tf.Tensor:\n return self._p_thermal * tf.math.exp(\n -(z + self._height * delta_t_frac *\n (tf.math.log(1.0 - delta_t_frac * tf.math.tanh(z / self._height)) -\n tf.math.log(1.0 + tf.math.tanh(z / self._height)) +\n z / self._height)) / h_sfc / (1.0 - delta_t_frac**2))","def CalcForceDistribution(self):\n\t\t\n\t\tself.F = self.s * (self.Tether - self.X)\n\t\t\n\t\treturn self.F","def Re_feed(F_mass, z_way, d_inner, n_pipe, mu_feed): \n return 0.785 * F_mass * z_way / (d_inner * n_pipe * mu_feed)","def boltzmann_radial_potential_linear_density_ebeam(\n r, current, r_e, e_kin, nl, kT, q, first_guess=None, ldu=None, max_step=500, rel_diff=1e-3\n ):\n # Solves the nonlinear radial poisson equation for a dynamic charge distribution following\n # the Boltzmann law\n # A * phi = b0 + bx (where b0 and bx are the static and dynamic terms)\n # Define cost function f = A * phi - b0 - bx\n # Compute jacobian J = A - diag(d bx_i / d phi_i)\n # Solve J y = f\n # Next guess: phi = phi - y\n # Iterate until adjustment is small\n cden = np.zeros(r.size)\n cden[r <= r_e] = -current/PI/r_e**2\n\n if ldu is None:\n ldu = fd_system_nonuniform_grid(r) # Set up tridiagonal system\n l, d, u = ldu\n\n nl = np.atleast_2d(np.asarray(nl))\n kT = np.atleast_2d(np.asarray(kT))\n q = np.atleast_2d(np.asarray(q))\n\n if first_guess is None:\n irho = np.zeros(r.size)\n irho[r <= r_e] = np.sum(q * Q_E * nl / (PI*r_e**2), axis=0)\n erho = cden/np.sqrt(2 * Q_E * e_kin/M_E)\n irho[r <= r_e] = np.minimum(-.95 * erho[r <= r_e], irho[r <= r_e])\n # if irho[0] < -erho[0]:\n phi = radial_potential_nonuniform_grid(r, erho + irho)\n # else:\n # phi = radial_potential_nonuniform_grid(r, erho)\n else:\n phi = first_guess\n\n for _ in range(max_step):\n # ion dist\n shape = np.exp(-q * (phi - phi.min())/kT)\n i_sr = np.atleast_2d(np.trapz(r*shape, r)).T\n nax = nl / 2 / PI / i_sr * np.atleast_2d(shape[:, 0]).T\n\n # dynamic rhs term\n _bx_a = - nax * q * shape * Q_E / EPS_0 # dynamic rhs term\n _bx_b = - cden/np.sqrt(2 * Q_E * (e_kin+phi)/M_E) / EPS_0\n _bx_a[:, -1] = 0 # boundary condition\n bx = np.sum(_bx_a, axis=0) + _bx_b\n\n # F = A.dot(phi) - (b0 + bx)\n f = _tridiag_targetfun(ldu, phi, bx)\n\n # Diagonal of the Jacobian df/dphi_i\n _c = np.zeros_like(shape)\n _c[:, :-1] = r[:-1] * (r[1:]-r[:-1]) * shape[:, :-1]\n j_d = -(np.sum(_bx_a * q/kT * (i_sr-_c)/i_sr, axis=0)\n + Q_E/M_E*_bx_b/(2 * Q_E * (e_kin+phi)/M_E)) # Diagonal of the Jacobian df/dphi_i\n\n y = tridiagonal_matrix_algorithm(l, d - j_d, u, f)\n res = np.max(np.abs(y[:-1]/phi[:-1]))\n phi = phi - y\n if res < rel_diff:\n break\n return phi, nax, shape","def dphi(x):\n return -kf(\n flapping_wing_frequency, flapping_angular_velocity_amplitude,\n flapping_acceleration_time_fraction, flapping_delay_time_fraction,\n x)","def dphi(x):\n return -kf(\n flapping_wing_frequency, flapping_angular_velocity_amplitude,\n flapping_acceleration_time_fraction, flapping_delay_time_fraction,\n x)","def get_angular_velocity(r, T):\n # http://www.hep.fsu.edu/~berg/teach/phy2048/0918.pdf\n # velocity = 2(pi)r/T\n return (2*math.pi*r)/T","def acceleration( x, u, m, rho, P, b, h):\n\t\n n = x.size\n a = np.zeros((n,1))\n\n for i in range(0, n):\n \n # damping & harmonic potential (0.5 x^2)\n a[i] = a[i] - u[i]*b - x[i]\n\n # quantum pressure (pairwise calculation)\n x_js = np.delete(x,i)\n P_js = np.delete(P,i)\n rho_js = np.delete(rho,i)\n # first, calculate vector between two particles\n uij = x[i] - x_js\n # calculate acceleration due to pressure\n fac = -m * (P[i]/rho[i]**2 + P_js/rho_js**2)\n pressure_a = fac * kernel( uij, h, '1' )\n # accumulate contributions to the acceleration\n a[i] = a[i] + np.sum(pressure_a)\n\n return a","def compute_vel(self, state, goal):\n\n \"\"\"\n Unicycle model control law:\n [v;w] = [kp 0 0; 0 ka kb]*[p;a;b]\n v = commanded linear velocity of robot\n w = commanded rotational velcoity of robot\n kp = gain parameter where kp > 0\n ka = gain parameter where ka - kp > 0\n kb = gain parameter where kb < 0\n p = distance from robot to goal\n a = angle between current robot heading and heading to goal\n b = error between current heading to goal and target end heading\n \"\"\"\n \n #print('state,goal,v,w')\n #print(state)\n #print(goal)\n\n xr = state[0][0] # m in world frame\n yr = state[1][0] # m in world frame\n thetar = state[2][0] #rads\n\n xg = goal[0] # m in world frame\n yg = goal[1] # m in world frame\n\n dy = yg - yr\n dx = xg - xr\n\n #print('')\n #print(state)\n #print(goal)\n \n # Calculate a\n a = -1*thetar + math.atan2(dy,dx)\n\n #print(a)\n\n if a > math.pi:\n a = a - 2*math.pi\n\n if a < -1*math.pi:\n a = a + 2*math.pi\n\n #print(a)\n\n # Set omega according to control law\n omega = self.ka*a\n if math.fabs(omega) > self.MAX_OMEGA:\n if omega > 0:\n omega = self.MAX_OMEGA\n else:\n omega = -1*self.MAX_OMEGA\n\n # Calculate P\n p = math.sqrt(dy*dy + dx*dx)\n\n # Set v \n v = self.kp*p\n if v > self.MAX_SPEED:\n v = self.MAX_SPEED\n\n # set the done value\n done = (p <= self.done_distance)\n\n #print(v)\n #print(omega)\n\n out_tuple = (v, omega, done)\n \n return out_tuple","def channel_flow(x_loc, y_loc, error, dt, u, v, p, rho, nu, nit, F):\n assert u.shape == (x_loc.shape[0], y_loc.shape[0])\n assert v.shape == (x_loc.shape[0], y_loc.shape[0])\n\n dx = x_loc[1:] - x_loc[0:-1]\n dy = y_loc[1:] - y_loc[0:-1]\n\n b = np.zeros((x_loc.shape[0], y_loc.shape[0]))\n u_diff = 1\n num_it = 0\n u_sol = u.copy()\n v_sol = v.copy()\n p_sol = p.copy()\n while u_diff > error:\n u = u_sol.copy()\n v = v_sol.copy()\n p = p_sol.copy()\n\n # Calculate b term in periodic poisson calculation\n b[1:-1, 1:-1] = (1 / dt) * ((u[2:, 1:-1] - u[0:-2, 1:-1]) \\\n / (2 * dx[0:-1]) + (v[1:-1, 2:] - v[1:-1, 0:-2]) / (2 * dy[0:-1])) \\\n - \\\n ((u[2:, 1:-1] - u[0:-2, 1:-1]) ** 2) / (4 * dx[0:-1] ** 2) \\\n - (u[1:-1, 2:] - u[1:-1, 0:-2]) * (v[2:, 1:-1] - v[0:-2, 1:-1]) \\\n / (4 * dx[0:-1] * dy[0:-1]) \\\n - ((v[1:-1, 2:] - v[1:-1, 0:-2]) ** 2) / (4 * dy[0:-1] ** 2)\n\n # Solve boundary conditions of b\n b[0, 1:-1] = (1 / dt) * ((u[1, 1:-1] - u[-1, 1:-1]) \\\n / (2 * dx[-1]) + (v[0, 2:] - v[0, 0:-2]) / (2 * dy[-1])) \\\n - \\\n ((u[1, 1:-1] - u[-1, 1:-1]) ** 2) / (4 * dx[-1] ** 2) \\\n - (u[0, 2:] - u[0, 0:-2]) * (v[1, 1:-1] - v[-1, 1:-1]) \\\n / (4 * dx[-1] * dy[-1]) \\\n - ((v[0, 2:] - v[0, 0:-2]) ** 2) / (4 * dy[-1] ** 2)\n\n b[-1, 1:-1] = (1 / dt) * ((u[0, 1:-1] - u[-2, 1:-1]) \\\n / (2 * dx[-1]) + (v[-1, 2:] - v[-1, 0:-2]) / (2 * dy[-1])) \\\n - \\\n ((u[0, 1:-1] - u[-2, 1:-1]) ** 2) / (4 * dx[-1] ** 2) \\\n - (u[-1, 2:] - u[-1, 0:-2]) * (v[0, 1:-1] - v[-2, 1:-1]) \\\n / (4 * dx[-1] * dy[-1]) \\\n - ((v[-1, 2:] - v[-1, 0:-2]) ** 2) / (4 * dy[-1] ** 2)\n\n # Calculate periodic poisson solution to pressure\n p_sol = NavierStokes.poisson_2d(x_loc, y_loc, p, b, nit, periodic_x=True, wall_y=True)\n\n # Calculate velocities\n u_sol[1:-1, 1:-1] = u[1:-1, 1:-1] - \\\n u[1:-1, 1:-1] * dt / (dx[0:-1]) * (u[1:-1, 1:-1] - u[0:-2, 1:-1]) \\\n - v[1:-1, 1:-1] * dt / (dy[0:-1]) * (u[1:-1, 1:-1] - u[1:-1, 0:-2]) \\\n - dt * (p[2:, 1:-1] - p[0:-2, 1:-1]) / (rho * 2 * dx[0:-1]) \\\n + nu * (dt/(dx[0:-1] ** 2) * (u[2:, 1:-1] - 2 * u[1:-1, 1:-1] + u[0:-2, 1:-1]) \\\n + (dt/(dy[0:-1] ** 2)) * (u[1:-1, 2:] - 2 * u[1:-1, 1:-1] + u[1:-1, 0:-2])) + F*dt\n\n v_sol[1:-1, 1:-1] = v[1:-1, 1:-1] - \\\n u[1:-1, 1:-1] * dt / (dx[0:-1]) * (v[1:-1, 1:-1] - v[0:-2, 1:-1]) \\\n - v[1:-1, 1:-1] * dt / (dy[0:-1]) * (v[1:-1, 1:-1] - v[1:-1, 0:-2]) \\\n - dt * (p[1:-1, 2:] - p[1:-1, 0:-2]) / (rho * 2 * dy[0:-1]) \\\n + nu * (dt/(dx[0:-1] ** 2) * (v[2:, 1:-1] - 2 * v[1:-1, 1:-1] + v[0:-2, 1:-1]) \\\n + (dt/(dy[0:-1] ** 2)) * (v[1:-1, 2:] - 2 * v[1:-1, 1:-1] + v[1:-1, 0:-2]))\n\n # B.Cs for channel flow\n u_sol[0, 1:-1] = u[0, 1:-1] - \\\n u[0, 1:-1] * dt / (dx[0]) * (u[0, 1:-1] - u[-1, 1:-1]) \\\n - v[0, 1:-1] * dt / (dy[0:-1]) * (u[0, 1:-1] - u[0, 0:-2]) \\\n - dt * (p[1, 1:-1] - p[-1, 1:-1]) / (rho * 2 * dx[0]) \\\n + nu * (dt/(dx[0] ** 2) * (u[1, 1:-1] - 2 * u[0, 1:-1] + u[-1, 1:-1]) \\\n + (dt/(dy[0:-1] ** 2)) * (u[0, 2:] - 2 * u[0, 1:-1] + u[0, 0:-2])) + F*dt\n\n u_sol[-1, 1:-1] = u[-1, 1:-1] - \\\n u[-1, 1:-1] * dt / (dx[-1]) * (u[-1, 1:-1] - u[-2, 1:-1]) \\\n - v[-1, 1:-1] * dt / (dy[0:-1]) * (u[-1, 1:-1] - u[-1, 0:-2]) \\\n - dt * (p[0, 1:-1] - p[-2, 1:-1]) / (rho * 2 * dx[-1]) \\\n + nu * (dt/(dx[-1] ** 2) * (u[0, 1:-1] - 2 * u[-1, 1:-1] + u[-2, 1:-1]) \\\n + (dt/(dy[0:-1] ** 2)) * (u[-1, 2:] - 2 * u[-1, 1:-1] + u[-1, 0:-2])) + F*dt\n\n v_sol[0, 1:-1] = v[0, 1:-1] - \\\n u[0, 1:-1] * dt / (dx[0]) * (v[0, 1:-1] - v[-1, 1:-1]) \\\n - v[0, 1:-1] * dt / (dy[0:-1]) * (v[0, 1:-1] - v[0, 0:-2]) \\\n - dt * (p[0, 2:] - p[0, 0:-2]) / (rho * 2 * dy[0:-1]) \\\n + nu * (dt/(dx[0] ** 2) * (v[1, 1:-1] - 2 * v[0, 1:-1] + v[-1, 1:-1]) \\\n + (dt/(dy[0:-1] ** 2)) * (v[0, 2:] - 2 * v[0, 1:-1] + v[0, 0:-2]))\n\n v_sol[-1, 1:-1] = v[-1, 1:-1] - \\\n u[-1, 1:-1] * dt / (dx[-1]) * (v[-1, 1:-1] - v[-2, 1:-1]) \\\n - v[-1, 1:-1] * dt / (dy[0:-1]) * (v[-1, 1:-1] - v[-1, 0:-2]) \\\n - dt * (p[-1, 2:] - p[-1, 0:-2]) / (rho * 2 * dy[0:-1]) \\\n + nu * (dt/(dx[-1] ** 2) * (v[0, 1:-1] - 2 * v[-1, 1:-1] + v[-2, 1:-1]) \\\n + (dt/(dy[0:-1] ** 2)) * (v[-1, 2:] - 2 * v[-1, 1:-1] + v[-1, 0:-2]))\n\n u_sol[:, 0] = 0\n u_sol[:, -1] = 0\n v_sol[:, 0] = 0\n v_sol[:, -1] = 0\n\n # Calculate difference between solutions\n u_diff = np.abs(np.sum(np.sqrt(u_sol**2 + v_sol**2)) - np.sum(np.sqrt(u ** 2 + v ** 2)))/u.size\n\n num_it += 1\n\n print num_it\n return u_sol, v_sol, p_sol","def bethe_gf_omega(z, t=1.0):\n half_bandwidth = 2 * t\n z_rel = z / half_bandwidth\n return 2. / half_bandwidth * z_rel * (1 - np.sqrt(1 - 1 / (z_rel * z_rel)))","def get_flow(self, pressure_drop, coeff): \n flow = coeff * pressure_drop**0.5\n return flow","def perron_term_fn(iteration_count, v, z):\n return -0.5 * z * (v + iteration_count - 0.5) / (\n (v + z + (iteration_count - 1.) / 2.) *\n (v + z + iteration_count / 2.))","def fermi(E,mu,T):\n if (E-mu)/T > 600:\n f=0\n\t\t\t\t\n else:\n f=1/(math.exp((E-mu)/(kB*T) )+1)\n return(f)","def thermal_factor(self, bfactor=1.0):\n return np.exp(-bfactor * (np.sin(self.angle) / self.xrd.wavelength) ** 2)","def flower(t,r,n,angle):\n for i in range(n):\n petal(t,r,angle)\n lt(t,360/n)","def f(r):\r\n\r\n x, y, vx, vy = r\r\n rcubed = np.sqrt(x**2 + y**2)**3\r\n\r\n fx = vx #return 1st parameter\r\n fy = vy #return 2nd parameter\r\n fvx = -1*G*M*(x/rcubed) #return 3rd parameter\r\n fvy = -1*G*M*(y/rcubed) #return last parameter\r\n return np.array([fx, fy, fvx, fvy])","def Y_force(omega_B, V_B, m):\n t = Symbol(\"t\")\n return m * (diff(V_B[1], t) + omega_B[2] * V_B[0] - omega_B[0] * V_B[2])","def driftRHS(field,drift_velocity,t,x):\n f = field.getValue(x)\n fs = np.linalg.norm(f)\n f = f/fs\n return -f*drift_velocity(fs)","def heterodyner_signal_from_f(self,f):\n omega = f*2*np.pi\n phvec = np.cumsum(omega)\n return np.exp(1j*phvec)","def main():\r\n #Drag Force Equation: 1/2 * rho * Cd * A * v^2\r\n\r\n #User-Defined Constants\r\n global m\r\n global v0\r\n global theta\r\n global rho #Fluid Density\r\n global A #Cross-sectional Area\r\n global Cd #Drag coefficient\r\n global tStep\r\n global g\r\n\r\n m = 1\r\n v0 = 30\r\n theta = math.radians(45)\r\n rho = 1.225\r\n A = 0.05\r\n Cd = 0.5 #A ball is approx. 0.5\r\n tStep = 0.005\r\n g = 9.8\r\n\r\n\r\n #Data Structures\r\n global tHist\r\n global xHist\r\n global yHist\r\n global thetaHist\r\n global vHist\r\n global vXHist\r\n global vYHist\r\n tHist = [] #list for all time steps\r\n xHist = [] #list for all x position steps\r\n yHist = [] #list for all y position steps\r\n thetaHist = [] #List for all theta at every time step\r\n vHist = [] #list for all velocities at every time step\r\n vXHist = [] #list for all x-axis velocities at every time step\r\n vYHist = [] #list for all y-axis velocities at every time step\r\n\r\n #Initialize intial values\r\n tHist.append(0.0)\r\n xHist.append(0.0)\r\n yHist.append(0.0)\r\n thetaHist.append(theta)\r\n vHist.append(v0)\r\n vXHist.append(v0 * math.cos(theta))\r\n vYHist.append(v0 * math.sin(theta))\r\n vTheta = math.atan(vYHist[0] / vXHist[0])\r\n # print(\"t: \" + str(tHist[0]))\r\n # print(\"x: \" + str(xHist[0]))\r\n # print(\"y: \" + str(yHist[0]))\r\n # print(\"v: \" + str(vHist[0]))\r\n # print(\"Vx: \" + str(vXHist[0]))\r\n # print(\"Vy: \" + str(vYHist[0]))\r\n\r\n #Convenience variables\r\n global k\r\n\r\n counter = 1\r\n #Loop until the y-displacement becomes negative (projectile reaches ground again)\r\n while True:\r\n tHist.append(counter * tStep) #increment time\r\n print(\"t: \" + str(tHist[counter]))\r\n\r\n #This large hunk is the solution to the net force differential equation in the x-axis\r\n # oneOverVX = (1/vXHist[counter-1]) + (((rho*A*Cd*math.cos(thetaHist[counter-1]))/(2*m))*(tStep)) #STABLE\r\n # oneOverVX = (1/vXHist[counter-1]) + (((rho*A*Cd)/(2*m))*(tStep))\r\n # oneOverVX = (1/vHist[counter-1]) + (((rho*A*Cd*math.cos(thetaHist[counter-1]))/(2*m))*(tStep))\r\n oneOverVX = (1/vXHist[counter-1]) + ((rho*A*Cd)/(2*m*math.cos(thetaHist[counter-1]))*(tStep)) #This is one over the solution for velocity in the x-axis net force differential equation\r\n vXHist.append(1 / oneOverVX) #Adding the velocity to the list of velocities\r\n\r\n vY0 = vYHist[counter-1] #Convenience variable\r\n # k = 0.5 * rho * A * Cd * math.sin(abs(thetaHist[counter-1])) #STABLE\r\n # k = 0.5 * rho * A * Cd\r\n k = (rho * A * Cd) / (2 * math.sin(abs(thetaHist[counter-1]))) #Convenience variable\r\n print(\"k: \" + str(k))\r\n print(\"vX: \" + str(vXHist[counter]))\r\n rootGMK = math.sqrt(g*m*k) #Convenience variable\r\n if vYHist[counter-1] > 0.0: #If the projectile is going upwards\r\n #Solving the y-axis differential equation for velocity\r\n equationRight = -rootGMK * ((tStep/m) - (math.atan((k*vY0)/(rootGMK))/rootGMK))\r\n vYHist.append((math.tan(equationRight) * rootGMK) / k)\r\n elif vYHist[counter-1] < 0.0: #If the projectile is going downwards\r\n #Solving the y-axis differential equation for velocity\r\n\r\n # Hand-solved integral\r\n # exponent = -(2*tStep*rootGMK)/m\r\n # numerator = g*m*math.exp(exponent) - math.exp(exponent)*vY0*rootGMK - vY0*rootGMK - g*m\r\n # denominator = math.exp(exponent)*(vY0-rootGMK) - vY0*k - rootGMK\r\n # vYHist.append(numerator / denominator)\r\n\r\n #Wolfram Alpha arctanh integral\r\n arctanh =(vY0*math.sqrt(k))/(math.sqrt(g*m))\r\n print(\"arctanh: \" + str(arctanh))\r\n equationRight = (np.arctanh(arctanh))/(rootGMK) - (tStep/m)\r\n vYHist.append(np.tanh(rootGMK * equationRight) * ((math.sqrt(g*m))/(math.sqrt(k))))\r\n else: #If current y velocity is 0\r\n vYHist.append(vY0 - g*tStep)\r\n print(\"vY: \" + str(vYHist[counter]))\r\n\r\n vHist.append(math.hypot(vXHist[counter], vYHist[counter])) #Calculate the net velocity and add it to the velocities list\r\n print(\"v: \" + str(vHist[counter]))\r\n thetaHist.append(math.atan(vYHist[counter]/vXHist[counter])) #Calculate the current angle based on the velocities and add it to the theta list\r\n print(\"0: \" + str(math.degrees(thetaHist[counter])))\r\n\r\n x0 = xHist[counter-1]\r\n y0 = yHist[counter-1]\r\n\r\n # yIntegral = trigintegrate()\r\n\r\n \"\"\"\r\n Note: What I wanted to do here was to integrate the velocity functions over the time interval to find the exact\r\n changes in position. Unfortunately, I was running short of time and decided it was not worth it to move forward with\r\n this final step, and instead worked on the presentation and testing different cases.\r\n \"\"\"\r\n xHist.append(x0 + vXHist[counter]*tStep) #Calculate new x position using x = x0 + vt\r\n yHist.append(y0 + vYHist[counter]*tStep) #Calculate new y position using y = y0 + vt\r\n print(\"x: \" + str(xHist[counter]))\r\n print(\"y: \" + str(yHist[counter]))\r\n print()\r\n\r\n # xHist.append(xHist[counter-1] + vXHist[counter-1]*tStep + 0.5*aXHist[counter-1]*tStep**2)\r\n # yHist.append(yHist[counter-1] + vYHist[counter-1]*tStep + 0.5*aYHist[counter-1]*tStep**2)\r\n # vXHist.append(vXHist[counter-1] + aXHist[counter-1]*tStep)\r\n # vYHist.append(vYHist[counter-1] + aYHist[counter-1]*tStep)\r\n # vHist.append(math.hypot(vXHist[counter], vYHist[counter]))\r\n #\r\n # vTheta = math.atan(vYHist[counter] / vXHist[counter])\r\n # xDragAccel = -0.5*rho*Cd*A*vHist[counter]**2*math.cos(vTheta) / m\r\n # yDragAccel = -math.copysign(0.5*rho*Cd*A*vHist[counter]**2*math.sin(vTheta) / m, vYHist[counter])\r\n #\r\n # aXHist.append(xDragAccel)\r\n # aYHist.append(-g*tStep + yDragAccel)\r\n\r\n if vYHist[counter-1] > 0.0 and vYHist[counter] < 0.0: #Check if the projectile has reached it's peak by checking for a critical point\r\n print(\"max height reached at time=\" + str(tHist[counter]))\r\n # break\r\n\r\n # print(\"t: \" + str(tHist[counter]))\r\n # print(\"x: \" + str(xHist[counter]))\r\n # print(\"y: \" + str(yHist[counter]))\r\n # print(\"Vx: \" + str(vXHist[counter]))\r\n # print(\"Vy: \" + str(vYHist[counter]))\r\n # print(\"Ax: \" + str(aXHist[counter]))\r\n # print(\"Ay: \" + str(aYHist[counter]))\r\n\r\n if yHist[counter] < 0 or counter > 99999: #End the loop if the projectile has reached the ground (or limit the number of iterations to avoid computer death)\r\n break\r\n\r\n counter += 1\r\n\r\n plotData()","def bounce(self, particle):\n\n\t\tif particle.x > self.width - (particle.size):\n\t\t\tparticle.x += ((particle.x + particle.size) - self.width)\n\t\t\tparticle.dx = particle.dx * -1\n\t\t\tparticle.dx = int(particle.dx * self.elasticity)\n\t\t\tparticle.dy = int(particle.dy * self.elasticity)\n\t\telif particle.x < (particle.size):\n\t\t\tparticle.x -= (particle.x - particle.size)\n\t\t\tparticle.dx = particle.dx * -1\n\t\t\tparticle.dx = int(particle.dx * self.elasticity)\n\t\t\tparticle.dy = int(particle.dy * self.elasticity)\n\n\t\tif particle.y > self.height - (particle.size):\n\t\t\tself.y = 2 * (self.height - particle.size) - particle.y\n\t\t\tparticle.dy = particle.dy * -1\n\t\t\tparticle.dx = int(particle.dx * self.elasticity)\n\t\t\tparticle.dy = int(particle.dy * self.elasticity)\n\t\telif particle.y < (particle.size):\n\t\t\tself.y = 2 * particle.size - particle.y\n\t\t\tparticle.dy = particle.dy * -1\n\t\t\tparticle.dx = int(particle.dx * self.elasticity)\n\t\t\tparticle.dy = int(particle.dy * self.elasticity)","def friction_model():\n return TimeWeakening()","def fppp(x):\n return (-2000.00*math.cos(10.0.x+1))","def test_turbulent_drag():\n fluid_vel = array((1., 0., 0.))\n vel = array((0., 0., 0.))\n emax = array((1.0e-8, 1.0e-8, 1.0e-8))\n drag = 0.44 * 3.0 / 32.0 * fluid_vel\n assert all(DM.turbulent_drag(fluid_velocity=fluid_vel, particle_velocity=vel,\n diameter=1.0, rho=1.0, rho_f=1.0, fluid_viscosity=1.0) - drag < emax)\n\n fluid_vel = array((0., 5., 0.))\n vel = array((0., 1., 0.))\n drag = 0.44 * 3.0 / 20.0 * vel\n\n assert all(DM.turbulent_drag(fluid_velocity=fluid_vel, particle_velocity=vel,\n diameter=10.0, rho=1.0, rho_f=1.0, fluid_viscosity=1.0) - drag < emax)"],"string":"[\n \"def eval_dryfriction():\\n # Environment\\n env = WAMBallInCupSim(num_dof=7, max_steps=1500)\\n\\n # Policy (random init)\\n policy_hparam = dict(num_feat_per_dim=12, bounds=(np.array([0.0]), np.array([1.0])))\\n policy = DualRBFLinearPolicy(env.spec, policy_hparam, dim_mask=2)\\n\\n # Do the rolllouts\\n t_all = []\\n qpos_all = []\\n dp_vals = [0.0, 0.3, 0.6, 0.9, 1.2]\\n print_cbt(f\\\"Run policy for stiction coefficients: {dp_vals}\\\")\\n for dpv in dp_vals:\\n env.reset(\\n domain_param=dict(\\n joint_1_dryfriction=dpv,\\n joint_2_dryfriction=dpv,\\n joint_3_dryfriction=dpv,\\n joint_4_dryfriction=dpv,\\n joint_5_dryfriction=dpv,\\n joint_6_dryfriction=dpv,\\n joint_7_dryfriction=dpv,\\n )\\n )\\n ro = rollout(env, policy, render_mode=RenderMode(video=False), eval=True)\\n t_all.append(ro.time[:-1])\\n qpos_all.append(ro.env_infos[\\\"qpos\\\"])\\n\\n # Plot\\n fig, ax = plt.subplots(nrows=env.num_dof, sharex=\\\"all\\\", figsize=(16, 7))\\n for i, idx_joint in enumerate([dof for dof in range(env.num_dof)]):\\n ax[i].set_prop_cycle(color=plt.get_cmap(\\\"cividis\\\")(np.linspace(0, 1, env.num_dof)))\\n ax[i].set_ylabel(f\\\"joint {idx_joint+1} pos [rad]\\\")\\n for j in range(len(dp_vals)):\\n ax[i].plot(t_all[j], qpos_all[j][:, idx_joint], ls=\\\"--\\\", label=f\\\"s = {dp_vals[j]}\\\")\\n if i == 0:\\n ax[i].legend(ncol=len(dp_vals))\\n ax[-1].set_xlabel(\\\"time [s]\\\")\\n plt.suptitle(\\\"Evaluation of joint stiction coefficients\\\")\\n plt.show()\",\n \"def integrateMomentum(p, dt, fluid_v, fvolpp, mu_g, rfluid):\\n\\n #integration constants\\n beta = 2.0/3.0 \\n alpha1 = -4.0/3.0 \\n alpha2 = 1.0/3.0 \\n dtbeta = dt * beta \\n\\n vel1 = p.vel[0] \\n pos1 = dtbeta * vel1 - alpha1 * p.pos[0] - alpha2 * p.pos[1] \\n rp = p.get_density() \\n D = p.get_diameter() \\n mdot = (p.mass[0] - p.mass[1]) / dt \\n \\n mfluid = rfluid * fvolpp + 1e-30 # mass of fluid around particle\\n fixedsrc = -alpha1 * p.vel[0] - alpha2 * p.vel[1] \\n volp = math.pi * D * D * D / 6.0 \\n volpp = fvolpp \\n # enhance drag function for large volume fraction\\n alphav = min(2.0, volp / max(volpp, 1e-30)) \\n \\n fp_vf = max((8.0 * alphav) ** 6.0 - 0.001, 0.0) \\n\\n #Integration loop\\n max_iterations = 20\\n for i in range(max_iterations): \\n #Update fluid velocity based on delta particle momentum\\n if i > 0: #Past first iteration\\n fluid_v = fluid_v - ((vel1 - p.vel[0]) * p.mass[0] / mfluid ) \\n\\n dv = abs(fluid_v - vel1) \\n Re = rfluid * D * dv / mu_g \\n # blowing Reynolds number\\n Re_b = abs(mdot / (D * mu_g * math.pi)) \\n a = 0.09 + 0.077 * math.exp(-0.4 * Re) \\n b = 0.4 + 0.77 * math.exp(-0.04 * Re) \\n denom = 1.0 + a * Re_b **b \\n\\n fpblow = (1. + 0.0545 * Re + 0.1 * math.sqrt(Re) * (1.0 - 0.03 * Re)) / denom + fp_vf \\n # Clift-Gauvin drag function (Crowe, 1998)\\n fpcg = 1.0 + 0.15 * Re ** 0.687 + 0.0175 * Re / (1.0 + 4.25e4 * (Re+1e-20) **-1.16) + fp_vf \\n # Choose drag function based on reynolds number. For high reynolds\\n # number use Clift Gauvin, otherwise use blowing reynolds number \\n if Re < 100:\\n fp = fpblow\\n else:\\n fp = fpcg\\n taup = rp * D ** 2 / (18.0 * mu_g * fp) \\n vcoef = dtbeta / taup \\n\\n # vel1 = (vcoef*fluid_v + fixedsrc)/(1.+vcoef) \\n f = (vcoef * fluid_v + fixedsrc) / (1.0 + vcoef) - vel1 \\n df = -vcoef * p.mass[0] / (mfluid * (1.0 + vcoef)) - 1.0 \\n vel1 -= -f/df \\n pos1 = dtbeta * vel1 - alpha1 * p.pos[0] - alpha2 * p.pos[1] \\n\\n # If iterated at least 2 times, check for convergence\\n if i > 1 and abs(f) / (abs(df) * (0.1 + abs(vel1))) < 1.0e-5 : \\n break \\n \\n # Now advance the particle momentum in time\\n p.vel[2] = p.vel[1] \\n p.vel[1] = p.vel[0] \\n p.vel[0] = vel1 \\n p.pos[1] = p.pos[0] \\n p.pos[0] = pos1\",\n \"def setFriction(self):\\n if int(self.vx) != 0:\\n self.ff = -abs(self.vx)/self.vx*self.mu*abs(self.fn)\\n else:\\n self.ff = 0\",\n \"def f(r,t):\\r\\n x = r[0]\\r\\n y = r[2]\\r\\n z = r[4]\\r\\n vx = r[1]\\r\\n vy = r[3]\\r\\n vz = r[5]\\r\\n velocity = np.sqrt(vx**2+vy**2+vz**2)\\r\\n #if np.abs(z)>eps:\\r\\n velocity = np.sqrt((vx+c*radius*wy)**2+(vy-c*radius*wx)**2+(-e*vz)**2)\\r\\n \\r\\n # equations for a cricket ball in motion\\r\\n return np.array([vx, (-k_d*velocity*vx+k_l*(wy*vz-wz*vy)),\\r\\n vy, (-k_d*velocity*vy+k_l*(wz*vx-wx*vz)),\\r\\n vz,(-k_d*velocity*vz+k_l*(wz*vy-wy*vx)-g)], float)\",\n \"def f_2(r,t):\\r\\n x = r[0]\\r\\n y = r[2]\\r\\n z = r[4]\\r\\n vx = r[1]\\r\\n vy = r[3]\\r\\n vz = r[5]\\r\\n # velocity equation for the ball after the bounce\\r\\n velocity = np.sqrt((vx+c*radius*wy)**2+(vy-c*radius*wx)**2+(-e*vz)**2)\\r\\n \\r\\n return np.array([vx, (-k_d*velocity*vx+k_l*(wy*vz-wz*vy)),\\r\\n vy, (-k_d*velocity*vy+k_l*(wz*vx-wx*vz)),\\r\\n vz,(-k_d*velocity*vz+k_l*(wz*vy-wy*vx)-g)], float)\",\n \"def particle_velocityV(V,F,dt,Rv,sigma,epsilon,D,N): \\n V += dt/2*(particle_forceV(Rv[-1], N, sigma, epsilon, D) + particle_forceV(Rv[-2], N, sigma, epsilon, D))\\n return V\",\n \"def calculate_angular_velocity(headings, fs):\\r\\n heading_vectors = np.array([np.cos(headings), np.sin(headings)]).T\\r\\n sin_angular_change = np.cross(heading_vectors[:-1], heading_vectors[1:])\\r\\n angular_velocity = np.arcsin(sin_angular_change) * float(fs)\\r\\n return angular_velocity\",\n \"def FlowRestriction(T_in, p_in, m_dot_out, d_inner, f):\\r\\n\\r\\n # Cross section\\r\\n A_cross = (np.pi/4)*(d_inner**2)\\r\\n\\r\\n # Assumption isenthalpic flow!\\r\\n h_in = hp.HeCalc(9, 0, 1, p_in, 2, T_in, 1) #J/kg\\r\\n\\r\\n # Iteration for the calculation of p_out even though the influence is probably negligible\\r\\n # I checked it and for 20 bar it really is negligible\\r\\n dp = 0.0\\r\\n p_out = 0.0\\r\\n for i in range(5):\\r\\n p_out = p_in - dp\\r\\n T_out = hp.HeCalc(2, 0, 1, p_out, 9, h_in, 1)\\r\\n Rho_out = hp.HeCalc(3, 0, 1, p_out, 2, T_out, 1) #kg/m³\\r\\n # Velocity of the outgoing flow\\r\\n u_out = m_dot_out/(A_cross*Rho_out) #m/s\\r\\n\\r\\n # Calculation of the dp with Bernoulli equation and resistance coefficient (see VDI Heatatlas 2013)\\r\\n dp = f * Rho_out * 0.5 * u_out**2\\r\\n\\r\\n\\r\\n h_out = hp.HeCalc(9, 0, 1, p_out, 2, T_out, 1)\\r\\n state_out = {\\\"h\\\": h_out, \\\"T\\\": T_out, \\\"p\\\": p_out}\\r\\n return state_out\",\n \"def CalcForce_aeroframe_DEP(V, CoefMatrix, x, rho, g):\\r\\n\\r\\n #Compute aero forces\\r\\n # here x must be of the form (alpha, beta, p, q, r, da, dr, de) (last one punctualy used)\\r\\n # set non dim for p,q,r\\r\\n nonDim=np.ones(7)\\r\\n nonDim[2]=g.b/(2*V)\\r\\n nonDim[3]=g.c/(2*V)\\r\\n nonDim[4]=g.b/(2*V)\\r\\n # F=np.dot(CoefMatrix,x[0:7]) # commented form, modification to account for symmetric drag increase of side slip\\r\\n F=np.zeros((3))\\r\\n M=np.zeros((3))\\r\\n xsym=np.copy(x[0:-1])\\r\\n xsym[1]=abs(xsym[1]) # make beta always positive since derivatives have already correct sign for drag and lift only\\r\\n xsym[-3]=abs(xsym[-3]) # make ailerons deflection always positive for drag increase and lift decrease\\r\\n xsym[-1]=abs(xsym[-1]) # make rudder deflection always positive for drag increase and lift decrease\\r\\n F[0]=np.dot(CoefMatrix[0],xsym)\\r\\n F[1]=np.dot(CoefMatrix[1],x[0:-1]) #side force\\r\\n F[2]=np.dot(CoefMatrix[2],xsym)\\r\\n M=np.dot(CoefMatrix[3:6,:],x[0:-1])\\r\\n# print(\\\"Printing moment coeff\\\")\\r\\n# print(M)\\r\\n\\r\\n \\r\\n #No need to project\\r\\n# alpha=x[0]\\r\\n# beta=x[1]\\r\\n# H=np.array([[math.cos(alpha)*math.sin(beta), -math.cos(alpha)*math.sin(beta), -math.sin(alpha)],[math.sin(beta), math.cos(beta), 0],[math.sin(alpha)*math.cos(beta), -math.sin(alpha)*math.sin(beta), math.cos(alpha)]])\\r\\n if V<=71 :\\r\\n Fbody=np.array([-F[0]-g.Cd0_fl,F[1],-F[2]-g.CL0_fl]) # add alpha=0 coefficients\\r\\n Moment=M+np.array([0,x[-1]*g.Cm_de+g.Cm0_fl,0])\\r\\n else:\\r\\n Fbody=np.array([-F[0]-g.Cd0,F[1],-F[2]-g.CL0]) # add alpha=0 coefficients\\r\\n Moment=M+np.array([0,x[-1]*g.Cm_de+g.Cm0,0])\\r\\n \\r\\n\\r\\n Fbody=0.5*V**2.0*rho*g.S*Fbody\\r\\n Moment=0.5*V**2.0*rho*g.S*g.b*Moment\\r\\n \\r\\n return np.append(Fbody, Moment)\",\n \"def velocity_field(xt,yt,x0,y0,velf,dia,tsr,solidity):\\n rad = dia/2.\\n rot = tsr*velf/rad\\n\\n # Calculating EMG distribution parameters\\n loc,spr,skw,scl = vorticity(tsr,solidity)\\n \\n # Translating the turbine position\\n x0t = x0 - xt\\n y0t = y0 - yt\\n \\n # Integration of the vorticity profile using Fortran code (vorticity.f90; _vortrun.so)\\n vel_vs = dblquad(_vortmodel.integrand,0.,35.*dia,lambda x: -4.*dia,lambda x: 4.*dia, args=(x0t,y0t,dia,loc[0],loc[1],loc[2],spr[0],spr[1],skw[0],skw[1],scl[0],scl[1],scl[2]))\\n \\n # Calculating velocity deficit\\n vel = (vel_vs[0]*(rot))/(2.*pi)\\n vel = (vel + velf)/velf # normalization of velocity\\n \\n return vel\",\n \"def force ( r, e ):\\n from math import isclose\\n\\n # Parameters of the Gay-Berne potential \\n # \\n # The key parameters are \\n # mu, nu ................ the exponents \\n # kappa and kappa' ....... the anisotropies \\n # kappa is the ratio of intermolecular separations \\n # sigma_e / sigma_s i.e. end-to-end / side-by-side \\n # kappa' is the ratio of well depths \\n # epsilon_s / epsilon_e i.e. side-by-side / end-to-end \\n # The derived parameters are chi and chi' \\n # chi = (kappa**2 - 1) / (kappa**2+1) \\n # chi' = (z - 1) / (z + 1)\\n # where z = (kappa') ** ( 1 / mu ) \\n # \\n # For convenience kappa' is spelt xappa, chi' is spelt xhi\\n # We choose units such that sigma_s = 1.0 and epsilon_0 = 1.0\\n # Two of the following three varieties should be commented out\\n\\n # Original Gay-Berne-deMiguel potential [J. Chem. Phys, 74, 3316; Mol. Phys. 74, 405 (1991)]\\n mu, nu, kappa, xappa = 2, 1, 3.0, 5.0\\n\\n # # Luckhurst-Phippen potential [Liq. Cryst., 8, 451 (1990)]\\n # mu, nu, kappa, xappa = 1, 2, 3.0, 5.0\\n\\n # # Berardi-Zannoni potential [J. Chem. Soc. Faraday Trans., 89, 4069 (1993)]\\n # mu, nu, kappa, xappa = 1, 3, 3.0, 5.0\\n\\n # Derived parameters\\n chi = (kappa**2 - 1.0) / (kappa**2+1.0)\\n xhi = (xappa**(1.0/mu) - 1.0) / (xappa**(1.0/mu) + 1.0)\\n\\n # Cutoff distance; normally we would use a larger value\\n r_cut = 4.0\\n \\n assert r.shape == (n,3), 'Incorrect shape of r'\\n assert e.shape == (n,3), 'Incorrect shape of e'\\n\\n # Notation to match appendix\\n i = 0\\n j = 1\\n\\n ei = e[i,:]\\n ej = e[j,:]\\n assert isclose(np.sum(ei**2),1.0), 'Non-unit vector {} {} {}'.format(*ei)\\n assert isclose(np.sum(ej**2),1.0), 'Non-unit vector {} {} {}'.format(*ej)\\n\\n rij = r[i,:] - r[j,:]\\n rij_mag = np.sqrt( np.sum(rij**2) ) # Magnitude of separation vector\\n sij = rij / rij_mag # Unit vector\\n ci = np.dot( ei, sij )\\n cj = np.dot( ej, sij )\\n cij = np.dot( ei, ej )\\n cp = ci + cj\\n cm = ci - cj\\n\\n # Sigma formula\\n cpchi = cp/(1.0+chi*cij)\\n cmchi = cm/(1.0-chi*cij)\\n sigma = 1.0/np.sqrt(1.0-0.5*chi*(cp*cpchi+cm*cmchi))\\n\\n # Epsilon formula\\n eps1 = 1.0/np.sqrt(1.0-(chi*cij)**2) # Depends on chi, not xhi\\n cpxhi = cp/(1.0+xhi*cij)\\n cmxhi = cm/(1.0-xhi*cij)\\n eps2 = 1.0-0.5*xhi*(cp*cpxhi+cm*cmxhi) # Depends on xhi\\n epsilon = (eps1**nu) * (eps2**mu)\\n\\n # Potential at rij\\n rho = rij_mag - sigma + 1.0\\n rho6 = 1.0 / rho**6\\n rho12 = rho6**2\\n rhoterm = 4.0*(rho12 - rho6) # Needed for forces and torques\\n drhoterm = -24.0 * (2.0 * rho12 - rho6) / rho # Needed for forces and torques\\n pot = epsilon*rhoterm\\n\\n # Potential at r_cut\\n rho = r_cut - sigma + 1.0\\n rho6 = 1.0 / rho**6\\n rho12 = rho6**2\\n cutterm = 4.0*(rho12 - rho6) # Needed for cutoff forces and torques\\n dcutterm = -24.0 * (2.0 * rho12 - rho6) / rho # Needed for cutoff forces and torques\\n pot = pot - epsilon * cutterm\\n\\n # Derivatives of sigma\\n prefac = 0.5*chi*sigma**3\\n dsig_dci = prefac*(cpchi+cmchi)\\n dsig_dcj = prefac*(cpchi-cmchi)\\n prefac = prefac*(0.5*chi)\\n dsig_dcij = -prefac*(cpchi**2-cmchi**2)\\n\\n # Derivatives of epsilon\\n prefac = -mu*xhi*(eps1**nu)*eps2**(mu-1)\\n deps_dci = prefac*(cpxhi+cmxhi)\\n deps_dcj = prefac*(cpxhi-cmxhi)\\n prefac = prefac*(0.5*xhi)\\n deps_dcij = -prefac*(cpxhi**2-cmxhi**2) # From derivative of eps2\\n deps_dcij = deps_dcij + nu*(chi**2)*(eps1**(nu+2))*(eps2**mu)*cij # From derivative of eps1\\n\\n # Derivatives of potential\\n dpot_drij = epsilon * drhoterm\\n dpot_dci = rhoterm * deps_dci - epsilon * drhoterm * dsig_dci\\n dpot_dcj = rhoterm * deps_dcj - epsilon * drhoterm * dsig_dcj\\n dpot_dcij = rhoterm * deps_dcij - epsilon * drhoterm * dsig_dcij\\n\\n # Standard formula for forces and torque gradients\\n fij = -dpot_drij*sij - dpot_dci*(ei-ci*sij)/rij_mag - dpot_dcj*(ej-cj*sij)/rij_mag\\n gi = dpot_dci*sij + dpot_dcij*ej\\n gj = dpot_dcj*sij + dpot_dcij*ei\\n\\n # Derivatives of potential at cutoff\\n dpot_drij = epsilon * dcutterm\\n dpot_dci = cutterm * deps_dci - epsilon * dcutterm * dsig_dci\\n dpot_dcj = cutterm * deps_dcj - epsilon * dcutterm * dsig_dcj\\n dpot_dcij = cutterm * deps_dcij - epsilon * dcutterm * dsig_dcij\\n\\n # Standard formula for forces and torque gradients (without dpot_drij term)\\n fij = fij + dpot_dci*(ei-ci*sij)/rij_mag + dpot_dcj*(ej-cj*sij)/rij_mag\\n gi = gi - ( dpot_dci*sij + dpot_dcij*ej ) \\n gj = gj - ( dpot_dcj*sij + dpot_dcij*ei ) \\n\\n # Final forces and torques\\n f = np.empty_like(r)\\n t = np.empty_like(r)\\n f[i,:] = fij\\n f[j,:] = -fij\\n t[i,:] = -np.cross(ei,gi)\\n t[j,:] = -np.cross(ej,gj)\\n\\n return pot, f, t\",\n \"def compute_f_ColeBrook(self, R, e, D):\\n # assume a starting correct value for the \\\"f\\\" on the right hand side (RHS)\\n # uses friction factor from Barr's equation as the starting value.\\n f_initial = self.compute_f_BARR(R, self.e, self.D)\\n\\n relative_roughness = e / D\\n a = relative_roughness / 3.71\\n b = 2.51 / (R * sqrt(f_initial))\\n\\n # Compute the f on the LHS ------ (1) \\n f_final = 1 / (-2 * log((a + b), 10))**2\\n \\n # Make sure friction factor is correct to at least 6 decimal place.\\n tolerance = 0.0000001\\n \\n # if the f on the LHS is not within tolerance limit,\\n # replace it on the RHS and recompute the f on the LHS till it's within\\n # tolerance limit.\\n while abs(f_final - f_initial) >= tolerance:\\n f_initial = f_final\\n b = 2.51 / (R * sqrt(f_initial))\\n f_final = 1 / (-2 * log((a + b), 10))**2\\n return f_final\",\n \"def friction_factor(v1: \\\"int\\\", v2: \\\"int\\\") -> \\\"int\\\":\",\n \"def step(self, f):\\n\\n NVTBerendsen.scale_velocities(self)\\n self.scale_positions_and_cell()\\n\\n #one step velocity verlet\\n atoms = self.atoms\\n p = self.atoms.get_momenta()\\n p += 0.5 * self.dt * f\\n\\n if self.fixcm:\\n # calculate the center of mass\\n # momentum and subtract it\\n psum = p.sum(axis=0) / float(len(p))\\n p = p - psum\\n\\n self.atoms.set_positions(self.atoms.get_positions() +\\n self.dt * p / self.atoms.get_masses()[:,np.newaxis])\\n\\n # We need to store the momenta on the atoms before calculating\\n # the forces, as in a parallel Asap calculation atoms may\\n # migrate during force calculations, and the momenta need to\\n # migrate along with the atoms. For the same reason, we\\n # cannot use self.masses in the line above.\\n\\n self.atoms.set_momenta(p)\\n f = self.atoms.get_forces()\\n atoms.set_momenta(self.atoms.get_momenta() + 0.5 * self.dt * f)\\n\\n\\n return f\",\n \"def after_bounce(x_f,y_f,z_f,vx_f,vy_f,vz_f,wx,wy,wz):\\r\\n # We define all the constants that we need to use\\r\\n g = 32.2 # gravitational constant in ft/ sec^2 \\r\\n radius = 0.116 # of the cricket ball in ft\\r\\n \\r\\n # We use the following two constants to calculate the value of constant_1\\r\\n #weight = 0.344 lb\\r\\n #ro = 0.075 lb/ft**2\\r\\n constant = 0.00461 # ro*area/(2*m)\\r\\n\\r\\n # C_d and C_l are constants for calculating the values of k_D and k_L\\r\\n c_d = 0.4\\r\\n c_l = 0.116\\r\\n k_d = c_d * constant # (c_d*ro*area)/(2*m)\\r\\n k_l = c_l * constant # (c_l*ro*area)/(2*m)\\r\\n \\r\\n # the initial and final time\\r\\n t_0 = 0.0 #s\\r\\n t_f = 3.0\\r\\n\\r\\n # number of steps and value of h \\r\\n N = 1000\\r\\n h = (t_f-t_0)/N\\r\\n\\r\\n e = 0.32 # coefficient of restitution\\r\\n c = 0.1 # constant for moisture level in the ground ranging from 0 to 1\\r\\n eps = 10E-2 # error constant\\r\\n \\r\\n def f_2(r,t):\\r\\n \\\"\\\"\\\"\\r\\n Helper function for using the fourth-order Runge Kutta (RK-4) method on the \\r\\n second order differential equations which help plot the ball's trajectory in its\\r\\n x, y and z axes after a bounce.\\r\\n \\\"\\\"\\\"\\r\\n x = r[0]\\r\\n y = r[2]\\r\\n z = r[4]\\r\\n vx = r[1]\\r\\n vy = r[3]\\r\\n vz = r[5]\\r\\n # velocity equation for the ball after the bounce\\r\\n velocity = np.sqrt((vx+c*radius*wy)**2+(vy-c*radius*wx)**2+(-e*vz)**2)\\r\\n \\r\\n return np.array([vx, (-k_d*velocity*vx+k_l*(wy*vz-wz*vy)),\\r\\n vy, (-k_d*velocity*vy+k_l*(wz*vx-wx*vz)),\\r\\n vz,(-k_d*velocity*vz+k_l*(wz*vy-wy*vx)-g)], float)\\r\\n \\r\\n # Applies RK-4 for each value of the position and velocity components\\r\\n t_after = np.arange(t_0, t_f, h)\\r\\n x_after = []\\r\\n y_after = []\\r\\n z_after = []\\r\\n r_after = np.array([x_f, vx_f, y_f, vy_f, z_f, vz_f], float)\\r\\n for t in t_after:\\r\\n # continues the function until it reaches the end of the pitch\\r\\n if r_after[0] <= (60+eps):\\r\\n x_after.append(abs(r_after[0]))\\r\\n y_after.append(abs(r_after[2]))\\r\\n z_after.append(abs(r_after[4]))\\r\\n k1 = h * f_2(r_after, t)\\r\\n k2 = h * f_2(r_after + 0.5 * k1, t + 0.5 * h)\\r\\n k3 = h * f_2(r_after + 0.5 * k2, t + 0.5 * h)\\r\\n k4 = h * f_2(r_after + k3, t + h)\\r\\n r_after += (k1 + 2 * k2 + 2 * k3 + k4) / 6\\r\\n \\r\\n # Makes a 3-D plot of the x, y and z axes representing the ball after hitting\\r\\n # the ground\\r\\n plt.figure(2)\\r\\n plot2 = plt.axes(projection=\\\"3d\\\")\\r\\n plot2.plot3D(x_after,y_after,z_after,'blue')\\r\\n plot2.set_xlabel('x')\\r\\n plot2.set_ylabel('y')\\r\\n plot2.set_zlabel('z')\\r\\n plot2.set_title('After Bounce')\\r\\n \\r\\n return x_after,y_after,z_after\",\n \"def u(self, f, rho=None, acceleration=None):\\n if rho is None:\\n rho = self.rho(f)\\n v = self.j(f) / rho\\n # apply correction due to forcing, which effectively averages the pre- and post-collision velocity\\n correction = 0.0\\n if acceleration is not None:\\n if len(acceleration.shape) == 1:\\n index = [Ellipsis] + [None]*self.D\\n acceleration = acceleration[index]\\n correction = acceleration / (2 * rho)\\n return v + correction\",\n \"def main():\\n \\n # Particle in SHO - c.f. Mocz & Succi (2015) Fig. 2\\n # parameters\\n n = 100 # number of particles\\n dt = 0.02 # timestep\\n nt = 100 # number of timesteps\\n nt_setup = 400 # number of timesteps to set up simulation\\n n_out = 25 # plot solution every nout steps\\n b = 4 # velocity damping for acquiring initial condition\\n m = 1/n # mass of SPH particle ( m * n = 1 normalizes |wavefunction|^2 to 1)\\n h = 40/n # smoothing length\\n t = 0. # time\\n\\n # plot potential\\n xx = np.linspace(-4.0, 4.0, num=400)\\n xx = np.reshape(xx,(xx.size,1))\\n fig = plt.plot(xx, 0.5*xx**2, linewidth=5, color=[0.7, 0.7, 0.9])\\n \\n # initialize\\n x = np.linspace(-3.0, 3.0, num=n)\\n x = np.reshape(x,(n,1))\\n u = np.zeros((n,1))\\n \\n rho = density( x, m, h )\\n P = pressure( x, rho, m, h )\\n a = acceleration( x, u, m, rho, P, b, h )\\n\\n # get v at t=-0.5*dt for the leap frog integrator using Euler's method\\n u_mhalf = u - 0.5 * dt * a\\n\\n # main loop (time evolution)\\n for i in np.arange(-nt_setup, nt): # negative time (t<0, i<0) is used to set up initial conditions\\n\\n # leap frog\\n u_phalf = u_mhalf + a*dt\\n x = x + u_phalf*dt\\n u = 0.5*(u_mhalf+u_phalf)\\n u_mhalf = u_phalf\\n if (i >= 0):\\n t = t + dt\\n print(\\\"%.2f\\\" % t)\\n \\n if (i == -1 ): # switch off damping before t=0\\n u = np.zeros((n,1)) + 1.0\\n u_mhalf = u\\n b = 0 # switch off damping at time t=0\\n \\n # update densities, pressures, accelerations\\n rho = density( x, m, h )\\n P = pressure( x, rho, m, h )\\n a = acceleration( x, u, m, rho, P, b, h)\\n \\n # plot solution every n_out steps\\n if( (i >= 0) and (i % n_out) == 0 ):\\n xx = np.linspace(-4.0, 4.0, num=400)\\n xx = np.reshape(xx,(xx.size,1))\\n rr = probeDensity(x, m, h, xx)\\n rr_exact = 1./np.sqrt(np.pi) * np.exp(-(xx-np.sin(t))**2/2.)**2\\n fig = plt.plot(xx, rr_exact, linewidth=2, color=[.6, .6, .6])\\n fig = plt.plot(xx, rr, linewidth=2, color=[1.*i/nt, 0, 1.-1.*i/nt], label='$t='+\\\"%.2f\\\" % t +'$')\\n # plot the t<0 damping process for fun\\n if( i==-nt_setup or i==-nt_setup*3/4 or i==-nt_setup/2 ):\\n xx = np.linspace(-4.0, 4.0, num=400)\\n xx = np.reshape(xx,(xx.size,1))\\n rr = probeDensity(x, m, h, xx)\\n fig = plt.plot(xx, rr, linewidth=1, color=[0.9, 0.9, 0.9])\\n \\n plt.legend()\\n plt.xlabel('$x$')\\n plt.ylabel('$|\\\\psi|^2$')\\n plt.axis([-2, 4, 0, 0.8])\\n plt.savefig('solution.pdf', aspect = 'normal', bbox_inches='tight', pad_inches = 0)\\n plt.close()\",\n \"def density_by_ideal_gas_law(\\n p: tf.Tensor,\\n r: tf.Tensor,\\n t: tf.Tensor,\\n ) -> tf.Tensor:\\n return p / r / t\",\n \"def calculer_force_traction(module_young, coefficient_poisson, longueur_fleche,\\n longueur_bras, longueur_corde):\\n return calculer_ressort(module_young, coefficient_poisson) * \\\\\\n calculer_longueur_deplacement(longueur_fleche, longueur_bras, longueur_corde)\",\n \"def FrictionF(self,eta):\\n return -3*3.14*eta*self.par.d*self.par.vel\",\n \"def _fv(self):\\n return self.beta * (self.x ** self.c)\",\n \"def get_f_aero_body(X, Usfc, P, Pdyn):\\n d_alpha = X[sv_alpha] - P.alpha0\\n rvel = X[sv_p:sv_r+1]*np.array([P.Bref, P.Cref, P.Bref])/2/P.Vref\\n\\n CL = P.CL0 + P.CL_alpha*d_alpha + P.CL_beta*X[sv_beta] +\\\\\\n np.dot(P.CL_omega,rvel) + np.dot(P.CL_sfc,Usfc)\\n\\n CD = P.CD0 + P.CD_k1*CL + P.CD_k2*(CL**2) + np.dot(P.CD_sfc,Usfc)\\n\\n CY = P.CY_alpha*d_alpha + P.CY_beta*X[sv_beta] +\\\\\\n np.dot(P.CY_omega,rvel) + np.dot(P.CY_sfc,Usfc)\\n \\n return Pdyn*P.Sref*np.dot(get_aero_to_body(X),[-CD, CY, -CL])\",\n \"def force ( r ):\\n \\n assert r.shape == (n,3), 'Incorrect shape of r'\\n\\n d = np.zeros_like(r) # Create d vectors (bonds)\\n d[1:n,:] = r[1:n,:] - r[0:n-1,:] # Compute d vectors (zero index not used)\\n\\n # Store C coefficients in a matrix\\n # In the general case we would not need to calculate every pair\\n # and also we would make use of the symmetry cc[a,b]=cc[b,a]\\n cc = np.zeros((n,n),dtype=np.float_) # Create C array (scalar products)\\n for a in range(1,n):\\n for b in range(1,n):\\n cc[a,b]=np.dot(d[a,:],d[b,:]) # Compute C array (zero indices not used)\\n\\n a = n-1 # For this test there is just one angle\\n\\n # Here is the potential as a function of cos(theta)\\n # For testing we use the simplest form: v= -cos(theta)\\n # The notation matches that used in the appendix\\n\\n prefac = 1.0 / np.sqrt(cc[a,a]*cc[a-1,a-1])\\n fac = cc[a,a-1]\\n pot = -prefac*fac # This is -cos(theta)\\n\\n # Here we include the derivative of the potential with respect to cos(theta) in the prefactor\\n # For this simple case it is -1, so the forces are simply gradients of cos(theta) as in the text\\n f = np.empty_like(r) # Create force array\\n fac1 = fac / cc[a,a]\\n fac2 = fac / cc[a-1,a-1]\\n f[a,:] = -prefac * ( fac1*d[a,:] - d[a-1,:] )\\n f[a-1,:] = prefac * ( fac1*d[a,:] - fac2*d[a-1,:] + d[a,:] - d[a-1,:] )\\n f[a-2,:] = prefac * ( fac2*d[a-1,:] - d[a,:] )\\n\\n return pot, f\",\n \"def velPulse (t,Dp,t1,Tp):\\r\\n\\tdiscretePulse=(Dp*math.pi)/(Tp*float(2))*np.cos(math.pi*(t-t1-Tp/float(2))/float(Tp))\\r\\n\\treturn discretePulse\",\n \"def define_ufl_body_force(self):\\n\\n if hasattr(self, 'ufl_body_force'):\\n return None\\n\\n # Set to None and exit if key is not in config dictionary.\\n if self.config['formulation']['body_force'] is None:\\n self.ufl_body_force = 0\\n self.ufl_body_force0 = 0\\n return None\\n\\n rho = self.config['material']['density']\\n b = self.config['formulation']['body_force']\\n xi = self.test_vector\\n\\n self.ufl_body_force = dlf.dot(xi, rho*b)*dlf.dx\\n\\n # Create a copy of the body force term to use at a different time step.\\n if self.config['formulation']['time']['unsteady'] and hasattr(b,'t'):\\n try:\\n cppcode = b.cppcode\\n except AttributeError:\\n cppcode = b._cppcode\\n b0 = dlf.Expression(cppcode, t=0.0,\\n element=self.vectorSpace.ufl_element())\\n self.ufl_body_force0 = dlf.dot(xi, rho*b0)*dlf.dx\\n else:\\n self.ufl_body_force0 = 0\\n\\n return None\",\n \"def set_f_exp(self):\\n self.flow_data.velocity = self.flow_data.flow / self.exh.area\\n self.flow_data.set_TempPres_dependents()\\n self.flow_data.Re_D = ( self.flow_data.velocity *\\n self.flow_data.rho * self.exh.D / self.flow_data.mu )\\n self.flow_data.f_exp = ( self.flow_data.pressure_drop * 1.e3 /\\n (0.5 * self.exh.length * self.exh.perimeter / self.exh.area *\\n self.flow_data.rho * self.flow_data.velocity**2) )\",\n \"def thermal_velocity(self, z, M_X, Omega_X, g_X = 1.5, stat = 'FD'):\\n if stat in ['FD','Fermi-Dirac','F','Fermi','fermions']: factor = 7/6.\\n elif stat in ['BE','Bose-Einstein','B','Bose','bosons'] : factor = 1.\\n else: raise ValueError(\\\"Statistics not known\\\")\\n #return factor*0.012*(1.+z)*(Omega_X/0.3)**(1./3.)*(self.h/0.65)**(2/3.)*M_X**(-4./3.)*(g_X/1.5)**(1./3.)\\n ratio_denominator = 93.14/(np.pi**4.*const.kB*self.T_cmb /(15*self.Gamma_nu**3.*self.omega_gamma*1.5*ss.zeta(3)))\\n return factor*0.03258*(1.+z)*(Omega_X/0.3)**(1./3.)*(self.h/0.7)**(2/3.)*M_X**(-4./3.)*(g_X/1.5)**(1./3.)*ratio_denominator**(-1/3)\",\n \"def calculate_drag_force(self, Uf, Vp):\\n\\n\\t\\tif self.B == None: \\n\\t\\t\\traise('The drag coefficient, B, must be set to calculate the drag force.')\\n\\n\\t\\tF = self.B*(Uf - Vp)\\n\\t\\treturn F\",\n \"def elasticP2Gpp(self, P, dt):\\n p_Jp = ti.static(self.p_Jp)\\n p_F = ti.static(self.p_F)\\n\\n h = self.cfg.elastic_h\\n mu, la = h * self.cfg.mu_0, h * self.cfg.lambda_0\\n\\n U, sig, V = ti.svd(p_F[P])\\n J = 1.0\\n\\n for d in ti.static(range(self.dim)):\\n new_sig = sig[d, d]\\n p_Jp[P] *= sig[d, d] / new_sig\\n sig[d, d] = new_sig\\n J *= new_sig\\n\\n force = self.kirchoff_FCR(p_F[P], U @ V.transpose(), J, mu, la)\\n force *= (-dt * self.cfg.p_vol * 4 * self.cfg.inv_dx ** 2)\\n\\n return force\",\n \"def Piping(T_in, p_in, m_dot, d_inner, l_pipe, f, epsilon_pipe, T_shield, N):\\r\\n\\r\\n ## Estimation of the influence of the arcs\\r\\n # Calculation according to VDI Heatatlas 2013\\r\\n # Assumption isoenthalpic flow\\r\\n state_Arc = FlowRestriction(T_in, p_in, m_dot, d_inner, f)\\r\\n p_Arc = state_Arc.get(\\\"p\\\")\\r\\n T_Arc = state_Arc.get(\\\"T\\\")\\r\\n\\r\\n ## Estimation of the influence of thermal radiation on the compressible flow\\r\\n\\r\\n # Emission coefficent for an enclosed vessel\\r\\n # Assuming much bigger hot surface -> emissivity of hot surface doesnt matter anymore, just the cold one\\r\\n # Thus the simple equation can be used\\r\\n q_pipe = epsilon_pipe * sp.constants.Stefan_Boltzmann * (T_shield**4 - T_Arc**4) #W\\r\\n\\r\\n # Calling of the function SimplePipe\\r\\n state_out = SimplePipe(T_Arc, p_Arc, m_dot, d_inner, l_pipe, N, 0, q_pipe)\\r\\n #Transfer results\\r\\n p_out = state_out.get(\\\"p\\\")\\r\\n T_out = state_out.get(\\\"T\\\")\\r\\n h_out = state_out.get(\\\"h\\\")\\r\\n state_out = {\\\"h\\\": h_out, \\\"T\\\": T_out, \\\"p\\\": p_out}\\r\\n\\r\\n return state_out\",\n \"def Step_DeadBiomass(Xo,Hinf,Cinf,Ninf,Ginf,QH,QC,QN,QG,Nc,decay,mort,Qc,X,dt,Vc):\\n\\treturn(Xo + (-0.1*Xo + (decay+mort)*Nc*(Qc+X))*dt) #Here the term with Q can be replaced with a specific biomass sedimentation flux\",\n \"def p2f(self):\\n\\n stale = self.m_f\\n self.m_f = self.v.b / self.m_v\",\n \"def ptf_function(x):\\n return ptf_coefficient + ptf_coefficient * (np.sin(\\n 2 * np.pi * flapping_wing_frequency *\\n (x - pitching_delay_time_fraction / flapping_wing_frequency)))**2\",\n \"def before_bounce(vx_0,vy_0,vz_0,wx,wy,wz):\\r\\n # We define all the constants that we need to use\\r\\n g = 32.2 # gravitational constant in ft/ sec^2 \\r\\n radius = 0.116 # of the cricket ball in ft\\r\\n\\r\\n # We use the following two constants to calculate the value of constant_1\\r\\n #weight = 0.344 lb\\r\\n #ro = 0.075 lb/ft**2\\r\\n constant = 0.00461 # ro*area/(2*m)\\r\\n\\r\\n # C_d and C_l are constants for calculating the values of k_D and k_L\\r\\n c_d = 0.4\\r\\n c_l = 0.116\\r\\n k_d = c_d * constant # (c_d*ro*area)/(2*m)\\r\\n k_l = c_l * constant # (c_l*ro*area)/(2*m)\\r\\n \\r\\n # the initial and final time\\r\\n t_0 = 0.0 #s\\r\\n t_f = 3.0\\r\\n\\r\\n # number of steps and value of h \\r\\n N = 1000\\r\\n h = (t_f-t_0)/N\\r\\n\\r\\n e = 0.32 # coefficient of restitution\\r\\n c = 0.1 # constant for moisture level in the ground ranging from 0 to 1\\r\\n eps = 10E-2 # error constant\\r\\n \\r\\n # the values of the initial position of the ball and its \\r\\n # x, y and z components\\r\\n x_0 = 1 #ft\\r\\n y_0 = 2 #ft\\r\\n z_0 = 7 #ft\\r\\n\\r\\n def f(r,t):\\r\\n \\\"\\\"\\\"\\r\\n Helper function for using the fourth-order Runge Kutta (RK-4) method on the \\r\\n second order differential equations which help plot the ball's trajectory in its\\r\\n x, y and z axes.\\r\\n \\\"\\\"\\\"\\r\\n x = r[0]\\r\\n y = r[2]\\r\\n z = r[4]\\r\\n vx = r[1]\\r\\n vy = r[3]\\r\\n vz = r[5]\\r\\n velocity = np.sqrt(vx**2+vy**2+vz**2)\\r\\n #if np.abs(z)>eps:\\r\\n velocity = np.sqrt((vx+c*radius*wy)**2+(vy-c*radius*wx)**2+(-e*vz)**2)\\r\\n \\r\\n # equations for a cricket ball in motion\\r\\n return np.array([vx, (-k_d*velocity*vx+k_l*(wy*vz-wz*vy)),\\r\\n vy, (-k_d*velocity*vy+k_l*(wz*vx-wx*vz)),\\r\\n vz,(-k_d*velocity*vz+k_l*(wz*vy-wy*vx)-g)], float)\\r\\n \\r\\n t_before = np.arange(t_0, t_f, h) #array of time \\r\\n x_before = [] \\r\\n y_before = []\\r\\n z_before = []\\r\\n r_before = np.array([x_0, vx_0, y_0, vy_0, z_0, vz_0], float)\\r\\n \\r\\n # Applies RK-4 for each value of the position and velocity components\\r\\n for t in t_before:\\r\\n if np.abs(r_before[4])>=eps and r_before[0] <= (60+eps): \\r\\n x_before.append(r_before[0])\\r\\n y_before.append(r_before[2])\\r\\n z_before.append(r_before[4])\\r\\n k1 = h * f(r_before, t)\\r\\n k2 = h * f(r_before + 0.5 * k1, t + 0.5 * h)\\r\\n k3 = h * f(r_before + 0.5 * k2, t + 0.5 * h)\\r\\n k4 = h * f(r_before + k3, t + h)\\r\\n r_before += (k1 + 2 * k2 + 2 * k3 + k4) / 6\\r\\n # sets the initial component values for after the bounce when z is 0\\r\\n x_f = r_before[0]\\r\\n y_f = r_before[2]\\r\\n z_f = r_before[4]\\r\\n vx_f = r_before[1]\\r\\n vy_f = r_before[3]\\r\\n vz_f = r_before[5]\\r\\n \\r\\n # Makes a 3-D plot of the x, y and z axes representing the ball before hitting\\r\\n # the ground\\r\\n plt.figure(1)\\r\\n plot1 = plt.axes(projection=\\\"3d\\\")\\r\\n plot1.plot3D(x_before,y_before,z_before,'blue')\\r\\n plot1.set_xlabel('x')\\r\\n plot1.set_ylabel('y')\\r\\n plot1.set_zlabel('z')\\r\\n plot1.set_title('Before Bounce')\\r\\n \\r\\n return x_f,y_f,z_f,vx_f,vy_f,vz_f,x_before,y_before,z_before\",\n \"def f(xx, uu, uref, t, p):\\n x1, x2, x3, x4 = xx # system state variables\\n u1, = uu # input variable\\n\\n l = 0.5 # length of the pendulum rod\\n g = 9.81 # gravitational acceleration\\n M = 1.0 # mass of the cart\\n m = 0.1 # mass of the pendulum\\n\\n s = sin(x3)\\n c = cos(x3)\\n\\n ff = np.array([ x2,\\n m*s*(-l*x4**2+g*c)/(M+m*s**2)+1/(M+m*s**2)*u1,\\n x4,\\n s*(-m*l*x4**2*c+g*(M+m))/(M*l+m*l*s**2)+c/(M*l+l*m*s**2)*u1\\n ])\\n return ff\",\n \"def __sending_flow(self, vm, beta, rhoc, qmax, rho):\\n if rho < 0.0:\\n return 0.0\\n elif rho < rhoc:\\n return float(vm * rho - vm * (rho ** 2) / beta)\\n else:\\n return float(qmax)\",\n \"def magnetic_pressure_gradient_force(self, method='spectral'):\\n import numpy as np\\n gradB, B = self.magnetic_gradient_tensor(method=method, return_B=True)\\n F = np.zeros_like(B)\\n for i in range(3):\\n for j in range(3):\\n F[i] -= B[j] * gradB[i,j]\\n return F\",\n \"def velocity_field(xt,yt,x0,y0,Vinf,dia,rot,chord,B,param=None,veltype='all',integration='simp',m=220,n=200):\\n rad = dia/2.\\n tsr = rad*fabs(rot)/Vinf\\n solidity = (chord*B)/rad\\n\\n # Translating the turbine position\\n x0t = x0 - xt\\n y0t = y0 - yt\\n\\n coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9 = coef_val()\\n\\n # Calculating EMG distribution parameters (based on polynomial surface fitting)\\n if param is None:\\n loc1 = _parameterval(tsr,solidity,coef0)\\n loc2 = _parameterval(tsr,solidity,coef1)\\n loc3 = _parameterval(tsr,solidity,coef2)\\n spr1 = _parameterval(tsr,solidity,coef3)\\n spr2 = _parameterval(tsr,solidity,coef4)\\n skw1 = _parameterval(tsr,solidity,coef5)\\n skw2 = _parameterval(tsr,solidity,coef6)\\n scl1 = _parameterval(tsr,solidity,coef7)\\n scl2 = _parameterval(tsr,solidity,coef8)\\n scl3 = _parameterval(tsr,solidity,coef9)\\n\\n else:\\n # Reading in EMG distribution parameters\\n loc1 = param[0]\\n loc2 = param[1]\\n loc3 = param[2]\\n spr1 = param[3]\\n spr2 = param[4]\\n skw1 = param[5]\\n skw2 = param[6]\\n scl1 = param[7]\\n scl2 = param[8]\\n scl3 = param[9]\\n\\n ###################################\\n if veltype == 'vort':\\n # VORTICITY CALCULATION (NO INTEGRATION)\\n if x0t < 0.:\\n vel = 0.\\n else:\\n vel = _vawtwake.vorticitystrength(x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)/rot\\n ###################################\\n else:\\n # Integration of the vorticity profile to calculate velocity\\n if integration == 'simp':\\n # SIMPSON'S RULE INTEGRATION (must use polynomial surface coefficients from VAWTPolySurfaceCoef.csv)\\n inte = 1 # Simpson's Rule\\n # inte = 2 # Trapezoidal Rule (optional ability of the code-- faster but less accurate)\\n\\n if param is not None:\\n print \\\"**** Using polynomial surface coefficients from VAWTPolySurfaceCoef.csv for Simpson's rule integration ****\\\"\\n\\n vel_xs,vel_ys = _vawtwake.vel_field(xt,yt,x0,y0,dia,rot,chord,B,Vinf,coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9,m,n,inte)\\n\\n if veltype == 'all':\\n vel = sqrt((vel_xs*Vinf + Vinf)**2 + (vel_ys*Vinf)**2)/Vinf\\n elif veltype == 'x':\\n vel = (vel_xs*Vinf + Vinf)/Vinf\\n elif veltype == 'y':\\n vel = vel_ys\\n elif veltype == 'ind':\\n vel = np.array([vel_xs,vel_ys])\\n ###################################\\n elif integration == 'gskr':\\n # 21-POINT GAUSS-KRONROD RULE QUADRATURE INTEGRATION\\n xbound = (scl3+5.)*dia\\n argval = (x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)\\n if veltype == 'all' or veltype == 'x' or veltype == 'ind':\\n vel_x = _dblquad(_vawtwake.integrandx,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\\n vel_xs = (vel_x[0]*fabs(rot))/(2.*pi)\\n if veltype == 'all' or veltype == 'y' or veltype == 'ind':\\n vel_y = _dblquad(_vawtwake.integrandy,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\\n vel_ys = (vel_y[0]*fabs(rot))/(2.*pi)\\n\\n if veltype == 'all':\\n vel = sqrt((vel_xs + Vinf)**2 + (vel_ys)**2)/Vinf\\n elif veltype == 'x':\\n vel = (vel_xs + Vinf)/Vinf\\n elif veltype == 'y':\\n vel = vel_ys/Vinf\\n elif veltype == 'ind':\\n vel = np.array([vel_xs,vel_ys])/Vinf\\n ###################################\\n\\n return vel\",\n \"def force_frenkel(r, epsilon, sigma, Rc, n):\\n\\n alpha=2*n*Rc**2*((1+2*n)/(2*n*(Rc**2-1)))**(2*n+1)\\n\\n Rc_term=(Rc/r)**2-1\\n sigma_term=(sigma/r)**2-1\\n first_term=-alpha*epsilon\\n second_term=(-2*sigma**2/(r**3))*Rc_term**(2*n)\\n third_term=-(Rc**2*4*n*Rc_term**(2*n-1)*sigma_term/r**3)\\n\\n# print \\\"The first term is %f, the second term is %f and the third is %f\\\"%(first_term, second_term, third_term)\\n\\n force=first_term*(second_term+third_term)\\n\\n return force\",\n \"def _term_probability(self, frequency, total_frequency, doc_length, total_doc_length):\\n if doc_length == 0:\\n p1 = 0\\n else:\\n p1 = frequency / doc_length\\n if total_doc_length == 0:\\n p2 = 0\\n else:\\n p2 = total_frequency / total_doc_length\\n return (1-self.omega) * p1 + self.omega * p2\",\n \"def force_12(alpha, r1, r2, R_particle, eps_particle, k, eps_out, eps_in,\\n fiber_radius, nmin, nmax, kzimax, E0_mod, nmin_sc, nmax_sc, case):\\n\\n dr = 1 / k * 1e-5\\n dz = dr\\n dtheta = 1e-5\\n\\n p1 = dipole_moment(1, r1, r2, R_particle, eps_particle, k, eps_out, eps_in,\\n fiber_radius, nmin, nmax, kzimax,\\n E0_mod, nmin_sc, nmax_sc, case)\\n p1c = p1.conjugate()\\n\\n # Fr\\n if alpha == 0:\\n r1plusdr = r1 + np.array([dr, 0, 0])\\n r1minusdr = r1 - np.array([dr, 0, 0])\\n Eplusr = total_loc_efield(1, r1plusdr, r2, k, case, nmin, nmax, kzimax,\\n fiber_radius, eps_out, eps_in, E0_mod,\\n nmin_sc, nmax_sc, R_particle, eps_particle)\\n Eminusr = total_loc_efield(1, r1minusdr, r2, k, case, nmin, nmax, kzimax,\\n fiber_radius, eps_out, eps_in, E0_mod,\\n nmin_sc, nmax_sc, R_particle, eps_particle)\\n grad_r = (Eplusr - Eminusr) / (2 * dr)\\n\\n return(0.5 * np.dot(p1c, grad_r).real)\\n # Ftheta\\n elif alpha == 1:\\n r1plusdtheta = r1 + np.array([0, dtheta, 0])\\n r1minusdtheta = r1 - np.array([0, dtheta, 0])\\n\\n Eplustheta = total_loc_efield(1, r1plusdtheta, r2, k, case, nmin, nmax, kzimax,\\n fiber_radius, eps_out, eps_in, E0_mod,\\n nmin_sc, nmax_sc, R_particle, eps_particle)\\n Eminustheta = total_loc_efield(1, r1minusdtheta, r2, k, case, nmin, nmax, kzimax,\\n fiber_radius, eps_out, eps_in, E0_mod,\\n nmin_sc, nmax_sc, R_particle, eps_particle)\\n grad_theta = (Eplustheta - Eminustheta) / (r1[0] * 2 * dtheta)\\n\\n return(0.5 * np.dot(p1c, grad_theta).real)\\n # Fz\\n elif alpha == 2:\\n r1plusdz = r1 + np.array([0, 0, dz])\\n r1minusdz = r1 - np.array([0, 0, dz])\\n\\n Eplusz = total_loc_efield(1, r1plusdz, r2, k, case, nmin, nmax, kzimax,\\n fiber_radius, eps_out, eps_in, E0_mod,\\n nmin_sc, nmax_sc, R_particle, eps_particle)\\n Eminusz = total_loc_efield(1, r1minusdz, r2, k, case, nmin, nmax, kzimax,\\n fiber_radius, eps_out, eps_in, E0_mod,\\n nmin_sc, nmax_sc, R_particle, eps_particle)\\n grad_z = (Eplusz - Eminusz) / (2 * dz)\\n\\n return(0.5 * np.dot(p1c, grad_z).real)\\n else:\\n print('alpha is out of range!')\\n return(0)\",\n \"def _compute_f(self, p, dh, dv):\\n return dh / (self.beta * p * dv)\",\n \"def stopping_length_function(initial_velocity=120, friction_coefficient=0.3):\\n g = 9.81\\n v0 = initial_velocity/3.6\\n mu = friction_coefficient\\n\\n return (1/2)*(v0**2/(mu*g))\",\n \"def duty_cycle_by_force(newton: float, profile: GripForceProfile) -> float:\\n if profile.min <= newton <= profile.max:\\n return sum(ele[1] * (newton ** ele[0]) for ele in profile.polynomial)\\n else:\\n raise ValueError(\\\"Gripper force out of bounds\\\")\",\n \"def compute_vel(self, state, goal):\\n\\n print(state)\\n print(goal)\\n dx = goal[0] - state[0]\\n dy = goal[1] - state[1]\\n theta = state[2]\\n rho = np.sqrt(dx**2+dy**2)\\n #alpha = np.minimum(-theta + np.arctan2(dy,dx), - theta + np.arctan2(dy,dx)+2*np.pi)\\n #beta = theta + alpha\\n beta = np.arctan2(dy,dx)\\n alpha = beta - theta\\n print(\\\"beta\\\", beta, \\\"theta\\\", theta, \\\"alpha1\\\", alpha)\\n\\tif (alpha > np.pi):\\n alpha = alpha - 2*np.pi\\n \\telse:\\n\\t if (alpha < -np.pi):\\n \\talpha = alpha + 2*np.pi\\n \\n v = self.kp*rho\\n omega = self.ka*alpha+self.kb*beta\\n print(alpha)\\n if (np.cos(alpha)*np.cos(alpha) < 0.2):\\n v = 0.1\\n else:\\n v = np.cos(alpha)*rho\\n\\n if (v < 0):\\n #exit()\\n\\t v = 0.1\\n\\t \\n if (v > self.MAX_SPEED):\\n v = self.MAX_SPEED\\n \\n omega = 4*alpha\\n if (omega > self.MAX_OMEGA):\\n omega = self.MAX_OMEGA\\n #if (omega < -self.MAX_OMEGA):\\n # omega = -self.MAX_OMEGA\\n\\n done = False\\n if (np.absolute(dx) < 0.01 and np.absolute(dy) < 0.01):\\n done = True\\n \\n\\treturn (v,omega,done)\",\n \"def applyForce(self, F, dT):\",\n \"def F(self, points):\\n\\n # Perform calculation in the frame parallel to the ellipse\\n # and then rotate it back\\n rotatedP = self.rotatePoint(points)\\n\\n surface_pot = np.sqrt((rotatedP[..., 0]/self.axisA[:, None])**2 + (rotatedP[..., 1]/self.axisB[:, None])**2)\\n\\n # Force = -grad potential\\n # swapaxes is necesary to make the created\\n # force array of shape [N_samples, N-particles, 2]\\n\\n surface_force = - 1/surface_pot[..., None]*np.array([\\n rotatedP[..., 0]/self.axisA[:, None]**2, \\n rotatedP[..., 1]/self.axisB[:, None]**2\\n ]).swapaxes(0, 2).swapaxes(0, 1) \\n\\n # Rotate back to the original frame\\n surface_force = self.rotatePoint(surface_force, -1)\\n\\n # Force is scaled with A_conf and applied only if the particle is outside\\n return self.A_conf*surface_force*self.isOutside(points)[:, :, None]\",\n \"def phase_velocity(refractive_index):\\n return cgs.c / refractive_index\",\n \"def friction(self, qd):\\n\\n qd = getvector(qd, self.n)\\n tau = np.zeros(self.n)\\n\\n for i in range(self.n):\\n tau[i] = self.links[i].friction(qd[i])\\n\\n return tau\",\n \"def fluxonium_potential(self):\\n return -0.5*(self.Ej * ((1+self.d)*cos(self.phis - 2. * pi * self.phi - 2. * pi * self.phiL) + (1-self.d)*cos(self.phis-2. * pi * self.phiL))) + self.El/2. * (self.phis) ** 2\\n #return -0.5*(self.Ej * cos(self.phis - 2. * pi * self.phi) + self.Ej * cos(self.phis)) + self.El/2. * (self.phis-self.phiL)** 2\",\n \"def force ( box, strain, r ):\\n\\n import numpy as np\\n from itertools import product\\n import math\\n \\n # It is assumed that positions are in units where box = 1\\n # Forces are calculated in units where sigma = 1 and epsilon = 1\\n # Lees-Edwards boundaries, in sliding brick arrangement\\n # Flow/gradient/vorticity directions are x/y/z == 0/1/2\\n # Uses neighbour lists\\n\\n n = r.shape[0]\\n\\n # Set up vectors to half the cells in neighbourhood of 3x3x3 cells in cubic lattice\\n # The cells are chosen so that if (d0,d1,d2) appears, then (-d0,-d1,-d2) does not.\\n # The last three cells are extra ones, to cope with the sheared system\\n d = np.array ( [ [ 0, 0, 0], [ 1, 0, 0], [ 1, 0, 1], [-1, 0, 1], [ 0, 0, 1], # 5 cells with d1=0\\n [ 1, 1, -1], [ 1, 1, 0], [ 1, 1, 1], # 3 cells with d0= 1, d1=1\\n [ 0, 1, -1], [ 0, 1, 0], [ 0, 1, 1], # 3 cells with d0= 0, d1=1\\n [-1, 1, -1], [-1, 1, 0], [-1, 1, 1], # 3 cells with d0=-1, d1=1\\n [-2, 1, -1], [-2, 1, 0], [-2, 1, 1] ] ) # 3 cells with d0=-2, d1=1\\n\\n r[:,0] = r[:,0] - np.rint(r[:,1])*strain # Extra correction in box=1 units\\n r = r - np.rint(r) # Ensure all atoms in periodic box\\n \\n sr2_ovr = 1.77 # Overlap threshold (pot > 100)\\n r_cut_box = r_cut / box\\n r_cut_box_sq = r_cut_box ** 2\\n box_sq = box ** 2\\n\\n # Initialize\\n f = np.zeros_like(r)\\n total = PotentialType ( pot=0.0, vir=0.0, pyx=0.0, lap=0.0, ovr=False )\\n\\n # Calculate cell index triplets\\n sc = math.floor(box/r_cut) # Number of cells along box edge\\n assert sc >= 3, 'System is too small for cells' # Guard against box being too small\\n c = np.floor((r+0.5)*sc).astype(np.int_) # N*3 array of cell indices for all atoms\\n assert np.all(c>=0) and np.all(c<sc), 'Index error' # Simplistic \\\"guard\\\" against roundoff\\n\\n shift = math.floor(strain*sc) # Strain measured in cell lengths\\n\\n if fast:\\n \\n # Build list of arrays, each array holding positions of atoms in a cell\\n # At the same time, define a matching set of force arrays in each cell\\n # i and j number the atoms in each cell; we do not refer explicitly to indices in r\\n rc, fc = [], [] # Initially empty lists of positions and forces\\n for ci in product(range(sc),repeat=3): # Triple loop over cells\\n mask = np.all(c==ci,axis=1) # Mask identifies atoms in this cell\\n rc.append(r[mask,:]) # Copy atom coordinates into array, add to list\\n fc.append(np.zeros_like(rc[-1])) # Zero corresponding forces, add to list\\n\\n for ci1, rci in enumerate(rc): # Loop over i-cells, getting all atoms in each i-cell as an array\\n ci = np.unravel_index(ci1,(sc,sc,sc)) # Get i-cell triple-indices\\n if rci.size==0: # Handle empty cell\\n continue\\n\\n # Set up correct neighbour cell indices\\n if ci[1]==sc-1: # i-cell is in the top layer\\n dd = d.copy() # Standard list copied, including extra 3 cells\\n dd[5:,0] = d[5:,0] - shift # All those looking up need adjustment in the x direction\\n else: # i-cell is not in top layer\\n dd = d[:-3,:].copy() # Last three extra cells are not needed; shift is not needed\\n \\n for dj in dd: # Loop over neighbouring j-cells\\n cj = ci + dj # Compute neighbour j-cell triple-indices\\n cj1 = np.ravel_multi_index(cj,(sc,sc,sc),mode='wrap') # Convert j-cell to single-index\\n rcj = rc[cj1] # Get atoms in j-cell as an array\\n if rcj.size==0: # Handle empty cell\\n continue\\n\\n rij = rci[:,np.newaxis,:]-rcj[np.newaxis,:,:] # Separation vectors for all i and j\\n rij[:,:,0] = rij[:,:,0] - np.rint(rij[:,:,1])*strain # Extra correction in box=1 units\\n rij = rij - np.rint(rij) # PBCs in box=1 units\\n rij_sq = np.sum(rij**2,axis=2) # Squared separations\\n in_range = rij_sq < r_cut_box_sq # Set flags for within cutoff\\n\\n if ci1==cj1:\\n np.fill_diagonal(in_range,False) # Eliminate i==j when i-cell==j-cell\\n np.fill_diagonal(rij_sq,1.0) # Avoid divide-by-zero below\\n\\n rij_sq = rij_sq * box_sq # Now in sigma=1 units\\n rij = rij * box # Now in sigma=1 units\\n sr2 = np.where ( in_range, 1.0/rij_sq, 0.0 ) # (sigma/rij)**2, only if in range\\n ovr = sr2 > sr2_ovr # Overlap if too close\\n sr6 = sr2 ** 3\\n sr12 = sr6 ** 2\\n pot = sr12 - sr6 # LJ potential (cut but not shifted)\\n vir = pot + sr12 # LJ virial\\n pot = np.where ( in_range, pot+0.25, 0.0 ) # WCA LJ pair potential (cut-and-shifted)\\n lap = ( 22.0*sr12 - 5.0*sr6 ) * sr2 # LJ Laplacian\\n fij = vir * sr2 # LJ scalar part of forces\\n fij = rij * fij[:,:,np.newaxis] # LJ pair forces\\n pyx = rij[:,:,1]*fij[:,:,0] # Off-diagonal element of pressure tensor\\n\\n if ci1==cj1: # Correct for double-counting ij and ji when i-cell==j-cell\\n fij = fij / 2\\n total = total + PotentialType ( pot=np.sum(pot)/2, vir=np.sum(vir)/2, \\n pyx=np.sum(pyx)/2, lap=np.sum(lap)/2, ovr=np.any(ovr) )\\n else:\\n total = total + PotentialType ( pot=np.sum(pot), vir=np.sum(vir), \\n pyx=np.sum(pyx), lap=np.sum(lap), ovr=np.any(ovr) )\\n\\n fc[ci1][:,:] = fc[ci1][:,:] + np.sum(fij,axis=1) # Aggregate force on atoms in i-cell\\n fc[cj1][:,:] = fc[cj1][:,:] - np.sum(fij,axis=0) # Aggregate force on atoms in j-cell\\n\\n # Copy forces from list of cell arrays to main force array\\n for ci in product(range(sc),repeat=3): # Triple loop over cells\\n mask = np.all(c==ci,axis=1) # Mask identifies atoms in this cell\\n ci1 = np.ravel_multi_index(ci,(sc,sc,sc),mode='wrap') # Single-index\\n f[mask,:] = fc[ci1] # Copy atom forces from correct cell\\n\\n else:\\n \\n # Build list of arrays, each array holding indices of atoms in a cell\\n # ki and kj are atom indices in the r array; i and j number the atoms in each cell\\n k_array = np.arange(n) # Atom indices 0..N-1\\n kc = [] # Initially empty list of indices\\n for ci in product(range(sc),repeat=3): # Triple loop over cells\\n mask = np.all(c==ci,axis=1) # Mask identifies atoms in this cell\\n kc.append(k_array[mask]) # Copy atom indices into array, add to list\\n\\n for ci1, kci in enumerate(kc): # Loop over i-cells, getting atom indices as an array\\n ci = np.unravel_index(ci1,(sc,sc,sc)) # Get i-cell triple-indices\\n\\n # Set up correct neighbour cell indices\\n if ci[1]==sc-1: # i-cell is in the top layer\\n dd = d # Standard list copied, including extra 3 cells\\n dd[5:,0] = dd[5:,0] - shift # All those looking up need adjustment in the x direction\\n else:\\n dd = d[:-3,:] # Last three extra cells are not needed; shift is not needed\\n\\n for dj in dd: # Loop over neighbouring j-cells\\n cj = ci + dj # Compute neighbour j-cell triple-indices\\n cj1 = np.ravel_multi_index(cj,(sc,sc,sc),mode='wrap') # Convert to single-index\\n kcj = kc[cj1] # Get indices of atoms in j-cell as an array\\n\\n for i, ki in enumerate(kci): # Loop over individual atoms in i-cell\\n j0 = i+1 if cj1==ci1 else 0 # Only look upwards if i-cell==j-cell\\n if j0 >= kcj.size: # Handles (redundantly) empty j-cell and the case \\n continue # where j-cell==i-cell and i is last atom\\n\\n for kj in kcj[j0:]: # Loop over individual atoms in j-cell\\n rij = r[ki,:]-r[kj,:] # Separation vector\\n rij[0] = rij[0] - np.rint(rij[1])*strain # Extra correction in box=1 units\\n rij = rij - np.rint(rij) # Periodic boundary conditions in box=1 units\\n rij_sq = np.sum(rij**2) # Squared separation\\n\\n if rij_sq < r_cut_box_sq: # Check within cutoff\\n rij_sq = rij_sq * box_sq # Now in sigma=1 units\\n rij = rij * box # Now in sigma=1 units\\n sr2 = 1.0 / rij_sq # (sigma/rij)**2\\n ovr = sr2 > sr2_ovr # Overlap if too close\\n sr6 = sr2 ** 3\\n sr12 = sr6 ** 2\\n pot = sr12 - sr6 # LJ potential (cut but not shifted)\\n vir = pot + sr12 # LJ virial\\n pot = pot + 0.25 # WCA LJ potential (cut-and-shifted)\\n lap = ( 22.0*sr12 - 5.0*sr6 ) * sr2 # LJ Laplacian\\n fij = rij * vir * sr2 # LJ forces\\n pyx = rij[1]*fij[0] # Off-diagonal element of pressure tensor\\n total = total + PotentialType ( pot=pot, vir=vir, pyx=pyx, lap=lap, ovr=ovr )\\n f[ki,:] = f[ki,:] + fij\\n f[kj,:] = f[kj,:] - fij\\n\\n # Multiply results by numerical factors\\n f = f * 24.0 # 24*epsilon\\n total.pot = total.pot * 4.0 # 4*epsilon\\n total.vir = total.vir * 24.0 / 3.0 # 24*epsilon and divide virial by 3\\n total.pyx = total.pyx * 24.0 # 24*epsilon\\n total.lap = total.lap * 24.0 * 2.0 # 24*epsilon and factor 2 for ij and ji\\n \\n return total, f\",\n \"def work_dos():\\n #potential = 2x**2+x**2y+y**2\\n x1,y1 = (2, -3)\\n x2,y2 = (-1, 2)\\n p1 = (2*(x1**2)) + ((x1**2)*y1) + (y1**2)\\n p2 = (2*(x2**2)) + ((x2**2)*y2) + (y2**2)\\n sol = p1 - p2\\n sol = abs(sol)\\n print(f'The vector field F=(4x+2xy,x2+2y) \\\\n'\\n 'along the curve C parametrized by r(t)=(3t−1,−5t+2) \\\\n '\\n f'for 0 ≤ t ≤ 1 is: {sol}')\",\n \"def calc_ertelPV(n2, bx, rel_vorticity, g=9.8,f=-1e-4):\\n\\n # vertical component\\n\\n qvert = (f+rel_vorticity)*n2\\n\\n # baroclinic component\\n qbc = -bx**2/f\\n\\n # Ertel PV\\n\\n ertelPV = qvert + qbc\\n\\n # If PV is unstable\\n fq = ertelPV*f # fq > 0 stable\\n\\n return ertelPV, qvert, qbc, fq\",\n \"def _f(red_component, green_component, blue_component):\\n return _e(red_component, green_component, blue_component, Z_FORE)\",\n \"def right_boundary(linkp, pn, H0, V0, H, V, links1, p, pump, valve, dt,\\n H10, V10, utype, dtype,\\n friction, dVdt, dVdx, dVdt10, dVdx10):\\n\\n # Properties of current pipe\\n link1 = [p[abs(i)-1] for i in links1]\\n f = linkp.roughness # unitless\\n D = linkp.diameter # m\\n g = 9.8 # m/s^2\\n a = linkp.wavev # m/s\\n n = linkp.number_of_segments # spatial discretization\\n KD = linkp.roughness_height\\n\\n # inner nodes\\n if friction == 'steady':\\n H[1:-1], V[1:-1] = inner_node_steady(linkp, H0, V0, dt, g)\\n elif friction == 'quasi-steady':\\n H[1:-1], V[1:-1] = inner_node_quasisteady(linkp, H0, V0, dt, g)\\n else:\\n H[1:-1], V[1:-1] = inner_node_unsteady(linkp, H0, V0, dt, g,\\n dVdx, dVdt)\\n\\n # Pipe start (inner boundary conditions)\\n V1 = V10; H1 = H10 # upstream node\\n V2 = V0[1]; H2 = H0[1] # downstream node\\n dVdx1 = dVdx10 ; dVdx2 = dVdx[0]\\n dVdt1 = dVdt10 ; dVdt2 = dVdt[1]\\n if utype[0] == 'Pipe':\\n if linkp.start_node.transient_node_type == 'SurgeTank':\\n shape = linkp.start_node.tank_shape\\n H[0], V[0], Qs = surge_tank(shape, link1, linkp,\\n H1, V1, H2, V2, dt, g, 0, np.sign(links1), [-1],\\n friction, dVdx1, dVdx2, dVdt1, dVdt2)\\n linkp.start_node.water_level = H[0]\\n linkp.start_node.tank_flow = Qs\\n if linkp.start_node.transient_node_type == 'Chamber':\\n shape = linkp.start_node.tank_shape\\n H[0], V[0], Qs, zp = air_chamber(shape, link1, linkp,\\n H1, V1, H2, V2, dt, g, 0, np.sign(links1), [-1],\\n friction, dVdx1, dVdx2, dVdt1, dVdt2)\\n linkp.start_node.water_level = zp\\n linkp.start_node.tank_flow = Qs\\n\\n else:\\n elev = linkp.start_node.elevation\\n emitter_coeff = linkp.start_node.emitter_coeff + linkp.start_node.demand_coeff\\n block_per = linkp.start_node.block_per\\n H[0], V[0] = add_leakage(emitter_coeff, block_per,link1, linkp, elev,\\n H1, V1, H2, V2, dt, g, 0, np.sign(links1), [-1],\\n friction, dVdx1, dVdx2, dVdt1, dVdt2)\\n\\n elif utype[0] == 'Pump':\\n pumpc = pump[0]\\n H[0], V[0] = pump_node(pumpc, link1, linkp,\\n H1, V1, H2, V2, dt, g, 0, np.sign(links1), [-1],\\n friction, dVdx1, dVdx2, dVdt1, dVdt2)\\n elif utype[0] == 'Valve':\\n valvec = valve[0]\\n H[0], V[0] = valve_node(valvec, link1, linkp,\\n H1, V1, H2, V2, dt, g, 0, np.sign(links1), [-1],\\n friction, dVdx1, dVdx2, dVdt1, dVdt2)\\n\\n # Pipe end (outer boundary conditions )\\n V1 = V0[n-1]; H1 = H0[n-1]\\n dVdx1 = dVdx[n-1]\\n dVdt1 = dVdt[n-1]\\n if dtype[0] == 'Reservoir' or dtype[0] == 'Tank':\\n H[n], V[n] = rev_end (H1, V1, H[n], n, a, g, f, D, dt,\\n KD, friction, dVdx1, dVdt1)\\n if dtype[0] == 'Valve':\\n H[n], V[n] = valve_end (H1, V1, V[n], n, a, g, f, D, dt,\\n KD, friction, dVdx1, dVdt1)\\n if dtype[0] == 'Junction':\\n elev = linkp.end_node.elevation\\n H[n], V[n] = dead_end (linkp ,H1, V1, elev, n, a, g, f, D, dt,\\n KD, friction, dVdx1, dVdt1)\\n\\n\\n return H, V\",\n \"def X_force(omega_B, V_B, m):\\n t = Symbol(\\\"t\\\")\\n return m * (diff(V_B[0], t) + omega_B[1] * V_B[2] - omega_B[2] * V_B[1])\",\n \"def get_delta_v_tot(f, e, a, P):\\n\\n coeff = (2.0*np.pi/P) * a / np.sqrt(1.0 - e*e)\\n delta_v_tot = coeff * (1.0 + 2.0*e*np.cos(f) + e*e) / 1.0e5\\n\\n return delta_v_tot\",\n \"def Fermi(En,T):\\n ev = 1.60218e-19\\n kb = 1.380e-23\\n return 1/(1+np.exp(En/(kb*T/ev)))\",\n \"def _redef_via_predef_eqn(self):\\r\\n time = self.current_T # + self.d_T\\r\\n\\r\\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\\\\r\\n (self.convect_coeff) \\r\\n self.Epsilon = self.d_T * self.thermal_conductivity / \\\\\\r\\n (self.density * self.heat_capacity)\\r\\n\\r\\n # Source term.\\r\\n def F_func(elem, eta):\\r\\n x = elem.local_to_global(eta)\\r\\n F = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n F -= self.Epsilon * self.redef_F_laplacian(x[0], x[1], time)\\r\\n F += self.redef_dTdt(x[0], x[1], time) * self.d_T\\r\\n return elem.funcs(eta) * F\\r\\n\\r\\n self.vF_vect_vol = et.elems_2_array(self.mesh,\\r\\n F_func,\\r\\n self.node_map,\\r\\n gauss_mult=2) # Use double gp_1D\\r\\n\\r\\n # Boundary term.\\r\\n def f_func(elem, eta):\\r\\n n = elem.guess_normal_vector_global(eta)\\r\\n f = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n x = elem.local_to_global(eta)\\r\\n # Evaluate our boundary term.\\r\\n f += self.Beta * self.redef_f_norm_grad(x[0], x[1], time, n)\\r\\n f += self.redef_dTdt(x[0], x[1], time) * self.d_T\\r\\n return elem.funcs(eta) * f\\r\\n\\r\\n self.vf_vect_bound = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n f_func,\\r\\n self.node_map,\\r\\n gauss_mult=2)\",\n \"def _term_probability(self, frequency, total_frequency, doc_length, total_doc_length):\\n omega = self.alpha / (doc_length + self.alpha)\\n if doc_length == 0:\\n p1 = 0\\n else:\\n p1 = frequency / doc_length\\n if total_doc_length == 0:\\n p2 = 0\\n else:\\n p2 = total_frequency / total_doc_length\\n return (1-omega) * p1 + omega * p2\",\n \"def pdfPR(self,x,p,r):\\n\\t\\treturn gammaF(x + r) / (factorial(x)*gammaF(r)) * p**r * (1-p)**x\",\n \"def Force_on_aircraft_in_body_reference_frame(m, V_B, V_dot_B, omega_B):\\n return m * (V_dot_B + omega_B.cross(V_B))\",\n \"def dalf(x):\\n # if pitch_start_time - ramp_constant_time <= x <= pitch_end_time + ramp_constant_time:\\n # f_t0 = smooth_factor * (x - pitch_start_time)\\n # f_t1 = smooth_factor * (x - p_acc_end_time)\\n # f_t2 = smooth_factor * (x - p_decc_start_time)\\n # f_t3 = smooth_factor * (x - pitch_end_time)\\n\\n # dalfx = (pitch_acceleration /\\n # 2) / smooth_factor * (logcosh(f_t0) - logcosh(f_t1) +\\n # logcosh(f_t3) - logcosh(f_t2))\\n # else:\\n # dalfx = 0\\n f_t0 = smooth_factor * (x - pitch_start_time)\\n f_t1 = smooth_factor * (x - p_acc_end_time)\\n f_t2 = smooth_factor * (x - p_decc_start_time)\\n f_t3 = smooth_factor * (x - pitch_end_time)\\n\\n dalfx = (pitch_acceleration / 2) / smooth_factor * (\\n logcosh(f_t0) - logcosh(f_t1) + logcosh(f_t3) - logcosh(f_t2))\\n return dalfx\",\n \"def cable_delay(params, f):\\n delay = params['delay'].value\\n phi = params['phi'].value\\n f_min = params['f_phi'].value\\n return np.exp(1j * (-2 * np.pi * (f - f_min) * delay + phi))\",\n \"def get_f_eng_body(X, U, P):\\n rho = patm.get_rho(-X[sv_z])\\n f_engines_body = np.zeros((P.eng_nb, 3))\\n for i in range(0, P.eng_nb):\\n thrust = U[i]*P.fmaxs[i]*math.pow((rho/P.rhois[i]),P.nrhos[i])*math.pow((X[sv_v]/P.Vis[i]),P.nVs[i]) \\n f_engines_body[i] = np.dot(P.eng_to_body[i], np.array([thrust, 0., 0.]))\\n return f_engines_body\",\n \"def friction_factor_2(v1: \\\"int\\\", v2: \\\"int\\\") -> \\\"int\\\":\",\n \"def calP(self):\\n N = len(self.listOfParticles)\\n m = self.listOfParticles[0].m\\n vsum = 0\\n for particle in self.listOfParticles:\\n vsum += particle.V.len()\\n A = np.pi*self.R**2\\n F = 0.5 * A * (2*self.R) * m * N * vsum**2\\n return F\",\n \"def Hertz_dipole_ff (r, p, R, phi, f, t=0, epsr=1.):\\r\\n nf = len(f)\\r\\n rprime = r-R # r'=r-R\\r\\n if ndim(p) < 2:\\r\\n magrprime = sqrt(sum((rprime)**2))\\r\\n magrprimep = tile(magrprime, (len(f),1)).T\\r\\n phip = tile(phi, (len(f),1))\\r\\n w = 2*pi*f # \\\\omega\\r\\n k = w/c # wave number\\r\\n krp = k*magrprimep # k|r'|\\r\\n rprime_cross_p = cross(rprime, p) # r'x p\\r\\n rp_c_p_c_rp = cross(rprime_cross_p, rprime) # (r' x p) x r'\\r\\n expfac = exp(1j*(w*t-krp+phip.T))/(4*pi*eps0*epsr)\\r\\n Ex = (w**2/(c**2*magrprimep**3) * expfac)* (tile(rp_c_p_c_rp[0],(nf,1))).T\\r\\n Ey = (w**2/(c**2*magrprimep**3) * expfac)* (tile(rp_c_p_c_rp[1],(nf,1))).T\\r\\n Ez = (w**2/(c**2*magrprimep**3) * expfac)* (tile(rp_c_p_c_rp[2],(nf,1))).T\\r\\n Bx = expfac/(magrprimep**2*c**3)*(w**2*tile(rprime_cross_p[0],(nf,1)).T)\\r\\n By = expfac/(magrprimep**2*c**3)*(w**2*tile(rprime_cross_p[1],(nf,1)).T)\\r\\n Bz = expfac/(magrprimep**2*c**3)*(w**2*tile(rprime_cross_p[2],(nf,1)).T)\\r\\n E = vstack((Ex,Ey,Ez))\\r\\n B = vstack((Bx,By,Bz))\\r\\n else:\\r\\n magrprime = sqrt(sum((rprime)**2,axis=1)) # |r'|\\r\\n magrprimep = tile(magrprime, (len(f),1)).T\\r\\n phip = tile(phi, (len(f),1))\\r\\n fp = tile(f,(len(magrprime),1))\\r\\n w = 2*pi*fp # \\\\omega\\r\\n k = w/c # wave number\\r\\n krp = k*magrprimep # k|r'|\\r\\n rprime_cross_p = cross(rprime, p) # r'x p\\r\\n rp_c_p_c_rp = cross(rprime_cross_p, rprime) # (r' x p) x r'\\r\\n expfac = exp(1j*(w*t-krp+phip.T))/(4*pi*eps0*epsr)\\r\\n Ex = (w**2/(c**2*magrprimep**3) * expfac)* (tile(rp_c_p_c_rp[:,0],(nf,1))).T\\r\\n Ey = (w**2/(c**2*magrprimep**3) * expfac)* (tile(rp_c_p_c_rp[:,1],(nf,1))).T\\r\\n Ez = (w**2/(c**2*magrprimep**3) * expfac)* (tile(rp_c_p_c_rp[:,2],(nf,1))).T\\r\\n Bx = expfac/(magrprimep**2*c**3)*(w**2*tile(rprime_cross_p[:,0],(nf,1)).T)\\r\\n By = expfac/(magrprimep**2*c**3)*(w**2*tile(rprime_cross_p[:,1],(nf,1)).T)\\r\\n Bz = expfac/(magrprimep**2*c**3)*(w**2*tile(rprime_cross_p[:,2],(nf,1)).T)\\r\\n E = vstack((sum(Ex,axis=0),sum(Ey,axis=0),sum(Ez,axis=0)))\\r\\n B = vstack((sum(Bx,axis=0),sum(By,axis=0),sum(Bz,axis=0)))\\r\\n return E,B\",\n \"def normal_velocity(self):\\n try:\\n a = self.params['nct']['a']\\n b = self.params['nct']['b']\\n # temp_log = self.get_log('Overburden_Pressure')\\n return normal(x=self.depth, a=a, b=b)\\n except KeyError:\\n print(\\\"No 'Overburden_Pressure' log found.\\\")\",\n \"def f(r, t=np.nan):\\n x, y, vx, vy = r\\n rcubed = np.sqrt(x**2 + y**2)**3\\n\\n fx = vx #return 1st parameter\\n fy = vy #return 2nd parameter\\n fvx = -G*M*x/rcubed #return 3rd parameter\\n fvy = -G*M*y/rcubed #return last parameter\\n \\n return np.array([fx, fy, fvx, fvy])\",\n \"def particle_initial_velocity(fignr,N,D,T,m,dim,kb):\\n V = np.zeros((3,N))\\n V[0:dim,:] = np.random.normal(0, kb*T/m, (dim,N))# / np.sqrt(T/(kb*m))\\n plotfunctions.velocity(fignr,N,V)\\n # Typical speed for particles\\n return V\",\n \"def define_ufl_velocity_equation(self):\\n\\n if hasattr(self, 'f1'):\\n return None\\n\\n if self.config['material']['type'] == 'viscous':\\n self.f1 = 0\\n return None\\n\\n if not self.config['formulation']['time']['unsteady']:\\n self.f1 = 0\\n return None\\n\\n theta = self.config['formulation']['time']['theta']\\n dt = self.config['formulation']['time']['dt']\\n f1 = self.displacement - self.displacement0 \\\\\\n - dt*(theta*self.velocity + (1.0 - theta)*self.velocity0)\\n\\n self.f1 = dlf.dot(self.test_vector, f1)*dlf.dx\\n\\n return None\",\n \"def aeroFus(F,alpha,beta,rp,V0=50.):\\r\\n a = alpha * m.pi/180;\\r\\n S_Cyl = 0.25 * m.pi * F.cD ** 2;\\r\\n f_F = (F.cL + F.bL + F.nL) / F.cD;\\r\\n FF = 1. + 2.2/(f_F ** (1.5)) - 0.9/(f_F ** (3.));\\r\\n gen = F.getGen();\\r\\n x = np.concatenate([np.linspace(0,gen[17,0],50), np.linspace(gen[17,0],gen[18,0],100),np.linspace(gen[18,0],gen[19,0],100)]);\\r\\n ReX = V0*x/(1.57e-5);\\r\\n delta = np.concatenate([[0.],4.92*x[1:50]/(ReX[1:50]**(0.5)),4.92*x[50]/(ReX[50]**(0.5))+0.35*(x[50:]-x[50])/(ReX[50:]**(0.2))]);\\r\\n rayon = np.interp(x,gen[:,0],gen[:,1])+delta;\\r\\n S = m.pi * rayon ** 2;\\r\\n cp_X = (x[1:]+x[:-1])*0.5;\\r\\n dS = S[1:]-S[:-1];\\r\\n \\r\\n CN_lin = np.sin(2.*a) * S[-1];\\r\\n CX_lin = np.sin(a)**2 * S[-1];\\r\\n L_lin = CN_lin * np.cos(a) - CX_lin * np.sin(a);\\r\\n D_lin = CN_lin * np.sin(a) + CX_lin * np.cos(a);\\r\\n M_lin = -np.sin(2.*a) * np.sum(dS*cp_X);\\r\\n ReF = V0 * cp_X /(1.57e-5);\\r\\n CF = np.concatenate([1.328/(ReF[:49]**0.5), 0.0442/(ReF[49:]**(1./6.))]);\\r\\n dX = x[1:]-x[:-1];\\r\\n r2 = np.interp(x,gen[:,0],gen[:,1]);\\r\\n ds = ((r2[1:]-r2[:-1])**2 + dX ** 2) ** (0.5);\\r\\n rMoy = (r2[1:]+r2[:-1])*0.5;\\r\\n dSW = 2*m.pi*ds*rMoy;\\r\\n Frot = np.sum(CF*dSW)*FF;\\r\\n D_Culot = 0.14*0.25 * m.pi * F.bD**2; # Lecture Aerodynamics 2A : slender body\\r\\n if F.bL/F.cD < 0.8:\\r\\n D_con = 1.4*S_Cyl;\\r\\n else:\\r\\n D_con = 1.4 * m.exp(-((F.bL/F.cD)-0.8)*3./0.8) * S_Cyl;\\r\\n D_windscreen = S_Cyl * 2.e-3;\\r\\n D_par = (Frot+D_Culot + D_con + D_windscreen);\\r\\n# D_par = (Frot + D_con + D_windscreen);\\r\\n Cx = 1.2; # Allen ensures that for transversal flow ok if no compressible effect\\r\\n L_visc = np.sin(a) * np.abs(np.sin(a)) * np.cos(a) * Cx * np.sum(2.*dX*rMoy);\\r\\n D_visc = np.abs(np.sin(a) ** 3) * Cx * np.sum(2.*dX*rMoy);\\r\\n M_visc = - Cx * np.abs(np.sin(a)) * np.sin(a) * np.sum(2.*dX*rMoy*cp_X);\\r\\n \\r\\n L = L_lin + L_visc - D_par * np.sin(a);\\r\\n D = D_lin + D_visc + D_par * np.cos(a);\\r\\n Moment = M_lin + M_visc;\\r\\n d = ((rp[0] + F.hDist) ** 2 + (rp[2]+F.vDist)**2)**(0.5);\\r\\n TP = np.arctan2(-(rp[2]+F.vDist),(rp[0]+F.hDist));\\r\\n M = Moment + d * L * np.cos(TP+a) + d * D * np.sin(TP + a);\\r\\n \\r\\n beta *= m.pi/180.;\\r\\n CN_lin = np.sin(2.*beta) * S[-1];\\r\\n CX_lin = np.sin(beta)**2 * S[-1];\\r\\n Y_lin = - CN_lin * np.cos(beta) + CX_lin * np.sin(beta);\\r\\n D_lin = CN_lin * np.sin(beta) + CX_lin * np.cos(beta);\\r\\n N_lin = np.sin(2.*beta) * np.sum(dS*cp_X);\\r\\n Y_visc = - np.sin(beta) * np.abs(np.sin(beta)) * np.cos(beta) * Cx * np.sum(2.*dX*rMoy);\\r\\n D_visc = np.abs(np.sin(beta) ** 3) * Cx * np.sum(2.*dX*rMoy);\\r\\n N_visc = Cx * np.abs(np.sin(beta)) * np.sin(beta) * np.sum(2.*dX*rMoy*cp_X);\\r\\n N = N_lin + N_visc;\\r\\n Y = Y_lin + Y_visc + D_par * np.sin(beta);\\r\\n Dt = D_lin + D_visc;\\r\\n D += Dt;\\r\\n dv = rp[0] + F.hDist;\\r\\n dvt = F.bL+F.cL+F.nL - dv;\\r\\n TPv = np.arctan2(-rp[1],(rp[0]+F.hDist));\\r\\n N += dv * (Y_lin + Y_visc) * m.cos(TPv - beta) + dv * Dt * m.sin(TPv-beta) + D_par * np.sin(beta) * dvt ;\\r\\n Y = 0.; \\r\\n return L,D,M,Y,N\",\n \"def build_rhs():\\n\\n def div(\\n coeff_rho,\\n momentum_x,\\n momentum_y,\\n momentum_z,\\n ):\\n \\\"\\\"\\\"Computes the divergence of the velocity field.\\\"\\\"\\\"\\n # Compute the fourth order derivative of the pressure for the face\\n # velocity correction.\\n p_corr = (\\n states['p']\\n if self._params.enable_rhie_chow_correction else states['dp'])\\n d4p_dx4 = self._kernel_op.apply_kernel_op_x(p_corr, 'k4d2x')\\n d4p_dy4 = self._kernel_op.apply_kernel_op_y(p_corr, 'k4d2y')\\n d4p_dz4 = self._kernel_op.apply_kernel_op_z(p_corr, 'k4d2z',\\n 'k4d2zsh')\\n\\n # Compute velocity gradient based on interpolated values on cell faces.\\n coeff_x = dt / (4. * coeff_rho * dx**2)\\n du = self._kernel_op.apply_kernel_op_x(momentum_x, 'kDx')\\n du_dx = [\\n du_i / (2. * dx) + coeff_x * d4p_dx4_i\\n for du_i, d4p_dx4_i in zip(du, d4p_dx4)\\n ]\\n\\n coeff_y = dt / (4. * coeff_rho * dy**2)\\n dv = self._kernel_op.apply_kernel_op_y(momentum_y, 'kDy')\\n dv_dy = [\\n dv_i / (2. * dy) + coeff_y * d4p_dy4_i\\n for dv_i, d4p_dy4_i in zip(dv, d4p_dy4)\\n ]\\n\\n coeff_z = dt / (4. * coeff_rho * dz**2)\\n dw = self._kernel_op.apply_kernel_op_z(momentum_z, 'kDz', 'kDzsh')\\n dw_dz = [\\n dw_i / (2. * dz) + coeff_z * d4p_dz4_i\\n for dw_i, d4p_dz4_i in zip(dw, d4p_dz4)\\n ]\\n\\n return [\\n du_dx_i + dv_dy_i + dw_dz_i\\n for du_dx_i, dv_dy_i, dw_dz_i in zip(du_dx, dv_dy, dw_dz)\\n ]\\n\\n def add_factor(\\n v,\\n factor,\\n ):\\n return [factor * v_i for v_i in v]\\n\\n b_terms = {\\n _B_TERM_SOURCE_RHO: add_factor(src_rho, inv_dt),\\n }\\n if isinstance(rho_info, ConstantDensityInfo):\\n b_terms.update({\\n _B_TERM_DIV:\\n add_factor(\\n div(rho_info.rho, states['u'], states['v'], states['w']),\\n inv_dt * rho_info.rho),\\n _B_TERM_DRHO_DT: [\\n tf.zeros_like(src_rho_i) for src_rho_i in src_rho\\n ],\\n })\\n\\n elif isinstance(rho_info, VariableDensityInfo):\\n b_terms.update({\\n _B_TERM_DIV:\\n add_factor(\\n div(1.0, states['rho_u'], states['rho_v'], states['rho_w']),\\n inv_dt),\\n _B_TERM_DRHO_DT:\\n add_factor(rho_info.drho_dt, inv_dt),\\n })\\n\\n else:\\n raise ValueError('`rho_info` has to be either `ConstantDensityInfo` or '\\n '`VariableDensityInfo`.')\\n\\n # pylint: disable=g-complex-comprehension\\n return [(div_i + drho_dt_i - src_rho_i)\\n for div_i, drho_dt_i, src_rho_i in zip(\\n b_terms[_B_TERM_DIV],\\n b_terms[_B_TERM_DRHO_DT],\\n b_terms[_B_TERM_SOURCE_RHO],\\n )], b_terms\\n # pylint: enable=g-complex-comprehension\",\n \"def fEqn(e, h, f, trap, Etrap, FHloss, stepsize):\\n fchange = (e * (trap - f) * Etrap\\n - f * h * FHloss)\\n return fchange * stepsize\",\n \"def pressure(z: tf.Tensor) -> tf.Tensor:\\n return self._p_thermal * tf.math.exp(\\n -(z + self._height * delta_t_frac *\\n (tf.math.log(1.0 - delta_t_frac * tf.math.tanh(z / self._height)) -\\n tf.math.log(1.0 + tf.math.tanh(z / self._height)) +\\n z / self._height)) / h_sfc / (1.0 - delta_t_frac**2))\",\n \"def CalcForceDistribution(self):\\n\\t\\t\\n\\t\\tself.F = self.s * (self.Tether - self.X)\\n\\t\\t\\n\\t\\treturn self.F\",\n \"def Re_feed(F_mass, z_way, d_inner, n_pipe, mu_feed): \\n return 0.785 * F_mass * z_way / (d_inner * n_pipe * mu_feed)\",\n \"def boltzmann_radial_potential_linear_density_ebeam(\\n r, current, r_e, e_kin, nl, kT, q, first_guess=None, ldu=None, max_step=500, rel_diff=1e-3\\n ):\\n # Solves the nonlinear radial poisson equation for a dynamic charge distribution following\\n # the Boltzmann law\\n # A * phi = b0 + bx (where b0 and bx are the static and dynamic terms)\\n # Define cost function f = A * phi - b0 - bx\\n # Compute jacobian J = A - diag(d bx_i / d phi_i)\\n # Solve J y = f\\n # Next guess: phi = phi - y\\n # Iterate until adjustment is small\\n cden = np.zeros(r.size)\\n cden[r <= r_e] = -current/PI/r_e**2\\n\\n if ldu is None:\\n ldu = fd_system_nonuniform_grid(r) # Set up tridiagonal system\\n l, d, u = ldu\\n\\n nl = np.atleast_2d(np.asarray(nl))\\n kT = np.atleast_2d(np.asarray(kT))\\n q = np.atleast_2d(np.asarray(q))\\n\\n if first_guess is None:\\n irho = np.zeros(r.size)\\n irho[r <= r_e] = np.sum(q * Q_E * nl / (PI*r_e**2), axis=0)\\n erho = cden/np.sqrt(2 * Q_E * e_kin/M_E)\\n irho[r <= r_e] = np.minimum(-.95 * erho[r <= r_e], irho[r <= r_e])\\n # if irho[0] < -erho[0]:\\n phi = radial_potential_nonuniform_grid(r, erho + irho)\\n # else:\\n # phi = radial_potential_nonuniform_grid(r, erho)\\n else:\\n phi = first_guess\\n\\n for _ in range(max_step):\\n # ion dist\\n shape = np.exp(-q * (phi - phi.min())/kT)\\n i_sr = np.atleast_2d(np.trapz(r*shape, r)).T\\n nax = nl / 2 / PI / i_sr * np.atleast_2d(shape[:, 0]).T\\n\\n # dynamic rhs term\\n _bx_a = - nax * q * shape * Q_E / EPS_0 # dynamic rhs term\\n _bx_b = - cden/np.sqrt(2 * Q_E * (e_kin+phi)/M_E) / EPS_0\\n _bx_a[:, -1] = 0 # boundary condition\\n bx = np.sum(_bx_a, axis=0) + _bx_b\\n\\n # F = A.dot(phi) - (b0 + bx)\\n f = _tridiag_targetfun(ldu, phi, bx)\\n\\n # Diagonal of the Jacobian df/dphi_i\\n _c = np.zeros_like(shape)\\n _c[:, :-1] = r[:-1] * (r[1:]-r[:-1]) * shape[:, :-1]\\n j_d = -(np.sum(_bx_a * q/kT * (i_sr-_c)/i_sr, axis=0)\\n + Q_E/M_E*_bx_b/(2 * Q_E * (e_kin+phi)/M_E)) # Diagonal of the Jacobian df/dphi_i\\n\\n y = tridiagonal_matrix_algorithm(l, d - j_d, u, f)\\n res = np.max(np.abs(y[:-1]/phi[:-1]))\\n phi = phi - y\\n if res < rel_diff:\\n break\\n return phi, nax, shape\",\n \"def dphi(x):\\n return -kf(\\n flapping_wing_frequency, flapping_angular_velocity_amplitude,\\n flapping_acceleration_time_fraction, flapping_delay_time_fraction,\\n x)\",\n \"def dphi(x):\\n return -kf(\\n flapping_wing_frequency, flapping_angular_velocity_amplitude,\\n flapping_acceleration_time_fraction, flapping_delay_time_fraction,\\n x)\",\n \"def get_angular_velocity(r, T):\\n # http://www.hep.fsu.edu/~berg/teach/phy2048/0918.pdf\\n # velocity = 2(pi)r/T\\n return (2*math.pi*r)/T\",\n \"def acceleration( x, u, m, rho, P, b, h):\\n\\t\\n n = x.size\\n a = np.zeros((n,1))\\n\\n for i in range(0, n):\\n \\n # damping & harmonic potential (0.5 x^2)\\n a[i] = a[i] - u[i]*b - x[i]\\n\\n # quantum pressure (pairwise calculation)\\n x_js = np.delete(x,i)\\n P_js = np.delete(P,i)\\n rho_js = np.delete(rho,i)\\n # first, calculate vector between two particles\\n uij = x[i] - x_js\\n # calculate acceleration due to pressure\\n fac = -m * (P[i]/rho[i]**2 + P_js/rho_js**2)\\n pressure_a = fac * kernel( uij, h, '1' )\\n # accumulate contributions to the acceleration\\n a[i] = a[i] + np.sum(pressure_a)\\n\\n return a\",\n \"def compute_vel(self, state, goal):\\n\\n \\\"\\\"\\\"\\n Unicycle model control law:\\n [v;w] = [kp 0 0; 0 ka kb]*[p;a;b]\\n v = commanded linear velocity of robot\\n w = commanded rotational velcoity of robot\\n kp = gain parameter where kp > 0\\n ka = gain parameter where ka - kp > 0\\n kb = gain parameter where kb < 0\\n p = distance from robot to goal\\n a = angle between current robot heading and heading to goal\\n b = error between current heading to goal and target end heading\\n \\\"\\\"\\\"\\n \\n #print('state,goal,v,w')\\n #print(state)\\n #print(goal)\\n\\n xr = state[0][0] # m in world frame\\n yr = state[1][0] # m in world frame\\n thetar = state[2][0] #rads\\n\\n xg = goal[0] # m in world frame\\n yg = goal[1] # m in world frame\\n\\n dy = yg - yr\\n dx = xg - xr\\n\\n #print('')\\n #print(state)\\n #print(goal)\\n \\n # Calculate a\\n a = -1*thetar + math.atan2(dy,dx)\\n\\n #print(a)\\n\\n if a > math.pi:\\n a = a - 2*math.pi\\n\\n if a < -1*math.pi:\\n a = a + 2*math.pi\\n\\n #print(a)\\n\\n # Set omega according to control law\\n omega = self.ka*a\\n if math.fabs(omega) > self.MAX_OMEGA:\\n if omega > 0:\\n omega = self.MAX_OMEGA\\n else:\\n omega = -1*self.MAX_OMEGA\\n\\n # Calculate P\\n p = math.sqrt(dy*dy + dx*dx)\\n\\n # Set v \\n v = self.kp*p\\n if v > self.MAX_SPEED:\\n v = self.MAX_SPEED\\n\\n # set the done value\\n done = (p <= self.done_distance)\\n\\n #print(v)\\n #print(omega)\\n\\n out_tuple = (v, omega, done)\\n \\n return out_tuple\",\n \"def channel_flow(x_loc, y_loc, error, dt, u, v, p, rho, nu, nit, F):\\n assert u.shape == (x_loc.shape[0], y_loc.shape[0])\\n assert v.shape == (x_loc.shape[0], y_loc.shape[0])\\n\\n dx = x_loc[1:] - x_loc[0:-1]\\n dy = y_loc[1:] - y_loc[0:-1]\\n\\n b = np.zeros((x_loc.shape[0], y_loc.shape[0]))\\n u_diff = 1\\n num_it = 0\\n u_sol = u.copy()\\n v_sol = v.copy()\\n p_sol = p.copy()\\n while u_diff > error:\\n u = u_sol.copy()\\n v = v_sol.copy()\\n p = p_sol.copy()\\n\\n # Calculate b term in periodic poisson calculation\\n b[1:-1, 1:-1] = (1 / dt) * ((u[2:, 1:-1] - u[0:-2, 1:-1]) \\\\\\n / (2 * dx[0:-1]) + (v[1:-1, 2:] - v[1:-1, 0:-2]) / (2 * dy[0:-1])) \\\\\\n - \\\\\\n ((u[2:, 1:-1] - u[0:-2, 1:-1]) ** 2) / (4 * dx[0:-1] ** 2) \\\\\\n - (u[1:-1, 2:] - u[1:-1, 0:-2]) * (v[2:, 1:-1] - v[0:-2, 1:-1]) \\\\\\n / (4 * dx[0:-1] * dy[0:-1]) \\\\\\n - ((v[1:-1, 2:] - v[1:-1, 0:-2]) ** 2) / (4 * dy[0:-1] ** 2)\\n\\n # Solve boundary conditions of b\\n b[0, 1:-1] = (1 / dt) * ((u[1, 1:-1] - u[-1, 1:-1]) \\\\\\n / (2 * dx[-1]) + (v[0, 2:] - v[0, 0:-2]) / (2 * dy[-1])) \\\\\\n - \\\\\\n ((u[1, 1:-1] - u[-1, 1:-1]) ** 2) / (4 * dx[-1] ** 2) \\\\\\n - (u[0, 2:] - u[0, 0:-2]) * (v[1, 1:-1] - v[-1, 1:-1]) \\\\\\n / (4 * dx[-1] * dy[-1]) \\\\\\n - ((v[0, 2:] - v[0, 0:-2]) ** 2) / (4 * dy[-1] ** 2)\\n\\n b[-1, 1:-1] = (1 / dt) * ((u[0, 1:-1] - u[-2, 1:-1]) \\\\\\n / (2 * dx[-1]) + (v[-1, 2:] - v[-1, 0:-2]) / (2 * dy[-1])) \\\\\\n - \\\\\\n ((u[0, 1:-1] - u[-2, 1:-1]) ** 2) / (4 * dx[-1] ** 2) \\\\\\n - (u[-1, 2:] - u[-1, 0:-2]) * (v[0, 1:-1] - v[-2, 1:-1]) \\\\\\n / (4 * dx[-1] * dy[-1]) \\\\\\n - ((v[-1, 2:] - v[-1, 0:-2]) ** 2) / (4 * dy[-1] ** 2)\\n\\n # Calculate periodic poisson solution to pressure\\n p_sol = NavierStokes.poisson_2d(x_loc, y_loc, p, b, nit, periodic_x=True, wall_y=True)\\n\\n # Calculate velocities\\n u_sol[1:-1, 1:-1] = u[1:-1, 1:-1] - \\\\\\n u[1:-1, 1:-1] * dt / (dx[0:-1]) * (u[1:-1, 1:-1] - u[0:-2, 1:-1]) \\\\\\n - v[1:-1, 1:-1] * dt / (dy[0:-1]) * (u[1:-1, 1:-1] - u[1:-1, 0:-2]) \\\\\\n - dt * (p[2:, 1:-1] - p[0:-2, 1:-1]) / (rho * 2 * dx[0:-1]) \\\\\\n + nu * (dt/(dx[0:-1] ** 2) * (u[2:, 1:-1] - 2 * u[1:-1, 1:-1] + u[0:-2, 1:-1]) \\\\\\n + (dt/(dy[0:-1] ** 2)) * (u[1:-1, 2:] - 2 * u[1:-1, 1:-1] + u[1:-1, 0:-2])) + F*dt\\n\\n v_sol[1:-1, 1:-1] = v[1:-1, 1:-1] - \\\\\\n u[1:-1, 1:-1] * dt / (dx[0:-1]) * (v[1:-1, 1:-1] - v[0:-2, 1:-1]) \\\\\\n - v[1:-1, 1:-1] * dt / (dy[0:-1]) * (v[1:-1, 1:-1] - v[1:-1, 0:-2]) \\\\\\n - dt * (p[1:-1, 2:] - p[1:-1, 0:-2]) / (rho * 2 * dy[0:-1]) \\\\\\n + nu * (dt/(dx[0:-1] ** 2) * (v[2:, 1:-1] - 2 * v[1:-1, 1:-1] + v[0:-2, 1:-1]) \\\\\\n + (dt/(dy[0:-1] ** 2)) * (v[1:-1, 2:] - 2 * v[1:-1, 1:-1] + v[1:-1, 0:-2]))\\n\\n # B.Cs for channel flow\\n u_sol[0, 1:-1] = u[0, 1:-1] - \\\\\\n u[0, 1:-1] * dt / (dx[0]) * (u[0, 1:-1] - u[-1, 1:-1]) \\\\\\n - v[0, 1:-1] * dt / (dy[0:-1]) * (u[0, 1:-1] - u[0, 0:-2]) \\\\\\n - dt * (p[1, 1:-1] - p[-1, 1:-1]) / (rho * 2 * dx[0]) \\\\\\n + nu * (dt/(dx[0] ** 2) * (u[1, 1:-1] - 2 * u[0, 1:-1] + u[-1, 1:-1]) \\\\\\n + (dt/(dy[0:-1] ** 2)) * (u[0, 2:] - 2 * u[0, 1:-1] + u[0, 0:-2])) + F*dt\\n\\n u_sol[-1, 1:-1] = u[-1, 1:-1] - \\\\\\n u[-1, 1:-1] * dt / (dx[-1]) * (u[-1, 1:-1] - u[-2, 1:-1]) \\\\\\n - v[-1, 1:-1] * dt / (dy[0:-1]) * (u[-1, 1:-1] - u[-1, 0:-2]) \\\\\\n - dt * (p[0, 1:-1] - p[-2, 1:-1]) / (rho * 2 * dx[-1]) \\\\\\n + nu * (dt/(dx[-1] ** 2) * (u[0, 1:-1] - 2 * u[-1, 1:-1] + u[-2, 1:-1]) \\\\\\n + (dt/(dy[0:-1] ** 2)) * (u[-1, 2:] - 2 * u[-1, 1:-1] + u[-1, 0:-2])) + F*dt\\n\\n v_sol[0, 1:-1] = v[0, 1:-1] - \\\\\\n u[0, 1:-1] * dt / (dx[0]) * (v[0, 1:-1] - v[-1, 1:-1]) \\\\\\n - v[0, 1:-1] * dt / (dy[0:-1]) * (v[0, 1:-1] - v[0, 0:-2]) \\\\\\n - dt * (p[0, 2:] - p[0, 0:-2]) / (rho * 2 * dy[0:-1]) \\\\\\n + nu * (dt/(dx[0] ** 2) * (v[1, 1:-1] - 2 * v[0, 1:-1] + v[-1, 1:-1]) \\\\\\n + (dt/(dy[0:-1] ** 2)) * (v[0, 2:] - 2 * v[0, 1:-1] + v[0, 0:-2]))\\n\\n v_sol[-1, 1:-1] = v[-1, 1:-1] - \\\\\\n u[-1, 1:-1] * dt / (dx[-1]) * (v[-1, 1:-1] - v[-2, 1:-1]) \\\\\\n - v[-1, 1:-1] * dt / (dy[0:-1]) * (v[-1, 1:-1] - v[-1, 0:-2]) \\\\\\n - dt * (p[-1, 2:] - p[-1, 0:-2]) / (rho * 2 * dy[0:-1]) \\\\\\n + nu * (dt/(dx[-1] ** 2) * (v[0, 1:-1] - 2 * v[-1, 1:-1] + v[-2, 1:-1]) \\\\\\n + (dt/(dy[0:-1] ** 2)) * (v[-1, 2:] - 2 * v[-1, 1:-1] + v[-1, 0:-2]))\\n\\n u_sol[:, 0] = 0\\n u_sol[:, -1] = 0\\n v_sol[:, 0] = 0\\n v_sol[:, -1] = 0\\n\\n # Calculate difference between solutions\\n u_diff = np.abs(np.sum(np.sqrt(u_sol**2 + v_sol**2)) - np.sum(np.sqrt(u ** 2 + v ** 2)))/u.size\\n\\n num_it += 1\\n\\n print num_it\\n return u_sol, v_sol, p_sol\",\n \"def bethe_gf_omega(z, t=1.0):\\n half_bandwidth = 2 * t\\n z_rel = z / half_bandwidth\\n return 2. / half_bandwidth * z_rel * (1 - np.sqrt(1 - 1 / (z_rel * z_rel)))\",\n \"def get_flow(self, pressure_drop, coeff): \\n flow = coeff * pressure_drop**0.5\\n return flow\",\n \"def perron_term_fn(iteration_count, v, z):\\n return -0.5 * z * (v + iteration_count - 0.5) / (\\n (v + z + (iteration_count - 1.) / 2.) *\\n (v + z + iteration_count / 2.))\",\n \"def fermi(E,mu,T):\\n if (E-mu)/T > 600:\\n f=0\\n\\t\\t\\t\\t\\n else:\\n f=1/(math.exp((E-mu)/(kB*T) )+1)\\n return(f)\",\n \"def thermal_factor(self, bfactor=1.0):\\n return np.exp(-bfactor * (np.sin(self.angle) / self.xrd.wavelength) ** 2)\",\n \"def flower(t,r,n,angle):\\n for i in range(n):\\n petal(t,r,angle)\\n lt(t,360/n)\",\n \"def f(r):\\r\\n\\r\\n x, y, vx, vy = r\\r\\n rcubed = np.sqrt(x**2 + y**2)**3\\r\\n\\r\\n fx = vx #return 1st parameter\\r\\n fy = vy #return 2nd parameter\\r\\n fvx = -1*G*M*(x/rcubed) #return 3rd parameter\\r\\n fvy = -1*G*M*(y/rcubed) #return last parameter\\r\\n return np.array([fx, fy, fvx, fvy])\",\n \"def Y_force(omega_B, V_B, m):\\n t = Symbol(\\\"t\\\")\\n return m * (diff(V_B[1], t) + omega_B[2] * V_B[0] - omega_B[0] * V_B[2])\",\n \"def driftRHS(field,drift_velocity,t,x):\\n f = field.getValue(x)\\n fs = np.linalg.norm(f)\\n f = f/fs\\n return -f*drift_velocity(fs)\",\n \"def heterodyner_signal_from_f(self,f):\\n omega = f*2*np.pi\\n phvec = np.cumsum(omega)\\n return np.exp(1j*phvec)\",\n \"def main():\\r\\n #Drag Force Equation: 1/2 * rho * Cd * A * v^2\\r\\n\\r\\n #User-Defined Constants\\r\\n global m\\r\\n global v0\\r\\n global theta\\r\\n global rho #Fluid Density\\r\\n global A #Cross-sectional Area\\r\\n global Cd #Drag coefficient\\r\\n global tStep\\r\\n global g\\r\\n\\r\\n m = 1\\r\\n v0 = 30\\r\\n theta = math.radians(45)\\r\\n rho = 1.225\\r\\n A = 0.05\\r\\n Cd = 0.5 #A ball is approx. 0.5\\r\\n tStep = 0.005\\r\\n g = 9.8\\r\\n\\r\\n\\r\\n #Data Structures\\r\\n global tHist\\r\\n global xHist\\r\\n global yHist\\r\\n global thetaHist\\r\\n global vHist\\r\\n global vXHist\\r\\n global vYHist\\r\\n tHist = [] #list for all time steps\\r\\n xHist = [] #list for all x position steps\\r\\n yHist = [] #list for all y position steps\\r\\n thetaHist = [] #List for all theta at every time step\\r\\n vHist = [] #list for all velocities at every time step\\r\\n vXHist = [] #list for all x-axis velocities at every time step\\r\\n vYHist = [] #list for all y-axis velocities at every time step\\r\\n\\r\\n #Initialize intial values\\r\\n tHist.append(0.0)\\r\\n xHist.append(0.0)\\r\\n yHist.append(0.0)\\r\\n thetaHist.append(theta)\\r\\n vHist.append(v0)\\r\\n vXHist.append(v0 * math.cos(theta))\\r\\n vYHist.append(v0 * math.sin(theta))\\r\\n vTheta = math.atan(vYHist[0] / vXHist[0])\\r\\n # print(\\\"t: \\\" + str(tHist[0]))\\r\\n # print(\\\"x: \\\" + str(xHist[0]))\\r\\n # print(\\\"y: \\\" + str(yHist[0]))\\r\\n # print(\\\"v: \\\" + str(vHist[0]))\\r\\n # print(\\\"Vx: \\\" + str(vXHist[0]))\\r\\n # print(\\\"Vy: \\\" + str(vYHist[0]))\\r\\n\\r\\n #Convenience variables\\r\\n global k\\r\\n\\r\\n counter = 1\\r\\n #Loop until the y-displacement becomes negative (projectile reaches ground again)\\r\\n while True:\\r\\n tHist.append(counter * tStep) #increment time\\r\\n print(\\\"t: \\\" + str(tHist[counter]))\\r\\n\\r\\n #This large hunk is the solution to the net force differential equation in the x-axis\\r\\n # oneOverVX = (1/vXHist[counter-1]) + (((rho*A*Cd*math.cos(thetaHist[counter-1]))/(2*m))*(tStep)) #STABLE\\r\\n # oneOverVX = (1/vXHist[counter-1]) + (((rho*A*Cd)/(2*m))*(tStep))\\r\\n # oneOverVX = (1/vHist[counter-1]) + (((rho*A*Cd*math.cos(thetaHist[counter-1]))/(2*m))*(tStep))\\r\\n oneOverVX = (1/vXHist[counter-1]) + ((rho*A*Cd)/(2*m*math.cos(thetaHist[counter-1]))*(tStep)) #This is one over the solution for velocity in the x-axis net force differential equation\\r\\n vXHist.append(1 / oneOverVX) #Adding the velocity to the list of velocities\\r\\n\\r\\n vY0 = vYHist[counter-1] #Convenience variable\\r\\n # k = 0.5 * rho * A * Cd * math.sin(abs(thetaHist[counter-1])) #STABLE\\r\\n # k = 0.5 * rho * A * Cd\\r\\n k = (rho * A * Cd) / (2 * math.sin(abs(thetaHist[counter-1]))) #Convenience variable\\r\\n print(\\\"k: \\\" + str(k))\\r\\n print(\\\"vX: \\\" + str(vXHist[counter]))\\r\\n rootGMK = math.sqrt(g*m*k) #Convenience variable\\r\\n if vYHist[counter-1] > 0.0: #If the projectile is going upwards\\r\\n #Solving the y-axis differential equation for velocity\\r\\n equationRight = -rootGMK * ((tStep/m) - (math.atan((k*vY0)/(rootGMK))/rootGMK))\\r\\n vYHist.append((math.tan(equationRight) * rootGMK) / k)\\r\\n elif vYHist[counter-1] < 0.0: #If the projectile is going downwards\\r\\n #Solving the y-axis differential equation for velocity\\r\\n\\r\\n # Hand-solved integral\\r\\n # exponent = -(2*tStep*rootGMK)/m\\r\\n # numerator = g*m*math.exp(exponent) - math.exp(exponent)*vY0*rootGMK - vY0*rootGMK - g*m\\r\\n # denominator = math.exp(exponent)*(vY0-rootGMK) - vY0*k - rootGMK\\r\\n # vYHist.append(numerator / denominator)\\r\\n\\r\\n #Wolfram Alpha arctanh integral\\r\\n arctanh =(vY0*math.sqrt(k))/(math.sqrt(g*m))\\r\\n print(\\\"arctanh: \\\" + str(arctanh))\\r\\n equationRight = (np.arctanh(arctanh))/(rootGMK) - (tStep/m)\\r\\n vYHist.append(np.tanh(rootGMK * equationRight) * ((math.sqrt(g*m))/(math.sqrt(k))))\\r\\n else: #If current y velocity is 0\\r\\n vYHist.append(vY0 - g*tStep)\\r\\n print(\\\"vY: \\\" + str(vYHist[counter]))\\r\\n\\r\\n vHist.append(math.hypot(vXHist[counter], vYHist[counter])) #Calculate the net velocity and add it to the velocities list\\r\\n print(\\\"v: \\\" + str(vHist[counter]))\\r\\n thetaHist.append(math.atan(vYHist[counter]/vXHist[counter])) #Calculate the current angle based on the velocities and add it to the theta list\\r\\n print(\\\"0: \\\" + str(math.degrees(thetaHist[counter])))\\r\\n\\r\\n x0 = xHist[counter-1]\\r\\n y0 = yHist[counter-1]\\r\\n\\r\\n # yIntegral = trigintegrate()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Note: What I wanted to do here was to integrate the velocity functions over the time interval to find the exact\\r\\n changes in position. Unfortunately, I was running short of time and decided it was not worth it to move forward with\\r\\n this final step, and instead worked on the presentation and testing different cases.\\r\\n \\\"\\\"\\\"\\r\\n xHist.append(x0 + vXHist[counter]*tStep) #Calculate new x position using x = x0 + vt\\r\\n yHist.append(y0 + vYHist[counter]*tStep) #Calculate new y position using y = y0 + vt\\r\\n print(\\\"x: \\\" + str(xHist[counter]))\\r\\n print(\\\"y: \\\" + str(yHist[counter]))\\r\\n print()\\r\\n\\r\\n # xHist.append(xHist[counter-1] + vXHist[counter-1]*tStep + 0.5*aXHist[counter-1]*tStep**2)\\r\\n # yHist.append(yHist[counter-1] + vYHist[counter-1]*tStep + 0.5*aYHist[counter-1]*tStep**2)\\r\\n # vXHist.append(vXHist[counter-1] + aXHist[counter-1]*tStep)\\r\\n # vYHist.append(vYHist[counter-1] + aYHist[counter-1]*tStep)\\r\\n # vHist.append(math.hypot(vXHist[counter], vYHist[counter]))\\r\\n #\\r\\n # vTheta = math.atan(vYHist[counter] / vXHist[counter])\\r\\n # xDragAccel = -0.5*rho*Cd*A*vHist[counter]**2*math.cos(vTheta) / m\\r\\n # yDragAccel = -math.copysign(0.5*rho*Cd*A*vHist[counter]**2*math.sin(vTheta) / m, vYHist[counter])\\r\\n #\\r\\n # aXHist.append(xDragAccel)\\r\\n # aYHist.append(-g*tStep + yDragAccel)\\r\\n\\r\\n if vYHist[counter-1] > 0.0 and vYHist[counter] < 0.0: #Check if the projectile has reached it's peak by checking for a critical point\\r\\n print(\\\"max height reached at time=\\\" + str(tHist[counter]))\\r\\n # break\\r\\n\\r\\n # print(\\\"t: \\\" + str(tHist[counter]))\\r\\n # print(\\\"x: \\\" + str(xHist[counter]))\\r\\n # print(\\\"y: \\\" + str(yHist[counter]))\\r\\n # print(\\\"Vx: \\\" + str(vXHist[counter]))\\r\\n # print(\\\"Vy: \\\" + str(vYHist[counter]))\\r\\n # print(\\\"Ax: \\\" + str(aXHist[counter]))\\r\\n # print(\\\"Ay: \\\" + str(aYHist[counter]))\\r\\n\\r\\n if yHist[counter] < 0 or counter > 99999: #End the loop if the projectile has reached the ground (or limit the number of iterations to avoid computer death)\\r\\n break\\r\\n\\r\\n counter += 1\\r\\n\\r\\n plotData()\",\n \"def bounce(self, particle):\\n\\n\\t\\tif particle.x > self.width - (particle.size):\\n\\t\\t\\tparticle.x += ((particle.x + particle.size) - self.width)\\n\\t\\t\\tparticle.dx = particle.dx * -1\\n\\t\\t\\tparticle.dx = int(particle.dx * self.elasticity)\\n\\t\\t\\tparticle.dy = int(particle.dy * self.elasticity)\\n\\t\\telif particle.x < (particle.size):\\n\\t\\t\\tparticle.x -= (particle.x - particle.size)\\n\\t\\t\\tparticle.dx = particle.dx * -1\\n\\t\\t\\tparticle.dx = int(particle.dx * self.elasticity)\\n\\t\\t\\tparticle.dy = int(particle.dy * self.elasticity)\\n\\n\\t\\tif particle.y > self.height - (particle.size):\\n\\t\\t\\tself.y = 2 * (self.height - particle.size) - particle.y\\n\\t\\t\\tparticle.dy = particle.dy * -1\\n\\t\\t\\tparticle.dx = int(particle.dx * self.elasticity)\\n\\t\\t\\tparticle.dy = int(particle.dy * self.elasticity)\\n\\t\\telif particle.y < (particle.size):\\n\\t\\t\\tself.y = 2 * particle.size - particle.y\\n\\t\\t\\tparticle.dy = particle.dy * -1\\n\\t\\t\\tparticle.dx = int(particle.dx * self.elasticity)\\n\\t\\t\\tparticle.dy = int(particle.dy * self.elasticity)\",\n \"def friction_model():\\n return TimeWeakening()\",\n \"def fppp(x):\\n return (-2000.00*math.cos(10.0.x+1))\",\n \"def test_turbulent_drag():\\n fluid_vel = array((1., 0., 0.))\\n vel = array((0., 0., 0.))\\n emax = array((1.0e-8, 1.0e-8, 1.0e-8))\\n drag = 0.44 * 3.0 / 32.0 * fluid_vel\\n assert all(DM.turbulent_drag(fluid_velocity=fluid_vel, particle_velocity=vel,\\n diameter=1.0, rho=1.0, rho_f=1.0, fluid_viscosity=1.0) - drag < emax)\\n\\n fluid_vel = array((0., 5., 0.))\\n vel = array((0., 1., 0.))\\n drag = 0.44 * 3.0 / 20.0 * vel\\n\\n assert all(DM.turbulent_drag(fluid_velocity=fluid_vel, particle_velocity=vel,\\n diameter=10.0, rho=1.0, rho_f=1.0, fluid_viscosity=1.0) - drag < emax)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58735925","0.58659214","0.58183104","0.5818074","0.5753325","0.5712705","0.5690621","0.5683825","0.56798863","0.5655784","0.56407523","0.5617837","0.56171685","0.5589888","0.5564685","0.5555104","0.555289","0.5549329","0.5536939","0.55097634","0.5494157","0.5489519","0.5483416","0.5463035","0.5455445","0.5450969","0.545018","0.54466975","0.5434983","0.5432205","0.5430152","0.5424204","0.5420789","0.5418307","0.5402542","0.5389449","0.53703535","0.5339696","0.53223044","0.5321929","0.5315653","0.5307489","0.5303313","0.5297497","0.5296275","0.5295749","0.52930796","0.52922034","0.5287003","0.5281635","0.52759093","0.5274123","0.5261308","0.5249312","0.52481365","0.52461857","0.5240939","0.5237027","0.52368295","0.5233794","0.5232323","0.5227671","0.5222717","0.52217335","0.52210176","0.5208051","0.51995146","0.5199477","0.51934326","0.518888","0.5186429","0.5178472","0.51697665","0.5169264","0.51627934","0.5160565","0.51596403","0.5158787","0.51565117","0.5155955","0.5155955","0.5147881","0.5142109","0.51362413","0.51340497","0.51339287","0.5128514","0.51199573","0.51174796","0.51170355","0.5113418","0.5112983","0.5107062","0.51051235","0.5104054","0.50980663","0.5090132","0.5086562","0.5086336","0.50839716"],"string":"[\n \"0.58735925\",\n \"0.58659214\",\n \"0.58183104\",\n \"0.5818074\",\n \"0.5753325\",\n \"0.5712705\",\n \"0.5690621\",\n \"0.5683825\",\n \"0.56798863\",\n \"0.5655784\",\n \"0.56407523\",\n \"0.5617837\",\n \"0.56171685\",\n \"0.5589888\",\n \"0.5564685\",\n \"0.5555104\",\n \"0.555289\",\n \"0.5549329\",\n \"0.5536939\",\n \"0.55097634\",\n \"0.5494157\",\n \"0.5489519\",\n \"0.5483416\",\n \"0.5463035\",\n \"0.5455445\",\n \"0.5450969\",\n \"0.545018\",\n \"0.54466975\",\n \"0.5434983\",\n \"0.5432205\",\n \"0.5430152\",\n \"0.5424204\",\n \"0.5420789\",\n \"0.5418307\",\n \"0.5402542\",\n \"0.5389449\",\n \"0.53703535\",\n \"0.5339696\",\n \"0.53223044\",\n \"0.5321929\",\n \"0.5315653\",\n \"0.5307489\",\n \"0.5303313\",\n \"0.5297497\",\n \"0.5296275\",\n \"0.5295749\",\n \"0.52930796\",\n \"0.52922034\",\n \"0.5287003\",\n \"0.5281635\",\n \"0.52759093\",\n \"0.5274123\",\n \"0.5261308\",\n \"0.5249312\",\n \"0.52481365\",\n \"0.52461857\",\n \"0.5240939\",\n \"0.5237027\",\n \"0.52368295\",\n \"0.5233794\",\n \"0.5232323\",\n \"0.5227671\",\n \"0.5222717\",\n \"0.52217335\",\n \"0.52210176\",\n \"0.5208051\",\n \"0.51995146\",\n \"0.5199477\",\n \"0.51934326\",\n \"0.518888\",\n \"0.5186429\",\n \"0.5178472\",\n \"0.51697665\",\n \"0.5169264\",\n \"0.51627934\",\n \"0.5160565\",\n \"0.51596403\",\n \"0.5158787\",\n \"0.51565117\",\n \"0.5155955\",\n \"0.5155955\",\n \"0.5147881\",\n \"0.5142109\",\n \"0.51362413\",\n \"0.51340497\",\n \"0.51339287\",\n \"0.5128514\",\n \"0.51199573\",\n \"0.51174796\",\n \"0.51170355\",\n \"0.5113418\",\n \"0.5112983\",\n \"0.5107062\",\n \"0.51051235\",\n \"0.5104054\",\n \"0.50980663\",\n \"0.5090132\",\n \"0.5086562\",\n \"0.5086336\",\n \"0.50839716\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58481055"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":19580,"cells":{"query":{"kind":"string","value":"Serve a file from the upload directory."},"document":{"kind":"string","value":"def download(path):\n return send_from_directory(UPLOAD_DIRECTORY, path, as_attachment=True)"},"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 uploaded_file(filename):\n return send_from_directory('/static/images/uploads/', filename)","def uploaded_file(filename):\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)","def uploaded_file(filename):\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)","def uploaded_file(filename):\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)","def uploaded_file(filename):\n return send_from_directory(app.config['UPLOAD_FOLDER'],\n filename)","def serve(file_path):\n\tfile_path = os.path.abspath(file_path)\n\tif os.path.exists(file_path):\n\t\tdest_path = get_path(os.path.basename(file_path))\n\t\tshutil.copyfile(file_path, dest_path)\n\telse:\n\t\traise Exception('File could not be located')","def _serve_file(request, filename, basepath):\n # Serve the file from the proper location\n if settings.DEBUG or settings.PYTHON_MEDIA:\n # Serve from Python\n return serve(request, filename, basepath)\n\n # Serve from Nginx\n content_type, encoding = mimetypes.guess_type(\n os.path.join(basepath, filename),\n )\n content_type = content_type or 'application/octet-stream'\n response = HttpResponse(content_type=content_type)\n if encoding:\n response['Content-Encoding'] = encoding\n try:\n iri_path = os.path.join(\n basepath[len(settings.SITE_ROOT):],\n filename,\n )\n # NGINX does not support non-ASCII characters in the header, so we\n # convert the IRI path to URI so it's compatible with what NGINX expects\n # as the header value.\n # https://github.com/benoitc/gunicorn/issues/1448\n # https://docs.djangoproject.com/en/1.11/ref/unicode/#uri-and-iri-handling\n x_accel_redirect = iri_to_uri(iri_path)\n response['X-Accel-Redirect'] = x_accel_redirect\n except UnicodeEncodeError:\n raise Http404\n\n return response","def _serve_file(self, abspath, params):\r\n relpath = os.path.relpath(abspath, self._root)\r\n breadcrumbs = self._create_breadcrumbs(relpath)\r\n link_path = urlparse.urlunparse([None, None, relpath, None, urllib.urlencode(params), None])\r\n args = self._default_template_args('file')\r\n args.update({ 'root_parent': os.path.dirname(self._root),\r\n 'breadcrumbs': breadcrumbs,\r\n 'link_path': link_path })\r\n self._send_content(self._renderer.render_name('base', args), 'text/html')","def upload():\n file = None\n if 'file' in request.files:\n file = request.files['file']\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n return json_response(\n message=\"Upload successful\",\n result=\"/v/{}\".format(filename)\n )\n return json_response(\n message=\"Invalid filename or extension (jpg, png, gif)\",\n status_code=500\n )","def handle_request(self,host,path,data=b''):\n\t\tif data:\n\t\t\tself.response_code(4,\"Uploads are not accepted.\")\n\t\t\treturn\n\t\tif not hasattr(self,\"root\"):\n\t\t\tself.response_code(5,\"Server is unable to handle requests at this time due to misconfiguration.\")\n\t\t\treturn\n\t\tself.root = os.path.abspath(self.root)\n\t\tif not (prefix:=os.path.abspath(os.path.join(self.root,host))).startswith(self.root):\n\t\t\tself.response_code(4,\"Cowardly refusing to serve file outside of root.\")\n\t\t\treturn\n\t\tif not (filepath:=os.path.abspath(os.path.join(prefix,unquote(path.lstrip(\"/\"))))).startswith(prefix):\n\t\t\tself.response_code(4,\"Cowardly refusing to serve file outside of root.\")\n\t\t\treturn\n\t\tif not os.path.exists(filepath):\n\t\t\tself.response_code(4,\"Not Found\")\n\t\t\treturn\n\t\tif os.path.isdir(filepath):\n\t\t\tif os.path.exists(os.path.join(filepath,\"index.gmi\")):\n\t\t\t\tfilepath = os.path.join(filepath,\"index.gmi\")\n\t\t\telse:\n\t\t\t\tself.response_code(5,\"Cowardly refusing to generate folder listing.\")\n\t\t\t\treturn\n\t\text = os.path.splitext(filepath)[1]\n\t\tmimetype = mimetypes.guess_type(filepath,False)\n\t\tif ext in self.OVERRIDE_MIMETYPES:\n\t\t\tmimetype = self.OVERRIDE_MIMETYPES[ext]\n\t\tmimetype = mimetype or \"application/octet-stream\"\n\t\twith open(filepath,\"rb\") as f:\n\t\t\tself.response_code(2,mimetype)\n\t\t\tshutil.copyfileobj(f,self.wfile)","def uploaded(self, filename):\n return send_from_directory(self.upload_path, filename)","def sendFile(self, path):\n\t\t# Either guess the mime type or just send it as a binary file.\n\t\tif self.guessMime:\n\t\t\tmimeType = mimetypes.guess_type(path)\n\t\telse:\n\t\t\tmimeType = \"application/octet-stream\"\n\n\t\t# Send the headers.\n\t\tself.send_response(200)\n\t\tself.send_header(\"Content-type\", mimeType)\n\t\tself.send_header(\"Content-Length\", os.path.getsize(path))\n\t\tself.end_headers()\n\t\t\n\t\tf = open(path, 'rb')\n\t\t# self.wfile is the output stream.\n\t\tshutil.copyfileobj(f, self.wfile)\n\t\tf.close()","def uploaded_file(image):\n return send_from_directory(app.config['UPLOAD_FOLDER'], image)","def serve_upload(conn, ssn_key, file_name, client_name):\n # get signal to begin upload\n request = aes.decrypt(ssn_key, conn.recv(1024))\n if request != SIG_START:\n conn.sendall(aes.encrypt(ssn_key, SIG_BAD))\n return print(\"Bob: something went wrong with file transfer\")\n response = aes.encrypt(ssn_key, SIG_GOOD)\n conn.sendall(response)\n print(\"Bob: beginning transfer for {}...\".format(file_name))\n\n # get file contents from client\n contents = list()\n completed_upload = False\n response = aes.encrypt(ssn_key, SIG_GOOD)\n while not completed_upload:\n request = aes.decrypt(ssn_key, conn.recv(1024))\n if request == SIG_END:\n completed_upload = True\n print(\"Bob: completed transfer for {}\".format(file_name))\n else:\n contents.append(request)\n conn.sendall(response)\n\n # save file to server folder\n file_path = \"{}/{}\".format(client_name, file_name)\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n with open(file_path, \"w\") as outputStream:\n outputStream.write(''.join(contents))\n print(\"Bob: file saved in {}\".format(file_path))","def serve_dir(port, dir_path):\n\n # Create index files, and store the list of their paths for cleanup later\n # This time, force no processing - this gives us a fast first-pass in terms\n # of page generation, but potentially slow serving for large image files\n print('Performing first pass index file generation')\n created_files = _create_index_files(dir_path, True)\n\n if (PIL_ENABLED):\n # If PIL is enabled, we'd like to process the HTML indexes to include\n # generated thumbnails - this slows down generation so we don't do it\n # first time around, but now we're serving it's good to do in the\n # background\n print('Performing PIL-enchanced optimised index file generation in background')\n background_indexer = BackgroundIndexFileGenerator(dir_path)\n background_indexer.run()\n\n # Run the server in the current location - this blocks until it's stopped\n _run_server(port)\n\n # Clean up the index files created earlier so we don't make a mess of\n # the image directories\n _clean_up(created_files)","def upload():\n return handle_upload(app, request)","def upload():\n\n file = request.files['query']\n filepath = upload_filepath(secure_filename(file.filename))\n file.save(filepath)\n classification = classify(filepath)\n classification['filename'] = file.filename\n return render_template('index.html', classification=classification)","def serve(files, port, mime):\n\tprint \"serving\", files, \"on\", port\n\t# Bind to any address.\n\taddress = ('', port)\n\tRequestHandler.files = files#[fName.rstrip('/') for fName in files]\n\tRequestHandler.guessMime = mime\n\thttpd = ThreadedHTTPServer(address, RequestHandler)\n\ttry:\n\t\thttpd.serve_forever()\n\texcept KeyboardInterrupt:\n\t\tprint \"Done.\"","def upload(self, filename, file_path):\n return","def SingleStaticServe(file_path):\n def get_file():\n mime, encoding = mimetypes.guess_type(file_path)\n return open(file_path, 'rb'), mime or 'application/octet-stream'\n\n class SingleStaticServe(GiottoProgram):\n controllers = ['http-get']\n model = [get_file]\n view = FileView()\n\n return SingleStaticServe()","def get(self):\n\n upload_url = blobstore.create_upload_url('/upload')\n\n self.response.headers['content-type'] = 'text/plain'\n self.response.out.write(upload_url)","def serve_file(path, content_type=None, disposition=None, name=None, delete=False):\n \n response = cherrypy.response\n \n # If path is relative, users should fix it by making path absolute.\n # That is, CherryPy should not guess where the application root is.\n # It certainly should *not* use cwd (since CP may be invoked from a\n # variety of paths). If using tools.static, you can make your relative\n # paths become absolute by supplying a value for \"tools.static.root\".\n if not os.path.isabs(path):\n raise ValueError(\"'%s' is not an absolute path.\" % path)\n \n try:\n st = os.stat(path)\n except OSError:\n raise cherrypy.NotFound()\n \n # Check if path is a directory.\n if stat.S_ISDIR(st.st_mode):\n # Let the caller deal with it as they like.\n raise cherrypy.NotFound()\n \n # Set the Last-Modified response header, so that\n # modified-since validation code can work.\n response.headers['Last-Modified'] = http.HTTPDate(st.st_mtime)\n cptools.validate_since()\n \n if content_type is None:\n # Set content-type based on filename extension\n ext = \"\"\n i = path.rfind('.')\n if i != -1:\n ext = path[i:].lower()\n content_type = mimetypes.types_map.get(ext, \"text/plain\")\n response.headers['Content-Type'] = content_type\n \n if disposition is not None:\n if name is None:\n name = os.path.basename(path)\n cd = '%s; filename=\"%s\"' % (disposition, name)\n response.headers[\"Content-Disposition\"] = cd\n \n # Set Content-Length and use an iterable (file object)\n # this way CP won't load the whole file in memory\n c_len = st.st_size\n if delete:\n flag = os.O_RDWR | os.O_EXCL\n if hasattr(os, 'O_BINARY'):\n flag |= os.O_BINARY\n if os.name == 'nt':\n flag |= os.O_TEMPORARY\n fd = os.open(path, flag)\n file = os.fdopen(fd, 'rb')\n bodyfile = file\n else:\n bodyfile = open(path, 'rb')\n \n # HTTP/1.0 didn't have Range/Accept-Ranges headers, or the 206 code\n if cherrypy.request.protocol >= (1, 1):\n response.headers[\"Accept-Ranges\"] = \"bytes\"\n r = http.get_ranges(cherrypy.request.headers.get('Range'), c_len)\n if r == []:\n response.headers['Content-Range'] = \"bytes */%s\" % c_len\n message = \"Invalid Range (first-byte-pos greater than Content-Length)\"\n raise cherrypy.HTTPError(416, message)\n if r:\n if len(r) == 1:\n # Return a single-part response.\n start, stop = r[0]\n if stop > c_len:\n stop = c_len\n r_len = stop - start\n response.status = \"206 Partial Content\"\n response.headers['Content-Range'] = (\"bytes %s-%s/%s\" %\n (start, stop - 1, c_len))\n response.headers['Content-Length'] = r_len\n bodyfile.seek(start)\n response.body = file_generator_limited(bodyfile, r_len)\n else:\n # Return a multipart/byteranges response.\n response.status = \"206 Partial Content\"\n import mimetools\n boundary = mimetools.choose_boundary()\n ct = \"multipart/byteranges; boundary=%s\" % boundary\n response.headers['Content-Type'] = ct\n if response.headers.has_key(\"Content-Length\"):\n # Delete Content-Length header so finalize() recalcs it.\n del response.headers[\"Content-Length\"]\n \n def file_ranges():\n # Apache compatibility:\n yield \"\\r\\n\"\n \n for start, stop in r:\n yield \"--\" + boundary\n yield \"\\r\\nContent-type: %s\" % content_type\n yield (\"\\r\\nContent-range: bytes %s-%s/%s\\r\\n\\r\\n\"\n % (start, stop - 1, c_len))\n bodyfile.seek(start)\n for chunk in file_generator_limited(bodyfile, stop-start):\n yield chunk\n yield \"\\r\\n\"\n # Final boundary\n yield \"--\" + boundary + \"--\"\n \n # Apache compatibility:\n yield \"\\r\\n\"\n response.body = file_ranges()\n else:\n response.headers['Content-Length'] = c_len\n response.body = bodyfile\n else:\n response.headers['Content-Length'] = c_len\n response.body = bodyfile\n response.stream = True\n return response.body","def upload_file():\n try:\n if 'file' not in request.files:\n flash(\"No file selected or file is too big (max size is %sB)\" % app.config['MAX_CONTENT_LENGTH'])\n return redirect(url_for('index'))\n\n file = request.files['file']\n str_in = file.read()\n file.close()\n\n str_out = io.BytesIO()\n str_out.write(process_text(str_in.decode('utf-8')).encode('utf-8'))\n str_out.seek(0)\n\n return send_file(\n str_out,\n attachment_filename=file.filename,\n as_attachment=True,\n mimetype='text/plain'\n )\n except RuntimeError:\n flash('Something bad happened')\n return redirect(url_for('index'))","def deliverFile(self, filename=None, docroot=None):\n if not docroot:\n docroot = self.docroot\n if not docroot or not os.path.isdir(docroot):\n if not filename: filename = \"index.html\"\n return self.sendNotFound(filename)\n\n if not filename:\n filename = \"index.htm\"\n if not os.path.exists(os.path.join(docroot, filename)):\n filename = \"index.html\"\n filename = filename.strip(os.sep)\n\n path = os.path.join(docroot, filename)\n if not os.path.exists(path) or not os.path.isfile(path):\n print >> self.estrm, \"vaologin: doc not found: \" + path\n return self.sendNotFound(filename)\n\n defbufsz = 1024000\n try:\n bufsz = int(self._getConfigParam(\"bufsize\", defbufsz))\n except TypeError, ex:\n bufsz = defbufsz\n\n ext = os.path.splitext(filename)[1]\n if ext and ext.startswith('.'):\n ext = ext[1:]\n mimetype = self.getMimeTypeFor(ext)\n self._sendheader()\n self.ostrm.write(\"Content-Type: %s\\r\\n\\r\\n\" % mimetype)\n\n fd = None\n try:\n fd = open(path, 'rb')\n data = fd.read(bufsz)\n while data:\n self.ostrm.write(data)\n data = fd.read(bufsz)\n\n except Exception, ex:\n print >> self.estrm, \"Problem sending file data: \" + str(ex)\n finally:\n if fd: fd.close()\n # self.ostrm.close()","async def send_file(self, filename, content_type=None, content_encoding=None, max_age=2592000, buf_size=128):\n try:\n # Get file size\n stat = os.stat(filename)\n slen = str(stat[6])\n self.add_header('Content-Length', slen)\n # Find content type\n if content_type:\n self.add_header('Content-Type', content_type)\n # Add content-encoding, if any\n if content_encoding:\n self.add_header('Content-Encoding', content_encoding)\n # Since this is static content is totally make sense\n # to tell browser to cache it, however, you can always\n # override it by setting max_age to zero\n self.add_header('Cache-Control', 'max-age={}, public'.format(max_age))\n with open(filename) as f:\n await self._send_headers()\n gc.collect()\n buf = bytearray(min(stat[6], buf_size))\n while True:\n size = f.readinto(buf)\n if size == 0:\n break\n await self.send(buf, sz=size)\n except OSError as e:\n # special handling for ENOENT / EACCESS\n if e.args[0] in (errno.ENOENT, errno.EACCES):\n raise HTTPException(404)\n else:\n raise","def StaticServe(base_path):\n def get_file(path):\n fullpath = base_path + path\n try:\n mime, encoding = mimetypes.guess_type(fullpath)\n return open(fullpath, 'rb'), mime or 'application/octet-stream'\n except IOError:\n raise DataNotFound(\"File does not exist\")\n\n class StaticServe(GiottoProgram):\n controllers = ['http-get']\n model = [get_file]\n view = FileView()\n\n return StaticServe()","def render_file(path: str):\n if path != \"\" and os.path.exists(os.path.join(current_app.config[\"UI_PATH\"], path)):\n _LOGGER.info(\n \"sending {} file from {}\".format(path, current_app.config[\"UI_PATH\"])\n )\n return send_from_directory(current_app.config[\"UI_PATH\"], path)\n else:\n _LOGGER.info(\n \"sending index.html file at {} from {}\".format(\n path, current_app.config[\"UI_PATH\"]\n )\n )\n return send_from_directory(current_app.config[\"UI_PATH\"], \"index.html\")","def post(self):\n filename = str(time.time())\n filepath = os.path.join(\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\n with open(filepath, 'bw') as uploadfile:\n chunk_size = 1024\n while True:\n chunk = request.stream.read(chunk_size)\n if len(chunk) == 0:\n break\n uploadfile.write(chunk)\n current_app.logger.info('file %s upload successfully', filename)\n return {'timestamp': filename}, http.HTTPStatus.CREATED","def do_GET(self):\n self.send_response(200)\n self.send_header(\"Content-type\", self.mimetype)\n self.end_headers()\n\n with open(filename, \"rb\") as file_:\n self.wfile.write(file_.read())\n file_.close()","def media(filename):\n media_path = flask.current_app.instance_path + '/media'\n return flask.send_from_directory(media_path, filename)","def upload_file():\n response = Response()\n\n size = 256, 256\n if request.method == 'POST':\n file = request.files['file']\n if file and allowed_file(file.filename):\n # Prevent dir traversal/NUL byte injection\n filename = secure_filename(file.filename)\n\n if not os.path.exists(app.config['UPLOAD_FOLDER']):\n os.makedirs(app.config['UPLOAD_FOLDER'])\n\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n im = Image.open(\n os.path.join(app.config['UPLOAD_FOLDER'], filename))\n im.thumbnail(size, Image.ANTIALIAS)\n im.save(app.config['UPLOAD_FOLDER'] + '/small_' + filename, \"PNG\")\n _upload_to_s3(filename)\n _upload_to_s3('small_{}'.format(filename))\n return response","def upload():\n\treturn render_template(\"upload.html\", title=\"Upload a file\")","def upload():\n global FILE_NAME\n target = os.path.join(APP_ROOT, \"images\")\n print(target)\n\n if not os.path.isdir(target):\n os.mkdir(target)\n\n for file in request.files.getlist(\"file\"):\n print(file)\n filename = file.filename\n destination = \"/\".join([target, filename])\n FILE_NAME = destination\n file.save(destination)\n return render_template(\"complete.html\")","def api_upload():\n return make_response(file_manager.save_uploaded_file(), 200)","def staticFile(path):\n logger.info('[FLASKWEB /fs] Static File Request for `%s`' % path)\n local = os.path.join(webapp.config['DIR'], path)\n if not os.path.exists(local):\n return returnError(\"File not found: %s\" % path, 404)\n if os.path.isdir(local):\n contents = sorted(os.listdir(local))\n for i, f in enumerate(contents):\n if os.path.isdir(f):\n contents[i] += '/'\n\n if request.headers['Accept'] == 'application/json':\n return jsonify(dict(cwd=local, contents=contents)), 200\n else:\n return render_template('listing.html', cwd=path, listing=contents), 200\n\n else:\n if 'stdout' in local or 'output' in local or local.split('.')[-1] in ['txt', 'yaml', 'yml', 'json', 'log']:\n with open(local, 'r') as file:\n # output = unicode(file.read(), 'utf-8')\n output = file.read()\n\n if request.headers['Accept'] == 'application/json':\n return output, 200\n else:\n return render_template(\"output.html\", output=output)\n\n return send_from_directory(webapp.config['DIR'], path)","def upload_file(self, file_path, file_name, output_path):","def upload_file():\n if request.method == 'POST':\n # check if the post request has the file part\n if 'img_file' not in request.files:\n return redirect(request.url)\n image = request.files['img_file']\n # if user does not select file, browser also\n # submit an empty part without filename\n if image.filename == '':\n return redirect(request.url)\n\n # save image_file\n save_path = os.path.join(app.config['UPLOAD_DIR'], image.filename)\n LOGGER.debug(save_path)\n if os.path.isfile(save_path):\n os.remove(save_path)\n image.save(save_path)\n\n category = _predict(FLAGS.hostport, save_path)\n return render_template('index.html', filename=save_path, category=category)\n return render_template('index.html')","def download_file(filename):\n return send_from_directory('uploads', filename, as_attachment=True)","def file_upload(self, req, folder_path):\n\t\tresult, filename = self.handle_upload(req, folder_path)\n\t\tfile_url = self.selected_root['url_callback'](req, folder_path, filename)\n\t\t\n\t\tself.content_type = 'text/html'\n\t\tself.content = [str(tags.script(type=\"text/javascript\")[\n\t\t\t\t\t\t\"window.parent.frames['frmUpload'].OnUploadCompleted(%s, '%s');\\n\" % (result, filename)\n\t\t\t\t\t\t])]","def upload_file(self, f):\n return self._telegraph.upload_file(f)","def serve(cls, path):\n path = path[6:] # strip \"media/\"\n path = path.replace(\"..\", \"\") ## .. tricks\n \n type = \"application/data\"\n \n if path.endswith(\"css\"):\n type = \"text/css\"\n elif path.endswith(\"js\"):\n type = \"text/javascript\"\n elif path.endswith(\"png\"):\n type = \"image/png\"\n elif path.endswith(\"gif\"):\n type = \"image/gif\"\n elif path.endswith(\"jpg\"):\n type = \"image/jpeg\"\n\n ## requests to /media/qx map to the qx framework directory\n if path.startswith(\"qx/\"):\n path = cls.qxbase + '/' + path[3:]\n elif not path.startswith(\"source/\"):\n path = \"source/\" + path \n\n data = open(os.path.join(cls.workingdir(), path), \"r\").read()\n return (type, data)","def upload_file(self, file_name, content):\n return self.files.upload(file_name, content)","def upload_file():\n retVal = None \n if request.method == 'POST' and upload_validated(request):\n retVal = render_successful_upload(request) \n else:\n retVal = render_index()\n return retVal","def upload():\n global video_file_name\n\n if request.method == 'POST' and 'file' in request.files:\n\n # clear uploads folder\n cleanup_uploads()\n\n # Video has been uploaded\n filename = video.save(request.files['file'])\n video_file_name[0] = filename\n\n # Process video on a new thread\n # threading.Thread(target=process_video, args=[os.path.join('uploads', filename)]).start()\n # threading.Thread(target=process_audio, args=[os.path.join('uploads', filename)]).start()\n threading.Thread(target=process_video, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\n threading.Thread(target=process_audio, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\n\n return jsonify({'success': True}), 200, {'ContentType': 'application/json'}\n\n elif request.method == 'GET':\n return render_template('home.html')","def handle_uploaded_file(f):\n path = settings.ABS_PATH + \"Server_data_visualization/uploads/executable\"\n destination = open(path, \"wb+\")\n for chunk in f.chunks():\n destination.write(chunk)\n destination.close()\n # os.chmod(path, stat.S_IXUSR | stat.S_IWUSR | stat.S_IRUSR)","def _handle_browse(self, relpath, params):\r\n abspath = os.path.normpath(os.path.join(self._root, relpath))\r\n if not abspath.startswith(self._root):\r\n raise ValueError # Prevent using .. to get files from anywhere other than root.\r\n if os.path.isdir(abspath):\r\n self._serve_dir(abspath, params)\r\n elif os.path.isfile(abspath):\r\n self._serve_file(abspath, params)","def sendFile(self, fullfilename):\n raise NotImplementedError(\"Implement this method in child class\")","def uploaded_file(filename):\n if app.config.get('UPLOAD_SCREENSHOTS_TO_S3', False):\n import boto\n from flask import redirect\n conn = boto.connect_s3()\n url = conn.generate_url(\n expires_in=long(60*60*2), # 2 hour expiry\n method='GET',\n bucket=app.config['S3_BUCKET'],\n key='{}/{}'.format(\n app.config.get('S3_FILES_PREFIX', 'sleepypuppy'),\n filename\n ),\n query_auth=True\n )\n url = _correct_s3_url(url)\n return redirect(url, 302)\n else:\n return send_from_directory(\n app.config['UPLOAD_FOLDER'],\n filename\n )","def uploaded_file(filename):\n if app.config.get('UPLOAD_SCREENSHOTS_TO_S3', False):\n import boto\n from flask import redirect\n conn = boto.connect_s3()\n url = conn.generate_url(\n expires_in=long(60 * 60 * 2), # 2 hour expiry\n method='GET',\n bucket=app.config['S3_BUCKET'],\n key='{}/{}'.format(\n app.config.get('S3_FILES_PREFIX', 'sleepypuppy'),\n filename\n ),\n query_auth=True\n )\n url = _correct_s3_url(url)\n return redirect(url, 302)\n else:\n return send_from_directory(\n app.config['UPLOAD_FOLDER'],\n filename\n )","def peer_server_upload(self, conn, data_received):\n try:\n file_size = os.path.getsize(SHARED_DIR+'/'+data_received['file_name'])\n f = open(SHARED_DIR+'/'+data_received['file_name'], 'rb')\n #print \"Hosting File: %s for download\" % data_received\n for chunk_start, chunk_size in self.get_chunks(file_size):\n file_chunk = f.read(chunk_size)\n conn.sendall(file_chunk)\n '''\n while True:\n data = f.readline()\n if data:\n conn.sendall(data)\n else:\n break\n '''\n f.close()\n conn.sendall('')\n conn.close()\n except Exception as e:\n print \"File Upload Error, %s\" % e","def _serve_dir(self, abspath, params):\r\n relpath = os.path.relpath(abspath, self._root)\r\n breadcrumbs = self._create_breadcrumbs(relpath)\r\n entries = [ {'link_path': os.path.join(relpath, e), 'name': e} for e in os.listdir(abspath)]\r\n args = self._default_template_args('dir')\r\n args.update({ 'root_parent': os.path.dirname(self._root),\r\n 'breadcrumbs': breadcrumbs,\r\n 'entries': entries,\r\n 'params': params })\r\n self._send_content(self._renderer.render_name('base', args), 'text/html')","def http_server(dir, ip, port, quiet):\n sys.stderr.write(\"Serving Image to Clair from http://%s:%d\\n\" % (ip, port))\n chdir(dir)\n if quiet:\n Handler = QuietSimpleHTTPHandler\n else:\n Handler = SimpleHTTPServer.SimpleHTTPRequestHandler\n httpd = socketserver.TCPServer((ip, port), Handler)\n httpd.serve_forever()","def send_file(self, src: PathLike, dest: PathLike, force: bool = False):","def main(filename: Path, port: int) -> None:\n LOG.info(f\"Serving Tacview file {filename} 127.0.0.1:{port}. ..\")\n loop = asyncio.get_event_loop()\n task = loop.create_task(serve_file(filename, port))\n try:\n loop.run_until_complete(task)\n except KeyboardInterrupt:\n LOG.info(\"Keyboard interupt!\")\n task.cancel()\n except Exception as err:\n task.cancel()\n raise err","def upload_file():\n response = Response()\n response.headers.add('Access-Control-Allow-Origin', request.headers.get(\"Origin\", None))\n response.headers.add('Access-Control-Allow-Methods', 'GET,POST')\n response.headers.add('Access-Control-Max-Age', '21600')\n response.headers.add('Access-Control-Allow-Credentials', 'true')\n response.headers.add('Access-Control-Allow-Headers', \"Origin, X-Requested-With, Content-Type, Accept\")\n\n size = 256, 256\n if request.method == 'POST':\n file = request.files['file']\n if file and allowed_file(file.filename):\n # Prevent dir traversal/NUL byte injection\n filename = secure_filename(file.filename)\n\n if not os.path.exists(app.config['UPLOAD_FOLDER']):\n os.makedirs(app.config['UPLOAD_FOLDER'])\n\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n im = Image.open(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n im.thumbnail(size, Image.ANTIALIAS)\n im.save(app.config['UPLOAD_FOLDER'] + '/small_' + filename, \"PNG\")\n _upload_to_s3(filename)\n _upload_to_s3('small_{}'.format(filename))\n return response","def index():\n return render_template(\"upload.html\")","def post(self):\n\n upload_files = self.get_uploads('file')\n blob_info = upload_files[0]\n self.redirect('/?upload_info=%s' % urllib.quote(blob_info.filename))","def fetch_file(self, path, content_type, response=settings.HTTP_OK):\n try:\n with open(path) as fp:\n self.fetch_content(fp.read(), content_type, response)\n except IOError:\n self.send_error(settings.HTTP_INTERNAL_SERVER_ERROR)","def download_file():\n\n if 'POST' == request.method:\n file_id = request.form['file_id']\n else:\n file_id = request.args.get('file_id')\n\n # 1 ==> example_1.tgz\n file_path = file_manager.get_file_path_from_id(file_id)\n print \"serving file: \" + file_path\n return send_file(file_path, as_attachment=True)","def handler ( self, environ, start_response ) :\n url = urlparse ( reconstruct_url ( environ ) ) \n \n if self.mount_point is not None:\n split_url = url.path.split ( self.mount_point, 1 ) \n serve_file = split_url [ 1 ] \n else:\n serve_file = url.path\n \n serve_file = serve_file.replace ( '%20', ' ' ) \n \n def do_get ( ):\n if serve_file.endswith ( '/' ) or os.path.isdir ( os.path.join ( self.path, serve_file ) ):\n if os.path.isdir ( os.path.join ( self.path, serve_file ) ):\n start_response ( '200 OK', [ ( 'Cache-Control', 'no-cache' ), ( 'Pragma','no-cache' ) ,\n ( 'Content-Type', 'text/html; charset=utf-8' ) ] )\n return [ \n '<html>' + \n '<br>'.join ( [ '<a href=\"%s/%s\">%s</a>' % ( serve_file.replace ( filename, '' ), filename, filename ) \n for filename in os.listdir ( os.path.join ( self.path, serve_file ) ) ] )\n + '</html>' \n ]\n else:\n logger.error ( 'failed to list directory %s/%s' % ( self.path, serve_file ) )\n start_response ( '404 Not found', [ ( 'Content-Type', 'text/plain') ] )\n return ['404 Not Found' ] \n \n try:\n if os.name == 'nt' or sys.platform == 'cygwin':\n f = open ( os.path.join ( self.path, serve_file ), 'rb' ) \n else:\n f = open ( os.path.join ( self.path, serve_file ), 'r' ) \n logger.debug ( 'opened file %s' % serve_file ) \n except IOError:\n logger.error ( 'failed to open file %s/%s' % ( self.path, serve_file ) )\n start_response ( '404 Not found', [ ( 'Content-Type', 'text/plain') ] )\n return [ '404 Not Found' ] \n\n start_response ( '200 OK', [ ( 'Cache-Control', 'no-cache' ), ( 'Pragma','no-cache' ),\n ( 'Content-Type', self.guess_content_type ( environ['PATH_INFO' ] ) ) ] )\n return [ f.read ( ) ] \n \n def do_put ( ):\n #Write file\n try:\n f = open ( os.path.join ( self.path, serve_file ) , 'w' ) \n logger.debug ( 'opened file for writing %s' % serve_file ) \n except:\n logger.error ( 'failed to open file for writiing %s/%s' % ( self.path, serve_file ) )\n start_response ( '403 Forbidden', [ ( 'Content-Type', 'text/plain' ) ] )\n return ['403 Forbidden' ] \n \n f.write ( environ [ 'wsgi.input' ] .read ( ) )\n \n def do_mkcollection ( ):\n pass\n \n http_method_map = { 'GET': do_get, 'PUT': do_put, 'MKCOLLECTION': do_mkcollection }\n return http_method_map[environ['REQUEST_METHOD' ] ] ( )","def static(self, filename):\n return send_from_directory(self.static_path, filename)","def _send_file(self, file_path: Path, stream_id: int) -> None:\n file_size = file_path.stat().st_size\n content_type, content_encoding = mimetypes.guess_type(str(file_path))\n response_headers = [\n (':status', '200'),\n ('content-length', str(file_size)),\n ('server', self._server_name)\n ]\n if content_type:\n response_headers.append(('content-type', content_type))\n if content_encoding:\n response_headers.append(('content-encoding', content_encoding))\n\n self._connection.send_headers(stream_id, response_headers)\n self._sock.sendall(self._connection.data_to_send())\n\n with file_path.open(mode='rb', buffering=0) as f:\n self._send_file_data(f, stream_id)","def index():\n\n #return 'file uploaded successfully'\n return render_template('index.html')","def files_serve(path):\n return flask.send_from_directory(\"static/js\", path)","def node_file_upload(ctx, file, filename):\n filepath = click.format_filename(file)\n filename = filename if filename else filepath\n\n with open(file, \"r\") as f:\n content = f.read()\n\n try:\n ctx.obj['node'].create_file(filename, content=content)\n except TimeoutError as e:\n logger.error('Error: %s' % e)\n exit(1)","def handle(req: bytes) -> str:\n\n try:\n pass\n except:\n dirname = os.path.dirname(__file__)\n path = os.path.join(dirname, 'html', 'upload.html')\n\n with (open(path, 'r')) as file:\n html = file.read()\n\n return html","def send_file():\n data = ARGS.data\n filename = ARGS.file\n outstream = \"POST||\" + filename + \"||\" + data\n CLIENT_SOCKET.send(outstream.encode())","def upload():\n print 'Loading upload page'\n\n if request.method == 'POST':\n if 'dvwfile' not in request.files:\n flash('No file given', 'danger')\n return render_template('upload.html')\n\n upload_file = request.files['dvwfile']\n # if user does not select file, browser also\n # submit a empty part without filename\n if upload_file.filename == '':\n flash('No selected file', 'danger')\n return render_template('upload.html')\n if upload_file and allowed_file(upload_file.filename):\n filename = secure_filename(upload_file.filename)\n upload_folder = os.path.join(app.config['UPLOAD_FOLDER'], str(session['user_id']))\n\n if not os.path.isdir(upload_folder):\n os.makedirs(upload_folder)\n\n upload_file.save(os.path.join(upload_folder, filename))\n\n upload_dict = {'user_id': session['user_id'], 'file_key': filename}\n datastore.insert_upload_row(engine, upload_dict)\n\n flash('File Uploaded!', 'info')\n\n print 'Uploaded File'\n return redirect(url_for('upload'))\n else:\n return render_template('upload.html')","def send_file_upload_request(self, view_name, filename, expected_response_code=200):\n url = reverse(view_name)\n\n client = Client()\n with open(filename) as file_upload:\n response = client.post(path=url, data={'file': file_upload})\n\n self.assertEqual(expected_response_code, response.status_code)\n\n return response","def Uploads():\n if request.method==\"POST\":\n if 'file' not in request.files:\n flash('No file part')\n return redirect(request.url)\n file=request.files[\"file\"]\n file.save(os.path.join(\"Solar\", file.filename))\n return render_template(\"index.html\", message = \"File Uploaded Successfuly\")\n return render_template(\"index.html\", message = \"Upload Solar Maintenance File\")\n return \"File Uploaded!\"","def do_GET(self):\n\n path = self.file_path\n\n if os.path.exists(path):\n # Symbolic link judgement.\n # Paths with denied symbolic links will pretend to be 404 errors.\n if args[TITLE_LOCAL_LINKS] and not (\"%s/\" % os.path.realpath(path)).startswith(os.getcwd() + \"/\"):\n return self.send_error(404, \"File not found\")\n elif args[TITLE_NO_LINKS]:\n # If all symbolic links are banned, then we must trace our\n # way down an existing path to make sure that no symbolic link exists\n curr = path\n while True:\n if os.path.islink(curr):\n return self.send_error(404, \"File not found\")\n if curr == path:\n break\n curr = os.path.dirname(path);\n\n f = None\n if os.path.isdir(path):\n\n if not getattr(self, common.ATTR_PATH, \"\").endswith(\"/\"):\n return self.send_redirect(\"%s/\" % getattr(self, common.ATTR_PATH, \"\"))\n\n for index in [\"index.html\", \"index.htm\"]:\n index = os.path.join(path, index)\n if os.path.exists(index):\n path = index\n break\n if path == self.file_path:\n return self.list_directory(path)\n\n return self.serve_file(path)","def upload(self):\n\n # Try to retrieve the file from the request\n file = None\n try:\n file = request.files[\"file\"]\n except KeyError as e:\n logging.warning(f\"File was not found in request: {e}.\")\n flash(\"No file given.\", \"error\")\n return redirect(request.url)\n except AttributeError as e:\n logging.warning(f\"Error: Request did not contain any files: {e}.\")\n flash(\"No file given.\", \"error\")\n return redirect(request.url)\n\n # Check if file was correctly uploaded\n if not file or len(file.filename) == 0:\n flash(\"No file selected for upload.\", \"message\")\n return redirect(request.url)\n\n \"\"\" Check if file has correct extension. Allowed extensions depend on\n the connector. To make the code more readable, group connectors\n with the same allowed file extensions together like this:\n if connector in ['someconnector', 'someotherconnector']:\n extensions = [...] \"\"\"\n\n if self.connector in [\"overtime\"]:\n allowed_extensions = OVERTIME_SUPPORTED_EXTENSIONS\n else:\n allowed_extensions = []\n\n if not allowed_file(file, allowed_extensions=allowed_extensions):\n flash(\"File extension not allowed.\", \"warning\")\n return redirect(request.url)\n\n \"\"\" File seems uploaded correctly and has correct extension.\n Generate a new record ID to keep track of the uploaded file.\n \"\"\"\n rec_id = generate_temp_record_id()\n\n # Save file to disk\n path = store_temp_file(file, record_id=rec_id)\n\n if not path:\n flash(\"Error saving file!\", \"error\")\n return redirect(request.url)\n\n \"\"\" If everything ended successfully, send the user to the\n confirmation page so he can review his changes \"\"\"\n\n return redirect(url_for(f\"{self.endpoint}.upload_confirm\", rec_id=rec_id))","def send_file_partial(path):\n print(\"Start stream response\")\n range_header = request.headers.get('Range', None)\n if not range_header: return send_file(path)\n\n size = os.path.getsize(path)\n byte1, byte2 = 0, None\n\n m = re.search('(\\d+)-(\\d*)', range_header)\n g = m.groups()\n\n if g[0]: byte1 = int(g[0])\n if g[1]: byte2 = int(g[1])\n\n length = size - byte1\n if byte2 is not None:\n length = byte2 - byte1\n\n with open(path, 'rb') as f:\n f.seek(byte1)\n data = f.read(length)\n\n rv = Response(data,\n 206,\n mimetype=mimetypes.guess_type(path)[0],\n direct_passthrough=True)\n rv.headers.add('Content-Range', 'bytes {0}-{1}/{2}'.format(byte1, byte1 + length - 1, size))\n rv.headers.add('Keep-Alive', 'no')\n print(\"Stream done\")\n return rv","def send(self, request, filepath):\n gzip = False\n if 'gzip' in request.headers[hdrs.ACCEPT_ENCODING]:\n gzip_path = filepath.with_name(filepath.name + '.gz')\n\n if gzip_path.is_file():\n filepath = gzip_path\n gzip = True\n\n st = filepath.stat()\n\n modsince = request.if_modified_since\n if modsince is not None and st.st_mtime <= modsince.timestamp():\n raise HTTPNotModified()\n\n ct, encoding = mimetypes.guess_type(str(filepath))\n if not ct:\n ct = 'application/octet-stream'\n\n resp = self._response_factory()\n resp.content_type = ct\n if encoding:\n resp.headers[hdrs.CONTENT_ENCODING] = encoding\n if gzip:\n resp.headers[hdrs.VARY] = hdrs.ACCEPT_ENCODING\n resp.last_modified = st.st_mtime\n\n # CACHE HACK\n if not request.app[KEY_DEVELOPMENT]:\n cache_time = 31 * 86400 # = 1 month\n resp.headers[hdrs.CACHE_CONTROL] = \"public, max-age={}\".format(\n cache_time)\n\n file_size = st.st_size\n\n resp.content_length = file_size\n with filepath.open('rb') as f:\n yield from self._sendfile(request, resp, f, file_size)\n\n return resp","def main():\n\tparser = optparse.OptionParser(usage=usage)\n\t# The network port to use.\n\tparser.add_option(\"-p\", \"--port\",\n\t\t\t\t\t dest=\"port\",\n\t\t\t\t\t default=80,\n\t\t\t\t\t metavar=\"PORT\",\n\t\t\t\t\t help=\"Serve the files from PORT. (Default 80)\")\n\t# Either guess the mime type, or just serve as binary.\n\tparser.add_option(\"-m\", \"--no-mime\",\n\t\t\t\t\t dest=\"mime\",\n\t\t\t\t\t default=True,\n\t\t\t\t\t action=\"store_false\",\n\t\t\t\t\t help=\"Don't try to guess mime types.\")\n\t\n\t(options, files) = parser.parse_args()\n\tserve(files, int(options.port), options.mime)","def upload_file(self):\n self.master.switch_frame(UploadFileView)","def send_head(self):\n path = self.translate_path(self.path)\n if os.path.basename(path).startswith(UPLOAD_LINK):\n return self.render_upload_form(os.path.dirname(self.path))\n return super(SimpleHTTPRequestHandlerWithUpload, self).send_head()","def send_file(cobj, dest, port, fname, hash, handler):\n pass","def serve(file: str, cache_age: int = 0, headers: Optional[Dict[str, str]] = None, status_override: int = None, range: str = None, max_bytes_per_request: int = None) -> Tuple[bytes, int, Dict[str, str]]:\n # prevent serving files outside of current / specified dir (prevents download of all system files)\n file = file.replace('../', '')\n if headers is None:\n headers = {}\n if not exists(file): # return 404 on file not exists\n return b'', 404, {}\n if 'Content-Type' not in headers: # if content-type is not already specified then guess from mimetype\n ctype, encoding = mimetypes.guess_type(file)\n if ctype is None or encoding is not None:\n ctype = 'application/octet-stream'\n headers['Content-Type'] = ctype\n with open(file, 'rb') as _in:\n if range is None: # 200 request\n lines = _in.read()\n else: # 206 Request\n lines, status_override = _handle_206(file, _in, headers, range, max_bytes_per_request)\n if cache_age > 0:\n headers['Cache-Control'] = f'max-age={cache_age}'\n elif not cache_age and file.split('.')[-1] != 'html' and not App.debug: # if cache_age is not specified and not an html file and not debug then autoset cache_age to 1 hour\n headers['Cache-Control'] = 'max-age=3600'\n return lines, status_override or 200, headers","def process(self):\n\n requested_file_path = self._get_requested_file_path()\n\n if os.path.exists(requested_file_path):\n self._respond_with_status_line(OK)\n self._respond_with_headers(file_path=requested_file_path,\n requested_headers=('Date',\n 'Content-Length',\n 'Content-Type',\n 'Server'))\n else:\n self._respond_with_status_line(NOT_FOUND)\n self._respond_with_headers(requested_headers=('Server',))","def send_file(file_name, path, ftp, callback):\n file_src = path + file_name\n if not os.path.isfile(file_src):\n log(\"Uploading file: '{}'. File not found. Skipping.\".format(file_src))\n return\n\n with open(file_src, \"rb\") as f:\n callback(\"Uploading file: {}. Status: {}\\n\".format(file_name, str(ftp.storbinary(\"STOR \" + file_name, f))))","def upload(path=None):\n # Get path and verify if it is valid\n base_path, directory, path = _normalize_path(path)\n\n if not settings['upload']:\n flash('File uploading is disabled.', 'error')\n return redirect(_get_dir_url('.files', path))\n\n form = UploadForm()\n if form.validate_on_submit():\n filename = op.join(directory, secure_filename(form.upload.data.filename))\n\n if op.exists(filename):\n flash('File \"%s\" already exists.' % form.upload.data.filename, 'error')\n else:\n try:\n save_file(filename, form.upload.data)\n return redirect(_get_dir_url('.files', path))\n except Exception, ex:\n flash('Failed to save file: %s' % ex, 'error')\n\n return render_template('files/form.html', form=form)","def upload(self, fullfilename, remotefolder=None):\n print(\"[Remote Server] Uploading %s to %s:%s\" %(fullfilename, self.server, self.remotefolder))\n\n if not self.status:\n return 0\n\n if remotefolder == None:\n remotefolder = self.remotefolder\n\n if not self.cd(remotefolder):\n return 0\n\n if not self.sendFile(fullfilename):\n print(\"[Remote Server] Error uploading file %s\" %fullfilename)\n return 0\n\n print(\"[Remote Server] upload finished successfully\")\n\n return 1","def homepage_redirect():\n return redirect('/upload_file')","def upload(request):\n # We pass the 'file_id' in the query string as a GET parameter. If\n # we read it from the POSTed data, WebOb would read all POSTed\n # data, which has various features and traps (like setting the\n # \"Content-Length\" header to 0) that we do not need since we are\n # going to read the data ourselves anyway.\n file_id = request.GET['X-Progress-ID']\n input_file, file_size, filename = get_file_from_request(request)\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n upload_dir = request.registry.settings['poulda.upload_dir']\n user_id = authenticated_userid(request)\n # We use a temporary path to detect unfinished uploads (post\n # mortem, not in the application itself).\n path = os.path.join(upload_dir, '_'.join((user_id, file_id)))\n u.tmp_path = path\n u.started = int(time.time())\n u.size = file_size\n u.state = u'uploading'\n session.flush()\n # We need to commit the transaction so that changes to the Upload\n # object can be seen by the other threads (which will serve the\n # 'progress' JSON view called by the upload page).\n transaction.commit()\n with open(path, 'w') as output:\n # We must read only 'file_size' bytes from the 'input_file',\n # not all of it since it also contains the MIME boundary.\n copy_to_file(input_file, file_size, output)\n final_path = filename[1 + filename.rfind(os.sep):]\n final_path = os.path.join(upload_dir, final_path)\n os.rename(path, final_path)\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n u.state = u'done'\n u.final_path = unicode(final_path, 'utf-8')\n return HTTPFound(location='success')","def upload_file():\n global gui\n print(request.data)\n if request.method == 'POST':\n # check if the post request has the file part\n if 'file' not in request.files:\n flash('No file part')\n return redirect(request.url)\n file = request.files['file']\n # if user does not select file, browser also\n # submit a empty part without filename\n if file.filename == '':\n flash('No selected file')\n return redirect(request.url)\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n print(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n gui.controller.main('openFile %s' % os.path.join(app.config['UPLOAD_FOLDER'], filename))\n return redirect('/data')\n return render_template('upload_file.html')","def upload_file():\r\n if not LOGGEDIN:\r\n return render_template(\"login_temp.html\", msg=\"\")\r\n\r\n if request.method == 'POST':\r\n firstname = flask.request.form[\"firstname\"]\r\n lastname = flask.request.form[\"lastname\"]\r\n city = flask.request.form[\"city\"]\r\n state = flask.request.form[\"state\"]\r\n status = flask.request.form[\"status\"]\r\n date = flask.request.form[\"date\"]\r\n photo = flask.request.form[\"photo\"]\r\n\r\n f_d = open(\"users/\" + lastname + firstname + \".txt\", \"a\")\r\n f_d.write(firstname + \"\\n\")\r\n f_d.write(lastname + \"\\n\")\r\n f_d.write(city + \"\\n\")\r\n f_d.write(state + \"\\n\")\r\n f_d.write(status + \"\\n\")\r\n f_d.write(date + \"\\n\")\r\n f_d.write(photo + \"\\n\")\r\n f_d.close()\r\n return render_template(\"home.html\")\r\n else:\r\n return render_template('check_in.html')","def upload_file(self, session, output, serverdir):\n name = output.metadata['filename']\n self.log.debug(\"uploading %r to %r as %r\",\n output.file.name, serverdir, name)\n\n kwargs = {}\n if self.blocksize is not None:\n kwargs['blocksize'] = self.blocksize\n self.log.debug(\"using blocksize %d\", self.blocksize)\n\n upload_logger = KojiUploadLogger(self.log)\n session.uploadWrapper(output.file.name, serverdir, name=name,\n callback=upload_logger.callback, **kwargs)\n path = os.path.join(serverdir, name)\n self.log.debug(\"uploaded %r\", path)\n return path","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)"],"string":"[\n \"def uploaded_file(filename):\\n return send_from_directory('/static/images/uploads/', filename)\",\n \"def uploaded_file(filename):\\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\",\n \"def uploaded_file(filename):\\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\",\n \"def uploaded_file(filename):\\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\",\n \"def uploaded_file(filename):\\n return send_from_directory(app.config['UPLOAD_FOLDER'],\\n filename)\",\n \"def serve(file_path):\\n\\tfile_path = os.path.abspath(file_path)\\n\\tif os.path.exists(file_path):\\n\\t\\tdest_path = get_path(os.path.basename(file_path))\\n\\t\\tshutil.copyfile(file_path, dest_path)\\n\\telse:\\n\\t\\traise Exception('File could not be located')\",\n \"def _serve_file(request, filename, basepath):\\n # Serve the file from the proper location\\n if settings.DEBUG or settings.PYTHON_MEDIA:\\n # Serve from Python\\n return serve(request, filename, basepath)\\n\\n # Serve from Nginx\\n content_type, encoding = mimetypes.guess_type(\\n os.path.join(basepath, filename),\\n )\\n content_type = content_type or 'application/octet-stream'\\n response = HttpResponse(content_type=content_type)\\n if encoding:\\n response['Content-Encoding'] = encoding\\n try:\\n iri_path = os.path.join(\\n basepath[len(settings.SITE_ROOT):],\\n filename,\\n )\\n # NGINX does not support non-ASCII characters in the header, so we\\n # convert the IRI path to URI so it's compatible with what NGINX expects\\n # as the header value.\\n # https://github.com/benoitc/gunicorn/issues/1448\\n # https://docs.djangoproject.com/en/1.11/ref/unicode/#uri-and-iri-handling\\n x_accel_redirect = iri_to_uri(iri_path)\\n response['X-Accel-Redirect'] = x_accel_redirect\\n except UnicodeEncodeError:\\n raise Http404\\n\\n return response\",\n \"def _serve_file(self, abspath, params):\\r\\n relpath = os.path.relpath(abspath, self._root)\\r\\n breadcrumbs = self._create_breadcrumbs(relpath)\\r\\n link_path = urlparse.urlunparse([None, None, relpath, None, urllib.urlencode(params), None])\\r\\n args = self._default_template_args('file')\\r\\n args.update({ 'root_parent': os.path.dirname(self._root),\\r\\n 'breadcrumbs': breadcrumbs,\\r\\n 'link_path': link_path })\\r\\n self._send_content(self._renderer.render_name('base', args), 'text/html')\",\n \"def upload():\\n file = None\\n if 'file' in request.files:\\n file = request.files['file']\\n if file and allowed_file(file.filename):\\n filename = secure_filename(file.filename)\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n return json_response(\\n message=\\\"Upload successful\\\",\\n result=\\\"/v/{}\\\".format(filename)\\n )\\n return json_response(\\n message=\\\"Invalid filename or extension (jpg, png, gif)\\\",\\n status_code=500\\n )\",\n \"def handle_request(self,host,path,data=b''):\\n\\t\\tif data:\\n\\t\\t\\tself.response_code(4,\\\"Uploads are not accepted.\\\")\\n\\t\\t\\treturn\\n\\t\\tif not hasattr(self,\\\"root\\\"):\\n\\t\\t\\tself.response_code(5,\\\"Server is unable to handle requests at this time due to misconfiguration.\\\")\\n\\t\\t\\treturn\\n\\t\\tself.root = os.path.abspath(self.root)\\n\\t\\tif not (prefix:=os.path.abspath(os.path.join(self.root,host))).startswith(self.root):\\n\\t\\t\\tself.response_code(4,\\\"Cowardly refusing to serve file outside of root.\\\")\\n\\t\\t\\treturn\\n\\t\\tif not (filepath:=os.path.abspath(os.path.join(prefix,unquote(path.lstrip(\\\"/\\\"))))).startswith(prefix):\\n\\t\\t\\tself.response_code(4,\\\"Cowardly refusing to serve file outside of root.\\\")\\n\\t\\t\\treturn\\n\\t\\tif not os.path.exists(filepath):\\n\\t\\t\\tself.response_code(4,\\\"Not Found\\\")\\n\\t\\t\\treturn\\n\\t\\tif os.path.isdir(filepath):\\n\\t\\t\\tif os.path.exists(os.path.join(filepath,\\\"index.gmi\\\")):\\n\\t\\t\\t\\tfilepath = os.path.join(filepath,\\\"index.gmi\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.response_code(5,\\\"Cowardly refusing to generate folder listing.\\\")\\n\\t\\t\\t\\treturn\\n\\t\\text = os.path.splitext(filepath)[1]\\n\\t\\tmimetype = mimetypes.guess_type(filepath,False)\\n\\t\\tif ext in self.OVERRIDE_MIMETYPES:\\n\\t\\t\\tmimetype = self.OVERRIDE_MIMETYPES[ext]\\n\\t\\tmimetype = mimetype or \\\"application/octet-stream\\\"\\n\\t\\twith open(filepath,\\\"rb\\\") as f:\\n\\t\\t\\tself.response_code(2,mimetype)\\n\\t\\t\\tshutil.copyfileobj(f,self.wfile)\",\n \"def uploaded(self, filename):\\n return send_from_directory(self.upload_path, filename)\",\n \"def sendFile(self, path):\\n\\t\\t# Either guess the mime type or just send it as a binary file.\\n\\t\\tif self.guessMime:\\n\\t\\t\\tmimeType = mimetypes.guess_type(path)\\n\\t\\telse:\\n\\t\\t\\tmimeType = \\\"application/octet-stream\\\"\\n\\n\\t\\t# Send the headers.\\n\\t\\tself.send_response(200)\\n\\t\\tself.send_header(\\\"Content-type\\\", mimeType)\\n\\t\\tself.send_header(\\\"Content-Length\\\", os.path.getsize(path))\\n\\t\\tself.end_headers()\\n\\t\\t\\n\\t\\tf = open(path, 'rb')\\n\\t\\t# self.wfile is the output stream.\\n\\t\\tshutil.copyfileobj(f, self.wfile)\\n\\t\\tf.close()\",\n \"def uploaded_file(image):\\n return send_from_directory(app.config['UPLOAD_FOLDER'], image)\",\n \"def serve_upload(conn, ssn_key, file_name, client_name):\\n # get signal to begin upload\\n request = aes.decrypt(ssn_key, conn.recv(1024))\\n if request != SIG_START:\\n conn.sendall(aes.encrypt(ssn_key, SIG_BAD))\\n return print(\\\"Bob: something went wrong with file transfer\\\")\\n response = aes.encrypt(ssn_key, SIG_GOOD)\\n conn.sendall(response)\\n print(\\\"Bob: beginning transfer for {}...\\\".format(file_name))\\n\\n # get file contents from client\\n contents = list()\\n completed_upload = False\\n response = aes.encrypt(ssn_key, SIG_GOOD)\\n while not completed_upload:\\n request = aes.decrypt(ssn_key, conn.recv(1024))\\n if request == SIG_END:\\n completed_upload = True\\n print(\\\"Bob: completed transfer for {}\\\".format(file_name))\\n else:\\n contents.append(request)\\n conn.sendall(response)\\n\\n # save file to server folder\\n file_path = \\\"{}/{}\\\".format(client_name, file_name)\\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\\n with open(file_path, \\\"w\\\") as outputStream:\\n outputStream.write(''.join(contents))\\n print(\\\"Bob: file saved in {}\\\".format(file_path))\",\n \"def serve_dir(port, dir_path):\\n\\n # Create index files, and store the list of their paths for cleanup later\\n # This time, force no processing - this gives us a fast first-pass in terms\\n # of page generation, but potentially slow serving for large image files\\n print('Performing first pass index file generation')\\n created_files = _create_index_files(dir_path, True)\\n\\n if (PIL_ENABLED):\\n # If PIL is enabled, we'd like to process the HTML indexes to include\\n # generated thumbnails - this slows down generation so we don't do it\\n # first time around, but now we're serving it's good to do in the\\n # background\\n print('Performing PIL-enchanced optimised index file generation in background')\\n background_indexer = BackgroundIndexFileGenerator(dir_path)\\n background_indexer.run()\\n\\n # Run the server in the current location - this blocks until it's stopped\\n _run_server(port)\\n\\n # Clean up the index files created earlier so we don't make a mess of\\n # the image directories\\n _clean_up(created_files)\",\n \"def upload():\\n return handle_upload(app, request)\",\n \"def upload():\\n\\n file = request.files['query']\\n filepath = upload_filepath(secure_filename(file.filename))\\n file.save(filepath)\\n classification = classify(filepath)\\n classification['filename'] = file.filename\\n return render_template('index.html', classification=classification)\",\n \"def serve(files, port, mime):\\n\\tprint \\\"serving\\\", files, \\\"on\\\", port\\n\\t# Bind to any address.\\n\\taddress = ('', port)\\n\\tRequestHandler.files = files#[fName.rstrip('/') for fName in files]\\n\\tRequestHandler.guessMime = mime\\n\\thttpd = ThreadedHTTPServer(address, RequestHandler)\\n\\ttry:\\n\\t\\thttpd.serve_forever()\\n\\texcept KeyboardInterrupt:\\n\\t\\tprint \\\"Done.\\\"\",\n \"def upload(self, filename, file_path):\\n return\",\n \"def SingleStaticServe(file_path):\\n def get_file():\\n mime, encoding = mimetypes.guess_type(file_path)\\n return open(file_path, 'rb'), mime or 'application/octet-stream'\\n\\n class SingleStaticServe(GiottoProgram):\\n controllers = ['http-get']\\n model = [get_file]\\n view = FileView()\\n\\n return SingleStaticServe()\",\n \"def get(self):\\n\\n upload_url = blobstore.create_upload_url('/upload')\\n\\n self.response.headers['content-type'] = 'text/plain'\\n self.response.out.write(upload_url)\",\n \"def serve_file(path, content_type=None, disposition=None, name=None, delete=False):\\n \\n response = cherrypy.response\\n \\n # If path is relative, users should fix it by making path absolute.\\n # That is, CherryPy should not guess where the application root is.\\n # It certainly should *not* use cwd (since CP may be invoked from a\\n # variety of paths). If using tools.static, you can make your relative\\n # paths become absolute by supplying a value for \\\"tools.static.root\\\".\\n if not os.path.isabs(path):\\n raise ValueError(\\\"'%s' is not an absolute path.\\\" % path)\\n \\n try:\\n st = os.stat(path)\\n except OSError:\\n raise cherrypy.NotFound()\\n \\n # Check if path is a directory.\\n if stat.S_ISDIR(st.st_mode):\\n # Let the caller deal with it as they like.\\n raise cherrypy.NotFound()\\n \\n # Set the Last-Modified response header, so that\\n # modified-since validation code can work.\\n response.headers['Last-Modified'] = http.HTTPDate(st.st_mtime)\\n cptools.validate_since()\\n \\n if content_type is None:\\n # Set content-type based on filename extension\\n ext = \\\"\\\"\\n i = path.rfind('.')\\n if i != -1:\\n ext = path[i:].lower()\\n content_type = mimetypes.types_map.get(ext, \\\"text/plain\\\")\\n response.headers['Content-Type'] = content_type\\n \\n if disposition is not None:\\n if name is None:\\n name = os.path.basename(path)\\n cd = '%s; filename=\\\"%s\\\"' % (disposition, name)\\n response.headers[\\\"Content-Disposition\\\"] = cd\\n \\n # Set Content-Length and use an iterable (file object)\\n # this way CP won't load the whole file in memory\\n c_len = st.st_size\\n if delete:\\n flag = os.O_RDWR | os.O_EXCL\\n if hasattr(os, 'O_BINARY'):\\n flag |= os.O_BINARY\\n if os.name == 'nt':\\n flag |= os.O_TEMPORARY\\n fd = os.open(path, flag)\\n file = os.fdopen(fd, 'rb')\\n bodyfile = file\\n else:\\n bodyfile = open(path, 'rb')\\n \\n # HTTP/1.0 didn't have Range/Accept-Ranges headers, or the 206 code\\n if cherrypy.request.protocol >= (1, 1):\\n response.headers[\\\"Accept-Ranges\\\"] = \\\"bytes\\\"\\n r = http.get_ranges(cherrypy.request.headers.get('Range'), c_len)\\n if r == []:\\n response.headers['Content-Range'] = \\\"bytes */%s\\\" % c_len\\n message = \\\"Invalid Range (first-byte-pos greater than Content-Length)\\\"\\n raise cherrypy.HTTPError(416, message)\\n if r:\\n if len(r) == 1:\\n # Return a single-part response.\\n start, stop = r[0]\\n if stop > c_len:\\n stop = c_len\\n r_len = stop - start\\n response.status = \\\"206 Partial Content\\\"\\n response.headers['Content-Range'] = (\\\"bytes %s-%s/%s\\\" %\\n (start, stop - 1, c_len))\\n response.headers['Content-Length'] = r_len\\n bodyfile.seek(start)\\n response.body = file_generator_limited(bodyfile, r_len)\\n else:\\n # Return a multipart/byteranges response.\\n response.status = \\\"206 Partial Content\\\"\\n import mimetools\\n boundary = mimetools.choose_boundary()\\n ct = \\\"multipart/byteranges; boundary=%s\\\" % boundary\\n response.headers['Content-Type'] = ct\\n if response.headers.has_key(\\\"Content-Length\\\"):\\n # Delete Content-Length header so finalize() recalcs it.\\n del response.headers[\\\"Content-Length\\\"]\\n \\n def file_ranges():\\n # Apache compatibility:\\n yield \\\"\\\\r\\\\n\\\"\\n \\n for start, stop in r:\\n yield \\\"--\\\" + boundary\\n yield \\\"\\\\r\\\\nContent-type: %s\\\" % content_type\\n yield (\\\"\\\\r\\\\nContent-range: bytes %s-%s/%s\\\\r\\\\n\\\\r\\\\n\\\"\\n % (start, stop - 1, c_len))\\n bodyfile.seek(start)\\n for chunk in file_generator_limited(bodyfile, stop-start):\\n yield chunk\\n yield \\\"\\\\r\\\\n\\\"\\n # Final boundary\\n yield \\\"--\\\" + boundary + \\\"--\\\"\\n \\n # Apache compatibility:\\n yield \\\"\\\\r\\\\n\\\"\\n response.body = file_ranges()\\n else:\\n response.headers['Content-Length'] = c_len\\n response.body = bodyfile\\n else:\\n response.headers['Content-Length'] = c_len\\n response.body = bodyfile\\n response.stream = True\\n return response.body\",\n \"def upload_file():\\n try:\\n if 'file' not in request.files:\\n flash(\\\"No file selected or file is too big (max size is %sB)\\\" % app.config['MAX_CONTENT_LENGTH'])\\n return redirect(url_for('index'))\\n\\n file = request.files['file']\\n str_in = file.read()\\n file.close()\\n\\n str_out = io.BytesIO()\\n str_out.write(process_text(str_in.decode('utf-8')).encode('utf-8'))\\n str_out.seek(0)\\n\\n return send_file(\\n str_out,\\n attachment_filename=file.filename,\\n as_attachment=True,\\n mimetype='text/plain'\\n )\\n except RuntimeError:\\n flash('Something bad happened')\\n return redirect(url_for('index'))\",\n \"def deliverFile(self, filename=None, docroot=None):\\n if not docroot:\\n docroot = self.docroot\\n if not docroot or not os.path.isdir(docroot):\\n if not filename: filename = \\\"index.html\\\"\\n return self.sendNotFound(filename)\\n\\n if not filename:\\n filename = \\\"index.htm\\\"\\n if not os.path.exists(os.path.join(docroot, filename)):\\n filename = \\\"index.html\\\"\\n filename = filename.strip(os.sep)\\n\\n path = os.path.join(docroot, filename)\\n if not os.path.exists(path) or not os.path.isfile(path):\\n print >> self.estrm, \\\"vaologin: doc not found: \\\" + path\\n return self.sendNotFound(filename)\\n\\n defbufsz = 1024000\\n try:\\n bufsz = int(self._getConfigParam(\\\"bufsize\\\", defbufsz))\\n except TypeError, ex:\\n bufsz = defbufsz\\n\\n ext = os.path.splitext(filename)[1]\\n if ext and ext.startswith('.'):\\n ext = ext[1:]\\n mimetype = self.getMimeTypeFor(ext)\\n self._sendheader()\\n self.ostrm.write(\\\"Content-Type: %s\\\\r\\\\n\\\\r\\\\n\\\" % mimetype)\\n\\n fd = None\\n try:\\n fd = open(path, 'rb')\\n data = fd.read(bufsz)\\n while data:\\n self.ostrm.write(data)\\n data = fd.read(bufsz)\\n\\n except Exception, ex:\\n print >> self.estrm, \\\"Problem sending file data: \\\" + str(ex)\\n finally:\\n if fd: fd.close()\\n # self.ostrm.close()\",\n \"async def send_file(self, filename, content_type=None, content_encoding=None, max_age=2592000, buf_size=128):\\n try:\\n # Get file size\\n stat = os.stat(filename)\\n slen = str(stat[6])\\n self.add_header('Content-Length', slen)\\n # Find content type\\n if content_type:\\n self.add_header('Content-Type', content_type)\\n # Add content-encoding, if any\\n if content_encoding:\\n self.add_header('Content-Encoding', content_encoding)\\n # Since this is static content is totally make sense\\n # to tell browser to cache it, however, you can always\\n # override it by setting max_age to zero\\n self.add_header('Cache-Control', 'max-age={}, public'.format(max_age))\\n with open(filename) as f:\\n await self._send_headers()\\n gc.collect()\\n buf = bytearray(min(stat[6], buf_size))\\n while True:\\n size = f.readinto(buf)\\n if size == 0:\\n break\\n await self.send(buf, sz=size)\\n except OSError as e:\\n # special handling for ENOENT / EACCESS\\n if e.args[0] in (errno.ENOENT, errno.EACCES):\\n raise HTTPException(404)\\n else:\\n raise\",\n \"def StaticServe(base_path):\\n def get_file(path):\\n fullpath = base_path + path\\n try:\\n mime, encoding = mimetypes.guess_type(fullpath)\\n return open(fullpath, 'rb'), mime or 'application/octet-stream'\\n except IOError:\\n raise DataNotFound(\\\"File does not exist\\\")\\n\\n class StaticServe(GiottoProgram):\\n controllers = ['http-get']\\n model = [get_file]\\n view = FileView()\\n\\n return StaticServe()\",\n \"def render_file(path: str):\\n if path != \\\"\\\" and os.path.exists(os.path.join(current_app.config[\\\"UI_PATH\\\"], path)):\\n _LOGGER.info(\\n \\\"sending {} file from {}\\\".format(path, current_app.config[\\\"UI_PATH\\\"])\\n )\\n return send_from_directory(current_app.config[\\\"UI_PATH\\\"], path)\\n else:\\n _LOGGER.info(\\n \\\"sending index.html file at {} from {}\\\".format(\\n path, current_app.config[\\\"UI_PATH\\\"]\\n )\\n )\\n return send_from_directory(current_app.config[\\\"UI_PATH\\\"], \\\"index.html\\\")\",\n \"def post(self):\\n filename = str(time.time())\\n filepath = os.path.join(\\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\\n with open(filepath, 'bw') as uploadfile:\\n chunk_size = 1024\\n while True:\\n chunk = request.stream.read(chunk_size)\\n if len(chunk) == 0:\\n break\\n uploadfile.write(chunk)\\n current_app.logger.info('file %s upload successfully', filename)\\n return {'timestamp': filename}, http.HTTPStatus.CREATED\",\n \"def do_GET(self):\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", self.mimetype)\\n self.end_headers()\\n\\n with open(filename, \\\"rb\\\") as file_:\\n self.wfile.write(file_.read())\\n file_.close()\",\n \"def media(filename):\\n media_path = flask.current_app.instance_path + '/media'\\n return flask.send_from_directory(media_path, filename)\",\n \"def upload_file():\\n response = Response()\\n\\n size = 256, 256\\n if request.method == 'POST':\\n file = request.files['file']\\n if file and allowed_file(file.filename):\\n # Prevent dir traversal/NUL byte injection\\n filename = secure_filename(file.filename)\\n\\n if not os.path.exists(app.config['UPLOAD_FOLDER']):\\n os.makedirs(app.config['UPLOAD_FOLDER'])\\n\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n im = Image.open(\\n os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n im.thumbnail(size, Image.ANTIALIAS)\\n im.save(app.config['UPLOAD_FOLDER'] + '/small_' + filename, \\\"PNG\\\")\\n _upload_to_s3(filename)\\n _upload_to_s3('small_{}'.format(filename))\\n return response\",\n \"def upload():\\n\\treturn render_template(\\\"upload.html\\\", title=\\\"Upload a file\\\")\",\n \"def upload():\\n global FILE_NAME\\n target = os.path.join(APP_ROOT, \\\"images\\\")\\n print(target)\\n\\n if not os.path.isdir(target):\\n os.mkdir(target)\\n\\n for file in request.files.getlist(\\\"file\\\"):\\n print(file)\\n filename = file.filename\\n destination = \\\"/\\\".join([target, filename])\\n FILE_NAME = destination\\n file.save(destination)\\n return render_template(\\\"complete.html\\\")\",\n \"def api_upload():\\n return make_response(file_manager.save_uploaded_file(), 200)\",\n \"def staticFile(path):\\n logger.info('[FLASKWEB /fs] Static File Request for `%s`' % path)\\n local = os.path.join(webapp.config['DIR'], path)\\n if not os.path.exists(local):\\n return returnError(\\\"File not found: %s\\\" % path, 404)\\n if os.path.isdir(local):\\n contents = sorted(os.listdir(local))\\n for i, f in enumerate(contents):\\n if os.path.isdir(f):\\n contents[i] += '/'\\n\\n if request.headers['Accept'] == 'application/json':\\n return jsonify(dict(cwd=local, contents=contents)), 200\\n else:\\n return render_template('listing.html', cwd=path, listing=contents), 200\\n\\n else:\\n if 'stdout' in local or 'output' in local or local.split('.')[-1] in ['txt', 'yaml', 'yml', 'json', 'log']:\\n with open(local, 'r') as file:\\n # output = unicode(file.read(), 'utf-8')\\n output = file.read()\\n\\n if request.headers['Accept'] == 'application/json':\\n return output, 200\\n else:\\n return render_template(\\\"output.html\\\", output=output)\\n\\n return send_from_directory(webapp.config['DIR'], path)\",\n \"def upload_file(self, file_path, file_name, output_path):\",\n \"def upload_file():\\n if request.method == 'POST':\\n # check if the post request has the file part\\n if 'img_file' not in request.files:\\n return redirect(request.url)\\n image = request.files['img_file']\\n # if user does not select file, browser also\\n # submit an empty part without filename\\n if image.filename == '':\\n return redirect(request.url)\\n\\n # save image_file\\n save_path = os.path.join(app.config['UPLOAD_DIR'], image.filename)\\n LOGGER.debug(save_path)\\n if os.path.isfile(save_path):\\n os.remove(save_path)\\n image.save(save_path)\\n\\n category = _predict(FLAGS.hostport, save_path)\\n return render_template('index.html', filename=save_path, category=category)\\n return render_template('index.html')\",\n \"def download_file(filename):\\n return send_from_directory('uploads', filename, as_attachment=True)\",\n \"def file_upload(self, req, folder_path):\\n\\t\\tresult, filename = self.handle_upload(req, folder_path)\\n\\t\\tfile_url = self.selected_root['url_callback'](req, folder_path, filename)\\n\\t\\t\\n\\t\\tself.content_type = 'text/html'\\n\\t\\tself.content = [str(tags.script(type=\\\"text/javascript\\\")[\\n\\t\\t\\t\\t\\t\\t\\\"window.parent.frames['frmUpload'].OnUploadCompleted(%s, '%s');\\\\n\\\" % (result, filename)\\n\\t\\t\\t\\t\\t\\t])]\",\n \"def upload_file(self, f):\\n return self._telegraph.upload_file(f)\",\n \"def serve(cls, path):\\n path = path[6:] # strip \\\"media/\\\"\\n path = path.replace(\\\"..\\\", \\\"\\\") ## .. tricks\\n \\n type = \\\"application/data\\\"\\n \\n if path.endswith(\\\"css\\\"):\\n type = \\\"text/css\\\"\\n elif path.endswith(\\\"js\\\"):\\n type = \\\"text/javascript\\\"\\n elif path.endswith(\\\"png\\\"):\\n type = \\\"image/png\\\"\\n elif path.endswith(\\\"gif\\\"):\\n type = \\\"image/gif\\\"\\n elif path.endswith(\\\"jpg\\\"):\\n type = \\\"image/jpeg\\\"\\n\\n ## requests to /media/qx map to the qx framework directory\\n if path.startswith(\\\"qx/\\\"):\\n path = cls.qxbase + '/' + path[3:]\\n elif not path.startswith(\\\"source/\\\"):\\n path = \\\"source/\\\" + path \\n\\n data = open(os.path.join(cls.workingdir(), path), \\\"r\\\").read()\\n return (type, data)\",\n \"def upload_file(self, file_name, content):\\n return self.files.upload(file_name, content)\",\n \"def upload_file():\\n retVal = None \\n if request.method == 'POST' and upload_validated(request):\\n retVal = render_successful_upload(request) \\n else:\\n retVal = render_index()\\n return retVal\",\n \"def upload():\\n global video_file_name\\n\\n if request.method == 'POST' and 'file' in request.files:\\n\\n # clear uploads folder\\n cleanup_uploads()\\n\\n # Video has been uploaded\\n filename = video.save(request.files['file'])\\n video_file_name[0] = filename\\n\\n # Process video on a new thread\\n # threading.Thread(target=process_video, args=[os.path.join('uploads', filename)]).start()\\n # threading.Thread(target=process_audio, args=[os.path.join('uploads', filename)]).start()\\n threading.Thread(target=process_video, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\\n threading.Thread(target=process_audio, args=[os.path.join(basedir, 'static', 'data', 'uploads', filename)]).start()\\n\\n return jsonify({'success': True}), 200, {'ContentType': 'application/json'}\\n\\n elif request.method == 'GET':\\n return render_template('home.html')\",\n \"def handle_uploaded_file(f):\\n path = settings.ABS_PATH + \\\"Server_data_visualization/uploads/executable\\\"\\n destination = open(path, \\\"wb+\\\")\\n for chunk in f.chunks():\\n destination.write(chunk)\\n destination.close()\\n # os.chmod(path, stat.S_IXUSR | stat.S_IWUSR | stat.S_IRUSR)\",\n \"def _handle_browse(self, relpath, params):\\r\\n abspath = os.path.normpath(os.path.join(self._root, relpath))\\r\\n if not abspath.startswith(self._root):\\r\\n raise ValueError # Prevent using .. to get files from anywhere other than root.\\r\\n if os.path.isdir(abspath):\\r\\n self._serve_dir(abspath, params)\\r\\n elif os.path.isfile(abspath):\\r\\n self._serve_file(abspath, params)\",\n \"def sendFile(self, fullfilename):\\n raise NotImplementedError(\\\"Implement this method in child class\\\")\",\n \"def uploaded_file(filename):\\n if app.config.get('UPLOAD_SCREENSHOTS_TO_S3', False):\\n import boto\\n from flask import redirect\\n conn = boto.connect_s3()\\n url = conn.generate_url(\\n expires_in=long(60*60*2), # 2 hour expiry\\n method='GET',\\n bucket=app.config['S3_BUCKET'],\\n key='{}/{}'.format(\\n app.config.get('S3_FILES_PREFIX', 'sleepypuppy'),\\n filename\\n ),\\n query_auth=True\\n )\\n url = _correct_s3_url(url)\\n return redirect(url, 302)\\n else:\\n return send_from_directory(\\n app.config['UPLOAD_FOLDER'],\\n filename\\n )\",\n \"def uploaded_file(filename):\\n if app.config.get('UPLOAD_SCREENSHOTS_TO_S3', False):\\n import boto\\n from flask import redirect\\n conn = boto.connect_s3()\\n url = conn.generate_url(\\n expires_in=long(60 * 60 * 2), # 2 hour expiry\\n method='GET',\\n bucket=app.config['S3_BUCKET'],\\n key='{}/{}'.format(\\n app.config.get('S3_FILES_PREFIX', 'sleepypuppy'),\\n filename\\n ),\\n query_auth=True\\n )\\n url = _correct_s3_url(url)\\n return redirect(url, 302)\\n else:\\n return send_from_directory(\\n app.config['UPLOAD_FOLDER'],\\n filename\\n )\",\n \"def peer_server_upload(self, conn, data_received):\\n try:\\n file_size = os.path.getsize(SHARED_DIR+'/'+data_received['file_name'])\\n f = open(SHARED_DIR+'/'+data_received['file_name'], 'rb')\\n #print \\\"Hosting File: %s for download\\\" % data_received\\n for chunk_start, chunk_size in self.get_chunks(file_size):\\n file_chunk = f.read(chunk_size)\\n conn.sendall(file_chunk)\\n '''\\n while True:\\n data = f.readline()\\n if data:\\n conn.sendall(data)\\n else:\\n break\\n '''\\n f.close()\\n conn.sendall('')\\n conn.close()\\n except Exception as e:\\n print \\\"File Upload Error, %s\\\" % e\",\n \"def _serve_dir(self, abspath, params):\\r\\n relpath = os.path.relpath(abspath, self._root)\\r\\n breadcrumbs = self._create_breadcrumbs(relpath)\\r\\n entries = [ {'link_path': os.path.join(relpath, e), 'name': e} for e in os.listdir(abspath)]\\r\\n args = self._default_template_args('dir')\\r\\n args.update({ 'root_parent': os.path.dirname(self._root),\\r\\n 'breadcrumbs': breadcrumbs,\\r\\n 'entries': entries,\\r\\n 'params': params })\\r\\n self._send_content(self._renderer.render_name('base', args), 'text/html')\",\n \"def http_server(dir, ip, port, quiet):\\n sys.stderr.write(\\\"Serving Image to Clair from http://%s:%d\\\\n\\\" % (ip, port))\\n chdir(dir)\\n if quiet:\\n Handler = QuietSimpleHTTPHandler\\n else:\\n Handler = SimpleHTTPServer.SimpleHTTPRequestHandler\\n httpd = socketserver.TCPServer((ip, port), Handler)\\n httpd.serve_forever()\",\n \"def send_file(self, src: PathLike, dest: PathLike, force: bool = False):\",\n \"def main(filename: Path, port: int) -> None:\\n LOG.info(f\\\"Serving Tacview file {filename} 127.0.0.1:{port}. ..\\\")\\n loop = asyncio.get_event_loop()\\n task = loop.create_task(serve_file(filename, port))\\n try:\\n loop.run_until_complete(task)\\n except KeyboardInterrupt:\\n LOG.info(\\\"Keyboard interupt!\\\")\\n task.cancel()\\n except Exception as err:\\n task.cancel()\\n raise err\",\n \"def upload_file():\\n response = Response()\\n response.headers.add('Access-Control-Allow-Origin', request.headers.get(\\\"Origin\\\", None))\\n response.headers.add('Access-Control-Allow-Methods', 'GET,POST')\\n response.headers.add('Access-Control-Max-Age', '21600')\\n response.headers.add('Access-Control-Allow-Credentials', 'true')\\n response.headers.add('Access-Control-Allow-Headers', \\\"Origin, X-Requested-With, Content-Type, Accept\\\")\\n\\n size = 256, 256\\n if request.method == 'POST':\\n file = request.files['file']\\n if file and allowed_file(file.filename):\\n # Prevent dir traversal/NUL byte injection\\n filename = secure_filename(file.filename)\\n\\n if not os.path.exists(app.config['UPLOAD_FOLDER']):\\n os.makedirs(app.config['UPLOAD_FOLDER'])\\n\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n im = Image.open(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n im.thumbnail(size, Image.ANTIALIAS)\\n im.save(app.config['UPLOAD_FOLDER'] + '/small_' + filename, \\\"PNG\\\")\\n _upload_to_s3(filename)\\n _upload_to_s3('small_{}'.format(filename))\\n return response\",\n \"def index():\\n return render_template(\\\"upload.html\\\")\",\n \"def post(self):\\n\\n upload_files = self.get_uploads('file')\\n blob_info = upload_files[0]\\n self.redirect('/?upload_info=%s' % urllib.quote(blob_info.filename))\",\n \"def fetch_file(self, path, content_type, response=settings.HTTP_OK):\\n try:\\n with open(path) as fp:\\n self.fetch_content(fp.read(), content_type, response)\\n except IOError:\\n self.send_error(settings.HTTP_INTERNAL_SERVER_ERROR)\",\n \"def download_file():\\n\\n if 'POST' == request.method:\\n file_id = request.form['file_id']\\n else:\\n file_id = request.args.get('file_id')\\n\\n # 1 ==> example_1.tgz\\n file_path = file_manager.get_file_path_from_id(file_id)\\n print \\\"serving file: \\\" + file_path\\n return send_file(file_path, as_attachment=True)\",\n \"def handler ( self, environ, start_response ) :\\n url = urlparse ( reconstruct_url ( environ ) ) \\n \\n if self.mount_point is not None:\\n split_url = url.path.split ( self.mount_point, 1 ) \\n serve_file = split_url [ 1 ] \\n else:\\n serve_file = url.path\\n \\n serve_file = serve_file.replace ( '%20', ' ' ) \\n \\n def do_get ( ):\\n if serve_file.endswith ( '/' ) or os.path.isdir ( os.path.join ( self.path, serve_file ) ):\\n if os.path.isdir ( os.path.join ( self.path, serve_file ) ):\\n start_response ( '200 OK', [ ( 'Cache-Control', 'no-cache' ), ( 'Pragma','no-cache' ) ,\\n ( 'Content-Type', 'text/html; charset=utf-8' ) ] )\\n return [ \\n '<html>' + \\n '<br>'.join ( [ '<a href=\\\"%s/%s\\\">%s</a>' % ( serve_file.replace ( filename, '' ), filename, filename ) \\n for filename in os.listdir ( os.path.join ( self.path, serve_file ) ) ] )\\n + '</html>' \\n ]\\n else:\\n logger.error ( 'failed to list directory %s/%s' % ( self.path, serve_file ) )\\n start_response ( '404 Not found', [ ( 'Content-Type', 'text/plain') ] )\\n return ['404 Not Found' ] \\n \\n try:\\n if os.name == 'nt' or sys.platform == 'cygwin':\\n f = open ( os.path.join ( self.path, serve_file ), 'rb' ) \\n else:\\n f = open ( os.path.join ( self.path, serve_file ), 'r' ) \\n logger.debug ( 'opened file %s' % serve_file ) \\n except IOError:\\n logger.error ( 'failed to open file %s/%s' % ( self.path, serve_file ) )\\n start_response ( '404 Not found', [ ( 'Content-Type', 'text/plain') ] )\\n return [ '404 Not Found' ] \\n\\n start_response ( '200 OK', [ ( 'Cache-Control', 'no-cache' ), ( 'Pragma','no-cache' ),\\n ( 'Content-Type', self.guess_content_type ( environ['PATH_INFO' ] ) ) ] )\\n return [ f.read ( ) ] \\n \\n def do_put ( ):\\n #Write file\\n try:\\n f = open ( os.path.join ( self.path, serve_file ) , 'w' ) \\n logger.debug ( 'opened file for writing %s' % serve_file ) \\n except:\\n logger.error ( 'failed to open file for writiing %s/%s' % ( self.path, serve_file ) )\\n start_response ( '403 Forbidden', [ ( 'Content-Type', 'text/plain' ) ] )\\n return ['403 Forbidden' ] \\n \\n f.write ( environ [ 'wsgi.input' ] .read ( ) )\\n \\n def do_mkcollection ( ):\\n pass\\n \\n http_method_map = { 'GET': do_get, 'PUT': do_put, 'MKCOLLECTION': do_mkcollection }\\n return http_method_map[environ['REQUEST_METHOD' ] ] ( )\",\n \"def static(self, filename):\\n return send_from_directory(self.static_path, filename)\",\n \"def _send_file(self, file_path: Path, stream_id: int) -> None:\\n file_size = file_path.stat().st_size\\n content_type, content_encoding = mimetypes.guess_type(str(file_path))\\n response_headers = [\\n (':status', '200'),\\n ('content-length', str(file_size)),\\n ('server', self._server_name)\\n ]\\n if content_type:\\n response_headers.append(('content-type', content_type))\\n if content_encoding:\\n response_headers.append(('content-encoding', content_encoding))\\n\\n self._connection.send_headers(stream_id, response_headers)\\n self._sock.sendall(self._connection.data_to_send())\\n\\n with file_path.open(mode='rb', buffering=0) as f:\\n self._send_file_data(f, stream_id)\",\n \"def index():\\n\\n #return 'file uploaded successfully'\\n return render_template('index.html')\",\n \"def files_serve(path):\\n return flask.send_from_directory(\\\"static/js\\\", path)\",\n \"def node_file_upload(ctx, file, filename):\\n filepath = click.format_filename(file)\\n filename = filename if filename else filepath\\n\\n with open(file, \\\"r\\\") as f:\\n content = f.read()\\n\\n try:\\n ctx.obj['node'].create_file(filename, content=content)\\n except TimeoutError as e:\\n logger.error('Error: %s' % e)\\n exit(1)\",\n \"def handle(req: bytes) -> str:\\n\\n try:\\n pass\\n except:\\n dirname = os.path.dirname(__file__)\\n path = os.path.join(dirname, 'html', 'upload.html')\\n\\n with (open(path, 'r')) as file:\\n html = file.read()\\n\\n return html\",\n \"def send_file():\\n data = ARGS.data\\n filename = ARGS.file\\n outstream = \\\"POST||\\\" + filename + \\\"||\\\" + data\\n CLIENT_SOCKET.send(outstream.encode())\",\n \"def upload():\\n print 'Loading upload page'\\n\\n if request.method == 'POST':\\n if 'dvwfile' not in request.files:\\n flash('No file given', 'danger')\\n return render_template('upload.html')\\n\\n upload_file = request.files['dvwfile']\\n # if user does not select file, browser also\\n # submit a empty part without filename\\n if upload_file.filename == '':\\n flash('No selected file', 'danger')\\n return render_template('upload.html')\\n if upload_file and allowed_file(upload_file.filename):\\n filename = secure_filename(upload_file.filename)\\n upload_folder = os.path.join(app.config['UPLOAD_FOLDER'], str(session['user_id']))\\n\\n if not os.path.isdir(upload_folder):\\n os.makedirs(upload_folder)\\n\\n upload_file.save(os.path.join(upload_folder, filename))\\n\\n upload_dict = {'user_id': session['user_id'], 'file_key': filename}\\n datastore.insert_upload_row(engine, upload_dict)\\n\\n flash('File Uploaded!', 'info')\\n\\n print 'Uploaded File'\\n return redirect(url_for('upload'))\\n else:\\n return render_template('upload.html')\",\n \"def send_file_upload_request(self, view_name, filename, expected_response_code=200):\\n url = reverse(view_name)\\n\\n client = Client()\\n with open(filename) as file_upload:\\n response = client.post(path=url, data={'file': file_upload})\\n\\n self.assertEqual(expected_response_code, response.status_code)\\n\\n return response\",\n \"def Uploads():\\n if request.method==\\\"POST\\\":\\n if 'file' not in request.files:\\n flash('No file part')\\n return redirect(request.url)\\n file=request.files[\\\"file\\\"]\\n file.save(os.path.join(\\\"Solar\\\", file.filename))\\n return render_template(\\\"index.html\\\", message = \\\"File Uploaded Successfuly\\\")\\n return render_template(\\\"index.html\\\", message = \\\"Upload Solar Maintenance File\\\")\\n return \\\"File Uploaded!\\\"\",\n \"def do_GET(self):\\n\\n path = self.file_path\\n\\n if os.path.exists(path):\\n # Symbolic link judgement.\\n # Paths with denied symbolic links will pretend to be 404 errors.\\n if args[TITLE_LOCAL_LINKS] and not (\\\"%s/\\\" % os.path.realpath(path)).startswith(os.getcwd() + \\\"/\\\"):\\n return self.send_error(404, \\\"File not found\\\")\\n elif args[TITLE_NO_LINKS]:\\n # If all symbolic links are banned, then we must trace our\\n # way down an existing path to make sure that no symbolic link exists\\n curr = path\\n while True:\\n if os.path.islink(curr):\\n return self.send_error(404, \\\"File not found\\\")\\n if curr == path:\\n break\\n curr = os.path.dirname(path);\\n\\n f = None\\n if os.path.isdir(path):\\n\\n if not getattr(self, common.ATTR_PATH, \\\"\\\").endswith(\\\"/\\\"):\\n return self.send_redirect(\\\"%s/\\\" % getattr(self, common.ATTR_PATH, \\\"\\\"))\\n\\n for index in [\\\"index.html\\\", \\\"index.htm\\\"]:\\n index = os.path.join(path, index)\\n if os.path.exists(index):\\n path = index\\n break\\n if path == self.file_path:\\n return self.list_directory(path)\\n\\n return self.serve_file(path)\",\n \"def upload(self):\\n\\n # Try to retrieve the file from the request\\n file = None\\n try:\\n file = request.files[\\\"file\\\"]\\n except KeyError as e:\\n logging.warning(f\\\"File was not found in request: {e}.\\\")\\n flash(\\\"No file given.\\\", \\\"error\\\")\\n return redirect(request.url)\\n except AttributeError as e:\\n logging.warning(f\\\"Error: Request did not contain any files: {e}.\\\")\\n flash(\\\"No file given.\\\", \\\"error\\\")\\n return redirect(request.url)\\n\\n # Check if file was correctly uploaded\\n if not file or len(file.filename) == 0:\\n flash(\\\"No file selected for upload.\\\", \\\"message\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" Check if file has correct extension. Allowed extensions depend on\\n the connector. To make the code more readable, group connectors\\n with the same allowed file extensions together like this:\\n if connector in ['someconnector', 'someotherconnector']:\\n extensions = [...] \\\"\\\"\\\"\\n\\n if self.connector in [\\\"overtime\\\"]:\\n allowed_extensions = OVERTIME_SUPPORTED_EXTENSIONS\\n else:\\n allowed_extensions = []\\n\\n if not allowed_file(file, allowed_extensions=allowed_extensions):\\n flash(\\\"File extension not allowed.\\\", \\\"warning\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" File seems uploaded correctly and has correct extension.\\n Generate a new record ID to keep track of the uploaded file.\\n \\\"\\\"\\\"\\n rec_id = generate_temp_record_id()\\n\\n # Save file to disk\\n path = store_temp_file(file, record_id=rec_id)\\n\\n if not path:\\n flash(\\\"Error saving file!\\\", \\\"error\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" If everything ended successfully, send the user to the\\n confirmation page so he can review his changes \\\"\\\"\\\"\\n\\n return redirect(url_for(f\\\"{self.endpoint}.upload_confirm\\\", rec_id=rec_id))\",\n \"def send_file_partial(path):\\n print(\\\"Start stream response\\\")\\n range_header = request.headers.get('Range', None)\\n if not range_header: return send_file(path)\\n\\n size = os.path.getsize(path)\\n byte1, byte2 = 0, None\\n\\n m = re.search('(\\\\d+)-(\\\\d*)', range_header)\\n g = m.groups()\\n\\n if g[0]: byte1 = int(g[0])\\n if g[1]: byte2 = int(g[1])\\n\\n length = size - byte1\\n if byte2 is not None:\\n length = byte2 - byte1\\n\\n with open(path, 'rb') as f:\\n f.seek(byte1)\\n data = f.read(length)\\n\\n rv = Response(data,\\n 206,\\n mimetype=mimetypes.guess_type(path)[0],\\n direct_passthrough=True)\\n rv.headers.add('Content-Range', 'bytes {0}-{1}/{2}'.format(byte1, byte1 + length - 1, size))\\n rv.headers.add('Keep-Alive', 'no')\\n print(\\\"Stream done\\\")\\n return rv\",\n \"def send(self, request, filepath):\\n gzip = False\\n if 'gzip' in request.headers[hdrs.ACCEPT_ENCODING]:\\n gzip_path = filepath.with_name(filepath.name + '.gz')\\n\\n if gzip_path.is_file():\\n filepath = gzip_path\\n gzip = True\\n\\n st = filepath.stat()\\n\\n modsince = request.if_modified_since\\n if modsince is not None and st.st_mtime <= modsince.timestamp():\\n raise HTTPNotModified()\\n\\n ct, encoding = mimetypes.guess_type(str(filepath))\\n if not ct:\\n ct = 'application/octet-stream'\\n\\n resp = self._response_factory()\\n resp.content_type = ct\\n if encoding:\\n resp.headers[hdrs.CONTENT_ENCODING] = encoding\\n if gzip:\\n resp.headers[hdrs.VARY] = hdrs.ACCEPT_ENCODING\\n resp.last_modified = st.st_mtime\\n\\n # CACHE HACK\\n if not request.app[KEY_DEVELOPMENT]:\\n cache_time = 31 * 86400 # = 1 month\\n resp.headers[hdrs.CACHE_CONTROL] = \\\"public, max-age={}\\\".format(\\n cache_time)\\n\\n file_size = st.st_size\\n\\n resp.content_length = file_size\\n with filepath.open('rb') as f:\\n yield from self._sendfile(request, resp, f, file_size)\\n\\n return resp\",\n \"def main():\\n\\tparser = optparse.OptionParser(usage=usage)\\n\\t# The network port to use.\\n\\tparser.add_option(\\\"-p\\\", \\\"--port\\\",\\n\\t\\t\\t\\t\\t dest=\\\"port\\\",\\n\\t\\t\\t\\t\\t default=80,\\n\\t\\t\\t\\t\\t metavar=\\\"PORT\\\",\\n\\t\\t\\t\\t\\t help=\\\"Serve the files from PORT. (Default 80)\\\")\\n\\t# Either guess the mime type, or just serve as binary.\\n\\tparser.add_option(\\\"-m\\\", \\\"--no-mime\\\",\\n\\t\\t\\t\\t\\t dest=\\\"mime\\\",\\n\\t\\t\\t\\t\\t default=True,\\n\\t\\t\\t\\t\\t action=\\\"store_false\\\",\\n\\t\\t\\t\\t\\t help=\\\"Don't try to guess mime types.\\\")\\n\\t\\n\\t(options, files) = parser.parse_args()\\n\\tserve(files, int(options.port), options.mime)\",\n \"def upload_file(self):\\n self.master.switch_frame(UploadFileView)\",\n \"def send_head(self):\\n path = self.translate_path(self.path)\\n if os.path.basename(path).startswith(UPLOAD_LINK):\\n return self.render_upload_form(os.path.dirname(self.path))\\n return super(SimpleHTTPRequestHandlerWithUpload, self).send_head()\",\n \"def send_file(cobj, dest, port, fname, hash, handler):\\n pass\",\n \"def serve(file: str, cache_age: int = 0, headers: Optional[Dict[str, str]] = None, status_override: int = None, range: str = None, max_bytes_per_request: int = None) -> Tuple[bytes, int, Dict[str, str]]:\\n # prevent serving files outside of current / specified dir (prevents download of all system files)\\n file = file.replace('../', '')\\n if headers is None:\\n headers = {}\\n if not exists(file): # return 404 on file not exists\\n return b'', 404, {}\\n if 'Content-Type' not in headers: # if content-type is not already specified then guess from mimetype\\n ctype, encoding = mimetypes.guess_type(file)\\n if ctype is None or encoding is not None:\\n ctype = 'application/octet-stream'\\n headers['Content-Type'] = ctype\\n with open(file, 'rb') as _in:\\n if range is None: # 200 request\\n lines = _in.read()\\n else: # 206 Request\\n lines, status_override = _handle_206(file, _in, headers, range, max_bytes_per_request)\\n if cache_age > 0:\\n headers['Cache-Control'] = f'max-age={cache_age}'\\n elif not cache_age and file.split('.')[-1] != 'html' and not App.debug: # if cache_age is not specified and not an html file and not debug then autoset cache_age to 1 hour\\n headers['Cache-Control'] = 'max-age=3600'\\n return lines, status_override or 200, headers\",\n \"def process(self):\\n\\n requested_file_path = self._get_requested_file_path()\\n\\n if os.path.exists(requested_file_path):\\n self._respond_with_status_line(OK)\\n self._respond_with_headers(file_path=requested_file_path,\\n requested_headers=('Date',\\n 'Content-Length',\\n 'Content-Type',\\n 'Server'))\\n else:\\n self._respond_with_status_line(NOT_FOUND)\\n self._respond_with_headers(requested_headers=('Server',))\",\n \"def send_file(file_name, path, ftp, callback):\\n file_src = path + file_name\\n if not os.path.isfile(file_src):\\n log(\\\"Uploading file: '{}'. File not found. Skipping.\\\".format(file_src))\\n return\\n\\n with open(file_src, \\\"rb\\\") as f:\\n callback(\\\"Uploading file: {}. Status: {}\\\\n\\\".format(file_name, str(ftp.storbinary(\\\"STOR \\\" + file_name, f))))\",\n \"def upload(path=None):\\n # Get path and verify if it is valid\\n base_path, directory, path = _normalize_path(path)\\n\\n if not settings['upload']:\\n flash('File uploading is disabled.', 'error')\\n return redirect(_get_dir_url('.files', path))\\n\\n form = UploadForm()\\n if form.validate_on_submit():\\n filename = op.join(directory, secure_filename(form.upload.data.filename))\\n\\n if op.exists(filename):\\n flash('File \\\"%s\\\" already exists.' % form.upload.data.filename, 'error')\\n else:\\n try:\\n save_file(filename, form.upload.data)\\n return redirect(_get_dir_url('.files', path))\\n except Exception, ex:\\n flash('Failed to save file: %s' % ex, 'error')\\n\\n return render_template('files/form.html', form=form)\",\n \"def upload(self, fullfilename, remotefolder=None):\\n print(\\\"[Remote Server] Uploading %s to %s:%s\\\" %(fullfilename, self.server, self.remotefolder))\\n\\n if not self.status:\\n return 0\\n\\n if remotefolder == None:\\n remotefolder = self.remotefolder\\n\\n if not self.cd(remotefolder):\\n return 0\\n\\n if not self.sendFile(fullfilename):\\n print(\\\"[Remote Server] Error uploading file %s\\\" %fullfilename)\\n return 0\\n\\n print(\\\"[Remote Server] upload finished successfully\\\")\\n\\n return 1\",\n \"def homepage_redirect():\\n return redirect('/upload_file')\",\n \"def upload(request):\\n # We pass the 'file_id' in the query string as a GET parameter. If\\n # we read it from the POSTed data, WebOb would read all POSTed\\n # data, which has various features and traps (like setting the\\n # \\\"Content-Length\\\" header to 0) that we do not need since we are\\n # going to read the data ourselves anyway.\\n file_id = request.GET['X-Progress-ID']\\n input_file, file_size, filename = get_file_from_request(request)\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n upload_dir = request.registry.settings['poulda.upload_dir']\\n user_id = authenticated_userid(request)\\n # We use a temporary path to detect unfinished uploads (post\\n # mortem, not in the application itself).\\n path = os.path.join(upload_dir, '_'.join((user_id, file_id)))\\n u.tmp_path = path\\n u.started = int(time.time())\\n u.size = file_size\\n u.state = u'uploading'\\n session.flush()\\n # We need to commit the transaction so that changes to the Upload\\n # object can be seen by the other threads (which will serve the\\n # 'progress' JSON view called by the upload page).\\n transaction.commit()\\n with open(path, 'w') as output:\\n # We must read only 'file_size' bytes from the 'input_file',\\n # not all of it since it also contains the MIME boundary.\\n copy_to_file(input_file, file_size, output)\\n final_path = filename[1 + filename.rfind(os.sep):]\\n final_path = os.path.join(upload_dir, final_path)\\n os.rename(path, final_path)\\n session = DBSession()\\n u = session.query(Upload).filter_by(id=file_id).one()\\n u.state = u'done'\\n u.final_path = unicode(final_path, 'utf-8')\\n return HTTPFound(location='success')\",\n \"def upload_file():\\n global gui\\n print(request.data)\\n if request.method == 'POST':\\n # check if the post request has the file part\\n if 'file' not in request.files:\\n flash('No file part')\\n return redirect(request.url)\\n file = request.files['file']\\n # if user does not select file, browser also\\n # submit a empty part without filename\\n if file.filename == '':\\n flash('No selected file')\\n return redirect(request.url)\\n if file and allowed_file(file.filename):\\n filename = secure_filename(file.filename)\\n print(file.filename)\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n gui.controller.main('openFile %s' % os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n return redirect('/data')\\n return render_template('upload_file.html')\",\n \"def upload_file():\\r\\n if not LOGGEDIN:\\r\\n return render_template(\\\"login_temp.html\\\", msg=\\\"\\\")\\r\\n\\r\\n if request.method == 'POST':\\r\\n firstname = flask.request.form[\\\"firstname\\\"]\\r\\n lastname = flask.request.form[\\\"lastname\\\"]\\r\\n city = flask.request.form[\\\"city\\\"]\\r\\n state = flask.request.form[\\\"state\\\"]\\r\\n status = flask.request.form[\\\"status\\\"]\\r\\n date = flask.request.form[\\\"date\\\"]\\r\\n photo = flask.request.form[\\\"photo\\\"]\\r\\n\\r\\n f_d = open(\\\"users/\\\" + lastname + firstname + \\\".txt\\\", \\\"a\\\")\\r\\n f_d.write(firstname + \\\"\\\\n\\\")\\r\\n f_d.write(lastname + \\\"\\\\n\\\")\\r\\n f_d.write(city + \\\"\\\\n\\\")\\r\\n f_d.write(state + \\\"\\\\n\\\")\\r\\n f_d.write(status + \\\"\\\\n\\\")\\r\\n f_d.write(date + \\\"\\\\n\\\")\\r\\n f_d.write(photo + \\\"\\\\n\\\")\\r\\n f_d.close()\\r\\n return render_template(\\\"home.html\\\")\\r\\n else:\\r\\n return render_template('check_in.html')\",\n \"def upload_file(self, session, output, serverdir):\\n name = output.metadata['filename']\\n self.log.debug(\\\"uploading %r to %r as %r\\\",\\n output.file.name, serverdir, name)\\n\\n kwargs = {}\\n if self.blocksize is not None:\\n kwargs['blocksize'] = self.blocksize\\n self.log.debug(\\\"using blocksize %d\\\", self.blocksize)\\n\\n upload_logger = KojiUploadLogger(self.log)\\n session.uploadWrapper(output.file.name, serverdir, name=name,\\n callback=upload_logger.callback, **kwargs)\\n path = os.path.join(serverdir, name)\\n self.log.debug(\\\"uploaded %r\\\", path)\\n return path\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72789335","0.7054051","0.7054051","0.7054051","0.7007223","0.69452924","0.6715913","0.6583728","0.65780145","0.65292954","0.63780475","0.6367171","0.62917274","0.6272483","0.62396723","0.62340975","0.6162114","0.6161394","0.613007","0.61287713","0.60720176","0.6068657","0.6054722","0.6037578","0.6031638","0.6025022","0.60125744","0.59936756","0.5986094","0.59848964","0.5983835","0.59780073","0.59476763","0.5935253","0.5931814","0.5927945","0.59157884","0.59053046","0.58746815","0.5858321","0.58582515","0.58520514","0.58273375","0.5824832","0.5812187","0.5793976","0.5786276","0.5785088","0.5781707","0.575948","0.57572436","0.5756332","0.5736699","0.5734161","0.5732205","0.5727702","0.57174677","0.5707239","0.5689271","0.56641304","0.56625885","0.5661188","0.561224","0.5595828","0.5574383","0.55712724","0.55574","0.5553937","0.5549649","0.5536944","0.5511941","0.5508366","0.5505693","0.54953796","0.5483876","0.5479175","0.5474022","0.54737306","0.54652035","0.5461063","0.5456787","0.545306","0.544956","0.5449232","0.54477763","0.5446612","0.54459167","0.5443491","0.54419565","0.54419565","0.54419565","0.54419565","0.54419565","0.54419565","0.54419565","0.54419565","0.54419565","0.54419565","0.54419565"],"string":"[\n \"0.72789335\",\n \"0.7054051\",\n \"0.7054051\",\n \"0.7054051\",\n \"0.7007223\",\n \"0.69452924\",\n \"0.6715913\",\n \"0.6583728\",\n \"0.65780145\",\n \"0.65292954\",\n \"0.63780475\",\n \"0.6367171\",\n \"0.62917274\",\n \"0.6272483\",\n \"0.62396723\",\n \"0.62340975\",\n \"0.6162114\",\n \"0.6161394\",\n \"0.613007\",\n \"0.61287713\",\n \"0.60720176\",\n \"0.6068657\",\n \"0.6054722\",\n \"0.6037578\",\n \"0.6031638\",\n \"0.6025022\",\n \"0.60125744\",\n \"0.59936756\",\n \"0.5986094\",\n \"0.59848964\",\n \"0.5983835\",\n \"0.59780073\",\n \"0.59476763\",\n \"0.5935253\",\n \"0.5931814\",\n \"0.5927945\",\n \"0.59157884\",\n \"0.59053046\",\n \"0.58746815\",\n \"0.5858321\",\n \"0.58582515\",\n \"0.58520514\",\n \"0.58273375\",\n \"0.5824832\",\n \"0.5812187\",\n \"0.5793976\",\n \"0.5786276\",\n \"0.5785088\",\n \"0.5781707\",\n \"0.575948\",\n \"0.57572436\",\n \"0.5756332\",\n \"0.5736699\",\n \"0.5734161\",\n \"0.5732205\",\n \"0.5727702\",\n \"0.57174677\",\n \"0.5707239\",\n \"0.5689271\",\n \"0.56641304\",\n \"0.56625885\",\n \"0.5661188\",\n \"0.561224\",\n \"0.5595828\",\n \"0.5574383\",\n \"0.55712724\",\n \"0.55574\",\n \"0.5553937\",\n \"0.5549649\",\n \"0.5536944\",\n \"0.5511941\",\n \"0.5508366\",\n \"0.5505693\",\n \"0.54953796\",\n \"0.5483876\",\n \"0.5479175\",\n \"0.5474022\",\n \"0.54737306\",\n \"0.54652035\",\n \"0.5461063\",\n \"0.5456787\",\n \"0.545306\",\n \"0.544956\",\n \"0.5449232\",\n \"0.54477763\",\n \"0.5446612\",\n \"0.54459167\",\n \"0.5443491\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\",\n \"0.54419565\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.561204"},"document_rank":{"kind":"string","value":"64"}}},{"rowIdx":19581,"cells":{"query":{"kind":"string","value":"Create a Plotly Dash 'A' element that downloads a file from the app."},"document":{"kind":"string","value":"def file_download_link(filename):\n location = f\"/{UPLOAD_DIRECTORY}/{filename}\"\n return html.A(filename, href=location)"},"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 file_download_link(filename):\n location = \"/download/{}\".format(urlquote(filename))\n return html.A(filename, href=location)","def download_link(request, job_id, filename):\n template_values = remote_view_util.fill_job_values(request, job_id)\n template_values = remote_view_util.fill_template_values(request, **template_values)\n template_values = catalog_view_util.fill_template_values(request, **template_values)\n template_values['title'] = 'Download area'\n template_values['file_name'] = filename\n return render_to_response('catalog/download_link.html',\n template_values)","def create_link(self):\n self.filename = App.get_running_app().root.ids.camera_screen.capture()\n self.url = FileSharer(self.filename).share()\n self.ids.label.text = self.url","def download(self,**attrs):\n\t\treturn super().download(**attrs)","def upload(cls, dashboard, filename, sharing=\"public\", auto_open=True):\n if sharing == \"public\":\n world_readable = True\n elif sharing == \"private\":\n world_readable = False\n elif sharing == \"secret\":\n world_readable = False\n\n data = {\n \"content\": json.dumps(dashboard),\n \"filename\": filename,\n \"world_readable\": world_readable,\n }\n\n file_info = _create_or_update(data, \"dashboard\")\n\n url = file_info[\"web_url\"]\n\n if sharing == \"secret\":\n url = add_share_key_to_url(url)\n\n if auto_open:\n webbrowser.open_new(file_info[\"web_url\"])\n\n return url","def createLink(self, downloadUrl, title):\n newUrl = downloadUrl.replace(\"details\", \"download\") \n return self.url + '/' + newUrl","def static(filename):\n return href.static(file=filename)","def download():\n \n browser.find_element_by_xpath('//*[@id=\"ctl00_contentPlaceHolder_divAllVariablesPerYear2012\"]/div[2]/div[2]/div[1]/a').click()","def generate(self, node, file):\n file.write(Html.generate_element('a', node.get_html_attributes(), node._argument))","def plot(figure_or_data, show_link=True, link_text='Export to plot.ly',\n validate=True, output_type='file', include_plotlyjs=True,\n filename='temp-plot.html', auto_open=True, image=None,\n image_filename='plot_image', image_width=800, image_height=600):\n if output_type not in ['div', 'file']:\n raise ValueError(\n \"`output_type` argument must be 'div' or 'file'. \"\n \"You supplied `\" + output_type + \"``\")\n if not filename.endswith('.html') and output_type == 'file':\n warnings.warn(\n \"Your filename `\" + filename + \"` didn't end with .html. \"\n \"Adding .html to the end of your file.\")\n filename += '.html'\n\n config = {}\n config['showLink'] = show_link\n config['linkText'] = link_text\n\n plot_html, plotdivid, width, height = _plot_html(\n figure_or_data, config, validate,\n '100%', '100%', global_requirejs=False)\n\n resize_script = ''\n if width == '100%' or height == '100%':\n resize_script = (\n ''\n '<script type=\"text/javascript\">'\n 'window.removeEventListener(\"resize\");'\n 'window.addEventListener(\"resize\", function(){{'\n 'Plotly.Plots.resize(document.getElementById(\"{id}\"));}});'\n '</script>'\n ).format(id=plotdivid)\n\n if output_type == 'file':\n with open(filename, 'w') as f:\n if include_plotlyjs:\n plotly_js_script = ''.join([\n '<script type=\"text/javascript\">',\n get_plotlyjs(),\n '</script>',\n ])\n else:\n plotly_js_script = ''\n\n if image:\n if image not in __IMAGE_FORMATS:\n raise ValueError('The image parameter must be one of the '\n 'following: {}'.format(__IMAGE_FORMATS))\n # if the check passes then download script is injected.\n # write the download script:\n script = get_image_download_script('plot')\n script = script.format(format=image,\n width=image_width,\n height=image_height,\n filename=image_filename,\n plot_id=plotdivid)\n else:\n script = ''\n\n f.write(''.join([\n '<html>',\n '<head><meta charset=\"utf-8\" /></head>',\n '<body>',\n plotly_js_script,\n plot_html,\n resize_script,\n script,\n '</body>',\n '</html>']))\n\n url = 'file://' + os.path.abspath(filename)\n if auto_open:\n webbrowser.open(url)\n\n return url\n\n elif output_type == 'div':\n if include_plotlyjs:\n return ''.join([\n '<div>',\n '<script type=\"text/javascript\">',\n get_plotlyjs(),\n '</script>',\n plot_html,\n '</div>'\n ])\n else:\n return plot_html","def download(self):\n\n with open(self.dataset_path) as dataset_file:\n dataset = json.load(dataset_file)\n\n path = \"\".join([POST_HIT_PATH, dataset[\"dataset\"][\"data_path\"]])\n if not os.path.exists(path):\n os.makedirs(path)\n\n protocole = dataset[\"dataset\"][\"protocole\"]\n\n download_links = []\n\n for resource in dataset[\"dataset\"][\"resources\"]:\n file_path = \"\".join([path, resource[\"filename\"]])\n\n #Check if the the download link has not been used before (One download link for all)\n if resource[\"download_link\"] not in download_links:\n \n print(\"DOWNLOADING : {}\".format(resource[\"filename\"]))\n f = urllib.request.urlopen(resource[\"download_link\"])\n data = f.read()\n with open(file_path, \"wb\") as donwload_file:\n donwload_file.write(data)\n\n download_links.append(resource[\"download_link\"])\n\n \n #Extract all files from the tar archives if necessary\n if tarfile.is_tarfile(file_path):\n tf = tarfile.open(file_path)\n tf.exractall()","def getDownloadLink(self):\n context = aq_inner(self.context)\n type = context.file.getContentType()\n extension = ''\n \n if BLOB_SUPPORT:\n if hasattr(context.file, 'getBlob'):\n # return a view that return the aquisition-wrapped object \n if type.startswith('audio/'):\n extension = '?e=.mp3'\n return context.absolute_url() + '/download' + extension\n \n # Fallback for media-files added before blob-support in operun.media.\n # context.file.absolute_url() doesn't return file-extensions, so we do some guessing. \n else:\n if type.startswith('audio/'):\n extension = '?e=.mp3'\n if type.startswith('video/'):\n extension = '?e=.flv'\n return context.file.absolute_url() + extension \n\n else:\n # get the file without plone.app.blob \n return context.absolute_url() + '/' + context.getFileName()","def handle_as_data_url(view: View, point: int, ext: str, encoded: str):\n\n # create a temporary file\n tmp_file = osp.join(TEMP_DIR, \"tmp_data_image.\" + ext)\n file_hash = int(hashlib.sha1(encoded.encode('utf-8')\n ).hexdigest(), 16) % (10 ** 8)\n name = str(file_hash) + \".\" + ext\n\n # Save downloaded data in the temporary file\n try:\n dst = open(tmp_file, \"wb\")\n dst.write(base64.b64decode(encoded))\n except Exception as e:\n print(e)\n return\n finally:\n dst.close()\n\n real_width, real_height, size = get_image_size(tmp_file)\n width, height = get_dimensions(view, tmp_file)\n size = str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'\n\n def on_navigate(href):\n\n if href == \"save\":\n save(tmp_file, name, \"data_url\")\n elif href == \"save_as\":\n convert(tmp_file, \"data_url\", name)\n else:\n sublime.active_window().open_file(tmp_file)\n\n view.show_popup(\n TEMPLATE % (width, height, ext, encoded, real_width, real_height, size),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def cli(ctx, dataset_collection_id, file_path):\n return ctx.gi.dataset_collections.download_dataset_collection(dataset_collection_id, file_path)","def get_download_url(self, ha):\n return create_ipa_url(ha)","def download_file(self, file_name):\n\n link = self.UTILS.element.getElement(('css selector', 'a[href=\"{}\"]'.format(file_name)),\n 'The file [{}] to download'.format(file_name), True, 10)\n link.tap()","def upload(cls, presentation, filename, sharing=\"public\", auto_open=True):\n if sharing == \"public\":\n world_readable = True\n elif sharing in [\"private\", \"secret\"]:\n world_readable = False\n else:\n raise _plotly_utils.exceptions.PlotlyError(SHARING_ERROR_MSG)\n data = {\n \"content\": json.dumps(presentation),\n \"filename\": filename,\n \"world_readable\": world_readable,\n }\n\n file_info = _create_or_update(data, \"spectacle_presentation\")\n\n url = file_info[\"web_url\"]\n\n if sharing == \"secret\":\n url = add_share_key_to_url(url)\n\n if auto_open:\n webbrowser.open_new(file_info[\"web_url\"])\n\n return url","def download(self, download_path):\n return","def create_figure_file():\n\n # Any plotly compliant dict or list that can be converted to json. You can use the Plotly python sdk to construct figures, by adding it to requirements.txt\n fig = {\"data\": [{\"x\": [\"giraffes\", \"orangutans\", \"monkeys\"],\n \"y\": [20, 14, 23],\n \"type\": \"bar\"\n }]}\n\n # Dump the dict to a plain text json file. Note that for more advanced data (e.g. including numpy arrays etc) you\n # may wish to use the serialiser provided with the plotly library\n name = analysis.output_dir + '/zoo_barchart.json'\n with open(name, 'w') as outfile:\n json.dump(fig, outfile)\n\n # You can either do this here, or in your main run() function definition (or basically anywhere else you like)...\n # but you need to add the created file (which is part of the analysis results) to the output results manifest. In\n # this case we do it here, which has the advantage of keeping file creation and manifesting together; but has the\n # disadvantage of needing to modify your code to pass the analysis around. If you're unable to alter the API of your\n # code; no problem - just do all your manifest creation separately (e.g. at the end of the run function)\n fig_data = {'name': name,\n 'short_caption': 'A shortened caption',\n 'caption': 'A longer caption, perhaps including some description of why on earth we would want to see a bar chart of different zoo animals'}\n # TODO add_to_manifest('figure', name, fig_data)","def download_link(self, handle):\n return None","def download():\n if auth.has_membership(1):\n user = \"Admin\"\n elif auth.has_membership(2):\n user = \"Examiner\"\n elif auth.has_membership(3):\n user = \"student\"\n elif auth.has_membership(5):\n user = \"Managment\"\n\n db.activity_log.insert( Title_entry=\"Download assignment\", \n referance_id=auth.user.id,\n remarks=\"content downloaded by {}\".format(user))\n db.commit()\n return response.download(request, db)","def download(self):\n pass","def download(self):\n pass","def download(args):\n with_dataset(args, Dataset._download)","def download_link(object_to_download, download_filename, download_link_text):\n if isinstance(object_to_download,pd.DataFrame):\n object_to_download = object_to_download.to_csv(index=False)\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode()).decode()\n\n return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","def download_link(object_to_download, download_filename, download_link_text):\n if isinstance(object_to_download,pd.DataFrame):\n object_to_download = object_to_download.to_csv(index=False)\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode()).decode()\n\n return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","def download_link(object_to_download, download_filename, download_link_text):\n if isinstance(object_to_download,pd.DataFrame):\n object_to_download = object_to_download.to_csv(index=False)\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode()).decode()\n\n return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","def download_file(self, parsed_event, input_dir_path):","def download_link(object_to_download, download_filename, download_link_text):\n if isinstance(object_to_download, pd.DataFrame):\n object_to_download = object_to_download.to_csv(index=False)\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode()).decode()\n\n return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","def download_link(object_to_download, download_filename, download_link_text):\n if isinstance(object_to_download, pd.DataFrame):\n object_to_download = object_to_download.to_csv(index=False, sep = ';')\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode(\"latin1\")).decode()\n\n return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","def start_download(url):\n return _add_download_to_deluge(url)","def url_to_file():\n urls = argToList(demisto.getArg('urls'))\n files = []\n for i in range(len(urls)):\n fileEntry = fileResult('url_' + str(i + 1), '[InternetShortcut]\\nURL=' + str(urls[i]))\n files.append(fileEntry)\n demisto.results(files)","def url(self):\n if not os.path.exists(self.path):\n self.save()\n return self.uset.url(os.path.join(self.folder, self.get_filename()))","def download():\n raise NotImplementedError","def export(request, slug):\n show = Show.objects.get(slug=slug)\n response = HttpResponse(show.data_file.read())\n response['Content-Disposition'] = f'attachment; filename={slug}.json'\n\n return response","def handle_as_data_url(view: sublime.View, point: int, ext: str, encoded: str):\n\n need_conversion = False\n # TODO: is this the only case ?\n if ext == \"svg+xml\":\n ext = \"svg\"\n need_conversion = True\n\n # create a temporary file\n temp_img = osp.join(TEMP_DIR, \"tmp_data_image.\" + ext)\n basename = str(int(hashlib.sha1(encoded.encode('utf-8')).hexdigest(), 16) % (10 ** 8))\n name = basename + \".\" + ext\n\n # Save downloaded data in the temporary file\n try:\n img = open(temp_img, \"wb\")\n img.write(base64.b64decode(encoded))\n except Exception as e:\n print(e)\n return\n finally:\n img.close()\n\n if need_conversion:\n ext = \".png\"\n\n conv_file = temp_img\n\n temp_png = osp.splitext(temp_img)[0] + \".png\"\n\n magick(temp_img, temp_png)\n\n with open(temp_png, \"rb\") as png:\n encoded = str(base64.b64encode(png.read()), \"utf-8\")\n\n temp_img = temp_png\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, name, \"data_url\")\n else:\n save(temp_img, name, \"data_url\")\n elif href == \"save_as\":\n if need_conversion:\n convert(conv_file, \"dat_url\", name)\n else:\n convert(temp_img, \"data_url\", name)\n else:\n sublime.active_window().open_file(temp_img)\n\n width, height, real_width, real_height, size = get_data(view, temp_img)\n\n view.show_popup(\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\n str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def download(self, *args, **kwargs):\n return wb.download(*args, **kwargs)","def menu_download_blender(self, event=None):\n self.link('http://www.blender.org/download/get-blender')","def restricted_download():\n aaa.require(fail_redirect='/login')\n return bottle.static_file('static_file', root='.')","def download(ctx: click.Context, **kwargs):\n root_commands.cmd_download(ctx.obj, **kwargs)","def download_files(self):","def download(self, account, code):\n\n url = Spider.BASE_URL + \"/p/%s/?taken-by=%s\" % (code, account)\n r = self.session.get(url)\n content_match = re.search(r\"<script.*?>\\s*?window._sharedData\\s*?=\\s*?({.*}).*?</script>\", r.text,\n re.MULTILINE)\n data = json.loads(content_match.group(1))\n media = data['entry_data']['PostPage'][0]['graphql']['shortcode_media']\n download_urls = []\n if media['__typename'] == 'GraphVideo': # video\n download_urls.append(media[\"video_url\"])\n if media['__typename'] == 'GraphImage': # image\n download_urls.append(media[\"display_url\"])\n if media['__typename'] == 'GraphSidecar': # slide\n nodes = media['edge_sidecar_to_children']['edges']\n for node in nodes:\n node = node['node']\n if node['is_video']:\n download_urls.append(node['video_url'])\n else:\n download_urls.append(node['display_url'])\n\n actual_download_dir = os.path.join(download_dir, account)\n if not os.path.isdir(actual_download_dir):\n os.mkdir(actual_download_dir)\n for url in download_urls:\n filename = os.path.join(actual_download_dir, url.split('/')[-1].split('?')[0])\n temp_name = filename + '.tmp'\n if os.path.isfile(filename):\n if self.spider.auto_stop:\n print('file', filename, \"already exists, exiting......\")\n sys.exit()\n print('file', filename, \"already exists, skipping\")\n else:\n print('downloading %s:' % filename)\n r = self.session.get(url, stream=True)\n content_length = int(r.headers['content-length'])\n curr = 0\n with open(temp_name, 'wb') as f:\n for chunk in r.iter_content(chunk_size=1024):\n f.write(chunk)\n curr += 1024\n progress(curr, content_length)\n os.rename(temp_name, filename)\n self.spider.item_count += 1","def package(request, name):\n return HttpResponse(get_koji_download_url(name), mimetype='application/json')","def package(request, name):\n return HttpResponse(get_koji_download_url(name), mimetype='application/json')","def _activity_endpoint(self, athlete, filename):\n return '{host}{athlete}/activity/{filename}'.format(\n host=self.host,\n athlete=quote_plus(athlete),\n filename=filename\n )","def get_table_download_link(df,location):\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\n href = f\"\"\"\n <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css\">\n <a href=\"data:file/csv;base64,{b64}\" download=\"{location}.csv\" style='text-decoration: inherit;'>\n <button style=\"background-color: DodgerBlue;border: none;color: white;padding: 12px 30px;cursor: pointer;font-size: 20px; display: block; \n margin-left: auto; font-size:100%;\n margin-right: auto;\n width: 40%;\"><i class=\"fa fa-download\"></i> Download {location}</button>\n </a>\n \n \"\"\"\n return href","def runBrowser(driver, url):\n\tdriver.get(url)\n\ttime.sleep(3) #REACT app need to sleep and wait app load.\n\tall_links=driver.execute_script('all_links = []; links = document.querySelectorAll(\".style-module--action--1Avvt>a\"); links.forEach(url => all_links.push(url.href)); return all_links');\n\tbar = IncrementalBar('📥 Icons Downloaded', max = len(all_links))\n\t\n\tfor i, link in enumerate(all_links):\n\t\tdriver.execute_script('''window.open(\"'''+link+'''\",\"_blank\");''')\n\t\tbar.next()\n\tprint('\\n')\n\tdriver.close()\n\tMessage.success('🎉 Download done!')","def download_file():\n\n if 'POST' == request.method:\n file_id = request.form['file_id']\n else:\n file_id = request.args.get('file_id')\n\n # 1 ==> example_1.tgz\n file_path = file_manager.get_file_path_from_id(file_id)\n print \"serving file: \" + file_path\n return send_file(file_path, as_attachment=True)","def download_with_callback(self, url, path=None, filename=None, headers=None, force=False, func=None):","def download(dataset_revision):\n return reverse('manageDatasets.download', 'microsites.urls',\n kwargs={'dataset_id': str(dataset_revision['dataset_id']), 'slug': dataset_revision['slug']})","def download():\n scope = request.args.get('scope')\n type = request.args.get('type')\n id = request.args.get('id')\n version = request.args.get('version')\n panel_name = request.args.get('name')\n\n if type == 'default':\n extension = 0\n else:\n extension = 25\n\n if scope == 'Panel':\n panel = get_regions_by_panelid(s, id, version, extension)\n elif scope == 'Virtual':\n panel = get_regions_by_vpanelid(s, id, version, extension)\n\n result = []\n\n for i in panel:\n line = []\n line.append(i.chrom)\n line.append(str(i.region_start))\n line.append(str(i.region_end))\n if not i.gene_name == '' and not i.gene_name == 'N/A':\n line.append(i.gene_name + ':' + i.name)\n else:\n line.append(i.name)\n result.append(line)\n\n bed = '\\n'.join(['\\t'.join(l) for l in result])\n\n bed_tool = BedTool(bed, from_string=True)\n bed_sorted = bed_tool.sort()\n bed_sorted_merged = bed_sorted.merge(c=4, o='collapse')\n\n if type == 'design':\n bed = create_design(bed_sorted_merged)\n filename = \"attachment; filename=\" + panel_name + \"_25bp_v\" + version + \"_\" + current_user.id + \"_\" + time.strftime(\n \"%d-%m-%Y\") + \".txt\"\n else:\n bed = create_bed(bed_sorted_merged)\n if type == \"extension\":\n filename = \"attachment; filename=\" + panel_name + \"_25bp_v\" + version + \"_\" + current_user.id + \"_\" + time.strftime(\n \"%d-%m-%Y\") + \".bed\"\n else:\n filename = \"attachment; filename=\" + panel_name + \"_v\" + version + \"_\" + current_user.id + \"_\" + time.strftime(\n \"%d-%m-%Y\") + \".bed\"\n\n return Response(\n bed,\n mimetype='test/plain',\n headers={\"Content-disposition\": filename\n }\n )","def get_table_download_link(df, file_name):\n if 'embedding_average' in df.columns:\n df = df.drop(columns='embedding_average')\n # df = results_output.drop(columns='embedding_average')\n # csv = df.to_csv(index=False)\n # b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\n # href = f'<a href=\"data:file/csv;base64,{encoded}\">Download Excel File</a> (right-click and save as <some_name>.csv)'\n # href = f'<a href=\"data:file/csv;base64,{b64}\">Download CSV File</a> (right-click and save as <some_name>.csv)'\n towrite = io.BytesIO()\n df.to_excel(towrite,index = False, encoding = 'UTF-8') # write to BytesIO buffer\n towrite.seek(0) # reset pointer\n encoded = base64.b64encode(towrite.read()).decode() # encoded object\n href = f'<a href=\"data:file/csv;base64,{encoded}\" download =\"{file_name}\">Download Excel File</a> (right-click and save as <some_name>.csv)'\n st.markdown(href, unsafe_allow_html=True)","def download_url(self, fname):\n if not fname in self.data:\n return ''\n url = '/'.join([\n self.context.absolute_url(),\n '@@download-file',\n self.id + ':' + fname\n ])\n return url","def get_table_download_link(df):\r\n\tval = to_excel(df)\r\n\tb64 = base64.b64encode(val).decode() # val looks like b'...'\r\n\thref=f'<a href=\"data:application/octet-stream;base64,{b64}\" download=\"captura.xlsx\" target=\"_blank\">Descargar: Haga clic derecho y guardar enlace como...</a>' # decode b'abc' => abc\t\r\n\treturn href","def main(url, localfile):\n ph.download_file(url, localfile)","def filedownload(df,filename): \n filename=filename + \".csv\"\n csv = df.to_csv(index=False)\n b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions\n href = f'<a href=\"data:file/csv;base64,{b64}\" download=%s>Download %s File</a>'%(filename,filename)\n return href","def download_it(fw, acquisition, file_name, input_path):\n\n safe = make_file_name_safe(file_name, replace_str='_')\n\n full_path = input_path + safe\n\n if acquisition.timestamp:\n if acquisition.timezone:\n created = acquisition.original_timestamp.isoformat()\n else:\n created = acquisition.timestamp.isoformat()\n else:\n created = 'unknown'\n\n rpt = 1\n while full_path in context.gear_dict['niftis']: # then repeated name\n full_path = input_path + str(rpt) + '_' + safe\n rpt += 1\n\n if os.path.isfile(full_path):\n log.info('File exists ' + file_name + ' -> ' +\\\n full_path + ' created ' + created)\n else:\n log.info('Downloading ' + file_name + ' -> ' +\\\n full_path + ' created ' + created)\n acquisition.download_file(file_name, full_path)\n\n full_file = fw.get_acquisition_file_info(acquisition.id, file_name)\n field_strength = full_file.info.get('MagneticFieldStrength')\n\n context.gear_dict['niftis'].append(full_path)\n context.gear_dict['file_names'].append(file_name)\n context.gear_dict['createds'].append(created)\n context.gear_dict['field_strength'].append(field_strength)","def get_url(self, filename, query_para=None, **url_args):\r\n from uliweb.core.html import Tag\r\n \r\n title = url_args.pop('title', filename)\r\n text = url_args.pop('text', title)\r\n query_para = query_para or {}\r\n return str(Tag('a', title, href=self.get_href(filename, **query_para), **url_args))","def download(exam, out, name_question, sid_question, compact):\n exam_json, template_questions, email_to_data_map, total = examtool.api.download.download(exam)\n examtool.api.download.export(template_questions, email_to_data_map, total, exam, out, name_question, sid_question, compact)","def download_link(object_to_download, download_filename, download_link_text):\n if isinstance(object_to_download,pd.DataFrame):\n object_to_download = object_to_download.to_csv(index=False)\n\n # some strings <-> bytes conversions necessary here\n b64 = base64.b64encode(object_to_download.encode()).decode()\n\n return f'<a href=\"data:file/csv;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'","def x_download():\n\t#_loadconfig()\n\tconf = _get_config()\n\t#print conf['xplane']\n\tdownload_url = conf['xplane']['download']\n\tlocal(\"wget -P %s %s\" % (navimport.conf.work_dir(\"/xplane_zips\"), download_url))","def ferry_data_download(URL):\n explanation = 'File exists'\n file_downloaded = True\n # Request if the thredds server is working, add .html to URL\n req = requests.get(URL + '.html')\n if req.status_code == 200:\n \"\"\"File exists and is good for download, so write file\"\"\"\n print('File is ok')\n explanation = 'Good URL, File downloaded'\n file_downloaded = True\n ferry = xr.open_dataset(URL)\n else:\n print('File not found or unavailable')\n explanation = ' File not found or unavailable'\n file_downloaded = False\n ferry = np.nan\n return (ferry, file_downloaded, explanation)","def _serve_file(self, abspath, params):\r\n relpath = os.path.relpath(abspath, self._root)\r\n breadcrumbs = self._create_breadcrumbs(relpath)\r\n link_path = urlparse.urlunparse([None, None, relpath, None, urllib.urlencode(params), None])\r\n args = self._default_template_args('file')\r\n args.update({ 'root_parent': os.path.dirname(self._root),\r\n 'breadcrumbs': breadcrumbs,\r\n 'link_path': link_path })\r\n self._send_content(self._renderer.render_name('base', args), 'text/html')","def _get_url(self, docket_number: str, docketEntryId: str) -> str:\n self.url = f\"https://public-api-green.dawson.ustaxcourt.gov/public-api/{docket_number}/{docketEntryId}/public-document-download-url\"\n if self.test_mode_enabled():\n # Don't fetch urls when running tests. Because it requires\n # a second api request.