path.join(self.production.event.repository.directory,\n # self.category))\n\n if self.production.event.repository:\n gps_file = self.production.get_timefile()\n coinc_file = self.production.get_coincfile()\n coinc_file = os.path.join(self.production.event.repository.directory, \"C01_offline\",\n coinc_file)\n ini = self.production.get_configuration().ini_loc\n ini = os.path.join(self.production.event.repository.directory, \"C01_offline\", ini)\n else:\n gps_file = \"gpstime.txt\"\n ini = os.path.join(self.production.event.meta['working directory'], f\"{self.production.name}.ini\")\n coinc_file = os.path.join(cwd, \"coinc.xml\")\n\n if self.production.get_meta(\"user\"):\n user = self.production.get_meta(\"user\")\n else:\n user = config.get(\"condor\", \"user\")\n self.production.set_meta(\"user\", user)\n\n os.environ['LIGO_USER_NAME'] = f\"{user}\"\n os.environ['LIGO_ACCOUNTING'] = f\"{config.get('pipelines', 'accounting')}\"\n\n try:\n calibration = config.get(\"general\", \"calibration\")\n except:\n calibration = \"C01\"\n\n approximant = self.production.meta['approximant']\n\n #ini.save()\n\n if self.production.rundir:\n rundir = os.path.relpath(self.production.rundir, os.getcwd())\n else:\n rundir = os.path.join(os.path.expanduser(\"~\"),\n self.production.event.name,\n self.production.name)\n self.production.rundir = rundir\n\n #lmax = self.production.meta['priors']['amp order']\n\n if \"lmax\" in self.production.meta:\n lmax = self.production.meta['lmax']\n elif \"HM\" in self.production.meta['approximant']:\n lmax = 4\n else:\n lmax = 2\n \n\n if \"cip jobs\" in self.production.meta:\n cip = self.production.meta['cip jobs']\n else:\n cip = 3\n \n \n command = [os.path.join(config.get(\"pipelines\", \"environment\"), \"bin\", \"util_RIFT_pseudo_pipe.py\"),\n \"--use-coinc\", coinc_file,\n \"--l-max\", f\"{lmax}\",\n \"--calibration\", f\"{calibration}\",\n \"--add-extrinsic\",\n \"--approx\", f\"{approximant}\",\n \"--cip-explode-jobs\", str(cip),\n \"--use-rundir\", rundir,\n \"--ile-force-gpu\",\n \"--use-ini\", ini\n ]\n print(\" \".join(command))\n # If a starting frequency is specified, add it\n if \"start-frequency\" in self.production.meta:\n command += [\"--fmin-template\", self.production.quality['start-frequency']]\n \n self.logger.info(\" \".join(command), production = self.production)\n\n # Placeholder LI grid bootstrapping; conditional on it existing and location specification\n \n if self.bootstrap:\n if self.bootstrap == \"manual\":\n if self.production.event.repository:\n bootstrap_file = os.path.join(self.production.event.repository.directory, \"C01_offline\", f\"{self.production.name}_bootstrap.xml.gz\")\n else:\n bootstrap_file = \"{self.production.name}_bootstrap.xml.gz\"\n else:\n raise PipelineException(f\"Unable to find the bootstrapping production for {self.production.name}.\",\n issue=self.production.event.issue_object,\n production=self.production.name)\n\n command += [\"--manual-initial-grid\", bootstrap_file]\n\n self.logger.info(command, production = self.production)\n os.chdir(self.production.event.meta['working directory'])\n pipe = subprocess.Popen(command, \n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n out, err = pipe.communicate()\n if err:\n self.production.status = \"stuck\"\n if hasattr(self.production.event, \"issue_object\"):\n self.logger.info(out, production = self.production)\n self.logger.error(err, production = self.production)\n raise PipelineException(f\"DAG file could not be created.\\n{command}\\n{out}\\n\\n{err}\",\n issue=self.production.event.issue_object,\n production=self.production.name)\n else:\n self.logger.info(out, production = self.production)\n self.logger.error(err, production = self.production)\n raise PipelineException(f\"DAG file could not be created.\\n{command}\\n{out}\\n\\n{err}\",\n production=self.production.name)\n else:\n if self.production.event.repository:\n os.chdir(self.production.rundir)\n for psdfile in self.production.get_psds(\"xml\"):\n ifo = psdfile.split(\"/\")[-1].split(\"_\")[1].split(\".\")[0]\n os.system(f\"cp {psdfile} {ifo}-psd.xml.gz\")\n\n #os.system(\"cat *_local.cache > local.cache\")\n\n if hasattr(self.production.event, \"issue_object\"):\n return PipelineLogger(message=out,\n issue=self.production.event.issue_object,\n production=self.production.name)\n else:\n return PipelineLogger(message=out,\n production=self.production.name)","def _setup_graph(self):\n pass","def _setup_graph(self):\n pass","def getBuild():","def _construct_graph(self):\n raise NotImplementedError","def Steering(cache, generations, input_queue, result_queue):\n\n # Generations that have pending tasks to be executed. Pending tasks are those\n # whose results are not ready. The tasks that have their results ready are\n # referenced to as ready tasks. Once there is no pending generation, the\n # algorithm terminates.\n waiting = generations\n\n # Record how many initial tasks there are. If there is no task at all, the\n # algorithm can terminate right away.\n num_tasks = 0\n\n # Submit all the tasks in the initial generations to the next stage of the\n # framework. The next stage can be the build/compilation stage.\n for generation in generations:\n # Only send the task that has not been performed before to the next stage.\n for task in [task for task in generation.Pool() if task not in cache]:\n result_queue.put(task)\n cache.add(task)\n num_tasks += 1\n\n # If there is no task to be executed at all, the algorithm returns right away.\n if not num_tasks:\n # Inform the next stage that there will be no more task.\n result_queue.put(pipeline_process.POISONPILL)\n return\n\n # The algorithm is done if there is no pending generation. A generation is\n # pending if it has pending task.\n while waiting:\n # Busy-waiting for the next task.\n if input_queue.empty():\n continue\n\n # If there is a task whose result is ready from the last stage of the\n # feedback loop, there will be one less pending task.\n\n task = input_queue.get()\n\n # Store the result of this ready task. Intermediate results can be used to\n # generate report for final result or be used to reboot from a crash from\n # the failure of any module of the framework.\n task.LogSteeringCost()\n\n # Find out which pending generation this ready task belongs to. This pending\n # generation will have one less pending task. The \"next\" expression iterates\n # the generations in waiting until the first generation whose UpdateTask\n # method returns true.\n generation = next(gen for gen in waiting if gen.UpdateTask(task))\n\n # If there is still any pending task, do nothing.\n if not generation.Done():\n continue\n\n # All the tasks in the generation are finished. The generation is ready to\n # produce the next generation.\n waiting.remove(generation)\n\n # Check whether a generation should generate the next generation.\n # A generation may not generate the next generation, e.g., because a\n # fixpoint has been reached, there has not been any improvement for a few\n # generations or a local maxima is reached.\n if not generation.IsImproved():\n continue\n\n for new_generation in generation.Next(cache):\n # Make sure that each generation should contain at least one task.\n assert new_generation.Pool()\n waiting.append(new_generation)\n\n # Send the tasks of the new generations to the next stage for execution.\n for new_task in new_generation.Pool():\n result_queue.put(new_task)\n cache.add(new_task)\n\n # Steering algorithm is finished and it informs the next stage that there will\n # be no more task.\n result_queue.put(pipeline_process.POISONPILL)","def dag_builder(conf):\n dag = DAG(dag_id=conf['dag_id'], schedule_interval=conf['schedule_interval'], start_date=conf['start_date'], catchup=conf['catchup'], default_args=conf['default_args'])\n task_conf = conf.get('tasks', [])\n dep_conf = conf.get('dependencies', [])\n tasks = {}\n if task_conf:\n tasks = attach_tasks(dag, task_conf)\n if dep_conf:\n build_flow(dep_conf, tasks)\n return dag","def run_task(self) -> Task:","def populate_graph(self):","def train_on_tasks(config):\n seed = config['seed']\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n\n tasks = config.pop('tasks')\n\n task_vis_params = config.pop('vis_params')\n\n # all_stats = []\n transfer_matrix = defaultdict(list)\n total_steps = 0\n\n if 'learner' in config:\n learner = config.pop('learner')\n else:\n learner_path = config.pop('learner_path')\n learner = torch.load(learner_path)\n task_level_tuning = config.pop('task_level_tuning')\n if task_level_tuning:\n ray_params = config.pop('ray_params')\n local_mode = config.pop('local_mode')\n redis_address = config.pop('redis_address')\n all_analysis = []\n selected_tags = []\n for t_id, (task, vis_p) in enumerate(zip(tasks, task_vis_params)):\n #todo sync transfer matrix\n static_params = dict(\n t_id=t_id, task=task, tasks=tasks, vis_p=vis_p,\n transfer_matrix=transfer_matrix, total_steps=total_steps\n )\n\n if task_level_tuning:\n if not ray.is_initialized():\n if local_mode:\n ray.init(local_mode=local_mode)\n else:\n ray.init(redis_address,\n log_to_driver=False,\n logging_level=logging.ERROR)\n\n config['static_params'] = static_params\n config['learner_path'] = learner_path\n config['seed'] += t_id\n\n # reporter = CLIReporter(max_progress_rows=10)\n # print(reporter._metric_columns)\n # print(reporter.DEFAULT_COLUMNS)\n # reporter.add_metric_column('avg_acc_val')\n # reporter.add_metric_column('total_params')\n # reporter.add_metric_column('fw_t')\n # reporter.add_metric_column('data_t')\n # reporter.add_metric_column('eval_t')\n # reporter.add_metric_column('epoch_t')\n # reporter.add_metric_column('total_t')\n # ray_params['progress_reporter'] = reporter\n analysis = tune.run(train_t, config=config, **ray_params)\n\n all_analysis.append(analysis)\n\n def get_key(trial):\n # return trial.last_result['avg_acc_val_so_far']\n return trial.last_result['best_val']\n best_trial = max(analysis.trials, key=get_key)\n for trial in analysis.trials:\n if trial != best_trial:\n trial_path = trial.logdir\n shutil.rmtree(trial_path)\n # am = np.argmax(list(map(get_key, analysis.trials)))\n # print(\"BEST IS {}: {}\".format(am, best_trial.last_result['avg_acc_val']))\n\n # t = best_trial.last_result['duration_iterations']\n total_steps = best_trial.last_result['total_steps']\n selected_tags.append(best_trial.experiment_tag)\n best_learner_path = os.path.join(best_trial.logdir, 'learner.pth')\n learner = torch.load(best_learner_path, map_location='cpu')\n shutil.rmtree(best_trial.logdir)\n\n #todo UPDATE LEARNER AND SAVE\n torch.save(learner, learner_path)\n else:\n rescaled, t, metrics, b_state_dict, \\\n stats = train_single_task(config=deepcopy(config), learner=learner,\n **static_params)\n\n # all_stats.append(stats)\n # update_rescaled(list(rescaled.values()), list(rescaled.keys()), tag,\n # g_task_vis, False)\n\n if task_level_tuning:\n return all_analysis, selected_tags\n else:\n save_path = path.join(tune.get_trial_dir(), 'learner.pth')\n logger.info('Saving {} to {}'.format(learner, save_path))\n torch.save(learner, save_path)","def test_restore_with_dependencies(self):\n task1 = self._remote_task()\n task1['id'] = 1\n task2 = self._remote_task()\n task2['id'] = 2\n task2['dependencies'] = [1]\n\n graph = self._restore_graph([task1, task2])\n assert len(graph.tasks) == 2\n assert graph._dependencies[2] == set([1])","def __post_init__(self) -> None:\n for node in self.task_graph:\n self.task_graph.nodes[node][\"info\"] = TaskInfo(TaskStatus.NOT_STARTED)","def __post_init__(self) -> None:\n for node in self.task_graph:\n self.task_graph.nodes[node][\"info\"] = TaskInfo(TaskStatus.NOT_STARTED)","def processTask(self):\n #Util.set_color(Util.FOREGROUND_YELLOW | Util.FOREGROUND_INTENSITY)\n #logging.info(\"cmd : %s\", self.ExecutionTask.get_cmd())\n #logging.info(\"param : %s\", self.ExecutionTask.get_param())\n #logging.info(\"ret : %s\", str(self.ExecutionTask.get_ret()))\n #logging.info(\"ipport : %s\", self.ExecutionTask.get_ipport())\n #Util.set_color(Util.FOREGROUND_WHITE)\n\n ##############################################################\n # Process for any commands without received messages.....\n ##############################################################\n if self.ExecutionTask.get_cmd() == 'PASS' or self.ExecutionTask.get_cmd() == 'FAIL':\n logging.debug(\"result is %s\", self.ExecutionTask.get_cmd())\n self.setStatus('STOP')\n self.setTestResult(self.ExecutionTask.get_cmd())\n return\n\n if self.ExecutionTask.get_cmd() == 'r_info':\n rinfo_result = self.ExecutionTask.get_param().split('!')\n\n if len(rinfo_result) > 1:\n msg = rinfo_result[1]\n logging.debug(\"%s\", msg)\n\n self.setStatus('STOP')\n self.setTestResult(rinfo_result[0])\n return\n\n if self.ExecutionTask.get_cmd() == 'ResultCheck':\n time.sleep(5)\n self.process_ResultCheck()\n return\n\n if self.ExecutionTask.get_cmd() == 'CheckThroughput':\n time.sleep(5)\n throughputChk = StreamHandler(self.test_mngr_initr)\n chk_result = throughputChk.processStreamResults(self.ExecutionTask.get_param())\n self.setCheckResult(chk_result)\n #if 'FAIL' in chk_result:\n # self.setStatus('STOP')\n return\n\n if self.ExecutionTask.get_cmd() == 'config_multi_subresults':\n self.process_config_multi_subresults()\n return\n\n ##############################################################\n # Process for any commands with received messages......\n ##############################################################\n status = \"\"\n retDict = self.ExecutionTask.get_ret()\n recvStr = \"\"\n if self.ExecutionTask.recv:\n recvStr = self.ExecutionTask.recv.rstrip('\\r\\n')\n #print \"recv : \" + recvStr\n \n if GlobalConfigFiles.curr_prog_name == \"WMMPS\" and \"sniffer_control_subtask\" in self.ExecutionTask.get_cmd():\n logging.debug('In WMMPS, before parsing the recvStr: %s' % recvStr)\n lines = re.split('\\n', recvStr)\n for line in lines:\n if re.search(\"RESULT\", line, re.I):\n if \"FAIL\" in line:\n self.setStatus('STOP')\n self.setTestResult('FAIL')\n logging.debug('set test result to FAIL')\n return\n if \"PASS\" in line:\n self.setTestResult('PASS')\n logging.debug('set test result to Pass')\n return\n return\n \n stitems = recvStr.split(',') \n if len(stitems) < 2:\n #logging.debug(\"Bypassing this cmd..\")\n return\n\n status = stitems[1]\n iDNB = TestScriptSymbolTable.get_value_from_sym_tab(\"iDNB\", TestScriptSymbolTable.test_script_sym_tab)\n iINV = TestScriptSymbolTable.get_value_from_sym_tab(\"iINV\", TestScriptSymbolTable.test_script_sym_tab) \n \n if iINV is None:\n iINV = 0\n \n if 'ERROR' in recvStr or 'INVALID' in recvStr and (iDNB == 0 or iDNB is None) and (iINV == 0 or iINV is None):\n #error case...\n logging.debug(\"Return ERROR or INVALID---> STOP process \")\n self.setStatus('STOP')\n self.setTestResult('FAIL')\n elif status != 'COMPLETE' and iDNB == 0 and iINV == 0:\n #incomplete case...(running?)\n logging.debug(\"Command %s not completed\", self.ExecutionTask.get_cmd())\n else:\n displayname = \"\"\n for tbd in self.test_mngr_initr.test_prog_mngr.test_prog.testbed_dev_list:\n if tbd.ctrlipaddr == self.ExecutionTask.get_ipport():\n displayname = tbd.displayname\n break\n \n if \"FAIL\" in recvStr and (iINV == 0 or iINV is None):\n if \"SNIFFER\" in displayname or \"sniffer\" in self.ExecutionTask.get_cmd():\n logging.info(\"Test Case Criteria Failure - Command returned FAIL\")\n self.setStatus('STOP')\n self.setTestResult('FAIL')\n\n elif self.ExecutionTask.get_cmd() == 'device_get_info':\n try:\n if displayname == '':\n self.tmsPacket.setDutDeviceInfo(recvStr)\n else:\n self.tmsPacket.setTestbedInfo(displayname, recvStr)\n\n #for validation\n self.setValidationInfo(displayname, recvStr)\n\n except OSError:\n logging.debug(\"exception -- device_get_info capi call\")\n elif self.ExecutionTask.get_cmd() == 'ca_get_version':\n self.setValidationInfo(displayname, recvStr)\n\n elif self.ExecutionTask.get_cmd() == 'sniffer_get_info':\n self.setValidationInfo('sniffer', recvStr)\n\n elif self.ExecutionTask.get_cmd() == 'sta_associate':\n time.sleep(10)\n\n if len(stitems) > 2:\n retParam = self.ExecutionTask.get_param().split(',')\n streamFlag = \"\"\n if len(retParam) > 4:\n streamFlag = retParam[3]\n\n if stitems[2] == 'streamID':\n streamHndler = StreamHandler(self.test_mngr_initr)\n logging.debug(\"stream config - streamID : %s\", stitems[3])\n if streamFlag == 'send':\n logging.debug(\"traffic config - send : streamInfo append\")\n streamPacket = streamInfo(\"%s\" % (stitems[3]), self.ExecutionTask.get_ipport(), -1, 'send',\n retParam[15], retParam[17], streamHndler.running_phase, streamHndler.RTPCount)\n streamHndler.add_streamInfo(streamPacket)\n streamHndler.RTPCount = streamHndler.RTPCount + 1\n\n elif streamFlag == 'receive':\n logging.debug(\"traffic config - receive : streamInfo append\")\n streamPacket = streamInfo(\"%s\" % (stitems[3]), self.ExecutionTask.get_ipport(), -1, 'receive',\n -1, -1, streamHndler.running_phase, -1)\n streamHndler.add_streamInfo(streamPacket)\n\n else:\n logging.debug(\"traffic config - else : \")\n\n\n\n if retParam[1] == 'Multicast':\n logging.debug(\"----MULTICAST----\")\n streamHndler.multicast = 1\n\n if self.ExecutionTask.get_cmd() != \"traffic_agent_send\":\n ret_val = \"%s\" %(stitems[3].strip())\n logging.debug(\"traffic config - ret_val : %s\", ret_val)\n setRetVal(getRetKey(retDict), ret_val)\n\n elif stitems[2].lower() == 'interfacetype':\n ret_val = (\"%s\" %(stitems[5]))\n setRetVal(getRetKey(retDict), ret_val)\n\n elif stitems[2].lower() == 'interfaceid':\n ret_val = stitems[3].split('_')[0]\n setRetVal(getRetKey(retDict), ret_val)\n\n elif self.ExecutionTask.get_cmd() == 'traffic_stop_ping':\n\n keyVal = retParam[1]\n #\"%s;%s\"%(retParam[1], self.ExecutionTask.get_ipport())\n setRetVal(keyVal, stitems[5])\n #print(\"%s = %s\" % (retParam[1], stitems[5]))\n pinginternalchk = TestScriptSymbolTable.get_value_from_sym_tab(\"PingInternalChk\", TestScriptSymbolTable.test_script_sym_tab)\n temp_key = getRetKey(self.ExecutionTask.get_ret())\n \n if \"$\" in temp_key:\n sent_reply = temp_key.split(',')\n #print \"SLIM==> ping result save...\"\n #print sent_reply[0]\n #print sent_reply[1]\n setRetVal(sent_reply[0], stitems[3])\n setRetVal(sent_reply[1], stitems[5]) \n\n setRetVal(\"$pingResp\", stitems[5])\n if pinginternalchk == '0':\n logging.debug(\"Ping Internal Check\")\n \n elif stitems[5] == '0':\n logging.debug (\"Test Case Criteria Failure - NO IP Connection -- Aborting the test\")\n self.setStatus('STOP')\n self.setTestResult('FAIL')\n else:\n if stitems[5] == '0':\n logging.debug (\"Test Case Criteria Failure - NO IP Connection -- Aborting the test\")\n self.setStatus('STOP')\n self.setTestResult('FAIL')\n else:\n if len(retDict) > 0:\n tempKey = getRetKey(retDict)\n temp_val = tempKey.split(',')\n count = 0\n item_len = len(stitems)\n for i in temp_val:\n if item_len > count + 3:\n setRetVal(i, stitems[3+count])\n count = count + 2\n\n if self.__status == 'STOP':\n logging.debug(\"generate final result if task stops.\")\n #self.generateFinalResult()\n else:\n pass\n #logging.debug(\"Continue---\")\n return","def build_nodes(self, tasks, clean_list):\n for name in clean_list:\n # add node itself if not in list of nodes\n self.nodes.setdefault(name, [])\n task = tasks[name]\n # reversing not required\n for dep_name in reversed(task.task_dep):\n if tasks[dep_name].subtask_of == name:\n rev_dep = self.nodes.setdefault(dep_name, [])\n rev_dep.append(name)","def build(_):","def _subgraph(self):\n return {\n 'type': 'SubgraphTask',\n 'id': 0,\n 'dependencies': [],\n 'parameters': {\n 'info': {},\n 'current_retries': 0,\n 'send_task_events': False,\n 'containing_subgraph': None,\n 'task_kwargs': {}\n }\n }","def dworker(self):\n\t\t\"\"\" {pid:{subkey:subvalue}} \"\"\"\n\t\t\"\"\" line colors in use: black, blue, red\"\"\"\n\t\t\"\"\" fill colors in use: cyan, yellow, orange, red\"\"\"\n\t\tif self.pslist:\n\t\t\tfor i in self.pslist:\n\t\t\t\tself.dgen(self.d, i[2], {\"name\":i[1], \"ppid\":i[3], \"created\":i[8], \"color\":\"black\"})\n\t\tif self.psscan:\n\t\t\tfor i in self.psscan:\n\t\t\t\tself.dgen(self.d, i[2], {\"name\":i[1], \"ppid\":i[3], \"created\":i[5], \"color\":\"blue\", \"fillcolor\":\"cyan\"})\n\t\t\t\tif len(i[6]) > 0:\n\t\t\t\t\tself.d[i[2]][\"exited\"]=i[6]\n\t\tif self.envars:\n\t\t\tfor i in self.envars:\n\t\t\t\tif i[3] == \"USERNAME\":\n\t\t\t\t\tself.dgen(self.d, i[0], {\"username\":i[4]})\n\t\tif self.netscan:\n\t\t\tfor i in self.netscan:\n\t\t\t\tself.dgen(self.d, i[5], {\"proto\":i[1], \"localaddr\":i[2], \"foreignaddr\":i[3], \"state\":i[4], \"conn-owner\":i[6], \"conn-created\":i[7]})\n\t\tif self.cmdline:\n\t\t\tfor i in self.cmdline:\n\t\t\t\tif len(i[2]) > 0:\n\t\t\t\t\tcfix = i[2].replace(\"\\\"\", \"\").replace(\"\\\\\", \"\\\\\\\\\").replace(\"{\", \"\\{\").replace(\"}\", \"\\}\")\n\t\t\t\t\tself.dgen(self.d, i[1], {\"cmdline\":cfix})\n\t\tif self.connscan:\n\t\t\tfor i in self.connscan:\n\t\t\t\tself.dgen(self.d, i[3], {\"loc_addr\":i[1], \"rem_addr\":i[2]})\n\t\tif self.apihooks:\n\t\t\tfor i in self.apihooks:\n\t\t\t\tself.dgen(self.d, i[3], {\"apihooks\": \"{} {} hooked {} via {}\".format(i[0], i[9], i[4], i[1]).replace(\"<\",\"\").replace(\">\",\"\"), \"fillcolor\":\"yellow\"})\n\t\tif self.malfind:\n\t\t\tfor i in self.malfind:\n\t\t\t\tif re.search('4d5a', i[6]):\n\t\t\t\t\tself.dgen(self.d, i[1], {\"malf_protection\":i[4], \"malf_bytes\":i[6][:10], \"fillcolor\":\"red\"})\n\t\t\t\telse:\n\t\t\t\t\tif \"malf_bytes\" not in self.d[i[1]].keys():\n\t\t\t\t\t\tself.dgen(self.d, i[1], {\"malf_protection\":i[4], \"malf_bytes\":i[6][:10], \"fillcolor\":\"orange\"})","def dispatch(self, queue):\n context = zmq.Context()\n socket = noBlockREQ(context)\n \n seedsQ1 = Queue()\n seedsQ2 = Queue()\n for address in self.seeds:\n seedsQ1.put(address)\n\n connectT = Thread(target=connectToSeeds, name=\"Connect to Seeds\", args=(socket, seedsQ1))\n connectT.start()\n\n toDisconnectQ = Queue()\n disconnectT = Thread(target=disconnectToSeeds, name=\"Disconnect to Seeds\", args=(socket, toDisconnectQ))\n disconnectT.start()\n\n pFindSeeds = Process(target=findSeeds, name=\"Find Seeds\", args=(set(self.seeds), [seedsQ1], [toDisconnectQ], log, 2000, 10, seedsQ2))\n pFindSeeds.start()\n\n pInput = Process(target=getSeedFromFile, name=\"Get seed from file\", args=(seedsQ1, seedsQ2))\n pInput.start()\n\n graph = {}\n depth = 1\n data = {}\n url_mapper = {url:f\"url_{i}\" for i, url in enumerate(self.urls)}\n \n src = set()\n while True: \n new_data = {}\n while len(self.urls):\n try:\n url = self.urls[0]\n self.urls.pop(0)\n self.urls.append(url)\n with counterSocketReq:\n socket.send_json((\"URL\", self.uuid, url))\n log.debug(f\"Send {url}\", \"dispatch\")\n response = socket.recv_pyobj()\n assert isinstance(response, tuple), f\"Bad response, expected <tuple> find {type(response)}\"\n assert len(response) == 2, \"bad response size\"\n assert response[0] == 'RESPONSE', \"Unexpected response format\"\n _, package = response\n log.debug(f\"Received a package with size: {len(package)}\", \"dispatch\")\n for recv_url, html in package.items():\n try:\n idx = self.urls.index(recv_url)\n log.info(f\"{recv_url} {GREEN}OK{RESET}\", \"dispatch\")\n new_data[recv_url] = html\n self.urls.pop(idx)\n except ValueError:\n log.debug(f'Unnecesary {recv_url}', 'dispatch')\n except AssertionError as e:\n log.error(e, \"dispatch\")\n except zmq.error.Again as e:\n log.debug(e, \"dispatch\")\n except Exception as e:\n log.error(e, \"dispatch\")\n time.sleep(0.8)\n \n log.info(f'Depth {depth} done', 'dispatch')\n for url, html in new_data.items():\n graph[url] = set()\n try:\n text = html.decode()\n soup = BeautifulSoup(html, 'html.parser')\n tags = soup.find_all(valid_tags)\n new_urls = [['src', 'href'][tag.has_attr('href')] for tag in tags]\n changes = []\n for i, attr in enumerate(new_urls):\n url_dir = urljoin(url, tags[i][attr])\n graph[url].add(url_dir)\n if url_dir not in url_mapper:\n url_mapper[url_dir] = f'url_{len(url_mapper)}'\n changes.append((tags[i][attr], url_mapper[url_dir]))\n if attr == 'src' or tags[i].name == 'link':\n src.add(url_dir)\n continue\n self.urls.append(url_dir)\n html = change_html(text, changes).encode()\n except UnicodeDecodeError:\n log.debug(f'{url} is not decodeable', 'dispatch')\n except: # BeautifulSoup strange exceptions related with his's logger\n pass\n new_data[url] = html\n data.update(new_data)\n self.urls = set(self.urls)\n self.urls.difference_update(self.old)\n self.old.update(self.urls)\n self.urls = list(self.urls)\n \n if depth > self.depth:\n break\n if depth == self.depth:\n src.difference_update(self.old)\n self.old.update(src)\n self.urls = list(src)\n depth += 1\n log.info(f\"Number of URLs to be requested for download: {RED}{len(self.urls)}{RESET}\", \"dispatch\")\n \n log.info(f\"Starting to write data\", \"dispatch\")\n for i, url in enumerate(self.originals):\n try:\n res = HtmlResponse(url=url, body=data[url], encoding='utf8')\n base = res.css('title::text')[0].get()\n except:\n base = f\"web_page_{i}\"\n try:\n os.makedirs(f'downloads/{base}-data')\n except:\n pass\n writer(f'downloads/{base}-data', url, set(), data, url_mapper, graph) \n \n html = data[url]\n if len(graph[url]) > 0:\n text = data[url].decode()\n changes = []\n for dep in graph[url]:\n name = url_mapper[dep]\n changes.append((name, f'{base}-data/{name}'))\n html = change_html(text, changes).encode()\n with open(f'downloads/{base}', 'wb') as fd:\n fd.write(html)\n \n log.info(f\"Dispatcher:{self.uuid} has completed his URLs succefully\", \"dispatch\")\n log.debug(f\"Dispatcher:{self.uuid} disconnecting from system\", \"dispatch\")\n #disconnect\n\n queue.put(True)\n pFindSeeds.terminate()\n pInput.terminate()","def process(self, sess):\n global send_counter\n \n #sess.run(self.sync) # copy weights from shared to local\n rollout = self.pull_batch_from_queue()\n batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)\n\n should_compute_summary = self.task == 0 and self.local_steps % 11 == 0\n\n if should_compute_summary:\n fetches = [self.summary_op, self.train_op]\n else:\n fetches = [self.train_op]\n\n\n feed_dict = {\n self.local_network.x: batch.si,\n self.ac: batch.a,\n self.adv: batch.adv,\n self.r: batch.r,\n self.local_network.state_in[0]: batch.features[0],\n self.local_network.state_in[1]: batch.features[1],\n }\n\n # Get current trainable variables\n # This is trainable variables\n fetched = sess.run(fetches, feed_dict=feed_dict)\n\n\n if self.num_workers > 1:\n sys.stdout.write('\\r' + str(self.local_steps))\n if self.local_steps % 100 == 0:\n global var0\n global var1\n var1 = sess.run(self.local_network.var_list) # After training\n if var0 != None:\n var_diff = [a - b for (a,b) in zip(var1, var0)]\n var_diff_data = pickle.dumps(var_diff, -1)\n print('Sync weights')\n self.msg_sent = socket_util.socket_send_data_chucks(self.sock, var_diff_data, self.mcast_destination, self.msg_sent)\n var0 = sess.run(self.local_network.var_list) # A list of numpy array\n\n # Handle each message in the socket queue\n while not self.inc_msg_q.empty():\n print('Apply remote gradients')\n # Process received grads_and_vars from other peers\n remote_var_diff_data = self.inc_msg_q.get(False)\n remote_var_diff = pickle.loads(remote_var_diff_data)\n\n add_op = [a+b for (a,b) in zip(self.local_network.var_list, remote_var_diff)]\n sess.run(add_op)\n\n if should_compute_summary:\n self.summary_writer.add_summary(tf.Summary.FromString(fetched[0]))\n self.summary_writer.flush()\n self.local_steps += 1","def run(self):\n while True:\n task = self.queue.get()\n if task is None:\n break\n fslfile = os.path.join(task.directory, task.fsl_file)\n # TODO: there must be a better way to set the workdir\n workdir = '/'.join(task.directory.split('/')[-3:])\n logger.info('Docker task %s %s workdir %s',\n task.id, task.fsl_file, workdir)\n fslcmds = ['save_model(close)',\n \"chdir('{}')\".format(workdir)]\n \n with open(fslfile) as f:\n fslcmds += f.readlines()\n r = [json.loads(s)\n for s in self.container.send_fsl(\n '\\n'.join(fslcmds), publish_receive)[:2]]\n try:\n if r[0]['status'] == 'ok':\n task.status = 'C'\n else:\n task.status = 'X'\n except:\n task.status = 'X'\n logger.info(\"Finished %s\", r)\n if self.stoponend:\n self.container.quit()\n # lets hope that docker will always restart this container\n self.queue.task_done()","def build_graph(self):\n n_classes = self.n_classes\n\n (self.feed('data')\n .conv(3, 3, 64, 1, 1, name='conv1_1', trainable=False)\n .conv(3, 3, 64, 1, 1, name='conv1_2', trainable=False)\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool1')\n .conv(3, 3, 128, 1, 1, name='conv2_1', trainable=False)\n .conv(3, 3, 128, 1, 1, name='conv2_2', trainable=False)\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool2')\n .conv(3, 3, 256, 1, 1, name='conv3_1')\n .conv(3, 3, 256, 1, 1, name='conv3_2')\n .conv(3, 3, 256, 1, 1, name='conv3_3')\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool3')\n .conv(3, 3, 512, 1, 1, name='conv4_1')\n .conv(3, 3, 512, 1, 1, name='conv4_2')\n .conv(3, 3, 512, 1, 1, name='conv4_3')\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool4')\n .conv(3, 3, 512, 1, 1, name='conv5_1')\n .conv(3, 3, 512, 1, 1, name='conv5_2')\n .conv(3, 3, 512, 1, 1, name='conv5_3'))\n\n self.compute_rDeRF() # dummy\n\n # Classification\n (self.feed('conv5_3')\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool6')\n .reshape(shape=(-1, 7, 7, 512), name='pool6_reshape')\n .fc(4096, name='fc6')\n .dropout(0.5, name='drop6')\n .fc(4096, name='fc7')\n .dropout(0.5, name='drop7')\n # .make_time(name='drop7_reduced')\n .fc(n_classes, relu=False, name='cls_score')\n .softmax(name='cls_prob'))\n pass","def __call__(self):\n\n link = hardlink.Command(dry_run=self.dry_run, force=True)\n\n # Load config.\n try:\n with open(self.config_path) as file:\n config = yaml.load(file.read())\n except Exception:\n self._err('Unable to load config: %s' % self.config_path)\n\n # Get JSON database for processed torrent status.\n database_path = config.get('db', DATABASE_PATH)\n logger.debug('Loading database: %s' % database_path)\n try:\n db = jsondict.JsonDict(database_path, autosave=True)\n except ValueError:\n self._err('Unable to load database: %s' % database_path)\n\n # Get mapped paths.\n mapped_paths = config.get('mapped_remote_paths', {}).items()\n\n # Get Transmission client.\n host = config.get('transmission_host', 'localhost')\n port = config.get('transmission_port', 9091)\n logger.debug('Connecting to Transmission: %s:%s' % (host, port))\n client = transmission.Transmission(host=host, port=port)\n\n # Get torrents from Transmission.\n try:\n torrents = client('torrent-get', fields=[\n 'downloadDir',\n 'id',\n 'name',\n 'percentDone',\n 'secondsSeeding',\n 'uploadRatio',\n ])['torrents']\n except requests.ConnectionError:\n self._err('Unable to connect to Transmission at %s:%s' % (\n host,\n port,\n ))\n\n # Store torrents found in a known torrents directory for later\n # processing.\n found_torrents = set()\n\n # Hard link or remove completed torrents.\n for torrent in torrents:\n remote_path = os.path.join(\n torrent['downloadDir'], torrent['name'])\n\n # Get local path for remote path.\n local_path = self.get_local_path(remote_path, mapped_paths)\n\n # Find matching torrents directory.\n for dir_config in config['torrent_dirs']:\n\n # Get config for torrent directory.\n download_dir = dir_config['download_dir']\n post_processing_dir = dir_config['post_processing_dir']\n ratio = dir_config.get('ratio')\n seed_days = dir_config.get('seed_days')\n\n if local_path.startswith(download_dir):\n found_torrents.add(local_path)\n\n # Get downloaded and seeding status.\n downloaded = bool(torrent['percentDone'] == 1)\n\n # Get processed status.\n processed = bool(downloaded and db.get(remote_path))\n\n # Get seeding status.\n seeding = bool(\n ratio and\n ratio > torrent['uploadRatio'] or\n seed_days and\n seed_days * DAY_SECONDS > torrent['secondsSeeding'])\n\n # Hard link downloaded torrents to the post processing\n # directory.\n if downloaded and not processed:\n logger.info('Processing torrent: %s' % local_path)\n destination = os.path.join(\n post_processing_dir,\n os.path.relpath(local_path, download_dir),\n )\n link(local_path, destination)\n if not self.dry_run:\n db[remote_path] = True\n\n # Remove processed torrents that have finished seeding.\n elif processed and not seeding:\n if not self.remove:\n logger.debug(\n 'Not removing inactive torrent: %s' %\n torrent['name'])\n else:\n logger.info(\n 'Removing inactive torrent: %s' %\n torrent['name'])\n if not self.dry_run:\n client(\n 'torrent-remove',\n ids=[torrent['id']],\n delete_local_data=True,\n )\n del db[remote_path]\n\n else:\n # Ignore torrents that are still downloading or\n # seeding.\n logger.debug(\n 'Skipping active torrent: %s' % local_path)\n\n # Log torrent data, regardless of action taken.\n logger.debug(' - Downloaded: %d%%' % (\n torrent['percentDone'] * 100))\n if ratio:\n logger.debug(' - Ratio: %.2f (%s)' % (\n torrent['uploadRatio'],\n ratio,\n ))\n if seed_days:\n logger.debug(' - Seed Time: %.2f (%s) days' % (\n torrent['secondsSeeding'] / DAY_SECONDS,\n seed_days,\n ))\n\n # We found a match. No need to continue.\n break\n\n # No matching torrents directory.\n else:\n logger.debug(\n 'Skipping torrent not located in any download directory: '\n '%s' % local_path)\n\n # Process orphaned torrent data in download directories.\n for dir_config in config['torrent_dirs']:\n download_dir = dir_config['download_dir']\n post_processing_dir = dir_config['post_processing_dir']\n\n # Skip download directories that don't exist.\n if not os.path.isdir(download_dir):\n continue\n\n # Compare every file and directory in the download directory to\n # previously found torrents. Remove anything already processed and\n # not belonging to a found torrent. Hard link everything else to\n # the post processing directory.\n for path in os.listdir(download_dir):\n\n # Ignore hidden files, e.g. dot-underscore files on OS X.\n if path.startswith('.'):\n continue\n\n local_path = os.path.join(download_dir, path)\n\n # Find matching torrent for this path.\n for found_torrent in found_torrents:\n if local_path.startswith(found_torrent):\n # We found a match. No need to continue.\n break\n\n # No matching torrent.\n else:\n\n # Get remote path for local path.\n remote_path = self.get_remote_path(\n local_path, mapped_paths)\n\n # Get processed status.\n processed = db.get(remote_path, False)\n\n # Hard link orphaned files that have not been processed.\n if not processed:\n logger.info(\n 'Processing orphaned file or directory: %s' %\n local_path)\n destination = os.path.join(\n post_processing_dir,\n os.path.relpath(local_path, download_dir),\n )\n link(local_path, destination)\n # No need to add path to database, it would be removed\n # immediately in the next code block.\n\n # Remove orphaned files that have been processed.\n logger.info(\n 'Removing orphaned file or directory: %s' %\n local_path)\n if not self.dry_run:\n try:\n shutil.rmtree(local_path)\n except OSError:\n os.remove(local_path)\n # Remove path from database.\n if remote_path in db:\n del db[remote_path]\n\n # Remove stale records in database. Convert database keys to list to\n # avoid `RuntimeError: dictionary changed size during iteration`.\n for remote_path in list(db):\n\n # Get local path for remote path.\n local_path = self.get_local_path(remote_path, mapped_paths)\n\n # Remove from database.\n if not os.path.exists(local_path):\n logger.debug(\n 'Removing stale record from database: %s' % remote_path)\n if not self.dry_run:\n del db[remote_path]"],"string":"[\n \"def build_taskgraph(tasks):\\n global taskgraph\\n for task in tasks:\\n if not task['id'] in taskgraph:\\n taskgraph.add_node(task[\\\"id\\\"])\\n data = str(task[\\\"DATA\\\"])\\n tr = TaskResources(ram=int(task['RAM']), cpu=int(\\n task['CPU']), hdd=int(task['HDD']))\\n taskgraph.node[task[\\\"id\\\"]] = [tr, data]\\n for dest in task[\\\"dest\\\"]:\\n taskgraph.add_edge(task[\\\"id\\\"], dest[\\\"id\\\"])\\n\\n return taskgraph\",\n \"def task():\",\n \"def tasks():\",\n \"def generate_tasks(self, task):\",\n \"def build() -> List[asyncio.Task]:\",\n \"def create_task():\",\n \"def task(self):\",\n \"def task(self):\",\n \"def build_curriculum(self,target_task,groups,setOfTasks):\\n connections = [] #list of graph connections\\n graph = Graph([],directed = True)\\n #Intra-group transfer\\n for g in groups:\\n group = groups[g]\\n #For all tasks inside each group\\n for i in range(len(group)):\\n currentTask = group[i][0]\\n maxTransferPot = -float('inf')\\n argMaxTask = None\\n # get task with maximum potential\\n for j in range(i+1,len(group)):\\n testTask = group[j][0]\\n if testTask != currentTask:\\n pot = self.transfer_potential(testTask,currentTask,graph.list_children(currentTask))\\n if pot > maxTransferPot:\\n maxTransferPot = pot\\n argMaxTask = testTask\\n if argMaxTask != None and maxTransferPot > self.thresholdTask:\\n # Add connection\\n connections.append((argMaxTask,currentTask))\\n graph.add(argMaxTask,currentTask)\\n \\n #Creating graph structure\\n graph = Graph(connections,directed = True)\\n \\n groupKeys = list(groups.keys())\\n #Inter-group transfer\\n for i in range(len(groupKeys)):\\n #Get task features of the first task in the group \\n #(this is equal for all tasks inside it)\\n g = groups[groupKeys[i]]\\n gFeatures = g[0][0].task_features()\\n #For all groups that have task features contained in g:\\n for j in range(i+1,len(groupKeys)):\\n gPrime = groups[groupKeys[j]]\\n gPrimeFeatures = gPrime[0][0].task_features()\\n \\n if task.is_contained(gPrimeFeatures,gFeatures):\\n #For all tasks in g, get the argmax task in gPrime and\\n #Possibly creates a link\\n for gTaskAndPot in g:\\n gTask = gTaskAndPot[0]\\n maxTransferPot = -float('inf')\\n argMaxTask = None\\n for gPrimeTaskAndPot in gPrime:\\n gPrimeTask = gPrimeTaskAndPot[0]\\n pot = self.transfer_potential(gPrimeTask,gTask,graph.list_children(currentTask))\\n if pot > maxTransferPot:\\n maxTransferPot = pot\\n argMaxTask = gPrimeTask\\n if argMaxTask != None and maxTransferPot > self.thresholdTask:\\n # Add connection\\n graph.add(argMaxTask,gTask)\\n connections.append((argMaxTask,gTask))\\n \\n #For all tasks with zero degree, a connection to the target-task is added\\n for currentTask in setOfTasks:\\n if graph.out_degree(currentTask) == 0:\\n graph.add(currentTask,target_task)\\n #The task graph is completed\\n self.taskGraph = graph\",\n \"def _build_graph(self):\\n pass\",\n \"def build_graph(self):\\n pass\",\n \"def task_gen(self):\\n pass\",\n \"def run(self):\\n\\n self._build_dependency_graph()\",\n \"def task_stagnant(task):\",\n \"def defineTasks(self,partition):\\n recv_slots = partition.recvSlices()\\n strm_slots = partition.streamSlices()\\n recvNodes = partition.recvNodesFromSlots()\\n strmNodes = partition.streamNodesFromSlots()\\n opt = '/'+self.manager.hostName()+'/'+partition.manager.name()+'/'+partition.name+'/'\\n cl0 = '/Class0'+opt\\n cl1 = '/Class1'+opt\\n\\n partition.setDataSources([])\\n tasks = []\\n pn = self.partitionName()\\n print '---------------------- Partition name is:',pn\\n for i in xrange(len(recv_slots)):\\n slot = recv_slots[i]\\n node = slot[:slot.find(':')]\\n sub_farm = 'SF%02d'%(i,)\\n short_name = sub_farm+'_SND' # Keep this name to ensure storageMon is working!\\n task = pn+'_'+node+'_'+short_name\\n tasks.append(node+'/'+task+'/'+short_name+'/RecStorageSend'+cl1+'(\\\"'+sub_farm+'\\\",'+str(i)+',)')\\n partition.setRecvSenders(tasks)\\n tasks = []\\n for i in xrange(len(strm_slots)):\\n slot = strm_slots[i]\\n node = slot[:slot.find(':')]\\n sub_farm = 'SF%02d'%(i,)\\n short_name = sub_farm+'_HLT' # Keep this name to ensure storageMon is working!\\n task = pn+'_'+node+'_'+short_name\\n tasks.append(node+'/'+task+'/'+short_name+'/RecStorageRecv'+cl1+'(\\\"'+sub_farm+'\\\",'+str(i)+',)')\\n partition.setStreamReceivers(tasks)\\n cnt = 0\\n tasks = []\\n infra = []\\n for j in recvNodes:\\n for itm in self.rcvInfra.data:\\n i,cl=itm.split('/')\\n infra.append(j+'/'+pn+'_'+j+'_'+i+'/'+i+'/'+i+'/'+cl+opt+'(\\\"'+str(cnt)+'\\\",)')\\n cnt = cnt + 1\\n partition.setRecvInfrastructure(infra)\\n partition.setRecvReceivers(tasks)\\n cnt = 0\\n tasks = []\\n infra = []\\n for j in strmNodes:\\n for itm in self.strInfra.data:\\n i,cl=itm.split('/')\\n infra.append(j+'/'+pn+'_'+j+'_'+i+'/'+i+'/'+i+'/'+cl+opt+'(\\\"'+str(cnt)+'\\\",)')\\n cnt = cnt + 1\\n partition.setStreamInfrastructure(infra)\\n partition.setStreamSenders(tasks)\\n if partition.saveTasks():\\n tasks = partition.collectTasks(tasks={},with_data_sources=0)\\n return tasks\\n return None\",\n \"def _build(self, replace=None, profile=False):\\n self.__node_dict.clear()\\n replace = dict() if replace is None else replace\\n\\n # check if there are item in the replace that is not in the graph\\n task_ids = set([task[TaskSpecSchema.task_id] for task in self])\\n for rkey in replace.keys():\\n if rkey not in task_ids:\\n warnings.warn(\\n 'Replace task-id {} not found in task-graph'.format(rkey),\\n RuntimeWarning)\\n\\n # instantiate node objects\\n for task in self:\\n task_id = task[TaskSpecSchema.task_id]\\n nodetype = task[TaskSpecSchema.node_type]\\n nodetype = nodetype if isinstance(nodetype, str) else \\\\\\n nodetype.__name__\\n if nodetype == OUTPUT_TYPE:\\n output_task = task\\n node = get_node_obj(output_task, tgraph_mixin=True)\\n else:\\n node = get_node_obj(task, replace.get(task_id), profile,\\n tgraph_mixin=True)\\n self.__node_dict[task_id] = node\\n\\n # build the graph\\n for task_id in self.__node_dict:\\n node = self.__node_dict[task_id]\\n task_inputs = node._task_obj[TaskSpecSchema.inputs]\\n for iport in task_inputs:\\n # node_inputs should be a dict with entries:\\n # {iport: taskid.oport}\\n input_task = task_inputs[iport].split('.')\\n dst_port = iport\\n\\n input_id = input_task[0]\\n # src_port = input_task[1] if len(input_task) > 1 else None\\n src_port = input_task[1]\\n\\n try:\\n input_node = self.__node_dict[input_id]\\n except KeyError:\\n raise LookupError(\\n 'Missing task \\\"{}\\\". Add task spec to TaskGraph.'\\n .format(input_id))\\n\\n node.inputs.append({\\n 'from_node': input_node,\\n 'from_port': src_port,\\n 'to_port': dst_port\\n })\\n # input_node.outputs.append(node)\\n input_node.outputs.append({\\n 'to_node': node,\\n 'to_port': dst_port,\\n 'from_port': src_port\\n })\",\n \"def invoke(self, msg, req):\\n node = Node.create()\\n node.acquire_lock()\\n\\n if msg.name == 'forward':\\n try:\\n with node.graph.as_default():\\n if node.num_devices == 5:\\n output, name = Model_5.forward(req['input'], req['next'], node)\\n Thread(target=self.send, args=(output, name, req['tag'])).start()\\n elif node.num_devices == 6:\\n output, name = Model_6.forward(req['input'], req['next'], node)\\n Thread(target=self.send, args=(output, name, req['tag'])).start()\\n elif node.num_devices == 7:\\n output, name = Model_7.forward(req['input'], req['next'], node)\\n Thread(target=self.send, args=(output, name, req['tag'])).start()\\n elif node.num_devices == 8:\\n output, name = Model_8.forward(req['input'], req['next'], node)\\n Thread(target=self.send, args=(output, name, req['tag'])).start()\\n\\n node.release_lock()\\n return\\n\\n except Exception, e:\\n node.log('Error', e.message)\\n elif msg.name == 'update':\\n \\\"\\\"\\\"update this node's task configuration,based on the received massage \\\"\\\"\\\"\\n try:\\n node.num_devices = req['num_devices']\\n available_ip = req['available_ip']\\n\\n update_ip(get_file(node.num_devices), available_ip)\\n load_ip(node)\\n\\n node.release_lock()\\n return\\n\\n except Exception, e:\\n node.log('Error', e.message)\\n\\n else:\\n raise schema.AvroException('unexpected message:', msg.getname())\",\n \"def get_graph(self, with_fix=False):\\n nodes = self.get_tasks()\\n if with_fix:\\n for n in nodes:\\n n.fix_arguments()\\n deps = self.get_dependencies()\\n graph = nx.DiGraph()\\n graph.add_nodes_from(nodes)\\n graph.add_edges_from( [d.edge() for d in deps] )\\n return graph\",\n \"def get_initial_graph(n_in, code_spec, n_out):\\n G = nx.MultiDiGraph()\\n\\n G.add_node(\\\"source\\\", label=\\\"SOURCE\\\", shape=\\\"cylinder\\\", color=\\\"gold\\\")\\n G.add_node(\\\"sink\\\", label=\\\"SINK\\\", shape=\\\"cylinder\\\", color=\\\"gold\\\")\\n\\n for task in tasks:\\n task_source_key = f\\\"{task.name}-source\\\"\\n G.add_node(task_source_key, label=task_source_key, shape=\\\"cylinder\\\", color=\\\"gold\\\")\\n G.add_edge(\\\"source\\\", task_source_key)\\n task_sink_key = f\\\"{task.name}-sink\\\"\\n G.add_node(task_sink_key, label=task_sink_key, shape=\\\"cylinder\\\", color=\\\"gold\\\")\\n G.add_edge(task_sink_key, \\\"sink\\\")\\n for i, input in enumerate(task.inputs):\\n input_key = f\\\"{task.name}-input-{i}\\\"\\n G.add_node(input_key, label=input_key, shape=\\\"circle\\\", color=\\\"blue\\\",\\n node_type=\\\"input\\\", input=input)\\n G.add_edge(task_source_key, input_key)\\n encoder_key = f\\\"{input_key}-encoder\\\"\\n G.add_node(encoder_key, label=encoder_key, shape=\\\"diamond\\\", color=\\\"black\\\",\\n node_type=\\\"encoder\\\", input=input, output=CODE)\\n G.add_edge(input_key, encoder_key)\\n for box_number in range(initial_boxes):\\n if box_number not in G.nodes():\\n G.add_node(box_number, label=box_number, shape=\\\"square\\\", color=\\\"black\\\")\\n G.add_edge(encoder_key, box_number)\\n for o, output in enumerate(task.outputs):\\n output_key = f\\\"{task.name}-output-{o}\\\"\\n G.add_node(output_key, label=output_key, shape=\\\"triangle\\\", color=\\\"red\\\",\\n node_type=\\\"output\\\", output=output)\\n G.add_edge(output_key, task_sink_key)\\n decoder_key = f\\\"{output_key}-decoder\\\"\\n G.add_node(decoder_key, label=decoder_key, shape=\\\"diamond\\\", color=\\\"black\\\",\\n node_type=\\\"decoder\\\", input=CODE, output=output)\\n for box_number in range(initial_boxes):\\n G.add_edge(box_number, decoder_key)\\n G.add_edge(decoder_key, output_key)\\n return G\",\n \"def collect(self):\\n while True:\\n if not self._queue.empty():\\n message = self._queue.get()\\n self.working_on = message['job_type']\\n else:\\n break\\n logging.info(\\\"Popped off message: {}\\\\n\\\".format(str(message)))\\n\\n if message['job_type'] == 'STOP':\\n break\\n\\n if message['job_type'] != 'MAINTAIN' and message['job_type'] != 'UPDATE':\\n raise ValueError('{} is not a recognized task type'.format(message['job_type']))\\n pass\\n\\n # Query all repos with repo url of given task\\n repoUrlSQL = s.sql.text(\\\"\\\"\\\"\\n SELECT min(repo_id) as repo_id FROM repo WHERE repo_git = '{}'\\n \\\"\\\"\\\".format(message['given']['github_url']))\\n repo_id = int(pd.read_sql(repoUrlSQL, self.db, params={}).iloc[0]['repo_id'])\\n\\n try:\\n if message['models'][0] == 'pull_requests':\\n self.pull_requests_model(message, repo_id)\\n elif message['models'][0] == 'pull_request_commits':\\n self.pull_request_commits_model(message, repo_id)\\n elif message['models'][0] == 'pull_request_files':\\n self.pull_requests_graphql(message, repo_id)\\n except Exception as e:\\n register_task_failure(self, message, repo_id, e)\\n pass\",\n \"def execute(self, g, inputs):\\n \\n if self._started:\\n raise ValueError(\\\"Traverser can only be started once.\\\")\\n self._started = True\\n \\n self._graph = graph.Graph()\\n self._graph = graphdecorator.DataGraphDecorator(self._graph)\\n self._graph = graphdecorator.RefineDecorator(self._graph)\\n self._graph = graphdecorator.ReadyDecorator(self._graph)\\n \\n # copy the graph into g\\n for tick in g.get_all_ticks():\\n self._graph.add_task(tick, g.get_task(tick), g.get_task_properties(tick))\\n for tick in g.get_all_ticks() + [graph.FINAL_TICK]:\\n for source, dest in g.get_in_connections(tick):\\n self._graph.connect(source, dest)\\n\\n # ingest graph inputs\\n self._graph.set_output_data(graph.START_TICK, inputs)\\n \\n self._iterate()\\n \\n return self._result\",\n \"def build_graph(self, graph, inst_name, port_nets):\\n return\",\n \"def collect(self):\\n while True:\\n if not self._queue.empty():\\n message = self._queue.get()\\n self.working_on = message['job_type']\\n else:\\n break\\n logging.info(\\\"Popped off message: {}\\\\n\\\".format(str(message)))\\n\\n if message['job_type'] == 'STOP':\\n break\\n\\n if message['job_type'] != 'MAINTAIN' and message['job_type'] != 'UPDATE':\\n raise ValueError('{} is not a recognized task type'.format(message['job_type']))\\n pass\\n\\n \\\"\\\"\\\" Query all repos with repo url of given task \\\"\\\"\\\"\\n repoUrlSQL = s.sql.text(\\\"\\\"\\\"\\n SELECT min(repo_id) as repo_id FROM repo WHERE repo_git = '{}'\\n \\\"\\\"\\\".format(message['given']['git_url']))\\n repo_id = int(pd.read_sql(repoUrlSQL, self.db, params={}).iloc[0]['repo_id'])\\n\\n try:\\n if message['models'][0] == 'badges':\\n self.badges_model(message, repo_id)\\n except Exception as e:\\n register_task_failure(self, logging, message, repo_id, e)\\n pass\",\n \"def build_graph(self):\\n raise NotImplementedError\",\n \"def _build_network(self):\\n pass\",\n \"def main():\\n \\n desc = \\\"\\\"\\\"TODO: ... ask me later! I'm on a deadline! ...\\\"\\\"\\\"\\n \\n parser = argparse.ArgumentParser(description=desc)\\n \\n parser.add_argument('config_file', type=str,\\n help=\\\"\\\"\\\"Path to a yaml formatted config file containing setup options for the graph.\\\"\\\"\\\")\\n \\n args = parser.parse_args()\\n \\n yopts = bunchify(yaml.load(open(args.config_file,'rU')))\\n \\n parser_list = setup_parsers(yopts)\\n \\n gBuilder = GraphBuilder(parser_list)\\n gBuilder.populate_registries()\\n gHandler = gBuilder.map_registries_to_graph()\\n\\n # init metric objs, RelHandler and Vote Handler\\n metrics = init_metrics()\\n relHandler = RelationsHandler(metrics)\\n voteHandler = VoteHandler(gHandler.graph)\\n voteHandler.set_vote_types(relHandler.get_vote_types(),weight_by='branch_length')\\n \\n gHandler.install_metric_handlers(rel_hndler=relHandler,vote_hndlr=voteHandler)\\n gHandler.measure_relations()\\n \\n # dump built construct as pickle\\n cPickle.dump(gHandler,open(yopts.outputs.graph_pickle,'w'))\",\n \"def octopus_tasks(self, msg, args):\\r\\n self.tasks.send_tasks(msg, args)\",\n \"def task(ctx, config):\\n pass\",\n \"def task1(self):\\n \\n pass\",\n \"def task_generate_tasks():\\n \\n yield {\\n 'basename': 'generate_tasks',\\n 'name': None,\\n # 'doc': 'docs for X',\\n 'watch': ['trains/'],\\n 'task_dep': ['create_folders'],\\n }\\n \\n for root, dirs, files in os.walk('trains/',topdown=False):\\n for f in files:\\n #print(f)\\n yield template_train_model(os.path.join(root,f))\",\n \"def task():\\n pass\",\n \"def task():\\n pass\",\n \"def build_graph(self):\\n self._build_model()\\n if self.mode == 'train':\\n self._build_train_op()\",\n \"def task2(self):\\n\\n pass\",\n \"def collect_tasks_fn(\\n tn: TraversalNode, data: Dict[CollectionAddress, GraphTask]\\n ) -> None:\\n if not tn.is_root_node():\\n data[tn.address] = GraphTask(tn, resources)\",\n \"def collect_tasks_fn(\\n tn: TraversalNode, data: Dict[CollectionAddress, GraphTask]\\n ) -> None:\\n if not tn.is_root_node():\\n data[tn.address] = GraphTask(tn, resources)\",\n \"def gen_graph(self):\",\n \"def build(self, graph):\\n self.results = None\\n\\n self.graph = graph\\n\\n self.__build_nodes()\\n self.__build_edges()\",\n \"def _build_computation_graph(self):\\n raise NotImplementedError\",\n \"def add_statistics_build_tasks(self, task_graph: TaskGraph,\\n **kwargs: Any) -> list[Task]:\\n raise NotImplementedError()\",\n \"def create_inference_tasks(task_queue, image_layer_path, convnet_path, \\n mask_layer_path, output_layer_path, output_block_start, output_block_size, \\n grid_size, patch_size, patch_overlap, cropping_margin_size,\\n output_key='output', num_output_channels=3, \\n image_mip=1, output_mip=1, mask_mip=3):\\n for z in tqdm(range(grid_size[0]), desc='z loop'):\\n for y in range(grid_size[1]):\\n for x in range(grid_size[2]):\\n output_offset = tuple(s+x*b for (s, x, b) in \\n zip(output_block_start, (z, y, x), \\n output_block_size))\\n task = InferenceTask(\\n image_layer_path=image_layer_path,\\n convnet_path=convnet_path,\\n mask_layer_path=mask_layer_path,\\n output_layer_path=output_layer_path,\\n output_offset=output_offset,\\n output_shape=output_block_size,\\n patch_size=patch_size, \\n patch_overlap=patch_overlap,\\n cropping_margin_size=cropping_margin_size,\\n output_key=output_key,\\n num_output_channels=num_output_channels,\\n image_mip=image_mip,\\n output_mip=output_mip,\\n mask_mip=mask_mip\\n )\\n task_queue.insert(task)\\n task_queue.wait('Uploading InferenceTasks')\\n\\n vol = CloudVolume(output_layer_path, mip=output_mip)\\n vol.provenance.processing.append({\\n 'method': {\\n 'task': 'InferenceTask',\\n 'image_layer_path': image_layer_path,\\n 'convnet_path': convnet_path,\\n 'mask_layer_path': mask_layer_path,\\n 'output_layer_path': output_layer_path,\\n 'output_offset': output_offset,\\n 'output_shape': output_block_size,\\n 'patch_size': patch_size,\\n 'patch_overlap': patch_overlap,\\n 'cropping_margin_size': cropping_margin_size,\\n 'output_key': output_key,\\n 'num_output_channels': num_output_channels,\\n 'image_mip': image_mip,\\n 'output_mip': output_mip,\\n 'mask_mip': mask_mip,\\n },\\n 'by': OPERATOR_CONTACT,\\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\\n })\\n vol.commit_provenance()\",\n \"def make_graph(self):\\n\\n # the root node\\n self.graph.node(self.playbook_filename, style=\\\"dotted\\\", id=\\\"root_node\\\")\\n\\n # loop through the plays\\n for play_counter, play in enumerate(self.playbook.get_plays(), 1):\\n\\n # the load basedir is relative to the playbook path\\n if play._included_path is not None:\\n self.data_loader.set_basedir(play._included_path)\\n else:\\n self.data_loader.set_basedir(self.playbook._basedir)\\n self.display.vvv(\\\"Loader basedir set to {}\\\".format(self.data_loader.get_basedir()))\\n\\n play_vars = self.variable_manager.get_vars(play)\\n play_hosts = [h.get_name() for h in self.inventory_manager.get_hosts(self.template(play.hosts, play_vars))]\\n play_name = \\\"Play #{}: {} ({})\\\".format(play_counter, clean_name(play.get_name()), len(play_hosts))\\n play_name = self.template(play_name, play_vars)\\n\\n self.display.banner(\\\"Graphing \\\" + play_name)\\n\\n play_id = \\\"play_\\\" + str(uuid.uuid4())\\n\\n self.graph_representation.add_node(play_id)\\n\\n with self.graph.subgraph(name=play_name) as play_subgraph:\\n color, play_font_color = get_play_colors(play)\\n # play node\\n play_subgraph.node(play_name, id=play_id, style=\\\"filled\\\", shape=\\\"box\\\", color=color,\\n fontcolor=play_font_color, tooltip=\\\" \\\".join(play_hosts))\\n\\n # edge from root node to plays\\n play_edge_id = \\\"edge_\\\" + str(uuid.uuid4())\\n play_subgraph.edge(self.playbook_filename, play_name, id=play_edge_id, style=\\\"bold\\\",\\n label=str(play_counter), color=color, fontcolor=color)\\n\\n # loop through the pre_tasks\\n self.display.v(\\\"Graphing pre_tasks...\\\")\\n nb_pre_tasks = 0\\n for pre_task_block in play.pre_tasks:\\n nb_pre_tasks = self._include_tasks_in_blocks(current_play=play, graph=play_subgraph,\\n parent_node_name=play_name, parent_node_id=play_id,\\n block=pre_task_block, color=color,\\n current_counter=nb_pre_tasks, play_vars=play_vars,\\n node_name_prefix=\\\"[pre_task] \\\")\\n\\n # loop through the roles\\n self.display.v(\\\"Graphing roles...\\\")\\n role_number = 0\\n for role in play.get_roles():\\n # Don't insert tasks from ``import/include_role``, preventing duplicate graphing\\n if role.from_include:\\n continue\\n\\n # the role object doesn't inherit the tags from the play. So we add it manually.\\n role.tags = role.tags + play.tags\\n if not role.evaluate_tags(only_tags=self.options.tags, skip_tags=self.options.skip_tags,\\n all_vars=play_vars):\\n self.display.vv(\\\"The role '{}' is skipped due to the tags.\\\".format(role.get_name()))\\n # Go to the next role\\n continue\\n\\n role_number += 1\\n role_name = \\\"[role] \\\" + clean_name(role.get_name())\\n\\n with self.graph.subgraph(name=role_name, node_attr={}) as role_subgraph:\\n current_counter = role_number + nb_pre_tasks\\n role_id = \\\"role_\\\" + str(uuid.uuid4())\\n edge_id = \\\"edge_\\\" + str(uuid.uuid4())\\n\\n role_subgraph.node(role_name, id=role_id)\\n # edge from play to role\\n role_subgraph.edge(play_name, role_name, label=str(current_counter), color=color,\\n fontcolor=color, id=edge_id)\\n\\n self.graph_representation.add_link(play_id, edge_id)\\n self.graph_representation.add_link(edge_id, role_id)\\n\\n # loop through the tasks of the roles\\n if self.options.include_role_tasks:\\n role_tasks_counter = 0\\n for block in role.compile(play):\\n role_tasks_counter = self._include_tasks_in_blocks(current_play=play,\\n graph=role_subgraph,\\n parent_node_name=role_name,\\n parent_node_id=role_id, block=block,\\n color=color, play_vars=play_vars,\\n current_counter=role_tasks_counter,\\n node_name_prefix=\\\"[task] \\\")\\n role_tasks_counter += 1\\n self.display.v(\\\"{} roles added to the graph\\\".format(role_number))\\n\\n # loop through the tasks\\n self.display.v(\\\"Graphing tasks...\\\")\\n nb_tasks = 0\\n for task_block in play.tasks:\\n nb_tasks = self._include_tasks_in_blocks(current_play=play, graph=play_subgraph,\\n parent_node_name=play_name, parent_node_id=play_id,\\n block=task_block, color=color,\\n current_counter=role_number + nb_pre_tasks,\\n play_vars=play_vars, node_name_prefix=\\\"[task] \\\")\\n\\n # loop through the post_tasks\\n self.display.v(\\\"Graphing post_tasks...\\\")\\n for post_task_block in play.post_tasks:\\n self._include_tasks_in_blocks(current_play=play, graph=play_subgraph, parent_node_name=play_name,\\n parent_node_id=play_id, block=post_task_block, color=color,\\n current_counter=nb_tasks, play_vars=play_vars,\\n node_name_prefix=\\\"[post_task] \\\")\\n\\n self.display.banner(\\\"Done graphing {}\\\".format(play_name))\\n self.display.display(\\\"\\\") # just an empty line\\n # moving to the next play\",\n \"def build_graph(self):\\n tf.logging.info('Building graph...')\\n t0 = time.time()\\n\\n self._add_placeholders()\\n\\n with tf.device(\\\"/gpu:%d\\\"%(config.gpu_selection)):\\n self._add_seq2seq()\\n\\n self.global_step = tf.Variable(0, name='global_step', trainable=False)\\n\\n\\n if self._mode == 'train':\\n self._add_train_op()\\n\\n self._summaries = tf.summary.merge_all()\\n\\n t1 = time.time()\\n tf.logging.info('Time to build graph: %i seconds', t1 - t0)\",\n \"def build():\",\n \"def dump_subgraph_for_debug(self):\\n\\n import pypipegraph2 as ppg\\n\\n nodes = []\\n seen = set()\\n edges = []\\n counter = [0]\\n node_to_counters = {}\\n\\n def descend(node):\\n if node in seen:\\n return\\n seen.add(node)\\n j = self.runner.jobs[node]\\n if isinstance(j, ppg.FileInvariant):\\n nodes.append(f\\\"Path('{counter[0]}').write_text('A')\\\")\\n nodes.append(f\\\"job_{counter[0]} = ppg.FileInvariant('{counter[0]}')\\\")\\n elif isinstance(j, ppg.ParameterInvariant):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.ParameterInvariant('{counter[0]}', 55)\\\"\\n )\\n elif isinstance(j, ppg.FunctionInvariant):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.FunctionInvariant('{counter[0]}', lambda: 55)\\\"\\n )\\n elif isinstance(j, ppg.SharedMultiFileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.SharedMultiFileGeneratingJob('{counter[0]}', {[x.name for x in j.files]!r}, dummy_smfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.TempFileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.TempFileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.FileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.FileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.MultiTempFileGeneratingJob):\\n files = [counter[0] + \\\"/\\\" + x.name for x in j.files]\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.MultiTempFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.MultiFileGeneratingJob):\\n files = [str(counter[0]) + \\\"/\\\" + x.name for x in j.files]\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.MultiFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.DataLoadingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.DataLoadingJob('{counter[0]}', lambda: None, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.AttributeLoadingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.AttributeLoadingJob('{counter[0]}', DummyObject(), 'attr_{counter[0]}', lambda: None, depend_on_function=False)\\\"\\n )\\n else:\\n raise ValueError(j)\\n node_to_counters[node] = counter[0]\\n counter[0] += 1\\n for parent in self.runner.dag.predecessors(node):\\n descend(parent)\\n\\n def build_edges(node):\\n for parent in self.runner.dag.predecessors(node):\\n edges.append(\\n f\\\"edges.append(('{node_to_counters[node]}', '{node_to_counters[parent]}'))\\\"\\n )\\n build_edges(parent)\\n\\n descend(self.job_id)\\n edges.append(\\\"edges = []\\\")\\n build_edges(self.job_id)\\n edges.extend(\\n [\\n \\\"for (a,b) in edges:\\\",\\n \\\" if a in ppg.global_pipegraph.jobs and b in ppg.global_pipegraph.jobs:\\\",\\n \\\" ppg.global_pipegraph.jobs[a].depends_on(ppg.global_pipegraph.jobs[b])\\\",\\n ]\\n )\\n with open(\\\"subgraph_debug.py\\\", \\\"w\\\") as op:\\n lines = \\\"\\\"\\\"\\nclass DummyObject:\\n pass\\n\\ndef dummy_smfg(files, prefix):\\n Path(prefix).mkdir(exist_ok=True, parents=True)\\n for f in files:\\n f.write_text(\\\"hello\\\")\\n\\n\\ndef dummy_mfg(files):\\n for f in files:\\n f.parent.mkdir(exist_ok=True, parents=True)\\n f.write_text(\\\"hello\\\")\\n\\ndef dummy_fg(of):\\n of.parent.mkdir(exist_ok=True, parents=True)\\n of.write_text(\\\"fg\\\")\\n\\n\\\"\\\"\\\".split(\\n \\\"\\\\n\\\"\\n )\\n lines += nodes\\n lines += edges\\n lines += [\\\"\\\", \\\"ppg.run()\\\", \\\"ppg.run\\\"]\\n\\n op.write(\\\"\\\\n\\\".join(\\\" \\\" + l for l in lines))\",\n \"def build_nodes_with_deps(self, tasks, task_name):\\n if task_name in self._processed:\\n return\\n else:\\n self._processed.add(task_name)\\n\\n # add node itself if not in list of nodes\\n self.nodes.setdefault(task_name, [])\\n task = tasks[task_name]\\n # reversing not required\\n for dep_name in reversed(task.setup_tasks + task.task_dep):\\n rev_dep = self.nodes.setdefault(dep_name, [])\\n rev_dep.append(task_name)\\n self.build_nodes_with_deps(tasks, dep_name)\",\n \"def build_graph(self):\\n self.import_tree(ZOO_PATH, self.import_zoo, self.verify_zoos)\\n self.import_tree(WILD_PATH, self.import_wild, self.verify_wilds)\\n self.import_tree(PANDA_PATH, self.import_redpanda, self.verify_pandas)\\n self.import_tree(MEDIA_PATH, self.import_media, self.verify_media)\",\n \"def build_tf_graph(self):\\n raise NotImplementedError\",\n \"def build_graph():\\n os.environ['CUDA_VISIBLE_DEVICES']= '0'\\n\\n # frozen_model = '/home/kevin/Codes/DeepNet/log/20180419_221132/frozen_model.pb'\\n # frozen_model = '/home/kevin/Downloads/deeplabv3_cityscapes_train/frozen_inference_graph.pb'\\n # frozen_model = '/home/kevin/Codes/EnvNet/RUNS/used3/frozen_model.pb'\\n frozen_model = '/home/kevin/Codes/DeepNet/log/20180716_212035/frozen_model1.pb'\\n graph = load_graph(frozen_model)\\n\\n for op in graph.get_operations():\\n print(op.name)\\n\\n ## model_envnet/frozen_model.pb\\n image_pl = graph.get_tensor_by_name('ImagePlaceholder:0')\\n pred_seg = graph.get_tensor_by_name('SemanticPredictions:0')\\n\\n ## model_deeplab/frozen_inference_graph.pb\\n # image_pl = graph.get_tensor_by_name('ImageTensor:0')\\n # pred_seg = graph.get_tensor_by_name('SemanticPredictions:0')\\n\\n # ## model_deepnet/frozen_model.pb\\n # image_pl = graph.get_tensor_by_name('ImagePlaceholder:0')\\n # pred_seg = graph.get_tensor_by_name('SemanticPredictions:0')\\n\\n config = tf.ConfigProto() \\n config.gpu_options.per_process_gpu_memory_fraction = 0.5\\n sess = tf.Session(graph=graph,config=config)\\n\\n return image_pl, pred_seg, sess\",\n \"def evaluate(self, g, tick, task, inputs):\\n # create the output file locally - relative output path is given by the model path of the given node. \\n context=inputs[CONTEXT]\\n transporter=context[TRANSPORTER]\\n parentdir=_dataflow_path(g, tick)\\n localpath=os.path.join(context[LOCALWORKDIR], parentdir, self.outputlistname)\\n if not os.path.exists(os.path.join(str(context[LOCALWORKDIR]), parentdir)):\\n os.makedirs(os.path.join(str(context[LOCALWORKDIR]), parentdir))\\n\\n outputlist=[] \\n for i in range(self.num_splits):\\n tuple_from_split=tuple([ inputs[\\\"%s_%i\\\" % (name,i+1)] for name in self.portnames ])\\n outputlist.append(tuple_from_split)\\n with open(localpath, 'w') as localfile:\\n pickle.dump(outputlist, localfile)\\n \\n # now compose the remote path where this file needs to be copied to\\n # use the transporter obtained the context has been initialized with\\n remotepath=os.path.join(str(context[WSROOT]), str(context[WORKDIR]), parentdir, self.outputlistname)\\n relativepath = os.path.join(parentdir, self.outputlistname)\\n d = transporter.upload_file(localpath, remotepath)\\n d.addCallback(lambda _: relativepath)\\n\\n def on_success(_path):\\n return EvalResult({self.outputlistname:_path})\\n\\n def on_failure(reason):\\n return Failure(ValueError((\\\"Exception while uploading file to %s. \\\\n\\\"\\n + \\\"Reason:%s.\\\")%(remotepath, reason.getTraceback())))\\n\\n d.addCallback(on_success)\\n d.addErrback(on_failure)\\n return d\",\n \"def parse(self, graph):\\n\\n # This function is very similar to its counterpart in the regular\\n # (non-tree-decomposing) parser. Read the comments there to understand how it\\n # works.\\n\\n start_time = time.clock()\\n log.chatter('parse...')\\n\\n # ensure that the input graph has its shortest-path table precomputed\\n graph.compute_fw_table()\\n\\n chart = ddict(set)\\n # TODO command line option to switch rule filtering on/off\\n pgrammar = [self.grammar[r] for r in self.grammar.reachable_rules(graph, None)] \\n queue = deque()\\n pending = set()\\n attempted = set()\\n visited = set()\\n terminal_lookup = ddict(set)\\n passive_item_lookup = ddict(set)\\n tree_node_lookup = ddict(set)\\n passive_item_rev_lookup = ddict(set)\\n tree_node_rev_lookup = ddict(set)\\n\\n for edge in graph.triples(nodelabels = self.nodelabels):\\n terminal_lookup[edge[1]].add(edge)\\n\\n for rule in pgrammar:\\n for leaf in rule.tree_leaves:\\n axiom = self.item_class(rule, leaf, graph, nodelabels = self.nodelabels)\\n queue.append(axiom)\\n pending.add(axiom)\\n assert leaf not in rule.tree_to_edge\\n\\n success = False\\n\\n while queue:\\n item = queue.popleft()\\n pending.remove(item)\\n visited.add(item)\\n log.debug('handling', item, item.subgraph)\\n\\n if item.target == Item.NONE:\\n log.debug(' none')\\n tree_node_lookup[item.self_key].add(item)\\n for ritem in tree_node_rev_lookup[item.self_key]:\\n if ritem not in pending:\\n queue.append(ritem)\\n pending.add(ritem)\\n\\n elif item.target == Item.ROOT:\\n log.debug(' root')\\n if self.is_goal(item):\\n chart['START'].add((item,))\\n success = True\\n log.debug(\\\"success!\\\")\\n\\n passive_item_lookup[item.self_key].add(item)\\n for ritem in passive_item_rev_lookup[item.self_key]:\\n if ritem not in pending:\\n log.debug(' retrieving', ritem)\\n queue.append(ritem)\\n pending.add(ritem)\\n\\n elif item.target == Item.TERMINAL:\\n log.debug(' terminal')\\n new_items = [item.terminal(edge) for edge in terminal_lookup[item.next_key]]\\n new_items = [i for i in new_items if i]\\n for nitem in new_items:\\n chart[nitem].add((item,))\\n if nitem not in pending and nitem not in visited:\\n log.debug(' new item!', nitem)\\n queue.append(nitem)\\n pending.add(nitem)\\n\\n else:\\n if item.target == Item.BINARY:\\n log.debug(' binary')\\n rev_lookup = tree_node_rev_lookup\\n lookup = tree_node_lookup\\n action = self.item_class.binary\\n elif item.target == Item.NONTERMINAL:\\n log.debug(' nonterminal')\\n rev_lookup = passive_item_rev_lookup\\n lookup = passive_item_lookup\\n action = self.item_class.nonterminal\\n else:\\n assert False\\n\\n rev_lookup[item.next_key].add(item)\\n for oitem in lookup[item.next_key]:\\n if (item, oitem) in attempted:\\n continue\\n attempted.add((item, oitem))\\n log.debug(' try', oitem, oitem.subgraph)\\n nitem = action(item, oitem)\\n if not nitem:\\n continue\\n log.debug(' new item!', nitem)\\n chart[nitem].add((item, oitem))\\n if nitem not in pending and nitem not in visited:\\n queue.append(nitem)\\n pending.add(nitem)\\n\\n if success:\\n log.chatter(' success!')\\n\\n etime = time.clock() - start_time\\n log.chatter('done in %.2fs' % etime)\\n return chart\",\n \"def build_graph(self):\\n if self.model == 'dense':\\n # ForecastNet with two densely connected hidden layers in a cell and Mixture Density Network outputs\\n self.outputs, self.mu, self.sigma, self.cost = forecastnet_graph(self.X,\\n self.Y,\\n self.hidden_dim,\\n self.out_seq_length,\\n self.is_training)\\n elif self.model == 'conv':\\n # ForecastNet with a convlutional neural network in a cell and Mixture Density Network outputs\\n self.outputs, self.mu, self.sigma, self.cost = forecastnet_conv_graph(self.X,\\n self.Y,\\n self.hidden_dim,\\n self.out_seq_length,\\n self.is_training)\\n elif self.model == 'dense2':\\n # ForecastNet with two densely connected hidden layers in a cell and linear outputs\\n self.outputs, self.cost = forecastnet_graph2(self.X,\\n self.Y,\\n self.hidden_dim,\\n self.out_seq_length,\\n self.is_training)\\n elif self.model == 'conv2':\\n # ForecastNet with a convolutional neural network in a cell and linear outputs\\n self.outputs, self.cost = forecastnet_conv_graph2(self.X,\\n self.Y,\\n self.hidden_dim,\\n self.out_seq_length,\\n self.is_training)\",\n \"def build(self):\\n self.logger.info('Rebuilding adjacency information')\\n self.edges = collections.defaultdict(list)\\n\\n topic_to_publisher = collections.defaultdict(list)\\n topic_to_subscribers = collections.defaultdict(list)\\n node_to_missing_deps = collections.defaultdict(list)\\n\\n result = True\\n\\n for node in self.nodes.values():\\n for topic in node.provided_topics.keys():\\n topic_to_publisher[topic].append(node)\\n\\n for topic in node.required_topics:\\n topic_to_subscribers[topic].append(node)\\n\\n for dep in node.additional_dependencies:\\n if dep not in self.nodes:\\n node_to_missing_deps[node].append(dep)\\n\\n if len(node_to_missing_deps) > 0:\\n result = False\\n msg = io.StringIO()\\n print('Found [{}] managed processes with missing dependencies'.format(len(node_to_missing_deps)), file=msg)\\n fmt = ' Managed process [{}] is missing [{}]'\\n\\n for (node, missing) in node_to_missing_deps.items():\\n print(fmt.format(node.name, ', '.join(missing)), file=msg)\\n self.logger.error(msg.getvalue())\\n\\n missing_publishers = []\\n for topic in topic_to_subscribers.keys():\\n if topic not in topic_to_publisher:\\n missing_publishers.append(topic)\\n\\n if len(missing_publishers) > 0:\\n result = False\\n msg = io.StringIO()\\n print('Found [{}] topics that do not have publishers'.format(len(missing_publishers)), file=msg)\\n fmt = ' Topic [{}] with subscribers [{}]'\\n\\n for topic in missing_publishers:\\n print(fmt.format(topic, ', '.join([x.name for x in topic_to_subscribers[topic]])), file=msg)\\n self.logger.error(msg.getvalue())\\n\\n if not result:\\n self.logger.error('Found errors when building adjacency information')\\n raise GraphBuildError(\\n 'Found errors when building adjacency information / graph edges. Check log for details')\\n\\n # Now we have enough information to build our edges. Phase 1: pub/sub stuff\\n for (topic, subscribers) in topic_to_subscribers.items():\\n publishers = topic_to_publisher[topic]\\n\\n for p in publishers:\\n for s in subscribers:\\n self.edges[p].append(s)\\n\\n # Phase 2: additional dependencies\\n for node in self.nodes.values():\\n for dep in node.additional_dependencies:\\n src = self.nodes[dep]\\n self.edges[src].append(node)\",\n \"def update_dependencies():\\r\\n\\r\\n if par['task'] == 'mnist':\\r\\n par['n_tasks'] = 100\\r\\n par['input_shape'] = [28, 28]\\r\\n par['n_input'] = np.product(par['input_shape'])\\r\\n par['n_output'] = 10\\r\\n elif par['task'] == 'omniglot':\\r\\n par['input_shape'] = [26, 26]\\r\\n par['n_input'] = 256 if par['conv_input'] else np.product(par['input_shape'])\\r\\n par['n_output'] = par['n_ways'] #par['n_meta_tasks'] + par['n_test_tasks']\\r\\n\\r\\n par['layer_dims'] = [par['n_input']] + par['hidden_layers'] + [par['n_output']]\\r\\n\\r\\n\\r\\n par['n_layers'] = len(par['layer_dims'])\\r\\n if par['task'] == 'mnist' or par['task'] == 'imagenet':\\r\\n par['labels_per_task'] = 10\\r\\n elif par['task'] == 'cifar':\\r\\n par['labels_per_task'] = 5\",\n \"def build_graph(self, graph, inst_name, port_nets):\\n self.add_graph_edges(graph, port_nets)\",\n \"def process_task(params):\\n params['task'](params)\",\n \"def build_inference_graph(self):\\n self.build_train_graph()\",\n \"def task_start_parsing():\\n add_task(url_for(\\\"task_queue_users\\\"))\\n add_task(url_for(\\\"task_clean_tmp_files\\\"))\\n return OK_RESPONSE\",\n \"def build_graph(self):\\n start_time = time.time()\\n\\n # init temp node\\n for value in self.domain:\\n node = TempDepNode(value)\\n self.nodes[value] = node\\n\\n attr_data = self.graph_data[self.attr_name]\\n print(f'{len(attr_data)} records in data')\\n\\n # init temp edge\\n for source_ix, value_i in tqdm(attr_data.items()):\\n visited = set()\\n for target_ix, value_j in attr_data[source_ix+1:].items():\\n if value_j in visited:\\n continue\\n else:\\n visited.add(value_j)\\n time_diff = self.graph_data[self.time][target_ix] - \\\\\\n self.graph_data[self.time][source_ix]\\n if time_diff > self.time_diff_threshold:\\n break\\n if (value_i, value_j) not in self.edges or (value_j, value_i) not in self.edges:\\n self.edges[(value_i, value_j)] = TempDepEdge(value_i, value_j)\\n self.edges[(value_j, value_i)] = TempDepEdge(value_j, value_i)\\n self.edges[(value_i, value_j)].add_event(time_diff)\\n if value_i != value_j:\\n self.edges[(value_j, value_i)].add_event(time_diff)\\n end_time = time.time()\\n print(f'{end_time-start_time} seconds for graph building')\",\n \"def __init__(self):\\n Task.__init__(self)\",\n \"def Run(self):\\n # print \\\"len of tostartFlows \\\", len(self.sched.toStartFlows)\\n # start all the flows along with updating related flow transfer time\\n while self.sched.toStartFlows:\\n # the first flow is with earliest startTime\\n curStartFlow = self.sched.toStartFlows[0]\\n # update flows if there are flows has already finished\\n while self.sched.runningFlows:\\n # the first flow is with earliest finishTime\\n toFinishFlow = self.sched.runningFlows[0]\\n if toFinishFlow.finishTime <= curStartFlow.startTime:\\n # remove this flow from running flows\\n self.sched.runningFlows.remove(toFinishFlow)\\n # add this flow to finished flows\\n self.sched.finishedFlows.append(toFinishFlow)\\n # Update related flow's transfer time in removing a flow\\n self.sched.UpdateFlow(toFinishFlow, \\\"remove\\\")\\n # Resort runningFlows by endTime\\n self.sched.runningFlows.sort(key=lambda x: x.finishTime)\\n \\n if self.Qlearning_enable == 1:\\n self.action = toFinishFlow.pathNodeIds\\n if len(self.action) == 5:\\n self.pre_state = self.state[:]\\n self.Update(toFinishFlow)\\n self.printQlearningLog()\\n #self.Update(self.pre_state, self.action, self.state, self.reward)\\n\\n else:\\n break\\n # insert current start flow to running list\\n if self.Qlearning_enable == 1:\\n self.routing.BuildPath(curStartFlow.startId, curStartFlow.endId, curStartFlow, self.state)\\n else:\\n self.routing.BuildPath(curStartFlow.startId, curStartFlow.endId, curStartFlow)\\n pathNodeIds = self.routing.GetPath(curStartFlow.startId, curStartFlow.endId)\\n curStartFlow.BuildPath(pathNodeIds)\\n self.sched.runningFlows.append(curStartFlow)\\n # Update related flow's transfer time in removing a flow\\n # self.lb(curStartFlow)\\n # self.topo.GetLinkOfLeastFlow()\\n # Step 1 find out which spine is less loaded\\n\\n # Hedera load balancing for spine leaf\\n # print self.topo.GetCoreLeastFlow()\\n # if self.topo.name == \\\"spineleaf\\\":\\n # if self.topo.GetCoreLeastFlow() not in curStartFlow.pathNodeIds:\\n # if len(curStartFlow.pathNodeIds) == 5:\\n # if curStartFlow.coflowId == 0:\\n # self.changeSpine(curStartFlow, self.topo.GetCoreLeastFlow())\\n # # print \\\"general flow reroute to spine {}\\\".format(self.topo.GetCoreLeastFlow().nodeId)\\n # else:\\n # self.changeSpine(curStartFlow,\\n # self.topo.GetCoreNode((curStartFlow.coflowId % self.topo.numOfCores)+1))\\n # print \\\"coflow reroute to spine {}\\\".format(self.topo.GetCoreNode((curStartFlow.coflowId % self.topo.numOfCores)+1).nodeId)\\n #print curStartFlow.pathNodeIds\\n # Less loaded in terms of more flows\\n\\n # update state and reward for Qlearning algorithm\\n self.sched.UpdateFlow(curStartFlow, \\\"insert\\\")\\n #self.updatenum += 1\\n #print \\\"updatenum= \\\",self.updatenum\\n if self.Qlearning_enable == 1:\\n self.action = curStartFlow.pathNodeIds\\n if len(self.action) == 5:\\n self.pre_state = self.state[:]\\n self.Update(curStartFlow)\\n self.printQlearningLog()\\n #self.Update(self.pre_state, self.action, self.state, self.reward)\\n\\n # Resort runningFlows by endTime\\n self.sched.runningFlows.sort(key=lambda x: x.finishTime)\\n # remove this flow from start list\\n self.sched.toStartFlows.remove(curStartFlow)\\n #print \\\"finished\\\"\\n #print len(self.sched.toStartFlows)\\n\\n # Now, all the flows are started\\n # Iteratively update flow's transfer time in running list until all the flows are finished\\n while self.sched.runningFlows:\\n # the first flow is always with earliest finish Time\\n curFinishFlow = self.sched.runningFlows[0]\\n # remove it from running list\\n self.sched.runningFlows.remove(curFinishFlow)\\n # insert it to finished flows\\n self.sched.finishedFlows.append(curFinishFlow)\\n # Update related flow's transfer time in removing a flow\\n self.sched.UpdateFlow(curFinishFlow, \\\"remove\\\")\\n # Resort runningFlows by endTime\\n self.sched.runningFlows.sort(key=lambda x: x.finishTime)\\n\\n # update state and reward for Qlearning algorithm\\n if self.Qlearning_enable == 1:\\n self.action = curFinishFlow.pathNodeIds\\n if len(self.action) == 5:\\n self.pre_state = self.state[:]\\n self.Update(curFinishFlow)\\n self.printQlearningLog()\\n #self.Update(self.pre_state, self.action, self.state, self.reward)\\n\\n\\n # Finally, all the flows are finished\\n self.sched.PrintFlows()\\n if self.Qlearning_enable == 1:\\n self.logf.close()\\n # print \\\"final stateId= \\\", self.stateId\",\n \"def buildGraph(self):\\n return None\",\n \"def _build_forward_graph(self):\\n\\n print('[*] Building a Neural Turing Machine.')\\n\\n self._initalize_state()\\n\\n # present start token\\n controller_out = self.controller.emit_feature_vector(self.start_token, self.r_t[0], reuse=None)\\n self._read_write(controller_out, reuse=None)\\n\\n # present inputs\\n print('Input chain: ')\\n for t in range(0, self.sequence_length):\\n print_progress(float(t + 1) / self.sequence_length)\\n\\n controller_out = self.controller.emit_feature_vector(self.inputs[t], self.r_t[-1], reuse=True)\\n self._read_write(controller_out, reuse=True)\\n\\n # present end token\\n controller_out = self.controller.emit_feature_vector(self.end_token, self.r_t[-1], reuse=True)\\n self._read_write(controller_out, reuse=True)\\n\\n # present outputs\\n print('Output chain: ')\\n for t in range(0, self.sequence_length):\\n print_progress(float(t + 1) / self.sequence_length)\\n\\n controller_out = self.controller.emit_feature_vector(self.zeros, self.r_t[-1], reuse=True)\\n self._read_write(controller_out, reuse=True)\\n\\n reuse = None if t == 0 else True\\n self.outputs.append(self._decode_read_vector(self.r_t[-1], reuse=reuse))\\n print('Done.')\",\n \"def new_tasks(self, extra):\\n\\n tasks = []\\n\\n try:\\n fd = open(self.params.command_file)\\n \\n self.result_dir = os.path.dirname(self.params.output)\\n \\n for line in fd:\\n command = line.strip()\\n\\n if not command:\\n # ignore black lines\\n continue\\n\\n cmd_args = _parse_command(command)\\n \\n # setting jobname\\n jobname = \\\"gc_gps-%s%s%s%s%s\\\" % (cmd_args['pos'],\\n cmd_args['realizations'],\\n cmd_args['snr'],\\n cmd_args['mast.h'],\\n cmd_args['sd.mast.o'])\\n\\n extra_args = extra.copy()\\n extra_args['jobname'] = jobname\\n # FIXME: ignore SessionBasedScript feature of customizing \\n # output folder\\n extra_args['output_dir'] = self.params.output\\n\\n extra_args['output_dir'] = extra_args['output_dir'].replace('NAME', os.path.join('.computation',jobname))\\n extra_args['output_dir'] = extra_args['output_dir'].replace('SESSION', os.path.join('.computation',jobname))\\n extra_args['output_dir'] = extra_args['output_dir'].replace('DATE', os.path.join('.computation',jobname))\\n extra_args['output_dir'] = extra_args['output_dir'].replace('TIME', os.path.join('.computation',jobname))\\n\\n self.log.debug(\\\"Creating Task for command: %s\\\" % command)\\n\\n tasks.append(GcgpsTask(\\n command,\\n self.params.R_source_folder,\\n self.result_dir,\\n self.params.input_dir,\\n **extra_args))\\n\\n except IOError, ioe:\\n self.log.error(\\\"Error while reading command file \\\" +\\n \\\"%s.\\\" % self.params.command_file +\\n \\\"Message: %s\\\" % ioe.message)\\n except Exception, ex:\\n self.log.error(\\\"Unexpected error. Error type: %s, Message: %s\\\" % (type(ex),str(ex)))\\n\\n finally:\\n fd.close()\\n\\n return tasks\",\n \"def _build_graph(self):\\n self._setup_placeholders()\\n self._embed()\\n self.p_emb = tf.concat([self.p_emb, tf.expand_dims(self.em, -1)], -1)\\n self._encode()\\n self._match()\\n self._fuse()\\n\\n with tf.variable_scope('boundary'):\\n self._decode()\\n with tf.variable_scope('content'):\\n self._content()\\n with tf.variable_scope('verif'):\\n self._verify()\\n\\n self._compute_loss()\",\n \"def task2_3():\",\n \"def build_graph(self):\\n\\t\\tself._create_placeholders()\\n\\t\\tself._create_embedding()\\n\\t\\tself._create_recurrent_layers()\\n\\t\\tself._create_de_embedding()\\n\\t\\tself._create_loss()\\n\\t\\tself._create_optimizer()\\n\\t\\tself._create_summaries()\",\n \"def _dispatcher_generator(self, selected_tasks):\\n # each selected task will create a tree (from dependencies) of\\n # tasks to be processed\\n tasks_to_run = list(reversed(selected_tasks))\\n node = None # current active ExecNode\\n\\n while True:\\n # get current node\\n if not node:\\n node = self._get_next_node(self.ready, tasks_to_run)\\n if not node:\\n if self.waiting:\\n # all tasks are waiting, hold on\\n processed = (yield \\\"hold on\\\")\\n self._update_waiting(processed)\\n continue\\n # we are done!\\n return\\n\\n # get next step from current node\\n next_step = node.step()\\n\\n # got None, nothing left for this generator\\n if next_step is None:\\n node = None\\n continue\\n\\n # got a task, send ExecNode to runner\\n if isinstance(next_step, Task):\\n processed = (yield self.nodes[next_step.name])\\n self._update_waiting(processed)\\n\\n # got new ExecNode, add to ready_queue\\n elif isinstance(next_step, ExecNode):\\n self.ready.append(next_step)\\n\\n # node just performed a delayed creation of tasks, restart\\n elif next_step == \\\"reset generator\\\":\\n node.reset_task(self.tasks[node.task.name],\\n self._add_task(node))\\n\\n # got 'wait', add ExecNode to waiting queue\\n else:\\n assert next_step == \\\"wait\\\"\\n self.waiting.add(node)\\n node = None\",\n \"def run(self):\\n with hp.a_temp_file() as fle:\\n fle.write(dedent(\\\"\\\"\\\"\\n ---\\n environments: { dev: {account_id: \\\"123\\\"} }\\n stacks: { app: {} }\\n \\\"\\\"\\\").encode('utf-8'))\\n fle.seek(0)\\n collector = Collector()\\n collector.prepare(fle.name, {'bespin': {'extra': \\\"\\\"}, \\\"command\\\": None, \\\"bash\\\": None})\\n\\n section = nodes.section()\\n section['ids'].append(\\\"available-tasks\\\")\\n\\n title = nodes.title()\\n title += nodes.Text(\\\"Default tasks\\\")\\n section += title\\n\\n for name, task in sorted(collector.configuration['task_finder'].tasks.items(), key=lambda x: len(x[0])):\\n\\n lines = [name] + [\\\" {0}\\\".format(line.strip()) for line in task.description.split('\\\\n')]\\n viewlist = ViewList()\\n for line in lines:\\n viewlist.append(line, name)\\n self.state.nested_parse(viewlist, self.content_offset, section)\\n\\n return [section]\",\n \"def task_parse_results():\\n pass\",\n \"def build_graph(self):\\n self.__create_placeholders()\\n self.__create_encoder()\\n self.__create_latent()\\n self.__create_decoder()\\n self.__create_loss()\\n self.__create_generate()\\n self.__create_reconstruct()\\n self.__create_optimizer()\\n self.__create_summary()\",\n \"def run():\\n ftpd_thread = mini_buildd.misc.run_as_thread(\\n mini_buildd.ftpd.run,\\n name=\\\"ftpd\\\",\\n bind=get().model.ftpd_bind,\\n queue=get().incoming_queue)\\n\\n builder_thread = mini_buildd.misc.run_as_thread(\\n mini_buildd.builder.run,\\n name=\\\"builder\\\",\\n daemon_=get())\\n\\n while True:\\n event = get().incoming_queue.get()\\n if event == \\\"SHUTDOWN\\\":\\n break\\n\\n try:\\n LOG.info(\\\"Status: {0} active packages, {1} changes waiting in incoming.\\\".\\n format(len(get().packages), get().incoming_queue.qsize()))\\n\\n changes = None\\n changes = mini_buildd.changes.Changes(event)\\n\\n if changes.type == changes.TYPE_BREQ:\\n # Build request: builder\\n\\n def queue_buildrequest(event):\\n \\\"\\\"\\\"Queue in extra thread so we don't block here in case builder is busy.\\\"\\\"\\\"\\n get().build_queue.put(event)\\n mini_buildd.misc.run_as_thread(queue_buildrequest, name=\\\"build queuer\\\", daemon=True, event=event)\\n\\n else:\\n # User upload or build result: packager\\n mini_buildd.packager.run(\\n daemon=get(),\\n changes=changes)\\n\\n except BaseException as e:\\n mini_buildd.config.log_exception(LOG, \\\"Invalid changes file\\\", e)\\n\\n # Try to notify\\n try:\\n with mini_buildd.misc.open_utf8(event, \\\"r\\\") as body:\\n subject = \\\"INVALID CHANGES: {c}: {e}\\\".format(c=event, e=e)\\n get().model.mbd_notify(subject, body.read())\\n except BaseException as e:\\n mini_buildd.config.log_exception(LOG, \\\"Invalid changes notify failed\\\", e)\\n\\n # Try to clean up\\n try:\\n if changes:\\n changes.remove()\\n else:\\n os.remove(event)\\n except BaseException as e:\\n mini_buildd.config.log_exception(LOG, \\\"Invalid changes cleanup failed\\\", e)\\n\\n finally:\\n get().incoming_queue.task_done()\\n\\n get().build_queue.put(\\\"SHUTDOWN\\\")\\n mini_buildd.ftpd.shutdown()\\n builder_thread.join()\\n ftpd_thread.join()\\n\\n # keyrings.close() is not called implicitly; this leaves tmp files around.\\n # There should be a nicer way, really...\\n try:\\n get().keyrings.close()\\n except BaseException:\\n pass\",\n \"def task_process(args):\\n if args.mode == 'change model':\\n for i in range(config.center_len):\\n h, w = config.center_list[i][0], config.center_list[i][1]\\n os.system('rm -rf ctpn_change_{}x{}.onnx'.format(h, w))\\n for i in range(config.center_len):\\n h, w = config.center_list[i][0], config.center_list[i][1]\\n os.system('{} change_model.py --input_path={}/ctpn_{}x{}.onnx --output_path={}/ctpn_change_{}x{}.onnx' \\\\\\n .format(args.interpreter, args.src_dir, h, w,args.res_dir, h, w)) \\n if args.mode == 'preprocess':\\n for i in range(config.center_len):\\n os.system('mkdir -p {}_{}x{}'.format(args.res_dir, config.center_list[i][0], config.center_list[i][1]))\\n os.system('{} ctpn_preprocess.py --src_dir={} --save_path={}' \\\\\\n .format(args.interpreter, args.src_dir, args.res_dir))\\n if args.mode == 'ais_infer':\\n fps_all = 0\\n os.system('mkdir -p {}/inf_output'.format(args.res_dir))\\n for i in range(config.center_len):\\n h, w = config.center_list[i][0], config.center_list[i][1]\\n\\n os.system('{} --model={} --input={}_{}x{} --dymHW {},{} --device {} --batchsize={} --output={}/inf_output' \\\\\\n .format(args.interpreter, args.om_path, args.src_dir ,h , w, h, w,args.device, args.batch_size, args.res_dir))\\n\\n sumary_path = glob.glob('{}/inf_output/*ary.json'.format(args.res_dir))[0]\\n with open(sumary_path, 'r') as f:\\n output = json.load(f)\\n throughput = output['throughput'] \\n fps_all = fps_all + throughput * config.center_count[i]\\n os.system('rm -f {}'.format(sumary_path))\\n os.system('mv {}/inf_output/*/*.bin {}'.format(args.res_dir, args.res_dir))\\n os.system('rm {}/inf_output -rf'.format(args.res_dir))\\n fps_all = fps_all / config.imgs_len\\n print(\\\"====performance data====\\\")\\n print('CTPN bs{} models fps:{}'.format(args.batch_size, fps_all))\",\n \"def execute(self):\\n # Ensure a true no-op (in particular, we don't want to GET the feed) if\\n # there are no Subtasks\\n if not any([self._tx_by_uuid, self._common_tx.subtasks,\\n self._post_exec]):\\n LOG.info(_(\\\"FeedTask %s has no Subtasks; no-op execution.\\\"),\\n self.name)\\n return\\n rets = {'wrapper_task_rets': {}}\\n try:\\n # Calling .wrapper_tasks will cause the feed to be fetched and\\n # WrapperTasks to be replicated, if not already done. Only do this\\n # if there exists at least one WrapperTask with Subtasks.\\n # (NB: It is legal to have a FeedTask that *only* has post-execs.)\\n if self._tx_by_uuid or self._common_tx.subtasks:\\n pflow = tf_uf.Flow(\\\"%s_parallel_flow\\\" % self.name)\\n pflow.add(*self.wrapper_tasks.values())\\n # Execute the parallel flow now so the results can be provided\\n # to any post-execs.\\n rets['wrapper_task_rets'] = self._process_subtask_rets(\\n tf_eng.run(\\n pflow, engine='parallel',\\n executor=ContextThreadPoolExecutor(self.max_workers)))\\n if self._post_exec:\\n flow = tf_lf.Flow('%s_post_execs' % self.name)\\n flow.add(*self._post_exec)\\n eng = tf_eng.load(flow, store=rets)\\n eng.run()\\n rets = eng.storage.fetch_all()\\n except tf_ex.WrappedFailure as wfail:\\n LOG.error(_(\\\"FeedTask %s experienced multiple exceptions. They \\\"\\n \\\"are logged individually below.\\\"), self.name)\\n for fail in wfail:\\n LOG.exception(fail.pformat(fail.traceback_str))\\n raise ex.MultipleExceptionsInFeedTask(self.name, wfail)\\n\\n # Let a non-wrapped exception (which happens if there's only one\\n # element in the feed) bubble up as-is.\\n\\n return rets\",\n \"def __init__(self, graph_a, graph_b):\\n\\n self.can_be_reprocessed = True\\n self.reasons = []\\n self._graph_a = graph_a\\n self._graph_b = graph_b\\n self._matched_recipe_inputs = {} # {Recipe Input B: Recipe Input A}\\n self._identical_nodes = {} # {Job Name B: Job Name A}\\n self._changed_nodes = {} # {Job Name B: Job Name A}\\n self._new_nodes = set() # {Job Name B}\\n self._deleted_nodes = set() # {Job Name A}\\n self._force_reprocess = set()\\n self._changes = {} # {Job Name B: [Change]}\\n self._topo_a_nodes = self._graph_a.get_topological_order() # Job names from A in topological order\\n self._topo_b_nodes = self._graph_b.get_topological_order() # Job names from B in topological order\\n self._unresolved_a_nodes = set(self._topo_a_nodes) # {Job Name A}\\n self._unresolved_b_nodes = set(self._topo_b_nodes) # {Job Name B}\\n\\n self._match_recipe_inputs()\\n self._match_identical_nodes()\\n self._match_changed_nodes()\\n self._new_nodes = self._unresolved_b_nodes\\n self._deleted_nodes = self._unresolved_a_nodes\\n self._unresolved_a_nodes = set()\\n self._unresolved_b_nodes = set()\",\n \"def viz_graph(self, show_ports=False, pydot_options=None):\\n import networkx as nx\\n G = nx.DiGraph()\\n if pydot_options:\\n G.graph['graph'] = pydot_options\\n # instantiate objects\\n for itask in self:\\n task_inputs = itask[TaskSpecSchema.inputs]\\n to_task = itask[TaskSpecSchema.task_id]\\n to_type = itask[TaskSpecSchema.node_type]\\n if to_task == \\\"\\\":\\n to_task = OUTPUT_TYPE\\n for iport_or_tid in task_inputs:\\n # iport_or_tid: it is either to_port or task id (tid) b/c\\n # if using ports API task_inputs is a dictionary otherwise\\n # task_inputs is a list.\\n taskin_and_oport = task_inputs[iport_or_tid] \\\\\\n if isinstance(task_inputs, dict) else iport_or_tid\\n isplit = taskin_and_oport.split('.')\\n from_task = isplit[0]\\n from_port = isplit[1] if len(isplit) > 1 else None\\n if show_ports and from_port is not None:\\n to_port = iport_or_tid\\n common_tip = taskin_and_oport\\n G.add_edge(from_task, common_tip, label=from_port)\\n G.add_edge(common_tip, to_task, label=to_port)\\n tnode = G.nodes[common_tip]\\n tnode.update({\\n # 'label': '',\\n 'shape': 'point'})\\n else:\\n G.add_edge(from_task, to_task)\\n\\n # draw output ports\\n if show_ports:\\n\\n if (to_type == OUTPUT_TYPE):\\n continue\\n task_node = get_node_obj(itask, tgraph_mixin=True)\\n # task_outputs = itask.get(TaskSpecSchema.outputs, [])\\n for pout in task_node._get_output_ports():\\n out_tip = '{}.{}'.format(\\n itask[TaskSpecSchema.task_id], pout)\\n G.add_edge(to_task, out_tip, label=pout)\\n tnode = G.nodes[out_tip]\\n tnode.update({\\n # 'label': '',\\n 'shape': 'point'})\\n return G\",\n \"def build_graph(self):\\n\\n\\n\\n self.inputs.append( #uint8\\n tf.placeholder(tf.float32, shape=[None, None, None, self.channel],\\n name='input/lr')) \\n\\n self.label.append(\\n tf.placeholder(tf.float32, shape=[None, None, None, self.channel],\\n name='label/hr'))\",\n \"def build_dag(self, user=None):\\n cwd = os.getcwd()\\n #os.chdir(self.production.event.meta['working directory'])\\n #os.chdir(os.path.join(self.production.event.repository.directory,\\n # self.category))\\n\\n if self.production.event.repository:\\n gps_file = self.production.get_timefile()\\n coinc_file = self.production.get_coincfile()\\n coinc_file = os.path.join(self.production.event.repository.directory, \\\"C01_offline\\\",\\n coinc_file)\\n ini = self.production.get_configuration().ini_loc\\n ini = os.path.join(self.production.event.repository.directory, \\\"C01_offline\\\", ini)\\n else:\\n gps_file = \\\"gpstime.txt\\\"\\n ini = os.path.join(self.production.event.meta['working directory'], f\\\"{self.production.name}.ini\\\")\\n coinc_file = os.path.join(cwd, \\\"coinc.xml\\\")\\n\\n if self.production.get_meta(\\\"user\\\"):\\n user = self.production.get_meta(\\\"user\\\")\\n else:\\n user = config.get(\\\"condor\\\", \\\"user\\\")\\n self.production.set_meta(\\\"user\\\", user)\\n\\n os.environ['LIGO_USER_NAME'] = f\\\"{user}\\\"\\n os.environ['LIGO_ACCOUNTING'] = f\\\"{config.get('pipelines', 'accounting')}\\\"\\n\\n try:\\n calibration = config.get(\\\"general\\\", \\\"calibration\\\")\\n except:\\n calibration = \\\"C01\\\"\\n\\n approximant = self.production.meta['approximant']\\n\\n #ini.save()\\n\\n if self.production.rundir:\\n rundir = os.path.relpath(self.production.rundir, os.getcwd())\\n else:\\n rundir = os.path.join(os.path.expanduser(\\\"~\\\"),\\n self.production.event.name,\\n self.production.name)\\n self.production.rundir = rundir\\n\\n #lmax = self.production.meta['priors']['amp order']\\n\\n if \\\"lmax\\\" in self.production.meta:\\n lmax = self.production.meta['lmax']\\n elif \\\"HM\\\" in self.production.meta['approximant']:\\n lmax = 4\\n else:\\n lmax = 2\\n \\n\\n if \\\"cip jobs\\\" in self.production.meta:\\n cip = self.production.meta['cip jobs']\\n else:\\n cip = 3\\n \\n \\n command = [os.path.join(config.get(\\\"pipelines\\\", \\\"environment\\\"), \\\"bin\\\", \\\"util_RIFT_pseudo_pipe.py\\\"),\\n \\\"--use-coinc\\\", coinc_file,\\n \\\"--l-max\\\", f\\\"{lmax}\\\",\\n \\\"--calibration\\\", f\\\"{calibration}\\\",\\n \\\"--add-extrinsic\\\",\\n \\\"--approx\\\", f\\\"{approximant}\\\",\\n \\\"--cip-explode-jobs\\\", str(cip),\\n \\\"--use-rundir\\\", rundir,\\n \\\"--ile-force-gpu\\\",\\n \\\"--use-ini\\\", ini\\n ]\\n print(\\\" \\\".join(command))\\n # If a starting frequency is specified, add it\\n if \\\"start-frequency\\\" in self.production.meta:\\n command += [\\\"--fmin-template\\\", self.production.quality['start-frequency']]\\n \\n self.logger.info(\\\" \\\".join(command), production = self.production)\\n\\n # Placeholder LI grid bootstrapping; conditional on it existing and location specification\\n \\n if self.bootstrap:\\n if self.bootstrap == \\\"manual\\\":\\n if self.production.event.repository:\\n bootstrap_file = os.path.join(self.production.event.repository.directory, \\\"C01_offline\\\", f\\\"{self.production.name}_bootstrap.xml.gz\\\")\\n else:\\n bootstrap_file = \\\"{self.production.name}_bootstrap.xml.gz\\\"\\n else:\\n raise PipelineException(f\\\"Unable to find the bootstrapping production for {self.production.name}.\\\",\\n issue=self.production.event.issue_object,\\n production=self.production.name)\\n\\n command += [\\\"--manual-initial-grid\\\", bootstrap_file]\\n\\n self.logger.info(command, production = self.production)\\n os.chdir(self.production.event.meta['working directory'])\\n pipe = subprocess.Popen(command, \\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT)\\n out, err = pipe.communicate()\\n if err:\\n self.production.status = \\\"stuck\\\"\\n if hasattr(self.production.event, \\\"issue_object\\\"):\\n self.logger.info(out, production = self.production)\\n self.logger.error(err, production = self.production)\\n raise PipelineException(f\\\"DAG file could not be created.\\\\n{command}\\\\n{out}\\\\n\\\\n{err}\\\",\\n issue=self.production.event.issue_object,\\n production=self.production.name)\\n else:\\n self.logger.info(out, production = self.production)\\n self.logger.error(err, production = self.production)\\n raise PipelineException(f\\\"DAG file could not be created.\\\\n{command}\\\\n{out}\\\\n\\\\n{err}\\\",\\n production=self.production.name)\\n else:\\n if self.production.event.repository:\\n os.chdir(self.production.rundir)\\n for psdfile in self.production.get_psds(\\\"xml\\\"):\\n ifo = psdfile.split(\\\"/\\\")[-1].split(\\\"_\\\")[1].split(\\\".\\\")[0]\\n os.system(f\\\"cp {psdfile} {ifo}-psd.xml.gz\\\")\\n\\n #os.system(\\\"cat *_local.cache > local.cache\\\")\\n\\n if hasattr(self.production.event, \\\"issue_object\\\"):\\n return PipelineLogger(message=out,\\n issue=self.production.event.issue_object,\\n production=self.production.name)\\n else:\\n return PipelineLogger(message=out,\\n production=self.production.name)\",\n \"def _setup_graph(self):\\n pass\",\n \"def _setup_graph(self):\\n pass\",\n \"def getBuild():\",\n \"def _construct_graph(self):\\n raise NotImplementedError\",\n \"def Steering(cache, generations, input_queue, result_queue):\\n\\n # Generations that have pending tasks to be executed. Pending tasks are those\\n # whose results are not ready. The tasks that have their results ready are\\n # referenced to as ready tasks. Once there is no pending generation, the\\n # algorithm terminates.\\n waiting = generations\\n\\n # Record how many initial tasks there are. If there is no task at all, the\\n # algorithm can terminate right away.\\n num_tasks = 0\\n\\n # Submit all the tasks in the initial generations to the next stage of the\\n # framework. The next stage can be the build/compilation stage.\\n for generation in generations:\\n # Only send the task that has not been performed before to the next stage.\\n for task in [task for task in generation.Pool() if task not in cache]:\\n result_queue.put(task)\\n cache.add(task)\\n num_tasks += 1\\n\\n # If there is no task to be executed at all, the algorithm returns right away.\\n if not num_tasks:\\n # Inform the next stage that there will be no more task.\\n result_queue.put(pipeline_process.POISONPILL)\\n return\\n\\n # The algorithm is done if there is no pending generation. A generation is\\n # pending if it has pending task.\\n while waiting:\\n # Busy-waiting for the next task.\\n if input_queue.empty():\\n continue\\n\\n # If there is a task whose result is ready from the last stage of the\\n # feedback loop, there will be one less pending task.\\n\\n task = input_queue.get()\\n\\n # Store the result of this ready task. Intermediate results can be used to\\n # generate report for final result or be used to reboot from a crash from\\n # the failure of any module of the framework.\\n task.LogSteeringCost()\\n\\n # Find out which pending generation this ready task belongs to. This pending\\n # generation will have one less pending task. The \\\"next\\\" expression iterates\\n # the generations in waiting until the first generation whose UpdateTask\\n # method returns true.\\n generation = next(gen for gen in waiting if gen.UpdateTask(task))\\n\\n # If there is still any pending task, do nothing.\\n if not generation.Done():\\n continue\\n\\n # All the tasks in the generation are finished. The generation is ready to\\n # produce the next generation.\\n waiting.remove(generation)\\n\\n # Check whether a generation should generate the next generation.\\n # A generation may not generate the next generation, e.g., because a\\n # fixpoint has been reached, there has not been any improvement for a few\\n # generations or a local maxima is reached.\\n if not generation.IsImproved():\\n continue\\n\\n for new_generation in generation.Next(cache):\\n # Make sure that each generation should contain at least one task.\\n assert new_generation.Pool()\\n waiting.append(new_generation)\\n\\n # Send the tasks of the new generations to the next stage for execution.\\n for new_task in new_generation.Pool():\\n result_queue.put(new_task)\\n cache.add(new_task)\\n\\n # Steering algorithm is finished and it informs the next stage that there will\\n # be no more task.\\n result_queue.put(pipeline_process.POISONPILL)\",\n \"def dag_builder(conf):\\n dag = DAG(dag_id=conf['dag_id'], schedule_interval=conf['schedule_interval'], start_date=conf['start_date'], catchup=conf['catchup'], default_args=conf['default_args'])\\n task_conf = conf.get('tasks', [])\\n dep_conf = conf.get('dependencies', [])\\n tasks = {}\\n if task_conf:\\n tasks = attach_tasks(dag, task_conf)\\n if dep_conf:\\n build_flow(dep_conf, tasks)\\n return dag\",\n \"def run_task(self) -> Task:\",\n \"def populate_graph(self):\",\n \"def train_on_tasks(config):\\n seed = config['seed']\\n torch.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n np.random.seed(seed)\\n random.seed(seed)\\n\\n tasks = config.pop('tasks')\\n\\n task_vis_params = config.pop('vis_params')\\n\\n # all_stats = []\\n transfer_matrix = defaultdict(list)\\n total_steps = 0\\n\\n if 'learner' in config:\\n learner = config.pop('learner')\\n else:\\n learner_path = config.pop('learner_path')\\n learner = torch.load(learner_path)\\n task_level_tuning = config.pop('task_level_tuning')\\n if task_level_tuning:\\n ray_params = config.pop('ray_params')\\n local_mode = config.pop('local_mode')\\n redis_address = config.pop('redis_address')\\n all_analysis = []\\n selected_tags = []\\n for t_id, (task, vis_p) in enumerate(zip(tasks, task_vis_params)):\\n #todo sync transfer matrix\\n static_params = dict(\\n t_id=t_id, task=task, tasks=tasks, vis_p=vis_p,\\n transfer_matrix=transfer_matrix, total_steps=total_steps\\n )\\n\\n if task_level_tuning:\\n if not ray.is_initialized():\\n if local_mode:\\n ray.init(local_mode=local_mode)\\n else:\\n ray.init(redis_address,\\n log_to_driver=False,\\n logging_level=logging.ERROR)\\n\\n config['static_params'] = static_params\\n config['learner_path'] = learner_path\\n config['seed'] += t_id\\n\\n # reporter = CLIReporter(max_progress_rows=10)\\n # print(reporter._metric_columns)\\n # print(reporter.DEFAULT_COLUMNS)\\n # reporter.add_metric_column('avg_acc_val')\\n # reporter.add_metric_column('total_params')\\n # reporter.add_metric_column('fw_t')\\n # reporter.add_metric_column('data_t')\\n # reporter.add_metric_column('eval_t')\\n # reporter.add_metric_column('epoch_t')\\n # reporter.add_metric_column('total_t')\\n # ray_params['progress_reporter'] = reporter\\n analysis = tune.run(train_t, config=config, **ray_params)\\n\\n all_analysis.append(analysis)\\n\\n def get_key(trial):\\n # return trial.last_result['avg_acc_val_so_far']\\n return trial.last_result['best_val']\\n best_trial = max(analysis.trials, key=get_key)\\n for trial in analysis.trials:\\n if trial != best_trial:\\n trial_path = trial.logdir\\n shutil.rmtree(trial_path)\\n # am = np.argmax(list(map(get_key, analysis.trials)))\\n # print(\\\"BEST IS {}: {}\\\".format(am, best_trial.last_result['avg_acc_val']))\\n\\n # t = best_trial.last_result['duration_iterations']\\n total_steps = best_trial.last_result['total_steps']\\n selected_tags.append(best_trial.experiment_tag)\\n best_learner_path = os.path.join(best_trial.logdir, 'learner.pth')\\n learner = torch.load(best_learner_path, map_location='cpu')\\n shutil.rmtree(best_trial.logdir)\\n\\n #todo UPDATE LEARNER AND SAVE\\n torch.save(learner, learner_path)\\n else:\\n rescaled, t, metrics, b_state_dict, \\\\\\n stats = train_single_task(config=deepcopy(config), learner=learner,\\n **static_params)\\n\\n # all_stats.append(stats)\\n # update_rescaled(list(rescaled.values()), list(rescaled.keys()), tag,\\n # g_task_vis, False)\\n\\n if task_level_tuning:\\n return all_analysis, selected_tags\\n else:\\n save_path = path.join(tune.get_trial_dir(), 'learner.pth')\\n logger.info('Saving {} to {}'.format(learner, save_path))\\n torch.save(learner, save_path)\",\n \"def test_restore_with_dependencies(self):\\n task1 = self._remote_task()\\n task1['id'] = 1\\n task2 = self._remote_task()\\n task2['id'] = 2\\n task2['dependencies'] = [1]\\n\\n graph = self._restore_graph([task1, task2])\\n assert len(graph.tasks) == 2\\n assert graph._dependencies[2] == set([1])\",\n \"def __post_init__(self) -> None:\\n for node in self.task_graph:\\n self.task_graph.nodes[node][\\\"info\\\"] = TaskInfo(TaskStatus.NOT_STARTED)\",\n \"def __post_init__(self) -> None:\\n for node in self.task_graph:\\n self.task_graph.nodes[node][\\\"info\\\"] = TaskInfo(TaskStatus.NOT_STARTED)\",\n \"def processTask(self):\\n #Util.set_color(Util.FOREGROUND_YELLOW | Util.FOREGROUND_INTENSITY)\\n #logging.info(\\\"cmd : %s\\\", self.ExecutionTask.get_cmd())\\n #logging.info(\\\"param : %s\\\", self.ExecutionTask.get_param())\\n #logging.info(\\\"ret : %s\\\", str(self.ExecutionTask.get_ret()))\\n #logging.info(\\\"ipport : %s\\\", self.ExecutionTask.get_ipport())\\n #Util.set_color(Util.FOREGROUND_WHITE)\\n\\n ##############################################################\\n # Process for any commands without received messages.....\\n ##############################################################\\n if self.ExecutionTask.get_cmd() == 'PASS' or self.ExecutionTask.get_cmd() == 'FAIL':\\n logging.debug(\\\"result is %s\\\", self.ExecutionTask.get_cmd())\\n self.setStatus('STOP')\\n self.setTestResult(self.ExecutionTask.get_cmd())\\n return\\n\\n if self.ExecutionTask.get_cmd() == 'r_info':\\n rinfo_result = self.ExecutionTask.get_param().split('!')\\n\\n if len(rinfo_result) > 1:\\n msg = rinfo_result[1]\\n logging.debug(\\\"%s\\\", msg)\\n\\n self.setStatus('STOP')\\n self.setTestResult(rinfo_result[0])\\n return\\n\\n if self.ExecutionTask.get_cmd() == 'ResultCheck':\\n time.sleep(5)\\n self.process_ResultCheck()\\n return\\n\\n if self.ExecutionTask.get_cmd() == 'CheckThroughput':\\n time.sleep(5)\\n throughputChk = StreamHandler(self.test_mngr_initr)\\n chk_result = throughputChk.processStreamResults(self.ExecutionTask.get_param())\\n self.setCheckResult(chk_result)\\n #if 'FAIL' in chk_result:\\n # self.setStatus('STOP')\\n return\\n\\n if self.ExecutionTask.get_cmd() == 'config_multi_subresults':\\n self.process_config_multi_subresults()\\n return\\n\\n ##############################################################\\n # Process for any commands with received messages......\\n ##############################################################\\n status = \\\"\\\"\\n retDict = self.ExecutionTask.get_ret()\\n recvStr = \\\"\\\"\\n if self.ExecutionTask.recv:\\n recvStr = self.ExecutionTask.recv.rstrip('\\\\r\\\\n')\\n #print \\\"recv : \\\" + recvStr\\n \\n if GlobalConfigFiles.curr_prog_name == \\\"WMMPS\\\" and \\\"sniffer_control_subtask\\\" in self.ExecutionTask.get_cmd():\\n logging.debug('In WMMPS, before parsing the recvStr: %s' % recvStr)\\n lines = re.split('\\\\n', recvStr)\\n for line in lines:\\n if re.search(\\\"RESULT\\\", line, re.I):\\n if \\\"FAIL\\\" in line:\\n self.setStatus('STOP')\\n self.setTestResult('FAIL')\\n logging.debug('set test result to FAIL')\\n return\\n if \\\"PASS\\\" in line:\\n self.setTestResult('PASS')\\n logging.debug('set test result to Pass')\\n return\\n return\\n \\n stitems = recvStr.split(',') \\n if len(stitems) < 2:\\n #logging.debug(\\\"Bypassing this cmd..\\\")\\n return\\n\\n status = stitems[1]\\n iDNB = TestScriptSymbolTable.get_value_from_sym_tab(\\\"iDNB\\\", TestScriptSymbolTable.test_script_sym_tab)\\n iINV = TestScriptSymbolTable.get_value_from_sym_tab(\\\"iINV\\\", TestScriptSymbolTable.test_script_sym_tab) \\n \\n if iINV is None:\\n iINV = 0\\n \\n if 'ERROR' in recvStr or 'INVALID' in recvStr and (iDNB == 0 or iDNB is None) and (iINV == 0 or iINV is None):\\n #error case...\\n logging.debug(\\\"Return ERROR or INVALID---> STOP process \\\")\\n self.setStatus('STOP')\\n self.setTestResult('FAIL')\\n elif status != 'COMPLETE' and iDNB == 0 and iINV == 0:\\n #incomplete case...(running?)\\n logging.debug(\\\"Command %s not completed\\\", self.ExecutionTask.get_cmd())\\n else:\\n displayname = \\\"\\\"\\n for tbd in self.test_mngr_initr.test_prog_mngr.test_prog.testbed_dev_list:\\n if tbd.ctrlipaddr == self.ExecutionTask.get_ipport():\\n displayname = tbd.displayname\\n break\\n \\n if \\\"FAIL\\\" in recvStr and (iINV == 0 or iINV is None):\\n if \\\"SNIFFER\\\" in displayname or \\\"sniffer\\\" in self.ExecutionTask.get_cmd():\\n logging.info(\\\"Test Case Criteria Failure - Command returned FAIL\\\")\\n self.setStatus('STOP')\\n self.setTestResult('FAIL')\\n\\n elif self.ExecutionTask.get_cmd() == 'device_get_info':\\n try:\\n if displayname == '':\\n self.tmsPacket.setDutDeviceInfo(recvStr)\\n else:\\n self.tmsPacket.setTestbedInfo(displayname, recvStr)\\n\\n #for validation\\n self.setValidationInfo(displayname, recvStr)\\n\\n except OSError:\\n logging.debug(\\\"exception -- device_get_info capi call\\\")\\n elif self.ExecutionTask.get_cmd() == 'ca_get_version':\\n self.setValidationInfo(displayname, recvStr)\\n\\n elif self.ExecutionTask.get_cmd() == 'sniffer_get_info':\\n self.setValidationInfo('sniffer', recvStr)\\n\\n elif self.ExecutionTask.get_cmd() == 'sta_associate':\\n time.sleep(10)\\n\\n if len(stitems) > 2:\\n retParam = self.ExecutionTask.get_param().split(',')\\n streamFlag = \\\"\\\"\\n if len(retParam) > 4:\\n streamFlag = retParam[3]\\n\\n if stitems[2] == 'streamID':\\n streamHndler = StreamHandler(self.test_mngr_initr)\\n logging.debug(\\\"stream config - streamID : %s\\\", stitems[3])\\n if streamFlag == 'send':\\n logging.debug(\\\"traffic config - send : streamInfo append\\\")\\n streamPacket = streamInfo(\\\"%s\\\" % (stitems[3]), self.ExecutionTask.get_ipport(), -1, 'send',\\n retParam[15], retParam[17], streamHndler.running_phase, streamHndler.RTPCount)\\n streamHndler.add_streamInfo(streamPacket)\\n streamHndler.RTPCount = streamHndler.RTPCount + 1\\n\\n elif streamFlag == 'receive':\\n logging.debug(\\\"traffic config - receive : streamInfo append\\\")\\n streamPacket = streamInfo(\\\"%s\\\" % (stitems[3]), self.ExecutionTask.get_ipport(), -1, 'receive',\\n -1, -1, streamHndler.running_phase, -1)\\n streamHndler.add_streamInfo(streamPacket)\\n\\n else:\\n logging.debug(\\\"traffic config - else : \\\")\\n\\n\\n\\n if retParam[1] == 'Multicast':\\n logging.debug(\\\"----MULTICAST----\\\")\\n streamHndler.multicast = 1\\n\\n if self.ExecutionTask.get_cmd() != \\\"traffic_agent_send\\\":\\n ret_val = \\\"%s\\\" %(stitems[3].strip())\\n logging.debug(\\\"traffic config - ret_val : %s\\\", ret_val)\\n setRetVal(getRetKey(retDict), ret_val)\\n\\n elif stitems[2].lower() == 'interfacetype':\\n ret_val = (\\\"%s\\\" %(stitems[5]))\\n setRetVal(getRetKey(retDict), ret_val)\\n\\n elif stitems[2].lower() == 'interfaceid':\\n ret_val = stitems[3].split('_')[0]\\n setRetVal(getRetKey(retDict), ret_val)\\n\\n elif self.ExecutionTask.get_cmd() == 'traffic_stop_ping':\\n\\n keyVal = retParam[1]\\n #\\\"%s;%s\\\"%(retParam[1], self.ExecutionTask.get_ipport())\\n setRetVal(keyVal, stitems[5])\\n #print(\\\"%s = %s\\\" % (retParam[1], stitems[5]))\\n pinginternalchk = TestScriptSymbolTable.get_value_from_sym_tab(\\\"PingInternalChk\\\", TestScriptSymbolTable.test_script_sym_tab)\\n temp_key = getRetKey(self.ExecutionTask.get_ret())\\n \\n if \\\"$\\\" in temp_key:\\n sent_reply = temp_key.split(',')\\n #print \\\"SLIM==> ping result save...\\\"\\n #print sent_reply[0]\\n #print sent_reply[1]\\n setRetVal(sent_reply[0], stitems[3])\\n setRetVal(sent_reply[1], stitems[5]) \\n\\n setRetVal(\\\"$pingResp\\\", stitems[5])\\n if pinginternalchk == '0':\\n logging.debug(\\\"Ping Internal Check\\\")\\n \\n elif stitems[5] == '0':\\n logging.debug (\\\"Test Case Criteria Failure - NO IP Connection -- Aborting the test\\\")\\n self.setStatus('STOP')\\n self.setTestResult('FAIL')\\n else:\\n if stitems[5] == '0':\\n logging.debug (\\\"Test Case Criteria Failure - NO IP Connection -- Aborting the test\\\")\\n self.setStatus('STOP')\\n self.setTestResult('FAIL')\\n else:\\n if len(retDict) > 0:\\n tempKey = getRetKey(retDict)\\n temp_val = tempKey.split(',')\\n count = 0\\n item_len = len(stitems)\\n for i in temp_val:\\n if item_len > count + 3:\\n setRetVal(i, stitems[3+count])\\n count = count + 2\\n\\n if self.__status == 'STOP':\\n logging.debug(\\\"generate final result if task stops.\\\")\\n #self.generateFinalResult()\\n else:\\n pass\\n #logging.debug(\\\"Continue---\\\")\\n return\",\n \"def build_nodes(self, tasks, clean_list):\\n for name in clean_list:\\n # add node itself if not in list of nodes\\n self.nodes.setdefault(name, [])\\n task = tasks[name]\\n # reversing not required\\n for dep_name in reversed(task.task_dep):\\n if tasks[dep_name].subtask_of == name:\\n rev_dep = self.nodes.setdefault(dep_name, [])\\n rev_dep.append(name)\",\n \"def build(_):\",\n \"def _subgraph(self):\\n return {\\n 'type': 'SubgraphTask',\\n 'id': 0,\\n 'dependencies': [],\\n 'parameters': {\\n 'info': {},\\n 'current_retries': 0,\\n 'send_task_events': False,\\n 'containing_subgraph': None,\\n 'task_kwargs': {}\\n }\\n }\",\n \"def dworker(self):\\n\\t\\t\\\"\\\"\\\" {pid:{subkey:subvalue}} \\\"\\\"\\\"\\n\\t\\t\\\"\\\"\\\" line colors in use: black, blue, red\\\"\\\"\\\"\\n\\t\\t\\\"\\\"\\\" fill colors in use: cyan, yellow, orange, red\\\"\\\"\\\"\\n\\t\\tif self.pslist:\\n\\t\\t\\tfor i in self.pslist:\\n\\t\\t\\t\\tself.dgen(self.d, i[2], {\\\"name\\\":i[1], \\\"ppid\\\":i[3], \\\"created\\\":i[8], \\\"color\\\":\\\"black\\\"})\\n\\t\\tif self.psscan:\\n\\t\\t\\tfor i in self.psscan:\\n\\t\\t\\t\\tself.dgen(self.d, i[2], {\\\"name\\\":i[1], \\\"ppid\\\":i[3], \\\"created\\\":i[5], \\\"color\\\":\\\"blue\\\", \\\"fillcolor\\\":\\\"cyan\\\"})\\n\\t\\t\\t\\tif len(i[6]) > 0:\\n\\t\\t\\t\\t\\tself.d[i[2]][\\\"exited\\\"]=i[6]\\n\\t\\tif self.envars:\\n\\t\\t\\tfor i in self.envars:\\n\\t\\t\\t\\tif i[3] == \\\"USERNAME\\\":\\n\\t\\t\\t\\t\\tself.dgen(self.d, i[0], {\\\"username\\\":i[4]})\\n\\t\\tif self.netscan:\\n\\t\\t\\tfor i in self.netscan:\\n\\t\\t\\t\\tself.dgen(self.d, i[5], {\\\"proto\\\":i[1], \\\"localaddr\\\":i[2], \\\"foreignaddr\\\":i[3], \\\"state\\\":i[4], \\\"conn-owner\\\":i[6], \\\"conn-created\\\":i[7]})\\n\\t\\tif self.cmdline:\\n\\t\\t\\tfor i in self.cmdline:\\n\\t\\t\\t\\tif len(i[2]) > 0:\\n\\t\\t\\t\\t\\tcfix = i[2].replace(\\\"\\\\\\\"\\\", \\\"\\\").replace(\\\"\\\\\\\\\\\", \\\"\\\\\\\\\\\\\\\\\\\").replace(\\\"{\\\", \\\"\\\\{\\\").replace(\\\"}\\\", \\\"\\\\}\\\")\\n\\t\\t\\t\\t\\tself.dgen(self.d, i[1], {\\\"cmdline\\\":cfix})\\n\\t\\tif self.connscan:\\n\\t\\t\\tfor i in self.connscan:\\n\\t\\t\\t\\tself.dgen(self.d, i[3], {\\\"loc_addr\\\":i[1], \\\"rem_addr\\\":i[2]})\\n\\t\\tif self.apihooks:\\n\\t\\t\\tfor i in self.apihooks:\\n\\t\\t\\t\\tself.dgen(self.d, i[3], {\\\"apihooks\\\": \\\"{} {} hooked {} via {}\\\".format(i[0], i[9], i[4], i[1]).replace(\\\"<\\\",\\\"\\\").replace(\\\">\\\",\\\"\\\"), \\\"fillcolor\\\":\\\"yellow\\\"})\\n\\t\\tif self.malfind:\\n\\t\\t\\tfor i in self.malfind:\\n\\t\\t\\t\\tif re.search('4d5a', i[6]):\\n\\t\\t\\t\\t\\tself.dgen(self.d, i[1], {\\\"malf_protection\\\":i[4], \\\"malf_bytes\\\":i[6][:10], \\\"fillcolor\\\":\\\"red\\\"})\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif \\\"malf_bytes\\\" not in self.d[i[1]].keys():\\n\\t\\t\\t\\t\\t\\tself.dgen(self.d, i[1], {\\\"malf_protection\\\":i[4], \\\"malf_bytes\\\":i[6][:10], \\\"fillcolor\\\":\\\"orange\\\"})\",\n \"def dispatch(self, queue):\\n context = zmq.Context()\\n socket = noBlockREQ(context)\\n \\n seedsQ1 = Queue()\\n seedsQ2 = Queue()\\n for address in self.seeds:\\n seedsQ1.put(address)\\n\\n connectT = Thread(target=connectToSeeds, name=\\\"Connect to Seeds\\\", args=(socket, seedsQ1))\\n connectT.start()\\n\\n toDisconnectQ = Queue()\\n disconnectT = Thread(target=disconnectToSeeds, name=\\\"Disconnect to Seeds\\\", args=(socket, toDisconnectQ))\\n disconnectT.start()\\n\\n pFindSeeds = Process(target=findSeeds, name=\\\"Find Seeds\\\", args=(set(self.seeds), [seedsQ1], [toDisconnectQ], log, 2000, 10, seedsQ2))\\n pFindSeeds.start()\\n\\n pInput = Process(target=getSeedFromFile, name=\\\"Get seed from file\\\", args=(seedsQ1, seedsQ2))\\n pInput.start()\\n\\n graph = {}\\n depth = 1\\n data = {}\\n url_mapper = {url:f\\\"url_{i}\\\" for i, url in enumerate(self.urls)}\\n \\n src = set()\\n while True: \\n new_data = {}\\n while len(self.urls):\\n try:\\n url = self.urls[0]\\n self.urls.pop(0)\\n self.urls.append(url)\\n with counterSocketReq:\\n socket.send_json((\\\"URL\\\", self.uuid, url))\\n log.debug(f\\\"Send {url}\\\", \\\"dispatch\\\")\\n response = socket.recv_pyobj()\\n assert isinstance(response, tuple), f\\\"Bad response, expected <tuple> find {type(response)}\\\"\\n assert len(response) == 2, \\\"bad response size\\\"\\n assert response[0] == 'RESPONSE', \\\"Unexpected response format\\\"\\n _, package = response\\n log.debug(f\\\"Received a package with size: {len(package)}\\\", \\\"dispatch\\\")\\n for recv_url, html in package.items():\\n try:\\n idx = self.urls.index(recv_url)\\n log.info(f\\\"{recv_url} {GREEN}OK{RESET}\\\", \\\"dispatch\\\")\\n new_data[recv_url] = html\\n self.urls.pop(idx)\\n except ValueError:\\n log.debug(f'Unnecesary {recv_url}', 'dispatch')\\n except AssertionError as e:\\n log.error(e, \\\"dispatch\\\")\\n except zmq.error.Again as e:\\n log.debug(e, \\\"dispatch\\\")\\n except Exception as e:\\n log.error(e, \\\"dispatch\\\")\\n time.sleep(0.8)\\n \\n log.info(f'Depth {depth} done', 'dispatch')\\n for url, html in new_data.items():\\n graph[url] = set()\\n try:\\n text = html.decode()\\n soup = BeautifulSoup(html, 'html.parser')\\n tags = soup.find_all(valid_tags)\\n new_urls = [['src', 'href'][tag.has_attr('href')] for tag in tags]\\n changes = []\\n for i, attr in enumerate(new_urls):\\n url_dir = urljoin(url, tags[i][attr])\\n graph[url].add(url_dir)\\n if url_dir not in url_mapper:\\n url_mapper[url_dir] = f'url_{len(url_mapper)}'\\n changes.append((tags[i][attr], url_mapper[url_dir]))\\n if attr == 'src' or tags[i].name == 'link':\\n src.add(url_dir)\\n continue\\n self.urls.append(url_dir)\\n html = change_html(text, changes).encode()\\n except UnicodeDecodeError:\\n log.debug(f'{url} is not decodeable', 'dispatch')\\n except: # BeautifulSoup strange exceptions related with his's logger\\n pass\\n new_data[url] = html\\n data.update(new_data)\\n self.urls = set(self.urls)\\n self.urls.difference_update(self.old)\\n self.old.update(self.urls)\\n self.urls = list(self.urls)\\n \\n if depth > self.depth:\\n break\\n if depth == self.depth:\\n src.difference_update(self.old)\\n self.old.update(src)\\n self.urls = list(src)\\n depth += 1\\n log.info(f\\\"Number of URLs to be requested for download: {RED}{len(self.urls)}{RESET}\\\", \\\"dispatch\\\")\\n \\n log.info(f\\\"Starting to write data\\\", \\\"dispatch\\\")\\n for i, url in enumerate(self.originals):\\n try:\\n res = HtmlResponse(url=url, body=data[url], encoding='utf8')\\n base = res.css('title::text')[0].get()\\n except:\\n base = f\\\"web_page_{i}\\\"\\n try:\\n os.makedirs(f'downloads/{base}-data')\\n except:\\n pass\\n writer(f'downloads/{base}-data', url, set(), data, url_mapper, graph) \\n \\n html = data[url]\\n if len(graph[url]) > 0:\\n text = data[url].decode()\\n changes = []\\n for dep in graph[url]:\\n name = url_mapper[dep]\\n changes.append((name, f'{base}-data/{name}'))\\n html = change_html(text, changes).encode()\\n with open(f'downloads/{base}', 'wb') as fd:\\n fd.write(html)\\n \\n log.info(f\\\"Dispatcher:{self.uuid} has completed his URLs succefully\\\", \\\"dispatch\\\")\\n log.debug(f\\\"Dispatcher:{self.uuid} disconnecting from system\\\", \\\"dispatch\\\")\\n #disconnect\\n\\n queue.put(True)\\n pFindSeeds.terminate()\\n pInput.terminate()\",\n \"def process(self, sess):\\n global send_counter\\n \\n #sess.run(self.sync) # copy weights from shared to local\\n rollout = self.pull_batch_from_queue()\\n batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)\\n\\n should_compute_summary = self.task == 0 and self.local_steps % 11 == 0\\n\\n if should_compute_summary:\\n fetches = [self.summary_op, self.train_op]\\n else:\\n fetches = [self.train_op]\\n\\n\\n feed_dict = {\\n self.local_network.x: batch.si,\\n self.ac: batch.a,\\n self.adv: batch.adv,\\n self.r: batch.r,\\n self.local_network.state_in[0]: batch.features[0],\\n self.local_network.state_in[1]: batch.features[1],\\n }\\n\\n # Get current trainable variables\\n # This is trainable variables\\n fetched = sess.run(fetches, feed_dict=feed_dict)\\n\\n\\n if self.num_workers > 1:\\n sys.stdout.write('\\\\r' + str(self.local_steps))\\n if self.local_steps % 100 == 0:\\n global var0\\n global var1\\n var1 = sess.run(self.local_network.var_list) # After training\\n if var0 != None:\\n var_diff = [a - b for (a,b) in zip(var1, var0)]\\n var_diff_data = pickle.dumps(var_diff, -1)\\n print('Sync weights')\\n self.msg_sent = socket_util.socket_send_data_chucks(self.sock, var_diff_data, self.mcast_destination, self.msg_sent)\\n var0 = sess.run(self.local_network.var_list) # A list of numpy array\\n\\n # Handle each message in the socket queue\\n while not self.inc_msg_q.empty():\\n print('Apply remote gradients')\\n # Process received grads_and_vars from other peers\\n remote_var_diff_data = self.inc_msg_q.get(False)\\n remote_var_diff = pickle.loads(remote_var_diff_data)\\n\\n add_op = [a+b for (a,b) in zip(self.local_network.var_list, remote_var_diff)]\\n sess.run(add_op)\\n\\n if should_compute_summary:\\n self.summary_writer.add_summary(tf.Summary.FromString(fetched[0]))\\n self.summary_writer.flush()\\n self.local_steps += 1\",\n \"def run(self):\\n while True:\\n task = self.queue.get()\\n if task is None:\\n break\\n fslfile = os.path.join(task.directory, task.fsl_file)\\n # TODO: there must be a better way to set the workdir\\n workdir = '/'.join(task.directory.split('/')[-3:])\\n logger.info('Docker task %s %s workdir %s',\\n task.id, task.fsl_file, workdir)\\n fslcmds = ['save_model(close)',\\n \\\"chdir('{}')\\\".format(workdir)]\\n \\n with open(fslfile) as f:\\n fslcmds += f.readlines()\\n r = [json.loads(s)\\n for s in self.container.send_fsl(\\n '\\\\n'.join(fslcmds), publish_receive)[:2]]\\n try:\\n if r[0]['status'] == 'ok':\\n task.status = 'C'\\n else:\\n task.status = 'X'\\n except:\\n task.status = 'X'\\n logger.info(\\\"Finished %s\\\", r)\\n if self.stoponend:\\n self.container.quit()\\n # lets hope that docker will always restart this container\\n self.queue.task_done()\",\n \"def build_graph(self):\\n n_classes = self.n_classes\\n\\n (self.feed('data')\\n .conv(3, 3, 64, 1, 1, name='conv1_1', trainable=False)\\n .conv(3, 3, 64, 1, 1, name='conv1_2', trainable=False)\\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool1')\\n .conv(3, 3, 128, 1, 1, name='conv2_1', trainable=False)\\n .conv(3, 3, 128, 1, 1, name='conv2_2', trainable=False)\\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool2')\\n .conv(3, 3, 256, 1, 1, name='conv3_1')\\n .conv(3, 3, 256, 1, 1, name='conv3_2')\\n .conv(3, 3, 256, 1, 1, name='conv3_3')\\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool3')\\n .conv(3, 3, 512, 1, 1, name='conv4_1')\\n .conv(3, 3, 512, 1, 1, name='conv4_2')\\n .conv(3, 3, 512, 1, 1, name='conv4_3')\\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool4')\\n .conv(3, 3, 512, 1, 1, name='conv5_1')\\n .conv(3, 3, 512, 1, 1, name='conv5_2')\\n .conv(3, 3, 512, 1, 1, name='conv5_3'))\\n\\n self.compute_rDeRF() # dummy\\n\\n # Classification\\n (self.feed('conv5_3')\\n .max_pool(2, 2, 2, 2, padding='VALID', name='pool6')\\n .reshape(shape=(-1, 7, 7, 512), name='pool6_reshape')\\n .fc(4096, name='fc6')\\n .dropout(0.5, name='drop6')\\n .fc(4096, name='fc7')\\n .dropout(0.5, name='drop7')\\n # .make_time(name='drop7_reduced')\\n .fc(n_classes, relu=False, name='cls_score')\\n .softmax(name='cls_prob'))\\n pass\",\n \"def __call__(self):\\n\\n link = hardlink.Command(dry_run=self.dry_run, force=True)\\n\\n # Load config.\\n try:\\n with open(self.config_path) as file:\\n config = yaml.load(file.read())\\n except Exception:\\n self._err('Unable to load config: %s' % self.config_path)\\n\\n # Get JSON database for processed torrent status.\\n database_path = config.get('db', DATABASE_PATH)\\n logger.debug('Loading database: %s' % database_path)\\n try:\\n db = jsondict.JsonDict(database_path, autosave=True)\\n except ValueError:\\n self._err('Unable to load database: %s' % database_path)\\n\\n # Get mapped paths.\\n mapped_paths = config.get('mapped_remote_paths', {}).items()\\n\\n # Get Transmission client.\\n host = config.get('transmission_host', 'localhost')\\n port = config.get('transmission_port', 9091)\\n logger.debug('Connecting to Transmission: %s:%s' % (host, port))\\n client = transmission.Transmission(host=host, port=port)\\n\\n # Get torrents from Transmission.\\n try:\\n torrents = client('torrent-get', fields=[\\n 'downloadDir',\\n 'id',\\n 'name',\\n 'percentDone',\\n 'secondsSeeding',\\n 'uploadRatio',\\n ])['torrents']\\n except requests.ConnectionError:\\n self._err('Unable to connect to Transmission at %s:%s' % (\\n host,\\n port,\\n ))\\n\\n # Store torrents found in a known torrents directory for later\\n # processing.\\n found_torrents = set()\\n\\n # Hard link or remove completed torrents.\\n for torrent in torrents:\\n remote_path = os.path.join(\\n torrent['downloadDir'], torrent['name'])\\n\\n # Get local path for remote path.\\n local_path = self.get_local_path(remote_path, mapped_paths)\\n\\n # Find matching torrents directory.\\n for dir_config in config['torrent_dirs']:\\n\\n # Get config for torrent directory.\\n download_dir = dir_config['download_dir']\\n post_processing_dir = dir_config['post_processing_dir']\\n ratio = dir_config.get('ratio')\\n seed_days = dir_config.get('seed_days')\\n\\n if local_path.startswith(download_dir):\\n found_torrents.add(local_path)\\n\\n # Get downloaded and seeding status.\\n downloaded = bool(torrent['percentDone'] == 1)\\n\\n # Get processed status.\\n processed = bool(downloaded and db.get(remote_path))\\n\\n # Get seeding status.\\n seeding = bool(\\n ratio and\\n ratio > torrent['uploadRatio'] or\\n seed_days and\\n seed_days * DAY_SECONDS > torrent['secondsSeeding'])\\n\\n # Hard link downloaded torrents to the post processing\\n # directory.\\n if downloaded and not processed:\\n logger.info('Processing torrent: %s' % local_path)\\n destination = os.path.join(\\n post_processing_dir,\\n os.path.relpath(local_path, download_dir),\\n )\\n link(local_path, destination)\\n if not self.dry_run:\\n db[remote_path] = True\\n\\n # Remove processed torrents that have finished seeding.\\n elif processed and not seeding:\\n if not self.remove:\\n logger.debug(\\n 'Not removing inactive torrent: %s' %\\n torrent['name'])\\n else:\\n logger.info(\\n 'Removing inactive torrent: %s' %\\n torrent['name'])\\n if not self.dry_run:\\n client(\\n 'torrent-remove',\\n ids=[torrent['id']],\\n delete_local_data=True,\\n )\\n del db[remote_path]\\n\\n else:\\n # Ignore torrents that are still downloading or\\n # seeding.\\n logger.debug(\\n 'Skipping active torrent: %s' % local_path)\\n\\n # Log torrent data, regardless of action taken.\\n logger.debug(' - Downloaded: %d%%' % (\\n torrent['percentDone'] * 100))\\n if ratio:\\n logger.debug(' - Ratio: %.2f (%s)' % (\\n torrent['uploadRatio'],\\n ratio,\\n ))\\n if seed_days:\\n logger.debug(' - Seed Time: %.2f (%s) days' % (\\n torrent['secondsSeeding'] / DAY_SECONDS,\\n seed_days,\\n ))\\n\\n # We found a match. No need to continue.\\n break\\n\\n # No matching torrents directory.\\n else:\\n logger.debug(\\n 'Skipping torrent not located in any download directory: '\\n '%s' % local_path)\\n\\n # Process orphaned torrent data in download directories.\\n for dir_config in config['torrent_dirs']:\\n download_dir = dir_config['download_dir']\\n post_processing_dir = dir_config['post_processing_dir']\\n\\n # Skip download directories that don't exist.\\n if not os.path.isdir(download_dir):\\n continue\\n\\n # Compare every file and directory in the download directory to\\n # previously found torrents. Remove anything already processed and\\n # not belonging to a found torrent. Hard link everything else to\\n # the post processing directory.\\n for path in os.listdir(download_dir):\\n\\n # Ignore hidden files, e.g. dot-underscore files on OS X.\\n if path.startswith('.'):\\n continue\\n\\n local_path = os.path.join(download_dir, path)\\n\\n # Find matching torrent for this path.\\n for found_torrent in found_torrents:\\n if local_path.startswith(found_torrent):\\n # We found a match. No need to continue.\\n break\\n\\n # No matching torrent.\\n else:\\n\\n # Get remote path for local path.\\n remote_path = self.get_remote_path(\\n local_path, mapped_paths)\\n\\n # Get processed status.\\n processed = db.get(remote_path, False)\\n\\n # Hard link orphaned files that have not been processed.\\n if not processed:\\n logger.info(\\n 'Processing orphaned file or directory: %s' %\\n local_path)\\n destination = os.path.join(\\n post_processing_dir,\\n os.path.relpath(local_path, download_dir),\\n )\\n link(local_path, destination)\\n # No need to add path to database, it would be removed\\n # immediately in the next code block.\\n\\n # Remove orphaned files that have been processed.\\n logger.info(\\n 'Removing orphaned file or directory: %s' %\\n local_path)\\n if not self.dry_run:\\n try:\\n shutil.rmtree(local_path)\\n except OSError:\\n os.remove(local_path)\\n # Remove path from database.\\n if remote_path in db:\\n del db[remote_path]\\n\\n # Remove stale records in database. Convert database keys to list to\\n # avoid `RuntimeError: dictionary changed size during iteration`.\\n for remote_path in list(db):\\n\\n # Get local path for remote path.\\n local_path = self.get_local_path(remote_path, mapped_paths)\\n\\n # Remove from database.\\n if not os.path.exists(local_path):\\n logger.debug(\\n 'Removing stale record from database: %s' % remote_path)\\n if not self.dry_run:\\n del db[remote_path]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65182334","0.63080335","0.6293795","0.6204706","0.6186549","0.61605525","0.61220175","0.61220175","0.61082584","0.6104672","0.59439194","0.591472","0.5882329","0.5751076","0.5699367","0.5686174","0.5679653","0.56747794","0.56723875","0.5661628","0.5650889","0.56284285","0.5594429","0.55911255","0.5589985","0.55585825","0.55433613","0.5537058","0.5522207","0.55097026","0.5494077","0.5494077","0.54900706","0.5488904","0.54728127","0.54728127","0.5464192","0.5461847","0.54608124","0.54445815","0.5443006","0.543599","0.54086107","0.5407722","0.5398848","0.5395352","0.53851736","0.5368783","0.5366378","0.53618026","0.5348882","0.5332784","0.53314686","0.5326782","0.5314284","0.53075546","0.5306602","0.53054285","0.5300558","0.52879226","0.52813345","0.52754724","0.526746","0.5264061","0.5263732","0.52601975","0.5257616","0.5245659","0.5242039","0.5240971","0.52384746","0.52262104","0.522482","0.5221646","0.5217755","0.5217752","0.52158934","0.5190281","0.5182435","0.5182435","0.5181743","0.5167194","0.5155172","0.5151939","0.5151838","0.5150962","0.51491296","0.51477945","0.51452434","0.51452434","0.51447403","0.5142132","0.5136674","0.51188254","0.51136297","0.51134044","0.5112322","0.5111501","0.51001227","0.51000017"],"string":"[\n \"0.65182334\",\n \"0.63080335\",\n \"0.6293795\",\n \"0.6204706\",\n \"0.6186549\",\n \"0.61605525\",\n \"0.61220175\",\n \"0.61220175\",\n \"0.61082584\",\n \"0.6104672\",\n \"0.59439194\",\n \"0.591472\",\n \"0.5882329\",\n \"0.5751076\",\n \"0.5699367\",\n \"0.5686174\",\n \"0.5679653\",\n \"0.56747794\",\n \"0.56723875\",\n \"0.5661628\",\n \"0.5650889\",\n \"0.56284285\",\n \"0.5594429\",\n \"0.55911255\",\n \"0.5589985\",\n \"0.55585825\",\n \"0.55433613\",\n \"0.5537058\",\n \"0.5522207\",\n \"0.55097026\",\n \"0.5494077\",\n \"0.5494077\",\n \"0.54900706\",\n \"0.5488904\",\n \"0.54728127\",\n \"0.54728127\",\n \"0.5464192\",\n \"0.5461847\",\n \"0.54608124\",\n \"0.54445815\",\n \"0.5443006\",\n \"0.543599\",\n \"0.54086107\",\n \"0.5407722\",\n \"0.5398848\",\n \"0.5395352\",\n \"0.53851736\",\n \"0.5368783\",\n \"0.5366378\",\n \"0.53618026\",\n \"0.5348882\",\n \"0.5332784\",\n \"0.53314686\",\n \"0.5326782\",\n \"0.5314284\",\n \"0.53075546\",\n \"0.5306602\",\n \"0.53054285\",\n \"0.5300558\",\n \"0.52879226\",\n \"0.52813345\",\n \"0.52754724\",\n \"0.526746\",\n \"0.5264061\",\n \"0.5263732\",\n \"0.52601975\",\n \"0.5257616\",\n \"0.5245659\",\n \"0.5242039\",\n \"0.5240971\",\n \"0.52384746\",\n \"0.52262104\",\n \"0.522482\",\n \"0.5221646\",\n \"0.5217755\",\n \"0.5217752\",\n \"0.52158934\",\n \"0.5190281\",\n \"0.5182435\",\n \"0.5182435\",\n \"0.5181743\",\n \"0.5167194\",\n \"0.5155172\",\n \"0.5151939\",\n \"0.5151838\",\n \"0.5150962\",\n \"0.51491296\",\n \"0.51477945\",\n \"0.51452434\",\n \"0.51452434\",\n \"0.51447403\",\n \"0.5142132\",\n \"0.5136674\",\n \"0.51188254\",\n \"0.51136297\",\n \"0.51134044\",\n \"0.5112322\",\n \"0.5111501\",\n \"0.51001227\",\n \"0.51000017\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72909814"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77482,"cells":{"query":{"kind":"string","value":"Set the edgenode's resources for debugging"},"document":{"kind":"string","value":"def set_resources():\n global available_resources\n global EdgenodeResources\n recv_json = request.get_json()\n for resourcename, value in recv_json.items():\n available_resources[resourcename] = value\n # TODO make this better\n EdgenodeResources = [TaskResources(ram=int(available_resources['RAM']), cpu=int(\n available_resources['CPU']), hdd=int(available_resources['HDD'])), available_resources['DATA']]\n\n print 'Available resources set to', EdgenodeResources\n return 'Available resources set to ' + str(available_resources)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def resources(self):","def resources(self, resources):\n self._resources = resources","def setOptManagerResources(o): # pylint: disable=global-statement\n # pylint: disable=global-statement\n global OPT_MANAGER_RESOURCES_PGAAS\n OPT_MANAGER_RESOURCES_PGAAS = \"{}/pgaas\".format(o)","def resources(self, resources):\n\n self._resources = resources","def resources(self, resources):\n\n self._resources = resources","def resources(self, resources):\n\n self._resources = resources","def resources(self, resources):\n\n self._resources = resources","def set_paths(self, specs, resources):\n self.install = 'install.xml'\n self.specs_path = path_format(specs)\n self.root = path_format(dirname(dirname(self.specs_path)) + '/')\n self.res_path = path_format(resources)\n self.resources['BASE'] = self.res_path\n self.specs['BASE'] = self.specs_path","def init_edra(self) -> None:\n ...","def __init__(self, basedir=None):\n # ------------------------------------------------------------------------\n super(Resources, self).__init__()\n self.xInitialize(basedir or \"resources\")","def __attrs_post_init__(self):\n self.path = (Path(CONFIG['conda_folder']) / 'envs' / self.name)","def set_jinja_globals():\n\n # The resource keys will not be present.\n ufo.app.logger.info('Start setting resources into jinja globals.\\n'\n 'Current jinja globals: %s' %\n ufo.app.jinja_env.globals.keys())\n\n ufo.app.jinja_env.globals['resources'] = json.dumps(_get_resources())\n\n # The resource keys should be present if set.\n ufo.app.logger.info('Finished setting resources into jinja globals.\\n'\n 'Current jinja globals: %s' %\n ufo.app.jinja_env.globals.keys())","def set_resource_data(self, resource, meta):\n super().set_resource_data(resource, meta)\n self._set_resource_temperature(resource)\n self._set_egs_plant_design_temperature()\n self._set_nameplate_to_match_resource_potential(resource)\n self._set_resource_potential_to_match_gross_output()\n self._set_costs()","def debug():\n # print(\" ++ debugging 'pyre.filesystem'\")\n # attach {Extent} as the metaclass of Node and Filesystem so we can verify that all\n # instances of these classes are properly garbage collected\n from ..patterns.Extent import Extent\n\n global _metaclass_Node\n _metaclass_Node = Extent\n # all done\n return","def resources(self, value):\n self._resource_objects = value","def __init__(self,resources):\n OeskObject.__init__(self,resources[0][0][0],resources[0][0][1])\n self.checkAndParse(resources)\n self.addAttribute('PRIORITY', hex(0))","def set_resource_data(self, resource, meta):","def resource_map(self):","def setup_dev():\n setup_general()","def _set_attr(self):\n self.as_skeletal = self._import_as_skeleton()\n self.materials = self._import_materials()\n self.textures = self._import_textures()","def setupResources(self):\r\n if self.initialized == True: return\r\n try:\r\n LOG(\"Configuring driver for context \" + repr(self.contextName))\r\n DriverManager.instance().setup(self.contextName)\r\n self.initialized = True\r\n \r\n except SpellException,ex:\r\n traceback.print_exc( file = sys.stderr )\r\n LOG(\"Could not setup driver: \" + repr(ex), LOG_ERROR)\r\n self.errorMessage = ex.message\r\n self.errorReason = ex.reason\r\n raise ex","def initNodePathAttribs(self, doReparent=1):\n self.callSetters('pos','x','y','z',\n 'hpr','h','p','r',\n 'scale','sx','sy','sz')\n if doReparent:\n self.callSetters('parentEntId')\n\n self.getNodePath().setName('%s-%s' %\n (self.__class__.__name__, self.entId))\n\n if __dev__:\n # for the editor\n self.getNodePath().setTag('entity', '1')","def setUp(self):\n self.test_resource = Resource(name='Test', slug='test', description='')\n self.test_resource.full_clean()\n self.test_resource.save()\n self.test_resource_two = Resource(name='Test Two', slug='testtwo', description='')\n self.test_resource_two.full_clean()\n self.test_resource_two.save()","def __init__(self):\r\n self.dev_conf_dir = self._get_dev_conf_dir()","def test_modify_response_descriptor_variables_library_variable_set_library_variable_set_resource_spaces(self):\n pass","def resources():\n check_resources()","def set_up():\n ResourcesManager().set_up()\n LocatorUtil().load_locators()","def test_load_response_descriptor_variables_library_variable_set_library_variable_set_resource_spaces(self):\n pass","def setDebug():\n\tglobal debug\n\tdebug = True","def resAlloc(self,band):\n\n if len(list(self.ues.keys()))>0:\n for ue in list(self.ues.keys()):\n self.ues[ue].prbs = band\n self.ues[ue].symb = self.TDDsmb\n # Print Resource Allocation\n self.printResAlloc()","def setUp(self):\n self.epath = 'flyeye/tests/fixtures'\n self.dpath = join(self.epath, 'disc.silhouette')","def setUp(self):\n self.test_resource = Resource(name='Test', slug='test', description='')\n self.test_resource.full_clean()\n self.test_resource.save()","def prepare(ctx, resource_config, **_):\n # Save the parameters\n ctx.instance.runtime_properties['resource_config'] = resource_config","def prepare(ctx, resource_config, **_):\n # Save the parameters\n ctx.instance.runtime_properties['resource_config'] = resource_config","def test_load_response_descriptor_projects_release_release_resource_spaces(self):\n pass","def test_new_resource(self):\n if verbosity>=3: print \"\\n\"+\"~\"*80\n for resource in cfg.new_resources:\n args=copy.deepcopy(resource)\n self.run_init_new_resource(**args)\n if verbosity>=3: print \"\\n\"+\"~\"*80","def test_set_node_properties(self):\n\n pass","def config(self):\n self._resource_manager = self._api._ixnetwork.ResourceManager\n self._ixn_vport = self._api._vport\n self._delete_vports()\n self._create_vports()\n self._create_capture()\n self._set_location()\n self._set_layer1()","def set_paths_gen(self, paths_gen): #w:\r\n self.paths_gen = paths_gen","def test_modify_response_descriptor_variables_library_variable_set_library_variable_set_resource(self):\n pass","def test_load_response_descriptor_variables_library_variable_set_library_variable_set_resource(self):\n pass","def resources(self):\n return self.__resources","def __setstate__(self, dict):\n\n\t\tself.__dict__ = dict\n\n\t\t# Set missing values to defaults.\n\t\tself._device = None\n\t\tself.resources = {}","def setUp(self):\n self.design = None","def setup(self):\n self.ae = None","def DevMode(self):\n for var in DEV_CONFIG:\n self.Cover(var, DEV_CONFIG[var], layer = SETTINGS_LAYER)","def set_device_parameters(request):\n def fin():\n request.cls.device.close()\n request.addfinalizer(fin)\n\n request.cls.driver = ros.ROSDriver\n request.cls.patched_driver = PatchedROSDevice\n request.cls.vendor = 'ros'\n parent_conftest.set_device_parameters(request)","def toggleResources(self):\n if self.game.myEmpire['viewResources'] == 0:\n self.game.myEmpire['viewResources'] = 1\n self.createResourceSims()\n else:\n self.game.myEmpire['viewResources'] = 0\n self.removeResourceSims()","def test_create_response_descriptor_variables_library_variable_set_library_variable_set_resource_spaces(self):\n pass","def set_debug(self, debug):\n self.debug = debug","def register_dcc_resource_path(resources_path):\n\n pass","def register_resources(self):\n raise NotImplementedError","def pre_config_node_create(self, resource_dict):\n pass","def resource(self, resource):\n self._resource = resource","def resource(self, resource):\n self._resource = resource","def assets():","def set_debug_mode(self):\n self.debug_mode = True","def __init__(self, resource_path):\n self.resource_path = resource_path","def set_defaults(self):\n for key, constraints in self.__class__.MODEL.items():\n if key not in self.resource:\n self.resource[key] = constraints[3]","def stand_by(self):\n\n if self.gpus is None:\n self['gpus'] = [0]\n\n # setup logging\n setup_logging(self.get_log_folder(), self.logging)\n\n # # prepare workspace\n # self.get_workspace_dir()\n\n # pretrained\n if \"pretrained\" in self:\n name = self.pretrained.name\n resources = self.pretrained.family.get(name)\n assert isinstance(resources, dict), ValueError(f\"Could not get the resource with name: {name}\")\n cache_dir = self.pretrained.cache_dir\n if cache_dir is None:\n cache_path = default_download_dir(name)\n else:\n cache_path = Path(cache_dir)\n cache_path = cache_path / name\n if not cache_path.exists():\n maybe_create_dir(str(cache_path))\n\n for k, item in resources.items():\n url = item.url\n suffix = item.suffix\n to_insert_paths = item.to_insert_paths\n to_replaces = item.to_replaces\n others = item.others\n # when specify urls of resource\n if url.startswith(\"http\"):\n filename = url.split(\"/\")[-1]\n file_path = cache_path / filename\n if not file_path.exists():\n try:\n maybe_download(url, str(cache_path))\n except Exception as e:\n logger.error(f\"Download from {url} failure!\", exc_info=True)\n raise e\n # assigning filename to the corresponding config variable.\n if to_insert_paths and file_path.exists():\n for to_insert_path in to_insert_paths:\n self.cascade_set(to_insert_path, str(file_path))\n # replace corresponding variable with file content\n if to_replaces and file_path.exists():\n replace_content = load_from_file(str(file_path))\n for to_replace in to_replaces:\n self.cascade_set(to_replace, replace_content)\n else: # when specify paths of resource which have downloaded.\n maybe_filename = Path(url)\n if maybe_filename.is_dir():\n if suffix:\n file_path = maybe_filename / suffix\n else:\n file_path = maybe_filename\n else:\n file_path = maybe_filename\n # assigning filename to the corresponding config variable.\n if to_insert_paths:\n for to_insert_path in to_insert_paths:\n self.cascade_set(to_insert_path, str(file_path))\n # replace corresponding variable with file content\n if to_replaces:\n replace_content = load_from_file(str(file_path))\n for to_replace in to_replaces:\n self.cascade_set(to_replace, replace_content)\n logging.info(f\"Prepare resource {k} from {url}, whose path is {str(file_path)}\")\n # others config\n if others:\n for _k, v in others.items():\n value = v.get(\"value\")\n other_to_replaces = v.get(\"to_replaces\")\n for other_to_replace in other_to_replaces:\n self.cascade_set(other_to_replace, value)\n\n # replace placeholder\n self._replace_placeholder(self)","def setObjectsDir(self,objects_dir):\n\t\tself.objects_dir=objects_dir","def getResource(self):\n pass;","def _set_conf_attrs():\n\tdebug_msg = \"initializing the configuration\"\n\tlogger.debug(debug_msg)","def setup(self):\n self.kernel = RunningKernel()\n self.setup_sanitize_files()","def pre_config_root_create(self, resource_dict):\n pass","def setup(self, context, entity, path, reduced_path, adapter, **kwargs):\n\n pass","def _set_attributes(self):","def add_resources(event):\n anuket_resources.need()","def setHandles(dmm, src, brd, log, err):\n global DMM, SRC, BRD, Log, Err\n DMM = dmm\n SRC = src\n BRD = brd\n Log = log\n Err = err","def test_create_response_descriptor_variables_library_variable_set_library_variable_set_resource(self):\n pass","def __init__(self):\n super(MemoryPersistence, self).__init__(descriptor)","def _set_device_path(self):\n pass","def upload_resources(directory, debug):\n from pybel.resources.deploy import deploy_directory\n\n set_debug_param(debug)\n deploy_directory(directory)","def __init__(self, debug=False):\n\n self.debug = debug\n self.default_path_aws = '/home/ubuntu/'","def setVirtualMachineOptions(self,node,vmid,post_data):\n data = self.connect('put',\"nodes/%s/qemu/%s/config\" % (node,vmid), post_data)\n return data","def reload( self ):\n\t\tCORE.info( 'Reloading resources: modules, categories' )\n\t\tmoduleManager.load()\n\t\tcategoryManager.load()\n\t\tRESOURCES.info( 'Reloading UCR variables' )\n\t\tucr.load()","def prepare_for_ESR(self): \r\n # Not implemented yet\r\n return","def _setup(self):","def _setup(self):","def test_got_resources(self, resources, monkeypatch):\n dev = qml.device(\"orquestra.qiskit\", wires=2, resources=resources)\n recorder = []\n mock_res_dict = {\"First\": {\"expval\": {\"list\": [123456789]}}}\n\n with monkeypatch.context() as m:\n\n # Record the resources that were passed\n get_resources_passed = lambda *args, **kwargs: recorder.append(\n kwargs.get(\"resources\", False)\n )\n m.setattr(\n pennylane_orquestra.orquestra_device, \"gen_expval_workflow\", get_resources_passed\n )\n\n # Disable submitting to the Orquestra platform by mocking Popen\n m.setattr(subprocess, \"Popen\", lambda *args, **kwargs: MockPopen())\n m.setattr(\n pennylane_orquestra.orquestra_device,\n \"loop_until_finished\",\n lambda *args, **kwargs: mock_res_dict,\n )\n\n @qml.qnode(dev)\n def circuit():\n qml.PauliX(0)\n return qml.expval(qml.PauliZ(0))\n\n assert circuit() == 123456789\n\n # Check that the resorces were passed correctly\n assert len(recorder) == 1\n assert recorder[0] == resources","def debug(self, debug):\n\n self._debug = debug","def _setup_for_wrangling_course(self):\n self._add_writer('node', NODES_FILENAME, NODE_FIELDS)\n self._add_writer('node_tags', NODE_TAGS_FILENAME, NODE_TAGS_FIELDS)\n self._add_writer('way', WAYS_FILENAME, WAY_FIELDS)\n self._add_writer('way_nodes', WAY_NODES_FILENAME, WAY_NODES_FIELDS)\n self._add_writer('way_tags', WAY_TAGS_FILENAME, WAY_TAGS_FIELDS)","def pre_config_node_update(self, resource_id, resource_dict):\n pass","def post_config_node_create(self, resource_dict):\n pass","def post_config_root_create(self, resource_dict):\n pass","def gen_resources(self):\n\n print \"\\t* Adding resources to compute template\"\n\n # add all the nets and subnets\n self.gen_net_resources()\n\n # add all routers\n self.gen_router_resources()\n\n # add all servers/intances\n self.gen_server_resources()","def elf_images_some(self, elf_images_some):\n\n self._elf_images_some = elf_images_some","def test_load_response_descriptor_events_event_event_resource_spaces(self):\n pass","def set_global_attributes(ds):\n ds.title = \"LPDM CO2 Concentration Footprints\"\n ds.summary = (\"Gridded CO2 concentration footprints from the output \"\n \"of the Lagrangian Particle Dispersion model \"\n \"described in Uliasz 1994.\")\n ds.Conventions = \"CF-1.6 ACDD-1.3\"\n ds.history = (\"{date:{acdd_format}} {user:s} \"\n \"created by {progname:s}\").format(\n date=RUN_DATE, user=os.environ[\"USER\"],\n acdd_format=ACDD_DATE,\n progname=sys.argv[0])\n ds.source = (\"Gridded outputs from LPDM v?.?.? \"\n \"written by Uliasz et al. and modified by Lauvaux\")\n ds.standard_name_vocabulary = \"CF Standard Name Table v32\"\n ds.date_created = \"{date:{acdd_format}}\".format(\n date=RUN_DATE, acdd_format=ACDD_DATE)\n ds.creator_name = \"Daniel Wesloh, Thomas Lauvaux\"\n ds.creator_institution = (\n \"The Pennsylvania State University \"\n \"Department of Meteorology and Atmospheric Science\")\n ds.date_modified = \"{date:{acdd_format}}\".format(\n date=RUN_DATE, acdd_format=ACDD_DATE)\n ds.date_metadata_modified = \"{date:{acdd_format}}\".format(\n date=RUN_DATE, acdd_format=ACDD_DATE)\n ds.product_version = \"Py_v1.0.0\"\n ds.references = \"\"\"Uliasz, M. 1994. Lagrangian particle dispersion modeling in mesoscale applications. Environ Model: Comput Methods and Softw for Simulat Environ Pollut and its Adverse Effects (CMP) 2 : 71-.\"\"\"\n\n ds.geospatial_vertical_min = 0\n ds.geospatial_vertical_max = CLOSE_TO_GROUND\n ds.geospatial_vertical_positive = \"up\"\n ds.geospatial_vertical_units = \"km AGL\"\n # Kind of a cross between Grid and Trajectory\n # Grid covers the first and last two axes;\n # trajectory covers third-to-last\n ds.cdm_data_type = \"Grid\"\n\n ds.institution = ds.creator_institution","def exercise_code_resource(self, exercise_code_resource):\n self._exercise_code_resource = exercise_code_resource","def set_elf(self, elf):\n\n self.logger.info('set elf ' + elf)\n rc = self.debugger.set_elf(elf)\n\n self.elf_set_status_sig.emit(rc)\n \n # Console message\n if rc is False:\n self.write_console_output_sig.emit(\n \"[%s] Could not set elf target.\" % DEBUG)\n\n return None\n\n self.elf = elf\n self.bp_func = None\n\n self.write_console_output_sig.emit(\n (\"[%s] Elf target set to %s\" % (DEBUG, elf)))\n \n # Debug symbols\n debug_sym = self.debugger.is_compiled_with_debug_symbols()\n self.has_compile_symbols_sig.emit(debug_sym)\n\n self.state = self.ExecStates.START_BP_SET","def resource(self, resource):\n\n self._resource = resource","def _set_config(self):\n\n self.config.data_path = \"http://{0}:{1}/db/data\".format(\n self.config.host,\n self.config.port)\n\n self.config.node_path = \"/\".join([self.config.data_path, \"node\"])\n self.config.headers = dict([])\n self.config.headers[\"get\"] = {\"Accept\": \"application/json\"}\n self.config.headers[\"put\"] = {\"Content-Type\": \"application/json\"}","def test_modify_response_descriptor_projects_release_release_resource_spaces(self):\n pass","def setUp(self):\n rebel[\"Name\"] = \"\"","def set_parameters(self, seed):\n self.file_location = self.CONFIG[seed]['file_location']\n self.url = self.CONFIG[seed]['url']\n self.pattern = self.CONFIG[seed]['pattern']\n self.date_type = self.CONFIG[seed]['date_type']\n self.ext = self.CONFIG[seed]['extension']\n self.rename = self.CONFIG[seed].get('rename', None)\n\n # Make the locations if they do not exist.\n if not os.path.isdir(self.file_location):\n os.mkdir(self.file_location)","def __init__(self, out_dir):\n self.env = Environment(loader=PackageLoader(\"emu\", \"templates\"))\n self.dest = out_dir","def setUp(self):\r\n self.full_id = 'edX/full/2012_Fall'\r\n self.toy_id = 'edX/toy/2012_Fall'","def saveSettings(self):\n self.genFiles.applyData()\n self.genGraph.applyData()","def _load_resources(self):\n puts = (getattr(self, 'project', None) or self).puts\n for resource_type, resource_cls in six.iteritems(AVAILABLE_RESOURCES):\n for name in self.settings.get(resource_type, {}):\n extra = {\n 'project': getattr(self, 'project', None) or self,\n 'app': self if hasattr(self, 'project') else None,\n }\n\n with indent(4 if hasattr(self, 'project') else 2):\n puts(colored.green(u\"✓ {}:{}\".format(resource_type, name)))\n\n self._resources[resource_type].append(\n resource_cls.factory(\n name=name,\n settings=self.settings.get(resource_type, {})[name],\n **extra\n )\n )"],"string":"[\n \"def resources(self):\",\n \"def resources(self, resources):\\n self._resources = resources\",\n \"def setOptManagerResources(o): # pylint: disable=global-statement\\n # pylint: disable=global-statement\\n global OPT_MANAGER_RESOURCES_PGAAS\\n OPT_MANAGER_RESOURCES_PGAAS = \\\"{}/pgaas\\\".format(o)\",\n \"def resources(self, resources):\\n\\n self._resources = resources\",\n \"def resources(self, resources):\\n\\n self._resources = resources\",\n \"def resources(self, resources):\\n\\n self._resources = resources\",\n \"def resources(self, resources):\\n\\n self._resources = resources\",\n \"def set_paths(self, specs, resources):\\n self.install = 'install.xml'\\n self.specs_path = path_format(specs)\\n self.root = path_format(dirname(dirname(self.specs_path)) + '/')\\n self.res_path = path_format(resources)\\n self.resources['BASE'] = self.res_path\\n self.specs['BASE'] = self.specs_path\",\n \"def init_edra(self) -> None:\\n ...\",\n \"def __init__(self, basedir=None):\\n # ------------------------------------------------------------------------\\n super(Resources, self).__init__()\\n self.xInitialize(basedir or \\\"resources\\\")\",\n \"def __attrs_post_init__(self):\\n self.path = (Path(CONFIG['conda_folder']) / 'envs' / self.name)\",\n \"def set_jinja_globals():\\n\\n # The resource keys will not be present.\\n ufo.app.logger.info('Start setting resources into jinja globals.\\\\n'\\n 'Current jinja globals: %s' %\\n ufo.app.jinja_env.globals.keys())\\n\\n ufo.app.jinja_env.globals['resources'] = json.dumps(_get_resources())\\n\\n # The resource keys should be present if set.\\n ufo.app.logger.info('Finished setting resources into jinja globals.\\\\n'\\n 'Current jinja globals: %s' %\\n ufo.app.jinja_env.globals.keys())\",\n \"def set_resource_data(self, resource, meta):\\n super().set_resource_data(resource, meta)\\n self._set_resource_temperature(resource)\\n self._set_egs_plant_design_temperature()\\n self._set_nameplate_to_match_resource_potential(resource)\\n self._set_resource_potential_to_match_gross_output()\\n self._set_costs()\",\n \"def debug():\\n # print(\\\" ++ debugging 'pyre.filesystem'\\\")\\n # attach {Extent} as the metaclass of Node and Filesystem so we can verify that all\\n # instances of these classes are properly garbage collected\\n from ..patterns.Extent import Extent\\n\\n global _metaclass_Node\\n _metaclass_Node = Extent\\n # all done\\n return\",\n \"def resources(self, value):\\n self._resource_objects = value\",\n \"def __init__(self,resources):\\n OeskObject.__init__(self,resources[0][0][0],resources[0][0][1])\\n self.checkAndParse(resources)\\n self.addAttribute('PRIORITY', hex(0))\",\n \"def set_resource_data(self, resource, meta):\",\n \"def resource_map(self):\",\n \"def setup_dev():\\n setup_general()\",\n \"def _set_attr(self):\\n self.as_skeletal = self._import_as_skeleton()\\n self.materials = self._import_materials()\\n self.textures = self._import_textures()\",\n \"def setupResources(self):\\r\\n if self.initialized == True: return\\r\\n try:\\r\\n LOG(\\\"Configuring driver for context \\\" + repr(self.contextName))\\r\\n DriverManager.instance().setup(self.contextName)\\r\\n self.initialized = True\\r\\n \\r\\n except SpellException,ex:\\r\\n traceback.print_exc( file = sys.stderr )\\r\\n LOG(\\\"Could not setup driver: \\\" + repr(ex), LOG_ERROR)\\r\\n self.errorMessage = ex.message\\r\\n self.errorReason = ex.reason\\r\\n raise ex\",\n \"def initNodePathAttribs(self, doReparent=1):\\n self.callSetters('pos','x','y','z',\\n 'hpr','h','p','r',\\n 'scale','sx','sy','sz')\\n if doReparent:\\n self.callSetters('parentEntId')\\n\\n self.getNodePath().setName('%s-%s' %\\n (self.__class__.__name__, self.entId))\\n\\n if __dev__:\\n # for the editor\\n self.getNodePath().setTag('entity', '1')\",\n \"def setUp(self):\\n self.test_resource = Resource(name='Test', slug='test', description='')\\n self.test_resource.full_clean()\\n self.test_resource.save()\\n self.test_resource_two = Resource(name='Test Two', slug='testtwo', description='')\\n self.test_resource_two.full_clean()\\n self.test_resource_two.save()\",\n \"def __init__(self):\\r\\n self.dev_conf_dir = self._get_dev_conf_dir()\",\n \"def test_modify_response_descriptor_variables_library_variable_set_library_variable_set_resource_spaces(self):\\n pass\",\n \"def resources():\\n check_resources()\",\n \"def set_up():\\n ResourcesManager().set_up()\\n LocatorUtil().load_locators()\",\n \"def test_load_response_descriptor_variables_library_variable_set_library_variable_set_resource_spaces(self):\\n pass\",\n \"def setDebug():\\n\\tglobal debug\\n\\tdebug = True\",\n \"def resAlloc(self,band):\\n\\n if len(list(self.ues.keys()))>0:\\n for ue in list(self.ues.keys()):\\n self.ues[ue].prbs = band\\n self.ues[ue].symb = self.TDDsmb\\n # Print Resource Allocation\\n self.printResAlloc()\",\n \"def setUp(self):\\n self.epath = 'flyeye/tests/fixtures'\\n self.dpath = join(self.epath, 'disc.silhouette')\",\n \"def setUp(self):\\n self.test_resource = Resource(name='Test', slug='test', description='')\\n self.test_resource.full_clean()\\n self.test_resource.save()\",\n \"def prepare(ctx, resource_config, **_):\\n # Save the parameters\\n ctx.instance.runtime_properties['resource_config'] = resource_config\",\n \"def prepare(ctx, resource_config, **_):\\n # Save the parameters\\n ctx.instance.runtime_properties['resource_config'] = resource_config\",\n \"def test_load_response_descriptor_projects_release_release_resource_spaces(self):\\n pass\",\n \"def test_new_resource(self):\\n if verbosity>=3: print \\\"\\\\n\\\"+\\\"~\\\"*80\\n for resource in cfg.new_resources:\\n args=copy.deepcopy(resource)\\n self.run_init_new_resource(**args)\\n if verbosity>=3: print \\\"\\\\n\\\"+\\\"~\\\"*80\",\n \"def test_set_node_properties(self):\\n\\n pass\",\n \"def config(self):\\n self._resource_manager = self._api._ixnetwork.ResourceManager\\n self._ixn_vport = self._api._vport\\n self._delete_vports()\\n self._create_vports()\\n self._create_capture()\\n self._set_location()\\n self._set_layer1()\",\n \"def set_paths_gen(self, paths_gen): #w:\\r\\n self.paths_gen = paths_gen\",\n \"def test_modify_response_descriptor_variables_library_variable_set_library_variable_set_resource(self):\\n pass\",\n \"def test_load_response_descriptor_variables_library_variable_set_library_variable_set_resource(self):\\n pass\",\n \"def resources(self):\\n return self.__resources\",\n \"def __setstate__(self, dict):\\n\\n\\t\\tself.__dict__ = dict\\n\\n\\t\\t# Set missing values to defaults.\\n\\t\\tself._device = None\\n\\t\\tself.resources = {}\",\n \"def setUp(self):\\n self.design = None\",\n \"def setup(self):\\n self.ae = None\",\n \"def DevMode(self):\\n for var in DEV_CONFIG:\\n self.Cover(var, DEV_CONFIG[var], layer = SETTINGS_LAYER)\",\n \"def set_device_parameters(request):\\n def fin():\\n request.cls.device.close()\\n request.addfinalizer(fin)\\n\\n request.cls.driver = ros.ROSDriver\\n request.cls.patched_driver = PatchedROSDevice\\n request.cls.vendor = 'ros'\\n parent_conftest.set_device_parameters(request)\",\n \"def toggleResources(self):\\n if self.game.myEmpire['viewResources'] == 0:\\n self.game.myEmpire['viewResources'] = 1\\n self.createResourceSims()\\n else:\\n self.game.myEmpire['viewResources'] = 0\\n self.removeResourceSims()\",\n \"def test_create_response_descriptor_variables_library_variable_set_library_variable_set_resource_spaces(self):\\n pass\",\n \"def set_debug(self, debug):\\n self.debug = debug\",\n \"def register_dcc_resource_path(resources_path):\\n\\n pass\",\n \"def register_resources(self):\\n raise NotImplementedError\",\n \"def pre_config_node_create(self, resource_dict):\\n pass\",\n \"def resource(self, resource):\\n self._resource = resource\",\n \"def resource(self, resource):\\n self._resource = resource\",\n \"def assets():\",\n \"def set_debug_mode(self):\\n self.debug_mode = True\",\n \"def __init__(self, resource_path):\\n self.resource_path = resource_path\",\n \"def set_defaults(self):\\n for key, constraints in self.__class__.MODEL.items():\\n if key not in self.resource:\\n self.resource[key] = constraints[3]\",\n \"def stand_by(self):\\n\\n if self.gpus is None:\\n self['gpus'] = [0]\\n\\n # setup logging\\n setup_logging(self.get_log_folder(), self.logging)\\n\\n # # prepare workspace\\n # self.get_workspace_dir()\\n\\n # pretrained\\n if \\\"pretrained\\\" in self:\\n name = self.pretrained.name\\n resources = self.pretrained.family.get(name)\\n assert isinstance(resources, dict), ValueError(f\\\"Could not get the resource with name: {name}\\\")\\n cache_dir = self.pretrained.cache_dir\\n if cache_dir is None:\\n cache_path = default_download_dir(name)\\n else:\\n cache_path = Path(cache_dir)\\n cache_path = cache_path / name\\n if not cache_path.exists():\\n maybe_create_dir(str(cache_path))\\n\\n for k, item in resources.items():\\n url = item.url\\n suffix = item.suffix\\n to_insert_paths = item.to_insert_paths\\n to_replaces = item.to_replaces\\n others = item.others\\n # when specify urls of resource\\n if url.startswith(\\\"http\\\"):\\n filename = url.split(\\\"/\\\")[-1]\\n file_path = cache_path / filename\\n if not file_path.exists():\\n try:\\n maybe_download(url, str(cache_path))\\n except Exception as e:\\n logger.error(f\\\"Download from {url} failure!\\\", exc_info=True)\\n raise e\\n # assigning filename to the corresponding config variable.\\n if to_insert_paths and file_path.exists():\\n for to_insert_path in to_insert_paths:\\n self.cascade_set(to_insert_path, str(file_path))\\n # replace corresponding variable with file content\\n if to_replaces and file_path.exists():\\n replace_content = load_from_file(str(file_path))\\n for to_replace in to_replaces:\\n self.cascade_set(to_replace, replace_content)\\n else: # when specify paths of resource which have downloaded.\\n maybe_filename = Path(url)\\n if maybe_filename.is_dir():\\n if suffix:\\n file_path = maybe_filename / suffix\\n else:\\n file_path = maybe_filename\\n else:\\n file_path = maybe_filename\\n # assigning filename to the corresponding config variable.\\n if to_insert_paths:\\n for to_insert_path in to_insert_paths:\\n self.cascade_set(to_insert_path, str(file_path))\\n # replace corresponding variable with file content\\n if to_replaces:\\n replace_content = load_from_file(str(file_path))\\n for to_replace in to_replaces:\\n self.cascade_set(to_replace, replace_content)\\n logging.info(f\\\"Prepare resource {k} from {url}, whose path is {str(file_path)}\\\")\\n # others config\\n if others:\\n for _k, v in others.items():\\n value = v.get(\\\"value\\\")\\n other_to_replaces = v.get(\\\"to_replaces\\\")\\n for other_to_replace in other_to_replaces:\\n self.cascade_set(other_to_replace, value)\\n\\n # replace placeholder\\n self._replace_placeholder(self)\",\n \"def setObjectsDir(self,objects_dir):\\n\\t\\tself.objects_dir=objects_dir\",\n \"def getResource(self):\\n pass;\",\n \"def _set_conf_attrs():\\n\\tdebug_msg = \\\"initializing the configuration\\\"\\n\\tlogger.debug(debug_msg)\",\n \"def setup(self):\\n self.kernel = RunningKernel()\\n self.setup_sanitize_files()\",\n \"def pre_config_root_create(self, resource_dict):\\n pass\",\n \"def setup(self, context, entity, path, reduced_path, adapter, **kwargs):\\n\\n pass\",\n \"def _set_attributes(self):\",\n \"def add_resources(event):\\n anuket_resources.need()\",\n \"def setHandles(dmm, src, brd, log, err):\\n global DMM, SRC, BRD, Log, Err\\n DMM = dmm\\n SRC = src\\n BRD = brd\\n Log = log\\n Err = err\",\n \"def test_create_response_descriptor_variables_library_variable_set_library_variable_set_resource(self):\\n pass\",\n \"def __init__(self):\\n super(MemoryPersistence, self).__init__(descriptor)\",\n \"def _set_device_path(self):\\n pass\",\n \"def upload_resources(directory, debug):\\n from pybel.resources.deploy import deploy_directory\\n\\n set_debug_param(debug)\\n deploy_directory(directory)\",\n \"def __init__(self, debug=False):\\n\\n self.debug = debug\\n self.default_path_aws = '/home/ubuntu/'\",\n \"def setVirtualMachineOptions(self,node,vmid,post_data):\\n data = self.connect('put',\\\"nodes/%s/qemu/%s/config\\\" % (node,vmid), post_data)\\n return data\",\n \"def reload( self ):\\n\\t\\tCORE.info( 'Reloading resources: modules, categories' )\\n\\t\\tmoduleManager.load()\\n\\t\\tcategoryManager.load()\\n\\t\\tRESOURCES.info( 'Reloading UCR variables' )\\n\\t\\tucr.load()\",\n \"def prepare_for_ESR(self): \\r\\n # Not implemented yet\\r\\n return\",\n \"def _setup(self):\",\n \"def _setup(self):\",\n \"def test_got_resources(self, resources, monkeypatch):\\n dev = qml.device(\\\"orquestra.qiskit\\\", wires=2, resources=resources)\\n recorder = []\\n mock_res_dict = {\\\"First\\\": {\\\"expval\\\": {\\\"list\\\": [123456789]}}}\\n\\n with monkeypatch.context() as m:\\n\\n # Record the resources that were passed\\n get_resources_passed = lambda *args, **kwargs: recorder.append(\\n kwargs.get(\\\"resources\\\", False)\\n )\\n m.setattr(\\n pennylane_orquestra.orquestra_device, \\\"gen_expval_workflow\\\", get_resources_passed\\n )\\n\\n # Disable submitting to the Orquestra platform by mocking Popen\\n m.setattr(subprocess, \\\"Popen\\\", lambda *args, **kwargs: MockPopen())\\n m.setattr(\\n pennylane_orquestra.orquestra_device,\\n \\\"loop_until_finished\\\",\\n lambda *args, **kwargs: mock_res_dict,\\n )\\n\\n @qml.qnode(dev)\\n def circuit():\\n qml.PauliX(0)\\n return qml.expval(qml.PauliZ(0))\\n\\n assert circuit() == 123456789\\n\\n # Check that the resorces were passed correctly\\n assert len(recorder) == 1\\n assert recorder[0] == resources\",\n \"def debug(self, debug):\\n\\n self._debug = debug\",\n \"def _setup_for_wrangling_course(self):\\n self._add_writer('node', NODES_FILENAME, NODE_FIELDS)\\n self._add_writer('node_tags', NODE_TAGS_FILENAME, NODE_TAGS_FIELDS)\\n self._add_writer('way', WAYS_FILENAME, WAY_FIELDS)\\n self._add_writer('way_nodes', WAY_NODES_FILENAME, WAY_NODES_FIELDS)\\n self._add_writer('way_tags', WAY_TAGS_FILENAME, WAY_TAGS_FIELDS)\",\n \"def pre_config_node_update(self, resource_id, resource_dict):\\n pass\",\n \"def post_config_node_create(self, resource_dict):\\n pass\",\n \"def post_config_root_create(self, resource_dict):\\n pass\",\n \"def gen_resources(self):\\n\\n print \\\"\\\\t* Adding resources to compute template\\\"\\n\\n # add all the nets and subnets\\n self.gen_net_resources()\\n\\n # add all routers\\n self.gen_router_resources()\\n\\n # add all servers/intances\\n self.gen_server_resources()\",\n \"def elf_images_some(self, elf_images_some):\\n\\n self._elf_images_some = elf_images_some\",\n \"def test_load_response_descriptor_events_event_event_resource_spaces(self):\\n pass\",\n \"def set_global_attributes(ds):\\n ds.title = \\\"LPDM CO2 Concentration Footprints\\\"\\n ds.summary = (\\\"Gridded CO2 concentration footprints from the output \\\"\\n \\\"of the Lagrangian Particle Dispersion model \\\"\\n \\\"described in Uliasz 1994.\\\")\\n ds.Conventions = \\\"CF-1.6 ACDD-1.3\\\"\\n ds.history = (\\\"{date:{acdd_format}} {user:s} \\\"\\n \\\"created by {progname:s}\\\").format(\\n date=RUN_DATE, user=os.environ[\\\"USER\\\"],\\n acdd_format=ACDD_DATE,\\n progname=sys.argv[0])\\n ds.source = (\\\"Gridded outputs from LPDM v?.?.? \\\"\\n \\\"written by Uliasz et al. and modified by Lauvaux\\\")\\n ds.standard_name_vocabulary = \\\"CF Standard Name Table v32\\\"\\n ds.date_created = \\\"{date:{acdd_format}}\\\".format(\\n date=RUN_DATE, acdd_format=ACDD_DATE)\\n ds.creator_name = \\\"Daniel Wesloh, Thomas Lauvaux\\\"\\n ds.creator_institution = (\\n \\\"The Pennsylvania State University \\\"\\n \\\"Department of Meteorology and Atmospheric Science\\\")\\n ds.date_modified = \\\"{date:{acdd_format}}\\\".format(\\n date=RUN_DATE, acdd_format=ACDD_DATE)\\n ds.date_metadata_modified = \\\"{date:{acdd_format}}\\\".format(\\n date=RUN_DATE, acdd_format=ACDD_DATE)\\n ds.product_version = \\\"Py_v1.0.0\\\"\\n ds.references = \\\"\\\"\\\"Uliasz, M. 1994. Lagrangian particle dispersion modeling in mesoscale applications. Environ Model: Comput Methods and Softw for Simulat Environ Pollut and its Adverse Effects (CMP) 2 : 71-.\\\"\\\"\\\"\\n\\n ds.geospatial_vertical_min = 0\\n ds.geospatial_vertical_max = CLOSE_TO_GROUND\\n ds.geospatial_vertical_positive = \\\"up\\\"\\n ds.geospatial_vertical_units = \\\"km AGL\\\"\\n # Kind of a cross between Grid and Trajectory\\n # Grid covers the first and last two axes;\\n # trajectory covers third-to-last\\n ds.cdm_data_type = \\\"Grid\\\"\\n\\n ds.institution = ds.creator_institution\",\n \"def exercise_code_resource(self, exercise_code_resource):\\n self._exercise_code_resource = exercise_code_resource\",\n \"def set_elf(self, elf):\\n\\n self.logger.info('set elf ' + elf)\\n rc = self.debugger.set_elf(elf)\\n\\n self.elf_set_status_sig.emit(rc)\\n \\n # Console message\\n if rc is False:\\n self.write_console_output_sig.emit(\\n \\\"[%s] Could not set elf target.\\\" % DEBUG)\\n\\n return None\\n\\n self.elf = elf\\n self.bp_func = None\\n\\n self.write_console_output_sig.emit(\\n (\\\"[%s] Elf target set to %s\\\" % (DEBUG, elf)))\\n \\n # Debug symbols\\n debug_sym = self.debugger.is_compiled_with_debug_symbols()\\n self.has_compile_symbols_sig.emit(debug_sym)\\n\\n self.state = self.ExecStates.START_BP_SET\",\n \"def resource(self, resource):\\n\\n self._resource = resource\",\n \"def _set_config(self):\\n\\n self.config.data_path = \\\"http://{0}:{1}/db/data\\\".format(\\n self.config.host,\\n self.config.port)\\n\\n self.config.node_path = \\\"/\\\".join([self.config.data_path, \\\"node\\\"])\\n self.config.headers = dict([])\\n self.config.headers[\\\"get\\\"] = {\\\"Accept\\\": \\\"application/json\\\"}\\n self.config.headers[\\\"put\\\"] = {\\\"Content-Type\\\": \\\"application/json\\\"}\",\n \"def test_modify_response_descriptor_projects_release_release_resource_spaces(self):\\n pass\",\n \"def setUp(self):\\n rebel[\\\"Name\\\"] = \\\"\\\"\",\n \"def set_parameters(self, seed):\\n self.file_location = self.CONFIG[seed]['file_location']\\n self.url = self.CONFIG[seed]['url']\\n self.pattern = self.CONFIG[seed]['pattern']\\n self.date_type = self.CONFIG[seed]['date_type']\\n self.ext = self.CONFIG[seed]['extension']\\n self.rename = self.CONFIG[seed].get('rename', None)\\n\\n # Make the locations if they do not exist.\\n if not os.path.isdir(self.file_location):\\n os.mkdir(self.file_location)\",\n \"def __init__(self, out_dir):\\n self.env = Environment(loader=PackageLoader(\\\"emu\\\", \\\"templates\\\"))\\n self.dest = out_dir\",\n \"def setUp(self):\\r\\n self.full_id = 'edX/full/2012_Fall'\\r\\n self.toy_id = 'edX/toy/2012_Fall'\",\n \"def saveSettings(self):\\n self.genFiles.applyData()\\n self.genGraph.applyData()\",\n \"def _load_resources(self):\\n puts = (getattr(self, 'project', None) or self).puts\\n for resource_type, resource_cls in six.iteritems(AVAILABLE_RESOURCES):\\n for name in self.settings.get(resource_type, {}):\\n extra = {\\n 'project': getattr(self, 'project', None) or self,\\n 'app': self if hasattr(self, 'project') else None,\\n }\\n\\n with indent(4 if hasattr(self, 'project') else 2):\\n puts(colored.green(u\\\"✓ {}:{}\\\".format(resource_type, name)))\\n\\n self._resources[resource_type].append(\\n resource_cls.factory(\\n name=name,\\n settings=self.settings.get(resource_type, {})[name],\\n **extra\\n )\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5880429","0.5852034","0.57128286","0.56084967","0.56084967","0.56084967","0.56084967","0.5529932","0.5283272","0.5241133","0.5212937","0.5193305","0.514852","0.51433814","0.51408863","0.5116762","0.50918823","0.5086745","0.5068364","0.5036334","0.50106084","0.5001991","0.50003874","0.49918106","0.49793562","0.49605638","0.49520162","0.49467567","0.4940319","0.49347645","0.4933845","0.49272606","0.49009234","0.49009234","0.4894851","0.48946977","0.48927957","0.4888527","0.48859447","0.487794","0.48628902","0.48589745","0.48492837","0.4846674","0.48404977","0.4831264","0.48309052","0.4830157","0.48263004","0.48236334","0.48152563","0.48150536","0.48095623","0.48042655","0.48042655","0.48023364","0.4794576","0.4790401","0.4783549","0.47793627","0.47672543","0.47584742","0.47582558","0.47500807","0.47402743","0.4738401","0.47346538","0.4729395","0.4720049","0.4706517","0.4704944","0.46999422","0.4697343","0.46948624","0.46920666","0.46750355","0.46654704","0.46617192","0.46617192","0.46581715","0.4653605","0.46534222","0.46501157","0.46464086","0.46432945","0.46427748","0.46352848","0.4633117","0.46308923","0.46273118","0.46262679","0.4621898","0.46196228","0.46170837","0.46154398","0.46142662","0.4612668","0.46103013","0.46064353","0.46017212"],"string":"[\n \"0.5880429\",\n \"0.5852034\",\n \"0.57128286\",\n \"0.56084967\",\n \"0.56084967\",\n \"0.56084967\",\n \"0.56084967\",\n \"0.5529932\",\n \"0.5283272\",\n \"0.5241133\",\n \"0.5212937\",\n \"0.5193305\",\n \"0.514852\",\n \"0.51433814\",\n \"0.51408863\",\n \"0.5116762\",\n \"0.50918823\",\n \"0.5086745\",\n \"0.5068364\",\n \"0.5036334\",\n \"0.50106084\",\n \"0.5001991\",\n \"0.50003874\",\n \"0.49918106\",\n \"0.49793562\",\n \"0.49605638\",\n \"0.49520162\",\n \"0.49467567\",\n \"0.4940319\",\n \"0.49347645\",\n \"0.4933845\",\n \"0.49272606\",\n \"0.49009234\",\n \"0.49009234\",\n \"0.4894851\",\n \"0.48946977\",\n \"0.48927957\",\n \"0.4888527\",\n \"0.48859447\",\n \"0.487794\",\n \"0.48628902\",\n \"0.48589745\",\n \"0.48492837\",\n \"0.4846674\",\n \"0.48404977\",\n \"0.4831264\",\n \"0.48309052\",\n \"0.4830157\",\n \"0.48263004\",\n \"0.48236334\",\n \"0.48152563\",\n \"0.48150536\",\n \"0.48095623\",\n \"0.48042655\",\n \"0.48042655\",\n \"0.48023364\",\n \"0.4794576\",\n \"0.4790401\",\n \"0.4783549\",\n \"0.47793627\",\n \"0.47672543\",\n \"0.47584742\",\n \"0.47582558\",\n \"0.47500807\",\n \"0.47402743\",\n \"0.4738401\",\n \"0.47346538\",\n \"0.4729395\",\n \"0.4720049\",\n \"0.4706517\",\n \"0.4704944\",\n \"0.46999422\",\n \"0.4697343\",\n \"0.46948624\",\n \"0.46920666\",\n \"0.46750355\",\n \"0.46654704\",\n \"0.46617192\",\n \"0.46617192\",\n \"0.46581715\",\n \"0.4653605\",\n \"0.46534222\",\n \"0.46501157\",\n \"0.46464086\",\n \"0.46432945\",\n \"0.46427748\",\n \"0.46352848\",\n \"0.4633117\",\n \"0.46308923\",\n \"0.46273118\",\n \"0.46262679\",\n \"0.4621898\",\n \"0.46196228\",\n \"0.46170837\",\n \"0.46154398\",\n \"0.46142662\",\n \"0.4612668\",\n \"0.46103013\",\n \"0.46064353\",\n \"0.46017212\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6285629"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77483,"cells":{"query":{"kind":"string","value":"Loads the textual templates of questions and saves them in a list."},"document":{"kind":"string","value":"def load_templates(self):\n\n self.templates = []\n\n if os.path.exists(\"question_templates.txt\"):\n for line in open(\"question_templates.txt\", \"r\"):\n self.templates.append(line.replace(\"\\n\", \"\"))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_questions(self, verbose=True):\n for question in self.question_list:\n question.load_question(self.data)","def load_templates(self):\n TemplateHandler.templates = []\n for template in os.listdir(TemplateHandler.templates_path):\n template_config = self.load_template_conf(template)\n if template_config is None:\n continue\n TemplateHandler.templates.append(template_config)","def ask_questions():\n print(story.prompts)\n prompts = story.prompts\n \n return render_template(\"questions.html\", prompts = prompts)","def load_corpus_questions():\r\n\tglobal search_criteria_dict, solution_dict, linked_abstracts_dict\r\n\tif os.path.exists(paths.path_data_questions_pickle):\r\n\t\tprint('\\nloading questions and answers')\r\n\t\tsearch_criteria_dict = pickle.load(open(paths.path_data_questions_pickle,\"rb\"))\r\n\t\tsolution_dict = pickle.load(open(paths.path_data_answers_pickle,\"rb\"))\r\n\t\tlinked_abstracts_dict = pickle.load(open(paths.path_data_linkedabstracts_pickle,\"rb\"))\r\n\t\t\r\n\t\tprint(len(search_criteria_dict))\r\n\t\tprint(len(solution_dict))\r\n\t\tprint(len(linked_abstracts_dict))\r\n\t\t\r\n\t\treturn True\r\n\telse:\r\n\t\treturn False","def collection(self):\n questions = []\n choice_list = []\n answers = []\n\n if self.form=='The correct German word':\n for i in range(self.num_ques):\n question, options, answer = self.generate_eng2ger()\n questions.append(question)\n choice_list.append(options)\n answers.append(answer)\n else:\n for i in range(self.num_ques):\n question, options, answer = self.generate_ger2eng()\n questions.append(question)\n choice_list.append(options)\n answers.append(answer)\n\n return questions, choice_list, answers","def _load_templates(cls):\n if cls._raw_templates is None:\n cls._raw_templates = fetch_rrlyrae_templates()","def load_questions_view(self, request):\n request.session['duplicates'] = []\n mm = LoadQuestionsMessageManager()\n try:\n form = XMLFileForm(request.POST, request.FILES)\n if form.is_valid():\n xmlfile = request.FILES['file']\n parser = XMLParser(xmlfile)\n for data in parser.parse_questions():\n self.load_question(data, mm, request)\n else:\n mm.form_is_valid = False\n mm.set_messages(request)\n print(reverse('admin:chm_xmlfile_add'))\n return redirect(reverse('admin:chm_xmlfile_add'))\n\n except XMLSyntaxError as err:\n mm.syntax_error = err\n\n mm.set_messages(request)\n return redirect(reverse('admin:chm_question_changelist'))","def gen_questions(self, number_of_questions):","def load_as_raw(self):\n\n # Q & A\n questions, answers = self.get_questions_answers()\n\n # Get vocabs\n\n # Step 4: cleaning the questions\n pprint('---- Step 4 cleaning questions ----')\n\n clean_questions = []\n for question in questions:\n clean_questions.append(clean_text(question))\n\n pprint(clean_questions, stream=Head(5))\n print('\\n\\n')\n \"\"\"\n Step 5: Clean the answers\n \"\"\"\n\n pprint('---- Step 5 cleaning answers ----')\n clean_answers = []\n for answer in answers:\n clean_answers.append(clean_text(answer))\n\n pprint(clean_answers, stream=Head(5))\n print('\\n\\n')\n \"\"\"\n Step 6: Creating a dictionary that maps each word to its number of occurences\n \"\"\"\n\n word2count = {}\n pprint('------ Step 6: counting words in questions ----')\n\n word2count = convert_word_to_count(word2count, clean_questions)\n\n pprint(word2count, stream=Head(5))\n print('\\n\\n')\n \"\"\"\n Step 7:\n For example, for a question: can we make this quick roxanne korrine and andrew barrett are having an incredibly horrendous public break up on the quad again\n It counts each word occurence such as \"can\" and accumulates the count into word2count dict\n \"\"\"\n pprint('------ Step 6: counting words in answers ----')\n\n word2count = convert_word_to_count(word2count, clean_answers)\n\n pprint(word2count, stream=Head(5))\n print('\\n\\n')\n\n keys = ['<unk>', '<s>', '</s>']\n\n \"\"\"\n Step 8: Creating word 2 int(count) by filtering words that are greater than the threshold\n \"\"\"\n\n pprint(\n '------ Step 8: questions_vocabs filtered by threshold (>) ----')\n threshold_questions = 20\n questions_vocabs = [] + keys\n for word, count in word2count.items():\n if count >= threshold_questions:\n if not word in questions_vocabs:\n questions_vocabs.append(word)\n\n pprint(questions_vocabs, stream=Head(5))\n print('\\n\\n')\n \"\"\"\n Step 9: Same as step 8 but for answers\n \"\"\"\n pprint(\n '------ Step 9: answers_vocabs filtered by threshold (>) ----')\n threshold_answers = 20\n answers_vocabs = [] + keys\n for word, count in word2count.items():\n if count >= threshold_answers:\n if not word in answers_vocabs:\n answers_vocabs.append(word)\n\n pprint(answers_vocabs, stream=Head(5))\n\n return questions, answers, questions_vocabs, answers_vocabs","def populate_fixtures():\n languages()\n words()","def _read_files(self):\n \n for langname in self.langnames:\n filename = f'data/word_lists/{langname}.txt'\n with open(filename) as f:\n index = self.langnames.index(langname)\n lang_list = getattr(self, f'word_list{index}')\n words = f.readlines()\n for word in words:\n fword = ''.join(char for char in word if char is not '\\n')\n lang_list.append(fword)\n f.close()\n return","def load_quests(self):\n\n raise NotImplementedError()","def generateISTemplates(course, wordlist, charLevels, translateDict, similarCharsList, supportsUppercase):\n\n levelsDict= {}\n stageByNumber = 1 # represents key of wordsByLevelDict to get wordlist value\n wordsByLengthDict = {}\n wordsByLevelDict = {}\n swappedTranslateDict = dict((v,k) for k,v in translateDict.iteritems()) # Keys are latin, values non-latin\n\n for word in wordlist:\n wordsByLengthDict = wordsByLength(word, wordsByLengthDict)\n wordsByLevelDict = wordsByLevel(word, charLevels, wordsByLevelDict)\n\n for stage in charLevels:\n\n if not wordsByLevelDict.has_key(stageByNumber): # Skip the uppercase stages in charLevels\n stageByNumber += 1\n continue\n\n charLevelsWordList = wordsByLevelDict[stageByNumber] # contains list of words suitable for current stage\n selectedOptions = makeWordOptions(charLevelsWordList, similarCharsList) # List of 3 sublists containing options or None\n\n if selectedOptions is not None:\n\n for i in range(len(selectedOptions)):\n generateLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\".qdef\")\n generateLevelAlternative(selectedOptions[i], translateDict, similarCharsList, name+str(stageByNumber)+\".qdef\")\n\n if supportsUppercase:\n generateUppercaseLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\".qdef\")\n\n generateEndOfQuiz(name+str(stageByNumber)+\".qdef\")\n stageByNumber += 1\n\n # generate practice levels\n for i in range(30):\n selectedWords = wordsByLengthDict[random.choice(wordsByLengthDict.keys())]\n if len(selectedWords) >= 3:\n quizRandomSelection = random.sample(selectedWords,3) # take random 4 items from list\n\n for word in quizRandomSelection:\n trickWord = findAndReplaceSimilarChar(word, quizRandomSelection, similarCharsList)\n if trickWord:\n quizRandomSelection.append(trickWord)\n break\n\n generateLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\"Practice.qdef\")\n generateLevelAlternative(quizRandomSelection, translateDict, similarCharsList, name+\"Practice.qdef\")\n\n if supportsUppercase:\n generateUppercaseLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\"Practice.qdef\")\n\n generateEndOfQuiz(name+\"Practice.qdef\")\n\n print \"New MUNI ROPOT templates has been created in: \" + target","def _load_answers(self, answer_list):\n # We are given a list of words that indicate what the answer\n # options are. By convention, the first word is probably the\n # correct answer; the others are incorrect answers. However,\n # we won't set this now because this convention may not hold.\n # We expect the SET_CORRECT OpalCommand to be used to set\n # which answers are correct or incorrect.\n # split the list of answers on commas.\n answers = answer_list.strip().split(',')\n\n # Shuffle answers to display them in a random order.\n random.shuffle(answers)\n\n # Load in the graphic for each answer.\n for answer in answers:\n toload = {}\n # Remove whitespace from name before using it.\n toload[\"name\"] = answer.strip()\n toload[\"tag\"] = \"PlayObject\"\n toload[\"slot\"] = answers.index(answer) + 1\n toload[\"draggable\"] = False\n toload[\"isAnswerSlot\"] = True\n self._ros_node.send_opal_command(\"LOAD_OBJECT\", json.dumps(toload))","def elastixTemplates():\n\t\ttransformations = []\n\t\tfileNames = os.listdir(AppVars.transformationsPath())\n\t\tfor fileName in fileNames:\n\t\t\tfullFileName = os.path.join(AppVars.transformationsPath(), fileName)\n\t\t\ttransformation = ParameterList()\n\t\t\tif transformation.loadFromFile(fullFileName):\n\t\t\t\ttransformations.append(transformation)\n\t\treturn transformations","def _collect_quizzes():\n data_path = join(dirname(abspath(__file__)), 'data')\n for _, _, filenames in os.walk(data_path):\n for filename in filenames:\n if filename.endswith('.yml'):\n quiz_type = filename.replace('.yml', '').capitalize()\n QUIZ_DICT[quiz_type] = []\n with open(join(data_path, filename), encoding='utf-8') as f:\n data = yaml.load(f)\n for class_name, settings in data.items():\n Q = type(class_name, (Quiz, ), settings)\n QUIZ_DICT[quiz_type].append(Q)\n QUIZ_DICT_FLAT[class_name] = Q","def load_data(squad_per_lang):\n question_set = QuestionSet()\n candidate_set = CandidateSet()\n\n for lang, squad in squad_per_lang.items():\n for question, answer, context, context_sentences, xling_id, context_id in (\n generate_examples(squad, lang)):\n question = Question(question, xling_id, lang)\n question_set.add(question)\n assert answer in context_sentences, (\n \"answer doesn't appear in context_sentences\")\n for sent_pos, sentence in enumerate(context_sentences):\n candidate = Candidate(sentence, context, lang, context_id, sent_pos)\n candidate = candidate_set.add_or_retrieve_candidate(candidate)\n if sentence == answer:\n candidate_set.update_xling_id(candidate, xling_id)\n print(\"Totals across languages: questions={}, candidates={}\".format(\n len(question_set.as_list()), len(candidate_set.as_list())))\n\n return question_set, candidate_set","def listQuestions(clean_user_name):\n DEVNULL = open(os.devnull, 'wb')\n question_names_pipe = subprocess.Popen(['ls', '-1', \"/home/\"+clean_user_name+\"/.question/questions\"], stdout=subprocess.PIPE, stderr=DEVNULL)\n question_names = question_names_pipe.stdout.read().split('\\n')\n question_names.pop()\n question_names.sort()\n for question_name in question_names:\n clean_question_name = question_name.rstrip(\"\\n\")\n if (clean_user_name != \"\" and clean_question_name != \"\"):\n generateQuestionList(clean_user_name, clean_question_name)","def _load_questions():\r\n page = request.args.get('page', type=int, default=1)\r\n content = request.args.get('content')\r\n if current_user.is_authenticated and content != 'all':\r\n query = current_user.followed_posts\r\n content = Post\r\n else:\r\n query = Post.query\r\n content = Post\r\n pagination = query.order_by(content.timestamp.desc()).paginate(page,\r\n per_page=current_app.config['FLASKY_POSTS_PER_PAGE'],\r\n error_out=False)\r\n macro = get_template_attribute(\"_add_questions.html\", \"render_posts\")\r\n posts = pagination.items\r\n return jsonify({'result': True,\r\n 'html': macro(posts),\r\n 'posts':[post.timestamp for post in posts],\r\n 'post_id':[post.id for post in posts]\r\n })","def loadTextFile(self):\n if self.tempFilePath is None or not MyFile.checkFileExists(self.tempFilePath):\n raise Exception(\"Temporary text file does not exist!\")\n\n io = Ioread()\n self.sentencesList = io.readFileContentList(self.tempFilePath)","def _create_examples(self, lines, kb_data, set_type):\n examples = []\n for idx, line in enumerate(lines):\n item = json.loads(line.strip())\n question_id = \"%s-%s\" % (set_type, idx)\n \n context_a_list = kb_data[idx]['answerA']\n context_b_list = kb_data[idx]['answerB']\n context_c_list = kb_data[idx]['answerC']\n\n context_a = \"\"\n for l in context_a_list[:1]:\n context_a += l.replace(\"\\n\",\". \")\n context_a = context_a[:-1]\n\n context_b = \"\"\n for l in context_b_list[:1]:\n context_b += l.replace(\"\\n\",\". \")\n context_b = context_b[:-1]\n\n context_c = \"\"\n for l in context_c_list[:1]:\n context_c += l.replace(\"\\n\",\". \")\n context_c = context_c[:-1]\n \n \n question = item[\"context\"] + item[\"question\"]\n endings = [item[\"answerA\"],item[\"answerB\"],item[\"answerC\"] ]\n label = item[\"correct\"]\n #race_id = \"%s-%s\" % (set_type, data_raw[\"race_id\"])\n #article = data_raw[\"article\"]\n #for i in range(len(data_raw[\"answers\"])):\n #truth = str(ord(data_raw[\"answers\"][i]) - ord(\"A\"))\n #question = data_raw[\"questions\"][i]\n #options = data_raw[\"options\"][i]\n\n examples.append(\n InputExample(\n example_id=question_id,\n question=question,\n contexts=[context_a,context_b,context_c],\n endings=[endings[0], endings[1], endings[2]],#, options[3]\n label=label,\n )\n )\n return examples","def get_questions():\n fields_dt = ['name', 'category', 'key', 'text']\n questions = frappe.db.get_list('Big Five Factor Model',\n fields=fields_dt)\n\n # Ordenamiendo random: se aplica sobre el objeto original\n suffle_data = random.shuffle(questions)\n\n return questions","def __init__(self, words, text):\n\n self.prompts = words\n self.template = text","def get_snippets(self, constraints):\n for ind, text in enumerate(self.__mongo_db.get({\"title\": \"Chapter 1\"})):\n self.__postgre_db.insert(\"texts\", {\"title\": text['title']})\n self.find_text_window(text['text'], text['id'], constraints)\n print(\"Chapter \" + str(text['id']) + \" done.\")","def read_in_templates(path, email_object=None):\n import os\n templates = {}\n\n for fle in os.listdir(path):\n with open(os.path.join(path, fle)) as _f:\n raw = \"\\n\".join(_f.readlines())\n templates[fle] = raw\n\n if email_object:\n email_object.use_templates(templates)\n else:\n return templates","def list_templates(self):\n raise NotImplementedError()","def clean_questions(self, q_dot=False):\n for q in self.data['questions']:\n temp = re.sub('[^ a-zA-Z0-9]', '', q['body'])\n temp = temp.strip().lower()\n # temp = self.stemming(temp).strip()\n self.cleaned_questions_q_dot += temp + '?\\n'\n self.cleaned_questions += temp + '\\n'\n self.types_q += q['type'].strip() + '\\n'","def generate_corpus():\n data = load_data()\n questions = [s.split(' ', 1)[1].lower() for s in data]\n return questions","def extract_questions_from_text(self, text):\n questions = []\n\n for match in self.QUESTION_RE.finditer(text):\n match_dict = match.groupdict()\n\n answer_type = match_dict['answer_type']\n number1 = match_dict.pop('number1')\n\n if answer_type == 'O':\n if re.search('(?i)to ask the Deputy President', match_dict['intro']):\n match_dict['dp_number'] = number1\n elif re.search('(?i)to ask the President', match_dict['intro']):\n match_dict['president_number'] = number1\n else:\n match_dict['oral_number'] = number1\n elif answer_type == 'W':\n match_dict['written_number'] = number1\n\n match_dict['translated'] = bool(match_dict['translated'])\n match_dict['questionto'] = match_dict['questionto'].replace(':', '')\n match_dict['questionto'] = self.correct_minister_title(match_dict['questionto'])\n\n questions.append(match_dict)\n\n return questions","def read_templates(folder):\n output = []\n for path, subdirs, files in os.walk(folder):\n for name in files:\n if name.endswith('.yml'):\n tpl = yaml.load(open(os.path.join(path, name)).read())\n tpl['template_name'] = name\n\n # Test if all required fields are in template:\n assert 'keywords' in tpl.keys(), 'Missing keywords field.'\n required_fields = ['date', 'amount', 'invoice_number']\n assert len(set(required_fields).intersection(tpl['fields'].keys())) == len(required_fields), \\\n 'Missing required key in template {} {}. Found {}'.format(name, path, tpl['fields'].keys())\n \n # Keywords as list, if only one.\n if type(tpl['keywords']) is not list:\n tpl['keywords'] = [tpl['keywords']]\n\n output.append(InvoiceTemplate(tpl))\n return output","def get_templates(self):\n\n data = self.request_from_server('templates')\n self.templates = data","def from_text(question_file, answer_file):\n with open(question_file, 'r', encoding='UTF8', errors='replace') as f:\n questions = f.readlines()\n\n with open(answer_file, 'r', encoding='UTF8', errors='replace') as f:\n answers = f.readlines()\n\n return QuestionSet([Question(question, answer) for question, answer in zip(questions, answers)])","def load_questions_from_web():\n # sending http request\n r = requests.get(http_api_questions)\n \n # loading the content to JSON object\n json_content = json.loads(r.content)\n \n # deleting unnecessary value from the JSON\n del json_content['response_code']\n\n # creating the questions dict\n questions = {}\n\n # questions from the JSON (in list [])\n questions_from_json = json_content['results']\n\n # looping through the questions, convert it to usual formatted question\n for index_question, question in enumerate(questions_from_json):\n\n # correct-answer\n correct_answer = question[\"correct_answer\"]\n\n # list of all answers\n all_answers = [correct_answer] + question[\"incorrect_answers\"]\n\n # shuffling the list [otherwise, the answer will always be in the first place]\n random.shuffle(all_answers)\n\n # finding the new index of the correct-answer\n correct_answer_index = all_answers.index(correct_answer)\n\n # important! - converting from 'XML\\CSS\\HTML' format to 'utf-8'\n fixed_replaced_question = question[\"question\"].replace(\"'\", \"'\").replace(\""\", \"'\")\n \n # discarding specific question if it still has unwelcomed characters\n if(fixed_replaced_question.find('#') != -1 or fixed_replaced_question.find('|') != -1):\n break\n \n # copying the question to the new dict\n questions[str(index_question)] = {\n \"question\" : fixed_replaced_question,\n \"answers\" : all_answers,\n \"correct\" : str(correct_answer_index + 1)\n }\n \n # returns the questions' dict\n return questions","def code_templates(self):\n\n one_of_languages = '|'.join(l if l != 'c++' else r'c\\+\\+' for l in Languages.all)\n pattern = r'^::[ \\t\\r\\f\\v]*(' + one_of_languages + r')[ \\t\\r\\f\\v]*$'\n parts = re.split(pattern, self.templates_data, flags=re.MULTILINE)\n parts = parts[1:]\n languages = parts[::2]\n # remove starting/ending empty lines, common indentation and ensure trailing newline\n templates = [textwrap.dedent(t).strip() for t in parts[1::2]]\n assert (len(languages) == len(templates))\n templates = dict(zip(languages, templates)) or {language: '' for language in Languages.all}\n for language, template in templates.items():\n if template:\n templates[language] = template + '\\n'\n else:\n templates[language] = Languages.default_templates[language]\n return templates","def realize_question(dialog, template, filter_objs):\n\n # Number of inputs.\n n_inputs = template.get('inputs', 0)\n # Sample a text template.\n text_sample = random.choice(template['text'])\n text_sample_index = template['text'].index(text_sample)\n\n # Extract attribute tags and get them into groups.\n tags = re.findall('(<[\\d\\w]*>)', text_sample)\n tag_groups = collections.defaultdict(list)\n for tag in tags:\n group_id = get_tag_group(tag)\n tag_groups[group_id].append(tag)\n\n # Sample a random element from filtered.\n arg_sample = random.choice(filter_objs)\n\n # Remove tags from text not allowed by filter_objs.\n for arg_ind in range(n_inputs):\n obj_sample = arg_sample['objects'][arg_ind]\n avail_attrs = obj_sample['optional'] + obj_sample['required']\n\n for ii in tag_groups[arg_ind][::-1]:\n if mapping(ii) not in avail_attrs:\n tag_groups[arg_ind].remove(ii)\n text_sample = replace_attribute(text_sample, ii, arg_sample, True)\n\n # Assert that all required attributes are present as tags.\n for attribute in obj_sample['required']:\n required_tag = inv_mapping(attribute, arg_ind)\n # Make an exception for <R> and <P>\n if required_tag == '<R>' and '<P>' in tag_groups[arg_ind]:\n continue\n assert required_tag in tag_groups[arg_ind], \\\n 'A required attribute is missing in template!'\n\n # Start compiling tags to keep.\n tags_to_keep = [inv_mapping(ii, arg_ind) for ii in obj_sample['required']]\n # Filter out those not present in text template.\n optional_tags = [inv_mapping(ii,arg_ind) for ii in obj_sample['optional']]\n optional_tags = [ii for ii in optional_tags if ii in tag_groups[arg_ind]]\n\n # If tags_to_keep is empty, sample from optional with (1:70, 2:25, 3:5).\n if len(optional_tags) > 0:\n if len(tags_to_keep) > 0:\n n_tags_sample = [0, 1, 2]\n else:\n n_tags_sample = [1, 2, 3]\n n_sample = np.random.choice(n_tags_sample, 1,\n p=gvars.METAINFO['probabilities'],\n replace=False)\n # Lower cap at the length of optional.\n n_sample = min(n_sample[0], len(optional_tags))\n if n_sample > 0:\n tags_to_keep += random.sample(optional_tags, n_sample)\n\n # Now create a dictionary of placeholders with actual attribute values.\n for tag in tag_groups[arg_ind]:\n remove = tag not in tags_to_keep\n text_sample = replace_attribute(text_sample, tag, arg_sample, remove)\n\n # Record info and merge scene graphs.\n dialog_datum = {'question': text_sample, 'answer': arg_sample['answer'],\n 'template': template['label']}\n dialog['template_info'].append(template.copy())\n del dialog['template_info'][-1]['text']\n dialog['template_info'][-1]['index'] = text_sample_index\n\n dialog['dialog'].append(dialog_datum)\n graph_item = arg_sample['graph']\n\n # If mergeable, add it to the objects list.\n dialog['graph'] = utils.merge_update_scene_graph(dialog['graph'], graph_item)\n\n # If there are volatile objects in the graph item, remove them.\n for obj in graph_item['objects'][::-1]:\n if obj.get('volatile', False): graph_item['objects'].remove(obj)\n dialog['graph']['history'].append(graph_item)\n return dialog","def create_populated_question(answer_states: List[bool], question_text: str = None):\n question = create_question(question_text)\n\n for state in answer_states:\n create_answer(question, state)\n\n return question","def get_question_templates(session: \"Session\", verbose: bool) -> Dict:\n return _get_dict(\n session,\n url_tail=f\"/{CoordConstsV2.RSC_QUESTION_TEMPLATES}\",\n params={CoordConstsV2.QP_VERBOSE: verbose},\n fail_fast=True,\n )","def main():\n\targs = getArgs()\n\tid_question = args.id_question\n\tlang = args.language\n\tdir_cp = None\n\twith open('config.json') as json_file:\n\t\tconfig_data = json.load(json_file)\n\t\tdir_cp = config_data['dir_cp']\n\n\t\n\t\n\t# sample_io = GetData(args.id_question).get_uri_io_sample()\n\ttemplate = FileUtil(id_question, dir_cp['path'], lang)\n\ttemplate.write_template()\n\t# print(sample_io)","def get_questions(self, obj):\n queryset = Question.objects.filter(sheet=obj)\n questions = []\n for q in queryset:\n questions.append(q.text)\n return questions","def template_list(self):\n return self.ezx.get_template_list()","def load_texts(self):\n\n db = mongo(self.text_dbname)\n\n print(\"Loading texts:\")\n # For each other, query the text database and add their works to texts to compare\n for author in self.authors:\n for text in db.texts.find({'author':author, 'language':\"latin\"}, no_cursor_timeout=True):\n self.texts_to_compare.append(text)\n print(\" -- loaded texts for\", author)\n\n return","def question_list():\r\n question_frame()\r\n list_box = Listbox(question_list_frame,width=100)\r\n # automatically opens and closes the file when not in use\r\n with open('Question_pool.txt', 'r') as fp:\r\n line = fp.readline()\r\n count = 1\r\n while line:\r\n list_box.insert(END,\"{}) {}\".format(count, line.split(',')[0]))\r\n list_box.grid(sticky=W)\r\n line = fp.readline()\r\n count += 1","def make_extra_questions_txt(self):\n raise NotImplementedError","def get_templates(self, template_name, **kwargs):\n text = render_template(\"{template}.txt\".format(template=template_name), **kwargs)\n return text","def _make_vocab_files(self):\n self.logger.info('making question vocab...' + self.opt.QUESTION_VOCAB_SPACE)\n qdic, _ = self.load_data(self.opt.QUESTION_VOCAB_SPACE)\n question_vocab = VQADataProvider.make_question_vocab(qdic, self.max_length)\n self.logger.info('making answer vocab...' + self.opt.ANSWER_VOCAB_SPACE)\n qdic, adic = self.load_data(self.opt.ANSWER_VOCAB_SPACE)\n answer_vocab = VQADataProvider.make_answer_vocab(adic, qdic, self.opt.MAX_ANSWER_VOCAB_SIZE, self.use_ocr)\n return question_vocab, answer_vocab","async def generate_question(self) -> None:\n topic = choice(list(self._topics.keys()))\n arrayList = await self.session.get(\"https://restcountries.eu/rest/v2\")\n arrayList = await arrayList.json() # get request to the country API\n countries = []\n \n for _ in range(4):\n country = choice(arrayList)\n del arrayList[arrayList.index(country)]\n countries.append(country)\n del arrayList\n \n country = choice(countries)\n del countries[countries.index(country)]\n self.question = f\"What is the {self._topics[topic]} of {country['name']}?\"\n self.correct_order = randint(0, 3)\n self.choices = [i[topic] for i in countries]\n self.choices.insert(self.correct_order, country[topic])\n del countries, topic","def readTemplates():\n\n # Compile HTML templates.\n templates = {}\n for tt in [ 'image', 'dirindex', 'allindex', 'trackindex', 'sortindex' ]:\n fn = 'template-%s' % tt + opts.htmlext\n ttext = readTemplate(fn)\n templates[ tt ] = compileTemplate(ttext, fn)\n\n fn = 'template-css.css'\n ttext = readTemplate(fn)\n templates[ 'css' ] = compileTemplate(ttext, fn)\n\n # Compile user-specified rc file.\n rcsfx = 'rc'\n templates[ rcsfx ] = []\n if opts.rc:\n try:\n tfile = open(opts.rc, \"r\")\n orc = tfile.read()\n tfile.close()\n except IOError, e:\n print >> sys.stderr, \"Error: can't open user rc file:\", opts.rc\n sys.exit(1)\n\n o = compileCode('', orc, opts.rc)\n templates[ rcsfx ] += [ o ]\n\n # Compile user-specified code.\n if opts.rccode:\n o = compileCode('', opts.rccode, \"rccode option\")\n templates[ rcsfx ] += [ o ]\n\n # Compile global rc file without HTML tags, just python code.\n code = readTemplate('template-%s' % rcsfx + '.py')\n o = compileCode('', code, tt)\n templates[ rcsfx ] += [ o ]\n\n return templates","def create_evaluation_template(client, survey_name):\n\n loremipsum = \"Lorem ipsum dolor sit amet, consecteteur adipiscing elit donec proin nulla vivamus. Augue donec a erat ve sagittis nisi rhoncus curabitur mauris. Nulla ipsum tortor sagittis adipiscing primis interdum suspendisse lobortis etiam risus nullam. Donec massa quam dis at nibh dolor netus quis. Purus etiam. Dolor neque nunc netus eget nulla faucibus vestibulum aenean class senectus. Porta dolor. Donec morbi. Felis lorem tempus luctus malesuada laoreet curae justo rhoncus ante facilisi parturient malesuada elit laoreet amet. Fusce augue nisi ligula praesent condimentum nascetur fringilla in id lectus per nunc. Lacus metus nisl orci odio maecenas adipiscing. Velit nulla a tempor class placerat ac condimentum nisi taciti at eros.\"\n\n loremipsum_A = \"A: \\n\" + loremipsum\n loremipsum_B = \"B: \\n\" + loremipsum\n\n # Create a new survey\n survey_id = client.create_survey(survey_name)\n # Create 2 more pages in the survey\n for i in range(0, 2):\n client.create_new_page(survey_id, str(i), loremipsum) # title and description\n\n # Get the page ids\n page_ids = client.get_pages_ids(survey_id) # There will be 3\n\n answers = [\"A\", \"B\"]\n question_title = \"Which of the following abstract is more relevant to the one above?\"\n for i, ID in enumerate(page_ids):\n client.update_title_description_of_page(survey_id, ID, \"Abstract\" + str(i), loremipsum)\n client.add_single_choice(survey_id, ID, question_title, answers)\n client.add_paragraph(survey_id, ID, loremipsum_A)\n client.add_paragraph(survey_id, ID, loremipsum_B)\n\n return survey_id","def ask_all():\n for q in Question.all_questions:\n print 'from', q.file\n q.ask()","def add_questions(self, questions):\n for question in questions:\n self.questions.append(question)","def create_type_question_ft(self):\n for i in range(0, len(self.cleaned_questions.splitlines())):\n self.type_question_ft += '__label__'+self.types_q.splitlines()[i] + ' ' + self.cleaned_questions.splitlines()[i] + '\\n'","def get_questions(qn_filepath):\n array = []\n with open(qn_filepath, 'rb') as file:\n for line in file:\n array.append(line)\n\n delete = ['<top>\\r\\n', '\\r\\n', '<desc> Description:\\r\\n', '</top>\\r\\n']\n for i in delete:\n array = list(filter((i).__ne__, array))\n final_array = []\n for i in range(len(array)):\n if i % 2 == 0:\n array[i] = array[i].split(' ')[-1].strip()\n array[i+1] = array[i+1].strip().split('?')[0]\n qn = Question(qn_id=int(array[i]), qn_text=array[i+1])\n final_array.append(qn)\n\n return final_array","def templatelist(cls):\n return cls._templatelist","def question(num):\n if request.method == 'GET':\n questionGroup = Question.query.filter(Question.num == num).from_self().all()\n answers = [answer.value for answer in questionGroup if '_text' in answer.variable]\n responses = [response.value for response in questionGroup if '_response' in response.variable]\n tooltips = [response.value for response in questionGroup if '_hover' in response.variable]\n questionType = questionGroup[0].question_type\n if questionType == 'Multiple Choice':\n return render_template('/demo/question-multiplechoice.html',\n question=questionGroup[0].question,\n choices=answers,\n responses=responses,\n tooltips=tooltips,\n # variables=variables,\n explanation=questionGroup[0].info,\n template=\"question-multiplechoice\")\n return render_template('/demo/question-freeform.html',\n question=questionGroup[0].question,\n choices=answers,\n responses=responses,\n tooltips=tooltips,\n # variables=variables,\n explanation=questionGroup[0].info,\n template=\"question-freeform\")","def load_skills():\n\n Skill.query.delete()\n\n # get all the qualifications text from postings\n postings = db.session.query(Posting.qualifications).all()\n # combine qualifications into a list\n all_skills = []\n with open('filler.txt') as filler:\n del_words = filler.read()\n for post in postings:\n words = post.qualifications.lower().split()\n # iterate through a list of those skills\n for word in words:\n word = word.strip(\"-()/\\,.:;* 1234567890\")\n # check to see if that word isn't in our filler document\n # if not, add it to the table\n if word not in del_words and word not in all_skills:\n all_skills.append(word)\n skill = Skill(skill=word)\n db.session.add(skill)\n db.session.commit()","def load_poems(self):\n file = open(self.name, \"r\")\n content = file.readlines()\n for i in content:\n self.add_msg_and_index(i.strip())","def ls():\n # TODO(rabrams) would be good to factor out the file specific stuff\n # to files module\n path = files.get_proj_dir_or_exit()\n mod_paths = glob.glob(os.path.join(path, \"solutions\", \"*.py\"))\n basenames = [os.path.basename(mod_path) for mod_path in mod_paths]\n with open(os.path.join(path, \"answers.json\")) as f:\n answers = json.load(f)\n\n problem_data = {}\n for basename in basenames:\n if basename == \"__init__.py\":\n continue\n problem = basename[:-len(\".py\")]\n if not problem.startswith(\"p\"):\n raise ValueError(\"Invalid module name found\", basename)\n try:\n number = int(problem[1:])\n except ValueError:\n raise ValueError(\"Invalid module name found\", basename)\n module = importlib.import_module(\"solutions.p{}\".format(number))\n\n fulldoc = module.