\n return self.url\n pdf_url = super()._download()[\"url\"]\n return pdf_url","def on_click(self) -> None:\n os.startfile(self.url) # noqa: S606","def save_local_interactive(fig: go.Figure, name):\n from plotly.offline import plot\n plot(fig, filename=name)","def _download_from_web(*, ds_name: str, ds_path: Path):\n import cgi\n import zipfile\n import httpx\n from tqdm import tqdm\n\n url = DATASET_OPTIONS[ds_name]['web']\n if ds_path.exists():\n print('Dataset directory already exists; remove it if you wish to '\n 're-download the dataset')\n return\n\n ds_path.mkdir(parents=True, exist_ok=True)\n\n with httpx.Client() as client:\n with client.stream('GET', url=url) as response:\n if not response.is_error:\n pass # All good!\n else:\n raise RuntimeError(\n f'Error {response.status_code} when trying '\n f'to download {url}')\n\n\n header = response.headers['content-disposition']\n _, params = cgi.parse_header(header)\n # where to store the archive\n outfile = ds_path / params['filename']\n remote_file_size = int(response.headers['content-length'])\n\n with open(outfile, mode='wb') as f:\n with tqdm(desc=params['filename'], initial=0,\n total=remote_file_size, unit='B',\n unit_scale=True, unit_divisor=1024,\n leave=False) as progress:\n num_bytes_downloaded = response.num_bytes_downloaded\n\n for chunk in response.iter_bytes():\n f.write(chunk)\n progress.update(response.num_bytes_downloaded -\n num_bytes_downloaded)\n num_bytes_downloaded = (response\n .num_bytes_downloaded)\n\n assert outfile.suffix == '.zip'\n\n with zipfile.ZipFile(outfile) as zip:\n for zip_info in zip.infolist():\n path_in_zip = Path(zip_info.filename)\n # omit top-level directory from Zip archive\n target_path = str(Path(*path_in_zip.parts[1:]))\n if str(target_path) in ('.', '..'):\n continue\n if zip_info.filename.endswith('/'):\n (ds_path / target_path).mkdir(parents=True, exist_ok=True)\n continue\n zip_info.filename = target_path\n print(f'Extracting: {target_path}')\n zip.extract(zip_info, ds_path)\n\n outfile.unlink()","def download_file(client, file_id):\n\n file_content = client.file(file_id).content()\n print(file_content)","def download_file(self):\r\n file_path = filedialog.askopenfilename(initialdir=\"/\",\r\n title=\"Select a File\",\r\n filetypes=((\"Torrent files\", \"*.torrent\"), (\"all files\", \"*.*\")))\r\n\r\n if file_path:\r\n download_bar = DownloadProgressBar(self.scroll_frame.interior, file_path)\r\n download_bar.pack()\r\n download_bar.start()","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)"],"string":"[\n \"def file_download_link(filename):\\n location = \\\"/download/{}\\\".format(urlquote(filename))\\n return html.A(filename, href=location)\",\n \"def download_link(request, job_id, filename):\\n template_values = remote_view_util.fill_job_values(request, job_id)\\n template_values = remote_view_util.fill_template_values(request, **template_values)\\n template_values = catalog_view_util.fill_template_values(request, **template_values)\\n template_values['title'] = 'Download area'\\n template_values['file_name'] = filename\\n return render_to_response('catalog/download_link.html',\\n template_values)\",\n \"def create_link(self):\\n self.filename = App.get_running_app().root.ids.camera_screen.capture()\\n self.url = FileSharer(self.filename).share()\\n self.ids.label.text = self.url\",\n \"def download(self,**attrs):\\n\\t\\treturn super().download(**attrs)\",\n \"def upload(cls, dashboard, filename, sharing=\\\"public\\\", auto_open=True):\\n if sharing == \\\"public\\\":\\n world_readable = True\\n elif sharing == \\\"private\\\":\\n world_readable = False\\n elif sharing == \\\"secret\\\":\\n world_readable = False\\n\\n data = {\\n \\\"content\\\": json.dumps(dashboard),\\n \\\"filename\\\": filename,\\n \\\"world_readable\\\": world_readable,\\n }\\n\\n file_info = _create_or_update(data, \\\"dashboard\\\")\\n\\n url = file_info[\\\"web_url\\\"]\\n\\n if sharing == \\\"secret\\\":\\n url = add_share_key_to_url(url)\\n\\n if auto_open:\\n webbrowser.open_new(file_info[\\\"web_url\\\"])\\n\\n return url\",\n \"def createLink(self, downloadUrl, title):\\n newUrl = downloadUrl.replace(\\\"details\\\", \\\"download\\\") \\n return self.url + '/' + newUrl\",\n \"def static(filename):\\n return href.static(file=filename)\",\n \"def download():\\n \\n browser.find_element_by_xpath('//*[@id=\\\"ctl00_contentPlaceHolder_divAllVariablesPerYear2012\\\"]/div[2]/div[2]/div[1]/a').click()\",\n \"def generate(self, node, file):\\n file.write(Html.generate_element('a', node.get_html_attributes(), node._argument))\",\n \"def plot(figure_or_data, show_link=True, link_text='Export to plot.ly',\\n validate=True, output_type='file', include_plotlyjs=True,\\n filename='temp-plot.html', auto_open=True, image=None,\\n image_filename='plot_image', image_width=800, image_height=600):\\n if output_type not in ['div', 'file']:\\n raise ValueError(\\n \\\"`output_type` argument must be 'div' or 'file'. \\\"\\n \\\"You supplied `\\\" + output_type + \\\"``\\\")\\n if not filename.endswith('.html') and output_type == 'file':\\n warnings.warn(\\n \\\"Your filename `\\\" + filename + \\\"` didn't end with .html. \\\"\\n \\\"Adding .html to the end of your file.\\\")\\n filename += '.html'\\n\\n config = {}\\n config['showLink'] = show_link\\n config['linkText'] = link_text\\n\\n plot_html, plotdivid, width, height = _plot_html(\\n figure_or_data, config, validate,\\n '100%', '100%', global_requirejs=False)\\n\\n resize_script = ''\\n if width == '100%' or height == '100%':\\n resize_script = (\\n ''\\n '<script type=\\\"text/javascript\\\">'\\n 'window.removeEventListener(\\\"resize\\\");'\\n 'window.addEventListener(\\\"resize\\\", function(){{'\\n 'Plotly.Plots.resize(document.getElementById(\\\"{id}\\\"));}});'\\n '</script>'\\n ).format(id=plotdivid)\\n\\n if output_type == 'file':\\n with open(filename, 'w') as f:\\n if include_plotlyjs:\\n plotly_js_script = ''.join([\\n '<script type=\\\"text/javascript\\\">',\\n get_plotlyjs(),\\n '</script>',\\n ])\\n else:\\n plotly_js_script = ''\\n\\n if image:\\n if image not in __IMAGE_FORMATS:\\n raise ValueError('The image parameter must be one of the '\\n 'following: {}'.format(__IMAGE_FORMATS))\\n # if the check passes then download script is injected.\\n # write the download script:\\n script = get_image_download_script('plot')\\n script = script.format(format=image,\\n width=image_width,\\n height=image_height,\\n filename=image_filename,\\n plot_id=plotdivid)\\n else:\\n script = ''\\n\\n f.write(''.join([\\n '<html>',\\n '<head><meta charset=\\\"utf-8\\\" /></head>',\\n '<body>',\\n plotly_js_script,\\n plot_html,\\n resize_script,\\n script,\\n '</body>',\\n '</html>']))\\n\\n url = 'file://' + os.path.abspath(filename)\\n if auto_open:\\n webbrowser.open(url)\\n\\n return url\\n\\n elif output_type == 'div':\\n if include_plotlyjs:\\n return ''.join([\\n '<div>',\\n '<script type=\\\"text/javascript\\\">',\\n get_plotlyjs(),\\n '</script>',\\n plot_html,\\n '</div>'\\n ])\\n else:\\n return plot_html\",\n \"def download(self):\\n\\n with open(self.dataset_path) as dataset_file:\\n dataset = json.load(dataset_file)\\n\\n path = \\\"\\\".join([POST_HIT_PATH, dataset[\\\"dataset\\\"][\\\"data_path\\\"]])\\n if not os.path.exists(path):\\n os.makedirs(path)\\n\\n protocole = dataset[\\\"dataset\\\"][\\\"protocole\\\"]\\n\\n download_links = []\\n\\n for resource in dataset[\\\"dataset\\\"][\\\"resources\\\"]:\\n file_path = \\\"\\\".join([path, resource[\\\"filename\\\"]])\\n\\n #Check if the the download link has not been used before (One download link for all)\\n if resource[\\\"download_link\\\"] not in download_links:\\n \\n print(\\\"DOWNLOADING : {}\\\".format(resource[\\\"filename\\\"]))\\n f = urllib.request.urlopen(resource[\\\"download_link\\\"])\\n data = f.read()\\n with open(file_path, \\\"wb\\\") as donwload_file:\\n donwload_file.write(data)\\n\\n download_links.append(resource[\\\"download_link\\\"])\\n\\n \\n #Extract all files from the tar archives if necessary\\n if tarfile.is_tarfile(file_path):\\n tf = tarfile.open(file_path)\\n tf.exractall()\",\n \"def getDownloadLink(self):\\n context = aq_inner(self.context)\\n type = context.file.getContentType()\\n extension = ''\\n \\n if BLOB_SUPPORT:\\n if hasattr(context.file, 'getBlob'):\\n # return a view that return the aquisition-wrapped object \\n if type.startswith('audio/'):\\n extension = '?e=.mp3'\\n return context.absolute_url() + '/download' + extension\\n \\n # Fallback for media-files added before blob-support in operun.media.\\n # context.file.absolute_url() doesn't return file-extensions, so we do some guessing. \\n else:\\n if type.startswith('audio/'):\\n extension = '?e=.mp3'\\n if type.startswith('video/'):\\n extension = '?e=.flv'\\n return context.file.absolute_url() + extension \\n\\n else:\\n # get the file without plone.app.blob \\n return context.absolute_url() + '/' + context.getFileName()\",\n \"def handle_as_data_url(view: View, point: int, ext: str, encoded: str):\\n\\n # create a temporary file\\n tmp_file = osp.join(TEMP_DIR, \\\"tmp_data_image.\\\" + ext)\\n file_hash = int(hashlib.sha1(encoded.encode('utf-8')\\n ).hexdigest(), 16) % (10 ** 8)\\n name = str(file_hash) + \\\".\\\" + ext\\n\\n # Save downloaded data in the temporary file\\n try:\\n dst = open(tmp_file, \\\"wb\\\")\\n dst.write(base64.b64decode(encoded))\\n except Exception as e:\\n print(e)\\n return\\n finally:\\n dst.close()\\n\\n real_width, real_height, size = get_image_size(tmp_file)\\n width, height = get_dimensions(view, tmp_file)\\n size = str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n save(tmp_file, name, \\\"data_url\\\")\\n elif href == \\\"save_as\\\":\\n convert(tmp_file, \\\"data_url\\\", name)\\n else:\\n sublime.active_window().open_file(tmp_file)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, ext, encoded, real_width, real_height, size),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def cli(ctx, dataset_collection_id, file_path):\\n return ctx.gi.dataset_collections.download_dataset_collection(dataset_collection_id, file_path)\",\n \"def get_download_url(self, ha):\\n return create_ipa_url(ha)\",\n \"def download_file(self, file_name):\\n\\n link = self.UTILS.element.getElement(('css selector', 'a[href=\\\"{}\\\"]'.format(file_name)),\\n 'The file [{}] to download'.format(file_name), True, 10)\\n link.tap()\",\n \"def upload(cls, presentation, filename, sharing=\\\"public\\\", auto_open=True):\\n if sharing == \\\"public\\\":\\n world_readable = True\\n elif sharing in [\\\"private\\\", \\\"secret\\\"]:\\n world_readable = False\\n else:\\n raise _plotly_utils.exceptions.PlotlyError(SHARING_ERROR_MSG)\\n data = {\\n \\\"content\\\": json.dumps(presentation),\\n \\\"filename\\\": filename,\\n \\\"world_readable\\\": world_readable,\\n }\\n\\n file_info = _create_or_update(data, \\\"spectacle_presentation\\\")\\n\\n url = file_info[\\\"web_url\\\"]\\n\\n if sharing == \\\"secret\\\":\\n url = add_share_key_to_url(url)\\n\\n if auto_open:\\n webbrowser.open_new(file_info[\\\"web_url\\\"])\\n\\n return url\",\n \"def download(self, download_path):\\n return\",\n \"def create_figure_file():\\n\\n # Any plotly compliant dict or list that can be converted to json. You can use the Plotly python sdk to construct figures, by adding it to requirements.txt\\n fig = {\\\"data\\\": [{\\\"x\\\": [\\\"giraffes\\\", \\\"orangutans\\\", \\\"monkeys\\\"],\\n \\\"y\\\": [20, 14, 23],\\n \\\"type\\\": \\\"bar\\\"\\n }]}\\n\\n # Dump the dict to a plain text json file. Note that for more advanced data (e.g. including numpy arrays etc) you\\n # may wish to use the serialiser provided with the plotly library\\n name = analysis.output_dir + '/zoo_barchart.json'\\n with open(name, 'w') as outfile:\\n json.dump(fig, outfile)\\n\\n # You can either do this here, or in your main run() function definition (or basically anywhere else you like)...\\n # but you need to add the created file (which is part of the analysis results) to the output results manifest. In\\n # this case we do it here, which has the advantage of keeping file creation and manifesting together; but has the\\n # disadvantage of needing to modify your code to pass the analysis around. If you're unable to alter the API of your\\n # code; no problem - just do all your manifest creation separately (e.g. at the end of the run function)\\n fig_data = {'name': name,\\n 'short_caption': 'A shortened caption',\\n 'caption': 'A longer caption, perhaps including some description of why on earth we would want to see a bar chart of different zoo animals'}\\n # TODO add_to_manifest('figure', name, fig_data)\",\n \"def download_link(self, handle):\\n return None\",\n \"def download():\\n if auth.has_membership(1):\\n user = \\\"Admin\\\"\\n elif auth.has_membership(2):\\n user = \\\"Examiner\\\"\\n elif auth.has_membership(3):\\n user = \\\"student\\\"\\n elif auth.has_membership(5):\\n user = \\\"Managment\\\"\\n\\n db.activity_log.insert( Title_entry=\\\"Download assignment\\\", \\n referance_id=auth.user.id,\\n remarks=\\\"content downloaded by {}\\\".format(user))\\n db.commit()\\n return response.download(request, db)\",\n \"def download(self):\\n pass\",\n \"def download(self):\\n pass\",\n \"def download(args):\\n with_dataset(args, Dataset._download)\",\n \"def download_link(object_to_download, download_filename, download_link_text):\\n if isinstance(object_to_download,pd.DataFrame):\\n object_to_download = object_to_download.to_csv(index=False)\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode()).decode()\\n\\n return f'<a href=\\\"data:file/txt;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"def download_link(object_to_download, download_filename, download_link_text):\\n if isinstance(object_to_download,pd.DataFrame):\\n object_to_download = object_to_download.to_csv(index=False)\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode()).decode()\\n\\n return f'<a href=\\\"data:file/txt;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"def download_link(object_to_download, download_filename, download_link_text):\\n if isinstance(object_to_download,pd.DataFrame):\\n object_to_download = object_to_download.to_csv(index=False)\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode()).decode()\\n\\n return f'<a href=\\\"data:file/txt;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"def download_file(self, parsed_event, input_dir_path):\",\n \"def download_link(object_to_download, download_filename, download_link_text):\\n if isinstance(object_to_download, pd.DataFrame):\\n object_to_download = object_to_download.to_csv(index=False)\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode()).decode()\\n\\n return f'<a href=\\\"data:file/txt;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"def download_link(object_to_download, download_filename, download_link_text):\\n if isinstance(object_to_download, pd.DataFrame):\\n object_to_download = object_to_download.to_csv(index=False, sep = ';')\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode(\\\"latin1\\\")).decode()\\n\\n return f'<a href=\\\"data:file/txt;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"def start_download(url):\\n return _add_download_to_deluge(url)\",\n \"def url_to_file():\\n urls = argToList(demisto.getArg('urls'))\\n files = []\\n for i in range(len(urls)):\\n fileEntry = fileResult('url_' + str(i + 1), '[InternetShortcut]\\\\nURL=' + str(urls[i]))\\n files.append(fileEntry)\\n demisto.results(files)\",\n \"def url(self):\\n if not os.path.exists(self.path):\\n self.save()\\n return self.uset.url(os.path.join(self.folder, self.get_filename()))\",\n \"def download():\\n raise NotImplementedError\",\n \"def export(request, slug):\\n show = Show.objects.get(slug=slug)\\n response = HttpResponse(show.data_file.read())\\n response['Content-Disposition'] = f'attachment; filename={slug}.json'\\n\\n return response\",\n \"def handle_as_data_url(view: sublime.View, point: int, ext: str, encoded: str):\\n\\n need_conversion = False\\n # TODO: is this the only case ?\\n if ext == \\\"svg+xml\\\":\\n ext = \\\"svg\\\"\\n need_conversion = True\\n\\n # create a temporary file\\n temp_img = osp.join(TEMP_DIR, \\\"tmp_data_image.\\\" + ext)\\n basename = str(int(hashlib.sha1(encoded.encode('utf-8')).hexdigest(), 16) % (10 ** 8))\\n name = basename + \\\".\\\" + ext\\n\\n # Save downloaded data in the temporary file\\n try:\\n img = open(temp_img, \\\"wb\\\")\\n img.write(base64.b64decode(encoded))\\n except Exception as e:\\n print(e)\\n return\\n finally:\\n img.close()\\n\\n if need_conversion:\\n ext = \\\".png\\\"\\n\\n conv_file = temp_img\\n\\n temp_png = osp.splitext(temp_img)[0] + \\\".png\\\"\\n\\n magick(temp_img, temp_png)\\n\\n with open(temp_png, \\\"rb\\\") as png:\\n encoded = str(base64.b64encode(png.read()), \\\"utf-8\\\")\\n\\n temp_img = temp_png\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, name, \\\"data_url\\\")\\n else:\\n save(temp_img, name, \\\"data_url\\\")\\n elif href == \\\"save_as\\\":\\n if need_conversion:\\n convert(conv_file, \\\"dat_url\\\", name)\\n else:\\n convert(temp_img, \\\"data_url\\\", name)\\n else:\\n sublime.active_window().open_file(temp_img)\\n\\n width, height, real_width, real_height, size = get_data(view, temp_img)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\\n str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def download(self, *args, **kwargs):\\n return wb.download(*args, **kwargs)\",\n \"def menu_download_blender(self, event=None):\\n self.link('http://www.blender.org/download/get-blender')\",\n \"def restricted_download():\\n aaa.require(fail_redirect='/login')\\n return bottle.static_file('static_file', root='.')\",\n \"def download(ctx: click.Context, **kwargs):\\n root_commands.cmd_download(ctx.obj, **kwargs)\",\n \"def download_files(self):\",\n \"def download(self, account, code):\\n\\n url = Spider.BASE_URL + \\\"/p/%s/?taken-by=%s\\\" % (code, account)\\n r = self.session.get(url)\\n content_match = re.search(r\\\"<script.*?>\\\\s*?window._sharedData\\\\s*?=\\\\s*?({.*}).*?</script>\\\", r.text,\\n re.MULTILINE)\\n data = json.loads(content_match.group(1))\\n media = data['entry_data']['PostPage'][0]['graphql']['shortcode_media']\\n download_urls = []\\n if media['__typename'] == 'GraphVideo': # video\\n download_urls.append(media[\\\"video_url\\\"])\\n if media['__typename'] == 'GraphImage': # image\\n download_urls.append(media[\\\"display_url\\\"])\\n if media['__typename'] == 'GraphSidecar': # slide\\n nodes = media['edge_sidecar_to_children']['edges']\\n for node in nodes:\\n node = node['node']\\n if node['is_video']:\\n download_urls.append(node['video_url'])\\n else:\\n download_urls.append(node['display_url'])\\n\\n actual_download_dir = os.path.join(download_dir, account)\\n if not os.path.isdir(actual_download_dir):\\n os.mkdir(actual_download_dir)\\n for url in download_urls:\\n filename = os.path.join(actual_download_dir, url.split('/')[-1].split('?')[0])\\n temp_name = filename + '.tmp'\\n if os.path.isfile(filename):\\n if self.spider.auto_stop:\\n print('file', filename, \\\"already exists, exiting......\\\")\\n sys.exit()\\n print('file', filename, \\\"already exists, skipping\\\")\\n else:\\n print('downloading %s:' % filename)\\n r = self.session.get(url, stream=True)\\n content_length = int(r.headers['content-length'])\\n curr = 0\\n with open(temp_name, 'wb') as f:\\n for chunk in r.iter_content(chunk_size=1024):\\n f.write(chunk)\\n curr += 1024\\n progress(curr, content_length)\\n os.rename(temp_name, filename)\\n self.spider.item_count += 1\",\n \"def package(request, name):\\n return HttpResponse(get_koji_download_url(name), mimetype='application/json')\",\n \"def package(request, name):\\n return HttpResponse(get_koji_download_url(name), mimetype='application/json')\",\n \"def _activity_endpoint(self, athlete, filename):\\n return '{host}{athlete}/activity/{filename}'.format(\\n host=self.host,\\n athlete=quote_plus(athlete),\\n filename=filename\\n )\",\n \"def get_table_download_link(df,location):\\n csv = df.to_csv(index=False)\\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\\n href = f\\\"\\\"\\\"\\n <link rel=\\\"stylesheet\\\" href=\\\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css\\\">\\n <a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"{location}.csv\\\" style='text-decoration: inherit;'>\\n <button style=\\\"background-color: DodgerBlue;border: none;color: white;padding: 12px 30px;cursor: pointer;font-size: 20px; display: block; \\n margin-left: auto; font-size:100%;\\n margin-right: auto;\\n width: 40%;\\\"><i class=\\\"fa fa-download\\\"></i> Download {location}</button>\\n </a>\\n \\n \\\"\\\"\\\"\\n return href\",\n \"def runBrowser(driver, url):\\n\\tdriver.get(url)\\n\\ttime.sleep(3) #REACT app need to sleep and wait app load.\\n\\tall_links=driver.execute_script('all_links = []; links = document.querySelectorAll(\\\".style-module--action--1Avvt>a\\\"); links.forEach(url => all_links.push(url.href)); return all_links');\\n\\tbar = IncrementalBar('📥 Icons Downloaded', max = len(all_links))\\n\\t\\n\\tfor i, link in enumerate(all_links):\\n\\t\\tdriver.execute_script('''window.open(\\\"'''+link+'''\\\",\\\"_blank\\\");''')\\n\\t\\tbar.next()\\n\\tprint('\\\\n')\\n\\tdriver.close()\\n\\tMessage.success('🎉 Download done!')\",\n \"def download_file():\\n\\n if 'POST' == request.method:\\n file_id = request.form['file_id']\\n else:\\n file_id = request.args.get('file_id')\\n\\n # 1 ==> example_1.tgz\\n file_path = file_manager.get_file_path_from_id(file_id)\\n print \\\"serving file: \\\" + file_path\\n return send_file(file_path, as_attachment=True)\",\n \"def download_with_callback(self, url, path=None, filename=None, headers=None, force=False, func=None):\",\n \"def download(dataset_revision):\\n return reverse('manageDatasets.download', 'microsites.urls',\\n kwargs={'dataset_id': str(dataset_revision['dataset_id']), 'slug': dataset_revision['slug']})\",\n \"def download():\\n scope = request.args.get('scope')\\n type = request.args.get('type')\\n id = request.args.get('id')\\n version = request.args.get('version')\\n panel_name = request.args.get('name')\\n\\n if type == 'default':\\n extension = 0\\n else:\\n extension = 25\\n\\n if scope == 'Panel':\\n panel = get_regions_by_panelid(s, id, version, extension)\\n elif scope == 'Virtual':\\n panel = get_regions_by_vpanelid(s, id, version, extension)\\n\\n result = []\\n\\n for i in panel:\\n line = []\\n line.append(i.chrom)\\n line.append(str(i.region_start))\\n line.append(str(i.region_end))\\n if not i.gene_name == '' and not i.gene_name == 'N/A':\\n line.append(i.gene_name + ':' + i.name)\\n else:\\n line.append(i.name)\\n result.append(line)\\n\\n bed = '\\\\n'.join(['\\\\t'.join(l) for l in result])\\n\\n bed_tool = BedTool(bed, from_string=True)\\n bed_sorted = bed_tool.sort()\\n bed_sorted_merged = bed_sorted.merge(c=4, o='collapse')\\n\\n if type == 'design':\\n bed = create_design(bed_sorted_merged)\\n filename = \\\"attachment; filename=\\\" + panel_name + \\\"_25bp_v\\\" + version + \\\"_\\\" + current_user.id + \\\"_\\\" + time.strftime(\\n \\\"%d-%m-%Y\\\") + \\\".txt\\\"\\n else:\\n bed = create_bed(bed_sorted_merged)\\n if type == \\\"extension\\\":\\n filename = \\\"attachment; filename=\\\" + panel_name + \\\"_25bp_v\\\" + version + \\\"_\\\" + current_user.id + \\\"_\\\" + time.strftime(\\n \\\"%d-%m-%Y\\\") + \\\".bed\\\"\\n else:\\n filename = \\\"attachment; filename=\\\" + panel_name + \\\"_v\\\" + version + \\\"_\\\" + current_user.id + \\\"_\\\" + time.strftime(\\n \\\"%d-%m-%Y\\\") + \\\".bed\\\"\\n\\n return Response(\\n bed,\\n mimetype='test/plain',\\n headers={\\\"Content-disposition\\\": filename\\n }\\n )\",\n \"def get_table_download_link(df, file_name):\\n if 'embedding_average' in df.columns:\\n df = df.drop(columns='embedding_average')\\n # df = results_output.drop(columns='embedding_average')\\n # csv = df.to_csv(index=False)\\n # b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\\n # href = f'<a href=\\\"data:file/csv;base64,{encoded}\\\">Download Excel File</a> (right-click and save as <some_name>.csv)'\\n # href = f'<a href=\\\"data:file/csv;base64,{b64}\\\">Download CSV File</a> (right-click and save as <some_name>.csv)'\\n towrite = io.BytesIO()\\n df.to_excel(towrite,index = False, encoding = 'UTF-8') # write to BytesIO buffer\\n towrite.seek(0) # reset pointer\\n encoded = base64.b64encode(towrite.read()).decode() # encoded object\\n href = f'<a href=\\\"data:file/csv;base64,{encoded}\\\" download =\\\"{file_name}\\\">Download Excel File</a> (right-click and save as <some_name>.csv)'\\n st.markdown(href, unsafe_allow_html=True)\",\n \"def download_url(self, fname):\\n if not fname in self.data:\\n return ''\\n url = '/'.join([\\n self.context.absolute_url(),\\n '@@download-file',\\n self.id + ':' + fname\\n ])\\n return url\",\n \"def get_table_download_link(df):\\r\\n\\tval = to_excel(df)\\r\\n\\tb64 = base64.b64encode(val).decode() # val looks like b'...'\\r\\n\\thref=f'<a href=\\\"data:application/octet-stream;base64,{b64}\\\" download=\\\"captura.xlsx\\\" target=\\\"_blank\\\">Descargar: Haga clic derecho y guardar enlace como...</a>' # decode b'abc' => abc\\t\\r\\n\\treturn href\",\n \"def main(url, localfile):\\n ph.download_file(url, localfile)\",\n \"def filedownload(df,filename): \\n filename=filename + \\\".csv\\\"\\n csv = df.to_csv(index=False)\\n b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions\\n href = f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=%s>Download %s File</a>'%(filename,filename)\\n return href\",\n \"def download_it(fw, acquisition, file_name, input_path):\\n\\n safe = make_file_name_safe(file_name, replace_str='_')\\n\\n full_path = input_path + safe\\n\\n if acquisition.timestamp:\\n if acquisition.timezone:\\n created = acquisition.original_timestamp.isoformat()\\n else:\\n created = acquisition.timestamp.isoformat()\\n else:\\n created = 'unknown'\\n\\n rpt = 1\\n while full_path in context.gear_dict['niftis']: # then repeated name\\n full_path = input_path + str(rpt) + '_' + safe\\n rpt += 1\\n\\n if os.path.isfile(full_path):\\n log.info('File exists ' + file_name + ' -> ' +\\\\\\n full_path + ' created ' + created)\\n else:\\n log.info('Downloading ' + file_name + ' -> ' +\\\\\\n full_path + ' created ' + created)\\n acquisition.download_file(file_name, full_path)\\n\\n full_file = fw.get_acquisition_file_info(acquisition.id, file_name)\\n field_strength = full_file.info.get('MagneticFieldStrength')\\n\\n context.gear_dict['niftis'].append(full_path)\\n context.gear_dict['file_names'].append(file_name)\\n context.gear_dict['createds'].append(created)\\n context.gear_dict['field_strength'].append(field_strength)\",\n \"def get_url(self, filename, query_para=None, **url_args):\\r\\n from uliweb.core.html import Tag\\r\\n \\r\\n title = url_args.pop('title', filename)\\r\\n text = url_args.pop('text', title)\\r\\n query_para = query_para or {}\\r\\n return str(Tag('a', title, href=self.get_href(filename, **query_para), **url_args))\",\n \"def download(exam, out, name_question, sid_question, compact):\\n exam_json, template_questions, email_to_data_map, total = examtool.api.download.download(exam)\\n examtool.api.download.export(template_questions, email_to_data_map, total, exam, out, name_question, sid_question, compact)\",\n \"def download_link(object_to_download, download_filename, download_link_text):\\n if isinstance(object_to_download,pd.DataFrame):\\n object_to_download = object_to_download.to_csv(index=False)\\n\\n # some strings <-> bytes conversions necessary here\\n b64 = base64.b64encode(object_to_download.encode()).decode()\\n\\n return f'<a href=\\\"data:file/csv;base64,{b64}\\\" download=\\\"{download_filename}\\\">{download_link_text}</a>'\",\n \"def x_download():\\n\\t#_loadconfig()\\n\\tconf = _get_config()\\n\\t#print conf['xplane']\\n\\tdownload_url = conf['xplane']['download']\\n\\tlocal(\\\"wget -P %s %s\\\" % (navimport.conf.work_dir(\\\"/xplane_zips\\\"), download_url))\",\n \"def ferry_data_download(URL):\\n explanation = 'File exists'\\n file_downloaded = True\\n # Request if the thredds server is working, add .html to URL\\n req = requests.get(URL + '.html')\\n if req.status_code == 200:\\n \\\"\\\"\\\"File exists and is good for download, so write file\\\"\\\"\\\"\\n print('File is ok')\\n explanation = 'Good URL, File downloaded'\\n file_downloaded = True\\n ferry = xr.open_dataset(URL)\\n else:\\n print('File not found or unavailable')\\n explanation = ' File not found or unavailable'\\n file_downloaded = False\\n ferry = np.nan\\n return (ferry, file_downloaded, explanation)\",\n \"def _serve_file(self, abspath, params):\\r\\n relpath = os.path.relpath(abspath, self._root)\\r\\n breadcrumbs = self._create_breadcrumbs(relpath)\\r\\n link_path = urlparse.urlunparse([None, None, relpath, None, urllib.urlencode(params), None])\\r\\n args = self._default_template_args('file')\\r\\n args.update({ 'root_parent': os.path.dirname(self._root),\\r\\n 'breadcrumbs': breadcrumbs,\\r\\n 'link_path': link_path })\\r\\n self._send_content(self._renderer.render_name('base', args), 'text/html')\",\n \"def _get_url(self, docket_number: str, docketEntryId: str) -> str:\\n self.url = f\\\"https://public-api-green.dawson.ustaxcourt.gov/public-api/{docket_number}/{docketEntryId}/public-document-download-url\\\"\\n if self.test_mode_enabled():\\n # Don't fetch urls when running tests. Because it requires\\n # a second api request.\\n return self.url\\n pdf_url = super()._download()[\\\"url\\\"]\\n return pdf_url\",\n \"def on_click(self) -> None:\\n os.startfile(self.url) # noqa: S606\",\n \"def save_local_interactive(fig: go.Figure, name):\\n from plotly.offline import plot\\n plot(fig, filename=name)\",\n \"def _download_from_web(*, ds_name: str, ds_path: Path):\\n import cgi\\n import zipfile\\n import httpx\\n from tqdm import tqdm\\n\\n url = DATASET_OPTIONS[ds_name]['web']\\n if ds_path.exists():\\n print('Dataset directory already exists; remove it if you wish to '\\n 're-download the dataset')\\n return\\n\\n ds_path.mkdir(parents=True, exist_ok=True)\\n\\n with httpx.Client() as client:\\n with client.stream('GET', url=url) as response:\\n if not response.is_error:\\n pass # All good!\\n else:\\n raise RuntimeError(\\n f'Error {response.status_code} when trying '\\n f'to download {url}')\\n\\n\\n header = response.headers['content-disposition']\\n _, params = cgi.parse_header(header)\\n # where to store the archive\\n outfile = ds_path / params['filename']\\n remote_file_size = int(response.headers['content-length'])\\n\\n with open(outfile, mode='wb') as f:\\n with tqdm(desc=params['filename'], initial=0,\\n total=remote_file_size, unit='B',\\n unit_scale=True, unit_divisor=1024,\\n leave=False) as progress:\\n num_bytes_downloaded = response.num_bytes_downloaded\\n\\n for chunk in response.iter_bytes():\\n f.write(chunk)\\n progress.update(response.num_bytes_downloaded -\\n num_bytes_downloaded)\\n num_bytes_downloaded = (response\\n .num_bytes_downloaded)\\n\\n assert outfile.suffix == '.zip'\\n\\n with zipfile.ZipFile(outfile) as zip:\\n for zip_info in zip.infolist():\\n path_in_zip = Path(zip_info.filename)\\n # omit top-level directory from Zip archive\\n target_path = str(Path(*path_in_zip.parts[1:]))\\n if str(target_path) in ('.', '..'):\\n continue\\n if zip_info.filename.endswith('/'):\\n (ds_path / target_path).mkdir(parents=True, exist_ok=True)\\n continue\\n zip_info.filename = target_path\\n print(f'Extracting: {target_path}')\\n zip.extract(zip_info, ds_path)\\n\\n outfile.unlink()\",\n \"def download_file(client, file_id):\\n\\n file_content = client.file(file_id).content()\\n print(file_content)\",\n \"def download_file(self):\\r\\n file_path = filedialog.askopenfilename(initialdir=\\\"/\\\",\\r\\n title=\\\"Select a File\\\",\\r\\n filetypes=((\\\"Torrent files\\\", \\\"*.torrent\\\"), (\\\"all files\\\", \\\"*.*\\\")))\\r\\n\\r\\n if file_path:\\r\\n download_bar = DownloadProgressBar(self.scroll_frame.interior, file_path)\\r\\n download_bar.pack()\\r\\n download_bar.start()\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6015341","0.56226736","0.55951804","0.552248","0.5511452","0.5382601","0.53783137","0.53105325","0.5288451","0.52870387","0.5222768","0.5213722","0.5212254","0.5193048","0.5184498","0.5182813","0.51763964","0.51701194","0.5161087","0.5158966","0.515461","0.51303816","0.51303816","0.5122473","0.5117201","0.5117201","0.5117201","0.5116391","0.51104707","0.5103775","0.5089216","0.5077563","0.50757045","0.5049742","0.5044619","0.50442827","0.50422025","0.4993394","0.49921045","0.49909386","0.4989442","0.49704725","0.4946703","0.4946703","0.4931549","0.49255216","0.4912732","0.49105704","0.4904758","0.48847485","0.4882602","0.48790917","0.48786524","0.4871491","0.4862566","0.48519808","0.4851676","0.48423308","0.4840048","0.48375463","0.4836064","0.4826259","0.48190573","0.48167333","0.4810051","0.4803351","0.4788997","0.47885603","0.47876346","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209","0.4786209"],"string":"[\n \"0.6015341\",\n \"0.56226736\",\n \"0.55951804\",\n \"0.552248\",\n \"0.5511452\",\n \"0.5382601\",\n \"0.53783137\",\n \"0.53105325\",\n \"0.5288451\",\n \"0.52870387\",\n \"0.5222768\",\n \"0.5213722\",\n \"0.5212254\",\n \"0.5193048\",\n \"0.5184498\",\n \"0.5182813\",\n \"0.51763964\",\n \"0.51701194\",\n \"0.5161087\",\n \"0.5158966\",\n \"0.515461\",\n \"0.51303816\",\n \"0.51303816\",\n \"0.5122473\",\n \"0.5117201\",\n \"0.5117201\",\n \"0.5117201\",\n \"0.5116391\",\n \"0.51104707\",\n \"0.5103775\",\n \"0.5089216\",\n \"0.5077563\",\n \"0.50757045\",\n \"0.5049742\",\n \"0.5044619\",\n \"0.50442827\",\n \"0.50422025\",\n \"0.4993394\",\n \"0.49921045\",\n \"0.49909386\",\n \"0.4989442\",\n \"0.49704725\",\n \"0.4946703\",\n \"0.4946703\",\n \"0.4931549\",\n \"0.49255216\",\n \"0.4912732\",\n \"0.49105704\",\n \"0.4904758\",\n \"0.48847485\",\n \"0.4882602\",\n \"0.48790917\",\n \"0.48786524\",\n \"0.4871491\",\n \"0.4862566\",\n \"0.48519808\",\n \"0.4851676\",\n \"0.48423308\",\n \"0.4840048\",\n \"0.48375463\",\n \"0.4836064\",\n \"0.4826259\",\n \"0.48190573\",\n \"0.48167333\",\n \"0.4810051\",\n \"0.4803351\",\n \"0.4788997\",\n \"0.47885603\",\n \"0.47876346\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\",\n \"0.4786209\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61712605"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19582,"cells":{"query":{"kind":"string","value":"Ground a node and look it up in the db"},"document":{"kind":"string","value":"def query (node, grounding, db):\n return db.funcVal(grounding.groundNode(node))"},"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 node_lookup(self, path, for_update=False):\n\n q = \"select node from nodes where path = ?\"\n self.execute(q, (path,))\n r = self.fetchone()\n if r is not None:\n return r[0]\n return None","def get_node(self, key: str) -> Node:","def get_node(cur, id):\n sql = \"\"\"\n SELECT\n *\n FROM\n nodes\n WHERE\n id = %s;\n \"\"\"\n if not isinstance(id, str):\n raise TypeError('ID must be type string (UUID4).')\n\n cur.execute(sql, (id, ))\n result = cur.fetchone()\n\n if result is None:\n raise exceptions.NodeNotFound(id)\n else:\n return NodeData(**result)","def get_node(self, key: str) -> Optional[Node]:","def exist_node(node):\n res = db.child(node).get()\n print(node)\n print(res)\n if res.pyres:\n return True\n else:\n return False","def get_info_by_node(conn, node): \n cur = conn.cursor()\n cur.execute(\"SELECT * FROM Info WHERE NodeID=?\", (node))","def findNode(self, target: hash.hash.Hash):\n for bucket in self.buckets:\n if bucket.inRange(nodeID):\n for node in bucket:\n if node.hash == target:\n return node\n \n return None\n return None","def node_by_id(self, identifier):\n for node in self.nodes:\n if node.identifier == identifier:\n return node\n raise Exception(\"Node '{0}' not available in {1}\".format(\n identifier, self.name))","def find_node(self, node):\r\n for tree_node in self.traverse():\r\n if tree_node.content == node:\r\n return tree_node\r\n raise LookupError(\"Given node does not exist on the tree\")","def get_object(self, shot, nodepath):\n checksum = hashlib.sha1(nodepath).hexdigest()\n\n node = Node.objects.get(shot__number=shot, path_checksum=checksum)\n node.data = node.read_primary_data()\n node.apply_filters(self.request)\n return node","def _get_node(self, key):\n\n index = self._hash_function(key) % self.capacity # Get the index by hashing the key\n node = self._buckets[index].contains(key) # Get the node with the key (if it exists)\n return node","def find(self, node):\n saveCursor = self.cursor\n if self.first() and self.cursor.nodeId == node.nodeId:\n return self.cursor\n while self.next():\n if self.cursor.nodeId == node.nodeId:\n return self.cursor\n self.cursor = saveCursor\n return None","def get_or_create_node(node_id, sqlite_db):\n\n if node_id is None:\n return None\n\n cursor = sqlite_db.cursor()\n query = \"SELECT * FROM nodes WHERE id='{}'\".format(node_id)\n cursor.execute(query)\n\n nodes = cursor.fetchall()\n\n if len(nodes) > 1:\n raise Exception(\"Multiple nodes found for single node ID\")\n\n if nodes:\n return nodes[0]\n\n node = Node.load(node_id)\n if node is None:\n return None\n\n node_public_date = None\n privacy_actions = NodeLog.find(\n Q('node', 'eq', node_id)\n & Q('action', 'in', [NodeLog.MADE_PUBLIC, NodeLog.MADE_PRIVATE])\n ).sort('-date')\n\n try:\n privacy_action = privacy_actions[0]\n except IndexError as e:\n pass\n else:\n if privacy_action.action == NodeLog.MADE_PUBLIC:\n node_public_date = privacy_action.date.isoformat()\n node_public_date = node_public_date[:-3] + 'Z'\n\n cursor.execute(\n u'INSERT INTO nodes (id, title, category, made_public_date) VALUES (?, ?, ?, ?)',\n (node_id, getattr(node, 'title'), getattr(node, 'category'), node_public_date)\n )\n sqlite_db.commit()\n return get_or_create_node(node_id, sqlite_db)","def get_node_by_id(self, id):\r\n for n in self.nodes:\r\n if n.id==id:\r\n return n\r\n return None","def get_node_by_id(self, node_name: Hashable):\n return self._meta.get_node(node_name)","def fillNode(node, grounding, db):\n gn = copy.deepcopy(node)\n gn.val = query(gn, grounding, db)\n return gn","def get_node(conn, name):\n datacenter_id = get_datacenter_id()\n\n for item in conn.list_servers(datacenter_id)[\"items\"]:\n if item[\"properties\"][\"name\"] == name:\n node = {\"id\": item[\"id\"]}\n node.update(item[\"properties\"])\n return node","def get_node(uuid, **fields):\n try:\n with session_for_read() as session:\n res = session.query(model.Node).filter_by(\n uuid=uuid, **fields).one()\n return model.Node(uuid=res.uuid, version_id=res.version_id,\n state=res.state, started_at=res.started_at,\n finished_at=res.finished_at, error=res.error,\n manage_boot=res.manage_boot)\n except (orm_errors.NoResultFound, orm_errors.StaleDataError):\n raise utils.NodeNotFoundInDBError()","def search_node(self, node_tup):\n #signature = hashlib.sha1(node_tup[0]+node_tup[4])\n app_process = sqlite3.connect('app_process::memory:', check_same_thread=False)\n app_process_cursor = app_process.cursor()\n out = app_process_cursor.execute(\"SELECT FROM nodes WHERE uname==(:uname)\", { \"uname\":node_tup[2] })\n \n app_process.commit()\n app_process.close()\n if not out:\n return (None,None,None,None) #LOL why am i doing this\n #**proceeds to not change it cuz its kinda funny \n return out","def getnode(self, id: int) -> node_data:\n return self.Nodes[id]","def get_node(self, key):\n pos = self._get_node_pos(key)\n if pos is None:\n return None\n return self._hashring[self._sorted_keys[pos]]","def nodeWithLabel(self, label):\r\n for nt in self.listNodes.keys(): \t# for all kind of nodes...\r\n for node in self.listNodes[nt]: \t# for all nodes of type <nt>\r\n if node.GGLabel.getValue() == label: # check if the node's label is what we are looking for...\r\n return node # a node has been found!\r\n return None # no appropriate node has been found \r","def get_resource(self, graph_db):\n node = neo_resource.get_node(graph_db, self.index)\n return node","def get_node(self, name, create=False):\n\n try:\n node = self.find_node(name, create)\n except KeyError:\n node = None\n return node","def get_node_b(name, taxid, pathway, topology, psi_mi_to_sql_object):\n\n # Testing if the node is already in the database\n node_dict = psi_mi_to_sql_object.get_node(name, node_tax_id=taxid)\n\n if not node_dict:\n node_dict = {\n \"name\": 'Uniprot:' + name,\n \"tax_id\": taxid,\n \"alt_accession\": None,\n 'pathways': pathway,\n \"aliases\": None,\n \"topology\": topology\n }\n\n return node_dict","def fetch_node(self, address):\n if self.id_ is not None:\n new_neofj = NeoFJ(address=address)\n fetch_node = new_neofj.get_node(alias=self.id_, label=self.label)\n self.name = fetch_node.properties['name']\n self.label = []\n while len(fetch_node.labels) > 0:\n self.label.append(fetch_node.labels.pop())","def get_node_a(name, taxid, pathway, topology, psi_mi_to_sql_object):\n\n # Testing if the node is already in the database\n node_dict = psi_mi_to_sql_object.get_node(name, node_tax_id=taxid)\n\n if not node_dict:\n node_dict = {\n \"name\" : 'Uniprot:' + name,\n \"tax_id\": taxid,\n \"alt_accession\": None,\n 'pathways': pathway,\n \"aliases\": None,\n \"topology\": topology\n }\n\n return node_dict","def get_node(self, value):\n\t\treturn self.adjacency_list[value]","def node(self, name):\r\n return self.nodes[name]","def get_node(self, _id):\n return self.make_request(\"GET\", \"nodes/\"+_id, {})","def initialize_node(db, c):\n\n # have we already called this function?\n if saq.SAQ_NODE_ID is not None:\n return\n\n saq.SAQ_NODE_ID = None\n\n # we always default to a local node so that it doesn't get used by remote nodes automatically\n c.execute(\"SELECT id FROM nodes WHERE name = %s\", (saq.SAQ_NODE,))\n row = c.fetchone()\n if row is not None:\n saq.SAQ_NODE_ID = row[0]\n logging.debug(\"got existing node id {} for {}\".format(saq.SAQ_NODE_ID, saq.SAQ_NODE))\n\n if saq.SAQ_NODE_ID is None:\n execute_with_retry(db, c, \"\"\"INSERT INTO nodes ( name, location, company_id, is_local, last_update ) \n VALUES ( %s, %s, %s, %s, NOW() )\"\"\", \n (saq.SAQ_NODE, saq.API_PREFIX, saq.COMPANY_ID, True),\n commit=True)\n\n c.execute(\"SELECT id FROM nodes WHERE name = %s\", (saq.SAQ_NODE,))\n row = c.fetchone()\n if row is None:\n logging.critical(\"unable to allocate a node_id from the database\")\n sys.exit(1)\n else:\n saq.SAQ_NODE_ID = row[0]\n logging.info(\"allocated node id {} for {}\".format(saq.SAQ_NODE_ID, saq.SAQ_NODE))","def get_node_info(self, obj_id, obj_type, pointer = None):\n\t\ttry:\n\t\t\tquery_str = \"SELECT obj->>'name', obj->>'status', obj->>'deleted', obj->>'type_full' FROM \" + obj_type + \" WHERE obj->>'id'='\" + str(obj_id) + \"'\"\n\t\t\tself.cur.execute(query_str)\n\t\t\tresult = self.cur.fetchall()[0]\n\t\t\tself.queries[query_str] = True\n\t\t\treturn result\n\t\texcept Exception as e:\n\t\t\tself.root_logger.info(obj_type + \" \" + str(obj_id) + \" (POINTED TO BY \" + pointer + \") DID NOT PARSE, POSSIBLY DOES NOT EXIST IN DATABASE\")\n\t\t\traise","def what_node(instance, node):\n map_dict = nodes_mapped(instance)\n\n for nd, ammentity in map_dict.items():\n if nd == node:\n return ammentity","def node(cls):\n return relationship.many_to_one(cls, 'node')","def node(cls):\n return relationship.many_to_one(cls, 'node')","def get_node_by_id(self, node_name: Hashable):\n try:\n return _node_to_metadata(\n self._g.V().has(ID, node_name).valueMap(True).toList()[0]\n )\n except IndexError as e:\n raise KeyError() from e","def node(self, node):\n nodes = [n for n in self.nodes if n.name == node]\n if nodes:\n return nodes\n else:\n raise Exception(\"Node {0} not available in {1}\".format(\n node, self.name))","def get_node(self, name=None, node_id=None):\n if node_id:\n return self._search_node(key=\"node_id\", value=node_id)\n elif name:\n return self._search_node(key=\"name\", value=name)\n else:\n raise ValueError(\"name or node_ide must be provided\")","def get(node, user_id):\n key = NodeCache.generate_key(node, user_id)\n db_node_cache = get_db_connector().node_cache.find({\n 'key': key\n }).sort('insertion_date', -1).limit(1)\n caches = list(db_node_cache)\n if len(caches):\n return NodeCache.from_dict(caches[0])\n else:\n return None","def ref(self, node):\n return self.node_map[node]","def get_node(self, ntype, nid):\n _check_type(ntype, self._node_index)\n if nid not in self._node_index[ntype]:\n raise KeyError(\"Node with id {0} does not exist.\".format(nid))\n return self._node_index[ntype][nid]","def find_node(self, type):\n pass","def get_node(self, name):\n return self._node_reg[name]","def get_node(self, node_ref):\n\n return Cache.get(node_ref)","def get_node(self, name):\n if name in self._nodes:\n return self._nodes[name]\n return None","def __getitem__(self, id : int) -> 'SpatialGraph.Node' :\n if id not in self.nodes.keys():\n raise IndexError(\"Index is out of bounds.\")\n return self.nodes[id]","def __getitem__(self, key: Union[int, str]) -> Node:\r\n node: Node = None\r\n if isinstance(key, int):\r\n node = self._nodes.get(key)\r\n if isinstance(key, str):\r\n node = self._node_name_map.get(key)\r\n\r\n if node is None:\r\n raise IndexError(\"Invalid key.\")\r\n\r\n return node","def find_node(self, name, create=False):\n\n name = self._validate_name(name)\n node = self.nodes.get(name)\n if node is None:\n if not create:\n raise KeyError\n node = self.node_factory()\n self.nodes[name] = node\n return node","def get_node_at_position(cur, node, position):\n sql = \"\"\"\n SELECT\n *\n FROM\n nodes\n WHERE\n parent=%s\n AND\n position=%s\n \"\"\"\n\n cur.execute(sql, (str(node), position))\n result = cur.fetchone()\n\n if result is None:\n raise ValueError('Node does not exist.')\n else:\n return NodeData(**result)","def select_node_by_label(conn, label):\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM Nodes WHERE label=?\", (label,))","def _bddnode(root, lo, hi):\n\t# print(\"_bddnode\")\n\tif lo is hi:\n\t\tnode = lo\n\telse:\n\t\tkey = (root, lo, hi)\n\t\ttry:\n\t\t\tnode = _NODES[key]\n\t\texcept KeyError:\n\t\t\tnode = _NODES[key] = BDDNode(*key)\n\treturn node","def get_node_by_name(self, node_name: str,\r\n can_validate_and_load: bool = False,\r\n external_id: Optional[str] = None) -> Optional[Node]:\r\n node: Node = self._node_name_map.get(node_name)\r\n if node is not None:\r\n return node\r\n\r\n db_node: DBNode = self._graph.database.Node.find_by_name(node_name)\r\n if db_node is None:\r\n if can_validate_and_load:\r\n node_name = self._graph.get_authentic_node_name(node_name)\r\n if node_name is not None:\r\n node = self._node_name_map.get(node_name)\r\n if node is not None:\r\n return node\r\n\r\n db_node = self._graph.database.Node.find_by_name(node_name)\r\n if db_node is None:\r\n self._internal_add_node(node_name=node_name,\r\n external_id=external_id,\r\n are_neighbors_cached=False,\r\n add_to_cache=True)\r\n else:\r\n self._internal_add_node(node_name=db_node.name,\r\n external_id=db_node.external_id,\r\n are_neighbors_cached=db_node.are_neighbors_cached,\r\n add_to_cache=False)\r\n else:\r\n return None\r\n else:\r\n self._internal_add_node(node_name=db_node.name,\r\n external_id=db_node.external_id,\r\n are_neighbors_cached=db_node.are_neighbors_cached,\r\n add_to_cache=False)\r\n\r\n node = self._node_name_map.get(node_name)\r\n\r\n # Trying to load the cached neighbors of the created node from the database could\r\n # cause a very-very-very deep recursion, so don't even think about doing it here.\r\n\r\n return node","def get_node():\n return TrieNode()","def get_node(self, id):\n\t\t# No node with given id\n\t\tif id not in self.nodes:\n\t\t\traise ValueError\n\n\t\treturn self.nodes[id]","def get_node(self, full_path, abort=True):\n if full_path in self.path2node:\n return self.path2node[full_path]\n elif abort:\n print \"Unable to get node for path\\n%s\" % full_path\n # return None\n traceback.print_stack()\n sys.exit(1)\n else:\n return None","def getNode(self, node_id):\n if node_id in self.nodes:\n return self.nodes[node_id]\n return None","def get(self, key):\n dkey = digest(key)\n _log.debug(\"Server:get %s\" % base64.b64encode(dkey))\n # if this node has it, return it\n exists, value = self.storage.get(dkey)\n if exists:\n return defer.succeed(value)\n node = Node(dkey)\n nearest = self.protocol.router.findNeighbors(node)\n if len(nearest) == 0:\n self.log.warning(\"There are no known neighbors to get key %s\" % key)\n return defer.succeed(None)\n spider = ValueSpiderCrawl(self.protocol, node, nearest, self.ksize, self.alpha)\n return spider.find()","def query_current_node_info(self, obj_id, obj_type):\n\t\tquery_str = \"SELECT obj FROM \" + str(obj_type) + \" WHERE obj->>'id'='\" + str(obj_id) + \"'\"\n\t\tself.cur.execute(query_str)\n\t\tresult = self.cur.fetchall()\n\t\treturn result[0][0]","def __getitem__(self, node):\n j = self._hash_function(node)\n bucket = self._T[j]\n if bucket is None:\n raise KeyError(node)\n return bucket[node]","def get_resource(self, graph_db, timestamp):\n node_name = self.hostname + '_' + 'Machine' + '_0'\n index = ('physical_resource', 'physical_name', node_name)\n node = neo_resource.add_node(graph_db, index, timestamp)\n return node","def test_node_object_exists():\n from priorityq import Node\n node = Node(0, [1, 2])\n assert node","def _add_node(self, node_name, node_type):\n q = 'MATCH (r:' + node_type + ') WHERE r.name=\"' \\\n + node_name + '\" RETURN r'\n results = self.db.query(q, returns=(client.Node, str, client.Node))\n res = self.db.labels.create(node_type)\n\n if (len(results) == 0):\n r = self.db.nodes.create(name=node_name)\n res.add(r)\n else:\n r = results[0][0]\n return r","def nodeFromName(self, name):\n for item in self.items():\n if isinstance(item, NodeItem):\n if item.name() == name:\n return item\n return None","def get(self, key):\n dkey = digest(key)\n # if this node has it, return it\n if self.storage.get(dkey) is not None:\n return defer.succeed(self.storage.get(dkey))\n node = Node(dkey)\n nearest = self.protocol.router.findNeighbors(node)\n if len(nearest) == 0:\n self.log.warning(\"There are no known neighbors to get key %s\" % key)\n return defer.succeed(None)\n spider = ValueSpiderCrawl(self.protocol, node, nearest, self.ksize, self.alpha)\n return spider.find()","def get_node(self, name):\n return self.source_net.nodes[name]","def get_node(self, x: int, y: int) -> MazeCell:\n node = self._nodes_by_row[y][x]\n assert node.x == x and node.y == y, f\"(node.x, node.y) == ({node.x}, {node.y}), but should be ({x}, {y})\"\n\n return node","def resolve(self, node):\n if isinstance(node, Job):\n return NodePointer(self.find(node))\n else:\n return node","def get_node(self, name):\n\n assert name in self.nodes\n return self.nodes[name]","def search(self, key):\n if key in self.key_list:\n return (self.nodes)[key]\n return None","def ex_get_node_by_name(self, name):\n domain = self._get_domain_for_name(name=name)\n node = self._to_node(domain=domain)\n return node","def get_node(self, string_key):\n pos = self.get_node_pos(string_key)\n if pos is None:\n return None\n return self.ring[ self._sorted_keys[pos] ]","def node_show(self, node):\n if node.instance_uuid:\n n = self.ironic_client.node.get_by_instance_uuid(\n node.instance_uuid)\n else:\n n = self.ironic_client.node.get(node.uuid)\n return n","def get_node(self):\n assert self._node_id is not None\n return self._get_info(self.EXPECTED)[self._node_id]","def _handle_gfk_field_node(self, node, field):\n if node.getElementsByTagName('None'):\n return None\n ct_key = node.getAttribute(\"d:to\").split(NATURAL_KEY_JOINER)\n ctype = ContentType.objects.get_by_natural_key(*ct_key)\n Model = ctype.model_class()\n if hasattr(Model._default_manager, 'get_by_natural_key'):\n value = getInnerText(node).strip()\n field_value = value.split(NATURAL_KEY_JOINER)\n obj = Model._default_manager.db_manager(self.db).get_by_natural_key(*field_value)\n return obj\n else:\n field_value = getInnerText(node).strip()\n return Model._default_manager.db_manager(self.db).get(pk=Model._meta.pk.to_python(field_value))","def test_lookup(graph):\n node1 = graph.lookup(0)\n assert str(node1) == \"<1, 2>\"\n\n node2 = graph.lookup(3)\n assert str(node2) == \"<1, 2>\"\n\n node3 = graph.lookup(1)\n assert str(node3) == \"<0, 2, 3>\"","def __getitem__(self, key):\n hash_ = self._hash(key)\n start = bisect.bisect(self._keys, hash_)\n if start == len(self._keys):\n start = 0\n return self._nodes[self._keys[start]]","def __getitem__(self, key):\n hash_ = self._hash(key)\n start = bisect.bisect(self._keys, hash_)\n if start == len(self._keys):\n start = 0\n return self._nodes[self._keys[start]]","def find_node(self, node_name):\n return self.labeled_nodes[node_name]","def _get_node(self, seq):\n cur_node = self.root\n for token_id in seq:\n children = cur_node.edges\n if not token_id in children:\n children[token_id] = SimpleNode()\n cur_node = children[token_id]\n return cur_node","def getNodeById(self, nodes, id):\n for item in nodes:\n if item.getProperty('id') == id:\n return item","def get_node(self, selector, index):\n\n self.arbor._setup_tree(self)\n self.arbor._grow_tree(self)\n indices = getattr(self, f\"_{selector}_field_indices\", None)\n if indices is None:\n raise RuntimeError(\"Bad selector.\")\n\n my_link = self.root._links[indices][index]\n return self.arbor._generate_tree_node(self.root, my_link)","def get_node(self, key, print_path=False):\r\n parent_node, search_node = self.__compare(key, method='search', print_path=print_path)\r\n self.__check_node(search_node)\r\n\r\n return search_node","def node_query(data, pk, room):\n node = \"queries/{}/{}\".format(room, Params.PREFIX['query'] + str(pk))\n if exist_node(node):\n res = db.child(node).update(data)\n else:\n print(\"creo\")\n res = db.child(node).set(data)\n if res:\n if \"succes\" in res:\n if res[\"success\"] != 1:\n logger.error(\"actualizando node query fallo \".format(node))","def findNode(self, name):\n return self._nodes.get(name)","def get_node_by_name(self, name):\n\n for node in self.nodes:\n if node.name == name:\n return node\n\n return None","def _getOrCreateByNum(self, nodeNum):\n if nodeNum == BROADCAST_NUM:\n raise Exception(\"Can not create/find nodenum by the broadcast num\")\n\n if nodeNum in self.nodesByNum:\n return self.nodesByNum[nodeNum]\n else:\n n = {\"num\": nodeNum} # Create a minimial node db entry\n self.nodesByNum[nodeNum] = n\n return n","def node(self, id=None, properties=None, **kwargs):\r\n\t\tif properties is None:\r\n\t\t\tproperties = {}\r\n\t\tif id is None:\r\n\t\t\tnodes = filter(self._nodes.values(), properties, **kwargs)\r\n\t\t\tif not nodes:\r\n\t\t\t\treturn None\r\n\t\t\telse:\r\n\t\t\t\treturn nodes[0]\r\n\t\telse:\r\n\t\t\tif id in self._nodes:\r\n\t\t\t\tresult_node = self._nodes[id]\r\n\t\t\t\tproperties.update(kwargs)\r\n\t\t\t\tfor key, value in properties.items():\r\n\t\t\t\t\tif key not in result_node.properties:\r\n\t\t\t\t\t\treturn None\r\n\t\t\t\t\tif result_node.properties[key] != value:\r\n\t\t\t\t\t\treturn None\r\n\t\t\t\treturn result_node\r\n\t\t\telse:\r\n\t\t\t\treturn None","def get_node(self, state):\n return self.nodes.get(Node.state_as_string(state))","def node(self, ident, time=None):\n if time is not None:\n ident = (ident, time)\n return self._nodes[ident]","def get_node(self):\n return self.__node","def read_node(\n *,\n storage: AbstractStorage = Depends(get_storage),\n node_id: str = Query(\n \"\",\n title=\"Node unique identifier\",\n description=\"The node's ID.\"\n ),\n is_user_authorized: str = Depends(authenticate)\n):\n node = storage.get_node(node_id)\n\n if not node:\n raise HTTPException(status_code=404, detail=\"Node not found\")\n\n return node","def find_node(self, value):\n for (fun, node) in self.__root.__fast_find:\n if fun(value):\n return node\n return None","def get_node(self):\r\n return self._node","def get_node(self, linode_id):\n try:\n print \"LI %s\" % linode_id\n return Linode.find(api_id=linode_id)\n except chube_api.linode_api.ApiError, e:\n print \"Looks like Linode's API is down:\"\n print\n print e\n sys.exit(1)","def node(env, node_name):\n envs = environments()\n check_env(env, envs)\n query = AndOperator()\n\n if env != '*':\n query.add(EqualsOperator(\"environment\", env))\n\n query.add(EqualsOperator(\"certname\", node_name))\n\n node = get_or_abort(puppetdb.node, node_name)\n facts = node.facts()\n return render_template(\n 'node.html',\n node=node,\n facts=yield_or_stop(facts),\n envs=envs,\n current_env=env,\n columns=REPORTS_COLUMNS[:2])","def search(self, item):\n # Find a node with the given item, if any\n node = self._find_node(item)\n # TODO: Return the node's data if found, or None\n return node.data if ... else None","def find_node(self, node_name):\n try:\n return next(x for x in self.all_nodes() if x.name == node_name)\n except StopIteration:\n merge_nodes = list(filter(lambda x: x.kind == 'Merge', self.all_nodes()))\n # return next(x for x in merge_nodes if x.data['art1'] == node_name or x.data['art2'] == node_name\n # or ('art3' in x.data and x.data['art3'] == node_name))\n for x in merge_nodes:\n if x.data['art1']['key'] == node_name or x.data['art2']['key'] == node_name \\\n or ('art3' in x.data and x.data['art3']['key'] == node_name):\n return x\n print(node_name)\n raise StopIteration()","def getNode(self):\n node = Entity.getNode(self)\n node.tag = 'relentity'\n return(node)","def read_node(name: str = '', value: str = '') -> Node:\n first_node = read_all_nodes(name=name, value=value).first()\n return first_node","def lookup(self, key):\n k = self.get_position(key)\n\n if self.keys[k] == key:\n return node.values[k]\n\n # Lookup in the child node.\n if self.refs[k+1] == None:\n return None\n return self.refs[k+1].lookup(key)"],"string":"[\n \"def node_lookup(self, path, for_update=False):\\n\\n q = \\\"select node from nodes where path = ?\\\"\\n self.execute(q, (path,))\\n r = self.fetchone()\\n if r is not None:\\n return r[0]\\n return None\",\n \"def get_node(self, key: str) -> Node:\",\n \"def get_node(cur, id):\\n sql = \\\"\\\"\\\"\\n SELECT\\n *\\n FROM\\n nodes\\n WHERE\\n id = %s;\\n \\\"\\\"\\\"\\n if not isinstance(id, str):\\n raise TypeError('ID must be type string (UUID4).')\\n\\n cur.execute(sql, (id, ))\\n result = cur.fetchone()\\n\\n if result is None:\\n raise exceptions.NodeNotFound(id)\\n else:\\n return NodeData(**result)\",\n \"def get_node(self, key: str) -> Optional[Node]:\",\n \"def exist_node(node):\\n res = db.child(node).get()\\n print(node)\\n print(res)\\n if res.pyres:\\n return True\\n else:\\n return False\",\n \"def get_info_by_node(conn, node): \\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM Info WHERE NodeID=?\\\", (node))\",\n \"def findNode(self, target: hash.hash.Hash):\\n for bucket in self.buckets:\\n if bucket.inRange(nodeID):\\n for node in bucket:\\n if node.hash == target:\\n return node\\n \\n return None\\n return None\",\n \"def node_by_id(self, identifier):\\n for node in self.nodes:\\n if node.identifier == identifier:\\n return node\\n raise Exception(\\\"Node '{0}' not available in {1}\\\".format(\\n identifier, self.name))\",\n \"def find_node(self, node):\\r\\n for tree_node in self.traverse():\\r\\n if tree_node.content == node:\\r\\n return tree_node\\r\\n raise LookupError(\\\"Given node does not exist on the tree\\\")\",\n \"def get_object(self, shot, nodepath):\\n checksum = hashlib.sha1(nodepath).hexdigest()\\n\\n node = Node.objects.get(shot__number=shot, path_checksum=checksum)\\n node.data = node.read_primary_data()\\n node.apply_filters(self.request)\\n return node\",\n \"def _get_node(self, key):\\n\\n index = self._hash_function(key) % self.capacity # Get the index by hashing the key\\n node = self._buckets[index].contains(key) # Get the node with the key (if it exists)\\n return node\",\n \"def find(self, node):\\n saveCursor = self.cursor\\n if self.first() and self.cursor.nodeId == node.nodeId:\\n return self.cursor\\n while self.next():\\n if self.cursor.nodeId == node.nodeId:\\n return self.cursor\\n self.cursor = saveCursor\\n return None\",\n \"def get_or_create_node(node_id, sqlite_db):\\n\\n if node_id is None:\\n return None\\n\\n cursor = sqlite_db.cursor()\\n query = \\\"SELECT * FROM nodes WHERE id='{}'\\\".format(node_id)\\n cursor.execute(query)\\n\\n nodes = cursor.fetchall()\\n\\n if len(nodes) > 1:\\n raise Exception(\\\"Multiple nodes found for single node ID\\\")\\n\\n if nodes:\\n return nodes[0]\\n\\n node = Node.load(node_id)\\n if node is None:\\n return None\\n\\n node_public_date = None\\n privacy_actions = NodeLog.find(\\n Q('node', 'eq', node_id)\\n & Q('action', 'in', [NodeLog.MADE_PUBLIC, NodeLog.MADE_PRIVATE])\\n ).sort('-date')\\n\\n try:\\n privacy_action = privacy_actions[0]\\n except IndexError as e:\\n pass\\n else:\\n if privacy_action.action == NodeLog.MADE_PUBLIC:\\n node_public_date = privacy_action.date.isoformat()\\n node_public_date = node_public_date[:-3] + 'Z'\\n\\n cursor.execute(\\n u'INSERT INTO nodes (id, title, category, made_public_date) VALUES (?, ?, ?, ?)',\\n (node_id, getattr(node, 'title'), getattr(node, 'category'), node_public_date)\\n )\\n sqlite_db.commit()\\n return get_or_create_node(node_id, sqlite_db)\",\n \"def get_node_by_id(self, id):\\r\\n for n in self.nodes:\\r\\n if n.id==id:\\r\\n return n\\r\\n return None\",\n \"def get_node_by_id(self, node_name: Hashable):\\n return self._meta.get_node(node_name)\",\n \"def fillNode(node, grounding, db):\\n gn = copy.deepcopy(node)\\n gn.val = query(gn, grounding, db)\\n return gn\",\n \"def get_node(conn, name):\\n datacenter_id = get_datacenter_id()\\n\\n for item in conn.list_servers(datacenter_id)[\\\"items\\\"]:\\n if item[\\\"properties\\\"][\\\"name\\\"] == name:\\n node = {\\\"id\\\": item[\\\"id\\\"]}\\n node.update(item[\\\"properties\\\"])\\n return node\",\n \"def get_node(uuid, **fields):\\n try:\\n with session_for_read() as session:\\n res = session.query(model.Node).filter_by(\\n uuid=uuid, **fields).one()\\n return model.Node(uuid=res.uuid, version_id=res.version_id,\\n state=res.state, started_at=res.started_at,\\n finished_at=res.finished_at, error=res.error,\\n manage_boot=res.manage_boot)\\n except (orm_errors.NoResultFound, orm_errors.StaleDataError):\\n raise utils.NodeNotFoundInDBError()\",\n \"def search_node(self, node_tup):\\n #signature = hashlib.sha1(node_tup[0]+node_tup[4])\\n app_process = sqlite3.connect('app_process::memory:', check_same_thread=False)\\n app_process_cursor = app_process.cursor()\\n out = app_process_cursor.execute(\\\"SELECT FROM nodes WHERE uname==(:uname)\\\", { \\\"uname\\\":node_tup[2] })\\n \\n app_process.commit()\\n app_process.close()\\n if not out:\\n return (None,None,None,None) #LOL why am i doing this\\n #**proceeds to not change it cuz its kinda funny \\n return out\",\n \"def getnode(self, id: int) -> node_data:\\n return self.Nodes[id]\",\n \"def get_node(self, key):\\n pos = self._get_node_pos(key)\\n if pos is None:\\n return None\\n return self._hashring[self._sorted_keys[pos]]\",\n \"def nodeWithLabel(self, label):\\r\\n for nt in self.listNodes.keys(): \\t# for all kind of nodes...\\r\\n for node in self.listNodes[nt]: \\t# for all nodes of type <nt>\\r\\n if node.GGLabel.getValue() == label: # check if the node's label is what we are looking for...\\r\\n return node # a node has been found!\\r\\n return None # no appropriate node has been found \\r\",\n \"def get_resource(self, graph_db):\\n node = neo_resource.get_node(graph_db, self.index)\\n return node\",\n \"def get_node(self, name, create=False):\\n\\n try:\\n node = self.find_node(name, create)\\n except KeyError:\\n node = None\\n return node\",\n \"def get_node_b(name, taxid, pathway, topology, psi_mi_to_sql_object):\\n\\n # Testing if the node is already in the database\\n node_dict = psi_mi_to_sql_object.get_node(name, node_tax_id=taxid)\\n\\n if not node_dict:\\n node_dict = {\\n \\\"name\\\": 'Uniprot:' + name,\\n \\\"tax_id\\\": taxid,\\n \\\"alt_accession\\\": None,\\n 'pathways': pathway,\\n \\\"aliases\\\": None,\\n \\\"topology\\\": topology\\n }\\n\\n return node_dict\",\n \"def fetch_node(self, address):\\n if self.id_ is not None:\\n new_neofj = NeoFJ(address=address)\\n fetch_node = new_neofj.get_node(alias=self.id_, label=self.label)\\n self.name = fetch_node.properties['name']\\n self.label = []\\n while len(fetch_node.labels) > 0:\\n self.label.append(fetch_node.labels.pop())\",\n \"def get_node_a(name, taxid, pathway, topology, psi_mi_to_sql_object):\\n\\n # Testing if the node is already in the database\\n node_dict = psi_mi_to_sql_object.get_node(name, node_tax_id=taxid)\\n\\n if not node_dict:\\n node_dict = {\\n \\\"name\\\" : 'Uniprot:' + name,\\n \\\"tax_id\\\": taxid,\\n \\\"alt_accession\\\": None,\\n 'pathways': pathway,\\n \\\"aliases\\\": None,\\n \\\"topology\\\": topology\\n }\\n\\n return node_dict\",\n \"def get_node(self, value):\\n\\t\\treturn self.adjacency_list[value]\",\n \"def node(self, name):\\r\\n return self.nodes[name]\",\n \"def get_node(self, _id):\\n return self.make_request(\\\"GET\\\", \\\"nodes/\\\"+_id, {})\",\n \"def initialize_node(db, c):\\n\\n # have we already called this function?\\n if saq.SAQ_NODE_ID is not None:\\n return\\n\\n saq.SAQ_NODE_ID = None\\n\\n # we always default to a local node so that it doesn't get used by remote nodes automatically\\n c.execute(\\\"SELECT id FROM nodes WHERE name = %s\\\", (saq.SAQ_NODE,))\\n row = c.fetchone()\\n if row is not None:\\n saq.SAQ_NODE_ID = row[0]\\n logging.debug(\\\"got existing node id {} for {}\\\".format(saq.SAQ_NODE_ID, saq.SAQ_NODE))\\n\\n if saq.SAQ_NODE_ID is None:\\n execute_with_retry(db, c, \\\"\\\"\\\"INSERT INTO nodes ( name, location, company_id, is_local, last_update ) \\n VALUES ( %s, %s, %s, %s, NOW() )\\\"\\\"\\\", \\n (saq.SAQ_NODE, saq.API_PREFIX, saq.COMPANY_ID, True),\\n commit=True)\\n\\n c.execute(\\\"SELECT id FROM nodes WHERE name = %s\\\", (saq.SAQ_NODE,))\\n row = c.fetchone()\\n if row is None:\\n logging.critical(\\\"unable to allocate a node_id from the database\\\")\\n sys.exit(1)\\n else:\\n saq.SAQ_NODE_ID = row[0]\\n logging.info(\\\"allocated node id {} for {}\\\".format(saq.SAQ_NODE_ID, saq.SAQ_NODE))\",\n \"def get_node_info(self, obj_id, obj_type, pointer = None):\\n\\t\\ttry:\\n\\t\\t\\tquery_str = \\\"SELECT obj->>'name', obj->>'status', obj->>'deleted', obj->>'type_full' FROM \\\" + obj_type + \\\" WHERE obj->>'id'='\\\" + str(obj_id) + \\\"'\\\"\\n\\t\\t\\tself.cur.execute(query_str)\\n\\t\\t\\tresult = self.cur.fetchall()[0]\\n\\t\\t\\tself.queries[query_str] = True\\n\\t\\t\\treturn result\\n\\t\\texcept Exception as e:\\n\\t\\t\\tself.root_logger.info(obj_type + \\\" \\\" + str(obj_id) + \\\" (POINTED TO BY \\\" + pointer + \\\") DID NOT PARSE, POSSIBLY DOES NOT EXIST IN DATABASE\\\")\\n\\t\\t\\traise\",\n \"def what_node(instance, node):\\n map_dict = nodes_mapped(instance)\\n\\n for nd, ammentity in map_dict.items():\\n if nd == node:\\n return ammentity\",\n \"def node(cls):\\n return relationship.many_to_one(cls, 'node')\",\n \"def node(cls):\\n return relationship.many_to_one(cls, 'node')\",\n \"def get_node_by_id(self, node_name: Hashable):\\n try:\\n return _node_to_metadata(\\n self._g.V().has(ID, node_name).valueMap(True).toList()[0]\\n )\\n except IndexError as e:\\n raise KeyError() from e\",\n \"def node(self, node):\\n nodes = [n for n in self.nodes if n.name == node]\\n if nodes:\\n return nodes\\n else:\\n raise Exception(\\\"Node {0} not available in {1}\\\".format(\\n node, self.name))\",\n \"def get_node(self, name=None, node_id=None):\\n if node_id:\\n return self._search_node(key=\\\"node_id\\\", value=node_id)\\n elif name:\\n return self._search_node(key=\\\"name\\\", value=name)\\n else:\\n raise ValueError(\\\"name or node_ide must be provided\\\")\",\n \"def get(node, user_id):\\n key = NodeCache.generate_key(node, user_id)\\n db_node_cache = get_db_connector().node_cache.find({\\n 'key': key\\n }).sort('insertion_date', -1).limit(1)\\n caches = list(db_node_cache)\\n if len(caches):\\n return NodeCache.from_dict(caches[0])\\n else:\\n return None\",\n \"def ref(self, node):\\n return self.node_map[node]\",\n \"def get_node(self, ntype, nid):\\n _check_type(ntype, self._node_index)\\n if nid not in self._node_index[ntype]:\\n raise KeyError(\\\"Node with id {0} does not exist.\\\".format(nid))\\n return self._node_index[ntype][nid]\",\n \"def find_node(self, type):\\n pass\",\n \"def get_node(self, name):\\n return self._node_reg[name]\",\n \"def get_node(self, node_ref):\\n\\n return Cache.get(node_ref)\",\n \"def get_node(self, name):\\n if name in self._nodes:\\n return self._nodes[name]\\n return None\",\n \"def __getitem__(self, id : int) -> 'SpatialGraph.Node' :\\n if id not in self.nodes.keys():\\n raise IndexError(\\\"Index is out of bounds.\\\")\\n return self.nodes[id]\",\n \"def __getitem__(self, key: Union[int, str]) -> Node:\\r\\n node: Node = None\\r\\n if isinstance(key, int):\\r\\n node = self._nodes.get(key)\\r\\n if isinstance(key, str):\\r\\n node = self._node_name_map.get(key)\\r\\n\\r\\n if node is None:\\r\\n raise IndexError(\\\"Invalid key.\\\")\\r\\n\\r\\n return node\",\n \"def find_node(self, name, create=False):\\n\\n name = self._validate_name(name)\\n node = self.nodes.get(name)\\n if node is None:\\n if not create:\\n raise KeyError\\n node = self.node_factory()\\n self.nodes[name] = node\\n return node\",\n \"def get_node_at_position(cur, node, position):\\n sql = \\\"\\\"\\\"\\n SELECT\\n *\\n FROM\\n nodes\\n WHERE\\n parent=%s\\n AND\\n position=%s\\n \\\"\\\"\\\"\\n\\n cur.execute(sql, (str(node), position))\\n result = cur.fetchone()\\n\\n if result is None:\\n raise ValueError('Node does not exist.')\\n else:\\n return NodeData(**result)\",\n \"def select_node_by_label(conn, label):\\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM Nodes WHERE label=?\\\", (label,))\",\n \"def _bddnode(root, lo, hi):\\n\\t# print(\\\"_bddnode\\\")\\n\\tif lo is hi:\\n\\t\\tnode = lo\\n\\telse:\\n\\t\\tkey = (root, lo, hi)\\n\\t\\ttry:\\n\\t\\t\\tnode = _NODES[key]\\n\\t\\texcept KeyError:\\n\\t\\t\\tnode = _NODES[key] = BDDNode(*key)\\n\\treturn node\",\n \"def get_node_by_name(self, node_name: str,\\r\\n can_validate_and_load: bool = False,\\r\\n external_id: Optional[str] = None) -> Optional[Node]:\\r\\n node: Node = self._node_name_map.get(node_name)\\r\\n if node is not None:\\r\\n return node\\r\\n\\r\\n db_node: DBNode = self._graph.database.Node.find_by_name(node_name)\\r\\n if db_node is None:\\r\\n if can_validate_and_load:\\r\\n node_name = self._graph.get_authentic_node_name(node_name)\\r\\n if node_name is not None:\\r\\n node = self._node_name_map.get(node_name)\\r\\n if node is not None:\\r\\n return node\\r\\n\\r\\n db_node = self._graph.database.Node.find_by_name(node_name)\\r\\n if db_node is None:\\r\\n self._internal_add_node(node_name=node_name,\\r\\n external_id=external_id,\\r\\n are_neighbors_cached=False,\\r\\n add_to_cache=True)\\r\\n else:\\r\\n self._internal_add_node(node_name=db_node.name,\\r\\n external_id=db_node.external_id,\\r\\n are_neighbors_cached=db_node.are_neighbors_cached,\\r\\n add_to_cache=False)\\r\\n else:\\r\\n return None\\r\\n else:\\r\\n self._internal_add_node(node_name=db_node.name,\\r\\n external_id=db_node.external_id,\\r\\n are_neighbors_cached=db_node.are_neighbors_cached,\\r\\n add_to_cache=False)\\r\\n\\r\\n node = self._node_name_map.get(node_name)\\r\\n\\r\\n # Trying to load the cached neighbors of the created node from the database could\\r\\n # cause a very-very-very deep recursion, so don't even think about doing it here.\\r\\n\\r\\n return node\",\n \"def get_node():\\n return TrieNode()\",\n \"def get_node(self, id):\\n\\t\\t# No node with given id\\n\\t\\tif id not in self.nodes:\\n\\t\\t\\traise ValueError\\n\\n\\t\\treturn self.nodes[id]\",\n \"def get_node(self, full_path, abort=True):\\n if full_path in self.path2node:\\n return self.path2node[full_path]\\n elif abort:\\n print \\\"Unable to get node for path\\\\n%s\\\" % full_path\\n # return None\\n traceback.print_stack()\\n sys.exit(1)\\n else:\\n return None\",\n \"def getNode(self, node_id):\\n if node_id in self.nodes:\\n return self.nodes[node_id]\\n return None\",\n \"def get(self, key):\\n dkey = digest(key)\\n _log.debug(\\\"Server:get %s\\\" % base64.b64encode(dkey))\\n # if this node has it, return it\\n exists, value = self.storage.get(dkey)\\n if exists:\\n return defer.succeed(value)\\n node = Node(dkey)\\n nearest = self.protocol.router.findNeighbors(node)\\n if len(nearest) == 0:\\n self.log.warning(\\\"There are no known neighbors to get key %s\\\" % key)\\n return defer.succeed(None)\\n spider = ValueSpiderCrawl(self.protocol, node, nearest, self.ksize, self.alpha)\\n return spider.find()\",\n \"def query_current_node_info(self, obj_id, obj_type):\\n\\t\\tquery_str = \\\"SELECT obj FROM \\\" + str(obj_type) + \\\" WHERE obj->>'id'='\\\" + str(obj_id) + \\\"'\\\"\\n\\t\\tself.cur.execute(query_str)\\n\\t\\tresult = self.cur.fetchall()\\n\\t\\treturn result[0][0]\",\n \"def __getitem__(self, node):\\n j = self._hash_function(node)\\n bucket = self._T[j]\\n if bucket is None:\\n raise KeyError(node)\\n return bucket[node]\",\n \"def get_resource(self, graph_db, timestamp):\\n node_name = self.hostname + '_' + 'Machine' + '_0'\\n index = ('physical_resource', 'physical_name', node_name)\\n node = neo_resource.add_node(graph_db, index, timestamp)\\n return node\",\n \"def test_node_object_exists():\\n from priorityq import Node\\n node = Node(0, [1, 2])\\n assert node\",\n \"def _add_node(self, node_name, node_type):\\n q = 'MATCH (r:' + node_type + ') WHERE r.name=\\\"' \\\\\\n + node_name + '\\\" RETURN r'\\n results = self.db.query(q, returns=(client.Node, str, client.Node))\\n res = self.db.labels.create(node_type)\\n\\n if (len(results) == 0):\\n r = self.db.nodes.create(name=node_name)\\n res.add(r)\\n else:\\n r = results[0][0]\\n return r\",\n \"def nodeFromName(self, name):\\n for item in self.items():\\n if isinstance(item, NodeItem):\\n if item.name() == name:\\n return item\\n return None\",\n \"def get(self, key):\\n dkey = digest(key)\\n # if this node has it, return it\\n if self.storage.get(dkey) is not None:\\n return defer.succeed(self.storage.get(dkey))\\n node = Node(dkey)\\n nearest = self.protocol.router.findNeighbors(node)\\n if len(nearest) == 0:\\n self.log.warning(\\\"There are no known neighbors to get key %s\\\" % key)\\n return defer.succeed(None)\\n spider = ValueSpiderCrawl(self.protocol, node, nearest, self.ksize, self.alpha)\\n return spider.find()\",\n \"def get_node(self, name):\\n return self.source_net.nodes[name]\",\n \"def get_node(self, x: int, y: int) -> MazeCell:\\n node = self._nodes_by_row[y][x]\\n assert node.x == x and node.y == y, f\\\"(node.x, node.y) == ({node.x}, {node.y}), but should be ({x}, {y})\\\"\\n\\n return node\",\n \"def resolve(self, node):\\n if isinstance(node, Job):\\n return NodePointer(self.find(node))\\n else:\\n return node\",\n \"def get_node(self, name):\\n\\n assert name in self.nodes\\n return self.nodes[name]\",\n \"def search(self, key):\\n if key in self.key_list:\\n return (self.nodes)[key]\\n return None\",\n \"def ex_get_node_by_name(self, name):\\n domain = self._get_domain_for_name(name=name)\\n node = self._to_node(domain=domain)\\n return node\",\n \"def get_node(self, string_key):\\n pos = self.get_node_pos(string_key)\\n if pos is None:\\n return None\\n return self.ring[ self._sorted_keys[pos] ]\",\n \"def node_show(self, node):\\n if node.instance_uuid:\\n n = self.ironic_client.node.get_by_instance_uuid(\\n node.instance_uuid)\\n else:\\n n = self.ironic_client.node.get(node.uuid)\\n return n\",\n \"def get_node(self):\\n assert self._node_id is not None\\n return self._get_info(self.EXPECTED)[self._node_id]\",\n \"def _handle_gfk_field_node(self, node, field):\\n if node.getElementsByTagName('None'):\\n return None\\n ct_key = node.getAttribute(\\\"d:to\\\").split(NATURAL_KEY_JOINER)\\n ctype = ContentType.objects.get_by_natural_key(*ct_key)\\n Model = ctype.model_class()\\n if hasattr(Model._default_manager, 'get_by_natural_key'):\\n value = getInnerText(node).strip()\\n field_value = value.split(NATURAL_KEY_JOINER)\\n obj = Model._default_manager.db_manager(self.db).get_by_natural_key(*field_value)\\n return obj\\n else:\\n field_value = getInnerText(node).strip()\\n return Model._default_manager.db_manager(self.db).get(pk=Model._meta.pk.to_python(field_value))\",\n \"def test_lookup(graph):\\n node1 = graph.lookup(0)\\n assert str(node1) == \\\"<1, 2>\\\"\\n\\n node2 = graph.lookup(3)\\n assert str(node2) == \\\"<1, 2>\\\"\\n\\n node3 = graph.lookup(1)\\n assert str(node3) == \\\"<0, 2, 3>\\\"\",\n \"def __getitem__(self, key):\\n hash_ = self._hash(key)\\n start = bisect.bisect(self._keys, hash_)\\n if start == len(self._keys):\\n start = 0\\n return self._nodes[self._keys[start]]\",\n \"def __getitem__(self, key):\\n hash_ = self._hash(key)\\n start = bisect.bisect(self._keys, hash_)\\n if start == len(self._keys):\\n start = 0\\n return self._nodes[self._keys[start]]\",\n \"def find_node(self, node_name):\\n return self.labeled_nodes[node_name]\",\n \"def _get_node(self, seq):\\n cur_node = self.root\\n for token_id in seq:\\n children = cur_node.edges\\n if not token_id in children:\\n children[token_id] = SimpleNode()\\n cur_node = children[token_id]\\n return cur_node\",\n \"def getNodeById(self, nodes, id):\\n for item in nodes:\\n if item.getProperty('id') == id:\\n return item\",\n \"def get_node(self, selector, index):\\n\\n self.arbor._setup_tree(self)\\n self.arbor._grow_tree(self)\\n indices = getattr(self, f\\\"_{selector}_field_indices\\\", None)\\n if indices is None:\\n raise RuntimeError(\\\"Bad selector.\\\")\\n\\n my_link = self.root._links[indices][index]\\n return self.arbor._generate_tree_node(self.root, my_link)\",\n \"def get_node(self, key, print_path=False):\\r\\n parent_node, search_node = self.__compare(key, method='search', print_path=print_path)\\r\\n self.__check_node(search_node)\\r\\n\\r\\n return search_node\",\n \"def node_query(data, pk, room):\\n node = \\\"queries/{}/{}\\\".format(room, Params.PREFIX['query'] + str(pk))\\n if exist_node(node):\\n res = db.child(node).update(data)\\n else:\\n print(\\\"creo\\\")\\n res = db.child(node).set(data)\\n if res:\\n if \\\"succes\\\" in res:\\n if res[\\\"success\\\"] != 1:\\n logger.error(\\\"actualizando node query fallo \\\".format(node))\",\n \"def findNode(self, name):\\n return self._nodes.get(name)\",\n \"def get_node_by_name(self, name):\\n\\n for node in self.nodes:\\n if node.name == name:\\n return node\\n\\n return None\",\n \"def _getOrCreateByNum(self, nodeNum):\\n if nodeNum == BROADCAST_NUM:\\n raise Exception(\\\"Can not create/find nodenum by the broadcast num\\\")\\n\\n if nodeNum in self.nodesByNum:\\n return self.nodesByNum[nodeNum]\\n else:\\n n = {\\\"num\\\": nodeNum} # Create a minimial node db entry\\n self.nodesByNum[nodeNum] = n\\n return n\",\n \"def node(self, id=None, properties=None, **kwargs):\\r\\n\\t\\tif properties is None:\\r\\n\\t\\t\\tproperties = {}\\r\\n\\t\\tif id is None:\\r\\n\\t\\t\\tnodes = filter(self._nodes.values(), properties, **kwargs)\\r\\n\\t\\t\\tif not nodes:\\r\\n\\t\\t\\t\\treturn None\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\treturn nodes[0]\\r\\n\\t\\telse:\\r\\n\\t\\t\\tif id in self._nodes:\\r\\n\\t\\t\\t\\tresult_node = self._nodes[id]\\r\\n\\t\\t\\t\\tproperties.update(kwargs)\\r\\n\\t\\t\\t\\tfor key, value in properties.items():\\r\\n\\t\\t\\t\\t\\tif key not in result_node.properties:\\r\\n\\t\\t\\t\\t\\t\\treturn None\\r\\n\\t\\t\\t\\t\\tif result_node.properties[key] != value:\\r\\n\\t\\t\\t\\t\\t\\treturn None\\r\\n\\t\\t\\t\\treturn result_node\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\treturn None\",\n \"def get_node(self, state):\\n return self.nodes.get(Node.state_as_string(state))\",\n \"def node(self, ident, time=None):\\n if time is not None:\\n ident = (ident, time)\\n return self._nodes[ident]\",\n \"def get_node(self):\\n return self.__node\",\n \"def read_node(\\n *,\\n storage: AbstractStorage = Depends(get_storage),\\n node_id: str = Query(\\n \\\"\\\",\\n title=\\\"Node unique identifier\\\",\\n description=\\\"The node's ID.\\\"\\n ),\\n is_user_authorized: str = Depends(authenticate)\\n):\\n node = storage.get_node(node_id)\\n\\n if not node:\\n raise HTTPException(status_code=404, detail=\\\"Node not found\\\")\\n\\n return node\",\n \"def find_node(self, value):\\n for (fun, node) in self.__root.__fast_find:\\n if fun(value):\\n return node\\n return None\",\n \"def get_node(self):\\r\\n return self._node\",\n \"def get_node(self, linode_id):\\n try:\\n print \\\"LI %s\\\" % linode_id\\n return Linode.find(api_id=linode_id)\\n except chube_api.linode_api.ApiError, e:\\n print \\\"Looks like Linode's API is down:\\\"\\n print\\n print e\\n sys.exit(1)\",\n \"def node(env, node_name):\\n envs = environments()\\n check_env(env, envs)\\n query = AndOperator()\\n\\n if env != '*':\\n query.add(EqualsOperator(\\\"environment\\\", env))\\n\\n query.add(EqualsOperator(\\\"certname\\\", node_name))\\n\\n node = get_or_abort(puppetdb.node, node_name)\\n facts = node.facts()\\n return render_template(\\n 'node.html',\\n node=node,\\n facts=yield_or_stop(facts),\\n envs=envs,\\n current_env=env,\\n columns=REPORTS_COLUMNS[:2])\",\n \"def search(self, item):\\n # Find a node with the given item, if any\\n node = self._find_node(item)\\n # TODO: Return the node's data if found, or None\\n return node.data if ... else None\",\n \"def find_node(self, node_name):\\n try:\\n return next(x for x in self.all_nodes() if x.name == node_name)\\n except StopIteration:\\n merge_nodes = list(filter(lambda x: x.kind == 'Merge', self.all_nodes()))\\n # return next(x for x in merge_nodes if x.data['art1'] == node_name or x.data['art2'] == node_name\\n # or ('art3' in x.data and x.data['art3'] == node_name))\\n for x in merge_nodes:\\n if x.data['art1']['key'] == node_name or x.data['art2']['key'] == node_name \\\\\\n or ('art3' in x.data and x.data['art3']['key'] == node_name):\\n return x\\n print(node_name)\\n raise StopIteration()\",\n \"def getNode(self):\\n node = Entity.getNode(self)\\n node.tag = 'relentity'\\n return(node)\",\n \"def read_node(name: str = '', value: str = '') -> Node:\\n first_node = read_all_nodes(name=name, value=value).first()\\n return first_node\",\n \"def lookup(self, key):\\n k = self.get_position(key)\\n\\n if self.keys[k] == key:\\n return node.values[k]\\n\\n # Lookup in the child node.\\n if self.refs[k+1] == None:\\n return None\\n return self.refs[k+1].lookup(key)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71134365","0.6668859","0.65132576","0.6439901","0.635995","0.635122","0.62648284","0.62508345","0.6188875","0.6179738","0.6151343","0.6136216","0.61359334","0.61337614","0.61033213","0.609807","0.6092274","0.6070642","0.6068153","0.6040271","0.6040146","0.6030616","0.60240555","0.6013662","0.6011176","0.59997046","0.5970677","0.59607077","0.59599024","0.59583604","0.5956845","0.5952452","0.59483534","0.59386027","0.59386027","0.5936018","0.59177566","0.59054947","0.5900712","0.58968544","0.5889473","0.58803284","0.5865468","0.5859611","0.5825566","0.5820215","0.5818018","0.5813548","0.5803164","0.58031267","0.58004206","0.5793792","0.57846725","0.5773456","0.57675034","0.57639265","0.57628286","0.57418185","0.5737594","0.57371825","0.57282376","0.5719553","0.5717326","0.57025087","0.5689436","0.5686653","0.5679766","0.56660664","0.5663705","0.5660415","0.56522757","0.5641359","0.56407213","0.5636784","0.55940264","0.55938995","0.55938995","0.5589834","0.55893","0.5585235","0.557724","0.5577215","0.55750936","0.557487","0.5570647","0.5568816","0.5550402","0.5547868","0.55459255","0.5540017","0.55292577","0.5523633","0.5521408","0.5519537","0.55179036","0.5506316","0.5504155","0.55031246","0.5501687","0.5493887"],"string":"[\n \"0.71134365\",\n \"0.6668859\",\n \"0.65132576\",\n \"0.6439901\",\n \"0.635995\",\n \"0.635122\",\n \"0.62648284\",\n \"0.62508345\",\n \"0.6188875\",\n \"0.6179738\",\n \"0.6151343\",\n \"0.6136216\",\n \"0.61359334\",\n \"0.61337614\",\n \"0.61033213\",\n \"0.609807\",\n \"0.6092274\",\n \"0.6070642\",\n \"0.6068153\",\n \"0.6040271\",\n \"0.6040146\",\n \"0.6030616\",\n \"0.60240555\",\n \"0.6013662\",\n \"0.6011176\",\n \"0.59997046\",\n \"0.5970677\",\n \"0.59607077\",\n \"0.59599024\",\n \"0.59583604\",\n \"0.5956845\",\n \"0.5952452\",\n \"0.59483534\",\n \"0.59386027\",\n \"0.59386027\",\n \"0.5936018\",\n \"0.59177566\",\n \"0.59054947\",\n \"0.5900712\",\n \"0.58968544\",\n \"0.5889473\",\n \"0.58803284\",\n \"0.5865468\",\n \"0.5859611\",\n \"0.5825566\",\n \"0.5820215\",\n \"0.5818018\",\n \"0.5813548\",\n \"0.5803164\",\n \"0.58031267\",\n \"0.58004206\",\n \"0.5793792\",\n \"0.57846725\",\n \"0.5773456\",\n \"0.57675034\",\n \"0.57639265\",\n \"0.57628286\",\n \"0.57418185\",\n \"0.5737594\",\n \"0.57371825\",\n \"0.57282376\",\n \"0.5719553\",\n \"0.5717326\",\n \"0.57025087\",\n \"0.5689436\",\n \"0.5686653\",\n \"0.5679766\",\n \"0.56660664\",\n \"0.5663705\",\n \"0.5660415\",\n \"0.56522757\",\n \"0.5641359\",\n \"0.56407213\",\n \"0.5636784\",\n \"0.55940264\",\n \"0.55938995\",\n \"0.55938995\",\n \"0.5589834\",\n \"0.55893\",\n \"0.5585235\",\n \"0.557724\",\n \"0.5577215\",\n \"0.55750936\",\n \"0.557487\",\n \"0.5570647\",\n \"0.5568816\",\n \"0.5550402\",\n \"0.5547868\",\n \"0.55459255\",\n \"0.5540017\",\n \"0.55292577\",\n \"0.5523633\",\n \"0.5521408\",\n \"0.5519537\",\n \"0.55179036\",\n \"0.5506316\",\n \"0.5504155\",\n \"0.55031246\",\n \"0.5501687\",\n \"0.5493887\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63679963"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":19583,"cells":{"query":{"kind":"string","value":"Return a grounded node, with the value for its functor according to db"},"document":{"kind":"string","value":"def fillNode(node, grounding, db):\n gn = copy.deepcopy(node)\n gn.val = query(gn, grounding, db)\n return gn"},"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 query (node, grounding, db):\n return db.funcVal(grounding.groundNode(node))","def _expr2bddnode(expr):\n\t# print(\"_expr2bddnode\")\n\tif expr.is_zero():\n\t\treturn BDDNODEZERO\n\telif expr.is_one():\n\t\treturn BDDNODEONE\n\telse:\n\t\ttop = expr.top\n\n\t\t# Register this variable\n\t\t_ = bddvar(top.names, top.indices)\n\n\t\troot = top.uniqid\n\t\tlo = _expr2bddnode(expr.restrict({top: 0}))\n\t\thi = _expr2bddnode(expr.restrict({top: 1}))\n\t\treturn _bddnode(root, lo, hi)","def __call__(self):\r\n new_node = Node()\r\n new_node.op = self\r\n return new_node","def __call__(self):\n new_node = Node()\n new_node.op = self\n return new_node","def __call__(self):\r\n new_node = Op.__call__(self)\r\n return new_node","def __call__(self):\n new_node = Op.__call__(self)\n return new_node","def make_tree(v,l,r):\r\n def dispatch(t):\r\n if(t==0):\r\n return v\r\n elif(t==1):\r\n return l\r\n elif (t==2):\r\n return r\r\n return dispatch","def get_node(self, value):\n\t\treturn self.adjacency_list[value]","def build():\n r = TreeNode(1)\n r.left = TreeNode(2)\n r.left.left = TreeNode(4)\n r.left.right = TreeNode(5)\n\n r.right = TreeNode(3)\n\n return r\n return TreeNode(3)","def binary_op(node_factory_function: Callable) -> Callable:\n\n @wraps(node_factory_function)\n def wrapper(left: NodeInput, right: NodeInput, *args: Any, **kwargs: Any) -> Node:\n left, right = as_nodes(left, right)\n node = node_factory_function(left, right, *args, **kwargs)\n node = _set_node_friendly_name(node, **kwargs)\n return node\n\n return wrapper","def make_dp_node(config, geounitNode):\n import programs.engine.primitives as primitives\n import re\n \n (file,queries_class_name) = config[BUDGET][QUERIESFILE].rsplit(\".\", 1)\n queries_module = __import__(file, fromlist=[queries_class_name])\n \n REGEX_CONFIG_DELIM = \"^\\s+|\\s*,\\s*|\\s+$\"\n \n levels = tuple(config[\"geodict\"][\"geolevel_names\"].split(\",\"))\n levels_reversed = list(reversed(levels))\n #index relative to topdown\n index = levels_reversed.index(geounitNode.geolevel)\n total_budget = float(config[\"budget\"][\"epsilon_budget_total\"])\n \n #check that geolevel_budget_prop adds to 1, if not raise exception\n geolevel_prop_budgets= [float(x) for x in re.split(REGEX_CONFIG_DELIM, config[\"budget\"][\"geolevel_budget_prop\"])]\n t_prop_sum = round(sum(geolevel_prop_budgets) , 5)\n if t_prop_sum != 1.0:\n raise Exception(\"Total Budget Proportion must add to 1.0 ; Current Sum: \" .format(t_prop_sum) ) \n \n dp_budget = total_budget * geolevel_prop_budgets[index]\n \n hist_shape = geounitNode.raw.shape\n\n if config.has_option(BUDGET,'DPqueries'):\n dp_query_prop = [float(x) for x in re.split(REGEX_CONFIG_DELIM, config[\"budget\"][\"queriesprop\"])]\n dp_query_names = re.split(das_utils.DELIM, config[BUDGET]['DPqueries'])\n queries_dict= getattr(queries_module, queries_class_name)(hist_shape, dp_query_names).calculateQueries().queries_dict\n\n else:\n dp_query_prop = [0.0,0.0]\n dp_query_names = None\n queries_dict = {}\n \n detailed_prop = float(config[\"budget\"][\"detailedprop\"])\n prop_sum = sum(dp_query_prop) + detailed_prop\n \n #sensitivity specification\n #defaults\n global_sensitivity = 2.0\n detailed_sens = global_sensitivity\n if dp_query_names:\n dp_query_sens = [global_sensitivity for query in dp_query_names]\n \n #change defaults\n if config.has_option(\"budget\",\"global_sensitivity\"):\n val = config[\"budget\"][\"global_sensitivity\"]\n if val != \"\":\n global_sensitivity = float(val)\n detailed_sens = global_sensitivity\n if dp_query_names:\n dp_query_sens = [global_sensitivity for query in dp_query_names]\n elif config.has_option(\"budget\",\"detailed_sens\") & config.has_option(\"budget\",\"dp_query_sens\"):\n val = config[\"budget\"][\"detailed_sensitivity\"]\n if val != \"\":\n detailed_sens = float(config[\"budget\"][\"detailed_sensitivity\"])\n if dp_query_names:\n dp_query_sens = [float(x) for x in re.split(REGEX_CONFIG_DELIM, config[\"budget\"][\"queries_sensitivity\"])]\n \n #check that budget proportion adds to 1, if not raise exception\n if prop_sum != 1.0:\n raise Exception(\"Geolevel Budget Proportion must add to 1.0 ; Current Sum: \" .format(prop_sum) )\n\n np.random.seed()\n \n dp_queries = {}\n if config.has_option('budget','DPqueries'):\n n_queries = len(dp_query_names)\n \n for i in range(n_queries):\n name = dp_query_names[i]\n query = queries_dict[name]\n budgetprop = float(dp_query_prop[i])\n sens = float(dp_query_sens[i])\n DPanswer, Var = primitives.geometric_mechanism(true_answer=query.answer(geounitNode.raw.toDense()), budget=budgetprop*dp_budget, sensitivity=sens, prng=np.random)\n dp_query = {name: cenquery.DPquery(query=query, DPanswer=np.array(DPanswer),\n epsilon = budgetprop*dp_budget, DPmechanism = \"geometric\",\n Var = Var)}\n dp_queries.update(dp_query)\n \n DPgeounitNode = geounitNode\n DPgeounitNode.dp_queries = dp_queries\n \n DPanswer, Var = primitives.geometric_mechanism(true_answer = geounitNode.raw.toDense(), budget=detailed_prop*dp_budget, sensitivity=detailed_sens, prng=np.random)\n \n query = cenquery.Query(array_dims = geounitNode.raw.shape)\n DPgeounitNode.dp = cenquery.DPquery(query=query, DPanswer=DPanswer,\n epsilon = detailed_prop*dp_budget, DPmechanism = \"geometric\",\n Var = Var)\n return DPgeounitNode","def fn(node):\n if node and node not in mp:\n temp = mp[node] = Node(node.val)\n temp.next, temp.random = fn(node.next), fn(node.random)\n return mp.get(node)","def _(self, node: BinaryOp):\n left = self.visit(node.left)\n right = self.visit(node.right)\n\n return f\"( {node.op} {left} {right} )\"","def _bddnode(root, lo, hi):\n\t# print(\"_bddnode\")\n\tif lo is hi:\n\t\tnode = lo\n\telse:\n\t\tkey = (root, lo, hi)\n\t\ttry:\n\t\t\tnode = _NODES[key]\n\t\texcept KeyError:\n\t\t\tnode = _NODES[key] = BDDNode(*key)\n\treturn node","def _gen_test_tree_1():\n tree = BinaryNode(5)\n tree.left = BinaryNode(5)\n return tree","def compute_tree(self, x):\n if (self.body in operators):\n try:\n return self.body(self.left.compute_tree(x), self.right.compute_tree(x))\n except:\n return float(\"inf\")\n elif self.body == 'x': return x\n else: return self.body","def _db(self, value):","def get_node(self, key: str) -> Node:","def _gen_test_tree_4():\n tree = BinaryNode(5)\n tree.left = BinaryNode(3)\n tree.left.left = BinaryNode(2)\n tree.left.right = BinaryNode(10)\n tree.right = BinaryNode(9)\n tree.right.left = BinaryNode(6)\n tree.right.right = BinaryNode(8)\n return tree","def _handle_gfk_field_node(self, node, field):\n if node.getElementsByTagName('None'):\n return None\n ct_key = node.getAttribute(\"d:to\").split(NATURAL_KEY_JOINER)\n ctype = ContentType.objects.get_by_natural_key(*ct_key)\n Model = ctype.model_class()\n if hasattr(Model._default_manager, 'get_by_natural_key'):\n value = getInnerText(node).strip()\n field_value = value.split(NATURAL_KEY_JOINER)\n obj = Model._default_manager.db_manager(self.db).get_by_natural_key(*field_value)\n return obj\n else:\n field_value = getInnerText(node).strip()\n return Model._default_manager.db_manager(self.db).get(pk=Model._meta.pk.to_python(field_value))","def ground(self, db, term, target=None, label=None, **kwdargs):\n if term.is_negated():\n negated = True\n term = -term\n elif term.functor in ('not', '\\+') and term.arity == 1:\n negated = True\n term = term.args[0]\n else:\n negated = False\n\n target, results = self._ground(db, term, target, silent_fail=False, **kwdargs)\n\n args_node = defaultdict(list)\n for args, node_id in results:\n if not is_ground(*args) and target.is_probabilistic(node_id):\n raise NonGroundQuery(term, db.lineno(term.location))\n args = tuple(args)\n args_node[args].append(node_id)\n for args, node_ids in args_node.items():\n if len(node_ids) > 1:\n node_id = target.add_or(node_ids)\n else:\n node_id = node_ids[0]\n term_store = term.with_args(*args)\n if negated:\n target.add_name(-term_store, target.negate(node_id), label)\n else:\n target.add_name(term_store, node_id, label)\n if not results:\n if negated:\n target.add_name(-term, target.TRUE, label)\n else:\n target.add_name(term, target.FALSE, label)\n\n return target","def _gen_test_tree_3():\n tree = BinaryNode(5)\n tree.left = BinaryNode(1)\n tree.left.left = BinaryNode(2)\n tree.left.right = BinaryNode(3)\n tree.right = BinaryNode(7)\n tree.right.left = BinaryNode(8)\n tree.right.right = BinaryNode(9)\n return tree","def _gen_test_tree_2():\n tree = BinaryNode(5)\n tree.left = BinaryNode(3)\n tree.left.left = BinaryNode(2)\n tree.left.left.left = BinaryNode(1)\n tree.left.right = BinaryNode(4)\n tree.right = BinaryNode(7)\n tree.right.left = BinaryNode(6)\n tree.right.right = BinaryNode(8)\n tree.right.right.right = BinaryNode(9)\n return tree","def value(self) -> global___Expression:","def value(self) -> global___Expression:","def __init__(self, value: object) -> None:\n self.value = value # to store node's data\n self.left = None # pointer to root of left subtree\n self.right = None # pointer to root of right subtree","def __init__(self, value: object) -> None:\n self.value = value # to store node's data\n self.left = None # pointer to root of left subtree\n self.right = None # pointer to root of right subtree","def build():\n # root = TreeNode(5)\n # root.left = TreeNode(2)\n # root.right = TreeNode(7)\n # return root\n\n \"\"\"\n 5\n / \\\n 2 6\n / \\\n 1 3\n [5,2,1,3,6]\n \"\"\"\n _5 = TreeNode(5)\n _2 = TreeNode(2)\n _6 = TreeNode(6)\n _1 = TreeNode(1)\n _3 = TreeNode(3)\n _5.left = _2\n _5.right = _6\n _2.left = _1\n _2.right = _3\n return _5","def __init__(self, left=None, right=None, feature=None, value=None, predict=None):\n self.left = left\n self.right = right\n self.feature = feature # column in which features is stored\n self.value = value # value to check against\n self.predict = predict # class to predict at this node","def _gen_test_tree_6():\n tree = BinaryNode(20)\n tree.left = BinaryNode(10)\n tree.right = BinaryNode(30)\n tree.left.right = BinaryNode(25)\n return tree","def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None","def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None","def __getitem__(self, t: Tuple[int, ...]) -> 'Tree':\n ...","def db(r):\n ey = gQ(r)\n gR = b.tcl(\n 'global no_gizmo; set no_gizmo 1; in %s {%s -New} ; return [value [stack 0].name]' % (ey.fullName(), r.Class()))\n group = b.toNode('.'.join((ey.fullName(), gR)))\n group.setSelected(False)\n if ew(r):\n for node, gS in ew(r).iteritems():\n for c in gS:\n node.setInput(c, group)\n\n for c in range(r.inputs()):\n group.setInput(c, r.input(c))\n\n group.setXYpos(r.xpos(), r.ypos())\n group.readKnobs(r.writeKnobs(b.TO_SCRIPT))\n b.delete(r)\n return group","def fn(node, val):\n if not node: return 0\n val = 10*val + node.val\n if not node.left and not node.right: return val \n return fn(node.left, val) + fn(node.right, val)","def _(self, node: ModelReturn):\n val = self.visit(node.value)\n return f\"( return {val} )\"","def create_child(self, value, fun=None):\n child = TaxonomyTree(value, self.weights, fun)\n self.children[value] = child\n child.parent = self\n child.level = self.level + 1\n child.__root = self.__root\n self.__root.__fast_find.append((child.fun, child))\n return child","def _get_collections_tree_query(sql_value, sql_oper=OP_EQ):\n col_list = [COL_NAME_COLL_PARENTID, COL_NAME_COLL_COLLID, COL_NAME_COLL_NAME, COL_NAME_COLL_COLLCOMMENT,\n COL_NAME_COLL_PRID, COL_NAME_COLL_IS_ACTIVE]\n catcoll_tbl_alias = \"c\"\n cat_coll_tbl = SQLTableExpr(TABLE_NAME_COLL, catcoll_tbl_alias)\n col_list_aliased = [SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_PARENTID),\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_COLLID),\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_NAME),\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_COLLCOMMENT),\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_PRID),\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_IS_ACTIVE)]\n\n start = GenericSQLSelect(col_list, False, [cat_coll_tbl],\n SQLBinaryExpr(COL_NAME_COLL_COLLID, sql_oper, sql_value))\n join = SQLJoinExpr(cat_coll_tbl, OP_INNER_JOIN, SQLTableExpr(GEN_RECUR_NAME, \"r\"),\n SQLBinaryExpr(SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_PARENTID), OP_EQ,\n SQLColumnExpr(\"r\", COL_NAME_COLL_COLLID)))\n stop = GenericSQLSelect(col_list_aliased, False, [join])\n outer = GenericSQLSelect(col_list, False, [GEN_RECUR_NAME])\n wexp = str(SQLConcatExpr(EXPR_WITH, SQLFuncExpr(GEN_RECUR_NAME, str(col_list)[1:-1].replace(\"'\", \"\"))))\n wexp = SQLBinaryExpr(wexp, OP_AS, SQLConcatExpr(start, OP_UNION_ALL, stop))\n return wexp, outer","def _get_node(self, seq):\n cur_node = self.root\n for token_id in seq:\n children = cur_node.edges\n if not token_id in children:\n children[token_id] = SimpleNode()\n cur_node = children[token_id]\n return cur_node","def tree(self) -> Node:\n return Node(self.to_string())","def view_record_byvalue(self):\n conn = rs.create_connection(\"dailyfantasyscraper.db\")\n value = val.get()\n cur = conn.cursor()\n cur.execute(\n \"SELECT * FROM rotowiredk WHERE value > ?\", (value,))\n result = cur.fetchall()\n conn.commit()\n conn.close()\n\n for item in result:\n print(item)\n tree.insert('', 'end', values=item)","def tree():\n return defaultdict(tree)","def __getattr__(self, attr):\n def factory(parent=None, **kwargs):\n return self.Node(parent, attr, **kwargs)\n return factory","def __arb__(self):\n if self.tree.total < 1:\n return None\n if self.tree.total % 2 == 0:\n return self.first()\n else:\n return self.last()","def Db(self, (k,t), (J,q), **params):\n Db = util.D(lambda q : self.b( (k,t), (J,q), **params ), q)\n return Db","def visit(self, node):","def visit(self, node):","def _gen_test_tree_5():\n tree = BinaryNode(30)\n tree.right = BinaryNode(30)\n return tree","def __init__(self, val):\n\n\t\tself.val = val\n\t\tself.left = None\n\t\tself.right = None","def on_graph(self, node):\n if node.value.parent is None:\n return self.graph_map[node.value]\n if node not in self.node_map:\n self.node_map[node] = self.convert_func(node.value)\n return self.node_map[node]","def __init__(self):\n self.idx = None\n self.val = None\n self.left = None\n self.right = None","def fn(node):\n if not node: return 0 \n ans = node.val + fn(node.left) + fn(node.right)\n vals.append(ans)\n return ans","def create_node(self, data):\n node = RealNode(data, layer=self)\n self.append_node(node)\n return node","def gremlin(self):\r\n initial = '{} = g.makeType().name(\"{}\").{}{}makeEdgeLabel()'\r\n primary_key = ''\r\n if self.primary_key:\r\n primary_key = \"primaryKey({}).\".format(self.primary_key)\r\n\r\n functional = \"functional().\" if self.functional else \"\"\r\n\r\n return initial.format(self.label, self.label, primary_key, functional)","def gQ(n):\n return b.toNode('.'.join(n.fullName().split('.')[:-1])) or b.root()","def nodeGetter(instance):\n if (instance, name) not in memo:\n subspace = subspaceFunction(instance)\n\n if subspace is None:\n assert False, \"Instance %s produced an empty subspace\" % instance\n\n memo[(instance,name)] = subspace.subspace(name)\n return memo[(instance,name)]","def query(self, key):\n arr_types = (\"forest\", \"prog\", \"tree\")\n if isinstance(key, tuple):\n if len(key) != 2:\n raise SyntaxError(\n \"Must be either 1 or 2 arguments.\")\n ftype, field = key\n if ftype not in arr_types:\n raise SyntaxError(\n f\"First argument must be one of {str(arr_types)}.\")\n if not isinstance(field, str):\n raise SyntaxError(\"Second argument must be a string.\")\n\n self.arbor._node_io.get_fields(self, fields=[field], root_only=False)\n indices = getattr(self, f\"_{ftype}_field_indices\")\n\n data_object = self.find_root()\n return data_object.field_data[field][indices]\n\n else:\n if not isinstance(key, str):\n raise SyntaxError(\"Single argument must be a string.\")\n\n # return the progenitor list or tree nodes in a list\n if key in arr_types:\n self.arbor._setup_tree(self)\n return getattr(self, f\"_{key}_nodes\")\n\n # return field value for this node\n self.arbor._node_io.get_fields(self, fields=[key],\n root_only=self.is_root)\n data_object = self.find_root()\n return data_object.field_data[key][self.tree_id]","def get_node_b(name, taxid, pathway, topology, psi_mi_to_sql_object):\n\n # Testing if the node is already in the database\n node_dict = psi_mi_to_sql_object.get_node(name, node_tax_id=taxid)\n\n if not node_dict:\n node_dict = {\n \"name\": 'Uniprot:' + name,\n \"tax_id\": taxid,\n \"alt_accession\": None,\n 'pathways': pathway,\n \"aliases\": None,\n \"topology\": topology\n }\n\n return node_dict","def createTracker3Node():\n return fQ()","def createTracker4Node():\n return fR()","def _create(self, context, values):\n # initialize load stats from existing instances:\n compute_node = db.compute_node_create(context, values)\n return compute_node","def from_db_value(self, value, expression, connection, context):\n return self.to_python(value)","def lookup(self, val):\n if val < self.val:\n if self.left is None:\n return None, None\n return self.left.lookup(val)\n elif val > self.val:\n if self.right is None:\n return None, None\n return self.right.lookup(val)\n else:\n return self","def node(self, func_or_name):\n\n self._compiled = None\n\n def _decorator(fn):\n self.graph[func_or_name] = fn\n return fn\n\n if callable(func_or_name):\n self.graph[func_or_name.__name__] = func_or_name\n return func_or_name\n else:\n return _decorator","def bad_tree():\n return OvalNode(\n node_id=1,\n node_type=\"value\",\n value=\"true\",\n children=[\n OvalNode(\n node_id=2,\n node_type=\"operator\",\n value=\"and\",\n children=[\n OvalNode(\n node_id=3,\n node_type=\"value\",\n value=\"true\",\n ),\n ]\n ),\n ]\n )","def __init__(self, formula = None, field_name = \"new_field\", analytical_type = \"unknown\",\r\n storage_type = \"unknown\"):\r\n super(DeriveNode, self).__init__()\r\n self.formula = formula\r\n self.field_name = field_name\r\n self.analytical_type = analytical_type\r\n self.storage_type = storage_type\r\n self._output_fields = None","def weight(tree):\n return root(tree)","def drupal_db_read(db_obj, db_cur, key_cv, value_cv):\n\n # get the chain type\n chain_type = get_drupal_chain_type(key_cv, value_cv)\n if not chain_type:\n nori.core.email_logger.error(\n'''Internal Error: invalid field list supplied in call to\ndrupal_db_read(); call was (in expanded notation):\n\ndrupal_db_read(\n db_obj={0},\n db_cur={1},\n key_cv={2},\n value_cv={3}\n)\n\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, key_cv, value_cv]))\n )\n sys.exit(nori.core.exitvals['internal']['num'])\n\n ########### assemble the query string and argument list ###########\n\n #\n # node -> field(s) (including term references)\n #\n if chain_type == 'n-f':\n # node details\n node_cv = key_cv[0]\n node_ident = node_cv[0]\n node_value_type = node_cv[1]\n if len(node_cv) > 2:\n node_value = node_cv[2]\n node_type = node_ident[1]\n node_id_type = node_ident[2]\n\n # handle node ID types\n if node_id_type == 'id':\n key_column = 'node.nid'\n elif node_id_type == 'title':\n key_column = 'node.title'\n\n # handle specified node value\n node_value_cond = ''\n if len(node_cv) > 2:\n node_value_cond = 'AND {0} = %s'.format(key_column)\n\n field_idents = {}\n field_value_types = {}\n field_values = []\n field_names = {}\n value_columns = []\n field_joins = []\n term_joins = []\n field_value_conds = []\n field_deleted_conds = []\n v_order_columns = []\n for i, field_cv in enumerate(value_cv):\n # field details\n field_idents[i] = field_cv[0]\n field_value_types[i] = field_cv[1]\n if len(field_cv) > 2:\n field_values.append(field_cv[2])\n field_names[i] = field_idents[i][1]\n\n # field join\n field_joins.append(\n 'LEFT JOIN field_data_field_{0} AS f{1}\\n'\n ' ON f{1}.entity_id = node.nid\\n'\n ' AND f{1}.revision_id = node.vid' .\n format(field_names[i], i)\n )\n\n # handle value types\n if field_value_types[i].startswith('term: '):\n value_columns.append('t{0}.name'.format(i))\n term_joins.append(\n 'LEFT JOIN taxonomy_term_data AS t{0}\\n'\n ' ON t{0}.tid = f{0}.field_{1}_tid' .\n format(i, field_names[i])\n )\n elif field_value_types[i] == 'ip':\n value_columns.append(\n 'f{0}.field_{1}_start'.format(i, field_names[i])\n )\n else:\n value_columns.append(\n 'f{0}.field_{1}_value'.format(i, field_names[i])\n )\n\n # handle specified field value\n if len(field_cv) > 2:\n field_value_conds.append(\n 'AND {0} = %s'.format(value_columns[-1])\n )\n\n # not deleted\n field_deleted_conds.append(\n 'AND f{0}.deleted = 0'.format(i)\n )\n\n # order column\n v_order_columns.append('f{0}.delta'.format(i))\n\n # query string and arguments\n query_str = (\n'''\nSELECT {0}, {1}\nFROM node\n{2}\n{3}\nWHERE node.vid IN\n (SELECT MAX(vid)\n FROM node_revision\n GROUP BY nid)\nAND node.type = %s\n{4}\n{5}\n{6}\nORDER BY node.title, node.nid, {7}\n''' .\n format(key_column, ', '.join(value_columns),\n '\\n'.join(field_joins),\n '\\n'.join(term_joins),\n node_value_cond,\n '\\n'.join(field_value_conds),\n '\\n'.join(field_deleted_conds),\n ', '.join(v_order_columns))\n )\n query_args = [node_type]\n if len(node_cv) > 2:\n query_args.append(node_value)\n query_args += field_values\n\n #\n # node -> relation -> node\n #\n elif chain_type == 'n-r-n':\n # key-node details\n k_node_cv = key_cv[0]\n k_node_ident = k_node_cv[0]\n k_node_value_type = k_node_cv[1]\n if len(k_node_cv) > 2:\n k_node_value = k_node_cv[2]\n k_node_type = k_node_ident[1]\n k_node_id_type = k_node_ident[2]\n\n # handle key-node ID types\n if k_node_id_type == 'id':\n node_key_column = 'k_node.nid'\n elif k_node_id_type == 'title':\n node_key_column = 'k_node.title'\n\n # handle specified key-node value\n k_node_value_cond = ''\n if len(k_node_cv) > 2:\n k_node_value_cond = 'AND {0} = %s'.format(node_key_column)\n\n # relation details\n relation_cv = key_cv[1]\n relation_ident = relation_cv[0]\n relation_type = relation_ident[1]\n\n # handle key relation-field\n relation_key_column = ''\n relation_field_join = ''\n relation_field_cond = ''\n relation_value_cond = ''\n if len(relation_ident) > 2:\n relation_field_name = relation_ident[2]\n relation_value_type = relation_cv[1]\n\n # field join\n relation_field_join = (\n 'LEFT JOIN field_data_field_{0} AS k_rf\\n'\n ' ON k_rf.entity_id = e2.entity_id\\n'\n ' AND k_rf.revision_id = e2.revision_id' .\n format(relation_field_name)\n )\n\n # conditions\n relation_field_cond = (\n \"AND k_rf.entity_type = 'relation'\\n\"\n \"AND k_rf.deleted = 0\"\n )\n\n # handle value type\n if relation_value_type.startswith('term: '):\n relation_key_column = 'k_rf_t.name'\n relation_field_join += (\n '\\nLEFT JOIN taxonomy_term_data AS k_rf_t\\n'\n 'ON k_rf_t.tid = k_rf.field_{0}_tid' .\n format(relation_field_name)\n )\n elif relation_value_type == 'ip':\n relation_key_column = (\n 'k_rf.field_{0}_start'.format(relation_field_name)\n )\n else:\n relation_key_column = (\n 'k_rf.field_{0}_value'.format(relation_field_name)\n )\n\n # handle specified field value\n if len(relation_cv) > 2:\n relation_value = relation_cv[2]\n relation_value_cond = (\n 'AND {0} = %s'.format(relation_key_column)\n )\n\n # value-node details\n v_node_cv = value_cv[0]\n v_node_ident = v_node_cv[0]\n v_node_value_type = v_node_cv[1]\n if len(v_node_cv) > 2:\n v_node_value = v_node_cv[2]\n v_node_type = v_node_ident[1]\n v_node_id_type = v_node_ident[2]\n\n # handle value-node ID types\n if v_node_id_type == 'id':\n value_column = 'v_node.nid'\n elif v_node_id_type == 'title':\n value_column = 'v_node.title'\n\n # handle value-node type\n extra_value_cols = ''\n v_node_type_cond = ''\n if v_node_type is None:\n extra_value_cols = ', v_node.type'\n else:\n v_node_type_cond = 'AND v_node.type = %s'\n\n # handle specified value-node value\n v_node_value_cond = ''\n if len(v_node_cv) > 2:\n v_node_value_cond = 'AND {0} = %s'.format(value_column)\n\n # query string and arguments\n query_str = (\n'''\nSELECT {0}, {1}{2}{3}\nFROM node AS k_node\nLEFT JOIN field_data_endpoints AS e1\n ON e1.endpoints_entity_id = k_node.nid\nLEFT JOIN field_data_endpoints AS e2\n ON e2.entity_id = e1.entity_id\n AND e2.revision_id = e1.revision_id\n AND e2.endpoints_r_index > e1.endpoints_r_index\n{4}\nLEFT JOIN node AS v_node\n ON v_node.nid = e2.endpoints_entity_id\nWHERE k_node.vid IN\n (SELECT MAX(vid)\n FROM node_revision\n GROUP BY nid)\nAND k_node.type = %s\n{5}\nAND e1.revision_id IN\n (SELECT MAX(vid)\n FROM relation_revision\n GROUP BY rid)\nAND e1.entity_type = 'relation'\nAND e1.bundle = %s\nAND e1.endpoints_entity_type = 'node'\nAND e1.deleted = 0\nAND e2.endpoints_entity_type = 'node'\nAND e2.deleted = 0\n{6}\n{7}\nAND v_node.vid IN\n (SELECT MAX(vid)\n FROM node_revision\n GROUP BY nid)\n{8}\n{9}\nORDER BY k_node.title, k_node.nid, e1.entity_id, v_node.title, v_node.nid\n''' .\n format(node_key_column,\n (relation_key_column + ', ') if relation_key_column\n else '',\n value_column,\n extra_value_cols,\n relation_field_join,\n k_node_value_cond,\n relation_field_cond,\n relation_value_cond,\n v_node_type_cond,\n v_node_value_cond)\n )\n query_args = [k_node_type]\n if len(k_node_cv) > 2:\n query_args.append(k_node_value)\n query_args.append(relation_type)\n if len(relation_ident) > 2 and len(relation_cv) > 2:\n query_args.append(relation_value)\n if v_node_type is not None:\n query_args.append(v_node_type)\n if len(v_node_cv) > 2:\n query_args.append(v_node_value)\n\n #\n # node -> relation & node -> relation_field(s) (incl. term refs)\n #\n elif chain_type == 'n-rn-rf':\n # node1 details\n node1_cv = key_cv[0]\n node1_ident = node1_cv[0]\n node1_value_type = node1_cv[1]\n if len(node1_cv) > 2:\n node1_value = node1_cv[2]\n node1_type = node1_ident[1]\n node1_id_type = node1_ident[2]\n\n # handle node1 ID types\n if node1_id_type == 'id':\n node1_key_column = 'node1.nid'\n elif node1_id_type == 'title':\n node1_key_column = 'node1.title'\n\n # handle specified node1 value\n node1_value_cond = ''\n if len(node1_cv) > 2:\n node1_value_cond = 'AND {0} = %s'.format(node1_key_column)\n\n # relation details\n relation_cv = key_cv[1]\n relation_ident = relation_cv[0]\n relation_type = relation_ident[1]\n\n # handle key relation-field\n relation_key_column = ''\n relation_field_join = ''\n relation_field_cond = ''\n relation_value_cond = ''\n if len(relation_ident) > 2:\n relation_field_name = relation_ident[2]\n relation_value_type = relation_cv[1]\n\n # field join\n relation_field_join = (\n 'LEFT JOIN field_data_field_{0} AS k_rf\\n'\n ' ON k_rf.entity_id = e2.entity_id\\n'\n ' AND k_rf.revision_id = e2.revision_id' .\n format(relation_field_name)\n )\n\n # conditions\n relation_field_cond = (\n \"AND k_rf.entity_type = 'relation'\\n\"\n \"AND k_rf.deleted = 0\"\n )\n\n # handle value type\n if relation_value_type.startswith('term: '):\n relation_key_column = 'k_rf_t.name'\n relation_field_join += (\n '\\nLEFT JOIN taxonomy_term_data AS k_rf_t\\n'\n 'ON k_rf_t.tid = k_rf.field_{0}_tid' .\n format(relation_field_name)\n )\n elif relation_value_type == 'ip':\n relation_key_column = (\n 'k_rf.field_{0}_start'.format(relation_field_name)\n )\n else:\n relation_key_column = (\n 'k_rf.field_{0}_value'.format(relation_field_name)\n )\n\n # handle specified field value\n if len(relation_cv) > 2:\n relation_value = relation_cv[2]\n relation_value_cond = (\n 'AND {0} = %s'.format(relation_key_column)\n )\n\n # node2 details\n node2_cv = key_cv[2]\n node2_ident = node2_cv[0]\n node2_value_type = node2_cv[1]\n if len(node2_cv) > 2:\n node2_value = node2_cv[2]\n node2_type = node2_ident[1]\n node2_id_type = node2_ident[2]\n\n # handle node2 ID types\n if node2_id_type == 'id':\n node2_key_column = 'node2.nid'\n elif node2_id_type == 'title':\n node2_key_column = 'node2.title'\n\n # handle specified node2 value\n node2_value_cond = ''\n if len(node2_cv) > 2:\n node2_value_cond = 'AND {0} = %s'.format(node2_key_column)\n\n field_idents = {}\n field_value_types = {}\n field_values = []\n field_names = {}\n value_columns = []\n field_joins = []\n term_joins = []\n field_entity_conds = []\n field_value_conds = []\n field_deleted_conds = []\n v_order_columns = []\n for i, field_cv in enumerate(value_cv):\n # field details\n field_idents[i] = field_cv[0]\n field_value_types[i] = field_cv[1]\n if len(field_cv) > 2:\n field_values.append(field_cv[2])\n field_names[i] = field_idents[i][1]\n\n # field join\n field_joins.append(\n 'LEFT JOIN field_data_field_{0} AS f{1}\\n'\n ' ON f{1}.entity_id = e2.entity_id\\n'\n ' AND f{1}.revision_id = e2.revision_id' .\n format(field_names[i], i)\n )\n\n # handle value types\n if field_value_types[i].startswith('term: '):\n value_columns.append('t{0}.name'.format(i))\n term_joins.append(\n 'LEFT JOIN taxonomy_term_data AS t{0}\\n'\n ' ON t{0}.tid = f{0}.field_{1}_tid' .\n format(i, field_names[i])\n )\n elif field_value_types[i] == 'ip':\n value_columns.append(\n 'f{0}.field_{1}_start'.format(i, field_names[i])\n )\n else:\n value_columns.append(\n 'f{0}.field_{1}_value'.format(i, field_names[i])\n )\n\n # field entity type\n field_entity_conds.append(\n \"AND f{0}.entity_type = 'relation'\".format(i)\n )\n\n # handle specified field value\n if len(field_cv) > 2:\n field_value_conds.append(\n 'AND {0} = %s'.format(value_columns[-1])\n )\n\n # not deleted\n field_deleted_conds.append(\n 'AND f{0}.deleted = 0'.format(i)\n )\n\n # order column\n v_order_columns.append('f{0}.delta'.format(i))\n\n # query string and arguments\n query_str = (\n'''\nSELECT {0}, {1}{2}, {3}\nFROM node AS node1\nLEFT JOIN field_data_endpoints AS e1\n ON e1.endpoints_entity_id = node1.nid\nLEFT JOIN field_data_endpoints AS e2\n ON e2.entity_id = e1.entity_id\n AND e2.revision_id = e1.revision_id\n AND e2.endpoints_r_index > e1.endpoints_r_index\n{4}\nLEFT JOIN node AS node2\n ON node2.nid = e2.endpoints_entity_id\n{5}\n{6}\nWHERE node1.vid IN\n (SELECT MAX(vid)\n FROM node_revision\n GROUP BY nid)\nAND node1.type = %s\n{7}\nAND e1.revision_id IN\n (SELECT MAX(vid)\n FROM relation_revision\n GROUP BY rid)\nAND e1.entity_type = 'relation'\nAND e1.bundle = %s\nAND e1.endpoints_entity_type = 'node'\nAND e1.deleted = 0\nAND e2.endpoints_entity_type = 'node'\nAND e2.deleted = 0\n{8}\n{9}\nAND node2.vid IN\n (SELECT MAX(vid)\n FROM node_revision\n GROUP BY nid)\nAND node2.type = %s\n{10}\n{11}\n{12}\n{13}\nORDER BY node1.title, node1.nid, e1.entity_id, {14}\n''' .\n format(node1_key_column,\n (relation_key_column + ', ') if relation_key_column\n else '',\n node2_key_column,\n ', '.join(value_columns),\n relation_field_join,\n '\\n'.join(field_joins),\n '\\n'.join(term_joins),\n node1_value_cond,\n relation_field_cond,\n relation_value_cond,\n node2_value_cond,\n '\\n'.join(field_entity_conds),\n '\\n'.join(field_value_conds),\n '\\n'.join(field_deleted_conds),\n ', '.join(v_order_columns))\n )\n query_args = [node1_type]\n if len(node1_cv) > 2:\n query_args.append(node1_value)\n query_args.append(relation_type)\n if len(relation_ident) > 2 and len(relation_cv) > 2:\n query_args.append(relation_value)\n query_args.append(node2_type)\n if len(node2_cv) > 2:\n query_args.append(node2_value)\n query_args += field_values\n\n #\n # node -> fc -> field(s) (including term references)\n #\n elif chain_type == 'n-fc-f':\n # node details\n node_cv = key_cv[0]\n node_ident = node_cv[0]\n node_value_type = node_cv[1]\n if len(node_cv) > 2:\n node_value = node_cv[2]\n node_type = node_ident[1]\n node_id_type = node_ident[2]\n\n # handle node ID types\n if node_id_type == 'id':\n key_column = 'node.nid'\n elif node_id_type == 'title':\n key_column = 'node.title'\n\n # handle specified node value\n node_value_cond = ''\n if len(node_cv) > 2:\n node_value_cond = 'AND {0} = %s'.format(key_column)\n\n # fc details\n fc_cv = key_cv[1]\n fc_ident = fc_cv[0]\n fc_value_type = fc_cv[1]\n if len(fc_cv) > 2:\n fc_value = fc_cv[2]\n fc_type = fc_ident[1]\n fc_id_type = fc_ident[2]\n\n # handle fc ID types\n if fc_id_type == 'id':\n extra_key_column = 'fci.item_id'\n elif fc_id_type == 'label':\n extra_key_column = 'fci.label'\n\n # handle specified fc value\n fc_value_cond = ''\n if len(fc_cv) > 2:\n fc_value_cond = 'AND {0} = %s'.format(extra_key_column)\n\n field_idents = {}\n field_value_types = {}\n field_values = []\n field_names = {}\n value_columns = []\n field_joins = []\n term_joins = []\n field_entity_conds = []\n field_value_conds = []\n field_deleted_conds = []\n v_order_columns = []\n for i, field_cv in enumerate(value_cv):\n # field details\n field_idents[i] = field_cv[0]\n field_value_types[i] = field_cv[1]\n if len(field_cv) > 2:\n field_values.append(field_cv[2])\n field_names[i] = field_idents[i][1]\n\n # field join\n field_joins.append(\n 'LEFT JOIN field_data_field_{0} AS f{1}\\n'\n ' ON f{1}.entity_id = fci.item_id\\n'\n ' AND f{1}.revision_id = fci.revision_id' .\n format(field_names[i], i)\n )\n\n # handle value types\n if field_value_types[i].startswith('term: '):\n value_columns.append('t{0}.name'.format(i))\n term_joins.append(\n 'LEFT JOIN taxonomy_term_data AS t{0}\\n'\n ' ON t{0}.tid = f{0}.field_{1}_tid' .\n format(i, field_names[i])\n )\n elif field_value_types[i] == 'ip':\n value_columns.append(\n 'f{0}.field_{1}_start'.format(i, field_names[i])\n )\n else:\n value_columns.append(\n 'f{0}.field_{1}_value'.format(i, field_names[i])\n )\n\n # field entity type\n field_entity_conds.append(\n \"AND f{0}.entity_type = 'field_collection_item'\".format(i)\n )\n\n # handle specified field value\n if len(field_cv) > 2:\n field_value_conds.append(\n 'AND {0} = %s'.format(value_columns[-1])\n )\n\n # not deleted\n field_deleted_conds.append(\n 'AND f{0}.deleted = 0'.format(i)\n )\n\n # order column\n v_order_columns.append('f{0}.delta'.format(i))\n\n # query string and arguments\n query_str = (\n'''\nSELECT {0}, {1}{2}\nFROM node\nLEFT JOIN field_data_field_{3} AS fcf\n ON fcf.entity_id = node.nid\n AND fcf.revision_id = node.vid\nLEFT JOIN field_collection_item as fci\n ON fci.item_id = fcf.field_{3}_value\n AND fci.revision_id = fcf.field_{3}_revision_id\n{4}\n{5}\nWHERE node.vid IN\n (SELECT MAX(vid)\n FROM node_revision\n GROUP BY nid)\nAND node.type = %s\n{6}\nAND fcf.entity_type = 'node'\nAND fcf.deleted = 0\nAND fci.revision_id IN\n (SELECT MAX(revision_id)\n FROM field_collection_item_revision\n GROUP BY item_id)\nAND fci.archived = 0\n{7}\n{8}\n{9}\n{10}\nORDER BY node.title, node.nid, fcf.delta, {11}\n''' .\n format(key_column,\n (extra_key_column + ', ') if extra_key_column else '',\n ', '.join(value_columns),\n fc_type,\n '\\n'.join(field_joins),\n '\\n'.join(term_joins),\n node_value_cond,\n fc_value_cond,\n '\\n'.join(field_entity_conds),\n '\\n'.join(field_value_conds),\n '\\n'.join(field_deleted_conds),\n ', '.join(v_order_columns))\n )\n query_args = [node_type]\n if len(node_cv) > 2:\n query_args.append(node_value)\n if len(fc_cv) > 2:\n query_args.append(fc_value)\n query_args += field_values\n\n ######################## execute the query ########################\n\n if not db_obj.execute(db_cur, query_str.strip(), query_args,\n has_results=True):\n return None\n ret = db_obj.fetchall(db_cur)\n if not ret[0]:\n return None\n if not ret[1]:\n return []\n return ret[1]","def construction(self):\n from sage.categories.pushout import AlgebraicClosureFunctor\n return (AlgebraicClosureFunctor(), self._real_field())","def build():\n root = TreeNode(3)\n root.left = TreeNode(2)\n root.right = TreeNode(4)\n root.right.right = TreeNode(7)\n root.right.left = TreeNode(5)\n return root","def __call__(self, value):\n self.right.left = self.__class__(value)\n self.right.left.right = self.right\n self.right = self.right.left\n self.right.left = self","def node(self):\n return Node(self)","def gremlin(self):\r\n initial = '{} = g.makeType().name(\"{}\").dataType({}.class).{}{}{}makePropertyKey()'\r\n func = ''\r\n idx = ''\r\n if self.functional:\r\n func = 'functional({}).'.format(\"true\" if self.locking else \"false\")\r\n if self.indexed:\r\n idx = 'indexed().'\r\n\r\n unique = \"unique().\" if self.unique else \"\"\r\n\r\n return initial.format(self.name, self.name, self.data_type, func, idx, unique)","def from_db_value(self, value, expression, connection):\n return self.to_python(value)","def from_db_value(self, value, expression, connection):\n # pylint: disable=unused-argument\n return self.to_python(value)","def __init__(self):\n self.left = None\n self.right = None\n self.depth = 0\n self.val = None\n self.id = None","def node(func, name=None):\n return NamedFunc(func, name)","def map_value(self) -> global___Expression.MapValue:","def tree2gv_helper(node: TreeNode, result: graphviz.Graph, id: int) -> None:\n idn = id\n result.node(idn, node.to_text(), shape=\"box\") # node.shape())\n if isinstance(node, LeafData):\n return\n if node.left is not None:\n idl = id + \"0\"\n tree2gv_helper(node.left, result, idl)\n result.edge(idn, idl)\n if node.right is not None:\n idr = id + \"1\"\n tree2gv_helper(node.right, result, idr)\n result.edge(idn, idr)","def get_terminal_node(\n self,\n feature_col: int,\n node: TreeSplits,\n feature_value: float,\n X: np.ndarray,\n y: np.ndarray,\n ):\n # Get the node type\n node_type = self.col_type_map[feature_col]\n\n if node_type == \"continuous\":\n # If no feature value is passed, this node is the leaf\n if feature_value is None:\n node.children = y\n self.n_nodes += 1\n # If a feature value is passed, create leaves as children\n else:\n # Get the above node\n above = X[:, feature_col] > feature_value\n\n # Add two children\n node.update(\n feature_col=feature_col,\n feature_value=feature_value,\n node_type=node_type,\n nodes={\n \"above\": TreeSplits(\n children=y[above]\n ), # Children are above points\n \"below\": TreeSplits(\n children=y[np.bitwise_not(above)]\n ), # Children are below points\n },\n )\n # Add two nodes to count\n self.n_nodes += 2\n else:\n # Get the valid values of the discrete column\n unique_x_vals = self.discrete_value_maps[feature_col]\n # Create the node\n node.update(\n feature_col=feature_col,\n feature_value=None,\n nodes={\n xval: TreeSplits(\n children=y[X[:, feature_col] == xval]\n ) # Add in the matching rows\n if np.any(X[:, feature_col] == xval) # If discrete values match\n else TreeSplits(\n children=y\n ) # Add in all the rows if there is no values match\n for xval in unique_x_vals\n },\n node_type=\"discrete\",\n )\n self.n_nodes += len(unique_x_vals) # increment node counter","def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None","def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None","def __getitem__(self, idx):\n\n pos = self.node_pos[self.n_cumsum[idx] : self.n_cumsum[idx + 1]]\n src = self.src[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]]\n dst = self.dst[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]]\n\n g = dgl_graph((src, dst))\n\n g.ndata[\"pos\"] = F.tensor(pos, dtype=F.data_type_dict[\"float32\"])\n g.ndata[\"attr\"] = F.tensor(\n self.node_attr[self.n_cumsum[idx] : self.n_cumsum[idx + 1]],\n dtype=F.data_type_dict[\"float32\"],\n )\n g.edata[\"edge_attr\"] = F.tensor(\n self.edge_attr[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]],\n dtype=F.data_type_dict[\"float32\"],\n )\n\n label = F.tensor(\n self.targets[idx][self.label_keys],\n dtype=F.data_type_dict[\"float32\"],\n )\n\n if self._transform is not None:\n g = self._transform(g)\n\n return g, label","def gen_fv(self, g, ng, fv):\n if fv.graph not in self.graphs:\n return self.gen_constant(g, ng, fv)","def value(d,o):\n # return memoized value if possible\n if (d,o) in v:\n return v[(d,o)]\n\n thisitem = int(t[d][o])\n # the total of a subtree that starts at the leaf, is just the value of the leaf\n if d == maxdepth:\n val = thisitem\n else:\n val = thisitem + max(value(d+1,o),value(d+1,o+1))\n\n v[(d,o)]=val\n return val","def fn(node):\n if not node: return 0, 0 # null node \n if node.left is node.right: return node.val, 0 # leaf node \n left, right = fn(node.left), fn(node.right) # post-order traversal \n return node.val+left[1]+right[1], max(left)+max(right)","def gdb_add_node(node, gdb, rdf, owl):\n gdb_node = gdb.nodes.create()\n node.set_node(gdb_node)\n gdb_node.labels.add([label.split('#')[-1] for label in node.get_labels()])\n for _, pro, obj in rdf.triples((node.get_uri(), None, None)):\n if (pro, RDF.type, owl.DatatypeProperty) in rdf:\n prop_name = pro.split('#')[-1]\n value = obj.split('#')[-1]\n gdb_node.set(prop_name, value)","def get_or_create_node(node_id, sqlite_db):\n\n if node_id is None:\n return None\n\n cursor = sqlite_db.cursor()\n query = \"SELECT * FROM nodes WHERE id='{}'\".format(node_id)\n cursor.execute(query)\n\n nodes = cursor.fetchall()\n\n if len(nodes) > 1:\n raise Exception(\"Multiple nodes found for single node ID\")\n\n if nodes:\n return nodes[0]\n\n node = Node.load(node_id)\n if node is None:\n return None\n\n node_public_date = None\n privacy_actions = NodeLog.find(\n Q('node', 'eq', node_id)\n & Q('action', 'in', [NodeLog.MADE_PUBLIC, NodeLog.MADE_PRIVATE])\n ).sort('-date')\n\n try:\n privacy_action = privacy_actions[0]\n except IndexError as e:\n pass\n else:\n if privacy_action.action == NodeLog.MADE_PUBLIC:\n node_public_date = privacy_action.date.isoformat()\n node_public_date = node_public_date[:-3] + 'Z'\n\n cursor.execute(\n u'INSERT INTO nodes (id, title, category, made_public_date) VALUES (?, ?, ?, ?)',\n (node_id, getattr(node, 'title'), getattr(node, 'category'), node_public_date)\n )\n sqlite_db.commit()\n return get_or_create_node(node_id, sqlite_db)","def __value_get(self):\n return from_pickle(self.db_value, db_obj=self)","def __create_internal_node_by_operator(operator: PatternStructure, sliding_window: timedelta, parent: Node = None):\n operator_type = operator.get_top_operator()\n if operator_type == SeqOperator:\n return SeqNode(sliding_window, parent)\n if operator_type == AndOperator:\n return AndNode(sliding_window, parent)\n if operator_type == KleeneClosureOperator:\n return KleeneClosureNode(sliding_window, operator.min_size, operator.max_size, parent)\n raise Exception(\"Unknown or unsupported operator %s\" % (operator_type,))","def build_node(self,key):\n\t\tchildren = self.edges.loc[self.edges[self.parentkey]==key]\n\t\tparent = self.edges.loc[self.edges[self.childkey]==key]\n\t\tcontext = {'link':key}\n\t\tmixed_children = [self.build_node(k) for k in children[self.childkey].values]\n\t\tnode = self.get_node(key,context,mixed_children)\n\t\treturn node","def _left(node):\n return 2 * node + 1","def fn(node):\n if not node: return #null node\n stack.append(node.val)\n if node.left is node.right: ans.append(\"->\".join(map(str, stack))) #leaf node\n fn(node.left) or fn(node.right)\n stack.pop()","def get_object(sv, expr):\r\n if expr in sv.Object: \r\n nod=sv.Object[expr] # object exists\r\n else: \r\n nod=add_object(sv, expr) # create node for intermediate expression\r\n nod.isvirtual=True # temporary node\r\n return nod","def db_values(self, db):","def node_mp(tup):\n return node_sp(*tup)","def _create_gemm(cls, op, op_t):\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\n\n node.attribute.extend([\n helper.make_attribute('alpha', float(op.alpha)),\n helper.make_attribute('beta', float(op.beta)),\n helper.make_attribute('transA', op.transA),\n helper.make_attribute('transB', op.transB),\n ])\n\n return node","def get_from_sqlite():\n graph = FileStructureProcessor(\"sqlite\")\n return graph.get_graph()","def Relation(self, paren=False):\n left = self.Addition(paren)\n if self.currtok[1].name in {\"GREATERTHAN\", \"LESSTHAN\", \"LET\", \"GET\"}:\n op = self.currtok[0]\n self.currtok = next(self.tg)\n right = self.Addition(paren)\n left = BinaryExpr(op, left, right, paren)\n return left","def _create_new(self, key):\n return AVLTreeNode(key)"],"string":"[\n \"def query (node, grounding, db):\\n return db.funcVal(grounding.groundNode(node))\",\n \"def _expr2bddnode(expr):\\n\\t# print(\\\"_expr2bddnode\\\")\\n\\tif expr.is_zero():\\n\\t\\treturn BDDNODEZERO\\n\\telif expr.is_one():\\n\\t\\treturn BDDNODEONE\\n\\telse:\\n\\t\\ttop = expr.top\\n\\n\\t\\t# Register this variable\\n\\t\\t_ = bddvar(top.names, top.indices)\\n\\n\\t\\troot = top.uniqid\\n\\t\\tlo = _expr2bddnode(expr.restrict({top: 0}))\\n\\t\\thi = _expr2bddnode(expr.restrict({top: 1}))\\n\\t\\treturn _bddnode(root, lo, hi)\",\n \"def __call__(self):\\r\\n new_node = Node()\\r\\n new_node.op = self\\r\\n return new_node\",\n \"def __call__(self):\\n new_node = Node()\\n new_node.op = self\\n return new_node\",\n \"def __call__(self):\\r\\n new_node = Op.__call__(self)\\r\\n return new_node\",\n \"def __call__(self):\\n new_node = Op.__call__(self)\\n return new_node\",\n \"def make_tree(v,l,r):\\r\\n def dispatch(t):\\r\\n if(t==0):\\r\\n return v\\r\\n elif(t==1):\\r\\n return l\\r\\n elif (t==2):\\r\\n return r\\r\\n return dispatch\",\n \"def get_node(self, value):\\n\\t\\treturn self.adjacency_list[value]\",\n \"def build():\\n r = TreeNode(1)\\n r.left = TreeNode(2)\\n r.left.left = TreeNode(4)\\n r.left.right = TreeNode(5)\\n\\n r.right = TreeNode(3)\\n\\n return r\\n return TreeNode(3)\",\n \"def binary_op(node_factory_function: Callable) -> Callable:\\n\\n @wraps(node_factory_function)\\n def wrapper(left: NodeInput, right: NodeInput, *args: Any, **kwargs: Any) -> Node:\\n left, right = as_nodes(left, right)\\n node = node_factory_function(left, right, *args, **kwargs)\\n node = _set_node_friendly_name(node, **kwargs)\\n return node\\n\\n return wrapper\",\n \"def make_dp_node(config, geounitNode):\\n import programs.engine.primitives as primitives\\n import re\\n \\n (file,queries_class_name) = config[BUDGET][QUERIESFILE].rsplit(\\\".\\\", 1)\\n queries_module = __import__(file, fromlist=[queries_class_name])\\n \\n REGEX_CONFIG_DELIM = \\\"^\\\\s+|\\\\s*,\\\\s*|\\\\s+$\\\"\\n \\n levels = tuple(config[\\\"geodict\\\"][\\\"geolevel_names\\\"].split(\\\",\\\"))\\n levels_reversed = list(reversed(levels))\\n #index relative to topdown\\n index = levels_reversed.index(geounitNode.geolevel)\\n total_budget = float(config[\\\"budget\\\"][\\\"epsilon_budget_total\\\"])\\n \\n #check that geolevel_budget_prop adds to 1, if not raise exception\\n geolevel_prop_budgets= [float(x) for x in re.split(REGEX_CONFIG_DELIM, config[\\\"budget\\\"][\\\"geolevel_budget_prop\\\"])]\\n t_prop_sum = round(sum(geolevel_prop_budgets) , 5)\\n if t_prop_sum != 1.0:\\n raise Exception(\\\"Total Budget Proportion must add to 1.0 ; Current Sum: \\\" .format(t_prop_sum) ) \\n \\n dp_budget = total_budget * geolevel_prop_budgets[index]\\n \\n hist_shape = geounitNode.raw.shape\\n\\n if config.has_option(BUDGET,'DPqueries'):\\n dp_query_prop = [float(x) for x in re.split(REGEX_CONFIG_DELIM, config[\\\"budget\\\"][\\\"queriesprop\\\"])]\\n dp_query_names = re.split(das_utils.DELIM, config[BUDGET]['DPqueries'])\\n queries_dict= getattr(queries_module, queries_class_name)(hist_shape, dp_query_names).calculateQueries().queries_dict\\n\\n else:\\n dp_query_prop = [0.0,0.0]\\n dp_query_names = None\\n queries_dict = {}\\n \\n detailed_prop = float(config[\\\"budget\\\"][\\\"detailedprop\\\"])\\n prop_sum = sum(dp_query_prop) + detailed_prop\\n \\n #sensitivity specification\\n #defaults\\n global_sensitivity = 2.0\\n detailed_sens = global_sensitivity\\n if dp_query_names:\\n dp_query_sens = [global_sensitivity for query in dp_query_names]\\n \\n #change defaults\\n if config.has_option(\\\"budget\\\",\\\"global_sensitivity\\\"):\\n val = config[\\\"budget\\\"][\\\"global_sensitivity\\\"]\\n if val != \\\"\\\":\\n global_sensitivity = float(val)\\n detailed_sens = global_sensitivity\\n if dp_query_names:\\n dp_query_sens = [global_sensitivity for query in dp_query_names]\\n elif config.has_option(\\\"budget\\\",\\\"detailed_sens\\\") & config.has_option(\\\"budget\\\",\\\"dp_query_sens\\\"):\\n val = config[\\\"budget\\\"][\\\"detailed_sensitivity\\\"]\\n if val != \\\"\\\":\\n detailed_sens = float(config[\\\"budget\\\"][\\\"detailed_sensitivity\\\"])\\n if dp_query_names:\\n dp_query_sens = [float(x) for x in re.split(REGEX_CONFIG_DELIM, config[\\\"budget\\\"][\\\"queries_sensitivity\\\"])]\\n \\n #check that budget proportion adds to 1, if not raise exception\\n if prop_sum != 1.0:\\n raise Exception(\\\"Geolevel Budget Proportion must add to 1.0 ; Current Sum: \\\" .format(prop_sum) )\\n\\n np.random.seed()\\n \\n dp_queries = {}\\n if config.has_option('budget','DPqueries'):\\n n_queries = len(dp_query_names)\\n \\n for i in range(n_queries):\\n name = dp_query_names[i]\\n query = queries_dict[name]\\n budgetprop = float(dp_query_prop[i])\\n sens = float(dp_query_sens[i])\\n DPanswer, Var = primitives.geometric_mechanism(true_answer=query.answer(geounitNode.raw.toDense()), budget=budgetprop*dp_budget, sensitivity=sens, prng=np.random)\\n dp_query = {name: cenquery.DPquery(query=query, DPanswer=np.array(DPanswer),\\n epsilon = budgetprop*dp_budget, DPmechanism = \\\"geometric\\\",\\n Var = Var)}\\n dp_queries.update(dp_query)\\n \\n DPgeounitNode = geounitNode\\n DPgeounitNode.dp_queries = dp_queries\\n \\n DPanswer, Var = primitives.geometric_mechanism(true_answer = geounitNode.raw.toDense(), budget=detailed_prop*dp_budget, sensitivity=detailed_sens, prng=np.random)\\n \\n query = cenquery.Query(array_dims = geounitNode.raw.shape)\\n DPgeounitNode.dp = cenquery.DPquery(query=query, DPanswer=DPanswer,\\n epsilon = detailed_prop*dp_budget, DPmechanism = \\\"geometric\\\",\\n Var = Var)\\n return DPgeounitNode\",\n \"def fn(node):\\n if node and node not in mp:\\n temp = mp[node] = Node(node.val)\\n temp.next, temp.random = fn(node.next), fn(node.random)\\n return mp.get(node)\",\n \"def _(self, node: BinaryOp):\\n left = self.visit(node.left)\\n right = self.visit(node.right)\\n\\n return f\\\"( {node.op} {left} {right} )\\\"\",\n \"def _bddnode(root, lo, hi):\\n\\t# print(\\\"_bddnode\\\")\\n\\tif lo is hi:\\n\\t\\tnode = lo\\n\\telse:\\n\\t\\tkey = (root, lo, hi)\\n\\t\\ttry:\\n\\t\\t\\tnode = _NODES[key]\\n\\t\\texcept KeyError:\\n\\t\\t\\tnode = _NODES[key] = BDDNode(*key)\\n\\treturn node\",\n \"def _gen_test_tree_1():\\n tree = BinaryNode(5)\\n tree.left = BinaryNode(5)\\n return tree\",\n \"def compute_tree(self, x):\\n if (self.body in operators):\\n try:\\n return self.body(self.left.compute_tree(x), self.right.compute_tree(x))\\n except:\\n return float(\\\"inf\\\")\\n elif self.body == 'x': return x\\n else: return self.body\",\n \"def _db(self, value):\",\n \"def get_node(self, key: str) -> Node:\",\n \"def _gen_test_tree_4():\\n tree = BinaryNode(5)\\n tree.left = BinaryNode(3)\\n tree.left.left = BinaryNode(2)\\n tree.left.right = BinaryNode(10)\\n tree.right = BinaryNode(9)\\n tree.right.left = BinaryNode(6)\\n tree.right.right = BinaryNode(8)\\n return tree\",\n \"def _handle_gfk_field_node(self, node, field):\\n if node.getElementsByTagName('None'):\\n return None\\n ct_key = node.getAttribute(\\\"d:to\\\").split(NATURAL_KEY_JOINER)\\n ctype = ContentType.objects.get_by_natural_key(*ct_key)\\n Model = ctype.model_class()\\n if hasattr(Model._default_manager, 'get_by_natural_key'):\\n value = getInnerText(node).strip()\\n field_value = value.split(NATURAL_KEY_JOINER)\\n obj = Model._default_manager.db_manager(self.db).get_by_natural_key(*field_value)\\n return obj\\n else:\\n field_value = getInnerText(node).strip()\\n return Model._default_manager.db_manager(self.db).get(pk=Model._meta.pk.to_python(field_value))\",\n \"def ground(self, db, term, target=None, label=None, **kwdargs):\\n if term.is_negated():\\n negated = True\\n term = -term\\n elif term.functor in ('not', '\\\\+') and term.arity == 1:\\n negated = True\\n term = term.args[0]\\n else:\\n negated = False\\n\\n target, results = self._ground(db, term, target, silent_fail=False, **kwdargs)\\n\\n args_node = defaultdict(list)\\n for args, node_id in results:\\n if not is_ground(*args) and target.is_probabilistic(node_id):\\n raise NonGroundQuery(term, db.lineno(term.location))\\n args = tuple(args)\\n args_node[args].append(node_id)\\n for args, node_ids in args_node.items():\\n if len(node_ids) > 1:\\n node_id = target.add_or(node_ids)\\n else:\\n node_id = node_ids[0]\\n term_store = term.with_args(*args)\\n if negated:\\n target.add_name(-term_store, target.negate(node_id), label)\\n else:\\n target.add_name(term_store, node_id, label)\\n if not results:\\n if negated:\\n target.add_name(-term, target.TRUE, label)\\n else:\\n target.add_name(term, target.FALSE, label)\\n\\n return target\",\n \"def _gen_test_tree_3():\\n tree = BinaryNode(5)\\n tree.left = BinaryNode(1)\\n tree.left.left = BinaryNode(2)\\n tree.left.right = BinaryNode(3)\\n tree.right = BinaryNode(7)\\n tree.right.left = BinaryNode(8)\\n tree.right.right = BinaryNode(9)\\n return tree\",\n \"def _gen_test_tree_2():\\n tree = BinaryNode(5)\\n tree.left = BinaryNode(3)\\n tree.left.left = BinaryNode(2)\\n tree.left.left.left = BinaryNode(1)\\n tree.left.right = BinaryNode(4)\\n tree.right = BinaryNode(7)\\n tree.right.left = BinaryNode(6)\\n tree.right.right = BinaryNode(8)\\n tree.right.right.right = BinaryNode(9)\\n return tree\",\n \"def value(self) -> global___Expression:\",\n \"def value(self) -> global___Expression:\",\n \"def __init__(self, value: object) -> None:\\n self.value = value # to store node's data\\n self.left = None # pointer to root of left subtree\\n self.right = None # pointer to root of right subtree\",\n \"def __init__(self, value: object) -> None:\\n self.value = value # to store node's data\\n self.left = None # pointer to root of left subtree\\n self.right = None # pointer to root of right subtree\",\n \"def build():\\n # root = TreeNode(5)\\n # root.left = TreeNode(2)\\n # root.right = TreeNode(7)\\n # return root\\n\\n \\\"\\\"\\\"\\n 5\\n / \\\\\\n 2 6\\n / \\\\\\n 1 3\\n [5,2,1,3,6]\\n \\\"\\\"\\\"\\n _5 = TreeNode(5)\\n _2 = TreeNode(2)\\n _6 = TreeNode(6)\\n _1 = TreeNode(1)\\n _3 = TreeNode(3)\\n _5.left = _2\\n _5.right = _6\\n _2.left = _1\\n _2.right = _3\\n return _5\",\n \"def __init__(self, left=None, right=None, feature=None, value=None, predict=None):\\n self.left = left\\n self.right = right\\n self.feature = feature # column in which features is stored\\n self.value = value # value to check against\\n self.predict = predict # class to predict at this node\",\n \"def _gen_test_tree_6():\\n tree = BinaryNode(20)\\n tree.left = BinaryNode(10)\\n tree.right = BinaryNode(30)\\n tree.left.right = BinaryNode(25)\\n return tree\",\n \"def __init__(self, value):\\n self.value = value\\n self.left = None\\n self.right = None\",\n \"def __init__(self, value):\\n self.value = value\\n self.left = None\\n self.right = None\",\n \"def __getitem__(self, t: Tuple[int, ...]) -> 'Tree':\\n ...\",\n \"def db(r):\\n ey = gQ(r)\\n gR = b.tcl(\\n 'global no_gizmo; set no_gizmo 1; in %s {%s -New} ; return [value [stack 0].name]' % (ey.fullName(), r.Class()))\\n group = b.toNode('.'.join((ey.fullName(), gR)))\\n group.setSelected(False)\\n if ew(r):\\n for node, gS in ew(r).iteritems():\\n for c in gS:\\n node.setInput(c, group)\\n\\n for c in range(r.inputs()):\\n group.setInput(c, r.input(c))\\n\\n group.setXYpos(r.xpos(), r.ypos())\\n group.readKnobs(r.writeKnobs(b.TO_SCRIPT))\\n b.delete(r)\\n return group\",\n \"def fn(node, val):\\n if not node: return 0\\n val = 10*val + node.val\\n if not node.left and not node.right: return val \\n return fn(node.left, val) + fn(node.right, val)\",\n \"def _(self, node: ModelReturn):\\n val = self.visit(node.value)\\n return f\\\"( return {val} )\\\"\",\n \"def create_child(self, value, fun=None):\\n child = TaxonomyTree(value, self.weights, fun)\\n self.children[value] = child\\n child.parent = self\\n child.level = self.level + 1\\n child.__root = self.__root\\n self.__root.__fast_find.append((child.fun, child))\\n return child\",\n \"def _get_collections_tree_query(sql_value, sql_oper=OP_EQ):\\n col_list = [COL_NAME_COLL_PARENTID, COL_NAME_COLL_COLLID, COL_NAME_COLL_NAME, COL_NAME_COLL_COLLCOMMENT,\\n COL_NAME_COLL_PRID, COL_NAME_COLL_IS_ACTIVE]\\n catcoll_tbl_alias = \\\"c\\\"\\n cat_coll_tbl = SQLTableExpr(TABLE_NAME_COLL, catcoll_tbl_alias)\\n col_list_aliased = [SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_PARENTID),\\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_COLLID),\\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_NAME),\\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_COLLCOMMENT),\\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_PRID),\\n SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_IS_ACTIVE)]\\n\\n start = GenericSQLSelect(col_list, False, [cat_coll_tbl],\\n SQLBinaryExpr(COL_NAME_COLL_COLLID, sql_oper, sql_value))\\n join = SQLJoinExpr(cat_coll_tbl, OP_INNER_JOIN, SQLTableExpr(GEN_RECUR_NAME, \\\"r\\\"),\\n SQLBinaryExpr(SQLColumnExpr(catcoll_tbl_alias, COL_NAME_COLL_PARENTID), OP_EQ,\\n SQLColumnExpr(\\\"r\\\", COL_NAME_COLL_COLLID)))\\n stop = GenericSQLSelect(col_list_aliased, False, [join])\\n outer = GenericSQLSelect(col_list, False, [GEN_RECUR_NAME])\\n wexp = str(SQLConcatExpr(EXPR_WITH, SQLFuncExpr(GEN_RECUR_NAME, str(col_list)[1:-1].replace(\\\"'\\\", \\\"\\\"))))\\n wexp = SQLBinaryExpr(wexp, OP_AS, SQLConcatExpr(start, OP_UNION_ALL, stop))\\n return wexp, outer\",\n \"def _get_node(self, seq):\\n cur_node = self.root\\n for token_id in seq:\\n children = cur_node.edges\\n if not token_id in children:\\n children[token_id] = SimpleNode()\\n cur_node = children[token_id]\\n return cur_node\",\n \"def tree(self) -> Node:\\n return Node(self.to_string())\",\n \"def view_record_byvalue(self):\\n conn = rs.create_connection(\\\"dailyfantasyscraper.db\\\")\\n value = val.get()\\n cur = conn.cursor()\\n cur.execute(\\n \\\"SELECT * FROM rotowiredk WHERE value > ?\\\", (value,))\\n result = cur.fetchall()\\n conn.commit()\\n conn.close()\\n\\n for item in result:\\n print(item)\\n tree.insert('', 'end', values=item)\",\n \"def tree():\\n return defaultdict(tree)\",\n \"def __getattr__(self, attr):\\n def factory(parent=None, **kwargs):\\n return self.Node(parent, attr, **kwargs)\\n return factory\",\n \"def __arb__(self):\\n if self.tree.total < 1:\\n return None\\n if self.tree.total % 2 == 0:\\n return self.first()\\n else:\\n return self.last()\",\n \"def Db(self, (k,t), (J,q), **params):\\n Db = util.D(lambda q : self.b( (k,t), (J,q), **params ), q)\\n return Db\",\n \"def visit(self, node):\",\n \"def visit(self, node):\",\n \"def _gen_test_tree_5():\\n tree = BinaryNode(30)\\n tree.right = BinaryNode(30)\\n return tree\",\n \"def __init__(self, val):\\n\\n\\t\\tself.val = val\\n\\t\\tself.left = None\\n\\t\\tself.right = None\",\n \"def on_graph(self, node):\\n if node.value.parent is None:\\n return self.graph_map[node.value]\\n if node not in self.node_map:\\n self.node_map[node] = self.convert_func(node.value)\\n return self.node_map[node]\",\n \"def __init__(self):\\n self.idx = None\\n self.val = None\\n self.left = None\\n self.right = None\",\n \"def fn(node):\\n if not node: return 0 \\n ans = node.val + fn(node.left) + fn(node.right)\\n vals.append(ans)\\n return ans\",\n \"def create_node(self, data):\\n node = RealNode(data, layer=self)\\n self.append_node(node)\\n return node\",\n \"def gremlin(self):\\r\\n initial = '{} = g.makeType().name(\\\"{}\\\").{}{}makeEdgeLabel()'\\r\\n primary_key = ''\\r\\n if self.primary_key:\\r\\n primary_key = \\\"primaryKey({}).\\\".format(self.primary_key)\\r\\n\\r\\n functional = \\\"functional().\\\" if self.functional else \\\"\\\"\\r\\n\\r\\n return initial.format(self.label, self.label, primary_key, functional)\",\n \"def gQ(n):\\n return b.toNode('.'.join(n.fullName().split('.')[:-1])) or b.root()\",\n \"def nodeGetter(instance):\\n if (instance, name) not in memo:\\n subspace = subspaceFunction(instance)\\n\\n if subspace is None:\\n assert False, \\\"Instance %s produced an empty subspace\\\" % instance\\n\\n memo[(instance,name)] = subspace.subspace(name)\\n return memo[(instance,name)]\",\n \"def query(self, key):\\n arr_types = (\\\"forest\\\", \\\"prog\\\", \\\"tree\\\")\\n if isinstance(key, tuple):\\n if len(key) != 2:\\n raise SyntaxError(\\n \\\"Must be either 1 or 2 arguments.\\\")\\n ftype, field = key\\n if ftype not in arr_types:\\n raise SyntaxError(\\n f\\\"First argument must be one of {str(arr_types)}.\\\")\\n if not isinstance(field, str):\\n raise SyntaxError(\\\"Second argument must be a string.\\\")\\n\\n self.arbor._node_io.get_fields(self, fields=[field], root_only=False)\\n indices = getattr(self, f\\\"_{ftype}_field_indices\\\")\\n\\n data_object = self.find_root()\\n return data_object.field_data[field][indices]\\n\\n else:\\n if not isinstance(key, str):\\n raise SyntaxError(\\\"Single argument must be a string.\\\")\\n\\n # return the progenitor list or tree nodes in a list\\n if key in arr_types:\\n self.arbor._setup_tree(self)\\n return getattr(self, f\\\"_{key}_nodes\\\")\\n\\n # return field value for this node\\n self.arbor._node_io.get_fields(self, fields=[key],\\n root_only=self.is_root)\\n data_object = self.find_root()\\n return data_object.field_data[key][self.tree_id]\",\n \"def get_node_b(name, taxid, pathway, topology, psi_mi_to_sql_object):\\n\\n # Testing if the node is already in the database\\n node_dict = psi_mi_to_sql_object.get_node(name, node_tax_id=taxid)\\n\\n if not node_dict:\\n node_dict = {\\n \\\"name\\\": 'Uniprot:' + name,\\n \\\"tax_id\\\": taxid,\\n \\\"alt_accession\\\": None,\\n 'pathways': pathway,\\n \\\"aliases\\\": None,\\n \\\"topology\\\": topology\\n }\\n\\n return node_dict\",\n \"def createTracker3Node():\\n return fQ()\",\n \"def createTracker4Node():\\n return fR()\",\n \"def _create(self, context, values):\\n # initialize load stats from existing instances:\\n compute_node = db.compute_node_create(context, values)\\n return compute_node\",\n \"def from_db_value(self, value, expression, connection, context):\\n return self.to_python(value)\",\n \"def lookup(self, val):\\n if val < self.val:\\n if self.left is None:\\n return None, None\\n return self.left.lookup(val)\\n elif val > self.val:\\n if self.right is None:\\n return None, None\\n return self.right.lookup(val)\\n else:\\n return self\",\n \"def node(self, func_or_name):\\n\\n self._compiled = None\\n\\n def _decorator(fn):\\n self.graph[func_or_name] = fn\\n return fn\\n\\n if callable(func_or_name):\\n self.graph[func_or_name.__name__] = func_or_name\\n return func_or_name\\n else:\\n return _decorator\",\n \"def bad_tree():\\n return OvalNode(\\n node_id=1,\\n node_type=\\\"value\\\",\\n value=\\\"true\\\",\\n children=[\\n OvalNode(\\n node_id=2,\\n node_type=\\\"operator\\\",\\n value=\\\"and\\\",\\n children=[\\n OvalNode(\\n node_id=3,\\n node_type=\\\"value\\\",\\n value=\\\"true\\\",\\n ),\\n ]\\n ),\\n ]\\n )\",\n \"def __init__(self, formula = None, field_name = \\\"new_field\\\", analytical_type = \\\"unknown\\\",\\r\\n storage_type = \\\"unknown\\\"):\\r\\n super(DeriveNode, self).__init__()\\r\\n self.formula = formula\\r\\n self.field_name = field_name\\r\\n self.analytical_type = analytical_type\\r\\n self.storage_type = storage_type\\r\\n self._output_fields = None\",\n \"def weight(tree):\\n return root(tree)\",\n \"def drupal_db_read(db_obj, db_cur, key_cv, value_cv):\\n\\n # get the chain type\\n chain_type = get_drupal_chain_type(key_cv, value_cv)\\n if not chain_type:\\n nori.core.email_logger.error(\\n'''Internal Error: invalid field list supplied in call to\\ndrupal_db_read(); call was (in expanded notation):\\n\\ndrupal_db_read(\\n db_obj={0},\\n db_cur={1},\\n key_cv={2},\\n value_cv={3}\\n)\\n\\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, key_cv, value_cv]))\\n )\\n sys.exit(nori.core.exitvals['internal']['num'])\\n\\n ########### assemble the query string and argument list ###########\\n\\n #\\n # node -> field(s) (including term references)\\n #\\n if chain_type == 'n-f':\\n # node details\\n node_cv = key_cv[0]\\n node_ident = node_cv[0]\\n node_value_type = node_cv[1]\\n if len(node_cv) > 2:\\n node_value = node_cv[2]\\n node_type = node_ident[1]\\n node_id_type = node_ident[2]\\n\\n # handle node ID types\\n if node_id_type == 'id':\\n key_column = 'node.nid'\\n elif node_id_type == 'title':\\n key_column = 'node.title'\\n\\n # handle specified node value\\n node_value_cond = ''\\n if len(node_cv) > 2:\\n node_value_cond = 'AND {0} = %s'.format(key_column)\\n\\n field_idents = {}\\n field_value_types = {}\\n field_values = []\\n field_names = {}\\n value_columns = []\\n field_joins = []\\n term_joins = []\\n field_value_conds = []\\n field_deleted_conds = []\\n v_order_columns = []\\n for i, field_cv in enumerate(value_cv):\\n # field details\\n field_idents[i] = field_cv[0]\\n field_value_types[i] = field_cv[1]\\n if len(field_cv) > 2:\\n field_values.append(field_cv[2])\\n field_names[i] = field_idents[i][1]\\n\\n # field join\\n field_joins.append(\\n 'LEFT JOIN field_data_field_{0} AS f{1}\\\\n'\\n ' ON f{1}.entity_id = node.nid\\\\n'\\n ' AND f{1}.revision_id = node.vid' .\\n format(field_names[i], i)\\n )\\n\\n # handle value types\\n if field_value_types[i].startswith('term: '):\\n value_columns.append('t{0}.name'.format(i))\\n term_joins.append(\\n 'LEFT JOIN taxonomy_term_data AS t{0}\\\\n'\\n ' ON t{0}.tid = f{0}.field_{1}_tid' .\\n format(i, field_names[i])\\n )\\n elif field_value_types[i] == 'ip':\\n value_columns.append(\\n 'f{0}.field_{1}_start'.format(i, field_names[i])\\n )\\n else:\\n value_columns.append(\\n 'f{0}.field_{1}_value'.format(i, field_names[i])\\n )\\n\\n # handle specified field value\\n if len(field_cv) > 2:\\n field_value_conds.append(\\n 'AND {0} = %s'.format(value_columns[-1])\\n )\\n\\n # not deleted\\n field_deleted_conds.append(\\n 'AND f{0}.deleted = 0'.format(i)\\n )\\n\\n # order column\\n v_order_columns.append('f{0}.delta'.format(i))\\n\\n # query string and arguments\\n query_str = (\\n'''\\nSELECT {0}, {1}\\nFROM node\\n{2}\\n{3}\\nWHERE node.vid IN\\n (SELECT MAX(vid)\\n FROM node_revision\\n GROUP BY nid)\\nAND node.type = %s\\n{4}\\n{5}\\n{6}\\nORDER BY node.title, node.nid, {7}\\n''' .\\n format(key_column, ', '.join(value_columns),\\n '\\\\n'.join(field_joins),\\n '\\\\n'.join(term_joins),\\n node_value_cond,\\n '\\\\n'.join(field_value_conds),\\n '\\\\n'.join(field_deleted_conds),\\n ', '.join(v_order_columns))\\n )\\n query_args = [node_type]\\n if len(node_cv) > 2:\\n query_args.append(node_value)\\n query_args += field_values\\n\\n #\\n # node -> relation -> node\\n #\\n elif chain_type == 'n-r-n':\\n # key-node details\\n k_node_cv = key_cv[0]\\n k_node_ident = k_node_cv[0]\\n k_node_value_type = k_node_cv[1]\\n if len(k_node_cv) > 2:\\n k_node_value = k_node_cv[2]\\n k_node_type = k_node_ident[1]\\n k_node_id_type = k_node_ident[2]\\n\\n # handle key-node ID types\\n if k_node_id_type == 'id':\\n node_key_column = 'k_node.nid'\\n elif k_node_id_type == 'title':\\n node_key_column = 'k_node.title'\\n\\n # handle specified key-node value\\n k_node_value_cond = ''\\n if len(k_node_cv) > 2:\\n k_node_value_cond = 'AND {0} = %s'.format(node_key_column)\\n\\n # relation details\\n relation_cv = key_cv[1]\\n relation_ident = relation_cv[0]\\n relation_type = relation_ident[1]\\n\\n # handle key relation-field\\n relation_key_column = ''\\n relation_field_join = ''\\n relation_field_cond = ''\\n relation_value_cond = ''\\n if len(relation_ident) > 2:\\n relation_field_name = relation_ident[2]\\n relation_value_type = relation_cv[1]\\n\\n # field join\\n relation_field_join = (\\n 'LEFT JOIN field_data_field_{0} AS k_rf\\\\n'\\n ' ON k_rf.entity_id = e2.entity_id\\\\n'\\n ' AND k_rf.revision_id = e2.revision_id' .\\n format(relation_field_name)\\n )\\n\\n # conditions\\n relation_field_cond = (\\n \\\"AND k_rf.entity_type = 'relation'\\\\n\\\"\\n \\\"AND k_rf.deleted = 0\\\"\\n )\\n\\n # handle value type\\n if relation_value_type.startswith('term: '):\\n relation_key_column = 'k_rf_t.name'\\n relation_field_join += (\\n '\\\\nLEFT JOIN taxonomy_term_data AS k_rf_t\\\\n'\\n 'ON k_rf_t.tid = k_rf.field_{0}_tid' .\\n format(relation_field_name)\\n )\\n elif relation_value_type == 'ip':\\n relation_key_column = (\\n 'k_rf.field_{0}_start'.format(relation_field_name)\\n )\\n else:\\n relation_key_column = (\\n 'k_rf.field_{0}_value'.format(relation_field_name)\\n )\\n\\n # handle specified field value\\n if len(relation_cv) > 2:\\n relation_value = relation_cv[2]\\n relation_value_cond = (\\n 'AND {0} = %s'.format(relation_key_column)\\n )\\n\\n # value-node details\\n v_node_cv = value_cv[0]\\n v_node_ident = v_node_cv[0]\\n v_node_value_type = v_node_cv[1]\\n if len(v_node_cv) > 2:\\n v_node_value = v_node_cv[2]\\n v_node_type = v_node_ident[1]\\n v_node_id_type = v_node_ident[2]\\n\\n # handle value-node ID types\\n if v_node_id_type == 'id':\\n value_column = 'v_node.nid'\\n elif v_node_id_type == 'title':\\n value_column = 'v_node.title'\\n\\n # handle value-node type\\n extra_value_cols = ''\\n v_node_type_cond = ''\\n if v_node_type is None:\\n extra_value_cols = ', v_node.type'\\n else:\\n v_node_type_cond = 'AND v_node.type = %s'\\n\\n # handle specified value-node value\\n v_node_value_cond = ''\\n if len(v_node_cv) > 2:\\n v_node_value_cond = 'AND {0} = %s'.format(value_column)\\n\\n # query string and arguments\\n query_str = (\\n'''\\nSELECT {0}, {1}{2}{3}\\nFROM node AS k_node\\nLEFT JOIN field_data_endpoints AS e1\\n ON e1.endpoints_entity_id = k_node.nid\\nLEFT JOIN field_data_endpoints AS e2\\n ON e2.entity_id = e1.entity_id\\n AND e2.revision_id = e1.revision_id\\n AND e2.endpoints_r_index > e1.endpoints_r_index\\n{4}\\nLEFT JOIN node AS v_node\\n ON v_node.nid = e2.endpoints_entity_id\\nWHERE k_node.vid IN\\n (SELECT MAX(vid)\\n FROM node_revision\\n GROUP BY nid)\\nAND k_node.type = %s\\n{5}\\nAND e1.revision_id IN\\n (SELECT MAX(vid)\\n FROM relation_revision\\n GROUP BY rid)\\nAND e1.entity_type = 'relation'\\nAND e1.bundle = %s\\nAND e1.endpoints_entity_type = 'node'\\nAND e1.deleted = 0\\nAND e2.endpoints_entity_type = 'node'\\nAND e2.deleted = 0\\n{6}\\n{7}\\nAND v_node.vid IN\\n (SELECT MAX(vid)\\n FROM node_revision\\n GROUP BY nid)\\n{8}\\n{9}\\nORDER BY k_node.title, k_node.nid, e1.entity_id, v_node.title, v_node.nid\\n''' .\\n format(node_key_column,\\n (relation_key_column + ', ') if relation_key_column\\n else '',\\n value_column,\\n extra_value_cols,\\n relation_field_join,\\n k_node_value_cond,\\n relation_field_cond,\\n relation_value_cond,\\n v_node_type_cond,\\n v_node_value_cond)\\n )\\n query_args = [k_node_type]\\n if len(k_node_cv) > 2:\\n query_args.append(k_node_value)\\n query_args.append(relation_type)\\n if len(relation_ident) > 2 and len(relation_cv) > 2:\\n query_args.append(relation_value)\\n if v_node_type is not None:\\n query_args.append(v_node_type)\\n if len(v_node_cv) > 2:\\n query_args.append(v_node_value)\\n\\n #\\n # node -> relation & node -> relation_field(s) (incl. term refs)\\n #\\n elif chain_type == 'n-rn-rf':\\n # node1 details\\n node1_cv = key_cv[0]\\n node1_ident = node1_cv[0]\\n node1_value_type = node1_cv[1]\\n if len(node1_cv) > 2:\\n node1_value = node1_cv[2]\\n node1_type = node1_ident[1]\\n node1_id_type = node1_ident[2]\\n\\n # handle node1 ID types\\n if node1_id_type == 'id':\\n node1_key_column = 'node1.nid'\\n elif node1_id_type == 'title':\\n node1_key_column = 'node1.title'\\n\\n # handle specified node1 value\\n node1_value_cond = ''\\n if len(node1_cv) > 2:\\n node1_value_cond = 'AND {0} = %s'.format(node1_key_column)\\n\\n # relation details\\n relation_cv = key_cv[1]\\n relation_ident = relation_cv[0]\\n relation_type = relation_ident[1]\\n\\n # handle key relation-field\\n relation_key_column = ''\\n relation_field_join = ''\\n relation_field_cond = ''\\n relation_value_cond = ''\\n if len(relation_ident) > 2:\\n relation_field_name = relation_ident[2]\\n relation_value_type = relation_cv[1]\\n\\n # field join\\n relation_field_join = (\\n 'LEFT JOIN field_data_field_{0} AS k_rf\\\\n'\\n ' ON k_rf.entity_id = e2.entity_id\\\\n'\\n ' AND k_rf.revision_id = e2.revision_id' .\\n format(relation_field_name)\\n )\\n\\n # conditions\\n relation_field_cond = (\\n \\\"AND k_rf.entity_type = 'relation'\\\\n\\\"\\n \\\"AND k_rf.deleted = 0\\\"\\n )\\n\\n # handle value type\\n if relation_value_type.startswith('term: '):\\n relation_key_column = 'k_rf_t.name'\\n relation_field_join += (\\n '\\\\nLEFT JOIN taxonomy_term_data AS k_rf_t\\\\n'\\n 'ON k_rf_t.tid = k_rf.field_{0}_tid' .\\n format(relation_field_name)\\n )\\n elif relation_value_type == 'ip':\\n relation_key_column = (\\n 'k_rf.field_{0}_start'.format(relation_field_name)\\n )\\n else:\\n relation_key_column = (\\n 'k_rf.field_{0}_value'.format(relation_field_name)\\n )\\n\\n # handle specified field value\\n if len(relation_cv) > 2:\\n relation_value = relation_cv[2]\\n relation_value_cond = (\\n 'AND {0} = %s'.format(relation_key_column)\\n )\\n\\n # node2 details\\n node2_cv = key_cv[2]\\n node2_ident = node2_cv[0]\\n node2_value_type = node2_cv[1]\\n if len(node2_cv) > 2:\\n node2_value = node2_cv[2]\\n node2_type = node2_ident[1]\\n node2_id_type = node2_ident[2]\\n\\n # handle node2 ID types\\n if node2_id_type == 'id':\\n node2_key_column = 'node2.nid'\\n elif node2_id_type == 'title':\\n node2_key_column = 'node2.title'\\n\\n # handle specified node2 value\\n node2_value_cond = ''\\n if len(node2_cv) > 2:\\n node2_value_cond = 'AND {0} = %s'.format(node2_key_column)\\n\\n field_idents = {}\\n field_value_types = {}\\n field_values = []\\n field_names = {}\\n value_columns = []\\n field_joins = []\\n term_joins = []\\n field_entity_conds = []\\n field_value_conds = []\\n field_deleted_conds = []\\n v_order_columns = []\\n for i, field_cv in enumerate(value_cv):\\n # field details\\n field_idents[i] = field_cv[0]\\n field_value_types[i] = field_cv[1]\\n if len(field_cv) > 2:\\n field_values.append(field_cv[2])\\n field_names[i] = field_idents[i][1]\\n\\n # field join\\n field_joins.append(\\n 'LEFT JOIN field_data_field_{0} AS f{1}\\\\n'\\n ' ON f{1}.entity_id = e2.entity_id\\\\n'\\n ' AND f{1}.revision_id = e2.revision_id' .\\n format(field_names[i], i)\\n )\\n\\n # handle value types\\n if field_value_types[i].startswith('term: '):\\n value_columns.append('t{0}.name'.format(i))\\n term_joins.append(\\n 'LEFT JOIN taxonomy_term_data AS t{0}\\\\n'\\n ' ON t{0}.tid = f{0}.field_{1}_tid' .\\n format(i, field_names[i])\\n )\\n elif field_value_types[i] == 'ip':\\n value_columns.append(\\n 'f{0}.field_{1}_start'.format(i, field_names[i])\\n )\\n else:\\n value_columns.append(\\n 'f{0}.field_{1}_value'.format(i, field_names[i])\\n )\\n\\n # field entity type\\n field_entity_conds.append(\\n \\\"AND f{0}.entity_type = 'relation'\\\".format(i)\\n )\\n\\n # handle specified field value\\n if len(field_cv) > 2:\\n field_value_conds.append(\\n 'AND {0} = %s'.format(value_columns[-1])\\n )\\n\\n # not deleted\\n field_deleted_conds.append(\\n 'AND f{0}.deleted = 0'.format(i)\\n )\\n\\n # order column\\n v_order_columns.append('f{0}.delta'.format(i))\\n\\n # query string and arguments\\n query_str = (\\n'''\\nSELECT {0}, {1}{2}, {3}\\nFROM node AS node1\\nLEFT JOIN field_data_endpoints AS e1\\n ON e1.endpoints_entity_id = node1.nid\\nLEFT JOIN field_data_endpoints AS e2\\n ON e2.entity_id = e1.entity_id\\n AND e2.revision_id = e1.revision_id\\n AND e2.endpoints_r_index > e1.endpoints_r_index\\n{4}\\nLEFT JOIN node AS node2\\n ON node2.nid = e2.endpoints_entity_id\\n{5}\\n{6}\\nWHERE node1.vid IN\\n (SELECT MAX(vid)\\n FROM node_revision\\n GROUP BY nid)\\nAND node1.type = %s\\n{7}\\nAND e1.revision_id IN\\n (SELECT MAX(vid)\\n FROM relation_revision\\n GROUP BY rid)\\nAND e1.entity_type = 'relation'\\nAND e1.bundle = %s\\nAND e1.endpoints_entity_type = 'node'\\nAND e1.deleted = 0\\nAND e2.endpoints_entity_type = 'node'\\nAND e2.deleted = 0\\n{8}\\n{9}\\nAND node2.vid IN\\n (SELECT MAX(vid)\\n FROM node_revision\\n GROUP BY nid)\\nAND node2.type = %s\\n{10}\\n{11}\\n{12}\\n{13}\\nORDER BY node1.title, node1.nid, e1.entity_id, {14}\\n''' .\\n format(node1_key_column,\\n (relation_key_column + ', ') if relation_key_column\\n else '',\\n node2_key_column,\\n ', '.join(value_columns),\\n relation_field_join,\\n '\\\\n'.join(field_joins),\\n '\\\\n'.join(term_joins),\\n node1_value_cond,\\n relation_field_cond,\\n relation_value_cond,\\n node2_value_cond,\\n '\\\\n'.join(field_entity_conds),\\n '\\\\n'.join(field_value_conds),\\n '\\\\n'.join(field_deleted_conds),\\n ', '.join(v_order_columns))\\n )\\n query_args = [node1_type]\\n if len(node1_cv) > 2:\\n query_args.append(node1_value)\\n query_args.append(relation_type)\\n if len(relation_ident) > 2 and len(relation_cv) > 2:\\n query_args.append(relation_value)\\n query_args.append(node2_type)\\n if len(node2_cv) > 2:\\n query_args.append(node2_value)\\n query_args += field_values\\n\\n #\\n # node -> fc -> field(s) (including term references)\\n #\\n elif chain_type == 'n-fc-f':\\n # node details\\n node_cv = key_cv[0]\\n node_ident = node_cv[0]\\n node_value_type = node_cv[1]\\n if len(node_cv) > 2:\\n node_value = node_cv[2]\\n node_type = node_ident[1]\\n node_id_type = node_ident[2]\\n\\n # handle node ID types\\n if node_id_type == 'id':\\n key_column = 'node.nid'\\n elif node_id_type == 'title':\\n key_column = 'node.title'\\n\\n # handle specified node value\\n node_value_cond = ''\\n if len(node_cv) > 2:\\n node_value_cond = 'AND {0} = %s'.format(key_column)\\n\\n # fc details\\n fc_cv = key_cv[1]\\n fc_ident = fc_cv[0]\\n fc_value_type = fc_cv[1]\\n if len(fc_cv) > 2:\\n fc_value = fc_cv[2]\\n fc_type = fc_ident[1]\\n fc_id_type = fc_ident[2]\\n\\n # handle fc ID types\\n if fc_id_type == 'id':\\n extra_key_column = 'fci.item_id'\\n elif fc_id_type == 'label':\\n extra_key_column = 'fci.label'\\n\\n # handle specified fc value\\n fc_value_cond = ''\\n if len(fc_cv) > 2:\\n fc_value_cond = 'AND {0} = %s'.format(extra_key_column)\\n\\n field_idents = {}\\n field_value_types = {}\\n field_values = []\\n field_names = {}\\n value_columns = []\\n field_joins = []\\n term_joins = []\\n field_entity_conds = []\\n field_value_conds = []\\n field_deleted_conds = []\\n v_order_columns = []\\n for i, field_cv in enumerate(value_cv):\\n # field details\\n field_idents[i] = field_cv[0]\\n field_value_types[i] = field_cv[1]\\n if len(field_cv) > 2:\\n field_values.append(field_cv[2])\\n field_names[i] = field_idents[i][1]\\n\\n # field join\\n field_joins.append(\\n 'LEFT JOIN field_data_field_{0} AS f{1}\\\\n'\\n ' ON f{1}.entity_id = fci.item_id\\\\n'\\n ' AND f{1}.revision_id = fci.revision_id' .\\n format(field_names[i], i)\\n )\\n\\n # handle value types\\n if field_value_types[i].startswith('term: '):\\n value_columns.append('t{0}.name'.format(i))\\n term_joins.append(\\n 'LEFT JOIN taxonomy_term_data AS t{0}\\\\n'\\n ' ON t{0}.tid = f{0}.field_{1}_tid' .\\n format(i, field_names[i])\\n )\\n elif field_value_types[i] == 'ip':\\n value_columns.append(\\n 'f{0}.field_{1}_start'.format(i, field_names[i])\\n )\\n else:\\n value_columns.append(\\n 'f{0}.field_{1}_value'.format(i, field_names[i])\\n )\\n\\n # field entity type\\n field_entity_conds.append(\\n \\\"AND f{0}.entity_type = 'field_collection_item'\\\".format(i)\\n )\\n\\n # handle specified field value\\n if len(field_cv) > 2:\\n field_value_conds.append(\\n 'AND {0} = %s'.format(value_columns[-1])\\n )\\n\\n # not deleted\\n field_deleted_conds.append(\\n 'AND f{0}.deleted = 0'.format(i)\\n )\\n\\n # order column\\n v_order_columns.append('f{0}.delta'.format(i))\\n\\n # query string and arguments\\n query_str = (\\n'''\\nSELECT {0}, {1}{2}\\nFROM node\\nLEFT JOIN field_data_field_{3} AS fcf\\n ON fcf.entity_id = node.nid\\n AND fcf.revision_id = node.vid\\nLEFT JOIN field_collection_item as fci\\n ON fci.item_id = fcf.field_{3}_value\\n AND fci.revision_id = fcf.field_{3}_revision_id\\n{4}\\n{5}\\nWHERE node.vid IN\\n (SELECT MAX(vid)\\n FROM node_revision\\n GROUP BY nid)\\nAND node.type = %s\\n{6}\\nAND fcf.entity_type = 'node'\\nAND fcf.deleted = 0\\nAND fci.revision_id IN\\n (SELECT MAX(revision_id)\\n FROM field_collection_item_revision\\n GROUP BY item_id)\\nAND fci.archived = 0\\n{7}\\n{8}\\n{9}\\n{10}\\nORDER BY node.title, node.nid, fcf.delta, {11}\\n''' .\\n format(key_column,\\n (extra_key_column + ', ') if extra_key_column else '',\\n ', '.join(value_columns),\\n fc_type,\\n '\\\\n'.join(field_joins),\\n '\\\\n'.join(term_joins),\\n node_value_cond,\\n fc_value_cond,\\n '\\\\n'.join(field_entity_conds),\\n '\\\\n'.join(field_value_conds),\\n '\\\\n'.join(field_deleted_conds),\\n ', '.join(v_order_columns))\\n )\\n query_args = [node_type]\\n if len(node_cv) > 2:\\n query_args.append(node_value)\\n if len(fc_cv) > 2:\\n query_args.append(fc_value)\\n query_args += field_values\\n\\n ######################## execute the query ########################\\n\\n if not db_obj.execute(db_cur, query_str.strip(), query_args,\\n has_results=True):\\n return None\\n ret = db_obj.fetchall(db_cur)\\n if not ret[0]:\\n return None\\n if not ret[1]:\\n return []\\n return ret[1]\",\n \"def construction(self):\\n from sage.categories.pushout import AlgebraicClosureFunctor\\n return (AlgebraicClosureFunctor(), self._real_field())\",\n \"def build():\\n root = TreeNode(3)\\n root.left = TreeNode(2)\\n root.right = TreeNode(4)\\n root.right.right = TreeNode(7)\\n root.right.left = TreeNode(5)\\n return root\",\n \"def __call__(self, value):\\n self.right.left = self.__class__(value)\\n self.right.left.right = self.right\\n self.right = self.right.left\\n self.right.left = self\",\n \"def node(self):\\n return Node(self)\",\n \"def gremlin(self):\\r\\n initial = '{} = g.makeType().name(\\\"{}\\\").dataType({}.class).{}{}{}makePropertyKey()'\\r\\n func = ''\\r\\n idx = ''\\r\\n if self.functional:\\r\\n func = 'functional({}).'.format(\\\"true\\\" if self.locking else \\\"false\\\")\\r\\n if self.indexed:\\r\\n idx = 'indexed().'\\r\\n\\r\\n unique = \\\"unique().\\\" if self.unique else \\\"\\\"\\r\\n\\r\\n return initial.format(self.name, self.name, self.data_type, func, idx, unique)\",\n \"def from_db_value(self, value, expression, connection):\\n return self.to_python(value)\",\n \"def from_db_value(self, value, expression, connection):\\n # pylint: disable=unused-argument\\n return self.to_python(value)\",\n \"def __init__(self):\\n self.left = None\\n self.right = None\\n self.depth = 0\\n self.val = None\\n self.id = None\",\n \"def node(func, name=None):\\n return NamedFunc(func, name)\",\n \"def map_value(self) -> global___Expression.MapValue:\",\n \"def tree2gv_helper(node: TreeNode, result: graphviz.Graph, id: int) -> None:\\n idn = id\\n result.node(idn, node.to_text(), shape=\\\"box\\\") # node.shape())\\n if isinstance(node, LeafData):\\n return\\n if node.left is not None:\\n idl = id + \\\"0\\\"\\n tree2gv_helper(node.left, result, idl)\\n result.edge(idn, idl)\\n if node.right is not None:\\n idr = id + \\\"1\\\"\\n tree2gv_helper(node.right, result, idr)\\n result.edge(idn, idr)\",\n \"def get_terminal_node(\\n self,\\n feature_col: int,\\n node: TreeSplits,\\n feature_value: float,\\n X: np.ndarray,\\n y: np.ndarray,\\n ):\\n # Get the node type\\n node_type = self.col_type_map[feature_col]\\n\\n if node_type == \\\"continuous\\\":\\n # If no feature value is passed, this node is the leaf\\n if feature_value is None:\\n node.children = y\\n self.n_nodes += 1\\n # If a feature value is passed, create leaves as children\\n else:\\n # Get the above node\\n above = X[:, feature_col] > feature_value\\n\\n # Add two children\\n node.update(\\n feature_col=feature_col,\\n feature_value=feature_value,\\n node_type=node_type,\\n nodes={\\n \\\"above\\\": TreeSplits(\\n children=y[above]\\n ), # Children are above points\\n \\\"below\\\": TreeSplits(\\n children=y[np.bitwise_not(above)]\\n ), # Children are below points\\n },\\n )\\n # Add two nodes to count\\n self.n_nodes += 2\\n else:\\n # Get the valid values of the discrete column\\n unique_x_vals = self.discrete_value_maps[feature_col]\\n # Create the node\\n node.update(\\n feature_col=feature_col,\\n feature_value=None,\\n nodes={\\n xval: TreeSplits(\\n children=y[X[:, feature_col] == xval]\\n ) # Add in the matching rows\\n if np.any(X[:, feature_col] == xval) # If discrete values match\\n else TreeSplits(\\n children=y\\n ) # Add in all the rows if there is no values match\\n for xval in unique_x_vals\\n },\\n node_type=\\\"discrete\\\",\\n )\\n self.n_nodes += len(unique_x_vals) # increment node counter\",\n \"def __init__(self, x):\\n self.val = x\\n self.left = None\\n self.right = None\",\n \"def __init__(self, x):\\n self.val = x\\n self.left = None\\n self.right = None\",\n \"def __getitem__(self, idx):\\n\\n pos = self.node_pos[self.n_cumsum[idx] : self.n_cumsum[idx + 1]]\\n src = self.src[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]]\\n dst = self.dst[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]]\\n\\n g = dgl_graph((src, dst))\\n\\n g.ndata[\\\"pos\\\"] = F.tensor(pos, dtype=F.data_type_dict[\\\"float32\\\"])\\n g.ndata[\\\"attr\\\"] = F.tensor(\\n self.node_attr[self.n_cumsum[idx] : self.n_cumsum[idx + 1]],\\n dtype=F.data_type_dict[\\\"float32\\\"],\\n )\\n g.edata[\\\"edge_attr\\\"] = F.tensor(\\n self.edge_attr[self.ne_cumsum[idx] : self.ne_cumsum[idx + 1]],\\n dtype=F.data_type_dict[\\\"float32\\\"],\\n )\\n\\n label = F.tensor(\\n self.targets[idx][self.label_keys],\\n dtype=F.data_type_dict[\\\"float32\\\"],\\n )\\n\\n if self._transform is not None:\\n g = self._transform(g)\\n\\n return g, label\",\n \"def gen_fv(self, g, ng, fv):\\n if fv.graph not in self.graphs:\\n return self.gen_constant(g, ng, fv)\",\n \"def value(d,o):\\n # return memoized value if possible\\n if (d,o) in v:\\n return v[(d,o)]\\n\\n thisitem = int(t[d][o])\\n # the total of a subtree that starts at the leaf, is just the value of the leaf\\n if d == maxdepth:\\n val = thisitem\\n else:\\n val = thisitem + max(value(d+1,o),value(d+1,o+1))\\n\\n v[(d,o)]=val\\n return val\",\n \"def fn(node):\\n if not node: return 0, 0 # null node \\n if node.left is node.right: return node.val, 0 # leaf node \\n left, right = fn(node.left), fn(node.right) # post-order traversal \\n return node.val+left[1]+right[1], max(left)+max(right)\",\n \"def gdb_add_node(node, gdb, rdf, owl):\\n gdb_node = gdb.nodes.create()\\n node.set_node(gdb_node)\\n gdb_node.labels.add([label.split('#')[-1] for label in node.get_labels()])\\n for _, pro, obj in rdf.triples((node.get_uri(), None, None)):\\n if (pro, RDF.type, owl.DatatypeProperty) in rdf:\\n prop_name = pro.split('#')[-1]\\n value = obj.split('#')[-1]\\n gdb_node.set(prop_name, value)\",\n \"def get_or_create_node(node_id, sqlite_db):\\n\\n if node_id is None:\\n return None\\n\\n cursor = sqlite_db.cursor()\\n query = \\\"SELECT * FROM nodes WHERE id='{}'\\\".format(node_id)\\n cursor.execute(query)\\n\\n nodes = cursor.fetchall()\\n\\n if len(nodes) > 1:\\n raise Exception(\\\"Multiple nodes found for single node ID\\\")\\n\\n if nodes:\\n return nodes[0]\\n\\n node = Node.load(node_id)\\n if node is None:\\n return None\\n\\n node_public_date = None\\n privacy_actions = NodeLog.find(\\n Q('node', 'eq', node_id)\\n & Q('action', 'in', [NodeLog.MADE_PUBLIC, NodeLog.MADE_PRIVATE])\\n ).sort('-date')\\n\\n try:\\n privacy_action = privacy_actions[0]\\n except IndexError as e:\\n pass\\n else:\\n if privacy_action.action == NodeLog.MADE_PUBLIC:\\n node_public_date = privacy_action.date.isoformat()\\n node_public_date = node_public_date[:-3] + 'Z'\\n\\n cursor.execute(\\n u'INSERT INTO nodes (id, title, category, made_public_date) VALUES (?, ?, ?, ?)',\\n (node_id, getattr(node, 'title'), getattr(node, 'category'), node_public_date)\\n )\\n sqlite_db.commit()\\n return get_or_create_node(node_id, sqlite_db)\",\n \"def __value_get(self):\\n return from_pickle(self.db_value, db_obj=self)\",\n \"def __create_internal_node_by_operator(operator: PatternStructure, sliding_window: timedelta, parent: Node = None):\\n operator_type = operator.get_top_operator()\\n if operator_type == SeqOperator:\\n return SeqNode(sliding_window, parent)\\n if operator_type == AndOperator:\\n return AndNode(sliding_window, parent)\\n if operator_type == KleeneClosureOperator:\\n return KleeneClosureNode(sliding_window, operator.min_size, operator.max_size, parent)\\n raise Exception(\\\"Unknown or unsupported operator %s\\\" % (operator_type,))\",\n \"def build_node(self,key):\\n\\t\\tchildren = self.edges.loc[self.edges[self.parentkey]==key]\\n\\t\\tparent = self.edges.loc[self.edges[self.childkey]==key]\\n\\t\\tcontext = {'link':key}\\n\\t\\tmixed_children = [self.build_node(k) for k in children[self.childkey].values]\\n\\t\\tnode = self.get_node(key,context,mixed_children)\\n\\t\\treturn node\",\n \"def _left(node):\\n return 2 * node + 1\",\n \"def fn(node):\\n if not node: return #null node\\n stack.append(node.val)\\n if node.left is node.right: ans.append(\\\"->\\\".join(map(str, stack))) #leaf node\\n fn(node.left) or fn(node.right)\\n stack.pop()\",\n \"def get_object(sv, expr):\\r\\n if expr in sv.Object: \\r\\n nod=sv.Object[expr] # object exists\\r\\n else: \\r\\n nod=add_object(sv, expr) # create node for intermediate expression\\r\\n nod.isvirtual=True # temporary node\\r\\n return nod\",\n \"def db_values(self, db):\",\n \"def node_mp(tup):\\n return node_sp(*tup)\",\n \"def _create_gemm(cls, op, op_t):\\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\\n\\n node.attribute.extend([\\n helper.make_attribute('alpha', float(op.alpha)),\\n helper.make_attribute('beta', float(op.beta)),\\n helper.make_attribute('transA', op.transA),\\n helper.make_attribute('transB', op.transB),\\n ])\\n\\n return node\",\n \"def get_from_sqlite():\\n graph = FileStructureProcessor(\\\"sqlite\\\")\\n return graph.get_graph()\",\n \"def Relation(self, paren=False):\\n left = self.Addition(paren)\\n if self.currtok[1].name in {\\\"GREATERTHAN\\\", \\\"LESSTHAN\\\", \\\"LET\\\", \\\"GET\\\"}:\\n op = self.currtok[0]\\n self.currtok = next(self.tg)\\n right = self.Addition(paren)\\n left = BinaryExpr(op, left, right, paren)\\n return left\",\n \"def _create_new(self, key):\\n return AVLTreeNode(key)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6327783","0.5721543","0.552557","0.54853195","0.53288656","0.5328032","0.52322","0.52084863","0.5185801","0.5182241","0.51591873","0.5155981","0.51427555","0.51391935","0.5104471","0.50723195","0.5068302","0.50631833","0.50392216","0.50341773","0.50243515","0.50085056","0.49975306","0.4986627","0.4986627","0.4986141","0.4986141","0.49841523","0.49735177","0.49597332","0.4953921","0.4953921","0.4952253","0.4945206","0.49444062","0.49341783","0.4906458","0.48952878","0.48796672","0.48788986","0.48740387","0.48632893","0.48608756","0.48581925","0.48460147","0.48424068","0.48424068","0.4834365","0.48322484","0.48298165","0.4824749","0.48208612","0.48086515","0.47977453","0.4765801","0.47650817","0.4760372","0.4756341","0.47515312","0.47514644","0.47485924","0.4742606","0.4733792","0.4726487","0.47221866","0.47195268","0.47154087","0.47008964","0.46987256","0.46967962","0.4693086","0.46914765","0.4687371","0.46867576","0.46866193","0.46823242","0.468001","0.46723413","0.4664469","0.46638936","0.46588805","0.46588805","0.46577644","0.46524715","0.46452114","0.46450898","0.46387434","0.46334305","0.46330366","0.46289462","0.4628803","0.46286887","0.462846","0.4620984","0.4619984","0.46195063","0.46166974","0.46143037","0.46051964","0.4601601"],"string":"[\n \"0.6327783\",\n \"0.5721543\",\n \"0.552557\",\n \"0.54853195\",\n \"0.53288656\",\n \"0.5328032\",\n \"0.52322\",\n \"0.52084863\",\n \"0.5185801\",\n \"0.5182241\",\n \"0.51591873\",\n \"0.5155981\",\n \"0.51427555\",\n \"0.51391935\",\n \"0.5104471\",\n \"0.50723195\",\n \"0.5068302\",\n \"0.50631833\",\n \"0.50392216\",\n \"0.50341773\",\n \"0.50243515\",\n \"0.50085056\",\n \"0.49975306\",\n \"0.4986627\",\n \"0.4986627\",\n \"0.4986141\",\n \"0.4986141\",\n \"0.49841523\",\n \"0.49735177\",\n \"0.49597332\",\n \"0.4953921\",\n \"0.4953921\",\n \"0.4952253\",\n \"0.4945206\",\n \"0.49444062\",\n \"0.49341783\",\n \"0.4906458\",\n \"0.48952878\",\n \"0.48796672\",\n \"0.48788986\",\n \"0.48740387\",\n \"0.48632893\",\n \"0.48608756\",\n \"0.48581925\",\n \"0.48460147\",\n \"0.48424068\",\n \"0.48424068\",\n \"0.4834365\",\n \"0.48322484\",\n \"0.48298165\",\n \"0.4824749\",\n \"0.48208612\",\n \"0.48086515\",\n \"0.47977453\",\n \"0.4765801\",\n \"0.47650817\",\n \"0.4760372\",\n \"0.4756341\",\n \"0.47515312\",\n \"0.47514644\",\n \"0.47485924\",\n \"0.4742606\",\n \"0.4733792\",\n \"0.4726487\",\n \"0.47221866\",\n \"0.47195268\",\n \"0.47154087\",\n \"0.47008964\",\n \"0.46987256\",\n \"0.46967962\",\n \"0.4693086\",\n \"0.46914765\",\n \"0.4687371\",\n \"0.46867576\",\n \"0.46866193\",\n \"0.46823242\",\n \"0.468001\",\n \"0.46723413\",\n \"0.4664469\",\n \"0.46638936\",\n \"0.46588805\",\n \"0.46588805\",\n \"0.46577644\",\n \"0.46524715\",\n \"0.46452114\",\n \"0.46450898\",\n \"0.46387434\",\n \"0.46334305\",\n \"0.46330366\",\n \"0.46289462\",\n \"0.4628803\",\n \"0.46286887\",\n \"0.462846\",\n \"0.4620984\",\n \"0.4619984\",\n \"0.46195063\",\n \"0.46166974\",\n \"0.46143037\",\n \"0.46051964\",\n \"0.4601601\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5392836"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":19584,"cells":{"query":{"kind":"string","value":"Locate the value for a grounded node and its parents in a rule set, return 1 if not found. For functors with binary ranges, when all parents match but child's value does not, return 1prob for other value."},"document":{"kind":"string","value":"def ruleMatch (ruleSet, node, parents):\n def getProb (node):\n for rule in ruleSet:\n #print rule\n if (rule.child.eq(node) and\n len(rule.parentList)==len(parents) and\n all([n[0].eq(n[1]) for n in zip(rule.parentList,parents)])):\n #print \"winning eq\", [n for n in zip(rule.parentList,parents)]\n return rule.prob\n else:\n return -1\n \n prob = getProb (node)\n if prob == -1 and functorRangeSize(node.functor) == 2:\n tn = copy.copy(node)\n tn.val = functorOtherValue(tn.functor, tn.val)\n prob = getProb (tn)\n if prob != -1:\n return 1 - prob\n else:\n return prob\n return prob"},"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 getValue(self):\n r = 1 if self.left.getValue() <= self.right.getValue() else 0\n return r","def find(self, node):\n if not node:\n return 0\n\n left = self.find(node.left)\n right = self.find(node.right)\n cur = 1 # node.val\n path = 1\n if left and node.left.val == node.val:\n path += left\n cur = left + 1\n\n if right and node.right.val == node.val:\n path += right\n if right > left:\n cur = right + 1\n\n self.ret = max(self.ret, path - 1)\n return cur","def find(self, node):\n if not node:\n return 0\n\n left = self.find(node.left)\n right = self.find(node.right)\n left_path = left + 1 if node.left and node.left.val == node.val else 0\n right_path = right + 1 if node.right and node.right.val == node.val else 0\n self.ret = max(self.ret, left_path + right_path)\n return max(left_path, right_path)","def getValue(self):\n r = 1 if self.left.getValue() >= self.right.getValue() else 0\n return r","def search(self):\n open_set = set()\n closed_set = set()\n open_set.add(self.start_node)\n\n # loop through all nodes until open set is empty to build neighbor map\n while open_set:\n current_node = open_set.pop()\n closed_set.add(current_node)\n for removed_cells, score, next_status in current_node.find_next_moves():\n open_status_set = [i.status for i in open_set]\n closed_status_set = [i.status for i in closed_set]\n if next_status in open_status_set:\n index = open_status_set.index(next_status)\n node = list(open_set)[index]\n elif next_status in closed_status_set:\n index = closed_status_set.index(next_status)\n node = list(closed_set)[index]\n else:\n node = PopstarsNode(next_status)\n open_set.add(node)\n node.parents.append(current_node)\n current_node.children[node].append(\n (score, removed_cells, True))\n current_node.update_parents()\n max_score = []\n for i in self.start_node.children:\n max_score += self.start_node.children[i]\n return max(max_score)[0]","def getValue(self):\n r = 1 if self.left.getValue() == self.right.getValue() else 0\n return r","def search(self, value):\n node = self\n parent = None\n\n while node is not None:\n\n if value == node.value:\n return node, parent, True\n\n elif value < node.value:\n parent = node\n node = node.left\n\n elif value > node.value:\n parent = node\n node = node.right\n\n return node, parent, False","def _find(self, value): \n # case 1: look deeper, left\n if self.value > value and self.left is not None:\n return self.left._find(value)\n\n # case 2: look deeper, right\n if self.value < value and self.right is not None:\n return self.right._find(value)\n\n # case 3: found it, or nothing to find\n else:\n return self","def fn(node):\n if not node: return 0\n return 1 + max(fn(node.left), fn(node.right))","def fn(node):\n if not node: return 0\n return 1 + max(fn(node.left), fn(node.right))","def get_node_value(succs, preds):\n ret = 1\n if succs == 0:\n ret *= NODE_ENTRY\n\n if preds == 0:\n ret *= NODE_EXIT\n\n ret *= NODE_NORMAL\n return ret","def getValue(self):\n r = 1 if self.left.getValue() > self.right.getValue() else 0\n return r","def getValue(self):\n r = 1 if self.left.getValue() != self.right.getValue() else 0\n return r","def count_value(tree,val):\r\n if (tree==None):\r\n return 0\r\n elif(value(tree)==val):\r\n return 1+count_value(left(tree), val)+count_value(right(tree), val)\r\n else:\r\n return count_value(left(tree), val)+count_value(right(tree), val)","def find(self, value):\n if self.value is None:\n raise BinaryTreeValueError(\"Value {} not in tree\")\n\n if self.value == value:\n return self.left_length\n\n elif value < self.value:\n # Value is in left side of tree\n return self.left.find(value)\n\n else:\n # Value is in right side of tree\n return self.right.find(value) + self.left_length + 1","def getValue(self):\n r = 1 if self.left.getValue() < self.right.getValue() else 0\n return r","def fn(node):\n lc = rc = 0\n lv = rv = node.val \n if node.left: lc, lv = fn(node.left)\n if node.right: rc, rv = fn(node.right)\n if lv == rv == node.val: return lc + rc + 1, node.val \n return lc + rc, None","def fn(node):\n if not node: return 0 \n h = 1 + max(fn(node.left), fn(node.right))\n seen.setdefault(h, []).append(node.val)\n return h","def getValue(self):\n if self.left.getValue() != 0.0:\n return 1.0\n if self.right.getValue() != 0.0:\n return 1.0\n return 0.0","def contains(self,value,parent= None):\n if value == self.node.value: \n return True\n if (value < self.node.value):\n if (self.node.left):\n return self.node.left.contains(value, self.node)\n else: \n return False\n else:\n if (self.node.right):\n return self.node.right.contains(value, self.node)\n else:\n return False","def fn(node):\n if node: \n (ln, lx), (rn, rx) = fn(node.left), fn(node.right)\n if node in (p, q): return node, 1 + lx + rx\n if ln and rn: return node, lx + rx\n return (ln, lx) if ln else (rn, rx)\n return None, 0","def fn(node):\n if not node: return 0 \n left, right = fn(node.left), fn(node.right)\n if node.val == x: \n cnt[0], cnt[1] = left, right\n return 1 + left + right","def heuristic_val(self, node, g_pos, goal_depth, nb_food,nb_ghost, depth):\n ret = 0\n if node.has_food:\n ret += 10 # food at that position\n x = node.i\n y = node.j\n for pos in g_pos:\n x1, y1, _, (scared,_),_,_ = pos\n if x == x1 and y == y1:\n if scared == 0:\n ret -= 1000000 # ghost at that position causing termination\n else:\n ret += 200\n\n if goal_depth >= 0:\n ret += goal_depth*50\n else:\n if self.maxDepth - depth < len(self.moves):\n ret -= dist(self.moves[self.maxDepth - depth],node)\n else:\n ret -= dist(self.goal, node)\n ret += nb_food * 10\n ret += 200*nb_ghost\n\n return ret","def fn(node):\n if not node: return 0\n if not node.left or not node.right: return 1 + fn(node.left) + fn(node.right)\n return 1 + min(fn(node.left), fn(node.right))","def _successor(self):\n if self.right is None:\n # get first rightward ancestor\n m = self\n n = m.parent\n while n is not None and m is n.right:\n m = n\n n = n.parent\n else:\n # get leftmost of right child\n n = self.right\n while n.left is not None:\n n = n.left\n return n","def _lookup(self, data):\n parent, current = None, self.root\n while current:\n if current < data: # data should be in right\n parent, current = current, current.right\n elif current > data: # data should be in left\n parent, current = current, current.left\n else: # equals\n return parent, current\n return parent, current","def getValue(self):\n if self.left.getValue() == 0.0:\n return 0.0\n if self.right.getValue() == 0.0:\n return 0.0\n return 1.0","def _find_positive_ancestor(self, refdata, seedindex): \n \n seedval = refdata[seedindex]\n if seedval > self.row_priors[seedindex]: \n return seedindex, -seedval/self.row_priors[seedindex]\n \n # find parents of seed\n parents = self.parents\n seedparents = parents[seedindex]\n parents_len = len(seedparents)\n if parents_len == 0:\n return None, 0\n elif parents_len == 1:\n return self._find_positive_ancestor(refdata, seedparents[0])\n elif parents_len == 2:\n # handle special case when there are only two items\n # instead of doing a general query and sort, pick best of two \n r0 = self._find_positive_ancestor(refdata, seedparents[0])\n r1 = self._find_positive_ancestor(refdata, seedparents[1])\n if r1[1] < r0[1]:\n return r1 \n return r0 \n \n # study multiple paths toward root, return most enriched\n result = [self._find_positive_ancestor(refdata, _) for _ in seedparents] \n return min(result, key=itemgetter(1))","def is_cousin(parent_db, A, B):\n parent_dict = {}\n for item in parent_db:\n if item[0] in parent_dict: #If parent is already in the dictionary, add this child to value (set of children)\n parent_dict[item[0]].add(item[1])\n else:\n parent_dict[item[0]] = {item[1]}\n\n child_dict = {}\n for item in parent_db:\n if item[1] in child_dict: #If child is already in the dictionary, add this parent to value (set of parents)\n child_dict[item[1]].add(item[0])\n else:\n child_dict[item[1]] = {item[0]}\n\n if A==B:\n return None\n\n for parent in parent_dict:\n if A in parent_dict[parent] and B in parent_dict[parent]: #Checking if they share the same parent\n return None\n\n grandparents_A = set()\n for parent in child_dict[A]: #Iterating through parents of A\n for grandparent in child_dict[parent]: #Iterating through parents of parents of A (grandparents of A)\n grandparents_A.add(grandparent)\n\n for parent in child_dict[B]: #Iterating through parents of B\n for grandparent in child_dict[parent]: #Iterating through parents of parents of B (grandparents of B)\n if grandparent in grandparents_A:\n return grandparent\n\n return None","def evaluate(self,parents):\n for i in range(10):\n try:\n sis,bro = self.crosser(parents)\n if sis.validate() and bro.validate():\n return sis,bro\n except SymbolError: break\n except NoneError:\n print \"hmmm. None for a parent value, try again\"\n print \" This often happens when 'ST' isn't included in rejects list\"\n\n raise ValueError","def nearest(self, value):\n coords = value[:2] # value only has 2 coords (x, y) right now, but it may have theta in the future\n hits = self.idx.nearest(self.make_bounding_box(coords), 1, objects=False)\n for hit in hits:\n # take the first index in the event of any ties\n return self.nodes[hit]\n \n \n \n #assert that value is valid here\n \"\"\"def recur(node, depth=0):\n closest, distance = node, self.cost(node.value, value)\n if depth < self.max_size:\n for child in node.children:\n (child_closest, child_distance) = recur(child, depth+1)\n if child_distance < distance:\n closest = child_closest\n distance = child_distance \n return closest, distance\n return recur(self.root)[0]\"\"\"","def fn(node):\n nonlocal ans\n if not node: return 0 \n sm = fn(node.left) + fn(node.right)\n if sm == node.val: ans += 1\n return sm + node.val","def __getitem__(self, value) -> Node:\n self.value = value\n self.next_value = None\n if value in map(lambda x: x.value, self.nodes):\n return value\n\n else:\n return False","def parent_medium(x, p, lower, upper, **_kwargs):\n ir = np.where(x < lower) # values under the range are repaired to a medium of x_min and parents value\n x[ir] = (lower[ir] + p[ir]) / 2.0\n ir = np.where(x > upper) # values over the range are repaired to a medium of x_max and parents value\n x[ir] = (upper[ir] + p[ir]) / 2.0\n return x","def get_value(self):\r\n if len(self.walk) == 0:\r\n return 0\r\n value = 0\r\n start = 0\r\n end = len(self.walk) - 1\r\n while start < end:\r\n i_segment = self.get_segment(start+1)\r\n if i_segment.value == 'RUNG':\r\n break\r\n start += 2\r\n while end >= 2:\r\n i_segment = self.get_segment(end-1)\r\n if i_segment.value == 'RUNG':\r\n break\r\n end -= 2\r\n j = start\r\n while j < end:\r\n j_node = self.get_node(j)\r\n j += 1\r\n j_segment = self.get_segment(j)\r\n j += 1\r\n if j_segment.value != 'RUNG':\r\n # if the node connector is not critical, try to find and skip a loop\r\n k = j\r\n while k < end:\r\n k_node = self.get_node(k)\r\n k += 1\r\n k_segment = self.get_segment(k)\r\n k += 1\r\n if k_segment.value == 'RUNG':\r\n break\r\n if k_node == j_node:\r\n # Only skippable nodes existed before returned to original node, so skip that loop.\r\n value += (k - j) * 10\r\n j = k\r\n j_node = k_node\r\n j_segment = k_segment\r\n break\r\n if j_segment.value == 'SCAFFOLD':\r\n value -= j_segment.a.distance_sq(j_segment.b)\r\n elif j_segment.value == 'RUNG':\r\n value -= j_segment.a.distance_sq(j_segment.b)\r\n return value","def expected_value(node, caller_relative_probabilities, caller_distances):\n\treturn np.dot(caller_relative_probabilities, caller_distances)","def find_position(self, val):\n edges = np.array(self.cell_edges)\n if val in edges:\n index = np.searchsorted(edges, val)\n return index, index\n else:\n edges -= val\n if edges[0] > 0:\n return -1, 0\n if edges[-1] < 0:\n return 0, -1\n index = 0\n for i, e in enumerate(edges):\n if e > 0:\n index = i\n break\n return index - 1, index","def scoreR(self) :\n if self.leafR() :\n return self.leafScore(), self\n else :\n games = self.R()\n min_g = games[0]\n min_score = min_g.scoreL()\n for g in games[1:] :\n score = g.scoreL()\n if score[0] < min_score[0] :\n min_g = g\n min_score = score\n return (min_score+(min_g,))","def evaluate(self, gstate: gamestate.Gamestate, move: util.Move):\n closed_set = []\n open_set = [gstate]\n came_from = parent\n gScore = float(\"inf\")\n gScore[0] = 0\n fScore = float(\"inf\")\n fScore[0] = heurtistic(gstate.pacman, gstate.win)\n while open_set:\n current = min(fScore)\n if current == gstate.win:\n return path(came_from, current)\n\n open_set.remove(current)\n closed_set.append(current)\n for neighbor in current:\n if neighbor in closed_set:\n continue\n if neighbor not in open_set:\n open_set.append(neighbor)\n tentative_gScore = gScore[current] + util.manhattan(current, neighbor)\n if tentative_gScore >= gScore[neighbor]:\n continue\n came_from[neighbor] = current\n gScore[neighbor] = tentative_gScore\n fScore[neighbor] = gScore[neighbor] + heuristic(neighbor, goal)\n return failure","def find_successor(self, node):\n try:\n if node.right:\n return self._find_smallest(node.right)\n if node.parent:\n while node == node.parent.right:\n node = node.parent\n return node.parent\n except AttributeError:\n return","def contains(self, value):\n\n node, parent, found = self.search(value)\n\n return found","def getnodeprobs(self, node_p=None, parent_states=None):\n node_p = self.getnodenamed(node_p) # Verify pointer.\n nstates = self.getnodenumberstates(node_p)\n numconds = self._numconditions(node_p)\n\n if numconds == 1: # Only one condition (no parents)\n # print('No Parents')\n resshape = (nstates,)\n resdim = 1\n else: # Need to return a 2D array.\n # print('Some Parents')\n resshape = (numconds, nstates)\n resdim = 2\n\n if not parent_states:\n parenttype = c_void_p\n else:\n # TODO: Can't specify custom number of parents\n parenttype = c_void_p\n # parenttype = ndpointer('int', ndim=1, shape=len(20,), flags='C')\n\n cnetica.GetNodeProbs_bn.argtypes = [c_void_p, parenttype]\n cnetica.GetNodeProbs_bn.restype = ndpointer(\n 'float32', ndim=resdim, shape=resshape, flags='C')\n # Get the exact right number format, otherwise it returns diff nums\n # pdb.set_trace()\n return cnetica.GetNodeProbs_bn(node_p, parent_states)","def count_property_range_hits(prop, node_dict, hits):\n\tres = []\n\t# sets tuple position to use in dict value\n\tswitcher = {\n \"length\": (0,(0,4000,8000,12000,16000,20000)),\n \"steps\": (1,(0,2,4,8,16,32)),\n \"cov\": (2,(1,10,100,1000,10000,100000)),\n \"cv\": (3, (0,0.05,0.10,0.15,0.20,0.25))\n }\n\tif prop not in switcher:\n\t\treturn res\n\ttup_pos = switcher[prop][0]\n\tnode_cnt = 0\n\tpos_cnt = 0\n\tfor ind in range(len(switcher[prop][1])-1):\n\t\tmin_val = switcher[prop][1][ind]\n\t\tmax_val = switcher[prop][1][ind+1]\n\t\tfor node in node_dict.keys():\n\t\t\tval = node_dict[node][tup_pos]\n\t\t\tif ind < len(switcher[prop][1])-2:\n\t\t\t\trange_test_val = (min_val <= val < max_val)\n\t\t\telse:\n\t\t\t\trange_test_val = (min_val <= val <= max_val)\n\t\t\t# print \"range bool is\", range_test_val\n\t\t\tif range_test_val:\n\t\t\t\tnode_cnt += 1\n\t\t\t\tif node in hits: pos_cnt += 1\n\t\tif node_cnt > 0:\n\t\t\tres.append( (pos_cnt, node_cnt, round(float(pos_cnt)/node_cnt,2)))\n\t\telse:\n\t\t\tres.append((0,0,0))\n\t\tnode_cnt = 0\n\t\tpos_cnt = 0\n\treturn res","def find_node_recursive(node, goal):\n return (goal, get_parent(node, goal))","def tree_find(T, x):\n if T.label is None:\n return False\n else:\n return x == T.label \\\n or (x < T.label and tree_find(T[0], x)) \\\n or (x > T.label and tree_find(T[1], x))","def test_value_in_tree_returns_true(balanced_7_nodes):\n assert balanced_7_nodes.contains(7)","def neighbors(self, val):\n if val in self._g:\n return self._g[val]\n else:\n raise ValueError('Node not in graph')","def value(d,o):\n # return memoized value if possible\n if (d,o) in v:\n return v[(d,o)]\n\n thisitem = int(t[d][o])\n # the total of a subtree that starts at the leaf, is just the value of the leaf\n if d == maxdepth:\n val = thisitem\n else:\n val = thisitem + max(value(d+1,o),value(d+1,o+1))\n\n v[(d,o)]=val\n return val","def fn(node):\n if node.val == 0: return (0, 1)\n if node.val == 1: return (1, 0)\n if node.left: lf, lt = fn(node.left)\n if node.right: rf, rt = fn(node.right)\n match node.val: \n case 2: return (lf+rf, min(lt, rt))\n case 3: return (min(lf, rf), lt+rt)\n case 4: return (min(lf+rf, lt+rt), min(lf+rt, lt+rf))\n case _: return (lt, lf) if node.left else (rt, rf)","def find(self, value):\n if self.value == value:\n return True\n else:\n if self.value > value:\n if not isinstance(self.left, Node):\n return False\n else:\n return self.left.find(value)\n elif self.value < value:\n if not isinstance(self.right, Node):\n return False\n else:\n return self.right.find(value)","def fn(node):\n if not node: return 0 \n ans = node.val + fn(node.left) + fn(node.right)\n vals.append(ans)\n return ans","def get_successor(self, key):\r\n parent_node, search_node = self.__compare(key, method='search')\r\n self.__check_node(search_node)\r\n\r\n if search_node.right_child.key:\r\n succ_node, _ = self.__compare(method='min', source=search_node.right_child)\r\n else:\r\n while search_node.key > parent_node.key:\r\n search_node = parent_node\r\n parent_node = parent_node.parent\r\n\r\n # if it reaches the root, means there is no predecessor\r\n if not parent_node:\r\n return NodeRBT(None, None)\r\n\r\n succ_node = parent_node\r\n\r\n return succ_node","def search(self) -> int:\n # crete node list\n for x in range(self.n):\n for y in range(self.n):\n if not self.grid[y][x] == 0:\n self.all_nodes.append((x, y))\n # recursively create paths\n i = 0\n paths = [[(0, 0)]]\n while i < self.n * self.n:\n paths = self.generate_paths(paths)\n if isinstance(paths, int):\n return paths\n i += 1\n\n return -1","def find_node(self, value):\n for (fun, node) in self.__root.__fast_find:\n if fun(value):\n return node\n return None","def __find(self, x, parent: 'Node'):\n found = None\n if parent.value == x:\n return parent\n\n for child in parent.children:\n if child.value == x:\n return child\n new_found = self.__find(x, parent=child)\n if new_found:\n found = new_found\n\n return found","def nearest_common_parent(self, go_ids=None):\n # Take the element at maximum depth\n ncp = max(self.common_parents(go_ids=go_ids), key=lambda t: self.dict_go[t].depth)\n return ncp","def fn(node):\n if not node or node in (p, q): return node\n left, right = fn(node.left), fn(node.right)\n if left and right: return node \n return left or right","def find_goal(self):\n\n self.separate_frontier()\n goal = self.centroid(self.regions)\n\t#rospy.loginfo(goal)\n self.centroidValue = goal\n self.paint_cells(self.centroidValue,self.frontier)\n\tif goal is not None and goal!=0:\n \tfinal = self.in_bounds(goal)\n \trospy.loginfo(\"Region Found\")\n \treturn final","def find_B(self):\n max_lb = 0\n for arc in self.arcs():\n lb = self.arc_info[arc[0]]['lower_bound']\n max_lb = max(max_lb, lb)\n n = len(self)\n m = len(list(self.edges()))\n return((m - n + 2)*max_lb)","def lowest_common_ancestor(node):\n pass","def getValue(self):\n if self.left.getValue() <= self.right.getValue():\n return self.left.getValue()\n\n return self.right.getValue()","def fn(u):\n if u == n-1: return 1 # boundary condition \n ans = 0\n for v, _ in graph[u]: \n if dist[u] > dist[v]: ans += fn(v)\n return ans","def getPeso(self, n1, n2):\n if n1 in self and n2 in self:\n if n2 in self.vertices[n1]:\n return self.vertices[n1][n2]\n elif n1 == n2:\n return 0\n return float(\"inf\")\n return False","def fn(node):\n if not node: return True, 0\n tf0, h0 = fn(node.left)\n tf1, h1 = fn(node.right)\n return tf0 and tf1 and abs(h0-h1) <= 1, 1 + max(h0, h1)","def select(self, board, c_puct):\n # the game rule has a random cases in the select procedure\n board.get_point()\n batch = self._children.get(board.point, None) # get this point's edge\n if not batch: return True, None # this node is the leaf\n return False, max(batch.items(),\n key=lambda act_node: act_node[1].get_value(c_puct))","def lookup(self, val):\n if val < self.val:\n if self.left is None:\n return None, None\n return self.left.lookup(val)\n elif val > self.val:\n if self.right is None:\n return None, None\n return self.right.lookup(val)\n else:\n return self","def question4(T,r,n1,n2):\n\n\tif(len(T)<=1):\t\t\t\t\t\t\t\t# Edge case : If the Tree only consists of a root and no children\n\t\treturn -1\n\n\tif(n1==None or n2==None):\t\t\t\t\t# Edge case : If n1 and n2 are not actually numbers\n\t\treturn -1\n\n\tlen_T = len(T)\n\tif(not n1 < len_T or not n2 < len_T):\t\t# Edge case : If the nodes gives in parameters do not actually exist in the tree\n\t\treturn -1\n\n\tn1_list = []\t\t\t\t\t\t\n\tn2_list = []\n\n\tfor i in range(len(T)):\t\t\t\t\t\t# Traverse the list and append all the parents of node1 if found in O(N)\n\t\tif T[i][n1]==1:\n\t\t\tn1_list.append(i)\n\n\tfor i in range(len(T)):\t\t\t\t\t\t# Traverse the list and append all the parents of node2 is found in O(N)\n\t\tif T[i][n2]:\n\t\t\tn2_list.append(i)\n\n\t\t\t\t\t\t\t\t\t\t\t\t# The root is a common ancestor of every node in the tree\n\tif not r in n1_list:\t\t\t\t\t\t# check if the root is in the list, if not, add it\n\t\tn1_list.append(r)\n\n\tif not r in n2_list:\t\t\t\t\t\t# check if the root is in the list, if not, add it\n\t\tn2_list.append(r)\n\n\tn1_list = reversed(n1_list)\t\t\t\t\t# Since we are operating on a binary tree, we sort\n\tfor i in n1_list:\t\t\t\t\t\t\t# in decending order to operate on the latest nodes\n\t\tif i in n2_list:\t\t\t\t\t\t# if a match is found, we know that it is the lowest common ancestor\n\t\t\treturn i \t\t\t\t\t\t\t# If nothing is found, the root node is bound to be returned. And it correct.","def getValue(self):\n if self.left.getValue() >= self.right.getValue():\n return self.left.getValue()\n\n return self.right.getValue()","def point_in_between(ob, row, cell, other_cell):\n if row:\n left = other_cell[0] < cell[0]\n if left:\n return ob.patt[0] == 1\n else:\n return ob.patt[2] == 1\n below = other_cell[1] < cell[1]\n if below:\n return ob.patt[1] == 0\n else:\n return ob.patt[1] == 2","def heuristicValueOfPosition(currPositions):\n hVal = 0;\n\n for y in range(1, n+1): #1,2,3\n for x in range(1, n+1):\n val = currPositions[y][x];\n if ((val == 0) or (goalPositions[val] == (y,x))): #val 0 means blank\n continue;\n else:\n hVal += abs(y-goalPositions[val][0]) + abs(x-goalPositions[val][1])\n\n return hVal;","def init_parent_lookup(parent_lookup, x, y):\n if x not in parent_lookup:\n parent_lookup[x] = x\n # single person family has himself as parent\n # maxCircle function decides\n if y not in parent_lookup:\n parent_lookup[y] = y","def find(p):\n if p != parent[p]:\n parent[p] = find(parent[p])\n return parent[p]","def lookup(self,entry):\n if self.type == 'v':\n return self\n v = entry[self.feature]\n assert v != None\n if self.type == 's':\n c = None\n try:\n c = self.children[v]\n except KeyError:\n #print \"Unseen value for feature\",self.feature,\": \",v\n best = None\n bestDist = float('inf')\n for (val,c) in self.children.iteritems():\n if abs(val - v) < bestDist:\n bestDist = abs(val - v)\n best = c\n c = best\n return c.lookup(entry)\n elif self.type == 'i':\n if v <= self.value:\n return self.children[0].lookup(entry)\n else:\n return self.children[1].lookup(entry)\n raise RuntimeError(\"Invalid DecisionTreeNode type?\")","def rank(self, value):\n i = 0\n n = len(self._tree)\n rank = 0\n count = 0\n while i < n:\n cur = self._tree[i]\n if value < cur:\n i = 2 * i + 1\n continue\n elif value > cur:\n rank += self._counts[i]\n # subtract off the right tree if exists\n nexti = 2 * i + 2\n if nexti < n:\n rank -= self._counts[nexti]\n i = nexti\n continue\n else:\n return (rank, count)\n else: # value == cur\n count = self._counts[i]\n lefti = 2 * i + 1\n if lefti < n:\n nleft = self._counts[lefti]\n count -= nleft\n rank += nleft\n righti = lefti + 1\n if righti < n:\n count -= self._counts[righti]\n return (rank, count)\n return (rank, count)","def rank(self, value):\n i = 0\n n = len(self._tree)\n rank = 0\n count = 0\n while i < n:\n cur = self._tree[i]\n if value < cur:\n i = 2 * i + 1\n continue\n elif value > cur:\n rank += self._counts[i]\n # subtract off the right tree if exists\n nexti = 2 * i + 2\n if nexti < n:\n rank -= self._counts[nexti]\n i = nexti\n continue\n else:\n return (rank, count)\n else: # value == cur\n count = self._counts[i]\n lefti = 2 * i + 1\n if lefti < n:\n nleft = self._counts[lefti]\n count -= nleft\n rank += nleft\n righti = lefti + 1\n if righti < n:\n count -= self._counts[righti]\n return (rank, count)\n return (rank, count)","def fn(node):\n if not node: return True, inf, -inf, 0, 0 # bst flag | min | max | sum\n ltf, lmn, lmx, lsm, lval = fn(node.left)\n rtf, rmn, rmx, rsm, rval = fn(node.right)\n lmn = min(lmn, node.val)\n rmx = max(rmx, node.val)\n sm = lsm + rsm + node.val \n if ltf and rtf and lmx < node.val < rmn: \n return True, lmn, rmx, sm, max(lval, rval, sm)\n return False, lmn, rmx, sm, max(lval, rval)","def search(self, val):\n if type(val) not in [int, float]:\n raise TypeError('This tree accepts numbers only.')\n current_node = self._root\n while current_node:\n if val == current_node._data:\n return current_node\n if val > current_node._data:\n current_node = current_node._rkid\n else:\n current_node = current_node._lkid\n return","def find(self, value):\n # initialize node as root\n node = self.root\n\n # find value\n while node != None:\n\n # value found: return node\n if node.value == value:\n return node\n\n # value is smaller than node: search in left sub tree\n elif node.value > value:\n node = node.left\n\n # value is bigger than node: search in right sub tree\n else:\n node = node.right\n\n # value not found: return None\n return None","def gomeroccupancyscore(pwm_dictionary, seq):\n if \"N\" in seq:\n return 0\n else:\n # pwm_length = len(pwm_dictionary)\n pwm_length = len(pwm_dictionary[\"A\"])\n gomer_occupancy = 1\n area_pwm_rc = rc_pwm(pwm_dictionary, pwm_length)\n for i in range(pwm_length - 1, 1, -1):\n prod_gomer = 1\n prod_gomer_rc = 1\n for j in range(pwm_length):\n if j <= i:\n prod_gomer *= 0.25\n prod_gomer_rc *= 0.25\n elif (j + i) > len(seq) - 1:\n prod_gomer *= 0.25\n prod_gomer_rc *= 0.25\n else:\n # print \"got to else\"\n s = seq[j + i]\n prod_gomer *= pwm_dictionary[s][j]\n prod_gomer_rc *= area_pwm_rc[s][j]\n gomer_occupancy *= (1 - prod_gomer) * (1 - prod_gomer_rc)\n for i in range(len(seq) - 1):\n prod_gomer = 1\n prod_gomer_rc = 1\n for j in range(pwm_length - 1):\n if (j + i) >= len(seq) - 1:\n prod_gomer *= 0.25\n prod_gomer_rc *= 0.25\n else:\n prod_gomer *= pwm_dictionary[seq[j + i]][j]\n prod_gomer_rc *= area_pwm_rc[seq[j + i]][j]\n gomer_occupancy *= (1 - prod_gomer) * (1 - prod_gomer_rc)\n gomer_occupancy = 1 - gomer_occupancy\n\n return gomer_occupancy","def findOverlapOrNearest(gs, ts, tree, start, end):\n #step 1, find overlaps\n rs = set()\n for i in range(start, end + 1):\n if i in gs:\n rs.add(gs[i])\n if len(rs) > 0:\n rs = list(rs)\n return rs, [0] * len(rs)\n #find the nearest one\n else:\n d, i = tree.query([(start + end) / 2], k=1)\n g = gs[ts[i][0]]\n #d = ts[i][0] - (start+end)/2\n d = int(d)\n return [g], [d]","def fn(node, val):\n if not node: return 0\n val = 10*val + node.val\n if not node.left and not node.right: return val \n return fn(node.left, val) + fn(node.right, val)","def _find_parents(self, user_profiles, new_infected_users):\n if (self._old_infection_state == 0).all():\n # Node is root\n return None\n # TODO: function is_following() based on code below:\n # candidates must have been previously infected\n prev_infected_users = np.where(self._old_infection_state > 0)[0]\n # candidates must be connected to newly infected users\n candidate_parents = user_profiles[:, prev_infected_users][new_infected_users]\n if not isinstance(candidate_parents, np.ndarray):\n candidate_parents = candidate_parents.toarray() # convert sparse to numpy if needed\n # randomly select parent out of those who were infected, use random multiplication\n candidate_parents = candidate_parents * np.random.rand(*candidate_parents.shape)\n parents = prev_infected_users[np.argmax(candidate_parents, axis=1)]\n return parents","def search(self, depth, state, curr_player):\n \n # enumerate all legal moves from this state\n legal_moves = []\n for i in range(7):\n # if column i is a legal move...\n if self.isLegalMove(i, state):\n # make the move in column i for curr_player\n temp = self.makeMove(state, i, curr_player)\n legal_moves.append(temp)\n \n # if this node (state) is a terminal node or depth == 0...\n if depth == 0 or len(legal_moves) == 0 or self.gameIsOver(state):\n # return the heuristic value of node\n return self.value(state, curr_player)\n \n # determine opponent's color\n if curr_player == self.colors[0]:\n opp_player = self.colors[1]\n else:\n opp_player = self.colors[0]\n\n alpha = -99999999\n for child in legal_moves:\n if child == None:\n print(\"child == None (search)\")\n alpha = max(alpha, -self.search(depth-1, child, opp_player))\n return alpha","def fn(node):\n if not node: return 0, -inf\n lh, lps = fn(node.left)\n rh, rps = fn(node.right)\n return node.val + max(0, lh, rh), max(lps, rps, node.val + max(0, lh) + max(0, rh))","def fn(node):\n nonlocal ans \n if not node: return 1\n left, right = fn(node.left), fn(node.right)\n if left == 0 or right == 0: \n ans += 1\n return 2 # add a camera \n if left == 2 or right == 2: return 1\n return 0","def lookup(self, data, parent=None):\n if data < self.data:\n if self.left is None:\n return None, None\n return self.left.lookup(data, self)\n elif data > self.data:\n if self.right is None:\n return None, None\n return self.right.lookup(data, self)\n else:\n return self, parent","def contain(graph, value):\n for node in graph:\n if node.value == value:\n return True\n\n return False","def contains(self, value):\n if self.graph == None:\n return\n \n else:\n return contain(self.graph, value)","def primer_binary_srch(self, primers):\n l = 0\n r = len(primers) - 1\n while l <= r:\n m = l + int((r - l) / 2) \n if primers[m].binds_to(self):\n return primers[m]\n elif primers[m] < self:\n l = m + 1\n else:\n r = m - 1\n return None","def get_successor(self, value):\n\t\tstack = [self]\n\t\tcurrent = None\n\t\tprevious = None\n\t\twhile not previous == value and stack:\n\t\t\titem = stack.pop()\n\t\t\tif isinstance(item, BSTreeNode):\n\t\t\t\tif item.right:\n\t\t\t\t\tstack.append(item.right)\n\t\t\t\tstack.append(item.value)\n\t\t\t\tif item.left:\n\t\t\t\t\tstack.append(item.left)\n\t\t\t\tcontinue\n\t\t\tprevious = current\n\t\t\tcurrent = item\n\t\treturn current if previous == value else None","def fn(node):\n if not node: return 0 \n ans = node.val + fn(node.left) + fn(node.right)\n freq[ans] += 1\n return ans","def fn(n):\n s, c = value[n], 1\n for nn in tree.get(n, []): \n ss, cc = fn(nn)\n s += ss\n c += cc \n return (s, c) if s != 0 else (0, 0)","def _find(self, item):\n stack = [self.root]\n while stack:\n node = stack.pop()\n if node.items and item in node.items:\n return True, node.active[node.items.index(item)], node\n elif not node.items:\n axis = node.cutdim\n cur = item[axis]\n median = node.cutval\n if median >= cur:\n stack.append(node.left)\n if median <= cur:\n stack.append(node.right)\n return False, False, None","def find(self, value, max_distance):\n\t\t# type: (Any, int) -> List[Tuple[int, Any]]\n\n\t\tnode = self.root\n\t\tret = [] # type: List[Tuple[int, Any]]\n\n\t\tif node is None:\n\t\t\treturn ret\n\n\t\tcandidates = [node] # is a deque better here?\n\n\t\twhile candidates:\n\t\t\tcandidate = candidates.pop()\n\t\t\tdistance = self.distance_func(value, candidate.value)\n\n\t\t\tif distance <= max_distance:\n\t\t\t\tret.append((distance, candidate.value))\n\n\t\t\t# instead of looking for candidates by searching,\n\t\t\t# one could also directly access the necessary keys in the dict\n\t\t\tfor d, bknode in candidate.leaves.items():\n\t\t\t\tlower = distance - max_distance\n\t\t\t\tupper = distance + max_distance\n\t\t\t\tif lower <= d <= upper:\n\t\t\t\t\tcandidates.append(bknode)\n\n\t\treturn ret","def side_of_parent(self):\n if self._node_empty(self._parent):\n return -1\n\n if self._parent.left is self:\n return 0\n elif self._parent.right is self:\n return 1\n else:\n raise ValueError('Node invalid.')","def determine_move_position(self):\n green_probs = []\n net_size = len(self.net)\n adjacents = self.net[self.current_pos].adjacents\n #Belief propagation:\n #Analyzes each position's probability of obtaining\n #green when measuring at a time t+1.\n for i in adjacents:\n accum = 0\n for j in range(0, net_size):\n distance = self.__get_distance(i-1, j)\n if distance == 0: #Probability of measure green at distance 0 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[0][0]\n elif distance == 1: #Probability of measure green at distance 1 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[1][0]\n elif distance == 2: #Probability of measure green at distance 2 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[2][0]\n elif distance == 3: #Probability of measure green at distance 3 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[3][0]\n else: #Probability of measure green at a distance >= 4 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[4][0]\n green_probs.append((i, accum))\n #Returns the position in which the probability of\n #obtaining green when measuring is the lowest.\n return min(green_probs, key=itemgetter(1))[0]","def get_effective_value(vals):\n lst = sorted(vals.values(), key=lambda x: -x[1])\n anchor = 0 # The highest priority non-relative value (with comb=None)\n while anchor < len(lst) and lst[anchor][2]:\n anchor += 1\n if lst[anchor][2]:\n ## TODO we should say which one :-D\n raise RuntimeError(\"Value set contains only relative values\")\n val = lst[anchor][0]\n for (relval, _, comb) in reversed(lst[:anchor]):\n val = comb(val, relval)\n return val","def neighbors(self, val):\n if val in self:\n return self[val]\n else:\n raise ValueError('Value not in graph')","def determine_measure_position(self):\n green_probs = []\n net_size = len(self.net)\n #Belief propagation:\n #Analyzes each position's probability of obtaining\n #green when measuring at a time t+1.\n for i in range(0, net_size):\n accum = 0\n for j in range(0, net_size):\n distance = self.__get_distance(i, j)\n if distance == 0: #Probability of measure green at distance 0 from 'i'.\n accum += self.net[i].value * self.ct[0][0]\n elif distance == 1: #Probability of measure green at distance 1 from 'i'.\n accum += self.net[i].value * self.ct[1][0]\n elif distance == 2: #Probability of measure green at distance 2 from 'i'.\n accum += self.net[i].value * self.ct[2][0]\n elif distance == 3: #Probability of measure green at distance 3 from 'i'.\n accum += self.net[i].value * self.ct[3][0]\n else: #Probability of measure green at a distance >= 4 from 'i'.\n accum += self.net[i].value * self.ct[4][0]\n green_probs.append(accum)\n #Returns the position in which the probability of\n #obtaining green when measuring is the highest.\n return self.net[np.argmax(green_probs)].id","def get_rank(self, pb):\n\n for rank in self.RANKS:\n start = self.RANKS[rank][\"ProgressStart\"]\n # 1 is not subtracted as we're calling range\n end = start + self.RANKS[rank][\"Progress\"]\n if pb in range(start, end):\n return int(rank)\n else:\n return 35"],"string":"[\n \"def getValue(self):\\n r = 1 if self.left.getValue() <= self.right.getValue() else 0\\n return r\",\n \"def find(self, node):\\n if not node:\\n return 0\\n\\n left = self.find(node.left)\\n right = self.find(node.right)\\n cur = 1 # node.val\\n path = 1\\n if left and node.left.val == node.val:\\n path += left\\n cur = left + 1\\n\\n if right and node.right.val == node.val:\\n path += right\\n if right > left:\\n cur = right + 1\\n\\n self.ret = max(self.ret, path - 1)\\n return cur\",\n \"def find(self, node):\\n if not node:\\n return 0\\n\\n left = self.find(node.left)\\n right = self.find(node.right)\\n left_path = left + 1 if node.left and node.left.val == node.val else 0\\n right_path = right + 1 if node.right and node.right.val == node.val else 0\\n self.ret = max(self.ret, left_path + right_path)\\n return max(left_path, right_path)\",\n \"def getValue(self):\\n r = 1 if self.left.getValue() >= self.right.getValue() else 0\\n return r\",\n \"def search(self):\\n open_set = set()\\n closed_set = set()\\n open_set.add(self.start_node)\\n\\n # loop through all nodes until open set is empty to build neighbor map\\n while open_set:\\n current_node = open_set.pop()\\n closed_set.add(current_node)\\n for removed_cells, score, next_status in current_node.find_next_moves():\\n open_status_set = [i.status for i in open_set]\\n closed_status_set = [i.status for i in closed_set]\\n if next_status in open_status_set:\\n index = open_status_set.index(next_status)\\n node = list(open_set)[index]\\n elif next_status in closed_status_set:\\n index = closed_status_set.index(next_status)\\n node = list(closed_set)[index]\\n else:\\n node = PopstarsNode(next_status)\\n open_set.add(node)\\n node.parents.append(current_node)\\n current_node.children[node].append(\\n (score, removed_cells, True))\\n current_node.update_parents()\\n max_score = []\\n for i in self.start_node.children:\\n max_score += self.start_node.children[i]\\n return max(max_score)[0]\",\n \"def getValue(self):\\n r = 1 if self.left.getValue() == self.right.getValue() else 0\\n return r\",\n \"def search(self, value):\\n node = self\\n parent = None\\n\\n while node is not None:\\n\\n if value == node.value:\\n return node, parent, True\\n\\n elif value < node.value:\\n parent = node\\n node = node.left\\n\\n elif value > node.value:\\n parent = node\\n node = node.right\\n\\n return node, parent, False\",\n \"def _find(self, value): \\n # case 1: look deeper, left\\n if self.value > value and self.left is not None:\\n return self.left._find(value)\\n\\n # case 2: look deeper, right\\n if self.value < value and self.right is not None:\\n return self.right._find(value)\\n\\n # case 3: found it, or nothing to find\\n else:\\n return self\",\n \"def fn(node):\\n if not node: return 0\\n return 1 + max(fn(node.left), fn(node.right))\",\n \"def fn(node):\\n if not node: return 0\\n return 1 + max(fn(node.left), fn(node.right))\",\n \"def get_node_value(succs, preds):\\n ret = 1\\n if succs == 0:\\n ret *= NODE_ENTRY\\n\\n if preds == 0:\\n ret *= NODE_EXIT\\n\\n ret *= NODE_NORMAL\\n return ret\",\n \"def getValue(self):\\n r = 1 if self.left.getValue() > self.right.getValue() else 0\\n return r\",\n \"def getValue(self):\\n r = 1 if self.left.getValue() != self.right.getValue() else 0\\n return r\",\n \"def count_value(tree,val):\\r\\n if (tree==None):\\r\\n return 0\\r\\n elif(value(tree)==val):\\r\\n return 1+count_value(left(tree), val)+count_value(right(tree), val)\\r\\n else:\\r\\n return count_value(left(tree), val)+count_value(right(tree), val)\",\n \"def find(self, value):\\n if self.value is None:\\n raise BinaryTreeValueError(\\\"Value {} not in tree\\\")\\n\\n if self.value == value:\\n return self.left_length\\n\\n elif value < self.value:\\n # Value is in left side of tree\\n return self.left.find(value)\\n\\n else:\\n # Value is in right side of tree\\n return self.right.find(value) + self.left_length + 1\",\n \"def getValue(self):\\n r = 1 if self.left.getValue() < self.right.getValue() else 0\\n return r\",\n \"def fn(node):\\n lc = rc = 0\\n lv = rv = node.val \\n if node.left: lc, lv = fn(node.left)\\n if node.right: rc, rv = fn(node.right)\\n if lv == rv == node.val: return lc + rc + 1, node.val \\n return lc + rc, None\",\n \"def fn(node):\\n if not node: return 0 \\n h = 1 + max(fn(node.left), fn(node.right))\\n seen.setdefault(h, []).append(node.val)\\n return h\",\n \"def getValue(self):\\n if self.left.getValue() != 0.0:\\n return 1.0\\n if self.right.getValue() != 0.0:\\n return 1.0\\n return 0.0\",\n \"def contains(self,value,parent= None):\\n if value == self.node.value: \\n return True\\n if (value < self.node.value):\\n if (self.node.left):\\n return self.node.left.contains(value, self.node)\\n else: \\n return False\\n else:\\n if (self.node.right):\\n return self.node.right.contains(value, self.node)\\n else:\\n return False\",\n \"def fn(node):\\n if node: \\n (ln, lx), (rn, rx) = fn(node.left), fn(node.right)\\n if node in (p, q): return node, 1 + lx + rx\\n if ln and rn: return node, lx + rx\\n return (ln, lx) if ln else (rn, rx)\\n return None, 0\",\n \"def fn(node):\\n if not node: return 0 \\n left, right = fn(node.left), fn(node.right)\\n if node.val == x: \\n cnt[0], cnt[1] = left, right\\n return 1 + left + right\",\n \"def heuristic_val(self, node, g_pos, goal_depth, nb_food,nb_ghost, depth):\\n ret = 0\\n if node.has_food:\\n ret += 10 # food at that position\\n x = node.i\\n y = node.j\\n for pos in g_pos:\\n x1, y1, _, (scared,_),_,_ = pos\\n if x == x1 and y == y1:\\n if scared == 0:\\n ret -= 1000000 # ghost at that position causing termination\\n else:\\n ret += 200\\n\\n if goal_depth >= 0:\\n ret += goal_depth*50\\n else:\\n if self.maxDepth - depth < len(self.moves):\\n ret -= dist(self.moves[self.maxDepth - depth],node)\\n else:\\n ret -= dist(self.goal, node)\\n ret += nb_food * 10\\n ret += 200*nb_ghost\\n\\n return ret\",\n \"def fn(node):\\n if not node: return 0\\n if not node.left or not node.right: return 1 + fn(node.left) + fn(node.right)\\n return 1 + min(fn(node.left), fn(node.right))\",\n \"def _successor(self):\\n if self.right is None:\\n # get first rightward ancestor\\n m = self\\n n = m.parent\\n while n is not None and m is n.right:\\n m = n\\n n = n.parent\\n else:\\n # get leftmost of right child\\n n = self.right\\n while n.left is not None:\\n n = n.left\\n return n\",\n \"def _lookup(self, data):\\n parent, current = None, self.root\\n while current:\\n if current < data: # data should be in right\\n parent, current = current, current.right\\n elif current > data: # data should be in left\\n parent, current = current, current.left\\n else: # equals\\n return parent, current\\n return parent, current\",\n \"def getValue(self):\\n if self.left.getValue() == 0.0:\\n return 0.0\\n if self.right.getValue() == 0.0:\\n return 0.0\\n return 1.0\",\n \"def _find_positive_ancestor(self, refdata, seedindex): \\n \\n seedval = refdata[seedindex]\\n if seedval > self.row_priors[seedindex]: \\n return seedindex, -seedval/self.row_priors[seedindex]\\n \\n # find parents of seed\\n parents = self.parents\\n seedparents = parents[seedindex]\\n parents_len = len(seedparents)\\n if parents_len == 0:\\n return None, 0\\n elif parents_len == 1:\\n return self._find_positive_ancestor(refdata, seedparents[0])\\n elif parents_len == 2:\\n # handle special case when there are only two items\\n # instead of doing a general query and sort, pick best of two \\n r0 = self._find_positive_ancestor(refdata, seedparents[0])\\n r1 = self._find_positive_ancestor(refdata, seedparents[1])\\n if r1[1] < r0[1]:\\n return r1 \\n return r0 \\n \\n # study multiple paths toward root, return most enriched\\n result = [self._find_positive_ancestor(refdata, _) for _ in seedparents] \\n return min(result, key=itemgetter(1))\",\n \"def is_cousin(parent_db, A, B):\\n parent_dict = {}\\n for item in parent_db:\\n if item[0] in parent_dict: #If parent is already in the dictionary, add this child to value (set of children)\\n parent_dict[item[0]].add(item[1])\\n else:\\n parent_dict[item[0]] = {item[1]}\\n\\n child_dict = {}\\n for item in parent_db:\\n if item[1] in child_dict: #If child is already in the dictionary, add this parent to value (set of parents)\\n child_dict[item[1]].add(item[0])\\n else:\\n child_dict[item[1]] = {item[0]}\\n\\n if A==B:\\n return None\\n\\n for parent in parent_dict:\\n if A in parent_dict[parent] and B in parent_dict[parent]: #Checking if they share the same parent\\n return None\\n\\n grandparents_A = set()\\n for parent in child_dict[A]: #Iterating through parents of A\\n for grandparent in child_dict[parent]: #Iterating through parents of parents of A (grandparents of A)\\n grandparents_A.add(grandparent)\\n\\n for parent in child_dict[B]: #Iterating through parents of B\\n for grandparent in child_dict[parent]: #Iterating through parents of parents of B (grandparents of B)\\n if grandparent in grandparents_A:\\n return grandparent\\n\\n return None\",\n \"def evaluate(self,parents):\\n for i in range(10):\\n try:\\n sis,bro = self.crosser(parents)\\n if sis.validate() and bro.validate():\\n return sis,bro\\n except SymbolError: break\\n except NoneError:\\n print \\\"hmmm. None for a parent value, try again\\\"\\n print \\\" This often happens when 'ST' isn't included in rejects list\\\"\\n\\n raise ValueError\",\n \"def nearest(self, value):\\n coords = value[:2] # value only has 2 coords (x, y) right now, but it may have theta in the future\\n hits = self.idx.nearest(self.make_bounding_box(coords), 1, objects=False)\\n for hit in hits:\\n # take the first index in the event of any ties\\n return self.nodes[hit]\\n \\n \\n \\n #assert that value is valid here\\n \\\"\\\"\\\"def recur(node, depth=0):\\n closest, distance = node, self.cost(node.value, value)\\n if depth < self.max_size:\\n for child in node.children:\\n (child_closest, child_distance) = recur(child, depth+1)\\n if child_distance < distance:\\n closest = child_closest\\n distance = child_distance \\n return closest, distance\\n return recur(self.root)[0]\\\"\\\"\\\"\",\n \"def fn(node):\\n nonlocal ans\\n if not node: return 0 \\n sm = fn(node.left) + fn(node.right)\\n if sm == node.val: ans += 1\\n return sm + node.val\",\n \"def __getitem__(self, value) -> Node:\\n self.value = value\\n self.next_value = None\\n if value in map(lambda x: x.value, self.nodes):\\n return value\\n\\n else:\\n return False\",\n \"def parent_medium(x, p, lower, upper, **_kwargs):\\n ir = np.where(x < lower) # values under the range are repaired to a medium of x_min and parents value\\n x[ir] = (lower[ir] + p[ir]) / 2.0\\n ir = np.where(x > upper) # values over the range are repaired to a medium of x_max and parents value\\n x[ir] = (upper[ir] + p[ir]) / 2.0\\n return x\",\n \"def get_value(self):\\r\\n if len(self.walk) == 0:\\r\\n return 0\\r\\n value = 0\\r\\n start = 0\\r\\n end = len(self.walk) - 1\\r\\n while start < end:\\r\\n i_segment = self.get_segment(start+1)\\r\\n if i_segment.value == 'RUNG':\\r\\n break\\r\\n start += 2\\r\\n while end >= 2:\\r\\n i_segment = self.get_segment(end-1)\\r\\n if i_segment.value == 'RUNG':\\r\\n break\\r\\n end -= 2\\r\\n j = start\\r\\n while j < end:\\r\\n j_node = self.get_node(j)\\r\\n j += 1\\r\\n j_segment = self.get_segment(j)\\r\\n j += 1\\r\\n if j_segment.value != 'RUNG':\\r\\n # if the node connector is not critical, try to find and skip a loop\\r\\n k = j\\r\\n while k < end:\\r\\n k_node = self.get_node(k)\\r\\n k += 1\\r\\n k_segment = self.get_segment(k)\\r\\n k += 1\\r\\n if k_segment.value == 'RUNG':\\r\\n break\\r\\n if k_node == j_node:\\r\\n # Only skippable nodes existed before returned to original node, so skip that loop.