__doc__ or \"\"\n parts = fulldoc.split(\"\\n\")\n problem_data[number] = {\n \"summary\": \"\" if len(parts) <= 1 else parts[1].strip(),\n \"answer\": answers.get(str(number), \"\"),\n }\n\n table = {\n \"problem\": [],\n \"summary\": [],\n \"answer\": [],\n }\n for number in sorted(problem_data):\n table[\"problem\"].append(number)\n table[\"summary\"].append(problem_data[number]['summary'])\n table[\"answer\"].append(problem_data[number]['answer'])\n print(tabulate.tabulate(table, headers=\"keys\"))","def load_as_files(self, lazy=True):\n # Handle lazy load\n if lazy:\n try:\n questions = read_file_data('questions', self.inputs_dir)\n answers = read_file_data('answers', self.inputs_dir)\n questions_vocabs = read_file_data('questions_vocabs', self.inputs_dir)\n answers_vocabs = read_file_data('answers_vocabs', self.inputs_dir)\n except Exception as e:\n print('Failed to lazy load !', e)\n questions, answers, questions_vocabs, answers_vocabs = self.sub.load_as_raw()\n save_file_data('questions', questions, self.inputs_dir)\n save_file_data('answers', answers, self.inputs_dir)\n save_file_data('questions_vocabs', questions_vocabs, self.inputs_dir)\n save_file_data('answers_vocabs', answers_vocabs, self.inputs_dir)\n else:\n questions, answers, questions_vocabs, answers_vocabs = self.sub.load_as_raw()\n\n # It will always use vi as src en as output\n X_train, X_test, y_train, y_test = train_test_split(questions, answers, test_size=0.2, random_state=42)\n X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n\n # Train\n save_list_to_file(X_train, '{}/train.vi'.format(self.inputs_dir))\n save_list_to_file(y_train, '{}/train.en'.format(self.inputs_dir))\n\n # Valid\n save_list_to_file(X_valid, '{}/dev.vi'.format(self.inputs_dir))\n save_list_to_file(y_valid, '{}/dev.en'.format(self.inputs_dir))\n\n # Test\n save_list_to_file(X_test, '{}/test.vi'.format(self.inputs_dir))\n save_list_to_file(y_test, '{}/test.en'.format(self.inputs_dir))\n\n # vocab\n save_list_to_file(questions_vocabs, '{}/vocab.vi'.format(self.inputs_dir))\n save_list_to_file(answers_vocabs, '{}/vocab.en'.format(self.inputs_dir))","def _fill_template(\n self,\n template: Dict[Text, Any],\n template_vars: Dict[Text, Any]\n ) -> Dict[Text, Any]:\n\n line_object_keys = [\"quickReply\", \"items\", \"action\", \"template\", \"actions\"]\n\n if type(template) == list:\n for item in template:\n self._fill_template(item, template_vars)\n else:\n self._fill_template_text(template, template_vars)\n for key in line_object_keys:\n if key in template:\n self._fill_template(template[key], template_vars)\n\n return template","def populate_game_questions():\n indices = random.sample(range(0, len(quizquestion.questions_all)), 5) # If user doesn't specify, choose 5 random questions\n return quizquestion.QuizQuestion.get_game_questions(indices)","def view_index():\n story_name = request.args.get(\"template_picker\")\n return render_template(\"questions.html\",\n story=story_templates[story_name],\n story_name=story_name)","def evaluate_questions(self):\n for question in self.question_list:\n question.evaluate_question()","def _create_examples(self, lines, set_type):\n examples = []\n for idx, line in enumerate(lines):\n item = json.loads(line.strip())\n question_id = \"%s-%s\" % (set_type, idx)\n context = item[\"context\"]\n question = item[\"question\"]\n endings = [item[\"answerA\"],item[\"answerB\"],item[\"answerC\"] ]\n label = item[\"correct\"]\n #race_id = \"%s-%s\" % (set_type, data_raw[\"race_id\"])\n #article = data_raw[\"article\"]\n #for i in range(len(data_raw[\"answers\"])):\n #truth = str(ord(data_raw[\"answers\"][i]) - ord(\"A\"))\n #question = data_raw[\"questions\"][i]\n #options = data_raw[\"options\"][i]\n\n examples.append(\n InputExample(\n example_id=question_id,\n question=question,\n contexts=[context,context,context],\n endings=[endings[0], endings[1], endings[2]],#, options[3]\n label=label,\n )\n )\n return examples","def use_templates(self, templates):\n self.htmls = templates","def question_list(request, question_views, template, questions_per_page=None,\n page_number=None, extra_context=None):\n view_id = request.GET.get('sort', None)\n view = dict([(q.id, q) for q in question_views]).get(view_id,\n question_views[0])\n if questions_per_page is None:\n questions_per_page = get_questions_per_page(request.user)\n paginator = Paginator(view.get_queryset(), questions_per_page)\n if page_number is None:\n page = get_page(request, paginator)\n else:\n page = paginator.page(page_number)\n populate_foreign_key_caches(User, ((page.object_list, (view.user,)),),\n fields=view.user_fields)\n context = {\n 'title': view.page_title,\n 'page': page,\n 'questions': page.object_list,\n 'current_view': view,\n 'question_views': question_views,\n }\n if extra_context is not None:\n context.update(extra_context)\n return render_to_response(template, context,\n context_instance=RequestContext(request))","def _GetTemplate(self):\n# First read default template.\n tmplt = self._LoadTemplate(c.preproc_template_default)\n tmplt['proc'] = self.topdir\n self.template_type = 'default'\n\n self.templates = []\n if self.template_file is not None:\n tmplt.update(self._LoadTemplate(self.template_file))\n self.template_type = 'command-line'\n self.templates.append(os.path.abspath(self.template_file))\n found_template = True\n else:\n# Find a study specific template file.\n study_template_file = self._FindTemplateFile('%s/..' % self.topdir)\n if study_template_file is not None:\n# Merge study template into default, study template has precedence.\n if self.verbose:\n print \"Using study template at \" + study_template_file\n tmplt.update(self._LoadTemplate(study_template_file))\n self.template_type = 'study-specific'\n self.templates.append(os.path.abspath(study_template_file))\n found_template = True\n else:\n found_template = False\n# Now look for a subject-specific template file.\n subject_template_file = self._FindTemplateFile('%s' % self.topdir)\n if subject_template_file is not None:\n# Merge subject template, subject template has precedence.\n if self.verbose:\n print \"Using subject-specific template at %s\" % \\\n subject_template_file\n tmplt.update(self._LoadTemplate(subject_template_file))\n self.template_type = 'study-specific'\n self.templates.append(os.path.abspath(subject_template_file))\n found_template = True\n\n if not found_template:\n raise RuntimeError('Could not find template file.')\n\n if tmplt.get('subject','same') == 'same':\n# Default subdirectory is same as data directory.\n tmplt['subject'] = self.topdir.split('/')[-1]\n else:\n if not isinstance(tmplt['subject'],str):\n errstr = 'preprocess: Invalid subject number. Be sure to ' + \\\n 'enclose the subject number item with double quotes.'\n raise RuntimeError(errstr)\n\n# Keys that apply to all EPIs.\n self.fsl_flip = tmplt.get('fsl_flip', False)\n if self.fsl_flip:\n self.flip_opts = '-LT'\n else:\n self.flip_opts = ''\n\n# Replace strings with python types.\n for key in tmplt.keys():\n if tmplt[key] == 'None':\n tmplt[key] = None\n elif key == 'True':\n tmplt[key] = True\n elif key == 'False':\n tmplt[key] = False\n return tmplt","def ttlist(qa,a1a,a0a,ans1,ans0,sentences):\n (questions,answers1,answers0)=sentences\n li=[]\n ones=0\n zeros=0\n for i in range(0,len(ans1)):\n li.append(q(qa[i],a1a[ones:ones+ans1[i]],a0a[zeros:zeros+ans0[i]],questions[i],\n answers1[ones:ones+ans1[i]],answers0[zeros:zeros+ans0[i]]))\n ones+=ans1[i]\n zeros+=ans0[i]\n return li","def question(length):\n\n length = len(responses)\n question_list = satisfaction_survey.questions\n asking_question = question_list[length].question\n the_choices = question_list[length].choices\n\n return render_template(\n 'questions_page.html',\n asking_question=asking_question,\n the_choices=the_choices\n )","def create_templates_database(dataset_path_list, db_file_path):\n paths_list = dataset_path_list\n\n templates = dict()\n for file in paths_list:\n\n image = face_recognition.load_image_file(file)\n tmp = face_recognition.face_encodings(image)\n if tmp:\n template = face_recognition.face_encodings(image)[0]\n if template.size != 0:\n templates[file] = template\n\n dump_dict_to_db(templates, db_file_path)","def process_input_files(list_input_files):\n global dict_models_results\n global list_spacy_docs\n \n for input_file in list_input_files:\n prefix = prefix_from_filename(input_file)\n \n with open(input_file) as f:\n list_cases = json.load(f)\n dict_models_results[prefix] = list_cases\n \n \n #extract list of questions from all vignettes and create a mapping page -> vignette question\n dict_questions = {}\n for prefix, list_cases in dict_models_results.items():\n for vignette in list_cases:\n dict_questions[vignette[\"book_page\"]] = vignette[\"question\"]\n \n \n for book_page,question in dict_questions.items():\n doc_q = load_bner_onto_tokens_extension(question, book_page)\n list_spacy_docs.append(doc_q)\n \n return","def __fill_all_templates__(self,configs):\n template_dir = configs['system'].get('Common_directories','template')\n sample_template = os.path.join(template_dir,configs['pipeline'].get('Template_files','sample'))\n system_template = os.path.join(template_dir,configs['pipeline'].get('Template_files','system'))\n qsub_template = os.path.join(template_dir,configs['pipeline'].get('Template_files','bcbio'))\n self.__fill_template__(sample_template,self.sample_file)\n self.__fill_template__(system_template,self.systems_file)\n self.__fill_template__(qsub_template,self.qsub_file)","def load_question(data, mm, request, ignore_similar=False):\n try:\n subject = Subject.objects.get(name=data['subject'])\n topic = Topic.objects.get(name=data['topic'], subject=subject)\n\n question = Question(text=data['question'], topic=topic)\n question.full_clean()\n answers = []\n for ans in data['answers']:\n answer = Answer()\n answer.text = ans['text']\n answer.is_correct = ans['is_correct']\n answers.append(answer)\n\n if question.similar_exists() and not ignore_similar:\n raise SimilarQuestionError(_(\"A similar question exists\"),\n data)\n else:\n question.save()\n for answer in answers:\n answer.question = question\n answer.full_clean()\n answer.save()\n mm.added.append(question)\n\n except Subject.DoesNotExist:\n mm.no_subject.append((data['subject'], data['question']))\n\n except Topic.DoesNotExist:\n mm.no_topic.append((data['topic'], data['question']))\n\n except ValidationError as err:\n mm.validation_error.append((err, data['question']))\n\n except SimilarQuestionError as err:\n request.session['duplicates'].append(err.data)","def premade():\n global all_questions\n\n choice(all_questions)\n temp = all_questions.pop(0) # Treat scrambled list like a queue\n question,answer = temp\n print(\"\\n\\n{}\".format(question))\n guess = get_user_input(str)\n\n if guess == \"T\": guess = True\n if guess == \"F\": guess = False\n\n if guess == answer:\n print(\"Correct the answer is: {}\".format(answer))\n else:\n print(\"Incorrect the answer is: {}, This question will be asked again.\".format(answer))\n all_questions.append(temp) # Add it back","def __loadListOfDocuments(self):\n\t\tfor value in default_paths():\n\t\t\titem = addNewListItemCalled([os.path.normpath(value)], self.ui.listWidget)\n\t\t\titem.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\t\t\tt = item.font()\n\t\t\tt.setItalic(True)\n\t\t\titem.setFont(t)\n\n\t\t# load up state from storage...\n\t\tpaths = QSettings().value(\"paths\").toList()\n\t\tfor value in paths:\n\t\t\tstr = os.path.normpath(value.toString())\n\t\t\tif str not in default_paths():\n\t\t\t\taddNewListItemCalled([str], self.ui.listWidget, mutable=True)","def questions(self) -> HTMLBody:\n\t\tif self.is_authorized:\n\t\t\tif session[\"user\"][\"is_diplomat\"] == \"True\":\n\t\t\t\tif request.method == \"GET\":\n\t\t\t\t\treturn render_template(\"questions.jinja2\", question_list=self.db.get_questions())\n\t\t\t\t# 1. Get the question id\n\t\t\t\t# 2. Get the response posted by the Diplomat\n\t\t\t\t# 3. Get the email associated with the question\n\t\t\t\t# 4. Create an email response for the question\n\t\t\t\t# 5. Send the response and delete the question\n\t\t\t\tprint(dict(request.form))\n\t\t\tflash(\"Only Engineering Diplomats may view the list of questions.\")\n\t\t\treturn redirect(url_for(\"index\"))\n\t\tflash(\"Please login first.\")\n\t\treturn redirect(url_for(\"login\"))","def deserialize(path):\n with open(path, 'rb') as f:\n temp = pickle.load(f)\n for q in temp.questions:\n q.on_deserialize()\n return temp","def list_templates(context):\n templates = get_oneoffixx_templates()\n template_group = context.REQUEST.form.get('form.widgets.template_group')\n terms = []\n\n for template in templates:\n terms.append(SimpleVocabulary.createTerm(\n template, template.template_id, template.title))\n\n # We filter templates when template_group has been selected\n if template_group is not None:\n favorites = get_oneoffixx_favorites()\n # Favorites are a special case\n if favorites and template_group[0] == favorites.get('id'):\n terms = [\n SimpleVocabulary.createTerm(\n OneOffixxTemplate(\n template, favorites.get('localizedName', '')),\n template.get('id'),\n template.get('localizedName'),\n )\n for template in favorites.get('templates')\n ]\n elif template_group[0] != '--NOVALUE--':\n terms = [term for term in terms if term.value.group == template_group[0]]\n\n return MutableObjectVocabulary(terms)","def generate(self, answers):\n\n text = self.template\n\n for (key, val) in answers.items():\n text = text.replace(\"{\" + key + \"}\", val)\n\n return text","def question_medtagger(self):\n self.save_on_file(self.cleaned_questions_q_dot, 'questions.txt')\n medpos_tag_out = self.medpos_tag('questions.txt')\n parsed_questions = self.parse(medpos_tag_out)\n parsed_questions_type = []\n parsed_questions_out = []\n for i in range(0, len(self.data['questions'])):\n # [ type, [ (w1, tag_w1) , (w2, tag_w2) ... (wn, tagwn) ] ]\n parsed_questions_type += [[self.data['questions'][i]['type']] + [parsed_questions[i]]]\n parsed_questions_out += [parsed_questions[i]]\n return parsed_questions_out, parsed_questions_type","def load_words(args, examples):\n def _insert(iterable):\n for w in iterable:\n w = Dictionary.normalize(w)\n if valid_words and w not in valid_words:\n continue\n words.add(w)\n if args.restrict_vocab and args.embedding_file:\n logger.info('Restricting to words in %s' % args.embedding_file)\n valid_words = index_embedding_words(args.embedding_file)\n logger.info('Num words in set = %d' % len(valid_words))\n else:\n valid_words = None\n\n words = set()\n for ex in examples:\n for p in ex['passage']:\n _insert(p)\n _insert(ex['question'])\n return words","def populate_questions(scores):\n \n print(\"populate_questions, scores: \", str(scores))\n\n try:\n return random.sample(range(len(quiz.list_fragen)), TOTAL_ROUNDS*len(scores))\n except ValueError:\n print(\"List of questions is too short.\")","def get_question_list(self, quiz):\n preguntas = []\n if quiz.preguntas_usadas is None:\n queryset = Pregunta.objects.filter(\n pool=self.diagnostico.banco).order_by('?').distinct()[:20]\n for pregunta in queryset:\n preguntas.append(PreguntasPrueba.objects.create(\n prueba=self.diagnostico, pregunta=pregunta))\n self.diagnostico.refresh_form_db()\n else:\n preguntas = [\n pregunta for pregunta in quiz.preguntas_usadas.all()]\n return preguntas","def template_list(call=None):\n templates = {}\n session = _get_session()\n vms = session.xenapi.VM.get_all()\n for vm in vms:\n record = session.xenapi.VM.get_record(vm)\n if record[\"is_a_template\"]:\n templates[record[\"name_label\"]] = record\n return templates","def question_new_translate():","def get_words():\n\tprompts = []\n\tfor prompt in story.prompts:\n\t\tprompts.append(prompt.replace('_', ' '))\n\n\treturn render_template(\"get-words.html\", prompts = prompts, key_prompts = story.prompts, num_of_prompts = len(prompts))","def parse_question_data(self):\n section = ''\n subsection = ''\n quest = ''\n # The data falls into 4 cases\n # 1. Sections\n # 2. subsections\n # 3. questions\n # 4. answers.\n\n for line in self.question_data: \n\n if \":\" in line: # case #2\n subsection = line.split(\":\")[1] # split the line on the : into an array but only take the [1] element\n debug(\"Subsection: %s\" % subsection)\n \n elif \".\" in line: # this is either a question or an answer?\n \n if line.split(\".\")[0].isdigit(): # case #3 it's a question, split on . into an array and take the element to the left and ask if it's a digit.\n quest = line # Since we know it's something like \"3. Are you a warlock?\" we stick that in the quest varable.\n debug(\"Question: %s\" % quest)\n # Create a question object and stick it in the dictonary with the key being the question (since we know it'll be unique)\n self.questions[quest] = question(section, subsection, quest) # I know it's redundant to have the key and have a value.\n \n elif line.startswith(\".\"): # case #4 answer All the answers startswith \".\" \n debug(\"Answer: %s\" % line)\n # take the question and append it to the answers array in the question object.\n self.questions[quest].answers.append(line[2:]) # Trim the first two characters off the answer since it's \". the answer\"\n \n else: # case #1 # This is section like AMERICAN DEMOCRACY\n section = line # load the line from the file into the section variable\n debug(\"Section = %s\" % section)","def save_collection(self, name):\n path = self._config._output\n pfile = open(path + '/' + name + '-all.tex', 'w')\n\n # Writes the header/configuration of the latex file\n ltxpreamble = open('latex/preamble-sol', 'r')\n for line in ltxpreamble:\n pfile.write(line)\n\n self._preamble(pfile)\n\n for i in range(self._quiz.size()):\n q = self._quiz.get_i_question(i)\n q.latex_complete_question(pfile, 'q'+str(i))\n\n self._closing(pfile)\n\n pfile.close()","def _load_templates(self, tasks, skip_tasks=()):\n name2kwargs = {}\n dir_ = Path('data/prompts')\n paths = (dir_/t for t in tasks) if tasks else dir_.iterdir()\n if skip_tasks: paths = (p for p in paths if p.stem not in skip_tasks)\n for path in paths:\n if not path.is_dir():\n if tasks: warnings.warn(f'{path} is not a directory.')\n continue\n name2kwargs[path.stem] = load_prompt(path.stem,\n verbose=self.verbose)\n return name2kwargs","def read_quiz_questions() -> pd.DataFrame:\n data_path = './data/quiz_question_data.txt'\n if not os.path.exists(data_path):\n raise Exception(f'Data not found in {data_path}')\n\n with open(data_path, 'r') as f:\n questions = f.read()\n\n return pd.DataFrame(ast.literal_eval(questions))","def _create_examples(self, lines, set_type):\n examples = []\n for (_, data) in enumerate(lines):\n passage = data[\"passage\"][\"text\"]\n for Q in data[\"passage\"][\"questions\"]:\n question = Q[\"question\"]\n for A in Q[\"answers\"]:\n guid = f\"{set_type}-{data['idx']-Q['idx']-A['idx']}\"\n examples.append(\n InputExample(\n guid=guid,\n text_a=passage,\n text_b=question + \" \" + A[\"text\"],\n label=str(A[\"label\"]),\n )\n )\n return examples","def _load(examples, f):\n\n for l in f:\n json_example = json.loads(l)\n if FLAGS.mode == 'long_answers' and not has_long_answer(json_example):\n continue\n\n elif FLAGS.mode == 'short_answers' and not has_short_answer(json_example):\n continue\n\n example = Example(json_example)\n examples[example.example_id] = example\n\n if len(examples) == FLAGS.max_examples:\n break","def create_question_template(xblock_id, question_template, image_url, resolver_selection, variables, answer_template):\n connection = mysql.connector.connect(**s.database)\n \n # clean_up_variables_and_expressions(fe_xblock, connection)\n \n insert_question_template(xblock_id, connection, question_template, image_url, resolver_selection, answer_template)\n \n create_variables(xblock_id, connection, variables)\n \n connection.commit()\n connection.close()","def create_quizz_question(classe, lecon):\n path_to_classe = \"..//data//questions//\" + classe\n path_to_questions = \"..//data//questions//\" + classe + \"//\" + lecon\n empty_questions_list = []\n # Enregistrement du fichier\n with open(path_to_questions, \"wb\") as file:\n writer = pickle.Pickler(file)\n writer.dump(empty_questions_list)\n\n # Enregistrement sur la liste\n with open(path_to_classe + '//liste_lecons', 'rb') as file:\n lecteur = pickle.Unpickler(file)\n liste_lecons = lecteur.load()\n liste_lecons.append(lecon)\n\n with open(path_to_classe + '//liste_lecons', 'wb') as file:\n writer = pickle.Pickler(file)\n writer.dump(liste_lecons)","def _create_examples(self, data_dir, set_type):\n\t\texamples = []\n\t\tinput_file_data = os.path.join(data_dir, \"data.tsv\")\n\t\twith open(input_file_data, \"r\", encoding=\"utf-8-sig\") as f:\n\t\t\tfor i, inp in enumerate(f):\n\t\t\t\tinps = inp.split('\\t') \n\t\t\t\tguid = \"%s-%s\" % (set_type, i)\n\t\t\t\ttext_inp = inps[1].strip()\n\t\t\t\ttext_out = inps[2].strip()\n\t\t\t\texamples.append(InputExample(guid=guid, text_inp=text_inp, text_out=text_out))\n\t\t\t\t\n\t\t\t# Sort these out before returning\n\t\t\texamples = sorted(examples, key=sort_inp_len)\n\t\t\treturn examples","def load_template_files(self):\n templates = dict()\n template_path = settings.CUSTOM_VERTO_TEMPLATES\n templates.update(self.read_template_files(template_path))\n if hasattr(self, \"extra_converter_templates_directory\"):\n directory = self.extra_converter_templates_directory\n template_path = os.path.join(template_path, directory)\n templates.update(self.read_template_files(template_path))\n return templates","def add_questions(self):\r\n for question_id in self.question_ids:\r\n question = self.questions[question_id]\r\n self.frame_layout.addWidget(question.create_frame(), alignment=QtCore.Qt.AlignLeft)","def store_all_content_info():\n all_content = ContentQueue(None, None)\n title = input('Title of your paragraph?')\n content = input('Content of your paragraph (single line)')\n images = image_option()\n enqueue_content(all_content, title, content, images)\n extra_content = add_paragraph_option()\n if len(extra_content) == 0:\n pass\n else:\n for i in range(len(extra_content)):\n enqueue_content(all_content, extra_content[i].p_title, extra_content[i].content, extra_content[i].image)\n\n return all_content","def get_questions():\n count = 1\n for i in range(6):\n r = requests.get('https://api.stackexchange.com/2.2/questions?filter=withbody&site=eosio&pagesize=100&page={}'.format(count))\n data = json.loads(r.text)\n for item in data['items']:\n own = item['owner']['user_id']\n dsp = item['owner']['display_name']\n try:\n owner = User.objects.get(username=own, se_display_name=dsp)\n except Exception:\n owner = None\n tags = item['tags']\n ts = []\n if owner:\n for tag in tags:\n t, created = Tag.objects.get_or_create(name=tag)\n ts.append(t)\n q = Question.objects.create(owner=owner, se_question_id=item['question_id'], title=item['title'], body=item[\n 'body'], se_link=item['link'], is_answered=item['is_answered'], score=item['score'])\n for t in ts:\n q.tags.add(t)\n q.save()\n count += 1\n print(count)","def update_templates():\n logging.info(\"Copying english po files to %s\" % POT_PATH)\n\n # post them to exposed URL\n ensure_dir(POT_PATH)\n shutil.copy(get_po_filepath(lang_code=\"en\", filename=\"django.po\"), os.path.join(POT_PATH, \"kalite.pot\"))\n shutil.copy(get_po_filepath(lang_code=\"en\", filename=\"djangojs.po\"), os.path.join(POT_PATH, \"kalitejs.pot\"))","def get_questions(self):\n for q in self._objects[\"Questions\"]:\n yield (q)\n\n return"],"string":"[\n \"def load_questions(self, verbose=True):\\n for question in self.question_list:\\n question.load_question(self.data)\",\n \"def load_templates(self):\\n TemplateHandler.templates = []\\n for template in os.listdir(TemplateHandler.templates_path):\\n template_config = self.load_template_conf(template)\\n if template_config is None:\\n continue\\n TemplateHandler.templates.append(template_config)\",\n \"def ask_questions():\\n print(story.prompts)\\n prompts = story.prompts\\n \\n return render_template(\\\"questions.html\\\", prompts = prompts)\",\n \"def load_corpus_questions():\\r\\n\\tglobal search_criteria_dict, solution_dict, linked_abstracts_dict\\r\\n\\tif os.path.exists(paths.path_data_questions_pickle):\\r\\n\\t\\tprint('\\\\nloading questions and answers')\\r\\n\\t\\tsearch_criteria_dict = pickle.load(open(paths.path_data_questions_pickle,\\\"rb\\\"))\\r\\n\\t\\tsolution_dict = pickle.load(open(paths.path_data_answers_pickle,\\\"rb\\\"))\\r\\n\\t\\tlinked_abstracts_dict = pickle.load(open(paths.path_data_linkedabstracts_pickle,\\\"rb\\\"))\\r\\n\\t\\t\\r\\n\\t\\tprint(len(search_criteria_dict))\\r\\n\\t\\tprint(len(solution_dict))\\r\\n\\t\\tprint(len(linked_abstracts_dict))\\r\\n\\t\\t\\r\\n\\t\\treturn True\\r\\n\\telse:\\r\\n\\t\\treturn False\",\n \"def collection(self):\\n questions = []\\n choice_list = []\\n answers = []\\n\\n if self.form=='The correct German word':\\n for i in range(self.num_ques):\\n question, options, answer = self.generate_eng2ger()\\n questions.append(question)\\n choice_list.append(options)\\n answers.append(answer)\\n else:\\n for i in range(self.num_ques):\\n question, options, answer = self.generate_ger2eng()\\n questions.append(question)\\n choice_list.append(options)\\n answers.append(answer)\\n\\n return questions, choice_list, answers\",\n \"def _load_templates(cls):\\n if cls._raw_templates is None:\\n cls._raw_templates = fetch_rrlyrae_templates()\",\n \"def load_questions_view(self, request):\\n request.session['duplicates'] = []\\n mm = LoadQuestionsMessageManager()\\n try:\\n form = XMLFileForm(request.POST, request.FILES)\\n if form.is_valid():\\n xmlfile = request.FILES['file']\\n parser = XMLParser(xmlfile)\\n for data in parser.parse_questions():\\n self.load_question(data, mm, request)\\n else:\\n mm.form_is_valid = False\\n mm.set_messages(request)\\n print(reverse('admin:chm_xmlfile_add'))\\n return redirect(reverse('admin:chm_xmlfile_add'))\\n\\n except XMLSyntaxError as err:\\n mm.syntax_error = err\\n\\n mm.set_messages(request)\\n return redirect(reverse('admin:chm_question_changelist'))\",\n \"def gen_questions(self, number_of_questions):\",\n \"def load_as_raw(self):\\n\\n # Q & A\\n questions, answers = self.get_questions_answers()\\n\\n # Get vocabs\\n\\n # Step 4: cleaning the questions\\n pprint('---- Step 4 cleaning questions ----')\\n\\n clean_questions = []\\n for question in questions:\\n clean_questions.append(clean_text(question))\\n\\n pprint(clean_questions, stream=Head(5))\\n print('\\\\n\\\\n')\\n \\\"\\\"\\\"\\n Step 5: Clean the answers\\n \\\"\\\"\\\"\\n\\n pprint('---- Step 5 cleaning answers ----')\\n clean_answers = []\\n for answer in answers:\\n clean_answers.append(clean_text(answer))\\n\\n pprint(clean_answers, stream=Head(5))\\n print('\\\\n\\\\n')\\n \\\"\\\"\\\"\\n Step 6: Creating a dictionary that maps each word to its number of occurences\\n \\\"\\\"\\\"\\n\\n word2count = {}\\n pprint('------ Step 6: counting words in questions ----')\\n\\n word2count = convert_word_to_count(word2count, clean_questions)\\n\\n pprint(word2count, stream=Head(5))\\n print('\\\\n\\\\n')\\n \\\"\\\"\\\"\\n Step 7:\\n For example, for a question: can we make this quick roxanne korrine and andrew barrett are having an incredibly horrendous public break up on the quad again\\n It counts each word occurence such as \\\"can\\\" and accumulates the count into word2count dict\\n \\\"\\\"\\\"\\n pprint('------ Step 6: counting words in answers ----')\\n\\n word2count = convert_word_to_count(word2count, clean_answers)\\n\\n pprint(word2count, stream=Head(5))\\n print('\\\\n\\\\n')\\n\\n keys = ['<unk>', '<s>', '</s>']\\n\\n \\\"\\\"\\\"\\n Step 8: Creating word 2 int(count) by filtering words that are greater than the threshold\\n \\\"\\\"\\\"\\n\\n pprint(\\n '------ Step 8: questions_vocabs filtered by threshold (>) ----')\\n threshold_questions = 20\\n questions_vocabs = [] + keys\\n for word, count in word2count.items():\\n if count >= threshold_questions:\\n if not word in questions_vocabs:\\n questions_vocabs.append(word)\\n\\n pprint(questions_vocabs, stream=Head(5))\\n print('\\\\n\\\\n')\\n \\\"\\\"\\\"\\n Step 9: Same as step 8 but for answers\\n \\\"\\\"\\\"\\n pprint(\\n '------ Step 9: answers_vocabs filtered by threshold (>) ----')\\n threshold_answers = 20\\n answers_vocabs = [] + keys\\n for word, count in word2count.items():\\n if count >= threshold_answers:\\n if not word in answers_vocabs:\\n answers_vocabs.append(word)\\n\\n pprint(answers_vocabs, stream=Head(5))\\n\\n return questions, answers, questions_vocabs, answers_vocabs\",\n \"def populate_fixtures():\\n languages()\\n words()\",\n \"def _read_files(self):\\n \\n for langname in self.langnames:\\n filename = f'data/word_lists/{langname}.txt'\\n with open(filename) as f:\\n index = self.langnames.index(langname)\\n lang_list = getattr(self, f'word_list{index}')\\n words = f.readlines()\\n for word in words:\\n fword = ''.join(char for char in word if char is not '\\\\n')\\n lang_list.append(fword)\\n f.close()\\n return\",\n \"def load_quests(self):\\n\\n raise NotImplementedError()\",\n \"def generateISTemplates(course, wordlist, charLevels, translateDict, similarCharsList, supportsUppercase):\\n\\n levelsDict= {}\\n stageByNumber = 1 # represents key of wordsByLevelDict to get wordlist value\\n wordsByLengthDict = {}\\n wordsByLevelDict = {}\\n swappedTranslateDict = dict((v,k) for k,v in translateDict.iteritems()) # Keys are latin, values non-latin\\n\\n for word in wordlist:\\n wordsByLengthDict = wordsByLength(word, wordsByLengthDict)\\n wordsByLevelDict = wordsByLevel(word, charLevels, wordsByLevelDict)\\n\\n for stage in charLevels:\\n\\n if not wordsByLevelDict.has_key(stageByNumber): # Skip the uppercase stages in charLevels\\n stageByNumber += 1\\n continue\\n\\n charLevelsWordList = wordsByLevelDict[stageByNumber] # contains list of words suitable for current stage\\n selectedOptions = makeWordOptions(charLevelsWordList, similarCharsList) # List of 3 sublists containing options or None\\n\\n if selectedOptions is not None:\\n\\n for i in range(len(selectedOptions)):\\n generateLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\\\".qdef\\\")\\n generateLevelAlternative(selectedOptions[i], translateDict, similarCharsList, name+str(stageByNumber)+\\\".qdef\\\")\\n\\n if supportsUppercase:\\n generateUppercaseLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\\\".qdef\\\")\\n\\n generateEndOfQuiz(name+str(stageByNumber)+\\\".qdef\\\")\\n stageByNumber += 1\\n\\n # generate practice levels\\n for i in range(30):\\n selectedWords = wordsByLengthDict[random.choice(wordsByLengthDict.keys())]\\n if len(selectedWords) >= 3:\\n quizRandomSelection = random.sample(selectedWords,3) # take random 4 items from list\\n\\n for word in quizRandomSelection:\\n trickWord = findAndReplaceSimilarChar(word, quizRandomSelection, similarCharsList)\\n if trickWord:\\n quizRandomSelection.append(trickWord)\\n break\\n\\n generateLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\\\"Practice.qdef\\\")\\n generateLevelAlternative(quizRandomSelection, translateDict, similarCharsList, name+\\\"Practice.qdef\\\")\\n\\n if supportsUppercase:\\n generateUppercaseLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\\\"Practice.qdef\\\")\\n\\n generateEndOfQuiz(name+\\\"Practice.qdef\\\")\\n\\n print \\\"New MUNI ROPOT templates has been created in: \\\" + target\",\n \"def _load_answers(self, answer_list):\\n # We are given a list of words that indicate what the answer\\n # options are. By convention, the first word is probably the\\n # correct answer; the others are incorrect answers. However,\\n # we won't set this now because this convention may not hold.\\n # We expect the SET_CORRECT OpalCommand to be used to set\\n # which answers are correct or incorrect.\\n # split the list of answers on commas.\\n answers = answer_list.strip().split(',')\\n\\n # Shuffle answers to display them in a random order.\\n random.shuffle(answers)\\n\\n # Load in the graphic for each answer.\\n for answer in answers:\\n toload = {}\\n # Remove whitespace from name before using it.\\n toload[\\\"name\\\"] = answer.strip()\\n toload[\\\"tag\\\"] = \\\"PlayObject\\\"\\n toload[\\\"slot\\\"] = answers.index(answer) + 1\\n toload[\\\"draggable\\\"] = False\\n toload[\\\"isAnswerSlot\\\"] = True\\n self._ros_node.send_opal_command(\\\"LOAD_OBJECT\\\", json.dumps(toload))\",\n \"def elastixTemplates():\\n\\t\\ttransformations = []\\n\\t\\tfileNames = os.listdir(AppVars.transformationsPath())\\n\\t\\tfor fileName in fileNames:\\n\\t\\t\\tfullFileName = os.path.join(AppVars.transformationsPath(), fileName)\\n\\t\\t\\ttransformation = ParameterList()\\n\\t\\t\\tif transformation.loadFromFile(fullFileName):\\n\\t\\t\\t\\ttransformations.append(transformation)\\n\\t\\treturn transformations\",\n \"def _collect_quizzes():\\n data_path = join(dirname(abspath(__file__)), 'data')\\n for _, _, filenames in os.walk(data_path):\\n for filename in filenames:\\n if filename.endswith('.yml'):\\n quiz_type = filename.replace('.yml', '').capitalize()\\n QUIZ_DICT[quiz_type] = []\\n with open(join(data_path, filename), encoding='utf-8') as f:\\n data = yaml.load(f)\\n for class_name, settings in data.items():\\n Q = type(class_name, (Quiz, ), settings)\\n QUIZ_DICT[quiz_type].append(Q)\\n QUIZ_DICT_FLAT[class_name] = Q\",\n \"def load_data(squad_per_lang):\\n question_set = QuestionSet()\\n candidate_set = CandidateSet()\\n\\n for lang, squad in squad_per_lang.items():\\n for question, answer, context, context_sentences, xling_id, context_id in (\\n generate_examples(squad, lang)):\\n question = Question(question, xling_id, lang)\\n question_set.add(question)\\n assert answer in context_sentences, (\\n \\\"answer doesn't appear in context_sentences\\\")\\n for sent_pos, sentence in enumerate(context_sentences):\\n candidate = Candidate(sentence, context, lang, context_id, sent_pos)\\n candidate = candidate_set.add_or_retrieve_candidate(candidate)\\n if sentence == answer:\\n candidate_set.update_xling_id(candidate, xling_id)\\n print(\\\"Totals across languages: questions={}, candidates={}\\\".format(\\n len(question_set.as_list()), len(candidate_set.as_list())))\\n\\n return question_set, candidate_set\",\n \"def listQuestions(clean_user_name):\\n DEVNULL = open(os.devnull, 'wb')\\n question_names_pipe = subprocess.Popen(['ls', '-1', \\\"/home/\\\"+clean_user_name+\\\"/.question/questions\\\"], stdout=subprocess.PIPE, stderr=DEVNULL)\\n question_names = question_names_pipe.stdout.read().split('\\\\n')\\n question_names.pop()\\n question_names.sort()\\n for question_name in question_names:\\n clean_question_name = question_name.rstrip(\\\"\\\\n\\\")\\n if (clean_user_name != \\\"\\\" and clean_question_name != \\\"\\\"):\\n generateQuestionList(clean_user_name, clean_question_name)\",\n \"def _load_questions():\\r\\n page = request.args.get('page', type=int, default=1)\\r\\n content = request.args.get('content')\\r\\n if current_user.is_authenticated and content != 'all':\\r\\n query = current_user.followed_posts\\r\\n content = Post\\r\\n else:\\r\\n query = Post.query\\r\\n content = Post\\r\\n pagination = query.order_by(content.timestamp.desc()).paginate(page,\\r\\n per_page=current_app.config['FLASKY_POSTS_PER_PAGE'],\\r\\n error_out=False)\\r\\n macro = get_template_attribute(\\\"_add_questions.html\\\", \\\"render_posts\\\")\\r\\n posts = pagination.items\\r\\n return jsonify({'result': True,\\r\\n 'html': macro(posts),\\r\\n 'posts':[post.timestamp for post in posts],\\r\\n 'post_id':[post.id for post in posts]\\r\\n })\",\n \"def loadTextFile(self):\\n if self.tempFilePath is None or not MyFile.checkFileExists(self.tempFilePath):\\n raise Exception(\\\"Temporary text file does not exist!\\\")\\n\\n io = Ioread()\\n self.sentencesList = io.readFileContentList(self.tempFilePath)\",\n \"def _create_examples(self, lines, kb_data, set_type):\\n examples = []\\n for idx, line in enumerate(lines):\\n item = json.loads(line.strip())\\n question_id = \\\"%s-%s\\\" % (set_type, idx)\\n \\n context_a_list = kb_data[idx]['answerA']\\n context_b_list = kb_data[idx]['answerB']\\n context_c_list = kb_data[idx]['answerC']\\n\\n context_a = \\\"\\\"\\n for l in context_a_list[:1]:\\n context_a += l.replace(\\\"\\\\n\\\",\\\". \\\")\\n context_a = context_a[:-1]\\n\\n context_b = \\\"\\\"\\n for l in context_b_list[:1]:\\n context_b += l.replace(\\\"\\\\n\\\",\\\". \\\")\\n context_b = context_b[:-1]\\n\\n context_c = \\\"\\\"\\n for l in context_c_list[:1]:\\n context_c += l.replace(\\\"\\\\n\\\",\\\". \\\")\\n context_c = context_c[:-1]\\n \\n \\n question = item[\\\"context\\\"] + item[\\\"question\\\"]\\n endings = [item[\\\"answerA\\\"],item[\\\"answerB\\\"],item[\\\"answerC\\\"] ]\\n label = item[\\\"correct\\\"]\\n #race_id = \\\"%s-%s\\\" % (set_type, data_raw[\\\"race_id\\\"])\\n #article = data_raw[\\\"article\\\"]\\n #for i in range(len(data_raw[\\\"answers\\\"])):\\n #truth = str(ord(data_raw[\\\"answers\\\"][i]) - ord(\\\"A\\\"))\\n #question = data_raw[\\\"questions\\\"][i]\\n #options = data_raw[\\\"options\\\"][i]\\n\\n examples.append(\\n InputExample(\\n example_id=question_id,\\n question=question,\\n contexts=[context_a,context_b,context_c],\\n endings=[endings[0], endings[1], endings[2]],#, options[3]\\n label=label,\\n )\\n )\\n return examples\",\n \"def get_questions():\\n fields_dt = ['name', 'category', 'key', 'text']\\n questions = frappe.db.get_list('Big Five Factor Model',\\n fields=fields_dt)\\n\\n # Ordenamiendo random: se aplica sobre el objeto original\\n suffle_data = random.shuffle(questions)\\n\\n return questions\",\n \"def __init__(self, words, text):\\n\\n self.prompts = words\\n self.template = text\",\n \"def get_snippets(self, constraints):\\n for ind, text in enumerate(self.__mongo_db.get({\\\"title\\\": \\\"Chapter 1\\\"})):\\n self.__postgre_db.insert(\\\"texts\\\", {\\\"title\\\": text['title']})\\n self.find_text_window(text['text'], text['id'], constraints)\\n print(\\\"Chapter \\\" + str(text['id']) + \\\" done.\\\")\",\n \"def read_in_templates(path, email_object=None):\\n import os\\n templates = {}\\n\\n for fle in os.listdir(path):\\n with open(os.path.join(path, fle)) as _f:\\n raw = \\\"\\\\n\\\".join(_f.readlines())\\n templates[fle] = raw\\n\\n if email_object:\\n email_object.use_templates(templates)\\n else:\\n return templates\",\n \"def list_templates(self):\\n raise NotImplementedError()\",\n \"def clean_questions(self, q_dot=False):\\n for q in self.data['questions']:\\n temp = re.sub('[^ a-zA-Z0-9]', '', q['body'])\\n temp = temp.strip().lower()\\n # temp = self.stemming(temp).strip()\\n self.cleaned_questions_q_dot += temp + '?\\\\n'\\n self.cleaned_questions += temp + '\\\\n'\\n self.types_q += q['type'].strip() + '\\\\n'\",\n \"def generate_corpus():\\n data = load_data()\\n questions = [s.split(' ', 1)[1].lower() for s in data]\\n return questions\",\n \"def extract_questions_from_text(self, text):\\n questions = []\\n\\n for match in self.QUESTION_RE.finditer(text):\\n match_dict = match.groupdict()\\n\\n answer_type = match_dict['answer_type']\\n number1 = match_dict.pop('number1')\\n\\n if answer_type == 'O':\\n if re.search('(?i)to ask the Deputy President', match_dict['intro']):\\n match_dict['dp_number'] = number1\\n elif re.search('(?i)to ask the President', match_dict['intro']):\\n match_dict['president_number'] = number1\\n else:\\n match_dict['oral_number'] = number1\\n elif answer_type == 'W':\\n match_dict['written_number'] = number1\\n\\n match_dict['translated'] = bool(match_dict['translated'])\\n match_dict['questionto'] = match_dict['questionto'].replace(':', '')\\n match_dict['questionto'] = self.correct_minister_title(match_dict['questionto'])\\n\\n questions.append(match_dict)\\n\\n return questions\",\n \"def read_templates(folder):\\n output = []\\n for path, subdirs, files in os.walk(folder):\\n for name in files:\\n if name.endswith('.yml'):\\n tpl = yaml.load(open(os.path.join(path, name)).read())\\n tpl['template_name'] = name\\n\\n # Test if all required fields are in template:\\n assert 'keywords' in tpl.keys(), 'Missing keywords field.'\\n required_fields = ['date', 'amount', 'invoice_number']\\n assert len(set(required_fields).intersection(tpl['fields'].keys())) == len(required_fields), \\\\\\n 'Missing required key in template {} {}. Found {}'.format(name, path, tpl['fields'].keys())\\n \\n # Keywords as list, if only one.\\n if type(tpl['keywords']) is not list:\\n tpl['keywords'] = [tpl['keywords']]\\n\\n output.append(InvoiceTemplate(tpl))\\n return output\",\n \"def get_templates(self):\\n\\n data = self.request_from_server('templates')\\n self.templates = data\",\n \"def from_text(question_file, answer_file):\\n with open(question_file, 'r', encoding='UTF8', errors='replace') as f:\\n questions = f.readlines()\\n\\n with open(answer_file, 'r', encoding='UTF8', errors='replace') as f:\\n answers = f.readlines()\\n\\n return QuestionSet([Question(question, answer) for question, answer in zip(questions, answers)])\",\n \"def load_questions_from_web():\\n # sending http request\\n r = requests.get(http_api_questions)\\n \\n # loading the content to JSON object\\n json_content = json.loads(r.content)\\n \\n # deleting unnecessary value from the JSON\\n del json_content['response_code']\\n\\n # creating the questions dict\\n questions = {}\\n\\n # questions from the JSON (in list [])\\n questions_from_json = json_content['results']\\n\\n # looping through the questions, convert it to usual formatted question\\n for index_question, question in enumerate(questions_from_json):\\n\\n # correct-answer\\n correct_answer = question[\\\"correct_answer\\\"]\\n\\n # list of all answers\\n all_answers = [correct_answer] + question[\\\"incorrect_answers\\\"]\\n\\n # shuffling the list [otherwise, the answer will always be in the first place]\\n random.shuffle(all_answers)\\n\\n # finding the new index of the correct-answer\\n correct_answer_index = all_answers.index(correct_answer)\\n\\n # important! - converting from 'XML\\\\CSS\\\\HTML' format to 'utf-8'\\n fixed_replaced_question = question[\\\"question\\\"].replace(\\\"'\\\", \\\"'\\\").replace(\\\""\\\", \\\"'\\\")\\n \\n # discarding specific question if it still has unwelcomed characters\\n if(fixed_replaced_question.find('#') != -1 or fixed_replaced_question.find('|') != -1):\\n break\\n \\n # copying the question to the new dict\\n questions[str(index_question)] = {\\n \\\"question\\\" : fixed_replaced_question,\\n \\\"answers\\\" : all_answers,\\n \\\"correct\\\" : str(correct_answer_index + 1)\\n }\\n \\n # returns the questions' dict\\n return questions\",\n \"def code_templates(self):\\n\\n one_of_languages = '|'.join(l if l != 'c++' else r'c\\\\+\\\\+' for l in Languages.all)\\n pattern = r'^::[ \\\\t\\\\r\\\\f\\\\v]*(' + one_of_languages + r')[ \\\\t\\\\r\\\\f\\\\v]*$'\\n parts = re.split(pattern, self.templates_data, flags=re.MULTILINE)\\n parts = parts[1:]\\n languages = parts[::2]\\n # remove starting/ending empty lines, common indentation and ensure trailing newline\\n templates = [textwrap.dedent(t).strip() for t in parts[1::2]]\\n assert (len(languages) == len(templates))\\n templates = dict(zip(languages, templates)) or {language: '' for language in Languages.all}\\n for language, template in templates.items():\\n if template:\\n templates[language] = template + '\\\\n'\\n else:\\n templates[language] = Languages.default_templates[language]\\n return templates\",\n \"def realize_question(dialog, template, filter_objs):\\n\\n # Number of inputs.\\n n_inputs = template.get('inputs', 0)\\n # Sample a text template.\\n text_sample = random.choice(template['text'])\\n text_sample_index = template['text'].index(text_sample)\\n\\n # Extract attribute tags and get them into groups.\\n tags = re.findall('(<[\\\\d\\\\w]*>)', text_sample)\\n tag_groups = collections.defaultdict(list)\\n for tag in tags:\\n group_id = get_tag_group(tag)\\n tag_groups[group_id].append(tag)\\n\\n # Sample a random element from filtered.\\n arg_sample = random.choice(filter_objs)\\n\\n # Remove tags from text not allowed by filter_objs.\\n for arg_ind in range(n_inputs):\\n obj_sample = arg_sample['objects'][arg_ind]\\n avail_attrs = obj_sample['optional'] + obj_sample['required']\\n\\n for ii in tag_groups[arg_ind][::-1]:\\n if mapping(ii) not in avail_attrs:\\n tag_groups[arg_ind].remove(ii)\\n text_sample = replace_attribute(text_sample, ii, arg_sample, True)\\n\\n # Assert that all required attributes are present as tags.\\n for attribute in obj_sample['required']:\\n required_tag = inv_mapping(attribute, arg_ind)\\n # Make an exception for <R> and <P>\\n if required_tag == '<R>' and '<P>' in tag_groups[arg_ind]:\\n continue\\n assert required_tag in tag_groups[arg_ind], \\\\\\n 'A required attribute is missing in template!'\\n\\n # Start compiling tags to keep.\\n tags_to_keep = [inv_mapping(ii, arg_ind) for ii in obj_sample['required']]\\n # Filter out those not present in text template.\\n optional_tags = [inv_mapping(ii,arg_ind) for ii in obj_sample['optional']]\\n optional_tags = [ii for ii in optional_tags if ii in tag_groups[arg_ind]]\\n\\n # If tags_to_keep is empty, sample from optional with (1:70, 2:25, 3:5).\\n if len(optional_tags) > 0:\\n if len(tags_to_keep) > 0:\\n n_tags_sample = [0, 1, 2]\\n else:\\n n_tags_sample = [1, 2, 3]\\n n_sample = np.random.choice(n_tags_sample, 1,\\n p=gvars.METAINFO['probabilities'],\\n replace=False)\\n # Lower cap at the length of optional.\\n n_sample = min(n_sample[0], len(optional_tags))\\n if n_sample > 0:\\n tags_to_keep += random.sample(optional_tags, n_sample)\\n\\n # Now create a dictionary of placeholders with actual attribute values.\\n for tag in tag_groups[arg_ind]:\\n remove = tag not in tags_to_keep\\n text_sample = replace_attribute(text_sample, tag, arg_sample, remove)\\n\\n # Record info and merge scene graphs.\\n dialog_datum = {'question': text_sample, 'answer': arg_sample['answer'],\\n 'template': template['label']}\\n dialog['template_info'].append(template.copy())\\n del dialog['template_info'][-1]['text']\\n dialog['template_info'][-1]['index'] = text_sample_index\\n\\n dialog['dialog'].append(dialog_datum)\\n graph_item = arg_sample['graph']\\n\\n # If mergeable, add it to the objects list.\\n dialog['graph'] = utils.merge_update_scene_graph(dialog['graph'], graph_item)\\n\\n # If there are volatile objects in the graph item, remove them.\\n for obj in graph_item['objects'][::-1]:\\n if obj.get('volatile', False): graph_item['objects'].remove(obj)\\n dialog['graph']['history'].append(graph_item)\\n return dialog\",\n \"def create_populated_question(answer_states: List[bool], question_text: str = None):\\n question = create_question(question_text)\\n\\n for state in answer_states:\\n create_answer(question, state)\\n\\n return question\",\n \"def get_question_templates(session: \\\"Session\\\", verbose: bool) -> Dict:\\n return _get_dict(\\n session,\\n url_tail=f\\\"/{CoordConstsV2.RSC_QUESTION_TEMPLATES}\\\",\\n params={CoordConstsV2.QP_VERBOSE: verbose},\\n fail_fast=True,\\n )\",\n \"def main():\\n\\targs = getArgs()\\n\\tid_question = args.id_question\\n\\tlang = args.language\\n\\tdir_cp = None\\n\\twith open('config.json') as json_file:\\n\\t\\tconfig_data = json.load(json_file)\\n\\t\\tdir_cp = config_data['dir_cp']\\n\\n\\t\\n\\t\\n\\t# sample_io = GetData(args.id_question).get_uri_io_sample()\\n\\ttemplate = FileUtil(id_question, dir_cp['path'], lang)\\n\\ttemplate.write_template()\\n\\t# print(sample_io)\",\n \"def get_questions(self, obj):\\n queryset = Question.objects.filter(sheet=obj)\\n questions = []\\n for q in queryset:\\n questions.append(q.text)\\n return questions\",\n \"def template_list(self):\\n return self.ezx.get_template_list()\",\n \"def load_texts(self):\\n\\n db = mongo(self.text_dbname)\\n\\n print(\\\"Loading texts:\\\")\\n # For each other, query the text database and add their works to texts to compare\\n for author in self.authors:\\n for text in db.texts.find({'author':author, 'language':\\\"latin\\\"}, no_cursor_timeout=True):\\n self.texts_to_compare.append(text)\\n print(\\\" -- loaded texts for\\\", author)\\n\\n return\",\n \"def question_list():\\r\\n question_frame()\\r\\n list_box = Listbox(question_list_frame,width=100)\\r\\n # automatically opens and closes the file when not in use\\r\\n with open('Question_pool.txt', 'r') as fp:\\r\\n line = fp.readline()\\r\\n count = 1\\r\\n while line:\\r\\n list_box.insert(END,\\\"{}) {}\\\".format(count, line.split(',')[0]))\\r\\n list_box.grid(sticky=W)\\r\\n line = fp.readline()\\r\\n count += 1\",\n \"def make_extra_questions_txt(self):\\n raise NotImplementedError\",\n \"def get_templates(self, template_name, **kwargs):\\n text = render_template(\\\"{template}.txt\\\".format(template=template_name), **kwargs)\\n return text\",\n \"def _make_vocab_files(self):\\n self.logger.info('making question vocab...' + self.opt.QUESTION_VOCAB_SPACE)\\n qdic, _ = self.load_data(self.opt.QUESTION_VOCAB_SPACE)\\n question_vocab = VQADataProvider.make_question_vocab(qdic, self.max_length)\\n self.logger.info('making answer vocab...' + self.opt.ANSWER_VOCAB_SPACE)\\n qdic, adic = self.load_data(self.opt.ANSWER_VOCAB_SPACE)\\n answer_vocab = VQADataProvider.make_answer_vocab(adic, qdic, self.opt.MAX_ANSWER_VOCAB_SIZE, self.use_ocr)\\n return question_vocab, answer_vocab\",\n \"async def generate_question(self) -> None:\\n topic = choice(list(self._topics.keys()))\\n arrayList = await self.session.get(\\\"https://restcountries.eu/rest/v2\\\")\\n arrayList = await arrayList.json() # get request to the country API\\n countries = []\\n \\n for _ in range(4):\\n country = choice(arrayList)\\n del arrayList[arrayList.index(country)]\\n countries.append(country)\\n del arrayList\\n \\n country = choice(countries)\\n del countries[countries.index(country)]\\n self.question = f\\\"What is the {self._topics[topic]} of {country['name']}?\\\"\\n self.correct_order = randint(0, 3)\\n self.choices = [i[topic] for i in countries]\\n self.choices.insert(self.correct_order, country[topic])\\n del countries, topic\",\n \"def readTemplates():\\n\\n # Compile HTML templates.\\n templates = {}\\n for tt in [ 'image', 'dirindex', 'allindex', 'trackindex', 'sortindex' ]:\\n fn = 'template-%s' % tt + opts.htmlext\\n ttext = readTemplate(fn)\\n templates[ tt ] = compileTemplate(ttext, fn)\\n\\n fn = 'template-css.css'\\n ttext = readTemplate(fn)\\n templates[ 'css' ] = compileTemplate(ttext, fn)\\n\\n # Compile user-specified rc file.\\n rcsfx = 'rc'\\n templates[ rcsfx ] = []\\n if opts.rc:\\n try:\\n tfile = open(opts.rc, \\\"r\\\")\\n orc = tfile.read()\\n tfile.close()\\n except IOError, e:\\n print >> sys.stderr, \\\"Error: can't open user rc file:\\\", opts.rc\\n sys.exit(1)\\n\\n o = compileCode('', orc, opts.rc)\\n templates[ rcsfx ] += [ o ]\\n\\n # Compile user-specified code.\\n if opts.rccode:\\n o = compileCode('', opts.rccode, \\\"rccode option\\\")\\n templates[ rcsfx ] += [ o ]\\n\\n # Compile global rc file without HTML tags, just python code.\\n code = readTemplate('template-%s' % rcsfx + '.py')\\n o = compileCode('', code, tt)\\n templates[ rcsfx ] += [ o ]\\n\\n return templates\",\n \"def create_evaluation_template(client, survey_name):\\n\\n loremipsum = \\\"Lorem ipsum dolor sit amet, consecteteur adipiscing elit donec proin nulla vivamus. Augue donec a erat ve sagittis nisi rhoncus curabitur mauris. Nulla ipsum tortor sagittis adipiscing primis interdum suspendisse lobortis etiam risus nullam. Donec massa quam dis at nibh dolor netus quis. Purus etiam. Dolor neque nunc netus eget nulla faucibus vestibulum aenean class senectus. Porta dolor. Donec morbi. Felis lorem tempus luctus malesuada laoreet curae justo rhoncus ante facilisi parturient malesuada elit laoreet amet. Fusce augue nisi ligula praesent condimentum nascetur fringilla in id lectus per nunc. Lacus metus nisl orci odio maecenas adipiscing. Velit nulla a tempor class placerat ac condimentum nisi taciti at eros.\\\"\\n\\n loremipsum_A = \\\"A: \\\\n\\\" + loremipsum\\n loremipsum_B = \\\"B: \\\\n\\\" + loremipsum\\n\\n # Create a new survey\\n survey_id = client.create_survey(survey_name)\\n # Create 2 more pages in the survey\\n for i in range(0, 2):\\n client.create_new_page(survey_id, str(i), loremipsum) # title and description\\n\\n # Get the page ids\\n page_ids = client.get_pages_ids(survey_id) # There will be 3\\n\\n answers = [\\\"A\\\", \\\"B\\\"]\\n question_title = \\\"Which of the following abstract is more relevant to the one above?\\\"\\n for i, ID in enumerate(page_ids):\\n client.update_title_description_of_page(survey_id, ID, \\\"Abstract\\\" + str(i), loremipsum)\\n client.add_single_choice(survey_id, ID, question_title, answers)\\n client.add_paragraph(survey_id, ID, loremipsum_A)\\n client.add_paragraph(survey_id, ID, loremipsum_B)\\n\\n return survey_id\",\n \"def ask_all():\\n for q in Question.all_questions:\\n print 'from', q.file\\n q.ask()\",\n \"def add_questions(self, questions):\\n for question in questions:\\n self.questions.append(question)\",\n \"def create_type_question_ft(self):\\n for i in range(0, len(self.cleaned_questions.splitlines())):\\n self.type_question_ft += '__label__'+self.types_q.splitlines()[i] + ' ' + self.cleaned_questions.splitlines()[i] + '\\\\n'\",\n \"def get_questions(qn_filepath):\\n array = []\\n with open(qn_filepath, 'rb') as file:\\n for line in file:\\n array.append(line)\\n\\n delete = ['<top>\\\\r\\\\n', '\\\\r\\\\n', '<desc> Description:\\\\r\\\\n', '</top>\\\\r\\\\n']\\n for i in delete:\\n array = list(filter((i).__ne__, array))\\n final_array = []\\n for i in range(len(array)):\\n if i % 2 == 0:\\n array[i] = array[i].split(' ')[-1].strip()\\n array[i+1] = array[i+1].strip().split('?')[0]\\n qn = Question(qn_id=int(array[i]), qn_text=array[i+1])\\n final_array.append(qn)\\n\\n return final_array\",\n \"def templatelist(cls):\\n return cls._templatelist\",\n \"def question(num):\\n if request.method == 'GET':\\n questionGroup = Question.query.filter(Question.num == num).from_self().all()\\n answers = [answer.value for answer in questionGroup if '_text' in answer.variable]\\n responses = [response.value for response in questionGroup if '_response' in response.variable]\\n tooltips = [response.value for response in questionGroup if '_hover' in response.variable]\\n questionType = questionGroup[0].question_type\\n if questionType == 'Multiple Choice':\\n return render_template('/demo/question-multiplechoice.html',\\n question=questionGroup[0].question,\\n choices=answers,\\n responses=responses,\\n tooltips=tooltips,\\n # variables=variables,\\n explanation=questionGroup[0].info,\\n template=\\\"question-multiplechoice\\\")\\n return render_template('/demo/question-freeform.html',\\n question=questionGroup[0].question,\\n choices=answers,\\n responses=responses,\\n tooltips=tooltips,\\n # variables=variables,\\n explanation=questionGroup[0].info,\\n template=\\\"question-freeform\\\")\",\n \"def load_skills():\\n\\n Skill.query.delete()\\n\\n # get all the qualifications text from postings\\n postings = db.session.query(Posting.qualifications).all()\\n # combine qualifications into a list\\n all_skills = []\\n with open('filler.txt') as filler:\\n del_words = filler.read()\\n for post in postings:\\n words = post.qualifications.lower().split()\\n # iterate through a list of those skills\\n for word in words:\\n word = word.strip(\\\"-()/\\\\,.:;* 1234567890\\\")\\n # check to see if that word isn't in our filler document\\n # if not, add it to the table\\n if word not in del_words and word not in all_skills:\\n all_skills.append(word)\\n skill = Skill(skill=word)\\n db.session.add(skill)\\n db.session.commit()\",\n \"def load_poems(self):\\n file = open(self.name, \\\"r\\\")\\n content = file.readlines()\\n for i in content:\\n self.add_msg_and_index(i.strip())\",\n \"def ls():\\n # TODO(rabrams) would be good to factor out the file specific stuff\\n # to files module\\n path = files.get_proj_dir_or_exit()\\n mod_paths = glob.glob(os.path.join(path, \\\"solutions\\\", \\\"*.py\\\"))\\n basenames = [os.path.basename(mod_path) for mod_path in mod_paths]\\n with open(os.path.join(path, \\\"answers.json\\\")) as f:\\n answers = json.load(f)\\n\\n problem_data = {}\\n for basename in basenames:\\n if basename == \\\"__init__.py\\\":\\n continue\\n problem = basename[:-len(\\\".py\\\")]\\n if not problem.startswith(\\\"p\\\"):\\n raise ValueError(\\\"Invalid module name found\\\", basename)\\n try:\\n number = int(problem[1:])\\n except ValueError:\\n raise ValueError(\\\"Invalid module name found\\\", basename)\\n module = importlib.import_module(\\\"solutions.p{}\\\".format(number))\\n\\n fulldoc = module.__doc__ or \\\"\\\"\\n parts = fulldoc.split(\\\"\\\\n\\\")\\n problem_data[number] = {\\n \\\"summary\\\": \\\"\\\" if len(parts) <= 1 else parts[1].strip(),\\n \\\"answer\\\": answers.get(str(number), \\\"\\\"),\\n }\\n\\n table = {\\n \\\"problem\\\": [],\\n \\\"summary\\\": [],\\n \\\"answer\\\": [],\\n }\\n for number in sorted(problem_data):\\n table[\\\"problem\\\"].append(number)\\n table[\\\"summary\\\"].append(problem_data[number]['summary'])\\n table[\\\"answer\\\"].append(problem_data[number]['answer'])\\n print(tabulate.tabulate(table, headers=\\\"keys\\\"))\",\n \"def load_as_files(self, lazy=True):\\n # Handle lazy load\\n if lazy:\\n try:\\n questions = read_file_data('questions', self.inputs_dir)\\n answers = read_file_data('answers', self.inputs_dir)\\n questions_vocabs = read_file_data('questions_vocabs', self.inputs_dir)\\n answers_vocabs = read_file_data('answers_vocabs', self.inputs_dir)\\n except Exception as e:\\n print('Failed to lazy load !', e)\\n questions, answers, questions_vocabs, answers_vocabs = self.sub.load_as_raw()\\n save_file_data('questions', questions, self.inputs_dir)\\n save_file_data('answers', answers, self.inputs_dir)\\n save_file_data('questions_vocabs', questions_vocabs, self.inputs_dir)\\n save_file_data('answers_vocabs', answers_vocabs, self.inputs_dir)\\n else:\\n questions, answers, questions_vocabs, answers_vocabs = self.sub.load_as_raw()\\n\\n # It will always use vi as src en as output\\n X_train, X_test, y_train, y_test = train_test_split(questions, answers, test_size=0.2, random_state=42)\\n X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\\n\\n # Train\\n save_list_to_file(X_train, '{}/train.vi'.format(self.inputs_dir))\\n save_list_to_file(y_train, '{}/train.en'.format(self.inputs_dir))\\n\\n # Valid\\n save_list_to_file(X_valid, '{}/dev.vi'.format(self.inputs_dir))\\n save_list_to_file(y_valid, '{}/dev.en'.format(self.inputs_dir))\\n\\n # Test\\n save_list_to_file(X_test, '{}/test.vi'.format(self.inputs_dir))\\n save_list_to_file(y_test, '{}/test.en'.format(self.inputs_dir))\\n\\n # vocab\\n save_list_to_file(questions_vocabs, '{}/vocab.vi'.format(self.inputs_dir))\\n save_list_to_file(answers_vocabs, '{}/vocab.en'.format(self.inputs_dir))\",\n \"def _fill_template(\\n self,\\n template: Dict[Text, Any],\\n template_vars: Dict[Text, Any]\\n ) -> Dict[Text, Any]:\\n\\n line_object_keys = [\\\"quickReply\\\", \\\"items\\\", \\\"action\\\", \\\"template\\\", \\\"actions\\\"]\\n\\n if type(template) == list:\\n for item in template:\\n self._fill_template(item, template_vars)\\n else:\\n self._fill_template_text(template, template_vars)\\n for key in line_object_keys:\\n if key in template:\\n self._fill_template(template[key], template_vars)\\n\\n return template\",\n \"def populate_game_questions():\\n indices = random.sample(range(0, len(quizquestion.questions_all)), 5) # If user doesn't specify, choose 5 random questions\\n return quizquestion.QuizQuestion.get_game_questions(indices)\",\n \"def view_index():\\n story_name = request.args.get(\\\"template_picker\\\")\\n return render_template(\\\"questions.html\\\",\\n story=story_templates[story_name],\\n story_name=story_name)\",\n \"def evaluate_questions(self):\\n for question in self.question_list:\\n question.evaluate_question()\",\n \"def _create_examples(self, lines, set_type):\\n examples = []\\n for idx, line in enumerate(lines):\\n item = json.loads(line.strip())\\n question_id = \\\"%s-%s\\\" % (set_type, idx)\\n context = item[\\\"context\\\"]\\n question = item[\\\"question\\\"]\\n endings = [item[\\\"answerA\\\"],item[\\\"answerB\\\"],item[\\\"answerC\\\"] ]\\n label = item[\\\"correct\\\"]\\n #race_id = \\\"%s-%s\\\" % (set_type, data_raw[\\\"race_id\\\"])\\n #article = data_raw[\\\"article\\\"]\\n #for i in range(len(data_raw[\\\"answers\\\"])):\\n #truth = str(ord(data_raw[\\\"answers\\\"][i]) - ord(\\\"A\\\"))\\n #question = data_raw[\\\"questions\\\"][i]\\n #options = data_raw[\\\"options\\\"][i]\\n\\n examples.append(\\n InputExample(\\n example_id=question_id,\\n question=question,\\n contexts=[context,context,context],\\n endings=[endings[0], endings[1], endings[2]],#, options[3]\\n label=label,\\n )\\n )\\n return examples\",\n \"def use_templates(self, templates):\\n self.htmls = templates\",\n \"def question_list(request, question_views, template, questions_per_page=None,\\n page_number=None, extra_context=None):\\n view_id = request.GET.get('sort', None)\\n view = dict([(q.id, q) for q in question_views]).get(view_id,\\n question_views[0])\\n if questions_per_page is None:\\n questions_per_page = get_questions_per_page(request.user)\\n paginator = Paginator(view.get_queryset(), questions_per_page)\\n if page_number is None:\\n page = get_page(request, paginator)\\n else:\\n page = paginator.page(page_number)\\n populate_foreign_key_caches(User, ((page.object_list, (view.user,)),),\\n fields=view.user_fields)\\n context = {\\n 'title': view.page_title,\\n 'page': page,\\n 'questions': page.object_list,\\n 'current_view': view,\\n 'question_views': question_views,\\n }\\n if extra_context is not None:\\n context.update(extra_context)\\n return render_to_response(template, context,\\n context_instance=RequestContext(request))\",\n \"def _GetTemplate(self):\\n# First read default template.\\n tmplt = self._LoadTemplate(c.preproc_template_default)\\n tmplt['proc'] = self.topdir\\n self.template_type = 'default'\\n\\n self.templates = []\\n if self.template_file is not None:\\n tmplt.update(self._LoadTemplate(self.template_file))\\n self.template_type = 'command-line'\\n self.templates.append(os.path.abspath(self.template_file))\\n found_template = True\\n else:\\n# Find a study specific template file.\\n study_template_file = self._FindTemplateFile('%s/..' % self.topdir)\\n if study_template_file is not None:\\n# Merge study template into default, study template has precedence.\\n if self.verbose:\\n print \\\"Using study template at \\\" + study_template_file\\n tmplt.update(self._LoadTemplate(study_template_file))\\n self.template_type = 'study-specific'\\n self.templates.append(os.path.abspath(study_template_file))\\n found_template = True\\n else:\\n found_template = False\\n# Now look for a subject-specific template file.\\n subject_template_file = self._FindTemplateFile('%s' % self.topdir)\\n if subject_template_file is not None:\\n# Merge subject template, subject template has precedence.\\n if self.verbose:\\n print \\\"Using subject-specific template at %s\\\" % \\\\\\n subject_template_file\\n tmplt.update(self._LoadTemplate(subject_template_file))\\n self.template_type = 'study-specific'\\n self.templates.append(os.path.abspath(subject_template_file))\\n found_template = True\\n\\n if not found_template:\\n raise RuntimeError('Could not find template file.')\\n\\n if tmplt.get('subject','same') == 'same':\\n# Default subdirectory is same as data directory.\\n tmplt['subject'] = self.topdir.split('/')[-1]\\n else:\\n if not isinstance(tmplt['subject'],str):\\n errstr = 'preprocess: Invalid subject number. Be sure to ' + \\\\\\n 'enclose the subject number item with double quotes.'\\n raise RuntimeError(errstr)\\n\\n# Keys that apply to all EPIs.\\n self.fsl_flip = tmplt.get('fsl_flip', False)\\n if self.fsl_flip:\\n self.flip_opts = '-LT'\\n else:\\n self.flip_opts = ''\\n\\n# Replace strings with python types.\\n for key in tmplt.keys():\\n if tmplt[key] == 'None':\\n tmplt[key] = None\\n elif key == 'True':\\n tmplt[key] = True\\n elif key == 'False':\\n tmplt[key] = False\\n return tmplt\",\n \"def ttlist(qa,a1a,a0a,ans1,ans0,sentences):\\n (questions,answers1,answers0)=sentences\\n li=[]\\n ones=0\\n zeros=0\\n for i in range(0,len(ans1)):\\n li.append(q(qa[i],a1a[ones:ones+ans1[i]],a0a[zeros:zeros+ans0[i]],questions[i],\\n answers1[ones:ones+ans1[i]],answers0[zeros:zeros+ans0[i]]))\\n ones+=ans1[i]\\n zeros+=ans0[i]\\n return li\",\n \"def question(length):\\n\\n length = len(responses)\\n question_list = satisfaction_survey.questions\\n asking_question = question_list[length].question\\n the_choices = question_list[length].choices\\n\\n return render_template(\\n 'questions_page.html',\\n asking_question=asking_question,\\n the_choices=the_choices\\n )\",\n \"def create_templates_database(dataset_path_list, db_file_path):\\n paths_list = dataset_path_list\\n\\n templates = dict()\\n for file in paths_list:\\n\\n image = face_recognition.load_image_file(file)\\n tmp = face_recognition.face_encodings(image)\\n if tmp:\\n template = face_recognition.face_encodings(image)[0]\\n if template.size != 0:\\n templates[file] = template\\n\\n dump_dict_to_db(templates, db_file_path)\",\n \"def process_input_files(list_input_files):\\n global dict_models_results\\n global list_spacy_docs\\n \\n for input_file in list_input_files:\\n prefix = prefix_from_filename(input_file)\\n \\n with open(input_file) as f:\\n list_cases = json.load(f)\\n dict_models_results[prefix] = list_cases\\n \\n \\n #extract list of questions from all vignettes and create a mapping page -> vignette question\\n dict_questions = {}\\n for prefix, list_cases in dict_models_results.items():\\n for vignette in list_cases:\\n dict_questions[vignette[\\\"book_page\\\"]] = vignette[\\\"question\\\"]\\n \\n \\n for book_page,question in dict_questions.items():\\n doc_q = load_bner_onto_tokens_extension(question, book_page)\\n list_spacy_docs.append(doc_q)\\n \\n return\",\n \"def __fill_all_templates__(self,configs):\\n template_dir = configs['system'].get('Common_directories','template')\\n sample_template = os.path.join(template_dir,configs['pipeline'].get('Template_files','sample'))\\n system_template = os.path.join(template_dir,configs['pipeline'].get('Template_files','system'))\\n qsub_template = os.path.join(template_dir,configs['pipeline'].get('Template_files','bcbio'))\\n self.__fill_template__(sample_template,self.sample_file)\\n self.__fill_template__(system_template,self.systems_file)\\n self.__fill_template__(qsub_template,self.qsub_file)\",\n \"def load_question(data, mm, request, ignore_similar=False):\\n try:\\n subject = Subject.objects.get(name=data['subject'])\\n topic = Topic.objects.get(name=data['topic'], subject=subject)\\n\\n question = Question(text=data['question'], topic=topic)\\n question.full_clean()\\n answers = []\\n for ans in data['answers']:\\n answer = Answer()\\n answer.text = ans['text']\\n answer.is_correct = ans['is_correct']\\n answers.append(answer)\\n\\n if question.similar_exists() and not ignore_similar:\\n raise SimilarQuestionError(_(\\\"A similar question exists\\\"),\\n data)\\n else:\\n question.save()\\n for answer in answers:\\n answer.question = question\\n answer.full_clean()\\n answer.save()\\n mm.added.append(question)\\n\\n except Subject.DoesNotExist:\\n mm.no_subject.append((data['subject'], data['question']))\\n\\n except Topic.DoesNotExist:\\n mm.no_topic.append((data['topic'], data['question']))\\n\\n except ValidationError as err:\\n mm.validation_error.append((err, data['question']))\\n\\n except SimilarQuestionError as err:\\n request.session['duplicates'].append(err.data)\",\n \"def premade():\\n global all_questions\\n\\n choice(all_questions)\\n temp = all_questions.pop(0) # Treat scrambled list like a queue\\n question,answer = temp\\n print(\\\"\\\\n\\\\n{}\\\".format(question))\\n guess = get_user_input(str)\\n\\n if guess == \\\"T\\\": guess = True\\n if guess == \\\"F\\\": guess = False\\n\\n if guess == answer:\\n print(\\\"Correct the answer is: {}\\\".format(answer))\\n else:\\n print(\\\"Incorrect the answer is: {}, This question will be asked again.\\\".format(answer))\\n all_questions.append(temp) # Add it back\",\n \"def __loadListOfDocuments(self):\\n\\t\\tfor value in default_paths():\\n\\t\\t\\titem = addNewListItemCalled([os.path.normpath(value)], self.ui.listWidget)\\n\\t\\t\\titem.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\\n\\t\\t\\tt = item.font()\\n\\t\\t\\tt.setItalic(True)\\n\\t\\t\\titem.setFont(t)\\n\\n\\t\\t# load up state from storage...\\n\\t\\tpaths = QSettings().value(\\\"paths\\\").toList()\\n\\t\\tfor value in paths:\\n\\t\\t\\tstr = os.path.normpath(value.toString())\\n\\t\\t\\tif str not in default_paths():\\n\\t\\t\\t\\taddNewListItemCalled([str], self.ui.listWidget, mutable=True)\",\n \"def questions(self) -> HTMLBody:\\n\\t\\tif self.is_authorized:\\n\\t\\t\\tif session[\\\"user\\\"][\\\"is_diplomat\\\"] == \\\"True\\\":\\n\\t\\t\\t\\tif request.method == \\\"GET\\\":\\n\\t\\t\\t\\t\\treturn render_template(\\\"questions.jinja2\\\", question_list=self.db.get_questions())\\n\\t\\t\\t\\t# 1. Get the question id\\n\\t\\t\\t\\t# 2. Get the response posted by the Diplomat\\n\\t\\t\\t\\t# 3. Get the email associated with the question\\n\\t\\t\\t\\t# 4. Create an email response for the question\\n\\t\\t\\t\\t# 5. Send the response and delete the question\\n\\t\\t\\t\\tprint(dict(request.form))\\n\\t\\t\\tflash(\\\"Only Engineering Diplomats may view the list of questions.\\\")\\n\\t\\t\\treturn redirect(url_for(\\\"index\\\"))\\n\\t\\tflash(\\\"Please login first.\\\")\\n\\t\\treturn redirect(url_for(\\\"login\\\"))\",\n \"def deserialize(path):\\n with open(path, 'rb') as f:\\n temp = pickle.load(f)\\n for q in temp.questions:\\n q.on_deserialize()\\n return temp\",\n \"def list_templates(context):\\n templates = get_oneoffixx_templates()\\n template_group = context.REQUEST.form.get('form.widgets.template_group')\\n terms = []\\n\\n for template in templates:\\n terms.append(SimpleVocabulary.createTerm(\\n template, template.template_id, template.title))\\n\\n # We filter templates when template_group has been selected\\n if template_group is not None:\\n favorites = get_oneoffixx_favorites()\\n # Favorites are a special case\\n if favorites and template_group[0] == favorites.get('id'):\\n terms = [\\n SimpleVocabulary.createTerm(\\n OneOffixxTemplate(\\n template, favorites.get('localizedName', '')),\\n template.get('id'),\\n template.get('localizedName'),\\n )\\n for template in favorites.get('templates')\\n ]\\n elif template_group[0] != '--NOVALUE--':\\n terms = [term for term in terms if term.value.group == template_group[0]]\\n\\n return MutableObjectVocabulary(terms)\",\n \"def generate(self, answers):\\n\\n text = self.template\\n\\n for (key, val) in answers.items():\\n text = text.replace(\\\"{\\\" + key + \\\"}\\\", val)\\n\\n return text\",\n \"def question_medtagger(self):\\n self.save_on_file(self.cleaned_questions_q_dot, 'questions.txt')\\n medpos_tag_out = self.medpos_tag('questions.txt')\\n parsed_questions = self.parse(medpos_tag_out)\\n parsed_questions_type = []\\n parsed_questions_out = []\\n for i in range(0, len(self.data['questions'])):\\n # [ type, [ (w1, tag_w1) , (w2, tag_w2) ... (wn, tagwn) ] ]\\n parsed_questions_type += [[self.data['questions'][i]['type']] + [parsed_questions[i]]]\\n parsed_questions_out += [parsed_questions[i]]\\n return parsed_questions_out, parsed_questions_type\",\n \"def load_words(args, examples):\\n def _insert(iterable):\\n for w in iterable:\\n w = Dictionary.normalize(w)\\n if valid_words and w not in valid_words:\\n continue\\n words.add(w)\\n if args.restrict_vocab and args.embedding_file:\\n logger.info('Restricting to words in %s' % args.embedding_file)\\n valid_words = index_embedding_words(args.embedding_file)\\n logger.info('Num words in set = %d' % len(valid_words))\\n else:\\n valid_words = None\\n\\n words = set()\\n for ex in examples:\\n for p in ex['passage']:\\n _insert(p)\\n _insert(ex['question'])\\n return words\",\n \"def populate_questions(scores):\\n \\n print(\\\"populate_questions, scores: \\\", str(scores))\\n\\n try:\\n return random.sample(range(len(quiz.list_fragen)), TOTAL_ROUNDS*len(scores))\\n except ValueError:\\n print(\\\"List of questions is too short.\\\")\",\n \"def get_question_list(self, quiz):\\n preguntas = []\\n if quiz.preguntas_usadas is None:\\n queryset = Pregunta.objects.filter(\\n pool=self.diagnostico.banco).order_by('?').distinct()[:20]\\n for pregunta in queryset:\\n preguntas.append(PreguntasPrueba.objects.create(\\n prueba=self.diagnostico, pregunta=pregunta))\\n self.diagnostico.refresh_form_db()\\n else:\\n preguntas = [\\n pregunta for pregunta in quiz.preguntas_usadas.all()]\\n return preguntas\",\n \"def template_list(call=None):\\n templates = {}\\n session = _get_session()\\n vms = session.xenapi.VM.get_all()\\n for vm in vms:\\n record = session.xenapi.VM.get_record(vm)\\n if record[\\\"is_a_template\\\"]:\\n templates[record[\\\"name_label\\\"]] = record\\n return templates\",\n \"def question_new_translate():\",\n \"def get_words():\\n\\tprompts = []\\n\\tfor prompt in story.prompts:\\n\\t\\tprompts.append(prompt.replace('_', ' '))\\n\\n\\treturn render_template(\\\"get-words.html\\\", prompts = prompts, key_prompts = story.prompts, num_of_prompts = len(prompts))\",\n \"def parse_question_data(self):\\n section = ''\\n subsection = ''\\n quest = ''\\n # The data falls into 4 cases\\n # 1. Sections\\n # 2. subsections\\n # 3. questions\\n # 4. answers.\\n\\n for line in self.question_data: \\n\\n if \\\":\\\" in line: # case #2\\n subsection = line.split(\\\":\\\")[1] # split the line on the : into an array but only take the [1] element\\n debug(\\\"Subsection: %s\\\" % subsection)\\n \\n elif \\\".\\\" in line: # this is either a question or an answer?\\n \\n if line.split(\\\".\\\")[0].isdigit(): # case #3 it's a question, split on . into an array and take the element to the left and ask if it's a digit.\\n quest = line # Since we know it's something like \\\"3. Are you a warlock?\\\" we stick that in the quest varable.\\n debug(\\\"Question: %s\\\" % quest)\\n # Create a question object and stick it in the dictonary with the key being the question (since we know it'll be unique)\\n self.questions[quest] = question(section, subsection, quest) # I know it's redundant to have the key and have a value.\\n \\n elif line.startswith(\\\".\\\"): # case #4 answer All the answers startswith \\\".\\\" \\n debug(\\\"Answer: %s\\\" % line)\\n # take the question and append it to the answers array in the question object.\\n self.questions[quest].answers.append(line[2:]) # Trim the first two characters off the answer since it's \\\". the answer\\\"\\n \\n else: # case #1 # This is section like AMERICAN DEMOCRACY\\n section = line # load the line from the file into the section variable\\n debug(\\\"Section = %s\\\" % section)\",\n \"def save_collection(self, name):\\n path = self._config._output\\n pfile = open(path + '/' + name + '-all.tex', 'w')\\n\\n # Writes the header/configuration of the latex file\\n ltxpreamble = open('latex/preamble-sol', 'r')\\n for line in ltxpreamble:\\n pfile.write(line)\\n\\n self._preamble(pfile)\\n\\n for i in range(self._quiz.size()):\\n q = self._quiz.get_i_question(i)\\n q.latex_complete_question(pfile, 'q'+str(i))\\n\\n self._closing(pfile)\\n\\n pfile.close()\",\n \"def _load_templates(self, tasks, skip_tasks=()):\\n name2kwargs = {}\\n dir_ = Path('data/prompts')\\n paths = (dir_/t for t in tasks) if tasks else dir_.iterdir()\\n if skip_tasks: paths = (p for p in paths if p.stem not in skip_tasks)\\n for path in paths:\\n if not path.is_dir():\\n if tasks: warnings.warn(f'{path} is not a directory.')\\n continue\\n name2kwargs[path.stem] = load_prompt(path.stem,\\n verbose=self.verbose)\\n return name2kwargs\",\n \"def read_quiz_questions() -> pd.DataFrame:\\n data_path = './data/quiz_question_data.txt'\\n if not os.path.exists(data_path):\\n raise Exception(f'Data not found in {data_path}')\\n\\n with open(data_path, 'r') as f:\\n questions = f.read()\\n\\n return pd.DataFrame(ast.literal_eval(questions))\",\n \"def _create_examples(self, lines, set_type):\\n examples = []\\n for (_, data) in enumerate(lines):\\n passage = data[\\\"passage\\\"][\\\"text\\\"]\\n for Q in data[\\\"passage\\\"][\\\"questions\\\"]:\\n question = Q[\\\"question\\\"]\\n for A in Q[\\\"answers\\\"]:\\n guid = f\\\"{set_type}-{data['idx']-Q['idx']-A['idx']}\\\"\\n examples.append(\\n InputExample(\\n guid=guid,\\n text_a=passage,\\n text_b=question + \\\" \\\" + A[\\\"text\\\"],\\n label=str(A[\\\"label\\\"]),\\n )\\n )\\n return examples\",\n \"def _load(examples, f):\\n\\n for l in f:\\n json_example = json.loads(l)\\n if FLAGS.mode == 'long_answers' and not has_long_answer(json_example):\\n continue\\n\\n elif FLAGS.mode == 'short_answers' and not has_short_answer(json_example):\\n continue\\n\\n example = Example(json_example)\\n examples[example.example_id] = example\\n\\n if len(examples) == FLAGS.max_examples:\\n break\",\n \"def create_question_template(xblock_id, question_template, image_url, resolver_selection, variables, answer_template):\\n connection = mysql.connector.connect(**s.database)\\n \\n # clean_up_variables_and_expressions(fe_xblock, connection)\\n \\n insert_question_template(xblock_id, connection, question_template, image_url, resolver_selection, answer_template)\\n \\n create_variables(xblock_id, connection, variables)\\n \\n connection.commit()\\n connection.close()\",\n \"def create_quizz_question(classe, lecon):\\n path_to_classe = \\\"..//data//questions//\\\" + classe\\n path_to_questions = \\\"..//data//questions//\\\" + classe + \\\"//\\\" + lecon\\n empty_questions_list = []\\n # Enregistrement du fichier\\n with open(path_to_questions, \\\"wb\\\") as file:\\n writer = pickle.Pickler(file)\\n writer.dump(empty_questions_list)\\n\\n # Enregistrement sur la liste\\n with open(path_to_classe + '//liste_lecons', 'rb') as file:\\n lecteur = pickle.Unpickler(file)\\n liste_lecons = lecteur.load()\\n liste_lecons.append(lecon)\\n\\n with open(path_to_classe + '//liste_lecons', 'wb') as file:\\n writer = pickle.Pickler(file)\\n writer.dump(liste_lecons)\",\n \"def _create_examples(self, data_dir, set_type):\\n\\t\\texamples = []\\n\\t\\tinput_file_data = os.path.join(data_dir, \\\"data.tsv\\\")\\n\\t\\twith open(input_file_data, \\\"r\\\", encoding=\\\"utf-8-sig\\\") as f:\\n\\t\\t\\tfor i, inp in enumerate(f):\\n\\t\\t\\t\\tinps = inp.split('\\\\t') \\n\\t\\t\\t\\tguid = \\\"%s-%s\\\" % (set_type, i)\\n\\t\\t\\t\\ttext_inp = inps[1].strip()\\n\\t\\t\\t\\ttext_out = inps[2].strip()\\n\\t\\t\\t\\texamples.append(InputExample(guid=guid, text_inp=text_inp, text_out=text_out))\\n\\t\\t\\t\\t\\n\\t\\t\\t# Sort these out before returning\\n\\t\\t\\texamples = sorted(examples, key=sort_inp_len)\\n\\t\\t\\treturn examples\",\n \"def load_template_files(self):\\n templates = dict()\\n template_path = settings.CUSTOM_VERTO_TEMPLATES\\n templates.update(self.read_template_files(template_path))\\n if hasattr(self, \\\"extra_converter_templates_directory\\\"):\\n directory = self.extra_converter_templates_directory\\n template_path = os.path.join(template_path, directory)\\n templates.update(self.read_template_files(template_path))\\n return templates\",\n \"def add_questions(self):\\r\\n for question_id in self.question_ids:\\r\\n question = self.questions[question_id]\\r\\n self.frame_layout.addWidget(question.create_frame(), alignment=QtCore.Qt.AlignLeft)\",\n \"def store_all_content_info():\\n all_content = ContentQueue(None, None)\\n title = input('Title of your paragraph?')\\n content = input('Content of your paragraph (single line)')\\n images = image_option()\\n enqueue_content(all_content, title, content, images)\\n extra_content = add_paragraph_option()\\n if len(extra_content) == 0:\\n pass\\n else:\\n for i in range(len(extra_content)):\\n enqueue_content(all_content, extra_content[i].p_title, extra_content[i].content, extra_content[i].image)\\n\\n return all_content\",\n \"def get_questions():\\n count = 1\\n for i in range(6):\\n r = requests.get('https://api.stackexchange.com/2.2/questions?filter=withbody&site=eosio&pagesize=100&page={}'.format(count))\\n data = json.loads(r.text)\\n for item in data['items']:\\n own = item['owner']['user_id']\\n dsp = item['owner']['display_name']\\n try:\\n owner = User.objects.get(username=own, se_display_name=dsp)\\n except Exception:\\n owner = None\\n tags = item['tags']\\n ts = []\\n if owner:\\n for tag in tags:\\n t, created = Tag.objects.get_or_create(name=tag)\\n ts.append(t)\\n q = Question.objects.create(owner=owner, se_question_id=item['question_id'], title=item['title'], body=item[\\n 'body'], se_link=item['link'], is_answered=item['is_answered'], score=item['score'])\\n for t in ts:\\n q.tags.add(t)\\n q.save()\\n count += 1\\n print(count)\",\n \"def update_templates():\\n logging.info(\\\"Copying english po files to %s\\\" % POT_PATH)\\n\\n # post them to exposed URL\\n ensure_dir(POT_PATH)\\n shutil.copy(get_po_filepath(lang_code=\\\"en\\\", filename=\\\"django.po\\\"), os.path.join(POT_PATH, \\\"kalite.pot\\\"))\\n shutil.copy(get_po_filepath(lang_code=\\\"en\\\", filename=\\\"djangojs.po\\\"), os.path.join(POT_PATH, \\\"kalitejs.pot\\\"))\",\n \"def get_questions(self):\\n for q in self._objects[\\\"Questions\\\"]:\\n yield (q)\\n\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6505029","0.5937621","0.5928935","0.5910248","0.5856114","0.5806971","0.57281137","0.5690211","0.56250936","0.56000656","0.5596543","0.55873555","0.55826813","0.55461633","0.5500507","0.5485882","0.54667103","0.5460839","0.5444884","0.5443755","0.54412305","0.5422903","0.54213494","0.5399723","0.5399593","0.5393683","0.5388224","0.53484225","0.5347501","0.5347423","0.53455824","0.5338381","0.5316879","0.5297822","0.5266027","0.52501476","0.5230088","0.5219007","0.521243","0.5209299","0.52037364","0.51990074","0.51895434","0.51889455","0.51804686","0.51553005","0.5147337","0.51398176","0.5137626","0.5125319","0.5124468","0.5119253","0.5117956","0.5116353","0.5110384","0.510851","0.51028675","0.51018244","0.509358","0.50918967","0.5082731","0.5078954","0.5073078","0.5050458","0.5048134","0.5047543","0.50268066","0.50180364","0.5014857","0.5014117","0.50123394","0.50053793","0.50004405","0.49948663","0.49941754","0.4993892","0.49932083","0.4986167","0.49752814","0.4974121","0.49733952","0.49695665","0.49665368","0.4954062","0.49527","0.49465978","0.4943717","0.4942656","0.49290922","0.49273264","0.49264598","0.4926162","0.4918041","0.4917974","0.49137956","0.49124157","0.49124023","0.49044412","0.49036154","0.49019223"],"string":"[\n \"0.6505029\",\n \"0.5937621\",\n \"0.5928935\",\n \"0.5910248\",\n \"0.5856114\",\n \"0.5806971\",\n \"0.57281137\",\n \"0.5690211\",\n \"0.56250936\",\n \"0.56000656\",\n \"0.5596543\",\n \"0.55873555\",\n \"0.55826813\",\n \"0.55461633\",\n \"0.5500507\",\n \"0.5485882\",\n \"0.54667103\",\n \"0.5460839\",\n \"0.5444884\",\n \"0.5443755\",\n \"0.54412305\",\n \"0.5422903\",\n \"0.54213494\",\n \"0.5399723\",\n \"0.5399593\",\n \"0.5393683\",\n \"0.5388224\",\n \"0.53484225\",\n \"0.5347501\",\n \"0.5347423\",\n \"0.53455824\",\n \"0.5338381\",\n \"0.5316879\",\n \"0.5297822\",\n \"0.5266027\",\n \"0.52501476\",\n \"0.5230088\",\n \"0.5219007\",\n \"0.521243\",\n \"0.5209299\",\n \"0.52037364\",\n \"0.51990074\",\n \"0.51895434\",\n \"0.51889455\",\n \"0.51804686\",\n \"0.51553005\",\n \"0.5147337\",\n \"0.51398176\",\n \"0.5137626\",\n \"0.5125319\",\n \"0.5124468\",\n \"0.5119253\",\n \"0.5117956\",\n \"0.5116353\",\n \"0.5110384\",\n \"0.510851\",\n \"0.51028675\",\n \"0.51018244\",\n \"0.509358\",\n \"0.50918967\",\n \"0.5082731\",\n \"0.5078954\",\n \"0.5073078\",\n \"0.5050458\",\n \"0.5048134\",\n \"0.5047543\",\n \"0.50268066\",\n \"0.50180364\",\n \"0.5014857\",\n \"0.5014117\",\n \"0.50123394\",\n \"0.50053793\",\n \"0.50004405\",\n \"0.49948663\",\n \"0.49941754\",\n \"0.4993892\",\n \"0.49932083\",\n \"0.4986167\",\n \"0.49752814\",\n \"0.4974121\",\n \"0.49733952\",\n \"0.49695665\",\n \"0.49665368\",\n \"0.4954062\",\n \"0.49527\",\n \"0.49465978\",\n \"0.4943717\",\n \"0.4942656\",\n \"0.49290922\",\n \"0.49273264\",\n \"0.49264598\",\n \"0.4926162\",\n \"0.4918041\",\n \"0.4917974\",\n \"0.49137956\",\n \"0.49124157\",\n \"0.49124023\",\n \"0.49044412\",\n \"0.49036154\",\n \"0.49019223\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8599687"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77484,"cells":{"query":{"kind":"string","value":"Uses the list of properties to generate a random question using the text file of question templates. If there are no more unknown properties, then the program can be completed."},"document":{"kind":"string","value":"def generate_language(self, properties):\n\n drink_property = self.find_drink_property(properties)\n\n if drink_property:\n return self.generate_sentence(drink_property)\n else:\n return \"\""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def generate_question(self) -> None:\n topic = choice(list(self._topics.keys()))\n arrayList = await self.session.get(\"https://restcountries.eu/rest/v2\")\n arrayList = await arrayList.json() # get request to the country API\n countries = []\n \n for _ in range(4):\n country = choice(arrayList)\n del arrayList[arrayList.index(country)]\n countries.append(country)\n del arrayList\n \n country = choice(countries)\n del countries[countries.index(country)]\n self.question = f\"What is the {self._topics[topic]} of {country['name']}?\"\n self.correct_order = randint(0, 3)\n self.choices = [i[topic] for i in countries]\n self.choices.insert(self.correct_order, country[topic])\n del countries, topic","def main():\n\n args = get_args()\n text = args.text\n mutations = args.mutations\n random.seed(args.seed)\n alpha = ''.join(sorted(string.ascii_letters + string.punctuation))\n\n new_text_line = []\n if os.path.isfile(text):\n print('You said: ', end='')\n for line in open(text, 'rt'):\n new_text_line = list(line.rstrip())\n print(f'\"{line.rstrip()}\"')\n len_text = len(line.rstrip())\n num_mutations = round(len_text * mutations)\n for i in random.sample(range(len_text), num_mutations):\n # print(f'i = {i}, char = {line[i]}, index = {alpha.find(line[i])}')\n # list.index 함수로 색인 위치 찾으면 없을때 error 발생. 있는지 확인하고 쓰던가 아니면 find 함수 쓸 것!!\n # find 반환값 : 색인 위치. 찾을 수 없는 경우 -1 반환\n new_text_line[i] = random.choice(alpha.replace(line[i], '')) # replace 함수는 line[i] 에 해당하는 문자가 alpha에 '있으면' 해당 문자를 치환한다. 없어도 error 안 남.\n print(f'I heard : \"' + ''.join(new_text_line)+ '\"')\n else:\n print(f'You said: \"{text}\"')\n new_text_line = list(text)\n len_text = len(text)\n num_mutations = round(len_text * mutations)\n for i in random.sample(range(len_text), num_mutations):\n new_text_line[i] = random.choice(alpha.replace(text[i], ''))\n print(f'I heard : \"' + ''.join(new_text_line)+ '\"')","def poem_generation(magnet,topic):\r\n e = inflect.engine()\r\n antonyms = []\r\n synonyms = []\r\n poem = \"\"\r\n verb = \"\"\r\n # plural\r\n if (e.singular_noun(topic) is False):\r\n verb = \"is\"\r\n else:\r\n verb = \"are\"\r\n \r\n for syn in wordnet.synsets(topic):\r\n for l in syn.lemmas():\r\n synonyms.append(l.name())\r\n if l.antonyms():\r\n antonyms.append(l.antonyms()[0].name())\r\n if (len(set(antonyms)) < 1):\r\n for syn in wordnet.synsets(RAND_LIST[random.randint(0,len(RAND_LIST)-1)]):\r\n for l in syn.lemmas():\r\n synonyms.append(l.name())\r\n if l.antonyms():\r\n antonyms.append(l.antonyms()[0].name())\r\n topic = topic.capitalize()\r\n for i in range(0,random.randint(5,15)):\r\n verse = random.randint(0,6)\r\n question = random.randint(0,6)\r\n # structure\r\n # antonyms\r\n if (verse < 2) and len(antonyms) > 0:\r\n ant_magnet = metaphor_magnet(antonyms[random.randint(0,len(antonyms)-1)])\r\n choice = random.randint(0,len(ant_magnet)-1)\r\n detail = ant_magnet[choice].split(\":\")\r\n if (question < 2):\r\n index = random.randint(0,len(QUESTION)-1)\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" not like an \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" not like a \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += topic + \" \" + verb + \" not like an \" + detail[0] + \" \" + detail[1] + \".\\n\" \r\n else:\r\n poem += topic + \" \" + verb + \" not like a \" + detail[0] + \" \" + detail[1] + \".\\n\" \r\n \r\n else:\r\n choice = random.randint(0,len(magnet)-1)\r\n detail = magnet[choice].split(\":\")\r\n if (question < 2):\r\n index = random.randint(0,len(QUESTION)-1)\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" like an \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" like a \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += topic + \" \" + verb + \" like an \" + detail[0] + \" \" + detail[1] + \"\\n\" \r\n else:\r\n poem += topic + \" \" + verb + \" like a \" + detail[0] + \" \" + detail[1] + \"\\n\" \r\n \r\n return poem","def main():\n\n args = get_args()\n random.seed(args.seed)\n wod = []\n\n for name, low, high in read_csv(args.file):\n reps = random.randint(low, high)\n if args.easy:\n reps = int(reps / 2)\n wod.append((name, reps))\n\n wod = random.sample(wod, k=args.num_exercises)\n print(tabulate(wod, headers=('Exercise', 'Reps')))","def randomPostToScreen():\n\ttxt_files = filter(lambda x: x.endswith('.txt'), os.listdir(bookpath))\n\tbook = random.choice(txt_files)\n\tprint generatePost(os.path.join(bookpath,book))\n\tprint '----------------------------'","def start_test(n_questions, questions_file):\r\n\tfile = open(\"questions/\"+questions_file, \"r\")\r\n\tc = 0\r\n\tcorrect = []\r\n\twrong = []\r\n\r\n\tfor i in range(3325):\r\n\t\tline = file.readline()\r\n\t\t\r\n\t\tif \"Answer: \" in line:\r\n\t\t\tc += 1\r\n\t\t\tif len(str(line[8:]).strip()) > 1:\r\n\t\t\t\tprint(\"\\nMultiple Choice\")\r\n\t\t\telse:\r\n\t\t\t\tprint(\"\\nSingle Choice\")\r\n\t\t\tansw = str(input(\"\\nAnswer: \")).upper()\r\n\t\t\tif answ == str(line[8:]).strip():\r\n\t\t\t\tcorrect.append(c)\r\n\t\t\telse:\r\n\t\t\t\twrong.append(c)\r\n\t\t\t\tprint(\"Correct answer: \", str(line[8:]))\r\n\t\t\tprint(\"\\n\",\"-\"*10, \"Correct: \", len(correct), \"-\"*10, \"\\n\", \"-\"*10, \"Wrong: \", len(wrong), \"-\"*10 )\r\n\t\t\tcontinue\r\n\r\n\t\tif c == n_questions:\r\n\t\t\tbreak\r\n\t\tprint(line)\r\n\r\n\tdict_result = {\"Corrects\":correct,\r\n\t\t\t\t\t\"Wrongs\":wrong,\r\n\t\t\t\t\t\"n_questions\":n_questions}\r\n\r\n\tfile.close()\r\n\r\n\treturn dict_result","def generateISTemplates(course, wordlist, charLevels, translateDict, similarCharsList, supportsUppercase):\n\n levelsDict= {}\n stageByNumber = 1 # represents key of wordsByLevelDict to get wordlist value\n wordsByLengthDict = {}\n wordsByLevelDict = {}\n swappedTranslateDict = dict((v,k) for k,v in translateDict.iteritems()) # Keys are latin, values non-latin\n\n for word in wordlist:\n wordsByLengthDict = wordsByLength(word, wordsByLengthDict)\n wordsByLevelDict = wordsByLevel(word, charLevels, wordsByLevelDict)\n\n for stage in charLevels:\n\n if not wordsByLevelDict.has_key(stageByNumber): # Skip the uppercase stages in charLevels\n stageByNumber += 1\n continue\n\n charLevelsWordList = wordsByLevelDict[stageByNumber] # contains list of words suitable for current stage\n selectedOptions = makeWordOptions(charLevelsWordList, similarCharsList) # List of 3 sublists containing options or None\n\n if selectedOptions is not None:\n\n for i in range(len(selectedOptions)):\n generateLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\".qdef\")\n generateLevelAlternative(selectedOptions[i], translateDict, similarCharsList, name+str(stageByNumber)+\".qdef\")\n\n if supportsUppercase:\n generateUppercaseLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\".qdef\")\n\n generateEndOfQuiz(name+str(stageByNumber)+\".qdef\")\n stageByNumber += 1\n\n # generate practice levels\n for i in range(30):\n selectedWords = wordsByLengthDict[random.choice(wordsByLengthDict.keys())]\n if len(selectedWords) >= 3:\n quizRandomSelection = random.sample(selectedWords,3) # take random 4 items from list\n\n for word in quizRandomSelection:\n trickWord = findAndReplaceSimilarChar(word, quizRandomSelection, similarCharsList)\n if trickWord:\n quizRandomSelection.append(trickWord)\n break\n\n generateLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\"Practice.qdef\")\n generateLevelAlternative(quizRandomSelection, translateDict, similarCharsList, name+\"Practice.qdef\")\n\n if supportsUppercase:\n generateUppercaseLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\"Practice.qdef\")\n\n generateEndOfQuiz(name+\"Practice.qdef\")\n\n print \"New MUNI ROPOT templates has been created in: \" + target","def create_undefined_question(query_data: dict, property_name: str):\n \n correct_subject = query_data[0][\"personLabel\"][\"value\"]\n del query_data[0][\"personLabel\"]\n\n correct_object = query_data[0][\"valueLabel\"][\"value\"]\n del query_data[0][\"valueLabel\"]\n\n alternatives = []\n for element in query_data[0]:\n alternatives.append(query_data[0][element][\"value\"])\n \n question_text = f\"{correct_subject} {property_name} _______?\"\n\n print(question_text)\n print(\"correct:\", correct_object)\n print(\"alternatives:\", alternatives)","def chooseSpecialties(self):\n #specialties = {}\n def createSpecialties(fileName):\n \"\"\" (str) -> None\n Helper function to create specialties dict from file.\n \"\"\"\n specialtyFile = open(fileName,'r')\n \n current_specialty = ''\n \n for line in specialtyFile:\n #If there is no text, go to next line\n if line == \"\\n\":\n pass\n #If line starts with \"~~\", set current_specialty to the specialty\n #on that line, and create a new key in the specialties dict and\n #set its value to an empty dict. Then input the first item in the\n #new dict as the specialty's decription\n elif line[:2] == \"~~\":\n current_specialty = line[2:line.index(\"-\")-1]\n specialties[current_specialty] = {}\n specialties[current_specialty]['story'] = line[line.index(\"-\")+1:-1]\n #If the line starts with \"Level,\" creates entries for which feats\n #the character will get at levels 1,3,6,9\n elif line[:5] == \"Level\":\n specialties[current_specialty][int(line[6:7])] = line[line.index(\":\")+2:-1]\n specialtyFile.close()\n\n #Create the specialties dict\n createSpecialties('Specialties.txt')\n\n\n #Make list of specialties\n specialties_list = []\n for i in specialties:\n specialties_list.append(i)\n specialties_list.sort()\n #Ask user which specialty they'd like, give them recommended specialty for their class\n class_recommendation = {'Barbarian':'reaper',\n 'Cleric':'mystical healer',\n 'Druid':'hedge magician',\n 'Fighter':'reaper',\n 'Monk':'skirmisher',\n 'Paladin':'defender',\n 'Ranger':'sharpshooter',\n 'Rogue':'specialist',\n 'Wizard':'hedge magician'\n }\n spec_choice = raw_input(\"Which specialty would you like?\\n\"\\\n \"Choose from: \\n\"+str(specialties_list)+\"\\n\\n\"\\\n \"The recommended specialty for your \"+self.classType+\" is \"+class_recommendation[self.classType].title()+\": \").title()\n print\n while spec_choice not in specialties_list:\n spec_choice = raw_input(\"Which specialty would you like?\\n\"\\\n \"Choose from: \\n\"+str(specialties_list)+\"\\n\\n\"\\\n \"The recommended specialty for your \"+self.classType+\" is \"+class_recommendation[self.classType].title()+\": \").title()\n print\n \n self.specialty = spec_choice\n self.specialtyStory = specialties[self.specialty]['story']","def gen_questions(self, number_of_questions):","def genQuestion(line):\r\n if type(line) is str: # If the passed variable is of type string.\r\n line = TextBlob(line) # Create object of type textblob.blob.TextBlob\r\n \r\n bucket = {} # Create an empty dictionary\r\n \r\n subject_list = []\r\n question_subject=\"\"\r\n answer_subject=\"\"\r\n for i,j in enumerate(line.tags): # line.tags are the parts-of-speach in English \r\n question_subject += j[0] + \" \"\r\n if (j[1] == \"NNP\" or j[1] == \"NNS\"): \r\n subject_list.append(j[0])\r\n if j[1] not in bucket:\r\n bucket[j[1]] = i # Add all tags to the dictionary or bucket variable\r\n \r\n if len(subject_list):\r\n random_subject_val = random.randint(0, len(subject_list)-1)\r\n question_subject = question_subject.replace(str(subject_list[random_subject_val]), \"______\")\r\n answer_subject = str(subject_list[random_subject_val])\r\n \r\n return question_subject, answer_subject","def main():\n\targs = getArgs()\n\tid_question = args.id_question\n\tlang = args.language\n\tdir_cp = None\n\twith open('config.json') as json_file:\n\t\tconfig_data = json.load(json_file)\n\t\tdir_cp = config_data['dir_cp']\n\n\t\n\t\n\t# sample_io = GetData(args.id_question).get_uri_io_sample()\n\ttemplate = FileUtil(id_question, dir_cp['path'], lang)\n\ttemplate.write_template()\n\t# print(sample_io)","def test_call_with_properties_file(self):\r\n id_to_taxonomy_file = NamedTemporaryFile(\r\n prefix='RdpTaxonAssignerTest_', suffix='.txt')\r\n id_to_taxonomy_file.write(rdp_id_to_taxonomy2)\r\n id_to_taxonomy_file.seek(0)\r\n\r\n app1 = RdpTaxonAssigner({\r\n 'id_to_taxonomy_fp': id_to_taxonomy_file.name,\r\n 'reference_sequences_fp': self.reference_seqs_file.name,\r\n })\r\n taxonomy_file, training_seqs_file = app1._generate_training_files()\r\n\r\n training_dir = mkdtemp(prefix='RdpTrainer_')\r\n training_results = train_rdp_classifier(\r\n training_seqs_file, taxonomy_file, training_dir)\r\n\r\n training_data_fp = training_results['properties'].name\r\n min_confidence = 0.80\r\n app2 = RdpTaxonAssigner({\r\n 'training_data_properties_fp': training_data_fp,\r\n 'Confidence': min_confidence,\r\n })\r\n\r\n expected = rdp_trained_test2_expected_dict\r\n\r\n # Since there is some variation in the assignments, run\r\n # 10 trials and make sure we get the expected result at least once\r\n num_trials = 10\r\n num_seqs = len(expected)\r\n seq_ids = expected.keys()\r\n assignment_comp_results = [False] * num_seqs\r\n expected_assignment_comp_results = [True] * num_seqs\r\n\r\n for i in range(num_trials):\r\n actual = app2(self.tmp_seq_filepath)\r\n # seq ids are the same, and all input sequences get a result\r\n self.assertEqual(actual.keys(), expected.keys())\r\n for j, seq_id in enumerate(seq_ids):\r\n # confidence is above threshold\r\n self.assertTrue(actual[seq_id][1] >= min_confidence)\r\n # confidence roughly matches expected\r\n assert_allclose(actual[seq_id][1], expected[seq_id][1],\r\n atol=0.1)\r\n # check if the assignment is correct -- this must happen\r\n # at least once per seq_id for the test to pass\r\n if actual[seq_id][0] == expected[seq_id][0]:\r\n assignment_comp_results[j] = True\r\n if assignment_comp_results == expected_assignment_comp_results:\r\n # break once we've seen a correct assignment for each seq\r\n break\r\n\r\n self.assertEqual(\r\n assignment_comp_results,\r\n expected_assignment_comp_results,\r\n \"Taxonomic assignments never correct in %d trials.\" % num_trials)","def choose_problems():\n\n problems = make_dict()\n\n res = random.sample(list(problems.items()), k = 3)\n # res2 = random.choice(list(problems.items()))\n # res3 = random.choice(list(problems.items()))\n\n res0 = res[0]\n res1 = res[1]\n res2 = res[2]\n\n return res0, res1, res2","def question_generator(self):\n self.status_conv = 'yes_no_question_asked'\n questions = config.questions\n if not self.voyage.get('voyageurs') and 'voyageur_add' not in self.infos_needed:\n self.infos_needed.append('voyageur_add')\n if self.infos_needed:\n if self.is_hotel_needed() and 'hotel' not in self.infos_needed and 'hotel' not in self.voyage:\n self.infos_needed.insert(1, 'hotel')\n self.hotel_asked = True\n key = self.infos_needed[0]\n self.info_asked = key\n return questions[key]\n else :\n self.status_conv = 'confirmation_asked'\n return self.conv_recap()","def generate_suggestion():\n age = age_input(\"Enter age: \")\n if age <= 5:\n age_5_younger = open_and_read_file(\"age_5_younger.txt\")\n print \"Why don't you {}?\".format(random.choice(age_5_younger))\n print\n\n elif (age > 5) and (age < 20):\n age_5_to_20 = open_and_read_file(\"age_5_to_20.txt\")\n print \"Why don't you {}?\".format(random.choice(age_5_to_20))\n print\n\n elif age >= 20:\n age_20_plus = open_and_read_file(\"age_20_plus.txt\")\n print \"Why don't you {}?\".format(random.choice(age_20_plus))\n print","def choose_properties(self, probability, minimize_ref_properties):\n assert self.property_names, \"Property group must not be empty!\"\n\n non_ref_prop_names = [\n name for name in self.property_names\n if not _is_ref_prop(name)\n ]\n\n if self.constraint_type == self.ONE:\n if minimize_ref_properties and non_ref_prop_names:\n chosen_props = [random.choice(non_ref_prop_names)]\n else:\n # Or should I have just stored the names as a list...?\n chosen_props = [\n stix2generator.utils.rand_iterable(self.property_names)\n ]\n\n elif self.constraint_type == self.AT_LEAST_ONE:\n\n # Choose one required prop; make the rest optional. If minimizing\n # reference properties and all are reference properties, we should\n # behave like \"one\" and choose exactly one. Choosing more would\n # not be \"minimal\".\n if minimize_ref_properties and non_ref_prop_names:\n required_prop = random.choice(non_ref_prop_names)\n else:\n required_prop = stix2generator.utils.rand_iterable(\n self.property_names\n )\n\n chosen_props = [required_prop]\n candidate_other_props = \\\n non_ref_prop_names if minimize_ref_properties \\\n else self.property_names\n\n chosen_props.extend(\n prop for prop in candidate_other_props\n if prop != required_prop and random.random() < probability\n )\n\n else:\n # ALL\n chosen_props = list(self.property_names)\n\n return chosen_props","def get_question():\n last_line = '\\n'\n question = \"\"\n\n print(\"Type in your question here:\")\n while last_line != \"\":\n current_line = input()\n if current_line == \"\":\n break\n question += current_line + \"\\n\"\n\n variables = {}\n\n # Special syntax variables\n rand = random.randint\n\n array_of_lines = question.split(\"\\n\")\n\n question_is_present = False\n\n for line in array_of_lines:\n if \"QUESTION\" in line:\n question_is_present = True\n break\n\n if question_is_present:\n variable_lines = []\n secret_lines = []\n question_lines = []\n\n for index in range(len(array_of_lines)):\n if array_of_lines[index] == \"SECRET\":\n secret_index = index\n if array_of_lines[index] == \"QUESTION\":\n question_index = index\n break\n\n # Adds variables to variable_lines\n for index in range(secret_index):\n variable_lines.append(array_of_lines[index])\n\n # Adds answer to secret_lines\n for index in range(secret_index + 1, question_index):\n secret_lines.append(array_of_lines[index])\n\n # Adds question lines to question_lines\n for index in range(question_index + 1, len(array_of_lines)):\n question_lines.append(array_of_lines[index])\n\n for line in variable_lines:\n variable, value = line.split(\"=\")\n variables[\"$\" + variable] = str(eval(value)).replace(\"_\", \" \")\n\n for index in range(len(secret_lines)):\n secret_lines[index] = science_utils.multiple_replace(line, variables)\n variable, value = secret_lines[index].split(\"=\")\n variables[\"answer\"] = str(eval(value)).replace(\"_\", \" \")\n\n for line in question_lines:\n question_lines[question_lines.index(line)] = science_utils.multiple_replace(line, variables)\n\n return question_lines, variables[\"answer\"]\n\n # This is the backup method, for when there is no QUESTION line found\n else:\n # Calculate values\n for word in array_of_lines:\n if \"=\" in word:\n variable, value = word.split(\"=\")\n variables[\"$\" + variable] = str(eval(value)).replace(\"_\", \" \")\n\n for words in array_of_lines:\n array_of_lines[array_of_lines.index(words)] = science_utils.multiple_replace(words, variables)\n\n to_return = []\n for line in array_of_lines:\n if \"=\" not in line:\n to_return.append(line)","def question_finder(self):\r\n with open('Questions.csv', mode='r') as csv_file:\r\n csv_reader = csv.DictReader(csv_file)\r\n for row in csv_reader:\r\n\r\n my_tuple = finding_best_match(row, \"Questions\", self.question.split(), 2)\r\n self.percentage_list.append(my_tuple)\r\n\r\n # Checks if a whole sentence is in the asked question\r\n if question_match(join_string(self.question.split()), join_string(row[\"Questions\"].split())):\r\n self.question_match_list.append(my_tuple)\r\n\r\n \"\"\"\r\n Gives user the most appropriate answer depending on the user's question\r\n And depending on the user's emotion\r\n \"\"\"\r\n # Find Random Index (This will later be changed)\r\n random_index = random.randint(2, len(max(self.percentage_list)) - 1)\r\n\r\n if max(self.percentage_list)[0] >= 60: # The question needs to be at least 65% right\r\n self.answer = max(self.percentage_list)[random_index]\r\n print(self.answer)\r\n\r\n elif len(self.question_match_list) > 0: # Or it needs to contain a specific sentence\r\n length_list = []\r\n for i in self.question_match_list:\r\n length_list.append(len(i[1]))\r\n self.answer = self.question_match_list[length_list.index(max(length_list))][random_index]\r\n print(self.answer)\r\n\r\n elif max(self.percentage_list)[0] >= 40:\r\n print(\"Random responses\")\r\n\r\n with open('Random Responses.csv') as f:\r\n max_n = sum(1 for _line in f)\r\n\r\n random_int = random.randint(1, max_n -1)\r\n with open('Random Responses.csv', mode='r') as csv_file:\r\n csv_reader = csv.DictReader(csv_file)\r\n for i in csv_reader:\r\n self.counter += 1\r\n if random_int == self.counter:\r\n self.answer = (i[\"Responses\"])\r\n print(self.answer)\r\n else:\r\n print(\"I am sorry. I don't understand this question\")\r\n\r\n #put answer and question in the file\r\n print()\r\n print(\"if its wrong please write an answer then ENTER\")\r\n print(\"or press ENTER to skip\")\r\n better_questions_answers(self.question)","def main(word_count=2, use_caps=False, use_leet=False, caps_percent=25, leet_percent=20):\n\n phrase = get_phrase(word_count)\n\n if use_caps:\n phrase = random_caps(phrase, caps_percent)\n\n if use_leet:\n phrase = random_characters(phrase, leet_percent)\n\n print(phrase)","def generate_question_and_answer(): # noqa: WPS210\n start_number = random.randint(1, 100)\n progression_step = random.randint(1, 10)\n progression_length = random.randint(5, 10)\n progression = generate_progression(\n start_number, progression_step, progression_length,\n )\n return hide_number(progression)","def generate(simulator, p, starts, goals, environment, r):\n num_gen_humans = min(len(starts), len(goals))\n print(\"Generating auto humans:\", num_gen_humans)\n from agents.humans.human_configs import HumanConfigs\n for i in range(num_gen_humans):\n start_config = generate_config_from_pos_3(starts[i])\n goal_config = generate_config_from_pos_3(goals[i])\n start_goal_configs = HumanConfigs(start_config, goal_config)\n human_i_name = \"auto_%04d\" % i\n # Generates a random human from the environment\n new_human_i = Human.generate_human_with_configs(\n start_goal_configs,\n generate_appearance=p.render_3D,\n name=human_i_name\n )\n # update renderer and get human traversible if it exists\n if p.render_3D:\n r.add_human(new_human_i)\n environment[\"human_traversible\"] = \\\n np.array(r.get_human_traversible())\n\n # Input human fields into simulator\n simulator.add_agent(new_human_i)","def make_final_text(text: str) -> str:\n baseline_text_template = \"\"\"Use this thread to discuss anything (within the rules of the subreddit):\n\n* What you didn't think was worthy of its own post\n* What club game you're most excited for\n* Where you're staying to watch a friendly\n* Which players should be called in\n{}\n* What the mods told you to re-post here\n* Etc\n\n### Schedules\n{}\n\"\"\"\n\n with open('random_dumb_questions.txt', 'r') as files:\n list_of_questions = files.readlines()\n\n question = list_of_questions[randint(0, len(list_of_questions))].replace('\\n', '')\n\n return baseline_text_template.format(question, text)","def load_templates(self):\n\n self.templates = []\n\n if os.path.exists(\"question_templates.txt\"):\n for line in open(\"question_templates.txt\", \"r\"):\n self.templates.append(line.replace(\"\\n\", \"\"))","def generateRandomInput(filename, num_people, travel_db):\n import random\n routes = []\n for i in range(num_people):\n route = travel_db.randomRoute()\n route.insert(0,\"Person \" + str(i)) # Add a name for each route.\n routes.append(route)\n if FileHandler.writeRoutesCSV(filename,routes): # If it's successful writing the file\n print(\"File {0} created successfully with {1} people.\".format(filename, num_people))\n else:\n print(\"File {0} could not be created.\".format(filename))","def generate(random, pid, autogen_tools, n):\n\n generator_path = autogen_tools.get_directory(__file__)\n\n template_path = path.join(generator_path, \"code.txt.template\")\n rendered_template_path = path.join(generator_path, \"code.txt\")\n\n autogen_tools.replace_source_tokens(\n template_path,\n {\"flag\": gen_code(n, \"Aviation House\")},\n rendered_template_path\n )\n\n code_link = autogen_tools.generate_resource_link(pid, \"code.txt\", title=\"Encrypted file\")\n\n return {\n \"resource_files\": {\n \"public\": [\n (rendered_template_path, \"code.txt\"),\n ],\n },\n \"static_files\": {\n },\n \"problem_updates\": {\n \"description\": \"<p>We've updated the system to AES. We heard that this is military grade encryption so that should fix everything</p><p>The team have stored the password in %s. Bet you can't get into it</p>\" % code_link\n }\n }","def read_properties(lines, options=None, scr=None):\n\n reader = None\n read_files = True\n\n if options is None:\n reader = read_properties_opt\n\n elif \"vfukui\" in options:\n reader = read_properties_fukui\n read_files = False\n\n elif \"vomega\" in options:\n reader = read_properties_omega\n read_files = False\n\n elif \"opt\" in options or \"ohess\" in options:\n reader = read_properties_opt\n\n else:\n reader = read_properties_sp\n\n properties = reader(lines)\n\n if scr is not None and read_files:\n # Parse file properties\n charges = get_mulliken_charges(scr=scr)\n bonds, bondorders = get_wbo(scr=scr)\n\n properties[\"mulliken_charges\"] = charges\n properties.update(get_cm5_charges(lines)) # Can return {} if not GFN1\n properties[\"bonds\"] = bonds\n properties[\"bondorders\"] = bondorders\n\n if \"vibspectrum\" in os.listdir(scr):\n properties[\"frequencies\"] = get_frequencies(scr=scr)\n\n return properties","def picksomechoices(question, answer):\n \"\"\" because of the way dict() works all 4 choices will be unique \"\"\"\n choices = dict()\n choices[question] = answer\n for choice in random.sample(nlist, 10):\n choices[choice[0]] = choice[1]\n if len(choices.keys()) > 3:\n break\n\n return choices","def picksomechoices(question, answer):\n \"\"\" because of the way dict() works all 4 choices will be unique \"\"\"\n choices = dict()\n choices[question] = answer\n for choice in random.sample(nlist, 10):\n choices[choice[0]] = choice[1]\n if len(choices.keys()) > 3:\n break\n\n return choices","def picksomechoices(question, answer):\n \"\"\" because of the way dict() works all 4 choices will be unique \"\"\"\n choices = dict()\n choices[question] = answer\n for choice in random.sample(nlist, 10):\n choices[choice[0]] = choice[1]\n if len(choices.keys()) > 3:\n break\n\n return choices","def picksomechoices(question, answer):\n \"\"\" because of the way dict() works all 4 choices will be unique \"\"\"\n choices = dict()\n choices[question] = answer\n for choice in random.sample(nlist, 10):\n choices[choice[0]] = choice[1]\n if len(choices.keys()) > 3:\n break\n\n return choices","def test_properties(self):\n input_file = self.copy_and_mark_for_cleanup(\"Fasta/f002\")\n\n cline = XXmotifCommandline(outdir=self.out_dir, seqfile=input_file)\n\n cline.revcomp = True\n cline.pseudo = 20\n cline.startmotif = \"ACGGGT\"\n\n self.standard_test_procedure(cline)","def notAFan_questions(user):\n questions = {\n 1: \"GBB: How old are you? \",\n 2: \"GBB: What do you like to do in your free time? \",\n 3: \"GBB: What is your ethnicity? \",\n 4: \"GBB: What did you eat for breakfast? \",\n 5: \"GBB: Are you an early bird or a night owl? \",\n 6: \"GBB: Do you like football? \"\n }\n\n while True:\n num = random.randint(1, 6)\n answered = user['personal questions asked']\n if num not in answered:\n user['personal questions asked'].append(num)\n return questions[num]\n if len(answered) == len(questions.keys()):\n return \"GBB: Looks like I know you so well that I don't even need to ask you a question! Type anything to proceed.\"","def gen_task(self, q_type, q_option):\n # check what it the task about\n if q_type == \"write\":\n question = [\"Please, let me know what you most like on \",\n \"Please, write a short sentence about \",\n \"Please, write the first experience you had with \"]\n select_question = random.choice(question)\n\n elif q_type == \"draw\":\n draw = [\n \"In a separeted piece of paper, please draw your favorite \",\n \"In a separeted piece of paper, please draw the first thing it comes to your mind when you think about \"\n ]\n select_question = random.choice(draw)\n\n # output the task based on the option input\n select_option = q_option\n\n # join everything\n task = select_question + select_option\n\n return task","def randomHelmet():\n return random.choice(HELMETS)","def _load_answers(self, answer_list):\n # We are given a list of words that indicate what the answer\n # options are. By convention, the first word is probably the\n # correct answer; the others are incorrect answers. However,\n # we won't set this now because this convention may not hold.\n # We expect the SET_CORRECT OpalCommand to be used to set\n # which answers are correct or incorrect.\n # split the list of answers on commas.\n answers = answer_list.strip().split(',')\n\n # Shuffle answers to display them in a random order.\n random.shuffle(answers)\n\n # Load in the graphic for each answer.\n for answer in answers:\n toload = {}\n # Remove whitespace from name before using it.\n toload[\"name\"] = answer.strip()\n toload[\"tag\"] = \"PlayObject\"\n toload[\"slot\"] = answers.index(answer) + 1\n toload[\"draggable\"] = False\n toload[\"isAnswerSlot\"] = True\n self._ros_node.send_opal_command(\"LOAD_OBJECT\", json.dumps(toload))","def random_text(random_file=None):\n if random_file is None:\n random_file = os.path.join(\n os.path.dirname(os.path.dirname(__file__)), \"data\", \"random.txt\"\n )\n with open(random_file) as file_used:\n return random.choice(list(file_used))","def boolean():\r\n i2 = question_amount_1.get()\r\n check = (only_int(i2))\r\n if not check:\r\n num_check(820, 210, 0)\r\n else:\r\n for it in range(5):\r\n variat(it)\r\n name = 'Операции булевой алгебры:'\r\n input_file_docx(name, name)\r\n for p in range(int(question_amount_1.get())):\r\n spechotr = ''\r\n sgnA = random.randint(0, 1)\r\n print()\r\n sgnB = random.randint(0, 1)\r\n print (\"A =\", sgnA, \"B =\", sgnB)\r\n spech = ''\r\n strv = ''\r\n ir = 1\r\n while ir < 4:\r\n nch = random.randint(1, 4)\r\n if nch == 1:\r\n spech = spech + \"¬A∨B\"\r\n strv = strv + \"not sgnA or sgnB \"\r\n if nch == 2:\r\n strv = strv + \"not sgnB or sgnA\"\r\n spech = spech + \"¬B∨A\"\r\n if nch == 3:\r\n strv = strv + \"(sgnB or sgnA)\"\r\n spech = spech + \"(B∨A)\"\r\n if nch == 4:\r\n strv = strv + \"(sgnA and sgnB)\"\r\n spech = spech + \"(A∧B)\"\r\n if ir < 3:\r\n strv = strv + \" and \"\r\n spech = spech + \"∧\"\r\n ir = ir + 1\r\n task = f'{p + 1}. A = {sgnA}, B = {sgnB} Какое значение принимает функция {spech} ?'\r\n rsh = eval(strv)\r\n answer = task + f' \\n Oтвет: {rsh}'\r\n input_file_docx(task, answer)\r\n lbvi = Label(window1, font=(\"Arial Bold\", 14), text=\"Выполнено \")\r\n lbvi.place(x=800, y=210)\r\n return","def load_test_troublemaker():\n return Troublemaker(\n \"Karen\",\n 700,\n 101892099,\n \"Scotia Bank\",\n 2000.00,\n [\n Budget(\"Games and Entertainment\", 200),\n Budget(\"Clothing and Accessories\", 200),\n Budget(\"Eating Out\", 200),\n Budget(\"Miscellaneous\", 200)\n ]\n )","def tst_random_set():\n final_wave, final_spec, final_z = desi_qso_templates(\n outfil='test_random_set.fits', N_perz=100, seed=12345)","def test_case_3(self):\n config_generator = Genesis()\n\n data = open_json_file('r1.json')\n\n task_1 = Task.new(data=data, name=\"R1_BASE_CONFIG\")\n task_2 = Task.new(data=data, name=\"R1_BASE_CONFIG_2\")\n\n config_generator.add_task(task_1)\n config_generator.add_task(task_2)\n\n results = config_generator.generate()\n self.assertIsInstance(results, dict)\n\n for task in config_generator.tasks:\n self.assertTrue(task.is_complete)\n self.assertIsInstance(task.rendered_data, str)","def test_generate_all_training(self):\n facade = ChatetteFacade.get_or_create()\n\n input_dir_path = \"tests/system-testing/inputs/generate-all/\"\n input_filenames = [\n \"simplest.chatette\", \"only-words.chatette\",\n \"words-and-groups.chatette\", \"alias.chatette\", \"include.chatette\",\n \"slot.chatette\", \"slotrolegroup.chatette\"\n ]\n for filename in input_filenames:\n file_path = os.path.join(input_dir_path, filename)\n facade.run(file_path)\n if not TestSystem.check_no_duplicates(facade.train_examples):\n pytest.fail(\n \"Some examples were generated several times \" +\n \"when dealing with file '\" + filename + \"'.\\nGenerated: \" + \\\n str(facade.train_examples)\n )\n legal_examples = TestSystem.get_legal_examples(file_path)\n for ex in facade.train_examples:\n formatted_ex = {\"intent\": ex.intent_name, \"text\": ex.text}\n if formatted_ex not in legal_examples:\n pytest.fail(\n str(formatted_ex) + \" is not a legal example for '\" + \\\n file_path + \"'\"\n )\n if len(legal_examples) != len(facade.train_examples):\n training_texts = [ex.text for ex in facade.train_examples]\n for legal_ex in legal_examples:\n if legal_ex[\"text\"] not in training_texts:\n pytest.fail(\n \"Example '\" + legal_ex[\"text\"] + \\\n \"' was not generated.\"\n )\n pytest.fail(\n \"An unknown example was not generated (\" + \\\n str(len(facade.train_examples)) + \\\n \" generated instead of \" + str(len(legal_examples)) + \\\n \").\\nGenerated: \" + str(facade.train_examples)\n )\n legal_syn = TestSystem.get_legal_synonyms(file_path)\n if legal_syn is not None:\n synonyms = AST.get_or_create().get_entities_synonyms()\n for key in synonyms:\n if key not in legal_syn:\n pytest.fail(\n \"'\" + key + \"' shouldn't have any synonyms.\"\n )\n for syn in synonyms[key]:\n if syn not in legal_syn[key]:\n pytest.fail(\n \"'\" + syn + \"' shouldn't be a synonym of '\" + \\\n key + \"'\"\n )","def generate_quest(self):\n\n if odds(3):\n\n quest_items = add_dicts_together(items[\"master\"], items[self.p.square.square_type])\n quest_item = random.choice(list(quest_items.keys()))\n\n i = Item(quest_item, 0, **quest_items[quest_item])\n self.inventory.append(i)\n\n quantity = {'super rare': '1',\n 'rare': '2',\n 'uncommon': '3',\n 'common': '6',\n 'super common': '15'}\n q = quantity[i.rarity]\n\n self.quest = i, int(q), f\"{self.p.name}, if you bring \" \\\n f\"me {q} {i.plural if int(q) > 1 else remove_little_words(i.name)}, \" \\\n f\"I will teach you a valuable skill.\"\n return\n elif odds(5):\n mobs = []\n for biome, building in buildings.items():\n for b, attributes in building.items():\n if attributes.get('mobs'):\n for k in attributes['mobs'].keys():\n mobs.append(k)\n for biome, mob in wild_mobs.items():\n for k in mob.keys():\n mobs.append(k)\n target = f\"{mobs[random.randint(0, len(mobs)-1)]} named {names[random.randint(0, len(names)-1)]}\"\n print(f\"Well, we'll keep this off the record, but I can arrange for some money to find its way \"\n f\"into your account if you make {colored(target, 'yellow')} disappear, if you know what I mean...\")\n self.p.hit_list.append(target)\n return False\n\n else:\n return None","def pirate():\n \n from random import choice\n d = {\n \"sir\": \"matey\", \"hotel\": \"fleabag inn\", \"student\": \"swabbie\",\n \"boy\": \"matey\", \"girl\": \"wench\", \"professor\": \"foul blaggart\",\n \"restaurant\": \"galley\", \"your\": \"ye\", \"excuse\": \"arr\", \"you\": \"ye\",\n \"students\": \"swabbies\", \"are\": \"be\", \"lawyer\": \"foul blaggart\",\n \"the\": \"th'\", \"restroom\": \"head\", \"my\": \"me\", \"hello\": \"avast\",\n \"is\": \"be\", \"man\": \"scurvy dog\", \"hey\": \"avast\", \"pirate\": \"scurvy pirate\",\n \"idiot\": \"flunder\", \"young\": \"ye\", \"the\": \"t'\", \"suck\": \"blow down\",\n \"fall\": \"hade\", \"happens\": \"be happening\", \"death\": \"Davy Jones' treasure chest\",\n \"always\": \"ever\", \"you're\": \"ye're\", \"girlfriend\": \"Lassie-Lucy\",\n \"with\": \"wit'\", \"everyone\": \"evr'un\"\n }\n #~ sentence = raw_input(\"What be yer sentence, ye old landlubber? \")\n sentences = [\"If the world didn't suck we'd all fall off\",\n \"What happens if you get scared half to death twice?\",\n \"Save water and shower with your girlfriend\",\n \"Always remember you're unique, just like everyone else\"]\n \n start = [\"Yarr, \", \"Yarr! \", \"Yarr-ha-harr! \", \"Skuttle me Skippers! \",\n \"Shiver me timbers! \", \"Ahoy, me hearties! \", \"Dogs ahoy! \",\n \"Hoist the mizzen!!! \", \"Hoist the colors!! \" ]\n end = [\". ARRRGHHHH!\", \". Avast ye varmint!\", \". Yarr?\",\n \". Savvyy?\", \". Yarr!\", \". Aye!\",\n \", ye bilge rat!\", \", ye mangy dog!\", \", ye scallywag!\"]\n\n s = choice(sentences)\n print s\n print choice(start) +\\\n ' '.join([d[i] if i in d else i for i in s.lower().split()]).capitalize()\\\n + choice(end)","def test_multi_template():\n data = []\n data.extend([\"{}_data.json\".format(i) for i in range(50)])\n data.extend([\"{}_log.csv\".format(i) for i in range(50)])\n data.extend([\"filename_{}.py\".format(i) for i in range(50)])\n data.extend([\"stuff_{}.py\".format(i) for i in range(50)])\n temp = data[:]\n random.shuffle(temp)\n assert data == sort(temp)","def random_data(self) -> (str, str):\n random_animal = random.choice(self.animals_list)\n synsets = wn.synsets(str(random_animal))\n definition = \"\"\n while True:\n if len(synsets) != 0:\n for synset in synsets:\n if synset.lexname() == 'noun.animal':\n definition = synset.definition()\n break\n else:\n random_animal = random.choice(self.animals_list)\n synsets = wn.synsets(str(random_animal))\n return random_animal, definition","def main():\n # Load and prep training files\n raw_speech_text = hg.load_training_file('trump_train.txt')\n speech_text = hg.prep_training(raw_speech_text)\n tweet_data = load_tweets('trump_tweets.json')\n raw_tweets = \"\"\n for dct in tweet_data:\n raw_tweets += \"{} \".format(dct['text'])\n tweets = hg.prep_training(raw_tweets)\n corpus = speech_text + tweets\n corpus = strip_punctuation(corpus)\n dict_1 = hg.map_one_to_one(corpus)\n dict_2 = hg.map_two_to_one(corpus)\n text = []\n \n # Introduction\n print(\"\\nTrump Speech Generator\\n\")\n print(\"Select words to add to speech\")\n print(\"\\'x\\' to exit\")\n print(\"\\'p\\' to add punctuation\")\n print(\"Select \\'p\\' before selecting the word you want to punctuate\")\n\n # Select first word\n options = corpus\n print ()\n selection = select_word(corpus)\n text.append(selection)\n \n # Select second word\n last = text[0]\n options = word_after_one(last, dict_1)\n print_text(text)\n selection = select_word(options)\n text.append(selection)\n \n # Select subsequent word\n while True:\n last = \"{} {}\".format(text[-2].strip(punctuation),\n text[-1].strip(punctuation))\n options = word_after_two(last, dict_2)\n if options == []:\n last = last.split()[1]\n options = word_after_one(last, dict_1)\n while options == []:\n last = random.choice(corpus)\n options = word_after_one(last, dict_1)\n print_text(text)\n selection = select_word(options)\n text.append(selection)\n \n print_text(text)","async def watdo(self, ctx, *args):\n choicelist = []\n for choice in args:\n choicelist.append(choice)\n result = random.choice(choicelist)\n await ctx.bot.send_message(ctx.message.channel, \"I pick {}!\".format(result))","def create_random_proposals(self): \r\n global MAX_NUMBER_PROPOSALS\r\n global LOCATIONS\r\n global CATEGORIES\r\n \r\n for i in range(MAX_NUMBER_PROPOSALS):\r\n description = \"\"\r\n location = locations_rv.rvs(size=1)[0]\r\n category = categories_rv.rvs(size=1)[0]\r\n budget = random.uniform(500000, 1000000)\r\n project = Project(i, description, category, budget, location)\r\n self.proposals.append(project)","def generate_question(self, num_question = 10):\n\t\t\n\t\tquestions = []\n\t\tfor q in range(num_question):\n\t\t\tfor r in range(2):\n\t\t\t\tquestion = np.zeros(self.question_dim, dtype = np.float32)\n\t\t\t\tcolor = np.random.randint(len(COLOR))\n\t\t\t\tquestion[color] = 1.0\n\t\t\t\tquestion[6 + r] = 1.0\n\t\t\t\tquestion_label = np.random.randint(3)\n\t\t\t\tquestion[8 + question_label] = 1.0\n\t\t\t\tquestions.append(question)\n\t\treturn questions","def choose_question():\r\n random_index_question = randint(1, question.num_question + 1)\r\n random_question = question.question[random_index_question]\r\n correct_answer = question.answer[random_index_question]\r\n return random_question, correct_answer","def random_word():\r\n # Opening the SOWPODS dictionary and getting a random word\r\n with open(r\"C:\\Users\\Santosh\\Desktop\\sowpods.txt\") as f:\r\n content = f.read()\r\n return random.choice(content.split())","def shuffle_question(self):\n r = random.SystemRandom()\n r.shuffle(self.question_list)","def generate(random, pid, autogen_tools, n):\n\n #Get a random build path\n generator_path = autogen_tools.get_directory(__file__)\n\n rendered_template_path = path.join(generator_path, \"encrypted\")\n\n key = \"xor_20134113\"\n flag = \"flag_\" + sha1((str(n) + key).encode('utf-8')).hexdigest()\n text = xor(\"You accessed all my secrets :(. But the juicy diary entries are in another castle! Here is the flag though: \" + flag, random.randint(0x1,0xff))\n\n with codecs.open(rendered_template_path, 'w', \"utf-8\") as out_file:\n out_file.write(text)\n\n encrypted_link = autogen_tools.generate_resource_link(pid, \"encrypted\", title=\"encrypted\")\n source_link = autogen_tools.generate_resource_link(pid, \"diary.py\", static=True, title=\"script\")\n\n return {\n \"resource_files\": {\n \"public\": [\n (rendered_template_path, \"encrypted\"),\n ],\n },\n \"static_files\": {\n \"public\": [\n (path.join(generator_path,\"diary.py\"), \"diary.py\")\n ]\n },\n \"problem_updates\": {\n \"description\": \"<p>A friend of yours has been using this %s to encrypt his diary. Being the nosy person you are, you must take a look! Can you decrypt it?</p><p>%s</p>\" % (source_link, encrypted_link)\n }\n }","def random_present_quiz_selector():\n\n go_again = True\n while go_again:\n number = random.randint(1, 3)\n if number == 1:\n begin_present_are_quiz()\n if number == 2:\n begin_present_ere_quiz()\n if number == 3:\n begin_present_ire_quiz()\n again = input(\"Continue? Y/N\\n\").lower()\n if again == \"n\":\n go_again = False","def setup(self):\n setup = RandomWordGenerator().get()\n self.formatted_word = ConvertWord().convert_to_dict(setup)\n self.underscore_word = HangmanUnderscoreDiagram(\n setup).create_hidden_word()\n self.failed_guesses = 0\n print(\"Hello\")\n self.has_won = False\n self.start_game(True)","def personal_questions(user):\n questions = {\n 1: 'GBB: How long have you been a fan of the Packers?',\n 2: 'GBB: Why are you a fan of the Packers?',\n 3: \"GBB: What team do you hate the most?\",\n 4: \"GBB: Who's your favorite player on the Packers?\",\n 5: \"GBB: Who's your least favorite player on the Packers?\",\n 6: \"GBB: Do you come from a family of Packer fans, or are you a lone ranger?\"\n }\n\n while True:\n num = random.randint(1, 6)\n answered = user['personal questions asked']\n if num not in answered:\n user['personal questions asked'].append(num)\n return questions[num]\n if len(answered) == len(questions.keys()):\n return \"GBB: Look's like I know you so well that I don't even need to ask you a question!\"","def generate_products():\n # initialize list of noun and adj\n num_products = 30\n products = [0] * num_products\n prices = [0] * num_products\n weights = [0] * num_products\n flammabilities = [0] * num_products\n\n # initlize random word object\n random = RandomWords()\n\n adj = [random.get_random_word(includePartOfSpeech=\"adjective\")\n for product in products]\n noun = [random.get_random_word(includePartOfSpeech=\"noun\")\n for product in products]\n products = [noun + \" \" + adj for noun, adj in zip(adj, noun)]\n\n prices = [random.randint(5, 100) for price in prices]\n weights = [random.randint(5, 100) for weight in weights]\n flammabilities = [random.randint(0.0, 2.5)\n for flammability in flammabilities]\n\n return products, prices, weights, flammabilities","def generate_sentence(self, complete_property):\n\n drink_property = complete_property.split(\": \")[0]\n property_type = complete_property.split(\": \")[1].split(\"// \")[1]\n template = self.get_template(property_type)\n question = template.replace(\"{\" + property_type + \"}\", drink_property)\n return self.get_correct_form(question)","def random_subjunctiveimp_quiz_selector():\n\n go_again = True\n while go_again:\n number = random.randint(1, 3)\n if number == 1:\n begin_subjunctiveimp_are_quiz()\n if number == 2:\n begin_subjunctiveimp_ere_quiz()\n if number == 3:\n begin_subjunctiveimp_ire_quiz()\n again = input(\"Continue? Y/N\\n\").lower()\n if again == \"n\":\n go_again = False","def make_phys():\n for rn in dcm_dict.keys():\n # PPG\n if not dcm_dict[rn]['ppg_file'] == 'File missing':\n # Files\n ppg_tsv = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-cardiac.tsv.gz')\n ppg_json = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-cardiac.json')\n # TSV\n gzip_file(dcm_dict[rn]['ppg_file'],ppg_tsv)\n # JSON\n data = OrderedDict()\n data['SamplingFrequency'] = 100.0\n data['StartTime'] = -30.0\n data['Columns'] = 'cardiac'\n with open(ppg_json, 'w') as ff:\n json.dump(data, ff,sort_keys=False, indent=4)\n # Respiration\n if not dcm_dict[rn]['resp_file'] == 'File missing':\n # Files\n resp_tsv = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-respiratory.tsv.gz')\n resp_json = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-respiratory.json')\n # TSV\n gzip_file(dcm_dict[rn]['resp_file'],resp_tsv)\n # JSON\n data = OrderedDict()\n data['SamplingFrequency'] = 25.0\n data['StartTime'] = -30.0\n data['Columns'] = 'respiratory'\n with open(resp_json, 'w') as ff:\n json.dump(data, ff,sort_keys=False, indent=4)\n # ECG\n # What to do if they have PPG and ECG?\n if not dcm_dict[rn]['ecg_file'] == 'File missing':\n # Files\n ecg_tsv = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-cardiac.tsv.gz')\n ecg_json = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-cardiac.json')\n # TSV\n gzip_file(dcm_dict[rn]['resp_file'],resp_tsv)\n # JSON\n data = OrderedDict()\n data['SamplingFrequency'] = 1000.0\n data['StartTime'] = -30.0\n data['Columns'] = 'cardiac'\n with open(resp_json, 'w') as ff:\n json.dump(data, ff,sort_keys=False, indent=4)","def random_conditional_quiz_selector():\n\n go_again = True\n while go_again:\n number = random.randint(1, 2)\n if number == 1:\n begin_conditionalpr_are_ere_quiz()\n if number == 2:\n begin_conditionalpr_ire_quiz()\n again = input(\"Continue? Y/N\\n\").lower()\n if again == \"n\":\n go_again = False","def get_random_quote():\n with open (\"./data/quotes.txt\", \"r\") as quotes:\n\n all_quotes = quotes.read().rstrip(\"\\n\").split(\"\\n\\n\")\n\n quote = choice(all_quotes)\n\n return quote","def _choose_templated_topics_persona(self):\n\n topic_bundles = random.sample(\n constants.TEMPLATE_PERSONAS_TOPICS, k=constants.TEMPLATE_PERSONAS_CHOICES\n ) # Each topic bundle is string of comma-seperated related topics, eg. \"book,author\"\n topics = []\n for tb in topic_bundles:\n topics.extend(tb.split(','))\n\n apprentice_agent = self._get_apprentice()\n persona_selection_form = [\n {\n 'type': 'choices',\n 'question': 'My character\\'s favorite ',\n 'choices': topics,\n },\n {'type': 'text', 'question': 'is '},\n {'type': 'text', 'question': 'Add something imaginative to refine it:'},\n ]\n _coordinator_send_message(\n apprentice_agent,\n message=constants.APPRENTICE_CHOOSE_PERSONA_TEMPLATE_REQUEST,\n task_data={'respond_with_form': persona_selection_form},\n )\n agent_response = self.receive_form_response(\n apprentice_agent, check_persona_overuse=True\n )\n\n rs = [r['response'] for r in agent_response['task_data']['form_responses']]\n assert len(rs) == 3, 'Template persona response form length is not 3.'\n topic, topic_item, extra_details = rs\n apprentice_persona = persona_from_template_values(\n topic, topic_item, extra_details\n )\n worker_name = self.worker_names[apprentice_agent]\n logging.info(f'Agent ({worker_name}) selected a persona: {apprentice_persona}')\n\n return apprentice_persona","def makeNonsense(part1, part2, part3, n=10):\n #convert to lists\n p1 = part1.split('\\n')\n p2 = part2.split('\\n')\n p3 = part3.split('\\n')\n #shuffle the lists\n [ random.shuffle( p ) for p in [p1,p2,p3] ]\n #concatinate the sentences\n sentence = []\n for k in range(n):\n try:\n s = p1[k] + ' ' + p2[k] + ' ' + p3[k] \n # THE LOUDER YOU YELL, THE RIGHTER YOU ARE!!\n s += '!!!'\n sentence.append(s)\n except IndexError:\n break\n return sentence","def generateSupplies(self):\n typePart = ['wrench','resistor','bulb','mushroom','coin']\n chosenPart = []\n for i in range(3):\n randomPart = choice(typePart)\n chosenPart.append(randomPart)\n typePart.remove(randomPart)\n for part in chosenPart:\n amount = randint(1,3)\n self._supplies.append(Node(part,amount))","def test_send_all(self):\n mailroom4.send_all()\n for person in donation_data:\n self.assertTrue(os.path.exists(person.replace(' ','_')+'.txt'))\n try:\n with open(person.replace(' ','_')+'.txt','r') as f:\n actual = f.read()\n except FileNotFoundError:\n print(\"The file wasn't created\")\n actual = ''\n expected = mailroom4.letter({'donor':person,'amount':donation_data[person][-1]})\n self.assertEqual(expected,actual)","def create_evaluation_template(client, survey_name):\n\n loremipsum = \"Lorem ipsum dolor sit amet, consecteteur adipiscing elit donec proin nulla vivamus. Augue donec a erat ve sagittis nisi rhoncus curabitur mauris. Nulla ipsum tortor sagittis adipiscing primis interdum suspendisse lobortis etiam risus nullam. Donec massa quam dis at nibh dolor netus quis. Purus etiam. Dolor neque nunc netus eget nulla faucibus vestibulum aenean class senectus. Porta dolor. Donec morbi. Felis lorem tempus luctus malesuada laoreet curae justo rhoncus ante facilisi parturient malesuada elit laoreet amet. Fusce augue nisi ligula praesent condimentum nascetur fringilla in id lectus per nunc. Lacus metus nisl orci odio maecenas adipiscing. Velit nulla a tempor class placerat ac condimentum nisi taciti at eros.\"\n\n loremipsum_A = \"A: \\n\" + loremipsum\n loremipsum_B = \"B: \\n\" + loremipsum\n\n # Create a new survey\n survey_id = client.create_survey(survey_name)\n # Create 2 more pages in the survey\n for i in range(0, 2):\n client.create_new_page(survey_id, str(i), loremipsum) # title and description\n\n # Get the page ids\n page_ids = client.get_pages_ids(survey_id) # There will be 3\n\n answers = [\"A\", \"B\"]\n question_title = \"Which of the following abstract is more relevant to the one above?\"\n for i, ID in enumerate(page_ids):\n client.update_title_description_of_page(survey_id, ID, \"Abstract\" + str(i), loremipsum)\n client.add_single_choice(survey_id, ID, question_title, answers)\n client.add_paragraph(survey_id, ID, loremipsum_A)\n client.add_paragraph(survey_id, ID, loremipsum_B)\n\n return survey_id","def pre_process(fname, num_ex, alt_speaker):\n conversation = []\n with PathManager.open(fname) as f:\n lines = f.readlines()\n random.shuffle(lines)\n lines = lines[:num_ex]\n for line in lines:\n data = json.loads(line)\n dialogue = data[\"dialog\"]\n for item in dialogue:\n speaker = item[0]['id']\n text = item[0]['text']\n conversation += [(speaker, text)]\n conversation += [(END_OF_CONVO, END_OF_CONVO)]\n\n return conversation","def play():\n display_starting_message()\n print(\"\")\n print(\"*\"*10)\n for question_number, question in enumerate(list_of_questions):\n print(question)\n print(\"\")\n for responses in list_of_questions[question]:\n print(responses)\n pick_one = input(\"pick one: \")\n check_murder_sauce(question, pick_one)\n\n murder_sauce_result(murder_sauce)","def main_questions(money, grain, people):\n quest_buy = [Q1, Q2, Q3, Q6, Q7]\n question = random.choice(quest_buy)\n print(question)\n answer = input()\n while answer.isdigit() is False:\n print(INPUT_INT_VALUE)\n answer = input()\n answer = int(answer)\n if question == Q1:\n money = money - answer * 12\n elif question == Q2:\n money -= answer * 14\n elif question == Q3:\n money -= answer * 13\n elif question == Q6:\n money -= answer * 10\n elif question == Q7:\n money -= answer * 15\n grain += answer\n\n quest_sell = [Q4, Q5, Q8, Q9, Q10]\n question_2 = random.choice(quest_sell)\n print(question_2)\n answer = input()\n while answer.isdigit() is False:\n print(INPUT_INT_VALUE)\n answer = input()\n answer = int(answer)\n if question == Q4:\n money += answer * 7\n elif question == Q5:\n money += answer * 5\n elif question == Q8:\n money += answer * 6\n elif question == Q9:\n money += answer * 9\n elif question == Q10:\n money += 8\n grain -= answer\n\n print(DISTRIBUTION_OF_GRAIN)\n answer_3 = input()\n while answer_3.isdigit() is False:\n print(INPUT_INT_VALUE)\n answer_3 = input()\n answer_3 = int(answer)\n grain -= answer_3\n if grain / people > 90:\n people *= 1.1\n elif grain / people < 40:\n people *= 0.9\n return int(money), int(grain), int(people)","def random_config_from_list(config_list_file):\n lines = tuple(open(config_list_file, 'r'))\n config_file = random.choice(lines).strip()\n print(\"[hypergan] config file chosen from list \", config_list_file, ' file:', config_file)\n return hg.configuration.Configuration.load(config_file+\".json\")","def main():\n # Read input from file, returns all objects\n objects = read_input()\n for obj in objects:\n try:\n # Generate the objects answer, yields new object\n obj = generate_answer(obj)\n except Exception:\n # If an error might occur that is not covered, catch it here! Continue where left off\n print('ERROR: An unrecoverable error occured during the processing of ' + obj.get(\n 'operation') + '. Continuing...')\n obj['answer'] = 'ERROR'\n\n print(obj) # TODO: Remove before production\n # Generate an output file\n print_output(objects)","def populate_game_questions():\n indices = random.sample(range(0, len(quizquestion.questions_all)), 5) # If user doesn't specify, choose 5 random questions\n return quizquestion.QuizQuestion.get_game_questions(indices)","def _generate_examples(self, filepath, split):\r\n if self.config.name == \"trex\":\r\n paths = filepath\r\n relations_path = paths[0]\r\n paths = paths[1:]\r\n all_rels = {}\r\n with open(relations_path, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n all_rels[data[\"relation\"]] = data\r\n id_ = -1\r\n for filepath in paths:\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n pred = all_rels.get(data[\"predicate_id\"], {})\r\n for evidences in data[\"evidences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"obj_uri\": str(data[\"obj_uri\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"sub_uri\": str(data[\"sub_uri\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"predicate_id\": str(data[\"predicate_id\"]),\r\n \"sub_surface\": str(evidences[\"sub_surface\"]),\r\n \"obj_surface\": str(evidences[\"obj_surface\"]),\r\n \"masked_sentence\": str(evidences[\"masked_sentence\"]),\r\n \"template\": str(pred.get(\"template\", \"\")),\r\n \"template_negated\": str(pred.get(\"template_negated\", \"\")),\r\n \"label\": str(pred.get(\"label\", \"\")),\r\n \"description\": str(pred.get(\"description\", \"\")),\r\n \"type\": str(pred.get(\"type\", \"\")),\r\n }\r\n elif self.config.name == \"conceptnet\":\r\n id_ = -1\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n if data.get(\"negated\") is not None:\r\n for masked_sentence, negated in zip(data[\"masked_sentences\"], data[\"negated\"]):\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"sub\": str(data.get(\"sub\", \"\")),\r\n \"obj\": str(data.get(\"obj\", \"\")),\r\n \"pred\": str(data[\"pred\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"negated\": str(negated),\r\n }\r\n else:\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"sub\": str(data.get(\"sub\", \"\")),\r\n \"obj\": str(data.get(\"obj\", \"\")),\r\n \"pred\": str(data[\"pred\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"negated\": str(\"\"),\r\n }\r\n elif self.config.name == \"squad\":\r\n id_ = -1\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"id\": str(data[\"id\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"negated\": str(data.get(\"negated\", \"\")),\r\n \"masked_sentence\": str(masked_sentence),\r\n }\r\n elif self.config.name == \"google_re\":\r\n id_ = -1\r\n paths = filepath\r\n for filepath in paths:\r\n # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py\r\n if \"place_of_birth\" in filepath:\r\n pred = {\r\n \"relation\": \"place_of_birth\",\r\n \"template\": \"[X] was born in [Y] .\",\r\n \"template_negated\": \"[X] was not born in [Y] .\",\r\n }\r\n elif \"date_of_birth\" in filepath:\r\n pred = {\r\n \"relation\": \"date_of_birth\",\r\n \"template\": \"[X] (born [Y]).\",\r\n \"template_negated\": \"[X] (not born [Y]).\",\r\n }\r\n else:\r\n pred = {\r\n \"relation\": \"place_of_death\",\r\n \"template\": \"[X] died in [Y] .\",\r\n \"template_negated\": \"[X] did not die in [Y] .\",\r\n }\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n for row in f:\r\n data = json.loads(row)\r\n for masked_sentence in data[\"masked_sentences\"]:\r\n id_ += 1\r\n yield id_, {\r\n \"pred\": str(data[\"pred\"]),\r\n \"sub\": str(data[\"sub\"]),\r\n \"obj\": str(data[\"obj\"]),\r\n \"evidences\": str(data[\"evidences\"]),\r\n \"judgments\": str(data[\"judgments\"]),\r\n \"sub_w\": str(data[\"sub_w\"]),\r\n \"sub_label\": str(data[\"sub_label\"]),\r\n \"sub_aliases\": str(data[\"sub_aliases\"]),\r\n \"obj_w\": str(data[\"obj_w\"]),\r\n \"obj_label\": str(data[\"obj_label\"]),\r\n \"obj_aliases\": str(data[\"obj_aliases\"]),\r\n \"uuid\": str(data[\"uuid\"]),\r\n \"masked_sentence\": str(masked_sentence),\r\n \"template\": str(pred[\"template\"]),\r\n \"template_negated\": str(pred[\"template_negated\"]),\r\n }","def generate_fantasy_title():\n d20 = random.randint(1, 20)\n if d20 <= 4:\n #genetive noun\n return fantasy_genetive[random.randint(0, len(fantasy_genetive) - 1)] + \" \" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\n elif d20 > 4 and d20 < 13: \n #The adj noun\n return \"The \" + fantasy_adj[random.randint(0, len(fantasy_adj) - 1)] + \" \" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\n elif d20 >= 13:\n #something of something\n return fantasy_noun[random.randint(0, len(fantasy_noun) - 1)] + \" of \" + fantasy_what_is_this[random.randint(0, len(fantasy_what_is_this) - 1)]","def realize_question(dialog, template, filter_objs):\n\n # Number of inputs.\n n_inputs = template.get('inputs', 0)\n # Sample a text template.\n text_sample = random.choice(template['text'])\n text_sample_index = template['text'].index(text_sample)\n\n # Extract attribute tags and get them into groups.\n tags = re.findall('(<[\\d\\w]*>)', text_sample)\n tag_groups = collections.defaultdict(list)\n for tag in tags:\n group_id = get_tag_group(tag)\n tag_groups[group_id].append(tag)\n\n # Sample a random element from filtered.\n arg_sample = random.choice(filter_objs)\n\n # Remove tags from text not allowed by filter_objs.\n for arg_ind in range(n_inputs):\n obj_sample = arg_sample['objects'][arg_ind]\n avail_attrs = obj_sample['optional'] + obj_sample['required']\n\n for ii in tag_groups[arg_ind][::-1]:\n if mapping(ii) not in avail_attrs:\n tag_groups[arg_ind].remove(ii)\n text_sample = replace_attribute(text_sample, ii, arg_sample, True)\n\n # Assert that all required attributes are present as tags.\n for attribute in obj_sample['required']:\n required_tag = inv_mapping(attribute, arg_ind)\n # Make an exception for <R> and <P>\n if required_tag == '<R>' and '<P>' in tag_groups[arg_ind]:\n continue\n assert required_tag in tag_groups[arg_ind], \\\n 'A required attribute is missing in template!'\n\n # Start compiling tags to keep.\n tags_to_keep = [inv_mapping(ii, arg_ind) for ii in obj_sample['required']]\n # Filter out those not present in text template.\n optional_tags = [inv_mapping(ii,arg_ind) for ii in obj_sample['optional']]\n optional_tags = [ii for ii in optional_tags if ii in tag_groups[arg_ind]]\n\n # If tags_to_keep is empty, sample from optional with (1:70, 2:25, 3:5).\n if len(optional_tags) > 0:\n if len(tags_to_keep) > 0:\n n_tags_sample = [0, 1, 2]\n else:\n n_tags_sample = [1, 2, 3]\n n_sample = np.random.choice(n_tags_sample, 1,\n p=gvars.METAINFO['probabilities'],\n replace=False)\n # Lower cap at the length of optional.\n n_sample = min(n_sample[0], len(optional_tags))\n if n_sample > 0:\n tags_to_keep += random.sample(optional_tags, n_sample)\n\n # Now create a dictionary of placeholders with actual attribute values.\n for tag in tag_groups[arg_ind]:\n remove = tag not in tags_to_keep\n text_sample = replace_attribute(text_sample, tag, arg_sample, remove)\n\n # Record info and merge scene graphs.\n dialog_datum = {'question': text_sample, 'answer': arg_sample['answer'],\n 'template': template['label']}\n dialog['template_info'].append(template.copy())\n del dialog['template_info'][-1]['text']\n dialog['template_info'][-1]['index'] = text_sample_index\n\n dialog['dialog'].append(dialog_datum)\n graph_item = arg_sample['graph']\n\n # If mergeable, add it to the objects list.\n dialog['graph'] = utils.merge_update_scene_graph(dialog['graph'], graph_item)\n\n # If there are volatile objects in the graph item, remove them.\n for obj in graph_item['objects'][::-1]:\n if obj.get('volatile', False): graph_item['objects'].remove(obj)\n dialog['graph']['history'].append(graph_item)\n return dialog","def q_v_random(n=1000):\n for i in range(5):\n p1_strategy = strategies.QStrategy('X')\n p2_strategy = strategies.RandomStrategy()\n p1 = player.Player('X', p1_strategy)\n p2 = player.Player('O', p2_strategy)\n board = tictactoe.Board()\n game = rl_game.Game(p1, p2, board)\n game.play_many(n)\n #p1.strategy.save_q()\n # Do not save gameplay against random opponent","def generate(self):\n\n self.event_type = random.choice(self.TYPES)\n self.event_item = EventGenerator.ITEM_GEN.generate_item()\n\n event_details = random.choice(self.DETAIL_DICT[self.event_type])\n\n self.event_message = event_details[0]\n self.event_title = event_details[1]\n self.event_text = event_details[2]\n\n self.event_widgets = event_details[3]\n\n self.accomplished = event_details[4]","def make_text(dictionary):\n\n capital_keys_list = [key for key in dictionary.keys() if key[0][0].isupper()] # create list of only keys that start with an uppercase letter\n starting_key = random.choice(capital_keys_list) # choose key (tuple) to start at\n new_text_list = list(starting_key) # add items in that tuple to list of created text\n \n punctuation = \"?.!\" # create punctuation string \n next_word = \" \" # initialize next_word\n\n while dictionary.get(starting_key) != None and next_word[-1] not in punctuation: # Continue until the key is not found in the dictionary and until the last word ends in punctuation\n value_list = dictionary[starting_key] # assign value of key (list)\n rand_index = random.randrange(0, len(value_list)) # choose random int w/in length of list\n next_word = value_list[rand_index] # find item at that random index\n\n new_text_list.append(next_word) # add next_word to list of created text\n\n starting_key = tuple(list(starting_key[1:]) + [next_word]) # create new tuple from second word of previous tuple + item at that index\n\n new_text_string = \" \".join(new_text_list) # join list into string\n return new_text_string # return new text","def ask_questions():\n print(story.prompts)\n prompts = story.prompts\n \n return render_template(\"questions.html\", prompts = prompts)","def test_missing_properties(self, properties: list[str]) -> None:\n issuer = unpaid_redemption()\n treq = treq_for_loopback_ristretto(issuer)\n d = treq.post(\n NOWHERE.child(\"v1\", \"redeem\").to_text().encode(\"ascii\"),\n dumps_utf8(dict.fromkeys(properties)),\n headers=Headers({\"content-type\": [\"application/json\"]}),\n )\n self.assertThat(\n d,\n succeeded(\n AfterPreprocessing(\n lambda response: response.code,\n Equals(BAD_REQUEST),\n ),\n ),\n )","def dummyProperties():\n global num_missing_properties\n num_missing_properties += 1 \n return ['main=appinventor.' + DUMMY_USER_NAME + '.' + DUMMY_PROJECT_NAME + '.Screen1\\n',\n 'name=' + DUMMY_PROJECT_NAME + '\\n',\n 'assets=../assets\\n',\n 'source=../src\\n',\n 'build=../build\\n',\n 'versioncode=1\\n',\n 'versionname=1.0\\n',\n 'useslocation=False\\n',\n 'aname=' + DUMMY_PROJECT_NAME + '\\n']","async def proo(pros):\n if not pros.text[0].isalpha() and pros.text[0] not in (\"/\", \"#\", \"@\", \"!\"):\n index = random.randint(0, len(PRO_STRINGS) - 1)\n reply_text = PRO_STRINGS[index]\n await pros.edit(reply_text)","def main(fileName, output, sample):\n fh = open(fileName)\n currentReview = None\n currentlyInReview = False\n allReviews = []\n for line in fh:\n line = line.strip()\n if line == END:\n currentlyInReview = False\n allReviews.append(currentReview)\n if currentlyInReview:\n currentReview += line\n currentReview += '\\n'\n if line == START:\n currentReview = \"\"\n currentlyInReview = True\n fh.close()\n if sample:\n reviews = random.sample(allReviews, sample)\n else:\n reviews = allReviews\n outFH = open(output, 'w')\n for review in reviews:\n outFH.write(review)\n outFH.close()","def nao_speech(possible_sentences):\n\n print(random.choice(possible_sentences))","def find_drink_property(self, properties):\n\n split_status = \"\"\n tries = 0\n\n while split_status != \"None\" and tries <= 5:\n random_drink = randint(0, len(properties) - 1)\n random_property = randint(1, len(properties[random_drink]) - 1)\n drink_property = properties[random_drink][random_property]\n split_status = drink_property.split(\": \")[1].split(\" //\")[0]\n tries += 1\n\n if split_status != \"None\" and tries > 5:\n drink_property = self.find_first_none(properties)\n return drink_property","def main():\n parser = OptionParser()\n parser.add_option(\"-u\", \"--update-index\", action=\"store_true\", \n dest=\"update_index\", default=False, \n help=\"Update index file\")\n parser.add_option(\"-r\", \"--randomise\", action=\"store_true\", \n dest=\"force_randomise\", default=False, \n help=\"Force randomisation when using search terms.\") \n # TODO: implement this\n #parser.add_option(\"-l\", \"--loop\", action=\"store_true\", \n # dest=\"loop_songs\", default=self.loop_songs,\n # help=\"Loop playlist\")\n parser.add_option(\"-l\", \"--list-only\", action=\"store_true\",\n dest=\"list_only\", default=False, help=\"List songs only\") \n parser.add_option(\"-c\", \"--config_file\",\n dest=\"config_file\", help=\"Configuration file\", default=DEFAULT_CONFIG_FILE)\n parser.add_option(\"-n\", \"--num-songs\",\n dest=\"num_songs\", help=\"Number of songs to generate/play\")\n\n\n (options, args) = parser.parse_args()\n \n # Try to create a playlist. \n have_playlist = False\n while not have_playlist: \n try: \n rmp = RandomMusicPlaylist(config_file=options.config_file, search_terms=args, update_index=options.update_index, \n force_randomise=options.force_randomise, \n list_only=options.list_only, num_songs=options.num_songs)\n have_playlist = True\n except MissingConfigFileError, err_msg:\n sys.stderr.write(\"%s\\n\" % err_msg)\n create_config_file(options.config_file, \n DEFAULT_HOME_DIR)\n \n rmp.play_music()","def generate_instance(num_facilities: int, num_towns: int, file_name: str) -> bool:\r\n\r\n facility_dictionary = [\r\n \"Perseus\",\r\n \"Andromeda\",\r\n \"Cassiopeia\",\r\n \"Cetus\",\r\n \"Cepheus\",\r\n \"Pegasus\",\r\n \"Auriga\",\r\n \"Hercules\",\r\n \"Sagitta\",\r\n \"Aquila\",\r\n \"Lyra\",\r\n \"Cygnus\",\r\n \"Hydra\",\r\n \"Crater\",\r\n \"Corvus\",\r\n \"Ophiuchus\",\r\n \"Serpens\",\r\n \"Centaurus\",\r\n \"Lupus\",\r\n \"Corona Australis\",\r\n \"Ara\",\r\n \"Orion\",\r\n \"Canis Major\",\r\n \"Canis Minor\",\r\n \"Lepus\",\r\n \"Monoceros\",\r\n \"Aries\",\r\n \"Taurus\",\r\n \"Gemini\",\r\n \"Cancer\",\r\n \"Leo\",\r\n \"Virgo\",\r\n \"Libra\",\r\n \"Scorpius\",\r\n \"Sagittarius\",\r\n \"Capricornus\",\r\n \"Aquarius\",\r\n \"Pisces\",\r\n \"Ursa Major\",\r\n \"Ursa Minor\",\r\n \"Draco\",\r\n \"Canes Venatici\",\r\n \"Boötes\",\r\n \"Corona Borealis\"\r\n ]\r\n towns_dictionary = [\r\n \"Rome\",\r\n \"Milan\",\r\n \"Naples\",\r\n \"Turin\",\r\n \"Florence\",\r\n \"Salerno\",\r\n \"Palermo\",\r\n \"Catania\",\r\n \"Genoa\",\r\n \"Bari\",\r\n \"Bologna\",\r\n \"Verona\",\r\n \"Pescara\",\r\n \"Cagliari\",\r\n \"Venice\",\r\n \"Messina\",\r\n \"Como\",\r\n \"Caserta\",\r\n \"Trieste\",\r\n \"Pisa\",\r\n \"Taranto\",\r\n \"Bergamo\",\r\n \"Reggio di Calabria\",\r\n \"Treviso\",\r\n \"Modena\",\r\n \"Parma\",\r\n \"Lecce\",\r\n \"Livorno\",\r\n \"Foggia\",\r\n \"Perugia\",\r\n \"Ravenna\",\r\n \"Ferrara\",\r\n \"Siracusa\",\r\n \"Sassari\",\r\n \"Udine\",\r\n \"Barletta\",\r\n \"Trento\",\r\n \"Brindisi\",\r\n \"Novara\",\r\n \"Ancona\",\r\n \"Soprabolzano\",\r\n \"Catanzaro\",\r\n \"Arezzo\",\r\n \"Marsala\",\r\n \"Asti\",\r\n \"Potenza\",\r\n \"Ragusa\",\r\n \"L’Aquila\",\r\n \"Benevento\",\r\n \"Civitavecchia\",\r\n \"Crotone\",\r\n \"Siena\",\r\n \"Campobasso\",\r\n \"Olbia\",\r\n \"Aosta\",\r\n \"Vibo Valentia\",\r\n \"Padova\",\r\n \"Savona\",\r\n \"Caltanissetta\",\r\n \"Vicenza\",\r\n \"Gorizia\",\r\n \"Rieti\",\r\n \"Grosseto\",\r\n \"Bolzano\",\r\n \"Massa\",\r\n \"Sanluri\",\r\n \"Latina\",\r\n \"Vercelli\",\r\n \"Belluno\",\r\n \"Cremona\",\r\n \"Oristano\",\r\n \"Mantova\",\r\n \"Prato\",\r\n \"Enna\",\r\n \"Lucca\",\r\n \"Viterbo\",\r\n \"Villacidro\",\r\n \"Trani\",\r\n \"Pavia\",\r\n \"Piacenza\",\r\n \"Monza\",\r\n \"Verbania\",\r\n \"Rimini\",\r\n \"Andria\",\r\n \"Fermo\",\r\n \"Nuoro\",\r\n \"Alessandria\",\r\n \"Matera\",\r\n \"Pistoia\",\r\n \"Reggio Emilia\",\r\n \"Frosinone\",\r\n \"Imperia\",\r\n \"Tortolì\",\r\n \"Iglesias\",\r\n \"Tempio Pausania\",\r\n \"Trapani\",\r\n \"Rovigo\",\r\n \"Teramo\",\r\n \"Sondrio\",\r\n \"Lanusei\",\r\n \"Biella\",\r\n \"Cosenza\",\r\n \"Cuneo\",\r\n \"Ascoli Piceno\",\r\n \"Avellino\",\r\n \"Chieti\",\r\n \"Terni\",\r\n \"Varese\",\r\n \"Forlì\",\r\n \"Lecco\",\r\n \"Carbonia\",\r\n \"Macerata\",\r\n \"La Spezia\",\r\n \"Pesaro\",\r\n \"Pordenone\",\r\n \"Lodi\",\r\n \"Brescia\",\r\n \"Agrigento\"\r\n ]\r\n\r\n if num_facilities > len(facility_dictionary) or num_towns > len(towns_dictionary):\r\n print(\"\\nToo many elements required:\")\r\n print(f\"\\tmax facilities: {len(facility_dictionary)}\")\r\n print(f\"\\tmax towns: {len(towns_dictionary)}\")\r\n return False\r\n\r\n if num_facilities < 1 or num_towns < 1:\r\n print(\"\\nFacility and towns elements must be more than 0\\n\")\r\n return False\r\n\r\n facilities = []\r\n towns = []\r\n hazards = []\r\n\r\n for i in range(num_facilities):\r\n facility = {\r\n \"facility_id\": i,\r\n \"name\": facility_dictionary[i],\r\n \"capacity\": math.floor(random.uniform(51, 300))\r\n }\r\n facilities.append(facility)\r\n\r\n for i in range(num_towns):\r\n town = {\r\n \"town_id\": i,\r\n \"name\": towns_dictionary[i],\r\n \"garbage\": math.floor(random.uniform(1, 50))\r\n }\r\n towns.append(town)\r\n\r\n for i in range(num_towns):\r\n town_hazards = []\r\n for j in range(num_facilities):\r\n town_hazards.append(math.floor(random.uniform(1, 100)))\r\n hazards.append(town_hazards)\r\n\r\n data = {\r\n \"towns\": towns,\r\n \"facilities\": facilities,\r\n \"hazards\": hazards\r\n }\r\n\r\n with open(f\"./instances/{file_name}.json\", 'w') as outfile:\r\n json.dump(data, outfile)\r\n\r\n return True","def random_entry(): \n\n files = list_entries()\n return random.choice(files)","def premade():\n global all_questions\n\n choice(all_questions)\n temp = all_questions.pop(0) # Treat scrambled list like a queue\n question,answer = temp\n print(\"\\n\\n{}\".format(question))\n guess = get_user_input(str)\n\n if guess == \"T\": guess = True\n if guess == \"F\": guess = False\n\n if guess == answer:\n print(\"Correct the answer is: {}\".format(answer))\n else:\n print(\"Incorrect the answer is: {}, This question will be asked again.\".format(answer))\n all_questions.append(temp) # Add it back","def make_music_rand():\n pass","def mimic_lecture(mimic_dict, starting_word=None, limit=10):\r\n\r\n if not starting_word:\r\n word = ''\r\n else:\r\n word = starting_word\r\n\r\n line_label = (divmod(x*3, 60) for x in numGen())\r\n text = word\r\n text = str(next(line_label)) + word\r\n line_mark = 0\r\n line_words = 1\r\n while line_mark < limit:\r\n if word not in mimic_dict:\r\n word = \"\"\r\n new_word = random.choice(mimic_dict[word])\r\n\r\n if line_words == 0:\r\n rand_silence = random.randint(1,10)\r\n if rand_silence <= 2:\r\n text = text + \"\\n\" + str(next(line_label))\r\n line_words = 0\r\n line_mark += 1\r\n word = new_word\r\n continue\r\n\r\n text = text + \" \" + new_word\r\n rand = random.randint(3,5)\r\n if line_words >= rand:\r\n text = text + \"\\n\" + str(next(line_label))\r\n line_words = 0\r\n line_mark += 1\r\n else:\r\n line_words += 1\r\n word = new_word\r\n return text","def answerQuestion(questionIndex):\n\n with open(\"validQuestionAnswers.txt\") as f:\n answers = f.readlines()\n answers = [x.strip() for x in answers] \n f.close()\n \n say(answers[questionIndex])","def test_create_all_letters():\n mailroom.create_all_letters()\n for donor in mailroom.donor_db:\n assert os.path.exists(f\"{donor}.txt\") is True","def generate_text(word_dict, num_words):\n\n current_word = '$'\n for i in range(num_words):\n wordlist = word_dict[current_word]\n next_word = random.choice(wordlist)\n print(next_word, end=' ')\n if '.' in next_word or '?' in next_word or '!' in next_word:\n current_word = '$'\n else:\n current_word = next_word\n print()","def gen_trip_question_alt(template_path,tui_url,trip):\n with open(template_path) as template_f:\n q_json = json.load(template_f)\n\n trip_data = set_trip_data(trip)\n\n q_json[1]['q']['url'] = tui_url.format(trip_data=trip_data)\n\n # Questions 4-6 are contextual, no need to instantiate \n pass\n\n return q_json","def custom_props():\r\n section = document.add_section()\r\n new_width, new_height = section.page_height, section.page_width\r\n section.orientation = WD_ORIENT.LANDSCAPE\r\n section.page_width = 7772400\r\n section.page_height = 10058400\r\n document.add_heading('Custom Properties', level=1)\r\n\r\n customproperties = get_qlik_sense.get_customprop()\r\n num_of_customproperties = len(customproperties)\r\n table = document.add_table(rows=num_of_customproperties+1, cols=3)\r\n table.style = 'Grid Table 1 Light Accent 1'\r\n row = table.rows[0]\r\n row.cells[0].text = 'name'\r\n row.cells[1].text = 'choice values'\r\n row.cells[2].text = 'object types'\r\n\r\n for customproperty in range(num_of_customproperties):\r\n row = table.rows[customproperty+1]\r\n row.cells[0].text = str(customproperties[customproperty][0])\r\n row.cells[1].text = ', '.join(customproperties[customproperty][1])\r\n row.cells[2].text = ', '.join(customproperties[customproperty][2])\r\n document.add_page_break()","async def randomChoice(self, ctx: commands.Context, *choices: str):\n if not choices:\n await ctx.reply(f\"Command failed - no arguments given.\\nEnter a sequence of arguments to choose from (you can use quotes for grouping).\", mention_author=False)\n elif len(choices)==1:\n await ctx.reply(f\"After some extremely randomized choosing from the one singular option that was given to choose from, the surprising result is:\\n{choices[0]}\", mention_author=False)\n else:\n await ctx.reply(f\"Randomly chosen result:\\n{random.choice(choices)}\", mention_author=False)","def generate_list_of_wrong_choices(possible_wrong_choices, no_of_wrong_choices):\n\n wrong_choices = random.sample(list(possible_wrong_choices), min(len(possible_wrong_choices), no_of_wrong_choices))\n\n # Return a list of the text of the wrong choices\n return [wrong_choice.text for wrong_choice in wrong_choices]","def __GenerateVariantsDistribution(self):\n np.random.seed(self.random_seed_parametr)\n try:\n Students = pd.read_excel(self.student_path)\n print('Load {}'.format(self.student_path))\n students_number = len(Students)\n\n self.__create_ALL_LR()\n Course_structure, variants_numbers = self.__generate_stracture()\n print('Generate stracture')\n Number_of_weaks = len(Course_structure)\n\n number_of_distribution = 0\n for WeakNumber in range(Number_of_weaks):\n for TaskNumber in range(Course_structure[WeakNumber]):\n Students['Week {0} Task {1}'.format(WeakNumber + 1, TaskNumber + 1)] = np.random.randint(\n variants_numbers[number_of_distribution], size=students_number)\n number_of_distribution += 1\n\n writer = pd.ExcelWriter(self.students_with_variants_path)\n print('Save {}'.format(self.students_with_variants_path))\n Students.to_excel(writer)\n writer.save()\n except:\n print('File with students doesnot exist')"],"string":"[\n \"async def generate_question(self) -> None:\\n topic = choice(list(self._topics.keys()))\\n arrayList = await self.session.get(\\\"https://restcountries.eu/rest/v2\\\")\\n arrayList = await arrayList.json() # get request to the country API\\n countries = []\\n \\n for _ in range(4):\\n country = choice(arrayList)\\n del arrayList[arrayList.index(country)]\\n countries.append(country)\\n del arrayList\\n \\n country = choice(countries)\\n del countries[countries.index(country)]\\n self.question = f\\\"What is the {self._topics[topic]} of {country['name']}?\\\"\\n self.correct_order = randint(0, 3)\\n self.choices = [i[topic] for i in countries]\\n self.choices.insert(self.correct_order, country[topic])\\n del countries, topic\",\n \"def main():\\n\\n args = get_args()\\n text = args.text\\n mutations = args.mutations\\n random.seed(args.seed)\\n alpha = ''.join(sorted(string.ascii_letters + string.punctuation))\\n\\n new_text_line = []\\n if os.path.isfile(text):\\n print('You said: ', end='')\\n for line in open(text, 'rt'):\\n new_text_line = list(line.rstrip())\\n print(f'\\\"{line.rstrip()}\\\"')\\n len_text = len(line.rstrip())\\n num_mutations = round(len_text * mutations)\\n for i in random.sample(range(len_text), num_mutations):\\n # print(f'i = {i}, char = {line[i]}, index = {alpha.find(line[i])}')\\n # list.index 함수로 색인 위치 찾으면 없을때 error 발생. 있는지 확인하고 쓰던가 아니면 find 함수 쓸 것!!\\n # find 반환값 : 색인 위치. 찾을 수 없는 경우 -1 반환\\n new_text_line[i] = random.choice(alpha.replace(line[i], '')) # replace 함수는 line[i] 에 해당하는 문자가 alpha에 '있으면' 해당 문자를 치환한다. 없어도 error 안 남.\\n print(f'I heard : \\\"' + ''.join(new_text_line)+ '\\\"')\\n else:\\n print(f'You said: \\\"{text}\\\"')\\n new_text_line = list(text)\\n len_text = len(text)\\n num_mutations = round(len_text * mutations)\\n for i in random.sample(range(len_text), num_mutations):\\n new_text_line[i] = random.choice(alpha.replace(text[i], ''))\\n print(f'I heard : \\\"' + ''.join(new_text_line)+ '\\\"')\",\n \"def poem_generation(magnet,topic):\\r\\n e = inflect.engine()\\r\\n antonyms = []\\r\\n synonyms = []\\r\\n poem = \\\"\\\"\\r\\n verb = \\\"\\\"\\r\\n # plural\\r\\n if (e.singular_noun(topic) is False):\\r\\n verb = \\\"is\\\"\\r\\n else:\\r\\n verb = \\\"are\\\"\\r\\n \\r\\n for syn in wordnet.synsets(topic):\\r\\n for l in syn.lemmas():\\r\\n synonyms.append(l.name())\\r\\n if l.antonyms():\\r\\n antonyms.append(l.antonyms()[0].name())\\r\\n if (len(set(antonyms)) < 1):\\r\\n for syn in wordnet.synsets(RAND_LIST[random.randint(0,len(RAND_LIST)-1)]):\\r\\n for l in syn.lemmas():\\r\\n synonyms.append(l.name())\\r\\n if l.antonyms():\\r\\n antonyms.append(l.antonyms()[0].name())\\r\\n topic = topic.capitalize()\\r\\n for i in range(0,random.randint(5,15)):\\r\\n verse = random.randint(0,6)\\r\\n question = random.randint(0,6)\\r\\n # structure\\r\\n # antonyms\\r\\n if (verse < 2) and len(antonyms) > 0:\\r\\n ant_magnet = metaphor_magnet(antonyms[random.randint(0,len(antonyms)-1)])\\r\\n choice = random.randint(0,len(ant_magnet)-1)\\r\\n detail = ant_magnet[choice].split(\\\":\\\")\\r\\n if (question < 2):\\r\\n index = random.randint(0,len(QUESTION)-1)\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" not like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" not like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += topic + \\\" \\\" + verb + \\\" not like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\".\\\\n\\\" \\r\\n else:\\r\\n poem += topic + \\\" \\\" + verb + \\\" not like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\".\\\\n\\\" \\r\\n \\r\\n else:\\r\\n choice = random.randint(0,len(magnet)-1)\\r\\n detail = magnet[choice].split(\\\":\\\")\\r\\n if (question < 2):\\r\\n index = random.randint(0,len(QUESTION)-1)\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += topic + \\\" \\\" + verb + \\\" like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"\\\\n\\\" \\r\\n else:\\r\\n poem += topic + \\\" \\\" + verb + \\\" like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"\\\\n\\\" \\r\\n \\r\\n return poem\",\n \"def main():\\n\\n args = get_args()\\n random.seed(args.seed)\\n wod = []\\n\\n for name, low, high in read_csv(args.file):\\n reps = random.randint(low, high)\\n if args.easy:\\n reps = int(reps / 2)\\n wod.append((name, reps))\\n\\n wod = random.sample(wod, k=args.num_exercises)\\n print(tabulate(wod, headers=('Exercise', 'Reps')))\",\n \"def randomPostToScreen():\\n\\ttxt_files = filter(lambda x: x.endswith('.txt'), os.listdir(bookpath))\\n\\tbook = random.choice(txt_files)\\n\\tprint generatePost(os.path.join(bookpath,book))\\n\\tprint '----------------------------'\",\n \"def start_test(n_questions, questions_file):\\r\\n\\tfile = open(\\\"questions/\\\"+questions_file, \\\"r\\\")\\r\\n\\tc = 0\\r\\n\\tcorrect = []\\r\\n\\twrong = []\\r\\n\\r\\n\\tfor i in range(3325):\\r\\n\\t\\tline = file.readline()\\r\\n\\t\\t\\r\\n\\t\\tif \\\"Answer: \\\" in line:\\r\\n\\t\\t\\tc += 1\\r\\n\\t\\t\\tif len(str(line[8:]).strip()) > 1:\\r\\n\\t\\t\\t\\tprint(\\\"\\\\nMultiple Choice\\\")\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tprint(\\\"\\\\nSingle Choice\\\")\\r\\n\\t\\t\\tansw = str(input(\\\"\\\\nAnswer: \\\")).upper()\\r\\n\\t\\t\\tif answ == str(line[8:]).strip():\\r\\n\\t\\t\\t\\tcorrect.append(c)\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\twrong.append(c)\\r\\n\\t\\t\\t\\tprint(\\\"Correct answer: \\\", str(line[8:]))\\r\\n\\t\\t\\tprint(\\\"\\\\n\\\",\\\"-\\\"*10, \\\"Correct: \\\", len(correct), \\\"-\\\"*10, \\\"\\\\n\\\", \\\"-\\\"*10, \\\"Wrong: \\\", len(wrong), \\\"-\\\"*10 )\\r\\n\\t\\t\\tcontinue\\r\\n\\r\\n\\t\\tif c == n_questions:\\r\\n\\t\\t\\tbreak\\r\\n\\t\\tprint(line)\\r\\n\\r\\n\\tdict_result = {\\\"Corrects\\\":correct,\\r\\n\\t\\t\\t\\t\\t\\\"Wrongs\\\":wrong,\\r\\n\\t\\t\\t\\t\\t\\\"n_questions\\\":n_questions}\\r\\n\\r\\n\\tfile.close()\\r\\n\\r\\n\\treturn dict_result\",\n \"def generateISTemplates(course, wordlist, charLevels, translateDict, similarCharsList, supportsUppercase):\\n\\n levelsDict= {}\\n stageByNumber = 1 # represents key of wordsByLevelDict to get wordlist value\\n wordsByLengthDict = {}\\n wordsByLevelDict = {}\\n swappedTranslateDict = dict((v,k) for k,v in translateDict.iteritems()) # Keys are latin, values non-latin\\n\\n for word in wordlist:\\n wordsByLengthDict = wordsByLength(word, wordsByLengthDict)\\n wordsByLevelDict = wordsByLevel(word, charLevels, wordsByLevelDict)\\n\\n for stage in charLevels:\\n\\n if not wordsByLevelDict.has_key(stageByNumber): # Skip the uppercase stages in charLevels\\n stageByNumber += 1\\n continue\\n\\n charLevelsWordList = wordsByLevelDict[stageByNumber] # contains list of words suitable for current stage\\n selectedOptions = makeWordOptions(charLevelsWordList, similarCharsList) # List of 3 sublists containing options or None\\n\\n if selectedOptions is not None:\\n\\n for i in range(len(selectedOptions)):\\n generateLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\\\".qdef\\\")\\n generateLevelAlternative(selectedOptions[i], translateDict, similarCharsList, name+str(stageByNumber)+\\\".qdef\\\")\\n\\n if supportsUppercase:\\n generateUppercaseLevel(random.choice(selectedOptions[i]), translateDict, selectedOptions[i], name+str(stageByNumber)+\\\".qdef\\\")\\n\\n generateEndOfQuiz(name+str(stageByNumber)+\\\".qdef\\\")\\n stageByNumber += 1\\n\\n # generate practice levels\\n for i in range(30):\\n selectedWords = wordsByLengthDict[random.choice(wordsByLengthDict.keys())]\\n if len(selectedWords) >= 3:\\n quizRandomSelection = random.sample(selectedWords,3) # take random 4 items from list\\n\\n for word in quizRandomSelection:\\n trickWord = findAndReplaceSimilarChar(word, quizRandomSelection, similarCharsList)\\n if trickWord:\\n quizRandomSelection.append(trickWord)\\n break\\n\\n generateLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\\\"Practice.qdef\\\")\\n generateLevelAlternative(quizRandomSelection, translateDict, similarCharsList, name+\\\"Practice.qdef\\\")\\n\\n if supportsUppercase:\\n generateUppercaseLevel(quizRandomSelection[0], translateDict, quizRandomSelection, name+\\\"Practice.qdef\\\")\\n\\n generateEndOfQuiz(name+\\\"Practice.qdef\\\")\\n\\n print \\\"New MUNI ROPOT templates has been created in: \\\" + target\",\n \"def create_undefined_question(query_data: dict, property_name: str):\\n \\n correct_subject = query_data[0][\\\"personLabel\\\"][\\\"value\\\"]\\n del query_data[0][\\\"personLabel\\\"]\\n\\n correct_object = query_data[0][\\\"valueLabel\\\"][\\\"value\\\"]\\n del query_data[0][\\\"valueLabel\\\"]\\n\\n alternatives = []\\n for element in query_data[0]:\\n alternatives.append(query_data[0][element][\\\"value\\\"])\\n \\n question_text = f\\\"{correct_subject} {property_name} _______?\\\"\\n\\n print(question_text)\\n print(\\\"correct:\\\", correct_object)\\n print(\\\"alternatives:\\\", alternatives)\",\n \"def chooseSpecialties(self):\\n #specialties = {}\\n def createSpecialties(fileName):\\n \\\"\\\"\\\" (str) -> None\\n Helper function to create specialties dict from file.\\n \\\"\\\"\\\"\\n specialtyFile = open(fileName,'r')\\n \\n current_specialty = ''\\n \\n for line in specialtyFile:\\n #If there is no text, go to next line\\n if line == \\\"\\\\n\\\":\\n pass\\n #If line starts with \\\"~~\\\", set current_specialty to the specialty\\n #on that line, and create a new key in the specialties dict and\\n #set its value to an empty dict. Then input the first item in the\\n #new dict as the specialty's decription\\n elif line[:2] == \\\"~~\\\":\\n current_specialty = line[2:line.index(\\\"-\\\")-1]\\n specialties[current_specialty] = {}\\n specialties[current_specialty]['story'] = line[line.index(\\\"-\\\")+1:-1]\\n #If the line starts with \\\"Level,\\\" creates entries for which feats\\n #the character will get at levels 1,3,6,9\\n elif line[:5] == \\\"Level\\\":\\n specialties[current_specialty][int(line[6:7])] = line[line.index(\\\":\\\")+2:-1]\\n specialtyFile.close()\\n\\n #Create the specialties dict\\n createSpecialties('Specialties.txt')\\n\\n\\n #Make list of specialties\\n specialties_list = []\\n for i in specialties:\\n specialties_list.append(i)\\n specialties_list.sort()\\n #Ask user which specialty they'd like, give them recommended specialty for their class\\n class_recommendation = {'Barbarian':'reaper',\\n 'Cleric':'mystical healer',\\n 'Druid':'hedge magician',\\n 'Fighter':'reaper',\\n 'Monk':'skirmisher',\\n 'Paladin':'defender',\\n 'Ranger':'sharpshooter',\\n 'Rogue':'specialist',\\n 'Wizard':'hedge magician'\\n }\\n spec_choice = raw_input(\\\"Which specialty would you like?\\\\n\\\"\\\\\\n \\\"Choose from: \\\\n\\\"+str(specialties_list)+\\\"\\\\n\\\\n\\\"\\\\\\n \\\"The recommended specialty for your \\\"+self.classType+\\\" is \\\"+class_recommendation[self.classType].title()+\\\": \\\").title()\\n print\\n while spec_choice not in specialties_list:\\n spec_choice = raw_input(\\\"Which specialty would you like?\\\\n\\\"\\\\\\n \\\"Choose from: \\\\n\\\"+str(specialties_list)+\\\"\\\\n\\\\n\\\"\\\\\\n \\\"The recommended specialty for your \\\"+self.classType+\\\" is \\\"+class_recommendation[self.classType].title()+\\\": \\\").title()\\n print\\n \\n self.specialty = spec_choice\\n self.specialtyStory = specialties[self.specialty]['story']\",\n \"def gen_questions(self, number_of_questions):\",\n \"def genQuestion(line):\\r\\n if type(line) is str: # If the passed variable is of type string.\\r\\n line = TextBlob(line) # Create object of type textblob.blob.TextBlob\\r\\n \\r\\n bucket = {} # Create an empty dictionary\\r\\n \\r\\n subject_list = []\\r\\n question_subject=\\\"\\\"\\r\\n answer_subject=\\\"\\\"\\r\\n for i,j in enumerate(line.tags): # line.tags are the parts-of-speach in English \\r\\n question_subject += j[0] + \\\" \\\"\\r\\n if (j[1] == \\\"NNP\\\" or j[1] == \\\"NNS\\\"): \\r\\n subject_list.append(j[0])\\r\\n if j[1] not in bucket:\\r\\n bucket[j[1]] = i # Add all tags to the dictionary or bucket variable\\r\\n \\r\\n if len(subject_list):\\r\\n random_subject_val = random.randint(0, len(subject_list)-1)\\r\\n question_subject = question_subject.replace(str(subject_list[random_subject_val]), \\\"______\\\")\\r\\n answer_subject = str(subject_list[random_subject_val])\\r\\n \\r\\n return question_subject, answer_subject\",\n \"def main():\\n\\targs = getArgs()\\n\\tid_question = args.id_question\\n\\tlang = args.language\\n\\tdir_cp = None\\n\\twith open('config.json') as json_file:\\n\\t\\tconfig_data = json.load(json_file)\\n\\t\\tdir_cp = config_data['dir_cp']\\n\\n\\t\\n\\t\\n\\t# sample_io = GetData(args.id_question).get_uri_io_sample()\\n\\ttemplate = FileUtil(id_question, dir_cp['path'], lang)\\n\\ttemplate.write_template()\\n\\t# print(sample_io)\",\n \"def test_call_with_properties_file(self):\\r\\n id_to_taxonomy_file = NamedTemporaryFile(\\r\\n prefix='RdpTaxonAssignerTest_', suffix='.txt')\\r\\n id_to_taxonomy_file.write(rdp_id_to_taxonomy2)\\r\\n id_to_taxonomy_file.seek(0)\\r\\n\\r\\n app1 = RdpTaxonAssigner({\\r\\n 'id_to_taxonomy_fp': id_to_taxonomy_file.name,\\r\\n 'reference_sequences_fp': self.reference_seqs_file.name,\\r\\n })\\r\\n taxonomy_file, training_seqs_file = app1._generate_training_files()\\r\\n\\r\\n training_dir = mkdtemp(prefix='RdpTrainer_')\\r\\n training_results = train_rdp_classifier(\\r\\n training_seqs_file, taxonomy_file, training_dir)\\r\\n\\r\\n training_data_fp = training_results['properties'].name\\r\\n min_confidence = 0.80\\r\\n app2 = RdpTaxonAssigner({\\r\\n 'training_data_properties_fp': training_data_fp,\\r\\n 'Confidence': min_confidence,\\r\\n })\\r\\n\\r\\n expected = rdp_trained_test2_expected_dict\\r\\n\\r\\n # Since there is some variation in the assignments, run\\r\\n # 10 trials and make sure we get the expected result at least once\\r\\n num_trials = 10\\r\\n num_seqs = len(expected)\\r\\n seq_ids = expected.keys()\\r\\n assignment_comp_results = [False] * num_seqs\\r\\n expected_assignment_comp_results = [True] * num_seqs\\r\\n\\r\\n for i in range(num_trials):\\r\\n actual = app2(self.tmp_seq_filepath)\\r\\n # seq ids are the same, and all input sequences get a result\\r\\n self.assertEqual(actual.keys(), expected.keys())\\r\\n for j, seq_id in enumerate(seq_ids):\\r\\n # confidence is above threshold\\r\\n self.assertTrue(actual[seq_id][1] >= min_confidence)\\r\\n # confidence roughly matches expected\\r\\n assert_allclose(actual[seq_id][1], expected[seq_id][1],\\r\\n atol=0.1)\\r\\n # check if the assignment is correct -- this must happen\\r\\n # at least once per seq_id for the test to pass\\r\\n if actual[seq_id][0] == expected[seq_id][0]:\\r\\n assignment_comp_results[j] = True\\r\\n if assignment_comp_results == expected_assignment_comp_results:\\r\\n # break once we've seen a correct assignment for each seq\\r\\n break\\r\\n\\r\\n self.assertEqual(\\r\\n assignment_comp_results,\\r\\n expected_assignment_comp_results,\\r\\n \\\"Taxonomic assignments never correct in %d trials.\\\" % num_trials)\",\n \"def choose_problems():\\n\\n problems = make_dict()\\n\\n res = random.sample(list(problems.items()), k = 3)\\n # res2 = random.choice(list(problems.items()))\\n # res3 = random.choice(list(problems.items()))\\n\\n res0 = res[0]\\n res1 = res[1]\\n res2 = res[2]\\n\\n return res0, res1, res2\",\n \"def question_generator(self):\\n self.status_conv = 'yes_no_question_asked'\\n questions = config.questions\\n if not self.voyage.get('voyageurs') and 'voyageur_add' not in self.infos_needed:\\n self.infos_needed.append('voyageur_add')\\n if self.infos_needed:\\n if self.is_hotel_needed() and 'hotel' not in self.infos_needed and 'hotel' not in self.voyage:\\n self.infos_needed.insert(1, 'hotel')\\n self.hotel_asked = True\\n key = self.infos_needed[0]\\n self.info_asked = key\\n return questions[key]\\n else :\\n self.status_conv = 'confirmation_asked'\\n return self.conv_recap()\",\n \"def generate_suggestion():\\n age = age_input(\\\"Enter age: \\\")\\n if age <= 5:\\n age_5_younger = open_and_read_file(\\\"age_5_younger.txt\\\")\\n print \\\"Why don't you {}?\\\".format(random.choice(age_5_younger))\\n print\\n\\n elif (age > 5) and (age < 20):\\n age_5_to_20 = open_and_read_file(\\\"age_5_to_20.txt\\\")\\n print \\\"Why don't you {}?\\\".format(random.choice(age_5_to_20))\\n print\\n\\n elif age >= 20:\\n age_20_plus = open_and_read_file(\\\"age_20_plus.txt\\\")\\n print \\\"Why don't you {}?\\\".format(random.choice(age_20_plus))\\n print\",\n \"def choose_properties(self, probability, minimize_ref_properties):\\n assert self.property_names, \\\"Property group must not be empty!\\\"\\n\\n non_ref_prop_names = [\\n name for name in self.property_names\\n if not _is_ref_prop(name)\\n ]\\n\\n if self.constraint_type == self.ONE:\\n if minimize_ref_properties and non_ref_prop_names:\\n chosen_props = [random.choice(non_ref_prop_names)]\\n else:\\n # Or should I have just stored the names as a list...?\\n chosen_props = [\\n stix2generator.utils.rand_iterable(self.property_names)\\n ]\\n\\n elif self.constraint_type == self.AT_LEAST_ONE:\\n\\n # Choose one required prop; make the rest optional. If minimizing\\n # reference properties and all are reference properties, we should\\n # behave like \\\"one\\\" and choose exactly one. Choosing more would\\n # not be \\\"minimal\\\".\\n if minimize_ref_properties and non_ref_prop_names:\\n required_prop = random.choice(non_ref_prop_names)\\n else:\\n required_prop = stix2generator.utils.rand_iterable(\\n self.property_names\\n )\\n\\n chosen_props = [required_prop]\\n candidate_other_props = \\\\\\n non_ref_prop_names if minimize_ref_properties \\\\\\n else self.property_names\\n\\n chosen_props.extend(\\n prop for prop in candidate_other_props\\n if prop != required_prop and random.random() < probability\\n )\\n\\n else:\\n # ALL\\n chosen_props = list(self.property_names)\\n\\n return chosen_props\",\n \"def get_question():\\n last_line = '\\\\n'\\n question = \\\"\\\"\\n\\n print(\\\"Type in your question here:\\\")\\n while last_line != \\\"\\\":\\n current_line = input()\\n if current_line == \\\"\\\":\\n break\\n question += current_line + \\\"\\\\n\\\"\\n\\n variables = {}\\n\\n # Special syntax variables\\n rand = random.randint\\n\\n array_of_lines = question.split(\\\"\\\\n\\\")\\n\\n question_is_present = False\\n\\n for line in array_of_lines:\\n if \\\"QUESTION\\\" in line:\\n question_is_present = True\\n break\\n\\n if question_is_present:\\n variable_lines = []\\n secret_lines = []\\n question_lines = []\\n\\n for index in range(len(array_of_lines)):\\n if array_of_lines[index] == \\\"SECRET\\\":\\n secret_index = index\\n if array_of_lines[index] == \\\"QUESTION\\\":\\n question_index = index\\n break\\n\\n # Adds variables to variable_lines\\n for index in range(secret_index):\\n variable_lines.append(array_of_lines[index])\\n\\n # Adds answer to secret_lines\\n for index in range(secret_index + 1, question_index):\\n secret_lines.append(array_of_lines[index])\\n\\n # Adds question lines to question_lines\\n for index in range(question_index + 1, len(array_of_lines)):\\n question_lines.append(array_of_lines[index])\\n\\n for line in variable_lines:\\n variable, value = line.split(\\\"=\\\")\\n variables[\\\"$\\\" + variable] = str(eval(value)).replace(\\\"_\\\", \\\" \\\")\\n\\n for index in range(len(secret_lines)):\\n secret_lines[index] = science_utils.multiple_replace(line, variables)\\n variable, value = secret_lines[index].split(\\\"=\\\")\\n variables[\\\"answer\\\"] = str(eval(value)).replace(\\\"_\\\", \\\" \\\")\\n\\n for line in question_lines:\\n question_lines[question_lines.index(line)] = science_utils.multiple_replace(line, variables)\\n\\n return question_lines, variables[\\\"answer\\\"]\\n\\n # This is the backup method, for when there is no QUESTION line found\\n else:\\n # Calculate values\\n for word in array_of_lines:\\n if \\\"=\\\" in word:\\n variable, value = word.split(\\\"=\\\")\\n variables[\\\"$\\\" + variable] = str(eval(value)).replace(\\\"_\\\", \\\" \\\")\\n\\n for words in array_of_lines:\\n array_of_lines[array_of_lines.index(words)] = science_utils.multiple_replace(words, variables)\\n\\n to_return = []\\n for line in array_of_lines:\\n if \\\"=\\\" not in line:\\n to_return.append(line)\",\n \"def question_finder(self):\\r\\n with open('Questions.csv', mode='r') as csv_file:\\r\\n csv_reader = csv.DictReader(csv_file)\\r\\n for row in csv_reader:\\r\\n\\r\\n my_tuple = finding_best_match(row, \\\"Questions\\\", self.question.split(), 2)\\r\\n self.percentage_list.append(my_tuple)\\r\\n\\r\\n # Checks if a whole sentence is in the asked question\\r\\n if question_match(join_string(self.question.split()), join_string(row[\\\"Questions\\\"].split())):\\r\\n self.question_match_list.append(my_tuple)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Gives user the most appropriate answer depending on the user's question\\r\\n And depending on the user's emotion\\r\\n \\\"\\\"\\\"\\r\\n # Find Random Index (This will later be changed)\\r\\n random_index = random.randint(2, len(max(self.percentage_list)) - 1)\\r\\n\\r\\n if max(self.percentage_list)[0] >= 60: # The question needs to be at least 65% right\\r\\n self.answer = max(self.percentage_list)[random_index]\\r\\n print(self.answer)\\r\\n\\r\\n elif len(self.question_match_list) > 0: # Or it needs to contain a specific sentence\\r\\n length_list = []\\r\\n for i in self.question_match_list:\\r\\n length_list.append(len(i[1]))\\r\\n self.answer = self.question_match_list[length_list.index(max(length_list))][random_index]\\r\\n print(self.answer)\\r\\n\\r\\n elif max(self.percentage_list)[0] >= 40:\\r\\n print(\\\"Random responses\\\")\\r\\n\\r\\n with open('Random Responses.csv') as f:\\r\\n max_n = sum(1 for _line in f)\\r\\n\\r\\n random_int = random.randint(1, max_n -1)\\r\\n with open('Random Responses.csv', mode='r') as csv_file:\\r\\n csv_reader = csv.DictReader(csv_file)\\r\\n for i in csv_reader:\\r\\n self.counter += 1\\r\\n if random_int == self.counter:\\r\\n self.answer = (i[\\\"Responses\\\"])\\r\\n print(self.answer)\\r\\n else:\\r\\n print(\\\"I am sorry. I don't understand this question\\\")\\r\\n\\r\\n #put answer and question in the file\\r\\n print()\\r\\n print(\\\"if its wrong please write an answer then ENTER\\\")\\r\\n print(\\\"or press ENTER to skip\\\")\\r\\n better_questions_answers(self.question)\",\n \"def main(word_count=2, use_caps=False, use_leet=False, caps_percent=25, leet_percent=20):\\n\\n phrase = get_phrase(word_count)\\n\\n if use_caps:\\n phrase = random_caps(phrase, caps_percent)\\n\\n if use_leet:\\n phrase = random_characters(phrase, leet_percent)\\n\\n print(phrase)\",\n \"def generate_question_and_answer(): # noqa: WPS210\\n start_number = random.randint(1, 100)\\n progression_step = random.randint(1, 10)\\n progression_length = random.randint(5, 10)\\n progression = generate_progression(\\n start_number, progression_step, progression_length,\\n )\\n return hide_number(progression)\",\n \"def generate(simulator, p, starts, goals, environment, r):\\n num_gen_humans = min(len(starts), len(goals))\\n print(\\\"Generating auto humans:\\\", num_gen_humans)\\n from agents.humans.human_configs import HumanConfigs\\n for i in range(num_gen_humans):\\n start_config = generate_config_from_pos_3(starts[i])\\n goal_config = generate_config_from_pos_3(goals[i])\\n start_goal_configs = HumanConfigs(start_config, goal_config)\\n human_i_name = \\\"auto_%04d\\\" % i\\n # Generates a random human from the environment\\n new_human_i = Human.generate_human_with_configs(\\n start_goal_configs,\\n generate_appearance=p.render_3D,\\n name=human_i_name\\n )\\n # update renderer and get human traversible if it exists\\n if p.render_3D:\\n r.add_human(new_human_i)\\n environment[\\\"human_traversible\\\"] = \\\\\\n np.array(r.get_human_traversible())\\n\\n # Input human fields into simulator\\n simulator.add_agent(new_human_i)\",\n \"def make_final_text(text: str) -> str:\\n baseline_text_template = \\\"\\\"\\\"Use this thread to discuss anything (within the rules of the subreddit):\\n\\n* What you didn't think was worthy of its own post\\n* What club game you're most excited for\\n* Where you're staying to watch a friendly\\n* Which players should be called in\\n{}\\n* What the mods told you to re-post here\\n* Etc\\n\\n### Schedules\\n{}\\n\\\"\\\"\\\"\\n\\n with open('random_dumb_questions.txt', 'r') as files:\\n list_of_questions = files.readlines()\\n\\n question = list_of_questions[randint(0, len(list_of_questions))].replace('\\\\n', '')\\n\\n return baseline_text_template.format(question, text)\",\n \"def load_templates(self):\\n\\n self.templates = []\\n\\n if os.path.exists(\\\"question_templates.txt\\\"):\\n for line in open(\\\"question_templates.txt\\\", \\\"r\\\"):\\n self.templates.append(line.replace(\\\"\\\\n\\\", \\\"\\\"))\",\n \"def generateRandomInput(filename, num_people, travel_db):\\n import random\\n routes = []\\n for i in range(num_people):\\n route = travel_db.randomRoute()\\n route.insert(0,\\\"Person \\\" + str(i)) # Add a name for each route.\\n routes.append(route)\\n if FileHandler.writeRoutesCSV(filename,routes): # If it's successful writing the file\\n print(\\\"File {0} created successfully with {1} people.\\\".format(filename, num_people))\\n else:\\n print(\\\"File {0} could not be created.\\\".format(filename))\",\n \"def generate(random, pid, autogen_tools, n):\\n\\n generator_path = autogen_tools.get_directory(__file__)\\n\\n template_path = path.join(generator_path, \\\"code.txt.template\\\")\\n rendered_template_path = path.join(generator_path, \\\"code.txt\\\")\\n\\n autogen_tools.replace_source_tokens(\\n template_path,\\n {\\\"flag\\\": gen_code(n, \\\"Aviation House\\\")},\\n rendered_template_path\\n )\\n\\n code_link = autogen_tools.generate_resource_link(pid, \\\"code.txt\\\", title=\\\"Encrypted file\\\")\\n\\n return {\\n \\\"resource_files\\\": {\\n \\\"public\\\": [\\n (rendered_template_path, \\\"code.txt\\\"),\\n ],\\n },\\n \\\"static_files\\\": {\\n },\\n \\\"problem_updates\\\": {\\n \\\"description\\\": \\\"<p>We've updated the system to AES. We heard that this is military grade encryption so that should fix everything</p><p>The team have stored the password in %s. Bet you can't get into it</p>\\\" % code_link\\n }\\n }\",\n \"def read_properties(lines, options=None, scr=None):\\n\\n reader = None\\n read_files = True\\n\\n if options is None:\\n reader = read_properties_opt\\n\\n elif \\\"vfukui\\\" in options:\\n reader = read_properties_fukui\\n read_files = False\\n\\n elif \\\"vomega\\\" in options:\\n reader = read_properties_omega\\n read_files = False\\n\\n elif \\\"opt\\\" in options or \\\"ohess\\\" in options:\\n reader = read_properties_opt\\n\\n else:\\n reader = read_properties_sp\\n\\n properties = reader(lines)\\n\\n if scr is not None and read_files:\\n # Parse file properties\\n charges = get_mulliken_charges(scr=scr)\\n bonds, bondorders = get_wbo(scr=scr)\\n\\n properties[\\\"mulliken_charges\\\"] = charges\\n properties.update(get_cm5_charges(lines)) # Can return {} if not GFN1\\n properties[\\\"bonds\\\"] = bonds\\n properties[\\\"bondorders\\\"] = bondorders\\n\\n if \\\"vibspectrum\\\" in os.listdir(scr):\\n properties[\\\"frequencies\\\"] = get_frequencies(scr=scr)\\n\\n return properties\",\n \"def picksomechoices(question, answer):\\n \\\"\\\"\\\" because of the way dict() works all 4 choices will be unique \\\"\\\"\\\"\\n choices = dict()\\n choices[question] = answer\\n for choice in random.sample(nlist, 10):\\n choices[choice[0]] = choice[1]\\n if len(choices.keys()) > 3:\\n break\\n\\n return choices\",\n \"def picksomechoices(question, answer):\\n \\\"\\\"\\\" because of the way dict() works all 4 choices will be unique \\\"\\\"\\\"\\n choices = dict()\\n choices[question] = answer\\n for choice in random.sample(nlist, 10):\\n choices[choice[0]] = choice[1]\\n if len(choices.keys()) > 3:\\n break\\n\\n return choices\",\n \"def picksomechoices(question, answer):\\n \\\"\\\"\\\" because of the way dict() works all 4 choices will be unique \\\"\\\"\\\"\\n choices = dict()\\n choices[question] = answer\\n for choice in random.sample(nlist, 10):\\n choices[choice[0]] = choice[1]\\n if len(choices.keys()) > 3:\\n break\\n\\n return choices\",\n \"def picksomechoices(question, answer):\\n \\\"\\\"\\\" because of the way dict() works all 4 choices will be unique \\\"\\\"\\\"\\n choices = dict()\\n choices[question] = answer\\n for choice in random.sample(nlist, 10):\\n choices[choice[0]] = choice[1]\\n if len(choices.keys()) > 3:\\n break\\n\\n return choices\",\n \"def test_properties(self):\\n input_file = self.copy_and_mark_for_cleanup(\\\"Fasta/f002\\\")\\n\\n cline = XXmotifCommandline(outdir=self.out_dir, seqfile=input_file)\\n\\n cline.revcomp = True\\n cline.pseudo = 20\\n cline.startmotif = \\\"ACGGGT\\\"\\n\\n self.standard_test_procedure(cline)\",\n \"def notAFan_questions(user):\\n questions = {\\n 1: \\\"GBB: How old are you? \\\",\\n 2: \\\"GBB: What do you like to do in your free time? \\\",\\n 3: \\\"GBB: What is your ethnicity? \\\",\\n 4: \\\"GBB: What did you eat for breakfast? \\\",\\n 5: \\\"GBB: Are you an early bird or a night owl? \\\",\\n 6: \\\"GBB: Do you like football? \\\"\\n }\\n\\n while True:\\n num = random.randint(1, 6)\\n answered = user['personal questions asked']\\n if num not in answered:\\n user['personal questions asked'].append(num)\\n return questions[num]\\n if len(answered) == len(questions.keys()):\\n return \\\"GBB: Looks like I know you so well that I don't even need to ask you a question! Type anything to proceed.\\\"\",\n \"def gen_task(self, q_type, q_option):\\n # check what it the task about\\n if q_type == \\\"write\\\":\\n question = [\\\"Please, let me know what you most like on \\\",\\n \\\"Please, write a short sentence about \\\",\\n \\\"Please, write the first experience you had with \\\"]\\n select_question = random.choice(question)\\n\\n elif q_type == \\\"draw\\\":\\n draw = [\\n \\\"In a separeted piece of paper, please draw your favorite \\\",\\n \\\"In a separeted piece of paper, please draw the first thing it comes to your mind when you think about \\\"\\n ]\\n select_question = random.choice(draw)\\n\\n # output the task based on the option input\\n select_option = q_option\\n\\n # join everything\\n task = select_question + select_option\\n\\n return task\",\n \"def randomHelmet():\\n return random.choice(HELMETS)\",\n \"def _load_answers(self, answer_list):\\n # We are given a list of words that indicate what the answer\\n # options are. By convention, the first word is probably the\\n # correct answer; the others are incorrect answers. However,\\n # we won't set this now because this convention may not hold.\\n # We expect the SET_CORRECT OpalCommand to be used to set\\n # which answers are correct or incorrect.\\n # split the list of answers on commas.\\n answers = answer_list.strip().split(',')\\n\\n # Shuffle answers to display them in a random order.\\n random.shuffle(answers)\\n\\n # Load in the graphic for each answer.\\n for answer in answers:\\n toload = {}\\n # Remove whitespace from name before using it.\\n toload[\\\"name\\\"] = answer.strip()\\n toload[\\\"tag\\\"] = \\\"PlayObject\\\"\\n toload[\\\"slot\\\"] = answers.index(answer) + 1\\n toload[\\\"draggable\\\"] = False\\n toload[\\\"isAnswerSlot\\\"] = True\\n self._ros_node.send_opal_command(\\\"LOAD_OBJECT\\\", json.dumps(toload))\",\n \"def random_text(random_file=None):\\n if random_file is None:\\n random_file = os.path.join(\\n os.path.dirname(os.path.dirname(__file__)), \\\"data\\\", \\\"random.txt\\\"\\n )\\n with open(random_file) as file_used:\\n return random.choice(list(file_used))\",\n \"def boolean():\\r\\n i2 = question_amount_1.get()\\r\\n check = (only_int(i2))\\r\\n if not check:\\r\\n num_check(820, 210, 0)\\r\\n else:\\r\\n for it in range(5):\\r\\n variat(it)\\r\\n name = 'Операции булевой алгебры:'\\r\\n input_file_docx(name, name)\\r\\n for p in range(int(question_amount_1.get())):\\r\\n spechotr = ''\\r\\n sgnA = random.randint(0, 1)\\r\\n print()\\r\\n sgnB = random.randint(0, 1)\\r\\n print (\\\"A =\\\", sgnA, \\\"B =\\\", sgnB)\\r\\n spech = ''\\r\\n strv = ''\\r\\n ir = 1\\r\\n while ir < 4:\\r\\n nch = random.randint(1, 4)\\r\\n if nch == 1:\\r\\n spech = spech + \\\"¬A∨B\\\"\\r\\n strv = strv + \\\"not sgnA or sgnB \\\"\\r\\n if nch == 2:\\r\\n strv = strv + \\\"not sgnB or sgnA\\\"\\r\\n spech = spech + \\\"¬B∨A\\\"\\r\\n if nch == 3:\\r\\n strv = strv + \\\"(sgnB or sgnA)\\\"\\r\\n spech = spech + \\\"(B∨A)\\\"\\r\\n if nch == 4:\\r\\n strv = strv + \\\"(sgnA and sgnB)\\\"\\r\\n spech = spech + \\\"(A∧B)\\\"\\r\\n if ir < 3:\\r\\n strv = strv + \\\" and \\\"\\r\\n spech = spech + \\\"∧\\\"\\r\\n ir = ir + 1\\r\\n task = f'{p + 1}. A = {sgnA}, B = {sgnB} Какое значение принимает функция {spech} ?'\\r\\n rsh = eval(strv)\\r\\n answer = task + f' \\\\n Oтвет: {rsh}'\\r\\n input_file_docx(task, answer)\\r\\n lbvi = Label(window1, font=(\\\"Arial Bold\\\", 14), text=\\\"Выполнено \\\")\\r\\n lbvi.place(x=800, y=210)\\r\\n return\",\n \"def load_test_troublemaker():\\n return Troublemaker(\\n \\\"Karen\\\",\\n 700,\\n 101892099,\\n \\\"Scotia Bank\\\",\\n 2000.00,\\n [\\n Budget(\\\"Games and Entertainment\\\", 200),\\n Budget(\\\"Clothing and Accessories\\\", 200),\\n Budget(\\\"Eating Out\\\", 200),\\n Budget(\\\"Miscellaneous\\\", 200)\\n ]\\n )\",\n \"def tst_random_set():\\n final_wave, final_spec, final_z = desi_qso_templates(\\n outfil='test_random_set.fits', N_perz=100, seed=12345)\",\n \"def test_case_3(self):\\n config_generator = Genesis()\\n\\n data = open_json_file('r1.json')\\n\\n task_1 = Task.new(data=data, name=\\\"R1_BASE_CONFIG\\\")\\n task_2 = Task.new(data=data, name=\\\"R1_BASE_CONFIG_2\\\")\\n\\n config_generator.add_task(task_1)\\n config_generator.add_task(task_2)\\n\\n results = config_generator.generate()\\n self.assertIsInstance(results, dict)\\n\\n for task in config_generator.tasks:\\n self.assertTrue(task.is_complete)\\n self.assertIsInstance(task.rendered_data, str)\",\n \"def test_generate_all_training(self):\\n facade = ChatetteFacade.get_or_create()\\n\\n input_dir_path = \\\"tests/system-testing/inputs/generate-all/\\\"\\n input_filenames = [\\n \\\"simplest.chatette\\\", \\\"only-words.chatette\\\",\\n \\\"words-and-groups.chatette\\\", \\\"alias.chatette\\\", \\\"include.chatette\\\",\\n \\\"slot.chatette\\\", \\\"slotrolegroup.chatette\\\"\\n ]\\n for filename in input_filenames:\\n file_path = os.path.join(input_dir_path, filename)\\n facade.run(file_path)\\n if not TestSystem.check_no_duplicates(facade.train_examples):\\n pytest.fail(\\n \\\"Some examples were generated several times \\\" +\\n \\\"when dealing with file '\\\" + filename + \\\"'.\\\\nGenerated: \\\" + \\\\\\n str(facade.train_examples)\\n )\\n legal_examples = TestSystem.get_legal_examples(file_path)\\n for ex in facade.train_examples:\\n formatted_ex = {\\\"intent\\\": ex.intent_name, \\\"text\\\": ex.text}\\n if formatted_ex not in legal_examples:\\n pytest.fail(\\n str(formatted_ex) + \\\" is not a legal example for '\\\" + \\\\\\n file_path + \\\"'\\\"\\n )\\n if len(legal_examples) != len(facade.train_examples):\\n training_texts = [ex.text for ex in facade.train_examples]\\n for legal_ex in legal_examples:\\n if legal_ex[\\\"text\\\"] not in training_texts:\\n pytest.fail(\\n \\\"Example '\\\" + legal_ex[\\\"text\\\"] + \\\\\\n \\\"' was not generated.\\\"\\n )\\n pytest.fail(\\n \\\"An unknown example was not generated (\\\" + \\\\\\n str(len(facade.train_examples)) + \\\\\\n \\\" generated instead of \\\" + str(len(legal_examples)) + \\\\\\n \\\").\\\\nGenerated: \\\" + str(facade.train_examples)\\n )\\n legal_syn = TestSystem.get_legal_synonyms(file_path)\\n if legal_syn is not None:\\n synonyms = AST.get_or_create().get_entities_synonyms()\\n for key in synonyms:\\n if key not in legal_syn:\\n pytest.fail(\\n \\\"'\\\" + key + \\\"' shouldn't have any synonyms.\\\"\\n )\\n for syn in synonyms[key]:\\n if syn not in legal_syn[key]:\\n pytest.fail(\\n \\\"'\\\" + syn + \\\"' shouldn't be a synonym of '\\\" + \\\\\\n key + \\\"'\\\"\\n )\",\n \"def generate_quest(self):\\n\\n if odds(3):\\n\\n quest_items = add_dicts_together(items[\\\"master\\\"], items[self.p.square.square_type])\\n quest_item = random.choice(list(quest_items.keys()))\\n\\n i = Item(quest_item, 0, **quest_items[quest_item])\\n self.inventory.append(i)\\n\\n quantity = {'super rare': '1',\\n 'rare': '2',\\n 'uncommon': '3',\\n 'common': '6',\\n 'super common': '15'}\\n q = quantity[i.rarity]\\n\\n self.quest = i, int(q), f\\\"{self.p.name}, if you bring \\\" \\\\\\n f\\\"me {q} {i.plural if int(q) > 1 else remove_little_words(i.name)}, \\\" \\\\\\n f\\\"I will teach you a valuable skill.\\\"\\n return\\n elif odds(5):\\n mobs = []\\n for biome, building in buildings.items():\\n for b, attributes in building.items():\\n if attributes.get('mobs'):\\n for k in attributes['mobs'].keys():\\n mobs.append(k)\\n for biome, mob in wild_mobs.items():\\n for k in mob.keys():\\n mobs.append(k)\\n target = f\\\"{mobs[random.randint(0, len(mobs)-1)]} named {names[random.randint(0, len(names)-1)]}\\\"\\n print(f\\\"Well, we'll keep this off the record, but I can arrange for some money to find its way \\\"\\n f\\\"into your account if you make {colored(target, 'yellow')} disappear, if you know what I mean...\\\")\\n self.p.hit_list.append(target)\\n return False\\n\\n else:\\n return None\",\n \"def pirate():\\n \\n from random import choice\\n d = {\\n \\\"sir\\\": \\\"matey\\\", \\\"hotel\\\": \\\"fleabag inn\\\", \\\"student\\\": \\\"swabbie\\\",\\n \\\"boy\\\": \\\"matey\\\", \\\"girl\\\": \\\"wench\\\", \\\"professor\\\": \\\"foul blaggart\\\",\\n \\\"restaurant\\\": \\\"galley\\\", \\\"your\\\": \\\"ye\\\", \\\"excuse\\\": \\\"arr\\\", \\\"you\\\": \\\"ye\\\",\\n \\\"students\\\": \\\"swabbies\\\", \\\"are\\\": \\\"be\\\", \\\"lawyer\\\": \\\"foul blaggart\\\",\\n \\\"the\\\": \\\"th'\\\", \\\"restroom\\\": \\\"head\\\", \\\"my\\\": \\\"me\\\", \\\"hello\\\": \\\"avast\\\",\\n \\\"is\\\": \\\"be\\\", \\\"man\\\": \\\"scurvy dog\\\", \\\"hey\\\": \\\"avast\\\", \\\"pirate\\\": \\\"scurvy pirate\\\",\\n \\\"idiot\\\": \\\"flunder\\\", \\\"young\\\": \\\"ye\\\", \\\"the\\\": \\\"t'\\\", \\\"suck\\\": \\\"blow down\\\",\\n \\\"fall\\\": \\\"hade\\\", \\\"happens\\\": \\\"be happening\\\", \\\"death\\\": \\\"Davy Jones' treasure chest\\\",\\n \\\"always\\\": \\\"ever\\\", \\\"you're\\\": \\\"ye're\\\", \\\"girlfriend\\\": \\\"Lassie-Lucy\\\",\\n \\\"with\\\": \\\"wit'\\\", \\\"everyone\\\": \\\"evr'un\\\"\\n }\\n #~ sentence = raw_input(\\\"What be yer sentence, ye old landlubber? \\\")\\n sentences = [\\\"If the world didn't suck we'd all fall off\\\",\\n \\\"What happens if you get scared half to death twice?\\\",\\n \\\"Save water and shower with your girlfriend\\\",\\n \\\"Always remember you're unique, just like everyone else\\\"]\\n \\n start = [\\\"Yarr, \\\", \\\"Yarr! \\\", \\\"Yarr-ha-harr! \\\", \\\"Skuttle me Skippers! \\\",\\n \\\"Shiver me timbers! \\\", \\\"Ahoy, me hearties! \\\", \\\"Dogs ahoy! \\\",\\n \\\"Hoist the mizzen!!! \\\", \\\"Hoist the colors!! \\\" ]\\n end = [\\\". ARRRGHHHH!\\\", \\\". Avast ye varmint!\\\", \\\". Yarr?\\\",\\n \\\". Savvyy?\\\", \\\". Yarr!\\\", \\\". Aye!\\\",\\n \\\", ye bilge rat!\\\", \\\", ye mangy dog!\\\", \\\", ye scallywag!\\\"]\\n\\n s = choice(sentences)\\n print s\\n print choice(start) +\\\\\\n ' '.join([d[i] if i in d else i for i in s.lower().split()]).capitalize()\\\\\\n + choice(end)\",\n \"def test_multi_template():\\n data = []\\n data.extend([\\\"{}_data.json\\\".format(i) for i in range(50)])\\n data.extend([\\\"{}_log.csv\\\".format(i) for i in range(50)])\\n data.extend([\\\"filename_{}.py\\\".format(i) for i in range(50)])\\n data.extend([\\\"stuff_{}.py\\\".format(i) for i in range(50)])\\n temp = data[:]\\n random.shuffle(temp)\\n assert data == sort(temp)\",\n \"def random_data(self) -> (str, str):\\n random_animal = random.choice(self.animals_list)\\n synsets = wn.synsets(str(random_animal))\\n definition = \\\"\\\"\\n while True:\\n if len(synsets) != 0:\\n for synset in synsets:\\n if synset.lexname() == 'noun.animal':\\n definition = synset.definition()\\n break\\n else:\\n random_animal = random.choice(self.animals_list)\\n synsets = wn.synsets(str(random_animal))\\n return random_animal, definition\",\n \"def main():\\n # Load and prep training files\\n raw_speech_text = hg.load_training_file('trump_train.txt')\\n speech_text = hg.prep_training(raw_speech_text)\\n tweet_data = load_tweets('trump_tweets.json')\\n raw_tweets = \\\"\\\"\\n for dct in tweet_data:\\n raw_tweets += \\\"{} \\\".format(dct['text'])\\n tweets = hg.prep_training(raw_tweets)\\n corpus = speech_text + tweets\\n corpus = strip_punctuation(corpus)\\n dict_1 = hg.map_one_to_one(corpus)\\n dict_2 = hg.map_two_to_one(corpus)\\n text = []\\n \\n # Introduction\\n print(\\\"\\\\nTrump Speech Generator\\\\n\\\")\\n print(\\\"Select words to add to speech\\\")\\n print(\\\"\\\\'x\\\\' to exit\\\")\\n print(\\\"\\\\'p\\\\' to add punctuation\\\")\\n print(\\\"Select \\\\'p\\\\' before selecting the word you want to punctuate\\\")\\n\\n # Select first word\\n options = corpus\\n print ()\\n selection = select_word(corpus)\\n text.append(selection)\\n \\n # Select second word\\n last = text[0]\\n options = word_after_one(last, dict_1)\\n print_text(text)\\n selection = select_word(options)\\n text.append(selection)\\n \\n # Select subsequent word\\n while True:\\n last = \\\"{} {}\\\".format(text[-2].strip(punctuation),\\n text[-1].strip(punctuation))\\n options = word_after_two(last, dict_2)\\n if options == []:\\n last = last.split()[1]\\n options = word_after_one(last, dict_1)\\n while options == []:\\n last = random.choice(corpus)\\n options = word_after_one(last, dict_1)\\n print_text(text)\\n selection = select_word(options)\\n text.append(selection)\\n \\n print_text(text)\",\n \"async def watdo(self, ctx, *args):\\n choicelist = []\\n for choice in args:\\n choicelist.append(choice)\\n result = random.choice(choicelist)\\n await ctx.bot.send_message(ctx.message.channel, \\\"I pick {}!\\\".format(result))\",\n \"def create_random_proposals(self): \\r\\n global MAX_NUMBER_PROPOSALS\\r\\n global LOCATIONS\\r\\n global CATEGORIES\\r\\n \\r\\n for i in range(MAX_NUMBER_PROPOSALS):\\r\\n description = \\\"\\\"\\r\\n location = locations_rv.rvs(size=1)[0]\\r\\n category = categories_rv.rvs(size=1)[0]\\r\\n budget = random.uniform(500000, 1000000)\\r\\n project = Project(i, description, category, budget, location)\\r\\n self.proposals.append(project)\",\n \"def generate_question(self, num_question = 10):\\n\\t\\t\\n\\t\\tquestions = []\\n\\t\\tfor q in range(num_question):\\n\\t\\t\\tfor r in range(2):\\n\\t\\t\\t\\tquestion = np.zeros(self.question_dim, dtype = np.float32)\\n\\t\\t\\t\\tcolor = np.random.randint(len(COLOR))\\n\\t\\t\\t\\tquestion[color] = 1.0\\n\\t\\t\\t\\tquestion[6 + r] = 1.0\\n\\t\\t\\t\\tquestion_label = np.random.randint(3)\\n\\t\\t\\t\\tquestion[8 + question_label] = 1.0\\n\\t\\t\\t\\tquestions.append(question)\\n\\t\\treturn questions\",\n \"def choose_question():\\r\\n random_index_question = randint(1, question.num_question + 1)\\r\\n random_question = question.question[random_index_question]\\r\\n correct_answer = question.answer[random_index_question]\\r\\n return random_question, correct_answer\",\n \"def random_word():\\r\\n # Opening the SOWPODS dictionary and getting a random word\\r\\n with open(r\\\"C:\\\\Users\\\\Santosh\\\\Desktop\\\\sowpods.txt\\\") as f:\\r\\n content = f.read()\\r\\n return random.choice(content.split())\",\n \"def shuffle_question(self):\\n r = random.SystemRandom()\\n r.shuffle(self.question_list)\",\n \"def generate(random, pid, autogen_tools, n):\\n\\n #Get a random build path\\n generator_path = autogen_tools.get_directory(__file__)\\n\\n rendered_template_path = path.join(generator_path, \\\"encrypted\\\")\\n\\n key = \\\"xor_20134113\\\"\\n flag = \\\"flag_\\\" + sha1((str(n) + key).encode('utf-8')).hexdigest()\\n text = xor(\\\"You accessed all my secrets :(. But the juicy diary entries are in another castle! Here is the flag though: \\\" + flag, random.randint(0x1,0xff))\\n\\n with codecs.open(rendered_template_path, 'w', \\\"utf-8\\\") as out_file:\\n out_file.write(text)\\n\\n encrypted_link = autogen_tools.generate_resource_link(pid, \\\"encrypted\\\", title=\\\"encrypted\\\")\\n source_link = autogen_tools.generate_resource_link(pid, \\\"diary.py\\\", static=True, title=\\\"script\\\")\\n\\n return {\\n \\\"resource_files\\\": {\\n \\\"public\\\": [\\n (rendered_template_path, \\\"encrypted\\\"),\\n ],\\n },\\n \\\"static_files\\\": {\\n \\\"public\\\": [\\n (path.join(generator_path,\\\"diary.py\\\"), \\\"diary.py\\\")\\n ]\\n },\\n \\\"problem_updates\\\": {\\n \\\"description\\\": \\\"<p>A friend of yours has been using this %s to encrypt his diary. Being the nosy person you are, you must take a look! Can you decrypt it?