\\r\\n value += (k - j) * 10\\r\\n j = k\\r\\n j_node = k_node\\r\\n j_segment = k_segment\\r\\n break\\r\\n if j_segment.value == 'SCAFFOLD':\\r\\n value -= j_segment.a.distance_sq(j_segment.b)\\r\\n elif j_segment.value == 'RUNG':\\r\\n value -= j_segment.a.distance_sq(j_segment.b)\\r\\n return value\",\n \"def expected_value(node, caller_relative_probabilities, caller_distances):\\n\\treturn np.dot(caller_relative_probabilities, caller_distances)\",\n \"def find_position(self, val):\\n edges = np.array(self.cell_edges)\\n if val in edges:\\n index = np.searchsorted(edges, val)\\n return index, index\\n else:\\n edges -= val\\n if edges[0] > 0:\\n return -1, 0\\n if edges[-1] < 0:\\n return 0, -1\\n index = 0\\n for i, e in enumerate(edges):\\n if e > 0:\\n index = i\\n break\\n return index - 1, index\",\n \"def scoreR(self) :\\n if self.leafR() :\\n return self.leafScore(), self\\n else :\\n games = self.R()\\n min_g = games[0]\\n min_score = min_g.scoreL()\\n for g in games[1:] :\\n score = g.scoreL()\\n if score[0] < min_score[0] :\\n min_g = g\\n min_score = score\\n return (min_score+(min_g,))\",\n \"def evaluate(self, gstate: gamestate.Gamestate, move: util.Move):\\n closed_set = []\\n open_set = [gstate]\\n came_from = parent\\n gScore = float(\\\"inf\\\")\\n gScore[0] = 0\\n fScore = float(\\\"inf\\\")\\n fScore[0] = heurtistic(gstate.pacman, gstate.win)\\n while open_set:\\n current = min(fScore)\\n if current == gstate.win:\\n return path(came_from, current)\\n\\n open_set.remove(current)\\n closed_set.append(current)\\n for neighbor in current:\\n if neighbor in closed_set:\\n continue\\n if neighbor not in open_set:\\n open_set.append(neighbor)\\n tentative_gScore = gScore[current] + util.manhattan(current, neighbor)\\n if tentative_gScore >= gScore[neighbor]:\\n continue\\n came_from[neighbor] = current\\n gScore[neighbor] = tentative_gScore\\n fScore[neighbor] = gScore[neighbor] + heuristic(neighbor, goal)\\n return failure\",\n \"def find_successor(self, node):\\n try:\\n if node.right:\\n return self._find_smallest(node.right)\\n if node.parent:\\n while node == node.parent.right:\\n node = node.parent\\n return node.parent\\n except AttributeError:\\n return\",\n \"def contains(self, value):\\n\\n node, parent, found = self.search(value)\\n\\n return found\",\n \"def getnodeprobs(self, node_p=None, parent_states=None):\\n node_p = self.getnodenamed(node_p) # Verify pointer.\\n nstates = self.getnodenumberstates(node_p)\\n numconds = self._numconditions(node_p)\\n\\n if numconds == 1: # Only one condition (no parents)\\n # print('No Parents')\\n resshape = (nstates,)\\n resdim = 1\\n else: # Need to return a 2D array.\\n # print('Some Parents')\\n resshape = (numconds, nstates)\\n resdim = 2\\n\\n if not parent_states:\\n parenttype = c_void_p\\n else:\\n # TODO: Can't specify custom number of parents\\n parenttype = c_void_p\\n # parenttype = ndpointer('int', ndim=1, shape=len(20,), flags='C')\\n\\n cnetica.GetNodeProbs_bn.argtypes = [c_void_p, parenttype]\\n cnetica.GetNodeProbs_bn.restype = ndpointer(\\n 'float32', ndim=resdim, shape=resshape, flags='C')\\n # Get the exact right number format, otherwise it returns diff nums\\n # pdb.set_trace()\\n return cnetica.GetNodeProbs_bn(node_p, parent_states)\",\n \"def count_property_range_hits(prop, node_dict, hits):\\n\\tres = []\\n\\t# sets tuple position to use in dict value\\n\\tswitcher = {\\n \\\"length\\\": (0,(0,4000,8000,12000,16000,20000)),\\n \\\"steps\\\": (1,(0,2,4,8,16,32)),\\n \\\"cov\\\": (2,(1,10,100,1000,10000,100000)),\\n \\\"cv\\\": (3, (0,0.05,0.10,0.15,0.20,0.25))\\n }\\n\\tif prop not in switcher:\\n\\t\\treturn res\\n\\ttup_pos = switcher[prop][0]\\n\\tnode_cnt = 0\\n\\tpos_cnt = 0\\n\\tfor ind in range(len(switcher[prop][1])-1):\\n\\t\\tmin_val = switcher[prop][1][ind]\\n\\t\\tmax_val = switcher[prop][1][ind+1]\\n\\t\\tfor node in node_dict.keys():\\n\\t\\t\\tval = node_dict[node][tup_pos]\\n\\t\\t\\tif ind < len(switcher[prop][1])-2:\\n\\t\\t\\t\\trange_test_val = (min_val <= val < max_val)\\n\\t\\t\\telse:\\n\\t\\t\\t\\trange_test_val = (min_val <= val <= max_val)\\n\\t\\t\\t# print \\\"range bool is\\\", range_test_val\\n\\t\\t\\tif range_test_val:\\n\\t\\t\\t\\tnode_cnt += 1\\n\\t\\t\\t\\tif node in hits: pos_cnt += 1\\n\\t\\tif node_cnt > 0:\\n\\t\\t\\tres.append( (pos_cnt, node_cnt, round(float(pos_cnt)/node_cnt,2)))\\n\\t\\telse:\\n\\t\\t\\tres.append((0,0,0))\\n\\t\\tnode_cnt = 0\\n\\t\\tpos_cnt = 0\\n\\treturn res\",\n \"def find_node_recursive(node, goal):\\n return (goal, get_parent(node, goal))\",\n \"def tree_find(T, x):\\n if T.label is None:\\n return False\\n else:\\n return x == T.label \\\\\\n or (x < T.label and tree_find(T[0], x)) \\\\\\n or (x > T.label and tree_find(T[1], x))\",\n \"def test_value_in_tree_returns_true(balanced_7_nodes):\\n assert balanced_7_nodes.contains(7)\",\n \"def neighbors(self, val):\\n if val in self._g:\\n return self._g[val]\\n else:\\n raise ValueError('Node not in graph')\",\n \"def value(d,o):\\n # return memoized value if possible\\n if (d,o) in v:\\n return v[(d,o)]\\n\\n thisitem = int(t[d][o])\\n # the total of a subtree that starts at the leaf, is just the value of the leaf\\n if d == maxdepth:\\n val = thisitem\\n else:\\n val = thisitem + max(value(d+1,o),value(d+1,o+1))\\n\\n v[(d,o)]=val\\n return val\",\n \"def fn(node):\\n if node.val == 0: return (0, 1)\\n if node.val == 1: return (1, 0)\\n if node.left: lf, lt = fn(node.left)\\n if node.right: rf, rt = fn(node.right)\\n match node.val: \\n case 2: return (lf+rf, min(lt, rt))\\n case 3: return (min(lf, rf), lt+rt)\\n case 4: return (min(lf+rf, lt+rt), min(lf+rt, lt+rf))\\n case _: return (lt, lf) if node.left else (rt, rf)\",\n \"def find(self, value):\\n if self.value == value:\\n return True\\n else:\\n if self.value > value:\\n if not isinstance(self.left, Node):\\n return False\\n else:\\n return self.left.find(value)\\n elif self.value < value:\\n if not isinstance(self.right, Node):\\n return False\\n else:\\n return self.right.find(value)\",\n \"def fn(node):\\n if not node: return 0 \\n ans = node.val + fn(node.left) + fn(node.right)\\n vals.append(ans)\\n return ans\",\n \"def get_successor(self, key):\\r\\n parent_node, search_node = self.__compare(key, method='search')\\r\\n self.__check_node(search_node)\\r\\n\\r\\n if search_node.right_child.key:\\r\\n succ_node, _ = self.__compare(method='min', source=search_node.right_child)\\r\\n else:\\r\\n while search_node.key > parent_node.key:\\r\\n search_node = parent_node\\r\\n parent_node = parent_node.parent\\r\\n\\r\\n # if it reaches the root, means there is no predecessor\\r\\n if not parent_node:\\r\\n return NodeRBT(None, None)\\r\\n\\r\\n succ_node = parent_node\\r\\n\\r\\n return succ_node\",\n \"def search(self) -> int:\\n # crete node list\\n for x in range(self.n):\\n for y in range(self.n):\\n if not self.grid[y][x] == 0:\\n self.all_nodes.append((x, y))\\n # recursively create paths\\n i = 0\\n paths = [[(0, 0)]]\\n while i < self.n * self.n:\\n paths = self.generate_paths(paths)\\n if isinstance(paths, int):\\n return paths\\n i += 1\\n\\n return -1\",\n \"def find_node(self, value):\\n for (fun, node) in self.__root.__fast_find:\\n if fun(value):\\n return node\\n return None\",\n \"def __find(self, x, parent: 'Node'):\\n found = None\\n if parent.value == x:\\n return parent\\n\\n for child in parent.children:\\n if child.value == x:\\n return child\\n new_found = self.__find(x, parent=child)\\n if new_found:\\n found = new_found\\n\\n return found\",\n \"def nearest_common_parent(self, go_ids=None):\\n # Take the element at maximum depth\\n ncp = max(self.common_parents(go_ids=go_ids), key=lambda t: self.dict_go[t].depth)\\n return ncp\",\n \"def fn(node):\\n if not node or node in (p, q): return node\\n left, right = fn(node.left), fn(node.right)\\n if left and right: return node \\n return left or right\",\n \"def find_goal(self):\\n\\n self.separate_frontier()\\n goal = self.centroid(self.regions)\\n\\t#rospy.loginfo(goal)\\n self.centroidValue = goal\\n self.paint_cells(self.centroidValue,self.frontier)\\n\\tif goal is not None and goal!=0:\\n \\tfinal = self.in_bounds(goal)\\n \\trospy.loginfo(\\\"Region Found\\\")\\n \\treturn final\",\n \"def find_B(self):\\n max_lb = 0\\n for arc in self.arcs():\\n lb = self.arc_info[arc[0]]['lower_bound']\\n max_lb = max(max_lb, lb)\\n n = len(self)\\n m = len(list(self.edges()))\\n return((m - n + 2)*max_lb)\",\n \"def lowest_common_ancestor(node):\\n pass\",\n \"def getValue(self):\\n if self.left.getValue() <= self.right.getValue():\\n return self.left.getValue()\\n\\n return self.right.getValue()\",\n \"def fn(u):\\n if u == n-1: return 1 # boundary condition \\n ans = 0\\n for v, _ in graph[u]: \\n if dist[u] > dist[v]: ans += fn(v)\\n return ans\",\n \"def getPeso(self, n1, n2):\\n if n1 in self and n2 in self:\\n if n2 in self.vertices[n1]:\\n return self.vertices[n1][n2]\\n elif n1 == n2:\\n return 0\\n return float(\\\"inf\\\")\\n return False\",\n \"def fn(node):\\n if not node: return True, 0\\n tf0, h0 = fn(node.left)\\n tf1, h1 = fn(node.right)\\n return tf0 and tf1 and abs(h0-h1) <= 1, 1 + max(h0, h1)\",\n \"def select(self, board, c_puct):\\n # the game rule has a random cases in the select procedure\\n board.get_point()\\n batch = self._children.get(board.point, None) # get this point's edge\\n if not batch: return True, None # this node is the leaf\\n return False, max(batch.items(),\\n key=lambda act_node: act_node[1].get_value(c_puct))\",\n \"def lookup(self, val):\\n if val < self.val:\\n if self.left is None:\\n return None, None\\n return self.left.lookup(val)\\n elif val > self.val:\\n if self.right is None:\\n return None, None\\n return self.right.lookup(val)\\n else:\\n return self\",\n \"def question4(T,r,n1,n2):\\n\\n\\tif(len(T)<=1):\\t\\t\\t\\t\\t\\t\\t\\t# Edge case : If the Tree only consists of a root and no children\\n\\t\\treturn -1\\n\\n\\tif(n1==None or n2==None):\\t\\t\\t\\t\\t# Edge case : If n1 and n2 are not actually numbers\\n\\t\\treturn -1\\n\\n\\tlen_T = len(T)\\n\\tif(not n1 < len_T or not n2 < len_T):\\t\\t# Edge case : If the nodes gives in parameters do not actually exist in the tree\\n\\t\\treturn -1\\n\\n\\tn1_list = []\\t\\t\\t\\t\\t\\t\\n\\tn2_list = []\\n\\n\\tfor i in range(len(T)):\\t\\t\\t\\t\\t\\t# Traverse the list and append all the parents of node1 if found in O(N)\\n\\t\\tif T[i][n1]==1:\\n\\t\\t\\tn1_list.append(i)\\n\\n\\tfor i in range(len(T)):\\t\\t\\t\\t\\t\\t# Traverse the list and append all the parents of node2 is found in O(N)\\n\\t\\tif T[i][n2]:\\n\\t\\t\\tn2_list.append(i)\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# The root is a common ancestor of every node in the tree\\n\\tif not r in n1_list:\\t\\t\\t\\t\\t\\t# check if the root is in the list, if not, add it\\n\\t\\tn1_list.append(r)\\n\\n\\tif not r in n2_list:\\t\\t\\t\\t\\t\\t# check if the root is in the list, if not, add it\\n\\t\\tn2_list.append(r)\\n\\n\\tn1_list = reversed(n1_list)\\t\\t\\t\\t\\t# Since we are operating on a binary tree, we sort\\n\\tfor i in n1_list:\\t\\t\\t\\t\\t\\t\\t# in decending order to operate on the latest nodes\\n\\t\\tif i in n2_list:\\t\\t\\t\\t\\t\\t# if a match is found, we know that it is the lowest common ancestor\\n\\t\\t\\treturn i \\t\\t\\t\\t\\t\\t\\t# If nothing is found, the root node is bound to be returned. And it correct.\",\n \"def getValue(self):\\n if self.left.getValue() >= self.right.getValue():\\n return self.left.getValue()\\n\\n return self.right.getValue()\",\n \"def point_in_between(ob, row, cell, other_cell):\\n if row:\\n left = other_cell[0] < cell[0]\\n if left:\\n return ob.patt[0] == 1\\n else:\\n return ob.patt[2] == 1\\n below = other_cell[1] < cell[1]\\n if below:\\n return ob.patt[1] == 0\\n else:\\n return ob.patt[1] == 2\",\n \"def heuristicValueOfPosition(currPositions):\\n hVal = 0;\\n\\n for y in range(1, n+1): #1,2,3\\n for x in range(1, n+1):\\n val = currPositions[y][x];\\n if ((val == 0) or (goalPositions[val] == (y,x))): #val 0 means blank\\n continue;\\n else:\\n hVal += abs(y-goalPositions[val][0]) + abs(x-goalPositions[val][1])\\n\\n return hVal;\",\n \"def init_parent_lookup(parent_lookup, x, y):\\n if x not in parent_lookup:\\n parent_lookup[x] = x\\n # single person family has himself as parent\\n # maxCircle function decides\\n if y not in parent_lookup:\\n parent_lookup[y] = y\",\n \"def find(p):\\n if p != parent[p]:\\n parent[p] = find(parent[p])\\n return parent[p]\",\n \"def lookup(self,entry):\\n if self.type == 'v':\\n return self\\n v = entry[self.feature]\\n assert v != None\\n if self.type == 's':\\n c = None\\n try:\\n c = self.children[v]\\n except KeyError:\\n #print \\\"Unseen value for feature\\\",self.feature,\\\": \\\",v\\n best = None\\n bestDist = float('inf')\\n for (val,c) in self.children.iteritems():\\n if abs(val - v) < bestDist:\\n bestDist = abs(val - v)\\n best = c\\n c = best\\n return c.lookup(entry)\\n elif self.type == 'i':\\n if v <= self.value:\\n return self.children[0].lookup(entry)\\n else:\\n return self.children[1].lookup(entry)\\n raise RuntimeError(\\\"Invalid DecisionTreeNode type?\\\")\",\n \"def rank(self, value):\\n i = 0\\n n = len(self._tree)\\n rank = 0\\n count = 0\\n while i < n:\\n cur = self._tree[i]\\n if value < cur:\\n i = 2 * i + 1\\n continue\\n elif value > cur:\\n rank += self._counts[i]\\n # subtract off the right tree if exists\\n nexti = 2 * i + 2\\n if nexti < n:\\n rank -= self._counts[nexti]\\n i = nexti\\n continue\\n else:\\n return (rank, count)\\n else: # value == cur\\n count = self._counts[i]\\n lefti = 2 * i + 1\\n if lefti < n:\\n nleft = self._counts[lefti]\\n count -= nleft\\n rank += nleft\\n righti = lefti + 1\\n if righti < n:\\n count -= self._counts[righti]\\n return (rank, count)\\n return (rank, count)\",\n \"def rank(self, value):\\n i = 0\\n n = len(self._tree)\\n rank = 0\\n count = 0\\n while i < n:\\n cur = self._tree[i]\\n if value < cur:\\n i = 2 * i + 1\\n continue\\n elif value > cur:\\n rank += self._counts[i]\\n # subtract off the right tree if exists\\n nexti = 2 * i + 2\\n if nexti < n:\\n rank -= self._counts[nexti]\\n i = nexti\\n continue\\n else:\\n return (rank, count)\\n else: # value == cur\\n count = self._counts[i]\\n lefti = 2 * i + 1\\n if lefti < n:\\n nleft = self._counts[lefti]\\n count -= nleft\\n rank += nleft\\n righti = lefti + 1\\n if righti < n:\\n count -= self._counts[righti]\\n return (rank, count)\\n return (rank, count)\",\n \"def fn(node):\\n if not node: return True, inf, -inf, 0, 0 # bst flag | min | max | sum\\n ltf, lmn, lmx, lsm, lval = fn(node.left)\\n rtf, rmn, rmx, rsm, rval = fn(node.right)\\n lmn = min(lmn, node.val)\\n rmx = max(rmx, node.val)\\n sm = lsm + rsm + node.val \\n if ltf and rtf and lmx < node.val < rmn: \\n return True, lmn, rmx, sm, max(lval, rval, sm)\\n return False, lmn, rmx, sm, max(lval, rval)\",\n \"def search(self, val):\\n if type(val) not in [int, float]:\\n raise TypeError('This tree accepts numbers only.')\\n current_node = self._root\\n while current_node:\\n if val == current_node._data:\\n return current_node\\n if val > current_node._data:\\n current_node = current_node._rkid\\n else:\\n current_node = current_node._lkid\\n return\",\n \"def find(self, value):\\n # initialize node as root\\n node = self.root\\n\\n # find value\\n while node != None:\\n\\n # value found: return node\\n if node.value == value:\\n return node\\n\\n # value is smaller than node: search in left sub tree\\n elif node.value > value:\\n node = node.left\\n\\n # value is bigger than node: search in right sub tree\\n else:\\n node = node.right\\n\\n # value not found: return None\\n return None\",\n \"def gomeroccupancyscore(pwm_dictionary, seq):\\n if \\\"N\\\" in seq:\\n return 0\\n else:\\n # pwm_length = len(pwm_dictionary)\\n pwm_length = len(pwm_dictionary[\\\"A\\\"])\\n gomer_occupancy = 1\\n area_pwm_rc = rc_pwm(pwm_dictionary, pwm_length)\\n for i in range(pwm_length - 1, 1, -1):\\n prod_gomer = 1\\n prod_gomer_rc = 1\\n for j in range(pwm_length):\\n if j <= i:\\n prod_gomer *= 0.25\\n prod_gomer_rc *= 0.25\\n elif (j + i) > len(seq) - 1:\\n prod_gomer *= 0.25\\n prod_gomer_rc *= 0.25\\n else:\\n # print \\\"got to else\\\"\\n s = seq[j + i]\\n prod_gomer *= pwm_dictionary[s][j]\\n prod_gomer_rc *= area_pwm_rc[s][j]\\n gomer_occupancy *= (1 - prod_gomer) * (1 - prod_gomer_rc)\\n for i in range(len(seq) - 1):\\n prod_gomer = 1\\n prod_gomer_rc = 1\\n for j in range(pwm_length - 1):\\n if (j + i) >= len(seq) - 1:\\n prod_gomer *= 0.25\\n prod_gomer_rc *= 0.25\\n else:\\n prod_gomer *= pwm_dictionary[seq[j + i]][j]\\n prod_gomer_rc *= area_pwm_rc[seq[j + i]][j]\\n gomer_occupancy *= (1 - prod_gomer) * (1 - prod_gomer_rc)\\n gomer_occupancy = 1 - gomer_occupancy\\n\\n return gomer_occupancy\",\n \"def findOverlapOrNearest(gs, ts, tree, start, end):\\n #step 1, find overlaps\\n rs = set()\\n for i in range(start, end + 1):\\n if i in gs:\\n rs.add(gs[i])\\n if len(rs) > 0:\\n rs = list(rs)\\n return rs, [0] * len(rs)\\n #find the nearest one\\n else:\\n d, i = tree.query([(start + end) / 2], k=1)\\n g = gs[ts[i][0]]\\n #d = ts[i][0] - (start+end)/2\\n d = int(d)\\n return [g], [d]\",\n \"def fn(node, val):\\n if not node: return 0\\n val = 10*val + node.val\\n if not node.left and not node.right: return val \\n return fn(node.left, val) + fn(node.right, val)\",\n \"def _find_parents(self, user_profiles, new_infected_users):\\n if (self._old_infection_state == 0).all():\\n # Node is root\\n return None\\n # TODO: function is_following() based on code below:\\n # candidates must have been previously infected\\n prev_infected_users = np.where(self._old_infection_state > 0)[0]\\n # candidates must be connected to newly infected users\\n candidate_parents = user_profiles[:, prev_infected_users][new_infected_users]\\n if not isinstance(candidate_parents, np.ndarray):\\n candidate_parents = candidate_parents.toarray() # convert sparse to numpy if needed\\n # randomly select parent out of those who were infected, use random multiplication\\n candidate_parents = candidate_parents * np.random.rand(*candidate_parents.shape)\\n parents = prev_infected_users[np.argmax(candidate_parents, axis=1)]\\n return parents\",\n \"def search(self, depth, state, curr_player):\\n \\n # enumerate all legal moves from this state\\n legal_moves = []\\n for i in range(7):\\n # if column i is a legal move...\\n if self.isLegalMove(i, state):\\n # make the move in column i for curr_player\\n temp = self.makeMove(state, i, curr_player)\\n legal_moves.append(temp)\\n \\n # if this node (state) is a terminal node or depth == 0...\\n if depth == 0 or len(legal_moves) == 0 or self.gameIsOver(state):\\n # return the heuristic value of node\\n return self.value(state, curr_player)\\n \\n # determine opponent's color\\n if curr_player == self.colors[0]:\\n opp_player = self.colors[1]\\n else:\\n opp_player = self.colors[0]\\n\\n alpha = -99999999\\n for child in legal_moves:\\n if child == None:\\n print(\\\"child == None (search)\\\")\\n alpha = max(alpha, -self.search(depth-1, child, opp_player))\\n return alpha\",\n \"def fn(node):\\n if not node: return 0, -inf\\n lh, lps = fn(node.left)\\n rh, rps = fn(node.right)\\n return node.val + max(0, lh, rh), max(lps, rps, node.val + max(0, lh) + max(0, rh))\",\n \"def fn(node):\\n nonlocal ans \\n if not node: return 1\\n left, right = fn(node.left), fn(node.right)\\n if left == 0 or right == 0: \\n ans += 1\\n return 2 # add a camera \\n if left == 2 or right == 2: return 1\\n return 0\",\n \"def lookup(self, data, parent=None):\\n if data < self.data:\\n if self.left is None:\\n return None, None\\n return self.left.lookup(data, self)\\n elif data > self.data:\\n if self.right is None:\\n return None, None\\n return self.right.lookup(data, self)\\n else:\\n return self, parent\",\n \"def contain(graph, value):\\n for node in graph:\\n if node.value == value:\\n return True\\n\\n return False\",\n \"def contains(self, value):\\n if self.graph == None:\\n return\\n \\n else:\\n return contain(self.graph, value)\",\n \"def primer_binary_srch(self, primers):\\n l = 0\\n r = len(primers) - 1\\n while l <= r:\\n m = l + int((r - l) / 2) \\n if primers[m].binds_to(self):\\n return primers[m]\\n elif primers[m] < self:\\n l = m + 1\\n else:\\n r = m - 1\\n return None\",\n \"def get_successor(self, value):\\n\\t\\tstack = [self]\\n\\t\\tcurrent = None\\n\\t\\tprevious = None\\n\\t\\twhile not previous == value and stack:\\n\\t\\t\\titem = stack.pop()\\n\\t\\t\\tif isinstance(item, BSTreeNode):\\n\\t\\t\\t\\tif item.right:\\n\\t\\t\\t\\t\\tstack.append(item.right)\\n\\t\\t\\t\\tstack.append(item.value)\\n\\t\\t\\t\\tif item.left:\\n\\t\\t\\t\\t\\tstack.append(item.left)\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tprevious = current\\n\\t\\t\\tcurrent = item\\n\\t\\treturn current if previous == value else None\",\n \"def fn(node):\\n if not node: return 0 \\n ans = node.val + fn(node.left) + fn(node.right)\\n freq[ans] += 1\\n return ans\",\n \"def fn(n):\\n s, c = value[n], 1\\n for nn in tree.get(n, []): \\n ss, cc = fn(nn)\\n s += ss\\n c += cc \\n return (s, c) if s != 0 else (0, 0)\",\n \"def _find(self, item):\\n stack = [self.root]\\n while stack:\\n node = stack.pop()\\n if node.items and item in node.items:\\n return True, node.active[node.items.index(item)], node\\n elif not node.items:\\n axis = node.cutdim\\n cur = item[axis]\\n median = node.cutval\\n if median >= cur:\\n stack.append(node.left)\\n if median <= cur:\\n stack.append(node.right)\\n return False, False, None\",\n \"def find(self, value, max_distance):\\n\\t\\t# type: (Any, int) -> List[Tuple[int, Any]]\\n\\n\\t\\tnode = self.root\\n\\t\\tret = [] # type: List[Tuple[int, Any]]\\n\\n\\t\\tif node is None:\\n\\t\\t\\treturn ret\\n\\n\\t\\tcandidates = [node] # is a deque better here?\\n\\n\\t\\twhile candidates:\\n\\t\\t\\tcandidate = candidates.pop()\\n\\t\\t\\tdistance = self.distance_func(value, candidate.value)\\n\\n\\t\\t\\tif distance <= max_distance:\\n\\t\\t\\t\\tret.append((distance, candidate.value))\\n\\n\\t\\t\\t# instead of looking for candidates by searching,\\n\\t\\t\\t# one could also directly access the necessary keys in the dict\\n\\t\\t\\tfor d, bknode in candidate.leaves.items():\\n\\t\\t\\t\\tlower = distance - max_distance\\n\\t\\t\\t\\tupper = distance + max_distance\\n\\t\\t\\t\\tif lower <= d <= upper:\\n\\t\\t\\t\\t\\tcandidates.append(bknode)\\n\\n\\t\\treturn ret\",\n \"def side_of_parent(self):\\n if self._node_empty(self._parent):\\n return -1\\n\\n if self._parent.left is self:\\n return 0\\n elif self._parent.right is self:\\n return 1\\n else:\\n raise ValueError('Node invalid.')\",\n \"def determine_move_position(self):\\n green_probs = []\\n net_size = len(self.net)\\n adjacents = self.net[self.current_pos].adjacents\\n #Belief propagation:\\n #Analyzes each position's probability of obtaining\\n #green when measuring at a time t+1.\\n for i in adjacents:\\n accum = 0\\n for j in range(0, net_size):\\n distance = self.__get_distance(i-1, j)\\n if distance == 0: #Probability of measure green at distance 0 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[0][0]\\n elif distance == 1: #Probability of measure green at distance 1 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[1][0]\\n elif distance == 2: #Probability of measure green at distance 2 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[2][0]\\n elif distance == 3: #Probability of measure green at distance 3 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[3][0]\\n else: #Probability of measure green at a distance >= 4 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[4][0]\\n green_probs.append((i, accum))\\n #Returns the position in which the probability of\\n #obtaining green when measuring is the lowest.\\n return min(green_probs, key=itemgetter(1))[0]\",\n \"def get_effective_value(vals):\\n lst = sorted(vals.values(), key=lambda x: -x[1])\\n anchor = 0 # The highest priority non-relative value (with comb=None)\\n while anchor < len(lst) and lst[anchor][2]:\\n anchor += 1\\n if lst[anchor][2]:\\n ## TODO we should say which one :-D\\n raise RuntimeError(\\\"Value set contains only relative values\\\")\\n val = lst[anchor][0]\\n for (relval, _, comb) in reversed(lst[:anchor]):\\n val = comb(val, relval)\\n return val\",\n \"def neighbors(self, val):\\n if val in self:\\n return self[val]\\n else:\\n raise ValueError('Value not in graph')\",\n \"def determine_measure_position(self):\\n green_probs = []\\n net_size = len(self.net)\\n #Belief propagation:\\n #Analyzes each position's probability of obtaining\\n #green when measuring at a time t+1.\\n for i in range(0, net_size):\\n accum = 0\\n for j in range(0, net_size):\\n distance = self.__get_distance(i, j)\\n if distance == 0: #Probability of measure green at distance 0 from 'i'.\\n accum += self.net[i].value * self.ct[0][0]\\n elif distance == 1: #Probability of measure green at distance 1 from 'i'.\\n accum += self.net[i].value * self.ct[1][0]\\n elif distance == 2: #Probability of measure green at distance 2 from 'i'.\\n accum += self.net[i].value * self.ct[2][0]\\n elif distance == 3: #Probability of measure green at distance 3 from 'i'.\\n accum += self.net[i].value * self.ct[3][0]\\n else: #Probability of measure green at a distance >= 4 from 'i'.\\n accum += self.net[i].value * self.ct[4][0]\\n green_probs.append(accum)\\n #Returns the position in which the probability of\\n #obtaining green when measuring is the highest.\\n return self.net[np.argmax(green_probs)].id\",\n \"def get_rank(self, pb):\\n\\n for rank in self.RANKS:\\n start = self.RANKS[rank][\\\"ProgressStart\\\"]\\n # 1 is not subtracted as we're calling range\\n end = start + self.RANKS[rank][\\\"Progress\\\"]\\n if pb in range(start, end):\\n return int(rank)\\n else:\\n return 35\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5753097","0.5747222","0.57390654","0.57036936","0.56338394","0.55915225","0.5584634","0.5555824","0.5537557","0.5537557","0.5511906","0.54953927","0.5484709","0.5481439","0.5480224","0.5455876","0.5380829","0.53786016","0.53774273","0.537409","0.53558004","0.5354007","0.5352297","0.53145224","0.5269906","0.5248043","0.52408797","0.523047","0.52191925","0.5195177","0.51855767","0.5183525","0.5175682","0.51659864","0.5164088","0.5120738","0.5080253","0.50667256","0.50636846","0.505802","0.5057419","0.5051633","0.5043914","0.50429237","0.5031719","0.50233406","0.5022002","0.5011954","0.5003718","0.4997623","0.4997362","0.49771866","0.49734616","0.49715364","0.4966727","0.49626485","0.49532142","0.4944355","0.4942076","0.49396846","0.49295416","0.49220344","0.4911556","0.49068248","0.49044433","0.48988238","0.48951027","0.48945916","0.4887467","0.48813412","0.48774204","0.48710787","0.4867792","0.48607457","0.48607457","0.48605734","0.48593235","0.48591387","0.485905","0.48518288","0.48454463","0.48408714","0.48327574","0.4828473","0.48242024","0.48210964","0.48175025","0.48139572","0.48115176","0.4806819","0.48050892","0.48046726","0.48041648","0.4799037","0.47974834","0.47972208","0.47891796","0.4780834","0.4774591","0.47724494"],"string":"[\n \"0.5753097\",\n \"0.5747222\",\n \"0.57390654\",\n \"0.57036936\",\n \"0.56338394\",\n \"0.55915225\",\n \"0.5584634\",\n \"0.5555824\",\n \"0.5537557\",\n \"0.5537557\",\n \"0.5511906\",\n \"0.54953927\",\n \"0.5484709\",\n \"0.5481439\",\n \"0.5480224\",\n \"0.5455876\",\n \"0.5380829\",\n \"0.53786016\",\n \"0.53774273\",\n \"0.537409\",\n \"0.53558004\",\n \"0.5354007\",\n \"0.5352297\",\n \"0.53145224\",\n \"0.5269906\",\n \"0.5248043\",\n \"0.52408797\",\n \"0.523047\",\n \"0.52191925\",\n \"0.5195177\",\n \"0.51855767\",\n \"0.5183525\",\n \"0.5175682\",\n \"0.51659864\",\n \"0.5164088\",\n \"0.5120738\",\n \"0.5080253\",\n \"0.50667256\",\n \"0.50636846\",\n \"0.505802\",\n \"0.5057419\",\n \"0.5051633\",\n \"0.5043914\",\n \"0.50429237\",\n \"0.5031719\",\n \"0.50233406\",\n \"0.5022002\",\n \"0.5011954\",\n \"0.5003718\",\n \"0.4997623\",\n \"0.4997362\",\n \"0.49771866\",\n \"0.49734616\",\n \"0.49715364\",\n \"0.4966727\",\n \"0.49626485\",\n \"0.49532142\",\n \"0.4944355\",\n \"0.4942076\",\n \"0.49396846\",\n \"0.49295416\",\n \"0.49220344\",\n \"0.4911556\",\n \"0.49068248\",\n \"0.49044433\",\n \"0.48988238\",\n \"0.48951027\",\n \"0.48945916\",\n \"0.4887467\",\n \"0.48813412\",\n \"0.48774204\",\n \"0.48710787\",\n \"0.4867792\",\n \"0.48607457\",\n \"0.48607457\",\n \"0.48605734\",\n \"0.48593235\",\n \"0.48591387\",\n \"0.485905\",\n \"0.48518288\",\n \"0.48454463\",\n \"0.48408714\",\n \"0.48327574\",\n \"0.4828473\",\n \"0.48242024\",\n \"0.48210964\",\n \"0.48175025\",\n \"0.48139572\",\n \"0.48115176\",\n \"0.4806819\",\n \"0.48050892\",\n \"0.48046726\",\n \"0.48041648\",\n \"0.4799037\",\n \"0.47974834\",\n \"0.47972208\",\n \"0.47891796\",\n \"0.4780834\",\n \"0.4774591\",\n \"0.47724494\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62124074"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19585,"cells":{"query":{"kind":"string","value":"Return default uniform distribution for the range of a functor"},"document":{"kind":"string","value":"def default(functor):\n return 1.0/functorRangeSize(functor)"},"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 _uniform(val_range):\r\n return np.random.uniform(val_range[0], val_range[1])","def initializeDistribution(self):\n self.minVal = min(math.exp(self.upperBound),math.exp(self.lowerBound))\n self.maxVal = max(math.exp(self.upperBound),math.exp(self.lowerBound))","def glorot_uniform(seed=None):\n return lambda shape, dtype, batch_ndims=0: _initialize( # pylint: disable=g-long-lambda\n shape, dtype, batch_ndims,\n scale=1., mode='fan_avg', distribution='uniform', seed=seed)","def initializeDistribution(self):\n if (self.lowerBoundUsed == False and self.upperBoundUsed == False):\n self._distribution = distribution1D.BasicWeibullDistribution(self.k,self.lambdaVar,self.low)\n self.lowerBound = self.low\n self.upperBound = sys.float_info.max\n else:\n if self.lowerBoundUsed == False:\n self.lowerBound = self.low\n if self.upperBoundUsed == False:\n self.upperBound = sys.float_info.max\n self._distribution = distribution1D.BasicWeibullDistribution(self.k,self.lambdaVar,self.lowerBound,self.upperBound,self.low)","def he_uniform(seed=None):\n # pylint: disable=line-too-long\n # pylint: enable=line-too-long\n return lambda shape, dtype, batch_ndims=0: _initialize( # pylint: disable=g-long-lambda\n shape, dtype, batch_ndims,\n scale=2., mode='fan_in', distribution='uniform', seed=seed)","def initializeDistribution(self):\n if (self.lowerBoundUsed == False and self.upperBoundUsed == False):\n self._distribution = distribution1D.BasicExponentialDistribution(self.lambdaVar,self.low)\n self.lowerBound = self.low\n self.upperBound = sys.float_info.max\n else:\n if self.lowerBoundUsed == False:\n self.lowerBound = self.low\n if self.upperBoundUsed == False:\n self.upperBound = sys.float_info.max\n self._distribution = distribution1D.BasicExponentialDistribution(self.lambdaVar,self.lowerBound,self.upperBound,self.low)","def default_quantile():\n return np.logspace(-5, 0, 100)","def _base_dist(self, *args, **kwargs):\n return tfd.TransformedDistribution(\n distribution=tfd.Uniform(low=0.0, high=1.0),\n bijector=tfp.bijectors.Invert(tfp.bijectors.Weibull(*args, **kwargs)),\n name=\"Weibull\",\n )","def random_from_bound(bound):\n if (isinstance(bound, tuple)):\n val = np.random.uniform(low = bound[0], high = bound[1])\n else:\n val = 0.0\n return val","def uniform(feature, bins):\n t = (feature.max()-feature.min())/bins\n return [t*i for i in range(1, bins)]","def dispatch_dg(distribution_u_want, a):\n return {\n 'bernoulli': lambda: a / (1. + a),\n 'geometric': lambda: a / (1. - a), # initialization can give divide by zero\n 'exponential': lambda: a,\n }.get(distribution_u_want, lambda: None)()","def uniform_dist(low, high):\n return sp_uniform(low, high - low)","def random_uniform_initializer(minval=0.0, maxval=1.0, seed=None,\n dtype=dtypes.float32):\n def _initializer(shape, dtype=_assert_float_dtype(dtype)):\n return random_ops.random_uniform(shape, minval, maxval, dtype, seed=seed)\n return _initializer","def uniform(a: float, b: float) -> float:\n ...","def uniform(self, key, min_value=0., max_value=1.):\n return min_value + self._random(key) * (max_value - min_value)","def loguniform_dist(low, high, base=10):\n return ExponentiateDistribution(sp_uniform(low, high - low), base=base)","def initializeDistribution(self):\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\n self._distribution = distribution1D.BasicLogisticDistribution(self.location,self.scale)\n else:\n if self.lowerBoundUsed == False:\n a = -sys.float_info.max\n else:\n a = self.lowerBound\n if self.upperBoundUsed == False:\n b = sys.float_info.max\n else:\n b = self.upperBound\n self._distribution = distribution1D.BasicLogisticDistribution(self.location,self.scale,a,b)","def get_standard_normal_distribution():\n return np.random.normal(0, 1)","def generate_number(lbound=1, ubound=100, mean=None, std=None):\n x = np.arange(lbound, ubound + 1)\n if mean and std:\n prob = stats.norm.pdf(x, loc=mean, scale=std)\n prob = prob / prob.sum() #normalize the probabilities so they sum up to 1\n else:\n prob = np.repeat(1 / len(x), len(x))\n num = np.random.choice(x, p=prob)\n return num","def discrete_uniform_sampler(upper_value):\n return int(np.random.random() * upper_value)","def test_uniform(self):\r\n\r\n s = np.random.uniform(-1.35, 0.5, 5000)\r\n plt.hist(s, 30, density=False)\r\n plt.xlabel('Interlayer point energy [eV]')\r\n plt.ylabel('Frequency')\r\n plt.show()","def _hardware_uniform(\n rng_key: Array,\n shape: Shape,\n dtype: jnp.dtype = np.float32,\n minval: Array = np.float32(0),\n maxval: Array = np.float32(1)\n) -> Array:\n del rng_key # non-deterministic prng.\n minval = jax.lax.convert_element_type(minval, dtype)\n maxval = jax.lax.convert_element_type(maxval, dtype)\n return jax.lax.rng_uniform(minval, maxval, shape)","def initializeDistribution(self):\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\n self._distribution = distribution1D.BasicPoissonDistribution(self.mu)\n self.lowerBound = 0.0\n self.upperBound = sys.float_info.max\n else:\n self.raiseAnError(IOError,'Truncated poisson not yet implemented')","def initializeDistribution(self):\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\n self._distribution = distribution1D.BasicLogNormalDistribution(self.mean,self.sigma,self.low)\n self.lowerBound = 0.0\n self.upperBound = sys.float_info.max\n else:\n if self.lowerBoundUsed == False:\n self.lowerBound = self.low\n if self.upperBoundUsed == False:\n self.upperBound = sys.float_info.max\n self._distribution = distribution1D.BasicLogNormalDistribution(self.mean,self.sigma,self.lowerBound,self.upperBound, self.low)","def uniform(lower, upper):\n\n return lower + random.random() * (upper - lower)","def uniform_weight_init(\n input_size: int,\n output_size: int,\n min_bounds: float = 0.0,\n max_bounds: float = 1.0,\n positive_ratio: Optional[float] = None,\n) -> t.Tensor:\n if input_size < 1:\n raise ValueError(\"input_size must be a positive integer.\")\n if output_size < 1:\n raise ValueError(\"output_size must be a positive integer.\")\n if min_bounds > max_bounds:\n raise ValueError(\"min_bounds must not be greater than max_bounds.\")\n if positive_ratio is not None:\n if positive_ratio > 1 or 0 > positive_ratio:\n raise ValueError(\n \"positive_ratio must be None, or must be between zero and one.\"\n )\n\n result = t.empty((input_size, output_size))\n uniform_(result, a=min_bounds, b=max_bounds)\n\n # TODO: test this.\n if positive_ratio is not None:\n bernoulli_distribution = Bernoulli(t.tensor([positive_ratio]))\n mask = bernoulli_distribution.sample((input_size, output_size)).squeeze().bool()\n result.abs_()\n result = result.where(mask, -result)\n\n return result","def initializeDistribution(self):\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\n self._distribution = distribution1D.BasicGeometricDistribution(self.p)\n else: self.raiseAnError(IOError,'Truncated Geometric not yet implemented')","def normal_upper_bound(probability: float, mu: float = 0, sigma: float = 1) -> float:\n return inverse_normal_cdf(probability, mu, sigma)","def uniform_random_value(l_boundary: float, r_boundary: float) -> float:\n return uniform(l_boundary, r_boundary)","def randrange(start: int, stop: int, step: int) -> int:\n ...","def WeightInitializer():\n return np.random.uniform(-1, 1)","def uniform_cdf(x):\n if x <0: return 0 #uniform random is never less than 0\n elif x < 1: return x #e.g. P(x <= 0.4) = 0.4\n else: return 1 #uniform random is always less than 1","def initializeDistribution(self):\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\n self._distribution = distribution1D.BasicBernoulliDistribution(self.p)\n else:\n self.raiseAnError(IOError,'Truncated Bernoulli not yet implemented')","def randrange(n, vmin, vmax):\n return (vmax - vmin) * np.random.rand(n) + vmin","def theoretical_distribution(x):\n a = 0\n b = 1\n if x < a:\n return 0\n elif x > b:\n return 1\n else:\n return float(x - a) / (b - a)","def uniform(self, size=None, low=0.0, high=1.0, ndim=None, dtype=None):\r\n return self.gen(uniform, size, low, high, ndim=ndim, dtype=dtype)","def dispatch_d_1s(distribution_u_want, a):\n return {\n 'bernoulli': lambda: 1 / (1. + a),\n 'geometric': lambda: 1. - a,\n 'exponential': lambda: -a,\n }.get(distribution_u_want, lambda: None)()","def _default_sampling_xrange(self):\n from scipy.stats import rv_continuous\n dataset = self.rvdist.rvs(1000) if rv_continuous in self.rvdist.__class__.__mro__ \\\n else self.rvdist.dataset\n scale = np.nanmax(dataset) - np.nanmin(dataset)\n return [np.nanmin(dataset) - scale*0.05, np.nanmax(dataset) + scale*0.05]","def normal_lower_bound(probability: float, mu: float = 0, sigma: float = 1) -> float:\n return inverse_normal_cdf(1 - probability, mu, sigma)","def log_uniform_sample(sample_range):\n log_min = np.log10(sample_range[0])\n log_max = np.log10(sample_range[1])\n u = np.random.rand()*(log_max-log_min) + log_min\n return np.power(10.0,u)","def rand_val(val_range):\r\n if isinstance(val_range, (list, tuple, np.ndarray)):\r\n return np.random.uniform(val_range[0], val_range[-1])\r\n # Assume val_range is a number\r\n return val_range","def uniform(random_state, size=None, low=0.0, high=1.0, ndim=None, dtype=None):\r\n low = tensor.as_tensor_variable(low)\r\n high = tensor.as_tensor_variable(high)\r\n if dtype is None:\r\n dtype = tensor.scal.upcast(theano.config.floatX, low.dtype, high.dtype)\r\n ndim, size, bcast = _infer_ndim_bcast(ndim, size, low, high)\r\n op = RandomFunction('uniform',\r\n tensor.TensorType(dtype=dtype, broadcastable=bcast))\r\n return op(random_state, size, low, high)","def normal_upper_bound(probability: float,\n mu: float = 0,\n sigma: float = 1) -> float:\n return inverse_normal_cdf(probability, mu, sigma)","def initializeDistribution(self):\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\n self._distribution = distribution1D.BasicBinomialDistribution(self.n,self.p)\n else:\n self.raiseAnError(IOError,'Truncated Binomial not yet implemented')","def x_uniform1(xlim, n):\n return linspace(xlim[0], xlim[1], n)","def normal_upper_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(probability, mu, sigma)","def normal_upper_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(probability, mu, sigma)","def normal_upper_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(probability, mu, sigma)","def normal_upper_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(probability, mu, sigma)","def normal_upper_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(probability, mu, sigma)","def initializeDistribution(self):\n self.checkDistParams()\n\n self.lowerBound = min(self.mapping.keys())\n self.upperBound = max(self.mapping.keys())","def rand_rate_uni( N, tmin, tmax, **kwargs):\n return np.random.uniform( tmin, tmax, size = N)","def peturb(param):\n ann = param.annotation\n if ann == inspect._empty:\n ann = 'normal'\n if type(ann)==str:\n if ann == 'normal':\n return param.default + np.random.normal()\n elif ann == 'positive':\n return abs(param.default + np.random.normal())\n elif type(ann) == tuple:\n # Get a number from uniform random distribution\n # bounded by values in the annotation tuple.\n if type(ann[0]) == float:\n return np.random.uniform(*ann)\n elif type(ann[0]) == int:\n return np.random.randint(*ann)\n else:\n print('Unrecognised function annotation.')","def UniformRV(low, high):\n return RV(ss.randint(low, high))","def normal_lower_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(1- probability, mu, sigma)","def normal_upper_bound(probability, mu=0, sigma=1):\r\n return ds_probability.inverse_normal_cdf(probability, mu, sigma)","def normal_lower_bound(probability: float,\n mu: float = 0,\n sigma: float = 1) -> float:\n return inverse_normal_cdf(1 - probability, mu, sigma)","def __call__(self, shape):\n return np.random.uniform(low=self.minval, high=self.maxval, size=shape)","def initializeDistribution(self):\n if (self.lowerBoundUsed == False and self.upperBoundUsed == False) or (self.min == self.lowerBound and self.max == self.upperBound):\n self._distribution = distribution1D.BasicTriangularDistribution(self.apex,self.min,self.max)\n else:\n self.raiseAnError(IOError,'Truncated triangular not yet implemented')","def __init__(self, mean_function: Callable, lower: Union[float, int], upper: Union[float, int],\n sample_size: int, seed: Optional[float] = None):\n super().__init__()\n np.random.seed(seed)\n assert callable(mean_function), \"intensity_function must be a callable!\"\n try:\n mean_function(np.array([1, 2]))\n self.mean_function: Callable = mean_function\n except ValueError:\n warn('Force vectorization of intensity function')\n self.mean_function: Callable = np.vectorize(mean_function)\n assert isinstance(upper, (int, float)), \"Wrong type of upper limit!\"\n assert isinstance(lower, (int, float)), \"Wrong type of lower limit!\"\n if seed is not None:\n assert isinstance(seed, (int, float)), \"Wrong type of seed!\"\n if np.sum(self.mean_function(np.random.uniform(lower, upper, int(1e6))) < 0) > 0:\n raise ValueError(\"Mean function must be greater than or equal to 0!\")\n if lower >= upper:\n raise ValueError(\"Wrong interval is specified! (lower {} >= upper {})\".format(lower, upper))\n self.lower: Union[float, int] = lower\n self.upper: Union[float, int] = upper\n self.scaler: Union[float, int] = mean_function(upper)\n self.sample_size: np.ndarray = np.random.poisson(lam=self.scaler * sample_size, size=1)","def normal_lower_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(1 - probability, mu, sigma)","def normal_lower_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(1 - probability, mu, sigma)","def normal_lower_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(1 - probability, mu, sigma)","def normal_lower_bound(probability, mu=0, sigma=1):\n return inverse_normal_cdf(1 - probability, mu, sigma)","def uniform(N=10000, left=0., right=3., n_bins_meas=10):\n\t# true distribution\n\ttrue = np.random.uniform(left, right, N)\n\t# measured distribution\n\tmeasured = true\n\t# Also return the generating true pdf\n\tx = np.linspace(-1, 5, 500)\n\tpdf = np.zeros([2, len(x)])\n\tpdf[0] = x\n\tpdf[1] = scs.uniform.pdf(x, left, right-left)\n\t# Default binning\n\tdefault_binning = np.linspace(np.amin(measured), np.amax(measured), n_bins_meas)\n\n\treturn measured, true, pdf, default_binning","def get_uniform_p_vals(self, min=1.5, max=9):\n rng = np.random.RandomState(12435)\n if self.power_activ == \"softplus\":\n p_vals = np.log(np.exp(rng.uniform(low=min, high=max, size=(self.num_units,))-1)-1)\n else:\n p_vals = np.sqrt(rng.uniform(low=min, high=max, size=(self.num_units,))-1)\n return p_vals","def rangeSample(val, minLim, maxLim):\n\tif val < minLim or val > maxLim:\n\t\tval = randint(minLim, maxLim)\n\treturn val","def uniform_cdf(x: float) -> float:\n if x < 0: return 0 # uniform random is never less than 0\n elif x < 1 : return x # e.g. P(X <= 0.4)\n else: return 1 # uniform random is always less than 1","def _make_distribution(series):\n arr = series.values\n return arr / np.sum(arr)","def generic_distribution(target, seeds, func):\n seeds = target[seeds]\n value = func.ppf(seeds)\n return value","def uniform(\n self, low: float = 0, high: float = 1, size: Optional[Iterable[int]] = None\n ):\n _seed = self._seed() if callable(self._seed) else self._seed\n return _uniform(\n low=low,\n high=high,\n size=size,\n seed=_seed,\n device=self._device,\n handle=self._handle,\n )","def xavier_init(dims, uniform=True):\n n_inputs,n_outputs = dims\n if uniform:\n # 6 was used in the paper.\n init_range = np.sqrt(6.0 / (n_inputs + n_outputs))\n return tf.random_uniform(shape=dims,minval=-init_range, maxval=init_range)\n else:\n # 3 gives us approximately the same limits as above since this repicks\n # values greater than 2 standard deviations from the mean.\n stddev = np.sqrt(3.0 / (n_inputs + n_outputs))\n return tf.truncated_normal(shape=dims,stddev=stddev)","def random_distribution():\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n return b / np.sum(b, 1)[:, None]","def __call__(self, *args, **kwargs):\n mu, sigma = self.condition(args, **kwargs)\n return tf.contrib.distributions.Normal(loc=mu, scale=sigma)","def uniform_sample(upper, num):\n sample = []\n for i in range(num):\n value = random.randint(0, upper - 1)\n sample.append(value)\n return sample","def random_distribution():\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n return b/np.sum(b, 1)[:,None]","def random_distribution():\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n return b/np.sum(b, 1)[:,None]","def get_hybrid_distribution():\n return get_discrete_distribution() + get_standard_normal_distribution()","def initial_vector(self):\n\n return asarray([np.random.uniform(l, u) for l, u in self.bounds])","def normal_lower_bound(probability, mu=0, sigma=1):\r\n return ds_probability.inverse_normal_cdf(1 - probability, mu, sigma)","def test_returns_custom_range(self):\n img = np.arange(5)\n\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\n self.assertEqual(actual.min(), 0)\n self.assertEqual(actual.max(), 255)","def rng_fn_scipy(cls, rng, *args, **kwargs):","def init_to_uniform(site, radius=2, skip_param=False):\n if site['type'] == 'sample' and not site['is_observed']:\n if isinstance(site['fn'], dist.TransformedDistribution):\n fn = site['fn'].base_dist\n else:\n fn = site['fn']\n value = numpyro.sample('_init', fn, sample_shape=site['kwargs']['sample_shape'])\n base_transform = biject_to(fn.support)\n unconstrained_value = numpyro.sample('_unconstrained_init', dist.Uniform(-radius, radius),\n sample_shape=np.shape(base_transform.inv(value)))\n return base_transform(unconstrained_value)\n\n if site['type'] == 'param' and not skip_param:\n # return base value of param site\n constraint = site['kwargs'].pop('constraint', real)\n transform = biject_to(constraint)\n value = site['args'][0]\n unconstrained_value = numpyro.sample('_unconstrained_init', dist.Uniform(-radius, radius),\n sample_shape=np.shape(transform.inv(value)))\n if isinstance(transform, ComposeTransform):\n base_transform = transform.parts[0]\n else:\n base_transform = transform\n return base_transform(unconstrained_value)","def range(self):\r\n\t\treturn max(self.sample) - min(self.sample)","def get_delta_distribution(non_zero_value):\n support = np.arange(0, MAX_MARK_VALUE + 1)\n probs = [0] * len(support)\n probs[non_zero_value] = 1.0\n return ProbabilityDistribution(support, probs)","def uniform(\n shape: Iterable[int],\n low: float = 0,\n high: float = 1,\n comp_node: Optional[CompNode] = None,\n comp_graph: Optional[CompGraph] = None,\n) -> Tensor:\n assert low < high, \"Uniform is not defined when low >= high\"\n\n comp_node, comp_graph = _use_default_if_none(comp_node, comp_graph)\n seed = _random_seed_generator().__next__()\n return low + (high - low) * mgb.opr.uniform_rng(\n shape, seed=seed, comp_node=comp_node, comp_graph=comp_graph\n )","def get_uniform(kernlen=13):\n kernel_raw = np.ones((kernlen, kernlen))\n kernel = kernel_raw/kernel_raw.sum()\n return theano.shared(kernel.astype(\"float32\"), borrow=True)","def random(target, element, seed=None, num_range=[0, 1]):\n range_size = num_range[1] - num_range[0]\n range_min = num_range[0]\n if seed is not None:\n np.random.seed(seed)\n value = np.random.rand(target._count(element),)\n value = value*range_size + range_min\n return value","def _WeightInit(self, stddev):\n return init_ops.truncated_normal_initializer(stddev=stddev)","def rng():\n return np.random.default_rng()","def dispatch_dh(distribution_u_want, a):\n return {\n 'bernoulli': lambda: a / ((1. + a) ** 2),\n 'geometric': lambda: a / ((1. - a) ** 2),\n 'exponential': lambda: 1. / (a ** 2),\n }.get(distribution_u_want, lambda: None)()","def uniform(low, high, size, dtype=np.float32):\n rng = np.random.default_rng(0)\n out = (high - low) * rng.random(size, dtype=dtype) + low\n return out","def stdProbabilityNorm(self):\n return 0.5","def range100(self):\r\n return self.init(100)","def _bernoulli_lower(self, p, n, delta):\n if p < 1e-6:\n return 0.\n else:\n lower = scipy.stats.beta.ppf(delta / 2, p * n, n - p * n + 1)\n return lower","def norm_hist(self, hist_lower, hist_upper, lower, upper, value) :\n norm = 1.\n if upper>lower :\n norm = hist_lower.Integral()+(hist_upper.Integral()-hist_lower.Integral())/abs(upper-lower)*(value-lower)\n return norm","def rand_sample_gauss():\n mean = float(NUM_UNIQUE_VALUES + 1) / 2\n while True:\n r = random.normalvariate(mean, DIST_PARAM)\n value = int(round(r))\n # Rejection sampling to cut off Gaussian to within [1, NUM_UNIQUE_VALUES]\n if 1 <= value <= NUM_UNIQUE_VALUES:\n break\n\n return value # true client value","def clamp(self, key):\n\t\treturn DiscreteDistribution({ k : 0. if k != key else 1. for k in self.keys() })","def rng():\n pass","def rng():\n pass"],"string":"[\n \"def _uniform(val_range):\\r\\n return np.random.uniform(val_range[0], val_range[1])\",\n \"def initializeDistribution(self):\\n self.minVal = min(math.exp(self.upperBound),math.exp(self.lowerBound))\\n self.maxVal = max(math.exp(self.upperBound),math.exp(self.lowerBound))\",\n \"def glorot_uniform(seed=None):\\n return lambda shape, dtype, batch_ndims=0: _initialize( # pylint: disable=g-long-lambda\\n shape, dtype, batch_ndims,\\n scale=1., mode='fan_avg', distribution='uniform', seed=seed)\",\n \"def initializeDistribution(self):\\n if (self.lowerBoundUsed == False and self.upperBoundUsed == False):\\n self._distribution = distribution1D.BasicWeibullDistribution(self.k,self.lambdaVar,self.low)\\n self.lowerBound = self.low\\n self.upperBound = sys.float_info.max\\n else:\\n if self.lowerBoundUsed == False:\\n self.lowerBound = self.low\\n if self.upperBoundUsed == False:\\n self.upperBound = sys.float_info.max\\n self._distribution = distribution1D.BasicWeibullDistribution(self.k,self.lambdaVar,self.lowerBound,self.upperBound,self.low)\",\n \"def he_uniform(seed=None):\\n # pylint: disable=line-too-long\\n # pylint: enable=line-too-long\\n return lambda shape, dtype, batch_ndims=0: _initialize( # pylint: disable=g-long-lambda\\n shape, dtype, batch_ndims,\\n scale=2., mode='fan_in', distribution='uniform', seed=seed)\",\n \"def initializeDistribution(self):\\n if (self.lowerBoundUsed == False and self.upperBoundUsed == False):\\n self._distribution = distribution1D.BasicExponentialDistribution(self.lambdaVar,self.low)\\n self.lowerBound = self.low\\n self.upperBound = sys.float_info.max\\n else:\\n if self.lowerBoundUsed == False:\\n self.lowerBound = self.low\\n if self.upperBoundUsed == False:\\n self.upperBound = sys.float_info.max\\n self._distribution = distribution1D.BasicExponentialDistribution(self.lambdaVar,self.lowerBound,self.upperBound,self.low)\",\n \"def default_quantile():\\n return np.logspace(-5, 0, 100)\",\n \"def _base_dist(self, *args, **kwargs):\\n return tfd.TransformedDistribution(\\n distribution=tfd.Uniform(low=0.0, high=1.0),\\n bijector=tfp.bijectors.Invert(tfp.bijectors.Weibull(*args, **kwargs)),\\n name=\\\"Weibull\\\",\\n )\",\n \"def random_from_bound(bound):\\n if (isinstance(bound, tuple)):\\n val = np.random.uniform(low = bound[0], high = bound[1])\\n else:\\n val = 0.0\\n return val\",\n \"def uniform(feature, bins):\\n t = (feature.max()-feature.min())/bins\\n return [t*i for i in range(1, bins)]\",\n \"def dispatch_dg(distribution_u_want, a):\\n return {\\n 'bernoulli': lambda: a / (1. + a),\\n 'geometric': lambda: a / (1. - a), # initialization can give divide by zero\\n 'exponential': lambda: a,\\n }.get(distribution_u_want, lambda: None)()\",\n \"def uniform_dist(low, high):\\n return sp_uniform(low, high - low)\",\n \"def random_uniform_initializer(minval=0.0, maxval=1.0, seed=None,\\n dtype=dtypes.float32):\\n def _initializer(shape, dtype=_assert_float_dtype(dtype)):\\n return random_ops.random_uniform(shape, minval, maxval, dtype, seed=seed)\\n return _initializer\",\n \"def uniform(a: float, b: float) -> float:\\n ...\",\n \"def uniform(self, key, min_value=0., max_value=1.):\\n return min_value + self._random(key) * (max_value - min_value)\",\n \"def loguniform_dist(low, high, base=10):\\n return ExponentiateDistribution(sp_uniform(low, high - low), base=base)\",\n \"def initializeDistribution(self):\\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\\n self._distribution = distribution1D.BasicLogisticDistribution(self.location,self.scale)\\n else:\\n if self.lowerBoundUsed == False:\\n a = -sys.float_info.max\\n else:\\n a = self.lowerBound\\n if self.upperBoundUsed == False:\\n b = sys.float_info.max\\n else:\\n b = self.upperBound\\n self._distribution = distribution1D.BasicLogisticDistribution(self.location,self.scale,a,b)\",\n \"def get_standard_normal_distribution():\\n return np.random.normal(0, 1)\",\n \"def generate_number(lbound=1, ubound=100, mean=None, std=None):\\n x = np.arange(lbound, ubound + 1)\\n if mean and std:\\n prob = stats.norm.pdf(x, loc=mean, scale=std)\\n prob = prob / prob.sum() #normalize the probabilities so they sum up to 1\\n else:\\n prob = np.repeat(1 / len(x), len(x))\\n num = np.random.choice(x, p=prob)\\n return num\",\n \"def discrete_uniform_sampler(upper_value):\\n return int(np.random.random() * upper_value)\",\n \"def test_uniform(self):\\r\\n\\r\\n s = np.random.uniform(-1.35, 0.5, 5000)\\r\\n plt.hist(s, 30, density=False)\\r\\n plt.xlabel('Interlayer point energy [eV]')\\r\\n plt.ylabel('Frequency')\\r\\n plt.show()\",\n \"def _hardware_uniform(\\n rng_key: Array,\\n shape: Shape,\\n dtype: jnp.dtype = np.float32,\\n minval: Array = np.float32(0),\\n maxval: Array = np.float32(1)\\n) -> Array:\\n del rng_key # non-deterministic prng.\\n minval = jax.lax.convert_element_type(minval, dtype)\\n maxval = jax.lax.convert_element_type(maxval, dtype)\\n return jax.lax.rng_uniform(minval, maxval, shape)\",\n \"def initializeDistribution(self):\\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\\n self._distribution = distribution1D.BasicPoissonDistribution(self.mu)\\n self.lowerBound = 0.0\\n self.upperBound = sys.float_info.max\\n else:\\n self.raiseAnError(IOError,'Truncated poisson not yet implemented')\",\n \"def initializeDistribution(self):\\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\\n self._distribution = distribution1D.BasicLogNormalDistribution(self.mean,self.sigma,self.low)\\n self.lowerBound = 0.0\\n self.upperBound = sys.float_info.max\\n else:\\n if self.lowerBoundUsed == False:\\n self.lowerBound = self.low\\n if self.upperBoundUsed == False:\\n self.upperBound = sys.float_info.max\\n self._distribution = distribution1D.BasicLogNormalDistribution(self.mean,self.sigma,self.lowerBound,self.upperBound, self.low)\",\n \"def uniform(lower, upper):\\n\\n return lower + random.random() * (upper - lower)\",\n \"def uniform_weight_init(\\n input_size: int,\\n output_size: int,\\n min_bounds: float = 0.0,\\n max_bounds: float = 1.0,\\n positive_ratio: Optional[float] = None,\\n) -> t.Tensor:\\n if input_size < 1:\\n raise ValueError(\\\"input_size must be a positive integer.\\\")\\n if output_size < 1:\\n raise ValueError(\\\"output_size must be a positive integer.\\\")\\n if min_bounds > max_bounds:\\n raise ValueError(\\\"min_bounds must not be greater than max_bounds.\\\")\\n if positive_ratio is not None:\\n if positive_ratio > 1 or 0 > positive_ratio:\\n raise ValueError(\\n \\\"positive_ratio must be None, or must be between zero and one.\\\"\\n )\\n\\n result = t.empty((input_size, output_size))\\n uniform_(result, a=min_bounds, b=max_bounds)\\n\\n # TODO: test this.\\n if positive_ratio is not None:\\n bernoulli_distribution = Bernoulli(t.tensor([positive_ratio]))\\n mask = bernoulli_distribution.sample((input_size, output_size)).squeeze().bool()\\n result.abs_()\\n result = result.where(mask, -result)\\n\\n return result\",\n \"def initializeDistribution(self):\\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\\n self._distribution = distribution1D.BasicGeometricDistribution(self.p)\\n else: self.raiseAnError(IOError,'Truncated Geometric not yet implemented')\",\n \"def normal_upper_bound(probability: float, mu: float = 0, sigma: float = 1) -> float:\\n return inverse_normal_cdf(probability, mu, sigma)\",\n \"def uniform_random_value(l_boundary: float, r_boundary: float) -> float:\\n return uniform(l_boundary, r_boundary)\",\n \"def randrange(start: int, stop: int, step: int) -> int:\\n ...\",\n \"def WeightInitializer():\\n return np.random.uniform(-1, 1)\",\n \"def uniform_cdf(x):\\n if x <0: return 0 #uniform random is never less than 0\\n elif x < 1: return x #e.g. P(x <= 0.4) = 0.4\\n else: return 1 #uniform random is always less than 1\",\n \"def initializeDistribution(self):\\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\\n self._distribution = distribution1D.BasicBernoulliDistribution(self.p)\\n else:\\n self.raiseAnError(IOError,'Truncated Bernoulli not yet implemented')\",\n \"def randrange(n, vmin, vmax):\\n return (vmax - vmin) * np.random.rand(n) + vmin\",\n \"def theoretical_distribution(x):\\n a = 0\\n b = 1\\n if x < a:\\n return 0\\n elif x > b:\\n return 1\\n else:\\n return float(x - a) / (b - a)\",\n \"def uniform(self, size=None, low=0.0, high=1.0, ndim=None, dtype=None):\\r\\n return self.gen(uniform, size, low, high, ndim=ndim, dtype=dtype)\",\n \"def dispatch_d_1s(distribution_u_want, a):\\n return {\\n 'bernoulli': lambda: 1 / (1. + a),\\n 'geometric': lambda: 1. - a,\\n 'exponential': lambda: -a,\\n }.get(distribution_u_want, lambda: None)()\",\n \"def _default_sampling_xrange(self):\\n from scipy.stats import rv_continuous\\n dataset = self.rvdist.rvs(1000) if rv_continuous in self.rvdist.__class__.__mro__ \\\\\\n else self.rvdist.dataset\\n scale = np.nanmax(dataset) - np.nanmin(dataset)\\n return [np.nanmin(dataset) - scale*0.05, np.nanmax(dataset) + scale*0.05]\",\n \"def normal_lower_bound(probability: float, mu: float = 0, sigma: float = 1) -> float:\\n return inverse_normal_cdf(1 - probability, mu, sigma)\",\n \"def log_uniform_sample(sample_range):\\n log_min = np.log10(sample_range[0])\\n log_max = np.log10(sample_range[1])\\n u = np.random.rand()*(log_max-log_min) + log_min\\n return np.power(10.0,u)\",\n \"def rand_val(val_range):\\r\\n if isinstance(val_range, (list, tuple, np.ndarray)):\\r\\n return np.random.uniform(val_range[0], val_range[-1])\\r\\n # Assume val_range is a number\\r\\n return val_range\",\n \"def uniform(random_state, size=None, low=0.0, high=1.0, ndim=None, dtype=None):\\r\\n low = tensor.as_tensor_variable(low)\\r\\n high = tensor.as_tensor_variable(high)\\r\\n if dtype is None:\\r\\n dtype = tensor.scal.upcast(theano.config.floatX, low.dtype, high.dtype)\\r\\n ndim, size, bcast = _infer_ndim_bcast(ndim, size, low, high)\\r\\n op = RandomFunction('uniform',\\r\\n tensor.TensorType(dtype=dtype, broadcastable=bcast))\\r\\n return op(random_state, size, low, high)\",\n \"def normal_upper_bound(probability: float,\\n mu: float = 0,\\n sigma: float = 1) -> float:\\n return inverse_normal_cdf(probability, mu, sigma)\",\n \"def initializeDistribution(self):\\n if self.lowerBoundUsed == False and self.upperBoundUsed == False:\\n self._distribution = distribution1D.BasicBinomialDistribution(self.n,self.p)\\n else:\\n self.raiseAnError(IOError,'Truncated Binomial not yet implemented')\",\n \"def x_uniform1(xlim, n):\\n return linspace(xlim[0], xlim[1], n)\",\n \"def normal_upper_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(probability, mu, sigma)\",\n \"def normal_upper_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(probability, mu, sigma)\",\n \"def normal_upper_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(probability, mu, sigma)\",\n \"def normal_upper_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(probability, mu, sigma)\",\n \"def normal_upper_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(probability, mu, sigma)\",\n \"def initializeDistribution(self):\\n self.checkDistParams()\\n\\n self.lowerBound = min(self.mapping.keys())\\n self.upperBound = max(self.mapping.keys())\",\n \"def rand_rate_uni( N, tmin, tmax, **kwargs):\\n return np.random.uniform( tmin, tmax, size = N)\",\n \"def peturb(param):\\n ann = param.annotation\\n if ann == inspect._empty:\\n ann = 'normal'\\n if type(ann)==str:\\n if ann == 'normal':\\n return param.default + np.random.normal()\\n elif ann == 'positive':\\n return abs(param.default + np.random.normal())\\n elif type(ann) == tuple:\\n # Get a number from uniform random distribution\\n # bounded by values in the annotation tuple.\\n if type(ann[0]) == float:\\n return np.random.uniform(*ann)\\n elif type(ann[0]) == int:\\n return np.random.randint(*ann)\\n else:\\n print('Unrecognised function annotation.')\",\n \"def UniformRV(low, high):\\n return RV(ss.randint(low, high))\",\n \"def normal_lower_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(1- probability, mu, sigma)\",\n \"def normal_upper_bound(probability, mu=0, sigma=1):\\r\\n return ds_probability.inverse_normal_cdf(probability, mu, sigma)\",\n \"def normal_lower_bound(probability: float,\\n mu: float = 0,\\n sigma: float = 1) -> float:\\n return inverse_normal_cdf(1 - probability, mu, sigma)\",\n \"def __call__(self, shape):\\n return np.random.uniform(low=self.minval, high=self.maxval, size=shape)\",\n \"def initializeDistribution(self):\\n if (self.lowerBoundUsed == False and self.upperBoundUsed == False) or (self.min == self.lowerBound and self.max == self.upperBound):\\n self._distribution = distribution1D.BasicTriangularDistribution(self.apex,self.min,self.max)\\n else:\\n self.raiseAnError(IOError,'Truncated triangular not yet implemented')\",\n \"def __init__(self, mean_function: Callable, lower: Union[float, int], upper: Union[float, int],\\n sample_size: int, seed: Optional[float] = None):\\n super().__init__()\\n np.random.seed(seed)\\n assert callable(mean_function), \\\"intensity_function must be a callable!\\\"\\n try:\\n mean_function(np.array([1, 2]))\\n self.mean_function: Callable = mean_function\\n except ValueError:\\n warn('Force vectorization of intensity function')\\n self.mean_function: Callable = np.vectorize(mean_function)\\n assert isinstance(upper, (int, float)), \\\"Wrong type of upper limit!\\\"\\n assert isinstance(lower, (int, float)), \\\"Wrong type of lower limit!\\\"\\n if seed is not None:\\n assert isinstance(seed, (int, float)), \\\"Wrong type of seed!\\\"\\n if np.sum(self.mean_function(np.random.uniform(lower, upper, int(1e6))) < 0) > 0:\\n raise ValueError(\\\"Mean function must be greater than or equal to 0!\\\")\\n if lower >= upper:\\n raise ValueError(\\\"Wrong interval is specified! (lower {} >= upper {})\\\".format(lower, upper))\\n self.lower: Union[float, int] = lower\\n self.upper: Union[float, int] = upper\\n self.scaler: Union[float, int] = mean_function(upper)\\n self.sample_size: np.ndarray = np.random.poisson(lam=self.scaler * sample_size, size=1)\",\n \"def normal_lower_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(1 - probability, mu, sigma)\",\n \"def normal_lower_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(1 - probability, mu, sigma)\",\n \"def normal_lower_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(1 - probability, mu, sigma)\",\n \"def normal_lower_bound(probability, mu=0, sigma=1):\\n return inverse_normal_cdf(1 - probability, mu, sigma)\",\n \"def uniform(N=10000, left=0., right=3., n_bins_meas=10):\\n\\t# true distribution\\n\\ttrue = np.random.uniform(left, right, N)\\n\\t# measured distribution\\n\\tmeasured = true\\n\\t# Also return the generating true pdf\\n\\tx = np.linspace(-1, 5, 500)\\n\\tpdf = np.zeros([2, len(x)])\\n\\tpdf[0] = x\\n\\tpdf[1] = scs.uniform.pdf(x, left, right-left)\\n\\t# Default binning\\n\\tdefault_binning = np.linspace(np.amin(measured), np.amax(measured), n_bins_meas)\\n\\n\\treturn measured, true, pdf, default_binning\",\n \"def get_uniform_p_vals(self, min=1.5, max=9):\\n rng = np.random.RandomState(12435)\\n if self.power_activ == \\\"softplus\\\":\\n p_vals = np.log(np.exp(rng.uniform(low=min, high=max, size=(self.num_units,))-1)-1)\\n else:\\n p_vals = np.sqrt(rng.uniform(low=min, high=max, size=(self.num_units,))-1)\\n return p_vals\",\n \"def rangeSample(val, minLim, maxLim):\\n\\tif val < minLim or val > maxLim:\\n\\t\\tval = randint(minLim, maxLim)\\n\\treturn val\",\n \"def uniform_cdf(x: float) -> float:\\n if x < 0: return 0 # uniform random is never less than 0\\n elif x < 1 : return x # e.g. P(X <= 0.4)\\n else: return 1 # uniform random is always less than 1\",\n \"def _make_distribution(series):\\n arr = series.values\\n return arr / np.sum(arr)\",\n \"def generic_distribution(target, seeds, func):\\n seeds = target[seeds]\\n value = func.ppf(seeds)\\n return value\",\n \"def uniform(\\n self, low: float = 0, high: float = 1, size: Optional[Iterable[int]] = None\\n ):\\n _seed = self._seed() if callable(self._seed) else self._seed\\n return _uniform(\\n low=low,\\n high=high,\\n size=size,\\n seed=_seed,\\n device=self._device,\\n handle=self._handle,\\n )\",\n \"def xavier_init(dims, uniform=True):\\n n_inputs,n_outputs = dims\\n if uniform:\\n # 6 was used in the paper.\\n init_range = np.sqrt(6.0 / (n_inputs + n_outputs))\\n return tf.random_uniform(shape=dims,minval=-init_range, maxval=init_range)\\n else:\\n # 3 gives us approximately the same limits as above since this repicks\\n # values greater than 2 standard deviations from the mean.\\n stddev = np.sqrt(3.0 / (n_inputs + n_outputs))\\n return tf.truncated_normal(shape=dims,stddev=stddev)\",\n \"def random_distribution():\\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\\n return b / np.sum(b, 1)[:, None]\",\n \"def __call__(self, *args, **kwargs):\\n mu, sigma = self.condition(args, **kwargs)\\n return tf.contrib.distributions.Normal(loc=mu, scale=sigma)\",\n \"def uniform_sample(upper, num):\\n sample = []\\n for i in range(num):\\n value = random.randint(0, upper - 1)\\n sample.append(value)\\n return sample\",\n \"def random_distribution():\\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\\n return b/np.sum(b, 1)[:,None]\",\n \"def random_distribution():\\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\\n return b/np.sum(b, 1)[:,None]\",\n \"def get_hybrid_distribution():\\n return get_discrete_distribution() + get_standard_normal_distribution()\",\n \"def initial_vector(self):\\n\\n return asarray([np.random.uniform(l, u) for l, u in self.bounds])\",\n \"def normal_lower_bound(probability, mu=0, sigma=1):\\r\\n return ds_probability.inverse_normal_cdf(1 - probability, mu, sigma)\",\n \"def test_returns_custom_range(self):\\n img = np.arange(5)\\n\\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\\n self.assertEqual(actual.min(), 0)\\n self.assertEqual(actual.max(), 255)\",\n \"def rng_fn_scipy(cls, rng, *args, **kwargs):\",\n \"def init_to_uniform(site, radius=2, skip_param=False):\\n if site['type'] == 'sample' and not site['is_observed']:\\n if isinstance(site['fn'], dist.TransformedDistribution):\\n fn = site['fn'].base_dist\\n else:\\n fn = site['fn']\\n value = numpyro.sample('_init', fn, sample_shape=site['kwargs']['sample_shape'])\\n base_transform = biject_to(fn.support)\\n unconstrained_value = numpyro.sample('_unconstrained_init', dist.Uniform(-radius, radius),\\n sample_shape=np.shape(base_transform.inv(value)))\\n return base_transform(unconstrained_value)\\n\\n if site['type'] == 'param' and not skip_param:\\n # return base value of param site\\n constraint = site['kwargs'].pop('constraint', real)\\n transform = biject_to(constraint)\\n value = site['args'][0]\\n unconstrained_value = numpyro.sample('_unconstrained_init', dist.Uniform(-radius, radius),\\n sample_shape=np.shape(transform.inv(value)))\\n if isinstance(transform, ComposeTransform):\\n base_transform = transform.parts[0]\\n else:\\n base_transform = transform\\n return base_transform(unconstrained_value)\",\n \"def range(self):\\r\\n\\t\\treturn max(self.sample) - min(self.sample)\",\n \"def get_delta_distribution(non_zero_value):\\n support = np.arange(0, MAX_MARK_VALUE + 1)\\n probs = [0] * len(support)\\n probs[non_zero_value] = 1.0\\n return ProbabilityDistribution(support, probs)\",\n \"def uniform(\\n shape: Iterable[int],\\n low: float = 0,\\n high: float = 1,\\n comp_node: Optional[CompNode] = None,\\n comp_graph: Optional[CompGraph] = None,\\n) -> Tensor:\\n assert low < high, \\\"Uniform is not defined when low >= high\\\"\\n\\n comp_node, comp_graph = _use_default_if_none(comp_node, comp_graph)\\n seed = _random_seed_generator().__next__()\\n return low + (high - low) * mgb.opr.uniform_rng(\\n shape, seed=seed, comp_node=comp_node, comp_graph=comp_graph\\n )\",\n \"def get_uniform(kernlen=13):\\n kernel_raw = np.ones((kernlen, kernlen))\\n kernel = kernel_raw/kernel_raw.sum()\\n return theano.shared(kernel.astype(\\\"float32\\\"), borrow=True)\",\n \"def random(target, element, seed=None, num_range=[0, 1]):\\n range_size = num_range[1] - num_range[0]\\n range_min = num_range[0]\\n if seed is not None:\\n np.random.seed(seed)\\n value = np.random.rand(target._count(element),)\\n value = value*range_size + range_min\\n return value\",\n \"def _WeightInit(self, stddev):\\n return init_ops.truncated_normal_initializer(stddev=stddev)\",\n \"def rng():\\n return np.random.default_rng()\",\n \"def dispatch_dh(distribution_u_want, a):\\n return {\\n 'bernoulli': lambda: a / ((1. + a) ** 2),\\n 'geometric': lambda: a / ((1. - a) ** 2),\\n 'exponential': lambda: 1. / (a ** 2),\\n }.get(distribution_u_want, lambda: None)()\",\n \"def uniform(low, high, size, dtype=np.float32):\\n rng = np.random.default_rng(0)\\n out = (high - low) * rng.random(size, dtype=dtype) + low\\n return out\",\n \"def stdProbabilityNorm(self):\\n return 0.5\",\n \"def range100(self):\\r\\n return self.init(100)\",\n \"def _bernoulli_lower(self, p, n, delta):\\n if p < 1e-6:\\n return 0.\\n else:\\n lower = scipy.stats.beta.ppf(delta / 2, p * n, n - p * n + 1)\\n return lower\",\n \"def norm_hist(self, hist_lower, hist_upper, lower, upper, value) :\\n norm = 1.\\n if upper>lower :\\n norm = hist_lower.Integral()+(hist_upper.Integral()-hist_lower.Integral())/abs(upper-lower)*(value-lower)\\n return norm\",\n \"def rand_sample_gauss():\\n mean = float(NUM_UNIQUE_VALUES + 1) / 2\\n while True:\\n r = random.normalvariate(mean, DIST_PARAM)\\n value = int(round(r))\\n # Rejection sampling to cut off Gaussian to within [1, NUM_UNIQUE_VALUES]\\n if 1 <= value <= NUM_UNIQUE_VALUES:\\n break\\n\\n return value # true client value\",\n \"def clamp(self, key):\\n\\t\\treturn DiscreteDistribution({ k : 0. if k != key else 1. for k in self.keys() })\",\n \"def rng():\\n pass\",\n \"def rng():\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67329484","0.63917905","0.634777","0.63349366","0.61998284","0.61475074","0.61413395","0.6124729","0.6119773","0.61055756","0.6086929","0.6079064","0.60665655","0.6043183","0.6039257","0.5985008","0.5984233","0.59805816","0.59690744","0.59467566","0.5941135","0.5927508","0.5901463","0.5896202","0.5884528","0.5881738","0.584001","0.5826473","0.5800217","0.5795453","0.5788206","0.57758445","0.57736397","0.5769886","0.5753157","0.574709","0.5724837","0.5724693","0.5716751","0.57142484","0.57098025","0.5699335","0.5696161","0.56876147","0.56846577","0.56800246","0.56800246","0.56800246","0.56800246","0.56800246","0.56520844","0.5639999","0.5621919","0.5617092","0.55995387","0.5583716","0.5582227","0.5580803","0.55778","0.55744326","0.55666","0.55666","0.55666","0.55666","0.5558013","0.55561835","0.5548622","0.55460644","0.55322754","0.55288166","0.55259126","0.55239064","0.5522373","0.5503022","0.54975265","0.54882705","0.54882705","0.5488019","0.54744434","0.5474315","0.5460565","0.5455774","0.5452424","0.5438572","0.54361296","0.54355365","0.54317766","0.54281145","0.5425375","0.5416201","0.54122734","0.5405398","0.54052305","0.5400126","0.5386262","0.53793275","0.5376463","0.53747374","0.5373311","0.5373311"],"string":"[\n \"0.67329484\",\n \"0.63917905\",\n \"0.634777\",\n \"0.63349366\",\n \"0.61998284\",\n \"0.61475074\",\n \"0.61413395\",\n \"0.6124729\",\n \"0.6119773\",\n \"0.61055756\",\n \"0.6086929\",\n \"0.6079064\",\n \"0.60665655\",\n \"0.6043183\",\n \"0.6039257\",\n \"0.5985008\",\n \"0.5984233\",\n \"0.59805816\",\n \"0.59690744\",\n \"0.59467566\",\n \"0.5941135\",\n \"0.5927508\",\n \"0.5901463\",\n \"0.5896202\",\n \"0.5884528\",\n \"0.5881738\",\n \"0.584001\",\n \"0.5826473\",\n \"0.5800217\",\n \"0.5795453\",\n \"0.5788206\",\n \"0.57758445\",\n \"0.57736397\",\n \"0.5769886\",\n \"0.5753157\",\n \"0.574709\",\n \"0.5724837\",\n \"0.5724693\",\n \"0.5716751\",\n \"0.57142484\",\n \"0.57098025\",\n \"0.5699335\",\n \"0.5696161\",\n \"0.56876147\",\n \"0.56846577\",\n \"0.56800246\",\n \"0.56800246\",\n \"0.56800246\",\n \"0.56800246\",\n \"0.56800246\",\n \"0.56520844\",\n \"0.5639999\",\n \"0.5621919\",\n \"0.5617092\",\n \"0.55995387\",\n \"0.5583716\",\n \"0.5582227\",\n \"0.5580803\",\n \"0.55778\",\n \"0.55744326\",\n \"0.55666\",\n \"0.55666\",\n \"0.55666\",\n \"0.55666\",\n \"0.5558013\",\n \"0.55561835\",\n \"0.5548622\",\n \"0.55460644\",\n \"0.55322754\",\n \"0.55288166\",\n \"0.55259126\",\n \"0.55239064\",\n \"0.5522373\",\n \"0.5503022\",\n \"0.54975265\",\n \"0.54882705\",\n \"0.54882705\",\n \"0.5488019\",\n \"0.54744434\",\n \"0.5474315\",\n \"0.5460565\",\n \"0.5455774\",\n \"0.5452424\",\n \"0.5438572\",\n \"0.54361296\",\n \"0.54355365\",\n \"0.54317766\",\n \"0.54281145\",\n \"0.5425375\",\n \"0.5416201\",\n \"0.54122734\",\n \"0.5405398\",\n \"0.54052305\",\n \"0.5400126\",\n \"0.5386262\",\n \"0.53793275\",\n \"0.5376463\",\n \"0.53747374\",\n \"0.5373311\",\n \"0.5373311\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7167602"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19586,"cells":{"query":{"kind":"string","value":"Look up the range for a functor"},"document":{"kind":"string","value":"def functorRange(functor):\n for (name, range) in functorRangeList:\n if functor == name:\n return range\n else:\n raise Exception (\"Functor \" + functor + \" not present in range list\")"},"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 _in_range_op(spec):","def _range_func(self, withscores, score_cast_func, decode_value_func=lambda x: x):\n if withscores:\n return lambda score_member: (decode_value_func(score_member[1]), score_cast_func(self._encode(score_member[0]))) # noqa\n else:\n return lambda score_member: decode_value_func(score_member[1])","def ranges(self, predicate):\n\n x = np.zeros(len(self)).astype(np.bool)\n for i, elem in enumerate(self.elements):\n x[i] = predicate(elem)\n\n return np.where(x)[0]","def range_(self):\n return self.bset.range_","def getRange(self, p_int): # real signature unknown; restored from __doc__\n pass","def test_get_range(self):\n pass","def GetScalarRange(self):\n ...","def get_lower_bound(self):\n return self._lower_bound","def get_lower_bound(self):\n return self._lower_bound","def range(self) -> ty.Tuple[float, float]:\r\n ...","def provider_range_lookup(self, record):\n pass","def get_bounds(f, lb=0, ub=None):\r\n lb_idx = np.searchsorted(f, lb, 'left')\r\n if ub == None:\r\n ub_idx = len(f)\r\n else:\r\n ub_idx = np.searchsorted(f, ub, 'right')\r\n\r\n return lb_idx, ub_idx","def range_(self):\n return tuple((e[0], e[-1]) for e in self.edges)","def get_ranges(self, tchain, kw):\n (lo, hi) = (\"min\", \"max\")\n ran = None\n for t in tchain:\n rstmt = t.search_one(kw)\n if rstmt is None: continue\n ran = [ i.split(\"..\") for i in rstmt.arg.split(\"|\") ]\n if ran[0][0] != 'min': lo = ran[0][0]\n if ran[-1][-1] != 'max': hi = ran[-1][-1]\n if ran is None: return None\n if len(ran) == 1:\n return [(lo, hi)]\n else:\n return [(lo, ran[0][-1])] + ran[1:-1] + [(ran[-1][0], hi)]","def range(self):\n return self.timerange()","def f_has_range(self):\n raise NotImplementedError(\"Should have implemented this.\")","def get_range(self) -> tuple[int, int]:\n return self.range_from, self.range_to","def range(self):\n\n return time_stat(self, stat=\"range\")","def lower_bound(self) -> float:\n ...","def _hit_range_get(self):\n return (self.hit_start, self.hit_end)","def range (self):\n return self._range","def range (self):\n return self._range","def range(self):\n return self.range_array","def visit_bound_predicate(self, predicate) -> T:","def _query_range_get(self):\n return (self.query_start, self.query_end)","def range(self) -> Tuple[Union[int, float], Union[int, float]]:\n return self._range","def _builtin_between(low, high, value, **k):\n mode = check_mode((low, high, value), ['iii', 'iiv'], functor='between', **k)\n low_v = int(low)\n high_v = int(high)\n if mode == 0: # Check\n value_v = int(value)\n if low_v <= value_v <= high_v:\n return [(low, high, value)]\n else: # Enumerate\n results = []\n for value_v in range(low_v, high_v + 1):\n results.append((low, high, Constant(value_v)))\n return results","def getRange(self):\n return self.range","def functorOtherValue(functor, val):\n range = functorRange(functor)\n assert len(range) == 2\n if val == range[0]:\n return range[1]\n else:\n return range[0]","def f_get_range(self, copy=True):\n raise NotImplementedError(\"Should have implemented this.\")","def define_range():\n\n def_range = {'lt': [0.0, 24.0],\n 'lon': [0.0, 360.0],\n 'angle': [0.0, 2.0 * np.pi]}\n\n return def_range","def limit_by(self, field, lower_bound=None, upper_bound=None):\n if lower_bound is not None and upper_bound is not None:\n indices = (self[field] >= lower_bound) \\\n & (self[field] <= upper_bound)\n elif lower_bound is None:\n indices = self[field] <= upper_bound\n elif upper_bound is None:\n indices = self[field] >= lower_bound\n else:\n raise ValueError(\"One bound must be set!\")\n return self[indices]","def _get_range(self):\n return tuple((0, m, 1) for m in self.level_shapes[0])","def calcrange(a4lim,data):\r\n a4range=N.intersect1d(N.where(data>a4lim[0])[0],N.where(data<a4lim[1])[0])\r\n return a4range","def _bound(x, min_value, max_value):\n return np.maximum(min_value, np.minimum(x, max_value))","def range(self):\n \n return self._range","def range_query(self, from_idx, to_idx):\n return self.prefix_query(to_idx) - self.prefix_query(from_idx - 1)","def _get_sight_range(self):\n raise NotImplementedError","def evaluate_comparison_range(node):\n return None","def query_range(tree, start_y, start_x, end_y, end_x):\n res = 0\n start_y -= 1\n\n while end_y > start_y:\n res += bit.query_range(tree[end_y], start_x, end_x)\n end_y -= (end_y & -end_y)\n\n while start_y > end_y:\n res -= bit.query_range(tree[start_y], start_x, end_x)\n start_y -= (start_y & -start_y)\n\n return res","def get_range_value(self, key):\n pass","def map_to_range(val, old_min, old_max, new_min, new_max):\n return new_max - (val - old_min) * (new_max - new_min) / (old_max - old_min)","def between(min, max):\n def func(x):\n return min <= x <= max\n return func","def evaluate_range(optree, update_interval=False, memoization_map=None):\n if memoization_map is None:\n memoization_map = {}\n init_interval = optree.get_interval()\n if not init_interval is None:\n return init_interval\n else:\n if optree in memoization_map:\n return memoization_map[optree]\n elif isinstance(optree, ML_LeafNode):\n op_range = optree.get_interval()\n elif is_comparison(optree):\n op_range = evaluate_comparison_range(optree)\n if update_interval:\n optree.set_interval(op_range)\n elif isinstance(optree, PlaceHolder):\n op_range = evaluate_range(optree.get_input(0),\n update_interval=update_interval,\n memoization_map=memoization_map)\n if update_interval:\n optree.set_interval(op_range)\n else:\n args_interval = tuple(\n evaluate_range(op, update_interval=update_interval,\n memoization_map=memoization_map\n ) for op in optree.get_inputs())\n args_interval_map = {op: op_interval for op, op_interval in zip(optree.inputs, args_interval)}\n # evaluate_range cannot rely on bare_range_function only as some\n # operations (e.g. CountLeadingZeros) do not base interval computation\n # on their inputs' intervals but on other parameters\n ops_interval_get = lambda op: args_interval_map[op]\n op_range = optree.range_function(optree.inputs,\n ops_interval_getter=ops_interval_get)\n if update_interval:\n optree.set_interval(op_range)\n Log.report(LOG_VERBOSE_EVALUATE_RANGE, \"range of {} is {}\", optree, op_range)\n memoization_map[optree] = op_range\n return op_range","def get_range(self, rel_name):\n return self._declaration[rel_name].range_type","def term_range(self):\n start = max([f.cohort_end for f in self.filters])\n end = MAX_NUM_COHORTS\n return range(start + 1, end + 1)","def __getitem__(self, *args):\n return _uhd_swig.range_vector_t___getitem__(self, *args)","def range(self):\n return self._upper - self._lower","def get_range(self):\n return time_to_range(self.get_time())","def get_range(self, field, deep=False, axis=None):\n variables = list(self.vars(deep, with_name=field))\n\n if not variables:\n raise KeyError(\"No variable named '%s' was found!\" % field)\n\n start = [np.nanmin(self[var], axis).item(0) for var in variables]\n end = [np.nanmax(self[var], axis).item(0) for var in variables]\n return min(start), max(end)","def get_range(df, col):\n return df[col].min(), df[col].max()","def global_range(self):\n raise NotImplementedError","def cal(num1, num2, range):\r\n setup(range)\r\n return Andcollections(num1, num2)","def get_xrange_indices(self, lower, upper) -> Tuple[int, int]:\n lower_index = np.argmax(self.x >= lower)\n upper_index = np.argmax(self.x >= upper)\n return int(lower_index), int(upper_index)","def GetIndexBounds(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def detect_range(self) -> Union[int, float]:\n return self.proto.detect_range","def map_bound(value, in_low, in_high, out_low, out_high):\n result = None\n\n if value <= in_low:\n result = out_low\n else:\n if value >= in_high:\n result = out_high\n else:\n # http://stackoverflow.com/a/5650012/574981\n result = out_low + (\n (out_high - out_low) * (value - in_low) / (in_high - in_low)\n )\n return result","def arange(module, start, end, step):\n _import_modules()\n if module in [np, ma, jnp]:\n return module.arange(start, end, step)\n elif module == torch:\n return module.arange(start, end, step, dtype=torch.float)\n elif module == tf:\n return tf.range(start, end, step)\n raise UnknownModuleException(f\"Module {module.__name__} not supported.\")","def range_callback(data):\n global D\n D.ranges = data.ranges","def check_range(num):\n for i in ranges.keys():\n if num in ranges[i]:\n return i","def targetRange(self):\n return self._getAttribute(Attribute.targetRange)","def range_(headers, data):\n\tcolumn_matrix=data.get_data(headers).getT() # get columns as rows, as this makes analysis much easier by just perfoming operations on column list directly\n\tif column_matrix==[]:\n\t\tprint \"wrong headers, not present in data Object\"\n\t\treturn []\n\tcolumn_max=column_matrix.max(1)\n\tcolumn_min=column_matrix.min(1)\n\tfinal=np.concatenate((column_min, column_max), axis=1)\n\t\n\trng=final.tolist()\n\treturn rng","def get_upper_bound(self):\n return self._upper_bound","def lrange(self, name, start, end):\r\n return self.format_inline('LRANGE', name, start, end)","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_range(g,h,d): # g: graph; h: head node; d: dependent node\n addresses = sorted(g.nodes.keys())\n h_index = addresses.index(h)\n d_index = addresses.index(d)\n sign = cmp(d_index,h_index)\n return addresses[h_index:d_index+sign:sign]","def functorRangeSize(functor):\n return len(functorRange(functor))","def range_table(self):\n raise NotImplementedError('Abstract method.')","def get_featureRange(self):\n\n return self.featureRange","def get_range(lst):\n return float(max(lst)) - float(min(lst))","def range(series):\n return min(series), max(series)","def range_field(self):\n return self.db.range_field","def GetTRange(self):\n ...","def _bi_range(start, end):\n if start == end:\n return (start,)\n\n elif end < start:\n return reversed(range(end, start + 1))\n\n else:\n return range(start, end + 1)","def upper_bound(self) -> float:\n ...","def map_range(x, in_min, in_max, out_min, out_max):\n mapped = (x-in_min) * (out_max - out_min) / (in_max-in_min) + out_min\n if out_min <= out_max:\n return max(min(mapped, out_max), out_min)\n return min(max(mapped, out_max), out_min)","def new_range(r):\n if isinstance(r, list) or isinstance(r, tuple) and len(r) == 2:\n lower = r[0]\n upper = r[1]\n else:\n lower = r\n upper = r\n lower = int(lower)\n upper = int(upper)\n return range(lower, upper + 1)","def query_range(self,i,j):\n assert 0 <= i <= j <= len(self)\n ans = self.__query_aux(0,0,len(self),i,j)\n return ans","def _lies_at_index_range(value):\n\ta = 0; b = len(keys)-1;\n\twhile a != b:\n\t\tc = (a+b)/2\n\t\tif value < dist[keys[c]][0]:\n\t\t\tb = c-1\n\t\telif value > dist[keys[c]][1]:\n\t\t\ta = c+1\n\t\telse:\n\t\t\treturn keys[c]\n\treturn keys[a]","def interval(self):\n return Intersection(*(a.interval for a in self.args))","def data_range(x):\n return max(x)-min(x)","def array_range(a, low, high, ref=None):\n if ref is None:\n ref = a\n return a[np.logical_and(ref >= low, ref < high)]","def get_range(n0: int, n1: int, ns: int) -> List[int]:\n # Return a range as a list\n def lrange(a, b, n=1) -> List[int]:\n return list(range(a, b, n))\n # Get the in-bounds part of the range\n n_range = lrange(max(0, n0), min(ns, n1))\n # Handle out-of-bounds indices by reflection across boundaries\n if n0 < 0:\n # Underflow\n n_range = lrange(-n0, 0, -1) + n_range\n if n1 > ns:\n # Overflow\n n_range = n_range + lrange(ns - 1, 2 * ns - n1 - 1, -1)\n\n return n_range","def open_range(start, stop, step):\n return np.arange(start, stop+step/2, step)","def _rangeQueryIntFeature(self):\n\n # create args\n minToGet = c_int64()\n maxToGet = c_int64()\n\n errorCode = VimbaDLL.featureIntRangeQuery(self._handle,\n self._name,\n byref(minToGet),\n byref(maxToGet))\n if errorCode != 0:\n raise VimbaException(errorCode)\n\n return (int(str(minToGet.value)), int(str(maxToGet.value)))","def getRange(self) -> Tuple[int, int]:\n return self.validator().bottom(), self.validator().top()","def range(self):\n return (self._start, self._end)","def range(self):\n lower, upper = sorted((self.y1, self.y2))\n return FloatRange(lower=lower, upper=upper)","def rangeFinder(self, startIndex, endIndex, list):\n max = list[startIndex]\n min = list[startIndex]\n for x in range(startIndex, endIndex):\n if list[x] > max:\n max = list[x]\n if list[x] < min:\n min = list[x]\n return max-min","def range_inclusive(start, stop):\n return range(start, stop + 1)","def xpointerRangeToFunction(self, nargs):\n libxml2mod.xmlXPtrRangeToFunction(self._o, nargs)","def subject(self) -> global___Range:","def range_params(self, ran, kw):\n specs = {\"range\": (SchemaNode(\"value\"),\n SchemaNode(\"param\").set_attr(\"name\",\"minInclusive\"),\n SchemaNode(\"param\").set_attr(\"name\",\"maxInclusive\")),\n \"length\": (SchemaNode(\"param\").set_attr(\"name\",\"length\"),\n SchemaNode(\"param\").set_attr(\"name\",\"minLength\"),\n SchemaNode(\"param\").set_attr(\"name\",\"maxLength\"))}\n (exact, min_, max_) = specs[kw]\n if (len(ran) == 1 or ran[0] == ran[1]) and ran[0][0] != \"m\":\n elem = exact\n elem.text = ran[0]\n return [elem]\n res = []\n if ran[0][0] != \"m\":\n elem = min_\n elem.text = ran[0]\n res.append(elem)\n if ran[1][0] != \"m\":\n elem = max_\n elem.text = ran[1]\n res.append(elem)\n return res","def bounds(self, start=None, finish=None):\n lower = start if start is not None else self.limits[0]\n upper = finish if finish is not None else self.limits[1]\n\n lower = lower + self.offsets[0]\n upper = upper + self.offsets[1]\n\n return (lower, upper)","def i_range(Pd):\n return xrange(i_min(Pd), i_max(Pd) + 1)","def getFeHRange(brand):\n return feh_range[brand]","def bounds(self): # -> tuple[()]:\n ...","def rangeSearch(self,x,y,node):\n l=[]\n start=self.find(node,x)\n while start.key<=y:\n if start.key>=x:\n l.append(start)\n start=self.next(start)\n return l","def Range(self, from: int, to: int) -> BaseVector:","def get_range(self, start=None, end=None):\n\n # handle the case of no data\n if self.data.shape[0] == 0 or self.source.data[\"index\"].shape[0] == 0:\n return None, None\n\n first_source_idx = self.source.data[\"index\"][0]\n last_source_idx = self.source.data[\"index\"][-1]\n\n # convert to timestamp if necessary\n if isinstance(self.data.index, pd.DatetimeIndex):\n start = pd.to_datetime(start, unit=\"ms\")\n end = pd.to_datetime(end, unit=\"ms\")\n first_source_idx = pd.to_datetime(first_source_idx, unit=\"ms\")\n last_source_idx = pd.to_datetime(last_source_idx, unit=\"ms\")\n\n # get new start and end\n if start is not None:\n if start < first_source_idx:\n start = max(self.data.index[0], start)\n elif start > last_source_idx:\n start = min(self.data.index[-1], start)\n elif start < self.data.index[0]:\n start = self.data.index[0]\n elif start > self.data.index[-1]:\n start = self.data.index[-1]\n elif len(self.source.data[\"index\"]) > 0:\n start = first_source_idx\n else:\n start = self.data.index[0]\n\n if end is not None:\n if end < first_source_idx:\n end = max(self.data.index[0], end)\n elif end > last_source_idx:\n end = min(self.data.index[-1], end)\n elif end < self.data.index[0]:\n end = self.data.index[0]\n elif end > self.data.index[-1]:\n end = self.data.index[-1]\n elif len(self.source.data[\"index\"]) > 0:\n end = last_source_idx\n else:\n end = self.data.index[-1]\n\n return start, end"],"string":"[\n \"def _in_range_op(spec):\",\n \"def _range_func(self, withscores, score_cast_func, decode_value_func=lambda x: x):\\n if withscores:\\n return lambda score_member: (decode_value_func(score_member[1]), score_cast_func(self._encode(score_member[0]))) # noqa\\n else:\\n return lambda score_member: decode_value_func(score_member[1])\",\n \"def ranges(self, predicate):\\n\\n x = np.zeros(len(self)).astype(np.bool)\\n for i, elem in enumerate(self.elements):\\n x[i] = predicate(elem)\\n\\n return np.where(x)[0]\",\n \"def range_(self):\\n return self.bset.range_\",\n \"def getRange(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def test_get_range(self):\\n pass\",\n \"def GetScalarRange(self):\\n ...\",\n \"def get_lower_bound(self):\\n return self._lower_bound\",\n \"def get_lower_bound(self):\\n return self._lower_bound\",\n \"def range(self) -> ty.Tuple[float, float]:\\r\\n ...\",\n \"def provider_range_lookup(self, record):\\n pass\",\n \"def get_bounds(f, lb=0, ub=None):\\r\\n lb_idx = np.searchsorted(f, lb, 'left')\\r\\n if ub == None:\\r\\n ub_idx = len(f)\\r\\n else:\\r\\n ub_idx = np.searchsorted(f, ub, 'right')\\r\\n\\r\\n return lb_idx, ub_idx\",\n \"def range_(self):\\n return tuple((e[0], e[-1]) for e in self.edges)\",\n \"def get_ranges(self, tchain, kw):\\n (lo, hi) = (\\\"min\\\", \\\"max\\\")\\n ran = None\\n for t in tchain:\\n rstmt = t.search_one(kw)\\n if rstmt is None: continue\\n ran = [ i.split(\\\"..\\\") for i in rstmt.arg.split(\\\"|\\\") ]\\n if ran[0][0] != 'min': lo = ran[0][0]\\n if ran[-1][-1] != 'max': hi = ran[-1][-1]\\n if ran is None: return None\\n if len(ran) == 1:\\n return [(lo, hi)]\\n else:\\n return [(lo, ran[0][-1])] + ran[1:-1] + [(ran[-1][0], hi)]\",\n \"def range(self):\\n return self.timerange()\",\n \"def f_has_range(self):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def get_range(self) -> tuple[int, int]:\\n return self.range_from, self.range_to\",\n \"def range(self):\\n\\n return time_stat(self, stat=\\\"range\\\")\",\n \"def lower_bound(self) -> float:\\n ...\",\n \"def _hit_range_get(self):\\n return (self.hit_start, self.hit_end)\",\n \"def range (self):\\n return self._range\",\n \"def range (self):\\n return self._range\",\n \"def range(self):\\n return self.range_array\",\n \"def visit_bound_predicate(self, predicate) -> T:\",\n \"def _query_range_get(self):\\n return (self.query_start, self.query_end)\",\n \"def range(self) -> Tuple[Union[int, float], Union[int, float]]:\\n return self._range\",\n \"def _builtin_between(low, high, value, **k):\\n mode = check_mode((low, high, value), ['iii', 'iiv'], functor='between', **k)\\n low_v = int(low)\\n high_v = int(high)\\n if mode == 0: # Check\\n value_v = int(value)\\n if low_v <= value_v <= high_v:\\n return [(low, high, value)]\\n else: # Enumerate\\n results = []\\n for value_v in range(low_v, high_v + 1):\\n results.append((low, high, Constant(value_v)))\\n return results\",\n \"def getRange(self):\\n return self.range\",\n \"def functorOtherValue(functor, val):\\n range = functorRange(functor)\\n assert len(range) == 2\\n if val == range[0]:\\n return range[1]\\n else:\\n return range[0]\",\n \"def f_get_range(self, copy=True):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def define_range():\\n\\n def_range = {'lt': [0.0, 24.0],\\n 'lon': [0.0, 360.0],\\n 'angle': [0.0, 2.0 * np.pi]}\\n\\n return def_range\",\n \"def limit_by(self, field, lower_bound=None, upper_bound=None):\\n if lower_bound is not None and upper_bound is not None:\\n indices = (self[field] >= lower_bound) \\\\\\n & (self[field] <= upper_bound)\\n elif lower_bound is None:\\n indices = self[field] <= upper_bound\\n elif upper_bound is None:\\n indices = self[field] >= lower_bound\\n else:\\n raise ValueError(\\\"One bound must be set!\\\")\\n return self[indices]\",\n \"def _get_range(self):\\n return tuple((0, m, 1) for m in self.level_shapes[0])\",\n \"def calcrange(a4lim,data):\\r\\n a4range=N.intersect1d(N.where(data>a4lim[0])[0],N.where(data<a4lim[1])[0])\\r\\n return a4range\",\n \"def _bound(x, min_value, max_value):\\n return np.maximum(min_value, np.minimum(x, max_value))\",\n \"def range(self):\\n \\n return self._range\",\n \"def range_query(self, from_idx, to_idx):\\n return self.prefix_query(to_idx) - self.prefix_query(from_idx - 1)\",\n \"def _get_sight_range(self):\\n raise NotImplementedError\",\n \"def evaluate_comparison_range(node):\\n return None\",\n \"def query_range(tree, start_y, start_x, end_y, end_x):\\n res = 0\\n start_y -= 1\\n\\n while end_y > start_y:\\n res += bit.query_range(tree[end_y], start_x, end_x)\\n end_y -= (end_y & -end_y)\\n\\n while start_y > end_y:\\n res -= bit.query_range(tree[start_y], start_x, end_x)\\n start_y -= (start_y & -start_y)\\n\\n return res\",\n \"def get_range_value(self, key):\\n pass\",\n \"def map_to_range(val, old_min, old_max, new_min, new_max):\\n return new_max - (val - old_min) * (new_max - new_min) / (old_max - old_min)\",\n \"def between(min, max):\\n def func(x):\\n return min <= x <= max\\n return func\",\n \"def evaluate_range(optree, update_interval=False, memoization_map=None):\\n if memoization_map is None:\\n memoization_map = {}\\n init_interval = optree.get_interval()\\n if not init_interval is None:\\n return init_interval\\n else:\\n if optree in memoization_map:\\n return memoization_map[optree]\\n elif isinstance(optree, ML_LeafNode):\\n op_range = optree.get_interval()\\n elif is_comparison(optree):\\n op_range = evaluate_comparison_range(optree)\\n if update_interval:\\n optree.set_interval(op_range)\\n elif isinstance(optree, PlaceHolder):\\n op_range = evaluate_range(optree.get_input(0),\\n update_interval=update_interval,\\n memoization_map=memoization_map)\\n if update_interval:\\n optree.set_interval(op_range)\\n else:\\n args_interval = tuple(\\n evaluate_range(op, update_interval=update_interval,\\n memoization_map=memoization_map\\n ) for op in optree.get_inputs())\\n args_interval_map = {op: op_interval for op, op_interval in zip(optree.inputs, args_interval)}\\n # evaluate_range cannot rely on bare_range_function only as some\\n # operations (e.g. CountLeadingZeros) do not base interval computation\\n # on their inputs' intervals but on other parameters\\n ops_interval_get = lambda op: args_interval_map[op]\\n op_range = optree.range_function(optree.inputs,\\n ops_interval_getter=ops_interval_get)\\n if update_interval:\\n optree.set_interval(op_range)\\n Log.report(LOG_VERBOSE_EVALUATE_RANGE, \\\"range of {} is {}\\\", optree, op_range)\\n memoization_map[optree] = op_range\\n return op_range\",\n \"def get_range(self, rel_name):\\n return self._declaration[rel_name].range_type\",\n \"def term_range(self):\\n start = max([f.cohort_end for f in self.filters])\\n end = MAX_NUM_COHORTS\\n return range(start + 1, end + 1)\",\n \"def __getitem__(self, *args):\\n return _uhd_swig.range_vector_t___getitem__(self, *args)\",\n \"def range(self):\\n return self._upper - self._lower\",\n \"def get_range(self):\\n return time_to_range(self.get_time())\",\n \"def get_range(self, field, deep=False, axis=None):\\n variables = list(self.vars(deep, with_name=field))\\n\\n if not variables:\\n raise KeyError(\\\"No variable named '%s' was found!\\\" % field)\\n\\n start = [np.nanmin(self[var], axis).item(0) for var in variables]\\n end = [np.nanmax(self[var], axis).item(0) for var in variables]\\n return min(start), max(end)\",\n \"def get_range(df, col):\\n return df[col].min(), df[col].max()\",\n \"def global_range(self):\\n raise NotImplementedError\",\n \"def cal(num1, num2, range):\\r\\n setup(range)\\r\\n return Andcollections(num1, num2)\",\n \"def get_xrange_indices(self, lower, upper) -> Tuple[int, int]:\\n lower_index = np.argmax(self.x >= lower)\\n upper_index = np.argmax(self.x >= upper)\\n return int(lower_index), int(upper_index)\",\n \"def GetIndexBounds(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def detect_range(self) -> Union[int, float]:\\n return self.proto.detect_range\",\n \"def map_bound(value, in_low, in_high, out_low, out_high):\\n result = None\\n\\n if value <= in_low:\\n result = out_low\\n else:\\n if value >= in_high:\\n result = out_high\\n else:\\n # http://stackoverflow.com/a/5650012/574981\\n result = out_low + (\\n (out_high - out_low) * (value - in_low) / (in_high - in_low)\\n )\\n return result\",\n \"def arange(module, start, end, step):\\n _import_modules()\\n if module in [np, ma, jnp]:\\n return module.arange(start, end, step)\\n elif module == torch:\\n return module.arange(start, end, step, dtype=torch.float)\\n elif module == tf:\\n return tf.range(start, end, step)\\n raise UnknownModuleException(f\\\"Module {module.__name__} not supported.\\\")\",\n \"def range_callback(data):\\n global D\\n D.ranges = data.ranges\",\n \"def check_range(num):\\n for i in ranges.keys():\\n if num in ranges[i]:\\n return i\",\n \"def targetRange(self):\\n return self._getAttribute(Attribute.targetRange)\",\n \"def range_(headers, data):\\n\\tcolumn_matrix=data.get_data(headers).getT() # get columns as rows, as this makes analysis much easier by just perfoming operations on column list directly\\n\\tif column_matrix==[]:\\n\\t\\tprint \\\"wrong headers, not present in data Object\\\"\\n\\t\\treturn []\\n\\tcolumn_max=column_matrix.max(1)\\n\\tcolumn_min=column_matrix.min(1)\\n\\tfinal=np.concatenate((column_min, column_max), axis=1)\\n\\t\\n\\trng=final.tolist()\\n\\treturn rng\",\n \"def get_upper_bound(self):\\n return self._upper_bound\",\n \"def lrange(self, name, start, end):\\r\\n return self.format_inline('LRANGE', name, start, end)\",\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_range(g,h,d): # g: graph; h: head node; d: dependent node\\n addresses = sorted(g.nodes.keys())\\n h_index = addresses.index(h)\\n d_index = addresses.index(d)\\n sign = cmp(d_index,h_index)\\n return addresses[h_index:d_index+sign:sign]\",\n \"def functorRangeSize(functor):\\n return len(functorRange(functor))\",\n \"def range_table(self):\\n raise NotImplementedError('Abstract method.')\",\n \"def get_featureRange(self):\\n\\n return self.featureRange\",\n \"def get_range(lst):\\n return float(max(lst)) - float(min(lst))\",\n \"def range(series):\\n return min(series), max(series)\",\n \"def range_field(self):\\n return self.db.range_field\",\n \"def GetTRange(self):\\n ...\",\n \"def _bi_range(start, end):\\n if start == end:\\n return (start,)\\n\\n elif end < start:\\n return reversed(range(end, start + 1))\\n\\n else:\\n return range(start, end + 1)\",\n \"def upper_bound(self) -> float:\\n ...\",\n \"def map_range(x, in_min, in_max, out_min, out_max):\\n mapped = (x-in_min) * (out_max - out_min) / (in_max-in_min) + out_min\\n if out_min <= out_max:\\n return max(min(mapped, out_max), out_min)\\n return min(max(mapped, out_max), out_min)\",\n \"def new_range(r):\\n if isinstance(r, list) or isinstance(r, tuple) and len(r) == 2:\\n lower = r[0]\\n upper = r[1]\\n else:\\n lower = r\\n upper = r\\n lower = int(lower)\\n upper = int(upper)\\n return range(lower, upper + 1)\",\n \"def query_range(self,i,j):\\n assert 0 <= i <= j <= len(self)\\n ans = self.__query_aux(0,0,len(self),i,j)\\n return ans\",\n \"def _lies_at_index_range(value):\\n\\ta = 0; b = len(keys)-1;\\n\\twhile a != b:\\n\\t\\tc = (a+b)/2\\n\\t\\tif value < dist[keys[c]][0]:\\n\\t\\t\\tb = c-1\\n\\t\\telif value > dist[keys[c]][1]:\\n\\t\\t\\ta = c+1\\n\\t\\telse:\\n\\t\\t\\treturn keys[c]\\n\\treturn keys[a]\",\n \"def interval(self):\\n return Intersection(*(a.interval for a in self.args))\",\n \"def data_range(x):\\n return max(x)-min(x)\",\n \"def array_range(a, low, high, ref=None):\\n if ref is None:\\n ref = a\\n return a[np.logical_and(ref >= low, ref < high)]\",\n \"def get_range(n0: int, n1: int, ns: int) -> List[int]:\\n # Return a range as a list\\n def lrange(a, b, n=1) -> List[int]:\\n return list(range(a, b, n))\\n # Get the in-bounds part of the range\\n n_range = lrange(max(0, n0), min(ns, n1))\\n # Handle out-of-bounds indices by reflection across boundaries\\n if n0 < 0:\\n # Underflow\\n n_range = lrange(-n0, 0, -1) + n_range\\n if n1 > ns:\\n # Overflow\\n n_range = n_range + lrange(ns - 1, 2 * ns - n1 - 1, -1)\\n\\n return n_range\",\n \"def open_range(start, stop, step):\\n return np.arange(start, stop+step/2, step)\",\n \"def _rangeQueryIntFeature(self):\\n\\n # create args\\n minToGet = c_int64()\\n maxToGet = c_int64()\\n\\n errorCode = VimbaDLL.featureIntRangeQuery(self._handle,\\n self._name,\\n byref(minToGet),\\n byref(maxToGet))\\n if errorCode != 0:\\n raise VimbaException(errorCode)\\n\\n return (int(str(minToGet.value)), int(str(maxToGet.value)))\",\n \"def getRange(self) -> Tuple[int, int]:\\n return self.validator().bottom(), self.validator().top()\",\n \"def range(self):\\n return (self._start, self._end)\",\n \"def range(self):\\n lower, upper = sorted((self.y1, self.y2))\\n return FloatRange(lower=lower, upper=upper)\",\n \"def rangeFinder(self, startIndex, endIndex, list):\\n max = list[startIndex]\\n min = list[startIndex]\\n for x in range(startIndex, endIndex):\\n if list[x] > max:\\n max = list[x]\\n if list[x] < min:\\n min = list[x]\\n return max-min\",\n \"def range_inclusive(start, stop):\\n return range(start, stop + 1)\",\n \"def xpointerRangeToFunction(self, nargs):\\n libxml2mod.xmlXPtrRangeToFunction(self._o, nargs)\",\n \"def subject(self) -> global___Range:\",\n \"def range_params(self, ran, kw):\\n specs = {\\\"range\\\": (SchemaNode(\\\"value\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"minInclusive\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"maxInclusive\\\")),\\n \\\"length\\\": (SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"length\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"minLength\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"maxLength\\\"))}\\n (exact, min_, max_) = specs[kw]\\n if (len(ran) == 1 or ran[0] == ran[1]) and ran[0][0] != \\\"m\\\":\\n elem = exact\\n elem.text = ran[0]\\n return [elem]\\n res = []\\n if ran[0][0] != \\\"m\\\":\\n elem = min_\\n elem.text = ran[0]\\n res.append(elem)\\n if ran[1][0] != \\\"m\\\":\\n elem = max_\\n elem.text = ran[1]\\n res.append(elem)\\n return res\",\n \"def bounds(self, start=None, finish=None):\\n lower = start if start is not None else self.limits[0]\\n upper = finish if finish is not None else self.limits[1]\\n\\n lower = lower + self.offsets[0]\\n upper = upper + self.offsets[1]\\n\\n return (lower, upper)\",\n \"def i_range(Pd):\\n return xrange(i_min(Pd), i_max(Pd) + 1)\",\n \"def getFeHRange(brand):\\n return feh_range[brand]\",\n \"def bounds(self): # -> tuple[()]:\\n ...\",\n \"def rangeSearch(self,x,y,node):\\n l=[]\\n start=self.find(node,x)\\n while start.key<=y:\\n if start.key>=x:\\n l.append(start)\\n start=self.next(start)\\n return l\",\n \"def Range(self, from: int, to: int) -> BaseVector:\",\n \"def get_range(self, start=None, end=None):\\n\\n # handle the case of no data\\n if self.data.shape[0] == 0 or self.source.data[\\\"index\\\"].shape[0] == 0:\\n return None, None\\n\\n first_source_idx = self.source.data[\\\"index\\\"][0]\\n last_source_idx = self.source.data[\\\"index\\\"][-1]\\n\\n # convert to timestamp if necessary\\n if isinstance(self.data.index, pd.DatetimeIndex):\\n start = pd.to_datetime(start, unit=\\\"ms\\\")\\n end = pd.to_datetime(end, unit=\\\"ms\\\")\\n first_source_idx = pd.to_datetime(first_source_idx, unit=\\\"ms\\\")\\n last_source_idx = pd.to_datetime(last_source_idx, unit=\\\"ms\\\")\\n\\n # get new start and end\\n if start is not None:\\n if start < first_source_idx:\\n start = max(self.data.index[0], start)\\n elif start > last_source_idx:\\n start = min(self.data.index[-1], start)\\n elif start < self.data.index[0]:\\n start = self.data.index[0]\\n elif start > self.data.index[-1]:\\n start = self.data.index[-1]\\n elif len(self.source.data[\\\"index\\\"]) > 0:\\n start = first_source_idx\\n else:\\n start = self.data.index[0]\\n\\n if end is not None:\\n if end < first_source_idx:\\n end = max(self.data.index[0], end)\\n elif end > last_source_idx:\\n end = min(self.data.index[-1], end)\\n elif end < self.data.index[0]:\\n end = self.data.index[0]\\n elif end > self.data.index[-1]:\\n end = self.data.index[-1]\\n elif len(self.source.data[\\\"index\\\"]) > 0:\\n end = last_source_idx\\n else:\\n end = self.data.index[-1]\\n\\n return start, end\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68109703","0.63385564","0.6327256","0.6227952","0.62047696","0.61920094","0.61834943","0.6165325","0.6165325","0.61133087","0.61056334","0.6076185","0.60739946","0.60496026","0.6020257","0.60012704","0.6000408","0.5993944","0.5981103","0.5969238","0.5917489","0.5917489","0.5914561","0.59062403","0.58986896","0.5891751","0.5876367","0.5875278","0.5871048","0.5856082","0.5848536","0.58430374","0.58429724","0.58400506","0.58295596","0.5822599","0.5812477","0.58103776","0.57989484","0.57796097","0.5761278","0.57610154","0.574285","0.57403946","0.5730047","0.5683561","0.56807214","0.56747866","0.567042","0.56645286","0.5652022","0.564783","0.56374824","0.56370413","0.5637008","0.5620599","0.5618049","0.5599556","0.55896467","0.5571734","0.556815","0.55579597","0.5552417","0.5549076","0.5537083","0.55353236","0.5524633","0.55204904","0.55093545","0.5506325","0.5497851","0.5497342","0.54968095","0.54833037","0.54730344","0.5471284","0.54692954","0.546703","0.54620713","0.54513377","0.544943","0.54450625","0.5443243","0.54392856","0.54275066","0.5424589","0.5422017","0.5418379","0.5411416","0.54060304","0.54056597","0.5401458","0.53986573","0.53947014","0.5393811","0.538828","0.53882784","0.5383574","0.538199","0.536653"],"string":"[\n \"0.68109703\",\n \"0.63385564\",\n \"0.6327256\",\n \"0.6227952\",\n \"0.62047696\",\n \"0.61920094\",\n \"0.61834943\",\n \"0.6165325\",\n \"0.6165325\",\n \"0.61133087\",\n \"0.61056334\",\n \"0.6076185\",\n \"0.60739946\",\n \"0.60496026\",\n \"0.6020257\",\n \"0.60012704\",\n \"0.6000408\",\n \"0.5993944\",\n \"0.5981103\",\n \"0.5969238\",\n \"0.5917489\",\n \"0.5917489\",\n \"0.5914561\",\n \"0.59062403\",\n \"0.58986896\",\n \"0.5891751\",\n \"0.5876367\",\n \"0.5875278\",\n \"0.5871048\",\n \"0.5856082\",\n \"0.5848536\",\n \"0.58430374\",\n \"0.58429724\",\n \"0.58400506\",\n \"0.58295596\",\n \"0.5822599\",\n \"0.5812477\",\n \"0.58103776\",\n \"0.57989484\",\n \"0.57796097\",\n \"0.5761278\",\n \"0.57610154\",\n \"0.574285\",\n \"0.57403946\",\n \"0.5730047\",\n \"0.5683561\",\n \"0.56807214\",\n \"0.56747866\",\n \"0.567042\",\n \"0.56645286\",\n \"0.5652022\",\n \"0.564783\",\n \"0.56374824\",\n \"0.56370413\",\n \"0.5637008\",\n \"0.5620599\",\n \"0.5618049\",\n \"0.5599556\",\n \"0.55896467\",\n \"0.5571734\",\n \"0.556815\",\n \"0.55579597\",\n \"0.5552417\",\n \"0.5549076\",\n \"0.5537083\",\n \"0.55353236\",\n \"0.5524633\",\n \"0.55204904\",\n \"0.55093545\",\n \"0.5506325\",\n \"0.5497851\",\n \"0.5497342\",\n \"0.54968095\",\n \"0.54833037\",\n \"0.54730344\",\n \"0.5471284\",\n \"0.54692954\",\n \"0.546703\",\n \"0.54620713\",\n \"0.54513377\",\n \"0.544943\",\n \"0.54450625\",\n \"0.5443243\",\n \"0.54392856\",\n \"0.54275066\",\n \"0.5424589\",\n \"0.5422017\",\n \"0.5418379\",\n \"0.5411416\",\n \"0.54060304\",\n \"0.54056597\",\n \"0.5401458\",\n \"0.53986573\",\n \"0.53947014\",\n \"0.5393811\",\n \"0.538828\",\n \"0.53882784\",\n \"0.5383574\",\n \"0.538199\",\n \"0.536653\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.839487"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19587,"cells":{"query":{"kind":"string","value":"Return cardinality of range for a functor"},"document":{"kind":"string","value":"def functorRangeSize(functor):\n return len(functorRange(functor))"},"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 cardinality(self):\n estimate = self._alpha * math.pow(self._m, 2) / sum(math.pow(2, -x) for x in self._registers)\n\n if estimate <= 2.5 * self._m:\n # get number of registers equal to zero\n empty_registers = self._registers.count(0)\n if empty_registers != 0:\n return self._linear_count(empty_registers)\n else:\n return estimate\n elif estimate <= ((1 << 32) / 30):\n return estimate\n else:\n return self._large_range_correction(estimate)","def functorRange(functor):\n for (name, range) in functorRangeList:\n if functor == name:\n return range\n else:\n raise Exception (\"Functor \" + functor + \" not present in range list\")","def interval_cardinality(self):\n return len(list(self.lower_contained_intervals()))","def cardinality(self):\n raise NotImplementedError()","def f_get_range_length(self):\n if not self.f_has_range():\n raise TypeError(\"Not applicable, parameter does not have a range\")\n elif hasattr(self, \"__len__\"):\n return len(self)\n else:\n raise NotImplementedError(\"Should have implemented this.\")","def cardinality(self):\n return int(ifac(self.size))","def compute_search_cardinality(params_space: List[Dict[str, Any]]) -> float:\n # check if search space is infinite\n is_infinite = any([param[\"type\"] == \"range\" for param in params_space])\n if is_infinite:\n return math.inf\n else:\n res = 1\n for param in params_space:\n if \"values\" in param:\n res *= len(param[\"values\"])\n return res","def count(predicate: Predicate[int]) -> Predicate[Sized]:\n\n def compare(sized: Sized, /) -> bool:\n return predicate(len(sized))\n\n return compare","def cardinality(self):\n from sage.arith.all import binomial\n n = self._size\n if n == 0:\n return Integer(1)\n return (2 * binomial(4 * n + 1, n - 1)) // (n * (n + 1))\n # return Integer(2 * factorial(4*n+1)/(factorial(n+1)*factorial(3*n+2)))","def _in_range_op(spec):","def count_oob(cube):\n out_range = (cube > 4).any(1) | (cube < -4).any(1)\n out_range = out_range.sum() / cube.shape[0]\n return out_range","def __len__(self):\n if self._data is None:\n return 0\n elif len(self._explored_range) > 0:\n return len(self._explored_range)\n else:\n return 1","def count(cls):\n return lambda x,y: ((type(x)==int) and [x+1] or ((y==None) and [1] or [2]))[0]","def get_event_count(event_times, start, end):\n mask = (event_times > start) & (event_times <= end)\n return event_times[mask].size","def GetScalarRange(self):\n ...","def _freqs(seq, lower_bound, upper_bound, k, normalized=False):\n freqs = []\n region_width = (upper_bound - lower_bound) / k \n\n for i in range(k):\n low = lower_bound + i * region_width\n high = lower_bound + i * region_width + region_width\n freqs.append( np.logical_and(seq >= low, seq < high).sum() )\n\n # because last interval has '[a;b]' - bounds, not '[a,b)'\n freqs[-1] += 1\n\n if normalized:\n freqs = np.array(freqs) / len(seq)\n\n return np.array(freqs), region_width","def test_cardinality(\n self, prior_name, min_bound, max_bound, precision, cardinality\n ):\n dim = Real(\n \"yolo\", prior_name, min_bound, max_bound, precision=precision, shape=None\n )\n assert dim.cardinality == cardinality\n dim = Real(\n \"yolo\", prior_name, min_bound, max_bound, precision=precision, shape=(2, 3)\n )\n assert dim.cardinality == cardinality ** (2 * 3)","def f_has_range(self):\n raise NotImplementedError(\"Should have implemented this.\")","def howmany_within_range(row, minimum, maximum):\n count = 0\n for n in row:\n if minimum <= n <= maximum:\n count = count + 1\n return count","def get_desired_count(value, lower, upper):\n if lower != -1 and value < lower:\n return lower\n if upper != -1 and value > upper:\n return upper\n return value","def test_cardinality(self):\n space = Space()\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", 2, 3, 4)\n dim = Categorical(\"yolo\", OrderedDict(zip(categories, probs)), shape=2)\n space.register(dim)\n dim = Integer(\"yolo2\", \"uniform\", -3, 6)\n space.register(dim)\n dim = Fidelity(\"epoch\", 1, 9, 3)\n space.register(dim)\n\n assert space.cardinality == (4**2) * (6 + 1) * 1\n\n dim = Integer(\"yolo3\", \"uniform\", -3, 2, shape=(3, 2))\n space.register(dim)\n assert space.cardinality == (4**2) * (6 + 1) * 1 * ((2 + 1) ** (3 * 2))\n\n dim = Real(\"yolo4\", \"norm\", 0.9)\n space.register(dim)\n assert np.inf == space.cardinality","def f02_03_countElemBetween(l, a, b):\n return sum([a < x < b for x in l])","def detect_range(self) -> Union[int, float]:\n return self.proto.detect_range","def count_if(iterable, pred, first=0, last=None):\n assert hasattr(iterable, '__getitem__')\n # Only slice for sub-ranges, slight performance improvement\n iterable = iterable if first == 0 and last is None else iterable[first:last]\n return sum(1 for x in iterable if pred(x))","def count_lorentz(fit_range, lorentz_array_2d):\n counter = 0\n for i in range(0, lorentz_array_2d.shape[0]):\n f0 = lorentz_array_2d[i][1]\n if f0 > fit_range[1] and f0 < fit_range[2]:\n counter += 1\n return counter","def countElements(self, nums):\n import sys\n max_n = -sys.maxint\n min_n = sys.maxint\n\n for n in nums:\n max_n = max(n, max_n)\n min_n = min(n, min_n)\n\n count = 0\n for n in nums:\n if min_n < n < max_n:\n count += 1\n return count","def default(functor):\n return 1.0/functorRangeSize(functor)","def boundaries_size(*args):\n return _ida_hexrays.boundaries_size(*args)","def count_property_range_hits(prop, node_dict, hits):\n\tres = []\n\t# sets tuple position to use in dict value\n\tswitcher = {\n \"length\": (0,(0,4000,8000,12000,16000,20000)),\n \"steps\": (1,(0,2,4,8,16,32)),\n \"cov\": (2,(1,10,100,1000,10000,100000)),\n \"cv\": (3, (0,0.05,0.10,0.15,0.20,0.25))\n }\n\tif prop not in switcher:\n\t\treturn res\n\ttup_pos = switcher[prop][0]\n\tnode_cnt = 0\n\tpos_cnt = 0\n\tfor ind in range(len(switcher[prop][1])-1):\n\t\tmin_val = switcher[prop][1][ind]\n\t\tmax_val = switcher[prop][1][ind+1]\n\t\tfor node in node_dict.keys():\n\t\t\tval = node_dict[node][tup_pos]\n\t\t\tif ind < len(switcher[prop][1])-2:\n\t\t\t\trange_test_val = (min_val <= val < max_val)\n\t\t\telse:\n\t\t\t\trange_test_val = (min_val <= val <= max_val)\n\t\t\t# print \"range bool is\", range_test_val\n\t\t\tif range_test_val:\n\t\t\t\tnode_cnt += 1\n\t\t\t\tif node in hits: pos_cnt += 1\n\t\tif node_cnt > 0:\n\t\t\tres.append( (pos_cnt, node_cnt, round(float(pos_cnt)/node_cnt,2)))\n\t\telse:\n\t\t\tres.append((0,0,0))\n\t\tnode_cnt = 0\n\t\tpos_cnt = 0\n\treturn res","def complexity(self, mode='#nodes'):\n if mode == '#nodes':\n return len(self.nodes)","def vsize(min, max):\n return lambda mate: any(min <= v <= max for v in mate['read_info'].v_list)","def compute_outliers(data: np.ndarray, data_range: typing.Tuple[float, float]) -> int:\n return np.count_nonzero((data >= data_range[0]) & (data < data_range[1]))","def range_width(self):\n return range(self.left, self.right + 1)","def get_bounds(f, lb=0, ub=None):\r\n lb_idx = np.searchsorted(f, lb, 'left')\r\n if ub == None:\r\n ub_idx = len(f)\r\n else:\r\n ub_idx = np.searchsorted(f, ub, 'right')\r\n\r\n return lb_idx, ub_idx","def _get_arity(callable):\n return len(inspect.signature(callable).parameters)","def _compute_register_bounds(cls, register_probs):\n return np.cumsum(register_probs)","def _compute_register_bounds(cls, register_probs):\n return np.cumsum(register_probs)","def _inside_op_range(self, idx):\n\n if idx < self._parameters.op_range[0]:\n return False\n return (self._parameters.op_range[1] < 0 or\n idx <= self._parameters.op_range[1])","def count_interval_index(arr1d, lower, upper):\n count = 0\n for ele in arr1d:\n if ele >= lower and ele <= upper:\n count = count + 1\n return count","def ranges(self, predicate):\n\n x = np.zeros(len(self)).astype(np.bool)\n for i, elem in enumerate(self.elements):\n x[i] = predicate(elem)\n\n return np.where(x)[0]","def count(pred, l):\n nl = [i for i in range(0,len(l)) if pred(l[i])]\n\n return len(nl)","def get_num_applies(self):\n ops = 0\n for _, remainder, _ in self:\n ops += len(remainder)\n return ops","def get_dof(self):\n return len(self.a_n)","def total_range_size(self) -> int:\n if not len(self):\n return 0\n regions = merge(self.data, bp=1)\n return regions.end.sum() - regions.start.sum()","def check_cardinality(df, cat_cols, threshold=8):\n high_card = {}\n low_card = {}\n for col in cat_cols:\n rank = len(Counter(df[col]))\n if rank <= threshold:\n low_card[col] = rank\n else:\n high_card[col] = rank\n return low_card, high_card","def __len__(self):\n # Product function that can handle iterables (np.product can't).\n product = partial(reduce, operator.mul)\n return sum(product(len(v) for v in p.values()) if p else 1\n for p in self.param_grid)","def test_basic_cardinality(self):\n dim = Real(\"yolo\", \"reciprocal\", 0.043, 2.3, precision=2)\n order_0012 = np.arange(43, 99 + 1)\n order_010 = np.arange(10, 99 + 1)\n order_23 = np.arange(10, 23 + 1)\n assert dim.cardinality == sum(map(len, [order_0012, order_010, order_23]))","def count_within(values, intervals):\n rvals = np.reshape(values, [-1, 1])\n intervals_m = np.array(intervals)\n rlow = intervals_m[:, 0].reshape(1, -1)\n rhigh = intervals_m[:, 1].reshape(1, -1)\n\n flags = (rlow <= rvals) * (rvals < rhigh)\n return np.sum(flags, axis=0)","def test_args_count_in_range(args: list, min: int, max: int) -> bool:\n\n\tcount = args_count(args)\n\treturn (count >= min and count <= max)","def get_upper_bound(self):\n return self._upper_bound","def term_range(self):\n start = max([f.cohort_end for f in self.filters])\n end = MAX_NUM_COHORTS\n return range(start + 1, end + 1)","def get_count_of_elements_by_condition(sequence):\n elements_and_indexes = {sequence[i]: i + 1\n for i in range(1, len(sequence) - 1)}\n filtered_values = filter(lambda element:\n 2 ** element[1] < element[0] < math.factorial(element[1]),\n elements_and_indexes.items())\n return len(dict(filtered_values))","def _compute_register_bounds(cls, num_values, probability):\n bits = np.arange(1, num_values + 1)\n probs = scipy.stats.geom.cdf(bits, probability)\n return probs / probs[-1]","def lenRange(start, stop, step=1):\n return (stop - start + step - 1 + 2 * (step < 0)) // step","def __len__(self):\n return self.count_of(CUBA.NODE)","def __len__(self):\n return len(self.constraints)","def bounds(self): # -> tuple[()]:\n ...","def count(\n type_signature: computation_types.Type, predicate: _TypePredicate\n) -> int:\n one_or_zero = lambda t: 1 if predicate(t) else 0\n return sum(map(one_or_zero, preorder_types(type_signature)))","def __len__(self):\n return len(np.where(np.logical_not(self.data.mask))[0])","def count_in_range(start, end, check):\n count = 0\n for val in range(start, end):\n if check(val):\n count += 1\n\n return count","def __len__(self):\n return self.start.dist(self.end)","def num_linearly_ind_features(self, S, eps=1e-11):\n return len(S[S >= eps])","def f_has_range(self):\n return len(self._explored_range) > 0","def II_eq_counts(tobin_series, num_bins):\n num_pbin = int(len(tobin_series) / num_bins)\n obs_list = tobin_series.sort_values().tolist()\n upper_bounds = [obs_list[(i + 1) * num_pbin] for i in range(num_bins)]\n lower_bounds = [0]\n lower_bounds += upper_bounds[:-1]\n return pd.IntervalIndex.from_arrays(lower_bounds, upper_bounds), upper_bounds","def integer_hist(a, int_range=None, open_range=False, relative=False):\n data = np.round(a).flatten()\n if int_range:\n values = np.arange(int(int_range[0]), int(int_range[1])+1)\n else:\n values = np.arange(int(data.min()), int(data.max())+1)\n N = values.size\n if relative:\n count = np.empty(N, 'd')\n else:\n count = np.empty(N, 'l')\n for bin, c in enumerate(values):\n if open_range and bin == N - 1:\n count[bin] = (data >= c).sum()\n else:\n count[bin] = (data == c).sum()\n if relative:\n count /= count.sum()\n return values, count","def __len__(self):\n return 9 # logsfr_ratios has 6 bins","def usable_occupancy(isotherm, p_min=5.0, p_max=100.0):\n\n occ_max = point_occupancy(isotherm, p_max)\n occ_min = point_occupancy(isotherm, p_min)\n\n usable_occ = occ_max - occ_min\n\n return usable_occ","def concave_max_binsearch(fn, lb, ub, num_iter=20):\n mid = tf.stop_gradient(.5 * lb + .5 * ub)\n f_mid = fn(mid)\n\n for _ in range(num_iter):\n # Calculate quartiles.\n lq = tf.stop_gradient(.75 * lb + .25 * ub)\n uq = tf.stop_gradient(.25 * lb + .75 * ub)\n f_lq = fn(lq)\n f_uq = fn(uq)\n\n # Identify three cases, recalling that fn is concave.\n # Case 1: f_lq > f_mid > f_uq\n # The maximum occurs in the range [lb, mid].\n # Case 2: f_lq > f_mid > f_uq\n # The maximum occurs in the range [mid, ub].\n # Case 3: f_lq < f_mid > f_uq\n # The maximum occurs in the range [lq, uq].\n case1 = f_lq > f_mid\n case2 = f_uq > f_mid\n lb, ub, mid, f_mid = (\n tf.where(case1, lb, tf.where(case2, mid, lq)),\n tf.where(case1, mid, tf.where(case2, ub, uq)),\n tf.where(case1, lq, tf.where(case2, uq, mid)),\n tf.where(case1, f_lq, tf.where(case2, f_uq, f_mid))\n )\n\n return mid, f_mid","def length(sequence):\r\n populated = tf.sign(tf.abs(sequence))\r\n length = tf.cast(tf.reduce_sum(populated, axis=1), tf.int32)\r\n mask = tf.cast(tf.expand_dims(populated, -1), tf.float32)\r\n return length, mask","def __len__(self):\n return self.nb_iterations","def nb_predicates(self) -> int:\n return 0","def fast_hist(a, b, n):\n # print(n)\n # print(b.max())\n k = (a >= 0) & (a < n)\n\n\n # a = np.floor(a)\n # a = a.astype(np.int)\n # print(a.max())\n # print(a.dtype)\n # print(a.shape)\n # print(type(a))\n\n return np.bincount((n * a[k].astype(int) + b[k]).astype(int), minlength=n ** 2).reshape(n, n)","def expected_counts(p0, T, n): \n M=T.shape[0]\n if n<=M:\n return ec_matrix_vector(p0, T, n)\n else:\n return ec_geometric_series(p0, T, n)","def overlap_size(self, begin, end=None):\n overlaps = self.overlaps(begin, end)\n if not overlaps:\n return 0\n\n if end is not None:\n # case end is given\n i0 = max(self.begin, begin)\n i1 = min(self.end, end)\n return i1 - i0\n # assume the type is interval, in other cases, an exception will be thrown\n i0 = max(self.begin, begin.begin)\n i1 = min(self.end, begin.end)\n return i1 - i0","def numel(self):\n return self.t.size","def c(ixs):\n return sum(range(1, sum((i > 0 for i in ixs)) + 1))","def count_ranges(a):\n ranges = []\n count = 0\n for i, v in enumerate(a):\n if v == 1: # same as previous value\n count += 1\n else:\n if count > 1:\n ranges.append([i, count]) # [end, length]\n count = 0\n return ranges","def __len__(self):\n return sum([1 for _ in self])","def count_qubits(operator):\n # Handle FermionOperator.\n if isinstance(operator, FermionOperator):\n num_qubits = 0\n for term in operator.terms:\n for ladder_operator in term:\n if ladder_operator[0] + 1 > num_qubits:\n num_qubits = ladder_operator[0] + 1\n return num_qubits\n\n # Handle QubitOperator.\n elif isinstance(operator, QubitOperator):\n num_qubits = 0\n for term in operator.terms:\n if term:\n if term[-1][0] + 1 > num_qubits:\n num_qubits = term[-1][0] + 1\n return num_qubits\n\n # Handle MajoranaOperator.\n if isinstance(operator, MajoranaOperator):\n num_qubits = 0\n for term in operator.terms:\n for majorana_index in term:\n if numpy.ceil((majorana_index + 1) / 2) > num_qubits:\n num_qubits = int(numpy.ceil((majorana_index + 1) / 2))\n return num_qubits\n\n # Handle DiagonalCoulombHamiltonian\n elif isinstance(operator, DiagonalCoulombHamiltonian):\n return operator.one_body.shape[0]\n\n # Handle PolynomialTensor\n elif isinstance(operator, PolynomialTensor):\n return operator.n_qubits\n\n # Raise for other classes.\n else:\n raise TypeError('Operator of invalid type.')","def freq(self) -> int:","def count_values_in_range(data: np.ndarray, ranges: np.ndarray) -> np.ndarray:\n\n results = np.full((data.shape[0], ranges.shape[0]), 0)\n for i in prange(data.shape[0]):\n for j in prange(ranges.shape[0]):\n lower_bound, upper_bound = ranges[j][0], ranges[j][1]\n results[i][j] = data[i][\n np.logical_and(data[i] >= lower_bound, data[i] <= upper_bound)\n ].shape[0]\n return results","def find_histogram_range(histogram):\n size = len(histogram)\n min_i = 0\n while min_i < size:\n if histogram[min_i] > 0:\n break\n min_i += 1\n\n max_i = size - 1\n while max_i >= 0:\n if histogram[max_i] > 0:\n break\n max_i -= 1\n return min_i, max_i","def quantize(input=None, min_range=None, max_range=None, out_type=_Null, out=None, name=None, **kwargs):\n return (0,)","def count(self, elem):\n if not self.step:\n return _coconut.float(\"inf\") if elem == self.start else 0\n return int(elem in self)","def find_range(reduced_dist_word_dim, range_limits):\n n_limits = len(range_limits)\n for limit in range(n_limits - 1):\n if (reduced_dist_word_dim > range_limits[limit]) and (reduced_dist_word_dim < range_limits[limit + 1]):\n return limit\n raise ValueError","def _compute_ndof(self, ai_type, ai_operator):\n li_dof = 1\n\n return li_dof","def __len__(self):\n nsamp = self.data.shape[-1]\n kernel = int(self.kernel * self.fs)\n stride = int(self.stride * self.fs)\n n_stride = int(np.ceil((nsamp - kernel) / stride) + 1)\n return max(0, n_stride)","def get_lower_bound(self):\n return self._lower_bound","def get_lower_bound(self):\n return self._lower_bound","def number_bits_in_cardinality(self,card):\n return 32 - self.count_lead_zs(card)","def __len__(self):\n return sum(f.count for f in self.filters)","def total_bounds(self):\n if not self.empty:\n return tuple(self.numba_rtree._bounds_tree[0, :])\n else:\n return tuple((np.nan,) * self.numba_rtree._bounds_tree.shape[1])","def calculate_occupancy(self):\n # TODO will need to be fixed now that using a dict and changed thresholds\n self.occupancy = [list(x > self.t for x in y) for y in self.counts]\n return self.occupancy","def num_cuts(self) -> Optional[int]:\n raise NotImplementedError(\n 'Sub-classes of CutSampler have to implement self.num_cuts'\n )","def __len__(self):\n return sum(abs(j) for (i, j) in self)","def test_range_argument(self):\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\n bins_range = (0, 2)\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\n hist_exp = np.array([1, 2, 0])\n\n bin_edges, hist, _, _ = hist_w_unc(\n self.input,\n bins=self.n_bins,\n bins_range=bins_range,\n normed=False,\n )\n\n # check if we end up with the same bin edges anyway\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\n np.testing.assert_array_almost_equal(hist_exp, hist)","def calc_priors(categories, data):\n counts = np.zeros(categories)\n for val in range(categories):\n counts[val] = np.count_nonzero(data.labels == val)\n return counts / len(data.labels)","def __len__(self):\n return self.end - self.begin","def computeFreq(self):\n for x in self.data:\n i = 0\n for interval in self.classesInterval:\n if interval[0] <= x <= interval[1]:\n self.frequencies[i] += 1\n break\n i += 1\n\n self.minFreq = self.frequencies[0]\n self.maxFreq = self.frequencies[0]\n for f in self.frequencies:\n if f < self.minFreq:\n self.minFreq = f\n elif f > self.maxFreq:\n self.maxFreq = f","def objectiveFunction(self):\n # start at zero\n satisfiedConstraints = 0\n # loop through all of the constraints\n for constraint in self.constraints:\n # if the constraint is satisfied, then increase the count\n if (constraint.satisfied(constraint.tail.value, constraint.head.value)):\n satisfiedConstraints += 1\n # return the count of satisfied constraints\n return satisfiedConstraints"],"string":"[\n \"def cardinality(self):\\n estimate = self._alpha * math.pow(self._m, 2) / sum(math.pow(2, -x) for x in self._registers)\\n\\n if estimate <= 2.5 * self._m:\\n # get number of registers equal to zero\\n empty_registers = self._registers.count(0)\\n if empty_registers != 0:\\n return self._linear_count(empty_registers)\\n else:\\n return estimate\\n elif estimate <= ((1 << 32) / 30):\\n return estimate\\n else:\\n return self._large_range_correction(estimate)\",\n \"def functorRange(functor):\\n for (name, range) in functorRangeList:\\n if functor == name:\\n return range\\n else:\\n raise Exception (\\\"Functor \\\" + functor + \\\" not present in range list\\\")\",\n \"def interval_cardinality(self):\\n return len(list(self.lower_contained_intervals()))\",\n \"def cardinality(self):\\n raise NotImplementedError()\",\n \"def f_get_range_length(self):\\n if not self.f_has_range():\\n raise TypeError(\\\"Not applicable, parameter does not have a range\\\")\\n elif hasattr(self, \\\"__len__\\\"):\\n return len(self)\\n else:\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def cardinality(self):\\n return int(ifac(self.size))\",\n \"def compute_search_cardinality(params_space: List[Dict[str, Any]]) -> float:\\n # check if search space is infinite\\n is_infinite = any([param[\\\"type\\\"] == \\\"range\\\" for param in params_space])\\n if is_infinite:\\n return math.inf\\n else:\\n res = 1\\n for param in params_space:\\n if \\\"values\\\" in param:\\n res *= len(param[\\\"values\\\"])\\n return res\",\n \"def count(predicate: Predicate[int]) -> Predicate[Sized]:\\n\\n def compare(sized: Sized, /) -> bool:\\n return predicate(len(sized))\\n\\n return compare\",\n \"def cardinality(self):\\n from sage.arith.all import binomial\\n n = self._size\\n if n == 0:\\n return Integer(1)\\n return (2 * binomial(4 * n + 1, n - 1)) // (n * (n + 1))\\n # return Integer(2 * factorial(4*n+1)/(factorial(n+1)*factorial(3*n+2)))\",\n \"def _in_range_op(spec):\",\n \"def count_oob(cube):\\n out_range = (cube > 4).any(1) | (cube < -4).any(1)\\n out_range = out_range.sum() / cube.shape[0]\\n return out_range\",\n \"def __len__(self):\\n if self._data is None:\\n return 0\\n elif len(self._explored_range) > 0:\\n return len(self._explored_range)\\n else:\\n return 1\",\n \"def count(cls):\\n return lambda x,y: ((type(x)==int) and [x+1] or ((y==None) and [1] or [2]))[0]\",\n \"def get_event_count(event_times, start, end):\\n mask = (event_times > start) & (event_times <= end)\\n return event_times[mask].size\",\n \"def GetScalarRange(self):\\n ...\",\n \"def _freqs(seq, lower_bound, upper_bound, k, normalized=False):\\n freqs = []\\n region_width = (upper_bound - lower_bound) / k \\n\\n for i in range(k):\\n low = lower_bound + i * region_width\\n high = lower_bound + i * region_width + region_width\\n freqs.append( np.logical_and(seq >= low, seq < high).sum() )\\n\\n # because last interval has '[a;b]' - bounds, not '[a,b)'\\n freqs[-1] += 1\\n\\n if normalized:\\n freqs = np.array(freqs) / len(seq)\\n\\n return np.array(freqs), region_width\",\n \"def test_cardinality(\\n self, prior_name, min_bound, max_bound, precision, cardinality\\n ):\\n dim = Real(\\n \\\"yolo\\\", prior_name, min_bound, max_bound, precision=precision, shape=None\\n )\\n assert dim.cardinality == cardinality\\n dim = Real(\\n \\\"yolo\\\", prior_name, min_bound, max_bound, precision=precision, shape=(2, 3)\\n )\\n assert dim.cardinality == cardinality ** (2 * 3)\",\n \"def f_has_range(self):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def howmany_within_range(row, minimum, maximum):\\n count = 0\\n for n in row:\\n if minimum <= n <= maximum:\\n count = count + 1\\n return count\",\n \"def get_desired_count(value, lower, upper):\\n if lower != -1 and value < lower:\\n return lower\\n if upper != -1 and value > upper:\\n return upper\\n return value\",\n \"def test_cardinality(self):\\n space = Space()\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", 2, 3, 4)\\n dim = Categorical(\\\"yolo\\\", OrderedDict(zip(categories, probs)), shape=2)\\n space.register(dim)\\n dim = Integer(\\\"yolo2\\\", \\\"uniform\\\", -3, 6)\\n space.register(dim)\\n dim = Fidelity(\\\"epoch\\\", 1, 9, 3)\\n space.register(dim)\\n\\n assert space.cardinality == (4**2) * (6 + 1) * 1\\n\\n dim = Integer(\\\"yolo3\\\", \\\"uniform\\\", -3, 2, shape=(3, 2))\\n space.register(dim)\\n assert space.cardinality == (4**2) * (6 + 1) * 1 * ((2 + 1) ** (3 * 2))\\n\\n dim = Real(\\\"yolo4\\\", \\\"norm\\\", 0.9)\\n space.register(dim)\\n assert np.inf == space.cardinality\",\n \"def f02_03_countElemBetween(l, a, b):\\n return sum([a < x < b for x in l])\",\n \"def detect_range(self) -> Union[int, float]:\\n return self.proto.detect_range\",\n \"def count_if(iterable, pred, first=0, last=None):\\n assert hasattr(iterable, '__getitem__')\\n # Only slice for sub-ranges, slight performance improvement\\n iterable = iterable if first == 0 and last is None else iterable[first:last]\\n return sum(1 for x in iterable if pred(x))\",\n \"def count_lorentz(fit_range, lorentz_array_2d):\\n counter = 0\\n for i in range(0, lorentz_array_2d.shape[0]):\\n f0 = lorentz_array_2d[i][1]\\n if f0 > fit_range[1] and f0 < fit_range[2]:\\n counter += 1\\n return counter\",\n \"def countElements(self, nums):\\n import sys\\n max_n = -sys.maxint\\n min_n = sys.maxint\\n\\n for n in nums:\\n max_n = max(n, max_n)\\n min_n = min(n, min_n)\\n\\n count = 0\\n for n in nums:\\n if min_n < n < max_n:\\n count += 1\\n return count\",\n \"def default(functor):\\n return 1.0/functorRangeSize(functor)\",\n \"def boundaries_size(*args):\\n return _ida_hexrays.boundaries_size(*args)\",\n \"def count_property_range_hits(prop, node_dict, hits):\\n\\tres = []\\n\\t# sets tuple position to use in dict value\\n\\tswitcher = {\\n \\\"length\\\": (0,(0,4000,8000,12000,16000,20000)),\\n \\\"steps\\\": (1,(0,2,4,8,16,32)),\\n \\\"cov\\\": (2,(1,10,100,1000,10000,100000)),\\n \\\"cv\\\": (3, (0,0.05,0.10,0.15,0.20,0.25))\\n }\\n\\tif prop not in switcher:\\n\\t\\treturn res\\n\\ttup_pos = switcher[prop][0]\\n\\tnode_cnt = 0\\n\\tpos_cnt = 0\\n\\tfor ind in range(len(switcher[prop][1])-1):\\n\\t\\tmin_val = switcher[prop][1][ind]\\n\\t\\tmax_val = switcher[prop][1][ind+1]\\n\\t\\tfor node in node_dict.keys():\\n\\t\\t\\tval = node_dict[node][tup_pos]\\n\\t\\t\\tif ind < len(switcher[prop][1])-2:\\n\\t\\t\\t\\trange_test_val = (min_val <= val < max_val)\\n\\t\\t\\telse:\\n\\t\\t\\t\\trange_test_val = (min_val <= val <= max_val)\\n\\t\\t\\t# print \\\"range bool is\\\", range_test_val\\n\\t\\t\\tif range_test_val:\\n\\t\\t\\t\\tnode_cnt += 1\\n\\t\\t\\t\\tif node in hits: pos_cnt += 1\\n\\t\\tif node_cnt > 0:\\n\\t\\t\\tres.append( (pos_cnt, node_cnt, round(float(pos_cnt)/node_cnt,2)))\\n\\t\\telse:\\n\\t\\t\\tres.append((0,0,0))\\n\\t\\tnode_cnt = 0\\n\\t\\tpos_cnt = 0\\n\\treturn res\",\n \"def complexity(self, mode='#nodes'):\\n if mode == '#nodes':\\n return len(self.nodes)\",\n \"def vsize(min, max):\\n return lambda mate: any(min <= v <= max for v in mate['read_info'].v_list)\",\n \"def compute_outliers(data: np.ndarray, data_range: typing.Tuple[float, float]) -> int:\\n return np.count_nonzero((data >= data_range[0]) & (data < data_range[1]))\",\n \"def range_width(self):\\n return range(self.left, self.right + 1)\",\n \"def get_bounds(f, lb=0, ub=None):\\r\\n lb_idx = np.searchsorted(f, lb, 'left')\\r\\n if ub == None:\\r\\n ub_idx = len(f)\\r\\n else:\\r\\n ub_idx = np.searchsorted(f, ub, 'right')\\r\\n\\r\\n return lb_idx, ub_idx\",\n \"def _get_arity(callable):\\n return len(inspect.signature(callable).parameters)\",\n \"def _compute_register_bounds(cls, register_probs):\\n return np.cumsum(register_probs)\",\n \"def _compute_register_bounds(cls, register_probs):\\n return np.cumsum(register_probs)\",\n \"def _inside_op_range(self, idx):\\n\\n if idx < self._parameters.op_range[0]:\\n return False\\n return (self._parameters.op_range[1] < 0 or\\n idx <= self._parameters.op_range[1])\",\n \"def count_interval_index(arr1d, lower, upper):\\n count = 0\\n for ele in arr1d:\\n if ele >= lower and ele <= upper:\\n count = count + 1\\n return count\",\n \"def ranges(self, predicate):\\n\\n x = np.zeros(len(self)).astype(np.bool)\\n for i, elem in enumerate(self.elements):\\n x[i] = predicate(elem)\\n\\n return np.where(x)[0]\",\n \"def count(pred, l):\\n nl = [i for i in range(0,len(l)) if pred(l[i])]\\n\\n return len(nl)\",\n \"def get_num_applies(self):\\n ops = 0\\n for _, remainder, _ in self:\\n ops += len(remainder)\\n return ops\",\n \"def get_dof(self):\\n return len(self.a_n)\",\n \"def total_range_size(self) -> int:\\n if not len(self):\\n return 0\\n regions = merge(self.data, bp=1)\\n return regions.end.sum() - regions.start.sum()\",\n \"def check_cardinality(df, cat_cols, threshold=8):\\n high_card = {}\\n low_card = {}\\n for col in cat_cols:\\n rank = len(Counter(df[col]))\\n if rank <= threshold:\\n low_card[col] = rank\\n else:\\n high_card[col] = rank\\n return low_card, high_card\",\n \"def __len__(self):\\n # Product function that can handle iterables (np.product can't).\\n product = partial(reduce, operator.mul)\\n return sum(product(len(v) for v in p.values()) if p else 1\\n for p in self.param_grid)\",\n \"def test_basic_cardinality(self):\\n dim = Real(\\\"yolo\\\", \\\"reciprocal\\\", 0.043, 2.3, precision=2)\\n order_0012 = np.arange(43, 99 + 1)\\n order_010 = np.arange(10, 99 + 1)\\n order_23 = np.arange(10, 23 + 1)\\n assert dim.cardinality == sum(map(len, [order_0012, order_010, order_23]))\",\n \"def count_within(values, intervals):\\n rvals = np.reshape(values, [-1, 1])\\n intervals_m = np.array(intervals)\\n rlow = intervals_m[:, 0].reshape(1, -1)\\n rhigh = intervals_m[:, 1].reshape(1, -1)\\n\\n flags = (rlow <= rvals) * (rvals < rhigh)\\n return np.sum(flags, axis=0)\",\n \"def test_args_count_in_range(args: list, min: int, max: int) -> bool:\\n\\n\\tcount = args_count(args)\\n\\treturn (count >= min and count <= max)\",\n \"def get_upper_bound(self):\\n return self._upper_bound\",\n \"def term_range(self):\\n start = max([f.cohort_end for f in self.filters])\\n end = MAX_NUM_COHORTS\\n return range(start + 1, end + 1)\",\n \"def get_count_of_elements_by_condition(sequence):\\n elements_and_indexes = {sequence[i]: i + 1\\n for i in range(1, len(sequence) - 1)}\\n filtered_values = filter(lambda element:\\n 2 ** element[1] < element[0] < math.factorial(element[1]),\\n elements_and_indexes.items())\\n return len(dict(filtered_values))\",\n \"def _compute_register_bounds(cls, num_values, probability):\\n bits = np.arange(1, num_values + 1)\\n probs = scipy.stats.geom.cdf(bits, probability)\\n return probs / probs[-1]\",\n \"def lenRange(start, stop, step=1):\\n return (stop - start + step - 1 + 2 * (step < 0)) // step\",\n \"def __len__(self):\\n return self.count_of(CUBA.NODE)\",\n \"def __len__(self):\\n return len(self.constraints)\",\n \"def bounds(self): # -> tuple[()]:\\n ...\",\n \"def count(\\n type_signature: computation_types.Type, predicate: _TypePredicate\\n) -> int:\\n one_or_zero = lambda t: 1 if predicate(t) else 0\\n return sum(map(one_or_zero, preorder_types(type_signature)))\",\n \"def __len__(self):\\n return len(np.where(np.logical_not(self.data.mask))[0])\",\n \"def count_in_range(start, end, check):\\n count = 0\\n for val in range(start, end):\\n if check(val):\\n count += 1\\n\\n return count\",\n \"def __len__(self):\\n return self.start.dist(self.end)\",\n \"def num_linearly_ind_features(self, S, eps=1e-11):\\n return len(S[S >= eps])\",\n \"def f_has_range(self):\\n return len(self._explored_range) > 0\",\n \"def II_eq_counts(tobin_series, num_bins):\\n num_pbin = int(len(tobin_series) / num_bins)\\n obs_list = tobin_series.sort_values().tolist()\\n upper_bounds = [obs_list[(i + 1) * num_pbin] for i in range(num_bins)]\\n lower_bounds = [0]\\n lower_bounds += upper_bounds[:-1]\\n return pd.IntervalIndex.from_arrays(lower_bounds, upper_bounds), upper_bounds\",\n \"def integer_hist(a, int_range=None, open_range=False, relative=False):\\n data = np.round(a).flatten()\\n if int_range:\\n values = np.arange(int(int_range[0]), int(int_range[1])+1)\\n else:\\n values = np.arange(int(data.min()), int(data.max())+1)\\n N = values.size\\n if relative:\\n count = np.empty(N, 'd')\\n else:\\n count = np.empty(N, 'l')\\n for bin, c in enumerate(values):\\n if open_range and bin == N - 1:\\n count[bin] = (data >= c).sum()\\n else:\\n count[bin] = (data == c).sum()\\n if relative:\\n count /= count.sum()\\n return values, count\",\n \"def __len__(self):\\n return 9 # logsfr_ratios has 6 bins\",\n \"def usable_occupancy(isotherm, p_min=5.0, p_max=100.0):\\n\\n occ_max = point_occupancy(isotherm, p_max)\\n occ_min = point_occupancy(isotherm, p_min)\\n\\n usable_occ = occ_max - occ_min\\n\\n return usable_occ\",\n \"def concave_max_binsearch(fn, lb, ub, num_iter=20):\\n mid = tf.stop_gradient(.5 * lb + .5 * ub)\\n f_mid = fn(mid)\\n\\n for _ in range(num_iter):\\n # Calculate quartiles.\\n lq = tf.stop_gradient(.75 * lb + .25 * ub)\\n uq = tf.stop_gradient(.25 * lb + .75 * ub)\\n f_lq = fn(lq)\\n f_uq = fn(uq)\\n\\n # Identify three cases, recalling that fn is concave.\\n # Case 1: f_lq > f_mid > f_uq\\n # The maximum occurs in the range [lb, mid].\\n # Case 2: f_lq > f_mid > f_uq\\n # The maximum occurs in the range [mid, ub].\\n # Case 3: f_lq < f_mid > f_uq\\n # The maximum occurs in the range [lq, uq].\\n case1 = f_lq > f_mid\\n case2 = f_uq > f_mid\\n lb, ub, mid, f_mid = (\\n tf.where(case1, lb, tf.where(case2, mid, lq)),\\n tf.where(case1, mid, tf.where(case2, ub, uq)),\\n tf.where(case1, lq, tf.where(case2, uq, mid)),\\n tf.where(case1, f_lq, tf.where(case2, f_uq, f_mid))\\n )\\n\\n return mid, f_mid\",\n \"def length(sequence):\\r\\n populated = tf.sign(tf.abs(sequence))\\r\\n length = tf.cast(tf.reduce_sum(populated, axis=1), tf.int32)\\r\\n mask = tf.cast(tf.expand_dims(populated, -1), tf.float32)\\r\\n return length, mask\",\n \"def __len__(self):\\n return self.nb_iterations\",\n \"def nb_predicates(self) -> int:\\n return 0\",\n \"def fast_hist(a, b, n):\\n # print(n)\\n # print(b.max())\\n k = (a >= 0) & (a < n)\\n\\n\\n # a = np.floor(a)\\n # a = a.astype(np.int)\\n # print(a.max())\\n # print(a.dtype)\\n # print(a.shape)\\n # print(type(a))\\n\\n return np.bincount((n * a[k].astype(int) + b[k]).astype(int), minlength=n ** 2).reshape(n, n)\",\n \"def expected_counts(p0, T, n): \\n M=T.shape[0]\\n if n<=M:\\n return ec_matrix_vector(p0, T, n)\\n else:\\n return ec_geometric_series(p0, T, n)\",\n \"def overlap_size(self, begin, end=None):\\n overlaps = self.overlaps(begin, end)\\n if not overlaps:\\n return 0\\n\\n if end is not None:\\n # case end is given\\n i0 = max(self.begin, begin)\\n i1 = min(self.end, end)\\n return i1 - i0\\n # assume the type is interval, in other cases, an exception will be thrown\\n i0 = max(self.begin, begin.begin)\\n i1 = min(self.end, begin.end)\\n return i1 - i0\",\n \"def numel(self):\\n return self.t.size\",\n \"def c(ixs):\\n return sum(range(1, sum((i > 0 for i in ixs)) + 1))\",\n \"def count_ranges(a):\\n ranges = []\\n count = 0\\n for i, v in enumerate(a):\\n if v == 1: # same as previous value\\n count += 1\\n else:\\n if count > 1:\\n ranges.append([i, count]) # [end, length]\\n count = 0\\n return ranges\",\n \"def __len__(self):\\n return sum([1 for _ in self])\",\n \"def count_qubits(operator):\\n # Handle FermionOperator.\\n if isinstance(operator, FermionOperator):\\n num_qubits = 0\\n for term in operator.terms:\\n for ladder_operator in term:\\n if ladder_operator[0] + 1 > num_qubits:\\n num_qubits = ladder_operator[0] + 1\\n return num_qubits\\n\\n # Handle QubitOperator.\\n elif isinstance(operator, QubitOperator):\\n num_qubits = 0\\n for term in operator.terms:\\n if term:\\n if term[-1][0] + 1 > num_qubits:\\n num_qubits = term[-1][0] + 1\\n return num_qubits\\n\\n # Handle MajoranaOperator.\\n if isinstance(operator, MajoranaOperator):\\n num_qubits = 0\\n for term in operator.terms:\\n for majorana_index in term:\\n if numpy.ceil((majorana_index + 1) / 2) > num_qubits:\\n num_qubits = int(numpy.ceil((majorana_index + 1) / 2))\\n return num_qubits\\n\\n # Handle DiagonalCoulombHamiltonian\\n elif isinstance(operator, DiagonalCoulombHamiltonian):\\n return operator.one_body.shape[0]\\n\\n # Handle PolynomialTensor\\n elif isinstance(operator, PolynomialTensor):\\n return operator.n_qubits\\n\\n # Raise for other classes.\\n else:\\n raise TypeError('Operator of invalid type.')\",\n \"def freq(self) -> int:\",\n \"def count_values_in_range(data: np.ndarray, ranges: np.ndarray) -> np.ndarray:\\n\\n results = np.full((data.shape[0], ranges.shape[0]), 0)\\n for i in prange(data.shape[0]):\\n for j in prange(ranges.shape[0]):\\n lower_bound, upper_bound = ranges[j][0], ranges[j][1]\\n results[i][j] = data[i][\\n np.logical_and(data[i] >= lower_bound, data[i] <= upper_bound)\\n ].shape[0]\\n return results\",\n \"def find_histogram_range(histogram):\\n size = len(histogram)\\n min_i = 0\\n while min_i < size:\\n if histogram[min_i] > 0:\\n break\\n min_i += 1\\n\\n max_i = size - 1\\n while max_i >= 0:\\n if histogram[max_i] > 0:\\n break\\n max_i -= 1\\n return min_i, max_i\",\n \"def quantize(input=None, min_range=None, max_range=None, out_type=_Null, out=None, name=None, **kwargs):\\n return (0,)\",\n \"def count(self, elem):\\n if not self.step:\\n return _coconut.float(\\\"inf\\\") if elem == self.start else 0\\n return int(elem in self)\",\n \"def find_range(reduced_dist_word_dim, range_limits):\\n n_limits = len(range_limits)\\n for limit in range(n_limits - 1):\\n if (reduced_dist_word_dim > range_limits[limit]) and (reduced_dist_word_dim < range_limits[limit + 1]):\\n return limit\\n raise ValueError\",\n \"def _compute_ndof(self, ai_type, ai_operator):\\n li_dof = 1\\n\\n return li_dof\",\n \"def __len__(self):\\n nsamp = self.data.shape[-1]\\n kernel = int(self.kernel * self.fs)\\n stride = int(self.stride * self.fs)\\n n_stride = int(np.ceil((nsamp - kernel) / stride) + 1)\\n return max(0, n_stride)\",\n \"def get_lower_bound(self):\\n return self._lower_bound\",\n \"def get_lower_bound(self):\\n return self._lower_bound\",\n \"def number_bits_in_cardinality(self,card):\\n return 32 - self.count_lead_zs(card)\",\n \"def __len__(self):\\n return sum(f.count for f in self.filters)\",\n \"def total_bounds(self):\\n if not self.empty:\\n return tuple(self.numba_rtree._bounds_tree[0, :])\\n else:\\n return tuple((np.nan,) * self.numba_rtree._bounds_tree.shape[1])\",\n \"def calculate_occupancy(self):\\n # TODO will need to be fixed now that using a dict and changed thresholds\\n self.occupancy = [list(x > self.t for x in y) for y in self.counts]\\n return self.occupancy\",\n \"def num_cuts(self) -> Optional[int]:\\n raise NotImplementedError(\\n 'Sub-classes of CutSampler have to implement self.num_cuts'\\n )\",\n \"def __len__(self):\\n return sum(abs(j) for (i, j) in self)\",\n \"def test_range_argument(self):\\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\\n bins_range = (0, 2)\\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\\n hist_exp = np.array([1, 2, 0])\\n\\n bin_edges, hist, _, _ = hist_w_unc(\\n self.input,\\n bins=self.n_bins,\\n bins_range=bins_range,\\n normed=False,\\n )\\n\\n # check if we end up with the same bin edges anyway\\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\\n np.testing.assert_array_almost_equal(hist_exp, hist)\",\n \"def calc_priors(categories, data):\\n counts = np.zeros(categories)\\n for val in range(categories):\\n counts[val] = np.count_nonzero(data.labels == val)\\n return counts / len(data.labels)\",\n \"def __len__(self):\\n return self.end - self.begin\",\n \"def computeFreq(self):\\n for x in self.data:\\n i = 0\\n for interval in self.classesInterval:\\n if interval[0] <= x <= interval[1]:\\n self.frequencies[i] += 1\\n break\\n i += 1\\n\\n self.minFreq = self.frequencies[0]\\n self.maxFreq = self.frequencies[0]\\n for f in self.frequencies:\\n if f < self.minFreq:\\n self.minFreq = f\\n elif f > self.maxFreq:\\n self.maxFreq = f\",\n \"def objectiveFunction(self):\\n # start at zero\\n satisfiedConstraints = 0\\n # loop through all of the constraints\\n for constraint in self.constraints:\\n # if the constraint is satisfied, then increase the count\\n if (constraint.satisfied(constraint.tail.value, constraint.head.value)):\\n satisfiedConstraints += 1\\n # return the count of satisfied constraints\\n return satisfiedConstraints\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6364935","0.6203069","0.6184483","0.6088533","0.5999436","0.5949017","0.5914987","0.5850817","0.5838362","0.58077663","0.57850456","0.56294644","0.55813044","0.5576867","0.5546719","0.55258554","0.55099475","0.5464677","0.5460476","0.54486597","0.54301196","0.542768","0.5427204","0.54044926","0.5389398","0.5384164","0.5373981","0.5367209","0.53636926","0.53592116","0.5355957","0.5332373","0.53319675","0.53251284","0.5319451","0.53063726","0.53063726","0.5306121","0.5298041","0.52854264","0.5275432","0.5268495","0.5242731","0.52417016","0.52388763","0.5236573","0.52352905","0.52299327","0.5229311","0.5200839","0.5200408","0.5195578","0.5193764","0.51872504","0.5182564","0.5180367","0.51761025","0.51722646","0.516604","0.5161762","0.51595634","0.5155614","0.51525927","0.5152441","0.5146775","0.5141098","0.51391745","0.51261926","0.51242566","0.51226354","0.51218075","0.511188","0.5103494","0.5092989","0.5090223","0.5087259","0.5083248","0.5063268","0.50616264","0.5056006","0.5054432","0.5052543","0.5051386","0.50504047","0.5049645","0.504738","0.5043703","0.50397307","0.50397307","0.5026735","0.50080633","0.5007743","0.5005429","0.5002298","0.49991274","0.49957728","0.49947956","0.4993911","0.4985443","0.49800125"],"string":"[\n \"0.6364935\",\n \"0.6203069\",\n \"0.6184483\",\n \"0.6088533\",\n \"0.5999436\",\n \"0.5949017\",\n \"0.5914987\",\n \"0.5850817\",\n \"0.5838362\",\n \"0.58077663\",\n \"0.57850456\",\n \"0.56294644\",\n \"0.55813044\",\n \"0.5576867\",\n \"0.5546719\",\n \"0.55258554\",\n \"0.55099475\",\n \"0.5464677\",\n \"0.5460476\",\n \"0.54486597\",\n \"0.54301196\",\n \"0.542768\",\n \"0.5427204\",\n \"0.54044926\",\n \"0.5389398\",\n \"0.5384164\",\n \"0.5373981\",\n \"0.5367209\",\n \"0.53636926\",\n \"0.53592116\",\n \"0.5355957\",\n \"0.5332373\",\n \"0.53319675\",\n \"0.53251284\",\n \"0.5319451\",\n \"0.53063726\",\n \"0.53063726\",\n \"0.5306121\",\n \"0.5298041\",\n \"0.52854264\",\n \"0.5275432\",\n \"0.5268495\",\n \"0.5242731\",\n \"0.52417016\",\n \"0.52388763\",\n \"0.5236573\",\n \"0.52352905\",\n \"0.52299327\",\n \"0.5229311\",\n \"0.5200839\",\n \"0.5200408\",\n \"0.5195578\",\n \"0.5193764\",\n \"0.51872504\",\n \"0.5182564\",\n \"0.5180367\",\n \"0.51761025\",\n \"0.51722646\",\n \"0.516604\",\n \"0.5161762\",\n \"0.51595634\",\n \"0.5155614\",\n \"0.51525927\",\n \"0.5152441\",\n \"0.5146775\",\n \"0.5141098\",\n \"0.51391745\",\n \"0.51261926\",\n \"0.51242566\",\n \"0.51226354\",\n \"0.51218075\",\n \"0.511188\",\n \"0.5103494\",\n \"0.5092989\",\n \"0.5090223\",\n \"0.5087259\",\n \"0.5083248\",\n \"0.5063268\",\n \"0.50616264\",\n \"0.5056006\",\n \"0.5054432\",\n \"0.5052543\",\n \"0.5051386\",\n \"0.50504047\",\n \"0.5049645\",\n \"0.504738\",\n \"0.5043703\",\n \"0.50397307\",\n \"0.50397307\",\n \"0.5026735\",\n \"0.50080633\",\n \"0.5007743\",\n \"0.5005429\",\n \"0.5002298\",\n \"0.49991274\",\n \"0.49957728\",\n \"0.49947956\",\n \"0.4993911\",\n \"0.4985443\",\n \"0.49800125\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.799879"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19588,"cells":{"query":{"kind":"string","value":"For functors with a binary range, return the other element"},"document":{"kind":"string","value":"def functorOtherValue(functor, val):\n range = functorRange(functor)\n assert len(range) == 2\n if val == range[0]:\n return range[1]\n else:\n return range[0]"},"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 functorRange(functor):\n for (name, range) in functorRangeList:\n if functor == name:\n return range\n else:\n raise Exception (\"Functor \" + functor + \" not present in range list\")","def ranges(self, predicate):\n\n x = np.zeros(len(self)).astype(np.bool)\n for i, elem in enumerate(self.elements):\n x[i] = predicate(elem)\n\n return np.where(x)[0]","def __or__(self, other):\n if not isinstance(other, Range):\n raise TypeError(\n f\"unsupported operand types for |: \"\n f\"{type(self).__name__!r} and {type(other).__name__!r}\"\n )\n\n if self == other:\n return Range(self.vmin, self.vmax)\n elif self.vmax < other.vmin - 1:\n return None\n elif self.vmin > other.vmax + 1:\n return None\n\n return Range(\n vmin=min(self.vmin, other.vmin),\n vmax=max(self.vmax, other.vmax)\n )","def _in_range_op(spec):","def __and__(self, other):\n if not isinstance(other, Range):\n raise TypeError(\n f\"unsupported operand types for &: \"\n f\"{type(self).__name__!r} and {type(other).__name__!r}\"\n )\n\n if self == other:\n return Range(self.vmin, self.vmax)\n elif self < other or self > other:\n return None\n\n return Range(\n vmin=max(self.vmin, other.vmin),\n vmax=min(self.vmax, other.vmax)\n )","def from_inclusive(a, b):\n c = int(b > a)*2-1\n return range(a, b+c, c)","def _range_func(self, withscores, score_cast_func, decode_value_func=lambda x: x):\n if withscores:\n return lambda score_member: (decode_value_func(score_member[1]), score_cast_func(self._encode(score_member[0]))) # noqa\n else:\n return lambda score_member: decode_value_func(score_member[1])","def _bisect_right(*args, **kwargs): # real signature unknown\n pass","def _builtin_between(low, high, value, **k):\n mode = check_mode((low, high, value), ['iii', 'iiv'], functor='between', **k)\n low_v = int(low)\n high_v = int(high)\n if mode == 0: # Check\n value_v = int(value)\n if low_v <= value_v <= high_v:\n return [(low, high, value)]\n else: # Enumerate\n results = []\n for value_v in range(low_v, high_v + 1):\n results.append((low, high, Constant(value_v)))\n return results","def cal(num1, num2, range):\r\n setup(range)\r\n return Andcollections(num1, num2)","def evaluate_comparison_range(node):\n return None","def range_(self):\n return tuple((e[0], e[-1]) for e in self.edges)","def _range_overapped(self, x, y):\n xs = set( range(x[0], x[1]))\n ys = set( range(y[0], y[1]))\n return xs.intersection(ys)","def in_range(x, a, b):\n return (x >= a and x <= b) or (x <= a and x >= b)","def between(min, max):\n def func(x):\n return min <= x <= max\n return func","def visit_bound_predicate(self, predicate) -> T:","def _range_contains(self, a, b):\n\t\treturn b[0] >= a[0] and b[-1] <= a[-1]","def other_above(self, element, first_successor):\n successors = list(self.above(element))\n if len(successors) != 2:\n raise ValueError(\"element is not binary in lattice\")\n elif successors[0] == first_successor:\n return successors[1]\n elif successors[1] == first_successor:\n return successors[0]\n else:\n raise ValueError(\"first_successor is not a successor of element in lattice\")","def __or__(self, other):\n return MyCustomNumber(self.value | other.value)","def _intersect_interval(self, other):\n interval = Intersection(self.interval, other.interval)\n return interval.inf, interval.sup","def dock_if_bound(f, v):\n return v[1:] if is_bound(f) else v","def __ge__(self, other):\n return self.master.abs2phy.__getitem__(pos=other)","def _less_than_or_equal_to_op(spec):","def __le__(self, other):\n return self.element() <= other.element()","def get(self, a: int, b: int) -> int:\n result = self.identity()\n q = [(1, 0, self.n2)]\n while q:\n k, left, right = q.pop()\n if a <= left and right <= b:\n result = self.binary(result, self.tree[k])\n continue\n m = (left + right) // 2\n k <<= 1\n if a < m and left < b:\n q.append((k, left, m))\n if a < right and left < m:\n q.append((k + 1, m, right))\n return result","def _bound(x, min_value, max_value):\n return np.maximum(min_value, np.minimum(x, max_value))","def __ge__(self, other):\n return self.element() >= other.element()","def _bi_range(start, end):\n if start == end:\n return (start,)\n\n elif end < start:\n return reversed(range(end, start + 1))\n\n else:\n return range(start, end + 1)","def __le__(self, other):\r\n # self <= other\r\n s = (self - other).share[0]\r\n return type(self)((s <= 0, self.exponent_type(0)))","def ge(self, y):\n return 1 - self.lt(y)","def interval(a, b):\n aIndex, bIndex = None, None\n for i in range(len(table)):\n fs = table[i]\n if aIndex == None and fs >= a: aIndex = i\n if bIndex == None and fs > b: bIndex = i\n if bIndex == None: bIndex = len(table)\n return bIndex - aIndex","def new_range(r):\n if isinstance(r, list) or isinstance(r, tuple) and len(r) == 2:\n lower = r[0]\n upper = r[1]\n else:\n lower = r\n upper = r\n lower = int(lower)\n upper = int(upper)\n return range(lower, upper + 1)","def __gt__(self, other):\n return self.element() > other.element()","def _apply_binary_op_elementwise(\n self: ConcreteStructuredMetricValue, other: ConcreteStructuredMetricValue,\n op: Callable[[float, float], float]) -> ConcreteStructuredMetricValue:\n ...","def __le__(self, other: Any) -> ColumnOperators:\n return self.operate(le, other)","def gt(self, x, y):\n return self.lt(y,x)","def subrange(x, onset=None, offset=None):\n return (y[(y >= onset) & ~(y > (offset))] for y in x)","def get_bounds(f, lb=0, ub=None):\r\n lb_idx = np.searchsorted(f, lb, 'left')\r\n if ub == None:\r\n ub_idx = len(f)\r\n else:\r\n ub_idx = np.searchsorted(f, ub, 'right')\r\n\r\n return lb_idx, ub_idx","def _get_sight_range(self):\n raise NotImplementedError","def in_range(start, end, x):\n if start <= end:\n return start <= x <= end\n else:\n return start <= x or x <= end","def __or__(self, other):\n @AccessFilter\n def f(*args, **kwargs):\n first = self(*args, **kwargs)\n if first is None:\n return other(*args, **kwargs)\n return first\n return f","def __le__(self,f2):\n return not self > f2 or self == f2","def __or__(self, other):\n\t\tif isinstance(other, int):\n\t\t\treturn self.value | other\n\t\telif type(self) is type(other):\n\t\t\treturn self.value | other.value","def _bisect_left(*args, **kwargs): # real signature unknown\n pass","def interval(self):\n return Intersection(*(a.interval for a in self.args))","def getx(v, lb, ub, i, B):\r\n x = lb + np.multiply((ub - lb), v)\r\n x[i] = B - (x.sum() - x[i])\r\n # Test if variable x[i] is within the bounds\r\n if x[i] <= ub[i] and x[i] >= lb[i]:\r\n return x\r\n else:\r\n return np.array([])","def map_bound(value, in_low, in_high, out_low, out_high):\n result = None\n\n if value <= in_low:\n result = out_low\n else:\n if value >= in_high:\n result = out_high\n else:\n # http://stackoverflow.com/a/5650012/574981\n result = out_low + (\n (out_high - out_low) * (value - in_low) / (in_high - in_low)\n )\n return result","def slice2(self, vs=None,xs=None):\n return self.condition2(vs,xs)","def bisect(f, lo=0, hi=None, eps=1e-9):\n lo_bool = f(lo)\n if hi is None:\n offset = 1\n while f(lo+offset) == lo_bool:\n offset *= 2\n hi = lo + offset\n else:\n assert f(hi) != lo_bool\n while hi - lo > eps:\n mid = (hi + lo) / 2\n if f(mid) == lo_bool:\n lo = mid\n else:\n hi = mid\n if lo_bool:\n return lo\n else:\n return hi","def foo_2(x, y):\n\tif x > y:\n\t\treturn x\n\treturn y","def __and__(self, other):\n return MyCustomNumber(self.value & other.value)","def __le__(self, other):\n return self.x ** 2 + self.y ** 2 <= other.x ** 2 + other.y ** 2","def query_range(self,i,j):\n assert 0 <= i <= j <= len(self)\n ans = self.__query_aux(0,0,len(self),i,j)\n return ans","def __le__(self, other):\n return self.lessThanOrEqual(other)","def range_inclusive(start, stop):\n return range(start, stop + 1)","def _inside_op_range(self, idx):\n\n if idx < self._parameters.op_range[0]:\n return False\n return (self._parameters.op_range[1] < 0 or\n idx <= self._parameters.op_range[1])","def __and__(self, other):\n return self >> (lambda _: other)","def get_xrange_indices(self, lower, upper) -> Tuple[int, int]:\n lower_index = np.argmax(self.x >= lower)\n upper_index = np.argmax(self.x >= upper)\n return int(lower_index), int(upper_index)","def __lt__(self, other: Any) -> ColumnOperators:\n return self.operate(lt, other)","def firstCrossIdx (i: int, wire1, wire2) -> Union [int, None]:\n try:\n if wire1 [i] in wire2:\n return i\n return F.tailR (firstCrossIdx) (i + 1, wire1, wire2)\n except IndexError:\n return None","def __sub__(self, other):\n return Base40Interval(self.base40 - other.base40)","def bisect(x_range, x_i):\n if x_i <= x_range[0]:\n return 0\n if x_i >= x_range[-1]:\n return len(x_range)-1\n \n for i in range(len(x_range)-1):\n if x_i > x_range[i] and x_i < x_range[i+1]:\n return (i, i+1)\n raise RuntimeError(\"No edges found.\")","def _less_than_op(spec):","def limit_by(self, field, lower_bound=None, upper_bound=None):\n if lower_bound is not None and upper_bound is not None:\n indices = (self[field] >= lower_bound) \\\n & (self[field] <= upper_bound)\n elif lower_bound is None:\n indices = self[field] <= upper_bound\n elif upper_bound is None:\n indices = self[field] >= lower_bound\n else:\n raise ValueError(\"One bound must be set!\")\n return self[indices]","def ranged_int(A, B=FLOAT_INF):\n\n class ranged_int(int):\n \"\"\"Int type in [A; B] range.\"\"\"\n\n def __init__(self, value):\n assert A <= int(value) <= B, value\n super(ranged_int, self).__init__()\n\n return ranged_int","def rect(T):\n return lambda t: (-T/2 <= t) & (t < T/2)","def boundaries_second(*args):\n return _ida_hexrays.boundaries_second(*args)","def array_range(a, low, high, ref=None):\n if ref is None:\n ref = a\n return a[np.logical_and(ref >= low, ref < high)]","def findSignificantBins(return_bins):\n \n count = 0\n range_even = []\n range_odd = []\n for i in xrange(1,len(return_bins['my_list'])):\n value_range = (return_bins['my_list'][i] - return_bins['my_list'][(i-1)])\n count = count + 1\n if (count % 2 == 0) :\n range_even.append(value_range)\n else: \n range_odd.append(value_range)\n\n print \"This is the even {}\".format(range_even)\n print \"This is the odd {}\".format(range_odd)\n if (return_bins.get('start_value') == 0) : \n return range_even\n else:\n return range_odd","def gt (x,y):\n\n return not le(x,y)","def higher_floor(person_1, person_2):\n return person_1 > person_2","def less(value, other):\n return value > other","def GetScalarRange(self):\n ...","def __ge__(self, other):\r\n # self >= other\r\n s = (self - other).share[0]\r\n return type(self)((s >= 0, self.exponent_type(0)))","def __le__(self, other):\r\n # self <= other <=> not (other < self)\r\n return 1 - runtime.lt(other, self)","def binary(self, state):\n val2 = pop(state)\n val1 = pop(state)\n # implicit deref\n if isinstance(val1, Reference):\n val1 = state.search(val1)\n if isinstance(val2, Reference):\n val2 = state.search(val2)\n \n if isinstance(val1, Numeric) and isinstance(val2, Numeric):\n return (val1, val2)\n else:\n raise TypeError(\"Tried to math non-numerics\")","def __ixor__(self, other: t.Any) -> te.Self:\n return self._op_inplace('__isub__', other)","def ge(self, x, y):\n return self.le(y,x)","def __ge__(self,f2):\n return self > f2 or self == f2","def gt(self, other):\n self._raise_if_null(other)\n if hasattr(other, 'end'):\n return self.begin >= other.end\n else:\n return self.begin > other","def __lt__(self,f2):\n return not self > f2","def other_under(self, element, first_predecessor):\n predecessors = list(self.under(element))\n if len(predecessors) != 2:\n raise ValueError(\"element is not binary in lattice\")\n elif predecessors[0] == first_predecessor:\n return predecessors[1]\n elif predecessors[1] == first_predecessor:\n return predecessors[0]\n else:\n raise ValueError(\"first_successor is not a successor of element in lattice\")","def range(self) -> ty.Tuple[float, float]:\r\n ...","def overlap_with(self, other):","def f_has_range(self):\n raise NotImplementedError(\"Should have implemented this.\")","def get_range(lst):\n return float(max(lst)) - float(min(lst))","def __nonzero__(self):\n return _uhd_swig.range_vector_t___nonzero__(self)","def bitrange(index, width, start, end):\n return index >> (width - end) & ((2 ** (end - start)) - 1)","def first2(x, y):\n y = np.asarray(y)\n return y[np.argsort(x)][0]","def __or__(self,other):\n #TODO: ensure that the \"left\" operand is a conditional itself.\n return compositeConditionalGenerator(left = self, right = other)","def get_range(self) -> tuple[int, int]:\n return self.range_from, self.range_to","def map_between(value, start, stop):\n\n return (stop - start) * value + start","def getRange(self, p_int): # real signature unknown; restored from __doc__\n pass","def closed_range(start, stop, step):\n return range(start, stop + 1, step)","def __gt__(self, other):\n return self >= other and not self <= other","def simplebounds(cls, val, lower, upper):\n if val < lower:\n val = lower\n if val > upper:\n val = upper\n return val","def mapRange(num, min1, max1, min2, max2, clamp=True):\n if(clamp and num < min1):\n return min2\n if(clamp and num > max1):\n return max2\n\n num1 = (num - min1) / (max1 - min1)\n num2 = (num1 * (max2 - min2)) + min2\n return num2","def range(x):\n try:\n return (min(min(y) for y in x), max(max(y) for y in x))\n except ValueError:\n return (None, None)","def lower_bound(self) -> float:\n ...","def in_range(cls, lhs, rhs):\n return rhs[0] <= lhs <= rhs[1]"],"string":"[\n \"def functorRange(functor):\\n for (name, range) in functorRangeList:\\n if functor == name:\\n return range\\n else:\\n raise Exception (\\\"Functor \\\" + functor + \\\" not present in range list\\\")\",\n \"def ranges(self, predicate):\\n\\n x = np.zeros(len(self)).astype(np.bool)\\n for i, elem in enumerate(self.elements):\\n x[i] = predicate(elem)\\n\\n return np.where(x)[0]\",\n \"def __or__(self, other):\\n if not isinstance(other, Range):\\n raise TypeError(\\n f\\\"unsupported operand types for |: \\\"\\n f\\\"{type(self).__name__!r} and {type(other).__name__!r}\\\"\\n )\\n\\n if self == other:\\n return Range(self.vmin, self.vmax)\\n elif self.vmax < other.vmin - 1:\\n return None\\n elif self.vmin > other.vmax + 1:\\n return None\\n\\n return Range(\\n vmin=min(self.vmin, other.vmin),\\n vmax=max(self.vmax, other.vmax)\\n )\",\n \"def _in_range_op(spec):\",\n \"def __and__(self, other):\\n if not isinstance(other, Range):\\n raise TypeError(\\n f\\\"unsupported operand types for &: \\\"\\n f\\\"{type(self).__name__!r} and {type(other).__name__!r}\\\"\\n )\\n\\n if self == other:\\n return Range(self.vmin, self.vmax)\\n elif self < other or self > other:\\n return None\\n\\n return Range(\\n vmin=max(self.vmin, other.vmin),\\n vmax=min(self.vmax, other.vmax)\\n )\",\n \"def from_inclusive(a, b):\\n c = int(b > a)*2-1\\n return range(a, b+c, c)\",\n \"def _range_func(self, withscores, score_cast_func, decode_value_func=lambda x: x):\\n if withscores:\\n return lambda score_member: (decode_value_func(score_member[1]), score_cast_func(self._encode(score_member[0]))) # noqa\\n else:\\n return lambda score_member: decode_value_func(score_member[1])\",\n \"def _bisect_right(*args, **kwargs): # real signature unknown\\n pass\",\n \"def _builtin_between(low, high, value, **k):\\n mode = check_mode((low, high, value), ['iii', 'iiv'], functor='between', **k)\\n low_v = int(low)\\n high_v = int(high)\\n if mode == 0: # Check\\n value_v = int(value)\\n if low_v <= value_v <= high_v:\\n return [(low, high, value)]\\n else: # Enumerate\\n results = []\\n for value_v in range(low_v, high_v + 1):\\n results.append((low, high, Constant(value_v)))\\n return results\",\n \"def cal(num1, num2, range):\\r\\n setup(range)\\r\\n return Andcollections(num1, num2)\",\n \"def evaluate_comparison_range(node):\\n return None\",\n \"def range_(self):\\n return tuple((e[0], e[-1]) for e in self.edges)\",\n \"def _range_overapped(self, x, y):\\n xs = set( range(x[0], x[1]))\\n ys = set( range(y[0], y[1]))\\n return xs.intersection(ys)\",\n \"def in_range(x, a, b):\\n return (x >= a and x <= b) or (x <= a and x >= b)\",\n \"def between(min, max):\\n def func(x):\\n return min <= x <= max\\n return func\",\n \"def visit_bound_predicate(self, predicate) -> T:\",\n \"def _range_contains(self, a, b):\\n\\t\\treturn b[0] >= a[0] and b[-1] <= a[-1]\",\n \"def other_above(self, element, first_successor):\\n successors = list(self.above(element))\\n if len(successors) != 2:\\n raise ValueError(\\\"element is not binary in lattice\\\")\\n elif successors[0] == first_successor:\\n return successors[1]\\n elif successors[1] == first_successor:\\n return successors[0]\\n else:\\n raise ValueError(\\\"first_successor is not a successor of element in lattice\\\")\",\n \"def __or__(self, other):\\n return MyCustomNumber(self.value | other.value)\",\n \"def _intersect_interval(self, other):\\n interval = Intersection(self.interval, other.interval)\\n return interval.inf, interval.sup\",\n \"def dock_if_bound(f, v):\\n return v[1:] if is_bound(f) else v\",\n \"def __ge__(self, other):\\n return self.master.abs2phy.__getitem__(pos=other)\",\n \"def _less_than_or_equal_to_op(spec):\",\n \"def __le__(self, other):\\n return self.element() <= other.element()\",\n \"def get(self, a: int, b: int) -> int:\\n result = self.identity()\\n q = [(1, 0, self.n2)]\\n while q:\\n k, left, right = q.pop()\\n if a <= left and right <= b:\\n result = self.binary(result, self.tree[k])\\n continue\\n m = (left + right) // 2\\n k <<= 1\\n if a < m and left < b:\\n q.append((k, left, m))\\n if a < right and left < m:\\n q.append((k + 1, m, right))\\n return result\",\n \"def _bound(x, min_value, max_value):\\n return np.maximum(min_value, np.minimum(x, max_value))\",\n \"def __ge__(self, other):\\n return self.element() >= other.element()\",\n \"def _bi_range(start, end):\\n if start == end:\\n return (start,)\\n\\n elif end < start:\\n return reversed(range(end, start + 1))\\n\\n else:\\n return range(start, end + 1)\",\n \"def __le__(self, other):\\r\\n # self <= other\\r\\n s = (self - other).share[0]\\r\\n return type(self)((s <= 0, self.exponent_type(0)))\",\n \"def ge(self, y):\\n return 1 - self.lt(y)\",\n \"def interval(a, b):\\n aIndex, bIndex = None, None\\n for i in range(len(table)):\\n fs = table[i]\\n if aIndex == None and fs >= a: aIndex = i\\n if bIndex == None and fs > b: bIndex = i\\n if bIndex == None: bIndex = len(table)\\n return bIndex - aIndex\",\n \"def new_range(r):\\n if isinstance(r, list) or isinstance(r, tuple) and len(r) == 2:\\n lower = r[0]\\n upper = r[1]\\n else:\\n lower = r\\n upper = r\\n lower = int(lower)\\n upper = int(upper)\\n return range(lower, upper + 1)\",\n \"def __gt__(self, other):\\n return self.element() > other.element()\",\n \"def _apply_binary_op_elementwise(\\n self: ConcreteStructuredMetricValue, other: ConcreteStructuredMetricValue,\\n op: Callable[[float, float], float]) -> ConcreteStructuredMetricValue:\\n ...\",\n \"def __le__(self, other: Any) -> ColumnOperators:\\n return self.operate(le, other)\",\n \"def gt(self, x, y):\\n return self.lt(y,x)\",\n \"def subrange(x, onset=None, offset=None):\\n return (y[(y >= onset) & ~(y > (offset))] for y in x)\",\n \"def get_bounds(f, lb=0, ub=None):\\r\\n lb_idx = np.searchsorted(f, lb, 'left')\\r\\n if ub == None:\\r\\n ub_idx = len(f)\\r\\n else:\\r\\n ub_idx = np.searchsorted(f, ub, 'right')\\r\\n\\r\\n return lb_idx, ub_idx\",\n \"def _get_sight_range(self):\\n raise NotImplementedError\",\n \"def in_range(start, end, x):\\n if start <= end:\\n return start <= x <= end\\n else:\\n return start <= x or x <= end\",\n \"def __or__(self, other):\\n @AccessFilter\\n def f(*args, **kwargs):\\n first = self(*args, **kwargs)\\n if first is None:\\n return other(*args, **kwargs)\\n return first\\n return f\",\n \"def __le__(self,f2):\\n return not self > f2 or self == f2\",\n \"def __or__(self, other):\\n\\t\\tif isinstance(other, int):\\n\\t\\t\\treturn self.value | other\\n\\t\\telif type(self) is type(other):\\n\\t\\t\\treturn self.value | other.value\",\n \"def _bisect_left(*args, **kwargs): # real signature unknown\\n pass\",\n \"def interval(self):\\n return Intersection(*(a.interval for a in self.args))\",\n \"def getx(v, lb, ub, i, B):\\r\\n x = lb + np.multiply((ub - lb), v)\\r\\n x[i] = B - (x.sum() - x[i])\\r\\n # Test if variable x[i] is within the bounds\\r\\n if x[i] <= ub[i] and x[i] >= lb[i]:\\r\\n return x\\r\\n else:\\r\\n return np.array([])\",\n \"def map_bound(value, in_low, in_high, out_low, out_high):\\n result = None\\n\\n if value <= in_low:\\n result = out_low\\n else:\\n if value >= in_high:\\n result = out_high\\n else:\\n # http://stackoverflow.com/a/5650012/574981\\n result = out_low + (\\n (out_high - out_low) * (value - in_low) / (in_high - in_low)\\n )\\n return result\",\n \"def slice2(self, vs=None,xs=None):\\n return self.condition2(vs,xs)\",\n \"def bisect(f, lo=0, hi=None, eps=1e-9):\\n lo_bool = f(lo)\\n if hi is None:\\n offset = 1\\n while f(lo+offset) == lo_bool:\\n offset *= 2\\n hi = lo + offset\\n else:\\n assert f(hi) != lo_bool\\n while hi - lo > eps:\\n mid = (hi + lo) / 2\\n if f(mid) == lo_bool:\\n lo = mid\\n else:\\n hi = mid\\n if lo_bool:\\n return lo\\n else:\\n return hi\",\n \"def foo_2(x, y):\\n\\tif x > y:\\n\\t\\treturn x\\n\\treturn y\",\n \"def __and__(self, other):\\n return MyCustomNumber(self.value & other.value)\",\n \"def __le__(self, other):\\n return self.x ** 2 + self.y ** 2 <= other.x ** 2 + other.y ** 2\",\n \"def query_range(self,i,j):\\n assert 0 <= i <= j <= len(self)\\n ans = self.__query_aux(0,0,len(self),i,j)\\n return ans\",\n \"def __le__(self, other):\\n return self.lessThanOrEqual(other)\",\n \"def range_inclusive(start, stop):\\n return range(start, stop + 1)\",\n \"def _inside_op_range(self, idx):\\n\\n if idx < self._parameters.op_range[0]:\\n return False\\n return (self._parameters.op_range[1] < 0 or\\n idx <= self._parameters.op_range[1])\",\n \"def __and__(self, other):\\n return self >> (lambda _: other)\",\n \"def get_xrange_indices(self, lower, upper) -> Tuple[int, int]:\\n lower_index = np.argmax(self.x >= lower)\\n upper_index = np.argmax(self.x >= upper)\\n return int(lower_index), int(upper_index)\",\n \"def __lt__(self, other: Any) -> ColumnOperators:\\n return self.operate(lt, other)\",\n \"def firstCrossIdx (i: int, wire1, wire2) -> Union [int, None]:\\n try:\\n if wire1 [i] in wire2:\\n return i\\n return F.tailR (firstCrossIdx) (i + 1, wire1, wire2)\\n except IndexError:\\n return None\",\n \"def __sub__(self, other):\\n return Base40Interval(self.base40 - other.base40)\",\n \"def bisect(x_range, x_i):\\n if x_i <= x_range[0]:\\n return 0\\n if x_i >= x_range[-1]:\\n return len(x_range)-1\\n \\n for i in range(len(x_range)-1):\\n if x_i > x_range[i] and x_i < x_range[i+1]:\\n return (i, i+1)\\n raise RuntimeError(\\\"No edges found.\\\")\",\n \"def _less_than_op(spec):\",\n \"def limit_by(self, field, lower_bound=None, upper_bound=None):\\n if lower_bound is not None and upper_bound is not None:\\n indices = (self[field] >= lower_bound) \\\\\\n & (self[field] <= upper_bound)\\n elif lower_bound is None:\\n indices = self[field] <= upper_bound\\n elif upper_bound is None:\\n indices = self[field] >= lower_bound\\n else:\\n raise ValueError(\\\"One bound must be set!\\\")\\n return self[indices]\",\n \"def ranged_int(A, B=FLOAT_INF):\\n\\n class ranged_int(int):\\n \\\"\\\"\\\"Int type in [A; B] range.\\\"\\\"\\\"\\n\\n def __init__(self, value):\\n assert A <= int(value) <= B, value\\n super(ranged_int, self).__init__()\\n\\n return ranged_int\",\n \"def rect(T):\\n return lambda t: (-T/2 <= t) & (t < T/2)\",\n \"def boundaries_second(*args):\\n return _ida_hexrays.boundaries_second(*args)\",\n \"def array_range(a, low, high, ref=None):\\n if ref is None:\\n ref = a\\n return a[np.logical_and(ref >= low, ref < high)]\",\n \"def findSignificantBins(return_bins):\\n \\n count = 0\\n range_even = []\\n range_odd = []\\n for i in xrange(1,len(return_bins['my_list'])):\\n value_range = (return_bins['my_list'][i] - return_bins['my_list'][(i-1)])\\n count = count + 1\\n if (count % 2 == 0) :\\n range_even.append(value_range)\\n else: \\n range_odd.append(value_range)\\n\\n print \\\"This is the even {}\\\".format(range_even)\\n print \\\"This is the odd {}\\\".format(range_odd)\\n if (return_bins.get('start_value') == 0) : \\n return range_even\\n else:\\n return range_odd\",\n \"def gt (x,y):\\n\\n return not le(x,y)\",\n \"def higher_floor(person_1, person_2):\\n return person_1 > person_2\",\n \"def less(value, other):\\n return value > other\",\n \"def GetScalarRange(self):\\n ...\",\n \"def __ge__(self, other):\\r\\n # self >= other\\r\\n s = (self - other).share[0]\\r\\n return type(self)((s >= 0, self.exponent_type(0)))\",\n \"def __le__(self, other):\\r\\n # self <= other <=> not (other < self)\\r\\n return 1 - runtime.lt(other, self)\",\n \"def binary(self, state):\\n val2 = pop(state)\\n val1 = pop(state)\\n # implicit deref\\n if isinstance(val1, Reference):\\n val1 = state.search(val1)\\n if isinstance(val2, Reference):\\n val2 = state.search(val2)\\n \\n if isinstance(val1, Numeric) and isinstance(val2, Numeric):\\n return (val1, val2)\\n else:\\n raise TypeError(\\\"Tried to math non-numerics\\\")\",\n \"def __ixor__(self, other: t.Any) -> te.Self:\\n return self._op_inplace('__isub__', other)\",\n \"def ge(self, x, y):\\n return self.le(y,x)\",\n \"def __ge__(self,f2):\\n return self > f2 or self == f2\",\n \"def gt(self, other):\\n self._raise_if_null(other)\\n if hasattr(other, 'end'):\\n return self.begin >= other.end\\n else:\\n return self.begin > other\",\n \"def __lt__(self,f2):\\n return not self > f2\",\n \"def other_under(self, element, first_predecessor):\\n predecessors = list(self.under(element))\\n if len(predecessors) != 2:\\n raise ValueError(\\\"element is not binary in lattice\\\")\\n elif predecessors[0] == first_predecessor:\\n return predecessors[1]\\n elif predecessors[1] == first_predecessor:\\n return predecessors[0]\\n else:\\n raise ValueError(\\\"first_successor is not a successor of element in lattice\\\")\",\n \"def range(self) -> ty.Tuple[float, float]:\\r\\n ...\",\n \"def overlap_with(self, other):\",\n \"def f_has_range(self):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def get_range(lst):\\n return float(max(lst)) - float(min(lst))\",\n \"def __nonzero__(self):\\n return _uhd_swig.range_vector_t___nonzero__(self)\",\n \"def bitrange(index, width, start, end):\\n return index >> (width - end) & ((2 ** (end - start)) - 1)\",\n \"def first2(x, y):\\n y = np.asarray(y)\\n return y[np.argsort(x)][0]\",\n \"def __or__(self,other):\\n #TODO: ensure that the \\\"left\\\" operand is a conditional itself.\\n return compositeConditionalGenerator(left = self, right = other)\",\n \"def get_range(self) -> tuple[int, int]:\\n return self.range_from, self.range_to\",\n \"def map_between(value, start, stop):\\n\\n return (stop - start) * value + start\",\n \"def getRange(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def closed_range(start, stop, step):\\n return range(start, stop + 1, step)\",\n \"def __gt__(self, other):\\n return self >= other and not self <= other\",\n \"def simplebounds(cls, val, lower, upper):\\n if val < lower:\\n val = lower\\n if val > upper:\\n val = upper\\n return val\",\n \"def mapRange(num, min1, max1, min2, max2, clamp=True):\\n if(clamp and num < min1):\\n return min2\\n if(clamp and num > max1):\\n return max2\\n\\n num1 = (num - min1) / (max1 - min1)\\n num2 = (num1 * (max2 - min2)) + min2\\n return num2\",\n \"def range(x):\\n try:\\n return (min(min(y) for y in x), max(max(y) for y in x))\\n except ValueError:\\n return (None, None)\",\n \"def lower_bound(self) -> float:\\n ...\",\n \"def in_range(cls, lhs, rhs):\\n return rhs[0] <= lhs <= rhs[1]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6828715","0.6507789","0.62094086","0.6026386","0.5969396","0.5849859","0.5836691","0.58243334","0.57149607","0.5674531","0.5671843","0.56514573","0.56158537","0.56145406","0.55590016","0.55359095","0.5534223","0.5493425","0.54904515","0.5454143","0.5448288","0.5446138","0.5445249","0.5415833","0.538504","0.53773594","0.53736085","0.53575164","0.53477377","0.5343283","0.5341681","0.53228456","0.5322211","0.53096414","0.5288807","0.5285822","0.5285201","0.5278714","0.52745605","0.5269914","0.5269201","0.52578217","0.52565134","0.52494746","0.52459955","0.5242417","0.52382475","0.52237815","0.52180094","0.52172464","0.5216097","0.52125174","0.5208588","0.5208545","0.5205986","0.52022296","0.518388","0.51786506","0.5176976","0.5175348","0.5174145","0.5167251","0.5164714","0.5162859","0.51591265","0.5158555","0.51577044","0.51498353","0.5140575","0.51341254","0.51334333","0.5111753","0.51099974","0.51048064","0.50978917","0.50884706","0.5082834","0.5077823","0.5070515","0.50667506","0.5065604","0.50653434","0.50643706","0.50626135","0.505797","0.5053616","0.50534225","0.50409937","0.50370514","0.50338465","0.50328803","0.5031893","0.5031711","0.5026824","0.50264657","0.5024882","0.5023028","0.5022496","0.5016192","0.50144506"],"string":"[\n \"0.6828715\",\n \"0.6507789\",\n \"0.62094086\",\n \"0.6026386\",\n \"0.5969396\",\n \"0.5849859\",\n \"0.5836691\",\n \"0.58243334\",\n \"0.57149607\",\n \"0.5674531\",\n \"0.5671843\",\n \"0.56514573\",\n \"0.56158537\",\n \"0.56145406\",\n \"0.55590016\",\n \"0.55359095\",\n \"0.5534223\",\n \"0.5493425\",\n \"0.54904515\",\n \"0.5454143\",\n \"0.5448288\",\n \"0.5446138\",\n \"0.5445249\",\n \"0.5415833\",\n \"0.538504\",\n \"0.53773594\",\n \"0.53736085\",\n \"0.53575164\",\n \"0.53477377\",\n \"0.5343283\",\n \"0.5341681\",\n \"0.53228456\",\n \"0.5322211\",\n \"0.53096414\",\n \"0.5288807\",\n \"0.5285822\",\n \"0.5285201\",\n \"0.5278714\",\n \"0.52745605\",\n \"0.5269914\",\n \"0.5269201\",\n \"0.52578217\",\n \"0.52565134\",\n \"0.52494746\",\n \"0.52459955\",\n \"0.5242417\",\n \"0.52382475\",\n \"0.52237815\",\n \"0.52180094\",\n \"0.52172464\",\n \"0.5216097\",\n \"0.52125174\",\n \"0.5208588\",\n \"0.5208545\",\n \"0.5205986\",\n \"0.52022296\",\n \"0.518388\",\n \"0.51786506\",\n \"0.5176976\",\n \"0.5175348\",\n \"0.5174145\",\n \"0.5167251\",\n \"0.5164714\",\n \"0.5162859\",\n \"0.51591265\",\n \"0.5158555\",\n \"0.51577044\",\n \"0.51498353\",\n \"0.5140575\",\n \"0.51341254\",\n \"0.51334333\",\n \"0.5111753\",\n \"0.51099974\",\n \"0.51048064\",\n \"0.50978917\",\n \"0.50884706\",\n \"0.5082834\",\n \"0.5077823\",\n \"0.5070515\",\n \"0.50667506\",\n \"0.5065604\",\n \"0.50653434\",\n \"0.50643706\",\n \"0.50626135\",\n \"0.505797\",\n \"0.5053616\",\n \"0.50534225\",\n \"0.50409937\",\n \"0.50370514\",\n \"0.50338465\",\n \"0.50328803\",\n \"0.5031893\",\n \"0.5031711\",\n \"0.5026824\",\n \"0.50264657\",\n \"0.5024882\",\n \"0.5023028\",\n \"0.5022496\",\n \"0.5016192\",\n \"0.50144506\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75341356"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19589,"cells":{"query":{"kind":"string","value":"Return the atoms, derived from the first entry in the joint probability table"},"document":{"kind":"string","value":"def atomList(joints):\n assert len(joints) > 0\n first = joints[0]\n functorList = first[1][:-2] # Second element of row, last two elements of that are joint prob and log prob\n atomList = []\n for (node,_) in functorList:\n atomList.append(node.functor+\"(\"+\",\".join(node.varList)+\")\")\n return atomList"},"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 jointProbabilities(constants, db, ruleList, bn):\n vars = bn.variableList()\n combs = generateCombos(vars, constants)\n joints = []\n for grounding in combs:\n joints.append((grounding, bn.jointProbs(grounding, db, ruleList)))\n return (vars, atomList(joints), joints)","def enumerate_joint_ask(X, e, P):\n Q = ProbDist(X) ## A probability distribution for X, initially empty\n Y = [v for v in P.variables if v != X and v not in e]\n for xi in P.values(X):\n Q[xi] = enumerate_joint(Y, extend(e, X, xi), P)\n return Q.normalize()","def prior_sample(self, bn):\n x = np.zeros(3)\n\n # first joint prob\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\n x[0] = random_choice[0]\n\n # Second Joint Prob\n if x[0] == 0.1:\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\n x[1] = random_choice\n elif x[0] == 0.9:\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\n x[1] = random_choice\n\n # Third Joint Prob\n if random_choice[0] == 0.8 or random_choice == 0.1:\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\n x[2] = random_choice\n else:\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\n x[2] = random_choice\n return x","def pon(self):\n return self.table[1, 1] / (self.table[0, 1] + self.table[1, 1])","def head2head(self, atoms):\n\n c1, c2 = atoms.keys()\n c1_ndx, c2_ndx = atoms.values()\n\n chain1, chain2 = self.determine_chains([c1, c2])\n\n # to get indexing right\n c1_ndx -= self.monomer.indices[chain1]['C1']\n c2_ndx -= self.monomer.indices[chain2]['C2']\n\n types = {'chain1': {'C1': 'c3', 'C2': 'c2', 'C3': 'c2', 'C4': 'c2', 'H1': 'hc', 'H2': 'hc', 'H3': 'ha',\n 'H4': 'ha', 'H5': 'ha'},\n 'chain2': {'C1': 'c3', 'C2': 'c2', 'C3': 'c2', 'C4': 'c3', 'H1': 'hc', 'H2': 'hc', 'H3': 'ha',\n 'H4': 'ha', 'H5': 'hc', 'D4': 'hc'}}\n\n # update types\n reacted_types = {'chain1': {c1_ndx + self.monomer.indices[chain1][a]: types['chain1'][a] for a in\n types['chain1'].keys()},\n 'chain2': {c2_ndx + self.monomer.indices[chain2][a]: types['chain2'][a] for a in\n types['chain2'].keys()}}\n\n # bond between carbons\n bonds = [[c1_ndx + self.monomer.indices[chain1]['C1'], c2_ndx + self.monomer.indices[chain2]['C1'], 'carbon']]\n\n # dummy bonds - 1 new bond between dummy atoms and carbon\n bonds += [[c2_ndx + self.monomer.indices[chain2]['C4'], c2_ndx + self.monomer.indices[chain2]['D4'], 'dummy']]\n\n # define indices of left-over radicals\n radicals = [c1_ndx + self.monomer.indices[chain1]['C2']]\n\n chain1_impropers = ['C1'] # [1]\n chain2_impropers = ['C1', 'C4'] # [1, 2]\n rm_improper = []\n for c in chain1_impropers:\n rm_improper.append([c1_ndx + self.monomer.indices[chain1][x] for x in self.monomer.impropers[chain1][c]])\n for c in chain2_impropers:\n rm_improper.append([c2_ndx + self.monomer.indices[chain2][x] for x in self.monomer.impropers[chain2][c]])\n\n # define terminated atoms\n terminated = [c1_ndx + self.monomer.indices[chain1]['C1'], c2_ndx + self.monomer.indices[chain2]['C1'],\n c2_ndx + self.monomer.indices[chain2]['C2']] # C2 terminated for now even though still alkene\n\n return reacted_types, bonds, radicals, rm_improper, terminated","def getFirstTorsionInds(self, extended):\n offset = 6 if extended else 0\n torsionInds = np.array(range(offset + 5, self.natoms * 3, 3))\n primaryTorsions = sorted(list(set(self._firstTorsionTInd)))\n return list(torsionInds[primaryTorsions])","def get_center_of_mass_allies(self,obs):","def posterior_first(self, word):\r\n prob = {}\r\n if word not in prob.keys():\r\n prob[word] = {\r\n pos: self.emission_probability[pos][word]\r\n * self.initial_probability[pos]\r\n if word in self.emission_probability[pos]\r\n else (1 / float(10 ** 10)) * self.initial_probability[pos]\r\n for pos in self.position_list\r\n }\r\n\r\n return prob[word]","def __generate_atoms__(self, pdb):\n\n atoms = [] # Maybe this can be a set \n # TODO: Here I can use self.structure.header['missing_residues'] to get a list of residues. It will have their seq and I can use this to make a sequential index\n for model in self.structure:\n residues = model.get_residues() # Biopython \n for residue in residues:\n full_id = residue.get_full_id()\n ins_code = full_id[3][2] \n this_model = str(int(full_id[1]) + 1) # BioPython starts at 0 and fr3d-python starts at 1. Add 1 to each model so unit ids match\n this_chain = full_id[2]\n component_number = full_id[3][1]\n if 'H' in full_id[3][0][0]:\n res_group = 'HETATM'\n else:\n res_group = 'ATOM'\n\n res = residue.get_resname().replace(\" \",\"\")\n\n if ins_code == \" \":\n ins_code = None\n\n for atom in residue:\n #drop numbers \n id = atom.id \n id = re.sub(r'\\d+', '',id)\n first = id[0]\n # logic to extract the type of atom from the id\n if 'C' == first: #Carbon\n atom_type = 'C' \n elif 'O' == first: #Ox\n atom_type = 'O'\n elif 'P' == first: #Phosphorus\n atom_type = 'P'\n elif 'N' == first: # nitrogen\n atom_type = 'N'\n else: #Magnesium, other ions\n atom_type = atom.id\n\n x = atom.coord[0]\n y = atom.coord[1]\n z = atom.coord[2]\n \n alt_id = atom.get_altloc()\n if alt_id == \" \":\n alt_id = None\n atoms.append(Atom(x=x, y=y, z=z,\n pdb=self.name,\n model=this_model,\n chain=this_chain,\n component_id=res,\n component_number=component_number,\n component_index=component_number,\n insertion_code=ins_code,\n alt_id= alt_id,\n group=res_group,\n type=atom_type,\n name=atom.get_name(),\n symmetry='1_555', #I haven't figured out how to extract symmetries from pdb files yet. Resort to identity\n polymeric=True)) # Need to find a way to parse this from biopython. Important, may be relevent in structures.py\n return atoms","def get_result(person_to_joint_assoc, joint_list):\n outputs = []\n for ridxPred in range(len(person_to_joint_assoc)):\n\n keypoints = np.zeros((18, 3))\n\n for part in range(18):\n index = int(person_to_joint_assoc[ridxPred, part])\n\n if -1 == index:\n keypoints[part, 0] = 0\n keypoints[part, 1] = 0\n keypoints[part, 2] = 0\n\n else:\n keypoints[part, 0] = joint_list[index, 0] + 0.5\n keypoints[part, 1] = joint_list[index, 1] + 0.5\n keypoints[part, 2] = 1.\n\n outputs.append(keypoints)\n return outputs","def GetBonds(Bonds):\n b = sorted([(min(x), max(x)) for x in Bonds])\n Bonds13, Bonds14 = [], []\n for (a1,b1) in b:\n #check for bonds with a1 at the center of a 1-3 interaction,\n #letting b1 be the higher number of the two flanking\n clist = [b2 for (a2,b2) in b if a2 == a1 and b2 < b1] + \\\n [a2 for (a2,b2) in b if b2 == a1 and a2 < b1]\n Bonds13.extend([(min(c,b1), max(c,b1)) for c in clist])\n #check for bonds with b1 at the center of a 1-3 interaction,\n #letting a1 be the higher number of the two flanking\n clist = [b2 for (a2,b2) in b if a2 == b1 and b2 < a1] + \\\n [a2 for (a2,b2) in b if b2 == b1 and a2 < a1]\n Bonds13.extend([(min(c,a1), max(c,a1)) for c in clist])\n #find atoms connected to a1\n clist = [b2 for (a2,b2) in b if a1==a2 and not b1==b2] +\\\n [a2 for (a2,b2) in b if a1==b2 and not b1==a2]\n #find atoms connected to b1\n dlist = [a2 for (a2,b2) in b if b1==b2 and not a1==a2] +\\\n [b2 for (a2,b2) in b if b1==a2 and not a1==b2]\n Bonds14.extend([(min(c,d), max(c,d)) for c in clist for d in dlist])\n Bonds1213 = b + Bonds13\n #sort\n Bonds1213.sort()\n Bonds14.sort()\n #get unique values in case of loops\n Bonds1213 = [x for (i,x) in enumerate(Bonds1213) if i == 0 or x != Bonds1213[i-1]]\n Bonds14 = [x for (i,x) in enumerate(Bonds14) if i == 0 or x != Bonds14[i-1]]\n #convert to arrays \n Bonds1213 = array(Bonds1213, int)\n Bonds14 = array(Bonds14, int)\n return Bonds1213, Bonds14","def get_joints(self, anno: List[Mapping[str, Any]]) -> np.ndarray:\n joints = []\n\n for i, obj in enumerate(anno):\n keypoints = np.array(obj[\"keypoints\"]).reshape([-1, 3])\n joints.append(keypoints)\n\n num_instances = len(joints)\n joints = np.array(joints, dtype=np.float32).reshape((num_instances, self.num_joints, 3))\n return joints","def get_prob(self, head):\n if len(self._head_vars) == 0 and head.size() > 0:\n return 0.\n\n trimmed_head = head.get_trimmed(self._head_vars)\n if trimmed_head in self._table:\n return self._table.get(trimmed_head)\n\n return 0.","def calc_pvecs_1mol(mol_crds, act_ats):\n nearest_neighbours = np.zeros((len(act_ats), 3, 3))\n at_inds = np.arange(len(mol_crds))\n at_map = {} # map at num to active at num\n\n # Loop over active atoms and calc nearest neighbours\n for count, iat in enumerate(act_ats):\n at_crd = mol_crds[iat]\n dists = np.linalg.norm(mol_crds - at_crd, axis=1)\n\n dist_mask = dists < 3.5\n nn_ats = at_inds[dist_mask][:3]\n if len(nn_ats) != 3:\n # Set the map at to the next closest one\n closest_at = K_min(list(dists), 2)\n at_map[count] = closest_at[1]\n continue\n else:\n # Make sure iat is the first atom\n nn_ats = nn_ats[nn_ats != iat][:2]\n nn_ats = [iat, *nn_ats]\n assert len(nn_ats) == 3\n\n nearest_neighbours[count] = mol_crds[nn_ats]\n\n # Set pvecs the same as the closest atom if we can't calculate them\n for at in at_map:\n nearest_neighbours[at] = nearest_neighbours[at_map[at]]\n\n pvecs = []\n for a1, a2, a3 in nearest_neighbours:\n v1 = a2 - a1\n v2 = a3 - a1\n pvec = np.cross(v1, v2)\n pvec /= np.linalg.norm(pvec)\n pvecs.append(pvec)\n\n return np.array(pvecs)","def evaljointbayes(fname):\n #read file and save lines to contents\n contents = []\n f = open(fname)\n for line in f:\n randomVar = line.rstrip().split()\n if randomVar[0] != 'END':\n contents.append(randomVar)\n print \"1. Read file\", fname, \"successfully.\"\n f.close()\n \n #count numbers of nodes and probabilities in each line\n length = len(contents)\n nodes, prob = [0] * length, [0] * length\n table = [] #save all probabilities for each node \n for num in range(0, length):\n tableline = []\n for i in contents[num]:\n try:\n j = float(i)\n prob[num] += 1\n tableline.append(j) \n except ValueError:\n if i != 'NONE':\n nodes[num] += 1 \n table.append(tableline) \n \n #print out the joint distribution formular\n print \"2. The joint distribution using this network is:\"\n nodelist = []\n for line in contents:\n nodelist.append(line[0])\n print \"P(\", printElement(nodelist), \") = \"\n for num in range(0, length):\n line = contents[num]\n if nodes[num] == 1:\n print \"P(\", line[0], \")\", \n else: \n print \"P(\", line[0], '|', printElement(line[1:nodes[num]]),\\\n \")\", \n if num == length - 1:\n print ' '\n else: \n print ' * ', \n \n #print out the full joint distribution table \n###This is the revised version using recursive calls and###\n###print out the cpt table to a .txt file###\n #further revision includes deleting depth by pop() table, contents and nodes\n #also, I can extract the parents in the previous step, then contents will not be used here\n fo=open(fname+'.zz.txt','w')\n result = 1.0\n depth = 0\n global additions, multiplications\n additions, multiplications = 0, 0\n fullCPT(nodelist, [], result, depth, fo, contents, table, nodes)\n fo.close()\n\n #print out result of step 3 \n print \"3. Additions and multiplications needed to calculate\",\\\n \"the joint distribution is:\", additions, \"and\", multiplications\n print \"The number of nodes in the network is: \", length\n\n #print out reselt of step 4\n spaceFull = int(math.pow(2,length)) - 1\n spaceBN = sum(prob)\n print \"4. Space this network saved is (Compactness): \", spaceBN, \"/\", \\\n spaceFull, \"=\", float(spaceBN) / float(spaceFull), '\\n'\n return","def _get_joints(self, anno, idx):\n num_people = len(anno)\n\n joints = np.zeros(\n (num_people, self.ann_info['num_joints'], 3), dtype=np.float32)\n\n for i, obj in enumerate(anno):\n joints[i, :self.ann_info['num_joints'], :3] = \\\n np.array(obj['keypoints']).reshape([-1, 3])\n\n img_info = self.coco.loadImgs(self.img_ids[idx])[0]\n orgsize = np.array([img_info['height'], img_info['width'], 1])\n\n return joints, orgsize","def setAtomPairs(self):\n atomPairs = []\n for item in self.condensedProperDihedrals:\n dih = item[0]\n atom1 = dih.atoms[0]\n atom2 = dih.atoms[3]\n pair = [atom1, atom2]\n if atomPairs.count(pair) == 0:\n atomPairs.append(pair)\n self.atomPairs = atomPairs # [[atom1, atom2], ...]\n self.printDebug(\"atomPairs done\")","def joint_pairs(self):\n return [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16], #17 body keypoints\n [20-3, 23-3], [21-3, 24-3], [22-3, 25-3], [26-3, 42-3], [27-3, 41-3], [28-3, 40-3], [29-3, 39-3], [30-3, 38-3], \n [31-3, 37-3], [32-3, 36-3], [33-3, 35-3], [43-3, 52-3], [44-3, 51-3], [45-3, 50-3], [46-3, 49-3], [47-3, 48-3], \n [62-3, 71-3], [63-3, 70-3], [64-3, 69-3], [65-3, 68-3], [66-3, 73-3], [67-3, 72-3], [57-3, 61-3], [58-3, 60-3],\n [74-3, 80-3], [75-3, 79-3], [76-3, 78-3], [87-3, 89-3], [93-3, 91-3], [86-3, 90-3], [85-3, 81-3], [84-3, 82-3],\n [94-3, 115-3], [95-3, 116-3], [96-3, 117-3], [97-3, 118-3], [98-3, 119-3], [99-3, 120-3], [100-3, 121-3],\n [101-3, 122-3], [102-3, 123-3], [103-3, 124-3], [104-3, 125-3], [105-3, 126-3], [106-3, 127-3], [107-3, 128-3],\n [108-3, 129-3], [109-3, 130-3], [110-3, 131-3], [111-3, 132-3], [112-3, 133-3], [113-3, 134-3], [114-3, 135-3]]","def atoms(self):\n return self.qc_mol.atoms + self.br_mol.atoms + self.pc_mol.atoms","def find_data_association(self, obs):\n prob = 0\n ass_obs = np.zeros((2,1))\n ass_jacobian = np.zeros((2,2))\n ass_adjcov = np.zeros((2,2))\n landmark_idx = -1\n for idx, landmark in enumerate(self.landmarks):\n predicted_obs, jacobian, adj_cov = self.compute_jacobians(landmark)\n p = multi_normal(np.transpose(np.array([obs])), predicted_obs, adj_cov)\n if p > prob:\n prob = p\n ass_obs = predicted_obs\n ass_jacobian = jacobian\n ass_adjcov = adj_cov\n landmark_idx = idx\n return prob, landmark_idx, ass_obs, ass_jacobian, ass_adjcov","def bond_atoms(atom_list):\n pass","def create_joint_prob_corr_table(to_dict=True):\n\n p = np.arange(0., 1.05, 0.05) # Range of marginal probability\n corr = np.arange(-1., 1.05, 0.05) # Range of correlations\n\n table = np.zeros(shape=(len(corr), len(p), len(p)))\n\n for i in range(len(corr)):\n\n sig = np.matrix([[1., corr[i]], [corr[i], 1.]])\n\n for j in range(len(p)):\n for k in range(j, len(p)):\n\n if corr[i] == -1:\n jp = max(0., p[j] + p[k] - 1.)\n elif corr[i] == 0.:\n jp = p[j] * p[k]\n elif corr[i] == 1.:\n jp = min(p[j], p[k])\n elif p[j] * p[k] == 0. or p[j] == 1 or p[k] == 1.:\n jp = p[j] * p[k]\n else:\n jp = mvnun(np.array([0., 0.]), np.array([np.inf, np.inf]),\n [norm.ppf(p[j]), norm.ppf(p[k])],\n sig)[0]\n\n table[i, j, k] = table[i, k, j] = jp\n\n if to_dict:\n n_table = {}\n\n # convert to dictionary:\n for j in range(len(p)):\n for k in range(j, len(p)):\n pj = round(p[j], 2)\n pk = round(p[k], 2)\n n_table[(pj, pk)] = n_table[(pj, pk)] = np.array((corr, table[:, j, k]))\n\n return n_table\n\n return table","def _propose_atoms_in_order(self, atom_group):\n atom_torsions= []\n logp = []\n assert len(atom_group) == len(set(atom_group)), \"There are duplicate atom indices in the list of atom proposal indices\"\n while len(atom_group) > 0:\n #initialise an eligible_torsions_list\n eligible_torsions_list = list()\n\n for atom_index in atom_group:\n\n # Find the shortest path up to length four from the atom in question:\n shortest_paths = nx.algorithms.single_source_shortest_path(self._residue_graph, atom_index, cutoff=4)\n\n # Loop through the destination and path of each path and append to eligible_torsions_list\n # if destination has a position and path[1:3] is a subset of atoms with positions\n for destination, path in shortest_paths.items():\n\n # Check if the path is length 4 (a torsion) and that the destination has a position. Continue if not.\n if len(path) != 4 or destination not in self._atoms_with_positions_set:\n continue\n\n # If the last atom is in atoms with positions, check to see if the others are also.\n # If they are, append the torsion to the list of possible torsions to propose\n if set(path[1:3]).issubset(self._atoms_with_positions_set):\n eligible_torsions_list.append(path)\n\n assert len(eligible_torsions_list) != 0, \"There is a connectivity issue; there are no torsions from which to choose\"\n #now we have to randomly choose a single torsion\n ntorsions = len(eligible_torsions_list)\n random_torsion_index = np.random.choice(range(ntorsions))\n random_torsion = eligible_torsions_list[random_torsion_index]\n\n #append random torsion to the atom_torsions and remove source atom from the atom_group\n chosen_atom_index = random_torsion[0]\n first_old_atom_index = random_torsion[1]\n atom_torsions.append(random_torsion)\n atom_group.remove(chosen_atom_index)\n\n #add atom to atoms with positions and corresponding set\n self._atoms_with_positions_set.add(chosen_atom_index)\n\n #add a bond from the new to the previous torsion atom in the _reference_connectivity_graph\n self._reference_connectivity_graph.add_edge(chosen_atom_index, first_old_atom_index)\n\n #add the log probability of the choice to logp\n logp.append(np.log(1./ntorsions))\n\n # Ensure that logp is not ill-defined\n assert len(logp) == len(atom_torsions), \"There is a mismatch in the size of the atom torsion proposals and the associated logps\"\n\n return atom_torsions, logp","def get_first(self):\n return self.A[1][0] if self.n > 0 else None","def position(self):\n return self.atoms.reshape((1,-1))","def atoms(self):\n return self._atoms","def initialize_record_distribution(states, actions, empty_atoms):\n dist = {}\n\n atom_shape = empty_atoms.shape\n\n # initial values, equal probability distribution\n val = np.full(shape=atom_shape, fill_value=1.0 / float(atom_shape[0]))\n\n for i in states:\n dist[i] = {}\n for j in actions:\n dist[i][j] = val.copy()\n\n return dist","def MAP(cpts, obs, terms):\r\n\r\n # a list to store the computed probabilities\r\n all_sums = []\r\n # initialize all terms to false\r\n for value in range(len(terms)):\r\n terms[value] = [terms[value], '0']\r\n search_array = terms + obs\r\n # if all terms are being watched, just call MPE\r\n if len(search_array) == len(cpts):\r\n return MPE(cpts, obs)\r\n # we need to know what terms we aren't interested in so we start with \r\n # or terms and observations and note the variables that appear in CPT but\r\n # not in those\r\n dont_count = []\r\n for var in cpts:\r\n if [var[0], '0'] not in search_array and [var[0], '1'] not in search_array:\r\n dont_count.append(var[0])\r\n terms.append([var[0],'1'])\r\n # sort the terms to ensure correct ordering\r\n terms.sort()\r\n # creates a list of all possible bit strings\r\n # just an easy way to create all possible truth assignments\r\n seq = [\"\".join(seq) for seq in itertools.product(\"01\", repeat=len(terms))]\r\n # loop through all possible truth assignments\r\n for j in range(len(seq)):\r\n # we initialize at probability = 100%\r\n chance = 1\r\n # assign the truth values\r\n for k in range(len(seq[j])):\r\n terms[k][1] = seq[j][k]\r\n # this computes the probability using the chaining rule\r\n for i in range(len(terms)):\r\n new_terms = terms[:-i-1] + obs\r\n new_terms.sort()\r\n chance *= probability(cpts,terms[-i-1], new_terms)\r\n # add the probabilities to our list\r\n all_sums.append(chance)\r\n combine = []\r\n # note all variables which weren't in obs or Vs\r\n for i in dont_count:\r\n combine.append(terms.index([i,'1']))\r\n # this will store the final probabilities\r\n final_array = [0] * len(seq)\r\n # another complicated looking loop, it just serves to combine probabilities\r\n # for example, if we have a CPT with x_1, x_2, x_3, x_4 and we observe \r\n # x_1 to be true and have Vs = [x_3, x_4] then we need to combine the \r\n # probabilities that are the same except for x_2 = true vs false\r\n for loc in combine:\r\n for sequence in range(len(seq)):\r\n for alt_sequence in range(sequence+1,len(seq)):\r\n if (seq[sequence][:loc] + seq[sequence][loc+1:]) == (seq[alt_sequence][:loc] + seq[alt_sequence][loc+1:]):\r\n final_array[sequence] = all_sums[sequence] + all_sums[alt_sequence]\r\n\r\n # get the truth assignment for the highest probability\r\n location = seq[final_array.index(max(final_array))]\r\n truth_assignment = []\r\n # place the truth assignment in a more readable fashion\r\n for value in range(len(terms)):\r\n if terms[value] in search_array:\r\n if location[value] == '0':\r\n truth_assignment.append(terms[value][0]+ ' = False')\r\n else:\r\n truth_assignment.append(terms[value][0]+ ' = True')\r\n return (truth_assignment)","def homo(a):\n return [ a[0]/a[3],\n a[1]/a[3],\n a[2]/a[3],\n 1 ]","def getActionProb(self, canonicalBoard, temp=1):\n for i in range(self.args.numMCTSSims):\n dir_noise = (i == 0 and self.dirichlet_noise)\n self.search(canonicalBoard, dirichlet_noise=dir_noise)\n\n s = self.game.stringRepresentation(canonicalBoard)\n counts = [\n self.Nsa[(s, a)] if (s, a) in self.Nsa else 0\n for a in range(self.game.getActionSize())\n ]\n\n if temp == 0:\n bestAs = np.array(np.argwhere(counts == np.max(counts))).flatten()\n bestA = np.random.choice(bestAs)\n probs = [0] * len(counts)\n probs[bestA] = 1\n return probs\n\n counts = [x**(1. / temp) for x in counts]\n counts_sum = float(sum(counts))\n probs = [x / counts_sum for x in counts]\n return probs","def joint_prob(network, assignment):\n prob = 1\n for a_key in assignment:\n conditions = []\n current = network[a_key]\n for parent in current['Parents']:\n conditions.append(True) if assignment[parent] else conditions.append(False)\n conditions = tuple(conditions)\n if not assignment[a_key]:\n prob *= (1 - current['CPT'].get(conditions))\n else:\n prob *= (current['CPT'].get(conditions)) \n return prob","def test_get_first_blast_hit_per_seq(self):\r\n p = BlastTaxonAssigner({})\r\n blast_hits = {'s1': [('blah', 0.0)],\r\n 's3': [('dsasd', 1e-42), ('rrr', 1e-12), ('qqq', 0.001)],\r\n 's2': []}\r\n expected = {'s1': ('blah', 0.0),\r\n 's3': ('dsasd', 1e-42),\r\n 's2': None}\r\n actual = p._get_first_blast_hit_per_seq(blast_hits)\r\n self.assertEqual(actual, expected)","def J_mpp(eta,Absorbed):\n return current_density(V_mpp(eta,Absorbed), eta,Absorbed)","def _get_tuple_of_probabilities(self):\n\t\tk_array = np.arange(-self.n,self.n+1,2)\n\t\tprobability_array = []\n\t\t\n\t\tfor k in k_array:\n\t\t\tprobability_array.append(self._calculate_probability(k))\n\t\t\t\n\t\treturn (k_array,probability_array)","def fom(self):\n return self.table[1, 0] / (self.table[0, 0] + self.table[1, 0])","def getAtoms(self):\n atomNameList = self.getFlagData('ATOM_NAME')\n atomTypeNameList = self.getFlagData('AMBER_ATOM_TYPE')\n self._atomTypeNameList = atomTypeNameList\n massList = self.getFlagData('MASS')\n chargeList = self.getFlagData('CHARGE')\n resIds = self.getFlagData('RESIDUE_POINTER') + [0]\n #uniqAtomTypeId = self.getFlagData('ATOM_TYPE_INDEX') # for LJ\n balanceChargeList = self.balanceCharges(chargeList)\n coords = self.getCoords()\n ACOEFs, BCOEFs = self.getABCOEFs()\n\n atoms = []\n atomTypes = []\n tmpList = [] # a list with unique atom types\n totalCharge = 0.0\n countRes = 0\n id = 0\n for atomName in atomNameList:\n atomTypeName = atomTypeNameList[id]\n if id + 1 == resIds[countRes]:\n resid = countRes #self.residueLabel[countRes]\n countRes += 1\n mass = massList[id]\n charge = balanceChargeList[id]\n totalCharge += charge\n coord = coords[id]\n ACOEF = ACOEFs[id]\n BCOEF = BCOEFs[id]\n atomType = AtomType(atomTypeName, mass, ACOEF, BCOEF)\n if atomTypeName not in tmpList:\n tmpList.append(atomTypeName)\n atomTypes.append(atomType)\n atom = Atom(atomName, atomType, id + 1, resid, mass, charge, coord)\n atoms.append(atom)\n id += 1\n\n if atomTypeName[0].islower():\n self.atomTypeSystem = 'gaff'\n else:\n self.atomTypeSystem = 'amber'\n\n self.printDebug('Balanced TotalCharge %13.10f' % float(totalCharge / qConv))\n self.totalCharge = int(totalCharge)\n\n self.atoms = atoms\n self.atomTypes = atomTypes\n\n self.pbc = None\n if len(coords) == len(atoms) + 2:\n self.pbc = [coords[-2], coords[-1]]\n self.printDebug(\"PBC = '%s\" % self.pbc)\n self.printDebug(\"getAtoms done\")","def probX(Xs, a, td, irts=Irts({}), prior=None, origmat=None, changed=[]):\n\n try:\n numnodes=len(a)\n except TypeError:\n raise Exception(a)\n reg=(1+1e-10) # nuisance parameter to prevent errors; can also use pinv instead of inv, but that's much slower\n identmat=np.identity(numnodes) * reg # pre-compute for tiny speed-up (only for non-IRT)\n\n probs=[]\n\n # generate transition matrix (from: column, to: row) from link matrix\n t=a/sum(a.astype(float))\n t=np.nan_to_num(t) # jumping/priming models can have nan in matrix, need to change to 0\n \n if (td.jumptype==\"stationary\") or (td.start_node==\"stationary\"):\n statdist=stationary(t)\n\n # U-INVITE probability excluding jumps, prior, and priming adjustments -- those come later\n for xnum, x in enumerate(Xs):\n x2=np.array(x)\n t2=t[x2[:,None],x2] # re-arrange transition matrix to be in list order\n prob=[]\n if td.start_node==\"stationary\":\n prob.append(statdist[x[0]]) # probability of X_1\n elif td.start_node==\"uniform\":\n prob.append(1.0/numnodes)\n\n # if impossible starting point, return immediately\n if (prob[-1]==0.0):\n try:\n return -np.inf, (x[0], x[1])\n except:\n return -np.inf, x[0]\n\n if (len(changed) > 0) and isinstance(origmat,list): # if updating prob. matrix based on specific link changes\n update=0 # reset for each list\n\n # flag if list contains perseverations\n if len(x) == len(set(x)):\n list_has_perseverations = False\n else:\n list_has_perseverations = True\n\n for curpos in range(1,len(x)):\n if (len(changed) > 0) and isinstance(origmat,list):\n if update==0: # first check if probability needs to be updated\n if (Xs[xnum][curpos-1] in changed): # (only AFTER first changed node has been reached)\n update=1\n else: # if not, take probability from old matrix\n prob.append(origmat[xnum][curpos])\n continue\n \n if list_has_perseverations: # a bit slower because matrix is being copied\n x2=np.array([i for i,j in enumerate(x) if (j not in x[:i]) and (i < curpos)]) # column ids for transient states excluding perseverations\n Q=t2[x2[:,None],x2] # excludes perseverations. could be sped if only performed when Q contains perseverations\n # as opposed to being done for every transition if a perseveration is in the list\n else: \n Q=t2[:curpos,:curpos] # old way when data does not include perseverations\n \n # td.censor_fault is necessary to model perservations in the data\n if td.censor_fault > 0.0:\n Q=np.multiply(Q, 1.0-td.censor_fault)\n \n if len(irts.data) > 0: # use this method only when passing IRTs\n numcols=len(Q)\n flist=[]\n newQ=np.zeros(numcols) # init to Q^0, for when r=1\n newQ[curpos-1]=1.0 # (using only one: row for efficiency)\n\n irt=irts.data[xnum][curpos-1]\n\n # precompute for small speedup\n if irts.irttype==\"gamma\":\n logbeta=np.log(irts.gamma_beta)\n logirt=np.log(irt)\n\n # normalize irt probabilities to avoid irt weighting\n if irts.irttype==\"gamma\":\n # r=alpha. probability of observing irt at r steps\n irtdist=[r*logbeta-math.lgamma(r)+(r-1)*logirt-irts.gamma_beta*irt for r in range(1,irts.rcutoff)]\n if irts.irttype==\"exgauss\":\n \n irtdist=[np.log(irts.exgauss_lambda/2.0)+(irts.exgauss_lambda/2.0)*(2.0*r+irts.exgauss_lambda*(irts.exgauss_sigma**2)-2*irt)+np.log(math.erfc((r+irts.exgauss_lambda*(irts.exgauss_sigma**2)-irt)/(np.sqrt(2)*irts.exgauss_sigma))) for r in range(1,irts.rcutoff)]\n\n for r in range(1,irts.rcutoff):\n innersum=0\n for k in range(numcols):\n num1=newQ[k] # probability of being at node k in r-1 steps\n num2=t2[curpos,k] # probability transitioning from k to absorbing node \n innersum=innersum+(num1*num2)\n\n # compute irt probability given r steps\n log_dist = irtdist[r-1] / sum(irtdist)\n\n if innersum > 0: # sometimes it's not possible to get to the target node in r steps\n flist.append(log_dist + np.log(innersum))\n\n newQ=np.inner(newQ,Q) # raise power by one\n\n f=sum([np.e**i for i in flist])\n prob.append(f) # probability of x_(t-1) to X_t\n else: # if no IRTs, use standard U-INVITE\n I=identmat[:len(Q),:len(Q)]\n \n # novel items are emitted with probability 1 when encountered. perseverations are emitted with probability td.censor_fault when encountered.\n if list_has_perseverations: # if list has perseverations. could speed up by only doing this step when a perseveration has been encountered\n x1=np.array([curpos]) # absorbing node\n #x2=np.array([i for i,j in enumerate(x) if (j not in x[:i]) and (i < curpos)]) # column ids for transient states excluding perseverations\n x2=np.array([i for i,j in enumerate(x) if (j not in x[i+1:curpos]) and (i < curpos)]) # column ids for transient states excluding perseverations\n R=t2[x1[:,None],x2][0] # why is [0] necessary here but not in the else case?\n \n if Xs[xnum][curpos] in Xs[xnum][:curpos]: # if absorbing state has appeared in list before...\n R=np.multiply(R,td.censor_fault)\n else: # if not a perseveration\n R=t2[curpos,:curpos] # old way\n \n ### test (when censor_fault=0) to see if absorbing distribution sums to 1... something is broken\n #total = []\n #x2=np.array([j for i,j in enumerate(x) if (i < curpos)]) # column ids for transient states excluding perseverations\n #N=np.linalg.solve(I-Q,I[-1])\n #for i in range(len(t)):\n # R=t[np.array([i])[:,None],x2]\n # B=np.dot(R,N)\n # total.append(B[0])\n # if B[0] > 1.0:\n # print(\"NONONO\")\n #print(\"total \", total)\n #R=t2[curpos,:curpos] # old way to reset\n ###\n \n N=np.linalg.solve(I-Q,I[-1])\n B=np.dot(R,N)\n if np.isnan(B):\n B=0.0\n prob.append(B)\n \n # alternative/original using matrix inverse\n #R=t2[curpos:,:curpos]\n #N=inv(I-Q)\n #B=np.dot(R,N) \n #prob.append(B[0,curpos-1])\n\n # if there's an impossible transition and no jumping/priming, return immediately\n if (prob[-1]==0.0) and (td.jump == 0.0) and (td.priming == 0.0):\n return -np.inf, (x[curpos-1], x[curpos])\n\n probs.append(prob)\n\n uinvite_probs = copy.deepcopy(probs) # store only u-invite transition probabilities (the computationally hard stuff) to avoid recomputing\n \n # adjust for jumping probability\n if td.jump > 0.0:\n if td.jumptype==\"uniform\":\n probs=addJumps(probs, td, numnodes=numnodes)\n elif td.jumptype==\"stationary\":\n probs=addJumps(probs, td, statdist=statdist, Xs=Xs)\n\n if (td.priming > 0.0):\n probs=adjustPriming(probs, td, Xs)\n\n # check for impossible transitions after priming and jumping\n for xnum, x in enumerate(probs):\n for inum, i in enumerate(x):\n if (i==0.0) and (inum==0):\n return -np.inf, (Xs[xnum][inum], Xs[xnum][inum+1]) # link to next item when first item is unreachable\n elif (i==0.0) and (inum > 0):\n return -np.inf, (Xs[xnum][inum-1], Xs[xnum][inum]) # link to previous item otherwise\n \n try:\n ll=sum([sum([np.log(j) for j in probs[i]]) for i in range(len(probs))])\n except:\n ll=-np.inf\n\n # include prior?\n if prior:\n priorlogprob = evalGraphPrior(a, prior)\n ll = ll + priorlogprob\n\n return ll, uinvite_probs","def joint_proba(self, X):\n return self.weights * self._bernoulli(X)","def base_atom(dict):\n for atom, values in dict.items():\n xyz = values[\"coor\"]\n if xyz[0] == xyz[1] == xyz[2] == 0:\n return atom","def getAtoms(self):\n return self.atoms","def muon(self) -> CellAtom:\n return self._cell_atoms[self._muon_index]","def joint_probabilities_from_transitions(ordered_pitch_types, transition_counts):\n first_pitch_totals = {first_pitch_type: sum(transition_counts[first_pitch_type].values())\n for first_pitch_type in ordered_pitch_types}\n\n total_transitions = sum(first_pitch_totals.values())\n\n markov_rows = []\n joint_probabilities = {}\n\n for first_pitch_type in ordered_pitch_types:\n first_pitch_transitions = transition_counts[first_pitch_type]\n joint_probabilities[first_pitch_type] = {}\n first_pitch_type_probability = float(first_pitch_totals[first_pitch_type]) / total_transitions\n\n second_pitch_total = sum(first_pitch_transitions.values())\n row = [first_pitch_type]\n\n for second_pitch_type in ordered_pitch_types:\n if second_pitch_total == 0:\n second_pitch_conditional_probability = 0\n\n else:\n second_pitch_conditional_probability = \\\n float(first_pitch_transitions[second_pitch_type]) / second_pitch_total\n\n row.append(second_pitch_conditional_probability)\n\n joint_probabilities[first_pitch_type][second_pitch_type] = \\\n first_pitch_type_probability * second_pitch_conditional_probability\n\n markov_rows.append(row)\n\n return joint_probabilities, markov_rows, total_transitions","def insert_nano_particle(atoms, nanoparticle):\n from scipy.spatial import cKDTree as KDTree\n np_pos = nanoparticle.get_positions()\n com = np.sum(np_pos, axis=0)/len(np_pos)\n np_pos -= com\n nanoparticle.set_positions(np_pos)\n\n cell = atoms.get_cell()\n diag = 0.5 * (cell[:, 0] + cell[:, 1] + cell[:, 2])\n at_pos = atoms.get_positions() - diag\n tree = KDTree(at_pos)\n\n used_indices = []\n for atom in nanoparticle:\n dists, closest_indx = tree.query(atom.position)\n if closest_indx in used_indices:\n raise RuntimeError(\"Two indices map to the same!\")\n atoms[closest_indx].symbol = atom.symbol\n used_indices.append(closest_indx)\n\n symbols = [atom.symbol for atom in atoms]\n return symbols","def atoms(self):\n return itertools.chain(self.rhs_atoms, self.lhs_atoms)","def _get_joint_positions_all(self, abs_input: [list, np.ndarray]):\n return np.copy(abs_input)","def changetype(atms,t1,t2, nt2):\n\n # find total number of atoms\n ntotal = len(atms)\n t1ids = []\n nt1 = 0;\n \n # find total number of t1 atoms and their ids\n for ia in range(ntotal):\n if atms[ia][3] == t1:\n t1ids.append(ia)\n nt1 +=1\n \n nt2 = int(nt2)\n if nt2 < 1:\n print \"$$$ ERROR: not enough atoms of type ## \" + str(t1) + \" ## \" + str(nt1) + \" are available to alloy $$$\"\n return\n\n np.random.shuffle(t1ids)\n rnd = t1ids[:nt2]\n for zombie in rnd:\n atms[zombie][3] = t2\n\n return (atms,rnd)","def fermionic_cells(self):\n cells = self.cells()\n cells_and_circles = self.all_cells()\n circles = [x for x in cells_and_circles if x not in cells]\n coords = [(i, jprime)\n for iprime, jprime in circles\n for i, j in circles\n if iprime > i\n ]\n coords.sort()\n return coords","def atoms(self):\n print('processing [ atoms ]')\n x = \"\"\"[ atoms ]\n; nr type resnr residu atom cgnr charge mass\n\"\"\"\n qtot = 0.0\n fmt = '{nr:6d} {type} 1 {residu} {atom} 1 {charge:14.11f} {mass:7.4f} ; qtot {qtot}\\n'\n for i, node in enumerate(self):\n vals = {}\n vals['nr'] = 1+i\n vals['type'] = node.atom.name\n vals['residu'] = self.molname\n vals['atom'] = node.atom.name\n vals['charge'] = node.properties['charge']\n qtot += node.properties['charge']\n vals['qtot'] = qtot\n vals['mass'] = node.properties['mass']\n x += fmt.format(**vals)\n return x + '\\n'","def naive_bn(data, attributes):\n bn = []\n attr = attributes['attr'].tolist()\n # each attribute is only dependent on the class node\n i = 0\n while (i < len(attr)-1):\n row = [attr[i], attr[-1]]\n bn.append(row)\n i= i + 1\n # frequency table \n freq = counts_table(data, attributes)\n # conditional probabilities and prior probabilities\n cond_probs, prior0, prior1 = conditional_probability(data, attributes, freq)\n\n return bn, cond_probs, prior0, prior1","def prepare_first_guess(\n self,\n rate_matrix_index,\n atomic_numbers,\n number_density,\n electron_density,\n ):\n first_guess = pd.Series(0.0, index=rate_matrix_index)\n for atomic_number in atomic_numbers:\n first_guess.loc[(atomic_number, 1)][0] = number_density.loc[\n atomic_number\n ]\n # TODO: After the first iteration, the new guess can be the old solution.\n first_guess = first_guess.values\n first_guess[-1] = electron_density\n return first_guess","def compute_conditional_distributions(joints):\n\n marginals = np.sum(joints, axis=1)\n posidx = marginals > 0\n \n conditionals = joints.copy()\n conditionals[posidx, :] /= marginals[posidx, None]\n conditionals[~posidx, :] = marginals\n assert np.isclose(np.sum(conditionals), len(conditionals))\n assert np.allclose(np.sum(conditionals, axis=1), 1.0)\n\n return conditionals","def _GetPseudoAAC1(ProteinSequence,lamda=10,weight=0.05):\n\trightpart=0.0\n\tfor i in range(lamda):\n\t\trightpart=rightpart+_GetSequenceOrderCorrelationFactor(ProteinSequence,k=i+1)\n\tAAC=GetAAComposition(ProteinSequence)\n\t\n\tresult={}\n\ttemp=1+weight*rightpart\n\tfor index,i in enumerate(AALetter):\n\t\tresult['PAAC'+str(index+1)]=round(AAC[i]/temp,3)\n\t\n\treturn result","def joint(G, xs=None):\n vars = G.vars() #: [var]\n facs = { f : G.N(f) for f in G.facs() } #: fac => vars\n\n dims = [G.node[x]['d'] for x in vars] #: [nat]\n _joint = ones(dims)\n\n for vals in itertools.product( *(xrange(d) for d in dims) ): # cartesian product\n _vars = dict(zip(vars,vals)) #: var => val\n vals = tuple(vals) # to index\n #print\n #print _vars\n for fac in facs:\n _vals = [_vars[v] for v in facs[fac]] # keep only fac's vars' vals\n #print '%s%s' % (fac, tuple(_vals))\n _joint[vals] *= G(fac, *_vals)\n\n Z = sum(_joint)\n\n return pd(_joint), Z","def joint_pairs(self):\n return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))","def probability_distribution(self):\n if not len(self.arms):\n return []\n\n possibility_of_pulling_others = self.epsilon / len(self.arms)\n possibility_of_pulling_best_arm = 1 - self.epsilon + possibility_of_pulling_others\n\n def arm_probability(arm):\n if arm != self.best_arm:\n return possibility_of_pulling_others\n else:\n return possibility_of_pulling_best_arm\n\n return [arm_probability(arm) for arm in self.arms]","def getBeliefDistribution(self):\n # This essentially gives a point to a location for each particle there, then \n # normalizes the point values so they add up to 1.\n dist = util.Counter()\n for part in self.particles: dist[part] += 1\n dist.normalize()\n return dist","def get_nearest_atom_inds(self):\n # Create empty data structure\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.natom)\n for iat in range(self.natom):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist[iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_ats[iat] = at_inds","def test_breed_certain_probability_all_in_cell():\n cell = topo.Jungle()\n for _ in range(100):\n cell.add_animal(animals.Herbivores(age=10, weight=100))\n cell.add_animal(animals.Carnivores(age=10, weight=100))\n cell.breed_all_animals_in_cell()\n assert len(cell.herbivore_list) == 200\n assert len(cell.carnivore_list) == 200","def atoms(self) -> Optional[Atoms]:\n return self._atoms","def margprobssites(self) :\n sitemargprobs=[]\n import scipy\n pmatrix=scipy.linalg.expm(self.q*self.v)\n for i in range(self.nsites) :\n initial=self.starts[i]\n final=self.finals[i]\n iindex=self.staspa.index(initial)\n findex=self.staspa.index(final)\n mp=pmatrix[iindex,findex]\n sitemargprobs.append(mp)\n return sitemargprobs","def primary(self):\n return Seq(''.join([r.aa for r in self.residues]), protein_alphabet)","def compute_for_one(smi):\n\n try:\n mol=Chem.MolFromSmiles(smi) \n prop = [Descriptors.ExactMolWt(mol), Descriptors.MolLogP(mol), QED.qed(mol)]\n except:\n prop = 'Not exist!'\n return prop","def adjoint(self):\n return self.cofactorMatrix().transpose()","def get_prob_for_distributions(p):\n w1 = p[0]\n mu1 = p[1]\n sigma1 = p[2]\n w2 = p[3]\n mu2 = p[4]\n sigma2 = p[5]\n w3 = p[6]\n mu3 = p[7]\n sigma3 = p[8]\n dist_range = (0, 4.330310991999920844e+01)\n x = np.linspace(dist_range[0], dist_range[1], 1000)\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\n p1 = A1 / (A1 + A2 + A3)\n p2 = A2 / (A1 + A2 + A3)\n p3 = A3 / (A1 + A2 + A3)\n return p1, p2, p3","def prob_list(n, p):\n ...\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\n#qn = 1 - pn\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\n list = []\n qn_a = 1\n qn_b = 1\n list.append(qn_a)\n list.append(qn_b)\n\n for i in range(n-2):\n i += 0\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\n list.append(qn_next)\n qn_a = qn_b\n qn_b = qn_next\n return list\n\n\n # returns list of probs. [q0,... qn]","def m1(self):\n return self.mass[0]","def prob_t_a_given_s(self, alignment_info):\n probability = 1.0\n MIN_PROB = IBMModel.MIN_PROB\n slots = Slots(len(alignment_info.trg_sentence) - 1)\n\n def null_generation_term():\n # Binomial distribution: B(m - null_fertility, p1)\n value = 1.0\n p1 = self.p1\n p0 = 1 - p1\n null_fertility = alignment_info.fertility_of_i(0)\n m = len(alignment_info.trg_sentence) - 1\n value *= pow(p1, null_fertility) * pow(p0, m - 2 * null_fertility)\n if value < MIN_PROB:\n return MIN_PROB\n\n # Combination: (m - null_fertility) choose null_fertility\n for i in range(1, null_fertility + 1):\n value *= (m - null_fertility - i + 1) / i\n return value\n\n def fertility_term():\n value = 1.0\n src_sentence = alignment_info.src_sentence\n for i in range(1, len(src_sentence)):\n fertility = alignment_info.fertility_of_i(i)\n value *= (\n factorial(fertility)\n * self.fertility_table[fertility][src_sentence[i]]\n )\n if value < MIN_PROB:\n return MIN_PROB\n return value\n\n def lexical_translation_term(j):\n t = alignment_info.trg_sentence[j]\n i = alignment_info.alignment[j]\n s = alignment_info.src_sentence[i]\n return self.translation_table[t][s]\n\n def vacancy_term(i):\n value = 1.0\n tablet = alignment_info.cepts[i]\n tablet_length = len(tablet)\n total_vacancies = slots.vacancies_at(len(slots))\n\n # case 1: NULL-aligned words\n if tablet_length == 0:\n return value\n\n # case 2: head word\n j = tablet[0]\n previous_cept = alignment_info.previous_cept(j)\n previous_center = alignment_info.center_of_cept(previous_cept)\n dv = slots.vacancies_at(j) - slots.vacancies_at(previous_center)\n max_v = total_vacancies - tablet_length + 1\n trg_class = self.trg_classes[alignment_info.trg_sentence[j]]\n value *= self.head_vacancy_table[dv][max_v][trg_class]\n slots.occupy(j) # mark position as occupied\n total_vacancies -= 1\n if value < MIN_PROB:\n return MIN_PROB\n\n # case 3: non-head words\n for k in range(1, tablet_length):\n previous_position = tablet[k - 1]\n previous_vacancies = slots.vacancies_at(previous_position)\n j = tablet[k]\n dv = slots.vacancies_at(j) - previous_vacancies\n max_v = total_vacancies - tablet_length + k + 1 - previous_vacancies\n trg_class = self.trg_classes[alignment_info.trg_sentence[j]]\n value *= self.non_head_vacancy_table[dv][max_v][trg_class]\n slots.occupy(j) # mark position as occupied\n total_vacancies -= 1\n if value < MIN_PROB:\n return MIN_PROB\n\n return value\n\n # end nested functions\n\n # Abort computation whenever probability falls below MIN_PROB at\n # any point, since MIN_PROB can be considered as zero\n probability *= null_generation_term()\n if probability < MIN_PROB:\n return MIN_PROB\n\n probability *= fertility_term()\n if probability < MIN_PROB:\n return MIN_PROB\n\n for j in range(1, len(alignment_info.trg_sentence)):\n probability *= lexical_translation_term(j)\n if probability < MIN_PROB:\n return MIN_PROB\n\n for i in range(1, len(alignment_info.src_sentence)):\n probability *= vacancy_term(i)\n if probability < MIN_PROB:\n return MIN_PROB\n\n return probability","def log_joint(self):\n return sum([\n self.log_marg_like(self.gamma, self.gamma0, self.lamb, self.nu),\n self._gamma0_distribution.logpdf(self.gamma0),\n self._nu_distribution.logpdf(self.nu),\n self._lambda_distribution.logpdf(self.lamb),\n self.probit_distribution(self.xi).logpdf(self.gamma),\n self._xi_distribution.logpdf(self.xi) if self.sample_xi else 0.0\n ])","def extract_bonds(self):\n atom_types = self.contents['Sub_ID']\n atom_ids = self.contents['ID']\n bond_list = []\n for key, value in self.bonds.items():\n a = value[0]\n b = value[1]\n\n A = np.asarray(atom_types).reshape(-1, 3)\n B = np.asarray(atom_ids).reshape(-1, 3)\n\n D = np.where(A == a, B, np.nan)\n E = np.where(A == b, B, np.nan)\n\n D = D[:, ~np.all(np.isnan(D), axis=0)]\n E = E[:, ~np.all(np.isnan(E), axis=0)]\n\n D_ = np.tile(D, (1, E.shape[1]))\n E_ = np.repeat(E, D.shape[1], axis=1)\n\n F = np.asarray([D_, E_]).T\n\n idd = np.ones((F.shape[1], F.shape[0])) * key\n # g = np.arange(1, )\n fi = np.arange(F.shape[1])\n iff = np.repeat(fi[:,np.newaxis], 2, axis=1)\n\n concate = np.concatenate((iff[:,:,np.newaxis], idd[:,:,np.newaxis], F.swapaxes(0, 1)), axis=-1)\n concate = concate.reshape(-1, 4)\n df = pd.DataFrame(data=concate, columns=['Mol_ID', 'Bond_type', 'Atom_1', 'Atom_2'])\n bond_list.append(df)\n self.bond_df = pd.concat(bond_list)\n self.num_bonds = len(self.bond_df)","def priorProb(self, state):\n actions = []\n for i in range(0, 10):\n actions.append(((i, i+1), random.uniform(0, 1))) \n \n return actions","def prediction_tuple(probabilities):\n prediction = [x for x, i in probabilities.items() if i == min(probabilities.values())][0]\n return (prediction, probabilities[prediction])","def atoms(self, symbol): \n # this is a stub implementation\n #return 10;\n if symbol not in _atomic_mass: raise KeyError( symbol + \" is not in the table\")\n if symbol in _atomic_mass and symbol not in self._gettokens():\n return 0\n #the method is similar to __iter__, just different return\n parse = re.findall(r'([A-Z][a-z]*)(\\d*)|(\\()|(\\))(\\d*)', str(self.dele_mole))\n if symbol in _atomic_mass and symbol in self._gettokens():\n sym_num = [collections.Counter()]\n for name, n1, left_open, right_open, n2 in parse:\n if name:\n sym_num[-1][name] += int(n1 or 1) \n if left_open:\n sym_num.append(collections.Counter())\n if right_open:\n top = sym_num.pop()\n for s in top:\n sym_num[-1][s] += top[s] * int(n2 or 1) \n return sym_num[-1][symbol]","def get_probability(self, reaction):\n return self.