</p><p>%s</p>\\\" % (source_link, encrypted_link)\\n }\\n }\",\n \"def random_present_quiz_selector():\\n\\n go_again = True\\n while go_again:\\n number = random.randint(1, 3)\\n if number == 1:\\n begin_present_are_quiz()\\n if number == 2:\\n begin_present_ere_quiz()\\n if number == 3:\\n begin_present_ire_quiz()\\n again = input(\\\"Continue? Y/N\\\\n\\\").lower()\\n if again == \\\"n\\\":\\n go_again = False\",\n \"def setup(self):\\n setup = RandomWordGenerator().get()\\n self.formatted_word = ConvertWord().convert_to_dict(setup)\\n self.underscore_word = HangmanUnderscoreDiagram(\\n setup).create_hidden_word()\\n self.failed_guesses = 0\\n print(\\\"Hello\\\")\\n self.has_won = False\\n self.start_game(True)\",\n \"def personal_questions(user):\\n questions = {\\n 1: 'GBB: How long have you been a fan of the Packers?',\\n 2: 'GBB: Why are you a fan of the Packers?',\\n 3: \\\"GBB: What team do you hate the most?\\\",\\n 4: \\\"GBB: Who's your favorite player on the Packers?\\\",\\n 5: \\\"GBB: Who's your least favorite player on the Packers?\\\",\\n 6: \\\"GBB: Do you come from a family of Packer fans, or are you a lone ranger?\\\"\\n }\\n\\n while True:\\n num = random.randint(1, 6)\\n answered = user['personal questions asked']\\n if num not in answered:\\n user['personal questions asked'].append(num)\\n return questions[num]\\n if len(answered) == len(questions.keys()):\\n return \\\"GBB: Look's like I know you so well that I don't even need to ask you a question!\\\"\",\n \"def generate_products():\\n # initialize list of noun and adj\\n num_products = 30\\n products = [0] * num_products\\n prices = [0] * num_products\\n weights = [0] * num_products\\n flammabilities = [0] * num_products\\n\\n # initlize random word object\\n random = RandomWords()\\n\\n adj = [random.get_random_word(includePartOfSpeech=\\\"adjective\\\")\\n for product in products]\\n noun = [random.get_random_word(includePartOfSpeech=\\\"noun\\\")\\n for product in products]\\n products = [noun + \\\" \\\" + adj for noun, adj in zip(adj, noun)]\\n\\n prices = [random.randint(5, 100) for price in prices]\\n weights = [random.randint(5, 100) for weight in weights]\\n flammabilities = [random.randint(0.0, 2.5)\\n for flammability in flammabilities]\\n\\n return products, prices, weights, flammabilities\",\n \"def generate_sentence(self, complete_property):\\n\\n drink_property = complete_property.split(\\\": \\\")[0]\\n property_type = complete_property.split(\\\": \\\")[1].split(\\\"// \\\")[1]\\n template = self.get_template(property_type)\\n question = template.replace(\\\"{\\\" + property_type + \\\"}\\\", drink_property)\\n return self.get_correct_form(question)\",\n \"def random_subjunctiveimp_quiz_selector():\\n\\n go_again = True\\n while go_again:\\n number = random.randint(1, 3)\\n if number == 1:\\n begin_subjunctiveimp_are_quiz()\\n if number == 2:\\n begin_subjunctiveimp_ere_quiz()\\n if number == 3:\\n begin_subjunctiveimp_ire_quiz()\\n again = input(\\\"Continue? Y/N\\\\n\\\").lower()\\n if again == \\\"n\\\":\\n go_again = False\",\n \"def make_phys():\\n for rn in dcm_dict.keys():\\n # PPG\\n if not dcm_dict[rn]['ppg_file'] == 'File missing':\\n # Files\\n ppg_tsv = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-cardiac.tsv.gz')\\n ppg_json = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-cardiac.json')\\n # TSV\\n gzip_file(dcm_dict[rn]['ppg_file'],ppg_tsv)\\n # JSON\\n data = OrderedDict()\\n data['SamplingFrequency'] = 100.0\\n data['StartTime'] = -30.0\\n data['Columns'] = 'cardiac'\\n with open(ppg_json, 'w') as ff:\\n json.dump(data, ff,sort_keys=False, indent=4)\\n # Respiration\\n if not dcm_dict[rn]['resp_file'] == 'File missing':\\n # Files\\n resp_tsv = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-respiratory.tsv.gz')\\n resp_json = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-respiratory.json')\\n # TSV\\n gzip_file(dcm_dict[rn]['resp_file'],resp_tsv)\\n # JSON\\n data = OrderedDict()\\n data['SamplingFrequency'] = 25.0\\n data['StartTime'] = -30.0\\n data['Columns'] = 'respiratory'\\n with open(resp_json, 'w') as ff:\\n json.dump(data, ff,sort_keys=False, indent=4)\\n # ECG\\n # What to do if they have PPG and ECG?\\n if not dcm_dict[rn]['ecg_file'] == 'File missing':\\n # Files\\n ecg_tsv = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-cardiac.tsv.gz')\\n ecg_json = os.path.join(out_dir,subject+'_'+dcm_dict[rn]['out_name']+'_physio-cardiac.json')\\n # TSV\\n gzip_file(dcm_dict[rn]['resp_file'],resp_tsv)\\n # JSON\\n data = OrderedDict()\\n data['SamplingFrequency'] = 1000.0\\n data['StartTime'] = -30.0\\n data['Columns'] = 'cardiac'\\n with open(resp_json, 'w') as ff:\\n json.dump(data, ff,sort_keys=False, indent=4)\",\n \"def random_conditional_quiz_selector():\\n\\n go_again = True\\n while go_again:\\n number = random.randint(1, 2)\\n if number == 1:\\n begin_conditionalpr_are_ere_quiz()\\n if number == 2:\\n begin_conditionalpr_ire_quiz()\\n again = input(\\\"Continue? Y/N\\\\n\\\").lower()\\n if again == \\\"n\\\":\\n go_again = False\",\n \"def get_random_quote():\\n with open (\\\"./data/quotes.txt\\\", \\\"r\\\") as quotes:\\n\\n all_quotes = quotes.read().rstrip(\\\"\\\\n\\\").split(\\\"\\\\n\\\\n\\\")\\n\\n quote = choice(all_quotes)\\n\\n return quote\",\n \"def _choose_templated_topics_persona(self):\\n\\n topic_bundles = random.sample(\\n constants.TEMPLATE_PERSONAS_TOPICS, k=constants.TEMPLATE_PERSONAS_CHOICES\\n ) # Each topic bundle is string of comma-seperated related topics, eg. \\\"book,author\\\"\\n topics = []\\n for tb in topic_bundles:\\n topics.extend(tb.split(','))\\n\\n apprentice_agent = self._get_apprentice()\\n persona_selection_form = [\\n {\\n 'type': 'choices',\\n 'question': 'My character\\\\'s favorite ',\\n 'choices': topics,\\n },\\n {'type': 'text', 'question': 'is '},\\n {'type': 'text', 'question': 'Add something imaginative to refine it:'},\\n ]\\n _coordinator_send_message(\\n apprentice_agent,\\n message=constants.APPRENTICE_CHOOSE_PERSONA_TEMPLATE_REQUEST,\\n task_data={'respond_with_form': persona_selection_form},\\n )\\n agent_response = self.receive_form_response(\\n apprentice_agent, check_persona_overuse=True\\n )\\n\\n rs = [r['response'] for r in agent_response['task_data']['form_responses']]\\n assert len(rs) == 3, 'Template persona response form length is not 3.'\\n topic, topic_item, extra_details = rs\\n apprentice_persona = persona_from_template_values(\\n topic, topic_item, extra_details\\n )\\n worker_name = self.worker_names[apprentice_agent]\\n logging.info(f'Agent ({worker_name}) selected a persona: {apprentice_persona}')\\n\\n return apprentice_persona\",\n \"def makeNonsense(part1, part2, part3, n=10):\\n #convert to lists\\n p1 = part1.split('\\\\n')\\n p2 = part2.split('\\\\n')\\n p3 = part3.split('\\\\n')\\n #shuffle the lists\\n [ random.shuffle( p ) for p in [p1,p2,p3] ]\\n #concatinate the sentences\\n sentence = []\\n for k in range(n):\\n try:\\n s = p1[k] + ' ' + p2[k] + ' ' + p3[k] \\n # THE LOUDER YOU YELL, THE RIGHTER YOU ARE!!\\n s += '!!!'\\n sentence.append(s)\\n except IndexError:\\n break\\n return sentence\",\n \"def generateSupplies(self):\\n typePart = ['wrench','resistor','bulb','mushroom','coin']\\n chosenPart = []\\n for i in range(3):\\n randomPart = choice(typePart)\\n chosenPart.append(randomPart)\\n typePart.remove(randomPart)\\n for part in chosenPart:\\n amount = randint(1,3)\\n self._supplies.append(Node(part,amount))\",\n \"def test_send_all(self):\\n mailroom4.send_all()\\n for person in donation_data:\\n self.assertTrue(os.path.exists(person.replace(' ','_')+'.txt'))\\n try:\\n with open(person.replace(' ','_')+'.txt','r') as f:\\n actual = f.read()\\n except FileNotFoundError:\\n print(\\\"The file wasn't created\\\")\\n actual = ''\\n expected = mailroom4.letter({'donor':person,'amount':donation_data[person][-1]})\\n self.assertEqual(expected,actual)\",\n \"def create_evaluation_template(client, survey_name):\\n\\n loremipsum = \\\"Lorem ipsum dolor sit amet, consecteteur adipiscing elit donec proin nulla vivamus. Augue donec a erat ve sagittis nisi rhoncus curabitur mauris. Nulla ipsum tortor sagittis adipiscing primis interdum suspendisse lobortis etiam risus nullam. Donec massa quam dis at nibh dolor netus quis. Purus etiam. Dolor neque nunc netus eget nulla faucibus vestibulum aenean class senectus. Porta dolor. Donec morbi. Felis lorem tempus luctus malesuada laoreet curae justo rhoncus ante facilisi parturient malesuada elit laoreet amet. Fusce augue nisi ligula praesent condimentum nascetur fringilla in id lectus per nunc. Lacus metus nisl orci odio maecenas adipiscing. Velit nulla a tempor class placerat ac condimentum nisi taciti at eros.\\\"\\n\\n loremipsum_A = \\\"A: \\\\n\\\" + loremipsum\\n loremipsum_B = \\\"B: \\\\n\\\" + loremipsum\\n\\n # Create a new survey\\n survey_id = client.create_survey(survey_name)\\n # Create 2 more pages in the survey\\n for i in range(0, 2):\\n client.create_new_page(survey_id, str(i), loremipsum) # title and description\\n\\n # Get the page ids\\n page_ids = client.get_pages_ids(survey_id) # There will be 3\\n\\n answers = [\\\"A\\\", \\\"B\\\"]\\n question_title = \\\"Which of the following abstract is more relevant to the one above?\\\"\\n for i, ID in enumerate(page_ids):\\n client.update_title_description_of_page(survey_id, ID, \\\"Abstract\\\" + str(i), loremipsum)\\n client.add_single_choice(survey_id, ID, question_title, answers)\\n client.add_paragraph(survey_id, ID, loremipsum_A)\\n client.add_paragraph(survey_id, ID, loremipsum_B)\\n\\n return survey_id\",\n \"def pre_process(fname, num_ex, alt_speaker):\\n conversation = []\\n with PathManager.open(fname) as f:\\n lines = f.readlines()\\n random.shuffle(lines)\\n lines = lines[:num_ex]\\n for line in lines:\\n data = json.loads(line)\\n dialogue = data[\\\"dialog\\\"]\\n for item in dialogue:\\n speaker = item[0]['id']\\n text = item[0]['text']\\n conversation += [(speaker, text)]\\n conversation += [(END_OF_CONVO, END_OF_CONVO)]\\n\\n return conversation\",\n \"def play():\\n display_starting_message()\\n print(\\\"\\\")\\n print(\\\"*\\\"*10)\\n for question_number, question in enumerate(list_of_questions):\\n print(question)\\n print(\\\"\\\")\\n for responses in list_of_questions[question]:\\n print(responses)\\n pick_one = input(\\\"pick one: \\\")\\n check_murder_sauce(question, pick_one)\\n\\n murder_sauce_result(murder_sauce)\",\n \"def main_questions(money, grain, people):\\n quest_buy = [Q1, Q2, Q3, Q6, Q7]\\n question = random.choice(quest_buy)\\n print(question)\\n answer = input()\\n while answer.isdigit() is False:\\n print(INPUT_INT_VALUE)\\n answer = input()\\n answer = int(answer)\\n if question == Q1:\\n money = money - answer * 12\\n elif question == Q2:\\n money -= answer * 14\\n elif question == Q3:\\n money -= answer * 13\\n elif question == Q6:\\n money -= answer * 10\\n elif question == Q7:\\n money -= answer * 15\\n grain += answer\\n\\n quest_sell = [Q4, Q5, Q8, Q9, Q10]\\n question_2 = random.choice(quest_sell)\\n print(question_2)\\n answer = input()\\n while answer.isdigit() is False:\\n print(INPUT_INT_VALUE)\\n answer = input()\\n answer = int(answer)\\n if question == Q4:\\n money += answer * 7\\n elif question == Q5:\\n money += answer * 5\\n elif question == Q8:\\n money += answer * 6\\n elif question == Q9:\\n money += answer * 9\\n elif question == Q10:\\n money += 8\\n grain -= answer\\n\\n print(DISTRIBUTION_OF_GRAIN)\\n answer_3 = input()\\n while answer_3.isdigit() is False:\\n print(INPUT_INT_VALUE)\\n answer_3 = input()\\n answer_3 = int(answer)\\n grain -= answer_3\\n if grain / people > 90:\\n people *= 1.1\\n elif grain / people < 40:\\n people *= 0.9\\n return int(money), int(grain), int(people)\",\n \"def random_config_from_list(config_list_file):\\n lines = tuple(open(config_list_file, 'r'))\\n config_file = random.choice(lines).strip()\\n print(\\\"[hypergan] config file chosen from list \\\", config_list_file, ' file:', config_file)\\n return hg.configuration.Configuration.load(config_file+\\\".json\\\")\",\n \"def main():\\n # Read input from file, returns all objects\\n objects = read_input()\\n for obj in objects:\\n try:\\n # Generate the objects answer, yields new object\\n obj = generate_answer(obj)\\n except Exception:\\n # If an error might occur that is not covered, catch it here! Continue where left off\\n print('ERROR: An unrecoverable error occured during the processing of ' + obj.get(\\n 'operation') + '. Continuing...')\\n obj['answer'] = 'ERROR'\\n\\n print(obj) # TODO: Remove before production\\n # Generate an output file\\n print_output(objects)\",\n \"def populate_game_questions():\\n indices = random.sample(range(0, len(quizquestion.questions_all)), 5) # If user doesn't specify, choose 5 random questions\\n return quizquestion.QuizQuestion.get_game_questions(indices)\",\n \"def _generate_examples(self, filepath, split):\\r\\n if self.config.name == \\\"trex\\\":\\r\\n paths = filepath\\r\\n relations_path = paths[0]\\r\\n paths = paths[1:]\\r\\n all_rels = {}\\r\\n with open(relations_path, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n all_rels[data[\\\"relation\\\"]] = data\\r\\n id_ = -1\\r\\n for filepath in paths:\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n pred = all_rels.get(data[\\\"predicate_id\\\"], {})\\r\\n for evidences in data[\\\"evidences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"obj_uri\\\": str(data[\\\"obj_uri\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"sub_uri\\\": str(data[\\\"sub_uri\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"predicate_id\\\": str(data[\\\"predicate_id\\\"]),\\r\\n \\\"sub_surface\\\": str(evidences[\\\"sub_surface\\\"]),\\r\\n \\\"obj_surface\\\": str(evidences[\\\"obj_surface\\\"]),\\r\\n \\\"masked_sentence\\\": str(evidences[\\\"masked_sentence\\\"]),\\r\\n \\\"template\\\": str(pred.get(\\\"template\\\", \\\"\\\")),\\r\\n \\\"template_negated\\\": str(pred.get(\\\"template_negated\\\", \\\"\\\")),\\r\\n \\\"label\\\": str(pred.get(\\\"label\\\", \\\"\\\")),\\r\\n \\\"description\\\": str(pred.get(\\\"description\\\", \\\"\\\")),\\r\\n \\\"type\\\": str(pred.get(\\\"type\\\", \\\"\\\")),\\r\\n }\\r\\n elif self.config.name == \\\"conceptnet\\\":\\r\\n id_ = -1\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n if data.get(\\\"negated\\\") is not None:\\r\\n for masked_sentence, negated in zip(data[\\\"masked_sentences\\\"], data[\\\"negated\\\"]):\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"sub\\\": str(data.get(\\\"sub\\\", \\\"\\\")),\\r\\n \\\"obj\\\": str(data.get(\\\"obj\\\", \\\"\\\")),\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"negated\\\": str(negated),\\r\\n }\\r\\n else:\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"sub\\\": str(data.get(\\\"sub\\\", \\\"\\\")),\\r\\n \\\"obj\\\": str(data.get(\\\"obj\\\", \\\"\\\")),\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"negated\\\": str(\\\"\\\"),\\r\\n }\\r\\n elif self.config.name == \\\"squad\\\":\\r\\n id_ = -1\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"id\\\": str(data[\\\"id\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"negated\\\": str(data.get(\\\"negated\\\", \\\"\\\")),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n }\\r\\n elif self.config.name == \\\"google_re\\\":\\r\\n id_ = -1\\r\\n paths = filepath\\r\\n for filepath in paths:\\r\\n # from https://github.com/facebookresearch/LAMA/blob/master/scripts/run_experiments.py\\r\\n if \\\"place_of_birth\\\" in filepath:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"place_of_birth\\\",\\r\\n \\\"template\\\": \\\"[X] was born in [Y] .\\\",\\r\\n \\\"template_negated\\\": \\\"[X] was not born in [Y] .\\\",\\r\\n }\\r\\n elif \\\"date_of_birth\\\" in filepath:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"date_of_birth\\\",\\r\\n \\\"template\\\": \\\"[X] (born [Y]).\\\",\\r\\n \\\"template_negated\\\": \\\"[X] (not born [Y]).\\\",\\r\\n }\\r\\n else:\\r\\n pred = {\\r\\n \\\"relation\\\": \\\"place_of_death\\\",\\r\\n \\\"template\\\": \\\"[X] died in [Y] .\\\",\\r\\n \\\"template_negated\\\": \\\"[X] did not die in [Y] .\\\",\\r\\n }\\r\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\r\\n for row in f:\\r\\n data = json.loads(row)\\r\\n for masked_sentence in data[\\\"masked_sentences\\\"]:\\r\\n id_ += 1\\r\\n yield id_, {\\r\\n \\\"pred\\\": str(data[\\\"pred\\\"]),\\r\\n \\\"sub\\\": str(data[\\\"sub\\\"]),\\r\\n \\\"obj\\\": str(data[\\\"obj\\\"]),\\r\\n \\\"evidences\\\": str(data[\\\"evidences\\\"]),\\r\\n \\\"judgments\\\": str(data[\\\"judgments\\\"]),\\r\\n \\\"sub_w\\\": str(data[\\\"sub_w\\\"]),\\r\\n \\\"sub_label\\\": str(data[\\\"sub_label\\\"]),\\r\\n \\\"sub_aliases\\\": str(data[\\\"sub_aliases\\\"]),\\r\\n \\\"obj_w\\\": str(data[\\\"obj_w\\\"]),\\r\\n \\\"obj_label\\\": str(data[\\\"obj_label\\\"]),\\r\\n \\\"obj_aliases\\\": str(data[\\\"obj_aliases\\\"]),\\r\\n \\\"uuid\\\": str(data[\\\"uuid\\\"]),\\r\\n \\\"masked_sentence\\\": str(masked_sentence),\\r\\n \\\"template\\\": str(pred[\\\"template\\\"]),\\r\\n \\\"template_negated\\\": str(pred[\\\"template_negated\\\"]),\\r\\n }\",\n \"def generate_fantasy_title():\\n d20 = random.randint(1, 20)\\n if d20 <= 4:\\n #genetive noun\\n return fantasy_genetive[random.randint(0, len(fantasy_genetive) - 1)] + \\\" \\\" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\\n elif d20 > 4 and d20 < 13: \\n #The adj noun\\n return \\\"The \\\" + fantasy_adj[random.randint(0, len(fantasy_adj) - 1)] + \\\" \\\" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\\n elif d20 >= 13:\\n #something of something\\n return fantasy_noun[random.randint(0, len(fantasy_noun) - 1)] + \\\" of \\\" + fantasy_what_is_this[random.randint(0, len(fantasy_what_is_this) - 1)]\",\n \"def realize_question(dialog, template, filter_objs):\\n\\n # Number of inputs.\\n n_inputs = template.get('inputs', 0)\\n # Sample a text template.\\n text_sample = random.choice(template['text'])\\n text_sample_index = template['text'].index(text_sample)\\n\\n # Extract attribute tags and get them into groups.\\n tags = re.findall('(<[\\\\d\\\\w]*>)', text_sample)\\n tag_groups = collections.defaultdict(list)\\n for tag in tags:\\n group_id = get_tag_group(tag)\\n tag_groups[group_id].append(tag)\\n\\n # Sample a random element from filtered.\\n arg_sample = random.choice(filter_objs)\\n\\n # Remove tags from text not allowed by filter_objs.\\n for arg_ind in range(n_inputs):\\n obj_sample = arg_sample['objects'][arg_ind]\\n avail_attrs = obj_sample['optional'] + obj_sample['required']\\n\\n for ii in tag_groups[arg_ind][::-1]:\\n if mapping(ii) not in avail_attrs:\\n tag_groups[arg_ind].remove(ii)\\n text_sample = replace_attribute(text_sample, ii, arg_sample, True)\\n\\n # Assert that all required attributes are present as tags.\\n for attribute in obj_sample['required']:\\n required_tag = inv_mapping(attribute, arg_ind)\\n # Make an exception for <R> and <P>\\n if required_tag == '<R>' and '<P>' in tag_groups[arg_ind]:\\n continue\\n assert required_tag in tag_groups[arg_ind], \\\\\\n 'A required attribute is missing in template!'\\n\\n # Start compiling tags to keep.\\n tags_to_keep = [inv_mapping(ii, arg_ind) for ii in obj_sample['required']]\\n # Filter out those not present in text template.\\n optional_tags = [inv_mapping(ii,arg_ind) for ii in obj_sample['optional']]\\n optional_tags = [ii for ii in optional_tags if ii in tag_groups[arg_ind]]\\n\\n # If tags_to_keep is empty, sample from optional with (1:70, 2:25, 3:5).\\n if len(optional_tags) > 0:\\n if len(tags_to_keep) > 0:\\n n_tags_sample = [0, 1, 2]\\n else:\\n n_tags_sample = [1, 2, 3]\\n n_sample = np.random.choice(n_tags_sample, 1,\\n p=gvars.METAINFO['probabilities'],\\n replace=False)\\n # Lower cap at the length of optional.\\n n_sample = min(n_sample[0], len(optional_tags))\\n if n_sample > 0:\\n tags_to_keep += random.sample(optional_tags, n_sample)\\n\\n # Now create a dictionary of placeholders with actual attribute values.\\n for tag in tag_groups[arg_ind]:\\n remove = tag not in tags_to_keep\\n text_sample = replace_attribute(text_sample, tag, arg_sample, remove)\\n\\n # Record info and merge scene graphs.\\n dialog_datum = {'question': text_sample, 'answer': arg_sample['answer'],\\n 'template': template['label']}\\n dialog['template_info'].append(template.copy())\\n del dialog['template_info'][-1]['text']\\n dialog['template_info'][-1]['index'] = text_sample_index\\n\\n dialog['dialog'].append(dialog_datum)\\n graph_item = arg_sample['graph']\\n\\n # If mergeable, add it to the objects list.\\n dialog['graph'] = utils.merge_update_scene_graph(dialog['graph'], graph_item)\\n\\n # If there are volatile objects in the graph item, remove them.\\n for obj in graph_item['objects'][::-1]:\\n if obj.get('volatile', False): graph_item['objects'].remove(obj)\\n dialog['graph']['history'].append(graph_item)\\n return dialog\",\n \"def q_v_random(n=1000):\\n for i in range(5):\\n p1_strategy = strategies.QStrategy('X')\\n p2_strategy = strategies.RandomStrategy()\\n p1 = player.Player('X', p1_strategy)\\n p2 = player.Player('O', p2_strategy)\\n board = tictactoe.Board()\\n game = rl_game.Game(p1, p2, board)\\n game.play_many(n)\\n #p1.strategy.save_q()\\n # Do not save gameplay against random opponent\",\n \"def generate(self):\\n\\n self.event_type = random.choice(self.TYPES)\\n self.event_item = EventGenerator.ITEM_GEN.generate_item()\\n\\n event_details = random.choice(self.DETAIL_DICT[self.event_type])\\n\\n self.event_message = event_details[0]\\n self.event_title = event_details[1]\\n self.event_text = event_details[2]\\n\\n self.event_widgets = event_details[3]\\n\\n self.accomplished = event_details[4]\",\n \"def make_text(dictionary):\\n\\n capital_keys_list = [key for key in dictionary.keys() if key[0][0].isupper()] # create list of only keys that start with an uppercase letter\\n starting_key = random.choice(capital_keys_list) # choose key (tuple) to start at\\n new_text_list = list(starting_key) # add items in that tuple to list of created text\\n \\n punctuation = \\\"?.!\\\" # create punctuation string \\n next_word = \\\" \\\" # initialize next_word\\n\\n while dictionary.get(starting_key) != None and next_word[-1] not in punctuation: # Continue until the key is not found in the dictionary and until the last word ends in punctuation\\n value_list = dictionary[starting_key] # assign value of key (list)\\n rand_index = random.randrange(0, len(value_list)) # choose random int w/in length of list\\n next_word = value_list[rand_index] # find item at that random index\\n\\n new_text_list.append(next_word) # add next_word to list of created text\\n\\n starting_key = tuple(list(starting_key[1:]) + [next_word]) # create new tuple from second word of previous tuple + item at that index\\n\\n new_text_string = \\\" \\\".join(new_text_list) # join list into string\\n return new_text_string # return new text\",\n \"def ask_questions():\\n print(story.prompts)\\n prompts = story.prompts\\n \\n return render_template(\\\"questions.html\\\", prompts = prompts)\",\n \"def test_missing_properties(self, properties: list[str]) -> None:\\n issuer = unpaid_redemption()\\n treq = treq_for_loopback_ristretto(issuer)\\n d = treq.post(\\n NOWHERE.child(\\\"v1\\\", \\\"redeem\\\").to_text().encode(\\\"ascii\\\"),\\n dumps_utf8(dict.fromkeys(properties)),\\n headers=Headers({\\\"content-type\\\": [\\\"application/json\\\"]}),\\n )\\n self.assertThat(\\n d,\\n succeeded(\\n AfterPreprocessing(\\n lambda response: response.code,\\n Equals(BAD_REQUEST),\\n ),\\n ),\\n )\",\n \"def dummyProperties():\\n global num_missing_properties\\n num_missing_properties += 1 \\n return ['main=appinventor.' + DUMMY_USER_NAME + '.' + DUMMY_PROJECT_NAME + '.Screen1\\\\n',\\n 'name=' + DUMMY_PROJECT_NAME + '\\\\n',\\n 'assets=../assets\\\\n',\\n 'source=../src\\\\n',\\n 'build=../build\\\\n',\\n 'versioncode=1\\\\n',\\n 'versionname=1.0\\\\n',\\n 'useslocation=False\\\\n',\\n 'aname=' + DUMMY_PROJECT_NAME + '\\\\n']\",\n \"async def proo(pros):\\n if not pros.text[0].isalpha() and pros.text[0] not in (\\\"/\\\", \\\"#\\\", \\\"@\\\", \\\"!\\\"):\\n index = random.randint(0, len(PRO_STRINGS) - 1)\\n reply_text = PRO_STRINGS[index]\\n await pros.edit(reply_text)\",\n \"def main(fileName, output, sample):\\n fh = open(fileName)\\n currentReview = None\\n currentlyInReview = False\\n allReviews = []\\n for line in fh:\\n line = line.strip()\\n if line == END:\\n currentlyInReview = False\\n allReviews.append(currentReview)\\n if currentlyInReview:\\n currentReview += line\\n currentReview += '\\\\n'\\n if line == START:\\n currentReview = \\\"\\\"\\n currentlyInReview = True\\n fh.close()\\n if sample:\\n reviews = random.sample(allReviews, sample)\\n else:\\n reviews = allReviews\\n outFH = open(output, 'w')\\n for review in reviews:\\n outFH.write(review)\\n outFH.close()\",\n \"def nao_speech(possible_sentences):\\n\\n print(random.choice(possible_sentences))\",\n \"def find_drink_property(self, properties):\\n\\n split_status = \\\"\\\"\\n tries = 0\\n\\n while split_status != \\\"None\\\" and tries <= 5:\\n random_drink = randint(0, len(properties) - 1)\\n random_property = randint(1, len(properties[random_drink]) - 1)\\n drink_property = properties[random_drink][random_property]\\n split_status = drink_property.split(\\\": \\\")[1].split(\\\" //\\\")[0]\\n tries += 1\\n\\n if split_status != \\\"None\\\" and tries > 5:\\n drink_property = self.find_first_none(properties)\\n return drink_property\",\n \"def main():\\n parser = OptionParser()\\n parser.add_option(\\\"-u\\\", \\\"--update-index\\\", action=\\\"store_true\\\", \\n dest=\\\"update_index\\\", default=False, \\n help=\\\"Update index file\\\")\\n parser.add_option(\\\"-r\\\", \\\"--randomise\\\", action=\\\"store_true\\\", \\n dest=\\\"force_randomise\\\", default=False, \\n help=\\\"Force randomisation when using search terms.\\\") \\n # TODO: implement this\\n #parser.add_option(\\\"-l\\\", \\\"--loop\\\", action=\\\"store_true\\\", \\n # dest=\\\"loop_songs\\\", default=self.loop_songs,\\n # help=\\\"Loop playlist\\\")\\n parser.add_option(\\\"-l\\\", \\\"--list-only\\\", action=\\\"store_true\\\",\\n dest=\\\"list_only\\\", default=False, help=\\\"List songs only\\\") \\n parser.add_option(\\\"-c\\\", \\\"--config_file\\\",\\n dest=\\\"config_file\\\", help=\\\"Configuration file\\\", default=DEFAULT_CONFIG_FILE)\\n parser.add_option(\\\"-n\\\", \\\"--num-songs\\\",\\n dest=\\\"num_songs\\\", help=\\\"Number of songs to generate/play\\\")\\n\\n\\n (options, args) = parser.parse_args()\\n \\n # Try to create a playlist. \\n have_playlist = False\\n while not have_playlist: \\n try: \\n rmp = RandomMusicPlaylist(config_file=options.config_file, search_terms=args, update_index=options.update_index, \\n force_randomise=options.force_randomise, \\n list_only=options.list_only, num_songs=options.num_songs)\\n have_playlist = True\\n except MissingConfigFileError, err_msg:\\n sys.stderr.write(\\\"%s\\\\n\\\" % err_msg)\\n create_config_file(options.config_file, \\n DEFAULT_HOME_DIR)\\n \\n rmp.play_music()\",\n \"def generate_instance(num_facilities: int, num_towns: int, file_name: str) -> bool:\\r\\n\\r\\n facility_dictionary = [\\r\\n \\\"Perseus\\\",\\r\\n \\\"Andromeda\\\",\\r\\n \\\"Cassiopeia\\\",\\r\\n \\\"Cetus\\\",\\r\\n \\\"Cepheus\\\",\\r\\n \\\"Pegasus\\\",\\r\\n \\\"Auriga\\\",\\r\\n \\\"Hercules\\\",\\r\\n \\\"Sagitta\\\",\\r\\n \\\"Aquila\\\",\\r\\n \\\"Lyra\\\",\\r\\n \\\"Cygnus\\\",\\r\\n \\\"Hydra\\\",\\r\\n \\\"Crater\\\",\\r\\n \\\"Corvus\\\",\\r\\n \\\"Ophiuchus\\\",\\r\\n \\\"Serpens\\\",\\r\\n \\\"Centaurus\\\",\\r\\n \\\"Lupus\\\",\\r\\n \\\"Corona Australis\\\",\\r\\n \\\"Ara\\\",\\r\\n \\\"Orion\\\",\\r\\n \\\"Canis Major\\\",\\r\\n \\\"Canis Minor\\\",\\r\\n \\\"Lepus\\\",\\r\\n \\\"Monoceros\\\",\\r\\n \\\"Aries\\\",\\r\\n \\\"Taurus\\\",\\r\\n \\\"Gemini\\\",\\r\\n \\\"Cancer\\\",\\r\\n \\\"Leo\\\",\\r\\n \\\"Virgo\\\",\\r\\n \\\"Libra\\\",\\r\\n \\\"Scorpius\\\",\\r\\n \\\"Sagittarius\\\",\\r\\n \\\"Capricornus\\\",\\r\\n \\\"Aquarius\\\",\\r\\n \\\"Pisces\\\",\\r\\n \\\"Ursa Major\\\",\\r\\n \\\"Ursa Minor\\\",\\r\\n \\\"Draco\\\",\\r\\n \\\"Canes Venatici\\\",\\r\\n \\\"Boötes\\\",\\r\\n \\\"Corona Borealis\\\"\\r\\n ]\\r\\n towns_dictionary = [\\r\\n \\\"Rome\\\",\\r\\n \\\"Milan\\\",\\r\\n \\\"Naples\\\",\\r\\n \\\"Turin\\\",\\r\\n \\\"Florence\\\",\\r\\n \\\"Salerno\\\",\\r\\n \\\"Palermo\\\",\\r\\n \\\"Catania\\\",\\r\\n \\\"Genoa\\\",\\r\\n \\\"Bari\\\",\\r\\n \\\"Bologna\\\",\\r\\n \\\"Verona\\\",\\r\\n \\\"Pescara\\\",\\r\\n \\\"Cagliari\\\",\\r\\n \\\"Venice\\\",\\r\\n \\\"Messina\\\",\\r\\n \\\"Como\\\",\\r\\n \\\"Caserta\\\",\\r\\n \\\"Trieste\\\",\\r\\n \\\"Pisa\\\",\\r\\n \\\"Taranto\\\",\\r\\n \\\"Bergamo\\\",\\r\\n \\\"Reggio di Calabria\\\",\\r\\n \\\"Treviso\\\",\\r\\n \\\"Modena\\\",\\r\\n \\\"Parma\\\",\\r\\n \\\"Lecce\\\",\\r\\n \\\"Livorno\\\",\\r\\n \\\"Foggia\\\",\\r\\n \\\"Perugia\\\",\\r\\n \\\"Ravenna\\\",\\r\\n \\\"Ferrara\\\",\\r\\n \\\"Siracusa\\\",\\r\\n \\\"Sassari\\\",\\r\\n \\\"Udine\\\",\\r\\n \\\"Barletta\\\",\\r\\n \\\"Trento\\\",\\r\\n \\\"Brindisi\\\",\\r\\n \\\"Novara\\\",\\r\\n \\\"Ancona\\\",\\r\\n \\\"Soprabolzano\\\",\\r\\n \\\"Catanzaro\\\",\\r\\n \\\"Arezzo\\\",\\r\\n \\\"Marsala\\\",\\r\\n \\\"Asti\\\",\\r\\n \\\"Potenza\\\",\\r\\n \\\"Ragusa\\\",\\r\\n \\\"L’Aquila\\\",\\r\\n \\\"Benevento\\\",\\r\\n \\\"Civitavecchia\\\",\\r\\n \\\"Crotone\\\",\\r\\n \\\"Siena\\\",\\r\\n \\\"Campobasso\\\",\\r\\n \\\"Olbia\\\",\\r\\n \\\"Aosta\\\",\\r\\n \\\"Vibo Valentia\\\",\\r\\n \\\"Padova\\\",\\r\\n \\\"Savona\\\",\\r\\n \\\"Caltanissetta\\\",\\r\\n \\\"Vicenza\\\",\\r\\n \\\"Gorizia\\\",\\r\\n \\\"Rieti\\\",\\r\\n \\\"Grosseto\\\",\\r\\n \\\"Bolzano\\\",\\r\\n \\\"Massa\\\",\\r\\n \\\"Sanluri\\\",\\r\\n \\\"Latina\\\",\\r\\n \\\"Vercelli\\\",\\r\\n \\\"Belluno\\\",\\r\\n \\\"Cremona\\\",\\r\\n \\\"Oristano\\\",\\r\\n \\\"Mantova\\\",\\r\\n \\\"Prato\\\",\\r\\n \\\"Enna\\\",\\r\\n \\\"Lucca\\\",\\r\\n \\\"Viterbo\\\",\\r\\n \\\"Villacidro\\\",\\r\\n \\\"Trani\\\",\\r\\n \\\"Pavia\\\",\\r\\n \\\"Piacenza\\\",\\r\\n \\\"Monza\\\",\\r\\n \\\"Verbania\\\",\\r\\n \\\"Rimini\\\",\\r\\n \\\"Andria\\\",\\r\\n \\\"Fermo\\\",\\r\\n \\\"Nuoro\\\",\\r\\n \\\"Alessandria\\\",\\r\\n \\\"Matera\\\",\\r\\n \\\"Pistoia\\\",\\r\\n \\\"Reggio Emilia\\\",\\r\\n \\\"Frosinone\\\",\\r\\n \\\"Imperia\\\",\\r\\n \\\"Tortolì\\\",\\r\\n \\\"Iglesias\\\",\\r\\n \\\"Tempio Pausania\\\",\\r\\n \\\"Trapani\\\",\\r\\n \\\"Rovigo\\\",\\r\\n \\\"Teramo\\\",\\r\\n \\\"Sondrio\\\",\\r\\n \\\"Lanusei\\\",\\r\\n \\\"Biella\\\",\\r\\n \\\"Cosenza\\\",\\r\\n \\\"Cuneo\\\",\\r\\n \\\"Ascoli Piceno\\\",\\r\\n \\\"Avellino\\\",\\r\\n \\\"Chieti\\\",\\r\\n \\\"Terni\\\",\\r\\n \\\"Varese\\\",\\r\\n \\\"Forlì\\\",\\r\\n \\\"Lecco\\\",\\r\\n \\\"Carbonia\\\",\\r\\n \\\"Macerata\\\",\\r\\n \\\"La Spezia\\\",\\r\\n \\\"Pesaro\\\",\\r\\n \\\"Pordenone\\\",\\r\\n \\\"Lodi\\\",\\r\\n \\\"Brescia\\\",\\r\\n \\\"Agrigento\\\"\\r\\n ]\\r\\n\\r\\n if num_facilities > len(facility_dictionary) or num_towns > len(towns_dictionary):\\r\\n print(\\\"\\\\nToo many elements required:\\\")\\r\\n print(f\\\"\\\\tmax facilities: {len(facility_dictionary)}\\\")\\r\\n print(f\\\"\\\\tmax towns: {len(towns_dictionary)}\\\")\\r\\n return False\\r\\n\\r\\n if num_facilities < 1 or num_towns < 1:\\r\\n print(\\\"\\\\nFacility and towns elements must be more than 0\\\\n\\\")\\r\\n return False\\r\\n\\r\\n facilities = []\\r\\n towns = []\\r\\n hazards = []\\r\\n\\r\\n for i in range(num_facilities):\\r\\n facility = {\\r\\n \\\"facility_id\\\": i,\\r\\n \\\"name\\\": facility_dictionary[i],\\r\\n \\\"capacity\\\": math.floor(random.uniform(51, 300))\\r\\n }\\r\\n facilities.append(facility)\\r\\n\\r\\n for i in range(num_towns):\\r\\n town = {\\r\\n \\\"town_id\\\": i,\\r\\n \\\"name\\\": towns_dictionary[i],\\r\\n \\\"garbage\\\": math.floor(random.uniform(1, 50))\\r\\n }\\r\\n towns.append(town)\\r\\n\\r\\n for i in range(num_towns):\\r\\n town_hazards = []\\r\\n for j in range(num_facilities):\\r\\n town_hazards.append(math.floor(random.uniform(1, 100)))\\r\\n hazards.append(town_hazards)\\r\\n\\r\\n data = {\\r\\n \\\"towns\\\": towns,\\r\\n \\\"facilities\\\": facilities,\\r\\n \\\"hazards\\\": hazards\\r\\n }\\r\\n\\r\\n with open(f\\\"./instances/{file_name}.json\\\", 'w') as outfile:\\r\\n json.dump(data, outfile)\\r\\n\\r\\n return True\",\n \"def random_entry(): \\n\\n files = list_entries()\\n return random.choice(files)\",\n \"def premade():\\n global all_questions\\n\\n choice(all_questions)\\n temp = all_questions.pop(0) # Treat scrambled list like a queue\\n question,answer = temp\\n print(\\\"\\\\n\\\\n{}\\\".format(question))\\n guess = get_user_input(str)\\n\\n if guess == \\\"T\\\": guess = True\\n if guess == \\\"F\\\": guess = False\\n\\n if guess == answer:\\n print(\\\"Correct the answer is: {}\\\".format(answer))\\n else:\\n print(\\\"Incorrect the answer is: {}, This question will be asked again.\\\".format(answer))\\n all_questions.append(temp) # Add it back\",\n \"def make_music_rand():\\n pass\",\n \"def mimic_lecture(mimic_dict, starting_word=None, limit=10):\\r\\n\\r\\n if not starting_word:\\r\\n word = ''\\r\\n else:\\r\\n word = starting_word\\r\\n\\r\\n line_label = (divmod(x*3, 60) for x in numGen())\\r\\n text = word\\r\\n text = str(next(line_label)) + word\\r\\n line_mark = 0\\r\\n line_words = 1\\r\\n while line_mark < limit:\\r\\n if word not in mimic_dict:\\r\\n word = \\\"\\\"\\r\\n new_word = random.choice(mimic_dict[word])\\r\\n\\r\\n if line_words == 0:\\r\\n rand_silence = random.randint(1,10)\\r\\n if rand_silence <= 2:\\r\\n text = text + \\\"\\\\n\\\" + str(next(line_label))\\r\\n line_words = 0\\r\\n line_mark += 1\\r\\n word = new_word\\r\\n continue\\r\\n\\r\\n text = text + \\\" \\\" + new_word\\r\\n rand = random.randint(3,5)\\r\\n if line_words >= rand:\\r\\n text = text + \\\"\\\\n\\\" + str(next(line_label))\\r\\n line_words = 0\\r\\n line_mark += 1\\r\\n else:\\r\\n line_words += 1\\r\\n word = new_word\\r\\n return text\",\n \"def answerQuestion(questionIndex):\\n\\n with open(\\\"validQuestionAnswers.txt\\\") as f:\\n answers = f.readlines()\\n answers = [x.strip() for x in answers] \\n f.close()\\n \\n say(answers[questionIndex])\",\n \"def test_create_all_letters():\\n mailroom.create_all_letters()\\n for donor in mailroom.donor_db:\\n assert os.path.exists(f\\\"{donor}.txt\\\") is True\",\n \"def generate_text(word_dict, num_words):\\n\\n current_word = '$'\\n for i in range(num_words):\\n wordlist = word_dict[current_word]\\n next_word = random.choice(wordlist)\\n print(next_word, end=' ')\\n if '.' in next_word or '?' in next_word or '!' in next_word:\\n current_word = '$'\\n else:\\n current_word = next_word\\n print()\",\n \"def gen_trip_question_alt(template_path,tui_url,trip):\\n with open(template_path) as template_f:\\n q_json = json.load(template_f)\\n\\n trip_data = set_trip_data(trip)\\n\\n q_json[1]['q']['url'] = tui_url.format(trip_data=trip_data)\\n\\n # Questions 4-6 are contextual, no need to instantiate \\n pass\\n\\n return q_json\",\n \"def custom_props():\\r\\n section = document.add_section()\\r\\n new_width, new_height = section.page_height, section.page_width\\r\\n section.orientation = WD_ORIENT.LANDSCAPE\\r\\n section.page_width = 7772400\\r\\n section.page_height = 10058400\\r\\n document.add_heading('Custom Properties', level=1)\\r\\n\\r\\n customproperties = get_qlik_sense.get_customprop()\\r\\n num_of_customproperties = len(customproperties)\\r\\n table = document.add_table(rows=num_of_customproperties+1, cols=3)\\r\\n table.style = 'Grid Table 1 Light Accent 1'\\r\\n row = table.rows[0]\\r\\n row.cells[0].text = 'name'\\r\\n row.cells[1].text = 'choice values'\\r\\n row.cells[2].text = 'object types'\\r\\n\\r\\n for customproperty in range(num_of_customproperties):\\r\\n row = table.rows[customproperty+1]\\r\\n row.cells[0].text = str(customproperties[customproperty][0])\\r\\n row.cells[1].text = ', '.join(customproperties[customproperty][1])\\r\\n row.cells[2].text = ', '.join(customproperties[customproperty][2])\\r\\n document.add_page_break()\",\n \"async def randomChoice(self, ctx: commands.Context, *choices: str):\\n if not choices:\\n await ctx.reply(f\\\"Command failed - no arguments given.\\\\nEnter a sequence of arguments to choose from (you can use quotes for grouping).\\\", mention_author=False)\\n elif len(choices)==1:\\n await ctx.reply(f\\\"After some extremely randomized choosing from the one singular option that was given to choose from, the surprising result is:\\\\n{choices[0]}\\\", mention_author=False)\\n else:\\n await ctx.reply(f\\\"Randomly chosen result:\\\\n{random.choice(choices)}\\\", mention_author=False)\",\n \"def generate_list_of_wrong_choices(possible_wrong_choices, no_of_wrong_choices):\\n\\n wrong_choices = random.sample(list(possible_wrong_choices), min(len(possible_wrong_choices), no_of_wrong_choices))\\n\\n # Return a list of the text of the wrong choices\\n return [wrong_choice.text for wrong_choice in wrong_choices]\",\n \"def __GenerateVariantsDistribution(self):\\n np.random.seed(self.random_seed_parametr)\\n try:\\n Students = pd.read_excel(self.student_path)\\n print('Load {}'.format(self.student_path))\\n students_number = len(Students)\\n\\n self.__create_ALL_LR()\\n Course_structure, variants_numbers = self.__generate_stracture()\\n print('Generate stracture')\\n Number_of_weaks = len(Course_structure)\\n\\n number_of_distribution = 0\\n for WeakNumber in range(Number_of_weaks):\\n for TaskNumber in range(Course_structure[WeakNumber]):\\n Students['Week {0} Task {1}'.format(WeakNumber + 1, TaskNumber + 1)] = np.random.randint(\\n variants_numbers[number_of_distribution], size=students_number)\\n number_of_distribution += 1\\n\\n writer = pd.ExcelWriter(self.students_with_variants_path)\\n print('Save {}'.format(self.students_with_variants_path))\\n Students.to_excel(writer)\\n writer.save()\\n except:\\n print('File with students doesnot exist')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6108525","0.58774537","0.58263105","0.5780516","0.5709611","0.56744903","0.5644065","0.5475492","0.54661214","0.5441473","0.5415947","0.53851706","0.5381618","0.53369546","0.53267866","0.5318371","0.5291741","0.5268244","0.5259975","0.5259326","0.52559954","0.525528","0.52311456","0.5218093","0.5155582","0.5149916","0.51446706","0.5137012","0.5137012","0.5137012","0.5137012","0.51330054","0.5102685","0.509423","0.5070152","0.50682575","0.5041892","0.50348294","0.5031404","0.50313973","0.5027113","0.50216615","0.5016848","0.50123256","0.5010663","0.5004551","0.5002648","0.5001574","0.4998201","0.4997834","0.498971","0.4980609","0.4973889","0.4969648","0.4967055","0.49662253","0.4959462","0.49499616","0.4949098","0.4947597","0.49395543","0.49261126","0.49160975","0.4913787","0.49133945","0.49114248","0.4895243","0.48951656","0.48555648","0.48509893","0.48503545","0.48473158","0.4844102","0.48430178","0.4838594","0.48314077","0.4830877","0.48282015","0.48276728","0.48200104","0.48143056","0.48106077","0.48043022","0.4803682","0.480182","0.48015055","0.48008862","0.48007244","0.47991937","0.47981364","0.47897717","0.4789371","0.47884232","0.47847345","0.47836965","0.4782914","0.47820136","0.4774395","0.47736028","0.47678763","0.47678596"],"string":"[\n \"0.6108525\",\n \"0.58774537\",\n \"0.58263105\",\n \"0.5780516\",\n \"0.5709611\",\n \"0.56744903\",\n \"0.5644065\",\n \"0.5475492\",\n \"0.54661214\",\n \"0.5441473\",\n \"0.5415947\",\n \"0.53851706\",\n \"0.5381618\",\n \"0.53369546\",\n \"0.53267866\",\n \"0.5318371\",\n \"0.5291741\",\n \"0.5268244\",\n \"0.5259975\",\n \"0.5259326\",\n \"0.52559954\",\n \"0.525528\",\n \"0.52311456\",\n \"0.5218093\",\n \"0.5155582\",\n \"0.5149916\",\n \"0.51446706\",\n \"0.5137012\",\n \"0.5137012\",\n \"0.5137012\",\n \"0.5137012\",\n \"0.51330054\",\n \"0.5102685\",\n \"0.509423\",\n \"0.5070152\",\n \"0.50682575\",\n \"0.5041892\",\n \"0.50348294\",\n \"0.5031404\",\n \"0.50313973\",\n \"0.5027113\",\n \"0.50216615\",\n \"0.5016848\",\n \"0.50123256\",\n \"0.5010663\",\n \"0.5004551\",\n \"0.5002648\",\n \"0.5001574\",\n \"0.4998201\",\n \"0.4997834\",\n \"0.498971\",\n \"0.4980609\",\n \"0.4973889\",\n \"0.4969648\",\n \"0.4967055\",\n \"0.49662253\",\n \"0.4959462\",\n \"0.49499616\",\n \"0.4949098\",\n \"0.4947597\",\n \"0.49395543\",\n \"0.49261126\",\n \"0.49160975\",\n \"0.4913787\",\n \"0.49133945\",\n \"0.49114248\",\n \"0.4895243\",\n \"0.48951656\",\n \"0.48555648\",\n \"0.48509893\",\n \"0.48503545\",\n \"0.48473158\",\n \"0.4844102\",\n \"0.48430178\",\n \"0.4838594\",\n \"0.48314077\",\n \"0.4830877\",\n \"0.48282015\",\n \"0.48276728\",\n \"0.48200104\",\n \"0.48143056\",\n \"0.48106077\",\n \"0.48043022\",\n \"0.4803682\",\n \"0.480182\",\n \"0.48015055\",\n \"0.48008862\",\n \"0.48007244\",\n \"0.47991937\",\n \"0.47981364\",\n \"0.47897717\",\n \"0.4789371\",\n \"0.47884232\",\n \"0.47847345\",\n \"0.47836965\",\n \"0.4782914\",\n \"0.47820136\",\n \"0.4774395\",\n \"0.47736028\",\n \"0.47678763\",\n \"0.47678596\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77485,"cells":{"query":{"kind":"string","value":"Generates two random numbers to get a random property about which there is no knowledge yet, which is indicated by 'None'. If there is knowledge, then this will either be indicated by 'True' or 'False'. If no 'None' is found for 5 times in a row, there is done a check to make sure that there are still 'Nones' in the list. Returns a property that needs knowledge or nothing if there is no more knowledge to gain."},"document":{"kind":"string","value":"def find_drink_property(self, properties):\n\n split_status = \"\"\n tries = 0\n\n while split_status != \"None\" and tries <= 5:\n random_drink = randint(0, len(properties) - 1)\n random_property = randint(1, len(properties[random_drink]) - 1)\n drink_property = properties[random_drink][random_property]\n split_status = drink_property.split(\": \")[1].split(\" //\")[0]\n tries += 1\n\n if split_status != \"None\" and tries > 5:\n drink_property = self.find_first_none(properties)\n return drink_property"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def random (self, checkfn=None):\n if len(self) == 0:\n return None\n return self.random_pick(checkfn=checkfn)[1]","def maybe(self):\n return random.getrandbits(1)","def random (self, checkfn=None):\n if len(self) == 0:\n return None\n if checkfn is None:\n return random.choice(self)\n else:\n item = random.choice(self)\n tries = len(self) * 5\n while not checkfn(item):\n item = random.choice(self)\n tries = tries - 1\n if tries <= 0:\n return None\n return item","def getRandom(self):\n import random\n res = -1\n len = 0\n head = self.head\n while head:\n if random.randint(0,len) == 0:\n res = head.val\n head = head.next\n len += 1\n return res","def getRandom(self) -> int:\n count = 0\n temp = self.head\n while temp:\n if random.randint(0,count)==0:\n res = temp.val\n temp = temp.next\n count+=1\n return res","def rand_zero_or_one(one_prob):\n if random.random() < one_prob:\n return 1\n return 0","def default_rule(neighbors, prob=0.9):\n\t\tfrom random import random\n\t\tp1,p2,p3 = neighbors\n\t\tif (p1) != (p2 or p3):\n\t\t\tif random() < prob:\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\treturn 0\n\t\telse:\n\t\t\tif random() < prob:\n\t\t\t\treturn 0\n\t\t\telse:\n\t\t\t\treturn 1","def find_first_none(self, properties):\n\n for drink in properties:\n iterate_drink = iter(drink)\n next(iterate_drink)\n for drink_property in iterate_drink:\n split_property = drink_property.split(\": \")[1].split(\" //\")[0]\n if split_property == \"None\":\n return drink_property\n return \"\"","def get_none1(self):\n pass","def confused(self, rand):\n return rand > 0","def decision():\n return random.random() > 0.5","def getRandom(self) -> int:\n import random \n \n count = 0\n node = self.head\n while node:\n if random.randint(0, count) == 0:\n ans = node.val\n node = node.next\n count += 1\n return ans","def getRandom(self):\n ans = self.head\n index = 1\n node = ans.next\n while node:\n value = random.randrange(0, index + 1)\n if value == 0:\n ans = node\n index += 1\n node = node.next\n return ans.val","def roll(self):\n roll = random.random()\n sum = 0\n for item in self.mask:\n sum += item.prob\n if sum >= roll: return item.elem\n return None","def random_test(self):\r\n return 1","def random_test(self):\r\n return 1","def generate_novel_prefix(self, random):\n assert not self.is_exhausted\n novel_prefix = bytearray()\n\n def append_int(n_bits, value):\n novel_prefix.extend(int_to_bytes(value, bits_to_bytes(n_bits)))\n\n current_node = self.root\n while True:\n assert not current_node.is_exhausted\n for i, (n_bits, value) in enumerate(\n zip(current_node.bit_lengths, current_node.values)\n ):\n if i in current_node.forced:\n append_int(n_bits, value)\n else:\n while True:\n k = random.getrandbits(n_bits)\n if k != value:\n append_int(n_bits, k)\n break\n # We've now found a value that is allowed to\n # vary, so what follows is not fixed.\n return bytes(novel_prefix)\n else:\n assert not isinstance(current_node.transition, (Conclusion, Killed))\n if current_node.transition is None:\n return bytes(novel_prefix)\n branch = current_node.transition\n assert isinstance(branch, Branch)\n n_bits = branch.bit_length\n\n check_counter = 0\n while True:\n k = random.getrandbits(n_bits)\n try:\n child = branch.children[k]\n except KeyError:\n append_int(n_bits, k)\n return bytes(novel_prefix)\n if not child.is_exhausted:\n append_int(n_bits, k)\n current_node = child\n break\n check_counter += 1\n # We don't expect this assertion to ever fire, but coverage\n # wants the loop inside to run if you have branch checking\n # on, hence the pragma.\n assert ( # pragma: no cover\n check_counter != 1000\n or len(branch.children) < (2**n_bits)\n or any(not v.is_exhausted for v in branch.children.values())\n )","def getRandom(self):\n result, node, index = self.node, self.node.next, 1\n\n while node:\n if random.random() < (1.0 / (index+1)):\n result = node\n node = node.next\n index += 1\n return result.val\n\n\n\n # Your Solution object will be instantiated and called as such:\n # obj = Solution(head)\n # param_1 = obj.getRandom()","def prove_NO() -> Proof:\n # Optional Task 6.9c","def get_next_unassigned_var(self):\n while(True):\n randomVal = random.randint(0, self.n - 1)\n if self.state[randomVal] is -1:\n return randomVal","def getRandom(self):\n if not self.head:\n return\n candidate, pv = self.head.val, 1\n tmp = self.head\n while tmp:\n if random.randint(1, pv) == 1:\n candidate = tmp.val\n tmp = tmp.next\n pv += 1\n return candidate","def choose_properties(self, probability, minimize_ref_properties):\n assert self.property_names, \"Property group must not be empty!\"\n\n non_ref_prop_names = [\n name for name in self.property_names\n if not _is_ref_prop(name)\n ]\n\n if self.constraint_type == self.ONE:\n if minimize_ref_properties and non_ref_prop_names:\n chosen_props = [random.choice(non_ref_prop_names)]\n else:\n # Or should I have just stored the names as a list...?\n chosen_props = [\n stix2generator.utils.rand_iterable(self.property_names)\n ]\n\n elif self.constraint_type == self.AT_LEAST_ONE:\n\n # Choose one required prop; make the rest optional. If minimizing\n # reference properties and all are reference properties, we should\n # behave like \"one\" and choose exactly one. Choosing more would\n # not be \"minimal\".\n if minimize_ref_properties and non_ref_prop_names:\n required_prop = random.choice(non_ref_prop_names)\n else:\n required_prop = stix2generator.utils.rand_iterable(\n self.property_names\n )\n\n chosen_props = [required_prop]\n candidate_other_props = \\\n non_ref_prop_names if minimize_ref_properties \\\n else self.property_names\n\n chosen_props.extend(\n prop for prop in candidate_other_props\n if prop != required_prop and random.random() < probability\n )\n\n else:\n # ALL\n chosen_props = list(self.property_names)\n\n return chosen_props","def _random_true_false(prob):\n # Get random probability between 0 and 1\n probability = tf.random_uniform(shape=[], minval=0., maxval=1., dtype=tf.float32)\n return tf.less(x=probability, y=prob)","def cointoss():\n return random.random() < 0.5","def getRandom(self):\n res = self.head.val\n cur = self.head.next\n count = 2\n\n while cur != None:\n if random() <= 1.0 / count:\n res = cur.val\n\n count += 1\n cur = cur.next\n return res","def conditioned(self):\n for name in self._nodes:\n node = self._nodes[name]\n if not isinstance(node, RandomVariable) or node.observed:\n yield name","def is_exhausted(self):\n return random.random() < 0.5","def pick_one(self):\n index = 0\n r = random.random()\n while r >= 0:\n r = r - self.normalised_fitness[index]\n index += 1\n index -= 1\n return self.population[index]","def simulate_null(data, col='orange'):\n df = data.loc[data[\"Factory\"].isin(np.array([\"Waco\", \"Yorkville\"]))]\n df = df[[col, \"Factory\"]]\n shuffled= (\n df[col]\n .sample(replace=False, frac=1)\n .reset_index(drop=True)\n )\n\n original_and_shuffled = (\n df\n .assign(**{'Shuffled Orange Amount': shuffled})\n )\n \n return abs(original_and_shuffled.groupby('Factory')[\"Shuffled Orange Amount\"].mean().diff()[1])","def test_rng_null(self):\n assert check_random_state(None) is np.random.mtrand._rand","def RandomEmptySpot(self):\n choice = int(np.random.choice(np.array([i for i in range(9) if self.state[i] == 0]), 1))\n return choice","def random_pop (self, checkfn=None):\n if len(self) == 0:\n return None\n item = random.randint(0, len(self)-1)\n if checkfn is not None:\n tries = len(self) * 5\n while not checkfn(self[item]):\n item = random.randint(0, len(self)-1)\n tries = tries - 1\n if tries <= 0:\n return None\n return self.pop(item)","def _rand_bool(self):\n\n return (self.np_random.randint(0, 2) == 0)","def gen_unused_random_num(self):\n num = random.randint(10 ** 20, 10 ** 21)\n while num in self.USED_RANDOM_NUMS:\n num = self.gen_unused_random_num()\n return num","def decision(self, probability):\n return random.random() < probability","def rs():\n return random.choice([-1,1])","def rs():\n return random.choice([-1,1])","def random_number():\n random_num = random.choice(empty)\n return random_num","def get_bprop_logical_not(self):\n\n def bprop(x, out, dout):\n return (zeros_like(x),)\n return bprop","def getRandom(self) -> int:\n result = self.head.val\n curr = self.head.next\n rand = random.Random()\n i = 2\n\n while curr:\n if rand.randrange(i) == 0:\n result = curr.val\n i += 1\n curr = curr.next\n\n return result","def computer_random():\n ok = False\n lotto_num_list = np.arange(1,45)\n while not ok:\n ci = np.random.choice(lotto_num_list,6,replace=False)\n tmp = np.where(ci == 0)\n (m, )= tmp[0].shape\n if(m == 0):\n ok = True\n return ci","def random_pick (self, checkfn=None):\n tweight = self.total_weight(checkfn=checkfn)\n if tweight == 0:\n return None, None\n\n n = random.uniform(0, tweight)\n\n for num, item in enumerate(self):\n if checkfn is not None and not checkfn(item):\n continue\n\n if item.weight >= n:\n return num, item\n n = n - item.weight\n\n return None, None","def random(self, n=1):\n # self.num_generated += n","def randomBoolean():\n return random.randint(0,99) < 50","def randomBoolean():\n return random.randint(0,99) < 50","def build_random_population(n: int)->Population:\n DEF_COO = 2\n v = [make_random_automaton(DEF_COO) for i in range(n)]\n return Population(v)","def getRandom(self) -> int:\n if self.counts:\n # key = list(self.counts.keys())\n return random.choice(self.counts.keys(), weights=list(self.counts.values()))\n return None","def init_state_rand(n):\n if DistEinsum.MPI_RANK == DistEinsum.MPI_ROOT:\n state = (cupy.random.random([2] * n) * 2.0 - 1.0) + (cupy.random.random([2] * n) * 2.0 - 1.0) * 1.0j\n state /= cupy.linalg.norm(state)\n else:\n state = None\n if DistEinsum.MPI_RANK == DistEinsum.MPI_ROOT:\n assert cupy.round(cupy.linalg.norm(state), 6) == 1.0\n else:\n state is None\n # print(state)\n return state","def atomic_nop(n_bytes):\n global ATOM_NOP_1B, ATOM_NOP_2B, ATOM_NOP_3B\n if n_bytes==1:\n return random.choice(ATOM_NOP_1B)\n elif n_bytes==2:\n return random.choice(ATOM_NOP_2B)\n elif n_bytes==3:\n return random.choice(ATOM_NOP_3B)\n else:\n raise ValueError('n_bytes should be in {1,2,3}')","def random_policy(self, pc):\n if list(self.state.P_k.keys()) or list(self.state.D_k):\n return random.choice(list(self.state.P_k.keys())+list(self.state.D_k))\n elif not(list(self.state.P_k.keys()) and list(self.state.D_k)):\n return 0\n else:\n raise ValueError('Check jobs remaining to be done.')","def totem_random():\n random_head()\n random_head()\n random_head()","def get_random_node(self):\n if random.randint(0, 100) > self.goal_sample_rate:\n random_node = self.Node(\n random.uniform(self.min_rand, self.max_rand),\n random.uniform(self.min_rand, self.max_rand),\n )\n else: # goal point sampling\n random_node = self.Node(self.end.x, self.end.y)\n return random_node","def init_one(self, _=0):\n some_srepr = self.simple_random_srepr()\n some_fitness, some_accuracy, some_ng = self.fitness(some_srepr)\n return some_fitness, some_accuracy, some_ng, some_srepr","def rand_field(length, chance):\n return [1 if random.random() < chance else 0 for _ in range(length)]","def __bool__(self) -> bool:\n return bool(mpmath.rand() < self.p)","def pull(self):\n chance = np.random.uniform()\n return chance < self.winning_prob","def __init__(self):\r\n self.my_piece = random.choice(self.pieces)\r\n self.opp = self.pieces[0] if self.my_piece == self.pieces[1] else self.pieces[1]","def generate_false_edges(true_edges, num_false_edges=5):\n nodes = list(set(chain.from_iterable(true_edges)))\n true_edges = set(true_edges)\n false_edges = set()\n \n while len(false_edges) < num_false_edges:\n # randomly sample two different nodes and check whether the pair exisit or not\n head, tail = np.random.choice(nodes, 2)\n if head != tail and ((head, tail) not in true_edges and (tail, head) not in true_edges) and ((head, tail) not in false_edges and (tail, head) not in false_edges):\n false_edges.add((head, tail)) \n false_edges = sorted(false_edges)\n \n return false_edges","def get_random_bool() -> bool:\n\n return random.choice([True, False])","def testDefault(self):\n for val in (1, {}, 'test', None):\n prop = make_prop(default=val)\n self.assertEqual(val, prop.interpret(recipe_api.PROPERTY_SENTINEL, {}))","def create_null_hypothesis(self):\n print(\"Calculating the null hypothesis...\")\n start = time.time()\n # file = open(\"data\\\\reddit.csv\", encoding=\"utf8\")\n # reader = csv.reader(file)\n # stops = nltk.corpus.stopwords.words('english')\n # next(reader)\n # sw_tok = EnglishStemmer()\n # for line in reader:\n # tokens = [sw_tok.stem(x) for x in nltk.RegexpTokenizer(r'\\w\\w+|U S').tokenize(line[4])]\n # self.all_dates[line[1]] = [x for x in tokens if x not in stops]\n # for word in self.all_dates[line[1]]:\n # self.alternative[word] = 0\n # if word in self.null.keys():\n # self.null[word] += 1\n # else:\n # self.null[word] = 1\n # self.total += 1\n # for word in self.null.keys():\n # self.null[word] /= self.total\n # print(\"time: \" + str(time.time() - start))\n # file = open(\"null.txt\", 'w', encoding=\"utf8\")\n # for key in self.null.keys():\n # file.write(key)\n # file.write(' ')\n # file.write(str(self.null[key]))\n # file.write('\\n')\n # file.close()\n # file = open(\"all_dates.txt\", 'w', encoding=\"utf8\")\n # for key in self.all_dates.keys():\n # file.write(key)\n # file.write(' ')\n # file.write(str(self.all_dates[key]))\n # file.write('\\n')\n # file.close()\n # with open(\"null.json\", 'w+') as f:\n # json.dump(self.null, f)\n # with open(\"all_dates.json\", 'w+') as f:\n # json.dump(self.all_dates, f)\n #\n with open(\"null.json\", 'r') as f:\n temp = json.loads(f.read())\n self.null = temp\n with open(\"all_dates.json\", 'r') as f:\n temp = json.loads(f.read())\n self.all_dates = temp\n self.alternative = {word: 0 for word in self.null.keys()}\n print(\"time: \" + str(time.time() - start))","def prove_NN() -> Proof:\n # Optional Task 6.7c","def boolean(self) -> bool:\n return self.random.choice([True, False])","def random():\n return constant(1)","def random(self):\r\n return random.randint(1, 4)","def tire_objectif() -> int:\n return random.randint(min_objectif, max_objectif)","def prove_NNE() -> Proof:\n # Optional Task 6.7b","def _random_not_singular(N):\n data = np.zeros((1, 1))\n while np.linalg.det(data) == 0:\n data = np.random.random((N, N)) + \\\n 1j * np.random.random((N, N)) - (0.5 + 0.5j)\n return data","def generate_random_list_or_string(self):\n if self.iteration_deep < self.max_iteration_deep:\n self.iteration_deep += 1\n value = self.generate_random_list(5)\n self.iteration_deep -= 1\n else:\n value = self.generate_random_value(str)\n return value","def test_example_field_is_none(self):\n self._example['weight'] = None\n output = self._gen.generate(\n example=self._example,\n model=self._model,\n dataset=self._dataset,\n config=self._config)\n self.assertNotEmpty(output)","def random_bool():\n return bool(randint(0, 1))","def testNoSpecialties(self):\n self.failUnlessEqual(self.person.getSpecialties(), [])","def generate_train(n: int, case: Case) -> List[bool]:\n if case is Case.Random:\n return [bool(randint(0, 1)) for _ in range(n)]\n elif case is Case.AllOn:\n return [True for _ in range(n)]\n elif case is Case.AllOffButStart:\n return [True] + [False for _ in range(n - 1)]","def randomize_value(self) -> None:","def random_boolean(percent=50):\n return random.randrange(100) < percent","def _u_naught_simple(self):\n # Random is better to give different multipliers in the subgradient phase\n return np.random.rand(self.mrows)*1.","def __init__(self):\n self.my_piece = random.choice(self.pieces)\n self.opp = self.pieces[0] if self.my_piece == self.pieces[1] else self.pieces[1]","def __init__(self):\n self.my_piece = random.choice(self.pieces)\n self.opp = self.pieces[0] if self.my_piece == self.pieces[1] else self.pieces[1]","def random_table(self):\n rule_set = []\n for i in range(self.k ** (2 * self.r + 1) - 1):\n g = np.random.rand()\n if g > self.lambda_param:\n g = 0\n else:\n g = np.random.randint(1, self.k)\n rule_set.append(g)\n rule_set.append(0)\n return rule_set","def copyCounterWithNoise(self, counter, prob):\n \"\"\"\n counter = deepcopy(counter)\n for i in range(self.nObjectTypes):\n noise = np.random.randint(1,5)\n if np.random.random() < prob:\n if np.random.random() > 0.5:\n counter[i] += noise # false detections\n else:\n counter[i] -= noise # missed detections\n if counter[i] < 0:\n counter[i]=0\n return counter\n \"\"\"\n counter = deepcopy(counter)\n for i in range(self.nObjectTypes):\n noise = np.random.randint(1,5)\n if np.random.random() > prob:\n continue\n if counter[i] == 0:\n # Noise could only be +1\n counter[i] = noise\n else:\n # Noise could be either +1 or -1\n if np.random.random() > 0.5:\n counter[i] += noise\n else:\n counter[i] -= noise\n if counter[i] < 0:\n counter[i]=0\n return counter","async def randomBool(self, ctx: commands.Context):\n await ctx.reply(f\"Randomly chosen result:\\n**{random.choice(['False', 'True'])}**\", mention_author=False)","def __rand__(self, otro):\n return self & otro","def getRandom(self):\n index = random.randrange(0, self.length)\n node = self.head\n while index:\n node = node.next\n index -= 1\n return node.val","def random(cls, n=2):\n angles = [Motion(math.pi*Vector.random(d=1), Vector.null(d=1),\n Vector.null(d=1)) for i in range(n)]\n lengths = [random.uniform(0, 1) for i in range(n)]\n masses = [random.uniform(0, 1) for i in range(n)]\n return cls(angles, lengths, masses)","def test_no_sweeps_property(self):\n expected_values = {\n 'no_timesteps': 1000,\n 'no_sweeps': 10,\n 'no_channels': 4,\n }\n test_rec = rt.Recording(\n np.zeros(\n [\n expected_values['no_channels'],\n expected_values['no_timesteps'],\n expected_values['no_sweeps'],\n ]\n ),\n dt=0.1,\n )\n self.assertEqual(\n test_rec.no_sweeps,\n expected_values['no_sweeps'],\n 'Expected {} for `no_sweeps` property; got {} instead.'.format(\n expected_values['no_sweeps'], test_rec.no_sweeps\n ),\n )","def make_random_move(self):\n choice = None\n options = []\n #generate full moves list\n for i in range(self.width):\n for j in range(self.height):\n #make sure move has not been made\n if (i,j) not in self.moves_made:\n #make sure move is not a mine\n if (i,j) not in self.mines:\n options.append((i,j))\n #if there are no options, return None\n if len(options) == 0:\n return None\n\n #pick a random option from generated list\n choice = random.choice(options)\n return choice\n\n \"\"\"\n For kicks and giggles I wrote this extra bit to determine a\n rough intuitive probability for each option based on the knowledge\n base, so rather than picking a choice randomly the AI can choose\n the option that is, at least intuitively, least likely to blow up.\n Better to take the 1/8 chance than the 1/3 chance, right?\n \"\"\"\n best_chance = 1\n #iterate through generated options\n for option in options:\n #Could set chance to 1/8, but the AI wouldn't actually know that. I\n #only know it because I can read the code...But for the purposes of this\n #drill we'll say the AI doesn't know how many bombs are placed.\n #Better then to pick a square we know nothing about than one that\n #has a 1/8 chance of exploding. Gather more information that way.\n chance = 0\n for sentence in self.knowledge:\n #look to see if current option is in sentences\n if option in sentence.cells:\n #use sentence count and length of cell set to calculate probability\n prob = sentence.count / len(sentence.cells)\n if prob > chance:\n #Looking for the highest explosive probability for this square\n chance = prob\n if chance < best_chance:\n #If this option has lower odds of exploding than current best, it becomes\n #the optimal\n best_chance = chance\n choice = option\n\n #return choice","def randomBool(p = 0.5):\n if random.random() < p: return True\n else: return False","def getNoWells(self):\n #code begins here \n return self.__nwells","def createRandom(protein):\n\n protein.occupied = []\n protein.aminoList = []\n\n for id in range(protein.proteinLength):\n protein.aminoList.append(Amino(id, protein.proteinString[id]))\n\n # Place the first and second amino acid\n if id in {0, 1}:\n thisCoordinate = [0, id]\n if protein.plane == \"3D\":\n thisCoordinate.append(0)\n protein.aminoList[id].coordinate = thisCoordinate\n protein.occupied.append(thisCoordinate)\n else:\n prevCo = protein.aminoList[(id - 1)].coordinate\n posCo = protein.getSurroundCo(prevCo, occupied=False)\n\n # If there are no surrounding coordinates available stop the folding\n if not posCo:\n protein.stability = 0\n return False\n\n coordinate = random.choice(posCo)\n protein.aminoList[id].coordinate = coordinate\n protein.occupied.append(coordinate)\n\n protein.stabilityUpdate(protein.aminoList[id])\n\n return True","def generatePiece(self):\n\n empty_tiles = []\n for y in range(BOARD_SIZE):\n for x in range(BOARD_SIZE):\n if self.grid[x][y].isEmpty():\n empty_tiles.append(self.grid[x][y])\n\n two_or_four = random.choice([2, 4])\n random.choice(empty_tiles).set(two_or_four)","def get_validation(self, n: Optional[int] = None, random: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:\n return self.__sample(self.test_data, self.test_los, n, random)","def _produce_none_relations(self):\n print \"Producing NONE-relations\"\n relations = self.relations\n events = self.events\n none_relations = []\n\n for source in events:\n for target in events:\n new_relation = Relation(\"NONE\", self.text_obj, source, target, RelationType.NONE)\n print new_relation\n\n if new_relation in relations:\n continue\n else:\n none_relations.append(new_relation)\n\n self.relations = self.relations + none_relations\n\n print \"Finished producing NONE-relations\"","def getRandom(self):\n # pick a random number from the list\n return random.choice(self.nums)","def computer_generate(self):\n return choice[random.randrange(3)]","def testWithoutNoise(self):\n self.checkMatching(self.references)","def nextMoveDecision(self):\n b = random.randint(1, 9) \n while (self.Occupied(b)):\n b = random.randint(1, 9) \n return b","def poblar_sansanito(n):\n\tcur.execute(\"\"\"\n\t\t\t\tSELECT nombre\n\t\t\t\tFROM poyo\"\"\")\n\tnombres = cur.fetchall()\n\t# Una lista de nombres, super grande\n\tfor i in range(n):\n\t\tnombre_elegido = choice(nombres)[0] \n\t\t# Elige nombre random de pokemon, pero existente\n\t\testado = choice(estados_permitidos) \n\t\t# Elige estado incluyendo el None\n\t\thptot_query = \"\"\"\n\t\t\t\t\tSELECT hptotal\n\t\t\t\t\tFROM poyo\n\t\t\t\t\tWHERE nombre = :1\"\"\"\n\t\tcur.execute(hptot_query, [nombre_elegido])\n\t\thpmax = cur.fetchall()\n\t\thpactual = randint(0, hpmax[0][0]) \n\t\t# Genera hpactual que no sea mayor que hpmax\n\t\tfecha_ingreso = generar_fecha()\n\t\tinsertar_pokemon(nombre_elegido,hpactual, estado, fecha_ingreso, False)","def generate_mixture_indicator(number_obeservations):\n mixture_indicator = np.random.randint(0, 2, (number_obeservations,1))\n return mixture_indicator","def test_no_prior(self):\n dim = Dimension(\"yolo\", None)\n print(dim._prior_name)\n assert dim.prior is None\n assert dim._prior_name == \"None\"","def create(ch, ran_n):\n l = ch.l / ch.num_of_links\n assert np.log2(len(\n ran_n)) % 1 == 0.0, \"The number of random variables have to be a power of 2 if binary\"\n rho_basic = maps.coupl(ch.em, *ch.rho_prep[:])\n rho_basic = maps.dp_sing(2 * l / ch.c, ch.T, *rho_basic)\n k = int(np.log2(len(ran_n)))\n rhos = [rho_basic[:] for i in range(2**k)]\n p_total = 1\n for i in range(k):\n new_rhos = []\n new_n = []\n for j in range(0, 2**k, 2):\n t_wait = ran_n[j + 1] * 2 * l / ch.c\n rho1 = maps.dp_doub(t_wait, ch.T, *rhos[j])\n rho, p = maps.distil(ch.lam_Dist, ch.p_dc,\n ch.p_dc, *(rho1 + rhos[j + 1]))\n p_total *= p\n new_rhos.append(rho)\n new_n.append(ran_n[j] + ran_n[j + 1])\n rhos = new_rhos\n ran_n = new_n\n k = k - 1\n assert len(rhos) == 1, \"Okay, something went wrong here, sorry for that!\"\n cond = np.random.random() <= p_total\n return cond, rhos[0]"],"string":"[\n \"def random (self, checkfn=None):\\n if len(self) == 0:\\n return None\\n return self.random_pick(checkfn=checkfn)[1]\",\n \"def maybe(self):\\n return random.getrandbits(1)\",\n \"def random (self, checkfn=None):\\n if len(self) == 0:\\n return None\\n if checkfn is None:\\n return random.choice(self)\\n else:\\n item = random.choice(self)\\n tries = len(self) * 5\\n while not checkfn(item):\\n item = random.choice(self)\\n tries = tries - 1\\n if tries <= 0:\\n return None\\n return item\",\n \"def getRandom(self):\\n import random\\n res = -1\\n len = 0\\n head = self.head\\n while head:\\n if random.randint(0,len) == 0:\\n res = head.val\\n head = head.next\\n len += 1\\n return res\",\n \"def getRandom(self) -> int:\\n count = 0\\n temp = self.head\\n while temp:\\n if random.randint(0,count)==0:\\n res = temp.val\\n temp = temp.next\\n count+=1\\n return res\",\n \"def rand_zero_or_one(one_prob):\\n if random.random() < one_prob:\\n return 1\\n return 0\",\n \"def default_rule(neighbors, prob=0.9):\\n\\t\\tfrom random import random\\n\\t\\tp1,p2,p3 = neighbors\\n\\t\\tif (p1) != (p2 or p3):\\n\\t\\t\\tif random() < prob:\\n\\t\\t\\t\\treturn 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\tif random() < prob:\\n\\t\\t\\t\\treturn 0\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn 1\",\n \"def find_first_none(self, properties):\\n\\n for drink in properties:\\n iterate_drink = iter(drink)\\n next(iterate_drink)\\n for drink_property in iterate_drink:\\n split_property = drink_property.split(\\\": \\\")[1].split(\\\" //\\\")[0]\\n if split_property == \\\"None\\\":\\n return drink_property\\n return \\\"\\\"\",\n \"def get_none1(self):\\n pass\",\n \"def confused(self, rand):\\n return rand > 0\",\n \"def decision():\\n return random.random() > 0.5\",\n \"def getRandom(self) -> int:\\n import random \\n \\n count = 0\\n node = self.head\\n while node:\\n if random.randint(0, count) == 0:\\n ans = node.val\\n node = node.next\\n count += 1\\n return ans\",\n \"def getRandom(self):\\n ans = self.head\\n index = 1\\n node = ans.next\\n while node:\\n value = random.randrange(0, index + 1)\\n if value == 0:\\n ans = node\\n index += 1\\n node = node.next\\n return ans.val\",\n \"def roll(self):\\n roll = random.random()\\n sum = 0\\n for item in self.mask:\\n sum += item.prob\\n if sum >= roll: return item.elem\\n return None\",\n \"def random_test(self):\\r\\n return 1\",\n \"def random_test(self):\\r\\n return 1\",\n \"def generate_novel_prefix(self, random):\\n assert not self.is_exhausted\\n novel_prefix = bytearray()\\n\\n def append_int(n_bits, value):\\n novel_prefix.extend(int_to_bytes(value, bits_to_bytes(n_bits)))\\n\\n current_node = self.root\\n while True:\\n assert not current_node.is_exhausted\\n for i, (n_bits, value) in enumerate(\\n zip(current_node.bit_lengths, current_node.values)\\n ):\\n if i in current_node.forced:\\n append_int(n_bits, value)\\n else:\\n while True:\\n k = random.getrandbits(n_bits)\\n if k != value:\\n append_int(n_bits, k)\\n break\\n # We've now found a value that is allowed to\\n # vary, so what follows is not fixed.\\n return bytes(novel_prefix)\\n else:\\n assert not isinstance(current_node.transition, (Conclusion, Killed))\\n if current_node.transition is None:\\n return bytes(novel_prefix)\\n branch = current_node.transition\\n assert isinstance(branch, Branch)\\n n_bits = branch.bit_length\\n\\n check_counter = 0\\n while True:\\n k = random.getrandbits(n_bits)\\n try:\\n child = branch.children[k]\\n except KeyError:\\n append_int(n_bits, k)\\n return bytes(novel_prefix)\\n if not child.is_exhausted:\\n append_int(n_bits, k)\\n current_node = child\\n break\\n check_counter += 1\\n # We don't expect this assertion to ever fire, but coverage\\n # wants the loop inside to run if you have branch checking\\n # on, hence the pragma.\\n assert ( # pragma: no cover\\n check_counter != 1000\\n or len(branch.children) < (2**n_bits)\\n or any(not v.is_exhausted for v in branch.children.values())\\n )\",\n \"def getRandom(self):\\n result, node, index = self.node, self.node.next, 1\\n\\n while node:\\n if random.random() < (1.0 / (index+1)):\\n result = node\\n node = node.next\\n index += 1\\n return result.val\\n\\n\\n\\n # Your Solution object will be instantiated and called as such:\\n # obj = Solution(head)\\n # param_1 = obj.getRandom()\",\n \"def prove_NO() -> Proof:\\n # Optional Task 6.9c\",\n \"def get_next_unassigned_var(self):\\n while(True):\\n randomVal = random.randint(0, self.n - 1)\\n if self.state[randomVal] is -1:\\n return randomVal\",\n \"def getRandom(self):\\n if not self.head:\\n return\\n candidate, pv = self.head.val, 1\\n tmp = self.head\\n while tmp:\\n if random.randint(1, pv) == 1:\\n candidate = tmp.val\\n tmp = tmp.next\\n pv += 1\\n return candidate\",\n \"def choose_properties(self, probability, minimize_ref_properties):\\n assert self.property_names, \\\"Property group must not be empty!\\\"\\n\\n non_ref_prop_names = [\\n name for name in self.property_names\\n if not _is_ref_prop(name)\\n ]\\n\\n if self.constraint_type == self.ONE:\\n if minimize_ref_properties and non_ref_prop_names:\\n chosen_props = [random.choice(non_ref_prop_names)]\\n else:\\n # Or should I have just stored the names as a list...?\\n chosen_props = [\\n stix2generator.utils.rand_iterable(self.property_names)\\n ]\\n\\n elif self.constraint_type == self.AT_LEAST_ONE:\\n\\n # Choose one required prop; make the rest optional. If minimizing\\n # reference properties and all are reference properties, we should\\n # behave like \\\"one\\\" and choose exactly one. Choosing more would\\n # not be \\\"minimal\\\".\\n if minimize_ref_properties and non_ref_prop_names:\\n required_prop = random.choice(non_ref_prop_names)\\n else:\\n required_prop = stix2generator.utils.rand_iterable(\\n self.property_names\\n )\\n\\n chosen_props = [required_prop]\\n candidate_other_props = \\\\\\n non_ref_prop_names if minimize_ref_properties \\\\\\n else self.property_names\\n\\n chosen_props.extend(\\n prop for prop in candidate_other_props\\n if prop != required_prop and random.random() < probability\\n )\\n\\n else:\\n # ALL\\n chosen_props = list(self.property_names)\\n\\n return chosen_props\",\n \"def _random_true_false(prob):\\n # Get random probability between 0 and 1\\n probability = tf.random_uniform(shape=[], minval=0., maxval=1., dtype=tf.float32)\\n return tf.less(x=probability, y=prob)\",\n \"def cointoss():\\n return random.random() < 0.5\",\n \"def getRandom(self):\\n res = self.head.val\\n cur = self.head.next\\n count = 2\\n\\n while cur != None:\\n if random() <= 1.0 / count:\\n res = cur.val\\n\\n count += 1\\n cur = cur.next\\n return res\",\n \"def conditioned(self):\\n for name in self._nodes:\\n node = self._nodes[name]\\n if not isinstance(node, RandomVariable) or node.observed:\\n yield name\",\n \"def is_exhausted(self):\\n return random.random() < 0.5\",\n \"def pick_one(self):\\n index = 0\\n r = random.random()\\n while r >= 0:\\n r = r - self.normalised_fitness[index]\\n index += 1\\n index -= 1\\n return self.population[index]\",\n \"def simulate_null(data, col='orange'):\\n df = data.loc[data[\\\"Factory\\\"].isin(np.array([\\\"Waco\\\", \\\"Yorkville\\\"]))]\\n df = df[[col, \\\"Factory\\\"]]\\n shuffled= (\\n df[col]\\n .sample(replace=False, frac=1)\\n .reset_index(drop=True)\\n )\\n\\n original_and_shuffled = (\\n df\\n .assign(**{'Shuffled Orange Amount': shuffled})\\n )\\n \\n return abs(original_and_shuffled.groupby('Factory')[\\\"Shuffled Orange Amount\\\"].mean().diff()[1])\",\n \"def test_rng_null(self):\\n assert check_random_state(None) is np.random.mtrand._rand\",\n \"def RandomEmptySpot(self):\\n choice = int(np.random.choice(np.array([i for i in range(9) if self.state[i] == 0]), 1))\\n return choice\",\n \"def random_pop (self, checkfn=None):\\n if len(self) == 0:\\n return None\\n item = random.randint(0, len(self)-1)\\n if checkfn is not None:\\n tries = len(self) * 5\\n while not checkfn(self[item]):\\n item = random.randint(0, len(self)-1)\\n tries = tries - 1\\n if tries <= 0:\\n return None\\n return self.pop(item)\",\n \"def _rand_bool(self):\\n\\n return (self.np_random.randint(0, 2) == 0)\",\n \"def gen_unused_random_num(self):\\n num = random.randint(10 ** 20, 10 ** 21)\\n while num in self.USED_RANDOM_NUMS:\\n num = self.gen_unused_random_num()\\n return num\",\n \"def decision(self, probability):\\n return random.random() < probability\",\n \"def rs():\\n return random.choice([-1,1])\",\n \"def rs():\\n return random.choice([-1,1])\",\n \"def random_number():\\n random_num = random.choice(empty)\\n return random_num\",\n \"def get_bprop_logical_not(self):\\n\\n def bprop(x, out, dout):\\n return (zeros_like(x),)\\n return bprop\",\n \"def getRandom(self) -> int:\\n result = self.head.val\\n curr = self.head.next\\n rand = random.Random()\\n i = 2\\n\\n while curr:\\n if rand.randrange(i) == 0:\\n result = curr.val\\n i += 1\\n curr = curr.next\\n\\n return result\",\n \"def computer_random():\\n ok = False\\n lotto_num_list = np.arange(1,45)\\n while not ok:\\n ci = np.random.choice(lotto_num_list,6,replace=False)\\n tmp = np.where(ci == 0)\\n (m, )= tmp[0].shape\\n if(m == 0):\\n ok = True\\n return ci\",\n \"def random_pick (self, checkfn=None):\\n tweight = self.total_weight(checkfn=checkfn)\\n if tweight == 0:\\n return None, None\\n\\n n = random.uniform(0, tweight)\\n\\n for num, item in enumerate(self):\\n if checkfn is not None and not checkfn(item):\\n continue\\n\\n if item.weight >= n:\\n return num, item\\n n = n - item.weight\\n\\n return None, None\",\n \"def random(self, n=1):\\n # self.num_generated += n\",\n \"def randomBoolean():\\n return random.randint(0,99) < 50\",\n \"def randomBoolean():\\n return random.randint(0,99) < 50\",\n \"def build_random_population(n: int)->Population:\\n DEF_COO = 2\\n v = [make_random_automaton(DEF_COO) for i in range(n)]\\n return Population(v)\",\n \"def getRandom(self) -> int:\\n if self.counts:\\n # key = list(self.counts.keys())\\n return random.choice(self.counts.keys(), weights=list(self.counts.values()))\\n return None\",\n \"def init_state_rand(n):\\n if DistEinsum.MPI_RANK == DistEinsum.MPI_ROOT:\\n state = (cupy.random.random([2] * n) * 2.0 - 1.0) + (cupy.random.random([2] * n) * 2.0 - 1.0) * 1.0j\\n state /= cupy.linalg.norm(state)\\n else:\\n state = None\\n if DistEinsum.MPI_RANK == DistEinsum.MPI_ROOT:\\n assert cupy.round(cupy.linalg.norm(state), 6) == 1.0\\n else:\\n state is None\\n # print(state)\\n return state\",\n \"def atomic_nop(n_bytes):\\n global ATOM_NOP_1B, ATOM_NOP_2B, ATOM_NOP_3B\\n if n_bytes==1:\\n return random.choice(ATOM_NOP_1B)\\n elif n_bytes==2:\\n return random.choice(ATOM_NOP_2B)\\n elif n_bytes==3:\\n return random.choice(ATOM_NOP_3B)\\n else:\\n raise ValueError('n_bytes should be in {1,2,3}')\",\n \"def random_policy(self, pc):\\n if list(self.state.P_k.keys()) or list(self.state.D_k):\\n return random.choice(list(self.state.P_k.keys())+list(self.state.D_k))\\n elif not(list(self.state.P_k.keys()) and list(self.state.D_k)):\\n return 0\\n else:\\n raise ValueError('Check jobs remaining to be done.')\",\n \"def totem_random():\\n random_head()\\n random_head()\\n random_head()\",\n \"def get_random_node(self):\\n if random.randint(0, 100) > self.goal_sample_rate:\\n random_node = self.Node(\\n random.uniform(self.min_rand, self.max_rand),\\n random.uniform(self.min_rand, self.max_rand),\\n )\\n else: # goal point sampling\\n random_node = self.Node(self.end.x, self.end.y)\\n return random_node\",\n \"def init_one(self, _=0):\\n some_srepr = self.simple_random_srepr()\\n some_fitness, some_accuracy, some_ng = self.fitness(some_srepr)\\n return some_fitness, some_accuracy, some_ng, some_srepr\",\n \"def rand_field(length, chance):\\n return [1 if random.random() < chance else 0 for _ in range(length)]\",\n \"def __bool__(self) -> bool:\\n return bool(mpmath.rand() < self.p)\",\n \"def pull(self):\\n chance = np.random.uniform()\\n return chance < self.winning_prob\",\n \"def __init__(self):\\r\\n self.my_piece = random.choice(self.pieces)\\r\\n self.opp = self.pieces[0] if self.my_piece == self.pieces[1] else self.pieces[1]\",\n \"def generate_false_edges(true_edges, num_false_edges=5):\\n nodes = list(set(chain.from_iterable(true_edges)))\\n true_edges = set(true_edges)\\n false_edges = set()\\n \\n while len(false_edges) < num_false_edges:\\n # randomly sample two different nodes and check whether the pair exisit or not\\n head, tail = np.random.choice(nodes, 2)\\n if head != tail and ((head, tail) not in true_edges and (tail, head) not in true_edges) and ((head, tail) not in false_edges and (tail, head) not in false_edges):\\n false_edges.add((head, tail)) \\n false_edges = sorted(false_edges)\\n \\n return false_edges\",\n \"def get_random_bool() -> bool:\\n\\n return random.choice([True, False])\",\n \"def testDefault(self):\\n for val in (1, {}, 'test', None):\\n prop = make_prop(default=val)\\n self.assertEqual(val, prop.interpret(recipe_api.PROPERTY_SENTINEL, {}))\",\n \"def create_null_hypothesis(self):\\n print(\\\"Calculating the null hypothesis...\\\")\\n start = time.time()\\n # file = open(\\\"data\\\\\\\\reddit.csv\\\", encoding=\\\"utf8\\\")\\n # reader = csv.reader(file)\\n # stops = nltk.corpus.stopwords.words('english')\\n # next(reader)\\n # sw_tok = EnglishStemmer()\\n # for line in reader:\\n # tokens = [sw_tok.stem(x) for x in nltk.RegexpTokenizer(r'\\\\w\\\\w+|U S').tokenize(line[4])]\\n # self.all_dates[line[1]] = [x for x in tokens if x not in stops]\\n # for word in self.all_dates[line[1]]:\\n # self.alternative[word] = 0\\n # if word in self.null.keys():\\n # self.null[word] += 1\\n # else:\\n # self.null[word] = 1\\n # self.total += 1\\n # for word in self.null.keys():\\n # self.null[word] /= self.total\\n # print(\\\"time: \\\" + str(time.time() - start))\\n # file = open(\\\"null.txt\\\", 'w', encoding=\\\"utf8\\\")\\n # for key in self.null.keys():\\n # file.write(key)\\n # file.write(' ')\\n # file.write(str(self.null[key]))\\n # file.write('\\\\n')\\n # file.close()\\n # file = open(\\\"all_dates.txt\\\", 'w', encoding=\\\"utf8\\\")\\n # for key in self.all_dates.keys():\\n # file.write(key)\\n # file.write(' ')\\n # file.write(str(self.all_dates[key]))\\n # file.write('\\\\n')\\n # file.close()\\n # with open(\\\"null.json\\\", 'w+') as f:\\n # json.dump(self.null, f)\\n # with open(\\\"all_dates.json\\\", 'w+') as f:\\n # json.dump(self.all_dates, f)\\n #\\n with open(\\\"null.json\\\", 'r') as f:\\n temp = json.loads(f.read())\\n self.null = temp\\n with open(\\\"all_dates.json\\\", 'r') as f:\\n temp = json.loads(f.read())\\n self.all_dates = temp\\n self.alternative = {word: 0 for word in self.null.keys()}\\n print(\\\"time: \\\" + str(time.time() - start))\",\n \"def prove_NN() -> Proof:\\n # Optional Task 6.7c\",\n \"def boolean(self) -> bool:\\n return self.random.choice([True, False])\",\n \"def random():\\n return constant(1)\",\n \"def random(self):\\r\\n return random.randint(1, 4)\",\n \"def tire_objectif() -> int:\\n return random.randint(min_objectif, max_objectif)\",\n \"def prove_NNE() -> Proof:\\n # Optional Task 6.7b\",\n \"def _random_not_singular(N):\\n data = np.zeros((1, 1))\\n while np.linalg.det(data) == 0:\\n data = np.random.random((N, N)) + \\\\\\n 1j * np.random.random((N, N)) - (0.5 + 0.5j)\\n return data\",\n \"def generate_random_list_or_string(self):\\n if self.iteration_deep < self.max_iteration_deep:\\n self.iteration_deep += 1\\n value = self.generate_random_list(5)\\n self.iteration_deep -= 1\\n else:\\n value = self.generate_random_value(str)\\n return value\",\n \"def test_example_field_is_none(self):\\n self._example['weight'] = None\\n output = self._gen.generate(\\n example=self._example,\\n model=self._model,\\n dataset=self._dataset,\\n config=self._config)\\n self.assertNotEmpty(output)\",\n \"def random_bool():\\n return bool(randint(0, 1))\",\n \"def testNoSpecialties(self):\\n self.failUnlessEqual(self.person.getSpecialties(), [])\",\n \"def generate_train(n: int, case: Case) -> List[bool]:\\n if case is Case.Random:\\n return [bool(randint(0, 1)) for _ in range(n)]\\n elif case is Case.AllOn:\\n return [True for _ in range(n)]\\n elif case is Case.AllOffButStart:\\n return [True] + [False for _ in range(n - 1)]\",\n \"def randomize_value(self) -> None:\",\n \"def random_boolean(percent=50):\\n return random.randrange(100) < percent\",\n \"def _u_naught_simple(self):\\n # Random is better to give different multipliers in the subgradient phase\\n return np.random.rand(self.mrows)*1.\",\n \"def __init__(self):\\n self.my_piece = random.choice(self.pieces)\\n self.opp = self.pieces[0] if self.my_piece == self.pieces[1] else self.pieces[1]\",\n \"def __init__(self):\\n self.my_piece = random.choice(self.pieces)\\n self.opp = self.pieces[0] if self.my_piece == self.pieces[1] else self.pieces[1]\",\n \"def random_table(self):\\n rule_set = []\\n for i in range(self.k ** (2 * self.r + 1) - 1):\\n g = np.random.rand()\\n if g > self.lambda_param:\\n g = 0\\n else:\\n g = np.random.randint(1, self.k)\\n rule_set.append(g)\\n rule_set.append(0)\\n return rule_set\",\n \"def copyCounterWithNoise(self, counter, prob):\\n \\\"\\\"\\\"\\n counter = deepcopy(counter)\\n for i in range(self.nObjectTypes):\\n noise = np.random.randint(1,5)\\n if np.random.random() < prob:\\n if np.random.random() > 0.5:\\n counter[i] += noise # false detections\\n else:\\n counter[i] -= noise # missed detections\\n if counter[i] < 0:\\n counter[i]=0\\n return counter\\n \\\"\\\"\\\"\\n counter = deepcopy(counter)\\n for i in range(self.nObjectTypes):\\n noise = np.random.randint(1,5)\\n if np.random.random() > prob:\\n continue\\n if counter[i] == 0:\\n # Noise could only be +1\\n counter[i] = noise\\n else:\\n # Noise could be either +1 or -1\\n if np.random.random() > 0.5:\\n counter[i] += noise\\n else:\\n counter[i] -= noise\\n if counter[i] < 0:\\n counter[i]=0\\n return counter\",\n \"async def randomBool(self, ctx: commands.Context):\\n await ctx.reply(f\\\"Randomly chosen result:\\\\n**{random.choice(['False', 'True'])}**\\\", mention_author=False)\",\n \"def __rand__(self, otro):\\n return self & otro\",\n \"def getRandom(self):\\n index = random.randrange(0, self.length)\\n node = self.head\\n while index:\\n node = node.next\\n index -= 1\\n return node.val\",\n \"def random(cls, n=2):\\n angles = [Motion(math.pi*Vector.random(d=1), Vector.null(d=1),\\n Vector.null(d=1)) for i in range(n)]\\n lengths = [random.uniform(0, 1) for i in range(n)]\\n masses = [random.uniform(0, 1) for i in range(n)]\\n return cls(angles, lengths, masses)\",\n \"def test_no_sweeps_property(self):\\n expected_values = {\\n 'no_timesteps': 1000,\\n 'no_sweeps': 10,\\n 'no_channels': 4,\\n }\\n test_rec = rt.Recording(\\n np.zeros(\\n [\\n expected_values['no_channels'],\\n expected_values['no_timesteps'],\\n expected_values['no_sweeps'],\\n ]\\n ),\\n dt=0.1,\\n )\\n self.assertEqual(\\n test_rec.no_sweeps,\\n expected_values['no_sweeps'],\\n 'Expected {} for `no_sweeps` property; got {} instead.'.format(\\n expected_values['no_sweeps'], test_rec.no_sweeps\\n ),\\n )\",\n \"def make_random_move(self):\\n choice = None\\n options = []\\n #generate full moves list\\n for i in range(self.width):\\n for j in range(self.height):\\n #make sure move has not been made\\n if (i,j) not in self.moves_made:\\n #make sure move is not a mine\\n if (i,j) not in self.mines:\\n options.append((i,j))\\n #if there are no options, return None\\n if len(options) == 0:\\n return None\\n\\n #pick a random option from generated list\\n choice = random.choice(options)\\n return choice\\n\\n \\\"\\\"\\\"\\n For kicks and giggles I wrote this extra bit to determine a\\n rough intuitive probability for each option based on the knowledge\\n base, so rather than picking a choice randomly the AI can choose\\n the option that is, at least intuitively, least likely to blow up.\\n Better to take the 1/8 chance than the 1/3 chance, right?\\n \\\"\\\"\\\"\\n best_chance = 1\\n #iterate through generated options\\n for option in options:\\n #Could set chance to 1/8, but the AI wouldn't actually know that. I\\n #only know it because I can read the code...But for the purposes of this\\n #drill we'll say the AI doesn't know how many bombs are placed.\\n #Better then to pick a square we know nothing about than one that\\n #has a 1/8 chance of exploding. Gather more information that way.\\n chance = 0\\n for sentence in self.knowledge:\\n #look to see if current option is in sentences\\n if option in sentence.cells:\\n #use sentence count and length of cell set to calculate probability\\n prob = sentence.count / len(sentence.cells)\\n if prob > chance:\\n #Looking for the highest explosive probability for this square\\n chance = prob\\n if chance < best_chance:\\n #If this option has lower odds of exploding than current best, it becomes\\n #the optimal\\n best_chance = chance\\n choice = option\\n\\n #return choice\",\n \"def randomBool(p = 0.5):\\n if random.random() < p: return True\\n else: return False\",\n \"def getNoWells(self):\\n #code begins here \\n return self.__nwells\",\n \"def createRandom(protein):\\n\\n protein.occupied = []\\n protein.aminoList = []\\n\\n for id in range(protein.proteinLength):\\n protein.aminoList.append(Amino(id, protein.proteinString[id]))\\n\\n # Place the first and second amino acid\\n if id in {0, 1}:\\n thisCoordinate = [0, id]\\n if protein.plane == \\\"3D\\\":\\n thisCoordinate.append(0)\\n protein.aminoList[id].coordinate = thisCoordinate\\n protein.occupied.append(thisCoordinate)\\n else:\\n prevCo = protein.aminoList[(id - 1)].coordinate\\n posCo = protein.getSurroundCo(prevCo, occupied=False)\\n\\n # If there are no surrounding coordinates available stop the folding\\n if not posCo:\\n protein.stability = 0\\n return False\\n\\n coordinate = random.choice(posCo)\\n protein.aminoList[id].coordinate = coordinate\\n protein.occupied.append(coordinate)\\n\\n protein.stabilityUpdate(protein.aminoList[id])\\n\\n return True\",\n \"def generatePiece(self):\\n\\n empty_tiles = []\\n for y in range(BOARD_SIZE):\\n for x in range(BOARD_SIZE):\\n if self.grid[x][y].isEmpty():\\n empty_tiles.append(self.grid[x][y])\\n\\n two_or_four = random.choice([2, 4])\\n random.choice(empty_tiles).set(two_or_four)\",\n \"def get_validation(self, n: Optional[int] = None, random: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:\\n return self.__sample(self.test_data, self.test_los, n, random)\",\n \"def _produce_none_relations(self):\\n print \\\"Producing NONE-relations\\\"\\n relations = self.relations\\n events = self.events\\n none_relations = []\\n\\n for source in events:\\n for target in events:\\n new_relation = Relation(\\\"NONE\\\", self.text_obj, source, target, RelationType.NONE)\\n print new_relation\\n\\n if new_relation in relations:\\n continue\\n else:\\n none_relations.append(new_relation)\\n\\n self.relations = self.relations + none_relations\\n\\n print \\\"Finished producing NONE-relations\\\"\",\n \"def getRandom(self):\\n # pick a random number from the list\\n return random.choice(self.nums)\",\n \"def computer_generate(self):\\n return choice[random.randrange(3)]\",\n \"def testWithoutNoise(self):\\n self.checkMatching(self.references)\",\n \"def nextMoveDecision(self):\\n b = random.randint(1, 9) \\n while (self.Occupied(b)):\\n b = random.randint(1, 9) \\n return b\",\n \"def poblar_sansanito(n):\\n\\tcur.execute(\\\"\\\"\\\"\\n\\t\\t\\t\\tSELECT nombre\\n\\t\\t\\t\\tFROM poyo\\\"\\\"\\\")\\n\\tnombres = cur.fetchall()\\n\\t# Una lista de nombres, super grande\\n\\tfor i in range(n):\\n\\t\\tnombre_elegido = choice(nombres)[0] \\n\\t\\t# Elige nombre random de pokemon, pero existente\\n\\t\\testado = choice(estados_permitidos) \\n\\t\\t# Elige estado incluyendo el None\\n\\t\\thptot_query = \\\"\\\"\\\"\\n\\t\\t\\t\\t\\tSELECT hptotal\\n\\t\\t\\t\\t\\tFROM poyo\\n\\t\\t\\t\\t\\tWHERE nombre = :1\\\"\\\"\\\"\\n\\t\\tcur.execute(hptot_query, [nombre_elegido])\\n\\t\\thpmax = cur.fetchall()\\n\\t\\thpactual = randint(0, hpmax[0][0]) \\n\\t\\t# Genera hpactual que no sea mayor que hpmax\\n\\t\\tfecha_ingreso = generar_fecha()\\n\\t\\tinsertar_pokemon(nombre_elegido,hpactual, estado, fecha_ingreso, False)\",\n \"def generate_mixture_indicator(number_obeservations):\\n mixture_indicator = np.random.randint(0, 2, (number_obeservations,1))\\n return mixture_indicator\",\n \"def test_no_prior(self):\\n dim = Dimension(\\\"yolo\\\", None)\\n print(dim._prior_name)\\n assert dim.prior is None\\n assert dim._prior_name == \\\"None\\\"\",\n \"def create(ch, ran_n):\\n l = ch.l / ch.num_of_links\\n assert np.log2(len(\\n ran_n)) % 1 == 0.0, \\\"The number of random variables have to be a power of 2 if binary\\\"\\n rho_basic = maps.coupl(ch.em, *ch.rho_prep[:])\\n rho_basic = maps.dp_sing(2 * l / ch.c, ch.T, *rho_basic)\\n k = int(np.log2(len(ran_n)))\\n rhos = [rho_basic[:] for i in range(2**k)]\\n p_total = 1\\n for i in range(k):\\n new_rhos = []\\n new_n = []\\n for j in range(0, 2**k, 2):\\n t_wait = ran_n[j + 1] * 2 * l / ch.c\\n rho1 = maps.dp_doub(t_wait, ch.T, *rhos[j])\\n rho, p = maps.distil(ch.lam_Dist, ch.p_dc,\\n ch.p_dc, *(rho1 + rhos[j + 1]))\\n p_total *= p\\n new_rhos.append(rho)\\n new_n.append(ran_n[j] + ran_n[j + 1])\\n rhos = new_rhos\\n ran_n = new_n\\n k = k - 1\\n assert len(rhos) == 1, \\\"Okay, something went wrong here, sorry for that!\\\"\\n cond = np.random.random() <= p_total\\n return cond, rhos[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5694869","0.5662974","0.56016517","0.55177194","0.544953","0.5439609","0.53909516","0.537335","0.5364247","0.5350749","0.53295314","0.5328545","0.52704114","0.5237583","0.5216445","0.5216445","0.5216065","0.5210262","0.5208821","0.5197762","0.5170702","0.5167708","0.51618165","0.51521516","0.5151923","0.5108511","0.5105862","0.50977933","0.50801617","0.5071661","0.5063624","0.50496984","0.5006902","0.50020975","0.49769998","0.49608147","0.49608147","0.49514127","0.49504545","0.49433804","0.4938936","0.49352372","0.4925981","0.4925337","0.4925337","0.49167755","0.49117857","0.49061552","0.4896479","0.48935735","0.48795104","0.48789442","0.48745012","0.4872132","0.4869769","0.4866418","0.48618668","0.48597693","0.48542133","0.4852056","0.48477477","0.48473835","0.48408306","0.48404184","0.48340917","0.48291266","0.48288897","0.4826616","0.48260993","0.48239893","0.4819696","0.4814941","0.48138243","0.4812199","0.48095974","0.48091716","0.4793871","0.4793871","0.4793044","0.47812754","0.47798404","0.47789103","0.47693083","0.47672388","0.4764264","0.476289","0.47604948","0.4759936","0.4756937","0.47562447","0.47538534","0.4752491","0.47446138","0.47440907","0.47413227","0.47412613","0.47377795","0.47337288","0.47330686","0.47269723"],"string":"[\n \"0.5694869\",\n \"0.5662974\",\n \"0.56016517\",\n \"0.55177194\",\n \"0.544953\",\n \"0.5439609\",\n \"0.53909516\",\n \"0.537335\",\n \"0.5364247\",\n \"0.5350749\",\n \"0.53295314\",\n \"0.5328545\",\n \"0.52704114\",\n \"0.5237583\",\n \"0.5216445\",\n \"0.5216445\",\n \"0.5216065\",\n \"0.5210262\",\n \"0.5208821\",\n \"0.5197762\",\n \"0.5170702\",\n \"0.5167708\",\n \"0.51618165\",\n \"0.51521516\",\n \"0.5151923\",\n \"0.5108511\",\n \"0.5105862\",\n \"0.50977933\",\n \"0.50801617\",\n \"0.5071661\",\n \"0.5063624\",\n \"0.50496984\",\n \"0.5006902\",\n \"0.50020975\",\n \"0.49769998\",\n \"0.49608147\",\n \"0.49608147\",\n \"0.49514127\",\n \"0.49504545\",\n \"0.49433804\",\n \"0.4938936\",\n \"0.49352372\",\n \"0.4925981\",\n \"0.4925337\",\n \"0.4925337\",\n \"0.49167755\",\n \"0.49117857\",\n \"0.49061552\",\n \"0.4896479\",\n \"0.48935735\",\n \"0.48795104\",\n \"0.48789442\",\n \"0.48745012\",\n \"0.4872132\",\n \"0.4869769\",\n \"0.4866418\",\n \"0.48618668\",\n \"0.48597693\",\n \"0.48542133\",\n \"0.4852056\",\n \"0.48477477\",\n \"0.48473835\",\n \"0.48408306\",\n \"0.48404184\",\n \"0.48340917\",\n \"0.48291266\",\n \"0.48288897\",\n \"0.4826616\",\n \"0.48260993\",\n \"0.48239893\",\n \"0.4819696\",\n \"0.4814941\",\n \"0.48138243\",\n \"0.4812199\",\n \"0.48095974\",\n \"0.48091716\",\n \"0.4793871\",\n \"0.4793871\",\n \"0.4793044\",\n \"0.47812754\",\n \"0.47798404\",\n \"0.47789103\",\n \"0.47693083\",\n \"0.47672388\",\n \"0.4764264\",\n \"0.476289\",\n \"0.47604948\",\n \"0.4759936\",\n \"0.4756937\",\n \"0.47562447\",\n \"0.47538534\",\n \"0.4752491\",\n \"0.47446138\",\n \"0.47440907\",\n \"0.47413227\",\n \"0.47412613\",\n \"0.47377795\",\n \"0.47337288\",\n \"0.47330686\",\n \"0.47269723\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5758818"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77486,"cells":{"query":{"kind":"string","value":"Finds the first occurrence of 'None' in the list of properties."},"document":{"kind":"string","value":"def find_first_none(self, properties):\n\n for drink in properties:\n iterate_drink = iter(drink)\n next(iterate_drink)\n for drink_property in iterate_drink:\n split_property = drink_property.split(\": \")[1].split(\" //\")[0]\n if split_property == \"None\":\n return drink_property\n return \"\""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_property(properties, key):\n matching = [prop.split('=').pop(1) for prop in properties\n if prop.startswith(key + '=')]\n if len(matching) > 1:\n raise ValueError('Property %r should appear only once in %r' %\n (key, properties))\n if '' in matching:\n raise ValueError('Invalid empty value for property %r in %r' %\n (key, properties))\n try:\n return matching.pop(0)\n except IndexError:\n return None","def _first_non_none_value(items: Iterable[tuple[bool | None, str] | None]) -> tuple[bool, str]:\n for item_opt in items:\n if item_opt is not None:\n item, reason = item_opt\n if item is not None:\n return item, reason\n return False, \"default\"","def one_or_none(self):\n return self._iter().one_or_none()","def filter_none(elems):\n return [x for x in elems if x is not None]","def __sort_properties(properties):\n return sorted(properties.items(), key=lambda x: (x[1] is None, x[1]))","def test_default_property_head(empty_list):\n assert empty_list.head is None\n assert empty_list._length == 0","def find_drink_property(self, properties):\n\n split_status = \"\"\n tries = 0\n\n while split_status != \"None\" and tries <= 5:\n random_drink = randint(0, len(properties) - 1)\n random_property = randint(1, len(properties[random_drink]) - 1)\n drink_property = properties[random_drink][random_property]\n split_status = drink_property.split(\": \")[1].split(\" //\")[0]\n tries += 1\n\n if split_status != \"None\" and tries > 5:\n drink_property = self.find_first_none(properties)\n return drink_property","def first(items):\n return next(iter(items or []), None)","def coalesce(*args):\n for arg in args:\n if arg is not None:\n return arg","def filter_none(x):\n if isinstance(x, dict):\n return {k: filter_none(v) for k, v in x.items() if v is not None}\n elif isinstance(x, list):\n return [filter_none(i) for i in x if x is not None]\n else:\n return x","def remove_none(obj: dict):\n return remap(\n obj, visit=lambda path, key, value: key is not None and value is not None\n )","def first(self) -> Optional[T]:\n if len(self.entry_finder) == 0:\n return None\n for (_, _, (item,)) in self.priority_queue:\n if item is not None:\n return cast(T, item)\n return None","def _replace_none(lst, repl=\"\"):\n return ['' if v is None else v for v in lst]","def _extract_non_empty_val(param_list):\n if param_list:\n value = param_list.pop(0)\n if value != '':\n return value\n return None","def filter_null(settings, null='__null__'):\n filtered = {}\n for k, v in settings.iteritems():\n if v == null:\n filtered[k] = None\n else:\n filtered[k] = v\n\n return filtered","def hd(lst):\n return lst[0] if lst else None","def _remove_none_values(dictionary):\n return list(map(dictionary.pop,\n [i for i in dictionary if dictionary[i] is None]))","def _advance_peek(ls: List[any]) -> Optional[any]:\r\n try:\r\n return ls[0]\r\n except IndexError:\r\n return None","def get_next_as_optional(iterator):\n return iterator.get_next_as_optional()","def _get_property(self, xpaths):\n result = None\n for xpath in xpaths:\n result = self[xpath]\n if len(result) > 0:\n break\n\n return result","def filter_null(item, keep_null=False, null_vals=None):\n\n if item[0] == 0:\n keep = True\n else:\n keep = bool(item[0])\n\n if null_vals and str(item[0]) in null_vals and keep:\n keep = False\n keep ^= keep_null\n\n return item if keep else None","def first(l):\n return next(iter(l), None)","def _find_or_none(string, substring, start):\n index = string.find(substring, start)\n return index if index != -1 else None","def null() -> SetupVal:\n return NullVal()","def first(self, **opts):\n try:\n return next(self.find(**opts))\n except StopIteration:\n if 'default' in opts:\n return opts['default']\n else:\n raise KeyError(\"no matching objects\")","def first(data, key):\n for i in data:\n if key(i):\n return i\n return None","def find_first_match(_list, _item):\n for _list_item in _list:\n if _item[\"pid\"] == _list_item[\"pid\"]:\n return _list_item\n return None","def first_unassigned_variable(assignment, csp):\r\n # print(first([var for var in csp.variables if var not in assignment]))\r\n\r\n return first([var for var in csp.variables if var not in assignment])","def first(l: iter, predicate):\n for ele in l:\n if predicate(ele):\n return ele\n raise RuntimeError(\"Found nothing to match predicate\")","def compare_with_none():\n value = {};\n if value is not None:\n print(\"value is not none\")\n else:\n print(\"value is none\")","def test_get_with_None_value(self):\n self.assertEqual(self.config.get('none_types','some_value'),None)\n self.assertEqual(self.config.get('none_types','some_value','something'),'something')","def get_nullable():\n changes = True\n while changes:\n changes = False\n for p in PRODUCTION_LIST:\n if not symbol_for_str(p.left).is_nullable:\n if p.right[0] == 'null':\n symbol_for_str(p.left).is_nullable = True\n changes = True\n continue\n else:\n right_is_nullable = symbol_for_str(p.right[0]).is_nullable\n # For X -> Y1 ... YN, Nullable(X) = Nullable(Y1) &\n # Nullable(Y2) ... & Nullable(YN)\n for r in p.right[1:]:\n if r.startswith('P'):\n continue\n right_is_nullable = right_is_nullable & symbol_for_str(\n r).is_nullable\n\n if right_is_nullable:\n changes = True\n symbol_for_str(p.left).is_nullable = True","def non_none(iterable: Iterable[Optional[_T]]) -> Iterable[_T]:\n return (x for x in iterable if x is not None)","def first(xs):\n if not xs:\n return None\n return xs[0]","def first(xs):\n if not xs:\n return None\n return xs[0]","def check_for_null(attribute, memberUrls):\n attributeNotFound =[]\n \n for url in memberUrls:\n member_data = utils.get_json(url)\n \n if member_data[attribute] is None:\n #TODO: TBD Could grab email here if speed was an issue\n attributeNotFound.append(url)\n return attributeNotFound","def not_none(item, alt=None):\r\n\r\n return item if item is not None else alt","def first(self):\n return self.head and self.head.value or None","def null_match():\n return Self._.match(\n lambda e=Example: e\n )","def find_atom_by_property(atoms: t.List[Atom], property_value: any, property_name: str = \"id\") -> Atom:\r\n\r\n if (all([hasattr(a, property_name) for a in atoms])):\r\n all_hits = list(filter(lambda a: a.id == property_value, atoms))\r\n if len(all_hits) == 0:\r\n raise ValueError('There is no atom with id: ' + str(id))\r\n elif len(all_hits) > 1:\r\n raise ValueError('There is more than one atom with id: ' + str(id))\r\n else:\r\n return all_hits[0]\r\n else:\r\n raise ValueError(\"atom does not have Property: \" + property_name + \" but: \" + str(vars(atoms[0])))","def return_none() -> None:\n pass","def test_single_match_returns_none(self):\n eq_(None,line_no_matches_ngreps(self.line,[\"foo\"]))","def is_none(obj):\n return obj is None","def test_get_none(self):\n self.assertTrue(self.tester.get('Not_a_Sample') is None)","def test_get_none(self):\n self.assertTrue(self.tester.get('Not_a_Sample') is None)","def get_list_of_present_entries(list_):\n\n _List = []\n for x in list_:\n if x not in _List:\n if not None:\n if not pd.isna(x):\n _List.append(x)\n return _List","def find_missing_pos(cons_pos_li, alt_class=False):\n i=0\n while i < len(cons_pos_li):\n if cons_pos_li[i][2]==\"\":\n cons_pos_li[i][2]=\"None\"\n # if alt_class:\n # cons_pos_li[i][0] += alt_class.lower() \n i+=1","def getXpathOptionalNode(elem, xpath):\n valueElems = elem.xpath(xpath)\n if len(valueElems) > 0:\n return valueElems[0]\n return None","def noneless(**kwargs):\n return {k: v for k, v in kwargs.items() if v is not None}","def test_given_none(self):\n llists = None\n result = solution(llists)\n self.assertEqual(None, result)","def test_null_value(self):\n lookup = ProbeSetLookup(lookup_data_fp=self.fp1)\n self.assertEqual(lookup.reverse_lookup('117_at'),[])","def _none_subst(self, *args):\n\n # Imports\n import numpy as np\n\n # Initialize argument list return value, and as None not found\n arglist = [a for a in args]\n none_found = False\n\n # Check for None values\n none_vals = list(map(lambda e: isinstance(e, type(None)), arglist))\n\n # Error if more than one None; handle if exactly one; pass through if\n # none.\n if np.count_nonzero(none_vals) > 1:\n raise ValueError(\n \"Multiple 'None' values [indices {0}] not supported\"\n .format(tuple(np.nonzero(none_vals)[0])))\n elif np.count_nonzero(none_vals) == 1:\n # Must be no iterables that are not strings. Thus, an element-wise\n # test for iterability and an element-wise test for stringiness\n # must give matching arrays\n if not all(np.equal(list(map(np.iterable, arglist)),\n list(map(lambda e: isinstance(e, str), arglist)))):\n raise ValueError(\n \"'None' as parameter invalid with non-str iterables\")\n ## end if\n\n # Parameters okay; replace the None with the appropriate range()\n none_found = True\n none_loc = np.nonzero(none_vals)[0][0]\n arglist[none_loc] = \\\n range(self.num_geoms if none_loc == 0 else self.num_atoms)\n ## end if\n\n # Return the arguments list and the none-found value\n return arglist","def _func_null_leaf(self, arr: list, search: str) -> list:\n return [a.get(search) for a in arr]","def first(self):\n try:\n return self.next()\n except StopIteration:\n return None","def null_getter(d, fld, default=\"-\"):\n return d.get(fld, default) or default","def extract(l):\n if l is None: return None\n if len(l) > 1:\n raise ValueError('More than 1 Value')\n try:\n return l[0]\n except IndexError:\n return None","def find_empty(counts): \n for index,count in enumerate(counts):\n if count == 0:\n return index\n return None","def preferred_property(detected_places: Dict[int, TypeProperty],\n property_order: List[str]) -> Optional[int]:\n for prop_dcid in property_order:\n for col_idx, type_prop in detected_places.items():\n if prop_dcid == type_prop.dc_property.dcid:\n return col_idx\n return None","def break_on_property(self, qualname: str) -> Optional[gdb.Breakpoint]:\n lower_prop = qualname.lower()\n\n for prop in self.context.debug_info.properties:\n if prop.name.lower() == lower_prop:\n break\n else:\n print('No such property: {}'.format(qualname))\n return None\n\n if prop.body_start is None:\n print('Cannot break on {}: it has no code'.format(prop.name))\n return None\n\n # Break on the first line of the property's first inner scope so that\n # we skip the prologue (all variable declarations).\n return gdb.Breakpoint('{}:{}'.format(self.context.debug_info.filename,\n prop.body_start))","def get_first_item(videos):\n\n return next(iter(videos or []), None)","def find_first_node_from_here_by_rule(start_node, select):\n try:\n return next(find_nodes_from_here_by_rule(start_node, select))\n except StopIteration:\n return None","def FirstTrue(values, default=None):\n for value in values:\n if value:\n return value\n return default","def changenonetoNone(s):\r\n if s=='None':\r\n return None\r\n else:\r\n return s","def none_comparison(func):\n @functools.wraps(func)\n def inner(obj1,obj2):\n if obj1 is not None and obj2 is not None:\n return func(obj1, obj2)\n if obj1 is None and obj2 is None:\n return []\n if obj1 is not None and obj2 is None:\n return Difference(f\"Second {obj1.__class__.__name__} is None\",(obj1,None))\n return Difference(f\"First {obj2.__class__.__name__} is None\",(None,obj2))\n return inner","def _advance(ls: List[any]) -> Optional[any]:\r\n try:\r\n return ls.pop(0)\r\n except IndexError:\r\n return None","def if_none(value: Any, default: Any):\n return value if value is not None else default","def _FindFirstCompoundMatch(matches):\n for m in matches:\n if isinstance(m.value, models.Compound):\n return m\n return None","def is_match(cells):\n if len(cells) == 1 and \"-\" not in cells:\n return list(cells)[0]\n return None","def first(collection):\n return next(iter(collection))","def first(collection):\n return next(iter(collection))","def replace_none(ret_st, pattern):\n curr_none = [i for i in range(len(fk_array)) if ret_st[i] == 'unmapped-none']\n while curr_none:\n temp_curr_none = curr_none[:MGET_CHUNK]\n curr_none = curr_none[MGET_CHUNK:]\n vals_array = rdb.mget([pattern.format(str(fk_array[i]).upper(), taxid, hint)\n for i in temp_curr_none])\n for i, val in zip(temp_curr_none, vals_array):\n if val is None:\n continue\n ret_st[i] = val.decode()","def or_none(cls, node):\n return node if isinstance(node, cls) else None","def test_display_search_with_field_with_none_tuple():\n result = display.display_search(None)\n\n assert result == \"No responses to list\"","def _get_simple_null_value(simple):\n return _SIMPLE_NULL_VALUES[simple]","def null_values(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetFunnelNullValueArgs']]]]:\n return pulumi.get(self, \"null_values\")","def testDefault(self):\n for val in (1, {}, 'test', None):\n prop = make_prop(default=val)\n self.assertEqual(val, prop.interpret(recipe_api.PROPERTY_SENTINEL, {}))","def get_next_as_optional(self):\n raise NotImplementedError(\"Iterator.get_next_as_optional()\")","def test_first_non_consecutive_none(self):\n\n allure.dynamic.title(\"Non is expected\")\n allure.dynamic.severity(allure.severity_level.NORMAL)\n allure.dynamic.description_html('<h3>Codewars badge:</h3>'\n '<img src=\"https://www.codewars.com/users/myFirstCode'\n '/badges/large\">'\n '<h3>Test Description:</h3>'\n \"<p></p>\")\n\n with allure.step(\"Pass a list with no non consecutive numbers\"):\n lst = [1, 2, 3, 4, 5, 6, 7, 8]\n expected = None\n\n print_log(list=lst, expected=expected)\n self.assertEqual(first_non_consecutive(lst), expected)\n\n with allure.step(\"Pass a list with no non consecutive numbers\"):\n lst = [31, 32]\n expected = None\n\n print_log(list=lst, expected=expected)\n self.assertEqual(first_non_consecutive(lst), expected)","def notNone(a):\n return a.count(None) != len(a)","def get_prop(node, name):\n title = node.get(\"title\")\n props = title.split(\";\")\n for prop in props:\n (key, args) = prop.split(None, 1)\n args = args.strip('\"')\n if key == name:\n return args\n return None","def _get_none(self, x, y):\n try:\n return self[x, y]\n except ArrayError:\n return None","def find_unassigned_table_cell(table):\n for row in range(len(table)):\n for column in range(len(table[row])):\n if table[row][column] is None:\n return row, column\n return row, column","def nonull(val):\n return val if not pd.isnull(val) else None","def first_true(iterable, default=False, pred=None):\n return next(filter(pred, iterable), default)","def filter_none(mongo_dict):\n for key in list(mongo_dict.keys()):\n val = mongo_dict[key]\n try:\n if val is None or math.isnan(val):\n mongo_dict.pop(key)\n except Exception:\n continue\n return mongo_dict","def get_none1(self):\n pass","def _dig_first(*pairs: Tuple[Mapping[str, Setting], str], ignore_empty: bool = False) -> Setting:\n if not pairs:\n raise ValueError(\"pairs cannot be empty\")\n\n for dict_like, key in pairs:\n if key in dict_like:\n value = dict_like[key]\n\n if ignore_empty and value == \"\":\n continue\n\n return value\n\n last_key = pairs[-1][1]\n raise KeyError(last_key)","def first(iterable: Iterable[T1], predicate: Callable[[T1], bool]) -> Union[T1, None]:\n for x in iterable:\n if predicate(x):\n return x\n return None","def get_first(self):\n return self.A[1][0] if self.n > 0 else None","def no_none(decorated):\n def _func(*args, **kwargs):\n \"\"\"wrap generator\"\"\"\n for value in decorated(*args, **kwargs):\n if value is not None:\n yield value\n return _func","def find_first_node_by_rule(root_node, select):\n try:\n return next(find_nodes_by_rule(root_node, select))\n except StopIteration:\n return None","def _get_iterative_null_value():\n return _ITERATIVE_NULL_VALUE","def _find(xs, predicate):\n for x in xs:\n if predicate(x):\n return x\n return None","def none(self, predicate):\n return not self.any(predicate)","def handle_empty_list(item):\n\n return None if len(item) == 0 else item","def property_autoparse(self, candidate_pattern, patterns):\n properties = None\n candidates = self.find_objects(regex=candidate_pattern)\n if len(candidates):\n properties = []\n else:\n return properties\n for candidate in candidates:\n properties.append(self.match_to_dict(line=candidate, patterns=patterns))\n return properties","def replace_empty_value(data: list) -> list:\n for book in data:\n for attribute in book:\n if book[attribute] == \"\":\n book[attribute] = \"None\"\n return data","def first_value(self):\n if not self.is_empty():\n return self.data[self.head]\n return None","def _check_none(self) -> PossibleResult[T]:\n if self.constructor == type(None):\n if not self.obj is None:\n raise DeserializeError(\n type(None), self.obj, self.new_depth, self.key\n )\n return self.obj # type: ignore\n return NO_RESULT","def find_first(self, field):\n for c in self.children:\n if c.name == field:\n return c\n child_result = c.find_first(field)\n if child_result is not None:\n return child_result\n return None"],"string":"[\n \"def _get_property(properties, key):\\n matching = [prop.split('=').pop(1) for prop in properties\\n if prop.startswith(key + '=')]\\n if len(matching) > 1:\\n raise ValueError('Property %r should appear only once in %r' %\\n (key, properties))\\n if '' in matching:\\n raise ValueError('Invalid empty value for property %r in %r' %\\n (key, properties))\\n try:\\n return matching.pop(0)\\n except IndexError:\\n return None\",\n \"def _first_non_none_value(items: Iterable[tuple[bool | None, str] | None]) -> tuple[bool, str]:\\n for item_opt in items:\\n if item_opt is not None:\\n item, reason = item_opt\\n if item is not None:\\n return item, reason\\n return False, \\\"default\\\"\",\n \"def one_or_none(self):\\n return self._iter().one_or_none()\",\n \"def filter_none(elems):\\n return [x for x in elems if x is not None]\",\n \"def __sort_properties(properties):\\n return sorted(properties.items(), key=lambda x: (x[1] is None, x[1]))\",\n \"def test_default_property_head(empty_list):\\n assert empty_list.head is None\\n assert empty_list._length == 0\",\n \"def find_drink_property(self, properties):\\n\\n split_status = \\\"\\\"\\n tries = 0\\n\\n while split_status != \\\"None\\\" and tries <= 5:\\n random_drink = randint(0, len(properties) - 1)\\n random_property = randint(1, len(properties[random_drink]) - 1)\\n drink_property = properties[random_drink][random_property]\\n split_status = drink_property.split(\\\": \\\")[1].split(\\\" //\\\")[0]\\n tries += 1\\n\\n if split_status != \\\"None\\\" and tries > 5:\\n drink_property = self.find_first_none(properties)\\n return drink_property\",\n \"def first(items):\\n return next(iter(items or []), None)\",\n \"def coalesce(*args):\\n for arg in args:\\n if arg is not None:\\n return arg\",\n \"def filter_none(x):\\n if isinstance(x, dict):\\n return {k: filter_none(v) for k, v in x.items() if v is not None}\\n elif isinstance(x, list):\\n return [filter_none(i) for i in x if x is not None]\\n else:\\n return x\",\n \"def remove_none(obj: dict):\\n return remap(\\n obj, visit=lambda path, key, value: key is not None and value is not None\\n )\",\n \"def first(self) -> Optional[T]:\\n if len(self.entry_finder) == 0:\\n return None\\n for (_, _, (item,)) in self.priority_queue:\\n if item is not None:\\n return cast(T, item)\\n return None\",\n \"def _replace_none(lst, repl=\\\"\\\"):\\n return ['' if v is None else v for v in lst]\",\n \"def _extract_non_empty_val(param_list):\\n if param_list:\\n value = param_list.pop(0)\\n if value != '':\\n return value\\n return None\",\n \"def filter_null(settings, null='__null__'):\\n filtered = {}\\n for k, v in settings.iteritems():\\n if v == null:\\n filtered[k] = None\\n else:\\n filtered[k] = v\\n\\n return filtered\",\n \"def hd(lst):\\n return lst[0] if lst else None\",\n \"def _remove_none_values(dictionary):\\n return list(map(dictionary.pop,\\n [i for i in dictionary if dictionary[i] is None]))\",\n \"def _advance_peek(ls: List[any]) -> Optional[any]:\\r\\n try:\\r\\n return ls[0]\\r\\n except IndexError:\\r\\n return None\",\n \"def get_next_as_optional(iterator):\\n return iterator.get_next_as_optional()\",\n \"def _get_property(self, xpaths):\\n result = None\\n for xpath in xpaths:\\n result = self[xpath]\\n if len(result) > 0:\\n break\\n\\n return result\",\n \"def filter_null(item, keep_null=False, null_vals=None):\\n\\n if item[0] == 0:\\n keep = True\\n else:\\n keep = bool(item[0])\\n\\n if null_vals and str(item[0]) in null_vals and keep:\\n keep = False\\n keep ^= keep_null\\n\\n return item if keep else None\",\n \"def first(l):\\n return next(iter(l), None)\",\n \"def _find_or_none(string, substring, start):\\n index = string.find(substring, start)\\n return index if index != -1 else None\",\n \"def null() -> SetupVal:\\n return NullVal()\",\n \"def first(self, **opts):\\n try:\\n return next(self.find(**opts))\\n except StopIteration:\\n if 'default' in opts:\\n return opts['default']\\n else:\\n raise KeyError(\\\"no matching objects\\\")\",\n \"def first(data, key):\\n for i in data:\\n if key(i):\\n return i\\n return None\",\n \"def find_first_match(_list, _item):\\n for _list_item in _list:\\n if _item[\\\"pid\\\"] == _list_item[\\\"pid\\\"]:\\n return _list_item\\n return None\",\n \"def first_unassigned_variable(assignment, csp):\\r\\n # print(first([var for var in csp.variables if var not in assignment]))\\r\\n\\r\\n return first([var for var in csp.variables if var not in assignment])\",\n \"def first(l: iter, predicate):\\n for ele in l:\\n if predicate(ele):\\n return ele\\n raise RuntimeError(\\\"Found nothing to match predicate\\\")\",\n \"def compare_with_none():\\n value = {};\\n if value is not None:\\n print(\\\"value is not none\\\")\\n else:\\n print(\\\"value is none\\\")\",\n \"def test_get_with_None_value(self):\\n self.assertEqual(self.config.get('none_types','some_value'),None)\\n self.assertEqual(self.config.get('none_types','some_value','something'),'something')\",\n \"def get_nullable():\\n changes = True\\n while changes:\\n changes = False\\n for p in PRODUCTION_LIST:\\n if not symbol_for_str(p.left).is_nullable:\\n if p.right[0] == 'null':\\n symbol_for_str(p.left).is_nullable = True\\n changes = True\\n continue\\n else:\\n right_is_nullable = symbol_for_str(p.right[0]).is_nullable\\n # For X -> Y1 ... YN, Nullable(X) = Nullable(Y1) &\\n # Nullable(Y2) ... & Nullable(YN)\\n for r in p.right[1:]:\\n if r.startswith('P'):\\n continue\\n right_is_nullable = right_is_nullable & symbol_for_str(\\n r).is_nullable\\n\\n if right_is_nullable:\\n changes = True\\n symbol_for_str(p.left).is_nullable = True\",\n \"def non_none(iterable: Iterable[Optional[_T]]) -> Iterable[_T]:\\n return (x for x in iterable if x is not None)\",\n \"def first(xs):\\n if not xs:\\n return None\\n return xs[0]\",\n \"def first(xs):\\n if not xs:\\n return None\\n return xs[0]\",\n \"def check_for_null(attribute, memberUrls):\\n attributeNotFound =[]\\n \\n for url in memberUrls:\\n member_data = utils.get_json(url)\\n \\n if member_data[attribute] is None:\\n #TODO: TBD Could grab email here if speed was an issue\\n attributeNotFound.append(url)\\n return attributeNotFound\",\n \"def not_none(item, alt=None):\\r\\n\\r\\n return item if item is not None else alt\",\n \"def first(self):\\n return self.head and self.head.value or None\",\n \"def null_match():\\n return Self._.match(\\n lambda e=Example: e\\n )\",\n \"def find_atom_by_property(atoms: t.List[Atom], property_value: any, property_name: str = \\\"id\\\") -> Atom:\\r\\n\\r\\n if (all([hasattr(a, property_name) for a in atoms])):\\r\\n all_hits = list(filter(lambda a: a.id == property_value, atoms))\\r\\n if len(all_hits) == 0:\\r\\n raise ValueError('There is no atom with id: ' + str(id))\\r\\n elif len(all_hits) > 1:\\r\\n raise ValueError('There is more than one atom with id: ' + str(id))\\r\\n else:\\r\\n return all_hits[0]\\r\\n else:\\r\\n raise ValueError(\\\"atom does not have Property: \\\" + property_name + \\\" but: \\\" + str(vars(atoms[0])))\",\n \"def return_none() -> None:\\n pass\",\n \"def test_single_match_returns_none(self):\\n eq_(None,line_no_matches_ngreps(self.line,[\\\"foo\\\"]))\",\n \"def is_none(obj):\\n return obj is None\",\n \"def test_get_none(self):\\n self.assertTrue(self.tester.get('Not_a_Sample') is None)\",\n \"def test_get_none(self):\\n self.assertTrue(self.tester.get('Not_a_Sample') is None)\",\n \"def get_list_of_present_entries(list_):\\n\\n _List = []\\n for x in list_:\\n if x not in _List:\\n if not None:\\n if not pd.isna(x):\\n _List.append(x)\\n return _List\",\n \"def find_missing_pos(cons_pos_li, alt_class=False):\\n i=0\\n while i < len(cons_pos_li):\\n if cons_pos_li[i][2]==\\\"\\\":\\n cons_pos_li[i][2]=\\\"None\\\"\\n # if alt_class:\\n # cons_pos_li[i][0] += alt_class.lower() \\n i+=1\",\n \"def getXpathOptionalNode(elem, xpath):\\n valueElems = elem.xpath(xpath)\\n if len(valueElems) > 0:\\n return valueElems[0]\\n return None\",\n \"def noneless(**kwargs):\\n return {k: v for k, v in kwargs.items() if v is not None}\",\n \"def test_given_none(self):\\n llists = None\\n result = solution(llists)\\n self.assertEqual(None, result)\",\n \"def test_null_value(self):\\n lookup = ProbeSetLookup(lookup_data_fp=self.fp1)\\n self.assertEqual(lookup.reverse_lookup('117_at'),[])\",\n \"def _none_subst(self, *args):\\n\\n # Imports\\n import numpy as np\\n\\n # Initialize argument list return value, and as None not found\\n arglist = [a for a in args]\\n none_found = False\\n\\n # Check for None values\\n none_vals = list(map(lambda e: isinstance(e, type(None)), arglist))\\n\\n # Error if more than one None; handle if exactly one; pass through if\\n # none.\\n if np.count_nonzero(none_vals) > 1:\\n raise ValueError(\\n \\\"Multiple 'None' values [indices {0}] not supported\\\"\\n .format(tuple(np.nonzero(none_vals)[0])))\\n elif np.count_nonzero(none_vals) == 1:\\n # Must be no iterables that are not strings. Thus, an element-wise\\n # test for iterability and an element-wise test for stringiness\\n # must give matching arrays\\n if not all(np.equal(list(map(np.iterable, arglist)),\\n list(map(lambda e: isinstance(e, str), arglist)))):\\n raise ValueError(\\n \\\"'None' as parameter invalid with non-str iterables\\\")\\n ## end if\\n\\n # Parameters okay; replace the None with the appropriate range()\\n none_found = True\\n none_loc = np.nonzero(none_vals)[0][0]\\n arglist[none_loc] = \\\\\\n range(self.num_geoms if none_loc == 0 else self.num_atoms)\\n ## end if\\n\\n # Return the arguments list and the none-found value\\n return arglist\",\n \"def _func_null_leaf(self, arr: list, search: str) -> list:\\n return [a.get(search) for a in arr]\",\n \"def first(self):\\n try:\\n return self.next()\\n except StopIteration:\\n return None\",\n \"def null_getter(d, fld, default=\\\"-\\\"):\\n return d.get(fld, default) or default\",\n \"def extract(l):\\n if l is None: return None\\n if len(l) > 1:\\n raise ValueError('More than 1 Value')\\n try:\\n return l[0]\\n except IndexError:\\n return None\",\n \"def find_empty(counts): \\n for index,count in enumerate(counts):\\n if count == 0:\\n return index\\n return None\",\n \"def preferred_property(detected_places: Dict[int, TypeProperty],\\n property_order: List[str]) -> Optional[int]:\\n for prop_dcid in property_order:\\n for col_idx, type_prop in detected_places.items():\\n if prop_dcid == type_prop.dc_property.dcid:\\n return col_idx\\n return None\",\n \"def break_on_property(self, qualname: str) -> Optional[gdb.Breakpoint]:\\n lower_prop = qualname.lower()\\n\\n for prop in self.context.debug_info.properties:\\n if prop.name.lower() == lower_prop:\\n break\\n else:\\n print('No such property: {}'.format(qualname))\\n return None\\n\\n if prop.body_start is None:\\n print('Cannot break on {}: it has no code'.format(prop.name))\\n return None\\n\\n # Break on the first line of the property's first inner scope so that\\n # we skip the prologue (all variable declarations).\\n return gdb.Breakpoint('{}:{}'.format(self.context.debug_info.filename,\\n prop.body_start))\",\n \"def get_first_item(videos):\\n\\n return next(iter(videos or []), None)\",\n \"def find_first_node_from_here_by_rule(start_node, select):\\n try:\\n return next(find_nodes_from_here_by_rule(start_node, select))\\n except StopIteration:\\n return None\",\n \"def FirstTrue(values, default=None):\\n for value in values:\\n if value:\\n return value\\n return default\",\n \"def changenonetoNone(s):\\r\\n if s=='None':\\r\\n return None\\r\\n else:\\r\\n return s\",\n \"def none_comparison(func):\\n @functools.wraps(func)\\n def inner(obj1,obj2):\\n if obj1 is not None and obj2 is not None:\\n return func(obj1, obj2)\\n if obj1 is None and obj2 is None:\\n return []\\n if obj1 is not None and obj2 is None:\\n return Difference(f\\\"Second {obj1.__class__.__name__} is None\\\",(obj1,None))\\n return Difference(f\\\"First {obj2.__class__.__name__} is None\\\",(None,obj2))\\n return inner\",\n \"def _advance(ls: List[any]) -> Optional[any]:\\r\\n try:\\r\\n return ls.pop(0)\\r\\n except IndexError:\\r\\n return None\",\n \"def if_none(value: Any, default: Any):\\n return value if value is not None else default\",\n \"def _FindFirstCompoundMatch(matches):\\n for m in matches:\\n if isinstance(m.value, models.Compound):\\n return m\\n return None\",\n \"def is_match(cells):\\n if len(cells) == 1 and \\\"-\\\" not in cells:\\n return list(cells)[0]\\n return None\",\n \"def first(collection):\\n return next(iter(collection))\",\n \"def first(collection):\\n return next(iter(collection))\",\n \"def replace_none(ret_st, pattern):\\n curr_none = [i for i in range(len(fk_array)) if ret_st[i] == 'unmapped-none']\\n while curr_none:\\n temp_curr_none = curr_none[:MGET_CHUNK]\\n curr_none = curr_none[MGET_CHUNK:]\\n vals_array = rdb.mget([pattern.format(str(fk_array[i]).upper(), taxid, hint)\\n for i in temp_curr_none])\\n for i, val in zip(temp_curr_none, vals_array):\\n if val is None:\\n continue\\n ret_st[i] = val.decode()\",\n \"def or_none(cls, node):\\n return node if isinstance(node, cls) else None\",\n \"def test_display_search_with_field_with_none_tuple():\\n result = display.display_search(None)\\n\\n assert result == \\\"No responses to list\\\"\",\n \"def _get_simple_null_value(simple):\\n return _SIMPLE_NULL_VALUES[simple]\",\n \"def null_values(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OneDashboardPageWidgetFunnelNullValueArgs']]]]:\\n return pulumi.get(self, \\\"null_values\\\")\",\n \"def testDefault(self):\\n for val in (1, {}, 'test', None):\\n prop = make_prop(default=val)\\n self.assertEqual(val, prop.interpret(recipe_api.PROPERTY_SENTINEL, {}))\",\n \"def get_next_as_optional(self):\\n raise NotImplementedError(\\\"Iterator.get_next_as_optional()\\\")\",\n \"def test_first_non_consecutive_none(self):\\n\\n allure.dynamic.title(\\\"Non is expected\\\")\\n allure.dynamic.severity(allure.severity_level.NORMAL)\\n allure.dynamic.description_html('<h3>Codewars badge:</h3>'\\n '<img src=\\\"https://www.codewars.com/users/myFirstCode'\\n '/badges/large\\\">'\\n '<h3>Test Description:</h3>'\\n \\\"<p></p>\\\")\\n\\n with allure.step(\\\"Pass a list with no non consecutive numbers\\\"):\\n lst = [1, 2, 3, 4, 5, 6, 7, 8]\\n expected = None\\n\\n print_log(list=lst, expected=expected)\\n self.assertEqual(first_non_consecutive(lst), expected)\\n\\n with allure.step(\\\"Pass a list with no non consecutive numbers\\\"):\\n lst = [31, 32]\\n expected = None\\n\\n print_log(list=lst, expected=expected)\\n self.assertEqual(first_non_consecutive(lst), expected)\",\n \"def notNone(a):\\n return a.count(None) != len(a)\",\n \"def get_prop(node, name):\\n title = node.get(\\\"title\\\")\\n props = title.split(\\\";\\\")\\n for prop in props:\\n (key, args) = prop.split(None, 1)\\n args = args.strip('\\\"')\\n if key == name:\\n return args\\n return None\",\n \"def _get_none(self, x, y):\\n try:\\n return self[x, y]\\n except ArrayError:\\n return None\",\n \"def find_unassigned_table_cell(table):\\n for row in range(len(table)):\\n for column in range(len(table[row])):\\n if table[row][column] is None:\\n return row, column\\n return row, column\",\n \"def nonull(val):\\n return val if not pd.isnull(val) else None\",\n \"def first_true(iterable, default=False, pred=None):\\n return next(filter(pred, iterable), default)\",\n \"def filter_none(mongo_dict):\\n for key in list(mongo_dict.keys()):\\n val = mongo_dict[key]\\n try:\\n if val is None or math.isnan(val):\\n mongo_dict.pop(key)\\n except Exception:\\n continue\\n return mongo_dict\",\n \"def get_none1(self):\\n pass\",\n \"def _dig_first(*pairs: Tuple[Mapping[str, Setting], str], ignore_empty: bool = False) -> Setting:\\n if not pairs:\\n raise ValueError(\\\"pairs cannot be empty\\\")\\n\\n for dict_like, key in pairs:\\n if key in dict_like:\\n value = dict_like[key]\\n\\n if ignore_empty and value == \\\"\\\":\\n continue\\n\\n return value\\n\\n last_key = pairs[-1][1]\\n raise KeyError(last_key)\",\n \"def first(iterable: Iterable[T1], predicate: Callable[[T1], bool]) -> Union[T1, None]:\\n for x in iterable:\\n if predicate(x):\\n return x\\n return None\",\n \"def get_first(self):\\n return self.A[1][0] if self.n > 0 else None\",\n \"def no_none(decorated):\\n def _func(*args, **kwargs):\\n \\\"\\\"\\\"wrap generator\\\"\\\"\\\"\\n for value in decorated(*args, **kwargs):\\n if value is not None:\\n yield value\\n return _func\",\n \"def find_first_node_by_rule(root_node, select):\\n try:\\n return next(find_nodes_by_rule(root_node, select))\\n except StopIteration:\\n return None\",\n \"def _get_iterative_null_value():\\n return _ITERATIVE_NULL_VALUE\",\n \"def _find(xs, predicate):\\n for x in xs:\\n if predicate(x):\\n return x\\n return None\",\n \"def none(self, predicate):\\n return not self.any(predicate)\",\n \"def handle_empty_list(item):\\n\\n return None if len(item) == 0 else item\",\n \"def property_autoparse(self, candidate_pattern, patterns):\\n properties = None\\n candidates = self.find_objects(regex=candidate_pattern)\\n if len(candidates):\\n properties = []\\n else:\\n return properties\\n for candidate in candidates:\\n properties.append(self.match_to_dict(line=candidate, patterns=patterns))\\n return properties\",\n \"def replace_empty_value(data: list) -> list:\\n for book in data:\\n for attribute in book:\\n if book[attribute] == \\\"\\\":\\n book[attribute] = \\\"None\\\"\\n return data\",\n \"def first_value(self):\\n if not self.is_empty():\\n return self.data[self.head]\\n return None\",\n \"def _check_none(self) -> PossibleResult[T]:\\n if self.constructor == type(None):\\n if not self.obj is None:\\n raise DeserializeError(\\n type(None), self.obj, self.new_depth, self.key\\n )\\n return self.obj # type: ignore\\n return NO_RESULT\",\n \"def find_first(self, field):\\n for c in self.children:\\n if c.name == field:\\n return c\\n child_result = c.find_first(field)\\n if child_result is not None:\\n return child_result\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6420576","0.6140944","0.56629044","0.5504727","0.5462736","0.5437577","0.53297234","0.52824485","0.5245571","0.52292776","0.5227138","0.5211976","0.51856893","0.5184328","0.5180561","0.5177724","0.516806","0.5162539","0.5130421","0.5128275","0.51195353","0.51194656","0.50852007","0.50845504","0.50818557","0.50604296","0.5044597","0.5038588","0.50366473","0.5029363","0.5023465","0.50177294","0.5013596","0.5009582","0.5009582","0.50002533","0.4995965","0.49899274","0.49896702","0.49762186","0.4972496","0.49417824","0.4937344","0.49135396","0.49135396","0.49112967","0.48939902","0.4883665","0.48809743","0.4862965","0.48575264","0.4850141","0.48491138","0.4843861","0.48366338","0.48243904","0.48207453","0.48189437","0.4804836","0.47937158","0.47929978","0.47915274","0.4764524","0.476189","0.47600994","0.47598663","0.47530845","0.4752108","0.4740772","0.4740772","0.473489","0.47320196","0.47312456","0.47302002","0.47226396","0.47225043","0.47182074","0.4712459","0.47065037","0.4695958","0.46829614","0.46804142","0.4675584","0.46668008","0.46620545","0.465943","0.46560565","0.46482638","0.46461043","0.46434987","0.46419168","0.46350327","0.46291876","0.4625689","0.4620075","0.46191484","0.46185964","0.46174094","0.46171677","0.4613281"],"string":"[\n \"0.6420576\",\n \"0.6140944\",\n \"0.56629044\",\n \"0.5504727\",\n \"0.5462736\",\n \"0.5437577\",\n \"0.53297234\",\n \"0.52824485\",\n \"0.5245571\",\n \"0.52292776\",\n \"0.5227138\",\n \"0.5211976\",\n \"0.51856893\",\n \"0.5184328\",\n \"0.5180561\",\n \"0.5177724\",\n \"0.516806\",\n \"0.5162539\",\n \"0.5130421\",\n \"0.5128275\",\n \"0.51195353\",\n \"0.51194656\",\n \"0.50852007\",\n \"0.50845504\",\n \"0.50818557\",\n \"0.50604296\",\n \"0.5044597\",\n \"0.5038588\",\n \"0.50366473\",\n \"0.5029363\",\n \"0.5023465\",\n \"0.50177294\",\n \"0.5013596\",\n \"0.5009582\",\n \"0.5009582\",\n \"0.50002533\",\n \"0.4995965\",\n \"0.49899274\",\n \"0.49896702\",\n \"0.49762186\",\n \"0.4972496\",\n \"0.49417824\",\n \"0.4937344\",\n \"0.49135396\",\n \"0.49135396\",\n \"0.49112967\",\n \"0.48939902\",\n \"0.4883665\",\n \"0.48809743\",\n \"0.4862965\",\n \"0.48575264\",\n \"0.4850141\",\n \"0.48491138\",\n \"0.4843861\",\n \"0.48366338\",\n \"0.48243904\",\n \"0.48207453\",\n \"0.48189437\",\n \"0.4804836\",\n \"0.47937158\",\n \"0.47929978\",\n \"0.47915274\",\n \"0.4764524\",\n \"0.476189\",\n \"0.47600994\",\n \"0.47598663\",\n \"0.47530845\",\n \"0.4752108\",\n \"0.4740772\",\n \"0.4740772\",\n \"0.473489\",\n \"0.47320196\",\n \"0.47312456\",\n \"0.47302002\",\n \"0.47226396\",\n \"0.47225043\",\n \"0.47182074\",\n \"0.4712459\",\n \"0.47065037\",\n \"0.4695958\",\n \"0.46829614\",\n \"0.46804142\",\n \"0.4675584\",\n \"0.46668008\",\n \"0.46620545\",\n \"0.465943\",\n \"0.46560565\",\n \"0.46482638\",\n \"0.46461043\",\n \"0.46434987\",\n \"0.46419168\",\n \"0.46350327\",\n \"0.46291876\",\n \"0.4625689\",\n \"0.4620075\",\n \"0.46191484\",\n \"0.46185964\",\n \"0.46174094\",\n \"0.46171677\",\n \"0.4613281\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8134218"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77487,"cells":{"query":{"kind":"string","value":"Uses the found drink property to generate a sentence from one of the templates."},"document":{"kind":"string","value":"def generate_sentence(self, complete_property):\n\n drink_property = complete_property.split(\": \")[0]\n property_type = complete_property.split(\": \")[1].split(\"// \")[1]\n template = self.get_template(property_type)\n question = template.replace(\"{\" + property_type + \"}\", drink_property)\n return self.get_correct_form(question)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_sentence(self):","def generate_language(self, properties):\n\n drink_property = self.find_drink_property(properties)\n\n if drink_property:\n return self.generate_sentence(drink_property)\n else:\n return \"\"","def linkTemplates(self, sentence):\n templates = sentence.templates\n sLength = len(sentence.tokens)\n \n # find the closest mention to each value. \n # In case of ties, use mention that appears before the quantity in sentence\n for qTemplate in templates.getList(self.quantityType): \n (mTemplate, dist) = templates.closestMention(qTemplate, self.mentionType)\n if mTemplate != None:\n # use distance between elements to estimate association probability\n prob = 1.0 - float(dist)/sLength\n if self.mentionType == 'outcome':\n qTemplate.outcome = mTemplate\n qTemplate.outcomeProb = prob\n elif self.mentionType == 'group':\n qTemplate.group = mTemplate\n qTemplate.groupProb = prob\n if self.quantityType == 'gs':\n mTemplate.addSize(qTemplate)","def generate(self):\n\n letter = ''\n\n for template_name in self.templates:\n template = self.templates[template_name]\n\n # Process all replacements ({...} syntax).\n replacements = re.finditer(REPLACEMENT_REGEX, template)\n for replacement in replacements:\n match = replacement.group()\n key = replacement.group(1)\n\n template = template.replace(match, self._lookup(key))\n\n # Process all conditionals (<...> syntax).\n conditionals = re.finditer(CONDITIONAL_REGEX, template)\n for conditional in conditionals:\n match = conditional.group()\n\n # Process each condition within the conditional ([...]... syntax).\n conditions = re.finditer(CONDITION_REGEX, match)\n for index, condition in enumerate(conditions):\n skill_type = condition.group(2)\n skill = condition.group(3)\n text = condition.group(4)\n\n # If the skill is empty, treat it as a catch all case.\n if not skill or skill in self._lookup(skill_type):\n template = template.replace(match, text)\n break\n\n letter += template\n\n return letter","def generate_description(self, which, world):\n global parsed_kd_des\n if parsed_kd_des is None:\n parsed_kd_des = util.parseDes('killdude_background', KillDudeBackground)[0]\n s = choice(parsed_kd_des.__dict__[which].strip().splitlines())\n substs = util.LambdaMap()\n #here we parse following options: $depart_village, $who (dude.name) and $name (dude.unique_name)\n substs['who'] = self.dude.name\n substs['name'] = self.dude.unique_name\n substs['current_city'] = lambda : current_city(world)\n substs['village_name'] = lambda : util.gen_name('city')\n\n res = string.Template(s).safe_substitute(substs)\n return res","def make_text(word_next_dict):\n\n final_text = []\n # randomly select a tuple from the dictionary\n start = random.choice(word_next_dict.keys())\n # If 1st word in tuple isn't capitalized, choose a new pair until one is\n while not start[0].isupper():\n start = random.choice(word_next_dict.keys())\n # Begin the list of random text with tuple (separated to 2 strings)\n final_text.append(start[0])\n final_text.append(start[1])\n\n # create random text list: find a word associated with last 2 and append list\n while len(final_text) < 12:\n # look for sentence endings and re-choose start tuple\n if '.' in final_text[-1]:\n start = random.choice(word_next_dict.keys())\n # make sure tuple begins with a capitalized string\n while not start[0].isupper():\n start = random.choice(word_next_dict.keys())\n final_text.append(start[0])\n final_text.append(start[1])\n last_words = (final_text[-2], final_text[-1]) \n next_guy = word_next_dict[last_words][random.randrange(len(word_next_dict[last_words]))] # finds length of wordlist, randomly picks \n final_text.append(next_guy)\n\n to_tweet = ' '.join(final_text) + '.'\n print to_tweet\n return to_tweet","def walk(self, count):\n curr_word = self.dictogram.sample()\n sentence = [curr_word]\n for i in range(1, count):\n curr_word = self[curr_word].sample()\n sentence.append(curr_word)\n return ' '.join(sentence) + '.'","def generate_sentence(self,t=20):\n return result","def generate_text(word_dict, num_words):\n\n current_word = '$'\n for i in range(num_words):\n wordlist = word_dict[current_word]\n next_word = random.choice(wordlist)\n print(next_word, end=' ')\n if '.' in next_word or '?' in next_word or '!' in next_word:\n current_word = '$'\n else:\n current_word = next_word\n print()","def generate_text(word_dict, num_words):\n current_word = '$'\n for i in range(num_words):\n word_list = word_dict[current_word]\n next_word = random.choice(word_list)\n print(next_word, end=' ')\n if next_word[-1] == '.' or next_word[-1] == '!' or next_word[-1] == '?':\n current_word = '$'\n elif next_word not in word_dict:\n current_word = '$'\n else:\n current_word = next_word\n print()","def sentence():\n res = random.choice(no_result)\n adr = random.choice(address)\n wik = random.choice(wiki)\n return {'res': res, 'adr': adr, 'wik': wik}","def get_random_phrase(self):\n return random.choice(self.phrases)","def generate_sentence():\n markov_chain = makeMarkovDict(\"text.txt\")\n\n # Pick a random word to begin with.\n first_word = random.choice(markov_chain.keys()) # Illegall\n\n # print first_word\n # random_choice = random.randint(0, len(markov_chain.keys()))\n # index = 0\n # first_word = \"\"\n # for word in markov_chain:\n # print word\n # if index == random_choice:\n # first_word = word\n # break\n # index += 1\n\n # Based on that word, call function to chose the next word.\n # print markov_chain[first_word]\n # print word_selection(markov_chain[first_word])\n\n lenght_of_sentence = 10\n sentence = [first_word] # First word already in there\n for i in range(lenght_of_sentence):\n sentence.append(word_selection(markov_chain[sentence[i]]))\n # Sentence after loop: ['fish', 'red', 'fish', 'two', 'fish', 'red', 'fish', 'red', 'fish', 'two', 'fish']\n\n # Cap with letter and add period at the end.\n final_sentece = \" \".join(sentence) + \".\"\n return final_sentece.capitalize()","def chooseRandomSentence(self):\r\n return self.__repo.chooseObject()","def generate(self, count=15):\n\n sentence = []\n print(\"self.word_dict\", self.word_dict)\n for i in range(count):\n first_tuple = random.choice(list(self.word_dict.keys())) # first word for our sentence\n first_word = random.choice(first_tuple)\n sentence.append(first_word)\n second_word = self.word_dict[first_tuple]\n # print(\"second_word\", second_word)\n next_word = second_word.sample()\n # print(\"next_word\", next_word)\n # first_tuple = second_word\n sentence.append(next_word)\n # end_tuple =\n sentence = ' '.join(sentence)\n return sentence + \".\"\n # for i in range(len(self.token)):\n # val = list(self.word_dict.values())[i]\n # print(len(val))\n # # print(\"val\", val)\n # next_word = val.sample()\n # sentence.append(next_word)\n # sentence = ' '.join(sentence)\n # return sentence + \".\"","def fixture_other_case() -> str:\n return \"angrybird\"","def news_speech():\n #Fetches data from API and creates global varibles.\n news_handle(news_fetch(config_fetcher('news_region'), config_fetcher('news_key')))\n #Creates a daily breifing using varibles\n news_daily_news = Markup((f\"The top headline for today is entitled: {title_1}, and was \\\nwritten by {author_1}. Here is a second headline, entitled: {title_2}, written by {author_2}.\"))\n return news_daily_news","def make_text(chains, n):\n\n text = \"\"\n # variable = \"Dog\"\n random_key = choice(chains.keys())\n #print random_key\n # while variable == \"Dog\":\n while True:\n if random_key[0][0].isupper():\n # variable = \"Cat\"\n break\n else: \n random_key = choice(chains.keys())\n \n text = \" \".join(random_key)\n #random_key[0] + \" \" + random_key[1]\n #Loops through dictionary called chains and raondomly \n #chooses a next word and adds it to string\n while True:\n\n random_add = choice(chains[random_key])\n # print random_key[1-n:]\n # print random_add\n new_list=list(random_key[1-n:])\n new_list.append(random_add)\n random_key = tuple(new_list)\n #random_key = tuple([random_key[1-n:], random_add])\n #test_key = random_key[n+1:]\n #print \"test random key: \", random_key, \"random add: \", random_add\n sequence = (text,random_add)\n text = \" \".join([text, random_add])\n #text = text + \" \" + random_add\n # if the key has no value, stop, you're at the end!\n if random_key not in chains:\n break\n elif random_key[n-1][-1] in [\".\", \"!\", \"?\"] and len(text) > 400:\n break\n\n return text","def persona_from_template_values(topic: str, topic_item: str, extra_details: str = ''):\n pers = f'My favorite {topic} is {topic_item}.'\n if extra_details:\n pers += f'\\n{extra_details}'\n return pers","def make_text(chains):\n\n # what word should we start on? \"Would you\"?\n # dicitonaries are unordered, so what does \"first key\" mean?\n # from which list you should pull out the random word?\n\n # key 1st word (a)\n # key 2nd word (b)\n # random word from value ([list]) (c)\n # value of b, c tuple\n\n key_selection = choice(chains.keys())\n\n words = [key_selection[0], key_selection[1]]\n\n while key_selection in chains:\n new_word = choice(chains[key_selection])\n\n words.append(new_word)\n\n key_selection = (key_selection[1], new_word,)\n\n\n\n # print first_word,second_word,third_word\n\n # random.choice(chains)\n\n # your code goes here\n\n return \" \".join(words)","def render(self, context):\n if not self.phrase:\n return ''\n t = context.template.engine.get_template('philosophy_phrases/phrase_popup.html')\n return t.render(template.Context({'phrase': self.phrase}))","def generate(self, answers):\n\n text = self.template\n\n for (key, val) in answers.items():\n text = text.replace(\"{\" + key + \"}\", val)\n\n return text","def generate_fantasy_title():\n d20 = random.randint(1, 20)\n if d20 <= 4:\n #genetive noun\n return fantasy_genetive[random.randint(0, len(fantasy_genetive) - 1)] + \" \" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\n elif d20 > 4 and d20 < 13: \n #The adj noun\n return \"The \" + fantasy_adj[random.randint(0, len(fantasy_adj) - 1)] + \" \" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\n elif d20 >= 13:\n #something of something\n return fantasy_noun[random.randint(0, len(fantasy_noun) - 1)] + \" of \" + fantasy_what_is_this[random.randint(0, len(fantasy_what_is_this) - 1)]","def generate_story():\n\n text = story.generate(request.args)\n \n return render_template(\"story.html\", text = text)","def create_word(self):\n return self.random.choice(CONSONANTS) + self.random.choice(VOWELS)","def substantiate():","def make_text(chains):\n\n words = []\n\n # Set random tuple to variable key\n key = choice(chains.keys())\n\n\n # First time through key in chains, unpack X - a tuple - to get \n # key[0], key[1] = key\n # print key0]\n # print key[2]\n # Append key[0], key[1] to words\n\n words.append(key[0])\n words.append(key[1])\n\n # While tuple is a key in chains, keep looping \n while key in chains:\n\n # Randomly choose a value from chains\n word = choice(chains[key])\n\n # Append word to words\n words.append(word)\n\n key = (key[1], word)\n\n # return \" \".join(words)\n\n # define variable of returned \" \".join(words) as markov-tweet\n markov_tweet = \" \".join(words)\n\n return markov_tweet","def make_text(chains):\n \n random_text = \" \"\n\n\n #We created current key which will be replaced each time this process\n #happens. Perhaps we can structure the repetition as a while so that\n #while the key is in the dictionary, it continues building the text string\n\n\n current_key = choice(chains.keys())\n\n while current_key in chains:\n chosen_word = choice(chains[current_key])\n #Gives us random word from a list of third words\n new_key = current_key[1], chosen_word\n #Pairs the last word of the current tuple with the randomly picked 3rd word\n random_text = random_text + \" \" + chosen_word\n #We want this to be a string\n current_key = new_key \n\n return random_text","def make_text(chains):\n\n #current key is equal to a random key in the dictionary\n current_key = choice(chains.keys())\n #text is a string equal to the first index of the tuple and the second index of the tuple\n text = current_key[0] + \" \" + current_key[1]\n #create a loop that will repeat until the function reaches the last bi-gram\n while True:\n #created a variable for a random value of the current key\n chosen_word = choice(chains[current_key])\n #equivalent to: if chosen_word is None\n if not chosen_word:\n break\n # if it's not the last key add the chosen_word to the text to string\n text = text + \" \" + chosen_word\n # select new key\n current_key = (current_key[1], chosen_word)\n \n return text","def make_first_recommendation() -> str:\r\n available_tanks = tank_search()\r\n fermentation_tanks = available_tanks[0]\r\n conditioning_tanks = available_tanks[1]\r\n fermentation_advice = available_tanks[2]\r\n conditioning_advice = available_tanks[3]\r\n more_beer = available_tanks[4]\r\n return render_template(\"make_first_recommendation.html\",\r\n fermentation_string=fermentation_tanks,\r\n conditioning_string=conditioning_tanks,\r\n first_advice=fermentation_advice,\r\n second_advice=conditioning_advice,\r\n next_page=more_beer)","def generate_sentence(model, opener_words):\n\n sentence=[]\n #sentences between 3 and 15 words\n length= random.randint(3,6)\n keys=model.keys()\n bigram=random.choice(list(keys))\n\n #choose a first word that can be a starter word\n while bigram[0] not in opener_words:\n bigram=random.choice(list(keys))\n #iterate until sentence is correct length\n for i in range(0,length):\n matches=[]\n found=False\n while not found:\n\n #search in keys for key[0] to match the bigram[1]\n for key in keys:\n regex=re.compile(r\"\\b%s\\b\"%bigram[1])\n result=regex.match(key[0])\n if result:\n matches.append(key)\n found=True\n if not found:\n matches=[]\n i=0\n bigram=random.choice(list(keys))\n sentence.pop()\n\n #add first member of bigram to sentence list\n sentence.append(bigram[1])\n #choose next bigram from the list of matches\n bigram=random.choice(matches)\n\n #combine strings from list\n return \" \".join(sentence)","def make_text(chain_dict):\n\n# This selects the first random key to begin the chain.\n \n random_key = random.choice(chain_dict.keys())\n\n# This creates a random text list to append a random value from the original random key.\n# The random key is then reassigned to a tuple containing the second key in the random keys tuple,\n# and adds the randomly generated value of the earlier random key tuple as the second tuple value. \n# Convert tuples in random_key to strings, then added all strings together, then added to empty list\n\n first_words = ' '.join(map(str,random_key)) \n random_text_list = [first_words]\n \n for i in range(1,100):\n #while random_key in chain_dict: \n next = random.choice(chain_dict[random_key]) \n random_text_list.append(next)\n random_key = (random_key[1],next)\n\n pretty_text = []\n pretty_text = ' '.join(map(str, random_text_list)) \n return pretty_text","def make_text(chains):\n keys_list = list(chains.keys())\n \n key = random.choice(keys_list)\n words = list(key)\n value = random.choice(chains[key])\n words.append(value)\n key = (key[1], value)\n while True:\n\n if key in chains:\n word = random.choice(chains[key])\n words.append(word)\n key = (key[1], word)\n else:\n break\n print(\" \".join(words))","def diss_person():\n\n player = request.args.get(\"nickname\")\n\n # compliment = choice(AWESOMENESS)\n diss = request.args.get(\"diss\")\n\n return \"\"\"\n <!doctype html>\n <html>\n <head>\n <title>An Insult</title>\n </head>\n <body>\n Hi, {player}! I think you're a {diss}!\n </body>\n </html>\n \"\"\".format(player=player, diss=diss)","def get_next_sentence(self, last_sentence=\"\"):\n return super(MeadSummaryGenerator, self).get_next_sentence(last_sentence)","def __str__(self):\n t = Template(\n \"\"\"\n <h4>$title</h4>\n $imgs\n $footnotes\n <hr/>\"\"\")\n # Return result.\n return t.substitute({\n \"title\": self.title,\n \"imgs\": self.render_images(),\n \"footnotes\": self.render_footnotes()\n })","def get_template(self):\n return self.sep.join([self.htmls[html] for html in self.lang]).format(**self.fields)","def make_text(chains):\n n = len(list(chains.keys())[0])\n words = []\n first_link = random.choice(list(chains.keys()))\n \n # words.append(first_link[0])\n # words.append(first_link[1])\n #print(words)\n i = 0\n\n \n # your code goes here\n # while i2 < len(words):\n # update_tuple = (words[i1], words[i2])\n # if update_tuple in chains.keys():\n # new_link = random.choice(chains[update_tuple])\n # i1 += 1\n # i2 += 1\n # words.append(new_link)\n # #print(new_link)\n # else:\n # break\n for item in first_link:\n words.append(item)\n # n2 = n + 2\n\n while i < len(words) - 1:\n update_tuple = tuple(words[i:n])\n # print(update_tuple)\n\n if update_tuple in chains.keys():\n new_link = random.choice(chains[update_tuple]) \n i += 1\n n += 1\n words.append(new_link)\n #print(new_link)\n else:\n break\n\n \n return ' '.join(words)","def _get_papy_sentence(self):\n sentences = [\n \"un crayon basique peut écrire jusqu’à 45 000 mots. \",\n \"la troisième langue officielle de la \"\n \"Nouvelle-Zélande est le langage des signes.\",\n \"si la Terre faisait la taille d’un grain de sable, \"\n \"le Soleil ferait la taille d’une orange.\",\n \"à la base, le papier bulle a été inventé \"\n \"pour servir de papier peint.\",\n ]\n chosen_sentence = random.choice(sentences)\n response = \"J'ai une histoire à te raconter : \" + chosen_sentence\n return response","def createSentence(self, n=0, v=0, o=0, p=0,prep=True):\n sentence = ''\n if not n:\n n = np.random.randint(1, 5)\n if not v:\n v = np.random.randint(1, 5)\n if not o:\n o = np.random.randint(1, 5)\n sentence += self.createPhrase(nwords=n) + ' '\n if sentence[:-1] not in ('mi', 'sina'):\n sentence += 'li '\n sentence += self.createPhrase(nwords=v) + ' e '\n sentence += self.createPhrase(nwords=o)\n if prep:\n if not p:\n p = np.random.randint(1, 5)\n sentence += ' ' + np.random.choice(self.prepositions) + ' ' + self.createPhrase(nwords=p)\n return sentence","def build_tweet():\n\n verb = conjugate(random.choice(verbs)['present'], tense=PARTICIPLE, parse=True).title()\n animal = random.choice(animals).title()\n food = random.choice(foods).title()\n noun = random.choice(nouns).title()\n\n band = food + \" \" + noun\n track = verb + \" \" + animal\n\n feature1 = clean_feature(random.choice(j['features']))\n feature2 = clean_feature(random.choice(j['features']))\n feature3 = random.choice(j['features'])\n\n dont = \"\"\n if random.randrange(100) <= 50:\n dont = \"don't \"\n\n s = \"I \" + dont + \"like the \" + feature1 + \" and \" + feature2 + \" with \" + \\\n feature3 + \" in \" + band + \"'s \" + '\"' + track + '\"'\n\n return s","def make_text(chains):\n\n key = random.choice(chains.keys())\n return_string = key[0] + \" \" + key[1]\n\n while chains[key] != None: #chains.get(key[-1]) != None: \n next_word = random.choice(chains[key])\n return_string = return_string + \" \" + next_word\n key = (key[1],next_word)\n\n return return_string","def read_template():\n\n text_msg = \"\"\"${PERSON_NAME} - Calling Campaign Summary - ${DATE}:\\n\n Total Called = ${TOTAL_CALLED}\\n\n Answered = ${ANSWERED}\\n\n Not Answered = ${NOT_ANSWERED}\\n\n Declines = ${DECLINES}\\n\n Remaining = ${REMAINING}\\n\n \\n\n Thank You.\"\"\"\n\n return Template(text_msg)","def markov_story():\n return render_template(\"markovstory.html\")","def markup_text(self, text):\n for moniker, name in S['names'].items():\n text = text.replace('${0}'.format(moniker.split('_')[1]), name)\n return text","def definition(request, word_to_lookup):\n return render(request, 'definition.html')","def generate_markov_text(d, size=40):\n\n li = [key for key in d.keys() if key[0][0].isupper()]\n key = random.choice(li)\n \n li = []\n first, second = key\n li.append(first)\n li.append(second)\n \n while True:\n while len(li)<size:\n try:\n third = random.choice(d[key])\n except KeyError:\n break\n li.append(third)\n key = (second, third)\n first, second = key\n \n return ' '.join(li)","def make_text(dictionary):\n\n capital_keys_list = [key for key in dictionary.keys() if key[0][0].isupper()] # create list of only keys that start with an uppercase letter\n starting_key = random.choice(capital_keys_list) # choose key (tuple) to start at\n new_text_list = list(starting_key) # add items in that tuple to list of created text\n \n punctuation = \"?.!\" # create punctuation string \n next_word = \" \" # initialize next_word\n\n while dictionary.get(starting_key) != None and next_word[-1] not in punctuation: # Continue until the key is not found in the dictionary and until the last word ends in punctuation\n value_list = dictionary[starting_key] # assign value of key (list)\n rand_index = random.randrange(0, len(value_list)) # choose random int w/in length of list\n next_word = value_list[rand_index] # find item at that random index\n\n new_text_list.append(next_word) # add next_word to list of created text\n\n starting_key = tuple(list(starting_key[1:]) + [next_word]) # create new tuple from second word of previous tuple + item at that index\n\n new_text_string = \" \".join(new_text_list) # join list into string\n return new_text_string # return new text","def SNB():\n return render_template('search_beer_by_name.html')","def greeting(sentence):\r\n for word in sentence.split():\r\n if word.lower() in GREETING_INPUTS:\r\n return random.choice(GREETING_RESPONSES)","def make_text(chains):\n words = []\n not_end_of_list = True\n # your code goes here\n \n # starts with a capital lettered word from source text\n capitalized_ngrams = []\n for key in chains.keys():\n # check if the first tuple in key[0][0]\n if key[0][0].isupper():\n capitalized_ngrams.append(key)\n \n \n\n selected_keys = list(capitalized_ngrams)\n count = 0\n while not_end_of_list:\n choice_n = choice(selected_keys)\n\n if count == 0:\n words.extend(choice_n)\n \n if chains[choice_n] and count <= 150:\n # as long as there is an option, picks a random element from dict list\n choose_next = choice(chains[choice_n])\n # adds new word to list\n words.append(choose_next)\n # creates a list of keys whose last item in tuple is item from list\n selected_keys = [x for x in chains.keys() if x == tuple([*choice_n[1:], choose_next])]\n # it is possible continues\n if selected_keys:\n pass\n else:\n not_end_of_list = False\n\n count += 1\n \n else:\n not_end_of_list = False\n\n return \" \".join(words)","def show_pet(self):\n pet = self.pet_factory.get_pet()\n print \"We have a lovely {}\".format(pet)\n print \"It says {}\".format(pet.speak())\n print \"We also have {}\".format(self.pet_factory.get_food())","def generate_excerpt():","def __str__(self):\n return self.underscoreSentence.get().__str__()","def random_sentence():\n article = random_article()\n random_sentence = random.choice(article.sentence_list)\n\n return {'sentence' : unicode(random_sentence),\n 'title' : unicode(article.title),\n 'revision_id' : article.revision_id }","def greeting(sentence):\n for word in sentence.split():\n if word.lower() in GREETING_INPUTS:\n return random.choice(GREETING_RESPONSES)","async def drinkify(self,ctx):\n artist = ctx.message.content[9:]\n drink = \"\"\n response = requests.get('http://drinkify.org/{}'.format(artist))\n if response.status_code == 200:\n root = html.fromstring(response.content)\n recipe = root.xpath('//ul[@class=\"recipe\"]/li/text()')\n for alc in recipe:\n drink +=\"{}\\n\".format(alc)\n instructions = root.xpath('normalize-space(//p[@class=\"instructions\"]/text())')\n drink += \"\\nInstructions:\\n{}\".format(instructions)\n else:\n return\n await ctx.bot.send_message(ctx.message.channel, drink)","def _get_sentence(sentence_data):\n return \" \".join([word for word, ne_tag in sentence_data])","def show_pet(self):\n pet = self.pet_factory.get_pet()\n\n print(\"this is a lovely \", pet)\n print(\"It says \", pet.speak())\n print(\"It eats \", self.pet_factory.get_food())","def get_random_phrase():\n return random.choices(PHRASES, WEIGHTS, k=1)[0]","def make_drink ():\n \n customer_pref = customer_order.drink_order()\n drink = []\n \n for pref in customer_pref:\n if customer_pref[pref] == True:\n drink.append(random.choice(ingredients[pref]))\n \n return drink","def render(self, template: str, **vars) -> str:","def nao_speech(possible_sentences):\n\n print(random.choice(possible_sentences))","def render_string(self, template: str, **vars) -> str:","def getSentence(self):\n return self.tokens[0].sentence","def generate_new_word(model: Dict[str, Set[str]]) -> str:\n all_keys = list(dict.keys(model))\n return random.choice(all_keys)","def make_text(chains):\n\n words = []\n\n # random_key = choice(chains.keys())\n\n # words.append(random_key[0])\n # words.append(random_key[1])\n\n # while random_key in chains.keys():\n\n # values_list = chains[random_key]\n\n # chosen_word = choice(values_list)\n\n # words.append(chosen_word)\n\n # new_key = (random_key[1], chosen_word)\n\n # random_key = new_key\n\n list_of_keys = chains.keys()\n\n random_key = choice(list_of_keys)\n\n while random_key[0].islower() or random_key[0] == \"--\":\n random_key = choice(list_of_keys)\n\n\n words.append(random_key[0])\n words.append(random_key[1])\n\n while random_key in chains.keys():\n random_value = choice(chains.get(random_key, None))\n\n words.append(random_value)\n new_key = (random_key[1], random_value)\n random_key = new_key\n if random_key[1].endswith(\".\"):\n break\n\n\n return \" \".join(words)","def create_word(self):\r\n\r\n template = self.word_constructions.get()\r\n word = \"\"\r\n for c in template:\r\n if c == \"v\":\r\n letter = self.get_letter(100)\r\n else:\r\n letter = self.get_letter(0)\r\n word += letter\r\n\r\n while not any(letter in self.vowels for letter in word):\r\n length = len(word)\r\n if length == 1:\r\n index = 0\r\n elif length == 2:\r\n index = random.randrange(0, 2)\r\n else:\r\n a = len(word) / 2\r\n index = a + random.randrange(-a / 2, a / 2)\r\n word = word[:index] + self.get_letter(100) + word[index + 1:]\r\n\r\n if random.random() > self.capital_chance:\r\n word = word.capitalize()\r\n self.words.append(word)\r\n self.word_count += 1\r\n return word","def get_template_names(self):\n if self.object.twfy_id or self.object.current_or_future_candidacies:\n return [\"people/person_detail.html\"]\n return [\"people/not_current_person_detail.html\"]","def linkTemplates(self, sentence):\n templates = sentence.templates\n cvList = templates.getList('cost_value')\n\n if len(cvList) < 2:\n return\n\n if sentence.abstract.id == '16895945':\n print '16895945', cvList\n\n parenDepth = 0\n newCostValueList = []\n tIdx = 0\n cvIdx = 0\n linkToEndOfParens = False\n while tIdx < len(sentence):\n token = sentence[tIdx]\n if token.isLeftParenthesis():\n parenDepth += 1\n elif token.isRightParenthesis():\n parenDepth -= 1\n if parenDepth is 0:\n linkToEndOfParens = False\n elif cvIdx < len(cvList) and token is cvList[cvIdx].token:\n # current token is a cost value\n if linkToEndOfParens is True:\n # currently need to link to last cost value in new list\n newCostValueList[-1].linkedValues.append(cvList[cvIdx])\n else:\n # this value does not get linked to any previous values\n # now check to see if it should be linked with *following* cost values\n newCostValueList.append(cvList[cvIdx])\n\n if parenDepth > 0:\n # inside parenthesis. assume all values inside the parens should be linked\n # however, use first (this) value in summary\n linkToEndOfParens = True\n else:\n # it is outside parenthesis.\n # check to see if it is followed by parenthesis (after possible units)\n if tIdx + 2 < len(sentence) and \\\n (sentence[tIdx + 1].isLeftParenthesis()\n or (sentence[tIdx + 1].isCurrencyWord() and sentence[tIdx + 2].isLeftParenthesis())):\n linkToEndOfParens = True\n cvIdx += 1\n tIdx += 1\n\n sentence.templates.setList('cost_value', newCostValueList)","def tex(self):\n self.cardinality = self.question.sorted_answers_cardinality(\n self.min_cardinality,\n self.group_together,\n self.group_by_letter_case,\n self.group_by_slugify,\n self.filter,\n self.sort_answer,\n other_question=self.other_question,\n )\n q1 = []\n q2 = []\n for answer_to_q1, cardinality_to_q2 in list(self.cardinality.items()):\n for answer_to_q2, number_of_time in list(cardinality_to_q2.items()):\n q1 += [answer_to_q1] * number_of_time\n q2 += [answer_to_q2] * number_of_time\n df = DataFrame(data={self.question.text: q1, self.other_question.text: q2})\n name = \"tex/q{}_vs_q{}\".format(self.question.pk, self.other_question.pk)\n sankey(df[self.question.text], df[self.other_question.text], aspect=20, fontsize=10, figureName=name)\n return Question2TexSankey.TEX_SKELETON % (\n name[4:],\n self.question.pk,\n self.other_question.pk,\n self.get_caption(),\n )","def make_text(chains, uppercase=True, break_on_first_punctuation=False, \n character_limit=True):\n\n random_key = choice(chains.keys())\n if uppercase:\n while True:\n if random_key[0][0].isalpha() and random_key[0][0].isupper():\n break\n else:\n random_key = choice(chains.keys())\n\n\n words = list(random_key)\n #words.extend(list(random_key))\n\n while True:\n random_value = choice(chains[random_key])\n if random_value is None:\n break \n words.append(random_value)\n if break_on_first_punctuation:\n if words[-1][-1] in [\"?\", \".\", \":\", \"!\"]:\n break \n next_key = random_key[1:] + (random_value,)\n random_key = next_key\n\n return_value = \" \".join(words)\n \n if character_limit:\n tweet_custom = return_value[:140]\n\n for i in range(len(tweet_custom)-1, 0, -1):\n if tweet_custom[i] in [\"?\", \".\", \"!\"]:\n return tweet_custom[:i+1]\n\n\n return return_value","def __str__(self):\n return f'{f\"The {self.word} \" if self.word.strip() != \"\" else \"\"}{self.name if self.name.strip() != \"\" else self.__class__.__name__}'","def tmpl_to_str(self, template_idx, o1_id, o2_id):\r\n template_str = self.template_generator.templates[template_idx]\r\n holes = template_str.count('OBJ')\r\n assert holes <= 2\r\n if holes <= 0:\r\n return template_str\r\n elif holes == 1:\r\n return template_str.replace('OBJ', self.vocab_act[o1_id])\r\n else:\r\n return template_str.replace('OBJ', self.vocab_act[o1_id], 1)\\\r\n .replace('OBJ', self.vocab_act[o2_id], 1)","def make_text(chains, n):\n\n first_key = random.choice(chains.keys(n))\n first_key_values = chains[first_key]\n third_word = random.choice(first_key_values)\n temp_list = [first_key[0], first_key[1], third_word]\n # for item in temp_list\n new_key = (first_key[1], third_word)\n\n while True:\n try:\n new_value = random.choice(chains[new_key])\n temp_list.append(new_value)\n new_first_word = new_key[1]\n new_key = (new_first_word, new_value)\n except KeyError:\n break\n\n text = \" \".join(temp_list)\n return text","def get_template_tag(self):\n return \"{% dataset \" + self.cleantitle + \" %}\"","def generate_text_uniform(model: List[str], n: int) -> str:\n # ACCUMULATOR: a list of the randomly-generated words so far\n words_so_far = []\n\n for _ in range(0, n):\n # Complete the following statement to choose a random word from model.\n random_word = random.choice(model)\n list.append(words_so_far, random_word)\n\n return str.join(' ', words_so_far)","def extract_words(tree: Tree, template: Tree) -> str:\n list_words = find_step(tree, template)\n return \" \".join(list_words)","def generate_text(model, field, seed, n=500):\n string = seed\n for i in range(n):\n indexes = field.numericalize(string)\n predictions = model(indexes.transpose(0, 1))\n last_output = predictions[-1]\n [most_probable] = torch.multinomial(last_output.exp(), 1)\n char = field.vocab.itos[most_probable]\n seed = seed[1:] + char\n string += char\n return string","def make_text(markov_chains):\n\n random_num = generate_random_number(markov_chains.keys())\n\n random_text = []\n\n start_words = generate_start_words(random_num, markov_chains.keys())\n \n random_text.extend(start_words)\n\n\n for i in range(500):\n word_tuple = (random_text[-2],random_text[-1])\n next_word = add_next_word(word_tuple, markov_chains)\n random_text.append(next_word)\n\n return random_text","def get_sentence(self):\r\n \r\n size = self.sentence_sizes.get()\r\n size += int(random.randrange(int(size * 0.8), int(size * 1.5)))\r\n\r\n sentence = \"\"\r\n opener = closer = None\r\n match_chance = self.punctuation_matched_chance\r\n \r\n for i in range(size):\r\n word = self.get_word()\r\n if self.last_word == word:\r\n # Retry to avoid repeats.\r\n word = self.get_word()\r\n self.last_word = word\r\n \r\n if i == 0:\r\n sentence += self.get_word().capitalize()\r\n elif opener:\r\n sentence += \" \" + opener + self.get_word()\r\n opener = None\r\n else:\r\n sentence += \" \" + self.get_word()\r\n \r\n if i != 0 and i != (size - 1):\r\n if random.random() > match_chance:\r\n if closer:\r\n sentence += closer\r\n closer = None\r\n match_chance = self.punctuation_matched_chance\r\n else:\r\n opener, closer = self.punctuation_matched.get()\r\n continue\r\n elif closer:\r\n # Make it increasingly likely to roll a closer\r\n match_chance *= 0.8\r\n if random.random() > self.punctuation_midline_chance:\r\n sentence += self.punctuation_midline.get()\r\n\r\n end_of_line = self.punctuation_endline.get()\r\n if closer:\r\n sentence = sentence.strip() + closer + end_of_line\r\n else:\r\n sentence = sentence.strip() + end_of_line\r\n return sentence, size","def get_foreign_phrase(self, src_sentence):\n fp = src_sentence[self.fp_start]\n for word in src_sentence[self.fp_start+1:self.fp_end]:\n fp += \" \" + word\n return fp","def _random_template_for(self, line_action: Text) -> Optional[Dict[Text, Any]]:\n\n if line_action in self.templates:\n return random.choice(self.templates[line_action])\n else:\n return None","def get_random_word(self):\n pass","def main(word_count=2, use_caps=False, use_leet=False, caps_percent=25, leet_percent=20):\n\n phrase = get_phrase(word_count)\n\n if use_caps:\n phrase = random_caps(phrase, caps_percent)\n\n if use_leet:\n phrase = random_characters(phrase, leet_percent)\n\n print(phrase)","def poem_generation(magnet,topic):\r\n e = inflect.engine()\r\n antonyms = []\r\n synonyms = []\r\n poem = \"\"\r\n verb = \"\"\r\n # plural\r\n if (e.singular_noun(topic) is False):\r\n verb = \"is\"\r\n else:\r\n verb = \"are\"\r\n \r\n for syn in wordnet.synsets(topic):\r\n for l in syn.lemmas():\r\n synonyms.append(l.name())\r\n if l.antonyms():\r\n antonyms.append(l.antonyms()[0].name())\r\n if (len(set(antonyms)) < 1):\r\n for syn in wordnet.synsets(RAND_LIST[random.randint(0,len(RAND_LIST)-1)]):\r\n for l in syn.lemmas():\r\n synonyms.append(l.name())\r\n if l.antonyms():\r\n antonyms.append(l.antonyms()[0].name())\r\n topic = topic.capitalize()\r\n for i in range(0,random.randint(5,15)):\r\n verse = random.randint(0,6)\r\n question = random.randint(0,6)\r\n # structure\r\n # antonyms\r\n if (verse < 2) and len(antonyms) > 0:\r\n ant_magnet = metaphor_magnet(antonyms[random.randint(0,len(antonyms)-1)])\r\n choice = random.randint(0,len(ant_magnet)-1)\r\n detail = ant_magnet[choice].split(\":\")\r\n if (question < 2):\r\n index = random.randint(0,len(QUESTION)-1)\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" not like an \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" not like a \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += topic + \" \" + verb + \" not like an \" + detail[0] + \" \" + detail[1] + \".\\n\" \r\n else:\r\n poem += topic + \" \" + verb + \" not like a \" + detail[0] + \" \" + detail[1] + \".\\n\" \r\n \r\n else:\r\n choice = random.randint(0,len(magnet)-1)\r\n detail = magnet[choice].split(\":\")\r\n if (question < 2):\r\n index = random.randint(0,len(QUESTION)-1)\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" like an \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n poem += QUESTION[index] + \" \" + verb + \" \" + topic + \" like a \" + detail[0] + \" \" + detail[1] + \"?\\n\" \r\n else:\r\n if (detail[0][0] in ['a','e','i','o','u']):\r\n poem += topic + \" \" + verb + \" like an \" + detail[0] + \" \" + detail[1] + \"\\n\" \r\n else:\r\n poem += topic + \" \" + verb + \" like a \" + detail[0] + \" \" + detail[1] + \"\\n\" \r\n \r\n return poem","def show_random_testimonial():\r\n\trandom_testimonial = Highlight.objects.filter(tags='testimonial')\r\n\tif random_testimonial != '':\r\n\t\treturn {'random_testimonial': random_testimonial}\r\n\telse:\r\n\t\treturn 'Nothing Here'","def test_clips_deftemplate_parse_text(self):\n G = CreateTestCarpetingGraph()\n \n nlpGraphProcessor = NLPResultGraphParser()\n res = BLNlpClipsRuleBase()\n seen = []\n nlpGraphProcessor.ParseObject(G, Strings.ndStep, seen, res)\n nlpGraphProcessor.ParseObject(G, \"CarpetingPattern\", seen, res)\n nlpGraphProcessor.ParseObject(G, \"ProcessSubStep\", seen, res)\n\n template = next(t for t in res.Deftemplates if t.TemplateName == Strings.ndStep)\n self.assertTrue(template != None)\n clipsText = template.ClipsConstruct()\n expTextLines = [\"(deftemplate ProcessStep\\n\", \"(slot Id)\\n\", \"(slot ProcessId)\\n\", \"(slot After)\\n\", \"\\n)\"]\n for expLine in expTextLines:\n self.assertTrue(expLine in clipsText)\n\n template = next(t for t in res.Deftemplates if t.TemplateName == \"CarpetingPattern\")\n self.assertTrue(template != None)\n clipsText = template.ClipsConstruct()\n expTextLines = [\"(deftemplate CarpetingPattern\\n\", \"(slot RotationAngle)\\n\", \"(slot ShiftRatio)\\n\", \"(slot ShiftDirection)\\n\", \"(slot Id)\", \"\\n)\"]\n for expLine in expTextLines:\n self.assertTrue(expLine in clipsText)\n\n template = next(t for t in res.Deftemplates if t.TemplateName == \"ProcessSubStep\")\n self.assertTrue(template != None)\n clipsText = template.ClipsConstruct()\n expTextLines = [\"(deftemplate ProcessSubStep\\n\", \"(slot StepNumber)\\n\", \"(slot Instruction)\\n\", \"(slot Id)\\n\", \"(slot ProcessStepId)\", \"\\n)\"]\n for expLine in expTextLines:\n self.assertTrue(expLine in clipsText)","def generate_sword_data(index):\n with open('./json/sword.json') as file:\n sword_data = json.loads(file.read())\n\n with open('./json/names.json') as file:\n name_rules = json.loads(file.read())\n\n name_grammar = tracery.Grammar(name_rules)\n name_grammar.add_modifiers(base_english)\n name_grammar.add_modifiers(extended_english)\n\n sword_data['name'] = f'Blade {index + 1}:\\n{name_grammar.flatten(\"#root#\")}'\n sword_data['tex'] = index\n sword_data['brokenTex'] = index\n sword_data['spriteAtlas'] = 'blades'\n sword_data['baseDamage'] = int(numpy.random.normal(10, 4))\n sword_data['randDamage'] = int(numpy.random.normal(10, 4))\n sword_data['durability'] = int(numpy.random.normal(100, 40))\n sword_data['knockback'] = numpy.random.normal(0.15, 0.025)\n sword_data['reach'] = numpy.random.normal(0.5, 0.125) + 0.25\n sword_data['speed'] = ((1 - (sword_data['baseDamage'] + sword_data['randDamage']) / 44) * 2.0) + 0.25\n sword_data['damageType'] = numpy.random.choice(\n [\n 'PHYSICAL',\n 'MAGIC',\n 'FIRE',\n 'ICE',\n 'LIGHTNING',\n 'POISON',\n 'HEALING',\n 'PARALYZE',\n 'VAMPIRE'\n ],\n p=[\n 0.5,\n 0.1,\n 0.1,\n 0.1,\n 0.1,\n 0.04,\n 0.0,\n 0.03,\n 0.03\n ]\n )\n sword_data['shader'] = {\n 'PHYSICAL': None,\n 'MAGIC': 'magic-item-purple',\n 'FIRE': 'magic-item-red',\n 'ICE': 'magic-item',\n 'LIGHTNING': 'magic-item-white',\n 'POISON': 'magic-item-green',\n 'HEALING': 'magic-item',\n 'PARALYZE': 'magic-item',\n 'VAMPIRE': 'magic-item-red'\n }[sword_data['damageType']]\n sword_data['attackAnimation'] = numpy.random.choice(\n [\n 'swordAttack',\n 'swordAttackSlow',\n 'daggerAttack',\n 'maceAttack'\n ],\n p=[\n 0.4,\n 0.2,\n 0.35,\n 0.05\n ]\n )\n sword_data['attackStrongAnimation'] = numpy.random.choice(\n [\n 'swordAttackStrong',\n 'thrustAttack',\n 'daggerAttackStrong',\n 'maceAttackStrong'\n ],\n p=[\n 0.4,\n 0.2,\n 0.35,\n 0.05\n ]\n )\n sword_data['chargeAnimation'] = numpy.random.choice(\n [\n 'swordCharge',\n 'thrustCharge',\n 'daggerCharge',\n 'maceCharge'\n ],\n p=[\n 0.35,\n 0.2,\n 0.35,\n 0.1\n ]\n )\n\n # Add a light?\n if (sword_data['damageType'] != 'PHYSICAL' and random.random() < 0.125):\n with open('./json/light.json') as file:\n light_data = json.loads(file.read())\n light_data['lightColor'] = damage_type_color[sword_data['damageType']]\n sword_data['attached'].append(light_data)\n\n return sword_data","def get(self, pos):\n return self.sentence[pos]","def plants (plant_name, plant_type):\n print (f\"\\n{plant_name.title()} is a {plant_type}. \\n\")","def inject_title(self,template,title):\n return re.sub('TITLE',title,template)","def _substitute(template, fuzzer, benchmark):\n return template.format(fuzzer=fuzzer, benchmark=benchmark)","def generate_name():\n return random.choice(ADJECTIVES) + \"_\" + random.choice(TOOLS)","def report(drink, resources):\n print(f\" Coffee: {resources['coffee']}gm\")\n print(f\" Water: {resources['water']}ml\")\n print(f\" Milk: {resources['water']}ml\")","def first_entrance(self):\n for item in self.items:\n if issubclass(item.__class__, items.Drink):\n drink_name = item.name[0].lower()\n break\n\n print(\"Location: \" + self.name)\n print(\"A simple room consisting of two couches. Each is accompanied by a potted plant and a table. \"\n \"On one sits a \", end=\"\")\n if drink_name == \"coffee\" or drink_name == \"tea\":\n print(\"cup of \" + drink_name + \".\")\n elif drink_name == \"water\":\n print(\"glass of water.\")\n elif drink_name == \"coke\":\n print(\"cold can of Coke.\")\n else:\n print(drink_name)\n print(\"pile of dust.\")","def pirate():\n \n from random import choice\n d = {\n \"sir\": \"matey\", \"hotel\": \"fleabag inn\", \"student\": \"swabbie\",\n \"boy\": \"matey\", \"girl\": \"wench\", \"professor\": \"foul blaggart\",\n \"restaurant\": \"galley\", \"your\": \"ye\", \"excuse\": \"arr\", \"you\": \"ye\",\n \"students\": \"swabbies\", \"are\": \"be\", \"lawyer\": \"foul blaggart\",\n \"the\": \"th'\", \"restroom\": \"head\", \"my\": \"me\", \"hello\": \"avast\",\n \"is\": \"be\", \"man\": \"scurvy dog\", \"hey\": \"avast\", \"pirate\": \"scurvy pirate\",\n \"idiot\": \"flunder\", \"young\": \"ye\", \"the\": \"t'\", \"suck\": \"blow down\",\n \"fall\": \"hade\", \"happens\": \"be happening\", \"death\": \"Davy Jones' treasure chest\",\n \"always\": \"ever\", \"you're\": \"ye're\", \"girlfriend\": \"Lassie-Lucy\",\n \"with\": \"wit'\", \"everyone\": \"evr'un\"\n }\n #~ sentence = raw_input(\"What be yer sentence, ye old landlubber? \")\n sentences = [\"If the world didn't suck we'd all fall off\",\n \"What happens if you get scared half to death twice?\",\n \"Save water and shower with your girlfriend\",\n \"Always remember you're unique, just like everyone else\"]\n \n start = [\"Yarr, \", \"Yarr! \", \"Yarr-ha-harr! \", \"Skuttle me Skippers! \",\n \"Shiver me timbers! \", \"Ahoy, me hearties! \", \"Dogs ahoy! \",\n \"Hoist the mizzen!!! \", \"Hoist the colors!! \" ]\n end = [\". ARRRGHHHH!\", \". Avast ye varmint!\", \". Yarr?\",\n \". Savvyy?\", \". Yarr!\", \". Aye!\",\n \", ye bilge rat!\", \", ye mangy dog!\", \", ye scallywag!\"]\n\n s = choice(sentences)\n print s\n print choice(start) +\\\n ' '.join([d[i] if i in d else i for i in s.lower().split()]).capitalize()\\\n + choice(end)","def teampreview(self, battle: AbstractBattle) -> str:\n return self.random_teampreview(battle)","def hello_world():\r\n sentence = '<p>Hello World! '+my_id+' <p>'\r\n return sentence","def generate(self, text):\n return self.generate_from_text(text)"],"string":"[\n \"def get_sentence(self):\",\n \"def generate_language(self, properties):\\n\\n drink_property = self.find_drink_property(properties)\\n\\n if drink_property:\\n return self.generate_sentence(drink_property)\\n else:\\n return \\\"\\\"\",\n \"def linkTemplates(self, sentence):\\n templates = sentence.templates\\n sLength = len(sentence.tokens)\\n \\n # find the closest mention to each value. \\n # In case of ties, use mention that appears before the quantity in sentence\\n for qTemplate in templates.getList(self.quantityType): \\n (mTemplate, dist) = templates.closestMention(qTemplate, self.mentionType)\\n if mTemplate != None:\\n # use distance between elements to estimate association probability\\n prob = 1.0 - float(dist)/sLength\\n if self.mentionType == 'outcome':\\n qTemplate.outcome = mTemplate\\n qTemplate.outcomeProb = prob\\n elif self.mentionType == 'group':\\n qTemplate.group = mTemplate\\n qTemplate.groupProb = prob\\n if self.quantityType == 'gs':\\n mTemplate.addSize(qTemplate)\",\n \"def generate(self):\\n\\n letter = ''\\n\\n for template_name in self.templates:\\n template = self.templates[template_name]\\n\\n # Process all replacements ({...} syntax).\\n replacements = re.finditer(REPLACEMENT_REGEX, template)\\n for replacement in replacements:\\n match = replacement.group()\\n key = replacement.group(1)\\n\\n template = template.replace(match, self._lookup(key))\\n\\n # Process all conditionals (<...> syntax).\\n conditionals = re.finditer(CONDITIONAL_REGEX, template)\\n for conditional in conditionals:\\n match = conditional.group()\\n\\n # Process each condition within the conditional ([...]... syntax).\\n conditions = re.finditer(CONDITION_REGEX, match)\\n for index, condition in enumerate(conditions):\\n skill_type = condition.group(2)\\n skill = condition.group(3)\\n text = condition.group(4)\\n\\n # If the skill is empty, treat it as a catch all case.\\n if not skill or skill in self._lookup(skill_type):\\n template = template.replace(match, text)\\n break\\n\\n letter += template\\n\\n return letter\",\n \"def generate_description(self, which, world):\\n global parsed_kd_des\\n if parsed_kd_des is None:\\n parsed_kd_des = util.parseDes('killdude_background', KillDudeBackground)[0]\\n s = choice(parsed_kd_des.__dict__[which].strip().splitlines())\\n substs = util.LambdaMap()\\n #here we parse following options: $depart_village, $who (dude.name) and $name (dude.unique_name)\\n substs['who'] = self.dude.name\\n substs['name'] = self.dude.unique_name\\n substs['current_city'] = lambda : current_city(world)\\n substs['village_name'] = lambda : util.gen_name('city')\\n\\n res = string.Template(s).safe_substitute(substs)\\n return res\",\n \"def make_text(word_next_dict):\\n\\n final_text = []\\n # randomly select a tuple from the dictionary\\n start = random.choice(word_next_dict.keys())\\n # If 1st word in tuple isn't capitalized, choose a new pair until one is\\n while not start[0].isupper():\\n start = random.choice(word_next_dict.keys())\\n # Begin the list of random text with tuple (separated to 2 strings)\\n final_text.append(start[0])\\n final_text.append(start[1])\\n\\n # create random text list: find a word associated with last 2 and append list\\n while len(final_text) < 12:\\n # look for sentence endings and re-choose start tuple\\n if '.' in final_text[-1]:\\n start = random.choice(word_next_dict.keys())\\n # make sure tuple begins with a capitalized string\\n while not start[0].isupper():\\n start = random.choice(word_next_dict.keys())\\n final_text.append(start[0])\\n final_text.append(start[1])\\n last_words = (final_text[-2], final_text[-1]) \\n next_guy = word_next_dict[last_words][random.randrange(len(word_next_dict[last_words]))] # finds length of wordlist, randomly picks \\n final_text.append(next_guy)\\n\\n to_tweet = ' '.join(final_text) + '.'\\n print to_tweet\\n return to_tweet\",\n \"def walk(self, count):\\n curr_word = self.dictogram.sample()\\n sentence = [curr_word]\\n for i in range(1, count):\\n curr_word = self[curr_word].sample()\\n sentence.append(curr_word)\\n return ' '.join(sentence) + '.'\",\n \"def generate_sentence(self,t=20):\\n return result\",\n \"def generate_text(word_dict, num_words):\\n\\n current_word = '$'\\n for i in range(num_words):\\n wordlist = word_dict[current_word]\\n next_word = random.choice(wordlist)\\n print(next_word, end=' ')\\n if '.' in next_word or '?' in next_word or '!' in next_word:\\n current_word = '$'\\n else:\\n current_word = next_word\\n print()\",\n \"def generate_text(word_dict, num_words):\\n current_word = '$'\\n for i in range(num_words):\\n word_list = word_dict[current_word]\\n next_word = random.choice(word_list)\\n print(next_word, end=' ')\\n if next_word[-1] == '.' or next_word[-1] == '!' or next_word[-1] == '?':\\n current_word = '$'\\n elif next_word not in word_dict:\\n current_word = '$'\\n else:\\n current_word = next_word\\n print()\",\n \"def sentence():\\n res = random.choice(no_result)\\n adr = random.choice(address)\\n wik = random.choice(wiki)\\n return {'res': res, 'adr': adr, 'wik': wik}\",\n \"def get_random_phrase(self):\\n return random.choice(self.phrases)\",\n \"def generate_sentence():\\n markov_chain = makeMarkovDict(\\\"text.txt\\\")\\n\\n # Pick a random word to begin with.\\n first_word = random.choice(markov_chain.keys()) # Illegall\\n\\n # print first_word\\n # random_choice = random.randint(0, len(markov_chain.keys()))\\n # index = 0\\n # first_word = \\\"\\\"\\n # for word in markov_chain:\\n # print word\\n # if index == random_choice:\\n # first_word = word\\n # break\\n # index += 1\\n\\n # Based on that word, call function to chose the next word.\\n # print markov_chain[first_word]\\n # print word_selection(markov_chain[first_word])\\n\\n lenght_of_sentence = 10\\n sentence = [first_word] # First word already in there\\n for i in range(lenght_of_sentence):\\n sentence.append(word_selection(markov_chain[sentence[i]]))\\n # Sentence after loop: ['fish', 'red', 'fish', 'two', 'fish', 'red', 'fish', 'red', 'fish', 'two', 'fish']\\n\\n # Cap with letter and add period at the end.\\n final_sentece = \\\" \\\".join(sentence) + \\\".\\\"\\n return final_sentece.capitalize()\",\n \"def chooseRandomSentence(self):\\r\\n return self.__repo.chooseObject()\",\n \"def generate(self, count=15):\\n\\n sentence = []\\n print(\\\"self.word_dict\\\", self.word_dict)\\n for i in range(count):\\n first_tuple = random.choice(list(self.word_dict.keys())) # first word for our sentence\\n first_word = random.choice(first_tuple)\\n sentence.append(first_word)\\n second_word = self.word_dict[first_tuple]\\n # print(\\\"second_word\\\", second_word)\\n next_word = second_word.sample()\\n # print(\\\"next_word\\\", next_word)\\n # first_tuple = second_word\\n sentence.append(next_word)\\n # end_tuple =\\n sentence = ' '.join(sentence)\\n return sentence + \\\".\\\"\\n # for i in range(len(self.token)):\\n # val = list(self.word_dict.values())[i]\\n # print(len(val))\\n # # print(\\\"val\\\", val)\\n # next_word = val.sample()\\n # sentence.append(next_word)\\n # sentence = ' '.join(sentence)\\n # return sentence + \\\".\\\"\",\n \"def fixture_other_case() -> str:\\n return \\\"angrybird\\\"\",\n \"def news_speech():\\n #Fetches data from API and creates global varibles.\\n news_handle(news_fetch(config_fetcher('news_region'), config_fetcher('news_key')))\\n #Creates a daily breifing using varibles\\n news_daily_news = Markup((f\\\"The top headline for today is entitled: {title_1}, and was \\\\\\nwritten by {author_1}. Here is a second headline, entitled: {title_2}, written by {author_2}.\\\"))\\n return news_daily_news\",\n \"def make_text(chains, n):\\n\\n text = \\\"\\\"\\n # variable = \\\"Dog\\\"\\n random_key = choice(chains.keys())\\n #print random_key\\n # while variable == \\\"Dog\\\":\\n while True:\\n if random_key[0][0].isupper():\\n # variable = \\\"Cat\\\"\\n break\\n else: \\n random_key = choice(chains.keys())\\n \\n text = \\\" \\\".join(random_key)\\n #random_key[0] + \\\" \\\" + random_key[1]\\n #Loops through dictionary called chains and raondomly \\n #chooses a next word and adds it to string\\n while True:\\n\\n random_add = choice(chains[random_key])\\n # print random_key[1-n:]\\n # print random_add\\n new_list=list(random_key[1-n:])\\n new_list.append(random_add)\\n random_key = tuple(new_list)\\n #random_key = tuple([random_key[1-n:], random_add])\\n #test_key = random_key[n+1:]\\n #print \\\"test random key: \\\", random_key, \\\"random add: \\\", random_add\\n sequence = (text,random_add)\\n text = \\\" \\\".join([text, random_add])\\n #text = text + \\\" \\\" + random_add\\n # if the key has no value, stop, you're at the end!\\n if random_key not in chains:\\n break\\n elif random_key[n-1][-1] in [\\\".\\\", \\\"!\\\", \\\"?\\\"] and len(text) > 400:\\n break\\n\\n return text\",\n \"def persona_from_template_values(topic: str, topic_item: str, extra_details: str = ''):\\n pers = f'My favorite {topic} is {topic_item}.'\\n if extra_details:\\n pers += f'\\\\n{extra_details}'\\n return pers\",\n \"def make_text(chains):\\n\\n # what word should we start on? \\\"Would you\\\"?\\n # dicitonaries are unordered, so what does \\\"first key\\\" mean?\\n # from which list you should pull out the random word?\\n\\n # key 1st word (a)\\n # key 2nd word (b)\\n # random word from value ([list]) (c)\\n # value of b, c tuple\\n\\n key_selection = choice(chains.keys())\\n\\n words = [key_selection[0], key_selection[1]]\\n\\n while key_selection in chains:\\n new_word = choice(chains[key_selection])\\n\\n words.append(new_word)\\n\\n key_selection = (key_selection[1], new_word,)\\n\\n\\n\\n # print first_word,second_word,third_word\\n\\n # random.choice(chains)\\n\\n # your code goes here\\n\\n return \\\" \\\".join(words)\",\n \"def render(self, context):\\n if not self.phrase:\\n return ''\\n t = context.template.engine.get_template('philosophy_phrases/phrase_popup.html')\\n return t.render(template.Context({'phrase': self.phrase}))\",\n \"def generate(self, answers):\\n\\n text = self.template\\n\\n for (key, val) in answers.items():\\n text = text.replace(\\\"{\\\" + key + \\\"}\\\", val)\\n\\n return text\",\n \"def generate_fantasy_title():\\n d20 = random.randint(1, 20)\\n if d20 <= 4:\\n #genetive noun\\n return fantasy_genetive[random.randint(0, len(fantasy_genetive) - 1)] + \\\" \\\" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\\n elif d20 > 4 and d20 < 13: \\n #The adj noun\\n return \\\"The \\\" + fantasy_adj[random.randint(0, len(fantasy_adj) - 1)] + \\\" \\\" + fantasy_noun[random.randint(0, len(fantasy_noun) - 1)]\\n elif d20 >= 13:\\n #something of something\\n return fantasy_noun[random.randint(0, len(fantasy_noun) - 1)] + \\\" of \\\" + fantasy_what_is_this[random.randint(0, len(fantasy_what_is_this) - 1)]\",\n \"def generate_story():\\n\\n text = story.generate(request.args)\\n \\n return render_template(\\\"story.html\\\", text = text)\",\n \"def create_word(self):\\n return self.random.choice(CONSONANTS) + self.random.choice(VOWELS)\",\n \"def substantiate():\",\n \"def make_text(chains):\\n\\n words = []\\n\\n # Set random tuple to variable key\\n key = choice(chains.keys())\\n\\n\\n # First time through key in chains, unpack X - a tuple - to get \\n # key[0], key[1] = key\\n # print key0]\\n # print key[2]\\n # Append key[0], key[1] to words\\n\\n words.append(key[0])\\n words.append(key[1])\\n\\n # While tuple is a key in chains, keep looping \\n while key in chains:\\n\\n # Randomly choose a value from chains\\n word = choice(chains[key])\\n\\n # Append word to words\\n words.append(word)\\n\\n key = (key[1], word)\\n\\n # return \\\" \\\".join(words)\\n\\n # define variable of returned \\\" \\\".join(words) as markov-tweet\\n markov_tweet = \\\" \\\".join(words)\\n\\n return markov_tweet\",\n \"def make_text(chains):\\n \\n random_text = \\\" \\\"\\n\\n\\n #We created current key which will be replaced each time this process\\n #happens. Perhaps we can structure the repetition as a while so that\\n #while the key is in the dictionary, it continues building the text string\\n\\n\\n current_key = choice(chains.keys())\\n\\n while current_key in chains:\\n chosen_word = choice(chains[current_key])\\n #Gives us random word from a list of third words\\n new_key = current_key[1], chosen_word\\n #Pairs the last word of the current tuple with the randomly picked 3rd word\\n random_text = random_text + \\\" \\\" + chosen_word\\n #We want this to be a string\\n current_key = new_key \\n\\n return random_text\",\n \"def make_text(chains):\\n\\n #current key is equal to a random key in the dictionary\\n current_key = choice(chains.keys())\\n #text is a string equal to the first index of the tuple and the second index of the tuple\\n text = current_key[0] + \\\" \\\" + current_key[1]\\n #create a loop that will repeat until the function reaches the last bi-gram\\n while True:\\n #created a variable for a random value of the current key\\n chosen_word = choice(chains[current_key])\\n #equivalent to: if chosen_word is None\\n if not chosen_word:\\n break\\n # if it's not the last key add the chosen_word to the text to string\\n text = text + \\\" \\\" + chosen_word\\n # select new key\\n current_key = (current_key[1], chosen_word)\\n \\n return text\",\n \"def make_first_recommendation() -> str:\\r\\n available_tanks = tank_search()\\r\\n fermentation_tanks = available_tanks[0]\\r\\n conditioning_tanks = available_tanks[1]\\r\\n fermentation_advice = available_tanks[2]\\r\\n conditioning_advice = available_tanks[3]\\r\\n more_beer = available_tanks[4]\\r\\n return render_template(\\\"make_first_recommendation.html\\\",\\r\\n fermentation_string=fermentation_tanks,\\r\\n conditioning_string=conditioning_tanks,\\r\\n first_advice=fermentation_advice,\\r\\n second_advice=conditioning_advice,\\r\\n next_page=more_beer)\",\n \"def generate_sentence(model, opener_words):\\n\\n sentence=[]\\n #sentences between 3 and 15 words\\n length= random.randint(3,6)\\n keys=model.keys()\\n bigram=random.choice(list(keys))\\n\\n #choose a first word that can be a starter word\\n while bigram[0] not in opener_words:\\n bigram=random.choice(list(keys))\\n #iterate until sentence is correct length\\n for i in range(0,length):\\n matches=[]\\n found=False\\n while not found:\\n\\n #search in keys for key[0] to match the bigram[1]\\n for key in keys:\\n regex=re.compile(r\\\"\\\\b%s\\\\b\\\"%bigram[1])\\n result=regex.match(key[0])\\n if result:\\n matches.append(key)\\n found=True\\n if not found:\\n matches=[]\\n i=0\\n bigram=random.choice(list(keys))\\n sentence.pop()\\n\\n #add first member of bigram to sentence list\\n sentence.append(bigram[1])\\n #choose next bigram from the list of matches\\n bigram=random.choice(matches)\\n\\n #combine strings from list\\n return \\\" \\\".join(sentence)\",\n \"def make_text(chain_dict):\\n\\n# This selects the first random key to begin the chain.\\n \\n random_key = random.choice(chain_dict.keys())\\n\\n# This creates a random text list to append a random value from the original random key.\\n# The random key is then reassigned to a tuple containing the second key in the random keys tuple,\\n# and adds the randomly generated value of the earlier random key tuple as the second tuple value. \\n# Convert tuples in random_key to strings, then added all strings together, then added to empty list\\n\\n first_words = ' '.join(map(str,random_key)) \\n random_text_list = [first_words]\\n \\n for i in range(1,100):\\n #while random_key in chain_dict: \\n next = random.choice(chain_dict[random_key]) \\n random_text_list.append(next)\\n random_key = (random_key[1],next)\\n\\n pretty_text = []\\n pretty_text = ' '.join(map(str, random_text_list)) \\n return pretty_text\",\n \"def make_text(chains):\\n keys_list = list(chains.keys())\\n \\n key = random.choice(keys_list)\\n words = list(key)\\n value = random.choice(chains[key])\\n words.append(value)\\n key = (key[1], value)\\n while True:\\n\\n if key in chains:\\n word = random.choice(chains[key])\\n words.append(word)\\n key = (key[1], word)\\n else:\\n break\\n print(\\\" \\\".join(words))\",\n \"def diss_person():\\n\\n player = request.args.get(\\\"nickname\\\")\\n\\n # compliment = choice(AWESOMENESS)\\n diss = request.args.get(\\\"diss\\\")\\n\\n return \\\"\\\"\\\"\\n <!doctype html>\\n <html>\\n <head>\\n <title>An Insult</title>\\n </head>\\n <body>\\n Hi, {player}! I think you're a {diss}!\\n </body>\\n </html>\\n \\\"\\\"\\\".format(player=player, diss=diss)\",\n \"def get_next_sentence(self, last_sentence=\\\"\\\"):\\n return super(MeadSummaryGenerator, self).get_next_sentence(last_sentence)\",\n \"def __str__(self):\\n t = Template(\\n \\\"\\\"\\\"\\n <h4>$title</h4>\\n $imgs\\n $footnotes\\n <hr/>\\\"\\\"\\\")\\n # Return result.\\n return t.substitute({\\n \\\"title\\\": self.title,\\n \\\"imgs\\\": self.render_images(),\\n \\\"footnotes\\\": self.render_footnotes()\\n })\",\n \"def get_template(self):\\n return self.sep.join([self.htmls[html] for html in self.lang]).format(**self.fields)\",\n \"def make_text(chains):\\n n = len(list(chains.keys())[0])\\n words = []\\n first_link = random.choice(list(chains.keys()))\\n \\n # words.append(first_link[0])\\n # words.append(first_link[1])\\n #print(words)\\n i = 0\\n\\n \\n # your code goes here\\n # while i2 < len(words):\\n # update_tuple = (words[i1], words[i2])\\n # if update_tuple in chains.keys():\\n # new_link = random.choice(chains[update_tuple])\\n # i1 += 1\\n # i2 += 1\\n # words.append(new_link)\\n # #print(new_link)\\n # else:\\n # break\\n for item in first_link:\\n words.append(item)\\n # n2 = n + 2\\n\\n while i < len(words) - 1:\\n update_tuple = tuple(words[i:n])\\n # print(update_tuple)\\n\\n if update_tuple in chains.keys():\\n new_link = random.choice(chains[update_tuple]) \\n i += 1\\n n += 1\\n words.append(new_link)\\n #print(new_link)\\n else:\\n break\\n\\n \\n return ' '.join(words)\",\n \"def _get_papy_sentence(self):\\n sentences = [\\n \\\"un crayon basique peut écrire jusqu’à 45 000 mots. \\\",\\n \\\"la troisième langue officielle de la \\\"\\n \\\"Nouvelle-Zélande est le langage des signes.\\\",\\n \\\"si la Terre faisait la taille d’un grain de sable, \\\"\\n \\\"le Soleil ferait la taille d’une orange.\\\",\\n \\\"à la base, le papier bulle a été inventé \\\"\\n \\\"pour servir de papier peint.\\\",\\n ]\\n chosen_sentence = random.choice(sentences)\\n response = \\\"J'ai une histoire à te raconter : \\\" + chosen_sentence\\n return response\",\n \"def createSentence(self, n=0, v=0, o=0, p=0,prep=True):\\n sentence = ''\\n if not n:\\n n = np.random.randint(1, 5)\\n if not v:\\n v = np.random.randint(1, 5)\\n if not o:\\n o = np.random.randint(1, 5)\\n sentence += self.createPhrase(nwords=n) + ' '\\n if sentence[:-1] not in ('mi', 'sina'):\\n sentence += 'li '\\n sentence += self.createPhrase(nwords=v) + ' e '\\n sentence += self.createPhrase(nwords=o)\\n if prep:\\n if not p:\\n p = np.random.randint(1, 5)\\n sentence += ' ' + np.random.choice(self.prepositions) + ' ' + self.createPhrase(nwords=p)\\n return sentence\",\n \"def build_tweet():\\n\\n verb = conjugate(random.choice(verbs)['present'], tense=PARTICIPLE, parse=True).title()\\n animal = random.choice(animals).title()\\n food = random.choice(foods).title()\\n noun = random.choice(nouns).title()\\n\\n band = food + \\\" \\\" + noun\\n track = verb + \\\" \\\" + animal\\n\\n feature1 = clean_feature(random.choice(j['features']))\\n feature2 = clean_feature(random.choice(j['features']))\\n feature3 = random.choice(j['features'])\\n\\n dont = \\\"\\\"\\n if random.randrange(100) <= 50:\\n dont = \\\"don't \\\"\\n\\n s = \\\"I \\\" + dont + \\\"like the \\\" + feature1 + \\\" and \\\" + feature2 + \\\" with \\\" + \\\\\\n feature3 + \\\" in \\\" + band + \\\"'s \\\" + '\\\"' + track + '\\\"'\\n\\n return s\",\n \"def make_text(chains):\\n\\n key = random.choice(chains.keys())\\n return_string = key[0] + \\\" \\\" + key[1]\\n\\n while chains[key] != None: #chains.get(key[-1]) != None: \\n next_word = random.choice(chains[key])\\n return_string = return_string + \\\" \\\" + next_word\\n key = (key[1],next_word)\\n\\n return return_string\",\n \"def read_template():\\n\\n text_msg = \\\"\\\"\\\"${PERSON_NAME} - Calling Campaign Summary - ${DATE}:\\\\n\\n Total Called = ${TOTAL_CALLED}\\\\n\\n Answered = ${ANSWERED}\\\\n\\n Not Answered = ${NOT_ANSWERED}\\\\n\\n Declines = ${DECLINES}\\\\n\\n Remaining = ${REMAINING}\\\\n\\n \\\\n\\n Thank You.\\\"\\\"\\\"\\n\\n return Template(text_msg)\",\n \"def markov_story():\\n return render_template(\\\"markovstory.html\\\")\",\n \"def markup_text(self, text):\\n for moniker, name in S['names'].items():\\n text = text.replace('${0}'.format(moniker.split('_')[1]), name)\\n return text\",\n \"def definition(request, word_to_lookup):\\n return render(request, 'definition.html')\",\n \"def generate_markov_text(d, size=40):\\n\\n li = [key for key in d.keys() if key[0][0].isupper()]\\n key = random.choice(li)\\n \\n li = []\\n first, second = key\\n li.append(first)\\n li.append(second)\\n \\n while True:\\n while len(li)<size:\\n try:\\n third = random.choice(d[key])\\n except KeyError:\\n break\\n li.append(third)\\n key = (second, third)\\n first, second = key\\n \\n return ' '.join(li)\",\n \"def make_text(dictionary):\\n\\n capital_keys_list = [key for key in dictionary.keys() if key[0][0].isupper()] # create list of only keys that start with an uppercase letter\\n starting_key = random.choice(capital_keys_list) # choose key (tuple) to start at\\n new_text_list = list(starting_key) # add items in that tuple to list of created text\\n \\n punctuation = \\\"?.!\\\" # create punctuation string \\n next_word = \\\" \\\" # initialize next_word\\n\\n while dictionary.get(starting_key) != None and next_word[-1] not in punctuation: # Continue until the key is not found in the dictionary and until the last word ends in punctuation\\n value_list = dictionary[starting_key] # assign value of key (list)\\n rand_index = random.randrange(0, len(value_list)) # choose random int w/in length of list\\n next_word = value_list[rand_index] # find item at that random index\\n\\n new_text_list.append(next_word) # add next_word to list of created text\\n\\n starting_key = tuple(list(starting_key[1:]) + [next_word]) # create new tuple from second word of previous tuple + item at that index\\n\\n new_text_string = \\\" \\\".join(new_text_list) # join list into string\\n return new_text_string # return new text\",\n \"def SNB():\\n return render_template('search_beer_by_name.html')\",\n \"def greeting(sentence):\\r\\n for word in sentence.split():\\r\\n if word.lower() in GREETING_INPUTS:\\r\\n return random.choice(GREETING_RESPONSES)\",\n \"def make_text(chains):\\n words = []\\n not_end_of_list = True\\n # your code goes here\\n \\n # starts with a capital lettered word from source text\\n capitalized_ngrams = []\\n for key in chains.keys():\\n # check if the first tuple in key[0][0]\\n if key[0][0].isupper():\\n capitalized_ngrams.append(key)\\n \\n \\n\\n selected_keys = list(capitalized_ngrams)\\n count = 0\\n while not_end_of_list:\\n choice_n = choice(selected_keys)\\n\\n if count == 0:\\n words.extend(choice_n)\\n \\n if chains[choice_n] and count <= 150:\\n # as long as there is an option, picks a random element from dict list\\n choose_next = choice(chains[choice_n])\\n # adds new word to list\\n words.append(choose_next)\\n # creates a list of keys whose last item in tuple is item from list\\n selected_keys = [x for x in chains.keys() if x == tuple([*choice_n[1:], choose_next])]\\n # it is possible continues\\n if selected_keys:\\n pass\\n else:\\n not_end_of_list = False\\n\\n count += 1\\n \\n else:\\n not_end_of_list = False\\n\\n return \\\" \\\".join(words)\",\n \"def show_pet(self):\\n pet = self.pet_factory.get_pet()\\n print \\\"We have a lovely {}\\\".format(pet)\\n print \\\"It says {}\\\".format(pet.speak())\\n print \\\"We also have {}\\\".format(self.pet_factory.get_food())\",\n \"def generate_excerpt():\",\n \"def __str__(self):\\n return self.underscoreSentence.get().__str__()\",\n \"def random_sentence():\\n article = random_article()\\n random_sentence = random.choice(article.sentence_list)\\n\\n return {'sentence' : unicode(random_sentence),\\n 'title' : unicode(article.title),\\n 'revision_id' : article.revision_id }\",\n \"def greeting(sentence):\\n for word in sentence.split():\\n if word.lower() in GREETING_INPUTS:\\n return random.choice(GREETING_RESPONSES)\",\n \"async def drinkify(self,ctx):\\n artist = ctx.message.content[9:]\\n drink = \\\"\\\"\\n response = requests.get('http://drinkify.org/{}'.format(artist))\\n if response.status_code == 200:\\n root = html.fromstring(response.content)\\n recipe = root.xpath('//ul[@class=\\\"recipe\\\"]/li/text()')\\n for alc in recipe:\\n drink +=\\\"{}\\\\n\\\".format(alc)\\n instructions = root.xpath('normalize-space(//p[@class=\\\"instructions\\\"]/text())')\\n drink += \\\"\\\\nInstructions:\\\\n{}\\\".format(instructions)\\n else:\\n return\\n await ctx.bot.send_message(ctx.message.channel, drink)\",\n \"def _get_sentence(sentence_data):\\n return \\\" \\\".join([word for word, ne_tag in sentence_data])\",\n \"def show_pet(self):\\n pet = self.pet_factory.get_pet()\\n\\n print(\\\"this is a lovely \\\", pet)\\n print(\\\"It says \\\", pet.speak())\\n print(\\\"It eats \\\", self.pet_factory.get_food())\",\n \"def get_random_phrase():\\n return random.choices(PHRASES, WEIGHTS, k=1)[0]\",\n \"def make_drink ():\\n \\n customer_pref = customer_order.drink_order()\\n drink = []\\n \\n for pref in customer_pref:\\n if customer_pref[pref] == True:\\n drink.append(random.choice(ingredients[pref]))\\n \\n return drink\",\n \"def render(self, template: str, **vars) -> str:\",\n \"def nao_speech(possible_sentences):\\n\\n print(random.choice(possible_sentences))\",\n \"def render_string(self, template: str, **vars) -> str:\",\n \"def getSentence(self):\\n return self.tokens[0].sentence\",\n \"def generate_new_word(model: Dict[str, Set[str]]) -> str:\\n all_keys = list(dict.keys(model))\\n return random.choice(all_keys)\",\n \"def make_text(chains):\\n\\n words = []\\n\\n # random_key = choice(chains.keys())\\n\\n # words.append(random_key[0])\\n # words.append(random_key[1])\\n\\n # while random_key in chains.keys():\\n\\n # values_list = chains[random_key]\\n\\n # chosen_word = choice(values_list)\\n\\n # words.append(chosen_word)\\n\\n # new_key = (random_key[1], chosen_word)\\n\\n # random_key = new_key\\n\\n list_of_keys = chains.keys()\\n\\n random_key = choice(list_of_keys)\\n\\n while random_key[0].islower() or random_key[0] == \\\"--\\\":\\n random_key = choice(list_of_keys)\\n\\n\\n words.append(random_key[0])\\n words.append(random_key[1])\\n\\n while random_key in chains.keys():\\n random_value = choice(chains.get(random_key, None))\\n\\n words.append(random_value)\\n new_key = (random_key[1], random_value)\\n random_key = new_key\\n if random_key[1].endswith(\\\".\\\"):\\n break\\n\\n\\n return \\\" \\\".join(words)\",\n \"def create_word(self):\\r\\n\\r\\n template = self.word_constructions.get()\\r\\n word = \\\"\\\"\\r\\n for c in template:\\r\\n if c == \\\"v\\\":\\r\\n letter = self.get_letter(100)\\r\\n else:\\r\\n letter = self.get_letter(0)\\r\\n word += letter\\r\\n\\r\\n while not any(letter in self.vowels for letter in word):\\r\\n length = len(word)\\r\\n if length == 1:\\r\\n index = 0\\r\\n elif length == 2:\\r\\n index = random.randrange(0, 2)\\r\\n else:\\r\\n a = len(word) / 2\\r\\n index = a + random.randrange(-a / 2, a / 2)\\r\\n word = word[:index] + self.get_letter(100) + word[index + 1:]\\r\\n\\r\\n if random.random() > self.capital_chance:\\r\\n word = word.capitalize()\\r\\n self.words.append(word)\\r\\n self.word_count += 1\\r\\n return word\",\n \"def get_template_names(self):\\n if self.object.twfy_id or self.object.current_or_future_candidacies:\\n return [\\\"people/person_detail.html\\\"]\\n return [\\\"people/not_current_person_detail.html\\\"]\",\n \"def linkTemplates(self, sentence):\\n templates = sentence.templates\\n cvList = templates.getList('cost_value')\\n\\n if len(cvList) < 2:\\n return\\n\\n if sentence.abstract.id == '16895945':\\n print '16895945', cvList\\n\\n parenDepth = 0\\n newCostValueList = []\\n tIdx = 0\\n cvIdx = 0\\n linkToEndOfParens = False\\n while tIdx < len(sentence):\\n token = sentence[tIdx]\\n if token.isLeftParenthesis():\\n parenDepth += 1\\n elif token.isRightParenthesis():\\n parenDepth -= 1\\n if parenDepth is 0:\\n linkToEndOfParens = False\\n elif cvIdx < len(cvList) and token is cvList[cvIdx].token:\\n # current token is a cost value\\n if linkToEndOfParens is True:\\n # currently need to link to last cost value in new list\\n newCostValueList[-1].linkedValues.append(cvList[cvIdx])\\n else:\\n # this value does not get linked to any previous values\\n # now check to see if it should be linked with *following* cost values\\n newCostValueList.append(cvList[cvIdx])\\n\\n if parenDepth > 0:\\n # inside parenthesis. assume all values inside the parens should be linked\\n # however, use first (this) value in summary\\n linkToEndOfParens = True\\n else:\\n # it is outside parenthesis.