__getitem__(reaction)","def test_atomic_masses():\n first = get_atomic_mass(\"As\")\n assert first == 74.9216\n \n second = get_atomic_mass(\"Be\")\n assert second == 9.012182\n\n third = get_atomic_mass(\"Li\")\n assert third == 6.941","def p1_pits(self):\n return self.state[:self.M]","def assignPositions(self):\n n = int(math.ceil(self.numAtoms**(1.0/3.0))) # Number of atoms in a direction\n particle = 0 # Particles placed so far\n \n for x in range(0, n):\n for y in range(0, n):\n for z in range(0, n):\n if (particle < self.numAtoms):\n self.atoms[particle].x = x * self.sigma\n self.atoms[particle].y = y * self.sigma \n self.atoms[particle].z = z * self.sigma\n particle += 1","def construct_joint(self, x):\n channel = x.reshape((self._crv_size, self._bound))\n channel /= channel.sum(axis=1, keepdims=True)\n channel[np.isnan(channel)] = self._mask[np.isnan(channel)]\n slc = (len(self._pmf.shape) - 1)*[np.newaxis] + 2*[colon]\n joint = self._pmf[..., np.newaxis] * channel[slc]\n\n return joint","def first(pair):\n\treturn pair[0]","def atoms(formula):\n pass\n # ======== YOUR CODE HERE ========","def focn(self):\n return self.table[1, 1] / (self.table[1, 0] + self.table[1, 1])","def conditionalize(prior, conditional, observed):\n\n # construct joint probability table (Step 1 of Master Method)\n joint = PGM2(prior, conditional)\n #print(joint.get_cell(('POX', 'NOSPOTS')))\n\n # update joint probability table after observing value of N1 (Steps 2 and 3 of Master Method)\n joint.update(observed, 1)\n\n # marginalize to get probability distribution for N0 (Step 4 of Master Method)\n posterior = joint.marginalize(0)\n\n return posterior","def _prob_map(self, x):\n d = self.manifold.shape[0]\n n = x.shape[1]\n maha = np.zeros(shape=(n, self.m))\n for w in range(self.m):\n mu = np.tile(self.manifold[:, w].reshape(d, 1),\n (1, n))\n maha[:, w] = np.sum((x-mu)**2, 0) / self.sigma**2\n tmp = np.exp(-0.5 * maha)\n return np.exp(-0.5 * maha)","def FK_pox(joint_angles, m_mat, s_lst):\n pass","def multinomial_pmf(sample, probabilities):\r\n # TODO\r\n a=[]\r\n b=[]\r\n i=0\r\n key_list=[]\r\n value_list=[]\r\n for key,value in sample.items():\r\n key_list.append(key)\r\n value_list.append(value)\r\n b=list(sample)\r\n while i< len(b):\r\n a.append(probabilities.keys()[probabilities.values().index(value_list[i])])\r\n\r\n\r\n return a","def bone_pairs(self):\n return ((0, 3), (1, 4), (2, 5), (10, 13), (11, 14), (12, 15))","def extract_monom(var, P0):\n \n x = var[0]\n th = var[1]\n \n P = [sp.Poly(p, *th) for p in P0]\n\n #which constraints are geometric in parameters theta, and don't involve current data x?\n geom = [sp.Poly(p, *x).degree() == 0 for p in P0]\n\n \n monom = [list(p.monoms()) for p in P]\n coeff = [np.array(p.coeffs()) for p in P]\n \n #supp_monom = np.unique(np.array(monom), axis=1)\n \n \n #I know this is ugly\n #find unique monomial terms in function/constraints\n #then output them in sorted lexicographical order\n \n supp_monom = np.flip(np.unique(np.array(sum(monom, [])), axis = 0), axis = 0)\n \n fb = [sp.lambdify(x, bi, \"numpy\") for bi in coeff]\n \n # if \"eq\" in cons.keys():\n # h = [sp.Poly(gi, *th) for gi in cons[\"eq\"]]\n \n # if \"ineq\" in cons.keys():\n # g = [sp.Poly(gi, *th) for gi in cons[\"ineq\"]]\n return {\"fb\": fb, \"monom_poly\": monom, \"coeff\": coeff, \"A_pre\": supp_monom, \"geom\": geom}","def _get_orbmom_per_atom(self, file, natom):\n #TODO implement\n return orbmom_at","def _prob_to_mass(self, prob):\n dictlist = []\n for i in range(len(prob)):\n dictlist.append([str(i+1), prob[i]])\n return MassFunction(dict(dictlist))","def one_from_cartesian_product_of_one_basis(self):\n return self.sum_of_monomials( zip( self._sets_keys(), (set.one_basis() for set in self._sets)) )","def marginalize(self, axis):\n \n dist = {}\n\n # -------------------------------------------------------------------------\n # YOUR CODE GOES HERE\n #\n \n # get relevant data based on the given random variable\n for i in self._table:\n if axis == 0:\n if i[0] in dist:\n dist[i[0]] += self._table[i]\n else:\n dist[i[0]] = self._table[i]\n else:\n if i[1] in dist:\n dist[i[1]] += self._table[i]\n else:\n dist[i[1]] = self._table[i]\n\n\n #\n # END OF YOUR CODE\n # ------------------------------------------------------------------------- \n\n return dist","def molecular_weight(elements):\n return (np.array([atomic_mass[i.upper()] for i in elements]).sum())","def RefAtom(Residue):\n\n RES = ['HOH','ALA','ARG','ASN','ASP','CYS','GLN','GLU','GLY','HIS','ILE','LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL']\n REF = [['O'],['O'], ['NH1','NH2'], ['OD1'], ['OD1', 'OD2'], ['SG'], ['OE1'], ['OE1', 'OE2'], ['O'], ['ND1', 'NE2'], ['O'], ['O'], ['NZ'], ['SD'], ['O'], ['O'], ['OG'], ['OG1'], ['O'], ['OH'], ['O']]\n return REF[RES.index(Residue)][:]","def select_corresponding(self, theory):\n\n Qth, Rth = theory\n return [None if x_data is None\n else (x_data.Q, np.interp(x_data.Q, Qth, x_th))\n for x_data, x_th in zip(self.xs, Rth)]","def comp_relation_probs(self, x_feats):\n # l : examples batch size\n # d : dimensionality of the (binary) feature space\n relation_scores = sparse.dot(x_feats, self.W) + self.Wb # [l, d] x [d, m] + [m] => [l, m]\n relation_probs = T.nnet.softmax(relation_scores)\n return relation_probs","def probabilidadObservable(obs,ket):\n valP,vectP = propiosObservable(obs)\n probs=[]\n calA=CalculadoraAvanzada()\n for v in vectP:\n p=calA.transitarVector(v,ket)\n probs.append(p)\n return probs","def find_biomass_precursors(reaction):\n return [met for met in reaction.reactants\n if met.id != 'atp_c' or met.id != 'h2o_c']","def joint_prob_to_sigma(joint_prob):\n\n table = create_joint_prob_corr_table()\n sigma = np.diag(np.ones(joint_prob.shape[0]))\n\n for i in range(sigma.shape[0]):\n for j in range(i + 1, sigma.shape[0]):\n r, jp = table[tuple(sorted((round(joint_prob[i, i], 2), round(joint_prob[j, j], 2))))]\n f = interpolate.interp1d(jp, r)\n sigma[i, j] = sigma[j, i] = f(joint_prob[i, j])\n\n return sigma","def get_atom_numbers_to_optimize(self):\n v = self.c.get(simulation_cell=True)\n return v.data.stoichiometry","def _process_atom(self, atom, body):\n if isinstance(atom, Or):\n # Annotated disjunction\n atoms = atom.to_list()\n else:\n atoms = [atom]\n\n atoms_out = []\n extra_clauses = []\n\n has_lfi_fact = False\n prior_probability = 0.0 # Sum of prior weights in AD.\n fixed_probability = 0.0 # Sum of fixed (i.e. non-learnable) weights in AD.\n\n num_random_weights = 0\n for atom in atoms:\n if atom.probability and atom.probability.functor == \"t\":\n try:\n start_value = float(atom.probability.args[0])\n prior_probability += float(start_value)\n except InstantiationError:\n # Can't be converted to float => take random\n num_random_weights += 1\n except ArithmeticError:\n num_random_weights += 1\n elif atom.probability and atom.probability.is_constant():\n fixed_probability += float(atom.probability)\n\n random_weights = [random.random() for _ in range(0, num_random_weights + 1)]\n norm_factor = (1.0 - prior_probability - fixed_probability) / sum(\n random_weights\n )\n random_weights = [r * norm_factor for r in random_weights]\n\n # First argument is probability available for learnable weights in the AD.\n self.add_ad(1.0 - fixed_probability, []) # TODO : this adds extra ad\n\n # Replace anonymous variables with non-anonymous variables.\n class ReplaceAnon(object):\n def __init__(self):\n self.cnt = 0\n\n def __getitem__(self, key):\n if key == \"_\":\n self.cnt += 1\n return Var(\"anon_%s\" % self.cnt)\n else:\n return Var(key)\n\n prob_args = []\n if isinstance(atom.probability, Term):\n for arg in atom.probability.args:\n if not isinstance(arg, Constant) and arg != Var(\"_\"):\n prob_args.append(arg)\n\n newcount = \"_\".join([str(self.count + count) for count in range(len(atoms))])\n\n factargs = []\n # probargs = ()\n for atom in atoms:\n q = list(atom.apply(ReplaceAnon()).args)\n for var in q:\n if var not in factargs:\n factargs.append(var)\n\n lfi_rule = Term(\n \"lfi_rule\", Constant(newcount), Term(\"t\", *prob_args), *factargs\n )\n\n if body is not None:\n extra_clauses.append(Clause(lfi_rule, body))\n\n for atom in atoms:\n if atom.probability and atom.probability.functor == \"t\":\n # t(_, X)::p(X, Y) :- body.\n #\n # Translate to\n # lfi_prob(1, t(X))::lfi_fact(1, t(X), X, Y).\n # lfi_rule(1, t(X), X, Y): - body.\n # p(X, Y): - lfi_body(1, t(X), X, Y).\n # lfi_body(1, t(X), X, Y): - lfi_par(1, t(X), X, Y), lfi_fact(1, t(X), X, Y).\n # lfi_par(1, t(X), X, Y): - lfi_rule(1, t(X), X, Y).\n #\n #\n # For annotated disjunction: t(_)::p1(X); t(_)::p2(X): - body.\n # lfi_prob(0, t)::lfi_fact(0, t, X); lfi_prob(1, t)::lfi_fact(1, t, X): - lfi_rule(0_1, t, X).\n # lfi_rule(0_1, t, X): - body.\n # p1(X): - lfi_body(0, t, X).\n # lfi_body(0, t, X): - lfi_par(0, t, X), lfi_fact(0, t, X).\n # lfi_par(0, t, X): - lfi_rule(0_1, t, X).\n # p2(X): - lfi_body(1, t, X).\n # lfi_body(1, t, X): - lfi_par(1, t, X), lfi_fact(1, t, X).\n # lfi_par(1, t, X): - lfi_rule(0_1, t, X).\n # ...\n has_lfi_fact = True\n # Learnable probability\n try:\n start_value = float(atom.probability.args[0])\n except InstantiationError:\n start_value = None\n except ArithmeticError:\n start_value = None\n\n atom1 = atom.apply(ReplaceAnon())\n\n # 1) Introduce a new LFI terms\n factargs = atom1.args\n lfi_fact = Term(\n \"lfi_fact\", Constant(self.count), Term(\"t\", *prob_args), *factargs\n )\n lfi_body = Term(\n \"lfi_body\", Constant(self.count), Term(\"t\", *prob_args), *factargs\n )\n lfi_par = Term(\n \"lfi_par\", Constant(self.count), Term(\"t\", *prob_args), *factargs\n )\n lfi_prob = Term(\"lfi_prob\", Constant(self.count), Term(\"t\", *prob_args))\n\n # 2) Replacement atom\n replacement = lfi_fact.with_probability(lfi_prob)\n\n # 3) Create redirection clause\n extra_clauses.append(Clause(atom1.with_probability(), lfi_body))\n extra_clauses.append(Clause(lfi_body, lfi_par & lfi_fact))\n\n if body is None:\n extra_clauses.append(Clause(lfi_par, Term(\"true\")))\n else:\n extra_clauses.append(Clause(lfi_par, lfi_rule))\n\n self.append_ad(len(self._weights))\n\n # 4) Set initial weight\n if start_value is None:\n # Assign a random weight initially\n start_value = random_weights.pop(-1)\n self._add_weight(start_value)\n\n # 5) Add name\n self.names.append(atom)\n self.bodies.append(lfi_body)\n self.parents.append(lfi_par)\n atoms_out.append(replacement)\n else:\n atoms_out.append(atom)\n\n self.verify_ad()\n\n if has_lfi_fact:\n if len(atoms) == 1 and body is None:\n # Non AD\n return [atoms_out[0]] + extra_clauses\n else:\n # AD\n if body is None:\n return [\n AnnotatedDisjunction(atoms_out, Term(\"true\"))\n ] + extra_clauses\n else:\n return [AnnotatedDisjunction(atoms_out, lfi_rule)] + extra_clauses\n else:\n if len(atoms) == 1:\n if body is None:\n return [atoms_out[0]]\n else:\n return [Clause(atoms_out[0], body)]\n else:\n if body is None:\n body = Term(\"true\")\n return [AnnotatedDisjunction(atoms_out, body)]","def GetPseudoAAC1(ProteinSequence,lamda=30,weight=0.05,AAP=[_Hydrophobicity,_hydrophilicity]):\n\trightpart=0.0\n\tfor i in range(lamda):\n\t\trightpart=rightpart+GetSequenceOrderCorrelationFactor(ProteinSequence,i+1,AAP)\n\tAAC=GetAAComposition(ProteinSequence)\n\t\n\tresult={}\n\ttemp=1+weight*rightpart\n\tfor index,i in enumerate(AALetter):\n\t\tresult['PAAC'+str(index+1)]=round(AAC[i]/temp,3)\n\t\n\treturn result"],"string":"[\n \"def jointProbabilities(constants, db, ruleList, bn):\\n vars = bn.variableList()\\n combs = generateCombos(vars, constants)\\n joints = []\\n for grounding in combs:\\n joints.append((grounding, bn.jointProbs(grounding, db, ruleList)))\\n return (vars, atomList(joints), joints)\",\n \"def enumerate_joint_ask(X, e, P):\\n Q = ProbDist(X) ## A probability distribution for X, initially empty\\n Y = [v for v in P.variables if v != X and v not in e]\\n for xi in P.values(X):\\n Q[xi] = enumerate_joint(Y, extend(e, X, xi), P)\\n return Q.normalize()\",\n \"def prior_sample(self, bn):\\n x = np.zeros(3)\\n\\n # first joint prob\\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\\n x[0] = random_choice[0]\\n\\n # Second Joint Prob\\n if x[0] == 0.1:\\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\\n x[1] = random_choice\\n elif x[0] == 0.9:\\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\\n x[1] = random_choice\\n\\n # Third Joint Prob\\n if random_choice[0] == 0.8 or random_choice == 0.1:\\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\\n x[2] = random_choice\\n else:\\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\\n x[2] = random_choice\\n return x\",\n \"def pon(self):\\n return self.table[1, 1] / (self.table[0, 1] + self.table[1, 1])\",\n \"def head2head(self, atoms):\\n\\n c1, c2 = atoms.keys()\\n c1_ndx, c2_ndx = atoms.values()\\n\\n chain1, chain2 = self.determine_chains([c1, c2])\\n\\n # to get indexing right\\n c1_ndx -= self.monomer.indices[chain1]['C1']\\n c2_ndx -= self.monomer.indices[chain2]['C2']\\n\\n types = {'chain1': {'C1': 'c3', 'C2': 'c2', 'C3': 'c2', 'C4': 'c2', 'H1': 'hc', 'H2': 'hc', 'H3': 'ha',\\n 'H4': 'ha', 'H5': 'ha'},\\n 'chain2': {'C1': 'c3', 'C2': 'c2', 'C3': 'c2', 'C4': 'c3', 'H1': 'hc', 'H2': 'hc', 'H3': 'ha',\\n 'H4': 'ha', 'H5': 'hc', 'D4': 'hc'}}\\n\\n # update types\\n reacted_types = {'chain1': {c1_ndx + self.monomer.indices[chain1][a]: types['chain1'][a] for a in\\n types['chain1'].keys()},\\n 'chain2': {c2_ndx + self.monomer.indices[chain2][a]: types['chain2'][a] for a in\\n types['chain2'].keys()}}\\n\\n # bond between carbons\\n bonds = [[c1_ndx + self.monomer.indices[chain1]['C1'], c2_ndx + self.monomer.indices[chain2]['C1'], 'carbon']]\\n\\n # dummy bonds - 1 new bond between dummy atoms and carbon\\n bonds += [[c2_ndx + self.monomer.indices[chain2]['C4'], c2_ndx + self.monomer.indices[chain2]['D4'], 'dummy']]\\n\\n # define indices of left-over radicals\\n radicals = [c1_ndx + self.monomer.indices[chain1]['C2']]\\n\\n chain1_impropers = ['C1'] # [1]\\n chain2_impropers = ['C1', 'C4'] # [1, 2]\\n rm_improper = []\\n for c in chain1_impropers:\\n rm_improper.append([c1_ndx + self.monomer.indices[chain1][x] for x in self.monomer.impropers[chain1][c]])\\n for c in chain2_impropers:\\n rm_improper.append([c2_ndx + self.monomer.indices[chain2][x] for x in self.monomer.impropers[chain2][c]])\\n\\n # define terminated atoms\\n terminated = [c1_ndx + self.monomer.indices[chain1]['C1'], c2_ndx + self.monomer.indices[chain2]['C1'],\\n c2_ndx + self.monomer.indices[chain2]['C2']] # C2 terminated for now even though still alkene\\n\\n return reacted_types, bonds, radicals, rm_improper, terminated\",\n \"def getFirstTorsionInds(self, extended):\\n offset = 6 if extended else 0\\n torsionInds = np.array(range(offset + 5, self.natoms * 3, 3))\\n primaryTorsions = sorted(list(set(self._firstTorsionTInd)))\\n return list(torsionInds[primaryTorsions])\",\n \"def get_center_of_mass_allies(self,obs):\",\n \"def posterior_first(self, word):\\r\\n prob = {}\\r\\n if word not in prob.keys():\\r\\n prob[word] = {\\r\\n pos: self.emission_probability[pos][word]\\r\\n * self.initial_probability[pos]\\r\\n if word in self.emission_probability[pos]\\r\\n else (1 / float(10 ** 10)) * self.initial_probability[pos]\\r\\n for pos in self.position_list\\r\\n }\\r\\n\\r\\n return prob[word]\",\n \"def __generate_atoms__(self, pdb):\\n\\n atoms = [] # Maybe this can be a set \\n # TODO: Here I can use self.structure.header['missing_residues'] to get a list of residues. It will have their seq and I can use this to make a sequential index\\n for model in self.structure:\\n residues = model.get_residues() # Biopython \\n for residue in residues:\\n full_id = residue.get_full_id()\\n ins_code = full_id[3][2] \\n this_model = str(int(full_id[1]) + 1) # BioPython starts at 0 and fr3d-python starts at 1. Add 1 to each model so unit ids match\\n this_chain = full_id[2]\\n component_number = full_id[3][1]\\n if 'H' in full_id[3][0][0]:\\n res_group = 'HETATM'\\n else:\\n res_group = 'ATOM'\\n\\n res = residue.get_resname().replace(\\\" \\\",\\\"\\\")\\n\\n if ins_code == \\\" \\\":\\n ins_code = None\\n\\n for atom in residue:\\n #drop numbers \\n id = atom.id \\n id = re.sub(r'\\\\d+', '',id)\\n first = id[0]\\n # logic to extract the type of atom from the id\\n if 'C' == first: #Carbon\\n atom_type = 'C' \\n elif 'O' == first: #Ox\\n atom_type = 'O'\\n elif 'P' == first: #Phosphorus\\n atom_type = 'P'\\n elif 'N' == first: # nitrogen\\n atom_type = 'N'\\n else: #Magnesium, other ions\\n atom_type = atom.id\\n\\n x = atom.coord[0]\\n y = atom.coord[1]\\n z = atom.coord[2]\\n \\n alt_id = atom.get_altloc()\\n if alt_id == \\\" \\\":\\n alt_id = None\\n atoms.append(Atom(x=x, y=y, z=z,\\n pdb=self.name,\\n model=this_model,\\n chain=this_chain,\\n component_id=res,\\n component_number=component_number,\\n component_index=component_number,\\n insertion_code=ins_code,\\n alt_id= alt_id,\\n group=res_group,\\n type=atom_type,\\n name=atom.get_name(),\\n symmetry='1_555', #I haven't figured out how to extract symmetries from pdb files yet. Resort to identity\\n polymeric=True)) # Need to find a way to parse this from biopython. Important, may be relevent in structures.py\\n return atoms\",\n \"def get_result(person_to_joint_assoc, joint_list):\\n outputs = []\\n for ridxPred in range(len(person_to_joint_assoc)):\\n\\n keypoints = np.zeros((18, 3))\\n\\n for part in range(18):\\n index = int(person_to_joint_assoc[ridxPred, part])\\n\\n if -1 == index:\\n keypoints[part, 0] = 0\\n keypoints[part, 1] = 0\\n keypoints[part, 2] = 0\\n\\n else:\\n keypoints[part, 0] = joint_list[index, 0] + 0.5\\n keypoints[part, 1] = joint_list[index, 1] + 0.5\\n keypoints[part, 2] = 1.\\n\\n outputs.append(keypoints)\\n return outputs\",\n \"def GetBonds(Bonds):\\n b = sorted([(min(x), max(x)) for x in Bonds])\\n Bonds13, Bonds14 = [], []\\n for (a1,b1) in b:\\n #check for bonds with a1 at the center of a 1-3 interaction,\\n #letting b1 be the higher number of the two flanking\\n clist = [b2 for (a2,b2) in b if a2 == a1 and b2 < b1] + \\\\\\n [a2 for (a2,b2) in b if b2 == a1 and a2 < b1]\\n Bonds13.extend([(min(c,b1), max(c,b1)) for c in clist])\\n #check for bonds with b1 at the center of a 1-3 interaction,\\n #letting a1 be the higher number of the two flanking\\n clist = [b2 for (a2,b2) in b if a2 == b1 and b2 < a1] + \\\\\\n [a2 for (a2,b2) in b if b2 == b1 and a2 < a1]\\n Bonds13.extend([(min(c,a1), max(c,a1)) for c in clist])\\n #find atoms connected to a1\\n clist = [b2 for (a2,b2) in b if a1==a2 and not b1==b2] +\\\\\\n [a2 for (a2,b2) in b if a1==b2 and not b1==a2]\\n #find atoms connected to b1\\n dlist = [a2 for (a2,b2) in b if b1==b2 and not a1==a2] +\\\\\\n [b2 for (a2,b2) in b if b1==a2 and not a1==b2]\\n Bonds14.extend([(min(c,d), max(c,d)) for c in clist for d in dlist])\\n Bonds1213 = b + Bonds13\\n #sort\\n Bonds1213.sort()\\n Bonds14.sort()\\n #get unique values in case of loops\\n Bonds1213 = [x for (i,x) in enumerate(Bonds1213) if i == 0 or x != Bonds1213[i-1]]\\n Bonds14 = [x for (i,x) in enumerate(Bonds14) if i == 0 or x != Bonds14[i-1]]\\n #convert to arrays \\n Bonds1213 = array(Bonds1213, int)\\n Bonds14 = array(Bonds14, int)\\n return Bonds1213, Bonds14\",\n \"def get_joints(self, anno: List[Mapping[str, Any]]) -> np.ndarray:\\n joints = []\\n\\n for i, obj in enumerate(anno):\\n keypoints = np.array(obj[\\\"keypoints\\\"]).reshape([-1, 3])\\n joints.append(keypoints)\\n\\n num_instances = len(joints)\\n joints = np.array(joints, dtype=np.float32).reshape((num_instances, self.num_joints, 3))\\n return joints\",\n \"def get_prob(self, head):\\n if len(self._head_vars) == 0 and head.size() > 0:\\n return 0.\\n\\n trimmed_head = head.get_trimmed(self._head_vars)\\n if trimmed_head in self._table:\\n return self._table.get(trimmed_head)\\n\\n return 0.\",\n \"def calc_pvecs_1mol(mol_crds, act_ats):\\n nearest_neighbours = np.zeros((len(act_ats), 3, 3))\\n at_inds = np.arange(len(mol_crds))\\n at_map = {} # map at num to active at num\\n\\n # Loop over active atoms and calc nearest neighbours\\n for count, iat in enumerate(act_ats):\\n at_crd = mol_crds[iat]\\n dists = np.linalg.norm(mol_crds - at_crd, axis=1)\\n\\n dist_mask = dists < 3.5\\n nn_ats = at_inds[dist_mask][:3]\\n if len(nn_ats) != 3:\\n # Set the map at to the next closest one\\n closest_at = K_min(list(dists), 2)\\n at_map[count] = closest_at[1]\\n continue\\n else:\\n # Make sure iat is the first atom\\n nn_ats = nn_ats[nn_ats != iat][:2]\\n nn_ats = [iat, *nn_ats]\\n assert len(nn_ats) == 3\\n\\n nearest_neighbours[count] = mol_crds[nn_ats]\\n\\n # Set pvecs the same as the closest atom if we can't calculate them\\n for at in at_map:\\n nearest_neighbours[at] = nearest_neighbours[at_map[at]]\\n\\n pvecs = []\\n for a1, a2, a3 in nearest_neighbours:\\n v1 = a2 - a1\\n v2 = a3 - a1\\n pvec = np.cross(v1, v2)\\n pvec /= np.linalg.norm(pvec)\\n pvecs.append(pvec)\\n\\n return np.array(pvecs)\",\n \"def evaljointbayes(fname):\\n #read file and save lines to contents\\n contents = []\\n f = open(fname)\\n for line in f:\\n randomVar = line.rstrip().split()\\n if randomVar[0] != 'END':\\n contents.append(randomVar)\\n print \\\"1. Read file\\\", fname, \\\"successfully.\\\"\\n f.close()\\n \\n #count numbers of nodes and probabilities in each line\\n length = len(contents)\\n nodes, prob = [0] * length, [0] * length\\n table = [] #save all probabilities for each node \\n for num in range(0, length):\\n tableline = []\\n for i in contents[num]:\\n try:\\n j = float(i)\\n prob[num] += 1\\n tableline.append(j) \\n except ValueError:\\n if i != 'NONE':\\n nodes[num] += 1 \\n table.append(tableline) \\n \\n #print out the joint distribution formular\\n print \\\"2. The joint distribution using this network is:\\\"\\n nodelist = []\\n for line in contents:\\n nodelist.append(line[0])\\n print \\\"P(\\\", printElement(nodelist), \\\") = \\\"\\n for num in range(0, length):\\n line = contents[num]\\n if nodes[num] == 1:\\n print \\\"P(\\\", line[0], \\\")\\\", \\n else: \\n print \\\"P(\\\", line[0], '|', printElement(line[1:nodes[num]]),\\\\\\n \\\")\\\", \\n if num == length - 1:\\n print ' '\\n else: \\n print ' * ', \\n \\n #print out the full joint distribution table \\n###This is the revised version using recursive calls and###\\n###print out the cpt table to a .txt file###\\n #further revision includes deleting depth by pop() table, contents and nodes\\n #also, I can extract the parents in the previous step, then contents will not be used here\\n fo=open(fname+'.zz.txt','w')\\n result = 1.0\\n depth = 0\\n global additions, multiplications\\n additions, multiplications = 0, 0\\n fullCPT(nodelist, [], result, depth, fo, contents, table, nodes)\\n fo.close()\\n\\n #print out result of step 3 \\n print \\\"3. Additions and multiplications needed to calculate\\\",\\\\\\n \\\"the joint distribution is:\\\", additions, \\\"and\\\", multiplications\\n print \\\"The number of nodes in the network is: \\\", length\\n\\n #print out reselt of step 4\\n spaceFull = int(math.pow(2,length)) - 1\\n spaceBN = sum(prob)\\n print \\\"4. Space this network saved is (Compactness): \\\", spaceBN, \\\"/\\\", \\\\\\n spaceFull, \\\"=\\\", float(spaceBN) / float(spaceFull), '\\\\n'\\n return\",\n \"def _get_joints(self, anno, idx):\\n num_people = len(anno)\\n\\n joints = np.zeros(\\n (num_people, self.ann_info['num_joints'], 3), dtype=np.float32)\\n\\n for i, obj in enumerate(anno):\\n joints[i, :self.ann_info['num_joints'], :3] = \\\\\\n np.array(obj['keypoints']).reshape([-1, 3])\\n\\n img_info = self.coco.loadImgs(self.img_ids[idx])[0]\\n orgsize = np.array([img_info['height'], img_info['width'], 1])\\n\\n return joints, orgsize\",\n \"def setAtomPairs(self):\\n atomPairs = []\\n for item in self.condensedProperDihedrals:\\n dih = item[0]\\n atom1 = dih.atoms[0]\\n atom2 = dih.atoms[3]\\n pair = [atom1, atom2]\\n if atomPairs.count(pair) == 0:\\n atomPairs.append(pair)\\n self.atomPairs = atomPairs # [[atom1, atom2], ...]\\n self.printDebug(\\\"atomPairs done\\\")\",\n \"def joint_pairs(self):\\n return [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16], #17 body keypoints\\n [20-3, 23-3], [21-3, 24-3], [22-3, 25-3], [26-3, 42-3], [27-3, 41-3], [28-3, 40-3], [29-3, 39-3], [30-3, 38-3], \\n [31-3, 37-3], [32-3, 36-3], [33-3, 35-3], [43-3, 52-3], [44-3, 51-3], [45-3, 50-3], [46-3, 49-3], [47-3, 48-3], \\n [62-3, 71-3], [63-3, 70-3], [64-3, 69-3], [65-3, 68-3], [66-3, 73-3], [67-3, 72-3], [57-3, 61-3], [58-3, 60-3],\\n [74-3, 80-3], [75-3, 79-3], [76-3, 78-3], [87-3, 89-3], [93-3, 91-3], [86-3, 90-3], [85-3, 81-3], [84-3, 82-3],\\n [94-3, 115-3], [95-3, 116-3], [96-3, 117-3], [97-3, 118-3], [98-3, 119-3], [99-3, 120-3], [100-3, 121-3],\\n [101-3, 122-3], [102-3, 123-3], [103-3, 124-3], [104-3, 125-3], [105-3, 126-3], [106-3, 127-3], [107-3, 128-3],\\n [108-3, 129-3], [109-3, 130-3], [110-3, 131-3], [111-3, 132-3], [112-3, 133-3], [113-3, 134-3], [114-3, 135-3]]\",\n \"def atoms(self):\\n return self.qc_mol.atoms + self.br_mol.atoms + self.pc_mol.atoms\",\n \"def find_data_association(self, obs):\\n prob = 0\\n ass_obs = np.zeros((2,1))\\n ass_jacobian = np.zeros((2,2))\\n ass_adjcov = np.zeros((2,2))\\n landmark_idx = -1\\n for idx, landmark in enumerate(self.landmarks):\\n predicted_obs, jacobian, adj_cov = self.compute_jacobians(landmark)\\n p = multi_normal(np.transpose(np.array([obs])), predicted_obs, adj_cov)\\n if p > prob:\\n prob = p\\n ass_obs = predicted_obs\\n ass_jacobian = jacobian\\n ass_adjcov = adj_cov\\n landmark_idx = idx\\n return prob, landmark_idx, ass_obs, ass_jacobian, ass_adjcov\",\n \"def bond_atoms(atom_list):\\n pass\",\n \"def create_joint_prob_corr_table(to_dict=True):\\n\\n p = np.arange(0., 1.05, 0.05) # Range of marginal probability\\n corr = np.arange(-1., 1.05, 0.05) # Range of correlations\\n\\n table = np.zeros(shape=(len(corr), len(p), len(p)))\\n\\n for i in range(len(corr)):\\n\\n sig = np.matrix([[1., corr[i]], [corr[i], 1.]])\\n\\n for j in range(len(p)):\\n for k in range(j, len(p)):\\n\\n if corr[i] == -1:\\n jp = max(0., p[j] + p[k] - 1.)\\n elif corr[i] == 0.:\\n jp = p[j] * p[k]\\n elif corr[i] == 1.:\\n jp = min(p[j], p[k])\\n elif p[j] * p[k] == 0. or p[j] == 1 or p[k] == 1.:\\n jp = p[j] * p[k]\\n else:\\n jp = mvnun(np.array([0., 0.]), np.array([np.inf, np.inf]),\\n [norm.ppf(p[j]), norm.ppf(p[k])],\\n sig)[0]\\n\\n table[i, j, k] = table[i, k, j] = jp\\n\\n if to_dict:\\n n_table = {}\\n\\n # convert to dictionary:\\n for j in range(len(p)):\\n for k in range(j, len(p)):\\n pj = round(p[j], 2)\\n pk = round(p[k], 2)\\n n_table[(pj, pk)] = n_table[(pj, pk)] = np.array((corr, table[:, j, k]))\\n\\n return n_table\\n\\n return table\",\n \"def _propose_atoms_in_order(self, atom_group):\\n atom_torsions= []\\n logp = []\\n assert len(atom_group) == len(set(atom_group)), \\\"There are duplicate atom indices in the list of atom proposal indices\\\"\\n while len(atom_group) > 0:\\n #initialise an eligible_torsions_list\\n eligible_torsions_list = list()\\n\\n for atom_index in atom_group:\\n\\n # Find the shortest path up to length four from the atom in question:\\n shortest_paths = nx.algorithms.single_source_shortest_path(self._residue_graph, atom_index, cutoff=4)\\n\\n # Loop through the destination and path of each path and append to eligible_torsions_list\\n # if destination has a position and path[1:3] is a subset of atoms with positions\\n for destination, path in shortest_paths.items():\\n\\n # Check if the path is length 4 (a torsion) and that the destination has a position. Continue if not.\\n if len(path) != 4 or destination not in self._atoms_with_positions_set:\\n continue\\n\\n # If the last atom is in atoms with positions, check to see if the others are also.\\n # If they are, append the torsion to the list of possible torsions to propose\\n if set(path[1:3]).issubset(self._atoms_with_positions_set):\\n eligible_torsions_list.append(path)\\n\\n assert len(eligible_torsions_list) != 0, \\\"There is a connectivity issue; there are no torsions from which to choose\\\"\\n #now we have to randomly choose a single torsion\\n ntorsions = len(eligible_torsions_list)\\n random_torsion_index = np.random.choice(range(ntorsions))\\n random_torsion = eligible_torsions_list[random_torsion_index]\\n\\n #append random torsion to the atom_torsions and remove source atom from the atom_group\\n chosen_atom_index = random_torsion[0]\\n first_old_atom_index = random_torsion[1]\\n atom_torsions.append(random_torsion)\\n atom_group.remove(chosen_atom_index)\\n\\n #add atom to atoms with positions and corresponding set\\n self._atoms_with_positions_set.add(chosen_atom_index)\\n\\n #add a bond from the new to the previous torsion atom in the _reference_connectivity_graph\\n self._reference_connectivity_graph.add_edge(chosen_atom_index, first_old_atom_index)\\n\\n #add the log probability of the choice to logp\\n logp.append(np.log(1./ntorsions))\\n\\n # Ensure that logp is not ill-defined\\n assert len(logp) == len(atom_torsions), \\\"There is a mismatch in the size of the atom torsion proposals and the associated logps\\\"\\n\\n return atom_torsions, logp\",\n \"def get_first(self):\\n return self.A[1][0] if self.n > 0 else None\",\n \"def position(self):\\n return self.atoms.reshape((1,-1))\",\n \"def atoms(self):\\n return self._atoms\",\n \"def initialize_record_distribution(states, actions, empty_atoms):\\n dist = {}\\n\\n atom_shape = empty_atoms.shape\\n\\n # initial values, equal probability distribution\\n val = np.full(shape=atom_shape, fill_value=1.0 / float(atom_shape[0]))\\n\\n for i in states:\\n dist[i] = {}\\n for j in actions:\\n dist[i][j] = val.copy()\\n\\n return dist\",\n \"def MAP(cpts, obs, terms):\\r\\n\\r\\n # a list to store the computed probabilities\\r\\n all_sums = []\\r\\n # initialize all terms to false\\r\\n for value in range(len(terms)):\\r\\n terms[value] = [terms[value], '0']\\r\\n search_array = terms + obs\\r\\n # if all terms are being watched, just call MPE\\r\\n if len(search_array) == len(cpts):\\r\\n return MPE(cpts, obs)\\r\\n # we need to know what terms we aren't interested in so we start with \\r\\n # or terms and observations and note the variables that appear in CPT but\\r\\n # not in those\\r\\n dont_count = []\\r\\n for var in cpts:\\r\\n if [var[0], '0'] not in search_array and [var[0], '1'] not in search_array:\\r\\n dont_count.append(var[0])\\r\\n terms.append([var[0],'1'])\\r\\n # sort the terms to ensure correct ordering\\r\\n terms.sort()\\r\\n # creates a list of all possible bit strings\\r\\n # just an easy way to create all possible truth assignments\\r\\n seq = [\\\"\\\".join(seq) for seq in itertools.product(\\\"01\\\", repeat=len(terms))]\\r\\n # loop through all possible truth assignments\\r\\n for j in range(len(seq)):\\r\\n # we initialize at probability = 100%\\r\\n chance = 1\\r\\n # assign the truth values\\r\\n for k in range(len(seq[j])):\\r\\n terms[k][1] = seq[j][k]\\r\\n # this computes the probability using the chaining rule\\r\\n for i in range(len(terms)):\\r\\n new_terms = terms[:-i-1] + obs\\r\\n new_terms.sort()\\r\\n chance *= probability(cpts,terms[-i-1], new_terms)\\r\\n # add the probabilities to our list\\r\\n all_sums.append(chance)\\r\\n combine = []\\r\\n # note all variables which weren't in obs or Vs\\r\\n for i in dont_count:\\r\\n combine.append(terms.index([i,'1']))\\r\\n # this will store the final probabilities\\r\\n final_array = [0] * len(seq)\\r\\n # another complicated looking loop, it just serves to combine probabilities\\r\\n # for example, if we have a CPT with x_1, x_2, x_3, x_4 and we observe \\r\\n # x_1 to be true and have Vs = [x_3, x_4] then we need to combine the \\r\\n # probabilities that are the same except for x_2 = true vs false\\r\\n for loc in combine:\\r\\n for sequence in range(len(seq)):\\r\\n for alt_sequence in range(sequence+1,len(seq)):\\r\\n if (seq[sequence][:loc] + seq[sequence][loc+1:]) == (seq[alt_sequence][:loc] + seq[alt_sequence][loc+1:]):\\r\\n final_array[sequence] = all_sums[sequence] + all_sums[alt_sequence]\\r\\n\\r\\n # get the truth assignment for the highest probability\\r\\n location = seq[final_array.index(max(final_array))]\\r\\n truth_assignment = []\\r\\n # place the truth assignment in a more readable fashion\\r\\n for value in range(len(terms)):\\r\\n if terms[value] in search_array:\\r\\n if location[value] == '0':\\r\\n truth_assignment.append(terms[value][0]+ ' = False')\\r\\n else:\\r\\n truth_assignment.append(terms[value][0]+ ' = True')\\r\\n return (truth_assignment)\",\n \"def homo(a):\\n return [ a[0]/a[3],\\n a[1]/a[3],\\n a[2]/a[3],\\n 1 ]\",\n \"def getActionProb(self, canonicalBoard, temp=1):\\n for i in range(self.args.numMCTSSims):\\n dir_noise = (i == 0 and self.dirichlet_noise)\\n self.search(canonicalBoard, dirichlet_noise=dir_noise)\\n\\n s = self.game.stringRepresentation(canonicalBoard)\\n counts = [\\n self.Nsa[(s, a)] if (s, a) in self.Nsa else 0\\n for a in range(self.game.getActionSize())\\n ]\\n\\n if temp == 0:\\n bestAs = np.array(np.argwhere(counts == np.max(counts))).flatten()\\n bestA = np.random.choice(bestAs)\\n probs = [0] * len(counts)\\n probs[bestA] = 1\\n return probs\\n\\n counts = [x**(1. / temp) for x in counts]\\n counts_sum = float(sum(counts))\\n probs = [x / counts_sum for x in counts]\\n return probs\",\n \"def joint_prob(network, assignment):\\n prob = 1\\n for a_key in assignment:\\n conditions = []\\n current = network[a_key]\\n for parent in current['Parents']:\\n conditions.append(True) if assignment[parent] else conditions.append(False)\\n conditions = tuple(conditions)\\n if not assignment[a_key]:\\n prob *= (1 - current['CPT'].get(conditions))\\n else:\\n prob *= (current['CPT'].get(conditions)) \\n return prob\",\n \"def test_get_first_blast_hit_per_seq(self):\\r\\n p = BlastTaxonAssigner({})\\r\\n blast_hits = {'s1': [('blah', 0.0)],\\r\\n 's3': [('dsasd', 1e-42), ('rrr', 1e-12), ('qqq', 0.001)],\\r\\n 's2': []}\\r\\n expected = {'s1': ('blah', 0.0),\\r\\n 's3': ('dsasd', 1e-42),\\r\\n 's2': None}\\r\\n actual = p._get_first_blast_hit_per_seq(blast_hits)\\r\\n self.assertEqual(actual, expected)\",\n \"def J_mpp(eta,Absorbed):\\n return current_density(V_mpp(eta,Absorbed), eta,Absorbed)\",\n \"def _get_tuple_of_probabilities(self):\\n\\t\\tk_array = np.arange(-self.n,self.n+1,2)\\n\\t\\tprobability_array = []\\n\\t\\t\\n\\t\\tfor k in k_array:\\n\\t\\t\\tprobability_array.append(self._calculate_probability(k))\\n\\t\\t\\t\\n\\t\\treturn (k_array,probability_array)\",\n \"def fom(self):\\n return self.table[1, 0] / (self.table[0, 0] + self.table[1, 0])\",\n \"def getAtoms(self):\\n atomNameList = self.getFlagData('ATOM_NAME')\\n atomTypeNameList = self.getFlagData('AMBER_ATOM_TYPE')\\n self._atomTypeNameList = atomTypeNameList\\n massList = self.getFlagData('MASS')\\n chargeList = self.getFlagData('CHARGE')\\n resIds = self.getFlagData('RESIDUE_POINTER') + [0]\\n #uniqAtomTypeId = self.getFlagData('ATOM_TYPE_INDEX') # for LJ\\n balanceChargeList = self.balanceCharges(chargeList)\\n coords = self.getCoords()\\n ACOEFs, BCOEFs = self.getABCOEFs()\\n\\n atoms = []\\n atomTypes = []\\n tmpList = [] # a list with unique atom types\\n totalCharge = 0.0\\n countRes = 0\\n id = 0\\n for atomName in atomNameList:\\n atomTypeName = atomTypeNameList[id]\\n if id + 1 == resIds[countRes]:\\n resid = countRes #self.residueLabel[countRes]\\n countRes += 1\\n mass = massList[id]\\n charge = balanceChargeList[id]\\n totalCharge += charge\\n coord = coords[id]\\n ACOEF = ACOEFs[id]\\n BCOEF = BCOEFs[id]\\n atomType = AtomType(atomTypeName, mass, ACOEF, BCOEF)\\n if atomTypeName not in tmpList:\\n tmpList.append(atomTypeName)\\n atomTypes.append(atomType)\\n atom = Atom(atomName, atomType, id + 1, resid, mass, charge, coord)\\n atoms.append(atom)\\n id += 1\\n\\n if atomTypeName[0].islower():\\n self.atomTypeSystem = 'gaff'\\n else:\\n self.atomTypeSystem = 'amber'\\n\\n self.printDebug('Balanced TotalCharge %13.10f' % float(totalCharge / qConv))\\n self.totalCharge = int(totalCharge)\\n\\n self.atoms = atoms\\n self.atomTypes = atomTypes\\n\\n self.pbc = None\\n if len(coords) == len(atoms) + 2:\\n self.pbc = [coords[-2], coords[-1]]\\n self.printDebug(\\\"PBC = '%s\\\" % self.pbc)\\n self.printDebug(\\\"getAtoms done\\\")\",\n \"def probX(Xs, a, td, irts=Irts({}), prior=None, origmat=None, changed=[]):\\n\\n try:\\n numnodes=len(a)\\n except TypeError:\\n raise Exception(a)\\n reg=(1+1e-10) # nuisance parameter to prevent errors; can also use pinv instead of inv, but that's much slower\\n identmat=np.identity(numnodes) * reg # pre-compute for tiny speed-up (only for non-IRT)\\n\\n probs=[]\\n\\n # generate transition matrix (from: column, to: row) from link matrix\\n t=a/sum(a.astype(float))\\n t=np.nan_to_num(t) # jumping/priming models can have nan in matrix, need to change to 0\\n \\n if (td.jumptype==\\\"stationary\\\") or (td.start_node==\\\"stationary\\\"):\\n statdist=stationary(t)\\n\\n # U-INVITE probability excluding jumps, prior, and priming adjustments -- those come later\\n for xnum, x in enumerate(Xs):\\n x2=np.array(x)\\n t2=t[x2[:,None],x2] # re-arrange transition matrix to be in list order\\n prob=[]\\n if td.start_node==\\\"stationary\\\":\\n prob.append(statdist[x[0]]) # probability of X_1\\n elif td.start_node==\\\"uniform\\\":\\n prob.append(1.0/numnodes)\\n\\n # if impossible starting point, return immediately\\n if (prob[-1]==0.0):\\n try:\\n return -np.inf, (x[0], x[1])\\n except:\\n return -np.inf, x[0]\\n\\n if (len(changed) > 0) and isinstance(origmat,list): # if updating prob. matrix based on specific link changes\\n update=0 # reset for each list\\n\\n # flag if list contains perseverations\\n if len(x) == len(set(x)):\\n list_has_perseverations = False\\n else:\\n list_has_perseverations = True\\n\\n for curpos in range(1,len(x)):\\n if (len(changed) > 0) and isinstance(origmat,list):\\n if update==0: # first check if probability needs to be updated\\n if (Xs[xnum][curpos-1] in changed): # (only AFTER first changed node has been reached)\\n update=1\\n else: # if not, take probability from old matrix\\n prob.append(origmat[xnum][curpos])\\n continue\\n \\n if list_has_perseverations: # a bit slower because matrix is being copied\\n x2=np.array([i for i,j in enumerate(x) if (j not in x[:i]) and (i < curpos)]) # column ids for transient states excluding perseverations\\n Q=t2[x2[:,None],x2] # excludes perseverations. could be sped if only performed when Q contains perseverations\\n # as opposed to being done for every transition if a perseveration is in the list\\n else: \\n Q=t2[:curpos,:curpos] # old way when data does not include perseverations\\n \\n # td.censor_fault is necessary to model perservations in the data\\n if td.censor_fault > 0.0:\\n Q=np.multiply(Q, 1.0-td.censor_fault)\\n \\n if len(irts.data) > 0: # use this method only when passing IRTs\\n numcols=len(Q)\\n flist=[]\\n newQ=np.zeros(numcols) # init to Q^0, for when r=1\\n newQ[curpos-1]=1.0 # (using only one: row for efficiency)\\n\\n irt=irts.data[xnum][curpos-1]\\n\\n # precompute for small speedup\\n if irts.irttype==\\\"gamma\\\":\\n logbeta=np.log(irts.gamma_beta)\\n logirt=np.log(irt)\\n\\n # normalize irt probabilities to avoid irt weighting\\n if irts.irttype==\\\"gamma\\\":\\n # r=alpha. probability of observing irt at r steps\\n irtdist=[r*logbeta-math.lgamma(r)+(r-1)*logirt-irts.gamma_beta*irt for r in range(1,irts.rcutoff)]\\n if irts.irttype==\\\"exgauss\\\":\\n \\n irtdist=[np.log(irts.exgauss_lambda/2.0)+(irts.exgauss_lambda/2.0)*(2.0*r+irts.exgauss_lambda*(irts.exgauss_sigma**2)-2*irt)+np.log(math.erfc((r+irts.exgauss_lambda*(irts.exgauss_sigma**2)-irt)/(np.sqrt(2)*irts.exgauss_sigma))) for r in range(1,irts.rcutoff)]\\n\\n for r in range(1,irts.rcutoff):\\n innersum=0\\n for k in range(numcols):\\n num1=newQ[k] # probability of being at node k in r-1 steps\\n num2=t2[curpos,k] # probability transitioning from k to absorbing node \\n innersum=innersum+(num1*num2)\\n\\n # compute irt probability given r steps\\n log_dist = irtdist[r-1] / sum(irtdist)\\n\\n if innersum > 0: # sometimes it's not possible to get to the target node in r steps\\n flist.append(log_dist + np.log(innersum))\\n\\n newQ=np.inner(newQ,Q) # raise power by one\\n\\n f=sum([np.e**i for i in flist])\\n prob.append(f) # probability of x_(t-1) to X_t\\n else: # if no IRTs, use standard U-INVITE\\n I=identmat[:len(Q),:len(Q)]\\n \\n # novel items are emitted with probability 1 when encountered. perseverations are emitted with probability td.censor_fault when encountered.\\n if list_has_perseverations: # if list has perseverations. could speed up by only doing this step when a perseveration has been encountered\\n x1=np.array([curpos]) # absorbing node\\n #x2=np.array([i for i,j in enumerate(x) if (j not in x[:i]) and (i < curpos)]) # column ids for transient states excluding perseverations\\n x2=np.array([i for i,j in enumerate(x) if (j not in x[i+1:curpos]) and (i < curpos)]) # column ids for transient states excluding perseverations\\n R=t2[x1[:,None],x2][0] # why is [0] necessary here but not in the else case?\\n \\n if Xs[xnum][curpos] in Xs[xnum][:curpos]: # if absorbing state has appeared in list before...\\n R=np.multiply(R,td.censor_fault)\\n else: # if not a perseveration\\n R=t2[curpos,:curpos] # old way\\n \\n ### test (when censor_fault=0) to see if absorbing distribution sums to 1... something is broken\\n #total = []\\n #x2=np.array([j for i,j in enumerate(x) if (i < curpos)]) # column ids for transient states excluding perseverations\\n #N=np.linalg.solve(I-Q,I[-1])\\n #for i in range(len(t)):\\n # R=t[np.array([i])[:,None],x2]\\n # B=np.dot(R,N)\\n # total.append(B[0])\\n # if B[0] > 1.0:\\n # print(\\\"NONONO\\\")\\n #print(\\\"total \\\", total)\\n #R=t2[curpos,:curpos] # old way to reset\\n ###\\n \\n N=np.linalg.solve(I-Q,I[-1])\\n B=np.dot(R,N)\\n if np.isnan(B):\\n B=0.0\\n prob.append(B)\\n \\n # alternative/original using matrix inverse\\n #R=t2[curpos:,:curpos]\\n #N=inv(I-Q)\\n #B=np.dot(R,N) \\n #prob.append(B[0,curpos-1])\\n\\n # if there's an impossible transition and no jumping/priming, return immediately\\n if (prob[-1]==0.0) and (td.jump == 0.0) and (td.priming == 0.0):\\n return -np.inf, (x[curpos-1], x[curpos])\\n\\n probs.append(prob)\\n\\n uinvite_probs = copy.deepcopy(probs) # store only u-invite transition probabilities (the computationally hard stuff) to avoid recomputing\\n \\n # adjust for jumping probability\\n if td.jump > 0.0:\\n if td.jumptype==\\\"uniform\\\":\\n probs=addJumps(probs, td, numnodes=numnodes)\\n elif td.jumptype==\\\"stationary\\\":\\n probs=addJumps(probs, td, statdist=statdist, Xs=Xs)\\n\\n if (td.priming > 0.0):\\n probs=adjustPriming(probs, td, Xs)\\n\\n # check for impossible transitions after priming and jumping\\n for xnum, x in enumerate(probs):\\n for inum, i in enumerate(x):\\n if (i==0.0) and (inum==0):\\n return -np.inf, (Xs[xnum][inum], Xs[xnum][inum+1]) # link to next item when first item is unreachable\\n elif (i==0.0) and (inum > 0):\\n return -np.inf, (Xs[xnum][inum-1], Xs[xnum][inum]) # link to previous item otherwise\\n \\n try:\\n ll=sum([sum([np.log(j) for j in probs[i]]) for i in range(len(probs))])\\n except:\\n ll=-np.inf\\n\\n # include prior?\\n if prior:\\n priorlogprob = evalGraphPrior(a, prior)\\n ll = ll + priorlogprob\\n\\n return ll, uinvite_probs\",\n \"def joint_proba(self, X):\\n return self.weights * self._bernoulli(X)\",\n \"def base_atom(dict):\\n for atom, values in dict.items():\\n xyz = values[\\\"coor\\\"]\\n if xyz[0] == xyz[1] == xyz[2] == 0:\\n return atom\",\n \"def getAtoms(self):\\n return self.atoms\",\n \"def muon(self) -> CellAtom:\\n return self._cell_atoms[self._muon_index]\",\n \"def joint_probabilities_from_transitions(ordered_pitch_types, transition_counts):\\n first_pitch_totals = {first_pitch_type: sum(transition_counts[first_pitch_type].values())\\n for first_pitch_type in ordered_pitch_types}\\n\\n total_transitions = sum(first_pitch_totals.values())\\n\\n markov_rows = []\\n joint_probabilities = {}\\n\\n for first_pitch_type in ordered_pitch_types:\\n first_pitch_transitions = transition_counts[first_pitch_type]\\n joint_probabilities[first_pitch_type] = {}\\n first_pitch_type_probability = float(first_pitch_totals[first_pitch_type]) / total_transitions\\n\\n second_pitch_total = sum(first_pitch_transitions.values())\\n row = [first_pitch_type]\\n\\n for second_pitch_type in ordered_pitch_types:\\n if second_pitch_total == 0:\\n second_pitch_conditional_probability = 0\\n\\n else:\\n second_pitch_conditional_probability = \\\\\\n float(first_pitch_transitions[second_pitch_type]) / second_pitch_total\\n\\n row.append(second_pitch_conditional_probability)\\n\\n joint_probabilities[first_pitch_type][second_pitch_type] = \\\\\\n first_pitch_type_probability * second_pitch_conditional_probability\\n\\n markov_rows.append(row)\\n\\n return joint_probabilities, markov_rows, total_transitions\",\n \"def insert_nano_particle(atoms, nanoparticle):\\n from scipy.spatial import cKDTree as KDTree\\n np_pos = nanoparticle.get_positions()\\n com = np.sum(np_pos, axis=0)/len(np_pos)\\n np_pos -= com\\n nanoparticle.set_positions(np_pos)\\n\\n cell = atoms.get_cell()\\n diag = 0.5 * (cell[:, 0] + cell[:, 1] + cell[:, 2])\\n at_pos = atoms.get_positions() - diag\\n tree = KDTree(at_pos)\\n\\n used_indices = []\\n for atom in nanoparticle:\\n dists, closest_indx = tree.query(atom.position)\\n if closest_indx in used_indices:\\n raise RuntimeError(\\\"Two indices map to the same!\\\")\\n atoms[closest_indx].symbol = atom.symbol\\n used_indices.append(closest_indx)\\n\\n symbols = [atom.symbol for atom in atoms]\\n return symbols\",\n \"def atoms(self):\\n return itertools.chain(self.rhs_atoms, self.lhs_atoms)\",\n \"def _get_joint_positions_all(self, abs_input: [list, np.ndarray]):\\n return np.copy(abs_input)\",\n \"def changetype(atms,t1,t2, nt2):\\n\\n # find total number of atoms\\n ntotal = len(atms)\\n t1ids = []\\n nt1 = 0;\\n \\n # find total number of t1 atoms and their ids\\n for ia in range(ntotal):\\n if atms[ia][3] == t1:\\n t1ids.append(ia)\\n nt1 +=1\\n \\n nt2 = int(nt2)\\n if nt2 < 1:\\n print \\\"$$$ ERROR: not enough atoms of type ## \\\" + str(t1) + \\\" ## \\\" + str(nt1) + \\\" are available to alloy $$$\\\"\\n return\\n\\n np.random.shuffle(t1ids)\\n rnd = t1ids[:nt2]\\n for zombie in rnd:\\n atms[zombie][3] = t2\\n\\n return (atms,rnd)\",\n \"def fermionic_cells(self):\\n cells = self.cells()\\n cells_and_circles = self.all_cells()\\n circles = [x for x in cells_and_circles if x not in cells]\\n coords = [(i, jprime)\\n for iprime, jprime in circles\\n for i, j in circles\\n if iprime > i\\n ]\\n coords.sort()\\n return coords\",\n \"def atoms(self):\\n print('processing [ atoms ]')\\n x = \\\"\\\"\\\"[ atoms ]\\n; nr type resnr residu atom cgnr charge mass\\n\\\"\\\"\\\"\\n qtot = 0.0\\n fmt = '{nr:6d} {type} 1 {residu} {atom} 1 {charge:14.11f} {mass:7.4f} ; qtot {qtot}\\\\n'\\n for i, node in enumerate(self):\\n vals = {}\\n vals['nr'] = 1+i\\n vals['type'] = node.atom.name\\n vals['residu'] = self.molname\\n vals['atom'] = node.atom.name\\n vals['charge'] = node.properties['charge']\\n qtot += node.properties['charge']\\n vals['qtot'] = qtot\\n vals['mass'] = node.properties['mass']\\n x += fmt.format(**vals)\\n return x + '\\\\n'\",\n \"def naive_bn(data, attributes):\\n bn = []\\n attr = attributes['attr'].tolist()\\n # each attribute is only dependent on the class node\\n i = 0\\n while (i < len(attr)-1):\\n row = [attr[i], attr[-1]]\\n bn.append(row)\\n i= i + 1\\n # frequency table \\n freq = counts_table(data, attributes)\\n # conditional probabilities and prior probabilities\\n cond_probs, prior0, prior1 = conditional_probability(data, attributes, freq)\\n\\n return bn, cond_probs, prior0, prior1\",\n \"def prepare_first_guess(\\n self,\\n rate_matrix_index,\\n atomic_numbers,\\n number_density,\\n electron_density,\\n ):\\n first_guess = pd.Series(0.0, index=rate_matrix_index)\\n for atomic_number in atomic_numbers:\\n first_guess.loc[(atomic_number, 1)][0] = number_density.loc[\\n atomic_number\\n ]\\n # TODO: After the first iteration, the new guess can be the old solution.\\n first_guess = first_guess.values\\n first_guess[-1] = electron_density\\n return first_guess\",\n \"def compute_conditional_distributions(joints):\\n\\n marginals = np.sum(joints, axis=1)\\n posidx = marginals > 0\\n \\n conditionals = joints.copy()\\n conditionals[posidx, :] /= marginals[posidx, None]\\n conditionals[~posidx, :] = marginals\\n assert np.isclose(np.sum(conditionals), len(conditionals))\\n assert np.allclose(np.sum(conditionals, axis=1), 1.0)\\n\\n return conditionals\",\n \"def _GetPseudoAAC1(ProteinSequence,lamda=10,weight=0.05):\\n\\trightpart=0.0\\n\\tfor i in range(lamda):\\n\\t\\trightpart=rightpart+_GetSequenceOrderCorrelationFactor(ProteinSequence,k=i+1)\\n\\tAAC=GetAAComposition(ProteinSequence)\\n\\t\\n\\tresult={}\\n\\ttemp=1+weight*rightpart\\n\\tfor index,i in enumerate(AALetter):\\n\\t\\tresult['PAAC'+str(index+1)]=round(AAC[i]/temp,3)\\n\\t\\n\\treturn result\",\n \"def joint(G, xs=None):\\n vars = G.vars() #: [var]\\n facs = { f : G.N(f) for f in G.facs() } #: fac => vars\\n\\n dims = [G.node[x]['d'] for x in vars] #: [nat]\\n _joint = ones(dims)\\n\\n for vals in itertools.product( *(xrange(d) for d in dims) ): # cartesian product\\n _vars = dict(zip(vars,vals)) #: var => val\\n vals = tuple(vals) # to index\\n #print\\n #print _vars\\n for fac in facs:\\n _vals = [_vars[v] for v in facs[fac]] # keep only fac's vars' vals\\n #print '%s%s' % (fac, tuple(_vals))\\n _joint[vals] *= G(fac, *_vals)\\n\\n Z = sum(_joint)\\n\\n return pd(_joint), Z\",\n \"def joint_pairs(self):\\n return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))\",\n \"def probability_distribution(self):\\n if not len(self.arms):\\n return []\\n\\n possibility_of_pulling_others = self.epsilon / len(self.arms)\\n possibility_of_pulling_best_arm = 1 - self.epsilon + possibility_of_pulling_others\\n\\n def arm_probability(arm):\\n if arm != self.best_arm:\\n return possibility_of_pulling_others\\n else:\\n return possibility_of_pulling_best_arm\\n\\n return [arm_probability(arm) for arm in self.arms]\",\n \"def getBeliefDistribution(self):\\n # This essentially gives a point to a location for each particle there, then \\n # normalizes the point values so they add up to 1.\\n dist = util.Counter()\\n for part in self.particles: dist[part] += 1\\n dist.normalize()\\n return dist\",\n \"def get_nearest_atom_inds(self):\\n # Create empty data structure\\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.natom)\\n for iat in range(self.natom):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist[iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_ats[iat] = at_inds\",\n \"def test_breed_certain_probability_all_in_cell():\\n cell = topo.Jungle()\\n for _ in range(100):\\n cell.add_animal(animals.Herbivores(age=10, weight=100))\\n cell.add_animal(animals.Carnivores(age=10, weight=100))\\n cell.breed_all_animals_in_cell()\\n assert len(cell.herbivore_list) == 200\\n assert len(cell.carnivore_list) == 200\",\n \"def atoms(self) -> Optional[Atoms]:\\n return self._atoms\",\n \"def margprobssites(self) :\\n sitemargprobs=[]\\n import scipy\\n pmatrix=scipy.linalg.expm(self.q*self.v)\\n for i in range(self.nsites) :\\n initial=self.starts[i]\\n final=self.finals[i]\\n iindex=self.staspa.index(initial)\\n findex=self.staspa.index(final)\\n mp=pmatrix[iindex,findex]\\n sitemargprobs.append(mp)\\n return sitemargprobs\",\n \"def primary(self):\\n return Seq(''.join([r.aa for r in self.residues]), protein_alphabet)\",\n \"def compute_for_one(smi):\\n\\n try:\\n mol=Chem.MolFromSmiles(smi) \\n prop = [Descriptors.ExactMolWt(mol), Descriptors.MolLogP(mol), QED.qed(mol)]\\n except:\\n prop = 'Not exist!'\\n return prop\",\n \"def adjoint(self):\\n return self.cofactorMatrix().transpose()\",\n \"def get_prob_for_distributions(p):\\n w1 = p[0]\\n mu1 = p[1]\\n sigma1 = p[2]\\n w2 = p[3]\\n mu2 = p[4]\\n sigma2 = p[5]\\n w3 = p[6]\\n mu3 = p[7]\\n sigma3 = p[8]\\n dist_range = (0, 4.330310991999920844e+01)\\n x = np.linspace(dist_range[0], dist_range[1], 1000)\\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\\n p1 = A1 / (A1 + A2 + A3)\\n p2 = A2 / (A1 + A2 + A3)\\n p3 = A3 / (A1 + A2 + A3)\\n return p1, p2, p3\",\n \"def prob_list(n, p):\\n ...\\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\\n#qn = 1 - pn\\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\\n list = []\\n qn_a = 1\\n qn_b = 1\\n list.append(qn_a)\\n list.append(qn_b)\\n\\n for i in range(n-2):\\n i += 0\\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\\n list.append(qn_next)\\n qn_a = qn_b\\n qn_b = qn_next\\n return list\\n\\n\\n # returns list of probs. [q0,... qn]\",\n \"def m1(self):\\n return self.mass[0]\",\n \"def prob_t_a_given_s(self, alignment_info):\\n probability = 1.0\\n MIN_PROB = IBMModel.MIN_PROB\\n slots = Slots(len(alignment_info.trg_sentence) - 1)\\n\\n def null_generation_term():\\n # Binomial distribution: B(m - null_fertility, p1)\\n value = 1.0\\n p1 = self.p1\\n p0 = 1 - p1\\n null_fertility = alignment_info.fertility_of_i(0)\\n m = len(alignment_info.trg_sentence) - 1\\n value *= pow(p1, null_fertility) * pow(p0, m - 2 * null_fertility)\\n if value < MIN_PROB:\\n return MIN_PROB\\n\\n # Combination: (m - null_fertility) choose null_fertility\\n for i in range(1, null_fertility + 1):\\n value *= (m - null_fertility - i + 1) / i\\n return value\\n\\n def fertility_term():\\n value = 1.0\\n src_sentence = alignment_info.src_sentence\\n for i in range(1, len(src_sentence)):\\n fertility = alignment_info.fertility_of_i(i)\\n value *= (\\n factorial(fertility)\\n * self.fertility_table[fertility][src_sentence[i]]\\n )\\n if value < MIN_PROB:\\n return MIN_PROB\\n return value\\n\\n def lexical_translation_term(j):\\n t = alignment_info.trg_sentence[j]\\n i = alignment_info.alignment[j]\\n s = alignment_info.src_sentence[i]\\n return self.translation_table[t][s]\\n\\n def vacancy_term(i):\\n value = 1.0\\n tablet = alignment_info.cepts[i]\\n tablet_length = len(tablet)\\n total_vacancies = slots.vacancies_at(len(slots))\\n\\n # case 1: NULL-aligned words\\n if tablet_length == 0:\\n return value\\n\\n # case 2: head word\\n j = tablet[0]\\n previous_cept = alignment_info.previous_cept(j)\\n previous_center = alignment_info.center_of_cept(previous_cept)\\n dv = slots.vacancies_at(j) - slots.vacancies_at(previous_center)\\n max_v = total_vacancies - tablet_length + 1\\n trg_class = self.trg_classes[alignment_info.trg_sentence[j]]\\n value *= self.head_vacancy_table[dv][max_v][trg_class]\\n slots.occupy(j) # mark position as occupied\\n total_vacancies -= 1\\n if value < MIN_PROB:\\n return MIN_PROB\\n\\n # case 3: non-head words\\n for k in range(1, tablet_length):\\n previous_position = tablet[k - 1]\\n previous_vacancies = slots.vacancies_at(previous_position)\\n j = tablet[k]\\n dv = slots.vacancies_at(j) - previous_vacancies\\n max_v = total_vacancies - tablet_length + k + 1 - previous_vacancies\\n trg_class = self.trg_classes[alignment_info.trg_sentence[j]]\\n value *= self.non_head_vacancy_table[dv][max_v][trg_class]\\n slots.occupy(j) # mark position as occupied\\n total_vacancies -= 1\\n if value < MIN_PROB:\\n return MIN_PROB\\n\\n return value\\n\\n # end nested functions\\n\\n # Abort computation whenever probability falls below MIN_PROB at\\n # any point, since MIN_PROB can be considered as zero\\n probability *= null_generation_term()\\n if probability < MIN_PROB:\\n return MIN_PROB\\n\\n probability *= fertility_term()\\n if probability < MIN_PROB:\\n return MIN_PROB\\n\\n for j in range(1, len(alignment_info.trg_sentence)):\\n probability *= lexical_translation_term(j)\\n if probability < MIN_PROB:\\n return MIN_PROB\\n\\n for i in range(1, len(alignment_info.src_sentence)):\\n probability *= vacancy_term(i)\\n if probability < MIN_PROB:\\n return MIN_PROB\\n\\n return probability\",\n \"def log_joint(self):\\n return sum([\\n self.log_marg_like(self.gamma, self.gamma0, self.lamb, self.nu),\\n self._gamma0_distribution.logpdf(self.gamma0),\\n self._nu_distribution.logpdf(self.nu),\\n self._lambda_distribution.logpdf(self.lamb),\\n self.probit_distribution(self.xi).logpdf(self.gamma),\\n self._xi_distribution.logpdf(self.xi) if self.sample_xi else 0.0\\n ])\",\n \"def extract_bonds(self):\\n atom_types = self.contents['Sub_ID']\\n atom_ids = self.contents['ID']\\n bond_list = []\\n for key, value in self.bonds.items():\\n a = value[0]\\n b = value[1]\\n\\n A = np.asarray(atom_types).reshape(-1, 3)\\n B = np.asarray(atom_ids).reshape(-1, 3)\\n\\n D = np.where(A == a, B, np.nan)\\n E = np.where(A == b, B, np.nan)\\n\\n D = D[:, ~np.all(np.isnan(D), axis=0)]\\n E = E[:, ~np.all(np.isnan(E), axis=0)]\\n\\n D_ = np.tile(D, (1, E.shape[1]))\\n E_ = np.repeat(E, D.shape[1], axis=1)\\n\\n F = np.asarray([D_, E_]).T\\n\\n idd = np.ones((F.shape[1], F.shape[0])) * key\\n # g = np.arange(1, )\\n fi = np.arange(F.shape[1])\\n iff = np.repeat(fi[:,np.newaxis], 2, axis=1)\\n\\n concate = np.concatenate((iff[:,:,np.newaxis], idd[:,:,np.newaxis], F.swapaxes(0, 1)), axis=-1)\\n concate = concate.reshape(-1, 4)\\n df = pd.DataFrame(data=concate, columns=['Mol_ID', 'Bond_type', 'Atom_1', 'Atom_2'])\\n bond_list.append(df)\\n self.bond_df = pd.concat(bond_list)\\n self.num_bonds = len(self.bond_df)\",\n \"def priorProb(self, state):\\n actions = []\\n for i in range(0, 10):\\n actions.append(((i, i+1), random.uniform(0, 1))) \\n \\n return actions\",\n \"def prediction_tuple(probabilities):\\n prediction = [x for x, i in probabilities.items() if i == min(probabilities.values())][0]\\n return (prediction, probabilities[prediction])\",\n \"def atoms(self, symbol): \\n # this is a stub implementation\\n #return 10;\\n if symbol not in _atomic_mass: raise KeyError( symbol + \\\" is not in the table\\\")\\n if symbol in _atomic_mass and symbol not in self._gettokens():\\n return 0\\n #the method is similar to __iter__, just different return\\n parse = re.findall(r'([A-Z][a-z]*)(\\\\d*)|(\\\\()|(\\\\))(\\\\d*)', str(self.dele_mole))\\n if symbol in _atomic_mass and symbol in self._gettokens():\\n sym_num = [collections.Counter()]\\n for name, n1, left_open, right_open, n2 in parse:\\n if name:\\n sym_num[-1][name] += int(n1 or 1) \\n if left_open:\\n sym_num.append(collections.Counter())\\n if right_open:\\n top = sym_num.pop()\\n for s in top:\\n sym_num[-1][s] += top[s] * int(n2 or 1) \\n return sym_num[-1][symbol]\",\n \"def get_probability(self, reaction):\\n return self.__getitem__(reaction)\",\n \"def test_atomic_masses():\\n first = get_atomic_mass(\\\"As\\\")\\n assert first == 74.9216\\n \\n second = get_atomic_mass(\\\"Be\\\")\\n assert second == 9.012182\\n\\n third = get_atomic_mass(\\\"Li\\\")\\n assert third == 6.941\",\n \"def p1_pits(self):\\n return self.state[:self.M]\",\n \"def assignPositions(self):\\n n = int(math.ceil(self.numAtoms**(1.0/3.0))) # Number of atoms in a direction\\n particle = 0 # Particles placed so far\\n \\n for x in range(0, n):\\n for y in range(0, n):\\n for z in range(0, n):\\n if (particle < self.numAtoms):\\n self.atoms[particle].x = x * self.sigma\\n self.atoms[particle].y = y * self.sigma \\n self.atoms[particle].z = z * self.sigma\\n particle += 1\",\n \"def construct_joint(self, x):\\n channel = x.reshape((self._crv_size, self._bound))\\n channel /= channel.sum(axis=1, keepdims=True)\\n channel[np.isnan(channel)] = self._mask[np.isnan(channel)]\\n slc = (len(self._pmf.shape) - 1)*[np.newaxis] + 2*[colon]\\n joint = self._pmf[..., np.newaxis] * channel[slc]\\n\\n return joint\",\n \"def first(pair):\\n\\treturn pair[0]\",\n \"def atoms(formula):\\n pass\\n # ======== YOUR CODE HERE ========\",\n \"def focn(self):\\n return self.table[1, 1] / (self.table[1, 0] + self.table[1, 1])\",\n \"def conditionalize(prior, conditional, observed):\\n\\n # construct joint probability table (Step 1 of Master Method)\\n joint = PGM2(prior, conditional)\\n #print(joint.get_cell(('POX', 'NOSPOTS')))\\n\\n # update joint probability table after observing value of N1 (Steps 2 and 3 of Master Method)\\n joint.update(observed, 1)\\n\\n # marginalize to get probability distribution for N0 (Step 4 of Master Method)\\n posterior = joint.marginalize(0)\\n\\n return posterior\",\n \"def _prob_map(self, x):\\n d = self.manifold.shape[0]\\n n = x.shape[1]\\n maha = np.zeros(shape=(n, self.m))\\n for w in range(self.m):\\n mu = np.tile(self.manifold[:, w].reshape(d, 1),\\n (1, n))\\n maha[:, w] = np.sum((x-mu)**2, 0) / self.sigma**2\\n tmp = np.exp(-0.5 * maha)\\n return np.exp(-0.5 * maha)\",\n \"def FK_pox(joint_angles, m_mat, s_lst):\\n pass\",\n \"def multinomial_pmf(sample, probabilities):\\r\\n # TODO\\r\\n a=[]\\r\\n b=[]\\r\\n i=0\\r\\n key_list=[]\\r\\n value_list=[]\\r\\n for key,value in sample.items():\\r\\n key_list.append(key)\\r\\n value_list.append(value)\\r\\n b=list(sample)\\r\\n while i< len(b):\\r\\n a.append(probabilities.keys()[probabilities.values().index(value_list[i])])\\r\\n\\r\\n\\r\\n return a\",\n \"def bone_pairs(self):\\n return ((0, 3), (1, 4), (2, 5), (10, 13), (11, 14), (12, 15))\",\n \"def extract_monom(var, P0):\\n \\n x = var[0]\\n th = var[1]\\n \\n P = [sp.Poly(p, *th) for p in P0]\\n\\n #which constraints are geometric in parameters theta, and don't involve current data x?\\n geom = [sp.Poly(p, *x).degree() == 0 for p in P0]\\n\\n \\n monom = [list(p.monoms()) for p in P]\\n coeff = [np.array(p.coeffs()) for p in P]\\n \\n #supp_monom = np.unique(np.array(monom), axis=1)\\n \\n \\n #I know this is ugly\\n #find unique monomial terms in function/constraints\\n #then output them in sorted lexicographical order\\n \\n supp_monom = np.flip(np.unique(np.array(sum(monom, [])), axis = 0), axis = 0)\\n \\n fb = [sp.lambdify(x, bi, \\\"numpy\\\") for bi in coeff]\\n \\n # if \\\"eq\\\" in cons.keys():\\n # h = [sp.Poly(gi, *th) for gi in cons[\\\"eq\\\"]]\\n \\n # if \\\"ineq\\\" in cons.keys():\\n # g = [sp.Poly(gi, *th) for gi in cons[\\\"ineq\\\"]]\\n return {\\\"fb\\\": fb, \\\"monom_poly\\\": monom, \\\"coeff\\\": coeff, \\\"A_pre\\\": supp_monom, \\\"geom\\\": geom}\",\n \"def _get_orbmom_per_atom(self, file, natom):\\n #TODO implement\\n return orbmom_at\",\n \"def _prob_to_mass(self, prob):\\n dictlist = []\\n for i in range(len(prob)):\\n dictlist.append([str(i+1), prob[i]])\\n return MassFunction(dict(dictlist))\",\n \"def one_from_cartesian_product_of_one_basis(self):\\n return self.sum_of_monomials( zip( self._sets_keys(), (set.one_basis() for set in self._sets)) )\",\n \"def marginalize(self, axis):\\n \\n dist = {}\\n\\n # -------------------------------------------------------------------------\\n # YOUR CODE GOES HERE\\n #\\n \\n # get relevant data based on the given random variable\\n for i in self._table:\\n if axis == 0:\\n if i[0] in dist:\\n dist[i[0]] += self._table[i]\\n else:\\n dist[i[0]] = self._table[i]\\n else:\\n if i[1] in dist:\\n dist[i[1]] += self._table[i]\\n else:\\n dist[i[1]] = self._table[i]\\n\\n\\n #\\n # END OF YOUR CODE\\n # ------------------------------------------------------------------------- \\n\\n return dist\",\n \"def molecular_weight(elements):\\n return (np.array([atomic_mass[i.upper()] for i in elements]).sum())\",\n \"def RefAtom(Residue):\\n\\n RES = ['HOH','ALA','ARG','ASN','ASP','CYS','GLN','GLU','GLY','HIS','ILE','LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL']\\n REF = [['O'],['O'], ['NH1','NH2'], ['OD1'], ['OD1', 'OD2'], ['SG'], ['OE1'], ['OE1', 'OE2'], ['O'], ['ND1', 'NE2'], ['O'], ['O'], ['NZ'], ['SD'], ['O'], ['O'], ['OG'], ['OG1'], ['O'], ['OH'], ['O']]\\n return REF[RES.index(Residue)][:]\",\n \"def select_corresponding(self, theory):\\n\\n Qth, Rth = theory\\n return [None if x_data is None\\n else (x_data.Q, np.interp(x_data.Q, Qth, x_th))\\n for x_data, x_th in zip(self.xs, Rth)]\",\n \"def comp_relation_probs(self, x_feats):\\n # l : examples batch size\\n # d : dimensionality of the (binary) feature space\\n relation_scores = sparse.dot(x_feats, self.W) + self.Wb # [l, d] x [d, m] + [m] => [l, m]\\n relation_probs = T.nnet.softmax(relation_scores)\\n return relation_probs\",\n \"def probabilidadObservable(obs,ket):\\n valP,vectP = propiosObservable(obs)\\n probs=[]\\n calA=CalculadoraAvanzada()\\n for v in vectP:\\n p=calA.transitarVector(v,ket)\\n probs.append(p)\\n return probs\",\n \"def find_biomass_precursors(reaction):\\n return [met for met in reaction.reactants\\n if met.id != 'atp_c' or met.id != 'h2o_c']\",\n \"def joint_prob_to_sigma(joint_prob):\\n\\n table = create_joint_prob_corr_table()\\n sigma = np.diag(np.ones(joint_prob.shape[0]))\\n\\n for i in range(sigma.shape[0]):\\n for j in range(i + 1, sigma.shape[0]):\\n r, jp = table[tuple(sorted((round(joint_prob[i, i], 2), round(joint_prob[j, j], 2))))]\\n f = interpolate.interp1d(jp, r)\\n sigma[i, j] = sigma[j, i] = f(joint_prob[i, j])\\n\\n return sigma\",\n \"def get_atom_numbers_to_optimize(self):\\n v = self.c.get(simulation_cell=True)\\n return v.data.stoichiometry\",\n \"def _process_atom(self, atom, body):\\n if isinstance(atom, Or):\\n # Annotated disjunction\\n atoms = atom.to_list()\\n else:\\n atoms = [atom]\\n\\n atoms_out = []\\n extra_clauses = []\\n\\n has_lfi_fact = False\\n prior_probability = 0.0 # Sum of prior weights in AD.\\n fixed_probability = 0.0 # Sum of fixed (i.e. non-learnable) weights in AD.\\n\\n num_random_weights = 0\\n for atom in atoms:\\n if atom.probability and atom.probability.functor == \\\"t\\\":\\n try:\\n start_value = float(atom.probability.args[0])\\n prior_probability += float(start_value)\\n except InstantiationError:\\n # Can't be converted to float => take random\\n num_random_weights += 1\\n except ArithmeticError:\\n num_random_weights += 1\\n elif atom.probability and atom.probability.is_constant():\\n fixed_probability += float(atom.probability)\\n\\n random_weights = [random.random() for _ in range(0, num_random_weights + 1)]\\n norm_factor = (1.0 - prior_probability - fixed_probability) / sum(\\n random_weights\\n )\\n random_weights = [r * norm_factor for r in random_weights]\\n\\n # First argument is probability available for learnable weights in the AD.\\n self.add_ad(1.0 - fixed_probability, []) # TODO : this adds extra ad\\n\\n # Replace anonymous variables with non-anonymous variables.\\n class ReplaceAnon(object):\\n def __init__(self):\\n self.cnt = 0\\n\\n def __getitem__(self, key):\\n if key == \\\"_\\\":\\n self.cnt += 1\\n return Var(\\\"anon_%s\\\" % self.cnt)\\n else:\\n return Var(key)\\n\\n prob_args = []\\n if isinstance(atom.probability, Term):\\n for arg in atom.probability.args:\\n if not isinstance(arg, Constant) and arg != Var(\\\"_\\\"):\\n prob_args.append(arg)\\n\\n newcount = \\\"_\\\".join([str(self.count + count) for count in range(len(atoms))])\\n\\n factargs = []\\n # probargs = ()\\n for atom in atoms:\\n q = list(atom.apply(ReplaceAnon()).args)\\n for var in q:\\n if var not in factargs:\\n factargs.append(var)\\n\\n lfi_rule = Term(\\n \\\"lfi_rule\\\", Constant(newcount), Term(\\\"t\\\", *prob_args), *factargs\\n )\\n\\n if body is not None:\\n extra_clauses.append(Clause(lfi_rule, body))\\n\\n for atom in atoms:\\n if atom.probability and atom.probability.functor == \\\"t\\\":\\n # t(_, X)::p(X, Y) :- body.\\n #\\n # Translate to\\n # lfi_prob(1, t(X))::lfi_fact(1, t(X), X, Y).\\n # lfi_rule(1, t(X), X, Y): - body.\\n # p(X, Y): - lfi_body(1, t(X), X, Y).\\n # lfi_body(1, t(X), X, Y): - lfi_par(1, t(X), X, Y), lfi_fact(1, t(X), X, Y).\\n # lfi_par(1, t(X), X, Y): - lfi_rule(1, t(X), X, Y).\\n #\\n #\\n # For annotated disjunction: t(_)::p1(X); t(_)::p2(X): - body.\\n # lfi_prob(0, t)::lfi_fact(0, t, X); lfi_prob(1, t)::lfi_fact(1, t, X): - lfi_rule(0_1, t, X).\\n # lfi_rule(0_1, t, X): - body.\\n # p1(X): - lfi_body(0, t, X).\\n # lfi_body(0, t, X): - lfi_par(0, t, X), lfi_fact(0, t, X).\\n # lfi_par(0, t, X): - lfi_rule(0_1, t, X).\\n # p2(X): - lfi_body(1, t, X).\\n # lfi_body(1, t, X): - lfi_par(1, t, X), lfi_fact(1, t, X).\\n # lfi_par(1, t, X): - lfi_rule(0_1, t, X).\\n # ...\\n has_lfi_fact = True\\n # Learnable probability\\n try:\\n start_value = float(atom.probability.args[0])\\n except InstantiationError:\\n start_value = None\\n except ArithmeticError:\\n start_value = None\\n\\n atom1 = atom.apply(ReplaceAnon())\\n\\n # 1) Introduce a new LFI terms\\n factargs = atom1.args\\n lfi_fact = Term(\\n \\\"lfi_fact\\\", Constant(self.count), Term(\\\"t\\\", *prob_args), *factargs\\n )\\n lfi_body = Term(\\n \\\"lfi_body\\\", Constant(self.count), Term(\\\"t\\\", *prob_args), *factargs\\n )\\n lfi_par = Term(\\n \\\"lfi_par\\\", Constant(self.count), Term(\\\"t\\\", *prob_args), *factargs\\n )\\n lfi_prob = Term(\\\"lfi_prob\\\", Constant(self.count), Term(\\\"t\\\", *prob_args))\\n\\n # 2) Replacement atom\\n replacement = lfi_fact.with_probability(lfi_prob)\\n\\n # 3) Create redirection clause\\n extra_clauses.append(Clause(atom1.with_probability(), lfi_body))\\n extra_clauses.append(Clause(lfi_body, lfi_par & lfi_fact))\\n\\n if body is None:\\n extra_clauses.append(Clause(lfi_par, Term(\\\"true\\\")))\\n else:\\n extra_clauses.append(Clause(lfi_par, lfi_rule))\\n\\n self.append_ad(len(self._weights))\\n\\n # 4) Set initial weight\\n if start_value is None:\\n # Assign a random weight initially\\n start_value = random_weights.pop(-1)\\n self._add_weight(start_value)\\n\\n # 5) Add name\\n self.names.append(atom)\\n self.bodies.append(lfi_body)\\n self.parents.append(lfi_par)\\n atoms_out.append(replacement)\\n else:\\n atoms_out.append(atom)\\n\\n self.verify_ad()\\n\\n if has_lfi_fact:\\n if len(atoms) == 1 and body is None:\\n # Non AD\\n return [atoms_out[0]] + extra_clauses\\n else:\\n # AD\\n if body is None:\\n return [\\n AnnotatedDisjunction(atoms_out, Term(\\\"true\\\"))\\n ] + extra_clauses\\n else:\\n return [AnnotatedDisjunction(atoms_out, lfi_rule)] + extra_clauses\\n else:\\n if len(atoms) == 1:\\n if body is None:\\n return [atoms_out[0]]\\n else:\\n return [Clause(atoms_out[0], body)]\\n else:\\n if body is None:\\n body = Term(\\\"true\\\")\\n return [AnnotatedDisjunction(atoms_out, body)]\",\n \"def GetPseudoAAC1(ProteinSequence,lamda=30,weight=0.05,AAP=[_Hydrophobicity,_hydrophilicity]):\\n\\trightpart=0.0\\n\\tfor i in range(lamda):\\n\\t\\trightpart=rightpart+GetSequenceOrderCorrelationFactor(ProteinSequence,i+1,AAP)\\n\\tAAC=GetAAComposition(ProteinSequence)\\n\\t\\n\\tresult={}\\n\\ttemp=1+weight*rightpart\\n\\tfor index,i in enumerate(AALetter):\\n\\t\\tresult['PAAC'+str(index+1)]=round(AAC[i]/temp,3)\\n\\t\\n\\treturn result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.594754","0.5753266","0.57007027","0.5424559","0.539321","0.5265403","0.52582085","0.52560824","0.5244419","0.5229816","0.5205455","0.51626754","0.51596713","0.5148809","0.51186013","0.51104856","0.51007","0.50988847","0.50907356","0.5085001","0.50847787","0.506436","0.5059798","0.5058871","0.5054824","0.50164527","0.49896","0.49687666","0.49669787","0.49666062","0.4966546","0.49497834","0.4947125","0.49454838","0.49405983","0.4933582","0.49314263","0.49219286","0.49093598","0.49089247","0.48925653","0.48846543","0.48838606","0.48738655","0.48688462","0.48684284","0.48671848","0.48668677","0.48619947","0.4850696","0.48498237","0.48446107","0.48434943","0.48427644","0.48390883","0.48324677","0.48303446","0.48224747","0.48213345","0.48184595","0.4816516","0.48140827","0.48084834","0.48070958","0.48068312","0.47980115","0.47979987","0.47966975","0.47966948","0.4787376","0.47794634","0.47779235","0.47713786","0.47659412","0.47651273","0.47631136","0.47619393","0.4761023","0.4743268","0.47416815","0.47375047","0.4734493","0.47342205","0.47298905","0.47272322","0.472042","0.47170517","0.47166625","0.4710581","0.47042146","0.47039995","0.469205","0.46903184","0.4688932","0.46862358","0.46825758","0.46758896","0.46746334","0.46735632","0.46709767"],"string":"[\n \"0.594754\",\n \"0.5753266\",\n \"0.57007027\",\n \"0.5424559\",\n \"0.539321\",\n \"0.5265403\",\n \"0.52582085\",\n \"0.52560824\",\n \"0.5244419\",\n \"0.5229816\",\n \"0.5205455\",\n \"0.51626754\",\n \"0.51596713\",\n \"0.5148809\",\n \"0.51186013\",\n \"0.51104856\",\n \"0.51007\",\n \"0.50988847\",\n \"0.50907356\",\n \"0.5085001\",\n \"0.50847787\",\n \"0.506436\",\n \"0.5059798\",\n \"0.5058871\",\n \"0.5054824\",\n \"0.50164527\",\n \"0.49896\",\n \"0.49687666\",\n \"0.49669787\",\n \"0.49666062\",\n \"0.4966546\",\n \"0.49497834\",\n \"0.4947125\",\n \"0.49454838\",\n \"0.49405983\",\n \"0.4933582\",\n \"0.49314263\",\n \"0.49219286\",\n \"0.49093598\",\n \"0.49089247\",\n \"0.48925653\",\n \"0.48846543\",\n \"0.48838606\",\n \"0.48738655\",\n \"0.48688462\",\n \"0.48684284\",\n \"0.48671848\",\n \"0.48668677\",\n \"0.48619947\",\n \"0.4850696\",\n \"0.48498237\",\n \"0.48446107\",\n \"0.48434943\",\n \"0.48427644\",\n \"0.48390883\",\n \"0.48324677\",\n \"0.48303446\",\n \"0.48224747\",\n \"0.48213345\",\n \"0.48184595\",\n \"0.4816516\",\n \"0.48140827\",\n \"0.48084834\",\n \"0.48070958\",\n \"0.48068312\",\n \"0.47980115\",\n \"0.47979987\",\n \"0.47966975\",\n \"0.47966948\",\n \"0.4787376\",\n \"0.47794634\",\n \"0.47779235\",\n \"0.47713786\",\n \"0.47659412\",\n \"0.47651273\",\n \"0.47631136\",\n \"0.47619393\",\n \"0.4761023\",\n \"0.4743268\",\n \"0.47416815\",\n \"0.47375047\",\n \"0.4734493\",\n \"0.47342205\",\n \"0.47298905\",\n \"0.47272322\",\n \"0.472042\",\n \"0.47170517\",\n \"0.47166625\",\n \"0.4710581\",\n \"0.47042146\",\n \"0.47039995\",\n \"0.469205\",\n \"0.46903184\",\n \"0.4688932\",\n \"0.46862358\",\n \"0.46825758\",\n \"0.46758896\",\n \"0.46746334\",\n \"0.46735632\",\n \"0.46709767\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63074166"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19590,"cells":{"query":{"kind":"string","value":"Compute the joint probabilities for all combinations of values"},"document":{"kind":"string","value":"def jointProbabilities(constants, db, ruleList, bn):\n vars = bn.variableList()\n combs = generateCombos(vars, constants)\n joints = []\n for grounding in combs:\n joints.append((grounding, bn.jointProbs(grounding, db, ruleList)))\n return (vars, atomList(joints), joints)"},"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 joint_proba(self, X):\n return self.weights * self._bernoulli(X)","def joint_prob(network, assignment):\n prob = 1\n for a_key in assignment:\n conditions = []\n current = network[a_key]\n for parent in current['Parents']:\n conditions.append(True) if assignment[parent] else conditions.append(False)\n conditions = tuple(conditions)\n if not assignment[a_key]:\n prob *= (1 - current['CPT'].get(conditions))\n else:\n prob *= (current['CPT'].get(conditions)) \n return prob","def generate_probabilities(self):\n k = 1\n v= 10\n for g in self.class_probabilities:\n curr_list = self.class_probabilities[g]\n for l in range(0,28):\n for w in range(0,28):\n total = float(curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2])\n curr_list[l][w][0] = (float(curr_list[l][w][0])+k)/(total + k*v) \n curr_list[l][w][1] = (float(curr_list[l][w][1])+k)/(total + k*v)\n curr_list[l][w][2] = (float(curr_list[l][w][2])+k)/(total + k*v)\n curr_list[l][w][3] = curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2]","def joint_probabilities_from_transitions(ordered_pitch_types, transition_counts):\n first_pitch_totals = {first_pitch_type: sum(transition_counts[first_pitch_type].values())\n for first_pitch_type in ordered_pitch_types}\n\n total_transitions = sum(first_pitch_totals.values())\n\n markov_rows = []\n joint_probabilities = {}\n\n for first_pitch_type in ordered_pitch_types:\n first_pitch_transitions = transition_counts[first_pitch_type]\n joint_probabilities[first_pitch_type] = {}\n first_pitch_type_probability = float(first_pitch_totals[first_pitch_type]) / total_transitions\n\n second_pitch_total = sum(first_pitch_transitions.values())\n row = [first_pitch_type]\n\n for second_pitch_type in ordered_pitch_types:\n if second_pitch_total == 0:\n second_pitch_conditional_probability = 0\n\n else:\n second_pitch_conditional_probability = \\\n float(first_pitch_transitions[second_pitch_type]) / second_pitch_total\n\n row.append(second_pitch_conditional_probability)\n\n joint_probabilities[first_pitch_type][second_pitch_type] = \\\n first_pitch_type_probability * second_pitch_conditional_probability\n\n markov_rows.append(row)\n\n return joint_probabilities, markov_rows, total_transitions","def probability(prods, prod_dict_As, count_dict):\n for p in prods:\n if p not in prod_dict_As:\n raise Exception(\"Think we cannot make the product {}.\".format(p))\n # Argh, Python, this is a reference!\n #possible_As = prod_dict_As[prods[0]]\n possible_As = set( prod_dict_As[prods[0]] )\n for p in prods[1:]:\n possible_As &= prod_dict_As[p]\n ret = []\n for A in possible_As:\n count = 1\n for p in prods:\n count *= count_dict[(p,A)]\n ret.append((A,count))\n return ret","def dealer_probs():\n # Pdf of any current hand (value, hard) and final value; p(v_f | v_c) where v_f = final value, v_c = current value\n probabilities = {}\n\n # End nodes: (value, True) for value >= 17 and (value, False) for value > 17\n # Dependencies (in order of increasing requirements):\n # Hard values, value >= 11, possiblity of bust, no possibility of going soft with an ace (value, True) depends on (value', True) for 17 > value' > value\n # Soft values, 17 >= value >= 11 (value, False) depends on (value', False) for 17 >= value' > value, (value', True) for 17 > value' > 11\n # Hard values, 11 > value >= 2 , no possibility of bust, possibility of going soft with an ace (value, True) depends on (value', True) for 17 > value' > value and (value', False) for 17 >= value' > 13\n\n\n # End nodes\n for value in xrange(17, 22):\n probabilities[(value, True)] = {value: 1.0}\n if value == 17: continue # on soft 17, dealer will still hit\n probabilities[(value, False)] = {value: 1.0}\n\n # Hard values, 17 > value >= 11, possibility of bust, no possibility of going soft with an ace\n for value in xrange(16, 10, -1):\n probabilities[(value, True)] = {}\n current_prob = probabilities[(value, True)]\n for next_card in xrange(1, min(10, 21-value)+1):\n next_prob = probabilities[(value + next_card, True)]\n for end_val in next_prob:\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\n\n # Soft values, 17 >= value >= 11\n for value in xrange(17, 10, -1):\n probabilities[(value, False)] = {}\n current_prob = probabilities[(value, False)]\n for next_card in xrange(1, 11):\n next_value = value + next_card\n hard = False\n if next_value > 21:\n next_value -= 10\n hard = True\n next_prob = probabilities[(next_value, hard)]\n for end_val in next_prob:\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\n\n # Hard values, 11 > value >= 2, no possibility of bust, possibility of going soft with an ace\n for value in xrange(10, 1, -1):\n probabilities[(value, True)] = {}\n current_prob = probabilities[(value, True)]\n for next_card in xrange(2, 12):\n next_value = value + next_card\n hard = (next_card != 11)\n next_prob = probabilities[(next_value, hard)]\n for end_val in next_prob:\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\n\n return probabilities","def probabilities(self):\n raise NotImplementedError","def compute_conditional_distributions(joints):\n\n marginals = np.sum(joints, axis=1)\n posidx = marginals > 0\n \n conditionals = joints.copy()\n conditionals[posidx, :] /= marginals[posidx, None]\n conditionals[~posidx, :] = marginals\n assert np.isclose(np.sum(conditionals), len(conditionals))\n assert np.allclose(np.sum(conditionals, axis=1), 1.0)\n\n return conditionals","def multinomial_prob(counts, probs):\n return nCkarray(*counts.values) * (probs ** counts).prod()","def CalculateProbabilities(self, beta_0, beta_1):\n denom = self.zero_zero + self.zero_one + self.one_zero + self.one_one + beta_0 + beta_1 - 2\n if denom != 0:\n self.prob_zero = min( max( (self.zero_zero + self.zero_one + beta_0 - 1) / denom, 0.0 ), 1.0 )\n self.prob_one = min( max( (self.one_zero + self.one_one + beta_1 - 1) / denom, 0.0 ), 1.0 )\n \n denom = self.zero_zero + self.one_zero + beta_0 + beta_1 - 2\n if denom != 0:\n self.prob_zero_given_zero = min( max( (self.zero_zero + beta_0 - 1) / denom, 0.0 ), 1.0 )\n self.prob_one_given_zero = min( max( (self.one_zero + beta_1 - 1) / denom, 0.0 ), 1.0 )\n \n denom = self.zero_one + self.one_one + beta_0 + beta_1 - 2\n if denom != 0:\n self.prob_zero_given_one = min( max( (self.zero_one + beta_0 - 1) / denom, 0.0 ), 1.0 )\n self.prob_one_given_one = min( max( (self.one_one + beta_1 - 1) / denom, 0.0 ), 1.0 )","def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n \"*** YOUR CODE HERE ***\"\n\t#Adds log(P(y)) to calculate P(y|f1,f2...)\n for label in self.legalLabels:\n\t\tlogJoint[label] += math.log(self.prior[label])\n\t#Adds log(P(f1|y)), log(P(f2|y))... to calculate P(y|f1, f2...)\n for key in datum:\n\t\t#if key == (7, 3):\n\t\t\t#print self.condprobs[key, 0]\n\t\tfor label in self.legalLabels:\n\t\t\t#print str(key) + str(datum[key])\n\t\t\tlogJoint[label] += math.log(self.condprobs[key, label][datum[key]])\n return logJoint","def get_components_probabilities(self):\n compsProbs={}\n for d in self.diagnoses:\n p = d.get_prob()\n for comp in d.get_diag():\n compsProbs[comp] = compsProbs.get(comp,0) + p\n return sorted(compsProbs.items(), key=lambda x: x[1], reverse=True)","def compute_joint_probability(token_list, token_probabilities, use_log_prob=False):\n\n log_prob = 0\n\n for word in token_list:\n\n # do not allow zero probabilites\n assert word in token_probabilities\n\n if use_log_prob:\n log_prob += token_probabilities[word]\n else:\n log_prob += log10(token_probabilities[word])\n\n if use_log_prob:\n return log_prob\n\n return 10**log_prob","def chance(n, p):\n total = 0.0\n for k in range(n+1):\n total += comb(n, k, exact=False) * p**k * (1-p) ** (n-k)\n return total","def _update_probabilities(self):\n pairs_last = copy.deepcopy(self.pairs)\n # pairs_last = [el for el in pairs]\n for src_p in range(self.frame_0.shape[0]):\n for seql_num, dst_p in enumerate(self.pairs[src_p][0]):\n Q = self._calculate_Q(src_p, dst_p, pairs_last)\n # update pair probability.\n self.pairs[src_p][1][seql_num] = pairs_last[src_p][1][seql_num] * (self.A + self.B * Q)\n\n # normalize probability\n self.pairs[src_p][1] = self.pairs[src_p][1] / self.pairs[src_p][1].sum()","def get_probs(self):\n\t\tprobArray = []\n\t\tfor combination in self.codepool:\n\t\t\tif self.feasible(combination):\n\t\t\t\tprobArray.append(self.get_probability(combination))\n\t\t\telse:\n\t\t\t\tprobArray.append(0)\n\t\tprobArray = np.array(probArray) / np.sum(probArray)\n\t\treturn probArray","def calculateLogJointProbabilities(self, datum):\n\tlogJoint = util.Counter()\n\t#want to calculate log(P(y)) + log(sum(P(fi|y)))\n\t#where y is a label\n\tfor label in self.legalLabels:\n\t\tlogJoint[label] = math.log(self.prior_distribution_prob[label])\n\t\tfor feature, value in datum.items():\n\t\t\tcp = self.conditional_prob[label][feature][value]\n\t\t\tif cp > 0: #condition check for values < 0 because log(0) is undefined and math domain error occurs\n\t\t\t\tlogJoint[label] += math.log(cp) #summing up\n\t\t\t\t\n\treturn logJoint","def get_prob_for_distributions(p):\n w1 = p[0]\n mu1 = p[1]\n sigma1 = p[2]\n w2 = p[3]\n mu2 = p[4]\n sigma2 = p[5]\n w3 = p[6]\n mu3 = p[7]\n sigma3 = p[8]\n dist_range = (0, 4.330310991999920844e+01)\n x = np.linspace(dist_range[0], dist_range[1], 1000)\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\n p1 = A1 / (A1 + A2 + A3)\n p2 = A2 / (A1 + A2 + A3)\n p3 = A3 / (A1 + A2 + A3)\n return p1, p2, p3","def caculate_prob(self):\n t_H = self.tree.depth()\n t_h = 1\n while(t_h <= t_H):\n t_hnodes = self.get_h(t_h)\n t_sum = 0\n t_hpro = []\n t_cpro = []\n for t_n in t_hnodes:\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\n t_node = self.tree.get_node(t_n)\n if t_node.is_leaf():\n t_node.data.append(0)\n continue\n t_childrens = self.tree.children(t_n)\n t_shang = 0\n for child in t_childrens:\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\n t_node.data.append(-t_shang)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n t_parentnode = self.tree.parent(t_n)\n if t_h > 1:\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n else:\n t_node.data.append((t_node.data[0] / t_sum))\n t_hpro.append((t_node.data[0] / t_sum))\n\n t_cpro.append(t_node.data[1])\n t_ndata = np.array(t_hpro)\n mean = np.mean(t_ndata)\n std = np.std(t_ndata,ddof=1)\n t_sdata = np.array(t_cpro)\n mean_s = np.mean(t_sdata)\n std_s = np.std(t_sdata,ddof=1)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n if(std != 0):\n t_node.data[2] = (t_node.data[2] - mean)/std\n else:\n t_node.data[2] = (t_node.data[2] - mean)\n if(mean_s == 0 and std_s ==0):\n t_node.data[1] = -100.0\n continue\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\n t_h = t_h + 1","def get_joint_outcomes(d):\n repeated_list = [[True, False]] * d\n oc = np.array(list(itertools.product(*repeated_list)))\n\n return oc","def MAP(cpts, obs, terms):\r\n\r\n # a list to store the computed probabilities\r\n all_sums = []\r\n # initialize all terms to false\r\n for value in range(len(terms)):\r\n terms[value] = [terms[value], '0']\r\n search_array = terms + obs\r\n # if all terms are being watched, just call MPE\r\n if len(search_array) == len(cpts):\r\n return MPE(cpts, obs)\r\n # we need to know what terms we aren't interested in so we start with \r\n # or terms and observations and note the variables that appear in CPT but\r\n # not in those\r\n dont_count = []\r\n for var in cpts:\r\n if [var[0], '0'] not in search_array and [var[0], '1'] not in search_array:\r\n dont_count.append(var[0])\r\n terms.append([var[0],'1'])\r\n # sort the terms to ensure correct ordering\r\n terms.sort()\r\n # creates a list of all possible bit strings\r\n # just an easy way to create all possible truth assignments\r\n seq = [\"\".join(seq) for seq in itertools.product(\"01\", repeat=len(terms))]\r\n # loop through all possible truth assignments\r\n for j in range(len(seq)):\r\n # we initialize at probability = 100%\r\n chance = 1\r\n # assign the truth values\r\n for k in range(len(seq[j])):\r\n terms[k][1] = seq[j][k]\r\n # this computes the probability using the chaining rule\r\n for i in range(len(terms)):\r\n new_terms = terms[:-i-1] + obs\r\n new_terms.sort()\r\n chance *= probability(cpts,terms[-i-1], new_terms)\r\n # add the probabilities to our list\r\n all_sums.append(chance)\r\n combine = []\r\n # note all variables which weren't in obs or Vs\r\n for i in dont_count:\r\n combine.append(terms.index([i,'1']))\r\n # this will store the final probabilities\r\n final_array = [0] * len(seq)\r\n # another complicated looking loop, it just serves to combine probabilities\r\n # for example, if we have a CPT with x_1, x_2, x_3, x_4 and we observe \r\n # x_1 to be true and have Vs = [x_3, x_4] then we need to combine the \r\n # probabilities that are the same except for x_2 = true vs false\r\n for loc in combine:\r\n for sequence in range(len(seq)):\r\n for alt_sequence in range(sequence+1,len(seq)):\r\n if (seq[sequence][:loc] + seq[sequence][loc+1:]) == (seq[alt_sequence][:loc] + seq[alt_sequence][loc+1:]):\r\n final_array[sequence] = all_sums[sequence] + all_sums[alt_sequence]\r\n\r\n # get the truth assignment for the highest probability\r\n location = seq[final_array.index(max(final_array))]\r\n truth_assignment = []\r\n # place the truth assignment in a more readable fashion\r\n for value in range(len(terms)):\r\n if terms[value] in search_array:\r\n if location[value] == '0':\r\n truth_assignment.append(terms[value][0]+ ' = False')\r\n else:\r\n truth_assignment.append(terms[value][0]+ ' = True')\r\n return (truth_assignment)","def prodgreqs_base(A):\n choices = [ list(range(xi+1)) for xi in A ]\n M = len(choices) + 1\n for yi in itertools.product(*choices):\n prod, freq = 1, 1\n for a, y, x in zip(range(2, M+1), yi, A):\n prod *= a ** y\n freq *= math.factorial(x) // math.factorial(y) // math.factorial(x-y)\n yield ProdFreqPair(prod, freq)","def calculate_probability(k: int, m: int, n: int) -> float:\n population = [\"AA\" for _ in range(k)] + [\"Aa\" for _ in range(m)] + [\"aa\" for _ in range(n)]\n pairings = it.combinations(population, 2)\n probabilities = [PROBABILITIES[pairing] for pairing in pairings]\n output = sum(probabilities) / len(probabilities)\n\n return output","def probability(distances):\n v = [1.0/(d + 1) for d in distances]\n s = sum(v)\n return [i/s for i in v]","def enumerate_joint_ask(X, e, P):\n Q = ProbDist(X) ## A probability distribution for X, initially empty\n Y = [v for v in P.variables if v != X and v not in e]\n for xi in P.values(X):\n Q[xi] = enumerate_joint(Y, extend(e, X, xi), P)\n return Q.normalize()","def proba(self, X):\n return np.sum(self.joint_proba(X), axis=-1)","def _get_selection_probabilities(self):\r\n probabilities = np.arange(1, self.population_size+1, dtype=float)[::-1]\r\n probabilities /= probabilities.sum()\r\n return probabilities","def get_probs(self, a):\n with torch.no_grad():\n probabilities = (np.array(self.priorities) ** a) / sum(np.array(self.priorities) ** a)\n return probabilities","def joint(G, xs=None):\n vars = G.vars() #: [var]\n facs = { f : G.N(f) for f in G.facs() } #: fac => vars\n\n dims = [G.node[x]['d'] for x in vars] #: [nat]\n _joint = ones(dims)\n\n for vals in itertools.product( *(xrange(d) for d in dims) ): # cartesian product\n _vars = dict(zip(vars,vals)) #: var => val\n vals = tuple(vals) # to index\n #print\n #print _vars\n for fac in facs:\n _vals = [_vars[v] for v in facs[fac]] # keep only fac's vars' vals\n #print '%s%s' % (fac, tuple(_vals))\n _joint[vals] *= G(fac, *_vals)\n\n Z = sum(_joint)\n\n return pd(_joint), Z","def comp_relation_probs(self, x_feats):\n # l : examples batch size\n # d : dimensionality of the (binary) feature space\n relation_scores = sparse.dot(x_feats, self.W) + self.Wb # [l, d] x [d, m] + [m] => [l, m]\n relation_probs = T.nnet.softmax(relation_scores)\n return relation_probs","def get_probs(self, *vars):\n freqs = self.freq_counts([self.data.get_column_view(v)[0] for v in vars], [len(v.values) for v in vars])\n k = np.prod([len(v.values) for v in vars])\n return (freqs + self.alpha) / (np.sum(freqs) + self.alpha*k)","def test_most_probable_value(self):\n with Pandas() as pd:\n if pd is None:\n return\n with Numpy() as np: # noqa\n if numpy is None:\n return\n sys.stderr.write(\"\\n\")\n\n df1 = pd.DataFrame(\n {'A': [0, 1, 2, 3, 4, 3, 2, 1, 1, 1], 'C': ['f1', 'f3', 'f4', 'f3', 'f4', 'f2', 'f2', 'f1', 'f3', 'f4']})\n df2 = pd.DataFrame(\n {'A': [2, 3, 4, 5, 7, 4, 6, 5, 7, 8], 'C': ['f7', 'f3', 'f5', 'f8', 'f9', 'f2', 'f3', 'f6', 'f7', 'f7']})\n\n # building 1d-, 2d-, and 3d-histogram (iteratively)\n hist0 = hg.Categorize(unit('C'))\n hist1 = hg.Categorize(unit('C'))\n hist2 = hg.SparselyBin(origin=0.0, binWidth=1.0, quantity=unit('A'))\n hist3 = hg.SparselyBin(origin=0.0, binWidth=1.0, quantity=unit('A'))\n\n # fill them\n hist0.fill.numpy(df1)\n hist1.fill.numpy(df2)\n hist2.fill.numpy(df1)\n hist3.fill.numpy(df2)\n\n assert hist0.mpv == 'f3'\n assert hist1.mpv == 'f7'\n assert hist2.mpv == 1.5\n assert hist3.mpv == 4.5","def _precompute_probabilities(self):\n\n d_graph = self.d_graph\n first_travel_done = set()\n\n nodes_generator = self.graph.nodes() if self.quiet \\\n else tqdm(self.graph.nodes(), desc='Computing transition probabilities')\n\n for source in nodes_generator:\n\n # Init probabilities dict for first travel\n if self.PROBABILITIES_KEY not in d_graph[source]:\n d_graph[source][self.PROBABILITIES_KEY] = dict()\n\n for current_node in self.graph.neighbors(source):\n\n # Init probabilities dict\n if self.PROBABILITIES_KEY not in d_graph[current_node]:\n d_graph[current_node][self.PROBABILITIES_KEY] = dict()\n\n unnormalized_weights = list()\n first_travel_weights = list()\n d_neighbors = list()\n\n # Calculate unnormalized weights\n for destination in self.graph.neighbors(current_node):\n\n p = self.sampling_strategy[current_node].get(self.P_KEY,\n self.p) if current_node in self.sampling_strategy else self.p\n q = self.sampling_strategy[current_node].get(self.Q_KEY,\n self.q) if current_node in self.sampling_strategy else self.q\n\n if destination == source: # Backwards probability\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / p\n elif destination in self.graph[source]: # If the neighbor is connected to the source\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1)\n else:\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / q\n\n # Assign the unnormalized sampling strategy weight, normalize during random walk\n unnormalized_weights.append(ss_weight)\n if current_node not in first_travel_done:\n first_travel_weights.append(self.graph[current_node][destination].get(self.weight_key, 1))\n d_neighbors.append(destination)\n\n # Normalize\n unnormalized_weights = np.array(unnormalized_weights)\n d_graph[current_node][self.PROBABILITIES_KEY][\n source] = unnormalized_weights / unnormalized_weights.sum()\n\n if current_node not in first_travel_done:\n unnormalized_weights = np.array(first_travel_weights)\n d_graph[current_node][self.FIRST_TRAVEL_KEY] = unnormalized_weights / unnormalized_weights.sum()\n first_travel_done.add(current_node)\n\n # Save neighbors\n d_graph[current_node][self.NEIGHBORS_KEY] = d_neighbors","def get_probs(self, states, actions):\n # YOUR CODE HERE\n \n # So we need to determine for every input state-action pair, what the resulting policy distribution is\n # This means that the input will be a single state and a single action per index. \n # We then need to determine if, according to our policy, the action should be taken (prob=1) \n # or not (prob=0)\n \n # state is a tuple of (player's current sum, dealer's single showing card, boolean for usable ace)\n probs = []\n for index, (state, action) in enumerate(zip(states, actions)):\n chosen_action = self.sample_action(state)\n if action == chosen_action:\n probs.append(1)\n else:\n probs.append(0)\n \n \n return np.array(probs)","def calc_probabilities(applications):\n sum_advantage = sum(app.get_advantage() for app in applications)\n return [app.get_advantage() / sum_advantage for app in applications]","def log_joint(self):\n return sum([\n self.log_marg_like(self.gamma, self.gamma0, self.lamb, self.nu),\n self._gamma0_distribution.logpdf(self.gamma0),\n self._nu_distribution.logpdf(self.nu),\n self._lambda_distribution.logpdf(self.lamb),\n self.probit_distribution(self.xi).logpdf(self.gamma),\n self._xi_distribution.logpdf(self.xi) if self.sample_xi else 0.0\n ])","def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n \n \"*** YOUR CODE HERE ***\"\n \n # -- OUR CODE HERE\n \n \n import math\n for label in self.legalLabels:\n sumThing = 0.0\n for pixel in self.conditionalProb[label]:\n if datum[pixel] is 1:\n #assert self.conditionalProb[label][pixel] < 1.0 # -- sanity check that the probability is valid\n sumThing += math.log((self.conditionalProb[label][pixel]*1.0))\n else:\n sumThing+=math.log(1-self.conditionalProb[label][pixel]*1.0)\n logJoint[label] = math.log(self.prior[label]*1.0) + sumThing*1.0\n \n\n \n \n import time\n #print \"logJoint is :: \", logJoint\n #time.sleep(2)\n \n \n # -- uses the conditional probability tables computed in the current iteration\n # -- in train and tune\n \n return logJoint","def set_uniform_probabilities(self, sentence_aligned_corpus):\n max_m = longest_target_sentence_length(sentence_aligned_corpus)\n\n # The maximum vacancy difference occurs when a word is placed in\n # the last available position m of the target sentence and the\n # previous word position has no vacancies.\n # The minimum is 1-max_v, when a word is placed in the first\n # available position and the previous word is placed beyond the\n # last available position.\n # Thus, the number of possible vacancy difference values is\n # (max_v) - (1-max_v) + 1 = 2 * max_v.\n if max_m > 0 and (1 / (2 * max_m)) < IBMModel.MIN_PROB:\n warnings.warn(\n \"A target sentence is too long (\"\n + str(max_m)\n + \" words). Results may be less accurate.\"\n )\n\n for max_v in range(1, max_m + 1):\n for dv in range(1, max_m + 1):\n initial_prob = 1 / (2 * max_v)\n self.head_vacancy_table[dv][max_v] = defaultdict(lambda: initial_prob)\n self.head_vacancy_table[-(dv - 1)][max_v] = defaultdict(\n lambda: initial_prob\n )\n self.non_head_vacancy_table[dv][max_v] = defaultdict(\n lambda: initial_prob\n )\n self.non_head_vacancy_table[-(dv - 1)][max_v] = defaultdict(\n lambda: initial_prob\n )","def joint_frequencies_combo(self, alleles):\n\n representations = [1 << i for i in range(len(alleles))]\n\n intrenal_hap_dict_per_group = {group2: self.build_intrenal_hap_dict(alleles, group2)\n for group2 in self.hap_dict_per_group}\n\n result = {}\n\n for c in representations:\n hap = {group2: internal[c] for group2, internal in intrenal_hap_dict_per_group.items()}\n result[c] = self.effective_joint_frequency(hap)\n\n for C in combinations(representations, 2):\n hap = {group2: internal[C[0]] & internal[C[1]] for group2, internal in intrenal_hap_dict_per_group.items()}\n result[C[0]|C[1]] = self.effective_joint_frequency(hap)\n\n for C in combinations(representations, 3):\n hap = {group2: internal[C[0]] & internal[C[1]] & internal[C[2]]\n for group2, internal in intrenal_hap_dict_per_group.items()}\n result[C[0]|C[1]|C[2]] = self.effective_joint_frequency(hap)\n\n for r in range(4,len(alleles)):\n for C in combinations(representations, r):\n hap = {group2: reduce(and_,itemgetter(*C)(internal))\n for group2, internal in intrenal_hap_dict_per_group.items()}\n result[sum(C)] = self.effective_joint_frequency(hap)\n\n if len(alleles)>=4:\n hap = {group2: reduce(and_,internal.values())\n for group2, internal in intrenal_hap_dict_per_group.items()}\n result[sum(representations)] = self.effective_joint_frequency(hap)\n\n return result","def entropy_permutation_test(ordered_pitch_types, single_pitch_pdf, conditional_joint_probabilities, total_transitions,\n n=1000):\n pitch_types, pitch_probabilities = zip(*single_pitch_pdf.items())\n permutation_entropies = []\n progress = progressbar.ProgressBar()\n\n for test_number in progress(xrange(n)):\n # create the new matrix\n permutation_counts = {}\n for first_pitch_type in ordered_pitch_types:\n permutation_counts[first_pitch_type] = {}\n for second_pitch_type in ordered_pitch_types:\n permutation_counts[first_pitch_type][second_pitch_type] = 0\n\n pitch_permutation = numpy.random.choice(pitch_types, total_transitions, p=pitch_probabilities)\n current_pitch = numpy.random.choice(pitch_types, p=pitch_probabilities)\n for next_pitch in pitch_permutation:\n permutation_counts[current_pitch][next_pitch] += 1\n current_pitch = next_pitch\n\n joint_probabilities, _, _ = joint_probabilities_from_transitions(ordered_pitch_types, permutation_counts)\n permutation_entropies.append(entropy_from_probability_matrix(joint_probabilities))\n\n joint_entropy = entropy_from_probability_matrix(conditional_joint_probabilities)\n # print 'Mean', numpy.mean(permutation_entropies)\n # print 'Standard deviation', numpy.std(permutation_entropies)\n # tdof, tloc, tscale = stats.t.fit(permutation_entropies)\n # print 'DF', tdof, 'Loc (mean)', tloc, 'Scale (SD)', tscale\n # t_score = (joint_entropy - tloc) / tscale\n # print stats.t.cdf(joint_entropy, df=tdof, loc=tloc, scale=tscale)\n\n mean, stddev = stats.norm.fit(permutation_entropies)\n print 'Mean = {mean}\\t StdDev = {stddev}'.format(mean=mean, stddev=stddev)\n z_score = (joint_entropy - mean) / stddev\n p_value = stats.norm.cdf(joint_entropy, mean, stddev)\n print 'The joint entropy has a Z-score of {z_score} which gives a P-value of {p_value}'.format(z_score=z_score,\n p_value=p_value)\n return z_score, p_value","def permutate_genome_percent(human, phix, bacteria):\n \n per = list(itertools.product(human, phix, bacteria))\n sum_per = [sum(i) for i in zip(*per)]\n \n #check percentage sum < 1\n if all(i > 1 for i in sum_per):\n print \"Some combinations of human, phix and bacteria greater than 1\"\n sys.exit(0)\n \n return per","def probability_from_internal(internal_values, constr):\n return internal_values / internal_values.sum()","def probability(cpts, term, obs):\r\n \r\n \r\n # term is a list e.g., ['x_1', '0']\r\n # flip refers to the assignment either '0' false or '1' true\r\n flip = term[1]\r\n # the term itself\r\n term = term[0]\r\n # accumulator variable\r\n answer = 0\r\n # this loop locates where in the CPT we're looking\r\n for clause in range(len(cpts)):\r\n if cpts[clause][0] == term:\r\n index = clause\r\n # focus on our term\r\n cpt = cpts[index]\r\n # this loop checks if there are no preconditions\r\n # if not, then we immediately know the probability and can return\r\n for m in range(len(cpt[1])):\r\n if cpt[1][m][-2][1] == '1':\r\n if cpt[1][m][0] == [[]]:\r\n answer = cpt[1][m][-1]\r\n # list of the variables we have observed\r\n have = []\r\n if obs != []:\r\n for k in obs:\r\n have.append(k[0])\r\n # list of variables we need to know in order to calculate the probability\r\n needed = []\r\n for prob in range(len(cpt[1])):\r\n for j in cpt[1][prob][0]:\r\n if j != []:\r\n if j[0] not in needed:\r\n needed.append(j[0])\r\n # conditional logic based on the known variables\r\n for required in needed:\r\n if required not in have:\r\n # deep copy our observations list\r\n obs2 = []\r\n obs3 = []\r\n for observs in obs:\r\n obs2.append(observs)\r\n obs3.append(observs)\r\n # if we need to know a variable but don't have it\r\n # then we allow it to be either 0 or 1\r\n obs3.append([required,'1'])\r\n obs2.append([required,'0'])\r\n # computes probability if the unknown term is true, times \r\n # the probability that the unknown term is true, plus the\r\n # probability if the unknown term is false, times the \r\n # probability that the unknown term is false\r\n answer = (probability(cpts, [term,flip], obs3) * probability(cpts, [required,'1'], obs)) + (probability(cpts, [term,flip], obs2) * (probability(cpts, [required,'0'], obs)))\r\n # this loop looks complicated but all it's doing is finding the correct\r\n # line in the CPT\r\n if cpt[1][prob][-2][1] == '1':\r\n count = 1\r\n for i in range(len(cpt[1][prob][0])):\r\n if cpt[1][prob][0][i] in obs:\r\n count *= 1\r\n else:\r\n count = 0\r\n if count == 1:\r\n answer += cpt[1][prob][-1]\r\n\r\n\r\n # this computes the probability that the term is true, so if we asked \r\n # for the probability that it is false, just return 1 - answer\r\n if flip == '0':\r\n return 1 - answer\r\n return answer","def probability(structure,seq, react=None):\n return energy_to_proba(get_ens_energy(seq,react),get_stru_energy(structure,seq,react))","def get_probability(self, combination):\n\n\t\tprob = 1\n\t\tfor i in np.arange(self.codelength):\n\t\t\tprob *= self.prior[combination[i]-1]\n\t\treturn prob","def cumprob(self):\r\n return self.probabilities.cumsum(-1)","def probability(series, params):\n\n prob = 1\n\n for result in series:\n\n prob *= params[result]\n\n return prob * params[\"die\"]","def prior_sample(self, bn):\n x = np.zeros(3)\n\n # first joint prob\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\n x[0] = random_choice[0]\n\n # Second Joint Prob\n if x[0] == 0.1:\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\n x[1] = random_choice\n elif x[0] == 0.9:\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\n x[1] = random_choice\n\n # Third Joint Prob\n if random_choice[0] == 0.8 or random_choice == 0.1:\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\n x[2] = random_choice\n else:\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\n x[2] = random_choice\n return x","def estimateProbabilities(X, C):\n p = []\n q = []\n r = []\n n = len(X)\n d = 9 # Dimension of the elements\n c = 2 # Number of classes\n for i in range(d):\n p_i = []\n q_i = []\n r_i = []\n for j in range(c):\n p_tmp = []\n q_tmp = []\n r_tmp = []\n elements = C[j] # Elements on the training set for class w_j\n nj = len(C[j])\n for k in elements:\n # p_i_j\n res = X[k][i]*(X[k][i] + 1)*0.5\n p_tmp.append(res)\n # q_i_j\n res = 1 - X[k][i]*X[k][i]\n q_tmp.append(res)\n # r_i_j\n res = X[k][i]*(X[k][i] - 1)*0.5\n r_tmp.append(res)\n p_i.append(sum(p_tmp) / float(nj))\n q_i.append(sum(q_tmp) / float(nj))\n r_i.append(sum(r_tmp) / float(nj))\n p.append(p_i)\n q.append(q_i)\n r.append(r_i)\n return (p, q, r)","def _generate_p(self):\n self._values, weights = zip(*self._weights.items())\n cumsum = list(itertools.accumulate(weights))\n total = cumsum[-1]\n self._p = [i / total for i in cumsum]","def _categorical(self, rewards, probs, dones):\n\n # Create local vars to keep code more concise\n vmin = self.vmin\n vmax = self.vmax\n atoms = self.atoms\n num_atoms = self.num_atoms\n gamma = self.gamma\n rollout = self.rollout\n\n # rewards/dones shape from [batchsize,] to [batchsize,1]\n rewards = rewards.unsqueeze(-1)\n dones = dones.unsqueeze(-1).type(torch.float)\n\n delta_z = (vmax - vmin) / (num_atoms - 1)\n\n projected_atoms = rewards + gamma**rollout * atoms * (1 - dones)\n projected_atoms.clamp_(vmin, vmax)\n b = (projected_atoms - vmin) / delta_z\n\n # It seems that on professional level GPUs (for instance on AWS), the\n # floating point math is accurate to the degree that a tensor printing\n # as 99.00000 might in fact be 99.000000001 in the backend, perhaps due\n # to binary imprecision, but resulting in 99.00000...ceil() evaluating\n # to 100 instead of 99. Forcibly reducing the precision to the minimum\n # seems to be the only solution to this problem, and presents no issues\n # to the accuracy of calculating lower/upper_bound correctly.\n precision = 1\n b = torch.round(b * 10**precision) / 10**precision\n lower_bound = b.floor()\n upper_bound = b.ceil()\n\n m_lower = (upper_bound + (lower_bound == upper_bound).float() - b) * probs\n m_upper = (b - lower_bound) * probs\n\n projected_probs = torch.tensor(np.zeros(probs.size())).to(self.device)\n\n for idx in range(probs.size(0)):\n projected_probs[idx].index_add_(0, lower_bound[idx].long(), m_lower[idx].double())\n projected_probs[idx].index_add_(0, upper_bound[idx].long(), m_upper[idx].double())\n return projected_probs.float()","def prob4():\n#raise NotImplementedError(\"Problem 4 Incomplete\")\n h = lambda x : x[0] < -1 and x[1] > 1\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))","def probability(n, k, p):\n prob = 0\n power = expotentation_by_squaring((1-p), n)\n count_mult = math.log(n, 2)\n p_fraction = p/(1-p)\n count_mult += 1\n for i in range(0, k+1):\n element = newton(n, i)*power\n prob += element\n power *= p_fraction\n count_mult += 2\n return prob, count_mult","def P(N,L) :\n accum = 0.0\n sign = 1.0\n for i in xrange(len(L)+1) :\n accum2 = 0.0\n for combin in combinations(L,i) :\n term = 1.0\n j = 0.0\n for Li in combin :\n term *= Li/(N-j)\n j += 1\n accum2 += term\n accum += sign*accum2\n sign *= -1.0\n return accum","def prob(self, x, y):\n p = self.tag_prob(y)\n for i in range(len(y)):\n p *= self.out_prob(x[i], y[i])\n\n return p","def cross(g1, g2):\n\n g1_haploid = product(*g1.split(\",\"))\n g2_haploid = product(*g2.split(\",\"))\n\n probs = defaultdict(float)\n\n count = 0\n for h1, h2 in product(g1_haploid, g2_haploid):\n offspring_g = \",\".join(\"\".join(sorted(i)) for i in zip(h1, h2))\n count += 1\n probs[offspring_g] += 1.0\n\n for gtype in probs.keys():\n probs[gtype] /= count\n\n return probs","def calc_choice_probs(Vals, sigma):\n\n # Assumes that NaNs have been replaced by -numpy.inf or similar\n if sigma == 0.0:\n # We could construct a linear index here and use unravel_index.\n Pflat = np.argmax(Vals, axis=0)\n Probs = np.zeros(Vals.shape)\n for i in range(Vals.shape[0]):\n Probs[i][Pflat == i] = 1\n return Probs\n\n maxV = np.max(Vals, axis=0)\n Probs = np.divide(\n np.exp((Vals - maxV) / sigma), np.sum(np.exp((Vals - maxV) / sigma), axis=0)\n )\n return Probs","def calcProbability(self):\n for attribute in self.attributes:\n index = self.F2I[attribute]\n features = set([self.train[i][0][index] for i in range(len(self.train))])\n for feature in features:\n #all the true and false\n result_t = list(filter(lambda x: x[1]== True, self.train))\n total_t = len(result_t)\n result_f = list(filter(lambda x: x[1]== False, self.train))\n total_f= len(result_f)\n #the probability for the feature if its true or false\n t = len(list(filter(lambda x: x[0][index] == feature, result_t)))\n f = len(list(filter(lambda x: x[0][index] == feature, result_f)))\n prob_yes= t/total_t\n prob_no = f/total_f\n #assign the probabilities to the dictionaries\n self.probs_yes[(index,feature)] = prob_yes\n self.probs_no[(index,feature)] = prob_no","def prob1(L):\n return min(L), max(L), sum(L)/len(L)\n raise NotImplementedError(\"Problem 1 Incomplete\")","def _data_prob(pi, multinomials, data):\n comp_prob = []\n for i, p_i in enumerate(pi):\n if multinomials[i].shape[0] == 1:\n mult = np.array(multinomials[i][0, data[:, 1].astype(int)].todense())\n else:\n mult = multinomials[i][data[:, 0].astype(int), data[:, 1].astype(int)]\n comp_prob.append(np.array(pi[i] * mult)[0])\n return comp_prob","def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n for cls in self.classes:\n class_probability = self.prior_prob[cls]\n for key, value in datum.items():\n relative_feature_values = self.likelihoods[cls][key]\n class_probability += math.log(relative_feature_values.get(datum[key], 0.01))\n\n logJoint[cls] = class_probability\n\n return logJoint","def get_probs(self, states, actions):\n # YOUR CODE HERE\n \n probs = np.ones(len(states))/2\n return probs","def predictability(self):\n temp = self.probs\n for n in range(10):\n temp = temp.dot(temp)\n final = temp[0,:]\n #Let's assume that all words have unique initial letters\n probs = map(len, self.words)\n probs = array(probs)\n probs = (probs + self.probs.max(1)-1)/probs\n return sum(final*probs)","def conditional_probability(data, attr, cp_table):\n # gets class names for dataframe manipulation\n classes = attr.tail(1)['vars'].tolist()\n classlist = [classes[0][0], classes[0][1]]\n class0 = classlist[0]\n class1 = classlist[1]\n # number of instances beloning to each class\n nclass0 = cp_table.loc[0, class0].sum()\n nclass1 = cp_table.loc[0, class1].sum()\n total = nclass0 + nclass1\n # all probabilities include a laplace est of 1\n prior0 = (nclass0 + 1) / (total + 2)\n prior1 = (nclass1 + 1) / (total + 2)\n list0 = []\n list1 = []\n for index, row in cp_table.iterrows():\n numattr = len(attr.loc[index, 'vars'])\n numer0 = row[class0] + 1\n numer1 = row[class1] + 1\n denom0 = nclass0 + (1 * numattr)\n denom1 = nclass1 + (1 * numattr)\n cp0 = numer0 / denom0\n cp1 = numer1 / denom1\n list0.append(cp0)\n list1.append(cp1)\n # replacing columns in previous table with cond probs\n del cp_table[class0]\n del cp_table[class1]\n cp_table[class0] = list0\n cp_table[class1] = list1\n \n return cp_table, prior0, prior1","def probabilities(self, x, y):\n return self.feed_and_return(x, y, self.network.a)","def generate_probabilities(self):\n dists = np.copy(self.distances)\n\n for i in range(self.number_towers):\n for j in range(self.number_towers):\n if self.method == 'distance_distribution':\n dists[i][j] = (\n -1 *\n (dists[i][j] ** 2) *\n xamtfos(dists[i][j] ** 2, self.sigma) *\n self.expander\n )\n elif self.method == 'distance_square':\n dists[i][j] = -1 * (dists[i][j] + 1) ** self.distance_power\n\n normalizer = dists.max().max() / 2\n dists -= normalizer\n\n return np.array([\n softmax(dists[i])\n for i in range(self.number_towers)\n ])","def alias_setup(probs):\n K = len(probs)\n q = np.zeros(K)\n J = np.zeros(K, dtype=np.int)\n\n smaller = []\n larger = []\n for kk, prob in enumerate(probs):\n q[kk] = K * prob\n if q[kk] < 1.0:\n smaller.append(kk)\n else:\n larger.append(kk)\n\n while len(smaller) > 0 and len(larger) > 0:\n small = smaller.pop()\n large = larger.pop()\n\n J[small] = large\n q[large] = q[large] + q[small] - 1.0\n if q[large] < 1.0:\n smaller.append(large)\n else:\n larger.append(large)\n\n return J, q","def selection_profiles_by_chance(true, compare):\n n_neurons, M = true.shape\n probabilities = np.zeros(n_neurons)\n\n for neuron in range(n_neurons):\n n = np.count_nonzero(true[neuron])\n N = np.count_nonzero(compare[neuron])\n rv = hypergeom(M=M, n=n, N=N)\n\n overlap = np.count_nonzero(true[neuron] * compare[neuron])\n probabilities[neuron] = 1 - rv.cdf(x=overlap)\n\n return probabilities","def test_posterior_logprobs(self):\n x = list(product([True, False], repeat=2))\n xs = list(e for e in product(x, repeat=3))\n all_obs = list(o for o in xs\n if all(any(e) and not all(e) for e in o))\n total = logsumexp(list(posterior_logprobs(np.array(obs), self.S, self.A, self.E)[1]\n for obs in all_obs))\n assert_allclose(total, np.log(1))","def Probability(rating1, rating2):\n return 1.0 * 1.0 / (1 + 1.0 * math.pow(10, 1.0 * (rating1 - rating2) / 400))","def boost_probability_for(fixation):\n probabilities = np.zeros(Number_of_locs) #MOD Number_of_locs deleted\n for possible_target_location in xrange(Number_of_locs): #MOD Number_of_locs deleted\n Lib_c.set_target(possible_target_location)\n probabilities[possible_target_location] = integrate.quad(\n Lib_c.function,\n -np.inf, np.inf,\n epsabs=0,\n limit=50,\n full_output=1\n )[0]\n return np.sum(Post_probs * probabilities) #MOD Post_probs deleted","def update_probs(flip,prob_coins,coins):\n if flip == 'H':\n joint_prob_sum = 0\n for x in range(len(prob_coins)):\n joint_prob_sum += (prob_coins[x] * coins[x])\n new_prob_coins = []\n for x in range(len(prob_coins)):\n new_prob_coin = prob_coins[x] * coins[x] / joint_prob_sum\n new_prob_coins.append(new_prob_coin)\n return new_prob_coins\n else:\n joint_prob_sum = 0\n for x in range(len(prob_coins)):\n joint_prob_sum += (prob_coins[x] * (1-coins[x]))\n new_prob_coins = []\n for x in range(len(prob_coins)):\n new_prob_coin = (prob_coins[x] * (1-coins[x])) / joint_prob_sum\n new_prob_coins.append(new_prob_coin)\n return new_prob_coins","def create_joint_prob_corr_table(to_dict=True):\n\n p = np.arange(0., 1.05, 0.05) # Range of marginal probability\n corr = np.arange(-1., 1.05, 0.05) # Range of correlations\n\n table = np.zeros(shape=(len(corr), len(p), len(p)))\n\n for i in range(len(corr)):\n\n sig = np.matrix([[1., corr[i]], [corr[i], 1.]])\n\n for j in range(len(p)):\n for k in range(j, len(p)):\n\n if corr[i] == -1:\n jp = max(0., p[j] + p[k] - 1.)\n elif corr[i] == 0.:\n jp = p[j] * p[k]\n elif corr[i] == 1.:\n jp = min(p[j], p[k])\n elif p[j] * p[k] == 0. or p[j] == 1 or p[k] == 1.:\n jp = p[j] * p[k]\n else:\n jp = mvnun(np.array([0., 0.]), np.array([np.inf, np.inf]),\n [norm.ppf(p[j]), norm.ppf(p[k])],\n sig)[0]\n\n table[i, j, k] = table[i, k, j] = jp\n\n if to_dict:\n n_table = {}\n\n # convert to dictionary:\n for j in range(len(p)):\n for k in range(j, len(p)):\n pj = round(p[j], 2)\n pk = round(p[k], 2)\n n_table[(pj, pk)] = n_table[(pj, pk)] = np.array((corr, table[:, j, k]))\n\n return n_table\n\n return table","def comb(n, k):\n return perm(n,k)/factorial(k)","def probDist(probList):\n\tdiscreteDist = []\n\tinvProbList = [] # 1 - probList\n\tnumEvents = len(probList)\n\tfor i in range(len(probList)): # watch out for floating-point rounding errors\n\t\tinvProbList.append(1-probList[i])\n\tpowerSet = []\n\teventList = [i for i in range(numEvents)]\n\t#print(eventList)\n\tfor i in range(numEvents+1):\n\t\tpowerSet.append(list(combinations(eventList,i)))\n\t#print(powerSet)\n\tfor subSet in powerSet: # subSets are grouped according to size, 0 to numEvents\n\t\t#print(subSet)\n\t\ttotalProb = 0\n\t\tfor subSubSet in subSet: # subSubSets are tuples (the actual subsets of the powerSet)\n\t\t\t#print(subSubSet)\n\t\t\tprob = 1\n\t\t\tfor ix in range(numEvents):\n\t\t\t\tif ix not in subSubSet:\n\t\t\t\t\t#print(ix,'loss')\n\t\t\t\t\tprob *= invProbList[ix]\n\t\t\t\telse:\n\t\t\t\t\t#print(ix,'win')\n\t\t\t\t\tprob *= probList[ix]\n\t\t\ttotalProb += prob\n\t\tdiscreteDist.append(totalProb)\n\treturn discreteDist","def prob4():\n\n\n N = 500000\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\n\n h = lambda x: x[0] < -1 and x[1] > 1\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\n\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\n\n return 1./N * np.sum(probability)","def p(self) -> Probability:\n ...","def probability_of_all_successes(p: float, r: int, n: int) -> float:\n\n if r == 1:\n return pow(p, n)\n elif n == 0:\n return 1\n else:\n result = 0\n for x in range(0, n+1):\n result += pow(p, x) * pow(1-p, n-x) * probability_of_all_successes(p, r-1, n-x)\n return result","def prob(self, sequence):\n prob = 1\n for event, context in self.extract_ngrams(sequence):\n prob *= self.cond_prob(event, context)\n return prob","def _proba(y):\n N = len(y)\n _, counts = np.unique(y, return_counts=True)\n return counts / N","def prob_list(n, p):\n ...\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\n#qn = 1 - pn\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\n list = []\n qn_a = 1\n qn_b = 1\n list.append(qn_a)\n list.append(qn_b)\n\n for i in range(n-2):\n i += 0\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\n list.append(qn_next)\n qn_a = qn_b\n qn_b = qn_next\n return list\n\n\n # returns list of probs. [q0,... qn]","def new_binomial_prefactor(s,l1,l2,PAx,PBx):\n with loops.Scope() as L:\n L.total = 0.\n L.t = 0\n for _ in L.while_range(lambda: L.t < s + 1):\n #TEMP TODO rewrite this. The cond_range causes a huge overhead.\n # Try Valeev implementation\n for _ in L.cond_range(((s - l1) <= L.t) & (L.t <= l2)):\n L.total += binomials[l1,s-L.t] * binomials[l2,L.t] * PAx[l1-s + L.t] * PBx[l2 - L.t]\n L.t += 1\n return L.total","def calc_prob(data):\n total = len(data)\n frequencies = sorted(Counter(data).items())\n probabilities = OrderedDict()\n for (key, value) in frequencies:\n probabilities[key] = value / total\n return probabilities","def collapse(probability, input_list):\n\n result = int((probability * len(input_list)) + 0.5)\n return min(result, len(input_list) - 1)","def initial_probabilities_from_trajectories(n_states, trajectories):\n p = np.zeros(n_states)\n\n for t in trajectories:\n p[t.transitions()[0][0]] += 1.0\n\n return p / len(trajectories)","def parameter_combinations(cls, raw=False):\r\n parameter_combinations = {'proportion': [0.1, 0.25, 0.5, 0.75,\r\n 1.0, 1.5, 2.0],\r\n 'n_neighbors': [3, 5, 7]}\r\n return cls.generate_parameter_combinations(parameter_combinations, raw)","def probability_array(self):\n q = self.apply_weights()\n return np.exp(q)/(1 + np.exp(q))","def joint_pairs(self):\n return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))","def prob_distr(self, x):\n return 1.0/x","def evaljointbayes(fname):\n #read file and save lines to contents\n contents = []\n f = open(fname)\n for line in f:\n randomVar = line.rstrip().split()\n if randomVar[0] != 'END':\n contents.append(randomVar)\n print \"1. Read file\", fname, \"successfully.\"\n f.close()\n \n #count numbers of nodes and probabilities in each line\n length = len(contents)\n nodes, prob = [0] * length, [0] * length\n table = [] #save all probabilities for each node \n for num in range(0, length):\n tableline = []\n for i in contents[num]:\n try:\n j = float(i)\n prob[num] += 1\n tableline.append(j) \n except ValueError:\n if i != 'NONE':\n nodes[num] += 1 \n table.append(tableline) \n \n #print out the joint distribution formular\n print \"2. The joint distribution using this network is:\"\n nodelist = []\n for line in contents:\n nodelist.append(line[0])\n print \"P(\", printElement(nodelist), \") = \"\n for num in range(0, length):\n line = contents[num]\n if nodes[num] == 1:\n print \"P(\", line[0], \")\", \n else: \n print \"P(\", line[0], '|', printElement(line[1:nodes[num]]),\\\n \")\", \n if num == length - 1:\n print ' '\n else: \n print ' * ', \n \n #print out the full joint distribution table \n###This is the revised version using recursive calls and###\n###print out the cpt table to a .txt file###\n #further revision includes deleting depth by pop() table, contents and nodes\n #also, I can extract the parents in the previous step, then contents will not be used here\n fo=open(fname+'.zz.txt','w')\n result = 1.0\n depth = 0\n global additions, multiplications\n additions, multiplications = 0, 0\n fullCPT(nodelist, [], result, depth, fo, contents, table, nodes)\n fo.close()\n\n #print out result of step 3 \n print \"3. Additions and multiplications needed to calculate\",\\\n \"the joint distribution is:\", additions, \"and\", multiplications\n print \"The number of nodes in the network is: \", length\n\n #print out reselt of step 4\n spaceFull = int(math.pow(2,length)) - 1\n spaceBN = sum(prob)\n print \"4. Space this network saved is (Compactness): \", spaceBN, \"/\", \\\n spaceFull, \"=\", float(spaceBN) / float(spaceFull), '\\n'\n return","def joint_entropy(P):\n P_nan = P.copy()\n P_nan[P_nan == 0] = np.nan\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))","def update_probabilities(self):\n self.probabilities = self.pheromones**self.EXP_PH * self.mcv**self.EXP_MCV","def _build_multinomial_weights(self) -> None:\n weights_obs = ramp_up_weights(\n len(self.obs), self.tpe.full_weight_num, self.tpe.equal_weight\n )\n counts_obs = numpy.bincount(\n self.obs, minlength=len(self.choices), weights=weights_obs\n )\n counts_obs = counts_obs + self.tpe.prior_weight\n self.weights = counts_obs / counts_obs.sum()","def _get_tuple_of_probabilities(self):\n\t\tk_array = np.arange(-self.n,self.n+1,2)\n\t\tprobability_array = []\n\t\t\n\t\tfor k in k_array:\n\t\t\tprobability_array.append(self._calculate_probability(k))\n\t\t\t\n\t\treturn (k_array,probability_array)","def prob_given(graph, posterior, prior):\n return graph.prob(merge(prior, posterior)) / graph.prob(prior)","def prob3():\n #set up the matrices\n solvers.options['show_progress'] = False\n c = np.array([4., 7., 6., 8., 8., 9.])\n\n G = np.array([[1.,1.,0.,0.,0.,0.],\n [-1.,-1.,0.,0.,0.,0.],\n [0.,0.,1.,1.,0.,0.],\n [0.,0.,-1.,-1.,0.,0.],\n [0.,0.,0.,0.,1.,1.],\n [0.,0.,0.,0.,-1.,-1.],\n [1.,0.,1.,0.,1.,0.],\n [-1.,0.,-1.,0.,-1.,0.],\n [0.,1.,0.,1.,0.,1.],\n [0.,-1.,0.,-1.,0.,-1.]])\n G = np.vstack((G, -1*np.eye(6)))\n h = np.array([7,-7,2,-2,4,-4,5,-5,8,-8,0,0,0,0,0,0],dtype=\"float\")\n\n #convert the matrices\n c = matrix(c)\n G = matrix(G)\n h = matrix(h)\n #solve the matrices\n sol = solvers.lp(c, G, h)\n return np.ravel(sol['x']), sol['primal objective']","def sample_discrete(probs):\r\n q = np.random.rand()\r\n i = 0\r\n p_sum = 0.0\r\n while p_sum < q:\r\n p_sum += probs[i]\r\n i += 1\r\n return i - 1","def get_expected_probability(probabilities):\n\n expected = dict()\n for a, b in cwr(probabilities.keys(), 2):\n if a == b:\n expected[\"\".join(sorted([a, b]))] = probabilities[a] * probabilities[b]\n else:\n expected[\"\".join(sorted([a, b]))] = 2 * (probabilities[a] * probabilities[b])\n\n return expected","def multinomial_pmf(sample, probabilities):\r\n # TODO\r\n a=[]\r\n b=[]\r\n i=0\r\n key_list=[]\r\n value_list=[]\r\n for key,value in sample.items():\r\n key_list.append(key)\r\n value_list.append(value)\r\n b=list(sample)\r\n while i< len(b):\r\n a.append(probabilities.keys()[probabilities.values().index(value_list[i])])\r\n\r\n\r\n return a","def log_prob(self, scores : torch.Tensor, permutations):\n s = torch.log(select_indices(scores, permutations))\n n = len(scores)\n p = self.upto if self.upto is not None else n - 1\n return -sum(\n torch.log(torch.exp((s[k:] - s[k]) * self.shape).sum(dim=0))\n for k in range(p))"],"string":"[\n \"def joint_proba(self, X):\\n return self.weights * self._bernoulli(X)\",\n \"def joint_prob(network, assignment):\\n prob = 1\\n for a_key in assignment:\\n conditions = []\\n current = network[a_key]\\n for parent in current['Parents']:\\n conditions.append(True) if assignment[parent] else conditions.append(False)\\n conditions = tuple(conditions)\\n if not assignment[a_key]:\\n prob *= (1 - current['CPT'].get(conditions))\\n else:\\n prob *= (current['CPT'].get(conditions)) \\n return prob\",\n \"def generate_probabilities(self):\\n k = 1\\n v= 10\\n for g in self.class_probabilities:\\n curr_list = self.class_probabilities[g]\\n for l in range(0,28):\\n for w in range(0,28):\\n total = float(curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2])\\n curr_list[l][w][0] = (float(curr_list[l][w][0])+k)/(total + k*v) \\n curr_list[l][w][1] = (float(curr_list[l][w][1])+k)/(total + k*v)\\n curr_list[l][w][2] = (float(curr_list[l][w][2])+k)/(total + k*v)\\n curr_list[l][w][3] = curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2]\",\n \"def joint_probabilities_from_transitions(ordered_pitch_types, transition_counts):\\n first_pitch_totals = {first_pitch_type: sum(transition_counts[first_pitch_type].values())\\n for first_pitch_type in ordered_pitch_types}\\n\\n total_transitions = sum(first_pitch_totals.values())\\n\\n markov_rows = []\\n joint_probabilities = {}\\n\\n for first_pitch_type in ordered_pitch_types:\\n first_pitch_transitions = transition_counts[first_pitch_type]\\n joint_probabilities[first_pitch_type] = {}\\n first_pitch_type_probability = float(first_pitch_totals[first_pitch_type]) / total_transitions\\n\\n second_pitch_total = sum(first_pitch_transitions.values())\\n row = [first_pitch_type]\\n\\n for second_pitch_type in ordered_pitch_types:\\n if second_pitch_total == 0:\\n second_pitch_conditional_probability = 0\\n\\n else:\\n second_pitch_conditional_probability = \\\\\\n float(first_pitch_transitions[second_pitch_type]) / second_pitch_total\\n\\n row.append(second_pitch_conditional_probability)\\n\\n joint_probabilities[first_pitch_type][second_pitch_type] = \\\\\\n first_pitch_type_probability * second_pitch_conditional_probability\\n\\n markov_rows.append(row)\\n\\n return joint_probabilities, markov_rows, total_transitions\",\n \"def probability(prods, prod_dict_As, count_dict):\\n for p in prods:\\n if p not in prod_dict_As:\\n raise Exception(\\\"Think we cannot make the product {}.\\\".format(p))\\n # Argh, Python, this is a reference!\\n #possible_As = prod_dict_As[prods[0]]\\n possible_As = set( prod_dict_As[prods[0]] )\\n for p in prods[1:]:\\n possible_As &= prod_dict_As[p]\\n ret = []\\n for A in possible_As:\\n count = 1\\n for p in prods:\\n count *= count_dict[(p,A)]\\n ret.append((A,count))\\n return ret\",\n \"def dealer_probs():\\n # Pdf of any current hand (value, hard) and final value; p(v_f | v_c) where v_f = final value, v_c = current value\\n probabilities = {}\\n\\n # End nodes: (value, True) for value >= 17 and (value, False) for value > 17\\n # Dependencies (in order of increasing requirements):\\n # Hard values, value >= 11, possiblity of bust, no possibility of going soft with an ace (value, True) depends on (value', True) for 17 > value' > value\\n # Soft values, 17 >= value >= 11 (value, False) depends on (value', False) for 17 >= value' > value, (value', True) for 17 > value' > 11\\n # Hard values, 11 > value >= 2 , no possibility of bust, possibility of going soft with an ace (value, True) depends on (value', True) for 17 > value' > value and (value', False) for 17 >= value' > 13\\n\\n\\n # End nodes\\n for value in xrange(17, 22):\\n probabilities[(value, True)] = {value: 1.0}\\n if value == 17: continue # on soft 17, dealer will still hit\\n probabilities[(value, False)] = {value: 1.0}\\n\\n # Hard values, 17 > value >= 11, possibility of bust, no possibility of going soft with an ace\\n for value in xrange(16, 10, -1):\\n probabilities[(value, True)] = {}\\n current_prob = probabilities[(value, True)]\\n for next_card in xrange(1, min(10, 21-value)+1):\\n next_prob = probabilities[(value + next_card, True)]\\n for end_val in next_prob:\\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\\n\\n # Soft values, 17 >= value >= 11\\n for value in xrange(17, 10, -1):\\n probabilities[(value, False)] = {}\\n current_prob = probabilities[(value, False)]\\n for next_card in xrange(1, 11):\\n next_value = value + next_card\\n hard = False\\n if next_value > 21:\\n next_value -= 10\\n hard = True\\n next_prob = probabilities[(next_value, hard)]\\n for end_val in next_prob:\\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\\n\\n # Hard values, 11 > value >= 2, no possibility of bust, possibility of going soft with an ace\\n for value in xrange(10, 1, -1):\\n probabilities[(value, True)] = {}\\n current_prob = probabilities[(value, True)]\\n for next_card in xrange(2, 12):\\n next_value = value + next_card\\n hard = (next_card != 11)\\n next_prob = probabilities[(next_value, hard)]\\n for end_val in next_prob:\\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\\n\\n return probabilities\",\n \"def probabilities(self):\\n raise NotImplementedError\",\n \"def compute_conditional_distributions(joints):\\n\\n marginals = np.sum(joints, axis=1)\\n posidx = marginals > 0\\n \\n conditionals = joints.copy()\\n conditionals[posidx, :] /= marginals[posidx, None]\\n conditionals[~posidx, :] = marginals\\n assert np.isclose(np.sum(conditionals), len(conditionals))\\n assert np.allclose(np.sum(conditionals, axis=1), 1.0)\\n\\n return conditionals\",\n \"def multinomial_prob(counts, probs):\\n return nCkarray(*counts.values) * (probs ** counts).prod()\",\n \"def CalculateProbabilities(self, beta_0, beta_1):\\n denom = self.zero_zero + self.zero_one + self.one_zero + self.one_one + beta_0 + beta_1 - 2\\n if denom != 0:\\n self.prob_zero = min( max( (self.zero_zero + self.zero_one + beta_0 - 1) / denom, 0.0 ), 1.0 )\\n self.prob_one = min( max( (self.one_zero + self.one_one + beta_1 - 1) / denom, 0.0 ), 1.0 )\\n \\n denom = self.zero_zero + self.one_zero + beta_0 + beta_1 - 2\\n if denom != 0:\\n self.prob_zero_given_zero = min( max( (self.zero_zero + beta_0 - 1) / denom, 0.0 ), 1.0 )\\n self.prob_one_given_zero = min( max( (self.one_zero + beta_1 - 1) / denom, 0.0 ), 1.0 )\\n \\n denom = self.zero_one + self.one_one + beta_0 + beta_1 - 2\\n if denom != 0:\\n self.prob_zero_given_one = min( max( (self.zero_one + beta_0 - 1) / denom, 0.0 ), 1.0 )\\n self.prob_one_given_one = min( max( (self.one_one + beta_1 - 1) / denom, 0.0 ), 1.0 )\",\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\t#Adds log(P(y)) to calculate P(y|f1,f2...)\\n for label in self.legalLabels:\\n\\t\\tlogJoint[label] += math.log(self.prior[label])\\n\\t#Adds log(P(f1|y)), log(P(f2|y))... to calculate P(y|f1, f2...)\\n for key in datum:\\n\\t\\t#if key == (7, 3):\\n\\t\\t\\t#print self.condprobs[key, 0]\\n\\t\\tfor label in self.legalLabels:\\n\\t\\t\\t#print str(key) + str(datum[key])\\n\\t\\t\\tlogJoint[label] += math.log(self.condprobs[key, label][datum[key]])\\n return logJoint\",\n \"def get_components_probabilities(self):\\n compsProbs={}\\n for d in self.diagnoses:\\n p = d.get_prob()\\n for comp in d.get_diag():\\n compsProbs[comp] = compsProbs.get(comp,0) + p\\n return sorted(compsProbs.items(), key=lambda x: x[1], reverse=True)\",\n \"def compute_joint_probability(token_list, token_probabilities, use_log_prob=False):\\n\\n log_prob = 0\\n\\n for word in token_list:\\n\\n # do not allow zero probabilites\\n assert word in token_probabilities\\n\\n if use_log_prob:\\n log_prob += token_probabilities[word]\\n else:\\n log_prob += log10(token_probabilities[word])\\n\\n if use_log_prob:\\n return log_prob\\n\\n return 10**log_prob\",\n \"def chance(n, p):\\n total = 0.0\\n for k in range(n+1):\\n total += comb(n, k, exact=False) * p**k * (1-p) ** (n-k)\\n return total\",\n \"def _update_probabilities(self):\\n pairs_last = copy.deepcopy(self.pairs)\\n # pairs_last = [el for el in pairs]\\n for src_p in range(self.frame_0.shape[0]):\\n for seql_num, dst_p in enumerate(self.pairs[src_p][0]):\\n Q = self._calculate_Q(src_p, dst_p, pairs_last)\\n # update pair probability.\\n self.pairs[src_p][1][seql_num] = pairs_last[src_p][1][seql_num] * (self.A + self.B * Q)\\n\\n # normalize probability\\n self.pairs[src_p][1] = self.pairs[src_p][1] / self.pairs[src_p][1].sum()\",\n \"def get_probs(self):\\n\\t\\tprobArray = []\\n\\t\\tfor combination in self.codepool:\\n\\t\\t\\tif self.feasible(combination):\\n\\t\\t\\t\\tprobArray.append(self.get_probability(combination))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprobArray.append(0)\\n\\t\\tprobArray = np.array(probArray) / np.sum(probArray)\\n\\t\\treturn probArray\",\n \"def calculateLogJointProbabilities(self, datum):\\n\\tlogJoint = util.Counter()\\n\\t#want to calculate log(P(y)) + log(sum(P(fi|y)))\\n\\t#where y is a label\\n\\tfor label in self.legalLabels:\\n\\t\\tlogJoint[label] = math.log(self.prior_distribution_prob[label])\\n\\t\\tfor feature, value in datum.items():\\n\\t\\t\\tcp = self.conditional_prob[label][feature][value]\\n\\t\\t\\tif cp > 0: #condition check for values < 0 because log(0) is undefined and math domain error occurs\\n\\t\\t\\t\\tlogJoint[label] += math.log(cp) #summing up\\n\\t\\t\\t\\t\\n\\treturn logJoint\",\n \"def get_prob_for_distributions(p):\\n w1 = p[0]\\n mu1 = p[1]\\n sigma1 = p[2]\\n w2 = p[3]\\n mu2 = p[4]\\n sigma2 = p[5]\\n w3 = p[6]\\n mu3 = p[7]\\n sigma3 = p[8]\\n dist_range = (0, 4.330310991999920844e+01)\\n x = np.linspace(dist_range[0], dist_range[1], 1000)\\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\\n p1 = A1 / (A1 + A2 + A3)\\n p2 = A2 / (A1 + A2 + A3)\\n p3 = A3 / (A1 + A2 + A3)\\n return p1, p2, p3\",\n \"def caculate_prob(self):\\n t_H = self.tree.depth()\\n t_h = 1\\n while(t_h <= t_H):\\n t_hnodes = self.get_h(t_h)\\n t_sum = 0\\n t_hpro = []\\n t_cpro = []\\n for t_n in t_hnodes:\\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\\n t_node = self.tree.get_node(t_n)\\n if t_node.is_leaf():\\n t_node.data.append(0)\\n continue\\n t_childrens = self.tree.children(t_n)\\n t_shang = 0\\n for child in t_childrens:\\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\\n t_node.data.append(-t_shang)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n t_parentnode = self.tree.parent(t_n)\\n if t_h > 1:\\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n else:\\n t_node.data.append((t_node.data[0] / t_sum))\\n t_hpro.append((t_node.data[0] / t_sum))\\n\\n t_cpro.append(t_node.data[1])\\n t_ndata = np.array(t_hpro)\\n mean = np.mean(t_ndata)\\n std = np.std(t_ndata,ddof=1)\\n t_sdata = np.array(t_cpro)\\n mean_s = np.mean(t_sdata)\\n std_s = np.std(t_sdata,ddof=1)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n if(std != 0):\\n t_node.data[2] = (t_node.data[2] - mean)/std\\n else:\\n t_node.data[2] = (t_node.data[2] - mean)\\n if(mean_s == 0 and std_s ==0):\\n t_node.data[1] = -100.0\\n continue\\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\\n t_h = t_h + 1\",\n \"def get_joint_outcomes(d):\\n repeated_list = [[True, False]] * d\\n oc = np.array(list(itertools.product(*repeated_list)))\\n\\n return oc\",\n \"def MAP(cpts, obs, terms):\\r\\n\\r\\n # a list to store the computed probabilities\\r\\n all_sums = []\\r\\n # initialize all terms to false\\r\\n for value in range(len(terms)):\\r\\n terms[value] = [terms[value], '0']\\r\\n search_array = terms + obs\\r\\n # if all terms are being watched, just call MPE\\r\\n if len(search_array) == len(cpts):\\r\\n return MPE(cpts, obs)\\r\\n # we need to know what terms we aren't interested in so we start with \\r\\n # or terms and observations and note the variables that appear in CPT but\\r\\n # not in those\\r\\n dont_count = []\\r\\n for var in cpts:\\r\\n if [var[0], '0'] not in search_array and [var[0], '1'] not in search_array:\\r\\n dont_count.append(var[0])\\r\\n terms.append([var[0],'1'])\\r\\n # sort the terms to ensure correct ordering\\r\\n terms.sort()\\r\\n # creates a list of all possible bit strings\\r\\n # just an easy way to create all possible truth assignments\\r\\n seq = [\\\"\\\".join(seq) for seq in itertools.product(\\\"01\\\", repeat=len(terms))]\\r\\n # loop through all possible truth assignments\\r\\n for j in range(len(seq)):\\r\\n # we initialize at probability = 100%\\r\\n chance = 1\\r\\n # assign the truth values\\r\\n for k in range(len(seq[j])):\\r\\n terms[k][1] = seq[j][k]\\r\\n # this computes the probability using the chaining rule\\r\\n for i in range(len(terms)):\\r\\n new_terms = terms[:-i-1] + obs\\r\\n new_terms.sort()\\r\\n chance *= probability(cpts,terms[-i-1], new_terms)\\r\\n # add the probabilities to our list\\r\\n all_sums.append(chance)\\r\\n combine = []\\r\\n # note all variables which weren't in obs or Vs\\r\\n for i in dont_count:\\r\\n combine.append(terms.index([i,'1']))\\r\\n # this will store the final probabilities\\r\\n final_array = [0] * len(seq)\\r\\n # another complicated looking loop, it just serves to combine probabilities\\r\\n # for example, if we have a CPT with x_1, x_2, x_3, x_4 and we observe \\r\\n # x_1 to be true and have Vs = [x_3, x_4] then we need to combine the \\r\\n # probabilities that are the same except for x_2 = true vs false\\r\\n for loc in combine:\\r\\n for sequence in range(len(seq)):\\r\\n for alt_sequence in range(sequence+1,len(seq)):\\r\\n if (seq[sequence][:loc] + seq[sequence][loc+1:]) == (seq[alt_sequence][:loc] + seq[alt_sequence][loc+1:]):\\r\\n final_array[sequence] = all_sums[sequence] + all_sums[alt_sequence]\\r\\n\\r\\n # get the truth assignment for the highest probability\\r\\n location = seq[final_array.index(max(final_array))]\\r\\n truth_assignment = []\\r\\n # place the truth assignment in a more readable fashion\\r\\n for value in range(len(terms)):\\r\\n if terms[value] in search_array:\\r\\n if location[value] == '0':\\r\\n truth_assignment.append(terms[value][0]+ ' = False')\\r\\n else:\\r\\n truth_assignment.append(terms[value][0]+ ' = True')\\r\\n return (truth_assignment)\",\n \"def prodgreqs_base(A):\\n choices = [ list(range(xi+1)) for xi in A ]\\n M = len(choices) + 1\\n for yi in itertools.product(*choices):\\n prod, freq = 1, 1\\n for a, y, x in zip(range(2, M+1), yi, A):\\n prod *= a ** y\\n freq *= math.factorial(x) // math.factorial(y) // math.factorial(x-y)\\n yield ProdFreqPair(prod, freq)\",\n \"def calculate_probability(k: int, m: int, n: int) -> float:\\n population = [\\\"AA\\\" for _ in range(k)] + [\\\"Aa\\\" for _ in range(m)] + [\\\"aa\\\" for _ in range(n)]\\n pairings = it.combinations(population, 2)\\n probabilities = [PROBABILITIES[pairing] for pairing in pairings]\\n output = sum(probabilities) / len(probabilities)\\n\\n return output\",\n \"def probability(distances):\\n v = [1.0/(d + 1) for d in distances]\\n s = sum(v)\\n return [i/s for i in v]\",\n \"def enumerate_joint_ask(X, e, P):\\n Q = ProbDist(X) ## A probability distribution for X, initially empty\\n Y = [v for v in P.variables if v != X and v not in e]\\n for xi in P.values(X):\\n Q[xi] = enumerate_joint(Y, extend(e, X, xi), P)\\n return Q.normalize()\",\n \"def proba(self, X):\\n return np.sum(self.joint_proba(X), axis=-1)\",\n \"def _get_selection_probabilities(self):\\r\\n probabilities = np.arange(1, self.population_size+1, dtype=float)[::-1]\\r\\n probabilities /= probabilities.sum()\\r\\n return probabilities\",\n \"def get_probs(self, a):\\n with torch.no_grad():\\n probabilities = (np.array(self.priorities) ** a) / sum(np.array(self.priorities) ** a)\\n return probabilities\",\n \"def joint(G, xs=None):\\n vars = G.vars() #: [var]\\n facs = { f : G.N(f) for f in G.facs() } #: fac => vars\\n\\n dims = [G.node[x]['d'] for x in vars] #: [nat]\\n _joint = ones(dims)\\n\\n for vals in itertools.product( *(xrange(d) for d in dims) ): # cartesian product\\n _vars = dict(zip(vars,vals)) #: var => val\\n vals = tuple(vals) # to index\\n #print\\n #print _vars\\n for fac in facs:\\n _vals = [_vars[v] for v in facs[fac]] # keep only fac's vars' vals\\n #print '%s%s' % (fac, tuple(_vals))\\n _joint[vals] *= G(fac, *_vals)\\n\\n Z = sum(_joint)\\n\\n return pd(_joint), Z\",\n \"def comp_relation_probs(self, x_feats):\\n # l : examples batch size\\n # d : dimensionality of the (binary) feature space\\n relation_scores = sparse.dot(x_feats, self.W) + self.Wb # [l, d] x [d, m] + [m] => [l, m]\\n relation_probs = T.nnet.softmax(relation_scores)\\n return relation_probs\",\n \"def get_probs(self, *vars):\\n freqs = self.freq_counts([self.data.get_column_view(v)[0] for v in vars], [len(v.values) for v in vars])\\n k = np.prod([len(v.values) for v in vars])\\n return (freqs + self.alpha) / (np.sum(freqs) + self.alpha*k)\",\n \"def test_most_probable_value(self):\\n with Pandas() as pd:\\n if pd is None:\\n return\\n with Numpy() as np: # noqa\\n if numpy is None:\\n return\\n sys.stderr.write(\\\"\\\\n\\\")\\n\\n df1 = pd.DataFrame(\\n {'A': [0, 1, 2, 3, 4, 3, 2, 1, 1, 1], 'C': ['f1', 'f3', 'f4', 'f3', 'f4', 'f2', 'f2', 'f1', 'f3', 'f4']})\\n df2 = pd.DataFrame(\\n {'A': [2, 3, 4, 5, 7, 4, 6, 5, 7, 8], 'C': ['f7', 'f3', 'f5', 'f8', 'f9', 'f2', 'f3', 'f6', 'f7', 'f7']})\\n\\n # building 1d-, 2d-, and 3d-histogram (iteratively)\\n hist0 = hg.Categorize(unit('C'))\\n hist1 = hg.Categorize(unit('C'))\\n hist2 = hg.SparselyBin(origin=0.0, binWidth=1.0, quantity=unit('A'))\\n hist3 = hg.SparselyBin(origin=0.0, binWidth=1.0, quantity=unit('A'))\\n\\n # fill them\\n hist0.fill.numpy(df1)\\n hist1.fill.numpy(df2)\\n hist2.fill.numpy(df1)\\n hist3.fill.numpy(df2)\\n\\n assert hist0.mpv == 'f3'\\n assert hist1.mpv == 'f7'\\n assert hist2.mpv == 1.5\\n assert hist3.mpv == 4.5\",\n \"def _precompute_probabilities(self):\\n\\n d_graph = self.d_graph\\n first_travel_done = set()\\n\\n nodes_generator = self.graph.nodes() if self.quiet \\\\\\n else tqdm(self.graph.nodes(), desc='Computing transition probabilities')\\n\\n for source in nodes_generator:\\n\\n # Init probabilities dict for first travel\\n if self.PROBABILITIES_KEY not in d_graph[source]:\\n d_graph[source][self.PROBABILITIES_KEY] = dict()\\n\\n for current_node in self.graph.neighbors(source):\\n\\n # Init probabilities dict\\n if self.PROBABILITIES_KEY not in d_graph[current_node]:\\n d_graph[current_node][self.PROBABILITIES_KEY] = dict()\\n\\n unnormalized_weights = list()\\n first_travel_weights = list()\\n d_neighbors = list()\\n\\n # Calculate unnormalized weights\\n for destination in self.graph.neighbors(current_node):\\n\\n p = self.sampling_strategy[current_node].get(self.P_KEY,\\n self.p) if current_node in self.sampling_strategy else self.p\\n q = self.sampling_strategy[current_node].get(self.Q_KEY,\\n self.q) if current_node in self.sampling_strategy else self.q\\n\\n if destination == source: # Backwards probability\\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / p\\n elif destination in self.graph[source]: # If the neighbor is connected to the source\\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1)\\n else:\\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / q\\n\\n # Assign the unnormalized sampling strategy weight, normalize during random walk\\n unnormalized_weights.append(ss_weight)\\n if current_node not in first_travel_done:\\n first_travel_weights.append(self.graph[current_node][destination].get(self.weight_key, 1))\\n d_neighbors.append(destination)\\n\\n # Normalize\\n unnormalized_weights = np.array(unnormalized_weights)\\n d_graph[current_node][self.PROBABILITIES_KEY][\\n source] = unnormalized_weights / unnormalized_weights.sum()\\n\\n if current_node not in first_travel_done:\\n unnormalized_weights = np.array(first_travel_weights)\\n d_graph[current_node][self.FIRST_TRAVEL_KEY] = unnormalized_weights / unnormalized_weights.sum()\\n first_travel_done.add(current_node)\\n\\n # Save neighbors\\n d_graph[current_node][self.NEIGHBORS_KEY] = d_neighbors\",\n \"def get_probs(self, states, actions):\\n # YOUR CODE HERE\\n \\n # So we need to determine for every input state-action pair, what the resulting policy distribution is\\n # This means that the input will be a single state and a single action per index. \\n # We then need to determine if, according to our policy, the action should be taken (prob=1) \\n # or not (prob=0)\\n \\n # state is a tuple of (player's current sum, dealer's single showing card, boolean for usable ace)\\n probs = []\\n for index, (state, action) in enumerate(zip(states, actions)):\\n chosen_action = self.sample_action(state)\\n if action == chosen_action:\\n probs.append(1)\\n else:\\n probs.append(0)\\n \\n \\n return np.array(probs)\",\n \"def calc_probabilities(applications):\\n sum_advantage = sum(app.get_advantage() for app in applications)\\n return [app.get_advantage() / sum_advantage for app in applications]\",\n \"def log_joint(self):\\n return sum([\\n self.log_marg_like(self.gamma, self.gamma0, self.lamb, self.nu),\\n self._gamma0_distribution.logpdf(self.gamma0),\\n self._nu_distribution.logpdf(self.nu),\\n self._lambda_distribution.logpdf(self.lamb),\\n self.probit_distribution(self.xi).logpdf(self.gamma),\\n self._xi_distribution.logpdf(self.xi) if self.sample_xi else 0.0\\n ])\",\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n \\n \\\"*** YOUR CODE HERE ***\\\"\\n \\n # -- OUR CODE HERE\\n \\n \\n import math\\n for label in self.legalLabels:\\n sumThing = 0.0\\n for pixel in self.conditionalProb[label]:\\n if datum[pixel] is 1:\\n #assert self.conditionalProb[label][pixel] < 1.0 # -- sanity check that the probability is valid\\n sumThing += math.log((self.conditionalProb[label][pixel]*1.0))\\n else:\\n sumThing+=math.log(1-self.conditionalProb[label][pixel]*1.0)\\n logJoint[label] = math.log(self.prior[label]*1.0) + sumThing*1.0\\n \\n\\n \\n \\n import time\\n #print \\\"logJoint is :: \\\", logJoint\\n #time.sleep(2)\\n \\n \\n # -- uses the conditional probability tables computed in the current iteration\\n # -- in train and tune\\n \\n return logJoint\",\n \"def set_uniform_probabilities(self, sentence_aligned_corpus):\\n max_m = longest_target_sentence_length(sentence_aligned_corpus)\\n\\n # The maximum vacancy difference occurs when a word is placed in\\n # the last available position m of the target sentence and the\\n # previous word position has no vacancies.\\n # The minimum is 1-max_v, when a word is placed in the first\\n # available position and the previous word is placed beyond the\\n # last available position.\\n # Thus, the number of possible vacancy difference values is\\n # (max_v) - (1-max_v) + 1 = 2 * max_v.\\n if max_m > 0 and (1 / (2 * max_m)) < IBMModel.MIN_PROB:\\n warnings.warn(\\n \\\"A target sentence is too long (\\\"\\n + str(max_m)\\n + \\\" words). Results may be less accurate.\\\"\\n )\\n\\n for max_v in range(1, max_m + 1):\\n for dv in range(1, max_m + 1):\\n initial_prob = 1 / (2 * max_v)\\n self.head_vacancy_table[dv][max_v] = defaultdict(lambda: initial_prob)\\n self.head_vacancy_table[-(dv - 1)][max_v] = defaultdict(\\n lambda: initial_prob\\n )\\n self.non_head_vacancy_table[dv][max_v] = defaultdict(\\n lambda: initial_prob\\n )\\n self.non_head_vacancy_table[-(dv - 1)][max_v] = defaultdict(\\n lambda: initial_prob\\n )\",\n \"def joint_frequencies_combo(self, alleles):\\n\\n representations = [1 << i for i in range(len(alleles))]\\n\\n intrenal_hap_dict_per_group = {group2: self.build_intrenal_hap_dict(alleles, group2)\\n for group2 in self.hap_dict_per_group}\\n\\n result = {}\\n\\n for c in representations:\\n hap = {group2: internal[c] for group2, internal in intrenal_hap_dict_per_group.items()}\\n result[c] = self.effective_joint_frequency(hap)\\n\\n for C in combinations(representations, 2):\\n hap = {group2: internal[C[0]] & internal[C[1]] for group2, internal in intrenal_hap_dict_per_group.items()}\\n result[C[0]|C[1]] = self.effective_joint_frequency(hap)\\n\\n for C in combinations(representations, 3):\\n hap = {group2: internal[C[0]] & internal[C[1]] & internal[C[2]]\\n for group2, internal in intrenal_hap_dict_per_group.items()}\\n result[C[0]|C[1]|C[2]] = self.effective_joint_frequency(hap)\\n\\n for r in range(4,len(alleles)):\\n for C in combinations(representations, r):\\n hap = {group2: reduce(and_,itemgetter(*C)(internal))\\n for group2, internal in intrenal_hap_dict_per_group.items()}\\n result[sum(C)] = self.effective_joint_frequency(hap)\\n\\n if len(alleles)>=4:\\n hap = {group2: reduce(and_,internal.values())\\n for group2, internal in intrenal_hap_dict_per_group.items()}\\n result[sum(representations)] = self.effective_joint_frequency(hap)\\n\\n return result\",\n \"def entropy_permutation_test(ordered_pitch_types, single_pitch_pdf, conditional_joint_probabilities, total_transitions,\\n n=1000):\\n pitch_types, pitch_probabilities = zip(*single_pitch_pdf.items())\\n permutation_entropies = []\\n progress = progressbar.ProgressBar()\\n\\n for test_number in progress(xrange(n)):\\n # create the new matrix\\n permutation_counts = {}\\n for first_pitch_type in ordered_pitch_types:\\n permutation_counts[first_pitch_type] = {}\\n for second_pitch_type in ordered_pitch_types:\\n permutation_counts[first_pitch_type][second_pitch_type] = 0\\n\\n pitch_permutation = numpy.random.choice(pitch_types, total_transitions, p=pitch_probabilities)\\n current_pitch = numpy.random.choice(pitch_types, p=pitch_probabilities)\\n for next_pitch in pitch_permutation:\\n permutation_counts[current_pitch][next_pitch] += 1\\n current_pitch = next_pitch\\n\\n joint_probabilities, _, _ = joint_probabilities_from_transitions(ordered_pitch_types, permutation_counts)\\n permutation_entropies.append(entropy_from_probability_matrix(joint_probabilities))\\n\\n joint_entropy = entropy_from_probability_matrix(conditional_joint_probabilities)\\n # print 'Mean', numpy.mean(permutation_entropies)\\n # print 'Standard deviation', numpy.std(permutation_entropies)\\n # tdof, tloc, tscale = stats.t.fit(permutation_entropies)\\n # print 'DF', tdof, 'Loc (mean)', tloc, 'Scale (SD)', tscale\\n # t_score = (joint_entropy - tloc) / tscale\\n # print stats.t.cdf(joint_entropy, df=tdof, loc=tloc, scale=tscale)\\n\\n mean, stddev = stats.norm.fit(permutation_entropies)\\n print 'Mean = {mean}\\\\t StdDev = {stddev}'.format(mean=mean, stddev=stddev)\\n z_score = (joint_entropy - mean) / stddev\\n p_value = stats.norm.cdf(joint_entropy, mean, stddev)\\n print 'The joint entropy has a Z-score of {z_score} which gives a P-value of {p_value}'.format(z_score=z_score,\\n p_value=p_value)\\n return z_score, p_value\",\n \"def permutate_genome_percent(human, phix, bacteria):\\n \\n per = list(itertools.product(human, phix, bacteria))\\n sum_per = [sum(i) for i in zip(*per)]\\n \\n #check percentage sum < 1\\n if all(i > 1 for i in sum_per):\\n print \\\"Some combinations of human, phix and bacteria greater than 1\\\"\\n sys.exit(0)\\n \\n return per\",\n \"def probability_from_internal(internal_values, constr):\\n return internal_values / internal_values.sum()\",\n \"def probability(cpts, term, obs):\\r\\n \\r\\n \\r\\n # term is a list e.g., ['x_1', '0']\\r\\n # flip refers to the assignment either '0' false or '1' true\\r\\n flip = term[1]\\r\\n # the term itself\\r\\n term = term[0]\\r\\n # accumulator variable\\r\\n answer = 0\\r\\n # this loop locates where in the CPT we're looking\\r\\n for clause in range(len(cpts)):\\r\\n if cpts[clause][0] == term:\\r\\n index = clause\\r\\n # focus on our term\\r\\n cpt = cpts[index]\\r\\n # this loop checks if there are no preconditions\\r\\n # if not, then we immediately know the probability and can return\\r\\n for m in range(len(cpt[1])):\\r\\n if cpt[1][m][-2][1] == '1':\\r\\n if cpt[1][m][0] == [[]]:\\r\\n answer = cpt[1][m][-1]\\r\\n # list of the variables we have observed\\r\\n have = []\\r\\n if obs != []:\\r\\n for k in obs:\\r\\n have.append(k[0])\\r\\n # list of variables we need to know in order to calculate the probability\\r\\n needed = []\\r\\n for prob in range(len(cpt[1])):\\r\\n for j in cpt[1][prob][0]:\\r\\n if j != []:\\r\\n if j[0] not in needed:\\r\\n needed.append(j[0])\\r\\n # conditional logic based on the known variables\\r\\n for required in needed:\\r\\n if required not in have:\\r\\n # deep copy our observations list\\r\\n obs2 = []\\r\\n obs3 = []\\r\\n for observs in obs:\\r\\n obs2.append(observs)\\r\\n obs3.append(observs)\\r\\n # if we need to know a variable but don't have it\\r\\n # then we allow it to be either 0 or 1\\r\\n obs3.append([required,'1'])\\r\\n obs2.append([required,'0'])\\r\\n # computes probability if the unknown term is true, times \\r\\n # the probability that the unknown term is true, plus the\\r\\n # probability if the unknown term is false, times the \\r\\n # probability that the unknown term is false\\r\\n answer = (probability(cpts, [term,flip], obs3) * probability(cpts, [required,'1'], obs)) + (probability(cpts, [term,flip], obs2) * (probability(cpts, [required,'0'], obs)))\\r\\n # this loop looks complicated but all it's doing is finding the correct\\r\\n # line in the CPT\\r\\n if cpt[1][prob][-2][1] == '1':\\r\\n count = 1\\r\\n for i in range(len(cpt[1][prob][0])):\\r\\n if cpt[1][prob][0][i] in obs:\\r\\n count *= 1\\r\\n else:\\r\\n count = 0\\r\\n if count == 1:\\r\\n answer += cpt[1][prob][-1]\\r\\n\\r\\n\\r\\n # this computes the probability that the term is true, so if we asked \\r\\n # for the probability that it is false, just return 1 - answer\\r\\n if flip == '0':\\r\\n return 1 - answer\\r\\n return answer\",\n \"def probability(structure,seq, react=None):\\n return energy_to_proba(get_ens_energy(seq,react),get_stru_energy(structure,seq,react))\",\n \"def get_probability(self, combination):\\n\\n\\t\\tprob = 1\\n\\t\\tfor i in np.arange(self.codelength):\\n\\t\\t\\tprob *= self.prior[combination[i]-1]\\n\\t\\treturn prob\",\n \"def cumprob(self):\\r\\n return self.probabilities.cumsum(-1)\",\n \"def probability(series, params):\\n\\n prob = 1\\n\\n for result in series:\\n\\n prob *= params[result]\\n\\n return prob * params[\\\"die\\\"]\",\n \"def prior_sample(self, bn):\\n x = np.zeros(3)\\n\\n # first joint prob\\n random_choice = np.random.choice(bn[0], 1, bn[0].all(), bn[0])\\n x[0] = random_choice[0]\\n\\n # Second Joint Prob\\n if x[0] == 0.1:\\n random_choice = np.random.choice(bn[1][0], 1, bn[1][0].all(), bn[1][0])\\n x[1] = random_choice\\n elif x[0] == 0.9:\\n random_choice = np.random.choice(bn[1][1], 1, bn[1][1].all(), bn[1][1])\\n x[1] = random_choice\\n\\n # Third Joint Prob\\n if random_choice[0] == 0.8 or random_choice == 0.1:\\n random_choice = np.random.choice(bn[2][0], 1, bn[2][0].all(), bn[2][0])\\n x[2] = random_choice\\n else:\\n random_choice = np.random.choice(bn[2][1], 1, bn[2][1].all(), bn[2][1])\\n x[2] = random_choice\\n return x\",\n \"def estimateProbabilities(X, C):\\n p = []\\n q = []\\n r = []\\n n = len(X)\\n d = 9 # Dimension of the elements\\n c = 2 # Number of classes\\n for i in range(d):\\n p_i = []\\n q_i = []\\n r_i = []\\n for j in range(c):\\n p_tmp = []\\n q_tmp = []\\n r_tmp = []\\n elements = C[j] # Elements on the training set for class w_j\\n nj = len(C[j])\\n for k in elements:\\n # p_i_j\\n res = X[k][i]*(X[k][i] + 1)*0.5\\n p_tmp.append(res)\\n # q_i_j\\n res = 1 - X[k][i]*X[k][i]\\n q_tmp.append(res)\\n # r_i_j\\n res = X[k][i]*(X[k][i] - 1)*0.5\\n r_tmp.append(res)\\n p_i.append(sum(p_tmp) / float(nj))\\n q_i.append(sum(q_tmp) / float(nj))\\n r_i.append(sum(r_tmp) / float(nj))\\n p.append(p_i)\\n q.append(q_i)\\n r.append(r_i)\\n return (p, q, r)\",\n \"def _generate_p(self):\\n self._values, weights = zip(*self._weights.items())\\n cumsum = list(itertools.accumulate(weights))\\n total = cumsum[-1]\\n self._p = [i / total for i in cumsum]\",\n \"def _categorical(self, rewards, probs, dones):\\n\\n # Create local vars to keep code more concise\\n vmin = self.vmin\\n vmax = self.vmax\\n atoms = self.atoms\\n num_atoms = self.num_atoms\\n gamma = self.gamma\\n rollout = self.rollout\\n\\n # rewards/dones shape from [batchsize,] to [batchsize,1]\\n rewards = rewards.unsqueeze(-1)\\n dones = dones.unsqueeze(-1).type(torch.float)\\n\\n delta_z = (vmax - vmin) / (num_atoms - 1)\\n\\n projected_atoms = rewards + gamma**rollout * atoms * (1 - dones)\\n projected_atoms.clamp_(vmin, vmax)\\n b = (projected_atoms - vmin) / delta_z\\n\\n # It seems that on professional level GPUs (for instance on AWS), the\\n # floating point math is accurate to the degree that a tensor printing\\n # as 99.00000 might in fact be 99.000000001 in the backend, perhaps due\\n # to binary imprecision, but resulting in 99.00000...ceil() evaluating\\n # to 100 instead of 99. Forcibly reducing the precision to the minimum\\n # seems to be the only solution to this problem, and presents no issues\\n # to the accuracy of calculating lower/upper_bound correctly.\\n precision = 1\\n b = torch.round(b * 10**precision) / 10**precision\\n lower_bound = b.floor()\\n upper_bound = b.ceil()\\n\\n m_lower = (upper_bound + (lower_bound == upper_bound).float() - b) * probs\\n m_upper = (b - lower_bound) * probs\\n\\n projected_probs = torch.tensor(np.zeros(probs.size())).to(self.device)\\n\\n for idx in range(probs.size(0)):\\n projected_probs[idx].index_add_(0, lower_bound[idx].long(), m_lower[idx].double())\\n projected_probs[idx].index_add_(0, upper_bound[idx].long(), m_upper[idx].double())\\n return projected_probs.float()\",\n \"def prob4():\\n#raise NotImplementedError(\\\"Problem 4 Incomplete\\\")\\n h = lambda x : x[0] < -1 and x[1] > 1\\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))\",\n \"def probability(n, k, p):\\n prob = 0\\n power = expotentation_by_squaring((1-p), n)\\n count_mult = math.log(n, 2)\\n p_fraction = p/(1-p)\\n count_mult += 1\\n for i in range(0, k+1):\\n element = newton(n, i)*power\\n prob += element\\n power *= p_fraction\\n count_mult += 2\\n return prob, count_mult\",\n \"def P(N,L) :\\n accum = 0.0\\n sign = 1.0\\n for i in xrange(len(L)+1) :\\n accum2 = 0.0\\n for combin in combinations(L,i) :\\n term = 1.0\\n j = 0.0\\n for Li in combin :\\n term *= Li/(N-j)\\n j += 1\\n accum2 += term\\n accum += sign*accum2\\n sign *= -1.0\\n return accum\",\n \"def prob(self, x, y):\\n p = self.tag_prob(y)\\n for i in range(len(y)):\\n p *= self.out_prob(x[i], y[i])\\n\\n return p\",\n \"def cross(g1, g2):\\n\\n g1_haploid = product(*g1.split(\\\",\\\"))\\n g2_haploid = product(*g2.split(\\\",\\\"))\\n\\n probs = defaultdict(float)\\n\\n count = 0\\n for h1, h2 in product(g1_haploid, g2_haploid):\\n offspring_g = \\\",\\\".join(\\\"\\\".join(sorted(i)) for i in zip(h1, h2))\\n count += 1\\n probs[offspring_g] += 1.0\\n\\n for gtype in probs.keys():\\n probs[gtype] /= count\\n\\n return probs\",\n \"def calc_choice_probs(Vals, sigma):\\n\\n # Assumes that NaNs have been replaced by -numpy.inf or similar\\n if sigma == 0.0:\\n # We could construct a linear index here and use unravel_index.\\n Pflat = np.argmax(Vals, axis=0)\\n Probs = np.zeros(Vals.shape)\\n for i in range(Vals.shape[0]):\\n Probs[i][Pflat == i] = 1\\n return Probs\\n\\n maxV = np.max(Vals, axis=0)\\n Probs = np.divide(\\n np.exp((Vals - maxV) / sigma), np.sum(np.exp((Vals - maxV) / sigma), axis=0)\\n )\\n return Probs\",\n \"def calcProbability(self):\\n for attribute in self.attributes:\\n index = self.F2I[attribute]\\n features = set([self.train[i][0][index] for i in range(len(self.train))])\\n for feature in features:\\n #all the true and false\\n result_t = list(filter(lambda x: x[1]== True, self.train))\\n total_t = len(result_t)\\n result_f = list(filter(lambda x: x[1]== False, self.train))\\n total_f= len(result_f)\\n #the probability for the feature if its true or false\\n t = len(list(filter(lambda x: x[0][index] == feature, result_t)))\\n f = len(list(filter(lambda x: x[0][index] == feature, result_f)))\\n prob_yes= t/total_t\\n prob_no = f/total_f\\n #assign the probabilities to the dictionaries\\n self.probs_yes[(index,feature)] = prob_yes\\n self.probs_no[(index,feature)] = prob_no\",\n \"def prob1(L):\\n return min(L), max(L), sum(L)/len(L)\\n raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\",\n \"def _data_prob(pi, multinomials, data):\\n comp_prob = []\\n for i, p_i in enumerate(pi):\\n if multinomials[i].shape[0] == 1:\\n mult = np.array(multinomials[i][0, data[:, 1].astype(int)].todense())\\n else:\\n mult = multinomials[i][data[:, 0].astype(int), data[:, 1].astype(int)]\\n comp_prob.append(np.array(pi[i] * mult)[0])\\n return comp_prob\",\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n for cls in self.classes:\\n class_probability = self.prior_prob[cls]\\n for key, value in datum.items():\\n relative_feature_values = self.likelihoods[cls][key]\\n class_probability += math.log(relative_feature_values.get(datum[key], 0.01))\\n\\n logJoint[cls] = class_probability\\n\\n return logJoint\",\n \"def get_probs(self, states, actions):\\n # YOUR CODE HERE\\n \\n probs = np.ones(len(states))/2\\n return probs\",\n \"def predictability(self):\\n temp = self.probs\\n for n in range(10):\\n temp = temp.dot(temp)\\n final = temp[0,:]\\n #Let's assume that all words have unique initial letters\\n probs = map(len, self.words)\\n probs = array(probs)\\n probs = (probs + self.probs.max(1)-1)/probs\\n return sum(final*probs)\",\n \"def conditional_probability(data, attr, cp_table):\\n # gets class names for dataframe manipulation\\n classes = attr.tail(1)['vars'].tolist()\\n classlist = [classes[0][0], classes[0][1]]\\n class0 = classlist[0]\\n class1 = classlist[1]\\n # number of instances beloning to each class\\n nclass0 = cp_table.loc[0, class0].sum()\\n nclass1 = cp_table.loc[0, class1].sum()\\n total = nclass0 + nclass1\\n # all probabilities include a laplace est of 1\\n prior0 = (nclass0 + 1) / (total + 2)\\n prior1 = (nclass1 + 1) / (total + 2)\\n list0 = []\\n list1 = []\\n for index, row in cp_table.iterrows():\\n numattr = len(attr.loc[index, 'vars'])\\n numer0 = row[class0] + 1\\n numer1 = row[class1] + 1\\n denom0 = nclass0 + (1 * numattr)\\n denom1 = nclass1 + (1 * numattr)\\n cp0 = numer0 / denom0\\n cp1 = numer1 / denom1\\n list0.append(cp0)\\n list1.append(cp1)\\n # replacing columns in previous table with cond probs\\n del cp_table[class0]\\n del cp_table[class1]\\n cp_table[class0] = list0\\n cp_table[class1] = list1\\n \\n return cp_table, prior0, prior1\",\n \"def probabilities(self, x, y):\\n return self.feed_and_return(x, y, self.network.a)\",\n \"def generate_probabilities(self):\\n dists = np.copy(self.distances)\\n\\n for i in range(self.number_towers):\\n for j in range(self.number_towers):\\n if self.method == 'distance_distribution':\\n dists[i][j] = (\\n -1 *\\n (dists[i][j] ** 2) *\\n xamtfos(dists[i][j] ** 2, self.sigma) *\\n self.expander\\n )\\n elif self.method == 'distance_square':\\n dists[i][j] = -1 * (dists[i][j] + 1) ** self.distance_power\\n\\n normalizer = dists.max().max() / 2\\n dists -= normalizer\\n\\n return np.array([\\n softmax(dists[i])\\n for i in range(self.number_towers)\\n ])\",\n \"def alias_setup(probs):\\n K = len(probs)\\n q = np.zeros(K)\\n J = np.zeros(K, dtype=np.int)\\n\\n smaller = []\\n larger = []\\n for kk, prob in enumerate(probs):\\n q[kk] = K * prob\\n if q[kk] < 1.0:\\n smaller.append(kk)\\n else:\\n larger.append(kk)\\n\\n while len(smaller) > 0 and len(larger) > 0:\\n small = smaller.pop()\\n large = larger.pop()\\n\\n J[small] = large\\n q[large] = q[large] + q[small] - 1.0\\n if q[large] < 1.0:\\n smaller.append(large)\\n else:\\n larger.append(large)\\n\\n return J, q\",\n \"def selection_profiles_by_chance(true, compare):\\n n_neurons, M = true.shape\\n probabilities = np.zeros(n_neurons)\\n\\n for neuron in range(n_neurons):\\n n = np.count_nonzero(true[neuron])\\n N = np.count_nonzero(compare[neuron])\\n rv = hypergeom(M=M, n=n, N=N)\\n\\n overlap = np.count_nonzero(true[neuron] * compare[neuron])\\n probabilities[neuron] = 1 - rv.cdf(x=overlap)\\n\\n return probabilities\",\n \"def test_posterior_logprobs(self):\\n x = list(product([True, False], repeat=2))\\n xs = list(e for e in product(x, repeat=3))\\n all_obs = list(o for o in xs\\n if all(any(e) and not all(e) for e in o))\\n total = logsumexp(list(posterior_logprobs(np.array(obs), self.S, self.A, self.E)[1]\\n for obs in all_obs))\\n assert_allclose(total, np.log(1))\",\n \"def Probability(rating1, rating2):\\n return 1.0 * 1.0 / (1 + 1.0 * math.pow(10, 1.0 * (rating1 - rating2) / 400))\",\n \"def boost_probability_for(fixation):\\n probabilities = np.zeros(Number_of_locs) #MOD Number_of_locs deleted\\n for possible_target_location in xrange(Number_of_locs): #MOD Number_of_locs deleted\\n Lib_c.set_target(possible_target_location)\\n probabilities[possible_target_location] = integrate.quad(\\n Lib_c.function,\\n -np.inf, np.inf,\\n epsabs=0,\\n limit=50,\\n full_output=1\\n )[0]\\n return np.sum(Post_probs * probabilities) #MOD Post_probs deleted\",\n \"def update_probs(flip,prob_coins,coins):\\n if flip == 'H':\\n joint_prob_sum = 0\\n for x in range(len(prob_coins)):\\n joint_prob_sum += (prob_coins[x] * coins[x])\\n new_prob_coins = []\\n for x in range(len(prob_coins)):\\n new_prob_coin = prob_coins[x] * coins[x] / joint_prob_sum\\n new_prob_coins.append(new_prob_coin)\\n return new_prob_coins\\n else:\\n joint_prob_sum = 0\\n for x in range(len(prob_coins)):\\n joint_prob_sum += (prob_coins[x] * (1-coins[x]))\\n new_prob_coins = []\\n for x in range(len(prob_coins)):\\n new_prob_coin = (prob_coins[x] * (1-coins[x])) / joint_prob_sum\\n new_prob_coins.append(new_prob_coin)\\n return new_prob_coins\",\n \"def create_joint_prob_corr_table(to_dict=True):\\n\\n p = np.arange(0., 1.05, 0.05) # Range of marginal probability\\n corr = np.arange(-1., 1.05, 0.05) # Range of correlations\\n\\n table = np.zeros(shape=(len(corr), len(p), len(p)))\\n\\n for i in range(len(corr)):\\n\\n sig = np.matrix([[1., corr[i]], [corr[i], 1.]])\\n\\n for j in range(len(p)):\\n for k in range(j, len(p)):\\n\\n if corr[i] == -1:\\n jp = max(0., p[j] + p[k] - 1.)\\n elif corr[i] == 0.:\\n jp = p[j] * p[k]\\n elif corr[i] == 1.:\\n jp = min(p[j], p[k])\\n elif p[j] * p[k] == 0. or p[j] == 1 or p[k] == 1.:\\n jp = p[j] * p[k]\\n else:\\n jp = mvnun(np.array([0., 0.]), np.array([np.inf, np.inf]),\\n [norm.ppf(p[j]), norm.ppf(p[k])],\\n sig)[0]\\n\\n table[i, j, k] = table[i, k, j] = jp\\n\\n if to_dict:\\n n_table = {}\\n\\n # convert to dictionary:\\n for j in range(len(p)):\\n for k in range(j, len(p)):\\n pj = round(p[j], 2)\\n pk = round(p[k], 2)\\n n_table[(pj, pk)] = n_table[(pj, pk)] = np.array((corr, table[:, j, k]))\\n\\n return n_table\\n\\n return table\",\n \"def comb(n, k):\\n return perm(n,k)/factorial(k)\",\n \"def probDist(probList):\\n\\tdiscreteDist = []\\n\\tinvProbList = [] # 1 - probList\\n\\tnumEvents = len(probList)\\n\\tfor i in range(len(probList)): # watch out for floating-point rounding errors\\n\\t\\tinvProbList.append(1-probList[i])\\n\\tpowerSet = []\\n\\teventList = [i for i in range(numEvents)]\\n\\t#print(eventList)\\n\\tfor i in range(numEvents+1):\\n\\t\\tpowerSet.append(list(combinations(eventList,i)))\\n\\t#print(powerSet)\\n\\tfor subSet in powerSet: # subSets are grouped according to size, 0 to numEvents\\n\\t\\t#print(subSet)\\n\\t\\ttotalProb = 0\\n\\t\\tfor subSubSet in subSet: # subSubSets are tuples (the actual subsets of the powerSet)\\n\\t\\t\\t#print(subSubSet)\\n\\t\\t\\tprob = 1\\n\\t\\t\\tfor ix in range(numEvents):\\n\\t\\t\\t\\tif ix not in subSubSet:\\n\\t\\t\\t\\t\\t#print(ix,'loss')\\n\\t\\t\\t\\t\\tprob *= invProbList[ix]\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t#print(ix,'win')\\n\\t\\t\\t\\t\\tprob *= probList[ix]\\n\\t\\t\\ttotalProb += prob\\n\\t\\tdiscreteDist.append(totalProb)\\n\\treturn discreteDist\",\n \"def prob4():\\n\\n\\n N = 500000\\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\\n\\n h = lambda x: x[0] < -1 and x[1] > 1\\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\\n\\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\\n\\n return 1./N * np.sum(probability)\",\n \"def p(self) -> Probability:\\n ...\",\n \"def probability_of_all_successes(p: float, r: int, n: int) -> float:\\n\\n if r == 1:\\n return pow(p, n)\\n elif n == 0:\\n return 1\\n else:\\n result = 0\\n for x in range(0, n+1):\\n result += pow(p, x) * pow(1-p, n-x) * probability_of_all_successes(p, r-1, n-x)\\n return result\",\n \"def prob(self, sequence):\\n prob = 1\\n for event, context in self.extract_ngrams(sequence):\\n prob *= self.cond_prob(event, context)\\n return prob\",\n \"def _proba(y):\\n N = len(y)\\n _, counts = np.unique(y, return_counts=True)\\n return counts / N\",\n \"def prob_list(n, p):\\n ...\\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\\n#qn = 1 - pn\\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\\n list = []\\n qn_a = 1\\n qn_b = 1\\n list.append(qn_a)\\n list.append(qn_b)\\n\\n for i in range(n-2):\\n i += 0\\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\\n list.append(qn_next)\\n qn_a = qn_b\\n qn_b = qn_next\\n return list\\n\\n\\n # returns list of probs. [q0,... qn]\",\n \"def new_binomial_prefactor(s,l1,l2,PAx,PBx):\\n with loops.Scope() as L:\\n L.total = 0.\\n L.t = 0\\n for _ in L.while_range(lambda: L.t < s + 1):\\n #TEMP TODO rewrite this. The cond_range causes a huge overhead.\\n # Try Valeev implementation\\n for _ in L.cond_range(((s - l1) <= L.t) & (L.t <= l2)):\\n L.total += binomials[l1,s-L.t] * binomials[l2,L.t] * PAx[l1-s + L.t] * PBx[l2 - L.t]\\n L.t += 1\\n return L.total\",\n \"def calc_prob(data):\\n total = len(data)\\n frequencies = sorted(Counter(data).items())\\n probabilities = OrderedDict()\\n for (key, value) in frequencies:\\n probabilities[key] = value / total\\n return probabilities\",\n \"def collapse(probability, input_list):\\n\\n result = int((probability * len(input_list)) + 0.5)\\n return min(result, len(input_list) - 1)\",\n \"def initial_probabilities_from_trajectories(n_states, trajectories):\\n p = np.zeros(n_states)\\n\\n for t in trajectories:\\n p[t.transitions()[0][0]] += 1.0\\n\\n return p / len(trajectories)\",\n \"def parameter_combinations(cls, raw=False):\\r\\n parameter_combinations = {'proportion': [0.1, 0.25, 0.5, 0.75,\\r\\n 1.0, 1.5, 2.0],\\r\\n 'n_neighbors': [3, 5, 7]}\\r\\n return cls.generate_parameter_combinations(parameter_combinations, raw)\",\n \"def probability_array(self):\\n q = self.apply_weights()\\n return np.exp(q)/(1 + np.exp(q))\",\n \"def joint_pairs(self):\\n return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))\",\n \"def prob_distr(self, x):\\n return 1.0/x\",\n \"def evaljointbayes(fname):\\n #read file and save lines to contents\\n contents = []\\n f = open(fname)\\n for line in f:\\n randomVar = line.rstrip().split()\\n if randomVar[0] != 'END':\\n contents.append(randomVar)\\n print \\\"1. Read file\\\", fname, \\\"successfully.\\\"\\n f.close()\\n \\n #count numbers of nodes and probabilities in each line\\n length = len(contents)\\n nodes, prob = [0] * length, [0] * length\\n table = [] #save all probabilities for each node \\n for num in range(0, length):\\n tableline = []\\n for i in contents[num]:\\n try:\\n j = float(i)\\n prob[num] += 1\\n tableline.append(j) \\n except ValueError:\\n if i != 'NONE':\\n nodes[num] += 1 \\n table.append(tableline) \\n \\n #print out the joint distribution formular\\n print \\\"2. The joint distribution using this network is:\\\"\\n nodelist = []\\n for line in contents:\\n nodelist.append(line[0])\\n print \\\"P(\\\", printElement(nodelist), \\\") = \\\"\\n for num in range(0, length):\\n line = contents[num]\\n if nodes[num] == 1:\\n print \\\"P(\\\", line[0], \\\")\\\", \\n else: \\n print \\\"P(\\\", line[0], '|', printElement(line[1:nodes[num]]),\\\\\\n \\\")\\\", \\n if num == length - 1:\\n print ' '\\n else: \\n print ' * ', \\n \\n #print out the full joint distribution table \\n###This is the revised version using recursive calls and###\\n###print out the cpt table to a .txt file###\\n #further revision includes deleting depth by pop() table, contents and nodes\\n #also, I can extract the parents in the previous step, then contents will not be used here\\n fo=open(fname+'.zz.txt','w')\\n result = 1.0\\n depth = 0\\n global additions, multiplications\\n additions, multiplications = 0, 0\\n fullCPT(nodelist, [], result, depth, fo, contents, table, nodes)\\n fo.close()\\n\\n #print out result of step 3 \\n print \\\"3. Additions and multiplications needed to calculate\\\",\\\\\\n \\\"the joint distribution is:\\\", additions, \\\"and\\\", multiplications\\n print \\\"The number of nodes in the network is: \\\", length\\n\\n #print out reselt of step 4\\n spaceFull = int(math.pow(2,length)) - 1\\n spaceBN = sum(prob)\\n print \\\"4. Space this network saved is (Compactness): \\\", spaceBN, \\\"/\\\", \\\\\\n spaceFull, \\\"=\\\", float(spaceBN) / float(spaceFull), '\\\\n'\\n return\",\n \"def joint_entropy(P):\\n P_nan = P.copy()\\n P_nan[P_nan == 0] = np.nan\\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))\",\n \"def update_probabilities(self):\\n self.probabilities = self.pheromones**self.EXP_PH * self.mcv**self.EXP_MCV\",\n \"def _build_multinomial_weights(self) -> None:\\n weights_obs = ramp_up_weights(\\n len(self.obs), self.tpe.full_weight_num, self.tpe.equal_weight\\n )\\n counts_obs = numpy.bincount(\\n self.obs, minlength=len(self.choices), weights=weights_obs\\n )\\n counts_obs = counts_obs + self.tpe.prior_weight\\n self.weights = counts_obs / counts_obs.sum()\",\n \"def _get_tuple_of_probabilities(self):\\n\\t\\tk_array = np.arange(-self.n,self.n+1,2)\\n\\t\\tprobability_array = []\\n\\t\\t\\n\\t\\tfor k in k_array:\\n\\t\\t\\tprobability_array.append(self._calculate_probability(k))\\n\\t\\t\\t\\n\\t\\treturn (k_array,probability_array)\",\n \"def prob_given(graph, posterior, prior):\\n return graph.prob(merge(prior, posterior)) / graph.prob(prior)\",\n \"def prob3():\\n #set up the matrices\\n solvers.options['show_progress'] = False\\n c = np.array([4., 7., 6., 8., 8., 9.])\\n\\n G = np.array([[1.,1.,0.,0.,0.,0.],\\n [-1.,-1.,0.,0.,0.,0.],\\n [0.,0.,1.,1.,0.,0.],\\n [0.,0.,-1.,-1.,0.,0.],\\n [0.,0.,0.,0.,1.,1.],\\n [0.,0.,0.,0.,-1.,-1.],\\n [1.,0.,1.,0.,1.,0.],\\n [-1.,0.,-1.,0.,-1.,0.],\\n [0.,1.,0.,1.,0.,1.],\\n [0.,-1.,0.,-1.,0.,-1.]])\\n G = np.vstack((G, -1*np.eye(6)))\\n h = np.array([7,-7,2,-2,4,-4,5,-5,8,-8,0,0,0,0,0,0],dtype=\\\"float\\\")\\n\\n #convert the matrices\\n c = matrix(c)\\n G = matrix(G)\\n h = matrix(h)\\n #solve the matrices\\n sol = solvers.lp(c, G, h)\\n return np.ravel(sol['x']), sol['primal objective']\",\n \"def sample_discrete(probs):\\r\\n q = np.random.rand()\\r\\n i = 0\\r\\n p_sum = 0.0\\r\\n while p_sum < q:\\r\\n p_sum += probs[i]\\r\\n i += 1\\r\\n return i - 1\",\n \"def get_expected_probability(probabilities):\\n\\n expected = dict()\\n for a, b in cwr(probabilities.keys(), 2):\\n if a == b:\\n expected[\\\"\\\".join(sorted([a, b]))] = probabilities[a] * probabilities[b]\\n else:\\n expected[\\\"\\\".join(sorted([a, b]))] = 2 * (probabilities[a] * probabilities[b])\\n\\n return expected\",\n \"def multinomial_pmf(sample, probabilities):\\r\\n # TODO\\r\\n a=[]\\r\\n b=[]\\r\\n i=0\\r\\n key_list=[]\\r\\n value_list=[]\\r\\n for key,value in sample.items():\\r\\n key_list.append(key)\\r\\n value_list.append(value)\\r\\n b=list(sample)\\r\\n while i< len(b):\\r\\n a.append(probabilities.keys()[probabilities.values().index(value_list[i])])\\r\\n\\r\\n\\r\\n return a\",\n \"def log_prob(self, scores : torch.Tensor, permutations):\\n s = torch.log(select_indices(scores, permutations))\\n n = len(scores)\\n p = self.upto if self.upto is not None else n - 1\\n return -sum(\\n torch.log(torch.exp((s[k:] - s[k]) * self.shape).sum(dim=0))\\n for k in range(p))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6887764","0.6807377","0.6577042","0.6448017","0.64271957","0.6388704","0.621264","0.61288685","0.6084318","0.60133076","0.59769166","0.59629256","0.5954798","0.5925714","0.5911252","0.5900835","0.5882425","0.5880176","0.5865823","0.5850093","0.580452","0.57948756","0.5785913","0.5762536","0.57624674","0.5760802","0.5755206","0.5739757","0.57310236","0.572803","0.5712486","0.57009035","0.5689562","0.5684528","0.5682139","0.5672222","0.56716394","0.5664358","0.5655259","0.5654425","0.5638729","0.56367886","0.56355655","0.5608994","0.5598662","0.55857736","0.55635923","0.5555379","0.5553738","0.55508614","0.55508333","0.554729","0.5544214","0.5544125","0.553783","0.5533712","0.55304754","0.5521562","0.5519848","0.55074114","0.548845","0.5485266","0.5482118","0.54684675","0.54613554","0.5460356","0.54549456","0.5441128","0.5440203","0.5430302","0.54278904","0.54262453","0.54191625","0.5418889","0.5415785","0.5413331","0.54124737","0.54097784","0.540788","0.5405831","0.5397085","0.5395609","0.53911096","0.53888625","0.5379086","0.53686583","0.5364772","0.5364628","0.5360841","0.53605944","0.5359919","0.53568035","0.53558886","0.5353082","0.53479886","0.5332916","0.5332179","0.5331092","0.5323","0.5322487"],"string":"[\n \"0.6887764\",\n \"0.6807377\",\n \"0.6577042\",\n \"0.6448017\",\n \"0.64271957\",\n \"0.6388704\",\n \"0.621264\",\n \"0.61288685\",\n \"0.6084318\",\n \"0.60133076\",\n \"0.59769166\",\n \"0.59629256\",\n \"0.5954798\",\n \"0.5925714\",\n \"0.5911252\",\n \"0.5900835\",\n \"0.5882425\",\n \"0.5880176\",\n \"0.5865823\",\n \"0.5850093\",\n \"0.580452\",\n \"0.57948756\",\n \"0.5785913\",\n \"0.5762536\",\n \"0.57624674\",\n \"0.5760802\",\n \"0.5755206\",\n \"0.5739757\",\n \"0.57310236\",\n \"0.572803\",\n \"0.5712486\",\n \"0.57009035\",\n \"0.5689562\",\n \"0.5684528\",\n \"0.5682139\",\n \"0.5672222\",\n \"0.56716394\",\n \"0.5664358\",\n \"0.5655259\",\n \"0.5654425\",\n \"0.5638729\",\n \"0.56367886\",\n \"0.56355655\",\n \"0.5608994\",\n \"0.5598662\",\n \"0.55857736\",\n \"0.55635923\",\n \"0.5555379\",\n \"0.5553738\",\n \"0.55508614\",\n \"0.55508333\",\n \"0.554729\",\n \"0.5544214\",\n \"0.5544125\",\n \"0.553783\",\n \"0.5533712\",\n \"0.55304754\",\n \"0.5521562\",\n \"0.5519848\",\n \"0.55074114\",\n \"0.548845\",\n \"0.5485266\",\n \"0.5482118\",\n \"0.54684675\",\n \"0.54613554\",\n \"0.5460356\",\n \"0.54549456\",\n \"0.5441128\",\n \"0.5440203\",\n \"0.5430302\",\n \"0.54278904\",\n \"0.54262453\",\n \"0.54191625\",\n \"0.5418889\",\n \"0.5415785\",\n \"0.5413331\",\n \"0.54124737\",\n \"0.54097784\",\n \"0.540788\",\n \"0.5405831\",\n \"0.5397085\",\n \"0.5395609\",\n \"0.53911096\",\n \"0.53888625\",\n \"0.5379086\",\n \"0.53686583\",\n \"0.5364772\",\n \"0.5364628\",\n \"0.5360841\",\n \"0.53605944\",\n \"0.5359919\",\n \"0.53568035\",\n \"0.53558886\",\n \"0.5353082\",\n \"0.53479886\",\n \"0.5332916\",\n \"0.5332179\",\n \"0.5331092\",\n \"0.5323\",\n \"0.5322487\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6838733"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":19591,"cells":{"query":{"kind":"string","value":"Generate all possible groundings (assignments of constants to variables)"},"document":{"kind":"string","value":"def generateCombos(vars,constants):\n # SUPER NOT GENERALIZED---TOO LATE AT NIGHT FOR ME TO DO RECURSIVE ALGORITHMS\n assert len(vars) == 2 and len(constants) == 2\n combs = []\n for c1 in constants:\n for c2 in constants:\n combs.append(Grounding([(vars[0], c1), (vars[1], c2)]))\n return combs"},"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 ground_operator(self, op_name):\n op = self.domain.operators[op_name]\n self._set_operator_groundspace( op_name, op.variable_list.items() )\n for ground in self._instantiate( op_name ):\n # print('grounded', ground)\n st = dict(ground)\n gop = Operator(op_name)\n gop.variable_list = st\n gop.precondition_pos = set( [ a.ground( st ) for a in op.precondition_pos ] )\n gop.precondition_neg = set( [ a.ground( st ) for a in op.precondition_neg ] )\n gop.effect_pos = set( [ a.ground( st ) for a in op.effect_pos ] )\n gop.effect_neg = set( [ a.ground( st ) for a in op.effect_neg ] )\n yield gop","def __compileVariables(self):\n state_variables = []\n state_diffs = []\n networks = []\n frames = []\n vars = []\n\n language = self.language\n\n instansiate_variables = {}\n\n self.obj_vars = self.object_variables()\n for key, var in self.variables.items(): # Grouping the variables\n size = self.size_of_variable(var)\n variable_type = var.type\n if not size or key in self.variables_not_included: # Skip variable\n print('Variable not included:\\tvar: {}'.format(var.label))\n continue\n if var.equation_list and variable_type in ['constant', \"network\"]:\n self.constantEquations.append(*var.equation_list)\n continue\n if variable_type in ['constant', 'frame', 'state']:\n # print(dir(var.units))\n units_pp = var.units.prettyPrint()\n doc_pp = var.doc\n if units_pp:\n units_doc_str = '{}, {}'.format(units_pp, doc_pp)\n else:\n units_doc_str = '{}'.format('Empty', doc_pp)\n instansiate_variables[key] = self.variable_dict(var)\n if var.compiled_index_list: # Index sets need compilation\n string_version = self.matrix_string_zeros(size, prefix = ' ')\n mat = np.zeros(size)\n index = str(var.index_structures) # Convert to string\n rep = self.mod_index[var.index_structures[0]].printable()\n string_w_comments = self.matrix_to_str_w_line_comments(mat, rep,\n prefix = ' ')\n width = 79 - 16 - len(index) - len(var.compiled) # 16 is others\n width2 = 79 - 8 - len(units_pp) - len(doc_pp)\n cons_str = '\\n{0} = {ar}({v: <{msg_box}} {com} {ind}\\n {st} ){v: <{w2}} {com} {udoc}'\n cons_var_str = cons_str.format(var.compiled,\n com = CODE[language][\"comment\"],\n ar = CODE[language][\"list\"],\n st = string_w_comments,\n udoc = units_doc_str,\n ind = index,\n w2 = width2,\n w = width,\n v = '')\n # cons_str = '{0} = {ar}({v: <{msg_box}} {com} {ind}\\n {st})'\n # cons_var_str = cons_str.format(var.compiled,\n # ar = CODE[language]['list'],\n # st = string_w_comments,\n # ind = index,\n # msg_box = width,\n # v = '')\n else:\n string_version = self.matrix_string_zeros(size, prefix = ' ')\n index = 'none'\n width = 79 - 16 - len(index) - len(var.compiled) # 16 is others\n width2 = 79 - 9 - len(units_pp) - len(doc_pp)\n cons_str = '\\n{0} = {array}({val: <{msg_box}} # {ind}\\n {st}\\n ) {h: <{w2}} {com} {udoc}'\n cons_var_str = cons_str.format(var.compiled,\n array = CODE[language][\"list\"],\n com = CODE[language][\"comment\"],\n udoc = units_doc_str,\n st = string_version,\n ind = index,\n w = width,\n w2 = width2,\n h = '',\n val = '')\n if variable_type in ['frame']:\n frames.append(cons_var_str)\n elif variable_type in ['state']:\n if var.label in self.state_variables:\n state_variables.append(cons_var_str)\n else:\n pass\n else:\n vars.append(cons_var_str)\n elif variable_type in [\"network\"]:\n mat = self.populateNetworkVariable(var)\n string_mat = self.matrix_to_string(mat, prefix = ' ')\n index = str(var.index_structures) # Convert to string\n width = 79 - 20 - len(index) - len(var.compiled) # 16 is others\n if width < 1:\n width = 1\n nt_str = '{0} = {array}({val: <{msg_box}} # {ind}\\n {st})'\n netw_var_str = nt_str.format(var.compiled,\n array = CODE[language][\"list\"],\n st = string_mat,\n ind = index,\n w = width,\n val = '')\n networks.append(netw_var_str)\n putData(instansiate_variables, self.variable_instantiate_file)\n return [state_variables, state_diffs, networks, frames, vars]","def compile_globulars():\n \n gc_frame = coord.Galactocentric(galcen_distance=8*u.kpc, z_sun=0*u.pc)\n frame_dict0 = gc_frame.__dict__\n old_keys = frame_dict0.keys()\n \n frame_dict = {}\n for k in ['galcen_distance', 'roll', 'galcen_v_sun', 'galcen_coord', 'z_sun']:\n frame_dict[k] = frame_dict0['_{}'.format(k)]\n \n t = Table.read('../data/gdr2_satellites_c3.txt', format='ascii')\n \n x = np.array([t['X']-8, t['Y'], t['Z']])*u.kpc\n v = np.array([t['U'], t['V'], t['W']])*u.km/u.s\n \n for i in range(3):\n v[i] = v[i] + gc_frame.galcen_v_sun.d_xyz[i]\n \n xgal = coord.Galactocentric(x, **frame_dict)\n xeq = xgal.transform_to(coord.ICRS)\n veq_ = gc.vgal_to_hel(xeq, v, galactocentric_frame=gc_frame)\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 # store observables\n data = {'name': t['Name'], 'ra': xeq.ra, 'dec': xeq.dec, 'distance': xeq.distance, 'pmra': veq[0], 'pmdec': veq[1], 'vr': veq[2]}\n \n tout = Table(data=data, names=('name', 'ra', 'dec', 'distance', 'pmra', 'pmdec', 'vr'))\n tout.pprint()\n tout.write('../data/positions_globular.fits', overwrite=True)","def gen_constant(self, g, ng, ct):\n pass","def set_ground_modes(self, basis=None, auto=True):\n if self._atmospheric_basis is None: # Presently, the ground can not yet be set independently of an atmosphere.\n print('Atmosphere modes not set up. Add an atmosphere before adding the ground!')\n print('Ground setup aborted.')\n return\n\n if auto:\n if self.gotemperature_params is None or isinstance(self.gotemperature_params, OceanicTemperatureParams):\n self.gotemperature_params = GroundTemperatureParams(self.scale_params)\n if self.ground_params is None:\n self.ground_params = GroundParams(self.scale_params)\n\n self.oceanic_params = None\n self._oceanic_basis = None\n\n if basis is not None:\n self.ground_basis = basis\n else:\n self.ground_basis = self._atmospheric_basis\n\n self._oceanic_var_string = list()\n self._oceanic_latex_var_string = list()\n self._ground_latex_var_string = list()\n self._ground_var_string = list()\n if self.dynamic_T:\n self._oceanic_latex_var_string.append(r', T_{{\\rm g},0}')\n self._oceanic_var_string.append(r'T_g_0')\n for i in range(1, self.nmod[1] + 1):\n self._ground_latex_var_string.append(r'delta T_{\\rm g,' + str(i) + \"}\")\n self._ground_var_string.append(r'delta_T_g_' + str(i))","def generate_source():\n \"\"\"their dependencies\"\"\"\n global dictionary_names, dictionary_slices\n src = \"\"\n for s in dictionary_slices:\n src += deconstruct(s)\n src += \" '\" + pointer_to_name(s)\n src += \"' define\\n\"\n return src + \"\\n\"","def generate_cfg(numStart, numNonterminals, numTerminals, numProductions, min_length, max_length, onlyunaryterminal,\n terminalprob, minrhs):\n grammar = cfg.ContextFreeGrammar()\n if numTerminals == -1:\n terminals = list(dictionary.generateDictionary(numProductions * max_length))\n else:\n terminals = list(dictionary.generateDictionary(numTerminals))\n nonterminals = []\n terminalCounter = 0\n start = []\n if numStart == 1:\n start.append(\"S\")\n else:\n for i in xrange(numStart):\n start.append(\"S_\" + str(i))\n for s in start:\n nonterminals.append(s)\n vcup = []\n for terminal in terminals:\n vcup.append(terminal)\n for i in range(numNonterminals - numStart):\n nt = \"NT\" + str(i)\n nonterminals.append(nt)\n vcup.append(nt)\n productionSet = set()\n obligatoryrhs = []\n for x in xrange(minrhs):\n for nt in nonterminals:\n obligatoryrhs.append(nt)\n while len(productionSet) < numProductions:\n if len(productionSet) < len(obligatoryrhs):\n lhs = obligatoryrhs[len(productionSet)]\n else:\n lhs = random.choice(nonterminals)\n rhs = []\n rhslength = random.randrange(min_length, max_length + 1)\n # print rhslength\n if rhslength == 1 and onlyunaryterminal:\n if numTerminals == -1:\n rhs.append(terminals[terminalCounter])\n terminalCounter += 1\n else:\n rhs.append(random.choice(terminals))\n else:\n for i in range(rhslength):\n if random.random() < terminalprob:\n if numTerminals == -1:\n rhs.append(terminals[terminalCounter])\n terminalCounter += 1\n else:\n rhs.append(random.choice(terminals))\n else:\n rhs.append(random.choice(nonterminals))\n prod = (lhs, tuple(rhs))\n if not prod in productionSet:\n productionSet.add(prod)\n # print prod\n for nt in nonterminals:\n n = 0\n for lhs, rhs in productionSet:\n for sym in rhs:\n if sym == nt:\n break\n else:\n # not on the rhs of any nonterminal.\n while True:\n lhs = random.choice(nonterminals)\n if lhs != nt:\n rhslength = random.randrange(min_length, max_length + 1)\n if rhslength == 1 and not onlyunaryterminal:\n productionSet.add((lhs, (nt,)))\n break\n elif rhslength > 1:\n position = random.choice(range(rhslength))\n rhs = []\n for i in range(rhslength):\n if i == position:\n rhs.append(nt)\n else:\n if random.random() < terminalprob:\n if numTerminals == -1:\n rhs.append(terminals[terminalCounter])\n terminalCounter += 1\n else:\n rhs.append(random.choice(terminals))\n else:\n rhs.append(random.choice(nonterminals))\n productionSet.add((lhs, tuple(rhs)))\n\n # now we have the set of weighted productions.\n grammar.productions = productionSet\n grammar.nonterminals = nonterminals\n grammar.terminals = terminals\n grammar.start_set = start\n return grammar.trim()","def create_gol_constants() -> None:\r\n\r\n self.INITIAL_RULE = 'B3/R23'\r\n self.INVALID_RULE_MESSAGE = (\r\n 'Invalid rule.\\n\\n'\r\n 'Set the rule in the format \"Bx/Ry\", where x and y are numbers of neighbors that:\\n'\r\n 'x: causes a birth of a cell\\n'\r\n 'y: allows a living cell to remain alive\\n\\n'\r\n 'Numbers 0 and 9 cannot belong to x and y.'\r\n )\r\n\r\n self.BOARD_WIDTH = 1000\r\n self.BOARD_HEIGHT = 1000\r\n self.BOARD_BG = (0, 0, 0)\r\n self.BOARD_STROKE = (50, 50, 50)\r\n self.BOARD_FILL = (255, 255, 255)\r\n\r\n self.IMAGE_MAX_WIDTH = 2000\r\n self.IMAGE_MAX_HEIGHT = 2000\r\n self.CELL_SIZES = [3, 5, 10, 20, 30, 50]\r\n self.INITIAL_ZOOM = len(self.CELL_SIZES) // 2\r\n\r\n self.TIMES_PER_GEN = [3000, 2000, 1500, 1000, 700, 400, 200, 100, 50]\r\n self.INITIAL_TIME_PER_GEN = len(self.TIMES_PER_GEN) // 2","def to_general(y: List[Symbol], yp: Symbol = 0, t: Symbol = t, constant_prefix: str = \"C\") -> Tuple[Symbol, List[Symbol]]:\n\n const_iter = numbered_symbols(prefix=constant_prefix, start=1)\n consts = []\n\n general = yp\n for y_ in y:\n const = next(const_iter)\n consts.append(const)\n general += const * y_\n general = constantsimp(general.collect(y_), consts)\n\n return general, consts","def _create_variables(self):\n\n \n with tf.name_scope(\"variable\"):\n if self.reg_type == 'L2':\n regularizer = tf.contrib.layers.l2_regularizer(scale=self.reg_scale)\n else:\n regularizer = tf.contrib.layers.l1_regularizer(scale=self.reg_scale)\n \n self.dim_lst = [self.dim_inputs] + self.dim_hidden_lst + [self.number_structures]\n print(self.dim_lst)\n \n self.W_lst = []\n self.b_lst = []\n for i in range(len(self.dim_lst)-1):\n self.W_lst.append(tf.get_variable(\n \"W{}\".format(i+1),\n shape=[self.dim_lst[i], self.dim_lst[i+1]],\n initializer=tf.contrib.layers.xavier_initializer(),\n regularizer=regularizer)\n )\n # not output layer, has bias term\n if i < len(self.dim_lst) - 2:\n self.b_lst.append(tf.get_variable(\"b{}\".format(i+1), shape=[self.dim_lst[i+1]]))","def define_variables(m):\r\n\r\n # Non-negative candidate capacity\r\n m.mu_1 = Var(m.G_C, m.Y, within=NonNegativeReals, initialize=0)\r\n\r\n # Solar build limits\r\n m.mu_2 = Var(m.Z, m.Y, within=NonNegativeReals, initialize=0)\r\n\r\n # Wind build limits\r\n m.mu_3 = Var(m.Z, m.Y, within=NonNegativeReals, initialize=0)\r\n\r\n # Storage build limits\r\n m.mu_4 = Var(m.Z, m.Y, within=NonNegativeReals, initialize=0)\r\n\r\n # Min power output (all generators excluding storage units)\r\n m.sigma_1 = Var(m.G.difference(m.G_STORAGE), m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max power output - existing thermal\r\n m.sigma_2 = Var(m.G_E_THERM, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max power output - candidate thermal\r\n m.sigma_3 = Var(m.G_C_THERM, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max power output - existing wind\r\n m.sigma_4 = Var(m.G_E_WIND, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max power output - candidate wind\r\n m.sigma_5 = Var(m.G_C_WIND, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max power output - existing solar\r\n m.sigma_6 = Var(m.G_E_SOLAR, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max power output - candidate solar\r\n m.sigma_7 = Var(m.G_C_SOLAR, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max power output - hydro\r\n m.sigma_8 = Var(m.G_E_HYDRO, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Min charging power - storage units\r\n m.sigma_9 = Var(m.G_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Min discharging power - storage_units\r\n m.sigma_10 = Var(m.G_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max charging power - existing storage\r\n m.sigma_11 = Var(m.G_E_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max charging power - candidate storage\r\n m.sigma_12 = Var(m.G_C_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max discharging power - existing storage\r\n m.sigma_13 = Var(m.G_E_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max discharging power - candidate storage\r\n m.sigma_14 = Var(m.G_C_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Min energy - storage units\r\n m.sigma_15 = Var(m.G_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max energy - existing storage units\r\n m.sigma_16 = Var(m.G_E_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max energy - candidate storage\r\n m.sigma_17 = Var(m.G_C_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Min energy - interval end\r\n m.sigma_18 = Var(m.G_STORAGE, m.Y, m.S, within=NonNegativeReals, initialize=0)\r\n\r\n # Max energy - interval end\r\n m.sigma_19 = Var(m.G_STORAGE, m.Y, m.S, within=NonNegativeReals, initialize=0)\r\n\r\n # Ramp-rate up (thermal and hydro generators)\r\n m.sigma_20 = Var(m.G_THERM.union(m.G_E_HYDRO), m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Ramp-rate down (thermal and hydro generators)\r\n m.sigma_23 = Var(m.G_THERM.union(m.G_E_HYDRO), m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Non-negative lost load power\r\n m.sigma_26 = Var(m.Z, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Min powerflow\r\n m.sigma_27 = Var(m.L, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Max powerflow\r\n m.sigma_28 = Var(m.L, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\r\n\r\n # Storage energy transition\r\n m.zeta_1 = Var(m.G_STORAGE, m.Y, m.S, m.T, initialize=0)\r\n\r\n # Power balance (locational marginal price)\r\n m.lamb = Var(m.Z, m.Y, m.S, m.T, initialize=0)\r\n\r\n return m","def build_schematic(self, bg=None):","def build_goal_generation(\n cls, constants: CType, mujoco_simulation: SType\n ) -> GoalGenerator:\n pass","def setup_steps(self):\n step1 = ground_step.Ground(5745, 495, 40, 44)\n step2 = ground_step.Ground(5788, 452, 40, 44)\n step3 = ground_step.Ground(5831, 409, 40, 44)\n step4 = ground_step.Ground(5874, 366, 40, 176)\n\n step5 = ground_step.Ground(6001, 366, 40, 176)\n step6 = ground_step.Ground(6044, 408, 40, 40)\n step7 = ground_step.Ground(6087, 452, 40, 40)\n step8 = ground_step.Ground(6130, 495, 40, 40)\n\n step9 = ground_step.Ground(6345, 495, 40, 40)\n step10 = ground_step.Ground(6388, 452, 40, 40)\n step11 = ground_step.Ground(6431, 409, 40, 40)\n step12 = ground_step.Ground(6474, 366, 40, 40)\n step13 = ground_step.Ground(6517, 366, 40, 176)\n\n step14 = ground_step.Ground(6644, 366, 40, 176)\n step15 = ground_step.Ground(6687, 408, 40, 40)\n step16 = ground_step.Ground(6728, 452, 40, 40)\n step17 = ground_step.Ground(6771, 495, 40, 40)\n\n step18 = ground_step.Ground(7760, 495, 40, 40)\n step19 = ground_step.Ground(7803, 452, 40, 40)\n step20 = ground_step.Ground(7845, 409, 40, 40)\n step21 = ground_step.Ground(7888, 366, 40, 40)\n step22 = ground_step.Ground(7931, 323, 40, 40)\n step23 = ground_step.Ground(7974, 280, 40, 40)\n step24 = ground_step.Ground(8017, 237, 40, 40)\n step25 = ground_step.Ground(8060, 194, 40, 40)\n step26 = ground_step.Ground(8103, 194, 40, 360)\n\n step27 = ground_step.Ground(8488, 495, 40, 40)\n\n self.step_group = pygame.sprite.Group(step1, step2,\n step3, step4,\n step5, step6,\n step7, step8,\n step9, step10,\n step11, step12,\n step13, step14,\n step15, step16,\n step17, step18,\n step19, step20,\n step21, step22,\n step23, step24,\n step25, step26,\n step27)","def initialize_sets(self):\n for block in self.blocks:\n # Insert phi nodes from SSA stage into the assignments of the block\n for phi in block.phis:\n block.gen.setdefault(phi, []).insert(0, phi)\n\n # Update the kill set with the variables that are assigned to in\n # the block\n block.kill = set(block.gen)\n block.output = set(block.gen)\n #for entry in block.bound:\n # block.i_kill |= self.assmts[entry].bit\n\n for assmts in self.assmts.itervalues():\n self.entry_point.i_gen |= assmts.bit\n self.entry_point.i_output = self.entry_point.i_gen","def gen_task0():\n argc = 1\n goal = 'f'\n premise = 'b'\n ctx, targets = list(), list()\n # Generate according to goal <- premise\n args = r_consts(argc)\n # Add the successful ground case\n ctx.append([(premise, args)])\n targets.append(((goal, args), 1))\n # Fail on non-matching constant\n args = args.copy()\n args[R.randrange(len(args))] = r_consts(1, args)[0]\n preds = r_preds(3)\n ctx.append([(preds[0], args)])\n targets.append(((goal, args), 0))\n # Add padding length dummy rule\n vs = r_vars(argc)\n ctx.append([(preds[1], vs), (preds[2], vs)])\n preds.extend([goal, premise])\n gen_task(ctx, targets, preds)","def gen_params(no_cultures):\n # Plate level\n kn = 0.1 # Nutrient diffusion\n ks = 0.1 # Signal diffusion\n b = 0.05 # Signal on cells effect constant\n a = 0.05 # Signal secretion constant\n # Culture level\n # Growth rate constant\n r_mean = 1.0\n r_var = 1.0\n r_params = [max(0.0, gauss(r_mean, r_var)) for i in range(no_cultures)]\n params = np.array([kn, ks, b, a] + r_params)\n return params","def variabilize(self):\n if self.nvars>=0:\n pass #already done\n else:\n varTab = syt.SymbolTable()\n def convertArgs(args):\n return map(lambda a: -varTab.getId(a) if isVariableAtom(a) else a, args)\n def convertGoal(g):\n return Goal(g.functor, convertArgs(g.args))\n if self.lhs: self.lhs = convertGoal(self.lhs)\n self.rhs = map(convertGoal, self.rhs)\n if self.features:\n self.features = map(convertGoal, self.features)\n if self.findall:\n self.findall = map(convertGoal, self.findall) \n self.variableList = varTab.getSymbolList()\n self.nvars = len(self.variableList)","def generate_powerset_bridge_constraints(problem):\n\n c_30 = _dynamic_constraint_30(problem)\n c_33 = _dynamic_constraint_33(problem)\n c_34 = _dynamic_constraint_34(problem)\n c_35 = _dynamic_constraint_35(problem)\n c_36 = _dynamic_constraint_36(problem)\n\n return c_30 & c_33 & c_34 & c_35 & c_36","def generate_all_locations(grid, shape):","def Gd():\n Pz=[8]\n Pp=[1,1]\n return Pz, Pp","def _generate_raw_environments(self, num, seed):","def ground(cls, nocc, nspins):\n from math import floor, ceil\n nspatials = int(nspins/2)\n alpha = [1]*ceil(nocc/2) + [0]*(nspatials-ceil(nocc/2))\n beta = [1]*floor(nocc/2) + [0]*(nspatials-floor(nocc/2))\n configuration = []\n for i,j in zip(alpha, beta):\n configuration.append(i)\n configuration.append(j)\n\n return cls(configuration)","def one_variation(self):\n\n globals_ = dict(\n # Physical constants\n g=9.81, # Gravitational acceleration [m/s^2]\n c=3e8, # Speed of Light [m/s]\n h=6.6262e-34, # Planck [Js]\n k=1.38e-23, # Boltzmann [J/K]\n R=8.31441, # k*NA [J/(mol*kg)]\n NA=6.0225e23, # Avogadro [1/mol]\n gamma=6.67e11, # Gravitational Constant [Nm^2/kg^2]num\n qe=1.60219e-19, # Elementary charge [C]\n # (e is not free unfortunately)\n e0=8.854187816e-12, # Permittivity of Vacuum [As/(Vm)]\n epsilon0=8.854187816e-12, # Permittivity of Vacuum [As/(Vm)]\n mu0=4e-7*pi, # Permeability of Vacuum [Vs/(Am)]\n K=9e9, # 1/(4*pi*epsilon0) [Vm/(As)]\n me=9.1095e-31, # The mass of electron [kg]\n mu=1.66056e-27, # Atomic mass unit [kg]\n sigma=5.67e-8, # Stefan-Boltzmann Constant\n )\n exec(function_import, globals_)\n for i in ('pi', 'e', 'sin', 'sind', 'asin'):\n assert i in globals_\n\n values = {}\n\n # For example there is a variable k, it is not equal to k (Planck const)\n for variable in self.variable_list:\n exec('%s = None' % variable, globals_, values)\n\n for const in self.const_list:\n exec('%(name)s = %(value)g' % const, values)\n\n for intv in self.interval_list:\n value = interval_.random(intv['interval'])\n if intv['name']:\n name = intv['name']\n exec('%s = float(%g)' % (name, value), globals_, values)\n\n compute_list = self.compute_list[:]\n number_of_uncomputable_formulas = 0\n # The number of the failed computation after\n # a successful computation.\n\n while compute_list:\n compute = compute_list[0]\n try:\n exec(compute['formula'], globals_, values)\n\n except (NameError, TypeError):\n compute_list.append(compute_list.pop(0))\n # It writes the first item to the end\n\n number_of_uncomputable_formulas += 1\n if number_of_uncomputable_formulas == len(compute_list):\n raise UncomputableError(self.code)\n self.is_computable = False\n return\n continue\n except ValueError:\n print('Value Error. Formula is:')\n print(compute['formula'])\n return\n\n compute_list.pop(0)\n number_of_uncomputable_formulas = 0\n command = '%(name)s = %(right)s' % compute\n exec(command, globals_, values)\n\n possibilities = next(self.possibilities_cycle)\n erased_elements = set(self.variable_list) - possibilities\n self.list.append((values, erased_elements))","def sat_generate_candidate_assignments(self):\n # YOUR CODE HERE\n short = min(len(c) for c in self.clauses)\n for c in self.clauses:\n if len(c) == short:\n return set(c.literals)\n # return (set(x.literals) for x in self.clauses if len(x) == min(len(c) for c in self.clauses))","def circuit():\n np.random.seed(1967)\n for gates in gates_per_layers:\n for gate in gates:\n qml.apply(gate)\n return qml.expval(qml.PauliZ(0))","def get_variables(test_case, name):\n\n test_case = test_case.lower()\n variables = {\n # Variables for control packet\n \"src_ip\": \"16.0.0.1\",\n \"dst_ip\": \"16.0.1.1\",\n \"src_net\": \"16.0.0.0\",\n \"dst_net\": \"16.0.1.0\",\n \"src_port\": \"1234\",\n \"dst_port\": \"1234\",\n \"src_mac\": \"01:02:03:04:05:06\",\n \"dst_mac\": \"10:20:30:40:50:60\"}\n\n test_vars = {\n \"macip\": {\n # MACs classified directly\n \"classify_src\": \"12:23:34:45:56:67\",\n \"classify_dst\": \"89:9A:AB:BC:CD:DE\",\n # MACs classified through mask\n \"classify_src2\": \"01:02:03:04:56:67\",\n \"classify_dst2\": \"89:9A:AB:BC:50:60\",\n \"src_mask\": \"00:00:00:00:FF:FF\",\n \"dst_mask\": \"FF:FF:FF:FF:00:00\"\n },\n \"l3_ip4\": {\n # IPs for DUT interface setup\n \"dut_to_tg_if1_ip\": \"16.0.0.2\",\n \"dut_to_tg_if2_ip\": \"192.168.0.2\",\n \"prefix_length\": 24,\n \"gateway\": \"192.168.0.1\",\n # classified networks\n \"classify_src_net\": \"16.0.2.0\",\n \"classify_dst_net\": \"16.0.3.0\",\n # IPs in classified networks\n \"classify_src\": \"16.0.2.1\",\n \"classify_dst\": \"16.0.3.1\",\n },\n \"l3_ip6\": {\n # Override control packet addresses with IPv6\n \"src_ip\": \"10::1\",\n \"dst_ip\": \"11::1\",\n \"dst_net\": \"11::\",\n # IPs for DUT interface setup\n \"dut_to_tg_if1_ip\": \"10::2\",\n \"dut_to_tg_if2_ip\": \"20::2\",\n \"prefix_length\": 64,\n \"gateway\": \"20::1\",\n # classified networks\n \"classify_src_net\": \"12::\",\n \"classify_dst_net\": \"13::\",\n # IPs in classified networks\n \"classify_src\": \"12::1\",\n \"classify_dst\": \"13::1\",\n },\n \"l4\": {\n # IPs for DUT interface and route setup\n \"dut_to_tg_if1_ip\": \"16.0.0.2\",\n \"dut_to_tg_if2_ip\": \"192.168.0.2\",\n \"prefix_length\": 24,\n \"gateway\": \"192.168.0.1\",\n \"classify_dst_net\": \"16.0.3.0\",\n # Ports in classified ranges\n \"classify_src\": 60000,\n \"classify_dst\": 61000,\n },\n \"mixed\": {\n # IPs for DUT interface and route setup\n \"dut_to_tg_if1_ip\": \"16.0.0.2\",\n \"dut_to_tg_if2_ip\": \"192.168.0.2\",\n \"prefix_length\": 24,\n \"gateway\": \"192.168.0.1\",\n \"classify_dst_net\": \"16.0.3.0\",\n # IPs in classified networks\n \"classify_src_ip\": \"16.0.2.1\",\n \"classify_dst_ip\": \"16.0.3.1\",\n # Ports in classified ranges\n \"classify_src_port\": 60000,\n \"classify_dst_port\": 61000,\n },\n \"icmp\": {\n # ICMP code and type for control packet\n \"icmp_type\": 0,\n \"icmp_code\": 0,\n # classified ICMP code and type\n \"classify_type\": 3,\n \"classify_code\": 3\n\n },\n \"icmpv6\": {\n # Override control packet addresses with IPv6\n \"src_ip\": \"10::1\",\n \"dst_ip\": \"11::1\",\n \"dst_net\": \"11::\",\n # IPs for DUT interface setup\n \"dut_to_tg_if1_ip\": \"10::2\",\n \"dut_to_tg_if2_ip\": \"20::2\",\n \"prefix_length\": 64,\n \"gateway\": \"20::1\",\n # classified networks\n \"classify_src_net\": \"12::\",\n \"classify_dst_net\": \"13::\",\n # ICMP code and type for control packet\n \"icmp_type\": 1,\n \"icmp_code\": 0,\n # classified ICMP code and type\n \"classify_type\": 4,\n \"classify_code\": 2\n\n },\n \"reflex\": {\n # IPs for DUT interface setup\n \"dut_to_tg_if1_ip\": \"16.0.0.2\",\n \"dut_to_tg_if2_ip\": \"192.168.0.2\",\n \"prefix_length\": 24,\n \"gateway\": \"192.168.0.1\",\n \"gateway2\": \"192.168.0.1\",\n # classified networks\n \"classify_src_net\": \"16.0.2.0\",\n \"classify_dst_net\": \"16.0.3.0\",\n # IPs in classified networks\n \"classify_src\": \"16.0.2.1\",\n \"classify_dst\": \"16.0.3.1\",\n },\n \"block_all\": {}\n }\n acl_data = {\n # ACL configuration for L2 tests\n\n \"macip\": {\n \"acl\": [{\n \"name\": name,\n \"type\": \"vpp-acl:vpp-macip-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"rule1\",\n \"matches\": {\n\n \"eth\": {\n \"source-mac-address\": test_vars[\"macip\"][\"classify_src\"],\n \"source-mac-address-mask\": test_vars[\"macip\"][\"src_mask\"]\n },\n \"ipv4\": {\n\n \"source-ipv4-network\": \"16.0.0.0/24\"\n }\n },\n\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:drop\"\n }\n },\n {\n \"name\": \"rule_all\",\n \"matches\": {\n\n \"eth\": {\n \"source-mac-address\": test_vars[\"macip\"][\"classify_src\"],\n \"source-mac-address-mask\": \"00:00:00:00:00:00\"\n },\n\n \"ipv4\": {\n \"source-ipv4-network\": \"0.0.0.0/0\"\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:accept\"\n }\n }\n ]}\n }\n ]\n },\n # ACL configuration for L3 IPv4 tests\n \"l3_ip4\": {\n \"acl\": [\n {\n \"name\": name,\n \"type\": \"vpp-acl:vpp-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"rule1\",\n \"matches\": {\n \"ipv4\": {\n \"destination-ipv4-network\": \"{0}/{1}\".format(\n test_vars[\"l3_ip4\"][\"classify_dst_net\"],\n test_vars[\"l3_ip4\"][\"prefix_length\"]),\n \"source-ipv4-network\": \"{0}/{1}\".format(\n test_vars[\"l3_ip4\"][\"classify_src_net\"],\n test_vars[\"l3_ip4\"][\"prefix_length\"])\n },\n \"udp\":{\n \"source-port\": {\n \"lower-port\": \"0\",\n \"upper-port\": \"65535\"\n },\n \"destination-port\": {\n \"lower-port\": \"0\",\n \"upper-port\": \"65535\"\n }\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:drop\"\n }\n },\n {\n \"name\": \"rule_all\",\n \"matches\": {\n \"ipv4\": {\n \"destination-ipv4-network\": \"0.0.0.0/0\",\n \"source-ipv4-network\": \"0.0.0.0/0\"\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:accept\"\n }\n }\n ]\n }\n }\n ]\n },\n # ACL settings for L3 IPv6 tests\n \"l3_ip6\": {\n \"acl\": [\n {\n \"name\": name,\n \"type\": \"vpp-acl:vpp-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"rule1\",\n \"matches\": {\n \"ipv6\": {\n \"destination-ipv6-network\": \"{0}/{1}\".format(\n test_vars[\"l3_ip6\"][\"classify_dst_net\"],\n test_vars[\"l3_ip6\"][\"prefix_length\"]),\n \"source-ipv6-network\": \"{0}/{1}\".format(\n test_vars[\"l3_ip6\"][\"classify_src_net\"],\n test_vars[\"l3_ip6\"][\"prefix_length\"])\n },\n \"udp\":{\n \"source-port\": {\n \"lower-port\": \"0\",\n \"upper-port\": \"65535\"\n },\n \"destination-port\": {\n \"lower-port\": \"0\",\n \"upper-port\": \"65535\"\n }\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:drop\"\n }\n },\n {\n \"name\": \"rule_all\",\n \"matches\": {\n \"ipv6\": {\n \"destination-ipv6-network\": \"0::0/0\",\n \"source-ipv6-network\": \"0::0/0\"\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:accept\"\n }\n }\n ]\n }\n }\n ]\n },\n # ACL configuration for L4 tests\n \"l4\": {\n \"acl\": [\n {\n \"name\": name,\n \"type\": \"vpp-acl:vpp-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"rule1\",\n \"matches\": {\n \"ipv4\": {\n \"source-ipv4-network\": \"0.0.0.0/0\"\n },\n \"tcp\": {\n \"source-port\": {\n \"lower-port\": test_vars[\"l4\"][\"classify_src\"],\n \"upper-port\": test_vars[\"l4\"][\"classify_src\"] + 10\n },\n \"destination-port\":{\n \"lower-port\": test_vars[\"l4\"][\"classify_dst\"],\n \"upper-port\": test_vars[\"l4\"][\"classify_dst\"] + 10\n }\n }\n },\n \"actions\":{\n \"forwarding\": \"ietf-access-control-list:drop\"\n }\n },\n {\n \"name\": \"rule_all\",\n \"matches\": {\n \"ipv4\": {\n \"source-ipv4-network\": \"0.0.0.0/0\",\n \"destination-ipv4-network\": \"0.0.0.0/0\"\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:accept\"\n }\n }\n ]\n }\n }\n ]\n },\n \"mixed\": {\n \"acl\": [\n {\n \"name\": name,\n \"type\": \"vpp-acl:vpp-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"ports\",\n \"matches\": {\n \"ipv4\": {\n \"source-ipv4-network\": \"0.0.0.0/0\"\n },\n \"tcp\": {\n \"source-port\": {\n \"lower-port\": test_vars[\"l4\"][\"classify_src\"],\n \"upper-port\": test_vars[\"l4\"][\"classify_src\"] + 10\n },\n \"destination-port\":{\n \"lower-port\": test_vars[\"l4\"][\"classify_dst\"],\n \"upper-port\": test_vars[\"l4\"][\"classify_dst\"] + 10\n }\n }\n },\n \"actions\":{\n \"forwarding\": \"ietf-access-control-list:drop\"\n }\n },\n {\n \"name\": \"rule_all\",\n \"matches\": {\n \"ipv4\": {\n \"destination-ipv4-network\": \"0.0.0.0/0\",\n \"source-ipv4-network\": \"0.0.0.0/0\"\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:accept\"\n }\n }\n ]\n }\n }\n ]\n },\n \"icmp\": {\n \"acl\": [\n {\n \"name\": name,\n \"type\": \"vpp-acl:vpp-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"rule1\",\n \"matches\": {\n \"ipv4\": {\n \"source-ipv4-network\": \"0.0.0.0/0\"\n },\n \"icmp\": {\n \"vpp-acl:vpp-icmp-ace\": {\n \"vpp-acl:icmp-type-range\": {\n \"first\": \"1\",\n \"last\": \"5\"\n },\n \"vpp-acl:icmp-code-range\": {\n \"first\": \"1\",\n \"last\": \"5\"\n }\n }\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:drop\"\n }\n },\n {\n \"name\": \"rule_all\",\n \"matches\": {\n \"ipv4\": {\n \"source-ipv4-network\": \"0.0.0.0/0\",\n \"destination-ipv4-network\": \"0.0.0.0/0\"\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:accept\"\n }\n }\n ]\n }\n }\n ]\n },\n \"icmpv6\": {\n \"acl\": [\n {\n \"name\": name,\n \"type\": \"vpp-acl:vpp-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"rule1\",\n \"matches\": {\n \"ipv6\": {\n \"source-ipv6-network\": \"::/0\",\n },\n \"icmp\": {\n \"vpp-acl:vpp-icmp-ace\": {\n \"vpp-acl:icmp-type-range\": {\n \"first\": \"1\",\n \"last\": \"5\"\n },\n \"vpp-acl:icmp-code-range\": {\n \"first\": \"1\",\n \"last\": \"5\"\n }\n }\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:drop\"\n }\n },\n {\n \"name\": \"rule_all\",\n \"matches\": {\n \"ipv6\": {\n \"destination-ipv6-network\": \"0::0/0\",\n \"source-ipv6-network\": \"::/0\",\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:accept\"\n }\n }\n ]\n }\n }\n ]\n },\n \"reflex\": {\n \"acl\": [\n {\n \"name\": name,\n \"type\": \"vpp-acl:vpp-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"rule1\",\n \"matches\": {\n \"ipv4\": {\n \"destination-ipv4-network\": \"{0}/{1}\".format(\n test_vars[\"reflex\"][\"classify_src_net\"],\n test_vars[\"reflex\"][\"prefix_length\"]),\n \"source-ipv4-network\": \"{0}/{1}\".format(\n test_vars[\"reflex\"][\"classify_dst_net\"],\n test_vars[\"reflex\"][\"prefix_length\"])\n }\n },\n \"actions\": {\n \"forwarding\": \"vpp-acl:accept-and-reflect\"\n }\n }\n ]\n }\n }\n ]\n },\n \"block_all\": {\n \"acl\": [\n {\n \"name\": name,\n \"type\": \"vpp-acl:vpp-acl\",\n \"aces\": {\n \"ace\": [\n {\n \"name\": \"rule_all\",\n \"matches\": {\n \"ipv4\": {\n \"destination-ipv4-network\": \"0.0.0.0/0\",\n \"source-ipv4-network\": \"0.0.0.0/0\"\n }\n },\n \"actions\": {\n \"forwarding\": \"ietf-access-control-list:drop\"\n }\n }\n ]\n }\n }\n ]\n },\n }\n\n try:\n ret_vars = {}\n ret_vars.update(variables)\n ret_vars.update(test_vars[test_case])\n ret_vars.update(\n {\"acl_settings\": acl_data[test_case]}\n )\n except KeyError:\n raise KeyError(\n \"Unrecognized test case {0}. Valid options are: {1}\".format(\n test_case, acl_data.keys()))\n return ret_vars","def global_parameter_space():\n return [list(range(7, 22)),\n list(range(12, 27)),\n list(range(25, 40)),\n list(permutations(range(1, 5)))]","def G():\n Pz=[40]\n Pp=[1,2,1]\n return Pz, Pp","def standardize_names_groundings(stmts):\n print('Standardize names to groundings')\n for stmt in stmts:\n for concept in stmt.agent_list():\n db_ns, db_id = concept.get_grounding()\n if db_id is not None:\n if isinstance(db_id, list):\n db_id = db_id[0][0].split('/')[-1]\n else:\n db_id = db_id.split('/')[-1]\n db_id = db_id.replace('|', ' ')\n db_id = db_id.replace('_', ' ')\n db_id = db_id.replace('ONT::', '')\n db_id = db_id.capitalize()\n concept.name = db_id\n return stmts\n \"\"\"\n for stmt in stmts:\n for idx, agent in enumerate(stmt.agent_list()):\n if 'UN' in agent.db_refs:\n all_un_scores = []\n for ev in stmt.evidence:\n agent_annots = ev.annotations.get('agents')\n if agent_annots and 'raw_grounding' in agent_annots and \\\n 'UN' in agent_annots['raw_grounding'][idx]:\n un_score = agent_annots['raw_grounding'][idx]['UN'][0][1]\n all_un_scores.append(un_score)\n if all_un_scores:\n noisy_or_score = 1 - numpy.prod([1-x for x in\n all_un_scores])\n print('%s -> %.2f' % (str(all_un_scores), noisy_or_score))\n agent.db_refs['UN'][0] = (agent.db_refs['UN'][0][0],\n noisy_or_score)\n \"\"\"","def prepare_for_sat(self):\n\n for state_group in self.state_groups:\n new_variables = set()\n for variable in state_group.variables():\n new_variables.add(variable.variable_name())\n\n self.add_variable_names(new_variables)\n\n for variable in new_variables:\n assert variable not in self.variable_to_state_group\n self.variable_to_state_group[variable] = state_group\n\n for clause in state_group.clauses():\n self.add_clause(clause)\n\n self.variable_names = sorted(self.variable_names)\n self.variable_name_to_index = {}\n\n # Assign SAT variables indicies to variable names\n for idx, variable_name in enumerate(self.variable_names):\n assert variable_name not in self.variable_name_to_index\n self.variable_name_to_index[variable_name] = idx + 1\n\n # Convert abstract clauses using variable names to SAT clauses\n concrete_clauses = set()\n for abstract_clause in self.abstract_clauses:\n for clause in abstract_clause.clauses():\n concrete_clause = []\n for part in clause:\n concrete_clause.append(part.variable(self))\n\n assert len(set(concrete_clause)) == len(concrete_clause)\n concrete_clauses.add(tuple(sorted(concrete_clause)))\n\n return sorted(concrete_clauses)","def boundVariablesToBase(FF, generators, variablesX, factorBase, m):\n FBR.<X> = PolynomialRing(FF, 1, order='degrevlex')\n baseBound = [FBR(1) for k in range(m)]\n \n #divide factorBase into m sets and create m-bounding polynomials\n for elem, k in factorBase.iteritems():\n baseBound[mod(k, m)] *= FBR(X - elem) #add to the m-th base \n \n if m == len(variablesX):\n for k in range(0,m):\n generators.append(baseBound[k](variablesX[k]))\n elif m == 1:\n for k in range(0,len(variablesX)):\n generators.append(baseBound[0](variablesX[k]))\n\n return generators","def setup(self):\n declared = []\n for obj in Rt.objective:\n var_list = split(\"[+*/-]\", obj)\n for v in var_list:\n if v not in declared:\n self.add_input(v)\n declared.append(v)\n self.add_output(\"Objective function \" + obj)","def generate():","def params(self) -> Iterable[sympy.Symbol]:\n for i in range(self.iterations):\n for p in range(len(self.qubits)):\n if (self.include_all_z or not\n numpy.isclose(self.orbital_energies[p], 0)):\n yield LetterWithSubscripts('U', p, i)\n for p, q in itertools.combinations(range(len(self.qubits)), 2):\n if (self.include_all_cz or not\n numpy.isclose(self.hamiltonian.two_body[p, q], 0)):\n yield LetterWithSubscripts('V', p, q, i)","def get_post_init_ops():\n # literally all variables, because it's better to sync optimizer-internal variables as well\n all_vars = tf.global_variables() + tf.local_variables()\n #var_by_name = dict([(v.name, v) for v in all_vars])\n var_by_name = dict()\n for v in all_vars:\n if v.name.startswith('tower_0'):\n split_name = v.name.split('/')\n realname = '/'.join(split_name[1:])\n var_by_name[realname] = v\n\n post_init_ops = []\n for v in all_vars:\n if not v.name.startswith('tower_'):\n continue\n if v.name.startswith('tower_0'):\n continue\n # in this trainer, the master name doesn't have the towerx/ prefix\n split_name = v.name.split('/')\n prefix = split_name[0]\n realname = '/'.join(split_name[1:])\n if 'AccumGrad' in realname:\n continue\n if 'counter' in realname:\n continue\n if prefix in realname:\n logger.error(\"[SyncMultiGPUReplicatedBuilder] variable \"\n \"{} has its prefix {} appears multiple times in its name!\".format(v.name, prefix))\n copy_from = var_by_name.get(realname)\n assert copy_from is not None, (realname, var_by_name.keys())\n #assert copy_from is not None, var_by_name.keys()\n post_init_ops.append(v.assign(copy_from.read_value()))\n return tf.group(*post_init_ops, name='sync_variables_from_main_tower')","def make_instructions(self):\n #de, aux, vers = self.rods\n de, aux, vers = 0, 1, 2\n n = self.num_rings\n\n self.recur(n, de, aux, vers)\n\n ### Add dummy tuple at end so I can look one move ahead on states\n self.instructions.append((0, 0, 0))","def generate_goal(self):\r\n\t\t# Creates a flat list of correct values\r\n\t\ttempList = [x for x in range(self.n**2)]\r\n\r\n\t\t# Nests those lists into a NxN\r\n\t\tBoardClass.goal = [tempList[self.n*i:self.n*(i+1)] for i in range(self.n)]\r\n\r\n\t\t# Creates a dictionary for the intended location of any specific tile. Used in\r\n\t\t# Manhatten Distance calculation.\r\n\t\tfor i in range(self.n**2):\r\n\t\t\trow = i // self.n\r\n\t\t\tcol = i % self.n\r\n\t\t\tBoardClass.goalTileLocations[i] = [row, col]","def doParametersOfInterest(self):\r\n if self.fg4fixed:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4[0]\")\r\n self.modelBuilder.doVar(\"r[1,0,4]\")\r\n print \"Fixing CMS_zz4l_fg4\"\r\n poi = \"r\"\r\n else:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg4\"):\r\n print \"have fg4 inside\"\r\n else:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4[0.,-1,1]\")\r\n poi = \"CMS_zz4l_fg4\"\r\n if self.cPOI:\r\n if self.modelBuilder.out.var(\"cww_zz\"):\r\n print \"have czz_ww inside\"\r\n else:\r\n self.modelBuilder.doVar(\"cww_zz[0.5,-10,10]\")\r\n poi += \",cww_zz\"\r\n\r\n if self.fg2POI:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg2\"):\r\n print \"have fg2 inside\"\r\n else:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg2[0.,0,1]\")\r\n poi += \",CMS_zz4l_fg2\"\r\n if self.muFloating:\r\n self.modelBuilder.doVar(\"r[1,0,2000]\")\r\n if self.muAsPOI:\r\n print \"Treating r as a POI\"\r\n poi += \",r\"\r\n else:\r\n self.modelBuilder.out.var(\"r\").setAttribute(\"flatParam\")\r\n if self.phiFloating:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg4phi\"):\r\n print \"have fg4phi inside\"\r\n else: \r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4phi[0.,-3.1415926,3.1415926]\")\r\n if self.phiPOI:\r\n poi += \",CMS_zz4l_fg4phi\"\r\n else:\r\n self.modelBuilder.out.var(\"CMS_zz4l_fg4phi\").setAttribute(\"flatParam\")\r\n if self.phi2Floating:\r\n #self.modelBuilder.doVar(\"CMS_zz4l_fg4phi[0.,-math.pi,math.pi]\")\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg2phi\"):\r\n print \"have fg2phi inside\"\r\n else: \r\n self.modelBuilder.doVar(\"CMS_zz4l_fg2phi[0.,-3.1415926,3.1415926]\")\r\n self.modelBuilder.out.var(\"CMS_zz4l_fg2phi\").setAttribute(\"flatParam\")\r\n \r\n self.modelBuilder.doSet(\"POI\",poi)","def gen_constant_graph(self, g, ng, ct):\n if ct.value in self.graphs:\n new_ct = Constant(self.get_graph(ct.value))\n self.repl[ct] = new_ct\n self.repl[ct.value] = new_ct\n else:\n self.gen_constant(g, ng, ct)","def all_different(variables) :\n constraints=[]\n for index,var in enumerate(variables):\n for sub_index in range(index+1,len(variables)):\n var1=var\n var2=variables[sub_index]\n new_constraint=Constraint(var1,var2,constraint_different)\n constraints.append(new_constraint)\n return constraints","def write_inp_all(self, type, template):\n \n for struct in self.structs:\n try:\n state = self.gEs.loc[struct,'Ground State']\n if state in self.states:\n self.assemble_inp(struct, template, state, type)\n except KeyError:\n print(\"Ground state missing for %s. Rerun whaler gs.\" % struct)","def _build_gains(self):\n flip_nibbles = True\n\n p = (100, 4, flip_nibbles)\n d = (1000, 4, flip_nibbles)\n i = (0, 4, flip_nibbles)\n il = (0, 4, flip_nibbles)\n ol = (255, 2)\n cl = (0, 2)\n el = (4000, 4, flip_nibbles)\n sr = (1, 2)\n db = (1, 2)\n sm = (1, 2)\n gains = self._build_hexstr(p, d, i, il, ol, cl, el, sr, db, sm)\n return \"F6{}\".format(gains)","def gen_constant(self, g, ng, ct):\n with About(ct.debug, self.relation):\n self.repl[(g, ct)] = sexp_to_node((P.J, ct), ng)","def generate_variable_names():\n while True:\n name = uuid.uuid4()\n yield f\"_{name.hex}\"","def build_net(nz=100):\n\tif opts.celeba:\n\t\tgen = get_wgen_celebA(nz=nz)\n\t\tdis = get_wdis_celebA(nz=nz)\n\n\tif opts.mnist:\n\t\tgen = get_wgen_mnist(nz=nz)\n\t\tdis = get_wdis_mnist(nz=nz)\n\n\treturn gen, dis","def makeSetup(self):\n startingRanks = [FLAG, SPY, SCOUT, SCOUT, MINER, MINER, GENERAL, MARSHALL, BOMB, BOMB]\n startingSpots = random.sample(self.getStartSpots(), len(startingRanks))\n pieces = []\n for i in range(len(startingRanks)):\n pieces += [Piece(startingRanks[i], startingSpots[i], self.index)]\n # print [(str(p), p.position) for p in pieces]\n return pieces","def items():\n for point in boolfunc.iter_points(inputs):\n gpnt = {v: val for v, val in point.items()\n if v not in unmapped}\n gval = gfunc.restrict(gpnt)\n # mapped function must be completely specified\n assert isinstance(gval, TTConstant)\n fpnt = {v: val for v, val in point.items()\n if v in unmapped}\n fpnt[gvar] = int(gval)\n yield func.restrict(fpnt).pcdata[0]","def build_net(nz=100):\n\tif opts.celeba:\n\t\tgen = get_gen_celebA(nz=nz)\n\t\tdis = get_dis_celebA(nz=nz)\n\n\tif opts.mnist:\n\t\tgen = get_gen_mnist(nz=nz)\n\t\tdis = get_dis_mnist(nz=nz)\n\n\treturn gen, dis","def get_structural_graph(self):\n mx = np.zeros(shape=(self.num_vars, self.num_vars))\n for var, inputs, _ in self.rules:\n ix1 = self.var_name_ndxs[var]\n for i in inputs:\n mx[i,ix1] = 1.0\n return mx","def init_cg(self):\n self.add_pc(2)\n self.pb[self.pc - 2] = \"ASSIGN\", _m(CodeGenerator.REGISTER_SIZE + CodeGenerator.INIT_MEMORY_VALUE, \"#\"), _m(\n self.top_sp)\n self.pb[self.pc - 1] = \"ASSIGN\", _m(self.top_sp), _m(self.top_sp, \"@\")\n self.init_global_func()\n self.make_output()","def doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBuilder.doVar('expr::e2(\"0.0917\",)')\n self.modelBuilder.doVar('expr::gs2(\"1.533\",)')\n\n # EFT Higgs basis couplings\n\n self.modelBuilder.doVar('cZ[0,-1,1]') \n self.modelBuilder.doVar(\"cZZ[0,-2,2]\") \n self.modelBuilder.doVar(\"cZZt[0,-2,2]\") \n self.modelBuilder.doVar(\"cZB[0,-6,6]\") \n\n poi='cZ,cZZ,cZZt,cZB'\n\n # Amplitude couplings from EFT couplings \n\n self.modelBuilder.doVar('expr::a1(\"@0+1\",cZ)') # (\"2*(@0+1)\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\n self.modelBuilder.doVar('expr::a2(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZ,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::a3(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZt,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::k1(\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\",cZB,e2,Lambda1,sinW,mZ)')\n self.modelBuilder.doVar('expr::k1L1(\"@0/pow(@1,2)\",k1,Lambda1)')\n\n ###### gamma_H ########\n\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\n\n self.modelBuilder.doVar('expr::kappa(\"1.0\",)')\n self.modelBuilder.doVar('expr::kappa_tilde(\"0.0\",)') \n\n self.modelBuilder.doVar('expr::a1_WW(\"@0\",a1)')\n self.modelBuilder.doVar('expr::a2_WW(\"@0*@0*@1\",cosW,a2)')\n self.modelBuilder.doVar('expr::a3_WW(\"@0*@0*@1\",cosW,a3)')\n self.modelBuilder.doVar('expr::k1_WW(\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\",cosW,sinW,k1,a2,Lambda1,mZ)')\n self.modelBuilder.doVar('expr::k2_k1(\"2*@0*@1*@2/(@0*@0 - @1*@1)\",cosW,sinW,k1)')\n self.modelBuilder.doVar('expr::k2_a2(\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\",cosW,sinW,mZ,a2,Lambda1)')\n self.modelBuilder.doVar('expr::k2(\"@0 + @1\",k2_k1,k2_a2)')\n\n # Determine gamma_H from VV couplings\n\n zz_expr = '\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \",a1,a2,a3,k1,k2'\n ww_expr = '\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \",a1_WW,a2_WW,a3_WW,k1_WW'\n zgamma_expr = '\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\",a1_WW,kappa,kappa_tilde'\n gg_expr = '\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\",kappa,kappa_tilde'\n bb_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n cc_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n tautau_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n mumu_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n gmgm_expr = '\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\",a1_WW,kappa,kappa_tilde'\n \n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\n\n self.modelBuilder.doVar('expr::gammaH(\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \n\n ###########################\n\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\"1V\") # Compensate for scaling of k1 templates \n self.g2V = GetCoupTerms(1,1,1,-0.0001,\"2V\") \n \n self.modelBuilder.doVar(\"expr::g2V_1(\\\"\"+str(self.g2V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1(\\\"((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1_Neg(\\\"-1*((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.doVar(\"expr::g2V_2(\\\"\"+str(self.g2V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2(\\\"((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2_Neg(\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.doVar(\"expr::g2V_3(\\\"\"+str(self.g2V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3(\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3_Neg(\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.doVar(\"expr::g2V_4(\\\"\"+str(self.g2V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4(\\\"((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4_Neg(\\\"-1*((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.doVar(\"expr::g2V_5(\\\"\"+str(self.g2V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5(\\\"((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5_Neg(\\\"-1*((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.doVar(\"expr::g2V_6(\\\"\"+str(self.g2V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6(\\\"((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6_Neg(\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.doVar(\"expr::g2V_7(\\\"\"+str(self.g2V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7(\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7_Neg(\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.doVar(\"expr::g2V_8(\\\"\"+str(self.g2V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8(\\\"((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8_Neg(\\\"-1*((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.doVar(\"expr::g2V_9(\\\"\"+str(self.g2V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9(\\\"((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9_Neg(\\\"-1*((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.doVar(\"expr::g2V_10(\\\"\"+str(self.g2V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10(\\\"((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10_Neg(\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.doVar(\"expr::g2V_11(\\\"\"+str(self.g2V[10])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11(\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11_Neg(\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.doVar(\"expr::g2V_12(\\\"\"+str(self.g2V[11])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12(\\\"((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12_Neg(\\\"-1*((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.doVar(\"expr::g2V_13(\\\"\"+str(self.g2V[12])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13(\\\"((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13_Neg(\\\"-1*((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.doVar(\"expr::g2V_14(\\\"\"+str(self.g2V[13])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14(\\\"((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14_Neg(\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.doVar(\"expr::g2V_15(\\\"\"+str(self.g2V[14])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15(\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15_Neg(\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.doVar(\"expr::g2V_16(\\\"\"+str(self.g2V[15])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16(\\\"((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16_Neg(\\\"-1*((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.doVar(\"expr::g2V_17(\\\"\"+str(self.g2V[16])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17(\\\"((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17_Neg(\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.doVar(\"expr::g2V_18(\\\"\"+str(self.g2V[17])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18(\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18_Neg(\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.doVar(\"expr::g2V_19(\\\"\"+str(self.g2V[18])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19(\\\"((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19_Neg(\\\"-1*((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.doVar(\"expr::g2V_20(\\\"\"+str(self.g2V[19])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20(\\\"((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20_Neg(\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.doVar(\"expr::g2V_21(\\\"\"+str(self.g2V[20])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21(\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21_Neg(\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.doVar(\"expr::g2V_22(\\\"\"+str(self.g2V[21])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22(\\\"((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22_Neg(\\\"-1*((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.doVar(\"expr::g2V_23(\\\"\"+str(self.g2V[22])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23(\\\"((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23_Neg(\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.doVar(\"expr::g2V_24(\\\"\"+str(self.g2V[23])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24(\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24_Neg(\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.doVar(\"expr::g2V_25(\\\"\"+str(self.g2V[24])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25(\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25_Neg(\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.doVar(\"expr::g2V_26(\\\"\"+str(self.g2V[25])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26(\\\"((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26_Neg(\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.doVar(\"expr::g2V_27(\\\"\"+str(self.g2V[26])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27(\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27_Neg(\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.doVar(\"expr::g2V_28(\\\"\"+str(self.g2V[27])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28(\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28_Neg(\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.doVar(\"expr::g2V_29(\\\"\"+str(self.g2V[28])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29(\\\"((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29_Neg(\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.doVar(\"expr::g2V_30(\\\"\"+str(self.g2V[29])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30(\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30_Neg(\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.doVar(\"expr::g2V_31(\\\"\"+str(self.g2V[30])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31(\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31_Neg(\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.doVar(\"expr::g2V_32(\\\"\"+str(self.g2V[31])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32(\\\"((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32_Neg(\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.doVar(\"expr::g2V_33(\\\"\"+str(self.g2V[32])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33(\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33_Neg(\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.doVar(\"expr::g2V_34(\\\"\"+str(self.g2V[33])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34(\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34_Neg(\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.doVar(\"expr::g2V_35(\\\"\"+str(self.g2V[34])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35(\\\"((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35_Neg(\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n \n self.modelBuilder.doVar(\"expr::g1V_1(\\\"\"+str(self.g1V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1(\\\"((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1_Neg(\\\"-1*((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.doVar(\"expr::g1V_2(\\\"\"+str(self.g1V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2(\\\"((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2_Neg(\\\"-1*((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.doVar(\"expr::g1V_3(\\\"\"+str(self.g1V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3(\\\"((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3_Neg(\\\"-1*((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.doVar(\"expr::g1V_4(\\\"\"+str(self.g1V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4(\\\"((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4_Neg(\\\"-1*((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.doVar(\"expr::g1V_5(\\\"\"+str(self.g1V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5(\\\"((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5_Neg(\\\"-1*((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.doVar(\"expr::g1V_6(\\\"\"+str(self.g1V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6(\\\"((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6_Neg(\\\"-1*((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.doVar(\"expr::g1V_7(\\\"\"+str(self.g1V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7(\\\"((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7_Neg(\\\"-1*((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.doVar(\"expr::g1V_8(\\\"\"+str(self.g1V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8(\\\"((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8_Neg(\\\"-1*((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.doVar(\"expr::g1V_9(\\\"\"+str(self.g1V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9(\\\"((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9_Neg(\\\"-1*((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.doVar(\"expr::g1V_10(\\\"\"+str(self.g1V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10(\\\"((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10_Neg(\\\"-1*((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n \n self.modelBuilder.doSet(\"POI\",poi)","def fetch_variables(self):\r\n fgraph = self.fgraph\r\n self.inputs = fgraph.inputs\r\n self.outputs = fgraph.outputs\r\n\r\n # list(fgraph.variables)\r\n # We need to include the not used inputs in our variables,\r\n # otherwise we can't pass them to the module.\r\n self.variables = [var for var in self.inputs if not len(var.clients)]\r\n self.variables += graph.variables(self.inputs, self.outputs)\r\n\r\n # The orphans field is listified to ensure a consistent order.\r\n #list(fgraph.orphans.difference(self.outputs))\r\n self.orphans = list(r for r in self.variables\r\n if isinstance(r, graph.Constant) and\r\n r not in self.inputs)\r\n self.temps = list(set(self.variables).difference(\r\n self.inputs).difference(self.outputs).difference(self.orphans))\r\n self.consts = []\r\n self.node_order = self.schedule(fgraph)","def test_basic_setup(self):\n random_vars = ['D', 'I', 'G', 'S', 'L']\n\n for rv in random_vars:\n self.assertTrue(rv in self.Gs.nodes)\n self.assertTrue(isinstance(self.Gs.nodes[rv], DiscreteNetworkNode))","def define_placeholders(self):\n sy_ob_no = tf.placeholder(shape=[None, self.ob_dim], name=\"ob\", dtype=tf.float32)\n if self.discrete:\n sy_ac_na = tf.placeholder(shape=[None], name=\"ac\", dtype=tf.int32)\n else:\n sy_ac_na = tf.placeholder(shape=[None, self.ac_dim], name=\"ac\", dtype=tf.float32)\n sy_adv_n = tf.placeholder(shape=[None], name=\"adv\", dtype=tf.float32)\n return sy_ob_no, sy_ac_na, sy_adv_n","def gencode(self):\n\n fullcode = self.code_cfg\n variables = codeconfig_getvars(fullcode)\n if len(variables) > 0:\n variables.sort(key=lambda x: len(x), reverse=True)\n for va in variables:\n if eval(\"self.\" + va[1:]) is None or eval(\"self.\" + va[1:]) == \"\":\n warnings.warn(\"Inserting None for \" + va, UserWarning)\n fullcode = fullcode.replace(va, str(eval(\"self.\" + va[1:])))\n # testing for sintax errors\n #compile(fullcode, \"<Test ModelCall Code>\\n\" + fullcode, 'exec')\n tmpfile = os.path.join(gettempdir(), \"TestModelCallCode.\" + self.complexity + \".py\")\n with open(tmpfile, \"w\") as f: f.write(fullcode)\n compile(fullcode, tmpfile, 'exec')\n return fullcode","def get_variables(self):\n return [self.g_t, self.m_t]","def gprog():\n return [('ip','ip','u')]+[k for x in ['c','1','2','3'] for k in gcopy(\"op\"+x, 'ip')]","def consts(consts):\n\n namespace = { }\n\n for c in consts:\n constname = c[\"constname\"]\n consttype = c[\"consttype\"]\n constval = c[\"constval\"]\n\n # Correct various values that won't evaluate in python.\n if constval == \"( SteamItemInstanceID_t ) ~ 0\":\n constval = \"-1\"\n elif constval == \"( ( uint32 ) 'd' << 16U ) | ( ( uint32 ) 'e' << 8U ) | ( uint32 ) 'v'\":\n constval = \"6579574\"\n else:\n constval = re.sub(r\"(0x[0-9a-fA-F]*)ull\", r\"\\1\", constval)\n\n # Evaluate the result, and place it into the namespace.\n value = eval(constval, namespace, namespace)\n namespace[constname] = value\n\n # Generate.\n mapped = map_type(consttype)\n\n if value > 0:\n p(f\"{constname} = {mapped}(0x{value:x})\")\n else:\n p(f\"{constname} = {mapped}({value})\")","def __updateNuclideBasesForSpecialCases():\n # Change the name of `AM242` to specific represent its ground state.\n am242g = byName[\"AM242\"]\n am242g.name = \"AM242G\"\n byName[\"AM242G\"] = am242g\n byDBName[byName[\"AM242G\"].getDatabaseName()] = am242g\n\n # Update the pointer of `AM242` to refer to `AM242M`.\n am242m = byName[\"AM242M\"]\n byName[\"AM242\"] = am242m\n byDBName[\"nAm242\"] = am242m\n byDBName[byName[\"AM242\"].getDatabaseName()] = am242m","def all(c):\n states(c)\n etc(c)\n prune(c)","def get_covariates(degree):\n\n base_variables = [\n \"smallm\",\n \"largem\",\n \"treatsmall\",\n \"treatlarge\",\n \"ssmall\",\n \"slarge\",\n \"streatsmall\",\n \"streatlarge\",\n ]\n if degree == 0:\n base = base_variables[0:4]\n return base\n if degree == 1:\n return base_variables\n else:\n for i in range(2, degree + 1):\n base_variables.append(f\"ssmall{i}\")\n base_variables.append(f\"slarge{i}\")\n base_variables.append(f\"streatsmall{i}\")\n base_variables.append(f\"streatlarge{i}\")\n return base_variables","def variables(self):\n for state in self.states:\n yield self.assert_state(state)\n yield self.deassert_state(state)","def generate_assignment(parameters):\n if len(parameters) == 0:\n yield []\n raise StopIteration()\n cp_pars = copy.deepcopy(parameters)\n par, values = cp_pars.popitem()\n for val in values:\n for r in generate_assignment(cp_pars):\n yield r + [(par,val)]","def generate_grammar(gram):\r\n c = 0\r\n while gram[c] != \"start_variable\": # find start variable\r\n c += 1\r\n start = gram[c+1]\r\n grammar = pcfg.PCFG(start) # create a PCFG with start and no rules\r\n while gram[c] != \"Grammar\": # find the index of the first rule\r\n c += 1\r\n c += 3\r\n\r\n while gram[c] != '###########':\r\n c = adding_rules_grammar(c, gram, grammar) # find each rule from the grammar and add it to the grammar\r\n c += 1\r\n\r\n while gram[c] != \"Lexicon\": # find the index of the first rule of the lexicon\r\n c += 1\r\n c += 3\r\n\r\n while c < len(gram):\r\n var = gram[c]\r\n c = adding_rules_lexicon(c, gram, grammar, var) # find each rule from the lexicon and add it to the grammar\r\n c += 1\r\n return grammar","def initializeVariables(F_n):\n bounds = [(-100, 100), (-10, 10), (-100, 100), (-100, 100), (-30, 30),\n (-100, 100), (-1.28, 1.28), (-500, 500), (-5.12, 5.12),\n (-32, 32), (-600, 600), (-50, 50), (-50, 50)]\n\n if F_n == 8:\n objectiveValue = -12569.5\n else:\n objectiveValue = 1e-8\n\n (lower, upper) = bounds[F_n - 1]\n return (lower, upper, objectiveValue)","def save_all_consts_to_memory():\n for key, value in symbol_table.dict.items():\n if value[2] is not None:\n save_to_memory(\"a\", \"b\", value[5], value[2])\n # assembler_generator.add_comment(\"# consts saved to memory\")","def Allcombos():\n\n global allcombos\n\n allcombos = []\n\n results = product(\"ABCDEF\", repeat=4)\n\n allcombos = resulttolist(results)\n\n return AIguessing(allcombos)","def generate(self):\n \n ##Dictionary of put values\n self.put = {}\n ##Dictionary of call values\n self.call = {}\n \n bottom = self.range[0]\n top = self.range[2]\n iter = self.range[1]\n \n if self.meth == 'bi':\n for E in range(bottom,top+iter,iter):\n solver = bi.binomial_euro(S=self.S,E=E,r=self.r,M=400,sigma=self.sigma,method='higham',T=self.T,opt='put')\n self.put[E] = solver.solve()\n del solver\n for E in range(bottom,top+iter,iter):\n solver = bi.binomial_euro(S=self.S,E=E,r=self.r,M=400,sigma=self.sigma,method='higham',T=self.T,opt='call')\n self.call[E] = solver.solve()\n del solver\n elif self.meth == 'mc':\n for E in range(bottom,top+iter,iter):\n solver = mc.mcfast_euro(S=self.S,E=E,r=self.r,M=20000,sigma=self.sigma,T=self.T,opt='put')\n self.put[E] = solver.solve()\n del solver\n for E in range(bottom,top+iter,iter):\n solver = mc.mcfast_euro(S=self.S,E=E,r=self.r,M=20000,sigma=self.sigma,T=self.T,opt='call')\n self.call[E] = solver.solve()\n del solver\n elif self.meth == 'all':\n pass","def generate_field(field):\n ship4 = [create_ship(4)]\n set_coordinates(ship4[0], 4)\n ship4[0].find_neighbor()\n ship3 = []\n for i in range(2):\n ship3.append(create_ship(3, ship4, ship3))\n set_coordinates(ship3[-1], 3, ship4, ship3[:-1])\n ship3[-1].find_neighbor()\n ship2 = []\n for i in range(3):\n ship2.append(create_ship(2, ship4, ship3, ship2))\n set_coordinates(ship2[-1], 2, ship4, ship3, ship2[:-1])\n ship2[-1].find_neighbor()\n ship1 = []\n for i in range(4):\n ship1.append(create_ship(1, ship4, ship3, ship2, ship1))\n ship1[-1].find_neighbor()\n\n for i in range(1, 5):\n for ship in eval('ship' + str(i)):\n for coor in ship.coordinates:\n field[coor[0]][coor[1]] = '■'\n ships = []\n for ship in ship4:\n ships.append(ship)\n for ship in ship3:\n ships.append(ship)\n for ship in ship2:\n ships.append(ship)\n for ship in ship1:\n ships.append(ship)\n return field, ships","def redef_vars(self):\r\n\r\n # Try using redefined source / boundary terms\r\n if self.redefined == True:\r\n self._redef_via_predef_eqn()\r\n else: # If they haven't been set you'll get an exception.\r\n self._redef_sp1_vars()","def generate_all_constraints(traj,policy,mdp):\n #print('generating all constraints')\n constraints = []\n traj_tmp = list(traj)\n #print(traj_tmp)\n #compute halfspace normals for all (s,a) pairs until terminal\n while(len(traj_tmp)>1):\n constraints += generate_half_space_normals(traj_tmp,policy,mdp)\n #print(constraints)\n traj_tmp.pop(0)\n #print('after pop',traj_tmp)\n return constraints","def all_net(configuration):\n net_dict_all = {\n \"design\" : ['H1', 'L1', 'V1' ],\n \"GW170817\" : ['H1', 'L1', 'V1' ],\n \"GW170814\" : ['H1', 'L1', 'V1' ],\n \"GW170817_without_Virgo\" : ['H1', 'L1' ],\n \"ET\" : [\"ET_L_Eu\", \"ET_L_Eu_2\"], # Triangular ET\n \"ET1\" : ['H1', 'L1', 'V1', 'ETdet1', 'ETdet2' ], # Triangular ET +LVC\n \"ET2\" : ['H1', 'L1', 'V1', 'ETdet1', 'ETdet3' ], # L-shaped at 2 places +LVC\n \"ET3\" : ['ETdet1', 'ETdet3', 'ETdet4'], # 3 L-shaped ET at three different places\n \"ET3L_EU\" : [\"ET_L_Eu\", \"ET_L_Aus_Eu\", \"ET_L_Argentina\"],\n \"3ET\" : [\"ET_L_US\", \"ET_L_Aus_US\", \"ET_L_Central_Africa\"],\n \"3CE\" : [\"CE_US\", \"CE_Aus_US\", \"CE_Central_Africa\"],\n \"1CE-ET\" : [\"CE_US\", \"ET_L_Eu\", \"ET_L_Eu_2\"],\n \"2CE-ET\" : [\"CE_US\", \"CE_Aus_US\", \"ET_L_Eu\", \"ET_L_Eu_2\"], #named 1 and 2 to distinguish from CE-ET (below) in Mills et al 2018.\n \"CE-ET\" : [\"CE_US\", \"CE_Aus_US\", \"ET_L_Eu\", \"ET_L_Eu_2\"],\n \"Voyager-ET\" : [\"LBB_H1\", \"LBB_L1\", \"LBB_I1\", \"ET_L_Eu\", \"ET_L_Eu_2\"],\n # next three networks are for calculating the impact of duty cycle on the Voyager-ET network\n \"VoyagerLI-ET\" : [\"LBB_L1\", \"LBB_I1\", \"ET_L_Eu\", \"ET_L_Eu_2\"],\n \"VoyagerHI-ET\" : [\"LBB_H1\", \"LBB_I1\", \"ET_L_Eu\", \"ET_L_Eu_2\"],\n \"VoyagerHL-ET\" : [\"LBB_H1\", \"LBB_L1\", \"ET_L_Eu\", \"ET_L_Eu_2\"],\n \n \"VoyagerETtri\" : [\"LBB_H1\", \"LBB_L1\", \"LBB_I1\", \"ET_Tri_Eu_1\", \"ET_Tri_Eu_2\", \"ET_Tri_Eu_3\"],\n \"Voyager\" : [\"LBB_H1\", \"LBB_L1\", \"LBB_I1\"],\n \"VoyagerWithAL\" : [\"LBB_H1\", \"LBB_L1\", \"LBB_I1\", \"ALV1\", \"ALK1\"],\n \"3_TriangularET\" : [\"ET_L_US\", \"ET_L_Aus_US\", \"ET_L_Central_Africa\",\"ET_L_US_2\", \"ET_L_Aus_US_2\", \"ET_L_Central_Africa_2\"],\n # for comparing to klimenko et al 2011:\n 'LHVA2' : [\"LBB_L1\",\"LBB_H1\",\"LBB_V1\",\"LBB_A-\"],\n 'LHVA' : [\"LBB_L1\",\"LBB_H1\",\"LBB_V1\",\"LBB_A\"],\n 'LHVJ' : [\"LBB_L1\",\"LBB_H1\",\"LBB_V1\",\"LBB_K1\"],\n 'LHVAJ' : [\"LBB_L1\",\"LBB_H1\",\"LBB_V1\",\"LBB_A\",\"LBB_K1\"],\n # for calculating alignment factor distributions in inclincation paper\n \"HL\" : [\"H1\", \"L1\"],\n \"HLV\" : [\"H1\", \"L1\", \"V1\" ],\n \"HLVK\" : [\"L1\",\"H1\",\"V1\",\"K1\"],\n \"HLVKI\" : [\"L1\",\"H1\",\"V1\",\"K1\", \"I1\"],\n \n\n #for optimizing the orientations of ET3L_EU w.r.t. polarization metric (see optimizing polarization notebook)\n #first optimize for the two detector network:\n \"ET2L_EU\" : [\"ET_L_Eu\", \"ET_L_Aus_Eu\"],\n \"2ET\" : [\"ET_L_US\", \"ET_L_Aus_US\"],\n #ranges\n }\n return(net_dict_all[configuration])","def base_mappings():\n return {\n 'from_1': {\n 'to_1': {\n 'mol_1': ({}, {}, []),\n 'mol_2': ({}, {}, []),\n },\n },\n }","def get_bases():\n\tbss = []\n\tfor es in MV.index:\n\t\tbs = []\n\t\tif es == ():\n\t\t\tbs.append(_1)\n\t\telse:\n\t\t\tfor js in es:\n\t\t\t\tbmv = reduce(operator.mul, map(lambda j: e[j], js))\n\t\t\t\tbs.append(bmv)\n\t\t\t\t\n\t\tbss.append(bs)\n\t\n\treturn bss","def test_create_tbg_neural_efficacies(self):\n np.random.seed(25432)\n m_act = 5.\n v_act = .05\n v_inact = .05\n cdef = [Condition(m_act=m_act, v_act=v_act, v_inact=v_inact)]\n npos = 5000\n labels = np.zeros((1, npos), dtype=int)\n labels[0, :npos / 2] = 1\n phy_params = phy.PHY_PARAMS_FRISTON00\n ne = phy.create_tbg_neural_efficacies(phy_params, cdef, labels)\n\n # check shape consistency:\n self.assertEqual(ne.shape, labels.shape)\n\n # check that moments are close to theoretical ones\n ne_act = ne[0, np.where(labels[0])]\n ne_inact = ne[0, np.where(labels[0] == 0)]\n m_act_theo = truncnorm.mean(0, phy_params['eps_max'], loc=m_act,\n scale=v_act ** .5)\n v_act_theo = truncnorm.var(0, phy_params['eps_max'], loc=m_act,\n scale=v_act ** .5)\n (ne_act.mean(), m_act_theo)\n npt.assert_approx_equal(ne_act.var(), v_act_theo, significant=2)\n\n m_inact_theo = truncnorm.mean(0, phy_params['eps_max'], loc=0.,\n scale=v_inact ** .5)\n v_inact_theo = truncnorm.var(0, phy_params['eps_max'], loc=0.,\n scale=v_inact ** .5)\n npt.assert_approx_equal(ne_inact.mean(), m_inact_theo, significant=2)\n npt.assert_approx_equal(ne_inact.var(), v_inact_theo, significant=2)\n npt.assert_array_less(ne, phy_params)\n npt.assert_array_less(0., ne)","def initial_domain(pre_condition=[]):\n one_day_shift = [ (0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1) ]\n\n possible_shifts = set( [ shift_mon + shift_tue + shift_wed + shift_thu + shift_fri + shift_sat + shift_sun\n for shift_mon in one_day_shift\n for shift_tue in one_day_shift\n for shift_wed in one_day_shift\n for shift_thu in one_day_shift\n for shift_fri in one_day_shift\n for shift_sat in one_day_shift\n for shift_sun in one_day_shift\n ] )\n\n print len(possible_shifts)\n\n for preprocess_func, args in pre_condition:\n if not args:\n possible_shifts = preprocess_func(possible_shifts)\n else:\n possible_shifts = preprocess_func(possible_shifts, *args)\n\n\n return possible_shifts","def make_bprod(self):\n rhs1 = random.choice(self.nonterminals)\n rhs2 = random.choice(self.nonterminals)\n lhs = random.choice(self.nonterminals)\n return (lhs, (rhs1, rhs2))","def change_variables((a,b,c,d), (n,r,m)): \n return ( n*a**2 + r*a*b + m*b**2, 2*(n*a*c + m*b*d) + r*(a*d + c*b), \\\n n*c**2 + r*c*d + m*d**2 )","def generate_rules(self):\n for rule in self._parser.conditionals:\n\n all_in_facts, matrix = self._generate_rules(rule)\n if all_in_facts is True:\n self.new_fact_from_facts(rule)\n else:\n facts = self._parser.conditionals[rule][1]\n #print(rule, facts, matrix)","def _make_circuit_instructions(n_qubits, depth, type_circuit):\n\n if type_circuit in [0, 1, 2]:\n\n # if type_circuit == 1:\n # if depth > 8:\n # raise ValueError(\n # \"For type-1 circuits, only at most depth=8 allowed!\"\n # )\n\n # define rotations for circuit in each layer, 0: identity, 1:X, 2:Y 3:Z\n ini_pauli = np.zeros([depth, n_qubits], dtype=int)\n\n # set first and second layer, rest comes later\n ini_pauli[0, :] = 2 # y rotation\n if depth > 1:\n ini_pauli[1, :] = 3 # z rotation\n\n # construct natural parameterized circuit\n # gives which type of entangling gates at each layer -- first entry is\n # first qubit index, second is second qubit index, third entry is type\n # of entangling gate\n entangling_gate_index_list = [[] for i in range(depth)]\n orderList = []\n for i in range(n_qubits//2):\n if i % 2 == 0:\n orderList.append(i//2)\n else:\n orderList.append((n_qubits-i)//2)\n\n if n_qubits > 1:\n shiftList = [orderList[0]]\n else:\n shiftList = []\n for i in range(1, n_qubits//2):\n shiftList.append(orderList[i])\n shiftList += shiftList[:-1]\n\n # this list gives which entangling gates are applied in which layer\n if type_circuit == 0:\n # deep natural PQC, includes non-nearest neighbor gates\n for j in range(min(len(shiftList), int(np.ceil(depth/2))-1)):\n entangling_gate_index_list[1+2*j] = [\n [2*i, (2*i+1+2*shiftList[j]) % n_qubits, 0]\n for i in range(n_qubits//2)\n ]\n elif type_circuit == 1:\n # only do 2 entangling layers at max, and only do gates with\n # nearest neighbor and no ring\n for j in range(min(len(shiftList), 3)):\n if j == 0:\n entangling_gate_index_list[1+2*j] = [\n [2*i, (2*i+1+2*shiftList[j]) % n_qubits, 0]\n for i in range(n_qubits//2)\n ]\n elif (j == 1 or j == 2):\n # exclude ring gate and gate 0,1 on third entangling layer\n entangling_gate_index_list[1+2*j] = [\n [2*i, (2*i+1+2*shiftList[j]) % n_qubits, 0]\n for i in range(1, n_qubits//2)\n ]\n\n elif type_circuit == 2:\n # only do 3 regular entangling layers in a ring topology, then two\n # more phase gates with next-nearst neighbor, which requires one\n # swap. This adds 4 more parameters\n for j in range(min(len(shiftList), 3)):\n entangling_gate_index_list[1+2*j] = [\n [2*i, (2*i+1+2*shiftList[j]) % n_qubits, 0]\n for i in range(n_qubits//2)\n ]\n # entangling_gate_index_list[1+2*3]=[[0,n_qubits-1,1],[0,1,0],[n_qubits-1,n_qubits-2,0]]\n # entangling_gate_index_list[1+2*3]=[[0,n_qubits-1,1],[0,1,0],[n_qubits-1,n_qubits-2,0]]\n entangling_gate_index_list[1+2*3] = [\n [n_qubits-1, 1, 0],\n [0, n_qubits-2, 0]\n ]\n\n for i in range(len(entangling_gate_index_list)-1):\n if len(entangling_gate_index_list[i]) > 0:\n for j in range(len(entangling_gate_index_list[i])):\n qubit_index = entangling_gate_index_list[i][j][0]\n ini_pauli[i+1, qubit_index] = 2\n if i+2 < depth:\n ini_pauli[i+2, qubit_index] = 3\n\n elif type_circuit == 3:\n\n ini_pauli = np.ones([depth, n_qubits], dtype=int)*2\n\n for i in range(1, depth, 2):\n ini_pauli[i, :] = 3\n\n if n_qubits % 2 == 0:\n # even qubits ALT circuit needs to get rid of boundary rotations at\n # even entangling layers\n for i in range(4, depth, 4):\n ini_pauli[i, 0] = 0\n ini_pauli[i, -1] = 0\n if i+1 < depth:\n ini_pauli[i+1, 0] = 0\n ini_pauli[i+1, -1] = 0\n else:\n # for odd qubits, get rid of boundary either on top or bottom qubit\n for i in range(2, depth, 4):\n ini_pauli[i, -1] = 0\n if i+1 < depth:\n ini_pauli[i+1, -1] = 0\n for i in range(4, depth, 4):\n ini_pauli[i, 0] = 0\n if i+1 < depth:\n ini_pauli[i+1, 0] = 0\n\n # CNOT entangling gates\n entangling_gate_index_list = [[] for i in range(depth)]\n counter = 0\n # third index indicates type of entangling gate\n for k in range(1, depth-1, 2):\n\n # place entangler every second layer, do not place any at last\n if counter % 2 == 0:\n # even layer\n entangling_gate_index_list[k] = [\n [2*j, 2*j+1, 1] for j in range(n_qubits//2)\n ]\n else:\n # odd layer\n entangling_gate_index_list[k] = [\n [2*j+1, 2*j+2, 1] for j in range((n_qubits-1)//2)\n ]\n counter += 1\n\n else:\n raise ValueError('type_circuit='+f'{type_circuit}'+' not recognised.')\n\n return ini_pauli, entangling_gate_index_list","def FindGroundstate(**args):\n\targs[\"imtime\"] = True\n\n\tprop = SetupProblem(**args)\n\n\tfor t in prop.Advance(10):\n\t\tE = prop.GetEnergy()\n\t\tprint \"t = %3.2f, E = %2.8f\" % (t, E)\n\n\treturn prop","def test_all_games_gen_1():\n gen1a = {zero, one, -one, star}\n gen1b = all_games_gen(1)\n assert gen1a == gen1b","def gen(length):\n return itertools.product(LABELS,repeat=length)","def _simulate_all_cells(self):\n for ID in tqdm(self.condition_dict, desc='Simulating cells'):\n for n in range(len(self.condition_dict[ID])):\n cond_dict = self.condition_dict[ID][n]\n g, tc, rsh_mult, rs_mult, Io_mult, Il_mult, nnsvth_mult = cond_dict['E'], cond_dict['Tc'], cond_dict[\n 'Rsh_mult'], cond_dict['Rs_mult'], cond_dict['Io_mult'], cond_dict['Il_mult'], cond_dict['nnsvth_mult']\n # calculate the 5 parameters for each set of cell conditions\n\n # Eventually, replace this with derived 5-parameters\n iph, io, rs, rsh, nnsvth = pvlib.pvsystem.calcparams_cec(effective_irradiance=g, temp_cell=tc,\n alpha_sc=self.cell_parameters['alpha_sc'],\n a_ref=self.cell_parameters['a_ref'],\n I_L_ref=self.cell_parameters['I_L_ref'],\n I_o_ref=self.cell_parameters['I_o_ref'],\n R_sh_ref=self.cell_parameters['R_sh_ref'],\n R_s=self.cell_parameters['R_s'],\n Adjust=self.cell_parameters['Adjust'])\n rs, rsh, io, iph, nnsvth = rs * rs_mult, rsh * \\\n rsh_mult, io * Io_mult, iph * Il_mult, nnsvth * nnsvth_mult\n\n # calculate cell IV curves by condition, rather than by cell index\n voc_est = pvlib.singlediode.estimate_voc(iph, io, nnsvth)\n v = voltage_pts(self.num_points_in_IV, voc_est,\n self.module_parameters['breakdown_voltage'])\n i = pvlib.singlediode.bishop88_i_from_v(v, iph, io, rs, rsh, nnsvth,\n breakdown_factor=self.module_parameters['breakdown_factor'],\n breakdown_voltage=self.module_parameters[\n 'breakdown_voltage'],\n breakdown_exp=self.module_parameters['breakdown_exp'])\n\n # @dev: Uncomment if debugging pvlib bishop88 simulation results\n # plt.plot(v,i)\n # plt.xlim(-5,v[-1])\n # plt.ylim(0,iph+1)\n # plt.title(f\"{ID}: {n} :: {rs},\"\n # f\"{rsh}, {io}, {iph}, {nnsvth}\")\n # plt.show()\n\n self.condition_dict[ID][n]['V'] = v\n self.condition_dict[ID][n]['I'] = i\n self.condition_dict[ID][n]['E'] = g\n self.condition_dict[ID][n]['Tc'] = tc\n return","def generate(self):\n for i in range(4):\n random_first = randomize_first_box()\n self.randomize(random_first)\n for i in range(9):\n random_pos = randomize_position()\n self.randomize(random_pos)\n self.board.solve()","def generate_random_starting_point(variables):\n variable_dict = {}\n\n for i in variables:\n variable_dict[variables[i - 1]] = 1 if random.random() < 0.5 else 0\n\n return variable_dict","def sample(self):\n assignments = {}\n for v in self.all_variables():\n for _ in range(gc.max_satisfy_tries):\n assignments[v] = v.sample()\n if self.check(assignments):\n break\n assert len(assignments) == len(self.all_variables())\n return assignments","def demo():\n # Create some nonterminals\n S, NP, VP, PP = nonterminals('S, NP, VP, PP')\n N, V, P, Det = nonterminals('N, V, P, Det')\n VP_slash_NP = VP/NP\n\n print 'Some nonterminals:', [S, NP, VP, PP, N, V, P, Det, VP/NP]\n print ' S.symbol() =>', `S.symbol()`\n print\n\n # Create some CFG Productions\n prods = [CFGProduction(S, [NP, VP]), CFGProduction(PP, [P, NP]),\n CFGProduction(NP, [Det, N]), CFGProduction(NP, [NP, PP]),\n CFGProduction(VP, [V, NP]), CFGProduction(VP, [VP, PP]),\n CFGProduction(Det, ['a']), CFGProduction(Det, ['the']),\n CFGProduction(N, ['dog']), CFGProduction(N, ['cat']), \n CFGProduction(V, ['chased']), CFGProduction(V, ['sat']),\n CFGProduction(P, ['on']), CFGProduction(P, ['in'])]\n\n prod = prods[2]\n print 'A CFG production:', `prod`\n print ' prod.lhs() =>', `prod.lhs()`\n print ' prod.rhs() =>', `prod.rhs()`\n print\n\n # Create and print a CFG\n cfg = CFG(S, prods)\n print 'A CFG grammar:', `cfg`\n print ' cfg.start() =>', `cfg.start()`\n print ' cfg.productions() =>',\n # Use string.replace(...) is to line-wrap the output.\n print `cfg.productions()`.replace(',', ',\\n'+' '*25)\n print\n\n # Create some probabilistic CFG Productions\n A, B, C = nonterminals('A, B, C')\n pcfg_prods = [PCFGProduction(A, [B, B], prob=0.3),\n PCFGProduction(A, [C, B, C], prob=0.7),\n PCFGProduction(B, [B, 'b'], prob=0.5),\n PCFGProduction(B, [C], prob=0.5),\n PCFGProduction(C, ['a'], prob=0.1),\n PCFGProduction(C, ['b'], prob=0.9)] \n \n pcfg_prod = pcfg_prods[2]\n print 'A PCFG production:', `pcfg_prod`\n print ' pcfg_prod.lhs() =>', `pcfg_prod.lhs()`\n print ' pcfg_prod.rhs() =>', `pcfg_prod.rhs()`\n print ' pcfg_prod.prob() =>', `pcfg_prod.prob()`\n print\n\n # Create and print a PCFG\n pcfg = PCFG(S, pcfg_prods)\n print 'A PCFG grammar:', `pcfg`\n print ' pcfg.start() =>', `pcfg.start()`\n print ' pcfg.productions() =>',\n # Use string.replace(...) is to line-wrap the output.\n print `pcfg.productions()`.replace(',', ',\\n'+' '*26)\n print","def get_predefined_constant_names_latex():\n return \"t_0/t_g\", \"t_g\", r\"\\dot{\\varepsilon}\", \\\n \"E_1\", \"E_3\", r\"\\nu_{21}\", r\"\\nu_{31}\"","def generate_pseudo_ground_truth_map(args: argparse.Namespace):\n data, ref_map = load_data(data_path=args.data_path, ref_map_path=args.ref_map_path)\n data = min_max_normalize_data(data=data)\n guided_image = get_guided_image(data=data)\n train_samples, train_labels, test_samples, test_labels = prepare_datasets(ref_map=ref_map,\n training_patch=args.training_patch)\n\n prediction = train_svm(data=data, test_labels=test_labels, test_samples=test_samples,\n train_labels=train_labels, train_samples=train_samples)\n\n updated_ref_map = construct_new_ref_map(labels=np.concatenate((train_labels, prediction)),\n samples=train_samples + test_samples,\n ref_map_shape=ref_map.shape)\n\n one_hot_ref_map = one_hot_map(ref_map=updated_ref_map.copy())\n\n print(\"SVM classification map similarity score according to GT map {0:5.2f}%\".format(\n ((ref_map == updated_ref_map).sum() / ref_map.size) * float(100)))\n\n window_size = 2 * (args.radius_size - 1) + 1\n\n improved_class_map = edge_preserving_filter(ref_map=one_hot_ref_map,\n window_size=window_size,\n guided_image=guided_image)\n\n np.save(os.path.join(args.dest_path, \"improved_classification_map_{}\".format(str(args.bands_num))),\n improved_class_map)","def init_c_code(self):\r\n subd = dict(\r\n zip(self.fgraph.inputs,\r\n [\"%%(i%i)s\" % i for i in xrange(len(self.fgraph.inputs))])\r\n + zip(self.fgraph.outputs,\r\n [\"%%(o%i)s\" % i for i in xrange(len(self.fgraph.outputs))]))\r\n\r\n for orphan in self.fgraph.variables: # fgraph.orphans:\r\n if orphan.owner is None and orphan not in self.fgraph.inputs:\r\n if isinstance(orphan, Constant):\r\n subd[orphan] = orphan.type.c_literal(orphan.data)\r\n else:\r\n raise ValueError(\r\n \"All orphans in the fgraph to Composite must\"\r\n \" be Constant instances.\")\r\n\r\n _c_code = \"{\\n\"\r\n self.nodenames = [\"%(nodename)s_\" + ('subnode%i' % j)\r\n for j, n in enumerate(self.fgraph.toposort())]\r\n\r\n i = 0\r\n for j, node in enumerate(self.fgraph.toposort()):\r\n for output in node.outputs:\r\n if output not in subd:\r\n i += 1\r\n name = \"V%%(id)s_tmp%i\" % i\r\n subd[output] = name\r\n _c_code += \"%s %s;\\n\" % (\r\n output.type.dtype_specs()[1], name)\r\n s = node.op.c_code(node,\r\n self.nodenames[j],\r\n [subd[input] for input in node.inputs],\r\n [subd[output] for output in node.outputs],\r\n dict(fail=\"%(fail)s\",\r\n id=\"%%(id)s_%i\" % j))\r\n _c_code += s\r\n _c_code += \"\\n\"\r\n _c_code += \"}\\n\"\r\n self._c_code = _c_code","def calc_variables ( ):\n\n # In this example we simulate using the shifted-force potential only\n # The values of < p_sf >, < e_sf > and density should be consistent (for this potential)\n # There are no long-range or delta corrections\n\n from averages_module import VariableType\n \n # Preliminary calculations\n vol = box**3 # Volume\n rho = n / vol # Density\n\n # Variables of interest, of class VariableType, containing three attributes:\n # .val: the instantaneous value\n # .nam: used for headings\n # .method: indicating averaging method\n # If not set below, .method adopts its default value of avg\n # The .nam and some other attributes need only be defined once, at the start of the program,\n # but for clarity and readability we assign all the values together below\n\n # Move acceptance ratio\n m_r = VariableType ( nam = 'Move ratio', val = m_ratio, instant = False )\n\n # Internal energy per molecule (shifted-force potential)\n # Ideal gas contribution (assuming nonlinear molecules) plus total PE divided by N\n e_sf = VariableType ( nam = 'E/N shifted force', val = 3.0*temperature + total.pot/n )\n\n # Pressure (shifted-force potential)\n # Ideal gas contribution plus total virial divided by V\n p_sf = VariableType ( nam = 'P shifted force', val = rho*temperature + total.vir/vol )\n\n # Collect together into a list for averaging\n return [ m_r, e_sf, p_sf ]","def _make_hard_copy_ops(target_vars, online_vars):\n return [(target_vars[var_name].assign(online_vars[var_name]))\n for var_name in target_vars.keys()]","def _init_costs(self, var_obj, edges):\n prob_tables = np.ones( var_obj.states* len(edges))\n util_tables = np.zeros( var_obj.states*len(edges))\n return np.concatenate( (prob_tables, util_tables) )","def generate_all_states(self):\n self.clingo = ClingoBridge() # reset clingo\n\n base = ('base', '')\n self.clingo.add_file('initial-states.lp')\n self.clingo.run([base])\n output = self.clingo.output\n\n num_states = int(len(output) / 2)\n\n states = np.full(num_states, object)\n for i in range(0, num_states):\n state_atoms = []\n for atom in output[i]:\n if atom.name == 'state':\n state_atoms.append(atom)\n states[i] = self.parse_state(state_atoms)\n return states","def _get_vars(symbol: Union[str, int]) -> str:\n if isinstance(symbol, str):\n return {\n 'circle': 'var b1=n.round(t,2);',\n 'square': 'var b1=n.round(t,2);',\n 'diamond': 'var b1=n.round(t*1.3,2);',\n 'hexagram': 'var b1=n.round(t,2);var b2=n.round(t/2,2);var b3=n.round(t*Math.sqrt(3)/2,2);'\n }[symbol]\n return {\n 37: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 38: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 39: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 40: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\n 34: 'var d1=n.round(t,2);',\n 33: 'var d1=n.round(t*1.4,2);',\n 35: 'var d1=n.round(t*1.2,2);var d2=n.round(t*0.85,2);',\n 36: 'var d1=n.round(t/2,2);var d2=n.round(t,2);'\n }[symbol]","def compile_classicals():\n \n gc_frame = coord.Galactocentric(galcen_distance=8*u.kpc, z_sun=0*u.pc)\n frame_dict0 = gc_frame.__dict__\n old_keys = frame_dict0.keys()\n \n frame_dict = {}\n for k in ['galcen_distance', 'roll', 'galcen_v_sun', 'galcen_coord', 'z_sun']:\n frame_dict[k] = frame_dict0['_{}'.format(k)]\n \n t = Table.read('../data/gdr2_satellites_c4.txt', format='ascii')\n \n x = np.array([t['X']-8, t['Y'], t['Z']])*u.kpc\n v = np.array([t['U'], t['V'], t['W']])*u.km/u.s\n \n for i in range(3):\n v[i] = v[i] + gc_frame.galcen_v_sun.d_xyz[i]\n \n xgal = coord.Galactocentric(x, **frame_dict)\n xeq = xgal.transform_to(coord.ICRS)\n veq_ = gc.vgal_to_hel(xeq, v, galactocentric_frame=gc_frame)\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 # store observables\n data = {'name': t['Name'], 'ra': xeq.ra, 'dec': xeq.dec, 'distance': xeq.distance, 'pmra': veq[0], 'pmdec': veq[1], 'vr': veq[2]}\n \n tout = Table(data=data, names=('name', 'ra', 'dec', 'distance', 'pmra', 'pmdec', 'vr'))\n tout.pprint()\n tout.write('../data/positions_classical.fits', overwrite=True)","def get_all_possible_moves():\r\n \"\"\"\r\n Creates the labels for the universal chess interface into an array and returns them\r\n \"\"\"\r\n labels_array = []\r\n letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']\r\n numbers = ['1', '2', '3', '4', '5', '6', '7', '8']\r\n promoted_to = ['q', 'r', 'b', 'n']\r\n\r\n for l1 in range(8):\r\n for n1 in range(8):\r\n destinations = [(t, n1) for t in range(8)] + \\\r\n [(l1, t) for t in range(8)] + \\\r\n [(l1 + t, n1 + t) for t in range(-7, 8)] + \\\r\n [(l1 + t, n1 - t) for t in range(-7, 8)] + \\\r\n [(l1 + a, n1 + b) for (a, b) in\r\n [(-2, -1), (-1, -2), (-2, 1), (1, -2), (2, -1), (-1, 2), (2, 1), (1, 2)]]\r\n for (l2, n2) in destinations:\r\n if (l1, n1) != (l2, n2) and l2 in range(8) and n2 in range(8):\r\n move = letters[l1] + numbers[n1] + letters[l2] + numbers[n2]\r\n labels_array.append(move)\r\n for l1 in range(8):\r\n l = letters[l1]\r\n for p in promoted_to:\r\n labels_array.append(l + '2' + l + '1' + p)\r\n labels_array.append(l + '7' + l + '8' + p)\r\n if l1 > 0:\r\n l_l = letters[l1 - 1]\r\n labels_array.append(l + '2' + l_l + '1' + p)\r\n labels_array.append(l + '7' + l_l + '8' + p)\r\n if l1 < 7:\r\n l_r = letters[l1 + 1]\r\n labels_array.append(l + '2' + l_r + '1' + p)\r\n labels_array.append(l + '7' + l_r + '8' + p)\r\n return labels_array","def random_var_triplets(global_vars1, global_vars2, templates):\n m1, m2 = type_var_map(global_vars1), type_var_map(global_vars2)\n mapping = []\n for t in templates:\n if t not in m1:\n continue\n if t not in m2:\n continue\n random_tuples = random_tuple_list(m1[t], m2[t])\n for tup in random_tuples:\n mapping.append((tup[0], tup[1], random.choice(templates[t]), t))\n return mapping"],"string":"[\n \"def ground_operator(self, op_name):\\n op = self.domain.operators[op_name]\\n self._set_operator_groundspace( op_name, op.variable_list.items() )\\n for ground in self._instantiate( op_name ):\\n # print('grounded', ground)\\n st = dict(ground)\\n gop = Operator(op_name)\\n gop.variable_list = st\\n gop.precondition_pos = set( [ a.ground( st ) for a in op.precondition_pos ] )\\n gop.precondition_neg = set( [ a.ground( st ) for a in op.precondition_neg ] )\\n gop.effect_pos = set( [ a.ground( st ) for a in op.effect_pos ] )\\n gop.effect_neg = set( [ a.ground( st ) for a in op.effect_neg ] )\\n yield gop\",\n \"def __compileVariables(self):\\n state_variables = []\\n state_diffs = []\\n networks = []\\n frames = []\\n vars = []\\n\\n language = self.language\\n\\n instansiate_variables = {}\\n\\n self.obj_vars = self.object_variables()\\n for key, var in self.variables.items(): # Grouping the variables\\n size = self.size_of_variable(var)\\n variable_type = var.type\\n if not size or key in self.variables_not_included: # Skip variable\\n print('Variable not included:\\\\tvar: {}'.format(var.label))\\n continue\\n if var.equation_list and variable_type in ['constant', \\\"network\\\"]:\\n self.constantEquations.append(*var.equation_list)\\n continue\\n if variable_type in ['constant', 'frame', 'state']:\\n # print(dir(var.units))\\n units_pp = var.units.prettyPrint()\\n doc_pp = var.doc\\n if units_pp:\\n units_doc_str = '{}, {}'.format(units_pp, doc_pp)\\n else:\\n units_doc_str = '{}'.format('Empty', doc_pp)\\n instansiate_variables[key] = self.variable_dict(var)\\n if var.compiled_index_list: # Index sets need compilation\\n string_version = self.matrix_string_zeros(size, prefix = ' ')\\n mat = np.zeros(size)\\n index = str(var.index_structures) # Convert to string\\n rep = self.mod_index[var.index_structures[0]].printable()\\n string_w_comments = self.matrix_to_str_w_line_comments(mat, rep,\\n prefix = ' ')\\n width = 79 - 16 - len(index) - len(var.compiled) # 16 is others\\n width2 = 79 - 8 - len(units_pp) - len(doc_pp)\\n cons_str = '\\\\n{0} = {ar}({v: <{msg_box}} {com} {ind}\\\\n {st} ){v: <{w2}} {com} {udoc}'\\n cons_var_str = cons_str.format(var.compiled,\\n com = CODE[language][\\\"comment\\\"],\\n ar = CODE[language][\\\"list\\\"],\\n st = string_w_comments,\\n udoc = units_doc_str,\\n ind = index,\\n w2 = width2,\\n w = width,\\n v = '')\\n # cons_str = '{0} = {ar}({v: <{msg_box}} {com} {ind}\\\\n {st})'\\n # cons_var_str = cons_str.format(var.compiled,\\n # ar = CODE[language]['list'],\\n # st = string_w_comments,\\n # ind = index,\\n # msg_box = width,\\n # v = '')\\n else:\\n string_version = self.matrix_string_zeros(size, prefix = ' ')\\n index = 'none'\\n width = 79 - 16 - len(index) - len(var.compiled) # 16 is others\\n width2 = 79 - 9 - len(units_pp) - len(doc_pp)\\n cons_str = '\\\\n{0} = {array}({val: <{msg_box}} # {ind}\\\\n {st}\\\\n ) {h: <{w2}} {com} {udoc}'\\n cons_var_str = cons_str.format(var.compiled,\\n array = CODE[language][\\\"list\\\"],\\n com = CODE[language][\\\"comment\\\"],\\n udoc = units_doc_str,\\n st = string_version,\\n ind = index,\\n w = width,\\n w2 = width2,\\n h = '',\\n val = '')\\n if variable_type in ['frame']:\\n frames.append(cons_var_str)\\n elif variable_type in ['state']:\\n if var.label in self.state_variables:\\n state_variables.append(cons_var_str)\\n else:\\n pass\\n else:\\n vars.append(cons_var_str)\\n elif variable_type in [\\\"network\\\"]:\\n mat = self.populateNetworkVariable(var)\\n string_mat = self.matrix_to_string(mat, prefix = ' ')\\n index = str(var.index_structures) # Convert to string\\n width = 79 - 20 - len(index) - len(var.compiled) # 16 is others\\n if width < 1:\\n width = 1\\n nt_str = '{0} = {array}({val: <{msg_box}} # {ind}\\\\n {st})'\\n netw_var_str = nt_str.format(var.compiled,\\n array = CODE[language][\\\"list\\\"],\\n st = string_mat,\\n ind = index,\\n w = width,\\n val = '')\\n networks.append(netw_var_str)\\n putData(instansiate_variables, self.variable_instantiate_file)\\n return [state_variables, state_diffs, networks, frames, vars]\",\n \"def compile_globulars():\\n \\n gc_frame = coord.Galactocentric(galcen_distance=8*u.kpc, z_sun=0*u.pc)\\n frame_dict0 = gc_frame.__dict__\\n old_keys = frame_dict0.keys()\\n \\n frame_dict = {}\\n for k in ['galcen_distance', 'roll', 'galcen_v_sun', 'galcen_coord', 'z_sun']:\\n frame_dict[k] = frame_dict0['_{}'.format(k)]\\n \\n t = Table.read('../data/gdr2_satellites_c3.txt', format='ascii')\\n \\n x = np.array([t['X']-8, t['Y'], t['Z']])*u.kpc\\n v = np.array([t['U'], t['V'], t['W']])*u.km/u.s\\n \\n for i in range(3):\\n v[i] = v[i] + gc_frame.galcen_v_sun.d_xyz[i]\\n \\n xgal = coord.Galactocentric(x, **frame_dict)\\n xeq = xgal.transform_to(coord.ICRS)\\n veq_ = gc.vgal_to_hel(xeq, v, galactocentric_frame=gc_frame)\\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 # store observables\\n data = {'name': t['Name'], 'ra': xeq.ra, 'dec': xeq.dec, 'distance': xeq.distance, 'pmra': veq[0], 'pmdec': veq[1], 'vr': veq[2]}\\n \\n tout = Table(data=data, names=('name', 'ra', 'dec', 'distance', 'pmra', 'pmdec', 'vr'))\\n tout.pprint()\\n tout.write('../data/positions_globular.fits', overwrite=True)\",\n \"def gen_constant(self, g, ng, ct):\\n pass\",\n \"def set_ground_modes(self, basis=None, auto=True):\\n if self._atmospheric_basis is None: # Presently, the ground can not yet be set independently of an atmosphere.\\n print('Atmosphere modes not set up. Add an atmosphere before adding the ground!')\\n print('Ground setup aborted.')\\n return\\n\\n if auto:\\n if self.gotemperature_params is None or isinstance(self.gotemperature_params, OceanicTemperatureParams):\\n self.gotemperature_params = GroundTemperatureParams(self.scale_params)\\n if self.ground_params is None:\\n self.ground_params = GroundParams(self.scale_params)\\n\\n self.oceanic_params = None\\n self._oceanic_basis = None\\n\\n if basis is not None:\\n self.ground_basis = basis\\n else:\\n self.ground_basis = self._atmospheric_basis\\n\\n self._oceanic_var_string = list()\\n self._oceanic_latex_var_string = list()\\n self._ground_latex_var_string = list()\\n self._ground_var_string = list()\\n if self.dynamic_T:\\n self._oceanic_latex_var_string.append(r', T_{{\\\\rm g},0}')\\n self._oceanic_var_string.append(r'T_g_0')\\n for i in range(1, self.nmod[1] + 1):\\n self._ground_latex_var_string.append(r'delta T_{\\\\rm g,' + str(i) + \\\"}\\\")\\n self._ground_var_string.append(r'delta_T_g_' + str(i))\",\n \"def generate_source():\\n \\\"\\\"\\\"their dependencies\\\"\\\"\\\"\\n global dictionary_names, dictionary_slices\\n src = \\\"\\\"\\n for s in dictionary_slices:\\n src += deconstruct(s)\\n src += \\\" '\\\" + pointer_to_name(s)\\n src += \\\"' define\\\\n\\\"\\n return src + \\\"\\\\n\\\"\",\n \"def generate_cfg(numStart, numNonterminals, numTerminals, numProductions, min_length, max_length, onlyunaryterminal,\\n terminalprob, minrhs):\\n grammar = cfg.ContextFreeGrammar()\\n if numTerminals == -1:\\n terminals = list(dictionary.generateDictionary(numProductions * max_length))\\n else:\\n terminals = list(dictionary.generateDictionary(numTerminals))\\n nonterminals = []\\n terminalCounter = 0\\n start = []\\n if numStart == 1:\\n start.append(\\\"S\\\")\\n else:\\n for i in xrange(numStart):\\n start.append(\\\"S_\\\" + str(i))\\n for s in start:\\n nonterminals.append(s)\\n vcup = []\\n for terminal in terminals:\\n vcup.append(terminal)\\n for i in range(numNonterminals - numStart):\\n nt = \\\"NT\\\" + str(i)\\n nonterminals.append(nt)\\n vcup.append(nt)\\n productionSet = set()\\n obligatoryrhs = []\\n for x in xrange(minrhs):\\n for nt in nonterminals:\\n obligatoryrhs.append(nt)\\n while len(productionSet) < numProductions:\\n if len(productionSet) < len(obligatoryrhs):\\n lhs = obligatoryrhs[len(productionSet)]\\n else:\\n lhs = random.choice(nonterminals)\\n rhs = []\\n rhslength = random.randrange(min_length, max_length + 1)\\n # print rhslength\\n if rhslength == 1 and onlyunaryterminal:\\n if numTerminals == -1:\\n rhs.append(terminals[terminalCounter])\\n terminalCounter += 1\\n else:\\n rhs.append(random.choice(terminals))\\n else:\\n for i in range(rhslength):\\n if random.random() < terminalprob:\\n if numTerminals == -1:\\n rhs.append(terminals[terminalCounter])\\n terminalCounter += 1\\n else:\\n rhs.append(random.choice(terminals))\\n else:\\n rhs.append(random.choice(nonterminals))\\n prod = (lhs, tuple(rhs))\\n if not prod in productionSet:\\n productionSet.add(prod)\\n # print prod\\n for nt in nonterminals:\\n n = 0\\n for lhs, rhs in productionSet:\\n for sym in rhs:\\n if sym == nt:\\n break\\n else:\\n # not on the rhs of any nonterminal.\\n while True:\\n lhs = random.choice(nonterminals)\\n if lhs != nt:\\n rhslength = random.randrange(min_length, max_length + 1)\\n if rhslength == 1 and not onlyunaryterminal:\\n productionSet.add((lhs, (nt,)))\\n break\\n elif rhslength > 1:\\n position = random.choice(range(rhslength))\\n rhs = []\\n for i in range(rhslength):\\n if i == position:\\n rhs.append(nt)\\n else:\\n if random.random() < terminalprob:\\n if numTerminals == -1:\\n rhs.append(terminals[terminalCounter])\\n terminalCounter += 1\\n else:\\n rhs.append(random.choice(terminals))\\n else:\\n rhs.append(random.choice(nonterminals))\\n productionSet.add((lhs, tuple(rhs)))\\n\\n # now we have the set of weighted productions.\\n grammar.productions = productionSet\\n grammar.nonterminals = nonterminals\\n grammar.terminals = terminals\\n grammar.start_set = start\\n return grammar.trim()\",\n \"def create_gol_constants() -> None:\\r\\n\\r\\n self.INITIAL_RULE = 'B3/R23'\\r\\n self.INVALID_RULE_MESSAGE = (\\r\\n 'Invalid rule.\\\\n\\\\n'\\r\\n 'Set the rule in the format \\\"Bx/Ry\\\", where x and y are numbers of neighbors that:\\\\n'\\r\\n 'x: causes a birth of a cell\\\\n'\\r\\n 'y: allows a living cell to remain alive\\\\n\\\\n'\\r\\n 'Numbers 0 and 9 cannot belong to x and y.'\\r\\n )\\r\\n\\r\\n self.BOARD_WIDTH = 1000\\r\\n self.BOARD_HEIGHT = 1000\\r\\n self.BOARD_BG = (0, 0, 0)\\r\\n self.BOARD_STROKE = (50, 50, 50)\\r\\n self.BOARD_FILL = (255, 255, 255)\\r\\n\\r\\n self.IMAGE_MAX_WIDTH = 2000\\r\\n self.IMAGE_MAX_HEIGHT = 2000\\r\\n self.CELL_SIZES = [3, 5, 10, 20, 30, 50]\\r\\n self.INITIAL_ZOOM = len(self.CELL_SIZES) // 2\\r\\n\\r\\n self.TIMES_PER_GEN = [3000, 2000, 1500, 1000, 700, 400, 200, 100, 50]\\r\\n self.INITIAL_TIME_PER_GEN = len(self.TIMES_PER_GEN) // 2\",\n \"def to_general(y: List[Symbol], yp: Symbol = 0, t: Symbol = t, constant_prefix: str = \\\"C\\\") -> Tuple[Symbol, List[Symbol]]:\\n\\n const_iter = numbered_symbols(prefix=constant_prefix, start=1)\\n consts = []\\n\\n general = yp\\n for y_ in y:\\n const = next(const_iter)\\n consts.append(const)\\n general += const * y_\\n general = constantsimp(general.collect(y_), consts)\\n\\n return general, consts\",\n \"def _create_variables(self):\\n\\n \\n with tf.name_scope(\\\"variable\\\"):\\n if self.reg_type == 'L2':\\n regularizer = tf.contrib.layers.l2_regularizer(scale=self.reg_scale)\\n else:\\n regularizer = tf.contrib.layers.l1_regularizer(scale=self.reg_scale)\\n \\n self.dim_lst = [self.dim_inputs] + self.dim_hidden_lst + [self.number_structures]\\n print(self.dim_lst)\\n \\n self.W_lst = []\\n self.b_lst = []\\n for i in range(len(self.dim_lst)-1):\\n self.W_lst.append(tf.get_variable(\\n \\\"W{}\\\".format(i+1),\\n shape=[self.dim_lst[i], self.dim_lst[i+1]],\\n initializer=tf.contrib.layers.xavier_initializer(),\\n regularizer=regularizer)\\n )\\n # not output layer, has bias term\\n if i < len(self.dim_lst) - 2:\\n self.b_lst.append(tf.get_variable(\\\"b{}\\\".format(i+1), shape=[self.dim_lst[i+1]]))\",\n \"def define_variables(m):\\r\\n\\r\\n # Non-negative candidate capacity\\r\\n m.mu_1 = Var(m.G_C, m.Y, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Solar build limits\\r\\n m.mu_2 = Var(m.Z, m.Y, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Wind build limits\\r\\n m.mu_3 = Var(m.Z, m.Y, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Storage build limits\\r\\n m.mu_4 = Var(m.Z, m.Y, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Min power output (all generators excluding storage units)\\r\\n m.sigma_1 = Var(m.G.difference(m.G_STORAGE), m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max power output - existing thermal\\r\\n m.sigma_2 = Var(m.G_E_THERM, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max power output - candidate thermal\\r\\n m.sigma_3 = Var(m.G_C_THERM, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max power output - existing wind\\r\\n m.sigma_4 = Var(m.G_E_WIND, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max power output - candidate wind\\r\\n m.sigma_5 = Var(m.G_C_WIND, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max power output - existing solar\\r\\n m.sigma_6 = Var(m.G_E_SOLAR, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max power output - candidate solar\\r\\n m.sigma_7 = Var(m.G_C_SOLAR, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max power output - hydro\\r\\n m.sigma_8 = Var(m.G_E_HYDRO, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Min charging power - storage units\\r\\n m.sigma_9 = Var(m.G_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Min discharging power - storage_units\\r\\n m.sigma_10 = Var(m.G_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max charging power - existing storage\\r\\n m.sigma_11 = Var(m.G_E_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max charging power - candidate storage\\r\\n m.sigma_12 = Var(m.G_C_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max discharging power - existing storage\\r\\n m.sigma_13 = Var(m.G_E_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max discharging power - candidate storage\\r\\n m.sigma_14 = Var(m.G_C_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Min energy - storage units\\r\\n m.sigma_15 = Var(m.G_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max energy - existing storage units\\r\\n m.sigma_16 = Var(m.G_E_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max energy - candidate storage\\r\\n m.sigma_17 = Var(m.G_C_STORAGE, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Min energy - interval end\\r\\n m.sigma_18 = Var(m.G_STORAGE, m.Y, m.S, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max energy - interval end\\r\\n m.sigma_19 = Var(m.G_STORAGE, m.Y, m.S, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Ramp-rate up (thermal and hydro generators)\\r\\n m.sigma_20 = Var(m.G_THERM.union(m.G_E_HYDRO), m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Ramp-rate down (thermal and hydro generators)\\r\\n m.sigma_23 = Var(m.G_THERM.union(m.G_E_HYDRO), m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Non-negative lost load power\\r\\n m.sigma_26 = Var(m.Z, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Min powerflow\\r\\n m.sigma_27 = Var(m.L, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Max powerflow\\r\\n m.sigma_28 = Var(m.L, m.Y, m.S, m.T, within=NonNegativeReals, initialize=0)\\r\\n\\r\\n # Storage energy transition\\r\\n m.zeta_1 = Var(m.G_STORAGE, m.Y, m.S, m.T, initialize=0)\\r\\n\\r\\n # Power balance (locational marginal price)\\r\\n m.lamb = Var(m.Z, m.Y, m.S, m.T, initialize=0)\\r\\n\\r\\n return m\",\n \"def build_schematic(self, bg=None):\",\n \"def build_goal_generation(\\n cls, constants: CType, mujoco_simulation: SType\\n ) -> GoalGenerator:\\n pass\",\n \"def setup_steps(self):\\n step1 = ground_step.Ground(5745, 495, 40, 44)\\n step2 = ground_step.Ground(5788, 452, 40, 44)\\n step3 = ground_step.Ground(5831, 409, 40, 44)\\n step4 = ground_step.Ground(5874, 366, 40, 176)\\n\\n step5 = ground_step.Ground(6001, 366, 40, 176)\\n step6 = ground_step.Ground(6044, 408, 40, 40)\\n step7 = ground_step.Ground(6087, 452, 40, 40)\\n step8 = ground_step.Ground(6130, 495, 40, 40)\\n\\n step9 = ground_step.Ground(6345, 495, 40, 40)\\n step10 = ground_step.Ground(6388, 452, 40, 40)\\n step11 = ground_step.Ground(6431, 409, 40, 40)\\n step12 = ground_step.Ground(6474, 366, 40, 40)\\n step13 = ground_step.Ground(6517, 366, 40, 176)\\n\\n step14 = ground_step.Ground(6644, 366, 40, 176)\\n step15 = ground_step.Ground(6687, 408, 40, 40)\\n step16 = ground_step.Ground(6728, 452, 40, 40)\\n step17 = ground_step.Ground(6771, 495, 40, 40)\\n\\n step18 = ground_step.Ground(7760, 495, 40, 40)\\n step19 = ground_step.Ground(7803, 452, 40, 40)\\n step20 = ground_step.Ground(7845, 409, 40, 40)\\n step21 = ground_step.Ground(7888, 366, 40, 40)\\n step22 = ground_step.Ground(7931, 323, 40, 40)\\n step23 = ground_step.Ground(7974, 280, 40, 40)\\n step24 = ground_step.Ground(8017, 237, 40, 40)\\n step25 = ground_step.Ground(8060, 194, 40, 40)\\n step26 = ground_step.Ground(8103, 194, 40, 360)\\n\\n step27 = ground_step.Ground(8488, 495, 40, 40)\\n\\n self.step_group = pygame.sprite.Group(step1, step2,\\n step3, step4,\\n step5, step6,\\n step7, step8,\\n step9, step10,\\n step11, step12,\\n step13, step14,\\n step15, step16,\\n step17, step18,\\n step19, step20,\\n step21, step22,\\n step23, step24,\\n step25, step26,\\n step27)\",\n \"def initialize_sets(self):\\n for block in self.blocks:\\n # Insert phi nodes from SSA stage into the assignments of the block\\n for phi in block.phis:\\n block.gen.setdefault(phi, []).insert(0, phi)\\n\\n # Update the kill set with the variables that are assigned to in\\n # the block\\n block.kill = set(block.gen)\\n block.output = set(block.gen)\\n #for entry in block.bound:\\n # block.i_kill |= self.assmts[entry].bit\\n\\n for assmts in self.assmts.itervalues():\\n self.entry_point.i_gen |= assmts.bit\\n self.entry_point.i_output = self.entry_point.i_gen\",\n \"def gen_task0():\\n argc = 1\\n goal = 'f'\\n premise = 'b'\\n ctx, targets = list(), list()\\n # Generate according to goal <- premise\\n args = r_consts(argc)\\n # Add the successful ground case\\n ctx.append([(premise, args)])\\n targets.append(((goal, args), 1))\\n # Fail on non-matching constant\\n args = args.copy()\\n args[R.randrange(len(args))] = r_consts(1, args)[0]\\n preds = r_preds(3)\\n ctx.append([(preds[0], args)])\\n targets.append(((goal, args), 0))\\n # Add padding length dummy rule\\n vs = r_vars(argc)\\n ctx.append([(preds[1], vs), (preds[2], vs)])\\n preds.extend([goal, premise])\\n gen_task(ctx, targets, preds)\",\n \"def gen_params(no_cultures):\\n # Plate level\\n kn = 0.1 # Nutrient diffusion\\n ks = 0.1 # Signal diffusion\\n b = 0.05 # Signal on cells effect constant\\n a = 0.05 # Signal secretion constant\\n # Culture level\\n # Growth rate constant\\n r_mean = 1.0\\n r_var = 1.0\\n r_params = [max(0.0, gauss(r_mean, r_var)) for i in range(no_cultures)]\\n params = np.array([kn, ks, b, a] + r_params)\\n return params\",\n \"def variabilize(self):\\n if self.nvars>=0:\\n pass #already done\\n else:\\n varTab = syt.SymbolTable()\\n def convertArgs(args):\\n return map(lambda a: -varTab.getId(a) if isVariableAtom(a) else a, args)\\n def convertGoal(g):\\n return Goal(g.functor, convertArgs(g.args))\\n if self.lhs: self.lhs = convertGoal(self.lhs)\\n self.rhs = map(convertGoal, self.rhs)\\n if self.features:\\n self.features = map(convertGoal, self.features)\\n if self.findall:\\n self.findall = map(convertGoal, self.findall) \\n self.variableList = varTab.getSymbolList()\\n self.nvars = len(self.variableList)\",\n \"def generate_powerset_bridge_constraints(problem):\\n\\n c_30 = _dynamic_constraint_30(problem)\\n c_33 = _dynamic_constraint_33(problem)\\n c_34 = _dynamic_constraint_34(problem)\\n c_35 = _dynamic_constraint_35(problem)\\n c_36 = _dynamic_constraint_36(problem)\\n\\n return c_30 & c_33 & c_34 & c_35 & c_36\",\n \"def generate_all_locations(grid, shape):\",\n \"def Gd():\\n Pz=[8]\\n Pp=[1,1]\\n return Pz, Pp\",\n \"def _generate_raw_environments(self, num, seed):\",\n \"def ground(cls, nocc, nspins):\\n from math import floor, ceil\\n nspatials = int(nspins/2)\\n alpha = [1]*ceil(nocc/2) + [0]*(nspatials-ceil(nocc/2))\\n beta = [1]*floor(nocc/2) + [0]*(nspatials-floor(nocc/2))\\n configuration = []\\n for i,j in zip(alpha, beta):\\n configuration.append(i)\\n configuration.append(j)\\n\\n return cls(configuration)\",\n \"def one_variation(self):\\n\\n globals_ = dict(\\n # Physical constants\\n g=9.81, # Gravitational acceleration [m/s^2]\\n c=3e8, # Speed of Light [m/s]\\n h=6.6262e-34, # Planck [Js]\\n k=1.38e-23, # Boltzmann [J/K]\\n R=8.31441, # k*NA [J/(mol*kg)]\\n NA=6.0225e23, # Avogadro [1/mol]\\n gamma=6.67e11, # Gravitational Constant [Nm^2/kg^2]num\\n qe=1.60219e-19, # Elementary charge [C]\\n # (e is not free unfortunately)\\n e0=8.854187816e-12, # Permittivity of Vacuum [As/(Vm)]\\n epsilon0=8.854187816e-12, # Permittivity of Vacuum [As/(Vm)]\\n mu0=4e-7*pi, # Permeability of Vacuum [Vs/(Am)]\\n K=9e9, # 1/(4*pi*epsilon0) [Vm/(As)]\\n me=9.1095e-31, # The mass of electron [kg]\\n mu=1.66056e-27, # Atomic mass unit [kg]\\n sigma=5.67e-8, # Stefan-Boltzmann Constant\\n )\\n exec(function_import, globals_)\\n for i in ('pi', 'e', 'sin', 'sind', 'asin'):\\n assert i in globals_\\n\\n values = {}\\n\\n # For example there is a variable k, it is not equal to k (Planck const)\\n for variable in self.variable_list:\\n exec('%s = None' % variable, globals_, values)\\n\\n for const in self.const_list:\\n exec('%(name)s = %(value)g' % const, values)\\n\\n for intv in self.interval_list:\\n value = interval_.random(intv['interval'])\\n if intv['name']:\\n name = intv['name']\\n exec('%s = float(%g)' % (name, value), globals_, values)\\n\\n compute_list = self.compute_list[:]\\n number_of_uncomputable_formulas = 0\\n # The number of the failed computation after\\n # a successful computation.\\n\\n while compute_list:\\n compute = compute_list[0]\\n try:\\n exec(compute['formula'], globals_, values)\\n\\n except (NameError, TypeError):\\n compute_list.append(compute_list.pop(0))\\n # It writes the first item to the end\\n\\n number_of_uncomputable_formulas += 1\\n if number_of_uncomputable_formulas == len(compute_list):\\n raise UncomputableError(self.code)\\n self.is_computable = False\\n return\\n continue\\n except ValueError:\\n print('Value Error. Formula is:')\\n print(compute['formula'])\\n return\\n\\n compute_list.pop(0)\\n number_of_uncomputable_formulas = 0\\n command = '%(name)s = %(right)s' % compute\\n exec(command, globals_, values)\\n\\n possibilities = next(self.possibilities_cycle)\\n erased_elements = set(self.variable_list) - possibilities\\n self.list.append((values, erased_elements))\",\n \"def sat_generate_candidate_assignments(self):\\n # YOUR CODE HERE\\n short = min(len(c) for c in self.clauses)\\n for c in self.clauses:\\n if len(c) == short:\\n return set(c.literals)\\n # return (set(x.literals) for x in self.clauses if len(x) == min(len(c) for c in self.clauses))\",\n \"def circuit():\\n np.random.seed(1967)\\n for gates in gates_per_layers:\\n for gate in gates:\\n qml.apply(gate)\\n return qml.expval(qml.PauliZ(0))\",\n \"def get_variables(test_case, name):\\n\\n test_case = test_case.lower()\\n variables = {\\n # Variables for control packet\\n \\\"src_ip\\\": \\\"16.0.0.1\\\",\\n \\\"dst_ip\\\": \\\"16.0.1.1\\\",\\n \\\"src_net\\\": \\\"16.0.0.0\\\",\\n \\\"dst_net\\\": \\\"16.0.1.0\\\",\\n \\\"src_port\\\": \\\"1234\\\",\\n \\\"dst_port\\\": \\\"1234\\\",\\n \\\"src_mac\\\": \\\"01:02:03:04:05:06\\\",\\n \\\"dst_mac\\\": \\\"10:20:30:40:50:60\\\"}\\n\\n test_vars = {\\n \\\"macip\\\": {\\n # MACs classified directly\\n \\\"classify_src\\\": \\\"12:23:34:45:56:67\\\",\\n \\\"classify_dst\\\": \\\"89:9A:AB:BC:CD:DE\\\",\\n # MACs classified through mask\\n \\\"classify_src2\\\": \\\"01:02:03:04:56:67\\\",\\n \\\"classify_dst2\\\": \\\"89:9A:AB:BC:50:60\\\",\\n \\\"src_mask\\\": \\\"00:00:00:00:FF:FF\\\",\\n \\\"dst_mask\\\": \\\"FF:FF:FF:FF:00:00\\\"\\n },\\n \\\"l3_ip4\\\": {\\n # IPs for DUT interface setup\\n \\\"dut_to_tg_if1_ip\\\": \\\"16.0.0.2\\\",\\n \\\"dut_to_tg_if2_ip\\\": \\\"192.168.0.2\\\",\\n \\\"prefix_length\\\": 24,\\n \\\"gateway\\\": \\\"192.168.0.1\\\",\\n # classified networks\\n \\\"classify_src_net\\\": \\\"16.0.2.0\\\",\\n \\\"classify_dst_net\\\": \\\"16.0.3.0\\\",\\n # IPs in classified networks\\n \\\"classify_src\\\": \\\"16.0.2.1\\\",\\n \\\"classify_dst\\\": \\\"16.0.3.1\\\",\\n },\\n \\\"l3_ip6\\\": {\\n # Override control packet addresses with IPv6\\n \\\"src_ip\\\": \\\"10::1\\\",\\n \\\"dst_ip\\\": \\\"11::1\\\",\\n \\\"dst_net\\\": \\\"11::\\\",\\n # IPs for DUT interface setup\\n \\\"dut_to_tg_if1_ip\\\": \\\"10::2\\\",\\n \\\"dut_to_tg_if2_ip\\\": \\\"20::2\\\",\\n \\\"prefix_length\\\": 64,\\n \\\"gateway\\\": \\\"20::1\\\",\\n # classified networks\\n \\\"classify_src_net\\\": \\\"12::\\\",\\n \\\"classify_dst_net\\\": \\\"13::\\\",\\n # IPs in classified networks\\n \\\"classify_src\\\": \\\"12::1\\\",\\n \\\"classify_dst\\\": \\\"13::1\\\",\\n },\\n \\\"l4\\\": {\\n # IPs for DUT interface and route setup\\n \\\"dut_to_tg_if1_ip\\\": \\\"16.0.0.2\\\",\\n \\\"dut_to_tg_if2_ip\\\": \\\"192.168.0.2\\\",\\n \\\"prefix_length\\\": 24,\\n \\\"gateway\\\": \\\"192.168.0.1\\\",\\n \\\"classify_dst_net\\\": \\\"16.0.3.0\\\",\\n # Ports in classified ranges\\n \\\"classify_src\\\": 60000,\\n \\\"classify_dst\\\": 61000,\\n },\\n \\\"mixed\\\": {\\n # IPs for DUT interface and route setup\\n \\\"dut_to_tg_if1_ip\\\": \\\"16.0.0.2\\\",\\n \\\"dut_to_tg_if2_ip\\\": \\\"192.168.0.2\\\",\\n \\\"prefix_length\\\": 24,\\n \\\"gateway\\\": \\\"192.168.0.1\\\",\\n \\\"classify_dst_net\\\": \\\"16.0.3.0\\\",\\n # IPs in classified networks\\n \\\"classify_src_ip\\\": \\\"16.0.2.1\\\",\\n \\\"classify_dst_ip\\\": \\\"16.0.3.1\\\",\\n # Ports in classified ranges\\n \\\"classify_src_port\\\": 60000,\\n \\\"classify_dst_port\\\": 61000,\\n },\\n \\\"icmp\\\": {\\n # ICMP code and type for control packet\\n \\\"icmp_type\\\": 0,\\n \\\"icmp_code\\\": 0,\\n # classified ICMP code and type\\n \\\"classify_type\\\": 3,\\n \\\"classify_code\\\": 3\\n\\n },\\n \\\"icmpv6\\\": {\\n # Override control packet addresses with IPv6\\n \\\"src_ip\\\": \\\"10::1\\\",\\n \\\"dst_ip\\\": \\\"11::1\\\",\\n \\\"dst_net\\\": \\\"11::\\\",\\n # IPs for DUT interface setup\\n \\\"dut_to_tg_if1_ip\\\": \\\"10::2\\\",\\n \\\"dut_to_tg_if2_ip\\\": \\\"20::2\\\",\\n \\\"prefix_length\\\": 64,\\n \\\"gateway\\\": \\\"20::1\\\",\\n # classified networks\\n \\\"classify_src_net\\\": \\\"12::\\\",\\n \\\"classify_dst_net\\\": \\\"13::\\\",\\n # ICMP code and type for control packet\\n \\\"icmp_type\\\": 1,\\n \\\"icmp_code\\\": 0,\\n # classified ICMP code and type\\n \\\"classify_type\\\": 4,\\n \\\"classify_code\\\": 2\\n\\n },\\n \\\"reflex\\\": {\\n # IPs for DUT interface setup\\n \\\"dut_to_tg_if1_ip\\\": \\\"16.0.0.2\\\",\\n \\\"dut_to_tg_if2_ip\\\": \\\"192.168.0.2\\\",\\n \\\"prefix_length\\\": 24,\\n \\\"gateway\\\": \\\"192.168.0.1\\\",\\n \\\"gateway2\\\": \\\"192.168.0.1\\\",\\n # classified networks\\n \\\"classify_src_net\\\": \\\"16.0.2.0\\\",\\n \\\"classify_dst_net\\\": \\\"16.0.3.0\\\",\\n # IPs in classified networks\\n \\\"classify_src\\\": \\\"16.0.2.1\\\",\\n \\\"classify_dst\\\": \\\"16.0.3.1\\\",\\n },\\n \\\"block_all\\\": {}\\n }\\n acl_data = {\\n # ACL configuration for L2 tests\\n\\n \\\"macip\\\": {\\n \\\"acl\\\": [{\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-macip-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"rule1\\\",\\n \\\"matches\\\": {\\n\\n \\\"eth\\\": {\\n \\\"source-mac-address\\\": test_vars[\\\"macip\\\"][\\\"classify_src\\\"],\\n \\\"source-mac-address-mask\\\": test_vars[\\\"macip\\\"][\\\"src_mask\\\"]\\n },\\n \\\"ipv4\\\": {\\n\\n \\\"source-ipv4-network\\\": \\\"16.0.0.0/24\\\"\\n }\\n },\\n\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:drop\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"rule_all\\\",\\n \\\"matches\\\": {\\n\\n \\\"eth\\\": {\\n \\\"source-mac-address\\\": test_vars[\\\"macip\\\"][\\\"classify_src\\\"],\\n \\\"source-mac-address-mask\\\": \\\"00:00:00:00:00:00\\\"\\n },\\n\\n \\\"ipv4\\\": {\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:accept\\\"\\n }\\n }\\n ]}\\n }\\n ]\\n },\\n # ACL configuration for L3 IPv4 tests\\n \\\"l3_ip4\\\": {\\n \\\"acl\\\": [\\n {\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"rule1\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"destination-ipv4-network\\\": \\\"{0}/{1}\\\".format(\\n test_vars[\\\"l3_ip4\\\"][\\\"classify_dst_net\\\"],\\n test_vars[\\\"l3_ip4\\\"][\\\"prefix_length\\\"]),\\n \\\"source-ipv4-network\\\": \\\"{0}/{1}\\\".format(\\n test_vars[\\\"l3_ip4\\\"][\\\"classify_src_net\\\"],\\n test_vars[\\\"l3_ip4\\\"][\\\"prefix_length\\\"])\\n },\\n \\\"udp\\\":{\\n \\\"source-port\\\": {\\n \\\"lower-port\\\": \\\"0\\\",\\n \\\"upper-port\\\": \\\"65535\\\"\\n },\\n \\\"destination-port\\\": {\\n \\\"lower-port\\\": \\\"0\\\",\\n \\\"upper-port\\\": \\\"65535\\\"\\n }\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:drop\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"rule_all\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"destination-ipv4-network\\\": \\\"0.0.0.0/0\\\",\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:accept\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n },\\n # ACL settings for L3 IPv6 tests\\n \\\"l3_ip6\\\": {\\n \\\"acl\\\": [\\n {\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"rule1\\\",\\n \\\"matches\\\": {\\n \\\"ipv6\\\": {\\n \\\"destination-ipv6-network\\\": \\\"{0}/{1}\\\".format(\\n test_vars[\\\"l3_ip6\\\"][\\\"classify_dst_net\\\"],\\n test_vars[\\\"l3_ip6\\\"][\\\"prefix_length\\\"]),\\n \\\"source-ipv6-network\\\": \\\"{0}/{1}\\\".format(\\n test_vars[\\\"l3_ip6\\\"][\\\"classify_src_net\\\"],\\n test_vars[\\\"l3_ip6\\\"][\\\"prefix_length\\\"])\\n },\\n \\\"udp\\\":{\\n \\\"source-port\\\": {\\n \\\"lower-port\\\": \\\"0\\\",\\n \\\"upper-port\\\": \\\"65535\\\"\\n },\\n \\\"destination-port\\\": {\\n \\\"lower-port\\\": \\\"0\\\",\\n \\\"upper-port\\\": \\\"65535\\\"\\n }\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:drop\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"rule_all\\\",\\n \\\"matches\\\": {\\n \\\"ipv6\\\": {\\n \\\"destination-ipv6-network\\\": \\\"0::0/0\\\",\\n \\\"source-ipv6-network\\\": \\\"0::0/0\\\"\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:accept\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n },\\n # ACL configuration for L4 tests\\n \\\"l4\\\": {\\n \\\"acl\\\": [\\n {\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"rule1\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n },\\n \\\"tcp\\\": {\\n \\\"source-port\\\": {\\n \\\"lower-port\\\": test_vars[\\\"l4\\\"][\\\"classify_src\\\"],\\n \\\"upper-port\\\": test_vars[\\\"l4\\\"][\\\"classify_src\\\"] + 10\\n },\\n \\\"destination-port\\\":{\\n \\\"lower-port\\\": test_vars[\\\"l4\\\"][\\\"classify_dst\\\"],\\n \\\"upper-port\\\": test_vars[\\\"l4\\\"][\\\"classify_dst\\\"] + 10\\n }\\n }\\n },\\n \\\"actions\\\":{\\n \\\"forwarding\\\": \\\"ietf-access-control-list:drop\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"rule_all\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\",\\n \\\"destination-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:accept\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n },\\n \\\"mixed\\\": {\\n \\\"acl\\\": [\\n {\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"ports\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n },\\n \\\"tcp\\\": {\\n \\\"source-port\\\": {\\n \\\"lower-port\\\": test_vars[\\\"l4\\\"][\\\"classify_src\\\"],\\n \\\"upper-port\\\": test_vars[\\\"l4\\\"][\\\"classify_src\\\"] + 10\\n },\\n \\\"destination-port\\\":{\\n \\\"lower-port\\\": test_vars[\\\"l4\\\"][\\\"classify_dst\\\"],\\n \\\"upper-port\\\": test_vars[\\\"l4\\\"][\\\"classify_dst\\\"] + 10\\n }\\n }\\n },\\n \\\"actions\\\":{\\n \\\"forwarding\\\": \\\"ietf-access-control-list:drop\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"rule_all\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"destination-ipv4-network\\\": \\\"0.0.0.0/0\\\",\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:accept\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n },\\n \\\"icmp\\\": {\\n \\\"acl\\\": [\\n {\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"rule1\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n },\\n \\\"icmp\\\": {\\n \\\"vpp-acl:vpp-icmp-ace\\\": {\\n \\\"vpp-acl:icmp-type-range\\\": {\\n \\\"first\\\": \\\"1\\\",\\n \\\"last\\\": \\\"5\\\"\\n },\\n \\\"vpp-acl:icmp-code-range\\\": {\\n \\\"first\\\": \\\"1\\\",\\n \\\"last\\\": \\\"5\\\"\\n }\\n }\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:drop\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"rule_all\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\",\\n \\\"destination-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:accept\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n },\\n \\\"icmpv6\\\": {\\n \\\"acl\\\": [\\n {\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"rule1\\\",\\n \\\"matches\\\": {\\n \\\"ipv6\\\": {\\n \\\"source-ipv6-network\\\": \\\"::/0\\\",\\n },\\n \\\"icmp\\\": {\\n \\\"vpp-acl:vpp-icmp-ace\\\": {\\n \\\"vpp-acl:icmp-type-range\\\": {\\n \\\"first\\\": \\\"1\\\",\\n \\\"last\\\": \\\"5\\\"\\n },\\n \\\"vpp-acl:icmp-code-range\\\": {\\n \\\"first\\\": \\\"1\\\",\\n \\\"last\\\": \\\"5\\\"\\n }\\n }\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:drop\\\"\\n }\\n },\\n {\\n \\\"name\\\": \\\"rule_all\\\",\\n \\\"matches\\\": {\\n \\\"ipv6\\\": {\\n \\\"destination-ipv6-network\\\": \\\"0::0/0\\\",\\n \\\"source-ipv6-network\\\": \\\"::/0\\\",\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:accept\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n },\\n \\\"reflex\\\": {\\n \\\"acl\\\": [\\n {\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"rule1\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"destination-ipv4-network\\\": \\\"{0}/{1}\\\".format(\\n test_vars[\\\"reflex\\\"][\\\"classify_src_net\\\"],\\n test_vars[\\\"reflex\\\"][\\\"prefix_length\\\"]),\\n \\\"source-ipv4-network\\\": \\\"{0}/{1}\\\".format(\\n test_vars[\\\"reflex\\\"][\\\"classify_dst_net\\\"],\\n test_vars[\\\"reflex\\\"][\\\"prefix_length\\\"])\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"vpp-acl:accept-and-reflect\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n },\\n \\\"block_all\\\": {\\n \\\"acl\\\": [\\n {\\n \\\"name\\\": name,\\n \\\"type\\\": \\\"vpp-acl:vpp-acl\\\",\\n \\\"aces\\\": {\\n \\\"ace\\\": [\\n {\\n \\\"name\\\": \\\"rule_all\\\",\\n \\\"matches\\\": {\\n \\\"ipv4\\\": {\\n \\\"destination-ipv4-network\\\": \\\"0.0.0.0/0\\\",\\n \\\"source-ipv4-network\\\": \\\"0.0.0.0/0\\\"\\n }\\n },\\n \\\"actions\\\": {\\n \\\"forwarding\\\": \\\"ietf-access-control-list:drop\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n },\\n }\\n\\n try:\\n ret_vars = {}\\n ret_vars.update(variables)\\n ret_vars.update(test_vars[test_case])\\n ret_vars.update(\\n {\\\"acl_settings\\\": acl_data[test_case]}\\n )\\n except KeyError:\\n raise KeyError(\\n \\\"Unrecognized test case {0}. Valid options are: {1}\\\".format(\\n test_case, acl_data.keys()))\\n return ret_vars\",\n \"def global_parameter_space():\\n return [list(range(7, 22)),\\n list(range(12, 27)),\\n list(range(25, 40)),\\n list(permutations(range(1, 5)))]\",\n \"def G():\\n Pz=[40]\\n Pp=[1,2,1]\\n return Pz, Pp\",\n \"def standardize_names_groundings(stmts):\\n print('Standardize names to groundings')\\n for stmt in stmts:\\n for concept in stmt.agent_list():\\n db_ns, db_id = concept.get_grounding()\\n if db_id is not None:\\n if isinstance(db_id, list):\\n db_id = db_id[0][0].split('/')[-1]\\n else:\\n db_id = db_id.split('/')[-1]\\n db_id = db_id.replace('|', ' ')\\n db_id = db_id.replace('_', ' ')\\n db_id = db_id.replace('ONT::', '')\\n db_id = db_id.capitalize()\\n concept.name = db_id\\n return stmts\\n \\\"\\\"\\\"\\n for stmt in stmts:\\n for idx, agent in enumerate(stmt.agent_list()):\\n if 'UN' in agent.db_refs:\\n all_un_scores = []\\n for ev in stmt.evidence:\\n agent_annots = ev.annotations.get('agents')\\n if agent_annots and 'raw_grounding' in agent_annots and \\\\\\n 'UN' in agent_annots['raw_grounding'][idx]:\\n un_score = agent_annots['raw_grounding'][idx]['UN'][0][1]\\n all_un_scores.append(un_score)\\n if all_un_scores:\\n noisy_or_score = 1 - numpy.prod([1-x for x in\\n all_un_scores])\\n print('%s -> %.2f' % (str(all_un_scores), noisy_or_score))\\n agent.db_refs['UN'][0] = (agent.db_refs['UN'][0][0],\\n noisy_or_score)\\n \\\"\\\"\\\"\",\n \"def prepare_for_sat(self):\\n\\n for state_group in self.state_groups:\\n new_variables = set()\\n for variable in state_group.variables():\\n new_variables.add(variable.variable_name())\\n\\n self.add_variable_names(new_variables)\\n\\n for variable in new_variables:\\n assert variable not in self.variable_to_state_group\\n self.variable_to_state_group[variable] = state_group\\n\\n for clause in state_group.clauses():\\n self.add_clause(clause)\\n\\n self.variable_names = sorted(self.variable_names)\\n self.variable_name_to_index = {}\\n\\n # Assign SAT variables indicies to variable names\\n for idx, variable_name in enumerate(self.variable_names):\\n assert variable_name not in self.variable_name_to_index\\n self.variable_name_to_index[variable_name] = idx + 1\\n\\n # Convert abstract clauses using variable names to SAT clauses\\n concrete_clauses = set()\\n for abstract_clause in self.abstract_clauses:\\n for clause in abstract_clause.clauses():\\n concrete_clause = []\\n for part in clause:\\n concrete_clause.append(part.variable(self))\\n\\n assert len(set(concrete_clause)) == len(concrete_clause)\\n concrete_clauses.add(tuple(sorted(concrete_clause)))\\n\\n return sorted(concrete_clauses)\",\n \"def boundVariablesToBase(FF, generators, variablesX, factorBase, m):\\n FBR.<X> = PolynomialRing(FF, 1, order='degrevlex')\\n baseBound = [FBR(1) for k in range(m)]\\n \\n #divide factorBase into m sets and create m-bounding polynomials\\n for elem, k in factorBase.iteritems():\\n baseBound[mod(k, m)] *= FBR(X - elem) #add to the m-th base \\n \\n if m == len(variablesX):\\n for k in range(0,m):\\n generators.append(baseBound[k](variablesX[k]))\\n elif m == 1:\\n for k in range(0,len(variablesX)):\\n generators.append(baseBound[0](variablesX[k]))\\n\\n return generators\",\n \"def setup(self):\\n declared = []\\n for obj in Rt.objective:\\n var_list = split(\\\"[+*/-]\\\", obj)\\n for v in var_list:\\n if v not in declared:\\n self.add_input(v)\\n declared.append(v)\\n self.add_output(\\\"Objective function \\\" + obj)\",\n \"def generate():\",\n \"def params(self) -> Iterable[sympy.Symbol]:\\n for i in range(self.iterations):\\n for p in range(len(self.qubits)):\\n if (self.include_all_z or not\\n numpy.isclose(self.orbital_energies[p], 0)):\\n yield LetterWithSubscripts('U', p, i)\\n for p, q in itertools.combinations(range(len(self.qubits)), 2):\\n if (self.include_all_cz or not\\n numpy.isclose(self.hamiltonian.two_body[p, q], 0)):\\n yield LetterWithSubscripts('V', p, q, i)\",\n \"def get_post_init_ops():\\n # literally all variables, because it's better to sync optimizer-internal variables as well\\n all_vars = tf.global_variables() + tf.local_variables()\\n #var_by_name = dict([(v.name, v) for v in all_vars])\\n var_by_name = dict()\\n for v in all_vars:\\n if v.name.startswith('tower_0'):\\n split_name = v.name.split('/')\\n realname = '/'.join(split_name[1:])\\n var_by_name[realname] = v\\n\\n post_init_ops = []\\n for v in all_vars:\\n if not v.name.startswith('tower_'):\\n continue\\n if v.name.startswith('tower_0'):\\n continue\\n # in this trainer, the master name doesn't have the towerx/ prefix\\n split_name = v.name.split('/')\\n prefix = split_name[0]\\n realname = '/'.join(split_name[1:])\\n if 'AccumGrad' in realname:\\n continue\\n if 'counter' in realname:\\n continue\\n if prefix in realname:\\n logger.error(\\\"[SyncMultiGPUReplicatedBuilder] variable \\\"\\n \\\"{} has its prefix {} appears multiple times in its name!\\\".format(v.name, prefix))\\n copy_from = var_by_name.get(realname)\\n assert copy_from is not None, (realname, var_by_name.keys())\\n #assert copy_from is not None, var_by_name.keys()\\n post_init_ops.append(v.assign(copy_from.read_value()))\\n return tf.group(*post_init_ops, name='sync_variables_from_main_tower')\",\n \"def make_instructions(self):\\n #de, aux, vers = self.rods\\n de, aux, vers = 0, 1, 2\\n n = self.num_rings\\n\\n self.recur(n, de, aux, vers)\\n\\n ### Add dummy tuple at end so I can look one move ahead on states\\n self.instructions.append((0, 0, 0))\",\n \"def generate_goal(self):\\r\\n\\t\\t# Creates a flat list of correct values\\r\\n\\t\\ttempList = [x for x in range(self.n**2)]\\r\\n\\r\\n\\t\\t# Nests those lists into a NxN\\r\\n\\t\\tBoardClass.goal = [tempList[self.n*i:self.n*(i+1)] for i in range(self.n)]\\r\\n\\r\\n\\t\\t# Creates a dictionary for the intended location of any specific tile. Used in\\r\\n\\t\\t# Manhatten Distance calculation.\\r\\n\\t\\tfor i in range(self.n**2):\\r\\n\\t\\t\\trow = i // self.n\\r\\n\\t\\t\\tcol = i % self.n\\r\\n\\t\\t\\tBoardClass.goalTileLocations[i] = [row, col]\",\n \"def doParametersOfInterest(self):\\r\\n if self.fg4fixed:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4[0]\\\")\\r\\n self.modelBuilder.doVar(\\\"r[1,0,4]\\\")\\r\\n print \\\"Fixing CMS_zz4l_fg4\\\"\\r\\n poi = \\\"r\\\"\\r\\n else:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg4\\\"):\\r\\n print \\\"have fg4 inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4[0.,-1,1]\\\")\\r\\n poi = \\\"CMS_zz4l_fg4\\\"\\r\\n if self.cPOI:\\r\\n if self.modelBuilder.out.var(\\\"cww_zz\\\"):\\r\\n print \\\"have czz_ww inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"cww_zz[0.5,-10,10]\\\")\\r\\n poi += \\\",cww_zz\\\"\\r\\n\\r\\n if self.fg2POI:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg2\\\"):\\r\\n print \\\"have fg2 inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg2[0.,0,1]\\\")\\r\\n poi += \\\",CMS_zz4l_fg2\\\"\\r\\n if self.muFloating:\\r\\n self.modelBuilder.doVar(\\\"r[1,0,2000]\\\")\\r\\n if self.muAsPOI:\\r\\n print \\\"Treating r as a POI\\\"\\r\\n poi += \\\",r\\\"\\r\\n else:\\r\\n self.modelBuilder.out.var(\\\"r\\\").setAttribute(\\\"flatParam\\\")\\r\\n if self.phiFloating:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg4phi\\\"):\\r\\n print \\\"have fg4phi inside\\\"\\r\\n else: \\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4phi[0.,-3.1415926,3.1415926]\\\")\\r\\n if self.phiPOI:\\r\\n poi += \\\",CMS_zz4l_fg4phi\\\"\\r\\n else:\\r\\n self.modelBuilder.out.var(\\\"CMS_zz4l_fg4phi\\\").setAttribute(\\\"flatParam\\\")\\r\\n if self.phi2Floating:\\r\\n #self.modelBuilder.doVar(\\\"CMS_zz4l_fg4phi[0.,-math.pi,math.pi]\\\")\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg2phi\\\"):\\r\\n print \\\"have fg2phi inside\\\"\\r\\n else: \\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg2phi[0.,-3.1415926,3.1415926]\\\")\\r\\n self.modelBuilder.out.var(\\\"CMS_zz4l_fg2phi\\\").setAttribute(\\\"flatParam\\\")\\r\\n \\r\\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def gen_constant_graph(self, g, ng, ct):\\n if ct.value in self.graphs:\\n new_ct = Constant(self.get_graph(ct.value))\\n self.repl[ct] = new_ct\\n self.repl[ct.value] = new_ct\\n else:\\n self.gen_constant(g, ng, ct)\",\n \"def all_different(variables) :\\n constraints=[]\\n for index,var in enumerate(variables):\\n for sub_index in range(index+1,len(variables)):\\n var1=var\\n var2=variables[sub_index]\\n new_constraint=Constraint(var1,var2,constraint_different)\\n constraints.append(new_constraint)\\n return constraints\",\n \"def write_inp_all(self, type, template):\\n \\n for struct in self.structs:\\n try:\\n state = self.gEs.loc[struct,'Ground State']\\n if state in self.states:\\n self.assemble_inp(struct, template, state, type)\\n except KeyError:\\n print(\\\"Ground state missing for %s. Rerun whaler gs.\\\" % struct)\",\n \"def _build_gains(self):\\n flip_nibbles = True\\n\\n p = (100, 4, flip_nibbles)\\n d = (1000, 4, flip_nibbles)\\n i = (0, 4, flip_nibbles)\\n il = (0, 4, flip_nibbles)\\n ol = (255, 2)\\n cl = (0, 2)\\n el = (4000, 4, flip_nibbles)\\n sr = (1, 2)\\n db = (1, 2)\\n sm = (1, 2)\\n gains = self._build_hexstr(p, d, i, il, ol, cl, el, sr, db, sm)\\n return \\\"F6{}\\\".format(gains)\",\n \"def gen_constant(self, g, ng, ct):\\n with About(ct.debug, self.relation):\\n self.repl[(g, ct)] = sexp_to_node((P.J, ct), ng)\",\n \"def generate_variable_names():\\n while True:\\n name = uuid.uuid4()\\n yield f\\\"_{name.hex}\\\"\",\n \"def build_net(nz=100):\\n\\tif opts.celeba:\\n\\t\\tgen = get_wgen_celebA(nz=nz)\\n\\t\\tdis = get_wdis_celebA(nz=nz)\\n\\n\\tif opts.mnist:\\n\\t\\tgen = get_wgen_mnist(nz=nz)\\n\\t\\tdis = get_wdis_mnist(nz=nz)\\n\\n\\treturn gen, dis\",\n \"def makeSetup(self):\\n startingRanks = [FLAG, SPY, SCOUT, SCOUT, MINER, MINER, GENERAL, MARSHALL, BOMB, BOMB]\\n startingSpots = random.sample(self.getStartSpots(), len(startingRanks))\\n pieces = []\\n for i in range(len(startingRanks)):\\n pieces += [Piece(startingRanks[i], startingSpots[i], self.index)]\\n # print [(str(p), p.position) for p in pieces]\\n return pieces\",\n \"def items():\\n for point in boolfunc.iter_points(inputs):\\n gpnt = {v: val for v, val in point.items()\\n if v not in unmapped}\\n gval = gfunc.restrict(gpnt)\\n # mapped function must be completely specified\\n assert isinstance(gval, TTConstant)\\n fpnt = {v: val for v, val in point.items()\\n if v in unmapped}\\n fpnt[gvar] = int(gval)\\n yield func.restrict(fpnt).pcdata[0]\",\n \"def build_net(nz=100):\\n\\tif opts.celeba:\\n\\t\\tgen = get_gen_celebA(nz=nz)\\n\\t\\tdis = get_dis_celebA(nz=nz)\\n\\n\\tif opts.mnist:\\n\\t\\tgen = get_gen_mnist(nz=nz)\\n\\t\\tdis = get_dis_mnist(nz=nz)\\n\\n\\treturn gen, dis\",\n \"def get_structural_graph(self):\\n mx = np.zeros(shape=(self.num_vars, self.num_vars))\\n for var, inputs, _ in self.rules:\\n ix1 = self.var_name_ndxs[var]\\n for i in inputs:\\n mx[i,ix1] = 1.0\\n return mx\",\n \"def init_cg(self):\\n self.add_pc(2)\\n self.pb[self.pc - 2] = \\\"ASSIGN\\\", _m(CodeGenerator.REGISTER_SIZE + CodeGenerator.INIT_MEMORY_VALUE, \\\"#\\\"), _m(\\n self.top_sp)\\n self.pb[self.pc - 1] = \\\"ASSIGN\\\", _m(self.top_sp), _m(self.top_sp, \\\"@\\\")\\n self.init_global_func()\\n self.make_output()\",\n \"def doParametersOfInterest(self):\\n \\n self.modelBuilder.doVar('expr::cosW(\\\"0.87681811112\\\",)')\\n self.modelBuilder.doVar('expr::sinW(\\\"0.48082221247\\\",)')\\n self.modelBuilder.doVar('expr::mZ(\\\"91.2\\\",)')\\n self.modelBuilder.doVar('expr::Lambda1(\\\"100.0\\\",)')\\n self.modelBuilder.doVar('expr::e2(\\\"0.0917\\\",)')\\n self.modelBuilder.doVar('expr::gs2(\\\"1.533\\\",)')\\n\\n # EFT Higgs basis couplings\\n\\n self.modelBuilder.doVar('cZ[0,-1,1]') \\n self.modelBuilder.doVar(\\\"cZZ[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZZt[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZB[0,-6,6]\\\") \\n\\n poi='cZ,cZZ,cZZt,cZB'\\n\\n # Amplitude couplings from EFT couplings \\n\\n self.modelBuilder.doVar('expr::a1(\\\"@0+1\\\",cZ)') # (\\\"2*(@0+1)\\\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\\n self.modelBuilder.doVar('expr::a2(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZ,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::a3(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZt,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::k1(\\\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\\\",cZB,e2,Lambda1,sinW,mZ)')\\n self.modelBuilder.doVar('expr::k1L1(\\\"@0/pow(@1,2)\\\",k1,Lambda1)')\\n\\n ###### gamma_H ########\\n\\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\\n\\n self.modelBuilder.doVar('expr::kappa(\\\"1.0\\\",)')\\n self.modelBuilder.doVar('expr::kappa_tilde(\\\"0.0\\\",)') \\n\\n self.modelBuilder.doVar('expr::a1_WW(\\\"@0\\\",a1)')\\n self.modelBuilder.doVar('expr::a2_WW(\\\"@0*@0*@1\\\",cosW,a2)')\\n self.modelBuilder.doVar('expr::a3_WW(\\\"@0*@0*@1\\\",cosW,a3)')\\n self.modelBuilder.doVar('expr::k1_WW(\\\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\\\",cosW,sinW,k1,a2,Lambda1,mZ)')\\n self.modelBuilder.doVar('expr::k2_k1(\\\"2*@0*@1*@2/(@0*@0 - @1*@1)\\\",cosW,sinW,k1)')\\n self.modelBuilder.doVar('expr::k2_a2(\\\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\\\",cosW,sinW,mZ,a2,Lambda1)')\\n self.modelBuilder.doVar('expr::k2(\\\"@0 + @1\\\",k2_k1,k2_a2)')\\n\\n # Determine gamma_H from VV couplings\\n\\n zz_expr = '\\\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \\\",a1,a2,a3,k1,k2'\\n ww_expr = '\\\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \\\",a1_WW,a2_WW,a3_WW,k1_WW'\\n zgamma_expr = '\\\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n gg_expr = '\\\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\\\",kappa,kappa_tilde'\\n bb_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n cc_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n tautau_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n mumu_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n gmgm_expr = '\\\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n \\n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\\n\\n self.modelBuilder.doVar('expr::gammaH(\\\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\\\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \\n\\n ###########################\\n\\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\\\"1V\\\") # Compensate for scaling of k1 templates \\n self.g2V = GetCoupTerms(1,1,1,-0.0001,\\\"2V\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g2V_1(\\\\\\\"\\\"+str(self.g2V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1(\\\\\\\"((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1_Neg(\\\\\\\"-1*((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_2(\\\\\\\"\\\"+str(self.g2V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2(\\\\\\\"((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2_Neg(\\\\\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_3(\\\\\\\"\\\"+str(self.g2V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3(\\\\\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_4(\\\\\\\"\\\"+str(self.g2V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4(\\\\\\\"((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4_Neg(\\\\\\\"-1*((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_5(\\\\\\\"\\\"+str(self.g2V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5(\\\\\\\"((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5_Neg(\\\\\\\"-1*((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_6(\\\\\\\"\\\"+str(self.g2V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6(\\\\\\\"((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6_Neg(\\\\\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_7(\\\\\\\"\\\"+str(self.g2V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7(\\\\\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_8(\\\\\\\"\\\"+str(self.g2V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8(\\\\\\\"((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8_Neg(\\\\\\\"-1*((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_9(\\\\\\\"\\\"+str(self.g2V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9(\\\\\\\"((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9_Neg(\\\\\\\"-1*((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_10(\\\\\\\"\\\"+str(self.g2V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10(\\\\\\\"((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10_Neg(\\\\\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_11(\\\\\\\"\\\"+str(self.g2V[10])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11(\\\\\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_12(\\\\\\\"\\\"+str(self.g2V[11])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12(\\\\\\\"((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12_Neg(\\\\\\\"-1*((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_13(\\\\\\\"\\\"+str(self.g2V[12])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13(\\\\\\\"((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13_Neg(\\\\\\\"-1*((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_14(\\\\\\\"\\\"+str(self.g2V[13])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14(\\\\\\\"((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14_Neg(\\\\\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_15(\\\\\\\"\\\"+str(self.g2V[14])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15(\\\\\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_16(\\\\\\\"\\\"+str(self.g2V[15])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16(\\\\\\\"((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16_Neg(\\\\\\\"-1*((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_17(\\\\\\\"\\\"+str(self.g2V[16])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17(\\\\\\\"((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17_Neg(\\\\\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_18(\\\\\\\"\\\"+str(self.g2V[17])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18(\\\\\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_19(\\\\\\\"\\\"+str(self.g2V[18])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19(\\\\\\\"((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19_Neg(\\\\\\\"-1*((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_20(\\\\\\\"\\\"+str(self.g2V[19])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20(\\\\\\\"((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20_Neg(\\\\\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_21(\\\\\\\"\\\"+str(self.g2V[20])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21(\\\\\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21_Neg(\\\\\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_22(\\\\\\\"\\\"+str(self.g2V[21])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22(\\\\\\\"((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22_Neg(\\\\\\\"-1*((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_23(\\\\\\\"\\\"+str(self.g2V[22])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23(\\\\\\\"((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23_Neg(\\\\\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_24(\\\\\\\"\\\"+str(self.g2V[23])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24(\\\\\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_25(\\\\\\\"\\\"+str(self.g2V[24])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25(\\\\\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_26(\\\\\\\"\\\"+str(self.g2V[25])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26(\\\\\\\"((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26_Neg(\\\\\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_27(\\\\\\\"\\\"+str(self.g2V[26])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27(\\\\\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_28(\\\\\\\"\\\"+str(self.g2V[27])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28(\\\\\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_29(\\\\\\\"\\\"+str(self.g2V[28])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29(\\\\\\\"((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29_Neg(\\\\\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_30(\\\\\\\"\\\"+str(self.g2V[29])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30(\\\\\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30_Neg(\\\\\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_31(\\\\\\\"\\\"+str(self.g2V[30])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31(\\\\\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31_Neg(\\\\\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_32(\\\\\\\"\\\"+str(self.g2V[31])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32(\\\\\\\"((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32_Neg(\\\\\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_33(\\\\\\\"\\\"+str(self.g2V[32])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33(\\\\\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33_Neg(\\\\\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_34(\\\\\\\"\\\"+str(self.g2V[33])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34(\\\\\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34_Neg(\\\\\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_35(\\\\\\\"\\\"+str(self.g2V[34])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35(\\\\\\\"((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35_Neg(\\\\\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g1V_1(\\\\\\\"\\\"+str(self.g1V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1(\\\\\\\"((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1_Neg(\\\\\\\"-1*((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_2(\\\\\\\"\\\"+str(self.g1V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2(\\\\\\\"((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2_Neg(\\\\\\\"-1*((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_3(\\\\\\\"\\\"+str(self.g1V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3(\\\\\\\"((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3_Neg(\\\\\\\"-1*((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_4(\\\\\\\"\\\"+str(self.g1V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4(\\\\\\\"((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4_Neg(\\\\\\\"-1*((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_5(\\\\\\\"\\\"+str(self.g1V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5(\\\\\\\"((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5_Neg(\\\\\\\"-1*((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_6(\\\\\\\"\\\"+str(self.g1V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6(\\\\\\\"((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6_Neg(\\\\\\\"-1*((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_7(\\\\\\\"\\\"+str(self.g1V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7(\\\\\\\"((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7_Neg(\\\\\\\"-1*((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_8(\\\\\\\"\\\"+str(self.g1V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8(\\\\\\\"((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8_Neg(\\\\\\\"-1*((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_9(\\\\\\\"\\\"+str(self.g1V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9(\\\\\\\"((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9_Neg(\\\\\\\"-1*((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_10(\\\\\\\"\\\"+str(self.g1V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10(\\\\\\\"((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10_Neg(\\\\\\\"-1*((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n \\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def fetch_variables(self):\\r\\n fgraph = self.fgraph\\r\\n self.inputs = fgraph.inputs\\r\\n self.outputs = fgraph.outputs\\r\\n\\r\\n # list(fgraph.variables)\\r\\n # We need to include the not used inputs in our variables,\\r\\n # otherwise we can't pass them to the module.\\r\\n self.variables = [var for var in self.inputs if not len(var.clients)]\\r\\n self.variables += graph.variables(self.inputs, self.outputs)\\r\\n\\r\\n # The orphans field is listified to ensure a consistent order.\\r\\n #list(fgraph.orphans.difference(self.outputs))\\r\\n self.orphans = list(r for r in self.variables\\r\\n if isinstance(r, graph.Constant) and\\r\\n r not in self.inputs)\\r\\n self.temps = list(set(self.variables).difference(\\r\\n self.inputs).difference(self.outputs).difference(self.orphans))\\r\\n self.consts = []\\r\\n self.node_order = self.schedule(fgraph)\",\n \"def test_basic_setup(self):\\n random_vars = ['D', 'I', 'G', 'S', 'L']\\n\\n for rv in random_vars:\\n self.assertTrue(rv in self.Gs.nodes)\\n self.assertTrue(isinstance(self.Gs.nodes[rv], DiscreteNetworkNode))\",\n \"def define_placeholders(self):\\n sy_ob_no = tf.placeholder(shape=[None, self.ob_dim], name=\\\"ob\\\", dtype=tf.float32)\\n if self.discrete:\\n sy_ac_na = tf.placeholder(shape=[None], name=\\\"ac\\\", dtype=tf.int32)\\n else:\\n sy_ac_na = tf.placeholder(shape=[None, self.ac_dim], name=\\\"ac\\\", dtype=tf.float32)\\n sy_adv_n = tf.placeholder(shape=[None], name=\\\"adv\\\", dtype=tf.float32)\\n return sy_ob_no, sy_ac_na, sy_adv_n\",\n \"def gencode(self):\\n\\n fullcode = self.code_cfg\\n variables = codeconfig_getvars(fullcode)\\n if len(variables) > 0:\\n variables.sort(key=lambda x: len(x), reverse=True)\\n for va in variables:\\n if eval(\\\"self.\\\" + va[1:]) is None or eval(\\\"self.\\\" + va[1:]) == \\\"\\\":\\n warnings.warn(\\\"Inserting None for \\\" + va, UserWarning)\\n fullcode = fullcode.replace(va, str(eval(\\\"self.\\\" + va[1:])))\\n # testing for sintax errors\\n #compile(fullcode, \\\"<Test ModelCall Code>\\\\n\\\" + fullcode, 'exec')\\n tmpfile = os.path.join(gettempdir(), \\\"TestModelCallCode.\\\" + self.complexity + \\\".py\\\")\\n with open(tmpfile, \\\"w\\\") as f: f.write(fullcode)\\n compile(fullcode, tmpfile, 'exec')\\n return fullcode\",\n \"def get_variables(self):\\n return [self.g_t, self.m_t]\",\n \"def gprog():\\n return [('ip','ip','u')]+[k for x in ['c','1','2','3'] for k in gcopy(\\\"op\\\"+x, 'ip')]\",\n \"def consts(consts):\\n\\n namespace = { }\\n\\n for c in consts:\\n constname = c[\\\"constname\\\"]\\n consttype = c[\\\"consttype\\\"]\\n constval = c[\\\"constval\\\"]\\n\\n # Correct various values that won't evaluate in python.\\n if constval == \\\"( SteamItemInstanceID_t ) ~ 0\\\":\\n constval = \\\"-1\\\"\\n elif constval == \\\"( ( uint32 ) 'd' << 16U ) | ( ( uint32 ) 'e' << 8U ) | ( uint32 ) 'v'\\\":\\n constval = \\\"6579574\\\"\\n else:\\n constval = re.sub(r\\\"(0x[0-9a-fA-F]*)ull\\\", r\\\"\\\\1\\\", constval)\\n\\n # Evaluate the result, and place it into the namespace.\\n value = eval(constval, namespace, namespace)\\n namespace[constname] = value\\n\\n # Generate.\\n mapped = map_type(consttype)\\n\\n if value > 0:\\n p(f\\\"{constname} = {mapped}(0x{value:x})\\\")\\n else:\\n p(f\\\"{constname} = {mapped}({value})\\\")\",\n \"def __updateNuclideBasesForSpecialCases():\\n # Change the name of `AM242` to specific represent its ground state.\\n am242g = byName[\\\"AM242\\\"]\\n am242g.name = \\\"AM242G\\\"\\n byName[\\\"AM242G\\\"] = am242g\\n byDBName[byName[\\\"AM242G\\\"].getDatabaseName()] = am242g\\n\\n # Update the pointer of `AM242` to refer to `AM242M`.\\n am242m = byName[\\\"AM242M\\\"]\\n byName[\\\"AM242\\\"] = am242m\\n byDBName[\\\"nAm242\\\"] = am242m\\n byDBName[byName[\\\"AM242\\\"].getDatabaseName()] = am242m\",\n \"def all(c):\\n states(c)\\n etc(c)\\n prune(c)\",\n \"def get_covariates(degree):\\n\\n base_variables = [\\n \\\"smallm\\\",\\n \\\"largem\\\",\\n \\\"treatsmall\\\",\\n \\\"treatlarge\\\",\\n \\\"ssmall\\\",\\n \\\"slarge\\\",\\n \\\"streatsmall\\\",\\n \\\"streatlarge\\\",\\n ]\\n if degree == 0:\\n base = base_variables[0:4]\\n return base\\n if degree == 1:\\n return base_variables\\n else:\\n for i in range(2, degree + 1):\\n base_variables.append(f\\\"ssmall{i}\\\")\\n base_variables.append(f\\\"slarge{i}\\\")\\n base_variables.append(f\\\"streatsmall{i}\\\")\\n base_variables.append(f\\\"streatlarge{i}\\\")\\n return base_variables\",\n \"def variables(self):\\n for state in self.states:\\n yield self.assert_state(state)\\n yield self.deassert_state(state)\",\n \"def generate_assignment(parameters):\\n if len(parameters) == 0:\\n yield []\\n raise StopIteration()\\n cp_pars = copy.deepcopy(parameters)\\n par, values = cp_pars.popitem()\\n for val in values:\\n for r in generate_assignment(cp_pars):\\n yield r + [(par,val)]\",\n \"def generate_grammar(gram):\\r\\n c = 0\\r\\n while gram[c] != \\\"start_variable\\\": # find start variable\\r\\n c += 1\\r\\n start = gram[c+1]\\r\\n grammar = pcfg.PCFG(start) # create a PCFG with start and no rules\\r\\n while gram[c] != \\\"Grammar\\\": # find the index of the first rule\\r\\n c += 1\\r\\n c += 3\\r\\n\\r\\n while gram[c] != '###########':\\r\\n c = adding_rules_grammar(c, gram, grammar) # find each rule from the grammar and add it to the grammar\\r\\n c += 1\\r\\n\\r\\n while gram[c] != \\\"Lexicon\\\": # find the index of the first rule of the lexicon\\r\\n c += 1\\r\\n c += 3\\r\\n\\r\\n while c < len(gram):\\r\\n var = gram[c]\\r\\n c = adding_rules_lexicon(c, gram, grammar, var) # find each rule from the lexicon and add it to the grammar\\r\\n c += 1\\r\\n return grammar\",\n \"def initializeVariables(F_n):\\n bounds = [(-100, 100), (-10, 10), (-100, 100), (-100, 100), (-30, 30),\\n (-100, 100), (-1.28, 1.28), (-500, 500), (-5.12, 5.12),\\n (-32, 32), (-600, 600), (-50, 50), (-50, 50)]\\n\\n if F_n == 8:\\n objectiveValue = -12569.5\\n else:\\n objectiveValue = 1e-8\\n\\n (lower, upper) = bounds[F_n - 1]\\n return (lower, upper, objectiveValue)\",\n \"def save_all_consts_to_memory():\\n for key, value in symbol_table.dict.items():\\n if value[2] is not None:\\n save_to_memory(\\\"a\\\", \\\"b\\\", value[5], value[2])\\n # assembler_generator.add_comment(\\\"# consts saved to memory\\\")\",\n \"def Allcombos():\\n\\n global allcombos\\n\\n allcombos = []\\n\\n results = product(\\\"ABCDEF\\\", repeat=4)\\n\\n allcombos = resulttolist(results)\\n\\n return AIguessing(allcombos)\",\n \"def generate(self):\\n \\n ##Dictionary of put values\\n self.put = {}\\n ##Dictionary of call values\\n self.call = {}\\n \\n bottom = self.range[0]\\n top = self.range[2]\\n iter = self.range[1]\\n \\n if self.meth == 'bi':\\n for E in range(bottom,top+iter,iter):\\n solver = bi.binomial_euro(S=self.S,E=E,r=self.r,M=400,sigma=self.sigma,method='higham',T=self.T,opt='put')\\n self.put[E] = solver.solve()\\n del solver\\n for E in range(bottom,top+iter,iter):\\n solver = bi.binomial_euro(S=self.S,E=E,r=self.r,M=400,sigma=self.sigma,method='higham',T=self.T,opt='call')\\n self.call[E] = solver.solve()\\n del solver\\n elif self.meth == 'mc':\\n for E in range(bottom,top+iter,iter):\\n solver = mc.mcfast_euro(S=self.S,E=E,r=self.r,M=20000,sigma=self.sigma,T=self.T,opt='put')\\n self.put[E] = solver.solve()\\n del solver\\n for E in range(bottom,top+iter,iter):\\n solver = mc.mcfast_euro(S=self.S,E=E,r=self.r,M=20000,sigma=self.sigma,T=self.T,opt='call')\\n self.call[E] = solver.solve()\\n del solver\\n elif self.meth == 'all':\\n pass\",\n \"def generate_field(field):\\n ship4 = [create_ship(4)]\\n set_coordinates(ship4[0], 4)\\n ship4[0].find_neighbor()\\n ship3 = []\\n for i in range(2):\\n ship3.append(create_ship(3, ship4, ship3))\\n set_coordinates(ship3[-1], 3, ship4, ship3[:-1])\\n ship3[-1].find_neighbor()\\n ship2 = []\\n for i in range(3):\\n ship2.append(create_ship(2, ship4, ship3, ship2))\\n set_coordinates(ship2[-1], 2, ship4, ship3, ship2[:-1])\\n ship2[-1].find_neighbor()\\n ship1 = []\\n for i in range(4):\\n ship1.append(create_ship(1, ship4, ship3, ship2, ship1))\\n ship1[-1].find_neighbor()\\n\\n for i in range(1, 5):\\n for ship in eval('ship' + str(i)):\\n for coor in ship.coordinates:\\n field[coor[0]][coor[1]] = '■'\\n ships = []\\n for ship in ship4:\\n ships.append(ship)\\n for ship in ship3:\\n ships.append(ship)\\n for ship in ship2:\\n ships.append(ship)\\n for ship in ship1:\\n ships.append(ship)\\n return field, ships\",\n \"def redef_vars(self):\\r\\n\\r\\n # Try using redefined source / boundary terms\\r\\n if self.redefined == True:\\r\\n self._redef_via_predef_eqn()\\r\\n else: # If they haven't been set you'll get an exception.\\r\\n self._redef_sp1_vars()\",\n \"def generate_all_constraints(traj,policy,mdp):\\n #print('generating all constraints')\\n constraints = []\\n traj_tmp = list(traj)\\n #print(traj_tmp)\\n #compute halfspace normals for all (s,a) pairs until terminal\\n while(len(traj_tmp)>1):\\n constraints += generate_half_space_normals(traj_tmp,policy,mdp)\\n #print(constraints)\\n traj_tmp.pop(0)\\n #print('after pop',traj_tmp)\\n return constraints\",\n \"def all_net(configuration):\\n net_dict_all = {\\n \\\"design\\\" : ['H1', 'L1', 'V1' ],\\n \\\"GW170817\\\" : ['H1', 'L1', 'V1' ],\\n \\\"GW170814\\\" : ['H1', 'L1', 'V1' ],\\n \\\"GW170817_without_Virgo\\\" : ['H1', 'L1' ],\\n \\\"ET\\\" : [\\\"ET_L_Eu\\\", \\\"ET_L_Eu_2\\\"], # Triangular ET\\n \\\"ET1\\\" : ['H1', 'L1', 'V1', 'ETdet1', 'ETdet2' ], # Triangular ET +LVC\\n \\\"ET2\\\" : ['H1', 'L1', 'V1', 'ETdet1', 'ETdet3' ], # L-shaped at 2 places +LVC\\n \\\"ET3\\\" : ['ETdet1', 'ETdet3', 'ETdet4'], # 3 L-shaped ET at three different places\\n \\\"ET3L_EU\\\" : [\\\"ET_L_Eu\\\", \\\"ET_L_Aus_Eu\\\", \\\"ET_L_Argentina\\\"],\\n \\\"3ET\\\" : [\\\"ET_L_US\\\", \\\"ET_L_Aus_US\\\", \\\"ET_L_Central_Africa\\\"],\\n \\\"3CE\\\" : [\\\"CE_US\\\", \\\"CE_Aus_US\\\", \\\"CE_Central_Africa\\\"],\\n \\\"1CE-ET\\\" : [\\\"CE_US\\\", \\\"ET_L_Eu\\\", \\\"ET_L_Eu_2\\\"],\\n \\\"2CE-ET\\\" : [\\\"CE_US\\\", \\\"CE_Aus_US\\\", \\\"ET_L_Eu\\\", \\\"ET_L_Eu_2\\\"], #named 1 and 2 to distinguish from CE-ET (below) in Mills et al 2018.\\n \\\"CE-ET\\\" : [\\\"CE_US\\\", \\\"CE_Aus_US\\\", \\\"ET_L_Eu\\\", \\\"ET_L_Eu_2\\\"],\\n \\\"Voyager-ET\\\" : [\\\"LBB_H1\\\", \\\"LBB_L1\\\", \\\"LBB_I1\\\", \\\"ET_L_Eu\\\", \\\"ET_L_Eu_2\\\"],\\n # next three networks are for calculating the impact of duty cycle on the Voyager-ET network\\n \\\"VoyagerLI-ET\\\" : [\\\"LBB_L1\\\", \\\"LBB_I1\\\", \\\"ET_L_Eu\\\", \\\"ET_L_Eu_2\\\"],\\n \\\"VoyagerHI-ET\\\" : [\\\"LBB_H1\\\", \\\"LBB_I1\\\", \\\"ET_L_Eu\\\", \\\"ET_L_Eu_2\\\"],\\n \\\"VoyagerHL-ET\\\" : [\\\"LBB_H1\\\", \\\"LBB_L1\\\", \\\"ET_L_Eu\\\", \\\"ET_L_Eu_2\\\"],\\n \\n \\\"VoyagerETtri\\\" : [\\\"LBB_H1\\\", \\\"LBB_L1\\\", \\\"LBB_I1\\\", \\\"ET_Tri_Eu_1\\\", \\\"ET_Tri_Eu_2\\\", \\\"ET_Tri_Eu_3\\\"],\\n \\\"Voyager\\\" : [\\\"LBB_H1\\\", \\\"LBB_L1\\\", \\\"LBB_I1\\\"],\\n \\\"VoyagerWithAL\\\" : [\\\"LBB_H1\\\", \\\"LBB_L1\\\", \\\"LBB_I1\\\", \\\"ALV1\\\", \\\"ALK1\\\"],\\n \\\"3_TriangularET\\\" : [\\\"ET_L_US\\\", \\\"ET_L_Aus_US\\\", \\\"ET_L_Central_Africa\\\",\\\"ET_L_US_2\\\", \\\"ET_L_Aus_US_2\\\", \\\"ET_L_Central_Africa_2\\\"],\\n # for comparing to klimenko et al 2011:\\n 'LHVA2' : [\\\"LBB_L1\\\",\\\"LBB_H1\\\",\\\"LBB_V1\\\",\\\"LBB_A-\\\"],\\n 'LHVA' : [\\\"LBB_L1\\\",\\\"LBB_H1\\\",\\\"LBB_V1\\\",\\\"LBB_A\\\"],\\n 'LHVJ' : [\\\"LBB_L1\\\",\\\"LBB_H1\\\",\\\"LBB_V1\\\",\\\"LBB_K1\\\"],\\n 'LHVAJ' : [\\\"LBB_L1\\\",\\\"LBB_H1\\\",\\\"LBB_V1\\\",\\\"LBB_A\\\",\\\"LBB_K1\\\"],\\n # for calculating alignment factor distributions in inclincation paper\\n \\\"HL\\\" : [\\\"H1\\\", \\\"L1\\\"],\\n \\\"HLV\\\" : [\\\"H1\\\", \\\"L1\\\", \\\"V1\\\" ],\\n \\\"HLVK\\\" : [\\\"L1\\\",\\\"H1\\\",\\\"V1\\\",\\\"K1\\\"],\\n \\\"HLVKI\\\" : [\\\"L1\\\",\\\"H1\\\",\\\"V1\\\",\\\"K1\\\", \\\"I1\\\"],\\n \\n\\n #for optimizing the orientations of ET3L_EU w.r.t. polarization metric (see optimizing polarization notebook)\\n #first optimize for the two detector network:\\n \\\"ET2L_EU\\\" : [\\\"ET_L_Eu\\\", \\\"ET_L_Aus_Eu\\\"],\\n \\\"2ET\\\" : [\\\"ET_L_US\\\", \\\"ET_L_Aus_US\\\"],\\n #ranges\\n }\\n return(net_dict_all[configuration])\",\n \"def base_mappings():\\n return {\\n 'from_1': {\\n 'to_1': {\\n 'mol_1': ({}, {}, []),\\n 'mol_2': ({}, {}, []),\\n },\\n },\\n }\",\n \"def get_bases():\\n\\tbss = []\\n\\tfor es in MV.index:\\n\\t\\tbs = []\\n\\t\\tif es == ():\\n\\t\\t\\tbs.append(_1)\\n\\t\\telse:\\n\\t\\t\\tfor js in es:\\n\\t\\t\\t\\tbmv = reduce(operator.mul, map(lambda j: e[j], js))\\n\\t\\t\\t\\tbs.append(bmv)\\n\\t\\t\\t\\t\\n\\t\\tbss.append(bs)\\n\\t\\n\\treturn bss\",\n \"def test_create_tbg_neural_efficacies(self):\\n np.random.seed(25432)\\n m_act = 5.\\n v_act = .05\\n v_inact = .05\\n cdef = [Condition(m_act=m_act, v_act=v_act, v_inact=v_inact)]\\n npos = 5000\\n labels = np.zeros((1, npos), dtype=int)\\n labels[0, :npos / 2] = 1\\n phy_params = phy.PHY_PARAMS_FRISTON00\\n ne = phy.create_tbg_neural_efficacies(phy_params, cdef, labels)\\n\\n # check shape consistency:\\n self.assertEqual(ne.shape, labels.shape)\\n\\n # check that moments are close to theoretical ones\\n ne_act = ne[0, np.where(labels[0])]\\n ne_inact = ne[0, np.where(labels[0] == 0)]\\n m_act_theo = truncnorm.mean(0, phy_params['eps_max'], loc=m_act,\\n scale=v_act ** .5)\\n v_act_theo = truncnorm.var(0, phy_params['eps_max'], loc=m_act,\\n scale=v_act ** .5)\\n (ne_act.mean(), m_act_theo)\\n npt.assert_approx_equal(ne_act.var(), v_act_theo, significant=2)\\n\\n m_inact_theo = truncnorm.mean(0, phy_params['eps_max'], loc=0.,\\n scale=v_inact ** .5)\\n v_inact_theo = truncnorm.var(0, phy_params['eps_max'], loc=0.,\\n scale=v_inact ** .5)\\n npt.assert_approx_equal(ne_inact.mean(), m_inact_theo, significant=2)\\n npt.assert_approx_equal(ne_inact.var(), v_inact_theo, significant=2)\\n npt.assert_array_less(ne, phy_params)\\n npt.assert_array_less(0., ne)\",\n \"def initial_domain(pre_condition=[]):\\n one_day_shift = [ (0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1) ]\\n\\n possible_shifts = set( [ shift_mon + shift_tue + shift_wed + shift_thu + shift_fri + shift_sat + shift_sun\\n for shift_mon in one_day_shift\\n for shift_tue in one_day_shift\\n for shift_wed in one_day_shift\\n for shift_thu in one_day_shift\\n for shift_fri in one_day_shift\\n for shift_sat in one_day_shift\\n for shift_sun in one_day_shift\\n ] )\\n\\n print len(possible_shifts)\\n\\n for preprocess_func, args in pre_condition:\\n if not args:\\n possible_shifts = preprocess_func(possible_shifts)\\n else:\\n possible_shifts = preprocess_func(possible_shifts, *args)\\n\\n\\n return possible_shifts\",\n \"def make_bprod(self):\\n rhs1 = random.choice(self.nonterminals)\\n rhs2 = random.choice(self.nonterminals)\\n lhs = random.choice(self.nonterminals)\\n return (lhs, (rhs1, rhs2))\",\n \"def change_variables((a,b,c,d), (n,r,m)): \\n return ( n*a**2 + r*a*b + m*b**2, 2*(n*a*c + m*b*d) + r*(a*d + c*b), \\\\\\n n*c**2 + r*c*d + m*d**2 )\",\n \"def generate_rules(self):\\n for rule in self._parser.conditionals:\\n\\n all_in_facts, matrix = self._generate_rules(rule)\\n if all_in_facts is True:\\n self.new_fact_from_facts(rule)\\n else:\\n facts = self._parser.conditionals[rule][1]\\n #print(rule, facts, matrix)\",\n \"def _make_circuit_instructions(n_qubits, depth, type_circuit):\\n\\n if type_circuit in [0, 1, 2]:\\n\\n # if type_circuit == 1:\\n # if depth > 8:\\n # raise ValueError(\\n # \\\"For type-1 circuits, only at most depth=8 allowed!\\\"\\n # )\\n\\n # define rotations for circuit in each layer, 0: identity, 1:X, 2:Y 3:Z\\n ini_pauli = np.zeros([depth, n_qubits], dtype=int)\\n\\n # set first and second layer, rest comes later\\n ini_pauli[0, :] = 2 # y rotation\\n if depth > 1:\\n ini_pauli[1, :] = 3 # z rotation\\n\\n # construct natural parameterized circuit\\n # gives which type of entangling gates at each layer -- first entry is\\n # first qubit index, second is second qubit index, third entry is type\\n # of entangling gate\\n entangling_gate_index_list = [[] for i in range(depth)]\\n orderList = []\\n for i in range(n_qubits//2):\\n if i % 2 == 0:\\n orderList.append(i//2)\\n else:\\n orderList.append((n_qubits-i)//2)\\n\\n if n_qubits > 1:\\n shiftList = [orderList[0]]\\n else:\\n shiftList = []\\n for i in range(1, n_qubits//2):\\n shiftList.append(orderList[i])\\n shiftList += shiftList[:-1]\\n\\n # this list gives which entangling gates are applied in which layer\\n if type_circuit == 0:\\n # deep natural PQC, includes non-nearest neighbor gates\\n for j in range(min(len(shiftList), int(np.ceil(depth/2))-1)):\\n entangling_gate_index_list[1+2*j] = [\\n [2*i, (2*i+1+2*shiftList[j]) % n_qubits, 0]\\n for i in range(n_qubits//2)\\n ]\\n elif type_circuit == 1:\\n # only do 2 entangling layers at max, and only do gates with\\n # nearest neighbor and no ring\\n for j in range(min(len(shiftList), 3)):\\n if j == 0:\\n entangling_gate_index_list[1+2*j] = [\\n [2*i, (2*i+1+2*shiftList[j]) % n_qubits, 0]\\n for i in range(n_qubits//2)\\n ]\\n elif (j == 1 or j == 2):\\n # exclude ring gate and gate 0,1 on third entangling layer\\n entangling_gate_index_list[1+2*j] = [\\n [2*i, (2*i+1+2*shiftList[j]) % n_qubits, 0]\\n for i in range(1, n_qubits//2)\\n ]\\n\\n elif type_circuit == 2:\\n # only do 3 regular entangling layers in a ring topology, then two\\n # more phase gates with next-nearst neighbor, which requires one\\n # swap. This adds 4 more parameters\\n for j in range(min(len(shiftList), 3)):\\n entangling_gate_index_list[1+2*j] = [\\n [2*i, (2*i+1+2*shiftList[j]) % n_qubits, 0]\\n for i in range(n_qubits//2)\\n ]\\n # entangling_gate_index_list[1+2*3]=[[0,n_qubits-1,1],[0,1,0],[n_qubits-1,n_qubits-2,0]]\\n # entangling_gate_index_list[1+2*3]=[[0,n_qubits-1,1],[0,1,0],[n_qubits-1,n_qubits-2,0]]\\n entangling_gate_index_list[1+2*3] = [\\n [n_qubits-1, 1, 0],\\n [0, n_qubits-2, 0]\\n ]\\n\\n for i in range(len(entangling_gate_index_list)-1):\\n if len(entangling_gate_index_list[i]) > 0:\\n for j in range(len(entangling_gate_index_list[i])):\\n qubit_index = entangling_gate_index_list[i][j][0]\\n ini_pauli[i+1, qubit_index] = 2\\n if i+2 < depth:\\n ini_pauli[i+2, qubit_index] = 3\\n\\n elif type_circuit == 3:\\n\\n ini_pauli = np.ones([depth, n_qubits], dtype=int)*2\\n\\n for i in range(1, depth, 2):\\n ini_pauli[i, :] = 3\\n\\n if n_qubits % 2 == 0:\\n # even qubits ALT circuit needs to get rid of boundary rotations at\\n # even entangling layers\\n for i in range(4, depth, 4):\\n ini_pauli[i, 0] = 0\\n ini_pauli[i, -1] = 0\\n if i+1 < depth:\\n ini_pauli[i+1, 0] = 0\\n ini_pauli[i+1, -1] = 0\\n else:\\n # for odd qubits, get rid of boundary either on top or bottom qubit\\n for i in range(2, depth, 4):\\n ini_pauli[i, -1] = 0\\n if i+1 < depth:\\n ini_pauli[i+1, -1] = 0\\n for i in range(4, depth, 4):\\n ini_pauli[i, 0] = 0\\n if i+1 < depth:\\n ini_pauli[i+1, 0] = 0\\n\\n # CNOT entangling gates\\n entangling_gate_index_list = [[] for i in range(depth)]\\n counter = 0\\n # third index indicates type of entangling gate\\n for k in range(1, depth-1, 2):\\n\\n # place entangler every second layer, do not place any at last\\n if counter % 2 == 0:\\n # even layer\\n entangling_gate_index_list[k] = [\\n [2*j, 2*j+1, 1] for j in range(n_qubits//2)\\n ]\\n else:\\n # odd layer\\n entangling_gate_index_list[k] = [\\n [2*j+1, 2*j+2, 1] for j in range((n_qubits-1)//2)\\n ]\\n counter += 1\\n\\n else:\\n raise ValueError('type_circuit='+f'{type_circuit}'+' not recognised.')\\n\\n return ini_pauli, entangling_gate_index_list\",\n \"def FindGroundstate(**args):\\n\\targs[\\\"imtime\\\"] = True\\n\\n\\tprop = SetupProblem(**args)\\n\\n\\tfor t in prop.Advance(10):\\n\\t\\tE = prop.GetEnergy()\\n\\t\\tprint \\\"t = %3.2f, E = %2.8f\\\" % (t, E)\\n\\n\\treturn prop\",\n \"def test_all_games_gen_1():\\n gen1a = {zero, one, -one, star}\\n gen1b = all_games_gen(1)\\n assert gen1a == gen1b\",\n \"def gen(length):\\n return itertools.product(LABELS,repeat=length)\",\n \"def _simulate_all_cells(self):\\n for ID in tqdm(self.condition_dict, desc='Simulating cells'):\\n for n in range(len(self.condition_dict[ID])):\\n cond_dict = self.condition_dict[ID][n]\\n g, tc, rsh_mult, rs_mult, Io_mult, Il_mult, nnsvth_mult = cond_dict['E'], cond_dict['Tc'], cond_dict[\\n 'Rsh_mult'], cond_dict['Rs_mult'], cond_dict['Io_mult'], cond_dict['Il_mult'], cond_dict['nnsvth_mult']\\n # calculate the 5 parameters for each set of cell conditions\\n\\n # Eventually, replace this with derived 5-parameters\\n iph, io, rs, rsh, nnsvth = pvlib.pvsystem.calcparams_cec(effective_irradiance=g, temp_cell=tc,\\n alpha_sc=self.cell_parameters['alpha_sc'],\\n a_ref=self.cell_parameters['a_ref'],\\n I_L_ref=self.cell_parameters['I_L_ref'],\\n I_o_ref=self.cell_parameters['I_o_ref'],\\n R_sh_ref=self.cell_parameters['R_sh_ref'],\\n R_s=self.cell_parameters['R_s'],\\n Adjust=self.cell_parameters['Adjust'])\\n rs, rsh, io, iph, nnsvth = rs * rs_mult, rsh * \\\\\\n rsh_mult, io * Io_mult, iph * Il_mult, nnsvth * nnsvth_mult\\n\\n # calculate cell IV curves by condition, rather than by cell index\\n voc_est = pvlib.singlediode.estimate_voc(iph, io, nnsvth)\\n v = voltage_pts(self.num_points_in_IV, voc_est,\\n self.module_parameters['breakdown_voltage'])\\n i = pvlib.singlediode.bishop88_i_from_v(v, iph, io, rs, rsh, nnsvth,\\n breakdown_factor=self.module_parameters['breakdown_factor'],\\n breakdown_voltage=self.module_parameters[\\n 'breakdown_voltage'],\\n breakdown_exp=self.module_parameters['breakdown_exp'])\\n\\n # @dev: Uncomment if debugging pvlib bishop88 simulation results\\n # plt.plot(v,i)\\n # plt.xlim(-5,v[-1])\\n # plt.ylim(0,iph+1)\\n # plt.title(f\\\"{ID}: {n} :: {rs},\\\"\\n # f\\\"{rsh}, {io}, {iph}, {nnsvth}\\\")\\n # plt.show()\\n\\n self.condition_dict[ID][n]['V'] = v\\n self.condition_dict[ID][n]['I'] = i\\n self.condition_dict[ID][n]['E'] = g\\n self.condition_dict[ID][n]['Tc'] = tc\\n return\",\n \"def generate(self):\\n for i in range(4):\\n random_first = randomize_first_box()\\n self.randomize(random_first)\\n for i in range(9):\\n random_pos = randomize_position()\\n self.randomize(random_pos)\\n self.board.solve()\",\n \"def generate_random_starting_point(variables):\\n variable_dict = {}\\n\\n for i in variables:\\n variable_dict[variables[i - 1]] = 1 if random.random() < 0.5 else 0\\n\\n return variable_dict\",\n \"def sample(self):\\n assignments = {}\\n for v in self.all_variables():\\n for _ in range(gc.max_satisfy_tries):\\n assignments[v] = v.sample()\\n if self.check(assignments):\\n break\\n assert len(assignments) == len(self.all_variables())\\n return assignments\",\n \"def demo():\\n # Create some nonterminals\\n S, NP, VP, PP = nonterminals('S, NP, VP, PP')\\n N, V, P, Det = nonterminals('N, V, P, Det')\\n VP_slash_NP = VP/NP\\n\\n print 'Some nonterminals:', [S, NP, VP, PP, N, V, P, Det, VP/NP]\\n print ' S.symbol() =>', `S.symbol()`\\n print\\n\\n # Create some CFG Productions\\n prods = [CFGProduction(S, [NP, VP]), CFGProduction(PP, [P, NP]),\\n CFGProduction(NP, [Det, N]), CFGProduction(NP, [NP, PP]),\\n CFGProduction(VP, [V, NP]), CFGProduction(VP, [VP, PP]),\\n CFGProduction(Det, ['a']), CFGProduction(Det, ['the']),\\n CFGProduction(N, ['dog']), CFGProduction(N, ['cat']), \\n CFGProduction(V, ['chased']), CFGProduction(V, ['sat']),\\n CFGProduction(P, ['on']), CFGProduction(P, ['in'])]\\n\\n prod = prods[2]\\n print 'A CFG production:', `prod`\\n print ' prod.lhs() =>', `prod.lhs()`\\n print ' prod.rhs() =>', `prod.rhs()`\\n print\\n\\n # Create and print a CFG\\n cfg = CFG(S, prods)\\n print 'A CFG grammar:', `cfg`\\n print ' cfg.start() =>', `cfg.start()`\\n print ' cfg.productions() =>',\\n # Use string.replace(...) is to line-wrap the output.\\n print `cfg.productions()`.replace(',', ',\\\\n'+' '*25)\\n print\\n\\n # Create some probabilistic CFG Productions\\n A, B, C = nonterminals('A, B, C')\\n pcfg_prods = [PCFGProduction(A, [B, B], prob=0.3),\\n PCFGProduction(A, [C, B, C], prob=0.7),\\n PCFGProduction(B, [B, 'b'], prob=0.5),\\n PCFGProduction(B, [C], prob=0.5),\\n PCFGProduction(C, ['a'], prob=0.1),\\n PCFGProduction(C, ['b'], prob=0.9)] \\n \\n pcfg_prod = pcfg_prods[2]\\n print 'A PCFG production:', `pcfg_prod`\\n print ' pcfg_prod.lhs() =>', `pcfg_prod.lhs()`\\n print ' pcfg_prod.rhs() =>', `pcfg_prod.rhs()`\\n print ' pcfg_prod.prob() =>', `pcfg_prod.prob()`\\n print\\n\\n # Create and print a PCFG\\n pcfg = PCFG(S, pcfg_prods)\\n print 'A PCFG grammar:', `pcfg`\\n print ' pcfg.start() =>', `pcfg.start()`\\n print ' pcfg.productions() =>',\\n # Use string.replace(...) is to line-wrap the output.\\n print `pcfg.productions()`.replace(',', ',\\\\n'+' '*26)\\n print\",\n \"def get_predefined_constant_names_latex():\\n return \\\"t_0/t_g\\\", \\\"t_g\\\", r\\\"\\\\dot{\\\\varepsilon}\\\", \\\\\\n \\\"E_1\\\", \\\"E_3\\\", r\\\"\\\\nu_{21}\\\", r\\\"\\\\nu_{31}\\\"\",\n \"def generate_pseudo_ground_truth_map(args: argparse.Namespace):\\n data, ref_map = load_data(data_path=args.data_path, ref_map_path=args.ref_map_path)\\n data = min_max_normalize_data(data=data)\\n guided_image = get_guided_image(data=data)\\n train_samples, train_labels, test_samples, test_labels = prepare_datasets(ref_map=ref_map,\\n training_patch=args.training_patch)\\n\\n prediction = train_svm(data=data, test_labels=test_labels, test_samples=test_samples,\\n train_labels=train_labels, train_samples=train_samples)\\n\\n updated_ref_map = construct_new_ref_map(labels=np.concatenate((train_labels, prediction)),\\n samples=train_samples + test_samples,\\n ref_map_shape=ref_map.shape)\\n\\n one_hot_ref_map = one_hot_map(ref_map=updated_ref_map.copy())\\n\\n print(\\\"SVM classification map similarity score according to GT map {0:5.2f}%\\\".format(\\n ((ref_map == updated_ref_map).sum() / ref_map.size) * float(100)))\\n\\n window_size = 2 * (args.radius_size - 1) + 1\\n\\n improved_class_map = edge_preserving_filter(ref_map=one_hot_ref_map,\\n window_size=window_size,\\n guided_image=guided_image)\\n\\n np.save(os.path.join(args.dest_path, \\\"improved_classification_map_{}\\\".format(str(args.bands_num))),\\n improved_class_map)\",\n \"def init_c_code(self):\\r\\n subd = dict(\\r\\n zip(self.fgraph.inputs,\\r\\n [\\\"%%(i%i)s\\\" % i for i in xrange(len(self.fgraph.inputs))])\\r\\n + zip(self.fgraph.outputs,\\r\\n [\\\"%%(o%i)s\\\" % i for i in xrange(len(self.fgraph.outputs))]))\\r\\n\\r\\n for orphan in self.fgraph.variables: # fgraph.orphans:\\r\\n if orphan.owner is None and orphan not in self.fgraph.inputs:\\r\\n if isinstance(orphan, Constant):\\r\\n subd[orphan] = orphan.type.c_literal(orphan.data)\\r\\n else:\\r\\n raise ValueError(\\r\\n \\\"All orphans in the fgraph to Composite must\\\"\\r\\n \\\" be Constant instances.\\\")\\r\\n\\r\\n _c_code = \\\"{\\\\n\\\"\\r\\n self.nodenames = [\\\"%(nodename)s_\\\" + ('subnode%i' % j)\\r\\n for j, n in enumerate(self.fgraph.toposort())]\\r\\n\\r\\n i = 0\\r\\n for j, node in enumerate(self.fgraph.toposort()):\\r\\n for output in node.outputs:\\r\\n if output not in subd:\\r\\n i += 1\\r\\n name = \\\"V%%(id)s_tmp%i\\\" % i\\r\\n subd[output] = name\\r\\n _c_code += \\\"%s %s;\\\\n\\\" % (\\r\\n output.type.dtype_specs()[1], name)\\r\\n s = node.op.c_code(node,\\r\\n self.nodenames[j],\\r\\n [subd[input] for input in node.inputs],\\r\\n [subd[output] for output in node.outputs],\\r\\n dict(fail=\\\"%(fail)s\\\",\\r\\n id=\\\"%%(id)s_%i\\\" % j))\\r\\n _c_code += s\\r\\n _c_code += \\\"\\\\n\\\"\\r\\n _c_code += \\\"}\\\\n\\\"\\r\\n self._c_code = _c_code\",\n \"def calc_variables ( ):\\n\\n # In this example we simulate using the shifted-force potential only\\n # The values of < p_sf >, < e_sf > and density should be consistent (for this potential)\\n # There are no long-range or delta corrections\\n\\n from averages_module import VariableType\\n \\n # Preliminary calculations\\n vol = box**3 # Volume\\n rho = n / vol # Density\\n\\n # Variables of interest, of class VariableType, containing three attributes:\\n # .val: the instantaneous value\\n # .nam: used for headings\\n # .method: indicating averaging method\\n # If not set below, .method adopts its default value of avg\\n # The .nam and some other attributes need only be defined once, at the start of the program,\\n # but for clarity and readability we assign all the values together below\\n\\n # Move acceptance ratio\\n m_r = VariableType ( nam = 'Move ratio', val = m_ratio, instant = False )\\n\\n # Internal energy per molecule (shifted-force potential)\\n # Ideal gas contribution (assuming nonlinear molecules) plus total PE divided by N\\n e_sf = VariableType ( nam = 'E/N shifted force', val = 3.0*temperature + total.pot/n )\\n\\n # Pressure (shifted-force potential)\\n # Ideal gas contribution plus total virial divided by V\\n p_sf = VariableType ( nam = 'P shifted force', val = rho*temperature + total.vir/vol )\\n\\n # Collect together into a list for averaging\\n return [ m_r, e_sf, p_sf ]\",\n \"def _make_hard_copy_ops(target_vars, online_vars):\\n return [(target_vars[var_name].assign(online_vars[var_name]))\\n for var_name in target_vars.keys()]\",\n \"def _init_costs(self, var_obj, edges):\\n prob_tables = np.ones( var_obj.states* len(edges))\\n util_tables = np.zeros( var_obj.states*len(edges))\\n return np.concatenate( (prob_tables, util_tables) )\",\n \"def generate_all_states(self):\\n self.clingo = ClingoBridge() # reset clingo\\n\\n base = ('base', '')\\n self.clingo.add_file('initial-states.lp')\\n self.clingo.run([base])\\n output = self.clingo.output\\n\\n num_states = int(len(output) / 2)\\n\\n states = np.full(num_states, object)\\n for i in range(0, num_states):\\n state_atoms = []\\n for atom in output[i]:\\n if atom.name == 'state':\\n state_atoms.append(atom)\\n states[i] = self.parse_state(state_atoms)\\n return states\",\n \"def _get_vars(symbol: Union[str, int]) -> str:\\n if isinstance(symbol, str):\\n return {\\n 'circle': 'var b1=n.round(t,2);',\\n 'square': 'var b1=n.round(t,2);',\\n 'diamond': 'var b1=n.round(t*1.3,2);',\\n 'hexagram': 'var b1=n.round(t,2);var b2=n.round(t/2,2);var b3=n.round(t*Math.sqrt(3)/2,2);'\\n }[symbol]\\n return {\\n 37: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 38: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 39: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 40: 'var d1=n.round(t*1.2,2);var d2=n.round(t*1.6,2);var d3=n.round(t*0.8,2);',\\n 34: 'var d1=n.round(t,2);',\\n 33: 'var d1=n.round(t*1.4,2);',\\n 35: 'var d1=n.round(t*1.2,2);var d2=n.round(t*0.85,2);',\\n 36: 'var d1=n.round(t/2,2);var d2=n.round(t,2);'\\n }[symbol]\",\n \"def compile_classicals():\\n \\n gc_frame = coord.Galactocentric(galcen_distance=8*u.kpc, z_sun=0*u.pc)\\n frame_dict0 = gc_frame.__dict__\\n old_keys = frame_dict0.keys()\\n \\n frame_dict = {}\\n for k in ['galcen_distance', 'roll', 'galcen_v_sun', 'galcen_coord', 'z_sun']:\\n frame_dict[k] = frame_dict0['_{}'.format(k)]\\n \\n t = Table.read('../data/gdr2_satellites_c4.txt', format='ascii')\\n \\n x = np.array([t['X']-8, t['Y'], t['Z']])*u.kpc\\n v = np.array([t['U'], t['V'], t['W']])*u.km/u.s\\n \\n for i in range(3):\\n v[i] = v[i] + gc_frame.galcen_v_sun.d_xyz[i]\\n \\n xgal = coord.Galactocentric(x, **frame_dict)\\n xeq = xgal.transform_to(coord.ICRS)\\n veq_ = gc.vgal_to_hel(xeq, v, galactocentric_frame=gc_frame)\\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 # store observables\\n data = {'name': t['Name'], 'ra': xeq.ra, 'dec': xeq.dec, 'distance': xeq.distance, 'pmra': veq[0], 'pmdec': veq[1], 'vr': veq[2]}\\n \\n tout = Table(data=data, names=('name', 'ra', 'dec', 'distance', 'pmra', 'pmdec', 'vr'))\\n tout.pprint()\\n tout.write('../data/positions_classical.fits', overwrite=True)\",\n \"def get_all_possible_moves():\\r\\n \\\"\\\"\\\"\\r\\n Creates the labels for the universal chess interface into an array and returns them\\r\\n \\\"\\\"\\\"\\r\\n labels_array = []\\r\\n letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']\\r\\n numbers = ['1', '2', '3', '4', '5', '6', '7', '8']\\r\\n promoted_to = ['q', 'r', 'b', 'n']\\r\\n\\r\\n for l1 in range(8):\\r\\n for n1 in range(8):\\r\\n destinations = [(t, n1) for t in range(8)] + \\\\\\r\\n [(l1, t) for t in range(8)] + \\\\\\r\\n [(l1 + t, n1 + t) for t in range(-7, 8)] + \\\\\\r\\n [(l1 + t, n1 - t) for t in range(-7, 8)] + \\\\\\r\\n [(l1 + a, n1 + b) for (a, b) in\\r\\n [(-2, -1), (-1, -2), (-2, 1), (1, -2), (2, -1), (-1, 2), (2, 1), (1, 2)]]\\r\\n for (l2, n2) in destinations:\\r\\n if (l1, n1) != (l2, n2) and l2 in range(8) and n2 in range(8):\\r\\n move = letters[l1] + numbers[n1] + letters[l2] + numbers[n2]\\r\\n labels_array.append(move)\\r\\n for l1 in range(8):\\r\\n l = letters[l1]\\r\\n for p in promoted_to:\\r\\n labels_array.append(l + '2' + l + '1' + p)\\r\\n labels_array.append(l + '7' + l + '8' + p)\\r\\n if l1 > 0:\\r\\n l_l = letters[l1 - 1]\\r\\n labels_array.append(l + '2' + l_l + '1' + p)\\r\\n labels_array.append(l + '7' + l_l + '8' + p)\\r\\n if l1 < 7:\\r\\n l_r = letters[l1 + 1]\\r\\n labels_array.append(l + '2' + l_r + '1' + p)\\r\\n labels_array.append(l + '7' + l_r + '8' + p)\\r\\n return labels_array\",\n \"def random_var_triplets(global_vars1, global_vars2, templates):\\n m1, m2 = type_var_map(global_vars1), type_var_map(global_vars2)\\n mapping = []\\n for t in templates:\\n if t not in m1:\\n continue\\n if t not in m2:\\n continue\\n random_tuples = random_tuple_list(m1[t], m2[t])\\n for tup in random_tuples:\\n mapping.append((tup[0], tup[1], random.choice(templates[t]), t))\\n return mapping\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.586086","0.5699041","0.5360215","0.53582025","0.53362375","0.53046995","0.5301871","0.52834463","0.521038","0.51668525","0.5156945","0.51415384","0.5131329","0.51303786","0.51272744","0.51083404","0.51081413","0.5107868","0.50947726","0.5074924","0.5066942","0.5065693","0.5064234","0.5060313","0.5053041","0.5035817","0.5030404","0.5029692","0.50229186","0.50115556","0.5010249","0.5007583","0.49999163","0.49994925","0.4999178","0.49932513","0.49669334","0.496498","0.49525055","0.49484497","0.4938907","0.4938428","0.49343708","0.4934077","0.49119788","0.491102","0.49024788","0.48963663","0.48918626","0.48896348","0.4880428","0.4879787","0.48789153","0.48777905","0.48754653","0.4866044","0.48658246","0.48650143","0.48611623","0.48580706","0.48532102","0.48472452","0.48427847","0.48382697","0.48297852","0.48288307","0.4827852","0.48273873","0.4823111","0.48170283","0.48152125","0.48147103","0.4805255","0.48020092","0.47991082","0.47979322","0.4793281","0.47931722","0.4790285","0.47844303","0.47836807","0.47832504","0.4781664","0.47806418","0.47782695","0.47760114","0.47749084","0.47677904","0.4765807","0.476507","0.4761114","0.47608018","0.4758255","0.4755528","0.47527078","0.47494936","0.47478554","0.47447804","0.47408846","0.4740165"],"string":"[\n \"0.586086\",\n \"0.5699041\",\n \"0.5360215\",\n \"0.53582025\",\n \"0.53362375\",\n \"0.53046995\",\n \"0.5301871\",\n \"0.52834463\",\n \"0.521038\",\n \"0.51668525\",\n \"0.5156945\",\n \"0.51415384\",\n \"0.5131329\",\n \"0.51303786\",\n \"0.51272744\",\n \"0.51083404\",\n \"0.51081413\",\n \"0.5107868\",\n \"0.50947726\",\n \"0.5074924\",\n \"0.5066942\",\n \"0.5065693\",\n \"0.5064234\",\n \"0.5060313\",\n \"0.5053041\",\n \"0.5035817\",\n \"0.5030404\",\n \"0.5029692\",\n \"0.50229186\",\n \"0.50115556\",\n \"0.5010249\",\n \"0.5007583\",\n \"0.49999163\",\n \"0.49994925\",\n \"0.4999178\",\n \"0.49932513\",\n \"0.49669334\",\n \"0.496498\",\n \"0.49525055\",\n \"0.49484497\",\n \"0.4938907\",\n \"0.4938428\",\n \"0.49343708\",\n \"0.4934077\",\n \"0.49119788\",\n \"0.491102\",\n \"0.49024788\",\n \"0.48963663\",\n \"0.48918626\",\n \"0.48896348\",\n \"0.4880428\",\n \"0.4879787\",\n \"0.48789153\",\n \"0.48777905\",\n \"0.48754653\",\n \"0.4866044\",\n \"0.48658246\",\n \"0.48650143\",\n \"0.48611623\",\n \"0.48580706\",\n \"0.48532102\",\n \"0.48472452\",\n \"0.48427847\",\n \"0.48382697\",\n \"0.48297852\",\n \"0.48288307\",\n \"0.4827852\",\n \"0.48273873\",\n \"0.4823111\",\n \"0.48170283\",\n \"0.48152125\",\n \"0.48147103\",\n \"0.4805255\",\n \"0.48020092\",\n \"0.47991082\",\n \"0.47979322\",\n \"0.4793281\",\n \"0.47931722\",\n \"0.4790285\",\n \"0.47844303\",\n \"0.47836807\",\n \"0.47832504\",\n \"0.4781664\",\n \"0.47806418\",\n \"0.47782695\",\n \"0.47760114\",\n \"0.47749084\",\n \"0.47677904\",\n \"0.4765807\",\n \"0.476507\",\n \"0.4761114\",\n \"0.47608018\",\n \"0.4758255\",\n \"0.4755528\",\n \"0.47527078\",\n \"0.47494936\",\n \"0.47478554\",\n \"0.47447804\",\n \"0.47408846\",\n \"0.4740165\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62475884"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19592,"cells":{"query":{"kind":"string","value":"` Given a joint probability table, format it for LaTeX. This function will have to be tailored for every paper. This function simply generates the {tabular} part of the table. The prologue and epilogue, including the caption and label, must be specified in the including file."},"document":{"kind":"string","value":"def formatJointTableForLaTeX(joints):\n (varList, atoms, probs) = joints\n cols = len(varList) + len (probs[0][1])\n with open(\"table1.tex\",\"w\") as out:\n out.write (\"\\\\begin{tabular}{|\" + \"|\".join([\"c\"]*(cols-2))+\"||c|c|}\\n\")\n out.write (\"\\\\hline\\n\")\n # Table header\n out.write (\" & \".join(varList) + \" & \" + \" & \".join([a for a in atoms]) + \" & Joint $p$ & ln~$p$ \\\\\\\\ \\\\hline\\n\")\n # Table rows\n logps = []\n for (grounding, probs) in probs:\n out.write (\" & \".join([val for (var, val) in grounding.varList]) + \" & \" +\n \" & \".join([str(n.val)+\" ({:.1f})\".format(p) for (n,p) in probs[:-2]]) +\n \" & {:.2f}\".format(probs[-2]) + \" & {:.2f}\".format(probs[-1]) + \"\\\\\\\\\\n\")\n logps.append(probs[-1])\n # A line to indicate there are further entries in the DB\n out.write(\" & \".join([\"\\ldots\"]*cols) + \"\\\\\\\\\\n\")\n # Close environment\n out.write (\"\\\\hline\\n\\\\end{tabular}\\n\")\n\n with open(\"tab1plogp.tex\",\"w\") as plogp:\n plogp.write(\"\\\\newcommand{\\\\pseudologp}{\"+\"{:.2f}\".format(sum(logps)/len(logps))+\"}\\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 generate_latex_table(true_hmm, sampled_hmm_list, conf=0.95, dt=1, time_unit='ms', obs_name='force', obs_units='pN', outfile=None):\n\n # confidence interval\n for sampled_hmm in sampled_hmm_list:\n sampled_hmm.set_confidence(conf)\n # dt\n dt = float(dt)\n # nstates\n nstates = sampled_hmm_list[0].nstates\n\n table = r\"\"\"\n\\begin{table*}\n\\caption{{\\bf Estimated mean model parameters and confidence intervals for synthetic timeseries data}}\n\\label{table:synthetic-confidence-intervals}\n\\begin{tabular*}{\\textwidth}{@{\\extracolsep{\\fill}}lccccc}\n\\hline\n& & & \\multicolumn{3}{c}{\\bf Estimated Model Parameters} \\\\ \\cline{4-6}\n\\multicolumn{2}{l}{\\bf Property} & \\bf True Value & \\bf 1 000 observations & \\bf 10 000 observations & \\bf 100 000 observations\\\\ \\hline\n\"\"\"\n # Stationary probability.\n for i in range(nstates):\n if (i == 0):\n table += '\\t\\tEquilibrium probability '\n table += '\\t\\t& $\\pi_{%d}$ & $%0.3f$' % (i+1, true_hmm.stationary_distribution[i])\n for sampled_hmm in sampled_hmm_list:\n p = sampled_hmm.stationary_distribution_mean\n p_lo, p_hi = sampled_hmm.stationary_distribution_conf\n table += ' & $%0.3f_{\\:%0.3f}^{\\:%0.3f}$ ' % (p[i], p_lo[i], p_hi[i])\n table += ' \\\\\\\\' + '\\n'\n table += '\\t\\t\\hline' + '\\n'\n\n # Transition probabilities.\n for i in range(nstates):\n for j in range(nstates):\n if (i == 0) and (j==0):\n table += '\\t\\tTransition probability ($\\Delta t = $%s) ' % (str(dt)+' '+time_unit)\n table += '\\t\\t& $T_{%d%d}$ & $%0.3f$' % (i+1, j+1, true_hmm.transition_matrix[i,j])\n for sampled_hmm in sampled_hmm_list:\n P = sampled_hmm.transition_matrix_mean\n P_lo, P_hi = sampled_hmm.transition_matrix_conf\n table += ' & $%0.3f_{\\:%0.3f}^{\\:%0.3f}$' % (P[i,j], P_lo[i,j], P_hi[i,j])\n table += ' \\\\\\\\' + '\\n'\n table += '\\t\\t\\hline' + '\\n'\n table += '\\t\\t\\hline' + '\\n'\n\n # Transition rates via pseudogenerator.\n index = 0\n for i in range(nstates):\n for j in range(nstates):\n if (i != j):\n if (index==0):\n table += '\\t\\tTransition rate (%s$^{-1}$) ' % time_unit\n Ktrue = compute_rate(true_hmm.transition_matrix, dt)\n table += '\\t\\t& $k_{%d%d}$ & $%2.3f$' % (i+1, j+1, Ktrue[i,j])\n for sampled_hmm in sampled_hmm_list:\n P = sampled_hmm.transition_matrix_mean\n P_lo, P_hi = sampled_hmm.transition_matrix_conf\n K = compute_rate(P, dt)\n K_lo = compute_rate(P_lo, dt)\n K_hi = compute_rate(P_hi, dt)\n table += ' & $%.3f_{\\:%.3f}^{\\:%.3f}$' % (K[i,j], K_lo[i,j], K_hi[i,j])\n index += 1\n table += ' \\\\\\\\' + '\\n'\n table += '\\t\\t\\hline' + '\\n'\n\n # State mean lifetimes.\n for i in range(nstates):\n if (i == 0):\n table += '\\t\\tState mean lifetime (%s) ' % time_unit\n l = true_hmm.lifetimes\n l *= dt\n table += '\\t\\t& $t_{%d}$ & $%.3f$' % (i+1, l[i])\n for sampled_hmm in sampled_hmm_list:\n l = sampled_hmm.lifetimes_mean\n l *= dt\n l_lo, l_hi = sampled_hmm.lifetimes_conf\n l_lo *= dt; l_hi *= dt\n table += ' & $%.3f_{\\:%.3f}^{\\:%.3f}$' % (l[i], l_lo[i], l_hi[i])\n table += ' \\\\\\\\' + '\\n'\n table += '\\t\\t\\hline' + '\\n'\n\n # State relaxation timescales.\n for i in range(nstates-1):\n if (i == 0):\n table += '\\t\\tRelaxation time (%s) ' % time_unit\n t = true_hmm.timescales\n t *= dt\n table += '\\t\\t& $\\\\tau_{%d}$ & $%.3f$' % (i+1, t[i])\n for sampled_hmm in sampled_hmm_list:\n t = sampled_hmm.timescales_mean\n t *= dt\n t_lo, t_hi = sampled_hmm.timescales_conf\n t_lo *= dt; t_hi *= dt\n table += ' & $%.3f_{\\:%.3f}^{\\:%.3f}$' % (t[i], t_lo[i], t_hi[i])\n table += ' \\\\\\\\' + '\\n'\n table += '\\t\\t\\hline' + '\\n'\n\n if True:\n table += '\\t\\t\\hline' + '\\n'\n\n # State mean forces.\n for i in range(nstates):\n if (i == 0):\n table += '\\t\\tState %s mean (%s) ' % (obs_name, obs_units)\n m = true_hmm.output_model.means\n table += '\\t\\t& $\\mu_{%d}$ & $%.3f$' % (i+1, m[i])\n for sampled_hmm in sampled_hmm_list:\n m = sampled_hmm.means_mean\n m_lo, m_hi = sampled_hmm.means_conf\n table += ' & $%.3f_{\\:%.3f}^{\\:%.3f}$' % (m[i], m_lo[i], m_hi[i])\n table += ' \\\\\\\\' + '\\n'\n table += '\\t\\t\\hline' + '\\n'\n\n # State force standard deviations.\n for i in range(nstates):\n if (i == 0):\n table += '\\t\\tState %s std dev (%s) ' % (obs_name, obs_units)\n s = true_hmm.output_model.sigmas\n table += '\\t\\t& $s_{%d}$ & $%.3f$' % (i+1, s[i])\n for sampled_hmm in sampled_hmm_list:\n s = sampled_hmm.sigmas_mean\n s_lo, s_hi = sampled_hmm.sigmas_conf\n table += ' & $%.3f_{\\:%.3f}^{\\:%.3f}$' % (s[i], s_lo[i], s_hi[i])\n table += ' \\\\\\\\' + '\\n'\n table += '\\t\\t\\hline' + '\\n'\n\n table += r\"\"\"\\hline\n\\end{tabular*}\n\\end{table*}\n\"\"\"\n\n # Write to file if desired.\n if outfile is not None:\n f = open(outfile,'w')\n f.write(table)\n f.close()\n\n return table","def generate_latex_table(dictionary,filename,location=\".\"):\n if type(filename) != str:\n raise TypeError('filename should be string')\n if type(dictionary) != dict:\n raise TypeError('dictionary should be dictionary')\n\n head_code = \"\"\"\\\\documentclass{article}\n%In the preamble section include the arabtex and utf8 packages\n\\\\usepackage{arabtex}\n\\\\usepackage{utf8}\n\\\\usepackage{longtable}\n\\\\usepackage{color, colortbl}\n\\\\usepackage{supertabular}\n\\\\usepackage{multicol}\n\\\\usepackage{geometry}\n\\\\geometry{left=.1in, right=.1in, top=.1in, bottom=.1in}\n\n\\\\begin{document}\n\\\\begin{multicols}{6}\n\\\\setcode{utf8}\n\n\\\\begin{center}\"\"\"\n\n tail_code = \"\"\"\\\\end{center}\n\\\\end{multicols}\n\\\\end{document}\"\"\"\n\n begin_table = \"\"\"\\\\begin{tabular}{ P{2cm} P{1cm}}\n\\\\textbf{words} & \\\\textbf{\\\\#} \\\\\\\\\n\\\\hline\n\\\\\\\\[0.01cm]\"\"\"\n end_table= \"\"\"\\\\end{tabular}\"\"\"\n rows_num = 40\n if location != '.':\n filename = location +\"/\"+ filename\n\n try:\n file = open(filename+'.tex', 'w', encoding='utf8')\n file.write(head_code+'\\n')\n n= int(len(dictionary)/rows_num)\n words = [(\"\\\\<\"+word+\"> & \"+str(frequancy)+' \\\\\\\\ \\n') for word, frequancy in dictionary.items()]\n start=0\n end=rows_num\n new_words = []\n for i in range(n):\n new_words = new_words+ [begin_table+'\\n'] +words[start:end] +[end_table+\" \\n\"]\n start=end\n end+=rows_num\n remain_words = len(dictionary) - rows_num*n\n if remain_words > 0:\n new_words += [begin_table+\" \\n\"]+ words[-1*remain_words:]+[end_table+\" \\n\"]\n for word in new_words:\n file.write(word)\n file.write(tail_code)\n file.close()\n return True\n except:\n return False","def setup_latex_table(self, tabletype, injected):\n self.texfile.write(r\"\\\\renewcommand{\\\\arraystretch}{1.6}\\n\")\n self.texfile.write(r\"\\n\")\n self.texfile.write(r\"\\\\begin{table}[t!]\\n\")\n self.texfile.write(r\" \\\\begin{center}\\n\")\n if tabletype == 'fiducial_fit_params':\n if injected:\n nextline = r\" \\\\begin{tabu} to 1.0\\\\textwidth \"\n nextline += r\"{| X[2.0,c] | X[1,c] | X[1,c] | X[1,c]\"\n nextline += r\" | X[1,c] | X[1,c] | X[1,c] | X[1,c] |}\\n\"\n self.texfile.write(nextline)\n self.texfile.write(r\" \\hline\\n\")\n nextline = r\" \\multirow{2}{*}{\\\\textbf{Parameter}} \"\n nextline += r\"& \\multirow{2}{*}{\\\\textbf{Inj}} \"\n nextline += r\"& \\multicolumn{3}{c|}{h0} \"\n nextline += r\"& \\multicolumn{3}{c|}{h1} \"\n nextline += r\"\\\\\\\\ \\cline{3-8}\"\n self.texfile.write(nextline)\n nextline = r\" & & Prior & Fit & \\(\\Delta\\) \"\n nextline += r\"& Prior & Fit & \\(\\Delta\\) \\\\\\\\ \\hline\\n\"\n self.texfile.write(nextline)\n else:\n nextline = r\" \\\\begin{tabu} to 1.0\\\\textwidth \"\n nextline += r\"{| X[c] | X[c] | X[c] |}\\n\"\n self.texfile.write(nextline)\n self.texfile.write(r\" \\hline\\n\")\n self.texfile.write(r\" Parameter & h0 & h1 \\\\\\\\ \\hline\\n\")\n elif tabletype == 'fiducial_fit_metrics':\n nextline = r\" \\\\begin{tabu} to 1.0\\\\textwidth \"\n nextline += r\"{| X[c] | X[c] | X[c] |}\\n\"\n self.texfile.write(nextline)\n self.texfile.write(r\" \\hline\\n\")\n self.texfile.write(r\" h0 & h1 & $\\Delta$ \\\\\\\\ \\hline\\n\")\n else:\n raise ValueError(\n \"This function is only for making fit metric or fit \"\n \"param tables in LaTeX. Got type %s\"%tabletype\n )","def texify_table(table, labels=None, row_labels=None, align='c'):\n rows = len(table)\n cols = len(table[0])\n if labels is not None and len(labels) != cols:\n raise Exception(\"Invalid argument value: labels.\")\n if row_labels is not None and len(row_labels) != rows:\n raise Exception(\"Invalid argument value: row_labels.\")\n # begin table\n s = \"\\\\begin{tabular}{\"\n if row_labels is not None: s += 'l|'\n s += align * cols\n s += \"}\\n\"\n s += \"\\\\toprule\\n\"\n # header\n if labels is not None:\n if row_labels is not None: s += ' & '\n s += \" & \".join(labels)\n s += \" \\\\\\\\ \\n\"\n s += \"\\\\midrule\\n\"\n # table\n for idx, row in enumerate(table):\n if row_labels is not None: s += row_labels[idx] + \" & \"\n s += \" & \".join(map(str, row))\n s += \" \\\\\\\\ \\n\"\n # end table\n s += \"\\\\bottomrule\\n\"\n s += \"\\\\end{tabular}\" \n return s","def latex_table():\n \n t = Table.read('../data/stream_origin.fits')\n N = len(t)\n \n f = open('../paper/stream_origin.tex', 'w')\n for i in range(N):\n t_ = t[i]\n for k in t_.colnames:\n if (t_[k]==np.nan) | (t_[k]=='nan'):\n t_[k] = '\\dots'\n #f.write('{:s} & {:s} & {:s} & {:s} & {:.1f}\\\\\\\\ \\n'.format(t_['Name'], t_['host'], t_['progenitor'], t_['type'], t_['feh']))\n line = '{:s} & {:s} & {:s} & {:s} & {:s}\\\\\\\\ \\n'.format(t_['Name'], t_['host'], t_['progenitor'], t_['friends'], t_['type'])\n f.write(line)\n print(line)\n \n f.close()","def tables(args):\n\n config_file = args.setupfn\n conf_base = os.path.basename(config_file).split('.')[0]\n statfile = os.path.join(args.outputdir,\n \"{}_radvel.stat\".format(conf_base))\n status = load_status(statfile)\n\n assert status.getboolean('mcmc', 'run'), \\\n \"Must run MCMC before making tables\"\n\n P, post = radvel.utils.initialize_posterior(config_file)\n post = radvel.posterior.load(status.get('fit', 'postfile'))\n chains = pd.read_csv(status.get('mcmc', 'chainfile'))\n minafactor = status.get('mcmc', 'minafactor')\n maxarchange = status.get('mcmc', 'maxarchange')\n maxgr = status.get('mcmc', 'maxgr')\n mintz = status.get('mcmc', 'mintz')\n if 'derive' in status.sections() and status.getboolean('derive', 'run'):\n dchains = pd.read_csv(status.get('derive', 'chainfile'))\n chains = chains.join(dchains, rsuffix='_derived')\n derived = True\n else:\n derived = False\n report = radvel.report.RadvelReport(P, post, chains, minafactor, maxarchange, maxgr, mintz, derived=derived)\n tabletex = radvel.report.TexTable(report)\n attrdict = {'priors': 'tab_prior_summary', 'rv': 'tab_rv',\n 'params': 'tab_params', 'derived': 'tab_derived',\n 'crit': 'tab_crit'}\n for tabtype in args.type:\n print(\"Generating LaTeX code for {} table\".format(tabtype))\n\n if tabtype == 'ic_compare':\n assert status.has_option('ic_compare', 'ic'), \\\n \"Must run Information Criteria comparison before making comparison tables\"\n\n compstats = eval(status.get('ic_compare', 'ic'))\n report = radvel.report.RadvelReport(\n P, post, chains, minafactor, maxarchange, maxgr, mintz, compstats=compstats\n )\n tabletex = radvel.report.TexTable(report)\n tex = tabletex.tab_comparison()\n elif tabtype == 'rv':\n tex = getattr(tabletex, attrdict[tabtype])(name_in_title=args.name_in_title, max_lines=None)\n elif tabtype == 'crit':\n tex = getattr(tabletex, attrdict[tabtype])(name_in_title=args.name_in_title)\n else:\n if tabtype == 'derived':\n assert status.has_option('derive', 'run'), \\\n \"Must run `radvel derive` before making derived parameter table\"\n assert tabtype in attrdict, 'Invalid Table Type %s ' % tabtype\n tex = getattr(tabletex, attrdict[tabtype])(name_in_title=args.name_in_title)\n\n saveto = os.path.join(\n args.outputdir, '{}_{}.tex'.format(conf_base, tabtype)\n )\n with open(saveto, 'w+') as f:\n f.write(tex)\n\n savestate = {'{}_tex'.format(tabtype): os.path.relpath(saveto)}\n save_status(statfile, 'table', savestate)","def to_latex_table(self, tab=\" \", caption=\"TODO\", label=\"TODO\"):\n return \"\".join(\n (\n \"\\\\begin{center}\\n\",\n f\"{tab}\\\\begin{{table}}[ht]\\n\",\n f\"{tab*2}\\\\centering\\n\",\n f'{tab*2}\\\\rowcolors{{2}}{{white}}{{gray!25}}\\n'\n f\"{tab*2}\\\\begin{{tabular}}{{crrrrrr}}\\n\",\n (\n f\"{tab*3}\\\\cellcolor[gray]{{0.7}} & \\\\multicolumn{{2}}{{c}}\"\n \"{BT\\\\cellcolor[gray]{0.7}} & \\\\multicolumn{2}{c}{BJ\"\n \"\\\\cellcolor[gray]{0.7}} & \\\\multicolumn{2}{c}\"\n \"{CBJ\\\\cellcolor[gray]{0.7}} \\\\\\\\\\n\"\n ),\n (\n f\"{tab*3}\\\\cellcolor[gray]{{0.7}} Test suite & \"\n \"\\\\multicolumn{1}{c}{\\\\cellcolor[gray]{0.7}Nodes} & \"\n \"\\\\multicolumn{1}{c}{\\\\cellcolor[gray]{0.7}Time(s)} & \"\n \"\\\\multicolumn{1}{c}{\\\\cellcolor[gray]{0.7}Nodes} & \"\n \"\\\\multicolumn{1}{c}{\\\\cellcolor[gray]{0.7}Time(s)} & \"\n \"\\\\multicolumn{1}{c}{\\\\cellcolor[gray]{0.7}Nodes} & \"\n \"\\\\multicolumn{1}{c}{\\\\cellcolor[gray]{0.7}Time(s)}\\\\\\\\\\n\"\n ),\n \"\".join(\n (\n f\"{tab*3}{i} & {bt.nodes_expanded} & {bt.time} \"\n f\"& {bj.nodes_expanded} & {bj.time} & {cbj.nodes_expanded} & \"\n f\"{cbj.time}\\\\\\\\\\n\"\n for i, (bt, bj, cbj) in enumerate(zip(*self.data))\n )\n ),\n f\"{tab*2}\\\\end{{tabular}}\\n\"\n f\"{tab*2}\\\\caption{{{caption}}}\\n\"\n f\"{tab*2}\\\\label{{tab:{label}}}\\n\"\n f\"{tab}\\\\end{{table}}\\n\"\n \"\\\\end{center}\",\n )\n )","def latex_table(samples, parameter_dict=None, labels=None):\n table = (\n \"\\\\begin{table}[hptb]\\n\\\\begin{ruledtabular}\\n\\\\begin{tabular}\"\n \"{l %s}\\n\" % (\"c \" * len(samples))\n )\n if labels:\n table += (\n \" & \" + \" & \".join(labels)\n )\n table += \"\\\\\\ \\n\\\\hline \\\\\\ \\n\"\n data = {i: i for i in samples[0].keys()}\n if parameter_dict is not None:\n import copy\n\n data = copy.deepcopy(parameter_dict)\n for param in parameter_dict.keys():\n if not all(param in samples_dict.keys() for samples_dict in samples):\n logger.warning(\n \"{} not in list of parameters. Not adding to \"\n \"table\".format(param)\n )\n data.pop(param)\n\n for param, desc in data.items():\n table += \"{}\".format(desc)\n for samples_dict in samples:\n median = samples_dict[param].average(type=\"median\")\n confidence = samples_dict[param].confidence_interval()\n table += (\n \" & $%s^{+%s}_{-%s}$\" % (\n np.round(median, 2),\n np.round(confidence[1] - median, 2),\n np.round(median - confidence[0], 2)\n )\n )\n table += \"\\\\\\ \\n\"\n table += (\n \"\\\\end{tabular}\\n\\\\end{ruledtabular}\\n\\\\caption{}\\n\\\\end{table}\"\n )\n return table","def ppg_table(signals):\n\n # TODO: add more features\n summary = {}\n\n summary[\"PPG_Rate_Mean\"] = np.mean(signals[\"PPG_Rate\"])\n summary[\"PPG_Rate_SD\"] = np.std(signals[\"PPG_Rate\"])\n summary_table = pd.DataFrame(summary, index=[0]) # .transpose()\n\n # Make HTML and Markdown versions\n html = '<h2 style=\"background-color: #D60574\">Summary table</h1>' + summary_table.to_html(\n index=None\n )\n\n try:\n md = summary_table.to_markdown(index=None)\n except ImportError:\n md = summary_table # in case printing markdown export fails\n return html, md","def insert_into_latex(body, url):\n latex_table_top = r\"\"\"\n% table automatically generated by rubberband, please have a look and check everything\n\\begin{table}\n\\caption{Performance comparison}\n\\label{tbl:rubberband_table}\n\\scriptsize\n\n\"\"\"\n latex_table_bottom = r\"\"\"\n\\end{table}\n\n\"\"\"\n return latex_table_top + body + latex_table_bottom + \"%% \" + url","def create_latex_table(data, id):\n bd = data['bd']\n sd = data['sd']\n \n filename = 'LatestResults.tex'\n file = r'..\\latex\\tables\\\\' + filename\n\n if os.path.exists(file):\n f_temp = os.path.splitext(file)[0] # without extension\n os.rename(file, f_temp + '_' + id + '.tex')\n\n f = codecs.open(file, 'w', 'utf-8')\n \n f.write('\\n' + r'\\begin{table}' + '\\n')\n f.write(r' \\centering' + '\\n')\n f.write(r' \\caption{Results for each drum instrument with batch sizes 64, 256 and 512.}' + '\\n')\n f.write(r' \\begin{tabular}{l c c c}' + '\\n')\n f.write(r' \\textbf{Batch size} & Metric & BD & SD \\\\' + '\\n')\n f.write(r' \\midrule' + '\\n')\n f.write(r' \\midrule' + '\\n')\n \n for batch_size in BATCHES:\n f.write(' ' + str(batch_size).rstrip('\\n'))\n # 0.805 +- 0.02\n f.write(r' & P & ' + r'$' + '{:.3}'.format(bd[batch_size]['p_mean']) + r' \\pm ' + '{:.3f}'.format(bd[batch_size]['p_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['p_mean']) + r' \\pm ' + '{:.3f}'.format(sd[batch_size]['p_std']) + '$' + r' \\\\' + '\\n')\n f.write(r' & R & ' + r'$' + '{:.3}'.format(bd[batch_size]['r_mean']) + r' \\pm ' + '{:.3f}'.format(bd[batch_size]['r_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['r_mean']) + r' \\pm ' + '{:.3f}'.format(sd[batch_size]['r_std']) + '$' + r' \\\\' + '\\n')\n f.write(r' & F & ' + r'$' + '{:.3}'.format(bd[batch_size]['f_mean']) + r' \\pm ' + '{:.3f}'.format(bd[batch_size]['f_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['f_mean']) + r' \\pm ' + '{:.3f}'.format(sd[batch_size]['f_std']) + '$' + r' \\\\' + '\\n')\n # Don't write horizontal line on the last batch.\n if batch_size != BATCHES[-1]:\n f.write(r' \\midrule' + '\\n')\n\n f.write(r' \\end{tabular}' + '\\n')\n f.write(r' \\label{tab:ResultsTable}' + '\\n')\n f.write(r'\\end{table}' + '\\n')\n f.close()","def print_table(table):\n for row in table:\n # Header column left justified\n print(\"{:<19}\".format(row[0]), end='')\n # Remaining columns right justified\n for col in row[1:]:\n print(\"{:>4}\".format(col), end='')\n print(\"\", end='\\n')","def make_table(ranked_means):\n fp = open(\"table.tex\", \"w\")\n fp.write(\"\"\"\\\\begin{tabular}{|l|c||l|c|}\n \\\\hline\n \\\\multicolumn{2}{|c||}{Slowest} & \\\\multicolumn{2}{|c|}{Fastest} \\\\\\\\ \\\\hline\n Feature & Rate & Feature & Rate \\\\\\\\ \\\\hline\n\"\"\")\n top_10 = ranked_means[0:10]\n bottom_10 = ranked_means[-10:]\n for ((f_rate, f_name),(s_rate,s_name)) in zip(top_10, bottom_10):\n f_name = f_name.split(\":\")[-1]\n f_name = f_name.rsplit(\" \", 1)[0] if f_name.endswith(\"(V)\") else f_name\n s_name = s_name.split(\":\")[-1]\n s_name = s_name.rsplit(\" \", 1)[0] if s_name.endswith(\"(V)\") else s_name\n fp.write(\" %s & %.2f & %s & %.2f \\\\\\\\ \\n\" % \\\n (f_name, f_rate, s_name, s_rate))\n fp.write(\"\\\\hline\\n\")\n fp.write(\"\\\\end{tabular}\\n\")\n fp.close()\n\n fp = open(\"supp_meaning_table.tex\", \"w\")\n fp.write(\"\"\"\\\\begin{tabular}{|l|c||l|c||l|c||l|c|}\n \\\\hline\n Meaning & Category & Meaning & Category & Meaning & Category & Meaning & Category\\\\\\\\ \\\\hline\n\n\"\"\")\n feature_names = [f.split(\":\")[-1] for (r,f) in ranked_means]\n feature_names.sort(key=lambda s: s.lower())\n col1 = feature_names[0:25]\n col2 = feature_names[25:50]\n col3 = feature_names[50:75]\n col4 = feature_names[75:]\n for a,b,c,d in zip(col1,col2,col3,col4):\n x,y,z,w = [get_meaning_category(i) or \"Excluded\" for i in (a,b,c,d)]\n # Lop off (V)s (we needed them above for get_meaning_category to work)\n a,b,c,d = [f.rsplit(\" \", 1)[0] if f.endswith(\"(V)\") else f for f in (a,b,c,d)]\n fp.write(\"%s & %s & %s & %s & %s & %s & %s & %s\\\\\\\\ \\n\" % (a, x, b, y, c, z, d, w))\n fp.write(\"\\\\hline\\n\")\n fp.write(\"\\\\end{tabular}\\n\")\n fp.close()","def latex(\r\n samples,\r\n median_pdf_model=True,\r\n sigma=3.0,\r\n name_to_label=True,\r\n include_name=True,\r\n include_quickmath=False,\r\n prefix=\"\",\r\n suffix=\"\"\r\n) -> str:\r\n\r\n values = values_from_samples(samples=samples, median_pdf_model=median_pdf_model)\r\n errors_at_sigma = samples.errors_at_sigma(sigma=sigma, as_instance=False)\r\n\r\n table = []\r\n\r\n for i in range(samples.model.prior_count):\r\n\r\n label_value = frm.parameter_result_latex_from(\r\n parameter_name=samples.model.parameter_names[i],\r\n value=values[i],\r\n errors=errors_at_sigma[i],\r\n superscript=samples.model.superscripts[i],\r\n name_to_label=name_to_label,\r\n include_name=include_name,\r\n include_quickmath=include_quickmath\r\n )\r\n\r\n table.append(f\"{label_value}\")\r\n\r\n table = \"\".join(table)[:-3]\r\n\r\n return f\"{prefix}{table}{suffix}\"","def to_latex_table(self, parameter_dict=None, save_to_file=None):\n import os\n\n if save_to_file is not None and os.path.isfile(\"{}\".format(save_to_file)):\n raise FileExistsError(\n \"The file {} already exists.\".format(save_to_file)\n )\n\n table = self.latex_table([self.samples_dict], parameter_dict)\n if save_to_file is None:\n print(table)\n elif os.path.isfile(\"{}\".format(save_to_file)):\n logger.warning(\n \"File {} already exists. Printing to stdout\".format(save_to_file)\n )\n print(table)\n else:\n with open(save_to_file, \"w\") as f:\n f.writelines([table])","def format_prettytable(table):\r\n for i, row in enumerate(table.rows):\r\n for j, item in enumerate(row):\r\n table.rows[i][j] = format_output(item)\r\n ptable = table.prettytable()\r\n ptable.hrules = FRAME\r\n ptable.horizontal_char = '.'\r\n ptable.vertical_char = ':'\r\n ptable.junction_char = ':'\r\n return ptable","def makeTexTable(\n tablefile, caption, sideways=False, footnotetext=None, clearpage=False, pos=\"h!\"\n):\n if sideways:\n tabletype = \"sidewaystable\"\n clearpage = True\n else:\n tabletype = \"table\"\n\n if clearpage:\n clearpagetext = r\"\\clearpage\"\n else:\n clearpagetext = \"\"\n\n if footnotetext is None:\n notes = \"\"\n else:\n notes = footnotetext\n\n tablestring = (\n dedent(\n r\"\"\"\n \\begin{%s}[%s]\n \\rowcolors{1}{CVCWhite}{CVCLightGrey}\n \\caption{%s}\n \\centering\n \\input{%s}\n \\end{%s}\n %s\n %s\n \"\"\"\n )\n % (tabletype, pos, caption, tablefile, tabletype, notes, clearpagetext)\n )\n return tablestring","def generate_supertable(*subtables):\n DEFAULT_VALUE = '-'\n # Build a set of all \"names\" to appear on the left of the table\n all_keys = set()\n for d in subtables:\n all_keys.update(d['data'].keys())\n\n # Sort the keys so there's a standard order\n all_keys = sorted(list(all_keys))\n # Create a list of table headings to pass to the template...\n table_headings = []\n # ... and a list for the colums, in matching order\n table_data = []\n for d in subtables:\n table_headings.append(d['title'])\n column = []\n for key in all_keys:\n column.append(d['data'].get(key, DEFAULT_VALUE))\n table_data.append(column)\n\n table_rows = []\n for col_number in range(len(all_keys)):\n row = []\n for row_number in range(len(subtables)):\n row.append(\n table_data[row_number][col_number]\n )\n table_rows.append(row)\n\n return generate_table_html(table_headings, all_keys, table_rows)","def prettyTable(self, heads, rows): \n # First calculate the maximum lengths for each column.\n lengths = map(len, heads)\n for row in rows:\n lengths = map(max, lengths, map(len, row))\n\n # Create a format string for the maximum lengths.\n formatString = (\"|{{:^{}}}\" * len(heads) + \"|\").format(*lengths)\n\n # Print the heads, then the contents.\n headLine = formatString.format(*heads)\n border = \"-\" * len(headLine)\n print(border)\n print(headLine)\n print(border)\n\n # Remake the format string right-justified.\n formatString = (\"|{{:>{}}}\" * len(heads) + \"|\").format(*lengths)\n for row in rows:\n print(formatString.format(*row))\n print(border)","def out(lam, eng, mat): # {{{1\n print(\"\\\\begin{table}[!htbp]\")\n print(\" \\\\renewcommand{\\\\arraystretch}{1.2}\")\n txt = \" \\\\caption{{\\\\label{{tab:{0}}}properties of {0}}}\"\n # Raw underscores in LaTeX text mode produce “Missing $” errors.\n texlname = lam.name.replace('_', '\\_')\n print(txt.format(texlname))\n print(\" \\\\centering\\\\footnotesize{\\\\rule{0pt}{10pt}\")\n print(\" \\\\tiny calculated by lamprop {}\\\\\\\\[3pt]}}\".format(__version__))\n if eng:\n _engprop(lam)\n if mat:\n _matrices(lam)\n print(\"\\\\end{table}\\n\") # 1}}}","def make_figure_table(image_files):\n cols = 2\n table_data = []\n row_data = []\n for i, fn in enumerate(image_files):\n row_data.append(p.Image(fn, 3.4 * u.inch, 3.0 * u.inch))\n if (i % cols) == (cols - 1):\n table_data.append(row_data)\n row_data = []\n\n # Determine if there are any images left to print\n if len(row_data) != 0:\n for i in range(len(row_data), cols):\n row_data.append(p.Paragraph(\"\", STYLES[\"body_style\"]))\n table_data.append(row_data)\n\n # Style this into a reportlab table and add to the story\n width = 3.75 * u.inch\n table = p.Table(table_data, colWidths=[width, width])\n table.setStyle(\n p.TableStyle(\n [\n (\"ALIGNMENT\", (0, 0), (-1, -1), \"CENTER\"),\n (\"VALIGN\", (0, 0), (-1, -1), \"CENTER\"),\n (\"TOPPADDING\", (0, 0), (-1, -1), 6.0),\n (\"BOTTOMPADDING\", (0, 0), (-1, -1), 6.0),\n ]\n )\n )\n return table","def print_para_table(s):\n if MODE == 1:\n t = [['Parameter', 'Value', 'Unit'],\n ['Number of bends', NBENDS, '/'], \n ['Width', WIDTH, 'm'],\n ['Depth', DEPTH, 'm'],\n ['Length', LAMBDA*(NBENDS+1), 'm'],\n ['Arc wavelength', LAMBDA, 'm'],\n ['Slope', SLOPE, '/'],\n ['Streamwise resolution', DS, 'm'],\n ['Transverse resolution', np.around(INTERVAL, decimals=4), 'm'],\n ['Streamwise # of pts', s.size + 2*int(LAMBDA/2/DS), '/'],\n ['Transverse # of pts', NUM*2+1, '/']]\n elif MODE == 2:\n if FNAME[0].islower():\n f = FNAME[0].upper() + FNAME[1:]\n else:\n f = FNAME\n t = [['Parameter', 'Value', 'Unit'],\n ['River name', f.rsplit('.', 1)[0], '/'],\n ['Width', WIDTH, 'm'],\n ['Depth', DEPTH, 'm'],\n ['Length', np.round(s[-1], decimals=2), 'm'],\n ['Slope', SLOPE, '/'],\n ['Streamwise resolution', np.round(np.mean(np.diff(s)), decimals=2), 'm'],\n ['Transverse resolution', np.round(INTERVAL, decimals=2), 'm'],\n ['Streamwise # of pts', s.size, '/'],\n ['Transverse # of pts', NUM*2+1, '/']]\n print(tabulate(t, tablefmt='psql', stralign='right', headers='firstrow'))","def _make_tex_table(self, tabletitle):\r\n stattable = (\r\n r\"\"\"\r\n \\begin{table}[h!]\r\n \\caption{%s}\r\n \\centering\r\n \\begin{tabular}{l l l l l}\r\n \\toprule\r\n \\textbf{Statistic} & \\textbf{Inlet} & \\textbf{Outlet} \\\\\"\"\"\r\n % tabletitle\r\n )\r\n\r\n stats = [\r\n {\"name\": \"Count\", \"attribute\": \"N\", \"rule\": \"top\", \"forceint\": True},\r\n {\"name\": \"Number of NDs\", \"attribute\": \"ND\", \"forceint\": True},\r\n {\"name\": \"Min; Max\", \"attribute\": [\"min\", \"max\"], \"twoval\": True},\r\n {\"name\": \"Mean\", \"attribute\": \"mean\"},\r\n {\r\n \"name\": \"(95\\% confidence interval)\",\r\n \"attribute\": \"mean_conf_interval\",\r\n \"twoval\": True,\r\n \"ci\": True,\r\n \"rule\": \"none\",\r\n },\r\n {\"name\": \"Standard Deviation\", \"attribute\": \"std\"},\r\n {\"name\": \"Log. Mean\", \"attribute\": \"logmean\"},\r\n {\r\n \"name\": \"(95\\% confidence interval)\",\r\n \"attribute\": \"logmean_conf_interval\",\r\n \"twoval\": True,\r\n \"ci\": True,\r\n \"rule\": \"none\",\r\n },\r\n {\"name\": \"Log. Standard Deviation\", \"attribute\": \"logstd\"},\r\n {\"name\": \"Geo. Mean\", \"attribute\": \"geomean\"},\r\n {\r\n \"name\": \"(95\\% confidence interval)\",\r\n \"attribute\": \"geomean_conf_interval\",\r\n \"twoval\": True,\r\n \"ci\": True,\r\n \"rule\": \"none\",\r\n },\r\n {\"name\": \"Coeff. of Variation\", \"attribute\": \"cov\"},\r\n {\"name\": \"Skewness\", \"attribute\": \"skew\"},\r\n {\"name\": \"Median\", \"attribute\": \"median\"},\r\n {\r\n \"name\": \"(95\\% confidence interval)\",\r\n \"attribute\": \"median_conf_interval\",\r\n \"twoval\": True,\r\n \"ci\": True,\r\n \"rule\": \"none\",\r\n },\r\n {\"name\": \"Quartiles\", \"attribute\": [\"pctl25\", \"pctl75\"], \"twoval\": True},\r\n {\r\n \"name\": \"Number of Pairs\",\r\n \"attribute\": \"n_pairs\",\r\n \"rule\": \"top\",\r\n \"fromdataset\": True,\r\n \"sigfigs\": 1,\r\n \"forceint\": True,\r\n },\r\n {\r\n \"name\": \"Wilcoxon p-value\",\r\n \"attribute\": \"wilcoxon_p\",\r\n \"fromdataset\": True,\r\n \"pval\": True,\r\n \"tex\": True,\r\n },\r\n {\r\n \"name\": \"Mann-Whitney p-value\",\r\n \"attribute\": \"mannwhitney_p\",\r\n \"fromdataset\": True,\r\n \"pval\": True,\r\n \"tex\": True,\r\n },\r\n ]\r\n for s in stats:\r\n stattable += self._tex_table_row(**s)\r\n\r\n stattable += r\"\"\"\r\n \\bottomrule\r\n \\end{tabular}\r\n \\end{table}\"\"\"\r\n\r\n return stattable + \"\\n\"","def pprint_table(out, table):\n\n\tcol_paddings = []\n\n\tfor i in range(len(table[0])):\n\t\tcol_paddings.append(get_max_width(table, i))\n\n\tfor row in table:\n\t\t# left col\n\t\tout.write(str(row[0]).ljust(col_paddings[0] + 1))\n\t\t\n\t\t# rest of the cols\n\t\tfor i in range(1, len(row)):\n\t\t\tout.write(str(row[i]).rjust(col_paddings[i] + 2))\n\t\t\n\t\tout.write('\\n')","def generate_problems_pdf(args):\n contents = generate_table(start_int=args.start_int, end_int=args.end_int, table_type=args.table_type)\n convert_latex_to_pdf(args.filename, contents=contents, view=True)\n remove_temporary_files(args.filename)","def _intermediary_to_markdown(tables, relationships):\n t = '\\n'.join(t.to_markdown() for t in tables)\n r = '\\n'.join(r.to_markdown() for r in relationships)\n return '{}\\n{}'.format(t, r)","def end_latex_table(self, tabletype):\n self.texfile.write(r\" \\end{tabu}\\n\")\n self.texfile.write(r\" \\end{center}\\n\")\n self.texfile.write(r\" \\\\vspace{-10pt}\\n\")\n newline = r\" \\caption{shows the fiducial fit \"\n if tabletype == \"fiducial_fit_params\":\n newline += \"parameters\"\n elif tabletype == \"fiducial_fit_metrics\":\n newline += \"metrics\"\n else:\n raise ValueError(\n \"This function is only for ending fit metric or fit \"\n \"param tables in LaTeX. Got type %s\"%tabletype\n )\n if self.detector is not None:\n newline += \" obtained with the %s\"%self.detector\n if self.selection is not None:\n newline += \" %s sample\"%self.selection\n if self.selection is not None:\n newline += \" obtained with the %s\"%self.selection\n newline += \" for h0 of %s\"%self.tex_axis_label(\n self.labels.dict['h0_name']\n )\n newline += \" and h1 of %s.\"%self.tex_axis_label(\n self.labels.dict['h1_name']\n )\n if self.labels.dict['data_name'] == '':\n newline += \" The truth is %s.\"%self.tex_axis_label(\n self.labels.dict['data_name']\n )\n else:\n newline += \" This is from an analysis performed on data.\"\n newline += \"}\\n\"\n self.texfile.write(newline)\n newline = r\" \\label{tab:\"\n if self.detector is not None:\n newline += self.detector\n if self.selection is not None:\n newline += self.selection\n newline += \"%stable}\\n\"%tabletype\n self.texfile.write(newline)\n self.texfile.write(r\"\\end{table}\\n\")","def generate_table(start_int=0, end_int=10, table_type='Addition'):\n lines = [r'\\documentclass{article}',\n r'\\usepackage{geometry}',\n r'\\geometry{landscape,a4paper,total={170mm,257mm},left=10mm,right=10mm,top=10mm}',\n r'\\usepackage{amsmath}',\n r'\\usepackage{amsfonts}',\n r'\\usepackage{amssymb}',\n r'\\usepackage{dcolumn}',\n r'\\newcolumntype{2}{D{.}{}{2.0}}',\n r'\\begin{document}',\n r'\\begin{large}',\n r'\\begin{center}',\n r'{\\Large ' + table_type + r' Table version 0.1\\par}',\n r'\\vspace*{25px}',\n r'\\renewcommand\\arraystretch{1.3}',\n r'\\setlength\\doublerulesep{0pt}',\n r'\\pagenumbering{gobble}',\n r'\\begin{tabular}{r||*{' + str(end_int - start_int + 1) + '}{3|}}']\n\n operator = {'Addition': r'$+$',\n 'Subtraction': r'$-$',\n 'Multiplication': r'$\\times$'}\n\n lines.append(operator[table_type] + ''.join([' & {} '.format(x) for x in range(start_int, end_int + 1)]) + r'\\\\')\n lines.append('\\hline\\hline')\n for i in range(start_int, end_int + 1):\n if table_type == 'Addition':\n lines.append(str(i) + ''.join([' & {} '.format(x + i) for x in range(start_int, end_int + 1)]) + r'\\\\')\n if table_type == 'Subtraction':\n lines.append(str(i) + ''.join([' & {} '.format(x - i) for x in range(start_int, end_int + 1)]) + r'\\\\')\n if table_type == 'Multiplication':\n lines.append(str(i) + ''.join([' & {} '.format(x * i) for x in range(start_int, end_int + 1)]) + r'\\\\')\n lines.append('\\hline')\n\n lines.append(r'\\end{tabular}')\n lines.append(r'\\end{center}')\n lines.append(r'\\end{large}')\n lines.append(r'\\end{document}')\n\n return '\\n'.join(lines)","def _latex(self, printer):\n return ''.join([\n self._latex_header, '{', printer.doprint(self.args[0]), '}'\n ])","def print_table(seqids, data, outputfile, separator='\\t'):\n\n tags = data.keys()\n with open(outputfile, 'w') as out:\n out.write(separator.join([\"#Sequence ID\"] + list(tags)) + \"\\n\")\n for s in seqids:\n out.write(s)\n for t in tags:\n out.write(\"{}{}\".format(separator, data[t].get(s, \"\")))\n out.write(\"\\n\")","def print_table(ledger):\n\n table = PrettyTable() # defines a PrettyTable object\n\n table.field_names = [\n \"hospital\",\n \"patient\",\n \"status\",\n \"nonce\",\n \"prev_hash\",\n \"a\",\n \"b\",\n \"c\",\n \"current_hash\",\n ] # define field names for table\n\n for block in ledger:\n table.add_row(\n [\n block[\"hospital\"],\n block[\"patient\"],\n block[\"status\"],\n block[\"nonce\"],\n block[\"prev_hash\"],\n block[\"a\"],\n block[\"b\"],\n block[\"c\"],\n block[\"current_hash\"],\n ]\n ) # add data to table\n\n print(\"\\n\\n\" + color.BOLD + \"Printing Your Ledger:\" + color.END)\n print(table) # print prettytable of patient info","def print_table(hdrs, data):\n tw = TextWrapper()\n # only difficult thing here is wrapping the cell if it exceeds the row length, and it could be\n # extended in multiple cells in the same row so we need to determine the longest cell...\n def get_row_string(column_widths, row_data, fmt_separator=\"|\"):\n # receive a list of ints representing each column width and a list of text data representing\n # data for each column and return single string line.\n fmt = []\n cols = []\n for index, width in enumerate(column_widths):\n fmt.append(\"{%s:<%s}\" % (index, width))\n if index<len(row_data):\n #text = \" \".join(row_data[index].strip().split())\n text = row_data[index]\n tw.width = width\n # to honor original user's return characters, we need to wrap each individual line\n wraps = []\n for line in text.split(\"\\n\"):\n wrapped = tw.wrap(line.strip())\n if len(wrapped) == 0:\n wraps+= [\"\"]\n else:\n wraps+= wrapped\n cols.append(wraps)\n else:\n cols.append([\"\"])\n fmt = \"%s%s%s\" % (fmt_separator, (\" %s \" % fmt_separator).join(fmt), fmt_separator)\n # expand all columns to the max length column\n max_col = max([len(c) for c in cols])\n for c in cols:\n c+= [\"\"]*(max_col - len(c))\n #logger.debug(\"fmt: %s\", fmt)\n #logger.debug(\"columns:%s max length:%s\\n%s\", len(cols), max_col, cols)\n # build final result string which is one or more lines of merged cells\n results = []\n for index in range(0, max_col):\n # grab this index from all columns to create a single row\n row = [c[index] for c in cols]\n results.append(fmt.format(*row))\n return \"\\n\".join(results)\n\n final_rows = []\n column_widths = [h.get(\"length\", 5) for h in hdrs]\n separator = [\"-\"*h.get(\"length\", 5) for h in hdrs]\n separator_string = get_row_string(column_widths, separator, fmt_separator=\"+\")\n final_rows.append(separator_string)\n final_rows.append(get_row_string(column_widths, [h.get(\"name\", \"\") for h in hdrs]))\n final_rows.append(separator_string)\n for row in data:\n final_rows.append(get_row_string(column_widths, row))\n final_rows.append(separator_string)\n print(\"\\n\".join(final_rows))","def ydump_table(doc, headings, rows, **kwargs):\n doc, tag, text, line = doc.ttl()\n with tag('table', **kwargs):\n with tag('tr'):\n for x in headings:\n line('th', str(x))\n for row in rows:\n with tag('tr'):\n for x in row:\n line('td', str(x))","def generate_table(columns, rows, plain=False, sort=None, reversesort=False):\n tbl = PrettyTable(columns)\n tbl.set_style(PLAIN_COLUMNS if plain else DEFAULT)\n tbl.header = not plain\n [tbl.add_row(x) for x in rows]\n tbl.align = 'l'\n\n if sort:\n tbl.sortby = sort\n\n tbl.reversesort = reversesort\n\n return tbl","def create_joint_prob_corr_table(to_dict=True):\n\n p = np.arange(0., 1.05, 0.05) # Range of marginal probability\n corr = np.arange(-1., 1.05, 0.05) # Range of correlations\n\n table = np.zeros(shape=(len(corr), len(p), len(p)))\n\n for i in range(len(corr)):\n\n sig = np.matrix([[1., corr[i]], [corr[i], 1.]])\n\n for j in range(len(p)):\n for k in range(j, len(p)):\n\n if corr[i] == -1:\n jp = max(0., p[j] + p[k] - 1.)\n elif corr[i] == 0.:\n jp = p[j] * p[k]\n elif corr[i] == 1.:\n jp = min(p[j], p[k])\n elif p[j] * p[k] == 0. or p[j] == 1 or p[k] == 1.:\n jp = p[j] * p[k]\n else:\n jp = mvnun(np.array([0., 0.]), np.array([np.inf, np.inf]),\n [norm.ppf(p[j]), norm.ppf(p[k])],\n sig)[0]\n\n table[i, j, k] = table[i, k, j] = jp\n\n if to_dict:\n n_table = {}\n\n # convert to dictionary:\n for j in range(len(p)):\n for k in range(j, len(p)):\n pj = round(p[j], 2)\n pk = round(p[k], 2)\n n_table[(pj, pk)] = n_table[(pj, pk)] = np.array((corr, table[:, j, k]))\n\n return n_table\n\n return table","def make_table(header, align_map=None, rows=None):\n t = PrettyTable()\n t.horizontal_char = t.vertical_char = t.junction_char = ' '\n t.field_names = header\n if align_map:\n for field, align in zip(header, align_map):\n t.align[field] = align\n if rows:\n for row in rows:\n if len(row) < len(t.field_names):\n continue\n try:\n t.add_row(row)\n except Exception as err:\n print_('fields:', t.field_names)\n print_('row:', row)\n print_('rows:', rows)\n raise err\n return t","def table(sentence, fill=1, placeholder=\"-\"):\n tags = [WORD, POS, IOB, CHUNK, ROLE, REL, PNP, ANCHOR, LEMMA]\n tags += [tag for tag in sentence.token if tag not in tags]\n def format(token, tag):\n # Returns the token tag as a string.\n if tag == WORD : s = token.string\n elif tag == POS : s = token.type\n elif tag == IOB : s = token.chunk and (token.index == token.chunk.start and \"B\" or \"I\")\n elif tag == CHUNK : s = token.chunk and token.chunk.type\n elif tag == ROLE : s = token.chunk and token.chunk.role\n elif tag == REL : s = token.chunk and token.chunk.relation and str(token.chunk.relation)\n elif tag == PNP : s = token.chunk and token.chunk.pnp and token.chunk.pnp.type\n elif tag == ANCHOR : s = token.chunk and token.chunk.anchor_id\n elif tag == LEMMA : s = token.lemma\n else : s = token.custom_tags.get(tag)\n return s or placeholder\n def outline(column, fill=1, padding=3, align=\"left\"):\n # Add spaces to each string in the column so they line out to the highest width.\n n = max([len(x) for x in column]+[fill])\n if align == \"left\" : return [x+\" \"*(n-len(x))+\" \"*padding for x in column]\n if align == \"right\" : return [\" \"*(n-len(x))+x+\" \"*padding for x in column]\n \n # Gather the tags of the tokens in the sentece per column.\n # If the IOB-tag is I-, mark the chunk tag with \"^\".\n # Add the tag names as headers in each column.\n columns = [[format(token, tag) for token in sentence] for tag in tags]\n columns[3] = [columns[3][i]+(iob == \"I\" and \" ^\" or \"\") for i, iob in enumerate(columns[2])]\n del columns[2]\n for i, header in enumerate(['word', 'tag', 'chunk', 'role', 'id', 'pnp', 'anchor', 'lemma']+tags[9:]):\n columns[i].insert(0, \"\")\n columns[i].insert(0, header.upper())\n # The left column (the word itself) is outlined to the right,\n # and has extra spacing so that words across sentences line out nicely below each other.\n for i, column in enumerate(columns):\n columns[i] = outline(column, fill+10*(i==0), align=(\"left\",\"right\")[i==0])\n # Anchor column is useful in MBSP but not in pattern.en.\n if not MBSP:\n del columns[6] \n # Create a string with one row (i.e., one token) per line.\n return \"\\n\".join([\"\".join([x[i] for x in columns]) for i in range(len(columns[0]))])","def to_latex_table(self, labels=\"all\", parameter_dict=None, save_to_file=None):\n import os\n\n if save_to_file is not None and os.path.isfile(\"{}\".format(save_to_file)):\n raise FileExistsError(\n \"The file {} already exists.\".format(save_to_file)\n )\n if labels != \"all\" and isinstance(labels, str) and labels not in self.labels:\n raise ValueError(\"The label %s does not exist.\" % (labels))\n elif labels == \"all\":\n labels = list(self.labels)\n elif isinstance(labels, str):\n labels = [labels]\n elif isinstance(labels, list):\n for ll in labels:\n if ll not in list(self.labels):\n raise ValueError(\"The label %s does not exist.\" % (ll))\n\n table = self.latex_table(\n [self.samples_dict[label] for label in labels], parameter_dict,\n labels=labels\n )\n if save_to_file is None:\n print(table)\n elif os.path.isfile(\"{}\".format(save_to_file)):\n logger.warning(\n \"File {} already exists. Printing to stdout\".format(save_to_file)\n )\n print(table)\n else:\n with open(save_to_file, \"w\") as f:\n f.writelines([table])","def perf2latex(latex_fn, all_perf, metrics_name, slice_v, cam_v):\n slice_num = slice_v.shape[0]\n\n f = open('%s'%latex_fn, 'w')\n f.write(\"\\\\documentclass{article}\\n\")\n f.write(\"\\\\usepackage[utf8]{inputenc}\\n\")\n f.write(\"\\\\usepackage{booktabs} \\n\")\n f.write(\"\\\\usepackage[]{float}\\n\")\n f.write(\"\\\\usepackage[margin=1.2in]{geometry}\\n\")\n f.write(\"\\\\begin{document}\\n\\n\")\n\n for m_name in metrics_name:\n print(m_name)\n f.write('\\\\begin{table}[tbh]\\n')\n f.write('\\\\begin{center}\\n')\n f.write('\\\\begin{tabular}{|*{%d}{c|}}\\n'%(slice_num + 1))\n f.write('\\\\hline\\n')\n f.write(' Survey')\n #for slice_idx, slice_id in enumerate(slice_cam_id[:-1]):\n for j, (slice_id, cam_id) in enumerate(zip(slice_v, cam_v)):\n f.write(' & %d\\_c%d'%(slice_id, cam_id))\n f.write(' \\\\\\\\ \\n')\n f.write('\\\\hline\\n')\n\n m = all_perf[m_name]\n print(m.shape)\n survey_num = m.shape[0]\n for i in range(survey_num):\n f.write('%d'%(i))\n for j in range(slice_num):\n f.write(' & %.3f'%m[i,j])\n f.write(' \\\\\\\\ \\n')\n\n f.write('\\\\hline\\n')\n f.write('\\\\end{tabular}\\n')\n f.write('\\\\end{center}\\n')\n f.write('\\\\caption{Metric: %s}\\n'%(m_name))\n f.write('\\\\end{table}\\n\\n\\n')\n\n f.write('\\\\end{document}\\n')\n print('\\\\end{document}\\n')\n \n f.close()","def table(name, components):\n table = PrettyTable([name])\n table.align[name] = 'l'\n [table.add_row([component['name'][0:-5]]) for component in components]\n return table","def create_table(categories:list)->str:\n\n total_spent = get_total_spent(categories)\n\n table = str()\n\n for row_num in range(11):\n row_label = 100 - row_num*10\n\n # Row label creation - ie 100| .. 90| ... 80| ...etc\n row = f\"{row_label:>3}|\"\n\n for category in categories:\n percentage = math.floor(category.total_spent/total_spent * 10) * 10\n if percentage >= row_label:\n row += ' o '\n else:\n row += ' '\n \n table += row + ' \\n'\n return table","def convert_table(mkd):\n\t\n\tmd_table_codes = re.findall(r\".*\\|.*\\n.*\\-.*(?:\\n.*\\|.*)*\", mkd, re.M)\n\tfor md_code in md_table_codes:\n\t\t\n\t\tmd_rows = re.findall(r\"(.*\\|.*)\", md_code, re.M)\n\t\theader = md_rows.pop(0)\n\t\tcolumn_count = md_rows.pop(0).count(\"-\")\n\n\t\ttex_code = \"\\\\begin{tabular}{|\"+\"l|\"*column_count+\"}\\n\\hline\\n\"\n\t\ttex_code += header.strip(\" |\").replace(\"|\", \"&\")+\" \\\\\\\\\\n\\hline\\n\"\n\t\tfor row in md_rows:\n\t\t\ttex_code += row.strip(\" |\").replace(\"|\", \"&\")+\" \\\\\\\\\\n\"\n\t\ttex_code += \"\\hline\\n\\end{tabular}\"\n\n\t\tmkd = mkd.replace(md_code, tex_code)\n\n\treturn mkd","def write_the_table(what):\n global count_row\n count_row += 1\n\n if what.get('rank') == 0:\n background_blue.append(count_row)\n\n struct = what.get('structure')\n link = what.get('link')\n exams_1 = what.get('exams_1')\n exams_2 = what.get('exams_2')\n exams_empty = [['', '', '', '', '', '', '', '', '', '', '', '']] \\\n if self.training.session_type != '1' else \\\n [['', '', '', '', '', '']]\n\n def formated(number):\n \"\"\"\n Remove trailing 0\n \"\"\"\n frac, whole = modf(number)\n if frac == 0:\n return int(whole)\n return str(number).rstrip('0')\n\n def write_exams(list_1, list_2):\n exam_table = []\n for ex_1, ex_2 in itertools.zip_longest(list_1, list_2):\n ex_1_table = [\n formated(ex_1.coefficient) if ex_1 is not None else '',\n [\n Paragraph(filter_content(ex_1.label) if ex_1 else '',\n self.styles['SmallNormal']),\n Paragraph(\n \"<para textColor=grey>\" + filter_content(ex_1.additionnal_info) \\\n if ex_1 and ex_1.additionnal_info \\\n else \"\" + \"</para\\>\",\n self.styles['SmallNormal'])\n ],\n ex_1.type_exam if ex_1 is not None else '',\n ex_1.text_duration if ex_1 is not None else '',\n '' if ex_1 is None \\\n else ex_1.convocation if not training_is_ccct \\\n else ex_1.get_type_ccct_display(),\n ex_1.eliminatory_grade if ex_1 is not None else '',\n ex_1.threshold_session_2 if ex_1 is not None else '',\n ]\n\n ex_2_table = [\n formated(ex_2.coefficient) if ex_2 is not None else '',\n [Paragraph(filter_content(ex_2.label) if ex_2 is not None else '', self.styles[\n 'SmallNormal']), Paragraph(\"<para textColor=grey\\\n >\" + ex_2.additionnal_info + \"</para\\\n >\" if ex_2.additionnal_info is not None else \"\",\n self.styles['SmallNormal'])],\n ex_2.type_exam if ex_2 is not None else '',\n ex_2.text_duration if ex_2 is not None else '',\n ex_2.eliminatory_grade if ex_2 is not None else '',\n ] if ex_2 is not None else ['', '', '', '', '']\n if self.training.session_type != '1':\n ex_1_table.extend(ex_2_table)\n else:\n ex_1_table.pop()\n exam_table.append(ex_1_table)\n exam_table = exam_table if len(exam_table) > 0 else exams_empty\n if exam_table == exams_empty:\n # TODO: calculate empty space to set rowHeights in order to\n # avoid blank in table\n pass\n inner_table = Table(\n exam_table, colWidths=width_exams, rowHeights=None)\n inner_table.setStyle(TableStyle(\n [('INNERGRID', (0, 0), (-1, -1), 0.1, colors.black),\n ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),\n ('ALIGN', (0, 0), (-1, -1), 'CENTER'),\n ('FONTSIZE', (0, 0), (-1, -1), 8),\n # ('LEFTPADDING', (0, 0), (-1, -1), 0),\n # ('RIGHTPADDING', (0, 0), (-1, -1), 0),\n ('BOTTOMPADDING', (0, 0), (-1, -1), 0),\n ('TOPPADDING', (0, 0), (-1, -1), 0),\n ]))\n return inner_table\n\n ref_scol = struct.ref_si_scol if struct.ref_si_scol else \"\" # FIX bug with rof data\n ref_data = (\n Paragraph(struct.ROF_ref, self.styles['CenterSmall']),\n Paragraph(ref_scol, self.styles['CenterSmall'])\n ) if self.reference == 'both' \\\n else Paragraph(struct.ROF_ref, self.styles['CenterSmall']) if self.reference == 'with_rof' \\\n else Paragraph(ref_scol, self.styles['CenterSmall']) if self.reference == 'with_si' \\\n else Paragraph('', self.styles['CenterSmall'])\n\n object_line = [\n Paragraph(\n \"<para leftIndent=%s>%s</para> \" % (what.get('rank')*10, filter_content(struct.label)),\n self.styles['SmallBold'] if what.get('rank') == 0 \\\n or what.get('structure').nature == 'UE' \\\n else self.styles['SmallNormal']\n ),\n Paragraph(\n struct.get_respens_name if not struct.external_name \\\n else struct.external_name,\n self.styles['CenterSmall'] if not struct.external_name else \\\n self.styles['CenterSmallItalic']\n ),\n [ref_data],\n '30' if self.training.degree_type.ROF_code in self.training_types_for_which_to_display_30_ects\\\n and struct.nature == 'SE'\\\n else struct.ECTS_credit if struct.ECTS_credit else '-',\n formated(link.coefficient) if link.coefficient else '',\n link.eliminatory_grade,\n write_exams(exams_1, exams_2)\n ]\n if self.respforms:\n if self.reference == 'without':\n object_line.pop(2)\n else:\n object_line.pop(1)\n if self.reference == 'without':\n object_line.pop(1)\n\n big_table.append(object_line)\n\n for e in what.get('children'):\n write_the_table(e)","def print_table(table, fieldnames):\n print(\"{:<19}\".format(fieldnames[0]), end='')\n for field in fieldnames[1:]:\n print(\"{:>6}\".format(field), end='')\n print(\"\")\n for name, row in table.items():\n # Header column left justified\n print(\"{:<19}\".format(name), end='')\n # Remaining columns right justified\n for field in fieldnames[1:]:\n print(\"{:>6}\".format(row[field]), end='')\n print(\"\", end='\\n')","def print_table(table, fieldnames):\n print(\"{:<19}\".format(fieldnames[0]), end='')\n for field in fieldnames[1:]:\n print(\"{:>6}\".format(field), end='')\n print(\"\")\n for name, row in table.items():\n # Header column left justified\n print(\"{:<19}\".format(name), end='')\n # Remaining columns right justified\n for field in fieldnames[1:]:\n print(\"{:>6}\".format(row[field]), end='')\n print(\"\", end='\\n')","def bonus_report_FR1_scalp(subj, scores, bonuses, nans_blank=True):\n ###############\n #\n # Part 1: Text Report\n #\n ###############\n\n outfile = '/data/eeg/scalp/ltp/FR1_scalp/bonus/%s_bonus_report.tsv' % subj\n\n # Create report header\n report = 'Session\\tRecall\\tRecall Bonus\\tMath Score\\tMath Bonus\\tTotal\\n'\n\n # Fill in report\n for i in range(len(bonuses)):\n report += '%d\\t%.1f%%\\t$%.2f\\t%.0f\\t$%.2f\\t$%.2f\\n' % \\\n (i, scores[i][0], bonuses[i][0], scores[i][1],\n bonuses[i][1], bonuses[i][2])\n report += 'Total\\t%.1f%%\\t$%.2f\\t%.0f\\t$%.2f\\t$%.2f\\n' % \\\n (np.nanmean([s[0] for s in scores]), np.nansum([b[0] for b in bonuses]),\n np.nansum([s[1] for s in scores]), np.nansum([b[1] for b in bonuses]),\n np.nansum([b[2] for b in bonuses]))\n report = report.strip()\n\n # Replace nans with blank entries if desired\n if nans_blank:\n for s in ('$nan', 'nan%', 'nan'):\n report = report.replace(s, '')\n\n # Write text report\n with open(outfile, 'w') as f:\n f.write(report)\n\n ###############\n #\n # Part 2: PDF Report\n #\n ###############\n\n tex_outfile = '/data/eeg/scalp/ltp/FR1_scalp/bonus/%s_bonus_report' % subj\n # Divide report into rows\n report = report.split('\\n')\n # Create list of column names\n header = report.pop(0).split('\\t')\n # Define the format of the LaTeX tabular -- one column for each item in the header\n fmt = ('X[r] ' * len(header)).strip()\n # Create LaTeX document\n geometry_options = dict(\n paperheight='6.5in',\n paperwidth='6in',\n margin='.5in'\n )\n doc = Document(page_numbers=False, geometry_options=geometry_options)\n\n with doc.create(Center()) as centered:\n doc.append(LargeText('Bonus Report: %s' % subj))\n doc.append(Command('par'))\n with centered.create(Tabu(fmt)) as data_table:\n data_table.add_row([''] * len(header))\n data_table.add_hline()\n data_table.add_row([''] * len(header))\n data_table.add_row(header, mapper=[bold])\n data_table.add_row([''] * len(header))\n data_table.add_hline()\n data_table.add_row([''] * len(header))\n for row in report[:-1]:\n data_table.add_row(row.split('\\t'))\n data_table.add_row([''] * len(header))\n data_table.add_hline()\n data_table.add_row([''] * len(header))\n data_table.add_row(report[-1].split('\\t'))\n doc.generate_pdf(tex_outfile, compiler='pdflatex')\n\n return outfile, tex_outfile + '.pdf'","def table(self, text):\n text = text + \"\\n\\n\"\n pattern = re.compile(r'^(?:table(_?%(s)s%(a)s%(c)s)\\.(.*?)\\n)?^(%(a)s%(c)s\\.? ?\\|.*\\|)[\\s]*\\n\\n'\n % {'s': self.table_span_re,\n 'a': self.align_re,\n 'c': self.c},\n re.S | re.M | re.U)\n return pattern.sub(self.fTable, text)","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 pprint_table(out, table, headers=None, output_format='pretty',\n separator=None, vertical=False, title=None):\n\n assert(isinstance(table, (list, tuple))), \"Invalid table type\"\n if headers:\n assert(isinstance(headers, (list, tuple))), \"Invalid headers type\"\n\n sep = separator if separator else \" \"\n\n def stringnify(obj):\n if isinstance(obj, (unicode, str)):\n return udec(obj)\n else:\n return str(obj)\n\n if headers:\n headers = map(stringnify, headers)\n table = [map(stringnify, row) for row in table]\n\n if output_format == \"json\":\n assert(headers is not None), \"json output format requires headers\"\n table = [dict(zip(headers, row)) for row in table]\n out.write(json.dumps(table, indent=4))\n out.write(\"\\n\")\n elif output_format == \"csv\":\n cw = csv.writer(out)\n if headers:\n table.insert(0, headers)\n table = map(functools.partial(map, uenc), table)\n cw.writerows(table)\n elif output_format == \"pretty\":\n if vertical:\n assert(len(table) == 1)\n row = table[0]\n max_key = max(map(len, headers))\n for row in table:\n for (k, v) in zip(headers, row):\n k = uenc(k.ljust(max_key))\n v = uenc(v)\n out.write(\"%s: %s\\n\" % (k, v))\n else:\n # Find out the max width of each column\n columns = [headers] + table if headers else table\n widths = [max(map(len, col)) for col in zip(*(columns))]\n\n t_length = sum(widths) + len(sep) * (len(widths) - 1)\n if title is not None:\n t_length = max(t_length, len(title))\n out.write(\"-\" * t_length + \"\\n\")\n out.write(title.center(t_length) + \"\\n\")\n out.write(\"-\" * t_length + \"\\n\")\n if headers:\n # pretty print the headers\n line = sep.join(uenc(v.rjust(w))\n for v, w in zip(headers, widths))\n out.write(line + \"\\n\")\n out.write(\"-\" * t_length + \"\\n\")\n\n # print the rest table\n for row in table:\n line = sep.join(uenc(v.rjust(w)) for v, w in zip(row, widths))\n out.write(line + \"\\n\")\n else:\n raise ValueError(\"Unknown output format '%s'\" % output_format)","def make_latex_table(scalar_metrics, threshold):\n latex = ''\n\n # head of the table\n latex += '\\\\begin{table} \\n'\n latex += '\\\\begin{center} \\n'\n latex += '\\\\begin{tabular}{ l | l } \\n'\n latex += '\\\\toprule \\\\ \\n'\n latex += '\\\\textbf{Metric} & \\\\textbf{Value} \\\\ \\midrule \\n'\n\n # body of the table\n # make 2D list for every section\n body_list = []\n body_list.append(['MSE', str(scalar_metrics['mse']) ])\n body_list.append(['RMSE', str(scalar_metrics['rmse']) ])\n body_list.append(['MAE', str(scalar_metrics['mae']) ])\n\n latex += array_as_latex_table(body_list)\n\n # footer of the table\n latex += '\\end{tabular} \\n'\n latex += '\\end{center} \\n'\n latex += '\\caption{Scaler performance metrics and default values} \\n'\n latex += '\\label{tab:TODO} \\n'\n latex += '\\end{table} \\n'\n\n return latex","def latex_table(result, *, decimal_places=3, label=None):\n if label is None:\n label = 'tbl:stat_results'\n\n table_df = result.rankdf\n columns = table_df.columns.to_list()\n if result.omnibus != 'bayes' and result.pvalue >= result.alpha or \\\n result.omnibus == 'bayes' and len({'smaller', 'larger'}.intersection(set(result.rankdf['decision']))) == 0:\n columns.remove('effect_size')\n columns.remove('magnitude')\n if result.posthoc == 'tukeyhsd':\n columns.remove('meanrank')\n columns.insert(columns.index('ci_lower'), 'CI')\n columns.remove('ci_lower')\n columns.remove('ci_upper')\n rename_map = {}\n if result.effect_size == 'cohen_d':\n rename_map['effect_size'] = '$d$'\n elif result.effect_size == 'cliff_delta':\n rename_map['effect_size'] = r'D-E-L-T-A'\n elif result.effect_size == 'akinshin_gamma':\n rename_map['effect_size'] = r'G-A-M-M-A'\n rename_map['magnitude'] = 'Magnitude'\n rename_map['mad'] = 'MAD'\n rename_map['median'] = 'MED'\n rename_map['meanrank'] = 'MR'\n rename_map['mean'] = 'M'\n rename_map['std'] = 'SD'\n rename_map['decision'] = 'Decision'\n format_string = '[{0[ci_lower]:.' + str(decimal_places) + 'f}, {0[ci_upper]:.' + str(decimal_places) + 'f}]'\n table_df['CI'] = table_df.agg(format_string.format, axis=1)\n table_df = table_df[columns]\n if result.omnibus == 'bayes':\n table_df.at[table_df.index[0], 'decision'] = '-'\n table_df = table_df.rename(rename_map, axis='columns')\n\n float_format = lambda x: (\"{:0.\" + str(decimal_places) + \"f}\").format(x) if not np.isnan(x) else '-'\n table_string = table_df.to_latex(float_format=float_format, na_rep='-').strip()\n table_string = table_string.replace('D-E-L-T-A', r'$\\delta$')\n table_string = table_string.replace('G-A-M-M-A', r'$\\gamma$')\n table_string = table_string.replace(r'p\\_equal', r'$P(\\textit{equal})$')\n table_string = table_string.replace(r'p\\_smaller', r'$P(\\textit{smaller})$')\n print(r\"\\begin{table}[h]\")\n print(r\"\\centering\")\n print(table_string)\n print(r\"\\caption{Summary of populations}\")\n print(r\"\\label{%s}\" % label)\n print(r\"\\end{table}\")","def print_table(self) -> None:\n if (self.probability_links == None):\n print(\"+--------+\")\n print(f\"| P({self.key:1s}) |\")\n print(\"+--------+\")\n print(f\"| {self.probability_values[0]:0.04f} |\")\n print(\"+--------+\")\n else:\n arg_len = 2 + len(' '.join(self.probability_links.keys()))\n param_len = 2 + \\\n max(6, len(\"P(A|)\" + \",\".join(self.probability_links.keys())))\n print(f\"+{'-'*arg_len}+{'-'*param_len}+\")\n print(\n f\"| {' '.join(self.probability_links.keys())} | P({self.key}|{','.join(self.probability_links.keys())}) |\")\n print(f\"+{'-'*arg_len}+{'-'*param_len}+\")\n for i in range(2**len(self.probability_links.keys())):\n # Gives us a string binary value to make truth table off of\n bool_key = f\"{i:0{len(self.probability_links.keys())}b}\"\n print(\n f\"| {' '.join(['T' if bool_key[j] == '0' else 'F' for j in range(len(self.probability_links.keys()))])} | {f'{self.probability_values[i]:0.04f}':<{param_len-1}s}|\")\n print(f\"+{'-'*arg_len}+{'-'*param_len}+\")","def __str__(self):\n if self.tabletitle:\n s = [\"<a name='%s'>%s</a><P>\" % (self.tabletitle, self.tabletitle)]\n else:\n s = []\n\n s.append('<TABLE border=%s cellpadding=%s cellspacing=%s width=\"%s\">\\n' % \\\n (self.border, self.cell_padding, self.cell_spacing, self.width))\n if self.tabletitle:\n s.append('<CAPTION align=%s><STRONG>%s</STRONG></CAPTION>\\n' % \\\n (self.caption_align, self.tabletitle))\n\n for i in range(len(self.body)):\n for j in range(len(self.body[i])):\n if type(self.body[i][j]) == type(''):\n #process cell contents to insert breaks for \\n char.\n if self.cell_line_breaks:\n self.body[i][j] = self.body[i][j].replace('\\n','<br>')\n else:\n self.body[i][j] = Text(self.body[i][j])\n\n # Initialize colspan property to 1 for each\n # heading column if user doesn't provide it.\n if self.heading:\n if not self.colspan:\n if type(self.heading[0]) == list:\n self.colspan = [1]*len(self.heading[0])\n else:\n self.colspan = [1]*len(self.heading)\n # Construct heading spec\n # can handle multi-row headings. colspan is a list specifying how many\n # columns the i-th element should span. Spanning only applies to the first\n # or only heading line.\n if self.heading:\n prefix = '<TR Align=' + self.heading_align + '> '\n postfix = '</TR>\\n'\n middle = ''\n if type(self.heading[0]) == type([]):\n for i in range(len(self.heading[0])):\n middle = middle + '<TH ColSpan=%s%s>' % \\\n (self.colspan[i], \\\n self.get_body_color(self.heading_color,i)) \\\n + str(self.heading[0][i]) +'</TH>'\n s.append(prefix + middle + postfix)\n for i in range(len(self.heading[1])):\n middle = middle + '<TH>' + str(self.heading[i]) +'</TH>'\n for heading_row in self.heading[1:]:\n for i in range(len(self.heading[1])):\n middle = middle + '<TH>' + heading_row[i] +'</TH>'\n s.append(prefix + middle + postfix)\n else:\n for i in range(len(self.heading)):\n middle = middle + '<TH ColSpan=%s%s>' % \\\n (self.colspan[i], \\\n self.get_body_color(self.heading_color,i)) \\\n + str(self.heading[i]) +'</TH>'\n s.append(prefix + middle + postfix)\n # construct the rows themselves\n stmp = '<TD Align=%s %s>'\n for row in self.body:\n s.append('<TR>')\n for i in range(len(row)):\n if i == 0 :\n ss1 = self.column1_align\n else:\n ss1 = self.cell_align\n s.append(stmp % (ss1, self.get_body_color(self.body_color,i)))\n s.append(str(row[i]))\n s.append('</TD>\\n')\n s.append('</TR>\\n')\n #close table\n s.append('</TABLE><P>\\n')\n return ''.join(s)","def generate_table(self, outtablename,\n cols=['A', 'B', 'AB'],\n generateTable=True):\n if generateTable:\n new_indices = ['time (s)', 'mean counts']\n for idx in self.data[cols].describe().index[2:]:\n new_indices.append(idx)\n outTable = self.data[cols].describe()\\\n .set_index(pd.Index(new_indices))\n outTable.to_latex(\n self.tables_dir + outtablename + \".tex\", float_format=\"%d\")\n print(\"Outtable: \", outTable)","def html_table(header_data, row_data):\n def make_header_cell(s):\n return '<th>{}</th>'.format(s)\n\n def make_cell(s):\n return '<td>{}</td>'.format(s)\n\n def make_row(s):\n return '<tr>{}</tr>'.format(s)\n headers = \" \".join([make_header_cell(h) for h in header_data])\n header_row = make_row(headers)\n rows = [make_row(\" \".join([make_cell(c) for c in row]))\n for row in row_data]\n rows = \"\\n\".join(rows)\n html = '<table>' + header_row + rows + '</table>'\n return html","def table(app, tuples, _asString=False, **options):\n\n display = app.display\n\n if not display.check(\"table\", options):\n return \"\"\n\n _browse = app._browse\n inNb = app.inNb\n\n api = app.api\n\n dContext = display.distill(options)\n end = dContext.end\n start = dContext.start\n withPassage = dContext.withPassage\n condensed = dContext.condensed\n condenseType = dContext.condenseType\n skipCols = dContext.skipCols\n\n ltr = _getLtr(app, dContext) or \"ltr\"\n\n item = condenseType if condensed else RESULT\n\n if condensed:\n tuples = condense(api, tuples, condenseType, multiple=True)\n skipCols = set()\n\n passageHead = f'</th><th class=\"tf {ltr}\">p' if withPassage is True else \"\"\n\n html = []\n one = True\n\n newOptions = display.consume(options, \"skipCols\")\n\n theseTuples = tuple(tupleEnum(tuples, start, end, LIMIT_TABLE, item, inNb))\n headerTypes = getHeaderTypes(app, theseTuples)\n\n for (i, tup) in theseTuples:\n if one:\n heads = '</th><th class=\"tf\">'.join(\n headerTypes.get(i, f\"column {i}\") for i in range(len(headerTypes))\n )\n html.append(\n f'<tr class=\"tf {ltr}\">'\n f'<th class=\"tf {ltr}\">n{passageHead}</th>'\n f'<th class=\"tf {ltr}\">{heads}</th>'\n f\"</tr>\"\n )\n one = False\n html.append(\n plainTuple(\n app,\n tup,\n seq=i,\n item=item,\n position=None,\n opened=False,\n _asString=True,\n skipCols=skipCols,\n **newOptions,\n )\n )\n html = \"<table>\" + \"\\n\".join(html) + \"</table>\"\n\n if _browse or _asString:\n return html\n dh(html, inNb=inNb)","def generate_table(self, rows):\n ...","def render_table_start(header, title):\n num_columns = len(header)\n entries = ['\\color[HTML]{FFFFFF}' + '{}'.format(clean_latex(x)) for x in header]\n if num_columns > 1: \n line = '} & {'.join(entries)\n else:\n line = entries[0]\n columns_format = '{|' + '|'.join(['l']*num_columns) + '|}'\n print('\\\\begin{tabular}' + '{}'.format(columns_format) + '\\n'\n ' ' + '\\\\hline\\n' +\n ' ' + '\\\\multicolumn{' + str(num_columns) + '}' +\n '{|c|}' + '{' + title + '}' + ' \\\\\\\\\\n' +\n ' ' + '\\\\rowcolor[HTML]{333333}\\n' +\n ' ' + '{' + line + '}' + ' \\\\\\\\' \n )","def get_table(header, floatPercission=4, *rows):\n\n table = PrettyTable(header)\n table.padding_width = 1\n for row in rows:\n # go through row and round floats\n for i in xrange(len(row)):\n if type(row[i]) is float:\n row[i] = round(row[i], floatPercission)\n table.add_row(row)\n return table","def print_table(outbuf, table, align):\n if len(table) == 0:\n return\n\n colwidths = None\n for row in table:\n if colwidths is None:\n colwidths = [len(x) for x in row]\n else:\n colwidths = [max(colwidths[i], len(x)) for i, x in enumerate(row)]\n\n for row in table:\n cells = []\n for i, cell in enumerate(row):\n padding = ' ' * (colwidths[i] - len(cell))\n if align[i] == 'r':\n cell = padding + cell\n elif i < len(row) - 1:\n # Do not pad the final column if left-aligned.\n cell += padding\n cells.append(cell.encode('utf-8', 'replace'))\n try:\n outbuf.write(b' '.join(cells) + b'\\n')\n except IOError: # pragma: no cover\n # Can happen on Windows if the pipe is closed early.\n pass","def print_table(table, exploded_at=[-1, -1]):\n\n # color codes just to look pretty\n NORMAL = '\\33[10m'\n BLUE_START = '\\33[104m'\n RED_START = '\\33[31m'\n PURPLE_START = '\\33[35m'\n GREEN_START = '\\33[92m'\n ORANGE_START = '\\33[93m'\n END = '\\033[0m'\n s = ' %s' % BLUE_START\n\n # print number headers along x-axis\n for i in range(0, width):\n s += \" %s\" % i\n if i < 10:\n s += \" \" * 2\n else:\n s += \" \"\n\n s += \"%s\\n\" % END\n # print letters for y-axis, + the relevant values in each coordinate\n # depending on table.\n for y in range(0, height):\n s += \"%s %s %s \\t\" % (BLUE_START, Minesweeper.letters[y], END)\n for x in range(0, width):\n value = table[y][x]\n if value == \"0\":\n s += \"%s%s%s\" % (NORMAL, value, END)\n elif value == \"1\":\n s += \"%s%s%s\" % (GREEN_START, value, END)\n elif value == \"2\":\n s += \"%s%s%s\" % (ORANGE_START, value, END)\n elif value == \"3\":\n s += \"%s%s%s\" % (RED_START, value, END)\n elif value == \"4\" or value == \"5\" or value == \"6\" or value == \"7\" or value == \"8\":\n s += \"%s%s%s\" % (PURPLE_START, value, END)\n # special\n elif value == \"-\":\n s += \"%s%s%s\" % (NORMAL, value, END)\n elif value == Minesweeper.BOMB:\n if y == exploded_at[0] and x == exploded_at[1]:\n # Make the bomb at the casualty site explode!\n s += \"%s%s%s\" % (RED_START, Minesweeper.EXPLOSION, END)\n else:\n # show normal bomb\n s += \"%s%s%s\" % (RED_START, value, END)\n elif value == Minesweeper.FLAG:\n s += \"%s%s%s\" % (RED_START, value, END)\n s += \" \" * 3\n s += \"\\n\"\n\n # use tabbing to space them nicely\n print s.expandtabs(3)","def console(pro):\n transect = pro['transect' ] \n nm = pro['nm120r' ].flatten()#[pro['m120swr_'][0,:]]\n t = pro['t120r' ].flatten()#[pro['m120swr_'][0,:]]\n sb = pro['sbliner' ].flatten()#[0][:-1]\n NASC = pro['NASC120swr'].flatten()#[pro['m120swr_'] ]\n pc = pro['pc120swr' ].flatten()#[pro['m120swr_'] ]\n \n # Preallocate table object\n table = io.StringIO()\n \n # Outline alignment and format for table lines, header, and data\n line = '+{:-^10}+{:-^11}+{:-^25}+{:-^8}+{:-^13}+{:-^11}+ \\n'\n header = '{:<9} | {:<9} | {:<23} | {:>6} | {:>11} |{:>12} \\n'\n data = '| {:<3d} | {:<9.3f} | {:<15} | {:>6.1f} | {:>11.2f} | {:>9.1f} | \\n'\n \n # Write table lines and header\n table.write(line.format('','','','','',''))\n table.write(header.format('| Transect','N. miles','Time','Seabed','NASC','% samples |')) \n table.write(line.format('','','','','','')) \n \n # Populate table with data\n for nmi, ti, sbi, NASCi, pci in zip(nm, t, sb, NASC, pc):\n table.write(data.format(transect, nmi, ti, sbi, NASCi, pci))\n \n # Close table with a line\n table.write(line[:-2].format('','','','','',''))\n \n # Print table in the console\n table = table.getvalue() \n print(table)","def make_HTML_table(l, other_frac, total, red, other_cat, fracs_labels_other,\r\n fracs_labels, dir_path, all_counts, level,\r\n prefs, pref_colors, background_color, label_color, chart_type,\r\n label, generate_image_type,\r\n plot_width, plot_height, bar_width, dpi, resize_nth_label,\r\n label_type, include_html_legend, include_html_counts):\r\n img_data = []\r\n\r\n # generate html for pie charts\r\n if chart_type == 'pie':\r\n # in the case the user wants to trim down the number of taxa\r\n if other_cat > 0:\r\n # first generate the pie charts containing an other group for all\r\n # taxa below the cutoff.\r\n fracs_labels_other.append((\"All Other Categories\", other_frac))\r\n title = TITLE_include % (l, total, total,\r\n len(fracs_labels_other), total - red, other_cat)\r\n all_taxons = [l]\r\n pie_charts_placement = []\r\n\r\n # make pie chart image\r\n pie = make_pie_chart(fracs_labels_other, dir_path, level,\r\n prefs, pref_colors, background_color, label_color,\r\n generate_image_type,\r\n plot_width, plot_height, bar_width, dpi,\r\n include_html_legend,\r\n props={'title': title})\r\n\r\n pie_charts_placement.append(pie[0] + ' ' + pie[1] +\r\n '</td></tr><tr><td>' + pie[2] +\r\n '</td></tr><tr><td class=\"ntitle\">')\r\n\r\n # second generate the pie charts where the other category is removed\r\n # and percents are recalculated\r\n title = TITLE_exclude % (l, red, total, len(fracs_labels),\r\n total - red, other_cat)\r\n\r\n # make pie chart image\r\n pie = make_pie_chart(fracs_labels, dir_path, level,\r\n prefs, pref_colors, background_color, label_color,\r\n generate_image_type,\r\n plot_width, plot_height, bar_width, dpi,\r\n include_html_legend,\r\n props={'title': title})\r\n\r\n pie_charts_placement.append(pie[0] + ' ' + pie[1] +\r\n '</td></tr><tr><td class=\"ntitle\">' +\r\n pie[2])\r\n\r\n all_taxons.extend(pie_charts_placement)\r\n all_taxons.extend((\" \", \" \"))\r\n\r\n # put the charts into the html image data\r\n img_data.append(TABLE_graph % tuple(all_taxons))\r\n img_data.append(DATA_TABLE_HTML % ''.join(all_counts))\r\n\r\n else:\r\n # if there is no category cutoff generate plots, without other cat\r\n title = TITLE % (l, total, total, len(fracs_labels_other))\r\n all_taxons = [l]\r\n\r\n # make pie chart image\r\n pie = make_pie_chart(fracs_labels_other, dir_path, level,\r\n prefs, pref_colors, background_color, label_color,\r\n generate_image_type,\r\n plot_width, plot_height, bar_width, dpi,\r\n include_html_legend,\r\n props={'title': title})\r\n\r\n all_taxons.extend(pie)\r\n\r\n # put the charts into the html image data\r\n img_data.append(TABLE_graph % tuple(all_taxons))\r\n img_data.append(DATA_TABLE_HTML % ''.join(all_counts))\r\n\r\n # generate html for bar and area charts\r\n elif chart_type == 'area' or chart_type == 'bar':\r\n\r\n taxa_percents = fracs_labels_other\r\n sample_ids = l\r\n taxa = other_cat\r\n\r\n all_categories = []\r\n title = TITLE % (label, total, total, len(fracs_labels_other))\r\n all_taxons = [label]\r\n\r\n # make area chart image\r\n area = make_area_bar_chart(sample_ids, taxa_percents, taxa, dir_path,\r\n level, prefs, pref_colors,\r\n background_color, label_color, chart_type,\r\n generate_image_type,\r\n plot_width, plot_height, bar_width, dpi,\r\n resize_nth_label, label_type,\r\n include_html_legend, include_html_counts,\r\n props={'title': title})\r\n\r\n all_taxons.extend(area)\r\n\r\n # put the charts into the html image data\r\n img_data.append(TABLE_graph % tuple(all_taxons))\r\n\r\n return img_data","def print_table2(df, eval_dir):\n\n out_file = os.path.join(eval_dir, 'table2.txt')\n\n\n with open(out_file, \"w\") as text_file:\n\n for idx, struc_name in enumerate(['LV', 'RV', 'Myo']):\n # new line\n header_string = ' & '\n line_string = '({}) '.format(struc_name)\n\n for p_idx, phase in enumerate(['ED', 'ES']):\n for measure in ['dice', 'assd', 'hd']:\n\n header_string += ' & {} ({}) '.format(phase, measure)\n\n dat = df.loc[(df['phase'] == phase) & (df['struc'] == struc_name)]\n\n if measure == 'dice':\n\n line_string += ' & {:.3f}\\,({:.3f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\n else:\n line_string += ' & {:.2f}\\,({:.2f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\n\n if p_idx == 0:\n header_string += ' & '\n line_string += ' & '\n\n header_string += ' \\\\\\\\ \\n'\n line_string += ' \\\\\\\\ \\n'\n\n if idx == 0:\n text_file.write(header_string)\n\n text_file.write(line_string)\n\n return 0","def generate_pdf(products, quantities, pdf_filename):\n # Construct a document.\n doc = reportlab.platypus.BaseDocTemplate(\n pdf_filename,\n pagesize=reportlab.lib.pagesizes.letter,\n title=\"Art Mart Inventory Sheet Check In/Out\",\n # showBoundary=True\n )\n\n # Construct a frame.\n page_width = doc.pagesize[0]\n page_height = doc.pagesize[1]\n frames = [\n reportlab.platypus.Frame(\n id=\"table_frame\",\n x1=0.25 * INCH,\n y1=1.60 * INCH,\n width=page_width - 0.45 * INCH,\n height=page_height - 3.60 * INCH,\n leftPadding=0,\n bottomPadding=0,\n rightPadding=0,\n topPadding=0\n )\n ]\n\n # Construct a template and add it to the document.\n doc.addPageTemplates(\n reportlab.platypus.PageTemplate(\n id=\"mytemplate\",\n frames=frames,\n onPage=on_page\n )\n )\n\n # Construct a story and add it to the document.\n col_widths = [\n 0.40 * INCH, # Existing Qty\n 1.15 * INCH, # Barcode\n 0.40 * INCH, # Qty Added\n 0.60 * INCH, # Price\n 5.00 * INCH, # Description\n 0.40 * INCH, # Total Qty\n ]\n col_header_style = reportlab.lib.styles.ParagraphStyle(\n name=\"col_header\",\n fontName=\"Helvetica-Bold\",\n fontSize=10\n )\n col_center_style = reportlab.lib.styles.ParagraphStyle(\n name=\"col_center\",\n fontName=\"Helvetica-Bold\",\n fontSize=10,\n alignment=reportlab.lib.enums.TA_CENTER\n )\n col_align_right_style = reportlab.lib.styles.ParagraphStyle(\n name=\"col_align_right\",\n fontName=\"Helvetica-Bold\",\n fontSize=10,\n alignment=reportlab.lib.enums.TA_RIGHT\n )\n # TableStyle cell formatting commands.\n styles = [\n # Whole table.\n ('FONTSIZE', (0, 0), (-1, -1), 10),\n ('TOPPADDING', (0, 1), (-1, -1), 0.26 * INCH),\n ('BOTTOMPADDING', (0, 1), (-1, -1), 0.10 * INCH),\n # Existing Qty\n ('LEFTPADDING', (0, 0), (0, -1), 0),\n # Barcode\n ('ALIGN', (1, 0), (1, 0), 'CENTER'),\n ('ALIGN', (1, 0), (1, -1), 'RIGHT'),\n ('FONT', (1, 0), (1, -1), 'Courier-Bold'),\n # Qty Added\n ('LEFTPADDING', (2, 0), (2, 0), 0),\n ('ALIGN', (2, 0), (2, 0), 'LEFT'),\n ('ALIGN', (2, 1), (2, -1), 'RIGHT'),\n ('FONT', (2, 0), (2, -1), 'Courier-Bold'),\n # Price\n ('ALIGN', (3, 0), (3, -1), 'RIGHT'),\n ('FONT', (3, 0), (3, -1), 'Courier-Bold'),\n # Description\n ('ALIGN', (4, 0), (4, -1), 'LEFT'),\n ('FONT', (4, 0), (4, -1), 'Helvetica'),\n # Total Qty\n ('LEFTPADDING', (5, 0), (5, -1), 0),\n ('ALIGN', (5, 0), (5, 0), 'LEFT'),\n # Other\n ('FONT', (0, 0), (-1, 0), 'Helvetica-Bold'),\n # ('GRID', (0, 0), (-1, -1), 1.0, BLACK)\n ]\n header_row = (\n reportlab.platypus.Paragraph(\"Existing<br/>Qty\", col_header_style),\n reportlab.platypus.Paragraph(\"Barcode<br/> \", col_center_style),\n reportlab.platypus.Paragraph(\"Qty<br/>Added\", col_header_style),\n reportlab.platypus.Paragraph(\n \"Price<br/> \",\n col_align_right_style\n ),\n reportlab.platypus.Paragraph(\n \"Description<br/> \",\n col_header_style\n ),\n reportlab.platypus.Paragraph(\"Total<br/>Qty\", col_header_style)\n )\n table_data = [header_row]\n for product in products:\n sku = product[\"SKU\"]\n if sku in quantities:\n quantity = quantities[sku]\n # Round price to nearest dollar.\n price = product[\"Price\"]\n price = \"${.0f}\".format(math.trunc(float(price) + 0.5))\n description = \"{}: {}\".format(\n product[\"Product Name\"],\n cctools.html_to_plain_text(product[\"Teaser\"])\n )\n description = description[:68]\n table_data.append(\n (\"_____\", sku, quantity, price, description, \"_____\")\n )\n table = reportlab.platypus.Table(\n data=table_data,\n colWidths=col_widths,\n style=styles,\n repeatRows=1\n )\n table.hAlign = \"LEFT\"\n story = [table]\n doc.build(story, canvasmaker=NumberedCanvas)","def write_latex_preamble(self):\n self.texfile.write(r\"\\n\")\n self.texfile.write(r\"\\documentclass[a4paper,12pt]{article}\\n\")\n self.texfile.write(r\"\\usepackage{tabu}\\n\")\n self.texfile.write(r\"\\usepackage{booktabs}\\n\")\n self.texfile.write(r\"\\usepackage[font=small,labelsep=space]{caption}\\n\")\n self.texfile.write(r\"\\usepackage[margin=2.5cm]{geometry}\\n\")\n self.texfile.write(r\"\\setlength{\\\\topmargin}{1.0cm}\\n\")\n self.texfile.write(r\"\\setlength{\\\\textheight}{22cm}\\n\")\n self.texfile.write(r\"\\usepackage{fancyhdr}\\n\")\n self.texfile.write(r\"\\pagestyle{fancy}\\n\")\n self.texfile.write(r\"\\\\fancyhf{}\\n\")\n self.texfile.write(r\"\\\\fancyhead[R]{\\leftmark}\\n\")\n self.texfile.write(r\"\\usepackage{multirow}\\n\")\n self.texfile.write(r\"\\n\")\n self.texfile.write(r\"\\\\begin{document}\\n\")\n self.texfile.write(r\"\\n\")","def table_gen(NamesL_pairs, p_pL, m_mL, p_mL, m_pL, p_valsL, p_vals_BonferoniL, RatiosL, p_valsL_divergent_convergent,\n p_valsL_divergent_convergent_BonferoniL, RatiosL_divergent_convergent, output_table):\n datafile = open(output_table, \"w\")\n datafile.write(\n \"Feature_1\" + '\\t' + \"Feature_2\" + \"\\t\" + \"plus_plus\" + '\\t' + \"minus_minus\" + '\\t' + \"plus_minus\" + '\\t' + \"minus_plus\" + '\\t' + \"p_value_same_opposite\" + '\\t' + \"p-value_same_opposite_Bonferoni_corrected\" + '\\t' + \"Ratio_same_opposite\" + '\\t' + \"p_value_divergent_convergent\" + '\\t' + \"p_value_divergent_convergent Bonferoni corrected\" + '\\t' + \"Ratio divergent convergent\" + '\\n')\n for i in range(len(NamesL_pairs)):\n datafile.write(\n NamesL_pairs[i][0] + '\\t' + NamesL_pairs[i][1] + '\\t' + str(p_pL[i]) + '\\t' + str(m_mL[i]) + '\\t' + str(\n p_mL[i]) + '\\t' + str(m_pL[i]) + '\\t' + str(p_valsL[i]) + '\\t' + str(p_vals_BonferoniL[i]) + '\\t' + str(\n RatiosL[i]) + '\\t' + str(p_valsL_divergent_convergent[i]) + '\\t' + str(\n p_valsL_divergent_convergent_BonferoniL[i]) + '\\t' + str(RatiosL_divergent_convergent[i]) + '\\n')\n datafile.close()\n return","def generate_pdf(request):\n reg_no = request.user.username\n user = get_object_or_404(User, username=reg_no)\n user_profile = user.get_profile()\n user_application = user_profile.application\n np = user_application.np\n \n response = HttpResponse(mimetype='application/pdf')\n response['Content-Disposition'] = 'attachment; filename=JAM2012_Allottment.pdf'\n \n elements = []\n doc = SimpleDocTemplate(response)\n \n formatted_time = time.ctime()\n styles = getSampleStyleSheet()\n styles.add(ParagraphStyle(name='Justify', alignment=TA_JUSTIFY))\n \n ptext = '<font size=15>JAM 2012 - Admissions.</font>' \n elements.append(Paragraph(ptext, styles[\"Justify\"]))\n elements.append(Spacer(4, 20))\n \n ptext = '<font size=12>Registration Number: %s</font>' % reg_no \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(1, 12))\n \n data = [] \n options = get_chosen_options(user) ##Put a check to show when the options chosen is empty\n \n if not(options):\n ptext = '<font size=12>No choices were selected.</font>' \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(1, 12))\n doc.build(elements) \n return response \n \n ptext = '<font size=12>The choices selected by me are as follows: </font>' \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(4, 30))\n \n counter = 1\n for opt in options:\n data.append([counter, opt.opt_code, opt.opt_location, opt.opt_name])\n counter = counter + 1\n \n t = Table(data)\n t.setStyle(TableStyle([('GRID',(0,0),(3,len(options)),1,colors.black),\n ('TEXTCOLOR',(0,0),(0,-1),colors.green)]))\n \n elements.append(t) \n \n elements.append(Spacer(4, 30))\n \n ptext = '<font size=12>I hereby declare that the order of preference given by me for my eligible programmes is final. </font>' \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(4, 25))\n \n ptext = '<font size=12>Signature of the Candidate</font>' \n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(4, 20))\n \n ptext = '<font size=12>%s</font>' % formatted_time\n elements.append(Paragraph(ptext, styles[\"Normal\"]))\n elements.append(Spacer(1, 12))\n \n doc.build(elements)\n \n return response","def delimToLaTeX(self,string,delim,linechange,style, math_var):\n # clearn the raw string \n string = string.rstrip()\n\n # generate a list of lists with each element is a list that represents a row\n text = self.escapeLaTeX(string)\n text = text.split(linechange)\n # split each row into cells by commas\n text = map(lambda x: x.split(delim), text)\n # print text\n\n # calculate the row number\n rows = len(text)\n\n # calculate the column number\n # if text[1] does not exist, that means this is not a proper table\n try:\n cols = len(text[0])\n except IndexError:\n print \"This is not a table! No columns exist!\"\n return\n\n # convert each cell by math indicator\n for i in range(rows):\n for j in range(cols):\n cell = text[i][j]\n text[i][j] = self.convertCell(cell,math_var)\n\n # generate a new table based of the lol\n # check each cell\n new_text = map(lambda x: ' & '.join(x), text)\n # print new_text\n new_text_str = ' \\\\\\\\ \\n'.join(new_text) + ' \\\\\\\\ \\n \\n'\n \n # cell style\n if (style == 'cells'):\n pass\n \n # simple style\n if (style == 'simple'): \n new_text_str = '\\\\begin{} \\n \\n'.format('{tabular}' + '{'+'l'*cols+'}') + new_text_str + '\\\\end{tabular}'\n \n # booktabs\n if (style == 'booktabs'):\n midrule_index = new_text_str.find('\\n')+1\n top = '\\\\toprule \\n' + new_text_str[:midrule_index]\n bottom = new_text_str[midrule_index:] + '\\\\bottomrule \\n'\n new_text_str = top + '\\\\midrule \\n' + bottom\n new_text_str = '\\\\begin{} \\n \\n'.format('{tabular}' + '{'+'l'*cols+'}') + new_text_str + '\\\\end{tabular}'\n\n # longtable\n if (style == 'longtable'):\n mid_index = new_text_str.find('\\n')+1\n first_row = new_text_str[:mid_index]\n rest = new_text_str[mid_index:]\n top = first_row + '\\\\endfirsthead \\n'+ first_row + '\\\\endhead \\n'\n top = top + '\\\\multicolumn{} \\n'.format('{' + str(cols) + '}' + '{'+'c'+'}'+'{Continued\\ldots} \\\\\\\\ \\n')\n top = top + '\\\\endfoot \\n\\\\hline \\n\\\\endlastfoot \\n \\n'\n new_text_str = top + rest\n new_text_str = '\\\\begin{} \\n \\n'.format('{longtable}' + '{'+'l'*cols+'}') + new_text_str + '\\\\end{longtable}'\n\n return new_text_str","def make_table_header(table_label, table_width,\n junction_char=\"+\", horizontal_char=\"-\",\n vertical_char=\"|\"):\n\n if len(table_label) >= (table_width - 2):\n raise ValueError(\n \"Table header %s is longer than total width of the table.\")\n\n label_and_space_width = table_width - len(table_label) - 2\n padding = 0 if label_and_space_width % 2 == 0 else 1\n\n half_table_width = label_and_space_width // 2\n left_spacing = (\" \" * half_table_width)\n right_spacing = (\" \" * (half_table_width + padding))\n\n border_line = \"\".join((junction_char,\n (horizontal_char * (table_width - 2)),\n junction_char,))\n\n label_line = \"\".join((vertical_char,\n left_spacing,\n table_label,\n right_spacing,\n vertical_char,))\n\n return \"\\n\".join((border_line, label_line,))","def print_table(self, table):\n raise NotImplementedError('print_table method not defined!')","def generate_table(rows):\n\n # - figure out column widths\n widths = [len(max(columns, key=len)) for columns in zip(*rows)]\n\n # - print the header\n header, data = rows[0], rows[1:]\n yield (\n ' | '.join(format(title, \"%ds\" % width) for width, title in zip(widths, header))\n )\n\n # Print the separator\n first_col = ''\n # - print the data\n for row in data:\n if first_col == '' and row[0] != '':\n # - print the separator\n yield '-+-'.join('-' * width for width in widths)\n first_col = row[0]\n\n yield (\n \" | \".join(format(cdata, \"%ds\" % width) for width, cdata in zip(widths, row))\n )","def make_html_table(filelist):\n pre=open(\"HTML_Header.txt\").read()\n out=[]\n for file in filelist:\n x=load_file(file)[1]\n out.append(\"<tr>\")\n out.append(\"<th>{}</th>\".format(x[0]))\n out.append(\"<th>{}</th>\".format(x[2]))\n out.append(\"<th>{}</th>\".format(x[1]))\n out.append(\"<th>{}</th>\".format(x[6]))\n out.append(\"<th>{}</th>\".format(x[7]))\n out.append(\"<th>{}</th>\".format(x[8]))\n out.append(\"<th>{}</th>\".format(x[9]))\n out.append(\"<th>{}</th>\".format(x[12]))\n out.append(\"<th>link</th>\")\n out.append(\"</tr>\")\n out.append(\"</table>\")\n \n for i in range(0,len(out)):\n pre=pre+out[i]+\"\\n\"\n \n path=os.getcwd()\n os.chdir(\"Ausgabe\")\n open(\"table.html\",\"w\").write(pre)\n os.chdir(path)","def gen_tag_year_report(\n lib, tags_ordered, n_papers_w_tag, years_ordered,\n actually_markdown=False):\n report = []\n if not actually_markdown:\n # Generate header.\n report.append(gen_header())\n report.append('<table class=\"table\">\\n')\n report.append(' <tr style=\"height: 8rem;\">\\n')\n report.append(' <th style=\"vertical-align: bottom;\"> Year </th>\\n')\n report.append(' <th style=\"vertical-align: bottom;\"> # Pubs </th>\\n')\n report.append(' <th> </th>\\n') # blank column before starting tags.\n for tag in tags_ordered:\n report.append(\n ' <th style=\"max-width: 3rem; vertical-align: middle; transform: translate(0rem, 2.2rem) rotate(290deg);\"> <a href=\"'\n + lib.gen_tag_url(tag)\n + '\">' + tag + '</a> </th>\\n')\n report.append(' </tr>\\n')\n\n # generate numbers of papers with tag per year\n all_papers_count = len(lib)\n for year in years_ordered:\n report.append(' <tr>\\n')\n n_papers_this_year = len(lib.get_pubs(year=year))\n report.append(' <th> ' + year + ' </th>\\n')\n\n year_count_style = gen_count_style(\n n_papers_this_year, all_papers_count)\n year_url = lib.gen_year_url(year)\n if year_url:\n report.append(\n ' <td {0}> <a href=\"{1}\"> {2} </a></td>\\n'.format(\n year_count_style, year_url, n_papers_this_year))\n else:\n report.append(\n ' <td {0}> {1} </td>\\n'.format(\n year_count_style, n_papers_this_year))\n report.append(' <td> </td>\\n') # blank column before starting tags.\n\n for tag in tags_ordered:\n papers_for_tag_year = lib.get_pubs(tag=tag, year=year)\n if papers_for_tag_year:\n n_papers_tag_year = len(papers_for_tag_year)\n style = gen_count_style(\n n_papers_tag_year, all_papers_count)\n tag_year_url = lib.gen_tag_year_url(tag, year)\n if tag_year_url:\n count_html = (\n '<a href=\"{0}\"> {1} </a>'.format(\n tag_year_url, n_papers_tag_year))\n else:\n count_html = \"{0}\".format(n_papers_tag_year)\n else:\n style = \"\"\n count_html = \"\"\n report.append(' <td ' + style + '> ' + count_html + ' </td>\\n')\n\n report.append(' </tr>\\n')\n\n # generate total line at bottom\n report.append(' <tr>\\n')\n report.append(' <th> Total </th>\\n')\n all_papers_style = gen_count_style(all_papers_count, all_papers_count)\n report.append(' <th ' + all_papers_style + '> '\n + str(all_papers_count) + ' </th>\\n')\n report.append(' <th> </th>\\n') # blank column before starting tags.\n\n for tag in tags_ordered:\n tag_count_style = gen_count_style(\n n_papers_w_tag[tag], all_papers_count)\n report.append(\n ' <th ' + tag_count_style + '> '\n + '<a href=\"' + lib.gen_tag_url(tag) + '\">'\n + str(n_papers_w_tag[tag]) + '</a> </th>\\n')\n\n report.append(' </tr>\\n')\n report.append('</table>\\n')\n if not actually_markdown:\n report.append(gen_footer())\n\n return report","def print_table(table):\n # transpose the table:\n table = map(list, zip(*table))\n # get the column width:\n col_width = [max(len(str(x)) for x in col) for col in zip(*table)]\n # print it to screen:\n print\n for line in table:\n print \"| \" + \" | \".join(\"{:{}}\".format(x, col_width[i]) for i, x in enumerate(line)) + \" |\"\n print","def print_table(headers, rows):\n try:\n if headers:\n print('\\n')\n print(tabulate.tabulate(\n rows, headers=headers,\n tablefmt=\"plain\", numalign=\"left\"\n ))\n print('\\n')\n except Exception as e:\n print(e.message)","def multiplicationTable(size=10,line_space=7):\n for column in range(1,size+1):\n row = ''\n for i in range(1, 11):\n if i*column > 9:\n row += str(i*column) + ' '*( (line_space+1) - len(str(i*column)) )\n continue\n row += str(i*column)+ ' '*line_space\n print(row)","def build_latex(file_list):\n eingabe=[]\n anhang_count=0\n anhaenge=[]\n anhaenge_file=[]\n for file in file_list:\n x=load_file(file)[1]\n eingabe.append(\"\\section{%s}\" %(x[2]))\n eingabe.append(\"\\subsection{Infos}\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}lX}\")\n eingabe.append(r\"\\textbf{Datum} & %s\\\\\" %(x[0]))\n eingabe.append(r\"\\textbf{Gremium} & %s\\\\\" %(x[1]))\n eingabe.append(r\"\\textbf{Anatrag/Beschluss wurde} & %s\\\\\" %(x[9]))\n x[11]=x[11].replace(\" \",\"\")\n kw=x[11].split(\",\")\n for i in range(0,len(kw)):\n if i==0:\n eingabe.append(r\"\\textbf{Keyword:} & %s\\\\\" %(kw[i]))\n else:\n eingabe.append(r\" & %s\\\\\" %(kw[i]))\n eingabe.append(\"\\end{tabularx}\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[6],x[7],x[8]))\n eingabe.append(\"\\end{tabularx}\")\n eingabe.append(\"\\subsection{Antrags/Beschlusstext}\")\n line_text=len(eingabe)\n eingabe.append(x[3])\n eingabe.append(\"\\subsection{Begründung}\")\n eingabe.append(x[4])\n if x[23]==\"Ja\" and x[24]!=\"\":\n delta=7\n anzahl=int((len(x)-23)/delta)\n if anzahl==1:\n eingabe.append(\"\\subsection{Änderungsantrag}\")\n eingabe.append(\"\\subsubsection*{Vorschlag}\")\n eingabe.append(x[24])\n eingabe.append(\"\\subsubsection*{Begründung}\")\n eingabe.append(x[25]+\"\\\\vspace{1.5ex} \\\\\\\\\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[26],x[27],x[28]))\n eingabe.append(r\"\\multicolumn{2}{@{}l}{\\textbf{Änderungsantrag wurde:}} & %s \\\\\" %(x[29]))\n eingabe.append(\"\\\\end{tabularx}\")\n else:\n eingabe.append(\"\\subsection{Änderungsanträge}\")\n for i in range(0,anzahl):\n eingabe.append(\"\\subsubsection{Änderungsvorschlag %s}\" %(i+1))\n eingabe.append(\"\\\\paragraph*{Vorschlag}\")\n eingabe.append(x[24+(delta*i)])\n eingabe.append(\"\\\\paragraph*{Begründung}\")\n eingabe.append(x[25+(delta*i)]+\"\\\\vspace{1.5ex} \\\\\\\\\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[26+(delta*i)],x[27+(delta*i)],x[28+(delta*i)]))\n eingabe.append(r\"\\multicolumn{2}{@{}l}{\\textbf{Änderungsantrag wurde:}} & %s \\\\\" %(x[29+(delta*i)]))\n eingabe.append(\"\\\\end{tabularx}\")\n if x[10]!=\"\":\n anhang=x[10].split(\",\")\n bennenung=x[11].split(\",\")\n eingabe[line_text]=eingabe[line_text]+\"\\\\\\\\ \\n Dieser Antrag enthält %s Anhänge: \" %(len(anhang))\n for i in range(0,len(anhang)):\n anhang_count=anhang_count+1\n anhaenge.append(\"\\section{%s - %s} \\label{An:%s}\" % (x[2],bennenung[i],str(anhang_count)))\n anhaenge.append(\"\\includepdf[pages=-]{%s}\" %(anhang[i]))\n anhaenge_file.append(anhang[i])\n if i!=len(anhang)-1:\n eingabe[line_text]=eingabe[line_text]+\"\\\\nameref{An:%s}, \" % (str(anhang_count))\n else:\n eingabe[line_text]=eingabe[line_text]+\"\\\\nameref{An:%s} \" % (str(anhang_count)) \n \n eingabe.append(\"\\\\newpage\") \n eingabe.append(\"\\\\appendix\") \n eingabe.append(\"\\\\pagenumbering{Roman}\") \n ausgabe=\"\"\n for i in range(0,len(eingabe)):\n ausgabe=ausgabe+eingabe[i]+\"\\n\"\n \n for i in range(0,len(anhaenge)):\n ausgabe=ausgabe+anhaenge[i]+\"\\n\"\n \n return ausgabe,anhaenge_file","def result_table(fmt='latex_booktabs'):\n \n names = [\n \"ETF EW.\",\n \"Antonacci ETF\",\n \"Antonacci ETF Inv. Vol.\",\n \"Futures EW.\",\n \"Antonacci Futures\",\n \"Antonacci Futures Inv. Vol.\",\n \"TSMOM Futures Low Vol.\",\n \"TSMOM Futures High Vol.\"\n ]\n\n # Get stats for each strategy\n s1 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=7, target_vol=40, periods=6, vol_weight=False)\n s2 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=2, target_vol=40, periods=6, vol_weight=False)\n s3 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=2, target_vol=40, periods=6, vol_weight=True)\n s4 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=47, target_vol=40, periods=6, vol_weight=False)\n s5 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=False)\n s6 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=True)\n s7 = calculate.stats_from_parameters(name='TSMOM', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=False)\n s8 = calculate.stats_from_parameters(name='TSMOM', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=100, periods=6, vol_weight=False)\n\n # The relevant columns from the summary data\n cols = [3, 4, 5, 6]\n num_assets = [7, 2, 2, 47, 10, 10, 47, 47]\n stats = [s1, s2, s3, s4, s5, s6, s7, s8]\n table = [names]\n \n # Collecting the results\n for i, col in enumerate(cols):\n col_list = [round(stat['summary'][col], 2) for stat in stats]\n table.append(col_list)\n\n table.append(num_assets)\n table = list(map(list, zip(*table))) # Transpose\n \n # Creating table headers\n headers = ['Strategy Name', 'Annual Return', 'Annual Vol.', 'Sharpe', 'Max. Drawdown', '# Assets']\n \n # Returning latex table\n tbl = tabulate(table, headers, tablefmt=fmt)\n print(tbl)\n \n return tbl","def print_table1(df, eval_dir):\n\n out_file = os.path.join(eval_dir, 'table1.txt')\n\n header_string = ' & '\n line_string = 'METHOD '\n\n\n for s_idx, struc_name in enumerate(['LV', 'RV', 'Myo']):\n for measure in ['dice', 'assd']:\n\n header_string += ' & {} ({}) '.format(measure, struc_name)\n\n dat = df.loc[df['struc'] == struc_name]\n\n if measure == 'dice':\n line_string += ' & {:.3f}\\,({:.3f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\n else:\n line_string += ' & {:.2f}\\,({:.2f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\n\n if s_idx < 2:\n header_string += ' & '\n line_string += ' & '\n\n header_string += ' \\\\\\\\ \\n'\n line_string += ' \\\\\\\\ \\n'\n\n with open(out_file, \"w\") as text_file:\n text_file.write(header_string)\n text_file.write(line_string)\n\n return 0","def table(self, doc, level, output):\n output('<table border=\"1\" cellpadding=\"2\">\\n')\n for row in doc.getRows()[0]:\n output(\"<tr>\\n\")\n for column in row.getColumns()[0]:\n str = ('<%s colspan=\"%s\" align=\"%s\" valign=\"%s\">'\n % (column.getType(),\n column.getSpan(),\n column.getAlign(),\n column.getValign()))\n output(str)\n for c in column.getChildNodes():\n getattr(self, self.element_types[c.getNodeName()]\n )(c, level, output)\n output(\"</\" + column.getType() + \">\\n\")\n output(\"</tr>\\n\")\n output(\"</table>\\n\")","def print_table(table, separator=' ', **kwargs):\n num_cols = max([len(row) for row in table])\n for idx, _ in enumerate(table):\n while len(table[idx]) < num_cols:\n table[idx].append('')\n widths = [max([len(str(cell)) for cell in [row[col_idx] for row in table]])\n for col_idx in range(len(table[0]))]\n for row in table:\n msg = ''\n for idx, cell in enumerate(row):\n msg += f'{cell:{widths[idx]}}{separator}'\n cprint(msg, **kwargs)","def format_participant_table(participants_f, url_prefix):\n personal_ids = sorted(parse_recipients(participants_f).keys())\n url_prefix = url_prefix if url_prefix.endswith('/') else url_prefix + '/'\n\n result = '<table class=\"data-table\">\\n<tr><th>Personal ID</th></tr>\\n'\n for personal_id in personal_ids:\n url = url_prefix + personal_id + '/index.html'\n result += '<tr><td><a href=\"%s\">%s</a></td></tr>\\n' % (url,\n personal_id)\n result += '</table>\\n'\n\n return result","def build_latex_standalone(x):\n eingabe=[]\n anhaenge_file=[]\n eingabe.append(\"\\\\title{%s}\" %(x[2]))\n eingabe.append(\"\\\\author{%s}\" %(x[1]))\n eingabe.append(\"\\\\date{%s}\" %(x[0]))\n eingabe.append(\"\\maketitle\") \n eingabe.append(\"\\section{Infos}\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}lX}\")\n eingabe.append(r\"\\textbf{Anatrag/Beschluss wurde} & %s\\\\\" %(x[9]))\n x[11]=x[11].replace(\" \",\"\")\n kw=x[11].split(\",\")\n for i in range(0,len(kw)):\n if i==0:\n eingabe.append(r\"\\textbf{Keyword:} & %s\\\\\" %(kw[i]))\n else:\n eingabe.append(r\" & %s\\\\\" %(kw[i]))\n eingabe.append(\"\\end{tabularx}\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[6],x[7],x[8]))\n eingabe.append(\"\\end{tabularx}\")\n eingabe.append(\"\\section{Antrags/Beschlusstext}\")\n eingabe.append(x[3])\n eingabe.append(\"\\section{Begründung}\")\n eingabe.append(x[4])\n if x[23]==\"Ja\" and x[24]!=\"\":\n delta=7\n anzahl=int((len(x)-23)/delta)\n if anzahl==1:\n eingabe.append(\"\\section{Änderungsantrag}\")\n eingabe.append(\"\\subsection*{Vorschlag}\")\n eingabe.append(x[24])\n eingabe.append(\"\\subsection*{Begründung}\")\n eingabe.append(x[25]+\"\\\\vspace{1.5ex} \\\\\\\\\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[26],x[27],x[28]))\n eingabe.append(r\"\\multicolumn{@{}2}{l}{\\textbf{Änderungsantrag wurde:}} & %s \\\\\" %(x[29]))\n eingabe.append(\"\\end{tabularx}\")\n else:\n eingabe.append(\"\\section{Änderungsanträge}\")\n for i in range(0,anzahl):\n eingabe.append(\"\\subsection{Änderungsvorschlag %s}\" %(i+1))\n eingabe.append(\"\\subsubsection*{Vorschlag}\")\n eingabe.append(x[24+(delta*i)])\n eingabe.append(\"\\subsubsection*{Begründung}\")\n eingabe.append(x[25+(delta*i)]+\"\\\\vspace{1.5ex} \\\\\\\\\")\n eingabe.append(\"\\\\begin{tabularx}{\\linewidth}{@{}XXX}\")\n eingabe.append(r\"\\textbf{Abstimmungsergebniss:}&&\\\\\")\n eingabe.append(r\"Zustimmung & Ablehnung & Enthaltungen \\\\\")\n eingabe.append(r\"{} & {} & {} \\\\\".format(x[26+(delta*i)],x[27+(delta*i)],x[28+(delta*i)]))\n eingabe.append(r\"\\multicolumn{@{}2}{l}{\\textbf{Änderungsantrag wurde:}} & %s \\\\\" %(x[29+(delta*i)]))\n eingabe.append(\"\\end{tabularx}\")\n if x[10]!