\\n # check to see if it is followed by parenthesis (after possible units)\\n if tIdx + 2 < len(sentence) and \\\\\\n (sentence[tIdx + 1].isLeftParenthesis()\\n or (sentence[tIdx + 1].isCurrencyWord() and sentence[tIdx + 2].isLeftParenthesis())):\\n linkToEndOfParens = True\\n cvIdx += 1\\n tIdx += 1\\n\\n sentence.templates.setList('cost_value', newCostValueList)\",\n \"def tex(self):\\n self.cardinality = self.question.sorted_answers_cardinality(\\n self.min_cardinality,\\n self.group_together,\\n self.group_by_letter_case,\\n self.group_by_slugify,\\n self.filter,\\n self.sort_answer,\\n other_question=self.other_question,\\n )\\n q1 = []\\n q2 = []\\n for answer_to_q1, cardinality_to_q2 in list(self.cardinality.items()):\\n for answer_to_q2, number_of_time in list(cardinality_to_q2.items()):\\n q1 += [answer_to_q1] * number_of_time\\n q2 += [answer_to_q2] * number_of_time\\n df = DataFrame(data={self.question.text: q1, self.other_question.text: q2})\\n name = \\\"tex/q{}_vs_q{}\\\".format(self.question.pk, self.other_question.pk)\\n sankey(df[self.question.text], df[self.other_question.text], aspect=20, fontsize=10, figureName=name)\\n return Question2TexSankey.TEX_SKELETON % (\\n name[4:],\\n self.question.pk,\\n self.other_question.pk,\\n self.get_caption(),\\n )\",\n \"def make_text(chains, uppercase=True, break_on_first_punctuation=False, \\n character_limit=True):\\n\\n random_key = choice(chains.keys())\\n if uppercase:\\n while True:\\n if random_key[0][0].isalpha() and random_key[0][0].isupper():\\n break\\n else:\\n random_key = choice(chains.keys())\\n\\n\\n words = list(random_key)\\n #words.extend(list(random_key))\\n\\n while True:\\n random_value = choice(chains[random_key])\\n if random_value is None:\\n break \\n words.append(random_value)\\n if break_on_first_punctuation:\\n if words[-1][-1] in [\\\"?\\\", \\\".\\\", \\\":\\\", \\\"!\\\"]:\\n break \\n next_key = random_key[1:] + (random_value,)\\n random_key = next_key\\n\\n return_value = \\\" \\\".join(words)\\n \\n if character_limit:\\n tweet_custom = return_value[:140]\\n\\n for i in range(len(tweet_custom)-1, 0, -1):\\n if tweet_custom[i] in [\\\"?\\\", \\\".\\\", \\\"!\\\"]:\\n return tweet_custom[:i+1]\\n\\n\\n return return_value\",\n \"def __str__(self):\\n return f'{f\\\"The {self.word} \\\" if self.word.strip() != \\\"\\\" else \\\"\\\"}{self.name if self.name.strip() != \\\"\\\" else self.__class__.__name__}'\",\n \"def tmpl_to_str(self, template_idx, o1_id, o2_id):\\r\\n template_str = self.template_generator.templates[template_idx]\\r\\n holes = template_str.count('OBJ')\\r\\n assert holes <= 2\\r\\n if holes <= 0:\\r\\n return template_str\\r\\n elif holes == 1:\\r\\n return template_str.replace('OBJ', self.vocab_act[o1_id])\\r\\n else:\\r\\n return template_str.replace('OBJ', self.vocab_act[o1_id], 1)\\\\\\r\\n .replace('OBJ', self.vocab_act[o2_id], 1)\",\n \"def make_text(chains, n):\\n\\n first_key = random.choice(chains.keys(n))\\n first_key_values = chains[first_key]\\n third_word = random.choice(first_key_values)\\n temp_list = [first_key[0], first_key[1], third_word]\\n # for item in temp_list\\n new_key = (first_key[1], third_word)\\n\\n while True:\\n try:\\n new_value = random.choice(chains[new_key])\\n temp_list.append(new_value)\\n new_first_word = new_key[1]\\n new_key = (new_first_word, new_value)\\n except KeyError:\\n break\\n\\n text = \\\" \\\".join(temp_list)\\n return text\",\n \"def get_template_tag(self):\\n return \\\"{% dataset \\\" + self.cleantitle + \\\" %}\\\"\",\n \"def generate_text_uniform(model: List[str], n: int) -> str:\\n # ACCUMULATOR: a list of the randomly-generated words so far\\n words_so_far = []\\n\\n for _ in range(0, n):\\n # Complete the following statement to choose a random word from model.\\n random_word = random.choice(model)\\n list.append(words_so_far, random_word)\\n\\n return str.join(' ', words_so_far)\",\n \"def extract_words(tree: Tree, template: Tree) -> str:\\n list_words = find_step(tree, template)\\n return \\\" \\\".join(list_words)\",\n \"def generate_text(model, field, seed, n=500):\\n string = seed\\n for i in range(n):\\n indexes = field.numericalize(string)\\n predictions = model(indexes.transpose(0, 1))\\n last_output = predictions[-1]\\n [most_probable] = torch.multinomial(last_output.exp(), 1)\\n char = field.vocab.itos[most_probable]\\n seed = seed[1:] + char\\n string += char\\n return string\",\n \"def make_text(markov_chains):\\n\\n random_num = generate_random_number(markov_chains.keys())\\n\\n random_text = []\\n\\n start_words = generate_start_words(random_num, markov_chains.keys())\\n \\n random_text.extend(start_words)\\n\\n\\n for i in range(500):\\n word_tuple = (random_text[-2],random_text[-1])\\n next_word = add_next_word(word_tuple, markov_chains)\\n random_text.append(next_word)\\n\\n return random_text\",\n \"def get_sentence(self):\\r\\n \\r\\n size = self.sentence_sizes.get()\\r\\n size += int(random.randrange(int(size * 0.8), int(size * 1.5)))\\r\\n\\r\\n sentence = \\\"\\\"\\r\\n opener = closer = None\\r\\n match_chance = self.punctuation_matched_chance\\r\\n \\r\\n for i in range(size):\\r\\n word = self.get_word()\\r\\n if self.last_word == word:\\r\\n # Retry to avoid repeats.\\r\\n word = self.get_word()\\r\\n self.last_word = word\\r\\n \\r\\n if i == 0:\\r\\n sentence += self.get_word().capitalize()\\r\\n elif opener:\\r\\n sentence += \\\" \\\" + opener + self.get_word()\\r\\n opener = None\\r\\n else:\\r\\n sentence += \\\" \\\" + self.get_word()\\r\\n \\r\\n if i != 0 and i != (size - 1):\\r\\n if random.random() > match_chance:\\r\\n if closer:\\r\\n sentence += closer\\r\\n closer = None\\r\\n match_chance = self.punctuation_matched_chance\\r\\n else:\\r\\n opener, closer = self.punctuation_matched.get()\\r\\n continue\\r\\n elif closer:\\r\\n # Make it increasingly likely to roll a closer\\r\\n match_chance *= 0.8\\r\\n if random.random() > self.punctuation_midline_chance:\\r\\n sentence += self.punctuation_midline.get()\\r\\n\\r\\n end_of_line = self.punctuation_endline.get()\\r\\n if closer:\\r\\n sentence = sentence.strip() + closer + end_of_line\\r\\n else:\\r\\n sentence = sentence.strip() + end_of_line\\r\\n return sentence, size\",\n \"def get_foreign_phrase(self, src_sentence):\\n fp = src_sentence[self.fp_start]\\n for word in src_sentence[self.fp_start+1:self.fp_end]:\\n fp += \\\" \\\" + word\\n return fp\",\n \"def _random_template_for(self, line_action: Text) -> Optional[Dict[Text, Any]]:\\n\\n if line_action in self.templates:\\n return random.choice(self.templates[line_action])\\n else:\\n return None\",\n \"def get_random_word(self):\\n pass\",\n \"def main(word_count=2, use_caps=False, use_leet=False, caps_percent=25, leet_percent=20):\\n\\n phrase = get_phrase(word_count)\\n\\n if use_caps:\\n phrase = random_caps(phrase, caps_percent)\\n\\n if use_leet:\\n phrase = random_characters(phrase, leet_percent)\\n\\n print(phrase)\",\n \"def poem_generation(magnet,topic):\\r\\n e = inflect.engine()\\r\\n antonyms = []\\r\\n synonyms = []\\r\\n poem = \\\"\\\"\\r\\n verb = \\\"\\\"\\r\\n # plural\\r\\n if (e.singular_noun(topic) is False):\\r\\n verb = \\\"is\\\"\\r\\n else:\\r\\n verb = \\\"are\\\"\\r\\n \\r\\n for syn in wordnet.synsets(topic):\\r\\n for l in syn.lemmas():\\r\\n synonyms.append(l.name())\\r\\n if l.antonyms():\\r\\n antonyms.append(l.antonyms()[0].name())\\r\\n if (len(set(antonyms)) < 1):\\r\\n for syn in wordnet.synsets(RAND_LIST[random.randint(0,len(RAND_LIST)-1)]):\\r\\n for l in syn.lemmas():\\r\\n synonyms.append(l.name())\\r\\n if l.antonyms():\\r\\n antonyms.append(l.antonyms()[0].name())\\r\\n topic = topic.capitalize()\\r\\n for i in range(0,random.randint(5,15)):\\r\\n verse = random.randint(0,6)\\r\\n question = random.randint(0,6)\\r\\n # structure\\r\\n # antonyms\\r\\n if (verse < 2) and len(antonyms) > 0:\\r\\n ant_magnet = metaphor_magnet(antonyms[random.randint(0,len(antonyms)-1)])\\r\\n choice = random.randint(0,len(ant_magnet)-1)\\r\\n detail = ant_magnet[choice].split(\\\":\\\")\\r\\n if (question < 2):\\r\\n index = random.randint(0,len(QUESTION)-1)\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" not like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" not like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += topic + \\\" \\\" + verb + \\\" not like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\".\\\\n\\\" \\r\\n else:\\r\\n poem += topic + \\\" \\\" + verb + \\\" not like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\".\\\\n\\\" \\r\\n \\r\\n else:\\r\\n choice = random.randint(0,len(magnet)-1)\\r\\n detail = magnet[choice].split(\\\":\\\")\\r\\n if (question < 2):\\r\\n index = random.randint(0,len(QUESTION)-1)\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n poem += QUESTION[index] + \\\" \\\" + verb + \\\" \\\" + topic + \\\" like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"?\\\\n\\\" \\r\\n else:\\r\\n if (detail[0][0] in ['a','e','i','o','u']):\\r\\n poem += topic + \\\" \\\" + verb + \\\" like an \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"\\\\n\\\" \\r\\n else:\\r\\n poem += topic + \\\" \\\" + verb + \\\" like a \\\" + detail[0] + \\\" \\\" + detail[1] + \\\"\\\\n\\\" \\r\\n \\r\\n return poem\",\n \"def show_random_testimonial():\\r\\n\\trandom_testimonial = Highlight.objects.filter(tags='testimonial')\\r\\n\\tif random_testimonial != '':\\r\\n\\t\\treturn {'random_testimonial': random_testimonial}\\r\\n\\telse:\\r\\n\\t\\treturn 'Nothing Here'\",\n \"def test_clips_deftemplate_parse_text(self):\\n G = CreateTestCarpetingGraph()\\n \\n nlpGraphProcessor = NLPResultGraphParser()\\n res = BLNlpClipsRuleBase()\\n seen = []\\n nlpGraphProcessor.ParseObject(G, Strings.ndStep, seen, res)\\n nlpGraphProcessor.ParseObject(G, \\\"CarpetingPattern\\\", seen, res)\\n nlpGraphProcessor.ParseObject(G, \\\"ProcessSubStep\\\", seen, res)\\n\\n template = next(t for t in res.Deftemplates if t.TemplateName == Strings.ndStep)\\n self.assertTrue(template != None)\\n clipsText = template.ClipsConstruct()\\n expTextLines = [\\\"(deftemplate ProcessStep\\\\n\\\", \\\"(slot Id)\\\\n\\\", \\\"(slot ProcessId)\\\\n\\\", \\\"(slot After)\\\\n\\\", \\\"\\\\n)\\\"]\\n for expLine in expTextLines:\\n self.assertTrue(expLine in clipsText)\\n\\n template = next(t for t in res.Deftemplates if t.TemplateName == \\\"CarpetingPattern\\\")\\n self.assertTrue(template != None)\\n clipsText = template.ClipsConstruct()\\n expTextLines = [\\\"(deftemplate CarpetingPattern\\\\n\\\", \\\"(slot RotationAngle)\\\\n\\\", \\\"(slot ShiftRatio)\\\\n\\\", \\\"(slot ShiftDirection)\\\\n\\\", \\\"(slot Id)\\\", \\\"\\\\n)\\\"]\\n for expLine in expTextLines:\\n self.assertTrue(expLine in clipsText)\\n\\n template = next(t for t in res.Deftemplates if t.TemplateName == \\\"ProcessSubStep\\\")\\n self.assertTrue(template != None)\\n clipsText = template.ClipsConstruct()\\n expTextLines = [\\\"(deftemplate ProcessSubStep\\\\n\\\", \\\"(slot StepNumber)\\\\n\\\", \\\"(slot Instruction)\\\\n\\\", \\\"(slot Id)\\\\n\\\", \\\"(slot ProcessStepId)\\\", \\\"\\\\n)\\\"]\\n for expLine in expTextLines:\\n self.assertTrue(expLine in clipsText)\",\n \"def generate_sword_data(index):\\n with open('./json/sword.json') as file:\\n sword_data = json.loads(file.read())\\n\\n with open('./json/names.json') as file:\\n name_rules = json.loads(file.read())\\n\\n name_grammar = tracery.Grammar(name_rules)\\n name_grammar.add_modifiers(base_english)\\n name_grammar.add_modifiers(extended_english)\\n\\n sword_data['name'] = f'Blade {index + 1}:\\\\n{name_grammar.flatten(\\\"#root#\\\")}'\\n sword_data['tex'] = index\\n sword_data['brokenTex'] = index\\n sword_data['spriteAtlas'] = 'blades'\\n sword_data['baseDamage'] = int(numpy.random.normal(10, 4))\\n sword_data['randDamage'] = int(numpy.random.normal(10, 4))\\n sword_data['durability'] = int(numpy.random.normal(100, 40))\\n sword_data['knockback'] = numpy.random.normal(0.15, 0.025)\\n sword_data['reach'] = numpy.random.normal(0.5, 0.125) + 0.25\\n sword_data['speed'] = ((1 - (sword_data['baseDamage'] + sword_data['randDamage']) / 44) * 2.0) + 0.25\\n sword_data['damageType'] = numpy.random.choice(\\n [\\n 'PHYSICAL',\\n 'MAGIC',\\n 'FIRE',\\n 'ICE',\\n 'LIGHTNING',\\n 'POISON',\\n 'HEALING',\\n 'PARALYZE',\\n 'VAMPIRE'\\n ],\\n p=[\\n 0.5,\\n 0.1,\\n 0.1,\\n 0.1,\\n 0.1,\\n 0.04,\\n 0.0,\\n 0.03,\\n 0.03\\n ]\\n )\\n sword_data['shader'] = {\\n 'PHYSICAL': None,\\n 'MAGIC': 'magic-item-purple',\\n 'FIRE': 'magic-item-red',\\n 'ICE': 'magic-item',\\n 'LIGHTNING': 'magic-item-white',\\n 'POISON': 'magic-item-green',\\n 'HEALING': 'magic-item',\\n 'PARALYZE': 'magic-item',\\n 'VAMPIRE': 'magic-item-red'\\n }[sword_data['damageType']]\\n sword_data['attackAnimation'] = numpy.random.choice(\\n [\\n 'swordAttack',\\n 'swordAttackSlow',\\n 'daggerAttack',\\n 'maceAttack'\\n ],\\n p=[\\n 0.4,\\n 0.2,\\n 0.35,\\n 0.05\\n ]\\n )\\n sword_data['attackStrongAnimation'] = numpy.random.choice(\\n [\\n 'swordAttackStrong',\\n 'thrustAttack',\\n 'daggerAttackStrong',\\n 'maceAttackStrong'\\n ],\\n p=[\\n 0.4,\\n 0.2,\\n 0.35,\\n 0.05\\n ]\\n )\\n sword_data['chargeAnimation'] = numpy.random.choice(\\n [\\n 'swordCharge',\\n 'thrustCharge',\\n 'daggerCharge',\\n 'maceCharge'\\n ],\\n p=[\\n 0.35,\\n 0.2,\\n 0.35,\\n 0.1\\n ]\\n )\\n\\n # Add a light?\\n if (sword_data['damageType'] != 'PHYSICAL' and random.random() < 0.125):\\n with open('./json/light.json') as file:\\n light_data = json.loads(file.read())\\n light_data['lightColor'] = damage_type_color[sword_data['damageType']]\\n sword_data['attached'].append(light_data)\\n\\n return sword_data\",\n \"def get(self, pos):\\n return self.sentence[pos]\",\n \"def plants (plant_name, plant_type):\\n print (f\\\"\\\\n{plant_name.title()} is a {plant_type}. \\\\n\\\")\",\n \"def inject_title(self,template,title):\\n return re.sub('TITLE',title,template)\",\n \"def _substitute(template, fuzzer, benchmark):\\n return template.format(fuzzer=fuzzer, benchmark=benchmark)\",\n \"def generate_name():\\n return random.choice(ADJECTIVES) + \\\"_\\\" + random.choice(TOOLS)\",\n \"def report(drink, resources):\\n print(f\\\" Coffee: {resources['coffee']}gm\\\")\\n print(f\\\" Water: {resources['water']}ml\\\")\\n print(f\\\" Milk: {resources['water']}ml\\\")\",\n \"def first_entrance(self):\\n for item in self.items:\\n if issubclass(item.__class__, items.Drink):\\n drink_name = item.name[0].lower()\\n break\\n\\n print(\\\"Location: \\\" + self.name)\\n print(\\\"A simple room consisting of two couches. Each is accompanied by a potted plant and a table. \\\"\\n \\\"On one sits a \\\", end=\\\"\\\")\\n if drink_name == \\\"coffee\\\" or drink_name == \\\"tea\\\":\\n print(\\\"cup of \\\" + drink_name + \\\".\\\")\\n elif drink_name == \\\"water\\\":\\n print(\\\"glass of water.\\\")\\n elif drink_name == \\\"coke\\\":\\n print(\\\"cold can of Coke.\\\")\\n else:\\n print(drink_name)\\n print(\\\"pile of dust.\\\")\",\n \"def pirate():\\n \\n from random import choice\\n d = {\\n \\\"sir\\\": \\\"matey\\\", \\\"hotel\\\": \\\"fleabag inn\\\", \\\"student\\\": \\\"swabbie\\\",\\n \\\"boy\\\": \\\"matey\\\", \\\"girl\\\": \\\"wench\\\", \\\"professor\\\": \\\"foul blaggart\\\",\\n \\\"restaurant\\\": \\\"galley\\\", \\\"your\\\": \\\"ye\\\", \\\"excuse\\\": \\\"arr\\\", \\\"you\\\": \\\"ye\\\",\\n \\\"students\\\": \\\"swabbies\\\", \\\"are\\\": \\\"be\\\", \\\"lawyer\\\": \\\"foul blaggart\\\",\\n \\\"the\\\": \\\"th'\\\", \\\"restroom\\\": \\\"head\\\", \\\"my\\\": \\\"me\\\", \\\"hello\\\": \\\"avast\\\",\\n \\\"is\\\": \\\"be\\\", \\\"man\\\": \\\"scurvy dog\\\", \\\"hey\\\": \\\"avast\\\", \\\"pirate\\\": \\\"scurvy pirate\\\",\\n \\\"idiot\\\": \\\"flunder\\\", \\\"young\\\": \\\"ye\\\", \\\"the\\\": \\\"t'\\\", \\\"suck\\\": \\\"blow down\\\",\\n \\\"fall\\\": \\\"hade\\\", \\\"happens\\\": \\\"be happening\\\", \\\"death\\\": \\\"Davy Jones' treasure chest\\\",\\n \\\"always\\\": \\\"ever\\\", \\\"you're\\\": \\\"ye're\\\", \\\"girlfriend\\\": \\\"Lassie-Lucy\\\",\\n \\\"with\\\": \\\"wit'\\\", \\\"everyone\\\": \\\"evr'un\\\"\\n }\\n #~ sentence = raw_input(\\\"What be yer sentence, ye old landlubber? \\\")\\n sentences = [\\\"If the world didn't suck we'd all fall off\\\",\\n \\\"What happens if you get scared half to death twice?\\\",\\n \\\"Save water and shower with your girlfriend\\\",\\n \\\"Always remember you're unique, just like everyone else\\\"]\\n \\n start = [\\\"Yarr, \\\", \\\"Yarr! \\\", \\\"Yarr-ha-harr! \\\", \\\"Skuttle me Skippers! \\\",\\n \\\"Shiver me timbers! \\\", \\\"Ahoy, me hearties! \\\", \\\"Dogs ahoy! \\\",\\n \\\"Hoist the mizzen!!! \\\", \\\"Hoist the colors!! \\\" ]\\n end = [\\\". ARRRGHHHH!\\\", \\\". Avast ye varmint!\\\", \\\". Yarr?\\\",\\n \\\". Savvyy?\\\", \\\". Yarr!\\\", \\\". Aye!\\\",\\n \\\", ye bilge rat!\\\", \\\", ye mangy dog!\\\", \\\", ye scallywag!\\\"]\\n\\n s = choice(sentences)\\n print s\\n print choice(start) +\\\\\\n ' '.join([d[i] if i in d else i for i in s.lower().split()]).capitalize()\\\\\\n + choice(end)\",\n \"def teampreview(self, battle: AbstractBattle) -> str:\\n return self.random_teampreview(battle)\",\n \"def hello_world():\\r\\n sentence = '<p>Hello World! '+my_id+' <p>'\\r\\n return sentence\",\n \"def generate(self, text):\\n return self.generate_from_text(text)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6083435","0.6067856","0.60557234","0.58162034","0.5800545","0.57799345","0.57570976","0.5742893","0.5580341","0.5577429","0.55747986","0.5573053","0.55707955","0.55657536","0.5488579","0.5484002","0.5479624","0.54288805","0.538444","0.5365156","0.5342022","0.5337323","0.53273505","0.5297109","0.5286542","0.5282722","0.5272583","0.52499604","0.52477074","0.5235527","0.5216671","0.52149326","0.51925135","0.5166232","0.51604706","0.5157955","0.51527715","0.51451004","0.5136965","0.51320016","0.51153904","0.5111234","0.5110408","0.5100398","0.50830436","0.5073559","0.50695807","0.5067821","0.50590014","0.50587195","0.5053647","0.505194","0.50500405","0.50303274","0.5020547","0.50196135","0.50146765","0.501267","0.5012565","0.5003991","0.5003159","0.49977773","0.49946365","0.4992683","0.49910188","0.49800408","0.49788472","0.49773404","0.49691865","0.4964057","0.49587184","0.49585918","0.49571228","0.49494192","0.49492648","0.494745","0.4941278","0.49403408","0.4938199","0.49349272","0.49332348","0.49226698","0.49226668","0.49181834","0.4916432","0.4914028","0.491243","0.4912324","0.49081033","0.49013776","0.48998916","0.48944497","0.48941955","0.4891118","0.4890495","0.4887842","0.4887085","0.48837283","0.48803404","0.4869664"],"string":"[\n \"0.6083435\",\n \"0.6067856\",\n \"0.60557234\",\n \"0.58162034\",\n \"0.5800545\",\n \"0.57799345\",\n \"0.57570976\",\n \"0.5742893\",\n \"0.5580341\",\n \"0.5577429\",\n \"0.55747986\",\n \"0.5573053\",\n \"0.55707955\",\n \"0.55657536\",\n \"0.5488579\",\n \"0.5484002\",\n \"0.5479624\",\n \"0.54288805\",\n \"0.538444\",\n \"0.5365156\",\n \"0.5342022\",\n \"0.5337323\",\n \"0.53273505\",\n \"0.5297109\",\n \"0.5286542\",\n \"0.5282722\",\n \"0.5272583\",\n \"0.52499604\",\n \"0.52477074\",\n \"0.5235527\",\n \"0.5216671\",\n \"0.52149326\",\n \"0.51925135\",\n \"0.5166232\",\n \"0.51604706\",\n \"0.5157955\",\n \"0.51527715\",\n \"0.51451004\",\n \"0.5136965\",\n \"0.51320016\",\n \"0.51153904\",\n \"0.5111234\",\n \"0.5110408\",\n \"0.5100398\",\n \"0.50830436\",\n \"0.5073559\",\n \"0.50695807\",\n \"0.5067821\",\n \"0.50590014\",\n \"0.50587195\",\n \"0.5053647\",\n \"0.505194\",\n \"0.50500405\",\n \"0.50303274\",\n \"0.5020547\",\n \"0.50196135\",\n \"0.50146765\",\n \"0.501267\",\n \"0.5012565\",\n \"0.5003991\",\n \"0.5003159\",\n \"0.49977773\",\n \"0.49946365\",\n \"0.4992683\",\n \"0.49910188\",\n \"0.49800408\",\n \"0.49788472\",\n \"0.49773404\",\n \"0.49691865\",\n \"0.4964057\",\n \"0.49587184\",\n \"0.49585918\",\n \"0.49571228\",\n \"0.49494192\",\n \"0.49492648\",\n \"0.494745\",\n \"0.4941278\",\n \"0.49403408\",\n \"0.4938199\",\n \"0.49349272\",\n \"0.49332348\",\n \"0.49226698\",\n \"0.49226668\",\n \"0.49181834\",\n \"0.4916432\",\n \"0.4914028\",\n \"0.491243\",\n \"0.4912324\",\n \"0.49081033\",\n \"0.49013776\",\n \"0.48998916\",\n \"0.48944497\",\n \"0.48941955\",\n \"0.4891118\",\n \"0.4890495\",\n \"0.4887842\",\n \"0.4887085\",\n \"0.48837283\",\n \"0.48803404\",\n \"0.4869664\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66812485"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77488,"cells":{"query":{"kind":"string","value":"Retrieves the correct template to ask a question with the found drink property and its type."},"document":{"kind":"string","value":"def get_template(self, property_type):\n\n valid_templates = [s for s in self.templates if \"{\" + property_type +\n \"}\" in s]\n return choice(valid_templates)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def question_generator(self):\n self.status_conv = 'yes_no_question_asked'\n questions = config.questions\n if not self.voyage.get('voyageurs') and 'voyageur_add' not in self.infos_needed:\n self.infos_needed.append('voyageur_add')\n if self.infos_needed:\n if self.is_hotel_needed() and 'hotel' not in self.infos_needed and 'hotel' not in self.voyage:\n self.infos_needed.insert(1, 'hotel')\n self.hotel_asked = True\n key = self.infos_needed[0]\n self.info_asked = key\n return questions[key]\n else :\n self.status_conv = 'confirmation_asked'\n return self.conv_recap()","def _get_template(self):\n # Get templates and put them in the order of importance:\n # 1. template specified in \"modules.yaml\"\n # 2. template specified in a package directly\n # 3. default template (must be defined, check in __init__)\n module_system_name = str(self.module.__name__).split(\".\")[-1]\n package_attribute = \"{0}_template\".format(module_system_name)\n choices = [\n self.conf.template,\n getattr(self.spec.package, package_attribute, None),\n self.default_template, # This is always defined at this point\n ]\n # Filter out false-ish values\n choices = list(filter(lambda x: bool(x), choices))\n # ... and return the first match\n return choices.pop(0)","def create_undefined_question(query_data: dict, property_name: str):\n \n correct_subject = query_data[0][\"personLabel\"][\"value\"]\n del query_data[0][\"personLabel\"]\n\n correct_object = query_data[0][\"valueLabel\"][\"value\"]\n del query_data[0][\"valueLabel\"]\n\n alternatives = []\n for element in query_data[0]:\n alternatives.append(query_data[0][element][\"value\"])\n \n question_text = f\"{correct_subject} {property_name} _______?\"\n\n print(question_text)\n print(\"correct:\", correct_object)\n print(\"alternatives:\", alternatives)","def generate_sentence(self, complete_property):\n\n drink_property = complete_property.split(\": \")[0]\n property_type = complete_property.split(\": \")[1].split(\"// \")[1]\n template = self.get_template(property_type)\n question = template.replace(\"{\" + property_type + \"}\", drink_property)\n return self.get_correct_form(question)","def picked_story(story_temp):\n form_prompts = STORIES[story_temp].prompts\n picked_story = story_temp\n\n return render_template(\"questions.html\", prompts = form_prompts, story_temp = picked_story)","def get_template(self):\n model = self.get_object()\n template_name = self.model_template_name or 'template'\n try:\n template_string = getattr(model, template_name)\n except AttributeError as e:\n raise ImproperlyConfigured(\n \"%(model)s is missing a template. Define \"\n \"%(model)s.template, %(cls)s.model_template_name \"\n \"or override %(cls)s.get_template().\" % {\n 'model': model.__class__.__name__,\n 'cls': self.__class__.__name__\n }\n )\n return template_string","def create_evaluation_template(client, survey_name):\n\n loremipsum = \"Lorem ipsum dolor sit amet, consecteteur adipiscing elit donec proin nulla vivamus. Augue donec a erat ve sagittis nisi rhoncus curabitur mauris. Nulla ipsum tortor sagittis adipiscing primis interdum suspendisse lobortis etiam risus nullam. Donec massa quam dis at nibh dolor netus quis. Purus etiam. Dolor neque nunc netus eget nulla faucibus vestibulum aenean class senectus. Porta dolor. Donec morbi. Felis lorem tempus luctus malesuada laoreet curae justo rhoncus ante facilisi parturient malesuada elit laoreet amet. Fusce augue nisi ligula praesent condimentum nascetur fringilla in id lectus per nunc. Lacus metus nisl orci odio maecenas adipiscing. Velit nulla a tempor class placerat ac condimentum nisi taciti at eros.\"\n\n loremipsum_A = \"A: \\n\" + loremipsum\n loremipsum_B = \"B: \\n\" + loremipsum\n\n # Create a new survey\n survey_id = client.create_survey(survey_name)\n # Create 2 more pages in the survey\n for i in range(0, 2):\n client.create_new_page(survey_id, str(i), loremipsum) # title and description\n\n # Get the page ids\n page_ids = client.get_pages_ids(survey_id) # There will be 3\n\n answers = [\"A\", \"B\"]\n question_title = \"Which of the following abstract is more relevant to the one above?\"\n for i, ID in enumerate(page_ids):\n client.update_title_description_of_page(survey_id, ID, \"Abstract\" + str(i), loremipsum)\n client.add_single_choice(survey_id, ID, question_title, answers)\n client.add_paragraph(survey_id, ID, loremipsum_A)\n client.add_paragraph(survey_id, ID, loremipsum_B)\n\n return survey_id","def question(num):\n if request.method == 'GET':\n questionGroup = Question.query.filter(Question.num == num).from_self().all()\n answers = [answer.value for answer in questionGroup if '_text' in answer.variable]\n responses = [response.value for response in questionGroup if '_response' in response.variable]\n tooltips = [response.value for response in questionGroup if '_hover' in response.variable]\n questionType = questionGroup[0].question_type\n if questionType == 'Multiple Choice':\n return render_template('/demo/question-multiplechoice.html',\n question=questionGroup[0].question,\n choices=answers,\n responses=responses,\n tooltips=tooltips,\n # variables=variables,\n explanation=questionGroup[0].info,\n template=\"question-multiplechoice\")\n return render_template('/demo/question-freeform.html',\n question=questionGroup[0].question,\n choices=answers,\n responses=responses,\n tooltips=tooltips,\n # variables=variables,\n explanation=questionGroup[0].info,\n template=\"question-freeform\")","def generate_property_template(self):\n template = {\n \"@id\": \"url or curie of the property\",\n \"@type\": \"rdf:Property\",\n \"rdfs:comment\": \"description of the property\",\n \"rdfs:label\": \"carmel case, should match @id\",\n \"schema:domainIncludes\": {\n \"@id\": \"class which use it as a property, could be list\"\n },\n \"schema:isPartOf\": {\n \"@id\": \"http://schema.biothings.io\"\n },\n \"schema:rangeIncludes\": {\n \"@id\": \"relates a property to a class that constitutes (one of) the expected type(s) for values of the property\"\n }\n }\n return template","def ask_questions():\n print(story.prompts)\n prompts = story.prompts\n \n return render_template(\"questions.html\", prompts = prompts)","def get_template(self):\n return self.template","def get_template(self):\n if self.get_website:\n return self.get_website.get_template()\n else:\n return default_entity.get_website.get_template()","def get_question(self):\n question = self.raw_question\n if question is not None:\n return {\n \"question\": self.raw_question\n }","def question(length):\n\n length = len(responses)\n question_list = satisfaction_survey.questions\n asking_question = question_list[length].question\n the_choices = question_list[length].choices\n\n return render_template(\n 'questions_page.html',\n asking_question=asking_question,\n the_choices=the_choices\n )","def get_random_template(self, curr_template, curr_meter, pref_pos=None, exclude=None):\n #gets all templates which end in curr_template and curr_meter\n poss_templates = [item for item in self.templates.keys() if item[-len(curr_template):] == curr_template and self.templates[item].split('_')[-len(curr_meter.split('_')):] == curr_meter.split('_')]\n if exclude: poss_templates = [x for x in poss_templates if x.split()[0] not in exclude] #if exclude is given, remove those ones\n if len(poss_templates) == 0: return False\n if pref_pos:\n n = len(poss_templates)\n template_scores = np.zeros(n)\n for i in range(n): #iterates through all valid templates\n score = 0\n for pos in poss_templates[i].split(): #iterates through each POS in the template\n if pos in pref_pos: score += pref_pos[pos] #adds the weight of that POS\n template_scores[i] = score\n\n #to normalize make all values positive\n template_scores += abs(min(template_scores)) + 1\n #then ensure sums to 1 ie is a distribution\n template_scores /= sum(template_scores)\n return np.random.choice(poss_templates, p=template_scores) #Very nifty function which chooses from a list with a custom distribution\n return random.choice(poss_templates)","def question(self):\n return self.details[KEY_QUESTION]","def get_typeform_answer(typeform_answers, field_id):\n\n for answer in typeform_answers:\n if field_id == answer['field']['id']:\n if answer['type'] == 'choice':\n return answer[answer['type']]['label']\n else:\n return answer[answer['type']]\n\n return None","def get_question_templates(session: \"Session\", verbose: bool) -> Dict:\n return _get_dict(\n session,\n url_tail=f\"/{CoordConstsV2.RSC_QUESTION_TEMPLATES}\",\n params={CoordConstsV2.QP_VERBOSE: verbose},\n fail_fast=True,\n )","def get_template(self):\n endpoint = \"/isam/wga_templates/dynurl_template\"\n response = self.client.get_json(endpoint)\n response.success = response.status_code == 200\n return response","def generate_answer(self):\n\n for model in Response.get_all_models():\n match = model.matches(self.request.question, self.request.element)\n if match: return model.solve(self.request.question, self.request.element)\n\n return \"I am unable to answer this question. If you think I should be able to answer\\n\" + \\\n \"this, please submit an issue or pull request at:\\n\" + \\\n \"https://github.com/jackcook/the-scientist/compare\"","def GetTemplate(self, _page_data):\n return self.template","def _random_template_for(self, line_action: Text) -> Optional[Dict[Text, Any]]:\n\n if line_action in self.templates:\n return random.choice(self.templates[line_action])\n else:\n return None","def get_template(type):\n # read model options file from Cloud Storage\n content = storage.read_file('templates/' + type + '.yaml')\n return Response(content, status=200, mimetype='application/text')","def data_for_question(self, question_type):\n\t\treturn {}","def get_tax_template(posting_date, args):\n\targs = frappe._dict(args)\n\tconditions = [\"\"\"(from_date is null or from_date = '' or from_date <= '{0}')\n\t\tand (to_date is null or to_date = '' or to_date >= '{0}')\"\"\".format(posting_date)]\n\n\tfor key, value in args.iteritems():\n\t\tif key==\"use_for_shopping_cart\":\n\t\t\tconditions.append(\"use_for_shopping_cart = {0}\".format(1 if value else 0))\n\t\tif key == 'customer_group' and value:\n\t\t\tcustomer_group_condition = get_customer_group_condition(value)\n\t\t\tconditions.append(\"ifnull({0}, '') in ('', {1})\".format(key, customer_group_condition))\n\t\telse:\n\t\t\tconditions.append(\"ifnull({0}, '') in ('', '{1}')\".format(key, frappe.db.escape(cstr(value))))\n\n\ttax_rule = frappe.db.sql(\"\"\"select * from `tabTax Rule`\n\t\twhere {0}\"\"\".format(\" and \".join(conditions)), as_dict = True)\n\n\tif not tax_rule:\n\t\treturn None\n\n\tfor rule in tax_rule:\n\t\trule.no_of_keys_matched = 0\n\t\tfor key in args:\n\t\t\tif rule.get(key): rule.no_of_keys_matched += 1\n\n\trule = sorted(tax_rule, lambda b, a: cmp(a.no_of_keys_matched, b.no_of_keys_matched) or cmp(a.priority, b.priority))[0]\n\n\ttax_template = rule.sales_tax_template or rule.purchase_tax_template\n\tdoctype = \"{0} Taxes and Charges Template\".format(rule.tax_type)\n\n\tif frappe.db.get_value(doctype, tax_template, 'disabled')==1:\n\t\treturn None\n\n\treturn tax_template","def drink_style_input():\n # Define answered style questions dictionary\n answers_style = {} \n # Loop through style questions\n for style, question in questions.iteritems():\n # Ask whether they like a drink style and set to lower case\n # Test if answer is yes, then add drink style and boolean to dictionary\n answers_style[style] = raw_input(questions[style] + \" Please answer (y)es or (n)o.\").lower() in [\"y\",\"yes\"]\n return answers_style","def get_question_type(type):\n\n if type == 'בחירה יחידה':\n return 'SingleChoice'\n elif type == 'בחירה מרובה':\n return 'MultipleChoice'\n elif type == 'שאלה פתוחה':\n return 'OpenQuestion'","def get_template(self, format):\n for pattern, converter in self._patterns:\n if converter.format == format:\n template = pattern.generate('{name}')\n if template:\n return template\n return '{name}' f'.{format}'","def relationship_template(cls):\n return relationship.many_to_one(cls, 'relationship_template')","def get_template(self):\n return self.sep.join([self.htmls[html] for html in self.lang]).format(**self.fields)","def getType(self):\n return \"Business Template\"","def get_substitute(self, category: str) -> str:\n\n substitute = self.select_substitute(category)\n return self.get_product_info(substitute[0][0])","def get_condition_template(request):\n\n try:\n age = int(request.POST.get(\"age\"))\n gender = int(request.POST.get(\"gender\"))\n hometownId = str(request.POST.get(\"hometownId\"))\n universityId = int(request.POST.get(\"universityId\"))\n schoolId = map(float, request.POST.getlist(\"schoolId[]\"))\n hobbiesId = map(float, request.POST.getlist(\"hobbiesId[]\"))\n templates = recommend_template(age, gender, hometownId, universityId, schoolId, hobbiesId)\n except:\n return JsonResponse({\"data\": []})\n else:\n return JsonResponse({\"data\": templates})","def template(self):\n return self.conf.get(\"template\", None)","def get_template_name(self):\n if self.template_name is not None:\n return self.template_name\n model_opts = self.queryset.model._meta\n return f\"{model_opts.app_label}/{model_opts.model_name}.html\"","def get_template_name(self):\n if self.template_name is not None:\n return self.template_name\n model_opts = self.queryset.model._meta\n return f\"{model_opts.app_label}/{model_opts.model_name}.html\"","def get_xpath_for_scrape_type(prod, scrape_type):\n xpath = None\n template = ScrapeTemplate.objects.filter(store=prod.store, scrape_type__name=scrape_type).first()\n\n if template:\n xpath = template.xpath\n\n return xpath","def get_template(self, name):\n return self.templates.get(name)","def choose_question():\r\n random_index_question = randint(1, question.num_question + 1)\r\n random_question = question.question[random_index_question]\r\n correct_answer = question.answer[random_index_question]\r\n return random_question, correct_answer","def _get_question(self, question_id):\n return get_object_or_404(Question, pk=question_id)","def detail_talk(request, pk, template=\"core/detail_talk.html\"):\n response = {\n 'talk': get_object_or_404(Talk, pk=pk)\n }\n return direct_to_template(request, template, response)","def question():\n\n # @todo: prep to populate question_l10n from question\n\n return s3_rest_controller(rheader = s3db.dc_rheader)","def _get_message_template(search_results: SearchResults) -> Text:\n msg_template = ''\n if search_results.checked_post.post_type == 'image':\n if len(search_results.matches) == 0:\n msg_template = DEFAULT_COMMENT_OC\n elif len(search_results.matches) == 1:\n msg_template = DEFAULT_REPOST_IMAGE_COMMENT_ONE_MATCH\n else:\n msg_template = DEFAULT_REPOST_IMAGE_COMMENT\n\n if search_results.checked_post.post_type == 'link':\n if len(search_results.matches) == 0:\n msg_template = LINK_OC\n else:\n msg_template = LINK_REPOST\n\n return msg_template","def template_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"template_name\")","def template(self):\n template_names = self.get_template_names()\n if template_names:\n return template_names[0]\n return None","def _choose_templated_topics_persona(self):\n\n topic_bundles = random.sample(\n constants.TEMPLATE_PERSONAS_TOPICS, k=constants.TEMPLATE_PERSONAS_CHOICES\n ) # Each topic bundle is string of comma-seperated related topics, eg. \"book,author\"\n topics = []\n for tb in topic_bundles:\n topics.extend(tb.split(','))\n\n apprentice_agent = self._get_apprentice()\n persona_selection_form = [\n {\n 'type': 'choices',\n 'question': 'My character\\'s favorite ',\n 'choices': topics,\n },\n {'type': 'text', 'question': 'is '},\n {'type': 'text', 'question': 'Add something imaginative to refine it:'},\n ]\n _coordinator_send_message(\n apprentice_agent,\n message=constants.APPRENTICE_CHOOSE_PERSONA_TEMPLATE_REQUEST,\n task_data={'respond_with_form': persona_selection_form},\n )\n agent_response = self.receive_form_response(\n apprentice_agent, check_persona_overuse=True\n )\n\n rs = [r['response'] for r in agent_response['task_data']['form_responses']]\n assert len(rs) == 3, 'Template persona response form length is not 3.'\n topic, topic_item, extra_details = rs\n apprentice_persona = persona_from_template_values(\n topic, topic_item, extra_details\n )\n worker_name = self.worker_names[apprentice_agent]\n logging.info(f'Agent ({worker_name}) selected a persona: {apprentice_persona}')\n\n return apprentice_persona","def template(self):\n return self._template","def template(self):\n return self._template","def template(self):\n return self._template","def get_template(self, templateType, blogid=1):\n return self.execute(\"metaWeblog.getTemplate\", self.appkey, blogid, self.username, self.password, templateType)","def get_field(entry, field):\n\n if field.name in entry.field_dict:\n if field.choices:\n return getattr(entry.object, \"get_%s_display\" % field.name)()\n return entry.field_dict[field.name]\n else:\n return settings.TEMPLATE_STRING_IF_INVALID","def test_get_relationship_templates(self):\n pass","def get_random_question(self):\n available_qs = self.available_qs\n if available_qs.exists():\n return random.choice(available_qs)","def detect_variant_template(self):\n # ====================================================================#\n # Check if Inputs Contains Variants Ids\n if \"variants\" not in self._in.keys() or not isinstance(self._in[\"variants\"], dict):\n return None\n # ====================================================================#\n # Walk on Variants Items\n for key, variant in self._in[\"variants\"].items():\n # Variant Object Splash Id is Here\n if \"id\" not in variant.keys() or not isinstance(variant[\"id\"], str):\n continue\n # Variant Object Splash Id is Valid\n product_id = ObjectsHelper.id(variant[\"id\"])\n if not isinstance(product_id, str) or int(product_id) <= 0:\n continue\n # Load Variant Product\n product = self.load(int(product_id))\n if product is False or not isinstance(product.product_tmpl_id[0].id, int):\n continue\n # Return product Template Id\n return product.product_tmpl_id[0].id\n\n return None","def template(self) -> Optional[pulumi.Input['InstanceTemplateSpecArgs']]:\n return pulumi.get(self, \"template\")","def template_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"template_name\")","def get_template(self ,template_name):\n\n found = False\n for template in self.templates:\n if template['name'] == template_name:\n found = True\n return template\n if not found:\n return None","def template_name(self, template_type: Union[TemplateType, str]) -> str:\n return self.options.get(\"templates\", {}).get(template_type, template_type)","def quiz_selection():\n\n verbs = crud.get_verbs()\n tenses = crud.get_tenses()\n\n return render_template(\"verb-conjugation.html\", verbs=verbs, tenses=tenses)","def ask_question(\n question: Text,\n input_type: Callable[[Text], Any],\n default: Optional[Any] = None,\n hide_input: Optional[bool] = False,\n) -> Any:\n if hide_input:\n if default:\n hint = \"**\"\n else:\n hint = \"\"\n\n return getpass.getpass(f\"{question} [{hint}] \")\n\n if default:\n ask = f\"{question} [{default}]\"\n else:\n ask = question\n\n answer = input(f\"{ask}: \")\n return input_type(answer)","def test_dynamic_template(hass: HomeAssistant) -> None:\n schema = vol.Schema(cv.dynamic_template)\n\n for value in (\n None,\n 1,\n \"{{ partial_print }\",\n \"{% if True %}Hello\",\n [\"test\"],\n \"just a string\",\n ):\n with pytest.raises(vol.Invalid):\n schema(value)\n\n options = (\n \"{{ beer }}\",\n \"{% if 1 == 1 %}Hello{% else %}World{% endif %}\",\n # Function added as an extension by Home Assistant\n \"{{ expand('group.foo')|map(attribute='entity_id')|list }}\",\n # Filter added as an extension by Home Assistant\n \"{{ ['group.foo']|expand|map(attribute='entity_id')|list }}\",\n )\n for value in options:\n schema(value)","def get_branded_template(self, brand, template_name, deprecated_template_name):\n\n # If the deprecated setting is defined, return it.\n try:\n return self.cfg.get(*deprecated_template_name)\n except configparser.NoOptionError:\n pass\n\n # If a brand hint is provided, attempt to use it if it is valid.\n if brand:\n if brand not in self.valid_brands:\n brand = None\n\n # If the brand hint is not valid, or not provided, fallback to the default brand.\n if not brand:\n brand = self.cfg.get(\"general\", \"brand.default\")\n\n root_template_path = self.cfg.get(\"general\", \"templates.path\")\n\n # Grab jinja template if it exists\n if os.path.exists(\n os.path.join(root_template_path, brand, template_name + \".j2\")\n ):\n return os.path.join(brand, template_name + \".j2\")\n else:\n return os.path.join(root_template_path, brand, template_name)","def get_layout_property_type(layout_name, property_name, base_url=DEFAULT_BASE_URL):\n res = commands.cyrest_get('apply/layouts/' + layout_name + '/parameters', base_url=base_url)\n param_types = {param['name']: param['type'] for param in res}\n return param_types[property_name]","def questionType(self):\n raise NotImplementedError","def apprentice_choose_persona(self):\n logging.info('Randomly choosing persona selection type.')\n choose_from_templates = (\n random.random() < constants.PROBABILITY_CHOOSING_TEMPLATE_PERSONA\n )\n if choose_from_templates:\n logging.info('Choosing persona persona from template.')\n resp = self._choose_templated_topics_persona()\n else:\n logging.info('Choosing persona persona from curated cases.')\n resp = self._choose_curated_persona()\n self._reset_to_text_response(self._get_apprentice())\n return resp","def fake_get_hint(_):\r\n return {'best_hint': 'This is the best hint.',\r\n 'rand_hint_1': 'A random hint',\r\n 'rand_hint_2': 'Another random hint',\r\n 'answer': '42.5'}","def get_by_json(self, key, value, item_type, default=None):\n session = self.DBSession()\n try:\n # baked query seem to not work with json\n query = (session.query(CurrentPropertySheet)\n # Rewrittent to use two separate joins per SQLAlchemy 2.0 requirements. -kmp 10-Apr-2023\n .join(CurrentPropertySheet.propsheet)\n .join(CurrentPropertySheet.resource)\n .filter(Resource.item_type == item_type,\n PropertySheet.properties[key].astext == value)\n )\n data = query.one()\n return data.resource\n except (NoResultFound, MultipleResultsFound):\n return default","def get_template(self, request, step, form):\n return 'forms/wizard.html'","def getItemTemplate(self):\n self.update()\n if self.resource_item_template is None:\n return None\n\n return ResourceItem(self.resource_item_template, self.__user__)","def detail(request, question_id):\n question = get_object_or_404(Question, pk=question_id)\n return render(request, 'polls/details.html', {'question': question})","def get_template(self, name):\n for template in self.templates:\n if template.name == name:\n assert isinstance(template, Template)\n return template\n return None","def get_template_names(self):\n if self.object.twfy_id or self.object.current_or_future_candidacies:\n return [\"people/person_detail.html\"]\n return [\"people/not_current_person_detail.html\"]","def make_drink ():\n \n customer_pref = customer_order.drink_order()\n drink = []\n \n for pref in customer_pref:\n if customer_pref[pref] == True:\n drink.append(random.choice(ingredients[pref]))\n \n return drink","def gen_trip_question_alt(template_path,tui_url,trip):\n with open(template_path) as template_f:\n q_json = json.load(template_f)\n\n trip_data = set_trip_data(trip)\n\n q_json[1]['q']['url'] = tui_url.format(trip_data=trip_data)\n\n # Questions 4-6 are contextual, no need to instantiate \n pass\n\n return q_json","def get_template(self, template):\n\n template_path = aj.config.data['email']['templates'].get(template, 'default')\n\n if template_path == 'default' or not os.path.isfile(template_path):\n template_path = DEFAULT_TEMPLATES[template]\n\n return template_path","def get_radio_input(question, selection):\n answer = st.radio(question, selection)\n return answer","def represent_in_drugbank(question):\n\tq = copy.deepcopy(question)\n \n # Full text search for question entities\n\tentities = []\n\tfor name in q['question_search']:\n\t\tif name[0] != '<':\n\t\t\tentities.append( search_entities(name,1) )\n\t\telse:\n\t\t\tentities.append(name)\n\tq['question_search'] = entities\n \n\t# Find entities of specific type\n\tentities=[]\n\tfor name in q['question_entities']:\n\t\tif name[0] != '<':\n\t\t\tentities.append(find_drugbank_entity(name,q['question_entity_type']))\n\t\telse:\n\t\t\tentities.append(name)\n\tq['question_entities'] = entities\n\n\t# Search for answer entities\n\tfor key, answer in enumerate(q['answers']): \n\t\tentities=[]\n\t\tfor name in answer:\n\t\t\tif name[0] != '<':\n\t\t\t\tentity = find_drugbank_entity(name)\n\t\t\t\tif entity:\n\t\t\t\t\tentities.append(entity)\n\t\t\t\telse:\n\t\t\t\t\tentities.append(search_entities(name,1))\n\t\t\telse:\n\t\t\t\tentities.append(name)\n\t\tq['answers'][key] = entities\n\n\treturn q","def detail_template(self):\n return '{}/{}.html'.format(self.object_name, self.detail_endpoint)","def read_template():\n\n text_msg = \"\"\"${PERSON_NAME} - Calling Campaign Summary - ${DATE}:\\n\n Total Called = ${TOTAL_CALLED}\\n\n Answered = ${ANSWERED}\\n\n Not Answered = ${NOT_ANSWERED}\\n\n Declines = ${DECLINES}\\n\n Remaining = ${REMAINING}\\n\n \\n\n Thank You.\"\"\"\n\n return Template(text_msg)","def get_question_of_answer(answer):\n dynamodb = boto3.resource(\"dynamodb\", region_name=\"eu-central-1\")\n question_table = dynamodb.Table(\"Questions\")\n\n question_id = answer.get(\"QuestionId\")\n # query topic_id of the question\n try:\n response = question_table.get_item(Key={\"QuestionId\": question_id})\n question = response[\"Item\"]\n except:\n print(\"No question found, returning None..\")\n return None\n return question","def get_template_name(self):\n template = None\n if self.template:\n template = self.template\n if not template:\n for p in self.get_ancestors(ascending=True):\n if p.template:\n template = p.template\n break\n if not template:\n template = settings.CMS_TEMPLATES[0][0]\n for t in settings.CMS_TEMPLATES:\n if t[0] == template:\n return t[1] \n return _(\"default\")","def template(self) -> 'outputs.PipelineTemplateResponse':\n return pulumi.get(self, \"template\")","def detail(self):\n url = '/question/%d' % self.id\n d = req.get(url)\n return parser.detail(d)","def question_page(question_id):\n return render_template('question.html', question_id=question_id)","def template_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"template_name\")","def relationship_template_or_type_validator(field, presentation, context):\n\n field.default_validate(presentation, context)\n\n value = getattr(presentation, field.name)\n if value is not None:\n relationship_templates = \\\n context.presentation.get('service_template', 'topology_template',\n 'relationship_templates') \\\n or {}\n if (value not in relationship_templates) and \\\n (get_type_by_name(context, value, 'relationship_types') is None):\n report_issue_for_unknown_type(context, presentation,\n 'relationship template or relationship type', field.name)","def grounding(self, user_preferences):\n #the preferences are stored in a list of three elements, p[0] for the area, p[1] for the price range and p[2] for the food type\n answer_template= \"So you would like me to find a restaurant \"\n p=user_preferences\n if p[0]:\n answer_template+=\"in the {} of town \".format(p[0])\n if p[1]:\n answer_template+=\"priced {}ly \".format(p[1])\n if p[2]:\n answer_template+=\"serving {} cuisine \".format(p[2])\n return answer_template.rstrip()+\". \"","def policy_template(cls):\n return relationship.many_to_one(cls, 'policy_template')","async def generate_question(self) -> None:\n topic = choice(list(self._topics.keys()))\n arrayList = await self.session.get(\"https://restcountries.eu/rest/v2\")\n arrayList = await arrayList.json() # get request to the country API\n countries = []\n \n for _ in range(4):\n country = choice(arrayList)\n del arrayList[arrayList.index(country)]\n countries.append(country)\n del arrayList\n \n country = choice(countries)\n del countries[countries.index(country)]\n self.question = f\"What is the {self._topics[topic]} of {country['name']}?\"\n self.correct_order = randint(0, 3)\n self.choices = [i[topic] for i in countries]\n self.choices.insert(self.correct_order, country[topic])\n del countries, topic","def get_reaction(reaction_type):\n\n if reaction_type == \"neg\":\n speechcon = \"<say-as interpret-as='interjection'>\" \\\n + random.choice(NEG_SPEECHCONS) + \"</say-as>\"\n ans = random.choice(NEG_ANS)\n elif reaction_type == \"pos\":\n speechcon = random.choice(POS_SPEECHCONS)\n ans = random.choice(POS_ANS)\n else:\n raise ValueError\n\n return speechcon + ans","def get_questionnaire(self, url, survey_path):\n pass","def standard_type_detail(request, standard_type):\n try:\n from .settings import STD_TYPE_SLUG_MAP\n context = {\n 'objects': Standard.objects.filter(\n standard_type=STD_TYPE_SLUG_MAP[standard_type]['key']\n ),\n 'standard_type': STD_TYPE_SLUG_MAP[standard_type]['name']\n }\n template = (\n \"curricula/%s_detail.html\" % standard_type,\n \"curricula/standard_type_detail.html\"\n )\n return TemplateResponse(request, template, context)\n except KeyError:\n raise Http404","def generate(self, answers):\n\n text = self.template\n\n for (key, val) in answers.items():\n text = text.replace(\"{\" + key + \"}\", val)\n\n return text","def get_response_example(self, opt_name, var_type, opt_values):\n if opt_name == 'previous' and var_type == 'uri':\n result = None\n elif var_type == 'uri':\n params = {i.group(0): 1 for i in self.find_param.finditer(self.current_path)}\n result = self.type_dict[var_type].format(self.current_path.format(**params))\n if opt_name == 'next':\n result += '?limit=1&offset=1'\n elif opt_name == 'count' and var_type == 'integer':\n result = 2\n elif var_type == 'array':\n items = opt_values.get('items', {}).get('$ref', None)\n item = 'array_example'\n if items:\n item = self.get_object_example(items.split('/')[-1])\n result = [item]\n elif var_type == 'autocomplete':\n result = opt_values.get('enum', [])[0]\n elif var_type in [None, 'object']:\n def_name = (opt_values.get('$ref') or '').split('/')[-1]\n result = self.get_object_example(def_name)\n elif var_type == 'select2':\n def_name = opt_values['x-options']['model']['$ref'].split('/')[-1]\n value_field_name = opt_values['x-options']['value_field']\n def_model = self.definitions[def_name].get('properties')\n value_field = def_model.get(value_field_name, None)\n var_type = value_field.get('format', None) or value_field.get('type', None)\n result = self.get_response_example(opt_name, var_type, def_model)\n else:\n var_type = var_type.replace('-', '_')\n result = opt_values.get('default', None) or self.type_dict.get(var_type, None)\n return result","def select_detail_substitute(self, user_answer_choice_id_substitute):\n self.cursor = self.data_base.cursor(MySQLCursorPrepared)\n self.cursor.execute(\"\"\"SELECT name_food, nutriscore, description, store, link\n FROM Food \n WHERE id = \n (SELECT id_substitute_chooses FROM Favorite WHERE id = {})\"\"\"\n .format(int(user_answer_choice_id_substitute)))\n info_substitute = self.cursor.fetchall()\n info_substitute = self.new_orm.transform_detail_substitute_to_object(info_substitute)\n return info_substitute","def _GetTemplate(self):\n# First read default template.\n tmplt = self._LoadTemplate(c.preproc_template_default)\n tmplt['proc'] = self.topdir\n self.template_type = 'default'\n\n self.templates = []\n if self.template_file is not None:\n tmplt.update(self._LoadTemplate(self.template_file))\n self.template_type = 'command-line'\n self.templates.append(os.path.abspath(self.template_file))\n found_template = True\n else:\n# Find a study specific template file.\n study_template_file = self._FindTemplateFile('%s/..' % self.topdir)\n if study_template_file is not None:\n# Merge study template into default, study template has precedence.\n if self.verbose:\n print \"Using study template at \" + study_template_file\n tmplt.update(self._LoadTemplate(study_template_file))\n self.template_type = 'study-specific'\n self.templates.append(os.path.abspath(study_template_file))\n found_template = True\n else:\n found_template = False\n# Now look for a subject-specific template file.\n subject_template_file = self._FindTemplateFile('%s' % self.topdir)\n if subject_template_file is not None:\n# Merge subject template, subject template has precedence.\n if self.verbose:\n print \"Using subject-specific template at %s\" % \\\n subject_template_file\n tmplt.update(self._LoadTemplate(subject_template_file))\n self.template_type = 'study-specific'\n self.templates.append(os.path.abspath(subject_template_file))\n found_template = True\n\n if not found_template:\n raise RuntimeError('Could not find template file.')\n\n if tmplt.get('subject','same') == 'same':\n# Default subdirectory is same as data directory.\n tmplt['subject'] = self.topdir.split('/')[-1]\n else:\n if not isinstance(tmplt['subject'],str):\n errstr = 'preprocess: Invalid subject number. Be sure to ' + \\\n 'enclose the subject number item with double quotes.'\n raise RuntimeError(errstr)\n\n# Keys that apply to all EPIs.\n self.fsl_flip = tmplt.get('fsl_flip', False)\n if self.fsl_flip:\n self.flip_opts = '-LT'\n else:\n self.flip_opts = ''\n\n# Replace strings with python types.\n for key in tmplt.keys():\n if tmplt[key] == 'None':\n tmplt[key] = None\n elif key == 'True':\n tmplt[key] = True\n elif key == 'False':\n tmplt[key] = False\n return tmplt","def get_type(cond, template, rep, subrep, sublevel, subtemplate):\n clean_rep = rep.replace(\"%\",\"\")\n clean_subrep = subrep.replace(\"%\",\"\")\n if subrep == '' and sublevel == '' and subtemplate == '':\n tetype = 1\n elif sublevel == 'location':\n tetype = 2\n elif sublevel == '' and subtemplate == '':\n tetype = 3\n elif sublevel == '':\n tetype = 4\n elif clean_rep == clean_subrep[:len(clean_rep)]:\n tetype = 5\n else:\n tetype = 6\n return tetype","def get_template(self, template):\n\n\n env = Environment(\n loader=FileSystemLoader('templates')\n )\n return env.get_template(template)","def _get_template(self):\n try:\n template_path = current_app.config.get('REPORT_TEMPLATE_PATH')\n template_code = Path(f'{template_path}/{self._get_template_filename()}').read_text()\n # substitute template parts\n template_code = self._substitute_template_parts(template_code)\n except Exception as err: # noqa: B902; just logging\n current_app.logger.error(err)\n raise err\n return template_code","def make_first_recommendation() -> str:\r\n available_tanks = tank_search()\r\n fermentation_tanks = available_tanks[0]\r\n conditioning_tanks = available_tanks[1]\r\n fermentation_advice = available_tanks[2]\r\n conditioning_advice = available_tanks[3]\r\n more_beer = available_tanks[4]\r\n return render_template(\"make_first_recommendation.html\",\r\n fermentation_string=fermentation_tanks,\r\n conditioning_string=conditioning_tanks,\r\n first_advice=fermentation_advice,\r\n second_advice=conditioning_advice,\r\n next_page=more_beer)"],"string":"[\n \"def question_generator(self):\\n self.status_conv = 'yes_no_question_asked'\\n questions = config.questions\\n if not self.voyage.get('voyageurs') and 'voyageur_add' not in self.infos_needed:\\n self.infos_needed.append('voyageur_add')\\n if self.infos_needed:\\n if self.is_hotel_needed() and 'hotel' not in self.infos_needed and 'hotel' not in self.voyage:\\n self.infos_needed.insert(1, 'hotel')\\n self.hotel_asked = True\\n key = self.infos_needed[0]\\n self.info_asked = key\\n return questions[key]\\n else :\\n self.status_conv = 'confirmation_asked'\\n return self.conv_recap()\",\n \"def _get_template(self):\\n # Get templates and put them in the order of importance:\\n # 1. template specified in \\\"modules.yaml\\\"\\n # 2. template specified in a package directly\\n # 3. default template (must be defined, check in __init__)\\n module_system_name = str(self.module.__name__).split(\\\".\\\")[-1]\\n package_attribute = \\\"{0}_template\\\".format(module_system_name)\\n choices = [\\n self.conf.template,\\n getattr(self.spec.package, package_attribute, None),\\n self.default_template, # This is always defined at this point\\n ]\\n # Filter out false-ish values\\n choices = list(filter(lambda x: bool(x), choices))\\n # ... and return the first match\\n return choices.pop(0)\",\n \"def create_undefined_question(query_data: dict, property_name: str):\\n \\n correct_subject = query_data[0][\\\"personLabel\\\"][\\\"value\\\"]\\n del query_data[0][\\\"personLabel\\\"]\\n\\n correct_object = query_data[0][\\\"valueLabel\\\"][\\\"value\\\"]\\n del query_data[0][\\\"valueLabel\\\"]\\n\\n alternatives = []\\n for element in query_data[0]:\\n alternatives.append(query_data[0][element][\\\"value\\\"])\\n \\n question_text = f\\\"{correct_subject} {property_name} _______?\\\"\\n\\n print(question_text)\\n print(\\\"correct:\\\", correct_object)\\n print(\\\"alternatives:\\\", alternatives)\",\n \"def generate_sentence(self, complete_property):\\n\\n drink_property = complete_property.split(\\\": \\\")[0]\\n property_type = complete_property.split(\\\": \\\")[1].split(\\\"// \\\")[1]\\n template = self.get_template(property_type)\\n question = template.replace(\\\"{\\\" + property_type + \\\"}\\\", drink_property)\\n return self.get_correct_form(question)\",\n \"def picked_story(story_temp):\\n form_prompts = STORIES[story_temp].prompts\\n picked_story = story_temp\\n\\n return render_template(\\\"questions.html\\\", prompts = form_prompts, story_temp = picked_story)\",\n \"def get_template(self):\\n model = self.get_object()\\n template_name = self.model_template_name or 'template'\\n try:\\n template_string = getattr(model, template_name)\\n except AttributeError as e:\\n raise ImproperlyConfigured(\\n \\\"%(model)s is missing a template. Define \\\"\\n \\\"%(model)s.template, %(cls)s.model_template_name \\\"\\n \\\"or override %(cls)s.get_template().\\\" % {\\n 'model': model.__class__.__name__,\\n 'cls': self.__class__.__name__\\n }\\n )\\n return template_string\",\n \"def create_evaluation_template(client, survey_name):\\n\\n loremipsum = \\\"Lorem ipsum dolor sit amet, consecteteur adipiscing elit donec proin nulla vivamus. Augue donec a erat ve sagittis nisi rhoncus curabitur mauris. Nulla ipsum tortor sagittis adipiscing primis interdum suspendisse lobortis etiam risus nullam. Donec massa quam dis at nibh dolor netus quis. Purus etiam. Dolor neque nunc netus eget nulla faucibus vestibulum aenean class senectus. Porta dolor. Donec morbi. Felis lorem tempus luctus malesuada laoreet curae justo rhoncus ante facilisi parturient malesuada elit laoreet amet. Fusce augue nisi ligula praesent condimentum nascetur fringilla in id lectus per nunc. Lacus metus nisl orci odio maecenas adipiscing. Velit nulla a tempor class placerat ac condimentum nisi taciti at eros.\\\"\\n\\n loremipsum_A = \\\"A: \\\\n\\\" + loremipsum\\n loremipsum_B = \\\"B: \\\\n\\\" + loremipsum\\n\\n # Create a new survey\\n survey_id = client.create_survey(survey_name)\\n # Create 2 more pages in the survey\\n for i in range(0, 2):\\n client.create_new_page(survey_id, str(i), loremipsum) # title and description\\n\\n # Get the page ids\\n page_ids = client.get_pages_ids(survey_id) # There will be 3\\n\\n answers = [\\\"A\\\", \\\"B\\\"]\\n question_title = \\\"Which of the following abstract is more relevant to the one above?\\\"\\n for i, ID in enumerate(page_ids):\\n client.update_title_description_of_page(survey_id, ID, \\\"Abstract\\\" + str(i), loremipsum)\\n client.add_single_choice(survey_id, ID, question_title, answers)\\n client.add_paragraph(survey_id, ID, loremipsum_A)\\n client.add_paragraph(survey_id, ID, loremipsum_B)\\n\\n return survey_id\",\n \"def question(num):\\n if request.method == 'GET':\\n questionGroup = Question.query.filter(Question.num == num).from_self().all()\\n answers = [answer.value for answer in questionGroup if '_text' in answer.variable]\\n responses = [response.value for response in questionGroup if '_response' in response.variable]\\n tooltips = [response.value for response in questionGroup if '_hover' in response.variable]\\n questionType = questionGroup[0].question_type\\n if questionType == 'Multiple Choice':\\n return render_template('/demo/question-multiplechoice.html',\\n question=questionGroup[0].question,\\n choices=answers,\\n responses=responses,\\n tooltips=tooltips,\\n # variables=variables,\\n explanation=questionGroup[0].info,\\n template=\\\"question-multiplechoice\\\")\\n return render_template('/demo/question-freeform.html',\\n question=questionGroup[0].question,\\n choices=answers,\\n responses=responses,\\n tooltips=tooltips,\\n # variables=variables,\\n explanation=questionGroup[0].info,\\n template=\\\"question-freeform\\\")\",\n \"def generate_property_template(self):\\n template = {\\n \\\"@id\\\": \\\"url or curie of the property\\\",\\n \\\"@type\\\": \\\"rdf:Property\\\",\\n \\\"rdfs:comment\\\": \\\"description of the property\\\",\\n \\\"rdfs:label\\\": \\\"carmel case, should match @id\\\",\\n \\\"schema:domainIncludes\\\": {\\n \\\"@id\\\": \\\"class which use it as a property, could be list\\\"\\n },\\n \\\"schema:isPartOf\\\": {\\n \\\"@id\\\": \\\"http://schema.biothings.io\\\"\\n },\\n \\\"schema:rangeIncludes\\\": {\\n \\\"@id\\\": \\\"relates a property to a class that constitutes (one of) the expected type(s) for values of the property\\\"\\n }\\n }\\n return template\",\n \"def ask_questions():\\n print(story.prompts)\\n prompts = story.prompts\\n \\n return render_template(\\\"questions.html\\\", prompts = prompts)\",\n \"def get_template(self):\\n return self.template\",\n \"def get_template(self):\\n if self.get_website:\\n return self.get_website.get_template()\\n else:\\n return default_entity.get_website.get_template()\",\n \"def get_question(self):\\n question = self.raw_question\\n if question is not None:\\n return {\\n \\\"question\\\": self.raw_question\\n }\",\n \"def question(length):\\n\\n length = len(responses)\\n question_list = satisfaction_survey.questions\\n asking_question = question_list[length].question\\n the_choices = question_list[length].choices\\n\\n return render_template(\\n 'questions_page.html',\\n asking_question=asking_question,\\n the_choices=the_choices\\n )\",\n \"def get_random_template(self, curr_template, curr_meter, pref_pos=None, exclude=None):\\n #gets all templates which end in curr_template and curr_meter\\n poss_templates = [item for item in self.templates.keys() if item[-len(curr_template):] == curr_template and self.templates[item].split('_')[-len(curr_meter.split('_')):] == curr_meter.split('_')]\\n if exclude: poss_templates = [x for x in poss_templates if x.split()[0] not in exclude] #if exclude is given, remove those ones\\n if len(poss_templates) == 0: return False\\n if pref_pos:\\n n = len(poss_templates)\\n template_scores = np.zeros(n)\\n for i in range(n): #iterates through all valid templates\\n score = 0\\n for pos in poss_templates[i].split(): #iterates through each POS in the template\\n if pos in pref_pos: score += pref_pos[pos] #adds the weight of that POS\\n template_scores[i] = score\\n\\n #to normalize make all values positive\\n template_scores += abs(min(template_scores)) + 1\\n #then ensure sums to 1 ie is a distribution\\n template_scores /= sum(template_scores)\\n return np.random.choice(poss_templates, p=template_scores) #Very nifty function which chooses from a list with a custom distribution\\n return random.choice(poss_templates)\",\n \"def question(self):\\n return self.details[KEY_QUESTION]\",\n \"def get_typeform_answer(typeform_answers, field_id):\\n\\n for answer in typeform_answers:\\n if field_id == answer['field']['id']:\\n if answer['type'] == 'choice':\\n return answer[answer['type']]['label']\\n else:\\n return answer[answer['type']]\\n\\n return None\",\n \"def get_question_templates(session: \\\"Session\\\", verbose: bool) -> Dict:\\n return _get_dict(\\n session,\\n url_tail=f\\\"/{CoordConstsV2.RSC_QUESTION_TEMPLATES}\\\",\\n params={CoordConstsV2.QP_VERBOSE: verbose},\\n fail_fast=True,\\n )\",\n \"def get_template(self):\\n endpoint = \\\"/isam/wga_templates/dynurl_template\\\"\\n response = self.client.get_json(endpoint)\\n response.success = response.status_code == 200\\n return response\",\n \"def generate_answer(self):\\n\\n for model in Response.get_all_models():\\n match = model.matches(self.request.question, self.request.element)\\n if match: return model.solve(self.request.question, self.request.element)\\n\\n return \\\"I am unable to answer this question. If you think I should be able to answer\\\\n\\\" + \\\\\\n \\\"this, please submit an issue or pull request at:\\\\n\\\" + \\\\\\n \\\"https://github.com/jackcook/the-scientist/compare\\\"\",\n \"def GetTemplate(self, _page_data):\\n return self.template\",\n \"def _random_template_for(self, line_action: Text) -> Optional[Dict[Text, Any]]:\\n\\n if line_action in self.templates:\\n return random.choice(self.templates[line_action])\\n else:\\n return None\",\n \"def get_template(type):\\n # read model options file from Cloud Storage\\n content = storage.read_file('templates/' + type + '.yaml')\\n return Response(content, status=200, mimetype='application/text')\",\n \"def data_for_question(self, question_type):\\n\\t\\treturn {}\",\n \"def get_tax_template(posting_date, args):\\n\\targs = frappe._dict(args)\\n\\tconditions = [\\\"\\\"\\\"(from_date is null or from_date = '' or from_date <= '{0}')\\n\\t\\tand (to_date is null or to_date = '' or to_date >= '{0}')\\\"\\\"\\\".format(posting_date)]\\n\\n\\tfor key, value in args.iteritems():\\n\\t\\tif key==\\\"use_for_shopping_cart\\\":\\n\\t\\t\\tconditions.append(\\\"use_for_shopping_cart = {0}\\\".format(1 if value else 0))\\n\\t\\tif key == 'customer_group' and value:\\n\\t\\t\\tcustomer_group_condition = get_customer_group_condition(value)\\n\\t\\t\\tconditions.append(\\\"ifnull({0}, '') in ('', {1})\\\".format(key, customer_group_condition))\\n\\t\\telse:\\n\\t\\t\\tconditions.append(\\\"ifnull({0}, '') in ('', '{1}')\\\".format(key, frappe.db.escape(cstr(value))))\\n\\n\\ttax_rule = frappe.db.sql(\\\"\\\"\\\"select * from `tabTax Rule`\\n\\t\\twhere {0}\\\"\\\"\\\".format(\\\" and \\\".join(conditions)), as_dict = True)\\n\\n\\tif not tax_rule:\\n\\t\\treturn None\\n\\n\\tfor rule in tax_rule:\\n\\t\\trule.no_of_keys_matched = 0\\n\\t\\tfor key in args:\\n\\t\\t\\tif rule.get(key): rule.no_of_keys_matched += 1\\n\\n\\trule = sorted(tax_rule, lambda b, a: cmp(a.no_of_keys_matched, b.no_of_keys_matched) or cmp(a.priority, b.priority))[0]\\n\\n\\ttax_template = rule.sales_tax_template or rule.purchase_tax_template\\n\\tdoctype = \\\"{0} Taxes and Charges Template\\\".format(rule.tax_type)\\n\\n\\tif frappe.db.get_value(doctype, tax_template, 'disabled')==1:\\n\\t\\treturn None\\n\\n\\treturn tax_template\",\n \"def drink_style_input():\\n # Define answered style questions dictionary\\n answers_style = {} \\n # Loop through style questions\\n for style, question in questions.iteritems():\\n # Ask whether they like a drink style and set to lower case\\n # Test if answer is yes, then add drink style and boolean to dictionary\\n answers_style[style] = raw_input(questions[style] + \\\" Please answer (y)es or (n)o.\\\").lower() in [\\\"y\\\",\\\"yes\\\"]\\n return answers_style\",\n \"def get_question_type(type):\\n\\n if type == 'בחירה יחידה':\\n return 'SingleChoice'\\n elif type == 'בחירה מרובה':\\n return 'MultipleChoice'\\n elif type == 'שאלה פתוחה':\\n return 'OpenQuestion'\",\n \"def get_template(self, format):\\n for pattern, converter in self._patterns:\\n if converter.format == format:\\n template = pattern.generate('{name}')\\n if template:\\n return template\\n return '{name}' f'.{format}'\",\n \"def relationship_template(cls):\\n return relationship.many_to_one(cls, 'relationship_template')\",\n \"def get_template(self):\\n return self.sep.join([self.htmls[html] for html in self.lang]).format(**self.fields)\",\n \"def getType(self):\\n return \\\"Business Template\\\"\",\n \"def get_substitute(self, category: str) -> str:\\n\\n substitute = self.select_substitute(category)\\n return self.get_product_info(substitute[0][0])\",\n \"def get_condition_template(request):\\n\\n try:\\n age = int(request.POST.get(\\\"age\\\"))\\n gender = int(request.POST.get(\\\"gender\\\"))\\n hometownId = str(request.POST.get(\\\"hometownId\\\"))\\n universityId = int(request.POST.get(\\\"universityId\\\"))\\n schoolId = map(float, request.POST.getlist(\\\"schoolId[]\\\"))\\n hobbiesId = map(float, request.POST.getlist(\\\"hobbiesId[]\\\"))\\n templates = recommend_template(age, gender, hometownId, universityId, schoolId, hobbiesId)\\n except:\\n return JsonResponse({\\\"data\\\": []})\\n else:\\n return JsonResponse({\\\"data\\\": templates})\",\n \"def template(self):\\n return self.conf.get(\\\"template\\\", None)\",\n \"def get_template_name(self):\\n if self.template_name is not None:\\n return self.template_name\\n model_opts = self.queryset.model._meta\\n return f\\\"{model_opts.app_label}/{model_opts.model_name}.html\\\"\",\n \"def get_template_name(self):\\n if self.template_name is not None:\\n return self.template_name\\n model_opts = self.queryset.model._meta\\n return f\\\"{model_opts.app_label}/{model_opts.model_name}.html\\\"\",\n \"def get_xpath_for_scrape_type(prod, scrape_type):\\n xpath = None\\n template = ScrapeTemplate.objects.filter(store=prod.store, scrape_type__name=scrape_type).first()\\n\\n if template:\\n xpath = template.xpath\\n\\n return xpath\",\n \"def get_template(self, name):\\n return self.templates.get(name)\",\n \"def choose_question():\\r\\n random_index_question = randint(1, question.num_question + 1)\\r\\n random_question = question.question[random_index_question]\\r\\n correct_answer = question.answer[random_index_question]\\r\\n return random_question, correct_answer\",\n \"def _get_question(self, question_id):\\n return get_object_or_404(Question, pk=question_id)\",\n \"def detail_talk(request, pk, template=\\\"core/detail_talk.html\\\"):\\n response = {\\n 'talk': get_object_or_404(Talk, pk=pk)\\n }\\n return direct_to_template(request, template, response)\",\n \"def question():\\n\\n # @todo: prep to populate question_l10n from question\\n\\n return s3_rest_controller(rheader = s3db.dc_rheader)\",\n \"def _get_message_template(search_results: SearchResults) -> Text:\\n msg_template = ''\\n if search_results.checked_post.post_type == 'image':\\n if len(search_results.matches) == 0:\\n msg_template = DEFAULT_COMMENT_OC\\n elif len(search_results.matches) == 1:\\n msg_template = DEFAULT_REPOST_IMAGE_COMMENT_ONE_MATCH\\n else:\\n msg_template = DEFAULT_REPOST_IMAGE_COMMENT\\n\\n if search_results.checked_post.post_type == 'link':\\n if len(search_results.matches) == 0:\\n msg_template = LINK_OC\\n else:\\n msg_template = LINK_REPOST\\n\\n return msg_template\",\n \"def template_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"template_name\\\")\",\n \"def template(self):\\n template_names = self.get_template_names()\\n if template_names:\\n return template_names[0]\\n return None\",\n \"def _choose_templated_topics_persona(self):\\n\\n topic_bundles = random.sample(\\n constants.TEMPLATE_PERSONAS_TOPICS, k=constants.TEMPLATE_PERSONAS_CHOICES\\n ) # Each topic bundle is string of comma-seperated related topics, eg. \\\"book,author\\\"\\n topics = []\\n for tb in topic_bundles:\\n topics.extend(tb.split(','))\\n\\n apprentice_agent = self._get_apprentice()\\n persona_selection_form = [\\n {\\n 'type': 'choices',\\n 'question': 'My character\\\\'s favorite ',\\n 'choices': topics,\\n },\\n {'type': 'text', 'question': 'is '},\\n {'type': 'text', 'question': 'Add something imaginative to refine it:'},\\n ]\\n _coordinator_send_message(\\n apprentice_agent,\\n message=constants.APPRENTICE_CHOOSE_PERSONA_TEMPLATE_REQUEST,\\n task_data={'respond_with_form': persona_selection_form},\\n )\\n agent_response = self.receive_form_response(\\n apprentice_agent, check_persona_overuse=True\\n )\\n\\n rs = [r['response'] for r in agent_response['task_data']['form_responses']]\\n assert len(rs) == 3, 'Template persona response form length is not 3.'\\n topic, topic_item, extra_details = rs\\n apprentice_persona = persona_from_template_values(\\n topic, topic_item, extra_details\\n )\\n worker_name = self.worker_names[apprentice_agent]\\n logging.info(f'Agent ({worker_name}) selected a persona: {apprentice_persona}')\\n\\n return apprentice_persona\",\n \"def template(self):\\n return self._template\",\n \"def template(self):\\n return self._template\",\n \"def template(self):\\n return self._template\",\n \"def get_template(self, templateType, blogid=1):\\n return self.execute(\\\"metaWeblog.getTemplate\\\", self.appkey, blogid, self.username, self.password, templateType)\",\n \"def get_field(entry, field):\\n\\n if field.name in entry.field_dict:\\n if field.choices:\\n return getattr(entry.object, \\\"get_%s_display\\\" % field.name)()\\n return entry.field_dict[field.name]\\n else:\\n return settings.TEMPLATE_STRING_IF_INVALID\",\n \"def test_get_relationship_templates(self):\\n pass\",\n \"def get_random_question(self):\\n available_qs = self.available_qs\\n if available_qs.exists():\\n return random.choice(available_qs)\",\n \"def detect_variant_template(self):\\n # ====================================================================#\\n # Check if Inputs Contains Variants Ids\\n if \\\"variants\\\" not in self._in.keys() or not isinstance(self._in[\\\"variants\\\"], dict):\\n return None\\n # ====================================================================#\\n # Walk on Variants Items\\n for key, variant in self._in[\\\"variants\\\"].items():\\n # Variant Object Splash Id is Here\\n if \\\"id\\\" not in variant.keys() or not isinstance(variant[\\\"id\\\"], str):\\n continue\\n # Variant Object Splash Id is Valid\\n product_id = ObjectsHelper.id(variant[\\\"id\\\"])\\n if not isinstance(product_id, str) or int(product_id) <= 0:\\n continue\\n # Load Variant Product\\n product = self.load(int(product_id))\\n if product is False or not isinstance(product.product_tmpl_id[0].id, int):\\n continue\\n # Return product Template Id\\n return product.product_tmpl_id[0].id\\n\\n return None\",\n \"def template(self) -> Optional[pulumi.Input['InstanceTemplateSpecArgs']]:\\n return pulumi.get(self, \\\"template\\\")\",\n \"def template_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"template_name\\\")\",\n \"def get_template(self ,template_name):\\n\\n found = False\\n for template in self.templates:\\n if template['name'] == template_name:\\n found = True\\n return template\\n if not found:\\n return None\",\n \"def template_name(self, template_type: Union[TemplateType, str]) -> str:\\n return self.options.get(\\\"templates\\\", {}).get(template_type, template_type)\",\n \"def quiz_selection():\\n\\n verbs = crud.get_verbs()\\n tenses = crud.get_tenses()\\n\\n return render_template(\\\"verb-conjugation.html\\\", verbs=verbs, tenses=tenses)\",\n \"def ask_question(\\n question: Text,\\n input_type: Callable[[Text], Any],\\n default: Optional[Any] = None,\\n hide_input: Optional[bool] = False,\\n) -> Any:\\n if hide_input:\\n if default:\\n hint = \\\"**\\\"\\n else:\\n hint = \\\"\\\"\\n\\n return getpass.getpass(f\\\"{question} [{hint}] \\\")\\n\\n if default:\\n ask = f\\\"{question} [{default}]\\\"\\n else:\\n ask = question\\n\\n answer = input(f\\\"{ask}: \\\")\\n return input_type(answer)\",\n \"def test_dynamic_template(hass: HomeAssistant) -> None:\\n schema = vol.Schema(cv.dynamic_template)\\n\\n for value in (\\n None,\\n 1,\\n \\\"{{ partial_print }\\\",\\n \\\"{% if True %}Hello\\\",\\n [\\\"test\\\"],\\n \\\"just a string\\\",\\n ):\\n with pytest.raises(vol.Invalid):\\n schema(value)\\n\\n options = (\\n \\\"{{ beer }}\\\",\\n \\\"{% if 1 == 1 %}Hello{% else %}World{% endif %}\\\",\\n # Function added as an extension by Home Assistant\\n \\\"{{ expand('group.foo')|map(attribute='entity_id')|list }}\\\",\\n # Filter added as an extension by Home Assistant\\n \\\"{{ ['group.foo']|expand|map(attribute='entity_id')|list }}\\\",\\n )\\n for value in options:\\n schema(value)\",\n \"def get_branded_template(self, brand, template_name, deprecated_template_name):\\n\\n # If the deprecated setting is defined, return it.\\n try:\\n return self.cfg.get(*deprecated_template_name)\\n except configparser.NoOptionError:\\n pass\\n\\n # If a brand hint is provided, attempt to use it if it is valid.\\n if brand:\\n if brand not in self.valid_brands:\\n brand = None\\n\\n # If the brand hint is not valid, or not provided, fallback to the default brand.\\n if not brand:\\n brand = self.cfg.get(\\\"general\\\", \\\"brand.default\\\")\\n\\n root_template_path = self.cfg.get(\\\"general\\\", \\\"templates.path\\\")\\n\\n # Grab jinja template if it exists\\n if os.path.exists(\\n os.path.join(root_template_path, brand, template_name + \\\".j2\\\")\\n ):\\n return os.path.join(brand, template_name + \\\".j2\\\")\\n else:\\n return os.path.join(root_template_path, brand, template_name)\",\n \"def get_layout_property_type(layout_name, property_name, base_url=DEFAULT_BASE_URL):\\n res = commands.cyrest_get('apply/layouts/' + layout_name + '/parameters', base_url=base_url)\\n param_types = {param['name']: param['type'] for param in res}\\n return param_types[property_name]\",\n \"def questionType(self):\\n raise NotImplementedError\",\n \"def apprentice_choose_persona(self):\\n logging.info('Randomly choosing persona selection type.')\\n choose_from_templates = (\\n random.random() < constants.PROBABILITY_CHOOSING_TEMPLATE_PERSONA\\n )\\n if choose_from_templates:\\n logging.info('Choosing persona persona from template.')\\n resp = self._choose_templated_topics_persona()\\n else:\\n logging.info('Choosing persona persona from curated cases.')\\n resp = self._choose_curated_persona()\\n self._reset_to_text_response(self._get_apprentice())\\n return resp\",\n \"def fake_get_hint(_):\\r\\n return {'best_hint': 'This is the best hint.',\\r\\n 'rand_hint_1': 'A random hint',\\r\\n 'rand_hint_2': 'Another random hint',\\r\\n 'answer': '42.5'}\",\n \"def get_by_json(self, key, value, item_type, default=None):\\n session = self.DBSession()\\n try:\\n # baked query seem to not work with json\\n query = (session.query(CurrentPropertySheet)\\n # Rewrittent to use two separate joins per SQLAlchemy 2.0 requirements. -kmp 10-Apr-2023\\n .join(CurrentPropertySheet.propsheet)\\n .join(CurrentPropertySheet.resource)\\n .filter(Resource.item_type == item_type,\\n PropertySheet.properties[key].astext == value)\\n )\\n data = query.one()\\n return data.resource\\n except (NoResultFound, MultipleResultsFound):\\n return default\",\n \"def get_template(self, request, step, form):\\n return 'forms/wizard.html'\",\n \"def getItemTemplate(self):\\n self.update()\\n if self.resource_item_template is None:\\n return None\\n\\n return ResourceItem(self.resource_item_template, self.__user__)\",\n \"def detail(request, question_id):\\n question = get_object_or_404(Question, pk=question_id)\\n return render(request, 'polls/details.html', {'question': question})\",\n \"def get_template(self, name):\\n for template in self.templates:\\n if template.name == name:\\n assert isinstance(template, Template)\\n return template\\n return None\",\n \"def get_template_names(self):\\n if self.object.twfy_id or self.object.current_or_future_candidacies:\\n return [\\\"people/person_detail.html\\\"]\\n return [\\\"people/not_current_person_detail.html\\\"]\",\n \"def make_drink ():\\n \\n customer_pref = customer_order.drink_order()\\n drink = []\\n \\n for pref in customer_pref:\\n if customer_pref[pref] == True:\\n drink.append(random.choice(ingredients[pref]))\\n \\n return drink\",\n \"def gen_trip_question_alt(template_path,tui_url,trip):\\n with open(template_path) as template_f:\\n q_json = json.load(template_f)\\n\\n trip_data = set_trip_data(trip)\\n\\n q_json[1]['q']['url'] = tui_url.format(trip_data=trip_data)\\n\\n # Questions 4-6 are contextual, no need to instantiate \\n pass\\n\\n return q_json\",\n \"def get_template(self, template):\\n\\n template_path = aj.config.data['email']['templates'].get(template, 'default')\\n\\n if template_path == 'default' or not os.path.isfile(template_path):\\n template_path = DEFAULT_TEMPLATES[template]\\n\\n return template_path\",\n \"def get_radio_input(question, selection):\\n answer = st.radio(question, selection)\\n return answer\",\n \"def represent_in_drugbank(question):\\n\\tq = copy.deepcopy(question)\\n \\n # Full text search for question entities\\n\\tentities = []\\n\\tfor name in q['question_search']:\\n\\t\\tif name[0] != '<':\\n\\t\\t\\tentities.append( search_entities(name,1) )\\n\\t\\telse:\\n\\t\\t\\tentities.append(name)\\n\\tq['question_search'] = entities\\n \\n\\t# Find entities of specific type\\n\\tentities=[]\\n\\tfor name in q['question_entities']:\\n\\t\\tif name[0] != '<':\\n\\t\\t\\tentities.append(find_drugbank_entity(name,q['question_entity_type']))\\n\\t\\telse:\\n\\t\\t\\tentities.append(name)\\n\\tq['question_entities'] = entities\\n\\n\\t# Search for answer entities\\n\\tfor key, answer in enumerate(q['answers']): \\n\\t\\tentities=[]\\n\\t\\tfor name in answer:\\n\\t\\t\\tif name[0] != '<':\\n\\t\\t\\t\\tentity = find_drugbank_entity(name)\\n\\t\\t\\t\\tif entity:\\n\\t\\t\\t\\t\\tentities.append(entity)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tentities.append(search_entities(name,1))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tentities.append(name)\\n\\t\\tq['answers'][key] = entities\\n\\n\\treturn q\",\n \"def detail_template(self):\\n return '{}/{}.html'.format(self.object_name, self.detail_endpoint)\",\n \"def read_template():\\n\\n text_msg = \\\"\\\"\\\"${PERSON_NAME} - Calling Campaign Summary - ${DATE}:\\\\n\\n Total Called = ${TOTAL_CALLED}\\\\n\\n Answered = ${ANSWERED}\\\\n\\n Not Answered = ${NOT_ANSWERED}\\\\n\\n Declines = ${DECLINES}\\\\n\\n Remaining = ${REMAINING}\\\\n\\n \\\\n\\n Thank You.\\\"\\\"\\\"\\n\\n return Template(text_msg)\",\n \"def get_question_of_answer(answer):\\n dynamodb = boto3.resource(\\\"dynamodb\\\", region_name=\\\"eu-central-1\\\")\\n question_table = dynamodb.Table(\\\"Questions\\\")\\n\\n question_id = answer.get(\\\"QuestionId\\\")\\n # query topic_id of the question\\n try:\\n response = question_table.get_item(Key={\\\"QuestionId\\\": question_id})\\n question = response[\\\"Item\\\"]\\n except:\\n print(\\\"No question found, returning None..\\\")\\n return None\\n return question\",\n \"def get_template_name(self):\\n template = None\\n if self.template:\\n template = self.template\\n if not template:\\n for p in self.get_ancestors(ascending=True):\\n if p.template:\\n template = p.template\\n break\\n if not template:\\n template = settings.CMS_TEMPLATES[0][0]\\n for t in settings.CMS_TEMPLATES:\\n if t[0] == template:\\n return t[1] \\n return _(\\\"default\\\")\",\n \"def template(self) -> 'outputs.PipelineTemplateResponse':\\n return pulumi.get(self, \\\"template\\\")\",\n \"def detail(self):\\n url = '/question/%d' % self.id\\n d = req.get(url)\\n return parser.detail(d)\",\n \"def question_page(question_id):\\n return render_template('question.html', question_id=question_id)\",\n \"def template_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"template_name\\\")\",\n \"def relationship_template_or_type_validator(field, presentation, context):\\n\\n field.default_validate(presentation, context)\\n\\n value = getattr(presentation, field.name)\\n if value is not None:\\n relationship_templates = \\\\\\n context.presentation.get('service_template', 'topology_template',\\n 'relationship_templates') \\\\\\n or {}\\n if (value not in relationship_templates) and \\\\\\n (get_type_by_name(context, value, 'relationship_types') is None):\\n report_issue_for_unknown_type(context, presentation,\\n 'relationship template or relationship type', field.name)\",\n \"def grounding(self, user_preferences):\\n #the preferences are stored in a list of three elements, p[0] for the area, p[1] for the price range and p[2] for the food type\\n answer_template= \\\"So you would like me to find a restaurant \\\"\\n p=user_preferences\\n if p[0]:\\n answer_template+=\\\"in the {} of town \\\".format(p[0])\\n if p[1]:\\n answer_template+=\\\"priced {}ly \\\".format(p[1])\\n if p[2]:\\n answer_template+=\\\"serving {} cuisine \\\".format(p[2])\\n return answer_template.rstrip()+\\\". \\\"\",\n \"def policy_template(cls):\\n return relationship.many_to_one(cls, 'policy_template')\",\n \"async def generate_question(self) -> None:\\n topic = choice(list(self._topics.keys()))\\n arrayList = await self.session.get(\\\"https://restcountries.eu/rest/v2\\\")\\n arrayList = await arrayList.json() # get request to the country API\\n countries = []\\n \\n for _ in range(4):\\n country = choice(arrayList)\\n del arrayList[arrayList.index(country)]\\n countries.append(country)\\n del arrayList\\n \\n country = choice(countries)\\n del countries[countries.index(country)]\\n self.question = f\\\"What is the {self._topics[topic]} of {country['name']}?\\\"\\n self.correct_order = randint(0, 3)\\n self.choices = [i[topic] for i in countries]\\n self.choices.insert(self.correct_order, country[topic])\\n del countries, topic\",\n \"def get_reaction(reaction_type):\\n\\n if reaction_type == \\\"neg\\\":\\n speechcon = \\\"<say-as interpret-as='interjection'>\\\" \\\\\\n + random.choice(NEG_SPEECHCONS) + \\\"</say-as>\\\"\\n ans = random.choice(NEG_ANS)\\n elif reaction_type == \\\"pos\\\":\\n speechcon = random.choice(POS_SPEECHCONS)\\n ans = random.choice(POS_ANS)\\n else:\\n raise ValueError\\n\\n return speechcon + ans\",\n \"def get_questionnaire(self, url, survey_path):\\n pass\",\n \"def standard_type_detail(request, standard_type):\\n try:\\n from .settings import STD_TYPE_SLUG_MAP\\n context = {\\n 'objects': Standard.objects.filter(\\n standard_type=STD_TYPE_SLUG_MAP[standard_type]['key']\\n ),\\n 'standard_type': STD_TYPE_SLUG_MAP[standard_type]['name']\\n }\\n template = (\\n \\\"curricula/%s_detail.html\\\" % standard_type,\\n \\\"curricula/standard_type_detail.html\\\"\\n )\\n return TemplateResponse(request, template, context)\\n except KeyError:\\n raise Http404\",\n \"def generate(self, answers):\\n\\n text = self.template\\n\\n for (key, val) in answers.items():\\n text = text.replace(\\\"{\\\" + key + \\\"}\\\", val)\\n\\n return text\",\n \"def get_response_example(self, opt_name, var_type, opt_values):\\n if opt_name == 'previous' and var_type == 'uri':\\n result = None\\n elif var_type == 'uri':\\n params = {i.group(0): 1 for i in self.find_param.finditer(self.current_path)}\\n result = self.type_dict[var_type].format(self.current_path.format(**params))\\n if opt_name == 'next':\\n result += '?limit=1&offset=1'\\n elif opt_name == 'count' and var_type == 'integer':\\n result = 2\\n elif var_type == 'array':\\n items = opt_values.get('items', {}).get('$ref', None)\\n item = 'array_example'\\n if items:\\n item = self.get_object_example(items.split('/')[-1])\\n result = [item]\\n elif var_type == 'autocomplete':\\n result = opt_values.get('enum', [])[0]\\n elif var_type in [None, 'object']:\\n def_name = (opt_values.get('$ref') or '').split('/')[-1]\\n result = self.get_object_example(def_name)\\n elif var_type == 'select2':\\n def_name = opt_values['x-options']['model']['$ref'].split('/')[-1]\\n value_field_name = opt_values['x-options']['value_field']\\n def_model = self.definitions[def_name].get('properties')\\n value_field = def_model.get(value_field_name, None)\\n var_type = value_field.get('format', None) or value_field.get('type', None)\\n result = self.get_response_example(opt_name, var_type, def_model)\\n else:\\n var_type = var_type.replace('-', '_')\\n result = opt_values.get('default', None) or self.type_dict.get(var_type, None)\\n return result\",\n \"def select_detail_substitute(self, user_answer_choice_id_substitute):\\n self.cursor = self.data_base.cursor(MySQLCursorPrepared)\\n self.cursor.execute(\\\"\\\"\\\"SELECT name_food, nutriscore, description, store, link\\n FROM Food \\n WHERE id = \\n (SELECT id_substitute_chooses FROM Favorite WHERE id = {})\\\"\\\"\\\"\\n .format(int(user_answer_choice_id_substitute)))\\n info_substitute = self.cursor.fetchall()\\n info_substitute = self.new_orm.transform_detail_substitute_to_object(info_substitute)\\n return info_substitute\",\n \"def _GetTemplate(self):\\n# First read default template.\\n tmplt = self._LoadTemplate(c.preproc_template_default)\\n tmplt['proc'] = self.topdir\\n self.template_type = 'default'\\n\\n self.templates = []\\n if self.template_file is not None:\\n tmplt.update(self._LoadTemplate(self.template_file))\\n self.template_type = 'command-line'\\n self.templates.append(os.path.abspath(self.template_file))\\n found_template = True\\n else:\\n# Find a study specific template file.\\n study_template_file = self._FindTemplateFile('%s/..' % self.topdir)\\n if study_template_file is not None:\\n# Merge study template into default, study template has precedence.\\n if self.verbose:\\n print \\\"Using study template at \\\" + study_template_file\\n tmplt.update(self._LoadTemplate(study_template_file))\\n self.template_type = 'study-specific'\\n self.templates.append(os.path.abspath(study_template_file))\\n found_template = True\\n else:\\n found_template = False\\n# Now look for a subject-specific template file.\\n subject_template_file = self._FindTemplateFile('%s' % self.topdir)\\n if subject_template_file is not None:\\n# Merge subject template, subject template has precedence.\\n if self.verbose:\\n print \\\"Using subject-specific template at %s\\\" % \\\\\\n subject_template_file\\n tmplt.update(self._LoadTemplate(subject_template_file))\\n self.template_type = 'study-specific'\\n self.templates.append(os.path.abspath(subject_template_file))\\n found_template = True\\n\\n if not found_template:\\n raise RuntimeError('Could not find template file.')\\n\\n if tmplt.get('subject','same') == 'same':\\n# Default subdirectory is same as data directory.\\n tmplt['subject'] = self.topdir.split('/')[-1]\\n else:\\n if not isinstance(tmplt['subject'],str):\\n errstr = 'preprocess: Invalid subject number. Be sure to ' + \\\\\\n 'enclose the subject number item with double quotes.'\\n raise RuntimeError(errstr)\\n\\n# Keys that apply to all EPIs.\\n self.fsl_flip = tmplt.get('fsl_flip', False)\\n if self.fsl_flip:\\n self.flip_opts = '-LT'\\n else:\\n self.flip_opts = ''\\n\\n# Replace strings with python types.\\n for key in tmplt.keys():\\n if tmplt[key] == 'None':\\n tmplt[key] = None\\n elif key == 'True':\\n tmplt[key] = True\\n elif key == 'False':\\n tmplt[key] = False\\n return tmplt\",\n \"def get_type(cond, template, rep, subrep, sublevel, subtemplate):\\n clean_rep = rep.replace(\\\"%\\\",\\\"\\\")\\n clean_subrep = subrep.replace(\\\"%\\\",\\\"\\\")\\n if subrep == '' and sublevel == '' and subtemplate == '':\\n tetype = 1\\n elif sublevel == 'location':\\n tetype = 2\\n elif sublevel == '' and subtemplate == '':\\n tetype = 3\\n elif sublevel == '':\\n tetype = 4\\n elif clean_rep == clean_subrep[:len(clean_rep)]:\\n tetype = 5\\n else:\\n tetype = 6\\n return tetype\",\n \"def get_template(self, template):\\n\\n\\n env = Environment(\\n loader=FileSystemLoader('templates')\\n )\\n return env.get_template(template)\",\n \"def _get_template(self):\\n try:\\n template_path = current_app.config.get('REPORT_TEMPLATE_PATH')\\n template_code = Path(f'{template_path}/{self._get_template_filename()}').read_text()\\n # substitute template parts\\n template_code = self._substitute_template_parts(template_code)\\n except Exception as err: # noqa: B902; just logging\\n current_app.logger.error(err)\\n raise err\\n return template_code\",\n \"def make_first_recommendation() -> str:\\r\\n available_tanks = tank_search()\\r\\n fermentation_tanks = available_tanks[0]\\r\\n conditioning_tanks = available_tanks[1]\\r\\n fermentation_advice = available_tanks[2]\\r\\n conditioning_advice = available_tanks[3]\\r\\n more_beer = available_tanks[4]\\r\\n return render_template(\\\"make_first_recommendation.html\\\",\\r\\n fermentation_string=fermentation_tanks,\\r\\n conditioning_string=conditioning_tanks,\\r\\n first_advice=fermentation_advice,\\r\\n second_advice=conditioning_advice,\\r\\n next_page=more_beer)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.55158","0.54558504","0.53866625","0.5272962","0.5254931","0.5225667","0.5201388","0.51010674","0.50510913","0.5043558","0.4993756","0.49860686","0.49789208","0.497429","0.49692836","0.49514955","0.49476779","0.4947658","0.49140137","0.49097073","0.49053505","0.48909205","0.48895472","0.4885545","0.48281682","0.48256525","0.48002145","0.47973177","0.47918388","0.47905597","0.47902557","0.47835016","0.4778756","0.4764708","0.47231388","0.47231388","0.46861774","0.46796036","0.46742326","0.46671343","0.4660614","0.4655659","0.46417075","0.4640956","0.46321145","0.4629595","0.46240485","0.46240485","0.46240485","0.4616008","0.4615169","0.46061614","0.46055573","0.4602852","0.46017742","0.4592724","0.45904762","0.45895815","0.4581316","0.45774704","0.45754656","0.4561356","0.45589992","0.45513773","0.45508283","0.44976714","0.44917265","0.44916025","0.44656876","0.44653046","0.4463963","0.4462005","0.44613138","0.44572967","0.44571948","0.44561905","0.44529212","0.4452031","0.44486272","0.44450644","0.4441355","0.44343212","0.4433915","0.4431769","0.44288838","0.44163564","0.44053176","0.43978444","0.4397656","0.43942323","0.43935677","0.4389823","0.43857023","0.43847808","0.43699232","0.43663004","0.43648902","0.4357991","0.43560937","0.43547025"],"string":"[\n \"0.55158\",\n \"0.54558504\",\n \"0.53866625\",\n \"0.5272962\",\n \"0.5254931\",\n \"0.5225667\",\n \"0.5201388\",\n \"0.51010674\",\n \"0.50510913\",\n \"0.5043558\",\n \"0.4993756\",\n \"0.49860686\",\n \"0.49789208\",\n \"0.497429\",\n \"0.49692836\",\n \"0.49514955\",\n \"0.49476779\",\n \"0.4947658\",\n \"0.49140137\",\n \"0.49097073\",\n \"0.49053505\",\n \"0.48909205\",\n \"0.48895472\",\n \"0.4885545\",\n \"0.48281682\",\n \"0.48256525\",\n \"0.48002145\",\n \"0.47973177\",\n \"0.47918388\",\n \"0.47905597\",\n \"0.47902557\",\n \"0.47835016\",\n \"0.4778756\",\n \"0.4764708\",\n \"0.47231388\",\n \"0.47231388\",\n \"0.46861774\",\n \"0.46796036\",\n \"0.46742326\",\n \"0.46671343\",\n \"0.4660614\",\n \"0.4655659\",\n \"0.46417075\",\n \"0.4640956\",\n \"0.46321145\",\n \"0.4629595\",\n \"0.46240485\",\n \"0.46240485\",\n \"0.46240485\",\n \"0.4616008\",\n \"0.4615169\",\n \"0.46061614\",\n \"0.46055573\",\n \"0.4602852\",\n \"0.46017742\",\n \"0.4592724\",\n \"0.45904762\",\n \"0.45895815\",\n \"0.4581316\",\n \"0.45774704\",\n \"0.45754656\",\n \"0.4561356\",\n \"0.45589992\",\n \"0.45513773\",\n \"0.45508283\",\n \"0.44976714\",\n \"0.44917265\",\n \"0.44916025\",\n \"0.44656876\",\n \"0.44653046\",\n \"0.4463963\",\n \"0.4462005\",\n \"0.44613138\",\n \"0.44572967\",\n \"0.44571948\",\n \"0.44561905\",\n \"0.44529212\",\n \"0.4452031\",\n \"0.44486272\",\n \"0.44450644\",\n \"0.4441355\",\n \"0.44343212\",\n \"0.4433915\",\n \"0.4431769\",\n \"0.44288838\",\n \"0.44163564\",\n \"0.44053176\",\n \"0.43978444\",\n \"0.4397656\",\n \"0.43942323\",\n \"0.43935677\",\n \"0.4389823\",\n \"0.43857023\",\n \"0.43847808\",\n \"0.43699232\",\n \"0.43663004\",\n \"0.43648902\",\n \"0.4357991\",\n \"0.43560937\",\n \"0.43547025\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5877344"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77489,"cells":{"query":{"kind":"string","value":"Returns the correct form of a question such that e.g. 'a(n) strainer' becomes 'a strainer'."},"document":{"kind":"string","value":"def get_correct_form(self, template):\n\n if \"(\" in template and \")\" in template:\n index = template.index(\")\")\n first_letter = template[index+2]\n if self.is_vowel(first_letter):\n template = template.replace(\"a(n)\", \"an\")\n else:\n template = template.replace(\"a(n)\", \"a\")\n return template"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def normalize_answer(s):\n\n def remove_articles(text):\n return re_art.sub(' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n return re_punc.sub(' ', text) # convert punctuation to spaces\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize_answer(s):\n\n def remove_articles(text):\n return re_art.sub(' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n return re_punc.sub(' ', text) # convert punctuation to spaces\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def clean_question(q):\n q = str(q)\n q = q.lower() # lowercase\n q = re.sub(r\"what's\", \"what is \", q)\n q = re.sub(r\"\\'ve\", \" have \", q)\n q = re.sub(r\"can't\", \"cannot \", q)\n q = re.sub(r\"n't\", \" not \", q)\n q = re.sub(r\"i'm\", \"i am \", q)\n q = re.sub(r\"\\'re\", \" are \", q)\n q = re.sub(r\"\\'d\", \" would \", q)\n q = re.sub(r\"\\'ll\", \" will \", q)\n q = re.sub(r\"[^A-Za-z0-9]\", \" \", q)\n q = TextBlob(q).tokens #tokenize\n return q","def normalize_answer(s):\n\tdef remove_articles(text):\n\t\treturn re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n\tdef white_space_fix(text):\n\t\treturn ' '.join(text.split())\n\n\tdef remove_punc(text):\n\t\texclude = set(string.punctuation)\n\t\treturn ''.join(ch for ch in text if ch not in exclude)\n\n\tdef lower(text):\n\t\treturn text.lower()\n\n\treturn white_space_fix(remove_articles(remove_punc(lower(s))))","def simplify_ques(question, debug=False):\n if not isinstance(question, str):\n raise TypeError('Please provide a string for a question.')\n\n if debug is True:\n load_json()\n\n neg = False\n qwords = question.lower().split()\n for i in qwords:\n if i in NEGATIVE_WORDS:\n neg = True\n\n cleanwords = [word for word in qwords if word.lower() not in REMOVE_WORDS]\n temp = ' '.join(cleanwords)\n\n clean_question = \"\"\n for letter in temp:\n if letter != \"?\" or letter != \"\\\"\" or letter != \"\\'\":\n clean_question = clean_question + letter\n\n return clean_question.lower(), neg","def normalize_answer(s):\n\n def remove_articles(text):\n return re.sub(r\"\\b(a|an|the)\\b\", \" \", text)\n\n def white_space_fix(text):\n return \" \".join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return \"\".join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize_answer(s):\n\n def remove_articles(text):\n return re.sub(r\"\\b(a|an|the)\\b\", \" \", text)\n\n def white_space_fix(text):\n return \" \".join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return \"\".join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize_answer(s):\n\n def remove_articles(text):\n regex = re.compile(r'\\b(a|an|the)\\b', re.UNICODE)\n return re.sub(regex, ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize_answer(s):\n\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize_answer(s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize_answer(s):\n def remove_articles(text):\n regex = re.compile(r'\\b(a|an|the)\\b', re.UNICODE)\n return re.sub(regex, ' ', text)\n def white_space_fix(text):\n return ' '.join(text.split())\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n def lower(text):\n return text.lower()\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def _normalize_answer(s):\n\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize_answer(s):\n\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(normalize(s)))))","def question_3(name: str) -> str:\n return \"My first name is\" + \" \" + name","def formula_answer_to_str(self, answer):\r\n return str(answer.values()[0])","def simplify(word, lexicon):\n\tsim = random.choice(lexicon[word])[1]\n\tif type(sim) == str:\n\t\tsim = [sim]\n\treturn \" \".join(sim)","def question_new_translate():","def normalize_answer(s):\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return \"\".join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return remove_punc(lower(s))","def reducedFormOne(self, equation):\n splitter = re.split('(\\+|\\-)', equation)\n newEquation = str()\n state = 0\n for token in splitter:\n if '(' in token or state > 0 or '[' in token:\n state += 1\n newEquation += token\n continue\n if ')' in token or ']' in token:\n state -= 1\n continue\n if '^' + self.var in token:\n newEquation += token\n self.validPolynome = False\n continue\n find = re.findall('(\\*|\\^|\\/)?(' + self.var + ')(\\^\\d+)?' , token)\n newVar = []\n for var in find:\n newVar.append(''.join(map(str,var)))\n for var in newVar:\n token = token.replace(var, '')\n if token != '+' and token != '-' and token != '':\n try:\n newEquation += str(eval(token.replace('^', '**'))) + ''.join(newVar)\n except:\n self.error = True\n continue\n else:\n newEquation += token\n return newEquation","def question_1(name: str) -> str:\n return \"Hello my name is\" + \" \" + name","def question(phrase):\n\n if phrase[-1] == '?':\n return 'Sure.'","def sanitize(formula):\n TR_UNICODE = {u\"·\": u\"*\", u\"−\": u\"-\"}\n return \"\".join(TR_UNICODE.get(ch, ch) for ch in formula if ch > \" \")","def possession_ques(analysis):\n\n #processing as statement\n phrase = statement(analysis)\n\n #We have to know if it is plural or singular\n if other_functions.plural_noun(analysis.sn) == 1:\n return ['whose'] + phrase[:len(phrase) - 1] + ['these'] + ['?']\n else:\n return ['whose'] + phrase[1:len(phrase) - 1] + ['this'] + ['?']","def question_5(first_name: str, last_name: str) -> str:\n return \"My full name is\" + \" \" + first_name + \" \" + last_name","def solution(s):","def w_question(analysis):\n if analysis.sv:\n #Opinion is a what question so we have to make some changes\n if analysis.sv[0].vrb_main[0].endswith('like'):\n verb = analysis.sv[0].vrb_main[0]\n analysis.sv[0].vrb_main[0] = verb[:len(verb) - 4] + 'think+of'\n\n #processing as yes or no question\n phrase = y_o_question(analysis)\n\n #Specific processing for invitation\n if analysis.aim == 'invitation':\n return ['how', 'about'] + phrase[1:]\n\n #Specific processing for classification\n if analysis.aim.startswith('classification'):\n aim_question = other_functions.list_rebuilding(analysis.aim)\n return ['what', 'kind', 'of'] + aim_question[1:] + phrase\n\n #It is an how question\n if other_functions.is_an_adj(analysis.aim) == 1:\n return ['how'] + [analysis.aim] + phrase\n elif analysis.aim == 'manner':\n return ['how'] + phrase\n\n if analysis.aim == 'thing' or analysis.aim == 'situation' or analysis.aim == 'explication' or analysis.aim == 'opinion':\n return ['what'] + phrase\n return ['what'] + [analysis.aim] + phrase","def short_form(self):\n return normalize_pred_string(self.string)","def solution(s):\n # check the input string is a valid length\n min_length = 1\n max_length = 101\n\n if len(s) < min_length or len(s) > max_length:\n return ''\n\n # need to handle integers between 1 and 10^100, so up to 101 decimal places of precision\n # The Python default is 28 significant figures\n # https://www.geeksforgeeks.org/setting-precision-in-python-using-decimal-module/\n getcontext().prec = max_length\n\n n = int(s)\n alpha = Decimal(2).sqrt()\n sequence = beatty_sequence(alpha, n)\n\n return str(int(sequence))","def quick_quiz(character_set):","def command_to_latex(q, index=0):\n open_index, close_index = -1, -1\n for q_index, i in enumerate(q):\n if i == '(':\n open_index = q_index\n elif i == ')':\n close_index = q_index\n break\n if open_index != -1:\n o = q[:open_index] + '@' + str(index) + q[close_index + 1:]\n m = q[open_index + 1:close_index]\n o_tex = command_to_latex(o, index + 1)\n m_tex = command_to_latex(m, index + 1)\n # Clean up redundant parentheses at recombination\n r_index = o_tex.find('@' + str(index))\n if o_tex[r_index - 1] == '{':\n return o_tex.replace('@'+str(index), m_tex)\n else:\n return o_tex.replace('@'+str(index),\n ' \\\\left (' + m_tex + ' \\\\right )')\n else:\n return parse_simple_eqn(q)","def question(func):\n return FillInTheBlank(_text_from_func(func), correct=Answer(repr(func()), is_code=True), is_code=True)","def format_answer(answer):\n # a regular expression matching the predicted outcome\n # by timbl.\n pattern = re.compile(r'CATEGORY \\{([^}]+)\\}')\n answer = pattern.search(answer)\n return answer and answer.group(1) or '0'","def simplify(phrase):\n \n num_actors = 0\n \n if phrase == \"\":\n return 0\n \n phrase_list = phrase.split()\n\n for word in phrase_list:\n if word == \"the\":\n num_actors += 1\n \n index = (2 * num_actors) - 1\n actor = phrase_list[index]\n verb = phrase_list[index + 1]\n \n del phrase_list[index]\n del phrase_list[index - 1]\n del phrase_list[index + 1]\n \n phrase_string = \"\"\n for word in phrase_list:\n phrase_string += word\n phrase_string += \" \"\n \n \n return \"the\" + actor + \"that\" + verb + simplify(phase_string)","def y_o_question(analysis):\n\n #init\n phrase = []\n\n #Recovering the subject\n subject = element_rebuilding.nom_struc_rebuilding(analysis.sn)\n\n if analysis.sv:\n #Recovering the end of the sentence\n phrase = element_rebuilding.end_question_rebuilding(phrase, analysis.sv, analysis.sn, analysis.aim)\n\n #We need special processing to find the position of the subject\n if analysis.sv[0].state == VerbalGroup.negative:\n phrase = phrase[0:2] + subject + phrase[2:]\n else:\n phrase = [phrase[0]] + subject + phrase[1:]\n\n #Recovering subsentences\n for s in analysis.sv[0].vrb_sub_sentence:\n phrase = phrase + sub_process(s)\n else:\n phrase = subject\n\n #Eliminate redundancies if there are\n phrase = other_functions.eliminate_redundancy(phrase)\n\n #If it is a question about the origin\n if analysis.aim == 'origin':\n return phrase + ['from'] + ['?']\n\n return phrase + ['?']","def reformulate_query(s):\n words = tokenize(s)\n tags = [tag for _, tag in pos_tag(words)]\n\n if tags[-1] == '.':\n words.pop()\n\n # what/who questions\n if tags[0] in set(['WP', 'WDT']):\n if tags[1] in set(['VBZ', 'VBD', 'VBP']):\n if tags[-1] is not 'IN':\n exact_query = '{0}\\s*{1}\\s*{2}'.format(' '.join(words[2:]),\n '(?:\\(.*\\))?', words[1])\n inexact_query = '{0} {1}'.format(' '.join(words[2:]), words[1])\n return exact_query, inexact_query\n return s, s","def plurals(num):\r\n if num != 1:\r\n return ('s')\r\n return ('')","def simplify(expression):\n q = []\n for x in expression:\n if x != \")\":\n q.append(x)\n else:\n subexp = \"\"\n while q:\n #print(q)\n c = q.pop()\n if c == \"(\":\n if len(q) and (q[-1] == \"+\" or q[-1] == \"-\"):\n sign = q.pop()\n else:\n sign = \"+\"\n subexp = signExp(subexp, sign)\n q.append(subexp)\n break\n else:\n subexp = c + subexp\n exp = \"\"\n while q:\n c = q.pop()\n exp = c + exp\n \n if len(exp) and exp[0] != \"+\" and exp[0] != \"-\":\n # Again if the first character is not a 'sign' make it a \"+\"\n exp = \"+\" + exp\n \n return exp","def question_4(name: str) -> str:\n return \"My first name is\" + \" \" + name.capitalize()","def clean_cq(cq: str) -> str:\n normalizers = {\n r\"[-\\\"\\'”]\": \"\", # remove all dashes and quotations\n r\"\\(.*?\\)\": \"\", # remove all ( )\n r\"[ \\t]+\": \" \", # remove all multiplied whitespaces\n r\" \\?\": \"?\", # remove whitespaces before ?\n r\"a single\": \"exactly 1\",\n r\"one\": '1',\n r\"a double\": 'exactly 2',\n r\"two\": '2',\n r\"three\": '3',\n r\"four\": '4',\n \"five\": '5'\n }\n\n for regex in normalizers:\n cq = re.sub(regex, normalizers[regex], cq)\n return cq","def numerical_answer_to_str(self, answer):\r\n return str(answer.values()[0])","def __str__(self):\n return f'{self.text}: {self.chs}, correct answer: {self.solution}'","def get_str(nr_rational):\n return f'{get_numarator(nr_rational)} / {get_numitor(nr_rational)}'","def normalize_answer(text):\n return ' '.join(re.findall(r\"\\w+\", text)).lower()","def question_12(one_string: str, two_string: str) -> str:\n return one_string + two_string","def question_6(first_name: str, last_name: str) -> str:\n return \"My full name is\" + \" \" + first_name.capitalize() + \" \" + last_name.capitalize()","def Problem9():\n return 'MSS'","def format_formula(self):\n label_formula = re.sub(\"([0-9])\", \"_\\\\1\", self.formula)\n label_formula = '$\\mathregular{'+label_formula+'}$'\n return label_formula","def question_2(name: str) -> str:\n return \"Hello my name is\" + \" \" + name.capitalize()","def ascii_qvalue(q, guard=0):\r\n value, poslist, neglist = unit_string(q.number, q.units, q.prefu)\r\n numbertext = ascii_number(value, sigfig(q.number, q.uncert) + guard)\r\n if len(neglist) == 1 and neglist[0][1] == 1:\r\n negtext = \"/\" + neglist[0][0]\r\n elif neglist:\r\n negtext = \"/(%s)\" % ascii_units(neglist)\r\n else:\r\n negtext = \"\"\r\n if not poslist:\r\n if not neglist:\r\n return numbertext\r\n return numbertext + \" 1\" + negtext\r\n return numbertext + \" \" + ascii_units(poslist) + negtext","def ire_subjunctivepr_quiz(verb, pronoun):\n return functions.conjugate_subjunctivepr_ere_verb(verb, pronoun, \"congiuntivo\")","def correct_penultimate_dactyl_chain(self, scansion: str) -> str:\n mark_list = string_utils.mark_list(scansion)\n vals = list(scansion.replace(\" \", \"\"))\n n_vals = (\n vals[:-7] + [self.constants.DACTYL + self.constants.DACTYL] + [vals[-1]]\n )\n corrected = \"\".join(n_vals)\n new_line = list(\" \" * len(scansion))\n for idx, car in enumerate(corrected):\n new_line[mark_list[idx]] = car\n return \"\".join(new_line)","def ire_future_quiz(verb, pronoun):\n return functions.conjugate_future_ire_verb(verb, pronoun, \"futuro\")","def generateParenthesis(self, n):\n sol = []\n \n def dfs(cur_str, o, c):\n if o==n and o==c:\n sol.append(cur_str)\n else:\n if o < n:\n dfs(cur_str + \"(\", o + 1, c)\n if c < o:\n dfs(cur_str + \")\", o, c+1)\n dfs(\"\", 0, 0)\n return sol","def _transform(s):\n return re.subn(r\"\\^?(-?\\d)\", r\"**\\g<1>\", s)[0]","def get_answer_phrase(question_sentence, answer_sentence):\n # TODO: UNCOMMENT TRY/CATCH BLOCK!\n try:\n question_sentence = remove_punctuation(question_sentence)\n answer_sentence = remove_punctuation(answer_sentence)\n\n question = formulate_question(question_sentence)\n answer = get_sentence(answer_sentence)\n\n # todo!!!!\n if question['qword'][0].lower() in [\"what\", \"which\"]:\n best_phrase = None\n for subtree in [\n tree.subtrees() for tree in\n get_parse_trees_with_tag(answer_sentence, \"NP\") +\n get_parse_trees_with_tag(answer_sentence, \"NX\")\n ]:\n for tree in subtree:\n baseline = text_analyzer.sentence_similarity(question_sentence, \" \".join(tree.leaves()))\n\n baseline = text_analyzer.sentence_similarity(question_sentence)\n\n elif question['qword'][0].lower() == \"when\":\n # get prepositional phrases\n prep_nodes = [d for d in answer.get_nodes if d['tag'] == \"prep\"]\n if prep_nodes:\n # todo: should this be the uppermost node (which'll be [0], always)?\n top_prep_string = \" \".join([x[0] for x in prep_nodes[0].get_pairs])\n if num_occurrences_time_regex(top_prep_string) > 0:\n return top_prep_string\n\n # todo: find a way to use my dependency parse here?\n prep_phrases = [x.leaves() for x in get_parse_trees_with_tag(answer_sentence, \"PP\")]\n\n if prep_phrases:\n return to_sentence(\n max(\n prep_phrases, key=lambda x: num_occurrences_time_regex(x)\n )\n )\n else:\n # todo: perhaps reconsider which one to return here. sentence length may be the wrong idea.\n if prep_phrases:\n return to_sentence(max(prep_phrases, key=lambda x: len(x)))\n\n elif question['qword'][0].lower() == \"where\":\n answer_chunks = get_top_ner_chunk_of_each_tag(answer_sentence, {\"GPE\"})\n\n untagged = [\n tagged[0][1] for tagged in [\n answer_chunks[tag] for tag in answer_chunks\n ]\n ]\n\n # get_dep_trees_with_tag(answer, \"prep\")\n prep_phrases = [tree.leaves() for tree in get_parse_trees_with_tag(answer_sentence, \"PP\")]\n\n # todo: strip preposition (e.g. \"in\") out of the answer\n if prep_phrases:\n return to_sentence(max(\n prep_phrases,\n key=lambda x: calculate_overlap(x, untagged, False)\n ))\n\n elif question['qword'][0].lower() in [\"who\", \"whose\", \"whom\"]:\n question_chunks = get_top_ner_chunk_of_each_tag(question_sentence)\n answer_chunks = get_top_ner_chunk_of_each_tag(answer_sentence)\n\n # todo: try something with checking the question tag with the answer tag\n # todo: consider stripping out the part of the answer with question entity in it...?\n\n untagged = [\n tagged[0][1] for tagged in [\n answer_chunks[tag] for tag in answer_chunks\n ]\n ]\n\n # todo: figure out what to do if not untagged\n if untagged:\n return to_sentence(max(untagged, key=lambda x: len(x)))\n\n elif question['qword'][0].lower() == \"why\":\n # q_verb = question.tuple\n # a_verb = answer.tuple\n\n parse_tree = next(CoreNLPParser().raw_parse(answer_sentence))\n to_vp_phrases = []\n prev_was_to = False\n for tree in parse_tree.subtrees():\n if tree.label() == \"VP\":\n for subtree in tree.subtrees():\n if prev_was_to:\n to_vp_phrases.append(subtree)\n prev_was_to = False\n elif subtree.label() == \"TO\":\n prev_was_to = True\n # todo: potentially strip out \"to\", and might consider including object?\n # todo: honestly, might just pick out things after \"to\"\n # if to_vp_phrases:\n # return to_sentence(min(\n # [tree.leaves() for tree in to_vp_phrases],\n # key=lambda x: calculate_overlap(to_vp_phrases, x)\n # ))\n\n # todo: finish debugging\n # vp_phrases = get_parse_trees_with_tag(answer_sentence, \"VP\")\n # to_phrases = []\n # if to_phrases:\n # return to_sentence(max(\n # to_phrases,\n # key=lambda x: len([])\n # ))\n\n # todo: soup up this absolute trash\n for i, word in enumerate(answer_sentence.split()):\n if word in [\"to\", \"so\", \"because\"]:\n return to_sentence(answer_sentence.split()[:i])\n\n # todo: try things with conjunctions, potentially? test.\n # conj_phrases = [tree.leaves() for tree in get_parse_trees_with_tag(answer_sentence, \"PP\")]\n\n elif question['qword'][0].lower() == \"how\":\n # TODO: look at \"QP\" parse tag for this!\n if any([\n # 'advmod' in [\n # pair[1] for pair in [\n # node.get_pairs[1] for node in question.get_nodes if node['tag'][0].lower() == 'w'\n # ]\n # ],\n get_parse_trees_with_tag(question_sentence, \"WHADJP\"),\n re.search(r\"much|many|tall|long\", question_sentence)\n ]):\n qp_phrases = get_parse_trees_with_tag(answer_sentence, \"QP\")\n if qp_phrases:\n return to_sentence(min(\n [tree.leaves() for tree in qp_phrases],\n key=lambda x: num_occurrences_quant_regex(x)\n ))\n\n # todo: non-measure cases! (mostly thinking about \"how did/does\")\n\n except:\n pass","def format_minimized_expression(prime_implicants):\n if not prime_implicants: # If no prime implicants, return 0\n return '0'\n elif prime_implicants[0] == '-' * len(prime_implicants[0]): # If all dashes, return 1\n return '1'\n\n result = ''\n for iteration, prime_implicant in enumerate(prime_implicants):\n for index, letter in enumerate(prime_implicant):\n if letter == '1':\n result += chr(ord('A') + index)\n elif letter == '0':\n result += chr(ord('A') + index) + \"'\"\n if iteration != (len(prime_implicants) - 1): # Check to see if last prime_implicant\n result += ' + '\n\n return result","def _normalize_hint_expression(hint_expression, hint_name):\n hdict = _extract_hint_operator_and_values(hint_expression, hint_name)\n result = hdict['op'].join([' %s ' % urlparse.quote(t)\n for t in hdict['values']])\n return (hdict['op'] + result).strip()","def fn(i, s=\"\", n=0):\n if i == len(word): return ans.append(s + (str(n) if n else \"\"))\n fn(i+1, s, n+1)\n fn(i+1, s + (str(n) if n else \"\") + word[i], 0)","def do(s):\r\n return get_AA_subs(generate_mutString(s))","def give_short():\n short = input('What DNA sequence would you like to match? ')\n short = short.upper()\n return short","def oversimplify(strng):\n s = strng.encode('latex').decode('utf-8')\n s = reg_simplify.sub('', s)\n return s","def simplify(phrase):\n list_phrase = phrase.split()\n \n first_noun = list_phrase[0:2]\n first_verb = list_phrase[-1]\n rest_list_phrase = list_phrase [2:-1]\n \n rest_phrase = phrase[len(first_noun[0] + first_noun[1]) + 2 :\n (-1 * len(first_verb)) - 1]\n #We calculate the length of the first two words and add 2 for the\n #number of spaces to start from the beginning of the rest of the phrase\n #and end at the beginning of the space before the last word by\n #subtracting the length of the last word by 1.\n \n #This is done because we are dealing with lists and strings concurrently.\n \n if len(rest_phrase) == 0:\n return first_noun[0] + \" \" + first_noun[1]\n \n return (simplify(rest_phrase) + \" that \" + first_verb + \" \" +\n first_noun[0] + \" \" + first_noun[1])","def createAnswer(self, input):\n\n input = input.strip()\n\n if len(input) == 1:\n if input in self.guessedChars:\n return \"Oled juba tähte \" + input + \" pakkunud. Paku midagi muud. \\nHetkel proovitud \" + ' '.join(\n self.guessedChars) + \"\\n\" + self.wordKnown\n else:\n self.addChar(input)\n if self.isWordSet():\n return self.answerIsSet(input)\n else:\n self.filterDict(input)\n if self.isWordSet():\n return self.answerIsSet(input)\n else:\n return \"Kahjuks tähte \" + input + \" sõnas ei ole. Vaja veel \" + str(\n self.wordKnown.count(\"_\")) + \" ära arvata. \\nHetkel proovitud \" + ' '.join(\n self.guessedChars) + \" \\n\" + self.wordKnown\n elif input == \"\":\n return \"Võiks midagi ikka sisestada ka...\\nHetkel proovitud \" + ' '.join(\n self.guessedChars) + \" \\n\" + self.wordKnown\n else:\n if input == \"aitab\":\n self.active = False\n return \"Kui aitab siis aitab. Sõna, mida ma mõtlesin, ma sulle ikkagi ei ütle. Jäägu see elu lõpuni \" \\\n \"Sind piinama.\"\n if self.word == input:\n self.active = False\n return \"Arvasid ära, mõtlesin tõesti sõna \" + self.word + \".\"\n else:\n self.removeWordFromDict(input)\n return \"Ei, ma kohe kindlasti ei mõelnud sõna \" + input + \"... Proovi veel. \\nHetkel proovitud \" \\\n \"\" \\\n \"\" \\\n \"\" + ' '.join(self.guessedChars) \\\n + \" \\n\" + self.wordKnown","def inask(question: str) -> str:\n answer = input(question)\n return answer","def mystery2(s):\r\n if len(s) <= 1:\r\n return s\r\n else:\r\n result_rest = mystery2(s[1:])\r\n if s[0] == s[-1]:\r\n return result_rest\r\n else:\r\n return result_rest + s[0]","def logic_program_form(self):\r\n s = ''\r\n return s","def ere_subjunctivepr_quiz(verb, pronoun):\n return functions.conjugate_subjunctivepr_ere_verb(verb, pronoun, \"congiuntivo\")","def scinot(string):\n\t\"\"\"If there is no '+' or '-' character in string, returns it as it is.\"\"\"\n\t\"\"\"If the argument is not string, returns the argument\"\"\"\n\tif type(string) != str:\n\t\treturn string\n\telse:\n\t\tretstr = string[0]\n\t\tfor char in string[1:]:\n\t\t\tif ((char == '-')|(char == '+')):\n\t\t\t\tretstr += 'E' + char\n\t\t\telse:\n\t\t\t\tretstr += char\n\t\t\n\t\treturn retstr","def lemma(self) -> str:","def parse_simple_eqn(equation=\"\"):\n # Define replacement rules.\n simple_replacements = [[' ', ''],\n ['**', '^'], ['*', ' \\\\cdot '],\n ['math.', ''], ['np.', ''],\n ['pi', '\\\\pi'] , ['tan', '\\\\tan'],\n ['cos', '\\\\cos'], ['sin', '\\\\sin'],\n ['sec', '\\\\sec'], ['csc', '\\\\csc']]\n complex_replacements = [['^', '{{{i1}}}^{{{i2}}}'],\n ['_', '{{{i1}}}_{{{i2}}}'],\n ['/', '\\\\frac{{{i1}}}{{{i2}}}'],\n ['sqrt','\\\\sqrt{{{i2}}}']]\n # Carry out simple replacements\n for pair in simple_replacements:\n equation = equation.replace(pair[0], pair[1])\n # Now complex replacements\n for item in ['*', '/', '+', '-', '^', '_', ',', 'sqrt']:\n equation = equation.replace(item, ' ' + item + ' ')\n q_split = equation.split()\n for index, item in enumerate(q_split):\n for pair in complex_replacements:\n if item == pair[0]:\n if item == 'sqrt':\n match_str = \" \".join(q_split[index:index+2])\n else:\n match_str = \" \".join(q_split[index-1:index+2])\n equation = equation.replace(match_str, pair[1].format(\n i1=q_split[index-1], i2=q_split[index+1]))\n return equation","def question1(s, t):\n if s is None or t is None:\n return \"Error: Please enter valid strings\"\n len_s = len(s)\n len_t = len(t)\n if len_s == 0 or len_s < len_t:\n return \"Error: Length of s cannot be smaller than length of t\"\n s = s.lower()\n t = t.lower()\n if len_t == 0:\n return True\n t = sorted(t)\n for i in range(len_s - len_t + 1):\n sub_s = s[i:i + len_t]\n if sorted(sub_s) == t:\n return True\n return False","def replaceMultiQuestionMark(text):\n text = re.sub(r\"(\\?)\\1+\", ' multiQuestion ', text)\n return text","def dispNextQuestion(q):\r\n print '\\n'*100\r\n print q.name+'\\n'+'-'*60+'\\n'+q.prompt+'\\n '+q.q\r\n\r\n for i, a in enumerate(q.options):\r\n print str(i+1)+')',a\r\n \r\n guess = raw_input('\\nEnter the number for the answer: ')\r\n return str(int(guess)-1)","def answer(question: str) -> int:\n phrases = split(r' (?!\\bby\\b)', question.replace('?', ''))\n length = len(phrases)-1\n\n if not any(word in OPERATIONS for word in phrases):\n try:\n return int(phrases[-1])\n except TypeError:\n raise ValueError('No numbers and/or operators in question.')\n\n stack = []\n\n for i, word in enumerate(phrases):\n if word.isdigit() and i != length and phrases[i+1] not in OPERATIONS:\n raise ValueError('Missing operation.')\n elif word not in OPERATIONS:\n continue\n\n try:\n a, b = stack[-1] if stack else int(phrases[i-1]), int(phrases[i+1])\n except (TypeError, IndexError):\n raise ValueError('Numbers not in valid order.')\n\n stack.append(OPERATIONS[word](a, b))\n\n return stack.pop()","def fmt(cls, n):\n return ''.join(c for c in n if c in cls.ALLOWED).lower()","def refrm(s):\n s2 = s[5:10] + s[4] + s[0:4]\n return s2","def rewritten_queries(question):\n # rewrites = []\n # tq = tokenize(question)\n # verb = tq[1] # 目前只处理 谁是XXX类 问题\n # rewrites.append(\n # RewrittenQuery(\"\\\"%s %s\\\"\" % (verb, \" \".join(tq[2:])),\n # QUOTED_QUERY_SCORE))\n # for j in range(2, len(tq)):\n # rewrites.append(\n # RewrittenQuery(\n # \"\\\"%s %s %s\\\"\" % (\n # \" \".join(tq[2:j + 1]), verb, \" \".join(tq[j + 1:])),\n # QUOTED_QUERY_SCORE))\n # rewrites.append(RewrittenQuery(\" \".join(tq[2:]), UNQUOTED_QUERY_SCORE))\n return [RewrittenQuery(question, 1)]","def question_13(one_string: str, two_string: str) -> str:\n return one_string.capitalize() + two_string.capitalize()","def tactic_simplify(tactic):\n names = re.split(\"[\\;\\.\\ \\(\\)]\", tactic)\n return names[0]","def __str__(self):\n if not self.b:\n return str(self.a)\n elif self.b==1:\n if not self.a:\n return 'sqrt(%s)' % (self.r)\n else:\n return '(%s + sqrt(%s))' % (self.a, self.r)\n else:\n if not self.a:\n return '(%s * sqrt(%s))' % (self.b, self.r)\n str_b = str(self.b)\n if str_b == '-1':\n str_b = ' - '\n elif str_b.startswith('-'):\n str_b = '- ' + str_b[1:] + ' *'\n else:\n str_b = '+ ' + str_b + ' *'\n return '(%s %s sqrt(%s))' % (self.a, str_b, self.r)","def __str__(self):\n termStrings = []\n for term in self.LHS:\n coefficient = term[0]\n unknownSet = term[1]\n\n termString = str(coefficient) + ' * '\n unknownStrings = []\n for unknown in unknownSet:\n unknownString = unknown[0].__class__.__name__ + '@' + str(id(unknown[0]))[-4:] + '.' + unknown[1] # last 4 digits of variable ID . attribute name\n unknownStrings.append(unknownString)\n termString += str.join(' * ', unknownStrings)\n termStrings.append(termString)\n\n termStrings = str.join(' + ', termStrings)\n return termStrings + ' = ' + str(self.RHS)","def SolveInput(s, n):\n\n if len(s) == 0:\n return\n elif s[0] == \"\":\n SolveInput(s[1:], n)\n else:\n result = SolveLine(list(filter(lambda a: a not in ['(', ')', ','],\n LexLine(s[0].replace(' ', '')) or [])), 0, n)\n if result and not type(result) == str:\n # def TestInput(solved, inputString):\n # \"\"\"Evaluates input using the python interpreter directly. Used\n # exclusively for testing/debugging purposes to validate output\n # correctness.\n # \"\"\"\n # def multiply(a, b):\n # return a * b\n # def add(a, b):\n # return a + b\n # correctness = solved == str(eval(inputString))\n # return solved + \" \" + str(correctness)\n # print (s[0], \"=\", TestInput(InfIntToStr(result, 0, n), s[0]))\n print (s[0], \"=\", InfIntToStr(result, 0, n))\n else:\n print (\"Invalid expression:\", s[0])\n SolveInput(s[1:], n)","def transform_one_sentence(sentence):\n words = list(map(str, nlp(sentence)))\n return ' '.join(list(map(spell.correction, words)))","def generateParenthesis(self, n: int) -> List[str]:\n\n def generate(result=[]):\n\n if len(result) == n * 2:\n # check if valid\n if is_valid(result):\n answer.append(\"\".join(result))\n\n else:\n result.append('(')\n generate(result)\n result.pop()\n result.append(')')\n generate(result)\n result.pop()\n\n def is_valid(result):\n balanced = 0\n\n for char in result:\n if char == '(':\n balanced += 1\n else:\n balanced -= 1\n\n if balanced < 0: return False\n return balanced == 0\n\n answer = []\n generate()\n return answer","def calc_conservation_string(aln):\n\n percids = calc_conservation(aln)\n\n # find identity positions\n identity = \"\"\n for pid in percids:\n if pid == 1:\n identity += \"*\"\n elif pid > .5:\n identity += \".\"\n else:\n identity += \" \"\n\n return identity","def normalize(q_1: Q, n: float = 1.0, q_type: str = \"U\") -> Q:\n\n end_q_type = f\"{q_1.q_type} {q_type}\"\n\n abs_q_inv = inverse(abs_of_q(q_1))\n n_q = product(product(q_1, abs_q_inv), Q([n, 0, 0, 0]))\n n_q.q_type = end_q_type\n n_q.representation = q_1.representation\n\n return n_q","def simplify(self, expr):\n return sy.simplify(expr)","def text_of_quotation(exp):\n return cadr(exp)","def test_str(self):\n q0 = sym.Symbol(\"q0\")\n q2 = sym.Symbol(\"q2\")\n f = (q0 + q2) / np.sqrt(2)\n\n rrt = RegRefTransform(f)\n\n assert rrt.__str__() == \"0.707106781186547*q0 + 0.707106781186547*q2\"","def answer_key(cls, input_num=2):\r\n return (\r\n \"%s_%d_1\" % (\r\n \"-\".join(['i4x', 'edX', 'capa_test', 'problem', 'SampleProblem%d' % cls.num]),\r\n input_num,\r\n )\r\n )","def simplify_chunks(self, input_text):\n string = ''\n chunks = re.split(r'([\\w\\.-]+|[\\(\\)\\*\\+])', input_text)\n chunks = [chunk.strip() for chunk in chunks]\n chunks = [chunk for chunk in chunks if chunk != '']\n\n classifiers = [\n 'is_integer', 'is_float', 'is_operator', 'is_constant', 'is_function'\n ]\n\n for chunk in chunks:\n for classifier in classifiers:\n result = getattr(self, classifier)(chunk)\n if result is not False:\n string += str(result) + ' '\n break\n\n # Replace '^' with '**' to evaluate exponents\n string = string.replace('^', '**')\n\n return string","def substitute_words(self, string):\n condensed_string = '_'.join(string.split())\n\n for word in self.math_words['words']:\n condensed_string = re.sub(\n '_'.join(word.split(' ')),\n self.math_words['words'][word],\n condensed_string\n )\n\n for number in self.math_words['numbers']:\n condensed_string = re.sub(\n number,\n str(self.math_words['numbers'][number]),\n condensed_string\n )\n\n for scale in self.math_words['scales']:\n condensed_string = re.sub(\n '_' + scale,\n ' ' + self.math_words['scales'][scale],\n condensed_string\n )\n\n condensed_string = condensed_string.split('_')\n for chunk_index in range(0, len(condensed_string)):\n value = ''\n\n try:\n value = str(eval(condensed_string[chunk_index]))\n\n condensed_string[chunk_index] = value\n except:\n pass\n\n for chunk_index in range(0, len(condensed_string)):\n condensed_chunk = condensed_string[chunk_index]\n if self.is_integer(condensed_chunk) or self.is_float(condensed_chunk):\n i = 1\n start_index = chunk_index\n end_index = -1\n while (chunk_index + i < len(condensed_string) and (self.is_integer(condensed_string[chunk_index + i]) or self.is_float(condensed_string[chunk_index + i]))):\n end_index = chunk_index + i\n i += 1\n\n for sub_chunk in range(start_index, end_index):\n condensed_string[sub_chunk] += ' +'\n\n condensed_string[start_index] = '( ' + condensed_string[start_index]\n condensed_string[end_index] += ' )'\n\n return ' '.join(condensed_string)","def get_star(self, index):\n ansNr = 1 # default answers (for multiple ones)\n chunk = \"(Choose \"\n firstTag = self.data.find(\"<\", index)\n chunkIndex = self.data.find(chunk, index, firstTag)\n if chunkIndex != -1:\n chunkIndex += len(chunk)\n number = \"\"\n while self.data[chunkIndex] != \")\":\n number += self.data[chunkIndex]\n chunkIndex += 1\n number = number.strip().lower()\n if number == \"two\":\n ansNr = 2\n elif number == \"three\":\n ansNr = 3\n elif number == \"four\":\n ansNr = 4 # i don't think this actually exists :)\n star = \"(*)\"\n last = index\n ans = \"\"\n while ansNr:\n index = self.data.find(star, last) - 1\n last = index + 1 + len(star)\n if index < 0:\n return False # invalid answer type\n # ok now we're good\n old = index\n while True:\n tag = True\n while self.data[index] != \">\":\n if tag and self.data[index].isspace():\n index -= 1\n continue\n if tag:\n ans = \"\\n\" + ans\n tag = False # found alphanumeric\n ans = self.data[index] + ans\n index -= 1\n if not self.check(old, index):\n return False\n ans = ans.strip()\n if tag and len(ans) > 0:\n break\n while self.data[index] != \"<\":\n index -= 1\n index -= 1\n ansNr -= 1\n ans = \"\\n\" + ans\n return ans.strip()","def end_question_rebuilding(sentence, sv, sn, aim):\n\n #Recovering the verb in the correct form\n phrase = vrb_ques_rebuilding(sv[0].vrb_tense, sv[0].vrb_main, sv[0].vrb_adv, sn, sv[0].state, aim)\n\n #We add the direct and indirect complement\n if sv[0].i_cmpl != [] and sv[0].i_cmpl[0].prep != []:\n phrase = phrase + nom_struc_rebuilding(sv[0].d_obj)\n for x in sv[0].i_cmpl:\n phrase = phrase + indirect_compl_rebuilding(x)\n else:\n if sv[0].i_cmpl:\n phrase = phrase + indirect_compl_rebuilding(sv[0].i_cmpl[0])\n phrase = phrase + nom_struc_rebuilding(sv[0].d_obj)\n\n #init\n x = 1\n while x < x < len(sv[0].i_cmpl):\n phrase = phrase + indirect_compl_rebuilding(sv[0].i_cmpl[x])\n x += 1\n\n flag = 0\n for j in ResourcePool().verb_need_to:\n if sv[0].vrb_main == [j]:\n flag = 1\n\n #If there is a second verb\n for k in sv[0].sv_sec:\n phrase = scd_vrb_rebuilding(k, phrase, flag)\n\n #We add the adverb of the sentence\n phrase = phrase + sv[0].advrb\n return sentence + phrase","def wrongtxt(answer, is_code=False):\n if not isinstance(answer, basestring):\n answer = repr(answer)\n def dec(func):\n func = _multiple_choice_passthrough(func)\n func.answers.append(Answer(answer, correct=False, is_code=is_code))\n func = _checkbox_if_multiple_answers_passthrough(func)\n func = _multiple_choice_if_wrongs_passthrough(func)\n return func\n return dec","def sentence(self) -> str:\n id_word = {nodeattr['position']-1: nodeattr['form']\n for nodeid, nodeattr in self.syntax_nodes.items()}\n return ' '.join([id_word[i] for i in range(max(list(id_word.keys()))+1)])","def get_equation(self):\n self.polynomials = dict(sorted(self.polynomials.items(), reverse=True))\n string = \"\"\n\n for index, polynomial in self.polynomials.items():\n polynomial = int(polynomial)\n index = int(index)\n\n if polynomial != 0:\n if polynomial < 0:\n string_pre = \" - \"\n else:\n string_pre = \" + \"\n\n if index != 0:\n string_append = \"x\"\n elif polynomial == 1 or polynomial == -1:\n string_append = str(abs(polynomial))\n else:\n string_append = \"\"\n\n if polynomial < 0:\n polynomial = abs(polynomial)\n\n if polynomial != 1:\n string_append = str(polynomial) + string_append\n\n if index != 0 and index != 1:\n string_append += \"^\" + str(index)\n\n string += string_pre + string_append\n\n if len(string) > 0:\n string = string[3:]\n else:\n string = \"0\"\n\n return string","def get_answer(s, i, j, res: list):\n if i == j:\n res.append('a[{}]'.format(i-1))\n else:\n res.append('(')\n get_answer(s, i, s[i][j], res)\n get_answer(s, s[i][j]+1, j, res)\n res.append(')')","def phred(q):\n n = int(q * 30 + 33)\n if n == 43:\n n += 1\n if n == 58:\n n += 1\n return chr(n)","def metaphlan_krona_string(input):\n s = []\n for f in input:\n name = bn(f).replace(\"_pe.krona\", \"\").replace(\"_se.krona\", \"\")\n s.append(f\"{f},{name}\")\n return \" \".join(s)"],"string":"[\n \"def normalize_answer(s):\\n\\n def remove_articles(text):\\n return re_art.sub(' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n def remove_punc(text):\\n return re_punc.sub(' ', text) # convert punctuation to spaces\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize_answer(s):\\n\\n def remove_articles(text):\\n return re_art.sub(' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n def remove_punc(text):\\n return re_punc.sub(' ', text) # convert punctuation to spaces\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def clean_question(q):\\n q = str(q)\\n q = q.lower() # lowercase\\n q = re.sub(r\\\"what's\\\", \\\"what is \\\", q)\\n q = re.sub(r\\\"\\\\'ve\\\", \\\" have \\\", q)\\n q = re.sub(r\\\"can't\\\", \\\"cannot \\\", q)\\n q = re.sub(r\\\"n't\\\", \\\" not \\\", q)\\n q = re.sub(r\\\"i'm\\\", \\\"i am \\\", q)\\n q = re.sub(r\\\"\\\\'re\\\", \\\" are \\\", q)\\n q = re.sub(r\\\"\\\\'d\\\", \\\" would \\\", q)\\n q = re.sub(r\\\"\\\\'ll\\\", \\\" will \\\", q)\\n q = re.sub(r\\\"[^A-Za-z0-9]\\\", \\\" \\\", q)\\n q = TextBlob(q).tokens #tokenize\\n return q\",\n \"def normalize_answer(s):\\n\\tdef remove_articles(text):\\n\\t\\treturn re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\n\\n\\tdef white_space_fix(text):\\n\\t\\treturn ' '.join(text.split())\\n\\n\\tdef remove_punc(text):\\n\\t\\texclude = set(string.punctuation)\\n\\t\\treturn ''.join(ch for ch in text if ch not in exclude)\\n\\n\\tdef lower(text):\\n\\t\\treturn text.lower()\\n\\n\\treturn white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def simplify_ques(question, debug=False):\\n if not isinstance(question, str):\\n raise TypeError('Please provide a string for a question.')\\n\\n if debug is True:\\n load_json()\\n\\n neg = False\\n qwords = question.lower().split()\\n for i in qwords:\\n if i in NEGATIVE_WORDS:\\n neg = True\\n\\n cleanwords = [word for word in qwords if word.lower() not in REMOVE_WORDS]\\n temp = ' '.join(cleanwords)\\n\\n clean_question = \\\"\\\"\\n for letter in temp:\\n if letter != \\\"?\\\" or letter != \\\"\\\\\\\"\\\" or letter != \\\"\\\\'\\\":\\n clean_question = clean_question + letter\\n\\n return clean_question.lower(), neg\",\n \"def normalize_answer(s):\\n\\n def remove_articles(text):\\n return re.sub(r\\\"\\\\b(a|an|the)\\\\b\\\", \\\" \\\", text)\\n\\n def white_space_fix(text):\\n return \\\" \\\".join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return \\\"\\\".join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize_answer(s):\\n\\n def remove_articles(text):\\n return re.sub(r\\\"\\\\b(a|an|the)\\\\b\\\", \\\" \\\", text)\\n\\n def white_space_fix(text):\\n return \\\" \\\".join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return \\\"\\\".join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize_answer(s):\\n\\n def remove_articles(text):\\n regex = re.compile(r'\\\\b(a|an|the)\\\\b', re.UNICODE)\\n return re.sub(regex, ' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return ''.join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize_answer(s):\\n\\n def remove_articles(text):\\n return re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return ''.join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize_answer(s):\\n def remove_articles(text):\\n return re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return ''.join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize_answer(s):\\n def remove_articles(text):\\n regex = re.compile(r'\\\\b(a|an|the)\\\\b', re.UNICODE)\\n return re.sub(regex, ' ', text)\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return ''.join(ch for ch in text if ch not in exclude)\\n def lower(text):\\n return text.lower()\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def _normalize_answer(s):\\n\\n def remove_articles(text):\\n return re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return ''.join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize_answer(s):\\n\\n def remove_articles(text):\\n return re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return ''.join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(normalize(s)))))\",\n \"def question_3(name: str) -> str:\\n return \\\"My first name is\\\" + \\\" \\\" + name\",\n \"def formula_answer_to_str(self, answer):\\r\\n return str(answer.values()[0])\",\n \"def simplify(word, lexicon):\\n\\tsim = random.choice(lexicon[word])[1]\\n\\tif type(sim) == str:\\n\\t\\tsim = [sim]\\n\\treturn \\\" \\\".join(sim)\",\n \"def question_new_translate():\",\n \"def normalize_answer(s):\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return \\\"\\\".join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return remove_punc(lower(s))\",\n \"def reducedFormOne(self, equation):\\n splitter = re.split('(\\\\+|\\\\-)', equation)\\n newEquation = str()\\n state = 0\\n for token in splitter:\\n if '(' in token or state > 0 or '[' in token:\\n state += 1\\n newEquation += token\\n continue\\n if ')' in token or ']' in token:\\n state -= 1\\n continue\\n if '^' + self.var in token:\\n newEquation += token\\n self.validPolynome = False\\n continue\\n find = re.findall('(\\\\*|\\\\^|\\\\/)?(' + self.var + ')(\\\\^\\\\d+)?' , token)\\n newVar = []\\n for var in find:\\n newVar.append(''.join(map(str,var)))\\n for var in newVar:\\n token = token.replace(var, '')\\n if token != '+' and token != '-' and token != '':\\n try:\\n newEquation += str(eval(token.replace('^', '**'))) + ''.join(newVar)\\n except:\\n self.error = True\\n continue\\n else:\\n newEquation += token\\n return newEquation\",\n \"def question_1(name: str) -> str:\\n return \\\"Hello my name is\\\" + \\\" \\\" + name\",\n \"def question(phrase):\\n\\n if phrase[-1] == '?':\\n return 'Sure.'\",\n \"def sanitize(formula):\\n TR_UNICODE = {u\\\"·\\\": u\\\"*\\\", u\\\"−\\\": u\\\"-\\\"}\\n return \\\"\\\".join(TR_UNICODE.get(ch, ch) for ch in formula if ch > \\\" \\\")\",\n \"def possession_ques(analysis):\\n\\n #processing as statement\\n phrase = statement(analysis)\\n\\n #We have to know if it is plural or singular\\n if other_functions.plural_noun(analysis.sn) == 1:\\n return ['whose'] + phrase[:len(phrase) - 1] + ['these'] + ['?']\\n else:\\n return ['whose'] + phrase[1:len(phrase) - 1] + ['this'] + ['?']\",\n \"def question_5(first_name: str, last_name: str) -> str:\\n return \\\"My full name is\\\" + \\\" \\\" + first_name + \\\" \\\" + last_name\",\n \"def solution(s):\",\n \"def w_question(analysis):\\n if analysis.sv:\\n #Opinion is a what question so we have to make some changes\\n if analysis.sv[0].vrb_main[0].endswith('like'):\\n verb = analysis.sv[0].vrb_main[0]\\n analysis.sv[0].vrb_main[0] = verb[:len(verb) - 4] + 'think+of'\\n\\n #processing as yes or no question\\n phrase = y_o_question(analysis)\\n\\n #Specific processing for invitation\\n if analysis.aim == 'invitation':\\n return ['how', 'about'] + phrase[1:]\\n\\n #Specific processing for classification\\n if analysis.aim.startswith('classification'):\\n aim_question = other_functions.list_rebuilding(analysis.aim)\\n return ['what', 'kind', 'of'] + aim_question[1:] + phrase\\n\\n #It is an how question\\n if other_functions.is_an_adj(analysis.aim) == 1:\\n return ['how'] + [analysis.aim] + phrase\\n elif analysis.aim == 'manner':\\n return ['how'] + phrase\\n\\n if analysis.aim == 'thing' or analysis.aim == 'situation' or analysis.aim == 'explication' or analysis.aim == 'opinion':\\n return ['what'] + phrase\\n return ['what'] + [analysis.aim] + phrase\",\n \"def short_form(self):\\n return normalize_pred_string(self.string)\",\n \"def solution(s):\\n # check the input string is a valid length\\n min_length = 1\\n max_length = 101\\n\\n if len(s) < min_length or len(s) > max_length:\\n return ''\\n\\n # need to handle integers between 1 and 10^100, so up to 101 decimal places of precision\\n # The Python default is 28 significant figures\\n # https://www.geeksforgeeks.org/setting-precision-in-python-using-decimal-module/\\n getcontext().prec = max_length\\n\\n n = int(s)\\n alpha = Decimal(2).sqrt()\\n sequence = beatty_sequence(alpha, n)\\n\\n return str(int(sequence))\",\n \"def quick_quiz(character_set):\",\n \"def command_to_latex(q, index=0):\\n open_index, close_index = -1, -1\\n for q_index, i in enumerate(q):\\n if i == '(':\\n open_index = q_index\\n elif i == ')':\\n close_index = q_index\\n break\\n if open_index != -1:\\n o = q[:open_index] + '@' + str(index) + q[close_index + 1:]\\n m = q[open_index + 1:close_index]\\n o_tex = command_to_latex(o, index + 1)\\n m_tex = command_to_latex(m, index + 1)\\n # Clean up redundant parentheses at recombination\\n r_index = o_tex.find('@' + str(index))\\n if o_tex[r_index - 1] == '{':\\n return o_tex.replace('@'+str(index), m_tex)\\n else:\\n return o_tex.replace('@'+str(index),\\n ' \\\\\\\\left (' + m_tex + ' \\\\\\\\right )')\\n else:\\n return parse_simple_eqn(q)\",\n \"def question(func):\\n return FillInTheBlank(_text_from_func(func), correct=Answer(repr(func()), is_code=True), is_code=True)\",\n \"def format_answer(answer):\\n # a regular expression matching the predicted outcome\\n # by timbl.\\n pattern = re.compile(r'CATEGORY \\\\{([^}]+)\\\\}')\\n answer = pattern.search(answer)\\n return answer and answer.group(1) or '0'\",\n \"def simplify(phrase):\\n \\n num_actors = 0\\n \\n if phrase == \\\"\\\":\\n return 0\\n \\n phrase_list = phrase.split()\\n\\n for word in phrase_list:\\n if word == \\\"the\\\":\\n num_actors += 1\\n \\n index = (2 * num_actors) - 1\\n actor = phrase_list[index]\\n verb = phrase_list[index + 1]\\n \\n del phrase_list[index]\\n del phrase_list[index - 1]\\n del phrase_list[index + 1]\\n \\n phrase_string = \\\"\\\"\\n for word in phrase_list:\\n phrase_string += word\\n phrase_string += \\\" \\\"\\n \\n \\n return \\\"the\\\" + actor + \\\"that\\\" + verb + simplify(phase_string)\",\n \"def y_o_question(analysis):\\n\\n #init\\n phrase = []\\n\\n #Recovering the subject\\n subject = element_rebuilding.nom_struc_rebuilding(analysis.sn)\\n\\n if analysis.sv:\\n #Recovering the end of the sentence\\n phrase = element_rebuilding.end_question_rebuilding(phrase, analysis.sv, analysis.sn, analysis.aim)\\n\\n #We need special processing to find the position of the subject\\n if analysis.sv[0].state == VerbalGroup.negative:\\n phrase = phrase[0:2] + subject + phrase[2:]\\n else:\\n phrase = [phrase[0]] + subject + phrase[1:]\\n\\n #Recovering subsentences\\n for s in analysis.sv[0].vrb_sub_sentence:\\n phrase = phrase + sub_process(s)\\n else:\\n phrase = subject\\n\\n #Eliminate redundancies if there are\\n phrase = other_functions.eliminate_redundancy(phrase)\\n\\n #If it is a question about the origin\\n if analysis.aim == 'origin':\\n return phrase + ['from'] + ['?']\\n\\n return phrase + ['?']\",\n \"def reformulate_query(s):\\n words = tokenize(s)\\n tags = [tag for _, tag in pos_tag(words)]\\n\\n if tags[-1] == '.':\\n words.pop()\\n\\n # what/who questions\\n if tags[0] in set(['WP', 'WDT']):\\n if tags[1] in set(['VBZ', 'VBD', 'VBP']):\\n if tags[-1] is not 'IN':\\n exact_query = '{0}\\\\s*{1}\\\\s*{2}'.format(' '.join(words[2:]),\\n '(?:\\\\(.*\\\\))?', words[1])\\n inexact_query = '{0} {1}'.format(' '.join(words[2:]), words[1])\\n return exact_query, inexact_query\\n return s, s\",\n \"def plurals(num):\\r\\n if num != 1:\\r\\n return ('s')\\r\\n return ('')\",\n \"def simplify(expression):\\n q = []\\n for x in expression:\\n if x != \\\")\\\":\\n q.append(x)\\n else:\\n subexp = \\\"\\\"\\n while q:\\n #print(q)\\n c = q.pop()\\n if c == \\\"(\\\":\\n if len(q) and (q[-1] == \\\"+\\\" or q[-1] == \\\"-\\\"):\\n sign = q.pop()\\n else:\\n sign = \\\"+\\\"\\n subexp = signExp(subexp, sign)\\n q.append(subexp)\\n break\\n else:\\n subexp = c + subexp\\n exp = \\\"\\\"\\n while q:\\n c = q.pop()\\n exp = c + exp\\n \\n if len(exp) and exp[0] != \\\"+\\\" and exp[0] != \\\"-\\\":\\n # Again if the first character is not a 'sign' make it a \\\"+\\\"\\n exp = \\\"+\\\" + exp\\n \\n return exp\",\n \"def question_4(name: str) -> str:\\n return \\\"My first name is\\\" + \\\" \\\" + name.capitalize()\",\n \"def clean_cq(cq: str) -> str:\\n normalizers = {\\n r\\\"[-\\\\\\\"\\\\'”]\\\": \\\"\\\", # remove all dashes and quotations\\n r\\\"\\\\(.*?\\\\)\\\": \\\"\\\", # remove all ( )\\n r\\\"[ \\\\t]+\\\": \\\" \\\", # remove all multiplied whitespaces\\n r\\\" \\\\?\\\": \\\"?\\\", # remove whitespaces before ?\\n r\\\"a single\\\": \\\"exactly 1\\\",\\n r\\\"one\\\": '1',\\n r\\\"a double\\\": 'exactly 2',\\n r\\\"two\\\": '2',\\n r\\\"three\\\": '3',\\n r\\\"four\\\": '4',\\n \\\"five\\\": '5'\\n }\\n\\n for regex in normalizers:\\n cq = re.sub(regex, normalizers[regex], cq)\\n return cq\",\n \"def numerical_answer_to_str(self, answer):\\r\\n return str(answer.values()[0])\",\n \"def __str__(self):\\n return f'{self.text}: {self.chs}, correct answer: {self.solution}'\",\n \"def get_str(nr_rational):\\n return f'{get_numarator(nr_rational)} / {get_numitor(nr_rational)}'\",\n \"def normalize_answer(text):\\n return ' '.join(re.findall(r\\\"\\\\w+\\\", text)).lower()\",\n \"def question_12(one_string: str, two_string: str) -> str:\\n return one_string + two_string\",\n \"def question_6(first_name: str, last_name: str) -> str:\\n return \\\"My full name is\\\" + \\\" \\\" + first_name.capitalize() + \\\" \\\" + last_name.capitalize()\",\n \"def Problem9():\\n return 'MSS'\",\n \"def format_formula(self):\\n label_formula = re.sub(\\\"([0-9])\\\", \\\"_\\\\\\\\1\\\", self.formula)\\n label_formula = '$\\\\mathregular{'+label_formula+'}$'\\n return label_formula\",\n \"def question_2(name: str) -> str:\\n return \\\"Hello my name is\\\" + \\\" \\\" + name.capitalize()\",\n \"def ascii_qvalue(q, guard=0):\\r\\n value, poslist, neglist = unit_string(q.number, q.units, q.prefu)\\r\\n numbertext = ascii_number(value, sigfig(q.number, q.uncert) + guard)\\r\\n if len(neglist) == 1 and neglist[0][1] == 1:\\r\\n negtext = \\\"/\\\" + neglist[0][0]\\r\\n elif neglist:\\r\\n negtext = \\\"/(%s)\\\" % ascii_units(neglist)\\r\\n else:\\r\\n negtext = \\\"\\\"\\r\\n if not poslist:\\r\\n if not neglist:\\r\\n return numbertext\\r\\n return numbertext + \\\" 1\\\" + negtext\\r\\n return numbertext + \\\" \\\" + ascii_units(poslist) + negtext\",\n \"def ire_subjunctivepr_quiz(verb, pronoun):\\n return functions.conjugate_subjunctivepr_ere_verb(verb, pronoun, \\\"congiuntivo\\\")\",\n \"def correct_penultimate_dactyl_chain(self, scansion: str) -> str:\\n mark_list = string_utils.mark_list(scansion)\\n vals = list(scansion.replace(\\\" \\\", \\\"\\\"))\\n n_vals = (\\n vals[:-7] + [self.constants.DACTYL + self.constants.DACTYL] + [vals[-1]]\\n )\\n corrected = \\\"\\\".join(n_vals)\\n new_line = list(\\\" \\\" * len(scansion))\\n for idx, car in enumerate(corrected):\\n new_line[mark_list[idx]] = car\\n return \\\"\\\".join(new_line)\",\n \"def ire_future_quiz(verb, pronoun):\\n return functions.conjugate_future_ire_verb(verb, pronoun, \\\"futuro\\\")\",\n \"def generateParenthesis(self, n):\\n sol = []\\n \\n def dfs(cur_str, o, c):\\n if o==n and o==c:\\n sol.append(cur_str)\\n else:\\n if o < n:\\n dfs(cur_str + \\\"(\\\", o + 1, c)\\n if c < o:\\n dfs(cur_str + \\\")\\\", o, c+1)\\n dfs(\\\"\\\", 0, 0)\\n return sol\",\n \"def _transform(s):\\n return re.subn(r\\\"\\\\^?(-?\\\\d)\\\", r\\\"**\\\\g<1>\\\", s)[0]\",\n \"def get_answer_phrase(question_sentence, answer_sentence):\\n # TODO: UNCOMMENT TRY/CATCH BLOCK!\\n try:\\n question_sentence = remove_punctuation(question_sentence)\\n answer_sentence = remove_punctuation(answer_sentence)\\n\\n question = formulate_question(question_sentence)\\n answer = get_sentence(answer_sentence)\\n\\n # todo!!!!\\n if question['qword'][0].lower() in [\\\"what\\\", \\\"which\\\"]:\\n best_phrase = None\\n for subtree in [\\n tree.subtrees() for tree in\\n get_parse_trees_with_tag(answer_sentence, \\\"NP\\\") +\\n get_parse_trees_with_tag(answer_sentence, \\\"NX\\\")\\n ]:\\n for tree in subtree:\\n baseline = text_analyzer.sentence_similarity(question_sentence, \\\" \\\".join(tree.leaves()))\\n\\n baseline = text_analyzer.sentence_similarity(question_sentence)\\n\\n elif question['qword'][0].lower() == \\\"when\\\":\\n # get prepositional phrases\\n prep_nodes = [d for d in answer.get_nodes if d['tag'] == \\\"prep\\\"]\\n if prep_nodes:\\n # todo: should this be the uppermost node (which'll be [0], always)?\\n top_prep_string = \\\" \\\".join([x[0] for x in prep_nodes[0].get_pairs])\\n if num_occurrences_time_regex(top_prep_string) > 0:\\n return top_prep_string\\n\\n # todo: find a way to use my dependency parse here?\\n prep_phrases = [x.leaves() for x in get_parse_trees_with_tag(answer_sentence, \\\"PP\\\")]\\n\\n if prep_phrases:\\n return to_sentence(\\n max(\\n prep_phrases, key=lambda x: num_occurrences_time_regex(x)\\n )\\n )\\n else:\\n # todo: perhaps reconsider which one to return here. sentence length may be the wrong idea.\\n if prep_phrases:\\n return to_sentence(max(prep_phrases, key=lambda x: len(x)))\\n\\n elif question['qword'][0].lower() == \\\"where\\\":\\n answer_chunks = get_top_ner_chunk_of_each_tag(answer_sentence, {\\\"GPE\\\"})\\n\\n untagged = [\\n tagged[0][1] for tagged in [\\n answer_chunks[tag] for tag in answer_chunks\\n ]\\n ]\\n\\n # get_dep_trees_with_tag(answer, \\\"prep\\\")\\n prep_phrases = [tree.leaves() for tree in get_parse_trees_with_tag(answer_sentence, \\\"PP\\\")]\\n\\n # todo: strip preposition (e.g. \\\"in\\\") out of the answer\\n if prep_phrases:\\n return to_sentence(max(\\n prep_phrases,\\n key=lambda x: calculate_overlap(x, untagged, False)\\n ))\\n\\n elif question['qword'][0].lower() in [\\\"who\\\", \\\"whose\\\", \\\"whom\\\"]:\\n question_chunks = get_top_ner_chunk_of_each_tag(question_sentence)\\n answer_chunks = get_top_ner_chunk_of_each_tag(answer_sentence)\\n\\n # todo: try something with checking the question tag with the answer tag\\n # todo: consider stripping out the part of the answer with question entity in it...?\\n\\n untagged = [\\n tagged[0][1] for tagged in [\\n answer_chunks[tag] for tag in answer_chunks\\n ]\\n ]\\n\\n # todo: figure out what to do if not untagged\\n if untagged:\\n return to_sentence(max(untagged, key=lambda x: len(x)))\\n\\n elif question['qword'][0].lower() == \\\"why\\\":\\n # q_verb = question.tuple\\n # a_verb = answer.tuple\\n\\n parse_tree = next(CoreNLPParser().raw_parse(answer_sentence))\\n to_vp_phrases = []\\n prev_was_to = False\\n for tree in parse_tree.subtrees():\\n if tree.label() == \\\"VP\\\":\\n for subtree in tree.subtrees():\\n if prev_was_to:\\n to_vp_phrases.append(subtree)\\n prev_was_to = False\\n elif subtree.label() == \\\"TO\\\":\\n prev_was_to = True\\n # todo: potentially strip out \\\"to\\\", and might consider including object?\\n # todo: honestly, might just pick out things after \\\"to\\\"\\n # if to_vp_phrases:\\n # return to_sentence(min(\\n # [tree.leaves() for tree in to_vp_phrases],\\n # key=lambda x: calculate_overlap(to_vp_phrases, x)\\n # ))\\n\\n # todo: finish debugging\\n # vp_phrases = get_parse_trees_with_tag(answer_sentence, \\\"VP\\\")\\n # to_phrases = []\\n # if to_phrases:\\n # return to_sentence(max(\\n # to_phrases,\\n # key=lambda x: len([])\\n # ))\\n\\n # todo: soup up this absolute trash\\n for i, word in enumerate(answer_sentence.split()):\\n if word in [\\\"to\\\", \\\"so\\\", \\\"because\\\"]:\\n return to_sentence(answer_sentence.split()[:i])\\n\\n # todo: try things with conjunctions, potentially? test.\\n # conj_phrases = [tree.leaves() for tree in get_parse_trees_with_tag(answer_sentence, \\\"PP\\\")]\\n\\n elif question['qword'][0].lower() == \\\"how\\\":\\n # TODO: look at \\\"QP\\\" parse tag for this!\\n if any([\\n # 'advmod' in [\\n # pair[1] for pair in [\\n # node.get_pairs[1] for node in question.get_nodes if node['tag'][0].lower() == 'w'\\n # ]\\n # ],\\n get_parse_trees_with_tag(question_sentence, \\\"WHADJP\\\"),\\n re.search(r\\\"much|many|tall|long\\\", question_sentence)\\n ]):\\n qp_phrases = get_parse_trees_with_tag(answer_sentence, \\\"QP\\\")\\n if qp_phrases:\\n return to_sentence(min(\\n [tree.leaves() for tree in qp_phrases],\\n key=lambda x: num_occurrences_quant_regex(x)\\n ))\\n\\n # todo: non-measure cases! (mostly thinking about \\\"how did/does\\\")\\n\\n except:\\n pass\",\n \"def format_minimized_expression(prime_implicants):\\n if not prime_implicants: # If no prime implicants, return 0\\n return '0'\\n elif prime_implicants[0] == '-' * len(prime_implicants[0]): # If all dashes, return 1\\n return '1'\\n\\n result = ''\\n for iteration, prime_implicant in enumerate(prime_implicants):\\n for index, letter in enumerate(prime_implicant):\\n if letter == '1':\\n result += chr(ord('A') + index)\\n elif letter == '0':\\n result += chr(ord('A') + index) + \\\"'\\\"\\n if iteration != (len(prime_implicants) - 1): # Check to see if last prime_implicant\\n result += ' + '\\n\\n return result\",\n \"def _normalize_hint_expression(hint_expression, hint_name):\\n hdict = _extract_hint_operator_and_values(hint_expression, hint_name)\\n result = hdict['op'].join([' %s ' % urlparse.quote(t)\\n for t in hdict['values']])\\n return (hdict['op'] + result).strip()\",\n \"def fn(i, s=\\\"\\\", n=0):\\n if i == len(word): return ans.append(s + (str(n) if n else \\\"\\\"))\\n fn(i+1, s, n+1)\\n fn(i+1, s + (str(n) if n else \\\"\\\") + word[i], 0)\",\n \"def do(s):\\r\\n return get_AA_subs(generate_mutString(s))\",\n \"def give_short():\\n short = input('What DNA sequence would you like to match? ')\\n short = short.upper()\\n return short\",\n \"def oversimplify(strng):\\n s = strng.encode('latex').decode('utf-8')\\n s = reg_simplify.sub('', s)\\n return s\",\n \"def simplify(phrase):\\n list_phrase = phrase.split()\\n \\n first_noun = list_phrase[0:2]\\n first_verb = list_phrase[-1]\\n rest_list_phrase = list_phrase [2:-1]\\n \\n rest_phrase = phrase[len(first_noun[0] + first_noun[1]) + 2 :\\n (-1 * len(first_verb)) - 1]\\n #We calculate the length of the first two words and add 2 for the\\n #number of spaces to start from the beginning of the rest of the phrase\\n #and end at the beginning of the space before the last word by\\n #subtracting the length of the last word by 1.\\n \\n #This is done because we are dealing with lists and strings concurrently.\\n \\n if len(rest_phrase) == 0:\\n return first_noun[0] + \\\" \\\" + first_noun[1]\\n \\n return (simplify(rest_phrase) + \\\" that \\\" + first_verb + \\\" \\\" +\\n first_noun[0] + \\\" \\\" + first_noun[1])\",\n \"def createAnswer(self, input):\\n\\n input = input.strip()\\n\\n if len(input) == 1:\\n if input in self.guessedChars:\\n return \\\"Oled juba tähte \\\" + input + \\\" pakkunud. Paku midagi muud. \\\\nHetkel proovitud \\\" + ' '.join(\\n self.guessedChars) + \\\"\\\\n\\\" + self.wordKnown\\n else:\\n self.addChar(input)\\n if self.isWordSet():\\n return self.answerIsSet(input)\\n else:\\n self.filterDict(input)\\n if self.isWordSet():\\n return self.answerIsSet(input)\\n else:\\n return \\\"Kahjuks tähte \\\" + input + \\\" sõnas ei ole. Vaja veel \\\" + str(\\n self.wordKnown.count(\\\"_\\\")) + \\\" ära arvata. \\\\nHetkel proovitud \\\" + ' '.join(\\n self.guessedChars) + \\\" \\\\n\\\" + self.wordKnown\\n elif input == \\\"\\\":\\n return \\\"Võiks midagi ikka sisestada ka...\\\\nHetkel proovitud \\\" + ' '.join(\\n self.guessedChars) + \\\" \\\\n\\\" + self.wordKnown\\n else:\\n if input == \\\"aitab\\\":\\n self.active = False\\n return \\\"Kui aitab siis aitab. Sõna, mida ma mõtlesin, ma sulle ikkagi ei ütle. Jäägu see elu lõpuni \\\" \\\\\\n \\\"Sind piinama.\\\"\\n if self.word == input:\\n self.active = False\\n return \\\"Arvasid ära, mõtlesin tõesti sõna \\\" + self.word + \\\".\\\"\\n else:\\n self.removeWordFromDict(input)\\n return \\\"Ei, ma kohe kindlasti ei mõelnud sõna \\\" + input + \\\"... Proovi veel. \\\\nHetkel proovitud \\\" \\\\\\n \\\"\\\" \\\\\\n \\\"\\\" \\\\\\n \\\"\\\" + ' '.join(self.guessedChars) \\\\\\n + \\\" \\\\n\\\" + self.wordKnown\",\n \"def inask(question: str) -> str:\\n answer = input(question)\\n return answer\",\n \"def mystery2(s):\\r\\n if len(s) <= 1:\\r\\n return s\\r\\n else:\\r\\n result_rest = mystery2(s[1:])\\r\\n if s[0] == s[-1]:\\r\\n return result_rest\\r\\n else:\\r\\n return result_rest + s[0]\",\n \"def logic_program_form(self):\\r\\n s = ''\\r\\n return s\",\n \"def ere_subjunctivepr_quiz(verb, pronoun):\\n return functions.conjugate_subjunctivepr_ere_verb(verb, pronoun, \\\"congiuntivo\\\")\",\n \"def scinot(string):\\n\\t\\\"\\\"\\\"If there is no '+' or '-' character in string, returns it as it is.\\\"\\\"\\\"\\n\\t\\\"\\\"\\\"If the argument is not string, returns the argument\\\"\\\"\\\"\\n\\tif type(string) != str:\\n\\t\\treturn string\\n\\telse:\\n\\t\\tretstr = string[0]\\n\\t\\tfor char in string[1:]:\\n\\t\\t\\tif ((char == '-')|(char == '+')):\\n\\t\\t\\t\\tretstr += 'E' + char\\n\\t\\t\\telse:\\n\\t\\t\\t\\tretstr += char\\n\\t\\t\\n\\t\\treturn retstr\",\n \"def lemma(self) -> str:\",\n \"def parse_simple_eqn(equation=\\\"\\\"):\\n # Define replacement rules.\\n simple_replacements = [[' ', ''],\\n ['**', '^'], ['*', ' \\\\\\\\cdot '],\\n ['math.', ''], ['np.', ''],\\n ['pi', '\\\\\\\\pi'] , ['tan', '\\\\\\\\tan'],\\n ['cos', '\\\\\\\\cos'], ['sin', '\\\\\\\\sin'],\\n ['sec', '\\\\\\\\sec'], ['csc', '\\\\\\\\csc']]\\n complex_replacements = [['^', '{{{i1}}}^{{{i2}}}'],\\n ['_', '{{{i1}}}_{{{i2}}}'],\\n ['/', '\\\\\\\\frac{{{i1}}}{{{i2}}}'],\\n ['sqrt','\\\\\\\\sqrt{{{i2}}}']]\\n # Carry out simple replacements\\n for pair in simple_replacements:\\n equation = equation.replace(pair[0], pair[1])\\n # Now complex replacements\\n for item in ['*', '/', '+', '-', '^', '_', ',', 'sqrt']:\\n equation = equation.replace(item, ' ' + item + ' ')\\n q_split = equation.split()\\n for index, item in enumerate(q_split):\\n for pair in complex_replacements:\\n if item == pair[0]:\\n if item == 'sqrt':\\n match_str = \\\" \\\".join(q_split[index:index+2])\\n else:\\n match_str = \\\" \\\".join(q_split[index-1:index+2])\\n equation = equation.replace(match_str, pair[1].format(\\n i1=q_split[index-1], i2=q_split[index+1]))\\n return equation\",\n \"def question1(s, t):\\n if s is None or t is None:\\n return \\\"Error: Please enter valid strings\\\"\\n len_s = len(s)\\n len_t = len(t)\\n if len_s == 0 or len_s < len_t:\\n return \\\"Error: Length of s cannot be smaller than length of t\\\"\\n s = s.lower()\\n t = t.lower()\\n if len_t == 0:\\n return True\\n t = sorted(t)\\n for i in range(len_s - len_t + 1):\\n sub_s = s[i:i + len_t]\\n if sorted(sub_s) == t:\\n return True\\n return False\",\n \"def replaceMultiQuestionMark(text):\\n text = re.sub(r\\\"(\\\\?)\\\\1+\\\", ' multiQuestion ', text)\\n return text\",\n \"def dispNextQuestion(q):\\r\\n print '\\\\n'*100\\r\\n print q.name+'\\\\n'+'-'*60+'\\\\n'+q.prompt+'\\\\n '+q.q\\r\\n\\r\\n for i, a in enumerate(q.options):\\r\\n print str(i+1)+')',a\\r\\n \\r\\n guess = raw_input('\\\\nEnter the number for the answer: ')\\r\\n return str(int(guess)-1)\",\n \"def answer(question: str) -> int:\\n phrases = split(r' (?!\\\\bby\\\\b)', question.replace('?', ''))\\n length = len(phrases)-1\\n\\n if not any(word in OPERATIONS for word in phrases):\\n try:\\n return int(phrases[-1])\\n except TypeError:\\n raise ValueError('No numbers and/or operators in question.')\\n\\n stack = []\\n\\n for i, word in enumerate(phrases):\\n if word.isdigit() and i != length and phrases[i+1] not in OPERATIONS:\\n raise ValueError('Missing operation.')\\n elif word not in OPERATIONS:\\n continue\\n\\n try:\\n a, b = stack[-1] if stack else int(phrases[i-1]), int(phrases[i+1])\\n except (TypeError, IndexError):\\n raise ValueError('Numbers not in valid order.')\\n\\n stack.append(OPERATIONS[word](a, b))\\n\\n return stack.pop()\",\n \"def fmt(cls, n):\\n return ''.join(c for c in n if c in cls.ALLOWED).lower()\",\n \"def refrm(s):\\n s2 = s[5:10] + s[4] + s[0:4]\\n return s2\",\n \"def rewritten_queries(question):\\n # rewrites = []\\n # tq = tokenize(question)\\n # verb = tq[1] # 目前只处理 谁是XXX类 问题\\n # rewrites.append(\\n # RewrittenQuery(\\\"\\\\\\\"%s %s\\\\\\\"\\\" % (verb, \\\" \\\".join(tq[2:])),\\n # QUOTED_QUERY_SCORE))\\n # for j in range(2, len(tq)):\\n # rewrites.append(\\n # RewrittenQuery(\\n # \\\"\\\\\\\"%s %s %s\\\\\\\"\\\" % (\\n # \\\" \\\".join(tq[2:j + 1]), verb, \\\" \\\".join(tq[j + 1:])),\\n # QUOTED_QUERY_SCORE))\\n # rewrites.append(RewrittenQuery(\\\" \\\".join(tq[2:]), UNQUOTED_QUERY_SCORE))\\n return [RewrittenQuery(question, 1)]\",\n \"def question_13(one_string: str, two_string: str) -> str:\\n return one_string.capitalize() + two_string.capitalize()\",\n \"def tactic_simplify(tactic):\\n names = re.split(\\\"[\\\\;\\\\.\\\\ \\\\(\\\\)]\\\", tactic)\\n return names[0]\",\n \"def __str__(self):\\n if not self.b:\\n return str(self.a)\\n elif self.b==1:\\n if not self.a:\\n return 'sqrt(%s)' % (self.r)\\n else:\\n return '(%s + sqrt(%s))' % (self.a, self.r)\\n else:\\n if not self.a:\\n return '(%s * sqrt(%s))' % (self.b, self.r)\\n str_b = str(self.b)\\n if str_b == '-1':\\n str_b = ' - '\\n elif str_b.startswith('-'):\\n str_b = '- ' + str_b[1:] + ' *'\\n else:\\n str_b = '+ ' + str_b + ' *'\\n return '(%s %s sqrt(%s))' % (self.a, str_b, self.r)\",\n \"def __str__(self):\\n termStrings = []\\n for term in self.LHS:\\n coefficient = term[0]\\n unknownSet = term[1]\\n\\n termString = str(coefficient) + ' * '\\n unknownStrings = []\\n for unknown in unknownSet:\\n unknownString = unknown[0].__class__.__name__ + '@' + str(id(unknown[0]))[-4:] + '.' + unknown[1] # last 4 digits of variable ID . attribute name\\n unknownStrings.append(unknownString)\\n termString += str.join(' * ', unknownStrings)\\n termStrings.append(termString)\\n\\n termStrings = str.join(' + ', termStrings)\\n return termStrings + ' = ' + str(self.RHS)\",\n \"def SolveInput(s, n):\\n\\n if len(s) == 0:\\n return\\n elif s[0] == \\\"\\\":\\n SolveInput(s[1:], n)\\n else:\\n result = SolveLine(list(filter(lambda a: a not in ['(', ')', ','],\\n LexLine(s[0].replace(' ', '')) or [])), 0, n)\\n if result and not type(result) == str:\\n # def TestInput(solved, inputString):\\n # \\\"\\\"\\\"Evaluates input using the python interpreter directly. Used\\n # exclusively for testing/debugging purposes to validate output\\n # correctness.\\n # \\\"\\\"\\\"\\n # def multiply(a, b):\\n # return a * b\\n # def add(a, b):\\n # return a + b\\n # correctness = solved == str(eval(inputString))\\n # return solved + \\\" \\\" + str(correctness)\\n # print (s[0], \\\"=\\\", TestInput(InfIntToStr(result, 0, n), s[0]))\\n print (s[0], \\\"=\\\", InfIntToStr(result, 0, n))\\n else:\\n print (\\\"Invalid expression:\\\", s[0])\\n SolveInput(s[1:], n)\",\n \"def transform_one_sentence(sentence):\\n words = list(map(str, nlp(sentence)))\\n return ' '.join(list(map(spell.correction, words)))\",\n \"def generateParenthesis(self, n: int) -> List[str]:\\n\\n def generate(result=[]):\\n\\n if len(result) == n * 2:\\n # check if valid\\n if is_valid(result):\\n answer.append(\\\"\\\".join(result))\\n\\n else:\\n result.append('(')\\n generate(result)\\n result.pop()\\n result.append(')')\\n generate(result)\\n result.pop()\\n\\n def is_valid(result):\\n balanced = 0\\n\\n for char in result:\\n if char == '(':\\n balanced += 1\\n else:\\n balanced -= 1\\n\\n if balanced < 0: return False\\n return balanced == 0\\n\\n answer = []\\n generate()\\n return answer\",\n \"def calc_conservation_string(aln):\\n\\n percids = calc_conservation(aln)\\n\\n # find identity positions\\n identity = \\\"\\\"\\n for pid in percids:\\n if pid == 1:\\n identity += \\\"*\\\"\\n elif pid > .5:\\n identity += \\\".\\\"\\n else:\\n identity += \\\" \\\"\\n\\n return identity\",\n \"def normalize(q_1: Q, n: float = 1.0, q_type: str = \\\"U\\\") -> Q:\\n\\n end_q_type = f\\\"{q_1.q_type} {q_type}\\\"\\n\\n abs_q_inv = inverse(abs_of_q(q_1))\\n n_q = product(product(q_1, abs_q_inv), Q([n, 0, 0, 0]))\\n n_q.q_type = end_q_type\\n n_q.representation = q_1.representation\\n\\n return n_q\",\n \"def simplify(self, expr):\\n return sy.simplify(expr)\",\n \"def text_of_quotation(exp):\\n return cadr(exp)\",\n \"def test_str(self):\\n q0 = sym.Symbol(\\\"q0\\\")\\n q2 = sym.Symbol(\\\"q2\\\")\\n f = (q0 + q2) / np.sqrt(2)\\n\\n rrt = RegRefTransform(f)\\n\\n assert rrt.__str__() == \\\"0.707106781186547*q0 + 0.707106781186547*q2\\\"\",\n \"def answer_key(cls, input_num=2):\\r\\n return (\\r\\n \\\"%s_%d_1\\\" % (\\r\\n \\\"-\\\".join(['i4x', 'edX', 'capa_test', 'problem', 'SampleProblem%d' % cls.num]),\\r\\n input_num,\\r\\n )\\r\\n )\",\n \"def simplify_chunks(self, input_text):\\n string = ''\\n chunks = re.split(r'([\\\\w\\\\.-]+|[\\\\(\\\\)\\\\*\\\\+])', input_text)\\n chunks = [chunk.strip() for chunk in chunks]\\n chunks = [chunk for chunk in chunks if chunk != '']\\n\\n classifiers = [\\n 'is_integer', 'is_float', 'is_operator', 'is_constant', 'is_function'\\n ]\\n\\n for chunk in chunks:\\n for classifier in classifiers:\\n result = getattr(self, classifier)(chunk)\\n if result is not False:\\n string += str(result) + ' '\\n break\\n\\n # Replace '^' with '**' to evaluate exponents\\n string = string.replace('^', '**')\\n\\n return string\",\n \"def substitute_words(self, string):\\n condensed_string = '_'.join(string.split())\\n\\n for word in self.math_words['words']:\\n condensed_string = re.sub(\\n '_'.join(word.split(' ')),\\n self.math_words['words'][word],\\n condensed_string\\n )\\n\\n for number in self.math_words['numbers']:\\n condensed_string = re.sub(\\n number,\\n str(self.math_words['numbers'][number]),\\n condensed_string\\n )\\n\\n for scale in self.math_words['scales']:\\n condensed_string = re.sub(\\n '_' + scale,\\n ' ' + self.math_words['scales'][scale],\\n condensed_string\\n )\\n\\n condensed_string = condensed_string.split('_')\\n for chunk_index in range(0, len(condensed_string)):\\n value = ''\\n\\n try:\\n value = str(eval(condensed_string[chunk_index]))\\n\\n condensed_string[chunk_index] = value\\n except:\\n pass\\n\\n for chunk_index in range(0, len(condensed_string)):\\n condensed_chunk = condensed_string[chunk_index]\\n if self.is_integer(condensed_chunk) or self.is_float(condensed_chunk):\\n i = 1\\n start_index = chunk_index\\n end_index = -1\\n while (chunk_index + i < len(condensed_string) and (self.is_integer(condensed_string[chunk_index + i]) or self.is_float(condensed_string[chunk_index + i]))):\\n end_index = chunk_index + i\\n i += 1\\n\\n for sub_chunk in range(start_index, end_index):\\n condensed_string[sub_chunk] += ' +'\\n\\n condensed_string[start_index] = '( ' + condensed_string[start_index]\\n condensed_string[end_index] += ' )'\\n\\n return ' '.join(condensed_string)\",\n \"def get_star(self, index):\\n ansNr = 1 # default answers (for multiple ones)\\n chunk = \\\"(Choose \\\"\\n firstTag = self.data.find(\\\"<\\\", index)\\n chunkIndex = self.data.find(chunk, index, firstTag)\\n if chunkIndex != -1:\\n chunkIndex += len(chunk)\\n number = \\\"\\\"\\n while self.data[chunkIndex] != \\\")\\\":\\n number += self.data[chunkIndex]\\n chunkIndex += 1\\n number = number.strip().lower()\\n if number == \\\"two\\\":\\n ansNr = 2\\n elif number == \\\"three\\\":\\n ansNr = 3\\n elif number == \\\"four\\\":\\n ansNr = 4 # i don't think this actually exists :)\\n star = \\\"(*)\\\"\\n last = index\\n ans = \\\"\\\"\\n while ansNr:\\n index = self.data.find(star, last) - 1\\n last = index + 1 + len(star)\\n if index < 0:\\n return False # invalid answer type\\n # ok now we're good\\n old = index\\n while True:\\n tag = True\\n while self.data[index] != \\\">\\\":\\n if tag and self.data[index].isspace():\\n index -= 1\\n continue\\n if tag:\\n ans = \\\"\\\\n\\\" + ans\\n tag = False # found alphanumeric\\n ans = self.data[index] + ans\\n index -= 1\\n if not self.check(old, index):\\n return False\\n ans = ans.strip()\\n if tag and len(ans) > 0:\\n break\\n while self.data[index] != \\\"<\\\":\\n index -= 1\\n index -= 1\\n ansNr -= 1\\n ans = \\\"\\\\n\\\" + ans\\n return ans.strip()\",\n \"def end_question_rebuilding(sentence, sv, sn, aim):\\n\\n #Recovering the verb in the correct form\\n phrase = vrb_ques_rebuilding(sv[0].vrb_tense, sv[0].vrb_main, sv[0].vrb_adv, sn, sv[0].state, aim)\\n\\n #We add the direct and indirect complement\\n if sv[0].i_cmpl != [] and sv[0].i_cmpl[0].prep != []:\\n phrase = phrase + nom_struc_rebuilding(sv[0].d_obj)\\n for x in sv[0].i_cmpl:\\n phrase = phrase + indirect_compl_rebuilding(x)\\n else:\\n if sv[0].i_cmpl:\\n phrase = phrase + indirect_compl_rebuilding(sv[0].i_cmpl[0])\\n phrase = phrase + nom_struc_rebuilding(sv[0].d_obj)\\n\\n #init\\n x = 1\\n while x < x < len(sv[0].i_cmpl):\\n phrase = phrase + indirect_compl_rebuilding(sv[0].i_cmpl[x])\\n x += 1\\n\\n flag = 0\\n for j in ResourcePool().verb_need_to:\\n if sv[0].vrb_main == [j]:\\n flag = 1\\n\\n #If there is a second verb\\n for k in sv[0].sv_sec:\\n phrase = scd_vrb_rebuilding(k, phrase, flag)\\n\\n #We add the adverb of the sentence\\n phrase = phrase + sv[0].advrb\\n return sentence + phrase\",\n \"def wrongtxt(answer, is_code=False):\\n if not isinstance(answer, basestring):\\n answer = repr(answer)\\n def dec(func):\\n func = _multiple_choice_passthrough(func)\\n func.answers.append(Answer(answer, correct=False, is_code=is_code))\\n func = _checkbox_if_multiple_answers_passthrough(func)\\n func = _multiple_choice_if_wrongs_passthrough(func)\\n return func\\n return dec\",\n \"def sentence(self) -> str:\\n id_word = {nodeattr['position']-1: nodeattr['form']\\n for nodeid, nodeattr in self.syntax_nodes.items()}\\n return ' '.join([id_word[i] for i in range(max(list(id_word.keys()))+1)])\",\n \"def get_equation(self):\\n self.polynomials = dict(sorted(self.polynomials.items(), reverse=True))\\n string = \\\"\\\"\\n\\n for index, polynomial in self.polynomials.items():\\n polynomial = int(polynomial)\\n index = int(index)\\n\\n if polynomial != 0:\\n if polynomial < 0:\\n string_pre = \\\" - \\\"\\n else:\\n string_pre = \\\" + \\\"\\n\\n if index != 0:\\n string_append = \\\"x\\\"\\n elif polynomial == 1 or polynomial == -1:\\n string_append = str(abs(polynomial))\\n else:\\n string_append = \\\"\\\"\\n\\n if polynomial < 0:\\n polynomial = abs(polynomial)\\n\\n if polynomial != 1:\\n string_append = str(polynomial) + string_append\\n\\n if index != 0 and index != 1:\\n string_append += \\\"^\\\" + str(index)\\n\\n string += string_pre + string_append\\n\\n if len(string) > 0:\\n string = string[3:]\\n else:\\n string = \\\"0\\\"\\n\\n return string\",\n \"def get_answer(s, i, j, res: list):\\n if i == j:\\n res.append('a[{}]'.format(i-1))\\n else:\\n res.append('(')\\n get_answer(s, i, s[i][j], res)\\n get_answer(s, s[i][j]+1, j, res)\\n res.append(')')\",\n \"def phred(q):\\n n = int(q * 30 + 33)\\n if n == 43:\\n n += 1\\n if n == 58:\\n n += 1\\n return chr(n)\",\n \"def metaphlan_krona_string(input):\\n s = []\\n for f in input:\\n name = bn(f).replace(\\\"_pe.krona\\\", \\\"\\\").replace(\\\"_se.krona\\\", \\\"\\\")\\n s.append(f\\\"{f},{name}\\\")\\n return \\\" \\\".join(s)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6192208","0.6192208","0.61774063","0.615062","0.6132909","0.6085301","0.6085301","0.6061046","0.6054664","0.6050931","0.60430825","0.5989373","0.59198713","0.5915252","0.58585846","0.58305085","0.57404023","0.5724126","0.5723947","0.5629233","0.5621194","0.5578402","0.55750614","0.5535756","0.55355287","0.55317163","0.55144686","0.5510986","0.5500427","0.5491534","0.54493916","0.544481","0.5404364","0.5390969","0.53759426","0.5360326","0.5333633","0.53254217","0.531748","0.5295013","0.52806437","0.5279203","0.5250996","0.5249606","0.52083397","0.5185604","0.51625204","0.5153487","0.5149785","0.5147087","0.5146038","0.51260144","0.51114106","0.5109518","0.51088995","0.5095789","0.5092014","0.50860554","0.5070412","0.50682783","0.50503623","0.5040337","0.5032746","0.50289506","0.50287","0.50275964","0.5023069","0.5007718","0.49967626","0.4991442","0.499114","0.49907893","0.4981487","0.49787652","0.49742767","0.49631828","0.49628735","0.49622607","0.4956245","0.49499738","0.49496907","0.49429184","0.4941329","0.49410638","0.4938871","0.49316743","0.49268046","0.4925857","0.49072284","0.49061814","0.49044415","0.48997974","0.4897506","0.48965982","0.4891795","0.48916462","0.4883098","0.4881859","0.48788294","0.48782912"],"string":"[\n \"0.6192208\",\n \"0.6192208\",\n \"0.61774063\",\n \"0.615062\",\n \"0.6132909\",\n \"0.6085301\",\n \"0.6085301\",\n \"0.6061046\",\n \"0.6054664\",\n \"0.6050931\",\n \"0.60430825\",\n \"0.5989373\",\n \"0.59198713\",\n \"0.5915252\",\n \"0.58585846\",\n \"0.58305085\",\n \"0.57404023\",\n \"0.5724126\",\n \"0.5723947\",\n \"0.5629233\",\n \"0.5621194\",\n \"0.5578402\",\n \"0.55750614\",\n \"0.5535756\",\n \"0.55355287\",\n \"0.55317163\",\n \"0.55144686\",\n \"0.5510986\",\n \"0.5500427\",\n \"0.5491534\",\n \"0.54493916\",\n \"0.544481\",\n \"0.5404364\",\n \"0.5390969\",\n \"0.53759426\",\n \"0.5360326\",\n \"0.5333633\",\n \"0.53254217\",\n \"0.531748\",\n \"0.5295013\",\n \"0.52806437\",\n \"0.5279203\",\n \"0.5250996\",\n \"0.5249606\",\n \"0.52083397\",\n \"0.5185604\",\n \"0.51625204\",\n \"0.5153487\",\n \"0.5149785\",\n \"0.5147087\",\n \"0.5146038\",\n \"0.51260144\",\n \"0.51114106\",\n \"0.5109518\",\n \"0.51088995\",\n \"0.5095789\",\n \"0.5092014\",\n \"0.50860554\",\n \"0.5070412\",\n \"0.50682783\",\n \"0.50503623\",\n \"0.5040337\",\n \"0.5032746\",\n \"0.50289506\",\n \"0.50287\",\n \"0.50275964\",\n \"0.5023069\",\n \"0.5007718\",\n \"0.49967626\",\n \"0.4991442\",\n \"0.499114\",\n \"0.49907893\",\n \"0.4981487\",\n \"0.49787652\",\n \"0.49742767\",\n \"0.49631828\",\n \"0.49628735\",\n \"0.49622607\",\n \"0.4956245\",\n \"0.49499738\",\n \"0.49496907\",\n \"0.49429184\",\n \"0.4941329\",\n \"0.49410638\",\n \"0.4938871\",\n \"0.49316743\",\n \"0.49268046\",\n \"0.4925857\",\n \"0.49072284\",\n \"0.49061814\",\n \"0.49044415\",\n \"0.48997974\",\n \"0.4897506\",\n \"0.48965982\",\n \"0.4891795\",\n \"0.48916462\",\n \"0.4883098\",\n \"0.4881859\",\n \"0.48788294\",\n \"0.48782912\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6070443"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":77490,"cells":{"query":{"kind":"string","value":"Returns a boolean that indicates whether a letter is a boolean."},"document":{"kind":"string","value":"def is_vowel(self, letter):\n\n if letter in (\"a\", \"e\", \"i\", \"o\", \"u\", \"A\", \"E\", \"I\", \"O\", \"U\"):\n return True\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def boolean(s):\r\n ss = str(s).lower()\r\n if ss in TRUTHY_STRINGS:\r\n return True\r\n elif ss in FALSY_STRINGS:\r\n return False\r\n else:\r\n raise ValueError(\"not a valid boolean value: \" + repr(s))","def __charToBool(self, char):\n\n if char == '1':\n return True\n elif char == '0':\n return False","def toBool( string ):\r\n return string == 'true'","def _get_bool(string):\n string = string.lower()\n return True if string == 'y' else False","def is_bool(self):\n answer = self._call('is_bool')\n return answer.yes","def str_to_bool(a):\n if a == 'True':\n return True\n else:\n return False","def boolean(raw):\n raw = raw.lower()\n if not raw in (\"true\", \"false\"):\n raise ValueError(\"Could not parse a boolean value from '%s'.\" % raw)\n return raw == \"true\"","def is_bool(string_):\n bools = [\"yes\", \"y\", \"1\", \"no\", \"n\", \"0\"]\n if string_.lower() in bools:\n return True\n return False","def str_to_bool(s):\n s = s.lower()\n if s not in {'true', 'false', '1', '0'}:\n raise ValueError('Bool expected, but got %r' % s)\n return s in {'true', '1'}","def string_to_boolean(word):\n if word.strip().lower() in ['true', 'yes', 'on', '1']:\n return True\n else:\n return False","def is_bool (self, phrase):\r\n \r\n return isinstance(phrase,bool)","def _isbool(string):\n return type(string) is bool or (\n isinstance(string, (bytes, str)) and string in (\"True\", \"False\")\n )","def Bool(arg):\n return arg.lower() in ('y', 'true', 't', '1')","def str2bool(v):\n return v.lower() in (\"yes\", \"true\", \"t\", \"1\")","def boolean(value):\n if isinstance(value, str):\n if value.lower() in [\"0\", \"no\", \"n\", \"false\", \"f\", \"off\"]:\n return False\n elif value.lower() in [\"1\", \"yes\", \"y\", \"true\", \"t\", \"on\"]:\n return True\n else:\n raise ValueError(\"Invalid truth value '%s'\" % value)\n elif isinstance(value, bool):\n return value\n else:\n raise TypeError(\"invalid type %s, expect bool or str\" % type(value))","def str_to_bool(text):\n return text and text.lower() in [\"true\", \"y\", \"yes\", \"1\"]","def str2bool(v):\n return v.lower() in (\"yes\", \"true\", \"t\", \"1\")","def boolean_string(s):\n if s is None:\n return False\n s = str(s)\n if s.lower() not in {'false', 'true'}:\n raise ValueError('Not a valid boolean string')\n return s.lower() == 'true'","def truth(text):\n lowered = str(text).lower()\n if lowered in frozenset(['y', 'yes', 'true']):\n return True\n elif lowered in frozenset(['n', 'no', 'false']):\n return False\n else:\n raise Error('Invalid truth value: %r' % text)","def str2bool(v): # type: ignore\n return v.lower() in (\"yes\", \"true\", \"t\", \"1\", \"on\")","def isbool(s):\n if type(s) in [int,float,bool]:\n return True\n elif (type(s) != str):\n return False\n return s in ['True','False']","def to_bool(s: str):\n if s.strip().lower() == \"y\":\n return True\n else:\n return False","def str_bool(s):\n if not s:\n return False\n if type(s) != str:\n # It's not a string and it's not falsy, soooo....\n return True\n s = s.lower()\n if s in [\"false\", \"0\", \"no\", \"n\"]:\n return False\n return True","def as_bool(value):\n return str(value).lower() in ('1', 'true', 'on', 'yes')","def isalpha(self) -> bool:\n pass","def str2bool(value):\n if value:\n return value.lower() in (\"true\",)\n else:\n return False","def str2bool(value):\n if value:\n return value.lower() in (\"true\",)\n else:\n return False","def strToBool(s):\n\tassert type(s) == str or type(s) == unicode\n\treturn s.lower() == \"true\"","def is_letter(c):\n return 'A' <= c <= 'Z' or 'a' <= c <= 'z'","def str2bool(val: str) -> bool:\n return val.lower() in (\"yes\", \"true\", \"t\", \"1\")","def _str2bool(self, v):\n return v.lower() in (\"yes\", \"true\", \"t\", \"1\")","def str_to_bool(s):\n if len(s) > 0 and s[0] in \"yYtT1\":\n return True\n return False","def string_to_bool(test_string):\n\treturn bool(test_string in [\"True\", \"true\", \"Yes\", \"yes\", \"Y\", \"y\"])","def is_bool(type_):\n return remove_alias(type_) in create_cv_types(cpptypes.bool_t())","def character(x):\n if (x==\"a\"or x==\"A\"or x==\"e\"or x==\"E\"or x==\"i\"or x==\"I\"or x==\"o\"or x==\"O\"or x==\"u\"or x==\"U\"):\n return('True')\n else:\n return('False')","def asbool(s):\n if s is None:\n return False\n if isinstance(s, bool):\n return s\n s = str(s).strip()\n return s.lower() in truthy","def asbool(s):\n if s is None:\n return False\n if isinstance(s, bool):\n return s\n s = str(s).strip()\n return s.lower() in truthy","def cast_bool(value: str) -> bool:\n return value.lower() in {\"true\", \"yes\", \"1\"}","def str2bool(self, val):\n return val.lower() in ('true','yes','t',1)","def to_boolean(self,string):\n if self.debug:\n print('to_boolean'+lineno())\n # FIXME\n sys.exit(1)\n #string.to_s.casecmp('true').zero?","def str2bool(s: str):\n\n if s.lower() in [\"true\", \"yes\", \"1\"]:\n return True\n elif s.lower() in [\"false\", \"no\", \"0\"]:\n return False\n else:\n print(\"Fehler: Boolean erwartet! %s ist nicht als Boolean interpretierbar\" % s)\n exit(1)","def getBool(string):\n return (True)","def asbool(value, true=(b'true', u'true'), false=(b'false', u'false')):\n value = value.strip().lower()\n if value in true: # might raise UnicodeWarning/BytesWarning\n return True\n if value in false:\n return False\n raise TypeError()","def isTrue(s):\n return s == \"Yes\"","def to_bool(bool_str: str):\n bool_str_lc = bool_str.lower()\n if bool_str_lc in ('true', '1', 't'):\n return True\n if bool_str_lc in ('false', '0', 'f', ''):\n return False\n raise ValueError(f'cannot interpret {bool_str} as a bool')","def interpret_boolean(input):\n if not input:\n return False\n\n input = input.lower().strip() if isinstance(input, basestring) else input\n\n return input in _trues","def string_to_bool(string, default=True):\n string = string.lower()\n if string in ('1', 'true', 't'):\n return True\n elif string in ('0', 'false', 'f'):\n return False\n return default","def to_bool(s):\n if isinstance(s, bool):\n return s\n elif s.lower() in ['true', '1']:\n return True\n elif s.lower() in ['false', '0']:\n return False\n else:\n raise ValueError(\"Can't cast '%s' to bool\" % (s))","def _parse_string_as_bool(self, bool_string):\n true_chars = ['t', 'T', 'y', 'Y', '1']\n false_chars = ['f', 'F', 'n', 'N', '0']\n if bool_string[0] in true_chars:\n return True\n if bool_string[0] in false_chars:\n return False\n\n raise tcError(\n \"Supplied string cannot be parsed as a bool: {0}\".format(bool_string))","def bool_flag(s):\n FALSY_STRINGS = {\"off\", \"false\", \"0\"}\n TRUTHY_STRINGS = {\"on\", \"true\", \"1\"}\n if s.lower() in FALSY_STRINGS:\n return False\n elif s.lower() in TRUTHY_STRINGS:\n return True\n else:\n raise argparse.ArgumentTypeError(\"invalid value for a boolean flag\")","def to_bool(value):\n return value == 'true ' or value == 'true'","def isalpha(a):\n return _vec_string(a, bool_, 'isalpha')","def bool_(val):\n if isinstance(val, six.string_types) and val.lower() == 'false':\n return False\n return bool(val)","def boolean(obj):\n\n try:\n text = obj.strip().lower()\n except AttributeError:\n return bool(obj)\n\n if text in ('yes', 'y', 'on', 'true', 't', '1'):\n return True\n elif text in ('no', 'n', 'off', 'false', 'f', '0'):\n return False\n else:\n raise ValueError(\"Unable to convert {!r} to a boolean value.\".format(text))","def str_to_bool(s):\n if isinstance(s, bool):\n return s\n elif not isinstance(s, six.string_types):\n raise TypeError('argument must be a string')\n\n true_values = ('true', 'on', '1')\n false_values = ('false', 'off', '0')\n\n if s.lower() in true_values:\n return True\n elif s.lower() in false_values:\n return False\n else:\n raise ValueError('not a recognized boolean value: %s'.format(s))","def booleanize(text):\n ltext = text.lower()\n if ltext == 'true':\n booleanized = True\n elif ltext == 'false':\n booleanized = False\n else:\n raise ValueError('A monk asked: Is \"{}\" true or false.'.format(text))\n return booleanized","def to_bool(value):\r\n return bool(value) or lower(value) in [\"true\", \"yes\", \"on\"]","def bool_flag(s):\n if s.lower() in FALSY_STRINGS:\n return False\n elif s.lower() in TRUTHY_STRINGS:\n return True\n else:\n raise argparse.ArgumentTypeError(\"invalid value for a boolean flag\")","def _str_to_bool(s):\n if s.lower() not in ['true', 'false']:\n raise ValueError('Argument needs to be a '\n 'boolean, got {}'.format(s))\n return {'true': True, 'false': False}[s.lower()]","def _str_to_bool(s):\n if s.lower() not in ['true', 'false']:\n raise ValueError('Argument needs to be a '\n 'boolean, got {}'.format(s))\n return {'true': True, 'false': False}[s.lower()]","def _str_to_bool(s):\n if s.lower() not in ['true', 'false']:\n raise ValueError('Argument needs to be a '\n 'boolean, got {}'.format(s))\n return {'true': True, 'false': False}[s.lower()]","def _str_to_bool(s):\n if s.lower() not in ['true', 'false']:\n raise ValueError('Argument needs to be a '\n 'boolean, got {}'.format(s))\n return {'true': True, 'false': False}[s.lower()]","def _str_to_bool(s):\r\n if s.lower() not in ['true', 'false']:\r\n raise ValueError('Argument needs to be a boolean, got {}'.format(s))\r\n return {'true': True, 'false': False}[s.lower()]","def str_to_bool(val: str) -> bool:\n\n val = val.lower()\n if val in {\n \"y\",\n \"yes\",\n \"yep\",\n \"yup\",\n \"t\",\n \"true\",\n \"on\",\n \"enable\",\n \"enabled\",\n \"1\",\n }:\n return True\n elif val in {\"n\", \"no\", \"f\", \"false\", \"off\", \"disable\", \"disabled\", \"0\"}:\n return False\n else:\n raise ValueError(f\"Invalid truth value {val}\")","def cast_str_to_bool(word: str) -> bool:\n word = word.upper()\n if word == \"N\":\n return False\n elif word == \"Y\":\n return True\n raise ValueError(\"%s is not a valid string to boolean!\" % str)","def str2bool(self, v):\n \tprint('Entering conversion function')\n return v.lower() in (\"yes\", \"true\", \"t\", \"1\")","def convert_string_to_bool(string):\n lower = string.lower()\n if lower == \"true\":\n return True\n elif lower == \"false\":\n return False\n else:\n raise ValueError(\n \"Invalid value %s while expecting boolean string\" % string)","def is_bool(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_bool)","def str2bool(s):\n if s == \"True\":\n return True\n elif s == \"False\":\n return False\n else:\n raise ValueError","def string_to_bool(value):\n return False if value.upper() == \"FALSE\" or value == \"0\" or value == \"\" else True","def str_to_bool(str):\r\n return False if str is None else str.lower() == \"true\"","def str2bool(self, boolstring):\n return boolstring.lower() in (\"yes\", \"true\", \"on\", \"1\", \"t\")","def stringTruth(x):\n if x is None:\n return False\n if x == '':\n return False\n if x == '0':\n return False\n lx = x.lower()\n if lx == 'false':\n return False\n if lx == 'f':\n return False\n return True","def check_bool_type(rep):\n rep = rep.lower()\n if rep in (\"true\", \"t\", \"yes\", \"y\"):\n return True\n elif rep in (\"false\", \"f\", \"no\", \"n\"):\n return False\n else:\n print(\"Enter a boolean type (True, T, False, F) !\")\n sys.exit(2)","def boolean(val):\n\tif val == \"True\" or val == \"1\":\n\t\treturn True\n\telse:\n\t\treturn False","def str_to_bool(argument):\n\n # Checks if the argument is already a boolean value.\n if isinstance(argument, bool): return argument\n\n # Returns boolean depending on the input string.\n if argument.lower() == \"true\" or argument.lower() == 't':\n return True\n elif argument.lower() == \"false\" or argument.lower() == 'f':\n return False\n\n # Returns an error if the value is not converted to a boolean value.\n return ArgumentTypeError(f\"Boolean value expected. Got \\\"{argument}\\\".\")","def str2bool(value) -> bool:\n if type(value) == type(''):\n if value.lower() in (\"yes\", \"y\", \"true\", \"t\", \"1\"):\n return True\n if value.lower() in (\"no\", \"n\", \"false\", \"f\", \"0\", \"\"):\n return False\n raise Exception('Invalid value for boolean conversion: ' + value)\n return bool(value)","def __str_to_bool(self, s):\n if s == 'True':\n return True\n elif s == 'False':\n return False\n else:\n raise ValueError","def alpha(self) -> bool:\n return \"a\" in self.modifier if self.modifier else False","def GetBool(self, variable):\n value = self.GetString(variable)\n return value and value.strip() == 'true'","def str2bool(s):\n if s.lower() not in ['true', 'false']:\n raise ValueError('Need bool; got %r' % s)\n return {'true': True, 'false': False}[s.lower()]","def is_letter(user_input):\n # If any characters is letter -> return boolean True else False\n if any(char.isalpha() for char in user_input):\n return True\n return False","def parse_boolean(val: str) -> str | bool:\n val = val.lower()\n if val in (\"y\", \"yes\", \"t\", \"true\", \"on\", \"1\"):\n return True\n if val in (\"n\", \"no\", \"f\", \"false\", \"off\", \"0\"):\n return False\n return val","def str2bool(v):\n\n if isinstance(v, bool):\n return v\n if v.lower() in ('yes', 'true', 't', 'y', '1'):\n return True\n elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n return False\n else:\n raise ArgumentTypeError('Boolean value expected.')","def str2bool(v) -> bool:\n\n if isinstance(v, bool):\n return v\n if v.lower() in ('yes', 'true', 't', 'y', '1'):\n return True\n elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n return False\n else:\n raise argparse.ArgumentTypeError('Boolean value expected.')","def to_bool(value: Union[bool, str]) -> bool:\n if isinstance(value, bool):\n return value\n if value == 'J':\n return True\n elif value == 'N':\n return False\n else:\n raise ValueError(f\"Expected 'J' or 'N', got {value!r}\")","def is_bool(value):\n try:\n strtobool(value)\n except ValueError:\n return False\n else:\n return True","def str2bool(string):\n \n string = \tstring.lower()\n if string == \"true\" or string == \"yes\" or string == \"1\":\n return True\n elif string == \"false\" or string == \"no\" or string == \"0\": \n return False\n else :\n\traise ValueError(\"Could not Interpret the boolean for %s\" %string)","def stringToBoolean(text):\n if text in (\"True\", \"1\"):\n return True\n if text in (\"False\", \"0\"):\n return False\n return text","def isalpha(self):\n return isalpha(self)","def is_bool_type(*args):\n return _ida_hexrays.is_bool_type(*args)","def str2bool(string: str) -> bool:\n if string.lower() in [\"yes\", \"true\", \"t\", \"y\", \"1\"]:\n return True\n if string.lower() in [\"no\", \"false\", \"f\", \"n\", \"0\"]:\n return False\n raise argparse.ArgumentTypeError(\"Boolean value expected.\")","def _is_alpha(argument):\n\n if not isinstance(argument, str):\n return False\n\n if argument.lower() == 'alpha':\n is_alpha = True\n else:\n argument, Z = _extract_charge_state(argument)\n\n if Z != 2:\n is_alpha = False\n elif argument[-2:] != '-4':\n is_alpha = False\n else:\n\n dash_position = argument.find('-')\n argument = argument[:dash_position]\n\n if argument.lower() == 'helium' or argument == 'He':\n is_alpha = True\n else:\n is_alpha = False\n\n return is_alpha","def strtobool(val: str) -> bool:\n val = val.lower()\n if val in ('y', 'yes', 't', 'true', 'on', '1'):\n return True\n if val in ('n', 'no', 'f', 'false', 'off', '0'):\n return False\n raise ValueError('invalid truth value {!r}'.format(val))","def str_to_bool(string):\n\n if string is not None:\n if string.lower() in [\"true\", \"yes\", \"1\", \"on\"]:\n return True\n elif string.lower() in[\"false\", \"no\", \"0\", \"off\"]:\n return False\n\n return None","def validate_bool(val):\n if type(val) is str:\n val = val.lower()\n\n if val in ('t', 'y', 'yes', 'on', 'true', '1', 1, True):\n return True\n\n elif val in ('f', 'n', 'no', 'off', 'false', '0', 0, False):\n return False\n\n else:\n raise ValueError('Could not convert \"%s\" to boolean!' % val)","def to_bool(value):\n if str(value).lower() in (\"yes\", \"y\", \"true\", \"t\", \"1\"):\n return True\n if str(value).lower() in (\"no\", \"n\", \"false\", \"f\", \"0\", \"0.0\", \"\", \"none\", \"[]\", \"{}\"):\n return False\n raise Exception('Invalid value for boolean conversion: ' + str(value))","def _parse_bool(val):\n return val.lower() in ('yes', 'true', 't', '1')","def bool_string(line):\n\n line = line.lower()\n\n bout = None\n if line in ['true', 't', '1', '']:\n bout = True\n elif line in ['false', 'f', '0']:\n bout = False\n\n if bout is None:\n raise ValueError('input not interpretable as a boolean')\n\n return bout","def _str_to_bool(val):\n if val.lower() in ('yes', 'true', 't', 'y', '1'):\n return True\n elif val.lower() in ('no', 'false', 'f', 'n', '0'):\n return False\n else:\n raise argparse.ArgumentTypeError('Boolean value expected.')","def str2bool(s, default=False):\n if not s:\n return False\n if s in ('', u''):\n return False\n s = s.lower()\n if s in (u'on', u'true', u'1', 'on', 'true', '1'):\n return True\n if s in (u'off', u'false', u'0', 'off', 'false', '0'):\n return False\n return default"],"string":"[\n \"def boolean(s):\\r\\n ss = str(s).lower()\\r\\n if ss in TRUTHY_STRINGS:\\r\\n return True\\r\\n elif ss in FALSY_STRINGS:\\r\\n return False\\r\\n else:\\r\\n raise ValueError(\\\"not a valid boolean value: \\\" + repr(s))\",\n \"def __charToBool(self, char):\\n\\n if char == '1':\\n return True\\n elif char == '0':\\n return False\",\n \"def toBool( string ):\\r\\n return string == 'true'\",\n \"def _get_bool(string):\\n string = string.lower()\\n return True if string == 'y' else False\",\n \"def is_bool(self):\\n answer = self._call('is_bool')\\n return answer.yes\",\n \"def str_to_bool(a):\\n if a == 'True':\\n return True\\n else:\\n return False\",\n \"def boolean(raw):\\n raw = raw.lower()\\n if not raw in (\\\"true\\\", \\\"false\\\"):\\n raise ValueError(\\\"Could not parse a boolean value from '%s'.\\\" % raw)\\n return raw == \\\"true\\\"\",\n \"def is_bool(string_):\\n bools = [\\\"yes\\\", \\\"y\\\", \\\"1\\\", \\\"no\\\", \\\"n\\\", \\\"0\\\"]\\n if string_.lower() in bools:\\n return True\\n return False\",\n \"def str_to_bool(s):\\n s = s.lower()\\n if s not in {'true', 'false', '1', '0'}:\\n raise ValueError('Bool expected, but got %r' % s)\\n return s in {'true', '1'}\",\n \"def string_to_boolean(word):\\n if word.strip().lower() in ['true', 'yes', 'on', '1']:\\n return True\\n else:\\n return False\",\n \"def is_bool (self, phrase):\\r\\n \\r\\n return isinstance(phrase,bool)\",\n \"def _isbool(string):\\n return type(string) is bool or (\\n isinstance(string, (bytes, str)) and string in (\\\"True\\\", \\\"False\\\")\\n )\",\n \"def Bool(arg):\\n return arg.lower() in ('y', 'true', 't', '1')\",\n \"def str2bool(v):\\n return v.lower() in (\\\"yes\\\", \\\"true\\\", \\\"t\\\", \\\"1\\\")\",\n \"def boolean(value):\\n if isinstance(value, str):\\n if value.lower() in [\\\"0\\\", \\\"no\\\", \\\"n\\\", \\\"false\\\", \\\"f\\\", \\\"off\\\"]:\\n return False\\n elif value.lower() in [\\\"1\\\", \\\"yes\\\", \\\"y\\\", \\\"true\\\", \\\"t\\\", \\\"on\\\"]:\\n return True\\n else:\\n raise ValueError(\\\"Invalid truth value '%s'\\\" % value)\\n elif isinstance(value, bool):\\n return value\\n else:\\n raise TypeError(\\\"invalid type %s, expect bool or str\\\" % type(value))\",\n \"def str_to_bool(text):\\n return text and text.lower() in [\\\"true\\\", \\\"y\\\", \\\"yes\\\", \\\"1\\\"]\",\n \"def str2bool(v):\\n return v.lower() in (\\\"yes\\\", \\\"true\\\", \\\"t\\\", \\\"1\\\")\",\n \"def boolean_string(s):\\n if s is None:\\n return False\\n s = str(s)\\n if s.lower() not in {'false', 'true'}:\\n raise ValueError('Not a valid boolean string')\\n return s.lower() == 'true'\",\n \"def truth(text):\\n lowered = str(text).lower()\\n if lowered in frozenset(['y', 'yes', 'true']):\\n return True\\n elif lowered in frozenset(['n', 'no', 'false']):\\n return False\\n else:\\n raise Error('Invalid truth value: %r' % text)\",\n \"def str2bool(v): # type: ignore\\n return v.lower() in (\\\"yes\\\", \\\"true\\\", \\\"t\\\", \\\"1\\\", \\\"on\\\")\",\n \"def isbool(s):\\n if type(s) in [int,float,bool]:\\n return True\\n elif (type(s) != str):\\n return False\\n return s in ['True','False']\",\n \"def to_bool(s: str):\\n if s.strip().lower() == \\\"y\\\":\\n return True\\n else:\\n return False\",\n \"def str_bool(s):\\n if not s:\\n return False\\n if type(s) != str:\\n # It's not a string and it's not falsy, soooo....\\n return True\\n s = s.lower()\\n if s in [\\\"false\\\", \\\"0\\\", \\\"no\\\", \\\"n\\\"]:\\n return False\\n return True\",\n \"def as_bool(value):\\n return str(value).lower() in ('1', 'true', 'on', 'yes')\",\n \"def isalpha(self) -> bool:\\n pass\",\n \"def str2bool(value):\\n if value:\\n return value.lower() in (\\\"true\\\",)\\n else:\\n return False\",\n \"def str2bool(value):\\n if value:\\n return value.lower() in (\\\"true\\\",)\\n else:\\n return False\",\n \"def strToBool(s):\\n\\tassert type(s) == str or type(s) == unicode\\n\\treturn s.lower() == \\\"true\\\"\",\n \"def is_letter(c):\\n return 'A' <= c <= 'Z' or 'a' <= c <= 'z'\",\n \"def str2bool(val: str) -> bool:\\n return val.lower() in (\\\"yes\\\", \\\"true\\\", \\\"t\\\", \\\"1\\\")\",\n \"def _str2bool(self, v):\\n return v.lower() in (\\\"yes\\\", \\\"true\\\", \\\"t\\\", \\\"1\\\")\",\n \"def str_to_bool(s):\\n if len(s) > 0 and s[0] in \\\"yYtT1\\\":\\n return True\\n return False\",\n \"def string_to_bool(test_string):\\n\\treturn bool(test_string in [\\\"True\\\", \\\"true\\\", \\\"Yes\\\", \\\"yes\\\", \\\"Y\\\", \\\"y\\\"])\",\n \"def is_bool(type_):\\n return remove_alias(type_) in create_cv_types(cpptypes.bool_t())\",\n \"def character(x):\\n if (x==\\\"a\\\"or x==\\\"A\\\"or x==\\\"e\\\"or x==\\\"E\\\"or x==\\\"i\\\"or x==\\\"I\\\"or x==\\\"o\\\"or x==\\\"O\\\"or x==\\\"u\\\"or x==\\\"U\\\"):\\n return('True')\\n else:\\n return('False')\",\n \"def asbool(s):\\n if s is None:\\n return False\\n if isinstance(s, bool):\\n return s\\n s = str(s).strip()\\n return s.lower() in truthy\",\n \"def asbool(s):\\n if s is None:\\n return False\\n if isinstance(s, bool):\\n return s\\n s = str(s).strip()\\n return s.lower() in truthy\",\n \"def cast_bool(value: str) -> bool:\\n return value.lower() in {\\\"true\\\", \\\"yes\\\", \\\"1\\\"}\",\n \"def str2bool(self, val):\\n return val.lower() in ('true','yes','t',1)\",\n \"def to_boolean(self,string):\\n if self.debug:\\n print('to_boolean'+lineno())\\n # FIXME\\n sys.exit(1)\\n #string.to_s.casecmp('true').zero?\",\n \"def str2bool(s: str):\\n\\n if s.lower() in [\\\"true\\\", \\\"yes\\\", \\\"1\\\"]:\\n return True\\n elif s.lower() in [\\\"false\\\", \\\"no\\\", \\\"0\\\"]:\\n return False\\n else:\\n print(\\\"Fehler: Boolean erwartet! %s ist nicht als Boolean interpretierbar\\\" % s)\\n exit(1)\",\n \"def getBool(string):\\n return (True)\",\n \"def asbool(value, true=(b'true', u'true'), false=(b'false', u'false')):\\n value = value.strip().lower()\\n if value in true: # might raise UnicodeWarning/BytesWarning\\n return True\\n if value in false:\\n return False\\n raise TypeError()\",\n \"def isTrue(s):\\n return s == \\\"Yes\\\"\",\n \"def to_bool(bool_str: str):\\n bool_str_lc = bool_str.lower()\\n if bool_str_lc in ('true', '1', 't'):\\n return True\\n if bool_str_lc in ('false', '0', 'f', ''):\\n return False\\n raise ValueError(f'cannot interpret {bool_str} as a bool')\",\n \"def interpret_boolean(input):\\n if not input:\\n return False\\n\\n input = input.lower().strip() if isinstance(input, basestring) else input\\n\\n return input in _trues\",\n \"def string_to_bool(string, default=True):\\n string = string.lower()\\n if string in ('1', 'true', 't'):\\n return True\\n elif string in ('0', 'false', 'f'):\\n return False\\n return default\",\n \"def to_bool(s):\\n if isinstance(s, bool):\\n return s\\n elif s.lower() in ['true', '1']:\\n return True\\n elif s.lower() in ['false', '0']:\\n return False\\n else:\\n raise ValueError(\\\"Can't cast '%s' to bool\\\" % (s))\",\n \"def _parse_string_as_bool(self, bool_string):\\n true_chars = ['t', 'T', 'y', 'Y', '1']\\n false_chars = ['f', 'F', 'n', 'N', '0']\\n if bool_string[0] in true_chars:\\n return True\\n if bool_string[0] in false_chars:\\n return False\\n\\n raise tcError(\\n \\\"Supplied string cannot be parsed as a bool: {0}\\\".format(bool_string))\",\n \"def bool_flag(s):\\n FALSY_STRINGS = {\\\"off\\\", \\\"false\\\", \\\"0\\\"}\\n TRUTHY_STRINGS = {\\\"on\\\", \\\"true\\\", \\\"1\\\"}\\n if s.lower() in FALSY_STRINGS:\\n return False\\n elif s.lower() in TRUTHY_STRINGS:\\n return True\\n else:\\n raise argparse.ArgumentTypeError(\\\"invalid value for a boolean flag\\\")\",\n \"def to_bool(value):\\n return value == 'true ' or value == 'true'\",\n \"def isalpha(a):\\n return _vec_string(a, bool_, 'isalpha')\",\n \"def bool_(val):\\n if isinstance(val, six.string_types) and val.lower() == 'false':\\n return False\\n return bool(val)\",\n \"def boolean(obj):\\n\\n try:\\n text = obj.strip().lower()\\n except AttributeError:\\n return bool(obj)\\n\\n if text in ('yes', 'y', 'on', 'true', 't', '1'):\\n return True\\n elif text in ('no', 'n', 'off', 'false', 'f', '0'):\\n return False\\n else:\\n raise ValueError(\\\"Unable to convert {!r} to a boolean value.\\\".format(text))\",\n \"def str_to_bool(s):\\n if isinstance(s, bool):\\n return s\\n elif not isinstance(s, six.string_types):\\n raise TypeError('argument must be a string')\\n\\n true_values = ('true', 'on', '1')\\n false_values = ('false', 'off', '0')\\n\\n if s.lower() in true_values:\\n return True\\n elif s.lower() in false_values:\\n return False\\n else:\\n raise ValueError('not a recognized boolean value: %s'.format(s))\",\n \"def booleanize(text):\\n ltext = text.lower()\\n if ltext == 'true':\\n booleanized = True\\n elif ltext == 'false':\\n booleanized = False\\n else:\\n raise ValueError('A monk asked: Is \\\"{}\\\" true or false.'.format(text))\\n return booleanized\",\n \"def to_bool(value):\\r\\n return bool(value) or lower(value) in [\\\"true\\\", \\\"yes\\\", \\\"on\\\"]\",\n \"def bool_flag(s):\\n if s.lower() in FALSY_STRINGS:\\n return False\\n elif s.lower() in TRUTHY_STRINGS:\\n return True\\n else:\\n raise argparse.ArgumentTypeError(\\\"invalid value for a boolean flag\\\")\",\n \"def _str_to_bool(s):\\n if s.lower() not in ['true', 'false']:\\n raise ValueError('Argument needs to be a '\\n 'boolean, got {}'.format(s))\\n return {'true': True, 'false': False}[s.lower()]\",\n \"def _str_to_bool(s):\\n if s.lower() not in ['true', 'false']:\\n raise ValueError('Argument needs to be a '\\n 'boolean, got {}'.format(s))\\n return {'true': True, 'false': False}[s.lower()]\",\n \"def _str_to_bool(s):\\n if s.lower() not in ['true', 'false']:\\n raise ValueError('Argument needs to be a '\\n 'boolean, got {}'.format(s))\\n return {'true': True, 'false': False}[s.lower()]\",\n \"def _str_to_bool(s):\\n if s.lower() not in ['true', 'false']:\\n raise ValueError('Argument needs to be a '\\n 'boolean, got {}'.format(s))\\n return {'true': True, 'false': False}[s.lower()]\",\n \"def _str_to_bool(s):\\r\\n if s.lower() not in ['true', 'false']:\\r\\n raise ValueError('Argument needs to be a boolean, got {}'.format(s))\\r\\n return {'true': True, 'false': False}[s.lower()]\",\n \"def str_to_bool(val: str) -> bool:\\n\\n val = val.lower()\\n if val in {\\n \\\"y\\\",\\n \\\"yes\\\",\\n \\\"yep\\\",\\n \\\"yup\\\",\\n \\\"t\\\",\\n \\\"true\\\",\\n \\\"on\\\",\\n \\\"enable\\\",\\n \\\"enabled\\\",\\n \\\"1\\\",\\n }:\\n return True\\n elif val in {\\\"n\\\", \\\"no\\\", \\\"f\\\", \\\"false\\\", \\\"off\\\", \\\"disable\\\", \\\"disabled\\\", \\\"0\\\"}:\\n return False\\n else:\\n raise ValueError(f\\\"Invalid truth value {val}\\\")\",\n \"def cast_str_to_bool(word: str) -> bool:\\n word = word.upper()\\n if word == \\\"N\\\":\\n return False\\n elif word == \\\"Y\\\":\\n return True\\n raise ValueError(\\\"%s is not a valid string to boolean!\\\" % str)\",\n \"def str2bool(self, v):\\n \\tprint('Entering conversion function')\\n return v.lower() in (\\\"yes\\\", \\\"true\\\", \\\"t\\\", \\\"1\\\")\",\n \"def convert_string_to_bool(string):\\n lower = string.lower()\\n if lower == \\\"true\\\":\\n return True\\n elif lower == \\\"false\\\":\\n return False\\n else:\\n raise ValueError(\\n \\\"Invalid value %s while expecting boolean string\\\" % string)\",\n \"def is_bool(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_bool)\",\n \"def str2bool(s):\\n if s == \\\"True\\\":\\n return True\\n elif s == \\\"False\\\":\\n return False\\n else:\\n raise ValueError\",\n \"def string_to_bool(value):\\n return False if value.upper() == \\\"FALSE\\\" or value == \\\"0\\\" or value == \\\"\\\" else True\",\n \"def str_to_bool(str):\\r\\n return False if str is None else str.lower() == \\\"true\\\"\",\n \"def str2bool(self, boolstring):\\n return boolstring.lower() in (\\\"yes\\\", \\\"true\\\", \\\"on\\\", \\\"1\\\", \\\"t\\\")\",\n \"def stringTruth(x):\\n if x is None:\\n return False\\n if x == '':\\n return False\\n if x == '0':\\n return False\\n lx = x.lower()\\n if lx == 'false':\\n return False\\n if lx == 'f':\\n return False\\n return True\",\n \"def check_bool_type(rep):\\n rep = rep.lower()\\n if rep in (\\\"true\\\", \\\"t\\\", \\\"yes\\\", \\\"y\\\"):\\n return True\\n elif rep in (\\\"false\\\", \\\"f\\\", \\\"no\\\", \\\"n\\\"):\\n return False\\n else:\\n print(\\\"Enter a boolean type (True, T, False, F) !\\\")\\n sys.exit(2)\",\n \"def boolean(val):\\n\\tif val == \\\"True\\\" or val == \\\"1\\\":\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\",\n \"def str_to_bool(argument):\\n\\n # Checks if the argument is already a boolean value.\\n if isinstance(argument, bool): return argument\\n\\n # Returns boolean depending on the input string.\\n if argument.lower() == \\\"true\\\" or argument.lower() == 't':\\n return True\\n elif argument.lower() == \\\"false\\\" or argument.lower() == 'f':\\n return False\\n\\n # Returns an error if the value is not converted to a boolean value.\\n return ArgumentTypeError(f\\\"Boolean value expected. Got \\\\\\\"{argument}\\\\\\\".\\\")\",\n \"def str2bool(value) -> bool:\\n if type(value) == type(''):\\n if value.lower() in (\\\"yes\\\", \\\"y\\\", \\\"true\\\", \\\"t\\\", \\\"1\\\"):\\n return True\\n if value.lower() in (\\\"no\\\", \\\"n\\\", \\\"false\\\", \\\"f\\\", \\\"0\\\", \\\"\\\"):\\n return False\\n raise Exception('Invalid value for boolean conversion: ' + value)\\n return bool(value)\",\n \"def __str_to_bool(self, s):\\n if s == 'True':\\n return True\\n elif s == 'False':\\n return False\\n else:\\n raise ValueError\",\n \"def alpha(self) -> bool:\\n return \\\"a\\\" in self.modifier if self.modifier else False\",\n \"def GetBool(self, variable):\\n value = self.GetString(variable)\\n return value and value.strip() == 'true'\",\n \"def str2bool(s):\\n if s.lower() not in ['true', 'false']:\\n raise ValueError('Need bool; got %r' % s)\\n return {'true': True, 'false': False}[s.lower()]\",\n \"def is_letter(user_input):\\n # If any characters is letter -> return boolean True else False\\n if any(char.isalpha() for char in user_input):\\n return True\\n return False\",\n \"def parse_boolean(val: str) -> str | bool:\\n val = val.lower()\\n if val in (\\\"y\\\", \\\"yes\\\", \\\"t\\\", \\\"true\\\", \\\"on\\\", \\\"1\\\"):\\n return True\\n if val in (\\\"n\\\", \\\"no\\\", \\\"f\\\", \\\"false\\\", \\\"off\\\", \\\"0\\\"):\\n return False\\n return val\",\n \"def str2bool(v):\\n\\n if isinstance(v, bool):\\n return v\\n if v.lower() in ('yes', 'true', 't', 'y', '1'):\\n return True\\n elif v.lower() in ('no', 'false', 'f', 'n', '0'):\\n return False\\n else:\\n raise ArgumentTypeError('Boolean value expected.')\",\n \"def str2bool(v) -> bool:\\n\\n if isinstance(v, bool):\\n return v\\n if v.lower() in ('yes', 'true', 't', 'y', '1'):\\n return True\\n elif v.lower() in ('no', 'false', 'f', 'n', '0'):\\n return False\\n else:\\n raise argparse.ArgumentTypeError('Boolean value expected.')