=\"\":\n #\\includepdf[pages=-]{Anhang/Geschaeftsordnung_Jugendausschuss.pdf}\n eingabe.append(\"\\\\appendix\")\n eingabe.append(\"\\section*{Anhang}\")\n anhang=x[10].split(\",\")\n bennenung=x[11].split(\",\")\n eingabe[14]=eingabe[14]+\"\\\\\\\\ \\n Dieser Antrag enthält %s Anhänge: \" %(len(anhang))\n for i in range(0,len(anhang)):\n eingabe.append(\"\\subsection*{%s} \\label{An:%s}\" % (bennenung[i],str(i+1)))\n eingabe.append(\"\\includepdf[pages=-]{%s}\" %(anhang[i]))\n anhaenge_file.append(anhang[i])\n if i!=len(anhang)-1:\n eingabe[14]=eingabe[14]+\"\\\\nameref{An:%s}, \" % (str(i+1))\n else:\n eingabe[14]=eingabe[14]+\"\\\\nameref{An:%s} \" % (str(i+1))\n \n\n \n ausgabe=\"\"\n for i in range(0,len(eingabe)):\n ausgabe=ausgabe+eingabe[i]+\"\\n\"\n \n return ausgabe,anhaenge_file","def print_table(rows, labels=None):\n if labels is None:\n labels = ROW_LABELS\n\n output_table = prettytable.PrettyTable()\n output_table.field_names = labels\n output_table.align = 'l'\n output_table.vrules = prettytable.prettytable.ALL\n output_table.hrules = prettytable.prettytable.HEADER\n\n for row in rows:\n row = [x.strip() for x in row]\n output_table.add_row(row)\n\n print output_table\n print ''","def print_table(table, title_list):\n\n # your goes code\n \n table.insert(0, title_list)\n # title listet 0.helyre teszi\n # your code\n\n lenght_list = [] # tartalmazza az összes szót\n for lines in table:\n for items in lines:\n lenght_list.append(items)\n\n longest_words_length = len(max(lenght_list, key=len))\n multiplier = len(title_list)*(longest_words_length+1)\n\n for sublist in table:\n print(\"|\\n|\", \"-\"*multiplier, \"|\")\n\n for j in sublist:\n print(\"|\", j, end = \" \"*(longest_words_length-len(j)))\n\n print(\"|\\n|\",\"-\"*multiplier, \"|\")","def prepare_table(self):\n i = 0\n for item in ['DN[-]', 'd_out[mm]', 'tl_trub[mm]', 'roztec_trub[mm]', 'delka[mm]', 'roztec_prep[mm]', 'vyska_prep[mm]']:\n self.table.insertColumn(i)\n self.table.setHorizontalHeaderItem(i, QTableWidgetItem(item))\n i += 1\n for item in ['tl_prep[mm]','pocet_prep[-]', 'pocet_trub[-]', 'TP[m/s]', 'MZP[m/s]', 'vykon [W]',\n 'tlak_ztraty[Pa]', 'hmotnost[kg]']:\n self.table.insertColumn(i)\n self.table.setHorizontalHeaderItem(i, QTableWidgetItem(item))\n i += 1","def markdown_table(self, which):\n if which == 'C':\n coef = 'C'\n elif which == 'c':\n coef = 'c'\n elif which == 'f':\n coef = 'f'\n str = '|order|'\n for i in range(1,N+1):\n str = str + '$%s_{%d}$ |' % (coef,i)\n str = str + '\\n|'\n for i in range(1,N+1):\n str = str + '-|'\n str = str + '\\n'\n for i in range(1,self.N+1):\n str = str + (self.dat[i]).markdown_row(self.N, which)\n return str","def line_latex(line: str) -> str:\r\n\r\n if not line:\r\n return \"\"\r\n\r\n if \"$$\" in line:\r\n return line\r\n\r\n # check for horizontal rule\r\n\r\n if line == \"***\" or line == \"---\" or line == \"___\":\r\n return \"\\\\noindent\\\\rule{\\\\textwidth}{0.4pt}\"\r\n\r\n if line[0:5] == \"#### \":\r\n return \"\\\\subsubsection{\" + line_latex(line[5:]) + \"}\"\r\n elif line[0:4] == \"### \":\r\n return \"\\\\subsection{\" + line_latex(line[4:]) + \"}\"\r\n elif line[0:3] == \"## \":\r\n return \"\\\\section{\" + line_latex(line[3:]) + \"}\"\r\n elif line[0:2] == \"# \":\r\n return \"\\\\title{\" + line_latex(line[2:]) + \"}\\n\" + \"\\\\maketitle\"\r\n\r\n # various formatting modes to be in\r\n math = False\r\n\r\n asterisks = 0\r\n bf = False\r\n it = False\r\n\r\n tildes = 0\r\n sout = False\r\n\r\n verb = False\r\n\r\n latex = \"\"\r\n\r\n for char in line:\r\n if char == \"`\":\r\n if math:\r\n latex += char\r\n elif verb:\r\n latex += \"|\"\r\n verb = False\r\n else:\r\n latex += \"\\\\verb|\"\r\n verb = True\r\n\r\n elif char == \"$\":\r\n if verb:\r\n latex += char\r\n elif math:\r\n latex += \"$\"\r\n math = False\r\n else:\r\n latex += \"$\"\r\n math = True\r\n\r\n elif char == \"*\":\r\n asterisks += 1\r\n elif char == \"~\":\r\n tildes += 1\r\n\r\n elif math or verb or (asterisks == 0 and tildes == 0):\r\n latex += char\r\n\r\n elif asterisks == 3:\r\n if bf or it:\r\n latex += \"}}\" + char\r\n asterisks = 0\r\n bf, it = False, False\r\n else:\r\n latex += \"\\\\textbf{\\\\textit{\" + char\r\n asterisks = 0\r\n bf, it, = True, True\r\n elif asterisks == 2:\r\n if bf:\r\n latex += \"}\" + char\r\n asterisks = 0\r\n bf = False\r\n else:\r\n latex += \"\\\\textbf{\" + char\r\n asterisks = 0\r\n bf = True\r\n elif asterisks == 1:\r\n if it:\r\n latex += \"}\" + char\r\n asterisks = 0\r\n it = False\r\n else:\r\n latex += \"\\\\textit{\" + char\r\n asterisks = 0\r\n it = True\r\n elif tildes == 2:\r\n if sout:\r\n latex += \"}\" + char\r\n tildes = 0\r\n sout = False\r\n else:\r\n latex += \"\\\\sout{\" + char\r\n tildes = 0\r\n sout = True\r\n elif tildes == 1:\r\n latex += \"~\" + char\r\n tildes = 0\r\n\r\n if bf and it:\r\n latex += \"}}\"\r\n elif bf or it:\r\n latex += \"}\"\r\n\r\n return latex","def _gen_html(self):\n yield \"\\n<table border=%r summary='a table'>\\n\" % self.border\n header = self.header\n for row in self:\n yield \"<tr>\\n \"\n for el in row:\n if header:\n yield \"<th>%s</th> \" % el\n else:\n yield '<td bgcolor=\"%s\">%s</td> ' % \\\n (getattr(row, \"color\", self.color), el)\n yield \"\\n</tr>\\n\"\n header = False\n yield \"</table>\\n\"","def generate_pr_table(start_ref, end_ref):\r\n header = \"|| Merged By || Author || Title || PR || JIRA || Verified? ||\"\r\n pr_link = \"[#{num}|https://github.com/edx/edx-platform/pull/{num}]\"\r\n user_link = \"[@{user}|https://github.com/{user}]\"\r\n rows = [header]\r\n prbe = prs_by_email(start_ref, end_ref)\r\n for email, pull_requests in prbe.items():\r\n for i, pull_request in enumerate(pull_requests):\r\n try:\r\n pr_info = get_pr_info(pull_request)\r\n title = pr_info[\"title\"] or \"\"\r\n body = pr_info[\"body\"] or \"\"\r\n author = pr_info[\"user\"][\"login\"]\r\n except requests.exceptions.RequestException as e:\r\n message = (\r\n \"Warning: could not fetch data for #{num}: \"\r\n \"{message}\".format(num=pull_request, message=e.message)\r\n )\r\n print(colorize(\"red\", message), file=sys.stderr)\r\n title = \"?\"\r\n body = \"?\"\r\n author = \"\"\r\n rows.append(\"| {merged_by} | {author} | {title} | {pull_request} | {jira} | {verified} |\".format(\r\n merged_by=email if i == 0 else \"\",\r\n author=user_link.format(user=author) if author else \"\",\r\n title=title.replace(\"|\", \"\\|\"),\r\n pull_request=pr_link.format(num=pull_request),\r\n jira=\", \".join(parse_ticket_references(body)),\r\n verified=\"\",\r\n ))\r\n return \"\\n\".join(rows)","def print_table(data_array, column_tag, row_tag, \n print_format = \"1.2f\", \n with_color_cell = True,\n colormap='Greys', colorscale=0.5, colorwrap=0, col_sep='', \n print_latex_table=True, print_text_table=True,\n print_format_along_row=True):\n if column_tag is None:\n column_tag = [\"\" for data in data_array[0, :]]\n if row_tag is None:\n row_tag = [\"\" for data in data_array]\n\n # check print_format\n if type(print_format) is not list:\n if print_format_along_row:\n # repeat the tag\n print_format = [print_format for x in row_tag]\n else:\n print_format = [print_format for x in column_tag]\n else:\n if print_format_along_row:\n assert len(print_format) == len(row_tag)\n else:\n assert len(print_format) == len(column_tag)\n\n\n # color configuration\n color_func = cm.get_cmap(colormap)\n data_idx = return_valid_number_idx(data_array)\n value_min = np.min(data_array[data_idx])\n value_max = np.max(data_array[data_idx])\n \n def get_latex_color(x):\n # return a color command for latex cell\n return return_latex_color_cell(x, value_min, value_max, \n colorscale, colorwrap, color_func)\n \n # maximum width for tags in 1st column\n row_tag_max_len = max([len(x) for x in row_tag])\n\n # maximum width for data and tags for other columns\n if print_format_along_row:\n tmp_len = []\n for idx, data_row in enumerate(data_array):\n tmp_len.append(\n max([len(\"{num:{form}}\".format(num=x, form=print_format[idx])) \\\n for x in data_row]))\n else:\n tmp_len = []\n for idx, data_col in enumerate(data_array.T):\n tmp_len.append(\n max([len(\"{num:{form}}\".format(num=x, form=print_format[idx])) \\\n for x in data_col]))\n col_tag_max_len = max([len(x) for x in column_tag] + tmp_len)\n \n # prepare buffer\n text_buffer = \"\"\n latex_buffer = \"\"\n \n # latex head\n latex_buffer += r\"\\begin{tabular}{\" \\\n + ''.join(['c' for x in column_tag + ['']]) + r\"}\" + \"\\n\"\n \n # head row\n # for latex\n hrow = [fill_cell(\"\", row_tag_max_len)] \\\n + [fill_cell(x, col_tag_max_len) for x in column_tag]\n latex_buffer += return_one_row_latex(hrow)\n # for plain text (add additional separator for each column)\n hrow = [fill_cell(\"\", row_tag_max_len, col_sep)] \\\n + [fill_cell(x, col_tag_max_len, col_sep) for x in column_tag]\n text_buffer += return_one_row_text(hrow)\n \n # contents\n row = data_array.shape[0]\n col = data_array.shape[1]\n for row_idx in np.arange(row):\n # row head\n row_content_latex = [fill_cell(row_tag[row_idx], row_tag_max_len)]\n row_content_text = [fill_cell(row_tag[row_idx],row_tag_max_len,col_sep)]\n \n # each column in the raw\n for col_idx in np.arange(col):\n\n if print_format_along_row:\n tmp_print_format = print_format[row_idx]\n else:\n tmp_print_format = print_format[col_idx]\n\n if is_valid_float(data_array[row_idx,col_idx]):\n num_str = \"{num:{form}}\".format(num=data_array[row_idx,col_idx],\n form=tmp_print_format)\n latex_color_cell = get_latex_color(data_array[row_idx,col_idx])\n elif type(data_array[row_idx,col_idx]) is str:\n num_str = \"{num:{form}}\".format(num=data_array[row_idx,col_idx],\n form=tmp_print_format)\n latex_color_cell = ''\n else:\n num_str = ''\n latex_color_cell = ''\n \n if not with_color_cell:\n latex_color_cell = ''\n \n row_content_text.append(\n fill_cell(num_str, col_tag_max_len, col_sep))\n\n row_content_latex.append(\n fill_cell(latex_color_cell + ' ' + num_str, col_tag_max_len))\n \n # latex table content\n latex_buffer += return_one_row_latex(row_content_latex)\n # text content\n text_buffer += return_one_row_text(row_content_text)\n \n latex_buffer += r\"\\end{tabular}\" + \"\\n\"\n\n if print_latex_table:\n print(latex_buffer)\n if print_text_table:\n print(text_buffer)\n return","def _construct_html_table(self, df: Table) -> str:\n string = attach_tag_tr('\\n'.join(map(attach_tag_th, df.columns)))\n stringified_df = _stringify_table(df)\n\n for (i, row_elements) in stringified_df.iterrows():\n # Commented out code is a bit sleaker, but restrictive\n #string += '\\n' + attach_tag_tr('\\n'.join(map(attach_tag_td,\n # row_elements)))\n table_content: List = []\n for col, val in row_elements.iteritems():\n if col == 'cost':\n table_content.append(attach_tag_td_rjust(val))\n else:\n table_content.append(attach_tag_td(val))\n\n string += '\\n' + attach_tag_tr('\\n'.join(table_content))\n\n return attach_tag_table(\n attach_tag_caption(f'All Costs of {self.trip_title}')\n + '\\n'\n + attach_tag_tbody(string))","def __print_work_table(table):\n print \"%-5s %-30s %5s %5s %5s %5s %5s\" % ('Act', 'Pred', 'Block', 'Dummy', 'Succ', 'start', 'end')\n for k, col in sorted(table.items()):\n print \"%-5s %-30s %5s %5s %5s %5s %5s\" % tuple(\n [str(k)] + [list(col[0])] + [str(col[i]) for i in range(1, len(col))])","def print_table(table):\n rest = table[1:]\n fmt = \"%-28s %-9s %-16s %s\"\n for row in rest:\n print(fmt % tuple(row))","def print_table(table, title_list):\n\n # your goes code\n cols = len(title_list)\n\n \n\n table.insert(0,title_list)\n\n for sublist in range(len(table)):\n if cols != len(table[sublist]):\n print('dataset does not match number of cols')\n quit()\n\n max_lenghts = []\n maxi = -1\n for sub_elem in range(cols): \n maxi = -1 \n for sublist in range(len(table)):\n if len(table[sublist][sub_elem]) > maxi:\n maxi = len(table[sublist][sub_elem])\n max_lenghts.append(maxi)\n \n\n \n\n sub_elem = 0\n \n for sublist in range(len(table)):\n if sublist == 0:\n while sub_elem < len(table[0]):\n \n if sub_elem == len(table[0])- 1:\n print('\\033[1;37;41m| {:^25} |'.format(table[sublist][sub_elem]), end =\"\")\n else:\n print('\\033[1;37;41m| {:^25} '.format(table[sublist][sub_elem]), end =\"\")\n sub_elem += 1\n \n print('\\033[0;32;48m\\n') \n sub_elem = 0 \n else:\n while sub_elem < len(table[0]):\n \n if sub_elem == len(table[0])- 1:\n print('\\033[0;37;44m| {:^25} |'.format(table[sublist][sub_elem]), end =\"\")\n else:\n print('\\033[0;37;44m| {:^25} '.format(table[sublist][sub_elem]), end =\"\")\n sub_elem += 1\n \n print('\\033[0;32;48m\\n') \n sub_elem = 0 \n print('\\033[0;37;48m\\n')\n table.pop(0)","def pretty_output(associations, population):\n\tcolumn_names = ['ld_snp_rsID', 'chrom', 'pos', 'GRCh38_chrom', 'GRCh38_pos', 'afr', 'amr', 'eas', 'eur', 'sas', 'gnomad', 'gnomad_sas', 'gnomad_oth', 'gnomad_asj', 'gnomad_nfe', 'gnomad_afr', 'gnomad_amr', 'gnomad_fin', 'gnomad_eas','gene_symbol', 'gene_id', 'gene_chrom', 'gene_tss', 'GRCh38_gene_chrom', 'GRCh38_gene_pos', 'disease_name', 'disease_efo_id', 'score', 'rank', 'r2', 'cluster_id', 'gwas_source', 'gwas_snp', 'gwas_pvalue', 'gwas_pvalue_description', 'gwas_odds_ratio', 'gwas_odds_ratio_ci_start', 'gwas_odds_ratio_ci_end', 'gwas_beta', 'gwas_size', 'gwas_pmid', 'gwas_study', 'gwas_reported_trait', 'ls_snp_is_gwas_snp', 'vep_terms', 'vep_sum', 'vep_mean'] + [\"GTEx_\" + tissue_name for tissue_name in postgap.Globals.ALL_TISSUES] + [source.display_name for source in postgap.Cisreg.sources + postgap.Reg.sources]\n\tif postgap.Globals.PERFORM_BAYESIAN: \n\t\tcolumn_names += [tissue_name + \"_CLPP\" for tissue_name in postgap.Globals.ALL_TISSUES]\n\theader = \"\\t\".join(column_names).encode('utf-8')\n\tcontent = filter(lambda X: len(X) > 0, [pretty_cluster_association(association, population) for association in associations])\n\treturn \"\\n\".join([header] + content)","def make_table_header(table_file, function_list):\n\tstring = '%10s' %(\" \")\n\tfor function in function_list:\n\t\tstring += \"\\t\"+'%32s' %(\"f_\"+str(function-1))\n\ttable_file.write(string+\"\\n\")\n\tstring = '%10s' %(\" \")+ \"\\t\" + \"------------\"*4*len(function_list)\n\ttable_file.write(string+\"\\n\")\n\treturn None","def generate_table(input_file, delim=\",\", header=True):\n input_file = os.path.abspath(input_file)\n if not os.path.exists(input_file):\n sys.exit(\"%s does not exist.\" % input_file)\n\n # Read in rows with user specified delimiter\n rows = read_rows(input_file, delim=delim)\n\n # Generate tabulars expected format\n labels = [\"column %s\" % x for x in range(len(rows[0]))]\n if header:\n labels = rows.pop(0)\n\n # Generate Tabular table to output\n table = Tabular(\n # Note that columns are specified here, so we provide a row (list) later\n columns=labels,\n style=dict(\n header_=dict(bold=True, transform=str.upper),\n # Default styling could be provided from some collection of styling files\n default_=dict(\n color=dict(\n lookup={\n \"Trix\": \"green\",\n \"110\": \"red\",\n \"100\": \"green\", # since no grey for now\n }\n )\n ),\n ),\n )\n\n # Add row to table. If columns aren't specified on init, provide dict here\n for row in rows:\n table(row)","def write_latex_table(machine, all_benchs, summary, diff, skipped, tex_file, num_splits,\n with_preamble=False, longtable=False, diff_vms=[]):\n\n num_benchmarks = len(all_benchs)\n all_vms = sorted(summary.keys())\n num_vms = len(summary)\n\n # decide how to lay out the splits\n num_vms_rounded = int(math.ceil(num_vms / float(num_splits)) * num_splits)\n vms_per_split = int(num_vms_rounded / float(num_splits))\n splits = [[] for x in xrange(num_splits)]\n vm_num = 0\n split_idx = 0\n for vm_idx in xrange(num_vms_rounded):\n if vm_idx < len(all_vms):\n vm = all_vms[vm_idx]\n else:\n vm = None\n splits[split_idx].append(vm)\n vm_num += 1\n if vm_num % vms_per_split == 0:\n split_idx += 1\n\n with open(tex_file, 'w') as fp:\n if with_preamble:\n fp.write(preamble(TITLE))\n if diff_vms:\n fp.write('\\\\centering{%%\\n\\\\Large{\\\\textbf{%s vs. %s}}%%\\n}\\n\\\\\\\\\\n~\\\\\\\\\\n\\n'\n % (diff_vms[0], diff_vms[1]))\n legends = get_latex_symbol_map() + ' \\\\\\\\ ' + legend()\n fp.write('\\\\centering %s' % legends)\n fp.write('\\n\\n\\n')\n if not longtable:\n fp.write('\\\\begin{landscape}\\n')\n fp.write('\\\\begin{table*}[hptb]\\n')\n fp.write('\\\\vspace{.8cm}\\n')\n fp.write('\\\\begin{adjustbox}{totalheight=12.4cm}\\n')\n # Emit table header.\n heads1 = TABLE_HEADINGS_START1 + '&'.join([TABLE_HEADINGS1] * num_splits)\n heads2 = TABLE_HEADINGS_START2 + '&'.join([TABLE_HEADINGS2] * num_splits)\n heads = '%s\\\\\\\\%s' % (heads1, heads2)\n if longtable:\n fp.write(start_longtable(TABLE_FORMAT, heads))\n else:\n fp.write(start_table(TABLE_FORMAT, heads))\n split_row_idx = 0\n for row_vms in zip(*splits):\n bench_idx = 0\n skipped_before = [b for (b, v) in skipped[SKIPPED_BEFORE] if v == row_vms[0]]\n skipped_after = [b for (b, v) in skipped[SKIPPED_AFTER] if v == row_vms[0]]\n for bench in sorted(all_benchs + skipped_before + skipped_after):\n row = []\n for vm in row_vms:\n if vm is None:\n continue # no more results\n try:\n this_summary = summary[vm][bench]\n except KeyError:\n if bench in skipped_before or bench in skipped_after:\n classification = '\\\\emph{Skipped}'\n else:\n classification = ''\n last_cpt = BLANK_CELL\n time_steady = BLANK_CELL\n last_mean = BLANK_CELL\n steady_iter_var = BLANK_CELL\n steady_time_var = BLANK_CELL\n else:\n if vm in diff and bench in diff[vm]:\n classification = colour_tex_cell(diff[vm][bench][CLASSIFICATIONS], this_summary['style'])\n last_cpt = colour_tex_cell(diff[vm][bench][STEADY_ITER], this_summary['last_cpt'])\n steady_iter_var = colour_tex_cell(diff[vm][bench][STEADY_ITER_VAR], this_summary['steady_iter_var'])\n time_steady = colour_tex_cell(diff[vm][bench][STEADY_ITER], this_summary['time_to_steady_state'])\n last_mean = colour_tex_cell(diff[vm][bench][STEADY_STATE_TIME], this_summary['last_mean'])\n steady_time_var = colour_tex_cell(diff[vm][bench][STEADY_STATE_TIME_VAR], this_summary['steady_time_var'])\n else:\n classification = this_summary['style']\n last_cpt = this_summary['last_cpt']\n steady_iter_var = this_summary['steady_iter_var']\n time_steady = this_summary['time_to_steady_state']\n last_mean = this_summary['last_mean']\n steady_time_var = this_summary['steady_time_var']\n classification = '\\\\multicolumn{1}{l}{%s}' % classification\n if classification == STYLE_SYMBOLS['flat']:\n last_cpt = BLANK_CELL\n time_steady = BLANK_CELL\n if last_cpt == '':\n last_cpt = BLANK_CELL\n if time_steady == '':\n time_steady = BLANK_CELL\n if last_mean == '':\n last_mean = BLANK_CELL\n\n if bench_idx == 0:\n if num_benchmarks == 10:\n fudge = 4\n elif num_benchmarks == 12:\n fudge = 5\n else:\n fudge = 0\n vm_cell = '\\\\multirow{%s}{*}{\\\\rotatebox[origin=c]{90}{%s}}' \\\n % (num_benchmarks + fudge, vm)\n else:\n vm_cell = ''\n row_add = [BLANK_CELL, vm_cell, classification, last_cpt,\n steady_iter_var, time_steady, last_mean, steady_time_var]\n if not row: # First bench in this row, needs the vm column.\n if vm in diff and bench in diff[vm]:\n bname = colour_tex_cell(diff[vm][bench][INTERSECTION], bench)\n else:\n bname = bench\n row.insert(0, escape(bname))\n row.extend(row_add)\n vm_idx += 1\n fp.write('&'.join(row))\n # Only -ve space row if not next to a midrule\n if not longtable and bench_idx < num_vms - 1:\n fp.write('\\\\\\\\[-3pt] \\n')\n else:\n fp.write('\\\\\\\\ \\n')\n bench_idx += 1\n if split_row_idx < vms_per_split - 1:\n if longtable:\n fp.write('\\\\hline\\n')\n else:\n fp.write('\\\\midrule\\n')\n split_row_idx += 1\n if longtable:\n fp.write(end_longtable())\n else:\n fp.write(end_table())\n if with_preamble:\n if not longtable:\n fp.write('\\\\end{adjustbox}\\n')\n fp.write('\\\\end{table*}\\n')\n fp.write('\\\\end{landscape}\\n')\n fp.write(end_document())"],"string":"[\n \"def generate_latex_table(true_hmm, sampled_hmm_list, conf=0.95, dt=1, time_unit='ms', obs_name='force', obs_units='pN', outfile=None):\\n\\n # confidence interval\\n for sampled_hmm in sampled_hmm_list:\\n sampled_hmm.set_confidence(conf)\\n # dt\\n dt = float(dt)\\n # nstates\\n nstates = sampled_hmm_list[0].nstates\\n\\n table = r\\\"\\\"\\\"\\n\\\\begin{table*}\\n\\\\caption{{\\\\bf Estimated mean model parameters and confidence intervals for synthetic timeseries data}}\\n\\\\label{table:synthetic-confidence-intervals}\\n\\\\begin{tabular*}{\\\\textwidth}{@{\\\\extracolsep{\\\\fill}}lccccc}\\n\\\\hline\\n& & & \\\\multicolumn{3}{c}{\\\\bf Estimated Model Parameters} \\\\\\\\ \\\\cline{4-6}\\n\\\\multicolumn{2}{l}{\\\\bf Property} & \\\\bf True Value & \\\\bf 1 000 observations & \\\\bf 10 000 observations & \\\\bf 100 000 observations\\\\\\\\ \\\\hline\\n\\\"\\\"\\\"\\n # Stationary probability.\\n for i in range(nstates):\\n if (i == 0):\\n table += '\\\\t\\\\tEquilibrium probability '\\n table += '\\\\t\\\\t& $\\\\pi_{%d}$ & $%0.3f$' % (i+1, true_hmm.stationary_distribution[i])\\n for sampled_hmm in sampled_hmm_list:\\n p = sampled_hmm.stationary_distribution_mean\\n p_lo, p_hi = sampled_hmm.stationary_distribution_conf\\n table += ' & $%0.3f_{\\\\:%0.3f}^{\\\\:%0.3f}$ ' % (p[i], p_lo[i], p_hi[i])\\n table += ' \\\\\\\\\\\\\\\\' + '\\\\n'\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n\\n # Transition probabilities.\\n for i in range(nstates):\\n for j in range(nstates):\\n if (i == 0) and (j==0):\\n table += '\\\\t\\\\tTransition probability ($\\\\Delta t = $%s) ' % (str(dt)+' '+time_unit)\\n table += '\\\\t\\\\t& $T_{%d%d}$ & $%0.3f$' % (i+1, j+1, true_hmm.transition_matrix[i,j])\\n for sampled_hmm in sampled_hmm_list:\\n P = sampled_hmm.transition_matrix_mean\\n P_lo, P_hi = sampled_hmm.transition_matrix_conf\\n table += ' & $%0.3f_{\\\\:%0.3f}^{\\\\:%0.3f}$' % (P[i,j], P_lo[i,j], P_hi[i,j])\\n table += ' \\\\\\\\\\\\\\\\' + '\\\\n'\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n\\n # Transition rates via pseudogenerator.\\n index = 0\\n for i in range(nstates):\\n for j in range(nstates):\\n if (i != j):\\n if (index==0):\\n table += '\\\\t\\\\tTransition rate (%s$^{-1}$) ' % time_unit\\n Ktrue = compute_rate(true_hmm.transition_matrix, dt)\\n table += '\\\\t\\\\t& $k_{%d%d}$ & $%2.3f$' % (i+1, j+1, Ktrue[i,j])\\n for sampled_hmm in sampled_hmm_list:\\n P = sampled_hmm.transition_matrix_mean\\n P_lo, P_hi = sampled_hmm.transition_matrix_conf\\n K = compute_rate(P, dt)\\n K_lo = compute_rate(P_lo, dt)\\n K_hi = compute_rate(P_hi, dt)\\n table += ' & $%.3f_{\\\\:%.3f}^{\\\\:%.3f}$' % (K[i,j], K_lo[i,j], K_hi[i,j])\\n index += 1\\n table += ' \\\\\\\\\\\\\\\\' + '\\\\n'\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n\\n # State mean lifetimes.\\n for i in range(nstates):\\n if (i == 0):\\n table += '\\\\t\\\\tState mean lifetime (%s) ' % time_unit\\n l = true_hmm.lifetimes\\n l *= dt\\n table += '\\\\t\\\\t& $t_{%d}$ & $%.3f$' % (i+1, l[i])\\n for sampled_hmm in sampled_hmm_list:\\n l = sampled_hmm.lifetimes_mean\\n l *= dt\\n l_lo, l_hi = sampled_hmm.lifetimes_conf\\n l_lo *= dt; l_hi *= dt\\n table += ' & $%.3f_{\\\\:%.3f}^{\\\\:%.3f}$' % (l[i], l_lo[i], l_hi[i])\\n table += ' \\\\\\\\\\\\\\\\' + '\\\\n'\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n\\n # State relaxation timescales.\\n for i in range(nstates-1):\\n if (i == 0):\\n table += '\\\\t\\\\tRelaxation time (%s) ' % time_unit\\n t = true_hmm.timescales\\n t *= dt\\n table += '\\\\t\\\\t& $\\\\\\\\tau_{%d}$ & $%.3f$' % (i+1, t[i])\\n for sampled_hmm in sampled_hmm_list:\\n t = sampled_hmm.timescales_mean\\n t *= dt\\n t_lo, t_hi = sampled_hmm.timescales_conf\\n t_lo *= dt; t_hi *= dt\\n table += ' & $%.3f_{\\\\:%.3f}^{\\\\:%.3f}$' % (t[i], t_lo[i], t_hi[i])\\n table += ' \\\\\\\\\\\\\\\\' + '\\\\n'\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n\\n if True:\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n\\n # State mean forces.\\n for i in range(nstates):\\n if (i == 0):\\n table += '\\\\t\\\\tState %s mean (%s) ' % (obs_name, obs_units)\\n m = true_hmm.output_model.means\\n table += '\\\\t\\\\t& $\\\\mu_{%d}$ & $%.3f$' % (i+1, m[i])\\n for sampled_hmm in sampled_hmm_list:\\n m = sampled_hmm.means_mean\\n m_lo, m_hi = sampled_hmm.means_conf\\n table += ' & $%.3f_{\\\\:%.3f}^{\\\\:%.3f}$' % (m[i], m_lo[i], m_hi[i])\\n table += ' \\\\\\\\\\\\\\\\' + '\\\\n'\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n\\n # State force standard deviations.\\n for i in range(nstates):\\n if (i == 0):\\n table += '\\\\t\\\\tState %s std dev (%s) ' % (obs_name, obs_units)\\n s = true_hmm.output_model.sigmas\\n table += '\\\\t\\\\t& $s_{%d}$ & $%.3f$' % (i+1, s[i])\\n for sampled_hmm in sampled_hmm_list:\\n s = sampled_hmm.sigmas_mean\\n s_lo, s_hi = sampled_hmm.sigmas_conf\\n table += ' & $%.3f_{\\\\:%.3f}^{\\\\:%.3f}$' % (s[i], s_lo[i], s_hi[i])\\n table += ' \\\\\\\\\\\\\\\\' + '\\\\n'\\n table += '\\\\t\\\\t\\\\hline' + '\\\\n'\\n\\n table += r\\\"\\\"\\\"\\\\hline\\n\\\\end{tabular*}\\n\\\\end{table*}\\n\\\"\\\"\\\"\\n\\n # Write to file if desired.\\n if outfile is not None:\\n f = open(outfile,'w')\\n f.write(table)\\n f.close()\\n\\n return table\",\n \"def generate_latex_table(dictionary,filename,location=\\\".\\\"):\\n if type(filename) != str:\\n raise TypeError('filename should be string')\\n if type(dictionary) != dict:\\n raise TypeError('dictionary should be dictionary')\\n\\n head_code = \\\"\\\"\\\"\\\\\\\\documentclass{article}\\n%In the preamble section include the arabtex and utf8 packages\\n\\\\\\\\usepackage{arabtex}\\n\\\\\\\\usepackage{utf8}\\n\\\\\\\\usepackage{longtable}\\n\\\\\\\\usepackage{color, colortbl}\\n\\\\\\\\usepackage{supertabular}\\n\\\\\\\\usepackage{multicol}\\n\\\\\\\\usepackage{geometry}\\n\\\\\\\\geometry{left=.1in, right=.1in, top=.1in, bottom=.1in}\\n\\n\\\\\\\\begin{document}\\n\\\\\\\\begin{multicols}{6}\\n\\\\\\\\setcode{utf8}\\n\\n\\\\\\\\begin{center}\\\"\\\"\\\"\\n\\n tail_code = \\\"\\\"\\\"\\\\\\\\end{center}\\n\\\\\\\\end{multicols}\\n\\\\\\\\end{document}\\\"\\\"\\\"\\n\\n begin_table = \\\"\\\"\\\"\\\\\\\\begin{tabular}{ P{2cm} P{1cm}}\\n\\\\\\\\textbf{words} & \\\\\\\\textbf{\\\\\\\\#} \\\\\\\\\\\\\\\\\\n\\\\\\\\hline\\n\\\\\\\\\\\\\\\\[0.01cm]\\\"\\\"\\\"\\n end_table= \\\"\\\"\\\"\\\\\\\\end{tabular}\\\"\\\"\\\"\\n rows_num = 40\\n if location != '.':\\n filename = location +\\\"/\\\"+ filename\\n\\n try:\\n file = open(filename+'.tex', 'w', encoding='utf8')\\n file.write(head_code+'\\\\n')\\n n= int(len(dictionary)/rows_num)\\n words = [(\\\"\\\\\\\\<\\\"+word+\\\"> & \\\"+str(frequancy)+' \\\\\\\\\\\\\\\\ \\\\n') for word, frequancy in dictionary.items()]\\n start=0\\n end=rows_num\\n new_words = []\\n for i in range(n):\\n new_words = new_words+ [begin_table+'\\\\n'] +words[start:end] +[end_table+\\\" \\\\n\\\"]\\n start=end\\n end+=rows_num\\n remain_words = len(dictionary) - rows_num*n\\n if remain_words > 0:\\n new_words += [begin_table+\\\" \\\\n\\\"]+ words[-1*remain_words:]+[end_table+\\\" \\\\n\\\"]\\n for word in new_words:\\n file.write(word)\\n file.write(tail_code)\\n file.close()\\n return True\\n except:\\n return False\",\n \"def setup_latex_table(self, tabletype, injected):\\n self.texfile.write(r\\\"\\\\\\\\renewcommand{\\\\\\\\arraystretch}{1.6}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\n\\\")\\n self.texfile.write(r\\\"\\\\\\\\begin{table}[t!]\\\\n\\\")\\n self.texfile.write(r\\\" \\\\\\\\begin{center}\\\\n\\\")\\n if tabletype == 'fiducial_fit_params':\\n if injected:\\n nextline = r\\\" \\\\\\\\begin{tabu} to 1.0\\\\\\\\textwidth \\\"\\n nextline += r\\\"{| X[2.0,c] | X[1,c] | X[1,c] | X[1,c]\\\"\\n nextline += r\\\" | X[1,c] | X[1,c] | X[1,c] | X[1,c] |}\\\\n\\\"\\n self.texfile.write(nextline)\\n self.texfile.write(r\\\" \\\\hline\\\\n\\\")\\n nextline = r\\\" \\\\multirow{2}{*}{\\\\\\\\textbf{Parameter}} \\\"\\n nextline += r\\\"& \\\\multirow{2}{*}{\\\\\\\\textbf{Inj}} \\\"\\n nextline += r\\\"& \\\\multicolumn{3}{c|}{h0} \\\"\\n nextline += r\\\"& \\\\multicolumn{3}{c|}{h1} \\\"\\n nextline += r\\\"\\\\\\\\\\\\\\\\ \\\\cline{3-8}\\\"\\n self.texfile.write(nextline)\\n nextline = r\\\" & & Prior & Fit & \\\\(\\\\Delta\\\\) \\\"\\n nextline += r\\\"& Prior & Fit & \\\\(\\\\Delta\\\\) \\\\\\\\\\\\\\\\ \\\\hline\\\\n\\\"\\n self.texfile.write(nextline)\\n else:\\n nextline = r\\\" \\\\\\\\begin{tabu} to 1.0\\\\\\\\textwidth \\\"\\n nextline += r\\\"{| X[c] | X[c] | X[c] |}\\\\n\\\"\\n self.texfile.write(nextline)\\n self.texfile.write(r\\\" \\\\hline\\\\n\\\")\\n self.texfile.write(r\\\" Parameter & h0 & h1 \\\\\\\\\\\\\\\\ \\\\hline\\\\n\\\")\\n elif tabletype == 'fiducial_fit_metrics':\\n nextline = r\\\" \\\\\\\\begin{tabu} to 1.0\\\\\\\\textwidth \\\"\\n nextline += r\\\"{| X[c] | X[c] | X[c] |}\\\\n\\\"\\n self.texfile.write(nextline)\\n self.texfile.write(r\\\" \\\\hline\\\\n\\\")\\n self.texfile.write(r\\\" h0 & h1 & $\\\\Delta$ \\\\\\\\\\\\\\\\ \\\\hline\\\\n\\\")\\n else:\\n raise ValueError(\\n \\\"This function is only for making fit metric or fit \\\"\\n \\\"param tables in LaTeX. Got type %s\\\"%tabletype\\n )\",\n \"def texify_table(table, labels=None, row_labels=None, align='c'):\\n rows = len(table)\\n cols = len(table[0])\\n if labels is not None and len(labels) != cols:\\n raise Exception(\\\"Invalid argument value: labels.\\\")\\n if row_labels is not None and len(row_labels) != rows:\\n raise Exception(\\\"Invalid argument value: row_labels.\\\")\\n # begin table\\n s = \\\"\\\\\\\\begin{tabular}{\\\"\\n if row_labels is not None: s += 'l|'\\n s += align * cols\\n s += \\\"}\\\\n\\\"\\n s += \\\"\\\\\\\\toprule\\\\n\\\"\\n # header\\n if labels is not None:\\n if row_labels is not None: s += ' & '\\n s += \\\" & \\\".join(labels)\\n s += \\\" \\\\\\\\\\\\\\\\ \\\\n\\\"\\n s += \\\"\\\\\\\\midrule\\\\n\\\"\\n # table\\n for idx, row in enumerate(table):\\n if row_labels is not None: s += row_labels[idx] + \\\" & \\\"\\n s += \\\" & \\\".join(map(str, row))\\n s += \\\" \\\\\\\\\\\\\\\\ \\\\n\\\"\\n # end table\\n s += \\\"\\\\\\\\bottomrule\\\\n\\\"\\n s += \\\"\\\\\\\\end{tabular}\\\" \\n return s\",\n \"def latex_table():\\n \\n t = Table.read('../data/stream_origin.fits')\\n N = len(t)\\n \\n f = open('../paper/stream_origin.tex', 'w')\\n for i in range(N):\\n t_ = t[i]\\n for k in t_.colnames:\\n if (t_[k]==np.nan) | (t_[k]=='nan'):\\n t_[k] = '\\\\dots'\\n #f.write('{:s} & {:s} & {:s} & {:s} & {:.1f}\\\\\\\\\\\\\\\\ \\\\n'.format(t_['Name'], t_['host'], t_['progenitor'], t_['type'], t_['feh']))\\n line = '{:s} & {:s} & {:s} & {:s} & {:s}\\\\\\\\\\\\\\\\ \\\\n'.format(t_['Name'], t_['host'], t_['progenitor'], t_['friends'], t_['type'])\\n f.write(line)\\n print(line)\\n \\n f.close()\",\n \"def tables(args):\\n\\n config_file = args.setupfn\\n conf_base = os.path.basename(config_file).split('.')[0]\\n statfile = os.path.join(args.outputdir,\\n \\\"{}_radvel.stat\\\".format(conf_base))\\n status = load_status(statfile)\\n\\n assert status.getboolean('mcmc', 'run'), \\\\\\n \\\"Must run MCMC before making tables\\\"\\n\\n P, post = radvel.utils.initialize_posterior(config_file)\\n post = radvel.posterior.load(status.get('fit', 'postfile'))\\n chains = pd.read_csv(status.get('mcmc', 'chainfile'))\\n minafactor = status.get('mcmc', 'minafactor')\\n maxarchange = status.get('mcmc', 'maxarchange')\\n maxgr = status.get('mcmc', 'maxgr')\\n mintz = status.get('mcmc', 'mintz')\\n if 'derive' in status.sections() and status.getboolean('derive', 'run'):\\n dchains = pd.read_csv(status.get('derive', 'chainfile'))\\n chains = chains.join(dchains, rsuffix='_derived')\\n derived = True\\n else:\\n derived = False\\n report = radvel.report.RadvelReport(P, post, chains, minafactor, maxarchange, maxgr, mintz, derived=derived)\\n tabletex = radvel.report.TexTable(report)\\n attrdict = {'priors': 'tab_prior_summary', 'rv': 'tab_rv',\\n 'params': 'tab_params', 'derived': 'tab_derived',\\n 'crit': 'tab_crit'}\\n for tabtype in args.type:\\n print(\\\"Generating LaTeX code for {} table\\\".format(tabtype))\\n\\n if tabtype == 'ic_compare':\\n assert status.has_option('ic_compare', 'ic'), \\\\\\n \\\"Must run Information Criteria comparison before making comparison tables\\\"\\n\\n compstats = eval(status.get('ic_compare', 'ic'))\\n report = radvel.report.RadvelReport(\\n P, post, chains, minafactor, maxarchange, maxgr, mintz, compstats=compstats\\n )\\n tabletex = radvel.report.TexTable(report)\\n tex = tabletex.tab_comparison()\\n elif tabtype == 'rv':\\n tex = getattr(tabletex, attrdict[tabtype])(name_in_title=args.name_in_title, max_lines=None)\\n elif tabtype == 'crit':\\n tex = getattr(tabletex, attrdict[tabtype])(name_in_title=args.name_in_title)\\n else:\\n if tabtype == 'derived':\\n assert status.has_option('derive', 'run'), \\\\\\n \\\"Must run `radvel derive` before making derived parameter table\\\"\\n assert tabtype in attrdict, 'Invalid Table Type %s ' % tabtype\\n tex = getattr(tabletex, attrdict[tabtype])(name_in_title=args.name_in_title)\\n\\n saveto = os.path.join(\\n args.outputdir, '{}_{}.tex'.format(conf_base, tabtype)\\n )\\n with open(saveto, 'w+') as f:\\n f.write(tex)\\n\\n savestate = {'{}_tex'.format(tabtype): os.path.relpath(saveto)}\\n save_status(statfile, 'table', savestate)\",\n \"def to_latex_table(self, tab=\\\" \\\", caption=\\\"TODO\\\", label=\\\"TODO\\\"):\\n return \\\"\\\".join(\\n (\\n \\\"\\\\\\\\begin{center}\\\\n\\\",\\n f\\\"{tab}\\\\\\\\begin{{table}}[ht]\\\\n\\\",\\n f\\\"{tab*2}\\\\\\\\centering\\\\n\\\",\\n f'{tab*2}\\\\\\\\rowcolors{{2}}{{white}}{{gray!25}}\\\\n'\\n f\\\"{tab*2}\\\\\\\\begin{{tabular}}{{crrrrrr}}\\\\n\\\",\\n (\\n f\\\"{tab*3}\\\\\\\\cellcolor[gray]{{0.7}} & \\\\\\\\multicolumn{{2}}{{c}}\\\"\\n \\\"{BT\\\\\\\\cellcolor[gray]{0.7}} & \\\\\\\\multicolumn{2}{c}{BJ\\\"\\n \\\"\\\\\\\\cellcolor[gray]{0.7}} & \\\\\\\\multicolumn{2}{c}\\\"\\n \\\"{CBJ\\\\\\\\cellcolor[gray]{0.7}} \\\\\\\\\\\\\\\\\\\\n\\\"\\n ),\\n (\\n f\\\"{tab*3}\\\\\\\\cellcolor[gray]{{0.7}} Test suite & \\\"\\n \\\"\\\\\\\\multicolumn{1}{c}{\\\\\\\\cellcolor[gray]{0.7}Nodes} & \\\"\\n \\\"\\\\\\\\multicolumn{1}{c}{\\\\\\\\cellcolor[gray]{0.7}Time(s)} & \\\"\\n \\\"\\\\\\\\multicolumn{1}{c}{\\\\\\\\cellcolor[gray]{0.7}Nodes} & \\\"\\n \\\"\\\\\\\\multicolumn{1}{c}{\\\\\\\\cellcolor[gray]{0.7}Time(s)} & \\\"\\n \\\"\\\\\\\\multicolumn{1}{c}{\\\\\\\\cellcolor[gray]{0.7}Nodes} & \\\"\\n \\\"\\\\\\\\multicolumn{1}{c}{\\\\\\\\cellcolor[gray]{0.7}Time(s)}\\\\\\\\\\\\\\\\\\\\n\\\"\\n ),\\n \\\"\\\".join(\\n (\\n f\\\"{tab*3}{i} & {bt.nodes_expanded} & {bt.time} \\\"\\n f\\\"& {bj.nodes_expanded} & {bj.time} & {cbj.nodes_expanded} & \\\"\\n f\\\"{cbj.time}\\\\\\\\\\\\\\\\\\\\n\\\"\\n for i, (bt, bj, cbj) in enumerate(zip(*self.data))\\n )\\n ),\\n f\\\"{tab*2}\\\\\\\\end{{tabular}}\\\\n\\\"\\n f\\\"{tab*2}\\\\\\\\caption{{{caption}}}\\\\n\\\"\\n f\\\"{tab*2}\\\\\\\\label{{tab:{label}}}\\\\n\\\"\\n f\\\"{tab}\\\\\\\\end{{table}}\\\\n\\\"\\n \\\"\\\\\\\\end{center}\\\",\\n )\\n )\",\n \"def latex_table(samples, parameter_dict=None, labels=None):\\n table = (\\n \\\"\\\\\\\\begin{table}[hptb]\\\\n\\\\\\\\begin{ruledtabular}\\\\n\\\\\\\\begin{tabular}\\\"\\n \\\"{l %s}\\\\n\\\" % (\\\"c \\\" * len(samples))\\n )\\n if labels:\\n table += (\\n \\\" & \\\" + \\\" & \\\".join(labels)\\n )\\n table += \\\"\\\\\\\\\\\\ \\\\n\\\\\\\\hline \\\\\\\\\\\\ \\\\n\\\"\\n data = {i: i for i in samples[0].keys()}\\n if parameter_dict is not None:\\n import copy\\n\\n data = copy.deepcopy(parameter_dict)\\n for param in parameter_dict.keys():\\n if not all(param in samples_dict.keys() for samples_dict in samples):\\n logger.warning(\\n \\\"{} not in list of parameters. Not adding to \\\"\\n \\\"table\\\".format(param)\\n )\\n data.pop(param)\\n\\n for param, desc in data.items():\\n table += \\\"{}\\\".format(desc)\\n for samples_dict in samples:\\n median = samples_dict[param].average(type=\\\"median\\\")\\n confidence = samples_dict[param].confidence_interval()\\n table += (\\n \\\" & $%s^{+%s}_{-%s}$\\\" % (\\n np.round(median, 2),\\n np.round(confidence[1] - median, 2),\\n np.round(median - confidence[0], 2)\\n )\\n )\\n table += \\\"\\\\\\\\\\\\ \\\\n\\\"\\n table += (\\n \\\"\\\\\\\\end{tabular}\\\\n\\\\\\\\end{ruledtabular}\\\\n\\\\\\\\caption{}\\\\n\\\\\\\\end{table}\\\"\\n )\\n return table\",\n \"def ppg_table(signals):\\n\\n # TODO: add more features\\n summary = {}\\n\\n summary[\\\"PPG_Rate_Mean\\\"] = np.mean(signals[\\\"PPG_Rate\\\"])\\n summary[\\\"PPG_Rate_SD\\\"] = np.std(signals[\\\"PPG_Rate\\\"])\\n summary_table = pd.DataFrame(summary, index=[0]) # .transpose()\\n\\n # Make HTML and Markdown versions\\n html = '<h2 style=\\\"background-color: #D60574\\\">Summary table</h1>' + summary_table.to_html(\\n index=None\\n )\\n\\n try:\\n md = summary_table.to_markdown(index=None)\\n except ImportError:\\n md = summary_table # in case printing markdown export fails\\n return html, md\",\n \"def insert_into_latex(body, url):\\n latex_table_top = r\\\"\\\"\\\"\\n% table automatically generated by rubberband, please have a look and check everything\\n\\\\begin{table}\\n\\\\caption{Performance comparison}\\n\\\\label{tbl:rubberband_table}\\n\\\\scriptsize\\n\\n\\\"\\\"\\\"\\n latex_table_bottom = r\\\"\\\"\\\"\\n\\\\end{table}\\n\\n\\\"\\\"\\\"\\n return latex_table_top + body + latex_table_bottom + \\\"%% \\\" + url\",\n \"def create_latex_table(data, id):\\n bd = data['bd']\\n sd = data['sd']\\n \\n filename = 'LatestResults.tex'\\n file = r'..\\\\latex\\\\tables\\\\\\\\' + filename\\n\\n if os.path.exists(file):\\n f_temp = os.path.splitext(file)[0] # without extension\\n os.rename(file, f_temp + '_' + id + '.tex')\\n\\n f = codecs.open(file, 'w', 'utf-8')\\n \\n f.write('\\\\n' + r'\\\\begin{table}' + '\\\\n')\\n f.write(r' \\\\centering' + '\\\\n')\\n f.write(r' \\\\caption{Results for each drum instrument with batch sizes 64, 256 and 512.}' + '\\\\n')\\n f.write(r' \\\\begin{tabular}{l c c c}' + '\\\\n')\\n f.write(r' \\\\textbf{Batch size} & Metric & BD & SD \\\\\\\\' + '\\\\n')\\n f.write(r' \\\\midrule' + '\\\\n')\\n f.write(r' \\\\midrule' + '\\\\n')\\n \\n for batch_size in BATCHES:\\n f.write(' ' + str(batch_size).rstrip('\\\\n'))\\n # 0.805 +- 0.02\\n f.write(r' & P & ' + r'$' + '{:.3}'.format(bd[batch_size]['p_mean']) + r' \\\\pm ' + '{:.3f}'.format(bd[batch_size]['p_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['p_mean']) + r' \\\\pm ' + '{:.3f}'.format(sd[batch_size]['p_std']) + '$' + r' \\\\\\\\' + '\\\\n')\\n f.write(r' & R & ' + r'$' + '{:.3}'.format(bd[batch_size]['r_mean']) + r' \\\\pm ' + '{:.3f}'.format(bd[batch_size]['r_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['r_mean']) + r' \\\\pm ' + '{:.3f}'.format(sd[batch_size]['r_std']) + '$' + r' \\\\\\\\' + '\\\\n')\\n f.write(r' & F & ' + r'$' + '{:.3}'.format(bd[batch_size]['f_mean']) + r' \\\\pm ' + '{:.3f}'.format(bd[batch_size]['f_std']) + '$' + r' & ' + r'$' + '{:.3}'.format(sd[batch_size]['f_mean']) + r' \\\\pm ' + '{:.3f}'.format(sd[batch_size]['f_std']) + '$' + r' \\\\\\\\' + '\\\\n')\\n # Don't write horizontal line on the last batch.\\n if batch_size != BATCHES[-1]:\\n f.write(r' \\\\midrule' + '\\\\n')\\n\\n f.write(r' \\\\end{tabular}' + '\\\\n')\\n f.write(r' \\\\label{tab:ResultsTable}' + '\\\\n')\\n f.write(r'\\\\end{table}' + '\\\\n')\\n f.close()\",\n \"def print_table(table):\\n for row in table:\\n # Header column left justified\\n print(\\\"{:<19}\\\".format(row[0]), end='')\\n # Remaining columns right justified\\n for col in row[1:]:\\n print(\\\"{:>4}\\\".format(col), end='')\\n print(\\\"\\\", end='\\\\n')\",\n \"def make_table(ranked_means):\\n fp = open(\\\"table.tex\\\", \\\"w\\\")\\n fp.write(\\\"\\\"\\\"\\\\\\\\begin{tabular}{|l|c||l|c|}\\n \\\\\\\\hline\\n \\\\\\\\multicolumn{2}{|c||}{Slowest} & \\\\\\\\multicolumn{2}{|c|}{Fastest} \\\\\\\\\\\\\\\\ \\\\\\\\hline\\n Feature & Rate & Feature & Rate \\\\\\\\\\\\\\\\ \\\\\\\\hline\\n\\\"\\\"\\\")\\n top_10 = ranked_means[0:10]\\n bottom_10 = ranked_means[-10:]\\n for ((f_rate, f_name),(s_rate,s_name)) in zip(top_10, bottom_10):\\n f_name = f_name.split(\\\":\\\")[-1]\\n f_name = f_name.rsplit(\\\" \\\", 1)[0] if f_name.endswith(\\\"(V)\\\") else f_name\\n s_name = s_name.split(\\\":\\\")[-1]\\n s_name = s_name.rsplit(\\\" \\\", 1)[0] if s_name.endswith(\\\"(V)\\\") else s_name\\n fp.write(\\\" %s & %.2f & %s & %.2f \\\\\\\\\\\\\\\\ \\\\n\\\" % \\\\\\n (f_name, f_rate, s_name, s_rate))\\n fp.write(\\\"\\\\\\\\hline\\\\n\\\")\\n fp.write(\\\"\\\\\\\\end{tabular}\\\\n\\\")\\n fp.close()\\n\\n fp = open(\\\"supp_meaning_table.tex\\\", \\\"w\\\")\\n fp.write(\\\"\\\"\\\"\\\\\\\\begin{tabular}{|l|c||l|c||l|c||l|c|}\\n \\\\\\\\hline\\n Meaning & Category & Meaning & Category & Meaning & Category & Meaning & Category\\\\\\\\\\\\\\\\ \\\\\\\\hline\\n\\n\\\"\\\"\\\")\\n feature_names = [f.split(\\\":\\\")[-1] for (r,f) in ranked_means]\\n feature_names.sort(key=lambda s: s.lower())\\n col1 = feature_names[0:25]\\n col2 = feature_names[25:50]\\n col3 = feature_names[50:75]\\n col4 = feature_names[75:]\\n for a,b,c,d in zip(col1,col2,col3,col4):\\n x,y,z,w = [get_meaning_category(i) or \\\"Excluded\\\" for i in (a,b,c,d)]\\n # Lop off (V)s (we needed them above for get_meaning_category to work)\\n a,b,c,d = [f.rsplit(\\\" \\\", 1)[0] if f.endswith(\\\"(V)\\\") else f for f in (a,b,c,d)]\\n fp.write(\\\"%s & %s & %s & %s & %s & %s & %s & %s\\\\\\\\\\\\\\\\ \\\\n\\\" % (a, x, b, y, c, z, d, w))\\n fp.write(\\\"\\\\\\\\hline\\\\n\\\")\\n fp.write(\\\"\\\\\\\\end{tabular}\\\\n\\\")\\n fp.close()\",\n \"def latex(\\r\\n samples,\\r\\n median_pdf_model=True,\\r\\n sigma=3.0,\\r\\n name_to_label=True,\\r\\n include_name=True,\\r\\n include_quickmath=False,\\r\\n prefix=\\\"\\\",\\r\\n suffix=\\\"\\\"\\r\\n) -> str:\\r\\n\\r\\n values = values_from_samples(samples=samples, median_pdf_model=median_pdf_model)\\r\\n errors_at_sigma = samples.errors_at_sigma(sigma=sigma, as_instance=False)\\r\\n\\r\\n table = []\\r\\n\\r\\n for i in range(samples.model.prior_count):\\r\\n\\r\\n label_value = frm.parameter_result_latex_from(\\r\\n parameter_name=samples.model.parameter_names[i],\\r\\n value=values[i],\\r\\n errors=errors_at_sigma[i],\\r\\n superscript=samples.model.superscripts[i],\\r\\n name_to_label=name_to_label,\\r\\n include_name=include_name,\\r\\n include_quickmath=include_quickmath\\r\\n )\\r\\n\\r\\n table.append(f\\\"{label_value}\\\")\\r\\n\\r\\n table = \\\"\\\".join(table)[:-3]\\r\\n\\r\\n return f\\\"{prefix}{table}{suffix}\\\"\",\n \"def to_latex_table(self, parameter_dict=None, save_to_file=None):\\n import os\\n\\n if save_to_file is not None and os.path.isfile(\\\"{}\\\".format(save_to_file)):\\n raise FileExistsError(\\n \\\"The file {} already exists.\\\".format(save_to_file)\\n )\\n\\n table = self.latex_table([self.samples_dict], parameter_dict)\\n if save_to_file is None:\\n print(table)\\n elif os.path.isfile(\\\"{}\\\".format(save_to_file)):\\n logger.warning(\\n \\\"File {} already exists. Printing to stdout\\\".format(save_to_file)\\n )\\n print(table)\\n else:\\n with open(save_to_file, \\\"w\\\") as f:\\n f.writelines([table])\",\n \"def format_prettytable(table):\\r\\n for i, row in enumerate(table.rows):\\r\\n for j, item in enumerate(row):\\r\\n table.rows[i][j] = format_output(item)\\r\\n ptable = table.prettytable()\\r\\n ptable.hrules = FRAME\\r\\n ptable.horizontal_char = '.'\\r\\n ptable.vertical_char = ':'\\r\\n ptable.junction_char = ':'\\r\\n return ptable\",\n \"def makeTexTable(\\n tablefile, caption, sideways=False, footnotetext=None, clearpage=False, pos=\\\"h!\\\"\\n):\\n if sideways:\\n tabletype = \\\"sidewaystable\\\"\\n clearpage = True\\n else:\\n tabletype = \\\"table\\\"\\n\\n if clearpage:\\n clearpagetext = r\\\"\\\\clearpage\\\"\\n else:\\n clearpagetext = \\\"\\\"\\n\\n if footnotetext is None:\\n notes = \\\"\\\"\\n else:\\n notes = footnotetext\\n\\n tablestring = (\\n dedent(\\n r\\\"\\\"\\\"\\n \\\\begin{%s}[%s]\\n \\\\rowcolors{1}{CVCWhite}{CVCLightGrey}\\n \\\\caption{%s}\\n \\\\centering\\n \\\\input{%s}\\n \\\\end{%s}\\n %s\\n %s\\n \\\"\\\"\\\"\\n )\\n % (tabletype, pos, caption, tablefile, tabletype, notes, clearpagetext)\\n )\\n return tablestring\",\n \"def generate_supertable(*subtables):\\n DEFAULT_VALUE = '-'\\n # Build a set of all \\\"names\\\" to appear on the left of the table\\n all_keys = set()\\n for d in subtables:\\n all_keys.update(d['data'].keys())\\n\\n # Sort the keys so there's a standard order\\n all_keys = sorted(list(all_keys))\\n # Create a list of table headings to pass to the template...\\n table_headings = []\\n # ... and a list for the colums, in matching order\\n table_data = []\\n for d in subtables:\\n table_headings.append(d['title'])\\n column = []\\n for key in all_keys:\\n column.append(d['data'].get(key, DEFAULT_VALUE))\\n table_data.append(column)\\n\\n table_rows = []\\n for col_number in range(len(all_keys)):\\n row = []\\n for row_number in range(len(subtables)):\\n row.append(\\n table_data[row_number][col_number]\\n )\\n table_rows.append(row)\\n\\n return generate_table_html(table_headings, all_keys, table_rows)\",\n \"def prettyTable(self, heads, rows): \\n # First calculate the maximum lengths for each column.\\n lengths = map(len, heads)\\n for row in rows:\\n lengths = map(max, lengths, map(len, row))\\n\\n # Create a format string for the maximum lengths.\\n formatString = (\\\"|{{:^{}}}\\\" * len(heads) + \\\"|\\\").format(*lengths)\\n\\n # Print the heads, then the contents.\\n headLine = formatString.format(*heads)\\n border = \\\"-\\\" * len(headLine)\\n print(border)\\n print(headLine)\\n print(border)\\n\\n # Remake the format string right-justified.\\n formatString = (\\\"|{{:>{}}}\\\" * len(heads) + \\\"|\\\").format(*lengths)\\n for row in rows:\\n print(formatString.format(*row))\\n print(border)\",\n \"def out(lam, eng, mat): # {{{1\\n print(\\\"\\\\\\\\begin{table}[!htbp]\\\")\\n print(\\\" \\\\\\\\renewcommand{\\\\\\\\arraystretch}{1.2}\\\")\\n txt = \\\" \\\\\\\\caption{{\\\\\\\\label{{tab:{0}}}properties of {0}}}\\\"\\n # Raw underscores in LaTeX text mode produce “Missing $” errors.\\n texlname = lam.name.replace('_', '\\\\_')\\n print(txt.format(texlname))\\n print(\\\" \\\\\\\\centering\\\\\\\\footnotesize{\\\\\\\\rule{0pt}{10pt}\\\")\\n print(\\\" \\\\\\\\tiny calculated by lamprop {}\\\\\\\\\\\\\\\\[3pt]}}\\\".format(__version__))\\n if eng:\\n _engprop(lam)\\n if mat:\\n _matrices(lam)\\n print(\\\"\\\\\\\\end{table}\\\\n\\\") # 1}}}\",\n \"def make_figure_table(image_files):\\n cols = 2\\n table_data = []\\n row_data = []\\n for i, fn in enumerate(image_files):\\n row_data.append(p.Image(fn, 3.4 * u.inch, 3.0 * u.inch))\\n if (i % cols) == (cols - 1):\\n table_data.append(row_data)\\n row_data = []\\n\\n # Determine if there are any images left to print\\n if len(row_data) != 0:\\n for i in range(len(row_data), cols):\\n row_data.append(p.Paragraph(\\\"\\\", STYLES[\\\"body_style\\\"]))\\n table_data.append(row_data)\\n\\n # Style this into a reportlab table and add to the story\\n width = 3.75 * u.inch\\n table = p.Table(table_data, colWidths=[width, width])\\n table.setStyle(\\n p.TableStyle(\\n [\\n (\\\"ALIGNMENT\\\", (0, 0), (-1, -1), \\\"CENTER\\\"),\\n (\\\"VALIGN\\\", (0, 0), (-1, -1), \\\"CENTER\\\"),\\n (\\\"TOPPADDING\\\", (0, 0), (-1, -1), 6.0),\\n (\\\"BOTTOMPADDING\\\", (0, 0), (-1, -1), 6.0),\\n ]\\n )\\n )\\n return table\",\n \"def print_para_table(s):\\n if MODE == 1:\\n t = [['Parameter', 'Value', 'Unit'],\\n ['Number of bends', NBENDS, '/'], \\n ['Width', WIDTH, 'm'],\\n ['Depth', DEPTH, 'm'],\\n ['Length', LAMBDA*(NBENDS+1), 'm'],\\n ['Arc wavelength', LAMBDA, 'm'],\\n ['Slope', SLOPE, '/'],\\n ['Streamwise resolution', DS, 'm'],\\n ['Transverse resolution', np.around(INTERVAL, decimals=4), 'm'],\\n ['Streamwise # of pts', s.size + 2*int(LAMBDA/2/DS), '/'],\\n ['Transverse # of pts', NUM*2+1, '/']]\\n elif MODE == 2:\\n if FNAME[0].islower():\\n f = FNAME[0].upper() + FNAME[1:]\\n else:\\n f = FNAME\\n t = [['Parameter', 'Value', 'Unit'],\\n ['River name', f.rsplit('.', 1)[0], '/'],\\n ['Width', WIDTH, 'm'],\\n ['Depth', DEPTH, 'm'],\\n ['Length', np.round(s[-1], decimals=2), 'm'],\\n ['Slope', SLOPE, '/'],\\n ['Streamwise resolution', np.round(np.mean(np.diff(s)), decimals=2), 'm'],\\n ['Transverse resolution', np.round(INTERVAL, decimals=2), 'm'],\\n ['Streamwise # of pts', s.size, '/'],\\n ['Transverse # of pts', NUM*2+1, '/']]\\n print(tabulate(t, tablefmt='psql', stralign='right', headers='firstrow'))\",\n \"def _make_tex_table(self, tabletitle):\\r\\n stattable = (\\r\\n r\\\"\\\"\\\"\\r\\n \\\\begin{table}[h!]\\r\\n \\\\caption{%s}\\r\\n \\\\centering\\r\\n \\\\begin{tabular}{l l l l l}\\r\\n \\\\toprule\\r\\n \\\\textbf{Statistic} & \\\\textbf{Inlet} & \\\\textbf{Outlet} \\\\\\\\\\\"\\\"\\\"\\r\\n % tabletitle\\r\\n )\\r\\n\\r\\n stats = [\\r\\n {\\\"name\\\": \\\"Count\\\", \\\"attribute\\\": \\\"N\\\", \\\"rule\\\": \\\"top\\\", \\\"forceint\\\": True},\\r\\n {\\\"name\\\": \\\"Number of NDs\\\", \\\"attribute\\\": \\\"ND\\\", \\\"forceint\\\": True},\\r\\n {\\\"name\\\": \\\"Min; Max\\\", \\\"attribute\\\": [\\\"min\\\", \\\"max\\\"], \\\"twoval\\\": True},\\r\\n {\\\"name\\\": \\\"Mean\\\", \\\"attribute\\\": \\\"mean\\\"},\\r\\n {\\r\\n \\\"name\\\": \\\"(95\\\\% confidence interval)\\\",\\r\\n \\\"attribute\\\": \\\"mean_conf_interval\\\",\\r\\n \\\"twoval\\\": True,\\r\\n \\\"ci\\\": True,\\r\\n \\\"rule\\\": \\\"none\\\",\\r\\n },\\r\\n {\\\"name\\\": \\\"Standard Deviation\\\", \\\"attribute\\\": \\\"std\\\"},\\r\\n {\\\"name\\\": \\\"Log. Mean\\\", \\\"attribute\\\": \\\"logmean\\\"},\\r\\n {\\r\\n \\\"name\\\": \\\"(95\\\\% confidence interval)\\\",\\r\\n \\\"attribute\\\": \\\"logmean_conf_interval\\\",\\r\\n \\\"twoval\\\": True,\\r\\n \\\"ci\\\": True,\\r\\n \\\"rule\\\": \\\"none\\\",\\r\\n },\\r\\n {\\\"name\\\": \\\"Log. Standard Deviation\\\", \\\"attribute\\\": \\\"logstd\\\"},\\r\\n {\\\"name\\\": \\\"Geo. Mean\\\", \\\"attribute\\\": \\\"geomean\\\"},\\r\\n {\\r\\n \\\"name\\\": \\\"(95\\\\% confidence interval)\\\",\\r\\n \\\"attribute\\\": \\\"geomean_conf_interval\\\",\\r\\n \\\"twoval\\\": True,\\r\\n \\\"ci\\\": True,\\r\\n \\\"rule\\\": \\\"none\\\",\\r\\n },\\r\\n {\\\"name\\\": \\\"Coeff. of Variation\\\", \\\"attribute\\\": \\\"cov\\\"},\\r\\n {\\\"name\\\": \\\"Skewness\\\", \\\"attribute\\\": \\\"skew\\\"},\\r\\n {\\\"name\\\": \\\"Median\\\", \\\"attribute\\\": \\\"median\\\"},\\r\\n {\\r\\n \\\"name\\\": \\\"(95\\\\% confidence interval)\\\",\\r\\n \\\"attribute\\\": \\\"median_conf_interval\\\",\\r\\n \\\"twoval\\\": True,\\r\\n \\\"ci\\\": True,\\r\\n \\\"rule\\\": \\\"none\\\",\\r\\n },\\r\\n {\\\"name\\\": \\\"Quartiles\\\", \\\"attribute\\\": [\\\"pctl25\\\", \\\"pctl75\\\"], \\\"twoval\\\": True},\\r\\n {\\r\\n \\\"name\\\": \\\"Number of Pairs\\\",\\r\\n \\\"attribute\\\": \\\"n_pairs\\\",\\r\\n \\\"rule\\\": \\\"top\\\",\\r\\n \\\"fromdataset\\\": True,\\r\\n \\\"sigfigs\\\": 1,\\r\\n \\\"forceint\\\": True,\\r\\n },\\r\\n {\\r\\n \\\"name\\\": \\\"Wilcoxon p-value\\\",\\r\\n \\\"attribute\\\": \\\"wilcoxon_p\\\",\\r\\n \\\"fromdataset\\\": True,\\r\\n \\\"pval\\\": True,\\r\\n \\\"tex\\\": True,\\r\\n },\\r\\n {\\r\\n \\\"name\\\": \\\"Mann-Whitney p-value\\\",\\r\\n \\\"attribute\\\": \\\"mannwhitney_p\\\",\\r\\n \\\"fromdataset\\\": True,\\r\\n \\\"pval\\\": True,\\r\\n \\\"tex\\\": True,\\r\\n },\\r\\n ]\\r\\n for s in stats:\\r\\n stattable += self._tex_table_row(**s)\\r\\n\\r\\n stattable += r\\\"\\\"\\\"\\r\\n \\\\bottomrule\\r\\n \\\\end{tabular}\\r\\n \\\\end{table}\\\"\\\"\\\"\\r\\n\\r\\n return stattable + \\\"\\\\n\\\"\",\n \"def pprint_table(out, table):\\n\\n\\tcol_paddings = []\\n\\n\\tfor i in range(len(table[0])):\\n\\t\\tcol_paddings.append(get_max_width(table, i))\\n\\n\\tfor row in table:\\n\\t\\t# left col\\n\\t\\tout.write(str(row[0]).ljust(col_paddings[0] + 1))\\n\\t\\t\\n\\t\\t# rest of the cols\\n\\t\\tfor i in range(1, len(row)):\\n\\t\\t\\tout.write(str(row[i]).rjust(col_paddings[i] + 2))\\n\\t\\t\\n\\t\\tout.write('\\\\n')\",\n \"def generate_problems_pdf(args):\\n contents = generate_table(start_int=args.start_int, end_int=args.end_int, table_type=args.table_type)\\n convert_latex_to_pdf(args.filename, contents=contents, view=True)\\n remove_temporary_files(args.filename)\",\n \"def _intermediary_to_markdown(tables, relationships):\\n t = '\\\\n'.join(t.to_markdown() for t in tables)\\n r = '\\\\n'.join(r.to_markdown() for r in relationships)\\n return '{}\\\\n{}'.format(t, r)\",\n \"def end_latex_table(self, tabletype):\\n self.texfile.write(r\\\" \\\\end{tabu}\\\\n\\\")\\n self.texfile.write(r\\\" \\\\end{center}\\\\n\\\")\\n self.texfile.write(r\\\" \\\\\\\\vspace{-10pt}\\\\n\\\")\\n newline = r\\\" \\\\caption{shows the fiducial fit \\\"\\n if tabletype == \\\"fiducial_fit_params\\\":\\n newline += \\\"parameters\\\"\\n elif tabletype == \\\"fiducial_fit_metrics\\\":\\n newline += \\\"metrics\\\"\\n else:\\n raise ValueError(\\n \\\"This function is only for ending fit metric or fit \\\"\\n \\\"param tables in LaTeX. Got type %s\\\"%tabletype\\n )\\n if self.detector is not None:\\n newline += \\\" obtained with the %s\\\"%self.detector\\n if self.selection is not None:\\n newline += \\\" %s sample\\\"%self.selection\\n if self.selection is not None:\\n newline += \\\" obtained with the %s\\\"%self.selection\\n newline += \\\" for h0 of %s\\\"%self.tex_axis_label(\\n self.labels.dict['h0_name']\\n )\\n newline += \\\" and h1 of %s.\\\"%self.tex_axis_label(\\n self.labels.dict['h1_name']\\n )\\n if self.labels.dict['data_name'] == '':\\n newline += \\\" The truth is %s.\\\"%self.tex_axis_label(\\n self.labels.dict['data_name']\\n )\\n else:\\n newline += \\\" This is from an analysis performed on data.\\\"\\n newline += \\\"}\\\\n\\\"\\n self.texfile.write(newline)\\n newline = r\\\" \\\\label{tab:\\\"\\n if self.detector is not None:\\n newline += self.detector\\n if self.selection is not None:\\n newline += self.selection\\n newline += \\\"%stable}\\\\n\\\"%tabletype\\n self.texfile.write(newline)\\n self.texfile.write(r\\\"\\\\end{table}\\\\n\\\")\",\n \"def generate_table(start_int=0, end_int=10, table_type='Addition'):\\n lines = [r'\\\\documentclass{article}',\\n r'\\\\usepackage{geometry}',\\n r'\\\\geometry{landscape,a4paper,total={170mm,257mm},left=10mm,right=10mm,top=10mm}',\\n r'\\\\usepackage{amsmath}',\\n r'\\\\usepackage{amsfonts}',\\n r'\\\\usepackage{amssymb}',\\n r'\\\\usepackage{dcolumn}',\\n r'\\\\newcolumntype{2}{D{.}{}{2.0}}',\\n r'\\\\begin{document}',\\n r'\\\\begin{large}',\\n r'\\\\begin{center}',\\n r'{\\\\Large ' + table_type + r' Table version 0.1\\\\par}',\\n r'\\\\vspace*{25px}',\\n r'\\\\renewcommand\\\\arraystretch{1.3}',\\n r'\\\\setlength\\\\doublerulesep{0pt}',\\n r'\\\\pagenumbering{gobble}',\\n r'\\\\begin{tabular}{r||*{' + str(end_int - start_int + 1) + '}{3|}}']\\n\\n operator = {'Addition': r'$+$',\\n 'Subtraction': r'$-$',\\n 'Multiplication': r'$\\\\times$'}\\n\\n lines.append(operator[table_type] + ''.join([' & {} '.format(x) for x in range(start_int, end_int + 1)]) + r'\\\\\\\\')\\n lines.append('\\\\hline\\\\hline')\\n for i in range(start_int, end_int + 1):\\n if table_type == 'Addition':\\n lines.append(str(i) + ''.join([' & {} '.format(x + i) for x in range(start_int, end_int + 1)]) + r'\\\\\\\\')\\n if table_type == 'Subtraction':\\n lines.append(str(i) + ''.join([' & {} '.format(x - i) for x in range(start_int, end_int + 1)]) + r'\\\\\\\\')\\n if table_type == 'Multiplication':\\n lines.append(str(i) + ''.join([' & {} '.format(x * i) for x in range(start_int, end_int + 1)]) + r'\\\\\\\\')\\n lines.append('\\\\hline')\\n\\n lines.append(r'\\\\end{tabular}')\\n lines.append(r'\\\\end{center}')\\n lines.append(r'\\\\end{large}')\\n lines.append(r'\\\\end{document}')\\n\\n return '\\\\n'.join(lines)\",\n \"def _latex(self, printer):\\n return ''.join([\\n self._latex_header, '{', printer.doprint(self.args[0]), '}'\\n ])\",\n \"def print_table(seqids, data, outputfile, separator='\\\\t'):\\n\\n tags = data.keys()\\n with open(outputfile, 'w') as out:\\n out.write(separator.join([\\\"#Sequence ID\\\"] + list(tags)) + \\\"\\\\n\\\")\\n for s in seqids:\\n out.write(s)\\n for t in tags:\\n out.write(\\\"{}{}\\\".format(separator, data[t].get(s, \\\"\\\")))\\n out.write(\\\"\\\\n\\\")\",\n \"def print_table(ledger):\\n\\n table = PrettyTable() # defines a PrettyTable object\\n\\n table.field_names = [\\n \\\"hospital\\\",\\n \\\"patient\\\",\\n \\\"status\\\",\\n \\\"nonce\\\",\\n \\\"prev_hash\\\",\\n \\\"a\\\",\\n \\\"b\\\",\\n \\\"c\\\",\\n \\\"current_hash\\\",\\n ] # define field names for table\\n\\n for block in ledger:\\n table.add_row(\\n [\\n block[\\\"hospital\\\"],\\n block[\\\"patient\\\"],\\n block[\\\"status\\\"],\\n block[\\\"nonce\\\"],\\n block[\\\"prev_hash\\\"],\\n block[\\\"a\\\"],\\n block[\\\"b\\\"],\\n block[\\\"c\\\"],\\n block[\\\"current_hash\\\"],\\n ]\\n ) # add data to table\\n\\n print(\\\"\\\\n\\\\n\\\" + color.BOLD + \\\"Printing Your Ledger:\\\" + color.END)\\n print(table) # print prettytable of patient info\",\n \"def print_table(hdrs, data):\\n tw = TextWrapper()\\n # only difficult thing here is wrapping the cell if it exceeds the row length, and it could be\\n # extended in multiple cells in the same row so we need to determine the longest cell...\\n def get_row_string(column_widths, row_data, fmt_separator=\\\"|\\\"):\\n # receive a list of ints representing each column width and a list of text data representing\\n # data for each column and return single string line.\\n fmt = []\\n cols = []\\n for index, width in enumerate(column_widths):\\n fmt.append(\\\"{%s:<%s}\\\" % (index, width))\\n if index<len(row_data):\\n #text = \\\" \\\".join(row_data[index].strip().split())\\n text = row_data[index]\\n tw.width = width\\n # to honor original user's return characters, we need to wrap each individual line\\n wraps = []\\n for line in text.split(\\\"\\\\n\\\"):\\n wrapped = tw.wrap(line.strip())\\n if len(wrapped) == 0:\\n wraps+= [\\\"\\\"]\\n else:\\n wraps+= wrapped\\n cols.append(wraps)\\n else:\\n cols.append([\\\"\\\"])\\n fmt = \\\"%s%s%s\\\" % (fmt_separator, (\\\" %s \\\" % fmt_separator).join(fmt), fmt_separator)\\n # expand all columns to the max length column\\n max_col = max([len(c) for c in cols])\\n for c in cols:\\n c+= [\\\"\\\"]*(max_col - len(c))\\n #logger.debug(\\\"fmt: %s\\\", fmt)\\n #logger.debug(\\\"columns:%s max length:%s\\\\n%s\\\", len(cols), max_col, cols)\\n # build final result string which is one or more lines of merged cells\\n results = []\\n for index in range(0, max_col):\\n # grab this index from all columns to create a single row\\n row = [c[index] for c in cols]\\n results.append(fmt.format(*row))\\n return \\\"\\\\n\\\".join(results)\\n\\n final_rows = []\\n column_widths = [h.get(\\\"length\\\", 5) for h in hdrs]\\n separator = [\\\"-\\\"*h.get(\\\"length\\\", 5) for h in hdrs]\\n separator_string = get_row_string(column_widths, separator, fmt_separator=\\\"+\\\")\\n final_rows.append(separator_string)\\n final_rows.append(get_row_string(column_widths, [h.get(\\\"name\\\", \\\"\\\") for h in hdrs]))\\n final_rows.append(separator_string)\\n for row in data:\\n final_rows.append(get_row_string(column_widths, row))\\n final_rows.append(separator_string)\\n print(\\\"\\\\n\\\".join(final_rows))\",\n \"def ydump_table(doc, headings, rows, **kwargs):\\n doc, tag, text, line = doc.ttl()\\n with tag('table', **kwargs):\\n with tag('tr'):\\n for x in headings:\\n line('th', str(x))\\n for row in rows:\\n with tag('tr'):\\n for x in row:\\n line('td', str(x))\",\n \"def generate_table(columns, rows, plain=False, sort=None, reversesort=False):\\n tbl = PrettyTable(columns)\\n tbl.set_style(PLAIN_COLUMNS if plain else DEFAULT)\\n tbl.header = not plain\\n [tbl.add_row(x) for x in rows]\\n tbl.align = 'l'\\n\\n if sort:\\n tbl.sortby = sort\\n\\n tbl.reversesort = reversesort\\n\\n return tbl\",\n \"def create_joint_prob_corr_table(to_dict=True):\\n\\n p = np.arange(0., 1.05, 0.05) # Range of marginal probability\\n corr = np.arange(-1., 1.05, 0.05) # Range of correlations\\n\\n table = np.zeros(shape=(len(corr), len(p), len(p)))\\n\\n for i in range(len(corr)):\\n\\n sig = np.matrix([[1., corr[i]], [corr[i], 1.]])\\n\\n for j in range(len(p)):\\n for k in range(j, len(p)):\\n\\n if corr[i] == -1:\\n jp = max(0., p[j] + p[k] - 1.)\\n elif corr[i] == 0.:\\n jp = p[j] * p[k]\\n elif corr[i] == 1.:\\n jp = min(p[j], p[k])\\n elif p[j] * p[k] == 0. or p[j] == 1 or p[k] == 1.:\\n jp = p[j] * p[k]\\n else:\\n jp = mvnun(np.array([0., 0.]), np.array([np.inf, np.inf]),\\n [norm.ppf(p[j]), norm.ppf(p[k])],\\n sig)[0]\\n\\n table[i, j, k] = table[i, k, j] = jp\\n\\n if to_dict:\\n n_table = {}\\n\\n # convert to dictionary:\\n for j in range(len(p)):\\n for k in range(j, len(p)):\\n pj = round(p[j], 2)\\n pk = round(p[k], 2)\\n n_table[(pj, pk)] = n_table[(pj, pk)] = np.array((corr, table[:, j, k]))\\n\\n return n_table\\n\\n return table\",\n \"def make_table(header, align_map=None, rows=None):\\n t = PrettyTable()\\n t.horizontal_char = t.vertical_char = t.junction_char = ' '\\n t.field_names = header\\n if align_map:\\n for field, align in zip(header, align_map):\\n t.align[field] = align\\n if rows:\\n for row in rows:\\n if len(row) < len(t.field_names):\\n continue\\n try:\\n t.add_row(row)\\n except Exception as err:\\n print_('fields:', t.field_names)\\n print_('row:', row)\\n print_('rows:', rows)\\n raise err\\n return t\",\n \"def table(sentence, fill=1, placeholder=\\\"-\\\"):\\n tags = [WORD, POS, IOB, CHUNK, ROLE, REL, PNP, ANCHOR, LEMMA]\\n tags += [tag for tag in sentence.token if tag not in tags]\\n def format(token, tag):\\n # Returns the token tag as a string.\\n if tag == WORD : s = token.string\\n elif tag == POS : s = token.type\\n elif tag == IOB : s = token.chunk and (token.index == token.chunk.start and \\\"B\\\" or \\\"I\\\")\\n elif tag == CHUNK : s = token.chunk and token.chunk.type\\n elif tag == ROLE : s = token.chunk and token.chunk.role\\n elif tag == REL : s = token.chunk and token.chunk.relation and str(token.chunk.relation)\\n elif tag == PNP : s = token.chunk and token.chunk.pnp and token.chunk.pnp.type\\n elif tag == ANCHOR : s = token.chunk and token.chunk.anchor_id\\n elif tag == LEMMA : s = token.lemma\\n else : s = token.custom_tags.get(tag)\\n return s or placeholder\\n def outline(column, fill=1, padding=3, align=\\\"left\\\"):\\n # Add spaces to each string in the column so they line out to the highest width.\\n n = max([len(x) for x in column]+[fill])\\n if align == \\\"left\\\" : return [x+\\\" \\\"*(n-len(x))+\\\" \\\"*padding for x in column]\\n if align == \\\"right\\\" : return [\\\" \\\"*(n-len(x))+x+\\\" \\\"*padding for x in column]\\n \\n # Gather the tags of the tokens in the sentece per column.\\n # If the IOB-tag is I-, mark the chunk tag with \\\"^\\\".\\n # Add the tag names as headers in each column.\\n columns = [[format(token, tag) for token in sentence] for tag in tags]\\n columns[3] = [columns[3][i]+(iob == \\\"I\\\" and \\\" ^\\\" or \\\"\\\") for i, iob in enumerate(columns[2])]\\n del columns[2]\\n for i, header in enumerate(['word', 'tag', 'chunk', 'role', 'id', 'pnp', 'anchor', 'lemma']+tags[9:]):\\n columns[i].insert(0, \\\"\\\")\\n columns[i].insert(0, header.upper())\\n # The left column (the word itself) is outlined to the right,\\n # and has extra spacing so that words across sentences line out nicely below each other.\\n for i, column in enumerate(columns):\\n columns[i] = outline(column, fill+10*(i==0), align=(\\\"left\\\",\\\"right\\\")[i==0])\\n # Anchor column is useful in MBSP but not in pattern.en.\\n if not MBSP:\\n del columns[6] \\n # Create a string with one row (i.e., one token) per line.\\n return \\\"\\\\n\\\".join([\\\"\\\".join([x[i] for x in columns]) for i in range(len(columns[0]))])\",\n \"def to_latex_table(self, labels=\\\"all\\\", parameter_dict=None, save_to_file=None):\\n import os\\n\\n if save_to_file is not None and os.path.isfile(\\\"{}\\\".format(save_to_file)):\\n raise FileExistsError(\\n \\\"The file {} already exists.\\\".format(save_to_file)\\n )\\n if labels != \\\"all\\\" and isinstance(labels, str) and labels not in self.labels:\\n raise ValueError(\\\"The label %s does not exist.\\\" % (labels))\\n elif labels == \\\"all\\\":\\n labels = list(self.labels)\\n elif isinstance(labels, str):\\n labels = [labels]\\n elif isinstance(labels, list):\\n for ll in labels:\\n if ll not in list(self.labels):\\n raise ValueError(\\\"The label %s does not exist.\\\" % (ll))\\n\\n table = self.latex_table(\\n [self.samples_dict[label] for label in labels], parameter_dict,\\n labels=labels\\n )\\n if save_to_file is None:\\n print(table)\\n elif os.path.isfile(\\\"{}\\\".format(save_to_file)):\\n logger.warning(\\n \\\"File {} already exists. Printing to stdout\\\".format(save_to_file)\\n )\\n print(table)\\n else:\\n with open(save_to_file, \\\"w\\\") as f:\\n f.writelines([table])\",\n \"def perf2latex(latex_fn, all_perf, metrics_name, slice_v, cam_v):\\n slice_num = slice_v.shape[0]\\n\\n f = open('%s'%latex_fn, 'w')\\n f.write(\\\"\\\\\\\\documentclass{article}\\\\n\\\")\\n f.write(\\\"\\\\\\\\usepackage[utf8]{inputenc}\\\\n\\\")\\n f.write(\\\"\\\\\\\\usepackage{booktabs} \\\\n\\\")\\n f.write(\\\"\\\\\\\\usepackage[]{float}\\\\n\\\")\\n f.write(\\\"\\\\\\\\usepackage[margin=1.2in]{geometry}\\\\n\\\")\\n f.write(\\\"\\\\\\\\begin{document}\\\\n\\\\n\\\")\\n\\n for m_name in metrics_name:\\n print(m_name)\\n f.write('\\\\\\\\begin{table}[tbh]\\\\n')\\n f.write('\\\\\\\\begin{center}\\\\n')\\n f.write('\\\\\\\\begin{tabular}{|*{%d}{c|}}\\\\n'%(slice_num + 1))\\n f.write('\\\\\\\\hline\\\\n')\\n f.write(' Survey')\\n #for slice_idx, slice_id in enumerate(slice_cam_id[:-1]):\\n for j, (slice_id, cam_id) in enumerate(zip(slice_v, cam_v)):\\n f.write(' & %d\\\\_c%d'%(slice_id, cam_id))\\n f.write(' \\\\\\\\\\\\\\\\ \\\\n')\\n f.write('\\\\\\\\hline\\\\n')\\n\\n m = all_perf[m_name]\\n print(m.shape)\\n survey_num = m.shape[0]\\n for i in range(survey_num):\\n f.write('%d'%(i))\\n for j in range(slice_num):\\n f.write(' & %.3f'%m[i,j])\\n f.write(' \\\\\\\\\\\\\\\\ \\\\n')\\n\\n f.write('\\\\\\\\hline\\\\n')\\n f.write('\\\\\\\\end{tabular}\\\\n')\\n f.write('\\\\\\\\end{center}\\\\n')\\n f.write('\\\\\\\\caption{Metric: %s}\\\\n'%(m_name))\\n f.write('\\\\\\\\end{table}\\\\n\\\\n\\\\n')\\n\\n f.write('\\\\\\\\end{document}\\\\n')\\n print('\\\\\\\\end{document}\\\\n')\\n \\n f.close()\",\n \"def table(name, components):\\n table = PrettyTable([name])\\n table.align[name] = 'l'\\n [table.add_row([component['name'][0:-5]]) for component in components]\\n return table\",\n \"def create_table(categories:list)->str:\\n\\n total_spent = get_total_spent(categories)\\n\\n table = str()\\n\\n for row_num in range(11):\\n row_label = 100 - row_num*10\\n\\n # Row label creation - ie 100| .. 90| ... 80| ...etc\\n row = f\\\"{row_label:>3}|\\\"\\n\\n for category in categories:\\n percentage = math.floor(category.total_spent/total_spent * 10) * 10\\n if percentage >= row_label:\\n row += ' o '\\n else:\\n row += ' '\\n \\n table += row + ' \\\\n'\\n return table\",\n \"def convert_table(mkd):\\n\\t\\n\\tmd_table_codes = re.findall(r\\\".*\\\\|.*\\\\n.*\\\\-.*(?:\\\\n.*\\\\|.*)*\\\", mkd, re.M)\\n\\tfor md_code in md_table_codes:\\n\\t\\t\\n\\t\\tmd_rows = re.findall(r\\\"(.*\\\\|.*)\\\", md_code, re.M)\\n\\t\\theader = md_rows.pop(0)\\n\\t\\tcolumn_count = md_rows.pop(0).count(\\\"-\\\")\\n\\n\\t\\ttex_code = \\\"\\\\\\\\begin{tabular}{|\\\"+\\\"l|\\\"*column_count+\\\"}\\\\n\\\\hline\\\\n\\\"\\n\\t\\ttex_code += header.strip(\\\" |\\\").replace(\\\"|\\\", \\\"&\\\")+\\\" \\\\\\\\\\\\\\\\\\\\n\\\\hline\\\\n\\\"\\n\\t\\tfor row in md_rows:\\n\\t\\t\\ttex_code += row.strip(\\\" |\\\").replace(\\\"|\\\", \\\"&\\\")+\\\" \\\\\\\\\\\\\\\\\\\\n\\\"\\n\\t\\ttex_code += \\\"\\\\hline\\\\n\\\\end{tabular}\\\"\\n\\n\\t\\tmkd = mkd.replace(md_code, tex_code)\\n\\n\\treturn mkd\",\n \"def write_the_table(what):\\n global count_row\\n count_row += 1\\n\\n if what.get('rank') == 0:\\n background_blue.append(count_row)\\n\\n struct = what.get('structure')\\n link = what.get('link')\\n exams_1 = what.get('exams_1')\\n exams_2 = what.get('exams_2')\\n exams_empty = [['', '', '', '', '', '', '', '', '', '', '', '']] \\\\\\n if self.training.session_type != '1' else \\\\\\n [['', '', '', '', '', '']]\\n\\n def formated(number):\\n \\\"\\\"\\\"\\n Remove trailing 0\\n \\\"\\\"\\\"\\n frac, whole = modf(number)\\n if frac == 0:\\n return int(whole)\\n return str(number).rstrip('0')\\n\\n def write_exams(list_1, list_2):\\n exam_table = []\\n for ex_1, ex_2 in itertools.zip_longest(list_1, list_2):\\n ex_1_table = [\\n formated(ex_1.coefficient) if ex_1 is not None else '',\\n [\\n Paragraph(filter_content(ex_1.label) if ex_1 else '',\\n self.styles['SmallNormal']),\\n Paragraph(\\n \\\"<para textColor=grey>\\\" + filter_content(ex_1.additionnal_info) \\\\\\n if ex_1 and ex_1.additionnal_info \\\\\\n else \\\"\\\" + \\\"</para\\\\>\\\",\\n self.styles['SmallNormal'])\\n ],\\n ex_1.type_exam if ex_1 is not None else '',\\n ex_1.text_duration if ex_1 is not None else '',\\n '' if ex_1 is None \\\\\\n else ex_1.convocation if not training_is_ccct \\\\\\n else ex_1.get_type_ccct_display(),\\n ex_1.eliminatory_grade if ex_1 is not None else '',\\n ex_1.threshold_session_2 if ex_1 is not None else '',\\n ]\\n\\n ex_2_table = [\\n formated(ex_2.coefficient) if ex_2 is not None else '',\\n [Paragraph(filter_content(ex_2.label) if ex_2 is not None else '', self.styles[\\n 'SmallNormal']), Paragraph(\\\"<para textColor=grey\\\\\\n >\\\" + ex_2.additionnal_info + \\\"</para\\\\\\n >\\\" if ex_2.additionnal_info is not None else \\\"\\\",\\n self.styles['SmallNormal'])],\\n ex_2.type_exam if ex_2 is not None else '',\\n ex_2.text_duration if ex_2 is not None else '',\\n ex_2.eliminatory_grade if ex_2 is not None else '',\\n ] if ex_2 is not None else ['', '', '', '', '']\\n if self.training.session_type != '1':\\n ex_1_table.extend(ex_2_table)\\n else:\\n ex_1_table.pop()\\n exam_table.append(ex_1_table)\\n exam_table = exam_table if len(exam_table) > 0 else exams_empty\\n if exam_table == exams_empty:\\n # TODO: calculate empty space to set rowHeights in order to\\n # avoid blank in table\\n pass\\n inner_table = Table(\\n exam_table, colWidths=width_exams, rowHeights=None)\\n inner_table.setStyle(TableStyle(\\n [('INNERGRID', (0, 0), (-1, -1), 0.1, colors.black),\\n ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),\\n ('ALIGN', (0, 0), (-1, -1), 'CENTER'),\\n ('FONTSIZE', (0, 0), (-1, -1), 8),\\n # ('LEFTPADDING', (0, 0), (-1, -1), 0),\\n # ('RIGHTPADDING', (0, 0), (-1, -1), 0),\\n ('BOTTOMPADDING', (0, 0), (-1, -1), 0),\\n ('TOPPADDING', (0, 0), (-1, -1), 0),\\n ]))\\n return inner_table\\n\\n ref_scol = struct.ref_si_scol if struct.ref_si_scol else \\\"\\\" # FIX bug with rof data\\n ref_data = (\\n Paragraph(struct.ROF_ref, self.styles['CenterSmall']),\\n Paragraph(ref_scol, self.styles['CenterSmall'])\\n ) if self.reference == 'both' \\\\\\n else Paragraph(struct.ROF_ref, self.styles['CenterSmall']) if self.reference == 'with_rof' \\\\\\n else Paragraph(ref_scol, self.styles['CenterSmall']) if self.reference == 'with_si' \\\\\\n else Paragraph('', self.styles['CenterSmall'])\\n\\n object_line = [\\n Paragraph(\\n \\\"<para leftIndent=%s>%s</para> \\\" % (what.get('rank')*10, filter_content(struct.label)),\\n self.styles['SmallBold'] if what.get('rank') == 0 \\\\\\n or what.get('structure').nature == 'UE' \\\\\\n else self.styles['SmallNormal']\\n ),\\n Paragraph(\\n struct.get_respens_name if not struct.external_name \\\\\\n else struct.external_name,\\n self.styles['CenterSmall'] if not struct.external_name else \\\\\\n self.styles['CenterSmallItalic']\\n ),\\n [ref_data],\\n '30' if self.training.degree_type.ROF_code in self.training_types_for_which_to_display_30_ects\\\\\\n and struct.nature == 'SE'\\\\\\n else struct.ECTS_credit if struct.ECTS_credit else '-',\\n formated(link.coefficient) if link.coefficient else '',\\n link.eliminatory_grade,\\n write_exams(exams_1, exams_2)\\n ]\\n if self.respforms:\\n if self.reference == 'without':\\n object_line.pop(2)\\n else:\\n object_line.pop(1)\\n if self.reference == 'without':\\n object_line.pop(1)\\n\\n big_table.append(object_line)\\n\\n for e in what.get('children'):\\n write_the_table(e)\",\n \"def print_table(table, fieldnames):\\n print(\\\"{:<19}\\\".format(fieldnames[0]), end='')\\n for field in fieldnames[1:]:\\n print(\\\"{:>6}\\\".format(field), end='')\\n print(\\\"\\\")\\n for name, row in table.items():\\n # Header column left justified\\n print(\\\"{:<19}\\\".format(name), end='')\\n # Remaining columns right justified\\n for field in fieldnames[1:]:\\n print(\\\"{:>6}\\\".format(row[field]), end='')\\n print(\\\"\\\", end='\\\\n')\",\n \"def print_table(table, fieldnames):\\n print(\\\"{:<19}\\\".format(fieldnames[0]), end='')\\n for field in fieldnames[1:]:\\n print(\\\"{:>6}\\\".format(field), end='')\\n print(\\\"\\\")\\n for name, row in table.items():\\n # Header column left justified\\n print(\\\"{:<19}\\\".format(name), end='')\\n # Remaining columns right justified\\n for field in fieldnames[1:]:\\n print(\\\"{:>6}\\\".format(row[field]), end='')\\n print(\\\"\\\", end='\\\\n')\",\n \"def bonus_report_FR1_scalp(subj, scores, bonuses, nans_blank=True):\\n ###############\\n #\\n # Part 1: Text Report\\n #\\n ###############\\n\\n outfile = '/data/eeg/scalp/ltp/FR1_scalp/bonus/%s_bonus_report.tsv' % subj\\n\\n # Create report header\\n report = 'Session\\\\tRecall\\\\tRecall Bonus\\\\tMath Score\\\\tMath Bonus\\\\tTotal\\\\n'\\n\\n # Fill in report\\n for i in range(len(bonuses)):\\n report += '%d\\\\t%.1f%%\\\\t$%.2f\\\\t%.0f\\\\t$%.2f\\\\t$%.2f\\\\n' % \\\\\\n (i, scores[i][0], bonuses[i][0], scores[i][1],\\n bonuses[i][1], bonuses[i][2])\\n report += 'Total\\\\t%.1f%%\\\\t$%.2f\\\\t%.0f\\\\t$%.2f\\\\t$%.2f\\\\n' % \\\\\\n (np.nanmean([s[0] for s in scores]), np.nansum([b[0] for b in bonuses]),\\n np.nansum([s[1] for s in scores]), np.nansum([b[1] for b in bonuses]),\\n np.nansum([b[2] for b in bonuses]))\\n report = report.strip()\\n\\n # Replace nans with blank entries if desired\\n if nans_blank:\\n for s in ('$nan', 'nan%', 'nan'):\\n report = report.replace(s, '')\\n\\n # Write text report\\n with open(outfile, 'w') as f:\\n f.write(report)\\n\\n ###############\\n #\\n # Part 2: PDF Report\\n #\\n ###############\\n\\n tex_outfile = '/data/eeg/scalp/ltp/FR1_scalp/bonus/%s_bonus_report' % subj\\n # Divide report into rows\\n report = report.split('\\\\n')\\n # Create list of column names\\n header = report.pop(0).split('\\\\t')\\n # Define the format of the LaTeX tabular -- one column for each item in the header\\n fmt = ('X[r] ' * len(header)).strip()\\n # Create LaTeX document\\n geometry_options = dict(\\n paperheight='6.5in',\\n paperwidth='6in',\\n margin='.5in'\\n )\\n doc = Document(page_numbers=False, geometry_options=geometry_options)\\n\\n with doc.create(Center()) as centered:\\n doc.append(LargeText('Bonus Report: %s' % subj))\\n doc.append(Command('par'))\\n with centered.create(Tabu(fmt)) as data_table:\\n data_table.add_row([''] * len(header))\\n data_table.add_hline()\\n data_table.add_row([''] * len(header))\\n data_table.add_row(header, mapper=[bold])\\n data_table.add_row([''] * len(header))\\n data_table.add_hline()\\n data_table.add_row([''] * len(header))\\n for row in report[:-1]:\\n data_table.add_row(row.split('\\\\t'))\\n data_table.add_row([''] * len(header))\\n data_table.add_hline()\\n data_table.add_row([''] * len(header))\\n data_table.add_row(report[-1].split('\\\\t'))\\n doc.generate_pdf(tex_outfile, compiler='pdflatex')\\n\\n return outfile, tex_outfile + '.pdf'\",\n \"def table(self, text):\\n text = text + \\\"\\\\n\\\\n\\\"\\n pattern = re.compile(r'^(?:table(_?%(s)s%(a)s%(c)s)\\\\.(.*?)\\\\n)?^(%(a)s%(c)s\\\\.? ?\\\\|.*\\\\|)[\\\\s]*\\\\n\\\\n'\\n % {'s': self.table_span_re,\\n 'a': self.align_re,\\n 'c': self.c},\\n re.S | re.M | re.U)\\n return pattern.sub(self.fTable, text)\",\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 pprint_table(out, table, headers=None, output_format='pretty',\\n separator=None, vertical=False, title=None):\\n\\n assert(isinstance(table, (list, tuple))), \\\"Invalid table type\\\"\\n if headers:\\n assert(isinstance(headers, (list, tuple))), \\\"Invalid headers type\\\"\\n\\n sep = separator if separator else \\\" \\\"\\n\\n def stringnify(obj):\\n if isinstance(obj, (unicode, str)):\\n return udec(obj)\\n else:\\n return str(obj)\\n\\n if headers:\\n headers = map(stringnify, headers)\\n table = [map(stringnify, row) for row in table]\\n\\n if output_format == \\\"json\\\":\\n assert(headers is not None), \\\"json output format requires headers\\\"\\n table = [dict(zip(headers, row)) for row in table]\\n out.write(json.dumps(table, indent=4))\\n out.write(\\\"\\\\n\\\")\\n elif output_format == \\\"csv\\\":\\n cw = csv.writer(out)\\n if headers:\\n table.insert(0, headers)\\n table = map(functools.partial(map, uenc), table)\\n cw.writerows(table)\\n elif output_format == \\\"pretty\\\":\\n if vertical:\\n assert(len(table) == 1)\\n row = table[0]\\n max_key = max(map(len, headers))\\n for row in table:\\n for (k, v) in zip(headers, row):\\n k = uenc(k.ljust(max_key))\\n v = uenc(v)\\n out.write(\\\"%s: %s\\\\n\\\" % (k, v))\\n else:\\n # Find out the max width of each column\\n columns = [headers] + table if headers else table\\n widths = [max(map(len, col)) for col in zip(*(columns))]\\n\\n t_length = sum(widths) + len(sep) * (len(widths) - 1)\\n if title is not None:\\n t_length = max(t_length, len(title))\\n out.write(\\\"-\\\" * t_length + \\\"\\\\n\\\")\\n out.write(title.center(t_length) + \\\"\\\\n\\\")\\n out.write(\\\"-\\\" * t_length + \\\"\\\\n\\\")\\n if headers:\\n # pretty print the headers\\n line = sep.join(uenc(v.rjust(w))\\n for v, w in zip(headers, widths))\\n out.write(line + \\\"\\\\n\\\")\\n out.write(\\\"-\\\" * t_length + \\\"\\\\n\\\")\\n\\n # print the rest table\\n for row in table:\\n line = sep.join(uenc(v.rjust(w)) for v, w in zip(row, widths))\\n out.write(line + \\\"\\\\n\\\")\\n else:\\n raise ValueError(\\\"Unknown output format '%s'\\\" % output_format)\",\n \"def make_latex_table(scalar_metrics, threshold):\\n latex = ''\\n\\n # head of the table\\n latex += '\\\\\\\\begin{table} \\\\n'\\n latex += '\\\\\\\\begin{center} \\\\n'\\n latex += '\\\\\\\\begin{tabular}{ l | l } \\\\n'\\n latex += '\\\\\\\\toprule \\\\\\\\ \\\\n'\\n latex += '\\\\\\\\textbf{Metric} & \\\\\\\\textbf{Value} \\\\\\\\ \\\\midrule \\\\n'\\n\\n # body of the table\\n # make 2D list for every section\\n body_list = []\\n body_list.append(['MSE', str(scalar_metrics['mse']) ])\\n body_list.append(['RMSE', str(scalar_metrics['rmse']) ])\\n body_list.append(['MAE', str(scalar_metrics['mae']) ])\\n\\n latex += array_as_latex_table(body_list)\\n\\n # footer of the table\\n latex += '\\\\end{tabular} \\\\n'\\n latex += '\\\\end{center} \\\\n'\\n latex += '\\\\caption{Scaler performance metrics and default values} \\\\n'\\n latex += '\\\\label{tab:TODO} \\\\n'\\n latex += '\\\\end{table} \\\\n'\\n\\n return latex\",\n \"def latex_table(result, *, decimal_places=3, label=None):\\n if label is None:\\n label = 'tbl:stat_results'\\n\\n table_df = result.rankdf\\n columns = table_df.columns.to_list()\\n if result.omnibus != 'bayes' and result.pvalue >= result.alpha or \\\\\\n result.omnibus == 'bayes' and len({'smaller', 'larger'}.intersection(set(result.rankdf['decision']))) == 0:\\n columns.remove('effect_size')\\n columns.remove('magnitude')\\n if result.posthoc == 'tukeyhsd':\\n columns.remove('meanrank')\\n columns.insert(columns.index('ci_lower'), 'CI')\\n columns.remove('ci_lower')\\n columns.remove('ci_upper')\\n rename_map = {}\\n if result.effect_size == 'cohen_d':\\n rename_map['effect_size'] = '$d$'\\n elif result.effect_size == 'cliff_delta':\\n rename_map['effect_size'] = r'D-E-L-T-A'\\n elif result.effect_size == 'akinshin_gamma':\\n rename_map['effect_size'] = r'G-A-M-M-A'\\n rename_map['magnitude'] = 'Magnitude'\\n rename_map['mad'] = 'MAD'\\n rename_map['median'] = 'MED'\\n rename_map['meanrank'] = 'MR'\\n rename_map['mean'] = 'M'\\n rename_map['std'] = 'SD'\\n rename_map['decision'] = 'Decision'\\n format_string = '[{0[ci_lower]:.' + str(decimal_places) + 'f}, {0[ci_upper]:.' + str(decimal_places) + 'f}]'\\n table_df['CI'] = table_df.agg(format_string.format, axis=1)\\n table_df = table_df[columns]\\n if result.omnibus == 'bayes':\\n table_df.at[table_df.index[0], 'decision'] = '-'\\n table_df = table_df.rename(rename_map, axis='columns')\\n\\n float_format = lambda x: (\\\"{:0.\\\" + str(decimal_places) + \\\"f}\\\").format(x) if not np.isnan(x) else '-'\\n table_string = table_df.to_latex(float_format=float_format, na_rep='-').strip()\\n table_string = table_string.replace('D-E-L-T-A', r'$\\\\delta$')\\n table_string = table_string.replace('G-A-M-M-A', r'$\\\\gamma$')\\n table_string = table_string.replace(r'p\\\\_equal', r'$P(\\\\textit{equal})$')\\n table_string = table_string.replace(r'p\\\\_smaller', r'$P(\\\\textit{smaller})$')\\n print(r\\\"\\\\begin{table}[h]\\\")\\n print(r\\\"\\\\centering\\\")\\n print(table_string)\\n print(r\\\"\\\\caption{Summary of populations}\\\")\\n print(r\\\"\\\\label{%s}\\\" % label)\\n print(r\\\"\\\\end{table}\\\")\",\n \"def print_table(self) -> None:\\n if (self.probability_links == None):\\n print(\\\"+--------+\\\")\\n print(f\\\"| P({self.key:1s}) |\\\")\\n print(\\\"+--------+\\\")\\n print(f\\\"| {self.probability_values[0]:0.04f} |\\\")\\n print(\\\"+--------+\\\")\\n else:\\n arg_len = 2 + len(' '.join(self.probability_links.keys()))\\n param_len = 2 + \\\\\\n max(6, len(\\\"P(A|)\\\" + \\\",\\\".join(self.probability_links.keys())))\\n print(f\\\"+{'-'*arg_len}+{'-'*param_len}+\\\")\\n print(\\n f\\\"| {' '.join(self.probability_links.keys())} | P({self.key}|{','.join(self.probability_links.keys())}) |\\\")\\n print(f\\\"+{'-'*arg_len}+{'-'*param_len}+\\\")\\n for i in range(2**len(self.probability_links.keys())):\\n # Gives us a string binary value to make truth table off of\\n bool_key = f\\\"{i:0{len(self.probability_links.keys())}b}\\\"\\n print(\\n f\\\"| {' '.join(['T' if bool_key[j] == '0' else 'F' for j in range(len(self.probability_links.keys()))])} | {f'{self.probability_values[i]:0.04f}':<{param_len-1}s}|\\\")\\n print(f\\\"+{'-'*arg_len}+{'-'*param_len}+\\\")\",\n \"def __str__(self):\\n if self.tabletitle:\\n s = [\\\"<a name='%s'>%s</a><P>\\\" % (self.tabletitle, self.tabletitle)]\\n else:\\n s = []\\n\\n s.append('<TABLE border=%s cellpadding=%s cellspacing=%s width=\\\"%s\\\">\\\\n' % \\\\\\n (self.border, self.cell_padding, self.cell_spacing, self.width))\\n if self.tabletitle:\\n s.append('<CAPTION align=%s><STRONG>%s</STRONG></CAPTION>\\\\n' % \\\\\\n (self.caption_align, self.tabletitle))\\n\\n for i in range(len(self.body)):\\n for j in range(len(self.body[i])):\\n if type(self.body[i][j]) == type(''):\\n #process cell contents to insert breaks for \\\\n char.\\n if self.cell_line_breaks:\\n self.body[i][j] = self.body[i][j].replace('\\\\n','<br>')\\n else:\\n self.body[i][j] = Text(self.body[i][j])\\n\\n # Initialize colspan property to 1 for each\\n # heading column if user doesn't provide it.\\n if self.heading:\\n if not self.colspan:\\n if type(self.heading[0]) == list:\\n self.colspan = [1]*len(self.heading[0])\\n else:\\n self.colspan = [1]*len(self.heading)\\n # Construct heading spec\\n # can handle multi-row headings. colspan is a list specifying how many\\n # columns the i-th element should span. Spanning only applies to the first\\n # or only heading line.\\n if self.heading:\\n prefix = '<TR Align=' + self.heading_align + '> '\\n postfix = '</TR>\\\\n'\\n middle = ''\\n if type(self.heading[0]) == type([]):\\n for i in range(len(self.heading[0])):\\n middle = middle + '<TH ColSpan=%s%s>' % \\\\\\n (self.colspan[i], \\\\\\n self.get_body_color(self.heading_color,i)) \\\\\\n + str(self.heading[0][i]) +'</TH>'\\n s.append(prefix + middle + postfix)\\n for i in range(len(self.heading[1])):\\n middle = middle + '<TH>' + str(self.heading[i]) +'</TH>'\\n for heading_row in self.heading[1:]:\\n for i in range(len(self.heading[1])):\\n middle = middle + '<TH>' + heading_row[i] +'</TH>'\\n s.append(prefix + middle + postfix)\\n else:\\n for i in range(len(self.heading)):\\n middle = middle + '<TH ColSpan=%s%s>' % \\\\\\n (self.colspan[i], \\\\\\n self.get_body_color(self.heading_color,i)) \\\\\\n + str(self.heading[i]) +'</TH>'\\n s.append(prefix + middle + postfix)\\n # construct the rows themselves\\n stmp = '<TD Align=%s %s>'\\n for row in self.body:\\n s.append('<TR>')\\n for i in range(len(row)):\\n if i == 0 :\\n ss1 = self.column1_align\\n else:\\n ss1 = self.cell_align\\n s.append(stmp % (ss1, self.get_body_color(self.body_color,i)))\\n s.append(str(row[i]))\\n s.append('</TD>\\\\n')\\n s.append('</TR>\\\\n')\\n #close table\\n s.append('</TABLE><P>\\\\n')\\n return ''.join(s)\",\n \"def generate_table(self, outtablename,\\n cols=['A', 'B', 'AB'],\\n generateTable=True):\\n if generateTable:\\n new_indices = ['time (s)', 'mean counts']\\n for idx in self.data[cols].describe().index[2:]:\\n new_indices.append(idx)\\n outTable = self.data[cols].describe()\\\\\\n .set_index(pd.Index(new_indices))\\n outTable.to_latex(\\n self.tables_dir + outtablename + \\\".tex\\\", float_format=\\\"%d\\\")\\n print(\\\"Outtable: \\\", outTable)\",\n \"def html_table(header_data, row_data):\\n def make_header_cell(s):\\n return '<th>{}</th>'.format(s)\\n\\n def make_cell(s):\\n return '<td>{}</td>'.format(s)\\n\\n def make_row(s):\\n return '<tr>{}</tr>'.format(s)\\n headers = \\\" \\\".join([make_header_cell(h) for h in header_data])\\n header_row = make_row(headers)\\n rows = [make_row(\\\" \\\".join([make_cell(c) for c in row]))\\n for row in row_data]\\n rows = \\\"\\\\n\\\".join(rows)\\n html = '<table>' + header_row + rows + '</table>'\\n return html\",\n \"def table(app, tuples, _asString=False, **options):\\n\\n display = app.display\\n\\n if not display.check(\\\"table\\\", options):\\n return \\\"\\\"\\n\\n _browse = app._browse\\n inNb = app.inNb\\n\\n api = app.api\\n\\n dContext = display.distill(options)\\n end = dContext.end\\n start = dContext.start\\n withPassage = dContext.withPassage\\n condensed = dContext.condensed\\n condenseType = dContext.condenseType\\n skipCols = dContext.skipCols\\n\\n ltr = _getLtr(app, dContext) or \\\"ltr\\\"\\n\\n item = condenseType if condensed else RESULT\\n\\n if condensed:\\n tuples = condense(api, tuples, condenseType, multiple=True)\\n skipCols = set()\\n\\n passageHead = f'</th><th class=\\\"tf {ltr}\\\">p' if withPassage is True else \\\"\\\"\\n\\n html = []\\n one = True\\n\\n newOptions = display.consume(options, \\\"skipCols\\\")\\n\\n theseTuples = tuple(tupleEnum(tuples, start, end, LIMIT_TABLE, item, inNb))\\n headerTypes = getHeaderTypes(app, theseTuples)\\n\\n for (i, tup) in theseTuples:\\n if one:\\n heads = '</th><th class=\\\"tf\\\">'.join(\\n headerTypes.get(i, f\\\"column {i}\\\") for i in range(len(headerTypes))\\n )\\n html.append(\\n f'<tr class=\\\"tf {ltr}\\\">'\\n f'<th class=\\\"tf {ltr}\\\">n{passageHead}</th>'\\n f'<th class=\\\"tf {ltr}\\\">{heads}</th>'\\n f\\\"</tr>\\\"\\n )\\n one = False\\n html.append(\\n plainTuple(\\n app,\\n tup,\\n seq=i,\\n item=item,\\n position=None,\\n opened=False,\\n _asString=True,\\n skipCols=skipCols,\\n **newOptions,\\n )\\n )\\n html = \\\"<table>\\\" + \\\"\\\\n\\\".join(html) + \\\"</table>\\\"\\n\\n if _browse or _asString:\\n return html\\n dh(html, inNb=inNb)\",\n \"def generate_table(self, rows):\\n ...\",\n \"def render_table_start(header, title):\\n num_columns = len(header)\\n entries = ['\\\\color[HTML]{FFFFFF}' + '{}'.format(clean_latex(x)) for x in header]\\n if num_columns > 1: \\n line = '} & {'.join(entries)\\n else:\\n line = entries[0]\\n columns_format = '{|' + '|'.join(['l']*num_columns) + '|}'\\n print('\\\\\\\\begin{tabular}' + '{}'.format(columns_format) + '\\\\n'\\n ' ' + '\\\\\\\\hline\\\\n' +\\n ' ' + '\\\\\\\\multicolumn{' + str(num_columns) + '}' +\\n '{|c|}' + '{' + title + '}' + ' \\\\\\\\\\\\\\\\\\\\n' +\\n ' ' + '\\\\\\\\rowcolor[HTML]{333333}\\\\n' +\\n ' ' + '{' + line + '}' + ' \\\\\\\\\\\\\\\\' \\n )\",\n \"def get_table(header, floatPercission=4, *rows):\\n\\n table = PrettyTable(header)\\n table.padding_width = 1\\n for row in rows:\\n # go through row and round floats\\n for i in xrange(len(row)):\\n if type(row[i]) is float:\\n row[i] = round(row[i], floatPercission)\\n table.add_row(row)\\n return table\",\n \"def print_table(outbuf, table, align):\\n if len(table) == 0:\\n return\\n\\n colwidths = None\\n for row in table:\\n if colwidths is None:\\n colwidths = [len(x) for x in row]\\n else:\\n colwidths = [max(colwidths[i], len(x)) for i, x in enumerate(row)]\\n\\n for row in table:\\n cells = []\\n for i, cell in enumerate(row):\\n padding = ' ' * (colwidths[i] - len(cell))\\n if align[i] == 'r':\\n cell = padding + cell\\n elif i < len(row) - 1:\\n # Do not pad the final column if left-aligned.\\n cell += padding\\n cells.append(cell.encode('utf-8', 'replace'))\\n try:\\n outbuf.write(b' '.join(cells) + b'\\\\n')\\n except IOError: # pragma: no cover\\n # Can happen on Windows if the pipe is closed early.\\n pass\",\n \"def print_table(table, exploded_at=[-1, -1]):\\n\\n # color codes just to look pretty\\n NORMAL = '\\\\33[10m'\\n BLUE_START = '\\\\33[104m'\\n RED_START = '\\\\33[31m'\\n PURPLE_START = '\\\\33[35m'\\n GREEN_START = '\\\\33[92m'\\n ORANGE_START = '\\\\33[93m'\\n END = '\\\\033[0m'\\n s = ' %s' % BLUE_START\\n\\n # print number headers along x-axis\\n for i in range(0, width):\\n s += \\\" %s\\\" % i\\n if i < 10:\\n s += \\\" \\\" * 2\\n else:\\n s += \\\" \\\"\\n\\n s += \\\"%s\\\\n\\\" % END\\n # print letters for y-axis, + the relevant values in each coordinate\\n # depending on table.\\n for y in range(0, height):\\n s += \\\"%s %s %s \\\\t\\\" % (BLUE_START, Minesweeper.letters[y], END)\\n for x in range(0, width):\\n value = table[y][x]\\n if value == \\\"0\\\":\\n s += \\\"%s%s%s\\\" % (NORMAL, value, END)\\n elif value == \\\"1\\\":\\n s += \\\"%s%s%s\\\" % (GREEN_START, value, END)\\n elif value == \\\"2\\\":\\n s += \\\"%s%s%s\\\" % (ORANGE_START, value, END)\\n elif value == \\\"3\\\":\\n s += \\\"%s%s%s\\\" % (RED_START, value, END)\\n elif value == \\\"4\\\" or value == \\\"5\\\" or value == \\\"6\\\" or value == \\\"7\\\" or value == \\\"8\\\":\\n s += \\\"%s%s%s\\\" % (PURPLE_START, value, END)\\n # special\\n elif value == \\\"-\\\":\\n s += \\\"%s%s%s\\\" % (NORMAL, value, END)\\n elif value == Minesweeper.BOMB:\\n if y == exploded_at[0] and x == exploded_at[1]:\\n # Make the bomb at the casualty site explode!\\n s += \\\"%s%s%s\\\" % (RED_START, Minesweeper.EXPLOSION, END)\\n else:\\n # show normal bomb\\n s += \\\"%s%s%s\\\" % (RED_START, value, END)\\n elif value == Minesweeper.FLAG:\\n s += \\\"%s%s%s\\\" % (RED_START, value, END)\\n s += \\\" \\\" * 3\\n s += \\\"\\\\n\\\"\\n\\n # use tabbing to space them nicely\\n print s.expandtabs(3)\",\n \"def console(pro):\\n transect = pro['transect' ] \\n nm = pro['nm120r' ].flatten()#[pro['m120swr_'][0,:]]\\n t = pro['t120r' ].flatten()#[pro['m120swr_'][0,:]]\\n sb = pro['sbliner' ].flatten()#[0][:-1]\\n NASC = pro['NASC120swr'].flatten()#[pro['m120swr_'] ]\\n pc = pro['pc120swr' ].flatten()#[pro['m120swr_'] ]\\n \\n # Preallocate table object\\n table = io.StringIO()\\n \\n # Outline alignment and format for table lines, header, and data\\n line = '+{:-^10}+{:-^11}+{:-^25}+{:-^8}+{:-^13}+{:-^11}+ \\\\n'\\n header = '{:<9} | {:<9} | {:<23} | {:>6} | {:>11} |{:>12} \\\\n'\\n data = '| {:<3d} | {:<9.3f} | {:<15} | {:>6.1f} | {:>11.2f} | {:>9.1f} | \\\\n'\\n \\n # Write table lines and header\\n table.write(line.format('','','','','',''))\\n table.write(header.format('| Transect','N. miles','Time','Seabed','NASC','% samples |')) \\n table.write(line.format('','','','','','')) \\n \\n # Populate table with data\\n for nmi, ti, sbi, NASCi, pci in zip(nm, t, sb, NASC, pc):\\n table.write(data.format(transect, nmi, ti, sbi, NASCi, pci))\\n \\n # Close table with a line\\n table.write(line[:-2].format('','','','','',''))\\n \\n # Print table in the console\\n table = table.getvalue() \\n print(table)\",\n \"def make_HTML_table(l, other_frac, total, red, other_cat, fracs_labels_other,\\r\\n fracs_labels, dir_path, all_counts, level,\\r\\n prefs, pref_colors, background_color, label_color, chart_type,\\r\\n label, generate_image_type,\\r\\n plot_width, plot_height, bar_width, dpi, resize_nth_label,\\r\\n label_type, include_html_legend, include_html_counts):\\r\\n img_data = []\\r\\n\\r\\n # generate html for pie charts\\r\\n if chart_type == 'pie':\\r\\n # in the case the user wants to trim down the number of taxa\\r\\n if other_cat > 0:\\r\\n # first generate the pie charts containing an other group for all\\r\\n # taxa below the cutoff.\\r\\n fracs_labels_other.append((\\\"All Other Categories\\\", other_frac))\\r\\n title = TITLE_include % (l, total, total,\\r\\n len(fracs_labels_other), total - red, other_cat)\\r\\n all_taxons = [l]\\r\\n pie_charts_placement = []\\r\\n\\r\\n # make pie chart image\\r\\n pie = make_pie_chart(fracs_labels_other, dir_path, level,\\r\\n prefs, pref_colors, background_color, label_color,\\r\\n generate_image_type,\\r\\n plot_width, plot_height, bar_width, dpi,\\r\\n include_html_legend,\\r\\n props={'title': title})\\r\\n\\r\\n pie_charts_placement.append(pie[0] + ' ' + pie[1] +\\r\\n '</td></tr><tr><td>' + pie[2] +\\r\\n '</td></tr><tr><td class=\\\"ntitle\\\">')\\r\\n\\r\\n # second generate the pie charts where the other category is removed\\r\\n # and percents are recalculated\\r\\n title = TITLE_exclude % (l, red, total, len(fracs_labels),\\r\\n total - red, other_cat)\\r\\n\\r\\n # make pie chart image\\r\\n pie = make_pie_chart(fracs_labels, dir_path, level,\\r\\n prefs, pref_colors, background_color, label_color,\\r\\n generate_image_type,\\r\\n plot_width, plot_height, bar_width, dpi,\\r\\n include_html_legend,\\r\\n props={'title': title})\\r\\n\\r\\n pie_charts_placement.append(pie[0] + ' ' + pie[1] +\\r\\n '</td></tr><tr><td class=\\\"ntitle\\\">' +\\r\\n pie[2])\\r\\n\\r\\n all_taxons.extend(pie_charts_placement)\\r\\n all_taxons.extend((\\\" \\\", \\\" \\\"))\\r\\n\\r\\n # put the charts into the html image data\\r\\n img_data.append(TABLE_graph % tuple(all_taxons))\\r\\n img_data.append(DATA_TABLE_HTML % ''.join(all_counts))\\r\\n\\r\\n else:\\r\\n # if there is no category cutoff generate plots, without other cat\\r\\n title = TITLE % (l, total, total, len(fracs_labels_other))\\r\\n all_taxons = [l]\\r\\n\\r\\n # make pie chart image\\r\\n pie = make_pie_chart(fracs_labels_other, dir_path, level,\\r\\n prefs, pref_colors, background_color, label_color,\\r\\n generate_image_type,\\r\\n plot_width, plot_height, bar_width, dpi,\\r\\n include_html_legend,\\r\\n props={'title': title})\\r\\n\\r\\n all_taxons.extend(pie)\\r\\n\\r\\n # put the charts into the html image data\\r\\n img_data.append(TABLE_graph % tuple(all_taxons))\\r\\n img_data.append(DATA_TABLE_HTML % ''.join(all_counts))\\r\\n\\r\\n # generate html for bar and area charts\\r\\n elif chart_type == 'area' or chart_type == 'bar':\\r\\n\\r\\n taxa_percents = fracs_labels_other\\r\\n sample_ids = l\\r\\n taxa = other_cat\\r\\n\\r\\n all_categories = []\\r\\n title = TITLE % (label, total, total, len(fracs_labels_other))\\r\\n all_taxons = [label]\\r\\n\\r\\n # make area chart image\\r\\n area = make_area_bar_chart(sample_ids, taxa_percents, taxa, dir_path,\\r\\n level, prefs, pref_colors,\\r\\n background_color, label_color, chart_type,\\r\\n generate_image_type,\\r\\n plot_width, plot_height, bar_width, dpi,\\r\\n resize_nth_label, label_type,\\r\\n include_html_legend, include_html_counts,\\r\\n props={'title': title})\\r\\n\\r\\n all_taxons.extend(area)\\r\\n\\r\\n # put the charts into the html image data\\r\\n img_data.append(TABLE_graph % tuple(all_taxons))\\r\\n\\r\\n return img_data\",\n \"def print_table2(df, eval_dir):\\n\\n out_file = os.path.join(eval_dir, 'table2.txt')\\n\\n\\n with open(out_file, \\\"w\\\") as text_file:\\n\\n for idx, struc_name in enumerate(['LV', 'RV', 'Myo']):\\n # new line\\n header_string = ' & '\\n line_string = '({}) '.format(struc_name)\\n\\n for p_idx, phase in enumerate(['ED', 'ES']):\\n for measure in ['dice', 'assd', 'hd']:\\n\\n header_string += ' & {} ({}) '.format(phase, measure)\\n\\n dat = df.loc[(df['phase'] == phase) & (df['struc'] == struc_name)]\\n\\n if measure == 'dice':\\n\\n line_string += ' & {:.3f}\\\\,({:.3f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\\n else:\\n line_string += ' & {:.2f}\\\\,({:.2f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\\n\\n if p_idx == 0:\\n header_string += ' & '\\n line_string += ' & '\\n\\n header_string += ' \\\\\\\\\\\\\\\\ \\\\n'\\n line_string += ' \\\\\\\\\\\\\\\\ \\\\n'\\n\\n if idx == 0:\\n text_file.write(header_string)\\n\\n text_file.write(line_string)\\n\\n return 0\",\n \"def generate_pdf(products, quantities, pdf_filename):\\n # Construct a document.\\n doc = reportlab.platypus.BaseDocTemplate(\\n pdf_filename,\\n pagesize=reportlab.lib.pagesizes.letter,\\n title=\\\"Art Mart Inventory Sheet Check In/Out\\\",\\n # showBoundary=True\\n )\\n\\n # Construct a frame.\\n page_width = doc.pagesize[0]\\n page_height = doc.pagesize[1]\\n frames = [\\n reportlab.platypus.Frame(\\n id=\\\"table_frame\\\",\\n x1=0.25 * INCH,\\n y1=1.60 * INCH,\\n width=page_width - 0.45 * INCH,\\n height=page_height - 3.60 * INCH,\\n leftPadding=0,\\n bottomPadding=0,\\n rightPadding=0,\\n topPadding=0\\n )\\n ]\\n\\n # Construct a template and add it to the document.\\n doc.addPageTemplates(\\n reportlab.platypus.PageTemplate(\\n id=\\\"mytemplate\\\",\\n frames=frames,\\n onPage=on_page\\n )\\n )\\n\\n # Construct a story and add it to the document.\\n col_widths = [\\n 0.40 * INCH, # Existing Qty\\n 1.15 * INCH, # Barcode\\n 0.40 * INCH, # Qty Added\\n 0.60 * INCH, # Price\\n 5.00 * INCH, # Description\\n 0.40 * INCH, # Total Qty\\n ]\\n col_header_style = reportlab.lib.styles.ParagraphStyle(\\n name=\\\"col_header\\\",\\n fontName=\\\"Helvetica-Bold\\\",\\n fontSize=10\\n )\\n col_center_style = reportlab.lib.styles.ParagraphStyle(\\n name=\\\"col_center\\\",\\n fontName=\\\"Helvetica-Bold\\\",\\n fontSize=10,\\n alignment=reportlab.lib.enums.TA_CENTER\\n )\\n col_align_right_style = reportlab.lib.styles.ParagraphStyle(\\n name=\\\"col_align_right\\\",\\n fontName=\\\"Helvetica-Bold\\\",\\n fontSize=10,\\n alignment=reportlab.lib.enums.TA_RIGHT\\n )\\n # TableStyle cell formatting commands.\\n styles = [\\n # Whole table.\\n ('FONTSIZE', (0, 0), (-1, -1), 10),\\n ('TOPPADDING', (0, 1), (-1, -1), 0.26 * INCH),\\n ('BOTTOMPADDING', (0, 1), (-1, -1), 0.10 * INCH),\\n # Existing Qty\\n ('LEFTPADDING', (0, 0), (0, -1), 0),\\n # Barcode\\n ('ALIGN', (1, 0), (1, 0), 'CENTER'),\\n ('ALIGN', (1, 0), (1, -1), 'RIGHT'),\\n ('FONT', (1, 0), (1, -1), 'Courier-Bold'),\\n # Qty Added\\n ('LEFTPADDING', (2, 0), (2, 0), 0),\\n ('ALIGN', (2, 0), (2, 0), 'LEFT'),\\n ('ALIGN', (2, 1), (2, -1), 'RIGHT'),\\n ('FONT', (2, 0), (2, -1), 'Courier-Bold'),\\n # Price\\n ('ALIGN', (3, 0), (3, -1), 'RIGHT'),\\n ('FONT', (3, 0), (3, -1), 'Courier-Bold'),\\n # Description\\n ('ALIGN', (4, 0), (4, -1), 'LEFT'),\\n ('FONT', (4, 0), (4, -1), 'Helvetica'),\\n # Total Qty\\n ('LEFTPADDING', (5, 0), (5, -1), 0),\\n ('ALIGN', (5, 0), (5, 0), 'LEFT'),\\n # Other\\n ('FONT', (0, 0), (-1, 0), 'Helvetica-Bold'),\\n # ('GRID', (0, 0), (-1, -1), 1.0, BLACK)\\n ]\\n header_row = (\\n reportlab.platypus.Paragraph(\\\"Existing<br/>Qty\\\", col_header_style),\\n reportlab.platypus.Paragraph(\\\"Barcode<br/> \\\", col_center_style),\\n reportlab.platypus.Paragraph(\\\"Qty<br/>Added\\\", col_header_style),\\n reportlab.platypus.Paragraph(\\n \\\"Price<br/> \\\",\\n col_align_right_style\\n ),\\n reportlab.platypus.Paragraph(\\n \\\"Description<br/> \\\",\\n col_header_style\\n ),\\n reportlab.platypus.Paragraph(\\\"Total<br/>Qty\\\", col_header_style)\\n )\\n table_data = [header_row]\\n for product in products:\\n sku = product[\\\"SKU\\\"]\\n if sku in quantities:\\n quantity = quantities[sku]\\n # Round price to nearest dollar.\\n price = product[\\\"Price\\\"]\\n price = \\\"${.0f}\\\".format(math.trunc(float(price) + 0.5))\\n description = \\\"{}: {}\\\".format(\\n product[\\\"Product Name\\\"],\\n cctools.html_to_plain_text(product[\\\"Teaser\\\"])\\n )\\n description = description[:68]\\n table_data.append(\\n (\\\"_____\\\", sku, quantity, price, description, \\\"_____\\\")\\n )\\n table = reportlab.platypus.Table(\\n data=table_data,\\n colWidths=col_widths,\\n style=styles,\\n repeatRows=1\\n )\\n table.hAlign = \\\"LEFT\\\"\\n story = [table]\\n doc.build(story, canvasmaker=NumberedCanvas)\",\n \"def write_latex_preamble(self):\\n self.texfile.write(r\\\"\\\\n\\\")\\n self.texfile.write(r\\\"\\\\documentclass[a4paper,12pt]{article}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\usepackage{tabu}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\usepackage{booktabs}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\usepackage[font=small,labelsep=space]{caption}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\usepackage[margin=2.5cm]{geometry}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\setlength{\\\\\\\\topmargin}{1.0cm}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\setlength{\\\\\\\\textheight}{22cm}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\usepackage{fancyhdr}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\pagestyle{fancy}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\\\\\fancyhf{}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\\\\\fancyhead[R]{\\\\leftmark}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\usepackage{multirow}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\n\\\")\\n self.texfile.write(r\\\"\\\\\\\\begin{document}\\\\n\\\")\\n self.texfile.write(r\\\"\\\\n\\\")\",\n \"def table_gen(NamesL_pairs, p_pL, m_mL, p_mL, m_pL, p_valsL, p_vals_BonferoniL, RatiosL, p_valsL_divergent_convergent,\\n p_valsL_divergent_convergent_BonferoniL, RatiosL_divergent_convergent, output_table):\\n datafile = open(output_table, \\\"w\\\")\\n datafile.write(\\n \\\"Feature_1\\\" + '\\\\t' + \\\"Feature_2\\\" + \\\"\\\\t\\\" + \\\"plus_plus\\\" + '\\\\t' + \\\"minus_minus\\\" + '\\\\t' + \\\"plus_minus\\\" + '\\\\t' + \\\"minus_plus\\\" + '\\\\t' + \\\"p_value_same_opposite\\\" + '\\\\t' + \\\"p-value_same_opposite_Bonferoni_corrected\\\" + '\\\\t' + \\\"Ratio_same_opposite\\\" + '\\\\t' + \\\"p_value_divergent_convergent\\\" + '\\\\t' + \\\"p_value_divergent_convergent Bonferoni corrected\\\" + '\\\\t' + \\\"Ratio divergent convergent\\\" + '\\\\n')\\n for i in range(len(NamesL_pairs)):\\n datafile.write(\\n NamesL_pairs[i][0] + '\\\\t' + NamesL_pairs[i][1] + '\\\\t' + str(p_pL[i]) + '\\\\t' + str(m_mL[i]) + '\\\\t' + str(\\n p_mL[i]) + '\\\\t' + str(m_pL[i]) + '\\\\t' + str(p_valsL[i]) + '\\\\t' + str(p_vals_BonferoniL[i]) + '\\\\t' + str(\\n RatiosL[i]) + '\\\\t' + str(p_valsL_divergent_convergent[i]) + '\\\\t' + str(\\n p_valsL_divergent_convergent_BonferoniL[i]) + '\\\\t' + str(RatiosL_divergent_convergent[i]) + '\\\\n')\\n datafile.close()\\n return\",\n \"def generate_pdf(request):\\n reg_no = request.user.username\\n user = get_object_or_404(User, username=reg_no)\\n user_profile = user.get_profile()\\n user_application = user_profile.application\\n np = user_application.np\\n \\n response = HttpResponse(mimetype='application/pdf')\\n response['Content-Disposition'] = 'attachment; filename=JAM2012_Allottment.pdf'\\n \\n elements = []\\n doc = SimpleDocTemplate(response)\\n \\n formatted_time = time.ctime()\\n styles = getSampleStyleSheet()\\n styles.add(ParagraphStyle(name='Justify', alignment=TA_JUSTIFY))\\n \\n ptext = '<font size=15>JAM 2012 - Admissions.</font>' \\n elements.append(Paragraph(ptext, styles[\\\"Justify\\\"]))\\n elements.append(Spacer(4, 20))\\n \\n ptext = '<font size=12>Registration Number: %s</font>' % reg_no \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(1, 12))\\n \\n data = [] \\n options = get_chosen_options(user) ##Put a check to show when the options chosen is empty\\n \\n if not(options):\\n ptext = '<font size=12>No choices were selected.</font>' \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(1, 12))\\n doc.build(elements) \\n return response \\n \\n ptext = '<font size=12>The choices selected by me are as follows: </font>' \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(4, 30))\\n \\n counter = 1\\n for opt in options:\\n data.append([counter, opt.opt_code, opt.opt_location, opt.opt_name])\\n counter = counter + 1\\n \\n t = Table(data)\\n t.setStyle(TableStyle([('GRID',(0,0),(3,len(options)),1,colors.black),\\n ('TEXTCOLOR',(0,0),(0,-1),colors.green)]))\\n \\n elements.append(t) \\n \\n elements.append(Spacer(4, 30))\\n \\n ptext = '<font size=12>I hereby declare that the order of preference given by me for my eligible programmes is final. </font>' \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(4, 25))\\n \\n ptext = '<font size=12>Signature of the Candidate</font>' \\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(4, 20))\\n \\n ptext = '<font size=12>%s</font>' % formatted_time\\n elements.append(Paragraph(ptext, styles[\\\"Normal\\\"]))\\n elements.append(Spacer(1, 12))\\n \\n doc.build(elements)\\n \\n return response\",\n \"def delimToLaTeX(self,string,delim,linechange,style, math_var):\\n # clearn the raw string \\n string = string.rstrip()\\n\\n # generate a list of lists with each element is a list that represents a row\\n text = self.escapeLaTeX(string)\\n text = text.split(linechange)\\n # split each row into cells by commas\\n text = map(lambda x: x.split(delim), text)\\n # print text\\n\\n # calculate the row number\\n rows = len(text)\\n\\n # calculate the column number\\n # if text[1] does not exist, that means this is not a proper table\\n try:\\n cols = len(text[0])\\n except IndexError:\\n print \\\"This is not a table! No columns exist!\\\"\\n return\\n\\n # convert each cell by math indicator\\n for i in range(rows):\\n for j in range(cols):\\n cell = text[i][j]\\n text[i][j] = self.convertCell(cell,math_var)\\n\\n # generate a new table based of the lol\\n # check each cell\\n new_text = map(lambda x: ' & '.join(x), text)\\n # print new_text\\n new_text_str = ' \\\\\\\\\\\\\\\\ \\\\n'.join(new_text) + ' \\\\\\\\\\\\\\\\ \\\\n \\\\n'\\n \\n # cell style\\n if (style == 'cells'):\\n pass\\n \\n # simple style\\n if (style == 'simple'): \\n new_text_str = '\\\\\\\\begin{} \\\\n \\\\n'.format('{tabular}' + '{'+'l'*cols+'}') + new_text_str + '\\\\\\\\end{tabular}'\\n \\n # booktabs\\n if (style == 'booktabs'):\\n midrule_index = new_text_str.find('\\\\n')+1\\n top = '\\\\\\\\toprule \\\\n' + new_text_str[:midrule_index]\\n bottom = new_text_str[midrule_index:] + '\\\\\\\\bottomrule \\\\n'\\n new_text_str = top + '\\\\\\\\midrule \\\\n' + bottom\\n new_text_str = '\\\\\\\\begin{} \\\\n \\\\n'.format('{tabular}' + '{'+'l'*cols+'}') + new_text_str + '\\\\\\\\end{tabular}'\\n\\n # longtable\\n if (style == 'longtable'):\\n mid_index = new_text_str.find('\\\\n')+1\\n first_row = new_text_str[:mid_index]\\n rest = new_text_str[mid_index:]\\n top = first_row + '\\\\\\\\endfirsthead \\\\n'+ first_row + '\\\\\\\\endhead \\\\n'\\n top = top + '\\\\\\\\multicolumn{} \\\\n'.format('{' + str(cols) + '}' + '{'+'c'+'}'+'{Continued\\\\ldots} \\\\\\\\\\\\\\\\ \\\\n')\\n top = top + '\\\\\\\\endfoot \\\\n\\\\\\\\hline \\\\n\\\\\\\\endlastfoot \\\\n \\\\n'\\n new_text_str = top + rest\\n new_text_str = '\\\\\\\\begin{} \\\\n \\\\n'.format('{longtable}' + '{'+'l'*cols+'}') + new_text_str + '\\\\\\\\end{longtable}'\\n\\n return new_text_str\",\n \"def make_table_header(table_label, table_width,\\n junction_char=\\\"+\\\", horizontal_char=\\\"-\\\",\\n vertical_char=\\\"|\\\"):\\n\\n if len(table_label) >= (table_width - 2):\\n raise ValueError(\\n \\\"Table header %s is longer than total width of the table.\\\")\\n\\n label_and_space_width = table_width - len(table_label) - 2\\n padding = 0 if label_and_space_width % 2 == 0 else 1\\n\\n half_table_width = label_and_space_width // 2\\n left_spacing = (\\\" \\\" * half_table_width)\\n right_spacing = (\\\" \\\" * (half_table_width + padding))\\n\\n border_line = \\\"\\\".join((junction_char,\\n (horizontal_char * (table_width - 2)),\\n junction_char,))\\n\\n label_line = \\\"\\\".join((vertical_char,\\n left_spacing,\\n table_label,\\n right_spacing,\\n vertical_char,))\\n\\n return \\\"\\\\n\\\".join((border_line, label_line,))\",\n \"def print_table(self, table):\\n raise NotImplementedError('print_table method not defined!')\",\n \"def generate_table(rows):\\n\\n # - figure out column widths\\n widths = [len(max(columns, key=len)) for columns in zip(*rows)]\\n\\n # - print the header\\n header, data = rows[0], rows[1:]\\n yield (\\n ' | '.join(format(title, \\\"%ds\\\" % width) for width, title in zip(widths, header))\\n )\\n\\n # Print the separator\\n first_col = ''\\n # - print the data\\n for row in data:\\n if first_col == '' and row[0] != '':\\n # - print the separator\\n yield '-+-'.join('-' * width for width in widths)\\n first_col = row[0]\\n\\n yield (\\n \\\" | \\\".join(format(cdata, \\\"%ds\\\" % width) for width, cdata in zip(widths, row))\\n )\",\n \"def make_html_table(filelist):\\n pre=open(\\\"HTML_Header.txt\\\").read()\\n out=[]\\n for file in filelist:\\n x=load_file(file)[1]\\n out.append(\\\"<tr>\\\")\\n out.append(\\\"<th>{}</th>\\\".format(x[0]))\\n out.append(\\\"<th>{}</th>\\\".format(x[2]))\\n out.append(\\\"<th>{}</th>\\\".format(x[1]))\\n out.append(\\\"<th>{}</th>\\\".format(x[6]))\\n out.append(\\\"<th>{}</th>\\\".format(x[7]))\\n out.append(\\\"<th>{}</th>\\\".format(x[8]))\\n out.append(\\\"<th>{}</th>\\\".format(x[9]))\\n out.append(\\\"<th>{}</th>\\\".format(x[12]))\\n out.append(\\\"<th>link</th>\\\")\\n out.append(\\\"</tr>\\\")\\n out.append(\\\"</table>\\\")\\n \\n for i in range(0,len(out)):\\n pre=pre+out[i]+\\\"\\\\n\\\"\\n \\n path=os.getcwd()\\n os.chdir(\\\"Ausgabe\\\")\\n open(\\\"table.html\\\",\\\"w\\\").write(pre)\\n os.chdir(path)\",\n \"def gen_tag_year_report(\\n lib, tags_ordered, n_papers_w_tag, years_ordered,\\n actually_markdown=False):\\n report = []\\n if not actually_markdown:\\n # Generate header.\\n report.append(gen_header())\\n report.append('<table class=\\\"table\\\">\\\\n')\\n report.append(' <tr style=\\\"height: 8rem;\\\">\\\\n')\\n report.append(' <th style=\\\"vertical-align: bottom;\\\"> Year </th>\\\\n')\\n report.append(' <th style=\\\"vertical-align: bottom;\\\"> # Pubs </th>\\\\n')\\n report.append(' <th> </th>\\\\n') # blank column before starting tags.\\n for tag in tags_ordered:\\n report.append(\\n ' <th style=\\\"max-width: 3rem; vertical-align: middle; transform: translate(0rem, 2.2rem) rotate(290deg);\\\"> <a href=\\\"'\\n + lib.gen_tag_url(tag)\\n + '\\\">' + tag + '</a> </th>\\\\n')\\n report.append(' </tr>\\\\n')\\n\\n # generate numbers of papers with tag per year\\n all_papers_count = len(lib)\\n for year in years_ordered:\\n report.append(' <tr>\\\\n')\\n n_papers_this_year = len(lib.get_pubs(year=year))\\n report.append(' <th> ' + year + ' </th>\\\\n')\\n\\n year_count_style = gen_count_style(\\n n_papers_this_year, all_papers_count)\\n year_url = lib.gen_year_url(year)\\n if year_url:\\n report.append(\\n ' <td {0}> <a href=\\\"{1}\\\"> {2} </a></td>\\\\n'.format(\\n year_count_style, year_url, n_papers_this_year))\\n else:\\n report.append(\\n ' <td {0}> {1} </td>\\\\n'.format(\\n year_count_style, n_papers_this_year))\\n report.append(' <td> </td>\\\\n') # blank column before starting tags.\\n\\n for tag in tags_ordered:\\n papers_for_tag_year = lib.get_pubs(tag=tag, year=year)\\n if papers_for_tag_year:\\n n_papers_tag_year = len(papers_for_tag_year)\\n style = gen_count_style(\\n n_papers_tag_year, all_papers_count)\\n tag_year_url = lib.gen_tag_year_url(tag, year)\\n if tag_year_url:\\n count_html = (\\n '<a href=\\\"{0}\\\"> {1} </a>'.format(\\n tag_year_url, n_papers_tag_year))\\n else:\\n count_html = \\\"{0}\\\".format(n_papers_tag_year)\\n else:\\n style = \\\"\\\"\\n count_html = \\\"\\\"\\n report.append(' <td ' + style + '> ' + count_html + ' </td>\\\\n')\\n\\n report.append(' </tr>\\\\n')\\n\\n # generate total line at bottom\\n report.append(' <tr>\\\\n')\\n report.append(' <th> Total </th>\\\\n')\\n all_papers_style = gen_count_style(all_papers_count, all_papers_count)\\n report.append(' <th ' + all_papers_style + '> '\\n + str(all_papers_count) + ' </th>\\\\n')\\n report.append(' <th> </th>\\\\n') # blank column before starting tags.\\n\\n for tag in tags_ordered:\\n tag_count_style = gen_count_style(\\n n_papers_w_tag[tag], all_papers_count)\\n report.append(\\n ' <th ' + tag_count_style + '> '\\n + '<a href=\\\"' + lib.gen_tag_url(tag) + '\\\">'\\n + str(n_papers_w_tag[tag]) + '</a> </th>\\\\n')\\n\\n report.append(' </tr>\\\\n')\\n report.append('</table>\\\\n')\\n if not actually_markdown:\\n report.append(gen_footer())\\n\\n return report\",\n \"def print_table(table):\\n # transpose the table:\\n table = map(list, zip(*table))\\n # get the column width:\\n col_width = [max(len(str(x)) for x in col) for col in zip(*table)]\\n # print it to screen:\\n print\\n for line in table:\\n print \\\"| \\\" + \\\" | \\\".join(\\\"{:{}}\\\".format(x, col_width[i]) for i, x in enumerate(line)) + \\\" |\\\"\\n print\",\n \"def print_table(headers, rows):\\n try:\\n if headers:\\n print('\\\\n')\\n print(tabulate.tabulate(\\n rows, headers=headers,\\n tablefmt=\\\"plain\\\", numalign=\\\"left\\\"\\n ))\\n print('\\\\n')\\n except Exception as e:\\n print(e.message)\",\n \"def multiplicationTable(size=10,line_space=7):\\n for column in range(1,size+1):\\n row = ''\\n for i in range(1, 11):\\n if i*column > 9:\\n row += str(i*column) + ' '*( (line_space+1) - len(str(i*column)) )\\n continue\\n row += str(i*column)+ ' '*line_space\\n print(row)\",\n \"def build_latex(file_list):\\n eingabe=[]\\n anhang_count=0\\n anhaenge=[]\\n anhaenge_file=[]\\n for file in file_list:\\n x=load_file(file)[1]\\n eingabe.append(\\\"\\\\section{%s}\\\" %(x[2]))\\n eingabe.append(\\\"\\\\subsection{Infos}\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}lX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Datum} & %s\\\\\\\\\\\" %(x[0]))\\n eingabe.append(r\\\"\\\\textbf{Gremium} & %s\\\\\\\\\\\" %(x[1]))\\n eingabe.append(r\\\"\\\\textbf{Anatrag/Beschluss wurde} & %s\\\\\\\\\\\" %(x[9]))\\n x[11]=x[11].replace(\\\" \\\",\\\"\\\")\\n kw=x[11].split(\\\",\\\")\\n for i in range(0,len(kw)):\\n if i==0:\\n eingabe.append(r\\\"\\\\textbf{Keyword:} & %s\\\\\\\\\\\" %(kw[i]))\\n else:\\n eingabe.append(r\\\" & %s\\\\\\\\\\\" %(kw[i]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[6],x[7],x[8]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n eingabe.append(\\\"\\\\subsection{Antrags/Beschlusstext}\\\")\\n line_text=len(eingabe)\\n eingabe.append(x[3])\\n eingabe.append(\\\"\\\\subsection{Begründung}\\\")\\n eingabe.append(x[4])\\n if x[23]==\\\"Ja\\\" and x[24]!=\\\"\\\":\\n delta=7\\n anzahl=int((len(x)-23)/delta)\\n if anzahl==1:\\n eingabe.append(\\\"\\\\subsection{Änderungsantrag}\\\")\\n eingabe.append(\\\"\\\\subsubsection*{Vorschlag}\\\")\\n eingabe.append(x[24])\\n eingabe.append(\\\"\\\\subsubsection*{Begründung}\\\")\\n eingabe.append(x[25]+\\\"\\\\\\\\vspace{1.5ex} \\\\\\\\\\\\\\\\\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[26],x[27],x[28]))\\n eingabe.append(r\\\"\\\\multicolumn{2}{@{}l}{\\\\textbf{Änderungsantrag wurde:}} & %s \\\\\\\\\\\" %(x[29]))\\n eingabe.append(\\\"\\\\\\\\end{tabularx}\\\")\\n else:\\n eingabe.append(\\\"\\\\subsection{Änderungsanträge}\\\")\\n for i in range(0,anzahl):\\n eingabe.append(\\\"\\\\subsubsection{Änderungsvorschlag %s}\\\" %(i+1))\\n eingabe.append(\\\"\\\\\\\\paragraph*{Vorschlag}\\\")\\n eingabe.append(x[24+(delta*i)])\\n eingabe.append(\\\"\\\\\\\\paragraph*{Begründung}\\\")\\n eingabe.append(x[25+(delta*i)]+\\\"\\\\\\\\vspace{1.5ex} \\\\\\\\\\\\\\\\\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[26+(delta*i)],x[27+(delta*i)],x[28+(delta*i)]))\\n eingabe.append(r\\\"\\\\multicolumn{2}{@{}l}{\\\\textbf{Änderungsantrag wurde:}} & %s \\\\\\\\\\\" %(x[29+(delta*i)]))\\n eingabe.append(\\\"\\\\\\\\end{tabularx}\\\")\\n if x[10]!=\\\"\\\":\\n anhang=x[10].split(\\\",\\\")\\n bennenung=x[11].split(\\\",\\\")\\n eingabe[line_text]=eingabe[line_text]+\\\"\\\\\\\\\\\\\\\\ \\\\n Dieser Antrag enthält %s Anhänge: \\\" %(len(anhang))\\n for i in range(0,len(anhang)):\\n anhang_count=anhang_count+1\\n anhaenge.append(\\\"\\\\section{%s - %s} \\\\label{An:%s}\\\" % (x[2],bennenung[i],str(anhang_count)))\\n anhaenge.append(\\\"\\\\includepdf[pages=-]{%s}\\\" %(anhang[i]))\\n anhaenge_file.append(anhang[i])\\n if i!=len(anhang)-1:\\n eingabe[line_text]=eingabe[line_text]+\\\"\\\\\\\\nameref{An:%s}, \\\" % (str(anhang_count))\\n else:\\n eingabe[line_text]=eingabe[line_text]+\\\"\\\\\\\\nameref{An:%s} \\\" % (str(anhang_count)) \\n \\n eingabe.append(\\\"\\\\\\\\newpage\\\") \\n eingabe.append(\\\"\\\\\\\\appendix\\\") \\n eingabe.append(\\\"\\\\\\\\pagenumbering{Roman}\\\") \\n ausgabe=\\\"\\\"\\n for i in range(0,len(eingabe)):\\n ausgabe=ausgabe+eingabe[i]+\\\"\\\\n\\\"\\n \\n for i in range(0,len(anhaenge)):\\n ausgabe=ausgabe+anhaenge[i]+\\\"\\\\n\\\"\\n \\n return ausgabe,anhaenge_file\",\n \"def result_table(fmt='latex_booktabs'):\\n \\n names = [\\n \\\"ETF EW.\\\",\\n \\\"Antonacci ETF\\\",\\n \\\"Antonacci ETF Inv. Vol.\\\",\\n \\\"Futures EW.\\\",\\n \\\"Antonacci Futures\\\",\\n \\\"Antonacci Futures Inv. Vol.\\\",\\n \\\"TSMOM Futures Low Vol.\\\",\\n \\\"TSMOM Futures High Vol.\\\"\\n ]\\n\\n # Get stats for each strategy\\n s1 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=7, target_vol=40, periods=6, vol_weight=False)\\n s2 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=2, target_vol=40, periods=6, vol_weight=False)\\n s3 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=2, target_vol=40, periods=6, vol_weight=True)\\n s4 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=47, target_vol=40, periods=6, vol_weight=False)\\n s5 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=False)\\n s6 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=True)\\n s7 = calculate.stats_from_parameters(name='TSMOM', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=False)\\n s8 = calculate.stats_from_parameters(name='TSMOM', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=100, periods=6, vol_weight=False)\\n\\n # The relevant columns from the summary data\\n cols = [3, 4, 5, 6]\\n num_assets = [7, 2, 2, 47, 10, 10, 47, 47]\\n stats = [s1, s2, s3, s4, s5, s6, s7, s8]\\n table = [names]\\n \\n # Collecting the results\\n for i, col in enumerate(cols):\\n col_list = [round(stat['summary'][col], 2) for stat in stats]\\n table.append(col_list)\\n\\n table.append(num_assets)\\n table = list(map(list, zip(*table))) # Transpose\\n \\n # Creating table headers\\n headers = ['Strategy Name', 'Annual Return', 'Annual Vol.', 'Sharpe', 'Max. Drawdown', '# Assets']\\n \\n # Returning latex table\\n tbl = tabulate(table, headers, tablefmt=fmt)\\n print(tbl)\\n \\n return tbl\",\n \"def print_table1(df, eval_dir):\\n\\n out_file = os.path.join(eval_dir, 'table1.txt')\\n\\n header_string = ' & '\\n line_string = 'METHOD '\\n\\n\\n for s_idx, struc_name in enumerate(['LV', 'RV', 'Myo']):\\n for measure in ['dice', 'assd']:\\n\\n header_string += ' & {} ({}) '.format(measure, struc_name)\\n\\n dat = df.loc[df['struc'] == struc_name]\\n\\n if measure == 'dice':\\n line_string += ' & {:.3f}\\\\,({:.3f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\\n else:\\n line_string += ' & {:.2f}\\\\,({:.2f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\\n\\n if s_idx < 2:\\n header_string += ' & '\\n line_string += ' & '\\n\\n header_string += ' \\\\\\\\\\\\\\\\ \\\\n'\\n line_string += ' \\\\\\\\\\\\\\\\ \\\\n'\\n\\n with open(out_file, \\\"w\\\") as text_file:\\n text_file.write(header_string)\\n text_file.write(line_string)\\n\\n return 0\",\n \"def table(self, doc, level, output):\\n output('<table border=\\\"1\\\" cellpadding=\\\"2\\\">\\\\n')\\n for row in doc.getRows()[0]:\\n output(\\\"<tr>\\\\n\\\")\\n for column in row.getColumns()[0]:\\n str = ('<%s colspan=\\\"%s\\\" align=\\\"%s\\\" valign=\\\"%s\\\">'\\n % (column.getType(),\\n column.getSpan(),\\n column.getAlign(),\\n column.getValign()))\\n output(str)\\n for c in column.getChildNodes():\\n getattr(self, self.element_types[c.getNodeName()]\\n )(c, level, output)\\n output(\\\"</\\\" + column.getType() + \\\">\\\\n\\\")\\n output(\\\"</tr>\\\\n\\\")\\n output(\\\"</table>\\\\n\\\")\",\n \"def print_table(table, separator=' ', **kwargs):\\n num_cols = max([len(row) for row in table])\\n for idx, _ in enumerate(table):\\n while len(table[idx]) < num_cols:\\n table[idx].append('')\\n widths = [max([len(str(cell)) for cell in [row[col_idx] for row in table]])\\n for col_idx in range(len(table[0]))]\\n for row in table:\\n msg = ''\\n for idx, cell in enumerate(row):\\n msg += f'{cell:{widths[idx]}}{separator}'\\n cprint(msg, **kwargs)\",\n \"def format_participant_table(participants_f, url_prefix):\\n personal_ids = sorted(parse_recipients(participants_f).keys())\\n url_prefix = url_prefix if url_prefix.endswith('/') else url_prefix + '/'\\n\\n result = '<table class=\\\"data-table\\\">\\\\n<tr><th>Personal ID</th></tr>\\\\n'\\n for personal_id in personal_ids:\\n url = url_prefix + personal_id + '/index.html'\\n result += '<tr><td><a href=\\\"%s\\\">%s</a></td></tr>\\\\n' % (url,\\n personal_id)\\n result += '</table>\\\\n'\\n\\n return result\",\n \"def build_latex_standalone(x):\\n eingabe=[]\\n anhaenge_file=[]\\n eingabe.append(\\\"\\\\\\\\title{%s}\\\" %(x[2]))\\n eingabe.append(\\\"\\\\\\\\author{%s}\\\" %(x[1]))\\n eingabe.append(\\\"\\\\\\\\date{%s}\\\" %(x[0]))\\n eingabe.append(\\\"\\\\maketitle\\\") \\n eingabe.append(\\\"\\\\section{Infos}\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}lX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Anatrag/Beschluss wurde} & %s\\\\\\\\\\\" %(x[9]))\\n x[11]=x[11].replace(\\\" \\\",\\\"\\\")\\n kw=x[11].split(\\\",\\\")\\n for i in range(0,len(kw)):\\n if i==0:\\n eingabe.append(r\\\"\\\\textbf{Keyword:} & %s\\\\\\\\\\\" %(kw[i]))\\n else:\\n eingabe.append(r\\\" & %s\\\\\\\\\\\" %(kw[i]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[6],x[7],x[8]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n eingabe.append(\\\"\\\\section{Antrags/Beschlusstext}\\\")\\n eingabe.append(x[3])\\n eingabe.append(\\\"\\\\section{Begründung}\\\")\\n eingabe.append(x[4])\\n if x[23]==\\\"Ja\\\" and x[24]!=\\\"\\\":\\n delta=7\\n anzahl=int((len(x)-23)/delta)\\n if anzahl==1:\\n eingabe.append(\\\"\\\\section{Änderungsantrag}\\\")\\n eingabe.append(\\\"\\\\subsection*{Vorschlag}\\\")\\n eingabe.append(x[24])\\n eingabe.append(\\\"\\\\subsection*{Begründung}\\\")\\n eingabe.append(x[25]+\\\"\\\\\\\\vspace{1.5ex} \\\\\\\\\\\\\\\\\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[26],x[27],x[28]))\\n eingabe.append(r\\\"\\\\multicolumn{@{}2}{l}{\\\\textbf{Änderungsantrag wurde:}} & %s \\\\\\\\\\\" %(x[29]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n else:\\n eingabe.append(\\\"\\\\section{Änderungsanträge}\\\")\\n for i in range(0,anzahl):\\n eingabe.append(\\\"\\\\subsection{Änderungsvorschlag %s}\\\" %(i+1))\\n eingabe.append(\\\"\\\\subsubsection*{Vorschlag}\\\")\\n eingabe.append(x[24+(delta*i)])\\n eingabe.append(\\\"\\\\subsubsection*{Begründung}\\\")\\n eingabe.append(x[25+(delta*i)]+\\\"\\\\\\\\vspace{1.5ex} \\\\\\\\\\\\\\\\\\\")\\n eingabe.append(\\\"\\\\\\\\begin{tabularx}{\\\\linewidth}{@{}XXX}\\\")\\n eingabe.append(r\\\"\\\\textbf{Abstimmungsergebniss:}&&\\\\\\\\\\\")\\n eingabe.append(r\\\"Zustimmung & Ablehnung & Enthaltungen \\\\\\\\\\\")\\n eingabe.append(r\\\"{} & {} & {} \\\\\\\\\\\".format(x[26+(delta*i)],x[27+(delta*i)],x[28+(delta*i)]))\\n eingabe.append(r\\\"\\\\multicolumn{@{}2}{l}{\\\\textbf{Änderungsantrag wurde:}} & %s \\\\\\\\\\\" %(x[29+(delta*i)]))\\n eingabe.append(\\\"\\\\end{tabularx}\\\")\\n if x[10]!=\\\"\\\":\\n #\\\\includepdf[pages=-]{Anhang/Geschaeftsordnung_Jugendausschuss.pdf}\\n eingabe.append(\\\"\\\\\\\\appendix\\\")\\n eingabe.append(\\\"\\\\section*{Anhang}\\\")\\n anhang=x[10].split(\\\",\\\")\\n bennenung=x[11].split(\\\",\\\")\\n eingabe[14]=eingabe[14]+\\\"\\\\\\\\\\\\\\\\ \\\\n Dieser Antrag enthält %s Anhänge: \\\" %(len(anhang))\\n for i in range(0,len(anhang)):\\n eingabe.append(\\\"\\\\subsection*{%s} \\\\label{An:%s}\\\" % (bennenung[i],str(i+1)))\\n eingabe.append(\\\"\\\\includepdf[pages=-]{%s}\\\" %(anhang[i]))\\n anhaenge_file.append(anhang[i])\\n if i!=len(anhang)-1:\\n eingabe[14]=eingabe[14]+\\\"\\\\\\\\nameref{An:%s}, \\\" % (str(i+1))\\n else:\\n eingabe[14]=eingabe[14]+\\\"\\\\\\\\nameref{An:%s} \\\" % (str(i+1))\\n \\n\\n \\n ausgabe=\\\"\\\"\\n for i in range(0,len(eingabe)):\\n ausgabe=ausgabe+eingabe[i]+\\\"\\\\n\\\"\\n \\n return ausgabe,anhaenge_file\",\n \"def print_table(rows, labels=None):\\n if labels is None:\\n labels = ROW_LABELS\\n\\n output_table = prettytable.PrettyTable()\\n output_table.field_names = labels\\n output_table.align = 'l'\\n output_table.vrules = prettytable.prettytable.ALL\\n output_table.hrules = prettytable.prettytable.HEADER\\n\\n for row in rows:\\n row = [x.strip() for x in row]\\n output_table.add_row(row)\\n\\n print output_table\\n print ''\",\n \"def print_table(table, title_list):\\n\\n # your goes code\\n \\n table.insert(0, title_list)\\n # title listet 0.helyre teszi\\n # your code\\n\\n lenght_list = [] # tartalmazza az összes szót\\n for lines in table:\\n for items in lines:\\n lenght_list.append(items)\\n\\n longest_words_length = len(max(lenght_list, key=len))\\n multiplier = len(title_list)*(longest_words_length+1)\\n\\n for sublist in table:\\n print(\\\"|\\\\n|\\\", \\\"-\\\"*multiplier, \\\"|\\\")\\n\\n for j in sublist:\\n print(\\\"|\\\", j, end = \\\" \\\"*(longest_words_length-len(j)))\\n\\n print(\\\"|\\\\n|\\\",\\\"-\\\"*multiplier, \\\"|\\\")\",\n \"def prepare_table(self):\\n i = 0\\n for item in ['DN[-]', 'd_out[mm]', 'tl_trub[mm]', 'roztec_trub[mm]', 'delka[mm]', 'roztec_prep[mm]', 'vyska_prep[mm]']:\\n self.table.insertColumn(i)\\n self.table.setHorizontalHeaderItem(i, QTableWidgetItem(item))\\n i += 1\\n for item in ['tl_prep[mm]','pocet_prep[-]', 'pocet_trub[-]', 'TP[m/s]', 'MZP[m/s]', 'vykon [W]',\\n 'tlak_ztraty[Pa]', 'hmotnost[kg]']:\\n self.table.insertColumn(i)\\n self.table.setHorizontalHeaderItem(i, QTableWidgetItem(item))\\n i += 1\",\n \"def markdown_table(self, which):\\n if which == 'C':\\n coef = 'C'\\n elif which == 'c':\\n coef = 'c'\\n elif which == 'f':\\n coef = 'f'\\n str = '|order|'\\n for i in range(1,N+1):\\n str = str + '$%s_{%d}$ |' % (coef,i)\\n str = str + '\\\\n|'\\n for i in range(1,N+1):\\n str = str + '-|'\\n str = str + '\\\\n'\\n for i in range(1,self.N+1):\\n str = str + (self.dat[i]).markdown_row(self.N, which)\\n return str\",\n \"def line_latex(line: str) -> str:\\r\\n\\r\\n if not line:\\r\\n return \\\"\\\"\\r\\n\\r\\n if \\\"$$\\\" in line:\\r\\n return line\\r\\n\\r\\n # check for horizontal rule\\r\\n\\r\\n if line == \\\"***\\\" or line == \\\"---\\\" or line == \\\"___\\\":\\r\\n return \\\"\\\\\\\\noindent\\\\\\\\rule{\\\\\\\\textwidth}{0.4pt}\\\"\\r\\n\\r\\n if line[0:5] == \\\"#### \\\":\\r\\n return \\\"\\\\\\\\subsubsection{\\\" + line_latex(line[5:]) + \\\"}\\\"\\r\\n elif line[0:4] == \\\"### \\\":\\r\\n return \\\"\\\\\\\\subsection{\\\" + line_latex(line[4:]) + \\\"}\\\"\\r\\n elif line[0:3] == \\\"## \\\":\\r\\n return \\\"\\\\\\\\section{\\\" + line_latex(line[3:]) + \\\"}\\\"\\r\\n elif line[0:2] == \\\"# \\\":\\r\\n return \\\"\\\\\\\\title{\\\" + line_latex(line[2:]) + \\\"}\\\\n\\\" + \\\"\\\\\\\\maketitle\\\"\\r\\n\\r\\n # various formatting modes to be in\\r\\n math = False\\r\\n\\r\\n asterisks = 0\\r\\n bf = False\\r\\n it = False\\r\\n\\r\\n tildes = 0\\r\\n sout = False\\r\\n\\r\\n verb = False\\r\\n\\r\\n latex = \\\"\\\"\\r\\n\\r\\n for char in line:\\r\\n if char == \\\"`\\\":\\r\\n if math:\\r\\n latex += char\\r\\n elif verb:\\r\\n latex += \\\"|\\\"\\r\\n verb = False\\r\\n else:\\r\\n latex += \\\"\\\\\\\\verb|\\\"\\r\\n verb = True\\r\\n\\r\\n elif char == \\\"$\\\":\\r\\n if verb:\\r\\n latex += char\\r\\n elif math:\\r\\n latex += \\\"$\\\"\\r\\n math = False\\r\\n else:\\r\\n latex += \\\"$\\\"\\r\\n math = True\\r\\n\\r\\n elif char == \\\"*\\\":\\r\\n asterisks += 1\\r\\n elif char == \\\"~\\\":\\r\\n tildes += 1\\r\\n\\r\\n elif math or verb or (asterisks == 0 and tildes == 0):\\r\\n latex += char\\r\\n\\r\\n elif asterisks == 3:\\r\\n if bf or it:\\r\\n latex += \\\"}}\\\" + char\\r\\n asterisks = 0\\r\\n bf, it = False, False\\r\\n else:\\r\\n latex += \\\"\\\\\\\\textbf{\\\\\\\\textit{\\\" + char\\r\\n asterisks = 0\\r\\n bf, it, = True, True\\r\\n elif asterisks == 2:\\r\\n if bf:\\r\\n latex += \\\"}\\\" + char\\r\\n asterisks = 0\\r\\n bf = False\\r\\n else:\\r\\n latex += \\\"\\\\\\\\textbf{\\\" + char\\r\\n asterisks = 0\\r\\n bf = True\\r\\n elif asterisks == 1:\\r\\n if it:\\r\\n latex += \\\"}\\\" + char\\r\\n asterisks = 0\\r\\n it = False\\r\\n else:\\r\\n latex += \\\"\\\\\\\\textit{\\\" + char\\r\\n asterisks = 0\\r\\n it = True\\r\\n elif tildes == 2:\\r\\n if sout:\\r\\n latex += \\\"}\\\" + char\\r\\n tildes = 0\\r\\n sout = False\\r\\n else:\\r\\n latex += \\\"\\\\\\\\sout{\\\" + char\\r\\n tildes = 0\\r\\n sout = True\\r\\n elif tildes == 1:\\r\\n latex += \\\"~\\\" + char\\r\\n tildes = 0\\r\\n\\r\\n if bf and it:\\r\\n latex += \\\"}}\\\"\\r\\n elif bf or it:\\r\\n latex += \\\"}\\\"\\r\\n\\r\\n return latex\",\n \"def _gen_html(self):\\n yield \\\"\\\\n<table border=%r summary='a table'>\\\\n\\\" % self.border\\n header = self.header\\n for row in self:\\n yield \\\"<tr>\\\\n \\\"\\n for el in row:\\n if header:\\n yield \\\"<th>%s</th> \\\" % el\\n else:\\n yield '<td bgcolor=\\\"%s\\\">%s</td> ' % \\\\\\n (getattr(row, \\\"color\\\", self.color), el)\\n yield \\\"\\\\n</tr>\\\\n\\\"\\n header = False\\n yield \\\"</table>\\\\n\\\"\",\n \"def generate_pr_table(start_ref, end_ref):\\r\\n header = \\\"|| Merged By || Author || Title || PR || JIRA || Verified? ||\\\"\\r\\n pr_link = \\\"[#{num}|https://github.com/edx/edx-platform/pull/{num}]\\\"\\r\\n user_link = \\\"[@{user}|https://github.com/{user}]\\\"\\r\\n rows = [header]\\r\\n prbe = prs_by_email(start_ref, end_ref)\\r\\n for email, pull_requests in prbe.items():\\r\\n for i, pull_request in enumerate(pull_requests):\\r\\n try:\\r\\n pr_info = get_pr_info(pull_request)\\r\\n title = pr_info[\\\"title\\\"] or \\\"\\\"\\r\\n body = pr_info[\\\"body\\\"] or \\\"\\\"\\r\\n author = pr_info[\\\"user\\\"][\\\"login\\\"]\\r\\n except requests.exceptions.RequestException as e:\\r\\n message = (\\r\\n \\\"Warning: could not fetch data for #{num}: \\\"\\r\\n \\\"{message}\\\".format(num=pull_request, message=e.message)\\r\\n )\\r\\n print(colorize(\\\"red\\\", message), file=sys.stderr)\\r\\n title = \\\"?\\\"\\r\\n body = \\\"?\\\"\\r\\n author = \\\"\\\"\\r\\n rows.append(\\\"| {merged_by} | {author} | {title} | {pull_request} | {jira} | {verified} |\\\".format(\\r\\n merged_by=email if i == 0 else \\\"\\\",\\r\\n author=user_link.format(user=author) if author else \\\"\\\",\\r\\n title=title.replace(\\\"|\\\", \\\"\\\\|\\\"),\\r\\n pull_request=pr_link.format(num=pull_request),\\r\\n jira=\\\", \\\".join(parse_ticket_references(body)),\\r\\n verified=\\\"\\\",\\r\\n ))\\r\\n return \\\"\\\\n\\\".join(rows)\",\n \"def print_table(data_array, column_tag, row_tag, \\n print_format = \\\"1.2f\\\", \\n with_color_cell = True,\\n colormap='Greys', colorscale=0.5, colorwrap=0, col_sep='', \\n print_latex_table=True, print_text_table=True,\\n print_format_along_row=True):\\n if column_tag is None:\\n column_tag = [\\\"\\\" for data in data_array[0, :]]\\n if row_tag is None:\\n row_tag = [\\\"\\\" for data in data_array]\\n\\n # check print_format\\n if type(print_format) is not list:\\n if print_format_along_row:\\n # repeat the tag\\n print_format = [print_format for x in row_tag]\\n else:\\n print_format = [print_format for x in column_tag]\\n else:\\n if print_format_along_row:\\n assert len(print_format) == len(row_tag)\\n else:\\n assert len(print_format) == len(column_tag)\\n\\n\\n # color configuration\\n color_func = cm.get_cmap(colormap)\\n data_idx = return_valid_number_idx(data_array)\\n value_min = np.min(data_array[data_idx])\\n value_max = np.max(data_array[data_idx])\\n \\n def get_latex_color(x):\\n # return a color command for latex cell\\n return return_latex_color_cell(x, value_min, value_max, \\n colorscale, colorwrap, color_func)\\n \\n # maximum width for tags in 1st column\\n row_tag_max_len = max([len(x) for x in row_tag])\\n\\n # maximum width for data and tags for other columns\\n if print_format_along_row:\\n tmp_len = []\\n for idx, data_row in enumerate(data_array):\\n tmp_len.append(\\n max([len(\\\"{num:{form}}\\\".format(num=x, form=print_format[idx])) \\\\\\n for x in data_row]))\\n else:\\n tmp_len = []\\n for idx, data_col in enumerate(data_array.T):\\n tmp_len.append(\\n max([len(\\\"{num:{form}}\\\".format(num=x, form=print_format[idx])) \\\\\\n for x in data_col]))\\n col_tag_max_len = max([len(x) for x in column_tag] + tmp_len)\\n \\n # prepare buffer\\n text_buffer = \\\"\\\"\\n latex_buffer = \\\"\\\"\\n \\n # latex head\\n latex_buffer += r\\\"\\\\begin{tabular}{\\\" \\\\\\n + ''.join(['c' for x in column_tag + ['']]) + r\\\"}\\\" + \\\"\\\\n\\\"\\n \\n # head row\\n # for latex\\n hrow = [fill_cell(\\\"\\\", row_tag_max_len)] \\\\\\n + [fill_cell(x, col_tag_max_len) for x in column_tag]\\n latex_buffer += return_one_row_latex(hrow)\\n # for plain text (add additional separator for each column)\\n hrow = [fill_cell(\\\"\\\", row_tag_max_len, col_sep)] \\\\\\n + [fill_cell(x, col_tag_max_len, col_sep) for x in column_tag]\\n text_buffer += return_one_row_text(hrow)\\n \\n # contents\\n row = data_array.shape[0]\\n col = data_array.shape[1]\\n for row_idx in np.arange(row):\\n # row head\\n row_content_latex = [fill_cell(row_tag[row_idx], row_tag_max_len)]\\n row_content_text = [fill_cell(row_tag[row_idx],row_tag_max_len,col_sep)]\\n \\n # each column in the raw\\n for col_idx in np.arange(col):\\n\\n if print_format_along_row:\\n tmp_print_format = print_format[row_idx]\\n else:\\n tmp_print_format = print_format[col_idx]\\n\\n if is_valid_float(data_array[row_idx,col_idx]):\\n num_str = \\\"{num:{form}}\\\".format(num=data_array[row_idx,col_idx],\\n form=tmp_print_format)\\n latex_color_cell = get_latex_color(data_array[row_idx,col_idx])\\n elif type(data_array[row_idx,col_idx]) is str:\\n num_str = \\\"{num:{form}}\\\".format(num=data_array[row_idx,col_idx],\\n form=tmp_print_format)\\n latex_color_cell = ''\\n else:\\n num_str = ''\\n latex_color_cell = ''\\n \\n if not with_color_cell:\\n latex_color_cell = ''\\n \\n row_content_text.append(\\n fill_cell(num_str, col_tag_max_len, col_sep))\\n\\n row_content_latex.append(\\n fill_cell(latex_color_cell + ' ' + num_str, col_tag_max_len))\\n \\n # latex table content\\n latex_buffer += return_one_row_latex(row_content_latex)\\n # text content\\n text_buffer += return_one_row_text(row_content_text)\\n \\n latex_buffer += r\\\"\\\\end{tabular}\\\" + \\\"\\\\n\\\"\\n\\n if print_latex_table:\\n print(latex_buffer)\\n if print_text_table:\\n print(text_buffer)\\n return\",\n \"def _construct_html_table(self, df: Table) -> str:\\n string = attach_tag_tr('\\\\n'.join(map(attach_tag_th, df.columns)))\\n stringified_df = _stringify_table(df)\\n\\n for (i, row_elements) in stringified_df.iterrows():\\n # Commented out code is a bit sleaker, but restrictive\\n #string += '\\\\n' + attach_tag_tr('\\\\n'.join(map(attach_tag_td,\\n # row_elements)))\\n table_content: List = []\\n for col, val in row_elements.iteritems():\\n if col == 'cost':\\n table_content.append(attach_tag_td_rjust(val))\\n else:\\n table_content.append(attach_tag_td(val))\\n\\n string += '\\\\n' + attach_tag_tr('\\\\n'.join(table_content))\\n\\n return attach_tag_table(\\n attach_tag_caption(f'All Costs of {self.trip_title}')\\n + '\\\\n'\\n + attach_tag_tbody(string))\",\n \"def __print_work_table(table):\\n print \\\"%-5s %-30s %5s %5s %5s %5s %5s\\\" % ('Act', 'Pred', 'Block', 'Dummy', 'Succ', 'start', 'end')\\n for k, col in sorted(table.items()):\\n print \\\"%-5s %-30s %5s %5s %5s %5s %5s\\\" % tuple(\\n [str(k)] + [list(col[0])] + [str(col[i]) for i in range(1, len(col))])\",\n \"def print_table(table):\\n rest = table[1:]\\n fmt = \\\"%-28s %-9s %-16s %s\\\"\\n for row in rest:\\n print(fmt % tuple(row))\",\n \"def print_table(table, title_list):\\n\\n # your goes code\\n cols = len(title_list)\\n\\n \\n\\n table.insert(0,title_list)\\n\\n for sublist in range(len(table)):\\n if cols != len(table[sublist]):\\n print('dataset does not match number of cols')\\n quit()\\n\\n max_lenghts = []\\n maxi = -1\\n for sub_elem in range(cols): \\n maxi = -1 \\n for sublist in range(len(table)):\\n if len(table[sublist][sub_elem]) > maxi:\\n maxi = len(table[sublist][sub_elem])\\n max_lenghts.append(maxi)\\n \\n\\n \\n\\n sub_elem = 0\\n \\n for sublist in range(len(table)):\\n if sublist == 0:\\n while sub_elem < len(table[0]):\\n \\n if sub_elem == len(table[0])- 1:\\n print('\\\\033[1;37;41m| {:^25} |'.format(table[sublist][sub_elem]), end =\\\"\\\")\\n else:\\n print('\\\\033[1;37;41m| {:^25} '.format(table[sublist][sub_elem]), end =\\\"\\\")\\n sub_elem += 1\\n \\n print('\\\\033[0;32;48m\\\\n') \\n sub_elem = 0 \\n else:\\n while sub_elem < len(table[0]):\\n \\n if sub_elem == len(table[0])- 1:\\n print('\\\\033[0;37;44m| {:^25} |'.format(table[sublist][sub_elem]), end =\\\"\\\")\\n else:\\n print('\\\\033[0;37;44m| {:^25} '.format(table[sublist][sub_elem]), end =\\\"\\\")\\n sub_elem += 1\\n \\n print('\\\\033[0;32;48m\\\\n') \\n sub_elem = 0 \\n print('\\\\033[0;37;48m\\\\n')\\n table.pop(0)\",\n \"def pretty_output(associations, population):\\n\\tcolumn_names = ['ld_snp_rsID', 'chrom', 'pos', 'GRCh38_chrom', 'GRCh38_pos', 'afr', 'amr', 'eas', 'eur', 'sas', 'gnomad', 'gnomad_sas', 'gnomad_oth', 'gnomad_asj', 'gnomad_nfe', 'gnomad_afr', 'gnomad_amr', 'gnomad_fin', 'gnomad_eas','gene_symbol', 'gene_id', 'gene_chrom', 'gene_tss', 'GRCh38_gene_chrom', 'GRCh38_gene_pos', 'disease_name', 'disease_efo_id', 'score', 'rank', 'r2', 'cluster_id', 'gwas_source', 'gwas_snp', 'gwas_pvalue', 'gwas_pvalue_description', 'gwas_odds_ratio', 'gwas_odds_ratio_ci_start', 'gwas_odds_ratio_ci_end', 'gwas_beta', 'gwas_size', 'gwas_pmid', 'gwas_study', 'gwas_reported_trait', 'ls_snp_is_gwas_snp', 'vep_terms', 'vep_sum', 'vep_mean'] + [\\\"GTEx_\\\" + tissue_name for tissue_name in postgap.Globals.ALL_TISSUES] + [source.display_name for source in postgap.Cisreg.sources + postgap.Reg.sources]\\n\\tif postgap.Globals.PERFORM_BAYESIAN: \\n\\t\\tcolumn_names += [tissue_name + \\\"_CLPP\\\" for tissue_name in postgap.Globals.ALL_TISSUES]\\n\\theader = \\\"\\\\t\\\".join(column_names).encode('utf-8')\\n\\tcontent = filter(lambda X: len(X) > 0, [pretty_cluster_association(association, population) for association in associations])\\n\\treturn \\\"\\\\n\\\".join([header] + content)\",\n \"def make_table_header(table_file, function_list):\\n\\tstring = '%10s' %(\\\" \\\")\\n\\tfor function in function_list:\\n\\t\\tstring += \\\"\\\\t\\\"+'%32s' %(\\\"f_\\\"+str(function-1))\\n\\ttable_file.write(string+\\\"\\\\n\\\")\\n\\tstring = '%10s' %(\\\" \\\")+ \\\"\\\\t\\\" + \\\"------------\\\"*4*len(function_list)\\n\\ttable_file.write(string+\\\"\\\\n\\\")\\n\\treturn None\",\n \"def generate_table(input_file, delim=\\\",\\\", header=True):\\n input_file = os.path.abspath(input_file)\\n if not os.path.exists(input_file):\\n sys.exit(\\\"%s does not exist.\\\" % input_file)\\n\\n # Read in rows with user specified delimiter\\n rows = read_rows(input_file, delim=delim)\\n\\n # Generate tabulars expected format\\n labels = [\\\"column %s\\\" % x for x in range(len(rows[0]))]\\n if header:\\n labels = rows.pop(0)\\n\\n # Generate Tabular table to output\\n table = Tabular(\\n # Note that columns are specified here, so we provide a row (list) later\\n columns=labels,\\n style=dict(\\n header_=dict(bold=True, transform=str.upper),\\n # Default styling could be provided from some collection of styling files\\n default_=dict(\\n color=dict(\\n lookup={\\n \\\"Trix\\\": \\\"green\\\",\\n \\\"110\\\": \\\"red\\\",\\n \\\"100\\\": \\\"green\\\", # since no grey for now\\n }\\n )\\n ),\\n ),\\n )\\n\\n # Add row to table. If columns aren't specified on init, provide dict here\\n for row in rows:\\n table(row)\",\n \"def write_latex_table(machine, all_benchs, summary, diff, skipped, tex_file, num_splits,\\n with_preamble=False, longtable=False, diff_vms=[]):\\n\\n num_benchmarks = len(all_benchs)\\n all_vms = sorted(summary.keys())\\n num_vms = len(summary)\\n\\n # decide how to lay out the splits\\n num_vms_rounded = int(math.ceil(num_vms / float(num_splits)) * num_splits)\\n vms_per_split = int(num_vms_rounded / float(num_splits))\\n splits = [[] for x in xrange(num_splits)]\\n vm_num = 0\\n split_idx = 0\\n for vm_idx in xrange(num_vms_rounded):\\n if vm_idx < len(all_vms):\\n vm = all_vms[vm_idx]\\n else:\\n vm = None\\n splits[split_idx].append(vm)\\n vm_num += 1\\n if vm_num % vms_per_split == 0:\\n split_idx += 1\\n\\n with open(tex_file, 'w') as fp:\\n if with_preamble:\\n fp.write(preamble(TITLE))\\n if diff_vms:\\n fp.write('\\\\\\\\centering{%%\\\\n\\\\\\\\Large{\\\\\\\\textbf{%s vs. %s}}%%\\\\n}\\\\n\\\\\\\\\\\\\\\\\\\\n~\\\\\\\\\\\\\\\\\\\\n\\\\n'\\n % (diff_vms[0], diff_vms[1]))\\n legends = get_latex_symbol_map() + ' \\\\\\\\\\\\\\\\ ' + legend()\\n fp.write('\\\\\\\\centering %s' % legends)\\n fp.write('\\\\n\\\\n\\\\n')\\n if not longtable:\\n fp.write('\\\\\\\\begin{landscape}\\\\n')\\n fp.write('\\\\\\\\begin{table*}[hptb]\\\\n')\\n fp.write('\\\\\\\\vspace{.8cm}\\\\n')\\n fp.write('\\\\\\\\begin{adjustbox}{totalheight=12.4cm}\\\\n')\\n # Emit table header.\\n heads1 = TABLE_HEADINGS_START1 + '&'.join([TABLE_HEADINGS1] * num_splits)\\n heads2 = TABLE_HEADINGS_START2 + '&'.join([TABLE_HEADINGS2] * num_splits)\\n heads = '%s\\\\\\\\\\\\\\\\%s' % (heads1, heads2)\\n if longtable:\\n fp.write(start_longtable(TABLE_FORMAT, heads))\\n else:\\n fp.write(start_table(TABLE_FORMAT, heads))\\n split_row_idx = 0\\n for row_vms in zip(*splits):\\n bench_idx = 0\\n skipped_before = [b for (b, v) in skipped[SKIPPED_BEFORE] if v == row_vms[0]]\\n skipped_after = [b for (b, v) in skipped[SKIPPED_AFTER] if v == row_vms[0]]\\n for bench in sorted(all_benchs + skipped_before + skipped_after):\\n row = []\\n for vm in row_vms:\\n if vm is None:\\n continue # no more results\\n try:\\n this_summary = summary[vm][bench]\\n except KeyError:\\n if bench in skipped_before or bench in skipped_after:\\n classification = '\\\\\\\\emph{Skipped}'\\n else:\\n classification = ''\\n last_cpt = BLANK_CELL\\n time_steady = BLANK_CELL\\n last_mean = BLANK_CELL\\n steady_iter_var = BLANK_CELL\\n steady_time_var = BLANK_CELL\\n else:\\n if vm in diff and bench in diff[vm]:\\n classification = colour_tex_cell(diff[vm][bench][CLASSIFICATIONS], this_summary['style'])\\n last_cpt = colour_tex_cell(diff[vm][bench][STEADY_ITER], this_summary['last_cpt'])\\n steady_iter_var = colour_tex_cell(diff[vm][bench][STEADY_ITER_VAR], this_summary['steady_iter_var'])\\n time_steady = colour_tex_cell(diff[vm][bench][STEADY_ITER], this_summary['time_to_steady_state'])\\n last_mean = colour_tex_cell(diff[vm][bench][STEADY_STATE_TIME], this_summary['last_mean'])\\n steady_time_var = colour_tex_cell(diff[vm][bench][STEADY_STATE_TIME_VAR], this_summary['steady_time_var'])\\n else:\\n classification = this_summary['style']\\n last_cpt = this_summary['last_cpt']\\n steady_iter_var = this_summary['steady_iter_var']\\n time_steady = this_summary['time_to_steady_state']\\n last_mean = this_summary['last_mean']\\n steady_time_var = this_summary['steady_time_var']\\n classification = '\\\\\\\\multicolumn{1}{l}{%s}' % classification\\n if classification == STYLE_SYMBOLS['flat']:\\n last_cpt = BLANK_CELL\\n time_steady = BLANK_CELL\\n if last_cpt == '':\\n last_cpt = BLANK_CELL\\n if time_steady == '':\\n time_steady = BLANK_CELL\\n if last_mean == '':\\n last_mean = BLANK_CELL\\n\\n if bench_idx == 0:\\n if num_benchmarks == 10:\\n fudge = 4\\n elif num_benchmarks == 12:\\n fudge = 5\\n else:\\n fudge = 0\\n vm_cell = '\\\\\\\\multirow{%s}{*}{\\\\\\\\rotatebox[origin=c]{90}{%s}}' \\\\\\n % (num_benchmarks + fudge, vm)\\n else:\\n vm_cell = ''\\n row_add = [BLANK_CELL, vm_cell, classification, last_cpt,\\n steady_iter_var, time_steady, last_mean, steady_time_var]\\n if not row: # First bench in this row, needs the vm column.\\n if vm in diff and bench in diff[vm]:\\n bname = colour_tex_cell(diff[vm][bench][INTERSECTION], bench)\\n else:\\n bname = bench\\n row.insert(0, escape(bname))\\n row.extend(row_add)\\n vm_idx += 1\\n fp.write('&'.join(row))\\n # Only -ve space row if not next to a midrule\\n if not longtable and bench_idx < num_vms - 1:\\n fp.write('\\\\\\\\\\\\\\\\[-3pt] \\\\n')\\n else:\\n fp.write('\\\\\\\\\\\\\\\\ \\\\n')\\n bench_idx += 1\\n if split_row_idx < vms_per_split - 1:\\n if longtable:\\n fp.write('\\\\\\\\hline\\\\n')\\n else:\\n fp.write('\\\\\\\\midrule\\\\n')\\n split_row_idx += 1\\n if longtable:\\n fp.write(end_longtable())\\n else:\\n fp.write(end_table())\\n if with_preamble:\\n if not longtable:\\n fp.write('\\\\\\\\end{adjustbox}\\\\n')\\n fp.write('\\\\\\\\end{table*}\\\\n')\\n fp.write('\\\\\\\\end{landscape}\\\\n')\\n fp.write(end_document())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6535216","0.6499372","0.6494908","0.6360739","0.6058271","0.605461","0.60403585","0.6008605","0.59048015","0.5904452","0.59012544","0.5884655","0.58371955","0.5809221","0.5790608","0.57705444","0.576766","0.57066625","0.5678048","0.56749755","0.5656739","0.5630516","0.5629062","0.5610359","0.5570405","0.55583566","0.5536824","0.551351","0.55069685","0.54920805","0.5481429","0.5467969","0.5467777","0.5455088","0.5444563","0.5444064","0.54353744","0.5432484","0.5429779","0.54130584","0.5395848","0.5387956","0.5380219","0.53724164","0.53724164","0.536492","0.535319","0.5350853","0.5345034","0.5313478","0.5293298","0.5284309","0.5265728","0.5261547","0.5255508","0.5249018","0.5242105","0.5239003","0.5238933","0.5232297","0.52309227","0.52295715","0.52258873","0.521663","0.5216516","0.5215366","0.5214292","0.52117944","0.52036315","0.52032846","0.5196517","0.5195829","0.51923305","0.5184104","0.5181042","0.51756436","0.51716167","0.51701313","0.5165314","0.5157185","0.51560706","0.5155845","0.5151929","0.5148572","0.5147132","0.51470166","0.5141462","0.5140087","0.51387143","0.5135818","0.51350594","0.5131044","0.5126564","0.51241624","0.51227957","0.5118773","0.51086295","0.51025635","0.5096706","0.50922436"],"string":"[\n \"0.6535216\",\n \"0.6499372\",\n \"0.6494908\",\n \"0.6360739\",\n \"0.6058271\",\n \"0.605461\",\n \"0.60403585\",\n \"0.6008605\",\n \"0.59048015\",\n \"0.5904452\",\n \"0.59012544\",\n \"0.5884655\",\n \"0.58371955\",\n \"0.5809221\",\n \"0.5790608\",\n \"0.57705444\",\n \"0.576766\",\n \"0.57066625\",\n \"0.5678048\",\n \"0.56749755\",\n \"0.5656739\",\n \"0.5630516\",\n \"0.5629062\",\n \"0.5610359\",\n \"0.5570405\",\n \"0.55583566\",\n \"0.5536824\",\n \"0.551351\",\n \"0.55069685\",\n \"0.54920805\",\n \"0.5481429\",\n \"0.5467969\",\n \"0.5467777\",\n \"0.5455088\",\n \"0.5444563\",\n \"0.5444064\",\n \"0.54353744\",\n \"0.5432484\",\n \"0.5429779\",\n \"0.54130584\",\n \"0.5395848\",\n \"0.5387956\",\n \"0.5380219\",\n \"0.53724164\",\n \"0.53724164\",\n \"0.536492\",\n \"0.535319\",\n \"0.5350853\",\n \"0.5345034\",\n \"0.5313478\",\n \"0.5293298\",\n \"0.5284309\",\n \"0.5265728\",\n \"0.5261547\",\n \"0.5255508\",\n \"0.5249018\",\n \"0.5242105\",\n \"0.5239003\",\n \"0.5238933\",\n \"0.5232297\",\n \"0.52309227\",\n \"0.52295715\",\n \"0.52258873\",\n \"0.521663\",\n \"0.5216516\",\n \"0.5215366\",\n \"0.5214292\",\n \"0.52117944\",\n \"0.52036315\",\n \"0.52032846\",\n \"0.5196517\",\n \"0.5195829\",\n \"0.51923305\",\n \"0.5184104\",\n \"0.5181042\",\n \"0.51756436\",\n \"0.51716167\",\n \"0.51701313\",\n \"0.5165314\",\n \"0.5157185\",\n \"0.51560706\",\n \"0.5155845\",\n \"0.5151929\",\n \"0.5148572\",\n \"0.5147132\",\n \"0.51470166\",\n \"0.5141462\",\n \"0.5140087\",\n \"0.51387143\",\n \"0.5135818\",\n \"0.51350594\",\n \"0.5131044\",\n \"0.5126564\",\n \"0.51241624\",\n \"0.51227957\",\n \"0.5118773\",\n \"0.51086295\",\n \"0.51025635\",\n \"0.5096706\",\n \"0.50922436\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8194145"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19593,"cells":{"query":{"kind":"string","value":"Take replay file from request Extract basic information about replay Return information to client or tell client data is invalid"},"document":{"kind":"string","value":"async def accept_replay(request, game):\n game = game.lower()\n replay_file = request.files.get('replay')\n if replay_file:\n if game == STARCRAFT:\n load_map = request.args.get(\"load_map\", False)\n result = await SC2Replay.analyze_replay(replay_file, load_map)\n if result:\n data = {\"result\": result,\n \"game\": STARCRAFT,\n \"success\": True}\n return sanic.response.json(data, status=200)\n else:\n data = {\n \"error\": \"Unable to parse game file.\",\n \"success\": False,\n \"game\": game\n }\n return sanic.response.json(data, status=500)\n else:\n data = {\n \"error\": \"Game not in list of games.\",\n \"success\": False,\n \"game\": game\n }\n return sanic.response.json(data, status=404)\n else:\n data = {\n \"error\": \"No replay file given.\",\n \"success\": False,\n \"game\": game\n }\n return sanic.response.json(data, status=500)"},"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 parse_replay(request, game):\n\n game = game.lower()\n replay_file = request.files.get(\"replay\")\n if replay_file:\n if game == STARCRAFT:\n basic, result = await SC2Replay.process_replay(replay_file, request.args.get(\"load_map\", False))\n if result:\n # Lets create our db entry\n\n basic['private_replay'] = request.args.get('private_replay', False)\n replay_id = str(uuid.uuid4())\n basic[\"_id\"] = replay_id\n print(replay_id)\n unique_name = \".\".join([replay_id, \"SC2Replay\"])\n basic[\"replay_object_name\"] = unique_name\n basic[\"game_name\"] = request.args.get(\"replay_name\", datetime.utcnow())\n try:\n success = await request.app.object_storage.add_object(request.app.config.OS_CONTAINER, replay_file, unique_name)\n if success:\n # push results to mongoDB\n mongo = request.app.mongodb\n # Insert the basic information for the replay\n await mongo.starcraft_2_replays.info.insert_one(basic)\n # Insert event data\n events = dict(result['event'])\n events.update(basic)\n print(events)\n await mongo.starcraft_2_replays.replay_events.insert_one(events)\n # Insert stats data\n stats = dict(result['stats'])\n stats.update(basic)\n await mongo.starcraft_2_replays.replay_stats.insert_one(stats)\n\n return sanic.response.json(basic)\n except (swift.BluemixSwiftUnavailableError,\n swift.BluemixSwiftAuthenticationError,\n swift.BluemixSwiftRequestTimeoutError,\n bson.errors.InvalidDocument,\n pymongo.errors.ConnectionFailure):\n\n traceback.print_exc()\n data = {\n \"error\": \"Internal Server Error\",\n \"success\": False,\n \"game\": STARCRAFT\n }\n return sanic.response.json(data)","def parse_replaydata(self):\n pass","def recv_read_response(self, recv_payload): \n\t#Only unpack the headers because we want to store the file data as binary\n\tunpacked_payload = struct.unpack('!H3IQ', recv_payload[:22])\n\tstatus = unpacked_payload[0:1][0]\n\tepoch_no = unpacked_payload[1:2][0]\n\thandle_no = unpacked_payload[2:3][0]\t\n\t\n\t#Check that file handle is the same, to make sure it is the same file request.\n\tif (self.epoch_no == epoch_no and self.handle_no == handle_no):\n\t start_position = unpacked_payload[3:4][0]\n\t num_bytes_been_read = unpacked_payload[4:5][0] \n\t # If we receive less bytes than the number we requested to read, this means that\n\t # end of file has been reached\n\t if (num_bytes_been_read < self.NUM_BYTES_TO_READ):\n\t\tself.eof = True\n\t data_to_write = recv_payload[22:]\t \n\t #If status field says that response contains real data: Append to file. Otherwise react \n\t #depending on error code received.\n\t #Status 00 = OK\n\t #Status 01 = Epoch no. of file handle doesnt match epoch no. of current invocation\n\t #Status 10 = No context found for file-handle and no data has been read\n\t #Status 11 = Context could be found but start position out of range\n\t if (status == 0b00):\n\t\tself.file_append.seek(start_position)\n\t\tself.file_append.write(data_to_write)\n\t elif (status == 0b01):\n\t\tprint(\"Error: Epoch no. of file handle doesnt match epoch no. of current invocation\")\n\t\tsys.exit()\n\t elif (status == 0b10):\n\t\tprint(\"Error: No context found for file-handle and no data has been read\")\n\t\tsys.exit()\n\t elif(status == 0b11):\n\t\tprint(\"Error: Context could be found but start position out of range\")\n\t\tsys.exit()\n\telse:\n\t print(\"Error: File handle does not match file handle stored in client. Wrong file received.\")\n\t sys.exit() \t \n\t#Then return control to read_service_loop() method so that next iteration of send_read_request \n\t#from new start position is called.\n return","def decode_replay(replay_file_obj):\n decoder = zstd.ZstdDecompressor()\n # Rewind to the beginning of the file obj, because\n # gcloud might have read it first\n replay_file_obj.seek(0)\n replay_data = replay_file_obj.read()\n try:\n decoded_data = decoder.decompress(replay_data)\n json_data = json.loads(decoded_data.decode('utf-8').strip())\n return json_data\n except zstd.ZstdError:\n # The replay file can't be decoded.\n return None\n finally:\n # Seek the replay file back to start so we can upload it.\n replay_file_obj.seek(0)","def recv_open_response(self, recv_payload):\n\n\tunpacked_payload = struct.unpack(\"!?Q2I\", recv_payload)\n # Read status field. If set to False, ignore remaining fields and \n\t# generate error msg (file not found) before exiting. \n\t# Each unpacked value is a tuple, so [0] accesses the value that we want\n\tstatus = unpacked_payload[0:1][0]\n\tif status == False:\n\t print \"Error: File not found.\"\n\t sys.exit()\n\t\n\t#If set to True, read remaining fields.\n\telif status == True:\n\t print(\"File found.\")\n\t self.file_length = unpacked_payload[1:2][0]\n\t self.epoch_no = unpacked_payload[2:3][0]\n\t self.handle_no = unpacked_payload[3:][0]\t \t \n\treturn","def processReadback(resp):\n a = np.fromstring(resp, dtype='<u1')\n return {\n 'build': a[51],\n 'serDAC': a[56],\n 'noPllLatch': bool((a[58] & 0x80) > 0),\n 'ackoutI2C': a[61],\n 'I2Cbytes': a[69:61:-1],\n 'executionCounter': (a[53] << 8) + a[52]\n }","def handle(self):\n\t\ttry:\n\t\t\trequest_line = self.rfile.readline().decode(\"ascii\")\n\t\t\tassert request_line.endswith(\"\\r\\n\"), \"Request line must end in CRLF\"\n\t\t\tparts = request_line.strip().split()\n\t\t\tassert len(parts)==3, \"Invalid request line\"\n\t\t\thost, path, content_length = parts\n\t\t\tif (content_length:=int(content_length))>0:\n\t\t\t\tdata = self.rfile.read(content_length)\n\t\t\telse:\n\t\t\t\tdata = b''\n\t\t\tself.handle_request(host,path,data)\n\t\texcept AssertionError as e:\n\t\t\tself.response_code(4,e.args[0])","def __read(self, bytes=31):\n raw_data = self.file_read.read(bytes)\n response = self.__get_response(raw_data)\n is_valid, error_code = self.__is_response_valid(response)\n if is_valid:\n char_list = self.__handle_raspi_glitch(response[1:])\n return str(''.join(char_list)), is_valid\n else:\n return error_code, is_valid","def get_response():\n result = ''\n line = ''\n while line != '\\n':\n result += line\n line = FROMFILE.readline()\n #print(\" I read line:[\"+line+\"]\")\n return result","def _get_data(self):\n response = self._get_raw_data()\n if response is None:\n # error has already been logged\n return None\n\n if response.startswith('ERROR'):\n self.error(\"received ERROR\")\n return None\n\n try:\n parsed = response.split(\"\\n\")\n except AttributeError:\n self.error(\"response is invalid/empty\")\n return None\n\n # split the response\n data = {}\n for line in parsed:\n if line.startswith('STAT'):\n try:\n t = line[5:].split(' ')\n data[t[0]] = t[1]\n except (IndexError, ValueError):\n self.debug(\"invalid line received: \" + str(line))\n pass\n\n if len(data) == 0:\n self.error(\"received data doesn't have any records\")\n return None\n\n # custom calculations\n try:\n data['avail'] = int(data['limit_maxbytes']) - int(data['bytes'])\n data['used'] = int(data['bytes'])\n except:\n pass\n\n return data","def parse_replay(replay):\n if replay is None:\n return None\n\n stats = GameStat(replay[\"num_players\"])\n stats.turns_total = len(replay['frames']) - 1\n for frame in replay[\"frames\"]:\n for event in frame.get(\"events\", []):\n player_tag = event[\"entity\"].get(\"owner\")\n if event[\"event\"] == \"spawned\":\n stats.ships_produced += 1\n stats.players[player_tag].ships_produced += 1\n elif event[\"event\"] == \"destroyed\":\n if event[\"entity\"][\"type\"] == \"ship\":\n stats.ships_destroyed += 1\n elif event[\"entity\"][\"type\"] == \"planet\":\n stats.planets_destroyed += 1\n if player_tag:\n stats.players[player_tag].planets_destroyed += 1\n elif event[\"event\"] == \"attack\":\n stats.players[player_tag].attacks_total += 1\n\n ships_alive_total = sum([len(ships) for ships in replay[\"frames\"][-1][\"ships\"].values()])\n for player_tag in stats.players.keys():\n stats.players[player_tag].ships_alive = len(replay[\"frames\"][-1][\"ships\"][str(player_tag)])\n # use max(1.0, ...) to avoid ZeroDivisionError\n stats.players[player_tag].ships_alive_ratio = 1.0 * stats.players[player_tag].ships_alive / max(1.0, stats.players[player_tag].ships_produced)\n stats.players[player_tag].ships_relative_ratio = 1.0 * stats.players[player_tag].ships_alive / max(1.0, ships_alive_total)\n\n for planet in replay[\"frames\"][-1][\"planets\"].values():\n if planet[\"owner\"] is not None:\n stats.players[planet[\"owner\"]].planets_controlled += 1\n\n return stats","def canned_ims2_response():\n return file_utils.response_file_to_json(test_data_dir + '/waveform_41177893.1')","def cmd_get(self, msg_dict):\r\n filename = msg_dict[\"filename\"]\r\n filename_abs_path = \"%s\" % msg_dict[\"current_directory\"] + \"/\" + filename\r\n # print(\"in the cmd_get server:\", filename_abs_path)\r\n if os.path.isfile(filename_abs_path):\r\n # print(\"@@@@\")\r\n file_size = os.stat(filename_abs_path).st_size\r\n self.request.send(str(file_size).encode())\r\n reply = self.request.recv(1024)\r\n server_md5 = hashlib.md5()\r\n if reply:\r\n with open(filename_abs_path, 'rb') as f:\r\n for line in f:\r\n self.request.send(line)\r\n server_md5.update(line)\r\n server_md5_value = server_md5.hexdigest()\r\n replay2 = self.request.recv(1024)\r\n # print(\"in the cmd_get server:\", replay2)\r\n self.request.send(server_md5_value.encode())","def check_file():\n #print('request=', request)\n #print('request.data=', request.data)\n #print('request.form=', request.form)\n #print('request.files=', request.files)\n #print('request.json=', request.json)\n qdata = None\n adata = None\n Q = None\n A = None\n if request.json:\n qdata = request.json.get('Q')\n adata = request.json.get('A')\n if 'Qfile' in request.files:\n qdata = request.files['Qfile'].read().decode('utf-8')\n if 'Afile' in request.files:\n adata = request.files['Afile'].read().decode('utf-8')\n\n #print('qdata\\n', qdata)\n #print('adata\\n', adata)\n try:\n if qdata:\n Q = adc2019.read_Q(qdata)\n if adata:\n A = adc2019.read_A(adata)\n if Q is None and A is None:\n return jsonify({'check_file': 'No data'})\n if Q is None:\n return jsonify({'check_file': 'A-ok'})\n if A is None:\n return jsonify({'check_file': 'Q-ok'})\n\n info = adc2019.check_data(Q, A)\n #print(info)\n info2 = info.copy()\n for k in ['count', 'corner', 'line_length', 'line_corner', 'ban_data_F']:\n info2[k] = str(info2[k])\n info2['check_file'] = 'ok'\n return jsonify(info2)\n except Exception as e:\n #traceback.print_exc()\n errinfo = ['ADC2019 rule violation'] + [str(i) for i in e.args]\n info = {'error': errinfo, 'stack_trace': traceback.format_exc()}\n return jsonify(info)\n\n return jsonify({'check_file': 'ok',\n 'value': 1234567,\n 'msg': '生麦生米生卵'})","def request_ims_data():\n input_check_results = input_validation.check_inputs(request)\n if input_check_results is not None:\n return error_response(input_check_results) \n\n time_range = request.json['timeRange']\n station = request.json['station']\n\n # Max msg_id size = 20 characters, so a UUID is too long; instead generate a random 20 digit number for\n # the random message id to represent this request and its associated response\n msg_id = str(random.randint(1,99999999999999999999))\n [request_filename, request_file_path, response_filename, response_file_path] = file_utils.create_filenames(msg_id, base_dir)\n \n # Write request to a file \n request_text = file_utils.create_waveform_request(msg_id, time_range, station) \n request_file = open(request_file_path, 'w')\n request_file.write(request_text)\n request_file.close()\n\n # Calling via subprocess is a little icky, but if we call this way:\n # nms_client.batch.batchclient.bootstrap_run()\n # then we have to manually mess with sys.argv to put the filename in the command line arguments. This is unlikely\n # to hold up well with multiple requests running simultaneously\n subprocess_output = subprocess.check_output(['/ims2/nms_client/bin/nms_client.sh', '-d', base_dir, '-f', response_filename, request_file_path])\n os.remove(request_file_path)\n\n # Read response file if it was written. \n if os.path.isfile(response_file_path):\n response_json = file_utils.response_file_to_json(response_file_path) \n os.remove(response_file_path) \n return response_json\n # No response file means there was some sort of error; return the output from calling the client. \n else:\n return error_response(subprocess_output)","def parse_mochad_line(self, line):\n # bail out unless it's an incoming RFSEC message\n if line[15:23] == 'Rx RFSEC':\n\n # decode receive RFSEC message. format is either:\n # 09/22 15:39:07 Rx RFSEC Addr: 21:26:80 Func: Contact_alert_min_DS10A\n # ~ or ~\n # 09/22 15:39:07 Rx RFSEC Addr: 0x80 Func: Motion_alert_SP554A\n line_list = line.split(' ')\n addr = line_list[5]\n func = line_list[7]\n\n func_dict = self.decode_func(func)\n\n return addr, {'func': func_dict}, 'security'\n\n# elif line[15:23] == 'Tx RFSEC':\n\n # decode send RFSEC message. format is either:\n # 09/22 15:39:07 Tx RFSEC Addr: 21:26:80 Func: Contact_alert_min_DS10A\n # ~ or ~\n # 09/22 15:39:07 Tx RFSEC Addr: 0x80 Func: Motion_alert_SP554A\n# line_list = line.split(' ')\n# addr = line_list[5]\n# func = line_list[7]\n#\n# func_dict = self.decode_func(func)\n#\n# return addr, {'func': func_dict}, 'trigger'\n\n elif line[15:20] == 'Rx RF':\n\n # decode receive RF message. format is:\n # 02/13 23:54:28 Rx RF HouseUnit: B1 Func: On\n line_list = line.split(' ')\n house_code = line_list[5];\n house_func = line_list[7]\n\n return house_code, {'func': house_func}, 'radio'\n\n elif line[15:20] == 'Rx PL':\n \n # decode receive PL message. format is:\n # 02/13 23:54:28 Rx PL HouseUnit: A1\n # 02/13 23:54:28 Rx PL House: A Func: On\n line_list = line.split(' ')\n if line[21:27] == 'HouseU':\n house_code = line_list[5]\n with open ('/root/.house_code', 'wb') as f:\n pickle.dump(house_code, f)\n else:\n house_func = line_list[7]\n with open ('/root/.house_code', 'rb') as f:\n house_code = pickle.load(f)\n return house_code, {'func': house_func}, 'powerline'\n \n elif line[15:20] == 'Tx PL':\n \n # decode send RF/PL message. format is:\n # 02/13 23:54:28 Tx PL HouseUnit: A1\n # 02/13 23:54:28 Tx PL House: A Func: On\n line_list = line.split(' ')\n if line[21:27] == 'HouseU':\n house_code = line_list[5]\n with open ('/root/.house_code', 'wb') as f:\n pickle.dump(house_code, f)\n else:\n house_func = line_list[7]\n with open ('/root/.house_code', 'rb') as f:\n house_code = pickle.load(f)\n return house_code, {'func': house_func}, 'button'\n \n return '', ''","def FILE_RTRV_record(self):\n eor = 0\n raw_record = []\n #record = []\n # record is a list of non-empty strings, \n # each string is a line of info from the reader\n word = ''\n linebuf = []\n while len(raw_record) < 1 and self.eof == 0 and eor == 0 :\n # we need to have something to reply.. \n while 1:\n # read 1 char at a time \n # until we get to the '>' prompt\n #\n c = self.tf.read(1)\n #\n #print c,\n #\n if len(c) != 1:\n self.eof = 1\n if self.debug > 2 :\n print \"FILE Raw Record: \",\n pprint.pprint(raw_record)\n return raw_record\n elif c == '>':\n eor = 1\n if self.debug > 2 :\n print \"FILE2 Raw Record: \",\n pprint.pprint(raw_record)\n return raw_record\n # \\r = CR , \\n = LF \n # (serial device uses CR + optionally LF, unix text only uses LF)\n # - new array entry but only if there is something to add \n elif c == '\\r' or c == '\\n':\n if word != '':\n linebuf.append(word)\n word = ''\n if linebuf != []:\n raw_record.append(linebuf)\n linebuf = []\n # split line into words\n elif c == ' ':\n if word != '':\n linebuf.append(word)\n word = ''\n # all other chars\n else : \n word = word + c\n \n time.sleep(0.001)","def get_server_logs(self):\n self.response.content\n binary_body = re.split('--==.*==', self.response.content)[2].split('\\r\\n')[5]\n\n f = StringIO.StringIO()\n f.write(bytearray(binary_body))\n\n memory_zip = ZipFile(f)\n zip_content = {name: memory_zip.read(name) for name in memory_zip.namelist()}\n oracc_log = zip_content['oracc.log']\n request_log = zip_content['request.log']\n\n # Check if server returns a lemmatised file\n autolem = None \n for key, value in zip_content.iteritems():\n if key.endswith(\"autolem.atf\"):\n autolem = value\n\n print zip_content.keys()\n print \"@\"*30\n print oracc_log\n print \"@\"*30\n print request_log\n print \"@\"*30\n if autolem:\n print autolem\n print \"@\"*30\n\n return oracc_log, request_log, autolem","def get_req_data(self, data):\n self.rdata=data\n if self.rdata == 1 or \\\n self.rdata == 0 or \\\n self.rdata == \"\":\n return 1\n rt=\"\"\n for ot in self.rdata.split('\\n'):\n ot = ot.rstrip('\\r\\n')\n logging.debug(\">>>>>>> Check value of output string: %s\" % ot)\n if not (ot.startswith(\"vRA \") or \\\n ot.startswith(\"JRE Version:\") or \\\n ot.startswith(\"CloudClient is \") or \\\n ot.startswith(\"true\") or \\\n len(ot) == 0):\n if ot.startswith(\"Error\"):\n print (\"Error: %s\" % ot.split('Error')[-1])\n sys.stdout.flush()\n sys.exit(1)\n else:\n logging.debug(\"SR# %s\" % ot)\n rt=ot\n break\n if not rt:\n print \"No service request is found: %s\" % rt\n return 1\n else:\n return rt","def view_full_response(line):\n reqs = yield load_reqlist(line)\n for req in reqs:\n if req.response:\n if len(reqs) > 1:\n print '-'*15 + (' %s ' % req.reqid) + '-'*15\n view_full_message(req.response)\n else:\n print \"Request %s does not have a response\" % req.reqid","def intercept_extractor(player_name, match_id, filepath, start_id=0):\n\n # define file path\n path = filepath + str(match_id) + \".json\"\n\n # read events json into a dataframe\n df_events = pd.read_json(path)\n\n # initiate an empty dictionary to store intercept values\n intercept_dict = defaultdict(dict)\n\n intercept_id = start_id\n\n # iterate through the events and extract intercept information specific to the player\n for i in range(len(df_events)):\n try:\n if (df_events.player[i][\"name\"]) == player_name:\n if df_events.type[i][\"name\"] == \"Interception\":\n\n # create a new entry in the dictionary with the key being the id\n\n # add event index to dictionary\n intercept_dict[intercept_id][\"index\"] = df_events.index[i + 1]\n\n # get the game state at the event index\n intercept_dict[intercept_id][\"gamestate\"] = game_state_extractor(\n df_events.index[i + 1],\n match_id=match_id,\n player_name=player_name,\n filepath=filepath,\n )\n\n # reading match_id\n intercept_dict[intercept_id][\"match_id\"] = match_id\n\n intercept_dict[intercept_id][\"start_X\"] = df_events.location[i][0]\n\n intercept_dict[intercept_id][\"start_Y\"] = df_events.location[i][1]\n\n intercept_dict[intercept_id][\"outcome\"] = df_events.iloc[i, :][\n \"interception\"\n ][\"outcome\"][\"name\"]\n\n intercept_id += 1\n except:\n pass\n\n return intercept_dict","def file_check( self ):\n try:\n file_handler = open( self.PATH_TO_SOURCE_FILE )\n log.info( 'annex requests found' )\n except Exception, e:\n message = 'no annex requests found; quitting\\n\\n'\n log.info( message )\n sys.exit( message )\n utf8_data = file_handler.read()\n assert type(utf8_data) == str, type(utf8_data)\n data = utf8_data.decode( 'utf-8' )\n return data","def readrecf(filename):\n\n rec_dict = {}\n with open(filename,'r') as recfile:\n line_tmp = \"\"\n while 1:\n if line_tmp == \"\":\n line = recfile.readline()\n else:\n line = line_tmp\n line_tmp = \"\"\n \n if line == \"\": # if End Of File\n break\n elif line == \"\\n\": # if blank line\n continue\n elif \"Catch\" in line:\n ind = line.find('=')\n rec_dict['iscatch'] = line[ind+1:]\n elif \"Chans\" in line:\n ind = line.find('=')\n rec_dict['num_channels'] = int(line[ind+1:])\n elif \"ADFREQ\" in line:\n ind = line.find('=')\n try:\n rec_dict['sample_freq'] = int(line[ind+1:])\n except ValueError:\n rec_dict['sample_freq'] = float(line[ind+1:])\n elif \"Samples\" in line:\n ind = line.find('=')\n rec_dict['num_samples'] = int(line[ind+1:])\n elif \"T After\" in line:\n ind = line.find('=')\n rec_dict['time_after'] = float(line[ind+1:])\n elif \"T Before\" in line:\n ind = line.find('=')\n rec_dict['time before'] = float(line[ind+1:])\n elif \"Output Sound File\" in line:\n ind = line.find('=')\n rec_dict['outfile'] = line[ind+1:]\n elif \"Thresholds\" in line:\n th_list = []\n while 1:\n line = recfile.readline()\n if line == \"\":\n break\n try:\n th_list.append(float(line))\n except ValueError: # because we reached next section\n line_tmp = line\n break\n rec_dict['thresholds'] = th_list\n if line == \"\":\n break\n elif \"Feedback information\" in line:\n fb_dict = {}\n while 1:\n line = recfile.readline()\n if line == \"\":\n break\n elif line == \"\\n\":\n continue\n ind = line.find(\"msec\")\n time = float(line[:ind-1])\n ind = line.find(\":\")\n fb_type = line[ind+2:]\n fb_dict[time] = fb_type\n rec_dict['feedback_info'] = fb_dict\n if line == \"\":\n break\n elif \"File created\" in line:\n header = [line]\n for counter in range(4):\n line = recfile.readline()\n header.append(line)\n rec_dict['header']=header\n return rec_dict","def get_info(raw_filename, epochs_filename):\n trans, fiducials, info = get_head_correct_info(\n raw_filename, epochs_filename)\n return info","def view_response_bytes(line):\n reqs = yield load_reqlist(line)\n for req in reqs:\n if req.response:\n if len(reqs) > 1:\n print '-'*15 + (' %s ' % req.reqid) + '-'*15\n print req.response.full_message\n else:\n print \"Request %s does not have a response\" % req.reqid","def readrecf(filename):\n\n rec_dict = {}\n with open(filename, 'r') as recfile:\n line_tmp = \"\"\n while 1:\n if line_tmp == \"\":\n line = recfile.readline()\n else:\n line = line_tmp\n line_tmp = \"\"\n\n if line == \"\": # if End Of File\n break\n elif line == \"\\n\": # if blank line\n continue\n elif \"Catch\" in line:\n ind = line.find('=')\n rec_dict['iscatch'] = line[ind + 1:]\n elif \"Chans\" in line:\n ind = line.find('=')\n rec_dict['num_channels'] = int(line[ind + 1:])\n elif \"ADFREQ\" in line:\n ind = line.find('=')\n try:\n rec_dict['sample_freq'] = int(line[ind + 1:])\n except ValueError:\n rec_dict['sample_freq'] = float(line[ind + 1:])\n elif \"Samples\" in line:\n ind = line.find('=')\n rec_dict['num_samples'] = int(line[ind + 1:])\n elif \"T After\" in line:\n ind = line.find('=')\n rec_dict['time_after'] = float(line[ind + 1:])\n elif \"T Before\" in line:\n ind = line.find('=')\n rec_dict['time before'] = float(line[ind + 1:])\n elif \"Output Sound File\" in line:\n ind = line.find('=')\n rec_dict['outfile'] = line[ind + 1:]\n elif \"Thresholds\" in line:\n th_list = []\n while 1:\n line = recfile.readline()\n if line == \"\":\n break\n try:\n th_list.append(float(line))\n except ValueError: # because we reached next section\n line_tmp = line\n break\n rec_dict['thresholds'] = th_list\n if line == \"\":\n break\n elif \"Feedback information\" in line:\n fb_dict = {}\n while 1:\n line = recfile.readline()\n if line == \"\":\n break\n elif line == \"\\n\":\n continue\n ind = line.find(\"msec\")\n time = float(line[:ind - 1])\n ind = line.find(\":\")\n fb_type = line[ind + 2:]\n fb_dict[time] = fb_type\n rec_dict['feedback_info'] = fb_dict\n if line == \"\":\n break\n elif \"File created\" in line:\n header = [line]\n for counter in range(4):\n line = recfile.readline()\n header.append(line)\n rec_dict['header'] = header\n return rec_dict","def test_vehicle_data_response(filename: str) -> None:\n response: models.KamereonVehicleDataResponse = fixtures.get_file_content_as_schema(\n filename, schemas.KamereonVehicleDataResponseSchema\n )\n response.raise_for_error_code()\n # Ensure the VIN is hidden\n assert response.data is not None\n assert response.data.id is not None\n assert response.data.id.startswith(\"VF1AAAA\")","def decode_replay_details(contents):\n decoder = VersionedDecoder(contents, typeinfos)\n return decoder.instance(game_details_typeid)","def test_process_response(self):\n t = self.create_request_object()\n response_content = u\"\"\" <Response ReferenceNumber=\"82e942b0-48e8-4cf4-b299-51e2b6a89a1b\"\n InboundODMFileOID=\"\"\n IsTransactionSuccessful=\"1\"\n SuccessStatistics=\"Rave objects touched: Subjects=0; Folders=0; Forms=0; Fields=0; LogLines=0\" NewRecords=\"\">\n </Response>\n \"\"\"\n req = mock.Mock(requests.Request, text=response_content)\n response = t.result(req)\n self.assertTrue(isinstance(response, RWSResponse))","def parse_meta(self):\n logger.info('parse_meta()')\n\n magic = self.replay.read_uint32()\n if (magic != FILE_MAGIC):\n raise InvalidReplayException()\n file_version = self.replay.read_uint32()\n lenght_in_ms = self.replay.read_uint32()\n network_version = self.replay.read_uint32()\n change_list = self.replay.read_uint32()\n friendly_name = self.replay.read_string()\n is_live = self.replay.read_bool()\n\n if file_version >= HistoryTypes.HISTORY_RECORDED_TIMESTAMP.value:\n time_stamp = self.replay.read_uint64()\n\n if file_version >= HistoryTypes.HISTORY_COMPRESSION.value:\n is_compressed = self.replay.read_bool()\n\n is_encrypted = False\n encryption_key = bytearray()\n if file_version >= HistoryTypes.HISTORY_ENCRYPTION.value:\n is_encrypted = self.replay.read_bool()\n encryption_key_size = self.replay.read_uint32()\n encryption_key = self.replay.read_bytes(encryption_key_size)\n\n if (not is_live and is_encrypted and len(encryption_key) == 0):\n logger.error(\n \"Completed replay is marked encrypted but has no key!\")\n raise InvalidReplayException()\n\n if (is_live and is_encrypted):\n logger.error(\n \"Replay is marked encrypted but not yet marked as completed!\")\n raise InvalidReplayException()\n\n self.meta = Meta(\n file_version=file_version,\n lenght_in_ms=lenght_in_ms,\n network_version=network_version,\n change_list=change_list,\n friendly_name=friendly_name,\n is_live=is_live,\n time_stamp=time_stamp,\n is_compressed=is_compressed,\n is_encrypted=is_encrypted,\n encryption_key=encryption_key,\n\n )","def _extract_info(self, data):\n if 'status' in data:\n if data['status'] == 'Already Downloaded':\n # Set self._return_code to already downloaded\n # and trash that key\n self._set_returncode(self.ALREADY)\n data['status'] = None\n\n if data['status'] == 'Filesize Abort':\n # Set self._return_code to filesize abort\n # and trash that key\n self._set_returncode(self.FILESIZE_ABORT)\n data['status'] = None","def _parse(self):\n with open(self._path, 'r') as file:\n try:\n line = file.readline()\n while line:\n if SENDING in line:\n self._req_set.add(self._get_request(line, True))\n line = file.readline()\n except Exception as err:\n print(\"Failed to read garbage collector log. Log was not a complete test log.\\n\"\n f\"{err!s}\")\n raise TestFailedException","def parseMeta(record):\n\n if 'csw:GetRecordByIdResponse' in record:\n log.info(\"Found 1 record\")\n if 'csw:Record' in json.loads(record)[\"csw:GetRecordByIdResponse\"]:\n recordRoot = (\n json.loads(record)[\"csw:GetRecordByIdResponse\"][\"csw:Record\"]\n )\n else:\n log.info(\"...But record is empty\")\n return\n elif 'csw:GetRecordsResponse' in record:\n if 'csw:SearchResults' in json.loads(record)[\"csw:GetRecordsResponse\"]:\n searchResults = (\n json.loads(record)[\"csw:GetRecordsResponse\"][\"csw:SearchResults\"]\n )\n\n else:\n log.info(\"...But record is empty\")\n return\n\n numberOfRecords = int(searchResults[\"@numberOfRecordsReturned\"])\n recordRoot = dict()\n\n if numberOfRecords == 0:\n log.warning(\"No records found\")\n return\n elif numberOfRecords == 1:\n log.info(\"Found 1 record\")\n recordRoot = searchResults[\"csw:Record\"]\n else:\n log.info(\"Found \" + str(numberOfRecords) + \" records\")\n recordRoot = searchResults[\"csw:Record\"][0]\n else:\n print(\"Could not parse GNOS response\")\n return\n\n if 'dc:identifier' in recordRoot:\n uuid = recordRoot[\"dc:identifier\"]\n else:\n uuid = 'null'\n\n if 'ows:BoundingBox' in recordRoot:\n recordBbox = recordRoot[\"ows:BoundingBox\"]\n\n if 'ows:LowerCorner' in recordBbox:\n bbox_lower = recordBbox[\"ows:LowerCorner\"]\n\n if 'ows:UpperCorner' in recordBbox:\n bbox_upper = recordBbox[\"ows:UpperCorner\"]\n\n bbox_a = float(bbox_lower.split(\" \")[0])\n bbox_b = float(bbox_lower.split(\" \")[1])\n bbox_c = float(bbox_upper.split(\" \")[0])\n bbox_d = float(bbox_upper.split(\" \")[1])\n bbox = [bbox_a, bbox_b, bbox_c, bbox_d]\n\n if '@crs' in recordBbox:\n crs = recordBbox[\"@crs\"]\n else:\n bbox = []\n crs = 'null'\n\n if 'dc:URI' in recordRoot:\n recordUri = recordRoot[\"dc:URI\"]\n if type(recordUri) is dict:\n if \"#text\" in recordUri:\n table = recordUri[\"#text\"]\n else:\n table = recordUri[0][\"#text\"]\n else:\n table = 'null'\n\n geodata_meta = GeodataMeta(\n uuid=uuid,\n bbox=bbox,\n crs=crs,\n table=table\n )\n\n return geodata_meta","def handle_client(self, client):\n request_data = client.recv(1024)\n request_lines = request_data.splitlines()\n for line in request_lines:\n print(line)\n request_start_line = request_lines[0]\n print(request_start_line.decode(\"utf-8\"))\n\n file_name = re.match(r\"\\w+ +(/[^ ]*) \", request_start_line.decode(\"utf-8\")).group(1)\n method = re.match(r\"(\\w+) +/[^ ]* \", request_start_line.decode(\"utf-8\")).group(1)\n\n env = {\n \"PATH_INFO\" : file_name,\n \"METHOD\": method\n }\n response_body = self.app(env, self.start_response)\n response = self.response_headers + \"\\r\\n\" + response_body\n print(\"response data: \", response)\n client.send(bytes(response, \"utf-8\"))\n client.close()","def parse_header(self, header):\n # \n \n # this is what the line'll look like:\n # e.g.: /logs/1\n # e.g.: /detailed/host/timestamp\n\n # get index of first slash\n first_slash = header.index('/')\n \n \n # splice the string now and remove any spaces\n requested_folder = header.strip('/')\n \n # check if it's just a slash\n if not requested_folder:\n # return a 0 meaning we want the latest log file\n return (0, 0)\n else:\n # check that it's a valid request\n detailed_request = requested_folder.split('/')\n # detailed_request should be of form /log/* where * is a number\n # two types of requests:\n # type 1: /log/* where * is a number\n # type 2: /detailed/node_name/timestamp\n # node_name: node name\n # timetamp is the timestamp of the run\n \n \n if len(detailed_request) == 2:\n # type 1 request\n # first entry is '' since there's a leading '/'\n if detailed_request[0] == 'log':\n # now get a valid number for a folder request\n try:\n log_number = int(detailed_request[1])\n except Exception, e:\n print \"Error obtaining log (request: \"+requested_folder+\")\"\n return (-1, str(e))\n else:\n return (0, log_number)\n else:\n return (-1, 'Invalid request (len 2)')\n elif len(detailed_request) == 3:\n # type 2 request\n if detailed_request[0] == 'detailed':\n nodename = detailed_request[1]\n timestamp = detailed_request[2]\n # verify that timestamp is a valid #\n try:\n timestamp_int = int(timestamp)\n except ValueError, ve:\n print 'Invalid timestamp requested, '+timestamp\n print ve\n return (-1, 'Invalid timestamp')\n else:\n # return the filepath as our response\n return (1, './detailed_logs/'+nodename+'/'+timestamp)\n \n \n else:\n return (-1, 'Invalid request (len 3)')\n \n else:\n # invalid!\n return (-1, 'Invalid detailed log request ('+str(detailed_request)+')')","def parse_lens_response_str(response):\n\n import re\n\n result = dict(MOSI=[], MISO=[], timedOut=False, interface_error=False)\n result['return_str'] = response\n\n for line in response:\n\n if \"Error\" in line:\n result[\"interface_error\"] = True\n\n if 'Timed out' in line:\n result['timedOut'] = True\n\n if 'fast mode' in line:\n result['fastMode'] = 1\n\n if 'slow mode' in line:\n result['fastMode'] = 0\n\n for ch in ['MOSI', 'MISO']:\n if ch in line:\n for match in re.finditer('Ox', line):\n result[ch].append(line[match.start() + 2:match.start() + 4])\n\n return result","def parse(lines): \n replied = len(lines)\n avg_delay, lost = 0, 0\n qos = 1.0\n \n if replied != 0:\n for line in lines:\n line.strip() #remove leading and trailing spaces\n \"\"\"\n Each line has the following fields:\n [status code] [reply time (seconds since epoch)] [source IP] [source url] [source query] [serving delay]\n \n e.g.:\n 200 1296756182 192.168.10.2 /home.php ?N=192 11045\n 200 1296756183 192.168.10.2 /home.php ?N=192 230036\n 200 1296756183 192.168.10.2 /home.php ?N=192 230684\n \"\"\"\n status, time, sourceIP, url, query, delay = line.split()\n \n time = int(time)\n delay = int(delay)\n \n if delay > DEADLINE:\n lost += 1\n avg_delay += delay\n avg_delay /= replied\n qos = (replied - lost) / replied\n\n return {'replied': replied, 'delay' : avg_delay, 'qos' : qos, 'lost': lost}","def view_request_info(line):\n args = shlex.split(line)\n if not args:\n raise PappyException(\"Request id is required\")\n reqids = args[0]\n\n reqs = yield load_reqlist(reqids)\n\n for req in reqs:\n print ''\n print_request_extended(req)\n print ''","def parse_reply(self, reply):\n lines = str(reply).split('\\n')\n seq_num = int(lines[1].split(' ')[1])\n\n # Process only if the server reply's sequence number is the same as the request's\n if seq_num == self.rtsp_seq:\n session = int(lines[2].split(' ')[1])\n # New RTSP session ID\n if self.session_id == 0:\n self.session_id = session\n\n # Process only if the session ID is the same\n if self.session_id == session:\n if int(lines[0].split(' ')[1]) == 200:\n if self.rtsp_command_send == self.SETUP:\n file_list = lines[3].split(' ')[1:]\n for file in file_list:\n self.video_list.addItem(file)\n\n self.state = self.READY\n self.open_rtp_port()\n self.open_rtcp_port()\n elif self.rtsp_command_send == self.PLAY:\n self.total_frame = int(lines[3].split(' ')[2])\n self.video_slider.setMaximum(self.total_frame)\n self.state = self.PLAYING\n threading.Thread(target=self.listen_rtp).start()\n self.play_event = threading.Event()\n self.play_event.clear()\n elif self.rtsp_command_send == self.PAUSE:\n self.state = self.READY\n self.play_event.set()\n elif self.rtsp_command_send == self.TEARDOWN:\n self.state = self.INIT\n self.teardown_ack = 1\n self.play_event.set()\n self.close()","def testReplayState1(t, env):\n c = env.c1\n c.init_connection()\n c.maketree([t.code])\n ops = c.use_obj(c.homedir + [t.code])\n ops += [c.open(t.code, 'vapor'), c.getfh_op()]\n _replay(c, ops, NFS4ERR_NOENT)","def readResponseFile(self):\n resp_filename = QFileDialog.getOpenFileName(self, \"Open Response File\", str(Path.home()), '') \n try:\n resp_file = open(resp_filename[0], 'r')\n except:\n print(\"Couldn't get any files from QFileDialog\")\n return\n\n try:\n response = readResponseArrayToResponse(resp_file.read().split('\\n'), resp_filename[0].split('/')[-1])\n except Exception as e:\n response = None\n print(\"Not a valid response file: {0}\".format(e))\n\n resp_file.close()\n\n if response is not None:\n self.setResponse(response)","def get_protocol_init_data(self):\n\t\tcontents = self.archive.read_file('replay.initData')\n\t\treturn self.protocol.decode_replay_initdata(contents)","def get_mockreturn(url, *args, **kwargs):\n filename = data_path(DATA_FILES['m101'])\n f = open(filename, 'r')\n text = f.read()\n retval = MockResponse(text)\n f.close()\n return retval","def __request_info_file(self, res, sock):\n udp_port = int(res[2:6])\n self.__send_request(\"03\" + self.file_name + str(self.file_size), sock)\n return udp_port","def reception():\n\tprint \"\"\n\tprint \" *****************************\"\n\tprint \" RECEPTION \t\t \"\n\tprint \" *****************************\"\n\t\n\t(port, packet_count, received_data_path) = interface.retrieve_info()\n\n\tprint \"\"\n\tprint \"Connexion parameters :\"\n\tprint \"Port = {}\".format(port) \n\tprint \"Packet_count = {}\".format(packet_count) \n\tprint \"Received_data_path = {}\".format(received_data_path)\n\tprint \"\"\n\n\tprint \"Let's connect the CC3200\"\n\tprint \"Or\"\n\tprint \"Run the client.py python script\"\n\tprint \"\"\n\n\tserver.receive_data(port, packet_count, received_data_path)\n\n\tprint \"to display the content of the file in an unix environement\"\n\tprint \"you can use the following command :\"\n\tprint \"hexdump -e \\'/4 \\\"%08X\\\\n\\\"\\' path_to_your_received_data_file.dat\"\n\tprint \"\"\n\n\tquestion = \"Proceed to integrity test ?\"\n\tif (interface.user_agree(question, \"no\")):\n\t\tintegrity_check.integrity_check_counter(received_data_path)\n\n\tprint \"\"\n\tprint \" *****************************\"\n\tprint \" END \t\t \"\n\tprint \" *****************************\"","def view_request_bytes(line):\n args = shlex.split(line)\n if not args:\n raise PappyException(\"Request id is required\")\n reqid = args[0]\n\n reqs = yield load_reqlist(reqid)\n for req in reqs:\n if len(reqs) > 1:\n print 'Request %s:' % req.reqid\n print req.full_message\n if len(reqs) > 1:\n print '-'*30\n print ''","def stats(self):\n stats = {\n 'lines' : '', # This will count the lines under each split\n 'status_code': self.status_code,\n 'content_type': self.mime,\n 'hop': self.hop_path[-1:],\n 'sum:content_length': self.content_length,\n 'host': self.host(),\n 'source': self.source\n }\n # Add in annotations:\n for annot in self.annotations:\n # Set a prefix based on what it is:\n prefix = ''\n if self.re_tries.match(annot):\n prefix = 'tries:'\n elif self.re_ip.match(annot):\n prefix = \"ip:\"\n # Only emit lines with annotations:\n if annot != \"-\":\n stats[\"%s%s\" % (prefix, annot)] = \"\"\n return stats","def _analyze_response(self, response):\n version_mo = self.VERSION_RE.search(response.get_body())\n admin_mo = self.ADMIN_URL_RE.search(response.get_body())\n author_mo = self.AUTHOR_URL_RE.search(response.get_body())\n\n if version_mo and admin_mo and author_mo:\n #Set the self._exec to false\n self._exec = False\n\n desc = 'The FrontPage Configuration Information file was found'\\\n ' at: \"%s\" and the version of FrontPage Server Extensions'\\\n ' is: \"%s\".'\n desc = desc % (response.get_url(), version_mo.group(1))\n\n i = Info('FrontPage configuration information', desc, response.id,\n self.get_name())\n i.set_url(response.get_url())\n i['version'] = version_mo.group(1)\n \n kb.kb.append(self, 'frontpage_version', i)\n om.out.information(i.get_desc())\n\n #\n # Handle the admin.exe file\n #\n self._analyze_admin(response, admin_mo)\n\n #\n # Handle the author.exe file\n #\n self._analyze_author(response, author_mo)\n\n else:\n # This is strange... we found a _vti_inf file, but there is no\n # frontpage information in it... IPS? WAF? honeypot?\n msg = '[IMPROVEMENT] Invalid frontPage configuration information'\\\n ' found at %s (id: %s).'\n msg = msg % (response.get_url(), response.id)\n om.out.debug(msg)","def report_extract_request(red):\n line_req = red.get_text_line(0) # First\n if REQ_KEY not in line_req: return None\n req_opts= line_req[line_req.index(REQ_KEY)+len(REQ_KEY):]\n return req_opts","def isResp(obxDict):\n readingCode = getReadingCode(obxDict)\n return readingCode == '76270-8'","def replay_url(self):\n if (\n self.cluster is None\n or self.steam_id is None\n or self.replay_salt is None\n ):\n return None\n else:\n return \"http://replay{0}.valve.net/570/{1}_{2}.dem.bz2\".format(\n self.cluster, self.steam_id, self.replay_salt\n )","def handle_file(self):\n query_cache_response = self.check_av_cache()\n cache_status_label = query_cache_response['response'][0]['status']['label']\n if cache_status_label == \"FOUND\":\n print(\"Results already exist in AV cache for file {}\".format(self.file_name))\n self.final_response = query_cache_response\n self.final_status_label = cache_status_label\n else:\n print(\"No results in AV cache before upload for file {}\".format(self.file_name))\n upload_response = self.upload_file()\n upload_status_label = upload_response[\"response\"][0][\"status\"][\"label\"]\n if upload_status_label == \"UPLOAD_SUCCESS\":\n query_response = self.query_file()\n query_status_label = query_response[\"response\"][0][\"status\"][\"label\"]\n print(\"Receiving Query response with av results for file {}. status: {}\".format(self.file_name,\n query_status_label))\n self.final_response = query_response\n self.final_status_label = query_status_label\n else:\n self.final_response = upload_response\n self.final_status_label = upload_status_label\n self.create_response_info(self.final_response)\n if self.final_status_label == \"FOUND\":\n signature = self.final_response[\"response\"][0][\"av\"][\"malware_info\"][\"signature_name\"]\n if signature:\n print(\"File {} was found malicious by AV. Signature : {}\".format(self.file_name, signature))\n else:\n print(\"File {} was found clean by AV\".format(self.file_name))","def handle_recording():\n\n recording_url = request.values.get(\"RecordingUrl\", None)\n\n resp = VoiceResponse()\n resp.say(\"Listen to your recorded message.\")\n resp.play(recording_url)\n resp.say(\"Goodbye.\")\n return str(resp)","def do_GET(self):\n try:\n \n # parse the requested page and see if it's valid\n parse_status, explanation_str = self.parse_header(self.path)\n \n # parse_status:\n # -1: error\n # 0: /log/* request\n # 1: /detailed/node/timestamp request\n print str(self.parse_header(self.path))\n \n explanation_str = str(explanation_str)\n \n # error\n if parse_status == -1:\n # invalid header, close the connection and die but notify user\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers()\n self.wfile.write('Invalid request ('+explanation_str+')')\n print '-1'\n return\n \n # 1: /detailed/node/timestamp request\n elif parse_status == 1:\n print '1'\n # just need to respond with the file that's contained in explanation_str\n # and once we verify that it exists, we're golden\n \n # path to the \"detailed\" file\n file_path = explanation_str\n \n if os.path.isfile(file_path):\n try:\n # TODO: make HTML here to nav around previous node things\n detailed_file_handle = open(file_path, 'r')\n self.send_response(200)\n self.send_header('Content-type',\t'text/plain')\n self.end_headers() \n self.wfile.write(detailed_file_handle.read())\n detailed_file_handle.close()\n return\n except Exception, e:\n print 'Error while sending detailed log file'\n print e\n return\n else:\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers()\n self.wfile.write('Invalid file request')\n return\n \n # 0: /log/* request\n elif parse_status == 0:\n print '0'\n # request was successfull, we just want the filename from index\n log_index = explanation_str\n \n success_status, log_filename = self.get_filename_from_index(log_index)\n \n if success_status == -1:\n # some kind of error of which the description is stored in log_filename\n #sockobj.send('The server encountered an error opening the file, please'+\\\n # ' try your request again')\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers() \n self.wfile.write('The server encountered an error opening the file, please'+\\\n ' try your request again')\n return\n \n # the file exists!\n # just dump the file at this point, and then...\n \n # send the HTML file\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers()\n self.send_html_file(log_filename, log_index)\n return\n\n # invalid type\n else:\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers()\n self.wfile.write('Invalid request type 2')\n return\n \n except IOError:\n self.send_error(404,'File Not Found: %s' % self.path)\n \n return","def _skip_replay(self, file):\n line = file.readline()\n while line and REPLAY_END not in line:\n line = file.readline()","def get_record_line_from(client_or_image_response):\n # NOTE AirSim uses WXYZ for quaternions, here we return XYZW. Also,\n # while it uses the client's pose, there's a slight difference\n # in the `position` captured by image_response, owing to the camera,\n # and there can be large ones in `orientation` when using MaxDegreeOfFreedom\n\n if isinstance(client := client_or_image_response, airsim.MultirotorClient):\n state = client.getMultirotorState()\n return state.timestamp, *ff.xyz_xyzw_of_client(state)\n\n elif isinstance(image_response := client_or_image_response, airsim.ImageResponse):\n return image_response.time_stamp, *ff.xyz_xyzw_of_image(image_response)\n\n else:\n assert False, f\"{type(client_or_image_response)=}\"","def request_file(self, file_path_on_server):\n # Create a RRQ\n packet = bytearray()\n packet.append(0)\n packet.append(1)\n name_barr = bytearray(file_path_on_server.encode('ascii'))\n packet += name_barr\n packet.append(0)\n mode = bytearray(\"octet\".encode('ascii'))\n packet += mode\n packet.append(0)\n return packet","def handle_message(self, data):\r\n print data\r\n\r\n #video stream starts\r\n if data[:13] == 'video_stream:':\r\n #port will be between 3000 to 7000\r\n port = int(data[13:17])\r\n self.partnum = int(data[18:])\r\n\r\n #creates a video file in cache\r\n if not exists(CASHE + movie_name + '\\\\'):\r\n makedirs(CASHE + movie_name + '\\\\')\r\n\r\n self.receive = Receiver(port, self.partnum, CASHE + movie_name + '\\\\')\r\n self.receive.start()\r\n\r\n #upload stream approved\r\n elif data[:16] == 'upload_approved:':\r\n port = int(data[16:])\r\n self.uploader = Uploader(port, upload_path)\r\n self.uploader.start()\r\n self.upload_num = 1\r\n\r\n elif data[:6] == 'parts:':\r\n if data[6:].isdigit():\r\n self.partnum = int(data[6:])\r\n\r\n elif data[:8] == 'invalid:':\r\n self.uploader = None\r\n if data[8:] == 'hash':\r\n self.upload_num = 2\r\n else:\r\n self.upload_num = 3\r\n\r\n print 'invalid upload'\r\n\r\n elif data == 'vid_not_found':\r\n self.partnum = -1\r\n print 'could not watch vid'\r\n\r\n elif data[:8] == 'results:':\r\n results = data[8:].split(':<!>:')\r\n self.res_list = [['Movie Name', 'views', 'grade']]\r\n for i in results:\r\n datas = i.split(':!:')\r\n self.res_list.append(datas)\r\n self.print_results = True\r\n '''if results == ['']:\r\n self.print_results = False\r\n else:\r\n self.print_results = True'''","def parsePkt(pkt):\r\n meta = dict()\r\n headers = dict()\r\n for h,pattern in SIP_PKT_PATTERNS.iteritems():\r\n if h in ['reqfirstline', 'respfirstline']:\r\n continue\r\n headers[h] = None\r\n match = pattern.search(pkt)\r\n if match:\r\n headers[h] = re.sub(h + ': ', '', match.group()).rstrip('\\r\\n')\r\n if h == 'User-Agent' and headers[h]:\r\n headers[h] = re.sub(\"Server: \", \"\", headers[h])\r\n match_1 = SIP_PKT_PATTERNS['respfirstline'].search(pkt)\r\n match_2 = SIP_PKT_PATTERNS['reqfirstline'].search(pkt)\r\n if match_1:\r\n meta['respfirstline'] = match_1.group().rstrip(' \\r\\n')\r\n meta['code'] = int(match_1.group('code'))\r\n elif match_2:\r\n meta['reqfirstline'] = match_2.group().rstrip(' \\r\\n')\r\n meta['code'] = None\r\n else:\r\n print \"can't parse rotten SIP pkt:\\r\\n%s\" %(pkt)\r\n return \r\n if meta['code'] == AUTHREQ \\\r\n or meta['code'] == PROXYAUTHREQ:\r\n meta['auth-header'] = dict()\r\n auth_match = re.search('(?P<www_or_proxy>(?:WWW|Proxy)-Authenticate): Digest (?P<other_meta>.*)\\r\\n', pkt)\r\n if auth_match:\r\n meta['auth-header']['type'] = auth_match.group('www_or_proxy')\r\n if meta['auth-header']['type'] == 'WWW-Auth-Header':\r\n meta['auth-header']['domain'] = re.search('domain=\"([-\\/\\\\:\\.a-zA-Z0-9]+)\"', other_meta).group(1)\r\n meta['auth-header']['qop'] = re.search('qop=\"([-\\/\\\\:\\.a-zA-Z0-9]+)\"', other_meta).group(1)\r\n meta['auth-header']['stale'] = re.search('stale=(?:True|False)', other_meta).group(1)\r\n meta['auth-header']['opaque'] = re.search('opaque=\"([-\\/\\\\:\\.a-zA-Z0-9]+)\"', other_meta).group(1)\r\n other_meta = auth_match.group('other_meta')\r\n algo_match = re.search('algorithm=([a-zA-Z0-9]+)', other_meta)\r\n meta['auth-header']['realm'] = re.search('realm=\"([-\\/\\\\:_\\.a-zA-Z0-9]+)\"', other_meta).group(1)\r\n meta['auth-header']['nonce'] = re.search('nonce=\"([-\\/\\\\+:_\\.a-zA-Z0-9]+)\"', other_meta).group(1)\r\n if algo_match:\r\n meta['auth-header']['algorithm'] = algo_match.group(1)\r\n else:\r\n meta['auth-header']['algorithm'] = 'MD5' \r\n else:\r\n del meta['auth-header']\r\n meta['headers'] = headers\r\n return meta","def __init__(self, line):\n (self.timestamp, self.status_code, self.content_length, self.url, self.hop_path, self.via,\n self.mime, self.thread, self.start_time_plus_duration, self.hash, self.source,\n self.annotation_string) = re.split(\" +\", line.strip(), maxsplit=11)\n # Account for any JSON 'extra info' ending, strip or split:\n if self.annotation_string.endswith(' {}'):\n self.annotation_string = self.annotation_string[:-3]\n elif ' {\"' in self.annotation_string and self.annotation_string.endswith('}'):\n self.annotation_string, self.extra_json = re.split(re.escape(' {\"'), self.annotation_string, maxsplit=1)\n self.extra_json = '{\"%s' % self.extra_json\n # And split out the annotations:\n self.annotations = self.annotation_string.split(',')\n\n # Some regexes:\n self.re_ip = re.compile('^\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}$')\n self.re_tries = re.compile('^\\d+t$')\n self.re_dol = re.compile('^dol:\\d+') # Discarded out-links - make a total?","def decode_replay_header(contents):\n decoder = VersionedDecoder(contents, typeinfos)\n return decoder.instance(replay_header_typeid)","def _read_header(\n self, header, filename, run_check_acceptability=True, background_lsts=True\n ):\n # get telescope information\n latitude = header[\"latitude\"][()]\n longitude = header[\"longitude\"][()]\n altitude = header[\"altitude\"][()]\n self.telescope_location_lat_lon_alt_degrees = (latitude, longitude, altitude)\n self.instrument = header[\"instrument\"][()].tobytes().decode(\"utf8\")\n self.telescope_name = header[\"telescope_name\"][()].tobytes().decode(\"utf8\")\n\n # get source information\n self.object_name = header[\"object_name\"][()].tobytes().decode(\"utf8\")\n\n # set history appropriately\n self.history = header[\"history\"][()].tobytes().decode(\"utf8\")\n if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):\n self.history += self.pyuvdata_version_str\n\n # check for vis_units\n if \"vis_units\" in header:\n self.vis_units = header[\"vis_units\"][()].tobytes().decode(\"utf8\")\n else:\n # default to uncalibrated data\n self.vis_units = \"UNCALIB\"\n\n # check for optional values\n if \"dut1\" in header:\n self.dut1 = float(header[\"dut1\"][()])\n if \"earth_omega\" in header:\n self.earth_omega = float(header[\"earth_omega\"][()])\n if \"gst0\" in header:\n self.gst0 = float(header[\"gst0\"][()])\n if \"rdate\" in header:\n self.rdate = header[\"rdate\"][()].tobytes().decode(\"utf8\")\n if \"timesys\" in header:\n self.timesys = header[\"timesys\"][()].tobytes().decode(\"utf8\")\n if \"x_orientation\" in header:\n self.x_orientation = header[\"x_orientation\"][()].tobytes().decode(\"utf8\")\n if \"blt_order\" in header:\n blt_order_str = header[\"blt_order\"][()].tobytes().decode(\"utf8\")\n self.blt_order = tuple(blt_order_str.split(\", \"))\n if self.blt_order == (\"bda\",):\n self._blt_order.form = (1,)\n\n if \"antenna_diameters\" in header:\n self.antenna_diameters = header[\"antenna_diameters\"][()]\n if \"uvplane_reference_time\" in header:\n self.uvplane_reference_time = int(header[\"uvplane_reference_time\"][()])\n if \"eq_coeffs\" in header:\n self.eq_coeffs = header[\"eq_coeffs\"][()]\n if \"eq_coeffs_convention\" in header:\n self.eq_coeffs_convention = (\n header[\"eq_coeffs_convention\"][()].tobytes().decode(\"utf8\")\n )\n\n # check for phasing information\n self.phase_type = header[\"phase_type\"][()].tobytes().decode(\"utf8\")\n if self.phase_type == \"phased\":\n self._set_phased()\n self.phase_center_ra = float(header[\"phase_center_ra\"][()])\n self.phase_center_dec = float(header[\"phase_center_dec\"][()])\n self.phase_center_epoch = float(header[\"phase_center_epoch\"][()])\n if \"phase_center_frame\" in header:\n self.phase_center_frame = (\n header[\"phase_center_frame\"][()].tobytes().decode(\"utf8\")\n )\n elif self.phase_type == \"drift\":\n self._set_drift()\n else:\n self._set_unknown_phase_type()\n\n # get antenna arrays\n # cast to native python int type\n self.Nants_data = int(header[\"Nants_data\"][()])\n self.Nants_telescope = int(header[\"Nants_telescope\"][()])\n self.ant_1_array = header[\"ant_1_array\"][:]\n self.ant_2_array = header[\"ant_2_array\"][:]\n self.antenna_names = [\n n.tobytes().decode(\"utf8\") for n in header[\"antenna_names\"][:]\n ]\n self.antenna_numbers = header[\"antenna_numbers\"][:]\n self.antenna_positions = header[\"antenna_positions\"][:]\n\n # set telescope params\n try:\n self.set_telescope_params()\n except ValueError as ve:\n warnings.warn(str(ve))\n\n # get baseline array\n self.baseline_array = self.antnums_to_baseline(\n self.ant_1_array, self.ant_2_array\n )\n self.Nbls = len(np.unique(self.baseline_array))\n\n # get uvw array\n self.uvw_array = header[\"uvw_array\"][:, :]\n\n # get time information\n self.time_array = header[\"time_array\"][:]\n integration_time = header[\"integration_time\"]\n self.integration_time = integration_time[:]\n proc = None\n if \"lst_array\" in header:\n self.lst_array = header[\"lst_array\"][:]\n # check that lst_array in file is self-consistent\n if run_check_acceptability:\n (\n latitude,\n longitude,\n altitude,\n ) = self.telescope_location_lat_lon_alt_degrees\n lst_array = uvutils.get_lst_for_time(\n self.time_array, latitude, longitude, altitude\n )\n if not np.all(\n np.isclose(\n self.lst_array,\n lst_array,\n rtol=self._lst_array.tols[0],\n atol=self._lst_array.tols[1],\n )\n ):\n warnings.warn(\n \"LST values stored in {file} are not self-consistent \"\n \"with time_array and telescope location. Consider \"\n \"recomputing with utils.get_lst_for_time.\".format(file=filename)\n )\n else:\n # compute lst_array from time_array and telescope location\n proc = self.set_lsts_from_time_array(background=background_lsts)\n\n # get frequency information\n self.freq_array = header[\"freq_array\"][:, :]\n self.channel_width = float(header[\"channel_width\"][()])\n self.spw_array = header[\"spw_array\"][:]\n\n # get polarization information\n self.polarization_array = header[\"polarization_array\"][:]\n\n # get data shapes\n self.Nfreqs = int(header[\"Nfreqs\"][()])\n self.Npols = int(header[\"Npols\"][()])\n self.Ntimes = int(header[\"Ntimes\"][()])\n self.Nblts = int(header[\"Nblts\"][()])\n self.Nspws = int(header[\"Nspws\"][()])\n\n # get extra_keywords\n if \"extra_keywords\" in header:\n self.extra_keywords = {}\n for key in header[\"extra_keywords\"].keys():\n if header[\"extra_keywords\"][key].dtype.type in (np.string_, np.object_):\n self.extra_keywords[key] = (\n header[\"extra_keywords\"][key][()].tobytes().decode(\"utf8\")\n )\n else:\n self.extra_keywords[key] = header[\"extra_keywords\"][key][()]\n\n if proc is not None:\n # if lsts are in the background wait for them to return\n proc.join()\n\n return","def read_data(self):\r\n # Verify length of response data\r\n length = self.read_until_null()\r\n message = self.read_until_null()\r\n if int(length) == len(message):\r\n return message\r\n else:\r\n raise ProtocolException(\"Length mismatch encountered while reading the Xdebug message\")","def media_file_info(self):\n\n if self.observationId and self.playerType == VLC:\n\n media = self.mediaplayer.get_media()\n\n logging.info(\"State: {}\".format(self.mediaplayer.get_state()))\n logging.info(\"Media (get_mrl): {}\".format(bytes_to_str(media.get_mrl())))\n logging.info(\"media.get_meta(0): {}\".format(media.get_meta(0)))\n logging.info(\n \"Track: {}/{}\".format(self.mediaplayer.video_get_track(), self.mediaplayer.video_get_track_count()))\n logging.info(\"number of media in media list: {}\".format(self.media_list.count()))\n logging.info(\"get time: {} duration: {}\".format(self.mediaplayer.get_time(), media.get_duration()))\n logging.info(\"Position: {} %\".format(self.mediaplayer.get_position()))\n logging.info(\"FPS: {}\".format(self.mediaplayer.get_fps()))\n logging.info(\"Rate: {}\".format(self.mediaplayer.get_rate()))\n logging.info(\"Video size: {}\".format(self.mediaplayer.video_get_size(0)))\n logging.info(\"Scale: {}\".format(self.mediaplayer.video_get_scale()))\n logging.info(\"Aspect ratio: {}\".format(self.mediaplayer.video_get_aspect_ratio()))\n logging.info(\"is seekable? {0}\".format(self.mediaplayer.is_seekable()))\n logging.info(\"has_vout? {0}\".format(self.mediaplayer.has_vout()))\n\n vlc_output = (\"State: {}<br>\"\n \"Media Resource Location: {}<br>\"\n \"File name: {}<br>\"\n \"Track: {}/{}<br>\"\n \"Number of media in media list: {}<br>\"\n \"get time: {}<br>\"\n \"duration: {}<br>\"\n \"Position: {} %<br>\"\n \"FPS: {}<br>\"\n \"Rate: {}<br>\"\n \"Video size: {}<br>\"\n \"Scale: {}<br>\"\n \"Aspect ratio: {}<br>\"\n \"is seekable? {}<br>\"\n \"has_vout? {}<br>\").format(self.mediaplayer.get_state(),\n bytes_to_str(media.get_mrl()),\n media.get_meta(0),\n self.mediaplayer.video_get_track(),\n self.mediaplayer.video_get_track_count(),\n self.media_list.count(),\n self.mediaplayer.get_time(),\n self.convertTime(media.get_duration() / 1000),\n self.mediaplayer.get_position(),\n self.mediaplayer.get_fps(),\n self.mediaplayer.get_rate(),\n self.mediaplayer.video_get_size(0),\n self.mediaplayer.video_get_scale(),\n self.mediaplayer.video_get_aspect_ratio(),\n \"Yes\" if self.mediaplayer.is_seekable() else \"No\",\n \"Yes\" if self.mediaplayer.has_vout() else \"No\"\n )\n\n self.results = dialog.ResultsWidget()\n self.results.setWindowTitle(programName + \" - Media file information\")\n self.results.ptText.setReadOnly(True)\n\n self.results.ptText.appendHtml(\"<b>VLC analysis</b><hr>\" + vlc_output)\n\n # FFmpeg analysis\n self.results.ptText.appendHtml(\"<br><b>FFmpeg analysis</b><hr>\")\n for nplayer in self.pj[OBSERVATIONS][self.observationId][FILE]:\n for filePath in self.pj[OBSERVATIONS][self.observationId][FILE][nplayer]:\n media_full_path = project_functions.media_full_path(filePath, self.projectFileName)\n # nframes, duration_ms, duration, fps, hasVideo, hasAudio = accurate_media_analysis(self.ffmpeg_bin, media_full_path)\n\n r = utilities.accurate_media_analysis2(self.ffmpeg_bin, media_full_path)\n nframes = r[\"frames_number\"]\n\n if \"error\" in r:\n self.results.ptText.appendHtml(\n \"File path: {filePath}<br><br>{error}<br><br>\".format(filePath=media_full_path,\n error=r[\"error\"]))\n else:\n self.results.ptText.appendHtml(\n \"File path: {}<br>Duration: {}<br>Bitrate: {}k<br>FPS: {}<br>Has video: {}<br>Has audio: {}<br><br>\".\n format(media_full_path, self.convertTime(r[\"duration\"]), r[\"bitrate\"], r[\"fps\"],\n r[\"has_video\"], r[\"has_audio\"]))\n\n self.results.ptText.appendHtml(\"Total duration: {} (hh:mm:ss.sss)\".\n format(self.convertTime(sum(self.duration) / 1000)))\n\n self.results.show()\n\n else:\n\n fn = QFileDialog(self).getOpenFileName(self, \"Select a media file\", \"\", \"Media files (*)\")\n filePath = fn[0] if type(fn) is tuple else fn\n\n if filePath:\n self.results = dialog.ResultsWidget()\n self.results.setWindowTitle(programName + \" - Media file information\")\n self.results.ptText.setReadOnly(True)\n self.results.ptText.appendHtml(\"<br><b>FFmpeg analysis</b><hr>\")\n # nframes, duration_ms, duration, fps, hasVideo, hasAudio = accurate_media_analysis(self.ffmpeg_bin, filePath)\n r = utilities.accurate_media_analysis2(self.ffmpeg_bin, filePath)\n if \"error\" in r:\n self.results.ptText.appendHtml(\n \"File path: {filePath}<br><br>{error}<br><br>\".format(filePath=filePath, error=r[\"error\"]))\n else:\n self.results.ptText.appendHtml(\n \"File path: {}<br>Duration: {}<br>Bitrate: {}k<br>FPS: {}<br>Has video: {}<br>Has audio: {}<br><br>\".\n format(filePath, self.convertTime(r[\"duration\"]), r[\"bitrate\"], r[\"fps\"], r[\"has_video\"],\n r[\"has_audio\"]))\n\n self.results.show()","def lineReceived(self, line):\n\n # Log request\n MaverickServerProtocol._logger.debug(\"Request received: %s\", line)\n\n # Pull out request name (e.g., \"REGISTER\") and arguments (unparsed)\n (requestName, _, requestArgsString) = line.partition(\" \")\n\n errMsg = None # If this gets set, there was an error\n if requestName in MaverickServerProtocol.VALID_REQUESTS:\n try:\n requestArgs = json.loads(requestArgsString,\n encoding=\"utf-8\")\n except ValueError:\n errMsg = \"Invalid JSON for arguments\"\n else:\n # Pull out the requirements for this request\n (tsCommand, expArgs, _) = \\\n MaverickServerProtocol.VALID_REQUESTS[requestName]\n\n if expArgs != set(requestArgs.keys()):\n # Give an error if not provided the correct arguments\n fStr = \"Invalid arguments, expected: {0}\"\n errMsg = fStr.format(\",\".join(list(expArgs)))\n else:\n try:\n import traceback\n # Dispatch command to TournamentSystem instance\n (successP, result) = tsCommand(self._ts, **requestArgs)\n\n except:\n tb = traceback.format_exc()\n print tb\n # Give an error if caught an exception\n errMsg = \"Uncaught exception\"\n\n else:\n if successP:\n # TODO (mattsh): check keys of response\n\n # Provide successful results to the user\n jsonStr = json.dumps(result,\n ensure_ascii=True,\n encoding=\"utf-8\")\n response = \"SUCCESS {0}\".format(jsonStr)\n if not successP:\n # Pull out structured error messages from func call\n errMsg = result[\"error\"]\n else:\n # Give an error if provided an invalid command\n errMsg = \"Unrecognized verb \\\"{0}\\\" in request\".format(requestName)\n\n # Respond to the client\n if errMsg is None:\n # Provide client with the response\n\n # Log successful response\n logStrF = \"RESPONSE [query=\\\"%s\\\"]: %s\"\n MaverickServerProtocol._logger.info(logStrF, line, response)\n\n # Send successful response\n self.sendLine(response)\n else:\n # Provide client with the error\n\n # Compute error response\n response = \"ERROR {0}\".format(errMsg, line)\n\n # Log error response\n logStrF = \"RESPONSE [query=\\\"%s\\\"]: %s\"\n MaverickServerProtocol._logger.info(logStrF, line, response)\n\n # Send error response\n self.sendLine(response)\n\n # Log the fact that the connection is being closed\n logStrF = \"Dropping connection to user after completion\"\n MaverickServerProtocol._logger.debug(logStrF)\n\n # Close connection after each request\n self.transport.loseConnection()","def setValidationInfo(self, displayname, recvstring):\n response = recvstring\n companyTestBed = \"\"\n modelTestBed = \"\"\n firmwareTestBed = \"\"\n Sniffer_WTS_VER = \"\"\n Sniffer_VendorName = \"\"\n Sniffer_DeviceModel = \"\"\n Sniffer_DeviceFirmware = \"\"\n ret_dict = {}\n\n if displayname.lower() == 'sniffer':\n #wfa_sniffer!sniffer_get_info!ID,$Sniffer_WTS_VER,$Sniffer_VendorName,$Sniffer_DeviceModel,$Sniffer_DeviceFirmware\n #status,COMPLETE,WfaSnifferVersion,$WfaSnifferVersion,SnifferSTA,$SnifferSTA,SwInfo,$DeviceSwInfo\\_$kernel_Ver,WiresharkVersion,$WiresharkInfo\\r\\n\n ret_items = response.split(',')\n \n \n if len(ret_items) > 9:\n Sniffer_WTS_VER = ret_items[3]\n Sniffer_VendorName = ret_items[5]\n Sniffer_DeviceModel = ret_items[7]\n Sniffer_DeviceFirmware = ret_items[9]\n else:\n if re.search(r\"status,COMPLETE\", response):\n if re.search(r\"WfaSnifferVersion\", response):\n posVendor = response.index('WfaSnifferVersion,') + len('WfaSnifferVersion,')\n data = response[posVendor:]\n data = data.lstrip()\n try:\n posSym = data.index(',')\n Sniffer_WTS_VER = data[:posSym]\n except Exception:\n Sniffer_WTS_VER = data.rstrip('\\n')\n\n if re.search(r\"SnifferSTA\", response):\n posVendor = response.index('SnifferSTA,') + len('SnifferSTA,')\n data = response[posVendor:]\n data = data.lstrip()\n try:\n posSym = data.index(',')\n Sniffer_VendorName = data[:posSym]\n except Exception:\n Sniffer_VendorName = data.rstrip('\\n')\n\n if re.search(r\"SwInfo\", response):\n posVendor = response.index('SwInfo,') + len('SwInfo,')\n data = response[posVendor:]\n data = data.lstrip()\n try:\n posSym = data.index(',')\n Sniffer_DeviceModel = data[:posSym]\n except Exception:\n Sniffer_DeviceModel = data.rstrip('\\n')\n\n if re.search(r\"WiresharkVersion\", response):\n posVendor = response.index('WiresharkVersion,') + len('WiresharkVersion,')\n data = response[posVendor:]\n data = data.lstrip()\n try:\n posSym = data.index(',')\n Sniffer_DeviceFirmware = data[:posSym]\n except Exception:\n Sniffer_DeviceFirmware = data.rstrip('\\n')\n\n setRetVal('$ca_version', Sniffer_WTS_VER)\n setRetVal('$tbd_info1', Sniffer_VendorName)\n setRetVal('$sw_version', Sniffer_DeviceModel)\n setRetVal('$tbd_info2', Sniffer_DeviceFirmware) \n\n ret_dict['ca_version'] = Sniffer_WTS_VER\n ret_dict['tbd_info1'] = Sniffer_VendorName\n ret_dict['sw_version'] = Sniffer_DeviceModel\n ret_dict['tbd_info2'] = Sniffer_DeviceFirmware\n\n for tbd in self.test_mngr_initr.test_prog_mngr.test_prog.testbed_dev_list:\n\n if tbd.dev_type == \"SNIFFER\":\n tbd.vendor = Sniffer_VendorName\n tbd.model = Sniffer_DeviceModel\n tbd.firmver = Sniffer_DeviceFirmware\n tbd.wtsver = Sniffer_WTS_VER\n tbd.validation_dict = ret_dict\n\n break\n\n\n else:\n if re.search(r\"status,COMPLETE\", response):\n if re.search(r\"vendor\", response):\n posVendor = response.index('vendor,') + 7\n data = response[posVendor:]\n data = data.lstrip()\n try:\n posSym = data.index(',')\n companyTestBed = data[:posSym]\n except Exception:\n companyTestBed = data.rstrip('\\n')\n\n if re.search(r\"model\", response):\n posVendor = response.index('model,') + 6\n data = response[posVendor:]\n data = data.lstrip()\n try:\n posSym = data.index(',')\n modelTestBed = data[:posSym]\n except Exception:\n modelTestBed = data.rstrip('\\n')\n\n if re.search(r\"version\", response):\n posVendor = response.index('version,') + 8\n data = response[posVendor:]\n data = data.lstrip()\n try:\n posSym = data.index(',')\n firmwareTestBed = data[:posSym]\n except Exception:\n firmwareTestBed = data.rstrip('\\n')\n \n if re.search(r\"firmware\", response):\n posVendor = response.index('firmware,') + 9\n data = response[posVendor:]\n data = data.lstrip()\n try:\n posSym = data.index(',')\n firmwareTestBed = data[:posSym]\n except Exception:\n firmwareTestBed = data.rstrip('\\n')\n \n \n \n for tbd in self.test_mngr_initr.test_prog_mngr.test_prog.testbed_dev_list:\n \n \n if tbd.ctrlipaddr == self.ExecutionTask.get_ipport().split(':')[0]:\n #\n if companyTestBed != \"\":\n tbd.vendor = companyTestBed\n if modelTestBed != \"\":\n tbd.model = modelTestBed\n if firmwareTestBed != \"\":\n if self.ExecutionTask.get_cmd() == \"ca_get_version\":\n tbd.ca_version = firmwareTestBed\n else:\n tbd.sw_version = firmwareTestBed\n \n if self.ExecutionTask.get_cmd() == \"ca_get_version\":\n \n tbd.validation_dict['ca_version'] = firmwareTestBed\n else:\n tbd.validation_dict['sw_version'] = firmwareTestBed \n tbd.validation_dict['vendor'] = companyTestBed\n tbd.validation_dict['model'] = modelTestBed \n \n break","def test_get_request_output(self):\n pass","def get_trace(captured_file, stream_num, rids):\n follow_stream = \"tshark -q -r {} -z follow,tcp,raw,{}\".format(captured_file, stream_num)\n stream_content = run_command(follow_stream, True)\n stream_all_res, stream_all_req = get_req_res(stream_content, rids)\n\n return stream_all_res, stream_all_req","def view_response_headers(line):\n reqs = yield load_reqlist(line)\n for req in reqs:\n if req.response:\n if len(reqs) > 1:\n print '-'*15 + (' %s ' % req.reqid) + '-'*15\n view_full_message(req.response, True)\n else:\n print \"Request %s does not have a response\" % req.reqid","def start_operation(self):\n\n # Check file\n is_good_file, info = self.check_file()\n\n if is_good_file:\n # Check input params from client\n is_error, json_params = self.check_text()\n\n if bool(is_error):\n return is_error, json_params\n else:\n # Start machine learning here\n from slashmlapi.app.slashml.ml_manager import MLManager\n\n # Path to zip file\n path_textfile = info['filename']\n print(json_params)\n results = MLManager.get_results(path_textfile, json_params, self.config, self.start_time)\n return True, results\n else:\n return is_good_file, info","def test_retrieve_partial_ok(self):\n content = 'Song of the Andoumboulou'\n offset = 5\n namespace = 'default'\n request = self.store_request(namespace, content)\n embedded = validate(\n request.upload_ticket, handlers_endpoints_v1.UPLOAD_MESSAGES[0])\n self.call_api('store_inline', self.message_to_dict(request), 200)\n retrieve_request = handlers_endpoints_v1.RetrieveRequest(\n digest=embedded['d'],\n namespace=handlers_endpoints_v1.Namespace(),\n offset=offset) # TODO(cmassaro): determine where offsets come from\n response = self.call_api(\n 'retrieve', self.message_to_dict(retrieve_request), 200)\n retrieved = response.json\n self.assertEqual(content[offset:], base64.b64decode(retrieved[u'content']))","def ftp_RETR(self, file):\n parsedform = parse_chunks(file)\n if parsedform:\n filename, framenum, chunks, user_or_cache = parsedform\n each_chunk_size = self.movie_LUT.chunk_size_lookup(filename)\n\n ## Check ID & Log appropriately\n if user_or_cache == 1:\n log_load('user', int(each_chunk_size) * len(chunks))\n else:\n log_load('cache', int(each_chunk_size) * len(chunks))\n\n try:\n # filename should be prefixed by \"file-\" in order to be valid.\n # frame number is expected to exist for this cache.\n chunksdir = 'video-' + filename\n framedir = filename + '.' + framenum + '.dir'\n path = self.movies_path + '/' + chunksdir + '/' + framedir\n # get chunks list and open up all files\n files = self.get_chunk_files(path, chunks)\n\n # if DEBUGGING_MSG:\n # print \"chunks requested:\", chunks\n # print 'chunksdir', chunksdir\n # print 'framedir', framedir\n # print 'path', path\n except OSError, err:\n why = ftpserver._strerror(err)\n self.respond('550 %s.' % why)\n\n producer = self.chunkproducer(files, self._current_type)\n self.push_dtp_data(producer, isproducer=True, file=None, cmd=\"RETR\")\n return","def handle(self) -> None:\r\n\r\n if self.data.get(\"message-id\") != None:\r\n if self.data[\"status\"] == \"error\":\r\n print(self.data[\"error\"])\r\n return\r\n else:\r\n requestData = self.obs.pendingResponses.pop(self.data[\"message-id\"])\r\n request = requestData[\"request-type\"]\r\n #Requests as of version 4.8.0\r\n\r\n #General\r\n if request == \"GetVersion\":\r\n pass\r\n\r\n elif request == \"GetAuthRequired\":\r\n if self.data[\"authRequired\"]:\r\n secret_string: str = self.obs.password + self.data[\"salt\"]\r\n secret_hash: sha256 = sha256(secret_string.encode(\"utf-8\"))\r\n secret: bytes = b64encode(secret_hash.digest())\r\n\r\n response_string: str = secret.decode(\"utf-8\") + self.data[\"challenge\"]\r\n response_hash: sha256 = sha256(response_string.encode(\"utf-8\"))\r\n response: bytes = b64encode(response_hash.digest())\r\n\r\n self.obs.requests.append({\r\n \"type\": \"Authenticate\",\r\n \"auth\": response.decode(\"utf-8\")})\r\n\r\n else:\r\n self.obs.requests.append({\"type\": \"GetSceneList\"})\r\n\r\n elif request == \"Authenticate\":\r\n self.obs.requests.append({\"type\": \"GetSceneList\"})\r\n\r\n elif request == \"SetHeartbeat\":\r\n #To be removed in 5.0.0\r\n pass\r\n\r\n elif request == \"SetFilenameFormatting\":\r\n pass\r\n\r\n elif request == \"GetFilenameFormatting\":\r\n pass\r\n\r\n elif request == \"GetStats\":\r\n pass\r\n\r\n elif request == \"BroadcastCustomMessage\":\r\n pass\r\n\r\n elif request == \"GetVideoInfo\":\r\n pass\r\n\r\n elif request == \"OpenProjector\":\r\n pass\r\n\r\n elif request == \"TriggerHotkeyByName\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"TriggerHotkeyBySequence\":\r\n #Unreleased\r\n pass\r\n\r\n #Media Control\r\n elif request == \"PlayPauseMedia\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"RestartMedia\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"StopMedia\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"NextMedia\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"PreviousMedia\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"GetMediaDuration\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"GetMediaTime\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"SetMediaTime\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"ScrubMedia\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"GetMediaState\":\r\n #Unreleased\r\n pass\r\n\r\n #Sources\r\n\r\n elif request == \"GetMediaSourcesList\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"GetSourcesList\":\r\n pass\r\n\r\n elif request == \"GetSourceTypesList\":\r\n pass\r\n\r\n elif request == \"GetVolume\":\r\n pass\r\n\r\n elif request == \"SetVolume\":\r\n pass\r\n\r\n elif request == \"GetMute\":\r\n pass\r\n\r\n elif request == \"SetMute\":\r\n pass\r\n\r\n elif request == \"ToggleMute\":\r\n pass\r\n\r\n elif request == \"GetAudioActive\":\r\n pass\r\n\r\n elif request == \"SetSourceName\":\r\n pass\r\n\r\n elif request == \"SetSyncOffset\":\r\n pass\r\n\r\n elif request == \"GetSyncOffset\":\r\n pass\r\n\r\n elif request == \"GetSourceSettings\":\r\n pass\r\n\r\n elif request == \"SetSourceSettings\":\r\n pass\r\n\r\n elif request == \"GetTextGDIPlusProperties\":\r\n pass\r\n\r\n elif request == \"SetTextGDIPlusProperties\":\r\n pass\r\n\r\n elif request == \"GetTextFreetype2Properties\":\r\n pass\r\n\r\n elif request == \"SetTextFreetype2Properties\":\r\n pass\r\n\r\n elif request == \"GetBrowserSourceProperties\":\r\n pass\r\n\r\n elif request == \"SetBrowserSourceProperties\":\r\n pass\r\n\r\n elif request == \"GetSpecialSources\":\r\n pass\r\n\r\n elif request == \"GetSourceFilters\":\r\n source = self.obs.getSource(requestData[\"sourceName\"])\r\n if source != None:\r\n for _filter in self.data[\"filters\"]:\r\n source.addFilter(_filter) #type: ignore\r\n\r\n elif request == \"GetSourceFilterInfo\":\r\n pass\r\n\r\n elif request == \"AddFilterToSource\":\r\n pass\r\n\r\n elif request == \"RemoveFilterFromSource\":\r\n pass\r\n\r\n elif request == \"ReorderSourceFilter\":\r\n pass\r\n\r\n elif request == \"MoveSourceFilter\":\r\n pass\r\n\r\n elif request == \"SetSourceFilterSettings\":\r\n pass\r\n\r\n elif request == \"SetSourceFilterVisibility\":\r\n pass\r\n \r\n elif request == \"GetAudioMonitorType\":\r\n pass\r\n\r\n elif request == \"SetAudioMonitorType\":\r\n pass\r\n\r\n elif request == \"TakeSourceScreenshot\":\r\n pass\r\n\r\n #Outpute\r\n elif request == \"ListOutputs\":\r\n pass\r\n\r\n elif request == \"GetOutputInfo\":\r\n pass\r\n\r\n elif request == \"StartOutput\":\r\n pass\r\n\r\n elif request == \"StopOutput\":\r\n pass\r\n\r\n #Profiles\r\n elif request == \"SetCurrentProfile\":\r\n pass\r\n\r\n elif request == \"GetCurrentProfile\":\r\n pass\r\n\r\n elif request == \"ListProfiles\":\r\n pass\r\n\r\n #Recording\r\n elif request == \"GetRecordingStatus\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"StartStopRecording\":\r\n pass\r\n\r\n elif request == \"StartRecording\":\r\n pass\r\n\r\n elif request == \"StopRecording\":\r\n pass\r\n\r\n elif request == \"PauseRecording\":\r\n pass\r\n\r\n elif request == \"ResumeRecording\":\r\n pass\r\n\r\n elif request == \"SetRecordingFolder\":\r\n pass\r\n\r\n elif request == \"GetRecordingFolder\":\r\n pass\r\n\r\n #Replay Buffer\r\n elif request == \"GetReplayBufferStatus\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"StartStopReplayBuffer\":\r\n pass\r\n\r\n elif request == \"StartReplayBuffer\":\r\n pass\r\n\r\n elif request == \"StopReplayBuffer\":\r\n pass\r\n\r\n elif request == \"SaveReplayBuffer\":\r\n pass\r\n\r\n #Scene Collections\r\n elif request == \"SetCurrentSceneCollection\":\r\n pass\r\n\r\n elif request == \"GetCurrentSceneCollection\":\r\n pass\r\n\r\n elif request == \"ListSceneCollections\":\r\n pass\r\n\r\n #Scene Items\r\n elif request == \"GetSceneItemList\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"GetSceneItemProperties\":\r\n pass\r\n\r\n elif request == \"SetSceneItemProperties\":\r\n pass\r\n\r\n elif request == \"ResetSceneItem\":\r\n pass\r\n\r\n elif request == \"SetSceneItemRender\":\r\n pass\r\n\r\n elif request == \"SetSceneItemPosition\":\r\n pass\r\n\r\n elif request == \"SetSceneItemTransform\":\r\n pass\r\n\r\n elif request == \"SetSceneItemCrop\":\r\n pass\r\n\r\n elif request == \"DeleteSceneItem\":\r\n pass\r\n\r\n elif request == \"AddSceneItem\":\r\n #Unreleased\r\n pass\r\n\r\n elif request == \"DuplicateSceneItem\":\r\n pass\r\n\r\n #Scenes\r\n elif request == \"SetCurrentScene\":\r\n pass\r\n\r\n elif request == \"GetCurrentScene\":\r\n self.obs.setCurrentScene(self.data[\"name\"])\r\n\r\n elif request == \"GetSceneList\":\r\n for scene in self.data[\"scenes\"]:\r\n self.obs.addScene(scene)\r\n self.obs.setCurrentScene(self.data[\"current-scene\"])\r\n\r\n elif request == \"CreateScene\":\r\n pass\r\n\r\n elif request == \"ReorderSceneItems\":\r\n pass\r\n\r\n elif request == \"SetSceneTransitionOverride\":\r\n pass\r\n\r\n elif request == \"RemoveSceneTransitionOverride\":\r\n pass\r\n\r\n elif request == \"GetSceneTransitionOverride\":\r\n pass\r\n\r\n #Streaming\r\n elif request == \"GetStreamingStatus\":\r\n pass\r\n\r\n elif request == \"StartStopStreaming\":\r\n pass\r\n\r\n elif request == \"StartStreaming\":\r\n pass\r\n\r\n elif request == \"StopStreaming\":\r\n pass\r\n\r\n elif request == \"SetStreamSettings\":\r\n pass\r\n\r\n elif request == \"GetStreamSettings\":\r\n pass\r\n\r\n elif request == \"SaveStreamSettings\":\r\n pass\r\n\r\n elif request == \"SendCaptions\":\r\n pass\r\n\r\n #Studio Mode\r\n elif request == \"GetStudioModeStatus\":\r\n pass\r\n\r\n elif request == \"GetPreviewScene\":\r\n pass\r\n\r\n elif request == \"SetPreviewScene\":\r\n pass\r\n\r\n elif request == \"TransitionToProgram\":\r\n pass\r\n\r\n elif request == \"EnableStudioMode\":\r\n pass\r\n\r\n elif request == \"DisableStudioMode\":\r\n pass\r\n\r\n elif request == \"ToggleStudioMode\":\r\n pass\r\n\r\n #Transitions\r\n elif request == \"GetTransitionList\":\r\n pass\r\n\r\n elif request == \"GetCurrentTransition\":\r\n pass\r\n\r\n elif request == \"SetCurrentTransition\":\r\n pass\r\n\r\n elif request == \"SetTransitionDuration\":\r\n pass\r\n\r\n elif request == \"GetTransitionDuration\":\r\n pass\r\n\r\n elif request == \"GetTransitionPosition\":\r\n pass\r\n\r\n else:\r\n print(f\"Unhandled response of type {request} and data {self.data}.\")\r\n\r\n \r\n\r\n else:\r\n event: str = self.data[\"update-type\"]\r\n #Events as of 4.8.0\r\n\r\n #Scenes\r\n if event == \"SwitchScenes\":\r\n self.obs.setCurrentScene(self.data[\"scene-name\"])\r\n\r\n elif event == \"ScenesChanged\":\r\n #self.obs.purgeScenes()\r\n pass\r\n\r\n elif event == \"SceneCollectionChanged\":\r\n pass\r\n\r\n elif event == \"SceneCollectionListChanged\":\r\n pass\r\n\r\n #Transitions\r\n elif event == \"SwitchTransition\":\r\n pass\r\n\r\n elif event == \"TransitionListChanged\":\r\n pass\r\n\r\n elif event == \"TransitionDurationChanged\":\r\n pass\r\n\r\n elif event == \"TransitionBegin\":\r\n pass\r\n\r\n elif event == \"TransitionEnd\":\r\n pass\r\n\r\n elif event == \"TransitionVideoEnd\":\r\n pass\r\n\r\n #Profiles\r\n elif event == \"ProfileChanged\":\r\n pass\r\n\r\n elif event == \"ProfileListChanged\":\r\n pass\r\n\r\n #Streaming\r\n elif event == \"StreamStarting\":\r\n pass\r\n\r\n elif event == \"StreamStarted\":\r\n pass\r\n\r\n elif event == \"StreamStopping\":\r\n pass\r\n\r\n elif event == \"StreamStopped\":\r\n pass\r\n\r\n elif event == \"StreamStatus\":\r\n pass\r\n\r\n #Recording\r\n elif event == \"RecordingStarting\":\r\n pass\r\n\r\n elif event == \"RecordingStarted\":\r\n pass\r\n\r\n elif event == \"RecordingStopping\":\r\n pass\r\n\r\n elif event == \"RecordingStopped\":\r\n pass\r\n\r\n elif event == \"RecordingPaused\":\r\n pass\r\n\r\n elif event == \"RecordingResumed\":\r\n pass\r\n\r\n #Replay Buffer\r\n elif event == \"ReplayStarting\":\r\n pass\r\n\r\n elif event == \"ReplayStarted\":\r\n pass\r\n\r\n elif event == \"ReplayStopping\":\r\n pass\r\n\r\n elif event == \"ReplayStopped\":\r\n pass\r\n\r\n #Other\r\n elif event == \"Exiting\":\r\n pass\r\n\r\n #General\r\n elif event == \"Heartbeat\":\r\n pass\r\n\r\n elif event == \"BroadcastCustomMessage\":\r\n pass\r\n\r\n #Sources\r\n elif event == \"SourceCreated\":\r\n pass\r\n\r\n elif event == \"SourceDestroyed\":\r\n pass\r\n\r\n elif event == \"SourceVolumeChanged\":\r\n pass\r\n\r\n elif event == \"SourceMuteStateChanged\":\r\n pass\r\n\r\n elif event == \"SourceAudioDeactivated\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"SourceAudioActivated\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"SourceAudioSyncOffsetChanged\":\r\n pass\r\n\r\n elif event == \"SourceAudioMixersChanged\":\r\n pass\r\n\r\n elif event == \"SourceRenamed\":\r\n pass\r\n\r\n elif event == \"SourceFilterAdded\":\r\n pass\r\n\r\n elif event == \"SourceFilterRemoved\":\r\n pass\r\n\r\n elif event == \"SourceFilterVisibilityChanged\":\r\n source = self.obs.getSource(self.data[\"sourceName\"])\r\n if source != None:\r\n _filter = source.getFilter(self.data[\"filterName\"]) #type: ignore\r\n if _filter != None:\r\n _filter.setVisible(self.data[\"filterEnabled\"]) #type: ignore\r\n\r\n elif event == \"SourceFiltersReordered\":\r\n pass\r\n\r\n #Media\r\n elif event == \"MediaPlaying\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"MediaPaused\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"MediaRestarted\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"MediaStopped\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"MediaNext\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"MediaPrevious\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"MediaStarted\":\r\n #Unreleased\r\n pass\r\n\r\n elif event == \"MediaEnded\":\r\n #Unreleased\r\n pass\r\n\r\n #Scene Items\r\n elif event == \"SceneItemOrderChanged\":\r\n pass\r\n\r\n elif event == \"SceneItemAdded\":\r\n pass\r\n\r\n elif event == \"SceneItemRemoved\":\r\n pass\r\n\r\n elif event == \"SceneItemVisibilityChanged\":\r\n scene = self.obs.getScene(self.data[\"scene-name\"])\r\n if scene != None:\r\n source = scene.getSource(self.data[\"item-name\"]) #type: ignore\r\n if source != None:\r\n source.setVisible(self.data[\"item-visible\"]) #type: ignore\r\n \r\n\r\n elif event == \"SceneItemLockChanged\":\r\n pass\r\n\r\n elif event == \"SceneItemTransformChanged\":\r\n pass\r\n\r\n elif event == \"SceneItemSelected\":\r\n pass\r\n\r\n elif event == \"SceneItemDeselected\":\r\n pass\r\n\r\n #Studio Mode\r\n elif event == \"PreviewSceneChanged\":\r\n pass\r\n\r\n elif event == \"StudioModeSwitched\":\r\n pass\r\n\r\n #Unhandled Events\r\n else:\r\n print(\"Unhandled event with data: \" + str(self.data))","def process(self):\n\n requested_file_path = self._get_requested_file_path()\n\n if os.path.exists(requested_file_path):\n self._respond_with_status_line(OK)\n self._respond_with_headers(file_path=requested_file_path,\n requested_headers=('Date',\n 'Content-Length',\n 'Content-Type',\n 'Server'))\n else:\n self._respond_with_status_line(NOT_FOUND)\n self._respond_with_headers(requested_headers=('Server',))","def parseMISPreamble(lines, flight, summarize=False):\n # Attempt to parse stuff from the Flight Plan ID bit. Fancy logic for\n # grabbing the fancy name, which didn't always exist\n try:\n flightid = regExper(lines, 'Flight Plan ID', howmany=1,\n keytype='key:val')\n fid = keyValuePair(flightid.group(), \"Flight Plan ID\", dtype=str)\n fid = fid.strip().split(\"_\")\n if fid[1] != '':\n try:\n flight.instrument = flight.instdict[fid[1].strip()]\n except:\n flight.instrument = ''\n if fid[2] != '':\n flight.fancyname = fid[2]\n except:\n fid = ['', '', '']\n\n # Grab the filename and date of MIS file creation\n filename = regExper(lines, 'Filename', howmany=1, keytype='key:val')\n flight.filename = keyValuePair(filename.group(), \"Filename\", dtype=str)\n\n # Note: the saved key is a timestamp, with a space in between stuff.\n saved = regExper(lines, 'Saved', howmany=1, keytype='key:dtime')\n flight.saved = keyValuePairDT(saved.group(), \"Saved\")\n\n # Search for two airports; first is takeoff, second is landing\n airports = regExper(lines, 'Airport', howmany=2, keytype='key:val')\n if airports is not None and len(airports) == 2:\n flight.origin = keyValuePair(airports[0].group(),\n \"Airport\", dtype=str)\n flight.destination = keyValuePair(airports[1].group(),\n \"Airport\", dtype=str)\n elif len(airports) != 2 or airports is None:\n print(\"WARNING: Couldn't find departure/arrival information!\")\n flight.origin = \"Unknown\"\n flight.destination = \"Unknown\"\n\n runway = regExper(lines, 'Runway', howmany=1, keytype='key:val')\n flight.drunway = keyValuePair(runway.group(), \"Runway\", dtype=str)\n\n legs = regExper(lines, 'Legs', howmany=1, keytype='key:val')\n flight.nlegs = keyValuePair(legs.group(), \"Legs\", dtype=int)\n\n mach = regExper(lines, 'Mach', howmany=1, keytype='key:val')\n flight.mach = keyValuePair(mach.group(), \"Mach\", dtype=float)\n\n takeoff = regExper(lines, 'Takeoff', howmany=1, keytype='key:dtime')\n flight.takeoff = keyValuePairDT(takeoff.group(), \"Takeoff\")\n\n obstime = regExper(lines, 'Obs Time', howmany=1, keytype='key:val')\n flight.obstime = keyValuePairTD(obstime.group(), \"Obs Time\")\n\n flttime = regExper(lines, 'Flt Time', howmany=1, keytype='key:val')\n flight.flighttime = keyValuePairTD(flttime.group(), \"Flt Time\")\n\n landing = regExper(lines, 'Landing', howmany=1, keytype='key:dtime')\n flight.landing = keyValuePairDT(landing.group(), \"Landing\")\n\n # NOTE: I hate fp. It sometimes doesn't write sunrise info.\n sunset = regExper(lines, 'Sunset', howmany=1, keytype='key:val')\n try:\n flight.sunset = keyValuePairTD(sunset.group(), \"Sunset\")\n except:\n flight.sunset = \"NONE\"\n\n sunrise = regExper(lines, 'Sunrise', howmany=1, keytype='key:val')\n try:\n flight.sunrise = keyValuePairTD(sunrise.group(), \"Sunrise\")\n except:\n flight.sunrise = \"NONE\"\n\n if summarize is True:\n print(flight.summarize())\n\n return flight","def header_postRsp(self):\n self.jobHealthy(4)\n contentBase = '/Reflect/RequestProcess/ports/analyze/0'\n # request : \n self.url = PROTOCOL + IP + ':' + str(PORT) + contentBase + '/post?'\n payload = {\"REPEAT\":str(self.tstCount)}\n print(\"** REQ: %s Payload len:%d\" % (self.url, len(payload)), flush=True)\n rsp = requests.post(url=self.url, data=payload)\n\n # response\n #print(\"RSP: %s\\nSTATUS:%s\\nCONTENT:%s\" % (rsp, rsp.status_code, rsp.text), flush=True)\n #print(\"RSP::%s\" % (rsp.text), flush=True)\n self.assertEqual(rsp.status_code, 200, \"incorrect completion code\")\n self.assertTrue(rsp.content.startswith(b\"RSP:\"), msg=\"preamble missing - data loss\")\n print(\"** RSP: expected: %d received: %d\" % (self.tstCount, len(rsp.text)))\n self.assertGreater(len(rsp.text), self.tstCount, msg=\"under fill count - data loss\" )","def parse(self):\n try:\n self.open_file()\n lines = list(self._file)\n\n if len(lines) > 0:\n text = ''.join(lines)\n regex = 'Song \\d+\\nStart (\\d+:\\d+:\\d+)\\nEnd (\\d+:\\d+:\\d+)\\nLength (\\d+.\\d+)'\n match = re.findall(regex, text)\n if len(match):\n starts = []\n ends = []\n lengths = []\n\n for i in range(len(match)):\n starts.append(match[i][0])\n ends.append(match[i][1])\n lengths.append(float(match[i][2]))\n\n for i in range(len(match)):\n self.debug_data.append({\n 'start':starts[i],'end':ends[i],'length':lengths[i]})\n\n match = re.search('T\\d_S(\\d{4})_.*.txt', self._filepath)\n if match:\n self._experiment_metadata['session_id'] = int(match.groups()[0])\n else:\n raise EIMParsingError(\"No valid session id found in filename %s\" % self._filepath)\n\n finally:\n if self._file and not self._file.closed:\n self.close_file()","def decode(r):\n ## Check length\n if len(r) != 60: return False\n ## Check cookie\n if r[0x0:0x2] != b'\\xef\\xfe': return False\n t = r[2]\n mac = \"%02x:%02x:%02x:%02x:%02x:%02x\" % struct.unpack(\"BBBBBB\",r[3:9])\n gateware = \"{0}.{1}\".format(r[0x09],r[0x15])\n radio_id = r[0x0a]\n temp = r[0x0b]\n use_eeprom_ip = (temp & 0x80) != 0\n use_eeprom_mac = (temp & 0x40) != 0\n favor_dhcp = (temp & 0x20) != 0\n eeprom_ip = \"%d:%d:%d:%d\" % struct.unpack(\"BBBB\",r[0x0d:0x11])\n eeprom_mac = \"%02x:%02x:%02x:%02x:%02x:%02x\" % struct.unpack(\"BBBBBB\",r[0x03:0x07]+r[0x11:0x13])\n receivers = r[0x13]\n temp = r[0x14]\n board_id = temp & 0x3f\n wideband_type = 0x03 & (temp >> 6)\n response_data = struct.unpack('!L',r[0x17:0x1b])[0]\n temp = r[0x1b]\n ext_cw_key = (temp & 0x80) != 0\n tx_on = (temp & 0x40) != 0\n adc_clip_cnt = temp & 0x03\n temperature = struct.unpack('!H',r[0x1c:0x1e])[0]\n # For best accuracy, 3.26 should be a user's measured 3.3V supply voltage.\n temperature = (3.26 * (temperature/4096.0) - 0.5)/0.01\n # TODO: Add proper power compoutation, maybe table interpolation like Quisk\n fwd_pwr = struct.unpack('!H',r[0x1e:0x20])[0]\n rev_pwr = struct.unpack('!H',r[0x20:0x22])[0]\n bias = struct.unpack('!H',r[0x22:0x24])[0]\n bias = ((3.26 * (bias/4096.0))/50.0)/0.04\n temp = r[0x24]\n txfifo_recovery = (temp & 0x80) != 0\n txfifo_msbs = (temp & 0x7f)\n return Response(t,mac,gateware,radio_id,use_eeprom_ip,use_eeprom_mac,favor_dhcp,eeprom_ip,eeprom_mac,\n receivers,board_id,wideband_type,response_data,ext_cw_key,tx_on,adc_clip_cnt,temperature,\n fwd_pwr,rev_pwr,bias,txfifo_recovery,txfifo_msbs)","def _decode(self):\n \n self.version = int(data_to_hex_str(self.packet[0])[2])\n self.header_len = int(data_to_hex_str(self.packet[0])[3]) * 4\n self.type_of_service = data_to_hex_str(self.packet[1:2])\n self.total_len = int(data_to_hex_str(self.packet[2:4]), 16)\n self.id = data_to_hex_str(self.packet[4:6])\n \n #parse the flags fields(reservedbit, don't fragment, more fragment)\n if ((ord(self.packet[6]) & (1 << 7)) != 0):\n self.flags_reservedbit = 1\n else:\n self.flags_reservedbit = 0\n #endof if\n \n if ((ord(self.packet[6]) & (1 << 6)) != 0):\n self.flags_dont_fragment = 1\n else:\n self.flags_dont_fragment = 0\n #endof if\n \n if ((ord(self.packet[6]) & (1 << 5)) != 0):\n self.flags_more_fragment = 1\n else:\n self.flags_more_fragment = 0\n #endof if\n \n #parse the offset field(in packet[6:7]): 00011111 & packet[6] (to filter flags) -->> get packet[6:7] in hex_str\n #tmp = str(31 & ord(self.packet[6]))\n self.fragment_offset = int(data_to_hex_str(self.packet[6:8]), 16)\n if (self.fragment_offset >= (1 << 13)):\n #take away the flags fields: 00011111 11111111 & self.fragment_offset\n self.fragment_offset = self.fragment_offset & ((1 << 13) - 1) \n \n self.TTL = ord(self.packet[8])\n self.protocol = IPPROTO[ord(self.packet[9])]\n self.header_checksum = data_to_hex_str(self.packet[10:12])\n \n self.src = str(ord(self.packet[12])) + '.' + str(ord(self.packet[13])) + '.' + \\\n str(ord(self.packet[14])) + '.' + str(ord(self.packet[15]))\n self.dst = str(ord(self.packet[16])) + '.' + str(ord(self.packet[17])) + '.' + \\\n str(ord(self.packet[18])) + '.' + str(ord(self.packet[19]))\n \n if (self.header_len > 20):\n self.opt_paddings = self.packet[20 : (self.header_len)]","def _validate_replay(self):\n if self.replay_tt.text != self.tt.text:\n raise ValueError(\"The replay text and text do not agree.\")\n\n if self.replay_tt.start_ts is None:\n raise ValueError(\"The replay was never started\")","def _extract_data(self, eopatch: EOPatch, responses: list[Any], shape: tuple[int, ...]) -> EOPatch:","def insert_bad_data():\n get_file_reply(files[2][0], files[2][1])","def from_dem(file, verbose=False):\n import subprocess, os\n from uuid import uuid4\n jarCall = list(REP_DEM_JARBASE)\n jarCall[-2] = file\n if not os.path.exists(OPTIONS.JB.SCRATCH): os.makedirs(OPTIONS.JB.SCRATCH)\n jarCall[-1] = os.path.abspath(os.path.join(OPTIONS.JB.SCRATCH, str(uuid4())))\n try:\n p = subprocess.Popen(jarCall, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = p.communicate()\n if (verbose):\n print out\n print err\n \n return Replay.from_csv(jarCall[-1])\n \n except Exception as e:\n print 'Invalid replay file. Unable to parse replay.'\n \n finally:\n if os.path.exists(jarCall[-1]):\n try: os.remove(jarCall[-1])\n except: print 'Unable to delete temporary file %s' % jarCall[-1]","def file_request(self):\n magic_number = int.to_bytes(int(0x497E), 2, byteorder='big') \n type_bytes = int.to_bytes(1, 1, byteorder='big')\n filename_in_bytes = bytes(self.file_name, 'utf-8') #ENCODE FILENAME IN BYTES\n try:\n filename_len = int.to_bytes(len(filename_in_bytes), 2, byteorder='big')\n except OverflowError:\n self.socket.close()\n sys.exit(\"ERROR: Filename is too long, Stack Overflow\") \n \n message_to_send = bytearray(magic_number+type_bytes+filename_len+filename_in_bytes)\n self.socket.send(message_to_send)\n \n #WAIT FOR FILE RESPONSE FROM SERVER\n try:\n self.socket.settimeout(1)\n while True: #LOOP TO RECIEVE DATA IN CHUNKS <= 4096 BYTES\n data = self.socket.recv(4096)\n if data:\n self.data += data\n else:\n break\n\n self.socket.settimeout(None)\n self.socket.close()\n\n except socket.timeout:\n self.socket.close()\n sys.exit(\"ERROR: Connection timed out\")","def view_full_request(line):\n args = shlex.split(line)\n if not args:\n raise PappyException(\"Request id is required\")\n reqid = args[0]\n\n reqs = yield load_reqlist(reqid)\n for req in reqs:\n if len(reqs) > 1:\n print 'Request %s:' % req.reqid\n view_full_message(req, try_ws=True)\n if len(reqs) > 1:\n print '-'*30\n print ''","def rinex_info(rinex_fname,\n nav_fname,\n work_path=None):\n if not os.path.isfile(rinex_fname):\n raise ValueError('RINEX observation file {} does not exist'.format(rinex_fname))\n if not os.path.isfile(nav_fname):\n raise ValueError('RINEX navigation file {} does not exist'.format(nav_fname))\n # information mapping\n info = {}\n def process_output(line):\n if line.startswith('Receiver type'):\n info['receiver'] = line.split(':')[1].split('(')[0].strip()\n elif line.lstrip().startswith('antenna WGS 84 (xyz)'):\n # make sure units are [m]\n assert line.rstrip().endswith('(m)')\n info['xyz'] = map(float, line.split(':')[1].split('(')[0].split())\n elif line.lstrip().startswith('antenna WGS 84 (geo)'):\n if line.split(':')[1].lstrip()[0] in ['N', 'S']:\n # skip arcmin, arcsec line\n pass\n else:\n lat, _, lon, _ = line.split(':')[1].split(None, 3)\n info['lat'] = float(lat)\n lon = float(lon)\n while lon > 180:\n lon -= 360\n info['lon'] = lon\n elif line.lstrip().startswith('WGS 84 height'):\n assert line.rstrip().endswith('m')\n info['height'] = float(line.split(':')[1].rstrip()[:-1])\n elif line.startswith('|qc - header| position'):\n # make sure units are [m]\n assert line.rstrip()[-1] == 'm'\n info['xyz error'] = float(line.split(':')[1].rstrip()[:-1])\n elif line.startswith('Observation interval'):\n info['interval'] = float(line.split(':')[1].split()[0])\n elif line.startswith('Moving average MP12'):\n info['MP12'] = float(line.split(':')[1].rstrip()[:-1])\n elif line.startswith('Moving average MP21'):\n info['MP21'] = float(line.split(':')[1].rstrip()[:-1])\n # query the RINEX file via teqc quality check --- process in given\n # work area to avoid intermediate file pollution\n with SmartTempDir(work_path) as work_path:\n intermediate_rinex_fname = replace_path(work_path, rinex_fname)\n os.symlink(os.path.abspath(rinex_fname),\n intermediate_rinex_fname)\n intermediate_nav_fname = replace_path(work_path, nav_fname)\n os.symlink(os.path.abspath(nav_fname),\n intermediate_nav_fname)\n sh.teqc('+qc',\n '+quiet',\n '-R',\n '-S',\n '-E',\n '-C',\n '-J',\n '-nav', intermediate_nav_fname,\n intermediate_rinex_fname,\n _cwd=work_path,\n _out=process_output,\n _err=sys.stderr)\n os.remove(intermediate_rinex_fname)\n os.remove(intermediate_nav_fname)\n return info","def get_file_name_from_resposne(r):\n if not r: \n return None\n return get_file_name_from_cd(r.headers.get())","def _main_header(self, hdr):\n d = {}\n # Called readDefAnalysis in OpenMIMS\n d['sample type'], d['data included'], d['sample x'], d['sample y'], \\\n d['analysis type'], d['user name'], d['sample z'], date, time = \\\n unpack(self._bo + '4i 32s 16s i 12x 16s 16s', hdr.read(112))\n\n d['data included'] = bool(d['data included'])\n d['user name'] = self._cleanup_string(d['user name'])\n d['analysis type'] = self._cleanup_string(d['analysis type']).lower()\n date = self._cleanup_string(date)\n time = self._cleanup_string(time)\n d['date'] = self._cleanup_date(date + ' ' + time)\n\n if self.header['file type'] in (27, 29, 39):\n # Called MaskImage/readMaskIm in OpenMIMS\n d['original filename'], d['analysis duration'], d['frames'], \\\n d['scan type'], d['magnification'], d['size type'], \\\n d['size detector'], d['beam blanking'], d['presputtering'], \\\n d['presputtering duration'] = \\\n unpack(self._bo + '16s 3i 3h 2x 3i', hdr.read(48))\n\n d['AutoCal'] = self._autocal(hdr)\n d['HVControl'] = {}\n d['HVControl']['hvcontrol enabled'] = False\n\n elif self.header['file type'] in (22, 41):\n # Called MaskSampleStageImage/readMaskIss in OpenMIMS\n d['original filename'], d['analysis duration'], d['scan type'], \\\n d['steps'], d['step size x'], d['step size y'], d['step size?'], \\\n d['step waittime'], d['frames'], d['beam blanking'], \\\n d['presputtering'], d['presputtering duration'] = \\\n unpack(self._bo + '16s 6i d 4i', hdr.read(64))\n\n d['scan type'] = _stage_scan_types.get(d['scan type'], str(d['scan type']))\n\n d['AutoCal'] = self._autocal(hdr)\n d['HVControl'] = self._hvcontrol(hdr)\n # Don't know if this unused byte needs to go after HVControl or after SigRef.\n hdr.seek(4, 1)\n\n elif self.header['file type'] in (21, 26):\n # Not in OpenMIMS\n # this bit same as image, 1 extra unused/unknown\n d['original filename'], d['analysis duration'], d['frames'], \\\n d['scan type'], d['magnification'], d['size type'], \\\n d['size detector'], d['beam blanking'], d['presputtering'], \\\n d['presputtering duration'] = \\\n unpack(self._bo + '16s 4x 3i 3h 2x 3i', hdr.read(52))\n\n # this bit same as stage scan\n d['AutoCal'] = self._autocal(hdr)\n d['HVControl'] = self._hvcontrol(hdr)\n\n # 24 bytes unknown, not sure if they go here or before AutoCal\n hdr.seek(24, 1)\n\n elif self.header['file type'] == 31:\n # Don't know if this is correct, all 0s anyway\n d['original filename'], d['scan type'], \\\n d['beam blanking'], d['presputtering'] = \\\n unpack(self._bo + '16s 3i 4x', hdr.read(32))\n\n elif self.header['file type'] == 35:\n d['original filename'], d['scan type'], d['analysis duration'], \\\n d['frames'], d['beam blanking'], d['presputtering'] = \\\n unpack(self._bo + '16s 5i 40x', hdr.read(76))\n\n d['AutoCal'] = self._autocal(hdr)\n d['HVControl'] = self._hvcontrol(hdr)\n\n else:\n raise TypeError('What type of image are you? {}'.format(self.header['file type']))\n\n # Continue main header for all types\n d['SigRef'] = self._sigref(hdr)\n d['masses'] = unpack(self._bo + 'i', hdr.read(4))[0]\n\n # scan type is set for stage scan analysis, set others\n if isinstance(d['scan type'], int):\n if d['scan type'] == 0:\n d['scan type'] = ''\n else:\n d['scan type'] = str(d['scan type'])\n\n d['beam blanking'] = bool(d['beam blanking'])\n d['presputtering'] = bool(d['presputtering'])\n d['original filename'] = self._cleanup_string(d['original filename'])\n\n if self.header['file type'] in (21, 26, 27, 29, 35, 39):\n if self.header['file version'] >= 4108:\n n = 60\n else:\n n = 10\n elif self.header['file type'] in (22, 31, 40, 41):\n n = 20\n else:\n n = 0\n\n # Not sure what this is, memory pointers? Not needed.\n # d['mass table ptr'] = unpack(self._bo + 2*n*'h', hdr.read(n*4))\n hdr.seek(n*4, 1)\n\n if self.header['file type'] in (21, 22, 26, 40, 41, 35):\n hdr.seek(4, 1) # 4 bytes unused\n\n # Mass table, dict by species label.\n d['MassTable'] = collections.OrderedDict()\n for m in range(d['masses']):\n mi = {}\n mi['trolley index'], unknown, mi['mass'], mi['matrix or trace'], \\\n mi['detector'], mi['wait time'], mi['frame count time'] = \\\n unpack(self._bo + '2i d 2i 2d', hdr.read(40))\n\n if self.header['file type'] == 31:\n if d['analysis type'].endswith('trolley step scan'):\n # start and end are in mm, step is in μm; convert to mm\n mi['radius start'], mi['radius end'], \\\n mi['radius step'], mi['b field bits'] = \\\n unpack(self._bo + '3d i', hdr.read(28))\n mi['radius step'] /= 1000\n else:\n mi['voltage start'], mi['voltage end'], \\\n mi['voltage step'], mi['b field bits'] = \\\n unpack(self._bo + '3d i', hdr.read(28))\n else:\n mi['offset'], mi['b field bits'] = unpack(self._bo + '2i', hdr.read(8))\n\n mi.update(self._species(hdr))\n\n if self.header['file type'] == 31:\n hdr.seek(4, 1)\n\n # Add correction controls, my own addition.\n mi['background corrected'] = False\n mi['deadtime corrected'] = False\n mi['yield corrected'] = False\n\n label = mi.pop('label')\n # This is true for NS50L and file version 4108.\n # Anywhere else different?\n # Maybe confirm this with the Trolleys dict,\n # there is an Esi trolley.\n if mi['trolley index'] == 8:\n label = 'SE'\n\n d['MassTable'][label] = mi\n\n # Create a few convenient lists\n d['label list'] = tuple(d['MassTable'].keys())\n d['label list fmt'] = tuple(format_species(m) for m in d['label list'])\n d['mass list'] = tuple(d['MassTable'][m]['mass'] for m in d['label list'])\n\n return d","def decode_request_content(self, datafile):\n content_type = self.headers.get(\"Content-Type\", \"notype\").lower()\n if 'application/x-www-form-urlencoded' in content_type:\n # The data is provided in a urlencoded format. Unencode it into\n # cgi FieldStorage/MiniFieldStorage objects in a form container\n form = cgi.FieldStorage(\n fp=datafile,\n headers=self.headers,\n environ=dict(REQUEST_METHOD='POST',\n CONTENT_TYPE=self.headers['Content-Type'])\n )\n itemdict = {}\n for item in form.list:\n if item.name == 'data':\n itemdict['data'] = \\\n SimpleLogRequestHandler.extract_form_fields(item)\n elif item.name == 'layout':\n # http://log4javascript.org/docs/manual.html#layouts\n itemdict['layout'] = item.value\n return itemdict\n else:\n self.send_response(501,\n \"Content-Type %r not supported\" % content_type)\n self.send_header(\"Content-length\", \"0\")\n self.end_headers()\n return None","def _parse_line(self, line):\n fields = line.split('|', 4) # stop splitting after fourth | found\n line_info = {'raw_message': line}\n if len(fields) == 5:\n line_info.update(dict(zip(self._fieldnames, fields)))\n return line_info","def handle(self): \n \n data = self.request[0].strip()\n self.socket = self.request[1]\n\n #split off first word of file, assume is filename\n filename,sep,data = data.partition(\" \")\n\n #assume is requesting file\n if not data:\n self.sendfile(filename)\n #assume we have to save the file since data was sent\n else:\n self.savefile(filename,data)\n\n return True","def test_recv_line(self):\n super(TestCisAsciiFileInput_local, self).test_recv_line()","def test_extract_receipt(self):\n\n # Test IAP Response without in_app list\n request = Request('DummyReceipt', use_production=True)\n ext_receipt = request._extract_receipt(self.iap_response)\n\n assert ext_receipt['status'] == 0 # 0 is normal\n assert ext_receipt['receipt']['product_id'] == 'TestProduction1'\n assert ext_receipt['receipt']['original_transaction_id'] == '1000000012345678' # original transaction id\n assert ext_receipt['receipt']['quantity'] == '1' # check quantity\n\n # Test IAP Response with in_app list\n request = Request('DummyReceipt', use_production=True)\n ext_receipt = request._extract_receipt(self.iap_response_in_app)\n\n assert ext_receipt['status'] == 0 # 0 is normal\n assert ext_receipt['receipt']['product_id'] == 'org.itunesiap'\n assert ext_receipt['receipt']['original_transaction_id'] == '1000000155718067' # original transaction id\n assert ext_receipt['receipt']['quantity'] == '1' # check quantity","async def read_request_line(self):\n while True:\n rl = await self.reader.readline()\n # skip empty lines\n if rl == b'\\r\\n' or rl == b'\\n':\n continue\n break\n rl_frags = rl.split()\n if len(rl_frags) != 3:\n raise HTTPException(400)\n self.method = rl_frags[0]\n url_frags = rl_frags[1].split(b'?', 1)\n self.path = url_frags[0]\n if len(url_frags) > 1:\n self.query_string = url_frags[1]","def process_packet(packet):\n if packet.haslayer(HTTPRequest):\n # if this packet is an HTTP Request\n # get the requested URL\n url = packet[HTTPRequest].Host.decode() + packet[HTTPRequest].Path.decode()\n # get the requester's IP Address\n ip = packet[IP].src\n # get the request method\n method = packet[HTTPRequest].Method.decode()\n print(\"\\n{GREEN}[+] \", ip, \"Requested \", url, \" with \", method)\n if show_raw and packet.haslayer(Raw) and method == \"POST\":\n # if show_raw flag is enabled, has raw data, and the requested method is \"POST\"\n # then show raw\n print(\"\\n{RED}[*] Some useful Raw data: \", packet[Raw].load)","def process_data(self):\n\t\t\n\t\tboundary = self.headers.plisttext.split('=')[1]\n\t\tremain = int(self.headers['content-length'])\n\t\t\n\t\t#Check whether file is started with boundary\n\t\tline = self.rfile.readline()\n\t\tremain -= len(line)\n\t\tif boundary not in line:\n\t\t\treturn False,\"File does not begin with boundary.\"\n\t\t\n\t\t#Get disposition info\t\t\n\t\tline = self.rfile.readline()\n\t\tremain -= len(line)\n\t\t\n\t\t#Check file info \n\t\tfilename = re.findall(r'Content-Disposition.*name=\"file\"; filename=\"(.*)\"',line)\n\t\tif not filename:\n\t\t\treturn False,\"Missing file name.\"\n\t\tpath = self.translate_path(self.path)\n\t\tprint path\n\t\tfilename = os.path.join(path,filename[0])\n\t\t\n\t\t#Check whether file name exists\n\t\twhile os.path.exists(filename):\n\t\t\tfilename = filename.split('.')[0]+'+'+filename.split('.')[1]\n\t\t\t\n\t\t\n\t\t#Get content type info\n\t\tline = self.rfile.readline()\n\t\tremain -= len(line)\n\t\tfiletype = re.findall(r'Content-Type: (.*).*',line)\n\t\tline = self.rfile.readline() #it is an empty line\n\t\tremain -= len(line)\n\t\t\n\t\t#Content begins, try writing data to file in server\n\t\ttry:\n\t\t\toutput = open(filename,'wb')\n\t\texcept IOError:\n\t\t\treturn False,\"Authority denied.\"\n\t\t\n\t\t#Write data\n\t\tfirstline = self.rfile.readline()\n\t\tremain -= len(firstline)\n\t\twhile remain > 0:\n\t\t\tline = self.rfile.readline()\n\t\t\tremain -= len(line)\n\t\t\tif boundary in line:\n\t\t\t\tfirstline = firstline[0:-1]\n\t\t\t\tif firstline[-1] == '\\r':\n\t\t\t\t\tfirstline = firstline[0:-1]\n\t\t\t\toutput.write(firstline)\n\t\t\t\toutput.close()\n\t\t\t\treturn True,\"File created.Path: %s\" % filename\n\t\t\telse:\n\t\t\t\toutput.write(firstline)\n\t\t\t\tfirstline = line\n\t\treturn False,\"Unexpected file end.\"","def get_metadata(diagnostics_dir, verbose=False):\n metafile = find_metadata_file(diagnostics_dir, 'mslist-2*txt', verbose=False)\n\n with open(metafile, 'r') as mslist_file:\n lines = mslist_file.readlines()\n\n nBlocks = 6 # these are the number of correlator cards (PILOT survey value)\n \n obs_metadata = ObservationMetadata()\n\n obs_date = 'Observed from'\n fields = 'Fields'\n code = 'Code'\n duration = 'Total elapsed time'\n antenna = 'antennas'\n frame = 'Frame'\n \n field_list = []\n\n for i in range(len(lines)):\n line = lines[i]\n if line.find(antenna) >=0:\n toks = line.split()\n obs_metadata.n_ant = toks[5][-2:]\n if line.find(obs_date) >=0:\n toks = line.split()\n obs_metadata.start_obs_date = toks[6]\n obs_metadata.end_obs_date = toks[8]\n if line.find(duration) >=0:\n toks = line.split()\n obs_metadata.tobs = float(toks[10]) # in second\n\n # Field details\n if line.find(fields) >=0:\n toks = line.split()\n obs_metadata.num_fields = int(toks[-1])\n\n if line.find(code) >= 0:\n for j in range(obs_metadata.num_fields):\n field_metadata = FieldMetadata()\n field_line = lines[i+j+1]\n toks = field_line.split()\n field_metadata.name = toks[5]\n field_metadata.ra = toks[6][:-5]\n field_metadata.dec = toks[7][:-4]\n field_metadata.num_rows = int(toks[9])\n obs_metadata.fields.append(field_metadata)\n\n if line.find(frame) >= 0:\n next_line = lines[i+1]\n toks = next_line.split()\n obs_metadata.total_obs_bw = float(toks[10])*nBlocks/1000.0 # kHz to MHz \n \n return obs_metadata #n_ant, start_obs_date, end_obs_date, tobs, field, ra, dec, total_obs_bw","def dump_response(line):\n # dump the data of a response\n args = shlex.split(line)\n reqs = yield load_reqlist(args[0])\n for req in reqs:\n if req.response:\n rsp = req.response\n if len(args) >= 2:\n fname = args[1]\n else:\n fname = req.path.split('/')[-1]\n\n with open(fname, 'w') as f:\n f.write(rsp.body)\n print 'Response data written to %s' % fname\n else:\n print 'Request %s does not have a response' % req.reqid","def extract(self, data):","def check_redemption_request(request: IRequest) -> Optional[bytes]:\n if request.requestHeaders.getRawHeaders(b\"content-type\") != [b\"application/json\"]:\n return bad_content_type(request)\n\n p = request.content.tell()\n content = request.content.read()\n request.content.seek(p)\n\n try:\n request_body = loads(content)\n except ValueError:\n return bad_request(request, None)\n\n expected_keys = {\"redeemVoucher\", \"redeemCounter\", \"redeemTokens\"}\n actual_keys = set(request_body.keys())\n if expected_keys != actual_keys:\n return bad_request(\n request,\n {\n \"success\": False,\n \"reason\": \"{} != {}\".format(\n expected_keys,\n actual_keys,\n ),\n },\n )\n return None"],"string":"[\n \"async def parse_replay(request, game):\\n\\n game = game.lower()\\n replay_file = request.files.get(\\\"replay\\\")\\n if replay_file:\\n if game == STARCRAFT:\\n basic, result = await SC2Replay.process_replay(replay_file, request.args.get(\\\"load_map\\\", False))\\n if result:\\n # Lets create our db entry\\n\\n basic['private_replay'] = request.args.get('private_replay', False)\\n replay_id = str(uuid.uuid4())\\n basic[\\\"_id\\\"] = replay_id\\n print(replay_id)\\n unique_name = \\\".\\\".join([replay_id, \\\"SC2Replay\\\"])\\n basic[\\\"replay_object_name\\\"] = unique_name\\n basic[\\\"game_name\\\"] = request.args.get(\\\"replay_name\\\", datetime.utcnow())\\n try:\\n success = await request.app.object_storage.add_object(request.app.config.OS_CONTAINER, replay_file, unique_name)\\n if success:\\n # push results to mongoDB\\n mongo = request.app.mongodb\\n # Insert the basic information for the replay\\n await mongo.starcraft_2_replays.info.insert_one(basic)\\n # Insert event data\\n events = dict(result['event'])\\n events.update(basic)\\n print(events)\\n await mongo.starcraft_2_replays.replay_events.insert_one(events)\\n # Insert stats data\\n stats = dict(result['stats'])\\n stats.update(basic)\\n await mongo.starcraft_2_replays.replay_stats.insert_one(stats)\\n\\n return sanic.response.json(basic)\\n except (swift.BluemixSwiftUnavailableError,\\n swift.BluemixSwiftAuthenticationError,\\n swift.BluemixSwiftRequestTimeoutError,\\n bson.errors.InvalidDocument,\\n pymongo.errors.ConnectionFailure):\\n\\n traceback.print_exc()\\n data = {\\n \\\"error\\\": \\\"Internal Server Error\\\",\\n \\\"success\\\": False,\\n \\\"game\\\": STARCRAFT\\n }\\n return sanic.response.json(data)\",\n \"def parse_replaydata(self):\\n pass\",\n \"def recv_read_response(self, recv_payload): \\n\\t#Only unpack the headers because we want to store the file data as binary\\n\\tunpacked_payload = struct.unpack('!H3IQ', recv_payload[:22])\\n\\tstatus = unpacked_payload[0:1][0]\\n\\tepoch_no = unpacked_payload[1:2][0]\\n\\thandle_no = unpacked_payload[2:3][0]\\t\\n\\t\\n\\t#Check that file handle is the same, to make sure it is the same file request.\\n\\tif (self.epoch_no == epoch_no and self.handle_no == handle_no):\\n\\t start_position = unpacked_payload[3:4][0]\\n\\t num_bytes_been_read = unpacked_payload[4:5][0] \\n\\t # If we receive less bytes than the number we requested to read, this means that\\n\\t # end of file has been reached\\n\\t if (num_bytes_been_read < self.NUM_BYTES_TO_READ):\\n\\t\\tself.eof = True\\n\\t data_to_write = recv_payload[22:]\\t \\n\\t #If status field says that response contains real data: Append to file. Otherwise react \\n\\t #depending on error code received.\\n\\t #Status 00 = OK\\n\\t #Status 01 = Epoch no. of file handle doesnt match epoch no. of current invocation\\n\\t #Status 10 = No context found for file-handle and no data has been read\\n\\t #Status 11 = Context could be found but start position out of range\\n\\t if (status == 0b00):\\n\\t\\tself.file_append.seek(start_position)\\n\\t\\tself.file_append.write(data_to_write)\\n\\t elif (status == 0b01):\\n\\t\\tprint(\\\"Error: Epoch no. of file handle doesnt match epoch no. of current invocation\\\")\\n\\t\\tsys.exit()\\n\\t elif (status == 0b10):\\n\\t\\tprint(\\\"Error: No context found for file-handle and no data has been read\\\")\\n\\t\\tsys.exit()\\n\\t elif(status == 0b11):\\n\\t\\tprint(\\\"Error: Context could be found but start position out of range\\\")\\n\\t\\tsys.exit()\\n\\telse:\\n\\t print(\\\"Error: File handle does not match file handle stored in client. Wrong file received.\\\")\\n\\t sys.exit() \\t \\n\\t#Then return control to read_service_loop() method so that next iteration of send_read_request \\n\\t#from new start position is called.\\n return\",\n \"def decode_replay(replay_file_obj):\\n decoder = zstd.ZstdDecompressor()\\n # Rewind to the beginning of the file obj, because\\n # gcloud might have read it first\\n replay_file_obj.seek(0)\\n replay_data = replay_file_obj.read()\\n try:\\n decoded_data = decoder.decompress(replay_data)\\n json_data = json.loads(decoded_data.decode('utf-8').strip())\\n return json_data\\n except zstd.ZstdError:\\n # The replay file can't be decoded.\\n return None\\n finally:\\n # Seek the replay file back to start so we can upload it.\\n replay_file_obj.seek(0)\",\n \"def recv_open_response(self, recv_payload):\\n\\n\\tunpacked_payload = struct.unpack(\\\"!?Q2I\\\", recv_payload)\\n # Read status field. If set to False, ignore remaining fields and \\n\\t# generate error msg (file not found) before exiting. \\n\\t# Each unpacked value is a tuple, so [0] accesses the value that we want\\n\\tstatus = unpacked_payload[0:1][0]\\n\\tif status == False:\\n\\t print \\\"Error: File not found.\\\"\\n\\t sys.exit()\\n\\t\\n\\t#If set to True, read remaining fields.\\n\\telif status == True:\\n\\t print(\\\"File found.\\\")\\n\\t self.file_length = unpacked_payload[1:2][0]\\n\\t self.epoch_no = unpacked_payload[2:3][0]\\n\\t self.handle_no = unpacked_payload[3:][0]\\t \\t \\n\\treturn\",\n \"def processReadback(resp):\\n a = np.fromstring(resp, dtype='<u1')\\n return {\\n 'build': a[51],\\n 'serDAC': a[56],\\n 'noPllLatch': bool((a[58] & 0x80) > 0),\\n 'ackoutI2C': a[61],\\n 'I2Cbytes': a[69:61:-1],\\n 'executionCounter': (a[53] << 8) + a[52]\\n }\",\n \"def handle(self):\\n\\t\\ttry:\\n\\t\\t\\trequest_line = self.rfile.readline().decode(\\\"ascii\\\")\\n\\t\\t\\tassert request_line.endswith(\\\"\\\\r\\\\n\\\"), \\\"Request line must end in CRLF\\\"\\n\\t\\t\\tparts = request_line.strip().split()\\n\\t\\t\\tassert len(parts)==3, \\\"Invalid request line\\\"\\n\\t\\t\\thost, path, content_length = parts\\n\\t\\t\\tif (content_length:=int(content_length))>0:\\n\\t\\t\\t\\tdata = self.rfile.read(content_length)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tdata = b''\\n\\t\\t\\tself.handle_request(host,path,data)\\n\\t\\texcept AssertionError as e:\\n\\t\\t\\tself.response_code(4,e.args[0])\",\n \"def __read(self, bytes=31):\\n raw_data = self.file_read.read(bytes)\\n response = self.__get_response(raw_data)\\n is_valid, error_code = self.__is_response_valid(response)\\n if is_valid:\\n char_list = self.__handle_raspi_glitch(response[1:])\\n return str(''.join(char_list)), is_valid\\n else:\\n return error_code, is_valid\",\n \"def get_response():\\n result = ''\\n line = ''\\n while line != '\\\\n':\\n result += line\\n line = FROMFILE.readline()\\n #print(\\\" I read line:[\\\"+line+\\\"]\\\")\\n return result\",\n \"def _get_data(self):\\n response = self._get_raw_data()\\n if response is None:\\n # error has already been logged\\n return None\\n\\n if response.startswith('ERROR'):\\n self.error(\\\"received ERROR\\\")\\n return None\\n\\n try:\\n parsed = response.split(\\\"\\\\n\\\")\\n except AttributeError:\\n self.error(\\\"response is invalid/empty\\\")\\n return None\\n\\n # split the response\\n data = {}\\n for line in parsed:\\n if line.startswith('STAT'):\\n try:\\n t = line[5:].split(' ')\\n data[t[0]] = t[1]\\n except (IndexError, ValueError):\\n self.debug(\\\"invalid line received: \\\" + str(line))\\n pass\\n\\n if len(data) == 0:\\n self.error(\\\"received data doesn't have any records\\\")\\n return None\\n\\n # custom calculations\\n try:\\n data['avail'] = int(data['limit_maxbytes']) - int(data['bytes'])\\n data['used'] = int(data['bytes'])\\n except:\\n pass\\n\\n return data\",\n \"def parse_replay(replay):\\n if replay is None:\\n return None\\n\\n stats = GameStat(replay[\\\"num_players\\\"])\\n stats.turns_total = len(replay['frames']) - 1\\n for frame in replay[\\\"frames\\\"]:\\n for event in frame.get(\\\"events\\\", []):\\n player_tag = event[\\\"entity\\\"].get(\\\"owner\\\")\\n if event[\\\"event\\\"] == \\\"spawned\\\":\\n stats.ships_produced += 1\\n stats.players[player_tag].ships_produced += 1\\n elif event[\\\"event\\\"] == \\\"destroyed\\\":\\n if event[\\\"entity\\\"][\\\"type\\\"] == \\\"ship\\\":\\n stats.ships_destroyed += 1\\n elif event[\\\"entity\\\"][\\\"type\\\"] == \\\"planet\\\":\\n stats.planets_destroyed += 1\\n if player_tag:\\n stats.players[player_tag].planets_destroyed += 1\\n elif event[\\\"event\\\"] == \\\"attack\\\":\\n stats.players[player_tag].attacks_total += 1\\n\\n ships_alive_total = sum([len(ships) for ships in replay[\\\"frames\\\"][-1][\\\"ships\\\"].values()])\\n for player_tag in stats.players.keys():\\n stats.players[player_tag].ships_alive = len(replay[\\\"frames\\\"][-1][\\\"ships\\\"][str(player_tag)])\\n # use max(1.0, ...) to avoid ZeroDivisionError\\n stats.players[player_tag].ships_alive_ratio = 1.0 * stats.players[player_tag].ships_alive / max(1.0, stats.players[player_tag].ships_produced)\\n stats.players[player_tag].ships_relative_ratio = 1.0 * stats.players[player_tag].ships_alive / max(1.0, ships_alive_total)\\n\\n for planet in replay[\\\"frames\\\"][-1][\\\"planets\\\"].values():\\n if planet[\\\"owner\\\"] is not None:\\n stats.players[planet[\\\"owner\\\"]].planets_controlled += 1\\n\\n return stats\",\n \"def canned_ims2_response():\\n return file_utils.response_file_to_json(test_data_dir + '/waveform_41177893.1')\",\n \"def cmd_get(self, msg_dict):\\r\\n filename = msg_dict[\\\"filename\\\"]\\r\\n filename_abs_path = \\\"%s\\\" % msg_dict[\\\"current_directory\\\"] + \\\"/\\\" + filename\\r\\n # print(\\\"in the cmd_get server:\\\", filename_abs_path)\\r\\n if os.path.isfile(filename_abs_path):\\r\\n # print(\\\"@@@@\\\")\\r\\n file_size = os.stat(filename_abs_path).st_size\\r\\n self.request.send(str(file_size).encode())\\r\\n reply = self.request.recv(1024)\\r\\n server_md5 = hashlib.md5()\\r\\n if reply:\\r\\n with open(filename_abs_path, 'rb') as f:\\r\\n for line in f:\\r\\n self.request.send(line)\\r\\n server_md5.update(line)\\r\\n server_md5_value = server_md5.hexdigest()\\r\\n replay2 = self.request.recv(1024)\\r\\n # print(\\\"in the cmd_get server:\\\", replay2)\\r\\n self.request.send(server_md5_value.encode())\",\n \"def check_file():\\n #print('request=', request)\\n #print('request.data=', request.data)\\n #print('request.form=', request.form)\\n #print('request.files=', request.files)\\n #print('request.json=', request.json)\\n qdata = None\\n adata = None\\n Q = None\\n A = None\\n if request.json:\\n qdata = request.json.get('Q')\\n adata = request.json.get('A')\\n if 'Qfile' in request.files:\\n qdata = request.files['Qfile'].read().decode('utf-8')\\n if 'Afile' in request.files:\\n adata = request.files['Afile'].read().decode('utf-8')\\n\\n #print('qdata\\\\n', qdata)\\n #print('adata\\\\n', adata)\\n try:\\n if qdata:\\n Q = adc2019.read_Q(qdata)\\n if adata:\\n A = adc2019.read_A(adata)\\n if Q is None and A is None:\\n return jsonify({'check_file': 'No data'})\\n if Q is None:\\n return jsonify({'check_file': 'A-ok'})\\n if A is None:\\n return jsonify({'check_file': 'Q-ok'})\\n\\n info = adc2019.check_data(Q, A)\\n #print(info)\\n info2 = info.copy()\\n for k in ['count', 'corner', 'line_length', 'line_corner', 'ban_data_F']:\\n info2[k] = str(info2[k])\\n info2['check_file'] = 'ok'\\n return jsonify(info2)\\n except Exception as e:\\n #traceback.print_exc()\\n errinfo = ['ADC2019 rule violation'] + [str(i) for i in e.args]\\n info = {'error': errinfo, 'stack_trace': traceback.format_exc()}\\n return jsonify(info)\\n\\n return jsonify({'check_file': 'ok',\\n 'value': 1234567,\\n 'msg': '生麦生米生卵'})\",\n \"def request_ims_data():\\n input_check_results = input_validation.check_inputs(request)\\n if input_check_results is not None:\\n return error_response(input_check_results) \\n\\n time_range = request.json['timeRange']\\n station = request.json['station']\\n\\n # Max msg_id size = 20 characters, so a UUID is too long; instead generate a random 20 digit number for\\n # the random message id to represent this request and its associated response\\n msg_id = str(random.randint(1,99999999999999999999))\\n [request_filename, request_file_path, response_filename, response_file_path] = file_utils.create_filenames(msg_id, base_dir)\\n \\n # Write request to a file \\n request_text = file_utils.create_waveform_request(msg_id, time_range, station) \\n request_file = open(request_file_path, 'w')\\n request_file.write(request_text)\\n request_file.close()\\n\\n # Calling via subprocess is a little icky, but if we call this way:\\n # nms_client.batch.batchclient.bootstrap_run()\\n # then we have to manually mess with sys.argv to put the filename in the command line arguments. This is unlikely\\n # to hold up well with multiple requests running simultaneously\\n subprocess_output = subprocess.check_output(['/ims2/nms_client/bin/nms_client.sh', '-d', base_dir, '-f', response_filename, request_file_path])\\n os.remove(request_file_path)\\n\\n # Read response file if it was written. \\n if os.path.isfile(response_file_path):\\n response_json = file_utils.response_file_to_json(response_file_path) \\n os.remove(response_file_path) \\n return response_json\\n # No response file means there was some sort of error; return the output from calling the client. \\n else:\\n return error_response(subprocess_output)\",\n \"def parse_mochad_line(self, line):\\n # bail out unless it's an incoming RFSEC message\\n if line[15:23] == 'Rx RFSEC':\\n\\n # decode receive RFSEC message. format is either:\\n # 09/22 15:39:07 Rx RFSEC Addr: 21:26:80 Func: Contact_alert_min_DS10A\\n # ~ or ~\\n # 09/22 15:39:07 Rx RFSEC Addr: 0x80 Func: Motion_alert_SP554A\\n line_list = line.split(' ')\\n addr = line_list[5]\\n func = line_list[7]\\n\\n func_dict = self.decode_func(func)\\n\\n return addr, {'func': func_dict}, 'security'\\n\\n# elif line[15:23] == 'Tx RFSEC':\\n\\n # decode send RFSEC message. format is either:\\n # 09/22 15:39:07 Tx RFSEC Addr: 21:26:80 Func: Contact_alert_min_DS10A\\n # ~ or ~\\n # 09/22 15:39:07 Tx RFSEC Addr: 0x80 Func: Motion_alert_SP554A\\n# line_list = line.split(' ')\\n# addr = line_list[5]\\n# func = line_list[7]\\n#\\n# func_dict = self.decode_func(func)\\n#\\n# return addr, {'func': func_dict}, 'trigger'\\n\\n elif line[15:20] == 'Rx RF':\\n\\n # decode receive RF message. format is:\\n # 02/13 23:54:28 Rx RF HouseUnit: B1 Func: On\\n line_list = line.split(' ')\\n house_code = line_list[5];\\n house_func = line_list[7]\\n\\n return house_code, {'func': house_func}, 'radio'\\n\\n elif line[15:20] == 'Rx PL':\\n \\n # decode receive PL message. format is:\\n # 02/13 23:54:28 Rx PL HouseUnit: A1\\n # 02/13 23:54:28 Rx PL House: A Func: On\\n line_list = line.split(' ')\\n if line[21:27] == 'HouseU':\\n house_code = line_list[5]\\n with open ('/root/.house_code', 'wb') as f:\\n pickle.dump(house_code, f)\\n else:\\n house_func = line_list[7]\\n with open ('/root/.house_code', 'rb') as f:\\n house_code = pickle.load(f)\\n return house_code, {'func': house_func}, 'powerline'\\n \\n elif line[15:20] == 'Tx PL':\\n \\n # decode send RF/PL message. format is:\\n # 02/13 23:54:28 Tx PL HouseUnit: A1\\n # 02/13 23:54:28 Tx PL House: A Func: On\\n line_list = line.split(' ')\\n if line[21:27] == 'HouseU':\\n house_code = line_list[5]\\n with open ('/root/.house_code', 'wb') as f:\\n pickle.dump(house_code, f)\\n else:\\n house_func = line_list[7]\\n with open ('/root/.house_code', 'rb') as f:\\n house_code = pickle.load(f)\\n return house_code, {'func': house_func}, 'button'\\n \\n return '', ''\",\n \"def FILE_RTRV_record(self):\\n eor = 0\\n raw_record = []\\n #record = []\\n # record is a list of non-empty strings, \\n # each string is a line of info from the reader\\n word = ''\\n linebuf = []\\n while len(raw_record) < 1 and self.eof == 0 and eor == 0 :\\n # we need to have something to reply.. \\n while 1:\\n # read 1 char at a time \\n # until we get to the '>' prompt\\n #\\n c = self.tf.read(1)\\n #\\n #print c,\\n #\\n if len(c) != 1:\\n self.eof = 1\\n if self.debug > 2 :\\n print \\\"FILE Raw Record: \\\",\\n pprint.pprint(raw_record)\\n return raw_record\\n elif c == '>':\\n eor = 1\\n if self.debug > 2 :\\n print \\\"FILE2 Raw Record: \\\",\\n pprint.pprint(raw_record)\\n return raw_record\\n # \\\\r = CR , \\\\n = LF \\n # (serial device uses CR + optionally LF, unix text only uses LF)\\n # - new array entry but only if there is something to add \\n elif c == '\\\\r' or c == '\\\\n':\\n if word != '':\\n linebuf.append(word)\\n word = ''\\n if linebuf != []:\\n raw_record.append(linebuf)\\n linebuf = []\\n # split line into words\\n elif c == ' ':\\n if word != '':\\n linebuf.append(word)\\n word = ''\\n # all other chars\\n else : \\n word = word + c\\n \\n time.sleep(0.001)\",\n \"def get_server_logs(self):\\n self.response.content\\n binary_body = re.split('--==.*==', self.response.content)[2].split('\\\\r\\\\n')[5]\\n\\n f = StringIO.StringIO()\\n f.write(bytearray(binary_body))\\n\\n memory_zip = ZipFile(f)\\n zip_content = {name: memory_zip.read(name) for name in memory_zip.namelist()}\\n oracc_log = zip_content['oracc.log']\\n request_log = zip_content['request.log']\\n\\n # Check if server returns a lemmatised file\\n autolem = None \\n for key, value in zip_content.iteritems():\\n if key.endswith(\\\"autolem.atf\\\"):\\n autolem = value\\n\\n print zip_content.keys()\\n print \\\"@\\\"*30\\n print oracc_log\\n print \\\"@\\\"*30\\n print request_log\\n print \\\"@\\\"*30\\n if autolem:\\n print autolem\\n print \\\"@\\\"*30\\n\\n return oracc_log, request_log, autolem\",\n \"def get_req_data(self, data):\\n self.rdata=data\\n if self.rdata == 1 or \\\\\\n self.rdata == 0 or \\\\\\n self.rdata == \\\"\\\":\\n return 1\\n rt=\\\"\\\"\\n for ot in self.rdata.split('\\\\n'):\\n ot = ot.rstrip('\\\\r\\\\n')\\n logging.debug(\\\">>>>>>> Check value of output string: %s\\\" % ot)\\n if not (ot.startswith(\\\"vRA \\\") or \\\\\\n ot.startswith(\\\"JRE Version:\\\") or \\\\\\n ot.startswith(\\\"CloudClient is \\\") or \\\\\\n ot.startswith(\\\"true\\\") or \\\\\\n len(ot) == 0):\\n if ot.startswith(\\\"Error\\\"):\\n print (\\\"Error: %s\\\" % ot.split('Error')[-1])\\n sys.stdout.flush()\\n sys.exit(1)\\n else:\\n logging.debug(\\\"SR# %s\\\" % ot)\\n rt=ot\\n break\\n if not rt:\\n print \\\"No service request is found: %s\\\" % rt\\n return 1\\n else:\\n return rt\",\n \"def view_full_response(line):\\n reqs = yield load_reqlist(line)\\n for req in reqs:\\n if req.response:\\n if len(reqs) > 1:\\n print '-'*15 + (' %s ' % req.reqid) + '-'*15\\n view_full_message(req.response)\\n else:\\n print \\\"Request %s does not have a response\\\" % req.reqid\",\n \"def intercept_extractor(player_name, match_id, filepath, start_id=0):\\n\\n # define file path\\n path = filepath + str(match_id) + \\\".json\\\"\\n\\n # read events json into a dataframe\\n df_events = pd.read_json(path)\\n\\n # initiate an empty dictionary to store intercept values\\n intercept_dict = defaultdict(dict)\\n\\n intercept_id = start_id\\n\\n # iterate through the events and extract intercept information specific to the player\\n for i in range(len(df_events)):\\n try:\\n if (df_events.player[i][\\\"name\\\"]) == player_name:\\n if df_events.type[i][\\\"name\\\"] == \\\"Interception\\\":\\n\\n # create a new entry in the dictionary with the key being the id\\n\\n # add event index to dictionary\\n intercept_dict[intercept_id][\\\"index\\\"] = df_events.index[i + 1]\\n\\n # get the game state at the event index\\n intercept_dict[intercept_id][\\\"gamestate\\\"] = game_state_extractor(\\n df_events.index[i + 1],\\n match_id=match_id,\\n player_name=player_name,\\n filepath=filepath,\\n )\\n\\n # reading match_id\\n intercept_dict[intercept_id][\\\"match_id\\\"] = match_id\\n\\n intercept_dict[intercept_id][\\\"start_X\\\"] = df_events.location[i][0]\\n\\n intercept_dict[intercept_id][\\\"start_Y\\\"] = df_events.location[i][1]\\n\\n intercept_dict[intercept_id][\\\"outcome\\\"] = df_events.iloc[i, :][\\n \\\"interception\\\"\\n ][\\\"outcome\\\"][\\\"name\\\"]\\n\\n intercept_id += 1\\n except:\\n pass\\n\\n return intercept_dict\",\n \"def file_check( self ):\\n try:\\n file_handler = open( self.PATH_TO_SOURCE_FILE )\\n log.info( 'annex requests found' )\\n except Exception, e:\\n message = 'no annex requests found; quitting\\\\n\\\\n'\\n log.info( message )\\n sys.exit( message )\\n utf8_data = file_handler.read()\\n assert type(utf8_data) == str, type(utf8_data)\\n data = utf8_data.decode( 'utf-8' )\\n return data\",\n \"def readrecf(filename):\\n\\n rec_dict = {}\\n with open(filename,'r') as recfile:\\n line_tmp = \\\"\\\"\\n while 1:\\n if line_tmp == \\\"\\\":\\n line = recfile.readline()\\n else:\\n line = line_tmp\\n line_tmp = \\\"\\\"\\n \\n if line == \\\"\\\": # if End Of File\\n break\\n elif line == \\\"\\\\n\\\": # if blank line\\n continue\\n elif \\\"Catch\\\" in line:\\n ind = line.find('=')\\n rec_dict['iscatch'] = line[ind+1:]\\n elif \\\"Chans\\\" in line:\\n ind = line.find('=')\\n rec_dict['num_channels'] = int(line[ind+1:])\\n elif \\\"ADFREQ\\\" in line:\\n ind = line.find('=')\\n try:\\n rec_dict['sample_freq'] = int(line[ind+1:])\\n except ValueError:\\n rec_dict['sample_freq'] = float(line[ind+1:])\\n elif \\\"Samples\\\" in line:\\n ind = line.find('=')\\n rec_dict['num_samples'] = int(line[ind+1:])\\n elif \\\"T After\\\" in line:\\n ind = line.find('=')\\n rec_dict['time_after'] = float(line[ind+1:])\\n elif \\\"T Before\\\" in line:\\n ind = line.find('=')\\n rec_dict['time before'] = float(line[ind+1:])\\n elif \\\"Output Sound File\\\" in line:\\n ind = line.find('=')\\n rec_dict['outfile'] = line[ind+1:]\\n elif \\\"Thresholds\\\" in line:\\n th_list = []\\n while 1:\\n line = recfile.readline()\\n if line == \\\"\\\":\\n break\\n try:\\n th_list.append(float(line))\\n except ValueError: # because we reached next section\\n line_tmp = line\\n break\\n rec_dict['thresholds'] = th_list\\n if line == \\\"\\\":\\n break\\n elif \\\"Feedback information\\\" in line:\\n fb_dict = {}\\n while 1:\\n line = recfile.readline()\\n if line == \\\"\\\":\\n break\\n elif line == \\\"\\\\n\\\":\\n continue\\n ind = line.find(\\\"msec\\\")\\n time = float(line[:ind-1])\\n ind = line.find(\\\":\\\")\\n fb_type = line[ind+2:]\\n fb_dict[time] = fb_type\\n rec_dict['feedback_info'] = fb_dict\\n if line == \\\"\\\":\\n break\\n elif \\\"File created\\\" in line:\\n header = [line]\\n for counter in range(4):\\n line = recfile.readline()\\n header.append(line)\\n rec_dict['header']=header\\n return rec_dict\",\n \"def get_info(raw_filename, epochs_filename):\\n trans, fiducials, info = get_head_correct_info(\\n raw_filename, epochs_filename)\\n return info\",\n \"def view_response_bytes(line):\\n reqs = yield load_reqlist(line)\\n for req in reqs:\\n if req.response:\\n if len(reqs) > 1:\\n print '-'*15 + (' %s ' % req.reqid) + '-'*15\\n print req.response.full_message\\n else:\\n print \\\"Request %s does not have a response\\\" % req.reqid\",\n \"def readrecf(filename):\\n\\n rec_dict = {}\\n with open(filename, 'r') as recfile:\\n line_tmp = \\\"\\\"\\n while 1:\\n if line_tmp == \\\"\\\":\\n line = recfile.readline()\\n else:\\n line = line_tmp\\n line_tmp = \\\"\\\"\\n\\n if line == \\\"\\\": # if End Of File\\n break\\n elif line == \\\"\\\\n\\\": # if blank line\\n continue\\n elif \\\"Catch\\\" in line:\\n ind = line.find('=')\\n rec_dict['iscatch'] = line[ind + 1:]\\n elif \\\"Chans\\\" in line:\\n ind = line.find('=')\\n rec_dict['num_channels'] = int(line[ind + 1:])\\n elif \\\"ADFREQ\\\" in line:\\n ind = line.find('=')\\n try:\\n rec_dict['sample_freq'] = int(line[ind + 1:])\\n except ValueError:\\n rec_dict['sample_freq'] = float(line[ind + 1:])\\n elif \\\"Samples\\\" in line:\\n ind = line.find('=')\\n rec_dict['num_samples'] = int(line[ind + 1:])\\n elif \\\"T After\\\" in line:\\n ind = line.find('=')\\n rec_dict['time_after'] = float(line[ind + 1:])\\n elif \\\"T Before\\\" in line:\\n ind = line.find('=')\\n rec_dict['time before'] = float(line[ind + 1:])\\n elif \\\"Output Sound File\\\" in line:\\n ind = line.find('=')\\n rec_dict['outfile'] = line[ind + 1:]\\n elif \\\"Thresholds\\\" in line:\\n th_list = []\\n while 1:\\n line = recfile.readline()\\n if line == \\\"\\\":\\n break\\n try:\\n th_list.append(float(line))\\n except ValueError: # because we reached next section\\n line_tmp = line\\n break\\n rec_dict['thresholds'] = th_list\\n if line == \\\"\\\":\\n break\\n elif \\\"Feedback information\\\" in line:\\n fb_dict = {}\\n while 1:\\n line = recfile.readline()\\n if line == \\\"\\\":\\n break\\n elif line == \\\"\\\\n\\\":\\n continue\\n ind = line.find(\\\"msec\\\")\\n time = float(line[:ind - 1])\\n ind = line.find(\\\":\\\")\\n fb_type = line[ind + 2:]\\n fb_dict[time] = fb_type\\n rec_dict['feedback_info'] = fb_dict\\n if line == \\\"\\\":\\n break\\n elif \\\"File created\\\" in line:\\n header = [line]\\n for counter in range(4):\\n line = recfile.readline()\\n header.append(line)\\n rec_dict['header'] = header\\n return rec_dict\",\n \"def test_vehicle_data_response(filename: str) -> None:\\n response: models.KamereonVehicleDataResponse = fixtures.get_file_content_as_schema(\\n filename, schemas.KamereonVehicleDataResponseSchema\\n )\\n response.raise_for_error_code()\\n # Ensure the VIN is hidden\\n assert response.data is not None\\n assert response.data.id is not None\\n assert response.data.id.startswith(\\\"VF1AAAA\\\")\",\n \"def decode_replay_details(contents):\\n decoder = VersionedDecoder(contents, typeinfos)\\n return decoder.instance(game_details_typeid)\",\n \"def test_process_response(self):\\n t = self.create_request_object()\\n response_content = u\\\"\\\"\\\" <Response ReferenceNumber=\\\"82e942b0-48e8-4cf4-b299-51e2b6a89a1b\\\"\\n InboundODMFileOID=\\\"\\\"\\n IsTransactionSuccessful=\\\"1\\\"\\n SuccessStatistics=\\\"Rave objects touched: Subjects=0; Folders=0; Forms=0; Fields=0; LogLines=0\\\" NewRecords=\\\"\\\">\\n </Response>\\n \\\"\\\"\\\"\\n req = mock.Mock(requests.Request, text=response_content)\\n response = t.result(req)\\n self.assertTrue(isinstance(response, RWSResponse))\",\n \"def parse_meta(self):\\n logger.info('parse_meta()')\\n\\n magic = self.replay.read_uint32()\\n if (magic != FILE_MAGIC):\\n raise InvalidReplayException()\\n file_version = self.replay.read_uint32()\\n lenght_in_ms = self.replay.read_uint32()\\n network_version = self.replay.read_uint32()\\n change_list = self.replay.read_uint32()\\n friendly_name = self.replay.read_string()\\n is_live = self.replay.read_bool()\\n\\n if file_version >= HistoryTypes.HISTORY_RECORDED_TIMESTAMP.value:\\n time_stamp = self.replay.read_uint64()\\n\\n if file_version >= HistoryTypes.HISTORY_COMPRESSION.value:\\n is_compressed = self.replay.read_bool()\\n\\n is_encrypted = False\\n encryption_key = bytearray()\\n if file_version >= HistoryTypes.HISTORY_ENCRYPTION.value:\\n is_encrypted = self.replay.read_bool()\\n encryption_key_size = self.replay.read_uint32()\\n encryption_key = self.replay.read_bytes(encryption_key_size)\\n\\n if (not is_live and is_encrypted and len(encryption_key) == 0):\\n logger.error(\\n \\\"Completed replay is marked encrypted but has no key!\\\")\\n raise InvalidReplayException()\\n\\n if (is_live and is_encrypted):\\n logger.error(\\n \\\"Replay is marked encrypted but not yet marked as completed!\\\")\\n raise InvalidReplayException()\\n\\n self.meta = Meta(\\n file_version=file_version,\\n lenght_in_ms=lenght_in_ms,\\n network_version=network_version,\\n change_list=change_list,\\n friendly_name=friendly_name,\\n is_live=is_live,\\n time_stamp=time_stamp,\\n is_compressed=is_compressed,\\n is_encrypted=is_encrypted,\\n encryption_key=encryption_key,\\n\\n )\",\n \"def _extract_info(self, data):\\n if 'status' in data:\\n if data['status'] == 'Already Downloaded':\\n # Set self._return_code to already downloaded\\n # and trash that key\\n self._set_returncode(self.ALREADY)\\n data['status'] = None\\n\\n if data['status'] == 'Filesize Abort':\\n # Set self._return_code to filesize abort\\n # and trash that key\\n self._set_returncode(self.FILESIZE_ABORT)\\n data['status'] = None\",\n \"def _parse(self):\\n with open(self._path, 'r') as file:\\n try:\\n line = file.readline()\\n while line:\\n if SENDING in line:\\n self._req_set.add(self._get_request(line, True))\\n line = file.readline()\\n except Exception as err:\\n print(\\\"Failed to read garbage collector log. Log was not a complete test log.\\\\n\\\"\\n f\\\"{err!s}\\\")\\n raise TestFailedException\",\n \"def parseMeta(record):\\n\\n if 'csw:GetRecordByIdResponse' in record:\\n log.info(\\\"Found 1 record\\\")\\n if 'csw:Record' in json.loads(record)[\\\"csw:GetRecordByIdResponse\\\"]:\\n recordRoot = (\\n json.loads(record)[\\\"csw:GetRecordByIdResponse\\\"][\\\"csw:Record\\\"]\\n )\\n else:\\n log.info(\\\"...But record is empty\\\")\\n return\\n elif 'csw:GetRecordsResponse' in record:\\n if 'csw:SearchResults' in json.loads(record)[\\\"csw:GetRecordsResponse\\\"]:\\n searchResults = (\\n json.loads(record)[\\\"csw:GetRecordsResponse\\\"][\\\"csw:SearchResults\\\"]\\n )\\n\\n else:\\n log.info(\\\"...But record is empty\\\")\\n return\\n\\n numberOfRecords = int(searchResults[\\\"@numberOfRecordsReturned\\\"])\\n recordRoot = dict()\\n\\n if numberOfRecords == 0:\\n log.warning(\\\"No records found\\\")\\n return\\n elif numberOfRecords == 1:\\n log.info(\\\"Found 1 record\\\")\\n recordRoot = searchResults[\\\"csw:Record\\\"]\\n else:\\n log.info(\\\"Found \\\" + str(numberOfRecords) + \\\" records\\\")\\n recordRoot = searchResults[\\\"csw:Record\\\"][0]\\n else:\\n print(\\\"Could not parse GNOS response\\\")\\n return\\n\\n if 'dc:identifier' in recordRoot:\\n uuid = recordRoot[\\\"dc:identifier\\\"]\\n else:\\n uuid = 'null'\\n\\n if 'ows:BoundingBox' in recordRoot:\\n recordBbox = recordRoot[\\\"ows:BoundingBox\\\"]\\n\\n if 'ows:LowerCorner' in recordBbox:\\n bbox_lower = recordBbox[\\\"ows:LowerCorner\\\"]\\n\\n if 'ows:UpperCorner' in recordBbox:\\n bbox_upper = recordBbox[\\\"ows:UpperCorner\\\"]\\n\\n bbox_a = float(bbox_lower.split(\\\" \\\")[0])\\n bbox_b = float(bbox_lower.split(\\\" \\\")[1])\\n bbox_c = float(bbox_upper.split(\\\" \\\")[0])\\n bbox_d = float(bbox_upper.split(\\\" \\\")[1])\\n bbox = [bbox_a, bbox_b, bbox_c, bbox_d]\\n\\n if '@crs' in recordBbox:\\n crs = recordBbox[\\\"@crs\\\"]\\n else:\\n bbox = []\\n crs = 'null'\\n\\n if 'dc:URI' in recordRoot:\\n recordUri = recordRoot[\\\"dc:URI\\\"]\\n if type(recordUri) is dict:\\n if \\\"#text\\\" in recordUri:\\n table = recordUri[\\\"#text\\\"]\\n else:\\n table = recordUri[0][\\\"#text\\\"]\\n else:\\n table = 'null'\\n\\n geodata_meta = GeodataMeta(\\n uuid=uuid,\\n bbox=bbox,\\n crs=crs,\\n table=table\\n )\\n\\n return geodata_meta\",\n \"def handle_client(self, client):\\n request_data = client.recv(1024)\\n request_lines = request_data.splitlines()\\n for line in request_lines:\\n print(line)\\n request_start_line = request_lines[0]\\n print(request_start_line.decode(\\\"utf-8\\\"))\\n\\n file_name = re.match(r\\\"\\\\w+ +(/[^ ]*) \\\", request_start_line.decode(\\\"utf-8\\\")).group(1)\\n method = re.match(r\\\"(\\\\w+) +/[^ ]* \\\", request_start_line.decode(\\\"utf-8\\\")).group(1)\\n\\n env = {\\n \\\"PATH_INFO\\\" : file_name,\\n \\\"METHOD\\\": method\\n }\\n response_body = self.app(env, self.start_response)\\n response = self.response_headers + \\\"\\\\r\\\\n\\\" + response_body\\n print(\\\"response data: \\\", response)\\n client.send(bytes(response, \\\"utf-8\\\"))\\n client.close()\",\n \"def parse_header(self, header):\\n # \\n \\n # this is what the line'll look like:\\n # e.g.: /logs/1\\n # e.g.: /detailed/host/timestamp\\n\\n # get index of first slash\\n first_slash = header.index('/')\\n \\n \\n # splice the string now and remove any spaces\\n requested_folder = header.strip('/')\\n \\n # check if it's just a slash\\n if not requested_folder:\\n # return a 0 meaning we want the latest log file\\n return (0, 0)\\n else:\\n # check that it's a valid request\\n detailed_request = requested_folder.split('/')\\n # detailed_request should be of form /log/* where * is a number\\n # two types of requests:\\n # type 1: /log/* where * is a number\\n # type 2: /detailed/node_name/timestamp\\n # node_name: node name\\n # timetamp is the timestamp of the run\\n \\n \\n if len(detailed_request) == 2:\\n # type 1 request\\n # first entry is '' since there's a leading '/'\\n if detailed_request[0] == 'log':\\n # now get a valid number for a folder request\\n try:\\n log_number = int(detailed_request[1])\\n except Exception, e:\\n print \\\"Error obtaining log (request: \\\"+requested_folder+\\\")\\\"\\n return (-1, str(e))\\n else:\\n return (0, log_number)\\n else:\\n return (-1, 'Invalid request (len 2)')\\n elif len(detailed_request) == 3:\\n # type 2 request\\n if detailed_request[0] == 'detailed':\\n nodename = detailed_request[1]\\n timestamp = detailed_request[2]\\n # verify that timestamp is a valid #\\n try:\\n timestamp_int = int(timestamp)\\n except ValueError, ve:\\n print 'Invalid timestamp requested, '+timestamp\\n print ve\\n return (-1, 'Invalid timestamp')\\n else:\\n # return the filepath as our response\\n return (1, './detailed_logs/'+nodename+'/'+timestamp)\\n \\n \\n else:\\n return (-1, 'Invalid request (len 3)')\\n \\n else:\\n # invalid!\\n return (-1, 'Invalid detailed log request ('+str(detailed_request)+')')\",\n \"def parse_lens_response_str(response):\\n\\n import re\\n\\n result = dict(MOSI=[], MISO=[], timedOut=False, interface_error=False)\\n result['return_str'] = response\\n\\n for line in response:\\n\\n if \\\"Error\\\" in line:\\n result[\\\"interface_error\\\"] = True\\n\\n if 'Timed out' in line:\\n result['timedOut'] = True\\n\\n if 'fast mode' in line:\\n result['fastMode'] = 1\\n\\n if 'slow mode' in line:\\n result['fastMode'] = 0\\n\\n for ch in ['MOSI', 'MISO']:\\n if ch in line:\\n for match in re.finditer('Ox', line):\\n result[ch].append(line[match.start() + 2:match.start() + 4])\\n\\n return result\",\n \"def parse(lines): \\n replied = len(lines)\\n avg_delay, lost = 0, 0\\n qos = 1.0\\n \\n if replied != 0:\\n for line in lines:\\n line.strip() #remove leading and trailing spaces\\n \\\"\\\"\\\"\\n Each line has the following fields:\\n [status code] [reply time (seconds since epoch)] [source IP] [source url] [source query] [serving delay]\\n \\n e.g.:\\n 200 1296756182 192.168.10.2 /home.php ?N=192 11045\\n 200 1296756183 192.168.10.2 /home.php ?N=192 230036\\n 200 1296756183 192.168.10.2 /home.php ?N=192 230684\\n \\\"\\\"\\\"\\n status, time, sourceIP, url, query, delay = line.split()\\n \\n time = int(time)\\n delay = int(delay)\\n \\n if delay > DEADLINE:\\n lost += 1\\n avg_delay += delay\\n avg_delay /= replied\\n qos = (replied - lost) / replied\\n\\n return {'replied': replied, 'delay' : avg_delay, 'qos' : qos, 'lost': lost}\",\n \"def view_request_info(line):\\n args = shlex.split(line)\\n if not args:\\n raise PappyException(\\\"Request id is required\\\")\\n reqids = args[0]\\n\\n reqs = yield load_reqlist(reqids)\\n\\n for req in reqs:\\n print ''\\n print_request_extended(req)\\n print ''\",\n \"def parse_reply(self, reply):\\n lines = str(reply).split('\\\\n')\\n seq_num = int(lines[1].split(' ')[1])\\n\\n # Process only if the server reply's sequence number is the same as the request's\\n if seq_num == self.rtsp_seq:\\n session = int(lines[2].split(' ')[1])\\n # New RTSP session ID\\n if self.session_id == 0:\\n self.session_id = session\\n\\n # Process only if the session ID is the same\\n if self.session_id == session:\\n if int(lines[0].split(' ')[1]) == 200:\\n if self.rtsp_command_send == self.SETUP:\\n file_list = lines[3].split(' ')[1:]\\n for file in file_list:\\n self.video_list.addItem(file)\\n\\n self.state = self.READY\\n self.open_rtp_port()\\n self.open_rtcp_port()\\n elif self.rtsp_command_send == self.PLAY:\\n self.total_frame = int(lines[3].split(' ')[2])\\n self.video_slider.setMaximum(self.total_frame)\\n self.state = self.PLAYING\\n threading.Thread(target=self.listen_rtp).start()\\n self.play_event = threading.Event()\\n self.play_event.clear()\\n elif self.rtsp_command_send == self.PAUSE:\\n self.state = self.READY\\n self.play_event.set()\\n elif self.rtsp_command_send == self.TEARDOWN:\\n self.state = self.INIT\\n self.teardown_ack = 1\\n self.play_event.set()\\n self.close()\",\n \"def testReplayState1(t, env):\\n c = env.c1\\n c.init_connection()\\n c.maketree([t.code])\\n ops = c.use_obj(c.homedir + [t.code])\\n ops += [c.open(t.code, 'vapor'), c.getfh_op()]\\n _replay(c, ops, NFS4ERR_NOENT)\",\n \"def readResponseFile(self):\\n resp_filename = QFileDialog.getOpenFileName(self, \\\"Open Response File\\\", str(Path.home()), '') \\n try:\\n resp_file = open(resp_filename[0], 'r')\\n except:\\n print(\\\"Couldn't get any files from QFileDialog\\\")\\n return\\n\\n try:\\n response = readResponseArrayToResponse(resp_file.read().split('\\\\n'), resp_filename[0].split('/')[-1])\\n except Exception as e:\\n response = None\\n print(\\\"Not a valid response file: {0}\\\".format(e))\\n\\n resp_file.close()\\n\\n if response is not None:\\n self.setResponse(response)\",\n \"def get_protocol_init_data(self):\\n\\t\\tcontents = self.archive.read_file('replay.initData')\\n\\t\\treturn self.protocol.decode_replay_initdata(contents)\",\n \"def get_mockreturn(url, *args, **kwargs):\\n filename = data_path(DATA_FILES['m101'])\\n f = open(filename, 'r')\\n text = f.read()\\n retval = MockResponse(text)\\n f.close()\\n return retval\",\n \"def __request_info_file(self, res, sock):\\n udp_port = int(res[2:6])\\n self.__send_request(\\\"03\\\" + self.file_name + str(self.file_size), sock)\\n return udp_port\",\n \"def reception():\\n\\tprint \\\"\\\"\\n\\tprint \\\" *****************************\\\"\\n\\tprint \\\" RECEPTION \\t\\t \\\"\\n\\tprint \\\" *****************************\\\"\\n\\t\\n\\t(port, packet_count, received_data_path) = interface.retrieve_info()\\n\\n\\tprint \\\"\\\"\\n\\tprint \\\"Connexion parameters :\\\"\\n\\tprint \\\"Port = {}\\\".format(port) \\n\\tprint \\\"Packet_count = {}\\\".format(packet_count) \\n\\tprint \\\"Received_data_path = {}\\\".format(received_data_path)\\n\\tprint \\\"\\\"\\n\\n\\tprint \\\"Let's connect the CC3200\\\"\\n\\tprint \\\"Or\\\"\\n\\tprint \\\"Run the client.py python script\\\"\\n\\tprint \\\"\\\"\\n\\n\\tserver.receive_data(port, packet_count, received_data_path)\\n\\n\\tprint \\\"to display the content of the file in an unix environement\\\"\\n\\tprint \\\"you can use the following command :\\\"\\n\\tprint \\\"hexdump -e \\\\'/4 \\\\\\\"%08X\\\\\\\\n\\\\\\\"\\\\' path_to_your_received_data_file.dat\\\"\\n\\tprint \\\"\\\"\\n\\n\\tquestion = \\\"Proceed to integrity test ?\\\"\\n\\tif (interface.user_agree(question, \\\"no\\\")):\\n\\t\\tintegrity_check.integrity_check_counter(received_data_path)\\n\\n\\tprint \\\"\\\"\\n\\tprint \\\" *****************************\\\"\\n\\tprint \\\" END \\t\\t \\\"\\n\\tprint \\\" *****************************\\\"\",\n \"def view_request_bytes(line):\\n args = shlex.split(line)\\n if not args:\\n raise PappyException(\\\"Request id is required\\\")\\n reqid = args[0]\\n\\n reqs = yield load_reqlist(reqid)\\n for req in reqs:\\n if len(reqs) > 1:\\n print 'Request %s:' % req.reqid\\n print req.full_message\\n if len(reqs) > 1:\\n print '-'*30\\n print ''\",\n \"def stats(self):\\n stats = {\\n 'lines' : '', # This will count the lines under each split\\n 'status_code': self.status_code,\\n 'content_type': self.mime,\\n 'hop': self.hop_path[-1:],\\n 'sum:content_length': self.content_length,\\n 'host': self.host(),\\n 'source': self.source\\n }\\n # Add in annotations:\\n for annot in self.annotations:\\n # Set a prefix based on what it is:\\n prefix = ''\\n if self.re_tries.match(annot):\\n prefix = 'tries:'\\n elif self.re_ip.match(annot):\\n prefix = \\\"ip:\\\"\\n # Only emit lines with annotations:\\n if annot != \\\"-\\\":\\n stats[\\\"%s%s\\\" % (prefix, annot)] = \\\"\\\"\\n return stats\",\n \"def _analyze_response(self, response):\\n version_mo = self.VERSION_RE.search(response.get_body())\\n admin_mo = self.ADMIN_URL_RE.search(response.get_body())\\n author_mo = self.AUTHOR_URL_RE.search(response.get_body())\\n\\n if version_mo and admin_mo and author_mo:\\n #Set the self._exec to false\\n self._exec = False\\n\\n desc = 'The FrontPage Configuration Information file was found'\\\\\\n ' at: \\\"%s\\\" and the version of FrontPage Server Extensions'\\\\\\n ' is: \\\"%s\\\".'\\n desc = desc % (response.get_url(), version_mo.group(1))\\n\\n i = Info('FrontPage configuration information', desc, response.id,\\n self.get_name())\\n i.set_url(response.get_url())\\n i['version'] = version_mo.group(1)\\n \\n kb.kb.append(self, 'frontpage_version', i)\\n om.out.information(i.get_desc())\\n\\n #\\n # Handle the admin.exe file\\n #\\n self._analyze_admin(response, admin_mo)\\n\\n #\\n # Handle the author.exe file\\n #\\n self._analyze_author(response, author_mo)\\n\\n else:\\n # This is strange... we found a _vti_inf file, but there is no\\n # frontpage information in it... IPS? WAF? honeypot?\\n msg = '[IMPROVEMENT] Invalid frontPage configuration information'\\\\\\n ' found at %s (id: %s).'\\n msg = msg % (response.get_url(), response.id)\\n om.out.debug(msg)\",\n \"def report_extract_request(red):\\n line_req = red.get_text_line(0) # First\\n if REQ_KEY not in line_req: return None\\n req_opts= line_req[line_req.index(REQ_KEY)+len(REQ_KEY):]\\n return req_opts\",\n \"def isResp(obxDict):\\n readingCode = getReadingCode(obxDict)\\n return readingCode == '76270-8'\",\n \"def replay_url(self):\\n if (\\n self.cluster is None\\n or self.steam_id is None\\n or self.replay_salt is None\\n ):\\n return None\\n else:\\n return \\\"http://replay{0}.valve.net/570/{1}_{2}.dem.bz2\\\".format(\\n self.cluster, self.steam_id, self.replay_salt\\n )\",\n \"def handle_file(self):\\n query_cache_response = self.check_av_cache()\\n cache_status_label = query_cache_response['response'][0]['status']['label']\\n if cache_status_label == \\\"FOUND\\\":\\n print(\\\"Results already exist in AV cache for file {}\\\".format(self.file_name))\\n self.final_response = query_cache_response\\n self.final_status_label = cache_status_label\\n else:\\n print(\\\"No results in AV cache before upload for file {}\\\".format(self.file_name))\\n upload_response = self.upload_file()\\n upload_status_label = upload_response[\\\"response\\\"][0][\\\"status\\\"][\\\"label\\\"]\\n if upload_status_label == \\\"UPLOAD_SUCCESS\\\":\\n query_response = self.query_file()\\n query_status_label = query_response[\\\"response\\\"][0][\\\"status\\\"][\\\"label\\\"]\\n print(\\\"Receiving Query response with av results for file {}. status: {}\\\".format(self.file_name,\\n query_status_label))\\n self.final_response = query_response\\n self.final_status_label = query_status_label\\n else:\\n self.final_response = upload_response\\n self.final_status_label = upload_status_label\\n self.create_response_info(self.final_response)\\n if self.final_status_label == \\\"FOUND\\\":\\n signature = self.final_response[\\\"response\\\"][0][\\\"av\\\"][\\\"malware_info\\\"][\\\"signature_name\\\"]\\n if signature:\\n print(\\\"File {} was found malicious by AV. Signature : {}\\\".format(self.file_name, signature))\\n else:\\n print(\\\"File {} was found clean by AV\\\".format(self.file_name))\",\n \"def handle_recording():\\n\\n recording_url = request.values.get(\\\"RecordingUrl\\\", None)\\n\\n resp = VoiceResponse()\\n resp.say(\\\"Listen to your recorded message.\\\")\\n resp.play(recording_url)\\n resp.say(\\\"Goodbye.\\\")\\n return str(resp)\",\n \"def do_GET(self):\\n try:\\n \\n # parse the requested page and see if it's valid\\n parse_status, explanation_str = self.parse_header(self.path)\\n \\n # parse_status:\\n # -1: error\\n # 0: /log/* request\\n # 1: /detailed/node/timestamp request\\n print str(self.parse_header(self.path))\\n \\n explanation_str = str(explanation_str)\\n \\n # error\\n if parse_status == -1:\\n # invalid header, close the connection and die but notify user\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers()\\n self.wfile.write('Invalid request ('+explanation_str+')')\\n print '-1'\\n return\\n \\n # 1: /detailed/node/timestamp request\\n elif parse_status == 1:\\n print '1'\\n # just need to respond with the file that's contained in explanation_str\\n # and once we verify that it exists, we're golden\\n \\n # path to the \\\"detailed\\\" file\\n file_path = explanation_str\\n \\n if os.path.isfile(file_path):\\n try:\\n # TODO: make HTML here to nav around previous node things\\n detailed_file_handle = open(file_path, 'r')\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/plain')\\n self.end_headers() \\n self.wfile.write(detailed_file_handle.read())\\n detailed_file_handle.close()\\n return\\n except Exception, e:\\n print 'Error while sending detailed log file'\\n print e\\n return\\n else:\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers()\\n self.wfile.write('Invalid file request')\\n return\\n \\n # 0: /log/* request\\n elif parse_status == 0:\\n print '0'\\n # request was successfull, we just want the filename from index\\n log_index = explanation_str\\n \\n success_status, log_filename = self.get_filename_from_index(log_index)\\n \\n if success_status == -1:\\n # some kind of error of which the description is stored in log_filename\\n #sockobj.send('The server encountered an error opening the file, please'+\\\\\\n # ' try your request again')\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers() \\n self.wfile.write('The server encountered an error opening the file, please'+\\\\\\n ' try your request again')\\n return\\n \\n # the file exists!\\n # just dump the file at this point, and then...\\n \\n # send the HTML file\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers()\\n self.send_html_file(log_filename, log_index)\\n return\\n\\n # invalid type\\n else:\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers()\\n self.wfile.write('Invalid request type 2')\\n return\\n \\n except IOError:\\n self.send_error(404,'File Not Found: %s' % self.path)\\n \\n return\",\n \"def _skip_replay(self, file):\\n line = file.readline()\\n while line and REPLAY_END not in line:\\n line = file.readline()\",\n \"def get_record_line_from(client_or_image_response):\\n # NOTE AirSim uses WXYZ for quaternions, here we return XYZW. Also,\\n # while it uses the client's pose, there's a slight difference\\n # in the `position` captured by image_response, owing to the camera,\\n # and there can be large ones in `orientation` when using MaxDegreeOfFreedom\\n\\n if isinstance(client := client_or_image_response, airsim.MultirotorClient):\\n state = client.getMultirotorState()\\n return state.timestamp, *ff.xyz_xyzw_of_client(state)\\n\\n elif isinstance(image_response := client_or_image_response, airsim.ImageResponse):\\n return image_response.time_stamp, *ff.xyz_xyzw_of_image(image_response)\\n\\n else:\\n assert False, f\\\"{type(client_or_image_response)=}\\\"\",\n \"def request_file(self, file_path_on_server):\\n # Create a RRQ\\n packet = bytearray()\\n packet.append(0)\\n packet.append(1)\\n name_barr = bytearray(file_path_on_server.encode('ascii'))\\n packet += name_barr\\n packet.append(0)\\n mode = bytearray(\\\"octet\\\".encode('ascii'))\\n packet += mode\\n packet.append(0)\\n return packet\",\n \"def handle_message(self, data):\\r\\n print data\\r\\n\\r\\n #video stream starts\\r\\n if data[:13] == 'video_stream:':\\r\\n #port will be between 3000 to 7000\\r\\n port = int(data[13:17])\\r\\n self.partnum = int(data[18:])\\r\\n\\r\\n #creates a video file in cache\\r\\n if not exists(CASHE + movie_name + '\\\\\\\\'):\\r\\n makedirs(CASHE + movie_name + '\\\\\\\\')\\r\\n\\r\\n self.receive = Receiver(port, self.partnum, CASHE + movie_name + '\\\\\\\\')\\r\\n self.receive.start()\\r\\n\\r\\n #upload stream approved\\r\\n elif data[:16] == 'upload_approved:':\\r\\n port = int(data[16:])\\r\\n self.uploader = Uploader(port, upload_path)\\r\\n self.uploader.start()\\r\\n self.upload_num = 1\\r\\n\\r\\n elif data[:6] == 'parts:':\\r\\n if data[6:].isdigit():\\r\\n self.partnum = int(data[6:])\\r\\n\\r\\n elif data[:8] == 'invalid:':\\r\\n self.uploader = None\\r\\n if data[8:] == 'hash':\\r\\n self.upload_num = 2\\r\\n else:\\r\\n self.upload_num = 3\\r\\n\\r\\n print 'invalid upload'\\r\\n\\r\\n elif data == 'vid_not_found':\\r\\n self.partnum = -1\\r\\n print 'could not watch vid'\\r\\n\\r\\n elif data[:8] == 'results:':\\r\\n results = data[8:].split(':<!>:')\\r\\n self.res_list = [['Movie Name', 'views', 'grade']]\\r\\n for i in results:\\r\\n datas = i.split(':!:')\\r\\n self.res_list.append(datas)\\r\\n self.print_results = True\\r\\n '''if results == ['']:\\r\\n self.print_results = False\\r\\n else:\\r\\n self.print_results = True'''\",\n \"def parsePkt(pkt):\\r\\n meta = dict()\\r\\n headers = dict()\\r\\n for h,pattern in SIP_PKT_PATTERNS.iteritems():\\r\\n if h in ['reqfirstline', 'respfirstline']:\\r\\n continue\\r\\n headers[h] = None\\r\\n match = pattern.search(pkt)\\r\\n if match:\\r\\n headers[h] = re.sub(h + ': ', '', match.group()).rstrip('\\\\r\\\\n')\\r\\n if h == 'User-Agent' and headers[h]:\\r\\n headers[h] = re.sub(\\\"Server: \\\", \\\"\\\", headers[h])\\r\\n match_1 = SIP_PKT_PATTERNS['respfirstline'].search(pkt)\\r\\n match_2 = SIP_PKT_PATTERNS['reqfirstline'].search(pkt)\\r\\n if match_1:\\r\\n meta['respfirstline'] = match_1.group().rstrip(' \\\\r\\\\n')\\r\\n meta['code'] = int(match_1.group('code'))\\r\\n elif match_2:\\r\\n meta['reqfirstline'] = match_2.group().rstrip(' \\\\r\\\\n')\\r\\n meta['code'] = None\\r\\n else:\\r\\n print \\\"can't parse rotten SIP pkt:\\\\r\\\\n%s\\\" %(pkt)\\r\\n return \\r\\n if meta['code'] == AUTHREQ \\\\\\r\\n or meta['code'] == PROXYAUTHREQ:\\r\\n meta['auth-header'] = dict()\\r\\n auth_match = re.search('(?P<www_or_proxy>(?:WWW|Proxy)-Authenticate): Digest (?P<other_meta>.*)\\\\r\\\\n', pkt)\\r\\n if auth_match:\\r\\n meta['auth-header']['type'] = auth_match.group('www_or_proxy')\\r\\n if meta['auth-header']['type'] == 'WWW-Auth-Header':\\r\\n meta['auth-header']['domain'] = re.search('domain=\\\"([-\\\\/\\\\\\\\:\\\\.a-zA-Z0-9]+)\\\"', other_meta).group(1)\\r\\n meta['auth-header']['qop'] = re.search('qop=\\\"([-\\\\/\\\\\\\\:\\\\.a-zA-Z0-9]+)\\\"', other_meta).group(1)\\r\\n meta['auth-header']['stale'] = re.search('stale=(?:True|False)', other_meta).group(1)\\r\\n meta['auth-header']['opaque'] = re.search('opaque=\\\"([-\\\\/\\\\\\\\:\\\\.a-zA-Z0-9]+)\\\"', other_meta).group(1)\\r\\n other_meta = auth_match.group('other_meta')\\r\\n algo_match = re.search('algorithm=([a-zA-Z0-9]+)', other_meta)\\r\\n meta['auth-header']['realm'] = re.search('realm=\\\"([-\\\\/\\\\\\\\:_\\\\.a-zA-Z0-9]+)\\\"', other_meta).group(1)\\r\\n meta['auth-header']['nonce'] = re.search('nonce=\\\"([-\\\\/\\\\\\\\+:_\\\\.a-zA-Z0-9]+)\\\"', other_meta).group(1)\\r\\n if algo_match:\\r\\n meta['auth-header']['algorithm'] = algo_match.group(1)\\r\\n else:\\r\\n meta['auth-header']['algorithm'] = 'MD5' \\r\\n else:\\r\\n del meta['auth-header']\\r\\n meta['headers'] = headers\\r\\n return meta\",\n \"def __init__(self, line):\\n (self.timestamp, self.status_code, self.content_length, self.url, self.hop_path, self.via,\\n self.mime, self.thread, self.start_time_plus_duration, self.hash, self.source,\\n self.annotation_string) = re.split(\\\" +\\\", line.strip(), maxsplit=11)\\n # Account for any JSON 'extra info' ending, strip or split:\\n if self.annotation_string.endswith(' {}'):\\n self.annotation_string = self.annotation_string[:-3]\\n elif ' {\\\"' in self.annotation_string and self.annotation_string.endswith('}'):\\n self.annotation_string, self.extra_json = re.split(re.escape(' {\\\"'), self.annotation_string, maxsplit=1)\\n self.extra_json = '{\\\"%s' % self.extra_json\\n # And split out the annotations:\\n self.annotations = self.annotation_string.split(',')\\n\\n # Some regexes:\\n self.re_ip = re.compile('^\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}$')\\n self.re_tries = re.compile('^\\\\d+t$')\\n self.re_dol = re.compile('^dol:\\\\d+') # Discarded out-links - make a total?\",\n \"def decode_replay_header(contents):\\n decoder = VersionedDecoder(contents, typeinfos)\\n return decoder.instance(replay_header_typeid)\",\n \"def _read_header(\\n self, header, filename, run_check_acceptability=True, background_lsts=True\\n ):\\n # get telescope information\\n latitude = header[\\\"latitude\\\"][()]\\n longitude = header[\\\"longitude\\\"][()]\\n altitude = header[\\\"altitude\\\"][()]\\n self.telescope_location_lat_lon_alt_degrees = (latitude, longitude, altitude)\\n self.instrument = header[\\\"instrument\\\"][()].tobytes().decode(\\\"utf8\\\")\\n self.telescope_name = header[\\\"telescope_name\\\"][()].tobytes().decode(\\\"utf8\\\")\\n\\n # get source information\\n self.object_name = header[\\\"object_name\\\"][()].tobytes().decode(\\\"utf8\\\")\\n\\n # set history appropriately\\n self.history = header[\\\"history\\\"][()].tobytes().decode(\\\"utf8\\\")\\n if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):\\n self.history += self.pyuvdata_version_str\\n\\n # check for vis_units\\n if \\\"vis_units\\\" in header:\\n self.vis_units = header[\\\"vis_units\\\"][()].tobytes().decode(\\\"utf8\\\")\\n else:\\n # default to uncalibrated data\\n self.vis_units = \\\"UNCALIB\\\"\\n\\n # check for optional values\\n if \\\"dut1\\\" in header:\\n self.dut1 = float(header[\\\"dut1\\\"][()])\\n if \\\"earth_omega\\\" in header:\\n self.earth_omega = float(header[\\\"earth_omega\\\"][()])\\n if \\\"gst0\\\" in header:\\n self.gst0 = float(header[\\\"gst0\\\"][()])\\n if \\\"rdate\\\" in header:\\n self.rdate = header[\\\"rdate\\\"][()].tobytes().decode(\\\"utf8\\\")\\n if \\\"timesys\\\" in header:\\n self.timesys = header[\\\"timesys\\\"][()].tobytes().decode(\\\"utf8\\\")\\n if \\\"x_orientation\\\" in header:\\n self.x_orientation = header[\\\"x_orientation\\\"][()].tobytes().decode(\\\"utf8\\\")\\n if \\\"blt_order\\\" in header:\\n blt_order_str = header[\\\"blt_order\\\"][()].tobytes().decode(\\\"utf8\\\")\\n self.blt_order = tuple(blt_order_str.split(\\\", \\\"))\\n if self.blt_order == (\\\"bda\\\",):\\n self._blt_order.form = (1,)\\n\\n if \\\"antenna_diameters\\\" in header:\\n self.antenna_diameters = header[\\\"antenna_diameters\\\"][()]\\n if \\\"uvplane_reference_time\\\" in header:\\n self.uvplane_reference_time = int(header[\\\"uvplane_reference_time\\\"][()])\\n if \\\"eq_coeffs\\\" in header:\\n self.eq_coeffs = header[\\\"eq_coeffs\\\"][()]\\n if \\\"eq_coeffs_convention\\\" in header:\\n self.eq_coeffs_convention = (\\n header[\\\"eq_coeffs_convention\\\"][()].tobytes().decode(\\\"utf8\\\")\\n )\\n\\n # check for phasing information\\n self.phase_type = header[\\\"phase_type\\\"][()].tobytes().decode(\\\"utf8\\\")\\n if self.phase_type == \\\"phased\\\":\\n self._set_phased()\\n self.phase_center_ra = float(header[\\\"phase_center_ra\\\"][()])\\n self.phase_center_dec = float(header[\\\"phase_center_dec\\\"][()])\\n self.phase_center_epoch = float(header[\\\"phase_center_epoch\\\"][()])\\n if \\\"phase_center_frame\\\" in header:\\n self.phase_center_frame = (\\n header[\\\"phase_center_frame\\\"][()].tobytes().decode(\\\"utf8\\\")\\n )\\n elif self.phase_type == \\\"drift\\\":\\n self._set_drift()\\n else:\\n self._set_unknown_phase_type()\\n\\n # get antenna arrays\\n # cast to native python int type\\n self.Nants_data = int(header[\\\"Nants_data\\\"][()])\\n self.Nants_telescope = int(header[\\\"Nants_telescope\\\"][()])\\n self.ant_1_array = header[\\\"ant_1_array\\\"][:]\\n self.ant_2_array = header[\\\"ant_2_array\\\"][:]\\n self.antenna_names = [\\n n.tobytes().decode(\\\"utf8\\\") for n in header[\\\"antenna_names\\\"][:]\\n ]\\n self.antenna_numbers = header[\\\"antenna_numbers\\\"][:]\\n self.antenna_positions = header[\\\"antenna_positions\\\"][:]\\n\\n # set telescope params\\n try:\\n self.set_telescope_params()\\n except ValueError as ve:\\n warnings.warn(str(ve))\\n\\n # get baseline array\\n self.baseline_array = self.antnums_to_baseline(\\n self.ant_1_array, self.ant_2_array\\n )\\n self.Nbls = len(np.unique(self.baseline_array))\\n\\n # get uvw array\\n self.uvw_array = header[\\\"uvw_array\\\"][:, :]\\n\\n # get time information\\n self.time_array = header[\\\"time_array\\\"][:]\\n integration_time = header[\\\"integration_time\\\"]\\n self.integration_time = integration_time[:]\\n proc = None\\n if \\\"lst_array\\\" in header:\\n self.lst_array = header[\\\"lst_array\\\"][:]\\n # check that lst_array in file is self-consistent\\n if run_check_acceptability:\\n (\\n latitude,\\n longitude,\\n altitude,\\n ) = self.telescope_location_lat_lon_alt_degrees\\n lst_array = uvutils.get_lst_for_time(\\n self.time_array, latitude, longitude, altitude\\n )\\n if not np.all(\\n np.isclose(\\n self.lst_array,\\n lst_array,\\n rtol=self._lst_array.tols[0],\\n atol=self._lst_array.tols[1],\\n )\\n ):\\n warnings.warn(\\n \\\"LST values stored in {file} are not self-consistent \\\"\\n \\\"with time_array and telescope location. Consider \\\"\\n \\\"recomputing with utils.get_lst_for_time.\\\".format(file=filename)\\n )\\n else:\\n # compute lst_array from time_array and telescope location\\n proc = self.set_lsts_from_time_array(background=background_lsts)\\n\\n # get frequency information\\n self.freq_array = header[\\\"freq_array\\\"][:, :]\\n self.channel_width = float(header[\\\"channel_width\\\"][()])\\n self.spw_array = header[\\\"spw_array\\\"][:]\\n\\n # get polarization information\\n self.polarization_array = header[\\\"polarization_array\\\"][:]\\n\\n # get data shapes\\n self.Nfreqs = int(header[\\\"Nfreqs\\\"][()])\\n self.Npols = int(header[\\\"Npols\\\"][()])\\n self.Ntimes = int(header[\\\"Ntimes\\\"][()])\\n self.Nblts = int(header[\\\"Nblts\\\"][()])\\n self.Nspws = int(header[\\\"Nspws\\\"][()])\\n\\n # get extra_keywords\\n if \\\"extra_keywords\\\" in header:\\n self.extra_keywords = {}\\n for key in header[\\\"extra_keywords\\\"].keys():\\n if header[\\\"extra_keywords\\\"][key].dtype.type in (np.string_, np.object_):\\n self.extra_keywords[key] = (\\n header[\\\"extra_keywords\\\"][key][()].tobytes().decode(\\\"utf8\\\")\\n )\\n else:\\n self.extra_keywords[key] = header[\\\"extra_keywords\\\"][key][()]\\n\\n if proc is not None:\\n # if lsts are in the background wait for them to return\\n proc.join()\\n\\n return\",\n \"def read_data(self):\\r\\n # Verify length of response data\\r\\n length = self.read_until_null()\\r\\n message = self.read_until_null()\\r\\n if int(length) == len(message):\\r\\n return message\\r\\n else:\\r\\n raise ProtocolException(\\\"Length mismatch encountered while reading the Xdebug message\\\")\",\n \"def media_file_info(self):\\n\\n if self.observationId and self.playerType == VLC:\\n\\n media = self.mediaplayer.get_media()\\n\\n logging.info(\\\"State: {}\\\".format(self.mediaplayer.get_state()))\\n logging.info(\\\"Media (get_mrl): {}\\\".format(bytes_to_str(media.get_mrl())))\\n logging.info(\\\"media.get_meta(0): {}\\\".format(media.get_meta(0)))\\n logging.info(\\n \\\"Track: {}/{}\\\".format(self.mediaplayer.video_get_track(), self.mediaplayer.video_get_track_count()))\\n logging.info(\\\"number of media in media list: {}\\\".format(self.media_list.count()))\\n logging.info(\\\"get time: {} duration: {}\\\".format(self.mediaplayer.get_time(), media.get_duration()))\\n logging.info(\\\"Position: {} %\\\".format(self.mediaplayer.get_position()))\\n logging.info(\\\"FPS: {}\\\".format(self.mediaplayer.get_fps()))\\n logging.info(\\\"Rate: {}\\\".format(self.mediaplayer.get_rate()))\\n logging.info(\\\"Video size: {}\\\".format(self.mediaplayer.video_get_size(0)))\\n logging.info(\\\"Scale: {}\\\".format(self.mediaplayer.video_get_scale()))\\n logging.info(\\\"Aspect ratio: {}\\\".format(self.mediaplayer.video_get_aspect_ratio()))\\n logging.info(\\\"is seekable? {0}\\\".format(self.mediaplayer.is_seekable()))\\n logging.info(\\\"has_vout? {0}\\\".format(self.mediaplayer.has_vout()))\\n\\n vlc_output = (\\\"State: {}<br>\\\"\\n \\\"Media Resource Location: {}<br>\\\"\\n \\\"File name: {}<br>\\\"\\n \\\"Track: {}/{}<br>\\\"\\n \\\"Number of media in media list: {}<br>\\\"\\n \\\"get time: {}<br>\\\"\\n \\\"duration: {}<br>\\\"\\n \\\"Position: {} %<br>\\\"\\n \\\"FPS: {}<br>\\\"\\n \\\"Rate: {}<br>\\\"\\n \\\"Video size: {}<br>\\\"\\n \\\"Scale: {}<br>\\\"\\n \\\"Aspect ratio: {}<br>\\\"\\n \\\"is seekable? {}<br>\\\"\\n \\\"has_vout? {}<br>\\\").format(self.mediaplayer.get_state(),\\n bytes_to_str(media.get_mrl()),\\n media.get_meta(0),\\n self.mediaplayer.video_get_track(),\\n self.mediaplayer.video_get_track_count(),\\n self.media_list.count(),\\n self.mediaplayer.get_time(),\\n self.convertTime(media.get_duration() / 1000),\\n self.mediaplayer.get_position(),\\n self.mediaplayer.get_fps(),\\n self.mediaplayer.get_rate(),\\n self.mediaplayer.video_get_size(0),\\n self.mediaplayer.video_get_scale(),\\n self.mediaplayer.video_get_aspect_ratio(),\\n \\\"Yes\\\" if self.mediaplayer.is_seekable() else \\\"No\\\",\\n \\\"Yes\\\" if self.mediaplayer.has_vout() else \\\"No\\\"\\n )\\n\\n self.results = dialog.ResultsWidget()\\n self.results.setWindowTitle(programName + \\\" - Media file information\\\")\\n self.results.ptText.setReadOnly(True)\\n\\n self.results.ptText.appendHtml(\\\"<b>VLC analysis</b><hr>\\\" + vlc_output)\\n\\n # FFmpeg analysis\\n self.results.ptText.appendHtml(\\\"<br><b>FFmpeg analysis</b><hr>\\\")\\n for nplayer in self.pj[OBSERVATIONS][self.observationId][FILE]:\\n for filePath in self.pj[OBSERVATIONS][self.observationId][FILE][nplayer]:\\n media_full_path = project_functions.media_full_path(filePath, self.projectFileName)\\n # nframes, duration_ms, duration, fps, hasVideo, hasAudio = accurate_media_analysis(self.ffmpeg_bin, media_full_path)\\n\\n r = utilities.accurate_media_analysis2(self.ffmpeg_bin, media_full_path)\\n nframes = r[\\\"frames_number\\\"]\\n\\n if \\\"error\\\" in r:\\n self.results.ptText.appendHtml(\\n \\\"File path: {filePath}<br><br>{error}<br><br>\\\".format(filePath=media_full_path,\\n error=r[\\\"error\\\"]))\\n else:\\n self.results.ptText.appendHtml(\\n \\\"File path: {}<br>Duration: {}<br>Bitrate: {}k<br>FPS: {}<br>Has video: {}<br>Has audio: {}<br><br>\\\".\\n format(media_full_path, self.convertTime(r[\\\"duration\\\"]), r[\\\"bitrate\\\"], r[\\\"fps\\\"],\\n r[\\\"has_video\\\"], r[\\\"has_audio\\\"]))\\n\\n self.results.ptText.appendHtml(\\\"Total duration: {} (hh:mm:ss.sss)\\\".\\n format(self.convertTime(sum(self.duration) / 1000)))\\n\\n self.results.show()\\n\\n else:\\n\\n fn = QFileDialog(self).getOpenFileName(self, \\\"Select a media file\\\", \\\"\\\", \\\"Media files (*)\\\")\\n filePath = fn[0] if type(fn) is tuple else fn\\n\\n if filePath:\\n self.results = dialog.ResultsWidget()\\n self.results.setWindowTitle(programName + \\\" - Media file information\\\")\\n self.results.ptText.setReadOnly(True)\\n self.results.ptText.appendHtml(\\\"<br><b>FFmpeg analysis</b><hr>\\\")\\n # nframes, duration_ms, duration, fps, hasVideo, hasAudio = accurate_media_analysis(self.ffmpeg_bin, filePath)\\n r = utilities.accurate_media_analysis2(self.ffmpeg_bin, filePath)\\n if \\\"error\\\" in r:\\n self.results.ptText.appendHtml(\\n \\\"File path: {filePath}<br><br>{error}<br><br>\\\".format(filePath=filePath, error=r[\\\"error\\\"]))\\n else:\\n self.results.ptText.appendHtml(\\n \\\"File path: {}<br>Duration: {}<br>Bitrate: {}k<br>FPS: {}<br>Has video: {}<br>Has audio: {}<br><br>\\\".\\n format(filePath, self.convertTime(r[\\\"duration\\\"]), r[\\\"bitrate\\\"], r[\\\"fps\\\"], r[\\\"has_video\\\"],\\n r[\\\"has_audio\\\"]))\\n\\n self.results.show()\",\n \"def lineReceived(self, line):\\n\\n # Log request\\n MaverickServerProtocol._logger.debug(\\\"Request received: %s\\\", line)\\n\\n # Pull out request name (e.g., \\\"REGISTER\\\") and arguments (unparsed)\\n (requestName, _, requestArgsString) = line.partition(\\\" \\\")\\n\\n errMsg = None # If this gets set, there was an error\\n if requestName in MaverickServerProtocol.VALID_REQUESTS:\\n try:\\n requestArgs = json.loads(requestArgsString,\\n encoding=\\\"utf-8\\\")\\n except ValueError:\\n errMsg = \\\"Invalid JSON for arguments\\\"\\n else:\\n # Pull out the requirements for this request\\n (tsCommand, expArgs, _) = \\\\\\n MaverickServerProtocol.VALID_REQUESTS[requestName]\\n\\n if expArgs != set(requestArgs.keys()):\\n # Give an error if not provided the correct arguments\\n fStr = \\\"Invalid arguments, expected: {0}\\\"\\n errMsg = fStr.format(\\\",\\\".join(list(expArgs)))\\n else:\\n try:\\n import traceback\\n # Dispatch command to TournamentSystem instance\\n (successP, result) = tsCommand(self._ts, **requestArgs)\\n\\n except:\\n tb = traceback.format_exc()\\n print tb\\n # Give an error if caught an exception\\n errMsg = \\\"Uncaught exception\\\"\\n\\n else:\\n if successP:\\n # TODO (mattsh): check keys of response\\n\\n # Provide successful results to the user\\n jsonStr = json.dumps(result,\\n ensure_ascii=True,\\n encoding=\\\"utf-8\\\")\\n response = \\\"SUCCESS {0}\\\".format(jsonStr)\\n if not successP:\\n # Pull out structured error messages from func call\\n errMsg = result[\\\"error\\\"]\\n else:\\n # Give an error if provided an invalid command\\n errMsg = \\\"Unrecognized verb \\\\\\\"{0}\\\\\\\" in request\\\".format(requestName)\\n\\n # Respond to the client\\n if errMsg is None:\\n # Provide client with the response\\n\\n # Log successful response\\n logStrF = \\\"RESPONSE [query=\\\\\\\"%s\\\\\\\"]: %s\\\"\\n MaverickServerProtocol._logger.info(logStrF, line, response)\\n\\n # Send successful response\\n self.sendLine(response)\\n else:\\n # Provide client with the error\\n\\n # Compute error response\\n response = \\\"ERROR {0}\\\".format(errMsg, line)\\n\\n # Log error response\\n logStrF = \\\"RESPONSE [query=\\\\\\\"%s\\\\\\\"]: %s\\\"\\n MaverickServerProtocol._logger.info(logStrF, line, response)\\n\\n # Send error response\\n self.sendLine(response)\\n\\n # Log the fact that the connection is being closed\\n logStrF = \\\"Dropping connection to user after completion\\\"\\n MaverickServerProtocol._logger.debug(logStrF)\\n\\n # Close connection after each request\\n self.transport.loseConnection()\",\n \"def setValidationInfo(self, displayname, recvstring):\\n response = recvstring\\n companyTestBed = \\\"\\\"\\n modelTestBed = \\\"\\\"\\n firmwareTestBed = \\\"\\\"\\n Sniffer_WTS_VER = \\\"\\\"\\n Sniffer_VendorName = \\\"\\\"\\n Sniffer_DeviceModel = \\\"\\\"\\n Sniffer_DeviceFirmware = \\\"\\\"\\n ret_dict = {}\\n\\n if displayname.lower() == 'sniffer':\\n #wfa_sniffer!sniffer_get_info!ID,$Sniffer_WTS_VER,$Sniffer_VendorName,$Sniffer_DeviceModel,$Sniffer_DeviceFirmware\\n #status,COMPLETE,WfaSnifferVersion,$WfaSnifferVersion,SnifferSTA,$SnifferSTA,SwInfo,$DeviceSwInfo\\\\_$kernel_Ver,WiresharkVersion,$WiresharkInfo\\\\r\\\\n\\n ret_items = response.split(',')\\n \\n \\n if len(ret_items) > 9:\\n Sniffer_WTS_VER = ret_items[3]\\n Sniffer_VendorName = ret_items[5]\\n Sniffer_DeviceModel = ret_items[7]\\n Sniffer_DeviceFirmware = ret_items[9]\\n else:\\n if re.search(r\\\"status,COMPLETE\\\", response):\\n if re.search(r\\\"WfaSnifferVersion\\\", response):\\n posVendor = response.index('WfaSnifferVersion,') + len('WfaSnifferVersion,')\\n data = response[posVendor:]\\n data = data.lstrip()\\n try:\\n posSym = data.index(',')\\n Sniffer_WTS_VER = data[:posSym]\\n except Exception:\\n Sniffer_WTS_VER = data.rstrip('\\\\n')\\n\\n if re.search(r\\\"SnifferSTA\\\", response):\\n posVendor = response.index('SnifferSTA,') + len('SnifferSTA,')\\n data = response[posVendor:]\\n data = data.lstrip()\\n try:\\n posSym = data.index(',')\\n Sniffer_VendorName = data[:posSym]\\n except Exception:\\n Sniffer_VendorName = data.rstrip('\\\\n')\\n\\n if re.search(r\\\"SwInfo\\\", response):\\n posVendor = response.index('SwInfo,') + len('SwInfo,')\\n data = response[posVendor:]\\n data = data.lstrip()\\n try:\\n posSym = data.index(',')\\n Sniffer_DeviceModel = data[:posSym]\\n except Exception:\\n Sniffer_DeviceModel = data.rstrip('\\\\n')\\n\\n if re.search(r\\\"WiresharkVersion\\\", response):\\n posVendor = response.index('WiresharkVersion,') + len('WiresharkVersion,')\\n data = response[posVendor:]\\n data = data.lstrip()\\n try:\\n posSym = data.index(',')\\n Sniffer_DeviceFirmware = data[:posSym]\\n except Exception:\\n Sniffer_DeviceFirmware = data.rstrip('\\\\n')\\n\\n setRetVal('$ca_version', Sniffer_WTS_VER)\\n setRetVal('$tbd_info1', Sniffer_VendorName)\\n setRetVal('$sw_version', Sniffer_DeviceModel)\\n setRetVal('$tbd_info2', Sniffer_DeviceFirmware) \\n\\n ret_dict['ca_version'] = Sniffer_WTS_VER\\n ret_dict['tbd_info1'] = Sniffer_VendorName\\n ret_dict['sw_version'] = Sniffer_DeviceModel\\n ret_dict['tbd_info2'] = Sniffer_DeviceFirmware\\n\\n for tbd in self.test_mngr_initr.test_prog_mngr.test_prog.testbed_dev_list:\\n\\n if tbd.dev_type == \\\"SNIFFER\\\":\\n tbd.vendor = Sniffer_VendorName\\n tbd.model = Sniffer_DeviceModel\\n tbd.firmver = Sniffer_DeviceFirmware\\n tbd.wtsver = Sniffer_WTS_VER\\n tbd.validation_dict = ret_dict\\n\\n break\\n\\n\\n else:\\n if re.search(r\\\"status,COMPLETE\\\", response):\\n if re.search(r\\\"vendor\\\", response):\\n posVendor = response.index('vendor,') + 7\\n data = response[posVendor:]\\n data = data.lstrip()\\n try:\\n posSym = data.index(',')\\n companyTestBed = data[:posSym]\\n except Exception:\\n companyTestBed = data.rstrip('\\\\n')\\n\\n if re.search(r\\\"model\\\", response):\\n posVendor = response.index('model,') + 6\\n data = response[posVendor:]\\n data = data.lstrip()\\n try:\\n posSym = data.index(',')\\n modelTestBed = data[:posSym]\\n except Exception:\\n modelTestBed = data.rstrip('\\\\n')\\n\\n if re.search(r\\\"version\\\", response):\\n posVendor = response.index('version,') + 8\\n data = response[posVendor:]\\n data = data.lstrip()\\n try:\\n posSym = data.index(',')\\n firmwareTestBed = data[:posSym]\\n except Exception:\\n firmwareTestBed = data.rstrip('\\\\n')\\n \\n if re.search(r\\\"firmware\\\", response):\\n posVendor = response.index('firmware,') + 9\\n data = response[posVendor:]\\n data = data.lstrip()\\n try:\\n posSym = data.index(',')\\n firmwareTestBed = data[:posSym]\\n except Exception:\\n firmwareTestBed = data.rstrip('\\\\n')\\n \\n \\n \\n for tbd in self.test_mngr_initr.test_prog_mngr.test_prog.testbed_dev_list:\\n \\n \\n if tbd.ctrlipaddr == self.ExecutionTask.get_ipport().split(':')[0]:\\n #\\n if companyTestBed != \\\"\\\":\\n tbd.vendor = companyTestBed\\n if modelTestBed != \\\"\\\":\\n tbd.model = modelTestBed\\n if firmwareTestBed != \\\"\\\":\\n if self.ExecutionTask.get_cmd() == \\\"ca_get_version\\\":\\n tbd.ca_version = firmwareTestBed\\n else:\\n tbd.sw_version = firmwareTestBed\\n \\n if self.ExecutionTask.get_cmd() == \\\"ca_get_version\\\":\\n \\n tbd.validation_dict['ca_version'] = firmwareTestBed\\n else:\\n tbd.validation_dict['sw_version'] = firmwareTestBed \\n tbd.validation_dict['vendor'] = companyTestBed\\n tbd.validation_dict['model'] = modelTestBed \\n \\n break\",\n \"def test_get_request_output(self):\\n pass\",\n \"def get_trace(captured_file, stream_num, rids):\\n follow_stream = \\\"tshark -q -r {} -z follow,tcp,raw,{}\\\".format(captured_file, stream_num)\\n stream_content = run_command(follow_stream, True)\\n stream_all_res, stream_all_req = get_req_res(stream_content, rids)\\n\\n return stream_all_res, stream_all_req\",\n \"def view_response_headers(line):\\n reqs = yield load_reqlist(line)\\n for req in reqs:\\n if req.response:\\n if len(reqs) > 1:\\n print '-'*15 + (' %s ' % req.reqid) + '-'*15\\n view_full_message(req.response, True)\\n else:\\n print \\\"Request %s does not have a response\\\" % req.reqid\",\n \"def start_operation(self):\\n\\n # Check file\\n is_good_file, info = self.check_file()\\n\\n if is_good_file:\\n # Check input params from client\\n is_error, json_params = self.check_text()\\n\\n if bool(is_error):\\n return is_error, json_params\\n else:\\n # Start machine learning here\\n from slashmlapi.app.slashml.ml_manager import MLManager\\n\\n # Path to zip file\\n path_textfile = info['filename']\\n print(json_params)\\n results = MLManager.get_results(path_textfile, json_params, self.config, self.start_time)\\n return True, results\\n else:\\n return is_good_file, info\",\n \"def test_retrieve_partial_ok(self):\\n content = 'Song of the Andoumboulou'\\n offset = 5\\n namespace = 'default'\\n request = self.store_request(namespace, content)\\n embedded = validate(\\n request.upload_ticket, handlers_endpoints_v1.UPLOAD_MESSAGES[0])\\n self.call_api('store_inline', self.message_to_dict(request), 200)\\n retrieve_request = handlers_endpoints_v1.RetrieveRequest(\\n digest=embedded['d'],\\n namespace=handlers_endpoints_v1.Namespace(),\\n offset=offset) # TODO(cmassaro): determine where offsets come from\\n response = self.call_api(\\n 'retrieve', self.message_to_dict(retrieve_request), 200)\\n retrieved = response.json\\n self.assertEqual(content[offset:], base64.b64decode(retrieved[u'content']))\",\n \"def ftp_RETR(self, file):\\n parsedform = parse_chunks(file)\\n if parsedform:\\n filename, framenum, chunks, user_or_cache = parsedform\\n each_chunk_size = self.movie_LUT.chunk_size_lookup(filename)\\n\\n ## Check ID & Log appropriately\\n if user_or_cache == 1:\\n log_load('user', int(each_chunk_size) * len(chunks))\\n else:\\n log_load('cache', int(each_chunk_size) * len(chunks))\\n\\n try:\\n # filename should be prefixed by \\\"file-\\\" in order to be valid.\\n # frame number is expected to exist for this cache.\\n chunksdir = 'video-' + filename\\n framedir = filename + '.' + framenum + '.dir'\\n path = self.movies_path + '/' + chunksdir + '/' + framedir\\n # get chunks list and open up all files\\n files = self.get_chunk_files(path, chunks)\\n\\n # if DEBUGGING_MSG:\\n # print \\\"chunks requested:\\\", chunks\\n # print 'chunksdir', chunksdir\\n # print 'framedir', framedir\\n # print 'path', path\\n except OSError, err:\\n why = ftpserver._strerror(err)\\n self.respond('550 %s.' % why)\\n\\n producer = self.chunkproducer(files, self._current_type)\\n self.push_dtp_data(producer, isproducer=True, file=None, cmd=\\\"RETR\\\")\\n return\",\n \"def handle(self) -> None:\\r\\n\\r\\n if self.data.get(\\\"message-id\\\") != None:\\r\\n if self.data[\\\"status\\\"] == \\\"error\\\":\\r\\n print(self.data[\\\"error\\\"])\\r\\n return\\r\\n else:\\r\\n requestData = self.obs.pendingResponses.pop(self.data[\\\"message-id\\\"])\\r\\n request = requestData[\\\"request-type\\\"]\\r\\n #Requests as of version 4.8.0\\r\\n\\r\\n #General\\r\\n if request == \\\"GetVersion\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetAuthRequired\\\":\\r\\n if self.data[\\\"authRequired\\\"]:\\r\\n secret_string: str = self.obs.password + self.data[\\\"salt\\\"]\\r\\n secret_hash: sha256 = sha256(secret_string.encode(\\\"utf-8\\\"))\\r\\n secret: bytes = b64encode(secret_hash.digest())\\r\\n\\r\\n response_string: str = secret.decode(\\\"utf-8\\\") + self.data[\\\"challenge\\\"]\\r\\n response_hash: sha256 = sha256(response_string.encode(\\\"utf-8\\\"))\\r\\n response: bytes = b64encode(response_hash.digest())\\r\\n\\r\\n self.obs.requests.append({\\r\\n \\\"type\\\": \\\"Authenticate\\\",\\r\\n \\\"auth\\\": response.decode(\\\"utf-8\\\")})\\r\\n\\r\\n else:\\r\\n self.obs.requests.append({\\\"type\\\": \\\"GetSceneList\\\"})\\r\\n\\r\\n elif request == \\\"Authenticate\\\":\\r\\n self.obs.requests.append({\\\"type\\\": \\\"GetSceneList\\\"})\\r\\n\\r\\n elif request == \\\"SetHeartbeat\\\":\\r\\n #To be removed in 5.0.0\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetFilenameFormatting\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetFilenameFormatting\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetStats\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"BroadcastCustomMessage\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetVideoInfo\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"OpenProjector\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"TriggerHotkeyByName\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"TriggerHotkeyBySequence\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n #Media Control\\r\\n elif request == \\\"PlayPauseMedia\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"RestartMedia\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"StopMedia\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"NextMedia\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"PreviousMedia\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetMediaDuration\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetMediaTime\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetMediaTime\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"ScrubMedia\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetMediaState\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n #Sources\\r\\n\\r\\n elif request == \\\"GetMediaSourcesList\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetSourcesList\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetSourceTypesList\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetVolume\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetVolume\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetMute\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetMute\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"ToggleMute\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetAudioActive\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSourceName\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSyncOffset\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetSyncOffset\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetSourceSettings\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSourceSettings\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetTextGDIPlusProperties\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetTextGDIPlusProperties\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetTextFreetype2Properties\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetTextFreetype2Properties\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetBrowserSourceProperties\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetBrowserSourceProperties\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetSpecialSources\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetSourceFilters\\\":\\r\\n source = self.obs.getSource(requestData[\\\"sourceName\\\"])\\r\\n if source != None:\\r\\n for _filter in self.data[\\\"filters\\\"]:\\r\\n source.addFilter(_filter) #type: ignore\\r\\n\\r\\n elif request == \\\"GetSourceFilterInfo\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"AddFilterToSource\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"RemoveFilterFromSource\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"ReorderSourceFilter\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"MoveSourceFilter\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSourceFilterSettings\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSourceFilterVisibility\\\":\\r\\n pass\\r\\n \\r\\n elif request == \\\"GetAudioMonitorType\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetAudioMonitorType\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"TakeSourceScreenshot\\\":\\r\\n pass\\r\\n\\r\\n #Outpute\\r\\n elif request == \\\"ListOutputs\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetOutputInfo\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StartOutput\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StopOutput\\\":\\r\\n pass\\r\\n\\r\\n #Profiles\\r\\n elif request == \\\"SetCurrentProfile\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetCurrentProfile\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"ListProfiles\\\":\\r\\n pass\\r\\n\\r\\n #Recording\\r\\n elif request == \\\"GetRecordingStatus\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"StartStopRecording\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StartRecording\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StopRecording\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"PauseRecording\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"ResumeRecording\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetRecordingFolder\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetRecordingFolder\\\":\\r\\n pass\\r\\n\\r\\n #Replay Buffer\\r\\n elif request == \\\"GetReplayBufferStatus\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"StartStopReplayBuffer\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StartReplayBuffer\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StopReplayBuffer\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SaveReplayBuffer\\\":\\r\\n pass\\r\\n\\r\\n #Scene Collections\\r\\n elif request == \\\"SetCurrentSceneCollection\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetCurrentSceneCollection\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"ListSceneCollections\\\":\\r\\n pass\\r\\n\\r\\n #Scene Items\\r\\n elif request == \\\"GetSceneItemList\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetSceneItemProperties\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSceneItemProperties\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"ResetSceneItem\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSceneItemRender\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSceneItemPosition\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSceneItemTransform\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSceneItemCrop\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"DeleteSceneItem\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"AddSceneItem\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif request == \\\"DuplicateSceneItem\\\":\\r\\n pass\\r\\n\\r\\n #Scenes\\r\\n elif request == \\\"SetCurrentScene\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetCurrentScene\\\":\\r\\n self.obs.setCurrentScene(self.data[\\\"name\\\"])\\r\\n\\r\\n elif request == \\\"GetSceneList\\\":\\r\\n for scene in self.data[\\\"scenes\\\"]:\\r\\n self.obs.addScene(scene)\\r\\n self.obs.setCurrentScene(self.data[\\\"current-scene\\\"])\\r\\n\\r\\n elif request == \\\"CreateScene\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"ReorderSceneItems\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetSceneTransitionOverride\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"RemoveSceneTransitionOverride\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetSceneTransitionOverride\\\":\\r\\n pass\\r\\n\\r\\n #Streaming\\r\\n elif request == \\\"GetStreamingStatus\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StartStopStreaming\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StartStreaming\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"StopStreaming\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetStreamSettings\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetStreamSettings\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SaveStreamSettings\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SendCaptions\\\":\\r\\n pass\\r\\n\\r\\n #Studio Mode\\r\\n elif request == \\\"GetStudioModeStatus\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetPreviewScene\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetPreviewScene\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"TransitionToProgram\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"EnableStudioMode\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"DisableStudioMode\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"ToggleStudioMode\\\":\\r\\n pass\\r\\n\\r\\n #Transitions\\r\\n elif request == \\\"GetTransitionList\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetCurrentTransition\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetCurrentTransition\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"SetTransitionDuration\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetTransitionDuration\\\":\\r\\n pass\\r\\n\\r\\n elif request == \\\"GetTransitionPosition\\\":\\r\\n pass\\r\\n\\r\\n else:\\r\\n print(f\\\"Unhandled response of type {request} and data {self.data}.\\\")\\r\\n\\r\\n \\r\\n\\r\\n else:\\r\\n event: str = self.data[\\\"update-type\\\"]\\r\\n #Events as of 4.8.0\\r\\n\\r\\n #Scenes\\r\\n if event == \\\"SwitchScenes\\\":\\r\\n self.obs.setCurrentScene(self.data[\\\"scene-name\\\"])\\r\\n\\r\\n elif event == \\\"ScenesChanged\\\":\\r\\n #self.obs.purgeScenes()\\r\\n pass\\r\\n\\r\\n elif event == \\\"SceneCollectionChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SceneCollectionListChanged\\\":\\r\\n pass\\r\\n\\r\\n #Transitions\\r\\n elif event == \\\"SwitchTransition\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"TransitionListChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"TransitionDurationChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"TransitionBegin\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"TransitionEnd\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"TransitionVideoEnd\\\":\\r\\n pass\\r\\n\\r\\n #Profiles\\r\\n elif event == \\\"ProfileChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"ProfileListChanged\\\":\\r\\n pass\\r\\n\\r\\n #Streaming\\r\\n elif event == \\\"StreamStarting\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"StreamStarted\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"StreamStopping\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"StreamStopped\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"StreamStatus\\\":\\r\\n pass\\r\\n\\r\\n #Recording\\r\\n elif event == \\\"RecordingStarting\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"RecordingStarted\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"RecordingStopping\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"RecordingStopped\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"RecordingPaused\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"RecordingResumed\\\":\\r\\n pass\\r\\n\\r\\n #Replay Buffer\\r\\n elif event == \\\"ReplayStarting\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"ReplayStarted\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"ReplayStopping\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"ReplayStopped\\\":\\r\\n pass\\r\\n\\r\\n #Other\\r\\n elif event == \\\"Exiting\\\":\\r\\n pass\\r\\n\\r\\n #General\\r\\n elif event == \\\"Heartbeat\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"BroadcastCustomMessage\\\":\\r\\n pass\\r\\n\\r\\n #Sources\\r\\n elif event == \\\"SourceCreated\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceDestroyed\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceVolumeChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceMuteStateChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceAudioDeactivated\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceAudioActivated\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceAudioSyncOffsetChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceAudioMixersChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceRenamed\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceFilterAdded\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceFilterRemoved\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SourceFilterVisibilityChanged\\\":\\r\\n source = self.obs.getSource(self.data[\\\"sourceName\\\"])\\r\\n if source != None:\\r\\n _filter = source.getFilter(self.data[\\\"filterName\\\"]) #type: ignore\\r\\n if _filter != None:\\r\\n _filter.setVisible(self.data[\\\"filterEnabled\\\"]) #type: ignore\\r\\n\\r\\n elif event == \\\"SourceFiltersReordered\\\":\\r\\n pass\\r\\n\\r\\n #Media\\r\\n elif event == \\\"MediaPlaying\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"MediaPaused\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"MediaRestarted\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"MediaStopped\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"MediaNext\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"MediaPrevious\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"MediaStarted\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n elif event == \\\"MediaEnded\\\":\\r\\n #Unreleased\\r\\n pass\\r\\n\\r\\n #Scene Items\\r\\n elif event == \\\"SceneItemOrderChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SceneItemAdded\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SceneItemRemoved\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SceneItemVisibilityChanged\\\":\\r\\n scene = self.obs.getScene(self.data[\\\"scene-name\\\"])\\r\\n if scene != None:\\r\\n source = scene.getSource(self.data[\\\"item-name\\\"]) #type: ignore\\r\\n if source != None:\\r\\n source.setVisible(self.data[\\\"item-visible\\\"]) #type: ignore\\r\\n \\r\\n\\r\\n elif event == \\\"SceneItemLockChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SceneItemTransformChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SceneItemSelected\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"SceneItemDeselected\\\":\\r\\n pass\\r\\n\\r\\n #Studio Mode\\r\\n elif event == \\\"PreviewSceneChanged\\\":\\r\\n pass\\r\\n\\r\\n elif event == \\\"StudioModeSwitched\\\":\\r\\n pass\\r\\n\\r\\n #Unhandled Events\\r\\n else:\\r\\n print(\\\"Unhandled event with data: \\\" + str(self.data))\",\n \"def process(self):\\n\\n requested_file_path = self._get_requested_file_path()\\n\\n if os.path.exists(requested_file_path):\\n self._respond_with_status_line(OK)\\n self._respond_with_headers(file_path=requested_file_path,\\n requested_headers=('Date',\\n 'Content-Length',\\n 'Content-Type',\\n 'Server'))\\n else:\\n self._respond_with_status_line(NOT_FOUND)\\n self._respond_with_headers(requested_headers=('Server',))\",\n \"def parseMISPreamble(lines, flight, summarize=False):\\n # Attempt to parse stuff from the Flight Plan ID bit. Fancy logic for\\n # grabbing the fancy name, which didn't always exist\\n try:\\n flightid = regExper(lines, 'Flight Plan ID', howmany=1,\\n keytype='key:val')\\n fid = keyValuePair(flightid.group(), \\\"Flight Plan ID\\\", dtype=str)\\n fid = fid.strip().split(\\\"_\\\")\\n if fid[1] != '':\\n try:\\n flight.instrument = flight.instdict[fid[1].strip()]\\n except:\\n flight.instrument = ''\\n if fid[2] != '':\\n flight.fancyname = fid[2]\\n except:\\n fid = ['', '', '']\\n\\n # Grab the filename and date of MIS file creation\\n filename = regExper(lines, 'Filename', howmany=1, keytype='key:val')\\n flight.filename = keyValuePair(filename.group(), \\\"Filename\\\", dtype=str)\\n\\n # Note: the saved key is a timestamp, with a space in between stuff.\\n saved = regExper(lines, 'Saved', howmany=1, keytype='key:dtime')\\n flight.saved = keyValuePairDT(saved.group(), \\\"Saved\\\")\\n\\n # Search for two airports; first is takeoff, second is landing\\n airports = regExper(lines, 'Airport', howmany=2, keytype='key:val')\\n if airports is not None and len(airports) == 2:\\n flight.origin = keyValuePair(airports[0].group(),\\n \\\"Airport\\\", dtype=str)\\n flight.destination = keyValuePair(airports[1].group(),\\n \\\"Airport\\\", dtype=str)\\n elif len(airports) != 2 or airports is None:\\n print(\\\"WARNING: Couldn't find departure/arrival information!\\\")\\n flight.origin = \\\"Unknown\\\"\\n flight.destination = \\\"Unknown\\\"\\n\\n runway = regExper(lines, 'Runway', howmany=1, keytype='key:val')\\n flight.drunway = keyValuePair(runway.group(), \\\"Runway\\\", dtype=str)\\n\\n legs = regExper(lines, 'Legs', howmany=1, keytype='key:val')\\n flight.nlegs = keyValuePair(legs.group(), \\\"Legs\\\", dtype=int)\\n\\n mach = regExper(lines, 'Mach', howmany=1, keytype='key:val')\\n flight.mach = keyValuePair(mach.group(), \\\"Mach\\\", dtype=float)\\n\\n takeoff = regExper(lines, 'Takeoff', howmany=1, keytype='key:dtime')\\n flight.takeoff = keyValuePairDT(takeoff.group(), \\\"Takeoff\\\")\\n\\n obstime = regExper(lines, 'Obs Time', howmany=1, keytype='key:val')\\n flight.obstime = keyValuePairTD(obstime.group(), \\\"Obs Time\\\")\\n\\n flttime = regExper(lines, 'Flt Time', howmany=1, keytype='key:val')\\n flight.flighttime = keyValuePairTD(flttime.group(), \\\"Flt Time\\\")\\n\\n landing = regExper(lines, 'Landing', howmany=1, keytype='key:dtime')\\n flight.landing = keyValuePairDT(landing.group(), \\\"Landing\\\")\\n\\n # NOTE: I hate fp. It sometimes doesn't write sunrise info.\\n sunset = regExper(lines, 'Sunset', howmany=1, keytype='key:val')\\n try:\\n flight.sunset = keyValuePairTD(sunset.group(), \\\"Sunset\\\")\\n except:\\n flight.sunset = \\\"NONE\\\"\\n\\n sunrise = regExper(lines, 'Sunrise', howmany=1, keytype='key:val')\\n try:\\n flight.sunrise = keyValuePairTD(sunrise.group(), \\\"Sunrise\\\")\\n except:\\n flight.sunrise = \\\"NONE\\\"\\n\\n if summarize is True:\\n print(flight.summarize())\\n\\n return flight\",\n \"def header_postRsp(self):\\n self.jobHealthy(4)\\n contentBase = '/Reflect/RequestProcess/ports/analyze/0'\\n # request : \\n self.url = PROTOCOL + IP + ':' + str(PORT) + contentBase + '/post?'\\n payload = {\\\"REPEAT\\\":str(self.tstCount)}\\n print(\\\"** REQ: %s Payload len:%d\\\" % (self.url, len(payload)), flush=True)\\n rsp = requests.post(url=self.url, data=payload)\\n\\n # response\\n #print(\\\"RSP: %s\\\\nSTATUS:%s\\\\nCONTENT:%s\\\" % (rsp, rsp.status_code, rsp.text), flush=True)\\n #print(\\\"RSP::%s\\\" % (rsp.text), flush=True)\\n self.assertEqual(rsp.status_code, 200, \\\"incorrect completion code\\\")\\n self.assertTrue(rsp.content.startswith(b\\\"RSP:\\\"), msg=\\\"preamble missing - data loss\\\")\\n print(\\\"** RSP: expected: %d received: %d\\\" % (self.tstCount, len(rsp.text)))\\n self.assertGreater(len(rsp.text), self.tstCount, msg=\\\"under fill count - data loss\\\" )\",\n \"def parse(self):\\n try:\\n self.open_file()\\n lines = list(self._file)\\n\\n if len(lines) > 0:\\n text = ''.join(lines)\\n regex = 'Song \\\\d+\\\\nStart (\\\\d+:\\\\d+:\\\\d+)\\\\nEnd (\\\\d+:\\\\d+:\\\\d+)\\\\nLength (\\\\d+.\\\\d+)'\\n match = re.findall(regex, text)\\n if len(match):\\n starts = []\\n ends = []\\n lengths = []\\n\\n for i in range(len(match)):\\n starts.append(match[i][0])\\n ends.append(match[i][1])\\n lengths.append(float(match[i][2]))\\n\\n for i in range(len(match)):\\n self.debug_data.append({\\n 'start':starts[i],'end':ends[i],'length':lengths[i]})\\n\\n match = re.search('T\\\\d_S(\\\\d{4})_.*.txt', self._filepath)\\n if match:\\n self._experiment_metadata['session_id'] = int(match.groups()[0])\\n else:\\n raise EIMParsingError(\\\"No valid session id found in filename %s\\\" % self._filepath)\\n\\n finally:\\n if self._file and not self._file.closed:\\n self.close_file()\",\n \"def decode(r):\\n ## Check length\\n if len(r) != 60: return False\\n ## Check cookie\\n if r[0x0:0x2] != b'\\\\xef\\\\xfe': return False\\n t = r[2]\\n mac = \\\"%02x:%02x:%02x:%02x:%02x:%02x\\\" % struct.unpack(\\\"BBBBBB\\\",r[3:9])\\n gateware = \\\"{0}.{1}\\\".format(r[0x09],r[0x15])\\n radio_id = r[0x0a]\\n temp = r[0x0b]\\n use_eeprom_ip = (temp & 0x80) != 0\\n use_eeprom_mac = (temp & 0x40) != 0\\n favor_dhcp = (temp & 0x20) != 0\\n eeprom_ip = \\\"%d:%d:%d:%d\\\" % struct.unpack(\\\"BBBB\\\",r[0x0d:0x11])\\n eeprom_mac = \\\"%02x:%02x:%02x:%02x:%02x:%02x\\\" % struct.unpack(\\\"BBBBBB\\\",r[0x03:0x07]+r[0x11:0x13])\\n receivers = r[0x13]\\n temp = r[0x14]\\n board_id = temp & 0x3f\\n wideband_type = 0x03 & (temp >> 6)\\n response_data = struct.unpack('!L',r[0x17:0x1b])[0]\\n temp = r[0x1b]\\n ext_cw_key = (temp & 0x80) != 0\\n tx_on = (temp & 0x40) != 0\\n adc_clip_cnt = temp & 0x03\\n temperature = struct.unpack('!H',r[0x1c:0x1e])[0]\\n # For best accuracy, 3.26 should be a user's measured 3.3V supply voltage.\\n temperature = (3.26 * (temperature/4096.0) - 0.5)/0.01\\n # TODO: Add proper power compoutation, maybe table interpolation like Quisk\\n fwd_pwr = struct.unpack('!H',r[0x1e:0x20])[0]\\n rev_pwr = struct.unpack('!H',r[0x20:0x22])[0]\\n bias = struct.unpack('!H',r[0x22:0x24])[0]\\n bias = ((3.26 * (bias/4096.0))/50.0)/0.04\\n temp = r[0x24]\\n txfifo_recovery = (temp & 0x80) != 0\\n txfifo_msbs = (temp & 0x7f)\\n return Response(t,mac,gateware,radio_id,use_eeprom_ip,use_eeprom_mac,favor_dhcp,eeprom_ip,eeprom_mac,\\n receivers,board_id,wideband_type,response_data,ext_cw_key,tx_on,adc_clip_cnt,temperature,\\n fwd_pwr,rev_pwr,bias,txfifo_recovery,txfifo_msbs)\",\n \"def _decode(self):\\n \\n self.version = int(data_to_hex_str(self.packet[0])[2])\\n self.header_len = int(data_to_hex_str(self.packet[0])[3]) * 4\\n self.type_of_service = data_to_hex_str(self.packet[1:2])\\n self.total_len = int(data_to_hex_str(self.packet[2:4]), 16)\\n self.id = data_to_hex_str(self.packet[4:6])\\n \\n #parse the flags fields(reservedbit, don't fragment, more fragment)\\n if ((ord(self.packet[6]) & (1 << 7)) != 0):\\n self.flags_reservedbit = 1\\n else:\\n self.flags_reservedbit = 0\\n #endof if\\n \\n if ((ord(self.packet[6]) & (1 << 6)) != 0):\\n self.flags_dont_fragment = 1\\n else:\\n self.flags_dont_fragment = 0\\n #endof if\\n \\n if ((ord(self.packet[6]) & (1 << 5)) != 0):\\n self.flags_more_fragment = 1\\n else:\\n self.flags_more_fragment = 0\\n #endof if\\n \\n #parse the offset field(in packet[6:7]): 00011111 & packet[6] (to filter flags) -->> get packet[6:7] in hex_str\\n #tmp = str(31 & ord(self.packet[6]))\\n self.fragment_offset = int(data_to_hex_str(self.packet[6:8]), 16)\\n if (self.fragment_offset >= (1 << 13)):\\n #take away the flags fields: 00011111 11111111 & self.fragment_offset\\n self.fragment_offset = self.fragment_offset & ((1 << 13) - 1) \\n \\n self.TTL = ord(self.packet[8])\\n self.protocol = IPPROTO[ord(self.packet[9])]\\n self.header_checksum = data_to_hex_str(self.packet[10:12])\\n \\n self.src = str(ord(self.packet[12])) + '.' + str(ord(self.packet[13])) + '.' + \\\\\\n str(ord(self.packet[14])) + '.' + str(ord(self.packet[15]))\\n self.dst = str(ord(self.packet[16])) + '.' + str(ord(self.packet[17])) + '.' + \\\\\\n str(ord(self.packet[18])) + '.' + str(ord(self.packet[19]))\\n \\n if (self.header_len > 20):\\n self.opt_paddings = self.packet[20 : (self.header_len)]\",\n \"def _validate_replay(self):\\n if self.replay_tt.text != self.tt.text:\\n raise ValueError(\\\"The replay text and text do not agree.\\\")\\n\\n if self.replay_tt.start_ts is None:\\n raise ValueError(\\\"The replay was never started\\\")\",\n \"def _extract_data(self, eopatch: EOPatch, responses: list[Any], shape: tuple[int, ...]) -> EOPatch:\",\n \"def insert_bad_data():\\n get_file_reply(files[2][0], files[2][1])\",\n \"def from_dem(file, verbose=False):\\n import subprocess, os\\n from uuid import uuid4\\n jarCall = list(REP_DEM_JARBASE)\\n jarCall[-2] = file\\n if not os.path.exists(OPTIONS.JB.SCRATCH): os.makedirs(OPTIONS.JB.SCRATCH)\\n jarCall[-1] = os.path.abspath(os.path.join(OPTIONS.JB.SCRATCH, str(uuid4())))\\n try:\\n p = subprocess.Popen(jarCall, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n out, err = p.communicate()\\n if (verbose):\\n print out\\n print err\\n \\n return Replay.from_csv(jarCall[-1])\\n \\n except Exception as e:\\n print 'Invalid replay file. Unable to parse replay.'\\n \\n finally:\\n if os.path.exists(jarCall[-1]):\\n try: os.remove(jarCall[-1])\\n except: print 'Unable to delete temporary file %s' % jarCall[-1]\",\n \"def file_request(self):\\n magic_number = int.to_bytes(int(0x497E), 2, byteorder='big') \\n type_bytes = int.to_bytes(1, 1, byteorder='big')\\n filename_in_bytes = bytes(self.file_name, 'utf-8') #ENCODE FILENAME IN BYTES\\n try:\\n filename_len = int.to_bytes(len(filename_in_bytes), 2, byteorder='big')\\n except OverflowError:\\n self.socket.close()\\n sys.exit(\\\"ERROR: Filename is too long, Stack Overflow\\\") \\n \\n message_to_send = bytearray(magic_number+type_bytes+filename_len+filename_in_bytes)\\n self.socket.send(message_to_send)\\n \\n #WAIT FOR FILE RESPONSE FROM SERVER\\n try:\\n self.socket.settimeout(1)\\n while True: #LOOP TO RECIEVE DATA IN CHUNKS <= 4096 BYTES\\n data = self.socket.recv(4096)\\n if data:\\n self.data += data\\n else:\\n break\\n\\n self.socket.settimeout(None)\\n self.socket.close()\\n\\n except socket.timeout:\\n self.socket.close()\\n sys.exit(\\\"ERROR: Connection timed out\\\")\",\n \"def view_full_request(line):\\n args = shlex.split(line)\\n if not args:\\n raise PappyException(\\\"Request id is required\\\")\\n reqid = args[0]\\n\\n reqs = yield load_reqlist(reqid)\\n for req in reqs:\\n if len(reqs) > 1:\\n print 'Request %s:' % req.reqid\\n view_full_message(req, try_ws=True)\\n if len(reqs) > 1:\\n print '-'*30\\n print ''\",\n \"def rinex_info(rinex_fname,\\n nav_fname,\\n work_path=None):\\n if not os.path.isfile(rinex_fname):\\n raise ValueError('RINEX observation file {} does not exist'.format(rinex_fname))\\n if not os.path.isfile(nav_fname):\\n raise ValueError('RINEX navigation file {} does not exist'.format(nav_fname))\\n # information mapping\\n info = {}\\n def process_output(line):\\n if line.startswith('Receiver type'):\\n info['receiver'] = line.split(':')[1].split('(')[0].strip()\\n elif line.lstrip().startswith('antenna WGS 84 (xyz)'):\\n # make sure units are [m]\\n assert line.rstrip().endswith('(m)')\\n info['xyz'] = map(float, line.split(':')[1].split('(')[0].split())\\n elif line.lstrip().startswith('antenna WGS 84 (geo)'):\\n if line.split(':')[1].lstrip()[0] in ['N', 'S']:\\n # skip arcmin, arcsec line\\n pass\\n else:\\n lat, _, lon, _ = line.split(':')[1].split(None, 3)\\n info['lat'] = float(lat)\\n lon = float(lon)\\n while lon > 180:\\n lon -= 360\\n info['lon'] = lon\\n elif line.lstrip().startswith('WGS 84 height'):\\n assert line.rstrip().endswith('m')\\n info['height'] = float(line.split(':')[1].rstrip()[:-1])\\n elif line.startswith('|qc - header| position'):\\n # make sure units are [m]\\n assert line.rstrip()[-1] == 'm'\\n info['xyz error'] = float(line.split(':')[1].rstrip()[:-1])\\n elif line.startswith('Observation interval'):\\n info['interval'] = float(line.split(':')[1].split()[0])\\n elif line.startswith('Moving average MP12'):\\n info['MP12'] = float(line.split(':')[1].rstrip()[:-1])\\n elif line.startswith('Moving average MP21'):\\n info['MP21'] = float(line.split(':')[1].rstrip()[:-1])\\n # query the RINEX file via teqc quality check --- process in given\\n # work area to avoid intermediate file pollution\\n with SmartTempDir(work_path) as work_path:\\n intermediate_rinex_fname = replace_path(work_path, rinex_fname)\\n os.symlink(os.path.abspath(rinex_fname),\\n intermediate_rinex_fname)\\n intermediate_nav_fname = replace_path(work_path, nav_fname)\\n os.symlink(os.path.abspath(nav_fname),\\n intermediate_nav_fname)\\n sh.teqc('+qc',\\n '+quiet',\\n '-R',\\n '-S',\\n '-E',\\n '-C',\\n '-J',\\n '-nav', intermediate_nav_fname,\\n intermediate_rinex_fname,\\n _cwd=work_path,\\n _out=process_output,\\n _err=sys.stderr)\\n os.remove(intermediate_rinex_fname)\\n os.remove(intermediate_nav_fname)\\n return info\",\n \"def get_file_name_from_resposne(r):\\n if not r: \\n return None\\n return get_file_name_from_cd(r.headers.get())\",\n \"def _main_header(self, hdr):\\n d = {}\\n # Called readDefAnalysis in OpenMIMS\\n d['sample type'], d['data included'], d['sample x'], d['sample y'], \\\\\\n d['analysis type'], d['user name'], d['sample z'], date, time = \\\\\\n unpack(self._bo + '4i 32s 16s i 12x 16s 16s', hdr.read(112))\\n\\n d['data included'] = bool(d['data included'])\\n d['user name'] = self._cleanup_string(d['user name'])\\n d['analysis type'] = self._cleanup_string(d['analysis type']).lower()\\n date = self._cleanup_string(date)\\n time = self._cleanup_string(time)\\n d['date'] = self._cleanup_date(date + ' ' + time)\\n\\n if self.header['file type'] in (27, 29, 39):\\n # Called MaskImage/readMaskIm in OpenMIMS\\n d['original filename'], d['analysis duration'], d['frames'], \\\\\\n d['scan type'], d['magnification'], d['size type'], \\\\\\n d['size detector'], d['beam blanking'], d['presputtering'], \\\\\\n d['presputtering duration'] = \\\\\\n unpack(self._bo + '16s 3i 3h 2x 3i', hdr.read(48))\\n\\n d['AutoCal'] = self._autocal(hdr)\\n d['HVControl'] = {}\\n d['HVControl']['hvcontrol enabled'] = False\\n\\n elif self.header['file type'] in (22, 41):\\n # Called MaskSampleStageImage/readMaskIss in OpenMIMS\\n d['original filename'], d['analysis duration'], d['scan type'], \\\\\\n d['steps'], d['step size x'], d['step size y'], d['step size?'], \\\\\\n d['step waittime'], d['frames'], d['beam blanking'], \\\\\\n d['presputtering'], d['presputtering duration'] = \\\\\\n unpack(self._bo + '16s 6i d 4i', hdr.read(64))\\n\\n d['scan type'] = _stage_scan_types.get(d['scan type'], str(d['scan type']))\\n\\n d['AutoCal'] = self._autocal(hdr)\\n d['HVControl'] = self._hvcontrol(hdr)\\n # Don't know if this unused byte needs to go after HVControl or after SigRef.\\n hdr.seek(4, 1)\\n\\n elif self.header['file type'] in (21, 26):\\n # Not in OpenMIMS\\n # this bit same as image, 1 extra unused/unknown\\n d['original filename'], d['analysis duration'], d['frames'], \\\\\\n d['scan type'], d['magnification'], d['size type'], \\\\\\n d['size detector'], d['beam blanking'], d['presputtering'], \\\\\\n d['presputtering duration'] = \\\\\\n unpack(self._bo + '16s 4x 3i 3h 2x 3i', hdr.read(52))\\n\\n # this bit same as stage scan\\n d['AutoCal'] = self._autocal(hdr)\\n d['HVControl'] = self._hvcontrol(hdr)\\n\\n # 24 bytes unknown, not sure if they go here or before AutoCal\\n hdr.seek(24, 1)\\n\\n elif self.header['file type'] == 31:\\n # Don't know if this is correct, all 0s anyway\\n d['original filename'], d['scan type'], \\\\\\n d['beam blanking'], d['presputtering'] = \\\\\\n unpack(self._bo + '16s 3i 4x', hdr.read(32))\\n\\n elif self.header['file type'] == 35:\\n d['original filename'], d['scan type'], d['analysis duration'], \\\\\\n d['frames'], d['beam blanking'], d['presputtering'] = \\\\\\n unpack(self._bo + '16s 5i 40x', hdr.read(76))\\n\\n d['AutoCal'] = self._autocal(hdr)\\n d['HVControl'] = self._hvcontrol(hdr)\\n\\n else:\\n raise TypeError('What type of image are you? {}'.format(self.header['file type']))\\n\\n # Continue main header for all types\\n d['SigRef'] = self._sigref(hdr)\\n d['masses'] = unpack(self._bo + 'i', hdr.read(4))[0]\\n\\n # scan type is set for stage scan analysis, set others\\n if isinstance(d['scan type'], int):\\n if d['scan type'] == 0:\\n d['scan type'] = ''\\n else:\\n d['scan type'] = str(d['scan type'])\\n\\n d['beam blanking'] = bool(d['beam blanking'])\\n d['presputtering'] = bool(d['presputtering'])\\n d['original filename'] = self._cleanup_string(d['original filename'])\\n\\n if self.header['file type'] in (21, 26, 27, 29, 35, 39):\\n if self.header['file version'] >= 4108:\\n n = 60\\n else:\\n n = 10\\n elif self.header['file type'] in (22, 31, 40, 41):\\n n = 20\\n else:\\n n = 0\\n\\n # Not sure what this is, memory pointers? Not needed.\\n # d['mass table ptr'] = unpack(self._bo + 2*n*'h', hdr.read(n*4))\\n hdr.seek(n*4, 1)\\n\\n if self.header['file type'] in (21, 22, 26, 40, 41, 35):\\n hdr.seek(4, 1) # 4 bytes unused\\n\\n # Mass table, dict by species label.\\n d['MassTable'] = collections.OrderedDict()\\n for m in range(d['masses']):\\n mi = {}\\n mi['trolley index'], unknown, mi['mass'], mi['matrix or trace'], \\\\\\n mi['detector'], mi['wait time'], mi['frame count time'] = \\\\\\n unpack(self._bo + '2i d 2i 2d', hdr.read(40))\\n\\n if self.header['file type'] == 31:\\n if d['analysis type'].endswith('trolley step scan'):\\n # start and end are in mm, step is in μm; convert to mm\\n mi['radius start'], mi['radius end'], \\\\\\n mi['radius step'], mi['b field bits'] = \\\\\\n unpack(self._bo + '3d i', hdr.read(28))\\n mi['radius step'] /= 1000\\n else:\\n mi['voltage start'], mi['voltage end'], \\\\\\n mi['voltage step'], mi['b field bits'] = \\\\\\n unpack(self._bo + '3d i', hdr.read(28))\\n else:\\n mi['offset'], mi['b field bits'] = unpack(self._bo + '2i', hdr.read(8))\\n\\n mi.update(self._species(hdr))\\n\\n if self.header['file type'] == 31:\\n hdr.seek(4, 1)\\n\\n # Add correction controls, my own addition.\\n mi['background corrected'] = False\\n mi['deadtime corrected'] = False\\n mi['yield corrected'] = False\\n\\n label = mi.pop('label')\\n # This is true for NS50L and file version 4108.\\n # Anywhere else different?\\n # Maybe confirm this with the Trolleys dict,\\n # there is an Esi trolley.\\n if mi['trolley index'] == 8:\\n label = 'SE'\\n\\n d['MassTable'][label] = mi\\n\\n # Create a few convenient lists\\n d['label list'] = tuple(d['MassTable'].keys())\\n d['label list fmt'] = tuple(format_species(m) for m in d['label list'])\\n d['mass list'] = tuple(d['MassTable'][m]['mass'] for m in d['label list'])\\n\\n return d\",\n \"def decode_request_content(self, datafile):\\n content_type = self.headers.get(\\\"Content-Type\\\", \\\"notype\\\").lower()\\n if 'application/x-www-form-urlencoded' in content_type:\\n # The data is provided in a urlencoded format. Unencode it into\\n # cgi FieldStorage/MiniFieldStorage objects in a form container\\n form = cgi.FieldStorage(\\n fp=datafile,\\n headers=self.headers,\\n environ=dict(REQUEST_METHOD='POST',\\n CONTENT_TYPE=self.headers['Content-Type'])\\n )\\n itemdict = {}\\n for item in form.list:\\n if item.name == 'data':\\n itemdict['data'] = \\\\\\n SimpleLogRequestHandler.extract_form_fields(item)\\n elif item.name == 'layout':\\n # http://log4javascript.org/docs/manual.html#layouts\\n itemdict['layout'] = item.value\\n return itemdict\\n else:\\n self.send_response(501,\\n \\\"Content-Type %r not supported\\\" % content_type)\\n self.send_header(\\\"Content-length\\\", \\\"0\\\")\\n self.end_headers()\\n return None\",\n \"def _parse_line(self, line):\\n fields = line.split('|', 4) # stop splitting after fourth | found\\n line_info = {'raw_message': line}\\n if len(fields) == 5:\\n line_info.update(dict(zip(self._fieldnames, fields)))\\n return line_info\",\n \"def handle(self): \\n \\n data = self.request[0].strip()\\n self.socket = self.request[1]\\n\\n #split off first word of file, assume is filename\\n filename,sep,data = data.partition(\\\" \\\")\\n\\n #assume is requesting file\\n if not data:\\n self.sendfile(filename)\\n #assume we have to save the file since data was sent\\n else:\\n self.savefile(filename,data)\\n\\n return True\",\n \"def test_recv_line(self):\\n super(TestCisAsciiFileInput_local, self).test_recv_line()\",\n \"def test_extract_receipt(self):\\n\\n # Test IAP Response without in_app list\\n request = Request('DummyReceipt', use_production=True)\\n ext_receipt = request._extract_receipt(self.iap_response)\\n\\n assert ext_receipt['status'] == 0 # 0 is normal\\n assert ext_receipt['receipt']['product_id'] == 'TestProduction1'\\n assert ext_receipt['receipt']['original_transaction_id'] == '1000000012345678' # original transaction id\\n assert ext_receipt['receipt']['quantity'] == '1' # check quantity\\n\\n # Test IAP Response with in_app list\\n request = Request('DummyReceipt', use_production=True)\\n ext_receipt = request._extract_receipt(self.iap_response_in_app)\\n\\n assert ext_receipt['status'] == 0 # 0 is normal\\n assert ext_receipt['receipt']['product_id'] == 'org.itunesiap'\\n assert ext_receipt['receipt']['original_transaction_id'] == '1000000155718067' # original transaction id\\n assert ext_receipt['receipt']['quantity'] == '1' # check quantity\",\n \"async def read_request_line(self):\\n while True:\\n rl = await self.reader.readline()\\n # skip empty lines\\n if rl == b'\\\\r\\\\n' or rl == b'\\\\n':\\n continue\\n break\\n rl_frags = rl.split()\\n if len(rl_frags) != 3:\\n raise HTTPException(400)\\n self.method = rl_frags[0]\\n url_frags = rl_frags[1].split(b'?', 1)\\n self.path = url_frags[0]\\n if len(url_frags) > 1:\\n self.query_string = url_frags[1]\",\n \"def process_packet(packet):\\n if packet.haslayer(HTTPRequest):\\n # if this packet is an HTTP Request\\n # get the requested URL\\n url = packet[HTTPRequest].Host.decode() + packet[HTTPRequest].Path.decode()\\n # get the requester's IP Address\\n ip = packet[IP].src\\n # get the request method\\n method = packet[HTTPRequest].Method.decode()\\n print(\\\"\\\\n{GREEN}[+] \\\", ip, \\\"Requested \\\", url, \\\" with \\\", method)\\n if show_raw and packet.haslayer(Raw) and method == \\\"POST\\\":\\n # if show_raw flag is enabled, has raw data, and the requested method is \\\"POST\\\"\\n # then show raw\\n print(\\\"\\\\n{RED}[*] Some useful Raw data: \\\", packet[Raw].load)\",\n \"def process_data(self):\\n\\t\\t\\n\\t\\tboundary = self.headers.plisttext.split('=')[1]\\n\\t\\tremain = int(self.headers['content-length'])\\n\\t\\t\\n\\t\\t#Check whether file is started with boundary\\n\\t\\tline = self.rfile.readline()\\n\\t\\tremain -= len(line)\\n\\t\\tif boundary not in line:\\n\\t\\t\\treturn False,\\\"File does not begin with boundary.\\\"\\n\\t\\t\\n\\t\\t#Get disposition info\\t\\t\\n\\t\\tline = self.rfile.readline()\\n\\t\\tremain -= len(line)\\n\\t\\t\\n\\t\\t#Check file info \\n\\t\\tfilename = re.findall(r'Content-Disposition.*name=\\\"file\\\"; filename=\\\"(.*)\\\"',line)\\n\\t\\tif not filename:\\n\\t\\t\\treturn False,\\\"Missing file name.\\\"\\n\\t\\tpath = self.translate_path(self.path)\\n\\t\\tprint path\\n\\t\\tfilename = os.path.join(path,filename[0])\\n\\t\\t\\n\\t\\t#Check whether file name exists\\n\\t\\twhile os.path.exists(filename):\\n\\t\\t\\tfilename = filename.split('.')[0]+'+'+filename.split('.')[1]\\n\\t\\t\\t\\n\\t\\t\\n\\t\\t#Get content type info\\n\\t\\tline = self.rfile.readline()\\n\\t\\tremain -= len(line)\\n\\t\\tfiletype = re.findall(r'Content-Type: (.*).*',line)\\n\\t\\tline = self.rfile.readline() #it is an empty line\\n\\t\\tremain -= len(line)\\n\\t\\t\\n\\t\\t#Content begins, try writing data to file in server\\n\\t\\ttry:\\n\\t\\t\\toutput = open(filename,'wb')\\n\\t\\texcept IOError:\\n\\t\\t\\treturn False,\\\"Authority denied.\\\"\\n\\t\\t\\n\\t\\t#Write data\\n\\t\\tfirstline = self.rfile.readline()\\n\\t\\tremain -= len(firstline)\\n\\t\\twhile remain > 0:\\n\\t\\t\\tline = self.rfile.readline()\\n\\t\\t\\tremain -= len(line)\\n\\t\\t\\tif boundary in line:\\n\\t\\t\\t\\tfirstline = firstline[0:-1]\\n\\t\\t\\t\\tif firstline[-1] == '\\\\r':\\n\\t\\t\\t\\t\\tfirstline = firstline[0:-1]\\n\\t\\t\\t\\toutput.write(firstline)\\n\\t\\t\\t\\toutput.close()\\n\\t\\t\\t\\treturn True,\\\"File created.Path: %s\\\" % filename\\n\\t\\t\\telse:\\n\\t\\t\\t\\toutput.write(firstline)\\n\\t\\t\\t\\tfirstline = line\\n\\t\\treturn False,\\\"Unexpected file end.\\\"\",\n \"def get_metadata(diagnostics_dir, verbose=False):\\n metafile = find_metadata_file(diagnostics_dir, 'mslist-2*txt', verbose=False)\\n\\n with open(metafile, 'r') as mslist_file:\\n lines = mslist_file.readlines()\\n\\n nBlocks = 6 # these are the number of correlator cards (PILOT survey value)\\n \\n obs_metadata = ObservationMetadata()\\n\\n obs_date = 'Observed from'\\n fields = 'Fields'\\n code = 'Code'\\n duration = 'Total elapsed time'\\n antenna = 'antennas'\\n frame = 'Frame'\\n \\n field_list = []\\n\\n for i in range(len(lines)):\\n line = lines[i]\\n if line.find(antenna) >=0:\\n toks = line.split()\\n obs_metadata.n_ant = toks[5][-2:]\\n if line.find(obs_date) >=0:\\n toks = line.split()\\n obs_metadata.start_obs_date = toks[6]\\n obs_metadata.end_obs_date = toks[8]\\n if line.find(duration) >=0:\\n toks = line.split()\\n obs_metadata.tobs = float(toks[10]) # in second\\n\\n # Field details\\n if line.find(fields) >=0:\\n toks = line.split()\\n obs_metadata.num_fields = int(toks[-1])\\n\\n if line.find(code) >= 0:\\n for j in range(obs_metadata.num_fields):\\n field_metadata = FieldMetadata()\\n field_line = lines[i+j+1]\\n toks = field_line.split()\\n field_metadata.name = toks[5]\\n field_metadata.ra = toks[6][:-5]\\n field_metadata.dec = toks[7][:-4]\\n field_metadata.num_rows = int(toks[9])\\n obs_metadata.fields.append(field_metadata)\\n\\n if line.find(frame) >= 0:\\n next_line = lines[i+1]\\n toks = next_line.split()\\n obs_metadata.total_obs_bw = float(toks[10])*nBlocks/1000.0 # kHz to MHz \\n \\n return obs_metadata #n_ant, start_obs_date, end_obs_date, tobs, field, ra, dec, total_obs_bw\",\n \"def dump_response(line):\\n # dump the data of a response\\n args = shlex.split(line)\\n reqs = yield load_reqlist(args[0])\\n for req in reqs:\\n if req.response:\\n rsp = req.response\\n if len(args) >= 2:\\n fname = args[1]\\n else:\\n fname = req.path.split('/')[-1]\\n\\n with open(fname, 'w') as f:\\n f.write(rsp.body)\\n print 'Response data written to %s' % fname\\n else:\\n print 'Request %s does not have a response' % req.reqid\",\n \"def extract(self, data):\",\n \"def check_redemption_request(request: IRequest) -> Optional[bytes]:\\n if request.requestHeaders.getRawHeaders(b\\\"content-type\\\") != [b\\\"application/json\\\"]:\\n return bad_content_type(request)\\n\\n p = request.content.tell()\\n content = request.content.read()\\n request.content.seek(p)\\n\\n try:\\n request_body = loads(content)\\n except ValueError:\\n return bad_request(request, None)\\n\\n expected_keys = {\\\"redeemVoucher\\\", \\\"redeemCounter\\\", \\\"redeemTokens\\\"}\\n actual_keys = set(request_body.keys())\\n if expected_keys != actual_keys:\\n return bad_request(\\n request,\\n {\\n \\\"success\\\": False,\\n \\\"reason\\\": \\\"{} != {}\\\".format(\\n expected_keys,\\n actual_keys,\\n ),\\n },\\n )\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66388625","0.6589973","0.561222","0.55880296","0.55790913","0.54335433","0.53088397","0.5289022","0.52817225","0.52215827","0.51933885","0.51402473","0.5130604","0.5128931","0.5114549","0.5101588","0.50950605","0.50752","0.50751144","0.50603235","0.50575024","0.5055748","0.5052026","0.5023965","0.49998227","0.49911895","0.49908215","0.49834695","0.49822846","0.49757367","0.49630618","0.49422124","0.49361855","0.49338982","0.4907871","0.49050808","0.4900873","0.48998192","0.48987216","0.4897284","0.4896594","0.48781562","0.48737168","0.4858136","0.48547205","0.4854442","0.4852321","0.48495948","0.4841918","0.48404014","0.48328242","0.48313913","0.4827432","0.48256043","0.4815854","0.48151082","0.4813623","0.48135972","0.48080173","0.4797475","0.47941667","0.4782859","0.47778833","0.47724155","0.47710633","0.47571924","0.47537673","0.47500247","0.47476244","0.47453615","0.47427553","0.47327134","0.47239017","0.47213492","0.47112238","0.46983114","0.4694763","0.4688415","0.4684919","0.4683196","0.46821684","0.4674099","0.4668203","0.46666867","0.46630812","0.46557415","0.46524724","0.4651611","0.46482885","0.46473375","0.46393347","0.46390086","0.46345985","0.46341974","0.4631663","0.46312502","0.46273756","0.46269923","0.4626244","0.46220353"],"string":"[\n \"0.66388625\",\n \"0.6589973\",\n \"0.561222\",\n \"0.55880296\",\n \"0.55790913\",\n \"0.54335433\",\n \"0.53088397\",\n \"0.5289022\",\n \"0.52817225\",\n \"0.52215827\",\n \"0.51933885\",\n \"0.51402473\",\n \"0.5130604\",\n \"0.5128931\",\n \"0.5114549\",\n \"0.5101588\",\n \"0.50950605\",\n \"0.50752\",\n \"0.50751144\",\n \"0.50603235\",\n \"0.50575024\",\n \"0.5055748\",\n \"0.5052026\",\n \"0.5023965\",\n \"0.49998227\",\n \"0.49911895\",\n \"0.49908215\",\n \"0.49834695\",\n \"0.49822846\",\n \"0.49757367\",\n \"0.49630618\",\n \"0.49422124\",\n \"0.49361855\",\n \"0.49338982\",\n \"0.4907871\",\n \"0.49050808\",\n \"0.4900873\",\n \"0.48998192\",\n \"0.48987216\",\n \"0.4897284\",\n \"0.4896594\",\n \"0.48781562\",\n \"0.48737168\",\n \"0.4858136\",\n \"0.48547205\",\n \"0.4854442\",\n \"0.4852321\",\n \"0.48495948\",\n \"0.4841918\",\n \"0.48404014\",\n \"0.48328242\",\n \"0.48313913\",\n \"0.4827432\",\n \"0.48256043\",\n \"0.4815854\",\n \"0.48151082\",\n \"0.4813623\",\n \"0.48135972\",\n \"0.48080173\",\n \"0.4797475\",\n \"0.47941667\",\n \"0.4782859\",\n \"0.47778833\",\n \"0.47724155\",\n \"0.47710633\",\n \"0.47571924\",\n \"0.47537673\",\n \"0.47500247\",\n \"0.47476244\",\n \"0.47453615\",\n \"0.47427553\",\n \"0.47327134\",\n \"0.47239017\",\n \"0.47213492\",\n \"0.47112238\",\n \"0.46983114\",\n \"0.4694763\",\n \"0.4688415\",\n \"0.4684919\",\n \"0.4683196\",\n \"0.46821684\",\n \"0.4674099\",\n \"0.4668203\",\n \"0.46666867\",\n \"0.46630812\",\n \"0.46557415\",\n \"0.46524724\",\n \"0.4651611\",\n \"0.46482885\",\n \"0.46473375\",\n \"0.46393347\",\n \"0.46390086\",\n \"0.46345985\",\n \"0.46341974\",\n \"0.4631663\",\n \"0.46312502\",\n \"0.46273756\",\n \"0.46269923\",\n \"0.4626244\",\n \"0.46220353\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6063962"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":19594,"cells":{"query":{"kind":"string","value":"Take replay file that was uploaded to ObjectStore and process data and store it to database and link to account"},"document":{"kind":"string","value":"async def parse_replay(request, game):\n\n game = game.lower()\n replay_file = request.files.get(\"replay\")\n if replay_file:\n if game == STARCRAFT:\n basic, result = await SC2Replay.process_replay(replay_file, request.args.get(\"load_map\", False))\n if result:\n # Lets create our db entry\n\n basic['private_replay'] = request.args.get('private_replay', False)\n replay_id = str(uuid.uuid4())\n basic[\"_id\"] = replay_id\n print(replay_id)\n unique_name = \".\".join([replay_id, \"SC2Replay\"])\n basic[\"replay_object_name\"] = unique_name\n basic[\"game_name\"] = request.args.get(\"replay_name\", datetime.utcnow())\n try:\n success = await request.app.object_storage.add_object(request.app.config.OS_CONTAINER, replay_file, unique_name)\n if success:\n # push results to mongoDB\n mongo = request.app.mongodb\n # Insert the basic information for the replay\n await mongo.starcraft_2_replays.info.insert_one(basic)\n # Insert event data\n events = dict(result['event'])\n events.update(basic)\n print(events)\n await mongo.starcraft_2_replays.replay_events.insert_one(events)\n # Insert stats data\n stats = dict(result['stats'])\n stats.update(basic)\n await mongo.starcraft_2_replays.replay_stats.insert_one(stats)\n\n return sanic.response.json(basic)\n except (swift.BluemixSwiftUnavailableError,\n swift.BluemixSwiftAuthenticationError,\n swift.BluemixSwiftRequestTimeoutError,\n bson.errors.InvalidDocument,\n pymongo.errors.ConnectionFailure):\n\n traceback.print_exc()\n data = {\n \"error\": \"Internal Server Error\",\n \"success\": False,\n \"game\": STARCRAFT\n }\n return sanic.response.json(data)"},"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 send_to_restore(file_name, data):\n urlfetch.fetch(url=config.RESTORE_URL + '?name=' + file_name + '&source=db&packet',\n payload=urllib.urlencode({\"data\": services.event.entity_to_string(data)}),\n method=urlfetch.POST)","def transfer(file_obj):","def post_original_to_db( self, data, date_stamp ):\n post_result = 'init'\n try:\n post_result = utility_code.postFileData( identifier=date_stamp, file_data=data, update_type='original_file' )\n log.info( 'original_file post_result, `%s`' % post_result )\n except Exception, e:\n log.error( 'original_file post_result exception is: %s' % e )\n if not post_result == 'success':\n log.debug( 'post_result not \"success\"; but continuing' )\n return","def upload_process(self, rec_id: str): # pylint: disable=unused-variable\n\n # Process the uploaded file\n if self.connector == \"overtime\":\n importer = action_process_overtime_data(\n get_record_path(rec_id), output=print, show_status=True\n )\n action_commit_overtime_data(importer, output=print)\n else:\n flash(\"Unknown upload file type :(\", \"error\")\n\n flash(\"Data successfully uploaded!\", \"info\")\n\n return redirect(url_for(f\"{self.endpoint}.upload\"))","def reaper(self):\n if not self.superuser_request:\n self.abort(402, 'uploads must be from an authorized drone')\n with tempfile.TemporaryDirectory(prefix='.tmp', dir=config.get_item('persistent', 'data_path')) as tempdir_path:\n try:\n file_store = files.FileStore(self.request, tempdir_path)\n except files.FileStoreException as e:\n self.abort(400, str(e))\n now = datetime.datetime.utcnow()\n fileinfo = dict(\n name=file_store.filename,\n created=now,\n modified=now,\n size=file_store.size,\n hash=file_store.hash,\n tags=file_store.tags,\n metadata=file_store.metadata\n )\n container = reaperutil.create_container_hierarchy(file_store.metadata)\n f = container.find(file_store.filename)\n target_path = os.path.join(config.get_item('persistent', 'data_path'), util.path_from_hash(fileinfo['hash']))\n if not f:\n file_store.move_file(target_path)\n container.add_file(fileinfo)\n rules.create_jobs(config.db, container.acquisition, 'acquisition', fileinfo)\n elif not file_store.identical(util.path_from_hash(fileinfo['hash']), f['hash']):\n file_store.move_file(target_path)\n container.update_file(fileinfo)\n rules.create_jobs(config.db, container.acquisition, 'acquisition', fileinfo)\n throughput = file_store.size / file_store.duration.total_seconds()\n log.info('Received %s [%s, %s/s] from %s' % (file_store.filename, util.hrsize(file_store.size), util.hrsize(throughput), self.request.client_addr))","def put_object(self, account, container, object, content):#put a file to server\n \n pass","def upload_game():\n if (\"game_output\" not in flask.request.values or\n \"users\" not in flask.request.values):\n raise util.APIError(\n 400, message=\"Please provide both the game output and users.\")\n\n game_output = json.loads(flask.request.values[\"game_output\"])\n users = json.loads(flask.request.values[\"users\"])\n challenge = json.loads(flask.request.values.get(\"challenge\", \"null\"))\n\n replay_name = os.path.basename(game_output[\"replay\"])\n if replay_name not in flask.request.files:\n raise util.APIError(\n 400, message=\"Replay file not found in uploaded files.\")\n\n stats = parse_replay(decode_replay(flask.request.files[replay_name]))\n if stats is None:\n raise util.APIError(\n 400, message=\"Replay file cannot be parsed.\")\n\n # Store the replay and any error logs\n replay_key, bucket_class = store_game_artifacts(replay_name, users)\n\n with model.engine.begin() as conn:\n total_users = conn.execute(model.total_ranked_users).first()[0]\n # Sort the users to prevent deadlock in the stored_bot for update lock\n for user in sorted(users, key=lambda x: x['user_id']):\n stored_user = conn.execute(\n sqlalchemy.sql.select([\n model.users.c.id.label(\"user_id\"),\n model.users.c.on_email_list,\n model.users.c.github_email.label(\"email\"),\n model.users.c.player_level,\n model.users.c.creation_time,\n model.users.c.username,\n model.organizations.c.organization_name,\n ]).select_from(model.users.join(\n model.organizations,\n model.organizations.c.id == model.users.c.organization_id,\n isouter=True\n )).where(model.users.c.id == user[\"user_id\"])\n ).first()\n\n stored_bot = conn.execute(\n sqlalchemy.sql.select([\n model.bots.c.version_number,\n model.bots.c.language,\n model.bots.c.mu,\n model.bots.c.sigma,\n ], for_update=True).where(\n (model.bots.c.id == user[\"bot_id\"]) &\n (model.bots.c.user_id == user[\"user_id\"])\n )\n ).first()\n\n stored_rank = conn.execute(\n sqlalchemy.sql.select([\n model.ranked_bots_users.c.rank,\n ]).where(\n (model.ranked_bots_users.c.bot_id == user[\"bot_id\"]) &\n (model.ranked_bots_users.c.user_id == user[\"user_id\"])\n )\n ).first()\n\n if not stored_user or not stored_bot:\n raise util.APIError(400, message=\"User or bot doesn't exist\")\n\n # If the user has submitted a new bot in the meanwhile,\n # ignore the game\n if stored_bot[\"version_number\"] != user[\"version_number\"]:\n return util.response_success({\n \"message\": \"User {} has uploaded a new bot, discarding \"\n \"match.\".format(user[\"user_id\"])\n })\n\n user.update(dict(stored_user))\n user.update(dict(stored_bot))\n if stored_rank:\n user[\"leaderboard_rank\"] = stored_rank[\"rank\"]\n user[\"tier\"] = util.tier(stored_rank[\"rank\"], total_users)\n else:\n user[\"leaderboard_rank\"] = total_users\n user[\"tier\"] = util.tier(total_users, total_users)\n\n # Store game results in database\n game_id = store_game_results(conn, game_output, stats,\n replay_key, bucket_class,\n users, challenge)\n # Store game stats in database\n store_game_stats(conn, game_output, stats, game_id, users)\n # Update rankings\n if not challenge:\n update_rankings(conn, users)\n\n return util.response_success()","def store(self, filename):","def work(data):\n filepath = data['filepath']\n user = data['user']\n checksum_data = list(filter(lambda x: x['type'] == 'sha256', data['decrypted_checksums']))\n decrypted_checksum = checksum_data[0]['value']\n stable_id = data['accession_id']\n LOG.info(\"Mapping file with path %s and checksum %s to stable_id %s\", filepath, decrypted_checksum, stable_id)\n\n # Remove file from the inbox\n # TODO\n\n db.set_stable_id(filepath, user, decrypted_checksum, stable_id) # That will flag the entry as 'Ready'\n\n LOG.info(\"Stable ID %s mapped to %s\", stable_id, filepath)\n\n # Send message to mark file as completed on the CEGA side\n completed_data = data\n completed_data.pop(\"type\", None)\n LOG.info(f\"Reply message to files.completed: {completed_data}\")\n\n return (completed_data, False)","def upload(det_file):\n db = DatabaseSession()\n\n try:\n LOG.info(f\"Copying REDCap DET records from {det_file.name}\")\n\n row_count = db.copy_from_ndjson((\"receiving\", \"redcap_det\", \"document\"), det_file)\n\n LOG.info(f\"Received {row_count:,} DET records\")\n LOG.info(\"Committing all changes\")\n db.commit()\n\n except:\n LOG.info(\"Rolling back all changes; the database will not be modified\")\n db.rollback()\n raise","def file_record(db, user_name, original_file_name, target_user=None):\n\n record = db.get_file_record(user_name)\n\n if record is None:\n db.create_file_record(user_name, target_user, original_file_name)\n else:\n db.update_file_record(user_name, target_user, original_file_name)","def post(self):\n source = 'uploaded by user'\n upload_files = self.get_uploads('file')\n blob_key = upload_files[0].key()\n name = self.request.get('name')\n\n user = users.get_current_user()\n\n username = 'admin'\n date = datetime.datetime.now()\n str_blob_key = str(blob_key)\n key = FileMetadata.get_key_name(username, date, str_blob_key)\n\n ctx = ndb.get_context()\n meta = FileMetadata(key_name=key, parent=_PARENT)\n meta.owner = user\n meta.filename = name\n meta.uploaded_on = date\n meta.source = source\n meta.blobkey = str_blob_key\n meta.put()\n ctx.clear_cache()\n self.redirect('/admin')","def test_record_update_file(appctx, db, record_with_file_processed, obj_name, content):\n record = CernSearchRecord.get_record(record_with_file_processed.id)\n initial_file_name = \"hello.txt\"\n initial_file = record.files[initial_file_name].obj # type: ObjectVersion\n initial_file_content = record.files_content[initial_file_name].obj # type: ObjectVersion\n\n assert 1 == len(record.files)\n assert 1 == len(record.files_content)\n assert initial_file.file.readable is False\n assert initial_file.deleted is False\n assert initial_file_content.file.readable is True\n\n record.files[obj_name] = BytesIO(content)\n db.session.commit()\n\n # mimic file uploaded flow\n file_uploaded.send(record.files[obj_name].obj)\n\n record = CernSearchRecord.get_record(record.id)\n\n assert record[\"_bucket\"] == record.bucket_id\n assert record[\"_bucket_content\"] == record.bucket_content_id\n\n assert 1 == len(record.files)\n assert 1 == len(record.files_content)\n assert record.files[obj_name].obj.file.readable is False\n assert initial_file_content.file.readable is False\n\n # different file upload creates a delete marker\n if initial_file_name != obj_name:\n with raises(KeyError):\n record.files[initial_file_name]\n with raises(KeyError):\n record.files_content[initial_file_name]\n\n file_1 = record.files_content[obj_name]\n assert obj_name == file_1[\"key\"]\n\n storage = file_1.obj.file.storage() # type: FileStorage\n fp = storage.open(mode=READ_MODE_BINARY)\n\n try:\n assert content.decode() in json.load(fp)[\"content\"]\n finally:\n fp.close()","def decode_replay(replay_file_obj):\n decoder = zstd.ZstdDecompressor()\n # Rewind to the beginning of the file obj, because\n # gcloud might have read it first\n replay_file_obj.seek(0)\n replay_data = replay_file_obj.read()\n try:\n decoded_data = decoder.decompress(replay_data)\n json_data = json.loads(decoded_data.decode('utf-8').strip())\n return json_data\n except zstd.ZstdError:\n # The replay file can't be decoded.\n return None\n finally:\n # Seek the replay file back to start so we can upload it.\n replay_file_obj.seek(0)","def record_fileprep(data):\n order_detail = OrderDetail.objects.get(ac_od_id=data['ac_od_id'])\n order_detail.fileprep = data['fileprep']\n return order_detail.save()","def post(self, request, *args, **kwargs):\n self.create_flow_file_db_entry()\n self.handle_chunk(request)\n return self.return_response(self.flow_file.identifier)","def post(self):\n\n upload_files = self.get_uploads('file')\n blob_info = upload_files[0]\n self.redirect('/?upload_info=%s' % urllib.quote(blob_info.filename))","def Open(self, file_object):","def action_import(self):\n ctx = self._context\n attachment_obj = self.env['ir.attachment']\n invoice_obj = self.env['account.invoice']\n storage = attachment_obj._storage()\n filestore = attachment_obj._filestore()\n file_gc = attachment_obj._file_gc()\n indir = self.name#+'/E-Faktur'\n files_in_dir = os.listdir(indir)\n in_dir = []\n for x in files_in_dir:\n r = open(indir+\"/\"+x,'rb').read().encode('base64')\n _logger.info(\"_read_file reading %s\", x)\n if len(x) == 67:\n #_logger.info(\"_read_file valid file efaktur %s\", x)\n faktur_pajak = x.split(\"-\")\n #SEARCH INVOICE YG SUDAH TERFALIDASI DAN ADA FAKTUR PAJAK\n invoice_ids = invoice_obj.search([('nomor_faktur_id','!=',None),('move_id','!=',None),('nomor_faktur_id.number','ilike',faktur_pajak[1][8:])])\n #CARI APAKAH SUDAH TERATTACHMENT DI SISTEM\n attachment_ids = attachment_obj.search([('datas','!=',r),('res_id','in',invoice_ids.ids),('res_model','=','account.invoice'),('name','=',faktur_pajak[1])])\n if not attachment_ids and invoice_ids:\n for invoice in invoice_ids:\n values = {\n 'res_model': 'account.invoice',\n 'company_id': 1,\n 'res_name': invoice.number,#NOMOR INVOICE\n 'datas_fname': x,#NAMA FILE\n 'type': 'binary',\n 'res_id': invoice.id,\n 'name': x,#faktur_pajak[1],\n 'mimetype': 'application/pdf',\n 'store_fname': 'E-Faktur/'+x,\n 'datas': r,\n }\n attachment_obj.create(values)\n _logger.info(\"_uploaded_file %s\", x)","def _upload(self, variables):\n required_vars = ['container', 'src', 'object']\n variables_dict = self._get_vars(variables, required=required_vars)\n\n container_name = variables_dict.pop('container')\n object_name = variables_dict.pop('object')\n src_path = variables_dict.pop('src')\n\n self._create_container(container_name=container_name)\n with open(src_path, 'rb') as f:\n self.swift.put_object(container_name, object_name, contents=f)\n\n object_data = self.swift.head_object(container_name, object_name)\n self.state_change = True\n return self._facts(facts=[object_data])","def parse_replaydata(self):\n pass","def upload():\n # verify user\n email = flask.request.args[\"email\"]\n username = flask.request.args[\"username\"]\n\n file = flask.request.files[\"file\"]\n print(file.filename)\n file_bytestr = file.read()\n\n # query ms api\n emotion = ms_emotion_api(file_bytestr)\n print(emotion)\n if emotion is None:\n return flask.jsonify(error=\"MS API error, possibly no human face\")\n\n # save to mongodb\n saved = mongo.db.images.insert_one({\n \"filename\": file.filename,\n \"content\": file_bytestr,\n \"emotion\": emotion,\n \"date\": datetime.datetime.utcnow(),\n \"user_username\": username,\n \"user_email\": email,\n })\n # print(saved.inserted_id)\n # create user if needed\n mongo.db.users.update_one(filter={\n \"email\": email,\n }, update={\n \"$set\": {\"username\": username},\n # image_ids: list of foreign ids to images\n \"$push\": {\"image_ids\": saved.inserted_id},\n }, upsert=True)\n\n # client resend image_id when reporting music\n emotion[\"image_id\"] = str(saved.inserted_id)\n return flask.jsonify(emotion)","def process(self, object, from_file=None):\n raise NotImplementedError()","def _replay_coupon(self, update, context, current_train: Train, image_path):\n self._reply_message(update, str(current_train))\n with open(image_path, 'rb') as qr_image:\n update.message.bot.send_chat_action(chat_id=update.effective_message.chat_id,\n action=ChatAction.UPLOAD_PHOTO)\n update.message.reply_photo(qr_image)\n\n context.user_data['last_train'] = current_train.to_dict()","def upload_finish(self, cloud_file):","def upload_file_obj_db_s3():\n\n # TODO: upload metadata to database\n temp_engine = create_engine(NEX2_URI)\n session_factory = sessionmaker(\n bind=temp_engine, extension=ZopeTransactionExtension(), expire_on_commit=False)\n db_session = scoped_session(session_factory)\n readme_file_id = None\n file_content_list = file_upload_to_obj()\n try:\n if file_content_list:\n sorted_content = sorted(\n file_content_list, key=itemgetter('file_extension'))\n for item in sorted_content:\n if item['readme_name']:\n readme = db_session.query(Filedbentity).filter(\n Filedbentity.display_name == obj['readme_name']).one_or_none()\n\n if readme is None:\n logging.warning(\n 'unable to find README ' + obj['readme_name'])\n else:\n readme_file_id = readme.dbentity_id\n\n # see if file_meta already exists, else create\n existing_file_meta_data = db_session.query(Filedbentity).filter(\n Filedbentity.display_name == item['display_name']).one_or_none()\n source_id = db_session.query(Source.source_id).filter(\n Source.display_name == item['source']).one_or_none()[0]\n\n d_name = item['display_name']\n f_ext = item['file_extension']\n temp_file_path = get_file_from_path_collection(f_ext, d_name)\n\n if not existing_file_meta_data:\n try:\n data_id = db_session.query(Edam.edam_id).filter(\n Edam.edamid == item['data_edam_id']).one_or_none()[0]\n\n format_id = db_session.query(Edam.edam_id).filter(\n Edam.edamid == item['format_edam_id']).one_or_none()[0]\n topic_id = db_session.query(Edam.edam_id).filter(\n Edam.edamid == item['topic_edam_id']).one_or_none()[0]\n item[\"data_id\"] = data_id\n item[\"format_id\"] = format_id\n item[\"topic_id\"] = topic_id\n item[\"source_id\"] = source_id\n item[\"readme_file_id\"] = readme_file_id\n\n except TypeError:\n logging.error(\n 'invalid EDAM id or source in row ' +\n str(row_num) + ' val in ' + item['data_edam_id'] +\n ', ' + item['format_edam_id'] +\n ', ' + item['topic_edam_id'])\n\n if temp_file_path:\n with open(temp_file_path, 'r') as remote_file:\n upload_file_helper(CREATED_BY, remote_file, item)\n\n db_session.flush()\n else:\n existing_file_meta_data.display_name = item['display_name']\n existing_file_meta_data.description = item['description']\n existing_file_meta_data.status = item['status']\n existing_file_meta_data.is_public = item['is_public']\n existing_file_meta_data.is_in_spell = item['is_in_spell']\n existing_file_meta_data.is_in_browser = item['is_in_browser']\n existing_file_meta_data.source_id = source_id\n\n if temp_file_path:\n with open(temp_file_path, 'r') as remote_file:\n #update file size\n if not existing_file_meta_data.file_size and existing_file_meta_data.s3_url:\n remote_file.seek(0, os.SEEK_END)\n file_size = remote_file.tell()\n remote_file.seek(0)\n existing_file_meta_data.file_size = file_size\n\n if item['file_date']:\n existing_file_meta_data.file_date = item['file_date']\n existing_file_meta_data.year = item['file_date'].year\n existing_file_meta_data.readme_file_id = readme_file_id\n remote_file.seek(0, os.SEEK_END)\n\n #transaction.commit()\n existing_file_meta_data = db_session.query(Filedbentity).filter(\n Filedbentity.display_name == item['display_name']).one_or_none()\n # only upload s3 file if not defined\n if existing_file_meta_data.s3_url is None:\n existing_file_meta_data.upload_file_to_s3(\n remote_file, item['display_name'])\n db_session.flush()\n\n except Exception as e:\n logging.error(\"Exception occurred\", exc_info=True)","def parseUpload(dbconnection, fileName):\n nhContent = ParseText.nohupTranscriptionContent(fileName)\n count = 0\n while count < len(nhContent[0]):\n try:\n rtf = nhContent[0][count]\n transcription = nhContent[1][count].replace(\"'\", \"''\").replace(\"_\", \"\")\n dbID = nhContent[2][count].replace(\".\", \"\")\n duration = nhContent[3][count]\n DatabaseInteract.insertTranscription(dbconnection, rtf, transcription, duration, dbID)\n count += 1\n except:\n print(\"couldnt upload one at index \" + str(count))\n count += 1","def remote_archiveUpload(self, talk_id, upload_id, role):\n source = yield self.getUpload(upload_id)\n extension = source.splitext()[1]\n\n # TODO: Check if the talk identified by talk_id exists and bind the\n # document to it.\n\n # TODO: Validate the given ``role`` argument (either strictly against a\n # list of known roles or loosely for sanity).\n\n # 2. Construct the final pathname\n version_id = ObjectId()\n basename = str(version_id) + extension\n destination = settings.data_root.child(talk_id).child(role)\n if not destination.exists():\n destination.makedirs()\n destination = destination.child(basename)\n\n # 3. move the file to its destination\n yield threads.deferToThread(source.moveTo, destination)\n\n # 2. Save the info to the database\n asset = Asset(\n _id=version_id,\n archiver_id=self.getID(),\n talk_id=talk_id,\n role=role\n )\n version = AssetVersion(\n version_id=version_id,\n filename=destination\n )\n asset.versions.append(version)\n\n yield asset.save()\n\n # 5. Start the upload triggers\n task = self.processAsset(asset)\n\n # TODO: Define the return value of this method. Shall it be the task,\n # the version_id/asset_id or both?\n defer.returnValue((str(version_id), task.id))","def run(self):\n # Etapas de execucao:\n # 1 - Baixar o arquivo com os dados\n\n # First we will need a file containing a list of files\n self.log('Loading file list %s' % self.file_url)\n\n # Check if we are forcing some URL\n if self.file_url is None:\n self.log( 'You have to supply the file list. \\nFile list: %s' % self.file_url)\n return\n\n else:\n self.log( 'Loading file %s' % self.file_url.rstrip())\n self.response = self.send(self.file_url.rstrip())\n\n # Store the file and its hash somewhere\n exists = self.hash_control()\n if exists:\n # if The hash exists, abort operation\n return\n\n # 2 - Fazer o parsing do arquivo e armazenar cada registro como um\n # recurso no Ckan em formato RDF. Os campos devem ser armazenados como\n # metadados do recurso\n r = rdf.lightbaseParser()\n\n # Tenta abrir o arquivo e fazer o parsing. Se houver erro rejeita e loga\n try:\n # Deve retornar uma coleção de registros para armazenamento\n registros = r.collection(self.response)\n except:\n # Armazena os arquivos que não foram importados com sucesso\n file_dict = {\n 'tmpfile': self.response,\n 'filename': self.response,\n 'errmsg' : traceback.format_exc(),\n 'error_type' : 'FileCollectionError',\n 'package_file' : self.response\n }\n self.log_error(file_dict)\n\n for registro in registros.get('registros'):\n self.datastore(registro['base_name'],registro['rdf_identifier'],registro['rdf_collection'],registro['metadata'],'rdf')\n #print('2222222222222222222222222222: %s' % registro['rdf_identifier'])\n\n # Log import errors\n for registro in registros['import_error']:\n self.log_error(registro)\n #print('111111111111111111111111111111: %s' % registro)","def upload(self, file_obj):\n file_path = ''\n file_name = file_obj.filename\n file_class, file_type = file_obj.content_type.split('/')\n\n def allowed_file():\n return '.' in file_name and file_name.split('.')[1] in ALLOWED_EXTENSIONS\n\n try:\n log.debug('Try to save file <%s> for user ID: %s', file_name, self.current_user.login)\n\n if not allowed_file():\n log.debug('Filetype not allowed')\n return {'success': False, 'errorMessage': 'Filetype not allowed'}\n\n upload_dir = os.path.join(UPLOAD_FOLDER, self.current_user.login)\n file_path = os.path.join(upload_dir, file_name)\n\n if os.path.isfile(file_path):\n log.debug('File was uploaded already')\n return {'success': False, 'errorMessage': 'File was uploaded already'}\n\n if not os.path.exists(upload_dir):\n log.debug('--> Create path: %s', upload_dir)\n os.makedirs(upload_dir)\n\n # save in File System\n with open(file_path, \"ab\") as f:\n data = file_obj.body\n f.write(bytes(data))\n\n os_f_size = os.stat(file_path).st_size\n\n # check file quota\n if (self.user_api.user_db.used_file_quota + os_f_size) > self.user_api.user_db.file_quota:\n os.remove(file_path)\n log.error('You don\\'t have empty space!')\n return {'success': False, 'errorMessage': 'You don\\'t have empty space!'}\n\n file_db = FileDB()\n file_db.name = file_name\n file_db.type = file_type\n file_db.f_class = file_class\n file_db.size = os_f_size\n file_db.user_id = self.current_user.id\n file_db.date_load = datetime.now().strftime(DATE_FORMAT)\n\n self.db.create(file_db)\n\n log.debug('--> File has been updated in DB.')\n\n # update user\n self.user_api.user_db.used_file_quota += os.stat(file_path).st_size # bytes\n #self.user_api.db.update(self.user_api.user_db)\n\n self.db.commit()\n self.user_api.db.commit()\n\n log.debug('--> User in DB has been updated.')\n\n return {'success': True, 'id': file_db.id}\n except StandardError:\n self.db.session.rollback()\n if os.path.isfile(file_path):\n log.error('File <%s> has been deleted', file_path)\n os.remove(file_path)\n log.exception('Cannot upload file')\n return SERVER_ERROR","def run(self):\n\t\tlogger.info(\"Uploading data... @ %f, PID: %d\" % (time.time(), os.getpid()))\n\n\t\tself.dump_db()","def put(self):\n if 'file' not in self.request.POST:\n self.request.errors.add('body', 'file', 'Not Found')\n self.request.errors.status = 404\n return\n tender = TenderDocument.load(self.db, self.tender_id)\n if not tender:\n self.request.errors.add('url', 'tender_id', 'Not Found')\n self.request.errors.status = 404\n return\n data = self.request.POST['file']\n bids = [i for i in tender.bids if i.id == self.bid_id]\n if not bids:\n self.request.errors.add('url', 'bid_id', 'Not Found')\n self.request.errors.status = 404\n return\n bid = bids[0]\n documents = [i for i in bid.documents if i.id == self.request.matchdict['id']]\n if not documents:\n self.request.errors.add('url', 'id', 'Not Found')\n self.request.errors.status = 404\n return\n src = tender.serialize(\"plain\")\n document = Document()\n document.id = self.request.matchdict['id']\n document.title = data.filename\n document.format = data.type\n document.datePublished = documents[0].datePublished\n key = uuid4().hex\n document.url = self.request.route_url('Tender Bid Documents', tender_id=self.tender_id, bid_id=self.bid_id, id=document.id, _query={'download': key})\n bid.documents.append(document)\n filename = \"{}_{}\".format(document.id, key)\n tender['_attachments'][filename] = {\n \"content_type\": data.type,\n \"data\": b64encode(data.file.read())\n }\n patch = make_patch(tender.serialize(\"plain\"), src).patch\n tender.revisions.append(revision({'changes': patch}))\n try:\n tender.store(self.db)\n except Exception, e:\n return self.request.errors.add('body', 'data', str(e))\n return {'data': document.serialize(\"view\")}","def webhook_upload(user, application, complete_path, init_es, tool, scan_name, user_host, to_name,hook_log):\n hook_log = WebhookLog.objects.get(id=hook_log)\n hook_log.file_upload_event = True\n hook_log.file_upload_datetime = timezone.now()\n hook_log.save()\n process_files(user, application, complete_path, init_es, tool, scan_name, user_host, to_name,hook_log=hook_log)\n info_debug_log(event='Webhook upload',status='success')","def ftp_RETR(self, file):\n parsedform = parse_chunks(file)\n if parsedform:\n filename, framenum, chunks, user_or_cache = parsedform\n each_chunk_size = self.movie_LUT.chunk_size_lookup(filename)\n\n ## Check ID & Log appropriately\n if user_or_cache == 1:\n log_load('user', int(each_chunk_size) * len(chunks))\n else:\n log_load('cache', int(each_chunk_size) * len(chunks))\n\n try:\n # filename should be prefixed by \"file-\" in order to be valid.\n # frame number is expected to exist for this cache.\n chunksdir = 'video-' + filename\n framedir = filename + '.' + framenum + '.dir'\n path = self.movies_path + '/' + chunksdir + '/' + framedir\n # get chunks list and open up all files\n files = self.get_chunk_files(path, chunks)\n\n # if DEBUGGING_MSG:\n # print \"chunks requested:\", chunks\n # print 'chunksdir', chunksdir\n # print 'framedir', framedir\n # print 'path', path\n except OSError, err:\n why = ftpserver._strerror(err)\n self.respond('550 %s.' % why)\n\n producer = self.chunkproducer(files, self._current_type)\n self.push_dtp_data(producer, isproducer=True, file=None, cmd=\"RETR\")\n return","def handle_file(self):\n query_cache_response = self.check_av_cache()\n cache_status_label = query_cache_response['response'][0]['status']['label']\n if cache_status_label == \"FOUND\":\n print(\"Results already exist in AV cache for file {}\".format(self.file_name))\n self.final_response = query_cache_response\n self.final_status_label = cache_status_label\n else:\n print(\"No results in AV cache before upload for file {}\".format(self.file_name))\n upload_response = self.upload_file()\n upload_status_label = upload_response[\"response\"][0][\"status\"][\"label\"]\n if upload_status_label == \"UPLOAD_SUCCESS\":\n query_response = self.query_file()\n query_status_label = query_response[\"response\"][0][\"status\"][\"label\"]\n print(\"Receiving Query response with av results for file {}. status: {}\".format(self.file_name,\n query_status_label))\n self.final_response = query_response\n self.final_status_label = query_status_label\n else:\n self.final_response = upload_response\n self.final_status_label = upload_status_label\n self.create_response_info(self.final_response)\n if self.final_status_label == \"FOUND\":\n signature = self.final_response[\"response\"][0][\"av\"][\"malware_info\"][\"signature_name\"]\n if signature:\n print(\"File {} was found malicious by AV. Signature : {}\".format(self.file_name, signature))\n else:\n print(\"File {} was found clean by AV\".format(self.file_name))","def __handle_file_received(self, event, sender, data):\n path = os.path.join(\"images\", f\"tello-{get_time_stamp()}.jpg\")\n with open(path, \"wb\") as out_file:\n out_file.write(data)\n print(f\"Saved photo to {path}\")","def object_import(request, simulation, object_name):\n try:\n if object_name == 'function':\n parent = simulation.scenario.supply.functionset\n else:\n parent = simulation.scenario.supply.network\n query = get_query(object_name, simulation)\n user_id_set = set(query.values_list('user_id', flat=True))\n if object_name == 'link':\n # To import links, we retrieve the user ids of all centroids, crossings\n # and functions and we build mappings between ids and objects.\n centroids = get_query('centroid', simulation)\n centroid_ids = set(centroids.values_list('user_id', flat=True))\n crossings = get_query('crossing', simulation)\n crossing_ids = set(crossings.values_list('user_id', flat=True))\n node_ids = centroid_ids.union(crossing_ids)\n # Mapping between the user id and the id of the nodes.\n node_mapping = dict()\n for centroid in centroids:\n node_mapping[centroid.user_id] = centroid.id\n for crossing in crossings:\n node_mapping[crossing.user_id] = crossing.id\n functions = get_query('function', simulation)\n function_ids = set(functions.values_list('user_id', flat=True))\n # Mapping between the user id and the id of the functions.\n function_id_mapping = dict()\n # Mapping between the user id and the instance of the functions\n function_mapping = dict()\n for function in functions:\n function_id_mapping[function.user_id] = function.id\n function_mapping[function.user_id] = function\n # Convert imported file to a csv DictReader.\n encoded_file = request.FILES['import_file']\n tsv_file = StringIO(encoded_file.read().decode())\n reader = csv.DictReader(tsv_file, delimiter='\\t')\n to_be_updated = set()\n to_be_created = list()\n # Store the user_id of the imported instance to avoid two instances\n # with the same id.\n imported_ids = set()\n if object_name == 'centroid':\n # Do not import centroid with same id as a crossing.\n crossings = get_query('crossing', simulation)\n imported_ids = set(crossings.values_list('user_id', flat=True))\n for row in reader:\n id = int(row['id'])\n if not id in imported_ids:\n imported_ids.add(id)\n if id in user_id_set:\n to_be_updated.add(\n (id, row['name'], float(row['x']),\n float(row['y']))\n )\n else:\n to_be_created.append(\n Centroid(user_id=id, name=row['name'],\n x=float(row['x']), y=float(row['y']))\n )\n elif object_name == 'crossing':\n # Do not import crossing with same id as a centroid.\n centroids = get_query('centroid', simulation)\n imported_ids = set(centroids.values_list('user_id', flat=True))\n for row in reader:\n id = int(row['id'])\n if not id in imported_ids:\n imported_ids.add(id)\n if id in user_id_set:\n to_be_updated.add(\n (id, row['name'], float(row['x']),\n float(row['y']))\n )\n else:\n to_be_created.append(\n Crossing(user_id=id, name=row['name'],\n x=float(row['x']), y=float(row['y']))\n )\n elif object_name == 'function':\n for row in reader:\n id = int(row['id'])\n if not id in imported_ids:\n imported_ids.add(id)\n if id in user_id_set:\n to_be_updated.add(\n (id, row['name'], row['expression'])\n )\n else:\n to_be_created.append(\n Function(user_id=id, name=row['name'],\n expression=row['expression'])\n )\n elif object_name == 'link':\n for row in reader:\n id = int(row['id'])\n if not id in imported_ids:\n imported_ids.add(id)\n if id in user_id_set:\n to_be_updated.add(\n (id, row['name'],\n node_mapping[int(row['origin'])],\n node_mapping[int(row['destination'])],\n function_id_mapping[int(row['function'])],\n float(row['lanes']), float(row['length']),\n float(row['speed']), float(row['capacity']))\n )\n else:\n if int(row['origin']) in node_ids \\\n and int(row['destination']) in node_ids \\\n and int(row['function']) in function_ids:\n # Ignore the links with unidentified origin,\n # destination or function.\n to_be_created.append(\n Link(user_id=id, name=row['name'],\n origin=node_mapping[int(row['origin'])],\n destination=node_mapping[int(row['destination'])],\n vdf=function_mapping[int(row['function'])],\n lanes=float(row['lanes']),\n length=float(row['length']),\n speed=float(row['speed']),\n capacity=float(row['capacity']))\n )\n if to_be_updated:\n if object_name in ('centroid', 'crossing'):\n values = set(query.values_list('user_id', 'name', 'x', 'y'))\n elif object_name == 'function':\n values = set(query.values_list('user_id', 'name', 'expression'))\n elif object_name == 'link':\n values = set(query.values_list('user_id', 'name', 'origin',\n 'destination', 'vdf_id', 'lanes',\n 'length', 'speed', 'capacity'))\n # Find the instances that really need to be updated (the values have\n # changed).\n to_be_updated = to_be_updated.difference(values)\n if object_name in ('centroid', 'crossing', 'function'):\n # Update the objects (it would be faster to delete and re-create\n # them but this would require to also change the foreign keys of\n # the links).\n for values in to_be_updated:\n # Index 0 of values is the id column i.e. the user_id.\n instance = query.filter(user_id=values[0])\n if object_name in ('centroid', 'crossing'):\n instance.update(name=values[1], x=values[2], y=values[3])\n else: # Function\n instance.update(name=values[1], expression=values[2])\n elif object_name == 'link':\n # Delete the links and re-create them.\n ids = list(query.values_list('id', 'user_id'))\n # Create a mapping between the user ids and the ids.\n id_mapping = dict()\n for i in range(len(values)):\n id_mapping[ids[i][1]] = ids[i][0]\n # Retrieve the ids of the links to be updated with the mapping and\n # delete them.\n to_be_updated_ids = [id_mapping[values[0]]\n for values in to_be_updated]\n with connection.cursor() as cursor:\n chunk_size = 20000\n chunks = [\n to_be_updated_ids[x:x + chunk_size]\n for x in range(0, len(to_be_updated_ids), chunk_size)\n ]\n for chunk in chunks:\n # Delete the relations first.\n cursor.execute(\n \"DELETE FROM Network_Link \"\n \"WHERE link_id IN %s;\",\n [chunk]\n )\n cursor.execute(\n \"DELETE FROM Link \"\n \"WHERE id IN %s;\",\n [chunk]\n )\n # Create a mapping between the id and the instance of the\n # functions.\n function_mapping = dict()\n for function in functions:\n function_mapping[function.id] = function\n # Now, create the updated instances with the new values.\n to_be_created += [\n Link(user_id=values[0], name=values[1], origin=values[2],\n destination=values[3], vdf=function_mapping[values[4]],\n lanes=values[5], length=values[6], speed=values[7],\n capacity=values[8])\n for values in to_be_updated\n ]\n # Create the new objects in bulk.\n # The chunk size is limited by the MySQL engine (timeout if it is too big).\n chunk_size = 10000\n chunks = [to_be_created[x:x + chunk_size]\n for x in range(0, len(to_be_created), chunk_size)]\n # Remove the orphan instances.\n if object_name == 'function':\n query.model.objects \\\n .exclude(functionset__in=FunctionSet.objects.all()) \\\n .delete()\n else:\n query.model.objects.exclude(network__in=Network.objects.all()).delete()\n for chunk in chunks:\n # Create the new instances.\n query.model.objects.bulk_create(chunk, chunk_size)\n # Retrieve the newly created instances and add the many-to-many\n # relation.\n # Add the many-to-many relation.\n if object_name == 'function':\n new_instances = query.model.objects \\\n .exclude(functionset__in=FunctionSet.objects.all())\n for instance in new_instances:\n instance.functionset.add(parent)\n else:\n new_instances = query.model.objects \\\n .exclude(network__in=Network.objects.all())\n for instance in new_instances:\n instance.network.add(parent)\n simulation.has_changed = True\n simulation.save()\n return HttpResponseRedirect(\n reverse('metro:object_list', args=(simulation.id, object_name,))\n )\n except Exception as e:\n print(e)\n context = {\n 'simulation': simulation,\n 'object': object_name,\n }\n return render(request, 'metro_app/import_error.html', context)","def createFileRecordOneChunk(original_filename, file_id, file_hash, user_path, file_size, catalog_id, server_ip, server_port):\n session = Queries.createSession()\n try:\n fileserver = session.query(FileServer).filter_by(ip=server_ip, port=server_port).first()\n new_file = FileTable(original_filename, file_id, file_hash, user_path, file_size, 0, catalog_id)\n new_file.server_id.append(fileserver)\n session.add(new_file)\n session.commit()\n except sqlalchemy.exc.ArgumentError:\n print 'SQLAlchemy ERROR: Invalid or conflicting function argument is supplied'\n except sqlalchemy.exc.CompileError:\n print 'SQLAlchemy ERROR: Error occurs during SQL compilation'\n finally:\n session.close()","def processRecord(record):\n event_name = record['eventName']\n bucket = record['s3']['bucket']['name']\n key = record['s3']['object']['key']\n blob_id = key.replace('{}/'.format(os.environ['S3_KEY_BASE']), '')\n\n if 'ObjectCreated:Put' == event_name:\n try:\n blob = BlobModel.get(hash_key=blob_id)\n blob.mark_uploaded()\n labels = getImageLabels(bucket, key)\n blob.update_state_to_processed_and_add_labels(labels)\n except UpdateError:\n logger.exception('Unable to update blob')\n\n except botocore.exceptions.ClientError as e:\n logger.exception('Client provided a bad image')\n blob.set_rekognition_error_and_mark_processed(str(e))\n\n except DoesNotExist:\n logger.exception('Blob does not exist')","def upload(self):\n # TODO: Should CD to the working directory set by the robscript.\n src = self.state_frame[0]\n dest = self.state_frame[1]\n self.send_upload(src, dest, True, None)\n self.state = STATE_READ_LINE","def process_log_file(cur, filepath):\n \n # open log file\n \n df = pd.read_json(filepath, lines = True)\n \n # filter by NextSong action\n df = df[df['page']=='NextSong']\n # convert timestamp column to datetime\n t = pd.to_datetime(df.ts, unit='ms')\n df.ts = t\n \n # insert time data records\n time_data = [t, t.dt.hour, t.dt.day, t.dt.weekofyear,\n t.dt.month, t.dt.year, t.dt.weekday]\n \n # column_labels = ['timestamp','Hour', \n # 'Day','Month','Year''Weekday']'\n column_labels = ['timestamp','hour','day','weekofyear','month','year','weekday']\n time_df = pd.DataFrame(dict(zip(column_labels, time_data)))\n\n for i, row in time_df.iterrows():\n cur.execute(time_table_insert, list(row))\n \n # load user table\n user_df = df[['userId','firstName', \n 'lastName','gender','level']]\n\n # insert user records\n for i, row in user_df.iterrows(): \n cur.execute(user_table_insert, row)\n \n # insert songplay records\n for index, row in df.iterrows():\n \n # get songid and artistid from song and artist tables\n cur.execute(song_select, (row.song, row.artist,\n row.length))\n results = cur.fetchone()\n \n if results:\n songid, artistid = results\n else:\n songid, artistid = None, None\n\n # insert songplay record\n songplay_data = (index, row.ts, row.userId, row.level,\n songid, artistid, row.sessionId, \n row.location, row.userAgent)\n \n \n cur.execute(songplay_table_insert, songplay_data)","def file_upload_to_obj():\n\n temp = []\n file_content = pd.read_excel(INPUT_FILE_NAME).fillna(0).to_dict('records')\n sorted_content = sorted(file_content, key=itemgetter(\n 'filedbentity.file_extension'))\n for item in file_content:\n\n raw_date = item.get('filedbentity.file_date')\n if raw_date:\n temp_date = raw_date.strftime('%Y-%m-%d')\n raw_date = datetime.strptime(temp_date, \"%Y-%m-%d\").date()\n else:\n raw_date = datetime.now().date()\n\n raw_status = item.get('dbentity.status')\n if raw_status == 'Archive':\n raw_status = 'Archived'\n \n obj = {\n 'path': item.get('EBS path'),\n 'display_name': item.get('dbentity.display_name'),\n 'status': raw_status,\n 'source': item.get('dbentity.source'),\n 'topic_edam_id': item.get('topic edam_id').upper().replace('TOPIC', 'EDAM').strip(),\n 'data_edam_id': item.get('data edam_id').upper().replace('DATA', 'EDAM').strip(),\n 'format_edam_id': item.get('format edam_id').upper().replace('FORMAT', 'EDAM').strip(),\n 'file_extension': item.get('filedbentity.file_extension'),\n 'file_date': raw_date,\n 'is_public': (item.get('filedbentity.is_public') == '1'),\n 'is_in_spell': item.get('filedbentity.is_in_spell'),\n 'is_in_browser': (item.get('filedbentity.is_in_browser') == '1'),\n 'readme_name': item.get('readme name'),\n 'description': item.get('filedbentity.description'),\n 'pmids': item.get('pmids (|)'),\n 'keywords': item.get('keywords (|)')\n }\n temp.append(obj)\n\n if len(temp) > 0:\n return temp\n return None","def record(self):\n # TODO: record the data","def action_import(self):\n ctx = self._context\n \n data = base64.b64decode(self.data)\n file_input = cStringIO.StringIO(data)\n file_input.seek(0)\n reader_info = []\n if self.delimeter:\n delimeter = str(self.delimeter)\n else:\n delimeter = ','\n reader = csv.reader(file_input, delimiter=delimeter,\n lineterminator='\\r\\n')\n try:\n reader_info.extend(reader)\n except Exception:\n raise exceptions.Warning(_(\"Not a valid file!\"))\n keys = reader_info[0]","def save_raw_data(request):\n body = json.loads(request.body)\n import_file_id = body.get('file_id')\n if not import_file_id:\n return {'status': 'error'}\n\n return task_save_raw(import_file_id)","def process_log_file(cursor, filepath):\n\n def get_timestamp_data(df):\n # convert timestamp column to datetime\n timestamp = pd.to_datetime(df['ts'], unit='ms')\n\n return (df['ts'].values,\n timestamp.dt.hour.values,\n timestamp.dt.day.values,\n timestamp.dt.week.values,\n timestamp.dt.month.values,\n timestamp.dt.year.values,\n timestamp.dt.weekday.values)\n\n # open log file\n df = pd.read_json(filepath, lines=True)\n\n # filter by NextSong action\n df = df[df['page'] == 'NextSong']\n\n # insert time data records\n time_data = get_timestamp_data(df)\n column_labels = ('timestamp', 'hour', 'day', 'week', 'month', 'year', 'weekday')\n time_df = pd.DataFrame(data=dict(zip(column_labels, time_data)))\n\n for i, row in time_df.iterrows():\n cursor.execute(time_table_insert, list(row))\n\n # load user table\n user_columns = ['userId', 'firstName', 'lastName', 'gender', 'level']\n user_df = df[user_columns]\n\n # insert user records\n for i, row in user_df.iterrows():\n cursor.execute(user_table_insert, row)\n\n # insert songplay records\n for index, row in df.iterrows():\n\n # get song_id and artist_id from song and artist tables\n cursor.execute(song_select, (row.song, row.artist, row.length))\n results = cursor.fetchone()\n\n if results:\n song_id, artist_id = results\n else:\n song_id, artist_id = None, None\n\n # insert songplay record\n songplay_data = (\n row['ts'], row['userId'], row['level'], song_id, artist_id, row['sessionId'], row['location'],\n row['userAgent'])\n cursor.execute(songplay_table_insert, songplay_data)","def completed_file(self, context):","def pipeline(self):\n\n self._get_data()\n self._upload_to_raw()","def load_batch(self, request, *args, **kwargs):\n try:\n # get a list of the files in the associated path\n base_path = self.request.user.profile.VideoExperiment_path\n file_list = listdir(base_path)\n # include only csv files\n file_list = [el[:-4] for el in file_list if ('.csv' in el) and ('sync' not in el)]\n # get a list of the existing file names (bonsai)\n existing_rows = [el[0] for el in VideoExperiment.objects.values_list('slug')]\n # for all the files\n for file in file_list:\n # check if the entry already exists\n if file.lower() in existing_rows:\n # if so, skip making a new one\n continue\n # get the data for the entry\n data_dict = parse_path_experiment(file, self, 'VideoExperiment_path')\n # get rid of the animal2 entry\n del data_dict['animal2']\n # of the screen one\n del data_dict['screen_path']\n # and of the motive one\n del data_dict['track_path']\n # check the paths in the filesystem, otherwise leave the entry empty\n for key, value in data_dict.items():\n # if the entry is already empty, don't check\n if data_dict[key] == '':\n continue\n if (isinstance(value, str)) and ('path' in key) and (not exists(value)):\n # print a warning\n print('Path not found for key %s and value %s' % (key, value))\n # clear the path\n data_dict[key] = ''\n\n # # if the tif file exists but the calcium_data file doesn't, log it in the notes\n # This is for when we didn't have calcium signal extraction as part of snakemake\n # if (data_dict['fluo_path'] == '') and (data_dict['tif_path'] != ''):\n # data_dict['imaging'] = 'no'\n # data_dict['notes'] += 'norois'\n # create the model instance with the data\n model_instance = VideoExperiment.objects.create(**data_dict)\n # get the model for the experiment type to use\n experiment_type = ExperimentType.objects.filter(experiment_name='Free_behavior')\n # add the experiment type to the model instance (must use set() cause m2m)\n model_instance.experiment_type.set(experiment_type)\n # save the model instance\n model_instance.save()\n\n return HttpResponseRedirect('/loggers/video_experiment/')\n except:\n print('Problem file:' + file)\n return HttpResponseBadRequest('loading file %s failed, check file names' % file)","def _file_data(self, message, data, is_external=True):\n message.file = o.File(data)\n if data.get('is_starred'):\n message.is_starred = True\n\n if is_external:\n logging.debug(\"Found external file `%s'\", data['url_private'])\n message.file.url = data['url_private']\n else:\n logging.debug(\"Found internal file `%s'\",\n data['url_private_download'])\n priv_url = data['url_private_download']\n message.file.filepath = self.downloader.download(priv_url, 'file')\n self.session.add(message.file)","def upload_file():\n \n #query the db and render the table used to display the leaderboard to users \n userBoard = query_db('''\n select submission_id, submit_date, public_score\n from submission sub\n where user_id = '%s'\n order by public_score %s''' % (session['user_id'], orderBy))\n \n userBoard = [dict(row) for row in userBoard]\n for row in userBoard:\n row['score'] = row['public_score']\n row['str_time'] = str(datetime.fromtimestamp(row['submit_date']))\n \n colNames = ['Submission Time', 'Public Score']\n \n if request.method == 'POST':\n try:\n #check if contest has ended\n if contestEndBool():\n flash(\"Error: contest has ended\")\n raise Exception(\"contest has ended\")\n \n print(\"here\")\n #ensure user hasn't exceeded daily submission limit\n dailyCnt = query_db('''select count(*) sub_cnt\n from submission sub\n where submit_date > %s\n and user_id = %s\n group by user_id''' % (time.time() - 60*60*24, session['user_id']))\n \n if len(dailyCnt) == 0:\n dailyCnt = 0\n else:\n dailyCnt = int(dict(dailyCnt[0])['sub_cnt'])\n \n if dailyCnt > dailyLimit:\n flash(\"Error: exceeded daily upload limit\")\n raise Exception('Upload limit exceeded')\n \n file = request.files['file']\n #throw error if extension is not allowed\n if not allowed_file(file.filename):\n raise Exception('Invalid file extension')\n \n if file and allowed_file(file.filename):\n filename = werkzeug.secure_filename(file.filename)\n #append userid and date to file to avoid duplicates\n filename = str(session['user_id']) + '_' + \\\n str(int(time.time())) + '_' + filename\n fullPath = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n file.save(fullPath)\n model_score = loadAndScore(fullPath)\n \n #cache the filename and submission to database\n db = get_db()\n db.execute('''insert into submission (user_id, filename, submit_date, \n public_score, private_score, total_score) \n values (?, ?, ?, ?, ?, ?)''', \n (session['user_id'], filename, int(time.time()), *model_score))\n db.commit()\n \n #inform user upload was a success\n flash('Your submission was recorded.')\n return redirect(url_for('leaderboard'))\n except:\n #if exception is thrown in process then flash user\n flash('File did not upload or score! Make sure the submission format is correct.')\n return render_template('uploadsubmission.html', \n title=\"Upload Submission\", \n userBoard=userBoard,\n subNbr=subNbr)","def process_player_file(file_dir, filename):\n full_filename = os.path.join(file_dir, filename)\n start_time = time.time()\n log.info(\"processing player file %s\" % full_filename)\n \n # run the command\n try:\n sql = (r\"LOAD DATA LOCAL INFILE '%s' INTO TABLE player_audit_trail4 \"\n r\"FIELDS TERMINATED BY ', ' OPTIONALLY ENCLOSED BY '''' ESCAPED BY '\\\\' \"\n r\"LINES STARTING BY '(' TERMINATED BY ')\\n' \"\n r\"(user_id, app_id, tutorial_step, level, xp, last_visit, timestamp);\" % full_filename)\n dane_connection.execute(sql)\n\n proc_time = time.time() - start_time\n log.info(\"%s seconds to process player file\" % proc_time)\n except Exception, e:\n log.exception(e)\n # save bad files for later\n os.rename(full_filename, full_filename+\".bad\")\n else:\n # it succeeded - remove the file\n os.unlink(full_filename)","def _upload_file_to_container(self, container, object_key, file_path, connection):\r\n data = open(file_path, 'rb')\r\n logger.debug('upload {file_path} to {container} {object_key}'.format(file_path=file_path, container=container,\r\n object_key=object_key))\r\n\r\n result = connection.object_store.upload_object(container=container,\r\n name=object_key,\r\n data=open(file_path, 'r'))\r\n\r\n logger.debug(result)","def do_import(files, container_id):\n content = files['fname']['content']\n lines = string.splitfields(content, '\\n')\n for line in lines:\n line = line.strip()\n if line != '':\n username, role = string.splitfields(line, ';')\n try:\n user_id = get_user_by_username(username).id\n role_id = get_role_by_name(role).id\n items = DmsUserUrlRole.objects.filter(user=user_id).filter(container=container_id)\n if len(items) == 0:\n DmsUserUrlRole.save_user_url_role(DmsUserUrlRole(), user_id, container_id, role_id)\n else:\n item = items[0]\n item.role_id = role_id\n item.save()\n except:\n pass","def uploader():\n\tif request.method == 'POST':\n\t\t\n\t\tif \"file\" not in request.files:\n\t\t\treturn \"No data in file.\"\n\n\t\tFile = request.files['file']\n\t\t\n\t\tif File.filename == \"\":\n\t\t\treturn \"No file selected.\"\n\t\t\n\t\tfilename, ext = secure_filename(File.filename).split('.')\n\t\t#Check if file stream exists and file tpye correct.\n\t\tif File and ext == \"hepmc\":\n\t\t\t#The file is a byte stream by default which is not compatible with the current version of hepmcio.\n\t\t\tstring_stream = io.StringIO(File.read().decode('utf-8'))\n\n\t\t\t#Get all events from file and jsonify them.\n\t\t\tevents = hepmcio.HepMCReader(string_stream).all_events()\n\t\t\thepMCEncoder = hepmcio_json.HepMCJSONEncoder()\n\t\t\tjsonified = [hepMCEncoder.encode(event) for event in events]\n\n\t\t\t#Each collection contains all the data in a file.\n\t\t\tif filename not in mongo.db.collection_names():\n\t\t\t\tcollection = mongo.db[filename]\n\t\t\t\tjsonDecoder = json.JSONDecoder()\n\n\t\t\t\t#MongoDB takes in Python objects and not JSON strings, so have to decode before adding documents.\n\t\t\t\tfor jsonObject in jsonified:\n\t\t\t\t\tjsonEvent = jsonDecoder.decode(jsonObject.evt)\n\t\t\t\t\tjsonParticles = [jsonDecoder.decode(p) for p in jsonObject.particles]\n\t\t\t\t\tjsonVertices = [jsonDecoder.decode(v) for v in jsonObject.vertices]\n\n\t\t\t\t\tcollection.insert_one(jsonEvent)\n\t\t\t\t\tcollection.insert_many(jsonParticles)\n\t\t\t\t\tcollection.insert_many(jsonVertices)\n\t\t\n\t\t\t\treturn \"Succesfully uploaded file.\"\n\t\t\t\n\t\t\treturn \"File already in database.\"\n\n\t\treturn \"Incorrect file type.\"","def process_scene_data(self, scene, data, tmp_dir):\n pass","def test_upload_file(self):\n\n uploadFile = os.path.join(testdatadir, \"upload.data\")\n r = gracedb.writeFile(eventId, uploadFile)\n self.assertEqual(r.status, 201) # CREATED\n r_content = r.json()\n link = r_content['permalink']\n\n self.assertEqual(\n open(uploadFile, 'r').read(),\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\n )\n\n self.assertEqual(\n open(uploadFile, 'r').read(),\n gracedb.get(link).read()\n )\n\n # Re-upload slightly different file.\n uploadFile2 = os.path.join(testdatadir, \"upload2.data\")\n r = gracedb.writeFile(\n eventId,\n filename=\"upload.data\",\n filecontents=open(uploadFile2, 'r'))\n self.assertEqual(r.status, 201) # CREATED\n r_content = r.json()\n link2 = r_content['permalink']\n\n self.assertEqual(\n open(uploadFile2, 'r').read(),\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\n )\n\n self.assertEqual(\n open(uploadFile2, 'r').read(),\n gracedb.get(link2).read()\n )\n\n self.assertNotEqual(link, link2)","def process_record(self, record):\n raise NotImplementedError('Process record needs to be customized')","def handle_flight_received(self, event, sender, data):\n path = f'{self.media_directory}/tello-{datetime.datetime.now().strftime(self.date_fmt)}.jpg' \n with open(path, 'wb') as out_file:\n out_file.write(data)\n log.info('Saved photo to %s' % path)","def store(request):\n if request.method == 'POST':\n username = request.session.get('username')\n password = request.session.get('password')\n storage = request.session.get('storage')\n inv = request.POST.get('inv')\n study = request.POST.get('study')\n metadata = request.POST.get('metadata')\n datafile = request.POST.get('datafile')\n disgenet = onto(request.POST.get('disgenet'), request.POST.get('edam'))[0]\n edam = onto(request.POST.get('disgenet'), request.POST.get('edam'))[1]\n if username == \"\" or username is None:\n login()\n else:\n pid = datafile\n metadata = metadata.split(',')\n if metadata is not None:\n for m in metadata:\n mfile = m.replace('[', '').replace(']', '').replace('\"', '').replace(' ', '')\n metafile = subprocess.Popen([\"curl -s -k -u\" + username + \":\" + password + \" \" + mfile[1:]],\n stdout=subprocess.PIPE, shell=True).communicate()[0]\n metaf = open(username + '/metafile.csv', 'w')\n metaf.write(metafile)\n metaf.close()\n filemeta = \"metafile.csv\"\n if \"This is the WebDAV interface. It can only be accessed by WebDAV clients such as the ownCloud desktop sync client.\" in metafile:\n createMetadata(request, datafile)\n filemeta = \"meta.txt\"\n call([\"curl -s -k -u \" + username + \":\" + password + \" -T \" + '\\'' + \"meta.txt\" + '\\'' +\n \" \" + storage + \"/\" + inv + \"/\" + study + \"/meta.txt\"], shell=True)\n with open(username + \"/\" + filemeta, 'rb') as csvfile:\n count = 0\n reader = csv.DictReader(csvfile)\n cnt = 0\n for row in reader:\n for p in pid.split(','):\n data = p.replace('[', '').replace(']', '').replace(\"'\", \"\").replace('\"', '').replace(' ', '')[1:]\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) + \"> <http://127.0.0.1:3030/ds/data?graph=\" +\n username.replace('@', '') + \"#pid> \\\"\" + data + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) + \"> <http://127.0.0.1:3030/ds/data?graph=\" +\n username.replace('@', '') + \"#investigation_id> \\\"\" + inv + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) + \"> <http://127.0.0.1:3030/ds/data?graph=\" +\n username.replace('@', '') + \"#group_id> \\\"\" + study + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) + \"> <http://127.0.0.1:3030/ds/data?graph=\" +\n username.replace('@', '') + \"#disgenet_iri> \\\"\" + disgenet + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) + \"> <http://127.0.0.1:3030/ds/data?graph=\" +\n username.replace('@', '') + \"#edam_iri> \\\"\" + edam + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) + \"> <http://127.0.0.1:3030/ds/data?graph=\" +\n username.replace('@', '') + \"#disease> \\\"\" + request.POST.get('disgenet') + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n if filemeta == \"meta.txt\":\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) + \"> <http://127.0.0.1:3030/ds/data?graph=\" +\n username.replace('@', '') + \"#meta> \\\"\" + storage + \"/\" + inv + \"/\" + study +\n \"/meta.txt\" + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n else:\n for m in metadata:\n mfile = m.replace('[', '').replace(']', '').replace('\"', '').replace(\"'\", \"\").replace(' ', '')\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) +\n \"> <http://127.0.0.1:3030/ds/data?graph=\" + username.replace('@', '') + \"#meta> \\\"\" + mfile[1:] +\n \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n headers = []\n for (k, v) in row.items():\n for h in range(0, len(k.split('\\t'))):\n if k.split('\\t')[h] != \"\":\n value = v.split('\\t')[h]\n header = k.split('\\t')[h]\n headers.append(header.replace('\"', ''))\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) +\n \"> <http://127.0.0.1:3030/ds/data?graph=\" + username.replace('@', '') + \"#\" + header.replace('\"', '') + \"> \\\"\" +\n value.replace('\"', '').replace('+', '%2B') + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n if \"sex\" not in headers:\n call([\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\" +\n username.replace('@', '') + \"> { <http://127.0.0.1:3030/\" + study + \"_\" + str(cnt) + \"> <http://127.0.0.1:3030/ds/data?graph=\" +\n username.replace('@', '') + \"#sex> \\\"\" + 'Unknown' + \"\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\"], shell=True)\n count += 1\n cnt += 1\n call([\"rm\", username + \"/metafile.csv\"])\n call([\"rm\", username + \"/meta.txt\"])\n return HttpResponseRedirect(reverse('index'))","def resumeupload():\n resumableChunkNumber = int(request.params['resumableChunkNumber'])\n resumableChunkSize = int(request.params['resumableChunkSize'])\n resumableIdentifier = request.params['resumableIdentifier']\n resumableTotalChunks = int(request.params['resumableTotalChunks'])\n ruModel = get_model('resumableUpload')\n resumableUploadObj = ruModel.get(ruModel.c.guid == resumableIdentifier)\n if request.method == 'GET':\n #----check if the chunk has uploaded\n if resumableUploadObj is not None and int(resumableUploadObj.currentChunk) >= resumableChunkNumber:\n return json({'status':'success'}, status=200)\n else:\n return json({'status':'404'}, status=404)\n else:\n try:\n resumableFilename = request.params['resumableFilename']\n # basePath = os.path.join(application_path(settings.TRANSFER.BASEUPLAOD), request.user.username, resumableIdentifier)\n ufile = request.files.get('file', None)\n if not ufile:\n return json({'status':'err', 'msg':'No file had been upload'}, status=400)\n rf = ResumeFile(fileobj=ufile, identify=resumableIdentifier, fileName=resumableFilename, username=request.user.username,\n totalChunk=resumableTotalChunks, currentChunk=resumableChunkNumber)\n rf.keepChunk(resumableChunkNumber)\n if resumableUploadObj is None:\n ruModel(guid=resumableIdentifier, currentChunk=resumableChunkNumber, totalChunk=resumableTotalChunks, state=0).save()\n else:\n ruModel.filter(ruModel.c.guid == resumableIdentifier).update(currentChunk=resumableChunkNumber)\n return json({'status':'ok'}, status=200)\n except Exception as e:\n return json({'msg':'keep file error'}, status=400)","def upload(self):\n\n # Try to retrieve the file from the request\n file = None\n try:\n file = request.files[\"file\"]\n except KeyError as e:\n logging.warning(f\"File was not found in request: {e}.\")\n flash(\"No file given.\", \"error\")\n return redirect(request.url)\n except AttributeError as e:\n logging.warning(f\"Error: Request did not contain any files: {e}.\")\n flash(\"No file given.\", \"error\")\n return redirect(request.url)\n\n # Check if file was correctly uploaded\n if not file or len(file.filename) == 0:\n flash(\"No file selected for upload.\", \"message\")\n return redirect(request.url)\n\n \"\"\" Check if file has correct extension. Allowed extensions depend on\n the connector. To make the code more readable, group connectors\n with the same allowed file extensions together like this:\n if connector in ['someconnector', 'someotherconnector']:\n extensions = [...] \"\"\"\n\n if self.connector in [\"overtime\"]:\n allowed_extensions = OVERTIME_SUPPORTED_EXTENSIONS\n else:\n allowed_extensions = []\n\n if not allowed_file(file, allowed_extensions=allowed_extensions):\n flash(\"File extension not allowed.\", \"warning\")\n return redirect(request.url)\n\n \"\"\" File seems uploaded correctly and has correct extension.\n Generate a new record ID to keep track of the uploaded file.\n \"\"\"\n rec_id = generate_temp_record_id()\n\n # Save file to disk\n path = store_temp_file(file, record_id=rec_id)\n\n if not path:\n flash(\"Error saving file!\", \"error\")\n return redirect(request.url)\n\n \"\"\" If everything ended successfully, send the user to the\n confirmation page so he can review his changes \"\"\"\n\n return redirect(url_for(f\"{self.endpoint}.upload_confirm\", rec_id=rec_id))","def sendRequest(event, context):\n file = event\n print(f\"Processing file: {file['name']}.\")\n\n filename = file['name']\n\n url = 'http://34.123.136.112:5000'\n myobj = {'filename': filename}\n\n x = requests.post(url, data = myobj)\n\n print(x.text)","def process_log_file(cur, filepath):\n \n # open log file\n df = pd.read_json(filepath,lines=True)\n\n # filter by NextSong action - i.e. get only listening music events from the logs\n df = df[(df.page == \"NextSong\")]\n\n # insert time records\n __insert_time_data(cur, df)\n \n # insert user records\n __insert_user_data(cur, df)\n \n # insert songplay records\n __insert_songplay_data(cur, df)\n \n # erase dataframe\n df = df.iloc[0:0]","def restore_input_from_recording(self, recorded_data, args, kwargs):\n file_path = self._get_file_path(args, kwargs)\n with open(file_path, \"wb\") as binary_file:\n __, file_content = self._deserialize_file(recorded_data)\n binary_file.write(file_content)\n\n return file_path","def processor(self, data):\n streaming_data = self.decoder.decodeData(data)\n # Add Your code here to process data and handle transport/storage","def processor(self, data):\n streaming_data = self.decoder.decodeData(data)\n # Add Your code here to process data and handle transport/storage","def processor(self, data):\n streaming_data = self.decoder.decodeData(data)\n # Add Your code here to process data and handle transport/storage","def processor(self, data):\n streaming_data = self.decoder.decodeData(data)\n # Add Your code here to process data and handle transport/storage","def processor(self, data):\n streaming_data = self.decoder.decodeData(data)\n # Add Your code here to process data and handle transport/storage","def processor(self, data):\n streaming_data = self.decoder.decodeData(data)\n # Add Your code here to process data and handle transport/storage","def upload(self, *route, **req_data):\n # Read the FieldStorage.\n file_desc = req_data['file']\n file_mimetype = req_data['mimetype']\n if not isinstance(file_desc, FieldStorage):\n # Python is dangerous when the type is incorrectly assumed.\n return Response(b'invalid request body', status='400 Bad Request')\n\n # Persist the file.\n data_id = get_bucket().put(file_desc.value)\n to_store = StoredFile(\n id=uuid4().hex,\n data_id=data_id,\n mimetype=file_mimetype,\n content_length=len(file_desc.value),\n original_name=file_desc.filename\n )\n StoredFile.collection().put(to_store)\n\n log_activity('%s uploaded file %s'%(\n context.user.link, to_store.access_link\n ))\n\n # Respond.\n return Response(\n bytes(to_store.access_url, 'utf-8'),\n status='201 Created'\n )","def start_recording(self):\n self.start_recording_and_saving_data(self.eeg_file_path)","def import_file(self):\n if self.session_filename is None:\n return\n\n try:\n with open(self.session_filename, \"rb\") as f:\n data = cPickle.loads(zlib.decompress(f.read()))\n except (IOError, zlib.error, cPickle.UnpicklingError):\n return\n\n # update the skip variable to pick up fuzzing from last test case.\n self._index_start = data[\"total_mutant_index\"]\n self.session_filename = data[\"session_filename\"]\n self.sleep_time = data[\"sleep_time\"]\n self.restart_sleep_time = data[\"restart_sleep_time\"]\n self.restart_interval = data[\"restart_interval\"]\n self.web_port = data[\"web_port\"]\n self._crash_threshold_node = data[\"crash_threshold\"]\n self.total_num_mutations = data[\"total_num_mutations\"]\n self.total_mutant_index = data[\"total_mutant_index\"]\n self.netmon_results = data[\"netmon_results\"]\n self.procmon_results = data[\"procmon_results\"]\n self.is_paused = data[\"is_paused\"]","def process_log_file(cur, filepath):\n # open log file\n data_frame = pd.read_json(filepath, lines=True)\n\n # filter by NextSong action\n data_frame = data_frame[data_frame.page == 'NextSong']\n\n # convert timestamp column to datetime\n time_value = pd.to_datetime(data_frame['ts'])\n\n # insert time data records\n time_data = (time_value, time_value.dt.year, time_value.dt.month,\n time_value.dt.isocalendar().week, time_value.dt.dayofweek,\n time_value.dt.day, time_value.dt.hour)\n\n column_labels = ('timestamp', 'year', 'month', 'week', 'weekday', 'day', 'hour')\n\n time_df = pd.DataFrame.from_dict(dict(zip(column_labels, time_data)))\n\n for i, row in time_df.iterrows():\n cur.execute(time_table_insert, list(row))\n\n # load user table\n user_df = data_frame[['userId', 'firstName', 'lastName', 'gender', 'level']]\n\n # insert user records\n for i, row in user_df.iterrows():\n cur.execute(user_table_insert, row)\n\n # insert songplay records\n for index, row in data_frame.iterrows():\n\n # get songid and artistid from song and artist tables\n cur.execute(song_select, (row.song, row.artist, row.length))\n results = cur.fetchone()\n\n if results:\n songid, artistid = results\n else:\n songid, artistid = None, None\n\n # insert songplay record\n songplay_data = row[['ts', 'userId', 'level', 'sessionId', 'location', 'userAgent']]. \\\n append(pd.Series([songid,\n artistid],\n index=[\n 'songid',\n 'artistid']))\n songplay_data['ts'] = pd.to_datetime(songplay_data['ts'])\n\n cur.execute(songplay_table_insert, songplay_data)","async def accept_replay(request, game):\n game = game.lower()\n replay_file = request.files.get('replay')\n if replay_file:\n if game == STARCRAFT:\n load_map = request.args.get(\"load_map\", False)\n result = await SC2Replay.analyze_replay(replay_file, load_map)\n if result:\n data = {\"result\": result,\n \"game\": STARCRAFT,\n \"success\": True}\n return sanic.response.json(data, status=200)\n else:\n data = {\n \"error\": \"Unable to parse game file.\",\n \"success\": False,\n \"game\": game\n }\n return sanic.response.json(data, status=500)\n else:\n data = {\n \"error\": \"Game not in list of games.\",\n \"success\": False,\n \"game\": game\n }\n return sanic.response.json(data, status=404)\n else:\n data = {\n \"error\": \"No replay file given.\",\n \"success\": False,\n \"game\": game\n }\n return sanic.response.json(data, status=500)","def train_enroller(self, train_files, enroller_file):\n # save something\n bob.io.base.save(_data, enroller_file)","def process(self,fileobj_out,fileobj_in):\n pass","def process_log_file(cur, filepath):\n # open log file\n df = get_file_df(filepath)\n\n # filter by NextSong action\n df = df[df['page'] == 'NextSong']\n\n # convert timestamp column to datetime\n df['ts'] = df['ts'].apply(lambda x: datetime.datetime.fromtimestamp(x/1000)) \n t = df\n \n time_data = []\n for td in t['ts']:\n wd = True if td.weekday() <=6 else False\n time_data.append([str(td.time()), td.hour, td.day, td.week, td.month, td.year, wd])\n column_labels = ('start_time', 'hour', 'day', 'week', 'month', 'year', 'weekday')\n\n # insert time data records\n time_df = pd.DataFrame(time_data, columns=column_labels)\n\n for i, row in time_df.iterrows():\n cur.execute(time_table_insert, list(row))\n\n # load user table\n user_df = df[['userId', 'firstName', 'lastName', 'gender', 'level']].copy()\n\n # insert user records\n for i, row in user_df.iterrows():\n cur.execute(user_table_insert, row)\n\n # insert songplay records\n for index, row in df.iterrows():\n \n # get songid and artistid from song and artist tables\n cur.execute(song_select, (row.song, row.artist, row.length))\n results = cur.fetchone()\n \n if results:\n songid, artistid = results\n else:\n songid, artistid = None, None\n\n # insert songplay record\n #user_id, level, song_id, artist_id, session_id, location, user_agent\n songplay_data = [row.userId, row.level, songid, artistid, row.sessionId, row.location, row.userAgent]\n cur.execute(songplay_table_insert, songplay_data)","def upload_from_file(self, file_obj, name_on_storage, **keyword_args):\n blob = self.bucket.blob(name_on_storage)\n blob.upload_from_file(file_obj, **keyword_args)\n print(f\"Upload object {name_on_storage}\")","def collection_post(self):\n if 'file' not in self.request.POST:\n self.request.errors.add('body', 'file', 'Not Found')\n self.request.errors.status = 404\n return\n tender = TenderDocument.load(self.db, self.tender_id)\n if not tender:\n self.request.errors.add('url', 'tender_id', 'Not Found')\n self.request.errors.status = 404\n return\n src = tender.serialize(\"plain\")\n bids = [i for i in tender.bids if i.id == self.bid_id]\n if not bids:\n self.request.errors.add('url', 'bid_id', 'Not Found')\n self.request.errors.status = 404\n return\n bid = bids[0]\n data = self.request.POST['file']\n document = Document()\n document.id = uuid4().hex\n document.title = data.filename\n document.format = data.type\n key = uuid4().hex\n document.url = self.request.route_url('Tender Bid Documents', tender_id=self.tender_id, bid_id=self.bid_id, id=document.id, _query={'download': key})\n bid.documents.append(document)\n filename = \"{}_{}\".format(document.id, key)\n tender['_attachments'][filename] = {\n \"content_type\": data.type,\n \"data\": b64encode(data.file.read())\n }\n patch = make_patch(tender.serialize(\"plain\"), src).patch\n tender.revisions.append(revision({'changes': patch}))\n try:\n tender.store(self.db)\n except Exception, e:\n return self.request.errors.add('body', 'data', str(e))\n self.request.response.status = 201\n self.request.response.headers['Location'] = self.request.route_url('Tender Bid Documents', tender_id=self.tender_id, bid_id=self.bid_id, id=document.id)\n return {'data': document.serialize(\"view\")}","def upload_sample(host='127.0.0.1', port=8000, path=\"\"):\n with Reader(path) as reader:\n new_user = {}\n user_id = reader.user_id\n if not validate_attr(user_id, -1, path):\n return\n new_user[\"user_id\"] = user_id\n\n username = reader.username\n if not validate_attr(username, \"\", path):\n return\n new_user[\"username\"] = username\n\n birthday = reader.birthday\n if not validate_attr(birthday, None, path):\n return\n try:\n birthday_timestamp = int(birthday.timestamp())\n except OverflowError:\n print(ERROR_PREFIX, \"datetime object raised OverflowError. Please recheck supplied data -\", path)\n return\n new_user[\"birthday\"] = birthday_timestamp\n\n gender = reader.gender\n if not validate_attr(gender, \"\", path):\n return\n new_user[\"gender\"] = gender\n\n base_url = \"\" if \"http\" in host else \"http://\"\n base_url += \"{}:{}\".format(host, port)\n parsers = upload_user(base_url, new_user)\n\n if parsers is not None: # if no error (on error, parsers = None)\n snapshot_id = 1\n for snapshot in reader:\n # print(snapshot.datetime, snapshot.color_image.width, snapshot.color_image.height)\n upload_snapshot(base_url, user_id, snapshot, snapshot_id, parsers)\n snapshot_id += 1","def process_log_file(cur, filepath):\n df = pd.read_json(filepath, lines=True)\n df = df.loc[df.page == 'NextSong', :]\n\n # insert time data records\n df['ts'] = pd.to_datetime(df.ts, unit='ms')\n t = df['ts']\n time_data = {\n 'start_time': t,\n 'hour': t.dt.hour,\n 'day': t.dt.day,\n 'week': t.dt.week,\n 'month': t.dt.month,\n 'year': t.dt.year,\n 'weekday': t.dt.weekday\n }\n time_df = pd.DataFrame(data=time_data)\n for i, row in time_df.iterrows():\n cur.execute(time_table_insert, list(row))\n\n # insert user records\n user_df = df.loc[:, ['userId', 'firstName', 'lastName', 'gender', 'level']]\n for i, row in user_df.iterrows():\n cur.execute(user_table_insert, row)\n\n # insert songplay records\n for index, row in df.iterrows():\n cur.execute(song_select, (row.song, row.artist, row.length))\n results = cur.fetchone()\n\n if results:\n songid, artistid = results\n else:\n songid, artistid = None, None\n\n songplay_data = {\n 'start_time': row.ts,\n 'user_id': row.userId,\n 'level': row.level,\n 'song_id': songid,\n 'artist_id': artistid,\n 'session_id': row.sessionId,\n 'location': row.location,\n 'user_agent': row.userAgent\n }\n cur.execute(songplay_table_insert, list(songplay_data.values()))","def process_log_file(cur, filepath):\r\n df=pd.read_json(filepath,lines=True)\r\n df2=df\r\n df=df[df['page']=='NextSong']\r\n ser=pd.to_datetime(df['ts'],unit='ms')\r\n times=[]\r\n for i in ser:\r\n times.append([i,i.hour,i.day,i.week,i.month,i.year,i.day_name()])\r\n for i in times:\r\n cur.execute(time_table_insert,i)\r\n df=df[['userId','firstName','lastName','gender','level']]\r\n for i,row in df.iterrows():\r\n cur.execute(users_table_insert,list(row))\r\n for i, row in df2.iterrows():\r\n cur.execute(song_select, (row.song, row.artist, row.length))\r\n res = cur.fetchone()\r\n if res:\r\n song_id, artist_id = res\r\n else:\r\n song_id, artist_id = None, None\r\n\r\n songplay_data = (\r\n i, pd.to_datetime(row.ts, unit='ms'),int(row.userId), row.level, song_id, artist_id, row.sessionId,\r\n row.location, row.userAgent)\r\n cur.execute(songplays_table_insert, songplay_data)","def process_one_file(guid, key, env):\n logging.info(f'Processing file: {key}')\n try:\n query_seq = read_s3_file(key).seq\n results = []\n for name, reference_seq in REFERENCE_RECORDS.items():\n offset = reference_seq.seq.find(query_seq)\n if offset != -1:\n result = {'filename': name,\n 'offset': offset,\n 'name': reference_seq.name,\n 'desc': reference_seq.description\n }\n results.append(result)\n logging.info(f'found in {name} at {offset}')\n update_database(guid, 'done', env, results)\n logging.info(f'Update succeeded for guid={guid} in env={env}')\n except Exception as err:\n report = {'time': str(datetime.utcnow()),\n 'guid': guid,\n 'env': env,\n 'key': key,\n 'trace' : traceback.format_exc()\n }\n results = [{'error' : report}]\n update_database(guid, 'error', env, results)\n raise","def __gitBisectCreateReplay(self):\n self.vcs.gitBisectCreateReplayFile(self.project.getProjectPath())","def storage_ipfs_upload_file(self, file_obj):\n # Request: upload a file\n # curl -X POST -H 'Content-Type: application/json' --data '{\"jsonrpc\":\"2.0\",\"method\":\"edgestore.PutFile\",\"params\":[{\"path\": \"theta-edge-store-demos/demos/image/data/smiley_explorer.png\"}],\"id\":1}' http://localhost:19888/rpc\n # Result\n # {\n # \"jsonrpc\": \"2.0\",\n # \"id\": 1,\n # \"result\": {\n # \"key\": \"0xbc0383809da9fb98c5755e3fa4f19f4ebc7e34308ab321246e4bb54e548fad04\",\n # \"relpath\": \"smiley_explorer.png\",\n # \"success\": true\n # }\n # }\n home_dir = expanduser(\"~\")\n file_path = os.path.join(home_dir, \"thetastore\")\n if not os.path.exists(file_path):\n os.makedirs(file_path, exist_ok=True)\n file_path = os.path.join(file_path, str(uuid.uuid4()))\n with open(file_path, \"wb\") as file_handle:\n file_handle.write(file_obj)\n\n request_data = {\"jsonrpc\":\"2.0\",\n \"method\":\"edgestore.PutFile\",\n \"params\":[{\"path\": file_path}],\n \"id\":1}\n res = httpx.post(\n self.ipfs_upload_uri,\n headers={\"Content-Type\": \"application/json\"},\n data=json.dumps(request_data),\n timeout=None,\n )\n content = res.json()\n result = content.get(\"result\", None)\n if result is None:\n return False, \"Failed to upload file(result is none).\"\n else:\n success = result.get(\"success\", False)\n if not success:\n return False, \"Failed to upload file(success if false).\"\n file_cid = result.get(\"key\", None)\n if file_cid is None:\n return False, \"Failed to upload file(key is none).\"\n return True, file_cid","def record(params, git_info = {}):\n print \"recording...\"\n\n try:\n # connect to MongoDB\n # config = json.load(open(os.environ.get('HOME') + \"/sandbox/config.json\"))\n config = json.load(open(os.environ.get('HOME') + \"/LSEMS/config.json\"))\n try:\n client = MongoClient(config[\"mongodb_url\"])\n except Exception as e:\n raise Exception(\"fail to connect to given MongoDB address: \" + DB_addr)\n\n # check and run the thing\n missing = checkKeys(params, ['data_set', 'src', 'type', 'param'])\n if len(missing) != 0:\n raise Exception(\"missing attribute\"+('s' if len(missing)!=1 else '')+\": \"+str(missing))\n\n params['time'] = asctime()\n params['commit_id'] = git_info['commit_id']\n params['name'] = git_info['name']\n repo_name = git_info['repo_name']\n params['repo_name'] = repo_name\n user = verifyUser(client, git_info['name'])\n\n exp = user.find_one({'exp_name': repo_name})\n if not exp:\n print 'adding new experiment '+repo_name+'...'\n user.insert({'exp_name': repo_name, 'exp_records':[]})\n old_records = user.find_one({'exp_name': repo_name})['exp_records']\n user.update({'exp_name': repo_name}, {'$set': {'exp_records': old_records + [params]}})\n\n print params\n #user.insert(params)\n client.close()\n return True,params\n except Exception as e:\n print e\n print \"Aborting...