\",\n \"def to_bool(value: Union[bool, str]) -> bool:\\n if isinstance(value, bool):\\n return value\\n if value == 'J':\\n return True\\n elif value == 'N':\\n return False\\n else:\\n raise ValueError(f\\\"Expected 'J' or 'N', got {value!r}\\\")\",\n \"def is_bool(value):\\n try:\\n strtobool(value)\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def str2bool(string):\\n \\n string = \\tstring.lower()\\n if string == \\\"true\\\" or string == \\\"yes\\\" or string == \\\"1\\\":\\n return True\\n elif string == \\\"false\\\" or string == \\\"no\\\" or string == \\\"0\\\": \\n return False\\n else :\\n\\traise ValueError(\\\"Could not Interpret the boolean for %s\\\" %string)\",\n \"def stringToBoolean(text):\\n if text in (\\\"True\\\", \\\"1\\\"):\\n return True\\n if text in (\\\"False\\\", \\\"0\\\"):\\n return False\\n return text\",\n \"def isalpha(self):\\n return isalpha(self)\",\n \"def is_bool_type(*args):\\n return _ida_hexrays.is_bool_type(*args)\",\n \"def str2bool(string: str) -> bool:\\n if string.lower() in [\\\"yes\\\", \\\"true\\\", \\\"t\\\", \\\"y\\\", \\\"1\\\"]:\\n return True\\n if string.lower() in [\\\"no\\\", \\\"false\\\", \\\"f\\\", \\\"n\\\", \\\"0\\\"]:\\n return False\\n raise argparse.ArgumentTypeError(\\\"Boolean value expected.\\\")\",\n \"def _is_alpha(argument):\\n\\n if not isinstance(argument, str):\\n return False\\n\\n if argument.lower() == 'alpha':\\n is_alpha = True\\n else:\\n argument, Z = _extract_charge_state(argument)\\n\\n if Z != 2:\\n is_alpha = False\\n elif argument[-2:] != '-4':\\n is_alpha = False\\n else:\\n\\n dash_position = argument.find('-')\\n argument = argument[:dash_position]\\n\\n if argument.lower() == 'helium' or argument == 'He':\\n is_alpha = True\\n else:\\n is_alpha = False\\n\\n return is_alpha\",\n \"def strtobool(val: str) -> bool:\\n val = val.lower()\\n if val in ('y', 'yes', 't', 'true', 'on', '1'):\\n return True\\n if val in ('n', 'no', 'f', 'false', 'off', '0'):\\n return False\\n raise ValueError('invalid truth value {!r}'.format(val))\",\n \"def str_to_bool(string):\\n\\n if string is not None:\\n if string.lower() in [\\\"true\\\", \\\"yes\\\", \\\"1\\\", \\\"on\\\"]:\\n return True\\n elif string.lower() in[\\\"false\\\", \\\"no\\\", \\\"0\\\", \\\"off\\\"]:\\n return False\\n\\n return None\",\n \"def validate_bool(val):\\n if type(val) is str:\\n val = val.lower()\\n\\n if val in ('t', 'y', 'yes', 'on', 'true', '1', 1, True):\\n return True\\n\\n elif val in ('f', 'n', 'no', 'off', 'false', '0', 0, False):\\n return False\\n\\n else:\\n raise ValueError('Could not convert \\\"%s\\\" to boolean!' % val)\",\n \"def to_bool(value):\\n if str(value).lower() in (\\\"yes\\\", \\\"y\\\", \\\"true\\\", \\\"t\\\", \\\"1\\\"):\\n return True\\n if str(value).lower() in (\\\"no\\\", \\\"n\\\", \\\"false\\\", \\\"f\\\", \\\"0\\\", \\\"0.0\\\", \\\"\\\", \\\"none\\\", \\\"[]\\\", \\\"{}\\\"):\\n return False\\n raise Exception('Invalid value for boolean conversion: ' + str(value))\",\n \"def _parse_bool(val):\\n return val.lower() in ('yes', 'true', 't', '1')\",\n \"def bool_string(line):\\n\\n line = line.lower()\\n\\n bout = None\\n if line in ['true', 't', '1', '']:\\n bout = True\\n elif line in ['false', 'f', '0']:\\n bout = False\\n\\n if bout is None:\\n raise ValueError('input not interpretable as a boolean')\\n\\n return bout\",\n \"def _str_to_bool(val):\\n if val.lower() in ('yes', 'true', 't', 'y', '1'):\\n return True\\n elif val.lower() in ('no', 'false', 'f', 'n', '0'):\\n return False\\n else:\\n raise argparse.ArgumentTypeError('Boolean value expected.')\",\n \"def str2bool(s, default=False):\\n if not s:\\n return False\\n if s in ('', u''):\\n return False\\n s = s.lower()\\n if s in (u'on', u'true', u'1', 'on', 'true', '1'):\\n return True\\n if s in (u'off', u'false', u'0', 'off', 'false', '0'):\\n return False\\n return default\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7187412","0.7018804","0.69296837","0.68297184","0.6738983","0.67321396","0.66973674","0.6673456","0.66617125","0.6620476","0.6616839","0.6589189","0.65791404","0.6567738","0.6567653","0.6552819","0.65400046","0.65357196","0.6515047","0.65084463","0.64890873","0.6481111","0.64801645","0.6478696","0.64776355","0.6462264","0.6462264","0.6457609","0.6455447","0.64527476","0.64321727","0.64258796","0.6421409","0.6407423","0.6403392","0.6398568","0.6398568","0.6385559","0.63804114","0.63735956","0.63732564","0.63719463","0.6368062","0.6350939","0.63405","0.6339794","0.6335935","0.6333062","0.6322576","0.6315336","0.63124084","0.63063717","0.6295482","0.6264591","0.6257176","0.62568265","0.6252873","0.6244316","0.62409073","0.62409073","0.62409073","0.62409073","0.6238162","0.62288064","0.6228342","0.62124115","0.6211237","0.61985123","0.6192196","0.6192147","0.618586","0.6184614","0.6176783","0.61720854","0.61634463","0.6162377","0.6150097","0.61453116","0.613276","0.61297685","0.61261725","0.612373","0.6119879","0.6113773","0.61053246","0.61028755","0.60956043","0.60891217","0.608654","0.6081574","0.60811305","0.60809594","0.60671663","0.60633844","0.60633755","0.6061278","0.6060342","0.6044292","0.60434455","0.6031848","0.602463"],"string":"[\n \"0.7187412\",\n \"0.7018804\",\n \"0.69296837\",\n \"0.68297184\",\n \"0.6738983\",\n \"0.67321396\",\n \"0.66973674\",\n \"0.6673456\",\n \"0.66617125\",\n \"0.6620476\",\n \"0.6616839\",\n \"0.6589189\",\n \"0.65791404\",\n \"0.6567738\",\n \"0.6567653\",\n \"0.6552819\",\n \"0.65400046\",\n \"0.65357196\",\n \"0.6515047\",\n \"0.65084463\",\n \"0.64890873\",\n \"0.6481111\",\n \"0.64801645\",\n \"0.6478696\",\n \"0.64776355\",\n \"0.6462264\",\n \"0.6462264\",\n \"0.6457609\",\n \"0.6455447\",\n \"0.64527476\",\n \"0.64321727\",\n \"0.64258796\",\n \"0.6421409\",\n \"0.6407423\",\n \"0.6403392\",\n \"0.6398568\",\n \"0.6398568\",\n \"0.6385559\",\n \"0.63804114\",\n \"0.63735956\",\n \"0.63732564\",\n \"0.63719463\",\n \"0.6368062\",\n \"0.6350939\",\n \"0.63405\",\n \"0.6339794\",\n \"0.6335935\",\n \"0.6333062\",\n \"0.6322576\",\n \"0.6315336\",\n \"0.63124084\",\n \"0.63063717\",\n \"0.6295482\",\n \"0.6264591\",\n \"0.6257176\",\n \"0.62568265\",\n \"0.6252873\",\n \"0.6244316\",\n \"0.62409073\",\n \"0.62409073\",\n \"0.62409073\",\n \"0.62409073\",\n \"0.6238162\",\n \"0.62288064\",\n \"0.6228342\",\n \"0.62124115\",\n \"0.6211237\",\n \"0.61985123\",\n \"0.6192196\",\n \"0.6192147\",\n \"0.618586\",\n \"0.6184614\",\n \"0.6176783\",\n \"0.61720854\",\n \"0.61634463\",\n \"0.6162377\",\n \"0.6150097\",\n \"0.61453116\",\n \"0.613276\",\n \"0.61297685\",\n \"0.61261725\",\n \"0.612373\",\n \"0.6119879\",\n \"0.6113773\",\n \"0.61053246\",\n \"0.61028755\",\n \"0.60956043\",\n \"0.60891217\",\n \"0.608654\",\n \"0.6081574\",\n \"0.60811305\",\n \"0.60809594\",\n \"0.60671663\",\n \"0.60633844\",\n \"0.60633755\",\n \"0.6061278\",\n \"0.6060342\",\n \"0.6044292\",\n \"0.60434455\",\n \"0.6031848\",\n \"0.602463\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77491,"cells":{"query":{"kind":"string","value":"Given a list of list of Results, return a named threetuple with the an item with the name of each function in self.stat_functions. Each of these items is itself a similar threetuple, but each item is, e.g., the maximum of the maximums of the results."},"document":{"kind":"string","value":"def statistics(self, results):\n nt = namedtuple(\"Statistic\", [n.__name__ for n in self.stat_functions])\n s = []\n for f_one in self.stat_functions:\n inside = []\n for f_two in self.stat_functions:\n inside.append(f_two(f_one(r.results) for r in results))\n s.append(nt(*inside))\n return nt(*s)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_func_tuples():\n func_tuples = [\n ('met_gumeJ1_3sopt_tr20', 'Rel-UME J1', 'C1-.'),\n ('met_gumeJ5_3sopt_tr20', 'Rel-UME J5', 'r-^'),\n ('met_gfssdJ1_3sopt_tr20', 'Rel-FSSD J1', 'C4--'),\n ('met_gfssdJ5_3sopt_tr20', 'Rel-FSSD J5', 'b-x'),\n\n ('met_gmmd_med', 'Rel-MMD', 'k-.'),\n ('met_gmmd_med_bounliphone', 'Rel-MMD medboun', 'k-'),\n\n ('met_gfssdJ1_3sopt_tr50', 'FSSD-opt3 J1', 'b-^'),\n ('met_gfssdJ5_3sopt_tr50', 'FSSD-opt3 J5', 'b-.h'),\n\n ('met_gumeJ1_2V_rand', 'UME-rand J1', 'r--^'),\n ('met_gumeJ1_1V_rand', 'UME-rand J1 1V', 'y-'),\n ('met_gumeJ2_2V_rand', 'UME-rand J2', 'g--^'),\n ('met_gumeJ3_2V_rand', 'UME-rand J3', 'b--^'),\n ('met_gumeJ5_2V_rand', 'UME-rand J5', 'k--^'),\n\n ('met_gumeJ1_2sopt_tr20', 'Rel-UME-opt2 J1', 'C2-.'),\n ('met_gumeJ5_2sopt_tr20', 'Rel-UME-opt2 J5', 'g-'),\n ('met_gumeJ1_2sopt_tr50', 'Rel-UME-opt2 J1', 'r-.h'),\n\n ('met_gumeJ1_3sopt_tr50', 'UME-opt3 J1', 'r-'),\n ('met_gumeJ5_3sopt_tr50', 'UME-opt3 J5', 'k-'),\n\n\n ]\n return func_tuples","def _get_attribute_functions(self, attributes):\n subqueries = []\n columns = []\n for attr in attributes:\n function = attributes[attr]\n if function == 'sum':\n sq = DBSession.query(\n self.db_item.id.label('item_id'),\n cast(self.db_value.value, Float).label('v')\n ). \\\n join(self.db_taggroup). \\\n join(\n self.db_tag,\n self.db_taggroup.id == self.db_tag.fk_tag_group). \\\n join(\n self.db_value,\n self.db_value.id == self.db_tag.fk_value). \\\n join(self.db_key, self.db_key.id == self.db_tag.fk_key). \\\n filter(self.db_key.key == attr). \\\n subquery()\n subqueries.append(sq)\n columns.append(func.sum(sq.c.v))\n elif function == 'count' or function == 'count distinct':\n if attr == 'Activity' or attr == 'Stakeholder':\n columns.append(func.count())\n else:\n sq = DBSession.query(\n self.db_item.id.label('item_id'),\n self.db_value.value.label('v')\n ). \\\n join(self.db_taggroup). \\\n join(\n self.db_tag,\n self.db_taggroup.id == self.db_tag.fk_tag_group). \\\n join(self.db_value). \\\n join(self.db_key). \\\n filter(self.db_key.key == attr). \\\n subquery()\n subqueries.append(sq)\n if (function == 'count distinct'):\n columns.append(func.count(distinct(sq.c.v)))\n else:\n columns.append(func.count(sq.c.v))\n return subqueries, columns","def get_all_metrics(\n self,\n metric_funcs: list,\n ):\n\n profile_results = {\"table\": {}, \"columns\": defaultdict(dict)}\n metric_list = [\n self.compute_metrics(*metric_func) for metric_func in metric_funcs\n ]\n for metric_result in metric_list:\n profile, column, metric_type = metric_result\n\n if metric_type == MetricTypes.Table.value:\n profile_results[\"table\"].update(profile)\n if metric_type == MetricTypes.System.value:\n profile_results[\"system\"] = profile\n else:\n if profile:\n profile_results[\"columns\"][column].update(\n {\n \"name\": column,\n \"timestamp\": datetime.now(tz=timezone.utc).timestamp(),\n **profile,\n }\n )\n return profile_results","def _groups_stat_transform(\n group_stats: List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]\n) -> Dict[str, torch.Tensor]:\n return {\n \"tp\": torch.stack([stat[0] for stat in group_stats]),\n \"fp\": torch.stack([stat[1] for stat in group_stats]),\n \"tn\": torch.stack([stat[2] for stat in group_stats]),\n \"fn\": torch.stack([stat[3] for stat in group_stats]),\n }","def organize_results(result_dicts_list, config_fn):\n\n metric_id = config_fn.get_metric_fn_id()[1]\n organized_results = []\n for results_list in result_dicts_list:\n for result_dict in results_list:\n dict_ = {}\n for key, value in result_dict.items():\n dict_[key] = value\n organized_results.append(dict_)\n pass\n\n return organized_results","def calculate_stats(time_list):\n time_array = np.array(time_list)\n\n median = np.median(time_array)\n mean = np.mean(time_array)\n std_dev = np.std(time_array)\n max_time = np.amax(time_array)\n min_time = np.amin(time_array)\n quantile_10 = np.quantile(time_array, 0.1)\n quantile_90 = np.quantile(time_array, 0.9)\n \n basic_key = [\"median\",\"mean\",\"std_dev\",\"min_time\",\"max_time\",\"quantile_10\",\"quantile_90\"]\n basic_value = [median,mean,std_dev,min_time,max_time,quantile_10,quantile_90]\n\n dict_basic = dict(zip(basic_key, basic_value))\n\n \n return pd.DataFrame(dict_basic, index = [0])","def getStatistics(results):\n results.sort(key=lambda x: x[0][0])\n hole1Values = [x[0][0] for x in results]\n logProbs = [x[1] for x in results]\n maxLogProb = max(logProbs)\n probs = [math.exp(x - maxLogProb) for x in logProbs]\n probs = [i / sum(probs) for i in probs]\n print(probs)\n expectedValue = sum(probs[i]*hole1Values[i] for i in xrange(len(probs)))\n stats = {}\n stats['avg'] = expectedValue\n stats['sd'] = sum(\n probs[i] * (expectedValue - hole1Values[i])**2 for i in range(len(probs))\n ) ** 0.5\n stats['map'] = hole1Values[max(xrange(len(probs)), key = lambda i: probs[i])]\n stats['pavg'] = probs[int(stats['avg']*(len(results) - 1))]\n stats['pmap'] = probs[int(stats['map']*(len(results) - 1))]\n return stats, hole1Values, logProbs","def get_output_distr(self, stat_list):\n out_probs = [[np.zeros(500) for _ in stat_list]\n for _ in range(self.task_count)]\n\n # for each task, get the posterior distribution for each predicted\n # output label\n for i in range(self.task_count):\n distr_vec = [\n stats.norm(loc=mu, scale=sigma) for mu, sigma in\n zip(self.output_mat['mu'][i, :],\n self.output_mat['sigma'][i, :])\n ]\n\n # calculate the range of possible predicted output values\n min_range = min(distr.ppf(0.01) for distr in distr_vec)\n max_range = max(distr.ppf(0.99) for distr in distr_vec)\n x_vals = np.linspace(min_range, max_range, 1000)\n\n # calculate the cumulative probability density function across all\n # label distributions at each possible predicted value\n for j, stat in enumerate(stat_list):\n out_probs[i][j] = [\n (x,\n np.mean([distr.pdf(x) for distr, st in\n zip(distr_vec, stat) if st])\n ) for x in x_vals\n ]\n\n return out_probs","def run_functions( functions, dir ):\n res = []\n for i in range(0, len(functions)):\n res.append(functions[i].execute(dir))\n return summarize_as_html(res)","def stat_names_listifier(stats, words_and_numbs=False):\n if words_and_numbs == False:\n list_stats = ' '.join(stats).split(', ')\n\n if int(len(list_stats)) == 1:\n column_name = [stat_name_ifs(list_stats[0])]\n return column_name\n else:\n list_of_columns = [stat_name_ifs(term) for term in list_stats]\n if 'Invalid' in list_of_columns:\n return 'Invalid'\n else:\n return list_of_columns\n\n elif words_and_numbs == True:\n items_to_assess = ' '.join(stats).split(', ')\n\n list_stats = [item.rsplit(' ', 1)[0] for item in items_to_assess]\n values_list = [item.split(' ')[-1] for item in items_to_assess]\n\n for item in values_list:\n try:\n int(item)\n except:\n return 'Invalid'\n\n if int(len(list_stats)) == 1:\n column_name = [stat_name_ifs(list_stats[0])]\n if column_name == 'Invalid':\n return 'Invalid'\n else:\n return column_name, values_list\n else:\n list_of_columns = [stat_name_ifs(term) for term in list_stats]\n if 'Invalid' in list_of_columns:\n return 'Invalid'\n else:\n return list_of_columns, values_list","def get_general_stats() ->List[BaseStat]:\n return [PositionalTendencies(),\n SpeedTendencies(),\n ItemGoals(),\n DropshotGoals(),\n DropshotBallPhaseTimes(),\n DropshotStats()\n ]","def get_best_for_models(results, models, w_ft=-0.25, w_mt=-10, w_f1=3.5, f1_type='macro', is_test=False):\n best_results = []\n\n for model in models:\n model_results = metrics.filter_results(results, models=[model], is_test=is_test)\n __sort_by_score(model_results, w_ft, w_mt, w_f1, f1_type)\n\n best_results.append(model_results[0])\n\n return best_results","def computeStats(results, independentVariable):\n common = {}\n\n # Collect lists of the values\n for v in results:\n test = v.__dict__[independentVariable]\n try:\n common[test].append(v)\n except:\n common[test] = [v]\n\n result = []\n # Sanity check for number of data items being summarized\n # print (\"Computing stats on \" + str(len(common.values()[0])))\n for measurements in list(common.values()):\n resultValues = {}\n resultValues[independentVariable] = measurements[0].__dict__[\n independentVariable\n ]\n # print (\"Measurement[\\\"\"+independentVariable+\"\\\"] : \" + str(resultValues[independentVariable]))\n fieldnames = list(measurements[0].__dict__.keys())\n fieldnames.remove(independentVariable)\n for stat in fieldnames:\n values = [m.__dict__[stat] for m in measurements]\n resultValues[stat] = mean(values)\n resultValues[stat + \"_SD\"] = standardDeviation(values, resultValues[stat])\n result.append(\n measurement(list(resultValues.keys()), list(resultValues.values()))\n )\n # print (\"Result: \" + str(result))\n return result","def aggregateFunction():\r\n global aggFunc\r\n aggFunc = []\r\n for objFunc in P_prime:\r\n aggFunc.append(objFunc[0]*FileSettings.settingsdict['weights'][0] +\r\n objFunc[1]*FileSettings.settingsdict['weights'][1] +\r\n objFunc[2]*FileSettings.settingsdict['weights'][2] +\r\n objFunc[3]*FileSettings.settingsdict['weights'][3])\r\n return aggFunc","def generate_metrics_querys(metrics: List, period: int = 30, stats: str = 'Sum') -> Tuple[List, Dict]:\r\n metricsquery = [] #type: List \r\n resultsquery = defaultdict(list) #type: DefaultDict\r\n for metric in metrics:\r\n identity = randomString() \r\n metricsquery.append({'Id': identity, 'MetricStat': {'Metric': metric, 'Period': period, 'Stat': stats} })\r\n resultsquery[identity].append({'query': {'MetricStat': {'Metric': metric, 'Period': period, 'Stat': stats}}})\r\n return metricsquery, dict(resultsquery)","def dictionary_of_metrics(items):\n \n np_list = np.array(items) #create an array of list to use numpy functions on list\n metric_dict = {'mean': np.mean(np_list).round(2),\n 'median': np.median(np_list).round(2),\n 'var': np.var(np_list, ddof=1).round(2),\n 'std': np.std(np_list, ddof=1).round(2),\n 'min': np.min(np_list).round(2),\n 'max': np.max(np_list).round(2),} #create a dictionary that calculates the five metrics\n \n return metric_dict #return result as a dictionary","def extract_stats(stat_list, prefix=\"\"):\n for stat in stat_list:\n data = stat.strip().split('\\n')\n value = data[0]\n\n # attempt to convert to number, and deal\n # with using 'k' as an abbreviation to 1000.\n try:\n if value[-1] == 'k':\n value = float(value[:-1])*1000\n else:\n value = float(value)\n except ValueError:\n pass\n\n name = prefix + data[1].replace(' ', '_')\n profile[name] = value","def get_system_pairs(self, lst, results, s_p_imp=False, s_p_w=False):\n \n system_pairs = []\n if isinstance(results, np.ndarray):\n cur = None\n curmax = None\n for i, item in enumerate(lst):\n if cur and item['sent'] != cur['sent']:\n system_pairs.append(cur)\n cur = None\n if not cur:\n curmax = results[i][1]\n cur = item\n if results[i][1] > curmax:\n curmax = results[i][1]\n cur = item\n\n if cur:\n system_pairs.append(cur)\n return system_pairs\n\n for i, item in enumerate(lst):\n if results[i]:\n system_pairs.append(item)\n return system_pairs","def stringify_results(self, results, format_fn=str):\n processed = []\n\n for row in results:\n processed.append([format_fn(v) for v in row])\n\n return processed","def MakeStats(query, levelParams):\r\n result = []\r\n rets = [ExtractPeriodReturns(\r\n query, levelParams.levelFuncs[name], levelParams.retNames[num])\r\n for (num, name) in enumerate(levelParams.levelNames)]\r\n for num, name in enumerate(levelParams.levelNames):\r\n stats = ComputeStats(rets[num])\r\n mult = levelParams.multipliers[name]\r\n result.append(\r\n '%s: ret = %.3f, vol = %.3f, sqCorrs = %.3f, %.3f, %.3f' % ((\r\n name.ljust(10), stats[0]*mult, stats[1]*math.sqrt(mult)\r\n ) + tuple(stats[2][0:3])))\r\n return '\\n'.join(result)","def get_metrics(self, slug_list):\n # meh. I should have been consistent here, but I'm lazy, so support these\n # value names instead of granularity names, but respect the min/max\n # granularity settings.\n keys = ['seconds', 'minutes', 'hours', 'day', 'week', 'month', 'year']\n key_mapping = {gran: key for gran, key in zip(GRANULARITIES, keys)}\n keys = [key_mapping[gran] for gran in self._granularities()]\n\n results = []\n for slug in slug_list:\n metrics = self.r.mget(*self._build_keys(slug))\n if any(metrics): # Only if we have data.\n results.append((slug, dict(zip(keys, metrics))))\n return results","def gen_stats_zip(filename_list, zipfilename, tier='tier2', csv_processor='ICOS-ETC', zip_processor='LBL_AMP', ts_format=\"%Y-%m-%d %H:%M:%S\"):\n log.info(\"Generating ZIP and stats for: {z}\".format(z=zipfilename))\n\n if not filename_list:\n return [], []\n\n today = datetime.now().strftime(ts_format)\n output_zip = zip_file_list(filename_list=filename_list, zipfilename=zipfilename)\n\n if output_zip != zipfilename:\n raise ONEFluxError(\"ZIP filenames differ: {i} {o}\".format(i=zipfilename, o=output_zip))\n\n size, md5sum, _ = file_stat(filename=zipfilename)\n zip_entry = [['\"{e}\"'.format(e=os.path.basename(zipfilename)),\n '{e}'.format(e=format(size)),\n '\"{e}\"'.format(e=md5sum),\n '\"{e}\"'.format(e=len(filename_list)),\n '\"{e}\"'.format(e=tier),\n '\"{e}\"'.format(e=zip_processor),\n '\"{e}\"'.format(e=today)], ]\n csv_entries = []\n for filename in filename_list:\n f_size, f_md5sum, f_timestamp_change = file_stat(filename=filename)\n f_today = f_timestamp_change.strftime(ts_format)\n entry = ['\"{e}\"'.format(e=os.path.basename(zipfilename)),\n '\"{e}\"'.format(e=os.path.basename(filename)),\n '{e}'.format(e=f_size),\n '\"{e}\"'.format(e=f_md5sum),\n '\"{e}\"'.format(e=csv_processor),\n '\"{e}\"'.format(e=f_today)]\n csv_entries.append(entry)\n\n return zip_entry, csv_entries","def analyze_results(results): #, result_nonprivate):\n res_dimensions = zip(*results)\n mean, std = [], []\n \n for resdim in res_dimensions:\n mean.append ( numpy.average(resdim) )\n std.append ( numpy.std(resdim) )\n\n return mean, std","def getGroupFuncs(self):\n\n funcs = []\n for p in self.Parameters:\n if p.arg_name[0:8] == \"Function\" and p.arg_value:\n fct, attr = p.arg_value.split(':')\n if fct and attr:\n funcs.append((fct, attr))\n if not funcs:\n funcs.append(('count', '*'))\n return funcs","def writeStatTables(allStats, overviewStats):\n\n # Create all stats \n statFileHandlers = {}\n statFiles = {}\n measures = ['recall', 'specificity', 'FPR', 'FNR', 'PBC', 'precision', 'f-measure']\n\n for measure in measures:\n statFileHandlers[measure] = open(measure + '-all.csv', 'w', newline='')\n statFiles[measure] = csv.writer(statFileHandlers[measure], delimiter=';')\n \n firstRowText = ''\n allMethods = []\n\n for categoryKey, category in allStats.items():\n\n # Check the number of subSubCategories inside the subCategories and issue a warning\n # if the count of subSubCategories for different subCategories does not match\n\n for subCategoryKey, subCategory in category.items():\n for subSubCategoryKey, subSubCategory in subCategory.items():\n if subSubCategoryKey not in allMethods:\n allMethods.append(subSubCategoryKey)\n\n\n firstRowText = [''] + allMethods\n \n for measure in measures:\n statFiles[measure].writerow(firstRowText)\n \n\n for categoryKey, category in allStats.items():\n for subCategoryKey, subCategory in category.items():\n\n rows = {'recall': [categoryKey + '/' + subCategoryKey], 'specificity' : [categoryKey + '/' + subCategoryKey], 'FPR' : [categoryKey + '/' + subCategoryKey], 'FNR' : [categoryKey + '/' + subCategoryKey], 'PBC' : [categoryKey + '/' + subCategoryKey], 'precision' : [categoryKey + '/' + subCategoryKey], 'f-measure': [categoryKey + '/' + subCategoryKey]}\n for method in allMethods:\n\n if method in subCategory:\n if isinstance(subCategory[method], (list, tuple, dict)):\n for methodKey, value in subCategory[method].items():\n rows[methodKey] = rows[methodKey] + [float(value)]\n else:\n print('Error')\n else:\n for measure in measures:\n rows[measure] = rows[measure] + [''] # Write empty entry at this column\n\n\n for measure in measures:\n statFiles[measure].writerow(rows[measure])\n\n for statFileHandlerKey, statFileHandler in statFileHandlers.items():\n statFileHandler.close()\n\n # 1) Overview, containing summarised numbers of the entire category, for each method\n # From the overview stats, summarise by method and folder\n overallRawStats = {} # Overall stats, summarised by method, i.e. Kim2015-no-blur\n siteRawStats = {} # Stats for each method, summarised by folder, i.e. Kim2015-no-blur/Egensevej\n\n for siteKey, site in overviewStats.items():\n if categoryKey not in siteRawStats:\n siteRawStats[siteKey] = {}\n\n for sequenceKey, sequence in site.items():\n if len(subCategory.items()) > 1:\n\n for methodKey, method in sequence.items():\n if methodKey not in siteRawStats[siteKey]:\n siteRawStats[siteKey][methodKey] = {}\n \n if methodKey not in overallRawStats:\n overallRawStats[methodKey] = {}\n\n for statKey, stat in method.items():\n if statKey not in siteRawStats[siteKey][methodKey]:\n siteRawStats[siteKey][methodKey][statKey] = float(stat)\n else:\n siteRawStats[siteKey][methodKey][statKey] += float(stat)\n\n\n if statKey not in overallRawStats[methodKey]:\n # Just add it\n overallRawStats[methodKey][statKey] = float(stat)\n else:\n # Otherwise add to list\n overallRawStats[methodKey][statKey] += float(stat)\n \n overallStatHandler = open('overallStats.csv', 'w', newline='')\n overallStatFile = csv.writer(overallStatHandler, delimiter=';') \n siteStatHandler = open('statsPerSite.csv', 'w', newline='')\n siteStatFile = csv.writer(siteStatHandler, delimiter=';')\n\n # Write the header row\n subSubCategoryHeader = [''] + [''] + measures\n overallStatFile.writerow(subSubCategoryHeader)\n siteStatFile.writerow([''] + subSubCategoryHeader)\n\n # Get the recall, specificity and other measures from the summarised numbers and write them to the stat files\n for methodName, rawStats in overallRawStats.items():\n stats = getStats(rawStats)\n\n names = methodName.split('/')\n row = names\n\n for measure in measures:\n row = row + ['{:f}'.format(stats[measure])]\n\n overallStatFile.writerow(row)\n\n\n for siteName, methodStats in siteRawStats.items():\n\n for methodName, rawStats in methodStats.items():\n stats = getStats(rawStats)\n\n row = [siteName] + methodName.split('/')\n\n for measure in measures:\n row = row + ['{:f}'.format(stats[measure])]\n\n siteStatFile.writerow(row)\n \n overallStatHandler.close();\n siteStatHandler.close();\n\n # Convert the commas from \".\" to \",\" to allow processing in Excel\n convertCommaInFile('overallStats.csv')\n convertCommaInFile('statsPerSite.csv')\n\n for measure in measures:\n convertCommaInFile(measure + '-all.csv')\n\n\n return","def funcsresults2lines(funcsresults):\n funcssimple = [(func, value) \n for func, value in funcsresults if not isinstance(value, list)]\n funcslist = [(func, value) \n for func, value in funcsresults if isinstance(value, list)]\n funcsbunchlist = [(func, values) for func, values in funcslist if values]\n funcsbunchlist = [(func, epbunchlist2html(values)) \n for func, values in funcsbunchlist \n if isinstance(values[0], EpBunch)]\n cleanfuncsresults = funcssimple + funcsbunchlist\n lines = [\"%s = %s\" % (func, value) for func, value in cleanfuncsresults]\n return lines","def collection_statistic(func: Callable) -> Callable:\n STATS_FUNCS[func.__name__] = func\n return func","def stat(lst):\n n = float(len(lst))\n mean = sum(lst) / n\n stdev = sqrt((sum(x * x for x in lst) / n) - (mean * mean))\n return mean, stdev","def get_result(self):\r\n return (self.x, self.lambd)","def format_multiple_results(results, features_to_show=[]):\n best_score_array = np.array([])\n for r in results:\n print r\n print r[-1]\n best_score_array = np.append(best_score_array, r[-1])\n\n print \"BEST SCORE ARRAY\", best_score_array\n print \"Best Score\"\n print \"Mean: \", best_score_array.mean()\n print \"Std: \", best_score_array.std()","def get_stats_on_profiles_using_named_tuple(profiles: list) -> \"Stats\":\n\n \n if type(profiles) != list:\n raise TypeError(\"A list needs to be provided as an argument\")\n\n if not len(profiles):\n raise ValueError(\"An empty list is provided\")\n\n Profile = namedtuple(\"Profile\", (\"job\", \"company\", \"ssn\", \"residence\", \"current_location\", \n \"blood_group\", \"website\", \"username\", \"name\", \"sex\", \"address\", \"mail\", \"birthdate\"))\n \n \n Stats = namedtuple(\"Stats\", (\"largest_blood_type\", \"mean_current_location\", \"oldest_person_age\", \"average_age\"))\n\n Stats.__doc__ = \"Represents the various statistics on the complete profile list\"\n Stats.largest_blood_type.__doc__ = \"This is the Blood Group of the maximum profiles in the dataset\"\n Stats.mean_current_location.__doc__ = \"This is the Mean of all the locations in the dataset\"\n Stats.oldest_person_age.__doc__ = \"The age of the oldest person in the dataset\"\n Stats.average_age.__doc__ = \"The average age of all the people in the dataset\"\n\n try:\n profiles_tuple = [Profile(**p) for p in profiles]\n except:\n raise ValueError(\"The valid list of fake profiles need to be sent.\")\n\n return Stats(\n largest_blood_type = Counter(p.blood_group for p in profiles_tuple).most_common(1)[0][0],\n mean_current_location = (sum(p.current_location[0] for p in profiles_tuple)/len(profiles_tuple),\n sum(p.current_location[1] for p in profiles_tuple)/len(profiles_tuple)),\n oldest_person_age = (date.today() - max(profiles_tuple, key = lambda item: (date.today() - item.birthdate).days).birthdate).days,\n average_age = sum((date.today() - p.birthdate).days for p in profiles_tuple)/len(profiles_tuple)\n )","def _metric_max_over_ground_truths(metric_fn: Callable[[str, str], Tensor], prediction: str, ground_truths: List[str]) ->Tensor:\n return max(metric_fn(prediction, truth) for truth in ground_truths)","def stats(self, ops=(min, max, np.median, sum)):\n names = [op.__name__ for op in ops]\n ops = [_zero_on_type_error(op) for op in ops]\n columns = [[op(column) for op in ops] for column in self.columns]\n table = self._with_columns(columns)\n stats = table._unused_label('statistic')\n table[stats] = names\n table.move_to_start(stats)\n return table","def get_stat(self,stat,**kwargs):\n stat=stat.lower()\n if stat.startswith('elem'): stat = 0#'elements'\n elif stat=='score': stat = 1#'score'\n elif stat.startswith('max'): stat = 2#'maximum'\n elif stat.startswith('point'): stat = 3#'points'\n elif stat.startswith('weight'): stat = 4#'weights'\n else: raise ValueError, '{} is not an accepted argument for stat'.format(stat)\n weighted = kwargs.get('weighted',False)\n counted = kwargs.get('counted',False)\n weighted,counted = map(bool,[weighted,counted])\n \n #generating ordered list of grades\n listy = []\n for gr in self._grades:\n val=None\n if gr.score is not None:\n val=gr.score\n if gr.getMaximum():\n val/=1.0*gr.getMaximum()\n if gr.getWeight() and weighted:\n val*=gr.getWeight()\n listy.append( (gr,val) )\n \n listy.sort(lambda a,b: cmp(a[1],b[1]),None,True)\n \n #trimming list (if counted)\n if counted and self.parentCategory.use_best_count and \\\n self.parentCategory.element_count is not None:\n listy = listy[0:self.parentCategory.element_count]\n listy = [x[0] for x in listy]\n \n if stat is 0:\n return listy\n \n #building current score, maximum, points, and weight\n curScore = 0\n curMax = 0\n curPoints = 0\n curWeight = 0\n for gr in listy:\n if gr.score is None:\n continue\n scr,mx,wgt,xtra = gr.score,gr.getMaximum(),gr.getWeight(),gr.extra_credit\n if wgt and weighted:\n scr=scr*wgt\n if mx:\n if weighted:\n curMax += mx*wgt\n else: curMax += mx\n curPoints+= 1.0*scr/mx\n else:\n curPoints+= scr\n if not xtra:\n curWeight+=wgt\n curScore += scr\n \n if stat is 1:\n return curScore\n if stat is 2:\n return curMax\n if stat is 3:\n if curMax:\n return 1.0*curPoints\n return None\n if stat is 4:\n return curWeight","def _summarize_as_html_helper( ret, diagnostics=True):\n summary = []\n summary.append(\"<table>\\n\")\n for i in range(0, len(ret)):\n result = ret[i]\n f = result.function_name\n summary.append(\"<tr>\")\n summary.append(\"<td>Question\")\n summary.append(str(i+1))\n summary.append(\":</td>\")\n summary.append(\"<td colspan='2'>Called function \")\n summary.append(escapeHTML(f) + \"()\")\n summary.append(\"</td>\")\n summary.append(\"</tr>\\n\")\n if result.internal_error:\n summary.append(\"<tr><td></td><td></td><td class='error'>\");\n summary.append(\"Server error. Please retry and if the problem persists report a bug.\\n\");\n summary.append(result.internal_error);\n summary.append(\"</td></tr>\\n\")\n elif result.error:\n summary.append(\"<tr><td></td><td></td><td class='error'>Student error. \")\n summary.append(escapeHTML(result.error) + \"</td></tr>\\n\")\n else:\n summary.append(\"<tr><td></td><td colspan='2'>Return values:</td></tr>\\n\")\n ret_values = result.ret\n for j in range(0, len(ret_values)):\n summary.append(\"<tr>\")\n summary.append(\"<td></td>\")\n summary.append(\"<td>Return value \" + str(j+1) + \"=</td>\")\n summary.append(\"<td>\" + str(ret_values[j]) + \"</td>\")\n summary.append(\"</tr>\\n\")\n if diagnostics:\n if result.internal_error:\n summary.append(\"<tr><td></td><td></td><td>Diagnostics</td></tr>\")\n else:\n summary.append(\"<tr><td></td><td></td><td>MATLAB Output</td></tr>\")\n summary.append(\"<tr><td></td><td></td><td><pre>\")\n if result.internal_error:\n summary.append( result.internal_error )\n else:\n summary.append( result.output )\n summary.append(\"</pre></td></tr>\")\n summary.append(\"</table>\\n\")\n summary = \"\".join(summary)\n return summary","def basic_stats_custom(x):\n def my_min(x):\n m = x[0]\n for elem in x:\n if elem < m:\n m = elem\n else:\n pass\n return m\n\n def my_max(x):\n m = x[0]\n for elem in x:\n if elem > m:\n m = elem\n else:\n pass\n return m\n\n def my_sum(x):\n acc = 0\n for elem in x:\n acc += elem\n return acc\n\n def my_len(x):\n acc = 0\n for elem in x:\n acc += 1\n return acc\n\n def my_mean(x):\n \"\"\"Return the mean of numbers in list. Since the task is not to use\n builtin functions, length is also defined anew (prior to this\n function definition).\n\n \"\"\"\n return my_sum(x) / my_len(x)\n return {\"Minimum: \": my_min(x), \"Maximum: \": my_max(x), \"Sum: \":\n my_sum(x), \"Mean: \": my_mean(x)}","def compute_metrics(self, results: list) -> dict:","def dist_stats(self,nn_list):\n\n nn_list = np.array(nn_list)\n d = self.dist[:,nn_list-1]\n\n mean = np.mean(d,axis=0)\n std = np.std(d,axis=0)\n\n return mean, std","def sort_result(self, result_list):\n case_map = {}\n case_types = []\n\n for status, test_case, output, trace in result_list:\n case_type = test_case.__class__\n\n if not case_map.has_key(case_type):\n case_map[case_type] = []\n case_types.append(case_type)\n\n case_map[case_type].append((status, test_case, output, trace))\n\n return [(typ, case_map[typ]) for typ in case_types]","def get_eval_metric_fns():\n metric_fns = {}\n metric_fns.update({\n \"metric/%s\" % name: tfr.metrics.make_ranking_metric_fn(name) for name in [\n tfr.metrics.RankingMetricKey.ARP,\n tfr.metrics.RankingMetricKey.MRR,\n ]\n })\n metric_fns.update({\n \"metric/ndcg@%d\" % topn: tfr.metrics.make_ranking_metric_fn(\n tfr.metrics.RankingMetricKey.NDCG, topn=topn)\n for topn in [1, 3, 5, 10]\n })\n return metric_fns","def group_results(results):\n equiv_classes = {}\n for tbl_prog, vis_spec in results:\n full_trace = vis_spec.eval()\n fronzen_trace = json.dumps(visual_trace.trace_to_table(full_trace), sort_keys=True)\n if fronzen_trace not in equiv_classes:\n equiv_classes[fronzen_trace] = []\n equiv_classes[fronzen_trace].append((tbl_prog, vis_spec))\n return equiv_classes","def define_result_as_arg_functions(self, functions):\n ordered = []\n for node in functions:\n ordered.append(node)\n if node.wrap.fortran is False:\n continue\n self.convert_result_as_arg(node, ordered)\n return ordered","def get_result(self):\r\n return (self.x, self.lambd, self.z)","def get_feature_statistics(results):\n to_be_deleted = []\n\n for result in results:\n if len(result.subset) != 6:\n to_be_deleted.append(result)\n\n length = len(results)\n feature_labels = datapoint_features\n statistics = {}\n\n for label in feature_labels:\n result_with = metrics.filter_results(results, features=[label])\n result_without = metrics.filter_results(results, without_features=[label])\n\n with_length = len(result_with)\n without_length = len(result_without)\n prevalence = with_length / length\n\n if prevalence != 0:\n avg_f1_dos = math.fsum([result.metrics['dos'].f1 for result in result_with]) / with_length\n avg_f1_fuzzy = math.fsum([result.metrics['fuzzy'].f1 for result in result_with]) / with_length\n avg_f1_imp = math.fsum([result.metrics['impersonation'].f1 for result in result_with]) / with_length\n else:\n avg_f1_dos = 0\n avg_f1_fuzzy = 0\n avg_f1_imp = 0\n\n avg_f1_without_dos = math.fsum([result.metrics['dos'].f1 for result in result_without]) / without_length\n avg_f1_without_fuzzy = math.fsum([result.metrics['fuzzy'].f1 for result in result_without]) / without_length\n avg_f1_without_imp = math.fsum([result.metrics['impersonation'].f1 for result in result_without]) / without_length\n avg_f1_diff_dos = avg_f1_without_dos - avg_f1_dos\n avg_f1_diff_fuzzy = avg_f1_without_fuzzy - avg_f1_fuzzy\n avg_f1_diff_imp = avg_f1_without_imp - avg_f1_imp\n\n statistics[label] = [prevalence, avg_f1_diff_dos, avg_f1_diff_fuzzy, avg_f1_diff_imp]\n\n return statistics","def create_gnuplot_statistic(statistic_entries):\n grouped_by_number_of_entries = {}\n for statistic in statistic_entries:\n key = statistic['max entries']\n if key not in grouped_by_number_of_entries:\n grouped_by_number_of_entries[key] = [statistic]\n else:\n grouped_by_number_of_entries[key].append(statistic)\n\n all_plots = multiplot(\"learn.py statistics\", title_font=(\"\", 18), plots_per_row=2)\n\n pos = 0\n max_pos = len(grouped_by_number_of_entries) - 1\n for key, statistic in grouped_by_number_of_entries.items():\n average_time_plot = plot()\n average_time_plot.set_ylabel(\"seconds\")\n if pos == max_pos:\n average_time_plot.set_xlabel(\"n'th test run\")\n average_time_plot.set_xtics(\"1\")\n average_time_plot.set_ytics(\"0.5\")\n average_time_plot.set_line_style(1, \"lc rgb \\\"#00ff00\\\" lw 2\")\n average_time_plot.set_fill_style(1, \"transparent solid 0.4 border\")\n values = list(enumerate([average(entry) for entry in statistic], 1))\n average_time_plot.add_curve(\"average times (max entries=%d)\" % key,\n values=values, mode=plot.FILLEDCURVES)\n\n all_plots.add_plot(average_time_plot)\n\n number_of_tests_plot = plot()\n number_of_tests_plot.set_ylabel(\"# tests\")\n if pos == max_pos:\n number_of_tests_plot.set_xlabel(\"n'th test run\")\n number_of_tests_plot.set_xtics(\"1\")\n number_of_tests_plot.set_ytics(\"1\")\n number_of_tests_plot.set_line_style(1, \"lc rgb \\\"#00ff00\\\" lw 2\")\n number_of_tests_plot.set_fill_style(1, \"transparent solid 0.4 border\")\n values = list(enumerate([entry['correct answers'] + entry['wrong answers']\n for entry in statistic], 1))\n number_of_tests_plot.add_curve(\"# of tests (max entries=%d)\" % key,\n values=values, mode=plot.FILLEDCURVES)\n\n all_plots.add_plot(number_of_tests_plot)\n pos += 1\n\n calculated_height = len(grouped_by_number_of_entries) * 250\n script(\"learn.gp\", all_plots, width=800, height=calculated_height).execute()","def get_eval_metric_fns(self, ):\n metric_fns = {}\n metric_fns.update({\n \"metric/%s\" % name: tfr.metrics.make_ranking_metric_fn(name) for name in [\n tfr.metrics.RankingMetricKey.ARP,\n tfr.metrics.RankingMetricKey.ORDERED_PAIR_ACCURACY,\n ]\n })\n metric_fns.update({\n \"metric/ndcg@%d\" % topn: tfr.metrics.make_ranking_metric_fn(\n tfr.metrics.RankingMetricKey.NDCG, topn=topn)\n for topn in [1, 3, 5, 10, 20]\n })\n return metric_fns","def get_team_stats() -> List[BaseStat]:\n return [PossessionStat(),\n TeamTendencies(),\n RelativeTendencies(),\n PerPossessionStat(),\n RumbleItemStat(),\n PreRumbleGoals(),\n DropshotStats()\n ]","def _func_list(name, size):\n return [hl.Func(\"%s_%d\" % (name, i)) for i in range(size)]","def _func_list(name, size):\n return [hl.Func(\"%s_%d\" % (name, i)) for i in range(size)]","def calc_metric(output, metrics):\n score = []\n for metric in metrics:\n metric_mod = __import__(\"sklearn.metrics\", fromlist=[metric])\n metric_func = getattr(metric_mod, metric)\n score.append(metric_func(output[0], output[1]))\n return score, output","def measure(funcs, args, comment='', verbose=False, number=1):\n if not comment:\n comment = repr(args)\n\n # measure performance\n results = []\n w = max(len(name) for name, _ in funcs)\n for name, f in funcs:\n results.append((measure_func(f, args, number=number), name))\n if verbose:\n print(\"{:{}s} {:>9s} {}\".format(\n name, w, human_seconds(results[-1][0]), comment))\n\n # print sorted results\n results.sort()\n mint = results[0][0] # minimal time\n ratios = [\"%5.2f\" % (t / mint,) for t, _ in results]\n maxratio_width = max(len(r) for r in ratios)\n # header\n print(\"{:{}s} {:>9s} {:>{}s} {}\".format(\n \"name\", w, \"time\", \"ratio\", maxratio_width, \"comment\"))\n ratios = [s.rjust(maxratio_width) for s in ratios]\n for (t, name), ratio in zip(results, ratios):\n print(\"{:{}s} {:>9s} {} {}\".format(\n name, w, human_seconds(t), ratio, comment))\n return results","def get_eval_metric_fns():\n\tmetric_fns = {}\n\tmetric_fns.update({\n\t\t\t\"metric/mrr@%d\" % topn: tfr.metrics.make_ranking_metric_fn(\n\t\t\t\t\ttfr.metrics.RankingMetricKey.MRR, topn=topn)\n\t\t\tfor topn in [10]\n\t})\n\tmetric_fns.update({\n\t\t\t\"metric/%s\" % name: tfr.metrics.make_ranking_metric_fn(name) for name in [\n\t\t\t\t\ttfr.metrics.RankingMetricKey.ARP,\n\t\t\t\t\t#tfr.metrics.RankingMetricKey.ORDERED_PAIR_ACCURACY,\n\t\t\t]\n\t})\n\tmetric_fns.update({\n\t\t\t\"metric/ndcg@%d\" % topn: tfr.metrics.make_ranking_metric_fn(\n\t\t\t\t\ttfr.metrics.RankingMetricKey.NDCG, topn=topn)\n\t\t\tfor topn in [1, 3, 5, 10]\n\t})\n\treturn metric_fns","def summarize(dataset):\n summaries = [(np.mean(attribute), np.std(attribute)) for attribute in zip(*dataset)]\n\n return summaries","def cmpthese(n=1, funcs=None, repeat=3):\n results = timethese(n=n, funcs=funcs, repeat=repeat)\n if isinstance(funcs, dict):\n results = results.values()\n\n results = sorted(results, key=lambda r: r[\"best_time\"])\n names = [r[\"name\"] for r in results]\n times = [r[\"best_time\"] for r in results]\n rates = [r[\"best_rate\"] for r in results]\n\n rows = []\n for row_idx, row_res in enumerate(results):\n row = []\n\n # Column 1 = performance\n row_rate = row_res[\"best_rate\"]\n\n for col_idx, col_res in enumerate(results):\n if row_idx == col_idx:\n out = 0\n else:\n col_rate = col_res[\"best_rate\"]\n out = row_rate / col_rate - 1\n\n row.append(out)\n rows.append(row)\n return {\"data\": rows, \"names\": names, \"times\": times, \"rates\": rates}","def dist_stats(self,nn_list):\n nn_list = np.array(nn_list)-1\n d = self.dist[:,nn_list]\n\n mean = np.mean(d,axis=0)\n std = np.std(d,axis=0)\n\n return mean, std","async def stat_table(self, data):\n\n table = \"\"\n table += tabulate([data[\"stats\"][1]], data[\"stats\"][0], tablefmt=\"grid\") + \"\\n\"\n table += tabulate([data[\"resist\"][1]], data[\"resist\"][0], tablefmt=\"grid\") + \"\\n\"\n if data[\"inherits\"] and data[\"inherits\"][0]:\n table += tabulate([data[\"inherits\"][1]], data[\"inherits\"][0], tablefmt=\"grid\") + \"\\n\"\n \n skills = tabulate(data[\"skills\"][1], data[\"skills\"][0], tablefmt=\"grid\")\n if len(skills) > 2000:\n counter = 0\n split_skills = []\n skills = skills.split(\"\\n\")\n skills = [\"\\n\".join(skills[8*i:min(8*(i+1)+1, len(skills))])\n for i in range(int(len(skills) / 8))]\n else:\n skills = [skills]\n\n results = [\"```\\n\" + table[:-1] + \"\\n```\"]\n for skill in skills:\n results.append(\"```\\n\" + skill + \"\\n```\")\n return results","def extract_statistics_output(\n partial_stats: List[Any],\n stats_generators: List[stats_generator.CombinerStatsGenerator]\n) -> statistics_pb2.DatasetFeatureStatisticsList:\n\n # We call compact before extract_output to guarentee that `compact()` is\n # called at least once, for testing coverage.\n outputs = [\n gen.extract_output(gen.compact(stats))\n for (gen, stats) in zip(stats_generators, partial_stats) # pytype: disable=attribute-error\n ]\n return _make_dataset_feature_statistics_list_proto(\n [_merge_dataset_feature_stats_protos(outputs)])","def _get_all(cls) -> Iterator[\"PermutationStatistic\"]:\n yield from (cls(name, func) for name, func in PermutationStatistic._STATISTICS)","def get_function(self, index = 0):\n return self._get_one_param('list_func')[index]","def process_assertion_list(self, cls, functions):","def create_functions(self, num_dimensions=400, min_threshold=0, max_threshold=255):\r\n functions = []\r\n for i in range(self.L):\r\n dimensions = np.random.randint(low = 0, \r\n high = num_dimensions,\r\n size = self.k)\r\n thresholds = np.random.randint(low = min_threshold, \r\n high = max_threshold + 1, \r\n size = self.k)\r\n\r\n functions.append(self.create_function(dimensions, thresholds))\r\n return functions","def computeStats(self, absList, statOut=None, errorOut=None):\n \n stats = EntityStats(self.entityTypes)\n for abs in absList:\n errorOut.write('---'+abs.id+'---\\n') \n \n # identify ALL annotated mentions, even in sentences we are not focused on\n# for sentence in abs.allSentences():\n# for mType in self.entityTypes:\n# aList = sentence.getAnnotatedMentions(mType, recomputeMentions=True)\n# \n# for sentence in abs.sentences:\n# for mType in self.entityTypes:\n# self.compareAnnotatedAndDetected(sentence, mType, \\\n# stats.irstats[mType], errorOut)\n\n\n for sentence in abs.allSentences():\n for mType in self.entityTypes:\n if sentence in abs.sentences:\n self.compareAnnotatedAndDetected(sentence, mType, \\\n stats.irstats[mType], errorOut)\n else: \n aList = sentence.getAnnotatedMentions(mType, recomputeMentions=True)\n \n\n stats.printStats()\n if statOut != None:\n stats.saveStats(statOut, keyPrefix='MF - ')\n \n return stats","def get_tuned_f1(results_df):\n df = results_df\n scores = []\n f1s = []\n lambdas = np.arange(.5, 5, .05)\n\n def add_weighted(df, lam):\n \"\"\" Calculates different weighted PMI values after already having mut_inf scores \"\"\"\n df['mut_inf_weighted'] = df.mut_inf + (lam - 1) * (df.head_conditional + df.tail_conditional) / 2.\n\n for lam in lambdas:\n ss = StandardScaler()\n add_weighted(df, lam=lam)\n model = GaussianMixture(2, n_init=1)\n dat = ss.fit_transform(df[['mut_inf_weighted']])\n pred = model.fit_predict(dat)\n score = model.aic(dat)\n f1 = f1_score((model.means_.argmax() == df.label), pred)\n scores.append(score)\n f1s.append(f1)\n\n scores = np.array(scores)\n f1s = np.array(f1s)\n lam = lambdas[scores.argmax()]\n\n optimal_lambda = lambdas[scores.argmax()]\n optimal_f1 = f1s[scores.argmax()]\n return optimal_f1, optimal_lambda","def calculate7NumberSummaryForOneList(ls, returnObj=None):\n\tfrom scipy import stats\t# for scoreatpercentile/percentileatscore to get quartiles\n\tif returnObj is None:\n\t\treturnObj = PassingData()\n\treturnObj.minimum = numpy.min(ls)\n\treturnObj.first_decile = stats.scoreatpercentile(ls, 10)\n\treturnObj.lower_quartile = stats.scoreatpercentile(ls, 25)\n\treturnObj.median = stats.scoreatpercentile(ls, 50)\n\treturnObj.upper_quartile = stats.scoreatpercentile(ls, 75)\n\treturnObj.last_decile = stats.scoreatpercentile(ls, 90)\n\treturnObj.maximum = numpy.max(ls)\n\treturn returnObj","def function(self, scores, multilabel):\n\n # Output functions\n # pylint: disable=C3001\n identity = lambda x: x\n sigmoid = lambda x: 1.0 / (1.0 + np.exp(-x))\n softmax = lambda x: np.exp(x) / np.sum(np.exp(x))\n function = identity if multilabel is None else sigmoid if multilabel else softmax\n\n # Apply output function\n return function(np.array(scores))","def summary(self, tmin=None, tmax=None, stats='basic'):\n output = {\n 'basic': {\n 'evp': 'Explained variance percentage',\n 'rmse': 'Root mean squared error',\n 'avg_dev': 'Average Deviation',\n 'rsq': 'Pearson R^2',\n 'bic': 'Bayesian Information Criterion',\n 'aic': 'Akaike Information Criterion'},\n }\n\n # get labels and method names for stats output\n if stats == 'all':\n # sort by key, label, method name\n selected_output = sorted([(k, l, f) for k, d in output.items()\n for f, l in d.items()])\n else:\n # sort by name, method name\n selected_output = sorted([(0, l, f) for f, l in\n output[stats].items()])\n\n # compute statistics\n labels_and_values = [(l, getattr(self, f)(tmin=tmin, tmax=tmax))\n for _, l, f in selected_output]\n labels, values = zip(*labels_and_values)\n\n stats = pd.DataFrame(index=list(labels), data=list(values),\n columns=['Value'])\n stats.index.name = 'Statistic'\n return stats","def get_statistics(pred, gt, num_cls=2):\n h,w = gt.shape\n statistics = []\n for i in range(num_cls):\n tp = np.sum((pred==i)&(gt==i))\n fp = np.sum((pred==i)&(gt!=i))\n fn = np.sum((pred!=i)&(gt==i)) \n statistics.append([tp, fp, fn])\n return statistics","def get_metrics_list(self, project, callback=None):\n metrics = sorted((yield gen.Task(self.client.smembers,\n self.get_metrics_key(project))))\n if callback:\n callback(metrics)","def calc_attribute_statistics(self, statistic_name):\n stats = {}\n for var, grids in self.attributes.items():\n if len(grids) > 1:\n stats[var] = getattr(np.array([getattr(np.ma.array(x, mask=self.masks[t] == 0), statistic_name)()\n for t, x in enumerate(grids)]), statistic_name)()\n else:\n stats[var] = getattr(np.ma.array(grids[0], mask=self.masks[0] == 0), statistic_name)()\n return stats","def _get_value(self, info):\n\n for function, data in info.items():\n for dimension, run_data in data.items():\n rezultat, local_mins, populations, fabicrated_args = run_data\n\n # prepare function\n function.set_args(fabicrated_args)\n run_f = function(dimensions=dimension)\n\n # get the best run\n best_run = None\n for run_id, value in populations.items():\n if not best_run:\n best_run = value\n elif (self._get_fit_pop(value, run_f, local_mins) <\n self._get_fit_pop(best_run, run_f, local_mins)):\n best_run = value\n\n # compute for the best run\n\n return 12","def _MpstatResults(\n metadata: Dict[str, Any],\n output: Dict[str, Any],\n interval: int,\n per_interval_samples: bool = False,\n ):\n start_timestamp = _ParseStartTime(output)\n samples = []\n hosts = output['sysstat']['hosts']\n\n for host in hosts:\n host_stats = host['statistics']\n number_of_cpus = host['number-of-cpus']\n metadata['nodename'] = host['nodename']\n\n samples += _GetCPUAverageMetrics(\n host_stats,\n number_of_cpus,\n metadata,\n start_timestamp)\n\n samples += _GetCPUAverageInterruptions(\n host_stats,\n number_of_cpus,\n metadata,\n start_timestamp)\n\n if per_interval_samples:\n samples += _GetPerIntervalSamples(\n host_stats,\n metadata,\n start_timestamp,\n interval)\n\n return samples","def _calc_frames(stats):\n timings = []\n callers = []\n for key, values in iteritems(stats.stats):\n timings.append(\n pd.Series(\n key + values[:-1],\n index=timing_colnames,\n )\n )\n for caller_key, caller_values in iteritems(values[-1]):\n callers.append(\n pd.Series(\n key + caller_key + caller_values,\n index=caller_columns,\n )\n )\n\n timings_df = pd.DataFrame(timings)\n callers_df = pd.DataFrame(callers)\n timings_df['filename:funcname'] = \\\n (timings_df['filename'] + ':' + timings_df['funcname'])\n timings_df = timings_df.groupby('filename:funcname').sum()\n return timings_df, callers_df","def get_player_stats() -> List[BaseStat]:\n return [BoostStat(),\n PositionalTendencies(),\n Averages(),\n BallDistanceStat(),\n ControlsStat(),\n SpeedTendencies(),\n CarryStat(),\n PerPossessionStat(),\n SpeedTendencies(),\n RumbleItemStat(),\n KickoffStat(),\n DropshotStats(),\n DemoStat()\n ]","def addfunctions(dtls, bunchdt):\n snames = [\n \"BuildingSurface:Detailed\",\n \"Wall:Detailed\",\n \"RoofCeiling:Detailed\",\n \"Floor:Detailed\",\n \"FenestrationSurface:Detailed\",\n \"Shading:Site:Detailed\",\n \"Shading:Building:Detailed\",\n \"Shading:Zone:Detailed\",\n ]\n for sname in snames:\n if sname.upper() in bunchdt:\n surfaces = bunchdt[sname.upper()]\n for surface in surfaces:\n func_dict = {\n \"area\": fh.area,\n \"height\": fh.height, # not working correctly\n \"width\": fh.width, # not working correctly\n \"azimuth\": fh.azimuth,\n \"tilt\": fh.tilt,\n \"coords\": fh.getcoords, # needed for debugging\n }\n try:\n surface.__functions.update(func_dict)\n except KeyError as e:\n surface.__functions = func_dict\n # add common functions\n # for name in dtls:\n # for idfobject in bunchdt[name]:\n # idfobject.__functions\n # idfobject['__functions']['fieldnames'] = fieldnames\n # idfobject['__functions']['fieldvalues'] = fieldvalues\n # idfobject['__functions']['getrange'] = GetRange(idfobject)\n # idfobject['__functions']['checkrange'] = CheckRange(idfobject)","def summarize_results(self, models, scores):\n mu = np.mean(scores)\n sigma = np.std(scores)\n best_model = models[np.argmax(scores)]\n best_score = max(scores)\n return best_model, best_score, mu, sigma","async def generate_leg_statistics(self) -> list:\n\n # General total amount of things\n amount_of_sessions = await self.bot.db.fetchval(\"SELECT COUNT(id) FROM legislature_sessions\")\n amount_of_bills = await self.bot.db.fetchval(\"SELECT COUNT(id) FROM legislature_bills\")\n amount_of_laws = await self.bot.db.fetchval(\"SELECT COUNT(law_id) FROM legislature_laws\")\n amount_of_motions = await self.bot.db.fetchval(\"SELECT COUNT(id) FROM legislature_motions\")\n\n # Sorted statistics by Discord Member\n amount_of_bills_by_submitter = self.count_rows_from_db_record(await self.bot.db.fetch(\"SELECT submitter FROM \"\n \"legislature_bills\"),\n 'submitter')\n amount_of_sessions_by_speaker = self.count_rows_from_db_record(await self.bot.db.fetch(\"SELECT speaker FROM \"\n \"legislature_sessions\"),\n 'speaker')\n query = \"\"\"SELECT submitter FROM legislature_bills AS b WHERE exists (SELECT 1 FROM legislature_laws l\n WHERE l.bill_id = b.id)\"\"\"\n amount_of_laws_by_submitter = self.count_rows_from_db_record(await self.bot.db.fetch(query), 'submitter')\n\n # Prettified sorted statistics by discord.Member\n pretty_top_submitter = self.get_pretty_stats(self.sort_dict_by_value(amount_of_bills_by_submitter), 'bills')\n\n pretty_top_speaker = self.get_pretty_stats(self.sort_dict_by_value(amount_of_sessions_by_speaker), 'sessions')\n\n pretty_top_lawmaker = self.get_pretty_stats(self.sort_dict_by_value(amount_of_laws_by_submitter), 'laws')\n\n return [amount_of_sessions, amount_of_bills, amount_of_laws, amount_of_motions,\n pretty_top_submitter, pretty_top_speaker, pretty_top_lawmaker]","def _parse_lighthouse_response(self, lighthouse_response, functions):\n # Keep all dict values relating to AWE functions and set the function\n # name as the key\n for function_name, audit in lighthouse_response[\"audits\"].items():\n if function_name in functions:\n yield (\n function_name,\n {\n # failing: the function was tested and it did not pass WCAG\n \"failing\": False if audit[\"score\"] == 1 else True,\n # applicable: the function can't be tested\n \"applicable\": audit[\"score\"] is not None,\n \"description\": audit[\"description\"],\n \"items\": list(\n self._parse_audit_items(\n audit.get(\"details\", {}).get(\"items\", []), function_name\n )\n ),\n },\n )","def many(self, tmin=None, tmax=None, stats=None):\n if not stats:\n stats = ['evp', 'rmse', 'rmsi', 'rsq']\n\n data = pd.DataFrame(index=[0], columns=stats)\n for k in stats:\n data.iloc[0][k] = (getattr(self, k)(tmin=tmin, tmax=tmax))\n\n return data","def map_functions(x, functions):\n res = []\n for func in functions:\n res.append(func(x))\n return res","def _prepare_stats_table(self, pinfos):\n\n stats_tbl = OrderedDict()\n stats_tbl[\"Title\"] = OrderedDict()\n for res in self.rsts:\n stats_tbl[res.reportid] = OrderedDict()\n\n for pinfo in pinfos:\n for colname in (pinfo[\"colname\"], pinfo[\"xcolname\"]):\n if colname in stats_tbl[\"Title\"]:\n continue\n\n # Each column name is represented by a row in the statistics table. Fill the \"Title\"\n # column.\n title_dict = stats_tbl[\"Title\"][colname] = OrderedDict()\n defs = self._refdefs.info[colname]\n\n if defs.get(\"unit\") == \"nanosecond\":\n # Convert nanoseconds to microseconds.\n unit = \"us\"\n else:\n unit = defs.get(\"short_unit\", \"\")\n\n title_dict[\"colname\"] = colname\n if unit:\n title_dict[\"colname\"] += f\", {unit}\"\n title_dict[\"coldescr\"] = defs[\"descr\"]\n\n title_dict[\"funcs\"] = OrderedDict()\n for funcname in self._stats_funcs:\n if funcname in self.rsts[0].cstats[colname]:\n title_dict[\"funcs\"][funcname] = RORawResult.get_stat_func_descr(funcname)\n\n # Now fill the values for each result.\n for res in self.rsts:\n res_dict = stats_tbl[res.reportid][colname] = OrderedDict()\n res_dict[\"funcs\"] = OrderedDict()\n\n for funcname in title_dict[\"funcs\"]:\n val = res.cstats[colname][funcname]\n fmt = \"{}\"\n if defs.get(\"unit\") == \"nanosecond\" and \"index\" not in funcname:\n val /= 1000\n fmt = \"{:.2f}\"\n if defs[\"type\"] == \"float\":\n fmt = \"{:.2f}\"\n\n fdict = res_dict[\"funcs\"][funcname] = OrderedDict()\n fdict[\"val\"] = fmt.format(val)\n fdict[\"raw_val\"] = val\n\n if self._refres.reportid == res.reportid:\n fdict[\"hovertext\"] = \"This is the reference result, other results \" \\\n \"are compared to this one.\"\n continue\n\n ref_fdict = stats_tbl[self._refres.reportid][colname][\"funcs\"][funcname]\n change = val - ref_fdict[\"raw_val\"]\n if ref_fdict[\"raw_val\"]:\n percent = (change / ref_fdict[\"raw_val\"]) * 100\n else:\n percent = change\n change = fmt.format(change) + unit\n percent = \"{:.1f}%\".format(percent)\n fdict[\"hovertext\"] = f\"Change: {change} ({percent})\"\n\n return stats_tbl","def _evaluate(self, function_name, x):\n stats = None\n for ii in range(self.nunique_vars):\n var = self.unique_variables[ii]\n indices = self.unique_variable_indices[ii]\n for jj in indices:\n stats_jj = np.atleast_1d(\n getattr(var.dist, function_name)(x[jj, :]))\n assert stats_jj.ndim == 1\n if stats is None:\n stats = np.empty((self.num_vars(), stats_jj.shape[0]))\n stats[jj] = stats_jj\n return stats","def get_as_rows(self, stats=(\"count\", \"mean\", \"stddev\", \"min\", \"max\")):\n return [\n row_from_keyed_values(\n [\n (\"summary\", stat)\n ] + [\n (col_name, self.get_stat(self.column_stat_helpers[col_name], stat))\n for col_name in self.col_names\n ]\n )\n for stat in stats\n ]","def get_th_mean_values(self, value_list):\n if not len(value_list):\n return None, None, None\n t, h, d = map(list, zip(*value_list))\n return (\n round(statistics.mean(t), 1),\n int(round(statistics.mean(h), 0)),\n round(statistics.mean(d), 1),\n )","def get_normalized_regret(evaluator_list):\n values = collections.defaultdict(list)\n for e in evaluator_list:\n values[e.task_name].append(e.get_normalized_regret())\n return _map(np.mean, values), _map(np.std, values)","def query_failure_rate_results(analysis_list):\n return {analysis['testId']: analysis for analysis in analysis_list}","def genHashFuncs(num_of_func, baskets):\n func_list = list()\n\n def build_func(param_a, param_b, param_m):\n def apply_funcs(input_x):\n return format((param_a * input_x + param_b) % param_m, 'b') \\\n .zfill(NUM_OF_BIT)\n\n return apply_funcs\n\n param_as = random.sample(range(1, sys.maxsize - 1), num_of_func)\n param_bs = random.sample(range(0, sys.maxsize - 1), num_of_func)\n for a, b in zip(param_as, param_bs):\n func_list.append(build_func(a, b, baskets))\n\n return func_list","def comparison_stats(df, els=None):\n if els is None:\n els = ['Li', 'Mg', 'Al', 'P', 'Ti', 'Y', 'La', 'Ce', 'Pr', 'Nd', 'Sm',\n 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Pb', 'Th',\n 'U']\n \n yl_stats = []\n \n for i, e in enumerate(els):\n x = df.loc[:, e + '_rd'].values\n yl = df.loc[:, e + '_la'].values\n \n yl_stats.append(summary_stats(x, yl, e))\n \n yl_stats = pd.concat(yl_stats).T\n \n return yl_stats.T","def GetMeansAndStdsFromList(lists):\n means = [np.mean(single_list) for single_list in lists]\n stds = [np.std(single_list) for single_list in lists]\n return means, stds","def apply_func(output, func):\n new_output = []\n for dict in output:\n mnemonic = copy.deepcopy(dict['mnemonic'])\n values = dict['values']\n new_values = func(values)\n new_output.append({'mnemonic': mnemonic, 'values': new_values})\n return new_output","def calc_metrics(metric_scores_list):\n\n N_split, N_miss, N_add, Q_P, Q_R, Q_F, N_gt, N_pred = [], [], [], [], [], [], [], []\n Q_rand, Q_jaccard, Q_aggregated_jaccard, Q_ctc, Q_piou = [], [], [], [], []\n tp, fp, fn = [], [], []\n\n for score in metric_scores_list:\n N_split.append(score['N_split']), N_miss.append(score['N_miss']), N_add.append(score['N_add'])\n Q_P.append(score['Q_P']), Q_R.append(score['Q_R']), Q_F.append(score['Q_F'])\n Q_rand.append(score['Q_rand']), Q_jaccard.append(score['Q_jaccard'])\n Q_aggregated_jaccard.append(score['Q_aggregated_jaccard'])\n if \"Q_ctc\" in score:\n Q_ctc.append(score['Q_ctc']), \n Q_piou.append(score['Q_piou'])\n N_gt.append(score['N_gt']), N_pred.append(score['N_pred'])\n tp.append(score['tp']), fp.append(score['fp']), fn.append(score['fn'])\n\n N_split, N_miss, N_add = np.array(N_split), np.array(N_miss), np.array(N_add)\n N_gt, N_pred = np.array(N_gt), np.array(N_pred)\n tp, fp, fn = np.array(tp), np.array(fp), np.array(fn)\n Q_P_macro, Q_R_macro, Q_F_macro = np.mean(np.array(Q_P)), np.mean(np.array(Q_R)), np.mean(np.array(Q_F))\n Q_P_micro = np.sum(tp) / (np.sum(tp) + np.sum(fp)) if (np.sum(tp) + np.sum(fp)) > 0 else 0\n Q_R_micro = np.sum(tp) / (np.sum(tp) + np.sum(fn)) if (np.sum(tp) + np.sum(fn)) > 0 else 0\n Q_rand_macro, Q_jaccard_macro = np.mean(np.array(Q_rand)), np.mean(np.array(Q_jaccard))\n Q_aggregated_jaccard_macro = np.mean(np.array(Q_aggregated_jaccard))\n Q_ctc_macro, Q_piou_macro = np.mean(np.array(Q_ctc)), np.mean(np.array(Q_piou))\n\n metrics = {\n 'Q_split_micro': float(np.sum(N_split) / np.sum(N_gt)),\n 'Q_split_macro': float(np.mean(N_split / N_gt)),\n 'Q_miss_micro': float(np.sum(N_miss) / np.sum(N_gt)),\n 'Q_miss_macro': float(np.mean(N_miss / N_gt)),\n 'Q_add_micro': float(np.sum(N_add) / np.sum(N_gt)),\n 'Q_add_macro': float(np.mean(N_add / N_gt)),\n 'N_gt': int(np.sum(N_gt)),\n 'N_pred': int(np.sum(N_pred)),\n 'Q_rand_macro': float(Q_rand_macro),\n 'Q_jaccard_macro': float(Q_jaccard_macro),\n 'Q_aggregated_jaccard_macro': float(Q_aggregated_jaccard_macro),\n 'Q_ctc_macro': float(Q_ctc_macro),\n 'Q_piou_macro': float(Q_piou_macro),\n 'Q_P_micro': float(Q_P_micro),\n 'Q_P_macro': float(Q_P_macro),\n 'Q_R_micro': float(Q_R_micro),\n 'Q_R_macro': float(Q_R_macro),\n 'Q_F_macro': float(Q_F_macro),\n 'Q_F_micro': float(2 * Q_P_micro * Q_R_micro / (Q_P_micro + Q_R_micro)) if (Q_P_micro + Q_R_micro) > 0 else 0\n }\n return metrics","def get_suffstat_names():\n params = ['sum_x', 'sum_x_squared']\n return params","def map_my(self, func: Callable[[Union[float, int]], int]) -> None:\n def list_func(lst: List[valueType]) -> List[valueType]:\n \"\"\"\n To apply the function/operation defined by users to every item in the list.\n :param lst: A list object like [element1, [element2, element3], element4].\n :return: A list that store the result of items after user-defined operation.\n \"\"\"\n tmp = [] # type: List[valueType]\n for e in lst:\n if isinstance(e, (list, set, tuple)):\n tmp.append(list_func(list(e)))\n else:\n if isinstance(e, (float, int)):\n tmp.append(func(e))\n else:\n raise Exception\n return tmp\n\n for head_node in self.hashTable:\n for node in head_node.singlyLinkedList:\n node.values = list_func(node.values)","def _compute_summary_stats(entries):\n result = {\n \"average\": None,\n \"median\": None,\n \"min\": None,\n \"max\": None,\n \"per66\": None,\n \"per75\": None,\n \"per90\": None,\n \"per95\": None,\n \"per99\": None,\n \"success_count\": len(entries['success']),\n \"error_count\": len(entries['error']),\n }\n # sort by the duration\n success = sorted(entries['success'], key=lambda x: x[1])\n if len(success) == 0:\n return result\n\n result['average'] = _compute_average(success)\n result['min'] = success[0][1]\n result['max'] = success[-1][1]\n result['median'] = _compute_percentile(success, 50)\n result['per66'] = _compute_percentile(success, 66)\n result['per75'] = _compute_percentile(success, 75)\n result['per90'] = _compute_percentile(success, 90)\n result['per95'] = _compute_percentile(success, 95)\n result['per99'] = _compute_percentile(success, 99)\n assert result['min'] <= result['median']\n assert result['median'] <= result['per66']\n assert result['per66'] <= result['per75']\n assert result['per75'] <= result['per90']\n assert result['per90'] <= result['per95']\n assert result['per95'] <= result['per99']\n assert result['per99'] <= result['max']\n return result","def get_results(cls):\n cls.all_hoechstzahls.sort(key=attrgetter('value', 'topic.category.weight'), reverse=True)\n for hoechstzahl in cls.all_hoechstzahls:\n yield hoechstzahl","def _sort_results(self, results: dict) -> List:\n return [results[url][\"display_name\"] for url in self.urls_list]","def _compute_user_stats():\n user_stats = []\n \n wmt16_group = Group.objects.filter(name='WMT16')\n wmt16_users = _get_active_users_for_group(wmt16_group)\n \n for user in wmt16_users:\n _user_stats = HIT.compute_status_for_user(user)\n _name = user.username\n _avg_time = seconds_to_timedelta(_user_stats[1])\n _total_time = seconds_to_timedelta(_user_stats[2])\n _data = (_name, _user_stats[0], _avg_time, _total_time)\n \n if _data[0] > 0:\n user_stats.append(_data)\n \n # Sort by total number of completed HITs.\n user_stats.sort(key=lambda x: x[1])\n user_stats.reverse()\n \n return user_stats","def process_bmi_list(cls, test_report_list):\n overweight_people_count = 0\n\n if not test_report_list or type(test_report_list) is not list:\n return (list(), overweight_people_count)\n\n for data in test_report_list:\n # ignore if data is not dict\n if type(data) is not dict:\n continue\n\n bmi = BMICalculator.calculate_bmi(data.get(HEIGHT_CM), data.get(WEIGHT_KG))\n\n if bmi:\n if bmi < LOWEST_NORMAL_WEIGHT_BMI:\n bmi_category, health_risk = UNDER_WEIGHT, MALNUTRITION_RISK\n elif bmi >= LOWEST_NORMAL_WEIGHT_BMI and bmi < LOWEST_OVER_WEIGHT_BMI:\n bmi_category, health_risk = NORMAL_WEIGHT, LOW_RISK\n elif bmi >= LOWEST_OVER_WEIGHT_BMI and bmi < LOWEST_MODERATELY_OBESE_BMI:\n bmi_category, health_risk = OVER_WEIGHT, ENHANCED_RISK\n overweight_people_count += 1\n elif bmi >= LOWEST_MODERATELY_OBESE_BMI and bmi < LOWEST_SEVERELY_OBESE_BMI:\n bmi_category, health_risk = MODERATELY_OBESE, MEDIUM_RISK\n elif bmi >= LOWEST_SEVERELY_OBESE_BMI and bmi < LOWEST_VERY_SEVERELY_OBESE_BMI:\n bmi_category, health_risk = SEVERELY_OBESE, HIGH_RISK\n elif bmi >= LOWEST_VERY_SEVERELY_OBESE_BMI:\n bmi_category, health_risk = VERY_SEVERELY_OBESE, VERY_HIGH_RISK\n else:\n bmi_category, health_risk = None, None\n \n data[BMI] = bmi\n data[BMI_CATEGORY] = bmi_category\n data[HEALTH_RISK] = health_risk\n \n return (test_report_list, overweight_people_count)","def bench_compare_multiple(logger, *param_groups, **fn_names):\n title_str, ans_str, time_str = \"Testing {name}:\",\"\\t{name}({params}) = {ans}\",\"\\tTime = {time}\"\n for name, fn in fn_names.items():\n str_params = {'name': name}\n print(title_str.format(**str_params))\n for pg in param_groups:\n str_params |= {'params': pg}\n try:\n start = time.time()\n result = fn(*pg)\n exec = time.time() - start\n except TypeError:\n start = time.time()\n result = fn(pg)\n exec = time.time() - start\n str_params |= {'time': exec, 'ans': result}\n print(ans_str.format(**str_params))\n print(time_str.format(**str_params))","def get_data_args(data_func, nfuncs):\n if data_func.__name__ == 'gg_1d':\n # first arg is sorted\n if nfuncs == 1:\n data_args = [{'a': 0.75, 'mu': 0.4, 'sigma': 0.3, 'beta': 2.0}]\n elif nfuncs == 2:\n data_args = [{'a': 0.2, 'mu': 0.4, 'sigma': 0.6, 'beta': 5.0},\n {'a': 0.55, 'mu': 0.4, 'sigma': 0.2, 'beta': 4.0}]\n elif nfuncs == 3:\n data_args = [{'a': 0.2, 'mu': 0.4, 'sigma': 0.6, 'beta': 5.0},\n {'a': 0.35, 'mu': 0.6, 'sigma': 0.07, 'beta': 2.0},\n {'a': 0.55, 'mu': 0.32, 'sigma': 0.14, 'beta': 6.0}]\n elif nfuncs == 4:\n data_args = [{'a': 0.2, 'mu': 0.3, 'sigma': 0.5, 'beta': 5.0},\n {'a': 0.4, 'mu': 0.65, 'sigma': 0.07, 'beta': 2.0},\n {'a': 0.6, 'mu': 0.25, 'sigma': 0.1, 'beta': 6.0},\n {'a': 0.9, 'mu': 0.95, 'sigma': 0.1, 'beta': 6.0}]\n elif data_func.__name__ == 'ta_1d':\n # first arg is sorted\n if nfuncs == 1:\n data_args = [{'a': 0.8, 'w_0': 0.0, 'w_1': 1.5}]\n elif nfuncs == 2:\n data_args = [{'a': 0.7, 'w_0': -1, 'w_1': 3},\n {'a': 0.9, 'w_0': 2, 'w_1': -3}]\n elif nfuncs == 3:\n data_args = [\n {'a': 0.6, 'w_0': -7, 'w_1': 8},\n {'a': 1, 'w_0': -1, 'w_1': 3},\n {'a': 1.4, 'w_0': 2, 'w_1': -3}]\n elif data_func.__name__ == 'gg_2d':\n # the order is (with first arg sorted):\n # [a_1, mu1_1, mu2_1, s1_1, s2_1, b1_1, b2_1, rot angle]\n if nfuncs == 1:\n data_args = [\n {'a': 0.8, 'mu1': 0.6, 'mu2': 0.6, 'sigma1': 0.1,\n 'sigma2': 0.2, 'beta1': 2, 'beta2': 2, 'omega': 0.1 * np.pi}]\n elif nfuncs == 2:\n data_args = [\n {'a': 0.5, 'mu1': 0.5, 'mu2': 0.4, 'sigma1': 0.4,\n 'sigma2': 0.2, 'beta1': 2, 'beta2': 2, 'omega': 0},\n {'a': 0.8, 'mu1': 0.5, 'mu2': 0.6, 'sigma1': 0.1,\n 'sigma2': 0.1, 'beta1': 2, 'beta2': 2, 'omega': 0}]\n elif nfuncs == 3:\n data_args = [\n {'a': 0.5, 'mu1': 0.3, 'mu2': 0.7, 'sigma1': 0.2,\n 'sigma2': 0.2, 'beta1': 2, 'beta2': 2, 'omega': 0},\n {'a': 0.7, 'mu1': 0.7, 'mu2': 0.6, 'sigma1': 0.15,\n 'sigma2': 0.15, 'beta1': 2, 'beta2': 2, 'omega': 0},\n {'a': 0.9, 'mu1': 0.4, 'mu2': 0.3, 'sigma1': 0.1,\n 'sigma2': 0.1, 'beta1': 2, 'beta2': 2, 'omega': 0}]\n try:\n data_args_list = []\n for name in bf.get_bf_param_names(data_func):\n data_args_list += [d[name] for d in data_args]\n return data_args_list\n except NameError:\n raise AssertionError('no data args found! func={} nfuncs={}'.format(\n data_func.__name__, nfuncs))","def statistics(list_of_int):\r\n\r\n if len(list_of_int) > 3:\r\n print('Input must be exactly 3 integers')\r\n return list_of_int\r\n\r\n my_sum = list_of_int[0] + list_of_int[1] + list_of_int[2]\r\n\r\n average_value = my_sum / 3\r\n\r\n\r\n if list_of_int[0] > list_of_int[1]:\r\n smallest_value = list_of_int[1]\r\n largest_value = list_of_int[0]\r\n\r\n if list_of_int[1] > list_of_int[2]:\r\n smallest_value = list_of_int[2]\r\n\r\n elif list_of_int[2] > list_of_int[0]:\r\n largest_value = list_of_int[2]\r\n \r\n elif list_of_int[1] > list_of_int[0]:\r\n smallest_value = list_of_int[0]\r\n largest_value = list_of_int[1]\r\n\r\n if list_of_int[0] > list_of_int[2]:\r\n smallest_value = list_of_int[2]\r\n\r\n elif list_of_int[2] > list_of_int[1]:\r\n largest_value = list_of_int[2]\r\n \r\n\r\n return [my_sum, average_value, smallest_value, largest_value]"],"string":"[\n \"def get_func_tuples():\\n func_tuples = [\\n ('met_gumeJ1_3sopt_tr20', 'Rel-UME J1', 'C1-.'),\\n ('met_gumeJ5_3sopt_tr20', 'Rel-UME J5', 'r-^'),\\n ('met_gfssdJ1_3sopt_tr20', 'Rel-FSSD J1', 'C4--'),\\n ('met_gfssdJ5_3sopt_tr20', 'Rel-FSSD J5', 'b-x'),\\n\\n ('met_gmmd_med', 'Rel-MMD', 'k-.'),\\n ('met_gmmd_med_bounliphone', 'Rel-MMD medboun', 'k-'),\\n\\n ('met_gfssdJ1_3sopt_tr50', 'FSSD-opt3 J1', 'b-^'),\\n ('met_gfssdJ5_3sopt_tr50', 'FSSD-opt3 J5', 'b-.h'),\\n\\n ('met_gumeJ1_2V_rand', 'UME-rand J1', 'r--^'),\\n ('met_gumeJ1_1V_rand', 'UME-rand J1 1V', 'y-'),\\n ('met_gumeJ2_2V_rand', 'UME-rand J2', 'g--^'),\\n ('met_gumeJ3_2V_rand', 'UME-rand J3', 'b--^'),\\n ('met_gumeJ5_2V_rand', 'UME-rand J5', 'k--^'),\\n\\n ('met_gumeJ1_2sopt_tr20', 'Rel-UME-opt2 J1', 'C2-.'),\\n ('met_gumeJ5_2sopt_tr20', 'Rel-UME-opt2 J5', 'g-'),\\n ('met_gumeJ1_2sopt_tr50', 'Rel-UME-opt2 J1', 'r-.h'),\\n\\n ('met_gumeJ1_3sopt_tr50', 'UME-opt3 J1', 'r-'),\\n ('met_gumeJ5_3sopt_tr50', 'UME-opt3 J5', 'k-'),\\n\\n\\n ]\\n return func_tuples\",\n \"def _get_attribute_functions(self, attributes):\\n subqueries = []\\n columns = []\\n for attr in attributes:\\n function = attributes[attr]\\n if function == 'sum':\\n sq = DBSession.query(\\n self.db_item.id.label('item_id'),\\n cast(self.db_value.value, Float).label('v')\\n ). \\\\\\n join(self.db_taggroup). \\\\\\n join(\\n self.db_tag,\\n self.db_taggroup.id == self.db_tag.fk_tag_group). \\\\\\n join(\\n self.db_value,\\n self.db_value.id == self.db_tag.fk_value). \\\\\\n join(self.db_key, self.db_key.id == self.db_tag.fk_key). \\\\\\n filter(self.db_key.key == attr). \\\\\\n subquery()\\n subqueries.append(sq)\\n columns.append(func.sum(sq.c.v))\\n elif function == 'count' or function == 'count distinct':\\n if attr == 'Activity' or attr == 'Stakeholder':\\n columns.append(func.count())\\n else:\\n sq = DBSession.query(\\n self.db_item.id.label('item_id'),\\n self.db_value.value.label('v')\\n ). \\\\\\n join(self.db_taggroup). \\\\\\n join(\\n self.db_tag,\\n self.db_taggroup.id == self.db_tag.fk_tag_group). \\\\\\n join(self.db_value). \\\\\\n join(self.db_key). \\\\\\n filter(self.db_key.key == attr). \\\\\\n subquery()\\n subqueries.append(sq)\\n if (function == 'count distinct'):\\n columns.append(func.count(distinct(sq.c.v)))\\n else:\\n columns.append(func.count(sq.c.v))\\n return subqueries, columns\",\n \"def get_all_metrics(\\n self,\\n metric_funcs: list,\\n ):\\n\\n profile_results = {\\\"table\\\": {}, \\\"columns\\\": defaultdict(dict)}\\n metric_list = [\\n self.compute_metrics(*metric_func) for metric_func in metric_funcs\\n ]\\n for metric_result in metric_list:\\n profile, column, metric_type = metric_result\\n\\n if metric_type == MetricTypes.Table.value:\\n profile_results[\\\"table\\\"].update(profile)\\n if metric_type == MetricTypes.System.value:\\n profile_results[\\\"system\\\"] = profile\\n else:\\n if profile:\\n profile_results[\\\"columns\\\"][column].update(\\n {\\n \\\"name\\\": column,\\n \\\"timestamp\\\": datetime.now(tz=timezone.utc).timestamp(),\\n **profile,\\n }\\n )\\n return profile_results\",\n \"def _groups_stat_transform(\\n group_stats: List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]\\n) -> Dict[str, torch.Tensor]:\\n return {\\n \\\"tp\\\": torch.stack([stat[0] for stat in group_stats]),\\n \\\"fp\\\": torch.stack([stat[1] for stat in group_stats]),\\n \\\"tn\\\": torch.stack([stat[2] for stat in group_stats]),\\n \\\"fn\\\": torch.stack([stat[3] for stat in group_stats]),\\n }\",\n \"def organize_results(result_dicts_list, config_fn):\\n\\n metric_id = config_fn.get_metric_fn_id()[1]\\n organized_results = []\\n for results_list in result_dicts_list:\\n for result_dict in results_list:\\n dict_ = {}\\n for key, value in result_dict.items():\\n dict_[key] = value\\n organized_results.append(dict_)\\n pass\\n\\n return organized_results\",\n \"def calculate_stats(time_list):\\n time_array = np.array(time_list)\\n\\n median = np.median(time_array)\\n mean = np.mean(time_array)\\n std_dev = np.std(time_array)\\n max_time = np.amax(time_array)\\n min_time = np.amin(time_array)\\n quantile_10 = np.quantile(time_array, 0.1)\\n quantile_90 = np.quantile(time_array, 0.9)\\n \\n basic_key = [\\\"median\\\",\\\"mean\\\",\\\"std_dev\\\",\\\"min_time\\\",\\\"max_time\\\",\\\"quantile_10\\\",\\\"quantile_90\\\"]\\n basic_value = [median,mean,std_dev,min_time,max_time,quantile_10,quantile_90]\\n\\n dict_basic = dict(zip(basic_key, basic_value))\\n\\n \\n return pd.DataFrame(dict_basic, index = [0])\",\n \"def getStatistics(results):\\n results.sort(key=lambda x: x[0][0])\\n hole1Values = [x[0][0] for x in results]\\n logProbs = [x[1] for x in results]\\n maxLogProb = max(logProbs)\\n probs = [math.exp(x - maxLogProb) for x in logProbs]\\n probs = [i / sum(probs) for i in probs]\\n print(probs)\\n expectedValue = sum(probs[i]*hole1Values[i] for i in xrange(len(probs)))\\n stats = {}\\n stats['avg'] = expectedValue\\n stats['sd'] = sum(\\n probs[i] * (expectedValue - hole1Values[i])**2 for i in range(len(probs))\\n ) ** 0.5\\n stats['map'] = hole1Values[max(xrange(len(probs)), key = lambda i: probs[i])]\\n stats['pavg'] = probs[int(stats['avg']*(len(results) - 1))]\\n stats['pmap'] = probs[int(stats['map']*(len(results) - 1))]\\n return stats, hole1Values, logProbs\",\n \"def get_output_distr(self, stat_list):\\n out_probs = [[np.zeros(500) for _ in stat_list]\\n for _ in range(self.task_count)]\\n\\n # for each task, get the posterior distribution for each predicted\\n # output label\\n for i in range(self.task_count):\\n distr_vec = [\\n stats.norm(loc=mu, scale=sigma) for mu, sigma in\\n zip(self.output_mat['mu'][i, :],\\n self.output_mat['sigma'][i, :])\\n ]\\n\\n # calculate the range of possible predicted output values\\n min_range = min(distr.ppf(0.01) for distr in distr_vec)\\n max_range = max(distr.ppf(0.99) for distr in distr_vec)\\n x_vals = np.linspace(min_range, max_range, 1000)\\n\\n # calculate the cumulative probability density function across all\\n # label distributions at each possible predicted value\\n for j, stat in enumerate(stat_list):\\n out_probs[i][j] = [\\n (x,\\n np.mean([distr.pdf(x) for distr, st in\\n zip(distr_vec, stat) if st])\\n ) for x in x_vals\\n ]\\n\\n return out_probs\",\n \"def run_functions( functions, dir ):\\n res = []\\n for i in range(0, len(functions)):\\n res.append(functions[i].execute(dir))\\n return summarize_as_html(res)\",\n \"def stat_names_listifier(stats, words_and_numbs=False):\\n if words_and_numbs == False:\\n list_stats = ' '.join(stats).split(', ')\\n\\n if int(len(list_stats)) == 1:\\n column_name = [stat_name_ifs(list_stats[0])]\\n return column_name\\n else:\\n list_of_columns = [stat_name_ifs(term) for term in list_stats]\\n if 'Invalid' in list_of_columns:\\n return 'Invalid'\\n else:\\n return list_of_columns\\n\\n elif words_and_numbs == True:\\n items_to_assess = ' '.join(stats).split(', ')\\n\\n list_stats = [item.rsplit(' ', 1)[0] for item in items_to_assess]\\n values_list = [item.split(' ')[-1] for item in items_to_assess]\\n\\n for item in values_list:\\n try:\\n int(item)\\n except:\\n return 'Invalid'\\n\\n if int(len(list_stats)) == 1:\\n column_name = [stat_name_ifs(list_stats[0])]\\n if column_name == 'Invalid':\\n return 'Invalid'\\n else:\\n return column_name, values_list\\n else:\\n list_of_columns = [stat_name_ifs(term) for term in list_stats]\\n if 'Invalid' in list_of_columns:\\n return 'Invalid'\\n else:\\n return list_of_columns, values_list\",\n \"def get_general_stats() ->List[BaseStat]:\\n return [PositionalTendencies(),\\n SpeedTendencies(),\\n ItemGoals(),\\n DropshotGoals(),\\n DropshotBallPhaseTimes(),\\n DropshotStats()\\n ]\",\n \"def get_best_for_models(results, models, w_ft=-0.25, w_mt=-10, w_f1=3.5, f1_type='macro', is_test=False):\\n best_results = []\\n\\n for model in models:\\n model_results = metrics.filter_results(results, models=[model], is_test=is_test)\\n __sort_by_score(model_results, w_ft, w_mt, w_f1, f1_type)\\n\\n best_results.append(model_results[0])\\n\\n return best_results\",\n \"def computeStats(results, independentVariable):\\n common = {}\\n\\n # Collect lists of the values\\n for v in results:\\n test = v.__dict__[independentVariable]\\n try:\\n common[test].append(v)\\n except:\\n common[test] = [v]\\n\\n result = []\\n # Sanity check for number of data items being summarized\\n # print (\\\"Computing stats on \\\" + str(len(common.values()[0])))\\n for measurements in list(common.values()):\\n resultValues = {}\\n resultValues[independentVariable] = measurements[0].__dict__[\\n independentVariable\\n ]\\n # print (\\\"Measurement[\\\\\\\"\\\"+independentVariable+\\\"\\\\\\\"] : \\\" + str(resultValues[independentVariable]))\\n fieldnames = list(measurements[0].__dict__.keys())\\n fieldnames.remove(independentVariable)\\n for stat in fieldnames:\\n values = [m.__dict__[stat] for m in measurements]\\n resultValues[stat] = mean(values)\\n resultValues[stat + \\\"_SD\\\"] = standardDeviation(values, resultValues[stat])\\n result.append(\\n measurement(list(resultValues.keys()), list(resultValues.values()))\\n )\\n # print (\\\"Result: \\\" + str(result))\\n return result\",\n \"def aggregateFunction():\\r\\n global aggFunc\\r\\n aggFunc = []\\r\\n for objFunc in P_prime:\\r\\n aggFunc.append(objFunc[0]*FileSettings.settingsdict['weights'][0] +\\r\\n objFunc[1]*FileSettings.settingsdict['weights'][1] +\\r\\n objFunc[2]*FileSettings.settingsdict['weights'][2] +\\r\\n objFunc[3]*FileSettings.settingsdict['weights'][3])\\r\\n return aggFunc\",\n \"def generate_metrics_querys(metrics: List, period: int = 30, stats: str = 'Sum') -> Tuple[List, Dict]:\\r\\n metricsquery = [] #type: List \\r\\n resultsquery = defaultdict(list) #type: DefaultDict\\r\\n for metric in metrics:\\r\\n identity = randomString() \\r\\n metricsquery.append({'Id': identity, 'MetricStat': {'Metric': metric, 'Period': period, 'Stat': stats} })\\r\\n resultsquery[identity].append({'query': {'MetricStat': {'Metric': metric, 'Period': period, 'Stat': stats}}})\\r\\n return metricsquery, dict(resultsquery)\",\n \"def dictionary_of_metrics(items):\\n \\n np_list = np.array(items) #create an array of list to use numpy functions on list\\n metric_dict = {'mean': np.mean(np_list).round(2),\\n 'median': np.median(np_list).round(2),\\n 'var': np.var(np_list, ddof=1).round(2),\\n 'std': np.std(np_list, ddof=1).round(2),\\n 'min': np.min(np_list).round(2),\\n 'max': np.max(np_list).round(2),} #create a dictionary that calculates the five metrics\\n \\n return metric_dict #return result as a dictionary\",\n \"def extract_stats(stat_list, prefix=\\\"\\\"):\\n for stat in stat_list:\\n data = stat.strip().split('\\\\n')\\n value = data[0]\\n\\n # attempt to convert to number, and deal\\n # with using 'k' as an abbreviation to 1000.\\n try:\\n if value[-1] == 'k':\\n value = float(value[:-1])*1000\\n else:\\n value = float(value)\\n except ValueError:\\n pass\\n\\n name = prefix + data[1].replace(' ', '_')\\n profile[name] = value\",\n \"def get_system_pairs(self, lst, results, s_p_imp=False, s_p_w=False):\\n \\n system_pairs = []\\n if isinstance(results, np.ndarray):\\n cur = None\\n curmax = None\\n for i, item in enumerate(lst):\\n if cur and item['sent'] != cur['sent']:\\n system_pairs.append(cur)\\n cur = None\\n if not cur:\\n curmax = results[i][1]\\n cur = item\\n if results[i][1] > curmax:\\n curmax = results[i][1]\\n cur = item\\n\\n if cur:\\n system_pairs.append(cur)\\n return system_pairs\\n\\n for i, item in enumerate(lst):\\n if results[i]:\\n system_pairs.append(item)\\n return system_pairs\",\n \"def stringify_results(self, results, format_fn=str):\\n processed = []\\n\\n for row in results:\\n processed.append([format_fn(v) for v in row])\\n\\n return processed\",\n \"def MakeStats(query, levelParams):\\r\\n result = []\\r\\n rets = [ExtractPeriodReturns(\\r\\n query, levelParams.levelFuncs[name], levelParams.retNames[num])\\r\\n for (num, name) in enumerate(levelParams.levelNames)]\\r\\n for num, name in enumerate(levelParams.levelNames):\\r\\n stats = ComputeStats(rets[num])\\r\\n mult = levelParams.multipliers[name]\\r\\n result.append(\\r\\n '%s: ret = %.3f, vol = %.3f, sqCorrs = %.3f, %.3f, %.3f' % ((\\r\\n name.ljust(10), stats[0]*mult, stats[1]*math.sqrt(mult)\\r\\n ) + tuple(stats[2][0:3])))\\r\\n return '\\\\n'.join(result)\",\n \"def get_metrics(self, slug_list):\\n # meh. I should have been consistent here, but I'm lazy, so support these\\n # value names instead of granularity names, but respect the min/max\\n # granularity settings.\\n keys = ['seconds', 'minutes', 'hours', 'day', 'week', 'month', 'year']\\n key_mapping = {gran: key for gran, key in zip(GRANULARITIES, keys)}\\n keys = [key_mapping[gran] for gran in self._granularities()]\\n\\n results = []\\n for slug in slug_list:\\n metrics = self.r.mget(*self._build_keys(slug))\\n if any(metrics): # Only if we have data.\\n results.append((slug, dict(zip(keys, metrics))))\\n return results\",\n \"def gen_stats_zip(filename_list, zipfilename, tier='tier2', csv_processor='ICOS-ETC', zip_processor='LBL_AMP', ts_format=\\\"%Y-%m-%d %H:%M:%S\\\"):\\n log.info(\\\"Generating ZIP and stats for: {z}\\\".format(z=zipfilename))\\n\\n if not filename_list:\\n return [], []\\n\\n today = datetime.now().strftime(ts_format)\\n output_zip = zip_file_list(filename_list=filename_list, zipfilename=zipfilename)\\n\\n if output_zip != zipfilename:\\n raise ONEFluxError(\\\"ZIP filenames differ: {i} {o}\\\".format(i=zipfilename, o=output_zip))\\n\\n size, md5sum, _ = file_stat(filename=zipfilename)\\n zip_entry = [['\\\"{e}\\\"'.format(e=os.path.basename(zipfilename)),\\n '{e}'.format(e=format(size)),\\n '\\\"{e}\\\"'.format(e=md5sum),\\n '\\\"{e}\\\"'.format(e=len(filename_list)),\\n '\\\"{e}\\\"'.format(e=tier),\\n '\\\"{e}\\\"'.format(e=zip_processor),\\n '\\\"{e}\\\"'.format(e=today)], ]\\n csv_entries = []\\n for filename in filename_list:\\n f_size, f_md5sum, f_timestamp_change = file_stat(filename=filename)\\n f_today = f_timestamp_change.strftime(ts_format)\\n entry = ['\\\"{e}\\\"'.format(e=os.path.basename(zipfilename)),\\n '\\\"{e}\\\"'.format(e=os.path.basename(filename)),\\n '{e}'.format(e=f_size),\\n '\\\"{e}\\\"'.format(e=f_md5sum),\\n '\\\"{e}\\\"'.format(e=csv_processor),\\n '\\\"{e}\\\"'.format(e=f_today)]\\n csv_entries.append(entry)\\n\\n return zip_entry, csv_entries\",\n \"def analyze_results(results): #, result_nonprivate):\\n res_dimensions = zip(*results)\\n mean, std = [], []\\n \\n for resdim in res_dimensions:\\n mean.append ( numpy.average(resdim) )\\n std.append ( numpy.std(resdim) )\\n\\n return mean, std\",\n \"def getGroupFuncs(self):\\n\\n funcs = []\\n for p in self.Parameters:\\n if p.arg_name[0:8] == \\\"Function\\\" and p.arg_value:\\n fct, attr = p.arg_value.split(':')\\n if fct and attr:\\n funcs.append((fct, attr))\\n if not funcs:\\n funcs.append(('count', '*'))\\n return funcs\",\n \"def writeStatTables(allStats, overviewStats):\\n\\n # Create all stats \\n statFileHandlers = {}\\n statFiles = {}\\n measures = ['recall', 'specificity', 'FPR', 'FNR', 'PBC', 'precision', 'f-measure']\\n\\n for measure in measures:\\n statFileHandlers[measure] = open(measure + '-all.csv', 'w', newline='')\\n statFiles[measure] = csv.writer(statFileHandlers[measure], delimiter=';')\\n \\n firstRowText = ''\\n allMethods = []\\n\\n for categoryKey, category in allStats.items():\\n\\n # Check the number of subSubCategories inside the subCategories and issue a warning\\n # if the count of subSubCategories for different subCategories does not match\\n\\n for subCategoryKey, subCategory in category.items():\\n for subSubCategoryKey, subSubCategory in subCategory.items():\\n if subSubCategoryKey not in allMethods:\\n allMethods.append(subSubCategoryKey)\\n\\n\\n firstRowText = [''] + allMethods\\n \\n for measure in measures:\\n statFiles[measure].writerow(firstRowText)\\n \\n\\n for categoryKey, category in allStats.items():\\n for subCategoryKey, subCategory in category.items():\\n\\n rows = {'recall': [categoryKey + '/' + subCategoryKey], 'specificity' : [categoryKey + '/' + subCategoryKey], 'FPR' : [categoryKey + '/' + subCategoryKey], 'FNR' : [categoryKey + '/' + subCategoryKey], 'PBC' : [categoryKey + '/' + subCategoryKey], 'precision' : [categoryKey + '/' + subCategoryKey], 'f-measure': [categoryKey + '/' + subCategoryKey]}\\n for method in allMethods:\\n\\n if method in subCategory:\\n if isinstance(subCategory[method], (list, tuple, dict)):\\n for methodKey, value in subCategory[method].items():\\n rows[methodKey] = rows[methodKey] + [float(value)]\\n else:\\n print('Error')\\n else:\\n for measure in measures:\\n rows[measure] = rows[measure] + [''] # Write empty entry at this column\\n\\n\\n for measure in measures:\\n statFiles[measure].writerow(rows[measure])\\n\\n for statFileHandlerKey, statFileHandler in statFileHandlers.items():\\n statFileHandler.close()\\n\\n # 1) Overview, containing summarised numbers of the entire category, for each method\\n # From the overview stats, summarise by method and folder\\n overallRawStats = {} # Overall stats, summarised by method, i.e. Kim2015-no-blur\\n siteRawStats = {} # Stats for each method, summarised by folder, i.e. Kim2015-no-blur/Egensevej\\n\\n for siteKey, site in overviewStats.items():\\n if categoryKey not in siteRawStats:\\n siteRawStats[siteKey] = {}\\n\\n for sequenceKey, sequence in site.items():\\n if len(subCategory.items()) > 1:\\n\\n for methodKey, method in sequence.items():\\n if methodKey not in siteRawStats[siteKey]:\\n siteRawStats[siteKey][methodKey] = {}\\n \\n if methodKey not in overallRawStats:\\n overallRawStats[methodKey] = {}\\n\\n for statKey, stat in method.items():\\n if statKey not in siteRawStats[siteKey][methodKey]:\\n siteRawStats[siteKey][methodKey][statKey] = float(stat)\\n else:\\n siteRawStats[siteKey][methodKey][statKey] += float(stat)\\n\\n\\n if statKey not in overallRawStats[methodKey]:\\n # Just add it\\n overallRawStats[methodKey][statKey] = float(stat)\\n else:\\n # Otherwise add to list\\n overallRawStats[methodKey][statKey] += float(stat)\\n \\n overallStatHandler = open('overallStats.csv', 'w', newline='')\\n overallStatFile = csv.writer(overallStatHandler, delimiter=';') \\n siteStatHandler = open('statsPerSite.csv', 'w', newline='')\\n siteStatFile = csv.writer(siteStatHandler, delimiter=';')\\n\\n # Write the header row\\n subSubCategoryHeader = [''] + [''] + measures\\n overallStatFile.writerow(subSubCategoryHeader)\\n siteStatFile.writerow([''] + subSubCategoryHeader)\\n\\n # Get the recall, specificity and other measures from the summarised numbers and write them to the stat files\\n for methodName, rawStats in overallRawStats.items():\\n stats = getStats(rawStats)\\n\\n names = methodName.split('/')\\n row = names\\n\\n for measure in measures:\\n row = row + ['{:f}'.format(stats[measure])]\\n\\n overallStatFile.writerow(row)\\n\\n\\n for siteName, methodStats in siteRawStats.items():\\n\\n for methodName, rawStats in methodStats.items():\\n stats = getStats(rawStats)\\n\\n row = [siteName] + methodName.split('/')\\n\\n for measure in measures:\\n row = row + ['{:f}'.format(stats[measure])]\\n\\n siteStatFile.writerow(row)\\n \\n overallStatHandler.close();\\n siteStatHandler.close();\\n\\n # Convert the commas from \\\".\\\" to \\\",\\\" to allow processing in Excel\\n convertCommaInFile('overallStats.csv')\\n convertCommaInFile('statsPerSite.csv')\\n\\n for measure in measures:\\n convertCommaInFile(measure + '-all.csv')\\n\\n\\n return\",\n \"def funcsresults2lines(funcsresults):\\n funcssimple = [(func, value) \\n for func, value in funcsresults if not isinstance(value, list)]\\n funcslist = [(func, value) \\n for func, value in funcsresults if isinstance(value, list)]\\n funcsbunchlist = [(func, values) for func, values in funcslist if values]\\n funcsbunchlist = [(func, epbunchlist2html(values)) \\n for func, values in funcsbunchlist \\n if isinstance(values[0], EpBunch)]\\n cleanfuncsresults = funcssimple + funcsbunchlist\\n lines = [\\\"%s = %s\\\" % (func, value) for func, value in cleanfuncsresults]\\n return lines\",\n \"def collection_statistic(func: Callable) -> Callable:\\n STATS_FUNCS[func.__name__] = func\\n return func\",\n \"def stat(lst):\\n n = float(len(lst))\\n mean = sum(lst) / n\\n stdev = sqrt((sum(x * x for x in lst) / n) - (mean * mean))\\n return mean, stdev\",\n \"def get_result(self):\\r\\n return (self.x, self.lambd)\",\n \"def format_multiple_results(results, features_to_show=[]):\\n best_score_array = np.array([])\\n for r in results:\\n print r\\n print r[-1]\\n best_score_array = np.append(best_score_array, r[-1])\\n\\n print \\\"BEST SCORE ARRAY\\\", best_score_array\\n print \\\"Best Score\\\"\\n print \\\"Mean: \\\", best_score_array.mean()\\n print \\\"Std: \\\", best_score_array.std()\",\n \"def get_stats_on_profiles_using_named_tuple(profiles: list) -> \\\"Stats\\\":\\n\\n \\n if type(profiles) != list:\\n raise TypeError(\\\"A list needs to be provided as an argument\\\")\\n\\n if not len(profiles):\\n raise ValueError(\\\"An empty list is provided\\\")\\n\\n Profile = namedtuple(\\\"Profile\\\", (\\\"job\\\", \\\"company\\\", \\\"ssn\\\", \\\"residence\\\", \\\"current_location\\\", \\n \\\"blood_group\\\", \\\"website\\\", \\\"username\\\", \\\"name\\\", \\\"sex\\\", \\\"address\\\", \\\"mail\\\", \\\"birthdate\\\"))\\n \\n \\n Stats = namedtuple(\\\"Stats\\\", (\\\"largest_blood_type\\\", \\\"mean_current_location\\\", \\\"oldest_person_age\\\", \\\"average_age\\\"))\\n\\n Stats.__doc__ = \\\"Represents the various statistics on the complete profile list\\\"\\n Stats.largest_blood_type.__doc__ = \\\"This is the Blood Group of the maximum profiles in the dataset\\\"\\n Stats.mean_current_location.__doc__ = \\\"This is the Mean of all the locations in the dataset\\\"\\n Stats.oldest_person_age.__doc__ = \\\"The age of the oldest person in the dataset\\\"\\n Stats.average_age.__doc__ = \\\"The average age of all the people in the dataset\\\"\\n\\n try:\\n profiles_tuple = [Profile(**p) for p in profiles]\\n except:\\n raise ValueError(\\\"The valid list of fake profiles need to be sent.\\\")\\n\\n return Stats(\\n largest_blood_type = Counter(p.blood_group for p in profiles_tuple).most_common(1)[0][0],\\n mean_current_location = (sum(p.current_location[0] for p in profiles_tuple)/len(profiles_tuple),\\n sum(p.current_location[1] for p in profiles_tuple)/len(profiles_tuple)),\\n oldest_person_age = (date.today() - max(profiles_tuple, key = lambda item: (date.today() - item.birthdate).days).birthdate).days,\\n average_age = sum((date.today() - p.birthdate).days for p in profiles_tuple)/len(profiles_tuple)\\n )\",\n \"def _metric_max_over_ground_truths(metric_fn: Callable[[str, str], Tensor], prediction: str, ground_truths: List[str]) ->Tensor:\\n return max(metric_fn(prediction, truth) for truth in ground_truths)\",\n \"def stats(self, ops=(min, max, np.median, sum)):\\n names = [op.__name__ for op in ops]\\n ops = [_zero_on_type_error(op) for op in ops]\\n columns = [[op(column) for op in ops] for column in self.columns]\\n table = self._with_columns(columns)\\n stats = table._unused_label('statistic')\\n table[stats] = names\\n table.move_to_start(stats)\\n return table\",\n \"def get_stat(self,stat,**kwargs):\\n stat=stat.lower()\\n if stat.startswith('elem'): stat = 0#'elements'\\n elif stat=='score': stat = 1#'score'\\n elif stat.startswith('max'): stat = 2#'maximum'\\n elif stat.startswith('point'): stat = 3#'points'\\n elif stat.startswith('weight'): stat = 4#'weights'\\n else: raise ValueError, '{} is not an accepted argument for stat'.format(stat)\\n weighted = kwargs.get('weighted',False)\\n counted = kwargs.get('counted',False)\\n weighted,counted = map(bool,[weighted,counted])\\n \\n #generating ordered list of grades\\n listy = []\\n for gr in self._grades:\\n val=None\\n if gr.score is not None:\\n val=gr.score\\n if gr.getMaximum():\\n val/=1.0*gr.getMaximum()\\n if gr.getWeight() and weighted:\\n val*=gr.getWeight()\\n listy.append( (gr,val) )\\n \\n listy.sort(lambda a,b: cmp(a[1],b[1]),None,True)\\n \\n #trimming list (if counted)\\n if counted and self.parentCategory.use_best_count and \\\\\\n self.parentCategory.element_count is not None:\\n listy = listy[0:self.parentCategory.element_count]\\n listy = [x[0] for x in listy]\\n \\n if stat is 0:\\n return listy\\n \\n #building current score, maximum, points, and weight\\n curScore = 0\\n curMax = 0\\n curPoints = 0\\n curWeight = 0\\n for gr in listy:\\n if gr.score is None:\\n continue\\n scr,mx,wgt,xtra = gr.score,gr.getMaximum(),gr.getWeight(),gr.extra_credit\\n if wgt and weighted:\\n scr=scr*wgt\\n if mx:\\n if weighted:\\n curMax += mx*wgt\\n else: curMax += mx\\n curPoints+= 1.0*scr/mx\\n else:\\n curPoints+= scr\\n if not xtra:\\n curWeight+=wgt\\n curScore += scr\\n \\n if stat is 1:\\n return curScore\\n if stat is 2:\\n return curMax\\n if stat is 3:\\n if curMax:\\n return 1.0*curPoints\\n return None\\n if stat is 4:\\n return curWeight\",\n \"def _summarize_as_html_helper( ret, diagnostics=True):\\n summary = []\\n summary.append(\\\"<table>\\\\n\\\")\\n for i in range(0, len(ret)):\\n result = ret[i]\\n f = result.function_name\\n summary.append(\\\"<tr>\\\")\\n summary.append(\\\"<td>Question\\\")\\n summary.append(str(i+1))\\n summary.append(\\\":</td>\\\")\\n summary.append(\\\"<td colspan='2'>Called function \\\")\\n summary.append(escapeHTML(f) + \\\"()\\\")\\n summary.append(\\\"</td>\\\")\\n summary.append(\\\"</tr>\\\\n\\\")\\n if result.internal_error:\\n summary.append(\\\"<tr><td></td><td></td><td class='error'>\\\");\\n summary.append(\\\"Server error. Please retry and if the problem persists report a bug.\\\\n\\\");\\n summary.append(result.internal_error);\\n summary.append(\\\"</td></tr>\\\\n\\\")\\n elif result.error:\\n summary.append(\\\"<tr><td></td><td></td><td class='error'>Student error. \\\")\\n summary.append(escapeHTML(result.error) + \\\"</td></tr>\\\\n\\\")\\n else:\\n summary.append(\\\"<tr><td></td><td colspan='2'>Return values:</td></tr>\\\\n\\\")\\n ret_values = result.ret\\n for j in range(0, len(ret_values)):\\n summary.append(\\\"<tr>\\\")\\n summary.append(\\\"<td></td>\\\")\\n summary.append(\\\"<td>Return value \\\" + str(j+1) + \\\"=</td>\\\")\\n summary.append(\\\"<td>\\\" + str(ret_values[j]) + \\\"</td>\\\")\\n summary.append(\\\"</tr>\\\\n\\\")\\n if diagnostics:\\n if result.internal_error:\\n summary.append(\\\"<tr><td></td><td></td><td>Diagnostics</td></tr>\\\")\\n else:\\n summary.append(\\\"<tr><td></td><td></td><td>MATLAB Output</td></tr>\\\")\\n summary.append(\\\"<tr><td></td><td></td><td><pre>\\\")\\n if result.internal_error:\\n summary.append( result.internal_error )\\n else:\\n summary.append( result.output )\\n summary.append(\\\"</pre></td></tr>\\\")\\n summary.append(\\\"</table>\\\\n\\\")\\n summary = \\\"\\\".join(summary)\\n return summary\",\n \"def basic_stats_custom(x):\\n def my_min(x):\\n m = x[0]\\n for elem in x:\\n if elem < m:\\n m = elem\\n else:\\n pass\\n return m\\n\\n def my_max(x):\\n m = x[0]\\n for elem in x:\\n if elem > m:\\n m = elem\\n else:\\n pass\\n return m\\n\\n def my_sum(x):\\n acc = 0\\n for elem in x:\\n acc += elem\\n return acc\\n\\n def my_len(x):\\n acc = 0\\n for elem in x:\\n acc += 1\\n return acc\\n\\n def my_mean(x):\\n \\\"\\\"\\\"Return the mean of numbers in list. Since the task is not to use\\n builtin functions, length is also defined anew (prior to this\\n function definition).\\n\\n \\\"\\\"\\\"\\n return my_sum(x) / my_len(x)\\n return {\\\"Minimum: \\\": my_min(x), \\\"Maximum: \\\": my_max(x), \\\"Sum: \\\":\\n my_sum(x), \\\"Mean: \\\": my_mean(x)}\",\n \"def compute_metrics(self, results: list) -> dict:\",\n \"def dist_stats(self,nn_list):\\n\\n nn_list = np.array(nn_list)\\n d = self.dist[:,nn_list-1]\\n\\n mean = np.mean(d,axis=0)\\n std = np.std(d,axis=0)\\n\\n return mean, std\",\n \"def sort_result(self, result_list):\\n case_map = {}\\n case_types = []\\n\\n for status, test_case, output, trace in result_list:\\n case_type = test_case.__class__\\n\\n if not case_map.has_key(case_type):\\n case_map[case_type] = []\\n case_types.append(case_type)\\n\\n case_map[case_type].append((status, test_case, output, trace))\\n\\n return [(typ, case_map[typ]) for typ in case_types]\",\n \"def get_eval_metric_fns():\\n metric_fns = {}\\n metric_fns.update({\\n \\\"metric/%s\\\" % name: tfr.metrics.make_ranking_metric_fn(name) for name in [\\n tfr.metrics.RankingMetricKey.ARP,\\n tfr.metrics.RankingMetricKey.MRR,\\n ]\\n })\\n metric_fns.update({\\n \\\"metric/ndcg@%d\\\" % topn: tfr.metrics.make_ranking_metric_fn(\\n tfr.metrics.RankingMetricKey.NDCG, topn=topn)\\n for topn in [1, 3, 5, 10]\\n })\\n return metric_fns\",\n \"def group_results(results):\\n equiv_classes = {}\\n for tbl_prog, vis_spec in results:\\n full_trace = vis_spec.eval()\\n fronzen_trace = json.dumps(visual_trace.trace_to_table(full_trace), sort_keys=True)\\n if fronzen_trace not in equiv_classes:\\n equiv_classes[fronzen_trace] = []\\n equiv_classes[fronzen_trace].append((tbl_prog, vis_spec))\\n return equiv_classes\",\n \"def define_result_as_arg_functions(self, functions):\\n ordered = []\\n for node in functions:\\n ordered.append(node)\\n if node.wrap.fortran is False:\\n continue\\n self.convert_result_as_arg(node, ordered)\\n return ordered\",\n \"def get_result(self):\\r\\n return (self.x, self.lambd, self.z)\",\n \"def get_feature_statistics(results):\\n to_be_deleted = []\\n\\n for result in results:\\n if len(result.subset) != 6:\\n to_be_deleted.append(result)\\n\\n length = len(results)\\n feature_labels = datapoint_features\\n statistics = {}\\n\\n for label in feature_labels:\\n result_with = metrics.filter_results(results, features=[label])\\n result_without = metrics.filter_results(results, without_features=[label])\\n\\n with_length = len(result_with)\\n without_length = len(result_without)\\n prevalence = with_length / length\\n\\n if prevalence != 0:\\n avg_f1_dos = math.fsum([result.metrics['dos'].f1 for result in result_with]) / with_length\\n avg_f1_fuzzy = math.fsum([result.metrics['fuzzy'].f1 for result in result_with]) / with_length\\n avg_f1_imp = math.fsum([result.metrics['impersonation'].f1 for result in result_with]) / with_length\\n else:\\n avg_f1_dos = 0\\n avg_f1_fuzzy = 0\\n avg_f1_imp = 0\\n\\n avg_f1_without_dos = math.fsum([result.metrics['dos'].f1 for result in result_without]) / without_length\\n avg_f1_without_fuzzy = math.fsum([result.metrics['fuzzy'].f1 for result in result_without]) / without_length\\n avg_f1_without_imp = math.fsum([result.metrics['impersonation'].f1 for result in result_without]) / without_length\\n avg_f1_diff_dos = avg_f1_without_dos - avg_f1_dos\\n avg_f1_diff_fuzzy = avg_f1_without_fuzzy - avg_f1_fuzzy\\n avg_f1_diff_imp = avg_f1_without_imp - avg_f1_imp\\n\\n statistics[label] = [prevalence, avg_f1_diff_dos, avg_f1_diff_fuzzy, avg_f1_diff_imp]\\n\\n return statistics\",\n \"def create_gnuplot_statistic(statistic_entries):\\n grouped_by_number_of_entries = {}\\n for statistic in statistic_entries:\\n key = statistic['max entries']\\n if key not in grouped_by_number_of_entries:\\n grouped_by_number_of_entries[key] = [statistic]\\n else:\\n grouped_by_number_of_entries[key].append(statistic)\\n\\n all_plots = multiplot(\\\"learn.py statistics\\\", title_font=(\\\"\\\", 18), plots_per_row=2)\\n\\n pos = 0\\n max_pos = len(grouped_by_number_of_entries) - 1\\n for key, statistic in grouped_by_number_of_entries.items():\\n average_time_plot = plot()\\n average_time_plot.set_ylabel(\\\"seconds\\\")\\n if pos == max_pos:\\n average_time_plot.set_xlabel(\\\"n'th test run\\\")\\n average_time_plot.set_xtics(\\\"1\\\")\\n average_time_plot.set_ytics(\\\"0.5\\\")\\n average_time_plot.set_line_style(1, \\\"lc rgb \\\\\\\"#00ff00\\\\\\\" lw 2\\\")\\n average_time_plot.set_fill_style(1, \\\"transparent solid 0.4 border\\\")\\n values = list(enumerate([average(entry) for entry in statistic], 1))\\n average_time_plot.add_curve(\\\"average times (max entries=%d)\\\" % key,\\n values=values, mode=plot.FILLEDCURVES)\\n\\n all_plots.add_plot(average_time_plot)\\n\\n number_of_tests_plot = plot()\\n number_of_tests_plot.set_ylabel(\\\"# tests\\\")\\n if pos == max_pos:\\n number_of_tests_plot.set_xlabel(\\\"n'th test run\\\")\\n number_of_tests_plot.set_xtics(\\\"1\\\")\\n number_of_tests_plot.set_ytics(\\\"1\\\")\\n number_of_tests_plot.set_line_style(1, \\\"lc rgb \\\\\\\"#00ff00\\\\\\\" lw 2\\\")\\n number_of_tests_plot.set_fill_style(1, \\\"transparent solid 0.4 border\\\")\\n values = list(enumerate([entry['correct answers'] + entry['wrong answers']\\n for entry in statistic], 1))\\n number_of_tests_plot.add_curve(\\\"# of tests (max entries=%d)\\\" % key,\\n values=values, mode=plot.FILLEDCURVES)\\n\\n all_plots.add_plot(number_of_tests_plot)\\n pos += 1\\n\\n calculated_height = len(grouped_by_number_of_entries) * 250\\n script(\\\"learn.gp\\\", all_plots, width=800, height=calculated_height).execute()\",\n \"def get_eval_metric_fns(self, ):\\n metric_fns = {}\\n metric_fns.update({\\n \\\"metric/%s\\\" % name: tfr.metrics.make_ranking_metric_fn(name) for name in [\\n tfr.metrics.RankingMetricKey.ARP,\\n tfr.metrics.RankingMetricKey.ORDERED_PAIR_ACCURACY,\\n ]\\n })\\n metric_fns.update({\\n \\\"metric/ndcg@%d\\\" % topn: tfr.metrics.make_ranking_metric_fn(\\n tfr.metrics.RankingMetricKey.NDCG, topn=topn)\\n for topn in [1, 3, 5, 10, 20]\\n })\\n return metric_fns\",\n \"def get_team_stats() -> List[BaseStat]:\\n return [PossessionStat(),\\n TeamTendencies(),\\n RelativeTendencies(),\\n PerPossessionStat(),\\n RumbleItemStat(),\\n PreRumbleGoals(),\\n DropshotStats()\\n ]\",\n \"def _func_list(name, size):\\n return [hl.Func(\\\"%s_%d\\\" % (name, i)) for i in range(size)]\",\n \"def _func_list(name, size):\\n return [hl.Func(\\\"%s_%d\\\" % (name, i)) for i in range(size)]\",\n \"def calc_metric(output, metrics):\\n score = []\\n for metric in metrics:\\n metric_mod = __import__(\\\"sklearn.metrics\\\", fromlist=[metric])\\n metric_func = getattr(metric_mod, metric)\\n score.append(metric_func(output[0], output[1]))\\n return score, output\",\n \"def measure(funcs, args, comment='', verbose=False, number=1):\\n if not comment:\\n comment = repr(args)\\n\\n # measure performance\\n results = []\\n w = max(len(name) for name, _ in funcs)\\n for name, f in funcs:\\n results.append((measure_func(f, args, number=number), name))\\n if verbose:\\n print(\\\"{:{}s} {:>9s} {}\\\".format(\\n name, w, human_seconds(results[-1][0]), comment))\\n\\n # print sorted results\\n results.sort()\\n mint = results[0][0] # minimal time\\n ratios = [\\\"%5.2f\\\" % (t / mint,) for t, _ in results]\\n maxratio_width = max(len(r) for r in ratios)\\n # header\\n print(\\\"{:{}s} {:>9s} {:>{}s} {}\\\".format(\\n \\\"name\\\", w, \\\"time\\\", \\\"ratio\\\", maxratio_width, \\\"comment\\\"))\\n ratios = [s.rjust(maxratio_width) for s in ratios]\\n for (t, name), ratio in zip(results, ratios):\\n print(\\\"{:{}s} {:>9s} {} {}\\\".format(\\n name, w, human_seconds(t), ratio, comment))\\n return results\",\n \"def get_eval_metric_fns():\\n\\tmetric_fns = {}\\n\\tmetric_fns.update({\\n\\t\\t\\t\\\"metric/mrr@%d\\\" % topn: tfr.metrics.make_ranking_metric_fn(\\n\\t\\t\\t\\t\\ttfr.metrics.RankingMetricKey.MRR, topn=topn)\\n\\t\\t\\tfor topn in [10]\\n\\t})\\n\\tmetric_fns.update({\\n\\t\\t\\t\\\"metric/%s\\\" % name: tfr.metrics.make_ranking_metric_fn(name) for name in [\\n\\t\\t\\t\\t\\ttfr.metrics.RankingMetricKey.ARP,\\n\\t\\t\\t\\t\\t#tfr.metrics.RankingMetricKey.ORDERED_PAIR_ACCURACY,\\n\\t\\t\\t]\\n\\t})\\n\\tmetric_fns.update({\\n\\t\\t\\t\\\"metric/ndcg@%d\\\" % topn: tfr.metrics.make_ranking_metric_fn(\\n\\t\\t\\t\\t\\ttfr.metrics.RankingMetricKey.NDCG, topn=topn)\\n\\t\\t\\tfor topn in [1, 3, 5, 10]\\n\\t})\\n\\treturn metric_fns\",\n \"def summarize(dataset):\\n summaries = [(np.mean(attribute), np.std(attribute)) for attribute in zip(*dataset)]\\n\\n return summaries\",\n \"def cmpthese(n=1, funcs=None, repeat=3):\\n results = timethese(n=n, funcs=funcs, repeat=repeat)\\n if isinstance(funcs, dict):\\n results = results.values()\\n\\n results = sorted(results, key=lambda r: r[\\\"best_time\\\"])\\n names = [r[\\\"name\\\"] for r in results]\\n times = [r[\\\"best_time\\\"] for r in results]\\n rates = [r[\\\"best_rate\\\"] for r in results]\\n\\n rows = []\\n for row_idx, row_res in enumerate(results):\\n row = []\\n\\n # Column 1 = performance\\n row_rate = row_res[\\\"best_rate\\\"]\\n\\n for col_idx, col_res in enumerate(results):\\n if row_idx == col_idx:\\n out = 0\\n else:\\n col_rate = col_res[\\\"best_rate\\\"]\\n out = row_rate / col_rate - 1\\n\\n row.append(out)\\n rows.append(row)\\n return {\\\"data\\\": rows, \\\"names\\\": names, \\\"times\\\": times, \\\"rates\\\": rates}\",\n \"def dist_stats(self,nn_list):\\n nn_list = np.array(nn_list)-1\\n d = self.dist[:,nn_list]\\n\\n mean = np.mean(d,axis=0)\\n std = np.std(d,axis=0)\\n\\n return mean, std\",\n \"async def stat_table(self, data):\\n\\n table = \\\"\\\"\\n table += tabulate([data[\\\"stats\\\"][1]], data[\\\"stats\\\"][0], tablefmt=\\\"grid\\\") + \\\"\\\\n\\\"\\n table += tabulate([data[\\\"resist\\\"][1]], data[\\\"resist\\\"][0], tablefmt=\\\"grid\\\") + \\\"\\\\n\\\"\\n if data[\\\"inherits\\\"] and data[\\\"inherits\\\"][0]:\\n table += tabulate([data[\\\"inherits\\\"][1]], data[\\\"inherits\\\"][0], tablefmt=\\\"grid\\\") + \\\"\\\\n\\\"\\n \\n skills = tabulate(data[\\\"skills\\\"][1], data[\\\"skills\\\"][0], tablefmt=\\\"grid\\\")\\n if len(skills) > 2000:\\n counter = 0\\n split_skills = []\\n skills = skills.split(\\\"\\\\n\\\")\\n skills = [\\\"\\\\n\\\".join(skills[8*i:min(8*(i+1)+1, len(skills))])\\n for i in range(int(len(skills) / 8))]\\n else:\\n skills = [skills]\\n\\n results = [\\\"```\\\\n\\\" + table[:-1] + \\\"\\\\n```\\\"]\\n for skill in skills:\\n results.append(\\\"```\\\\n\\\" + skill + \\\"\\\\n```\\\")\\n return results\",\n \"def extract_statistics_output(\\n partial_stats: List[Any],\\n stats_generators: List[stats_generator.CombinerStatsGenerator]\\n) -> statistics_pb2.DatasetFeatureStatisticsList:\\n\\n # We call compact before extract_output to guarentee that `compact()` is\\n # called at least once, for testing coverage.\\n outputs = [\\n gen.extract_output(gen.compact(stats))\\n for (gen, stats) in zip(stats_generators, partial_stats) # pytype: disable=attribute-error\\n ]\\n return _make_dataset_feature_statistics_list_proto(\\n [_merge_dataset_feature_stats_protos(outputs)])\",\n \"def _get_all(cls) -> Iterator[\\\"PermutationStatistic\\\"]:\\n yield from (cls(name, func) for name, func in PermutationStatistic._STATISTICS)\",\n \"def get_function(self, index = 0):\\n return self._get_one_param('list_func')[index]\",\n \"def process_assertion_list(self, cls, functions):\",\n \"def create_functions(self, num_dimensions=400, min_threshold=0, max_threshold=255):\\r\\n functions = []\\r\\n for i in range(self.L):\\r\\n dimensions = np.random.randint(low = 0, \\r\\n high = num_dimensions,\\r\\n size = self.k)\\r\\n thresholds = np.random.randint(low = min_threshold, \\r\\n high = max_threshold + 1, \\r\\n size = self.k)\\r\\n\\r\\n functions.append(self.create_function(dimensions, thresholds))\\r\\n return functions\",\n \"def computeStats(self, absList, statOut=None, errorOut=None):\\n \\n stats = EntityStats(self.entityTypes)\\n for abs in absList:\\n errorOut.write('---'+abs.id+'---\\\\n') \\n \\n # identify ALL annotated mentions, even in sentences we are not focused on\\n# for sentence in abs.allSentences():\\n# for mType in self.entityTypes:\\n# aList = sentence.getAnnotatedMentions(mType, recomputeMentions=True)\\n# \\n# for sentence in abs.sentences:\\n# for mType in self.entityTypes:\\n# self.compareAnnotatedAndDetected(sentence, mType, \\\\\\n# stats.irstats[mType], errorOut)\\n\\n\\n for sentence in abs.allSentences():\\n for mType in self.entityTypes:\\n if sentence in abs.sentences:\\n self.compareAnnotatedAndDetected(sentence, mType, \\\\\\n stats.irstats[mType], errorOut)\\n else: \\n aList = sentence.getAnnotatedMentions(mType, recomputeMentions=True)\\n \\n\\n stats.printStats()\\n if statOut != None:\\n stats.saveStats(statOut, keyPrefix='MF - ')\\n \\n return stats\",\n \"def get_tuned_f1(results_df):\\n df = results_df\\n scores = []\\n f1s = []\\n lambdas = np.arange(.5, 5, .05)\\n\\n def add_weighted(df, lam):\\n \\\"\\\"\\\" Calculates different weighted PMI values after already having mut_inf scores \\\"\\\"\\\"\\n df['mut_inf_weighted'] = df.mut_inf + (lam - 1) * (df.head_conditional + df.tail_conditional) / 2.\\n\\n for lam in lambdas:\\n ss = StandardScaler()\\n add_weighted(df, lam=lam)\\n model = GaussianMixture(2, n_init=1)\\n dat = ss.fit_transform(df[['mut_inf_weighted']])\\n pred = model.fit_predict(dat)\\n score = model.aic(dat)\\n f1 = f1_score((model.means_.argmax() == df.label), pred)\\n scores.append(score)\\n f1s.append(f1)\\n\\n scores = np.array(scores)\\n f1s = np.array(f1s)\\n lam = lambdas[scores.argmax()]\\n\\n optimal_lambda = lambdas[scores.argmax()]\\n optimal_f1 = f1s[scores.argmax()]\\n return optimal_f1, optimal_lambda\",\n \"def calculate7NumberSummaryForOneList(ls, returnObj=None):\\n\\tfrom scipy import stats\\t# for scoreatpercentile/percentileatscore to get quartiles\\n\\tif returnObj is None:\\n\\t\\treturnObj = PassingData()\\n\\treturnObj.minimum = numpy.min(ls)\\n\\treturnObj.first_decile = stats.scoreatpercentile(ls, 10)\\n\\treturnObj.lower_quartile = stats.scoreatpercentile(ls, 25)\\n\\treturnObj.median = stats.scoreatpercentile(ls, 50)\\n\\treturnObj.upper_quartile = stats.scoreatpercentile(ls, 75)\\n\\treturnObj.last_decile = stats.scoreatpercentile(ls, 90)\\n\\treturnObj.maximum = numpy.max(ls)\\n\\treturn returnObj\",\n \"def function(self, scores, multilabel):\\n\\n # Output functions\\n # pylint: disable=C3001\\n identity = lambda x: x\\n sigmoid = lambda x: 1.0 / (1.0 + np.exp(-x))\\n softmax = lambda x: np.exp(x) / np.sum(np.exp(x))\\n function = identity if multilabel is None else sigmoid if multilabel else softmax\\n\\n # Apply output function\\n return function(np.array(scores))\",\n \"def summary(self, tmin=None, tmax=None, stats='basic'):\\n output = {\\n 'basic': {\\n 'evp': 'Explained variance percentage',\\n 'rmse': 'Root mean squared error',\\n 'avg_dev': 'Average Deviation',\\n 'rsq': 'Pearson R^2',\\n 'bic': 'Bayesian Information Criterion',\\n 'aic': 'Akaike Information Criterion'},\\n }\\n\\n # get labels and method names for stats output\\n if stats == 'all':\\n # sort by key, label, method name\\n selected_output = sorted([(k, l, f) for k, d in output.items()\\n for f, l in d.items()])\\n else:\\n # sort by name, method name\\n selected_output = sorted([(0, l, f) for f, l in\\n output[stats].items()])\\n\\n # compute statistics\\n labels_and_values = [(l, getattr(self, f)(tmin=tmin, tmax=tmax))\\n for _, l, f in selected_output]\\n labels, values = zip(*labels_and_values)\\n\\n stats = pd.DataFrame(index=list(labels), data=list(values),\\n columns=['Value'])\\n stats.index.name = 'Statistic'\\n return stats\",\n \"def get_statistics(pred, gt, num_cls=2):\\n h,w = gt.shape\\n statistics = []\\n for i in range(num_cls):\\n tp = np.sum((pred==i)&(gt==i))\\n fp = np.sum((pred==i)&(gt!=i))\\n fn = np.sum((pred!=i)&(gt==i)) \\n statistics.append([tp, fp, fn])\\n return statistics\",\n \"def get_metrics_list(self, project, callback=None):\\n metrics = sorted((yield gen.Task(self.client.smembers,\\n self.get_metrics_key(project))))\\n if callback:\\n callback(metrics)\",\n \"def calc_attribute_statistics(self, statistic_name):\\n stats = {}\\n for var, grids in self.attributes.items():\\n if len(grids) > 1:\\n stats[var] = getattr(np.array([getattr(np.ma.array(x, mask=self.masks[t] == 0), statistic_name)()\\n for t, x in enumerate(grids)]), statistic_name)()\\n else:\\n stats[var] = getattr(np.ma.array(grids[0], mask=self.masks[0] == 0), statistic_name)()\\n return stats\",\n \"def _get_value(self, info):\\n\\n for function, data in info.items():\\n for dimension, run_data in data.items():\\n rezultat, local_mins, populations, fabicrated_args = run_data\\n\\n # prepare function\\n function.set_args(fabicrated_args)\\n run_f = function(dimensions=dimension)\\n\\n # get the best run\\n best_run = None\\n for run_id, value in populations.items():\\n if not best_run:\\n best_run = value\\n elif (self._get_fit_pop(value, run_f, local_mins) <\\n self._get_fit_pop(best_run, run_f, local_mins)):\\n best_run = value\\n\\n # compute for the best run\\n\\n return 12\",\n \"def _MpstatResults(\\n metadata: Dict[str, Any],\\n output: Dict[str, Any],\\n interval: int,\\n per_interval_samples: bool = False,\\n ):\\n start_timestamp = _ParseStartTime(output)\\n samples = []\\n hosts = output['sysstat']['hosts']\\n\\n for host in hosts:\\n host_stats = host['statistics']\\n number_of_cpus = host['number-of-cpus']\\n metadata['nodename'] = host['nodename']\\n\\n samples += _GetCPUAverageMetrics(\\n host_stats,\\n number_of_cpus,\\n metadata,\\n start_timestamp)\\n\\n samples += _GetCPUAverageInterruptions(\\n host_stats,\\n number_of_cpus,\\n metadata,\\n start_timestamp)\\n\\n if per_interval_samples:\\n samples += _GetPerIntervalSamples(\\n host_stats,\\n metadata,\\n start_timestamp,\\n interval)\\n\\n return samples\",\n \"def _calc_frames(stats):\\n timings = []\\n callers = []\\n for key, values in iteritems(stats.stats):\\n timings.append(\\n pd.Series(\\n key + values[:-1],\\n index=timing_colnames,\\n )\\n )\\n for caller_key, caller_values in iteritems(values[-1]):\\n callers.append(\\n pd.Series(\\n key + caller_key + caller_values,\\n index=caller_columns,\\n )\\n )\\n\\n timings_df = pd.DataFrame(timings)\\n callers_df = pd.DataFrame(callers)\\n timings_df['filename:funcname'] = \\\\\\n (timings_df['filename'] + ':' + timings_df['funcname'])\\n timings_df = timings_df.groupby('filename:funcname').sum()\\n return timings_df, callers_df\",\n \"def get_player_stats() -> List[BaseStat]:\\n return [BoostStat(),\\n PositionalTendencies(),\\n Averages(),\\n BallDistanceStat(),\\n ControlsStat(),\\n SpeedTendencies(),\\n CarryStat(),\\n PerPossessionStat(),\\n SpeedTendencies(),\\n RumbleItemStat(),\\n KickoffStat(),\\n DropshotStats(),\\n DemoStat()\\n ]\",\n \"def addfunctions(dtls, bunchdt):\\n snames = [\\n \\\"BuildingSurface:Detailed\\\",\\n \\\"Wall:Detailed\\\",\\n \\\"RoofCeiling:Detailed\\\",\\n \\\"Floor:Detailed\\\",\\n \\\"FenestrationSurface:Detailed\\\",\\n \\\"Shading:Site:Detailed\\\",\\n \\\"Shading:Building:Detailed\\\",\\n \\\"Shading:Zone:Detailed\\\",\\n ]\\n for sname in snames:\\n if sname.upper() in bunchdt:\\n surfaces = bunchdt[sname.upper()]\\n for surface in surfaces:\\n func_dict = {\\n \\\"area\\\": fh.area,\\n \\\"height\\\": fh.height, # not working correctly\\n \\\"width\\\": fh.width, # not working correctly\\n \\\"azimuth\\\": fh.azimuth,\\n \\\"tilt\\\": fh.tilt,\\n \\\"coords\\\": fh.getcoords, # needed for debugging\\n }\\n try:\\n surface.__functions.update(func_dict)\\n except KeyError as e:\\n surface.__functions = func_dict\\n # add common functions\\n # for name in dtls:\\n # for idfobject in bunchdt[name]:\\n # idfobject.__functions\\n # idfobject['__functions']['fieldnames'] = fieldnames\\n # idfobject['__functions']['fieldvalues'] = fieldvalues\\n # idfobject['__functions']['getrange'] = GetRange(idfobject)\\n # idfobject['__functions']['checkrange'] = CheckRange(idfobject)\",\n \"def summarize_results(self, models, scores):\\n mu = np.mean(scores)\\n sigma = np.std(scores)\\n best_model = models[np.argmax(scores)]\\n best_score = max(scores)\\n return best_model, best_score, mu, sigma\",\n \"async def generate_leg_statistics(self) -> list:\\n\\n # General total amount of things\\n amount_of_sessions = await self.bot.db.fetchval(\\\"SELECT COUNT(id) FROM legislature_sessions\\\")\\n amount_of_bills = await self.bot.db.fetchval(\\\"SELECT COUNT(id) FROM legislature_bills\\\")\\n amount_of_laws = await self.bot.db.fetchval(\\\"SELECT COUNT(law_id) FROM legislature_laws\\\")\\n amount_of_motions = await self.bot.db.fetchval(\\\"SELECT COUNT(id) FROM legislature_motions\\\")\\n\\n # Sorted statistics by Discord Member\\n amount_of_bills_by_submitter = self.count_rows_from_db_record(await self.bot.db.fetch(\\\"SELECT submitter FROM \\\"\\n \\\"legislature_bills\\\"),\\n 'submitter')\\n amount_of_sessions_by_speaker = self.count_rows_from_db_record(await self.bot.db.fetch(\\\"SELECT speaker FROM \\\"\\n \\\"legislature_sessions\\\"),\\n 'speaker')\\n query = \\\"\\\"\\\"SELECT submitter FROM legislature_bills AS b WHERE exists (SELECT 1 FROM legislature_laws l\\n WHERE l.bill_id = b.id)\\\"\\\"\\\"\\n amount_of_laws_by_submitter = self.count_rows_from_db_record(await self.bot.db.fetch(query), 'submitter')\\n\\n # Prettified sorted statistics by discord.Member\\n pretty_top_submitter = self.get_pretty_stats(self.sort_dict_by_value(amount_of_bills_by_submitter), 'bills')\\n\\n pretty_top_speaker = self.get_pretty_stats(self.sort_dict_by_value(amount_of_sessions_by_speaker), 'sessions')\\n\\n pretty_top_lawmaker = self.get_pretty_stats(self.sort_dict_by_value(amount_of_laws_by_submitter), 'laws')\\n\\n return [amount_of_sessions, amount_of_bills, amount_of_laws, amount_of_motions,\\n pretty_top_submitter, pretty_top_speaker, pretty_top_lawmaker]\",\n \"def _parse_lighthouse_response(self, lighthouse_response, functions):\\n # Keep all dict values relating to AWE functions and set the function\\n # name as the key\\n for function_name, audit in lighthouse_response[\\\"audits\\\"].items():\\n if function_name in functions:\\n yield (\\n function_name,\\n {\\n # failing: the function was tested and it did not pass WCAG\\n \\\"failing\\\": False if audit[\\\"score\\\"] == 1 else True,\\n # applicable: the function can't be tested\\n \\\"applicable\\\": audit[\\\"score\\\"] is not None,\\n \\\"description\\\": audit[\\\"description\\\"],\\n \\\"items\\\": list(\\n self._parse_audit_items(\\n audit.get(\\\"details\\\", {}).get(\\\"items\\\", []), function_name\\n )\\n ),\\n },\\n )\",\n \"def many(self, tmin=None, tmax=None, stats=None):\\n if not stats:\\n stats = ['evp', 'rmse', 'rmsi', 'rsq']\\n\\n data = pd.DataFrame(index=[0], columns=stats)\\n for k in stats:\\n data.iloc[0][k] = (getattr(self, k)(tmin=tmin, tmax=tmax))\\n\\n return data\",\n \"def map_functions(x, functions):\\n res = []\\n for func in functions:\\n res.append(func(x))\\n return res\",\n \"def _prepare_stats_table(self, pinfos):\\n\\n stats_tbl = OrderedDict()\\n stats_tbl[\\\"Title\\\"] = OrderedDict()\\n for res in self.rsts:\\n stats_tbl[res.reportid] = OrderedDict()\\n\\n for pinfo in pinfos:\\n for colname in (pinfo[\\\"colname\\\"], pinfo[\\\"xcolname\\\"]):\\n if colname in stats_tbl[\\\"Title\\\"]:\\n continue\\n\\n # Each column name is represented by a row in the statistics table. Fill the \\\"Title\\\"\\n # column.\\n title_dict = stats_tbl[\\\"Title\\\"][colname] = OrderedDict()\\n defs = self._refdefs.info[colname]\\n\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\":\\n # Convert nanoseconds to microseconds.\\n unit = \\\"us\\\"\\n else:\\n unit = defs.get(\\\"short_unit\\\", \\\"\\\")\\n\\n title_dict[\\\"colname\\\"] = colname\\n if unit:\\n title_dict[\\\"colname\\\"] += f\\\", {unit}\\\"\\n title_dict[\\\"coldescr\\\"] = defs[\\\"descr\\\"]\\n\\n title_dict[\\\"funcs\\\"] = OrderedDict()\\n for funcname in self._stats_funcs:\\n if funcname in self.rsts[0].cstats[colname]:\\n title_dict[\\\"funcs\\\"][funcname] = RORawResult.get_stat_func_descr(funcname)\\n\\n # Now fill the values for each result.\\n for res in self.rsts:\\n res_dict = stats_tbl[res.reportid][colname] = OrderedDict()\\n res_dict[\\\"funcs\\\"] = OrderedDict()\\n\\n for funcname in title_dict[\\\"funcs\\\"]:\\n val = res.cstats[colname][funcname]\\n fmt = \\\"{}\\\"\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\" and \\\"index\\\" not in funcname:\\n val /= 1000\\n fmt = \\\"{:.2f}\\\"\\n if defs[\\\"type\\\"] == \\\"float\\\":\\n fmt = \\\"{:.2f}\\\"\\n\\n fdict = res_dict[\\\"funcs\\\"][funcname] = OrderedDict()\\n fdict[\\\"val\\\"] = fmt.format(val)\\n fdict[\\\"raw_val\\\"] = val\\n\\n if self._refres.reportid == res.reportid:\\n fdict[\\\"hovertext\\\"] = \\\"This is the reference result, other results \\\" \\\\\\n \\\"are compared to this one.\\\"\\n continue\\n\\n ref_fdict = stats_tbl[self._refres.reportid][colname][\\\"funcs\\\"][funcname]\\n change = val - ref_fdict[\\\"raw_val\\\"]\\n if ref_fdict[\\\"raw_val\\\"]:\\n percent = (change / ref_fdict[\\\"raw_val\\\"]) * 100\\n else:\\n percent = change\\n change = fmt.format(change) + unit\\n percent = \\\"{:.1f}%\\\".format(percent)\\n fdict[\\\"hovertext\\\"] = f\\\"Change: {change} ({percent})\\\"\\n\\n return stats_tbl\",\n \"def _evaluate(self, function_name, x):\\n stats = None\\n for ii in range(self.nunique_vars):\\n var = self.unique_variables[ii]\\n indices = self.unique_variable_indices[ii]\\n for jj in indices:\\n stats_jj = np.atleast_1d(\\n getattr(var.dist, function_name)(x[jj, :]))\\n assert stats_jj.ndim == 1\\n if stats is None:\\n stats = np.empty((self.num_vars(), stats_jj.shape[0]))\\n stats[jj] = stats_jj\\n return stats\",\n \"def get_as_rows(self, stats=(\\\"count\\\", \\\"mean\\\", \\\"stddev\\\", \\\"min\\\", \\\"max\\\")):\\n return [\\n row_from_keyed_values(\\n [\\n (\\\"summary\\\", stat)\\n ] + [\\n (col_name, self.get_stat(self.column_stat_helpers[col_name], stat))\\n for col_name in self.col_names\\n ]\\n )\\n for stat in stats\\n ]\",\n \"def get_th_mean_values(self, value_list):\\n if not len(value_list):\\n return None, None, None\\n t, h, d = map(list, zip(*value_list))\\n return (\\n round(statistics.mean(t), 1),\\n int(round(statistics.mean(h), 0)),\\n round(statistics.mean(d), 1),\\n )\",\n \"def get_normalized_regret(evaluator_list):\\n values = collections.defaultdict(list)\\n for e in evaluator_list:\\n values[e.task_name].append(e.get_normalized_regret())\\n return _map(np.mean, values), _map(np.std, values)\",\n \"def query_failure_rate_results(analysis_list):\\n return {analysis['testId']: analysis for analysis in analysis_list}\",\n \"def genHashFuncs(num_of_func, baskets):\\n func_list = list()\\n\\n def build_func(param_a, param_b, param_m):\\n def apply_funcs(input_x):\\n return format((param_a * input_x + param_b) % param_m, 'b') \\\\\\n .zfill(NUM_OF_BIT)\\n\\n return apply_funcs\\n\\n param_as = random.sample(range(1, sys.maxsize - 1), num_of_func)\\n param_bs = random.sample(range(0, sys.maxsize - 1), num_of_func)\\n for a, b in zip(param_as, param_bs):\\n func_list.append(build_func(a, b, baskets))\\n\\n return func_list\",\n \"def comparison_stats(df, els=None):\\n if els is None:\\n els = ['Li', 'Mg', 'Al', 'P', 'Ti', 'Y', 'La', 'Ce', 'Pr', 'Nd', 'Sm',\\n 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Pb', 'Th',\\n 'U']\\n \\n yl_stats = []\\n \\n for i, e in enumerate(els):\\n x = df.loc[:, e + '_rd'].values\\n yl = df.loc[:, e + '_la'].values\\n \\n yl_stats.append(summary_stats(x, yl, e))\\n \\n yl_stats = pd.concat(yl_stats).T\\n \\n return yl_stats.T\",\n \"def GetMeansAndStdsFromList(lists):\\n means = [np.mean(single_list) for single_list in lists]\\n stds = [np.std(single_list) for single_list in lists]\\n return means, stds\",\n \"def apply_func(output, func):\\n new_output = []\\n for dict in output:\\n mnemonic = copy.deepcopy(dict['mnemonic'])\\n values = dict['values']\\n new_values = func(values)\\n new_output.append({'mnemonic': mnemonic, 'values': new_values})\\n return new_output\",\n \"def calc_metrics(metric_scores_list):\\n\\n N_split, N_miss, N_add, Q_P, Q_R, Q_F, N_gt, N_pred = [], [], [], [], [], [], [], []\\n Q_rand, Q_jaccard, Q_aggregated_jaccard, Q_ctc, Q_piou = [], [], [], [], []\\n tp, fp, fn = [], [], []\\n\\n for score in metric_scores_list:\\n N_split.append(score['N_split']), N_miss.append(score['N_miss']), N_add.append(score['N_add'])\\n Q_P.append(score['Q_P']), Q_R.append(score['Q_R']), Q_F.append(score['Q_F'])\\n Q_rand.append(score['Q_rand']), Q_jaccard.append(score['Q_jaccard'])\\n Q_aggregated_jaccard.append(score['Q_aggregated_jaccard'])\\n if \\\"Q_ctc\\\" in score:\\n Q_ctc.append(score['Q_ctc']), \\n Q_piou.append(score['Q_piou'])\\n N_gt.append(score['N_gt']), N_pred.append(score['N_pred'])\\n tp.append(score['tp']), fp.append(score['fp']), fn.append(score['fn'])\\n\\n N_split, N_miss, N_add = np.array(N_split), np.array(N_miss), np.array(N_add)\\n N_gt, N_pred = np.array(N_gt), np.array(N_pred)\\n tp, fp, fn = np.array(tp), np.array(fp), np.array(fn)\\n Q_P_macro, Q_R_macro, Q_F_macro = np.mean(np.array(Q_P)), np.mean(np.array(Q_R)), np.mean(np.array(Q_F))\\n Q_P_micro = np.sum(tp) / (np.sum(tp) + np.sum(fp)) if (np.sum(tp) + np.sum(fp)) > 0 else 0\\n Q_R_micro = np.sum(tp) / (np.sum(tp) + np.sum(fn)) if (np.sum(tp) + np.sum(fn)) > 0 else 0\\n Q_rand_macro, Q_jaccard_macro = np.mean(np.array(Q_rand)), np.mean(np.array(Q_jaccard))\\n Q_aggregated_jaccard_macro = np.mean(np.array(Q_aggregated_jaccard))\\n Q_ctc_macro, Q_piou_macro = np.mean(np.array(Q_ctc)), np.mean(np.array(Q_piou))\\n\\n metrics = {\\n 'Q_split_micro': float(np.sum(N_split) / np.sum(N_gt)),\\n 'Q_split_macro': float(np.mean(N_split / N_gt)),\\n 'Q_miss_micro': float(np.sum(N_miss) / np.sum(N_gt)),\\n 'Q_miss_macro': float(np.mean(N_miss / N_gt)),\\n 'Q_add_micro': float(np.sum(N_add) / np.sum(N_gt)),\\n 'Q_add_macro': float(np.mean(N_add / N_gt)),\\n 'N_gt': int(np.sum(N_gt)),\\n 'N_pred': int(np.sum(N_pred)),\\n 'Q_rand_macro': float(Q_rand_macro),\\n 'Q_jaccard_macro': float(Q_jaccard_macro),\\n 'Q_aggregated_jaccard_macro': float(Q_aggregated_jaccard_macro),\\n 'Q_ctc_macro': float(Q_ctc_macro),\\n 'Q_piou_macro': float(Q_piou_macro),\\n 'Q_P_micro': float(Q_P_micro),\\n 'Q_P_macro': float(Q_P_macro),\\n 'Q_R_micro': float(Q_R_micro),\\n 'Q_R_macro': float(Q_R_macro),\\n 'Q_F_macro': float(Q_F_macro),\\n 'Q_F_micro': float(2 * Q_P_micro * Q_R_micro / (Q_P_micro + Q_R_micro)) if (Q_P_micro + Q_R_micro) > 0 else 0\\n }\\n return metrics\",\n \"def get_suffstat_names():\\n params = ['sum_x', 'sum_x_squared']\\n return params\",\n \"def map_my(self, func: Callable[[Union[float, int]], int]) -> None:\\n def list_func(lst: List[valueType]) -> List[valueType]:\\n \\\"\\\"\\\"\\n To apply the function/operation defined by users to every item in the list.\\n :param lst: A list object like [element1, [element2, element3], element4].\\n :return: A list that store the result of items after user-defined operation.\\n \\\"\\\"\\\"\\n tmp = [] # type: List[valueType]\\n for e in lst:\\n if isinstance(e, (list, set, tuple)):\\n tmp.append(list_func(list(e)))\\n else:\\n if isinstance(e, (float, int)):\\n tmp.append(func(e))\\n else:\\n raise Exception\\n return tmp\\n\\n for head_node in self.hashTable:\\n for node in head_node.singlyLinkedList:\\n node.values = list_func(node.values)\",\n \"def _compute_summary_stats(entries):\\n result = {\\n \\\"average\\\": None,\\n \\\"median\\\": None,\\n \\\"min\\\": None,\\n \\\"max\\\": None,\\n \\\"per66\\\": None,\\n \\\"per75\\\": None,\\n \\\"per90\\\": None,\\n \\\"per95\\\": None,\\n \\\"per99\\\": None,\\n \\\"success_count\\\": len(entries['success']),\\n \\\"error_count\\\": len(entries['error']),\\n }\\n # sort by the duration\\n success = sorted(entries['success'], key=lambda x: x[1])\\n if len(success) == 0:\\n return result\\n\\n result['average'] = _compute_average(success)\\n result['min'] = success[0][1]\\n result['max'] = success[-1][1]\\n result['median'] = _compute_percentile(success, 50)\\n result['per66'] = _compute_percentile(success, 66)\\n result['per75'] = _compute_percentile(success, 75)\\n result['per90'] = _compute_percentile(success, 90)\\n result['per95'] = _compute_percentile(success, 95)\\n result['per99'] = _compute_percentile(success, 99)\\n assert result['min'] <= result['median']\\n assert result['median'] <= result['per66']\\n assert result['per66'] <= result['per75']\\n assert result['per75'] <= result['per90']\\n assert result['per90'] <= result['per95']\\n assert result['per95'] <= result['per99']\\n assert result['per99'] <= result['max']\\n return result\",\n \"def get_results(cls):\\n cls.all_hoechstzahls.sort(key=attrgetter('value', 'topic.category.weight'), reverse=True)\\n for hoechstzahl in cls.all_hoechstzahls:\\n yield hoechstzahl\",\n \"def _sort_results(self, results: dict) -> List:\\n return [results[url][\\\"display_name\\\"] for url in self.urls_list]\",\n \"def _compute_user_stats():\\n user_stats = []\\n \\n wmt16_group = Group.objects.filter(name='WMT16')\\n wmt16_users = _get_active_users_for_group(wmt16_group)\\n \\n for user in wmt16_users:\\n _user_stats = HIT.compute_status_for_user(user)\\n _name = user.username\\n _avg_time = seconds_to_timedelta(_user_stats[1])\\n _total_time = seconds_to_timedelta(_user_stats[2])\\n _data = (_name, _user_stats[0], _avg_time, _total_time)\\n \\n if _data[0] > 0:\\n user_stats.append(_data)\\n \\n # Sort by total number of completed HITs.\\n user_stats.sort(key=lambda x: x[1])\\n user_stats.reverse()\\n \\n return user_stats\",\n \"def process_bmi_list(cls, test_report_list):\\n overweight_people_count = 0\\n\\n if not test_report_list or type(test_report_list) is not list:\\n return (list(), overweight_people_count)\\n\\n for data in test_report_list:\\n # ignore if data is not dict\\n if type(data) is not dict:\\n continue\\n\\n bmi = BMICalculator.calculate_bmi(data.get(HEIGHT_CM), data.get(WEIGHT_KG))\\n\\n if bmi:\\n if bmi < LOWEST_NORMAL_WEIGHT_BMI:\\n bmi_category, health_risk = UNDER_WEIGHT, MALNUTRITION_RISK\\n elif bmi >= LOWEST_NORMAL_WEIGHT_BMI and bmi < LOWEST_OVER_WEIGHT_BMI:\\n bmi_category, health_risk = NORMAL_WEIGHT, LOW_RISK\\n elif bmi >= LOWEST_OVER_WEIGHT_BMI and bmi < LOWEST_MODERATELY_OBESE_BMI:\\n bmi_category, health_risk = OVER_WEIGHT, ENHANCED_RISK\\n overweight_people_count += 1\\n elif bmi >= LOWEST_MODERATELY_OBESE_BMI and bmi < LOWEST_SEVERELY_OBESE_BMI:\\n bmi_category, health_risk = MODERATELY_OBESE, MEDIUM_RISK\\n elif bmi >= LOWEST_SEVERELY_OBESE_BMI and bmi < LOWEST_VERY_SEVERELY_OBESE_BMI:\\n bmi_category, health_risk = SEVERELY_OBESE, HIGH_RISK\\n elif bmi >= LOWEST_VERY_SEVERELY_OBESE_BMI:\\n bmi_category, health_risk = VERY_SEVERELY_OBESE, VERY_HIGH_RISK\\n else:\\n bmi_category, health_risk = None, None\\n \\n data[BMI] = bmi\\n data[BMI_CATEGORY] = bmi_category\\n data[HEALTH_RISK] = health_risk\\n \\n return (test_report_list, overweight_people_count)\",\n \"def bench_compare_multiple(logger, *param_groups, **fn_names):\\n title_str, ans_str, time_str = \\\"Testing {name}:\\\",\\\"\\\\t{name}({params}) = {ans}\\\",\\\"\\\\tTime = {time}\\\"\\n for name, fn in fn_names.items():\\n str_params = {'name': name}\\n print(title_str.format(**str_params))\\n for pg in param_groups:\\n str_params |= {'params': pg}\\n try:\\n start = time.time()\\n result = fn(*pg)\\n exec = time.time() - start\\n except TypeError:\\n start = time.time()\\n result = fn(pg)\\n exec = time.time() - start\\n str_params |= {'time': exec, 'ans': result}\\n print(ans_str.format(**str_params))\\n print(time_str.format(**str_params))\",\n \"def get_data_args(data_func, nfuncs):\\n if data_func.__name__ == 'gg_1d':\\n # first arg is sorted\\n if nfuncs == 1:\\n data_args = [{'a': 0.75, 'mu': 0.4, 'sigma': 0.3, 'beta': 2.0}]\\n elif nfuncs == 2:\\n data_args = [{'a': 0.2, 'mu': 0.4, 'sigma': 0.6, 'beta': 5.0},\\n {'a': 0.55, 'mu': 0.4, 'sigma': 0.2, 'beta': 4.0}]\\n elif nfuncs == 3:\\n data_args = [{'a': 0.2, 'mu': 0.4, 'sigma': 0.6, 'beta': 5.0},\\n {'a': 0.35, 'mu': 0.6, 'sigma': 0.07, 'beta': 2.0},\\n {'a': 0.55, 'mu': 0.32, 'sigma': 0.14, 'beta': 6.0}]\\n elif nfuncs == 4:\\n data_args = [{'a': 0.2, 'mu': 0.3, 'sigma': 0.5, 'beta': 5.0},\\n {'a': 0.4, 'mu': 0.65, 'sigma': 0.07, 'beta': 2.0},\\n {'a': 0.6, 'mu': 0.25, 'sigma': 0.1, 'beta': 6.0},\\n {'a': 0.9, 'mu': 0.95, 'sigma': 0.1, 'beta': 6.0}]\\n elif data_func.__name__ == 'ta_1d':\\n # first arg is sorted\\n if nfuncs == 1:\\n data_args = [{'a': 0.8, 'w_0': 0.0, 'w_1': 1.5}]\\n elif nfuncs == 2:\\n data_args = [{'a': 0.7, 'w_0': -1, 'w_1': 3},\\n {'a': 0.9, 'w_0': 2, 'w_1': -3}]\\n elif nfuncs == 3:\\n data_args = [\\n {'a': 0.6, 'w_0': -7, 'w_1': 8},\\n {'a': 1, 'w_0': -1, 'w_1': 3},\\n {'a': 1.4, 'w_0': 2, 'w_1': -3}]\\n elif data_func.__name__ == 'gg_2d':\\n # the order is (with first arg sorted):\\n # [a_1, mu1_1, mu2_1, s1_1, s2_1, b1_1, b2_1, rot angle]\\n if nfuncs == 1:\\n data_args = [\\n {'a': 0.8, 'mu1': 0.6, 'mu2': 0.6, 'sigma1': 0.1,\\n 'sigma2': 0.2, 'beta1': 2, 'beta2': 2, 'omega': 0.1 * np.pi}]\\n elif nfuncs == 2:\\n data_args = [\\n {'a': 0.5, 'mu1': 0.5, 'mu2': 0.4, 'sigma1': 0.4,\\n 'sigma2': 0.2, 'beta1': 2, 'beta2': 2, 'omega': 0},\\n {'a': 0.8, 'mu1': 0.5, 'mu2': 0.6, 'sigma1': 0.1,\\n 'sigma2': 0.1, 'beta1': 2, 'beta2': 2, 'omega': 0}]\\n elif nfuncs == 3:\\n data_args = [\\n {'a': 0.5, 'mu1': 0.3, 'mu2': 0.7, 'sigma1': 0.2,\\n 'sigma2': 0.2, 'beta1': 2, 'beta2': 2, 'omega': 0},\\n {'a': 0.7, 'mu1': 0.7, 'mu2': 0.6, 'sigma1': 0.15,\\n 'sigma2': 0.15, 'beta1': 2, 'beta2': 2, 'omega': 0},\\n {'a': 0.9, 'mu1': 0.4, 'mu2': 0.3, 'sigma1': 0.1,\\n 'sigma2': 0.1, 'beta1': 2, 'beta2': 2, 'omega': 0}]\\n try:\\n data_args_list = []\\n for name in bf.get_bf_param_names(data_func):\\n data_args_list += [d[name] for d in data_args]\\n return data_args_list\\n except NameError:\\n raise AssertionError('no data args found! func={} nfuncs={}'.format(\\n data_func.__name__, nfuncs))\",\n \"def statistics(list_of_int):\\r\\n\\r\\n if len(list_of_int) > 3:\\r\\n print('Input must be exactly 3 integers')\\r\\n return list_of_int\\r\\n\\r\\n my_sum = list_of_int[0] + list_of_int[1] + list_of_int[2]\\r\\n\\r\\n average_value = my_sum / 3\\r\\n\\r\\n\\r\\n if list_of_int[0] > list_of_int[1]:\\r\\n smallest_value = list_of_int[1]\\r\\n largest_value = list_of_int[0]\\r\\n\\r\\n if list_of_int[1] > list_of_int[2]:\\r\\n smallest_value = list_of_int[2]\\r\\n\\r\\n elif list_of_int[2] > list_of_int[0]:\\r\\n largest_value = list_of_int[2]\\r\\n \\r\\n elif list_of_int[1] > list_of_int[0]:\\r\\n smallest_value = list_of_int[0]\\r\\n largest_value = list_of_int[1]\\r\\n\\r\\n if list_of_int[0] > list_of_int[2]:\\r\\n smallest_value = list_of_int[2]\\r\\n\\r\\n elif list_of_int[2] > list_of_int[1]:\\r\\n largest_value = list_of_int[2]\\r\\n \\r\\n\\r\\n return [my_sum, average_value, smallest_value, largest_value]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56247234","0.5412346","0.52866143","0.5263205","0.5087793","0.50818396","0.50043947","0.50017667","0.49602503","0.49288338","0.49267918","0.48980197","0.4889592","0.488622","0.48502252","0.4846984","0.4838837","0.4835714","0.48321322","0.4820766","0.4813563","0.47997853","0.4779614","0.47726384","0.47702277","0.47675222","0.4749616","0.4737774","0.4734445","0.47304836","0.47234637","0.47183064","0.47162068","0.47118992","0.46919656","0.46907225","0.46838793","0.4680327","0.4673356","0.46635917","0.4652053","0.46512574","0.46407548","0.4634465","0.463191","0.46261945","0.4621516","0.46207088","0.46207088","0.46079496","0.460431","0.46034217","0.4595037","0.45883116","0.4576299","0.4575072","0.45673043","0.45561677","0.4542717","0.45314822","0.45306873","0.45276737","0.452734","0.4527116","0.45266166","0.45252836","0.4525031","0.45213005","0.45139688","0.45014584","0.4500489","0.4486213","0.4484228","0.4483053","0.44827276","0.4473113","0.44720447","0.44712386","0.44669512","0.44662791","0.4457241","0.44562992","0.44552687","0.44545466","0.44524768","0.4450982","0.44504306","0.4449969","0.44498888","0.44487652","0.44478038","0.4447123","0.4437221","0.44329438","0.4424899","0.44246936","0.4416174","0.44102135","0.44073185","0.44032052"],"string":"[\n \"0.56247234\",\n \"0.5412346\",\n \"0.52866143\",\n \"0.5263205\",\n \"0.5087793\",\n \"0.50818396\",\n \"0.50043947\",\n \"0.50017667\",\n \"0.49602503\",\n \"0.49288338\",\n \"0.49267918\",\n \"0.48980197\",\n \"0.4889592\",\n \"0.488622\",\n \"0.48502252\",\n \"0.4846984\",\n \"0.4838837\",\n \"0.4835714\",\n \"0.48321322\",\n \"0.4820766\",\n \"0.4813563\",\n \"0.47997853\",\n \"0.4779614\",\n \"0.47726384\",\n \"0.47702277\",\n \"0.47675222\",\n \"0.4749616\",\n \"0.4737774\",\n \"0.4734445\",\n \"0.47304836\",\n \"0.47234637\",\n \"0.47183064\",\n \"0.47162068\",\n \"0.47118992\",\n \"0.46919656\",\n \"0.46907225\",\n \"0.46838793\",\n \"0.4680327\",\n \"0.4673356\",\n \"0.46635917\",\n \"0.4652053\",\n \"0.46512574\",\n \"0.46407548\",\n \"0.4634465\",\n \"0.463191\",\n \"0.46261945\",\n \"0.4621516\",\n \"0.46207088\",\n \"0.46207088\",\n \"0.46079496\",\n \"0.460431\",\n \"0.46034217\",\n \"0.4595037\",\n \"0.45883116\",\n \"0.4576299\",\n \"0.4575072\",\n \"0.45673043\",\n \"0.45561677\",\n \"0.4542717\",\n \"0.45314822\",\n \"0.45306873\",\n \"0.45276737\",\n \"0.452734\",\n \"0.4527116\",\n \"0.45266166\",\n \"0.45252836\",\n \"0.4525031\",\n \"0.45213005\",\n \"0.45139688\",\n \"0.45014584\",\n \"0.4500489\",\n \"0.4486213\",\n \"0.4484228\",\n \"0.4483053\",\n \"0.44827276\",\n \"0.4473113\",\n \"0.44720447\",\n \"0.44712386\",\n \"0.44669512\",\n \"0.44662791\",\n \"0.4457241\",\n \"0.44562992\",\n \"0.44552687\",\n \"0.44545466\",\n \"0.44524768\",\n \"0.4450982\",\n \"0.44504306\",\n \"0.4449969\",\n \"0.44498888\",\n \"0.44487652\",\n \"0.44478038\",\n \"0.4447123\",\n \"0.4437221\",\n \"0.44329438\",\n \"0.4424899\",\n \"0.44246936\",\n \"0.4416174\",\n \"0.44102135\",\n \"0.44073185\",\n \"0.44032052\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7310715"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77492,"cells":{"query":{"kind":"string","value":"Displays up to 12 charaters on the hp33401a"},"document":{"kind":"string","value":"def hp34401a_lcd_disp(hp_meter, message):\n hp_meter.write(\"DISP:TEXT:CLE\")\n hp_meter.write(\"DISP:TEXT '\" + str(message) + \"'\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def banner_ascii():\n print(\"\")\n print(f\"\\n{RED} Steganography Tool{RESET}\")\n print(f\"{RED} Made By {RESET}\")\n print(f\"{RED} Ehthe Samul Islam Laskar USN:1DS16CS712 {RESET}\")\n print(f\"{RED} B Padma USN:1DS19CS420{RESET}\")\n print(f\"{RED} Nikhil D Kanyal USN:1DS17CS731{RESET}\")\n print(f\"{YELLOW}Type 'help' to see commands{RESET}\")","def n_char(self,char,n,w=1,h=1):\n for i in range(n):\n self.esprint(char,w,h)","def basic_char_setup( self ):\n\t\tsmall_bar = 3 # number of points per bar\n\t\twide_bar = round(small_bar * 2.25,0) # 2.25 x small_bar\n\t\tdpl = 50 # dots per line 300dpi/6lpi = 50dpl\n\t\tself._nb = bytes( self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ( '*c%02ia%ib0P' % (small_bar, self.bc_height*dpl) ) + \n\t\t\t\t\t\t self.owner.PRINTER_ESC + \n\t\t\t\t\t\t (\"*p+%02iX\" % small_bar) )\n\t\tself._wb = bytes( self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ('*c%02ia%ib0P' % (wide_bar, self.bc_height*dpl) )+\n\t\t\t\t\t\t self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ('*p+%02iX' % wide_bar ) )\n\t\tself._ns = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % small_bar ) )\n\t\tself._ws = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % wide_bar ) )\n \n\t\t# DONE nb = bc39_esc+\"*c\"+TRANSFORM(small_bar,'99')+\"a\"+Alltrim(STR(bc39_height*dpl))+\"b0P\"+bc39_esc+\"*p+\"+TRANSFORM(small_bar,'99')+\"X\"\n\t\t# DONE wb = bc39_esc+\"*c\"+TRANSFORM(wide_bar,'99')+\"a\"+Alltrim(STR(bc39_height*dpl))+\"b0P\"+bc39_esc+\"*p+\"+TRANSFORM(wide_bar,'99')+\"X\"\n\t\t# DONE ns = bc39_esc+\"*p+\"+TRANSFORM(small_bar,'99')+\"X\"\n\t\t# DONE ws = bc39_esc+\"*p+\"+TRANSFORM(wide_bar,'99')+\"X\"\n \n\t\t# adjust cusor position to start at top of line and return to bottom of line\n\t\tself._bc_start = bytes( self.owner.PRINTER_ESC + '*p-50Y' )\n\t\tself._bc_end = bytes( self.owner.PRINTER_ESC + '*p+50Y' )\n\t\t# DONE bc39_start = bc39_esc+\"*p-50Y\"\n\t\t# DONE bc39_END = bc39_esc+\"*p+50Y\"\n\n\t\t# setup the structure allowing to print the code codebar section for various LETTERS\n\t\tself._char39 = { u'1' : 'wb+ns+nb+ws+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'2' : 'nb+ns+wb+ws+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'3' : 'wb+ns+wb+ws+nb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'4' : 'nb+ns+nb+ws+wb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'5' : 'wb+ns+nb+ws+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'6' : 'nb+ns+wb+ws+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'7' : 'nb+ns+nb+ws+nb+ns+wb+ns+wb' , \n\t\t\t\t\t\t u'8' : 'wb+ns+nb+ws+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'9' : 'nb+ns+wb+ws+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'0' : 'nb+ns+nb+ws+wb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'A' : 'wb+ns+nb+ns+nb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'B' : 'nb+ns+wb+ns+nb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'C' : 'wb+ns+wb+ns+nb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'D' : 'nb+ns+nb+ns+wb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'E' : 'wb+ns+nb+ns+wb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'F' : 'nb+ns+wb+ns+wb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'G' : 'nb+ns+nb+ns+nb+ws+wb+ns+wb' , \n\t\t\t\t\t\t u'H' : 'wb+ns+nb+ns+nb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'I' : 'nb+ns+wb+ns+nb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'J' : 'nb+ns+nb+ns+wb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'K' : 'wb+ns+nb+ns+nb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'L' : 'nb+ns+wb+ns+nb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'M' : 'wb+ns+wb+ns+nb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'N' : 'nb+ns+nb+ns+wb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'O' : 'wb+ns+nb+ns+wb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'P' : 'nb+ns+wb+ns+wb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'Q' : 'nb+ns+nb+ns+nb+ns+wb+ws+wb' , \n\t\t\t\t\t\t u'R' : 'wb+ns+nb+ns+nb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'S' : 'nb+ns+wb+ns+nb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'T' : 'nb+ns+nb+ns+wb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'U' : 'wb+ws+nb+ns+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'V' : 'nb+ws+wb+ns+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'W' : 'wb+ws+wb+ns+nb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'X' : 'nb+ws+nb+ns+wb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'Y' : 'wb+ws+nb+ns+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'Z' : 'nb+ws+wb+ns+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'-' : 'nb+ws+nb+ns+nb+ns+wb+ns+wb' , \n\t\t\t\t\t\t u'.' : 'wb+ws+nb+ns+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u' ' : 'nb+ws+wb+ns+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'*' : 'nb+ws+nb+ns+wb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'$' : 'nb+ws+nb+ws+nb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'/' : 'nb+ws+nb+ws+nb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'+' : 'nb+ws+nb+ns+nb+ws+nb+ws+nb' , \n\t\t\t\t\t\t u'%' : 'nb+ns+nb+ws+nb+ws+nb+ws+nb' }","def displayHand(hand: d_si) -> None:\n for letter in hand.keys():\n for _ in range(hand[letter]):\n print(letter,end=\" \")\n print()","def print_chars(self):\n for v in voc.split('\\n'):\n pair = v.split(',')\n print(pair[0], pair[1], '\\t', self.epi.xsampa_list(pair[0]))","def display_hand(hand):\n letters = \"\"\n\n for i in hand.keys():\n\n for j in range(hand[i]):\n letters += i\n\n return letters","def display_characters(self):\n return f'{self.character_set}'","def display_string(text_area_no: int) -> str:\n if text_area_no == 1:\n text = ''\n for v in utterances['P1']:\n text += v + '\\n'\n return text\n elif text_area_no == 2:\n text = ''\n for v in utterances['S1']:\n text += v + '\\n'\n return text\n elif text_area_no == 3:\n text = ''\n for v in utterances['S2']:\n text += v + '\\n'\n return text\n elif text_area_no == 4:\n text = ''\n for v in utterances['S3']:\n text += v + '\\n'\n return text\n elif text_area_no == 5:\n text = ''\n for v in utterances['S4']:\n text += v + '\\n'\n return text\n elif text_area_no == 6:\n text = ''\n for v in utterances['S4']:\n text += v + '\\n'\n return text\n elif text_area_no == 7:\n text = ''\n for v in utterances['C1']:\n text += v + '\\n'\n return text\n elif text_area_no == 8:\n text = ''\n for v in utterances['C2']:\n text += v + '\\n'\n return text","def display_s(s, font, screen, lcd, size=5, x=0, y=0):\n i = 0\n spacing = size * .11\n s = str(s)\n char = s[0]\n char_w, char_h = font[char].size\n for c in s:\n display_c(c,font,screen,lcd,size,(int(i*spacing*char_w)+x),y)\n i += 1\n return screen","def tower(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC+\"255D\"+self.ESC+\"0;1;44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"58C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"68C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"30m\"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"0;44m\"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"1m\"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"52C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"43m\"+self.A223+self.A223+self.ESC+\"0;43m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"1m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"40C\"+self.ESC+\"43m\"+self.ESC+\"0;34;43m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"30m\"+self.A223+self.A223+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40mYou reach th\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"42Ce castle and fight your way up the\"+self.ESC+\"C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A178+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"30C\"+self.ESC+\"43m\"+self.ESC+\"1;36;40mtower!\"+self.ESC+\"41C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A219+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"24C\"+self.ESC+\"44m\"+self.ESC+\"40m\"+self.A221+self.ESC+\"44m\"+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"48C\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A178+self.A177+self.A177+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A177+self.A177+self.A177+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40mYou blindly slash at any and all who\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"66C stand in \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A177+self.A176+self.A177+self.A176+self.A178+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A176+self.A177+self.A177+self.A219+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"30C\"+self.ESC+\"43m\"+self.ESC+\"1;36;40myour path - screaming to find the prisoner \"+self.ESC+\"4C\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"77C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40myou seek - and the jailer of your he\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"66Cart.\"+self.ESC+\"7C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.A176+self.A177+self.ESC+\"33m\"+self.A176+self.ESC+\"32m\"+self.A177+self.A176+self.A177+self.ESC+\"33m\"+self.A178+self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A178+self.A176+self.A177+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"23C\"+self.ESC+\"44m\"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"48C\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A176+self.A177+self.A177+self.A177+self.A176+self.A178+self.A177+self.ESC+\"33m\"+self.A176+self.A177+self.A178+self.ESC+\"32m\"+self.A177+self.A177+self.A178+self.A176+self.A178+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"43m\"+self.A220+self.A220+self.ESC+\"0;43m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"34C\"+self.ESC+\"43m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.ESC+\"0;34;43m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"76C\"+self.ESC+\"43m\"+self.ESC+\"30m\"+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A177+self.A176+self.A177+self.A176+self.A176+self.ESC+\"33m\"+self.A176+self.A177+self.A178+self.A177+self.A177+self.ESC+\"32m\"+self.A177+self.A178+self.A177+self.A177+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A222+self.ESC+\"44m\"+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A221+self.A219+self.ESC+\"1;44m\"+self.A219+self.ESC+\"40m\"+self.A221+self.ESC+\"44m\"+self.A219+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\"+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"44C\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"43m\"+self.A219+self.ESC+\"34m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"33;40m\"+self.A176+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"37C\"+self.ESC+\"44m\"+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"1;37;40mYou make it to the top and \\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"74C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"40C\"+self.ESC+\"44m \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"4C\"+self.ESC+\"1;37;40mthrow open the door...\"+self.ESC+\"6C\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"74C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A178+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A177+self.ESC+\"1;42m\"+self.A178+self.A219+self.ESC+\"30;44m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"31C\"+self.ESC+\"44m\"+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"1;32;42m\"+self.A177+self.A178+self.A178+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"53C\"+self.ESC+\"32m\"+self.A178+self.ESC+\"1;42m\"+self.A219+self.A219+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A219+self.A219+self.ESC+\"0;33m\"+self.A176+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"20C\"+self.ESC+\"44m\"+self.ESC+\"1;32;42m\"+self.A178+self.A219+self.ESC+\"0;32m\"+self.A176+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;30;44m\"+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"32C\"+self.ESC+\"44m\"+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"46C\"+self.ESC+\"42m\"+self.ESC+\"44m \"+self.ESC+\"0;32m\"+self.A177+self.A178+self.A176+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A176+self.A178+self.ESC+\"1;42m\"+self.A219+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"42m\"+self.A176+self.A177+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"63C\"+self.ESC+\"42m\"+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"42m \"+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A178+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A178+self.A219+self.ESC+\"0;32m\"+self.A178+self.A219+self.ESC+\"42m \"+self.ESC+\"40m\"+self.A177+self.A177+self.A176+self.A176+self.A177+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"21C\"+self.ESC+\"1;42m\"+self.A178+self.A176+self.ESC+\"30;40m\"+self.A219+self.ESC+\"0;32m\"+self.A177+self.A177+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"30;40m\"+self.A219+self.A219+self.ESC+\"0m\"+self.A219+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"36C\"+self.ESC+\"0;32m\"+self.A177+self.A178+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.A178+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.A219+\" \"+self.A178+\" \"+self.ESC+\"0;32m\"+self.A177+self.A176+self.A176+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"52C\"+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.A178+self.A176+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"63C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"42m\"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A176+self.A176+self.A178+self.A219+self.A219+self.A178+self.A178+self.A176+self.A176+\" \"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.A177+self.A178+self.A176+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"26C\"+self.ESC+\"42m\"+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"0;32m\"+self.A177+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A177+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"36C\"+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.A177+self.A177+self.A177+self.ESC+\"1;42m\"+self.A219+self.A176+self.ESC+\"0;32m\"+self.A178+self.A178+self.A177+self.A176+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"51C\"+self.ESC+\"42m\"+self.ESC+\"0;32m\"+self.A177+self.A176+self.A177+self.A178+self.A178+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A177+self.A178+self.ESC+\"1;42m\"+self.A177+self.A178+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"64C\"+self.ESC+\"44m \"+self.ESC+\"37m<MORE> \"+self.ESC+\"40m\\r\\n\"\n\t\treturn thismsg","def code_display(self):\r\n return u'%s-%s-%s' % (self.code[:3], self.code[3:6], self.code[6:])","def display_letters(word, guesses):\n pass","def errors_icons(self):\n msg_errors_lifes = ''\n for i in range(0,5):\n if self.letters_wrong <= i:\n msg_errors_lifes += ' ♥ '\n else:\n msg_errors_lifes += ' ☠ ' \n return msg_errors_lifes","def __card_display(self):\n return ''.join([(each_number if (i == 0 or i % 4 != 0) else ' ' + each_number)\n for i, each_number in enumerate(self.card_number)])","async def charinfo(self, ctx: Context, *, characters: str) -> None:\n match = re.match(r\"<(a?):(\\w+):(\\d+)>\", characters)\n if match:\n embed = Embed(\n title=\"Non-Character Detected\",\n description=\"Only unicode characters can be processed, but a custom Discord emoji \" \"was found. Please remove it and try again.\",\n )\n embed.colour = Color.red()\n await ctx.send(embed=embed)\n return\n\n if len(characters) > 25:\n embed = Embed(title=f\"Too many characters ({len(characters)}/25)\")\n embed.colour = Color.red()\n await ctx.send(embed=embed)\n return\n\n def get_info(char: str) -> Tuple[str, str]:\n digit = f\"{ord(char):x}\"\n if len(digit) <= 4:\n u_code = f\"\\\\u{digit:>04}\"\n else:\n u_code = f\"\\\\U{digit:>08}\"\n url = f\"https://www.compart.com/en/unicode/U+{digit:>04}\"\n name = f\"[{unicodedata.name(char, '')}]({url})\"\n info = f\"`{u_code.ljust(10)}`: {name} - {char}\"\n return info, u_code\n\n charlist, rawlist = zip(*(get_info(c) for c in characters))\n\n embed = Embed(description=\"\\n\".join(charlist))\n embed.set_author(name=\"Character Info\")\n\n if len(characters) > 1:\n embed.add_field(name=\"Raw\", value=f\"`{''.join(rawlist)}`\", inline=False)\n\n await ctx.send(embed=embed)","async def charinfo(self, ctx: Context, *, characters: str) -> None:\n match = re.match(r\"<(a?):(\\w+):(\\d+)>\", characters)\n if match:\n await messages.send_denial(\n ctx,\n \"**Non-Character Detected**\\n\"\n \"Only unicode characters can be processed, but a custom Discord emoji \"\n \"was found. Please remove it and try again.\"\n )\n return\n\n if len(characters) > 50:\n await messages.send_denial(ctx, f\"Too many characters ({len(characters)}/50)\")\n return\n\n def get_info(char: str) -> tuple[str, str]:\n digit = f\"{ord(char):x}\"\n if len(digit) <= 4:\n u_code = f\"\\\\u{digit:>04}\"\n else:\n u_code = f\"\\\\U{digit:>08}\"\n url = f\"https://www.compart.com/en/unicode/U+{digit:>04}\"\n name = f\"[{unicodedata.name(char, '')}]({url})\"\n info = f\"`{u_code.ljust(10)}`: {name} - {utils.escape_markdown(char)}\"\n return info, u_code\n\n char_list, raw_list = zip(*(get_info(c) for c in characters), strict=True)\n embed = Embed().set_author(name=\"Character Info\")\n\n if len(characters) > 1:\n # Maximum length possible is 502 out of 1024, so there's no need to truncate.\n embed.add_field(name=\"Full Raw Text\", value=f\"`{''.join(raw_list)}`\", inline=False)\n\n await LinePaginator.paginate(char_list, ctx, embed, max_lines=10, max_size=2000, empty=False)","def displayHand(hand):\r\n for letter in hand.keys():\r\n for j in range(hand[letter]):\r\n print(letter,end=\" \") # print all on the same line\r\n print() # print an empty line\r","def print_hint_board(self):\n board = \"\"\n count = 1 \n for i in range(3):#need to change this in the future\n for j in range(3):#need to change this in the future\n board += str(count) \n count += 1\n if j != 2:#need to change this in the future\n board += \" | \"\n board += \"\\n\"\n return board","def show_theme_message(width):\n print_dotted_line()\n print_bold(\"Attack of The Orcs v0.0.5:\")\n msg = (\n \"\"\"\n Hrabri vitez Talion vraća se iz boja i želi predahnuti u jednom seocetu. \n U seocu se nalazi 5 kućica i želi stupiti u jednu od njih. \n Hrabri vitez ne zna da u ovom području ima neprijatelja.\n Odluči se za jedna od vrata ...\n \"\"\")\n\n print(textwrap.fill(msg, width=width))","def header(text, char=\"*\", repeat=80):\n print(f\"\\n{char*repeat}\")\n print(f\"{char*2} {text.upper()}\")\n print(f\"{char*repeat}\")","def display_char(self) -> None:\r\n print(self.char if self.was_guessed else '_', end=' ')","def make_display_word(secret_word):\n return ('_ ' * len(secret_word))","def __repr__(self):\r\n numLetters = self.numLetters\r\n S = ''\r\n S += 3*'\\n'\r\n S += ' '\r\n for i in range(numLetters):\r\n S += self.currentBoard[i] + ' '\r\n\r\n return S","def header(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC + \"0m \" +self.A220 + self.A220 + self.A220 + self.A220 + self.A220 +\" \" + self.ESC + \"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A220+self.A220+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mS\"+self.ESC+\"0;31mAGA\"+self.ESC+\"37m \"+self.A219+self.A219+self.ESC+\"30;47mo\"+self.ESC+\"37;40m\"+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mO\"+self.ESC+\"0;31mF THE\"+self.ESC+\"37m \"+self.A219+self.A219+self.ESC+\"30;47mO\"+self.ESC+\"37;40m\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.A220+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mR\"+self.ESC+\"0;31mED\"+self.ESC+\"37m \"+self.A219+self.ESC+\"30;47mo\"+self.ESC+\"37;40m\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.A223+self.A219+self.ESC+\"1;47m\"+self.A176+self.A219+self.A219+self.A219+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m \"+self.ESC+\"1mD\"+self.ESC+\"0;31mRAGON 0.9.9\"+self.ESC+\"37m \"+self.A223+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.A219+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"0m \"+self.A219+self.ESC+\"1;47m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m concept\"+self.ESC+\"37m \"+self.A223+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A220+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A220+self.ESC+\"0m \"+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"1;41m\"+self.A176+self.A178+\" \"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m Seth Robinson \"+self.ESC+\"37m\"+self.A222+\" \"+self.A223+self.A223+self.ESC+\"1;47m\"+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A223+self.A223+self.ESC+\"0m \"+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+self.A223+\" \"+self.ESC+\"0m\"+self.A223+self.A219+self.A220+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m by\"+self.ESC+\"0m \"+self.A219+\" \"+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A220+self.A220+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0m \"+self.A223+self.ESC+\"1;47m\"+self.A176+self.A219+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A178+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A176+\" \"+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1;34m J\"+self.ESC+\"0;34m.\"+self.ESC+\"1mT\"+self.ESC+\"0;34m.\"+self.ESC+\"1mS\"+self.ESC+\"0;34mage\"+self.ESC+\"0m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.A220+self.ESC+\"1;47m\"+self.A177+self.A176+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A220+self.A220+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0m \"+self.A223+self.ESC+\"1;47m\"+self.A177+self.A219+self.A219+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A176+self.A178+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"1;41m\"+self.A176+self.A178+self.A176+self.A176+self.A177+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.A219+\" \"+self.ESC+\"1;47m\"+self.A176+self.A177+self.A219+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"41m\"+self.A223+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+\" \"+self.A220+self.A220+self.A220+self.A220+self.A223+self.A220+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A177+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A178+\" \"+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A178+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.A219+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+\" \"+self.A176+self.A176+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A178+self.A178+self.A219+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+self.ESC+\"37m \"+self.ESC+\"31m\"+self.A178+self.A177+self.A177+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A177+self.A178+self.A219+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A219+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"1;41m\"+self.A178+\" \"+self.A177+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A178+self.A177+self.A219+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.A223+self.ESC+\"1;41m\"+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A223+\" \"+self.A220+self.A220+\" \"+self.A223+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A221+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A223+self.A223+self.A223+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A178+self.A177+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A177+self.A219+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.A178+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.A223+self.A223+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A178+self.A178+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.A223+self.A178+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+\" \"+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.ESC+\"1;30;47m\"+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.A219+self.ESC+\"1;41m\"+self.A176+self.A176+self.ESC+\"0;31m\"+self.A221+\" \"+self.ESC+\"1;5;32m\"+self.A220+self.A220+self.A223+\" \"+self.ESC+\"0;31;41m \"+self.ESC+\"40m\"+self.A178+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A220+self.A220+self.A220+self.A220+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.A177+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;5;32m\"+self.A219+self.A219+self.A223+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.A219+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A178+self.A223+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"1;5;32m\"+self.A223+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.A219+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A223+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A177+self.A219+self.A178+self.ESC+\"37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+self.ESC+\"0;31m\"+self.A221+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+\" \"+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+self.A219+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A176+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A177+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.A221+\" \"+self.ESC+\"31;41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A177+self.A177+self.A176+self.A178+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A220+self.A220+\" \"+self.ESC+\"1;41m\"+self.A178+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0m\"+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A176+self.A177+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.A178+\" \"+self.ESC+\"1;41m\"+self.A177+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A176+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A223+self.ESC+\"1m\"+self.A219+self.A220+\" \"+self.ESC+\"0;31;41m \"+self.A177+self.A178+self.A176+self.A176+\" \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A176+self.A178+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A223+self.ESC+\"1;47m\"+self.A223+self.ESC+\"40m\"+self.A219+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A178+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;37;47m\"+self.A222+self.ESC+\"40m\"+self.A221+self.A223+self.A220+\" \"+self.ESC+\"0;31m\"+self.A177+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A221+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+\" \"+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A223+self.ESC+\"31m\"+self.A222+self.ESC+\"1;41m\"+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;37;47m\"+self.A222+self.ESC+\"40m\"+self.A221+\" \"+self.ESC+\"47m\"+self.A222+self.ESC+\"40m\"+self.A221+\" \"+self.ESC+\"0;31m\"+self.A178+self.A177+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"33m\"+self.A220+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A220+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+\" \"+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"31;41m \"+self.A176+self.A176+self.ESC+\"37;40m \"+self.A220+self.ESC+\"1m\"+self.A219+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A219+self.A221+\" \"+self.A223+\" \"+self.A223+self.A220+\" \"+self.ESC+\"0;31;41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A178+self.A177+self.A176+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A220+self.ESC+\"1m\"+self.A219+\" \"+self.A219+self.A221+\" \"+self.A223+\" \"+self.A220+\" \"+self.A223+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"1;33;43m\"+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A219+self.A220+self.A222+self.ESC+\"1;41m\"+self.A219+self.A177+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.A178+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A223+self.ESC+\"1m\"+self.A220+\" \"+self.A223+\" \"+self.A220+self.A223+self.A220+\" \"+self.A223+\" \"+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"1;33m\"+self.A223+self.ESC+\"43m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0;33m\"+self.A220+self.A219+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A177+self.A178+\" \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+self.A178+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"30mÙ\"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A223+self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.A223+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+\" \"+self.A219+\" \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"31m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.A176+self.A176+\" \"+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.ESC+\"1;33;43m\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A177+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.ESC+\"1;33;43m\"+self.A219+self.A178+self.ESC+\"40m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.A220+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.A220+self.A220+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.A223+\" \"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A177+\" \"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A178+self.ESC+\"0;33m\"+self.A220+self.A219+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A177+self.A177+self.A176+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A223+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A178+\" \"+self.A177+\" \"+self.A176+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A219+self.A178+self.A178+self.A177+self.A177+self.A176+\" \"+self.A176+\" \"+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"0;37m \"+self.ESC+\"1;33m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+\" \"+self.ESC+\"30;41m \"+self.ESC+\"1;31mShatterstar [W/X] \"+self.ESC+\"0;37;40m \"+self.ESC+\"30;41m \"+self.ESC+\"37;40m \"+self.ESC+\"30;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\treturn thismsg","def print_title():\r\n HANGMAN_ASCII_ART = \"\"\"welcome to the game hangman\r\n _ _ \r\n | | | | \r\n | |__| | __ _ _ __ __ _ _ __ ___ __ _ _ __ \r\n | __ |/ _` | '_ \\ / _` | '_ ` _ \\ / _` | '_ \\ \r\n | | | | (_| | | | | (_| | | | | | | (_| | | | |\r\n |_| |_|\\__,_|_| |_|\\__, |_| |_| |_|\\__,_|_| |_|\r\n __/ | \r\n |___/\r\n\"\"\"\r\n print(HANGMAN_ASCII_ART)","def horizontal_char(self):\n ...","def print_character( self, ordinal ):\n print( '--- %s ----------------' % chr(ordinal) )\n if not( ordinal in self.characters ):\n print( '%s (%i) is not present in characters' % (chr(ordinal), ordinal) )\n print( 'Ordinal: %i' % ordinal )\n print( 'Char has descender: %s' % self.char_has_descender( ordinal ) )\n print( 'width, height = %i, %i' % (self.characters[ordinal].width, self.characters[ordinal].height) )\n # print( 'bitmap_top from base = %i' % self.glyphs[ordinal].bitmap_top )\n\n if self.char_has_descender( ordinal ):\n print( 'width, height = %i, %i (with %i px descender already included)' % (self.characters[ordinal].width, self.characters[ordinal].height, self.descender_size ) )\n else:\n # Sometime, Char-Size + Descender Size GOES OVER the font size --> reduce the descender\n _descender = self.ajusted_descender_size( ordinal )\n print( 'width, height = %i, %i (with %i px ajusted_descender added)' % (self.characters[ordinal].width, self.characters[ordinal].height + _descender, _descender ) )\n\n print( self.characters[ordinal] )","def displayHand(hand):\n for letter in hand.keys():\n for j in range(hand[letter]):\n print letter, # print all on the same line\n print # print an empty line","def show_word(self):\n self.display_word = len(self.chosen_word) * \"_ \"\n Donatello.draw_word(self.display_word)\n return self.display_word","def print_rangoli(size):\n alphabets = \"abcdefghijklmnopqrstuvwxyz\"\n character_length = 4 * size -3\n for i in range(1, size * 2):\n index = size - abs(i - size)\n pattern = alphabets[size - index: size]\n pattern = pattern[::-1] + pattern[1:]\n pattern = '-'.join(list(pattern))\n lines = \"-\" * ((character_length - len(pattern)) // 2)\n row = \"{0}{1}{2}\".format(lines, pattern, lines)\n print(row)","def show_instructions():\n\n print('4-digit Code has been set. Digits in range 1 to 8. You have 12 turns to break it.')","def display():\n screen.addch(head[0],head[1],'x')","def display(missed_letters, correct_letters, secret_word):\n print(HANGMAN[len(missed_letters)])\n print()\n\n print('Missed letters:', end=' ')\n for letter in missed_letters:\n print(letter, end=' ')\n print()\n\n dashed_entry = ''\n\n for letter in secret_word:\n if letter == ' ':\n dashed_entry += ' '\n elif letter in correct_letters:\n dashed_entry += letter\n else:\n dashed_entry += '-'\n\n for letter in dashed_entry: # show the secret word with spaces in between each letter\n print(letter, end=' ')\n print()","def abdul(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC+\"1;33m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30m\"+self.A220+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.A220+self.ESC+\"1;43m\"+self.A219+self.A178+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A223+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"33m\"+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"C\"+self.A254+self.ESC+\"33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A219+self.ESC+\"1;43m\"+self.A177+self.A176+self.ESC+\"C\"+self.A176+self.ESC+\"C\"+self.ESC+\"37;40mSaga\"+self.ESC+\"Cof\"+self.ESC+\"Cthe\"+self.ESC+\"CRed\"+self.ESC+\"CDragon\"+self.ESC+\"C-\"+self.ESC+\"C\"+self.ESC+\"33mAbduls\"+self.ESC+\"CArmour \"+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A177+self.ESC+\"0;32m\"+self.A220+self.ESC+\"C\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"2C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"2C\"+self.ESC+\"0;33m\"+self.A223+self.A219+self.ESC+\"C\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"33m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"33m\"+self.A220+self.A219+self.A223+self.ESC+\"37m\"+self.A220+self.ESC+\"1;47m\"+self.A176+self.ESC+\"0m\"+self.A219+self.A223+self.ESC+\"1;30;47m\"+self.A176+self.A176+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A177+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1;30m\"+self.A223+self.ESC+\"4C\"+self.ESC+\"0;33mBehind\"+self.ESC+\"Cthe\"+self.ESC+\"Cdesk\"+self.ESC+\"Cof\"+self.ESC+\"Cthe\"+self.ESC+\"Carmour\"+self.ESC+\"Cshop\"+self.ESC+\"Cis\"+self.ESC+\"Can\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"37;40m\"+self.A223+self.ESC+\"1;47m\"+self.A177+self.A176+self.ESC+\"C\"+self.ESC+\"0m\"+self.A219+self.A219+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A178+self.ESC+\"C\"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"4C\"+self.ESC+\"0;33mamazingly\"+self.ESC+\"Cattractive\"+self.ESC+\"Clooking\"+self.ESC+\"Cfemale - she seems\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"33;40m\"+self.A223+self.ESC+\"1;37m\"+self.A220+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30m\"+self.A220+self.A220+self.A223+self.ESC+\"47m\"+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.ESC+\"1;30;43m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"0;32m\"+self.A220+self.A254+self.ESC+\"3C\"+self.ESC+\"33mbusy, doing her mails but she\"+self.ESC+\"Casks\"+self.ESC+\"C\\\"\"+self.ESC+\"1mHow\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A223+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"1;37;47m\"+self.A178+self.ESC+\"40m\"+self.A222+self.A222+self.ESC+\"47m\"+self.A176+self.ESC+\"C\"+self.ESC+\"30m\"+self.A176+self.ESC+\"C\"+self.A177+self.ESC+\"40m\"+self.A220+self.ESC+\"47m\"+self.A178+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"47m\"+self.A219+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A223+self.ESC+\"32m\"+self.A220+self.A178+self.ESC+\"6C\"+self.ESC+\"1;33mmay\"+self.ESC+\"CI\"+self.ESC+\"Cbe\"+self.ESC+\"Cof\"+self.ESC+\"Cservice?\"+self.ESC+\"0;33m\\\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"C\"+self.A220+self.A220+self.ESC+\"C\"+self.ESC+\"1;37m\"+self.A223+self.ESC+\"47m\"+self.A178+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A177+self.ESC+\"0m\"+self.A220+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0m\"+self.A220+self.ESC+\"1;30m\"+self.A223+self.A223+self.A220+self.ESC+\"47m\"+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.ESC+\"33;40m\"+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1;33;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A220+self.A223+self.ESC+\"C\"+self.A220+self.ESC+\"1;43m\"+self.A176+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1;37m\"+self.A223+self.ESC+\"0m\"+self.A220+self.ESC+\"2C\"+self.A220+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.A176+self.ESC+\"40m\"+self.A223+self.ESC+\"47m\"+self.A219+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"1;30m\"+self.A220+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mB\"+self.ESC+\"0;33m]\"+self.ESC+\"1muy\"+self.ESC+\"CArmour\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A220+self.A223+self.ESC+\"1;37m\"+self.A220+self.A220+self.ESC+\"0m\"+self.A223+self.ESC+\"C\"+self.ESC+\"1;47m\"+self.A223+self.ESC+\"0m\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"40m\"+self.A220+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"1;40m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mS\"+self.ESC+\"0;33m]\"+self.ESC+\"1mell\"+self.ESC+\"CArmour\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"1;37;47m\"+self.A219+self.A178+self.ESC+\"40m\"+self.A220+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A220+self.A220+self.A220+self.A220+self.A219+self.A220+self.A223+self.ESC+\"1;30m\"+self.A220+self.A220+self.A219+self.A219+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mY\"+self.ESC+\"0;33m]\"+self.ESC+\"1mour\"+self.ESC+\"CStats\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A178+self.ESC+\"0;33m\"+self.A223+self.A220+self.A223+self.ESC+\"30;43m\"+self.A176+self.A223+self.ESC+\"1;37;40m\"+self.A223+self.A223+self.ESC+\"47m\"+self.A178+self.A177+self.A176+self.A176+self.ESC+\"0m\"+self.A219+self.A219+self.A223+self.ESC+\"1;30m\"+self.A220+self.A176+self.A177+self.A178+self.A223+self.A223+self.ESC+\"0;33m\"+self.A220+self.A219+self.ESC+\"1;30m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mR\"+self.ESC+\"0;33m]\"+self.ESC+\"1meturn\"+self.ESC+\"Cto\"+self.ESC+\"CTown\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A177+self.ESC+\"0;33m\"+self.A219+self.A220+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A220+self.ESC+\"C\"+self.ESC+\"33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.A220+self.A220+self.A219+self.ESC+\"30;43m\"+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A220+self.A254+self.ESC+\"C\"+self.ESC+\"1;30m\"+self.A220+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"33;43m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"C\"+self.A178+self.A254+self.ESC+\"33m\"+self.A219+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A223+self.A220+self.ESC+\"1;43m\"+self.A176+self.ESC+\"0;33m\"+self.A220+self.A223+self.A220+self.A223+self.A223+self.A220+self.ESC+\"3C\"+self.ESC+\"1;30m\"+self.A177+self.A220+self.ESC+\"2C\"+self.ESC+\"33m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"33m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.A220+self.ESC+\"32m\"+self.A254+self.ESC+\"33m\"+self.A223+self.A219+self.A219+self.A220+self.A223+self.A220+self.A220+self.A223+self.A223+self.ESC+\"1;30m\"+self.A220+self.A220+self.A219+self.ESC+\"2C\"+self.A220+self.A178+self.A220+self.ESC+\"C\"+self.ESC+\"33;43m\"+self.A177+self.ESC+\"2C\"+self.ESC+\"0m \"+self.ESC+\"3C\"+self.ESC+\"33m\"+self.A220+\"\\r\\n\"\n\t\tthismsg += self.A223+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.A223+self.ESC+\"0m\\r\\n\"\n\t\treturn thismsg","def printunichars(row):\n print(\"Title:\")\n print(row[0].encode('utf-8'))\n print(\"Body:\")\n print(row[1].encode('utf-8'))\n print(\"Ref:\")\n print(row[2].encode('utf-8'))\n print(\"Url:\")\n print(row[3].encode('utf-8'))","def display_c(c, font, screen, lcd, size=5, x=0, y=0):\n char = font[str(c)]\n width, height = char.size\n \"\"\"\n if not(size == 10):\n size /= 10.0\n width = int(round(size*width))\n height = int(round(size*height))\n char.resize((width,height))\n \"\"\"\n size = int(round(size * 10))\n images.display_img(char,screen,lcd,size,x,y)","def pretty_hebrew(val):\n return 'font-size:20px; font-family: Times New Roman; text-align: right; max-width: 500px'","def print_table(table):\r\n print('/-----------------------------------------------------------------------------------\\\\')\r\n for item in table:\r\n\r\n while len(item[1]) <= 22:\r\n item[1] += ' '\r\n\r\n while len(item[2]) <= 27:\r\n item[2] += ' '\r\n\r\n while len(item[0]) <= 15:\r\n item[0] += ' '\r\n\r\n print('| '+item[0]+' | '+item[1]+'| '+item[2]+' |')\r\n\r\n print('\\\\-----------------------------------------------------------------------------------/')","def afficher_damier_ascii(infojeu):\n lignes = []\n lignes += list(\"Légende: 1=\"+ str(infojeu[\"joueurs\"][0][\"nom\"])+\n ', 2='+str(infojeu[\"joueurs\"][1][\"nom\"]) + \"\\n\")\n lignes += list(\" \"+\"-\"*35+\"\\n\")\n for i in range(1, 10):\n lignes += str(10-i) + \" | \"\n for j in range(1, 9):\n strplayer = \".\"\n if [j, 10-i] == infojeu[\"joueurs\"][0][\"pos\"]:\n strplayer = \"1\"\n elif [j, 10-i] == infojeu[\"joueurs\"][1][\"pos\"]:\n strplayer = \"2\"\n if [j+1, 10-i] in infojeu[\"murs\"][\"verticaux\"]:\n lignes += list(strplayer + \" | \")\n elif [j+1, 9-i] in infojeu[\"murs\"][\"verticaux\"]:\n lignes += list(strplayer + \" | \")\n else:\n lignes += list(strplayer + \" \")\n if [9, 10-i] == infojeu[\"joueurs\"][0][\"pos\"]:\n lignes += list(\"1 |\")\n elif [9, 10-i] == infojeu[\"joueurs\"][1][\"pos\"]:\n lignes += list(\"2 |\")\n else:\n lignes += list(\". |\")\n if i != 9:\n lignes += list(\"\\n |\")\n for k in range(1, 9):\n if i != 9:\n if [k, 10-i] in infojeu[\"murs\"][\"horizontaux\"]:\n lignes += list(\"----\")\n elif [k-1, 10-i] in infojeu[\"murs\"][\"horizontaux\"] and \\\n [k+1, 9-i] in infojeu[\"murs\"][\"verticaux\"]:\n lignes += list(\"---|\")\n elif [k-1, 10-i] in infojeu[\"murs\"][\"horizontaux\"]:\n lignes += list(\"--- \")\n elif [k+1, 9-i] in infojeu[\"murs\"][\"verticaux\"]:\n lignes += list(\" |\")\n else:\n lignes += list(\" \")\n if i != 9:\n if [8, 10-i] in infojeu[\"murs\"][\"horizontaux\"]:\n lignes += list(\"---|\")\n else:\n lignes += list(\" |\")\n lignes += list(\"\\n\")\n lignes += list(\"--|\"+ \"-\"*35+\"\\n\")\n lignes += list(\" | 1 2 3 4 5 6 7 8 9\")\n lignes = ''.join(lignes)\n print(lignes)","def affiche_plateau(plateau):\n remplissage = ' ' * 30\n print(remplissage, end='')\n for i in range(1, 7, 2):\n print(\" \" + str(i), sep='', end='')\n print()\n for i in range(get_nb_lignes(plateau[0])):\n print(remplissage, end='')\n if i % 2 == 0:\n print(' ', sep='', end='')\n else:\n print(str(i), sep='', end='')\n for j in range(get_nb_colonnes(plateau[0])):\n print(to_char(get_valeur(plateau[0], i, j)), end='')\n if i % 2 == 0:\n print(' ', sep='', end='')\n else:\n print(str(i), sep='', end='')\n print()\n print(' ', sep='', end='')\n print(remplissage, end='')\n for i in range(1, 7, 2):\n print(\" \" + str(i), sep='', end='')\n print()","def display(self):\n print (\"+\" + \"-\"*self.size + \"+\")\n for i in range(self.size):\n terrain_strs = [Terrain.display_string(self.array[j, i]) for j in range(self.size)]\n print(\"|\" + \"\".join(terrain_strs) + \"|\")\n print (\"+\" + \"-\"*self.size + \"+\")","def get_equipment(self):\n s = ''\n for i in range(12, 16):\n s += ' ' + str(self.dna[i])\n return s","def supercombiner(bot, ev):\n # ported from jenni\n s = 'u'\n for i in iter(range(1, 3000)):\n if unicodedata.category(chr(i)) == \"Mn\":\n s += chr(i)\n if len(s) > 100:\n break\n bot.say(s)","def label(mi_, ma_):\n\treturn \"caractères Unicode des points de code {} à {}\".format(mi_, ma_)","def show( self):\n def symbol( i):\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\n \n X, Y = np.max( self.board.positions, 0)\n # -2 to indicate outside board.\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \n for x, y in self.board.positions:\n display[x, y] = -1 # -1 to indicate unoccupied\n for p, i in self.occupation.items():\n x, y = self.board.positions[p]\n display[x, y] = i\n for x in xrange(X+1):\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\n print s","def print_screen(history):\n\n # Extract the angles from the distance data. Usually this won't change \n # from scan to scan, but there are scanning algorithms where that may not \n # be the case \n angles = []\n for h in history:\n angles = list(set(angles + h.keys())) \n angles.sort()\n\n # Create a 2D grid of characters. Essentially a \"screen buffer\"\n buff = {}\n for angle in angles:\n buff[angle] = ' '.rjust(120)\n\n \n blips = ['.', '*', '#', '@']\n blips = blips[-len(history):] # if we only have 2, take last 2 blips\n\n # Plot blips onto buffer \n for h in history:\n blip = blips.pop(0) if len(blips) else '.'\n for angle in angles:\n if angle not in h: continue\n dist = h[angle]\n if dist < 120:\n buff[angle] = set_char_at(buff[angle], dist, blip)\n\n # Output\n print \"\\n\\n\\n\"\n for angle in angles:\n obstacle = 'x' if '@' in buff[angle][0:30] else ' '\n print \"%s %s %s\" % (str(angle).rjust(5), obstacle, buff[angle])\n print '20cm'.rjust(30) + '50cm'.rjust(30) + '1m'.rjust(50)","def the_display(self):\r\n return f\"\"\"\r\n {self.display[0]}\\n\r\n {self.display[1]}\\n\r\n {self.display[2]}\\n\r\n {self.display[3]}\\n\r\n {self.display[4]}\\n\r\n \"\"\"","def reveal_occupants(idx, huts):\n msg = \"\"\n print(\"展示小屋内部情况...\")\n for i in range(len(huts)):\n occupant_info = \"<%d:%s>\" % (i + 1, huts[i])\n if i + 1 == idx:\n occupant_info = \"\\033[1m\" + occupant_info + \"\\033[0m\"\n msg += occupant_info + \" \"\n\n print(\"\\t\" + msg)\n print_dotted_line()","def create_random_string(total_character):\n feed=string.printable\n words=\"\"\n i=0\n while i < total_character:\n words += feed[random.randrange(0,len(feed)-1)]\n i+=1\n return words","def __str__(self):\n allowed = ['!', '@', '#', '$', '%', '^', '&', '*', '/', '.', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n returnstring = \"\"\n for row in self.positions:\n for char in row:\n if char.isupper() or char == 'r' or char in allowed:\n returnstring += \"| \" + char + \" \"\n else:\n returnstring += \"| \" + \"_\" + \" \"\n returnstring += \"\\n\"\n return returnstring","def render(self, s):\n if s < self.num_states - 1:\n a = np.zeros((self.height, self.width), dtype=np.unicode_)\n # mark all locations as inaccessible first\n a[:, :] = u'█'\n # regular fields\n for c in self.locs:\n a[c[1], c[0]] = u'░'\n # mark exits\n for c in self.exitsP:\n a[c[1], c[0]] = 'E'\n for c in self.exitsN:\n a[c[1], c[0]] = 'F'\n # player character\n c = self.locs[s]\n a[c[1], c[0]] = u'☺'\n a = [' '.join(row) for row in a]\n a = '\\n'.join(reversed(a))\n return a\n else:\n return 'Game over!'","def _string_data(self, data):\n print(\"_string_data:\")\n string_to_print = []\n for i in data[::2]:\n string_to_print.append(chr(i))\n print(string_to_print)","def pretty(self):\n #table = [\"\".join([\"%8s \" % s for s in self.alpha.getSymbols()])]\n table = []\n for row in PWM.getFreq(self):\n table.append(\"\".join([\"%8.6f \" % y for y in row]))\n return table","def get_char_to_display(in_value):\n\treturn '#' if in_value else ' '","def phoneDisplay(number):\n return number[0:3] + \" · \" + number[3:6] + \" · \" + number[6:10]","def _make_sysex_message(self, text_message, display_index):\n if len(text_message) > 28:\n text_message = text_message[:27]\n sysex_message = [240, 0, 0, 102, 23, 18, min(display_index, 3) * 28]\n filled = text_message.ljust(28)\n for c in filled:\n sysex_message.append(ord(c))\n sysex_message.append(247)\n return sysex_message","def get_formatted_text(self, n_cols):","def int_21H_2(self):\r\n byte_DL = self.registers['DX'].get_byte(0)\r\n char_DL = byte_DL.decode(\"ascii\") # zdekodowany znak do wyswietlenia\r\n self.GUI.set_output(char_DL)","def help():\n print(f\"{YELLOW}zwc{RESET} - Hidden messages using Zero Width Characters.\")","def printPokemon():\n print(\" _ \")\n print(\" _ __ ___ | | _____ _ __ ___ ___ _ __ \")\n print(\" | '_ \\ / _ \\| |/ / _ \\ '_ ` _ \\ / _ \\| '_ \\ \")\n print(\" | |_) | (_) | < __/ | | | | | (_) | | | |\")\n print(\" | .__/ \\___/|_|\\_\\___|_| |_| |_|\\___/|_| |_|\")\n print(\" |_| \")","def print_cards(cards):\r\n string = ''\r\n for c in cards:\r\n suit = c[0]\r\n if suit == 1:\r\n suit = \"\\u2665\" # heart\r\n elif suit == 2:\r\n suit = \"\\u2660\" # Spade\r\n elif suit == 3:\r\n suit = \"\\u2666\" # Diamond\r\n else:\r\n suit = \"\\u2663\" # club\r\n\r\n num = c[1]\r\n if num == 11:\r\n num = 'J'\r\n elif num == 12:\r\n num = 'Q'\r\n elif num == 13:\r\n num = 'K'\r\n else:\r\n num = str(num)\r\n\r\n string = string + num + suit + ' '\r\n return string","def char(runtime_addr, n=1):\n\n set_formatter(runtime_addr, n, mainformatter.char_formatter)","def enlabel(mi_, ma_):\n\treturn \"Unicode characters from {} to {} codepoints\".format(mi_, ma_)","def print_data_line(line, max_name, max_roomcode, max_type):\n print(\"|\", end='')\n print(line[0], end='')\n print(\"|\", end='')\n print(line[1], end='')\n print(\" \" * (max_name - len(line[1])), end='')\n print(\"|\", end='')\n print(line[4], end='')\n print(\" \" * (max_type - len(line[4])), end='')\n print(\"|\", end='')\n print(line[2], end='')\n print(\" \" * (max_roomcode - len(line[2])), end='')\n print(\"|\", end='')\n print(line[3], end='')\n print(\"|\")","def _full_content():\n return string.ascii_letters","def print_map(self):\n y_max,x_max = map(max, zip(*self.mp.keys()))\n for row in range(0,y_max+1):\n msg = []\n for k in range(0,x_max+1):\n msg.append(chr(self.mp[row,k]))\n print(\"\".join(msg))","def report_index(self, index):\n i = 0\n for k, data in self.matches[index].items():\n if i != 0:\n print\n print fmt(\"['%c': charset - chars]\" % k, MAGENTA)\n print fmt(sorted([x for x in data[\"charset\"]]), WHITE)\n print fmt(data[\"chars\"], WHITE)\n i = 1","def gen_beat_output(e):\n return [playback_char(e,t) for t in range(70000)]","def head_plain():\n print (hair_buzz())\n print (eye_narrow())\n print (nose_triangle())\n print (mouth_smile())\n print (chin_plain())","def __str__(self):\n local_s = 'F30A: '\n local_s += '\\n'\n return local_s","def delimit_output(num=25):\n print('-'*num)","def display(self):\n print('')\n print(\" ---------------------------------\")\n counter = 0\n for row in self.positions:\n counter += 1\n line = f'{counter}: |'\n for space in row:\n if isinstance(space, str):\n line += f' {space} |'\n else:\n starter = ' '\n ender = '|'\n if space.team == 'white':\n piece = stylize(space.symbol+' ', colored.fg(\"light_blue\"))\n else:\n piece = stylize(space.symbol+' ', colored.fg(\"light_red\"))\n line += starter+piece+ender\n print(line)\n print(\" ---------------------------------\")\n print(\" | A | B | C | D | E | F | G | H |\\n\")","def generate(length):\n alpha = map(chr, range(97, 123))\n alpha.append(' ')\n result = \"\"\n for x in range(length):\n result += alpha[random.randrange(0,27)]\n return result","def string(self):\n text = \"\"\n for char, qty in self.chars.items():\n text += char * qty\n return \"\".join(sorted(text))","def display(self):\n lines = []\n lines.append(\" \".join([str(x + 1) for x in range(self.board.columns)]))\n for row in self.board.board:\n new_row = \"\".join([c for c in row])\n new_row = new_row.replace(\"0\", \" \")\n new_row = new_row.replace(\"1\", colored(f\"{PLAYER1_CHR} \", PLAYER1_COLOR))\n new_row = new_row.replace(\"2\", colored(f\"{PLAYER2_CHR} \", PLAYER2_COLOR))\n lines.append(new_row)\n return \"\\n\".join(lines)","def display_board(self):\n print(f\"{BREAK_STRING}\\n\")\n print(f\"STOCK \\t WASTE \\t\\t\\t\\tFOUNDATION\\n\")\n print(f\"{self.stock}\\t{self.waste}\\t\\t\\t\\t{self.foundations['clubs']}\\t{self.foundations['diamonds']}\"\n f\"\\t{self.foundations['hearts']}\\t{self.foundations['spades']}\\n\")\n print(f\"\\nTABLEAU\\n\")\n for num in range(1, 8):\n print(f\"{num} {self.tableaus[num-1]}\")\n print(f\"{BREAK_STRING}\\n\")","def Display2(self, rain, humidity, lang):\n if lang == 'eng':\n string = rain + '% Rain\\n' + humidity + '% Humidity'\n return string\n else:\n return 0\n # Not Yet Supported","def display(self):\n for row in range(self.height):\n for col in range(self.width):\n char = '#' if self.pixels[row * self.width + col] else '.'\n print(char, end='')\n print()\n print()","def designer_pdf_viewer(h, word):\n a = 97\n alphabet = dict()\n for i in range(len(h)):\n alphabet.update({chr(a): h[i]})\n a += 1\n\n max_height = 0\n for i in word:\n if i in alphabet.keys():\n height = alphabet[i]\n if height > max_height:\n max_height = height\n\n return len(word) * 1 * max_height","def render(state, c_choice, h_choice):\r\n\r\n chars = {\r\n 2: h_choice,\r\n 1: c_choice,\r\n 0: ' '\r\n }\r\n str_line = '--------------------------------------------------'\r\n\r\n print('\\n' + str_line)\r\n for row in state:\r\n for cell in row:\r\n #print(cell)\r\n symbol = chars[cell]\r\n print(f'| {symbol} |', end='')\r\n print('\\n' + str_line)","def display_game(game,grid_size):\n row_wall = '----'*(grid_size+1)\n #first row\n str_print = ' | '\n for i in range(grid_size):\n #samll number\n if i < 9:\n str_print = str_print+str(i+1)+' | '\n else:\n str_print = str_print+str(i+1)+'| '\n print(str_print[:-1])\n print(row_wall)\n #2 to end row\n for i in range(grid_size):\n str_print = ALPHA[i]+' | '\n for j in range(grid_size):\n str_print = str_print+game[i*grid_size+j]+' | '\n print(str_print[:-1])\n print(row_wall)","def __str__(self):\n return textwrap.fill('{:064b}'.format(self.num), 8)","def __str__(self):\n table_data = [\n ['', 'C', 'G', 'A', 'T'],\n ['total', str(self.total['C']), str(self.total['G']), str(self.total['A']), str(self.total['T'])],\n ['reverse half strand', str(self.reverse['C']), str(self.reverse['G']), str(self.reverse['A']),\n str(self.reverse['T'])],\n ['forward half strand', str(self.forward['C']), str(self.forward['G']), str(self.forward['A']),\n str(self.forward['T'])]\n ]\n table = AsciiTable(table_data)\n return \"Generation #{}\\n\".format(self.epoch) + table.table","def render(self, s):\n if s < self.num_states - 1:\n a = np.zeros((self.height, self.width), dtype=np.unicode_)\n # mark all locations as inaccessible first\n a[:, :] = u'█'\n # regular fields\n for c in self.locs:\n a[c[1], c[0]] = u'░'\n # mark exits\n for c in self.exitsP:\n a[c[1], c[0]] = 'E'\n for c in self.exitsN:\n a[c[1], c[0]] = 'F'\n # player and ghost location index\n ip = s % len(self.locs)\n ig = s // len(self.locs)\n # player character\n cp = self.locs[ip]\n a[cp[1], cp[0]] = u'☺'\n # ghost character\n cg = self.locs[ig]\n a[cg[1], cg[0]] = 'G'\n a = [' '.join(row) for row in a]\n a = '\\n'.join(reversed(a))\n return a\n else:\n return 'Game over!'","def helptext(self):\n return \"\"","def KingWen(font, hexid):\n\n ret = []\n ch = 0x4DC0 + hexid - 1\n exec 'char = u\\'\\u%4x\\'' % ch\n lines = GetFontPixels(font, char, 'R', ' ')\n ret.append(' ' * 8)\n for line in lines[1::2]:\n hex_chars = ''.join(line[1:-1])\n ret.append(hex_chars[1:-1])\n ret.append(' ' * 8)\n return ret","def __str__(self):\n result = \"\"\n for i in range(3):\n for j in range(3):\n if self.board[i][j] == 5:\n result += \" x\"\n elif self.board[i][j] == 7:\n result += \" о\"\n else:\n result += \" #\"\n result += \"\\n\"\n return result","def show(self):\n print('\\n'+'\\n'.join([' '.join([['.', 'O', 'X'][self.board[3*j + i]]\n for i in range(3)]) for j in range(3)]))","def print_title(title):\n print \"\\n\"+\"#\"*32+\"\\n# \"+title+\"\\n\"+\"#\"*32+\"\\n\"","async def aesthetic(self, ctx, *, text):\n out = \"\"\n for char in text:\n out += utils.fullwidth_transform.get(char, char)\n await ctx.send(out)","def n_char_generate(self,char,n):\n return char*n","def render(self):\n for r in range(self.y_size):\n line = ''\n for c in range(self.x_size):\n glyph = self.MAP_GLYPH_TABLE[self.grid_data[r][c]]\n\n # overwrite with player\n if r == self.player_y and c == self.player_x:\n glyph = self.PLAYER_GLYPH_TABLE[self.player_heading]\n\n line += glyph\n print(line)\n\n print('\\n' * (20 - self.y_size))","def _display_pattern(self):\n # Technically we are supposed to look up this pattern in each language.\n # Practically, it's the same in every language except Chinese, where the\n # parentheses are full-width.\n if self._disp_pattern is not None:\n return self._disp_pattern\n if self.distance(Language.get('zh')) <= 25:\n self._disp_pattern = \"{0}（{1}）\"\n else:\n self._disp_pattern = \"{0} ({1})\"\n return self._disp_pattern","def get_char_echo(self) -> str:\n ...","def show_frame_info(self):\n if self.font:\n font_surface = self.font.render(\"Sample Number: \" + \\\n str(self.data_row_num-self.num_lndmrks) + \\\n \" of \" + \\\n str(self.tot_rows - self.data_start_row), \\\n False, self.GREEN)\n pts = (self.screen_width / 3),3\n self.screen.blit(font_surface,pts)","def encode_this( self, charCode ):\n values = []\n # Character Bitmap\n bmp = self.characters[charCode]\n # Nbre of entry in the dictionnary = Character width\n nbr_entry = bmp.width\n # Number of bytes per entry (=font height / 8bits.)\n # Vera_19 = 19 point height => 19/8 = 2.375 => must be coded on 3 bytes\n nbr_bytes = ceil(self.max_height / 8)\n if ((self.max_height%8) == 0): # Encoded font must always starts with 0b1, so if the font has 8 points height (or a multiple)\n nbr_bytes += 1 # we have to create more room to receive that bit.\n nbr_bits = nbr_bytes * 8\n\n # If we are coding 19 bits on X bytes, we will have to pad the left most\n # bits with 1\n pad_bits = nbr_bits - self.max_height\n pad_value = 0\n for i in range( pad_bits ):\n # debug: print( 'shift by %i' % (nbr_bits-1-i) )\n pad_value = pad_value + ( 1<<(nbr_bits-1-i))\n\n # Building the values\n for iw in range( bmp.width ):\n value = pad_value\n # Debug: print( '---------------------------' )\n for ih in range( bmp.height ):\n _ih = bmp.height-1-ih # Must start by the \"bottom\" of the character\n\n # Align on the BottomLine\n _height_extra_shift = self.max_height-bmp.height # The character may have 10 pixels height on a 19 point height font --> shift properly to the left!\n\n # Align on the BaseLine (if it applies)\n # check if we do need to insert a descender space (in pixel) under the character\n if ( len(self.descender_ordinals)>0 ) and not( self.char_has_descender( charCode ) ):\n # move up the normal character baseline\n _height_extra_shift = _height_extra_shift - self.ajusted_descender_size( charCode )\n\n # SPECIAL ALIGNMENT\n # check for special alignment instruction (~,comma,\", etc)\n _align = self.special_align_ordinals.get( charCode, None )\n if _align == 'T':\n # special alignment ON TOP -> reset shifting\n _height_extra_shift = 0\n elif _align == 'M':\n # Special alignment IN MIDLLE of baseline and top\n _height_extra_shift = _height_extra_shift - ( self.max_height-bmp.height-self.ajusted_descender_size( charCode ))//2\n elif _align == 'B':\n # Specoam alignment TO BOTTOM (so re-reset the _height_extra_shift on the BottomLine)\n _height_extra_shift = self.max_height-bmp.height\n\n # TODO: replacing the descender feature by glyph.bitmap_top does not work properly\n # for every characters\n # _height_extra_shift = self.max_height - self.glyphs[ charCode ].bitmap_top\n\n # print( 'coding bit %i, extra shift of %i FOR VALUE %s' % (_ih, _height_extra_shift, bmp.pixel_at( iw, _ih )) )\n if bmp.pixel_at( iw, _ih ):\n value = value + (1 << (_ih+_height_extra_shift))\n values.append( value )\n\n\n return values","def print_character(character):\n for k, v in character.items():\n print(str(k) + ': ' + str(v)) # Prints every key:value pair in character dictionary\n if len(character) == 8:\n print(\"Inventory:\" + str(character[7])) # If there is an inventory, then print it as well","def display(values):\n boxes = cross(rows, cols)\n # This finds the max number of characters in each box and adds one.\n # the max will be '123456789' so 9 + 1 = 10\n width = 1+max(len(values[s])for s in boxes)\n\n # This will create 30 dashes separated with a \"+\" 3x\n line = '+' .join(['-'*(width*3)]*3)\n\n # This builds the board. It iterates through [A1, A2, etc.] and then\n # uses the .center method to center whatever value is in that cell.\n # It adds a | in-between columns 3 & 6 otherwise an empty string.\n # It prints a line if the row is C or F\n for r in rows:\n print(''.join(values[r+c].center(width)+('|' if c in '36' else '')\n for c in cols))\n if r in 'CF': print(line)\n return","def prescription(self):\n prescription = \"\\n{0:>10}\\t{1:>10}\\t{2:>10}\\t{3:>10}\\n\".format(\"R\",\"Material\",\"d\",\"diameter\")\n for surface in self.lensSurfaces():\n prescription += \"{0:>10.2f}\\t{1:>10}\\t{2:>10.2f}\\t{3:>10.2f}\\n\".format(surface.R, str(surface.mat), surface.spacing, surface.diameter)\n return prescription","def print_all_letters(all_letters):\n letters_msg = ' '.join(all_letters)\n print(\"\\nLetters suggested so far:\")\n print(letters_msg.upper())","def get_background(self):\n s = ''\n for i in range(9, 11):\n s += ' ' + str(self.dna[i])\n return s"],"string":"[\n \"def banner_ascii():\\n print(\\\"\\\")\\n print(f\\\"\\\\n{RED} Steganography Tool{RESET}\\\")\\n print(f\\\"{RED} Made By {RESET}\\\")\\n print(f\\\"{RED} Ehthe Samul Islam Laskar USN:1DS16CS712 {RESET}\\\")\\n print(f\\\"{RED} B Padma USN:1DS19CS420{RESET}\\\")\\n print(f\\\"{RED} Nikhil D Kanyal USN:1DS17CS731{RESET}\\\")\\n print(f\\\"{YELLOW}Type 'help' to see commands{RESET}\\\")\",\n \"def n_char(self,char,n,w=1,h=1):\\n for i in range(n):\\n self.esprint(char,w,h)\",\n \"def basic_char_setup( self ):\\n\\t\\tsmall_bar = 3 # number of points per bar\\n\\t\\twide_bar = round(small_bar * 2.25,0) # 2.25 x small_bar\\n\\t\\tdpl = 50 # dots per line 300dpi/6lpi = 50dpl\\n\\t\\tself._nb = bytes( self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ( '*c%02ia%ib0P' % (small_bar, self.bc_height*dpl) ) + \\n\\t\\t\\t\\t\\t\\t self.owner.PRINTER_ESC + \\n\\t\\t\\t\\t\\t\\t (\\\"*p+%02iX\\\" % small_bar) )\\n\\t\\tself._wb = bytes( self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ('*c%02ia%ib0P' % (wide_bar, self.bc_height*dpl) )+\\n\\t\\t\\t\\t\\t\\t self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ('*p+%02iX' % wide_bar ) )\\n\\t\\tself._ns = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % small_bar ) )\\n\\t\\tself._ws = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % wide_bar ) )\\n \\n\\t\\t# DONE nb = bc39_esc+\\\"*c\\\"+TRANSFORM(small_bar,'99')+\\\"a\\\"+Alltrim(STR(bc39_height*dpl))+\\\"b0P\\\"+bc39_esc+\\\"*p+\\\"+TRANSFORM(small_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE wb = bc39_esc+\\\"*c\\\"+TRANSFORM(wide_bar,'99')+\\\"a\\\"+Alltrim(STR(bc39_height*dpl))+\\\"b0P\\\"+bc39_esc+\\\"*p+\\\"+TRANSFORM(wide_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE ns = bc39_esc+\\\"*p+\\\"+TRANSFORM(small_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE ws = bc39_esc+\\\"*p+\\\"+TRANSFORM(wide_bar,'99')+\\\"X\\\"\\n \\n\\t\\t# adjust cusor position to start at top of line and return to bottom of line\\n\\t\\tself._bc_start = bytes( self.owner.PRINTER_ESC + '*p-50Y' )\\n\\t\\tself._bc_end = bytes( self.owner.PRINTER_ESC + '*p+50Y' )\\n\\t\\t# DONE bc39_start = bc39_esc+\\\"*p-50Y\\\"\\n\\t\\t# DONE bc39_END = bc39_esc+\\\"*p+50Y\\\"\\n\\n\\t\\t# setup the structure allowing to print the code codebar section for various LETTERS\\n\\t\\tself._char39 = { u'1' : 'wb+ns+nb+ws+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'2' : 'nb+ns+wb+ws+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'3' : 'wb+ns+wb+ws+nb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'4' : 'nb+ns+nb+ws+wb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'5' : 'wb+ns+nb+ws+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'6' : 'nb+ns+wb+ws+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'7' : 'nb+ns+nb+ws+nb+ns+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'8' : 'wb+ns+nb+ws+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'9' : 'nb+ns+wb+ws+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'0' : 'nb+ns+nb+ws+wb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'A' : 'wb+ns+nb+ns+nb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'B' : 'nb+ns+wb+ns+nb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'C' : 'wb+ns+wb+ns+nb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'D' : 'nb+ns+nb+ns+wb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'E' : 'wb+ns+nb+ns+wb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'F' : 'nb+ns+wb+ns+wb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'G' : 'nb+ns+nb+ns+nb+ws+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'H' : 'wb+ns+nb+ns+nb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'I' : 'nb+ns+wb+ns+nb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'J' : 'nb+ns+nb+ns+wb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'K' : 'wb+ns+nb+ns+nb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'L' : 'nb+ns+wb+ns+nb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'M' : 'wb+ns+wb+ns+nb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'N' : 'nb+ns+nb+ns+wb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'O' : 'wb+ns+nb+ns+wb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'P' : 'nb+ns+wb+ns+wb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'Q' : 'nb+ns+nb+ns+nb+ns+wb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'R' : 'wb+ns+nb+ns+nb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'S' : 'nb+ns+wb+ns+nb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'T' : 'nb+ns+nb+ns+wb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'U' : 'wb+ws+nb+ns+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'V' : 'nb+ws+wb+ns+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'W' : 'wb+ws+wb+ns+nb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'X' : 'nb+ws+nb+ns+wb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'Y' : 'wb+ws+nb+ns+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'Z' : 'nb+ws+wb+ns+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'-' : 'nb+ws+nb+ns+nb+ns+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'.' : 'wb+ws+nb+ns+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u' ' : 'nb+ws+wb+ns+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'*' : 'nb+ws+nb+ns+wb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'$' : 'nb+ws+nb+ws+nb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'/' : 'nb+ws+nb+ws+nb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'+' : 'nb+ws+nb+ns+nb+ws+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'%' : 'nb+ns+nb+ws+nb+ws+nb+ws+nb' }\",\n \"def displayHand(hand: d_si) -> None:\\n for letter in hand.keys():\\n for _ in range(hand[letter]):\\n print(letter,end=\\\" \\\")\\n print()\",\n \"def print_chars(self):\\n for v in voc.split('\\\\n'):\\n pair = v.split(',')\\n print(pair[0], pair[1], '\\\\t', self.epi.xsampa_list(pair[0]))\",\n \"def display_hand(hand):\\n letters = \\\"\\\"\\n\\n for i in hand.keys():\\n\\n for j in range(hand[i]):\\n letters += i\\n\\n return letters\",\n \"def display_characters(self):\\n return f'{self.character_set}'\",\n \"def display_string(text_area_no: int) -> str:\\n if text_area_no == 1:\\n text = ''\\n for v in utterances['P1']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 2:\\n text = ''\\n for v in utterances['S1']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 3:\\n text = ''\\n for v in utterances['S2']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 4:\\n text = ''\\n for v in utterances['S3']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 5:\\n text = ''\\n for v in utterances['S4']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 6:\\n text = ''\\n for v in utterances['S4']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 7:\\n text = ''\\n for v in utterances['C1']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 8:\\n text = ''\\n for v in utterances['C2']:\\n text += v + '\\\\n'\\n return text\",\n \"def display_s(s, font, screen, lcd, size=5, x=0, y=0):\\n i = 0\\n spacing = size * .11\\n s = str(s)\\n char = s[0]\\n char_w, char_h = font[char].size\\n for c in s:\\n display_c(c,font,screen,lcd,size,(int(i*spacing*char_w)+x),y)\\n i += 1\\n return screen\",\n \"def tower(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC+\\\"255D\\\"+self.ESC+\\\"0;1;44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"58C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"68C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"30m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"0;44m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"1m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"52C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"43m\\\"+self.A223+self.A223+self.ESC+\\\"0;43m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"40C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"0;34;43m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"30m\\\"+self.A223+self.A223+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40mYou reach th\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"42Ce castle and fight your way up the\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A178+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"30C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"1;36;40mtower!\\\"+self.ESC+\\\"41C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A219+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"24C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"48C\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A178+self.A177+self.A177+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A177+self.A177+self.A177+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40mYou blindly slash at any and all who\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"66C stand in \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A177+self.A176+self.A177+self.A176+self.A178+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A176+self.A177+self.A177+self.A219+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"30C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"1;36;40myour path - screaming to find the prisoner \\\"+self.ESC+\\\"4C\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"77C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40myou seek - and the jailer of your he\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"66Cart.\\\"+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.A176+self.A177+self.ESC+\\\"33m\\\"+self.A176+self.ESC+\\\"32m\\\"+self.A177+self.A176+self.A177+self.ESC+\\\"33m\\\"+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A178+self.A176+self.A177+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"23C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"48C\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A176+self.A177+self.A177+self.A177+self.A176+self.A178+self.A177+self.ESC+\\\"33m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"32m\\\"+self.A177+self.A177+self.A178+self.A176+self.A178+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"43m\\\"+self.A220+self.A220+self.ESC+\\\"0;43m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"34C\\\"+self.ESC+\\\"43m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"0;34;43m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"76C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"30m\\\"+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A177+self.A176+self.A177+self.A176+self.A176+self.ESC+\\\"33m\\\"+self.A176+self.A177+self.A178+self.A177+self.A177+self.ESC+\\\"32m\\\"+self.A177+self.A178+self.A177+self.A177+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A222+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A221+self.A219+self.ESC+\\\"1;44m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"44m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"44C\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"34m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"33;40m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"37C\\\"+self.ESC+\\\"44m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"1;37;40mYou make it to the top and \\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"74C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"40C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"4C\\\"+self.ESC+\\\"1;37;40mthrow open the door...\\\"+self.ESC+\\\"6C\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"74C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A178+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A178+self.A219+self.ESC+\\\"30;44m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"31C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"1;32;42m\\\"+self.A177+self.A178+self.A178+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"53C\\\"+self.ESC+\\\"32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+self.ESC+\\\"0;33m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"20C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1;32;42m\\\"+self.A178+self.A219+self.ESC+\\\"0;32m\\\"+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;30;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"32C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"46C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"42m\\\"+self.A176+self.A177+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"63C\\\"+self.ESC+\\\"42m\\\"+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"42m \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.A219+self.ESC+\\\"0;32m\\\"+self.A178+self.A219+self.ESC+\\\"42m \\\"+self.ESC+\\\"40m\\\"+self.A177+self.A177+self.A176+self.A176+self.A177+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"21C\\\"+self.ESC+\\\"1;42m\\\"+self.A178+self.A176+self.ESC+\\\"30;40m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A177+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"30;40m\\\"+self.A219+self.A219+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"36C\\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.A178+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+\\\" \\\"+self.A178+\\\" \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A176+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"52C\\\"+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.A178+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"63C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"42m\\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.A176+self.A178+self.A219+self.A219+self.A178+self.A178+self.A176+self.A176+\\\" \\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A177+self.A178+self.A176+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"26C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A177+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"36C\\\"+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.A177+self.A177+self.A177+self.ESC+\\\"1;42m\\\"+self.A219+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.A178+self.A177+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"51C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A177+self.A178+self.A178+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A177+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"64C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"37m<MORE> \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def code_display(self):\\r\\n return u'%s-%s-%s' % (self.code[:3], self.code[3:6], self.code[6:])\",\n \"def display_letters(word, guesses):\\n pass\",\n \"def errors_icons(self):\\n msg_errors_lifes = ''\\n for i in range(0,5):\\n if self.letters_wrong <= i:\\n msg_errors_lifes += ' ♥ '\\n else:\\n msg_errors_lifes += ' ☠ ' \\n return msg_errors_lifes\",\n \"def __card_display(self):\\n return ''.join([(each_number if (i == 0 or i % 4 != 0) else ' ' + each_number)\\n for i, each_number in enumerate(self.card_number)])\",\n \"async def charinfo(self, ctx: Context, *, characters: str) -> None:\\n match = re.match(r\\\"<(a?):(\\\\w+):(\\\\d+)>\\\", characters)\\n if match:\\n embed = Embed(\\n title=\\\"Non-Character Detected\\\",\\n description=\\\"Only unicode characters can be processed, but a custom Discord emoji \\\" \\\"was found. Please remove it and try again.\\\",\\n )\\n embed.colour = Color.red()\\n await ctx.send(embed=embed)\\n return\\n\\n if len(characters) > 25:\\n embed = Embed(title=f\\\"Too many characters ({len(characters)}/25)\\\")\\n embed.colour = Color.red()\\n await ctx.send(embed=embed)\\n return\\n\\n def get_info(char: str) -> Tuple[str, str]:\\n digit = f\\\"{ord(char):x}\\\"\\n if len(digit) <= 4:\\n u_code = f\\\"\\\\\\\\u{digit:>04}\\\"\\n else:\\n u_code = f\\\"\\\\\\\\U{digit:>08}\\\"\\n url = f\\\"https://www.compart.com/en/unicode/U+{digit:>04}\\\"\\n name = f\\\"[{unicodedata.name(char, '')}]({url})\\\"\\n info = f\\\"`{u_code.ljust(10)}`: {name} - {char}\\\"\\n return info, u_code\\n\\n charlist, rawlist = zip(*(get_info(c) for c in characters))\\n\\n embed = Embed(description=\\\"\\\\n\\\".join(charlist))\\n embed.set_author(name=\\\"Character Info\\\")\\n\\n if len(characters) > 1:\\n embed.add_field(name=\\\"Raw\\\", value=f\\\"`{''.join(rawlist)}`\\\", inline=False)\\n\\n await ctx.send(embed=embed)\",\n \"async def charinfo(self, ctx: Context, *, characters: str) -> None:\\n match = re.match(r\\\"<(a?):(\\\\w+):(\\\\d+)>\\\", characters)\\n if match:\\n await messages.send_denial(\\n ctx,\\n \\\"**Non-Character Detected**\\\\n\\\"\\n \\\"Only unicode characters can be processed, but a custom Discord emoji \\\"\\n \\\"was found. Please remove it and try again.\\\"\\n )\\n return\\n\\n if len(characters) > 50:\\n await messages.send_denial(ctx, f\\\"Too many characters ({len(characters)}/50)\\\")\\n return\\n\\n def get_info(char: str) -> tuple[str, str]:\\n digit = f\\\"{ord(char):x}\\\"\\n if len(digit) <= 4:\\n u_code = f\\\"\\\\\\\\u{digit:>04}\\\"\\n else:\\n u_code = f\\\"\\\\\\\\U{digit:>08}\\\"\\n url = f\\\"https://www.compart.com/en/unicode/U+{digit:>04}\\\"\\n name = f\\\"[{unicodedata.name(char, '')}]({url})\\\"\\n info = f\\\"`{u_code.ljust(10)}`: {name} - {utils.escape_markdown(char)}\\\"\\n return info, u_code\\n\\n char_list, raw_list = zip(*(get_info(c) for c in characters), strict=True)\\n embed = Embed().set_author(name=\\\"Character Info\\\")\\n\\n if len(characters) > 1:\\n # Maximum length possible is 502 out of 1024, so there's no need to truncate.\\n embed.add_field(name=\\\"Full Raw Text\\\", value=f\\\"`{''.join(raw_list)}`\\\", inline=False)\\n\\n await LinePaginator.paginate(char_list, ctx, embed, max_lines=10, max_size=2000, empty=False)\",\n \"def displayHand(hand):\\r\\n for letter in hand.keys():\\r\\n for j in range(hand[letter]):\\r\\n print(letter,end=\\\" \\\") # print all on the same line\\r\\n print() # print an empty line\\r\",\n \"def print_hint_board(self):\\n board = \\\"\\\"\\n count = 1 \\n for i in range(3):#need to change this in the future\\n for j in range(3):#need to change this in the future\\n board += str(count) \\n count += 1\\n if j != 2:#need to change this in the future\\n board += \\\" | \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def show_theme_message(width):\\n print_dotted_line()\\n print_bold(\\\"Attack of The Orcs v0.0.5:\\\")\\n msg = (\\n \\\"\\\"\\\"\\n Hrabri vitez Talion vraća se iz boja i želi predahnuti u jednom seocetu. \\n U seocu se nalazi 5 kućica i želi stupiti u jednu od njih. \\n Hrabri vitez ne zna da u ovom području ima neprijatelja.\\n Odluči se za jedna od vrata ...\\n \\\"\\\"\\\")\\n\\n print(textwrap.fill(msg, width=width))\",\n \"def header(text, char=\\\"*\\\", repeat=80):\\n print(f\\\"\\\\n{char*repeat}\\\")\\n print(f\\\"{char*2} {text.upper()}\\\")\\n print(f\\\"{char*repeat}\\\")\",\n \"def display_char(self) -> None:\\r\\n print(self.char if self.was_guessed else '_', end=' ')\",\n \"def make_display_word(secret_word):\\n return ('_ ' * len(secret_word))\",\n \"def __repr__(self):\\r\\n numLetters = self.numLetters\\r\\n S = ''\\r\\n S += 3*'\\\\n'\\r\\n S += ' '\\r\\n for i in range(numLetters):\\r\\n S += self.currentBoard[i] + ' '\\r\\n\\r\\n return S\",\n \"def header(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC + \\\"0m \\\" +self.A220 + self.A220 + self.A220 + self.A220 + self.A220 +\\\" \\\" + self.ESC + \\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A220+self.A220+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mS\\\"+self.ESC+\\\"0;31mAGA\\\"+self.ESC+\\\"37m \\\"+self.A219+self.A219+self.ESC+\\\"30;47mo\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mO\\\"+self.ESC+\\\"0;31mF THE\\\"+self.ESC+\\\"37m \\\"+self.A219+self.A219+self.ESC+\\\"30;47mO\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.A220+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mR\\\"+self.ESC+\\\"0;31mED\\\"+self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"30;47mo\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.A223+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m \\\"+self.ESC+\\\"1mD\\\"+self.ESC+\\\"0;31mRAGON 0.9.9\\\"+self.ESC+\\\"37m \\\"+self.A223+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.A219+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"0m \\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m concept\\\"+self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+\\\" \\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m Seth Robinson \\\"+self.ESC+\\\"37m\\\"+self.A222+\\\" \\\"+self.A223+self.A223+self.ESC+\\\"1;47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A223+self.A223+self.ESC+\\\"0m \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+self.A219+self.A220+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m by\\\"+self.ESC+\\\"0m \\\"+self.A219+\\\" \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A176+self.A219+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A176+\\\" \\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;34m J\\\"+self.ESC+\\\"0;34m.\\\"+self.ESC+\\\"1mT\\\"+self.ESC+\\\"0;34m.\\\"+self.ESC+\\\"1mS\\\"+self.ESC+\\\"0;34mage\\\"+self.ESC+\\\"0m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A177+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+self.A178+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+self.A176+self.A176+self.A177+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"41m\\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A223+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A177+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A178+\\\" \\\"+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A178+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.A219+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A178+self.A219+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A178+self.A177+self.A177+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A178+self.A219+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A219+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A178+\\\" \\\"+self.A177+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A177+self.A219+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A223+\\\" \\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A178+self.A177+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A219+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.A223+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A178+self.A178+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.A178+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+\\\" \\\"+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.A219+self.ESC+\\\"1;41m\\\"+self.A176+self.A176+self.ESC+\\\"0;31m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"1;5;32m\\\"+self.A220+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;5;32m\\\"+self.A219+self.A219+self.A223+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.A219+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;5;32m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.A219+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A177+self.A219+self.A178+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A221+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+\\\" \\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A177+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.A221+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A177+self.A177+self.A176+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A178+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A176+self.A177+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A178+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A177+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A176+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"1m\\\"+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.A177+self.A178+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A176+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A223+self.ESC+\\\"40m\\\"+self.A219+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A178+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;37;47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A177+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+\\\" \\\"+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;37;47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A178+self.A177+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"33m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+\\\" \\\"+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A176+self.ESC+\\\"37;40m \\\"+self.A220+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A219+self.A221+\\\" \\\"+self.A223+\\\" \\\"+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A178+self.A177+self.A176+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A220+self.ESC+\\\"1m\\\"+self.A219+\\\" \\\"+self.A219+self.A221+\\\" \\\"+self.A223+\\\" \\\"+self.A220+\\\" \\\"+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A219+self.A220+self.A222+self.ESC+\\\"1;41m\\\"+self.A219+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.A178+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A223+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.A223+\\\" \\\"+self.A220+self.A223+self.A220+\\\" \\\"+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"30mÙ\\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.A223+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.A176+self.A176+\\\" \\\"+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A177+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A220+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A177+\\\" \\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A177+self.A177+self.A176+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A178+\\\" \\\"+self.A177+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A219+self.A178+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.A176+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"0;37m \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"1;31mShatterstar [W/X] \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def print_title():\\r\\n HANGMAN_ASCII_ART = \\\"\\\"\\\"welcome to the game hangman\\r\\n _ _ \\r\\n | | | | \\r\\n | |__| | __ _ _ __ __ _ _ __ ___ __ _ _ __ \\r\\n | __ |/ _` | '_ \\\\ / _` | '_ ` _ \\\\ / _` | '_ \\\\ \\r\\n | | | | (_| | | | | (_| | | | | | | (_| | | | |\\r\\n |_| |_|\\\\__,_|_| |_|\\\\__, |_| |_| |_|\\\\__,_|_| |_|\\r\\n __/ | \\r\\n |___/\\r\\n\\\"\\\"\\\"\\r\\n print(HANGMAN_ASCII_ART)\",\n \"def horizontal_char(self):\\n ...\",\n \"def print_character( self, ordinal ):\\n print( '--- %s ----------------' % chr(ordinal) )\\n if not( ordinal in self.characters ):\\n print( '%s (%i) is not present in characters' % (chr(ordinal), ordinal) )\\n print( 'Ordinal: %i' % ordinal )\\n print( 'Char has descender: %s' % self.char_has_descender( ordinal ) )\\n print( 'width, height = %i, %i' % (self.characters[ordinal].width, self.characters[ordinal].height) )\\n # print( 'bitmap_top from base = %i' % self.glyphs[ordinal].bitmap_top )\\n\\n if self.char_has_descender( ordinal ):\\n print( 'width, height = %i, %i (with %i px descender already included)' % (self.characters[ordinal].width, self.characters[ordinal].height, self.descender_size ) )\\n else:\\n # Sometime, Char-Size + Descender Size GOES OVER the font size --> reduce the descender\\n _descender = self.ajusted_descender_size( ordinal )\\n print( 'width, height = %i, %i (with %i px ajusted_descender added)' % (self.characters[ordinal].width, self.characters[ordinal].height + _descender, _descender ) )\\n\\n print( self.characters[ordinal] )\",\n \"def displayHand(hand):\\n for letter in hand.keys():\\n for j in range(hand[letter]):\\n print letter, # print all on the same line\\n print # print an empty line\",\n \"def show_word(self):\\n self.display_word = len(self.chosen_word) * \\\"_ \\\"\\n Donatello.draw_word(self.display_word)\\n return self.display_word\",\n \"def print_rangoli(size):\\n alphabets = \\\"abcdefghijklmnopqrstuvwxyz\\\"\\n character_length = 4 * size -3\\n for i in range(1, size * 2):\\n index = size - abs(i - size)\\n pattern = alphabets[size - index: size]\\n pattern = pattern[::-1] + pattern[1:]\\n pattern = '-'.join(list(pattern))\\n lines = \\\"-\\\" * ((character_length - len(pattern)) // 2)\\n row = \\\"{0}{1}{2}\\\".format(lines, pattern, lines)\\n print(row)\",\n \"def show_instructions():\\n\\n print('4-digit Code has been set. Digits in range 1 to 8. You have 12 turns to break it.')\",\n \"def display():\\n screen.addch(head[0],head[1],'x')\",\n \"def display(missed_letters, correct_letters, secret_word):\\n print(HANGMAN[len(missed_letters)])\\n print()\\n\\n print('Missed letters:', end=' ')\\n for letter in missed_letters:\\n print(letter, end=' ')\\n print()\\n\\n dashed_entry = ''\\n\\n for letter in secret_word:\\n if letter == ' ':\\n dashed_entry += ' '\\n elif letter in correct_letters:\\n dashed_entry += letter\\n else:\\n dashed_entry += '-'\\n\\n for letter in dashed_entry: # show the secret word with spaces in between each letter\\n print(letter, end=' ')\\n print()\",\n \"def abdul(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30m\\\"+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.A220+self.ESC+\\\"1;43m\\\"+self.A219+self.A178+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A223+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"33m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.A254+self.ESC+\\\"33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A219+self.ESC+\\\"1;43m\\\"+self.A177+self.A176+self.ESC+\\\"C\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"37;40mSaga\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"CRed\\\"+self.ESC+\\\"CDragon\\\"+self.ESC+\\\"C-\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"33mAbduls\\\"+self.ESC+\\\"CArmour \\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A177+self.ESC+\\\"0;32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A219+self.ESC+\\\"C\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"33m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"33m\\\"+self.A220+self.A219+self.A223+self.ESC+\\\"37m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A223+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A176+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;33mBehind\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"Cdesk\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"Carmour\\\"+self.ESC+\\\"Cshop\\\"+self.ESC+\\\"Cis\\\"+self.ESC+\\\"Can\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"37;40m\\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;33mamazingly\\\"+self.ESC+\\\"Cattractive\\\"+self.ESC+\\\"Clooking\\\"+self.ESC+\\\"Cfemale - she seems\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"1;37m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"47m\\\"+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;32m\\\"+self.A220+self.A254+self.ESC+\\\"3C\\\"+self.ESC+\\\"33mbusy, doing her mails but she\\\"+self.ESC+\\\"Casks\\\"+self.ESC+\\\"C\\\\\\\"\\\"+self.ESC+\\\"1mHow\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A223+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37;47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A222+self.A222+self.ESC+\\\"47m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"30m\\\"+self.A176+self.ESC+\\\"C\\\"+self.A177+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"47m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.A178+self.ESC+\\\"6C\\\"+self.ESC+\\\"1;33mmay\\\"+self.ESC+\\\"CI\\\"+self.ESC+\\\"Cbe\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cservice?\\\"+self.ESC+\\\"0;33m\\\\\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"C\\\"+self.A220+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37m\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A177+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;33;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+self.A223+self.ESC+\\\"C\\\"+self.A220+self.ESC+\\\"1;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1;37m\\\"+self.A223+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"2C\\\"+self.A220+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A176+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mB\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1muy\\\"+self.ESC+\\\"CArmour\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A220+self.A223+self.ESC+\\\"1;37m\\\"+self.A220+self.A220+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"1;47m\\\"+self.A223+self.ESC+\\\"0m\\\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"1;40m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mS\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1mell\\\"+self.ESC+\\\"CArmour\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37;47m\\\"+self.A219+self.A178+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.A220+self.A220+self.A219+self.A220+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A219+self.A219+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mY\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1mour\\\"+self.ESC+\\\"CStats\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"0;33m\\\"+self.A223+self.A220+self.A223+self.ESC+\\\"30;43m\\\"+self.A176+self.A223+self.ESC+\\\"1;37;40m\\\"+self.A223+self.A223+self.ESC+\\\"47m\\\"+self.A178+self.A177+self.A176+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A176+self.A177+self.A178+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mR\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1meturn\\\"+self.ESC+\\\"Cto\\\"+self.ESC+\\\"CTown\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A177+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A254+self.ESC+\\\"C\\\"+self.ESC+\\\"1;30m\\\"+self.A220+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"33;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"C\\\"+self.A178+self.A254+self.ESC+\\\"33m\\\"+self.A219+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A223+self.A220+self.ESC+\\\"1;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A220+self.A223+self.A220+self.A223+self.A223+self.A220+self.ESC+\\\"3C\\\"+self.ESC+\\\"1;30m\\\"+self.A177+self.A220+self.ESC+\\\"2C\\\"+self.ESC+\\\"33m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"33m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"32m\\\"+self.A254+self.ESC+\\\"33m\\\"+self.A223+self.A219+self.A219+self.A220+self.A223+self.A220+self.A220+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"2C\\\"+self.A220+self.A178+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"33;43m\\\"+self.A177+self.ESC+\\\"2C\\\"+self.ESC+\\\"0m \\\"+self.ESC+\\\"3C\\\"+self.ESC+\\\"33m\\\"+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.A223+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.A223+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def printunichars(row):\\n print(\\\"Title:\\\")\\n print(row[0].encode('utf-8'))\\n print(\\\"Body:\\\")\\n print(row[1].encode('utf-8'))\\n print(\\\"Ref:\\\")\\n print(row[2].encode('utf-8'))\\n print(\\\"Url:\\\")\\n print(row[3].encode('utf-8'))\",\n \"def display_c(c, font, screen, lcd, size=5, x=0, y=0):\\n char = font[str(c)]\\n width, height = char.size\\n \\\"\\\"\\\"\\n if not(size == 10):\\n size /= 10.0\\n width = int(round(size*width))\\n height = int(round(size*height))\\n char.resize((width,height))\\n \\\"\\\"\\\"\\n size = int(round(size * 10))\\n images.display_img(char,screen,lcd,size,x,y)\",\n \"def pretty_hebrew(val):\\n return 'font-size:20px; font-family: Times New Roman; text-align: right; max-width: 500px'\",\n \"def print_table(table):\\r\\n print('/-----------------------------------------------------------------------------------\\\\\\\\')\\r\\n for item in table:\\r\\n\\r\\n while len(item[1]) <= 22:\\r\\n item[1] += ' '\\r\\n\\r\\n while len(item[2]) <= 27:\\r\\n item[2] += ' '\\r\\n\\r\\n while len(item[0]) <= 15:\\r\\n item[0] += ' '\\r\\n\\r\\n print('| '+item[0]+' | '+item[1]+'| '+item[2]+' |')\\r\\n\\r\\n print('\\\\\\\\-----------------------------------------------------------------------------------/')\",\n \"def afficher_damier_ascii(infojeu):\\n lignes = []\\n lignes += list(\\\"Légende: 1=\\\"+ str(infojeu[\\\"joueurs\\\"][0][\\\"nom\\\"])+\\n ', 2='+str(infojeu[\\\"joueurs\\\"][1][\\\"nom\\\"]) + \\\"\\\\n\\\")\\n lignes += list(\\\" \\\"+\\\"-\\\"*35+\\\"\\\\n\\\")\\n for i in range(1, 10):\\n lignes += str(10-i) + \\\" | \\\"\\n for j in range(1, 9):\\n strplayer = \\\".\\\"\\n if [j, 10-i] == infojeu[\\\"joueurs\\\"][0][\\\"pos\\\"]:\\n strplayer = \\\"1\\\"\\n elif [j, 10-i] == infojeu[\\\"joueurs\\\"][1][\\\"pos\\\"]:\\n strplayer = \\\"2\\\"\\n if [j+1, 10-i] in infojeu[\\\"murs\\\"][\\\"verticaux\\\"]:\\n lignes += list(strplayer + \\\" | \\\")\\n elif [j+1, 9-i] in infojeu[\\\"murs\\\"][\\\"verticaux\\\"]:\\n lignes += list(strplayer + \\\" | \\\")\\n else:\\n lignes += list(strplayer + \\\" \\\")\\n if [9, 10-i] == infojeu[\\\"joueurs\\\"][0][\\\"pos\\\"]:\\n lignes += list(\\\"1 |\\\")\\n elif [9, 10-i] == infojeu[\\\"joueurs\\\"][1][\\\"pos\\\"]:\\n lignes += list(\\\"2 |\\\")\\n else:\\n lignes += list(\\\". |\\\")\\n if i != 9:\\n lignes += list(\\\"\\\\n |\\\")\\n for k in range(1, 9):\\n if i != 9:\\n if [k, 10-i] in infojeu[\\\"murs\\\"][\\\"horizontaux\\\"]:\\n lignes += list(\\\"----\\\")\\n elif [k-1, 10-i] in infojeu[\\\"murs\\\"][\\\"horizontaux\\\"] and \\\\\\n [k+1, 9-i] in infojeu[\\\"murs\\\"][\\\"verticaux\\\"]:\\n lignes += list(\\\"---|\\\")\\n elif [k-1, 10-i] in infojeu[\\\"murs\\\"][\\\"horizontaux\\\"]:\\n lignes += list(\\\"--- \\\")\\n elif [k+1, 9-i] in infojeu[\\\"murs\\\"][\\\"verticaux\\\"]:\\n lignes += list(\\\" |\\\")\\n else:\\n lignes += list(\\\" \\\")\\n if i != 9:\\n if [8, 10-i] in infojeu[\\\"murs\\\"][\\\"horizontaux\\\"]:\\n lignes += list(\\\"---|\\\")\\n else:\\n lignes += list(\\\" |\\\")\\n lignes += list(\\\"\\\\n\\\")\\n lignes += list(\\\"--|\\\"+ \\\"-\\\"*35+\\\"\\\\n\\\")\\n lignes += list(\\\" | 1 2 3 4 5 6 7 8 9\\\")\\n lignes = ''.join(lignes)\\n print(lignes)\",\n \"def affiche_plateau(plateau):\\n remplissage = ' ' * 30\\n print(remplissage, end='')\\n for i in range(1, 7, 2):\\n print(\\\" \\\" + str(i), sep='', end='')\\n print()\\n for i in range(get_nb_lignes(plateau[0])):\\n print(remplissage, end='')\\n if i % 2 == 0:\\n print(' ', sep='', end='')\\n else:\\n print(str(i), sep='', end='')\\n for j in range(get_nb_colonnes(plateau[0])):\\n print(to_char(get_valeur(plateau[0], i, j)), end='')\\n if i % 2 == 0:\\n print(' ', sep='', end='')\\n else:\\n print(str(i), sep='', end='')\\n print()\\n print(' ', sep='', end='')\\n print(remplissage, end='')\\n for i in range(1, 7, 2):\\n print(\\\" \\\" + str(i), sep='', end='')\\n print()\",\n \"def display(self):\\n print (\\\"+\\\" + \\\"-\\\"*self.size + \\\"+\\\")\\n for i in range(self.size):\\n terrain_strs = [Terrain.display_string(self.array[j, i]) for j in range(self.size)]\\n print(\\\"|\\\" + \\\"\\\".join(terrain_strs) + \\\"|\\\")\\n print (\\\"+\\\" + \\\"-\\\"*self.size + \\\"+\\\")\",\n \"def get_equipment(self):\\n s = ''\\n for i in range(12, 16):\\n s += ' ' + str(self.dna[i])\\n return s\",\n \"def supercombiner(bot, ev):\\n # ported from jenni\\n s = 'u'\\n for i in iter(range(1, 3000)):\\n if unicodedata.category(chr(i)) == \\\"Mn\\\":\\n s += chr(i)\\n if len(s) > 100:\\n break\\n bot.say(s)\",\n \"def label(mi_, ma_):\\n\\treturn \\\"caractères Unicode des points de code {} à {}\\\".format(mi_, ma_)\",\n \"def show( self):\\n def symbol( i):\\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\\n \\n X, Y = np.max( self.board.positions, 0)\\n # -2 to indicate outside board.\\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \\n for x, y in self.board.positions:\\n display[x, y] = -1 # -1 to indicate unoccupied\\n for p, i in self.occupation.items():\\n x, y = self.board.positions[p]\\n display[x, y] = i\\n for x in xrange(X+1):\\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\\n print s\",\n \"def print_screen(history):\\n\\n # Extract the angles from the distance data. Usually this won't change \\n # from scan to scan, but there are scanning algorithms where that may not \\n # be the case \\n angles = []\\n for h in history:\\n angles = list(set(angles + h.keys())) \\n angles.sort()\\n\\n # Create a 2D grid of characters. Essentially a \\\"screen buffer\\\"\\n buff = {}\\n for angle in angles:\\n buff[angle] = ' '.rjust(120)\\n\\n \\n blips = ['.', '*', '#', '@']\\n blips = blips[-len(history):] # if we only have 2, take last 2 blips\\n\\n # Plot blips onto buffer \\n for h in history:\\n blip = blips.pop(0) if len(blips) else '.'\\n for angle in angles:\\n if angle not in h: continue\\n dist = h[angle]\\n if dist < 120:\\n buff[angle] = set_char_at(buff[angle], dist, blip)\\n\\n # Output\\n print \\\"\\\\n\\\\n\\\\n\\\"\\n for angle in angles:\\n obstacle = 'x' if '@' in buff[angle][0:30] else ' '\\n print \\\"%s %s %s\\\" % (str(angle).rjust(5), obstacle, buff[angle])\\n print '20cm'.rjust(30) + '50cm'.rjust(30) + '1m'.rjust(50)\",\n \"def the_display(self):\\r\\n return f\\\"\\\"\\\"\\r\\n {self.display[0]}\\\\n\\r\\n {self.display[1]}\\\\n\\r\\n {self.display[2]}\\\\n\\r\\n {self.display[3]}\\\\n\\r\\n {self.display[4]}\\\\n\\r\\n \\\"\\\"\\\"\",\n \"def reveal_occupants(idx, huts):\\n msg = \\\"\\\"\\n print(\\\"展示小屋内部情况...\\\")\\n for i in range(len(huts)):\\n occupant_info = \\\"<%d:%s>\\\" % (i + 1, huts[i])\\n if i + 1 == idx:\\n occupant_info = \\\"\\\\033[1m\\\" + occupant_info + \\\"\\\\033[0m\\\"\\n msg += occupant_info + \\\" \\\"\\n\\n print(\\\"\\\\t\\\" + msg)\\n print_dotted_line()\",\n \"def create_random_string(total_character):\\n feed=string.printable\\n words=\\\"\\\"\\n i=0\\n while i < total_character:\\n words += feed[random.randrange(0,len(feed)-1)]\\n i+=1\\n return words\",\n \"def __str__(self):\\n allowed = ['!', '@', '#', '$', '%', '^', '&', '*', '/', '.', '1', '2', '3', '4', '5', '6', '7', '8', '9']\\n returnstring = \\\"\\\"\\n for row in self.positions:\\n for char in row:\\n if char.isupper() or char == 'r' or char in allowed:\\n returnstring += \\\"| \\\" + char + \\\" \\\"\\n else:\\n returnstring += \\\"| \\\" + \\\"_\\\" + \\\" \\\"\\n returnstring += \\\"\\\\n\\\"\\n return returnstring\",\n \"def render(self, s):\\n if s < self.num_states - 1:\\n a = np.zeros((self.height, self.width), dtype=np.unicode_)\\n # mark all locations as inaccessible first\\n a[:, :] = u'█'\\n # regular fields\\n for c in self.locs:\\n a[c[1], c[0]] = u'░'\\n # mark exits\\n for c in self.exitsP:\\n a[c[1], c[0]] = 'E'\\n for c in self.exitsN:\\n a[c[1], c[0]] = 'F'\\n # player character\\n c = self.locs[s]\\n a[c[1], c[0]] = u'☺'\\n a = [' '.join(row) for row in a]\\n a = '\\\\n'.join(reversed(a))\\n return a\\n else:\\n return 'Game over!'\",\n \"def _string_data(self, data):\\n print(\\\"_string_data:\\\")\\n string_to_print = []\\n for i in data[::2]:\\n string_to_print.append(chr(i))\\n print(string_to_print)\",\n \"def pretty(self):\\n #table = [\\\"\\\".join([\\\"%8s \\\" % s for s in self.alpha.getSymbols()])]\\n table = []\\n for row in PWM.getFreq(self):\\n table.append(\\\"\\\".join([\\\"%8.6f \\\" % y for y in row]))\\n return table\",\n \"def get_char_to_display(in_value):\\n\\treturn '#' if in_value else ' '\",\n \"def phoneDisplay(number):\\n return number[0:3] + \\\" · \\\" + number[3:6] + \\\" · \\\" + number[6:10]\",\n \"def _make_sysex_message(self, text_message, display_index):\\n if len(text_message) > 28:\\n text_message = text_message[:27]\\n sysex_message = [240, 0, 0, 102, 23, 18, min(display_index, 3) * 28]\\n filled = text_message.ljust(28)\\n for c in filled:\\n sysex_message.append(ord(c))\\n sysex_message.append(247)\\n return sysex_message\",\n \"def get_formatted_text(self, n_cols):\",\n \"def int_21H_2(self):\\r\\n byte_DL = self.registers['DX'].get_byte(0)\\r\\n char_DL = byte_DL.decode(\\\"ascii\\\") # zdekodowany znak do wyswietlenia\\r\\n self.GUI.set_output(char_DL)\",\n \"def help():\\n print(f\\\"{YELLOW}zwc{RESET} - Hidden messages using Zero Width Characters.\\\")\",\n \"def printPokemon():\\n print(\\\" _ \\\")\\n print(\\\" _ __ ___ | | _____ _ __ ___ ___ _ __ \\\")\\n print(\\\" | '_ \\\\ / _ \\\\| |/ / _ \\\\ '_ ` _ \\\\ / _ \\\\| '_ \\\\ \\\")\\n print(\\\" | |_) | (_) | < __/ | | | | | (_) | | | |\\\")\\n print(\\\" | .__/ \\\\___/|_|\\\\_\\\\___|_| |_| |_|\\\\___/|_| |_|\\\")\\n print(\\\" |_| \\\")\",\n \"def print_cards(cards):\\r\\n string = ''\\r\\n for c in cards:\\r\\n suit = c[0]\\r\\n if suit == 1:\\r\\n suit = \\\"\\\\u2665\\\" # heart\\r\\n elif suit == 2:\\r\\n suit = \\\"\\\\u2660\\\" # Spade\\r\\n elif suit == 3:\\r\\n suit = \\\"\\\\u2666\\\" # Diamond\\r\\n else:\\r\\n suit = \\\"\\\\u2663\\\" # club\\r\\n\\r\\n num = c[1]\\r\\n if num == 11:\\r\\n num = 'J'\\r\\n elif num == 12:\\r\\n num = 'Q'\\r\\n elif num == 13:\\r\\n num = 'K'\\r\\n else:\\r\\n num = str(num)\\r\\n\\r\\n string = string + num + suit + ' '\\r\\n return string\",\n \"def char(runtime_addr, n=1):\\n\\n set_formatter(runtime_addr, n, mainformatter.char_formatter)\",\n \"def enlabel(mi_, ma_):\\n\\treturn \\\"Unicode characters from {} to {} codepoints\\\".format(mi_, ma_)\",\n \"def print_data_line(line, max_name, max_roomcode, max_type):\\n print(\\\"|\\\", end='')\\n print(line[0], end='')\\n print(\\\"|\\\", end='')\\n print(line[1], end='')\\n print(\\\" \\\" * (max_name - len(line[1])), end='')\\n print(\\\"|\\\", end='')\\n print(line[4], end='')\\n print(\\\" \\\" * (max_type - len(line[4])), end='')\\n print(\\\"|\\\", end='')\\n print(line[2], end='')\\n print(\\\" \\\" * (max_roomcode - len(line[2])), end='')\\n print(\\\"|\\\", end='')\\n print(line[3], end='')\\n print(\\\"|\\\")\",\n \"def _full_content():\\n return string.ascii_letters\",\n \"def print_map(self):\\n y_max,x_max = map(max, zip(*self.mp.keys()))\\n for row in range(0,y_max+1):\\n msg = []\\n for k in range(0,x_max+1):\\n msg.append(chr(self.mp[row,k]))\\n print(\\\"\\\".join(msg))\",\n \"def report_index(self, index):\\n i = 0\\n for k, data in self.matches[index].items():\\n if i != 0:\\n print\\n print fmt(\\\"['%c': charset - chars]\\\" % k, MAGENTA)\\n print fmt(sorted([x for x in data[\\\"charset\\\"]]), WHITE)\\n print fmt(data[\\\"chars\\\"], WHITE)\\n i = 1\",\n \"def gen_beat_output(e):\\n return [playback_char(e,t) for t in range(70000)]\",\n \"def head_plain():\\n print (hair_buzz())\\n print (eye_narrow())\\n print (nose_triangle())\\n print (mouth_smile())\\n print (chin_plain())\",\n \"def __str__(self):\\n local_s = 'F30A: '\\n local_s += '\\\\n'\\n return local_s\",\n \"def delimit_output(num=25):\\n print('-'*num)\",\n \"def display(self):\\n print('')\\n print(\\\" ---------------------------------\\\")\\n counter = 0\\n for row in self.positions:\\n counter += 1\\n line = f'{counter}: |'\\n for space in row:\\n if isinstance(space, str):\\n line += f' {space} |'\\n else:\\n starter = ' '\\n ender = '|'\\n if space.team == 'white':\\n piece = stylize(space.symbol+' ', colored.fg(\\\"light_blue\\\"))\\n else:\\n piece = stylize(space.symbol+' ', colored.fg(\\\"light_red\\\"))\\n line += starter+piece+ender\\n print(line)\\n print(\\\" ---------------------------------\\\")\\n print(\\\" | A | B | C | D | E | F | G | H |\\\\n\\\")\",\n \"def generate(length):\\n alpha = map(chr, range(97, 123))\\n alpha.append(' ')\\n result = \\\"\\\"\\n for x in range(length):\\n result += alpha[random.randrange(0,27)]\\n return result\",\n \"def string(self):\\n text = \\\"\\\"\\n for char, qty in self.chars.items():\\n text += char * qty\\n return \\\"\\\".join(sorted(text))\",\n \"def display(self):\\n lines = []\\n lines.append(\\\" \\\".join([str(x + 1) for x in range(self.board.columns)]))\\n for row in self.board.board:\\n new_row = \\\"\\\".join([c for c in row])\\n new_row = new_row.replace(\\\"0\\\", \\\" \\\")\\n new_row = new_row.replace(\\\"1\\\", colored(f\\\"{PLAYER1_CHR} \\\", PLAYER1_COLOR))\\n new_row = new_row.replace(\\\"2\\\", colored(f\\\"{PLAYER2_CHR} \\\", PLAYER2_COLOR))\\n lines.append(new_row)\\n return \\\"\\\\n\\\".join(lines)\",\n \"def display_board(self):\\n print(f\\\"{BREAK_STRING}\\\\n\\\")\\n print(f\\\"STOCK \\\\t WASTE \\\\t\\\\t\\\\t\\\\tFOUNDATION\\\\n\\\")\\n print(f\\\"{self.stock}\\\\t{self.waste}\\\\t\\\\t\\\\t\\\\t{self.foundations['clubs']}\\\\t{self.foundations['diamonds']}\\\"\\n f\\\"\\\\t{self.foundations['hearts']}\\\\t{self.foundations['spades']}\\\\n\\\")\\n print(f\\\"\\\\nTABLEAU\\\\n\\\")\\n for num in range(1, 8):\\n print(f\\\"{num} {self.tableaus[num-1]}\\\")\\n print(f\\\"{BREAK_STRING}\\\\n\\\")\",\n \"def Display2(self, rain, humidity, lang):\\n if lang == 'eng':\\n string = rain + '% Rain\\\\n' + humidity + '% Humidity'\\n return string\\n else:\\n return 0\\n # Not Yet Supported\",\n \"def display(self):\\n for row in range(self.height):\\n for col in range(self.width):\\n char = '#' if self.pixels[row * self.width + col] else '.'\\n print(char, end='')\\n print()\\n print()\",\n \"def designer_pdf_viewer(h, word):\\n a = 97\\n alphabet = dict()\\n for i in range(len(h)):\\n alphabet.update({chr(a): h[i]})\\n a += 1\\n\\n max_height = 0\\n for i in word:\\n if i in alphabet.keys():\\n height = alphabet[i]\\n if height > max_height:\\n max_height = height\\n\\n return len(word) * 1 * max_height\",\n \"def render(state, c_choice, h_choice):\\r\\n\\r\\n chars = {\\r\\n 2: h_choice,\\r\\n 1: c_choice,\\r\\n 0: ' '\\r\\n }\\r\\n str_line = '--------------------------------------------------'\\r\\n\\r\\n print('\\\\n' + str_line)\\r\\n for row in state:\\r\\n for cell in row:\\r\\n #print(cell)\\r\\n symbol = chars[cell]\\r\\n print(f'| {symbol} |', end='')\\r\\n print('\\\\n' + str_line)\",\n \"def display_game(game,grid_size):\\n row_wall = '----'*(grid_size+1)\\n #first row\\n str_print = ' | '\\n for i in range(grid_size):\\n #samll number\\n if i < 9:\\n str_print = str_print+str(i+1)+' | '\\n else:\\n str_print = str_print+str(i+1)+'| '\\n print(str_print[:-1])\\n print(row_wall)\\n #2 to end row\\n for i in range(grid_size):\\n str_print = ALPHA[i]+' | '\\n for j in range(grid_size):\\n str_print = str_print+game[i*grid_size+j]+' | '\\n print(str_print[:-1])\\n print(row_wall)\",\n \"def __str__(self):\\n return textwrap.fill('{:064b}'.format(self.num), 8)\",\n \"def __str__(self):\\n table_data = [\\n ['', 'C', 'G', 'A', 'T'],\\n ['total', str(self.total['C']), str(self.total['G']), str(self.total['A']), str(self.total['T'])],\\n ['reverse half strand', str(self.reverse['C']), str(self.reverse['G']), str(self.reverse['A']),\\n str(self.reverse['T'])],\\n ['forward half strand', str(self.forward['C']), str(self.forward['G']), str(self.forward['A']),\\n str(self.forward['T'])]\\n ]\\n table = AsciiTable(table_data)\\n return \\\"Generation #{}\\\\n\\\".format(self.epoch) + table.table\",\n \"def render(self, s):\\n if s < self.num_states - 1:\\n a = np.zeros((self.height, self.width), dtype=np.unicode_)\\n # mark all locations as inaccessible first\\n a[:, :] = u'█'\\n # regular fields\\n for c in self.locs:\\n a[c[1], c[0]] = u'░'\\n # mark exits\\n for c in self.exitsP:\\n a[c[1], c[0]] = 'E'\\n for c in self.exitsN:\\n a[c[1], c[0]] = 'F'\\n # player and ghost location index\\n ip = s % len(self.locs)\\n ig = s // len(self.locs)\\n # player character\\n cp = self.locs[ip]\\n a[cp[1], cp[0]] = u'☺'\\n # ghost character\\n cg = self.locs[ig]\\n a[cg[1], cg[0]] = 'G'\\n a = [' '.join(row) for row in a]\\n a = '\\\\n'.join(reversed(a))\\n return a\\n else:\\n return 'Game over!'\",\n \"def helptext(self):\\n return \\\"\\\"\",\n \"def KingWen(font, hexid):\\n\\n ret = []\\n ch = 0x4DC0 + hexid - 1\\n exec 'char = u\\\\'\\\\u%4x\\\\'' % ch\\n lines = GetFontPixels(font, char, 'R', ' ')\\n ret.append(' ' * 8)\\n for line in lines[1::2]:\\n hex_chars = ''.join(line[1:-1])\\n ret.append(hex_chars[1:-1])\\n ret.append(' ' * 8)\\n return ret\",\n \"def __str__(self):\\n result = \\\"\\\"\\n for i in range(3):\\n for j in range(3):\\n if self.board[i][j] == 5:\\n result += \\\" x\\\"\\n elif self.board[i][j] == 7:\\n result += \\\" о\\\"\\n else:\\n result += \\\" #\\\"\\n result += \\\"\\\\n\\\"\\n return result\",\n \"def show(self):\\n print('\\\\n'+'\\\\n'.join([' '.join([['.', 'O', 'X'][self.board[3*j + i]]\\n for i in range(3)]) for j in range(3)]))\",\n \"def print_title(title):\\n print \\\"\\\\n\\\"+\\\"#\\\"*32+\\\"\\\\n# \\\"+title+\\\"\\\\n\\\"+\\\"#\\\"*32+\\\"\\\\n\\\"\",\n \"async def aesthetic(self, ctx, *, text):\\n out = \\\"\\\"\\n for char in text:\\n out += utils.fullwidth_transform.get(char, char)\\n await ctx.send(out)\",\n \"def n_char_generate(self,char,n):\\n return char*n\",\n \"def render(self):\\n for r in range(self.y_size):\\n line = ''\\n for c in range(self.x_size):\\n glyph = self.MAP_GLYPH_TABLE[self.grid_data[r][c]]\\n\\n # overwrite with player\\n if r == self.player_y and c == self.player_x:\\n glyph = self.PLAYER_GLYPH_TABLE[self.player_heading]\\n\\n line += glyph\\n print(line)\\n\\n print('\\\\n' * (20 - self.y_size))\",\n \"def _display_pattern(self):\\n # Technically we are supposed to look up this pattern in each language.\\n # Practically, it's the same in every language except Chinese, where the\\n # parentheses are full-width.\\n if self._disp_pattern is not None:\\n return self._disp_pattern\\n if self.distance(Language.get('zh')) <= 25:\\n self._disp_pattern = \\\"{0}（{1}）\\\"\\n else:\\n self._disp_pattern = \\\"{0} ({1})\\\"\\n return self._disp_pattern\",\n \"def get_char_echo(self) -> str:\\n ...\",\n \"def show_frame_info(self):\\n if self.font:\\n font_surface = self.font.render(\\\"Sample Number: \\\" + \\\\\\n str(self.data_row_num-self.num_lndmrks) + \\\\\\n \\\" of \\\" + \\\\\\n str(self.tot_rows - self.data_start_row), \\\\\\n False, self.GREEN)\\n pts = (self.screen_width / 3),3\\n self.screen.blit(font_surface,pts)\",\n \"def encode_this( self, charCode ):\\n values = []\\n # Character Bitmap\\n bmp = self.characters[charCode]\\n # Nbre of entry in the dictionnary = Character width\\n nbr_entry = bmp.width\\n # Number of bytes per entry (=font height / 8bits.)\\n # Vera_19 = 19 point height => 19/8 = 2.375 => must be coded on 3 bytes\\n nbr_bytes = ceil(self.max_height / 8)\\n if ((self.max_height%8) == 0): # Encoded font must always starts with 0b1, so if the font has 8 points height (or a multiple)\\n nbr_bytes += 1 # we have to create more room to receive that bit.\\n nbr_bits = nbr_bytes * 8\\n\\n # If we are coding 19 bits on X bytes, we will have to pad the left most\\n # bits with 1\\n pad_bits = nbr_bits - self.max_height\\n pad_value = 0\\n for i in range( pad_bits ):\\n # debug: print( 'shift by %i' % (nbr_bits-1-i) )\\n pad_value = pad_value + ( 1<<(nbr_bits-1-i))\\n\\n # Building the values\\n for iw in range( bmp.width ):\\n value = pad_value\\n # Debug: print( '---------------------------' )\\n for ih in range( bmp.height ):\\n _ih = bmp.height-1-ih # Must start by the \\\"bottom\\\" of the character\\n\\n # Align on the BottomLine\\n _height_extra_shift = self.max_height-bmp.height # The character may have 10 pixels height on a 19 point height font --> shift properly to the left!\\n\\n # Align on the BaseLine (if it applies)\\n # check if we do need to insert a descender space (in pixel) under the character\\n if ( len(self.descender_ordinals)>0 ) and not( self.char_has_descender( charCode ) ):\\n # move up the normal character baseline\\n _height_extra_shift = _height_extra_shift - self.ajusted_descender_size( charCode )\\n\\n # SPECIAL ALIGNMENT\\n # check for special alignment instruction (~,comma,\\\", etc)\\n _align = self.special_align_ordinals.get( charCode, None )\\n if _align == 'T':\\n # special alignment ON TOP -> reset shifting\\n _height_extra_shift = 0\\n elif _align == 'M':\\n # Special alignment IN MIDLLE of baseline and top\\n _height_extra_shift = _height_extra_shift - ( self.max_height-bmp.height-self.ajusted_descender_size( charCode ))//2\\n elif _align == 'B':\\n # Specoam alignment TO BOTTOM (so re-reset the _height_extra_shift on the BottomLine)\\n _height_extra_shift = self.max_height-bmp.height\\n\\n # TODO: replacing the descender feature by glyph.bitmap_top does not work properly\\n # for every characters\\n # _height_extra_shift = self.max_height - self.glyphs[ charCode ].bitmap_top\\n\\n # print( 'coding bit %i, extra shift of %i FOR VALUE %s' % (_ih, _height_extra_shift, bmp.pixel_at( iw, _ih )) )\\n if bmp.pixel_at( iw, _ih ):\\n value = value + (1 << (_ih+_height_extra_shift))\\n values.append( value )\\n\\n\\n return values\",\n \"def print_character(character):\\n for k, v in character.items():\\n print(str(k) + ': ' + str(v)) # Prints every key:value pair in character dictionary\\n if len(character) == 8:\\n print(\\\"Inventory:\\\" + str(character[7])) # If there is an inventory, then print it as well\",\n \"def display(values):\\n boxes = cross(rows, cols)\\n # This finds the max number of characters in each box and adds one.\\n # the max will be '123456789' so 9 + 1 = 10\\n width = 1+max(len(values[s])for s in boxes)\\n\\n # This will create 30 dashes separated with a \\\"+\\\" 3x\\n line = '+' .join(['-'*(width*3)]*3)\\n\\n # This builds the board. It iterates through [A1, A2, etc.] and then\\n # uses the .center method to center whatever value is in that cell.\\n # It adds a | in-between columns 3 & 6 otherwise an empty string.\\n # It prints a line if the row is C or F\\n for r in rows:\\n print(''.join(values[r+c].center(width)+('|' if c in '36' else '')\\n for c in cols))\\n if r in 'CF': print(line)\\n return\",\n \"def prescription(self):\\n prescription = \\\"\\\\n{0:>10}\\\\t{1:>10}\\\\t{2:>10}\\\\t{3:>10}\\\\n\\\".format(\\\"R\\\",\\\"Material\\\",\\\"d\\\",\\\"diameter\\\")\\n for surface in self.lensSurfaces():\\n prescription += \\\"{0:>10.2f}\\\\t{1:>10}\\\\t{2:>10.2f}\\\\t{3:>10.2f}\\\\n\\\".format(surface.R, str(surface.mat), surface.spacing, surface.diameter)\\n return prescription\",\n \"def print_all_letters(all_letters):\\n letters_msg = ' '.join(all_letters)\\n print(\\\"\\\\nLetters suggested so far:\\\")\\n print(letters_msg.upper())\",\n \"def get_background(self):\\n s = ''\\n for i in range(9, 11):\\n s += ' ' + str(self.dna[i])\\n return s\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6712049","0.61855406","0.6153275","0.61067873","0.6099449","0.6087882","0.6048082","0.5993004","0.5990485","0.5968788","0.5943066","0.5904404","0.58474976","0.58384794","0.58171725","0.5817065","0.5815135","0.57878107","0.57636875","0.576346","0.5755653","0.57465637","0.5744279","0.5740341","0.572631","0.5719523","0.5717393","0.5703657","0.5683052","0.5680681","0.56730884","0.5653822","0.5644273","0.56438214","0.5637591","0.5635627","0.5612918","0.56111276","0.56032777","0.5595207","0.55945164","0.5591549","0.5591213","0.55911577","0.5587661","0.55834395","0.5583319","0.55808014","0.5576008","0.5571199","0.5570152","0.55699307","0.5567285","0.55658174","0.55567074","0.5556279","0.5549114","0.5535192","0.55301714","0.55279225","0.5525998","0.552105","0.5514921","0.5511407","0.550259","0.5498962","0.54851997","0.54825026","0.54778105","0.5475279","0.54717344","0.5462483","0.5461626","0.5458188","0.5454309","0.545367","0.54463106","0.54461807","0.54430264","0.5439486","0.54317755","0.54314774","0.5418557","0.5413765","0.5410247","0.54091537","0.54060435","0.54048884","0.54027265","0.5390563","0.53879684","0.538447","0.5381975","0.5379342","0.5370237","0.5367795","0.5366733","0.5365832","0.5365624","0.53622216","0.53620124"],"string":"[\n \"0.6712049\",\n \"0.61855406\",\n \"0.6153275\",\n \"0.61067873\",\n \"0.6099449\",\n \"0.6087882\",\n \"0.6048082\",\n \"0.5993004\",\n \"0.5990485\",\n \"0.5968788\",\n \"0.5943066\",\n \"0.5904404\",\n \"0.58474976\",\n \"0.58384794\",\n \"0.58171725\",\n \"0.5817065\",\n \"0.5815135\",\n \"0.57878107\",\n \"0.57636875\",\n \"0.576346\",\n \"0.5755653\",\n \"0.57465637\",\n \"0.5744279\",\n \"0.5740341\",\n \"0.572631\",\n \"0.5719523\",\n \"0.5717393\",\n \"0.5703657\",\n \"0.5683052\",\n \"0.5680681\",\n \"0.56730884\",\n \"0.5653822\",\n \"0.5644273\",\n \"0.56438214\",\n \"0.5637591\",\n \"0.5635627\",\n \"0.5612918\",\n \"0.56111276\",\n \"0.56032777\",\n \"0.5595207\",\n \"0.55945164\",\n \"0.5591549\",\n \"0.5591213\",\n \"0.55911577\",\n \"0.5587661\",\n \"0.55834395\",\n \"0.5583319\",\n \"0.55808014\",\n \"0.5576008\",\n \"0.5571199\",\n \"0.5570152\",\n \"0.55699307\",\n \"0.5567285\",\n \"0.55658174\",\n \"0.55567074\",\n \"0.5556279\",\n \"0.5549114\",\n \"0.5535192\",\n \"0.55301714\",\n \"0.55279225\",\n \"0.5525998\",\n \"0.552105\",\n \"0.5514921\",\n \"0.5511407\",\n \"0.550259\",\n \"0.5498962\",\n \"0.54851997\",\n \"0.54825026\",\n \"0.54778105\",\n \"0.5475279\",\n \"0.54717344\",\n \"0.5462483\",\n \"0.5461626\",\n \"0.5458188\",\n \"0.5454309\",\n \"0.545367\",\n \"0.54463106\",\n \"0.54461807\",\n \"0.54430264\",\n \"0.5439486\",\n \"0.54317755\",\n \"0.54314774\",\n \"0.5418557\",\n \"0.5413765\",\n \"0.5410247\",\n \"0.54091537\",\n \"0.54060435\",\n \"0.54048884\",\n \"0.54027265\",\n \"0.5390563\",\n \"0.53879684\",\n \"0.538447\",\n \"0.5381975\",\n \"0.5379342\",\n \"0.5370237\",\n \"0.5367795\",\n \"0.5366733\",\n \"0.5365832\",\n \"0.5365624\",\n \"0.53622216\",\n \"0.53620124\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77493,"cells":{"query":{"kind":"string","value":"Measures voltage in auto range and auto resolution returns the voltage in float"},"document":{"kind":"string","value":"def hp34401a_read_voltage(hp_meter):\n hp_meter.write(\"MEAS:VOLT:DC? DEF,DEF\")\n return float(hp_meter.read())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def ac_voltage(self):\n return float(self.get_ac_voltage())","def meas_voltage(instrument, v_range=10, resolution=0.003):\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))","def get_voltage(self):\n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\n voltage = float(re.findall(pattern,summary).pop())\n return voltage","def get_voltage(self):\n status = self.get_status_response()\n volts = status[20] + (status[21] * 0x100) + (status[22] * 0x10000) + (status[23] * 0x1000000)\n volts = float(volts)\n volts /= (1000.0 * 1000.0)\n return volts\n #end get_voltage","def hp34401a_read_voltage_rng_res(hp_meter , v_range, v_resolution):\n hp_meter.write(\"MEAS:VOLT:DC? \" + str(v_range) + \" , \" + str(v_resolution))\n return float(hp_meter.read())","def reference_voltage(self) -> float:\n return self._ref_voltage","def read_actual_voltage(self):\n function_string = 'V' + self.output + 'O?'\n value_string = self.scpi_comm(function_string)\n LOGGER.warn(value_string)\n time.sleep(0.1) # This might only be necessary on LAN interface\n try:\n value = float(value_string.replace('V', ''))\n except ValueError:\n value = -999999\n return value","def read_set_voltage(self):\n function_string = 'V' + self.output + '?'\n value_string = self.scpi_comm(function_string)\n try:\n value = float(value_string.replace('V' + self.output, ''))\n except ValueError:\n value = -9997\n return value","def voltage(self):\n\t\treturn self._voltage","def get_voltage(self):\n print(\"voici le voltage de la batterie\")","def v(self):\n\n # TODO This translation formula works, but needs simplified.\n\n # PWM duration can go from 0 to 4095 with 4095 representing max rpm\n# print(\"MuleBot.v MuleBot.dcMotorPWMDurationLeft:\", MuleBot.dcMotorPWMDurationLeft)\n speed_percentage = float(MuleBot.dcMotorPWMDurationLeft) / 4095.0\n# print(\"speed_percentage: \", speed_percentage)\n\n rpm = speed_percentage * self.motorMaxRPM\n# print(\"rpm: \", rpm)\n\n secondsPerMinute = 60\n revs_per_second = rpm / secondsPerMinute\n# print(\"--revs_per_second\", revs_per_second)\n\n inches_per_rev = 2.0 * math.pi * MuleBot.WHEEL_RADIUS\n INCHES_PER_METER = 39.3701\n meters_per_rev = inches_per_rev / INCHES_PER_METER\n# print(\"--meters_per_rev\", meters_per_rev)\n\n meters_per_second = meters_per_rev * revs_per_second\n\n# print(\"--meters_per_second: \", meters_per_second)\n return meters_per_second","def read_voltage(self):\n self.write(':FETC?')\n msg = self.read()\n #print ('dmm msg = ', msg)\n v = msg.split(',')[0].rstrip('NVDC').strip()\n if v[-1] == 'R':\n return float(v[:-1])\n else:\n return float(v)","def voltage(self):\n return self.outputValue()","def sm_measure_voltage(self,num_readings=1):\n self.sm.set_measurement_function(\"VOLTAGE\")\n self.sm.format_readings(\"VOLTAGE\")\n return average(self.sm.take_measurement(num_readings))","def get_voltage(self):\n self._raise_not_implemented()","def outputValue(self):\n string = self.ask('od;E;')\n string = re.sub(r'^(NDCV|NDCA|EDCV)', r'', string)\n self.notify('voltage', float(string))\n return float(string)","def dmm_measure_voltage(self,range=None,res=None):\n if range is None:\n return self.dmm.measure_voltage()\n elif res is None:\n return self.dmm.measure_voltage(volt_range=range)\n else:\n return self.dmm.measure_voltage(volt_range=range,resolution=res)","def calibV(self):\n # clear buffer in case of errors\n self.flushInput()\n \n if (self.model == 'GDS'):\n self.write(':CHAN'+str(ch)+':SCAL?\\n')\n # returns V/div, turn it into multiplicative factor\n # between digitizer and actual volts\n vmult = float(self.readline()) * 10./255.\n # GDS includes vertical offset in the data returned.\n voff = 0.\n elif (self.model == 'TDS'):\n self.write('WFMPre:YMUlt?\\n')\n # formula I am using later is from TDS manual, so this\n # is straightforward.\n vmult = float(self.readline())\n self.write('WFMPre:YOFf?\\n')\n voff = float(self.readline())\n \n # clear buffer in case of errors\n self.flushInput()\n\n return (vmult, voff)","def get_uv_value(self):\n return float(int(self.data[2]) / 10)","def calc_out_voltage(self, input_photocurrent_file):\n pass","def fetch(self):\n return read_voltage()","def proximity_voltage(x):\n y = VOLTAGE_TASK['value']\n rate = 1 / (y * 0.02)\n diff = abs(y - x)\n return 1 - rate * diff","def get_meas_time_voltage(instrument):\n return float(instrument.query('SENSE:VOLT:DC:APER?'))","def raw_ldr_voltage(self) -> int:\n self._update_analog_value_cache()\n return self.analog_cache.ldr_voltage","def voltage_conversion(self):\r\n\t\tvoltage = ((self.data[0] * 256 + self.data[1]) / 65536.0) * 5.0\r\n\t\t\r\n\t\treturn {'v' : voltage}","def voltage(self) -> int:\n return int(self._device_info[\"CurrentVoltage\"])","def get_resistance(self):\n adc = ADC(self.pin)\n value = adc.read()\n if value == 0:\n return -1\n\n return (4095./value - 1.) * self.RLOAD # ESP32 maksimi, ESP8266:lle arvo on 1023","def voltage(analog_pin):\n return \"%0.2f\" % inVolts(analogRead(analog_pin))","def get_voltage(self):\n return self.environment.get_voltage(self.neuron_id)","def get_fan_speed_volts(self):\n return self.__fan_speed_raw * PSCU.FAN_VREF","def get_setVoltage(self):\n self.read(\":VOLT?\")","def vol(self) -> float:\n return float(self.tsdf.pct_change().std() * np.sqrt(self.periods_in_a_year))","def get_volt(data):\n volt = (data * 5.0) / 255\n #volt = round(volt, 1)\n return volt","def get_vrms(self, ch: int) -> float:\n cmd = \":measure:vrms? cycle,ac,channel{0}\".format(ch)\n return float(self.query(cmd))","def present_input_voltage(self):\n return self._read(MX_PRESENT_INPUT_VOLTAGE)","def get_voltage_and_current(self):\n return self.voltage_and_current","def value(self) -> float:","def get_voltage(self, c):\n if 'slot_number' in c.keys():\n slot_number = c['slot_number'] \n yield self.open_comm(c, slot_number)\n yield self.write(c, 'VOLT?')\n voltage = yield self.read(c)\n yield self.close_comm(c, slot_number)\n \n try:\n voltage = float(voltage)\n except(ValueError):\n old_voltage = voltage\n voltage = ''.join([i for i in voltage if (i.isdigit() or i == '-' or i == '.')])\n voltage = float(voltage)\n print ('The value ' + old_voltage + ' was returned by the SIM928, this value was automatically converted to ' + voltage + '.')\n \n value = voltage * units.V\n \n # try:\n # voltage = yield self.query(c,\n # slot_number, \n # \"VOLT?\")\n # try:\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # try:\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # try:\n # value = voltage * units.V\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # value = voltage * units.V\n # else:\n # raise ValueError(self.no_selection_msg())\n returnValue(value)","def toVoltage(self, range=None, turnOn=False):\n self.write('F1;E')\n c = None\n if range == 1 or range == '10mV':\n c = 'R2'\n elif range == 2 or range == '100mV':\n c = 'R3'\n elif range == 3 or range == '1V':\n c = 'R4'\n elif range == 4 or range == '10V':\n c = 'R5'\n elif range == 5 or range == '30V':\n c = 'R6'\n if c is not None:\n self.write(c + ';E')\n if turnOn:\n self.turnOn()","def read_vref(self) -> float:","def getMeasurement(self):\n if(ADConverterSettings.useRealAD):\n print \"Real AD not activated\"\n #return self.adcdac.read_adc_voltage(1)\n else:\n return self.__readMeasurementFromFile()","def raw_zener_voltage(self) -> int:\n self._update_analog_value_cache()\n return self.analog_cache.zener_voltage","def get_channel_v_unit(self)->float:\n return self.__channel_v_unit","def vol(x):\r\n return pi*(topdia(x)/2000.)**2 * length (x)","def temperature() -> float:","def to_float(self) -> float:\n return (1.0 * self.as_int) / sensor_fixed_point_conversion","def read_core_vbat(self) -> float:","def hp3458a_read_voltage(hp_meter):\n hp_meter.write(\"TARM SGL\")\n return float(hp_meter.read())","def Take_Voltage_Measurement(self):\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n nidaq.DAQmxClearTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def Take_Voltage_Measurement(self):\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n nidaq.DAQmxClearTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def calc_voltage(self) -> (Volt, None):\n power: ComType = complex(0)\n power_unit: str = ''\n amp: NumType = 0\n amp_unit: str = ''\n ohm: ComType = complex(0)\n ohm_unit: str = ''\n\n if self._amp_exists and self._ohm_exists:\n if hasattr(self._obj1, 'amps'):\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\n elif hasattr(self._obj1, 'ohm'):\n ohm, ohm_unit = self._obj1.ohm, self._obj1.ohm_unit\n if hasattr(self._obj2, 'amps'):\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\n elif hasattr(self._obj2, 'ohm'):\n ohm, ohm_unit = self._obj2.ohm, self._obj2.ohm_unit\n volt = amp * ohm\n volt_unit: str = f'Volts ({amp_unit}*{ohm_unit})'\n\n elif self._amp_exists and self._power_exists:\n if hasattr(self._obj1, 'amps'):\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\n elif hasattr(self._obj1, 'power'):\n power, power_unit = self._obj1.power, self._obj1.power_unit\n if hasattr(self._obj2, 'amps'):\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\n elif hasattr(self._obj2, 'power'):\n power, power_unit = self._obj2.power, self._obj2.power_unit\n volt = power / amp\n volt_unit: str = f'Volts ({power_unit}/{amp_unit})'\n\n else:\n return None\n\n return Volt(volt, volt_unit, self._obj1.frequency, self._obj1.freq_unit)","def voltage(self, voltage=None):\n if voltage is not None:\n # set output voltage on all phases\n # self.ts.log_debug('voltage: %s, type: %s' % (voltage, type(voltage)))\n if type(voltage) is not list and type(voltage) is not tuple:\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\n' % (voltage, voltage, voltage))\n v1 = voltage\n v2 = voltage\n v3 = voltage\n else:\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\n' % (voltage[0], voltage[0], voltage[0])) # use the first value in the 3 phase list\n v1 = voltage[0]\n v2 = voltage[0]\n v3 = voltage[0]","def get_fan_set_point_volts(self):\n return self.__fan_set_point_raw * PSCU.FAN_VREF","def _voltage_get(self):\n #print \"latest=%s\" % self.connection.latestResults\n if self.channelNumber in self.connection.latestResults:\n return self.connection.latestResults[self.channelNumber]\n else:\n return -999","def ret_vol_ratio(self) -> float:\n return self.geo_ret / self.vol","def _voltage_get(self, channelNumber):\n #print \"latest=%s\" % self.connection.latestResults\n if channelNumber in self.connection.latestResults:\n return self.connection.latestResults[channelNumber]\n else:\n return scipy.NaN","def get_voltage_rating(self):\n summary = self.get_version_summary()\n pattern = '\\$.*? .*? .*? .*? .*? (.*?) .*? .*? .*? \\r\\n' \n rating = int(re.findall(pattern,summary).pop())\n return rating","def get_resistance(self):\n adc = ADC(self.pin)\n value = adc.read()\n if value == 0:\n return -1\n\n return (4095./value - 1.) * self.RLOAD","def get_ambient_light(self, ldr_voltage: Optional[int] = None) -> float:\n if ldr_voltage is None:\n ldr_voltage = self.raw_ldr_voltage\n\n # TODO: this conversion algorithm is straight from the manual but it seems odd.\n # It goes \"to infinity\" as ldr_voltage nears 1023 (hence the clamp, I guess)\n # Clarify.\n if ldr_voltage > 1022:\n ldr_voltage = 1022\n if ldr_voltage < 1:\n ldr_voltage = 1\n\n return self.ldr_pull_up_resistance / ((1023 / ldr_voltage) - 1)","def volts(self) -> NumType:\n return self._volt","def get_vsolar(self):\n return self.read_register(4098, 1, 3)","def calc_V(A):\n return 1. / calc_rV(A)","def value(self) -> float:\n ...","def value(self) -> float:\n ...","def value(self) -> float:\n ...","def ac_voltage(self, ac):\n self.set_ac_voltage(f'{ac}' if self._is_min_max(ac) else f'{ac} V')","def get_perfect_voltage_for_a_day(start, freq):\n surface_tilt = _munich_location.latitude\n surface_azimuth = 180 # pointing south\n date_range = pd.date_range(start=start,\n end=start + dt.timedelta(\n hours=23, minutes=59, seconds=59),\n freq=freq, tz=_munich_location.tz)\n\n clearsky_estimations = _munich_location.get_clearsky(date_range)\n dni_extra = irradiance.extraradiation(date_range)\n solar_position = solarposition.get_solarposition(\n date_range, _munich_location.latitude, _munich_location.longitude)\n airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'])\n pressure = atmosphere.alt2pres(_munich_location.altitude)\n am_abs = atmosphere.absoluteairmass(airmass, pressure)\n\n total_irrad = irradiance.total_irrad(surface_tilt,\n surface_azimuth,\n solar_position['apparent_zenith'],\n solar_position['azimuth'],\n clearsky_estimations['dni'],\n clearsky_estimations['ghi'],\n clearsky_estimations['dhi'],\n dni_extra=dni_extra,\n model='haydavies')\n\n temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], 0, 15)\n aoi = irradiance.aoi(surface_tilt, surface_azimuth,\n solar_position['apparent_zenith'],\n solar_position['azimuth'])\n # add 0.0001 to avoid np.log(0) and warnings about that\n effective_irradiance = pvsystem.sapm_effective_irradiance(\n total_irrad['poa_direct'], total_irrad['poa_diffuse'], am_abs,\n aoi, _sandia_module) + 0.0001\n sapm = pvsystem.sapm(effective_irradiance, temps['temp_cell'],\n _sandia_module)\n\n return sapm['p_mp'] * _module_count","def charging_current_and_voltage(self):\n done, data = self._request('GG')\n if done:\n milliamps = float(data[0])\n millivolts = float(data[1])\n return {\n 'amps': float(milliamps) / 1000 if milliamps > 0 else 0.0,\n 'volts': float(millivolts) / 1000 if millivolts > 0 else 0.0\n }\n\n raise EvseError","def get_voltage_no_load(self):\n return self.__voltage_no_load","def getavgvel(self):\n if self.total_time:\n return (6.28)/(self.total_time)","def estimated_voltage(clocks, clock_threshold, voltage_scale):\n return [1 + ((clock > clock_threshold) * (1e-3 * voltage_scale * (clock-clock_threshold))) for clock in clocks]","def get_vbat(self):\n return self.read_register(4096, 1, 3)","def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\n self.min_volts = min_volts\n self.max_volts = max_volts\n self.sample_rate = sample_rate\n self.num_samples = num_samples\n self.channel = device + \"/\" + AIchan\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\"\",\n DAQmx_Val_NRSE,\n float64(self.min_volts),float64(self.max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))","def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\n self.min_volts = min_volts\n self.max_volts = max_volts\n self.sample_rate = sample_rate\n self.num_samples = num_samples\n self.channel = device + \"/\" + AIchan\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\"\",\n DAQmx_Val_NRSE,\n float64(self.min_volts),float64(self.max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))","def last_voltage(self):\n return self._last_voltage","def get_voltage(self, i_sup, t, *args, **kwargs):\r\n raise NotImplementedError","def angular_velocity(self):\n trig = gyro_trigger_mode.GET_ANGULAR_VELOCITY_TRIGGER_READ\n if self.__trigger == trig:\n self.read_and_update_angle()\n\n adjusted = (self.__angle - self.__offset)\n if self.__factorSet:\n return adjusted / self.__degPerSecondFactor\n return adjusted","def get_pump_set_point_volts(self):\n return self.__pump_set_point_raw * PSCU.PUMP_VREF","def get_position_volts(self):\n return self.__position_raw * PSCU.POSITION_VREF","def get_vmax(self, ch_id: int) -> float:\n return float(self.query(':measure:vmax? channel{}'.format(ch_id)))","def get_value(\n self\n ) -> float:\n\n return self.average","def get_value(self, xval: float, wvtype: str, wv_id: int, precision=6) -> float:\n fmt = ':measure:vtime? {0:.%dg},{1}{2}' % precision\n return float(self.query(fmt.format(xval, wvtype, wv_id)))","def convert_fuelconsumption(self, event):\n try:\n #Compare other unit to one unit(liters/100 kilometer)\n current_value, current_unit = float(\"0\" + str(self.v.get())), self.dropdown.get()\n if current_value != 0:\n unit_comp = {\"car(2014 US Average)\": 9.260417, \"gallon(UK)/100 miles\": 2.824809, \"gallon(US)/100 miles\": 2.352146, \"kilometer/liter\": 100.0 / (current_value ** 2), \"liters/100 kilometer\": 1.0, \"liters/meter\": 100000.0, \"miles/gallon(UK)\": 282.480936 / (current_value ** 2), \"miles/gallon(US)\": 235.214583 / (current_value ** 2)}\n else: #In case current_value == 0, it will error coz number division by zero.\n unit_comp = {\"car(2014 US Average)\": 1.0, \"gallon(UK)/100 miles\": 1.0, \"gallon(US)/100 miles\": 1.0, \"kilometer/liter\": 1.0, \"liters/100 kilometer\": 1.0, \"liters/meter\": 1.0, \"miles/gallon(UK)\": 1.0, \"miles/gallon(US)\": 1.0}\n value_comp, printer = current_value * unit_comp[current_unit], \"\"\n unit_list = sorted(unit_comp.keys())\n unit_list.remove(current_unit)\n for unit in unit_list:\n printer += \"To %s \" % unit + \" \" * (max([len(i) for i in unit_list]) - len(unit)) + str(value_comp / unit_comp[unit]) + [\"\", \"\\n\"][unit_list[-1] != unit]\n except ValueError: #In case user enter the other type of value, not Int or Float\n printer = \"Value is invalid.\"\n self.print_text(printer)","def valor(self):\n nbits = len(self.gens)\n dec = int(self.gens,2)\n real = self.min+dec*((self.max-self.min)/(math.pow(2,nbits)-1))\n return real","def get_fullscale(self, ch_id: int) -> float:\n return float(self.query(':channel{0}:range?'.format(ch_id)))","def get_max_cell_voltage(self): \n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. .*? .*? .*? .*? (.*?) . .*? .*? . . . .*?'\n maxv = float(re.findall(pattern,summary).pop())\n return maxv","def get_vcond(lambdam, taum):\n return 2 * lambdam / taum","def vratio(self):\n return self.run_command('vratio')[0]","def convert_volts(self,data,places):\n volts = (data * self.max) / float(255)\n volts = round(volts,places)\n return volts","def to_real(self):\n return _uhd_swig.sensor_value_t_to_real(self)","def READ_PRESSURE_SENSOR():\n return 15.246","def get(self) -> float:\n ...","def calcPfePres(voltage: float):\n # V → Torr\n exponent = 1.667 * voltage - 11.46\n pres = 10**exponent\n return pres","def E2V(E):\n# for energy in mev returns velocity in m/s\n return sqrt(E/5.227e-6)","def measure_vdc(self, rng = 'DEF', res = 'DEF', unit = 'V', samp = 1):\n self.configure_vdc(rng, res, unit)\n self.write_to_serial(':samp:coun ' + str(samp))\n if samp == 1:\n return float(self.query_serial('read?'))\n else:\n samples = self.query_serial('read?').split(',')\n sampleresult = []\n for sample in samples:\n sampleresult.append(float(sample))\n return sampleresult","def _update_voltageSetpoint_fromGUI(self):\n self.voltageSetpoint = self.widgets['p_setpoint'].value()/self.gain","def vega(self, spot_price, implied_vol):\n option = self.Option.option_ql\n process = self.evaluation.get_bsmprocess_cnstvol(self.rf, spot_price, implied_vol)\n engine = util.get_engine(process, self.engineType)\n option.setPricingEngine(engine)\n vega = option.vega()/100.0\n # price1 = self.option_price(spot_price, implied_vol)\n # price2 = self.option_price(spot_price, implied_vol + 0.01)\n # vega = price2 - price1\n return vega","def getSingleMeasurement(self, ch = \"CH1\"):\n\t\tcounter = 1\n\t\t# added try except block to take care of times when get_waveform() has a problem.\n\t\t# this seems to take a long time. Calling for data from oscilloscope is slow... May need to\n\t\t# Take many samples at once, receive data all at once.\n\t\twhile True:\n\t\t\ttry:\t\t\n\t\t\t\twaveform = self.osc.get_waveform(source = ch, start = 0, stop = 0)\n\t\t\t\tbreak\n\t\t\texcept:\n\t\t\t\tprint(\"Retry: \" + str(counter))\n\t\t\t\tcounter += 1\n\t\tfor x,y in waveform:\n\t\t\tvoltage = y\n\t\treturn voltage","def VACF(df,conversion = \"x\"):\n #conversion from pixels to micrometers\n if conversion == \"y\":\n df = df/1200*633\n else:\n df = df/1600*844\n #computes the velocity in one direction between the frames\n dif = pd.DataFrame()\n\n for i in range(1,len(df.T)):\n dif[i-1] = velocity(df[i-1],df[i])\n vel = []\n for i in range(len(dif)):\n vel.append(tidynamics.acf(dif.T[i]))\n\n #return the velocities in array\n return np.array(vel)","def meas_resistance(instrument):\n return float(instrument.query('MEAS:RESistance?'))"],"string":"[\n \"def ac_voltage(self):\\n return float(self.get_ac_voltage())\",\n \"def meas_voltage(instrument, v_range=10, resolution=0.003):\\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))\",\n \"def get_voltage(self):\\n summary = \\\" \\\".join(self.get_summary().split())\\n pattern = '\\\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\\n voltage = float(re.findall(pattern,summary).pop())\\n return voltage\",\n \"def get_voltage(self):\\n status = self.get_status_response()\\n volts = status[20] + (status[21] * 0x100) + (status[22] * 0x10000) + (status[23] * 0x1000000)\\n volts = float(volts)\\n volts /= (1000.0 * 1000.0)\\n return volts\\n #end get_voltage\",\n \"def hp34401a_read_voltage_rng_res(hp_meter , v_range, v_resolution):\\n hp_meter.write(\\\"MEAS:VOLT:DC? \\\" + str(v_range) + \\\" , \\\" + str(v_resolution))\\n return float(hp_meter.read())\",\n \"def reference_voltage(self) -> float:\\n return self._ref_voltage\",\n \"def read_actual_voltage(self):\\n function_string = 'V' + self.output + 'O?'\\n value_string = self.scpi_comm(function_string)\\n LOGGER.warn(value_string)\\n time.sleep(0.1) # This might only be necessary on LAN interface\\n try:\\n value = float(value_string.replace('V', ''))\\n except ValueError:\\n value = -999999\\n return value\",\n \"def read_set_voltage(self):\\n function_string = 'V' + self.output + '?'\\n value_string = self.scpi_comm(function_string)\\n try:\\n value = float(value_string.replace('V' + self.output, ''))\\n except ValueError:\\n value = -9997\\n return value\",\n \"def voltage(self):\\n\\t\\treturn self._voltage\",\n \"def get_voltage(self):\\n print(\\\"voici le voltage de la batterie\\\")\",\n \"def v(self):\\n\\n # TODO This translation formula works, but needs simplified.\\n\\n # PWM duration can go from 0 to 4095 with 4095 representing max rpm\\n# print(\\\"MuleBot.v MuleBot.dcMotorPWMDurationLeft:\\\", MuleBot.dcMotorPWMDurationLeft)\\n speed_percentage = float(MuleBot.dcMotorPWMDurationLeft) / 4095.0\\n# print(\\\"speed_percentage: \\\", speed_percentage)\\n\\n rpm = speed_percentage * self.motorMaxRPM\\n# print(\\\"rpm: \\\", rpm)\\n\\n secondsPerMinute = 60\\n revs_per_second = rpm / secondsPerMinute\\n# print(\\\"--revs_per_second\\\", revs_per_second)\\n\\n inches_per_rev = 2.0 * math.pi * MuleBot.WHEEL_RADIUS\\n INCHES_PER_METER = 39.3701\\n meters_per_rev = inches_per_rev / INCHES_PER_METER\\n# print(\\\"--meters_per_rev\\\", meters_per_rev)\\n\\n meters_per_second = meters_per_rev * revs_per_second\\n\\n# print(\\\"--meters_per_second: \\\", meters_per_second)\\n return meters_per_second\",\n \"def read_voltage(self):\\n self.write(':FETC?')\\n msg = self.read()\\n #print ('dmm msg = ', msg)\\n v = msg.split(',')[0].rstrip('NVDC').strip()\\n if v[-1] == 'R':\\n return float(v[:-1])\\n else:\\n return float(v)\",\n \"def voltage(self):\\n return self.outputValue()\",\n \"def sm_measure_voltage(self,num_readings=1):\\n self.sm.set_measurement_function(\\\"VOLTAGE\\\")\\n self.sm.format_readings(\\\"VOLTAGE\\\")\\n return average(self.sm.take_measurement(num_readings))\",\n \"def get_voltage(self):\\n self._raise_not_implemented()\",\n \"def outputValue(self):\\n string = self.ask('od;E;')\\n string = re.sub(r'^(NDCV|NDCA|EDCV)', r'', string)\\n self.notify('voltage', float(string))\\n return float(string)\",\n \"def dmm_measure_voltage(self,range=None,res=None):\\n if range is None:\\n return self.dmm.measure_voltage()\\n elif res is None:\\n return self.dmm.measure_voltage(volt_range=range)\\n else:\\n return self.dmm.measure_voltage(volt_range=range,resolution=res)\",\n \"def calibV(self):\\n # clear buffer in case of errors\\n self.flushInput()\\n \\n if (self.model == 'GDS'):\\n self.write(':CHAN'+str(ch)+':SCAL?\\\\n')\\n # returns V/div, turn it into multiplicative factor\\n # between digitizer and actual volts\\n vmult = float(self.readline()) * 10./255.\\n # GDS includes vertical offset in the data returned.\\n voff = 0.\\n elif (self.model == 'TDS'):\\n self.write('WFMPre:YMUlt?\\\\n')\\n # formula I am using later is from TDS manual, so this\\n # is straightforward.\\n vmult = float(self.readline())\\n self.write('WFMPre:YOFf?\\\\n')\\n voff = float(self.readline())\\n \\n # clear buffer in case of errors\\n self.flushInput()\\n\\n return (vmult, voff)\",\n \"def get_uv_value(self):\\n return float(int(self.data[2]) / 10)\",\n \"def calc_out_voltage(self, input_photocurrent_file):\\n pass\",\n \"def fetch(self):\\n return read_voltage()\",\n \"def proximity_voltage(x):\\n y = VOLTAGE_TASK['value']\\n rate = 1 / (y * 0.02)\\n diff = abs(y - x)\\n return 1 - rate * diff\",\n \"def get_meas_time_voltage(instrument):\\n return float(instrument.query('SENSE:VOLT:DC:APER?'))\",\n \"def raw_ldr_voltage(self) -> int:\\n self._update_analog_value_cache()\\n return self.analog_cache.ldr_voltage\",\n \"def voltage_conversion(self):\\r\\n\\t\\tvoltage = ((self.data[0] * 256 + self.data[1]) / 65536.0) * 5.0\\r\\n\\t\\t\\r\\n\\t\\treturn {'v' : voltage}\",\n \"def voltage(self) -> int:\\n return int(self._device_info[\\\"CurrentVoltage\\\"])\",\n \"def get_resistance(self):\\n adc = ADC(self.pin)\\n value = adc.read()\\n if value == 0:\\n return -1\\n\\n return (4095./value - 1.) * self.RLOAD # ESP32 maksimi, ESP8266:lle arvo on 1023\",\n \"def voltage(analog_pin):\\n return \\\"%0.2f\\\" % inVolts(analogRead(analog_pin))\",\n \"def get_voltage(self):\\n return self.environment.get_voltage(self.neuron_id)\",\n \"def get_fan_speed_volts(self):\\n return self.__fan_speed_raw * PSCU.FAN_VREF\",\n \"def get_setVoltage(self):\\n self.read(\\\":VOLT?\\\")\",\n \"def vol(self) -> float:\\n return float(self.tsdf.pct_change().std() * np.sqrt(self.periods_in_a_year))\",\n \"def get_volt(data):\\n volt = (data * 5.0) / 255\\n #volt = round(volt, 1)\\n return volt\",\n \"def get_vrms(self, ch: int) -> float:\\n cmd = \\\":measure:vrms? cycle,ac,channel{0}\\\".format(ch)\\n return float(self.query(cmd))\",\n \"def present_input_voltage(self):\\n return self._read(MX_PRESENT_INPUT_VOLTAGE)\",\n \"def get_voltage_and_current(self):\\n return self.voltage_and_current\",\n \"def value(self) -> float:\",\n \"def get_voltage(self, c):\\n if 'slot_number' in c.keys():\\n slot_number = c['slot_number'] \\n yield self.open_comm(c, slot_number)\\n yield self.write(c, 'VOLT?')\\n voltage = yield self.read(c)\\n yield self.close_comm(c, slot_number)\\n \\n try:\\n voltage = float(voltage)\\n except(ValueError):\\n old_voltage = voltage\\n voltage = ''.join([i for i in voltage if (i.isdigit() or i == '-' or i == '.')])\\n voltage = float(voltage)\\n print ('The value ' + old_voltage + ' was returned by the SIM928, this value was automatically converted to ' + voltage + '.')\\n \\n value = voltage * units.V\\n \\n # try:\\n # voltage = yield self.query(c,\\n # slot_number, \\n # \\\"VOLT?\\\")\\n # try:\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # try:\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # try:\\n # value = voltage * units.V\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # value = voltage * units.V\\n # else:\\n # raise ValueError(self.no_selection_msg())\\n returnValue(value)\",\n \"def toVoltage(self, range=None, turnOn=False):\\n self.write('F1;E')\\n c = None\\n if range == 1 or range == '10mV':\\n c = 'R2'\\n elif range == 2 or range == '100mV':\\n c = 'R3'\\n elif range == 3 or range == '1V':\\n c = 'R4'\\n elif range == 4 or range == '10V':\\n c = 'R5'\\n elif range == 5 or range == '30V':\\n c = 'R6'\\n if c is not None:\\n self.write(c + ';E')\\n if turnOn:\\n self.turnOn()\",\n \"def read_vref(self) -> float:\",\n \"def getMeasurement(self):\\n if(ADConverterSettings.useRealAD):\\n print \\\"Real AD not activated\\\"\\n #return self.adcdac.read_adc_voltage(1)\\n else:\\n return self.__readMeasurementFromFile()\",\n \"def raw_zener_voltage(self) -> int:\\n self._update_analog_value_cache()\\n return self.analog_cache.zener_voltage\",\n \"def get_channel_v_unit(self)->float:\\n return self.__channel_v_unit\",\n \"def vol(x):\\r\\n return pi*(topdia(x)/2000.)**2 * length (x)\",\n \"def temperature() -> float:\",\n \"def to_float(self) -> float:\\n return (1.0 * self.as_int) / sensor_fixed_point_conversion\",\n \"def read_core_vbat(self) -> float:\",\n \"def hp3458a_read_voltage(hp_meter):\\n hp_meter.write(\\\"TARM SGL\\\")\\n return float(hp_meter.read())\",\n \"def Take_Voltage_Measurement(self):\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n nidaq.DAQmxClearTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def Take_Voltage_Measurement(self):\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n nidaq.DAQmxClearTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def calc_voltage(self) -> (Volt, None):\\n power: ComType = complex(0)\\n power_unit: str = ''\\n amp: NumType = 0\\n amp_unit: str = ''\\n ohm: ComType = complex(0)\\n ohm_unit: str = ''\\n\\n if self._amp_exists and self._ohm_exists:\\n if hasattr(self._obj1, 'amps'):\\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\\n elif hasattr(self._obj1, 'ohm'):\\n ohm, ohm_unit = self._obj1.ohm, self._obj1.ohm_unit\\n if hasattr(self._obj2, 'amps'):\\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\\n elif hasattr(self._obj2, 'ohm'):\\n ohm, ohm_unit = self._obj2.ohm, self._obj2.ohm_unit\\n volt = amp * ohm\\n volt_unit: str = f'Volts ({amp_unit}*{ohm_unit})'\\n\\n elif self._amp_exists and self._power_exists:\\n if hasattr(self._obj1, 'amps'):\\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\\n elif hasattr(self._obj1, 'power'):\\n power, power_unit = self._obj1.power, self._obj1.power_unit\\n if hasattr(self._obj2, 'amps'):\\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\\n elif hasattr(self._obj2, 'power'):\\n power, power_unit = self._obj2.power, self._obj2.power_unit\\n volt = power / amp\\n volt_unit: str = f'Volts ({power_unit}/{amp_unit})'\\n\\n else:\\n return None\\n\\n return Volt(volt, volt_unit, self._obj1.frequency, self._obj1.freq_unit)\",\n \"def voltage(self, voltage=None):\\n if voltage is not None:\\n # set output voltage on all phases\\n # self.ts.log_debug('voltage: %s, type: %s' % (voltage, type(voltage)))\\n if type(voltage) is not list and type(voltage) is not tuple:\\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\\\n' % (voltage, voltage, voltage))\\n v1 = voltage\\n v2 = voltage\\n v3 = voltage\\n else:\\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\\\n' % (voltage[0], voltage[0], voltage[0])) # use the first value in the 3 phase list\\n v1 = voltage[0]\\n v2 = voltage[0]\\n v3 = voltage[0]\",\n \"def get_fan_set_point_volts(self):\\n return self.__fan_set_point_raw * PSCU.FAN_VREF\",\n \"def _voltage_get(self):\\n #print \\\"latest=%s\\\" % self.connection.latestResults\\n if self.channelNumber in self.connection.latestResults:\\n return self.connection.latestResults[self.channelNumber]\\n else:\\n return -999\",\n \"def ret_vol_ratio(self) -> float:\\n return self.geo_ret / self.vol\",\n \"def _voltage_get(self, channelNumber):\\n #print \\\"latest=%s\\\" % self.connection.latestResults\\n if channelNumber in self.connection.latestResults:\\n return self.connection.latestResults[channelNumber]\\n else:\\n return scipy.NaN\",\n \"def get_voltage_rating(self):\\n summary = self.get_version_summary()\\n pattern = '\\\\$.*? .*? .*? .*? .*? (.*?) .*? .*? .*? \\\\r\\\\n' \\n rating = int(re.findall(pattern,summary).pop())\\n return rating\",\n \"def get_resistance(self):\\n adc = ADC(self.pin)\\n value = adc.read()\\n if value == 0:\\n return -1\\n\\n return (4095./value - 1.) * self.RLOAD\",\n \"def get_ambient_light(self, ldr_voltage: Optional[int] = None) -> float:\\n if ldr_voltage is None:\\n ldr_voltage = self.raw_ldr_voltage\\n\\n # TODO: this conversion algorithm is straight from the manual but it seems odd.\\n # It goes \\\"to infinity\\\" as ldr_voltage nears 1023 (hence the clamp, I guess)\\n # Clarify.\\n if ldr_voltage > 1022:\\n ldr_voltage = 1022\\n if ldr_voltage < 1:\\n ldr_voltage = 1\\n\\n return self.ldr_pull_up_resistance / ((1023 / ldr_voltage) - 1)\",\n \"def volts(self) -> NumType:\\n return self._volt\",\n \"def get_vsolar(self):\\n return self.read_register(4098, 1, 3)\",\n \"def calc_V(A):\\n return 1. / calc_rV(A)\",\n \"def value(self) -> float:\\n ...\",\n \"def value(self) -> float:\\n ...\",\n \"def value(self) -> float:\\n ...\",\n \"def ac_voltage(self, ac):\\n self.set_ac_voltage(f'{ac}' if self._is_min_max(ac) else f'{ac} V')\",\n \"def get_perfect_voltage_for_a_day(start, freq):\\n surface_tilt = _munich_location.latitude\\n surface_azimuth = 180 # pointing south\\n date_range = pd.date_range(start=start,\\n end=start + dt.timedelta(\\n hours=23, minutes=59, seconds=59),\\n freq=freq, tz=_munich_location.tz)\\n\\n clearsky_estimations = _munich_location.get_clearsky(date_range)\\n dni_extra = irradiance.extraradiation(date_range)\\n solar_position = solarposition.get_solarposition(\\n date_range, _munich_location.latitude, _munich_location.longitude)\\n airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'])\\n pressure = atmosphere.alt2pres(_munich_location.altitude)\\n am_abs = atmosphere.absoluteairmass(airmass, pressure)\\n\\n total_irrad = irradiance.total_irrad(surface_tilt,\\n surface_azimuth,\\n solar_position['apparent_zenith'],\\n solar_position['azimuth'],\\n clearsky_estimations['dni'],\\n clearsky_estimations['ghi'],\\n clearsky_estimations['dhi'],\\n dni_extra=dni_extra,\\n model='haydavies')\\n\\n temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], 0, 15)\\n aoi = irradiance.aoi(surface_tilt, surface_azimuth,\\n solar_position['apparent_zenith'],\\n solar_position['azimuth'])\\n # add 0.0001 to avoid np.log(0) and warnings about that\\n effective_irradiance = pvsystem.sapm_effective_irradiance(\\n total_irrad['poa_direct'], total_irrad['poa_diffuse'], am_abs,\\n aoi, _sandia_module) + 0.0001\\n sapm = pvsystem.sapm(effective_irradiance, temps['temp_cell'],\\n _sandia_module)\\n\\n return sapm['p_mp'] * _module_count\",\n \"def charging_current_and_voltage(self):\\n done, data = self._request('GG')\\n if done:\\n milliamps = float(data[0])\\n millivolts = float(data[1])\\n return {\\n 'amps': float(milliamps) / 1000 if milliamps > 0 else 0.0,\\n 'volts': float(millivolts) / 1000 if millivolts > 0 else 0.0\\n }\\n\\n raise EvseError\",\n \"def get_voltage_no_load(self):\\n return self.__voltage_no_load\",\n \"def getavgvel(self):\\n if self.total_time:\\n return (6.28)/(self.total_time)\",\n \"def estimated_voltage(clocks, clock_threshold, voltage_scale):\\n return [1 + ((clock > clock_threshold) * (1e-3 * voltage_scale * (clock-clock_threshold))) for clock in clocks]\",\n \"def get_vbat(self):\\n return self.read_register(4096, 1, 3)\",\n \"def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\\n self.min_volts = min_volts\\n self.max_volts = max_volts\\n self.sample_rate = sample_rate\\n self.num_samples = num_samples\\n self.channel = device + \\\"/\\\" + AIchan\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(self.min_volts),float64(self.max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\",\n \"def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\\n self.min_volts = min_volts\\n self.max_volts = max_volts\\n self.sample_rate = sample_rate\\n self.num_samples = num_samples\\n self.channel = device + \\\"/\\\" + AIchan\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(self.min_volts),float64(self.max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\",\n \"def last_voltage(self):\\n return self._last_voltage\",\n \"def get_voltage(self, i_sup, t, *args, **kwargs):\\r\\n raise NotImplementedError\",\n \"def angular_velocity(self):\\n trig = gyro_trigger_mode.GET_ANGULAR_VELOCITY_TRIGGER_READ\\n if self.__trigger == trig:\\n self.read_and_update_angle()\\n\\n adjusted = (self.__angle - self.__offset)\\n if self.__factorSet:\\n return adjusted / self.__degPerSecondFactor\\n return adjusted\",\n \"def get_pump_set_point_volts(self):\\n return self.__pump_set_point_raw * PSCU.PUMP_VREF\",\n \"def get_position_volts(self):\\n return self.__position_raw * PSCU.POSITION_VREF\",\n \"def get_vmax(self, ch_id: int) -> float:\\n return float(self.query(':measure:vmax? channel{}'.format(ch_id)))\",\n \"def get_value(\\n self\\n ) -> float:\\n\\n return self.average\",\n \"def get_value(self, xval: float, wvtype: str, wv_id: int, precision=6) -> float:\\n fmt = ':measure:vtime? {0:.%dg},{1}{2}' % precision\\n return float(self.query(fmt.format(xval, wvtype, wv_id)))\",\n \"def convert_fuelconsumption(self, event):\\n try:\\n #Compare other unit to one unit(liters/100 kilometer)\\n current_value, current_unit = float(\\\"0\\\" + str(self.v.get())), self.dropdown.get()\\n if current_value != 0:\\n unit_comp = {\\\"car(2014 US Average)\\\": 9.260417, \\\"gallon(UK)/100 miles\\\": 2.824809, \\\"gallon(US)/100 miles\\\": 2.352146, \\\"kilometer/liter\\\": 100.0 / (current_value ** 2), \\\"liters/100 kilometer\\\": 1.0, \\\"liters/meter\\\": 100000.0, \\\"miles/gallon(UK)\\\": 282.480936 / (current_value ** 2), \\\"miles/gallon(US)\\\": 235.214583 / (current_value ** 2)}\\n else: #In case current_value == 0, it will error coz number division by zero.\\n unit_comp = {\\\"car(2014 US Average)\\\": 1.0, \\\"gallon(UK)/100 miles\\\": 1.0, \\\"gallon(US)/100 miles\\\": 1.0, \\\"kilometer/liter\\\": 1.0, \\\"liters/100 kilometer\\\": 1.0, \\\"liters/meter\\\": 1.0, \\\"miles/gallon(UK)\\\": 1.0, \\\"miles/gallon(US)\\\": 1.0}\\n value_comp, printer = current_value * unit_comp[current_unit], \\\"\\\"\\n unit_list = sorted(unit_comp.keys())\\n unit_list.remove(current_unit)\\n for unit in unit_list:\\n printer += \\\"To %s \\\" % unit + \\\" \\\" * (max([len(i) for i in unit_list]) - len(unit)) + str(value_comp / unit_comp[unit]) + [\\\"\\\", \\\"\\\\n\\\"][unit_list[-1] != unit]\\n except ValueError: #In case user enter the other type of value, not Int or Float\\n printer = \\\"Value is invalid.\\\"\\n self.print_text(printer)\",\n \"def valor(self):\\n nbits = len(self.gens)\\n dec = int(self.gens,2)\\n real = self.min+dec*((self.max-self.min)/(math.pow(2,nbits)-1))\\n return real\",\n \"def get_fullscale(self, ch_id: int) -> float:\\n return float(self.query(':channel{0}:range?'.format(ch_id)))\",\n \"def get_max_cell_voltage(self): \\n summary = \\\" \\\".join(self.get_summary().split())\\n pattern = '\\\\$.... .. .*? .*? .*? .*? (.*?) . .*? .*? . . . .*?'\\n maxv = float(re.findall(pattern,summary).pop())\\n return maxv\",\n \"def get_vcond(lambdam, taum):\\n return 2 * lambdam / taum\",\n \"def vratio(self):\\n return self.run_command('vratio')[0]\",\n \"def convert_volts(self,data,places):\\n volts = (data * self.max) / float(255)\\n volts = round(volts,places)\\n return volts\",\n \"def to_real(self):\\n return _uhd_swig.sensor_value_t_to_real(self)\",\n \"def READ_PRESSURE_SENSOR():\\n return 15.246\",\n \"def get(self) -> float:\\n ...\",\n \"def calcPfePres(voltage: float):\\n # V → Torr\\n exponent = 1.667 * voltage - 11.46\\n pres = 10**exponent\\n return pres\",\n \"def E2V(E):\\n# for energy in mev returns velocity in m/s\\n return sqrt(E/5.227e-6)\",\n \"def measure_vdc(self, rng = 'DEF', res = 'DEF', unit = 'V', samp = 1):\\n self.configure_vdc(rng, res, unit)\\n self.write_to_serial(':samp:coun ' + str(samp))\\n if samp == 1:\\n return float(self.query_serial('read?'))\\n else:\\n samples = self.query_serial('read?').split(',')\\n sampleresult = []\\n for sample in samples:\\n sampleresult.append(float(sample))\\n return sampleresult\",\n \"def _update_voltageSetpoint_fromGUI(self):\\n self.voltageSetpoint = self.widgets['p_setpoint'].value()/self.gain\",\n \"def vega(self, spot_price, implied_vol):\\n option = self.Option.option_ql\\n process = self.evaluation.get_bsmprocess_cnstvol(self.rf, spot_price, implied_vol)\\n engine = util.get_engine(process, self.engineType)\\n option.setPricingEngine(engine)\\n vega = option.vega()/100.0\\n # price1 = self.option_price(spot_price, implied_vol)\\n # price2 = self.option_price(spot_price, implied_vol + 0.01)\\n # vega = price2 - price1\\n return vega\",\n \"def getSingleMeasurement(self, ch = \\\"CH1\\\"):\\n\\t\\tcounter = 1\\n\\t\\t# added try except block to take care of times when get_waveform() has a problem.\\n\\t\\t# this seems to take a long time. Calling for data from oscilloscope is slow... May need to\\n\\t\\t# Take many samples at once, receive data all at once.\\n\\t\\twhile True:\\n\\t\\t\\ttry:\\t\\t\\n\\t\\t\\t\\twaveform = self.osc.get_waveform(source = ch, start = 0, stop = 0)\\n\\t\\t\\t\\tbreak\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tprint(\\\"Retry: \\\" + str(counter))\\n\\t\\t\\t\\tcounter += 1\\n\\t\\tfor x,y in waveform:\\n\\t\\t\\tvoltage = y\\n\\t\\treturn voltage\",\n \"def VACF(df,conversion = \\\"x\\\"):\\n #conversion from pixels to micrometers\\n if conversion == \\\"y\\\":\\n df = df/1200*633\\n else:\\n df = df/1600*844\\n #computes the velocity in one direction between the frames\\n dif = pd.DataFrame()\\n\\n for i in range(1,len(df.T)):\\n dif[i-1] = velocity(df[i-1],df[i])\\n vel = []\\n for i in range(len(dif)):\\n vel.append(tidynamics.acf(dif.T[i]))\\n\\n #return the velocities in array\\n return np.array(vel)\",\n \"def meas_resistance(instrument):\\n return float(instrument.query('MEAS:RESistance?'))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7460715","0.72376","0.7180077","0.71739894","0.7143595","0.69467497","0.6899874","0.6868156","0.6855484","0.67759335","0.6678318","0.6674746","0.66508025","0.6561424","0.6534731","0.65066266","0.65000963","0.6477398","0.6432846","0.6412113","0.6365112","0.63559425","0.6315891","0.62513286","0.6233522","0.6230869","0.62299657","0.6224726","0.62220496","0.6214991","0.61886","0.61519915","0.61219096","0.61133313","0.6109801","0.61078835","0.6100609","0.6091751","0.6054846","0.6048133","0.60254663","0.6007312","0.5977056","0.59751695","0.5966601","0.59646237","0.5946324","0.5941724","0.59323776","0.59323776","0.5928323","0.59127325","0.5895794","0.58903265","0.58739585","0.58674663","0.5866161","0.5850168","0.58497846","0.584659","0.5835049","0.58167255","0.57938176","0.57938176","0.57938176","0.57914543","0.57818455","0.57761985","0.57758635","0.5772706","0.5770615","0.5756861","0.57550234","0.57550234","0.57507735","0.57290506","0.57267994","0.5713069","0.5703421","0.5699523","0.5682172","0.5678719","0.56731194","0.56658214","0.56554544","0.5648297","0.56479216","0.5642044","0.56370765","0.5634103","0.56318766","0.56289905","0.5628316","0.562253","0.5619499","0.56081986","0.5606985","0.56038743","0.55968064","0.5594343"],"string":"[\n \"0.7460715\",\n \"0.72376\",\n \"0.7180077\",\n \"0.71739894\",\n \"0.7143595\",\n \"0.69467497\",\n \"0.6899874\",\n \"0.6868156\",\n \"0.6855484\",\n \"0.67759335\",\n \"0.6678318\",\n \"0.6674746\",\n \"0.66508025\",\n \"0.6561424\",\n \"0.6534731\",\n \"0.65066266\",\n \"0.65000963\",\n \"0.6477398\",\n \"0.6432846\",\n \"0.6412113\",\n \"0.6365112\",\n \"0.63559425\",\n \"0.6315891\",\n \"0.62513286\",\n \"0.6233522\",\n \"0.6230869\",\n \"0.62299657\",\n \"0.6224726\",\n \"0.62220496\",\n \"0.6214991\",\n \"0.61886\",\n \"0.61519915\",\n \"0.61219096\",\n \"0.61133313\",\n \"0.6109801\",\n \"0.61078835\",\n \"0.6100609\",\n \"0.6091751\",\n \"0.6054846\",\n \"0.6048133\",\n \"0.60254663\",\n \"0.6007312\",\n \"0.5977056\",\n \"0.59751695\",\n \"0.5966601\",\n \"0.59646237\",\n \"0.5946324\",\n \"0.5941724\",\n \"0.59323776\",\n \"0.59323776\",\n \"0.5928323\",\n \"0.59127325\",\n \"0.5895794\",\n \"0.58903265\",\n \"0.58739585\",\n \"0.58674663\",\n \"0.5866161\",\n \"0.5850168\",\n \"0.58497846\",\n \"0.584659\",\n \"0.5835049\",\n \"0.58167255\",\n \"0.57938176\",\n \"0.57938176\",\n \"0.57938176\",\n \"0.57914543\",\n \"0.57818455\",\n \"0.57761985\",\n \"0.57758635\",\n \"0.5772706\",\n \"0.5770615\",\n \"0.5756861\",\n \"0.57550234\",\n \"0.57550234\",\n \"0.57507735\",\n \"0.57290506\",\n \"0.57267994\",\n \"0.5713069\",\n \"0.5703421\",\n \"0.5699523\",\n \"0.5682172\",\n \"0.5678719\",\n \"0.56731194\",\n \"0.56658214\",\n \"0.56554544\",\n \"0.5648297\",\n \"0.56479216\",\n \"0.5642044\",\n \"0.56370765\",\n \"0.5634103\",\n \"0.56318766\",\n \"0.56289905\",\n \"0.5628316\",\n \"0.562253\",\n \"0.5619499\",\n \"0.56081986\",\n \"0.5606985\",\n \"0.56038743\",\n \"0.55968064\",\n \"0.5594343\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64051443"},"document_rank":{"kind":"string","value":"20"}}},{"rowIdx":77494,"cells":{"query":{"kind":"string","value":"Measures voltage with specified range and resolution returns the voltage in float"},"document":{"kind":"string","value":"def hp34401a_read_voltage_rng_res(hp_meter , v_range, v_resolution):\n hp_meter.write(\"MEAS:VOLT:DC? \" + str(v_range) + \" , \" + str(v_resolution))\n return float(hp_meter.read())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def meas_voltage(instrument, v_range=10, resolution=0.003):\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))","def dmm_measure_voltage(self,range=None,res=None):\n if range is None:\n return self.dmm.measure_voltage()\n elif res is None:\n return self.dmm.measure_voltage(volt_range=range)\n else:\n return self.dmm.measure_voltage(volt_range=range,resolution=res)","def get_voltage(self):\n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\n voltage = float(re.findall(pattern,summary).pop())\n return voltage","def get_voltage(self):\n status = self.get_status_response()\n volts = status[20] + (status[21] * 0x100) + (status[22] * 0x10000) + (status[23] * 0x1000000)\n volts = float(volts)\n volts /= (1000.0 * 1000.0)\n return volts\n #end get_voltage","def sm_measure_voltage(self,num_readings=1):\n self.sm.set_measurement_function(\"VOLTAGE\")\n self.sm.format_readings(\"VOLTAGE\")\n return average(self.sm.take_measurement(num_readings))","def read_voltage(self):\n self.write(':FETC?')\n msg = self.read()\n #print ('dmm msg = ', msg)\n v = msg.split(',')[0].rstrip('NVDC').strip()\n if v[-1] == 'R':\n return float(v[:-1])\n else:\n return float(v)","def ac_voltage(self):\n return float(self.get_ac_voltage())","def read_set_voltage(self):\n function_string = 'V' + self.output + '?'\n value_string = self.scpi_comm(function_string)\n try:\n value = float(value_string.replace('V' + self.output, ''))\n except ValueError:\n value = -9997\n return value","def read_actual_voltage(self):\n function_string = 'V' + self.output + 'O?'\n value_string = self.scpi_comm(function_string)\n LOGGER.warn(value_string)\n time.sleep(0.1) # This might only be necessary on LAN interface\n try:\n value = float(value_string.replace('V', ''))\n except ValueError:\n value = -999999\n return value","def reference_voltage(self) -> float:\n return self._ref_voltage","def get_voltage(self):\n self._raise_not_implemented()","def voltage(self):\n\t\treturn self._voltage","def hp34401a_read_voltage(hp_meter):\n hp_meter.write(\"MEAS:VOLT:DC? DEF,DEF\")\n return float(hp_meter.read())","def get_voltage(self):\n print(\"voici le voltage de la batterie\")","def v(self):\n\n # TODO This translation formula works, but needs simplified.\n\n # PWM duration can go from 0 to 4095 with 4095 representing max rpm\n# print(\"MuleBot.v MuleBot.dcMotorPWMDurationLeft:\", MuleBot.dcMotorPWMDurationLeft)\n speed_percentage = float(MuleBot.dcMotorPWMDurationLeft) / 4095.0\n# print(\"speed_percentage: \", speed_percentage)\n\n rpm = speed_percentage * self.motorMaxRPM\n# print(\"rpm: \", rpm)\n\n secondsPerMinute = 60\n revs_per_second = rpm / secondsPerMinute\n# print(\"--revs_per_second\", revs_per_second)\n\n inches_per_rev = 2.0 * math.pi * MuleBot.WHEEL_RADIUS\n INCHES_PER_METER = 39.3701\n meters_per_rev = inches_per_rev / INCHES_PER_METER\n# print(\"--meters_per_rev\", meters_per_rev)\n\n meters_per_second = meters_per_rev * revs_per_second\n\n# print(\"--meters_per_second: \", meters_per_second)\n return meters_per_second","def voltage(analog_pin):\n return \"%0.2f\" % inVolts(analogRead(analog_pin))","def calc_out_voltage(self, input_photocurrent_file):\n pass","def get_uv_value(self):\n return float(int(self.data[2]) / 10)","def toVoltage(self, range=None, turnOn=False):\n self.write('F1;E')\n c = None\n if range == 1 or range == '10mV':\n c = 'R2'\n elif range == 2 or range == '100mV':\n c = 'R3'\n elif range == 3 or range == '1V':\n c = 'R4'\n elif range == 4 or range == '10V':\n c = 'R5'\n elif range == 5 or range == '30V':\n c = 'R6'\n if c is not None:\n self.write(c + ';E')\n if turnOn:\n self.turnOn()","def resolution_range(self) -> Optional[float]:\n return self._get_property(RESOLUTION_RANGE_PROP, float)","def vol_from_var(self, level: float = 0.95, interpolation: str = 'lower') -> float:\n return float(-np.sqrt(self.periods_in_a_year) *\n self.var_down_func(level, interpolation=interpolation) / ss.norm.ppf(level))","def voltage(self):\n return self.outputValue()","def get_meas_time_voltage(instrument):\n return float(instrument.query('SENSE:VOLT:DC:APER?'))","def get_volt(data):\n volt = (data * 5.0) / 255\n #volt = round(volt, 1)\n return volt","def voltage_conversion(self):\r\n\t\tvoltage = ((self.data[0] * 256 + self.data[1]) / 65536.0) * 5.0\r\n\t\t\r\n\t\treturn {'v' : voltage}","def vol(self) -> float:\n return float(self.tsdf.pct_change().std() * np.sqrt(self.periods_in_a_year))","def Take_Voltage_Measurement(self):\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n nidaq.DAQmxClearTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def Take_Voltage_Measurement(self):\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n nidaq.DAQmxClearTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def vol(x):\r\n return pi*(topdia(x)/2000.)**2 * length (x)","def measure_vdc(self, rng = 'DEF', res = 'DEF', unit = 'V', samp = 1):\n self.configure_vdc(rng, res, unit)\n self.write_to_serial(':samp:coun ' + str(samp))\n if samp == 1:\n return float(self.query_serial('read?'))\n else:\n samples = self.query_serial('read?').split(',')\n sampleresult = []\n for sample in samples:\n sampleresult.append(float(sample))\n return sampleresult","def ret_vol_ratio_func(self, months_from_last: int = None,\n from_date: dt.date = None, to_date: dt.date = None) -> float:\n return self.geo_ret_func(months_from_last, from_date, to_date) / self.vol_func(months_from_last, from_date,\n to_date)","def calibV(self):\n # clear buffer in case of errors\n self.flushInput()\n \n if (self.model == 'GDS'):\n self.write(':CHAN'+str(ch)+':SCAL?\\n')\n # returns V/div, turn it into multiplicative factor\n # between digitizer and actual volts\n vmult = float(self.readline()) * 10./255.\n # GDS includes vertical offset in the data returned.\n voff = 0.\n elif (self.model == 'TDS'):\n self.write('WFMPre:YMUlt?\\n')\n # formula I am using later is from TDS manual, so this\n # is straightforward.\n vmult = float(self.readline())\n self.write('WFMPre:YOFf?\\n')\n voff = float(self.readline())\n \n # clear buffer in case of errors\n self.flushInput()\n\n return (vmult, voff)","def get_channel_v_unit(self)->float:\n return self.__channel_v_unit","def proximity_voltage(x):\n y = VOLTAGE_TASK['value']\n rate = 1 / (y * 0.02)\n diff = abs(y - x)\n return 1 - rate * diff","def get_voltage(self):\n return self.environment.get_voltage(self.neuron_id)","def get_perfect_voltage_for_a_day(start, freq):\n surface_tilt = _munich_location.latitude\n surface_azimuth = 180 # pointing south\n date_range = pd.date_range(start=start,\n end=start + dt.timedelta(\n hours=23, minutes=59, seconds=59),\n freq=freq, tz=_munich_location.tz)\n\n clearsky_estimations = _munich_location.get_clearsky(date_range)\n dni_extra = irradiance.extraradiation(date_range)\n solar_position = solarposition.get_solarposition(\n date_range, _munich_location.latitude, _munich_location.longitude)\n airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'])\n pressure = atmosphere.alt2pres(_munich_location.altitude)\n am_abs = atmosphere.absoluteairmass(airmass, pressure)\n\n total_irrad = irradiance.total_irrad(surface_tilt,\n surface_azimuth,\n solar_position['apparent_zenith'],\n solar_position['azimuth'],\n clearsky_estimations['dni'],\n clearsky_estimations['ghi'],\n clearsky_estimations['dhi'],\n dni_extra=dni_extra,\n model='haydavies')\n\n temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], 0, 15)\n aoi = irradiance.aoi(surface_tilt, surface_azimuth,\n solar_position['apparent_zenith'],\n solar_position['azimuth'])\n # add 0.0001 to avoid np.log(0) and warnings about that\n effective_irradiance = pvsystem.sapm_effective_irradiance(\n total_irrad['poa_direct'], total_irrad['poa_diffuse'], am_abs,\n aoi, _sandia_module) + 0.0001\n sapm = pvsystem.sapm(effective_irradiance, temps['temp_cell'],\n _sandia_module)\n\n return sapm['p_mp'] * _module_count","def get_vrms(self, ch: int) -> float:\n cmd = \":measure:vrms? cycle,ac,channel{0}\".format(ch)\n return float(self.query(cmd))","def estimated_voltage(clocks, clock_threshold, voltage_scale):\n return [1 + ((clock > clock_threshold) * (1e-3 * voltage_scale * (clock-clock_threshold))) for clock in clocks]","def vol_from_var_func(self, level: float = 0.95, months_from_last: int = None,\n from_date: dt.date = None, to_date: dt.date = None, interpolation: str = 'lower',\n drift_adjust: bool = False, periods_in_a_year_fixed: int = None) -> float:\n earlier, later = self.calc_range(months_from_last, from_date, to_date)\n if periods_in_a_year_fixed:\n time_factor = periods_in_a_year_fixed\n else:\n fraction = (later - earlier).days / 365.25\n how_many = self.tsdf.loc[earlier:later].count(numeric_only=True)\n time_factor = how_many / fraction\n if drift_adjust:\n return float((-np.sqrt(time_factor) / ss.norm.ppf(level)) *\n (self.var_down_func(level, months_from_last, from_date, to_date, interpolation) -\n self.tsdf.loc[earlier:later].pct_change().sum() /\n len(self.tsdf.loc[earlier:later].pct_change())))\n else:\n return float(-np.sqrt(time_factor) *\n self.var_down_func(level, months_from_last, from_date, to_date,\n interpolation) / ss.norm.ppf(level))","def hp3458a_read_voltage(hp_meter):\n hp_meter.write(\"TARM SGL\")\n return float(hp_meter.read())","def get_voltage(self, c):\n if 'slot_number' in c.keys():\n slot_number = c['slot_number'] \n yield self.open_comm(c, slot_number)\n yield self.write(c, 'VOLT?')\n voltage = yield self.read(c)\n yield self.close_comm(c, slot_number)\n \n try:\n voltage = float(voltage)\n except(ValueError):\n old_voltage = voltage\n voltage = ''.join([i for i in voltage if (i.isdigit() or i == '-' or i == '.')])\n voltage = float(voltage)\n print ('The value ' + old_voltage + ' was returned by the SIM928, this value was automatically converted to ' + voltage + '.')\n \n value = voltage * units.V\n \n # try:\n # voltage = yield self.query(c,\n # slot_number, \n # \"VOLT?\")\n # try:\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # try:\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # try:\n # value = voltage * units.V\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # value = voltage * units.V\n # else:\n # raise ValueError(self.no_selection_msg())\n returnValue(value)","def read_vref(self) -> float:","def convert_volts(self,data,places):\n volts = (data * self.max) / float(255)\n volts = round(volts,places)\n return volts","def Take_Voltage_Measurement(self,timeout=10.0):\n self.timeout = timeout\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def get_volume_from_points(min_pt, max_pt):\n vol = (max_pt[0] - min_pt[0]) * (max_pt[1] - min_pt[1]) * (max_pt[2] - min_pt[2])\n return vol","def raw_ldr_voltage(self) -> int:\n self._update_analog_value_cache()\n return self.analog_cache.ldr_voltage","def meas_resistance(instrument):\n return float(instrument.query('MEAS:RESistance?'))","def _voltage_get(self, channelNumber):\n #print \"latest=%s\" % self.connection.latestResults\n if channelNumber in self.connection.latestResults:\n return self.connection.latestResults[channelNumber]\n else:\n return scipy.NaN","def ret_vol_ratio(self) -> float:\n return self.geo_ret / self.vol","def get_fan_speed_volts(self):\n return self.__fan_speed_raw * PSCU.FAN_VREF","def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\n self.min_volts = min_volts\n self.max_volts = max_volts\n self.sample_rate = sample_rate\n self.num_samples = num_samples\n self.channel = device + \"/\" + AIchan\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\"\",\n DAQmx_Val_NRSE,\n float64(self.min_volts),float64(self.max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))","def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\n self.min_volts = min_volts\n self.max_volts = max_volts\n self.sample_rate = sample_rate\n self.num_samples = num_samples\n self.channel = device + \"/\" + AIchan\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\"\",\n DAQmx_Val_NRSE,\n float64(self.min_volts),float64(self.max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))","def get_ambient_light(self, ldr_voltage: Optional[int] = None) -> float:\n if ldr_voltage is None:\n ldr_voltage = self.raw_ldr_voltage\n\n # TODO: this conversion algorithm is straight from the manual but it seems odd.\n # It goes \"to infinity\" as ldr_voltage nears 1023 (hence the clamp, I guess)\n # Clarify.\n if ldr_voltage > 1022:\n ldr_voltage = 1022\n if ldr_voltage < 1:\n ldr_voltage = 1\n\n return self.ldr_pull_up_resistance / ((1023 / ldr_voltage) - 1)","def Take_Voltage_Measurement(self,timeout=10.0):\n self.timeout = timeout\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.samples_per_sec),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples_per_ch)))\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_per_chan_read_by_DAQ = int32()\n data = numpy.zeros((self.num_samples_per_ch*self.num_channels),dtype=numpy.float64)\n\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples_per_ch),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples_per_ch*self.num_channels,ctypes.byref(samples_per_chan_read_by_DAQ),None))\n\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n\n if (samples_per_chan_read_by_DAQ.value *self.num_channels) != (self.num_samples_per_ch*self.num_channels):\n raise RuntimeError(\"Measurement Error: incorrect number of samples taken\")\n else:\n data_list = []\n start_index = 0\n end_index = self.num_samples_per_ch\n\n for i in range(self.num_channels):\n data_list.append(data[start_index:end_index])\n start_index += self.num_samples_per_ch\n end_index += self.num_samples_per_ch\n\n return data_list","def voltage(self) -> int:\n return int(self._device_info[\"CurrentVoltage\"])","def get_ai_voltage(self, ai_channel, num_samples=1, max_range=10):\n voltages_pickle = self._service.exposed_get_ai_voltage(\n ai_channel=ai_channel,\n num_samples=num_samples,\n max_range=max_range\n )\n return pickle.loads(voltages_pickle)","def _calculate_norm_vol(dna_concs, ng=5, min_vol=2.5, max_vol=3500,\n resolution=2.5):\n sample_vols = ng / np.nan_to_num(dna_concs) * 1000\n sample_vols = np.clip(sample_vols, min_vol, max_vol)\n sample_vols = np.round(sample_vols / resolution) * resolution\n return sample_vols","def fetch(self):\n return read_voltage()","def to_float(self) -> float:\n return (1.0 * self.as_int) / sensor_fixed_point_conversion","def get_resistance(self):\n adc = ADC(self.pin)\n value = adc.read()\n if value == 0:\n return -1\n\n return (4095./value - 1.) * self.RLOAD # ESP32 maksimi, ESP8266:lle arvo on 1023","def dmm_measure_resistance(self,range=None,res=None):\n if range is None:\n return self.dmm.measure_resistance()\n elif res is None:\n return self.dmm.measure_resistance(res_range=range)\n else:\n return self.dmm.measure_resistance(res_range=range,resolution=res)","def VResolution(self, *args):\n return _Adaptor3d.Adaptor3d_Surface_VResolution(self, *args)","def get_value(self, xval: float, wvtype: str, wv_id: int, precision=6) -> float:\n fmt = ':measure:vtime? {0:.%dg},{1}{2}' % precision\n return float(self.query(fmt.format(xval, wvtype, wv_id)))","def outputValue(self):\n string = self.ask('od;E;')\n string = re.sub(r'^(NDCV|NDCA|EDCV)', r'', string)\n self.notify('voltage', float(string))\n return float(string)","def get_setVoltage(self):\n self.read(\":VOLT?\")","def getRangeMM(self) -> float:\n ...","def present_input_voltage(self):\n return self._read(MX_PRESENT_INPUT_VOLTAGE)","def calc_voltage(self) -> (Volt, None):\n power: ComType = complex(0)\n power_unit: str = ''\n amp: NumType = 0\n amp_unit: str = ''\n ohm: ComType = complex(0)\n ohm_unit: str = ''\n\n if self._amp_exists and self._ohm_exists:\n if hasattr(self._obj1, 'amps'):\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\n elif hasattr(self._obj1, 'ohm'):\n ohm, ohm_unit = self._obj1.ohm, self._obj1.ohm_unit\n if hasattr(self._obj2, 'amps'):\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\n elif hasattr(self._obj2, 'ohm'):\n ohm, ohm_unit = self._obj2.ohm, self._obj2.ohm_unit\n volt = amp * ohm\n volt_unit: str = f'Volts ({amp_unit}*{ohm_unit})'\n\n elif self._amp_exists and self._power_exists:\n if hasattr(self._obj1, 'amps'):\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\n elif hasattr(self._obj1, 'power'):\n power, power_unit = self._obj1.power, self._obj1.power_unit\n if hasattr(self._obj2, 'amps'):\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\n elif hasattr(self._obj2, 'power'):\n power, power_unit = self._obj2.power, self._obj2.power_unit\n volt = power / amp\n volt_unit: str = f'Volts ({power_unit}/{amp_unit})'\n\n else:\n return None\n\n return Volt(volt, volt_unit, self._obj1.frequency, self._obj1.freq_unit)","def get_voltage_and_current(self):\n return self.voltage_and_current","def getMeasurement(self):\n if(ADConverterSettings.useRealAD):\n print \"Real AD not activated\"\n #return self.adcdac.read_adc_voltage(1)\n else:\n return self.__readMeasurementFromFile()","def get_voltage(self, i_sup, t, *args, **kwargs):\r\n raise NotImplementedError","def charging_current_and_voltage(self):\n done, data = self._request('GG')\n if done:\n milliamps = float(data[0])\n millivolts = float(data[1])\n return {\n 'amps': float(milliamps) / 1000 if milliamps > 0 else 0.0,\n 'volts': float(millivolts) / 1000 if millivolts > 0 else 0.0\n }\n\n raise EvseError","def _voltage_get(self):\n #print \"latest=%s\" % self.connection.latestResults\n if self.channelNumber in self.connection.latestResults:\n return self.connection.latestResults[self.channelNumber]\n else:\n return -999","def to_float(self): \n return (self._num / self._den)","def get_fullscale(self, ch_id: int) -> float:\n return float(self.query(':channel{0}:range?'.format(ch_id)))","def E2V(E):\n# for energy in mev returns velocity in m/s\n return sqrt(E/5.227e-6)","def value(self) -> float:","def validateVoltage( self, name, voltage ):\n channel = self.d[name]\n (MIN,MAX) = channel.limits\n if not MIN <= voltage <= MAX: raise Exception('Invalid voltage {}'.format(voltage))","def get_voltage(self, channel):\n self.check_validity()\n\n channel = int(channel)\n\n return self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_VOLTAGE, (channel,), 'B', 12, 'i')","def fluxRatio_fromVmag(Vmag):\n fluxRatio = 10.**(-0.4*Vmag)\n return fluxRatio","def __q2v_cf(self, w, rhom, q):\n return float(q / (rhom + q / w))","def calc_V(A):\n return 1. / calc_rV(A)","def Take_Voltage_Measurement(self,timeout=10.0):\n self.timeout = timeout\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_per_chan_read = int32()\n data = numpy.zeros((self.num_samples*self.num_channels),dtype=numpy.float64)\n\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples*self.num_channels,ctypes.byref(samples_per_chan_read),None))\n\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n\n if (samples_per_chan_read.value *self.num_channels) != (self.num_samples*self.num_channels):\n raise RuntimeError(\"Measurement Error: incorrect number of samples taken\")\n else:\n data_list = []\n start_index = 0\n end_index = self.num_samples\n\n for i in range(self.num_channels):\n data_list.append(data[start_index:end_index])\n start_index += self.num_samples\n end_index += self.num_samples\n\n return data_list","def rvs(self):\n return float(self.interp(random.rand()))","def compute_Flocal(config):\n \n vlow = config['vlow']\n vhigh = config['vhigh']\n vdef = config['vdef']\n lo_restfreq = config[\"DOPPLERTRACKFREQ\"]\n\n velocity = (vlow + vhigh) * 0.5\n vd = Vdef()\n vd.compute_local_frame_with_vdef(vdef, velocity,\n lo_restfreq, velocity)\n # this better be the same as vlow since i sent in the avg\n cur_vhigh = vd.get_vhigh()\n cur_vlow = vd.get_vlow()\n if cur_vhigh != cur_vlow:\n \"PANIC: How can the avg velocities differ!!!!!\"\n \n return cur_vhigh","def get_vsolar(self):\n return self.read_register(4098, 1, 3)","def get_range(cls, data: tuple or list) -> float:\n cls._data_validation(data)\n max_ = cls.get_max(data)\n min_ = cls.get_min(data)\n return float(max_ - min_)","def get_vmax(self, ch_id: int) -> float:\n return float(self.query(':measure:vmax? channel{}'.format(ch_id)))","def get_vcond(lambdam, taum):\n return 2 * lambdam / taum","def get_resistance(self):\n adc = ADC(self.pin)\n value = adc.read()\n if value == 0:\n return -1\n\n return (4095./value - 1.) * self.RLOAD","def Config_Finite_Voltage_Measurement(self,AIchans,min_volts,max_volts,sample_rate,num_samples):\n self.num_channels = len(AIchans.split(','))\n self.num_samples = num_samples\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,AIchans,\"\",\n DAQmx_Val_NRSE,\n float64(min_volts),float64(max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))","def read_adc_voltage(self, channel, mode):\n if (channel > 2) or (channel < 1):\n raise ValueError('read_adc_voltage: channel out of range')\n if (mode > 1) or (mode < 0):\n raise ValueError('read_adc_voltage: mode out of range')\n raw = self.read_adc_raw(channel, mode)\n voltage = (self.__adcrefvoltage / 4096) * raw\n return voltage","def range_flux(self):\n return self.max_flux - self.min_flux","def get_volume(cls) -> float:\n raise NotImplementedError","def _rangeQueryFloatFeature(self):\n\n # create args\n minToGet = c_double()\n maxToGet = c_double()\n\n errorCode = VimbaDLL.featureFloatRangeQuery(self._handle,\n self._name,\n byref(minToGet),\n byref(maxToGet))\n if errorCode != 0:\n raise VimbaException(errorCode)\n\n return (minToGet.value, maxToGet.value)","def read_voltage_stepsize(self):\n function_string = 'DELTAV' + self.output + '?'\n return self.scpi_comm(function_string)","def calcPfePres(voltage: float):\n # V → Torr\n exponent = 1.667 * voltage - 11.46\n pres = 10**exponent\n return pres","def analytic_value_VaR(x):\n mu_H = -15 * x + 10 * x ** 2\n # z = 0.67448975 # VaR 0.75\n # z = .7978845608028654 # CVaR 0.5\n z = 1.27111 # CVaR 0.75\n sigma_H = np.sqrt(16 * x ** 2 + 4 * x ** 4)\n return mu_H + z * sigma_H","def request_voltage_and_current(self):\n self.voltage_and_current = self.current_sensors.get_channel_voltage_and_current(self.channel)\n return self.voltage_and_current","def volts(self) -> NumType:\n return self._volt"],"string":"[\n \"def meas_voltage(instrument, v_range=10, resolution=0.003):\\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))\",\n \"def dmm_measure_voltage(self,range=None,res=None):\\n if range is None:\\n return self.dmm.measure_voltage()\\n elif res is None:\\n return self.dmm.measure_voltage(volt_range=range)\\n else:\\n return self.dmm.measure_voltage(volt_range=range,resolution=res)\",\n \"def get_voltage(self):\\n summary = \\\" \\\".join(self.get_summary().split())\\n pattern = '\\\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\\n voltage = float(re.findall(pattern,summary).pop())\\n return voltage\",\n \"def get_voltage(self):\\n status = self.get_status_response()\\n volts = status[20] + (status[21] * 0x100) + (status[22] * 0x10000) + (status[23] * 0x1000000)\\n volts = float(volts)\\n volts /= (1000.0 * 1000.0)\\n return volts\\n #end get_voltage\",\n \"def sm_measure_voltage(self,num_readings=1):\\n self.sm.set_measurement_function(\\\"VOLTAGE\\\")\\n self.sm.format_readings(\\\"VOLTAGE\\\")\\n return average(self.sm.take_measurement(num_readings))\",\n \"def read_voltage(self):\\n self.write(':FETC?')\\n msg = self.read()\\n #print ('dmm msg = ', msg)\\n v = msg.split(',')[0].rstrip('NVDC').strip()\\n if v[-1] == 'R':\\n return float(v[:-1])\\n else:\\n return float(v)\",\n \"def ac_voltage(self):\\n return float(self.get_ac_voltage())\",\n \"def read_set_voltage(self):\\n function_string = 'V' + self.output + '?'\\n value_string = self.scpi_comm(function_string)\\n try:\\n value = float(value_string.replace('V' + self.output, ''))\\n except ValueError:\\n value = -9997\\n return value\",\n \"def read_actual_voltage(self):\\n function_string = 'V' + self.output + 'O?'\\n value_string = self.scpi_comm(function_string)\\n LOGGER.warn(value_string)\\n time.sleep(0.1) # This might only be necessary on LAN interface\\n try:\\n value = float(value_string.replace('V', ''))\\n except ValueError:\\n value = -999999\\n return value\",\n \"def reference_voltage(self) -> float:\\n return self._ref_voltage\",\n \"def get_voltage(self):\\n self._raise_not_implemented()\",\n \"def voltage(self):\\n\\t\\treturn self._voltage\",\n \"def hp34401a_read_voltage(hp_meter):\\n hp_meter.write(\\\"MEAS:VOLT:DC? DEF,DEF\\\")\\n return float(hp_meter.read())\",\n \"def get_voltage(self):\\n print(\\\"voici le voltage de la batterie\\\")\",\n \"def v(self):\\n\\n # TODO This translation formula works, but needs simplified.\\n\\n # PWM duration can go from 0 to 4095 with 4095 representing max rpm\\n# print(\\\"MuleBot.v MuleBot.dcMotorPWMDurationLeft:\\\", MuleBot.dcMotorPWMDurationLeft)\\n speed_percentage = float(MuleBot.dcMotorPWMDurationLeft) / 4095.0\\n# print(\\\"speed_percentage: \\\", speed_percentage)\\n\\n rpm = speed_percentage * self.motorMaxRPM\\n# print(\\\"rpm: \\\", rpm)\\n\\n secondsPerMinute = 60\\n revs_per_second = rpm / secondsPerMinute\\n# print(\\\"--revs_per_second\\\", revs_per_second)\\n\\n inches_per_rev = 2.0 * math.pi * MuleBot.WHEEL_RADIUS\\n INCHES_PER_METER = 39.3701\\n meters_per_rev = inches_per_rev / INCHES_PER_METER\\n# print(\\\"--meters_per_rev\\\", meters_per_rev)\\n\\n meters_per_second = meters_per_rev * revs_per_second\\n\\n# print(\\\"--meters_per_second: \\\", meters_per_second)\\n return meters_per_second\",\n \"def voltage(analog_pin):\\n return \\\"%0.2f\\\" % inVolts(analogRead(analog_pin))\",\n \"def calc_out_voltage(self, input_photocurrent_file):\\n pass\",\n \"def get_uv_value(self):\\n return float(int(self.data[2]) / 10)\",\n \"def toVoltage(self, range=None, turnOn=False):\\n self.write('F1;E')\\n c = None\\n if range == 1 or range == '10mV':\\n c = 'R2'\\n elif range == 2 or range == '100mV':\\n c = 'R3'\\n elif range == 3 or range == '1V':\\n c = 'R4'\\n elif range == 4 or range == '10V':\\n c = 'R5'\\n elif range == 5 or range == '30V':\\n c = 'R6'\\n if c is not None:\\n self.write(c + ';E')\\n if turnOn:\\n self.turnOn()\",\n \"def resolution_range(self) -> Optional[float]:\\n return self._get_property(RESOLUTION_RANGE_PROP, float)\",\n \"def vol_from_var(self, level: float = 0.95, interpolation: str = 'lower') -> float:\\n return float(-np.sqrt(self.periods_in_a_year) *\\n self.var_down_func(level, interpolation=interpolation) / ss.norm.ppf(level))\",\n \"def voltage(self):\\n return self.outputValue()\",\n \"def get_meas_time_voltage(instrument):\\n return float(instrument.query('SENSE:VOLT:DC:APER?'))\",\n \"def get_volt(data):\\n volt = (data * 5.0) / 255\\n #volt = round(volt, 1)\\n return volt\",\n \"def voltage_conversion(self):\\r\\n\\t\\tvoltage = ((self.data[0] * 256 + self.data[1]) / 65536.0) * 5.0\\r\\n\\t\\t\\r\\n\\t\\treturn {'v' : voltage}\",\n \"def vol(self) -> float:\\n return float(self.tsdf.pct_change().std() * np.sqrt(self.periods_in_a_year))\",\n \"def Take_Voltage_Measurement(self):\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n nidaq.DAQmxClearTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def Take_Voltage_Measurement(self):\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n nidaq.DAQmxClearTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def vol(x):\\r\\n return pi*(topdia(x)/2000.)**2 * length (x)\",\n \"def measure_vdc(self, rng = 'DEF', res = 'DEF', unit = 'V', samp = 1):\\n self.configure_vdc(rng, res, unit)\\n self.write_to_serial(':samp:coun ' + str(samp))\\n if samp == 1:\\n return float(self.query_serial('read?'))\\n else:\\n samples = self.query_serial('read?').split(',')\\n sampleresult = []\\n for sample in samples:\\n sampleresult.append(float(sample))\\n return sampleresult\",\n \"def ret_vol_ratio_func(self, months_from_last: int = None,\\n from_date: dt.date = None, to_date: dt.date = None) -> float:\\n return self.geo_ret_func(months_from_last, from_date, to_date) / self.vol_func(months_from_last, from_date,\\n to_date)\",\n \"def calibV(self):\\n # clear buffer in case of errors\\n self.flushInput()\\n \\n if (self.model == 'GDS'):\\n self.write(':CHAN'+str(ch)+':SCAL?\\\\n')\\n # returns V/div, turn it into multiplicative factor\\n # between digitizer and actual volts\\n vmult = float(self.readline()) * 10./255.\\n # GDS includes vertical offset in the data returned.\\n voff = 0.\\n elif (self.model == 'TDS'):\\n self.write('WFMPre:YMUlt?\\\\n')\\n # formula I am using later is from TDS manual, so this\\n # is straightforward.\\n vmult = float(self.readline())\\n self.write('WFMPre:YOFf?\\\\n')\\n voff = float(self.readline())\\n \\n # clear buffer in case of errors\\n self.flushInput()\\n\\n return (vmult, voff)\",\n \"def get_channel_v_unit(self)->float:\\n return self.__channel_v_unit\",\n \"def proximity_voltage(x):\\n y = VOLTAGE_TASK['value']\\n rate = 1 / (y * 0.02)\\n diff = abs(y - x)\\n return 1 - rate * diff\",\n \"def get_voltage(self):\\n return self.environment.get_voltage(self.neuron_id)\",\n \"def get_perfect_voltage_for_a_day(start, freq):\\n surface_tilt = _munich_location.latitude\\n surface_azimuth = 180 # pointing south\\n date_range = pd.date_range(start=start,\\n end=start + dt.timedelta(\\n hours=23, minutes=59, seconds=59),\\n freq=freq, tz=_munich_location.tz)\\n\\n clearsky_estimations = _munich_location.get_clearsky(date_range)\\n dni_extra = irradiance.extraradiation(date_range)\\n solar_position = solarposition.get_solarposition(\\n date_range, _munich_location.latitude, _munich_location.longitude)\\n airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'])\\n pressure = atmosphere.alt2pres(_munich_location.altitude)\\n am_abs = atmosphere.absoluteairmass(airmass, pressure)\\n\\n total_irrad = irradiance.total_irrad(surface_tilt,\\n surface_azimuth,\\n solar_position['apparent_zenith'],\\n solar_position['azimuth'],\\n clearsky_estimations['dni'],\\n clearsky_estimations['ghi'],\\n clearsky_estimations['dhi'],\\n dni_extra=dni_extra,\\n model='haydavies')\\n\\n temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], 0, 15)\\n aoi = irradiance.aoi(surface_tilt, surface_azimuth,\\n solar_position['apparent_zenith'],\\n solar_position['azimuth'])\\n # add 0.0001 to avoid np.log(0) and warnings about that\\n effective_irradiance = pvsystem.sapm_effective_irradiance(\\n total_irrad['poa_direct'], total_irrad['poa_diffuse'], am_abs,\\n aoi, _sandia_module) + 0.0001\\n sapm = pvsystem.sapm(effective_irradiance, temps['temp_cell'],\\n _sandia_module)\\n\\n return sapm['p_mp'] * _module_count\",\n \"def get_vrms(self, ch: int) -> float:\\n cmd = \\\":measure:vrms? cycle,ac,channel{0}\\\".format(ch)\\n return float(self.query(cmd))\",\n \"def estimated_voltage(clocks, clock_threshold, voltage_scale):\\n return [1 + ((clock > clock_threshold) * (1e-3 * voltage_scale * (clock-clock_threshold))) for clock in clocks]\",\n \"def vol_from_var_func(self, level: float = 0.95, months_from_last: int = None,\\n from_date: dt.date = None, to_date: dt.date = None, interpolation: str = 'lower',\\n drift_adjust: bool = False, periods_in_a_year_fixed: int = None) -> float:\\n earlier, later = self.calc_range(months_from_last, from_date, to_date)\\n if periods_in_a_year_fixed:\\n time_factor = periods_in_a_year_fixed\\n else:\\n fraction = (later - earlier).days / 365.25\\n how_many = self.tsdf.loc[earlier:later].count(numeric_only=True)\\n time_factor = how_many / fraction\\n if drift_adjust:\\n return float((-np.sqrt(time_factor) / ss.norm.ppf(level)) *\\n (self.var_down_func(level, months_from_last, from_date, to_date, interpolation) -\\n self.tsdf.loc[earlier:later].pct_change().sum() /\\n len(self.tsdf.loc[earlier:later].pct_change())))\\n else:\\n return float(-np.sqrt(time_factor) *\\n self.var_down_func(level, months_from_last, from_date, to_date,\\n interpolation) / ss.norm.ppf(level))\",\n \"def hp3458a_read_voltage(hp_meter):\\n hp_meter.write(\\\"TARM SGL\\\")\\n return float(hp_meter.read())\",\n \"def get_voltage(self, c):\\n if 'slot_number' in c.keys():\\n slot_number = c['slot_number'] \\n yield self.open_comm(c, slot_number)\\n yield self.write(c, 'VOLT?')\\n voltage = yield self.read(c)\\n yield self.close_comm(c, slot_number)\\n \\n try:\\n voltage = float(voltage)\\n except(ValueError):\\n old_voltage = voltage\\n voltage = ''.join([i for i in voltage if (i.isdigit() or i == '-' or i == '.')])\\n voltage = float(voltage)\\n print ('The value ' + old_voltage + ' was returned by the SIM928, this value was automatically converted to ' + voltage + '.')\\n \\n value = voltage * units.V\\n \\n # try:\\n # voltage = yield self.query(c,\\n # slot_number, \\n # \\\"VOLT?\\\")\\n # try:\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # try:\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # try:\\n # value = voltage * units.V\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # value = voltage * units.V\\n # else:\\n # raise ValueError(self.no_selection_msg())\\n returnValue(value)\",\n \"def read_vref(self) -> float:\",\n \"def convert_volts(self,data,places):\\n volts = (data * self.max) / float(255)\\n volts = round(volts,places)\\n return volts\",\n \"def Take_Voltage_Measurement(self,timeout=10.0):\\n self.timeout = timeout\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def get_volume_from_points(min_pt, max_pt):\\n vol = (max_pt[0] - min_pt[0]) * (max_pt[1] - min_pt[1]) * (max_pt[2] - min_pt[2])\\n return vol\",\n \"def raw_ldr_voltage(self) -> int:\\n self._update_analog_value_cache()\\n return self.analog_cache.ldr_voltage\",\n \"def meas_resistance(instrument):\\n return float(instrument.query('MEAS:RESistance?'))\",\n \"def _voltage_get(self, channelNumber):\\n #print \\\"latest=%s\\\" % self.connection.latestResults\\n if channelNumber in self.connection.latestResults:\\n return self.connection.latestResults[channelNumber]\\n else:\\n return scipy.NaN\",\n \"def ret_vol_ratio(self) -> float:\\n return self.geo_ret / self.vol\",\n \"def get_fan_speed_volts(self):\\n return self.__fan_speed_raw * PSCU.FAN_VREF\",\n \"def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\\n self.min_volts = min_volts\\n self.max_volts = max_volts\\n self.sample_rate = sample_rate\\n self.num_samples = num_samples\\n self.channel = device + \\\"/\\\" + AIchan\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(self.min_volts),float64(self.max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\",\n \"def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\\n self.min_volts = min_volts\\n self.max_volts = max_volts\\n self.sample_rate = sample_rate\\n self.num_samples = num_samples\\n self.channel = device + \\\"/\\\" + AIchan\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(self.min_volts),float64(self.max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\",\n \"def get_ambient_light(self, ldr_voltage: Optional[int] = None) -> float:\\n if ldr_voltage is None:\\n ldr_voltage = self.raw_ldr_voltage\\n\\n # TODO: this conversion algorithm is straight from the manual but it seems odd.\\n # It goes \\\"to infinity\\\" as ldr_voltage nears 1023 (hence the clamp, I guess)\\n # Clarify.\\n if ldr_voltage > 1022:\\n ldr_voltage = 1022\\n if ldr_voltage < 1:\\n ldr_voltage = 1\\n\\n return self.ldr_pull_up_resistance / ((1023 / ldr_voltage) - 1)\",\n \"def Take_Voltage_Measurement(self,timeout=10.0):\\n self.timeout = timeout\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.samples_per_sec),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples_per_ch)))\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_per_chan_read_by_DAQ = int32()\\n data = numpy.zeros((self.num_samples_per_ch*self.num_channels),dtype=numpy.float64)\\n\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples_per_ch),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples_per_ch*self.num_channels,ctypes.byref(samples_per_chan_read_by_DAQ),None))\\n\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n\\n if (samples_per_chan_read_by_DAQ.value *self.num_channels) != (self.num_samples_per_ch*self.num_channels):\\n raise RuntimeError(\\\"Measurement Error: incorrect number of samples taken\\\")\\n else:\\n data_list = []\\n start_index = 0\\n end_index = self.num_samples_per_ch\\n\\n for i in range(self.num_channels):\\n data_list.append(data[start_index:end_index])\\n start_index += self.num_samples_per_ch\\n end_index += self.num_samples_per_ch\\n\\n return data_list\",\n \"def voltage(self) -> int:\\n return int(self._device_info[\\\"CurrentVoltage\\\"])\",\n \"def get_ai_voltage(self, ai_channel, num_samples=1, max_range=10):\\n voltages_pickle = self._service.exposed_get_ai_voltage(\\n ai_channel=ai_channel,\\n num_samples=num_samples,\\n max_range=max_range\\n )\\n return pickle.loads(voltages_pickle)\",\n \"def _calculate_norm_vol(dna_concs, ng=5, min_vol=2.5, max_vol=3500,\\n resolution=2.5):\\n sample_vols = ng / np.nan_to_num(dna_concs) * 1000\\n sample_vols = np.clip(sample_vols, min_vol, max_vol)\\n sample_vols = np.round(sample_vols / resolution) * resolution\\n return sample_vols\",\n \"def fetch(self):\\n return read_voltage()\",\n \"def to_float(self) -> float:\\n return (1.0 * self.as_int) / sensor_fixed_point_conversion\",\n \"def get_resistance(self):\\n adc = ADC(self.pin)\\n value = adc.read()\\n if value == 0:\\n return -1\\n\\n return (4095./value - 1.) * self.RLOAD # ESP32 maksimi, ESP8266:lle arvo on 1023\",\n \"def dmm_measure_resistance(self,range=None,res=None):\\n if range is None:\\n return self.dmm.measure_resistance()\\n elif res is None:\\n return self.dmm.measure_resistance(res_range=range)\\n else:\\n return self.dmm.measure_resistance(res_range=range,resolution=res)\",\n \"def VResolution(self, *args):\\n return _Adaptor3d.Adaptor3d_Surface_VResolution(self, *args)\",\n \"def get_value(self, xval: float, wvtype: str, wv_id: int, precision=6) -> float:\\n fmt = ':measure:vtime? {0:.%dg},{1}{2}' % precision\\n return float(self.query(fmt.format(xval, wvtype, wv_id)))\",\n \"def outputValue(self):\\n string = self.ask('od;E;')\\n string = re.sub(r'^(NDCV|NDCA|EDCV)', r'', string)\\n self.notify('voltage', float(string))\\n return float(string)\",\n \"def get_setVoltage(self):\\n self.read(\\\":VOLT?\\\")\",\n \"def getRangeMM(self) -> float:\\n ...\",\n \"def present_input_voltage(self):\\n return self._read(MX_PRESENT_INPUT_VOLTAGE)\",\n \"def calc_voltage(self) -> (Volt, None):\\n power: ComType = complex(0)\\n power_unit: str = ''\\n amp: NumType = 0\\n amp_unit: str = ''\\n ohm: ComType = complex(0)\\n ohm_unit: str = ''\\n\\n if self._amp_exists and self._ohm_exists:\\n if hasattr(self._obj1, 'amps'):\\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\\n elif hasattr(self._obj1, 'ohm'):\\n ohm, ohm_unit = self._obj1.ohm, self._obj1.ohm_unit\\n if hasattr(self._obj2, 'amps'):\\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\\n elif hasattr(self._obj2, 'ohm'):\\n ohm, ohm_unit = self._obj2.ohm, self._obj2.ohm_unit\\n volt = amp * ohm\\n volt_unit: str = f'Volts ({amp_unit}*{ohm_unit})'\\n\\n elif self._amp_exists and self._power_exists:\\n if hasattr(self._obj1, 'amps'):\\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\\n elif hasattr(self._obj1, 'power'):\\n power, power_unit = self._obj1.power, self._obj1.power_unit\\n if hasattr(self._obj2, 'amps'):\\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\\n elif hasattr(self._obj2, 'power'):\\n power, power_unit = self._obj2.power, self._obj2.power_unit\\n volt = power / amp\\n volt_unit: str = f'Volts ({power_unit}/{amp_unit})'\\n\\n else:\\n return None\\n\\n return Volt(volt, volt_unit, self._obj1.frequency, self._obj1.freq_unit)\",\n \"def get_voltage_and_current(self):\\n return self.voltage_and_current\",\n \"def getMeasurement(self):\\n if(ADConverterSettings.useRealAD):\\n print \\\"Real AD not activated\\\"\\n #return self.adcdac.read_adc_voltage(1)\\n else:\\n return self.__readMeasurementFromFile()\",\n \"def get_voltage(self, i_sup, t, *args, **kwargs):\\r\\n raise NotImplementedError\",\n \"def charging_current_and_voltage(self):\\n done, data = self._request('GG')\\n if done:\\n milliamps = float(data[0])\\n millivolts = float(data[1])\\n return {\\n 'amps': float(milliamps) / 1000 if milliamps > 0 else 0.0,\\n 'volts': float(millivolts) / 1000 if millivolts > 0 else 0.0\\n }\\n\\n raise EvseError\",\n \"def _voltage_get(self):\\n #print \\\"latest=%s\\\" % self.connection.latestResults\\n if self.channelNumber in self.connection.latestResults:\\n return self.connection.latestResults[self.channelNumber]\\n else:\\n return -999\",\n \"def to_float(self): \\n return (self._num / self._den)\",\n \"def get_fullscale(self, ch_id: int) -> float:\\n return float(self.query(':channel{0}:range?'.format(ch_id)))\",\n \"def E2V(E):\\n# for energy in mev returns velocity in m/s\\n return sqrt(E/5.227e-6)\",\n \"def value(self) -> float:\",\n \"def validateVoltage( self, name, voltage ):\\n channel = self.d[name]\\n (MIN,MAX) = channel.limits\\n if not MIN <= voltage <= MAX: raise Exception('Invalid voltage {}'.format(voltage))\",\n \"def get_voltage(self, channel):\\n self.check_validity()\\n\\n channel = int(channel)\\n\\n return self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_VOLTAGE, (channel,), 'B', 12, 'i')\",\n \"def fluxRatio_fromVmag(Vmag):\\n fluxRatio = 10.**(-0.4*Vmag)\\n return fluxRatio\",\n \"def __q2v_cf(self, w, rhom, q):\\n return float(q / (rhom + q / w))\",\n \"def calc_V(A):\\n return 1. / calc_rV(A)\",\n \"def Take_Voltage_Measurement(self,timeout=10.0):\\n self.timeout = timeout\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_per_chan_read = int32()\\n data = numpy.zeros((self.num_samples*self.num_channels),dtype=numpy.float64)\\n\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples*self.num_channels,ctypes.byref(samples_per_chan_read),None))\\n\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n\\n if (samples_per_chan_read.value *self.num_channels) != (self.num_samples*self.num_channels):\\n raise RuntimeError(\\\"Measurement Error: incorrect number of samples taken\\\")\\n else:\\n data_list = []\\n start_index = 0\\n end_index = self.num_samples\\n\\n for i in range(self.num_channels):\\n data_list.append(data[start_index:end_index])\\n start_index += self.num_samples\\n end_index += self.num_samples\\n\\n return data_list\",\n \"def rvs(self):\\n return float(self.interp(random.rand()))\",\n \"def compute_Flocal(config):\\n \\n vlow = config['vlow']\\n vhigh = config['vhigh']\\n vdef = config['vdef']\\n lo_restfreq = config[\\\"DOPPLERTRACKFREQ\\\"]\\n\\n velocity = (vlow + vhigh) * 0.5\\n vd = Vdef()\\n vd.compute_local_frame_with_vdef(vdef, velocity,\\n lo_restfreq, velocity)\\n # this better be the same as vlow since i sent in the avg\\n cur_vhigh = vd.get_vhigh()\\n cur_vlow = vd.get_vlow()\\n if cur_vhigh != cur_vlow:\\n \\\"PANIC: How can the avg velocities differ!!!!!\\\"\\n \\n return cur_vhigh\",\n \"def get_vsolar(self):\\n return self.read_register(4098, 1, 3)\",\n \"def get_range(cls, data: tuple or list) -> float:\\n cls._data_validation(data)\\n max_ = cls.get_max(data)\\n min_ = cls.get_min(data)\\n return float(max_ - min_)\",\n \"def get_vmax(self, ch_id: int) -> float:\\n return float(self.query(':measure:vmax? channel{}'.format(ch_id)))\",\n \"def get_vcond(lambdam, taum):\\n return 2 * lambdam / taum\",\n \"def get_resistance(self):\\n adc = ADC(self.pin)\\n value = adc.read()\\n if value == 0:\\n return -1\\n\\n return (4095./value - 1.) * self.RLOAD\",\n \"def Config_Finite_Voltage_Measurement(self,AIchans,min_volts,max_volts,sample_rate,num_samples):\\n self.num_channels = len(AIchans.split(','))\\n self.num_samples = num_samples\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,AIchans,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(min_volts),float64(max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\",\n \"def read_adc_voltage(self, channel, mode):\\n if (channel > 2) or (channel < 1):\\n raise ValueError('read_adc_voltage: channel out of range')\\n if (mode > 1) or (mode < 0):\\n raise ValueError('read_adc_voltage: mode out of range')\\n raw = self.read_adc_raw(channel, mode)\\n voltage = (self.__adcrefvoltage / 4096) * raw\\n return voltage\",\n \"def range_flux(self):\\n return self.max_flux - self.min_flux\",\n \"def get_volume(cls) -> float:\\n raise NotImplementedError\",\n \"def _rangeQueryFloatFeature(self):\\n\\n # create args\\n minToGet = c_double()\\n maxToGet = c_double()\\n\\n errorCode = VimbaDLL.featureFloatRangeQuery(self._handle,\\n self._name,\\n byref(minToGet),\\n byref(maxToGet))\\n if errorCode != 0:\\n raise VimbaException(errorCode)\\n\\n return (minToGet.value, maxToGet.value)\",\n \"def read_voltage_stepsize(self):\\n function_string = 'DELTAV' + self.output + '?'\\n return self.scpi_comm(function_string)\",\n \"def calcPfePres(voltage: float):\\n # V → Torr\\n exponent = 1.667 * voltage - 11.46\\n pres = 10**exponent\\n return pres\",\n \"def analytic_value_VaR(x):\\n mu_H = -15 * x + 10 * x ** 2\\n # z = 0.67448975 # VaR 0.75\\n # z = .7978845608028654 # CVaR 0.5\\n z = 1.27111 # CVaR 0.75\\n sigma_H = np.sqrt(16 * x ** 2 + 4 * x ** 4)\\n return mu_H + z * sigma_H\",\n \"def request_voltage_and_current(self):\\n self.voltage_and_current = self.current_sensors.get_channel_voltage_and_current(self.channel)\\n return self.voltage_and_current\",\n \"def volts(self) -> NumType:\\n return self._volt\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8078823","0.7291037","0.67395306","0.67171156","0.63865334","0.63084126","0.62544125","0.6248933","0.62172204","0.61805737","0.6111289","0.6101917","0.6081985","0.60785013","0.60742426","0.60578114","0.6011588","0.59588313","0.59566176","0.59343493","0.58906895","0.5874022","0.586478","0.5859189","0.5855696","0.5851702","0.5849764","0.5849764","0.58477575","0.58397275","0.57960206","0.5789083","0.5786005","0.5771295","0.576187","0.57378113","0.571671","0.5710845","0.56960547","0.56720895","0.567077","0.56446916","0.5629","0.5617213","0.5607382","0.5580846","0.5572248","0.5560239","0.5556799","0.5539988","0.5514117","0.5514117","0.55128574","0.55126864","0.551153","0.55090576","0.55089694","0.55089575","0.5508662","0.55058944","0.5503638","0.54865533","0.54847795","0.5476771","0.54691076","0.5467145","0.5458414","0.54475933","0.5434454","0.5425172","0.5422324","0.5421408","0.5395585","0.5394833","0.5393429","0.5393404","0.53727245","0.53608805","0.53529245","0.5352387","0.5348029","0.5345444","0.5345305","0.5342413","0.5337943","0.53043914","0.53018093","0.5287061","0.5286652","0.5279284","0.52766186","0.5270468","0.52684253","0.52670693","0.5266753","0.5261435","0.5251253","0.52323323","0.52307063","0.5228533"],"string":"[\n \"0.8078823\",\n \"0.7291037\",\n \"0.67395306\",\n \"0.67171156\",\n \"0.63865334\",\n \"0.63084126\",\n \"0.62544125\",\n \"0.6248933\",\n \"0.62172204\",\n \"0.61805737\",\n \"0.6111289\",\n \"0.6101917\",\n \"0.6081985\",\n \"0.60785013\",\n \"0.60742426\",\n \"0.60578114\",\n \"0.6011588\",\n \"0.59588313\",\n \"0.59566176\",\n \"0.59343493\",\n \"0.58906895\",\n \"0.5874022\",\n \"0.586478\",\n \"0.5859189\",\n \"0.5855696\",\n \"0.5851702\",\n \"0.5849764\",\n \"0.5849764\",\n \"0.58477575\",\n \"0.58397275\",\n \"0.57960206\",\n \"0.5789083\",\n \"0.5786005\",\n \"0.5771295\",\n \"0.576187\",\n \"0.57378113\",\n \"0.571671\",\n \"0.5710845\",\n \"0.56960547\",\n \"0.56720895\",\n \"0.567077\",\n \"0.56446916\",\n \"0.5629\",\n \"0.5617213\",\n \"0.5607382\",\n \"0.5580846\",\n \"0.5572248\",\n \"0.5560239\",\n \"0.5556799\",\n \"0.5539988\",\n \"0.5514117\",\n \"0.5514117\",\n \"0.55128574\",\n \"0.55126864\",\n \"0.551153\",\n \"0.55090576\",\n \"0.55089694\",\n \"0.55089575\",\n \"0.5508662\",\n \"0.55058944\",\n \"0.5503638\",\n \"0.54865533\",\n \"0.54847795\",\n \"0.5476771\",\n \"0.54691076\",\n \"0.5467145\",\n \"0.5458414\",\n \"0.54475933\",\n \"0.5434454\",\n \"0.5425172\",\n \"0.5422324\",\n \"0.5421408\",\n \"0.5395585\",\n \"0.5394833\",\n \"0.5393429\",\n \"0.5393404\",\n \"0.53727245\",\n \"0.53608805\",\n \"0.53529245\",\n \"0.5352387\",\n \"0.5348029\",\n \"0.5345444\",\n \"0.5345305\",\n \"0.5342413\",\n \"0.5337943\",\n \"0.53043914\",\n \"0.53018093\",\n \"0.5287061\",\n \"0.5286652\",\n \"0.5279284\",\n \"0.52766186\",\n \"0.5270468\",\n \"0.52684253\",\n \"0.52670693\",\n \"0.5266753\",\n \"0.5261435\",\n \"0.5251253\",\n \"0.52323323\",\n \"0.52307063\",\n \"0.5228533\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7613944"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":77495,"cells":{"query":{"kind":"string","value":"Displays up to 16 charaters on the hp2458a"},"document":{"kind":"string","value":"def hp3458a_lcd_disp(hp_meter, message):\n hp_meter.write(\"DISP 3\")\n hp_meter.write(\"DISP 2 '\" + str(message) + \"'\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def banner_ascii():\n print(\"\")\n print(f\"\\n{RED} Steganography Tool{RESET}\")\n print(f\"{RED} Made By {RESET}\")\n print(f\"{RED} Ehthe Samul Islam Laskar USN:1DS16CS712 {RESET}\")\n print(f\"{RED} B Padma USN:1DS19CS420{RESET}\")\n print(f\"{RED} Nikhil D Kanyal USN:1DS17CS731{RESET}\")\n print(f\"{YELLOW}Type 'help' to see commands{RESET}\")","def tower(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC+\"255D\"+self.ESC+\"0;1;44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"58C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"68C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"30m\"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"0;44m\"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"1m\"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"52C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"43m\"+self.A223+self.A223+self.ESC+\"0;43m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"1m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"40C\"+self.ESC+\"43m\"+self.ESC+\"0;34;43m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"30m\"+self.A223+self.A223+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40mYou reach th\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"42Ce castle and fight your way up the\"+self.ESC+\"C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A178+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"30C\"+self.ESC+\"43m\"+self.ESC+\"1;36;40mtower!\"+self.ESC+\"41C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A219+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"24C\"+self.ESC+\"44m\"+self.ESC+\"40m\"+self.A221+self.ESC+\"44m\"+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"48C\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A178+self.A177+self.A177+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A177+self.A177+self.A177+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40mYou blindly slash at any and all who\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"66C stand in \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A177+self.A176+self.A177+self.A176+self.A178+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A176+self.A177+self.A177+self.A219+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"30C\"+self.ESC+\"43m\"+self.ESC+\"1;36;40myour path - screaming to find the prisoner \"+self.ESC+\"4C\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"77C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40myou seek - and the jailer of your he\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"66Cart.\"+self.ESC+\"7C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.A176+self.A177+self.ESC+\"33m\"+self.A176+self.ESC+\"32m\"+self.A177+self.A176+self.A177+self.ESC+\"33m\"+self.A178+self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A178+self.A176+self.A177+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"23C\"+self.ESC+\"44m\"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"48C\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A176+self.A177+self.A177+self.A177+self.A176+self.A178+self.A177+self.ESC+\"33m\"+self.A176+self.A177+self.A178+self.ESC+\"32m\"+self.A177+self.A177+self.A178+self.A176+self.A178+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"43m\"+self.A220+self.A220+self.ESC+\"0;43m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"34C\"+self.ESC+\"43m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.ESC+\"0;34;43m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"76C\"+self.ESC+\"43m\"+self.ESC+\"30m\"+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A177+self.A176+self.A177+self.A176+self.A176+self.ESC+\"33m\"+self.A176+self.A177+self.A178+self.A177+self.A177+self.ESC+\"32m\"+self.A177+self.A178+self.A177+self.A177+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A222+self.ESC+\"44m\"+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A221+self.A219+self.ESC+\"1;44m\"+self.A219+self.ESC+\"40m\"+self.A221+self.ESC+\"44m\"+self.A219+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\"+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"44C\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"43m\"+self.A219+self.ESC+\"34m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"33;40m\"+self.A176+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"37C\"+self.ESC+\"44m\"+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"1;37;40mYou make it to the top and \\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"74C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"40C\"+self.ESC+\"44m \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"4C\"+self.ESC+\"1;37;40mthrow open the door...\"+self.ESC+\"6C\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"74C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A178+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A177+self.ESC+\"1;42m\"+self.A178+self.A219+self.ESC+\"30;44m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"31C\"+self.ESC+\"44m\"+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"1;32;42m\"+self.A177+self.A178+self.A178+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"53C\"+self.ESC+\"32m\"+self.A178+self.ESC+\"1;42m\"+self.A219+self.A219+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A219+self.A219+self.ESC+\"0;33m\"+self.A176+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"20C\"+self.ESC+\"44m\"+self.ESC+\"1;32;42m\"+self.A178+self.A219+self.ESC+\"0;32m\"+self.A176+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;30;44m\"+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"32C\"+self.ESC+\"44m\"+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"46C\"+self.ESC+\"42m\"+self.ESC+\"44m \"+self.ESC+\"0;32m\"+self.A177+self.A178+self.A176+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A176+self.A178+self.ESC+\"1;42m\"+self.A219+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"42m\"+self.A176+self.A177+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"63C\"+self.ESC+\"42m\"+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"42m \"+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A178+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A178+self.A219+self.ESC+\"0;32m\"+self.A178+self.A219+self.ESC+\"42m \"+self.ESC+\"40m\"+self.A177+self.A177+self.A176+self.A176+self.A177+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"21C\"+self.ESC+\"1;42m\"+self.A178+self.A176+self.ESC+\"30;40m\"+self.A219+self.ESC+\"0;32m\"+self.A177+self.A177+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"30;40m\"+self.A219+self.A219+self.ESC+\"0m\"+self.A219+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"36C\"+self.ESC+\"0;32m\"+self.A177+self.A178+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.A178+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.A219+\" \"+self.A178+\" \"+self.ESC+\"0;32m\"+self.A177+self.A176+self.A176+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"52C\"+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.A178+self.A176+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"63C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"42m\"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A176+self.A176+self.A178+self.A219+self.A219+self.A178+self.A178+self.A176+self.A176+\" \"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.A177+self.A178+self.A176+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"26C\"+self.ESC+\"42m\"+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"0;32m\"+self.A177+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A177+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"36C\"+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.A177+self.A177+self.A177+self.ESC+\"1;42m\"+self.A219+self.A176+self.ESC+\"0;32m\"+self.A178+self.A178+self.A177+self.A176+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"51C\"+self.ESC+\"42m\"+self.ESC+\"0;32m\"+self.A177+self.A176+self.A177+self.A178+self.A178+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A177+self.A178+self.ESC+\"1;42m\"+self.A177+self.A178+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"64C\"+self.ESC+\"44m \"+self.ESC+\"37m<MORE> \"+self.ESC+\"40m\\r\\n\"\n\t\treturn thismsg","def basic_char_setup( self ):\n\t\tsmall_bar = 3 # number of points per bar\n\t\twide_bar = round(small_bar * 2.25,0) # 2.25 x small_bar\n\t\tdpl = 50 # dots per line 300dpi/6lpi = 50dpl\n\t\tself._nb = bytes( self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ( '*c%02ia%ib0P' % (small_bar, self.bc_height*dpl) ) + \n\t\t\t\t\t\t self.owner.PRINTER_ESC + \n\t\t\t\t\t\t (\"*p+%02iX\" % small_bar) )\n\t\tself._wb = bytes( self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ('*c%02ia%ib0P' % (wide_bar, self.bc_height*dpl) )+\n\t\t\t\t\t\t self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ('*p+%02iX' % wide_bar ) )\n\t\tself._ns = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % small_bar ) )\n\t\tself._ws = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % wide_bar ) )\n \n\t\t# DONE nb = bc39_esc+\"*c\"+TRANSFORM(small_bar,'99')+\"a\"+Alltrim(STR(bc39_height*dpl))+\"b0P\"+bc39_esc+\"*p+\"+TRANSFORM(small_bar,'99')+\"X\"\n\t\t# DONE wb = bc39_esc+\"*c\"+TRANSFORM(wide_bar,'99')+\"a\"+Alltrim(STR(bc39_height*dpl))+\"b0P\"+bc39_esc+\"*p+\"+TRANSFORM(wide_bar,'99')+\"X\"\n\t\t# DONE ns = bc39_esc+\"*p+\"+TRANSFORM(small_bar,'99')+\"X\"\n\t\t# DONE ws = bc39_esc+\"*p+\"+TRANSFORM(wide_bar,'99')+\"X\"\n \n\t\t# adjust cusor position to start at top of line and return to bottom of line\n\t\tself._bc_start = bytes( self.owner.PRINTER_ESC + '*p-50Y' )\n\t\tself._bc_end = bytes( self.owner.PRINTER_ESC + '*p+50Y' )\n\t\t# DONE bc39_start = bc39_esc+\"*p-50Y\"\n\t\t# DONE bc39_END = bc39_esc+\"*p+50Y\"\n\n\t\t# setup the structure allowing to print the code codebar section for various LETTERS\n\t\tself._char39 = { u'1' : 'wb+ns+nb+ws+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'2' : 'nb+ns+wb+ws+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'3' : 'wb+ns+wb+ws+nb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'4' : 'nb+ns+nb+ws+wb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'5' : 'wb+ns+nb+ws+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'6' : 'nb+ns+wb+ws+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'7' : 'nb+ns+nb+ws+nb+ns+wb+ns+wb' , \n\t\t\t\t\t\t u'8' : 'wb+ns+nb+ws+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'9' : 'nb+ns+wb+ws+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'0' : 'nb+ns+nb+ws+wb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'A' : 'wb+ns+nb+ns+nb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'B' : 'nb+ns+wb+ns+nb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'C' : 'wb+ns+wb+ns+nb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'D' : 'nb+ns+nb+ns+wb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'E' : 'wb+ns+nb+ns+wb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'F' : 'nb+ns+wb+ns+wb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'G' : 'nb+ns+nb+ns+nb+ws+wb+ns+wb' , \n\t\t\t\t\t\t u'H' : 'wb+ns+nb+ns+nb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'I' : 'nb+ns+wb+ns+nb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'J' : 'nb+ns+nb+ns+wb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'K' : 'wb+ns+nb+ns+nb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'L' : 'nb+ns+wb+ns+nb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'M' : 'wb+ns+wb+ns+nb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'N' : 'nb+ns+nb+ns+wb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'O' : 'wb+ns+nb+ns+wb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'P' : 'nb+ns+wb+ns+wb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'Q' : 'nb+ns+nb+ns+nb+ns+wb+ws+wb' , \n\t\t\t\t\t\t u'R' : 'wb+ns+nb+ns+nb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'S' : 'nb+ns+wb+ns+nb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'T' : 'nb+ns+nb+ns+wb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'U' : 'wb+ws+nb+ns+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'V' : 'nb+ws+wb+ns+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'W' : 'wb+ws+wb+ns+nb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'X' : 'nb+ws+nb+ns+wb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'Y' : 'wb+ws+nb+ns+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'Z' : 'nb+ws+wb+ns+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'-' : 'nb+ws+nb+ns+nb+ns+wb+ns+wb' , \n\t\t\t\t\t\t u'.' : 'wb+ws+nb+ns+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u' ' : 'nb+ws+wb+ns+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'*' : 'nb+ws+nb+ns+wb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'$' : 'nb+ws+nb+ws+nb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'/' : 'nb+ws+nb+ws+nb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'+' : 'nb+ws+nb+ns+nb+ws+nb+ws+nb' , \n\t\t\t\t\t\t u'%' : 'nb+ns+nb+ws+nb+ws+nb+ws+nb' }","def displayHand(hand: d_si) -> None:\n for letter in hand.keys():\n for _ in range(hand[letter]):\n print(letter,end=\" \")\n print()","def display_s(s, font, screen, lcd, size=5, x=0, y=0):\n i = 0\n spacing = size * .11\n s = str(s)\n char = s[0]\n char_w, char_h = font[char].size\n for c in s:\n display_c(c,font,screen,lcd,size,(int(i*spacing*char_w)+x),y)\n i += 1\n return screen","def display_c(c, font, screen, lcd, size=5, x=0, y=0):\n char = font[str(c)]\n width, height = char.size\n \"\"\"\n if not(size == 10):\n size /= 10.0\n width = int(round(size*width))\n height = int(round(size*height))\n char.resize((width,height))\n \"\"\"\n size = int(round(size * 10))\n images.display_img(char,screen,lcd,size,x,y)","def abdul(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC+\"1;33m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30m\"+self.A220+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.A220+self.ESC+\"1;43m\"+self.A219+self.A178+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A223+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"33m\"+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"C\"+self.A254+self.ESC+\"33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A219+self.ESC+\"1;43m\"+self.A177+self.A176+self.ESC+\"C\"+self.A176+self.ESC+\"C\"+self.ESC+\"37;40mSaga\"+self.ESC+\"Cof\"+self.ESC+\"Cthe\"+self.ESC+\"CRed\"+self.ESC+\"CDragon\"+self.ESC+\"C-\"+self.ESC+\"C\"+self.ESC+\"33mAbduls\"+self.ESC+\"CArmour \"+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A177+self.ESC+\"0;32m\"+self.A220+self.ESC+\"C\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"2C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"2C\"+self.ESC+\"0;33m\"+self.A223+self.A219+self.ESC+\"C\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"33m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"33m\"+self.A220+self.A219+self.A223+self.ESC+\"37m\"+self.A220+self.ESC+\"1;47m\"+self.A176+self.ESC+\"0m\"+self.A219+self.A223+self.ESC+\"1;30;47m\"+self.A176+self.A176+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A177+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1;30m\"+self.A223+self.ESC+\"4C\"+self.ESC+\"0;33mBehind\"+self.ESC+\"Cthe\"+self.ESC+\"Cdesk\"+self.ESC+\"Cof\"+self.ESC+\"Cthe\"+self.ESC+\"Carmour\"+self.ESC+\"Cshop\"+self.ESC+\"Cis\"+self.ESC+\"Can\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"37;40m\"+self.A223+self.ESC+\"1;47m\"+self.A177+self.A176+self.ESC+\"C\"+self.ESC+\"0m\"+self.A219+self.A219+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A178+self.ESC+\"C\"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"4C\"+self.ESC+\"0;33mamazingly\"+self.ESC+\"Cattractive\"+self.ESC+\"Clooking\"+self.ESC+\"Cfemale - she seems\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"33;40m\"+self.A223+self.ESC+\"1;37m\"+self.A220+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30m\"+self.A220+self.A220+self.A223+self.ESC+\"47m\"+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.ESC+\"1;30;43m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"0;32m\"+self.A220+self.A254+self.ESC+\"3C\"+self.ESC+\"33mbusy, doing her mails but she\"+self.ESC+\"Casks\"+self.ESC+\"C\\\"\"+self.ESC+\"1mHow\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A223+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"1;37;47m\"+self.A178+self.ESC+\"40m\"+self.A222+self.A222+self.ESC+\"47m\"+self.A176+self.ESC+\"C\"+self.ESC+\"30m\"+self.A176+self.ESC+\"C\"+self.A177+self.ESC+\"40m\"+self.A220+self.ESC+\"47m\"+self.A178+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"47m\"+self.A219+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A223+self.ESC+\"32m\"+self.A220+self.A178+self.ESC+\"6C\"+self.ESC+\"1;33mmay\"+self.ESC+\"CI\"+self.ESC+\"Cbe\"+self.ESC+\"Cof\"+self.ESC+\"Cservice?\"+self.ESC+\"0;33m\\\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"C\"+self.A220+self.A220+self.ESC+\"C\"+self.ESC+\"1;37m\"+self.A223+self.ESC+\"47m\"+self.A178+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A177+self.ESC+\"0m\"+self.A220+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0m\"+self.A220+self.ESC+\"1;30m\"+self.A223+self.A223+self.A220+self.ESC+\"47m\"+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.ESC+\"33;40m\"+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1;33;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A220+self.A223+self.ESC+\"C\"+self.A220+self.ESC+\"1;43m\"+self.A176+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1;37m\"+self.A223+self.ESC+\"0m\"+self.A220+self.ESC+\"2C\"+self.A220+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.A176+self.ESC+\"40m\"+self.A223+self.ESC+\"47m\"+self.A219+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"1;30m\"+self.A220+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mB\"+self.ESC+\"0;33m]\"+self.ESC+\"1muy\"+self.ESC+\"CArmour\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A220+self.A223+self.ESC+\"1;37m\"+self.A220+self.A220+self.ESC+\"0m\"+self.A223+self.ESC+\"C\"+self.ESC+\"1;47m\"+self.A223+self.ESC+\"0m\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"40m\"+self.A220+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"1;40m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mS\"+self.ESC+\"0;33m]\"+self.ESC+\"1mell\"+self.ESC+\"CArmour\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"1;37;47m\"+self.A219+self.A178+self.ESC+\"40m\"+self.A220+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A220+self.A220+self.A220+self.A220+self.A219+self.A220+self.A223+self.ESC+\"1;30m\"+self.A220+self.A220+self.A219+self.A219+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mY\"+self.ESC+\"0;33m]\"+self.ESC+\"1mour\"+self.ESC+\"CStats\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A178+self.ESC+\"0;33m\"+self.A223+self.A220+self.A223+self.ESC+\"30;43m\"+self.A176+self.A223+self.ESC+\"1;37;40m\"+self.A223+self.A223+self.ESC+\"47m\"+self.A178+self.A177+self.A176+self.A176+self.ESC+\"0m\"+self.A219+self.A219+self.A223+self.ESC+\"1;30m\"+self.A220+self.A176+self.A177+self.A178+self.A223+self.A223+self.ESC+\"0;33m\"+self.A220+self.A219+self.ESC+\"1;30m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mR\"+self.ESC+\"0;33m]\"+self.ESC+\"1meturn\"+self.ESC+\"Cto\"+self.ESC+\"CTown\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A177+self.ESC+\"0;33m\"+self.A219+self.A220+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A220+self.ESC+\"C\"+self.ESC+\"33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.A220+self.A220+self.A219+self.ESC+\"30;43m\"+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A220+self.A254+self.ESC+\"C\"+self.ESC+\"1;30m\"+self.A220+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"33;43m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"C\"+self.A178+self.A254+self.ESC+\"33m\"+self.A219+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A223+self.A220+self.ESC+\"1;43m\"+self.A176+self.ESC+\"0;33m\"+self.A220+self.A223+self.A220+self.A223+self.A223+self.A220+self.ESC+\"3C\"+self.ESC+\"1;30m\"+self.A177+self.A220+self.ESC+\"2C\"+self.ESC+\"33m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"33m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.A220+self.ESC+\"32m\"+self.A254+self.ESC+\"33m\"+self.A223+self.A219+self.A219+self.A220+self.A223+self.A220+self.A220+self.A223+self.A223+self.ESC+\"1;30m\"+self.A220+self.A220+self.A219+self.ESC+\"2C\"+self.A220+self.A178+self.A220+self.ESC+\"C\"+self.ESC+\"33;43m\"+self.A177+self.ESC+\"2C\"+self.ESC+\"0m \"+self.ESC+\"3C\"+self.ESC+\"33m\"+self.A220+\"\\r\\n\"\n\t\tthismsg += self.A223+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.A223+self.ESC+\"0m\\r\\n\"\n\t\treturn thismsg","def n_char(self,char,n,w=1,h=1):\n for i in range(n):\n self.esprint(char,w,h)","def int_21H_2(self):\r\n byte_DL = self.registers['DX'].get_byte(0)\r\n char_DL = byte_DL.decode(\"ascii\") # zdekodowany znak do wyswietlenia\r\n self.GUI.set_output(char_DL)","def display_hand(hand):\n letters = \"\"\n\n for i in hand.keys():\n\n for j in range(hand[i]):\n letters += i\n\n return letters","def header(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC + \"0m \" +self.A220 + self.A220 + self.A220 + self.A220 + self.A220 +\" \" + self.ESC + \"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A220+self.A220+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mS\"+self.ESC+\"0;31mAGA\"+self.ESC+\"37m \"+self.A219+self.A219+self.ESC+\"30;47mo\"+self.ESC+\"37;40m\"+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mO\"+self.ESC+\"0;31mF THE\"+self.ESC+\"37m \"+self.A219+self.A219+self.ESC+\"30;47mO\"+self.ESC+\"37;40m\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.A220+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mR\"+self.ESC+\"0;31mED\"+self.ESC+\"37m \"+self.A219+self.ESC+\"30;47mo\"+self.ESC+\"37;40m\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.A223+self.A219+self.ESC+\"1;47m\"+self.A176+self.A219+self.A219+self.A219+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m \"+self.ESC+\"1mD\"+self.ESC+\"0;31mRAGON 0.9.9\"+self.ESC+\"37m \"+self.A223+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.A219+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"0m \"+self.A219+self.ESC+\"1;47m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m concept\"+self.ESC+\"37m \"+self.A223+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A220+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A220+self.ESC+\"0m \"+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"1;41m\"+self.A176+self.A178+\" \"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m Seth Robinson \"+self.ESC+\"37m\"+self.A222+\" \"+self.A223+self.A223+self.ESC+\"1;47m\"+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A223+self.A223+self.ESC+\"0m \"+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+self.A223+\" \"+self.ESC+\"0m\"+self.A223+self.A219+self.A220+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m by\"+self.ESC+\"0m \"+self.A219+\" \"+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A220+self.A220+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0m \"+self.A223+self.ESC+\"1;47m\"+self.A176+self.A219+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A178+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A176+\" \"+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1;34m J\"+self.ESC+\"0;34m.\"+self.ESC+\"1mT\"+self.ESC+\"0;34m.\"+self.ESC+\"1mS\"+self.ESC+\"0;34mage\"+self.ESC+\"0m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.A220+self.ESC+\"1;47m\"+self.A177+self.A176+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A220+self.A220+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0m \"+self.A223+self.ESC+\"1;47m\"+self.A177+self.A219+self.A219+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A176+self.A178+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"1;41m\"+self.A176+self.A178+self.A176+self.A176+self.A177+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.A219+\" \"+self.ESC+\"1;47m\"+self.A176+self.A177+self.A219+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"41m\"+self.A223+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+\" \"+self.A220+self.A220+self.A220+self.A220+self.A223+self.A220+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A177+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A178+\" \"+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A178+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.A219+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+\" \"+self.A176+self.A176+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A178+self.A178+self.A219+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+self.ESC+\"37m \"+self.ESC+\"31m\"+self.A178+self.A177+self.A177+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A177+self.A178+self.A219+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A219+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"1;41m\"+self.A178+\" \"+self.A177+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A178+self.A177+self.A219+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.A223+self.ESC+\"1;41m\"+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A223+\" \"+self.A220+self.A220+\" \"+self.A223+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A221+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A223+self.A223+self.A223+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A178+self.A177+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A177+self.A219+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.A178+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.A223+self.A223+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A178+self.A178+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.A223+self.A178+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+\" \"+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.ESC+\"1;30;47m\"+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.A219+self.ESC+\"1;41m\"+self.A176+self.A176+self.ESC+\"0;31m\"+self.A221+\" \"+self.ESC+\"1;5;32m\"+self.A220+self.A220+self.A223+\" \"+self.ESC+\"0;31;41m \"+self.ESC+\"40m\"+self.A178+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A220+self.A220+self.A220+self.A220+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.A177+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;5;32m\"+self.A219+self.A219+self.A223+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.A219+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A178+self.A223+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"1;5;32m\"+self.A223+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.A219+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A223+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A177+self.A219+self.A178+self.ESC+\"37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+self.ESC+\"0;31m\"+self.A221+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+\" \"+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+self.A219+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A176+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A177+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.A221+\" \"+self.ESC+\"31;41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A177+self.A177+self.A176+self.A178+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A220+self.A220+\" \"+self.ESC+\"1;41m\"+self.A178+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0m\"+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A176+self.A177+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.A178+\" \"+self.ESC+\"1;41m\"+self.A177+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A176+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A223+self.ESC+\"1m\"+self.A219+self.A220+\" \"+self.ESC+\"0;31;41m \"+self.A177+self.A178+self.A176+self.A176+\" \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A176+self.A178+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A223+self.ESC+\"1;47m\"+self.A223+self.ESC+\"40m\"+self.A219+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A178+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;37;47m\"+self.A222+self.ESC+\"40m\"+self.A221+self.A223+self.A220+\" \"+self.ESC+\"0;31m\"+self.A177+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A221+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+\" \"+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A223+self.ESC+\"31m\"+self.A222+self.ESC+\"1;41m\"+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;37;47m\"+self.A222+self.ESC+\"40m\"+self.A221+\" \"+self.ESC+\"47m\"+self.A222+self.ESC+\"40m\"+self.A221+\" \"+self.ESC+\"0;31m\"+self.A178+self.A177+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"33m\"+self.A220+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A220+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+\" \"+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"31;41m \"+self.A176+self.A176+self.ESC+\"37;40m \"+self.A220+self.ESC+\"1m\"+self.A219+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A219+self.A221+\" \"+self.A223+\" \"+self.A223+self.A220+\" \"+self.ESC+\"0;31;41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A178+self.A177+self.A176+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A220+self.ESC+\"1m\"+self.A219+\" \"+self.A219+self.A221+\" \"+self.A223+\" \"+self.A220+\" \"+self.A223+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"1;33;43m\"+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A219+self.A220+self.A222+self.ESC+\"1;41m\"+self.A219+self.A177+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.A178+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A223+self.ESC+\"1m\"+self.A220+\" \"+self.A223+\" \"+self.A220+self.A223+self.A220+\" \"+self.A223+\" \"+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"1;33m\"+self.A223+self.ESC+\"43m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0;33m\"+self.A220+self.A219+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A177+self.A178+\" \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+self.A178+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"30mÙ\"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A223+self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.A223+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+\" \"+self.A219+\" \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"31m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.A176+self.A176+\" \"+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.ESC+\"1;33;43m\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A177+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.ESC+\"1;33;43m\"+self.A219+self.A178+self.ESC+\"40m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.A220+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.A220+self.A220+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.A223+\" \"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A177+\" \"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A178+self.ESC+\"0;33m\"+self.A220+self.A219+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A177+self.A177+self.A176+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A223+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A178+\" \"+self.A177+\" \"+self.A176+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A219+self.A178+self.A178+self.A177+self.A177+self.A176+\" \"+self.A176+\" \"+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"0;37m \"+self.ESC+\"1;33m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+\" \"+self.ESC+\"30;41m \"+self.ESC+\"1;31mShatterstar [W/X] \"+self.ESC+\"0;37;40m \"+self.ESC+\"30;41m \"+self.ESC+\"37;40m \"+self.ESC+\"30;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\treturn thismsg","def display():\n screen.addch(head[0],head[1],'x')","def gen_beat_output(e):\n return [playback_char(e,t) for t in range(70000)]","async def charinfo(self, ctx: Context, *, characters: str) -> None:\n match = re.match(r\"<(a?):(\\w+):(\\d+)>\", characters)\n if match:\n embed = Embed(\n title=\"Non-Character Detected\",\n description=\"Only unicode characters can be processed, but a custom Discord emoji \" \"was found. Please remove it and try again.\",\n )\n embed.colour = Color.red()\n await ctx.send(embed=embed)\n return\n\n if len(characters) > 25:\n embed = Embed(title=f\"Too many characters ({len(characters)}/25)\")\n embed.colour = Color.red()\n await ctx.send(embed=embed)\n return\n\n def get_info(char: str) -> Tuple[str, str]:\n digit = f\"{ord(char):x}\"\n if len(digit) <= 4:\n u_code = f\"\\\\u{digit:>04}\"\n else:\n u_code = f\"\\\\U{digit:>08}\"\n url = f\"https://www.compart.com/en/unicode/U+{digit:>04}\"\n name = f\"[{unicodedata.name(char, '')}]({url})\"\n info = f\"`{u_code.ljust(10)}`: {name} - {char}\"\n return info, u_code\n\n charlist, rawlist = zip(*(get_info(c) for c in characters))\n\n embed = Embed(description=\"\\n\".join(charlist))\n embed.set_author(name=\"Character Info\")\n\n if len(characters) > 1:\n embed.add_field(name=\"Raw\", value=f\"`{''.join(rawlist)}`\", inline=False)\n\n await ctx.send(embed=embed)","def display(self):\n for row in range(self.height):\n for col in range(self.width):\n char = '#' if self.pixels[row * self.width + col] else '.'\n print(char, end='')\n print()\n print()","def print_chars(self):\n for v in voc.split('\\n'):\n pair = v.split(',')\n print(pair[0], pair[1], '\\t', self.epi.xsampa_list(pair[0]))","def show_theme_message(width):\n print_dotted_line()\n print_bold(\"Attack of The Orcs v0.0.5:\")\n msg = (\n \"\"\"\n Hrabri vitez Talion vraća se iz boja i želi predahnuti u jednom seocetu. \n U seocu se nalazi 5 kućica i želi stupiti u jednu od njih. \n Hrabri vitez ne zna da u ovom području ima neprijatelja.\n Odluči se za jedna od vrata ...\n \"\"\")\n\n print(textwrap.fill(msg, width=width))","def createGlyphs(input):\r\n\r\n command_open = subprocess.Popen([\"start\", \"cmd\",\"/k\", 'echo {}'.format(input)], shell = True)\r\n\r\n time.sleep(2) #time for window to appear\r\n\r\n #print win32gui.FindWindow(None, \"C:\\Windows\\system32\\cmd.exe\")\r\n hwnd = win32gui.FindWindow(None, \"C:\\Windows\\system32\\cmd.exe\")\r\n win32gui.SetForegroundWindow(hwnd)\r\n bbox = win32gui.GetWindowRect(hwnd)\r\n img = ImageGrab.grab(bbox)\r\n #img.show()\r\n\r\n length_of_chars = len(input) * 8 # +2 to compensate for the quotation marks\r\n combined_chars = img.crop((10,34,length_of_chars+9,45))\r\n #combined_chars.show()\r\n\r\n #nonetype error was caused by chaining the .show() wiutth the rest of the stuff\r\n\r\n chars = {x:\"\" for x in input}\r\n\r\n for i, each in enumerate(range(8,combined_chars.size[0]+9,8)): #starts from 1, and +8 to compensate for the -8 below VVV\r\n #if i not in bad_indexes: #this is to avoid the first and last double quotation marks\r\n current_char = input[i]\r\n \r\n indiv_char = combined_chars.crop((each-8,0,each,11))\r\n \r\n w, h = indiv_char.size #should be 8 wide by 9 high\r\n\r\n for i2, pixel in enumerate(indiv_char.getdata()): #tuple values can either be (0, 0, 0) or (192,192,192) for the default terminal colours\r\n if pixel == (192,192,192): \r\n chars[current_char] += u\"\\u2588\"\r\n\r\n else:\r\n chars[current_char] += \" \"\r\n\r\n if i2 % w == w-1: # we want it too look decent so overflow is neeeded onto the next lines\r\n # ^^^ before it was i2 % w == 0, but that lead to a trail behind character, so whats before 0? -1! so w-1!!!\r\n chars[current_char] += \"\\n\"\r\n\r\n chars[current_char] = chars[current_char][:-1] #this is to remove the last \"\\n\"\r\n\r\n return chars","async def charinfo(self, ctx: Context, *, characters: str) -> None:\n match = re.match(r\"<(a?):(\\w+):(\\d+)>\", characters)\n if match:\n await messages.send_denial(\n ctx,\n \"**Non-Character Detected**\\n\"\n \"Only unicode characters can be processed, but a custom Discord emoji \"\n \"was found. Please remove it and try again.\"\n )\n return\n\n if len(characters) > 50:\n await messages.send_denial(ctx, f\"Too many characters ({len(characters)}/50)\")\n return\n\n def get_info(char: str) -> tuple[str, str]:\n digit = f\"{ord(char):x}\"\n if len(digit) <= 4:\n u_code = f\"\\\\u{digit:>04}\"\n else:\n u_code = f\"\\\\U{digit:>08}\"\n url = f\"https://www.compart.com/en/unicode/U+{digit:>04}\"\n name = f\"[{unicodedata.name(char, '')}]({url})\"\n info = f\"`{u_code.ljust(10)}`: {name} - {utils.escape_markdown(char)}\"\n return info, u_code\n\n char_list, raw_list = zip(*(get_info(c) for c in characters), strict=True)\n embed = Embed().set_author(name=\"Character Info\")\n\n if len(characters) > 1:\n # Maximum length possible is 502 out of 1024, so there's no need to truncate.\n embed.add_field(name=\"Full Raw Text\", value=f\"`{''.join(raw_list)}`\", inline=False)\n\n await LinePaginator.paginate(char_list, ctx, embed, max_lines=10, max_size=2000, empty=False)","def _text16(self, font, text, x0, y0, color=WHITE, background=BLACK):\n for char in text:\n ch = ord(char)\n if (font.FIRST <= ch < font.LAST\n and x0+font.WIDTH <= self.width\n and y0+font.HEIGHT <= self.height):\n\n each = 16\n if font.HEIGHT == 16:\n passes = 2\n size = 32\n else:\n passes = 4\n size = 64\n\n for line in range(passes):\n idx = (ch-font.FIRST)*size+(each*line)\n buffer = struct.pack(\n '>128H',\n color if font.FONT[idx] & _BIT7 else background,\n color if font.FONT[idx] & _BIT6 else background,\n color if font.FONT[idx] & _BIT5 else background,\n color if font.FONT[idx] & _BIT4 else background,\n color if font.FONT[idx] & _BIT3 else background,\n color if font.FONT[idx] & _BIT2 else background,\n color if font.FONT[idx] & _BIT1 else background,\n color if font.FONT[idx] & _BIT0 else background,\n color if font.FONT[idx+1] & _BIT7 else background,\n color if font.FONT[idx+1] & _BIT6 else background,\n color if font.FONT[idx+1] & _BIT5 else background,\n color if font.FONT[idx+1] & _BIT4 else background,\n color if font.FONT[idx+1] & _BIT3 else background,\n color if font.FONT[idx+1] & _BIT2 else background,\n color if font.FONT[idx+1] & _BIT1 else background,\n color if font.FONT[idx+1] & _BIT0 else background,\n color if font.FONT[idx+2] & _BIT7 else background,\n color if font.FONT[idx+2] & _BIT6 else background,\n color if font.FONT[idx+2] & _BIT5 else background,\n color if font.FONT[idx+2] & _BIT4 else background,\n color if font.FONT[idx+2] & _BIT3 else background,\n color if font.FONT[idx+2] & _BIT2 else background,\n color if font.FONT[idx+2] & _BIT1 else background,\n color if font.FONT[idx+2] & _BIT0 else background,\n color if font.FONT[idx+3] & _BIT7 else background,\n color if font.FONT[idx+3] & _BIT6 else background,\n color if font.FONT[idx+3] & _BIT5 else background,\n color if font.FONT[idx+3] & _BIT4 else background,\n color if font.FONT[idx+3] & _BIT3 else background,\n color if font.FONT[idx+3] & _BIT2 else background,\n color if font.FONT[idx+3] & _BIT1 else background,\n color if font.FONT[idx+3] & _BIT0 else background,\n color if font.FONT[idx+4] & _BIT7 else background,\n color if font.FONT[idx+4] & _BIT6 else background,\n color if font.FONT[idx+4] & _BIT5 else background,\n color if font.FONT[idx+4] & _BIT4 else background,\n color if font.FONT[idx+4] & _BIT3 else background,\n color if font.FONT[idx+4] & _BIT2 else background,\n color if font.FONT[idx+4] & _BIT1 else background,\n color if font.FONT[idx+4] & _BIT0 else background,\n color if font.FONT[idx+5] & _BIT7 else background,\n color if font.FONT[idx+5] & _BIT6 else background,\n color if font.FONT[idx+5] & _BIT5 else background,\n color if font.FONT[idx+5] & _BIT4 else background,\n color if font.FONT[idx+5] & _BIT3 else background,\n color if font.FONT[idx+5] & _BIT2 else background,\n color if font.FONT[idx+5] & _BIT1 else background,\n color if font.FONT[idx+5] & _BIT0 else background,\n color if font.FONT[idx+6] & _BIT7 else background,\n color if font.FONT[idx+6] & _BIT6 else background,\n color if font.FONT[idx+6] & _BIT5 else background,\n color if font.FONT[idx+6] & _BIT4 else background,\n color if font.FONT[idx+6] & _BIT3 else background,\n color if font.FONT[idx+6] & _BIT2 else background,\n color if font.FONT[idx+6] & _BIT1 else background,\n color if font.FONT[idx+6] & _BIT0 else background,\n color if font.FONT[idx+7] & _BIT7 else background,\n color if font.FONT[idx+7] & _BIT6 else background,\n color if font.FONT[idx+7] & _BIT5 else background,\n color if font.FONT[idx+7] & _BIT4 else background,\n color if font.FONT[idx+7] & _BIT3 else background,\n color if font.FONT[idx+7] & _BIT2 else background,\n color if font.FONT[idx+7] & _BIT1 else background,\n color if font.FONT[idx+7] & _BIT0 else background,\n color if font.FONT[idx+8] & _BIT7 else background,\n color if font.FONT[idx+8] & _BIT6 else background,\n color if font.FONT[idx+8] & _BIT5 else background,\n color if font.FONT[idx+8] & _BIT4 else background,\n color if font.FONT[idx+8] & _BIT3 else background,\n color if font.FONT[idx+8] & _BIT2 else background,\n color if font.FONT[idx+8] & _BIT1 else background,\n color if font.FONT[idx+8] & _BIT0 else background,\n color if font.FONT[idx+9] & _BIT7 else background,\n color if font.FONT[idx+9] & _BIT6 else background,\n color if font.FONT[idx+9] & _BIT5 else background,\n color if font.FONT[idx+9] & _BIT4 else background,\n color if font.FONT[idx+9] & _BIT3 else background,\n color if font.FONT[idx+9] & _BIT2 else background,\n color if font.FONT[idx+9] & _BIT1 else background,\n color if font.FONT[idx+9] & _BIT0 else background,\n color if font.FONT[idx+10] & _BIT7 else background,\n color if font.FONT[idx+10] & _BIT6 else background,\n color if font.FONT[idx+10] & _BIT5 else background,\n color if font.FONT[idx+10] & _BIT4 else background,\n color if font.FONT[idx+10] & _BIT3 else background,\n color if font.FONT[idx+10] & _BIT2 else background,\n color if font.FONT[idx+10] & _BIT1 else background,\n color if font.FONT[idx+10] & _BIT0 else background,\n color if font.FONT[idx+11] & _BIT7 else background,\n color if font.FONT[idx+11] & _BIT6 else background,\n color if font.FONT[idx+11] & _BIT5 else background,\n color if font.FONT[idx+11] & _BIT4 else background,\n color if font.FONT[idx+11] & _BIT3 else background,\n color if font.FONT[idx+11] & _BIT2 else background,\n color if font.FONT[idx+11] & _BIT1 else background,\n color if font.FONT[idx+11] & _BIT0 else background,\n color if font.FONT[idx+12] & _BIT7 else background,\n color if font.FONT[idx+12] & _BIT6 else background,\n color if font.FONT[idx+12] & _BIT5 else background,\n color if font.FONT[idx+12] & _BIT4 else background,\n color if font.FONT[idx+12] & _BIT3 else background,\n color if font.FONT[idx+12] & _BIT2 else background,\n color if font.FONT[idx+12] & _BIT1 else background,\n color if font.FONT[idx+12] & _BIT0 else background,\n color if font.FONT[idx+13] & _BIT7 else background,\n color if font.FONT[idx+13] & _BIT6 else background,\n color if font.FONT[idx+13] & _BIT5 else background,\n color if font.FONT[idx+13] & _BIT4 else background,\n color if font.FONT[idx+13] & _BIT3 else background,\n color if font.FONT[idx+13] & _BIT2 else background,\n color if font.FONT[idx+13] & _BIT1 else background,\n color if font.FONT[idx+13] & _BIT0 else background,\n color if font.FONT[idx+14] & _BIT7 else background,\n color if font.FONT[idx+14] & _BIT6 else background,\n color if font.FONT[idx+14] & _BIT5 else background,\n color if font.FONT[idx+14] & _BIT4 else background,\n color if font.FONT[idx+14] & _BIT3 else background,\n color if font.FONT[idx+14] & _BIT2 else background,\n color if font.FONT[idx+14] & _BIT1 else background,\n color if font.FONT[idx+14] & _BIT0 else background,\n color if font.FONT[idx+15] & _BIT7 else background,\n color if font.FONT[idx+15] & _BIT6 else background,\n color if font.FONT[idx+15] & _BIT5 else background,\n color if font.FONT[idx+15] & _BIT4 else background,\n color if font.FONT[idx+15] & _BIT3 else background,\n color if font.FONT[idx+15] & _BIT2 else background,\n color if font.FONT[idx+15] & _BIT1 else background,\n color if font.FONT[idx+15] & _BIT0 else background\n )\n self.blit_buffer(buffer, x0, y0+8*line, 16, 8)\n x0 += font.WIDTH","def show(self):\n # Disable IRQ to improve speed\n with NoIRQ():\n for chip in range(NB_CHIPS):\n self._select(chip)\n row = 0 if chip in (0, 1) else 1\n col = 0 if chip in (0, 2) else 1\n data = self.get_ht1632_data(row, col)\n green = (is_green(value) for value in data)\n red = (is_red(value) for value in data)\n self._write_data(green, red)","def displayHand(hand):\r\n for letter in hand.keys():\r\n for j in range(hand[letter]):\r\n print(letter,end=\" \") # print all on the same line\r\n print() # print an empty line\r","def print_screen(history):\n\n # Extract the angles from the distance data. Usually this won't change \n # from scan to scan, but there are scanning algorithms where that may not \n # be the case \n angles = []\n for h in history:\n angles = list(set(angles + h.keys())) \n angles.sort()\n\n # Create a 2D grid of characters. Essentially a \"screen buffer\"\n buff = {}\n for angle in angles:\n buff[angle] = ' '.rjust(120)\n\n \n blips = ['.', '*', '#', '@']\n blips = blips[-len(history):] # if we only have 2, take last 2 blips\n\n # Plot blips onto buffer \n for h in history:\n blip = blips.pop(0) if len(blips) else '.'\n for angle in angles:\n if angle not in h: continue\n dist = h[angle]\n if dist < 120:\n buff[angle] = set_char_at(buff[angle], dist, blip)\n\n # Output\n print \"\\n\\n\\n\"\n for angle in angles:\n obstacle = 'x' if '@' in buff[angle][0:30] else ' '\n print \"%s %s %s\" % (str(angle).rjust(5), obstacle, buff[angle])\n print '20cm'.rjust(30) + '50cm'.rjust(30) + '1m'.rjust(50)","def wepbuy(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC+\"14C\"+self.ESC+\"1;34m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"0;34m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"0;34m\"+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;34m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;34m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"46m\"+self.A178+self.ESC+\"40m\"+self.A223+self.ESC+\"2C\"+self.ESC+\"37m The\"+self.ESC+\"CSaga\"+self.ESC+\"Cof\"+self.ESC+\"Cthe\"+self.ESC+\"CRed\"+self.ESC+\"CDragon\"+self.ESC+\"C-\"+self.ESC+\"C\"+self.ESC+\"34mWeapons\"+self.ESC+\"CList \"+self.ESC+\"C\"+self.A220+self.ESC+\"46m\"+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"46m\"+self.A178+self.ESC+\"44m\"+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"0;34m\"+self.A223+self.ESC+\"1m\"+self.A223+self.A223+self.A223+self.ESC+\"0;34m\"+self.A223+self.ESC+\"1m\"+self.A223+self.A223+self.ESC+\"0;34m\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"0;34m\"+self.A223+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"0;34m\"+self.A223+self.ESC+\"1m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"46m\"+self.A178+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"46m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"0;36mWeapons\"+self.ESC+\"27CPrice\"+self.ESC+\"7C\"+self.ESC+\"1;34;44m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"44m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"40m1.\"+self.ESC+\"CStick\"+self.ESC+\"0;34m..................................\"+self.ESC+\"36m200\"+self.ESC+\"C\"+self.ESC+\"1;34;44m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"44m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"40m2.\"+self.ESC+\"CDagger\"+self.ESC+\"0;34m...............................\"+self.ESC+\"36m1,000\"+self.ESC+\"C\"+self.ESC+\"1;34;44m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"44m\"+self.A178+self.ESC+\"2C\"+self.ESC+\"40m3.\"+self.ESC+\"CShort\"+self.ESC+\"CSword\"+self.ESC+\"0;34m..........................\"+self.ESC+\"36m3,000\"+self.ESC+\"C\"+self.ESC+\"1;34;44m\"+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"44m\"+self.A177+self.ESC+\"2C\"+self.ESC+\"40m4.\"+self.ESC+\"CLong\"+self.ESC+\"CSword\"+self.ESC+\"0;34m..........................\"+self.ESC+\"36m10,000\"+self.ESC+\"C\"+self.ESC+\"1;34;44m\"+self.A177+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"44m\"+self.A176+self.ESC+\"2C\"+self.ESC+\"40m5.\"+self.ESC+\"CHuge\"+self.ESC+\"CAxe\"+self.ESC+\"0;34m............................\"+self.ESC+\"36m30,000\"+self.ESC+\"C\"+self.ESC+\"1;34;44m\"+self.A176+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"0;34m\"+self.A223+self.ESC+\"2C\"+self.ESC+\"1m6.\"+self.ESC+\"CBone\"+self.ESC+\"CCruncher\"+self.ESC+\"0;34m......................\"+self.ESC+\"36m100,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A176+self.ESC+\"2C\"+self.ESC+\"1m7.\"+self.ESC+\"CTwin\"+self.ESC+\"CSwords\"+self.ESC+\"0;34m........................\"+self.ESC+\"36m150,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A176+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A177+self.ESC+\"2C\"+self.ESC+\"1m8.\"+self.ESC+\"CPower\"+self.ESC+\"CAxe\"+self.ESC+\"0;34m..........................\"+self.ESC+\"36m200,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A177+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A178+self.ESC+\"2C\"+self.ESC+\"1m9.\"+self.ESC+\"CAble's\"+self.ESC+\"CSword\"+self.ESC+\"0;34m.......................\"+self.ESC+\"36m400,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A178+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A219+self.ESC+\"C\"+self.ESC+\"1m10.\"+self.ESC+\"CWan's\"+self.ESC+\"CWeapon\"+self.ESC+\"0;34m.....................\"+self.ESC+\"36m1,000,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A223+self.ESC+\"C\"+self.ESC+\"1m11.\"+self.ESC+\"CSpear\"+self.ESC+\"COf\"+self.ESC+\"CGold\"+self.ESC+\"0;34m....................\"+self.ESC+\"36m4,000,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A219+self.ESC+\"C\"+self.ESC+\"1m12.\"+self.ESC+\"CCrystal\"+self.ESC+\"CShard\"+self.ESC+\"0;34m...................\"+self.ESC+\"36m10,000,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A219+self.ESC+\"C\"+self.ESC+\"1m13.\"+self.ESC+\"CNiras's\"+self.ESC+\"CTeeth\"+self.ESC+\"0;34m...................\"+self.ESC+\"36m40,000,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A219+self.ESC+\"C\"+self.ESC+\"1m14.\"+self.ESC+\"CBlood\"+self.ESC+\"CSword\"+self.ESC+\"0;34m....................\"+self.ESC+\"36m100,000,000\"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.A219+self.ESC+\"C\"+self.ESC+\"1m15.\"+self.ESC+\"CDeath\"+self.ESC+\"CSword\"+self.ESC+\"0;34m....................\"+self.ESC+\"36m400,000,000\"+self.ESC+\"C\"+self.ESC+\"1;34;44m\"+self.A176+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"14C\"+self.ESC+\"44m\"+self.A176+self.ESC+\"0;34m\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.A220+self.A178+self.ESC+\"C\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A178+self.ESC+\"C\"+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.A220+self.A220+self.ESC+\"C\"+self.A220+self.A176+self.ESC+\"C\"+self.A220+self.ESC+\"C\"+self.A220+self.A220+self.A220+self.A220+self.A178+self.A220+self.A220+self.A220+self.A220+self.A176+self.A220+self.A220+self.A219+self.ESC+\"1;44m\"+self.A176+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"0m\\r\\n\";\n\t\treturn thismsg","def KingWen(font, hexid):\n\n ret = []\n ch = 0x4DC0 + hexid - 1\n exec 'char = u\\'\\u%4x\\'' % ch\n lines = GetFontPixels(font, char, 'R', ' ')\n ret.append(' ' * 8)\n for line in lines[1::2]:\n hex_chars = ''.join(line[1:-1])\n ret.append(hex_chars[1:-1])\n ret.append(' ' * 8)\n return ret","def help():\n print(f\"{YELLOW}zwc{RESET} - Hidden messages using Zero Width Characters.\")","def __card_display(self):\n return ''.join([(each_number if (i == 0 or i % 4 != 0) else ' ' + each_number)\n for i, each_number in enumerate(self.card_number)])","def write_display(self):\n for i, value in enumerate(self.buffer):\n self.bus.write_byte_data(self.address, i, value)","def _make_sysex_message(self, text_message, display_index):\n if len(text_message) > 28:\n text_message = text_message[:27]\n sysex_message = [240, 0, 0, 102, 23, 18, min(display_index, 3) * 28]\n filled = text_message.ljust(28)\n for c in filled:\n sysex_message.append(ord(c))\n sysex_message.append(247)\n return sysex_message","def show_fixedblock(self):\n fb = self.station.get_raw_fixed_block(unbuffered=True)\n for i, ptr in enumerate(range(len(fb))):\n print('%02x' % fb[ptr], end=' ')\n if (i+1) % 16 == 0:\n print()","def displayHand(hand):\n for letter in hand.keys():\n for j in range(hand[letter]):\n print letter, # print all on the same line\n print # print an empty line","def encode_this( self, charCode ):\n values = []\n # Character Bitmap\n bmp = self.characters[charCode]\n # Nbre of entry in the dictionnary = Character width\n nbr_entry = bmp.width\n # Number of bytes per entry (=font height / 8bits.)\n # Vera_19 = 19 point height => 19/8 = 2.375 => must be coded on 3 bytes\n nbr_bytes = ceil(self.max_height / 8)\n if ((self.max_height%8) == 0): # Encoded font must always starts with 0b1, so if the font has 8 points height (or a multiple)\n nbr_bytes += 1 # we have to create more room to receive that bit.\n nbr_bits = nbr_bytes * 8\n\n # If we are coding 19 bits on X bytes, we will have to pad the left most\n # bits with 1\n pad_bits = nbr_bits - self.max_height\n pad_value = 0\n for i in range( pad_bits ):\n # debug: print( 'shift by %i' % (nbr_bits-1-i) )\n pad_value = pad_value + ( 1<<(nbr_bits-1-i))\n\n # Building the values\n for iw in range( bmp.width ):\n value = pad_value\n # Debug: print( '---------------------------' )\n for ih in range( bmp.height ):\n _ih = bmp.height-1-ih # Must start by the \"bottom\" of the character\n\n # Align on the BottomLine\n _height_extra_shift = self.max_height-bmp.height # The character may have 10 pixels height on a 19 point height font --> shift properly to the left!\n\n # Align on the BaseLine (if it applies)\n # check if we do need to insert a descender space (in pixel) under the character\n if ( len(self.descender_ordinals)>0 ) and not( self.char_has_descender( charCode ) ):\n # move up the normal character baseline\n _height_extra_shift = _height_extra_shift - self.ajusted_descender_size( charCode )\n\n # SPECIAL ALIGNMENT\n # check for special alignment instruction (~,comma,\", etc)\n _align = self.special_align_ordinals.get( charCode, None )\n if _align == 'T':\n # special alignment ON TOP -> reset shifting\n _height_extra_shift = 0\n elif _align == 'M':\n # Special alignment IN MIDLLE of baseline and top\n _height_extra_shift = _height_extra_shift - ( self.max_height-bmp.height-self.ajusted_descender_size( charCode ))//2\n elif _align == 'B':\n # Specoam alignment TO BOTTOM (so re-reset the _height_extra_shift on the BottomLine)\n _height_extra_shift = self.max_height-bmp.height\n\n # TODO: replacing the descender feature by glyph.bitmap_top does not work properly\n # for every characters\n # _height_extra_shift = self.max_height - self.glyphs[ charCode ].bitmap_top\n\n # print( 'coding bit %i, extra shift of %i FOR VALUE %s' % (_ih, _height_extra_shift, bmp.pixel_at( iw, _ih )) )\n if bmp.pixel_at( iw, _ih ):\n value = value + (1 << (_ih+_height_extra_shift))\n values.append( value )\n\n\n return values","def hbar(value):\n bar = '\\xe2\\x96\\x88'*(value/8)\n if value%8:\n bar += '\\xe2\\x96' + chr(0x90 - value%8)\n return bar","def draw(self):\n res = ''\n # ANSI code to clear the screen\n #res += chr(27) + \"[2J\"\n for position, value in enumerate(self.board.tttboard):\n if value is None:\n res += str(position)\n #sys.stdout.write(str(position))\n else:\n res += str(value)\n #sys.stdout.write(str(value))\n\n if (position + 1) % 3 != 0:\n res += str('|')\n #sys.stdout.write('|')\n else:\n #print ''\n\n res += str('\\n')\n if position == 2 or position == 5:\n #print '-' * 5\n\n res += '-' * 5\n res += str('\\n')\n return res","def show( self):\n def symbol( i):\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\n \n X, Y = np.max( self.board.positions, 0)\n # -2 to indicate outside board.\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \n for x, y in self.board.positions:\n display[x, y] = -1 # -1 to indicate unoccupied\n for p, i in self.occupation.items():\n x, y = self.board.positions[p]\n display[x, y] = i\n for x in xrange(X+1):\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\n print s","def display_char(self) -> None:\r\n print(self.char if self.was_guessed else '_', end=' ')","def terminal_diagram(self):\n lines = [[unichr(9632) for x in range(k)]\n for k in self[1]]\n lines += [[unichr(9632) for x in range(k)]\n + [unichr(9675)] for k in self[0]]\n lines.sort(reverse=True, key=len)\n for line in lines:\n print(' '.join(line))\n print('')","def int_21H_1(self):\r\n\r\n ascii_char = self.GUI.get_key_value() # ten do w wczytania\r\n self.registers['AX'].move_into(ascii_char, 0, is_int=True) # zapisanie kodu ascii do AXL\r","def display_characters(self):\n return f'{self.character_set}'","def show_frame_info(self):\n if self.font:\n font_surface = self.font.render(\"Sample Number: \" + \\\n str(self.data_row_num-self.num_lndmrks) + \\\n \" of \" + \\\n str(self.tot_rows - self.data_start_row), \\\n False, self.GREEN)\n pts = (self.screen_width / 3),3\n self.screen.blit(font_surface,pts)","def display_content(com,message):\n #message = message.encode('utf-8')\n #message = message.decode('ascii', 'ignore')\n safeMsg = filter(lambda x: x in string.printable, message)\n safeMsg = safeMsg.replace('\\n', ' ')\n print \"ALPHA: \", safeMsg\n try:\n #com = serial.Serial(config.devalpha, 9600, timeout=3)\n #com.close()\n #com.open()\n comstr = config.alpha['display'] + safeMsg + config.alpha['eot']\n com.write(comstr)\n #com.close()\n except serial.SerialException as e:\n logging.warning(\"Serial exception: \"+str(e))","def code_display(self):\r\n return u'%s-%s-%s' % (self.code[:3], self.code[3:6], self.code[6:])","def errors_icons(self):\n msg_errors_lifes = ''\n for i in range(0,5):\n if self.letters_wrong <= i:\n msg_errors_lifes += ' ♥ '\n else:\n msg_errors_lifes += ' ☠ ' \n return msg_errors_lifes","def pretty_hebrew(val):\n return 'font-size:20px; font-family: Times New Roman; text-align: right; max-width: 500px'","def __str__(self):\n return f\"{self._desc:16s}\"","def __repr__(self):\r\n numLetters = self.numLetters\r\n S = ''\r\n S += 3*'\\n'\r\n S += ' '\r\n for i in range(numLetters):\r\n S += self.currentBoard[i] + ' '\r\n\r\n return S","def sn(self):\n\t\tstring = []\n\t\tresp = [0x00]\n\t\tself.spi.transfer([0x10], [0x00], 1)\n\t\ttime.sleep(9e-3)\n\t\tfor i in range(60):\n\t\t\tself.spi.transfer([0x00], resp, 1)\n\t\t\tstring.append(chr(resp[0]))\n\t\ttime.sleep(0.1)\n\t\treturn ''.join(string).strip()","def rendermsg(self,msg):\n return ' '.join(['%02x'%ord(x) for x in msg])","def __str__(self):\n return textwrap.fill('{:064b}'.format(self.num), 8)","def DebugFormat(self):\n print FormatAsBits((self.output, self.out_boff))\n for i in xrange(self.idx_byte*8 + self.idx_boff - 1):\n if not i % 8:\n sys.stdout.write(\"|\")\n sys.stdout.write(\"-\")\n print \"^\"","def showHexaView (self, data, dataType, shortName):\n self.hexLogsView.display( data = data, dataType = dataType, shortName = shortName)","def make_control_character():\n # Add one character made up of one codepoint each from\n # (High Surrogates + High Private Use Surrogates) and Low Surrogates.\n # We expect each such pair to behave as a single high-codepoint\n # character.\n controls = ('0000', '001F')\n return [unicode_char(char)\n for char in range(int(controls[0], 16), int(controls[1], 16)+1)]","def unH(s):\n return ''.join([chr(int(s[i:i+2],16)) for i in range(2, len(s),2)])","def print_short(sp) :\n sp.print_peak_data_short()","def pretty(self):\n #table = [\"\".join([\"%8s \" % s for s in self.alpha.getSymbols()])]\n table = []\n for row in PWM.getFreq(self):\n table.append(\"\".join([\"%8.6f \" % y for y in row]))\n return table","def H(s):\n return 'H_' + ''.join(['%02x' % ord(x) for x in s])","def __show_protocol__(self, data):\n t_16 = t_16_Bit_Options()\n t_8 = t_8_Bit_Options()\n t_var = t_8_Bit_Options()\n print('Received ' + str(len(data)) + ' Bytest')\n\n #----------------------------------------------------------------------\n print('='*80)\n print('Handling Protocol response: ' + hexlify(data))\n #----------------------------------------------------------------------\n print('='*80)\n print('Overhead Bytes: ' + hexlify(data[:BP_TOOL.OVERHEAD]))\n print('Number of UINT16 bitstream data = ' + str(data[BP_TOOL.UINT16S]))\n print('Number of UINT8 bitstream data = ' + str(data[BP_TOOL.UINT8S]))\n print('Number of var bitstream data = ' + str(data[BP_TOOL.VARS]))\n print('Follow = ' + str(self.get_follow(data)))\n print('Length = ' + str(self.get_length(data)))\n start = self.get_follow_and_length(data)\n end = start + BP_TOOL.SIZE_FOLLOW + BP_TOOL.SIZE_LEN\n print('Following bytes and length = ' + hexlify(data[start:end]))\n #----------------------------------------------------------------------\n print('='*80)\n bits = self.get_16bit_options_bits(data)\n values = self.get_16bit_options(data)\n options = self.get_options_requested(bits)\n\n # Display the options if exist\n if len(options):\n print('UINT16 bits...... : ' + hexlify(bits))\n print('UINT16 data...... : ' + hexlify(values))\n print('UINT16 Num of opts ... : ' + str(len(values) // 2))\n print('UINT16 options... : ' + str(options))\n print('-'*80)\n for x in range(len(options)):\n value = (values[x*2] << 8) | (values[x*2 + 1])\n opt = options[x]\n t_16.set_value(opt, value)\n print('Option: ' + t_16.options[opt]['name'] + ' ' + str(value))\n pprint.pprint(t_16.options)\n else:\n print('No 16 bit options')\n\n #----------------------------------------------------------------------\n print('-'*80)\n bits = self.get_8bit_options_bits(data)\n values = self.get_8bit_options(data)\n options = self.get_options_requested(bits)\n # Display the options if exist\n if len(options):\n print('UINT8 bits...... : ' + hexlify(bits))\n print('UINT8 data...... : ' + hexlify(values))\n print('UINT8 options... : ' + str(options))\n print('-'*80)\n for x in range(len(options)):\n value = values[x]\n opt = options[x]\n t_8.set_value(opt, value)\n print('Option: ' + t_8.options[x]['name'] + ' ' + str(value))\n pprint.pprint(t_8.options)\n else:\n print('No 8 bit options')\n\n #----------------------------------------------------------------------\n print('-'*80)\n bits = self.get_var_options_bits(data)\n values = self.get_var_options(data)\n print('VARS !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n # Display the options if exist\n if len(values):\n pprint.pprint(values)\n else:\n print('No var bit options')\n\n print('VAR options... : ' + str(self.get_options_requested(bits)))\n print('VARS !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n print('-'*80)","def test_repr_format(self):\n t = OneHotEncode(3)\n assert t.repr_format(\"asfa\") == \"OneHotEncode(asfa)\"","def print_character( self, ordinal ):\n print( '--- %s ----------------' % chr(ordinal) )\n if not( ordinal in self.characters ):\n print( '%s (%i) is not present in characters' % (chr(ordinal), ordinal) )\n print( 'Ordinal: %i' % ordinal )\n print( 'Char has descender: %s' % self.char_has_descender( ordinal ) )\n print( 'width, height = %i, %i' % (self.characters[ordinal].width, self.characters[ordinal].height) )\n # print( 'bitmap_top from base = %i' % self.glyphs[ordinal].bitmap_top )\n\n if self.char_has_descender( ordinal ):\n print( 'width, height = %i, %i (with %i px descender already included)' % (self.characters[ordinal].width, self.characters[ordinal].height, self.descender_size ) )\n else:\n # Sometime, Char-Size + Descender Size GOES OVER the font size --> reduce the descender\n _descender = self.ajusted_descender_size( ordinal )\n print( 'width, height = %i, %i (with %i px ajusted_descender added)' % (self.characters[ordinal].width, self.characters[ordinal].height + _descender, _descender ) )\n\n print( self.characters[ordinal] )","def show_instructions():\n\n print('4-digit Code has been set. Digits in range 1 to 8. You have 12 turns to break it.')","def supercombiner(bot, ev):\n # ported from jenni\n s = 'u'\n for i in iter(range(1, 3000)):\n if unicodedata.category(chr(i)) == \"Mn\":\n s += chr(i)\n if len(s) > 100:\n break\n bot.say(s)","def print_hint_board(self):\n board = \"\"\n count = 1 \n for i in range(3):#need to change this in the future\n for j in range(3):#need to change this in the future\n board += str(count) \n count += 1\n if j != 2:#need to change this in the future\n board += \" | \"\n board += \"\\n\"\n return board","def display(self):\n self.command(SSD1306_COLUMNADDR)\n self.command(0) # Column start address. (0 = reset)\n self.command(self.width-1) # Column end address.\n self.command(SSD1306_PAGEADDR)\n self.command(0) # Page start address. (0 = reset)\n self.command(self._pages-1) # Page end address.\n # Write buffer data.\n if self._spi is not None:\n # Set DC high for data.\n self._gpio.set_high(self._dc)\n # Write buffer.\n self._spi.write(self._buffer)\n else:\n for i in range(0, len(self._buffer), 16):\n control = 0x40 # Co = 0, DC = 0\n self._i2c.writeList(control, self._buffer[i:i+16])","def char(runtime_addr, n=1):\n\n set_formatter(runtime_addr, n, mainformatter.char_formatter)","def render(state, c_choice, h_choice):\r\n\r\n chars = {\r\n 2: h_choice,\r\n 1: c_choice,\r\n 0: ' '\r\n }\r\n str_line = '--------------------------------------------------'\r\n\r\n print('\\n' + str_line)\r\n for row in state:\r\n for cell in row:\r\n #print(cell)\r\n symbol = chars[cell]\r\n print(f'| {symbol} |', end='')\r\n print('\\n' + str_line)","def device_extension_formatting(info):\n return ('\\n\\t'+' '*(device_maxwidth+3)).join(info.split())","def hexDump(bytes):\n for i in range(len(bytes)):\n sys.stdout.write(\"%2x \" % (ord(bytes[i])))\n if (i+1) % 8 == 0:\n print repr(bytes[i-7:i+1])\n\n if(len(bytes) % 8 != 0):\n print string.rjust(\"\", 11), repr(bytes[i-7:i+1])","def print_hex(pbtData, szBytes):\n for szPos in range(szBytes):\n sys.stdout.write(\"%02x \" % convBytes(pbtData[szPos]))\n print('')","def render(self):\n for r in range(self.y_size):\n line = ''\n for c in range(self.x_size):\n glyph = self.MAP_GLYPH_TABLE[self.grid_data[r][c]]\n\n # overwrite with player\n if r == self.player_y and c == self.player_x:\n glyph = self.PLAYER_GLYPH_TABLE[self.player_heading]\n\n line += glyph\n print(line)\n\n print('\\n' * (20 - self.y_size))","def nice_output(self):\n return 'Pitch: {0} at {1}: {2}'.format(\n self.pitch_type, self.start_speed, self.des)","def line(self):\n\t\treturn self.ESC+\"32m-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\"+self.ESC+\"0m\\r\\n\"","def get_equipment(self):\n s = ''\n for i in range(12, 16):\n s += ' ' + str(self.dna[i])\n return s","def print(self):\r\n base = 8 * self.width\r\n print(base * \"-\")\r\n for x in range(self.height):\r\n output = \"\"\r\n for y in range(self.width):\r\n output = output + self.board[x][y] + \"|\"\r\n print(\"|\" + output)\r\n print(base * \"-\")","def get_char_echo(self) -> str:\n ...","def output(self):\n try:\n sys.stdout.write(chr(self.tape.current_cell()%256)) # Wrapping fits it into ascii codes\n except:\n print \"Error -001\"","def print(self):\n board_string = ''\n for y in range(self.size):\n if y == 0:\n board_string += '+ '\n for x in range(self.size):\n board_string += str(x+1) + ' '\n board_string += '\\n'\n board_string += (1+3*self.size)*'-'\n board_string += '\\n'\n board_string += str(y+1)+'|'+y*' '\n \n for x in range(self.size):\n board_string += ' '\n if self.board[y,x] == HexBoard.BLUE:\n board_string += self.char_player1\n elif self.board[y,x] == HexBoard.RED:\n board_string += self.char_player2\n else: \n board_string += self.char_empty\n board_string += '\\n'\n board_string = board_string.strip()\n\n print(board_string)","def hp34401a_lcd_disp(hp_meter, message):\n hp_meter.write(\"DISP:TEXT:CLE\")\n hp_meter.write(\"DISP:TEXT '\" + str(message) + \"'\")","def render(self, s):\n if s < self.num_states - 1:\n a = np.zeros((self.height, self.width), dtype=np.unicode_)\n # mark all locations as inaccessible first\n a[:, :] = u'█'\n # regular fields\n for c in self.locs:\n a[c[1], c[0]] = u'░'\n # mark exits\n for c in self.exitsP:\n a[c[1], c[0]] = 'E'\n for c in self.exitsN:\n a[c[1], c[0]] = 'F'\n # player character\n c = self.locs[s]\n a[c[1], c[0]] = u'☺'\n a = [' '.join(row) for row in a]\n a = '\\n'.join(reversed(a))\n return a\n else:\n return 'Game over!'","def delimit_output(num=25):\n print('-'*num)","def Display2(self, rain, humidity, lang):\n if lang == 'eng':\n string = rain + '% Rain\\n' + humidity + '% Humidity'\n return string\n else:\n return 0\n # Not Yet Supported","def _make_totp(self):\n secret = struct.pack('> Q', int(time.time()) / DEFAULT_STEP).ljust(64, chr(0x0))\n response = self.yk.challenge_response(secret, slot=self.slot.get())\n # format with appropriate number of leading zeros\n fmt = '%.' + str(self.digits.get()) + 'i'\n totp_str = fmt % (yubico.yubico_util.hotp_truncate(response, length=self.digits.get()))\n return totp_str","def display(field):\n side = int(math.sqrt(len(field))) # in number of elements (tiles)\n \n def pos():\n cy, cx = win.getyx()\n stdscr.addstr(0, 0, \"cy: \"+str(cy)+\", cx: \"+str(cx))\n\n def br():\n while True:\n c = stdscr.getch()\n if c == curses.KEY_RIGHT:\n break\n win.refresh()\n\n win.addstr(0, 0, '┏')\n for _ in range(side-1):\n win.addstr('━━━━━━')\n win.addstr('┳')\n win.addstr('━━━━━━')\n win.addstr('┓ ')\n\n for y in range(side):\n \n win.addstr('┃')\n for x in range(side):\n #stdscr.addstr(0, 0, \"side: \" + str(x))\n idx = y * side + x\n if field[idx] == 0:\n win.addstr(' '.center(6))\n else:\n n = field[idx]\n color = curses.color_pair(0)\n if n < 0:\n field[idx] *= -1\n n = field[idx]\n color = curses.A_BOLD | curses.A_STANDOUT\n elif n == 4:\n color = curses.color_pair(3)\n elif n == 8:\n color = curses.color_pair(4)\n elif n >= 16:\n color = curses.color_pair(1)\n \n #win.addstr(str(n).center(6), color)\n \n n = str(n)\n left = (6-len(n)) // 2\n right = 6 - (left + len(n))\n win.addstr(left*' ')\n win.addstr(n, color)\n win.addstr(right*' ')\n\n \n win.addstr('┃')\n win.addstr(' ')\n if y == side-1:\n break\n else: \n win.addstr('┣')\n for _ in range(side-1):\n win.addstr('━━━━━━')\n win.addstr('╋')\n win.addstr('━━━━━━')\n win.addstr('┫ ')\n \n win.addstr('┗')\n for _ in range(side-1):\n win.addstr('━━━━━━')\n win.addstr('┻')\n win.addstr('━━━━━━')\n #pos()\n #br()\n win.addstr('┛')\n\n #win.border()\n win.refresh()","def render(board):\n #The top of the board render, which gives enough space for each square based on max square value\n spot_length = len(str(len(board)))\n rv = '-'*((spot_length+1)*len(board))+'-\\n' \n\n #Create a print statement for each line\n for i in range(len(board)): \n line = '|' \n\n #For each number in line, add to print line \n for j in range(len(board[i])): \n spot = str(board[i][j]) \n #If spot is a zero, replace for visuals with '_' \n if spot == '0': \n spot = '_'*spot_length\n #Add number to print line with a separator '|'\n line += ' '*(spot_length-len(spot)) + spot + '|' \n\n #Print the rendered line\n rv += line + '\\n' \n\n #Print the bottom of the board\n rv += '-'*((spot_length+1)*len(board))+'-' \n return rv","def Display1(self, high, low, windSpeed, units_Speed, windDir, lang):\n if lang == 'eng':\n string = 'Temp H:' + high + ' L:' + low + '\\nWind ' + windSpeed + units_Speed + ' ' + windDir\n return string\n else:\n return 0\n # Not Yet Supported","def display(self):\n print (\"+\" + \"-\"*self.size + \"+\")\n for i in range(self.size):\n terrain_strs = [Terrain.display_string(self.array[j, i]) for j in range(self.size)]\n print(\"|\" + \"\".join(terrain_strs) + \"|\")\n print (\"+\" + \"-\"*self.size + \"+\")","def Print(self, s, color=(229, 153, 153, 255)):\r\n self.screen.blit(self.font.render(s, True, color), (5, self.textLine))\r\n self.textLine += 15","def display_event(player,player_name,event,count_line):\n player_name = 'you !'\n color_player= Back.RED\n if player == 'player1':\n color_player= Back.BLUE\n\n if event=='intro' :\n\n l0='█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████'\n l1='██████████████████████████████████████████████████████++████████++███████████████████████████████████████████████████████'\n l2='███████+++█████████████████++██████████+++████████████++███++█████+++███████████████++███████████████++██████████++██████'\n l3='+++++++███++++++++++████;████:++██+++██+++██++++++++++++++++++++++███+++++++████████++███████+++██+++██+++++++█████+++██+'\n l4='+++++++***++*****+++++++++++++++++***+++++++**++++++++**+++++***++++++++++++++***++++++++++++***++:++++++***++***++++++++'\n l5='**+++██████**████*****+++*******************++*******;███*******++***████*++**+++*******+++*******+++**████:*******+++++*'\n l6='*****██:::**:::██********::*******;::************::**██:██******::***██*██*******************:::******██*:::*************'\n l7='*****██;,::,:::██:::*██**██**:████;:::█████;,,::;**::██:██:::***::***██::██:;██**;██::████:**██:*██::*██::::::█████:::,:*'\n l8='::;:;██::::::::██::::██::██::██::██::██:::::::::::::::███::::::::::::██::██::██:█:██:::::██::██:███:::██:::::██::;:::::::'\n l9=':::::█████:::::██::::██::██::██::██::██::::::::::::::██::::::::::::::██;:██::██:█:██:::::██::███:::::██████::██::::::::::'\n l10=':::::██::::::::██::::██::██::██████:::████:::::::::,,██:████:::::::::██,:██::██:█;██:;█████::██:::::::██::::::████:::::::'\n l11=':::::██:::::,,,██::::██,,██::██:,,:::::,,██:::,,,::,,██,,██::::::::::██::██,:██:█:██:██:,██::██:,,::::██:,,,:::::██,,,:::'\n l12=',,:::██:::,,,,,██:::.,████,,,██:,,,,,,,::██:::,,,,,,,██,,██,,,,,::,,,██,██,,,,██:██,:██,,██,,██,::,,,:██,,,,,,:::██,,,,,:'\n l13=',,,,,██████,,██████,,,,██,,,,,████,,,█████,,,,,,,,,,,,███,██,,,,,,,,,████,,,,,██,██,,,█████,,██,,,,,,,██,,,,,█████,,:,,,,'\n l14=',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,'\n l15=',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,'\n la1=Back.WHITE+Fore.BLACK+' GROUPE 42 : EMILIE LAURENT, JOFFREY BIENVENU, JONATHAN MAROIT ET SYLVAIN PIRLOT '\n\n\n\n\n line_list = [l0,l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12,l13,l14,l15,la1]\n if count_line < len(line_list):\n print Fore.RED+ Back.BLACK+line_list[count_line]\n\n\n\n elif event=='game_over':\n d0=(Fore.BLACK+Back.WHITE)+' '+('The loser is: '+player_name)+' '\n d1='██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████'\n d2='███████████████████████████████████████ ████████████████████████████████████ ███████████████████████████████████████'\n d3='███████████████████████████████████████ ████████████████████████████████████ ███████████████████████████████████████'\n d4='███████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████'\n d5='███████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████'\n d6='███████████████████████████████████████████████████ ███████████████████████████████████████████████████'\n d7='███████████████████████████████████████████████████ ████████ ████████ ███████████████████████████████████████████████████'\n d8='███████████████████████████████████████████████████ ████████ ████████ ███████████████████████████████████████████████████'\n d9='███████████████████████████████████████████████████ ████████ ████████ ███████████████████████████████████████████████████'\n d10='███████████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████████'\n d11='███████████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████████'\n d12='███████████████████████████████████████████████████ ████ ███████████████████████████████████████████████████'\n d13='███████████████████████████████████████████████████ ████ ███████████████████████████████████████████████████'\n d14='███████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████'\n d15='███████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████'\n d16='███████████████████████████████████████ ████████ ████ ████ ████████ ███████████████████████████████████████'\n d17='███████████████████████████████████████ ████████ ████ ████ ████████ ███████████████████████████████████████'\n d18='██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████'\n d19=(Fore.BLACK+Back.WHITE)+' '\n death_list=[d0,d1,d2,d3,d4,d5,d6,d7,d8,d9,d10,d11,d12,d13,d14,d15,d16,d17,d18]\n if count_line < len(death_list):\n print Fore.BLACK+color_player+death_list[count_line]\n\n\n elif event == 'win':\n\n w0=Back.BLACK+Fore.WHITE+' THE '\n wa=' '\n w1=' ██ █ ██ █ ██ ██ ████ ██ ██ ██ ██ ██████ █████ █████ '\n w2=' █████████ ███ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ █ ███ █ '\n w3=' ███ █ ███ █ ██ ██ ██ ███ ██ ███ ██ ██ ██ ██ █ █ ██ █ '\n w4=' ██ █ ██ █ ██ █ ██ ██ ████ ██ ████ ██ ██ ██ ██ █ █ █ ██ █ '\n w5=' █ █ █ █ ██ █ ██ ██ ██ ████ ██ ████ █████ █████ █ █ █ '\n w6=' █ █ ██ █ ██ ██ ██ ███ ██ ███ ██ ██ ██ █ █ █ '\n w7=' █ █ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ █ █ █ '\n w8=' █ █████████ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ███ █ █ '\n w9=' ███ █████████ ██ ██ ████ ██ ██ ██ ██ ██████ ██ ██ ██ ██ ██ █ '\n wb=' █ █ █ █ ████ '\n wb1=' '\n w10=Back.BLACK+Fore.WHITE+' IS '\n w11=Back.BLACK+Fore.WHITE+' '+player_name+' '\n win_list=[w0,wa,w1,w2,w3,w4,w5,w6,w7,w8,w9,wb,wb1,w10,w11]\n if count_line < len(win_list):\n print Fore.BLACK+color_player+win_list[count_line]","def _string_data(self, data):\n print(\"_string_data:\")\n string_to_print = []\n for i in data[::2]:\n string_to_print.append(chr(i))\n print(string_to_print)","def repr2col(tups):\n quant = tuple(round(comp * 255) for comp in tups)\n if quant[3] != 255: return \"\".join([\"{0:02x}\".format(m) for m in quant])\n quant = quant[:3]\n for w in shortaliases:\n if quant == shortaliases[w]: return w\n return \"#\" + \"\".join([\"{:02x}\".format(n) if max(n % 17 for n in quant) else \"{:x}\".format(n >> 4) for n in quant])","def print_cards(cards):\r\n string = ''\r\n for c in cards:\r\n suit = c[0]\r\n if suit == 1:\r\n suit = \"\\u2665\" # heart\r\n elif suit == 2:\r\n suit = \"\\u2660\" # Spade\r\n elif suit == 3:\r\n suit = \"\\u2666\" # Diamond\r\n else:\r\n suit = \"\\u2663\" # club\r\n\r\n num = c[1]\r\n if num == 11:\r\n num = 'J'\r\n elif num == 12:\r\n num = 'Q'\r\n elif num == 13:\r\n num = 'K'\r\n else:\r\n num = str(num)\r\n\r\n string = string + num + suit + ' '\r\n return string","def rock():\n typer.echo(\"🤖🤘\")","def show_packet_info(scapyshark, packet):\n\n # Grab the actual packet\n packet = scapyshark._top_box.get_packet(packet)\n\n #\n # Middle Box\n #\n \n scapyshark._middle_box.base_widget.body.clear()\n\n for line in packet.show(dump=True).split(\"\\n\"):\n scapyshark._middle_box.base_widget.body.append(urwid.Text(line))\n\n #\n # Bottom Box\n #\n\n scapyshark._bottom_box.base_widget.body.clear()\n\n b = bytes(packet)\n first_line = 0\n\n for i in range(0, len(b), 16):\n b_chunk = b[i:i+16]\n\n line = \":\".join(\"{:02x}\".format(c) for c in b_chunk)\n\n if first_line == 0:\n first_line = len(line)\n else:\n if len(line) != first_line:\n line += ' '*(first_line - len(line))\n\n line += ' | '\n\n for c in b_chunk:\n c2 = chr(c)\n if c2 in printable:\n line += c2\n else:\n line += '.'\n\n scapyshark._bottom_box.base_widget.body.append(urwid.Text(line))","def horizontal_char(self):\n ...","def display(tab):\n screen = [None] * 64\n for i in range(8):\n for j in range(8):\n if tab[i][j] == 1:\n sense.set_pixel(i, j, 255, 0, 0) if STATES[index_state] == \"temp\" else sense.set_pixel(i, j, 0, 0, 255)\n else:\n sense.set_pixel(i, j, 0, 0, 0)","def reveal_occupants(idx, huts):\n msg = \"\"\n print(\"展示小屋内部情况...\")\n for i in range(len(huts)):\n occupant_info = \"<%d:%s>\" % (i + 1, huts[i])\n if i + 1 == idx:\n occupant_info = \"\\033[1m\" + occupant_info + \"\\033[0m\"\n msg += occupant_info + \" \"\n\n print(\"\\t\" + msg)\n print_dotted_line()","def display(values):\n boxes = cross(rows, cols)\n # This finds the max number of characters in each box and adds one.\n # the max will be '123456789' so 9 + 1 = 10\n width = 1+max(len(values[s])for s in boxes)\n\n # This will create 30 dashes separated with a \"+\" 3x\n line = '+' .join(['-'*(width*3)]*3)\n\n # This builds the board. It iterates through [A1, A2, etc.] and then\n # uses the .center method to center whatever value is in that cell.\n # It adds a | in-between columns 3 & 6 otherwise an empty string.\n # It prints a line if the row is C or F\n for r in rows:\n print(''.join(values[r+c].center(width)+('|' if c in '36' else '')\n for c in cols))\n if r in 'CF': print(line)\n return","def hAKI(self):\n\n aki = self.get_AKI()\n return \":\".join((\"%02X\" % ord(i) for i in aki)) if aki else \"\"","def display_string(text_area_no: int) -> str:\n if text_area_no == 1:\n text = ''\n for v in utterances['P1']:\n text += v + '\\n'\n return text\n elif text_area_no == 2:\n text = ''\n for v in utterances['S1']:\n text += v + '\\n'\n return text\n elif text_area_no == 3:\n text = ''\n for v in utterances['S2']:\n text += v + '\\n'\n return text\n elif text_area_no == 4:\n text = ''\n for v in utterances['S3']:\n text += v + '\\n'\n return text\n elif text_area_no == 5:\n text = ''\n for v in utterances['S4']:\n text += v + '\\n'\n return text\n elif text_area_no == 6:\n text = ''\n for v in utterances['S4']:\n text += v + '\\n'\n return text\n elif text_area_no == 7:\n text = ''\n for v in utterances['C1']:\n text += v + '\\n'\n return text\n elif text_area_no == 8:\n text = ''\n for v in utterances['C2']:\n text += v + '\\n'\n return text","def show_device_information_long(self):\n\n for device in self._devices:\n print(\"\")\n if device['Device Type'].startswith(\"enclosu\"):\n if device.get('Device Type'):\n print(\"{0:>32}: {1}\".format(\"Device Type\", device['Device Type']))\n if device['Device Type Description']:\n print(\"{0:>32}: {1}\".format(\"Device Description\", device['Device Type Description']))\n if device.get('SCSI Device Name'):\n print(\"{0:>32}: {1}\".format(\"SCSI Device Name\", device['SCSI Device Name']))\n if device.get('Product Identification'):\n print(\"{0:>32}: {1}\".format(\"Product Identification\", device['Product Identification']))\n if device.get('Vendor Identification'):\n print(\"{0:>32}: {1}\".format(\"Vendor Identification\", device['Vendor Identification']))\n if device.get('Firmware Version'):\n print(\"{0:>32}: {1}\".format(\"Firmware Version\", device['Firmware Version']))\n if device.get('Serial Number'):\n print(\"{0:>32}: {1}\".format(\"Serial Number\", device['Serial Number']))\n if device.get('SAS Address'):\n print(\"{0:>32}: {1}\".format(\"SAS Address\", device['SAS Address']))\n else:\n if device.get('Device Type'):\n print(\"{0:>32}: {1}\".format(\"Device Type\", device['Device Type']))\n if device['Device Type Description']:\n print(\"{0:>32}: {1}\".format(\"Device Description\", device['Device Type Description']))\n if device.get('Linux Device Name'):\n print(\"{0:>32}: {1}\".format(\"Linux Device Name\", device['Linux Device Name']))\n if device.get('SCSI Device Name'):\n print(\"{0:>32}: {1}\".format(\"SCSI Device Name\", device['SCSI Device Name']))\n if device.get('Product Identification'):\n print(\"{0:>32}: {1}\".format(\"Product Identification\", device['Product Identification']))\n if device.get('Vendor Identification'):\n print(\"{0:>32}: {1}\".format(\"Vendor Identification\", device['Vendor Identification']))\n if device.get('Firmware Version'):\n print(\"{0:>32}: {1}\".format(\"Firmware Version\", device['Firmware Version']))\n if device.get('Serial Number'):\n print(\"{0:>32}: {1}\".format(\"Serial Number\", device['Serial Number']))\n if device.get('Drive Capacity'):\n print(\"{0:>32}: {1}\".format(\"Drive Capacity\", device['Drive Capacity']))\n if device.get('Block Length'):\n print(\"{0:>32}: {1}\".format(\"Block Length\", device['Block Length']))\n if device.get('Power On Hours'):\n print(\"{0:>32}: {1}\".format(\"Power On Hours\", device['Power On Hours']))\n if device.get('Current Temperature'):\n print(\"{0:>32}: {1}\".format(\"Current Temperature\", device['Current Temperature']))\n if device.get('SAS Address'):\n print(\"{0:>32}: {1}\".format(\"SAS Address\", device['SAS Address']))\n if device.get('Enclosure Device'):\n print(\"{0:>32}: {1}\".format(\"Enclosure Device\", device['Enclosure Device']))\n if device.get('Enclosure Slot'):\n print(\"{0:>32}: {1}\".format(\"Enclosure Slot\", device['Enclosure Slot']))\n if device.get('Slot Description'):\n print(\"{0:>32}: {1}\".format(\"Slot Desciption\", device['Slot Description']))\n\n if len(self._devices):\n print(\"\")","def my_banner(bannerString):\n print(len(bannerString) * \"!\")\n print(bannerString)\n print(len(bannerString) * \"!\")"],"string":"[\n \"def banner_ascii():\\n print(\\\"\\\")\\n print(f\\\"\\\\n{RED} Steganography Tool{RESET}\\\")\\n print(f\\\"{RED} Made By {RESET}\\\")\\n print(f\\\"{RED} Ehthe Samul Islam Laskar USN:1DS16CS712 {RESET}\\\")\\n print(f\\\"{RED} B Padma USN:1DS19CS420{RESET}\\\")\\n print(f\\\"{RED} Nikhil D Kanyal USN:1DS17CS731{RESET}\\\")\\n print(f\\\"{YELLOW}Type 'help' to see commands{RESET}\\\")\",\n \"def tower(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC+\\\"255D\\\"+self.ESC+\\\"0;1;44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"58C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"68C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"30m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"0;44m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"1m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"52C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"43m\\\"+self.A223+self.A223+self.ESC+\\\"0;43m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"40C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"0;34;43m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"30m\\\"+self.A223+self.A223+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40mYou reach th\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"42Ce castle and fight your way up the\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A178+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"30C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"1;36;40mtower!\\\"+self.ESC+\\\"41C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A219+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"24C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"48C\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A178+self.A177+self.A177+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A177+self.A177+self.A177+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40mYou blindly slash at any and all who\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"66C stand in \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A177+self.A176+self.A177+self.A176+self.A178+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A176+self.A177+self.A177+self.A219+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"30C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"1;36;40myour path - screaming to find the prisoner \\\"+self.ESC+\\\"4C\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"77C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40myou seek - and the jailer of your he\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"66Cart.\\\"+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.A176+self.A177+self.ESC+\\\"33m\\\"+self.A176+self.ESC+\\\"32m\\\"+self.A177+self.A176+self.A177+self.ESC+\\\"33m\\\"+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A178+self.A176+self.A177+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"23C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"48C\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A176+self.A177+self.A177+self.A177+self.A176+self.A178+self.A177+self.ESC+\\\"33m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"32m\\\"+self.A177+self.A177+self.A178+self.A176+self.A178+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"43m\\\"+self.A220+self.A220+self.ESC+\\\"0;43m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"34C\\\"+self.ESC+\\\"43m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"0;34;43m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"76C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"30m\\\"+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A177+self.A176+self.A177+self.A176+self.A176+self.ESC+\\\"33m\\\"+self.A176+self.A177+self.A178+self.A177+self.A177+self.ESC+\\\"32m\\\"+self.A177+self.A178+self.A177+self.A177+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A222+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A221+self.A219+self.ESC+\\\"1;44m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"44m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"44C\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"34m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"33;40m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"37C\\\"+self.ESC+\\\"44m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"1;37;40mYou make it to the top and \\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"74C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"40C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"4C\\\"+self.ESC+\\\"1;37;40mthrow open the door...\\\"+self.ESC+\\\"6C\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"74C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A178+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A178+self.A219+self.ESC+\\\"30;44m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"31C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"1;32;42m\\\"+self.A177+self.A178+self.A178+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"53C\\\"+self.ESC+\\\"32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+self.ESC+\\\"0;33m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"20C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1;32;42m\\\"+self.A178+self.A219+self.ESC+\\\"0;32m\\\"+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;30;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"32C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"46C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"42m\\\"+self.A176+self.A177+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"63C\\\"+self.ESC+\\\"42m\\\"+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"42m \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.A219+self.ESC+\\\"0;32m\\\"+self.A178+self.A219+self.ESC+\\\"42m \\\"+self.ESC+\\\"40m\\\"+self.A177+self.A177+self.A176+self.A176+self.A177+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"21C\\\"+self.ESC+\\\"1;42m\\\"+self.A178+self.A176+self.ESC+\\\"30;40m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A177+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"30;40m\\\"+self.A219+self.A219+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"36C\\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.A178+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+\\\" \\\"+self.A178+\\\" \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A176+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"52C\\\"+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.A178+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"63C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"42m\\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.A176+self.A178+self.A219+self.A219+self.A178+self.A178+self.A176+self.A176+\\\" \\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A177+self.A178+self.A176+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"26C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A177+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"36C\\\"+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.A177+self.A177+self.A177+self.ESC+\\\"1;42m\\\"+self.A219+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.A178+self.A177+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"51C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A177+self.A178+self.A178+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A177+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"64C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"37m<MORE> \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def basic_char_setup( self ):\\n\\t\\tsmall_bar = 3 # number of points per bar\\n\\t\\twide_bar = round(small_bar * 2.25,0) # 2.25 x small_bar\\n\\t\\tdpl = 50 # dots per line 300dpi/6lpi = 50dpl\\n\\t\\tself._nb = bytes( self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ( '*c%02ia%ib0P' % (small_bar, self.bc_height*dpl) ) + \\n\\t\\t\\t\\t\\t\\t self.owner.PRINTER_ESC + \\n\\t\\t\\t\\t\\t\\t (\\\"*p+%02iX\\\" % small_bar) )\\n\\t\\tself._wb = bytes( self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ('*c%02ia%ib0P' % (wide_bar, self.bc_height*dpl) )+\\n\\t\\t\\t\\t\\t\\t self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ('*p+%02iX' % wide_bar ) )\\n\\t\\tself._ns = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % small_bar ) )\\n\\t\\tself._ws = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % wide_bar ) )\\n \\n\\t\\t# DONE nb = bc39_esc+\\\"*c\\\"+TRANSFORM(small_bar,'99')+\\\"a\\\"+Alltrim(STR(bc39_height*dpl))+\\\"b0P\\\"+bc39_esc+\\\"*p+\\\"+TRANSFORM(small_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE wb = bc39_esc+\\\"*c\\\"+TRANSFORM(wide_bar,'99')+\\\"a\\\"+Alltrim(STR(bc39_height*dpl))+\\\"b0P\\\"+bc39_esc+\\\"*p+\\\"+TRANSFORM(wide_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE ns = bc39_esc+\\\"*p+\\\"+TRANSFORM(small_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE ws = bc39_esc+\\\"*p+\\\"+TRANSFORM(wide_bar,'99')+\\\"X\\\"\\n \\n\\t\\t# adjust cusor position to start at top of line and return to bottom of line\\n\\t\\tself._bc_start = bytes( self.owner.PRINTER_ESC + '*p-50Y' )\\n\\t\\tself._bc_end = bytes( self.owner.PRINTER_ESC + '*p+50Y' )\\n\\t\\t# DONE bc39_start = bc39_esc+\\\"*p-50Y\\\"\\n\\t\\t# DONE bc39_END = bc39_esc+\\\"*p+50Y\\\"\\n\\n\\t\\t# setup the structure allowing to print the code codebar section for various LETTERS\\n\\t\\tself._char39 = { u'1' : 'wb+ns+nb+ws+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'2' : 'nb+ns+wb+ws+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'3' : 'wb+ns+wb+ws+nb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'4' : 'nb+ns+nb+ws+wb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'5' : 'wb+ns+nb+ws+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'6' : 'nb+ns+wb+ws+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'7' : 'nb+ns+nb+ws+nb+ns+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'8' : 'wb+ns+nb+ws+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'9' : 'nb+ns+wb+ws+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'0' : 'nb+ns+nb+ws+wb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'A' : 'wb+ns+nb+ns+nb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'B' : 'nb+ns+wb+ns+nb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'C' : 'wb+ns+wb+ns+nb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'D' : 'nb+ns+nb+ns+wb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'E' : 'wb+ns+nb+ns+wb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'F' : 'nb+ns+wb+ns+wb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'G' : 'nb+ns+nb+ns+nb+ws+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'H' : 'wb+ns+nb+ns+nb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'I' : 'nb+ns+wb+ns+nb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'J' : 'nb+ns+nb+ns+wb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'K' : 'wb+ns+nb+ns+nb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'L' : 'nb+ns+wb+ns+nb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'M' : 'wb+ns+wb+ns+nb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'N' : 'nb+ns+nb+ns+wb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'O' : 'wb+ns+nb+ns+wb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'P' : 'nb+ns+wb+ns+wb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'Q' : 'nb+ns+nb+ns+nb+ns+wb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'R' : 'wb+ns+nb+ns+nb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'S' : 'nb+ns+wb+ns+nb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'T' : 'nb+ns+nb+ns+wb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'U' : 'wb+ws+nb+ns+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'V' : 'nb+ws+wb+ns+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'W' : 'wb+ws+wb+ns+nb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'X' : 'nb+ws+nb+ns+wb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'Y' : 'wb+ws+nb+ns+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'Z' : 'nb+ws+wb+ns+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'-' : 'nb+ws+nb+ns+nb+ns+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'.' : 'wb+ws+nb+ns+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u' ' : 'nb+ws+wb+ns+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'*' : 'nb+ws+nb+ns+wb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'$' : 'nb+ws+nb+ws+nb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'/' : 'nb+ws+nb+ws+nb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'+' : 'nb+ws+nb+ns+nb+ws+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'%' : 'nb+ns+nb+ws+nb+ws+nb+ws+nb' }\",\n \"def displayHand(hand: d_si) -> None:\\n for letter in hand.keys():\\n for _ in range(hand[letter]):\\n print(letter,end=\\\" \\\")\\n print()\",\n \"def display_s(s, font, screen, lcd, size=5, x=0, y=0):\\n i = 0\\n spacing = size * .11\\n s = str(s)\\n char = s[0]\\n char_w, char_h = font[char].size\\n for c in s:\\n display_c(c,font,screen,lcd,size,(int(i*spacing*char_w)+x),y)\\n i += 1\\n return screen\",\n \"def display_c(c, font, screen, lcd, size=5, x=0, y=0):\\n char = font[str(c)]\\n width, height = char.size\\n \\\"\\\"\\\"\\n if not(size == 10):\\n size /= 10.0\\n width = int(round(size*width))\\n height = int(round(size*height))\\n char.resize((width,height))\\n \\\"\\\"\\\"\\n size = int(round(size * 10))\\n images.display_img(char,screen,lcd,size,x,y)\",\n \"def abdul(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30m\\\"+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.A220+self.ESC+\\\"1;43m\\\"+self.A219+self.A178+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A223+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"33m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.A254+self.ESC+\\\"33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A219+self.ESC+\\\"1;43m\\\"+self.A177+self.A176+self.ESC+\\\"C\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"37;40mSaga\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"CRed\\\"+self.ESC+\\\"CDragon\\\"+self.ESC+\\\"C-\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"33mAbduls\\\"+self.ESC+\\\"CArmour \\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A177+self.ESC+\\\"0;32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A219+self.ESC+\\\"C\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"33m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"33m\\\"+self.A220+self.A219+self.A223+self.ESC+\\\"37m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A223+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A176+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;33mBehind\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"Cdesk\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"Carmour\\\"+self.ESC+\\\"Cshop\\\"+self.ESC+\\\"Cis\\\"+self.ESC+\\\"Can\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"37;40m\\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;33mamazingly\\\"+self.ESC+\\\"Cattractive\\\"+self.ESC+\\\"Clooking\\\"+self.ESC+\\\"Cfemale - she seems\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"1;37m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"47m\\\"+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;32m\\\"+self.A220+self.A254+self.ESC+\\\"3C\\\"+self.ESC+\\\"33mbusy, doing her mails but she\\\"+self.ESC+\\\"Casks\\\"+self.ESC+\\\"C\\\\\\\"\\\"+self.ESC+\\\"1mHow\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A223+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37;47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A222+self.A222+self.ESC+\\\"47m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"30m\\\"+self.A176+self.ESC+\\\"C\\\"+self.A177+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"47m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.A178+self.ESC+\\\"6C\\\"+self.ESC+\\\"1;33mmay\\\"+self.ESC+\\\"CI\\\"+self.ESC+\\\"Cbe\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cservice?\\\"+self.ESC+\\\"0;33m\\\\\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"C\\\"+self.A220+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37m\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A177+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;33;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+self.A223+self.ESC+\\\"C\\\"+self.A220+self.ESC+\\\"1;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1;37m\\\"+self.A223+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"2C\\\"+self.A220+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A176+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mB\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1muy\\\"+self.ESC+\\\"CArmour\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A220+self.A223+self.ESC+\\\"1;37m\\\"+self.A220+self.A220+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"1;47m\\\"+self.A223+self.ESC+\\\"0m\\\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"1;40m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mS\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1mell\\\"+self.ESC+\\\"CArmour\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37;47m\\\"+self.A219+self.A178+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.A220+self.A220+self.A219+self.A220+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A219+self.A219+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mY\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1mour\\\"+self.ESC+\\\"CStats\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"0;33m\\\"+self.A223+self.A220+self.A223+self.ESC+\\\"30;43m\\\"+self.A176+self.A223+self.ESC+\\\"1;37;40m\\\"+self.A223+self.A223+self.ESC+\\\"47m\\\"+self.A178+self.A177+self.A176+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A176+self.A177+self.A178+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mR\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1meturn\\\"+self.ESC+\\\"Cto\\\"+self.ESC+\\\"CTown\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A177+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A254+self.ESC+\\\"C\\\"+self.ESC+\\\"1;30m\\\"+self.A220+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"33;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"C\\\"+self.A178+self.A254+self.ESC+\\\"33m\\\"+self.A219+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A223+self.A220+self.ESC+\\\"1;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A220+self.A223+self.A220+self.A223+self.A223+self.A220+self.ESC+\\\"3C\\\"+self.ESC+\\\"1;30m\\\"+self.A177+self.A220+self.ESC+\\\"2C\\\"+self.ESC+\\\"33m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"33m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"32m\\\"+self.A254+self.ESC+\\\"33m\\\"+self.A223+self.A219+self.A219+self.A220+self.A223+self.A220+self.A220+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"2C\\\"+self.A220+self.A178+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"33;43m\\\"+self.A177+self.ESC+\\\"2C\\\"+self.ESC+\\\"0m \\\"+self.ESC+\\\"3C\\\"+self.ESC+\\\"33m\\\"+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.A223+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.A223+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def n_char(self,char,n,w=1,h=1):\\n for i in range(n):\\n self.esprint(char,w,h)\",\n \"def int_21H_2(self):\\r\\n byte_DL = self.registers['DX'].get_byte(0)\\r\\n char_DL = byte_DL.decode(\\\"ascii\\\") # zdekodowany znak do wyswietlenia\\r\\n self.GUI.set_output(char_DL)\",\n \"def display_hand(hand):\\n letters = \\\"\\\"\\n\\n for i in hand.keys():\\n\\n for j in range(hand[i]):\\n letters += i\\n\\n return letters\",\n \"def header(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC + \\\"0m \\\" +self.A220 + self.A220 + self.A220 + self.A220 + self.A220 +\\\" \\\" + self.ESC + \\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A220+self.A220+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mS\\\"+self.ESC+\\\"0;31mAGA\\\"+self.ESC+\\\"37m \\\"+self.A219+self.A219+self.ESC+\\\"30;47mo\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mO\\\"+self.ESC+\\\"0;31mF THE\\\"+self.ESC+\\\"37m \\\"+self.A219+self.A219+self.ESC+\\\"30;47mO\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.A220+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mR\\\"+self.ESC+\\\"0;31mED\\\"+self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"30;47mo\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.A223+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m \\\"+self.ESC+\\\"1mD\\\"+self.ESC+\\\"0;31mRAGON 0.9.9\\\"+self.ESC+\\\"37m \\\"+self.A223+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.A219+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"0m \\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m concept\\\"+self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+\\\" \\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m Seth Robinson \\\"+self.ESC+\\\"37m\\\"+self.A222+\\\" \\\"+self.A223+self.A223+self.ESC+\\\"1;47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A223+self.A223+self.ESC+\\\"0m \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+self.A219+self.A220+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m by\\\"+self.ESC+\\\"0m \\\"+self.A219+\\\" \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A176+self.A219+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A176+\\\" \\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;34m J\\\"+self.ESC+\\\"0;34m.\\\"+self.ESC+\\\"1mT\\\"+self.ESC+\\\"0;34m.\\\"+self.ESC+\\\"1mS\\\"+self.ESC+\\\"0;34mage\\\"+self.ESC+\\\"0m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A177+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+self.A178+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+self.A176+self.A176+self.A177+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"41m\\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A223+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A177+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A178+\\\" \\\"+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A178+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.A219+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A178+self.A219+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A178+self.A177+self.A177+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A178+self.A219+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A219+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A178+\\\" \\\"+self.A177+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A177+self.A219+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A223+\\\" \\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A178+self.A177+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A219+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.A223+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A178+self.A178+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.A178+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+\\\" \\\"+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.A219+self.ESC+\\\"1;41m\\\"+self.A176+self.A176+self.ESC+\\\"0;31m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"1;5;32m\\\"+self.A220+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;5;32m\\\"+self.A219+self.A219+self.A223+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.A219+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;5;32m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.A219+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A177+self.A219+self.A178+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A221+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+\\\" \\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A177+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.A221+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A177+self.A177+self.A176+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A178+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A176+self.A177+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A178+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A177+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A176+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"1m\\\"+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.A177+self.A178+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A176+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A223+self.ESC+\\\"40m\\\"+self.A219+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A178+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;37;47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A177+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+\\\" \\\"+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;37;47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A178+self.A177+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"33m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+\\\" \\\"+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A176+self.ESC+\\\"37;40m \\\"+self.A220+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A219+self.A221+\\\" \\\"+self.A223+\\\" \\\"+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A178+self.A177+self.A176+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A220+self.ESC+\\\"1m\\\"+self.A219+\\\" \\\"+self.A219+self.A221+\\\" \\\"+self.A223+\\\" \\\"+self.A220+\\\" \\\"+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A219+self.A220+self.A222+self.ESC+\\\"1;41m\\\"+self.A219+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.A178+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A223+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.A223+\\\" \\\"+self.A220+self.A223+self.A220+\\\" \\\"+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"30mÙ\\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.A223+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.A176+self.A176+\\\" \\\"+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A177+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A220+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A177+\\\" \\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A177+self.A177+self.A176+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A178+\\\" \\\"+self.A177+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A219+self.A178+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.A176+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"0;37m \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"1;31mShatterstar [W/X] \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def display():\\n screen.addch(head[0],head[1],'x')\",\n \"def gen_beat_output(e):\\n return [playback_char(e,t) for t in range(70000)]\",\n \"async def charinfo(self, ctx: Context, *, characters: str) -> None:\\n match = re.match(r\\\"<(a?):(\\\\w+):(\\\\d+)>\\\", characters)\\n if match:\\n embed = Embed(\\n title=\\\"Non-Character Detected\\\",\\n description=\\\"Only unicode characters can be processed, but a custom Discord emoji \\\" \\\"was found. Please remove it and try again.\\\",\\n )\\n embed.colour = Color.red()\\n await ctx.send(embed=embed)\\n return\\n\\n if len(characters) > 25:\\n embed = Embed(title=f\\\"Too many characters ({len(characters)}/25)\\\")\\n embed.colour = Color.red()\\n await ctx.send(embed=embed)\\n return\\n\\n def get_info(char: str) -> Tuple[str, str]:\\n digit = f\\\"{ord(char):x}\\\"\\n if len(digit) <= 4:\\n u_code = f\\\"\\\\\\\\u{digit:>04}\\\"\\n else:\\n u_code = f\\\"\\\\\\\\U{digit:>08}\\\"\\n url = f\\\"https://www.compart.com/en/unicode/U+{digit:>04}\\\"\\n name = f\\\"[{unicodedata.name(char, '')}]({url})\\\"\\n info = f\\\"`{u_code.ljust(10)}`: {name} - {char}\\\"\\n return info, u_code\\n\\n charlist, rawlist = zip(*(get_info(c) for c in characters))\\n\\n embed = Embed(description=\\\"\\\\n\\\".join(charlist))\\n embed.set_author(name=\\\"Character Info\\\")\\n\\n if len(characters) > 1:\\n embed.add_field(name=\\\"Raw\\\", value=f\\\"`{''.join(rawlist)}`\\\", inline=False)\\n\\n await ctx.send(embed=embed)\",\n \"def display(self):\\n for row in range(self.height):\\n for col in range(self.width):\\n char = '#' if self.pixels[row * self.width + col] else '.'\\n print(char, end='')\\n print()\\n print()\",\n \"def print_chars(self):\\n for v in voc.split('\\\\n'):\\n pair = v.split(',')\\n print(pair[0], pair[1], '\\\\t', self.epi.xsampa_list(pair[0]))\",\n \"def show_theme_message(width):\\n print_dotted_line()\\n print_bold(\\\"Attack of The Orcs v0.0.5:\\\")\\n msg = (\\n \\\"\\\"\\\"\\n Hrabri vitez Talion vraća se iz boja i želi predahnuti u jednom seocetu. \\n U seocu se nalazi 5 kućica i želi stupiti u jednu od njih. \\n Hrabri vitez ne zna da u ovom području ima neprijatelja.\\n Odluči se za jedna od vrata ...\\n \\\"\\\"\\\")\\n\\n print(textwrap.fill(msg, width=width))\",\n \"def createGlyphs(input):\\r\\n\\r\\n command_open = subprocess.Popen([\\\"start\\\", \\\"cmd\\\",\\\"/k\\\", 'echo {}'.format(input)], shell = True)\\r\\n\\r\\n time.sleep(2) #time for window to appear\\r\\n\\r\\n #print win32gui.FindWindow(None, \\\"C:\\\\Windows\\\\system32\\\\cmd.exe\\\")\\r\\n hwnd = win32gui.FindWindow(None, \\\"C:\\\\Windows\\\\system32\\\\cmd.exe\\\")\\r\\n win32gui.SetForegroundWindow(hwnd)\\r\\n bbox = win32gui.GetWindowRect(hwnd)\\r\\n img = ImageGrab.grab(bbox)\\r\\n #img.show()\\r\\n\\r\\n length_of_chars = len(input) * 8 # +2 to compensate for the quotation marks\\r\\n combined_chars = img.crop((10,34,length_of_chars+9,45))\\r\\n #combined_chars.show()\\r\\n\\r\\n #nonetype error was caused by chaining the .show() wiutth the rest of the stuff\\r\\n\\r\\n chars = {x:\\\"\\\" for x in input}\\r\\n\\r\\n for i, each in enumerate(range(8,combined_chars.size[0]+9,8)): #starts from 1, and +8 to compensate for the -8 below VVV\\r\\n #if i not in bad_indexes: #this is to avoid the first and last double quotation marks\\r\\n current_char = input[i]\\r\\n \\r\\n indiv_char = combined_chars.crop((each-8,0,each,11))\\r\\n \\r\\n w, h = indiv_char.size #should be 8 wide by 9 high\\r\\n\\r\\n for i2, pixel in enumerate(indiv_char.getdata()): #tuple values can either be (0, 0, 0) or (192,192,192) for the default terminal colours\\r\\n if pixel == (192,192,192): \\r\\n chars[current_char] += u\\\"\\\\u2588\\\"\\r\\n\\r\\n else:\\r\\n chars[current_char] += \\\" \\\"\\r\\n\\r\\n if i2 % w == w-1: # we want it too look decent so overflow is neeeded onto the next lines\\r\\n # ^^^ before it was i2 % w == 0, but that lead to a trail behind character, so whats before 0? -1! so w-1!!!\\r\\n chars[current_char] += \\\"\\\\n\\\"\\r\\n\\r\\n chars[current_char] = chars[current_char][:-1] #this is to remove the last \\\"\\\\n\\\"\\r\\n\\r\\n return chars\",\n \"async def charinfo(self, ctx: Context, *, characters: str) -> None:\\n match = re.match(r\\\"<(a?):(\\\\w+):(\\\\d+)>\\\", characters)\\n if match:\\n await messages.send_denial(\\n ctx,\\n \\\"**Non-Character Detected**\\\\n\\\"\\n \\\"Only unicode characters can be processed, but a custom Discord emoji \\\"\\n \\\"was found. Please remove it and try again.\\\"\\n )\\n return\\n\\n if len(characters) > 50:\\n await messages.send_denial(ctx, f\\\"Too many characters ({len(characters)}/50)\\\")\\n return\\n\\n def get_info(char: str) -> tuple[str, str]:\\n digit = f\\\"{ord(char):x}\\\"\\n if len(digit) <= 4:\\n u_code = f\\\"\\\\\\\\u{digit:>04}\\\"\\n else:\\n u_code = f\\\"\\\\\\\\U{digit:>08}\\\"\\n url = f\\\"https://www.compart.com/en/unicode/U+{digit:>04}\\\"\\n name = f\\\"[{unicodedata.name(char, '')}]({url})\\\"\\n info = f\\\"`{u_code.ljust(10)}`: {name} - {utils.escape_markdown(char)}\\\"\\n return info, u_code\\n\\n char_list, raw_list = zip(*(get_info(c) for c in characters), strict=True)\\n embed = Embed().set_author(name=\\\"Character Info\\\")\\n\\n if len(characters) > 1:\\n # Maximum length possible is 502 out of 1024, so there's no need to truncate.\\n embed.add_field(name=\\\"Full Raw Text\\\", value=f\\\"`{''.join(raw_list)}`\\\", inline=False)\\n\\n await LinePaginator.paginate(char_list, ctx, embed, max_lines=10, max_size=2000, empty=False)\",\n \"def _text16(self, font, text, x0, y0, color=WHITE, background=BLACK):\\n for char in text:\\n ch = ord(char)\\n if (font.FIRST <= ch < font.LAST\\n and x0+font.WIDTH <= self.width\\n and y0+font.HEIGHT <= self.height):\\n\\n each = 16\\n if font.HEIGHT == 16:\\n passes = 2\\n size = 32\\n else:\\n passes = 4\\n size = 64\\n\\n for line in range(passes):\\n idx = (ch-font.FIRST)*size+(each*line)\\n buffer = struct.pack(\\n '>128H',\\n color if font.FONT[idx] & _BIT7 else background,\\n color if font.FONT[idx] & _BIT6 else background,\\n color if font.FONT[idx] & _BIT5 else background,\\n color if font.FONT[idx] & _BIT4 else background,\\n color if font.FONT[idx] & _BIT3 else background,\\n color if font.FONT[idx] & _BIT2 else background,\\n color if font.FONT[idx] & _BIT1 else background,\\n color if font.FONT[idx] & _BIT0 else background,\\n color if font.FONT[idx+1] & _BIT7 else background,\\n color if font.FONT[idx+1] & _BIT6 else background,\\n color if font.FONT[idx+1] & _BIT5 else background,\\n color if font.FONT[idx+1] & _BIT4 else background,\\n color if font.FONT[idx+1] & _BIT3 else background,\\n color if font.FONT[idx+1] & _BIT2 else background,\\n color if font.FONT[idx+1] & _BIT1 else background,\\n color if font.FONT[idx+1] & _BIT0 else background,\\n color if font.FONT[idx+2] & _BIT7 else background,\\n color if font.FONT[idx+2] & _BIT6 else background,\\n color if font.FONT[idx+2] & _BIT5 else background,\\n color if font.FONT[idx+2] & _BIT4 else background,\\n color if font.FONT[idx+2] & _BIT3 else background,\\n color if font.FONT[idx+2] & _BIT2 else background,\\n color if font.FONT[idx+2] & _BIT1 else background,\\n color if font.FONT[idx+2] & _BIT0 else background,\\n color if font.FONT[idx+3] & _BIT7 else background,\\n color if font.FONT[idx+3] & _BIT6 else background,\\n color if font.FONT[idx+3] & _BIT5 else background,\\n color if font.FONT[idx+3] & _BIT4 else background,\\n color if font.FONT[idx+3] & _BIT3 else background,\\n color if font.FONT[idx+3] & _BIT2 else background,\\n color if font.FONT[idx+3] & _BIT1 else background,\\n color if font.FONT[idx+3] & _BIT0 else background,\\n color if font.FONT[idx+4] & _BIT7 else background,\\n color if font.FONT[idx+4] & _BIT6 else background,\\n color if font.FONT[idx+4] & _BIT5 else background,\\n color if font.FONT[idx+4] & _BIT4 else background,\\n color if font.FONT[idx+4] & _BIT3 else background,\\n color if font.FONT[idx+4] & _BIT2 else background,\\n color if font.FONT[idx+4] & _BIT1 else background,\\n color if font.FONT[idx+4] & _BIT0 else background,\\n color if font.FONT[idx+5] & _BIT7 else background,\\n color if font.FONT[idx+5] & _BIT6 else background,\\n color if font.FONT[idx+5] & _BIT5 else background,\\n color if font.FONT[idx+5] & _BIT4 else background,\\n color if font.FONT[idx+5] & _BIT3 else background,\\n color if font.FONT[idx+5] & _BIT2 else background,\\n color if font.FONT[idx+5] & _BIT1 else background,\\n color if font.FONT[idx+5] & _BIT0 else background,\\n color if font.FONT[idx+6] & _BIT7 else background,\\n color if font.FONT[idx+6] & _BIT6 else background,\\n color if font.FONT[idx+6] & _BIT5 else background,\\n color if font.FONT[idx+6] & _BIT4 else background,\\n color if font.FONT[idx+6] & _BIT3 else background,\\n color if font.FONT[idx+6] & _BIT2 else background,\\n color if font.FONT[idx+6] & _BIT1 else background,\\n color if font.FONT[idx+6] & _BIT0 else background,\\n color if font.FONT[idx+7] & _BIT7 else background,\\n color if font.FONT[idx+7] & _BIT6 else background,\\n color if font.FONT[idx+7] & _BIT5 else background,\\n color if font.FONT[idx+7] & _BIT4 else background,\\n color if font.FONT[idx+7] & _BIT3 else background,\\n color if font.FONT[idx+7] & _BIT2 else background,\\n color if font.FONT[idx+7] & _BIT1 else background,\\n color if font.FONT[idx+7] & _BIT0 else background,\\n color if font.FONT[idx+8] & _BIT7 else background,\\n color if font.FONT[idx+8] & _BIT6 else background,\\n color if font.FONT[idx+8] & _BIT5 else background,\\n color if font.FONT[idx+8] & _BIT4 else background,\\n color if font.FONT[idx+8] & _BIT3 else background,\\n color if font.FONT[idx+8] & _BIT2 else background,\\n color if font.FONT[idx+8] & _BIT1 else background,\\n color if font.FONT[idx+8] & _BIT0 else background,\\n color if font.FONT[idx+9] & _BIT7 else background,\\n color if font.FONT[idx+9] & _BIT6 else background,\\n color if font.FONT[idx+9] & _BIT5 else background,\\n color if font.FONT[idx+9] & _BIT4 else background,\\n color if font.FONT[idx+9] & _BIT3 else background,\\n color if font.FONT[idx+9] & _BIT2 else background,\\n color if font.FONT[idx+9] & _BIT1 else background,\\n color if font.FONT[idx+9] & _BIT0 else background,\\n color if font.FONT[idx+10] & _BIT7 else background,\\n color if font.FONT[idx+10] & _BIT6 else background,\\n color if font.FONT[idx+10] & _BIT5 else background,\\n color if font.FONT[idx+10] & _BIT4 else background,\\n color if font.FONT[idx+10] & _BIT3 else background,\\n color if font.FONT[idx+10] & _BIT2 else background,\\n color if font.FONT[idx+10] & _BIT1 else background,\\n color if font.FONT[idx+10] & _BIT0 else background,\\n color if font.FONT[idx+11] & _BIT7 else background,\\n color if font.FONT[idx+11] & _BIT6 else background,\\n color if font.FONT[idx+11] & _BIT5 else background,\\n color if font.FONT[idx+11] & _BIT4 else background,\\n color if font.FONT[idx+11] & _BIT3 else background,\\n color if font.FONT[idx+11] & _BIT2 else background,\\n color if font.FONT[idx+11] & _BIT1 else background,\\n color if font.FONT[idx+11] & _BIT0 else background,\\n color if font.FONT[idx+12] & _BIT7 else background,\\n color if font.FONT[idx+12] & _BIT6 else background,\\n color if font.FONT[idx+12] & _BIT5 else background,\\n color if font.FONT[idx+12] & _BIT4 else background,\\n color if font.FONT[idx+12] & _BIT3 else background,\\n color if font.FONT[idx+12] & _BIT2 else background,\\n color if font.FONT[idx+12] & _BIT1 else background,\\n color if font.FONT[idx+12] & _BIT0 else background,\\n color if font.FONT[idx+13] & _BIT7 else background,\\n color if font.FONT[idx+13] & _BIT6 else background,\\n color if font.FONT[idx+13] & _BIT5 else background,\\n color if font.FONT[idx+13] & _BIT4 else background,\\n color if font.FONT[idx+13] & _BIT3 else background,\\n color if font.FONT[idx+13] & _BIT2 else background,\\n color if font.FONT[idx+13] & _BIT1 else background,\\n color if font.FONT[idx+13] & _BIT0 else background,\\n color if font.FONT[idx+14] & _BIT7 else background,\\n color if font.FONT[idx+14] & _BIT6 else background,\\n color if font.FONT[idx+14] & _BIT5 else background,\\n color if font.FONT[idx+14] & _BIT4 else background,\\n color if font.FONT[idx+14] & _BIT3 else background,\\n color if font.FONT[idx+14] & _BIT2 else background,\\n color if font.FONT[idx+14] & _BIT1 else background,\\n color if font.FONT[idx+14] & _BIT0 else background,\\n color if font.FONT[idx+15] & _BIT7 else background,\\n color if font.FONT[idx+15] & _BIT6 else background,\\n color if font.FONT[idx+15] & _BIT5 else background,\\n color if font.FONT[idx+15] & _BIT4 else background,\\n color if font.FONT[idx+15] & _BIT3 else background,\\n color if font.FONT[idx+15] & _BIT2 else background,\\n color if font.FONT[idx+15] & _BIT1 else background,\\n color if font.FONT[idx+15] & _BIT0 else background\\n )\\n self.blit_buffer(buffer, x0, y0+8*line, 16, 8)\\n x0 += font.WIDTH\",\n \"def show(self):\\n # Disable IRQ to improve speed\\n with NoIRQ():\\n for chip in range(NB_CHIPS):\\n self._select(chip)\\n row = 0 if chip in (0, 1) else 1\\n col = 0 if chip in (0, 2) else 1\\n data = self.get_ht1632_data(row, col)\\n green = (is_green(value) for value in data)\\n red = (is_red(value) for value in data)\\n self._write_data(green, red)\",\n \"def displayHand(hand):\\r\\n for letter in hand.keys():\\r\\n for j in range(hand[letter]):\\r\\n print(letter,end=\\\" \\\") # print all on the same line\\r\\n print() # print an empty line\\r\",\n \"def print_screen(history):\\n\\n # Extract the angles from the distance data. Usually this won't change \\n # from scan to scan, but there are scanning algorithms where that may not \\n # be the case \\n angles = []\\n for h in history:\\n angles = list(set(angles + h.keys())) \\n angles.sort()\\n\\n # Create a 2D grid of characters. Essentially a \\\"screen buffer\\\"\\n buff = {}\\n for angle in angles:\\n buff[angle] = ' '.rjust(120)\\n\\n \\n blips = ['.', '*', '#', '@']\\n blips = blips[-len(history):] # if we only have 2, take last 2 blips\\n\\n # Plot blips onto buffer \\n for h in history:\\n blip = blips.pop(0) if len(blips) else '.'\\n for angle in angles:\\n if angle not in h: continue\\n dist = h[angle]\\n if dist < 120:\\n buff[angle] = set_char_at(buff[angle], dist, blip)\\n\\n # Output\\n print \\\"\\\\n\\\\n\\\\n\\\"\\n for angle in angles:\\n obstacle = 'x' if '@' in buff[angle][0:30] else ' '\\n print \\\"%s %s %s\\\" % (str(angle).rjust(5), obstacle, buff[angle])\\n print '20cm'.rjust(30) + '50cm'.rjust(30) + '1m'.rjust(50)\",\n \"def wepbuy(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC+\\\"14C\\\"+self.ESC+\\\"1;34m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"0;34m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"0;34m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;34m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;34m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"46m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"2C\\\"+self.ESC+\\\"37m The\\\"+self.ESC+\\\"CSaga\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"CRed\\\"+self.ESC+\\\"CDragon\\\"+self.ESC+\\\"C-\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34mWeapons\\\"+self.ESC+\\\"CList \\\"+self.ESC+\\\"C\\\"+self.A220+self.ESC+\\\"46m\\\"+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"46m\\\"+self.A178+self.ESC+\\\"44m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"46m\\\"+self.A178+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"46m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;36mWeapons\\\"+self.ESC+\\\"27CPrice\\\"+self.ESC+\\\"7C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"44m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"40m1.\\\"+self.ESC+\\\"CStick\\\"+self.ESC+\\\"0;34m..................................\\\"+self.ESC+\\\"36m200\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"44m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"40m2.\\\"+self.ESC+\\\"CDagger\\\"+self.ESC+\\\"0;34m...............................\\\"+self.ESC+\\\"36m1,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"44m\\\"+self.A178+self.ESC+\\\"2C\\\"+self.ESC+\\\"40m3.\\\"+self.ESC+\\\"CShort\\\"+self.ESC+\\\"CSword\\\"+self.ESC+\\\"0;34m..........................\\\"+self.ESC+\\\"36m3,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"44m\\\"+self.A177+self.ESC+\\\"2C\\\"+self.ESC+\\\"40m4.\\\"+self.ESC+\\\"CLong\\\"+self.ESC+\\\"CSword\\\"+self.ESC+\\\"0;34m..........................\\\"+self.ESC+\\\"36m10,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A177+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"44m\\\"+self.A176+self.ESC+\\\"2C\\\"+self.ESC+\\\"40m5.\\\"+self.ESC+\\\"CHuge\\\"+self.ESC+\\\"CAxe\\\"+self.ESC+\\\"0;34m............................\\\"+self.ESC+\\\"36m30,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A176+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"0;34m\\\"+self.A223+self.ESC+\\\"2C\\\"+self.ESC+\\\"1m6.\\\"+self.ESC+\\\"CBone\\\"+self.ESC+\\\"CCruncher\\\"+self.ESC+\\\"0;34m......................\\\"+self.ESC+\\\"36m100,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A176+self.ESC+\\\"2C\\\"+self.ESC+\\\"1m7.\\\"+self.ESC+\\\"CTwin\\\"+self.ESC+\\\"CSwords\\\"+self.ESC+\\\"0;34m........................\\\"+self.ESC+\\\"36m150,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A176+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A177+self.ESC+\\\"2C\\\"+self.ESC+\\\"1m8.\\\"+self.ESC+\\\"CPower\\\"+self.ESC+\\\"CAxe\\\"+self.ESC+\\\"0;34m..........................\\\"+self.ESC+\\\"36m200,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A177+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A178+self.ESC+\\\"2C\\\"+self.ESC+\\\"1m9.\\\"+self.ESC+\\\"CAble's\\\"+self.ESC+\\\"CSword\\\"+self.ESC+\\\"0;34m.......................\\\"+self.ESC+\\\"36m400,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A178+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1m10.\\\"+self.ESC+\\\"CWan's\\\"+self.ESC+\\\"CWeapon\\\"+self.ESC+\\\"0;34m.....................\\\"+self.ESC+\\\"36m1,000,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"1m11.\\\"+self.ESC+\\\"CSpear\\\"+self.ESC+\\\"COf\\\"+self.ESC+\\\"CGold\\\"+self.ESC+\\\"0;34m....................\\\"+self.ESC+\\\"36m4,000,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1m12.\\\"+self.ESC+\\\"CCrystal\\\"+self.ESC+\\\"CShard\\\"+self.ESC+\\\"0;34m...................\\\"+self.ESC+\\\"36m10,000,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1m13.\\\"+self.ESC+\\\"CNiras's\\\"+self.ESC+\\\"CTeeth\\\"+self.ESC+\\\"0;34m...................\\\"+self.ESC+\\\"36m40,000,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1m14.\\\"+self.ESC+\\\"CBlood\\\"+self.ESC+\\\"CSword\\\"+self.ESC+\\\"0;34m....................\\\"+self.ESC+\\\"36m100,000,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1m15.\\\"+self.ESC+\\\"CDeath\\\"+self.ESC+\\\"CSword\\\"+self.ESC+\\\"0;34m....................\\\"+self.ESC+\\\"36m400,000,000\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A176+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"14C\\\"+self.ESC+\\\"44m\\\"+self.A176+self.ESC+\\\"0;34m\\\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.A220+self.A178+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A178+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A176+self.ESC+\\\"C\\\"+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A220+self.A220+self.A178+self.A220+self.A220+self.A220+self.A220+self.A176+self.A220+self.A220+self.A219+self.ESC+\\\"1;44m\\\"+self.A176+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"0m\\\\r\\\\n\\\";\\n\\t\\treturn thismsg\",\n \"def KingWen(font, hexid):\\n\\n ret = []\\n ch = 0x4DC0 + hexid - 1\\n exec 'char = u\\\\'\\\\u%4x\\\\'' % ch\\n lines = GetFontPixels(font, char, 'R', ' ')\\n ret.append(' ' * 8)\\n for line in lines[1::2]:\\n hex_chars = ''.join(line[1:-1])\\n ret.append(hex_chars[1:-1])\\n ret.append(' ' * 8)\\n return ret\",\n \"def help():\\n print(f\\\"{YELLOW}zwc{RESET} - Hidden messages using Zero Width Characters.\\\")\",\n \"def __card_display(self):\\n return ''.join([(each_number if (i == 0 or i % 4 != 0) else ' ' + each_number)\\n for i, each_number in enumerate(self.card_number)])\",\n \"def write_display(self):\\n for i, value in enumerate(self.buffer):\\n self.bus.write_byte_data(self.address, i, value)\",\n \"def _make_sysex_message(self, text_message, display_index):\\n if len(text_message) > 28:\\n text_message = text_message[:27]\\n sysex_message = [240, 0, 0, 102, 23, 18, min(display_index, 3) * 28]\\n filled = text_message.ljust(28)\\n for c in filled:\\n sysex_message.append(ord(c))\\n sysex_message.append(247)\\n return sysex_message\",\n \"def show_fixedblock(self):\\n fb = self.station.get_raw_fixed_block(unbuffered=True)\\n for i, ptr in enumerate(range(len(fb))):\\n print('%02x' % fb[ptr], end=' ')\\n if (i+1) % 16 == 0:\\n print()\",\n \"def displayHand(hand):\\n for letter in hand.keys():\\n for j in range(hand[letter]):\\n print letter, # print all on the same line\\n print # print an empty line\",\n \"def encode_this( self, charCode ):\\n values = []\\n # Character Bitmap\\n bmp = self.characters[charCode]\\n # Nbre of entry in the dictionnary = Character width\\n nbr_entry = bmp.width\\n # Number of bytes per entry (=font height / 8bits.)\\n # Vera_19 = 19 point height => 19/8 = 2.375 => must be coded on 3 bytes\\n nbr_bytes = ceil(self.max_height / 8)\\n if ((self.max_height%8) == 0): # Encoded font must always starts with 0b1, so if the font has 8 points height (or a multiple)\\n nbr_bytes += 1 # we have to create more room to receive that bit.\\n nbr_bits = nbr_bytes * 8\\n\\n # If we are coding 19 bits on X bytes, we will have to pad the left most\\n # bits with 1\\n pad_bits = nbr_bits - self.max_height\\n pad_value = 0\\n for i in range( pad_bits ):\\n # debug: print( 'shift by %i' % (nbr_bits-1-i) )\\n pad_value = pad_value + ( 1<<(nbr_bits-1-i))\\n\\n # Building the values\\n for iw in range( bmp.width ):\\n value = pad_value\\n # Debug: print( '---------------------------' )\\n for ih in range( bmp.height ):\\n _ih = bmp.height-1-ih # Must start by the \\\"bottom\\\" of the character\\n\\n # Align on the BottomLine\\n _height_extra_shift = self.max_height-bmp.height # The character may have 10 pixels height on a 19 point height font --> shift properly to the left!\\n\\n # Align on the BaseLine (if it applies)\\n # check if we do need to insert a descender space (in pixel) under the character\\n if ( len(self.descender_ordinals)>0 ) and not( self.char_has_descender( charCode ) ):\\n # move up the normal character baseline\\n _height_extra_shift = _height_extra_shift - self.ajusted_descender_size( charCode )\\n\\n # SPECIAL ALIGNMENT\\n # check for special alignment instruction (~,comma,\\\", etc)\\n _align = self.special_align_ordinals.get( charCode, None )\\n if _align == 'T':\\n # special alignment ON TOP -> reset shifting\\n _height_extra_shift = 0\\n elif _align == 'M':\\n # Special alignment IN MIDLLE of baseline and top\\n _height_extra_shift = _height_extra_shift - ( self.max_height-bmp.height-self.ajusted_descender_size( charCode ))//2\\n elif _align == 'B':\\n # Specoam alignment TO BOTTOM (so re-reset the _height_extra_shift on the BottomLine)\\n _height_extra_shift = self.max_height-bmp.height\\n\\n # TODO: replacing the descender feature by glyph.bitmap_top does not work properly\\n # for every characters\\n # _height_extra_shift = self.max_height - self.glyphs[ charCode ].bitmap_top\\n\\n # print( 'coding bit %i, extra shift of %i FOR VALUE %s' % (_ih, _height_extra_shift, bmp.pixel_at( iw, _ih )) )\\n if bmp.pixel_at( iw, _ih ):\\n value = value + (1 << (_ih+_height_extra_shift))\\n values.append( value )\\n\\n\\n return values\",\n \"def hbar(value):\\n bar = '\\\\xe2\\\\x96\\\\x88'*(value/8)\\n if value%8:\\n bar += '\\\\xe2\\\\x96' + chr(0x90 - value%8)\\n return bar\",\n \"def draw(self):\\n res = ''\\n # ANSI code to clear the screen\\n #res += chr(27) + \\\"[2J\\\"\\n for position, value in enumerate(self.board.tttboard):\\n if value is None:\\n res += str(position)\\n #sys.stdout.write(str(position))\\n else:\\n res += str(value)\\n #sys.stdout.write(str(value))\\n\\n if (position + 1) % 3 != 0:\\n res += str('|')\\n #sys.stdout.write('|')\\n else:\\n #print ''\\n\\n res += str('\\\\n')\\n if position == 2 or position == 5:\\n #print '-' * 5\\n\\n res += '-' * 5\\n res += str('\\\\n')\\n return res\",\n \"def show( self):\\n def symbol( i):\\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\\n \\n X, Y = np.max( self.board.positions, 0)\\n # -2 to indicate outside board.\\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \\n for x, y in self.board.positions:\\n display[x, y] = -1 # -1 to indicate unoccupied\\n for p, i in self.occupation.items():\\n x, y = self.board.positions[p]\\n display[x, y] = i\\n for x in xrange(X+1):\\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\\n print s\",\n \"def display_char(self) -> None:\\r\\n print(self.char if self.was_guessed else '_', end=' ')\",\n \"def terminal_diagram(self):\\n lines = [[unichr(9632) for x in range(k)]\\n for k in self[1]]\\n lines += [[unichr(9632) for x in range(k)]\\n + [unichr(9675)] for k in self[0]]\\n lines.sort(reverse=True, key=len)\\n for line in lines:\\n print(' '.join(line))\\n print('')\",\n \"def int_21H_1(self):\\r\\n\\r\\n ascii_char = self.GUI.get_key_value() # ten do w wczytania\\r\\n self.registers['AX'].move_into(ascii_char, 0, is_int=True) # zapisanie kodu ascii do AXL\\r\",\n \"def display_characters(self):\\n return f'{self.character_set}'\",\n \"def show_frame_info(self):\\n if self.font:\\n font_surface = self.font.render(\\\"Sample Number: \\\" + \\\\\\n str(self.data_row_num-self.num_lndmrks) + \\\\\\n \\\" of \\\" + \\\\\\n str(self.tot_rows - self.data_start_row), \\\\\\n False, self.GREEN)\\n pts = (self.screen_width / 3),3\\n self.screen.blit(font_surface,pts)\",\n \"def display_content(com,message):\\n #message = message.encode('utf-8')\\n #message = message.decode('ascii', 'ignore')\\n safeMsg = filter(lambda x: x in string.printable, message)\\n safeMsg = safeMsg.replace('\\\\n', ' ')\\n print \\\"ALPHA: \\\", safeMsg\\n try:\\n #com = serial.Serial(config.devalpha, 9600, timeout=3)\\n #com.close()\\n #com.open()\\n comstr = config.alpha['display'] + safeMsg + config.alpha['eot']\\n com.write(comstr)\\n #com.close()\\n except serial.SerialException as e:\\n logging.warning(\\\"Serial exception: \\\"+str(e))\",\n \"def code_display(self):\\r\\n return u'%s-%s-%s' % (self.code[:3], self.code[3:6], self.code[6:])\",\n \"def errors_icons(self):\\n msg_errors_lifes = ''\\n for i in range(0,5):\\n if self.letters_wrong <= i:\\n msg_errors_lifes += ' ♥ '\\n else:\\n msg_errors_lifes += ' ☠ ' \\n return msg_errors_lifes\",\n \"def pretty_hebrew(val):\\n return 'font-size:20px; font-family: Times New Roman; text-align: right; max-width: 500px'\",\n \"def __str__(self):\\n return f\\\"{self._desc:16s}\\\"\",\n \"def __repr__(self):\\r\\n numLetters = self.numLetters\\r\\n S = ''\\r\\n S += 3*'\\\\n'\\r\\n S += ' '\\r\\n for i in range(numLetters):\\r\\n S += self.currentBoard[i] + ' '\\r\\n\\r\\n return S\",\n \"def sn(self):\\n\\t\\tstring = []\\n\\t\\tresp = [0x00]\\n\\t\\tself.spi.transfer([0x10], [0x00], 1)\\n\\t\\ttime.sleep(9e-3)\\n\\t\\tfor i in range(60):\\n\\t\\t\\tself.spi.transfer([0x00], resp, 1)\\n\\t\\t\\tstring.append(chr(resp[0]))\\n\\t\\ttime.sleep(0.1)\\n\\t\\treturn ''.join(string).strip()\",\n \"def rendermsg(self,msg):\\n return ' '.join(['%02x'%ord(x) for x in msg])\",\n \"def __str__(self):\\n return textwrap.fill('{:064b}'.format(self.num), 8)\",\n \"def DebugFormat(self):\\n print FormatAsBits((self.output, self.out_boff))\\n for i in xrange(self.idx_byte*8 + self.idx_boff - 1):\\n if not i % 8:\\n sys.stdout.write(\\\"|\\\")\\n sys.stdout.write(\\\"-\\\")\\n print \\\"^\\\"\",\n \"def showHexaView (self, data, dataType, shortName):\\n self.hexLogsView.display( data = data, dataType = dataType, shortName = shortName)\",\n \"def make_control_character():\\n # Add one character made up of one codepoint each from\\n # (High Surrogates + High Private Use Surrogates) and Low Surrogates.\\n # We expect each such pair to behave as a single high-codepoint\\n # character.\\n controls = ('0000', '001F')\\n return [unicode_char(char)\\n for char in range(int(controls[0], 16), int(controls[1], 16)+1)]\",\n \"def unH(s):\\n return ''.join([chr(int(s[i:i+2],16)) for i in range(2, len(s),2)])\",\n \"def print_short(sp) :\\n sp.print_peak_data_short()\",\n \"def pretty(self):\\n #table = [\\\"\\\".join([\\\"%8s \\\" % s for s in self.alpha.getSymbols()])]\\n table = []\\n for row in PWM.getFreq(self):\\n table.append(\\\"\\\".join([\\\"%8.6f \\\" % y for y in row]))\\n return table\",\n \"def H(s):\\n return 'H_' + ''.join(['%02x' % ord(x) for x in s])\",\n \"def __show_protocol__(self, data):\\n t_16 = t_16_Bit_Options()\\n t_8 = t_8_Bit_Options()\\n t_var = t_8_Bit_Options()\\n print('Received ' + str(len(data)) + ' Bytest')\\n\\n #----------------------------------------------------------------------\\n print('='*80)\\n print('Handling Protocol response: ' + hexlify(data))\\n #----------------------------------------------------------------------\\n print('='*80)\\n print('Overhead Bytes: ' + hexlify(data[:BP_TOOL.OVERHEAD]))\\n print('Number of UINT16 bitstream data = ' + str(data[BP_TOOL.UINT16S]))\\n print('Number of UINT8 bitstream data = ' + str(data[BP_TOOL.UINT8S]))\\n print('Number of var bitstream data = ' + str(data[BP_TOOL.VARS]))\\n print('Follow = ' + str(self.get_follow(data)))\\n print('Length = ' + str(self.get_length(data)))\\n start = self.get_follow_and_length(data)\\n end = start + BP_TOOL.SIZE_FOLLOW + BP_TOOL.SIZE_LEN\\n print('Following bytes and length = ' + hexlify(data[start:end]))\\n #----------------------------------------------------------------------\\n print('='*80)\\n bits = self.get_16bit_options_bits(data)\\n values = self.get_16bit_options(data)\\n options = self.get_options_requested(bits)\\n\\n # Display the options if exist\\n if len(options):\\n print('UINT16 bits...... : ' + hexlify(bits))\\n print('UINT16 data...... : ' + hexlify(values))\\n print('UINT16 Num of opts ... : ' + str(len(values) // 2))\\n print('UINT16 options... : ' + str(options))\\n print('-'*80)\\n for x in range(len(options)):\\n value = (values[x*2] << 8) | (values[x*2 + 1])\\n opt = options[x]\\n t_16.set_value(opt, value)\\n print('Option: ' + t_16.options[opt]['name'] + ' ' + str(value))\\n pprint.pprint(t_16.options)\\n else:\\n print('No 16 bit options')\\n\\n #----------------------------------------------------------------------\\n print('-'*80)\\n bits = self.get_8bit_options_bits(data)\\n values = self.get_8bit_options(data)\\n options = self.get_options_requested(bits)\\n # Display the options if exist\\n if len(options):\\n print('UINT8 bits...... : ' + hexlify(bits))\\n print('UINT8 data...... : ' + hexlify(values))\\n print('UINT8 options... : ' + str(options))\\n print('-'*80)\\n for x in range(len(options)):\\n value = values[x]\\n opt = options[x]\\n t_8.set_value(opt, value)\\n print('Option: ' + t_8.options[x]['name'] + ' ' + str(value))\\n pprint.pprint(t_8.options)\\n else:\\n print('No 8 bit options')\\n\\n #----------------------------------------------------------------------\\n print('-'*80)\\n bits = self.get_var_options_bits(data)\\n values = self.get_var_options(data)\\n print('VARS !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\\n # Display the options if exist\\n if len(values):\\n pprint.pprint(values)\\n else:\\n print('No var bit options')\\n\\n print('VAR options... : ' + str(self.get_options_requested(bits)))\\n print('VARS !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\\n print('-'*80)\",\n \"def test_repr_format(self):\\n t = OneHotEncode(3)\\n assert t.repr_format(\\\"asfa\\\") == \\\"OneHotEncode(asfa)\\\"\",\n \"def print_character( self, ordinal ):\\n print( '--- %s ----------------' % chr(ordinal) )\\n if not( ordinal in self.characters ):\\n print( '%s (%i) is not present in characters' % (chr(ordinal), ordinal) )\\n print( 'Ordinal: %i' % ordinal )\\n print( 'Char has descender: %s' % self.char_has_descender( ordinal ) )\\n print( 'width, height = %i, %i' % (self.characters[ordinal].width, self.characters[ordinal].height) )\\n # print( 'bitmap_top from base = %i' % self.glyphs[ordinal].bitmap_top )\\n\\n if self.char_has_descender( ordinal ):\\n print( 'width, height = %i, %i (with %i px descender already included)' % (self.characters[ordinal].width, self.characters[ordinal].height, self.descender_size ) )\\n else:\\n # Sometime, Char-Size + Descender Size GOES OVER the font size --> reduce the descender\\n _descender = self.ajusted_descender_size( ordinal )\\n print( 'width, height = %i, %i (with %i px ajusted_descender added)' % (self.characters[ordinal].width, self.characters[ordinal].height + _descender, _descender ) )\\n\\n print( self.characters[ordinal] )\",\n \"def show_instructions():\\n\\n print('4-digit Code has been set. Digits in range 1 to 8. You have 12 turns to break it.')\",\n \"def supercombiner(bot, ev):\\n # ported from jenni\\n s = 'u'\\n for i in iter(range(1, 3000)):\\n if unicodedata.category(chr(i)) == \\\"Mn\\\":\\n s += chr(i)\\n if len(s) > 100:\\n break\\n bot.say(s)\",\n \"def print_hint_board(self):\\n board = \\\"\\\"\\n count = 1 \\n for i in range(3):#need to change this in the future\\n for j in range(3):#need to change this in the future\\n board += str(count) \\n count += 1\\n if j != 2:#need to change this in the future\\n board += \\\" | \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def display(self):\\n self.command(SSD1306_COLUMNADDR)\\n self.command(0) # Column start address. (0 = reset)\\n self.command(self.width-1) # Column end address.\\n self.command(SSD1306_PAGEADDR)\\n self.command(0) # Page start address. (0 = reset)\\n self.command(self._pages-1) # Page end address.\\n # Write buffer data.\\n if self._spi is not None:\\n # Set DC high for data.\\n self._gpio.set_high(self._dc)\\n # Write buffer.\\n self._spi.write(self._buffer)\\n else:\\n for i in range(0, len(self._buffer), 16):\\n control = 0x40 # Co = 0, DC = 0\\n self._i2c.writeList(control, self._buffer[i:i+16])\",\n \"def char(runtime_addr, n=1):\\n\\n set_formatter(runtime_addr, n, mainformatter.char_formatter)\",\n \"def render(state, c_choice, h_choice):\\r\\n\\r\\n chars = {\\r\\n 2: h_choice,\\r\\n 1: c_choice,\\r\\n 0: ' '\\r\\n }\\r\\n str_line = '--------------------------------------------------'\\r\\n\\r\\n print('\\\\n' + str_line)\\r\\n for row in state:\\r\\n for cell in row:\\r\\n #print(cell)\\r\\n symbol = chars[cell]\\r\\n print(f'| {symbol} |', end='')\\r\\n print('\\\\n' + str_line)\",\n \"def device_extension_formatting(info):\\n return ('\\\\n\\\\t'+' '*(device_maxwidth+3)).join(info.split())\",\n \"def hexDump(bytes):\\n for i in range(len(bytes)):\\n sys.stdout.write(\\\"%2x \\\" % (ord(bytes[i])))\\n if (i+1) % 8 == 0:\\n print repr(bytes[i-7:i+1])\\n\\n if(len(bytes) % 8 != 0):\\n print string.rjust(\\\"\\\", 11), repr(bytes[i-7:i+1])\",\n \"def print_hex(pbtData, szBytes):\\n for szPos in range(szBytes):\\n sys.stdout.write(\\\"%02x \\\" % convBytes(pbtData[szPos]))\\n print('')\",\n \"def render(self):\\n for r in range(self.y_size):\\n line = ''\\n for c in range(self.x_size):\\n glyph = self.MAP_GLYPH_TABLE[self.grid_data[r][c]]\\n\\n # overwrite with player\\n if r == self.player_y and c == self.player_x:\\n glyph = self.PLAYER_GLYPH_TABLE[self.player_heading]\\n\\n line += glyph\\n print(line)\\n\\n print('\\\\n' * (20 - self.y_size))\",\n \"def nice_output(self):\\n return 'Pitch: {0} at {1}: {2}'.format(\\n self.pitch_type, self.start_speed, self.des)\",\n \"def line(self):\\n\\t\\treturn self.ESC+\\\"32m-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\",\n \"def get_equipment(self):\\n s = ''\\n for i in range(12, 16):\\n s += ' ' + str(self.dna[i])\\n return s\",\n \"def print(self):\\r\\n base = 8 * self.width\\r\\n print(base * \\\"-\\\")\\r\\n for x in range(self.height):\\r\\n output = \\\"\\\"\\r\\n for y in range(self.width):\\r\\n output = output + self.board[x][y] + \\\"|\\\"\\r\\n print(\\\"|\\\" + output)\\r\\n print(base * \\\"-\\\")\",\n \"def get_char_echo(self) -> str:\\n ...\",\n \"def output(self):\\n try:\\n sys.stdout.write(chr(self.tape.current_cell()%256)) # Wrapping fits it into ascii codes\\n except:\\n print \\\"Error -001\\\"\",\n \"def print(self):\\n board_string = ''\\n for y in range(self.size):\\n if y == 0:\\n board_string += '+ '\\n for x in range(self.size):\\n board_string += str(x+1) + ' '\\n board_string += '\\\\n'\\n board_string += (1+3*self.size)*'-'\\n board_string += '\\\\n'\\n board_string += str(y+1)+'|'+y*' '\\n \\n for x in range(self.size):\\n board_string += ' '\\n if self.board[y,x] == HexBoard.BLUE:\\n board_string += self.char_player1\\n elif self.board[y,x] == HexBoard.RED:\\n board_string += self.char_player2\\n else: \\n board_string += self.char_empty\\n board_string += '\\\\n'\\n board_string = board_string.strip()\\n\\n print(board_string)\",\n \"def hp34401a_lcd_disp(hp_meter, message):\\n hp_meter.write(\\\"DISP:TEXT:CLE\\\")\\n hp_meter.write(\\\"DISP:TEXT '\\\" + str(message) + \\\"'\\\")\",\n \"def render(self, s):\\n if s < self.num_states - 1:\\n a = np.zeros((self.height, self.width), dtype=np.unicode_)\\n # mark all locations as inaccessible first\\n a[:, :] = u'█'\\n # regular fields\\n for c in self.locs:\\n a[c[1], c[0]] = u'░'\\n # mark exits\\n for c in self.exitsP:\\n a[c[1], c[0]] = 'E'\\n for c in self.exitsN:\\n a[c[1], c[0]] = 'F'\\n # player character\\n c = self.locs[s]\\n a[c[1], c[0]] = u'☺'\\n a = [' '.join(row) for row in a]\\n a = '\\\\n'.join(reversed(a))\\n return a\\n else:\\n return 'Game over!'\",\n \"def delimit_output(num=25):\\n print('-'*num)\",\n \"def Display2(self, rain, humidity, lang):\\n if lang == 'eng':\\n string = rain + '% Rain\\\\n' + humidity + '% Humidity'\\n return string\\n else:\\n return 0\\n # Not Yet Supported\",\n \"def _make_totp(self):\\n secret = struct.pack('> Q', int(time.time()) / DEFAULT_STEP).ljust(64, chr(0x0))\\n response = self.yk.challenge_response(secret, slot=self.slot.get())\\n # format with appropriate number of leading zeros\\n fmt = '%.' + str(self.digits.get()) + 'i'\\n totp_str = fmt % (yubico.yubico_util.hotp_truncate(response, length=self.digits.get()))\\n return totp_str\",\n \"def display(field):\\n side = int(math.sqrt(len(field))) # in number of elements (tiles)\\n \\n def pos():\\n cy, cx = win.getyx()\\n stdscr.addstr(0, 0, \\\"cy: \\\"+str(cy)+\\\", cx: \\\"+str(cx))\\n\\n def br():\\n while True:\\n c = stdscr.getch()\\n if c == curses.KEY_RIGHT:\\n break\\n win.refresh()\\n\\n win.addstr(0, 0, '┏')\\n for _ in range(side-1):\\n win.addstr('━━━━━━')\\n win.addstr('┳')\\n win.addstr('━━━━━━')\\n win.addstr('┓ ')\\n\\n for y in range(side):\\n \\n win.addstr('┃')\\n for x in range(side):\\n #stdscr.addstr(0, 0, \\\"side: \\\" + str(x))\\n idx = y * side + x\\n if field[idx] == 0:\\n win.addstr(' '.center(6))\\n else:\\n n = field[idx]\\n color = curses.color_pair(0)\\n if n < 0:\\n field[idx] *= -1\\n n = field[idx]\\n color = curses.A_BOLD | curses.A_STANDOUT\\n elif n == 4:\\n color = curses.color_pair(3)\\n elif n == 8:\\n color = curses.color_pair(4)\\n elif n >= 16:\\n color = curses.color_pair(1)\\n \\n #win.addstr(str(n).center(6), color)\\n \\n n = str(n)\\n left = (6-len(n)) // 2\\n right = 6 - (left + len(n))\\n win.addstr(left*' ')\\n win.addstr(n, color)\\n win.addstr(right*' ')\\n\\n \\n win.addstr('┃')\\n win.addstr(' ')\\n if y == side-1:\\n break\\n else: \\n win.addstr('┣')\\n for _ in range(side-1):\\n win.addstr('━━━━━━')\\n win.addstr('╋')\\n win.addstr('━━━━━━')\\n win.addstr('┫ ')\\n \\n win.addstr('┗')\\n for _ in range(side-1):\\n win.addstr('━━━━━━')\\n win.addstr('┻')\\n win.addstr('━━━━━━')\\n #pos()\\n #br()\\n win.addstr('┛')\\n\\n #win.border()\\n win.refresh()\",\n \"def render(board):\\n #The top of the board render, which gives enough space for each square based on max square value\\n spot_length = len(str(len(board)))\\n rv = '-'*((spot_length+1)*len(board))+'-\\\\n' \\n\\n #Create a print statement for each line\\n for i in range(len(board)): \\n line = '|' \\n\\n #For each number in line, add to print line \\n for j in range(len(board[i])): \\n spot = str(board[i][j]) \\n #If spot is a zero, replace for visuals with '_' \\n if spot == '0': \\n spot = '_'*spot_length\\n #Add number to print line with a separator '|'\\n line += ' '*(spot_length-len(spot)) + spot + '|' \\n\\n #Print the rendered line\\n rv += line + '\\\\n' \\n\\n #Print the bottom of the board\\n rv += '-'*((spot_length+1)*len(board))+'-' \\n return rv\",\n \"def Display1(self, high, low, windSpeed, units_Speed, windDir, lang):\\n if lang == 'eng':\\n string = 'Temp H:' + high + ' L:' + low + '\\\\nWind ' + windSpeed + units_Speed + ' ' + windDir\\n return string\\n else:\\n return 0\\n # Not Yet Supported\",\n \"def display(self):\\n print (\\\"+\\\" + \\\"-\\\"*self.size + \\\"+\\\")\\n for i in range(self.size):\\n terrain_strs = [Terrain.display_string(self.array[j, i]) for j in range(self.size)]\\n print(\\\"|\\\" + \\\"\\\".join(terrain_strs) + \\\"|\\\")\\n print (\\\"+\\\" + \\\"-\\\"*self.size + \\\"+\\\")\",\n \"def Print(self, s, color=(229, 153, 153, 255)):\\r\\n self.screen.blit(self.font.render(s, True, color), (5, self.textLine))\\r\\n self.textLine += 15\",\n \"def display_event(player,player_name,event,count_line):\\n player_name = 'you !'\\n color_player= Back.RED\\n if player == 'player1':\\n color_player= Back.BLUE\\n\\n if event=='intro' :\\n\\n l0='█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████'\\n l1='██████████████████████████████████████████████████████++████████++███████████████████████████████████████████████████████'\\n l2='███████+++█████████████████++██████████+++████████████++███++█████+++███████████████++███████████████++██████████++██████'\\n l3='+++++++███++++++++++████;████:++██+++██+++██++++++++++++++++++++++███+++++++████████++███████+++██+++██+++++++█████+++██+'\\n l4='+++++++***++*****+++++++++++++++++***+++++++**++++++++**+++++***++++++++++++++***++++++++++++***++:++++++***++***++++++++'\\n l5='**+++██████**████*****+++*******************++*******;███*******++***████*++**+++*******+++*******+++**████:*******+++++*'\\n l6='*****██:::**:::██********::*******;::************::**██:██******::***██*██*******************:::******██*:::*************'\\n l7='*****██;,::,:::██:::*██**██**:████;:::█████;,,::;**::██:██:::***::***██::██:;██**;██::████:**██:*██::*██::::::█████:::,:*'\\n l8='::;:;██::::::::██::::██::██::██::██::██:::::::::::::::███::::::::::::██::██::██:█:██:::::██::██:███:::██:::::██::;:::::::'\\n l9=':::::█████:::::██::::██::██::██::██::██::::::::::::::██::::::::::::::██;:██::██:█:██:::::██::███:::::██████::██::::::::::'\\n l10=':::::██::::::::██::::██::██::██████:::████:::::::::,,██:████:::::::::██,:██::██:█;██:;█████::██:::::::██::::::████:::::::'\\n l11=':::::██:::::,,,██::::██,,██::██:,,:::::,,██:::,,,::,,██,,██::::::::::██::██,:██:█:██:██:,██::██:,,::::██:,,,:::::██,,,:::'\\n l12=',,:::██:::,,,,,██:::.,████,,,██:,,,,,,,::██:::,,,,,,,██,,██,,,,,::,,,██,██,,,,██:██,:██,,██,,██,::,,,:██,,,,,,:::██,,,,,:'\\n l13=',,,,,██████,,██████,,,,██,,,,,████,,,█████,,,,,,,,,,,,███,██,,,,,,,,,████,,,,,██,██,,,█████,,██,,,,,,,██,,,,,█████,,:,,,,'\\n l14=',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,'\\n l15=',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,'\\n la1=Back.WHITE+Fore.BLACK+' GROUPE 42 : EMILIE LAURENT, JOFFREY BIENVENU, JONATHAN MAROIT ET SYLVAIN PIRLOT '\\n\\n\\n\\n\\n line_list = [l0,l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12,l13,l14,l15,la1]\\n if count_line < len(line_list):\\n print Fore.RED+ Back.BLACK+line_list[count_line]\\n\\n\\n\\n elif event=='game_over':\\n d0=(Fore.BLACK+Back.WHITE)+' '+('The loser is: '+player_name)+' '\\n d1='██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████'\\n d2='███████████████████████████████████████ ████████████████████████████████████ ███████████████████████████████████████'\\n d3='███████████████████████████████████████ ████████████████████████████████████ ███████████████████████████████████████'\\n d4='███████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████'\\n d5='███████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████'\\n d6='███████████████████████████████████████████████████ ███████████████████████████████████████████████████'\\n d7='███████████████████████████████████████████████████ ████████ ████████ ███████████████████████████████████████████████████'\\n d8='███████████████████████████████████████████████████ ████████ ████████ ███████████████████████████████████████████████████'\\n d9='███████████████████████████████████████████████████ ████████ ████████ ███████████████████████████████████████████████████'\\n d10='███████████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████████'\\n d11='███████████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████████'\\n d12='███████████████████████████████████████████████████ ████ ███████████████████████████████████████████████████'\\n d13='███████████████████████████████████████████████████ ████ ███████████████████████████████████████████████████'\\n d14='███████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████'\\n d15='███████████████████████████████████████████████ ████ ████ ███████████████████████████████████████████████'\\n d16='███████████████████████████████████████ ████████ ████ ████ ████████ ███████████████████████████████████████'\\n d17='███████████████████████████████████████ ████████ ████ ████ ████████ ███████████████████████████████████████'\\n d18='██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████'\\n d19=(Fore.BLACK+Back.WHITE)+' '\\n death_list=[d0,d1,d2,d3,d4,d5,d6,d7,d8,d9,d10,d11,d12,d13,d14,d15,d16,d17,d18]\\n if count_line < len(death_list):\\n print Fore.BLACK+color_player+death_list[count_line]\\n\\n\\n elif event == 'win':\\n\\n w0=Back.BLACK+Fore.WHITE+' THE '\\n wa=' '\\n w1=' ██ █ ██ █ ██ ██ ████ ██ ██ ██ ██ ██████ █████ █████ '\\n w2=' █████████ ███ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ █ ███ █ '\\n w3=' ███ █ ███ █ ██ ██ ██ ███ ██ ███ ██ ██ ██ ██ █ █ ██ █ '\\n w4=' ██ █ ██ █ ██ █ ██ ██ ████ ██ ████ ██ ██ ██ ██ █ █ █ ██ █ '\\n w5=' █ █ █ █ ██ █ ██ ██ ██ ████ ██ ████ █████ █████ █ █ █ '\\n w6=' █ █ ██ █ ██ ██ ██ ███ ██ ███ ██ ██ ██ █ █ █ '\\n w7=' █ █ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ █ █ █ '\\n w8=' █ █████████ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ███ █ █ '\\n w9=' ███ █████████ ██ ██ ████ ██ ██ ██ ██ ██████ ██ ██ ██ ██ ██ █ '\\n wb=' █ █ █ █ ████ '\\n wb1=' '\\n w10=Back.BLACK+Fore.WHITE+' IS '\\n w11=Back.BLACK+Fore.WHITE+' '+player_name+' '\\n win_list=[w0,wa,w1,w2,w3,w4,w5,w6,w7,w8,w9,wb,wb1,w10,w11]\\n if count_line < len(win_list):\\n print Fore.BLACK+color_player+win_list[count_line]\",\n \"def _string_data(self, data):\\n print(\\\"_string_data:\\\")\\n string_to_print = []\\n for i in data[::2]:\\n string_to_print.append(chr(i))\\n print(string_to_print)\",\n \"def repr2col(tups):\\n quant = tuple(round(comp * 255) for comp in tups)\\n if quant[3] != 255: return \\\"\\\".join([\\\"{0:02x}\\\".format(m) for m in quant])\\n quant = quant[:3]\\n for w in shortaliases:\\n if quant == shortaliases[w]: return w\\n return \\\"#\\\" + \\\"\\\".join([\\\"{:02x}\\\".format(n) if max(n % 17 for n in quant) else \\\"{:x}\\\".format(n >> 4) for n in quant])\",\n \"def print_cards(cards):\\r\\n string = ''\\r\\n for c in cards:\\r\\n suit = c[0]\\r\\n if suit == 1:\\r\\n suit = \\\"\\\\u2665\\\" # heart\\r\\n elif suit == 2:\\r\\n suit = \\\"\\\\u2660\\\" # Spade\\r\\n elif suit == 3:\\r\\n suit = \\\"\\\\u2666\\\" # Diamond\\r\\n else:\\r\\n suit = \\\"\\\\u2663\\\" # club\\r\\n\\r\\n num = c[1]\\r\\n if num == 11:\\r\\n num = 'J'\\r\\n elif num == 12:\\r\\n num = 'Q'\\r\\n elif num == 13:\\r\\n num = 'K'\\r\\n else:\\r\\n num = str(num)\\r\\n\\r\\n string = string + num + suit + ' '\\r\\n return string\",\n \"def rock():\\n typer.echo(\\\"🤖🤘\\\")\",\n \"def show_packet_info(scapyshark, packet):\\n\\n # Grab the actual packet\\n packet = scapyshark._top_box.get_packet(packet)\\n\\n #\\n # Middle Box\\n #\\n \\n scapyshark._middle_box.base_widget.body.clear()\\n\\n for line in packet.show(dump=True).split(\\\"\\\\n\\\"):\\n scapyshark._middle_box.base_widget.body.append(urwid.Text(line))\\n\\n #\\n # Bottom Box\\n #\\n\\n scapyshark._bottom_box.base_widget.body.clear()\\n\\n b = bytes(packet)\\n first_line = 0\\n\\n for i in range(0, len(b), 16):\\n b_chunk = b[i:i+16]\\n\\n line = \\\":\\\".join(\\\"{:02x}\\\".format(c) for c in b_chunk)\\n\\n if first_line == 0:\\n first_line = len(line)\\n else:\\n if len(line) != first_line:\\n line += ' '*(first_line - len(line))\\n\\n line += ' | '\\n\\n for c in b_chunk:\\n c2 = chr(c)\\n if c2 in printable:\\n line += c2\\n else:\\n line += '.'\\n\\n scapyshark._bottom_box.base_widget.body.append(urwid.Text(line))\",\n \"def horizontal_char(self):\\n ...\",\n \"def display(tab):\\n screen = [None] * 64\\n for i in range(8):\\n for j in range(8):\\n if tab[i][j] == 1:\\n sense.set_pixel(i, j, 255, 0, 0) if STATES[index_state] == \\\"temp\\\" else sense.set_pixel(i, j, 0, 0, 255)\\n else:\\n sense.set_pixel(i, j, 0, 0, 0)\",\n \"def reveal_occupants(idx, huts):\\n msg = \\\"\\\"\\n print(\\\"展示小屋内部情况...\\\")\\n for i in range(len(huts)):\\n occupant_info = \\\"<%d:%s>\\\" % (i + 1, huts[i])\\n if i + 1 == idx:\\n occupant_info = \\\"\\\\033[1m\\\" + occupant_info + \\\"\\\\033[0m\\\"\\n msg += occupant_info + \\\" \\\"\\n\\n print(\\\"\\\\t\\\" + msg)\\n print_dotted_line()\",\n \"def display(values):\\n boxes = cross(rows, cols)\\n # This finds the max number of characters in each box and adds one.\\n # the max will be '123456789' so 9 + 1 = 10\\n width = 1+max(len(values[s])for s in boxes)\\n\\n # This will create 30 dashes separated with a \\\"+\\\" 3x\\n line = '+' .join(['-'*(width*3)]*3)\\n\\n # This builds the board. It iterates through [A1, A2, etc.] and then\\n # uses the .center method to center whatever value is in that cell.\\n # It adds a | in-between columns 3 & 6 otherwise an empty string.\\n # It prints a line if the row is C or F\\n for r in rows:\\n print(''.join(values[r+c].center(width)+('|' if c in '36' else '')\\n for c in cols))\\n if r in 'CF': print(line)\\n return\",\n \"def hAKI(self):\\n\\n aki = self.get_AKI()\\n return \\\":\\\".join((\\\"%02X\\\" % ord(i) for i in aki)) if aki else \\\"\\\"\",\n \"def display_string(text_area_no: int) -> str:\\n if text_area_no == 1:\\n text = ''\\n for v in utterances['P1']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 2:\\n text = ''\\n for v in utterances['S1']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 3:\\n text = ''\\n for v in utterances['S2']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 4:\\n text = ''\\n for v in utterances['S3']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 5:\\n text = ''\\n for v in utterances['S4']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 6:\\n text = ''\\n for v in utterances['S4']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 7:\\n text = ''\\n for v in utterances['C1']:\\n text += v + '\\\\n'\\n return text\\n elif text_area_no == 8:\\n text = ''\\n for v in utterances['C2']:\\n text += v + '\\\\n'\\n return text\",\n \"def show_device_information_long(self):\\n\\n for device in self._devices:\\n print(\\\"\\\")\\n if device['Device Type'].startswith(\\\"enclosu\\\"):\\n if device.get('Device Type'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Device Type\\\", device['Device Type']))\\n if device['Device Type Description']:\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Device Description\\\", device['Device Type Description']))\\n if device.get('SCSI Device Name'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"SCSI Device Name\\\", device['SCSI Device Name']))\\n if device.get('Product Identification'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Product Identification\\\", device['Product Identification']))\\n if device.get('Vendor Identification'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Vendor Identification\\\", device['Vendor Identification']))\\n if device.get('Firmware Version'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Firmware Version\\\", device['Firmware Version']))\\n if device.get('Serial Number'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Serial Number\\\", device['Serial Number']))\\n if device.get('SAS Address'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"SAS Address\\\", device['SAS Address']))\\n else:\\n if device.get('Device Type'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Device Type\\\", device['Device Type']))\\n if device['Device Type Description']:\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Device Description\\\", device['Device Type Description']))\\n if device.get('Linux Device Name'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Linux Device Name\\\", device['Linux Device Name']))\\n if device.get('SCSI Device Name'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"SCSI Device Name\\\", device['SCSI Device Name']))\\n if device.get('Product Identification'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Product Identification\\\", device['Product Identification']))\\n if device.get('Vendor Identification'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Vendor Identification\\\", device['Vendor Identification']))\\n if device.get('Firmware Version'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Firmware Version\\\", device['Firmware Version']))\\n if device.get('Serial Number'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Serial Number\\\", device['Serial Number']))\\n if device.get('Drive Capacity'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Drive Capacity\\\", device['Drive Capacity']))\\n if device.get('Block Length'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Block Length\\\", device['Block Length']))\\n if device.get('Power On Hours'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Power On Hours\\\", device['Power On Hours']))\\n if device.get('Current Temperature'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Current Temperature\\\", device['Current Temperature']))\\n if device.get('SAS Address'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"SAS Address\\\", device['SAS Address']))\\n if device.get('Enclosure Device'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Enclosure Device\\\", device['Enclosure Device']))\\n if device.get('Enclosure Slot'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Enclosure Slot\\\", device['Enclosure Slot']))\\n if device.get('Slot Description'):\\n print(\\\"{0:>32}: {1}\\\".format(\\\"Slot Desciption\\\", device['Slot Description']))\\n\\n if len(self._devices):\\n print(\\\"\\\")\",\n \"def my_banner(bannerString):\\n print(len(bannerString) * \\\"!\\\")\\n print(bannerString)\\n print(len(bannerString) * \\\"!\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6711596","0.6433494","0.63968563","0.61818796","0.6086967","0.6044831","0.6038081","0.60282725","0.59977597","0.5977302","0.59293854","0.5919693","0.5907645","0.5782015","0.5761691","0.5756993","0.57358706","0.57197696","0.5708366","0.5704662","0.56909454","0.5684595","0.5679914","0.5670678","0.56292725","0.56278217","0.5620461","0.56092983","0.56073296","0.5598926","0.55911535","0.5586724","0.5576811","0.5560953","0.55562544","0.55428666","0.55396307","0.55353117","0.5534349","0.5531812","0.5522807","0.55225164","0.55211616","0.5520708","0.55167395","0.55124396","0.550546","0.5492788","0.5483706","0.5483653","0.54834133","0.5482733","0.5480341","0.5476328","0.5471763","0.54516995","0.5450902","0.54489243","0.5447248","0.5445256","0.54434574","0.5443118","0.5439508","0.5433216","0.5414248","0.5413049","0.5406762","0.540489","0.5402284","0.54003006","0.5400104","0.5398108","0.5395595","0.53943384","0.53907365","0.53880817","0.5386218","0.53861415","0.5385374","0.5377441","0.5370458","0.53694487","0.5361044","0.53574044","0.5354865","0.53482884","0.5345847","0.5345106","0.53439766","0.53430945","0.53354734","0.5334604","0.5333744","0.5327385","0.53263104","0.5323524","0.53215986","0.5320869","0.5317823","0.53171605"],"string":"[\n \"0.6711596\",\n \"0.6433494\",\n \"0.63968563\",\n \"0.61818796\",\n \"0.6086967\",\n \"0.6044831\",\n \"0.6038081\",\n \"0.60282725\",\n \"0.59977597\",\n \"0.5977302\",\n \"0.59293854\",\n \"0.5919693\",\n \"0.5907645\",\n \"0.5782015\",\n \"0.5761691\",\n \"0.5756993\",\n \"0.57358706\",\n \"0.57197696\",\n \"0.5708366\",\n \"0.5704662\",\n \"0.56909454\",\n \"0.5684595\",\n \"0.5679914\",\n \"0.5670678\",\n \"0.56292725\",\n \"0.56278217\",\n \"0.5620461\",\n \"0.56092983\",\n \"0.56073296\",\n \"0.5598926\",\n \"0.55911535\",\n \"0.5586724\",\n \"0.5576811\",\n \"0.5560953\",\n \"0.55562544\",\n \"0.55428666\",\n \"0.55396307\",\n \"0.55353117\",\n \"0.5534349\",\n \"0.5531812\",\n \"0.5522807\",\n \"0.55225164\",\n \"0.55211616\",\n \"0.5520708\",\n \"0.55167395\",\n \"0.55124396\",\n \"0.550546\",\n \"0.5492788\",\n \"0.5483706\",\n \"0.5483653\",\n \"0.54834133\",\n \"0.5482733\",\n \"0.5480341\",\n \"0.5476328\",\n \"0.5471763\",\n \"0.54516995\",\n \"0.5450902\",\n \"0.54489243\",\n \"0.5447248\",\n \"0.5445256\",\n \"0.54434574\",\n \"0.5443118\",\n \"0.5439508\",\n \"0.5433216\",\n \"0.5414248\",\n \"0.5413049\",\n \"0.5406762\",\n \"0.540489\",\n \"0.5402284\",\n \"0.54003006\",\n \"0.5400104\",\n \"0.5398108\",\n \"0.5395595\",\n \"0.53943384\",\n \"0.53907365\",\n \"0.53880817\",\n \"0.5386218\",\n \"0.53861415\",\n \"0.5385374\",\n \"0.5377441\",\n \"0.5370458\",\n \"0.53694487\",\n \"0.5361044\",\n \"0.53574044\",\n \"0.5354865\",\n \"0.53482884\",\n \"0.5345847\",\n \"0.5345106\",\n \"0.53439766\",\n \"0.53430945\",\n \"0.53354734\",\n \"0.5334604\",\n \"0.5333744\",\n \"0.5327385\",\n \"0.53263104\",\n \"0.5323524\",\n \"0.53215986\",\n \"0.5320869\",\n \"0.5317823\",\n \"0.53171605\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5474368"},"document_rank":{"kind":"string","value":"54"}}},{"rowIdx":77496,"cells":{"query":{"kind":"string","value":"Starts a self test"},"document":{"kind":"string","value":"def hp3458a_self_test(hp_meter):\n hp_meter.write(\"TEST\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def startTestRun(self):","def runTests(self):\n \n pass","def test_run_started(self):","def runtest(self):","def __main() :\n launchTests()","def startTestHook(self):","def test_start(self):\n self.fail(\"write a test\")","def run_test(self):\n raise NotImplementedError","def RunTest(self):\n self.TestLs()\n self.TestTerminate()\n self.TestMultipleProcesses()","def startTest(asset):","def test_script(self) -> None:\n main()","def main():\n run_test_all()","def run_tests(self):\n raise NotImplementedError","def run(self, test, env):\n\n raise NotImplementedError","def test_start_test(self):\n self.protocol.startTest(self.test)\n self.assertEqual(self.io.getvalue(), compat._b(\n \"test: %s\\n\" % self.test.id()))","def run_starter(self, expect_to_fail=False):","def run_self_test(self):\n par_values = list(SELF_TEST_LIST)\n\n\n if True:\n # Only ERASynth+ and ERASynth++ have this functionality\n par_values += [(\"reference_tcxo_ocxo\", \"tcxo\")]\n\n num_tests = len(par_values)\n for i, (name, val) in enumerate(par_values):\n print(f\"\\r[{i+1:2d}/{num_tests}] Running...\", end=\"\")\n self.set(name, val)\n\n print(\"\\nDone!\")","def startTestRun(self, test):\n self.runTime= time.time()\n self.logger.debug(\"\\nBeginning ForceBalance test suite at %s\\n\" % time.strftime('%x %X %Z'))","def runTest(self):\n self.testsActivated = True\n self.session = subprocess.Popen([\"sudo\", \"python3\", TEST_RUNNER_PATH], preexec_fn=os.setsid)","def start_test(self, request):\n request.worker.start_test(request.message.test_id)\n\n return SuccessReply()","def start_test_exec(cls):\n time_str = cls.get_current_time()\n os.system(\"robot -l ./logs/log_{0}.html -r ./logs/report_{0}.html -o ./logs/output_{0}.xml \\\n ./test_suite/{1}\".format(time_str, test_suite))","def testInit(self):\n self.globalInit()\n self.test.start()","def test_begin(self):","def do_test(self):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tif not self.build['dotest']:\n\t\t\tself.log('Tests configured off, not running',level=logging.DEBUG)\n\t\t\treturn\n\t\t# Test in reverse order\n\t\tself.log('PHASE: test', level=logging.DEBUG)\n\t\tself.stop_all()\n\t\tself.start_all()\n\t\tfor module_id in self.module_ids(rev=True):\n\t\t\t# Only test if it's installed.\n\t\t\tif self.is_installed(self.shutit_map[module_id]):\n\t\t\t\tself.log('RUNNING TEST ON: ' + module_id, level=logging.DEBUG)\n\t\t\t\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\n\t\t\t\tif not self.shutit_map[module_id].test(self):\n\t\t\t\t\tself.fail(module_id + ' failed on test', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\n\t\t\t\tself.logout(echo=False)","def start_test_run(self, request):\n request.worker.initialize_test_run(request.message.tests,\n request.message.run_data)\n\n return SuccessReply()","def run_main(): # pragma: no cover\n RunTestsCLI.run()","def startTest(testname, host):\r\n host, UNDI = getBoxInfo()\r\n runID = uuid.uuid5(uuid.NAMESPACE_DNS, host)\r\n lg.info(\"Test, %s, run started on %s/%s with runID %s\"%(testname, host, UNDI, runID.hex))\r\n def abort():\r\n lg.info(\"Aborting test run %s for test %s on %s/%s\"%(runID.hex, testname, host, UNDI))\r\n return\r\n def end():\r\n lg.info(\"Ending test run %s for test %s on %s/%s\"%(runID.hex, testname, host, UNDI))\r\n return\r\n return end, abort","def startTest(self, test):\n self.start()\n test.status = None\n test.errors = None\n test.test_item = self.service.start_nose_item(self, test)\n self.setupLoghandler()","def startTest(self, test):\n self._timer = time()","def setUp(self):\r\n print('---------------------------------------------\\n')\r\n print('STARTING TEST...')","def tests():","def runDotNetTest():\r\n global test\r\n test = DotNetTest()\r\n test.show()","def run(context, path=\"\"):\n common.success(f\"Tests {path} running \")\n return start.run_python(\n context,\n f\"-m pytest {path}\"\n )","def test_example_runs(self):\n run_example(\n verbose=False,\n testapp=self.testapp,\n )","def run(self):\n self.speed_test.start()","def main():\n\n parser = argparse.ArgumentParser(prog=\"run_test.py\",\n formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument('id', help=\"Id of a test\")\n args = parser.parse_args()\n\n configure_logger()\n\n test_info = TESTS.get(args.id, None)\n if not test_info:\n test_info.log.error(f'{args.id} does not exist')\n exit(ErrorCode.CRITICAL)\n os.environ['DISPLAY'] = \":0.0\"\n\n test = Test(args.id, test_info)\n result = test.run()\n\n test.log.info('#' * 80)\n if not result:\n test.log.error('TEST FAILED')\n else:\n test.log.info('TEST PASSED')\n test.log.info('#' * 80)\n exit(not result)","def test_single_test_case():\n pass","def startTestRun(self):\n self.startTime = time.time()\n # Really verbose information\n if self.verbose > 2:\n self.stream.writeln(self.colors.bold(pretty_version() + \"\\n\"))","def test():\n import unittest\n\n tests = unittest.TestLoader().discover(\"tests\")\n unittest.TextTestRunner(verbosity=2).run(tests)","def _run_local_tests(self, *args, **kwargs):\n pass","def test_class_started(self, cls):","def test_basic_execution(self):","def runTest(self):\n unittest.main()\n ChoreTest.clean_up()","def tests():\n api.local('nosetests')","def test(self):\n pass","def test():\n nose.run()","def runTest(self):\n return True","def test_jam_attempt(self):\n self.run_test_suites(self.jam_test_suite_list)","def runTest(self):\n self.setUp()\n self.test_FiberDistance1()","def test_runner(self):\n run(HelloWorldApi, [\"/bin/test_api.py app_root/bin/libs\", \"app_root/bin/libs\"], sys.stdin.buffer,\n sys.__stdout__.buffer)","def run_tests(self):\n\n self.manifest_path = os.path.join('tests',\n 'functional',\n 'manifest.ini')\n TestRun.run_tests(self)","def run_test(test_name):\n\n print 'Running %s_test...' % test_name\n os.system('./test_%s.py' % test_name)\n print","def testing(self):\n print('test successful')","def run_tests(self):\n manifest = manifestparser.TestManifest(\n manifests=[os.path.join(self.repository_path, self.manifest_path)],\n strict=False)\n\n tests = manifest.active_tests(**mozinfo.info)\n self._mozmill.run(tests, self.options.restart)\n\n # Whenever a test fails it has to be marked, so we quit with the correct exit code\n self.last_failed_tests = self.last_failed_tests or self._mozmill.results.fails\n\n self.testrun_index += 1","def setUp(self):\n self.p = Process(target = start_server)\n self.p.start()\n time.sleep(0.5)","def start_next_test(self):\n next_test_num = self.test_numbers.popleft()\n self.tests.append(\n self.TEST(\n process=Popen(COMMANDS[next_test_num],\n stdout=PIPE,\n stderr=PIPE),\n number=next_test_num))","def setUp(self):\n self.p = Process(target = start_server)\n self.p.start()\n time.sleep(0.5)\n #init_fakeDB()\n time.sleep(0.5)","def setUp(self):\n self.p = Process(target = start_server)\n self.p.start()\n time.sleep(0.5)\n #init_fakeDB()\n time.sleep(0.5)","def setUp(self):\n self.p = Process(target = start_server)\n self.p.start()\n time.sleep(0.5)\n #init_fakeDB()\n time.sleep(0.5)","def test_start_scan(self):\n pass","def runTest(self):\n self.setUp()\n self.test_ExtendSpine1()","def test_run_ended(self):","def TestOneStep(self):\n pass","def start_new_test_thread(self):\n GObject.timeout_add(100, self.on_timeout, None)\n def run_tests():\n with self.lock:\n for l, t in zip(self.liststore, self.liststore.tests):\n if l[2]:\n t.test()\n for l, t in zip(self.liststore, self.liststore.tests):\n if l[2]:\n print(t.results)\n \n if not self.test_thread:\n self.test_thread = threading.Thread(target=run_tests)\n self.test_thread.start()","def initTest(self, myargs):\n return","def test(self, args=None, init_args=None, config=None, verbose=False):\n self.ctest(args=args, init_args=init_args, config=config, verbose=verbose)","def test_base(self):\n self.render_config_template(\n )\n\n proc = self.start_beat()\n self.wait_until(lambda: self.log_contains(\"mockbeat start running.\"))\n proc.check_kill_and_wait()\n assert self.log_contains(\"mockbeat stopped.\")","def start_fixture(self):\n pass","def test_runGame(self):\n # this is tested by playing the game. No good way to unit test this.\n pass","def test_create_run(self):\n pass","def test_1():","def runTest(self):\n self.setUp()\n self.test_ProstateReporting1()","def test_get_run(self):\n pass","def test_start(self):\n magic_hat = Game()\n result = Game.start(magic_hat)\n self.assertEqual(result, game.STATUS_PLAYING)","def test():\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n pass","def test():\n loader = unittest.TestLoader()\n suite = loader.discover(os.path.dirname(__file__))\n runner = unittest.TextTestRunner()\n runner.run(suite)","def run(self):\n if self.all:\n cmd = self.apply_options(self.test_all_cmd)\n self.call_and_exit(cmd)\n else:\n cmds = (self.apply_options(self.unit_test_cmd, (\"coverage\",)),)\n if self.coverage:\n cmds += (self.apply_options(self.coverage_cmd),)\n self.call_in_sequence(cmds)","def test():\r\n import unittest\r\n tests=unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_base(self):\n self.render_config_template(\n path=os.path.abspath(self.working_dir) + \"/log/*\",\n local=True,\n )\n\n functionbeat_proc = self.start_beat()\n self.wait_until(lambda: self.log_contains(\"functionbeat is running\"))\n exit_code = functionbeat_proc.kill_and_wait()\n assert exit_code == 0","def start_test_instance(test_name=None):\n env.warn_only = True\n if test_name is not None:\n instances = [test_name]\n else:\n output = run('ls -1 %s' % env.site_root)\n instances = [x.strip() for x in output.split(\"\\n\")]\n for item in instances:\n sudo(\"start %s\" % item.strip())","def runTest(self):\r\n self.setUp()","def startTest(self, test):\n\n super(ForceBalanceTestResult, self).startTest(test)\n self.logger.debug(\"\\n>>> Starting %s\\n\" % test.id())\n self.logger.info(\">>> \" + test.shortDescription())","def test_base(self):\n self.render_config_template(\n path=os.path.abspath(self.working_dir) + \"/log/*\"\n )\n\n sqsbeat_proc = self.start_beat()\n self.wait_until(lambda: self.log_contains(\"sqsbeat is running\"))\n exit_code = sqsbeat_proc.kill_and_wait()\n assert exit_code == 0","def run_tests(self):\n\n self.manifest_path = os.path.join('tests',\n 'remote',\n 'manifest.ini')\n TestRun.run_tests(self)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\r\n import unittest\r\n tests = unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\r\n import unittest\r\n tests = unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def startTestRun(self, event):\n self.prof = cProfile.Profile()\n event.executeTests = self.prof.runcall","def startTest(self, test):\n test = proto_test(test)\n self.start_time = time.time()\n self.reinitialize()\n if self.start_callback:\n self.start_callback(test)","def main_tester():\n create_tester_paths()\n _logger.info(' -- tester init done setting up paths and db file.')","def test_Demo(self):\n self._run(self._example_scenarios, \"Demo\")","def runTest(self):\n self.setUp()\n self.test_Animator1()","def runTest(self):\n self.setUp()\n self.test_JupyterNotebooks1()","def setUp(self):\n MainTests.setUp(self)","def run(self):\r\n self.dut.create_device_and_transport(self.config_module.config_data)\r\n self.logger.info(\"Run the Test Case\")","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)"],"string":"[\n \"def startTestRun(self):\",\n \"def runTests(self):\\n \\n pass\",\n \"def test_run_started(self):\",\n \"def runtest(self):\",\n \"def __main() :\\n launchTests()\",\n \"def startTestHook(self):\",\n \"def test_start(self):\\n self.fail(\\\"write a test\\\")\",\n \"def run_test(self):\\n raise NotImplementedError\",\n \"def RunTest(self):\\n self.TestLs()\\n self.TestTerminate()\\n self.TestMultipleProcesses()\",\n \"def startTest(asset):\",\n \"def test_script(self) -> None:\\n main()\",\n \"def main():\\n run_test_all()\",\n \"def run_tests(self):\\n raise NotImplementedError\",\n \"def run(self, test, env):\\n\\n raise NotImplementedError\",\n \"def test_start_test(self):\\n self.protocol.startTest(self.test)\\n self.assertEqual(self.io.getvalue(), compat._b(\\n \\\"test: %s\\\\n\\\" % self.test.id()))\",\n \"def run_starter(self, expect_to_fail=False):\",\n \"def run_self_test(self):\\n par_values = list(SELF_TEST_LIST)\\n\\n\\n if True:\\n # Only ERASynth+ and ERASynth++ have this functionality\\n par_values += [(\\\"reference_tcxo_ocxo\\\", \\\"tcxo\\\")]\\n\\n num_tests = len(par_values)\\n for i, (name, val) in enumerate(par_values):\\n print(f\\\"\\\\r[{i+1:2d}/{num_tests}] Running...\\\", end=\\\"\\\")\\n self.set(name, val)\\n\\n print(\\\"\\\\nDone!\\\")\",\n \"def startTestRun(self, test):\\n self.runTime= time.time()\\n self.logger.debug(\\\"\\\\nBeginning ForceBalance test suite at %s\\\\n\\\" % time.strftime('%x %X %Z'))\",\n \"def runTest(self):\\n self.testsActivated = True\\n self.session = subprocess.Popen([\\\"sudo\\\", \\\"python3\\\", TEST_RUNNER_PATH], preexec_fn=os.setsid)\",\n \"def start_test(self, request):\\n request.worker.start_test(request.message.test_id)\\n\\n return SuccessReply()\",\n \"def start_test_exec(cls):\\n time_str = cls.get_current_time()\\n os.system(\\\"robot -l ./logs/log_{0}.html -r ./logs/report_{0}.html -o ./logs/output_{0}.xml \\\\\\n ./test_suite/{1}\\\".format(time_str, test_suite))\",\n \"def testInit(self):\\n self.globalInit()\\n self.test.start()\",\n \"def test_begin(self):\",\n \"def do_test(self):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tif not self.build['dotest']:\\n\\t\\t\\tself.log('Tests configured off, not running',level=logging.DEBUG)\\n\\t\\t\\treturn\\n\\t\\t# Test in reverse order\\n\\t\\tself.log('PHASE: test', level=logging.DEBUG)\\n\\t\\tself.stop_all()\\n\\t\\tself.start_all()\\n\\t\\tfor module_id in self.module_ids(rev=True):\\n\\t\\t\\t# Only test if it's installed.\\n\\t\\t\\tif self.is_installed(self.shutit_map[module_id]):\\n\\t\\t\\t\\tself.log('RUNNING TEST ON: ' + module_id, level=logging.DEBUG)\\n\\t\\t\\t\\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\\n\\t\\t\\t\\tif not self.shutit_map[module_id].test(self):\\n\\t\\t\\t\\t\\tself.fail(module_id + ' failed on test', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\\n\\t\\t\\t\\tself.logout(echo=False)\",\n \"def start_test_run(self, request):\\n request.worker.initialize_test_run(request.message.tests,\\n request.message.run_data)\\n\\n return SuccessReply()\",\n \"def run_main(): # pragma: no cover\\n RunTestsCLI.run()\",\n \"def startTest(testname, host):\\r\\n host, UNDI = getBoxInfo()\\r\\n runID = uuid.uuid5(uuid.NAMESPACE_DNS, host)\\r\\n lg.info(\\\"Test, %s, run started on %s/%s with runID %s\\\"%(testname, host, UNDI, runID.hex))\\r\\n def abort():\\r\\n lg.info(\\\"Aborting test run %s for test %s on %s/%s\\\"%(runID.hex, testname, host, UNDI))\\r\\n return\\r\\n def end():\\r\\n lg.info(\\\"Ending test run %s for test %s on %s/%s\\\"%(runID.hex, testname, host, UNDI))\\r\\n return\\r\\n return end, abort\",\n \"def startTest(self, test):\\n self.start()\\n test.status = None\\n test.errors = None\\n test.test_item = self.service.start_nose_item(self, test)\\n self.setupLoghandler()\",\n \"def startTest(self, test):\\n self._timer = time()\",\n \"def setUp(self):\\r\\n print('---------------------------------------------\\\\n')\\r\\n print('STARTING TEST...')\",\n \"def tests():\",\n \"def runDotNetTest():\\r\\n global test\\r\\n test = DotNetTest()\\r\\n test.show()\",\n \"def run(context, path=\\\"\\\"):\\n common.success(f\\\"Tests {path} running \\\")\\n return start.run_python(\\n context,\\n f\\\"-m pytest {path}\\\"\\n )\",\n \"def test_example_runs(self):\\n run_example(\\n verbose=False,\\n testapp=self.testapp,\\n )\",\n \"def run(self):\\n self.speed_test.start()\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(prog=\\\"run_test.py\\\",\\n formatter_class=argparse.RawTextHelpFormatter)\\n parser.add_argument('id', help=\\\"Id of a test\\\")\\n args = parser.parse_args()\\n\\n configure_logger()\\n\\n test_info = TESTS.get(args.id, None)\\n if not test_info:\\n test_info.log.error(f'{args.id} does not exist')\\n exit(ErrorCode.CRITICAL)\\n os.environ['DISPLAY'] = \\\":0.0\\\"\\n\\n test = Test(args.id, test_info)\\n result = test.run()\\n\\n test.log.info('#' * 80)\\n if not result:\\n test.log.error('TEST FAILED')\\n else:\\n test.log.info('TEST PASSED')\\n test.log.info('#' * 80)\\n exit(not result)\",\n \"def test_single_test_case():\\n pass\",\n \"def startTestRun(self):\\n self.startTime = time.time()\\n # Really verbose information\\n if self.verbose > 2:\\n self.stream.writeln(self.colors.bold(pretty_version() + \\\"\\\\n\\\"))\",\n \"def test():\\n import unittest\\n\\n tests = unittest.TestLoader().discover(\\\"tests\\\")\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def _run_local_tests(self, *args, **kwargs):\\n pass\",\n \"def test_class_started(self, cls):\",\n \"def test_basic_execution(self):\",\n \"def runTest(self):\\n unittest.main()\\n ChoreTest.clean_up()\",\n \"def tests():\\n api.local('nosetests')\",\n \"def test(self):\\n pass\",\n \"def test():\\n nose.run()\",\n \"def runTest(self):\\n return True\",\n \"def test_jam_attempt(self):\\n self.run_test_suites(self.jam_test_suite_list)\",\n \"def runTest(self):\\n self.setUp()\\n self.test_FiberDistance1()\",\n \"def test_runner(self):\\n run(HelloWorldApi, [\\\"/bin/test_api.py app_root/bin/libs\\\", \\\"app_root/bin/libs\\\"], sys.stdin.buffer,\\n sys.__stdout__.buffer)\",\n \"def run_tests(self):\\n\\n self.manifest_path = os.path.join('tests',\\n 'functional',\\n 'manifest.ini')\\n TestRun.run_tests(self)\",\n \"def run_test(test_name):\\n\\n print 'Running %s_test...' % test_name\\n os.system('./test_%s.py' % test_name)\\n print\",\n \"def testing(self):\\n print('test successful')\",\n \"def run_tests(self):\\n manifest = manifestparser.TestManifest(\\n manifests=[os.path.join(self.repository_path, self.manifest_path)],\\n strict=False)\\n\\n tests = manifest.active_tests(**mozinfo.info)\\n self._mozmill.run(tests, self.options.restart)\\n\\n # Whenever a test fails it has to be marked, so we quit with the correct exit code\\n self.last_failed_tests = self.last_failed_tests or self._mozmill.results.fails\\n\\n self.testrun_index += 1\",\n \"def setUp(self):\\n self.p = Process(target = start_server)\\n self.p.start()\\n time.sleep(0.5)\",\n \"def start_next_test(self):\\n next_test_num = self.test_numbers.popleft()\\n self.tests.append(\\n self.TEST(\\n process=Popen(COMMANDS[next_test_num],\\n stdout=PIPE,\\n stderr=PIPE),\\n number=next_test_num))\",\n \"def setUp(self):\\n self.p = Process(target = start_server)\\n self.p.start()\\n time.sleep(0.5)\\n #init_fakeDB()\\n time.sleep(0.5)\",\n \"def setUp(self):\\n self.p = Process(target = start_server)\\n self.p.start()\\n time.sleep(0.5)\\n #init_fakeDB()\\n time.sleep(0.5)\",\n \"def setUp(self):\\n self.p = Process(target = start_server)\\n self.p.start()\\n time.sleep(0.5)\\n #init_fakeDB()\\n time.sleep(0.5)\",\n \"def test_start_scan(self):\\n pass\",\n \"def runTest(self):\\n self.setUp()\\n self.test_ExtendSpine1()\",\n \"def test_run_ended(self):\",\n \"def TestOneStep(self):\\n pass\",\n \"def start_new_test_thread(self):\\n GObject.timeout_add(100, self.on_timeout, None)\\n def run_tests():\\n with self.lock:\\n for l, t in zip(self.liststore, self.liststore.tests):\\n if l[2]:\\n t.test()\\n for l, t in zip(self.liststore, self.liststore.tests):\\n if l[2]:\\n print(t.results)\\n \\n if not self.test_thread:\\n self.test_thread = threading.Thread(target=run_tests)\\n self.test_thread.start()\",\n \"def initTest(self, myargs):\\n return\",\n \"def test(self, args=None, init_args=None, config=None, verbose=False):\\n self.ctest(args=args, init_args=init_args, config=config, verbose=verbose)\",\n \"def test_base(self):\\n self.render_config_template(\\n )\\n\\n proc = self.start_beat()\\n self.wait_until(lambda: self.log_contains(\\\"mockbeat start running.\\\"))\\n proc.check_kill_and_wait()\\n assert self.log_contains(\\\"mockbeat stopped.\\\")\",\n \"def start_fixture(self):\\n pass\",\n \"def test_runGame(self):\\n # this is tested by playing the game. No good way to unit test this.\\n pass\",\n \"def test_create_run(self):\\n pass\",\n \"def test_1():\",\n \"def runTest(self):\\n self.setUp()\\n self.test_ProstateReporting1()\",\n \"def test_get_run(self):\\n pass\",\n \"def test_start(self):\\n magic_hat = Game()\\n result = Game.start(magic_hat)\\n self.assertEqual(result, game.STATUS_PLAYING)\",\n \"def test():\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n pass\",\n \"def test():\\n loader = unittest.TestLoader()\\n suite = loader.discover(os.path.dirname(__file__))\\n runner = unittest.TextTestRunner()\\n runner.run(suite)\",\n \"def run(self):\\n if self.all:\\n cmd = self.apply_options(self.test_all_cmd)\\n self.call_and_exit(cmd)\\n else:\\n cmds = (self.apply_options(self.unit_test_cmd, (\\\"coverage\\\",)),)\\n if self.coverage:\\n cmds += (self.apply_options(self.coverage_cmd),)\\n self.call_in_sequence(cmds)\",\n \"def test():\\r\\n import unittest\\r\\n tests=unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_base(self):\\n self.render_config_template(\\n path=os.path.abspath(self.working_dir) + \\\"/log/*\\\",\\n local=True,\\n )\\n\\n functionbeat_proc = self.start_beat()\\n self.wait_until(lambda: self.log_contains(\\\"functionbeat is running\\\"))\\n exit_code = functionbeat_proc.kill_and_wait()\\n assert exit_code == 0\",\n \"def start_test_instance(test_name=None):\\n env.warn_only = True\\n if test_name is not None:\\n instances = [test_name]\\n else:\\n output = run('ls -1 %s' % env.site_root)\\n instances = [x.strip() for x in output.split(\\\"\\\\n\\\")]\\n for item in instances:\\n sudo(\\\"start %s\\\" % item.strip())\",\n \"def runTest(self):\\r\\n self.setUp()\",\n \"def startTest(self, test):\\n\\n super(ForceBalanceTestResult, self).startTest(test)\\n self.logger.debug(\\\"\\\\n>>> Starting %s\\\\n\\\" % test.id())\\n self.logger.info(\\\">>> \\\" + test.shortDescription())\",\n \"def test_base(self):\\n self.render_config_template(\\n path=os.path.abspath(self.working_dir) + \\\"/log/*\\\"\\n )\\n\\n sqsbeat_proc = self.start_beat()\\n self.wait_until(lambda: self.log_contains(\\\"sqsbeat is running\\\"))\\n exit_code = sqsbeat_proc.kill_and_wait()\\n assert exit_code == 0\",\n \"def run_tests(self):\\n\\n self.manifest_path = os.path.join('tests',\\n 'remote',\\n 'manifest.ini')\\n TestRun.run_tests(self)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\r\\n import unittest\\r\\n tests = unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\r\\n import unittest\\r\\n tests = unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def startTestRun(self, event):\\n self.prof = cProfile.Profile()\\n event.executeTests = self.prof.runcall\",\n \"def startTest(self, test):\\n test = proto_test(test)\\n self.start_time = time.time()\\n self.reinitialize()\\n if self.start_callback:\\n self.start_callback(test)\",\n \"def main_tester():\\n create_tester_paths()\\n _logger.info(' -- tester init done setting up paths and db file.')\",\n \"def test_Demo(self):\\n self._run(self._example_scenarios, \\\"Demo\\\")\",\n \"def runTest(self):\\n self.setUp()\\n self.test_Animator1()\",\n \"def runTest(self):\\n self.setUp()\\n self.test_JupyterNotebooks1()\",\n \"def setUp(self):\\n MainTests.setUp(self)\",\n \"def run(self):\\r\\n self.dut.create_device_and_transport(self.config_module.config_data)\\r\\n self.logger.info(\\\"Run the Test Case\\\")\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8505261","0.79985905","0.7970492","0.7886812","0.7796331","0.76125836","0.75867486","0.7508232","0.7442587","0.74282044","0.7393508","0.7370026","0.7354305","0.7352568","0.7300997","0.72973377","0.72624886","0.71843565","0.7175111","0.7169313","0.71427715","0.7134971","0.71145165","0.7077856","0.70632374","0.70033264","0.69984066","0.6997823","0.69503284","0.6910205","0.6895906","0.68952304","0.6864245","0.6853798","0.6851675","0.6838149","0.6832104","0.6827467","0.68159544","0.68138885","0.6813532","0.681145","0.67922527","0.67671406","0.6751722","0.674287","0.67405593","0.67375433","0.67333436","0.6713955","0.6706847","0.6705894","0.6704516","0.6697838","0.6686246","0.6685565","0.66786104","0.66786104","0.66786104","0.66714746","0.6665001","0.6653644","0.66466826","0.66384083","0.66356725","0.66252714","0.6623213","0.6616604","0.6612763","0.6602307","0.65901375","0.6589113","0.65857095","0.65773255","0.65606606","0.65606606","0.65606606","0.6559582","0.65573555","0.6551604","0.6551106","0.654748","0.6544267","0.6538756","0.65381426","0.6536535","0.653488","0.6534256","0.6529706","0.6529706","0.6528876","0.6527598","0.65250707","0.65248114","0.6514269","0.65103406","0.65067166","0.6499105","0.6495412","0.6495412","0.6495412"],"string":"[\n \"0.8505261\",\n \"0.79985905\",\n \"0.7970492\",\n \"0.7886812\",\n \"0.7796331\",\n \"0.76125836\",\n \"0.75867486\",\n \"0.7508232\",\n \"0.7442587\",\n \"0.74282044\",\n \"0.7393508\",\n \"0.7370026\",\n \"0.7354305\",\n \"0.7352568\",\n \"0.7300997\",\n \"0.72973377\",\n \"0.72624886\",\n \"0.71843565\",\n \"0.7175111\",\n \"0.7169313\",\n \"0.71427715\",\n \"0.7134971\",\n \"0.71145165\",\n \"0.7077856\",\n \"0.70632374\",\n \"0.70033264\",\n \"0.69984066\",\n \"0.6997823\",\n \"0.69503284\",\n \"0.6910205\",\n \"0.6895906\",\n \"0.68952304\",\n \"0.6864245\",\n \"0.6853798\",\n \"0.6851675\",\n \"0.6838149\",\n \"0.6832104\",\n \"0.6827467\",\n \"0.68159544\",\n \"0.68138885\",\n \"0.6813532\",\n \"0.681145\",\n \"0.67922527\",\n \"0.67671406\",\n \"0.6751722\",\n \"0.674287\",\n \"0.67405593\",\n \"0.67375433\",\n \"0.67333436\",\n \"0.6713955\",\n \"0.6706847\",\n \"0.6705894\",\n \"0.6704516\",\n \"0.6697838\",\n \"0.6686246\",\n \"0.6685565\",\n \"0.66786104\",\n \"0.66786104\",\n \"0.66786104\",\n \"0.66714746\",\n \"0.6665001\",\n \"0.6653644\",\n \"0.66466826\",\n \"0.66384083\",\n \"0.66356725\",\n \"0.66252714\",\n \"0.6623213\",\n \"0.6616604\",\n \"0.6612763\",\n \"0.6602307\",\n \"0.65901375\",\n \"0.6589113\",\n \"0.65857095\",\n \"0.65773255\",\n \"0.65606606\",\n \"0.65606606\",\n \"0.65606606\",\n \"0.6559582\",\n \"0.65573555\",\n \"0.6551604\",\n \"0.6551106\",\n \"0.654748\",\n \"0.6544267\",\n \"0.6538756\",\n \"0.65381426\",\n \"0.6536535\",\n \"0.653488\",\n \"0.6534256\",\n \"0.6529706\",\n \"0.6529706\",\n \"0.6528876\",\n \"0.6527598\",\n \"0.65250707\",\n \"0.65248114\",\n \"0.6514269\",\n \"0.65103406\",\n \"0.65067166\",\n \"0.6499105\",\n \"0.6495412\",\n \"0.6495412\",\n \"0.6495412\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77497,"cells":{"query":{"kind":"string","value":"Initializes the meter to DC voltage measurment in the 10V range"},"document":{"kind":"string","value":"def hp3458a_init(hp_meter):\n hp_meter.write(\"RESET\")\n hp_meter.write(\"TARM HOLD\")\n hp_meter.write(\"FUNC DCV\") # Set to DC voltage measurment\n hp_meter.write(\"RANGE 10\") # Set to 10V range\n hp_meter.write(\"LFREQ LINE\") # Measure line freq and set rejection filter\n hp_meter.write(\"NPLC 1\")\n hp_meter.write(\"AZERO ONCE\")\n hp_meter.write(\"FIXEDZ ON\") # Fixed input impedance"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def configure_vdc(self, rng, res, unit = 'V'):\n self.write_to_serial(':conf:volt:dc ' + str(rng) + ',' + str(res))# + unit)","def setvoltages(self):\n pass","def meas_voltage(instrument, v_range=10, resolution=0.003):\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))","def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\n self.min_volts = min_volts\n self.max_volts = max_volts\n self.sample_rate = sample_rate\n self.num_samples = num_samples\n self.channel = device + \"/\" + AIchan\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\"\",\n DAQmx_Val_NRSE,\n float64(self.min_volts),float64(self.max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))","def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\n self.min_volts = min_volts\n self.max_volts = max_volts\n self.sample_rate = sample_rate\n self.num_samples = num_samples\n self.channel = device + \"/\" + AIchan\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\"\",\n DAQmx_Val_NRSE,\n float64(self.min_volts),float64(self.max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))","def ccd(self):\n self.spectrum = self.spectrum","def __init__(self,initial_v,v_select=0,max_dev_semitones=1):\n self.v=initial_v\n self.v_select=v_select\n self.max_dev_semitones=max_dev_semitones","def calibrate_meter(self):\r\n print(\"meter calibrated\")","def __init__(self, dx0=0.0, min_cutoff=1.0, beta=0.0,\n d_cutoff=1.0):\n self.min_cutoff = float(min_cutoff)\n self.beta = float(beta)\n self.d_cutoff = float(d_cutoff)\n self.dx_prev = float(dx0)","def testDensityCenterVelocity(self):\n known_dcv = np.array([0.24275266063732542, 0.25474645145914782, 0.32455563530545328])\n np.testing.assert_allclose(nb.dc_vel, known_dcv)","def _dvolume_dR(self):\n\n dvdr = 0.\n\n return dvdr","def get_voltage(self):\n print(\"voici le voltage de la batterie\")","def set_control_voltage(self, value):\n self._curr_output_voltage = value\n self._update_output_voltage_label()","def set_dac_voltage(self, channel, voltage):\n if (channel > 2) or (channel < 1):\n raise ValueError('set_dac_voltage: DAC channel needs to be 1 or 2')\n if (voltage >= 0.0) and (voltage < self.maxdacvoltage):\n rawval = (voltage / 2.048) * 4096 / self.gain\n self.set_dac_raw(channel, int(rawval))\n else:\n raise ValueError('set_dac_voltage: voltage out of range')\n return","def set_dac_voltage(self, channel, voltage):\n if (channel > 2) or (channel < 1):\n raise ValueError('set_dac_voltage: DAC channel needs to be 1 or 2')\n if (voltage >= 0.0) and (voltage < self.maxdacvoltage):\n rawval = (voltage / 2.048) * 4096 / self.gain\n self.set_dac_raw(channel, int(rawval))\n else:\n raise ValueError('set_dac_voltage: voltage out of range')\n return","def __init__(self):\n self.data0 = [] # This will hold data from ADC0\n self.data1 = [] # This will hold data from ADC1\n self.dev = _configure_device()","def set_voltage_data(self):\n self.voltage_record = {}\n self.threshold_value = {}\n for l in self.network.layers:\n if 'v' in self.network.layers[l].__dict__:\n self.voltage_record[l] = self.network.monitors['{:}_voltages'.format(l)].get('v')\n if 'thresh' in self.network.layers[l].__dict__:\n self.threshold_value[l] = self.network.layers[l].thresh","def hp34401a_read_voltage(hp_meter):\n hp_meter.write(\"MEAS:VOLT:DC? DEF,DEF\")\n return float(hp_meter.read())","def initRunningVals(self):\n self.r_Vm = [0.0]*self.mirror.dataPoints\n self.r_Va = [0.0]*self.mirror.dataPoints","def dmm_measure_voltage(self,range=None,res=None):\n if range is None:\n return self.dmm.measure_voltage()\n elif res is None:\n return self.dmm.measure_voltage(volt_range=range)\n else:\n return self.dmm.measure_voltage(volt_range=range,resolution=res)","def set_voltage(self, millivolts):\n assert 3060 <= millivolts <= 10680, \"Voltage must be between 3,060 and 10,680 mV.\"\n assert self.instr == self.INSTR_EXT, \"Please switch to extended instruction set first.\"\n self.voltage = millivolts\n basevoltage = millivolts - 3060\n incrementor = basevoltage // 60\n code = 0x80 & incrementor\n self.command([code])","def __init__(self, gain_factor=1):\n if (gain_factor != 1) and (gain_factor != 2):\n raise ValueError('DAC __init__: Invalid gain factor. \\\n Must be 1 or 2')\n else:\n self.gain = gain_factor\n self.maxdacvoltage = self.__dacMaxOutput__[self.gain]","def __init__(self, timestep=1.0 * simtk.unit.femtoseconds):\n\n super(VelocityVerletIntegrator, self).__init__(timestep)\n\n self.addPerDofVariable(\"x1\", 0)\n\n self.addUpdateContextState()\n self.addComputePerDof(\"v\", \"v+0.5*dt*f/m\")\n self.addComputePerDof(\"x\", \"x+dt*v\")\n self.addComputePerDof(\"x1\", \"x\")\n self.addConstrainPositions()\n self.addComputePerDof(\"v\", \"v+0.5*dt*f/m+(x-x1)/dt\")\n self.addConstrainVelocities()","def __init__(\n self,\n voltage_v,\n remaining_percent):\n self.voltage_v = voltage_v\n self.remaining_percent = remaining_percent","def updatePWM(self):\n v_dc = self.dcmotorSpeed * self.dcmotor_sgn # changed \"vr\" to \"v_dc\", \"rightSpeed\" to \"dcmotorSpeed\" and \"right_sgn\" to dcmotor_sgn\", RFMH_2019_02_26\n pwm_dc = self.PWMvalue(v_dc, self.DC_MOTOR_MIN_PWM,\n self.DC_MOTOR_MAX_PWM) # changed \"pwmr\" to \"pwm_dc\" and \"vr\" to \"v_dc\" and adjusted both orange constants to \"DC_MOTOR_MIN_PWM\" AND \"DC_MOTOR_MAX_PWM\", RFMH_2019_02_26\n\n # TODO: Fix this debug message. I am trying to port this code over from an old version, and I do not know\n # what v and u are supposed to be here. Timothy Scott, 5.11.2019\n # if self.debug: # where the duck does the \"u\" come from?!?, RFMH_2019_02_26\n # print(\"v = %5.3f, u = %5.3f, v_dc = %5.3f, pwm_dc = %3d\" % (\n # v, u, v_dc, pwm_dc)) # deleted \"vl\" and \"pwml\" and adjust \"vr\" to \"v_dc\" to \"pwm_dc\"\n\n if math.fabs(v_dc) < self.SPEED_TOLERANCE: # changed v_r to v_dc in if loop , RFMH_2019_02_28\n DcMotorMode = Adafruit_MotorHAT.RELEASE\n pwm_dc = 0\n elif v_dc > 0:\n DcMotorMode = Adafruit_MotorHAT.FORWARD\n elif v_dc < 0:\n DcMotorMode = Adafruit_MotorHAT.BACKWARD\n\n if not self.old_pwm_dc == pwm_dc:\n self.DcMotor.setSpeed(pwm_dc) # changed rightMotor to DcMotor and pwmr to pwm_dc , RFMH_2019_02_28\n self.DcMotor.run(DcMotorMode)\n\n self.old_pwm_dc = pwm_dc","def set_voltage(self, c, voltage):\n if abs(voltage['V']) < (.00001):\n voltage = 0 * units.V\n \n dev = self.selectedDevice(c)\n if 'slot_number' in c.keys():\n slot_number = c['slot_number']\n self.open_comm(c, slot_number)\n output_state = yield self.get_output_state(c)\n if not output_state:\n try:\n yield self.set_output_state(c, True)\n except:\n self.initialize_mainframe(c)\n yield self.set_output_state(c, True)\n write_str = \"VOLT \"+ str(voltage['V'])\n try:\n yield self.write(c, write_str)\n self.wait_for_completion(c)\n except:\n self.initialize_mainframe(c)\n yield self.write(c, write_str)\n self.wait_for_completion(c)\n self.close_comm(c, slot_number)\n else:\n raise ValueError(self.no_selection_msg())","def __init__(\n self, voltage={0:(0, 0)}, rate=500, repetitions=1,\n board_name='cDAQ1Mod1', voltage_limits=None, num_channels=7):\n self.board_name = board_name #Check Measurement and Automation Explorer\n self._taskHandle = ctypes.c_void_p(0)\n self.num_channels = num_channels\n DAQmxErrChk(api.DAQmxCreateTask(\"\", ctypes.byref(self._taskHandle)))\n DAQmxErrChk(api.DAQmxCreateAOVoltageChan(\n self._taskHandle,\n self.board_name + \"/ao0:%i\"%(num_channels - 1),\n \"\",\n ctypes.c_double(-10.0), #Minimum voltage\n ctypes.c_double(10.0), #Maximum voltage\n 10348, #DAQmx_Val_Volts; don't question it!\n ctypes.c_void_p(0), #NULL\n ))\n self.num_points_written = ctypes.c_long(0)\n self._unwritten_voltages = False\n self._unplayed_voltages = False\n self.set_voltage_and_timing(voltage, rate, repetitions, voltage_limits)\n return None","def read_voltage(self):\n self.write(':FETC?')\n msg = self.read()\n #print ('dmm msg = ', msg)\n v = msg.split(',')[0].rstrip('NVDC').strip()\n if v[-1] == 'R':\n return float(v[:-1])\n else:\n return float(v)","def ac_voltage(self, ac):\n self.set_ac_voltage(f'{ac}' if self._is_min_max(ac) else f'{ac} V')","def hp34401a_read_voltage_rng_res(hp_meter , v_range, v_resolution):\n hp_meter.write(\"MEAS:VOLT:DC? \" + str(v_range) + \" , \" + str(v_resolution))\n return float(hp_meter.read())","def _set_dac(self, channel, mvoltage):\n proceed = True\n\n if self.check_setpoints():\n cur_val = self.get('dac{}'.format(channel))\n # dac range in mV / 16 bits FIXME make range depend on polarity\n byte_res = self.full_range / 2**16\n # eps is a magic number to correct for an offset in the values\n # the IVVI returns (i.e. setting 0 returns byte_res/2 = 0.030518\n # with rounding\n eps = 0.0001\n\n proceed = False\n\n if (mvoltage > (cur_val + byte_res / 2 + eps) or\n mvoltage < (cur_val - byte_res / 2 - eps)):\n proceed = True\n\n if self.dac_set_sleep() > 0.0:\n time.sleep(self.dac_set_sleep())\n\n # only update the value if it is different from the previous one\n # this saves time in setting values, set cmd takes ~650ms\n if proceed:\n polarity_corrected = mvoltage - self.pol_num[channel - 1]\n byte_val = self._mvoltage_to_bytes(polarity_corrected)\n message = bytes([2, 1, channel]) + byte_val\n\n reply = self.ask(message)\n self._time_last_update = 0 # ensures get command will update\n\n return reply","def get_voltage(self):\n status = self.get_status_response()\n volts = status[20] + (status[21] * 0x100) + (status[22] * 0x10000) + (status[23] * 0x1000000)\n volts = float(volts)\n volts /= (1000.0 * 1000.0)\n return volts\n #end get_voltage","def __init__(self, dzdt, v, e, D):\n self.V = dzdt\n self.v = v\n self.e = e\n self.D = D\n self.laminar_label = \"Laminar\"\n self.critical_label = \"Critical\"\n self.turbulent_label = \"Turbulent\"","def __init__(self, D, K):\n\t\tself.D = D \n\t\tself.K = K \n\t\tself.V = np.zeros((D+1,K))\n\t\treturn","def init_dds(self, freqmult=10, channels=0):\n\n self.reset()\n\n self.set_freqmult(freqmult)\n self._set_channels(channels)\n self._io_update()\n\n self.currents = [1, 1, 1, 1]\n self.frequencies = [0, 0, 0, 0]\n self.amplitudes = [1, 1, 1, 1]\n self.phases = [0, 0, 0, 0]","def get_voltages(self):\n if self.v is None or self.dirty is True:\n v = self.simulator.get_voltages()\n n_compartments = self.neuron_collection.total_compartments()\n self.v = np.array(v).reshape([len(v) / n_compartments, n_compartments])\n\n self.dirty = False\n t = int(self.T / self.dt)\n return self.v[:t, :]","def Init(self):\n # dac address 1136 to 1139 should all 255\n #只需要最低字节\n rd_data = (struct.unpack('4c',self.Run_Command(self.board_def.CTRL_RD_DAC1,addr,0))[0] for addr in range(1136,1140))\n rd_data = (struct.unpack('4c',self.Run_Command(self.board_def.CTRL_RD_DAC2,addr,0))[0] for addr in range(1136,1140))\n\n for data in rd_data:\n print(data)\n # print ('Print something I can understand: {}'.format(repr(chunks)))\n # print ram_data\n return rd_data","def __init__(self, dr_ds: DatasetReader) -> None:\n super().__init__()\n\n self.dr_ds = dr_ds\n try:\n self.cmap = dr_ds.colormap(1)\n except ValueError:\n pass\n\n crs = dr_ds.crs\n res = dr_ds.res[0]\n\n with WarpedVRT(dr_ds, crs=crs) as dr:\n minx, miny, maxx, maxy = dr.bounds\n\n mint: float = 0\n maxt: float = sys.maxsize\n\n coords = (minx, maxx, miny, maxy, mint, maxt)\n self.index.insert(0, coords, 'dr')\n\n self._crs = cast(CRS, crs)\n self.res = cast(float, res)","def __init__(self, voltage=6, capacity=220, stateBounds = np.array([0.2,0.9]), storedEnergy = None):\n\t\tself.energyCapacity = voltage*capacity\n\t\tself.storedEnergy = storedEnergy or self.energyCapacity/2.0\n\t\tself.minState = stateBounds[0]\n\t\tself.maxState = stateBounds[1]","def __init__(self, description, vdma=None):\n super().__init__(description)\n self._vdma = vdma\n self._color = self.color_convert\n self._pixel = self.pixel_unpack\n self._hdmi = self.frontend","def v(self):\n\n # TODO This translation formula works, but needs simplified.\n\n # PWM duration can go from 0 to 4095 with 4095 representing max rpm\n# print(\"MuleBot.v MuleBot.dcMotorPWMDurationLeft:\", MuleBot.dcMotorPWMDurationLeft)\n speed_percentage = float(MuleBot.dcMotorPWMDurationLeft) / 4095.0\n# print(\"speed_percentage: \", speed_percentage)\n\n rpm = speed_percentage * self.motorMaxRPM\n# print(\"rpm: \", rpm)\n\n secondsPerMinute = 60\n revs_per_second = rpm / secondsPerMinute\n# print(\"--revs_per_second\", revs_per_second)\n\n inches_per_rev = 2.0 * math.pi * MuleBot.WHEEL_RADIUS\n INCHES_PER_METER = 39.3701\n meters_per_rev = inches_per_rev / INCHES_PER_METER\n# print(\"--meters_per_rev\", meters_per_rev)\n\n meters_per_second = meters_per_rev * revs_per_second\n\n# print(\"--meters_per_second: \", meters_per_second)\n return meters_per_second","def __init__(self):\n\n super().__init__()\n\n self.active = True\n self.driver = Driver.instance()\n self.sensor_manager = SensorManager.instance()\n\n self.pwm = Adafruit_PCA9685.PCA9685(address=0x40, busnum=1) # create PCA9685-object at I2C-port\n self.pwm.set_pwm_freq(50)\n\n GPIO.setwarnings(False)\n GPIO.setmode(GPIO.BCM)\n GPIO.setup(20, GPIO.OUT)\n GPIO.setup(21, GPIO.OUT)\n GPIO.setup(26, GPIO.OUT)\n self.driven_distance = 0","def calibV(self):\n # clear buffer in case of errors\n self.flushInput()\n \n if (self.model == 'GDS'):\n self.write(':CHAN'+str(ch)+':SCAL?\\n')\n # returns V/div, turn it into multiplicative factor\n # between digitizer and actual volts\n vmult = float(self.readline()) * 10./255.\n # GDS includes vertical offset in the data returned.\n voff = 0.\n elif (self.model == 'TDS'):\n self.write('WFMPre:YMUlt?\\n')\n # formula I am using later is from TDS manual, so this\n # is straightforward.\n vmult = float(self.readline())\n self.write('WFMPre:YOFf?\\n')\n voff = float(self.readline())\n \n # clear buffer in case of errors\n self.flushInput()\n\n return (vmult, voff)","def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.device_and_channel,\"\",\n DAQmx_Val_NRSE,\n float64(self.min_volts),float64(self.max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))\n self._CHK(nidaq.DAQmxCfgAnlgEdgeRefTrig(self.task_handle,self.device_and_channel,self.slope,\n float64(self.trig_level),self.num_pre_trig_samples))","def voltage_conversion(self):\r\n\t\tvoltage = ((self.data[0] * 256 + self.data[1]) / 65536.0) * 5.0\r\n\t\t\r\n\t\treturn {'v' : voltage}","def Take_Voltage_Measurement(self):\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n nidaq.DAQmxClearTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def Take_Voltage_Measurement(self):\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n nidaq.DAQmxClearTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def measure_vdc(self, rng = 'DEF', res = 'DEF', unit = 'V', samp = 1):\n self.configure_vdc(rng, res, unit)\n self.write_to_serial(':samp:coun ' + str(samp))\n if samp == 1:\n return float(self.query_serial('read?'))\n else:\n samples = self.query_serial('read?').split(',')\n sampleresult = []\n for sample in samples:\n sampleresult.append(float(sample))\n return sampleresult","def voltage(self, voltage=None):\n if voltage is not None:\n # set output voltage on all phases\n # self.ts.log_debug('voltage: %s, type: %s' % (voltage, type(voltage)))\n if type(voltage) is not list and type(voltage) is not tuple:\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\n' % (voltage, voltage, voltage))\n v1 = voltage\n v2 = voltage\n v3 = voltage\n else:\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\n' % (voltage[0], voltage[0], voltage[0])) # use the first value in the 3 phase list\n v1 = voltage[0]\n v2 = voltage[0]\n v3 = voltage[0]","def __init__(self,initial_meter_reading, initial_date):\r\n\t\tself.initial_meter_reading = initial_meter_reading\r\n\t\tself.initial_date = initial_date\r\n\t\tself.total_units_consumed = 0\r\n\t\tself.total_amount_spent = 0","def def_dc(m, x, v=None, r_min=0.0045):\n calc_vdc = v is not None\n x_ = x.copy()\n xdc = np.zeros(3)\n vdc = np.zeros(3)\n v_ = v.copy() if calc_vdc else None\n r_lim = np.sqrt(np.max(np.sum(x ** 2, axis=1)))\n num_iter = 0\n\n while (r_lim > r_min) and (num_iter < 100):\n ncm, mcm, xcm, vcm = cenmas_lim(m, x_, v_, r_lim)\n if (mcm > 0) and (ncm > 100):\n x_ -= xcm\n xdc += xcm\n if calc_vdc:\n v_ -= vcm\n vdc += vcm\n else:\n break\n r_lim *= 0.8\n num_iter += 1\n\n if calc_vdc:\n return xdc, vdc\n else:\n return xdc","def set_adc_refvoltage(self, voltage):\n if (voltage >= 0.0) and (voltage <= 7.0):\n self.__adcrefvoltage = voltage\n else:\n raise ValueError('set_adc_refvoltage: reference voltage \\\n out of range')\n return","def setUp(self):\n\n # Set thresholds for testing\n self.highvoltage_value = 1\n self.lowvoltage_value = -1\n\n self.highvoltage_rule = HighVoltage(self.highvoltage_value)\n self.lowvoltage_rule = LowVoltage(self.lowvoltage_value)\n\n self.channels = 32","def analogDaSetZero(self, Startchannel, Stopchannel=None, Debug=0): \n self.bib.DapiSpecialCommand.argtypes = \\\n [c_ulong, c_ulong, c_ulong, c_ulong, c_ulong]\n self.bib.DapiSpecialCommand.restype = None\n if Stopchannel == None or Stopchannel == Startchannel:\n #Stopchannel = \n for Startchannel in range(Startchannel, Startchannel+1):\n self.bib.DapiSpecialCommand(self.handle, self.DAPI_SPECIAL_CMD_DA, \\\n self.DAPI_SPECIAL_DA_PAR_DA_LOAD_DEFAULT, Startchannel, 0) \n print(\"D/A Channel\",Startchannel,\"set to zero volts.\")\n else:\n for Startchannel in range(Startchannel, Stopchannel+1):\n self.bib.DapiSpecialCommand(self.handle, self.DAPI_SPECIAL_CMD_DA, \\\n self.DAPI_SPECIAL_DA_PAR_DA_LOAD_DEFAULT, Startchannel, 0) \n print(\"D/A Channel\",Startchannel,\"set to zero volts.\")","def __init__(self, dmm: \"Keithley_6500\", channel: int, **kwargs) -> None:\n super().__init__(dmm, f\"ch{channel}\", **kwargs)\n self.channel = channel\n self.dmm = dmm\n\n self.add_parameter('resistance',\n unit='Ohm',\n label=f'Resistance CH{self.channel}',\n get_parser=float,\n get_cmd=partial(self._measure, 'RES'))\n\n self.add_parameter('resistance_4w',\n unit='Ohm',\n label=f'Resistance (4-wire) CH{self.channel}',\n get_parser=float,\n get_cmd=partial(self._measure, 'FRES'))\n\n self.add_parameter('voltage_dc',\n unit='V',\n label=f'DC Voltage CH{self.channel}',\n get_parser=float,\n get_cmd=partial(self._measure, 'VOLT'))\n\n self.add_parameter('current_dc',\n unit='A',\n label=f'DC current CH{self.channel}',\n get_parser=float,\n get_cmd=partial(self._measure, 'CURR'))","def __init__(self, r=None, c=None, v=None):\n if r is None and c is None and v is None:\n self.r = RESISTANCE_LIF\n self.c = CAPACITANCE_LIF\n self.v = VOLTAGE_THRESHOLD_LIF\n else:\n self.r, self.c, self.v = r, c, v","def Take_Voltage_Measurement(self,timeout=10.0):\n self.timeout = timeout\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.samples_per_sec),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples_per_ch)))\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_per_chan_read_by_DAQ = int32()\n data = numpy.zeros((self.num_samples_per_ch*self.num_channels),dtype=numpy.float64)\n\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples_per_ch),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples_per_ch*self.num_channels,ctypes.byref(samples_per_chan_read_by_DAQ),None))\n\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n\n if (samples_per_chan_read_by_DAQ.value *self.num_channels) != (self.num_samples_per_ch*self.num_channels):\n raise RuntimeError(\"Measurement Error: incorrect number of samples taken\")\n else:\n data_list = []\n start_index = 0\n end_index = self.num_samples_per_ch\n\n for i in range(self.num_channels):\n data_list.append(data[start_index:end_index])\n start_index += self.num_samples_per_ch\n end_index += self.num_samples_per_ch\n\n return data_list","def setup_dd(self, company):\n self.company = company\n self.apr = .03\n self.minimum_balance = 1000.0","def __init__(self, minv, maxv, name = \"Amplitude Oscillator\"):\n Oscillator.__init__(self, name + \" %.2f, %.2f\" % (minv, maxv))\n if minv > maxv:\n x = maxv; maxv = minv; minv = x\n self.min = minv\n self.max = maxv","def ac_voltage(self):\n return float(self.get_ac_voltage())","def populateDict(self):\n for dev in self.dcDict:\n range = self.dcDict[dev]['range']\n for devChannel in self.dcDict[dev]['devChannels']:\n channel = self.dcDict[dev]['devChannels'][devChannel]['channel']\n comstring = str(channel)+'r'\n yield self.ser.write(comstring)\n encoded = yield self.ser.read(3)\n seq = int(binascii.hexlify(encoded[0:2]),16)\n voltage = round(range[0] + float(seq) / (2**16 - 1) * float(range[1]-range[0]),2)\n self.dcDict[dev]['devChannels'][devChannel]['value'] = voltage","def set_voltage(self, v, ch): \n self.write(\"VSET\" + str(ch) + \":\" + str(v) + \"\\n\")","def set_vcon (dev, volts=3.3, enable=True):\n if enable:\n v=float(volts)\n d=int(round(22e3*(v/.62-1) / 100e3 * 127))\n d=max(d,0)\n d=min(d,127)\n log.debug ( \"vcon pot value: %d\" % d )\n #dev.set('UXN1330','vcon_pot', d )\n\n dev.set('UXN1330','vcon_en', 1 if enable else 0 )\n # convert back to volts so user can check against input\n return 0 if not enable else .62*(d/127.*100e3/22e3+1)","def voltage(self):\n\t\treturn self._voltage","def __init__(self, dr_type='', value='', m_vol='', m_page=''):\n self.cid = None\n self.dr_type = dr_type\n self.value = value\n self.m_vol = m_vol\n self.m_page = m_page","def __init__(self, w0=6):\n self.w0 = w0\n if w0 == 6:\n # value of C_d from TC98\n self.C_d = 0.776","def set_voltage(self, v):\n self.environment.set_voltage(self.neuron_id, v)","def set_adc_refvoltage(self, voltage):\n\n if (voltage >= 0.0) and (voltage <= 5.5):\n self.__adcrefvoltage = voltage\n else:\n raise ValueError('set_adc_refvoltage: reference voltage \\\n out of range')\n return","def __init__(self):\n #MdvData. __init__(self,model.target_fragments)\n #self.mdv = {}\n self.mdvtc ={}\n self.mode = \"timecourse\"","def init(self):\n self.AOMBoxConnection = pyArdDAC.ARD_DAC(HOST=self.AOMBox_IP, PORT=8888, DEBUG=False)#connects to arduino in High frequency Na AOM box\n #channel number should be defined in subclass\n self.INTEGER_MIN = 0\n self.INTEGER_MAX = 65535\n self.VOLTAGE_MIN = 0.0\n self.VOLTAGE_MAX = 5.0\n self.initialised=True\n return \"%s init successful\" % self.hardwareActionName","def __init__(self, x_len, num=(50, 0), den=(1, 50), dt=0.002, x_init=None):\n derivative_filter = signal.cont2discrete((num, den), dt)\n self.b = derivative_filter[0].ravel().astype(np.float32)\n self.a = derivative_filter[1].astype(np.float32)\n if x_init is None:\n self.z = np.zeros((max(len(self.a), len(self.b)) - 1, x_len),\n dtype=np.float32)\n else:\n self.set_initial_state(x_init)","def __init__(self, dms, num_dms=-1, min_dm_size=-1):\r\n self._num_dms = num_dms\r\n self._min_dm_size = min_dm_size\r\n self.DistanceMatrices = dms","def get_voltage(self):\n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\n voltage = float(re.findall(pattern,summary).pop())\n return voltage","def __init__(\n self,\n channel,\n discharge_pin,\n leds_object=None,\n temperature_sensors=None,\n current_sensors=None,\n low_voltage_cutoff=2.5,\n start_discharge_voltage_cutoff=3,\n temperature_cutoff=50,\n ):\n self.channel = str(channel)\n self.low_voltage_cutoff = low_voltage_cutoff\n self.start_discharge_voltage_cutoff = start_discharge_voltage_cutoff\n self.temperature_cutoff = temperature_cutoff\n self.discharge_pin = Pin(discharge_pin, Pin.OUT)\n self.leds = leds_object\n self.temperature_sensors = temperature_sensors\n self.current_sensors = current_sensors\n self.state = \"empty\"\n self.led_state = 0\n self.discharge_stats = None\n self.temperature = 0\n\n self.voltage_and_current = self.request_voltage_and_current()\n self.current = self.request_current()\n\n self.discharge_pin.off()","def configure_adc(self, rng, res, unit = 'A'):\n self.write_to_serial(':conf:curr:dc ' + str(rng) + ',' + str(res))# + unit)","def __init__(self, dm1, dm2, cdm):\r\n super(PartialMantel, self).__init__([dm1, dm2, cdm], num_dms=3,\r\n min_dm_size=3)","def __init__(self, temperature=298.0 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\n\n # Compute constants.\n kT = kB * temperature\n gamma = collision_rate\n\n # Create a new custom integrator.\n super(VVVRIntegrator, self).__init__(timestep)\n\n #\n # Integrator initialization.\n #\n self.addGlobalVariable(\"kT\", kT) # thermal energy\n self.addGlobalVariable(\"b\", numpy.exp(-gamma * timestep)) # velocity mixing parameter\n self.addPerDofVariable(\"sigma\", 0)\n self.addPerDofVariable(\"x1\", 0) # position before application of constraints\n\n #\n # Allow context updating here.\n #\n self.addUpdateContextState()\n\n #\n # Pre-computation.\n # This only needs to be done once, but it needs to be done for each degree of freedom.\n # Could move this to initialization?\n #\n self.addComputePerDof(\"sigma\", \"sqrt(kT/m)\")\n\n #\n # Velocity perturbation.\n #\n self.addComputePerDof(\"v\", \"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\")\n self.addConstrainVelocities()\n\n #\n # Metropolized symplectic step.\n #\n self.addComputePerDof(\"v\", \"v + 0.5*dt*f/m\")\n self.addComputePerDof(\"x\", \"x + v*dt\")\n self.addComputePerDof(\"x1\", \"x\")\n self.addConstrainPositions()\n self.addComputePerDof(\"v\", \"v + 0.5*dt*f/m + (x-x1)/dt\")\n self.addConstrainVelocities()\n\n #\n # Velocity randomization\n #\n self.addComputePerDof(\"v\", \"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\")\n self.addConstrainVelocities()","def initialise_calibration(self):\n for i in range(0, self.NUM_SENSORS):\n self.calibratedMax[i] = 0\n self.calibratedMin[i] = self.READING_TIMEOUT","def set_voltage(self):\r\n\t\tself.voltage = int(input(\"Enter the Value from (0-65536)= \"))\r\n\t\twhile self.voltage > 65536 :\r\n\t\t\tself.voltage = int(input(\"Enter the Value from (0-65536)= \"))\r\n\t\t\r\n\t\tself.data = [self.voltage >> 8, self.voltage & 0xFF]\r\n\t\t\r\n\t\treturn self.data","def Config_Finite_Voltage_Measurement(self,AIchans,min_volts,max_volts,sample_rate,num_samples):\n self.num_channels = len(AIchans.split(','))\n self.num_samples = num_samples\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,AIchans,\"\",\n DAQmx_Val_NRSE,\n float64(min_volts),float64(max_volts),\n DAQmx_Val_Volts,None))\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(sample_rate),\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\n uInt64(self.num_samples)))","def get_voltage(self, c):\n if 'slot_number' in c.keys():\n slot_number = c['slot_number'] \n yield self.open_comm(c, slot_number)\n yield self.write(c, 'VOLT?')\n voltage = yield self.read(c)\n yield self.close_comm(c, slot_number)\n \n try:\n voltage = float(voltage)\n except(ValueError):\n old_voltage = voltage\n voltage = ''.join([i for i in voltage if (i.isdigit() or i == '-' or i == '.')])\n voltage = float(voltage)\n print ('The value ' + old_voltage + ' was returned by the SIM928, this value was automatically converted to ' + voltage + '.')\n \n value = voltage * units.V\n \n # try:\n # voltage = yield self.query(c,\n # slot_number, \n # \"VOLT?\")\n # try:\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # try:\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # try:\n # value = voltage * units.V\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # value = voltage * units.V\n # else:\n # raise ValueError(self.no_selection_msg())\n returnValue(value)","def analogDaSetVolt(self, Channel, Voltage, Debug=0):\n self.bib.DapiDASetVolt.argtypes = [c_ulong, c_ulong, c_float]\n self.bib.DapiDASetVolt.restype = None\n self.bib.DapiDASetVolt(self.handle, Channel, Voltage)\n if Debug == 1:\n print(\"D/A Channel\",Channel,\"set. Voltage is\",Voltage)\n self.lastError()\n self.lastErrorText()\n #return(voltage)","def get_setVoltage(self):\n self.read(\":VOLT?\")","def __init__(self, gainFactor=1):\n\n # Define SPI bus and init\n self.__spiDAC = spidev.SpiDev()\n self.__spiDAC.open(0, 1)\n self.__spiDAC.max_speed_hz = (20000000)\n\n if (gainFactor != 1) and (gainFactor != 2):\n raise ValueError('DAC __init__: Invalid gain factor. \\\n Must be 1 or 2')\n else:\n self.gain = gainFactor\n\n self.maxdacvoltage = self.__dacMaxOutput__[self.gain]","def __init__(self, diameter, sigma_color, sigma_space):\n self.v = 0\n self._diameter = diameter\n self._sigma_color = sigma_color\n self._sigma_space = sigma_space","def test_velocity(self):\n sol = Mader(p_cj=3.0e11, d_cj=8.0e5, gamma=3.0, u_piston=0.0)\n # r must contain 2 elements, otherwise the density and pressure are nan\n r = np.array([0.7, 0.8])\n t = 6.25e-6\n solrt = sol(r, t)\n np.testing.assert_allclose(solrt.velocity[0], 144000.0)","def set_voltage(self, value):\n self.write(\":VOLT {}V\".format(value))","def read_adc_voltage(self, channel, mode):\n if (channel > 2) or (channel < 1):\n raise ValueError('read_adc_voltage: channel out of range')\n if (mode > 1) or (mode < 0):\n raise ValueError('read_adc_voltage: mode out of range')\n raw = self.read_adc_raw(channel, mode)\n voltage = (self.__adcrefvoltage / 4096) * raw\n return voltage","def test_str_voltage(self):\n xknx = XKNX()\n sensor = Sensor(\n xknx, \"TestSensor\", group_address_state=\"1/2/3\", value_type=\"voltage\"\n )\n sensor.sensor_value.payload = DPTArray(\n (\n 0x6D,\n 0xBF,\n )\n )\n\n self.assertEqual(sensor.resolve_state(), 120504.32)\n self.assertEqual(sensor.unit_of_measurement(), \"mV\")\n self.assertEqual(sensor.ha_device_class(), None)","def init(self, id_dds, dds):\n self.info('Initialized with dds %r' % id_dds) \n self._dds = dds\n self.id_dds = id_dds","def __init__(\n self,\n velocity_north_m_s,\n velocity_east_m_s,\n velocity_down_m_s):\n self.velocity_north_m_s = velocity_north_m_s\n self.velocity_east_m_s = velocity_east_m_s\n self.velocity_down_m_s = velocity_down_m_s","def set_channel_v_unit(self , channel_v_unit:float): \n self.__channel_v_unit = channel_v_unit","def __init__(self):\n super().__init__()\n self.metric = 'SEGVOL'","def setup_continuous_scans(self, monitor=None):\n if monitor is None:\n monitor = self.monitor\n else:\n self.monitor = monitor\n\n # Lets grab the laser\n laser = self.devices[monitor['laser']['name']]\n if 'wavelength_sweeps' not in monitor['laser']['params']:\n monitor['laser']['params']['wavelength_sweeps'] = 0 # This will generate the laser to sweep always.\n\n # Clear the array to start afresh\n for d in self.daqs:\n self.daqs[d]['monitor'] = []\n\n # Lets see what happens with the devices to monitor\n devices_to_monitor = monitor['detectors']\n\n for dev in devices_to_monitor:\n self.daqs[dev.properties['connection']['device']]['monitor'].append(dev)\n\n # Lets calculate the conditions of the scan\n num_points = (monitor['laser']['params']['stop_wavelength'] - monitor['laser']['params']['start_wavelength'])/ monitor['laser']['params']['interval_trigger']\n num_points = int(round(num_points.m_as('')))+1\n\n # Some DAQ cards behave strangely when dealing with even/odd number of points.\n # Using an even number of data points makes it behave correctly 'always'\n #if num_points % 2 != 0:\n # # Increase the stop wavelength in one step in order to have an extra data point\n # monitor['laser']['params']['stop_wavelength'] += monitor['laser']['params']['interval_trigger']\n # num_points += 1\n\n accuracy = monitor['laser']['params']['interval_trigger'] / monitor['laser']['params']['wavelength_speed']\n\n approx_time_to_scan = (monitor['laser']['params']['stop_wavelength'] - monitor['laser']['params']['start_wavelength']) / monitor['laser']['params'][\n 'wavelength_speed']\n\n laser.apply_values(monitor['laser']['params'])\n monitor['approx_time_to_scan'] = approx_time_to_scan\n \n wl_sweeps = int(monitor['laser']['params']['wavelength_sweeps'])\n if wl_sweeps > 0:\n num_points = num_points * wl_sweeps\n if monitor['laser']['params']['sweep_mode'] in ('ContTwo', 'StepTwo'):\n num_points = num_points*2\n sampling = 'continuous'\n else:\n sampling = 'continuous'\n conditions = {\n 'accuracy': accuracy,\n 'points': config.ni_buffer\n }\n # Then setup the ADQs\n for d in self.daqs:\n daq = self.daqs[d] # Get the DAQ from the dictionary of daqs.\n daq_driver = self.devices[d] # Gets the link to the DAQ\n if len(daq['monitor']) > 0:\n print('DAQ: %s' % d)\n devs_to_monitor = daq['monitor'] # daqs dictionary groups the channels by daq to which they are plugged\n print('Devs to monitor:')\n print(devs_to_monitor)\n conditions['devices'] = devs_to_monitor\n if 'trigger' not in monitor['daq']:\n raise Exception('A trigger has to be specified in the daq section of the monitor')\n conditions['trigger'] = monitor['daq']['trigger']\n print('Trigger: %s' % conditions['trigger'])\n if conditions['trigger'] == 'external':\n # It should specify also which port to use\n conditions['trigger_source'] = monitor['daq']['trigger_source']\n print('Trigger source: %s' % conditions['trigger_source'])\n elif conditions['trigger'] == 'internal':\n if monitor['daq']['trigger_source'] is not None:\n raise Warning('Specifying trigger even if set to Internal.')\n else:\n raise Exception('Trigger not recognized. Check the config. Only external or internal can be used.')\n\n if 'start_source' in self.monitor['daq']:\n if monitor['daq']['start_source'] is not None:\n conditions['start_source'] = monitor['daq']['start_source']\n conditions['start_mode'] = 'digital'\n else:\n conditions['start_mode'] = 'software'\n\n conditions['sampling'] = sampling\n daq['monitor_task'] = daq_driver.driver.analog_input_setup(conditions)\n self.daqs[d] = daq # Store it back to the class variable\n print('Task number: %s' % self.daqs[d]['monitor_task'])\n # Store the values back to the class\n self.monitor = monitor\n self.read_continuous_scans() # Empties the memory of the DAQ before starting a new measurement","def reset(self):\r\n return self.get_voltage(0, 0)","def __init__(self, drift_detector, size):\r\n self.drift_detector = drift_detector\r\n self.sample = 0\r\n self.reservoir = Reservoir(size)\r\n self.buffer = Buffer(size)\r\n self.confidence = 0.05\r\n self.recent_interval = []\r\n self.timestamp = 0\r\n self.vol_drift_found = False\r\n self.drift_found = False\r\n self.pre_drift_point = -1\r\n self.rolling_index = 0\r\n for i in range(size * 2 + 1):\r\n self.recent_interval.append(0.0)","def read_adc_voltage(self, channel, mode):\n if (mode < 0) or (mode > 1):\n raise ValueError('read_adc_voltage: mode out of range')\n if (channel > 4) and (mode == 1):\n raise ValueError('read_adc_voltage: channel out of range')\n if (channel > 8) or (channel < 1):\n raise ValueError('read_adc_voltage: channel out of range')\n\n raw = self.read_adc_raw(channel, mode)\n voltage = (self.__adcrefvoltage / 4096) * raw\n return voltage","def Add_Analog_Voltage_Channel(self,channel,min=-10.0,max=10.0):\n self.num_channels += 1\n self.channel = self.device + \"/\" + channel\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\"\",\n DAQmx_Val_NRSE,\n float64(min),float64(max),\n DAQmx_Val_Volts,None))","def _set_output_voltage_from_label(self):\n if (~self._is_stabilizing): #Only updates value if we are not stabilizing, otherwise the PID loop will be driving the output voltage\n #as opposed to the user.\n self._curr_output_voltage = self.widgets['p_outputVoltage'].value()\n self._ao_client.set_ao_voltage(self._ao_channel, self._curr_output_voltage)","def __init__(self, name, value, description = 'Numeric Data', limits = None, initvalue = None, unit = ''):\n super().__init__(name=name, description=description, value=value, limits=limits , initvalue=initvalue, unit = unit)","def voltage(self):\n return self.outputValue()"],"string":"[\n \"def configure_vdc(self, rng, res, unit = 'V'):\\n self.write_to_serial(':conf:volt:dc ' + str(rng) + ',' + str(res))# + unit)\",\n \"def setvoltages(self):\\n pass\",\n \"def meas_voltage(instrument, v_range=10, resolution=0.003):\\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))\",\n \"def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\\n self.min_volts = min_volts\\n self.max_volts = max_volts\\n self.sample_rate = sample_rate\\n self.num_samples = num_samples\\n self.channel = device + \\\"/\\\" + AIchan\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(self.min_volts),float64(self.max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\",\n \"def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\\n self.min_volts = min_volts\\n self.max_volts = max_volts\\n self.sample_rate = sample_rate\\n self.num_samples = num_samples\\n self.channel = device + \\\"/\\\" + AIchan\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(self.min_volts),float64(self.max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\",\n \"def ccd(self):\\n self.spectrum = self.spectrum\",\n \"def __init__(self,initial_v,v_select=0,max_dev_semitones=1):\\n self.v=initial_v\\n self.v_select=v_select\\n self.max_dev_semitones=max_dev_semitones\",\n \"def calibrate_meter(self):\\r\\n print(\\\"meter calibrated\\\")\",\n \"def __init__(self, dx0=0.0, min_cutoff=1.0, beta=0.0,\\n d_cutoff=1.0):\\n self.min_cutoff = float(min_cutoff)\\n self.beta = float(beta)\\n self.d_cutoff = float(d_cutoff)\\n self.dx_prev = float(dx0)\",\n \"def testDensityCenterVelocity(self):\\n known_dcv = np.array([0.24275266063732542, 0.25474645145914782, 0.32455563530545328])\\n np.testing.assert_allclose(nb.dc_vel, known_dcv)\",\n \"def _dvolume_dR(self):\\n\\n dvdr = 0.\\n\\n return dvdr\",\n \"def get_voltage(self):\\n print(\\\"voici le voltage de la batterie\\\")\",\n \"def set_control_voltage(self, value):\\n self._curr_output_voltage = value\\n self._update_output_voltage_label()\",\n \"def set_dac_voltage(self, channel, voltage):\\n if (channel > 2) or (channel < 1):\\n raise ValueError('set_dac_voltage: DAC channel needs to be 1 or 2')\\n if (voltage >= 0.0) and (voltage < self.maxdacvoltage):\\n rawval = (voltage / 2.048) * 4096 / self.gain\\n self.set_dac_raw(channel, int(rawval))\\n else:\\n raise ValueError('set_dac_voltage: voltage out of range')\\n return\",\n \"def set_dac_voltage(self, channel, voltage):\\n if (channel > 2) or (channel < 1):\\n raise ValueError('set_dac_voltage: DAC channel needs to be 1 or 2')\\n if (voltage >= 0.0) and (voltage < self.maxdacvoltage):\\n rawval = (voltage / 2.048) * 4096 / self.gain\\n self.set_dac_raw(channel, int(rawval))\\n else:\\n raise ValueError('set_dac_voltage: voltage out of range')\\n return\",\n \"def __init__(self):\\n self.data0 = [] # This will hold data from ADC0\\n self.data1 = [] # This will hold data from ADC1\\n self.dev = _configure_device()\",\n \"def set_voltage_data(self):\\n self.voltage_record = {}\\n self.threshold_value = {}\\n for l in self.network.layers:\\n if 'v' in self.network.layers[l].__dict__:\\n self.voltage_record[l] = self.network.monitors['{:}_voltages'.format(l)].get('v')\\n if 'thresh' in self.network.layers[l].__dict__:\\n self.threshold_value[l] = self.network.layers[l].thresh\",\n \"def hp34401a_read_voltage(hp_meter):\\n hp_meter.write(\\\"MEAS:VOLT:DC? DEF,DEF\\\")\\n return float(hp_meter.read())\",\n \"def initRunningVals(self):\\n self.r_Vm = [0.0]*self.mirror.dataPoints\\n self.r_Va = [0.0]*self.mirror.dataPoints\",\n \"def dmm_measure_voltage(self,range=None,res=None):\\n if range is None:\\n return self.dmm.measure_voltage()\\n elif res is None:\\n return self.dmm.measure_voltage(volt_range=range)\\n else:\\n return self.dmm.measure_voltage(volt_range=range,resolution=res)\",\n \"def set_voltage(self, millivolts):\\n assert 3060 <= millivolts <= 10680, \\\"Voltage must be between 3,060 and 10,680 mV.\\\"\\n assert self.instr == self.INSTR_EXT, \\\"Please switch to extended instruction set first.\\\"\\n self.voltage = millivolts\\n basevoltage = millivolts - 3060\\n incrementor = basevoltage // 60\\n code = 0x80 & incrementor\\n self.command([code])\",\n \"def __init__(self, gain_factor=1):\\n if (gain_factor != 1) and (gain_factor != 2):\\n raise ValueError('DAC __init__: Invalid gain factor. \\\\\\n Must be 1 or 2')\\n else:\\n self.gain = gain_factor\\n self.maxdacvoltage = self.__dacMaxOutput__[self.gain]\",\n \"def __init__(self, timestep=1.0 * simtk.unit.femtoseconds):\\n\\n super(VelocityVerletIntegrator, self).__init__(timestep)\\n\\n self.addPerDofVariable(\\\"x1\\\", 0)\\n\\n self.addUpdateContextState()\\n self.addComputePerDof(\\\"v\\\", \\\"v+0.5*dt*f/m\\\")\\n self.addComputePerDof(\\\"x\\\", \\\"x+dt*v\\\")\\n self.addComputePerDof(\\\"x1\\\", \\\"x\\\")\\n self.addConstrainPositions()\\n self.addComputePerDof(\\\"v\\\", \\\"v+0.5*dt*f/m+(x-x1)/dt\\\")\\n self.addConstrainVelocities()\",\n \"def __init__(\\n self,\\n voltage_v,\\n remaining_percent):\\n self.voltage_v = voltage_v\\n self.remaining_percent = remaining_percent\",\n \"def updatePWM(self):\\n v_dc = self.dcmotorSpeed * self.dcmotor_sgn # changed \\\"vr\\\" to \\\"v_dc\\\", \\\"rightSpeed\\\" to \\\"dcmotorSpeed\\\" and \\\"right_sgn\\\" to dcmotor_sgn\\\", RFMH_2019_02_26\\n pwm_dc = self.PWMvalue(v_dc, self.DC_MOTOR_MIN_PWM,\\n self.DC_MOTOR_MAX_PWM) # changed \\\"pwmr\\\" to \\\"pwm_dc\\\" and \\\"vr\\\" to \\\"v_dc\\\" and adjusted both orange constants to \\\"DC_MOTOR_MIN_PWM\\\" AND \\\"DC_MOTOR_MAX_PWM\\\", RFMH_2019_02_26\\n\\n # TODO: Fix this debug message. I am trying to port this code over from an old version, and I do not know\\n # what v and u are supposed to be here. Timothy Scott, 5.11.2019\\n # if self.debug: # where the duck does the \\\"u\\\" come from?!?, RFMH_2019_02_26\\n # print(\\\"v = %5.3f, u = %5.3f, v_dc = %5.3f, pwm_dc = %3d\\\" % (\\n # v, u, v_dc, pwm_dc)) # deleted \\\"vl\\\" and \\\"pwml\\\" and adjust \\\"vr\\\" to \\\"v_dc\\\" to \\\"pwm_dc\\\"\\n\\n if math.fabs(v_dc) < self.SPEED_TOLERANCE: # changed v_r to v_dc in if loop , RFMH_2019_02_28\\n DcMotorMode = Adafruit_MotorHAT.RELEASE\\n pwm_dc = 0\\n elif v_dc > 0:\\n DcMotorMode = Adafruit_MotorHAT.FORWARD\\n elif v_dc < 0:\\n DcMotorMode = Adafruit_MotorHAT.BACKWARD\\n\\n if not self.old_pwm_dc == pwm_dc:\\n self.DcMotor.setSpeed(pwm_dc) # changed rightMotor to DcMotor and pwmr to pwm_dc , RFMH_2019_02_28\\n self.DcMotor.run(DcMotorMode)\\n\\n self.old_pwm_dc = pwm_dc\",\n \"def set_voltage(self, c, voltage):\\n if abs(voltage['V']) < (.00001):\\n voltage = 0 * units.V\\n \\n dev = self.selectedDevice(c)\\n if 'slot_number' in c.keys():\\n slot_number = c['slot_number']\\n self.open_comm(c, slot_number)\\n output_state = yield self.get_output_state(c)\\n if not output_state:\\n try:\\n yield self.set_output_state(c, True)\\n except:\\n self.initialize_mainframe(c)\\n yield self.set_output_state(c, True)\\n write_str = \\\"VOLT \\\"+ str(voltage['V'])\\n try:\\n yield self.write(c, write_str)\\n self.wait_for_completion(c)\\n except:\\n self.initialize_mainframe(c)\\n yield self.write(c, write_str)\\n self.wait_for_completion(c)\\n self.close_comm(c, slot_number)\\n else:\\n raise ValueError(self.no_selection_msg())\",\n \"def __init__(\\n self, voltage={0:(0, 0)}, rate=500, repetitions=1,\\n board_name='cDAQ1Mod1', voltage_limits=None, num_channels=7):\\n self.board_name = board_name #Check Measurement and Automation Explorer\\n self._taskHandle = ctypes.c_void_p(0)\\n self.num_channels = num_channels\\n DAQmxErrChk(api.DAQmxCreateTask(\\\"\\\", ctypes.byref(self._taskHandle)))\\n DAQmxErrChk(api.DAQmxCreateAOVoltageChan(\\n self._taskHandle,\\n self.board_name + \\\"/ao0:%i\\\"%(num_channels - 1),\\n \\\"\\\",\\n ctypes.c_double(-10.0), #Minimum voltage\\n ctypes.c_double(10.0), #Maximum voltage\\n 10348, #DAQmx_Val_Volts; don't question it!\\n ctypes.c_void_p(0), #NULL\\n ))\\n self.num_points_written = ctypes.c_long(0)\\n self._unwritten_voltages = False\\n self._unplayed_voltages = False\\n self.set_voltage_and_timing(voltage, rate, repetitions, voltage_limits)\\n return None\",\n \"def read_voltage(self):\\n self.write(':FETC?')\\n msg = self.read()\\n #print ('dmm msg = ', msg)\\n v = msg.split(',')[0].rstrip('NVDC').strip()\\n if v[-1] == 'R':\\n return float(v[:-1])\\n else:\\n return float(v)\",\n \"def ac_voltage(self, ac):\\n self.set_ac_voltage(f'{ac}' if self._is_min_max(ac) else f'{ac} V')\",\n \"def hp34401a_read_voltage_rng_res(hp_meter , v_range, v_resolution):\\n hp_meter.write(\\\"MEAS:VOLT:DC? \\\" + str(v_range) + \\\" , \\\" + str(v_resolution))\\n return float(hp_meter.read())\",\n \"def _set_dac(self, channel, mvoltage):\\n proceed = True\\n\\n if self.check_setpoints():\\n cur_val = self.get('dac{}'.format(channel))\\n # dac range in mV / 16 bits FIXME make range depend on polarity\\n byte_res = self.full_range / 2**16\\n # eps is a magic number to correct for an offset in the values\\n # the IVVI returns (i.e. setting 0 returns byte_res/2 = 0.030518\\n # with rounding\\n eps = 0.0001\\n\\n proceed = False\\n\\n if (mvoltage > (cur_val + byte_res / 2 + eps) or\\n mvoltage < (cur_val - byte_res / 2 - eps)):\\n proceed = True\\n\\n if self.dac_set_sleep() > 0.0:\\n time.sleep(self.dac_set_sleep())\\n\\n # only update the value if it is different from the previous one\\n # this saves time in setting values, set cmd takes ~650ms\\n if proceed:\\n polarity_corrected = mvoltage - self.pol_num[channel - 1]\\n byte_val = self._mvoltage_to_bytes(polarity_corrected)\\n message = bytes([2, 1, channel]) + byte_val\\n\\n reply = self.ask(message)\\n self._time_last_update = 0 # ensures get command will update\\n\\n return reply\",\n \"def get_voltage(self):\\n status = self.get_status_response()\\n volts = status[20] + (status[21] * 0x100) + (status[22] * 0x10000) + (status[23] * 0x1000000)\\n volts = float(volts)\\n volts /= (1000.0 * 1000.0)\\n return volts\\n #end get_voltage\",\n \"def __init__(self, dzdt, v, e, D):\\n self.V = dzdt\\n self.v = v\\n self.e = e\\n self.D = D\\n self.laminar_label = \\\"Laminar\\\"\\n self.critical_label = \\\"Critical\\\"\\n self.turbulent_label = \\\"Turbulent\\\"\",\n \"def __init__(self, D, K):\\n\\t\\tself.D = D \\n\\t\\tself.K = K \\n\\t\\tself.V = np.zeros((D+1,K))\\n\\t\\treturn\",\n \"def init_dds(self, freqmult=10, channels=0):\\n\\n self.reset()\\n\\n self.set_freqmult(freqmult)\\n self._set_channels(channels)\\n self._io_update()\\n\\n self.currents = [1, 1, 1, 1]\\n self.frequencies = [0, 0, 0, 0]\\n self.amplitudes = [1, 1, 1, 1]\\n self.phases = [0, 0, 0, 0]\",\n \"def get_voltages(self):\\n if self.v is None or self.dirty is True:\\n v = self.simulator.get_voltages()\\n n_compartments = self.neuron_collection.total_compartments()\\n self.v = np.array(v).reshape([len(v) / n_compartments, n_compartments])\\n\\n self.dirty = False\\n t = int(self.T / self.dt)\\n return self.v[:t, :]\",\n \"def Init(self):\\n # dac address 1136 to 1139 should all 255\\n #只需要最低字节\\n rd_data = (struct.unpack('4c',self.Run_Command(self.board_def.CTRL_RD_DAC1,addr,0))[0] for addr in range(1136,1140))\\n rd_data = (struct.unpack('4c',self.Run_Command(self.board_def.CTRL_RD_DAC2,addr,0))[0] for addr in range(1136,1140))\\n\\n for data in rd_data:\\n print(data)\\n # print ('Print something I can understand: {}'.format(repr(chunks)))\\n # print ram_data\\n return rd_data\",\n \"def __init__(self, dr_ds: DatasetReader) -> None:\\n super().__init__()\\n\\n self.dr_ds = dr_ds\\n try:\\n self.cmap = dr_ds.colormap(1)\\n except ValueError:\\n pass\\n\\n crs = dr_ds.crs\\n res = dr_ds.res[0]\\n\\n with WarpedVRT(dr_ds, crs=crs) as dr:\\n minx, miny, maxx, maxy = dr.bounds\\n\\n mint: float = 0\\n maxt: float = sys.maxsize\\n\\n coords = (minx, maxx, miny, maxy, mint, maxt)\\n self.index.insert(0, coords, 'dr')\\n\\n self._crs = cast(CRS, crs)\\n self.res = cast(float, res)\",\n \"def __init__(self, voltage=6, capacity=220, stateBounds = np.array([0.2,0.9]), storedEnergy = None):\\n\\t\\tself.energyCapacity = voltage*capacity\\n\\t\\tself.storedEnergy = storedEnergy or self.energyCapacity/2.0\\n\\t\\tself.minState = stateBounds[0]\\n\\t\\tself.maxState = stateBounds[1]\",\n \"def __init__(self, description, vdma=None):\\n super().__init__(description)\\n self._vdma = vdma\\n self._color = self.color_convert\\n self._pixel = self.pixel_unpack\\n self._hdmi = self.frontend\",\n \"def v(self):\\n\\n # TODO This translation formula works, but needs simplified.\\n\\n # PWM duration can go from 0 to 4095 with 4095 representing max rpm\\n# print(\\\"MuleBot.v MuleBot.dcMotorPWMDurationLeft:\\\", MuleBot.dcMotorPWMDurationLeft)\\n speed_percentage = float(MuleBot.dcMotorPWMDurationLeft) / 4095.0\\n# print(\\\"speed_percentage: \\\", speed_percentage)\\n\\n rpm = speed_percentage * self.motorMaxRPM\\n# print(\\\"rpm: \\\", rpm)\\n\\n secondsPerMinute = 60\\n revs_per_second = rpm / secondsPerMinute\\n# print(\\\"--revs_per_second\\\", revs_per_second)\\n\\n inches_per_rev = 2.0 * math.pi * MuleBot.WHEEL_RADIUS\\n INCHES_PER_METER = 39.3701\\n meters_per_rev = inches_per_rev / INCHES_PER_METER\\n# print(\\\"--meters_per_rev\\\", meters_per_rev)\\n\\n meters_per_second = meters_per_rev * revs_per_second\\n\\n# print(\\\"--meters_per_second: \\\", meters_per_second)\\n return meters_per_second\",\n \"def __init__(self):\\n\\n super().__init__()\\n\\n self.active = True\\n self.driver = Driver.instance()\\n self.sensor_manager = SensorManager.instance()\\n\\n self.pwm = Adafruit_PCA9685.PCA9685(address=0x40, busnum=1) # create PCA9685-object at I2C-port\\n self.pwm.set_pwm_freq(50)\\n\\n GPIO.setwarnings(False)\\n GPIO.setmode(GPIO.BCM)\\n GPIO.setup(20, GPIO.OUT)\\n GPIO.setup(21, GPIO.OUT)\\n GPIO.setup(26, GPIO.OUT)\\n self.driven_distance = 0\",\n \"def calibV(self):\\n # clear buffer in case of errors\\n self.flushInput()\\n \\n if (self.model == 'GDS'):\\n self.write(':CHAN'+str(ch)+':SCAL?\\\\n')\\n # returns V/div, turn it into multiplicative factor\\n # between digitizer and actual volts\\n vmult = float(self.readline()) * 10./255.\\n # GDS includes vertical offset in the data returned.\\n voff = 0.\\n elif (self.model == 'TDS'):\\n self.write('WFMPre:YMUlt?\\\\n')\\n # formula I am using later is from TDS manual, so this\\n # is straightforward.\\n vmult = float(self.readline())\\n self.write('WFMPre:YOFf?\\\\n')\\n voff = float(self.readline())\\n \\n # clear buffer in case of errors\\n self.flushInput()\\n\\n return (vmult, voff)\",\n \"def Config_Finite_Voltage_Measurement(self,device,AIchan,min_volts,max_volts,sample_rate,num_samples):\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.device_and_channel,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(self.min_volts),float64(self.max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\\n self._CHK(nidaq.DAQmxCfgAnlgEdgeRefTrig(self.task_handle,self.device_and_channel,self.slope,\\n float64(self.trig_level),self.num_pre_trig_samples))\",\n \"def voltage_conversion(self):\\r\\n\\t\\tvoltage = ((self.data[0] * 256 + self.data[1]) / 65536.0) * 5.0\\r\\n\\t\\t\\r\\n\\t\\treturn {'v' : voltage}\",\n \"def Take_Voltage_Measurement(self):\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n nidaq.DAQmxClearTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def Take_Voltage_Measurement(self):\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n nidaq.DAQmxClearTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def measure_vdc(self, rng = 'DEF', res = 'DEF', unit = 'V', samp = 1):\\n self.configure_vdc(rng, res, unit)\\n self.write_to_serial(':samp:coun ' + str(samp))\\n if samp == 1:\\n return float(self.query_serial('read?'))\\n else:\\n samples = self.query_serial('read?').split(',')\\n sampleresult = []\\n for sample in samples:\\n sampleresult.append(float(sample))\\n return sampleresult\",\n \"def voltage(self, voltage=None):\\n if voltage is not None:\\n # set output voltage on all phases\\n # self.ts.log_debug('voltage: %s, type: %s' % (voltage, type(voltage)))\\n if type(voltage) is not list and type(voltage) is not tuple:\\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\\\n' % (voltage, voltage, voltage))\\n v1 = voltage\\n v2 = voltage\\n v3 = voltage\\n else:\\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\\\n' % (voltage[0], voltage[0], voltage[0])) # use the first value in the 3 phase list\\n v1 = voltage[0]\\n v2 = voltage[0]\\n v3 = voltage[0]\",\n \"def __init__(self,initial_meter_reading, initial_date):\\r\\n\\t\\tself.initial_meter_reading = initial_meter_reading\\r\\n\\t\\tself.initial_date = initial_date\\r\\n\\t\\tself.total_units_consumed = 0\\r\\n\\t\\tself.total_amount_spent = 0\",\n \"def def_dc(m, x, v=None, r_min=0.0045):\\n calc_vdc = v is not None\\n x_ = x.copy()\\n xdc = np.zeros(3)\\n vdc = np.zeros(3)\\n v_ = v.copy() if calc_vdc else None\\n r_lim = np.sqrt(np.max(np.sum(x ** 2, axis=1)))\\n num_iter = 0\\n\\n while (r_lim > r_min) and (num_iter < 100):\\n ncm, mcm, xcm, vcm = cenmas_lim(m, x_, v_, r_lim)\\n if (mcm > 0) and (ncm > 100):\\n x_ -= xcm\\n xdc += xcm\\n if calc_vdc:\\n v_ -= vcm\\n vdc += vcm\\n else:\\n break\\n r_lim *= 0.8\\n num_iter += 1\\n\\n if calc_vdc:\\n return xdc, vdc\\n else:\\n return xdc\",\n \"def set_adc_refvoltage(self, voltage):\\n if (voltage >= 0.0) and (voltage <= 7.0):\\n self.__adcrefvoltage = voltage\\n else:\\n raise ValueError('set_adc_refvoltage: reference voltage \\\\\\n out of range')\\n return\",\n \"def setUp(self):\\n\\n # Set thresholds for testing\\n self.highvoltage_value = 1\\n self.lowvoltage_value = -1\\n\\n self.highvoltage_rule = HighVoltage(self.highvoltage_value)\\n self.lowvoltage_rule = LowVoltage(self.lowvoltage_value)\\n\\n self.channels = 32\",\n \"def analogDaSetZero(self, Startchannel, Stopchannel=None, Debug=0): \\n self.bib.DapiSpecialCommand.argtypes = \\\\\\n [c_ulong, c_ulong, c_ulong, c_ulong, c_ulong]\\n self.bib.DapiSpecialCommand.restype = None\\n if Stopchannel == None or Stopchannel == Startchannel:\\n #Stopchannel = \\n for Startchannel in range(Startchannel, Startchannel+1):\\n self.bib.DapiSpecialCommand(self.handle, self.DAPI_SPECIAL_CMD_DA, \\\\\\n self.DAPI_SPECIAL_DA_PAR_DA_LOAD_DEFAULT, Startchannel, 0) \\n print(\\\"D/A Channel\\\",Startchannel,\\\"set to zero volts.\\\")\\n else:\\n for Startchannel in range(Startchannel, Stopchannel+1):\\n self.bib.DapiSpecialCommand(self.handle, self.DAPI_SPECIAL_CMD_DA, \\\\\\n self.DAPI_SPECIAL_DA_PAR_DA_LOAD_DEFAULT, Startchannel, 0) \\n print(\\\"D/A Channel\\\",Startchannel,\\\"set to zero volts.\\\")\",\n \"def __init__(self, dmm: \\\"Keithley_6500\\\", channel: int, **kwargs) -> None:\\n super().__init__(dmm, f\\\"ch{channel}\\\", **kwargs)\\n self.channel = channel\\n self.dmm = dmm\\n\\n self.add_parameter('resistance',\\n unit='Ohm',\\n label=f'Resistance CH{self.channel}',\\n get_parser=float,\\n get_cmd=partial(self._measure, 'RES'))\\n\\n self.add_parameter('resistance_4w',\\n unit='Ohm',\\n label=f'Resistance (4-wire) CH{self.channel}',\\n get_parser=float,\\n get_cmd=partial(self._measure, 'FRES'))\\n\\n self.add_parameter('voltage_dc',\\n unit='V',\\n label=f'DC Voltage CH{self.channel}',\\n get_parser=float,\\n get_cmd=partial(self._measure, 'VOLT'))\\n\\n self.add_parameter('current_dc',\\n unit='A',\\n label=f'DC current CH{self.channel}',\\n get_parser=float,\\n get_cmd=partial(self._measure, 'CURR'))\",\n \"def __init__(self, r=None, c=None, v=None):\\n if r is None and c is None and v is None:\\n self.r = RESISTANCE_LIF\\n self.c = CAPACITANCE_LIF\\n self.v = VOLTAGE_THRESHOLD_LIF\\n else:\\n self.r, self.c, self.v = r, c, v\",\n \"def Take_Voltage_Measurement(self,timeout=10.0):\\n self.timeout = timeout\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(self.samples_per_sec),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples_per_ch)))\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_per_chan_read_by_DAQ = int32()\\n data = numpy.zeros((self.num_samples_per_ch*self.num_channels),dtype=numpy.float64)\\n\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples_per_ch),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples_per_ch*self.num_channels,ctypes.byref(samples_per_chan_read_by_DAQ),None))\\n\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n\\n if (samples_per_chan_read_by_DAQ.value *self.num_channels) != (self.num_samples_per_ch*self.num_channels):\\n raise RuntimeError(\\\"Measurement Error: incorrect number of samples taken\\\")\\n else:\\n data_list = []\\n start_index = 0\\n end_index = self.num_samples_per_ch\\n\\n for i in range(self.num_channels):\\n data_list.append(data[start_index:end_index])\\n start_index += self.num_samples_per_ch\\n end_index += self.num_samples_per_ch\\n\\n return data_list\",\n \"def setup_dd(self, company):\\n self.company = company\\n self.apr = .03\\n self.minimum_balance = 1000.0\",\n \"def __init__(self, minv, maxv, name = \\\"Amplitude Oscillator\\\"):\\n Oscillator.__init__(self, name + \\\" %.2f, %.2f\\\" % (minv, maxv))\\n if minv > maxv:\\n x = maxv; maxv = minv; minv = x\\n self.min = minv\\n self.max = maxv\",\n \"def ac_voltage(self):\\n return float(self.get_ac_voltage())\",\n \"def populateDict(self):\\n for dev in self.dcDict:\\n range = self.dcDict[dev]['range']\\n for devChannel in self.dcDict[dev]['devChannels']:\\n channel = self.dcDict[dev]['devChannels'][devChannel]['channel']\\n comstring = str(channel)+'r'\\n yield self.ser.write(comstring)\\n encoded = yield self.ser.read(3)\\n seq = int(binascii.hexlify(encoded[0:2]),16)\\n voltage = round(range[0] + float(seq) / (2**16 - 1) * float(range[1]-range[0]),2)\\n self.dcDict[dev]['devChannels'][devChannel]['value'] = voltage\",\n \"def set_voltage(self, v, ch): \\n self.write(\\\"VSET\\\" + str(ch) + \\\":\\\" + str(v) + \\\"\\\\n\\\")\",\n \"def set_vcon (dev, volts=3.3, enable=True):\\n if enable:\\n v=float(volts)\\n d=int(round(22e3*(v/.62-1) / 100e3 * 127))\\n d=max(d,0)\\n d=min(d,127)\\n log.debug ( \\\"vcon pot value: %d\\\" % d )\\n #dev.set('UXN1330','vcon_pot', d )\\n\\n dev.set('UXN1330','vcon_en', 1 if enable else 0 )\\n # convert back to volts so user can check against input\\n return 0 if not enable else .62*(d/127.*100e3/22e3+1)\",\n \"def voltage(self):\\n\\t\\treturn self._voltage\",\n \"def __init__(self, dr_type='', value='', m_vol='', m_page=''):\\n self.cid = None\\n self.dr_type = dr_type\\n self.value = value\\n self.m_vol = m_vol\\n self.m_page = m_page\",\n \"def __init__(self, w0=6):\\n self.w0 = w0\\n if w0 == 6:\\n # value of C_d from TC98\\n self.C_d = 0.776\",\n \"def set_voltage(self, v):\\n self.environment.set_voltage(self.neuron_id, v)\",\n \"def set_adc_refvoltage(self, voltage):\\n\\n if (voltage >= 0.0) and (voltage <= 5.5):\\n self.__adcrefvoltage = voltage\\n else:\\n raise ValueError('set_adc_refvoltage: reference voltage \\\\\\n out of range')\\n return\",\n \"def __init__(self):\\n #MdvData. __init__(self,model.target_fragments)\\n #self.mdv = {}\\n self.mdvtc ={}\\n self.mode = \\\"timecourse\\\"\",\n \"def init(self):\\n self.AOMBoxConnection = pyArdDAC.ARD_DAC(HOST=self.AOMBox_IP, PORT=8888, DEBUG=False)#connects to arduino in High frequency Na AOM box\\n #channel number should be defined in subclass\\n self.INTEGER_MIN = 0\\n self.INTEGER_MAX = 65535\\n self.VOLTAGE_MIN = 0.0\\n self.VOLTAGE_MAX = 5.0\\n self.initialised=True\\n return \\\"%s init successful\\\" % self.hardwareActionName\",\n \"def __init__(self, x_len, num=(50, 0), den=(1, 50), dt=0.002, x_init=None):\\n derivative_filter = signal.cont2discrete((num, den), dt)\\n self.b = derivative_filter[0].ravel().astype(np.float32)\\n self.a = derivative_filter[1].astype(np.float32)\\n if x_init is None:\\n self.z = np.zeros((max(len(self.a), len(self.b)) - 1, x_len),\\n dtype=np.float32)\\n else:\\n self.set_initial_state(x_init)\",\n \"def __init__(self, dms, num_dms=-1, min_dm_size=-1):\\r\\n self._num_dms = num_dms\\r\\n self._min_dm_size = min_dm_size\\r\\n self.DistanceMatrices = dms\",\n \"def get_voltage(self):\\n summary = \\\" \\\".join(self.get_summary().split())\\n pattern = '\\\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\\n voltage = float(re.findall(pattern,summary).pop())\\n return voltage\",\n \"def __init__(\\n self,\\n channel,\\n discharge_pin,\\n leds_object=None,\\n temperature_sensors=None,\\n current_sensors=None,\\n low_voltage_cutoff=2.5,\\n start_discharge_voltage_cutoff=3,\\n temperature_cutoff=50,\\n ):\\n self.channel = str(channel)\\n self.low_voltage_cutoff = low_voltage_cutoff\\n self.start_discharge_voltage_cutoff = start_discharge_voltage_cutoff\\n self.temperature_cutoff = temperature_cutoff\\n self.discharge_pin = Pin(discharge_pin, Pin.OUT)\\n self.leds = leds_object\\n self.temperature_sensors = temperature_sensors\\n self.current_sensors = current_sensors\\n self.state = \\\"empty\\\"\\n self.led_state = 0\\n self.discharge_stats = None\\n self.temperature = 0\\n\\n self.voltage_and_current = self.request_voltage_and_current()\\n self.current = self.request_current()\\n\\n self.discharge_pin.off()\",\n \"def configure_adc(self, rng, res, unit = 'A'):\\n self.write_to_serial(':conf:curr:dc ' + str(rng) + ',' + str(res))# + unit)\",\n \"def __init__(self, dm1, dm2, cdm):\\r\\n super(PartialMantel, self).__init__([dm1, dm2, cdm], num_dms=3,\\r\\n min_dm_size=3)\",\n \"def __init__(self, temperature=298.0 * simtk.unit.kelvin, collision_rate=91.0 / simtk.unit.picoseconds, timestep=1.0 * simtk.unit.femtoseconds):\\n\\n # Compute constants.\\n kT = kB * temperature\\n gamma = collision_rate\\n\\n # Create a new custom integrator.\\n super(VVVRIntegrator, self).__init__(timestep)\\n\\n #\\n # Integrator initialization.\\n #\\n self.addGlobalVariable(\\\"kT\\\", kT) # thermal energy\\n self.addGlobalVariable(\\\"b\\\", numpy.exp(-gamma * timestep)) # velocity mixing parameter\\n self.addPerDofVariable(\\\"sigma\\\", 0)\\n self.addPerDofVariable(\\\"x1\\\", 0) # position before application of constraints\\n\\n #\\n # Allow context updating here.\\n #\\n self.addUpdateContextState()\\n\\n #\\n # Pre-computation.\\n # This only needs to be done once, but it needs to be done for each degree of freedom.\\n # Could move this to initialization?\\n #\\n self.addComputePerDof(\\\"sigma\\\", \\\"sqrt(kT/m)\\\")\\n\\n #\\n # Velocity perturbation.\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\\\")\\n self.addConstrainVelocities()\\n\\n #\\n # Metropolized symplectic step.\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"v + 0.5*dt*f/m\\\")\\n self.addComputePerDof(\\\"x\\\", \\\"x + v*dt\\\")\\n self.addComputePerDof(\\\"x1\\\", \\\"x\\\")\\n self.addConstrainPositions()\\n self.addComputePerDof(\\\"v\\\", \\\"v + 0.5*dt*f/m + (x-x1)/dt\\\")\\n self.addConstrainVelocities()\\n\\n #\\n # Velocity randomization\\n #\\n self.addComputePerDof(\\\"v\\\", \\\"sqrt(b)*v + sqrt(1-b)*sigma*gaussian\\\")\\n self.addConstrainVelocities()\",\n \"def initialise_calibration(self):\\n for i in range(0, self.NUM_SENSORS):\\n self.calibratedMax[i] = 0\\n self.calibratedMin[i] = self.READING_TIMEOUT\",\n \"def set_voltage(self):\\r\\n\\t\\tself.voltage = int(input(\\\"Enter the Value from (0-65536)= \\\"))\\r\\n\\t\\twhile self.voltage > 65536 :\\r\\n\\t\\t\\tself.voltage = int(input(\\\"Enter the Value from (0-65536)= \\\"))\\r\\n\\t\\t\\r\\n\\t\\tself.data = [self.voltage >> 8, self.voltage & 0xFF]\\r\\n\\t\\t\\r\\n\\t\\treturn self.data\",\n \"def Config_Finite_Voltage_Measurement(self,AIchans,min_volts,max_volts,sample_rate,num_samples):\\n self.num_channels = len(AIchans.split(','))\\n self.num_samples = num_samples\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,AIchans,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(min_volts),float64(max_volts),\\n DAQmx_Val_Volts,None))\\n self._CHK(nidaq.DAQmxCfgSampClkTiming(self.task_handle,Internal_Clock,float64(sample_rate),\\n DAQmx_Val_Rising,DAQmx_Val_FiniteSamps,\\n uInt64(self.num_samples)))\",\n \"def get_voltage(self, c):\\n if 'slot_number' in c.keys():\\n slot_number = c['slot_number'] \\n yield self.open_comm(c, slot_number)\\n yield self.write(c, 'VOLT?')\\n voltage = yield self.read(c)\\n yield self.close_comm(c, slot_number)\\n \\n try:\\n voltage = float(voltage)\\n except(ValueError):\\n old_voltage = voltage\\n voltage = ''.join([i for i in voltage if (i.isdigit() or i == '-' or i == '.')])\\n voltage = float(voltage)\\n print ('The value ' + old_voltage + ' was returned by the SIM928, this value was automatically converted to ' + voltage + '.')\\n \\n value = voltage * units.V\\n \\n # try:\\n # voltage = yield self.query(c,\\n # slot_number, \\n # \\\"VOLT?\\\")\\n # try:\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # try:\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # try:\\n # value = voltage * units.V\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # value = voltage * units.V\\n # else:\\n # raise ValueError(self.no_selection_msg())\\n returnValue(value)\",\n \"def analogDaSetVolt(self, Channel, Voltage, Debug=0):\\n self.bib.DapiDASetVolt.argtypes = [c_ulong, c_ulong, c_float]\\n self.bib.DapiDASetVolt.restype = None\\n self.bib.DapiDASetVolt(self.handle, Channel, Voltage)\\n if Debug == 1:\\n print(\\\"D/A Channel\\\",Channel,\\\"set. Voltage is\\\",Voltage)\\n self.lastError()\\n self.lastErrorText()\\n #return(voltage)\",\n \"def get_setVoltage(self):\\n self.read(\\\":VOLT?\\\")\",\n \"def __init__(self, gainFactor=1):\\n\\n # Define SPI bus and init\\n self.__spiDAC = spidev.SpiDev()\\n self.__spiDAC.open(0, 1)\\n self.__spiDAC.max_speed_hz = (20000000)\\n\\n if (gainFactor != 1) and (gainFactor != 2):\\n raise ValueError('DAC __init__: Invalid gain factor. \\\\\\n Must be 1 or 2')\\n else:\\n self.gain = gainFactor\\n\\n self.maxdacvoltage = self.__dacMaxOutput__[self.gain]\",\n \"def __init__(self, diameter, sigma_color, sigma_space):\\n self.v = 0\\n self._diameter = diameter\\n self._sigma_color = sigma_color\\n self._sigma_space = sigma_space\",\n \"def test_velocity(self):\\n sol = Mader(p_cj=3.0e11, d_cj=8.0e5, gamma=3.0, u_piston=0.0)\\n # r must contain 2 elements, otherwise the density and pressure are nan\\n r = np.array([0.7, 0.8])\\n t = 6.25e-6\\n solrt = sol(r, t)\\n np.testing.assert_allclose(solrt.velocity[0], 144000.0)\",\n \"def set_voltage(self, value):\\n self.write(\\\":VOLT {}V\\\".format(value))\",\n \"def read_adc_voltage(self, channel, mode):\\n if (channel > 2) or (channel < 1):\\n raise ValueError('read_adc_voltage: channel out of range')\\n if (mode > 1) or (mode < 0):\\n raise ValueError('read_adc_voltage: mode out of range')\\n raw = self.read_adc_raw(channel, mode)\\n voltage = (self.__adcrefvoltage / 4096) * raw\\n return voltage\",\n \"def test_str_voltage(self):\\n xknx = XKNX()\\n sensor = Sensor(\\n xknx, \\\"TestSensor\\\", group_address_state=\\\"1/2/3\\\", value_type=\\\"voltage\\\"\\n )\\n sensor.sensor_value.payload = DPTArray(\\n (\\n 0x6D,\\n 0xBF,\\n )\\n )\\n\\n self.assertEqual(sensor.resolve_state(), 120504.32)\\n self.assertEqual(sensor.unit_of_measurement(), \\\"mV\\\")\\n self.assertEqual(sensor.ha_device_class(), None)\",\n \"def init(self, id_dds, dds):\\n self.info('Initialized with dds %r' % id_dds) \\n self._dds = dds\\n self.id_dds = id_dds\",\n \"def __init__(\\n self,\\n velocity_north_m_s,\\n velocity_east_m_s,\\n velocity_down_m_s):\\n self.velocity_north_m_s = velocity_north_m_s\\n self.velocity_east_m_s = velocity_east_m_s\\n self.velocity_down_m_s = velocity_down_m_s\",\n \"def set_channel_v_unit(self , channel_v_unit:float): \\n self.__channel_v_unit = channel_v_unit\",\n \"def __init__(self):\\n super().__init__()\\n self.metric = 'SEGVOL'\",\n \"def setup_continuous_scans(self, monitor=None):\\n if monitor is None:\\n monitor = self.monitor\\n else:\\n self.monitor = monitor\\n\\n # Lets grab the laser\\n laser = self.devices[monitor['laser']['name']]\\n if 'wavelength_sweeps' not in monitor['laser']['params']:\\n monitor['laser']['params']['wavelength_sweeps'] = 0 # This will generate the laser to sweep always.\\n\\n # Clear the array to start afresh\\n for d in self.daqs:\\n self.daqs[d]['monitor'] = []\\n\\n # Lets see what happens with the devices to monitor\\n devices_to_monitor = monitor['detectors']\\n\\n for dev in devices_to_monitor:\\n self.daqs[dev.properties['connection']['device']]['monitor'].append(dev)\\n\\n # Lets calculate the conditions of the scan\\n num_points = (monitor['laser']['params']['stop_wavelength'] - monitor['laser']['params']['start_wavelength'])/ monitor['laser']['params']['interval_trigger']\\n num_points = int(round(num_points.m_as('')))+1\\n\\n # Some DAQ cards behave strangely when dealing with even/odd number of points.\\n # Using an even number of data points makes it behave correctly 'always'\\n #if num_points % 2 != 0:\\n # # Increase the stop wavelength in one step in order to have an extra data point\\n # monitor['laser']['params']['stop_wavelength'] += monitor['laser']['params']['interval_trigger']\\n # num_points += 1\\n\\n accuracy = monitor['laser']['params']['interval_trigger'] / monitor['laser']['params']['wavelength_speed']\\n\\n approx_time_to_scan = (monitor['laser']['params']['stop_wavelength'] - monitor['laser']['params']['start_wavelength']) / monitor['laser']['params'][\\n 'wavelength_speed']\\n\\n laser.apply_values(monitor['laser']['params'])\\n monitor['approx_time_to_scan'] = approx_time_to_scan\\n \\n wl_sweeps = int(monitor['laser']['params']['wavelength_sweeps'])\\n if wl_sweeps > 0:\\n num_points = num_points * wl_sweeps\\n if monitor['laser']['params']['sweep_mode'] in ('ContTwo', 'StepTwo'):\\n num_points = num_points*2\\n sampling = 'continuous'\\n else:\\n sampling = 'continuous'\\n conditions = {\\n 'accuracy': accuracy,\\n 'points': config.ni_buffer\\n }\\n # Then setup the ADQs\\n for d in self.daqs:\\n daq = self.daqs[d] # Get the DAQ from the dictionary of daqs.\\n daq_driver = self.devices[d] # Gets the link to the DAQ\\n if len(daq['monitor']) > 0:\\n print('DAQ: %s' % d)\\n devs_to_monitor = daq['monitor'] # daqs dictionary groups the channels by daq to which they are plugged\\n print('Devs to monitor:')\\n print(devs_to_monitor)\\n conditions['devices'] = devs_to_monitor\\n if 'trigger' not in monitor['daq']:\\n raise Exception('A trigger has to be specified in the daq section of the monitor')\\n conditions['trigger'] = monitor['daq']['trigger']\\n print('Trigger: %s' % conditions['trigger'])\\n if conditions['trigger'] == 'external':\\n # It should specify also which port to use\\n conditions['trigger_source'] = monitor['daq']['trigger_source']\\n print('Trigger source: %s' % conditions['trigger_source'])\\n elif conditions['trigger'] == 'internal':\\n if monitor['daq']['trigger_source'] is not None:\\n raise Warning('Specifying trigger even if set to Internal.')\\n else:\\n raise Exception('Trigger not recognized. Check the config. Only external or internal can be used.')\\n\\n if 'start_source' in self.monitor['daq']:\\n if monitor['daq']['start_source'] is not None:\\n conditions['start_source'] = monitor['daq']['start_source']\\n conditions['start_mode'] = 'digital'\\n else:\\n conditions['start_mode'] = 'software'\\n\\n conditions['sampling'] = sampling\\n daq['monitor_task'] = daq_driver.driver.analog_input_setup(conditions)\\n self.daqs[d] = daq # Store it back to the class variable\\n print('Task number: %s' % self.daqs[d]['monitor_task'])\\n # Store the values back to the class\\n self.monitor = monitor\\n self.read_continuous_scans() # Empties the memory of the DAQ before starting a new measurement\",\n \"def reset(self):\\r\\n return self.get_voltage(0, 0)\",\n \"def __init__(self, drift_detector, size):\\r\\n self.drift_detector = drift_detector\\r\\n self.sample = 0\\r\\n self.reservoir = Reservoir(size)\\r\\n self.buffer = Buffer(size)\\r\\n self.confidence = 0.05\\r\\n self.recent_interval = []\\r\\n self.timestamp = 0\\r\\n self.vol_drift_found = False\\r\\n self.drift_found = False\\r\\n self.pre_drift_point = -1\\r\\n self.rolling_index = 0\\r\\n for i in range(size * 2 + 1):\\r\\n self.recent_interval.append(0.0)\",\n \"def read_adc_voltage(self, channel, mode):\\n if (mode < 0) or (mode > 1):\\n raise ValueError('read_adc_voltage: mode out of range')\\n if (channel > 4) and (mode == 1):\\n raise ValueError('read_adc_voltage: channel out of range')\\n if (channel > 8) or (channel < 1):\\n raise ValueError('read_adc_voltage: channel out of range')\\n\\n raw = self.read_adc_raw(channel, mode)\\n voltage = (self.__adcrefvoltage / 4096) * raw\\n return voltage\",\n \"def Add_Analog_Voltage_Channel(self,channel,min=-10.0,max=10.0):\\n self.num_channels += 1\\n self.channel = self.device + \\\"/\\\" + channel\\n self._CHK(nidaq.DAQmxCreateAIVoltageChan(self.task_handle,self.channel,\\\"\\\",\\n DAQmx_Val_NRSE,\\n float64(min),float64(max),\\n DAQmx_Val_Volts,None))\",\n \"def _set_output_voltage_from_label(self):\\n if (~self._is_stabilizing): #Only updates value if we are not stabilizing, otherwise the PID loop will be driving the output voltage\\n #as opposed to the user.\\n self._curr_output_voltage = self.widgets['p_outputVoltage'].value()\\n self._ao_client.set_ao_voltage(self._ao_channel, self._curr_output_voltage)\",\n \"def __init__(self, name, value, description = 'Numeric Data', limits = None, initvalue = None, unit = ''):\\n super().__init__(name=name, description=description, value=value, limits=limits , initvalue=initvalue, unit = unit)\",\n \"def voltage(self):\\n return self.outputValue()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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float"},"document":{"kind":"string","value":"def hp3458a_read_voltage(hp_meter):\n hp_meter.write(\"TARM SGL\")\n return float(hp_meter.read())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_voltage(self):\n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\n voltage = float(re.findall(pattern,summary).pop())\n return voltage","def get_voltage(self):\n status = self.get_status_response()\n volts = status[20] + (status[21] * 0x100) + (status[22] * 0x10000) + (status[23] * 0x1000000)\n volts = float(volts)\n volts /= (1000.0 * 1000.0)\n return volts\n #end get_voltage","def voltage(self):\n\t\treturn self._voltage","def ac_voltage(self):\n return float(self.get_ac_voltage())","def voltage(self):\n return self.outputValue()","def meas_voltage(instrument, v_range=10, resolution=0.003):\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))","def sm_measure_voltage(self,num_readings=1):\n self.sm.set_measurement_function(\"VOLTAGE\")\n self.sm.format_readings(\"VOLTAGE\")\n return average(self.sm.take_measurement(num_readings))","def read_voltage(self):\n self.write(':FETC?')\n msg = self.read()\n #print ('dmm msg = ', msg)\n v = msg.split(',')[0].rstrip('NVDC').strip()\n if v[-1] == 'R':\n return float(v[:-1])\n else:\n return float(v)","def get_voltage(self):\n print(\"voici le voltage de la batterie\")","def reference_voltage(self) -> float:\n return self._ref_voltage","def get_voltage(self):\n self._raise_not_implemented()","def read_actual_voltage(self):\n function_string = 'V' + self.output + 'O?'\n value_string = self.scpi_comm(function_string)\n LOGGER.warn(value_string)\n time.sleep(0.1) # This might only be necessary on LAN interface\n try:\n value = float(value_string.replace('V', ''))\n except ValueError:\n value = -999999\n return value","def get_meas_time_voltage(instrument):\n return float(instrument.query('SENSE:VOLT:DC:APER?'))","def get_voltage(self):\n return self.environment.get_voltage(self.neuron_id)","def read_set_voltage(self):\n function_string = 'V' + self.output + '?'\n value_string = self.scpi_comm(function_string)\n try:\n value = float(value_string.replace('V' + self.output, ''))\n except ValueError:\n value = -9997\n return value","def hp34401a_read_voltage(hp_meter):\n hp_meter.write(\"MEAS:VOLT:DC? DEF,DEF\")\n return float(hp_meter.read())","def voltage(self) -> int:\n return int(self._device_info[\"CurrentVoltage\"])","def calc_out_voltage(self, input_photocurrent_file):\n pass","def voltage(analog_pin):\n return \"%0.2f\" % inVolts(analogRead(analog_pin))","def get_uv_value(self):\n return float(int(self.data[2]) / 10)","def vol(self) -> float:\n return float(self.tsdf.pct_change().std() * np.sqrt(self.periods_in_a_year))","def read_vref(self) -> float:","def voltage_conversion(self):\r\n\t\tvoltage = ((self.data[0] * 256 + self.data[1]) / 65536.0) * 5.0\r\n\t\t\r\n\t\treturn {'v' : voltage}","def v(self):\n\n # TODO This translation formula works, but needs simplified.\n\n # PWM duration can go from 0 to 4095 with 4095 representing max rpm\n# print(\"MuleBot.v MuleBot.dcMotorPWMDurationLeft:\", MuleBot.dcMotorPWMDurationLeft)\n speed_percentage = float(MuleBot.dcMotorPWMDurationLeft) / 4095.0\n# print(\"speed_percentage: \", speed_percentage)\n\n rpm = speed_percentage * self.motorMaxRPM\n# print(\"rpm: \", rpm)\n\n secondsPerMinute = 60\n revs_per_second = rpm / secondsPerMinute\n# print(\"--revs_per_second\", revs_per_second)\n\n inches_per_rev = 2.0 * math.pi * MuleBot.WHEEL_RADIUS\n INCHES_PER_METER = 39.3701\n meters_per_rev = inches_per_rev / INCHES_PER_METER\n# print(\"--meters_per_rev\", meters_per_rev)\n\n meters_per_second = meters_per_rev * revs_per_second\n\n# print(\"--meters_per_second: \", meters_per_second)\n return meters_per_second","def calibV(self):\n # clear buffer in case of errors\n self.flushInput()\n \n if (self.model == 'GDS'):\n self.write(':CHAN'+str(ch)+':SCAL?\\n')\n # returns V/div, turn it into multiplicative factor\n # between digitizer and actual volts\n vmult = float(self.readline()) * 10./255.\n # GDS includes vertical offset in the data returned.\n voff = 0.\n elif (self.model == 'TDS'):\n self.write('WFMPre:YMUlt?\\n')\n # formula I am using later is from TDS manual, so this\n # is straightforward.\n vmult = float(self.readline())\n self.write('WFMPre:YOFf?\\n')\n voff = float(self.readline())\n \n # clear buffer in case of errors\n self.flushInput()\n\n return (vmult, voff)","def dmm_measure_voltage(self,range=None,res=None):\n if range is None:\n return self.dmm.measure_voltage()\n elif res is None:\n return self.dmm.measure_voltage(volt_range=range)\n else:\n return self.dmm.measure_voltage(volt_range=range,resolution=res)","def fetch(self):\n return read_voltage()","def get_channel_v_unit(self)->float:\n return self.__channel_v_unit","def get_voltage_and_current(self):\n return self.voltage_and_current","def Take_Voltage_Measurement(self):\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n nidaq.DAQmxClearTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def Take_Voltage_Measurement(self):\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n nidaq.DAQmxClearTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def outputValue(self):\n string = self.ask('od;E;')\n string = re.sub(r'^(NDCV|NDCA|EDCV)', r'', string)\n self.notify('voltage', float(string))\n return float(string)","def get_setVoltage(self):\n self.read(\":VOLT?\")","def get_volt(data):\n volt = (data * 5.0) / 255\n #volt = round(volt, 1)\n return volt","def present_input_voltage(self):\n return self._read(MX_PRESENT_INPUT_VOLTAGE)","def hp34401a_read_voltage_rng_res(hp_meter , v_range, v_resolution):\n hp_meter.write(\"MEAS:VOLT:DC? \" + str(v_range) + \" , \" + str(v_resolution))\n return float(hp_meter.read())","def ve(self) -> float:\n a = np.sum(np.abs(self.predicted - self.true))\n b = np.sum(self.true)\n return float(1 - (a / b))","def get_vrms(self, ch: int) -> float:\n cmd = \":measure:vrms? cycle,ac,channel{0}\".format(ch)\n return float(self.query(cmd))","def get_voltage(self, c):\n if 'slot_number' in c.keys():\n slot_number = c['slot_number'] \n yield self.open_comm(c, slot_number)\n yield self.write(c, 'VOLT?')\n voltage = yield self.read(c)\n yield self.close_comm(c, slot_number)\n \n try:\n voltage = float(voltage)\n except(ValueError):\n old_voltage = voltage\n voltage = ''.join([i for i in voltage if (i.isdigit() or i == '-' or i == '.')])\n voltage = float(voltage)\n print ('The value ' + old_voltage + ' was returned by the SIM928, this value was automatically converted to ' + voltage + '.')\n \n value = voltage * units.V\n \n # try:\n # voltage = yield self.query(c,\n # slot_number, \n # \"VOLT?\")\n # try:\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # try:\n # voltage = float(voltage)\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # try:\n # value = voltage * units.V\n # except:\n # self.initialize_mainframe(c)\n # voltage = yield self.query(c,\n # slot_number,\n # \"VOLT?\")\n # voltage = float(voltage)\n # value = voltage * units.V\n # else:\n # raise ValueError(self.no_selection_msg())\n returnValue(value)","def vol(x):\r\n return pi*(topdia(x)/2000.)**2 * length (x)","def value(self) -> float:","def calc_V(A):\n return 1. / calc_rV(A)","def _voltage_get(self, channelNumber):\n #print \"latest=%s\" % self.connection.latestResults\n if channelNumber in self.connection.latestResults:\n return self.connection.latestResults[channelNumber]\n else:\n return scipy.NaN","def raw_zener_voltage(self) -> int:\n self._update_analog_value_cache()\n return self.analog_cache.zener_voltage","def getMeasurement(self):\n if(ADConverterSettings.useRealAD):\n print \"Real AD not activated\"\n #return self.adcdac.read_adc_voltage(1)\n else:\n return self.__readMeasurementFromFile()","def getavgvel(self):\n if self.total_time:\n return (6.28)/(self.total_time)","def volts(self) -> NumType:\n return self._volt","def __q2v_ff(self, vm, beta, q):\n return float((vm * beta - np.sqrt(np.power(vm * beta, 2) - 4 * vm * beta * q)) / (2 * vm))","def E2V(E):\n# for energy in mev returns velocity in m/s\n return sqrt(E/5.227e-6)","def _voltage_get(self):\n #print \"latest=%s\" % self.connection.latestResults\n if self.channelNumber in self.connection.latestResults:\n return self.connection.latestResults[self.channelNumber]\n else:\n return -999","def compute_volume(self) -> float:\n return (\n (1 if self.clockwise else -1)\n * np.sum(\n np.linalg.det(\n np.dstack(\n (\n self.vertices[self._faces[:, 0]],\n self.vertices[self._faces[:, 1]],\n self.vertices[self._faces[:, 2]],\n )\n )\n )\n )\n / 6\n )","def get_fan_speed_volts(self):\n return self.__fan_speed_raw * PSCU.FAN_VREF","def voltage(self, voltage=None):\n if voltage is not None:\n # set output voltage on all phases\n # self.ts.log_debug('voltage: %s, type: %s' % (voltage, type(voltage)))\n if type(voltage) is not list and type(voltage) is not tuple:\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\n' % (voltage, voltage, voltage))\n v1 = voltage\n v2 = voltage\n v3 = voltage\n else:\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\n' % (voltage[0], voltage[0], voltage[0])) # use the first value in the 3 phase list\n v1 = voltage[0]\n v2 = voltage[0]\n v3 = voltage[0]","def calc_voltage(self) -> (Volt, None):\n power: ComType = complex(0)\n power_unit: str = ''\n amp: NumType = 0\n amp_unit: str = ''\n ohm: ComType = complex(0)\n ohm_unit: str = ''\n\n if self._amp_exists and self._ohm_exists:\n if hasattr(self._obj1, 'amps'):\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\n elif hasattr(self._obj1, 'ohm'):\n ohm, ohm_unit = self._obj1.ohm, self._obj1.ohm_unit\n if hasattr(self._obj2, 'amps'):\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\n elif hasattr(self._obj2, 'ohm'):\n ohm, ohm_unit = self._obj2.ohm, self._obj2.ohm_unit\n volt = amp * ohm\n volt_unit: str = f'Volts ({amp_unit}*{ohm_unit})'\n\n elif self._amp_exists and self._power_exists:\n if hasattr(self._obj1, 'amps'):\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\n elif hasattr(self._obj1, 'power'):\n power, power_unit = self._obj1.power, self._obj1.power_unit\n if hasattr(self._obj2, 'amps'):\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\n elif hasattr(self._obj2, 'power'):\n power, power_unit = self._obj2.power, self._obj2.power_unit\n volt = power / amp\n volt_unit: str = f'Volts ({power_unit}/{amp_unit})'\n\n else:\n return None\n\n return Volt(volt, volt_unit, self._obj1.frequency, self._obj1.freq_unit)","def raw_ldr_voltage(self) -> int:\n self._update_analog_value_cache()\n return self.analog_cache.ldr_voltage","def get_voltage_rating(self):\n summary = self.get_version_summary()\n pattern = '\\$.*? .*? .*? .*? .*? (.*?) .*? .*? .*? \\r\\n' \n rating = int(re.findall(pattern,summary).pop())\n return rating","def get_value(self, xval: float, wvtype: str, wv_id: int, precision=6) -> float:\n fmt = ':measure:vtime? {0:.%dg},{1}{2}' % precision\n return float(self.query(fmt.format(xval, wvtype, wv_id)))","def get_voltages(self):\n if self.v is None or self.dirty is True:\n v = self.simulator.get_voltages()\n n_compartments = self.neuron_collection.total_compartments()\n self.v = np.array(v).reshape([len(v) / n_compartments, n_compartments])\n\n self.dirty = False\n t = int(self.T / self.dt)\n return self.v[:t, :]","def volume_error(self) -> float:\n # TODO written formula and executed formula are different.\n ve = np.sum(self.predicted - self.true) / np.sum(self.true)\n return float(ve)","def get_vmax(self, ch_id: int) -> float:\n return float(self.query(':measure:vmax? channel{}'.format(ch_id)))","def proximity_voltage(x):\n y = VOLTAGE_TASK['value']\n rate = 1 / (y * 0.02)\n diff = abs(y - x)\n return 1 - rate * diff","def dvdt(self, args: Dict) -> float:\n if self.channel_bool['leak']:\n i_leak: float = self.leak.i(args['v'])\n else:\n i_leak: float = 0.\n \n if self.channel_bool['nav']:\n i_nav: float = self.nav.i(args['v'], h=args['h_nav'])\n else:\n i_nav: float = 0.\n\n if self.channel_bool['kvhh']:\n i_kvhh: float = self.kvhh.i(args['v'], n=args['n_kvhh'])\n else:\n i_kvhh: float = 0.\n\n if self.channel_bool['kva']:\n i_kva: float = self.kva.i(args['v'], h=args['h_kva'])\n else:\n i_kva: float = 0.\n\n if self.channel_bool['kvsi']:\n i_kvsi: float = self.kvsi.i(args['v'], m=args['m_kvsi'])\n else:\n i_kvsi: float = 0.\n\n if self.channel_bool['cav']:\n i_cav: float = self.cav.i(args['v'])\n else:\n i_cav: float = 0.\n\n if self.channel_bool['kca']:\n i_kca: float = self.kca.i(args['v'], ca=args['ca'])\n else:\n i_kca: float = 0.\n \n if self.channel_bool['nap']:\n i_nap: float = self.nap.i(args['v'])\n else:\n i_nap: float = 0.\n\n if self.channel_bool['kir']:\n i_kir: float = self.kir.i(args['v'])\n else:\n i_kir: float = 0.\n\n if self.channel_bool['ampar']:\n i_ampar: float = self.ampar.i(args['v'], s=args['s_ampar'])\n else:\n i_ampar: float = 0.\n\n if self.channel_bool['nmdar']:\n i_nmdar: float = self.nmdar.i(args['v'], s=args['s_nmdar'])\n else:\n i_nmdar: float = 0.\n\n if self.channel_bool['gabar']:\n i_gabar: float = self.gabar.i(args['v'], s=args['s_gabar'])\n else:\n i_gabar: float = 0.\n\n return ((-10.0*self.params.area \n * (i_leak\n + i_nav \n + i_kvhh \n + i_kva \n + i_kvsi \n + i_cav \n + i_kca \n + i_nap \n + i_kir) \n - (i_ampar \n + i_nmdar \n + i_gabar))\n / (10.0*self.params.cm*self.params.area))","def vonMises(self):\n s = self.voigt\n return ((1 / 2) * ((s[0] - s[1])**2\n + (s[1] - s[2])**2\n + (s[2] - s[0])**2\n + 6 * (s[3]**2 + s[4]**2 + s[5]**2))) ** (1 / 2)","def dv(self):\n return self.dvdlogdp.mul(self.dlogdp)","def measure_vdc(self, rng = 'DEF', res = 'DEF', unit = 'V', samp = 1):\n self.configure_vdc(rng, res, unit)\n self.write_to_serial(':samp:coun ' + str(samp))\n if samp == 1:\n return float(self.query_serial('read?'))\n else:\n samples = self.query_serial('read?').split(',')\n sampleresult = []\n for sample in samples:\n sampleresult.append(float(sample))\n return sampleresult","def getBatteryVoltage(self):\n return self.values[BATTERY_VOLTAGE]","def Take_Voltage_Measurement(self,timeout=10.0):\n self.timeout = timeout\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\n samples_read = int32()\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\n DAQmx_Val_GroupByChannel,data.ctypes.data,\n self.num_samples,ctypes.byref(samples_read),None))\n if self.task_handle.value != 0:\n nidaq.DAQmxStopTask(self.task_handle)\n if samples_read.value != self.num_samples:\n return 'error'\n else:\n return data","def _getBusVoltageLambdaSensor(self):\n muVmin = array([b.mu_vmin for b in self.market.case.connected_buses])\n muVmax = array([b.mu_vmax for b in self.market.case.connected_buses])\n muVmin = -1.0 * muVmin\n diff = muVmin + muVmax\n return diff","def get_voltage(self, channel):\n self.check_validity()\n\n channel = int(channel)\n\n return self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_VOLTAGE, (channel,), 'B', 12, 'i')","def get_velocity(self):\n return self.momentum/self.mass","def get_volume(cls) -> float:\n raise NotImplementedError","def get_V(self):\n return self.V","def last_voltage(self):\n return self._last_voltage","def get_voltage_no_load(self):\n return self.__voltage_no_load","def get_vcond(lambdam, taum):\n return 2 * lambdam / taum","def get_voltage(self, i_sup, t, *args, **kwargs):\r\n raise NotImplementedError","def read_core_vbat(self) -> float:","def read_adc_voltage(self, channel, mode):\n if (channel > 2) or (channel < 1):\n raise ValueError('read_adc_voltage: channel out of range')\n if (mode > 1) or (mode < 0):\n raise ValueError('read_adc_voltage: mode out of range')\n raw = self.read_adc_raw(channel, mode)\n voltage = (self.__adcrefvoltage / 4096) * raw\n return voltage","def charging_current_and_voltage(self):\n done, data = self._request('GG')\n if done:\n milliamps = float(data[0])\n millivolts = float(data[1])\n return {\n 'amps': float(milliamps) / 1000 if milliamps > 0 else 0.0,\n 'volts': float(millivolts) / 1000 if millivolts > 0 else 0.0\n }\n\n raise EvseError","def value(self) -> float:\n ...","def value(self) -> float:\n ...","def value(self) -> float:\n ...","def request_voltage_and_current(self):\n self.voltage_and_current = self.current_sensors.get_channel_voltage_and_current(self.channel)\n return self.voltage_and_current","def analogAdGetVolt(self, Channel, Debug=0):\n self.bib.DapiADGetVolt.argtypes = [c_ulong, c_ulong]\n self.bib.DapiADGetVolt.restype = c_float\n voltage = self.bib.DapiADGetVolt(self.handle, Channel)\n if Debug == 1:\n print(\"A/D Channel\",Channel,\"read. Voltage is\",voltage)\n self.lastError()\n self.lastErrorText()\n return(voltage)","def get_value(\n self\n ) -> float:\n\n return self.average","def get_vbat(self):\n return self.read_register(4096, 1, 3)","def delta_v_calc(mass_initial,\n mass_final,\n v_exhaust,\n ):\n\n return v_exhaust * math.log(mass_initial / mass_final)","def fla (mva, vnom):\r\n x=mva*1000000\r\n y=(vnom*1000)\r\n z=round(x/y,3)\r\n return z","def __float__(self):\n return self.num/self.denom","def __float__(self):\n return self.num/self.denom","def read_adc_voltage(self, channel, mode):\n if (mode < 0) or (mode > 1):\n raise ValueError('read_adc_voltage: mode out of range')\n if (channel > 4) and (mode == 1):\n raise ValueError('read_adc_voltage: channel out of range')\n if (channel > 8) or (channel < 1):\n raise ValueError('read_adc_voltage: channel out of range')\n\n raw = self.read_adc_raw(channel, mode)\n voltage = (self.__adcrefvoltage / 4096) * raw\n return voltage","def calcDVavg(supplyvol, demandvol):\n dvavg = (supplyvol - demandvol)/(0.5 * (supplyvol + demandvol))\n return dvavg","def read_voltage_stepsize(self):\n function_string = 'DELTAV' + self.output + '?'\n return self.scpi_comm(function_string)","def dvdt(self, args: List[float]) -> float:\n v, h_nav, n_kvhh, h_kva, m_kvsi, s_ampar, _, s_nmdar, s_gabar, ca = args\n return ((-10.0*self.params.area \n * (self.leak.i(v)\n + self.nav.i(v, h=h_nav) \n + self.kvhh.i(v, n=n_kvhh)\n + self.kva.i(v, h=h_kva)\n + self.kvsi.i(v, m=m_kvsi)\n + self.cav.i(v)\n + self.kca.i(v, ca=ca)\n + self.nap.i(v)\n + self.kir.i(v))\n - (self.ampar.i(v, s=s_ampar)\n + self.nmdar.i(v, s=s_nmdar)\n + self.gabar.i(v, s=s_gabar))) \n / (10.0*self.params.cm*self.params.area))","def to_float(self): \n return (self._num / self._den)","def get_position_volts(self):\n return self.__position_raw * PSCU.POSITION_VREF","def total_volume(self):","def vol_from_var(self, level: float = 0.95, interpolation: str = 'lower') -> float:\n return float(-np.sqrt(self.periods_in_a_year) *\n self.var_down_func(level, interpolation=interpolation) / ss.norm.ppf(level))","def format_function_voltage(value):\n if value == -999:\n return \"Not in Use\"\n else:\n return \"%0.3f V\" % (value)","def get_max_cell_voltage(self): \n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. .*? .*? .*? .*? (.*?) . .*? .*? . . . .*?'\n maxv = float(re.findall(pattern,summary).pop())\n return maxv"],"string":"[\n \"def get_voltage(self):\\n summary = \\\" \\\".join(self.get_summary().split())\\n pattern = '\\\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\\n voltage = float(re.findall(pattern,summary).pop())\\n return voltage\",\n \"def get_voltage(self):\\n status = self.get_status_response()\\n volts = status[20] + (status[21] * 0x100) + (status[22] * 0x10000) + (status[23] * 0x1000000)\\n volts = float(volts)\\n volts /= (1000.0 * 1000.0)\\n return volts\\n #end get_voltage\",\n \"def voltage(self):\\n\\t\\treturn self._voltage\",\n \"def ac_voltage(self):\\n return float(self.get_ac_voltage())\",\n \"def voltage(self):\\n return self.outputValue()\",\n \"def meas_voltage(instrument, v_range=10, resolution=0.003):\\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))\",\n \"def sm_measure_voltage(self,num_readings=1):\\n self.sm.set_measurement_function(\\\"VOLTAGE\\\")\\n self.sm.format_readings(\\\"VOLTAGE\\\")\\n return average(self.sm.take_measurement(num_readings))\",\n \"def read_voltage(self):\\n self.write(':FETC?')\\n msg = self.read()\\n #print ('dmm msg = ', msg)\\n v = msg.split(',')[0].rstrip('NVDC').strip()\\n if v[-1] == 'R':\\n return float(v[:-1])\\n else:\\n return float(v)\",\n \"def get_voltage(self):\\n print(\\\"voici le voltage de la batterie\\\")\",\n \"def reference_voltage(self) -> float:\\n return self._ref_voltage\",\n \"def get_voltage(self):\\n self._raise_not_implemented()\",\n \"def read_actual_voltage(self):\\n function_string = 'V' + self.output + 'O?'\\n value_string = self.scpi_comm(function_string)\\n LOGGER.warn(value_string)\\n time.sleep(0.1) # This might only be necessary on LAN interface\\n try:\\n value = float(value_string.replace('V', ''))\\n except ValueError:\\n value = -999999\\n return value\",\n \"def get_meas_time_voltage(instrument):\\n return float(instrument.query('SENSE:VOLT:DC:APER?'))\",\n \"def get_voltage(self):\\n return self.environment.get_voltage(self.neuron_id)\",\n \"def read_set_voltage(self):\\n function_string = 'V' + self.output + '?'\\n value_string = self.scpi_comm(function_string)\\n try:\\n value = float(value_string.replace('V' + self.output, ''))\\n except ValueError:\\n value = -9997\\n return value\",\n \"def hp34401a_read_voltage(hp_meter):\\n hp_meter.write(\\\"MEAS:VOLT:DC? DEF,DEF\\\")\\n return float(hp_meter.read())\",\n \"def voltage(self) -> int:\\n return int(self._device_info[\\\"CurrentVoltage\\\"])\",\n \"def calc_out_voltage(self, input_photocurrent_file):\\n pass\",\n \"def voltage(analog_pin):\\n return \\\"%0.2f\\\" % inVolts(analogRead(analog_pin))\",\n \"def get_uv_value(self):\\n return float(int(self.data[2]) / 10)\",\n \"def vol(self) -> float:\\n return float(self.tsdf.pct_change().std() * np.sqrt(self.periods_in_a_year))\",\n \"def read_vref(self) -> float:\",\n \"def voltage_conversion(self):\\r\\n\\t\\tvoltage = ((self.data[0] * 256 + self.data[1]) / 65536.0) * 5.0\\r\\n\\t\\t\\r\\n\\t\\treturn {'v' : voltage}\",\n \"def v(self):\\n\\n # TODO This translation formula works, but needs simplified.\\n\\n # PWM duration can go from 0 to 4095 with 4095 representing max rpm\\n# print(\\\"MuleBot.v MuleBot.dcMotorPWMDurationLeft:\\\", MuleBot.dcMotorPWMDurationLeft)\\n speed_percentage = float(MuleBot.dcMotorPWMDurationLeft) / 4095.0\\n# print(\\\"speed_percentage: \\\", speed_percentage)\\n\\n rpm = speed_percentage * self.motorMaxRPM\\n# print(\\\"rpm: \\\", rpm)\\n\\n secondsPerMinute = 60\\n revs_per_second = rpm / secondsPerMinute\\n# print(\\\"--revs_per_second\\\", revs_per_second)\\n\\n inches_per_rev = 2.0 * math.pi * MuleBot.WHEEL_RADIUS\\n INCHES_PER_METER = 39.3701\\n meters_per_rev = inches_per_rev / INCHES_PER_METER\\n# print(\\\"--meters_per_rev\\\", meters_per_rev)\\n\\n meters_per_second = meters_per_rev * revs_per_second\\n\\n# print(\\\"--meters_per_second: \\\", meters_per_second)\\n return meters_per_second\",\n \"def calibV(self):\\n # clear buffer in case of errors\\n self.flushInput()\\n \\n if (self.model == 'GDS'):\\n self.write(':CHAN'+str(ch)+':SCAL?\\\\n')\\n # returns V/div, turn it into multiplicative factor\\n # between digitizer and actual volts\\n vmult = float(self.readline()) * 10./255.\\n # GDS includes vertical offset in the data returned.\\n voff = 0.\\n elif (self.model == 'TDS'):\\n self.write('WFMPre:YMUlt?\\\\n')\\n # formula I am using later is from TDS manual, so this\\n # is straightforward.\\n vmult = float(self.readline())\\n self.write('WFMPre:YOFf?\\\\n')\\n voff = float(self.readline())\\n \\n # clear buffer in case of errors\\n self.flushInput()\\n\\n return (vmult, voff)\",\n \"def dmm_measure_voltage(self,range=None,res=None):\\n if range is None:\\n return self.dmm.measure_voltage()\\n elif res is None:\\n return self.dmm.measure_voltage(volt_range=range)\\n else:\\n return self.dmm.measure_voltage(volt_range=range,resolution=res)\",\n \"def fetch(self):\\n return read_voltage()\",\n \"def get_channel_v_unit(self)->float:\\n return self.__channel_v_unit\",\n \"def get_voltage_and_current(self):\\n return self.voltage_and_current\",\n \"def Take_Voltage_Measurement(self):\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n nidaq.DAQmxClearTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def Take_Voltage_Measurement(self):\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n nidaq.DAQmxClearTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def outputValue(self):\\n string = self.ask('od;E;')\\n string = re.sub(r'^(NDCV|NDCA|EDCV)', r'', string)\\n self.notify('voltage', float(string))\\n return float(string)\",\n \"def get_setVoltage(self):\\n self.read(\\\":VOLT?\\\")\",\n \"def get_volt(data):\\n volt = (data * 5.0) / 255\\n #volt = round(volt, 1)\\n return volt\",\n \"def present_input_voltage(self):\\n return self._read(MX_PRESENT_INPUT_VOLTAGE)\",\n \"def hp34401a_read_voltage_rng_res(hp_meter , v_range, v_resolution):\\n hp_meter.write(\\\"MEAS:VOLT:DC? \\\" + str(v_range) + \\\" , \\\" + str(v_resolution))\\n return float(hp_meter.read())\",\n \"def ve(self) -> float:\\n a = np.sum(np.abs(self.predicted - self.true))\\n b = np.sum(self.true)\\n return float(1 - (a / b))\",\n \"def get_vrms(self, ch: int) -> float:\\n cmd = \\\":measure:vrms? cycle,ac,channel{0}\\\".format(ch)\\n return float(self.query(cmd))\",\n \"def get_voltage(self, c):\\n if 'slot_number' in c.keys():\\n slot_number = c['slot_number'] \\n yield self.open_comm(c, slot_number)\\n yield self.write(c, 'VOLT?')\\n voltage = yield self.read(c)\\n yield self.close_comm(c, slot_number)\\n \\n try:\\n voltage = float(voltage)\\n except(ValueError):\\n old_voltage = voltage\\n voltage = ''.join([i for i in voltage if (i.isdigit() or i == '-' or i == '.')])\\n voltage = float(voltage)\\n print ('The value ' + old_voltage + ' was returned by the SIM928, this value was automatically converted to ' + voltage + '.')\\n \\n value = voltage * units.V\\n \\n # try:\\n # voltage = yield self.query(c,\\n # slot_number, \\n # \\\"VOLT?\\\")\\n # try:\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # try:\\n # voltage = float(voltage)\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # try:\\n # value = voltage * units.V\\n # except:\\n # self.initialize_mainframe(c)\\n # voltage = yield self.query(c,\\n # slot_number,\\n # \\\"VOLT?\\\")\\n # voltage = float(voltage)\\n # value = voltage * units.V\\n # else:\\n # raise ValueError(self.no_selection_msg())\\n returnValue(value)\",\n \"def vol(x):\\r\\n return pi*(topdia(x)/2000.)**2 * length (x)\",\n \"def value(self) -> float:\",\n \"def calc_V(A):\\n return 1. / calc_rV(A)\",\n \"def _voltage_get(self, channelNumber):\\n #print \\\"latest=%s\\\" % self.connection.latestResults\\n if channelNumber in self.connection.latestResults:\\n return self.connection.latestResults[channelNumber]\\n else:\\n return scipy.NaN\",\n \"def raw_zener_voltage(self) -> int:\\n self._update_analog_value_cache()\\n return self.analog_cache.zener_voltage\",\n \"def getMeasurement(self):\\n if(ADConverterSettings.useRealAD):\\n print \\\"Real AD not activated\\\"\\n #return self.adcdac.read_adc_voltage(1)\\n else:\\n return self.__readMeasurementFromFile()\",\n \"def getavgvel(self):\\n if self.total_time:\\n return (6.28)/(self.total_time)\",\n \"def volts(self) -> NumType:\\n return self._volt\",\n \"def __q2v_ff(self, vm, beta, q):\\n return float((vm * beta - np.sqrt(np.power(vm * beta, 2) - 4 * vm * beta * q)) / (2 * vm))\",\n \"def E2V(E):\\n# for energy in mev returns velocity in m/s\\n return sqrt(E/5.227e-6)\",\n \"def _voltage_get(self):\\n #print \\\"latest=%s\\\" % self.connection.latestResults\\n if self.channelNumber in self.connection.latestResults:\\n return self.connection.latestResults[self.channelNumber]\\n else:\\n return -999\",\n \"def compute_volume(self) -> float:\\n return (\\n (1 if self.clockwise else -1)\\n * np.sum(\\n np.linalg.det(\\n np.dstack(\\n (\\n self.vertices[self._faces[:, 0]],\\n self.vertices[self._faces[:, 1]],\\n self.vertices[self._faces[:, 2]],\\n )\\n )\\n )\\n )\\n / 6\\n )\",\n \"def get_fan_speed_volts(self):\\n return self.__fan_speed_raw * PSCU.FAN_VREF\",\n \"def voltage(self, voltage=None):\\n if voltage is not None:\\n # set output voltage on all phases\\n # self.ts.log_debug('voltage: %s, type: %s' % (voltage, type(voltage)))\\n if type(voltage) is not list and type(voltage) is not tuple:\\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\\\n' % (voltage, voltage, voltage))\\n v1 = voltage\\n v2 = voltage\\n v3 = voltage\\n else:\\n self.cmd(':AC:SETB:VOLT PERC,%0.1f,%0.1f,%0.1f\\\\n' % (voltage[0], voltage[0], voltage[0])) # use the first value in the 3 phase list\\n v1 = voltage[0]\\n v2 = voltage[0]\\n v3 = voltage[0]\",\n \"def calc_voltage(self) -> (Volt, None):\\n power: ComType = complex(0)\\n power_unit: str = ''\\n amp: NumType = 0\\n amp_unit: str = ''\\n ohm: ComType = complex(0)\\n ohm_unit: str = ''\\n\\n if self._amp_exists and self._ohm_exists:\\n if hasattr(self._obj1, 'amps'):\\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\\n elif hasattr(self._obj1, 'ohm'):\\n ohm, ohm_unit = self._obj1.ohm, self._obj1.ohm_unit\\n if hasattr(self._obj2, 'amps'):\\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\\n elif hasattr(self._obj2, 'ohm'):\\n ohm, ohm_unit = self._obj2.ohm, self._obj2.ohm_unit\\n volt = amp * ohm\\n volt_unit: str = f'Volts ({amp_unit}*{ohm_unit})'\\n\\n elif self._amp_exists and self._power_exists:\\n if hasattr(self._obj1, 'amps'):\\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\\n elif hasattr(self._obj1, 'power'):\\n power, power_unit = self._obj1.power, self._obj1.power_unit\\n if hasattr(self._obj2, 'amps'):\\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\\n elif hasattr(self._obj2, 'power'):\\n power, power_unit = self._obj2.power, self._obj2.power_unit\\n volt = power / amp\\n volt_unit: str = f'Volts ({power_unit}/{amp_unit})'\\n\\n else:\\n return None\\n\\n return Volt(volt, volt_unit, self._obj1.frequency, self._obj1.freq_unit)\",\n \"def raw_ldr_voltage(self) -> int:\\n self._update_analog_value_cache()\\n return self.analog_cache.ldr_voltage\",\n \"def get_voltage_rating(self):\\n summary = self.get_version_summary()\\n pattern = '\\\\$.*? .*? .*? .*? .*? (.*?) .*? .*? .*? \\\\r\\\\n' \\n rating = int(re.findall(pattern,summary).pop())\\n return rating\",\n \"def get_value(self, xval: float, wvtype: str, wv_id: int, precision=6) -> float:\\n fmt = ':measure:vtime? {0:.%dg},{1}{2}' % precision\\n return float(self.query(fmt.format(xval, wvtype, wv_id)))\",\n \"def get_voltages(self):\\n if self.v is None or self.dirty is True:\\n v = self.simulator.get_voltages()\\n n_compartments = self.neuron_collection.total_compartments()\\n self.v = np.array(v).reshape([len(v) / n_compartments, n_compartments])\\n\\n self.dirty = False\\n t = int(self.T / self.dt)\\n return self.v[:t, :]\",\n \"def volume_error(self) -> float:\\n # TODO written formula and executed formula are different.\\n ve = np.sum(self.predicted - self.true) / np.sum(self.true)\\n return float(ve)\",\n \"def get_vmax(self, ch_id: int) -> float:\\n return float(self.query(':measure:vmax? channel{}'.format(ch_id)))\",\n \"def proximity_voltage(x):\\n y = VOLTAGE_TASK['value']\\n rate = 1 / (y * 0.02)\\n diff = abs(y - x)\\n return 1 - rate * diff\",\n \"def dvdt(self, args: Dict) -> float:\\n if self.channel_bool['leak']:\\n i_leak: float = self.leak.i(args['v'])\\n else:\\n i_leak: float = 0.\\n \\n if self.channel_bool['nav']:\\n i_nav: float = self.nav.i(args['v'], h=args['h_nav'])\\n else:\\n i_nav: float = 0.\\n\\n if self.channel_bool['kvhh']:\\n i_kvhh: float = self.kvhh.i(args['v'], n=args['n_kvhh'])\\n else:\\n i_kvhh: float = 0.\\n\\n if self.channel_bool['kva']:\\n i_kva: float = self.kva.i(args['v'], h=args['h_kva'])\\n else:\\n i_kva: float = 0.\\n\\n if self.channel_bool['kvsi']:\\n i_kvsi: float = self.kvsi.i(args['v'], m=args['m_kvsi'])\\n else:\\n i_kvsi: float = 0.\\n\\n if self.channel_bool['cav']:\\n i_cav: float = self.cav.i(args['v'])\\n else:\\n i_cav: float = 0.\\n\\n if self.channel_bool['kca']:\\n i_kca: float = self.kca.i(args['v'], ca=args['ca'])\\n else:\\n i_kca: float = 0.\\n \\n if self.channel_bool['nap']:\\n i_nap: float = self.nap.i(args['v'])\\n else:\\n i_nap: float = 0.\\n\\n if self.channel_bool['kir']:\\n i_kir: float = self.kir.i(args['v'])\\n else:\\n i_kir: float = 0.\\n\\n if self.channel_bool['ampar']:\\n i_ampar: float = self.ampar.i(args['v'], s=args['s_ampar'])\\n else:\\n i_ampar: float = 0.\\n\\n if self.channel_bool['nmdar']:\\n i_nmdar: float = self.nmdar.i(args['v'], s=args['s_nmdar'])\\n else:\\n i_nmdar: float = 0.\\n\\n if self.channel_bool['gabar']:\\n i_gabar: float = self.gabar.i(args['v'], s=args['s_gabar'])\\n else:\\n i_gabar: float = 0.\\n\\n return ((-10.0*self.params.area \\n * (i_leak\\n + i_nav \\n + i_kvhh \\n + i_kva \\n + i_kvsi \\n + i_cav \\n + i_kca \\n + i_nap \\n + i_kir) \\n - (i_ampar \\n + i_nmdar \\n + i_gabar))\\n / (10.0*self.params.cm*self.params.area))\",\n \"def vonMises(self):\\n s = self.voigt\\n return ((1 / 2) * ((s[0] - s[1])**2\\n + (s[1] - s[2])**2\\n + (s[2] - s[0])**2\\n + 6 * (s[3]**2 + s[4]**2 + s[5]**2))) ** (1 / 2)\",\n \"def dv(self):\\n return self.dvdlogdp.mul(self.dlogdp)\",\n \"def measure_vdc(self, rng = 'DEF', res = 'DEF', unit = 'V', samp = 1):\\n self.configure_vdc(rng, res, unit)\\n self.write_to_serial(':samp:coun ' + str(samp))\\n if samp == 1:\\n return float(self.query_serial('read?'))\\n else:\\n samples = self.query_serial('read?').split(',')\\n sampleresult = []\\n for sample in samples:\\n sampleresult.append(float(sample))\\n return sampleresult\",\n \"def getBatteryVoltage(self):\\n return self.values[BATTERY_VOLTAGE]\",\n \"def Take_Voltage_Measurement(self,timeout=10.0):\\n self.timeout = timeout\\n self._CHK(nidaq.DAQmxStartTask(self.task_handle))\\n samples_read = int32()\\n data = numpy.zeros((self.num_samples,),dtype=numpy.float64)\\n self._CHK(nidaq.DAQmxReadAnalogF64(self.task_handle,uInt32(self.num_samples),float64(self.timeout),\\n DAQmx_Val_GroupByChannel,data.ctypes.data,\\n self.num_samples,ctypes.byref(samples_read),None))\\n if self.task_handle.value != 0:\\n nidaq.DAQmxStopTask(self.task_handle)\\n if samples_read.value != self.num_samples:\\n return 'error'\\n else:\\n return data\",\n \"def _getBusVoltageLambdaSensor(self):\\n muVmin = array([b.mu_vmin for b in self.market.case.connected_buses])\\n muVmax = array([b.mu_vmax for b in self.market.case.connected_buses])\\n muVmin = -1.0 * muVmin\\n diff = muVmin + muVmax\\n return diff\",\n \"def get_voltage(self, channel):\\n self.check_validity()\\n\\n channel = int(channel)\\n\\n return self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_GET_VOLTAGE, (channel,), 'B', 12, 'i')\",\n \"def get_velocity(self):\\n return self.momentum/self.mass\",\n \"def get_volume(cls) -> float:\\n raise NotImplementedError\",\n \"def get_V(self):\\n return self.V\",\n \"def last_voltage(self):\\n return self._last_voltage\",\n \"def get_voltage_no_load(self):\\n return self.__voltage_no_load\",\n \"def get_vcond(lambdam, taum):\\n return 2 * lambdam / taum\",\n \"def get_voltage(self, i_sup, t, *args, **kwargs):\\r\\n raise NotImplementedError\",\n \"def read_core_vbat(self) -> float:\",\n \"def read_adc_voltage(self, channel, mode):\\n if (channel > 2) or (channel < 1):\\n raise ValueError('read_adc_voltage: channel out of range')\\n if (mode > 1) or (mode < 0):\\n raise ValueError('read_adc_voltage: mode out of range')\\n raw = self.read_adc_raw(channel, mode)\\n voltage = (self.__adcrefvoltage / 4096) * raw\\n return voltage\",\n \"def charging_current_and_voltage(self):\\n done, data = self._request('GG')\\n if done:\\n milliamps = float(data[0])\\n millivolts = float(data[1])\\n return {\\n 'amps': float(milliamps) / 1000 if milliamps > 0 else 0.0,\\n 'volts': float(millivolts) / 1000 if millivolts > 0 else 0.0\\n }\\n\\n raise EvseError\",\n \"def value(self) -> float:\\n ...\",\n \"def value(self) -> float:\\n ...\",\n \"def value(self) -> float:\\n ...\",\n \"def request_voltage_and_current(self):\\n self.voltage_and_current = self.current_sensors.get_channel_voltage_and_current(self.channel)\\n return self.voltage_and_current\",\n \"def analogAdGetVolt(self, Channel, Debug=0):\\n self.bib.DapiADGetVolt.argtypes = [c_ulong, c_ulong]\\n self.bib.DapiADGetVolt.restype = c_float\\n voltage = self.bib.DapiADGetVolt(self.handle, Channel)\\n if Debug == 1:\\n print(\\\"A/D Channel\\\",Channel,\\\"read. Voltage is\\\",voltage)\\n self.lastError()\\n self.lastErrorText()\\n return(voltage)\",\n \"def get_value(\\n self\\n ) -> float:\\n\\n return self.average\",\n \"def get_vbat(self):\\n return self.read_register(4096, 1, 3)\",\n \"def delta_v_calc(mass_initial,\\n mass_final,\\n v_exhaust,\\n ):\\n\\n return v_exhaust * math.log(mass_initial / mass_final)\",\n \"def fla (mva, vnom):\\r\\n x=mva*1000000\\r\\n y=(vnom*1000)\\r\\n z=round(x/y,3)\\r\\n return z\",\n \"def __float__(self):\\n return self.num/self.denom\",\n \"def __float__(self):\\n return self.num/self.denom\",\n \"def read_adc_voltage(self, channel, mode):\\n if (mode < 0) or (mode > 1):\\n raise ValueError('read_adc_voltage: mode out of range')\\n if (channel > 4) and (mode == 1):\\n raise ValueError('read_adc_voltage: channel out of range')\\n if (channel > 8) or (channel < 1):\\n raise ValueError('read_adc_voltage: channel out of range')\\n\\n raw = self.read_adc_raw(channel, mode)\\n voltage = (self.__adcrefvoltage / 4096) * raw\\n return voltage\",\n \"def calcDVavg(supplyvol, demandvol):\\n dvavg = (supplyvol - demandvol)/(0.5 * (supplyvol + demandvol))\\n return dvavg\",\n \"def read_voltage_stepsize(self):\\n function_string = 'DELTAV' + self.output + '?'\\n return self.scpi_comm(function_string)\",\n \"def dvdt(self, args: List[float]) -> float:\\n v, h_nav, n_kvhh, h_kva, m_kvsi, s_ampar, _, s_nmdar, s_gabar, ca = args\\n return ((-10.0*self.params.area \\n * (self.leak.i(v)\\n + self.nav.i(v, h=h_nav) \\n + self.kvhh.i(v, n=n_kvhh)\\n + self.kva.i(v, h=h_kva)\\n + self.kvsi.i(v, m=m_kvsi)\\n + self.cav.i(v)\\n + self.kca.i(v, ca=ca)\\n + self.nap.i(v)\\n + self.kir.i(v))\\n - (self.ampar.i(v, s=s_ampar)\\n + self.nmdar.i(v, s=s_nmdar)\\n + self.gabar.i(v, s=s_gabar))) \\n / (10.0*self.params.cm*self.params.area))\",\n \"def to_float(self): \\n return (self._num / self._den)\",\n \"def get_position_volts(self):\\n return self.__position_raw * PSCU.POSITION_VREF\",\n \"def total_volume(self):\",\n \"def vol_from_var(self, level: float = 0.95, interpolation: str = 'lower') -> float:\\n return float(-np.sqrt(self.periods_in_a_year) *\\n self.var_down_func(level, interpolation=interpolation) / ss.norm.ppf(level))\",\n \"def format_function_voltage(value):\\n if value == -999:\\n return \\\"Not in Use\\\"\\n else:\\n return \\\"%0.3f V\\\" % (value)\",\n \"def get_max_cell_voltage(self): \\n summary = \\\" \\\".join(self.get_summary().split())\\n pattern = '\\\\$.... .. .*? .*? .*? .*? (.*?) . .*? .*? . . . .*?'\\n maxv = float(re.findall(pattern,summary).pop())\\n return maxv\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80243504","0.76661944","0.75472015","0.74645776","0.7437857","0.73945224","0.7392207","0.7350186","0.73374736","0.7213724","0.7169046","0.7097468","0.70568234","0.7049625","0.7021389","0.6937337","0.6860303","0.68288875","0.66705036","0.66682374","0.6655914","0.6644716","0.6638835","0.6615325","0.6613023","0.6585681","0.6582362","0.65554535","0.64984536","0.6465046","0.6465046","0.64295316","0.63401586","0.63341874","0.63140225","0.63051146","0.6295046","0.6293342","0.62801975","0.6274929","0.6269015","0.62609327","0.62301165","0.6215816","0.62128747","0.62060046","0.61881125","0.6180322","0.615874","0.61575896","0.6141532","0.61387306","0.6135455","0.6125309","0.6117584","0.60996103","0.60882944","0.60879344","0.6045197","0.6035636","0.60227555","0.60199535","0.6019015","0.5991061","0.5988956","0.5978193","0.5967327","0.5958416","0.59559345","0.59512156","0.5948521","0.594162","0.5941048","0.594101","0.5937671","0.5931661","0.59311205","0.59218013","0.5911772","0.5909889","0.5909889","0.5909889","0.5901092","0.58848137","0.58822364","0.5878423","0.58601016","0.5842212","0.5834388","0.5834388","0.58281857","0.582373","0.5817754","0.58143765","0.5812908","0.5810157","0.58090013","0.5807079","0.5802897","0.57874876"],"string":"[\n \"0.80243504\",\n \"0.76661944\",\n \"0.75472015\",\n \"0.74645776\",\n \"0.7437857\",\n \"0.73945224\",\n \"0.7392207\",\n \"0.7350186\",\n \"0.73374736\",\n \"0.7213724\",\n \"0.7169046\",\n \"0.7097468\",\n \"0.70568234\",\n \"0.7049625\",\n \"0.7021389\",\n \"0.6937337\",\n \"0.6860303\",\n \"0.68288875\",\n \"0.66705036\",\n \"0.66682374\",\n \"0.6655914\",\n \"0.6644716\",\n \"0.6638835\",\n \"0.6615325\",\n \"0.6613023\",\n \"0.6585681\",\n \"0.6582362\",\n \"0.65554535\",\n \"0.64984536\",\n \"0.6465046\",\n \"0.6465046\",\n \"0.64295316\",\n \"0.63401586\",\n \"0.63341874\",\n \"0.63140225\",\n \"0.63051146\",\n \"0.6295046\",\n \"0.6293342\",\n \"0.62801975\",\n \"0.6274929\",\n \"0.6269015\",\n \"0.62609327\",\n \"0.62301165\",\n \"0.6215816\",\n \"0.62128747\",\n \"0.62060046\",\n \"0.61881125\",\n \"0.6180322\",\n \"0.615874\",\n \"0.61575896\",\n \"0.6141532\",\n \"0.61387306\",\n \"0.6135455\",\n \"0.6125309\",\n \"0.6117584\",\n \"0.60996103\",\n \"0.60882944\",\n \"0.60879344\",\n \"0.6045197\",\n \"0.6035636\",\n \"0.60227555\",\n \"0.60199535\",\n \"0.6019015\",\n \"0.5991061\",\n \"0.5988956\",\n \"0.5978193\",\n \"0.5967327\",\n \"0.5958416\",\n \"0.59559345\",\n \"0.59512156\",\n \"0.5948521\",\n \"0.594162\",\n \"0.5941048\",\n \"0.594101\",\n \"0.5937671\",\n \"0.5931661\",\n \"0.59311205\",\n \"0.59218013\",\n \"0.5911772\",\n \"0.5909889\",\n \"0.5909889\",\n \"0.5909889\",\n \"0.5901092\",\n \"0.58848137\",\n \"0.58822364\",\n \"0.5878423\",\n \"0.58601016\",\n \"0.5842212\",\n \"0.5834388\",\n \"0.5834388\",\n \"0.58281857\",\n \"0.582373\",\n \"0.5817754\",\n \"0.58143765\",\n \"0.5812908\",\n \"0.5810157\",\n \"0.58090013\",\n \"0.5807079\",\n \"0.5802897\",\n \"0.57874876\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6485079"},"document_rank":{"kind":"string","value":"29"}}},{"rowIdx":77499,"cells":{"query":{"kind":"string","value":"Connect to the resource manager returns the visa avalable resources"},"document":{"kind":"string","value":"def resource_manager():\n return visa.ResourceManager()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def resources(self):\n return self.__resources","def resources(self):","def resources(self):\n return self._resources","def resources(self):\n return self._resources","def resources(self):\n return self._resources","def resources(self) -> pulumi.Output[Sequence['outputs.MachineExtensionResponse']]:\n return pulumi.get(self, \"resources\")","def resources():\n check_resources()","def resources(self) -> Sequence['outputs.GetResourcesResourceResult']:\n return pulumi.get(self, \"resources\")","def get_resources(self):\n return []","def set_resources():\n global available_resources\n global EdgenodeResources\n recv_json = request.get_json()\n for resourcename, value in recv_json.items():\n available_resources[resourcename] = value\n # TODO make this better\n EdgenodeResources = [TaskResources(ram=int(available_resources['RAM']), cpu=int(\n available_resources['CPU']), hdd=int(available_resources['HDD'])), available_resources['DATA']]\n\n print 'Available resources set to', EdgenodeResources\n return 'Available resources set to ' + str(available_resources)","def resources(self):\r\n return self.page.object_list","def resources(self) -> \"Resources\":\n return self._resources","def getResources(self):\n\t\treturn deepcopy(self.server.resources)","def resources(self):\n return [self]","def resources(self):\n return list(self.get_resources_for_type(gdef.ResType_All))","def GetAllResourcesSample():\n client = CreateClient()\n # Unlike client.GetResources, this returns a list of resources\n for resource in client.GetAllResources():\n PrintResource(resource)","def create_resources(self) -> List[ResourceDescription]:\r\n return self.resources","def get_resources():\n return Response(f\"{Resource.get_all_resources()}\", 200, mimetype='text/plain')","def get_resources():\n global __res\n if __res == None:\n __init_resources()\n return __res","def get_resources():\n global __res\n if __res == None:\n __init_resources()\n return __res","def get_resources():\n global __res\n if __res == None:\n __init_resources()\n return __res","def get_resources():\r\n global __res\r\n if __res == None:\r\n __init_resources()\r\n return __res","def get_resources():\r\n global __res\r\n if __res == None:\r\n __init_resources()\r\n return __res","def get_resources():\r\n global __res\r\n if __res == None:\r\n __init_resources()\r\n return __res","def list(self):\n resources = self._os_resource_manager.list()\n resource_list = []\n for resource in resources:\n resource_list.append(self._resource_class(id=resource.id,\n name=resource.name))\n return resource_list","def resources(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"resources\")","def resources(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"resources\")","def resources(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"resources\")","def resources(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"resources\")","def __init__(self):\n import visa\n\n rm = visa.ResourceManager()\n target = 'Agilent Technologies,8163B,MY48208514,V5.25(72637)'\n\n for dev in rm.list_resources():\n try:\n inst = rm.open_resource(dev)\n name = inst.query('*IDN?') # Agilent Technologies,8163B,MY48208514,V5.25(72637)\n if target in name:\n # TODO: check that the slot contains the correct module\n self._inst = inst\n except:\n continue\n\n if self._inst is None:\n raise RuntimeError(\"Target resource {} cannot be found in the VISA resource manager\".format(target))\n print(\"Connected to \" + self.id())","def resources(self):\n\n return self.FIXTURE.resources_collection(self)","def list_resources(self):\n self.workersResponded = 0\n print(\"Main thread\", threading.get_ident())\n\n for addr in self.equipment_model.get_addr_list():\n self.equipment_model.reset_index(addr)\n self.equipment_model.set_connected(addr, 2)\n\n if not self.worker_pool.is_init():\n w = self.worker_pool.create_worker(addr)\n #Signals from worker\n w.signal_connected.connect(self.slot_connected)\n w.signal_not_connected.connect(self.slot_not_connected)\n w.signal_write_success.connect(self.parent.slot_write_success)\n w.signal_query_success.connect(self.parent.slot_query_success)\n w.signal_error.connect(self.parent.slot_error)\n\n self.next_connection(addr)\n \n self.worker_pool.set_init(True)","def resources(self):\n res = []\n for resource in self._resources:\n res = res + resource.resources()\n\n return res","def resources(self):\n res = []\n for resource in self._resources:\n res = res + resource.resources()\n\n return res","def test_get_virtualization_realm_resources(self):\n pass","def cmd_list_resources(config=DEFAULT_LINUX_PATH):\n config = load_config_file(expand_config_path(config))\n px = connection_proxmox(config[\"proxmox\"])\n try:\n if config[\"pools\"]:\n l, h = list_resources(px, config[\"pools\"])\n return tabulate(l, h)\n else:\n print(\"Dick 'pools' is empty\")\n except KeyError:\n print(\"Missing 'pools' dict in config file\")\n sys.exit(1)","def get_all_resources(self) -> typing.List:\n\n session = self.session()\n\n try:\n available_peers = session\\\n .query(\n ResourceTable.peerIp,\n ResourceTable.peerPort,\n ResourceTable.resourcePath,\n ResourceTable.resourceName,\n ResourceTable.resourceHash\n )\\\n .group_by(ResourceTable.peerId, ResourceTable.resourceHash)\\\n .all()\n\n return available_peers\n\n finally:\n session.close()","def test_show_vcs_resources(mgmt_session):\n vcs_resource = rift.vcs.vcs.VcsResource(mgmt_session)\n vcs_resource_info = None\n\n # Get vcs resources\n vcs_resource_info = vcs_resource.get_vcs_resource()\n\n # Verify there are VM entries in the vcs resource info container\n vms = [vm for vm in vcs_resource_info.vm]\n if len(vms) == 0:\n raise AssertionError(\"No entries found in vcs resource info\")","def GetResourcesSample():\n client = CreateClient()\n # Get a feed and print it\n feed = client.GetResources()\n PrintFeed(feed)","def get_resources(genome_build, fasta_ref, data):\n config = tz.get_in([\"config\", CONFIG_KEY], data)\n find_fn = _find_file(config)\n def normalize(f):\n return _get_id_fname(f)[-1]\n return sret.get_resources(genome_build, fasta_ref, config,\n data, _open_remote, _list(config), find_fn, normalize)","def get_resources():\n user_id = session[\"email\"]\n resources = fm.get_resources(user_id)\n returned_val = dict(resources=resources)\n return jsonify(returned_val)","def get_resources(self, **extra_args):\n return [lrms for lrms in self.resources.itervalues()]","def _get_all_resources(self):\n all_resources = []\n for resource in ResourceModel.scan():\n all_resources.append(resource)\n return all_resources","def show_instances():\n return get_instances()","def ResourceList(self):\n url = AddToUrl(self, 'https://api.spiget.org/v2/resources?')\n return ApiSearch(url)","def get_resources(self):\n return list(self.get_inputs()) + list(self.get_outputs())","def get_indexes(self):\n resources = []\n\n data = self.get_resource_data(type='sources')\n data['url'] = constants.URLS['sources'] % data\n resources.append(data)\n\n for arch in self.data['arch']:\n data = self.get_resource_data(type='packages', arch=arch)\n data['url'] = constants.URLS['packages'] % data\n resources.append(data)\n return resources","def get_pecan_resources(self):\n return []","def get_available_resource(self, nodename):\n LOG.debug(\"get_available_resource\")\n\n dictval = self._host.properties\n\n return dictval","def resources(self) -> [ListResourcesResponse]:\n resources = []\n\n layers = self.layers\n for layer_name in layers:\n layer_arns = self.layer_version_arns(layer_name)\n for arn in layer_arns:\n list_resources_response = ListResourcesResponse(\n service=self.service, account_id=self.current_account_id, arn=arn, region=self.region,\n resource_type=self.resource_type, name=layer_name)\n resources.append(list_resources_response)\n return resources","def print_resources(self) -> None:\n for resource in self._request_get(self.url_base + 'documentacao'):\n print(\n \"Nome: {},\\nUrl: {},\\n\".format(\n resource['name'],\n self._format_url_to_resource(resource['url']),\n )\n )","def init_physical_resources():\n test_physical_resources = []\n\n # add info to list in memory, one by one, following signature values\n phys_resrc_ID = 1\n phys_resrc_name = \"small-cavium-1\"\n phys_resrc_info = \"Jump server in Arm pod, 48 cores, 64G RAM, 447G SSD, aarch64 Cavium ThunderX, Ubuntu OS\"\n phys_resrc_IPAddress = \"10.10.50.12\"\n phys_resrc_MACAddress = \"00-14-22-01-23-45\"\n\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\n phys_resrc_info,\n phys_resrc_IPAddress,\n phys_resrc_MACAddress))\n\n phys_resrc_ID = 2\n phys_resrc_name = \"medium-cavium-1\"\n phys_resrc_info = \"Jump server in New York pod, 96 cores, 64G RAM, 447G SSD, aarch64 Cavium ThunderX, Ubuntu OS\"\n phys_resrc_IPAddress = \"30.31.32.33\"\n phys_resrc_MACAddress = \"0xb3:22:05:c1:aa:82\"\n\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\n phys_resrc_info,\n phys_resrc_IPAddress,\n phys_resrc_MACAddress))\n\n phys_resrc_ID = 3\n phys_resrc_name = \"mega-cavium-666\"\n phys_resrc_info = \"Jump server in Las Vegas, 1024 cores, 1024G RAM, 6666G SSD, aarch64 Cavium ThunderX, Ubuntu OS\"\n phys_resrc_IPAddress = \"54.53.52.51\"\n phys_resrc_MACAddress = \"01-23-45-67-89-ab\"\n\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\n phys_resrc_info,\n phys_resrc_IPAddress,\n phys_resrc_MACAddress))\n\n\n # write list to binary file\n write_list_bin(test_physical_resources, FILE_PHYSICAL_RESOURCES)\n\n return test_physical_resources","def getResources(self, folder):\n\n #-------------------- \n # Get the resource JSON\n #-------------------- \n folder += \"/resources\"\n resources = self.__getJson(folder)\n #print(\"%s %s\"%(, folder))\n #print(\" Got resources: '%s'\"%(str(resources)))\n\n\n\n #-------------------- \n # Filter the JSONs\n #-------------------- \n resourceNames = []\n for r in resources:\n if 'label' in r:\n resourceNames.append(r['label'])\n #print(\"FOUND RESOURCE ('%s') : %s\"%(folder, r['label']))\n elif 'Name' in r:\n resourceNames.append(r['Name'])\n #print(\"FOUND RESOURCE ('%s') : %s\"%(folder, r['Name']))\n\n return resourceNames","async def websocket_lovelace_resources(\n hass: HomeAssistant,\n connection: websocket_api.ActiveConnection,\n msg: dict[str, Any],\n) -> None:\n resources = hass.data[DOMAIN][\"resources\"]\n\n if not resources.loaded:\n await resources.async_load()\n resources.loaded = True\n\n connection.send_result(msg[\"id\"], resources.async_items())","def get_resources():\n # Acquire the lock...\n get_resources_lock.acquire()\n\n # ...but always release it\n try:\n # Construct the dictionaries as copies from nanny\n (limits,usage) = nanny.get_resource_information()\n\n\n # Calculate all the usage's\n pid = os.getpid()\n\n # Get CPU and memory, this is thread specific\n if ostype in [\"Linux\", \"Darwin\"]:\n \n # Get CPU first, then memory\n usage[\"cpu\"] = os_api.get_process_cpu_time(pid)\n\n # This uses the cached PID data from the CPU check\n usage[\"memory\"] = os_api.get_process_rss()\n\n # Get the thread specific CPU usage\n usage[\"threadcpu\"] = os_api.get_current_thread_cpu_time() \n\n\n # Windows Specific versions\n elif ostype in [\"Windows\"]:\n \n # Get the CPU time\n usage[\"cpu\"] = windows_api.get_process_cpu_time(pid)\n\n # Get the memory, use the resident set size\n usage[\"memory\"] = windows_api.process_memory_info(pid)['WorkingSetSize'] \n\n # Get thread-level CPU \n usage[\"threadcpu\"] = windows_api.get_current_thread_cpu_time()\n\n # Unknown OS\n else:\n raise EnvironmentError(\"Unsupported Platform!\")\n\n # Use the cached disk used amount\n usage[\"diskused\"] = cached_disk_used\n\n finally:\n # Release the lock\n get_resources_lock.release()\n\n # Copy the stop times\n stoptimes = process_stopped_timeline[:]\n\n # Return the dictionaries and the stoptimes\n return (limits,usage,stoptimes)","def register_resources(self):\n raise NotImplementedError","def getCatalogs():","def get_logical_links(self, osdf_config):\n\n config = osdf_config.deployment\n aai_url = config[\"aaiUrl\"]\n aai_req_url = aai_url + config[\"aaiGetLinksUrl\"]\n\n response = requests.get(aai_req_url,headers=self.aai_headers,auth=HTTPBasicAuth(\"AAI\", \"AAI\"),verify=False)\n if response.status_code == 200:\n return response.json()","def load_all_resources():\n\n # Load the fonts\n ResourcesManager._load_font(\"Munro.ttf\")\n\n # Load images\n ResourcesManager.HIBER_NATION_IMG = ResourcesManager._load_image(\"hiber_nation.png\")\n ResourcesManager.SHIP_IMG = ResourcesManager._load_image(\"ship.png\")\n ResourcesManager.MISSILE_IMG = ResourcesManager._load_image(\"missile.png\")\n\n # Load sounds\n # ResourcesManager.MENU_MUSIC = ResourcesManager._load_sound(\"menu.ogg\")","def sx_get_active_vrids(handle): \n try:\n vrid_list = []\n \n vrid_cnt_p = new_uint32_t_p()\n uint32_t_p_assign(vrid_cnt_p, 0)\n vrid_key_p = new_sx_router_id_t_p()\n sx_router_id_t_p_assign(vrid_key_p, 0)\n vrid_key = sx_router_id_t_p_value(vrid_key_p)\n \n rc = sx_api_router_vrid_iter_get(handle, SX_ACCESS_CMD_GET, vrid_key, None, None, vrid_cnt_p)\n sx_check_rc(rc)\n \n vrid_cnt = uint32_t_p_value(vrid_cnt_p)\n vrid_list_p = new_sx_router_id_t_arr(vrid_cnt)\n \n rc = sx_api_router_vrid_iter_get(handle, SX_ACCESS_CMD_GET_FIRST, vrid_key, None, vrid_list_p, vrid_cnt_p)\n sx_check_rc(rc)\n \n vrid_cnt = uint32_t_p_value(vrid_cnt_p)\n for i in range(0, vrid_cnt):\n vrid = sx_router_id_t_arr_getitem(vrid_list_p, i)\n vrid_list.append(vrid)\n\n return vrid_list\n\n finally:\n delete_sx_router_id_t_arr(vrid_list_p)\n delete_sx_router_id_t_p(vrid_key_p)\n delete_uint32_t_p(vrid_cnt_p)","def ls():\n # TODO: listing all availabe containers form sequence\n return","def test_get_cloud_resources(self):\n pass","def _get_resources():\n return {\n 'searchPageUrl': flask.url_for('search_page'),\n 'searchJsonUrl': flask.url_for('search'),\n 'userAddIconUrl': flask.url_for('static', filename='img/add-users.svg'),\n 'logoutUrl': flask.url_for('logout'),\n 'settingsUrl': flask.url_for('setup') + '#settingsDisplayTemplate',\n 'listAdminUrl': flask.url_for('admin_list'),\n 'addAdminUrl': flask.url_for('add_admin'),\n 'changeAdminPasswordUrl': flask.url_for('change_admin_password'),\n 'removeAdminUrl': flask.url_for('delete_admin'),\n 'loginUrl': flask.url_for('login'),\n 'recaptchaKey': ufo.app.config.get('RECAPTCHA_SITE_KEY', ''),\n 'setupUrl': flask.url_for('setup'),\n 'setupAdminUrl': flask.url_for('setup_admin'),\n 'setupOauthUrl': flask.url_for('setup_oauth'),\n 'download_chrome_policy': flask.url_for('download_chrome_policy'),\n 'policy_filename': 'chrome_policy.json',\n 'proxyServerAddUrl': flask.url_for('proxyserver_add'),\n 'proxyServerAddIconUrl': flask.url_for('static',\n filename='img/add-servers.svg'),\n 'proxyServerInverseAddIconUrl': flask.url_for(\n 'static', filename='img/add-servers-inverse.svg'),\n 'proxyServerListId': 'proxyList',\n 'proxyServerListUrl': flask.url_for('proxyserver_list'),\n 'listLimit': 10,\n 'proxyServerDetailsButtonId': 'serverDetailsButton',\n 'editButtonId': 'serverEditButton',\n 'proxyServerDetailsOverlayId': 'serverDetailsOverlay',\n 'proxyServerEditUrl': flask.url_for('proxyserver_edit'),\n 'proxyServerDeleteUrl': flask.url_for('proxyserver_delete'),\n 'proxyServerIconUrl': flask.url_for('static', filename='img/server.svg'),\n 'proxyServerAddButtonId': 'addServerButton',\n 'proxyServerModalId': 'serverModal',\n 'textAreaMaxRows': 10,\n 'ipInput': 'ipInput',\n 'nameInput': 'nameInput',\n 'sshPrivateKeyInput': 'sshPrivateKeyInput',\n 'hostPublicKeyInput': 'hostPublicKeyInput',\n 'getSettingsUrl': flask.url_for('get_settings'),\n 'settingsEditUrl': flask.url_for('edit_settings'),\n 'userAddUrl': flask.url_for('add_user'),\n 'userInverseAddIconUrl': flask.url_for(\n 'static', filename='img/add-users-inverse.svg'),\n 'userListId': 'userList',\n 'userListUrl': flask.url_for('user_list'),\n 'revokeToggleUrl': flask.url_for('user_toggle_revoked'),\n 'rotateKeysUrl': flask.url_for('user_get_new_key_pair'),\n 'inviteCodeUrl': flask.url_for('user_get_invite_code'),\n 'userDeleteUrl': flask.url_for('delete_user'),\n 'userDetailsButtonId': 'userDetailsButton',\n 'userDetailsOverlayId': 'userDetailsOverlay',\n 'userIconUrl': flask.url_for('static', filename='img/user.svg'),\n 'userAddButtonId': 'addUserButton',\n 'userModalId': 'userModal',\n 'groupAddTabId': 'groupAddTab',\n 'groupAddFormId': 'groupAdd',\n 'groupAddInputName': 'group_key',\n 'userAddTabId': 'userAddTab',\n 'userAddFormId': 'userAdd',\n 'userAddInputName': 'user_key',\n 'domainAddTabId': 'domainAddTab',\n 'domainAddFormId': 'domainAdd',\n 'manualAddTabId': 'manualAddTab',\n 'manualAddFormId': 'manualAdd',\n 'regexes': regex.REGEXES_AND_ERRORS_DICTIONARY,\n 'jsonPrefix': ufo.XSSI_PREFIX,\n 'maxFailedLoginsBeforeRecaptcha': ufo.MAX_FAILED_LOGINS_BEFORE_RECAPTCHA,\n 'userAddListFlipperId': 'userAddListFlipper',\n 'proxyServerAddListFlipperId': 'proxyServerAddListFlipper',\n 'userAddTabsId': 'userAddTabs',\n 'proxyServerAddFormId': 'serverAddFormHolder',\n }","def create_external_resources(self) -> List[ResourceDescription]:\r\n return effects.get_effect_resources()","def getResources( dummy = False , retryPeriod = 0.5 ) :\n\n resources = {}\n\n\n # --------------------------------------------------\n # Add GPS\n # --------------------------------------------------\n import Gps\n if Config.IS_MONSTRO :\n resources.update({ \"gps\" : Gps.getGps( dummy = dummy ) })\n else :\n resources.update({ \"gps\" : Gps.getGps( dummy = True ) })\n\n\n if not dummy :\n\n # --------------------------------------------------\n # Add roboMote\n # --------------------------------------------------\n import RoboMote\n\n # find the com ports associated with motes\n done = False\n while not done :\n\n moteComs = findMotes()\n if (not moteComs) and (retryPeriod > 0) :\n time.sleep(retryPeriod)\n continue\n\n roboMote = None\n for i in range(len(moteComs)-1,-1,-1) :\n moteCom = moteComs[i]\n if RoboMote.isRoboMote( moteCom ) :\n del moteComs[i] \n roboMote = RoboMote.RoboMote( moteCom )\n done = True\n break\n\n if not roboMote :\n if ( retryPeriod > 0 ) :\n time.sleep(retryPeriod)\n continue\n else :\n raise RoboMote.RoboMoteException , \"Could not connect to the mote providing RoboMote\"\n\n # Add the roboMote to the resource list\n resources.update({ \"roboMote\" : roboMote })\n\n\n return resources","def show_networks():\n return get_networks()","async def get_resources(self, **kwargs) -> dict:\n resources = await self.request.get(self._base_path, **kwargs)\n self._sanitize_resources(resources)\n return resources","def show_resource_pool(client, private_cloud, resource_pool, location):\n return client.get(location, private_cloud, resource_pool)","def get_available_resource(self, nodename):\n curent_time = time.time()\n if curent_time - self.cleanup_time > CONF.azure.cleanup_span:\n self.cleanup_time = curent_time\n self._cleanup_deleted_os_disks()\n self._cleanup_deleted_nics()\n usage_family = 'basicAFamily'\n try:\n page = self.compute.usage.list(CONF.azure.location)\n except Exception as e:\n msg = six.text_type(e)\n LOG.exception(msg)\n ex = exception.ComputeUsageListFailure(reason=six.text_type(e))\n raise ex\n usages = [i for i in page]\n cores = 0\n cores_used = 0\n for i in usages:\n if hasattr(i, 'name') and hasattr(i.name, 'value'):\n if usage_family == i.name.value:\n cores = i.limit if hasattr(i, 'limit') else 0\n cores_used = i.current_value \\\n if hasattr(i, 'current_value') else 0\n break\n return {'vcpus': cores,\n 'memory_mb': 100000000,\n 'local_gb': 100000000,\n 'vcpus_used': cores_used,\n 'memory_mb_used': 0,\n 'local_gb_used': 0,\n 'hypervisor_type': hv_type.HYPERV,\n 'hypervisor_version': 300,\n 'hypervisor_hostname': nodename,\n 'cpu_info': '{\"model\": [\"Intel(R) Xeon(R) CPU E5-2670 0 @ '\n '2.60GHz\"], \"topology\": {\"cores\": 16, \"threads\": '\n '32}}',\n 'supported_instances': [(arch.I686, hv_type.HYPERV,\n vm_mode.HVM),\n (arch.X86_64, hv_type.HYPERV,\n vm_mode.HVM)],\n 'numa_topology': None\n }","def _get_info_about_available_resources(self, min_ram, min_hdd, min_vcpus):\n vms_count = 0\n for hypervisor in self.nova_cli.hypervisors.list():\n if hypervisor.free_ram_mb >= min_ram:\n if hypervisor.free_disk_gb >= min_hdd:\n if hypervisor.vcpus - hypervisor.vcpus_used >= min_vcpus:\n # We need to determine how many VMs we can run\n # on this hypervisor\n free_cpu = hypervisor.vcpus - hypervisor.vcpus_used\n k1 = int(hypervisor.free_ram_mb / min_ram)\n k2 = int(hypervisor.free_disk_gb / min_hdd)\n k3 = int(free_cpu / min_vcpus)\n vms_count += min(k1, k2, k3)\n return vms_count","def _get_resources(self):\n while not self.is_stopped():\n pools = self.poolsonline.get_pools()\n tx_source = self.txonline.get_txsource()\n mempoolstate = self.mempool.state\n if mempoolstate and pools and tx_source:\n return pools, tx_source, mempoolstate\n # Resources aren't available due to some error elsewhere,\n # so get rid of stats to avoid giving stale stats to others.\n self.stats = None\n self.sleep(5)\n raise StopIteration","def views(self):\r\n return resources.Views(self)","def list_resource_pool(client, private_cloud, location):\n return client.list(location, private_cloud)","def GetResourceAclSample():\n client = CreateClient()\n for resource in client.GetResources(limit=5).entry:\n acl_feed = client.GetResourceAcl(resource)\n for acl in acl_feed.entry:\n print acl.role.value, acl.scope.type, acl.scope.value","def test_list_resources(self, rabbitmq):\n assert rabbitmq.list_vhosts() == ['/vhost']\n assert rabbitmq.list_users() == [('user', ['administrator'])]\n assert rabbitmq.list_queues() == []","def add_resources(event):\n anuket_resources.need()","def list():\n rino.remote.list()","def view(self, *args, **kwargs):\n return self._resources_manager.view(*args, **kwargs)","def dedicated_resources(self) -> Optional[Sequence['outputs.AiEndpointDeployedModelDedicatedResource']]:\n return pulumi.get(self, \"dedicated_resources\")","def list(self, **kwargs):\n\n return self.getResourceManager() \\\n .getSdk() \\\n .hosts \\\n .list(**kwargs)","def getResource(self):\n pass;","def find_resources(self):\n path = self.get_path('resources')\n\n if not path:\n rsoup = self.soup\n else:\n rsoup = BeautifulSoup(open(path))\n\n self.parse_resources(rsoup)","def get_resources(self, resource_type=None):\n for application in self.applications:\n for r in application.get_resources(resource_type):\n yield r\n\n for r in BaseResourceContainer.get_resources(self, resource_type):\n yield r","def get_available_atlases_cli():\n from .roi_extractor import get_available_atlases\n get_available_atlases()","def resource_map(self):","def _load_resources(self):\n puts = (getattr(self, 'project', None) or self).puts\n for resource_type, resource_cls in six.iteritems(AVAILABLE_RESOURCES):\n for name in self.settings.get(resource_type, {}):\n extra = {\n 'project': getattr(self, 'project', None) or self,\n 'app': self if hasattr(self, 'project') else None,\n }\n\n with indent(4 if hasattr(self, 'project') else 2):\n puts(colored.green(u\"✓ {}:{}\".format(resource_type, name)))\n\n self._resources[resource_type].append(\n resource_cls.factory(\n name=name,\n settings=self.settings.get(resource_type, {})[name],\n **extra\n )\n )","def query_resources(self, request):\n desc = ResourceDescriptor.decode(request.message.descriptors)\n self.logger.debug(\"Looking for resources with description %r\", desc)\n\n # query for resources that are usable and match the descriptors\n query = (Q(is_usable=True, **desc.properties))\n matches = desc.type.objects.filter(query)\n\n if matches.count() == 0:\n raise ResourceDoesNotExistError(\"No existing resource meets \"\n \"the requirements: %r\" % desc)\n\n query_result = [resource for resource in matches]\n\n return ResourcesReply(resources=query_result)","def list_resources(px, pools):\n result = []\n for pool in pools:\n for i in px.pools.get(pool)[\"members\"]:\n result.append(\n {\n \"pool\": pool,\n \"vmid\": i[\"vmid\"],\n \"name\": i[\"name\"],\n \"status\": i[\"status\"],\n \"type\": i[\"type\"],\n }\n )\n return result, {\n \"pool\": \"pool(s)\",\n \"vmid\": \"vmid\",\n \"name\": \"name\",\n \"status\": \"status\",\n \"type\": \"type\",\n }","def get_resource_urls():\n base_url = 'http://developer.pardot.com/'\n pattern = re.compile(\n r'(?ims)\\<a [^>]*?href=\"(kb/api-version-3/[^>]*?/)\"[^>]*?\\>'\n r'[^<]*?\\</a\\>')\n response = requests.get(base_url)\n return [\n '%s/%s' % (base_url, url) for url in pattern.findall(response.text)]","def dashboards(self):\r\n return resources.Dashboards(self)","def init_cloud_virtual_resources():\n test_cldvirt_resources = []\n\n # add info to list in memory, one by one, following signature values\n cldvirtres_ID = 1\n cldvirtres_name = \"nova-compute-1\"\n cldvirtres_info = \"nova VM in Arm pod\"\n cldvirtres_IPAddress = \"50.60.70.80\"\n cldvirtres_URL = \"http://50.60.70.80:8080\"\n cldvirtres_related_phys_rsrcIDs = [1,3]\n\n test_cldvirt_resources.append(CloudVirtualResource(cldvirtres_ID, cldvirtres_name,\n cldvirtres_info,\n cldvirtres_IPAddress,\n cldvirtres_URL,\n cldvirtres_related_phys_rsrcIDs))\n\n cldvirtres_ID = 2\n cldvirtres_name = \"nova-compute-2\"\n cldvirtres_info = \"nova VM in LaaS\"\n cldvirtres_IPAddress = \"50.60.70.80\"\n cldvirtres_URL = \"http://50.60.70.80:8080\"\n cldvirtres_related_phys_rsrcIDs = [2,3]\n\n test_cldvirt_resources.append(CloudVirtualResource(cldvirtres_ID, cldvirtres_name,\n cldvirtres_info,\n cldvirtres_IPAddress,\n cldvirtres_URL,\n cldvirtres_related_phys_rsrcIDs))\n\n cldvirtres_ID = 3\n cldvirtres_name = \"nova-compute-3\"\n cldvirtres_info = \"nova VM in x86 pod\"\n cldvirtres_IPAddress = \"50.60.70.80\"\n cldvirtres_URL = \"http://50.60.70.80:8080\"\n cldvirtres_related_phys_rsrcIDs = [1]\n\n test_cldvirt_resources.append(CloudVirtualResource(cldvirtres_ID, cldvirtres_name,\n cldvirtres_info,\n cldvirtres_IPAddress,\n cldvirtres_URL,\n cldvirtres_related_phys_rsrcIDs))\n\n\n # write list to binary file\n write_list_bin(test_cldvirt_resources, FILE_CLOUD_RESOURCES)\n\n return test_cldvirt_resources","def get_resources(self, resource_url):\n response = self.response(resource_url)\n body = response[0]\n return ResourceParser.extract_resources(body)","def has_resources(self):\r\n return hasattr(self, 'resources') and self.resources","def test_get_deployment_resources(self):\n pass","def get_resources(self):\n res = set()\n res.update(self.get_inputs())\n res.update(self.get_outputs())\n return res","def get_inventory(self, context):\n # See below some example code demonstrating how to return the resource structure\n # and attributes. In real life, of course, if the actual values are not static,\n # this code would be preceded by some SNMP/other calls to get the actual resource information\n '''\n # Add sub resources details\n sub_resources = [ AutoLoadResource(model ='Generic Chassis',name= 'Chassis 1', relative_address='1'),\n AutoLoadResource(model='Generic Module',name= 'Module 1',relative_address= '1/1'),\n AutoLoadResource(model='Generic Port',name= 'Port 1', relative_address='1/1/1'),\n AutoLoadResource(model='Generic Port', name='Port 2', relative_address='1/1/2'),\n AutoLoadResource(model='Generic Power Port', name='Power Port', relative_address='1/PP1')]\n\n\n attributes = [ AutoLoadAttribute(relative_address='', attribute_name='Location', attribute_value='Santa Clara Lab'),\n AutoLoadAttribute('', 'Model', 'Catalyst 3850'),\n AutoLoadAttribute('', 'Vendor', 'Cisco'),\n AutoLoadAttribute('1', 'Serial Number', 'JAE053002JD'),\n AutoLoadAttribute('1', 'Model', 'WS-X4232-GB-RJ'),\n AutoLoadAttribute('1/1', 'Model', 'WS-X4233-GB-EJ'),\n AutoLoadAttribute('1/1', 'Serial Number', 'RVE056702UD'),\n AutoLoadAttribute('1/1/1', 'MAC Address', 'fe80::e10c:f055:f7f1:bb7t16'),\n AutoLoadAttribute('1/1/1', 'IPv4 Address', '192.168.10.7'),\n AutoLoadAttribute('1/1/2', 'MAC Address', 'te67::e40c:g755:f55y:gh7w36'),\n AutoLoadAttribute('1/1/2', 'IPv4 Address', '192.168.10.9'),\n AutoLoadAttribute('1/PP1', 'Model', 'WS-X4232-GB-RJ'),\n AutoLoadAttribute('1/PP1', 'Port Description', 'Power'),\n AutoLoadAttribute('1/PP1', 'Serial Number', 'RVE056702UD')]\n\n return AutoLoadDetails(sub_resources,attributes)\n '''\n\n self._log(context, 'Begin autoload')\n resources = []\n attributes = []\n\n\n attributes.append(AutoLoadAttribute('', 'replication_address', self.get_replication_address(context)))\n attributes.append(AutoLoadAttribute('', 'connection_key', self.get_connection_key(context)))\n\n networks = self._get_newtork_interfaces(context)\n self._log(context, 'got networks')\n\n controllers = self._get_controllers(context)\n self._log(context, 'got controllers')\n ports = self._get_ports(context)\n\n model = None\n for controller in controllers:\n self._log(context, 'Processing ctrlt: ' + controller['name'] + ':' + controller['model'])\n resources.append(AutoLoadResource(model='Generic Storage Controller', name=controller['name'],\n relative_address=controller['name']))\n if model is None:\n model = controller['model']\n\n attributes.append(AutoLoadAttribute('', 'Model', model))\n\n for network in networks:\n self._log(context, 'Processing netwk: ' + network['name'] + ':' + str(network['address']))\n net_name = network['name']\n controller = net_name.split('.')[0]\n if 'vir0' in controller or 'vir1' in controller:\n attributes.append(AutoLoadAttribute('',str(controller + '_address'), str(network['address'])))\n continue\n if 'vir' in controller:\n continue\n if 'management' not in network['services']:\n continue\n resources.append(AutoLoadResource(model='Storage Network Port', name=net_name,\n relative_address=controller.upper() + '/' + str(network['address'])))\n\n for port in ports:\n if port['iqn'] is not None:\n port_name = port['name']\n controller = port_name.split('.')[0]\n resources.append(AutoLoadResource(model='iSCSI Storage Port', name=port['name'],\n relative_address=controller + '/' + port['portal']))\n attributes.append(AutoLoadAttribute(controller + '/' + port['portal'], 'iqn', port['iqn']))\n elif port['wwn'] is not None:\n port_name = port['name']\n controller = port_name.split('.')[0]\n resources.append(AutoLoadResource(model='FC Storage Port', name=port['name'],\n relative_address=controller + '/' + port['name'].split('.')[1]))\n attributes.append(AutoLoadAttribute(controller + '/' + port['name'].split('.')[1], 'wwn', port['wwn']))\n\n return AutoLoadDetails(resources, attributes)","def test_ListResources(self, zipped=False):\n from openflow.dummyom.models import DummyOM\n slice_urn, cred = self.create_ch_slice()\n options = dict(geni_compressed=zipped, geni_available=True)\n rspec = wrap_xmlrpc_call(\n self.am_client.ListResources,\n [cred, options], {}, settings.TIMEOUT)\n \n logger.debug(\"Got Advertisement RSpec: \\n%s\" % rspec)\n \n if zipped:\n import zlib, base64\n rspec = zlib.decompress(base64.b64decode(rspec))\n \n # Create switches and links\n self.switches, self.links = parse_rspec(rspec)\n \n # check the number of switches and links\n num_links = sum([len(d.get_switches()) for d in DummyOM.objects.all()])\n self.assertEqual(len(self.switches),\n num_links)\n self.assertEqual(len(self.links),\n settings.NUM_LINKS_PER_AGG * settings.NUM_DUMMY_OMS)","def resource_navigation_links(self) -> Sequence['outputs.ResourceNavigationLinkResponse']:\n return pulumi.get(self, \"resource_navigation_links\")","def get_transifex_resources(self):\n RESOURCES_API_25 = (\n f\"organizations/{self.organization_slug}/projects/\"\n f\"{self.project_slug}/resources/\"\n )\n return self.request25(\"get\", RESOURCES_API_25).json()","def automatic_resources(self) -> Optional[Sequence['outputs.AiEndpointDeployedModelAutomaticResource']]:\n return pulumi.get(self, \"automatic_resources\")"],"string":"[\n \"def resources(self):\\n return self.__resources\",\n \"def resources(self):\",\n \"def resources(self):\\n return self._resources\",\n \"def resources(self):\\n return self._resources\",\n \"def resources(self):\\n return self._resources\",\n \"def resources(self) -> pulumi.Output[Sequence['outputs.MachineExtensionResponse']]:\\n return pulumi.get(self, \\\"resources\\\")\",\n \"def resources():\\n check_resources()\",\n \"def resources(self) -> Sequence['outputs.GetResourcesResourceResult']:\\n return pulumi.get(self, \\\"resources\\\")\",\n \"def get_resources(self):\\n return []\",\n \"def set_resources():\\n global available_resources\\n global EdgenodeResources\\n recv_json = request.get_json()\\n for resourcename, value in recv_json.items():\\n available_resources[resourcename] = value\\n # TODO make this better\\n EdgenodeResources = [TaskResources(ram=int(available_resources['RAM']), cpu=int(\\n available_resources['CPU']), hdd=int(available_resources['HDD'])), available_resources['DATA']]\\n\\n print 'Available resources set to', EdgenodeResources\\n return 'Available resources set to ' + str(available_resources)\",\n \"def resources(self):\\r\\n return self.page.object_list\",\n \"def resources(self) -> \\\"Resources\\\":\\n return self._resources\",\n \"def getResources(self):\\n\\t\\treturn deepcopy(self.server.resources)\",\n \"def resources(self):\\n return [self]\",\n \"def resources(self):\\n return list(self.get_resources_for_type(gdef.ResType_All))\",\n \"def GetAllResourcesSample():\\n client = CreateClient()\\n # Unlike client.GetResources, this returns a list of resources\\n for resource in client.GetAllResources():\\n PrintResource(resource)\",\n \"def create_resources(self) -> List[ResourceDescription]:\\r\\n return self.resources\",\n \"def get_resources():\\n return Response(f\\\"{Resource.get_all_resources()}\\\", 200, mimetype='text/plain')\",\n \"def get_resources():\\n global __res\\n if __res == None:\\n __init_resources()\\n return __res\",\n \"def get_resources():\\n global __res\\n if __res == None:\\n __init_resources()\\n return __res\",\n \"def get_resources():\\n global __res\\n if __res == None:\\n __init_resources()\\n return __res\",\n \"def get_resources():\\r\\n global __res\\r\\n if __res == None:\\r\\n __init_resources()\\r\\n return __res\",\n \"def get_resources():\\r\\n global __res\\r\\n if __res == None:\\r\\n __init_resources()\\r\\n return __res\",\n \"def get_resources():\\r\\n global __res\\r\\n if __res == None:\\r\\n __init_resources()\\r\\n return __res\",\n \"def list(self):\\n resources = self._os_resource_manager.list()\\n resource_list = []\\n for resource in resources:\\n resource_list.append(self._resource_class(id=resource.id,\\n name=resource.name))\\n return resource_list\",\n \"def resources(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"resources\\\")\",\n \"def resources(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"resources\\\")\",\n \"def resources(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"resources\\\")\",\n \"def resources(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"resources\\\")\",\n \"def __init__(self):\\n import visa\\n\\n rm = visa.ResourceManager()\\n target = 'Agilent Technologies,8163B,MY48208514,V5.25(72637)'\\n\\n for dev in rm.list_resources():\\n try:\\n inst = rm.open_resource(dev)\\n name = inst.query('*IDN?') # Agilent Technologies,8163B,MY48208514,V5.25(72637)\\n if target in name:\\n # TODO: check that the slot contains the correct module\\n self._inst = inst\\n except:\\n continue\\n\\n if self._inst is None:\\n raise RuntimeError(\\\"Target resource {} cannot be found in the VISA resource manager\\\".format(target))\\n print(\\\"Connected to \\\" + self.id())\",\n \"def resources(self):\\n\\n return self.FIXTURE.resources_collection(self)\",\n \"def list_resources(self):\\n self.workersResponded = 0\\n print(\\\"Main thread\\\", threading.get_ident())\\n\\n for addr in self.equipment_model.get_addr_list():\\n self.equipment_model.reset_index(addr)\\n self.equipment_model.set_connected(addr, 2)\\n\\n if not self.worker_pool.is_init():\\n w = self.worker_pool.create_worker(addr)\\n #Signals from worker\\n w.signal_connected.connect(self.slot_connected)\\n w.signal_not_connected.connect(self.slot_not_connected)\\n w.signal_write_success.connect(self.parent.slot_write_success)\\n w.signal_query_success.connect(self.parent.slot_query_success)\\n w.signal_error.connect(self.parent.slot_error)\\n\\n self.next_connection(addr)\\n \\n self.worker_pool.set_init(True)\",\n \"def resources(self):\\n res = []\\n for resource in self._resources:\\n res = res + resource.resources()\\n\\n return res\",\n \"def resources(self):\\n res = []\\n for resource in self._resources:\\n res = res + resource.resources()\\n\\n return res\",\n \"def test_get_virtualization_realm_resources(self):\\n pass\",\n \"def cmd_list_resources(config=DEFAULT_LINUX_PATH):\\n config = load_config_file(expand_config_path(config))\\n px = connection_proxmox(config[\\\"proxmox\\\"])\\n try:\\n if config[\\\"pools\\\"]:\\n l, h = list_resources(px, config[\\\"pools\\\"])\\n return tabulate(l, h)\\n else:\\n print(\\\"Dick 'pools' is empty\\\")\\n except KeyError:\\n print(\\\"Missing 'pools' dict in config file\\\")\\n sys.exit(1)\",\n \"def get_all_resources(self) -> typing.List:\\n\\n session = self.session()\\n\\n try:\\n available_peers = session\\\\\\n .query(\\n ResourceTable.peerIp,\\n ResourceTable.peerPort,\\n ResourceTable.resourcePath,\\n ResourceTable.resourceName,\\n ResourceTable.resourceHash\\n )\\\\\\n .group_by(ResourceTable.peerId, ResourceTable.resourceHash)\\\\\\n .all()\\n\\n return available_peers\\n\\n finally:\\n session.close()\",\n \"def test_show_vcs_resources(mgmt_session):\\n vcs_resource = rift.vcs.vcs.VcsResource(mgmt_session)\\n vcs_resource_info = None\\n\\n # Get vcs resources\\n vcs_resource_info = vcs_resource.get_vcs_resource()\\n\\n # Verify there are VM entries in the vcs resource info container\\n vms = [vm for vm in vcs_resource_info.vm]\\n if len(vms) == 0:\\n raise AssertionError(\\\"No entries found in vcs resource info\\\")\",\n \"def GetResourcesSample():\\n client = CreateClient()\\n # Get a feed and print it\\n feed = client.GetResources()\\n PrintFeed(feed)\",\n \"def get_resources(genome_build, fasta_ref, data):\\n config = tz.get_in([\\\"config\\\", CONFIG_KEY], data)\\n find_fn = _find_file(config)\\n def normalize(f):\\n return _get_id_fname(f)[-1]\\n return sret.get_resources(genome_build, fasta_ref, config,\\n data, _open_remote, _list(config), find_fn, normalize)\",\n \"def get_resources():\\n user_id = session[\\\"email\\\"]\\n resources = fm.get_resources(user_id)\\n returned_val = dict(resources=resources)\\n return jsonify(returned_val)\",\n \"def get_resources(self, **extra_args):\\n return [lrms for lrms in self.resources.itervalues()]\",\n \"def _get_all_resources(self):\\n all_resources = []\\n for resource in ResourceModel.scan():\\n all_resources.append(resource)\\n return all_resources\",\n \"def show_instances():\\n return get_instances()\",\n \"def ResourceList(self):\\n url = AddToUrl(self, 'https://api.spiget.org/v2/resources?')\\n return ApiSearch(url)\",\n \"def get_resources(self):\\n return list(self.get_inputs()) + list(self.get_outputs())\",\n \"def get_indexes(self):\\n resources = []\\n\\n data = self.get_resource_data(type='sources')\\n data['url'] = constants.URLS['sources'] % data\\n resources.append(data)\\n\\n for arch in self.data['arch']:\\n data = self.get_resource_data(type='packages', arch=arch)\\n data['url'] = constants.URLS['packages'] % data\\n resources.append(data)\\n return resources\",\n \"def get_pecan_resources(self):\\n return []\",\n \"def get_available_resource(self, nodename):\\n LOG.debug(\\\"get_available_resource\\\")\\n\\n dictval = self._host.properties\\n\\n return dictval\",\n \"def resources(self) -> [ListResourcesResponse]:\\n resources = []\\n\\n layers = self.layers\\n for layer_name in layers:\\n layer_arns = self.layer_version_arns(layer_name)\\n for arn in layer_arns:\\n list_resources_response = ListResourcesResponse(\\n service=self.service, account_id=self.current_account_id, arn=arn, region=self.region,\\n resource_type=self.resource_type, name=layer_name)\\n resources.append(list_resources_response)\\n return resources\",\n \"def print_resources(self) -> None:\\n for resource in self._request_get(self.url_base + 'documentacao'):\\n print(\\n \\\"Nome: {},\\\\nUrl: {},\\\\n\\\".format(\\n resource['name'],\\n self._format_url_to_resource(resource['url']),\\n )\\n )\",\n \"def init_physical_resources():\\n test_physical_resources = []\\n\\n # add info to list in memory, one by one, following signature values\\n phys_resrc_ID = 1\\n phys_resrc_name = \\\"small-cavium-1\\\"\\n phys_resrc_info = \\\"Jump server in Arm pod, 48 cores, 64G RAM, 447G SSD, aarch64 Cavium ThunderX, Ubuntu OS\\\"\\n phys_resrc_IPAddress = \\\"10.10.50.12\\\"\\n phys_resrc_MACAddress = \\\"00-14-22-01-23-45\\\"\\n\\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\\n phys_resrc_info,\\n phys_resrc_IPAddress,\\n phys_resrc_MACAddress))\\n\\n phys_resrc_ID = 2\\n phys_resrc_name = \\\"medium-cavium-1\\\"\\n phys_resrc_info = \\\"Jump server in New York pod, 96 cores, 64G RAM, 447G SSD, aarch64 Cavium ThunderX, Ubuntu OS\\\"\\n phys_resrc_IPAddress = \\\"30.31.32.33\\\"\\n phys_resrc_MACAddress = \\\"0xb3:22:05:c1:aa:82\\\"\\n\\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\\n phys_resrc_info,\\n phys_resrc_IPAddress,\\n phys_resrc_MACAddress))\\n\\n phys_resrc_ID = 3\\n phys_resrc_name = \\\"mega-cavium-666\\\"\\n phys_resrc_info = \\\"Jump server in Las Vegas, 1024 cores, 1024G RAM, 6666G SSD, aarch64 Cavium ThunderX, Ubuntu OS\\\"\\n phys_resrc_IPAddress = \\\"54.53.52.51\\\"\\n phys_resrc_MACAddress = \\\"01-23-45-67-89-ab\\\"\\n\\n test_physical_resources.append(PhysicalResource(phys_resrc_ID, phys_resrc_name,\\n phys_resrc_info,\\n phys_resrc_IPAddress,\\n phys_resrc_MACAddress))\\n\\n\\n # write list to binary file\\n write_list_bin(test_physical_resources, FILE_PHYSICAL_RESOURCES)\\n\\n return test_physical_resources\",\n \"def getResources(self, folder):\\n\\n #-------------------- \\n # Get the resource JSON\\n #-------------------- \\n folder += \\\"/resources\\\"\\n resources = self.__getJson(folder)\\n #print(\\\"%s %s\\\"%(, folder))\\n #print(\\\" Got resources: '%s'\\\"%(str(resources)))\\n\\n\\n\\n #-------------------- \\n # Filter the JSONs\\n #-------------------- \\n resourceNames = []\\n for r in resources:\\n if 'label' in r:\\n resourceNames.append(r['label'])\\n #print(\\\"FOUND RESOURCE ('%s') : %s\\\"%(folder, r['label']))\\n elif 'Name' in r:\\n resourceNames.append(r['Name'])\\n #print(\\\"FOUND RESOURCE ('%s') : %s\\\"%(folder, r['Name']))\\n\\n return resourceNames\",\n \"async def websocket_lovelace_resources(\\n hass: HomeAssistant,\\n connection: websocket_api.ActiveConnection,\\n msg: dict[str, Any],\\n) -> None:\\n resources = hass.data[DOMAIN][\\\"resources\\\"]\\n\\n if not resources.loaded:\\n await resources.async_load()\\n resources.loaded = True\\n\\n connection.send_result(msg[\\\"id\\\"], resources.async_items())\",\n \"def get_resources():\\n # Acquire the lock...\\n get_resources_lock.acquire()\\n\\n # ...but always release it\\n try:\\n # Construct the dictionaries as copies from nanny\\n (limits,usage) = nanny.get_resource_information()\\n\\n\\n # Calculate all the usage's\\n pid = os.getpid()\\n\\n # Get CPU and memory, this is thread specific\\n if ostype in [\\\"Linux\\\", \\\"Darwin\\\"]:\\n \\n # Get CPU first, then memory\\n usage[\\\"cpu\\\"] = os_api.get_process_cpu_time(pid)\\n\\n # This uses the cached PID data from the CPU check\\n usage[\\\"memory\\\"] = os_api.get_process_rss()\\n\\n # Get the thread specific CPU usage\\n usage[\\\"threadcpu\\\"] = os_api.get_current_thread_cpu_time() \\n\\n\\n # Windows Specific versions\\n elif ostype in [\\\"Windows\\\"]:\\n \\n # Get the CPU time\\n usage[\\\"cpu\\\"] = windows_api.get_process_cpu_time(pid)\\n\\n # Get the memory, use the resident set size\\n usage[\\\"memory\\\"] = windows_api.process_memory_info(pid)['WorkingSetSize'] \\n\\n # Get thread-level CPU \\n usage[\\\"threadcpu\\\"] = windows_api.get_current_thread_cpu_time()\\n\\n # Unknown OS\\n else:\\n raise EnvironmentError(\\\"Unsupported Platform!\\\")\\n\\n # Use the cached disk used amount\\n usage[\\\"diskused\\\"] = cached_disk_used\\n\\n finally:\\n # Release the lock\\n get_resources_lock.release()\\n\\n # Copy the stop times\\n stoptimes = process_stopped_timeline[:]\\n\\n # Return the dictionaries and the stoptimes\\n return (limits,usage,stoptimes)\",\n \"def register_resources(self):\\n raise NotImplementedError\",\n \"def getCatalogs():\",\n \"def get_logical_links(self, osdf_config):\\n\\n config = osdf_config.deployment\\n aai_url = config[\\\"aaiUrl\\\"]\\n aai_req_url = aai_url + config[\\\"aaiGetLinksUrl\\\"]\\n\\n response = requests.get(aai_req_url,headers=self.aai_headers,auth=HTTPBasicAuth(\\\"AAI\\\", \\\"AAI\\\"),verify=False)\\n if response.status_code == 200:\\n return response.json()\",\n \"def load_all_resources():\\n\\n # Load the fonts\\n ResourcesManager._load_font(\\\"Munro.ttf\\\")\\n\\n # Load images\\n ResourcesManager.HIBER_NATION_IMG = ResourcesManager._load_image(\\\"hiber_nation.png\\\")\\n ResourcesManager.SHIP_IMG = ResourcesManager._load_image(\\\"ship.png\\\")\\n ResourcesManager.MISSILE_IMG = ResourcesManager._load_image(\\\"missile.png\\\")\\n\\n # Load sounds\\n # ResourcesManager.MENU_MUSIC = ResourcesManager._load_sound(\\\"menu.ogg\\\")\",\n \"def sx_get_active_vrids(handle): \\n try:\\n vrid_list = []\\n \\n vrid_cnt_p = new_uint32_t_p()\\n uint32_t_p_assign(vrid_cnt_p, 0)\\n vrid_key_p = new_sx_router_id_t_p()\\n sx_router_id_t_p_assign(vrid_key_p, 0)\\n vrid_key = sx_router_id_t_p_value(vrid_key_p)\\n \\n rc = sx_api_router_vrid_iter_get(handle, SX_ACCESS_CMD_GET, vrid_key, None, None, vrid_cnt_p)\\n sx_check_rc(rc)\\n \\n vrid_cnt = uint32_t_p_value(vrid_cnt_p)\\n vrid_list_p = new_sx_router_id_t_arr(vrid_cnt)\\n \\n rc = sx_api_router_vrid_iter_get(handle, SX_ACCESS_CMD_GET_FIRST, vrid_key, None, vrid_list_p, vrid_cnt_p)\\n sx_check_rc(rc)\\n \\n vrid_cnt = uint32_t_p_value(vrid_cnt_p)\\n for i in range(0, vrid_cnt):\\n vrid = sx_router_id_t_arr_getitem(vrid_list_p, i)\\n vrid_list.append(vrid)\\n\\n return vrid_list\\n\\n finally:\\n delete_sx_router_id_t_arr(vrid_list_p)\\n delete_sx_router_id_t_p(vrid_key_p)\\n delete_uint32_t_p(vrid_cnt_p)\",\n \"def ls():\\n # TODO: listing all availabe containers form sequence\\n return\",\n \"def test_get_cloud_resources(self):\\n pass\",\n \"def _get_resources():\\n return {\\n 'searchPageUrl': flask.url_for('search_page'),\\n 'searchJsonUrl': flask.url_for('search'),\\n 'userAddIconUrl': flask.url_for('static', filename='img/add-users.svg'),\\n 'logoutUrl': flask.url_for('logout'),\\n 'settingsUrl': flask.url_for('setup') + '#settingsDisplayTemplate',\\n 'listAdminUrl': flask.url_for('admin_list'),\\n 'addAdminUrl': flask.url_for('add_admin'),\\n 'changeAdminPasswordUrl': flask.url_for('change_admin_password'),\\n 'removeAdminUrl': flask.url_for('delete_admin'),\\n 'loginUrl': flask.url_for('login'),\\n 'recaptchaKey': ufo.app.config.get('RECAPTCHA_SITE_KEY', ''),\\n 'setupUrl': flask.url_for('setup'),\\n 'setupAdminUrl': flask.url_for('setup_admin'),\\n 'setupOauthUrl': flask.url_for('setup_oauth'),\\n 'download_chrome_policy': flask.url_for('download_chrome_policy'),\\n 'policy_filename': 'chrome_policy.json',\\n 'proxyServerAddUrl': flask.url_for('proxyserver_add'),\\n 'proxyServerAddIconUrl': flask.url_for('static',\\n filename='img/add-servers.svg'),\\n 'proxyServerInverseAddIconUrl': flask.url_for(\\n 'static', filename='img/add-servers-inverse.svg'),\\n 'proxyServerListId': 'proxyList',\\n 'proxyServerListUrl': flask.url_for('proxyserver_list'),\\n 'listLimit': 10,\\n 'proxyServerDetailsButtonId': 'serverDetailsButton',\\n 'editButtonId': 'serverEditButton',\\n 'proxyServerDetailsOverlayId': 'serverDetailsOverlay',\\n 'proxyServerEditUrl': flask.url_for('proxyserver_edit'),\\n 'proxyServerDeleteUrl': flask.url_for('proxyserver_delete'),\\n 'proxyServerIconUrl': flask.url_for('static', filename='img/server.svg'),\\n 'proxyServerAddButtonId': 'addServerButton',\\n 'proxyServerModalId': 'serverModal',\\n 'textAreaMaxRows': 10,\\n 'ipInput': 'ipInput',\\n 'nameInput': 'nameInput',\\n 'sshPrivateKeyInput': 'sshPrivateKeyInput',\\n 'hostPublicKeyInput': 'hostPublicKeyInput',\\n 'getSettingsUrl': flask.url_for('get_settings'),\\n 'settingsEditUrl': flask.url_for('edit_settings'),\\n 'userAddUrl': flask.url_for('add_user'),\\n 'userInverseAddIconUrl': flask.url_for(\\n 'static', filename='img/add-users-inverse.svg'),\\n 'userListId': 'userList',\\n 'userListUrl': flask.url_for('user_list'),\\n 'revokeToggleUrl': flask.url_for('user_toggle_revoked'),\\n 'rotateKeysUrl': flask.url_for('user_get_new_key_pair'),\\n 'inviteCodeUrl': flask.url_for('user_get_invite_code'),\\n 'userDeleteUrl': flask.url_for('delete_user'),\\n 'userDetailsButtonId': 'userDetailsButton',\\n 'userDetailsOverlayId': 'userDetailsOverlay',\\n 'userIconUrl': flask.url_for('static', filename='img/user.svg'),\\n 'userAddButtonId': 'addUserButton',\\n 'userModalId': 'userModal',\\n 'groupAddTabId': 'groupAddTab',\\n 'groupAddFormId': 'groupAdd',\\n 'groupAddInputName': 'group_key',\\n 'userAddTabId': 'userAddTab',\\n 'userAddFormId': 'userAdd',\\n 'userAddInputName': 'user_key',\\n 'domainAddTabId': 'domainAddTab',\\n 'domainAddFormId': 'domainAdd',\\n 'manualAddTabId': 'manualAddTab',\\n 'manualAddFormId': 'manualAdd',\\n 'regexes': regex.REGEXES_AND_ERRORS_DICTIONARY,\\n 'jsonPrefix': ufo.XSSI_PREFIX,\\n 'maxFailedLoginsBeforeRecaptcha': ufo.MAX_FAILED_LOGINS_BEFORE_RECAPTCHA,\\n 'userAddListFlipperId': 'userAddListFlipper',\\n 'proxyServerAddListFlipperId': 'proxyServerAddListFlipper',\\n 'userAddTabsId': 'userAddTabs',\\n 'proxyServerAddFormId': 'serverAddFormHolder',\\n }\",\n \"def create_external_resources(self) -> List[ResourceDescription]:\\r\\n return effects.get_effect_resources()\",\n \"def getResources( dummy = False , retryPeriod = 0.5 ) :\\n\\n resources = {}\\n\\n\\n # --------------------------------------------------\\n # Add GPS\\n # --------------------------------------------------\\n import Gps\\n if Config.IS_MONSTRO :\\n resources.update({ \\\"gps\\\" : Gps.getGps( dummy = dummy ) })\\n else :\\n resources.update({ \\\"gps\\\" : Gps.getGps( dummy = True ) })\\n\\n\\n if not dummy :\\n\\n # --------------------------------------------------\\n # Add roboMote\\n # --------------------------------------------------\\n import RoboMote\\n\\n # find the com ports associated with motes\\n done = False\\n while not done :\\n\\n moteComs = findMotes()\\n if (not moteComs) and (retryPeriod > 0) :\\n time.sleep(retryPeriod)\\n continue\\n\\n roboMote = None\\n for i in range(len(moteComs)-1,-1,-1) :\\n moteCom = moteComs[i]\\n if RoboMote.isRoboMote( moteCom ) :\\n del moteComs[i] \\n roboMote = RoboMote.RoboMote( moteCom )\\n done = True\\n break\\n\\n if not roboMote :\\n if ( retryPeriod > 0 ) :\\n time.sleep(retryPeriod)\\n continue\\n else :\\n raise RoboMote.RoboMoteException , \\\"Could not connect to the mote providing RoboMote\\\"\\n\\n # Add the roboMote to the resource list\\n resources.update({ \\\"roboMote\\\" : roboMote })\\n\\n\\n return resources\",\n \"def show_networks():\\n return get_networks()\",\n \"async def get_resources(self, **kwargs) -> dict:\\n resources = await self.request.get(self._base_path, **kwargs)\\n self._sanitize_resources(resources)\\n return resources\",\n \"def show_resource_pool(client, private_cloud, resource_pool, location):\\n return client.get(location, private_cloud, resource_pool)\",\n \"def get_available_resource(self, nodename):\\n curent_time = time.time()\\n if curent_time - self.cleanup_time > CONF.azure.cleanup_span:\\n self.cleanup_time = curent_time\\n self._cleanup_deleted_os_disks()\\n self._cleanup_deleted_nics()\\n usage_family = 'basicAFamily'\\n try:\\n page = self.compute.usage.list(CONF.azure.location)\\n except Exception as e:\\n msg = six.text_type(e)\\n LOG.exception(msg)\\n ex = exception.ComputeUsageListFailure(reason=six.text_type(e))\\n raise ex\\n usages = [i for i in page]\\n cores = 0\\n cores_used = 0\\n for i in usages:\\n if hasattr(i, 'name') and hasattr(i.name, 'value'):\\n if usage_family == i.name.value:\\n cores = i.limit if hasattr(i, 'limit') else 0\\n cores_used = i.current_value \\\\\\n if hasattr(i, 'current_value') else 0\\n break\\n return {'vcpus': cores,\\n 'memory_mb': 100000000,\\n 'local_gb': 100000000,\\n 'vcpus_used': cores_used,\\n 'memory_mb_used': 0,\\n 'local_gb_used': 0,\\n 'hypervisor_type': hv_type.HYPERV,\\n 'hypervisor_version': 300,\\n 'hypervisor_hostname': nodename,\\n 'cpu_info': '{\\\"model\\\": [\\\"Intel(R) Xeon(R) CPU E5-2670 0 @ '\\n '2.60GHz\\\"], \\\"topology\\\": {\\\"cores\\\": 16, \\\"threads\\\": '\\n '32}}',\\n 'supported_instances': [(arch.I686, hv_type.HYPERV,\\n vm_mode.HVM),\\n (arch.X86_64, hv_type.HYPERV,\\n vm_mode.HVM)],\\n 'numa_topology': None\\n }\",\n \"def _get_info_about_available_resources(self, min_ram, min_hdd, min_vcpus):\\n vms_count = 0\\n for hypervisor in self.nova_cli.hypervisors.list():\\n if hypervisor.free_ram_mb >= min_ram:\\n if hypervisor.free_disk_gb >= min_hdd:\\n if hypervisor.vcpus - hypervisor.vcpus_used >= min_vcpus:\\n # We need to determine how many VMs we can run\\n # on this hypervisor\\n free_cpu = hypervisor.vcpus - hypervisor.vcpus_used\\n k1 = int(hypervisor.free_ram_mb / min_ram)\\n k2 = int(hypervisor.free_disk_gb / min_hdd)\\n k3 = int(free_cpu / min_vcpus)\\n vms_count += min(k1, k2, k3)\\n return vms_count\",\n \"def _get_resources(self):\\n while not self.is_stopped():\\n pools = self.poolsonline.get_pools()\\n tx_source = self.txonline.get_txsource()\\n mempoolstate = self.mempool.state\\n if mempoolstate and pools and tx_source:\\n return pools, tx_source, mempoolstate\\n # Resources aren't available due to some error elsewhere,\\n # so get rid of stats to avoid giving stale stats to others.\\n self.stats = None\\n self.sleep(5)\\n raise StopIteration\",\n \"def views(self):\\r\\n return resources.Views(self)\",\n \"def list_resource_pool(client, private_cloud, location):\\n return client.list(location, private_cloud)\",\n \"def GetResourceAclSample():\\n client = CreateClient()\\n for resource in client.GetResources(limit=5).entry:\\n acl_feed = client.GetResourceAcl(resource)\\n for acl in acl_feed.entry:\\n print acl.role.value, acl.scope.type, acl.scope.value\",\n \"def test_list_resources(self, rabbitmq):\\n assert rabbitmq.list_vhosts() == ['/vhost']\\n assert rabbitmq.list_users() == [('user', ['administrator'])]\\n assert rabbitmq.list_queues() == []\",\n \"def add_resources(event):\\n anuket_resources.need()\",\n \"def list():\\n rino.remote.list()\",\n \"def view(self, *args, **kwargs):\\n return self._resources_manager.view(*args, **kwargs)\",\n \"def dedicated_resources(self) -> Optional[Sequence['outputs.AiEndpointDeployedModelDedicatedResource']]:\\n return pulumi.get(self, \\\"dedicated_resources\\\")\",\n \"def list(self, **kwargs):\\n\\n return self.getResourceManager() \\\\\\n .getSdk() \\\\\\n .hosts \\\\\\n .list(**kwargs)\",\n \"def getResource(self):\\n pass;\",\n \"def find_resources(self):\\n path = self.get_path('resources')\\n\\n if not path:\\n rsoup = self.soup\\n else:\\n rsoup = BeautifulSoup(open(path))\\n\\n self.parse_resources(rsoup)\",\n \"def get_resources(self, resource_type=None):\\n for application in self.applications:\\n for r in application.get_resources(resource_type):\\n yield r\\n\\n for r in BaseResourceContainer.get_resources(self, resource_type):\\n yield r\",\n \"def get_available_atlases_cli():\\n from .roi_extractor import get_available_atlases\\n get_available_atlases()\",\n \"def resource_map(self):\",\n \"def _load_resources(self):\\n puts = (getattr(self, 'project', None) or self).puts\\n for resource_type, resource_cls in six.iteritems(AVAILABLE_RESOURCES):\\n for name in self.settings.get(resource_type, {}):\\n extra = {\\n 'project': getattr(self, 'project', None) or self,\\n 'app': self if hasattr(self, 'project') else None,\\n }\\n\\n with indent(4 if hasattr(self, 'project') else 2):\\n puts(colored.green(u\\\"✓ {}:{}\\\".format(resource_type, name)))\\n\\n self._resources[resource_type].append(\\n resource_cls.factory(\\n name=name,\\n settings=self.settings.get(resource_type, {})[name],\\n **extra\\n )\\n )\",\n \"def query_resources(self, request):\\n desc = ResourceDescriptor.decode(request.message.descriptors)\\n self.logger.debug(\\\"Looking for resources with description %r\\\", desc)\\n\\n # query for resources that are usable and match the descriptors\\n query = (Q(is_usable=True, **desc.properties))\\n matches = desc.type.objects.filter(query)\\n\\n if matches.count() == 0:\\n raise ResourceDoesNotExistError(\\\"No existing resource meets \\\"\\n \\\"the requirements: %r\\\" % desc)\\n\\n query_result = [resource for resource in matches]\\n\\n return ResourcesReply(resources=query_result)\",\n \"def list_resources(px, pools):\\n result = []\\n for pool in pools:\\n for i in px.pools.get(pool)[\\\"members\\\"]:\\n result.append(\\n {\\n \\\"pool\\\": pool,\\n \\\"vmid\\\": i[\\\"vmid\\\"],\\n \\\"name\\\": i[\\\"name\\\"],\\n \\\"status\\\": i[\\\"status\\\"],\\n \\\"type\\\": i[\\\"type\\\"],\\n }\\n )\\n return result, {\\n \\\"pool\\\": \\\"pool(s)\\\",\\n \\\"vmid\\\": \\\"vmid\\\",\\n \\\"name\\\": \\\"name\\\",\\n \\\"status\\\": \\\"status\\\",\\n \\\"type\\\": \\\"type\\\",\\n }\",\n \"def get_resource_urls():\\n base_url = 'http://developer.pardot.com/'\\n pattern = re.compile(\\n r'(?ims)\\\\<a [^>]*?href=\\\"(kb/api-version-3/[^>]*?/)\\\"[^>]*?\\\\>'\\n r'[^<]*?\\\\</a\\\\>')\\n response = requests.get(base_url)\\n return [\\n '%s/%s' % (base_url, url) for url in pattern.findall(response.text)]\",\n \"def dashboards(self):\\r\\n return resources.Dashboards(self)\",\n \"def init_cloud_virtual_resources():\\n test_cldvirt_resources = []\\n\\n # add info to list in memory, one by one, following signature values\\n cldvirtres_ID = 1\\n cldvirtres_name = \\\"nova-compute-1\\\"\\n cldvirtres_info = \\\"nova VM in Arm pod\\\"\\n cldvirtres_IPAddress = \\\"50.60.70.80\\\"\\n cldvirtres_URL = \\\"http://50.60.70.80:8080\\\"\\n cldvirtres_related_phys_rsrcIDs = [1,3]\\n\\n test_cldvirt_resources.append(CloudVirtualResource(cldvirtres_ID, cldvirtres_name,\\n cldvirtres_info,\\n cldvirtres_IPAddress,\\n cldvirtres_URL,\\n cldvirtres_related_phys_rsrcIDs))\\n\\n cldvirtres_ID = 2\\n cldvirtres_name = \\\"nova-compute-2\\\"\\n cldvirtres_info = \\\"nova VM in LaaS\\\"\\n cldvirtres_IPAddress = \\\"50.60.70.80\\\"\\n cldvirtres_URL = \\\"http://50.60.70.80:8080\\\"\\n cldvirtres_related_phys_rsrcIDs = [2,3]\\n\\n test_cldvirt_resources.append(CloudVirtualResource(cldvirtres_ID, cldvirtres_name,\\n cldvirtres_info,\\n cldvirtres_IPAddress,\\n cldvirtres_URL,\\n cldvirtres_related_phys_rsrcIDs))\\n\\n cldvirtres_ID = 3\\n cldvirtres_name = \\\"nova-compute-3\\\"\\n cldvirtres_info = \\\"nova VM in x86 pod\\\"\\n cldvirtres_IPAddress = \\\"50.60.70.80\\\"\\n cldvirtres_URL = \\\"http://50.60.70.80:8080\\\"\\n cldvirtres_related_phys_rsrcIDs = [1]\\n\\n test_cldvirt_resources.append(CloudVirtualResource(cldvirtres_ID, cldvirtres_name,\\n cldvirtres_info,\\n cldvirtres_IPAddress,\\n cldvirtres_URL,\\n cldvirtres_related_phys_rsrcIDs))\\n\\n\\n # write list to binary file\\n write_list_bin(test_cldvirt_resources, FILE_CLOUD_RESOURCES)\\n\\n return test_cldvirt_resources\",\n \"def get_resources(self, resource_url):\\n response = self.response(resource_url)\\n body = response[0]\\n return ResourceParser.extract_resources(body)\",\n \"def has_resources(self):\\r\\n return hasattr(self, 'resources') and self.resources\",\n \"def test_get_deployment_resources(self):\\n pass\",\n \"def get_resources(self):\\n res = set()\\n res.update(self.get_inputs())\\n res.update(self.get_outputs())\\n return res\",\n \"def get_inventory(self, context):\\n # See below some example code demonstrating how to return the resource structure\\n # and attributes. In real life, of course, if the actual values are not static,\\n # this code would be preceded by some SNMP/other calls to get the actual resource information\\n '''\\n # Add sub resources details\\n sub_resources = [ AutoLoadResource(model ='Generic Chassis',name= 'Chassis 1', relative_address='1'),\\n AutoLoadResource(model='Generic Module',name= 'Module 1',relative_address= '1/1'),\\n AutoLoadResource(model='Generic Port',name= 'Port 1', relative_address='1/1/1'),\\n AutoLoadResource(model='Generic Port', name='Port 2', relative_address='1/1/2'),\\n AutoLoadResource(model='Generic Power Port', name='Power Port', relative_address='1/PP1')]\\n\\n\\n attributes = [ AutoLoadAttribute(relative_address='', attribute_name='Location', attribute_value='Santa Clara Lab'),\\n AutoLoadAttribute('', 'Model', 'Catalyst 3850'),\\n AutoLoadAttribute('', 'Vendor', 'Cisco'),\\n AutoLoadAttribute('1', 'Serial Number', 'JAE053002JD'),\\n AutoLoadAttribute('1', 'Model', 'WS-X4232-GB-RJ'),\\n AutoLoadAttribute('1/1', 'Model', 'WS-X4233-GB-EJ'),\\n AutoLoadAttribute('1/1', 'Serial Number', 'RVE056702UD'),\\n AutoLoadAttribute('1/1/1', 'MAC Address', 'fe80::e10c:f055:f7f1:bb7t16'),\\n AutoLoadAttribute('1/1/1', 'IPv4 Address', '192.168.10.7'),\\n AutoLoadAttribute('1/1/2', 'MAC Address', 'te67::e40c:g755:f55y:gh7w36'),\\n AutoLoadAttribute('1/1/2', 'IPv4 Address', '192.168.10.9'),\\n AutoLoadAttribute('1/PP1', 'Model', 'WS-X4232-GB-RJ'),\\n AutoLoadAttribute('1/PP1', 'Port Description', 'Power'),\\n AutoLoadAttribute('1/PP1', 'Serial Number', 'RVE056702UD')]\\n\\n return AutoLoadDetails(sub_resources,attributes)\\n '''\\n\\n self._log(context, 'Begin autoload')\\n resources = []\\n attributes = []\\n\\n\\n attributes.append(AutoLoadAttribute('', 'replication_address', self.get_replication_address(context)))\\n attributes.append(AutoLoadAttribute('', 'connection_key', self.get_connection_key(context)))\\n\\n networks = self._get_newtork_interfaces(context)\\n self._log(context, 'got networks')\\n\\n controllers = self._get_controllers(context)\\n self._log(context, 'got controllers')\\n ports = self._get_ports(context)\\n\\n model = None\\n for controller in controllers:\\n self._log(context, 'Processing ctrlt: ' + controller['name'] + ':' + controller['model'])\\n resources.append(AutoLoadResource(model='Generic Storage Controller', name=controller['name'],\\n relative_address=controller['name']))\\n if model is None:\\n model = controller['model']\\n\\n attributes.append(AutoLoadAttribute('', 'Model', model))\\n\\n for network in networks:\\n self._log(context, 'Processing netwk: ' + network['name'] + ':' + str(network['address']))\\n net_name = network['name']\\n controller = net_name.split('.')[0]\\n if 'vir0' in controller or 'vir1' in controller:\\n attributes.append(AutoLoadAttribute('',str(controller + '_address'), str(network['address'])))\\n continue\\n if 'vir' in controller:\\n continue\\n if 'management' not in network['services']:\\n continue\\n resources.append(AutoLoadResource(model='Storage Network Port', name=net_name,\\n relative_address=controller.upper() + '/' + str(network['address'])))\\n\\n for port in ports:\\n if port['iqn'] is not None:\\n port_name = port['name']\\n controller = port_name.split('.')[0]\\n resources.append(AutoLoadResource(model='iSCSI Storage Port', name=port['name'],\\n relative_address=controller + '/' + port['portal']))\\n attributes.append(AutoLoadAttribute(controller + '/' + port['portal'], 'iqn', port['iqn']))\\n elif port['wwn'] is not None:\\n port_name = port['name']\\n controller = port_name.split('.')[0]\\n resources.append(AutoLoadResource(model='FC Storage Port', name=port['name'],\\n relative_address=controller + '/' + port['name'].split('.')[1]))\\n attributes.append(AutoLoadAttribute(controller + '/' + port['name'].split('.')[1], 'wwn', port['wwn']))\\n\\n return AutoLoadDetails(resources, attributes)\",\n \"def test_ListResources(self, zipped=False):\\n from openflow.dummyom.models import DummyOM\\n slice_urn, cred = self.create_ch_slice()\\n options = dict(geni_compressed=zipped, geni_available=True)\\n rspec = wrap_xmlrpc_call(\\n self.am_client.ListResources,\\n [cred, options], {}, settings.TIMEOUT)\\n \\n logger.debug(\\\"Got Advertisement RSpec: \\\\n%s\\\" % rspec)\\n \\n if zipped:\\n import zlib, base64\\n rspec = zlib.decompress(base64.b64decode(rspec))\\n \\n # Create switches and links\\n self.switches, self.links = parse_rspec(rspec)\\n \\n # check the number of switches and links\\n num_links = sum([len(d.get_switches()) for d in DummyOM.objects.all()])\\n self.assertEqual(len(self.switches),\\n num_links)\\n self.assertEqual(len(self.links),\\n settings.NUM_LINKS_PER_AGG * settings.NUM_DUMMY_OMS)\",\n \"def resource_navigation_links(self) -> Sequence['outputs.ResourceNavigationLinkResponse']:\\n return pulumi.get(self, \\\"resource_navigation_links\\\")\",\n \"def get_transifex_resources(self):\\n RESOURCES_API_25 = (\\n f\\\"organizations/{self.organization_slug}/projects/\\\"\\n f\\\"{self.project_slug}/resources/\\\"\\n )\\n return self.request25(\\\"get\\\", RESOURCES_API_25).json()\",\n \"def automatic_resources(self) -> Optional[Sequence['outputs.AiEndpointDeployedModelAutomaticResource']]:\\n return pulumi.get(self, \\\"automatic_resources\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67140317","0.66184807","0.6323841","0.6323841","0.6323841","0.6289765","0.6227922","0.61894625","0.6173168","0.61393124","0.61299413","0.6100624","0.60945547","0.6082597","0.60609436","0.6056553","0.60070026","0.60023284","0.5967622","0.5967622","0.5967622","0.5952064","0.5952064","0.5952064","0.58885795","0.5888549","0.5888549","0.5888549","0.5888549","0.5803307","0.57903254","0.5766855","0.5740832","0.5740832","0.5730709","0.57017004","0.56819814","0.5678319","0.5661085","0.5658316","0.5625088","0.5585858","0.5574713","0.55658567","0.5562803","0.551667","0.55150694","0.5504397","0.5494585","0.5491069","0.5490005","0.5475679","0.54706526","0.54681116","0.5466244","0.5444974","0.5444552","0.53926617","0.5382331","0.5364927","0.53538775","0.5351199","0.5327365","0.5319943","0.5314716","0.52980834","0.5290724","0.5286888","0.527554","0.5272142","0.52685994","0.525","0.5248325","0.523659","0.52217436","0.52184254","0.5216704","0.5211716","0.5205583","0.5191537","0.5187146","0.51825607","0.5172475","0.5168921","0.51609397","0.51601815","0.51517123","0.5148292","0.5139101","0.51346993","0.5128017","0.5127499","0.51267046","0.51235","0.51226413","0.5114125","0.51140106","0.51121145","0.50855625","0.5083149"],"string":"[\n \"0.67140317\",\n \"0.66184807\",\n \"0.6323841\",\n \"0.6323841\",\n \"0.6323841\",\n \"0.6289765\",\n \"0.6227922\",\n \"0.61894625\",\n \"0.6173168\",\n \"0.61393124\",\n \"0.61299413\",\n \"0.6100624\",\n \"0.60945547\",\n \"0.6082597\",\n \"0.60609436\",\n \"0.6056553\",\n \"0.60070026\",\n \"0.60023284\",\n \"0.5967622\",\n \"0.5967622\",\n \"0.5967622\",\n \"0.5952064\",\n \"0.5952064\",\n \"0.5952064\",\n \"0.58885795\",\n \"0.5888549\",\n \"0.5888549\",\n \"0.5888549\",\n \"0.5888549\",\n \"0.5803307\",\n \"0.57903254\",\n \"0.5766855\",\n \"0.5740832\",\n \"0.5740832\",\n \"0.5730709\",\n \"0.57017004\",\n \"0.56819814\",\n \"0.5678319\",\n \"0.5661085\",\n \"0.5658316\",\n \"0.5625088\",\n \"0.5585858\",\n \"0.5574713\",\n \"0.55658567\",\n \"0.5562803\",\n \"0.551667\",\n \"0.55150694\",\n \"0.5504397\",\n \"0.5494585\",\n \"0.5491069\",\n \"0.5490005\",\n \"0.5475679\",\n \"0.54706526\",\n \"0.54681116\",\n \"0.5466244\",\n \"0.5444974\",\n \"0.5444552\",\n \"0.53926617\",\n \"0.5382331\",\n \"0.5364927\",\n \"0.53538775\",\n \"0.5351199\",\n \"0.5327365\",\n \"0.5319943\",\n \"0.5314716\",\n \"0.52980834\",\n \"0.5290724\",\n \"0.5286888\",\n \"0.527554\",\n \"0.5272142\",\n \"0.52685994\",\n \"0.525\",\n \"0.5248325\",\n \"0.523659\",\n \"0.52217436\",\n \"0.52184254\",\n \"0.5216704\",\n \"0.5211716\",\n \"0.5205583\",\n \"0.5191537\",\n \"0.5187146\",\n \"0.51825607\",\n \"0.5172475\",\n \"0.5168921\",\n \"0.51609397\",\n \"0.51601815\",\n \"0.51517123\",\n \"0.5148292\",\n \"0.5139101\",\n \"0.51346993\",\n \"0.5128017\",\n \"0.5127499\",\n \"0.51267046\",\n \"0.51235\",\n \"0.51226413\",\n \"0.5114125\",\n \"0.51140106\",\n \"0.51121145\",\n \"0.50855625\",\n \"0.5083149\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6919788"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":774,"numItemsPerPage":100,"numTotalItems":95772,"offset":77400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjM5MTEyNywianRpIjoiODUwY2EyYmItOTA0NC00MTQ0LWJjYTUtMGQ3MmUyMmJmNmM3Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYzOTQ3MjcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.dgbAF7PVxMR-CmeDN9FSWvHFPOvQV9Zw49zYI6FS_JNnwOnAWCEzyHTKD98VnT_Uq_rdkiCJaP-_3wERn2bRDQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
r""" Enable or disable specified input image. If enabling input image, optional custom settings can be specified in customArgs. | def enableInputImageByName(self, inputImage, enabled=True, customArgs=None):
if inputImage not in getInputImageTypes():
self.logger.warning('Input image type %s is not recognized', inputImage)
return
if enabled:
if customArgs is None:
customArgs = {}
self.logger.debug('Enablin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enableInputImages(self, **inputImages):\n self.logger.debug('Updating enabled input images types with %s', inputImages)\n self.inputImages.update(inputImages)\n self.logger.debug('Enabled input images types: %s', self.inputImages)",
"def setEnabled(*args):",
"def setEnabled(*args):",
"def setEna... | [
"0.7061782",
"0.62825954",
"0.62825954",
"0.62825954",
"0.62825954",
"0.62825954",
"0.62825954",
"0.62825954",
"0.62825954",
"0.62825954",
"0.62825954",
"0.62825954",
"0.59351027",
"0.581261",
"0.581261",
"0.581261",
"0.581261",
"0.581261",
"0.581261",
"0.581261",
"0.581261",... | 0.75504714 | 0 |
Enable input images, with optionally custom settings, which are applied to the respective input image. Settings specified here override those in kwargs. | def enableInputImages(self, **inputImages):
self.logger.debug('Updating enabled input images types with %s', inputImages)
self.inputImages.update(inputImages)
self.logger.debug('Enabled input images types: %s', self.inputImages) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enableInputImageByName(self, inputImage, enabled=True, customArgs=None):\n if inputImage not in getInputImageTypes():\n self.logger.warning('Input image type %s is not recognized', inputImage)\n return\n\n if enabled:\n if customArgs is None:\n customArgs = {}\n self.logger.d... | [
"0.75860304",
"0.71119815",
"0.6290015",
"0.59151906",
"0.5834432",
"0.56261206",
"0.560337",
"0.5568557",
"0.5532304",
"0.55164206",
"0.55091166",
"0.5507203",
"0.54913306",
"0.53785723",
"0.5370891",
"0.53520036",
"0.53520036",
"0.53520036",
"0.53520036",
"0.53520036",
"0.5... | 0.8008344 | 0 |
Enable all classes and all features. | def enableAllFeatures(self):
self.logger.debug('Enabling all features in all feature classes')
for featureClassName in self.getFeatureClassNames():
self.enabledFeatures[featureClassName] = []
self.logger.debug('Enabled features: %s', self.enabledFeatures) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enable(self, *args, **kwargs):\n pass",
"def _enable(self):\n self.debug_log(\"Enabling...\")\n self._register_handlers()",
"def enable(cls):\r\n cls.disable()\r\n sys.meta_path.insert(0, cls())",
"def enable(self):\n pass",
"def enable(self):\n self.ena... | [
"0.65616536",
"0.64384973",
"0.63570607",
"0.63292027",
"0.627972",
"0.62389576",
"0.6151444",
"0.61159676",
"0.60977143",
"0.6018554",
"0.5983644",
"0.5950242",
"0.5950242",
"0.5927329",
"0.58994263",
"0.5877411",
"0.58279175",
"0.5823262",
"0.58101696",
"0.57101333",
"0.570... | 0.8068366 | 0 |
Enable or disable all features in given class. | def enableFeatureClassByName(self, featureClass, enabled=True):
if featureClass not in self.getFeatureClassNames():
self.logger.warning('Feature class %s is not recognized', featureClass)
return
if enabled:
self.logger.debug('Enabling all features in class %s', featureClass)
self.enable... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enableAllFeatures(self):\n self.logger.debug('Enabling all features in all feature classes')\n for featureClassName in self.getFeatureClassNames():\n self.enabledFeatures[featureClassName] = []\n self.logger.debug('Enabled features: %s', self.enabledFeatures)",
"def enableFeaturesByName(self, *... | [
"0.7448473",
"0.589912",
"0.57723415",
"0.57723415",
"0.57723415",
"0.57723415",
"0.57723415",
"0.57723415",
"0.57723415",
"0.57723415",
"0.57723415",
"0.57723415",
"0.57723415",
"0.547834",
"0.544477",
"0.53477913",
"0.5314227",
"0.5266131",
"0.5256946",
"0.5222854",
"0.5169... | 0.680213 | 1 |
Specify which features to enable. Key is feature class name, value is a list of enabled feature names. To enable all features for a class, provide the class name with an empty list or None as value. Settings for feature classes specified in enabledFeatures.keys are updated, settings for feature classes not yet present ... | def enableFeaturesByName(self, **enabledFeatures):
self.logger.debug('Updating enabled features with %s', enabledFeatures)
self.enabledFeatures.update(enabledFeatures)
self.logger.debug('Enabled features: %s', self.enabledFeatures) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enableFeatureClassByName(self, featureClass, enabled=True):\n if featureClass not in self.getFeatureClassNames():\n self.logger.warning('Feature class %s is not recognized', featureClass)\n return\n\n if enabled:\n self.logger.debug('Enabling all features in class %s', featureClass)\n ... | [
"0.7588935",
"0.7220157",
"0.6214421",
"0.6084086",
"0.59338534",
"0.59016854",
"0.58781534",
"0.5852401",
"0.5772626",
"0.5720475",
"0.5615681",
"0.5437084",
"0.54323184",
"0.5419842",
"0.5319318",
"0.53126454",
"0.5277723",
"0.5259413",
"0.5245193",
"0.5208251",
"0.5188673"... | 0.7475316 | 1 |
Preprocess the image and labelmap. If ImageFilePath is a string, it is loaded as SimpleITK Image and assigned to image, if it already is a SimpleITK Image, it is just assigned to image. All other cases are ignored (nothing calculated). Equal approach is used for assignment of mask using MaskFilePath. If normalizing is ... | def loadImage(self, ImageFilePath, MaskFilePath):
self.logger.info('Loading image and mask')
if isinstance(ImageFilePath, six.string_types) and os.path.exists(ImageFilePath):
image = sitk.ReadImage(ImageFilePath)
elif isinstance(ImageFilePath, sitk.SimpleITK.Image):
image = ImageFilePath
els... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess(path, path2 , scale):\n image = imread(path)\n label_ = imread(path2)\n\n #label_ = modcrop(label, scale)\n\n # Must be normalized\n input_ = image / 255.\n label_ = label_ / 255.\n\n #input_ = scipy.ndimage.interpolation.zoom(label_, (1./scale), prefilter=False)\n #input_ = scipy.ndimage.in... | [
"0.65085906",
"0.61711144",
"0.6159926",
"0.61240757",
"0.6091799",
"0.60409606",
"0.59914386",
"0.5914176",
"0.5849112",
"0.58378875",
"0.580039",
"0.5790209",
"0.57787704",
"0.5715158",
"0.5674759",
"0.55987114",
"0.5583947",
"0.55767995",
"0.5576684",
"0.5569302",
"0.55604... | 0.6324117 | 1 |
Generates provenance information for reproducibility. Takes the original image & mask filepath, as well as the resampled mask which is passed to the feature classes. Returns a dictionary with keynames coded as | def getProvenance(self, imageFilepath, maskFilepath, mask):
self.logger.info('Adding additional extraction information')
provenanceVector = collections.OrderedDict()
generalinfoClass = generalinfo.GeneralInfo(imageFilepath, maskFilepath, mask, self.kwargs, self.inputImages)
for k, v in six.iteritems(ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocessing(fish_class):\n\n\t# encode fish_class into integer\n\tfish_label = fish_labels[fish_class]\n\t\n\t# return a list of image directories for each image\n\timg_handles = glob.glob(data_dir + fish_class + '/' + '*.jpg')\n\t\n\t# build an empty list to store each img as np.ndarray\n\timgs = []\n\t\... | [
"0.552891",
"0.5448059",
"0.5411503",
"0.5371006",
"0.53595996",
"0.53296924",
"0.53267634",
"0.5300106",
"0.5253858",
"0.51922536",
"0.5189962",
"0.5188522",
"0.511655",
"0.51083356",
"0.50774795",
"0.50622326",
"0.5058947",
"0.5045965",
"0.50381124",
"0.50327414",
"0.502445... | 0.6158426 | 0 |
Compute signature using image, mask, \\kwargs settings. This function computes the signature for just the passed image (original or derived), it does not preprocess or apply a filter to the passed image. Features / Classes to use for calculation of signature are defined in | def computeFeatures(self, image, mask, inputImageName, **kwargs):
featureVector = collections.OrderedDict()
# Calculate feature classes
for featureClassName, enabledFeatures in six.iteritems(self.enabledFeatures):
# Handle calculation of shape features separately
if featureClassName == 'shape':... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_image_signature(self):\n pass",
"def generate_signature(self, image: Image) -> np.array:\n # Step 1: Load image as array of grey-levels\n im_array = preprocess_image(image)\n\n # Step 2a: Determine cropping boundaries\n if self.crop_percentiles is not None:... | [
"0.63266903",
"0.6138613",
"0.5865123",
"0.57880235",
"0.57880235",
"0.57880235",
"0.57880235",
"0.57880235",
"0.57880235",
"0.55053073",
"0.54949194",
"0.54736435",
"0.54736435",
"0.54573",
"0.54460126",
"0.5432876",
"0.5386486",
"0.53640884",
"0.5354486",
"0.5339103",
"0.53... | 0.0 | -1 |
Returns a list of all possible feature classes. | def getFeatureClassNames(self):
return self.featureClasses.keys() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def classes(self):\n return list(self._classes_generator())",
"def all(cls):\n results = []\n for name in FEATURES:\n feat = cls.get_by_name(name)\n if feat is None:\n feat = cls(name=name)\n cls.query.session.add(feat)\n results... | [
"0.7083753",
"0.7047303",
"0.6986615",
"0.693242",
"0.6733783",
"0.6730487",
"0.67188895",
"0.6682392",
"0.6679798",
"0.66244197",
"0.65775675",
"0.65715474",
"0.65669453",
"0.65622187",
"0.65523064",
"0.65395606",
"0.65354544",
"0.65196466",
"0.65172577",
"0.65092283",
"0.64... | 0.76183635 | 0 |
Returns a list of all possible features in provided featureClass | def getFeatureNames(self, featureClassName):
return self.featureClasses[featureClassName].getFeatureNames() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_features(self) :\n raise NotImplementedError",
"def all(cls):\n results = []\n for name in FEATURES:\n feat = cls.get_by_name(name)\n if feat is None:\n feat = cls(name=name)\n cls.query.session.add(feat)\n results.ap... | [
"0.6993939",
"0.68624085",
"0.6709082",
"0.669176",
"0.6688182",
"0.6592365",
"0.65730655",
"0.6553046",
"0.64251375",
"0.6407249",
"0.62570727",
"0.62361485",
"0.6235838",
"0.622333",
"0.6219865",
"0.61453277",
"0.609679",
"0.60952693",
"0.6093235",
"0.60358524",
"0.602832",... | 0.700178 | 0 |
Break each set of measurements into a subset a range of ~30120 seconds Use polyfit to find the best fit second order polynomial Find the inflection point of the best fit polynomial If the polyfit inflection point is very close to the median time point in the record An inflection is found | def find_inflections(study, record, sensor, segment, range):
# check if the inflections have already been found
path = [study, 'analyzed', 'inflections', 'all_times', str(range), record, segment]
pathJoined = os.path.join(*path)
file = os.path.join(pathJoined, sensor + ".csv")
if os.path.isfile(fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def findpeakl(data, zp, noise, bottom, top, r):\n length = len(data)\n width = (top - bottom)/(length - 1)\n absdata = []\n peak = []\n for i in range(length):\n absdata.append(abs(data[i] - zp[i]))\n i = 0\n fsnum = 0#fitting section number\n fsec = []#fitting section\n fdata = [... | [
"0.5755418",
"0.5700004",
"0.56831235",
"0.5657567",
"0.56494856",
"0.5626742",
"0.5622285",
"0.5621709",
"0.5592336",
"0.55063874",
"0.5498446",
"0.5471435",
"0.5460565",
"0.5431857",
"0.54292023",
"0.54071164",
"0.5399318",
"0.5387999",
"0.5365317",
"0.5360944",
"0.53576726... | 0.0 | -1 |
constant step to simplify algorithm | def compute_step(X):
return MOVING_STEP | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def step_constant(step):\n return step",
"def _step(self) -> None:",
"def step_linear_double(step):\n return step * 2",
"def horde_step(self, observation):",
"def step(self, state):",
"def step(self, s, a):\n raise NotImplementedError",
"def _step(self):\n pass",
"def warmup_step(... | [
"0.6762189",
"0.66126466",
"0.6571635",
"0.6309831",
"0.61970043",
"0.616405",
"0.61469406",
"0.61422324",
"0.61208844",
"0.608428",
"0.60831004",
"0.60501164",
"0.60283834",
"0.60187936",
"0.60081095",
"0.60065466",
"0.59746146",
"0.5946914",
"0.59426427",
"0.59346056",
"0.5... | 0.6568499 | 3 |
Produce figure 1 of the paper. | def fig_1():
epoch = 3
N = 60000
Nr = N
K = 32
n_iter = 256
Nstar = 16
data = 'dr10'
factor = 100.
features = ['psf_mag', 'model_colors', 'psf_minus_model']
filters = ['r', 'ug gr ri iz', 'ugriz']
message = 'pm_mc_pmm_r_all_all'
model = 'xdmodel_%s_%d_%d_%d_%d_%s.pkl' % (... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Figure4Main(self, supplemental1=False):\n if not supplemental1:\n example_cells = [5, 9, 17, 30]\n else:\n example_cells = [2, 6, 10, 11, 13, 18]\n\n start_letter = \"A\"\n parent_figure = None\n\n if not supplemental1:\n sizer = {\n ... | [
"0.6279921",
"0.61718154",
"0.6133319",
"0.5995697",
"0.5886824",
"0.58511084",
"0.5758559",
"0.57020164",
"0.56793135",
"0.5660195",
"0.5649378",
"0.55890405",
"0.5529864",
"0.55229014",
"0.5513478",
"0.55114144",
"0.5506495",
"0.54989177",
"0.5493746",
"0.54935914",
"0.5489... | 0.6285657 | 0 |
Produce figure 2 of the paper. | def fig_2():
epoch = 3
N = 60000
Nr = N
K = 32
n_iter = 256
Nstar = 16
data = 'dr10'
factor = 100.
features = ['psf_mag', 'model_colors', 'psf_minus_model']
filters = ['r', 'ug gr ri iz', 'ugriz']
message = 'pm_mc_pmm_r_all_all'
model = 'xdmodel_%s_%d_%d_%d_%d_%s.pkl' % (... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eps2(filename1, filename2, npair, ofname=\"output\",nstart=0, showfig=True):\n\n\tofnametex = ofname+\".tex\"\n\t\t\n\tf = open(ofnametex, 'w')\n\t\n\t\n\tf.write('''\\documentclass {article}\n\t\\usepackage{python}\n\t\\usepackage{graphicx}\n\t\\usepackage[top=1cm, bottom=1cm, left=1cm, right=2cm]{geometry}... | [
"0.6445488",
"0.6045485",
"0.59777915",
"0.59749174",
"0.5964026",
"0.5928806",
"0.5914323",
"0.5909216",
"0.5863343",
"0.58378446",
"0.5804495",
"0.5793671",
"0.577509",
"0.5739826",
"0.5738572",
"0.5731333",
"0.5709291",
"0.57086104",
"0.5707525",
"0.5670021",
"0.5585405",
... | 0.64443517 | 1 |
Produce figure 3 of the paper. | def fig_3():
epoch = 3
N = 60000
Nr = N
K = 32
n_iter = 256
Nstar = 16
data = 'dr10'
factor = 100.
features = ['psf_mag', 'model_colors', 'psf_minus_model']
filters = ['r', 'ug gr ri iz', 'ugriz']
message = 'pm_mc_pmm_r_all_all'
model = 'xdmodel_%s_%d_%d_%d_%d_%s.pkl' % (... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _figure_3():\n\n dataset_id = 3\n pkl_file = _pkl_file_path(dataset_id)\n with open(pkl_file, 'rb') as f:\n data = pickle.load(f)\n\n cdata = data[:, 33]\n seconds = np.arange(data.shape[0]) * 1. / 250\n\n plt.xlim(right=seconds[-1])\n plt.plot(seconds, cdata, color='black', linesty... | [
"0.6577245",
"0.6428742",
"0.6147097",
"0.6141355",
"0.61092234",
"0.5974064",
"0.59260553",
"0.59151864",
"0.5909971",
"0.5865524",
"0.58302325",
"0.5776051",
"0.5730802",
"0.5729746",
"0.5728845",
"0.5714727",
"0.5675011",
"0.5672505",
"0.5665346",
"0.56564784",
"0.56522834... | 0.6272048 | 2 |
Produce figure 4 of the paper. | def fig_4():
epoch = 3
N = 60000
Nr = N
K = 32
n_iter = 256
Nstar = 16
data = 'dr10'
factor = 100.
features = ['psf_mag', 'model_colors', 'psf_minus_model']
filters = ['r', 'ug gr ri iz', 'ugriz']
message = 'pm_mc_pmm_r_all_all'
model = 'xdmodel_%s_%d_%d_%d_%d_%s.pkl' % (... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eps4(filename1, filename2, filename3, filename4, nquad, ofname=\"output\",nstart=0, showfig=True):\n\n\tofnametex = ofname+\".tex\"\n\t\t\n\tf = open(ofnametex, 'w')\n\t\n\t\n\tf.write('''\\documentclass{article}\n\t\\usepackage{python}\n\t\\usepackage{graphicx}\n\t\\usepackage[top=1cm, bottom=1cm, left=1cm, r... | [
"0.6742629",
"0.6706074",
"0.66961974",
"0.63015217",
"0.6081837",
"0.60569793",
"0.58787054",
"0.58424",
"0.58161134",
"0.57563984",
"0.56880033",
"0.5668921",
"0.5633899",
"0.56335175",
"0.5615796",
"0.55709904",
"0.5535165",
"0.5531884",
"0.55115294",
"0.5468825",
"0.54641... | 0.68666595 | 0 |
Produce figure 5 of the paper. | def fig_5():
epoch = 3
N = 60000
Nr = N
K = 32
n_iter = 256
Nstar = 16
data = 'dr10'
factor = 100.
features = ['psf_mag', 'model_colors', 'psf_minus_model']
filters = ['r', 'ug gr ri iz', 'ugriz']
message = 'pm_mc_pmm_r_all_all'
model = 'xdmodel_%s_%d_%d_%d_%d_%s.pkl' % (... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eps5(filename1, filename2, filename3, filename4, filename5, nquad, ofname=\"output\",nstart=0, showfig=True):\n\n\tofnametex = ofname+\".tex\"\n\t\t\n\tf = open(ofnametex, 'w')\n\t\n\t\n\tf.write('''\\documentclass{article}\n\t\\usepackage{python}\n\t\\usepackage{graphicx}\n\t\\usepackage[top=1cm, bottom=1cm, ... | [
"0.71369654",
"0.6271395",
"0.6266325",
"0.62185913",
"0.6215509",
"0.6197117",
"0.6187425",
"0.6151191",
"0.59776217",
"0.5976559",
"0.5971548",
"0.5950157",
"0.59359837",
"0.59034",
"0.58745664",
"0.57873493",
"0.57561326",
"0.5732278",
"0.56726986",
"0.565986",
"0.56571025... | 0.709452 | 1 |
Produce figure 5 of the paper. | def fig_6():
epoch = 3
N = 60000
Nr = N
K = 32
n_iter = 256
Nstar = 16
data = 'dr10'
factor = 100.
features = ['psf_mag', 'model_colors', 'psf_minus_model']
filters = ['r', 'ug gr ri iz', 'ugriz']
message = 'pm_mc_pmm_r_all_all'
model = 'xdmodel_%s_%d_%d_%d_%d_%s.pkl' % (... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eps5(filename1, filename2, filename3, filename4, filename5, nquad, ofname=\"output\",nstart=0, showfig=True):\n\n\tofnametex = ofname+\".tex\"\n\t\t\n\tf = open(ofnametex, 'w')\n\t\n\t\n\tf.write('''\\documentclass{article}\n\t\\usepackage{python}\n\t\\usepackage{graphicx}\n\t\\usepackage[top=1cm, bottom=1cm, ... | [
"0.71369654",
"0.709452",
"0.6266325",
"0.62185913",
"0.6215509",
"0.6197117",
"0.6187425",
"0.6151191",
"0.59776217",
"0.5976559",
"0.5971548",
"0.5950157",
"0.59359837",
"0.59034",
"0.58745664",
"0.57873493",
"0.57561326",
"0.5732278",
"0.56726986",
"0.565986",
"0.56571025"... | 0.6271395 | 2 |
Bin up errors and return medians. | def bininator(magbins, dlt, mags, err):
meds = np.zeros_like(magbins)
for i in range(len(magbins)):
ind = (mags > magbins[i] - dlt) & (mags <= magbins[i] + dlt)
ind = ind & (err < 10.)
meds[i] = np.median(err[ind])
return meds | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mad(errors):\n med = np.median(errors)\n return np.median(np.abs(errors - med))",
"def medianError(root='./', align='align/align_d_rms_t',\n poly='polyfit_d_points/fit', points='points_d/',\n youngOnly=False, adjustPoints=False):\n # Load up our starlist\n s = starse... | [
"0.66861254",
"0.6022347",
"0.59237236",
"0.54876673",
"0.546723",
"0.54648757",
"0.5420873",
"0.5376365",
"0.5285055",
"0.5280651",
"0.5267501",
"0.52321583",
"0.5231567",
"0.5227237",
"0.5216346",
"0.51923925",
"0.51872134",
"0.51550794",
"0.512808",
"0.51256",
"0.51256",
... | 0.56515837 | 3 |
Plot the median error rates for single epoch, dr10, coadd, and posteriors. | def error_rates(epoch, model, features, filters, figname, fgal=0.5,
idx=-1, N=10000):
Xsingle, Xsinglecov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features,
filters, use_single=Fa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_errors(self):\n\n plt.title(\"Prediction Error\")\n plt.plot(self.errors)\n plt.ylabel(\"MSE (Mean Squared Error)\")\n plt.xlabel(\"Iteration\")\n plt.show()",
"def plot_train_test_errors(train_errors, test_errors, lambda_str , K , path, rng):\n plt.plot(range(rng),... | [
"0.5919546",
"0.5884253",
"0.58668184",
"0.5824854",
"0.5785445",
"0.57845217",
"0.57657117",
"0.575321",
"0.5747122",
"0.5738861",
"0.5735035",
"0.5716799",
"0.5704525",
"0.56938636",
"0.5692793",
"0.56819844",
"0.5668775",
"0.5664179",
"0.5662615",
"0.5659408",
"0.5657302",... | 0.59027916 | 1 |
Plot the data and contours for objects called star/galaxy. | def contours_and_data(epoch, model, features, filters, figname, fgal=0.5,
idx=-1, data='s82', N=60000):
if data == 's82':
# fetch Stripe 82 data
X, Xcov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, featur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot(self):\n y = self.projection\n mpl.scatter(y[:, 0], y[:, 1], c=self.data_class)\n mpl.show()",
"def plot_star_classes(obj_catalog):\n\n fig = plt.figure(num=None,figsize=(8,8), dpi=100)\n ax = fig.add_subplot(1,1,1)\n\n phot_class = obj_catalog.phot_star_class\n sclass =... | [
"0.6209733",
"0.6076914",
"0.5989726",
"0.5939084",
"0.5917941",
"0.5910307",
"0.59081817",
"0.5907614",
"0.58976495",
"0.5897461",
"0.5884078",
"0.58648926",
"0.58311903",
"0.5803733",
"0.58007014",
"0.57809025",
"0.5694475",
"0.565386",
"0.56447524",
"0.5631204",
"0.5627771... | 0.0 | -1 |
Plot the posterior for a star and a galaxy at r=21. | def posteriors_plot(model, features, filters, figname, fgal=0.5, N=60000,
idx=-1, seed=123):
# fetch DR10 data
X, Xcov = fetch_prepped_dr10data(N, fgal, features, filters)
# only data within window
dlt = 0.05
ind = (X[:, 0] > 21 - dlt) & (X[:, 0] < 21 + dlt)
X = X[ind]
X... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_posterior(x_train, y_train, x_test, kernel, params): \n\n\tparams = combine_params(params)\n\tfor p in params:\n\t\ty, mu, sigma_sq = sample_GP_posterior(x_train, y_train, x_test, \n\t\t\t\t\t\t\t\t\t\t\t zero_mean, kernel, p)\n\t\tmake_plot(x_test, y)\n\t\tplt.plot(x_train, y_train, 'ko')\n\t\tplt.gca()... | [
"0.63534963",
"0.6190498",
"0.5884882",
"0.58564085",
"0.5829954",
"0.58232963",
"0.57771003",
"0.57724506",
"0.57369727",
"0.5728631",
"0.57180935",
"0.5702705",
"0.56918395",
"0.5669759",
"0.5668919",
"0.5601508",
"0.5599467",
"0.55959314",
"0.55415785",
"0.55062276",
"0.54... | 0.58094907 | 6 |
Plot the single epoch and xd posts versus coadd | def xx_plot(epoch, model, features, filters, figname, fgal=0.5):
# fetch Stripe 82 data
X, Xcov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features,
filters, use_single=False)
N = 20000
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_per_epoch(d, d_label, title):\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.scatter(range(1,len(d)+1), d, c=\"b\", s=6, marker=\"o\", label=d_label)\n ax.set_xlabel(\"Epoch\")\n ax.set_ylabel(d_label)\n ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n ax.set_title(tit... | [
"0.6697393",
"0.6640096",
"0.639575",
"0.62434304",
"0.62414646",
"0.60780275",
"0.60544884",
"0.6052113",
"0.60176635",
"0.59831333",
"0.5908956",
"0.58954054",
"0.58913213",
"0.5875567",
"0.5859042",
"0.58542186",
"0.583645",
"0.5824845",
"0.5823258",
"0.5806909",
"0.579064... | 0.6113482 | 5 |
Plot the CMD for an SDSS cluster, both raw data and XD posteriors. | def glob_cmd(model, globular_name, features, filters, figname, mag=0,
color={2:(-4, -3)}, xlim=(-0.5, 2), ylim=(23, 16)):
# fetch data for the cluster
X, Xcov = fetch_glob_data(globular_name, features, filters)
# unpickle the XD model
if type(model) == str:
f = open(model, 'rb')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot(self):\n\t\tif (2 <= len(self.X) <= 3):\n\t\t\tvDataFrame(self.name, self.cursor).scatter(columns = self.X, catcol = \"dbscan_cluster\", max_cardinality = 100, max_nb_points = 10000)\n\t\telse:\n\t\t\traise ValueError(\"Clustering Plots are only available in 2D or 3D\")",
"def sse_plot(self):\n d... | [
"0.6540148",
"0.6198114",
"0.6179473",
"0.608459",
"0.6043866",
"0.5790243",
"0.5789512",
"0.57833713",
"0.57762975",
"0.57742345",
"0.57710123",
"0.57588714",
"0.5717733",
"0.5688315",
"0.5664204",
"0.56527424",
"0.56500745",
"0.56235504",
"0.5611287",
"0.5598483",
"0.559178... | 0.0 | -1 |
Plot the single epoch and xd posts versus coadd | def misclass_plot(epoch, model, features, filters, figname, fgal=0.5, idx=-1):
# fetch Stripe 82 data
X, Xcov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features,
filters, use_single=False)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_per_epoch(d, d_label, title):\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.scatter(range(1,len(d)+1), d, c=\"b\", s=6, marker=\"o\", label=d_label)\n ax.set_xlabel(\"Epoch\")\n ax.set_ylabel(d_label)\n ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n ax.set_title(tit... | [
"0.6697393",
"0.6640096",
"0.639575",
"0.62434304",
"0.62414646",
"0.6113482",
"0.60780275",
"0.60544884",
"0.6052113",
"0.60176635",
"0.59831333",
"0.5908956",
"0.58954054",
"0.58913213",
"0.5875567",
"0.5859042",
"0.58542186",
"0.583645",
"0.5824845",
"0.5823258",
"0.580690... | 0.57604045 | 25 |
Plot the single epoch and xd post histograms for psfmodel of stars. | def psfminusmodel_plot(epoch, model, features, filters, figname, fgal=0.5,
idx=-1):
# fetch Stripe 82 data
X, Xcov = fetch_prepped_s82data(epoch, fgal, features, filters)
Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features,
fil... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fig_2():\n epoch = 3\n N = 60000\n Nr = N\n K = 32\n n_iter = 256\n Nstar = 16\n data = 'dr10'\n factor = 100.\n features = ['psf_mag', 'model_colors', 'psf_minus_model']\n filters = ['r', 'ug gr ri iz', 'ugriz']\n message = 'pm_mc_pmm_r_all_all'\n model = 'xdmodel_%s_%d_%d_... | [
"0.68504846",
"0.68190914",
"0.6615435",
"0.64206356",
"0.63476354",
"0.6309046",
"0.6266294",
"0.6252298",
"0.6166132",
"0.613872",
"0.6133059",
"0.6095309",
"0.6094693",
"0.6094177",
"0.6077771",
"0.60618454",
"0.60509294",
"0.60507536",
"0.6030458",
"0.5987387",
"0.5984168... | 0.6640184 | 2 |
Compare quality of classifcation for a model with the s82 coadd. Should be a model trained on s82 single epoch data. | def s82_star_galaxy_classification(model_parms_file, epoch, Nstar,
features, filters, r_pmm, figname,
threshold=0., Nthreads=4):
# get the data
single, singlecov = fetch_matched_s82data(epoch, features=features,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compareAB(model1_name, model2_name, X_test_B, X_test_S, analysis_dir=\"Analysis/\"):\n #Load best weights\n model = tf.keras.models.load_model(\"Models/\"+model1_name)\n bkg_preds1 = model.predict(X_test_B).flatten()\n sig_preds1 = model.predict(X_test_S).flatten()\n\n model = tf.keras.models.lo... | [
"0.62082326",
"0.6184853",
"0.60857576",
"0.60311025",
"0.5970569",
"0.5959846",
"0.5899067",
"0.5848085",
"0.5829245",
"0.5823377",
"0.5774531",
"0.57647103",
"0.5750128",
"0.5733305",
"0.5724385",
"0.56959975",
"0.5674769",
"0.5669577",
"0.56046885",
"0.55895644",
"0.558356... | 0.56420314 | 18 |
get the dict of all nodes to their IP in the network | async def get_node_to_ips(
self, client: OpenrCtrlCppClient.Async, area: Optional[str] = None
) -> Dict:
node_dict = {}
keyDumpParams = self.buildKvStoreKeyDumpParams(Consts.PREFIX_DB_MARKER)
resp = Publication()
if not self.area_feature:
resp = await client.getK... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getNodes(self):\n nodes = [{\"address\": \"http://0.0.0.0:100\"}\n ,{\"address\": \"http://0.0.0.0:200\"}\n ,{\"address\": \"http://0.0.0.0:300\"}\n ,{\"address\": \"http://0.0.0.0:400\"}\n ,{\"address\": \"http://0.0.0.0:500\"}]\n retur... | [
"0.72978073",
"0.7055782",
"0.69536096",
"0.6807267",
"0.666083",
"0.6659717",
"0.65648925",
"0.6539925",
"0.64667284",
"0.64580387",
"0.64431643",
"0.6430201",
"0.6353768",
"0.63483626",
"0.63344246",
"0.62853783",
"0.62787384",
"0.6269251",
"0.6256344",
"0.6248679",
"0.6243... | 0.6633458 | 6 |
get routable IP address of node from it's prefix database | def get_node_ip(self, prefix_db: openr_types.PrefixDatabase) -> Any:
# First look for LOOPBACK prefix
for prefix_entry in prefix_db.prefixEntries:
if prefix_entry.type == network_types.PrefixType.LOOPBACK:
return ipnetwork.sprint_addr(prefix_entry.prefix.prefixAddress.addr)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_rug_address():\n net = netaddr.IPNetwork(ULA_PREFIX)\n return str(netaddr.IPAddress(net.first + 1))",
"def get_node_ip(\n self,\n name,\n ):\n pass",
"def ip(self, prefix = None):\n ip = self._update_ips()\n if prefix is None:\n return self... | [
"0.67512614",
"0.672374",
"0.66232747",
"0.6416445",
"0.6413486",
"0.64080024",
"0.6393339",
"0.63819945",
"0.62922806",
"0.6266255",
"0.61608344",
"0.6154852",
"0.6154319",
"0.61521226",
"0.6094634",
"0.6090791",
"0.6051159",
"0.6035991",
"0.6009145",
"0.59926647",
"0.598004... | 0.76153773 | 0 |
print the Kv Store peers | def print_peers(self, host_id: str, peers_list: Dict[str, Any]) -> None:
caption = "{}'s peers".format(host_id)
rows = []
column_labels = ["Peer", "State", "Address", "Port", "Area"]
for area, peers in peers_list.items():
for (peer, peerspec) in sorted(peers.items(), key=l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printNode(self):\n print(\"Instance ID :\" + str(self.instanceID))\n print(\"Counter value :\" + str(self.counter))\n print(\"appData :\")\n for i in range(0, len(self.appData)):\n print(self.appData[i])\n print(\"syncDataStructure :\")\n for key, value in s... | [
"0.67486143",
"0.6583794",
"0.64443433",
"0.63070315",
"0.597289",
"0.5909516",
"0.5815585",
"0.5787694",
"0.57851905",
"0.5736432",
"0.5707676",
"0.5669401",
"0.5662586",
"0.5660323",
"0.56579095",
"0.5654499",
"0.5636523",
"0.56351084",
"0.56335396",
"0.5626399",
"0.5602864... | 0.5975985 | 4 |
print keys from raw publication from KvStore | def print_kvstore_keys(
self, resp: Dict[str, Publication], ttl: bool, json: bool
) -> None:
# Export in json format if enabled
if json:
all_kv = {}
for _, kv in resp.items():
all_kv.update(kv.keyVals)
# Force set value to None
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_kvstore_values(\n self,\n resp: Publication,\n area: Optional[str] = None,\n ) -> None:\n\n rows = []\n for key, value in sorted(resp.keyVals.items(), key=lambda x: x[0]):\n val = self.deserialize_kvstore_publication(key, value)\n if not val:\n ... | [
"0.65500253",
"0.602648",
"0.602648",
"0.5896136",
"0.5843619",
"0.5818693",
"0.5740994",
"0.57164586",
"0.5690575",
"0.5682321",
"0.564288",
"0.56066865",
"0.5548096",
"0.55289364",
"0.5522452",
"0.5501682",
"0.5500842",
"0.54946166",
"0.5470928",
"0.5458189",
"0.54509246",
... | 0.6805582 | 0 |
Fetch the keyval publication for each area specified in areas via thrift call | async def fetch_keyvals(
self,
client: OpenrCtrlCppClient.Async,
areas: Set[Any],
keyDumpParams: KeyDumpParams,
) -> Dict[str, Publication]:
area_to_publication_dict = {}
for area in areas:
area_to_publication_dict[area] = await client.getKvStoreKeyValsFi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_kvstore_values(\n self,\n resp: Publication,\n area: Optional[str] = None,\n ) -> None:\n\n rows = []\n for key, value in sorted(resp.keyVals.items(), key=lambda x: x[0]):\n val = self.deserialize_kvstore_publication(key, value)\n if not val:\n ... | [
"0.5258662",
"0.50673336",
"0.49245554",
"0.48266882",
"0.48198405",
"0.47868848",
"0.4764812",
"0.47608864",
"0.474016",
"0.4707932",
"0.47043625",
"0.47033048",
"0.46736437",
"0.46550706",
"0.46489",
"0.46427444",
"0.46238628",
"0.46221396",
"0.46108213",
"0.4608701",
"0.46... | 0.7031373 | 0 |
run method that invokes _run with client and arguments | def run(self, *args, **kwargs) -> int:
async def _wrapper() -> int:
ret_val: Optional[int] = 0
async with get_openr_ctrl_cpp_client(
self.host,
self.cli_opts,
client_type=ClientType.THRIFT_ROCKET_CLIENT_TYPE,
) as client:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _run(self, client: OpenrCtrl.Client, *args, **kwargs) -> None:\n\n raise NotImplementedError",
"async def run():\n # Get the arguments from the parser\n args = client.arguments\n\n # If the help argument was used, return\n if hasattr(args, \"help\"):\n return\n # Otherwise, check... | [
"0.79941505",
"0.70497537",
"0.701971",
"0.69282407",
"0.6797228",
"0.67657524",
"0.67541826",
"0.6748296",
"0.67078376",
"0.67068344",
"0.66644394",
"0.6659236",
"0.6595019",
"0.6587069",
"0.6586537",
"0.65689015",
"0.6527023",
"0.64988273",
"0.64684373",
"0.6371168",
"0.636... | 0.6248496 | 24 |
classify kvstore prefix and return the corresponding deserialized obj | def deserialize_kvstore_publication(self, key, value):
options = {
Consts.PREFIX_DB_MARKER: openr_types.PrefixDatabase,
Consts.ADJ_DB_MARKER: openr_types.AdjacencyDatabase,
}
prefix_type = key.split(":")[0] + ":"
if prefix_type in options.keys():
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extract_immediate_prefix(obj_key:str)->str:\n immed_prefix = \"\"\n if len(obj_key.split(\"/\")) > 1:\n immed_prefix = obj_key.split(\"/\")[-2]\n \n return immed_prefix",
"def _get_prefix_attributes(self, prefix):\n return filter_dict_by_prefix(self.__dict__, prefix)",
"def get_p... | [
"0.5711935",
"0.55419487",
"0.5494264",
"0.54277533",
"0.5371517",
"0.52974343",
"0.52290225",
"0.5219558",
"0.52009475",
"0.5164571",
"0.5137503",
"0.51101",
"0.5093739",
"0.5079489",
"0.506517",
"0.5060772",
"0.5027817",
"0.50255066",
"0.50075495",
"0.5004343",
"0.49997222"... | 0.590767 | 0 |
print values from raw publication from KvStore | def print_kvstore_values(
self,
resp: Publication,
area: Optional[str] = None,
) -> None:
rows = []
for key, value in sorted(resp.keyVals.items(), key=lambda x: x[0]):
val = self.deserialize_kvstore_publication(key, value)
if not val:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_contents(self):\n print self.values",
"def show_vdcs_detail(self):\n for v in self.vdcs:\n print self.vdcs[v]",
"def show_vdcs(self):\n for v in self.vdcs:\n print v",
"def printMixData(self):\n\t\tprint \"OPERATED MIXNODE: Name: %s, address: (%d, %s), Pub... | [
"0.63312674",
"0.6113396",
"0.5822299",
"0.5791831",
"0.5784153",
"0.577308",
"0.5701333",
"0.56527925",
"0.5638593",
"0.561655",
"0.5576552",
"0.5510589",
"0.55091405",
"0.55068046",
"0.55068046",
"0.55062485",
"0.5444003",
"0.54272056",
"0.5409797",
"0.53990006",
"0.5387225... | 0.68602514 | 0 |
Build graph of adjacencies and return list of connected node from current nodeid | def get_connected_nodes(self, adj_keys: Publication, node_id: str) -> Set[str]:
import networkx as nx
edges = set()
graph = nx.Graph()
for adj_value in adj_keys.keyVals.values():
if adj_value.value:
adj_db = deserialize(openr_types.AdjacencyDatabase, adj_valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _graph_connected_component(graph, node_id):\n n_node = graph.shape[0]\n if sparse.issparse(graph):\n # speed up row-wise access to boolean connection mask\n graph = graph.tocsr()\n connected_nodes = np.zeros(n_node, dtype=np.bool)\n nodes_to_explore = np.zeros(n_node, dtype=np.bool)\n... | [
"0.65930283",
"0.6570428",
"0.648398",
"0.6338957",
"0.63259256",
"0.63064647",
"0.6305456",
"0.6290016",
"0.62735987",
"0.6261877",
"0.62043774",
"0.61851615",
"0.61685765",
"0.6158463",
"0.61408174",
"0.613847",
"0.6134505",
"0.60813373",
"0.60753703",
"0.60753703",
"0.6072... | 0.6298708 | 7 |
Print kvstore nodes information. Their loopback and reachability information. | def print_kvstore_nodes(
self,
connected_nodes: Set[str],
prefix_keys: Publication,
host_id: str,
node_area: Optional[Dict[str, str]] = None,
) -> None:
def _parse_loopback_addrs(addrs, value):
v4_addrs = addrs["v4"]
v6_addrs = addrs["v6"]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printNode(self):\n print(\"Instance ID :\" + str(self.instanceID))\n print(\"Counter value :\" + str(self.counter))\n print(\"appData :\")\n for i in range(0, len(self.appData)):\n print(self.appData[i])\n print(\"syncDataStructure :\")\n for key, value in s... | [
"0.72202766",
"0.6942311",
"0.66287684",
"0.6514881",
"0.63812757",
"0.6361851",
"0.6216663",
"0.6204839",
"0.62037903",
"0.6164678",
"0.6160873",
"0.6086526",
"0.6082106",
"0.60566115",
"0.6032304",
"0.60018545",
"0.59969217",
"0.59411764",
"0.593119",
"0.59187895",
"0.59098... | 0.6914931 | 2 |
get the kvs of a set of nodes | def dump_nodes_kvs(
self, nodes: set, all_nodes_to_ips: Dict, area: Optional[str] = None
):
kv_dict = {}
for node in nodes:
node_ip = all_nodes_to_ips.get(node, node)
kv = utils.dump_node_kvs(self.cli_opts, node_ip, area)
if kv is not None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_partitions(V,k):\n k_subs = k_subset(V,k)\n k_subs = uniq_subsets(k_subs)\n\n return k_subs",
"def kruskal(self):\n k = self.cc().max() + 1\n E = 2 * self.V - 2\n V = self.V\n Kedges = np.zeros((E, 2)).astype(np.int_)\n Kweights = np.zeros(E)\n iw = np.... | [
"0.59882766",
"0.5957845",
"0.59310323",
"0.5925573",
"0.57926774",
"0.57625383",
"0.57398385",
"0.5710748",
"0.56699103",
"0.56671077",
"0.56493664",
"0.564675",
"0.56295794",
"0.56295794",
"0.5627649",
"0.5616906",
"0.5609812",
"0.560659",
"0.5582245",
"0.5545183",
"0.55447... | 0.5574408 | 19 |
Takes StreamSubscriberInfo from thrift and returns list[str] (aka row) representing the subscriber | def get_subscriber_row(self, stream_session_info):
uptime = "unknown"
last_msg_time = "unknown"
if (
stream_session_info.uptime is not None
and stream_session_info.last_msg_sent_time is not None
):
uptime_str = str(
datetime.timedelta(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subscribers(id):\n return core.query(schema.streamBySubscribers, id)",
"def get_subscribers(self) -> Iterator[Any]:\n for subscription in self._subscriptions[self.id]:\n yield subscription.subscriber",
"def test_successful_subscriptions_list_subscribers(self) -> None:\n result =... | [
"0.61119455",
"0.53708094",
"0.51381725",
"0.5079605",
"0.5024306",
"0.49607125",
"0.49371487",
"0.49270263",
"0.4903581",
"0.4871267",
"0.48365107",
"0.48138362",
"0.48064584",
"0.47757068",
"0.4767832",
"0.47622314",
"0.47521242",
"0.47241798",
"0.4713322",
"0.4707597",
"0.... | 0.7634182 | 0 |
Checks if the local node is advertising atleast one adjacency key and one prefix key to the kvstore. Returns a boolean for each key type. | def _validate_local_key_advertisement(
self, area_to_publication_dict: Dict[str, Publication]
) -> Tuple[bool, bool]:
keys = []
for _, publication in area_to_publication_dict.items():
keys.extend(list(publication.keyVals.keys()))
is_adj_advertised = False
is_pre... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _print_local_node_advertising_check(\n self, is_advertising_adj: bool, is_advertising_prefix: bool\n ) -> None:\n\n is_pass = is_advertising_adj and is_advertising_prefix\n click.echo(\n self.validation_result_str(\n \"kvStore\",\n \"prefix and a... | [
"0.6733836",
"0.6396705",
"0.6381344",
"0.62376046",
"0.57620454",
"0.56948906",
"0.55602145",
"0.55404294",
"0.5508798",
"0.5494694",
"0.5464157",
"0.54515123",
"0.5451274",
"0.54367054",
"0.5413227",
"0.540365",
"0.5388373",
"0.53826374",
"0.53594834",
"0.5344876",
"0.53239... | 0.667407 | 1 |
Checks if all peers are in INITIALIZED state, | def _validate_peer_state(
self, area_to_peers: Dict[str, Dict[str, PeerSpec]]
) -> Dict[str, Dict[str, PeerSpec]]:
invalid_peers = defaultdict(dict)
for (area, peers) in area_to_peers.items():
for (peer_name, peer_spec) in peers.items():
if peer_spec.state != KvS... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_initial_states(self):\n return len(self.initial_states()) > 0",
"def is_initialized(self) -> bool:\n return (\n (self._exchange_params_by_currency_id is not None)\n and (self._utility_params_by_good_id is not None)\n and (self._transaction_fees is not None)\... | [
"0.684354",
"0.6604674",
"0.6497527",
"0.6300411",
"0.6294015",
"0.60291374",
"0.60048556",
"0.59718686",
"0.59673285",
"0.5922915",
"0.59170294",
"0.5847167",
"0.5826648",
"0.58103687",
"0.5803412",
"0.5773058",
"0.5765966",
"0.5762333",
"0.57160574",
"0.57032996",
"0.570051... | 0.5603364 | 34 |
Checks if the ttl of each key is above 3/4 of the max ttl | def _validate_key_ttl(
self,
publications: Dict[str, Publication],
ttl: int,
threshold: float = 0.75,
) -> Dict[str, Publication]:
area_to_invalid_keyvals = {}
for area, publication in publications.items():
invalid_keyvals = {
k: v
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_maxttl_greater_doc_expiry(self):\n for bucket in self.buckets:\n self._load_json(bucket, self.num_items, exp=int(self.maxttl)-100)\n self.sleep(int(self.maxttl-100), \"waiting for all docs to expire per maxTTL rule...\")\n self.expire_pager(self.servers)\n self.sleep... | [
"0.7159066",
"0.6974216",
"0.68057406",
"0.6800872",
"0.6693853",
"0.66240424",
"0.656756",
"0.62757313",
"0.609933",
"0.5843171",
"0.57584894",
"0.57158446",
"0.5664364",
"0.5661062",
"0.5621551",
"0.5562735",
"0.54820377",
"0.5463845",
"0.54153395",
"0.5397436",
"0.5379184"... | 0.6633002 | 5 |
Prints result of the ttl check along with information about keys with too low ttl The ttl printed is the ttl of the key right before the check occured | def _print_key_ttl_check(
self,
area_to_invalid_keys: Dict[str, Publication],
ttl: int,
threshold: float = 0.75,
) -> None:
click.echo(
self.validation_result_str(
"kvStore", "Key TTL Check", len(area_to_invalid_keys) == 0
)
)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ttl(self, key):\n return self._command(b'PTTL', key, handler=lambda ms: .001 * ms)",
"def get_ttl(self, keyword, key):",
"def ttl(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"ttl\")",
"def ttl(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"ttl\")",
"def ttl(se... | [
"0.66567254",
"0.6301414",
"0.6223945",
"0.6223945",
"0.6101219",
"0.5965713",
"0.5690484",
"0.5654071",
"0.56501955",
"0.56454045",
"0.56354886",
"0.5547257",
"0.5547257",
"0.5547257",
"0.5527442",
"0.55273604",
"0.55273604",
"0.549073",
"0.5475836",
"0.5457758",
"0.5383536"... | 0.7880797 | 0 |
Prints the result of the peer state check and a list of peers not in INITIALIZED state if there are any | def _print_peer_state_check(
self,
invalid_peers: Dict[str, Dict[str, PeerSpec]],
host_id: str,
) -> None:
click.echo(
self.validation_result_str(
"kvStore", "peer state check", len(invalid_peers) == 0
)
)
if len(invalid_peers... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_all_peer_status(self):\n statuses = self.pybird.get_peer_status()\n\n self.assertEquals(statuses[0]['name'], \"PS1\")\n self.assertEquals(statuses[0]['state'], \"Passive\")\n self.assertEquals(statuses[1]['name'], \"PS2\")\n self.assertEquals(statuses[1]['state'], \"Esta... | [
"0.6495497",
"0.6251295",
"0.6225992",
"0.6186012",
"0.61554384",
"0.60008013",
"0.57276255",
"0.5577009",
"0.5554054",
"0.55529517",
"0.5551861",
"0.55037373",
"0.5487797",
"0.5483634",
"0.54702264",
"0.54596984",
"0.54097784",
"0.5396765",
"0.5389472",
"0.5365949",
"0.53658... | 0.7865517 | 0 |
Prints the result of the validation check and the which keys are not being advertised if the validation check fails | def _print_local_node_advertising_check(
self, is_advertising_adj: bool, is_advertising_prefix: bool
) -> None:
is_pass = is_advertising_adj and is_advertising_prefix
click.echo(
self.validation_result_str(
"kvStore",
"prefix and adjacency key adv... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkResultsCorrection(self, result, valid_keys):\n for key in result:\n if key not in valid_keys:\n print(\"[ERROR] Key '%s' does not exist.\" % key)\n return False\n return True",
"def run_missing_value_check():\n print(\"\\n### CHECKING FOR MISSING... | [
"0.6539935",
"0.6483713",
"0.6392351",
"0.63436186",
"0.6324393",
"0.6318017",
"0.6257398",
"0.62552106",
"0.62457097",
"0.623596",
"0.6196751",
"0.6127988",
"0.6032944",
"0.59989476",
"0.5983608",
"0.59258735",
"0.5917128",
"0.5881947",
"0.58657074",
"0.58457834",
"0.5807657... | 0.0 | -1 |
returns a generator of degenerated sequences in scrambled order | def degenerate2(s):
from lasagna.utils import base_repr
n = s.count('N')
seed = hash(s) % (2**32 - 1)
rng = random.Random(seed)
random_base_ix = lambda: base_repr(rng.randint(0, 4**(n + 1) - 1), 4, n + 1)[::-1]
while True:
bases = ['ACTG'[int(j)] for j in random_base_ix()]
s2 = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate(self) -> List[str]:\n\n self._reset()\n\n res = self._get_interactions(\n random.randint(self._min_seq_len, self._max_seq_len))\n\n self._add_guarded_first_named_alloc(res)\n\n if random.randint(0, 1):\n # Add some noise between source and destinat... | [
"0.6526458",
"0.6346598",
"0.61941546",
"0.60155445",
"0.5956522",
"0.59486526",
"0.5940059",
"0.5862646",
"0.5813078",
"0.57996607",
"0.5775033",
"0.57735795",
"0.5773163",
"0.5760917",
"0.5757377",
"0.5739533",
"0.57337964",
"0.5724858",
"0.56738037",
"0.5655347",
"0.564458... | 0.67052954 | 0 |
start a new session to start app | def initialize(self, device, platform, version, app, package, activity):
desired_caps = dict()
desired_caps['platformName'] = platform
desired_caps['platformVersion'] = version
desired_caps['deviceName'] = device
desired_caps['app'] = app
desired_caps['appPackage'] = pack... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def start_session(self):\n\t\t...",
"def new_session(self):\n self.command(\"new\")",
"def start_session(self):\r\n ee.Initialize()",
"def startapp():",
"def start():\r\n\r\n userName = userLogin.login()\r\n runApp(userName)",
"def main():\n\n run_manual_session()\n # run_... | [
"0.7681355",
"0.70464206",
"0.70276684",
"0.6876244",
"0.68088776",
"0.6754588",
"0.6729491",
"0.6623179",
"0.6590256",
"0.65724987",
"0.6539288",
"0.64773875",
"0.6455504",
"0.6455504",
"0.6455504",
"0.6455504",
"0.64209837",
"0.64031553",
"0.64031553",
"0.63755596",
"0.6355... | 0.0 | -1 |
close app and quit session | def quit(self):
self.driver.close_app()
self.driver.quit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quit_app(self):\n self._socket_client.receiver_controller.stop_app()",
"def quit_app(self):\n self._socket_client.receiver_controller.stop_app()",
"def _endApp(self):\n try:\n self._smApp.close()\n except OSError:\n pass # igore OSErrors like 'No such ... | [
"0.7794985",
"0.77792263",
"0.75582784",
"0.7548624",
"0.75133824",
"0.7507985",
"0.7497864",
"0.74760485",
"0.7460032",
"0.7431441",
"0.73626417",
"0.72017676",
"0.7183301",
"0.717902",
"0.71531713",
"0.71192175",
"0.7060073",
"0.70542777",
"0.70294344",
"0.7022385",
"0.6983... | 0.71123177 | 16 |
start a new session to start app | def start(self, device, platform, version, app, package, activity):
self.initialize(device, platform, version, app, package, activity) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def start_session(self):\n\t\t...",
"def new_session(self):\n self.command(\"new\")",
"def start_session(self):\r\n ee.Initialize()",
"def startapp():",
"def start():\r\n\r\n userName = userLogin.login()\r\n runApp(userName)",
"def main():\n\n run_manual_session()\n # run_... | [
"0.7681355",
"0.70464206",
"0.70276684",
"0.6876244",
"0.68088776",
"0.6754588",
"0.6729491",
"0.6623179",
"0.6590256",
"0.65724987",
"0.6539288",
"0.64773875",
"0.6455504",
"0.6455504",
"0.6455504",
"0.6455504",
"0.64209837",
"0.64031553",
"0.64031553",
"0.63755596",
"0.6355... | 0.0 | -1 |
close app and restart session | def restart(self, device, platform, version, app, package, activity):
self.quit()
self.initialize(device, platform, version, app, package, activity) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop():\n app = get_vistrails_application()\n app.finishSession()\n app.save_configuration()\n app.destroy()",
"def exitApplication(self):\n try:\n self.repository.saveSession()\n print(\"Session saved :).\")\n except IOError:\n print(\"Could not sav... | [
"0.7208908",
"0.7080692",
"0.69472945",
"0.68616325",
"0.6811587",
"0.6711037",
"0.6690465",
"0.66641724",
"0.6650588",
"0.6638569",
"0.6549663",
"0.64569604",
"0.64456123",
"0.638764",
"0.6374605",
"0.6374515",
"0.63323486",
"0.628264",
"0.6239681",
"0.61703646",
"0.61612785... | 0.0 | -1 |
remove app from android device | def remove(self, package):
self.driver.remove_app(package) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_app(self):\n \n pass",
"def uninstall_app(self, package, keepdata=False):\n return self.adb.uninstall(package, keepdata)",
"def app_delete(self, name):\n self.core.api.os.shell.cmd('{0} delete app /app.name:\"{1}\"'.format(self.APP_CMD, name))",
"def remove_app(self, a... | [
"0.74033237",
"0.72458357",
"0.717659",
"0.7134077",
"0.70731086",
"0.70726144",
"0.70547813",
"0.6990547",
"0.6977605",
"0.6866595",
"0.68093693",
"0.6765531",
"0.67247427",
"0.667689",
"0.66132075",
"0.6551885",
"0.65018636",
"0.6441072",
"0.6422958",
"0.6422323",
"0.639456... | 0.6690135 | 13 |
Determines whether some element of an observable sequence satisfies a condition if present, else if some items are in the sequence. | def some(self, predicate=None):
source = self
def subscribe(observer):
def on_next(_):
observer.on_next(True)
observer.on_completed()
def on_error():
observer.on_next(False)
observer.on_completed()
return source.subscribe(on_next, observer... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forall(seq,cond):\n for x in seq:\n if not cond(x): return False\n return True",
"def forall(seq,cond):\n for x in seq:\n if not cond(x): return False\n return True",
"def any(seq, pred=None):\n for elem in itertools.ifilter(pred, seq):\n return True\n return False",
... | [
"0.6489119",
"0.6489119",
"0.6322223",
"0.62787896",
"0.62787896",
"0.6172666",
"0.6091153",
"0.59761876",
"0.5815191",
"0.5802926",
"0.5781212",
"0.56770223",
"0.56426907",
"0.56333035",
"0.5621713",
"0.5620997",
"0.5583275",
"0.5533149",
"0.5532246",
"0.55276513",
"0.547698... | 0.5921078 | 8 |
Compute the losses for a target batch, given a set of potential predictions. | def loss_grid(self, output, target):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_losses(self, predictions, targets):\n smpl_weight = targets['target_smpl_weight']\n\n losses = {}\n if self.loss_beta is not None:\n losses['loss_beta'] = self.loss_beta(\n predictions['pred_shape'] * smpl_weight,\n targets['target_beta'] * ... | [
"0.7635991",
"0.723824",
"0.723824",
"0.66826284",
"0.66593707",
"0.66399896",
"0.65882856",
"0.6515454",
"0.64670026",
"0.6433944",
"0.64061284",
"0.6397278",
"0.63851756",
"0.63324416",
"0.6295115",
"0.62873447",
"0.6285234",
"0.6284424",
"0.62837875",
"0.62794286",
"0.6277... | 0.0 | -1 |
Parse directories holding extracted frames from standard benchmarks | def parse_directory(path, key_func=lambda x: x[-11:],
rgb_prefix='img_',
flow_x_prefix='flow_x_',
flow_y_prefix='flow_y_',
level=1):
print('parse frames under folder {}'.format(path))
if level == 1:
frame_folders = glob.glob... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_directory(path, rgb_prefix='img_', flow_x_prefix='flow_x_', flow_y_prefix='flow_y_'):\n print('parse frames under folder {}'.format(path))\n frame_folders = glob.glob(os.path.join(path, '*'))\n\n def count_files(directory, prefix_list):\n lst = os.listdir(directory)\n cnt_list = [l... | [
"0.6806591",
"0.61956006",
"0.61574477",
"0.60362446",
"0.60252094",
"0.6013178",
"0.6008159",
"0.6006396",
"0.5959281",
"0.59024763",
"0.5896736",
"0.58548516",
"0.58537865",
"0.5809266",
"0.5792813",
"0.57871157",
"0.57588553",
"0.57568353",
"0.5742853",
"0.57063025",
"0.56... | 0.6455126 | 1 |
This function identifies the habitat area of a patch in which an individual is. | def identify_habarea(indiv_xy_position, habarea_map):
row=int(indiv_xy_position[0])
col=int(indiv_xy_position[1])
habarea=habarea_map[row][col]
return habarea | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def patch_areas(patch_ids):\n\n return np.bincount(patch_ids.reshape((-1,)))[1:]",
"def area(self):",
"def get_area(self):\n raise NotImplementedError()",
"def get_area(option_set):\n return option_set & AREA_MASK",
"def area(self, tileID):\n pass",
"def area(self):\n ...",
"... | [
"0.6068956",
"0.60643595",
"0.59910935",
"0.59910876",
"0.5972108",
"0.59208566",
"0.5889996",
"0.58316416",
"0.5813654",
"0.5810717",
"0.5796046",
"0.5765837",
"0.5745804",
"0.5711808",
"0.57045734",
"0.5694406",
"0.5686349",
"0.567481",
"0.56726205",
"0.56686926",
"0.566484... | 0.700036 | 0 |
Extract probability distributions obtained for each class of the classifier | def save_prod_dist(model,test_data):
y_label = 0
modeldist_probs = []
test = [sentence[0] for sentence in test_data]
y_labels = [sentence[1] for sentence in test_data]
for pdist in model.prob_classify_many(test):
modeldist_probs.append([pdist.prob('AIMX'), pdist.prob('OWNX'),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _class_distribution(y):\n unique, counts = np.unique(y, return_counts = True)\n\n percentages = counts / np.sum(counts)\n\n return unique, counts, percentages",
"def classProbs(observation, tree, classes):\n res = classify(observation, tree) #res = results\n total = sum(res.values())\n prob... | [
"0.7313617",
"0.7230231",
"0.7090465",
"0.6929822",
"0.6908797",
"0.68230456",
"0.6800955",
"0.66687405",
"0.63816804",
"0.6374652",
"0.631745",
"0.6301458",
"0.62666416",
"0.62589633",
"0.62549794",
"0.620444",
"0.6203373",
"0.61895466",
"0.6188604",
"0.61534476",
"0.6131033... | 0.58812314 | 38 |
Hobday et al. (2016) marine heat wave definition | def detect(
t: np.ndarray,
temp: np.ndarray,
climatologyPeriod=[None, None],
pctile=90,
windowHalfWidth=5,
smoothPercentile=True,
smoothPercentileWidth=31,
minDuration=5,
joinAcrossGaps=True,
maxGap=2,
maxPadLength=False,
coldSpells=False,
alternateClimatology: Option... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def manipulate_heat_data(self): \n self.exh.T_array = ( 0.5 * (self.exh.T_inlet_array +\n self.exh.T_outlet_array) + 273.15)\n self.exh.delta_T_array = ( self.exh.T_inlet_array -\n self.exh.T_outlet_array )\n \n self.cool.delta_T_array = ( self.cool.T_inlet_array -... | [
"0.64778084",
"0.61752194",
"0.6094722",
"0.6049435",
"0.6026047",
"0.598912",
"0.5969268",
"0.59183013",
"0.5851902",
"0.5799416",
"0.5755162",
"0.57525724",
"0.57220525",
"0.57075334",
"0.5683096",
"0.56653666",
"0.5665189",
"0.56563634",
"0.5644805",
"0.5637388",
"0.563225... | 0.0 | -1 |
Generate vectors for year, month, dayofmonth, and dayofyear create vectors of length = `len(t)` for DAY, MONTH, YEAR | def generate_time_vectors(t) -> Tuple:
T = len(t)
year = np.zeros((T))
month = np.zeros((T))
day = np.zeros((T))
doy = np.zeros((T))
for i in range(T):
year[i] = date.fromordinal(t[i]).year
month[i] = date.fromordinal(t[i]).month
day[i] = date.fromordinal(t[i]).day
# ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def arrayofyears(month1,day1,year1,month2,day2,year2):\n return (arrayofdaysmonthsyears(month1,day1,year1,month2,day2,year2))[2]",
"def generate_months(nmonths, year=2022, month=1, day=1):\n return np.asarray([\n f'{year:04d}-{month+i:02d}-{day:02d}T00:00:00.000000'\n for i in range(nmonths)\... | [
"0.6626277",
"0.6265367",
"0.62616616",
"0.61773056",
"0.61604315",
"0.6143473",
"0.60762256",
"0.5977502",
"0.5961502",
"0.5944625",
"0.59250414",
"0.5923237",
"0.58977413",
"0.5889735",
"0.5883183",
"0.5864521",
"0.5854827",
"0.5771225",
"0.57699937",
"0.57558084",
"0.57406... | 0.7770098 | 0 |
Calculate the climatology (mean) for each day and threshold (90pctile) | def calculate_daily_climatology(
pctile,
windowHalfWidth,
lenClimYear,
smoothPercentile,
smoothPercentileWidth,
thresh_climYear, # empty array
seas_climYear, # empty array
clim, # empty dict
feb29,
doyClim,
clim_start,
clim_end,
tempClim,
temp,
):
# Loop ov... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_percentiles_pediatrics_new():\n df_cdc = pd.read_csv(Path(\"growthviz-data/ext/growthfile_cdc_ext.csv.gz\"))\n df_who = pd.read_csv(Path(\"growthviz-data/ext/growthfile_who.csv.gz\"))\n df = df_cdc.merge(df_who, on=[\"agedays\", \"sex\"], how=\"left\")\n\n # Add weighting columns to support s... | [
"0.5650315",
"0.5634501",
"0.55678546",
"0.55586326",
"0.5522147",
"0.54622644",
"0.53760904",
"0.5370891",
"0.53564376",
"0.5344963",
"0.53288364",
"0.53195065",
"0.5314005",
"0.53047764",
"0.5268559",
"0.52627236",
"0.52579826",
"0.5235522",
"0.5231728",
"0.52182317",
"0.52... | 0.59774745 | 0 |
Calculate a smoothed time series. Uses a running average of an input time series using uniform window. | def smooth_climatologies(thresh_climYear, seas_climYear, smoothPercentileWidth):
# If the climatology contains NaNs, then assume it is a <365-day year and deal accordingly
if np.sum(np.isnan(seas_climYear)) + np.sum(np.isnan(thresh_climYear)):
valid = ~np.isnan(thresh_climYear)
thresh_climYear[v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moving_average(data, temporal_window=100):\n window = np.ones(temporal_window) / temporal_window\n return np.convolve(data, window, 'valid')",
"def runavg(ts, w):\n # Original length of ts\n N = len(ts)\n # make ts three-fold periodic\n ts = np.append(ts, np.append(ts, ts))\n # smooth by... | [
"0.6991072",
"0.6959405",
"0.6856522",
"0.68367374",
"0.6827277",
"0.6805591",
"0.6766678",
"0.6719291",
"0.67090917",
"0.67002445",
"0.6687949",
"0.6687138",
"0.6659968",
"0.66362816",
"0.6632409",
"0.6625122",
"0.6621746",
"0.66188395",
"0.66104066",
"0.6603746",
"0.6579043... | 0.0 | -1 |
Set all remaining missing temp values equal to the climatology | def fill_missing_temp_vals_with_climatology(temp: List, clim: Dict):
temp[np.isnan(temp)] = clim["seas"][np.isnan(temp)]
return temp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def interpolate_none(self):\n\n # Reset processed data\n self.u_processed_mps = np.copy(self.u_mps)\n self.v_processed_mps = np.copy(self.v_mps)\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan",
"de... | [
"0.60011417",
"0.5966284",
"0.58717424",
"0.57598615",
"0.56755286",
"0.5633607",
"0.5623653",
"0.5571434",
"0.5541537",
"0.5529372",
"0.55014426",
"0.54828835",
"0.5442271",
"0.5440241",
"0.54358196",
"0.5429773",
"0.5426778",
"0.54202056",
"0.54152465",
"0.54128855",
"0.540... | 0.7074793 | 0 |
Calculate a time series of "True" when threshold is exceeded, "False" otherwise | def find_exceedences(temp, clim):
exceed_bool = temp - clim["thresh"]
exceed_bool[exceed_bool <= 0] = False
exceed_bool[exceed_bool > 0] = True
# Find contiguous regions of exceed_bool = True
events, n_events = ndimage.label(exceed_bool)
return events, n_events | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def time_to_threshold(data, threshold, above=True, forecast_length=3600, interval_width=.95):\n # needs other input format: dataframe with two columns, ds and y\n model = Prophet(interval_width=interval_width)\n try:\n data.columns = ['ds', 'y']\n model.fit(data)\n except:\n print(... | [
"0.6474931",
"0.62953085",
"0.6285641",
"0.6216803",
"0.6081091",
"0.60358685",
"0.59739476",
"0.59377205",
"0.5912156",
"0.5896608",
"0.5830032",
"0.582725",
"0.578036",
"0.5776547",
"0.5772676",
"0.5752825",
"0.57133037",
"0.5700873",
"0.5662195",
"0.56507903",
"0.5636293",... | 0.0 | -1 |
Find all MHW events of duration >= minDuration Updates the mhw dictionary object. | def find_consecutive_exceedences_above_threshold(
events, n_events, mhw, joinAcrossGaps, maxGap
):
for ev in range(1, n_events + 1): # for each event
event_duration = (events == ev).sum()
if event_duration < minDuration: # is it longer than threshold?
continue
# extract the... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def duration_filter(ids_and_durations, minute_threshold):\n delta_sec = minute_threshold * 60 # Threshold passed in second to fit 'timedelta'.\n\n short_videos = [video[0] for video in ids_and_durations if video[1] <= timedelta(seconds=delta_sec)]\n long_videos = [video[0] for video in ids_and_durations ... | [
"0.54400396",
"0.4907825",
"0.4809929",
"0.4713698",
"0.4688717",
"0.46803078",
"0.4660191",
"0.46311387",
"0.46072233",
"0.45317474",
"0.45250195",
"0.44913363",
"0.4469598",
"0.44641155",
"0.44362915",
"0.44328907",
"0.44158658",
"0.4409055",
"0.43937212",
"0.43847978",
"0.... | 0.51633 | 1 |
Link heat waves that occur before and after a short gap (gap must be no longer than maxGap) | def join_events_across_gaps(maxGap, mhw):
# Calculate gap length for each consecutive pair of events
# from the start of the NEXT EVENT to the end of the PRIOR EVENT
gaps = np.array(mhw["time_start"][1:]) - np.array(mhw["time_end"][0:-1]) - 1
if len(gaps) > 0:
while gaps.min() <= maxGap:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SplitGap(data,gapsize,medwin,fluxdiff):\n \n # defining new empty lists and stuff\n pcount=0\n istamps=[]\n outData={}\n \n data['x'].mask = data['UnMasked']\n data['y'].mask = data['UnMasked']\n data['yerr'].mask = data['UnMasked']\n \n # median smoothing the lightcurve\n m... | [
"0.64434713",
"0.5801542",
"0.56155133",
"0.56064266",
"0.55303115",
"0.54668903",
"0.54343146",
"0.53865033",
"0.5327515",
"0.53137743",
"0.5306543",
"0.5272721",
"0.5268985",
"0.52571553",
"0.52498835",
"0.52097887",
"0.51800275",
"0.5167571",
"0.5143609",
"0.51013005",
"0.... | 0.58150005 | 1 |
Calculate marine heat wave properties | def calculate_event_characteristics(mhw, t, clim, temp):
mhw["n_events"] = len(mhw["time_start"])
# for each event caclulate the characteristics
for ev in range(mhw["n_events"]):
mhw["date_start"].append(date.fromordinal(mhw["time_start"][ev]))
mhw["date_end"].append(date.fromordinal(mhw["t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def manipulate_heat_data(self): \n self.exh.T_array = ( 0.5 * (self.exh.T_inlet_array +\n self.exh.T_outlet_array) + 273.15)\n self.exh.delta_T_array = ( self.exh.T_inlet_array -\n self.exh.T_outlet_array )\n \n self.cool.delta_T_array = ( self.cool.T_inlet_array -... | [
"0.63388246",
"0.59296554",
"0.58371013",
"0.5741513",
"0.5706787",
"0.5706253",
"0.5702298",
"0.559826",
"0.5589624",
"0.5559046",
"0.55509424",
"0.5541427",
"0.5477874",
"0.54478437",
"0.54435796",
"0.54355675",
"0.5431773",
"0.54197073",
"0.54046607",
"0.5366216",
"0.53429... | 0.0 | -1 |
find the peak time for that event (INTENSITY) | def find_peak(mhw, mhw_relSeas, ev, tt_start):
tt_peak = np.argmax(mhw_relSeas)
mhw["time_peak"].append(mhw["time_start"][ev] + tt_peak)
mhw["date_peak"].append(date.fromordinal(mhw["time_start"][ev] + tt_peak))
mhw["index_peak"].append(tt_start + tt_peak)
return mhw, tt_peak | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def peak_time(self):\n return np.array([self.wftime[ch][self.waveform[ch].argmax()] for ch in range(self.nchannels)])",
"def peak(self):\n pass",
"def find_alert_time(self) -> None:\n \n # Also not clear from the paper how to doe this,\n # use the first 10 data points in the ligh... | [
"0.7247735",
"0.6565663",
"0.64705825",
"0.64565206",
"0.6371662",
"0.62771463",
"0.62676597",
"0.61803085",
"0.61606777",
"0.61517483",
"0.6071804",
"0.6058603",
"0.60551894",
"0.6047215",
"0.60207856",
"0.5992068",
"0.5982672",
"0.5955427",
"0.5916465",
"0.59072816",
"0.590... | 0.6051851 | 13 |
Requires getting MHW strength at "start" and "end" | def rates_of_onset_and_decline(mhw, tt_start, tt_end, tt_peak, mhw_relSeas, temp, clim):
## For START of event
if tt_start > 0:
mhw_relSeas_start = 0.5 * (
mhw_relSeas[0] + temp[tt_start - 1] - clim["seas"][tt_start - 1]
)
mhw["rate_onset"].append(
(mhw_relSeas[tt... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strength(self) -> float:\n ...",
"def test_W_start(self):\t\t\n self.assertAlmostEqual(attempt.W[0], 12)",
"def test_sw2():\n B1 = 100\n B2 = 200\n h = 18\n t = 1\n H = h + 2 * t\n E1 = 20000\n E2 = 10000\n sections = ((B1, t, 0, E1), (B2, t, h + t, E2))\n EI, top, ... | [
"0.611811",
"0.58543104",
"0.54258955",
"0.53168124",
"0.527644",
"0.525759",
"0.52391386",
"0.5229558",
"0.5170525",
"0.5161343",
"0.5128514",
"0.511606",
"0.5106863",
"0.50794834",
"0.50664425",
"0.50602925",
"0.5058106",
"0.5052371",
"0.50257456",
"0.49980128",
"0.49905187... | 0.0 | -1 |
Return input array [1D numpy array] with all nan values removed | def nonans(array):
return array[~np.isnan(array)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_nans(arr, nan_rows=None):\n # Remove NaNs\n nconfigs, nt = arr.shape\n if nan_rows is None:\n mask = np.isfinite(arr)\n else:\n mask = np.array([n for n in np.arange(nconfigs) if n not in nan_rows])\n return arr[mask].reshape(-1, nt)",
"def removeNans(data):\n for i in ... | [
"0.77405065",
"0.7408961",
"0.7318458",
"0.72726125",
"0.70151496",
"0.6928957",
"0.6729866",
"0.6716962",
"0.65393853",
"0.65328234",
"0.64841986",
"0.6428685",
"0.6409222",
"0.6259641",
"0.6246225",
"0.62417316",
"0.62114847",
"0.62003404",
"0.61770093",
"0.6146045",
"0.614... | 0.7642145 | 1 |
Linearly interpolate over missing data (NaNs) in a time series. | def pad(data, maxPadLength=False):
data_padded = data.copy()
bad_indexes = np.isnan(data)
good_indexes = np.logical_not(bad_indexes)
good_data = data[good_indexes]
interpolated = np.interp(
bad_indexes.nonzero()[0], good_indexes.nonzero()[0], good_data
)
data_padded[bad_indexes] = in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def interpolate_missing(y):\n if y.isna().any():\n y = y.interpolate(method='linear', limit_direction='both')\n return y",
"def fill_missing_data_points(data):\n return data.interpolate()",
"def check_and_interpolate_nans(df):\n nan_count = df.isna().sum().sum()\n if nan_count > 0:\n ... | [
"0.7629114",
"0.7248559",
"0.71787924",
"0.7115637",
"0.70973015",
"0.7010133",
"0.69124943",
"0.68163675",
"0.68025076",
"0.67993385",
"0.6695243",
"0.6660976",
"0.66580003",
"0.6648088",
"0.6600599",
"0.6532182",
"0.6478971",
"0.6434493",
"0.63816845",
"0.6318016",
"0.62516... | 0.0 | -1 |
Performs a running average of an input time series using uniform window of width w. This function assumes that the input time series is periodic. | def runavg(ts, w):
# Original length of ts
N = len(ts)
# make ts three-fold periodic
ts = np.append(ts, np.append(ts, ts))
# smooth by convolution with a window of equal weights
ts_smooth = np.convolve(ts, np.ones(w) / w, mode="same")
# Only output central section, of length equal to the ori... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moving_average(data, temporal_window=100):\n window = np.ones(temporal_window) / temporal_window\n return np.convolve(data, window, 'valid')",
"def moving_average(data, window_size):\n window= np.ones(int(window_size))/float(window_size)\n return np.convolve(data, window, 'same')",
"def running... | [
"0.72755015",
"0.7019223",
"0.6980032",
"0.69147635",
"0.6886311",
"0.6849971",
"0.67949075",
"0.67820406",
"0.66995203",
"0.66644543",
"0.6640706",
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"0.64019024",
"0.6376318",
"0.63155216",
"0.6246977",
"0.6240341",
"0.62263185",
"0.622... | 0.7964289 | 0 |
Get all cover devices and setup them via config entry. | async def async_setup_entry(
hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback
) -> None:
entities = []
for gateway in hass.data[DOMAIN][entry.entry_id]["gateways"]:
for device in gateway.multi_level_switch_devices:
for multi_level_switch in device.multi_l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_devices():",
"async def async_setup_entry(hass, config_entry, async_add_entities):\n entities = []\n gateway = hass.data[DOMAIN][GATEWAYS_KEY][config_entry.entry_id]\n gateway.discover()\n for device in list(gateway.devices.values()): #TODO filter by type cover\n device_type = SELVE_C... | [
"0.65474904",
"0.6335435",
"0.6185986",
"0.60845584",
"0.59925437",
"0.5884936",
"0.5849123",
"0.58073187",
"0.58024555",
"0.5795098",
"0.579066",
"0.57888335",
"0.57872635",
"0.578623",
"0.578623",
"0.57815707",
"0.5763278",
"0.57449365",
"0.57423085",
"0.5685192",
"0.568451... | 0.58917755 | 5 |
Initialize a climate entity within devolo Home Control. | def __init__(
self, homecontrol: HomeControl, device_instance: Zwave, element_uid: str
) -> None:
super().__init__(
homecontrol=homecontrol,
device_instance=device_instance,
element_uid=element_uid,
)
self._attr_device_class = CoverDeviceClass.BLI... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n \n self._citydb = CityDB()\n self._depression = 6 # Set default depression in degrees",
"def test_init(self, fixture_environment):\n\n # Generate city object\n city_object = cit.City(environment=fixture_environment)\n\n # Check inheritance from c... | [
"0.5964611",
"0.5734109",
"0.5678026",
"0.5661269",
"0.56197786",
"0.5596879",
"0.5563419",
"0.5556939",
"0.55399334",
"0.552751",
"0.546134",
"0.54588443",
"0.5418741",
"0.5418111",
"0.5384016",
"0.53809404",
"0.53809404",
"0.53710335",
"0.53685933",
"0.53409564",
"0.5339822... | 0.0 | -1 |
Return the current position. 0 is closed. 100 is open. | def current_cover_position(self) -> int:
return int(self._value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPos(self):\n return self.__current_pos",
"def _get_pos(self):\n return self._pos",
"def get_pos(self):\r\n return self.pos",
"def tell(self):\n _complain_ifclosed(self._closed)\n return self._position",
"def get_pos(self):\n return self.pos",
"def getPosit... | [
"0.7595",
"0.7341735",
"0.72841847",
"0.72672564",
"0.72579455",
"0.7145039",
"0.7109477",
"0.7088855",
"0.7027087",
"0.7010361",
"0.7010361",
"0.69745183",
"0.6952913",
"0.6940286",
"0.69308114",
"0.69220424",
"0.69075656",
"0.69002414",
"0.68650085",
"0.6839626",
"0.6835894... | 0.6384917 | 74 |
Return if the blind is closed or not. | def is_closed(self) -> bool:
return not bool(self._value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _isclose(self):\n return self.dp.state()==PyTango.DevState.CLOSE",
"def is_closed(self) -> bool:",
"def is_closed(self):\r\n return self.position == SinglePhaseBreaker.CLOSED",
"def isClosed(self):\n pass",
"def is_closed(self) -> bool:\n lopen = self.left_boundary['open']\n... | [
"0.787848",
"0.7705696",
"0.770256",
"0.7663994",
"0.765039",
"0.7616886",
"0.7553198",
"0.7548185",
"0.7519958",
"0.7517888",
"0.7515429",
"0.743554",
"0.74284077",
"0.73875725",
"0.7385514",
"0.7315057",
"0.7315057",
"0.7308602",
"0.7180794",
"0.7131144",
"0.7065028",
"0.... | 0.71877086 | 18 |
Set the blind to the given position. | def set_cover_position(self, **kwargs: Any) -> None:
self._multi_level_switch_property.set(kwargs["position"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setPosition(position):",
"def setPosition(self, *args):\n return _libsbml.BoundingBox_setPosition(self, *args)",
"def bias(self, value):\n self.mbmod.bias = value",
"def set_position(self, x, y):\n self.tx = -x\n self.ty = -y",
"def SetBlindMode(self, agwFlags):\r\n\r\n ... | [
"0.6343822",
"0.60181844",
"0.5908633",
"0.58790344",
"0.5868035",
"0.58086485",
"0.5772332",
"0.5769417",
"0.5769417",
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"0.5769417",
"0.5769417",
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"0.5769417",
"0.5769417",
"0.5769417",
"0.5769417",
"0.57621205",
"0.57330173",
"0.5698484"... | 0.0 | -1 |
Convert something in [1,1] to [0,1] | def normalize_to_prob(inp):
return (inp + 1)/2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zero_to_ones(L):\n return [-1 if val == 0 else 1 for val in L]",
"def invert_zero_one(sequence):\n return [1 - code for code in sequence]",
"def intconv(arg):\n return([arg[0], int(arg[1])] + arg[2:])",
"def invert_v(val):\n return [val[0],1-val[1]]",
"def binary_converter(probs):\n re... | [
"0.6727341",
"0.66507214",
"0.6430734",
"0.6361047",
"0.6328756",
"0.6125416",
"0.6044102",
"0.59924",
"0.5975442",
"0.5952622",
"0.5947834",
"0.59292996",
"0.58282095",
"0.58242655",
"0.5808415",
"0.5786242",
"0.5779058",
"0.575488",
"0.5746223",
"0.57387036",
"0.5726756",
... | 0.0 | -1 |
Assigns a score to the coursePlan based on the rarity of the course, the number of courses this one makes available in the future, and the users need and preference for a particular type of elective | def heuristics(course, suggestedPlan, user):
score = course.score
bonus = 0
return score + bonus | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _score_to_decision(self, score):",
"def scoring(self):\n pass",
"def score(self):\n raise NotImplementedError()",
"def _cost_caught_by_police(self):\n if self.fine_frequency != 0:\n if self.number_of_courses % self.fine_frequency == 0 and self.number_of_courses != 0:\n ... | [
"0.60130614",
"0.58570117",
"0.5749423",
"0.5732798",
"0.56482965",
"0.55836266",
"0.55628306",
"0.5561281",
"0.5544241",
"0.54613525",
"0.54533947",
"0.54433405",
"0.54193467",
"0.5417941",
"0.54135525",
"0.54113394",
"0.5402817",
"0.54014575",
"0.53850657",
"0.538445",
"0.5... | 0.7116717 | 0 |
Takes a user object and generates the shortest path to graduation. | def automated(user):
# Start should be a Plan() and will include any classes the user has already taken to this point. It should have
# accurate counts of its course typesTaken before starting the search. Goal is the end state desired by the user.
# Usually this will be the gradReqs for the users Curriculu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_pathways(users, job_graph, debug, min_likelihood_thr=0.2):\r\n start_time = time.time()\r\n __print_msg('Computing career pathways...', debug)\r\n user_pathways = {}\r\n tot_users = len(users)\r\n i = 0\r\n for user, user_jobs in users.items():\r\n user_pathway = compute_user_p... | [
"0.584734",
"0.5654354",
"0.55678916",
"0.5486526",
"0.54030484",
"0.538108",
"0.53505796",
"0.5349955",
"0.53403103",
"0.5308392",
"0.5268799",
"0.5247845",
"0.5236312",
"0.5192242",
"0.5121435",
"0.5113122",
"0.51009756",
"0.5100681",
"0.50975037",
"0.5097221",
"0.50766873"... | 0.5956283 | 0 |
This function is called to check if a username / password combination is valid. | def check_auth(username, password):
return username == 'jeffkoons' and password == 'likesweirdbaloons' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_auth(username, password, expected_user, expected_pw):\n return username == expected_user and password == expected_pw",
"def validate_authentication(self, username, password):\n return self.user_table[username]['pwd'] == password",
"def check_auth(username, password):\n return usernam... | [
"0.82211864",
"0.8146436",
"0.7975914",
"0.7962432",
"0.7885527",
"0.78588223",
"0.78382313",
"0.78288996",
"0.7815222",
"0.77856815",
"0.77700555",
"0.7760794",
"0.7754975",
"0.77548647",
"0.7750886",
"0.7749223",
"0.77322245",
"0.7715806",
"0.7687129",
"0.7685904",
"0.76736... | 0.7798579 | 9 |
Sends a 401 response that enables basic auth | def authenticate():
return Response(
'Could not verify your access level for that URL.\n'
'You have to login with proper credentials', 401,
{'WWW-Authenticate': 'Basic realm="Login Required"'}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def authenticate():\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\"api\"'})",
"def authenticate():\n return Response(\n '', 401, {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )",
"def authenticate():\n return Response(\n 'You have to login with pro... | [
"0.8095378",
"0.80943215",
"0.7960329",
"0.785324",
"0.7844362",
"0.78259075",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
"0.7812201",
... | 0.7749071 | 35 |
graph from the task json with TaskResource named tuples living in nodes | def build_taskgraph(tasks):
global taskgraph
for task in tasks:
if not task['id'] in taskgraph:
taskgraph.add_node(task["id"])
data = str(task["DATA"])
tr = TaskResources(ram=int(task['RAM']), cpu=int(
task['CPU']), hdd=int(task['HDD']))
taskgraph.node[tas... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def task_relation_json(self) -> List[Dict]:\n if not self.tasks:\n return [self.tasks]\n else:\n self._handle_root_relation()\n return [tr.get_define() for tr in self._task_relations]",
"def _subgraph(self):\n return {\n 'type': 'SubgraphTask',\n ... | [
"0.6246238",
"0.6081832",
"0.6065744",
"0.6018206",
"0.6018206",
"0.58417726",
"0.5779049",
"0.5658437",
"0.5583271",
"0.55294436",
"0.55002636",
"0.5468481",
"0.54389864",
"0.53757447",
"0.5373088",
"0.53719306",
"0.53705764",
"0.5357756",
"0.53515995",
"0.5348182",
"0.53369... | 0.71598744 | 0 |
receive and build taskgraph | def tasks_recv():
recv_json = request.get_json()
build_taskgraph(recv_json)
return 'ok' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_taskgraph(tasks):\n global taskgraph\n for task in tasks:\n if not task['id'] in taskgraph:\n taskgraph.add_node(task[\"id\"])\n data = str(task[\"DATA\"])\n tr = TaskResources(ram=int(task['RAM']), cpu=int(\n task['CPU']), hdd=int(task['HDD']))\n t... | [
"0.65182334",
"0.63080335",
"0.6293795",
"0.6204706",
"0.6186549",
"0.61605525",
"0.61220175",
"0.61220175",
"0.61082584",
"0.6104672",
"0.59439194",
"0.591472",
"0.5882329",
"0.5751076",
"0.5699367",
"0.5686174",
"0.5679653",
"0.56747794",
"0.56723875",
"0.5661628",
"0.56508... | 0.72909814 | 0 |
Set the edgenode's resources for debugging | def set_resources():
global available_resources
global EdgenodeResources
recv_json = request.get_json()
for resourcename, value in recv_json.items():
available_resources[resourcename] = value
# TODO make this better
EdgenodeResources = [TaskResources(ram=int(available_resources['RAM']), ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resources(self):",
"def resources(self, resources):\n self._resources = resources",
"def setOptManagerResources(o): # pylint: disable=global-statement\n # pylint: disable=global-statement\n global OPT_MANAGER_RESOURCES_PGAAS\n OPT_MANAGER_RESOURCES_PGAAS = \"{}/pgaas\".format(o)",
"def resource... | [
"0.5880429",
"0.5852034",
"0.57128286",
"0.56084967",
"0.56084967",
"0.56084967",
"0.56084967",
"0.5529932",
"0.5283272",
"0.5241133",
"0.5212937",
"0.5193305",
"0.514852",
"0.51433814",
"0.51408863",
"0.5116762",
"0.50918823",
"0.5086745",
"0.5068364",
"0.5036334",
"0.501060... | 0.6285629 | 0 |
Loads the textual templates of questions and saves them in a list. | def load_templates(self):
self.templates = []
if os.path.exists("question_templates.txt"):
for line in open("question_templates.txt", "r"):
self.templates.append(line.replace("\n", "")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_questions(self, verbose=True):\n for question in self.question_list:\n question.load_question(self.data)",
"def load_templates(self):\n TemplateHandler.templates = []\n for template in os.listdir(TemplateHandler.templates_path):\n template_config = self.load_te... | [
"0.6505029",
"0.5937621",
"0.5928935",
"0.5910248",
"0.5856114",
"0.5806971",
"0.57281137",
"0.5690211",
"0.56250936",
"0.56000656",
"0.5596543",
"0.55873555",
"0.55826813",
"0.55461633",
"0.5500507",
"0.5485882",
"0.54667103",
"0.5460839",
"0.5444884",
"0.5443755",
"0.544123... | 0.8599687 | 0 |
Uses the list of properties to generate a random question using the text file of question templates. If there are no more unknown properties, then the program can be completed. | def generate_language(self, properties):
drink_property = self.find_drink_property(properties)
if drink_property:
return self.generate_sentence(drink_property)
else:
return "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def generate_question(self) -> None:\n topic = choice(list(self._topics.keys()))\n arrayList = await self.session.get(\"https://restcountries.eu/rest/v2\")\n arrayList = await arrayList.json() # get request to the country API\n countries = []\n \n for _ in range(4):\... | [
"0.6108525",
"0.58774537",
"0.58263105",
"0.5780516",
"0.5709611",
"0.56744903",
"0.5644065",
"0.5475492",
"0.54661214",
"0.5441473",
"0.5415947",
"0.53851706",
"0.5381618",
"0.53369546",
"0.53267866",
"0.5318371",
"0.5291741",
"0.5268244",
"0.5259975",
"0.5259326",
"0.525599... | 0.0 | -1 |
Generates two random numbers to get a random property about which there is no knowledge yet, which is indicated by 'None'. If there is knowledge, then this will either be indicated by 'True' or 'False'. If no 'None' is found for 5 times in a row, there is done a check to make sure that there are still 'Nones' in the li... | def find_drink_property(self, properties):
split_status = ""
tries = 0
while split_status != "None" and tries <= 5:
random_drink = randint(0, len(properties) - 1)
random_property = randint(1, len(properties[random_drink]) - 1)
drink_property = properties[ran... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random (self, checkfn=None):\n if len(self) == 0:\n return None\n return self.random_pick(checkfn=checkfn)[1]",
"def maybe(self):\n return random.getrandbits(1)",
"def random (self, checkfn=None):\n if len(self) == 0:\n return None\n if checkfn is No... | [
"0.5694869",
"0.5662974",
"0.56016517",
"0.55177194",
"0.544953",
"0.5439609",
"0.53909516",
"0.537335",
"0.5364247",
"0.5350749",
"0.53295314",
"0.5328545",
"0.52704114",
"0.5237583",
"0.5216445",
"0.5216445",
"0.5216065",
"0.5210262",
"0.5208821",
"0.5197762",
"0.5170702",
... | 0.5758818 | 0 |
Finds the first occurrence of 'None' in the list of properties. | def find_first_none(self, properties):
for drink in properties:
iterate_drink = iter(drink)
next(iterate_drink)
for drink_property in iterate_drink:
split_property = drink_property.split(": ")[1].split(" //")[0]
if split_property == "None":
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_property(properties, key):\n matching = [prop.split('=').pop(1) for prop in properties\n if prop.startswith(key + '=')]\n if len(matching) > 1:\n raise ValueError('Property %r should appear only once in %r' %\n (key, properties))\n if '' in matching:\... | [
"0.6420576",
"0.6140944",
"0.56629044",
"0.5504727",
"0.5462736",
"0.5437577",
"0.53297234",
"0.52824485",
"0.5245571",
"0.52292776",
"0.5227138",
"0.5211976",
"0.51856893",
"0.5184328",
"0.5180561",
"0.5177724",
"0.516806",
"0.5162539",
"0.5130421",
"0.5128275",
"0.51195353"... | 0.8134218 | 0 |
Uses the found drink property to generate a sentence from one of the templates. | def generate_sentence(self, complete_property):
drink_property = complete_property.split(": ")[0]
property_type = complete_property.split(": ")[1].split("// ")[1]
template = self.get_template(property_type)
question = template.replace("{" + property_type + "}", drink_property)
r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sentence(self):",
"def generate_language(self, properties):\n\n drink_property = self.find_drink_property(properties)\n\n if drink_property:\n return self.generate_sentence(drink_property)\n else:\n return \"\"",
"def linkTemplates(self, sentence):\n templa... | [
"0.6083435",
"0.6067856",
"0.60557234",
"0.58162034",
"0.5800545",
"0.57799345",
"0.57570976",
"0.5742893",
"0.5580341",
"0.5577429",
"0.55747986",
"0.5573053",
"0.55707955",
"0.55657536",
"0.5488579",
"0.5484002",
"0.5479624",
"0.54288805",
"0.538444",
"0.5365156",
"0.534202... | 0.66812485 | 0 |
Retrieves the correct template to ask a question with the found drink property and its type. | def get_template(self, property_type):
valid_templates = [s for s in self.templates if "{" + property_type +
"}" in s]
return choice(valid_templates) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def question_generator(self):\n self.status_conv = 'yes_no_question_asked'\n questions = config.questions\n if not self.voyage.get('voyageurs') and 'voyageur_add' not in self.infos_needed:\n self.infos_needed.append('voyageur_add')\n if self.infos_needed:\n if self... | [
"0.55158",
"0.54558504",
"0.53866625",
"0.5272962",
"0.5254931",
"0.5225667",
"0.5201388",
"0.51010674",
"0.50510913",
"0.5043558",
"0.4993756",
"0.49860686",
"0.49789208",
"0.497429",
"0.49692836",
"0.49514955",
"0.49476779",
"0.4947658",
"0.49140137",
"0.49097073",
"0.49053... | 0.5877344 | 0 |
Returns the correct form of a question such that e.g. 'a(n) strainer' becomes 'a strainer'. | def get_correct_form(self, template):
if "(" in template and ")" in template:
index = template.index(")")
first_letter = template[index+2]
if self.is_vowel(first_letter):
template = template.replace("a(n)", "an")
else:
template = t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize_answer(s):\n\n def remove_articles(text):\n return re_art.sub(' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n return re_punc.sub(' ', text) # convert punctuation to spaces\n\n def lower(text):\n return tex... | [
"0.6192208",
"0.6192208",
"0.61774063",
"0.615062",
"0.6132909",
"0.6085301",
"0.6085301",
"0.6061046",
"0.6054664",
"0.6050931",
"0.60430825",
"0.5989373",
"0.59198713",
"0.5915252",
"0.58585846",
"0.58305085",
"0.57404023",
"0.5724126",
"0.5723947",
"0.5629233",
"0.5621194"... | 0.6070443 | 7 |
Returns a boolean that indicates whether a letter is a boolean. | def is_vowel(self, letter):
if letter in ("a", "e", "i", "o", "u", "A", "E", "I", "O", "U"):
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def boolean(s):\r\n ss = str(s).lower()\r\n if ss in TRUTHY_STRINGS:\r\n return True\r\n elif ss in FALSY_STRINGS:\r\n return False\r\n else:\r\n raise ValueError(\"not a valid boolean value: \" + repr(s))",
"def __charToBool(self, char):\n\n if char == '1':\n r... | [
"0.7187412",
"0.7018804",
"0.69296837",
"0.68297184",
"0.6738983",
"0.67321396",
"0.66973674",
"0.6673456",
"0.66617125",
"0.6620476",
"0.6616839",
"0.6589189",
"0.65791404",
"0.6567738",
"0.6567653",
"0.6552819",
"0.65400046",
"0.65357196",
"0.6515047",
"0.65084463",
"0.6489... | 0.0 | -1 |
Given a list of list of Results, return a named threetuple with the an item with the name of each function in self.stat_functions. Each of these items is itself a similar threetuple, but each item is, e.g., the maximum of the maximums of the results. | def statistics(self, results):
nt = namedtuple("Statistic", [n.__name__ for n in self.stat_functions])
s = []
for f_one in self.stat_functions:
inside = []
for f_two in self.stat_functions:
inside.append(f_two(f_one(r.results) for r in results))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_func_tuples():\n func_tuples = [\n ('met_gumeJ1_3sopt_tr20', 'Rel-UME J1', 'C1-.'),\n ('met_gumeJ5_3sopt_tr20', 'Rel-UME J5', 'r-^'),\n ('met_gfssdJ1_3sopt_tr20', 'Rel-FSSD J1', 'C4--'),\n ('met_gfssdJ5_3sopt_tr20', 'Rel-FSSD J5', 'b-x'),\n\n ('met_... | [
"0.56247234",
"0.5412346",
"0.52866143",
"0.5263205",
"0.5087793",
"0.50818396",
"0.50043947",
"0.50017667",
"0.49602503",
"0.49288338",
"0.49267918",
"0.48980197",
"0.4889592",
"0.488622",
"0.48502252",
"0.4846984",
"0.4838837",
"0.4835714",
"0.48321322",
"0.4820766",
"0.481... | 0.7310715 | 0 |
Displays up to 12 charaters on the hp33401a | def hp34401a_lcd_disp(hp_meter, message):
hp_meter.write("DISP:TEXT:CLE")
hp_meter.write("DISP:TEXT '" + str(message) + "'") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def banner_ascii():\n print(\"\")\n print(f\"\\n{RED} Steganography Tool{RESET}\")\n print(f\"{RED} Made By {RESET}\")\n print(f\"{RED} Ehthe Samul Islam Laskar USN:1DS16CS712 {RESET}\")\n print(f\"{RED} B Padma USN:1DS19CS420{RESET}\")\n print(... | [
"0.6712049",
"0.61855406",
"0.6153275",
"0.61067873",
"0.6099449",
"0.6087882",
"0.6048082",
"0.5993004",
"0.5990485",
"0.5968788",
"0.5943066",
"0.5904404",
"0.58474976",
"0.58384794",
"0.58171725",
"0.5817065",
"0.5815135",
"0.57878107",
"0.57636875",
"0.576346",
"0.5755653... | 0.0 | -1 |
Measures voltage in auto range and auto resolution returns the voltage in float | def hp34401a_read_voltage(hp_meter):
hp_meter.write("MEAS:VOLT:DC? DEF,DEF")
return float(hp_meter.read()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ac_voltage(self):\n return float(self.get_ac_voltage())",
"def meas_voltage(instrument, v_range=10, resolution=0.003):\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))",
"def get_voltage(self):\n summary = \" \".join(self.get_summary().split())\n pa... | [
"0.7460715",
"0.72376",
"0.7180077",
"0.71739894",
"0.7143595",
"0.69467497",
"0.6899874",
"0.6868156",
"0.6855484",
"0.67759335",
"0.6678318",
"0.6674746",
"0.66508025",
"0.6561424",
"0.6534731",
"0.65066266",
"0.65000963",
"0.6477398",
"0.6432846",
"0.6412113",
"0.6365112",... | 0.64051443 | 20 |
Measures voltage with specified range and resolution returns the voltage in float | def hp34401a_read_voltage_rng_res(hp_meter , v_range, v_resolution):
hp_meter.write("MEAS:VOLT:DC? " + str(v_range) + " , " + str(v_resolution))
return float(hp_meter.read()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def meas_voltage(instrument, v_range=10, resolution=0.003):\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, resolution)))",
"def dmm_measure_voltage(self,range=None,res=None):\n if range is None:\n return self.dmm.measure_voltage()\n elif res is None:\n ... | [
"0.8078823",
"0.7291037",
"0.67395306",
"0.67171156",
"0.63865334",
"0.63084126",
"0.62544125",
"0.6248933",
"0.62172204",
"0.61805737",
"0.6111289",
"0.6101917",
"0.6081985",
"0.60785013",
"0.60742426",
"0.60578114",
"0.6011588",
"0.59588313",
"0.59566176",
"0.59343493",
"0.... | 0.7613944 | 1 |
Displays up to 16 charaters on the hp2458a | def hp3458a_lcd_disp(hp_meter, message):
hp_meter.write("DISP 3")
hp_meter.write("DISP 2 '" + str(message) + "'") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def banner_ascii():\n print(\"\")\n print(f\"\\n{RED} Steganography Tool{RESET}\")\n print(f\"{RED} Made By {RESET}\")\n print(f\"{RED} Ehthe Samul Islam Laskar USN:1DS16CS712 {RESET}\")\n print(f\"{RED} B Padma USN:1DS19CS420{RESET}\")\n print(... | [
"0.6711596",
"0.6433494",
"0.63968563",
"0.61818796",
"0.6086967",
"0.6044831",
"0.6038081",
"0.60282725",
"0.59977597",
"0.5977302",
"0.59293854",
"0.5919693",
"0.5907645",
"0.5782015",
"0.5761691",
"0.5756993",
"0.57358706",
"0.57197696",
"0.5708366",
"0.5704662",
"0.569094... | 0.5474368 | 54 |
Starts a self test | def hp3458a_self_test(hp_meter):
hp_meter.write("TEST") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def startTestRun(self):",
"def runTests(self):\n \n pass",
"def test_run_started(self):",
"def runtest(self):",
"def __main() :\n launchTests()",
"def startTestHook(self):",
"def test_start(self):\n self.fail(\"write a test\")",
"def run_test(self):\n raise NotImplementedEr... | [
"0.8505261",
"0.79985905",
"0.7970492",
"0.7886812",
"0.7796331",
"0.76125836",
"0.75867486",
"0.7508232",
"0.7442587",
"0.74282044",
"0.7393508",
"0.7370026",
"0.7354305",
"0.7352568",
"0.7300997",
"0.72973377",
"0.72624886",
"0.71843565",
"0.7175111",
"0.7169313",
"0.714277... | 0.0 | -1 |
Initializes the meter to DC voltage measurment in the 10V range | def hp3458a_init(hp_meter):
hp_meter.write("RESET")
hp_meter.write("TARM HOLD")
hp_meter.write("FUNC DCV") # Set to DC voltage measurment
hp_meter.write("RANGE 10") # Set to 10V range
hp_meter.write("LFREQ LINE") # Measure line freq and set rejection filter
hp_meter.write("NPLC 1")
hp_meter.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure_vdc(self, rng, res, unit = 'V'):\n self.write_to_serial(':conf:volt:dc ' + str(rng) + ',' + str(res))# + unit)",
"def setvoltages(self):\n pass",
"def meas_voltage(instrument, v_range=10, resolution=0.003):\n return float(instrument.query('MEAS:VOLTage:DC? %s,%s' % (v_range, reso... | [
"0.6578889",
"0.5966041",
"0.5959843",
"0.58004075",
"0.58004075",
"0.57944673",
"0.5790686",
"0.5748484",
"0.5745046",
"0.5723111",
"0.57070476",
"0.56898814",
"0.5680734",
"0.5679567",
"0.5679567",
"0.56482434",
"0.5646847",
"0.5607881",
"0.56076604",
"0.5604224",
"0.560302... | 0.0 | -1 |
Measures voltage returns the voltage in float | def hp3458a_read_voltage(hp_meter):
hp_meter.write("TARM SGL")
return float(hp_meter.read()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_voltage(self):\n summary = \" \".join(self.get_summary().split())\n pattern = '\\$.... .. (.*?) .*? .*? .*? .*? . .*? .*? . . . .*?'\n voltage = float(re.findall(pattern,summary).pop())\n return voltage",
"def get_voltage(self):\n status = self.get_status_response()\n ... | [
"0.80243504",
"0.76661944",
"0.75472015",
"0.74645776",
"0.7437857",
"0.73945224",
"0.7392207",
"0.7350186",
"0.73374736",
"0.7213724",
"0.7169046",
"0.7097468",
"0.70568234",
"0.7049625",
"0.7021389",
"0.6937337",
"0.6860303",
"0.68288875",
"0.66705036",
"0.66682374",
"0.665... | 0.6485079 | 29 |
Connect to the resource manager returns the visa avalable resources | def resource_manager():
return visa.ResourceManager() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resources(self):\n return self.__resources",
"def resources(self):",
"def resources(self):\n return self._resources",
"def resources(self):\n return self._resources",
"def resources(self):\n return self._resources",
"def resources(self) -> pulumi.Output[Sequence['outputs.Machi... | [
"0.67140317",
"0.66184807",
"0.6323841",
"0.6323841",
"0.6323841",
"0.6289765",
"0.6227922",
"0.61894625",
"0.6173168",
"0.61393124",
"0.61299413",
"0.6100624",
"0.60945547",
"0.6082597",
"0.60609436",
"0.6056553",
"0.60070026",
"0.60023284",
"0.5967622",
"0.5967622",
"0.5967... | 0.6919788 | 0 |
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