\"\n return False,{}","def post(self):\n upload_files = upload_images(request.files, 'users', 'user')\n record = request.json if request.content_type == 'application/json' else request.form\n \"\"\" Values assign to Category Model \"\"\"\n pprint.pprint(record)\n model_record = UserModel(\n record['name'], \n record['password'], \n record['email'],\n record['mobile'] if 'mobile' in record else None, \n record['userType'] if 'userType' in record else None,\n upload_files['fileArr']\n )\n \"\"\" Model converts to document like json object \"\"\"\n record_document = model_record.to_document()\n \"\"\" Below line will insert record and get objectID \"\"\"\n insertedId = col_users.insert_one(record_document).inserted_id\n \"\"\" Below line does files move from one place to another \"\"\"\n if len(upload_files['fileArr']) > 0:\n moved_file(upload_files['fileArr'], upload_files['folder'], str(insertedId)) \n\n return jsonify(str(insertedId))","def seed_user_data(filename):\n\n #open file and go through it line by line\n log_file = open(filename)\n\n for line in log_file:\n data = line.strip().split(\"|\") #data is a list\n\n #get data from split line\n id = int(data[0])\n age = int(data[1])\n zip = data[4]\n\n #create a new record and add it to the queue\n new_user = User(user_id=id, age=age, zipcode=zip)\n db.session.add(new_user)\n\n #commit changes\n db.session.commit()","def execute_request(self, request):\n request.input_content = self.read_file(request.input_content) if request.is_input_file else [\n request.input_content]\n poke_request = PokeObjectRequest(request.mode, request.input_content, request.expanded)\n loop = asyncio.get_event_loop()\n result = loop.run_until_complete(poke_request.execute())\n self.print_output(result, request.output)","def start(self, filename):\n\n self.db.init_tables()\n self.db.single_insert_camera(filename)\n self.vitess.mydb.commit()","async def _upload(self) -> None:\n\n # filename given?\n filename = str(uuid.uuid4()) if self.filename is None else self.filename\n\n # check\n if self._upload_path is None:\n raise ValueError(\"No upload URL given.\")\n\n # send data and return image ID\n async with aiohttp.ClientSession() as session:\n data = aiohttp.FormData()\n data.add_field(\"file\", self._buffer, filename=self.filename)\n async with session.post(self._upload_path, auth=self._auth, data=data, timeout=self._timeout) as response:\n if response.status == 401:\n log.error(\"Wrong credentials for uploading file.\")\n raise FileNotFoundError\n elif response.status != 200:\n log.error(f\"Could not upload file to filecache: {response.status} {response.reason}\")\n raise FileNotFoundError","def process(self):\n self.extract()\n self.transform()\n self.load()","def __download(self, downloadType):\n\n downloadAudit = {}\n downloadAudit['download_type'] = downloadType\n downloadAudit['remote_url'] = self.remoteUrl#get the url that is being requested\n downloadAudit['download_start_time'] = datetime.now()#capture the date when the url was accessed\n #first make an entry into the db stating that a download operation is to be attempted\n downloadAudit['comment'] = 'Starting download operation'\n newRecordId = self.__auditFileRequest(downloadAudit)\n\n downloadAudit = {}\n try:\n req = urllib2.Request(self.remoteUrl)\n r = urllib2.urlopen(req)\n except URLError, urle:\n if hasattr(urle, 'reason'):\n downloadAudit['comment'] = urle.reason\n else:\n downloadAudit['comment'] = urle.__str__()\n \n self.__auditFileRequest(downloadAudit, newRecordId)\n \n return None #just return since there has been an error in connecting with the remote server\n\n try:\n downloadAudit['local_file_path'] = '/' + self.localRepoDir + '/' + downloadType + '/' + downloadType + '-' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '.xls'\n\n file = open(settings.APPLICATION_SETTINGS['MCH_HOME'] + downloadAudit['local_file_path'], 'wb')\n file.write(r.read())\n\n #headerInfo = r.info()\n\n isFileToBeProcessed = False #default is not to process file\n\n #before downloading, check to see if the remote file is more recent than the last file that was downloaded, whose\n #information is in the db\n latestFetch = self.__recentDownload(downloadType)\n\n if latestFetch:\n downloadAudit['file_size'] = latestFetch[0]\n file.flush()#make sure all the content is written to file \n os.fsync(file.fileno())\n if latestFetch[0] != os.path.getsize(file.name):\n isFileToBeProcessed = True\n else:\n isFileToBeProcessed = True\n\n if isFileToBeProcessed:\n downloadAudit['file_size'] = os.path.getsize(file.name)\n downloadAudit['file_modification_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n downloadAudit['download_completion_time'] = datetime.now()\n #now make an audit entry into the database\n downloadAudit['comment'] = 'New file to be processed.'\n else:\n os.remove(file.name)#remove the file since it looks like it has already been downloaded and processed\n #now make an audit entry into the database\n downloadAudit['comment'] = 'File already downloaded, purged it from the file system.'\n\n self.__auditFileRequest(downloadAudit, newRecordId)\n file.close()\n except Exception as e:\n self.logger.exception('\\n Unknown fatal error occured during the downloading of the raw files. %s' % str(e))\n\n return newRecordId","def process_log_file(cur, filepath):\n\n # open log file\n df = pd.read_json(filepath, lines=True)\n\n # filter by NextSong action\n df = df[df['page'] == 'NextSong']\n\n # convert timestamp column to datetime\n t = pd.to_datetime(df['ts'], unit='ms')\n\n # insert time data records\n time_data = (t, t.dt.hour, t.dt.day,\n t.dt.month, t.dt.year, t.dt.day_name())\n column_labels = ('start_time', 'hour', 'day', 'month', 'year', 'weekday')\n time_df = pd.DataFrame(dict(zip(column_labels, time_data)))\n\n # bulk insert time_df into Postgres\n # create a csv like object\n output = io.StringIO()\n time_df.to_csv(output, sep='\\t', index=False, header=False)\n # move the pointer to start of the file\n output.seek(0)\n # creating a temp table to handle conflict due to duplicate insert\n # ref: https://stackoverflow.com/questions/48019381/how-postgresql-copy-to-stdin-with-csv-do-on-conflic-do-update\n cur.execute(time_tmp_table)\n # copy data from csv to temp table\n cur.copy_from(output, 'tmp_table')\n # merge temp table with main table\n cur.execute(time_table_bulk_insert)\n\n # load user table\n user_df = df[['userId', 'firstName', 'lastName', 'gender', 'level']]\n\n # insert user records\n for i, row in user_df.iterrows():\n cur.execute(user_table_insert, row)\n\n # insert songplay records\n # create a csv like object\n output = io.StringIO()\n for index, row in df.iterrows():\n\n # get songid and artistid from song and artist tables\n cur.execute(song_select, (row.song, row.artist, row.length))\n results = cur.fetchone()\n\n if results:\n songid, artistid = results\n else:\n songid, artistid = None, None\n # insert songplay record\n songplay_data = (t[index], row.userId, row.level, songid,\n artistid, row.sessionId, row.location, row.userAgent)\n # write to csv like object\n output.write('\\t'.join(map(clean_csv_value, songplay_data)) + '\\n')\n # move the pointer to start of the csv like object\n output.seek(0)\n # columns to insert (songplay_id is a serial insert)\n columns = ['start_time', 'user_id', 'level', 'song_id',\n 'artist_id', 'session_id', 'location', 'user_agent']\n # copy data to songplays table\n cur.copy_from(output, 'songplays', columns=columns)","def ftp_CACHEDATA(self, line):\n #print 'WE ARE INSIDE OF FTPCACHEDATA THANK GOD!'\n (line, chunksize_username_metadata) = line.split('?')\n (chunk_size, user_name) = chunksize_username_metadata.split('_')\n raw_cache_list = line.split('_')\n cache_dicts = []\n for raw_cache_string in raw_cache_list:\n cache_dict = create_cache_json(raw_cache_string, chunk_size, user_name)\n if cache_dict != {}:\n cache_dicts.append(cache_dict)\n if len(cache_dicts) != 0:\n #take cache_dicts, convert to dict so that it can be encoded\n dumped_dicts = json.dumps(cache_dicts)\n ##\n send_cache_data_to_tracker(tracker_address, dumped_dicts)\n #my_dict = ({ 'cache_data', cache_dicts})\n #urllib.urlencode(my_dict)\n #make function in helper.py that sends the post to the tracker url as JSON\n \n \n f = open('user_log/' + user_name + '.txt','w')\n #f.write(json.dumps(cache_dicts))\n f.write(dumped_dicts)\n f.close()\n #cache_dicts_length = len(cache_dicts)\n #f.write('[')\n #for i in range(0,cache_dicts_length):\n # val = cache_dicts[i]\n # f.write(json.dumps(val))\n # if(i != cache_dicts_length - 1):\n # f.write(',')\n # f.write('\\n')\n #f.write(']')\n #f.close()\n \n #self.push_dtp_data(line, isproducer=False, cmd='CDAT')\n self.respond(\"200 success.\")","def replay_delete(sender, instance, **kwargs):\n pass\n # Temporarily disabled\n\n #print(\"deleting file from S3\")\n # False so FileField doesn't save the model\n #instance.file.delete(False)","def run_replay_analysis(replay, cluster_endpoint, start_time, end_time, bucket_url, iam_role, user, tag='',\n workload=\"\",\n is_serverless=False, secret_name=None, nlb_nat_dns=None, complete=True, stats=None,\n summary=None):\n\n logger = logging.getLogger(\"SimpleReplayLogger\")\n s3_client = boto3.client('s3')\n cluster = cluster_dict(cluster_endpoint, is_serverless, start_time, end_time)\n cluster[\"is_serverless\"] = is_serverless\n cluster[\"secret_name\"] = secret_name\n cluster[\"host\"] = nlb_nat_dns if nlb_nat_dns != None else cluster[\"host\"]\n\n if type(bucket_url) is str:\n bucket = bucket_dict(bucket_url)\n\n logger.debug(bucket)\n\n logger.info(f\"Running analysis for replay: {replay}\")\n replay_path = f\"{bucket['prefix']}analysis/{replay}\"\n\n # unload from cluster\n queries = unload(bucket, iam_role, cluster, user, replay)\n info = create_json(replay, cluster, workload, complete, stats, tag)\n try:\n boto3.resource('s3').Bucket(bucket.get('bucket_name')).upload_file(info, f\"{replay_path}/{info}\")\n except ClientError as e:\n logger.error(f\"{e} Could not upload info. Confirm IAM permissions include S3::PutObject.\")\n\n if is_serverless:\n exit(0)\n else:\n report = Report(cluster, replay, bucket, replay_path, tag, complete)\n\n try:\n # iterate through query csv results and import\n for q in queries:\n get_raw_data(report, bucket, replay_path, q)\n\n except s3_client.exceptions.NoSuchKey as e:\n logger.error(f\"{e} Raw data does not exist in S3. Error in replay analysis.\")\n exit(-1)\n except Exception as e:\n logger.error(f\"{e}: Data read failed. Error in replay analysis.\")\n exit(-1)\n\n # generate replay_id_report.pdf and info.json\n logger.info(f\"Generating report.\")\n pdf = pdf_gen(report, summary)\n\n s3_resource = boto3.resource('s3')\n # upload to s3 and output presigned urls\n try:\n s3_resource.Bucket(bucket.get('bucket_name')).upload_file(pdf, f\"{replay_path}/out/{pdf}\")\n s3_resource.Bucket(bucket.get('bucket_name')).upload_file(info, f\"{replay_path}/out/{info}\")\n analysis_summary(bucket.get('url'), replay)\n except ClientError as e:\n logger.error(f\"{e} Could not upload report. Confirm IAM permissions include S3::PutObject.\")\n exit(-1)","def start_recording_and_saving_data(self, eeg_file_path):\n # set up variables\n self.e_event, self.e_state, user_id, n_samples, n_sam, composer_port, secs, state = self.setup_var()\n # pointers\n n_samples_taken = ct.pointer(n_samples)\n data = ct.pointer(ct.c_double(0))\n user = ct.pointer(user_id)\n ready_to_collect = False\n # start connection\n print \"Connecting...\"\n if self.libEDK.EE_EngineConnect(\"Emotiv Systems-5\") != 0: # connection failed\n print \"Emotiv Engine start up failed.\"\n self.stop_connection()\n sys.exit(1)\n print \"Connected! Start receiving data...\"\n self._recording = True\n # write data to file\n self.f = open(eeg_file_path, 'w')\n # write header\n for header in Constants.HEADER:\n # last one in header\n if header != \"TIMESTAMP\" and not self.f.closed:\n self.f.write(header + \",\")\n elif not self.f.closed:\n self.f.write(header + \"\\n\")\n h_data = self.libEDK.EE_DataCreate()\n self.libEDK.EE_DataSetBufferSizeInSec(secs)\n # start recording\n while self._recording:\n state = self.libEDK.EE_EngineGetNextEvent(self.e_event)\n if state == 0:\n event_type = self.libEDK.EE_EmoEngineEventGetType(self.e_event)\n self.libEDK.EE_EmoEngineEventGetUserId(self.e_event, user)\n # add user\n if event_type == 16:\n print \"User added\"\n self.libEDK.EE_DataAcquisitionEnable(user_id, True)\n ready_to_collect = True\n if ready_to_collect:\n self.libEDK.EE_DataUpdateHandle(0, h_data)\n self.libEDK.EE_DataGetNumberOfSample(h_data,n_samples_taken)\n # print \"Updated :\", n_samples_taken[0]\n if n_samples_taken[0] != 0:\n n_sam = n_samples_taken[0]\n arr = (ct.c_double*n_samples_taken[0])()\n ct.cast(arr, ct.POINTER(ct.c_double))\n # self.libEDK.EE_DataGet(h_data, 3,byref(arr), nSam)\n data = np.array('d') # zeros(n_samples_taken[0],double)\n for sampleIdx in range(n_samples_taken[0]):\n # channel 14 + loc data 6 + timestamp 1 = 23\n data = np.zeros(23)\n for i in range(22):\n self.libEDK.EE_DataGet(h_data, Constants.TARGET_CHANNEL_LIST[i], ct.byref(arr), n_sam)\n if not self.f.closed:\n print >>self.f,arr[sampleIdx],\",\",\n data[i] = arr[sampleIdx]\n # write our own time stamp\n t = time.time()\n data[-1] = t\n if not self.f.closed:\n print >>self.f,t,\n # put data onto out buffer queue\n self.out_buffer_queue.put(data)\n # switch line\n if not self.f.closed:\n print >>self.f,'\\n',\n self.f.flush()\n # not sure the use of this in the original program...\n self.libEDK.EE_DataFree(h_data)"],"string":"[\n \"def send_to_restore(file_name, data):\\n urlfetch.fetch(url=config.RESTORE_URL + '?name=' + file_name + '&source=db&packet',\\n payload=urllib.urlencode({\\\"data\\\": services.event.entity_to_string(data)}),\\n method=urlfetch.POST)\",\n \"def transfer(file_obj):\",\n \"def post_original_to_db( self, data, date_stamp ):\\n post_result = 'init'\\n try:\\n post_result = utility_code.postFileData( identifier=date_stamp, file_data=data, update_type='original_file' )\\n log.info( 'original_file post_result, `%s`' % post_result )\\n except Exception, e:\\n log.error( 'original_file post_result exception is: %s' % e )\\n if not post_result == 'success':\\n log.debug( 'post_result not \\\"success\\\"; but continuing' )\\n return\",\n \"def upload_process(self, rec_id: str): # pylint: disable=unused-variable\\n\\n # Process the uploaded file\\n if self.connector == \\\"overtime\\\":\\n importer = action_process_overtime_data(\\n get_record_path(rec_id), output=print, show_status=True\\n )\\n action_commit_overtime_data(importer, output=print)\\n else:\\n flash(\\\"Unknown upload file type :(\\\", \\\"error\\\")\\n\\n flash(\\\"Data successfully uploaded!\\\", \\\"info\\\")\\n\\n return redirect(url_for(f\\\"{self.endpoint}.upload\\\"))\",\n \"def reaper(self):\\n if not self.superuser_request:\\n self.abort(402, 'uploads must be from an authorized drone')\\n with tempfile.TemporaryDirectory(prefix='.tmp', dir=config.get_item('persistent', 'data_path')) as tempdir_path:\\n try:\\n file_store = files.FileStore(self.request, tempdir_path)\\n except files.FileStoreException as e:\\n self.abort(400, str(e))\\n now = datetime.datetime.utcnow()\\n fileinfo = dict(\\n name=file_store.filename,\\n created=now,\\n modified=now,\\n size=file_store.size,\\n hash=file_store.hash,\\n tags=file_store.tags,\\n metadata=file_store.metadata\\n )\\n container = reaperutil.create_container_hierarchy(file_store.metadata)\\n f = container.find(file_store.filename)\\n target_path = os.path.join(config.get_item('persistent', 'data_path'), util.path_from_hash(fileinfo['hash']))\\n if not f:\\n file_store.move_file(target_path)\\n container.add_file(fileinfo)\\n rules.create_jobs(config.db, container.acquisition, 'acquisition', fileinfo)\\n elif not file_store.identical(util.path_from_hash(fileinfo['hash']), f['hash']):\\n file_store.move_file(target_path)\\n container.update_file(fileinfo)\\n rules.create_jobs(config.db, container.acquisition, 'acquisition', fileinfo)\\n throughput = file_store.size / file_store.duration.total_seconds()\\n log.info('Received %s [%s, %s/s] from %s' % (file_store.filename, util.hrsize(file_store.size), util.hrsize(throughput), self.request.client_addr))\",\n \"def put_object(self, account, container, object, content):#put a file to server\\n \\n pass\",\n \"def upload_game():\\n if (\\\"game_output\\\" not in flask.request.values or\\n \\\"users\\\" not in flask.request.values):\\n raise util.APIError(\\n 400, message=\\\"Please provide both the game output and users.\\\")\\n\\n game_output = json.loads(flask.request.values[\\\"game_output\\\"])\\n users = json.loads(flask.request.values[\\\"users\\\"])\\n challenge = json.loads(flask.request.values.get(\\\"challenge\\\", \\\"null\\\"))\\n\\n replay_name = os.path.basename(game_output[\\\"replay\\\"])\\n if replay_name not in flask.request.files:\\n raise util.APIError(\\n 400, message=\\\"Replay file not found in uploaded files.\\\")\\n\\n stats = parse_replay(decode_replay(flask.request.files[replay_name]))\\n if stats is None:\\n raise util.APIError(\\n 400, message=\\\"Replay file cannot be parsed.\\\")\\n\\n # Store the replay and any error logs\\n replay_key, bucket_class = store_game_artifacts(replay_name, users)\\n\\n with model.engine.begin() as conn:\\n total_users = conn.execute(model.total_ranked_users).first()[0]\\n # Sort the users to prevent deadlock in the stored_bot for update lock\\n for user in sorted(users, key=lambda x: x['user_id']):\\n stored_user = conn.execute(\\n sqlalchemy.sql.select([\\n model.users.c.id.label(\\\"user_id\\\"),\\n model.users.c.on_email_list,\\n model.users.c.github_email.label(\\\"email\\\"),\\n model.users.c.player_level,\\n model.users.c.creation_time,\\n model.users.c.username,\\n model.organizations.c.organization_name,\\n ]).select_from(model.users.join(\\n model.organizations,\\n model.organizations.c.id == model.users.c.organization_id,\\n isouter=True\\n )).where(model.users.c.id == user[\\\"user_id\\\"])\\n ).first()\\n\\n stored_bot = conn.execute(\\n sqlalchemy.sql.select([\\n model.bots.c.version_number,\\n model.bots.c.language,\\n model.bots.c.mu,\\n model.bots.c.sigma,\\n ], for_update=True).where(\\n (model.bots.c.id == user[\\\"bot_id\\\"]) &\\n (model.bots.c.user_id == user[\\\"user_id\\\"])\\n )\\n ).first()\\n\\n stored_rank = conn.execute(\\n sqlalchemy.sql.select([\\n model.ranked_bots_users.c.rank,\\n ]).where(\\n (model.ranked_bots_users.c.bot_id == user[\\\"bot_id\\\"]) &\\n (model.ranked_bots_users.c.user_id == user[\\\"user_id\\\"])\\n )\\n ).first()\\n\\n if not stored_user or not stored_bot:\\n raise util.APIError(400, message=\\\"User or bot doesn't exist\\\")\\n\\n # If the user has submitted a new bot in the meanwhile,\\n # ignore the game\\n if stored_bot[\\\"version_number\\\"] != user[\\\"version_number\\\"]:\\n return util.response_success({\\n \\\"message\\\": \\\"User {} has uploaded a new bot, discarding \\\"\\n \\\"match.\\\".format(user[\\\"user_id\\\"])\\n })\\n\\n user.update(dict(stored_user))\\n user.update(dict(stored_bot))\\n if stored_rank:\\n user[\\\"leaderboard_rank\\\"] = stored_rank[\\\"rank\\\"]\\n user[\\\"tier\\\"] = util.tier(stored_rank[\\\"rank\\\"], total_users)\\n else:\\n user[\\\"leaderboard_rank\\\"] = total_users\\n user[\\\"tier\\\"] = util.tier(total_users, total_users)\\n\\n # Store game results in database\\n game_id = store_game_results(conn, game_output, stats,\\n replay_key, bucket_class,\\n users, challenge)\\n # Store game stats in database\\n store_game_stats(conn, game_output, stats, game_id, users)\\n # Update rankings\\n if not challenge:\\n update_rankings(conn, users)\\n\\n return util.response_success()\",\n \"def store(self, filename):\",\n \"def work(data):\\n filepath = data['filepath']\\n user = data['user']\\n checksum_data = list(filter(lambda x: x['type'] == 'sha256', data['decrypted_checksums']))\\n decrypted_checksum = checksum_data[0]['value']\\n stable_id = data['accession_id']\\n LOG.info(\\\"Mapping file with path %s and checksum %s to stable_id %s\\\", filepath, decrypted_checksum, stable_id)\\n\\n # Remove file from the inbox\\n # TODO\\n\\n db.set_stable_id(filepath, user, decrypted_checksum, stable_id) # That will flag the entry as 'Ready'\\n\\n LOG.info(\\\"Stable ID %s mapped to %s\\\", stable_id, filepath)\\n\\n # Send message to mark file as completed on the CEGA side\\n completed_data = data\\n completed_data.pop(\\\"type\\\", None)\\n LOG.info(f\\\"Reply message to files.completed: {completed_data}\\\")\\n\\n return (completed_data, False)\",\n \"def upload(det_file):\\n db = DatabaseSession()\\n\\n try:\\n LOG.info(f\\\"Copying REDCap DET records from {det_file.name}\\\")\\n\\n row_count = db.copy_from_ndjson((\\\"receiving\\\", \\\"redcap_det\\\", \\\"document\\\"), det_file)\\n\\n LOG.info(f\\\"Received {row_count:,} DET records\\\")\\n LOG.info(\\\"Committing all changes\\\")\\n db.commit()\\n\\n except:\\n LOG.info(\\\"Rolling back all changes; the database will not be modified\\\")\\n db.rollback()\\n raise\",\n \"def file_record(db, user_name, original_file_name, target_user=None):\\n\\n record = db.get_file_record(user_name)\\n\\n if record is None:\\n db.create_file_record(user_name, target_user, original_file_name)\\n else:\\n db.update_file_record(user_name, target_user, original_file_name)\",\n \"def post(self):\\n source = 'uploaded by user'\\n upload_files = self.get_uploads('file')\\n blob_key = upload_files[0].key()\\n name = self.request.get('name')\\n\\n user = users.get_current_user()\\n\\n username = 'admin'\\n date = datetime.datetime.now()\\n str_blob_key = str(blob_key)\\n key = FileMetadata.get_key_name(username, date, str_blob_key)\\n\\n ctx = ndb.get_context()\\n meta = FileMetadata(key_name=key, parent=_PARENT)\\n meta.owner = user\\n meta.filename = name\\n meta.uploaded_on = date\\n meta.source = source\\n meta.blobkey = str_blob_key\\n meta.put()\\n ctx.clear_cache()\\n self.redirect('/admin')\",\n \"def test_record_update_file(appctx, db, record_with_file_processed, obj_name, content):\\n record = CernSearchRecord.get_record(record_with_file_processed.id)\\n initial_file_name = \\\"hello.txt\\\"\\n initial_file = record.files[initial_file_name].obj # type: ObjectVersion\\n initial_file_content = record.files_content[initial_file_name].obj # type: ObjectVersion\\n\\n assert 1 == len(record.files)\\n assert 1 == len(record.files_content)\\n assert initial_file.file.readable is False\\n assert initial_file.deleted is False\\n assert initial_file_content.file.readable is True\\n\\n record.files[obj_name] = BytesIO(content)\\n db.session.commit()\\n\\n # mimic file uploaded flow\\n file_uploaded.send(record.files[obj_name].obj)\\n\\n record = CernSearchRecord.get_record(record.id)\\n\\n assert record[\\\"_bucket\\\"] == record.bucket_id\\n assert record[\\\"_bucket_content\\\"] == record.bucket_content_id\\n\\n assert 1 == len(record.files)\\n assert 1 == len(record.files_content)\\n assert record.files[obj_name].obj.file.readable is False\\n assert initial_file_content.file.readable is False\\n\\n # different file upload creates a delete marker\\n if initial_file_name != obj_name:\\n with raises(KeyError):\\n record.files[initial_file_name]\\n with raises(KeyError):\\n record.files_content[initial_file_name]\\n\\n file_1 = record.files_content[obj_name]\\n assert obj_name == file_1[\\\"key\\\"]\\n\\n storage = file_1.obj.file.storage() # type: FileStorage\\n fp = storage.open(mode=READ_MODE_BINARY)\\n\\n try:\\n assert content.decode() in json.load(fp)[\\\"content\\\"]\\n finally:\\n fp.close()\",\n \"def decode_replay(replay_file_obj):\\n decoder = zstd.ZstdDecompressor()\\n # Rewind to the beginning of the file obj, because\\n # gcloud might have read it first\\n replay_file_obj.seek(0)\\n replay_data = replay_file_obj.read()\\n try:\\n decoded_data = decoder.decompress(replay_data)\\n json_data = json.loads(decoded_data.decode('utf-8').strip())\\n return json_data\\n except zstd.ZstdError:\\n # The replay file can't be decoded.\\n return None\\n finally:\\n # Seek the replay file back to start so we can upload it.\\n replay_file_obj.seek(0)\",\n \"def record_fileprep(data):\\n order_detail = OrderDetail.objects.get(ac_od_id=data['ac_od_id'])\\n order_detail.fileprep = data['fileprep']\\n return order_detail.save()\",\n \"def post(self, request, *args, **kwargs):\\n self.create_flow_file_db_entry()\\n self.handle_chunk(request)\\n return self.return_response(self.flow_file.identifier)\",\n \"def post(self):\\n\\n upload_files = self.get_uploads('file')\\n blob_info = upload_files[0]\\n self.redirect('/?upload_info=%s' % urllib.quote(blob_info.filename))\",\n \"def Open(self, file_object):\",\n \"def action_import(self):\\n ctx = self._context\\n attachment_obj = self.env['ir.attachment']\\n invoice_obj = self.env['account.invoice']\\n storage = attachment_obj._storage()\\n filestore = attachment_obj._filestore()\\n file_gc = attachment_obj._file_gc()\\n indir = self.name#+'/E-Faktur'\\n files_in_dir = os.listdir(indir)\\n in_dir = []\\n for x in files_in_dir:\\n r = open(indir+\\\"/\\\"+x,'rb').read().encode('base64')\\n _logger.info(\\\"_read_file reading %s\\\", x)\\n if len(x) == 67:\\n #_logger.info(\\\"_read_file valid file efaktur %s\\\", x)\\n faktur_pajak = x.split(\\\"-\\\")\\n #SEARCH INVOICE YG SUDAH TERFALIDASI DAN ADA FAKTUR PAJAK\\n invoice_ids = invoice_obj.search([('nomor_faktur_id','!=',None),('move_id','!=',None),('nomor_faktur_id.number','ilike',faktur_pajak[1][8:])])\\n #CARI APAKAH SUDAH TERATTACHMENT DI SISTEM\\n attachment_ids = attachment_obj.search([('datas','!=',r),('res_id','in',invoice_ids.ids),('res_model','=','account.invoice'),('name','=',faktur_pajak[1])])\\n if not attachment_ids and invoice_ids:\\n for invoice in invoice_ids:\\n values = {\\n 'res_model': 'account.invoice',\\n 'company_id': 1,\\n 'res_name': invoice.number,#NOMOR INVOICE\\n 'datas_fname': x,#NAMA FILE\\n 'type': 'binary',\\n 'res_id': invoice.id,\\n 'name': x,#faktur_pajak[1],\\n 'mimetype': 'application/pdf',\\n 'store_fname': 'E-Faktur/'+x,\\n 'datas': r,\\n }\\n attachment_obj.create(values)\\n _logger.info(\\\"_uploaded_file %s\\\", x)\",\n \"def _upload(self, variables):\\n required_vars = ['container', 'src', 'object']\\n variables_dict = self._get_vars(variables, required=required_vars)\\n\\n container_name = variables_dict.pop('container')\\n object_name = variables_dict.pop('object')\\n src_path = variables_dict.pop('src')\\n\\n self._create_container(container_name=container_name)\\n with open(src_path, 'rb') as f:\\n self.swift.put_object(container_name, object_name, contents=f)\\n\\n object_data = self.swift.head_object(container_name, object_name)\\n self.state_change = True\\n return self._facts(facts=[object_data])\",\n \"def parse_replaydata(self):\\n pass\",\n \"def upload():\\n # verify user\\n email = flask.request.args[\\\"email\\\"]\\n username = flask.request.args[\\\"username\\\"]\\n\\n file = flask.request.files[\\\"file\\\"]\\n print(file.filename)\\n file_bytestr = file.read()\\n\\n # query ms api\\n emotion = ms_emotion_api(file_bytestr)\\n print(emotion)\\n if emotion is None:\\n return flask.jsonify(error=\\\"MS API error, possibly no human face\\\")\\n\\n # save to mongodb\\n saved = mongo.db.images.insert_one({\\n \\\"filename\\\": file.filename,\\n \\\"content\\\": file_bytestr,\\n \\\"emotion\\\": emotion,\\n \\\"date\\\": datetime.datetime.utcnow(),\\n \\\"user_username\\\": username,\\n \\\"user_email\\\": email,\\n })\\n # print(saved.inserted_id)\\n # create user if needed\\n mongo.db.users.update_one(filter={\\n \\\"email\\\": email,\\n }, update={\\n \\\"$set\\\": {\\\"username\\\": username},\\n # image_ids: list of foreign ids to images\\n \\\"$push\\\": {\\\"image_ids\\\": saved.inserted_id},\\n }, upsert=True)\\n\\n # client resend image_id when reporting music\\n emotion[\\\"image_id\\\"] = str(saved.inserted_id)\\n return flask.jsonify(emotion)\",\n \"def process(self, object, from_file=None):\\n raise NotImplementedError()\",\n \"def _replay_coupon(self, update, context, current_train: Train, image_path):\\n self._reply_message(update, str(current_train))\\n with open(image_path, 'rb') as qr_image:\\n update.message.bot.send_chat_action(chat_id=update.effective_message.chat_id,\\n action=ChatAction.UPLOAD_PHOTO)\\n update.message.reply_photo(qr_image)\\n\\n context.user_data['last_train'] = current_train.to_dict()\",\n \"def upload_finish(self, cloud_file):\",\n \"def upload_file_obj_db_s3():\\n\\n # TODO: upload metadata to database\\n temp_engine = create_engine(NEX2_URI)\\n session_factory = sessionmaker(\\n bind=temp_engine, extension=ZopeTransactionExtension(), expire_on_commit=False)\\n db_session = scoped_session(session_factory)\\n readme_file_id = None\\n file_content_list = file_upload_to_obj()\\n try:\\n if file_content_list:\\n sorted_content = sorted(\\n file_content_list, key=itemgetter('file_extension'))\\n for item in sorted_content:\\n if item['readme_name']:\\n readme = db_session.query(Filedbentity).filter(\\n Filedbentity.display_name == obj['readme_name']).one_or_none()\\n\\n if readme is None:\\n logging.warning(\\n 'unable to find README ' + obj['readme_name'])\\n else:\\n readme_file_id = readme.dbentity_id\\n\\n # see if file_meta already exists, else create\\n existing_file_meta_data = db_session.query(Filedbentity).filter(\\n Filedbentity.display_name == item['display_name']).one_or_none()\\n source_id = db_session.query(Source.source_id).filter(\\n Source.display_name == item['source']).one_or_none()[0]\\n\\n d_name = item['display_name']\\n f_ext = item['file_extension']\\n temp_file_path = get_file_from_path_collection(f_ext, d_name)\\n\\n if not existing_file_meta_data:\\n try:\\n data_id = db_session.query(Edam.edam_id).filter(\\n Edam.edamid == item['data_edam_id']).one_or_none()[0]\\n\\n format_id = db_session.query(Edam.edam_id).filter(\\n Edam.edamid == item['format_edam_id']).one_or_none()[0]\\n topic_id = db_session.query(Edam.edam_id).filter(\\n Edam.edamid == item['topic_edam_id']).one_or_none()[0]\\n item[\\\"data_id\\\"] = data_id\\n item[\\\"format_id\\\"] = format_id\\n item[\\\"topic_id\\\"] = topic_id\\n item[\\\"source_id\\\"] = source_id\\n item[\\\"readme_file_id\\\"] = readme_file_id\\n\\n except TypeError:\\n logging.error(\\n 'invalid EDAM id or source in row ' +\\n str(row_num) + ' val in ' + item['data_edam_id'] +\\n ', ' + item['format_edam_id'] +\\n ', ' + item['topic_edam_id'])\\n\\n if temp_file_path:\\n with open(temp_file_path, 'r') as remote_file:\\n upload_file_helper(CREATED_BY, remote_file, item)\\n\\n db_session.flush()\\n else:\\n existing_file_meta_data.display_name = item['display_name']\\n existing_file_meta_data.description = item['description']\\n existing_file_meta_data.status = item['status']\\n existing_file_meta_data.is_public = item['is_public']\\n existing_file_meta_data.is_in_spell = item['is_in_spell']\\n existing_file_meta_data.is_in_browser = item['is_in_browser']\\n existing_file_meta_data.source_id = source_id\\n\\n if temp_file_path:\\n with open(temp_file_path, 'r') as remote_file:\\n #update file size\\n if not existing_file_meta_data.file_size and existing_file_meta_data.s3_url:\\n remote_file.seek(0, os.SEEK_END)\\n file_size = remote_file.tell()\\n remote_file.seek(0)\\n existing_file_meta_data.file_size = file_size\\n\\n if item['file_date']:\\n existing_file_meta_data.file_date = item['file_date']\\n existing_file_meta_data.year = item['file_date'].year\\n existing_file_meta_data.readme_file_id = readme_file_id\\n remote_file.seek(0, os.SEEK_END)\\n\\n #transaction.commit()\\n existing_file_meta_data = db_session.query(Filedbentity).filter(\\n Filedbentity.display_name == item['display_name']).one_or_none()\\n # only upload s3 file if not defined\\n if existing_file_meta_data.s3_url is None:\\n existing_file_meta_data.upload_file_to_s3(\\n remote_file, item['display_name'])\\n db_session.flush()\\n\\n except Exception as e:\\n logging.error(\\\"Exception occurred\\\", exc_info=True)\",\n \"def parseUpload(dbconnection, fileName):\\n nhContent = ParseText.nohupTranscriptionContent(fileName)\\n count = 0\\n while count < len(nhContent[0]):\\n try:\\n rtf = nhContent[0][count]\\n transcription = nhContent[1][count].replace(\\\"'\\\", \\\"''\\\").replace(\\\"_\\\", \\\"\\\")\\n dbID = nhContent[2][count].replace(\\\".\\\", \\\"\\\")\\n duration = nhContent[3][count]\\n DatabaseInteract.insertTranscription(dbconnection, rtf, transcription, duration, dbID)\\n count += 1\\n except:\\n print(\\\"couldnt upload one at index \\\" + str(count))\\n count += 1\",\n \"def remote_archiveUpload(self, talk_id, upload_id, role):\\n source = yield self.getUpload(upload_id)\\n extension = source.splitext()[1]\\n\\n # TODO: Check if the talk identified by talk_id exists and bind the\\n # document to it.\\n\\n # TODO: Validate the given ``role`` argument (either strictly against a\\n # list of known roles or loosely for sanity).\\n\\n # 2. Construct the final pathname\\n version_id = ObjectId()\\n basename = str(version_id) + extension\\n destination = settings.data_root.child(talk_id).child(role)\\n if not destination.exists():\\n destination.makedirs()\\n destination = destination.child(basename)\\n\\n # 3. move the file to its destination\\n yield threads.deferToThread(source.moveTo, destination)\\n\\n # 2. Save the info to the database\\n asset = Asset(\\n _id=version_id,\\n archiver_id=self.getID(),\\n talk_id=talk_id,\\n role=role\\n )\\n version = AssetVersion(\\n version_id=version_id,\\n filename=destination\\n )\\n asset.versions.append(version)\\n\\n yield asset.save()\\n\\n # 5. Start the upload triggers\\n task = self.processAsset(asset)\\n\\n # TODO: Define the return value of this method. Shall it be the task,\\n # the version_id/asset_id or both?\\n defer.returnValue((str(version_id), task.id))\",\n \"def run(self):\\n # Etapas de execucao:\\n # 1 - Baixar o arquivo com os dados\\n\\n # First we will need a file containing a list of files\\n self.log('Loading file list %s' % self.file_url)\\n\\n # Check if we are forcing some URL\\n if self.file_url is None:\\n self.log( 'You have to supply the file list. \\\\nFile list: %s' % self.file_url)\\n return\\n\\n else:\\n self.log( 'Loading file %s' % self.file_url.rstrip())\\n self.response = self.send(self.file_url.rstrip())\\n\\n # Store the file and its hash somewhere\\n exists = self.hash_control()\\n if exists:\\n # if The hash exists, abort operation\\n return\\n\\n # 2 - Fazer o parsing do arquivo e armazenar cada registro como um\\n # recurso no Ckan em formato RDF. Os campos devem ser armazenados como\\n # metadados do recurso\\n r = rdf.lightbaseParser()\\n\\n # Tenta abrir o arquivo e fazer o parsing. Se houver erro rejeita e loga\\n try:\\n # Deve retornar uma coleção de registros para armazenamento\\n registros = r.collection(self.response)\\n except:\\n # Armazena os arquivos que não foram importados com sucesso\\n file_dict = {\\n 'tmpfile': self.response,\\n 'filename': self.response,\\n 'errmsg' : traceback.format_exc(),\\n 'error_type' : 'FileCollectionError',\\n 'package_file' : self.response\\n }\\n self.log_error(file_dict)\\n\\n for registro in registros.get('registros'):\\n self.datastore(registro['base_name'],registro['rdf_identifier'],registro['rdf_collection'],registro['metadata'],'rdf')\\n #print('2222222222222222222222222222: %s' % registro['rdf_identifier'])\\n\\n # Log import errors\\n for registro in registros['import_error']:\\n self.log_error(registro)\\n #print('111111111111111111111111111111: %s' % registro)\",\n \"def upload(self, file_obj):\\n file_path = ''\\n file_name = file_obj.filename\\n file_class, file_type = file_obj.content_type.split('/')\\n\\n def allowed_file():\\n return '.' in file_name and file_name.split('.')[1] in ALLOWED_EXTENSIONS\\n\\n try:\\n log.debug('Try to save file <%s> for user ID: %s', file_name, self.current_user.login)\\n\\n if not allowed_file():\\n log.debug('Filetype not allowed')\\n return {'success': False, 'errorMessage': 'Filetype not allowed'}\\n\\n upload_dir = os.path.join(UPLOAD_FOLDER, self.current_user.login)\\n file_path = os.path.join(upload_dir, file_name)\\n\\n if os.path.isfile(file_path):\\n log.debug('File was uploaded already')\\n return {'success': False, 'errorMessage': 'File was uploaded already'}\\n\\n if not os.path.exists(upload_dir):\\n log.debug('--> Create path: %s', upload_dir)\\n os.makedirs(upload_dir)\\n\\n # save in File System\\n with open(file_path, \\\"ab\\\") as f:\\n data = file_obj.body\\n f.write(bytes(data))\\n\\n os_f_size = os.stat(file_path).st_size\\n\\n # check file quota\\n if (self.user_api.user_db.used_file_quota + os_f_size) > self.user_api.user_db.file_quota:\\n os.remove(file_path)\\n log.error('You don\\\\'t have empty space!')\\n return {'success': False, 'errorMessage': 'You don\\\\'t have empty space!'}\\n\\n file_db = FileDB()\\n file_db.name = file_name\\n file_db.type = file_type\\n file_db.f_class = file_class\\n file_db.size = os_f_size\\n file_db.user_id = self.current_user.id\\n file_db.date_load = datetime.now().strftime(DATE_FORMAT)\\n\\n self.db.create(file_db)\\n\\n log.debug('--> File has been updated in DB.')\\n\\n # update user\\n self.user_api.user_db.used_file_quota += os.stat(file_path).st_size # bytes\\n #self.user_api.db.update(self.user_api.user_db)\\n\\n self.db.commit()\\n self.user_api.db.commit()\\n\\n log.debug('--> User in DB has been updated.')\\n\\n return {'success': True, 'id': file_db.id}\\n except StandardError:\\n self.db.session.rollback()\\n if os.path.isfile(file_path):\\n log.error('File <%s> has been deleted', file_path)\\n os.remove(file_path)\\n log.exception('Cannot upload file')\\n return SERVER_ERROR\",\n \"def run(self):\\n\\t\\tlogger.info(\\\"Uploading data... @ %f, PID: %d\\\" % (time.time(), os.getpid()))\\n\\n\\t\\tself.dump_db()\",\n \"def put(self):\\n if 'file' not in self.request.POST:\\n self.request.errors.add('body', 'file', 'Not Found')\\n self.request.errors.status = 404\\n return\\n tender = TenderDocument.load(self.db, self.tender_id)\\n if not tender:\\n self.request.errors.add('url', 'tender_id', 'Not Found')\\n self.request.errors.status = 404\\n return\\n data = self.request.POST['file']\\n bids = [i for i in tender.bids if i.id == self.bid_id]\\n if not bids:\\n self.request.errors.add('url', 'bid_id', 'Not Found')\\n self.request.errors.status = 404\\n return\\n bid = bids[0]\\n documents = [i for i in bid.documents if i.id == self.request.matchdict['id']]\\n if not documents:\\n self.request.errors.add('url', 'id', 'Not Found')\\n self.request.errors.status = 404\\n return\\n src = tender.serialize(\\\"plain\\\")\\n document = Document()\\n document.id = self.request.matchdict['id']\\n document.title = data.filename\\n document.format = data.type\\n document.datePublished = documents[0].datePublished\\n key = uuid4().hex\\n document.url = self.request.route_url('Tender Bid Documents', tender_id=self.tender_id, bid_id=self.bid_id, id=document.id, _query={'download': key})\\n bid.documents.append(document)\\n filename = \\\"{}_{}\\\".format(document.id, key)\\n tender['_attachments'][filename] = {\\n \\\"content_type\\\": data.type,\\n \\\"data\\\": b64encode(data.file.read())\\n }\\n patch = make_patch(tender.serialize(\\\"plain\\\"), src).patch\\n tender.revisions.append(revision({'changes': patch}))\\n try:\\n tender.store(self.db)\\n except Exception, e:\\n return self.request.errors.add('body', 'data', str(e))\\n return {'data': document.serialize(\\\"view\\\")}\",\n \"def webhook_upload(user, application, complete_path, init_es, tool, scan_name, user_host, to_name,hook_log):\\n hook_log = WebhookLog.objects.get(id=hook_log)\\n hook_log.file_upload_event = True\\n hook_log.file_upload_datetime = timezone.now()\\n hook_log.save()\\n process_files(user, application, complete_path, init_es, tool, scan_name, user_host, to_name,hook_log=hook_log)\\n info_debug_log(event='Webhook upload',status='success')\",\n \"def ftp_RETR(self, file):\\n parsedform = parse_chunks(file)\\n if parsedform:\\n filename, framenum, chunks, user_or_cache = parsedform\\n each_chunk_size = self.movie_LUT.chunk_size_lookup(filename)\\n\\n ## Check ID & Log appropriately\\n if user_or_cache == 1:\\n log_load('user', int(each_chunk_size) * len(chunks))\\n else:\\n log_load('cache', int(each_chunk_size) * len(chunks))\\n\\n try:\\n # filename should be prefixed by \\\"file-\\\" in order to be valid.\\n # frame number is expected to exist for this cache.\\n chunksdir = 'video-' + filename\\n framedir = filename + '.' + framenum + '.dir'\\n path = self.movies_path + '/' + chunksdir + '/' + framedir\\n # get chunks list and open up all files\\n files = self.get_chunk_files(path, chunks)\\n\\n # if DEBUGGING_MSG:\\n # print \\\"chunks requested:\\\", chunks\\n # print 'chunksdir', chunksdir\\n # print 'framedir', framedir\\n # print 'path', path\\n except OSError, err:\\n why = ftpserver._strerror(err)\\n self.respond('550 %s.' % why)\\n\\n producer = self.chunkproducer(files, self._current_type)\\n self.push_dtp_data(producer, isproducer=True, file=None, cmd=\\\"RETR\\\")\\n return\",\n \"def handle_file(self):\\n query_cache_response = self.check_av_cache()\\n cache_status_label = query_cache_response['response'][0]['status']['label']\\n if cache_status_label == \\\"FOUND\\\":\\n print(\\\"Results already exist in AV cache for file {}\\\".format(self.file_name))\\n self.final_response = query_cache_response\\n self.final_status_label = cache_status_label\\n else:\\n print(\\\"No results in AV cache before upload for file {}\\\".format(self.file_name))\\n upload_response = self.upload_file()\\n upload_status_label = upload_response[\\\"response\\\"][0][\\\"status\\\"][\\\"label\\\"]\\n if upload_status_label == \\\"UPLOAD_SUCCESS\\\":\\n query_response = self.query_file()\\n query_status_label = query_response[\\\"response\\\"][0][\\\"status\\\"][\\\"label\\\"]\\n print(\\\"Receiving Query response with av results for file {}. status: {}\\\".format(self.file_name,\\n query_status_label))\\n self.final_response = query_response\\n self.final_status_label = query_status_label\\n else:\\n self.final_response = upload_response\\n self.final_status_label = upload_status_label\\n self.create_response_info(self.final_response)\\n if self.final_status_label == \\\"FOUND\\\":\\n signature = self.final_response[\\\"response\\\"][0][\\\"av\\\"][\\\"malware_info\\\"][\\\"signature_name\\\"]\\n if signature:\\n print(\\\"File {} was found malicious by AV. Signature : {}\\\".format(self.file_name, signature))\\n else:\\n print(\\\"File {} was found clean by AV\\\".format(self.file_name))\",\n \"def __handle_file_received(self, event, sender, data):\\n path = os.path.join(\\\"images\\\", f\\\"tello-{get_time_stamp()}.jpg\\\")\\n with open(path, \\\"wb\\\") as out_file:\\n out_file.write(data)\\n print(f\\\"Saved photo to {path}\\\")\",\n \"def object_import(request, simulation, object_name):\\n try:\\n if object_name == 'function':\\n parent = simulation.scenario.supply.functionset\\n else:\\n parent = simulation.scenario.supply.network\\n query = get_query(object_name, simulation)\\n user_id_set = set(query.values_list('user_id', flat=True))\\n if object_name == 'link':\\n # To import links, we retrieve the user ids of all centroids, crossings\\n # and functions and we build mappings between ids and objects.\\n centroids = get_query('centroid', simulation)\\n centroid_ids = set(centroids.values_list('user_id', flat=True))\\n crossings = get_query('crossing', simulation)\\n crossing_ids = set(crossings.values_list('user_id', flat=True))\\n node_ids = centroid_ids.union(crossing_ids)\\n # Mapping between the user id and the id of the nodes.\\n node_mapping = dict()\\n for centroid in centroids:\\n node_mapping[centroid.user_id] = centroid.id\\n for crossing in crossings:\\n node_mapping[crossing.user_id] = crossing.id\\n functions = get_query('function', simulation)\\n function_ids = set(functions.values_list('user_id', flat=True))\\n # Mapping between the user id and the id of the functions.\\n function_id_mapping = dict()\\n # Mapping between the user id and the instance of the functions\\n function_mapping = dict()\\n for function in functions:\\n function_id_mapping[function.user_id] = function.id\\n function_mapping[function.user_id] = function\\n # Convert imported file to a csv DictReader.\\n encoded_file = request.FILES['import_file']\\n tsv_file = StringIO(encoded_file.read().decode())\\n reader = csv.DictReader(tsv_file, delimiter='\\\\t')\\n to_be_updated = set()\\n to_be_created = list()\\n # Store the user_id of the imported instance to avoid two instances\\n # with the same id.\\n imported_ids = set()\\n if object_name == 'centroid':\\n # Do not import centroid with same id as a crossing.\\n crossings = get_query('crossing', simulation)\\n imported_ids = set(crossings.values_list('user_id', flat=True))\\n for row in reader:\\n id = int(row['id'])\\n if not id in imported_ids:\\n imported_ids.add(id)\\n if id in user_id_set:\\n to_be_updated.add(\\n (id, row['name'], float(row['x']),\\n float(row['y']))\\n )\\n else:\\n to_be_created.append(\\n Centroid(user_id=id, name=row['name'],\\n x=float(row['x']), y=float(row['y']))\\n )\\n elif object_name == 'crossing':\\n # Do not import crossing with same id as a centroid.\\n centroids = get_query('centroid', simulation)\\n imported_ids = set(centroids.values_list('user_id', flat=True))\\n for row in reader:\\n id = int(row['id'])\\n if not id in imported_ids:\\n imported_ids.add(id)\\n if id in user_id_set:\\n to_be_updated.add(\\n (id, row['name'], float(row['x']),\\n float(row['y']))\\n )\\n else:\\n to_be_created.append(\\n Crossing(user_id=id, name=row['name'],\\n x=float(row['x']), y=float(row['y']))\\n )\\n elif object_name == 'function':\\n for row in reader:\\n id = int(row['id'])\\n if not id in imported_ids:\\n imported_ids.add(id)\\n if id in user_id_set:\\n to_be_updated.add(\\n (id, row['name'], row['expression'])\\n )\\n else:\\n to_be_created.append(\\n Function(user_id=id, name=row['name'],\\n expression=row['expression'])\\n )\\n elif object_name == 'link':\\n for row in reader:\\n id = int(row['id'])\\n if not id in imported_ids:\\n imported_ids.add(id)\\n if id in user_id_set:\\n to_be_updated.add(\\n (id, row['name'],\\n node_mapping[int(row['origin'])],\\n node_mapping[int(row['destination'])],\\n function_id_mapping[int(row['function'])],\\n float(row['lanes']), float(row['length']),\\n float(row['speed']), float(row['capacity']))\\n )\\n else:\\n if int(row['origin']) in node_ids \\\\\\n and int(row['destination']) in node_ids \\\\\\n and int(row['function']) in function_ids:\\n # Ignore the links with unidentified origin,\\n # destination or function.\\n to_be_created.append(\\n Link(user_id=id, name=row['name'],\\n origin=node_mapping[int(row['origin'])],\\n destination=node_mapping[int(row['destination'])],\\n vdf=function_mapping[int(row['function'])],\\n lanes=float(row['lanes']),\\n length=float(row['length']),\\n speed=float(row['speed']),\\n capacity=float(row['capacity']))\\n )\\n if to_be_updated:\\n if object_name in ('centroid', 'crossing'):\\n values = set(query.values_list('user_id', 'name', 'x', 'y'))\\n elif object_name == 'function':\\n values = set(query.values_list('user_id', 'name', 'expression'))\\n elif object_name == 'link':\\n values = set(query.values_list('user_id', 'name', 'origin',\\n 'destination', 'vdf_id', 'lanes',\\n 'length', 'speed', 'capacity'))\\n # Find the instances that really need to be updated (the values have\\n # changed).\\n to_be_updated = to_be_updated.difference(values)\\n if object_name in ('centroid', 'crossing', 'function'):\\n # Update the objects (it would be faster to delete and re-create\\n # them but this would require to also change the foreign keys of\\n # the links).\\n for values in to_be_updated:\\n # Index 0 of values is the id column i.e. the user_id.\\n instance = query.filter(user_id=values[0])\\n if object_name in ('centroid', 'crossing'):\\n instance.update(name=values[1], x=values[2], y=values[3])\\n else: # Function\\n instance.update(name=values[1], expression=values[2])\\n elif object_name == 'link':\\n # Delete the links and re-create them.\\n ids = list(query.values_list('id', 'user_id'))\\n # Create a mapping between the user ids and the ids.\\n id_mapping = dict()\\n for i in range(len(values)):\\n id_mapping[ids[i][1]] = ids[i][0]\\n # Retrieve the ids of the links to be updated with the mapping and\\n # delete them.\\n to_be_updated_ids = [id_mapping[values[0]]\\n for values in to_be_updated]\\n with connection.cursor() as cursor:\\n chunk_size = 20000\\n chunks = [\\n to_be_updated_ids[x:x + chunk_size]\\n for x in range(0, len(to_be_updated_ids), chunk_size)\\n ]\\n for chunk in chunks:\\n # Delete the relations first.\\n cursor.execute(\\n \\\"DELETE FROM Network_Link \\\"\\n \\\"WHERE link_id IN %s;\\\",\\n [chunk]\\n )\\n cursor.execute(\\n \\\"DELETE FROM Link \\\"\\n \\\"WHERE id IN %s;\\\",\\n [chunk]\\n )\\n # Create a mapping between the id and the instance of the\\n # functions.\\n function_mapping = dict()\\n for function in functions:\\n function_mapping[function.id] = function\\n # Now, create the updated instances with the new values.\\n to_be_created += [\\n Link(user_id=values[0], name=values[1], origin=values[2],\\n destination=values[3], vdf=function_mapping[values[4]],\\n lanes=values[5], length=values[6], speed=values[7],\\n capacity=values[8])\\n for values in to_be_updated\\n ]\\n # Create the new objects in bulk.\\n # The chunk size is limited by the MySQL engine (timeout if it is too big).\\n chunk_size = 10000\\n chunks = [to_be_created[x:x + chunk_size]\\n for x in range(0, len(to_be_created), chunk_size)]\\n # Remove the orphan instances.\\n if object_name == 'function':\\n query.model.objects \\\\\\n .exclude(functionset__in=FunctionSet.objects.all()) \\\\\\n .delete()\\n else:\\n query.model.objects.exclude(network__in=Network.objects.all()).delete()\\n for chunk in chunks:\\n # Create the new instances.\\n query.model.objects.bulk_create(chunk, chunk_size)\\n # Retrieve the newly created instances and add the many-to-many\\n # relation.\\n # Add the many-to-many relation.\\n if object_name == 'function':\\n new_instances = query.model.objects \\\\\\n .exclude(functionset__in=FunctionSet.objects.all())\\n for instance in new_instances:\\n instance.functionset.add(parent)\\n else:\\n new_instances = query.model.objects \\\\\\n .exclude(network__in=Network.objects.all())\\n for instance in new_instances:\\n instance.network.add(parent)\\n simulation.has_changed = True\\n simulation.save()\\n return HttpResponseRedirect(\\n reverse('metro:object_list', args=(simulation.id, object_name,))\\n )\\n except Exception as e:\\n print(e)\\n context = {\\n 'simulation': simulation,\\n 'object': object_name,\\n }\\n return render(request, 'metro_app/import_error.html', context)\",\n \"def createFileRecordOneChunk(original_filename, file_id, file_hash, user_path, file_size, catalog_id, server_ip, server_port):\\n session = Queries.createSession()\\n try:\\n fileserver = session.query(FileServer).filter_by(ip=server_ip, port=server_port).first()\\n new_file = FileTable(original_filename, file_id, file_hash, user_path, file_size, 0, catalog_id)\\n new_file.server_id.append(fileserver)\\n session.add(new_file)\\n session.commit()\\n except sqlalchemy.exc.ArgumentError:\\n print 'SQLAlchemy ERROR: Invalid or conflicting function argument is supplied'\\n except sqlalchemy.exc.CompileError:\\n print 'SQLAlchemy ERROR: Error occurs during SQL compilation'\\n finally:\\n session.close()\",\n \"def processRecord(record):\\n event_name = record['eventName']\\n bucket = record['s3']['bucket']['name']\\n key = record['s3']['object']['key']\\n blob_id = key.replace('{}/'.format(os.environ['S3_KEY_BASE']), '')\\n\\n if 'ObjectCreated:Put' == event_name:\\n try:\\n blob = BlobModel.get(hash_key=blob_id)\\n blob.mark_uploaded()\\n labels = getImageLabels(bucket, key)\\n blob.update_state_to_processed_and_add_labels(labels)\\n except UpdateError:\\n logger.exception('Unable to update blob')\\n\\n except botocore.exceptions.ClientError as e:\\n logger.exception('Client provided a bad image')\\n blob.set_rekognition_error_and_mark_processed(str(e))\\n\\n except DoesNotExist:\\n logger.exception('Blob does not exist')\",\n \"def upload(self):\\n # TODO: Should CD to the working directory set by the robscript.\\n src = self.state_frame[0]\\n dest = self.state_frame[1]\\n self.send_upload(src, dest, True, None)\\n self.state = STATE_READ_LINE\",\n \"def process_log_file(cur, filepath):\\n \\n # open log file\\n \\n df = pd.read_json(filepath, lines = True)\\n \\n # filter by NextSong action\\n df = df[df['page']=='NextSong']\\n # convert timestamp column to datetime\\n t = pd.to_datetime(df.ts, unit='ms')\\n df.ts = t\\n \\n # insert time data records\\n time_data = [t, t.dt.hour, t.dt.day, t.dt.weekofyear,\\n t.dt.month, t.dt.year, t.dt.weekday]\\n \\n # column_labels = ['timestamp','Hour', \\n # 'Day','Month','Year''Weekday']'\\n column_labels = ['timestamp','hour','day','weekofyear','month','year','weekday']\\n time_df = pd.DataFrame(dict(zip(column_labels, time_data)))\\n\\n for i, row in time_df.iterrows():\\n cur.execute(time_table_insert, list(row))\\n \\n # load user table\\n user_df = df[['userId','firstName', \\n 'lastName','gender','level']]\\n\\n # insert user records\\n for i, row in user_df.iterrows(): \\n cur.execute(user_table_insert, row)\\n \\n # insert songplay records\\n for index, row in df.iterrows():\\n \\n # get songid and artistid from song and artist tables\\n cur.execute(song_select, (row.song, row.artist,\\n row.length))\\n results = cur.fetchone()\\n \\n if results:\\n songid, artistid = results\\n else:\\n songid, artistid = None, None\\n\\n # insert songplay record\\n songplay_data = (index, row.ts, row.userId, row.level,\\n songid, artistid, row.sessionId, \\n row.location, row.userAgent)\\n \\n \\n cur.execute(songplay_table_insert, songplay_data)\",\n \"def file_upload_to_obj():\\n\\n temp = []\\n file_content = pd.read_excel(INPUT_FILE_NAME).fillna(0).to_dict('records')\\n sorted_content = sorted(file_content, key=itemgetter(\\n 'filedbentity.file_extension'))\\n for item in file_content:\\n\\n raw_date = item.get('filedbentity.file_date')\\n if raw_date:\\n temp_date = raw_date.strftime('%Y-%m-%d')\\n raw_date = datetime.strptime(temp_date, \\\"%Y-%m-%d\\\").date()\\n else:\\n raw_date = datetime.now().date()\\n\\n raw_status = item.get('dbentity.status')\\n if raw_status == 'Archive':\\n raw_status = 'Archived'\\n \\n obj = {\\n 'path': item.get('EBS path'),\\n 'display_name': item.get('dbentity.display_name'),\\n 'status': raw_status,\\n 'source': item.get('dbentity.source'),\\n 'topic_edam_id': item.get('topic edam_id').upper().replace('TOPIC', 'EDAM').strip(),\\n 'data_edam_id': item.get('data edam_id').upper().replace('DATA', 'EDAM').strip(),\\n 'format_edam_id': item.get('format edam_id').upper().replace('FORMAT', 'EDAM').strip(),\\n 'file_extension': item.get('filedbentity.file_extension'),\\n 'file_date': raw_date,\\n 'is_public': (item.get('filedbentity.is_public') == '1'),\\n 'is_in_spell': item.get('filedbentity.is_in_spell'),\\n 'is_in_browser': (item.get('filedbentity.is_in_browser') == '1'),\\n 'readme_name': item.get('readme name'),\\n 'description': item.get('filedbentity.description'),\\n 'pmids': item.get('pmids (|)'),\\n 'keywords': item.get('keywords (|)')\\n }\\n temp.append(obj)\\n\\n if len(temp) > 0:\\n return temp\\n return None\",\n \"def record(self):\\n # TODO: record the data\",\n \"def action_import(self):\\n ctx = self._context\\n \\n data = base64.b64decode(self.data)\\n file_input = cStringIO.StringIO(data)\\n file_input.seek(0)\\n reader_info = []\\n if self.delimeter:\\n delimeter = str(self.delimeter)\\n else:\\n delimeter = ','\\n reader = csv.reader(file_input, delimiter=delimeter,\\n lineterminator='\\\\r\\\\n')\\n try:\\n reader_info.extend(reader)\\n except Exception:\\n raise exceptions.Warning(_(\\\"Not a valid file!\\\"))\\n keys = reader_info[0]\",\n \"def save_raw_data(request):\\n body = json.loads(request.body)\\n import_file_id = body.get('file_id')\\n if not import_file_id:\\n return {'status': 'error'}\\n\\n return task_save_raw(import_file_id)\",\n \"def process_log_file(cursor, filepath):\\n\\n def get_timestamp_data(df):\\n # convert timestamp column to datetime\\n timestamp = pd.to_datetime(df['ts'], unit='ms')\\n\\n return (df['ts'].values,\\n timestamp.dt.hour.values,\\n timestamp.dt.day.values,\\n timestamp.dt.week.values,\\n timestamp.dt.month.values,\\n timestamp.dt.year.values,\\n timestamp.dt.weekday.values)\\n\\n # open log file\\n df = pd.read_json(filepath, lines=True)\\n\\n # filter by NextSong action\\n df = df[df['page'] == 'NextSong']\\n\\n # insert time data records\\n time_data = get_timestamp_data(df)\\n column_labels = ('timestamp', 'hour', 'day', 'week', 'month', 'year', 'weekday')\\n time_df = pd.DataFrame(data=dict(zip(column_labels, time_data)))\\n\\n for i, row in time_df.iterrows():\\n cursor.execute(time_table_insert, list(row))\\n\\n # load user table\\n user_columns = ['userId', 'firstName', 'lastName', 'gender', 'level']\\n user_df = df[user_columns]\\n\\n # insert user records\\n for i, row in user_df.iterrows():\\n cursor.execute(user_table_insert, row)\\n\\n # insert songplay records\\n for index, row in df.iterrows():\\n\\n # get song_id and artist_id from song and artist tables\\n cursor.execute(song_select, (row.song, row.artist, row.length))\\n results = cursor.fetchone()\\n\\n if results:\\n song_id, artist_id = results\\n else:\\n song_id, artist_id = None, None\\n\\n # insert songplay record\\n songplay_data = (\\n row['ts'], row['userId'], row['level'], song_id, artist_id, row['sessionId'], row['location'],\\n row['userAgent'])\\n cursor.execute(songplay_table_insert, songplay_data)\",\n \"def completed_file(self, context):\",\n \"def pipeline(self):\\n\\n self._get_data()\\n self._upload_to_raw()\",\n \"def load_batch(self, request, *args, **kwargs):\\n try:\\n # get a list of the files in the associated path\\n base_path = self.request.user.profile.VideoExperiment_path\\n file_list = listdir(base_path)\\n # include only csv files\\n file_list = [el[:-4] for el in file_list if ('.csv' in el) and ('sync' not in el)]\\n # get a list of the existing file names (bonsai)\\n existing_rows = [el[0] for el in VideoExperiment.objects.values_list('slug')]\\n # for all the files\\n for file in file_list:\\n # check if the entry already exists\\n if file.lower() in existing_rows:\\n # if so, skip making a new one\\n continue\\n # get the data for the entry\\n data_dict = parse_path_experiment(file, self, 'VideoExperiment_path')\\n # get rid of the animal2 entry\\n del data_dict['animal2']\\n # of the screen one\\n del data_dict['screen_path']\\n # and of the motive one\\n del data_dict['track_path']\\n # check the paths in the filesystem, otherwise leave the entry empty\\n for key, value in data_dict.items():\\n # if the entry is already empty, don't check\\n if data_dict[key] == '':\\n continue\\n if (isinstance(value, str)) and ('path' in key) and (not exists(value)):\\n # print a warning\\n print('Path not found for key %s and value %s' % (key, value))\\n # clear the path\\n data_dict[key] = ''\\n\\n # # if the tif file exists but the calcium_data file doesn't, log it in the notes\\n # This is for when we didn't have calcium signal extraction as part of snakemake\\n # if (data_dict['fluo_path'] == '') and (data_dict['tif_path'] != ''):\\n # data_dict['imaging'] = 'no'\\n # data_dict['notes'] += 'norois'\\n # create the model instance with the data\\n model_instance = VideoExperiment.objects.create(**data_dict)\\n # get the model for the experiment type to use\\n experiment_type = ExperimentType.objects.filter(experiment_name='Free_behavior')\\n # add the experiment type to the model instance (must use set() cause m2m)\\n model_instance.experiment_type.set(experiment_type)\\n # save the model instance\\n model_instance.save()\\n\\n return HttpResponseRedirect('/loggers/video_experiment/')\\n except:\\n print('Problem file:' + file)\\n return HttpResponseBadRequest('loading file %s failed, check file names' % file)\",\n \"def _file_data(self, message, data, is_external=True):\\n message.file = o.File(data)\\n if data.get('is_starred'):\\n message.is_starred = True\\n\\n if is_external:\\n logging.debug(\\\"Found external file `%s'\\\", data['url_private'])\\n message.file.url = data['url_private']\\n else:\\n logging.debug(\\\"Found internal file `%s'\\\",\\n data['url_private_download'])\\n priv_url = data['url_private_download']\\n message.file.filepath = self.downloader.download(priv_url, 'file')\\n self.session.add(message.file)\",\n \"def upload_file():\\n \\n #query the db and render the table used to display the leaderboard to users \\n userBoard = query_db('''\\n select submission_id, submit_date, public_score\\n from submission sub\\n where user_id = '%s'\\n order by public_score %s''' % (session['user_id'], orderBy))\\n \\n userBoard = [dict(row) for row in userBoard]\\n for row in userBoard:\\n row['score'] = row['public_score']\\n row['str_time'] = str(datetime.fromtimestamp(row['submit_date']))\\n \\n colNames = ['Submission Time', 'Public Score']\\n \\n if request.method == 'POST':\\n try:\\n #check if contest has ended\\n if contestEndBool():\\n flash(\\\"Error: contest has ended\\\")\\n raise Exception(\\\"contest has ended\\\")\\n \\n print(\\\"here\\\")\\n #ensure user hasn't exceeded daily submission limit\\n dailyCnt = query_db('''select count(*) sub_cnt\\n from submission sub\\n where submit_date > %s\\n and user_id = %s\\n group by user_id''' % (time.time() - 60*60*24, session['user_id']))\\n \\n if len(dailyCnt) == 0:\\n dailyCnt = 0\\n else:\\n dailyCnt = int(dict(dailyCnt[0])['sub_cnt'])\\n \\n if dailyCnt > dailyLimit:\\n flash(\\\"Error: exceeded daily upload limit\\\")\\n raise Exception('Upload limit exceeded')\\n \\n file = request.files['file']\\n #throw error if extension is not allowed\\n if not allowed_file(file.filename):\\n raise Exception('Invalid file extension')\\n \\n if file and allowed_file(file.filename):\\n filename = werkzeug.secure_filename(file.filename)\\n #append userid and date to file to avoid duplicates\\n filename = str(session['user_id']) + '_' + \\\\\\n str(int(time.time())) + '_' + filename\\n fullPath = os.path.join(app.config['UPLOAD_FOLDER'], filename)\\n file.save(fullPath)\\n model_score = loadAndScore(fullPath)\\n \\n #cache the filename and submission to database\\n db = get_db()\\n db.execute('''insert into submission (user_id, filename, submit_date, \\n public_score, private_score, total_score) \\n values (?, ?, ?, ?, ?, ?)''', \\n (session['user_id'], filename, int(time.time()), *model_score))\\n db.commit()\\n \\n #inform user upload was a success\\n flash('Your submission was recorded.')\\n return redirect(url_for('leaderboard'))\\n except:\\n #if exception is thrown in process then flash user\\n flash('File did not upload or score! Make sure the submission format is correct.')\\n return render_template('uploadsubmission.html', \\n title=\\\"Upload Submission\\\", \\n userBoard=userBoard,\\n subNbr=subNbr)\",\n \"def process_player_file(file_dir, filename):\\n full_filename = os.path.join(file_dir, filename)\\n start_time = time.time()\\n log.info(\\\"processing player file %s\\\" % full_filename)\\n \\n # run the command\\n try:\\n sql = (r\\\"LOAD DATA LOCAL INFILE '%s' INTO TABLE player_audit_trail4 \\\"\\n r\\\"FIELDS TERMINATED BY ', ' OPTIONALLY ENCLOSED BY '''' ESCAPED BY '\\\\\\\\' \\\"\\n r\\\"LINES STARTING BY '(' TERMINATED BY ')\\\\n' \\\"\\n r\\\"(user_id, app_id, tutorial_step, level, xp, last_visit, timestamp);\\\" % full_filename)\\n dane_connection.execute(sql)\\n\\n proc_time = time.time() - start_time\\n log.info(\\\"%s seconds to process player file\\\" % proc_time)\\n except Exception, e:\\n log.exception(e)\\n # save bad files for later\\n os.rename(full_filename, full_filename+\\\".bad\\\")\\n else:\\n # it succeeded - remove the file\\n os.unlink(full_filename)\",\n \"def _upload_file_to_container(self, container, object_key, file_path, connection):\\r\\n data = open(file_path, 'rb')\\r\\n logger.debug('upload {file_path} to {container} {object_key}'.format(file_path=file_path, container=container,\\r\\n object_key=object_key))\\r\\n\\r\\n result = connection.object_store.upload_object(container=container,\\r\\n name=object_key,\\r\\n data=open(file_path, 'r'))\\r\\n\\r\\n logger.debug(result)\",\n \"def do_import(files, container_id):\\n content = files['fname']['content']\\n lines = string.splitfields(content, '\\\\n')\\n for line in lines:\\n line = line.strip()\\n if line != '':\\n username, role = string.splitfields(line, ';')\\n try:\\n user_id = get_user_by_username(username).id\\n role_id = get_role_by_name(role).id\\n items = DmsUserUrlRole.objects.filter(user=user_id).filter(container=container_id)\\n if len(items) == 0:\\n DmsUserUrlRole.save_user_url_role(DmsUserUrlRole(), user_id, container_id, role_id)\\n else:\\n item = items[0]\\n item.role_id = role_id\\n item.save()\\n except:\\n pass\",\n \"def uploader():\\n\\tif request.method == 'POST':\\n\\t\\t\\n\\t\\tif \\\"file\\\" not in request.files:\\n\\t\\t\\treturn \\\"No data in file.\\\"\\n\\n\\t\\tFile = request.files['file']\\n\\t\\t\\n\\t\\tif File.filename == \\\"\\\":\\n\\t\\t\\treturn \\\"No file selected.\\\"\\n\\t\\t\\n\\t\\tfilename, ext = secure_filename(File.filename).split('.')\\n\\t\\t#Check if file stream exists and file tpye correct.\\n\\t\\tif File and ext == \\\"hepmc\\\":\\n\\t\\t\\t#The file is a byte stream by default which is not compatible with the current version of hepmcio.\\n\\t\\t\\tstring_stream = io.StringIO(File.read().decode('utf-8'))\\n\\n\\t\\t\\t#Get all events from file and jsonify them.\\n\\t\\t\\tevents = hepmcio.HepMCReader(string_stream).all_events()\\n\\t\\t\\thepMCEncoder = hepmcio_json.HepMCJSONEncoder()\\n\\t\\t\\tjsonified = [hepMCEncoder.encode(event) for event in events]\\n\\n\\t\\t\\t#Each collection contains all the data in a file.\\n\\t\\t\\tif filename not in mongo.db.collection_names():\\n\\t\\t\\t\\tcollection = mongo.db[filename]\\n\\t\\t\\t\\tjsonDecoder = json.JSONDecoder()\\n\\n\\t\\t\\t\\t#MongoDB takes in Python objects and not JSON strings, so have to decode before adding documents.\\n\\t\\t\\t\\tfor jsonObject in jsonified:\\n\\t\\t\\t\\t\\tjsonEvent = jsonDecoder.decode(jsonObject.evt)\\n\\t\\t\\t\\t\\tjsonParticles = [jsonDecoder.decode(p) for p in jsonObject.particles]\\n\\t\\t\\t\\t\\tjsonVertices = [jsonDecoder.decode(v) for v in jsonObject.vertices]\\n\\n\\t\\t\\t\\t\\tcollection.insert_one(jsonEvent)\\n\\t\\t\\t\\t\\tcollection.insert_many(jsonParticles)\\n\\t\\t\\t\\t\\tcollection.insert_many(jsonVertices)\\n\\t\\t\\n\\t\\t\\t\\treturn \\\"Succesfully uploaded file.\\\"\\n\\t\\t\\t\\n\\t\\t\\treturn \\\"File already in database.\\\"\\n\\n\\t\\treturn \\\"Incorrect file type.\\\"\",\n \"def process_scene_data(self, scene, data, tmp_dir):\\n pass\",\n \"def test_upload_file(self):\\n\\n uploadFile = os.path.join(testdatadir, \\\"upload.data\\\")\\n r = gracedb.writeFile(eventId, uploadFile)\\n self.assertEqual(r.status, 201) # CREATED\\n r_content = r.json()\\n link = r_content['permalink']\\n\\n self.assertEqual(\\n open(uploadFile, 'r').read(),\\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\\n )\\n\\n self.assertEqual(\\n open(uploadFile, 'r').read(),\\n gracedb.get(link).read()\\n )\\n\\n # Re-upload slightly different file.\\n uploadFile2 = os.path.join(testdatadir, \\\"upload2.data\\\")\\n r = gracedb.writeFile(\\n eventId,\\n filename=\\\"upload.data\\\",\\n filecontents=open(uploadFile2, 'r'))\\n self.assertEqual(r.status, 201) # CREATED\\n r_content = r.json()\\n link2 = r_content['permalink']\\n\\n self.assertEqual(\\n open(uploadFile2, 'r').read(),\\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\\n )\\n\\n self.assertEqual(\\n open(uploadFile2, 'r').read(),\\n gracedb.get(link2).read()\\n )\\n\\n self.assertNotEqual(link, link2)\",\n \"def process_record(self, record):\\n raise NotImplementedError('Process record needs to be customized')\",\n \"def handle_flight_received(self, event, sender, data):\\n path = f'{self.media_directory}/tello-{datetime.datetime.now().strftime(self.date_fmt)}.jpg' \\n with open(path, 'wb') as out_file:\\n out_file.write(data)\\n log.info('Saved photo to %s' % path)\",\n \"def store(request):\\n if request.method == 'POST':\\n username = request.session.get('username')\\n password = request.session.get('password')\\n storage = request.session.get('storage')\\n inv = request.POST.get('inv')\\n study = request.POST.get('study')\\n metadata = request.POST.get('metadata')\\n datafile = request.POST.get('datafile')\\n disgenet = onto(request.POST.get('disgenet'), request.POST.get('edam'))[0]\\n edam = onto(request.POST.get('disgenet'), request.POST.get('edam'))[1]\\n if username == \\\"\\\" or username is None:\\n login()\\n else:\\n pid = datafile\\n metadata = metadata.split(',')\\n if metadata is not None:\\n for m in metadata:\\n mfile = m.replace('[', '').replace(']', '').replace('\\\"', '').replace(' ', '')\\n metafile = subprocess.Popen([\\\"curl -s -k -u\\\" + username + \\\":\\\" + password + \\\" \\\" + mfile[1:]],\\n stdout=subprocess.PIPE, shell=True).communicate()[0]\\n metaf = open(username + '/metafile.csv', 'w')\\n metaf.write(metafile)\\n metaf.close()\\n filemeta = \\\"metafile.csv\\\"\\n if \\\"This is the WebDAV interface. It can only be accessed by WebDAV clients such as the ownCloud desktop sync client.\\\" in metafile:\\n createMetadata(request, datafile)\\n filemeta = \\\"meta.txt\\\"\\n call([\\\"curl -s -k -u \\\" + username + \\\":\\\" + password + \\\" -T \\\" + '\\\\'' + \\\"meta.txt\\\" + '\\\\'' +\\n \\\" \\\" + storage + \\\"/\\\" + inv + \\\"/\\\" + study + \\\"/meta.txt\\\"], shell=True)\\n with open(username + \\\"/\\\" + filemeta, 'rb') as csvfile:\\n count = 0\\n reader = csv.DictReader(csvfile)\\n cnt = 0\\n for row in reader:\\n for p in pid.split(','):\\n data = p.replace('[', '').replace(']', '').replace(\\\"'\\\", \\\"\\\").replace('\\\"', '').replace(' ', '')[1:]\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) + \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" +\\n username.replace('@', '') + \\\"#pid> \\\\\\\"\\\" + data + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) + \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" +\\n username.replace('@', '') + \\\"#investigation_id> \\\\\\\"\\\" + inv + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) + \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" +\\n username.replace('@', '') + \\\"#group_id> \\\\\\\"\\\" + study + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) + \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" +\\n username.replace('@', '') + \\\"#disgenet_iri> \\\\\\\"\\\" + disgenet + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) + \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" +\\n username.replace('@', '') + \\\"#edam_iri> \\\\\\\"\\\" + edam + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) + \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" +\\n username.replace('@', '') + \\\"#disease> \\\\\\\"\\\" + request.POST.get('disgenet') + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n if filemeta == \\\"meta.txt\\\":\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) + \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" +\\n username.replace('@', '') + \\\"#meta> \\\\\\\"\\\" + storage + \\\"/\\\" + inv + \\\"/\\\" + study +\\n \\\"/meta.txt\\\" + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n else:\\n for m in metadata:\\n mfile = m.replace('[', '').replace(']', '').replace('\\\"', '').replace(\\\"'\\\", \\\"\\\").replace(' ', '')\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) +\\n \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" + username.replace('@', '') + \\\"#meta> \\\\\\\"\\\" + mfile[1:] +\\n \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n headers = []\\n for (k, v) in row.items():\\n for h in range(0, len(k.split('\\\\t'))):\\n if k.split('\\\\t')[h] != \\\"\\\":\\n value = v.split('\\\\t')[h]\\n header = k.split('\\\\t')[h]\\n headers.append(header.replace('\\\"', ''))\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) +\\n \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" + username.replace('@', '') + \\\"#\\\" + header.replace('\\\"', '') + \\\"> \\\\\\\"\\\" +\\n value.replace('\\\"', '').replace('+', '%2B') + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n if \\\"sex\\\" not in headers:\\n call([\\\"curl http://127.0.0.1:3030/ds/update -X POST --data 'update=INSERT DATA { GRAPH <http://127.0.0.1:3030/ds/data/\\\" +\\n username.replace('@', '') + \\\"> { <http://127.0.0.1:3030/\\\" + study + \\\"_\\\" + str(cnt) + \\\"> <http://127.0.0.1:3030/ds/data?graph=\\\" +\\n username.replace('@', '') + \\\"#sex> \\\\\\\"\\\" + 'Unknown' + \\\"\\\\\\\" } }' -H 'Accept: text/plain,*/*;q=0.9'\\\"], shell=True)\\n count += 1\\n cnt += 1\\n call([\\\"rm\\\", username + \\\"/metafile.csv\\\"])\\n call([\\\"rm\\\", username + \\\"/meta.txt\\\"])\\n return HttpResponseRedirect(reverse('index'))\",\n \"def resumeupload():\\n resumableChunkNumber = int(request.params['resumableChunkNumber'])\\n resumableChunkSize = int(request.params['resumableChunkSize'])\\n resumableIdentifier = request.params['resumableIdentifier']\\n resumableTotalChunks = int(request.params['resumableTotalChunks'])\\n ruModel = get_model('resumableUpload')\\n resumableUploadObj = ruModel.get(ruModel.c.guid == resumableIdentifier)\\n if request.method == 'GET':\\n #----check if the chunk has uploaded\\n if resumableUploadObj is not None and int(resumableUploadObj.currentChunk) >= resumableChunkNumber:\\n return json({'status':'success'}, status=200)\\n else:\\n return json({'status':'404'}, status=404)\\n else:\\n try:\\n resumableFilename = request.params['resumableFilename']\\n # basePath = os.path.join(application_path(settings.TRANSFER.BASEUPLAOD), request.user.username, resumableIdentifier)\\n ufile = request.files.get('file', None)\\n if not ufile:\\n return json({'status':'err', 'msg':'No file had been upload'}, status=400)\\n rf = ResumeFile(fileobj=ufile, identify=resumableIdentifier, fileName=resumableFilename, username=request.user.username,\\n totalChunk=resumableTotalChunks, currentChunk=resumableChunkNumber)\\n rf.keepChunk(resumableChunkNumber)\\n if resumableUploadObj is None:\\n ruModel(guid=resumableIdentifier, currentChunk=resumableChunkNumber, totalChunk=resumableTotalChunks, state=0).save()\\n else:\\n ruModel.filter(ruModel.c.guid == resumableIdentifier).update(currentChunk=resumableChunkNumber)\\n return json({'status':'ok'}, status=200)\\n except Exception as e:\\n return json({'msg':'keep file error'}, status=400)\",\n \"def upload(self):\\n\\n # Try to retrieve the file from the request\\n file = None\\n try:\\n file = request.files[\\\"file\\\"]\\n except KeyError as e:\\n logging.warning(f\\\"File was not found in request: {e}.\\\")\\n flash(\\\"No file given.\\\", \\\"error\\\")\\n return redirect(request.url)\\n except AttributeError as e:\\n logging.warning(f\\\"Error: Request did not contain any files: {e}.\\\")\\n flash(\\\"No file given.\\\", \\\"error\\\")\\n return redirect(request.url)\\n\\n # Check if file was correctly uploaded\\n if not file or len(file.filename) == 0:\\n flash(\\\"No file selected for upload.\\\", \\\"message\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" Check if file has correct extension. Allowed extensions depend on\\n the connector. To make the code more readable, group connectors\\n with the same allowed file extensions together like this:\\n if connector in ['someconnector', 'someotherconnector']:\\n extensions = [...] \\\"\\\"\\\"\\n\\n if self.connector in [\\\"overtime\\\"]:\\n allowed_extensions = OVERTIME_SUPPORTED_EXTENSIONS\\n else:\\n allowed_extensions = []\\n\\n if not allowed_file(file, allowed_extensions=allowed_extensions):\\n flash(\\\"File extension not allowed.\\\", \\\"warning\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" File seems uploaded correctly and has correct extension.\\n Generate a new record ID to keep track of the uploaded file.\\n \\\"\\\"\\\"\\n rec_id = generate_temp_record_id()\\n\\n # Save file to disk\\n path = store_temp_file(file, record_id=rec_id)\\n\\n if not path:\\n flash(\\\"Error saving file!\\\", \\\"error\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" If everything ended successfully, send the user to the\\n confirmation page so he can review his changes \\\"\\\"\\\"\\n\\n return redirect(url_for(f\\\"{self.endpoint}.upload_confirm\\\", rec_id=rec_id))\",\n \"def sendRequest(event, context):\\n file = event\\n print(f\\\"Processing file: {file['name']}.\\\")\\n\\n filename = file['name']\\n\\n url = 'http://34.123.136.112:5000'\\n myobj = {'filename': filename}\\n\\n x = requests.post(url, data = myobj)\\n\\n print(x.text)\",\n \"def process_log_file(cur, filepath):\\n \\n # open log file\\n df = pd.read_json(filepath,lines=True)\\n\\n # filter by NextSong action - i.e. get only listening music events from the logs\\n df = df[(df.page == \\\"NextSong\\\")]\\n\\n # insert time records\\n __insert_time_data(cur, df)\\n \\n # insert user records\\n __insert_user_data(cur, df)\\n \\n # insert songplay records\\n __insert_songplay_data(cur, df)\\n \\n # erase dataframe\\n df = df.iloc[0:0]\",\n \"def restore_input_from_recording(self, recorded_data, args, kwargs):\\n file_path = self._get_file_path(args, kwargs)\\n with open(file_path, \\\"wb\\\") as binary_file:\\n __, file_content = self._deserialize_file(recorded_data)\\n binary_file.write(file_content)\\n\\n return file_path\",\n \"def processor(self, data):\\n streaming_data = self.decoder.decodeData(data)\\n # Add Your code here to process data and handle transport/storage\",\n \"def processor(self, data):\\n streaming_data = self.decoder.decodeData(data)\\n # Add Your code here to process data and handle transport/storage\",\n \"def processor(self, data):\\n streaming_data = self.decoder.decodeData(data)\\n # Add Your code here to process data and handle transport/storage\",\n \"def processor(self, data):\\n streaming_data = self.decoder.decodeData(data)\\n # Add Your code here to process data and handle transport/storage\",\n \"def processor(self, data):\\n streaming_data = self.decoder.decodeData(data)\\n # Add Your code here to process data and handle transport/storage\",\n \"def processor(self, data):\\n streaming_data = self.decoder.decodeData(data)\\n # Add Your code here to process data and handle transport/storage\",\n \"def upload(self, *route, **req_data):\\n # Read the FieldStorage.\\n file_desc = req_data['file']\\n file_mimetype = req_data['mimetype']\\n if not isinstance(file_desc, FieldStorage):\\n # Python is dangerous when the type is incorrectly assumed.\\n return Response(b'invalid request body', status='400 Bad Request')\\n\\n # Persist the file.\\n data_id = get_bucket().put(file_desc.value)\\n to_store = StoredFile(\\n id=uuid4().hex,\\n data_id=data_id,\\n mimetype=file_mimetype,\\n content_length=len(file_desc.value),\\n original_name=file_desc.filename\\n )\\n StoredFile.collection().put(to_store)\\n\\n log_activity('%s uploaded file %s'%(\\n context.user.link, to_store.access_link\\n ))\\n\\n # Respond.\\n return Response(\\n bytes(to_store.access_url, 'utf-8'),\\n status='201 Created'\\n )\",\n \"def start_recording(self):\\n self.start_recording_and_saving_data(self.eeg_file_path)\",\n \"def import_file(self):\\n if self.session_filename is None:\\n return\\n\\n try:\\n with open(self.session_filename, \\\"rb\\\") as f:\\n data = cPickle.loads(zlib.decompress(f.read()))\\n except (IOError, zlib.error, cPickle.UnpicklingError):\\n return\\n\\n # update the skip variable to pick up fuzzing from last test case.\\n self._index_start = data[\\\"total_mutant_index\\\"]\\n self.session_filename = data[\\\"session_filename\\\"]\\n self.sleep_time = data[\\\"sleep_time\\\"]\\n self.restart_sleep_time = data[\\\"restart_sleep_time\\\"]\\n self.restart_interval = data[\\\"restart_interval\\\"]\\n self.web_port = data[\\\"web_port\\\"]\\n self._crash_threshold_node = data[\\\"crash_threshold\\\"]\\n self.total_num_mutations = data[\\\"total_num_mutations\\\"]\\n self.total_mutant_index = data[\\\"total_mutant_index\\\"]\\n self.netmon_results = data[\\\"netmon_results\\\"]\\n self.procmon_results = data[\\\"procmon_results\\\"]\\n self.is_paused = data[\\\"is_paused\\\"]\",\n \"def process_log_file(cur, filepath):\\n # open log file\\n data_frame = pd.read_json(filepath, lines=True)\\n\\n # filter by NextSong action\\n data_frame = data_frame[data_frame.page == 'NextSong']\\n\\n # convert timestamp column to datetime\\n time_value = pd.to_datetime(data_frame['ts'])\\n\\n # insert time data records\\n time_data = (time_value, time_value.dt.year, time_value.dt.month,\\n time_value.dt.isocalendar().week, time_value.dt.dayofweek,\\n time_value.dt.day, time_value.dt.hour)\\n\\n column_labels = ('timestamp', 'year', 'month', 'week', 'weekday', 'day', 'hour')\\n\\n time_df = pd.DataFrame.from_dict(dict(zip(column_labels, time_data)))\\n\\n for i, row in time_df.iterrows():\\n cur.execute(time_table_insert, list(row))\\n\\n # load user table\\n user_df = data_frame[['userId', 'firstName', 'lastName', 'gender', 'level']]\\n\\n # insert user records\\n for i, row in user_df.iterrows():\\n cur.execute(user_table_insert, row)\\n\\n # insert songplay records\\n for index, row in data_frame.iterrows():\\n\\n # get songid and artistid from song and artist tables\\n cur.execute(song_select, (row.song, row.artist, row.length))\\n results = cur.fetchone()\\n\\n if results:\\n songid, artistid = results\\n else:\\n songid, artistid = None, None\\n\\n # insert songplay record\\n songplay_data = row[['ts', 'userId', 'level', 'sessionId', 'location', 'userAgent']]. \\\\\\n append(pd.Series([songid,\\n artistid],\\n index=[\\n 'songid',\\n 'artistid']))\\n songplay_data['ts'] = pd.to_datetime(songplay_data['ts'])\\n\\n cur.execute(songplay_table_insert, songplay_data)\",\n \"async def accept_replay(request, game):\\n game = game.lower()\\n replay_file = request.files.get('replay')\\n if replay_file:\\n if game == STARCRAFT:\\n load_map = request.args.get(\\\"load_map\\\", False)\\n result = await SC2Replay.analyze_replay(replay_file, load_map)\\n if result:\\n data = {\\\"result\\\": result,\\n \\\"game\\\": STARCRAFT,\\n \\\"success\\\": True}\\n return sanic.response.json(data, status=200)\\n else:\\n data = {\\n \\\"error\\\": \\\"Unable to parse game file.\\\",\\n \\\"success\\\": False,\\n \\\"game\\\": game\\n }\\n return sanic.response.json(data, status=500)\\n else:\\n data = {\\n \\\"error\\\": \\\"Game not in list of games.\\\",\\n \\\"success\\\": False,\\n \\\"game\\\": game\\n }\\n return sanic.response.json(data, status=404)\\n else:\\n data = {\\n \\\"error\\\": \\\"No replay file given.\\\",\\n \\\"success\\\": False,\\n \\\"game\\\": game\\n }\\n return sanic.response.json(data, status=500)\",\n \"def train_enroller(self, train_files, enroller_file):\\n # save something\\n bob.io.base.save(_data, enroller_file)\",\n \"def process(self,fileobj_out,fileobj_in):\\n pass\",\n \"def process_log_file(cur, filepath):\\n # open log file\\n df = get_file_df(filepath)\\n\\n # filter by NextSong action\\n df = df[df['page'] == 'NextSong']\\n\\n # convert timestamp column to datetime\\n df['ts'] = df['ts'].apply(lambda x: datetime.datetime.fromtimestamp(x/1000)) \\n t = df\\n \\n time_data = []\\n for td in t['ts']:\\n wd = True if td.weekday() <=6 else False\\n time_data.append([str(td.time()), td.hour, td.day, td.week, td.month, td.year, wd])\\n column_labels = ('start_time', 'hour', 'day', 'week', 'month', 'year', 'weekday')\\n\\n # insert time data records\\n time_df = pd.DataFrame(time_data, columns=column_labels)\\n\\n for i, row in time_df.iterrows():\\n cur.execute(time_table_insert, list(row))\\n\\n # load user table\\n user_df = df[['userId', 'firstName', 'lastName', 'gender', 'level']].copy()\\n\\n # insert user records\\n for i, row in user_df.iterrows():\\n cur.execute(user_table_insert, row)\\n\\n # insert songplay records\\n for index, row in df.iterrows():\\n \\n # get songid and artistid from song and artist tables\\n cur.execute(song_select, (row.song, row.artist, row.length))\\n results = cur.fetchone()\\n \\n if results:\\n songid, artistid = results\\n else:\\n songid, artistid = None, None\\n\\n # insert songplay record\\n #user_id, level, song_id, artist_id, session_id, location, user_agent\\n songplay_data = [row.userId, row.level, songid, artistid, row.sessionId, row.location, row.userAgent]\\n cur.execute(songplay_table_insert, songplay_data)\",\n \"def upload_from_file(self, file_obj, name_on_storage, **keyword_args):\\n blob = self.bucket.blob(name_on_storage)\\n blob.upload_from_file(file_obj, **keyword_args)\\n print(f\\\"Upload object {name_on_storage}\\\")\",\n \"def collection_post(self):\\n if 'file' not in self.request.POST:\\n self.request.errors.add('body', 'file', 'Not Found')\\n self.request.errors.status = 404\\n return\\n tender = TenderDocument.load(self.db, self.tender_id)\\n if not tender:\\n self.request.errors.add('url', 'tender_id', 'Not Found')\\n self.request.errors.status = 404\\n return\\n src = tender.serialize(\\\"plain\\\")\\n bids = [i for i in tender.bids if i.id == self.bid_id]\\n if not bids:\\n self.request.errors.add('url', 'bid_id', 'Not Found')\\n self.request.errors.status = 404\\n return\\n bid = bids[0]\\n data = self.request.POST['file']\\n document = Document()\\n document.id = uuid4().hex\\n document.title = data.filename\\n document.format = data.type\\n key = uuid4().hex\\n document.url = self.request.route_url('Tender Bid Documents', tender_id=self.tender_id, bid_id=self.bid_id, id=document.id, _query={'download': key})\\n bid.documents.append(document)\\n filename = \\\"{}_{}\\\".format(document.id, key)\\n tender['_attachments'][filename] = {\\n \\\"content_type\\\": data.type,\\n \\\"data\\\": b64encode(data.file.read())\\n }\\n patch = make_patch(tender.serialize(\\\"plain\\\"), src).patch\\n tender.revisions.append(revision({'changes': patch}))\\n try:\\n tender.store(self.db)\\n except Exception, e:\\n return self.request.errors.add('body', 'data', str(e))\\n self.request.response.status = 201\\n self.request.response.headers['Location'] = self.request.route_url('Tender Bid Documents', tender_id=self.tender_id, bid_id=self.bid_id, id=document.id)\\n return {'data': document.serialize(\\\"view\\\")}\",\n \"def upload_sample(host='127.0.0.1', port=8000, path=\\\"\\\"):\\n with Reader(path) as reader:\\n new_user = {}\\n user_id = reader.user_id\\n if not validate_attr(user_id, -1, path):\\n return\\n new_user[\\\"user_id\\\"] = user_id\\n\\n username = reader.username\\n if not validate_attr(username, \\\"\\\", path):\\n return\\n new_user[\\\"username\\\"] = username\\n\\n birthday = reader.birthday\\n if not validate_attr(birthday, None, path):\\n return\\n try:\\n birthday_timestamp = int(birthday.timestamp())\\n except OverflowError:\\n print(ERROR_PREFIX, \\\"datetime object raised OverflowError. Please recheck supplied data -\\\", path)\\n return\\n new_user[\\\"birthday\\\"] = birthday_timestamp\\n\\n gender = reader.gender\\n if not validate_attr(gender, \\\"\\\", path):\\n return\\n new_user[\\\"gender\\\"] = gender\\n\\n base_url = \\\"\\\" if \\\"http\\\" in host else \\\"http://\\\"\\n base_url += \\\"{}:{}\\\".format(host, port)\\n parsers = upload_user(base_url, new_user)\\n\\n if parsers is not None: # if no error (on error, parsers = None)\\n snapshot_id = 1\\n for snapshot in reader:\\n # print(snapshot.datetime, snapshot.color_image.width, snapshot.color_image.height)\\n upload_snapshot(base_url, user_id, snapshot, snapshot_id, parsers)\\n snapshot_id += 1\",\n \"def process_log_file(cur, filepath):\\n df = pd.read_json(filepath, lines=True)\\n df = df.loc[df.page == 'NextSong', :]\\n\\n # insert time data records\\n df['ts'] = pd.to_datetime(df.ts, unit='ms')\\n t = df['ts']\\n time_data = {\\n 'start_time': t,\\n 'hour': t.dt.hour,\\n 'day': t.dt.day,\\n 'week': t.dt.week,\\n 'month': t.dt.month,\\n 'year': t.dt.year,\\n 'weekday': t.dt.weekday\\n }\\n time_df = pd.DataFrame(data=time_data)\\n for i, row in time_df.iterrows():\\n cur.execute(time_table_insert, list(row))\\n\\n # insert user records\\n user_df = df.loc[:, ['userId', 'firstName', 'lastName', 'gender', 'level']]\\n for i, row in user_df.iterrows():\\n cur.execute(user_table_insert, row)\\n\\n # insert songplay records\\n for index, row in df.iterrows():\\n cur.execute(song_select, (row.song, row.artist, row.length))\\n results = cur.fetchone()\\n\\n if results:\\n songid, artistid = results\\n else:\\n songid, artistid = None, None\\n\\n songplay_data = {\\n 'start_time': row.ts,\\n 'user_id': row.userId,\\n 'level': row.level,\\n 'song_id': songid,\\n 'artist_id': artistid,\\n 'session_id': row.sessionId,\\n 'location': row.location,\\n 'user_agent': row.userAgent\\n }\\n cur.execute(songplay_table_insert, list(songplay_data.values()))\",\n \"def process_log_file(cur, filepath):\\r\\n df=pd.read_json(filepath,lines=True)\\r\\n df2=df\\r\\n df=df[df['page']=='NextSong']\\r\\n ser=pd.to_datetime(df['ts'],unit='ms')\\r\\n times=[]\\r\\n for i in ser:\\r\\n times.append([i,i.hour,i.day,i.week,i.month,i.year,i.day_name()])\\r\\n for i in times:\\r\\n cur.execute(time_table_insert,i)\\r\\n df=df[['userId','firstName','lastName','gender','level']]\\r\\n for i,row in df.iterrows():\\r\\n cur.execute(users_table_insert,list(row))\\r\\n for i, row in df2.iterrows():\\r\\n cur.execute(song_select, (row.song, row.artist, row.length))\\r\\n res = cur.fetchone()\\r\\n if res:\\r\\n song_id, artist_id = res\\r\\n else:\\r\\n song_id, artist_id = None, None\\r\\n\\r\\n songplay_data = (\\r\\n i, pd.to_datetime(row.ts, unit='ms'),int(row.userId), row.level, song_id, artist_id, row.sessionId,\\r\\n row.location, row.userAgent)\\r\\n cur.execute(songplays_table_insert, songplay_data)\",\n \"def process_one_file(guid, key, env):\\n logging.info(f'Processing file: {key}')\\n try:\\n query_seq = read_s3_file(key).seq\\n results = []\\n for name, reference_seq in REFERENCE_RECORDS.items():\\n offset = reference_seq.seq.find(query_seq)\\n if offset != -1:\\n result = {'filename': name,\\n 'offset': offset,\\n 'name': reference_seq.name,\\n 'desc': reference_seq.description\\n }\\n results.append(result)\\n logging.info(f'found in {name} at {offset}')\\n update_database(guid, 'done', env, results)\\n logging.info(f'Update succeeded for guid={guid} in env={env}')\\n except Exception as err:\\n report = {'time': str(datetime.utcnow()),\\n 'guid': guid,\\n 'env': env,\\n 'key': key,\\n 'trace' : traceback.format_exc()\\n }\\n results = [{'error' : report}]\\n update_database(guid, 'error', env, results)\\n raise\",\n \"def __gitBisectCreateReplay(self):\\n self.vcs.gitBisectCreateReplayFile(self.project.getProjectPath())\",\n \"def storage_ipfs_upload_file(self, file_obj):\\n # Request: upload a file\\n # curl -X POST -H 'Content-Type: application/json' --data '{\\\"jsonrpc\\\":\\\"2.0\\\",\\\"method\\\":\\\"edgestore.PutFile\\\",\\\"params\\\":[{\\\"path\\\": \\\"theta-edge-store-demos/demos/image/data/smiley_explorer.png\\\"}],\\\"id\\\":1}' http://localhost:19888/rpc\\n # Result\\n # {\\n # \\\"jsonrpc\\\": \\\"2.0\\\",\\n # \\\"id\\\": 1,\\n # \\\"result\\\": {\\n # \\\"key\\\": \\\"0xbc0383809da9fb98c5755e3fa4f19f4ebc7e34308ab321246e4bb54e548fad04\\\",\\n # \\\"relpath\\\": \\\"smiley_explorer.png\\\",\\n # \\\"success\\\": true\\n # }\\n # }\\n home_dir = expanduser(\\\"~\\\")\\n file_path = os.path.join(home_dir, \\\"thetastore\\\")\\n if not os.path.exists(file_path):\\n os.makedirs(file_path, exist_ok=True)\\n file_path = os.path.join(file_path, str(uuid.uuid4()))\\n with open(file_path, \\\"wb\\\") as file_handle:\\n file_handle.write(file_obj)\\n\\n request_data = {\\\"jsonrpc\\\":\\\"2.0\\\",\\n \\\"method\\\":\\\"edgestore.PutFile\\\",\\n \\\"params\\\":[{\\\"path\\\": file_path}],\\n \\\"id\\\":1}\\n res = httpx.post(\\n self.ipfs_upload_uri,\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"},\\n data=json.dumps(request_data),\\n timeout=None,\\n )\\n content = res.json()\\n result = content.get(\\\"result\\\", None)\\n if result is None:\\n return False, \\\"Failed to upload file(result is none).\\\"\\n else:\\n success = result.get(\\\"success\\\", False)\\n if not success:\\n return False, \\\"Failed to upload file(success if false).\\\"\\n file_cid = result.get(\\\"key\\\", None)\\n if file_cid is None:\\n return False, \\\"Failed to upload file(key is none).\\\"\\n return True, file_cid\",\n \"def record(params, git_info = {}):\\n print \\\"recording...\\\"\\n\\n try:\\n # connect to MongoDB\\n # config = json.load(open(os.environ.get('HOME') + \\\"/sandbox/config.json\\\"))\\n config = json.load(open(os.environ.get('HOME') + \\\"/LSEMS/config.json\\\"))\\n try:\\n client = MongoClient(config[\\\"mongodb_url\\\"])\\n except Exception as e:\\n raise Exception(\\\"fail to connect to given MongoDB address: \\\" + DB_addr)\\n\\n # check and run the thing\\n missing = checkKeys(params, ['data_set', 'src', 'type', 'param'])\\n if len(missing) != 0:\\n raise Exception(\\\"missing attribute\\\"+('s' if len(missing)!=1 else '')+\\\": \\\"+str(missing))\\n\\n params['time'] = asctime()\\n params['commit_id'] = git_info['commit_id']\\n params['name'] = git_info['name']\\n repo_name = git_info['repo_name']\\n params['repo_name'] = repo_name\\n user = verifyUser(client, git_info['name'])\\n\\n exp = user.find_one({'exp_name': repo_name})\\n if not exp:\\n print 'adding new experiment '+repo_name+'...'\\n user.insert({'exp_name': repo_name, 'exp_records':[]})\\n old_records = user.find_one({'exp_name': repo_name})['exp_records']\\n user.update({'exp_name': repo_name}, {'$set': {'exp_records': old_records + [params]}})\\n\\n print params\\n #user.insert(params)\\n client.close()\\n return True,params\\n except Exception as e:\\n print e\\n print \\\"Aborting...\\\"\\n return False,{}\",\n \"def post(self):\\n upload_files = upload_images(request.files, 'users', 'user')\\n record = request.json if request.content_type == 'application/json' else request.form\\n \\\"\\\"\\\" Values assign to Category Model \\\"\\\"\\\"\\n pprint.pprint(record)\\n model_record = UserModel(\\n record['name'], \\n record['password'], \\n record['email'],\\n record['mobile'] if 'mobile' in record else None, \\n record['userType'] if 'userType' in record else None,\\n upload_files['fileArr']\\n )\\n \\\"\\\"\\\" Model converts to document like json object \\\"\\\"\\\"\\n record_document = model_record.to_document()\\n \\\"\\\"\\\" Below line will insert record and get objectID \\\"\\\"\\\"\\n insertedId = col_users.insert_one(record_document).inserted_id\\n \\\"\\\"\\\" Below line does files move from one place to another \\\"\\\"\\\"\\n if len(upload_files['fileArr']) > 0:\\n moved_file(upload_files['fileArr'], upload_files['folder'], str(insertedId)) \\n\\n return jsonify(str(insertedId))\",\n \"def seed_user_data(filename):\\n\\n #open file and go through it line by line\\n log_file = open(filename)\\n\\n for line in log_file:\\n data = line.strip().split(\\\"|\\\") #data is a list\\n\\n #get data from split line\\n id = int(data[0])\\n age = int(data[1])\\n zip = data[4]\\n\\n #create a new record and add it to the queue\\n new_user = User(user_id=id, age=age, zipcode=zip)\\n db.session.add(new_user)\\n\\n #commit changes\\n db.session.commit()\",\n \"def execute_request(self, request):\\n request.input_content = self.read_file(request.input_content) if request.is_input_file else [\\n request.input_content]\\n poke_request = PokeObjectRequest(request.mode, request.input_content, request.expanded)\\n loop = asyncio.get_event_loop()\\n result = loop.run_until_complete(poke_request.execute())\\n self.print_output(result, request.output)\",\n \"def start(self, filename):\\n\\n self.db.init_tables()\\n self.db.single_insert_camera(filename)\\n self.vitess.mydb.commit()\",\n \"async def _upload(self) -> None:\\n\\n # filename given?\\n filename = str(uuid.uuid4()) if self.filename is None else self.filename\\n\\n # check\\n if self._upload_path is None:\\n raise ValueError(\\\"No upload URL given.\\\")\\n\\n # send data and return image ID\\n async with aiohttp.ClientSession() as session:\\n data = aiohttp.FormData()\\n data.add_field(\\\"file\\\", self._buffer, filename=self.filename)\\n async with session.post(self._upload_path, auth=self._auth, data=data, timeout=self._timeout) as response:\\n if response.status == 401:\\n log.error(\\\"Wrong credentials for uploading file.\\\")\\n raise FileNotFoundError\\n elif response.status != 200:\\n log.error(f\\\"Could not upload file to filecache: {response.status} {response.reason}\\\")\\n raise FileNotFoundError\",\n \"def process(self):\\n self.extract()\\n self.transform()\\n self.load()\",\n \"def __download(self, downloadType):\\n\\n downloadAudit = {}\\n downloadAudit['download_type'] = downloadType\\n downloadAudit['remote_url'] = self.remoteUrl#get the url that is being requested\\n downloadAudit['download_start_time'] = datetime.now()#capture the date when the url was accessed\\n #first make an entry into the db stating that a download operation is to be attempted\\n downloadAudit['comment'] = 'Starting download operation'\\n newRecordId = self.__auditFileRequest(downloadAudit)\\n\\n downloadAudit = {}\\n try:\\n req = urllib2.Request(self.remoteUrl)\\n r = urllib2.urlopen(req)\\n except URLError, urle:\\n if hasattr(urle, 'reason'):\\n downloadAudit['comment'] = urle.reason\\n else:\\n downloadAudit['comment'] = urle.__str__()\\n \\n self.__auditFileRequest(downloadAudit, newRecordId)\\n \\n return None #just return since there has been an error in connecting with the remote server\\n\\n try:\\n downloadAudit['local_file_path'] = '/' + self.localRepoDir + '/' + downloadType + '/' + downloadType + '-' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '.xls'\\n\\n file = open(settings.APPLICATION_SETTINGS['MCH_HOME'] + downloadAudit['local_file_path'], 'wb')\\n file.write(r.read())\\n\\n #headerInfo = r.info()\\n\\n isFileToBeProcessed = False #default is not to process file\\n\\n #before downloading, check to see if the remote file is more recent than the last file that was downloaded, whose\\n #information is in the db\\n latestFetch = self.__recentDownload(downloadType)\\n\\n if latestFetch:\\n downloadAudit['file_size'] = latestFetch[0]\\n file.flush()#make sure all the content is written to file \\n os.fsync(file.fileno())\\n if latestFetch[0] != os.path.getsize(file.name):\\n isFileToBeProcessed = True\\n else:\\n isFileToBeProcessed = True\\n\\n if isFileToBeProcessed:\\n downloadAudit['file_size'] = os.path.getsize(file.name)\\n downloadAudit['file_modification_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n downloadAudit['download_completion_time'] = datetime.now()\\n #now make an audit entry into the database\\n downloadAudit['comment'] = 'New file to be processed.'\\n else:\\n os.remove(file.name)#remove the file since it looks like it has already been downloaded and processed\\n #now make an audit entry into the database\\n downloadAudit['comment'] = 'File already downloaded, purged it from the file system.'\\n\\n self.__auditFileRequest(downloadAudit, newRecordId)\\n file.close()\\n except Exception as e:\\n self.logger.exception('\\\\n Unknown fatal error occured during the downloading of the raw files. %s' % str(e))\\n\\n return newRecordId\",\n \"def process_log_file(cur, filepath):\\n\\n # open log file\\n df = pd.read_json(filepath, lines=True)\\n\\n # filter by NextSong action\\n df = df[df['page'] == 'NextSong']\\n\\n # convert timestamp column to datetime\\n t = pd.to_datetime(df['ts'], unit='ms')\\n\\n # insert time data records\\n time_data = (t, t.dt.hour, t.dt.day,\\n t.dt.month, t.dt.year, t.dt.day_name())\\n column_labels = ('start_time', 'hour', 'day', 'month', 'year', 'weekday')\\n time_df = pd.DataFrame(dict(zip(column_labels, time_data)))\\n\\n # bulk insert time_df into Postgres\\n # create a csv like object\\n output = io.StringIO()\\n time_df.to_csv(output, sep='\\\\t', index=False, header=False)\\n # move the pointer to start of the file\\n output.seek(0)\\n # creating a temp table to handle conflict due to duplicate insert\\n # ref: https://stackoverflow.com/questions/48019381/how-postgresql-copy-to-stdin-with-csv-do-on-conflic-do-update\\n cur.execute(time_tmp_table)\\n # copy data from csv to temp table\\n cur.copy_from(output, 'tmp_table')\\n # merge temp table with main table\\n cur.execute(time_table_bulk_insert)\\n\\n # load user table\\n user_df = df[['userId', 'firstName', 'lastName', 'gender', 'level']]\\n\\n # insert user records\\n for i, row in user_df.iterrows():\\n cur.execute(user_table_insert, row)\\n\\n # insert songplay records\\n # create a csv like object\\n output = io.StringIO()\\n for index, row in df.iterrows():\\n\\n # get songid and artistid from song and artist tables\\n cur.execute(song_select, (row.song, row.artist, row.length))\\n results = cur.fetchone()\\n\\n if results:\\n songid, artistid = results\\n else:\\n songid, artistid = None, None\\n # insert songplay record\\n songplay_data = (t[index], row.userId, row.level, songid,\\n artistid, row.sessionId, row.location, row.userAgent)\\n # write to csv like object\\n output.write('\\\\t'.join(map(clean_csv_value, songplay_data)) + '\\\\n')\\n # move the pointer to start of the csv like object\\n output.seek(0)\\n # columns to insert (songplay_id is a serial insert)\\n columns = ['start_time', 'user_id', 'level', 'song_id',\\n 'artist_id', 'session_id', 'location', 'user_agent']\\n # copy data to songplays table\\n cur.copy_from(output, 'songplays', columns=columns)\",\n \"def ftp_CACHEDATA(self, line):\\n #print 'WE ARE INSIDE OF FTPCACHEDATA THANK GOD!'\\n (line, chunksize_username_metadata) = line.split('?')\\n (chunk_size, user_name) = chunksize_username_metadata.split('_')\\n raw_cache_list = line.split('_')\\n cache_dicts = []\\n for raw_cache_string in raw_cache_list:\\n cache_dict = create_cache_json(raw_cache_string, chunk_size, user_name)\\n if cache_dict != {}:\\n cache_dicts.append(cache_dict)\\n if len(cache_dicts) != 0:\\n #take cache_dicts, convert to dict so that it can be encoded\\n dumped_dicts = json.dumps(cache_dicts)\\n ##\\n send_cache_data_to_tracker(tracker_address, dumped_dicts)\\n #my_dict = ({ 'cache_data', cache_dicts})\\n #urllib.urlencode(my_dict)\\n #make function in helper.py that sends the post to the tracker url as JSON\\n \\n \\n f = open('user_log/' + user_name + '.txt','w')\\n #f.write(json.dumps(cache_dicts))\\n f.write(dumped_dicts)\\n f.close()\\n #cache_dicts_length = len(cache_dicts)\\n #f.write('[')\\n #for i in range(0,cache_dicts_length):\\n # val = cache_dicts[i]\\n # f.write(json.dumps(val))\\n # if(i != cache_dicts_length - 1):\\n # f.write(',')\\n # f.write('\\\\n')\\n #f.write(']')\\n #f.close()\\n \\n #self.push_dtp_data(line, isproducer=False, cmd='CDAT')\\n self.respond(\\\"200 success.\\\")\",\n \"def replay_delete(sender, instance, **kwargs):\\n pass\\n # Temporarily disabled\\n\\n #print(\\\"deleting file from S3\\\")\\n # False so FileField doesn't save the model\\n #instance.file.delete(False)\",\n \"def run_replay_analysis(replay, cluster_endpoint, start_time, end_time, bucket_url, iam_role, user, tag='',\\n workload=\\\"\\\",\\n is_serverless=False, secret_name=None, nlb_nat_dns=None, complete=True, stats=None,\\n summary=None):\\n\\n logger = logging.getLogger(\\\"SimpleReplayLogger\\\")\\n s3_client = boto3.client('s3')\\n cluster = cluster_dict(cluster_endpoint, is_serverless, start_time, end_time)\\n cluster[\\\"is_serverless\\\"] = is_serverless\\n cluster[\\\"secret_name\\\"] = secret_name\\n cluster[\\\"host\\\"] = nlb_nat_dns if nlb_nat_dns != None else cluster[\\\"host\\\"]\\n\\n if type(bucket_url) is str:\\n bucket = bucket_dict(bucket_url)\\n\\n logger.debug(bucket)\\n\\n logger.info(f\\\"Running analysis for replay: {replay}\\\")\\n replay_path = f\\\"{bucket['prefix']}analysis/{replay}\\\"\\n\\n # unload from cluster\\n queries = unload(bucket, iam_role, cluster, user, replay)\\n info = create_json(replay, cluster, workload, complete, stats, tag)\\n try:\\n boto3.resource('s3').Bucket(bucket.get('bucket_name')).upload_file(info, f\\\"{replay_path}/{info}\\\")\\n except ClientError as e:\\n logger.error(f\\\"{e} Could not upload info. Confirm IAM permissions include S3::PutObject.\\\")\\n\\n if is_serverless:\\n exit(0)\\n else:\\n report = Report(cluster, replay, bucket, replay_path, tag, complete)\\n\\n try:\\n # iterate through query csv results and import\\n for q in queries:\\n get_raw_data(report, bucket, replay_path, q)\\n\\n except s3_client.exceptions.NoSuchKey as e:\\n logger.error(f\\\"{e} Raw data does not exist in S3. Error in replay analysis.\\\")\\n exit(-1)\\n except Exception as e:\\n logger.error(f\\\"{e}: Data read failed. Error in replay analysis.\\\")\\n exit(-1)\\n\\n # generate replay_id_report.pdf and info.json\\n logger.info(f\\\"Generating report.\\\")\\n pdf = pdf_gen(report, summary)\\n\\n s3_resource = boto3.resource('s3')\\n # upload to s3 and output presigned urls\\n try:\\n s3_resource.Bucket(bucket.get('bucket_name')).upload_file(pdf, f\\\"{replay_path}/out/{pdf}\\\")\\n s3_resource.Bucket(bucket.get('bucket_name')).upload_file(info, f\\\"{replay_path}/out/{info}\\\")\\n analysis_summary(bucket.get('url'), replay)\\n except ClientError as e:\\n logger.error(f\\\"{e} Could not upload report. Confirm IAM permissions include S3::PutObject.\\\")\\n exit(-1)\",\n \"def start_recording_and_saving_data(self, eeg_file_path):\\n # set up variables\\n self.e_event, self.e_state, user_id, n_samples, n_sam, composer_port, secs, state = self.setup_var()\\n # pointers\\n n_samples_taken = ct.pointer(n_samples)\\n data = ct.pointer(ct.c_double(0))\\n user = ct.pointer(user_id)\\n ready_to_collect = False\\n # start connection\\n print \\\"Connecting...\\\"\\n if self.libEDK.EE_EngineConnect(\\\"Emotiv Systems-5\\\") != 0: # connection failed\\n print \\\"Emotiv Engine start up failed.\\\"\\n self.stop_connection()\\n sys.exit(1)\\n print \\\"Connected! Start receiving data...\\\"\\n self._recording = True\\n # write data to file\\n self.f = open(eeg_file_path, 'w')\\n # write header\\n for header in Constants.HEADER:\\n # last one in header\\n if header != \\\"TIMESTAMP\\\" and not self.f.closed:\\n self.f.write(header + \\\",\\\")\\n elif not self.f.closed:\\n self.f.write(header + \\\"\\\\n\\\")\\n h_data = self.libEDK.EE_DataCreate()\\n self.libEDK.EE_DataSetBufferSizeInSec(secs)\\n # start recording\\n while self._recording:\\n state = self.libEDK.EE_EngineGetNextEvent(self.e_event)\\n if state == 0:\\n event_type = self.libEDK.EE_EmoEngineEventGetType(self.e_event)\\n self.libEDK.EE_EmoEngineEventGetUserId(self.e_event, user)\\n # add user\\n if event_type == 16:\\n print \\\"User added\\\"\\n self.libEDK.EE_DataAcquisitionEnable(user_id, True)\\n ready_to_collect = True\\n if ready_to_collect:\\n self.libEDK.EE_DataUpdateHandle(0, h_data)\\n self.libEDK.EE_DataGetNumberOfSample(h_data,n_samples_taken)\\n # print \\\"Updated :\\\", n_samples_taken[0]\\n if n_samples_taken[0] != 0:\\n n_sam = n_samples_taken[0]\\n arr = (ct.c_double*n_samples_taken[0])()\\n ct.cast(arr, ct.POINTER(ct.c_double))\\n # self.libEDK.EE_DataGet(h_data, 3,byref(arr), nSam)\\n data = np.array('d') # zeros(n_samples_taken[0],double)\\n for sampleIdx in range(n_samples_taken[0]):\\n # channel 14 + loc data 6 + timestamp 1 = 23\\n data = np.zeros(23)\\n for i in range(22):\\n self.libEDK.EE_DataGet(h_data, Constants.TARGET_CHANNEL_LIST[i], ct.byref(arr), n_sam)\\n if not self.f.closed:\\n print >>self.f,arr[sampleIdx],\\\",\\\",\\n data[i] = arr[sampleIdx]\\n # write our own time stamp\\n t = time.time()\\n data[-1] = t\\n if not self.f.closed:\\n print >>self.f,t,\\n # put data onto out buffer queue\\n self.out_buffer_queue.put(data)\\n # switch line\\n if not self.f.closed:\\n print >>self.f,'\\\\n',\\n self.f.flush()\\n # not sure the use of this in the original program...\\n self.libEDK.EE_DataFree(h_data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59654164","0.5942923","0.58948725","0.58847404","0.5826352","0.57525676","0.5647753","0.56387746","0.5543896","0.55360585","0.5521623","0.5506403","0.5493576","0.5484211","0.54712117","0.54331356","0.5429796","0.53877634","0.5359096","0.5353566","0.5341829","0.5311284","0.52975315","0.52895296","0.52578443","0.5256056","0.5206786","0.51986015","0.51896536","0.51839703","0.51820856","0.51779884","0.5166233","0.51523906","0.5150375","0.51446867","0.5141375","0.51238894","0.5122929","0.5122604","0.5091778","0.50906223","0.50816315","0.5077998","0.5073066","0.50600445","0.50570613","0.50552267","0.5053191","0.50520384","0.5048662","0.50391626","0.50300527","0.5029595","0.5027283","0.5014238","0.5009907","0.4999114","0.49989045","0.49981984","0.49954027","0.49950105","0.49872318","0.49863362","0.49844515","0.4979874","0.4979874","0.4979874","0.4979874","0.4979874","0.4979874","0.49754718","0.49700138","0.49675342","0.49638376","0.49608782","0.49544787","0.49524835","0.49505058","0.49477264","0.49411052","0.49387234","0.4929567","0.49237332","0.49209777","0.4918255","0.49126163","0.49119872","0.49098596","0.49069256","0.490487","0.49005327","0.49002635","0.4898472","0.4897752","0.48968726","0.48967007","0.48921454","0.4887173","0.4886068"],"string":"[\n \"0.59654164\",\n \"0.5942923\",\n \"0.58948725\",\n \"0.58847404\",\n \"0.5826352\",\n \"0.57525676\",\n \"0.5647753\",\n \"0.56387746\",\n \"0.5543896\",\n \"0.55360585\",\n \"0.5521623\",\n \"0.5506403\",\n \"0.5493576\",\n \"0.5484211\",\n \"0.54712117\",\n \"0.54331356\",\n \"0.5429796\",\n \"0.53877634\",\n \"0.5359096\",\n \"0.5353566\",\n \"0.5341829\",\n \"0.5311284\",\n \"0.52975315\",\n \"0.52895296\",\n \"0.52578443\",\n \"0.5256056\",\n \"0.5206786\",\n \"0.51986015\",\n \"0.51896536\",\n \"0.51839703\",\n \"0.51820856\",\n \"0.51779884\",\n \"0.5166233\",\n \"0.51523906\",\n \"0.5150375\",\n \"0.51446867\",\n \"0.5141375\",\n \"0.51238894\",\n \"0.5122929\",\n \"0.5122604\",\n \"0.5091778\",\n \"0.50906223\",\n \"0.50816315\",\n \"0.5077998\",\n \"0.5073066\",\n \"0.50600445\",\n \"0.50570613\",\n \"0.50552267\",\n \"0.5053191\",\n \"0.50520384\",\n \"0.5048662\",\n \"0.50391626\",\n \"0.50300527\",\n \"0.5029595\",\n \"0.5027283\",\n \"0.5014238\",\n \"0.5009907\",\n \"0.4999114\",\n \"0.49989045\",\n \"0.49981984\",\n \"0.49954027\",\n \"0.49950105\",\n \"0.49872318\",\n \"0.49863362\",\n \"0.49844515\",\n \"0.4979874\",\n \"0.4979874\",\n \"0.4979874\",\n \"0.4979874\",\n \"0.4979874\",\n \"0.4979874\",\n \"0.49754718\",\n \"0.49700138\",\n \"0.49675342\",\n \"0.49638376\",\n \"0.49608782\",\n \"0.49544787\",\n \"0.49524835\",\n \"0.49505058\",\n \"0.49477264\",\n \"0.49411052\",\n \"0.49387234\",\n \"0.4929567\",\n \"0.49237332\",\n \"0.49209777\",\n \"0.4918255\",\n \"0.49126163\",\n \"0.49119872\",\n \"0.49098596\",\n \"0.49069256\",\n \"0.490487\",\n \"0.49005327\",\n \"0.49002635\",\n \"0.4898472\",\n \"0.4897752\",\n \"0.48968726\",\n \"0.48967007\",\n \"0.48921454\",\n \"0.4887173\",\n \"0.4886068\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62600714"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19595,"cells":{"query":{"kind":"string","value":"_execute_ map the request name provided to an ID"},"document":{"kind":"string","value":"def execute(self, requestName, conn = None, trans = False):\n self.sql = \"SELECT request_id from reqmgr_request WHERE \"\n self.sql += \"request_name=:request_name\"\n binds = {\"request_name\": requestName}\n reqID = self.dbi.processData(self.sql, binds, conn = conn, transaction = trans)\n result = self.formatOne(reqID)\n if result == []:\n return None\n return result[0]"},"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 _execute(self, _):\r\n pass","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def execute():","def request(query):","def perform_action(self, action_id: int) -> None:\r\n ...","def _send_request(self, code):\n msg_id = uuid4().hex\n message = KernelClient.__create_execute_request(msg_id, code)\n\n # create response-queue and add to map for this msg_id\n self.response_queues[msg_id] = queue.Queue()\n\n self.kernel_socket.send(message)\n\n return msg_id","def submit(id, host):","def getEventIDName(*args, **kwargs):\n pass","def execute(self, response):\n raise NotImplementedError()","def process_actions(parameter: str, action_name: str) -> str:\n\n try:\n if action_name == \"login\":\n return create_followup_event_data(parameter[2])\n elif action_name == \"get_exam_date\":\n return create_data_response(DatabaseExtractor.get_exam_date(parameter[1]))\n elif action_name == \"get_assessment_form\":\n return create_data_response(DatabaseExtractor.get_assessment_form(parameter[1]))\n elif action_name == \"get_contact_mail\":\n return create_data_response(DatabaseExtractor.get_contact_mail(parameter[1]))\n elif action_name == \"get_contact_name\":\n return create_data_response(DatabaseExtractor.get_contact_name(parameter[1]))\n elif action_name == \"get_contact_phone\":\n return create_data_response(DatabaseExtractor.get_contact_phone(parameter[1]))\n elif action_name == \"get_contact_website\":\n return create_data_response(DatabaseExtractor.get_contact_website(parameter[1]))\n elif action_name == \"get_office\":\n return create_data_response(DatabaseExtractor.get_contact_office(parameter[1]))\n elif action_name == \"get_teaching_form\":\n return create_data_response(DatabaseExtractor.get_teaching_form(parameter[1]))\n elif action_name == \"get_course_name\":\n return create_data_response(DatabaseExtractor.get_course_name(parameter[1]))\n elif action_name == \"get_credit\":\n return create_data_response(DatabaseExtractor.get_credit(parameter[1]))\n elif action_name == \"get_url\":\n return create_data_response(DatabaseExtractor.get_url(parameter[1]))\n elif action_name == \"get_prereq_knowledge\":\n return create_data_response(DatabaseExtractor.get_prereq_knowledge(parameter[1]))\n elif action_name == \"get_course_content\":\n return create_data_response(DatabaseExtractor.get_course_content(parameter[1]))\n elif action_name == \"get_course_material\":\n return create_data_response(DatabaseExtractor.get_course_material(parameter[1]))\n elif action_name == \"get_teaching_form\":\n return create_data_response(DatabaseExtractor.get_teaching_form(parameter[1]))\n elif action_name == \"get_exercise_status\":\n return create_data_response(DatabaseExtractor.get_exercise_status(parameter[1], parameter[0]))\n elif action_name==\"get_exercise_scheme_approval\":\n return create_data_response(DatabaseExtractor.get_exercise_scheme_approval(parameter[1], parameter[0]))\n elif action_name==\"get_exercises_left\":\n return create_data_response(DatabaseExtractor.get_exercises_left(parameter[1], parameter[0]))\n elif action_name == \"get_next_event\":\n return create_data_response(DatabaseExtractor.get_next_event(username=parameter[0]))\n elif action_name == \"get_next_assignment\":\n return create_data_response(DatabaseExtractor.get_next_assignment(username=parameter[0]))\n elif action_name == \"get_this_weeks_schedule\":\n return create_data_response(DatabaseExtractor.get_this_week_schedule(username=parameter[0]))\n elif action_name == \"get_next_weeks_schedule\":\n return create_data_response(DatabaseExtractor.get_next_week_schedule(username=parameter[0]))\n elif action_name == \"get_next_weeks_events\":\n return create_data_response(DatabaseExtractor.get_next_weeks_events(username=parameter[0]))\n elif action_name == \"get_next_weeks_assignments\":\n return create_data_response(DatabaseExtractor.get_next_weeks_assignments(username=parameter[0]))\n elif action_name == \"get_this_weeks_assignments\":\n return create_data_response(DatabaseExtractor.get_this_weeks_assignments(username=parameter[0]))\n elif action_name == \"get_this_weeks_events\":\n return create_data_response(DatabaseExtractor.get_this_weeks_events(username=parameter[0]))\n elif action_name == \"get_exam_dates\":\n return create_data_response(DatabaseExtractor.get_exam_dates(username=parameter[0]))\n elif action_name == \"get_days_until_first_exam\":\n return create_data_response(DatabaseExtractor.get_days_until_first_exam(username=parameter[0]))\n elif action_name == \"get_course_codes\":\n return create_data_response(DatabaseExtractor.get_course_codes(username=parameter[0]))\n elif action_name == \"get_course_names\":\n return create_data_response(DatabaseExtractor.get_course_names(username=parameter[0]))\n elif action_name == \"get_number_of_courses\":\n return create_data_response(DatabaseExtractor.get_number_of_courses(username=parameter[0]))\n elif action_name == \"get_today_assignments\":\n return create_data_response(DatabaseExtractor.get_today_assignments(username=parameter[0]))\n elif action_name == \"get_tomorrow_assignments\":\n return create_data_response(DatabaseExtractor.get_tomorrow_assignments(username=parameter[0]))\n elif action_name == \"get_today_events\":\n return create_data_response(DatabaseExtractor.get_today_events(username=parameter[0]))\n elif action_name == \"get_tomorrow_events\":\n return create_data_response(DatabaseExtractor.get_tomorrow_events(username=parameter[0]))\n else:\n return create_data_response(\"I didn't understand anything, you probably broke me :(\")\n\n except:\n return create_data_response(\"Sorry, i can not answer that.\")","def _cmd(self, name, *args, **kwargs):\n if name in ['FETCH', 'SORT', 'STORE', 'COPY', 'SEARCH']:\n try:\n typ, data = self.m.uid(name, *args)\n except imaplib.IMAP4.error as e:\n raise ImapError(e)\n if typ == \"NO\":\n raise ImapError(data)\n if name == 'FETCH':\n return FetchResponseParser().parse(data)\n return data\n\n try:\n typ, data = self.m._simple_command(name, *args)\n except imaplib.IMAP4.error as e:\n raise ImapError(e)\n if typ == \"NO\":\n raise ImapError(data)\n if 'responses' not in kwargs:\n if name not in self.m.untagged_responses:\n return None\n return self.m.untagged_responses.pop(name)\n res = []\n for r in kwargs['responses']:\n if r not in self.m.untagged_responses:\n return None\n res.append(self.m.untagged_responses.pop(r))\n return res","def request_execute_plan(\n self,\n location: \"sy.workers.BaseWorker\",\n response_ids: List[Union[str, int]],\n *args,\n **kwargs,\n ) -> object:\n plan_name = f\"plan{self.id}\"\n # args, _, _ = hook_args.unwrap_args_from_function(\n # plan_name, args, {}\n # )\n args = [args, response_ids]\n\n command = (\"execute_plan\", self.id_at_location, args, kwargs)\n\n response = self.owner.send_command(\n message=command, recipient=location, return_ids=response_ids\n )\n response = hook_args.hook_response(plan_name, response, wrap_type=FrameworkTensor[0])\n response.garbage_collect_data = False\n return response","def PostSearchesByID(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def process_id(job_id):\n pass # Not implemented yet","def taskid(name):\n return \"(select id from event_type where name = '{}')\".format(name)","def _add_identifiers_to_request(request, response_id):\n meta = request.meta\n meta['__id'] = response_id\n return request.replace(meta=meta)","def execute(self, name=None, clientRequestToken=None):\n params = {'name': name if name else self.resource_id}\n if clientRequestToken:\n params.update({\"clientRequestToken\": clientRequestToken})\n self.logger.debug('Executing {resource_type} with parameters:'\n ' {params}'.format(resource_type=self.type_name,\n params=params))\n\n return self.client.start_pipeline_execution(**params)","def execute(self, *args, **kwargs):","def execute(self, *args, **kwargs):","async def generic_action(self, request):\n pass","def id(self, *args, **kwargs) -> Any:\n pass","def _execute_action(self, action):\n cmd, _self, args_, kwargs_, return_values = (\n action.name,\n action.target, # target is equivalent to the \"self\" in a method\n action.args,\n action.kwargs,\n action.return_ids,\n )\n _self = self._fetch_placeholders_from_ids(_self)\n args_ = self._fetch_placeholders_from_ids(args_)\n kwargs_ = self._fetch_placeholders_from_ids(kwargs_)\n return_values = self._fetch_placeholders_from_ids(return_values)\n\n # We can only instantiate placeholders, filter them\n return_placeholders = []\n Role.nested_object_traversal(\n return_values, lambda ph: return_placeholders.append(ph), PlaceHolder\n )\n\n if _self is None:\n method = self._fetch_package_method(cmd)\n response = method(*args_, **kwargs_)\n else:\n response = getattr(_self, cmd)(*args_, **kwargs_)\n\n if not isinstance(response, (tuple, list)):\n response = (response,)\n\n PlaceHolder.instantiate_placeholders(return_placeholders, response)","def dispatch(intent_request):\n\n logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))\n return diningsuggestions_intent(intent_request)","def _ExecuteRequest(request):\n service = request.service_name()\n method = request.method()\n service_methods = remote_api_services.SERVICE_PB_MAP.get(service, {})\n request_class, response_class = service_methods.get(method, (None, None))\n if not request_class:\n raise apiproxy_errors.CallNotFoundError('%s.%s does not exist' % (service,\n method))\n\n request_data = request_class()\n request_data.ParseFromString(request.request())\n response_data = response_class()\n\n def MakeRequest():\n apiproxy_stub_map.MakeSyncCall(service, method, request_data,\n response_data)\n\n\n\n if service in THREAD_SAFE_SERVICES:\n MakeRequest()\n else:\n with GLOBAL_API_LOCK:\n MakeRequest()\n return response_data","def process_incoming_request(private_key: bytes, feed_id: bytes, name: str) -> str:\n try:\n package = shareBuffer[name]\n except KeyError:\n raise SecretSharingError(\"Someone requested a non-existent share.\")\n return core.create_sub_event(core.E_TYPE.REPLY, sk=private_key, pk=feed_id, name=name, shard=package)","def execute(self, **payload):\n pass","async def request(self):\n # TODO: validate the state\n message = Message(self.name_path)\n await self.issue_command(Command(message))","def run(self, args_: Tuple, result_ids: List[Union[str, int]]):\n # TODO: can we reuse result_ids?\n return self.__call__(*args_)","async def submit_data(self, value: Any, request_id: str) -> Any:\r\n\r\n raise NotImplementedError","def process_request(self, req, resp, resource, params):","def create_command_from_request(request: RequestInterface):","async def get_by_name(request: web.Request) -> web.Response:\n try:\n name = request.match_info.get('script_name', None)\n storage = connect.get_storage_async()\n cf_mgr = ConfigurationManager(storage)\n payload = PayloadBuilder().SELECT(\"name\", \"steps\", \"acl\").WHERE(['name', '=', name]).payload()\n result = await storage.query_tbl_with_payload('control_script', payload)\n if 'rows' in result:\n if result['rows']:\n rows = result['rows'][0]\n rows['configuration'] = {}\n rows['schedule'] = {}\n try:\n # Add configuration to script\n cat_name = \"{}-automation-script\".format(rows['name'])\n get_category = await cf_mgr.get_category_all_items(cat_name)\n if get_category is not None:\n rows['configuration'] = {\"categoryName\": cat_name}\n rows['configuration'].update(get_category)\n # Add schedule to script\n sch = await server.Server.scheduler.get_schedule_by_name(rows['name'])\n rows['schedule'] = {\n 'id': str(sch.schedule_id),\n 'name': sch.name,\n 'processName': sch.process_name,\n 'type': Schedule.Type(int(sch.schedule_type)).name,\n 'repeat': 0,\n 'time': 0,\n 'day': sch.day,\n 'exclusive': sch.exclusive,\n 'enabled': sch.enabled\n }\n except:\n pass\n else:\n raise NameNotFoundError('Script with name {} is not found.'.format(name))\n else:\n raise StorageServerError(result)\n except StorageServerError as err:\n msg = \"Storage error: {}\".format(str(err))\n raise web.HTTPInternalServerError(reason=msg, body=json.dumps({\"message\": msg}))\n except NameNotFoundError as err:\n msg = str(err)\n raise web.HTTPNotFound(reason=msg, body=json.dumps({\"message\": msg}))\n except Exception as ex:\n msg = str(ex)\n _logger.error(ex, \"Get Control script by name failed.\")\n raise web.HTTPInternalServerError(reason=msg, body=json.dumps({\"message\": msg}))\n else:\n return web.json_response(rows)","def execute():\n pass","def task_trigger_raw(self, request):\n input_json = os.path.join(self._tmpdir, 'task_trigger_raw_input.json')\n with open(input_json, 'wb') as f:\n json.dump({'requests': [request]}, f)\n output_json = os.path.join(self._tmpdir, 'task_trigger_raw_output.json')\n args = [\n '-json-input',\n input_json,\n '-json-output',\n output_json,\n ]\n ret = self._run_swarming('spawn-tasks', args)\n assert ret == 0, 'Failed to spawn a task. exit_code=%d, args=%s' % (ret,\n args)\n with open(output_json, 'rb') as f:\n data = json.load(f)\n task_id = data['tasks'][0]['task_id']\n logging.debug('task_id = %s', task_id)\n return task_id","def request(self, cr, uid, ids,context=None):\n return self.write(cr, uid, ids, {'state':'request'}, context=context)","def __getattr__(self, name):\n return functools.partial(self._obj.request, self._api_prefix + name)","def calltask(self, name, **vars):\n if name in self._tasks:\n for entry in self._tasks[name]:\n entry.execute(vars)\n else:\n raise Error(\"No such task: {0}\".format(name))","def _extract_id_from_batch_response(r, name='id'):\n names = name + 's'\n if names in r:\n # soon-to-be deprecated batch reponse\n if 'errors' in r and r['errors']:\n raise GeneralException(r['errors'][0]['desc'])\n id = r[names][0]\n else:\n # new-style simplified api response\n id = r[name]\n return int(id)","def handleRequest(self, access_id, msg):\n log.msg('handling engine request for %s' % access_id)\n try:\n engine_client = yield self.backend.getEngine(access_id)\n log.msg('got engine Client %s' % str(engine_client))\n except InvalidAccessId:\n err = {'status':'ERR', 'response':'InvalidAccessId'}\n log.err('InvalidAccessId %s' % access_id)\n defer.returnValue(err)\n\n result = yield engine_client.send(msg)\n sucs = {'status':'OK', 'response':result}\n defer.returnValue(sucs)","def process_task(params):\n params['task'](params)","def process_outgoing_request(private_key: bytes, feed_id: bytes, name: str, password: str):\n return core.create_sub_event(t=core.E_TYPE.REQUEST, sk=private_key, pk=feed_id, name=name, password=password)","def __getattr__ (self, name) :\n\t\treturn functools.partial( self.run, name )","def execute_req4(catalog, req_country ,req_tag, n_sample):\n return controller.execute_req4(catalog, req_country ,req_tag, n_sample)","def run_injection(self, injection):\n\n self.make_request(injection)","async def handle_request(self, request: aioweb.request.Request):","def __call__(self):\n if grinder.runNumber == 0: self.initialSleep()\n (param1, param2) = self.getParam()\n self.request1(param1, param2)","def dispatch(intent_request):\n\n #logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))\n\n intent_name = intent_request['currentIntent']['name']\n print(\"asdd\")\n print(intent_name)\n \n # Dispatch to your bot's intent handlers\n if intent_name == 'GreetingIntent':\n return greeting_intent(intent_request)\n elif intent_name == 'DiningSuggestionsIntent':\n return dining_suggestion_intent(intent_request)\n elif intent_name == 'ThankYouIntent':\n return thank_you_intent(intent_request)\n\n raise Exception('Intent with name ' + intent_name + ' not supported')","def get_action_id(output):\n return output['Action queued with id']","def execute(*args):","def exposed_execute(self, text):\n execute(text, PublicService.exposed_namespace)","def call(self, name, *args, **kwargs):\r\n return self.client.call(self.name, name, *args, **kwargs)","def name(inp, mode, **kwargs):\n return name.dispatch(inp, mode, **kwargs)","def _retrieve_task_id(job_name, res_id, job_dict):\n if job_dict:\n workers = list(job_dict.keys())\n for worker in workers:\n for job in job_dict[worker]:\n if 'name' in job:\n if job['name'] == job_name:\n if res_id in job['args']:\n return job['id']\n elif 'request' in job:\n scheduled_job = job['request']\n if 'name' in scheduled_job:\n if scheduled_job['name'] == job_name:\n if res_id in scheduled_job['args']:\n return scheduled_job['id']\n\n return None","def export_getCurrentExecutionOrder(self,requestName):\n\n if type(requestName) in StringTypes:\n result = requestDB._getRequestAttribute('RequestID',requestName=requestName)\n if not result['OK']:\n return result\n requestID = result['Value']\n else:\n requestID = requestName\n\n result = requestDB.getCurrentExecutionOrder(requestID)\n return result","def execute_action(self, action_name, *args, **kw):\n action_dict = {}\n for robot_id in self._robot_group._robots_id_in_group_list:\n robot_module = self._robot_group.all_robots_dict[robot_id].get_module(self._module_name)\n action_dict[robot_id] = getattr(robot_module, action_name)(*args, **kw)\n logger.info(\"Multi Module robot id {0}: begin to execute the action\".format(robot_id))\n multi_action = MultiAction(action_dict)\n return multi_action","def output_name_to_id(self, name):\n for i, o in list(r.outputs.items()):\n if o.name == name:\n return i","def __call__(self, *args, **kwargs):\n result_ids = [sy.ID_PROVIDER.pop()]\n\n response = self.request_execute_plan(self.location, result_ids, *args)\n\n return response","def _get_id(key):\n id = request.args.get(key)\n if not id:\n raise NoIDError()\n else:\n return id","def execute(self, conn, name=\"\", transaction = False):\n sql = self.sql\n sql += \" WHERE S.SITE_NAME = :site_name\" \n binds = {\"site_name\":name}\n result = self.dbi.processData(sql, binds, conn, transaction)\n plist = self.formatDict(result)\n\tif len(plist) < 1: return -1\n return plist[0][\"site_id\"]","def execute(self) -> typing.Dict[str, typing.Any]:\n headers = {\n \"User-Agent\": \"{zenora.__name__} {zenora.__version__}\",\n \"Authorization\": f\"{self.token}\",\n }\n if self.headers:\n headers = self.headers\n\n if self.json:\n r = requests.request(\n method=self.method,\n url=self.url,\n headers=headers,\n json=self.json,\n )\n else:\n r = requests.request(\n method=self.method,\n url=self.url,\n headers=headers,\n data=self.form_data,\n )\n\n return raise_error_or_return(r) # type: ignore[return-value]","def execute_task(task_name, uuid, args, kwargs):\n driver = registry.get_driver() # Here is the main reason we have a singleton\n task_to_run = driver.get_task(task_name)\n log.info('Executing task {}'.format(task_name))\n log.debug('with param {}, {}'.format(args, kwargs))\n try:\n ret = TaskResponse(uuid, 'DONE',\n task_to_run.execute(*args, **kwargs))\n except Exception as exc:\n log.error('Error {} while running task {} with param {}, {}'\n ''.format(exc, task_name, args, kwargs))\n ret = TaskResponse(uuid, 'ERROR',\n exception=exc,\n traceback=sys.exc_info[2])\n\n ret = ret.to_dict()\n log.info('task {} executed'.format(task_name))\n log.debug('task returns {}'.format(ret))\n return ret","def test_named_params(self):\n req = '{\"jsonrpc\": \"2.0\", \"method\": \"subtract\", \"params\": {\"subtrahend\": 23, \"minuend\": 42}, \"id\": 3}'\n resp = '{\"jsonrpc\": \"2.0\", \"result\": 19, \"id\": 3}'\n status = 200\n r_status, r_resp = self.exec_handler(req)\n self.assertEqual(r_status, status)\n self.assertEqual(r_resp, resp)\n\n req = '{\"jsonrpc\": \"2.0\", \"method\": \"subtract\", \"params\": {\"minuend\": 42, \"subtrahend\": 23}, \"id\": 4}'\n resp = '{\"jsonrpc\": \"2.0\", \"result\": 19, \"id\": 4}'\n status = 200\n r_status, r_resp = self.exec_handler(req)\n self.assertEqual(r_status, status)\n self.assertEqual(r_resp, resp)","def execute_req1(catalog, req_category, req_country, n_sample):\n return controller.execute_req1(catalog, req_category, req_country, n_sample)","def fetch_execute(self):\n\n op_code = self.mem.read(self.reg.ip)\n self.reg.ip_inc()\n addr = self.mem.read(self.reg.ip)\n self.reg.ip_inc()\n\n # Execute the instruction on addr.\n self.op_codes[op_code.num](addr)","def execute_query(self, *args, **kwargs):","def _executeOperation(self, request:CSERequest, reqRi:str) -> Result:\n\t\t# Execute the actual operation\n\t\trequest.args.operation == Operation.RETRIEVE and (operationResult := CSE.dispatcher.processRetrieveRequest(request, request.headers.originator)) is not None\n\t\trequest.args.operation == Operation.CREATE and (operationResult := CSE.dispatcher.processCreateRequest(request, request.headers.originator)) is not None\n\t\trequest.args.operation == Operation.UPDATE and (operationResult := CSE.dispatcher.processUpdateRequest(request, request.headers.originator)) is not None\n\t\trequest.args.operation == Operation.DELETE and (operationResult := CSE.dispatcher.processDeleteRequest(request, request.headers.originator)) is not None\n\n\t\t# Retrieve the <request> resource\n\t\tif (res := CSE.dispatcher.retrieveResource(reqRi)).resource is None:\t\n\t\t\treturn Result(status=False) \t\t\t\t\t\t\t\t\t\t\t\t\t\t# No idea what we should do if this fails\n\t\treqres = res.resource\n\n\t\t# Fill the <request>\n\t\treqres['ors'] = {\t# operationResult\n\t\t\t'rsc'\t: operationResult.rsc,\n\t\t\t'rqi'\t: reqres.rid,\n\t\t\t'to'\t: request.id,\n\t\t\t'fr'\t: reqres.org,\n\t\t\t'ot'\t: reqres['mi/ot'],\n\t\t\t'rset'\t: reqres.et\n\t\t}\n\t\tif operationResult.rsc in [ RC.OK, RC.created, RC.updated, RC.deleted ] :\t\t\t# OK, created, updated, deleted -> resource\n\t\t\treqres['rs'] = RequestStatus.COMPLETED\n\t\t\tif operationResult.resource is not None:\n\t\t\t\treqres['ors/pc'] = operationResult.resource.asDict()\n\t\telse:\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Error\n\t\t\treqres['rs'] = RequestStatus.FAILED\n\t\t\tif operationResult.dbg is not None:\n\t\t\t\treqres['ors/pc'] = { 'm2m:dbg' : operationResult.dbg }\n\n\t\t# Update in DB\n\t\treqres.dbUpdate()\n\n\t\treturn Result(resource=reqres, status=True)","def _handle_request(self, info, desired=None):\r\n debug_print('%s request:' % info.name)\r\n\r\n editor = info.editor\r\n if ((not editor.is_python_like())\r\n or sourcecode.is_keyword(info.obj)\r\n or editor.in_comment_or_string()):\r\n desired = 'fallback'\r\n\r\n self.pending = (info, desired)\r\n if not self.busy:\r\n self._handle_pending()","def _process_request(self, request):\n try:\n self._validate_rpc_request(request)\n except ValueError as err:\n return self._build_rpc_error(None, RpcErrors.INVALID_REQUEST, err, keep_null_id=True)\n\n id = request.get('id', None)\n\n try:\n method = getattr(rpc, request['method'])\n except AttributeError as err:\n return self._build_rpc_error(id, RpcErrors.METHOD_NOT_FOUND, err)\n\n try:\n params = request.get('params', None)\n if params is None:\n result = method()\n elif isinstance(params, list):\n result = method(*params)\n elif isinstance(params, dict):\n result = method(**params)\n\n return self._build_rpc_result(id, result)\n\n except TypeError as err:\n return self._build_rpc_error(id, RpcErrors.INVALID_PARAMS, err)\n except Exception as err:\n return self._build_rpc_error(id, RpcErrors.INTERNAL_ERROR, err)","def _exec(self, bound_names, args, kw):\n if not kw.has_key('args'):\n kw['args'] = args\n bound_names['options'] = kw\n\n try:\n response = self.REQUEST.RESPONSE\n if not response.headers.has_key('content-type'):\n response.setHeader('content-type', self.content_type)\n except AttributeError:\n pass\n \n security = getSecurityManager()\n bound_names['user'] = security.getUser()\n \n # Retrieve the value from the cache.\n keyset = None\n if self.ZCacheable_isCachingEnabled():\n # Prepare a cache key.\n keyset = {'here': self._getContext(),\n 'bound_names': bound_names}\n result = self.ZCacheable_get(keywords=keyset)\n if result is not None:\n # Got a cached value.\n return result\n\n # Execute the template in a new security context.\n security.addContext(self)\n try:\n result = self._render_as(extra_context=bound_names,\n RESPONSE=response)\n if keyset is not None:\n # Store the result in the cache.\n self.ZCacheable_set(result, keywords=keyset)\n return result\n finally:\n security.removeContext(self)","def urlfor( name, **matchdict ) :","def _Dynamic_RunQuery(self, query, query_result, request_id=None):\n if query.has_transaction():\n if not query.has_ancestor():\n raise apiproxy_errors.ApplicationError(\n datastore_pb.Error.BAD_REQUEST,\n 'Only ancestor queries are allowed inside transactions.')\n (filters, orders) = datastore_index.Normalize(query.filter_list(),\n query.order_list(), [])\n \n old_datastore_stub_util.FillUsersInQuery(filters)\n\n if not query.has_app():\n query.set_app(self.project_id)\n self.__ValidateAppId(query.app())\n\n self._RemoteSend(query, query_result, \"RunQuery\", request_id)\n results = query_result.result_list()\n for result in results:\n old_datastore_stub_util.PrepareSpecialPropertiesForLoad(result)\n\n last_cursor = None\n if query_result.has_compiled_cursor():\n last_cursor = query_result.compiled_cursor()\n\n if query_result.more_results():\n new_cursor = InternalCursor(query, last_cursor, len(results))\n cursor_id = self.__getCursorID()\n cursor = query_result.mutable_cursor()\n cursor.set_app(self.project_id)\n cursor.set_cursor(cursor_id)\n self.__queries[cursor_id] = new_cursor\n\n if query.compile():\n compiled_query = query_result.mutable_compiled_query()\n compiled_query.set_keys_only(query.keys_only())\n compiled_query.mutable_primaryscan().set_index_name(query.Encode())","def execute(self):","def execute(self):","def execute(self):","def execute(self):","def _resolve_name(self, name, service=None):\n service = service or self.service\n if re.match(r'^[-a-f0-9]{30,}$', name):\n return name\n\n try:\n request_cls = getattr(services, service).GetAllRequest\n except AttributeError:\n raise NameResolutionError('Name resolution unavailable for {}'.format(service))\n\n request = request_cls.from_dict(dict(name=name, only_fields=['name', 'id']))\n # from_dict will ignore unrecognised keyword arguments - not all GetAll's have only_fields\n response = getattr(self._session.send_api(request), service)\n matches = [db_object for db_object in response if name.lower() == db_object.name.lower()]\n\n def truncated_bullet_list(format_string, elements, callback, **kwargs):\n if len(elements) > self.MAX_SUGGESTIONS:\n kwargs.update(\n dict(details=' (showing {}/{})'.format(self.MAX_SUGGESTIONS, len(elements)), suffix='\\n...'))\n else:\n kwargs.update(dict(details='', suffix=''))\n bullet_list = '\\n'.join('* {}'.format(callback(item)) for item in elements[:self.MAX_SUGGESTIONS])\n return format_string.format(bullet_list, **kwargs)\n\n if len(matches) == 1:\n return matches.pop().id\n elif len(matches) > 1:\n message = truncated_bullet_list(\n 'Found multiple {service} with name \"{name}\"{details}:\\n{}{suffix}',\n matches,\n callback=attrgetter('id'),\n **locals())\n self.exit(message)\n\n message = 'Could not find {} with name/id \"{}\"'.format(service.rstrip('s'), name)\n\n if not response:\n raise NameResolutionError(message)\n\n suggestions = truncated_bullet_list(\n '. Did you mean this?{details}\\n{}{suffix}',\n sorted(response, key=attrgetter('name')),\n lambda db_object: '({}) {}'.format(db_object.id, db_object.name)\n )\n raise NameResolutionError(message, suggestions)","def __call__( request, c ):","def __call__( request, c ):","async def _execute(self):","def _send_execute_command(self):\n client = SBusClient(self.storlet_pipe_path)\n try:\n resp = client.execute(self.srequest.params, self.remote_fds)\n if not resp.status:\n raise StorletRuntimeException(\"Failed to send execute command\")\n\n if not resp.task_id:\n raise StorletRuntimeException(\"Missing task id\")\n else:\n self.task_id = resp.task_id\n except SBusClientException:\n raise StorletRuntimeException(\"Failed to send execute command\")","def execute(self, *args, **kwargs):\n pass","def dispatch(intent_request):\n\n logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))\n\n intent_name = intent_request['currentIntent']['name']\n\n # Dispatch to your bot's intent handlers\n if intent_name == 'Help':\n return order_help(intent_request)\n elif intent_name == 'FastFood':\n return order_snacks(intent_request)\n elif intent_name == 'Beverages':\n return order_beverages(intent_request)\n elif intent_name == 'Admin':\n return admin(intent_request)\n raise Exception('Intent with name ' + intent_name + ' not supported')","def _execute(self):\n\n action_name = self.action.lower()\n method_name = action_name.replace('node', 'do')\n method = getattr(self, method_name, None)\n\n if method is None:\n reason = 'Unsupported action: %s' % self.action\n EVENT.error(self, consts.PHASE_ERROR, reason)\n return self.RES_ERROR, reason\n\n return method()","def execute(self) -> Any:\n return self.function(**self.kwargs)","def tasks_rpc():\n # First check that this is a legitimate request from the coordinator\n authenticate_coordinator()\n action, task_id, release_id = validate_action(request.get_json(force=True))\n # Call into action\n return ROUTES[action](task_id, release_id)","def dispatch(intent_request):\n\n logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))\n\n intent_name = intent_request['currentIntent']['name']\n\n # Dispatch to your bot's intent handlers\n if intent_name == 'ScheduleMeeting':\n return schedule_meeting(intent_request)\n raise Exception('Intent with name ' + intent_name + ' not supported')","def request(self, msg):\n\t\tif msg.command in ('AUTH', 'EXIT', 'GET', 'SET', 'VERSION', 'COMMAND', 'UPLOAD'):\n\t\t\tmethod = 'handle_request_%s' % (msg.command.lower(),)\n\t\telse:\n\t\t\tmethod = 'handle_request_unknown'\n\n\t\tself.execute(method, msg)","def reqid(self) -> str:","def reqid(self) -> str:"],"string":"[\n \"def _execute(self, _):\\r\\n pass\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def execute():\",\n \"def request(query):\",\n \"def perform_action(self, action_id: int) -> None:\\r\\n ...\",\n \"def _send_request(self, code):\\n msg_id = uuid4().hex\\n message = KernelClient.__create_execute_request(msg_id, code)\\n\\n # create response-queue and add to map for this msg_id\\n self.response_queues[msg_id] = queue.Queue()\\n\\n self.kernel_socket.send(message)\\n\\n return msg_id\",\n \"def submit(id, host):\",\n \"def getEventIDName(*args, **kwargs):\\n pass\",\n \"def execute(self, response):\\n raise NotImplementedError()\",\n \"def process_actions(parameter: str, action_name: str) -> str:\\n\\n try:\\n if action_name == \\\"login\\\":\\n return create_followup_event_data(parameter[2])\\n elif action_name == \\\"get_exam_date\\\":\\n return create_data_response(DatabaseExtractor.get_exam_date(parameter[1]))\\n elif action_name == \\\"get_assessment_form\\\":\\n return create_data_response(DatabaseExtractor.get_assessment_form(parameter[1]))\\n elif action_name == \\\"get_contact_mail\\\":\\n return create_data_response(DatabaseExtractor.get_contact_mail(parameter[1]))\\n elif action_name == \\\"get_contact_name\\\":\\n return create_data_response(DatabaseExtractor.get_contact_name(parameter[1]))\\n elif action_name == \\\"get_contact_phone\\\":\\n return create_data_response(DatabaseExtractor.get_contact_phone(parameter[1]))\\n elif action_name == \\\"get_contact_website\\\":\\n return create_data_response(DatabaseExtractor.get_contact_website(parameter[1]))\\n elif action_name == \\\"get_office\\\":\\n return create_data_response(DatabaseExtractor.get_contact_office(parameter[1]))\\n elif action_name == \\\"get_teaching_form\\\":\\n return create_data_response(DatabaseExtractor.get_teaching_form(parameter[1]))\\n elif action_name == \\\"get_course_name\\\":\\n return create_data_response(DatabaseExtractor.get_course_name(parameter[1]))\\n elif action_name == \\\"get_credit\\\":\\n return create_data_response(DatabaseExtractor.get_credit(parameter[1]))\\n elif action_name == \\\"get_url\\\":\\n return create_data_response(DatabaseExtractor.get_url(parameter[1]))\\n elif action_name == \\\"get_prereq_knowledge\\\":\\n return create_data_response(DatabaseExtractor.get_prereq_knowledge(parameter[1]))\\n elif action_name == \\\"get_course_content\\\":\\n return create_data_response(DatabaseExtractor.get_course_content(parameter[1]))\\n elif action_name == \\\"get_course_material\\\":\\n return create_data_response(DatabaseExtractor.get_course_material(parameter[1]))\\n elif action_name == \\\"get_teaching_form\\\":\\n return create_data_response(DatabaseExtractor.get_teaching_form(parameter[1]))\\n elif action_name == \\\"get_exercise_status\\\":\\n return create_data_response(DatabaseExtractor.get_exercise_status(parameter[1], parameter[0]))\\n elif action_name==\\\"get_exercise_scheme_approval\\\":\\n return create_data_response(DatabaseExtractor.get_exercise_scheme_approval(parameter[1], parameter[0]))\\n elif action_name==\\\"get_exercises_left\\\":\\n return create_data_response(DatabaseExtractor.get_exercises_left(parameter[1], parameter[0]))\\n elif action_name == \\\"get_next_event\\\":\\n return create_data_response(DatabaseExtractor.get_next_event(username=parameter[0]))\\n elif action_name == \\\"get_next_assignment\\\":\\n return create_data_response(DatabaseExtractor.get_next_assignment(username=parameter[0]))\\n elif action_name == \\\"get_this_weeks_schedule\\\":\\n return create_data_response(DatabaseExtractor.get_this_week_schedule(username=parameter[0]))\\n elif action_name == \\\"get_next_weeks_schedule\\\":\\n return create_data_response(DatabaseExtractor.get_next_week_schedule(username=parameter[0]))\\n elif action_name == \\\"get_next_weeks_events\\\":\\n return create_data_response(DatabaseExtractor.get_next_weeks_events(username=parameter[0]))\\n elif action_name == \\\"get_next_weeks_assignments\\\":\\n return create_data_response(DatabaseExtractor.get_next_weeks_assignments(username=parameter[0]))\\n elif action_name == \\\"get_this_weeks_assignments\\\":\\n return create_data_response(DatabaseExtractor.get_this_weeks_assignments(username=parameter[0]))\\n elif action_name == \\\"get_this_weeks_events\\\":\\n return create_data_response(DatabaseExtractor.get_this_weeks_events(username=parameter[0]))\\n elif action_name == \\\"get_exam_dates\\\":\\n return create_data_response(DatabaseExtractor.get_exam_dates(username=parameter[0]))\\n elif action_name == \\\"get_days_until_first_exam\\\":\\n return create_data_response(DatabaseExtractor.get_days_until_first_exam(username=parameter[0]))\\n elif action_name == \\\"get_course_codes\\\":\\n return create_data_response(DatabaseExtractor.get_course_codes(username=parameter[0]))\\n elif action_name == \\\"get_course_names\\\":\\n return create_data_response(DatabaseExtractor.get_course_names(username=parameter[0]))\\n elif action_name == \\\"get_number_of_courses\\\":\\n return create_data_response(DatabaseExtractor.get_number_of_courses(username=parameter[0]))\\n elif action_name == \\\"get_today_assignments\\\":\\n return create_data_response(DatabaseExtractor.get_today_assignments(username=parameter[0]))\\n elif action_name == \\\"get_tomorrow_assignments\\\":\\n return create_data_response(DatabaseExtractor.get_tomorrow_assignments(username=parameter[0]))\\n elif action_name == \\\"get_today_events\\\":\\n return create_data_response(DatabaseExtractor.get_today_events(username=parameter[0]))\\n elif action_name == \\\"get_tomorrow_events\\\":\\n return create_data_response(DatabaseExtractor.get_tomorrow_events(username=parameter[0]))\\n else:\\n return create_data_response(\\\"I didn't understand anything, you probably broke me :(\\\")\\n\\n except:\\n return create_data_response(\\\"Sorry, i can not answer that.\\\")\",\n \"def _cmd(self, name, *args, **kwargs):\\n if name in ['FETCH', 'SORT', 'STORE', 'COPY', 'SEARCH']:\\n try:\\n typ, data = self.m.uid(name, *args)\\n except imaplib.IMAP4.error as e:\\n raise ImapError(e)\\n if typ == \\\"NO\\\":\\n raise ImapError(data)\\n if name == 'FETCH':\\n return FetchResponseParser().parse(data)\\n return data\\n\\n try:\\n typ, data = self.m._simple_command(name, *args)\\n except imaplib.IMAP4.error as e:\\n raise ImapError(e)\\n if typ == \\\"NO\\\":\\n raise ImapError(data)\\n if 'responses' not in kwargs:\\n if name not in self.m.untagged_responses:\\n return None\\n return self.m.untagged_responses.pop(name)\\n res = []\\n for r in kwargs['responses']:\\n if r not in self.m.untagged_responses:\\n return None\\n res.append(self.m.untagged_responses.pop(r))\\n return res\",\n \"def request_execute_plan(\\n self,\\n location: \\\"sy.workers.BaseWorker\\\",\\n response_ids: List[Union[str, int]],\\n *args,\\n **kwargs,\\n ) -> object:\\n plan_name = f\\\"plan{self.id}\\\"\\n # args, _, _ = hook_args.unwrap_args_from_function(\\n # plan_name, args, {}\\n # )\\n args = [args, response_ids]\\n\\n command = (\\\"execute_plan\\\", self.id_at_location, args, kwargs)\\n\\n response = self.owner.send_command(\\n message=command, recipient=location, return_ids=response_ids\\n )\\n response = hook_args.hook_response(plan_name, response, wrap_type=FrameworkTensor[0])\\n response.garbage_collect_data = False\\n return response\",\n \"def PostSearchesByID(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def process_id(job_id):\\n pass # Not implemented yet\",\n \"def taskid(name):\\n return \\\"(select id from event_type where name = '{}')\\\".format(name)\",\n \"def _add_identifiers_to_request(request, response_id):\\n meta = request.meta\\n meta['__id'] = response_id\\n return request.replace(meta=meta)\",\n \"def execute(self, name=None, clientRequestToken=None):\\n params = {'name': name if name else self.resource_id}\\n if clientRequestToken:\\n params.update({\\\"clientRequestToken\\\": clientRequestToken})\\n self.logger.debug('Executing {resource_type} with parameters:'\\n ' {params}'.format(resource_type=self.type_name,\\n params=params))\\n\\n return self.client.start_pipeline_execution(**params)\",\n \"def execute(self, *args, **kwargs):\",\n \"def execute(self, *args, **kwargs):\",\n \"async def generic_action(self, request):\\n pass\",\n \"def id(self, *args, **kwargs) -> Any:\\n pass\",\n \"def _execute_action(self, action):\\n cmd, _self, args_, kwargs_, return_values = (\\n action.name,\\n action.target, # target is equivalent to the \\\"self\\\" in a method\\n action.args,\\n action.kwargs,\\n action.return_ids,\\n )\\n _self = self._fetch_placeholders_from_ids(_self)\\n args_ = self._fetch_placeholders_from_ids(args_)\\n kwargs_ = self._fetch_placeholders_from_ids(kwargs_)\\n return_values = self._fetch_placeholders_from_ids(return_values)\\n\\n # We can only instantiate placeholders, filter them\\n return_placeholders = []\\n Role.nested_object_traversal(\\n return_values, lambda ph: return_placeholders.append(ph), PlaceHolder\\n )\\n\\n if _self is None:\\n method = self._fetch_package_method(cmd)\\n response = method(*args_, **kwargs_)\\n else:\\n response = getattr(_self, cmd)(*args_, **kwargs_)\\n\\n if not isinstance(response, (tuple, list)):\\n response = (response,)\\n\\n PlaceHolder.instantiate_placeholders(return_placeholders, response)\",\n \"def dispatch(intent_request):\\n\\n logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))\\n return diningsuggestions_intent(intent_request)\",\n \"def _ExecuteRequest(request):\\n service = request.service_name()\\n method = request.method()\\n service_methods = remote_api_services.SERVICE_PB_MAP.get(service, {})\\n request_class, response_class = service_methods.get(method, (None, None))\\n if not request_class:\\n raise apiproxy_errors.CallNotFoundError('%s.%s does not exist' % (service,\\n method))\\n\\n request_data = request_class()\\n request_data.ParseFromString(request.request())\\n response_data = response_class()\\n\\n def MakeRequest():\\n apiproxy_stub_map.MakeSyncCall(service, method, request_data,\\n response_data)\\n\\n\\n\\n if service in THREAD_SAFE_SERVICES:\\n MakeRequest()\\n else:\\n with GLOBAL_API_LOCK:\\n MakeRequest()\\n return response_data\",\n \"def process_incoming_request(private_key: bytes, feed_id: bytes, name: str) -> str:\\n try:\\n package = shareBuffer[name]\\n except KeyError:\\n raise SecretSharingError(\\\"Someone requested a non-existent share.\\\")\\n return core.create_sub_event(core.E_TYPE.REPLY, sk=private_key, pk=feed_id, name=name, shard=package)\",\n \"def execute(self, **payload):\\n pass\",\n \"async def request(self):\\n # TODO: validate the state\\n message = Message(self.name_path)\\n await self.issue_command(Command(message))\",\n \"def run(self, args_: Tuple, result_ids: List[Union[str, int]]):\\n # TODO: can we reuse result_ids?\\n return self.__call__(*args_)\",\n \"async def submit_data(self, value: Any, request_id: str) -> Any:\\r\\n\\r\\n raise NotImplementedError\",\n \"def process_request(self, req, resp, resource, params):\",\n \"def create_command_from_request(request: RequestInterface):\",\n \"async def get_by_name(request: web.Request) -> web.Response:\\n try:\\n name = request.match_info.get('script_name', None)\\n storage = connect.get_storage_async()\\n cf_mgr = ConfigurationManager(storage)\\n payload = PayloadBuilder().SELECT(\\\"name\\\", \\\"steps\\\", \\\"acl\\\").WHERE(['name', '=', name]).payload()\\n result = await storage.query_tbl_with_payload('control_script', payload)\\n if 'rows' in result:\\n if result['rows']:\\n rows = result['rows'][0]\\n rows['configuration'] = {}\\n rows['schedule'] = {}\\n try:\\n # Add configuration to script\\n cat_name = \\\"{}-automation-script\\\".format(rows['name'])\\n get_category = await cf_mgr.get_category_all_items(cat_name)\\n if get_category is not None:\\n rows['configuration'] = {\\\"categoryName\\\": cat_name}\\n rows['configuration'].update(get_category)\\n # Add schedule to script\\n sch = await server.Server.scheduler.get_schedule_by_name(rows['name'])\\n rows['schedule'] = {\\n 'id': str(sch.schedule_id),\\n 'name': sch.name,\\n 'processName': sch.process_name,\\n 'type': Schedule.Type(int(sch.schedule_type)).name,\\n 'repeat': 0,\\n 'time': 0,\\n 'day': sch.day,\\n 'exclusive': sch.exclusive,\\n 'enabled': sch.enabled\\n }\\n except:\\n pass\\n else:\\n raise NameNotFoundError('Script with name {} is not found.'.format(name))\\n else:\\n raise StorageServerError(result)\\n except StorageServerError as err:\\n msg = \\\"Storage error: {}\\\".format(str(err))\\n raise web.HTTPInternalServerError(reason=msg, body=json.dumps({\\\"message\\\": msg}))\\n except NameNotFoundError as err:\\n msg = str(err)\\n raise web.HTTPNotFound(reason=msg, body=json.dumps({\\\"message\\\": msg}))\\n except Exception as ex:\\n msg = str(ex)\\n _logger.error(ex, \\\"Get Control script by name failed.\\\")\\n raise web.HTTPInternalServerError(reason=msg, body=json.dumps({\\\"message\\\": msg}))\\n else:\\n return web.json_response(rows)\",\n \"def execute():\\n pass\",\n \"def task_trigger_raw(self, request):\\n input_json = os.path.join(self._tmpdir, 'task_trigger_raw_input.json')\\n with open(input_json, 'wb') as f:\\n json.dump({'requests': [request]}, f)\\n output_json = os.path.join(self._tmpdir, 'task_trigger_raw_output.json')\\n args = [\\n '-json-input',\\n input_json,\\n '-json-output',\\n output_json,\\n ]\\n ret = self._run_swarming('spawn-tasks', args)\\n assert ret == 0, 'Failed to spawn a task. exit_code=%d, args=%s' % (ret,\\n args)\\n with open(output_json, 'rb') as f:\\n data = json.load(f)\\n task_id = data['tasks'][0]['task_id']\\n logging.debug('task_id = %s', task_id)\\n return task_id\",\n \"def request(self, cr, uid, ids,context=None):\\n return self.write(cr, uid, ids, {'state':'request'}, context=context)\",\n \"def __getattr__(self, name):\\n return functools.partial(self._obj.request, self._api_prefix + name)\",\n \"def calltask(self, name, **vars):\\n if name in self._tasks:\\n for entry in self._tasks[name]:\\n entry.execute(vars)\\n else:\\n raise Error(\\\"No such task: {0}\\\".format(name))\",\n \"def _extract_id_from_batch_response(r, name='id'):\\n names = name + 's'\\n if names in r:\\n # soon-to-be deprecated batch reponse\\n if 'errors' in r and r['errors']:\\n raise GeneralException(r['errors'][0]['desc'])\\n id = r[names][0]\\n else:\\n # new-style simplified api response\\n id = r[name]\\n return int(id)\",\n \"def handleRequest(self, access_id, msg):\\n log.msg('handling engine request for %s' % access_id)\\n try:\\n engine_client = yield self.backend.getEngine(access_id)\\n log.msg('got engine Client %s' % str(engine_client))\\n except InvalidAccessId:\\n err = {'status':'ERR', 'response':'InvalidAccessId'}\\n log.err('InvalidAccessId %s' % access_id)\\n defer.returnValue(err)\\n\\n result = yield engine_client.send(msg)\\n sucs = {'status':'OK', 'response':result}\\n defer.returnValue(sucs)\",\n \"def process_task(params):\\n params['task'](params)\",\n \"def process_outgoing_request(private_key: bytes, feed_id: bytes, name: str, password: str):\\n return core.create_sub_event(t=core.E_TYPE.REQUEST, sk=private_key, pk=feed_id, name=name, password=password)\",\n \"def __getattr__ (self, name) :\\n\\t\\treturn functools.partial( self.run, name )\",\n \"def execute_req4(catalog, req_country ,req_tag, n_sample):\\n return controller.execute_req4(catalog, req_country ,req_tag, n_sample)\",\n \"def run_injection(self, injection):\\n\\n self.make_request(injection)\",\n \"async def handle_request(self, request: aioweb.request.Request):\",\n \"def __call__(self):\\n if grinder.runNumber == 0: self.initialSleep()\\n (param1, param2) = self.getParam()\\n self.request1(param1, param2)\",\n \"def dispatch(intent_request):\\n\\n #logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))\\n\\n intent_name = intent_request['currentIntent']['name']\\n print(\\\"asdd\\\")\\n print(intent_name)\\n \\n # Dispatch to your bot's intent handlers\\n if intent_name == 'GreetingIntent':\\n return greeting_intent(intent_request)\\n elif intent_name == 'DiningSuggestionsIntent':\\n return dining_suggestion_intent(intent_request)\\n elif intent_name == 'ThankYouIntent':\\n return thank_you_intent(intent_request)\\n\\n raise Exception('Intent with name ' + intent_name + ' not supported')\",\n \"def get_action_id(output):\\n return output['Action queued with id']\",\n \"def execute(*args):\",\n \"def exposed_execute(self, text):\\n execute(text, PublicService.exposed_namespace)\",\n \"def call(self, name, *args, **kwargs):\\r\\n return self.client.call(self.name, name, *args, **kwargs)\",\n \"def name(inp, mode, **kwargs):\\n return name.dispatch(inp, mode, **kwargs)\",\n \"def _retrieve_task_id(job_name, res_id, job_dict):\\n if job_dict:\\n workers = list(job_dict.keys())\\n for worker in workers:\\n for job in job_dict[worker]:\\n if 'name' in job:\\n if job['name'] == job_name:\\n if res_id in job['args']:\\n return job['id']\\n elif 'request' in job:\\n scheduled_job = job['request']\\n if 'name' in scheduled_job:\\n if scheduled_job['name'] == job_name:\\n if res_id in scheduled_job['args']:\\n return scheduled_job['id']\\n\\n return None\",\n \"def export_getCurrentExecutionOrder(self,requestName):\\n\\n if type(requestName) in StringTypes:\\n result = requestDB._getRequestAttribute('RequestID',requestName=requestName)\\n if not result['OK']:\\n return result\\n requestID = result['Value']\\n else:\\n requestID = requestName\\n\\n result = requestDB.getCurrentExecutionOrder(requestID)\\n return result\",\n \"def execute_action(self, action_name, *args, **kw):\\n action_dict = {}\\n for robot_id in self._robot_group._robots_id_in_group_list:\\n robot_module = self._robot_group.all_robots_dict[robot_id].get_module(self._module_name)\\n action_dict[robot_id] = getattr(robot_module, action_name)(*args, **kw)\\n logger.info(\\\"Multi Module robot id {0}: begin to execute the action\\\".format(robot_id))\\n multi_action = MultiAction(action_dict)\\n return multi_action\",\n \"def output_name_to_id(self, name):\\n for i, o in list(r.outputs.items()):\\n if o.name == name:\\n return i\",\n \"def __call__(self, *args, **kwargs):\\n result_ids = [sy.ID_PROVIDER.pop()]\\n\\n response = self.request_execute_plan(self.location, result_ids, *args)\\n\\n return response\",\n \"def _get_id(key):\\n id = request.args.get(key)\\n if not id:\\n raise NoIDError()\\n else:\\n return id\",\n \"def execute(self, conn, name=\\\"\\\", transaction = False):\\n sql = self.sql\\n sql += \\\" WHERE S.SITE_NAME = :site_name\\\" \\n binds = {\\\"site_name\\\":name}\\n result = self.dbi.processData(sql, binds, conn, transaction)\\n plist = self.formatDict(result)\\n\\tif len(plist) < 1: return -1\\n return plist[0][\\\"site_id\\\"]\",\n \"def execute(self) -> typing.Dict[str, typing.Any]:\\n headers = {\\n \\\"User-Agent\\\": \\\"{zenora.__name__} {zenora.__version__}\\\",\\n \\\"Authorization\\\": f\\\"{self.token}\\\",\\n }\\n if self.headers:\\n headers = self.headers\\n\\n if self.json:\\n r = requests.request(\\n method=self.method,\\n url=self.url,\\n headers=headers,\\n json=self.json,\\n )\\n else:\\n r = requests.request(\\n method=self.method,\\n url=self.url,\\n headers=headers,\\n data=self.form_data,\\n )\\n\\n return raise_error_or_return(r) # type: ignore[return-value]\",\n \"def execute_task(task_name, uuid, args, kwargs):\\n driver = registry.get_driver() # Here is the main reason we have a singleton\\n task_to_run = driver.get_task(task_name)\\n log.info('Executing task {}'.format(task_name))\\n log.debug('with param {}, {}'.format(args, kwargs))\\n try:\\n ret = TaskResponse(uuid, 'DONE',\\n task_to_run.execute(*args, **kwargs))\\n except Exception as exc:\\n log.error('Error {} while running task {} with param {}, {}'\\n ''.format(exc, task_name, args, kwargs))\\n ret = TaskResponse(uuid, 'ERROR',\\n exception=exc,\\n traceback=sys.exc_info[2])\\n\\n ret = ret.to_dict()\\n log.info('task {} executed'.format(task_name))\\n log.debug('task returns {}'.format(ret))\\n return ret\",\n \"def test_named_params(self):\\n req = '{\\\"jsonrpc\\\": \\\"2.0\\\", \\\"method\\\": \\\"subtract\\\", \\\"params\\\": {\\\"subtrahend\\\": 23, \\\"minuend\\\": 42}, \\\"id\\\": 3}'\\n resp = '{\\\"jsonrpc\\\": \\\"2.0\\\", \\\"result\\\": 19, \\\"id\\\": 3}'\\n status = 200\\n r_status, r_resp = self.exec_handler(req)\\n self.assertEqual(r_status, status)\\n self.assertEqual(r_resp, resp)\\n\\n req = '{\\\"jsonrpc\\\": \\\"2.0\\\", \\\"method\\\": \\\"subtract\\\", \\\"params\\\": {\\\"minuend\\\": 42, \\\"subtrahend\\\": 23}, \\\"id\\\": 4}'\\n resp = '{\\\"jsonrpc\\\": \\\"2.0\\\", \\\"result\\\": 19, \\\"id\\\": 4}'\\n status = 200\\n r_status, r_resp = self.exec_handler(req)\\n self.assertEqual(r_status, status)\\n self.assertEqual(r_resp, resp)\",\n \"def execute_req1(catalog, req_category, req_country, n_sample):\\n return controller.execute_req1(catalog, req_category, req_country, n_sample)\",\n \"def fetch_execute(self):\\n\\n op_code = self.mem.read(self.reg.ip)\\n self.reg.ip_inc()\\n addr = self.mem.read(self.reg.ip)\\n self.reg.ip_inc()\\n\\n # Execute the instruction on addr.\\n self.op_codes[op_code.num](addr)\",\n \"def execute_query(self, *args, **kwargs):\",\n \"def _executeOperation(self, request:CSERequest, reqRi:str) -> Result:\\n\\t\\t# Execute the actual operation\\n\\t\\trequest.args.operation == Operation.RETRIEVE and (operationResult := CSE.dispatcher.processRetrieveRequest(request, request.headers.originator)) is not None\\n\\t\\trequest.args.operation == Operation.CREATE and (operationResult := CSE.dispatcher.processCreateRequest(request, request.headers.originator)) is not None\\n\\t\\trequest.args.operation == Operation.UPDATE and (operationResult := CSE.dispatcher.processUpdateRequest(request, request.headers.originator)) is not None\\n\\t\\trequest.args.operation == Operation.DELETE and (operationResult := CSE.dispatcher.processDeleteRequest(request, request.headers.originator)) is not None\\n\\n\\t\\t# Retrieve the <request> resource\\n\\t\\tif (res := CSE.dispatcher.retrieveResource(reqRi)).resource is None:\\t\\n\\t\\t\\treturn Result(status=False) \\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# No idea what we should do if this fails\\n\\t\\treqres = res.resource\\n\\n\\t\\t# Fill the <request>\\n\\t\\treqres['ors'] = {\\t# operationResult\\n\\t\\t\\t'rsc'\\t: operationResult.rsc,\\n\\t\\t\\t'rqi'\\t: reqres.rid,\\n\\t\\t\\t'to'\\t: request.id,\\n\\t\\t\\t'fr'\\t: reqres.org,\\n\\t\\t\\t'ot'\\t: reqres['mi/ot'],\\n\\t\\t\\t'rset'\\t: reqres.et\\n\\t\\t}\\n\\t\\tif operationResult.rsc in [ RC.OK, RC.created, RC.updated, RC.deleted ] :\\t\\t\\t# OK, created, updated, deleted -> resource\\n\\t\\t\\treqres['rs'] = RequestStatus.COMPLETED\\n\\t\\t\\tif operationResult.resource is not None:\\n\\t\\t\\t\\treqres['ors/pc'] = operationResult.resource.asDict()\\n\\t\\telse:\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# Error\\n\\t\\t\\treqres['rs'] = RequestStatus.FAILED\\n\\t\\t\\tif operationResult.dbg is not None:\\n\\t\\t\\t\\treqres['ors/pc'] = { 'm2m:dbg' : operationResult.dbg }\\n\\n\\t\\t# Update in DB\\n\\t\\treqres.dbUpdate()\\n\\n\\t\\treturn Result(resource=reqres, status=True)\",\n \"def _handle_request(self, info, desired=None):\\r\\n debug_print('%s request:' % info.name)\\r\\n\\r\\n editor = info.editor\\r\\n if ((not editor.is_python_like())\\r\\n or sourcecode.is_keyword(info.obj)\\r\\n or editor.in_comment_or_string()):\\r\\n desired = 'fallback'\\r\\n\\r\\n self.pending = (info, desired)\\r\\n if not self.busy:\\r\\n self._handle_pending()\",\n \"def _process_request(self, request):\\n try:\\n self._validate_rpc_request(request)\\n except ValueError as err:\\n return self._build_rpc_error(None, RpcErrors.INVALID_REQUEST, err, keep_null_id=True)\\n\\n id = request.get('id', None)\\n\\n try:\\n method = getattr(rpc, request['method'])\\n except AttributeError as err:\\n return self._build_rpc_error(id, RpcErrors.METHOD_NOT_FOUND, err)\\n\\n try:\\n params = request.get('params', None)\\n if params is None:\\n result = method()\\n elif isinstance(params, list):\\n result = method(*params)\\n elif isinstance(params, dict):\\n result = method(**params)\\n\\n return self._build_rpc_result(id, result)\\n\\n except TypeError as err:\\n return self._build_rpc_error(id, RpcErrors.INVALID_PARAMS, err)\\n except Exception as err:\\n return self._build_rpc_error(id, RpcErrors.INTERNAL_ERROR, err)\",\n \"def _exec(self, bound_names, args, kw):\\n if not kw.has_key('args'):\\n kw['args'] = args\\n bound_names['options'] = kw\\n\\n try:\\n response = self.REQUEST.RESPONSE\\n if not response.headers.has_key('content-type'):\\n response.setHeader('content-type', self.content_type)\\n except AttributeError:\\n pass\\n \\n security = getSecurityManager()\\n bound_names['user'] = security.getUser()\\n \\n # Retrieve the value from the cache.\\n keyset = None\\n if self.ZCacheable_isCachingEnabled():\\n # Prepare a cache key.\\n keyset = {'here': self._getContext(),\\n 'bound_names': bound_names}\\n result = self.ZCacheable_get(keywords=keyset)\\n if result is not None:\\n # Got a cached value.\\n return result\\n\\n # Execute the template in a new security context.\\n security.addContext(self)\\n try:\\n result = self._render_as(extra_context=bound_names,\\n RESPONSE=response)\\n if keyset is not None:\\n # Store the result in the cache.\\n self.ZCacheable_set(result, keywords=keyset)\\n return result\\n finally:\\n security.removeContext(self)\",\n \"def urlfor( name, **matchdict ) :\",\n \"def _Dynamic_RunQuery(self, query, query_result, request_id=None):\\n if query.has_transaction():\\n if not query.has_ancestor():\\n raise apiproxy_errors.ApplicationError(\\n datastore_pb.Error.BAD_REQUEST,\\n 'Only ancestor queries are allowed inside transactions.')\\n (filters, orders) = datastore_index.Normalize(query.filter_list(),\\n query.order_list(), [])\\n \\n old_datastore_stub_util.FillUsersInQuery(filters)\\n\\n if not query.has_app():\\n query.set_app(self.project_id)\\n self.__ValidateAppId(query.app())\\n\\n self._RemoteSend(query, query_result, \\\"RunQuery\\\", request_id)\\n results = query_result.result_list()\\n for result in results:\\n old_datastore_stub_util.PrepareSpecialPropertiesForLoad(result)\\n\\n last_cursor = None\\n if query_result.has_compiled_cursor():\\n last_cursor = query_result.compiled_cursor()\\n\\n if query_result.more_results():\\n new_cursor = InternalCursor(query, last_cursor, len(results))\\n cursor_id = self.__getCursorID()\\n cursor = query_result.mutable_cursor()\\n cursor.set_app(self.project_id)\\n cursor.set_cursor(cursor_id)\\n self.__queries[cursor_id] = new_cursor\\n\\n if query.compile():\\n compiled_query = query_result.mutable_compiled_query()\\n compiled_query.set_keys_only(query.keys_only())\\n compiled_query.mutable_primaryscan().set_index_name(query.Encode())\",\n \"def execute(self):\",\n \"def execute(self):\",\n \"def execute(self):\",\n \"def execute(self):\",\n \"def _resolve_name(self, name, service=None):\\n service = service or self.service\\n if re.match(r'^[-a-f0-9]{30,}$', name):\\n return name\\n\\n try:\\n request_cls = getattr(services, service).GetAllRequest\\n except AttributeError:\\n raise NameResolutionError('Name resolution unavailable for {}'.format(service))\\n\\n request = request_cls.from_dict(dict(name=name, only_fields=['name', 'id']))\\n # from_dict will ignore unrecognised keyword arguments - not all GetAll's have only_fields\\n response = getattr(self._session.send_api(request), service)\\n matches = [db_object for db_object in response if name.lower() == db_object.name.lower()]\\n\\n def truncated_bullet_list(format_string, elements, callback, **kwargs):\\n if len(elements) > self.MAX_SUGGESTIONS:\\n kwargs.update(\\n dict(details=' (showing {}/{})'.format(self.MAX_SUGGESTIONS, len(elements)), suffix='\\\\n...'))\\n else:\\n kwargs.update(dict(details='', suffix=''))\\n bullet_list = '\\\\n'.join('* {}'.format(callback(item)) for item in elements[:self.MAX_SUGGESTIONS])\\n return format_string.format(bullet_list, **kwargs)\\n\\n if len(matches) == 1:\\n return matches.pop().id\\n elif len(matches) > 1:\\n message = truncated_bullet_list(\\n 'Found multiple {service} with name \\\"{name}\\\"{details}:\\\\n{}{suffix}',\\n matches,\\n callback=attrgetter('id'),\\n **locals())\\n self.exit(message)\\n\\n message = 'Could not find {} with name/id \\\"{}\\\"'.format(service.rstrip('s'), name)\\n\\n if not response:\\n raise NameResolutionError(message)\\n\\n suggestions = truncated_bullet_list(\\n '. Did you mean this?{details}\\\\n{}{suffix}',\\n sorted(response, key=attrgetter('name')),\\n lambda db_object: '({}) {}'.format(db_object.id, db_object.name)\\n )\\n raise NameResolutionError(message, suggestions)\",\n \"def __call__( request, c ):\",\n \"def __call__( request, c ):\",\n \"async def _execute(self):\",\n \"def _send_execute_command(self):\\n client = SBusClient(self.storlet_pipe_path)\\n try:\\n resp = client.execute(self.srequest.params, self.remote_fds)\\n if not resp.status:\\n raise StorletRuntimeException(\\\"Failed to send execute command\\\")\\n\\n if not resp.task_id:\\n raise StorletRuntimeException(\\\"Missing task id\\\")\\n else:\\n self.task_id = resp.task_id\\n except SBusClientException:\\n raise StorletRuntimeException(\\\"Failed to send execute command\\\")\",\n \"def execute(self, *args, **kwargs):\\n pass\",\n \"def dispatch(intent_request):\\n\\n logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))\\n\\n intent_name = intent_request['currentIntent']['name']\\n\\n # Dispatch to your bot's intent handlers\\n if intent_name == 'Help':\\n return order_help(intent_request)\\n elif intent_name == 'FastFood':\\n return order_snacks(intent_request)\\n elif intent_name == 'Beverages':\\n return order_beverages(intent_request)\\n elif intent_name == 'Admin':\\n return admin(intent_request)\\n raise Exception('Intent with name ' + intent_name + ' not supported')\",\n \"def _execute(self):\\n\\n action_name = self.action.lower()\\n method_name = action_name.replace('node', 'do')\\n method = getattr(self, method_name, None)\\n\\n if method is None:\\n reason = 'Unsupported action: %s' % self.action\\n EVENT.error(self, consts.PHASE_ERROR, reason)\\n return self.RES_ERROR, reason\\n\\n return method()\",\n \"def execute(self) -> Any:\\n return self.function(**self.kwargs)\",\n \"def tasks_rpc():\\n # First check that this is a legitimate request from the coordinator\\n authenticate_coordinator()\\n action, task_id, release_id = validate_action(request.get_json(force=True))\\n # Call into action\\n return ROUTES[action](task_id, release_id)\",\n \"def dispatch(intent_request):\\n\\n logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))\\n\\n intent_name = intent_request['currentIntent']['name']\\n\\n # Dispatch to your bot's intent handlers\\n if intent_name == 'ScheduleMeeting':\\n return schedule_meeting(intent_request)\\n raise Exception('Intent with name ' + intent_name + ' not supported')\",\n \"def request(self, msg):\\n\\t\\tif msg.command in ('AUTH', 'EXIT', 'GET', 'SET', 'VERSION', 'COMMAND', 'UPLOAD'):\\n\\t\\t\\tmethod = 'handle_request_%s' % (msg.command.lower(),)\\n\\t\\telse:\\n\\t\\t\\tmethod = 'handle_request_unknown'\\n\\n\\t\\tself.execute(method, msg)\",\n \"def reqid(self) -> str:\",\n \"def reqid(self) -> str:\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.55304587","0.5463426","0.5463426","0.5463426","0.5463426","0.5463426","0.5463426","0.5463426","0.5463426","0.5463426","0.5463426","0.5463426","0.5463426","0.5370444","0.5351898","0.53309184","0.5321808","0.5240275","0.5239346","0.52391875","0.52137643","0.52130556","0.52076656","0.5206376","0.51634085","0.51417744","0.5138334","0.513091","0.5120133","0.5120133","0.51169455","0.511308","0.5071282","0.50684184","0.5063238","0.50207436","0.5008017","0.50075054","0.49988467","0.49983695","0.49961016","0.4990312","0.4978331","0.49663132","0.49656558","0.49484235","0.4942481","0.49358094","0.49326205","0.49273914","0.49256858","0.4925279","0.49181458","0.49167037","0.49128228","0.49124318","0.49104154","0.4902597","0.4898146","0.48975655","0.48895562","0.48825824","0.48763943","0.4874445","0.4872475","0.48695856","0.4865276","0.4864173","0.48616028","0.48588294","0.48552337","0.48421198","0.48378265","0.48369476","0.48342955","0.48129514","0.48038658","0.48013076","0.4795977","0.47822645","0.47804523","0.47778976","0.47775945","0.47775945","0.47775945","0.47775945","0.47728255","0.4772696","0.4772696","0.4772175","0.47715607","0.47607327","0.47582325","0.47580978","0.4755617","0.4752285","0.4750455","0.47492743","0.47460216","0.47460216"],"string":"[\n \"0.55304587\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5463426\",\n \"0.5370444\",\n \"0.5351898\",\n \"0.53309184\",\n \"0.5321808\",\n \"0.5240275\",\n \"0.5239346\",\n \"0.52391875\",\n \"0.52137643\",\n \"0.52130556\",\n \"0.52076656\",\n \"0.5206376\",\n \"0.51634085\",\n \"0.51417744\",\n \"0.5138334\",\n \"0.513091\",\n \"0.5120133\",\n \"0.5120133\",\n \"0.51169455\",\n \"0.511308\",\n \"0.5071282\",\n \"0.50684184\",\n \"0.5063238\",\n \"0.50207436\",\n \"0.5008017\",\n \"0.50075054\",\n \"0.49988467\",\n \"0.49983695\",\n \"0.49961016\",\n \"0.4990312\",\n \"0.4978331\",\n \"0.49663132\",\n \"0.49656558\",\n \"0.49484235\",\n \"0.4942481\",\n \"0.49358094\",\n \"0.49326205\",\n \"0.49273914\",\n \"0.49256858\",\n \"0.4925279\",\n \"0.49181458\",\n \"0.49167037\",\n \"0.49128228\",\n \"0.49124318\",\n \"0.49104154\",\n \"0.4902597\",\n \"0.4898146\",\n \"0.48975655\",\n \"0.48895562\",\n \"0.48825824\",\n \"0.48763943\",\n \"0.4874445\",\n \"0.4872475\",\n \"0.48695856\",\n \"0.4865276\",\n \"0.4864173\",\n \"0.48616028\",\n \"0.48588294\",\n \"0.48552337\",\n \"0.48421198\",\n \"0.48378265\",\n \"0.48369476\",\n \"0.48342955\",\n \"0.48129514\",\n \"0.48038658\",\n \"0.48013076\",\n \"0.4795977\",\n \"0.47822645\",\n \"0.47804523\",\n \"0.47778976\",\n \"0.47775945\",\n \"0.47775945\",\n \"0.47775945\",\n \"0.47775945\",\n \"0.47728255\",\n \"0.4772696\",\n \"0.4772696\",\n \"0.4772175\",\n \"0.47715607\",\n \"0.47607327\",\n \"0.47582325\",\n \"0.47580978\",\n \"0.4755617\",\n \"0.4752285\",\n \"0.4750455\",\n \"0.47492743\",\n \"0.47460216\",\n \"0.47460216\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5936447"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19596,"cells":{"query":{"kind":"string","value":"Update the matrix with the coordinates of the operation."},"document":{"kind":"string","value":"def update_matrix(self, ope, mat):\n ope_coord = []\n for coord in self.coord_name:\n if np.isnan(ope[coord]):\n return\n ope_coord.append(int(ope[coord]))\n mat[tuple(ope_coord)] += 1"},"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 update(mat) -> np.ndarray:\n return mat","def update(self, row, col):\n self.update_m(row, col)\n self.update_ix(row, col)\n self.update_iy(row, col)","def _UpdateCoords(self, new_coords):\n for i in range(self.mol.n_atoms):\n for j in range(const.NUMDIM):\n self.mol.atoms[i].coords[j] = new_coords[i][j]","def matrix_add():","def execute(self):\n self.W = self.X+self.y+self.a\n self.Z = 2*self.W","def newMatrix(self):\n self.matrix = makeMatrix()\n for row in range(self.matrix.getHeight()):\n for column in range(self.matrix.getWidth()):\n self.canvasGrid[row][column].draw(self.matrix[row][column])","def __update_matrix(self, old_matrix_view):\n # if we've cleaned dirt - we will see it on our next move, so we substitute only unseen cells\n # which are marked with \"o\"\n new_matrix_view = []\n for row in range(self.matrix_rows):\n new_matrix_view.append([char for char in input()])\n\n if old_matrix_view:\n for row in range(self.matrix_rows):\n for col in range(self.matrix_cols):\n if new_matrix_view[row][col] == \"o\":\n new_matrix_view[row][col] = old_matrix_view[row][col]\n\n return new_matrix_view","def _update_loc(self) -> None:\n self.state[:, :, Boids.Attr.LOC] += self.state[:, :, Boids.Attr.VEL]\n # wrap-around the simulated environment\n self.state[:, :, Boids.Attr.LOC] %= np.expand_dims(self.env_bounds, axis=1)","def update_F_matrix(self, F_matrix):\n self.F_matrix = F_matrix","def __pos__(self):\r\n return mat4(map(lambda x: +x, self.mlist))","def mult_operation(self):\n arg1 = self.memory[self.memory[self._cursor + 1]]\n arg2 = self.memory[self.memory[self._cursor + 2]]\n arg3 = self.memory[self._cursor + 3]\n self.memory[arg3] = arg1 * arg2\n print(f'Cursor: {self._cursor}\\tAssigning position {arg3} with value {arg1 * arg2}')\n self._cursor += 4\n return","def _update_coords(self, change=None):\n if self.node_id:\n x, y = self.layout[self.node_id]\n self.coords = (x - self.dist, x + self.dist, y - self.dist, y + self.dist)","def mult_operation(self):\n n1 = self.memory[self.memory[self._cursor + 1]]\n n2 = self.memory[self.memory[self._cursor + 2]]\n position = self.memory[self._cursor + 3]\n self.memory[position] = n1 * n2\n # print(f'Cursor: {self._cursor}\\tAssigning position {position} with value {n1} * {n2} = {n1 * n2}')\n return","def update(self) -> np.ndarray:\r\n raise NotImplementedError(\"`update` method is not implemented!\")","def update_E(self):\n self.grid.E[:, 0, :, :] = self.grid.E[:, -1, :, :]","def update(self, operation, operand0, operand1, operand2):\n self.operation = operation\n self.operand0 = operand0\n self.operand1 = operand1\n self.operand2 = operand2","def apply_xfm(self, mtx):\n self.coords = np.dot(np.c_[self.coords, np.ones(len(self.coords))],\n mtx.T)[:, 3]","def update_location(self):\n if self.simulation:\n return (self.y, self.x)\n else:\n raise NotImplementedError\n\n self.y = new_y\n self.x = new_x\n\n return (new_y, new_x)","def update_E(self):\n self.grid.E[:, :, 0, :] = self.grid.E[:, :, -1, :]","def _update(self):\n self.parametrize_beam()\n self.update_ranks()\n self._points = tf.reshape(self._endpoint, (1, 2)) * tf.reshape(self._ranks, (-1, 1))","def execute(self):\n self.z = self.x + self.y","def update_grid(self):\n # Check to see if we have moved squares\n _new_grid = self.calc_grid()\n if _new_grid == self._grid:\n return\n # Remove from old square and add to new square\n self.target._grid[self._grid][self._type].discard(self)\n self.target._grid[_new_grid][self._type].add(self)\n # Update coordinates\n self._grid = _new_grid","def update_task_location_vector(self):\n for counter, task in enumerate(self.tasks):\n location = task.getloc()\n if location[0] == 0:\n vectorized_task_loc = location[1]\n elif location[0] == 1:\n vectorized_task_loc = 4 + location[1]\n elif location[0] == 2:\n vectorized_task_loc = 8 + location[1]\n else: # location[0] == 3\n vectorized_task_loc = 12 + location[1]\n self.how_many_tasks_in_each_square[0][vectorized_task_loc] += 1\n self.task_locations[0][counter] = vectorized_task_loc\n # print(location)\n # print(self.how_many_tasks_in_each_square)","def update_positions(self, grid):\r\n self.grid = grid","def process(self, mat):","def update_system_matrices(self, A, B):\n self._A = A\n self._B = B","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 update_E(self):\n self.grid.E[0, :, :, :] = self.grid.E[-1, :, :, :]","def updateModel(self, X, Y):\n self.X = X\n self.Y = Y","def add_OP(self, OP):\n \n if len(self.OPs) == self.size: # matrix is full, check for swaps\n mut_info = []\n existing = []\n for i in range(len(self.OPs)):\n mi, label = self.mut.distance(self.OPs[i], OP)\n mut_info.append(mi)\n product = 1\n for j in range(len(self.OPs)):\n if not i == j:\n product = product * self.matrix[i][j]\n existing.append(product)\n update = False\n difference = None\n for i in range(len(self.OPs)):\n candidate_info = 1\n for j in range(len(self.OPs)):\n if not i == j:\n candidate_info = candidate_info * mut_info[j]\n if candidate_info > existing[i]:\n update = True\n if difference == None:\n difference = candidate_info - existing[i]\n old_OP = i\n else:\n if (candidate_info - existing[i]) > difference:\n difference = candidate_info - existing[i]\n old_OP = i\n if update == True: # swapping out an OP\n mi, label = self.mut.distance(OP, OP)\n mut_info[old_OP] = mi\n self.matrix[old_OP] = mut_info\n self.OPs[old_OP] = OP\n for i in range(len(self.OPs)):\n self.matrix[i][old_OP] = mut_info[i]\n else: # adding an OP when there are fewer than self.size\n distances = []\n for i in range(len(self.OPs)):\n mi,label = self.mut.distance(OP, self.OPs[i])\n distances.append(mi)\n for i in range(len(self.OPs)):\n mut_info = distances[i]\n self.matrix[i].append(mut_info)\n self.matrix[len(self.OPs)].append(mut_info)\n mi, label = self.mut.distance(OP, OP)\n #mi = dask.compute(mi)\n self.matrix[len(self.OPs)].append(mi)\n self.OPs.append(OP)","def cambiovelocidad(self,x,y):\n self.change_x += x\n self.change_y += y","def update_output(self, latent_mat, weight_mat, y_list):","def update_E(self):\n self.grid.E[self.loc] += (\n self.grid.courant_number\n * self.grid.inverse_permittivity[self.loc]\n * self.phi_E\n )","def _apply_operation(self, state, operation):\n if operation.__class__.__name__ == \"Identity\":\n return state\n if operation.name == \"GlobalPhase\":\n return self._apply_global_phase(state, operation)\n wires = operation.wires\n\n if str(operation.name) in self._apply_ops: # cast to string because of Tensor\n shift = int(self._ndim(state) > self.num_wires)\n axes = [ax + shift for ax in self.wires.indices(wires)]\n return self._apply_ops[operation.name](state, axes)\n\n matrix = self._asarray(self._get_unitary_matrix(operation), dtype=self.C_DTYPE)\n\n if operation in diagonal_in_z_basis:\n return self._apply_diagonal_unitary(state, matrix, wires)\n if len(wires) <= 2:\n # Einsum is faster for small gates\n return self._apply_unitary_einsum(state, matrix, wires)\n\n return self._apply_unitary(state, matrix, wires)","def compute_upd(self, move):","def rowReduce(self):\n myMatrix = Matrix(self.Matrix)\n print(\"This is the row reduced echelon form of your matrix: \\n\", myMatrix.rref())","def update_poi (POIn, POInm1, new, current_cell_mask):\n row, col = cuda.grid(2)\n\n if row < POIn.shape[0] and col < POIn.shape[1]:\n POIn[row,col] = 0 \n if current_cell_mask[row,col] == True:\n POIn[row,col] = POInm1[row,col] + new[row,col]","def update_coords(self, coords):\n Mol = copy.deepcopy(self.m)\n c1 = Mol.GetConformer(-1)\n for i in range(self.na):\n coords_i = Point3D()\n coords_i.x, coords_i.y, coords_i.z = coords[i]\n c1.SetAtomPosition(i, coords_i)\n self.m = Mol","def move(self,move):\n for x in range(len(self.coord)):\n self.coord[x] = np.array([y+np.array(move) for y in self.coord[x]])\n return self","def update(self):\n self.x += self.vx\n self.y += self.vy","def temp_update(self):\n a_w = self.k / self.dx\n a_e = self.k / self.dx\n a_n = self.k / self.dy\n a_s = self.k / self.dy\n a_p = a_w + a_e + a_n + a_s + self.rho * self.cp * self.dx / self.dt\n for i, j in ti.ndrange((1, self.nx - 1), (1, self.ny - 1)):\n self.T[i,\n j] = (a_w * self.T[i - 1, j] + a_e * self.T[i + 1, j] +\n a_s * self.T[i, j - 1] + a_n * self.T[i, j + 1]) / a_p","def change_basis(self, U_global):\n self.matrix = U_global @ self.matrix @ np.conj(U_global).T","def add_operation(self):\n n1 = self.memory[self.memory[self._cursor + 1]]\n n2 = self.memory[self.memory[self._cursor + 2]]\n position = self.memory[self._cursor + 3]\n self.memory[position] = n1 + n2\n # print(f'Cursor: {self._cursor}\\tAssigning position {position} with value {n1} + {n2} = {n1 + n2}')\n return","def update_value(self, (features, action), parameters):\n features = np.array(features)\n new_value = features.dot(parameters)\n features = list(features)\n\n self.__setitem__((features, action), new_value)","def move(self, state, move_cmd, i, j):\r\n new_state = self.clone_state(state)\r\n coordinate_change = self.action_dic[self.reflection_dic[move_cmd]]\r\n new_state[i][j], new_state[i + coordinate_change[0]][j + coordinate_change[1]] = \\\r\n new_state[i + coordinate_change[0]][j + coordinate_change[1]]\\\r\n , new_state[i][j]\r\n return new_state","def vec_matrix_update(self, A, y, P, evecr):\n\n l = y.shape[1]\n A_old = A[:, :(P) * l]\n\n y = np.matrix(y)\n\n if evecr.size:\n new_cols = y * evecr\n A_old = np.concatenate((A_old, new_cols[P:-1]), axis=1)\n\n N = A.shape[0]\n A_old = np.concatenate((A_old, np.ones([N, 1])), axis=1)\n\n return np.array(A_old)","def _update_cells(self):\n for row_number in range(self.number_cells_y):\n for col_number in range(self.number_cells_x):\n if self.to_be_updated[row_number][col_number]:\n self.cells[row_number][col_number].update()","def update_r_tensor():\n A_op = linear_operator_r(num_w*num_o, D*num_rep, tensors_A, model.x_r, model.xs_l, model.x_orb)\n model.x_r[:,:] = __ridge_complex_lsqr(num_w*num_o, D * num_rep, A_op, y, model.alpha, atol=atol_lsqr, comm=comm).reshape((D, num_rep))","def _update(self):\n self._execute_lane_changes()\n self._execute_forward_movement()","def pull(self,x,y):\n\t\tself.x_sum -= np.sum(x,axis=0)[:,np.newaxis]\n\t\tself.y_sum -= np.sum(y,axis=0)[:,np.newaxis]\n\t\tself.xy_sum -= np.matmul(np.transpose(x),y)\n\t\tself.xx_sum -= np.matmul(np.transpose(x),x)\n\t\tself.yy_sum -= np.matmul(np.transpose(y),y)\n\t\tself.n -= np.shape(x)[0]","def fast_update_col(self,j,vals):\n dataptr = self.col_view[:,j].data\n self.X.data[dataptr] = vals","def _setMatrixRow(self, row):\n item = self._item()\n if item is not None:\n matrix = item.getMatrix()\n matrix[self._index, :] = row.x(), row.y(), row.z()\n item.setMatrix(matrix)","def perform(self, node, ins, outs):\r\n x, = ins\r\n z, = outs\r\n # zero-dimensional matrices ...\r\n if x.shape[0] == 0 or x.shape[1] == 0:\r\n z[0] = node.outputs[0].type.value_zeros((0,))\r\n return\r\n\r\n if x.shape[0] < x.shape[1]:\r\n rval = x[:, 0]\r\n else:\r\n rval = x[0]\r\n\r\n rval.strides = (x.strides[0] + x.strides[1],)\r\n if self.view:\r\n z[0] = rval\r\n else:\r\n z[0] = rval.copy()","def add_operation(self):\n arg1 = self.memory[self.memory[self._cursor + 1]]\n arg2 = self.memory[self.memory[self._cursor + 2]]\n arg3 = self.memory[self._cursor + 3]\n self.memory[arg3] = arg1 + arg2\n # print(f'Cursor: {self._cursor}\\tAssigning position {position} with value {n1 + n2}')\n self._cursor += 4\n return","def table_move_update():\n pos = self.variables.table.get_current_position()\n self.table_move_ui.x_move.setProperty(\"value\", int(pos[0]))\n self.table_move_ui.y_move.setProperty(\"value\", int(pos[1]))\n self.table_move_ui.z_move.setProperty(\"value\", int(pos[2]))","def compute_coordinates(self):\n self._x, self._y = self.board.index_to_coordinates(self.index)","def rotate(self, matrix):\n newCoord = np.zeros(self.coord.shape)\n newCoord[:,0]=matrix[0,0]+matrix[0,1]*self.coord[:,0]+matrix[0,2]*self.coord[:,1]+matrix[0,3]*self.coord[:,2]\n newCoord[:,1]=matrix[1,0]+matrix[1,1]*self.coord[:,0]+matrix[1,2]*self.coord[:,1]+matrix[1,3]*self.coord[:,2]\n newCoord[:,2]=matrix[2,0]+matrix[2,1]*self.coord[:,0]+matrix[2,2]*self.coord[:,1]+matrix[2,3]*self.coord[:,2]\n self.coord = deepcopy(newCoord)","def _increase_coordinates(coordinates, x, y):\n orig_x, orig_y = coordinates[\"x\"], coordinates[\"y\"]\n coordinates[\"x\"], coordinates[\"y\"] = orig_x + x, orig_y + y","def _calc_matrix(self):\n\t\tz = self.zoom\n\t\talloc = self.allocation\n\t\tif self.image:\n\t\t\tiw, ih = self.image.get_width(), self.image.get_height()\n\t\telse:\n\t\t\tiw, ih = 0, 0\n#\t\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\n\t\t\n\t\ti2w = cairo.Matrix(\n\t\t\tz,0,\n\t\t\t0,z,\n\t\t\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \n\t\t\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\n\t\t\t)\n\t\t\n\t\tself._i2w_matrix = i2w\n\t\t\n\t\tw2i = cairo.Matrix(*i2w) #copy\n\t\tw2i.invert()\n\t\tself._w2i_matrix = w2i","def make_move(self, row:int, col:int,curr_move):\n self.array[row][col] = curr_move","def reiniciarMatrix(self):\n self.matrixMAPA = []\n self.rellenarMatrix()","def current_state(self):\n return self.matrix\n #return tuple([tuple(i) for i in self.matrix]) # + [(self.rows, self.cols)])","def run(self):\n\n self.sess.run(self.update_operations)","def update(self):\n self.m.update()","def update(self, action): \n self.memory.pop(-1) \n self.memory.insert(0, [self.last_state.cpu().numpy(), action.cpu().numpy()])\n\n self.last_action = action","def move(self, action):\n ligne = self.location_[0] + self.actions_[action][0]\n column = self.location_[1] + self.actions_[action][1]\n newLocation = (ligne, column)\n self.location_ = newLocation\n newState = (self.location_[0] * self.width ) + self.location_[1]\n\n if self.location_[0] == 0 and self.location_[0] == 0:\n return 0\n\n return newState","def move(self, row, col):\n self.row = row\n self.col = col\n self.calculate_window_position()","def update(self, xnew, ynew):\n # define old snapshots to be discarded\n xold, yold = self.Xw[:,0], self.Yw[:,0]\n # Update recent w snapshots\n self.Xw = np.column_stack((self.Xw[:,1:], xnew))\n self.Yw = np.column_stack((self.Yw[:,1:], ynew))\n \n # direct rank-2 update\n # define matrices\n U, V = np.column_stack((xold, xnew)), np.column_stack((yold, ynew))\n C = np.diag([-(self.weighting)**(self.w),1])\n # compute PkU matrix matrix product beforehand\n PkU = self.P.dot(U)\n # compute AkU matrix matrix product beforehand\n AkU = self.A.dot(U)\n # compute Gamma\n Gamma = np.linalg.inv(np.linalg.inv(C)+U.T.dot(PkU))\n # update A\n self.A += (V-AkU).dot(Gamma).dot(PkU.T)\n # update P\n self.P = (self.P - PkU.dot(Gamma).dot(PkU.T))/self.weighting\n # ensure P is SPD by taking its symmetric part\n self.P = (self.P + self.P.T)/2\n \n # time step + 1\n self.timestep += 1","def update(self):\n self.bpos_x += 3","def __update_state(self, x0, u):\n N = int(len(u) / 2)\n lower_triangular_ones_matrix = np.tril(np.ones((N, N)))\n kron = np.kron(lower_triangular_ones_matrix, np.eye(2))\n\n new_state = np.vstack([np.eye(2)] * int(N)) @ x0 + kron @ u * self.nmpc_timestep\n\n return new_state","def update(self, x):\n pass","def rebuildMatrixCache(self):\n self.converterYUR = Mat4.convertMat(CSYupRight, self.lens.getCoordinateSystem()) * self.lens.getProjectionMat()","def __setitem__(self, key, val):\n x, y = key\n self.matrix[y][x] = val","def move (self):\n\t\tself.x += self.direction[0]\n\t\tself.y += self.direction[1]\n\t\tself.row = (self.y - 15) / 30\n\t\tself.col = (self.x - 15) / 30\n\t\tself.rowcol = (self.row,self.col)\n\t\tself.draw()","def rs_edit_upd(obj):\n verts = [x.co for x in obj.data.vertices]\n if verts[0] != Vector():\n fix = Vector(verts[0])\n for i in range(len(verts)):\n obj.data.vertices[i].co = obj.data.vertices[i].co - fix\n\n obj.data.update()\n obj.location += fix\n verts[1] = (verts[2] + verts[0]) / 2\n verts[3] = (verts[2] + verts[4]) / 2\n verts[5] = (verts[4] + verts[6]) / 2\n verts[7] = (verts[6] + verts[0]) / 2\n for i in range(len(verts)):\n obj.data.vertices[i].co = verts[i]\n\n obj.data.update()","def update(self):\n for i in range(self.min_y, self.max_y + 1):\n for j in range(self.min_x, self.max_x + 1):\n try:\n DIMENSIONAL_ARRAY[i-1][j-1] = self.lis[i-self.min_y][j-self.min_x]\n except IndexError:\n pass","def move(i, j):\n global X\n global Y\n X += i\n Y += j","def inc_rstate(self):\r\n #self.rstate = ff_2p134(self.rstate)\r\n self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)\r\n assert self.rstate.dtype == numpy.int32","def update(self, X, Y):\n\n # (Equation 5a, b)\n #\n # | XX XY |\n # | YX YY |\n #\n if np.ndim(X) > 1:\n k, m = np.shape(X)\n x = np.dot(X.T, Y)\n\n # Update the statistics given a block of data (in the following\n # order: XX, XY, YX, YY)\n self.__prod[:m, :m] += np.dot(X.T, X)\n self.__prod[:m, m:] += x\n self.__prod[m:, :m] += x.T\n self.__prod[m:, m:] += np.dot(Y.T, Y)\n self.__weight += k\n\n else:\n m = np.size(X)\n x = np.outer(X, Y)\n\n # Update the statistics given a single datum.\n self.__prod[:m, :m] += np.outer(X, X)\n self.__prod[:m, m:] += x\n self.__prod[m:, :m] += x.T\n self.__prod[m:, m:] += np.outer(Y, Y)\n self.__weight += 1","def UpdateCostMatrix( self, extraXs ):\n for x in extraXs:\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\n self.C.append(newRow)","def gather_state(self):\n self.x[0,0:self.n] = self.m[0:self.n]\n self.x[1,0:self.n] = self.r[0:self.n,0]\n self.x[2,0:self.n] = self.r[0:self.n,1]\n self.x[3,0:self.n] = self.r[0:self.n,2]\n self.x[4,0:self.n] = self.v[0:self.n,0]\n self.x[5,0:self.n] = self.v[0:self.n,1]\n self.x[6,0:self.n] = self.v[0:self.n,2]\n return(self.x)","def update(self):\n self.setVector(0.15, 0.0)","def update_Atoms(self,Theta=None,update_jacobian=False):\n if Theta is not None:\n self.Theta = Theta # If necessary, update Theta\n if update_jacobian:\n self.Atoms, self.Jacobians = self.compute_Atoms_matrix(return_jacobian=True)\n else:\n self.Atoms = self.compute_Atoms_matrix(return_jacobian=False)","def trace(self):\n if not Matrix.is_square(self):\n self._logger.error(\n \"cannot calculate the trace of non square matrix\")\n raise TypeError(\"cannot calculate the trace of non square matrix\")\n\n if self._coord_format != constants.MatrixCoordinateDefault:\n self._logger.error(\"invalid coordinate format\")\n raise NotImplementedError(\"invalid coordinate format\")\n\n return self._data.filter(\n lambda m: m[0] == m[1]\n ).reduce(\n lambda a, b: a[2] + b[2]\n )","def _apply_correction(self):\n np.copyto(self.network.weights, self.correction_matrix)","def solvetop(self, debug=False, debugfunc=print):\n for y,x in reversed(self.coords(False)):\n if x == y:\n if debug:\n debugfunc(\"Scale Row \"+str(y)+\" By 1/\"+str(self.retrieve(y,x)))\n self.scalerow(y, 1.0/self.retrieve(y,x))\n if debug > 1:\n debugfunc(self)\n elif x < self.y and x > y:\n if debug:\n debugfunc(\"Add To Row \"+str(y)+\" Row \"+str(x)+\" Scaled By \"+str(-1.0*self.retrieve(y,x)))\n self.addrow(y, self.scaledrow(x, -1.0*self.retrieve(y,x)))\n if debug > 1:\n debugfunc(self)","def result(self, row, col, move):\n start = (row, col)\n end = self.updateCell(row, col, move)\n\n return self.change(start, end)","def store(self, code, change):\n self.matrix[code] = change['new']","def clear(self):\n for y in range(len(self.matrix)):\n for x in range(len(self.matrix[0])):\n self.matrix[y-1][x-1] = (0,0,0)","def current_update():\n # Compute the multiplier coefficient:\n ci = dt / (L * dx)\n for k in range(0, nx-1):\n I[k] = I[k] - (ci * (V[k + 1] - V[k]))","def update_state(self):\n self.reset_state()\n for piece in self.pieces:\n coordinates = piece.get_block_positions()\n for coor in coordinates:\n x, y = coor\n self.state[y][x] = piece","def DrawMatrix(self, image):\n for row in range(min(image.shape[1], 64)):\n for col in range(min(image.shape[0], 64)):\n self.matrix.SetPixel(col, row, image[row][col][2], image[row][col][1], image[row][col][0])","def overlayMatrix(board_object, item_object, x, y):\n board_matrix = board_object.returnMatrixBoard()\n item_matrix = item_object.returnMatrix()\n k = 0\n l = 0\n for i in range(x, x + item_object.length):\n for j in range(y, y + item_object.width):\n board_matrix[i][j] = item_matrix[k][l]\n l += 1\n k += 1\n l = 0\n board_object.editBoard(board_matrix)","def _calc_coords(self):\n i = num.outer(num.arange(self.size[0]), num.ones(self.size[1]))\n i0 = self.pos[0] + (i * self.space[0])\n i1 = self.pos[0] + (i * self.space[0]) + self.bub[0]\n\n j = num.outer(num.ones(self.size[0]), num.arange(self.size[1]))\n j0 = self.pos[1] + (j * self.space[1])\n j1 = self.pos[1] + (j * self.space[1]) + self.bub[1]\n\n self.coords = num.dstack((i0, i1, j0, j1)).astype('i')","def set_position(self, updated):\n self.buff_x = updated[0]\n self.buff_y = updated[1]","def update_rec(self):\n import copy\n \n self.leftrec, self.rightrec = copy.copy(self.rec), copy.copy(self.rec)\n self.leftrec[2*self.dim + 1], self.rightrec[2*self.dim] = self.node.dimension[self.dim], self.node.dimension[self.dim]","def update(self):\n if self.dir == \"r\":\n self.vx = 10\n self.vy = 0\n elif self.dir == \"l\":\n self.vx = -10\n self.vy = 0\n elif self.dir == \"u\":\n self.vx = 0\n self.vy = -10\n elif self.dir == \"d\":\n self.vx = 0\n self.vy = 10\n elif self.dir == \"None\":\n self.vx = 0\n self.vy = 0\n self.x += self.vx\n self.y += self.vy","def set_contribution(self):\r\n \r\n # WX = X*Q\r\n # WY = Y*Q\r\n # see how these are assembled in the Farm class.\r\n \r\n # x-row of big X:\r\n self.x = np.hstack((np.hstack((\\\r\n [xi + ni for xi, ni in zip(self.cx, self.bn)]\\\r\n )), np.hstack((\\\r\n [yi + ti for yi, ti in zip(self.cy, self.bt)]\\\r\n ))))\r\n \r\n # y-row of big Y:\r\n self.y = np.hstack((np.hstack((self.cy)), np.hstack((\\\r\n [-xi for xi in self.cx]))))","def matrix_apply_to_2d(data, matrix: np.matrix):\n from scipy import mgrid\n\n cx = data.shape[0] / 2\n cy = data.shape[1] / 2\n\n # Calculate the new coordinates of every point\n grid = mgrid[-cx:data.shape[0]-cx, -cy:data.shape[1]-cy]\n temp = grid.reshape((2, grid.size / 2))\n # Add the fourth dimension (just 1s but needed for the computations)\n newrow = np.ones(grid.size / 2)\n temp = np.vstack([temp, newrow])\n # Use the matrix to calculate the new positions of every point\n temp = np.dot(matrix, temp)\n # Delete the fourth dimension\n temp = np.delete(temp, 2, axis=0)\n temp = np.array(temp)\n grid = np.reshape(temp, (2, data.shape[0], data.shape[1]))\n\n grid[0] += cx\n grid[1] += cy\n\n from scipy.ndimage.interpolation import map_coordinates\n d = map_coordinates(data, grid, order=3)\n\n return d","def update_position(self):\n self.current_position = utility_methods.cylindrical(self.current_position + self.rotation)\n\n self.rotation_list.append(self.current_position)"],"string":"[\n \"def update(mat) -> np.ndarray:\\n return mat\",\n \"def update(self, row, col):\\n self.update_m(row, col)\\n self.update_ix(row, col)\\n self.update_iy(row, col)\",\n \"def _UpdateCoords(self, new_coords):\\n for i in range(self.mol.n_atoms):\\n for j in range(const.NUMDIM):\\n self.mol.atoms[i].coords[j] = new_coords[i][j]\",\n \"def matrix_add():\",\n \"def execute(self):\\n self.W = self.X+self.y+self.a\\n self.Z = 2*self.W\",\n \"def newMatrix(self):\\n self.matrix = makeMatrix()\\n for row in range(self.matrix.getHeight()):\\n for column in range(self.matrix.getWidth()):\\n self.canvasGrid[row][column].draw(self.matrix[row][column])\",\n \"def __update_matrix(self, old_matrix_view):\\n # if we've cleaned dirt - we will see it on our next move, so we substitute only unseen cells\\n # which are marked with \\\"o\\\"\\n new_matrix_view = []\\n for row in range(self.matrix_rows):\\n new_matrix_view.append([char for char in input()])\\n\\n if old_matrix_view:\\n for row in range(self.matrix_rows):\\n for col in range(self.matrix_cols):\\n if new_matrix_view[row][col] == \\\"o\\\":\\n new_matrix_view[row][col] = old_matrix_view[row][col]\\n\\n return new_matrix_view\",\n \"def _update_loc(self) -> None:\\n self.state[:, :, Boids.Attr.LOC] += self.state[:, :, Boids.Attr.VEL]\\n # wrap-around the simulated environment\\n self.state[:, :, Boids.Attr.LOC] %= np.expand_dims(self.env_bounds, axis=1)\",\n \"def update_F_matrix(self, F_matrix):\\n self.F_matrix = F_matrix\",\n \"def __pos__(self):\\r\\n return mat4(map(lambda x: +x, self.mlist))\",\n \"def mult_operation(self):\\n arg1 = self.memory[self.memory[self._cursor + 1]]\\n arg2 = self.memory[self.memory[self._cursor + 2]]\\n arg3 = self.memory[self._cursor + 3]\\n self.memory[arg3] = arg1 * arg2\\n print(f'Cursor: {self._cursor}\\\\tAssigning position {arg3} with value {arg1 * arg2}')\\n self._cursor += 4\\n return\",\n \"def _update_coords(self, change=None):\\n if self.node_id:\\n x, y = self.layout[self.node_id]\\n self.coords = (x - self.dist, x + self.dist, y - self.dist, y + self.dist)\",\n \"def mult_operation(self):\\n n1 = self.memory[self.memory[self._cursor + 1]]\\n n2 = self.memory[self.memory[self._cursor + 2]]\\n position = self.memory[self._cursor + 3]\\n self.memory[position] = n1 * n2\\n # print(f'Cursor: {self._cursor}\\\\tAssigning position {position} with value {n1} * {n2} = {n1 * n2}')\\n return\",\n \"def update(self) -> np.ndarray:\\r\\n raise NotImplementedError(\\\"`update` method is not implemented!\\\")\",\n \"def update_E(self):\\n self.grid.E[:, 0, :, :] = self.grid.E[:, -1, :, :]\",\n \"def update(self, operation, operand0, operand1, operand2):\\n self.operation = operation\\n self.operand0 = operand0\\n self.operand1 = operand1\\n self.operand2 = operand2\",\n \"def apply_xfm(self, mtx):\\n self.coords = np.dot(np.c_[self.coords, np.ones(len(self.coords))],\\n mtx.T)[:, 3]\",\n \"def update_location(self):\\n if self.simulation:\\n return (self.y, self.x)\\n else:\\n raise NotImplementedError\\n\\n self.y = new_y\\n self.x = new_x\\n\\n return (new_y, new_x)\",\n \"def update_E(self):\\n self.grid.E[:, :, 0, :] = self.grid.E[:, :, -1, :]\",\n \"def _update(self):\\n self.parametrize_beam()\\n self.update_ranks()\\n self._points = tf.reshape(self._endpoint, (1, 2)) * tf.reshape(self._ranks, (-1, 1))\",\n \"def execute(self):\\n self.z = self.x + self.y\",\n \"def update_grid(self):\\n # Check to see if we have moved squares\\n _new_grid = self.calc_grid()\\n if _new_grid == self._grid:\\n return\\n # Remove from old square and add to new square\\n self.target._grid[self._grid][self._type].discard(self)\\n self.target._grid[_new_grid][self._type].add(self)\\n # Update coordinates\\n self._grid = _new_grid\",\n \"def update_task_location_vector(self):\\n for counter, task in enumerate(self.tasks):\\n location = task.getloc()\\n if location[0] == 0:\\n vectorized_task_loc = location[1]\\n elif location[0] == 1:\\n vectorized_task_loc = 4 + location[1]\\n elif location[0] == 2:\\n vectorized_task_loc = 8 + location[1]\\n else: # location[0] == 3\\n vectorized_task_loc = 12 + location[1]\\n self.how_many_tasks_in_each_square[0][vectorized_task_loc] += 1\\n self.task_locations[0][counter] = vectorized_task_loc\\n # print(location)\\n # print(self.how_many_tasks_in_each_square)\",\n \"def update_positions(self, grid):\\r\\n self.grid = grid\",\n \"def process(self, mat):\",\n \"def update_system_matrices(self, A, B):\\n self._A = A\\n self._B = B\",\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 update_E(self):\\n self.grid.E[0, :, :, :] = self.grid.E[-1, :, :, :]\",\n \"def updateModel(self, X, Y):\\n self.X = X\\n self.Y = Y\",\n \"def add_OP(self, OP):\\n \\n if len(self.OPs) == self.size: # matrix is full, check for swaps\\n mut_info = []\\n existing = []\\n for i in range(len(self.OPs)):\\n mi, label = self.mut.distance(self.OPs[i], OP)\\n mut_info.append(mi)\\n product = 1\\n for j in range(len(self.OPs)):\\n if not i == j:\\n product = product * self.matrix[i][j]\\n existing.append(product)\\n update = False\\n difference = None\\n for i in range(len(self.OPs)):\\n candidate_info = 1\\n for j in range(len(self.OPs)):\\n if not i == j:\\n candidate_info = candidate_info * mut_info[j]\\n if candidate_info > existing[i]:\\n update = True\\n if difference == None:\\n difference = candidate_info - existing[i]\\n old_OP = i\\n else:\\n if (candidate_info - existing[i]) > difference:\\n difference = candidate_info - existing[i]\\n old_OP = i\\n if update == True: # swapping out an OP\\n mi, label = self.mut.distance(OP, OP)\\n mut_info[old_OP] = mi\\n self.matrix[old_OP] = mut_info\\n self.OPs[old_OP] = OP\\n for i in range(len(self.OPs)):\\n self.matrix[i][old_OP] = mut_info[i]\\n else: # adding an OP when there are fewer than self.size\\n distances = []\\n for i in range(len(self.OPs)):\\n mi,label = self.mut.distance(OP, self.OPs[i])\\n distances.append(mi)\\n for i in range(len(self.OPs)):\\n mut_info = distances[i]\\n self.matrix[i].append(mut_info)\\n self.matrix[len(self.OPs)].append(mut_info)\\n mi, label = self.mut.distance(OP, OP)\\n #mi = dask.compute(mi)\\n self.matrix[len(self.OPs)].append(mi)\\n self.OPs.append(OP)\",\n \"def cambiovelocidad(self,x,y):\\n self.change_x += x\\n self.change_y += y\",\n \"def update_output(self, latent_mat, weight_mat, y_list):\",\n \"def update_E(self):\\n self.grid.E[self.loc] += (\\n self.grid.courant_number\\n * self.grid.inverse_permittivity[self.loc]\\n * self.phi_E\\n )\",\n \"def _apply_operation(self, state, operation):\\n if operation.__class__.__name__ == \\\"Identity\\\":\\n return state\\n if operation.name == \\\"GlobalPhase\\\":\\n return self._apply_global_phase(state, operation)\\n wires = operation.wires\\n\\n if str(operation.name) in self._apply_ops: # cast to string because of Tensor\\n shift = int(self._ndim(state) > self.num_wires)\\n axes = [ax + shift for ax in self.wires.indices(wires)]\\n return self._apply_ops[operation.name](state, axes)\\n\\n matrix = self._asarray(self._get_unitary_matrix(operation), dtype=self.C_DTYPE)\\n\\n if operation in diagonal_in_z_basis:\\n return self._apply_diagonal_unitary(state, matrix, wires)\\n if len(wires) <= 2:\\n # Einsum is faster for small gates\\n return self._apply_unitary_einsum(state, matrix, wires)\\n\\n return self._apply_unitary(state, matrix, wires)\",\n \"def compute_upd(self, move):\",\n \"def rowReduce(self):\\n myMatrix = Matrix(self.Matrix)\\n print(\\\"This is the row reduced echelon form of your matrix: \\\\n\\\", myMatrix.rref())\",\n \"def update_poi (POIn, POInm1, new, current_cell_mask):\\n row, col = cuda.grid(2)\\n\\n if row < POIn.shape[0] and col < POIn.shape[1]:\\n POIn[row,col] = 0 \\n if current_cell_mask[row,col] == True:\\n POIn[row,col] = POInm1[row,col] + new[row,col]\",\n \"def update_coords(self, coords):\\n Mol = copy.deepcopy(self.m)\\n c1 = Mol.GetConformer(-1)\\n for i in range(self.na):\\n coords_i = Point3D()\\n coords_i.x, coords_i.y, coords_i.z = coords[i]\\n c1.SetAtomPosition(i, coords_i)\\n self.m = Mol\",\n \"def move(self,move):\\n for x in range(len(self.coord)):\\n self.coord[x] = np.array([y+np.array(move) for y in self.coord[x]])\\n return self\",\n \"def update(self):\\n self.x += self.vx\\n self.y += self.vy\",\n \"def temp_update(self):\\n a_w = self.k / self.dx\\n a_e = self.k / self.dx\\n a_n = self.k / self.dy\\n a_s = self.k / self.dy\\n a_p = a_w + a_e + a_n + a_s + self.rho * self.cp * self.dx / self.dt\\n for i, j in ti.ndrange((1, self.nx - 1), (1, self.ny - 1)):\\n self.T[i,\\n j] = (a_w * self.T[i - 1, j] + a_e * self.T[i + 1, j] +\\n a_s * self.T[i, j - 1] + a_n * self.T[i, j + 1]) / a_p\",\n \"def change_basis(self, U_global):\\n self.matrix = U_global @ self.matrix @ np.conj(U_global).T\",\n \"def add_operation(self):\\n n1 = self.memory[self.memory[self._cursor + 1]]\\n n2 = self.memory[self.memory[self._cursor + 2]]\\n position = self.memory[self._cursor + 3]\\n self.memory[position] = n1 + n2\\n # print(f'Cursor: {self._cursor}\\\\tAssigning position {position} with value {n1} + {n2} = {n1 + n2}')\\n return\",\n \"def update_value(self, (features, action), parameters):\\n features = np.array(features)\\n new_value = features.dot(parameters)\\n features = list(features)\\n\\n self.__setitem__((features, action), new_value)\",\n \"def move(self, state, move_cmd, i, j):\\r\\n new_state = self.clone_state(state)\\r\\n coordinate_change = self.action_dic[self.reflection_dic[move_cmd]]\\r\\n new_state[i][j], new_state[i + coordinate_change[0]][j + coordinate_change[1]] = \\\\\\r\\n new_state[i + coordinate_change[0]][j + coordinate_change[1]]\\\\\\r\\n , new_state[i][j]\\r\\n return new_state\",\n \"def vec_matrix_update(self, A, y, P, evecr):\\n\\n l = y.shape[1]\\n A_old = A[:, :(P) * l]\\n\\n y = np.matrix(y)\\n\\n if evecr.size:\\n new_cols = y * evecr\\n A_old = np.concatenate((A_old, new_cols[P:-1]), axis=1)\\n\\n N = A.shape[0]\\n A_old = np.concatenate((A_old, np.ones([N, 1])), axis=1)\\n\\n return np.array(A_old)\",\n \"def _update_cells(self):\\n for row_number in range(self.number_cells_y):\\n for col_number in range(self.number_cells_x):\\n if self.to_be_updated[row_number][col_number]:\\n self.cells[row_number][col_number].update()\",\n \"def update_r_tensor():\\n A_op = linear_operator_r(num_w*num_o, D*num_rep, tensors_A, model.x_r, model.xs_l, model.x_orb)\\n model.x_r[:,:] = __ridge_complex_lsqr(num_w*num_o, D * num_rep, A_op, y, model.alpha, atol=atol_lsqr, comm=comm).reshape((D, num_rep))\",\n \"def _update(self):\\n self._execute_lane_changes()\\n self._execute_forward_movement()\",\n \"def pull(self,x,y):\\n\\t\\tself.x_sum -= np.sum(x,axis=0)[:,np.newaxis]\\n\\t\\tself.y_sum -= np.sum(y,axis=0)[:,np.newaxis]\\n\\t\\tself.xy_sum -= np.matmul(np.transpose(x),y)\\n\\t\\tself.xx_sum -= np.matmul(np.transpose(x),x)\\n\\t\\tself.yy_sum -= np.matmul(np.transpose(y),y)\\n\\t\\tself.n -= np.shape(x)[0]\",\n \"def fast_update_col(self,j,vals):\\n dataptr = self.col_view[:,j].data\\n self.X.data[dataptr] = vals\",\n \"def _setMatrixRow(self, row):\\n item = self._item()\\n if item is not None:\\n matrix = item.getMatrix()\\n matrix[self._index, :] = row.x(), row.y(), row.z()\\n item.setMatrix(matrix)\",\n \"def perform(self, node, ins, outs):\\r\\n x, = ins\\r\\n z, = outs\\r\\n # zero-dimensional matrices ...\\r\\n if x.shape[0] == 0 or x.shape[1] == 0:\\r\\n z[0] = node.outputs[0].type.value_zeros((0,))\\r\\n return\\r\\n\\r\\n if x.shape[0] < x.shape[1]:\\r\\n rval = x[:, 0]\\r\\n else:\\r\\n rval = x[0]\\r\\n\\r\\n rval.strides = (x.strides[0] + x.strides[1],)\\r\\n if self.view:\\r\\n z[0] = rval\\r\\n else:\\r\\n z[0] = rval.copy()\",\n \"def add_operation(self):\\n arg1 = self.memory[self.memory[self._cursor + 1]]\\n arg2 = self.memory[self.memory[self._cursor + 2]]\\n arg3 = self.memory[self._cursor + 3]\\n self.memory[arg3] = arg1 + arg2\\n # print(f'Cursor: {self._cursor}\\\\tAssigning position {position} with value {n1 + n2}')\\n self._cursor += 4\\n return\",\n \"def table_move_update():\\n pos = self.variables.table.get_current_position()\\n self.table_move_ui.x_move.setProperty(\\\"value\\\", int(pos[0]))\\n self.table_move_ui.y_move.setProperty(\\\"value\\\", int(pos[1]))\\n self.table_move_ui.z_move.setProperty(\\\"value\\\", int(pos[2]))\",\n \"def compute_coordinates(self):\\n self._x, self._y = self.board.index_to_coordinates(self.index)\",\n \"def rotate(self, matrix):\\n newCoord = np.zeros(self.coord.shape)\\n newCoord[:,0]=matrix[0,0]+matrix[0,1]*self.coord[:,0]+matrix[0,2]*self.coord[:,1]+matrix[0,3]*self.coord[:,2]\\n newCoord[:,1]=matrix[1,0]+matrix[1,1]*self.coord[:,0]+matrix[1,2]*self.coord[:,1]+matrix[1,3]*self.coord[:,2]\\n newCoord[:,2]=matrix[2,0]+matrix[2,1]*self.coord[:,0]+matrix[2,2]*self.coord[:,1]+matrix[2,3]*self.coord[:,2]\\n self.coord = deepcopy(newCoord)\",\n \"def _increase_coordinates(coordinates, x, y):\\n orig_x, orig_y = coordinates[\\\"x\\\"], coordinates[\\\"y\\\"]\\n coordinates[\\\"x\\\"], coordinates[\\\"y\\\"] = orig_x + x, orig_y + y\",\n \"def _calc_matrix(self):\\n\\t\\tz = self.zoom\\n\\t\\talloc = self.allocation\\n\\t\\tif self.image:\\n\\t\\t\\tiw, ih = self.image.get_width(), self.image.get_height()\\n\\t\\telse:\\n\\t\\t\\tiw, ih = 0, 0\\n#\\t\\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\\n\\t\\t\\n\\t\\ti2w = cairo.Matrix(\\n\\t\\t\\tz,0,\\n\\t\\t\\t0,z,\\n\\t\\t\\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \\n\\t\\t\\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\\n\\t\\t\\t)\\n\\t\\t\\n\\t\\tself._i2w_matrix = i2w\\n\\t\\t\\n\\t\\tw2i = cairo.Matrix(*i2w) #copy\\n\\t\\tw2i.invert()\\n\\t\\tself._w2i_matrix = w2i\",\n \"def make_move(self, row:int, col:int,curr_move):\\n self.array[row][col] = curr_move\",\n \"def reiniciarMatrix(self):\\n self.matrixMAPA = []\\n self.rellenarMatrix()\",\n \"def current_state(self):\\n return self.matrix\\n #return tuple([tuple(i) for i in self.matrix]) # + [(self.rows, self.cols)])\",\n \"def run(self):\\n\\n self.sess.run(self.update_operations)\",\n \"def update(self):\\n self.m.update()\",\n \"def update(self, action): \\n self.memory.pop(-1) \\n self.memory.insert(0, [self.last_state.cpu().numpy(), action.cpu().numpy()])\\n\\n self.last_action = action\",\n \"def move(self, action):\\n ligne = self.location_[0] + self.actions_[action][0]\\n column = self.location_[1] + self.actions_[action][1]\\n newLocation = (ligne, column)\\n self.location_ = newLocation\\n newState = (self.location_[0] * self.width ) + self.location_[1]\\n\\n if self.location_[0] == 0 and self.location_[0] == 0:\\n return 0\\n\\n return newState\",\n \"def move(self, row, col):\\n self.row = row\\n self.col = col\\n self.calculate_window_position()\",\n \"def update(self, xnew, ynew):\\n # define old snapshots to be discarded\\n xold, yold = self.Xw[:,0], self.Yw[:,0]\\n # Update recent w snapshots\\n self.Xw = np.column_stack((self.Xw[:,1:], xnew))\\n self.Yw = np.column_stack((self.Yw[:,1:], ynew))\\n \\n # direct rank-2 update\\n # define matrices\\n U, V = np.column_stack((xold, xnew)), np.column_stack((yold, ynew))\\n C = np.diag([-(self.weighting)**(self.w),1])\\n # compute PkU matrix matrix product beforehand\\n PkU = self.P.dot(U)\\n # compute AkU matrix matrix product beforehand\\n AkU = self.A.dot(U)\\n # compute Gamma\\n Gamma = np.linalg.inv(np.linalg.inv(C)+U.T.dot(PkU))\\n # update A\\n self.A += (V-AkU).dot(Gamma).dot(PkU.T)\\n # update P\\n self.P = (self.P - PkU.dot(Gamma).dot(PkU.T))/self.weighting\\n # ensure P is SPD by taking its symmetric part\\n self.P = (self.P + self.P.T)/2\\n \\n # time step + 1\\n self.timestep += 1\",\n \"def update(self):\\n self.bpos_x += 3\",\n \"def __update_state(self, x0, u):\\n N = int(len(u) / 2)\\n lower_triangular_ones_matrix = np.tril(np.ones((N, N)))\\n kron = np.kron(lower_triangular_ones_matrix, np.eye(2))\\n\\n new_state = np.vstack([np.eye(2)] * int(N)) @ x0 + kron @ u * self.nmpc_timestep\\n\\n return new_state\",\n \"def update(self, x):\\n pass\",\n \"def rebuildMatrixCache(self):\\n self.converterYUR = Mat4.convertMat(CSYupRight, self.lens.getCoordinateSystem()) * self.lens.getProjectionMat()\",\n \"def __setitem__(self, key, val):\\n x, y = key\\n self.matrix[y][x] = val\",\n \"def move (self):\\n\\t\\tself.x += self.direction[0]\\n\\t\\tself.y += self.direction[1]\\n\\t\\tself.row = (self.y - 15) / 30\\n\\t\\tself.col = (self.x - 15) / 30\\n\\t\\tself.rowcol = (self.row,self.col)\\n\\t\\tself.draw()\",\n \"def rs_edit_upd(obj):\\n verts = [x.co for x in obj.data.vertices]\\n if verts[0] != Vector():\\n fix = Vector(verts[0])\\n for i in range(len(verts)):\\n obj.data.vertices[i].co = obj.data.vertices[i].co - fix\\n\\n obj.data.update()\\n obj.location += fix\\n verts[1] = (verts[2] + verts[0]) / 2\\n verts[3] = (verts[2] + verts[4]) / 2\\n verts[5] = (verts[4] + verts[6]) / 2\\n verts[7] = (verts[6] + verts[0]) / 2\\n for i in range(len(verts)):\\n obj.data.vertices[i].co = verts[i]\\n\\n obj.data.update()\",\n \"def update(self):\\n for i in range(self.min_y, self.max_y + 1):\\n for j in range(self.min_x, self.max_x + 1):\\n try:\\n DIMENSIONAL_ARRAY[i-1][j-1] = self.lis[i-self.min_y][j-self.min_x]\\n except IndexError:\\n pass\",\n \"def move(i, j):\\n global X\\n global Y\\n X += i\\n Y += j\",\n \"def inc_rstate(self):\\r\\n #self.rstate = ff_2p134(self.rstate)\\r\\n self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)\\r\\n assert self.rstate.dtype == numpy.int32\",\n \"def update(self, X, Y):\\n\\n # (Equation 5a, b)\\n #\\n # | XX XY |\\n # | YX YY |\\n #\\n if np.ndim(X) > 1:\\n k, m = np.shape(X)\\n x = np.dot(X.T, Y)\\n\\n # Update the statistics given a block of data (in the following\\n # order: XX, XY, YX, YY)\\n self.__prod[:m, :m] += np.dot(X.T, X)\\n self.__prod[:m, m:] += x\\n self.__prod[m:, :m] += x.T\\n self.__prod[m:, m:] += np.dot(Y.T, Y)\\n self.__weight += k\\n\\n else:\\n m = np.size(X)\\n x = np.outer(X, Y)\\n\\n # Update the statistics given a single datum.\\n self.__prod[:m, :m] += np.outer(X, X)\\n self.__prod[:m, m:] += x\\n self.__prod[m:, :m] += x.T\\n self.__prod[m:, m:] += np.outer(Y, Y)\\n self.__weight += 1\",\n \"def UpdateCostMatrix( self, extraXs ):\\n for x in extraXs:\\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\\n self.C.append(newRow)\",\n \"def gather_state(self):\\n self.x[0,0:self.n] = self.m[0:self.n]\\n self.x[1,0:self.n] = self.r[0:self.n,0]\\n self.x[2,0:self.n] = self.r[0:self.n,1]\\n self.x[3,0:self.n] = self.r[0:self.n,2]\\n self.x[4,0:self.n] = self.v[0:self.n,0]\\n self.x[5,0:self.n] = self.v[0:self.n,1]\\n self.x[6,0:self.n] = self.v[0:self.n,2]\\n return(self.x)\",\n \"def update(self):\\n self.setVector(0.15, 0.0)\",\n \"def update_Atoms(self,Theta=None,update_jacobian=False):\\n if Theta is not None:\\n self.Theta = Theta # If necessary, update Theta\\n if update_jacobian:\\n self.Atoms, self.Jacobians = self.compute_Atoms_matrix(return_jacobian=True)\\n else:\\n self.Atoms = self.compute_Atoms_matrix(return_jacobian=False)\",\n \"def trace(self):\\n if not Matrix.is_square(self):\\n self._logger.error(\\n \\\"cannot calculate the trace of non square matrix\\\")\\n raise TypeError(\\\"cannot calculate the trace of non square matrix\\\")\\n\\n if self._coord_format != constants.MatrixCoordinateDefault:\\n self._logger.error(\\\"invalid coordinate format\\\")\\n raise NotImplementedError(\\\"invalid coordinate format\\\")\\n\\n return self._data.filter(\\n lambda m: m[0] == m[1]\\n ).reduce(\\n lambda a, b: a[2] + b[2]\\n )\",\n \"def _apply_correction(self):\\n np.copyto(self.network.weights, self.correction_matrix)\",\n \"def solvetop(self, debug=False, debugfunc=print):\\n for y,x in reversed(self.coords(False)):\\n if x == y:\\n if debug:\\n debugfunc(\\\"Scale Row \\\"+str(y)+\\\" By 1/\\\"+str(self.retrieve(y,x)))\\n self.scalerow(y, 1.0/self.retrieve(y,x))\\n if debug > 1:\\n debugfunc(self)\\n elif x < self.y and x > y:\\n if debug:\\n debugfunc(\\\"Add To Row \\\"+str(y)+\\\" Row \\\"+str(x)+\\\" Scaled By \\\"+str(-1.0*self.retrieve(y,x)))\\n self.addrow(y, self.scaledrow(x, -1.0*self.retrieve(y,x)))\\n if debug > 1:\\n debugfunc(self)\",\n \"def result(self, row, col, move):\\n start = (row, col)\\n end = self.updateCell(row, col, move)\\n\\n return self.change(start, end)\",\n \"def store(self, code, change):\\n self.matrix[code] = change['new']\",\n \"def clear(self):\\n for y in range(len(self.matrix)):\\n for x in range(len(self.matrix[0])):\\n self.matrix[y-1][x-1] = (0,0,0)\",\n \"def current_update():\\n # Compute the multiplier coefficient:\\n ci = dt / (L * dx)\\n for k in range(0, nx-1):\\n I[k] = I[k] - (ci * (V[k + 1] - V[k]))\",\n \"def update_state(self):\\n self.reset_state()\\n for piece in self.pieces:\\n coordinates = piece.get_block_positions()\\n for coor in coordinates:\\n x, y = coor\\n self.state[y][x] = piece\",\n \"def DrawMatrix(self, image):\\n for row in range(min(image.shape[1], 64)):\\n for col in range(min(image.shape[0], 64)):\\n self.matrix.SetPixel(col, row, image[row][col][2], image[row][col][1], image[row][col][0])\",\n \"def overlayMatrix(board_object, item_object, x, y):\\n board_matrix = board_object.returnMatrixBoard()\\n item_matrix = item_object.returnMatrix()\\n k = 0\\n l = 0\\n for i in range(x, x + item_object.length):\\n for j in range(y, y + item_object.width):\\n board_matrix[i][j] = item_matrix[k][l]\\n l += 1\\n k += 1\\n l = 0\\n board_object.editBoard(board_matrix)\",\n \"def _calc_coords(self):\\n i = num.outer(num.arange(self.size[0]), num.ones(self.size[1]))\\n i0 = self.pos[0] + (i * self.space[0])\\n i1 = self.pos[0] + (i * self.space[0]) + self.bub[0]\\n\\n j = num.outer(num.ones(self.size[0]), num.arange(self.size[1]))\\n j0 = self.pos[1] + (j * self.space[1])\\n j1 = self.pos[1] + (j * self.space[1]) + self.bub[1]\\n\\n self.coords = num.dstack((i0, i1, j0, j1)).astype('i')\",\n \"def set_position(self, updated):\\n self.buff_x = updated[0]\\n self.buff_y = updated[1]\",\n \"def update_rec(self):\\n import copy\\n \\n self.leftrec, self.rightrec = copy.copy(self.rec), copy.copy(self.rec)\\n self.leftrec[2*self.dim + 1], self.rightrec[2*self.dim] = self.node.dimension[self.dim], self.node.dimension[self.dim]\",\n \"def update(self):\\n if self.dir == \\\"r\\\":\\n self.vx = 10\\n self.vy = 0\\n elif self.dir == \\\"l\\\":\\n self.vx = -10\\n self.vy = 0\\n elif self.dir == \\\"u\\\":\\n self.vx = 0\\n self.vy = -10\\n elif self.dir == \\\"d\\\":\\n self.vx = 0\\n self.vy = 10\\n elif self.dir == \\\"None\\\":\\n self.vx = 0\\n self.vy = 0\\n self.x += self.vx\\n self.y += self.vy\",\n \"def set_contribution(self):\\r\\n \\r\\n # WX = X*Q\\r\\n # WY = Y*Q\\r\\n # see how these are assembled in the Farm class.\\r\\n \\r\\n # x-row of big X:\\r\\n self.x = np.hstack((np.hstack((\\\\\\r\\n [xi + ni for xi, ni in zip(self.cx, self.bn)]\\\\\\r\\n )), np.hstack((\\\\\\r\\n [yi + ti for yi, ti in zip(self.cy, self.bt)]\\\\\\r\\n ))))\\r\\n \\r\\n # y-row of big Y:\\r\\n self.y = np.hstack((np.hstack((self.cy)), np.hstack((\\\\\\r\\n [-xi for xi in self.cx]))))\",\n \"def matrix_apply_to_2d(data, matrix: np.matrix):\\n from scipy import mgrid\\n\\n cx = data.shape[0] / 2\\n cy = data.shape[1] / 2\\n\\n # Calculate the new coordinates of every point\\n grid = mgrid[-cx:data.shape[0]-cx, -cy:data.shape[1]-cy]\\n temp = grid.reshape((2, grid.size / 2))\\n # Add the fourth dimension (just 1s but needed for the computations)\\n newrow = np.ones(grid.size / 2)\\n temp = np.vstack([temp, newrow])\\n # Use the matrix to calculate the new positions of every point\\n temp = np.dot(matrix, temp)\\n # Delete the fourth dimension\\n temp = np.delete(temp, 2, axis=0)\\n temp = np.array(temp)\\n grid = np.reshape(temp, (2, data.shape[0], data.shape[1]))\\n\\n grid[0] += cx\\n grid[1] += cy\\n\\n from scipy.ndimage.interpolation import map_coordinates\\n d = map_coordinates(data, grid, order=3)\\n\\n return d\",\n \"def update_position(self):\\n self.current_position = utility_methods.cylindrical(self.current_position + self.rotation)\\n\\n self.rotation_list.append(self.current_position)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64133954","0.6272482","0.6020012","0.60073805","0.59557956","0.5955312","0.5928968","0.5921737","0.590957","0.586239","0.58351856","0.58159703","0.579463","0.5779063","0.57384187","0.57379836","0.56909096","0.56815416","0.56763625","0.5650514","0.5626454","0.56264246","0.56151754","0.5615047","0.5613081","0.5602278","0.55958956","0.559345","0.5553566","0.5552968","0.55343556","0.5522872","0.55154645","0.5508955","0.55035305","0.5485752","0.54845256","0.5484448","0.5478628","0.5474521","0.54727936","0.5456831","0.54517126","0.5447755","0.54472923","0.543701","0.54349977","0.5419732","0.5412178","0.5404413","0.53969336","0.5382135","0.53812796","0.537808","0.5368181","0.53642225","0.5363563","0.5341594","0.53406775","0.53346413","0.5334065","0.5333882","0.53189945","0.531625","0.53091234","0.53009427","0.5299947","0.5290334","0.5289465","0.5272282","0.5271403","0.5266549","0.52566266","0.5250817","0.52335066","0.52323705","0.52308935","0.52224296","0.52140844","0.5210298","0.51964664","0.51907694","0.518765","0.5184238","0.5180858","0.51799536","0.51743877","0.5166659","0.5162849","0.51619226","0.51591843","0.5153303","0.5147828","0.5145681","0.5145606","0.5144082","0.5142883","0.51419955","0.5141408","0.51362056"],"string":"[\n \"0.64133954\",\n \"0.6272482\",\n \"0.6020012\",\n \"0.60073805\",\n \"0.59557956\",\n \"0.5955312\",\n \"0.5928968\",\n \"0.5921737\",\n \"0.590957\",\n \"0.586239\",\n \"0.58351856\",\n \"0.58159703\",\n \"0.579463\",\n \"0.5779063\",\n \"0.57384187\",\n \"0.57379836\",\n \"0.56909096\",\n \"0.56815416\",\n \"0.56763625\",\n \"0.5650514\",\n \"0.5626454\",\n \"0.56264246\",\n \"0.56151754\",\n \"0.5615047\",\n \"0.5613081\",\n \"0.5602278\",\n \"0.55958956\",\n \"0.559345\",\n \"0.5553566\",\n \"0.5552968\",\n \"0.55343556\",\n \"0.5522872\",\n \"0.55154645\",\n \"0.5508955\",\n \"0.55035305\",\n \"0.5485752\",\n \"0.54845256\",\n \"0.5484448\",\n \"0.5478628\",\n \"0.5474521\",\n \"0.54727936\",\n \"0.5456831\",\n \"0.54517126\",\n \"0.5447755\",\n \"0.54472923\",\n \"0.543701\",\n \"0.54349977\",\n \"0.5419732\",\n \"0.5412178\",\n \"0.5404413\",\n \"0.53969336\",\n \"0.5382135\",\n \"0.53812796\",\n \"0.537808\",\n \"0.5368181\",\n \"0.53642225\",\n \"0.5363563\",\n \"0.5341594\",\n \"0.53406775\",\n \"0.53346413\",\n \"0.5334065\",\n \"0.5333882\",\n \"0.53189945\",\n \"0.531625\",\n \"0.53091234\",\n \"0.53009427\",\n \"0.5299947\",\n \"0.5290334\",\n \"0.5289465\",\n \"0.5272282\",\n \"0.5271403\",\n \"0.5266549\",\n \"0.52566266\",\n \"0.5250817\",\n \"0.52335066\",\n \"0.52323705\",\n \"0.52308935\",\n \"0.52224296\",\n \"0.52140844\",\n \"0.5210298\",\n \"0.51964664\",\n \"0.51907694\",\n \"0.518765\",\n \"0.5184238\",\n \"0.5180858\",\n \"0.51799536\",\n \"0.51743877\",\n \"0.5166659\",\n \"0.5162849\",\n \"0.51619226\",\n \"0.51591843\",\n \"0.5153303\",\n \"0.5147828\",\n \"0.5145681\",\n \"0.5145606\",\n \"0.5144082\",\n \"0.5142883\",\n \"0.51419955\",\n \"0.5141408\",\n \"0.51362056\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6219569"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":19597,"cells":{"query":{"kind":"string","value":"Use float16 for faster IO during training."},"document":{"kind":"string","value":"def save_float16_npy(data, path):\n np.save(path, data.astype(np.float16))"},"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 _update_use_bfloat16(configs, use_bfloat16):\n configs[\"train_config\"].use_bfloat16 = use_bfloat16","def data_type():\n if FLAGS.use_fp16:\n return tf.float16\n else:\n return tf.float32","def data_type():\n if FLAGS.use_fp16:\n return tf.float16\n else:\n return tf.float32","def read(reader: BitStreamReader, _index: int) -> float:\n\n return reader.readFloat16()","def float_to_int_16(x):\n return np.float16(x).view(np.int16)","def benchmark_fp16_synth_forward_batch16(self):\n params = self._shared_params()._replace(batch_size=16, use_fp16=True)\n self._run_benchmark(params)","def _float_to_16_bit_sample(value):\n sample = int(32767.0 * value)\n byte0 = sample & 255\n byte1 = (sample >> 8) & 255\n return byte0, byte1","def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):\n return self._cast_floating_to(params, jnp.float16, mask)","def dtype_float(dtype: DType):\n return promote_dtypes(dtype, np.float16)","def test_convert_float16_to_float32(in_dtype):\n check_type_supported(in_dtype)\n\n f16_input = torch.tensor(range(-int(2 ** (16 - 1)), int(2 ** (16 - 1))), dtype=torch.int16).view(in_dtype)\n f32_output = convert_float_to_float32(f16_input)\n\n nan = f16_input.isnan()\n assert torch.all(f32_output[nan].isnan())\n inf = f16_input.isinf()\n assert torch.all(f32_output[inf].isinf())\n other = torch.logical_not(torch.logical_or(nan, inf))\n assert torch.all(f16_input[other] == f32_output[other])","def cshort(queue=None):\n return np.float16","def bfloat16_to_float32(tensor):\n if tensor.dtype == tf.bfloat16:\n return tf.cast(tensor, dtype=tf.float32)\n else:\n return tensor","def benchmark_fp16_synth_forward_batch128(self):\n params = self._shared_params()._replace(batch_size=128, use_fp16=True)\n self._run_benchmark(params)","def write(writer: BitStreamWriter, value: float) -> None:\n\n writer.writeFloat16(value)","def benchmark_fp16_xla_synth_forward_batch16(self):\n params = self._shared_params()._replace(\n batch_size=16, use_fp16=True, xla=True)\n self._run_benchmark(params)","def convert_uint16_to_float_ifneed(self, actual_np, expect_np):\n if actual_np.dtype == np.uint16:\n if expect_np.dtype in [np.float32, np.float64]:\n actual_np = convert_uint16_to_float(actual_np)\n self.rtol = 1.0e-2\n elif actual_np.dtype == np.float16:\n self.rtol = 1.0e-3\n else:\n self.rtol = 1.0e-5\n if (\n expect_np.dtype == np.uint16\n and actual_np.dtype == np.uint16\n ):\n nonlocal atol\n expect_np = convert_uint16_to_float(expect_np)\n actual_np = convert_uint16_to_float(actual_np)\n atol = max(atol, 0.03)\n return actual_np, expect_np","def _shorts2float(lo_byte_pair, hi_byte_pair):\n\tba = bytearray(struct.pack(\"HH\", lo_byte_pair, hi_byte_pair))\n\t[f] = struct.unpack('f', ba)\n\treturn f","def test_S2L1C_float32_uint16(self):\n test_dir = os.path.dirname(os.path.realpath(__file__))\n cache_folder = os.path.join(test_dir, 'cache_test')\n\n if os.path.exists(cache_folder):\n shutil.rmtree(cache_folder)\n\n task = SentinelHubInputTask(\n bands_feature=(FeatureType.DATA, 'BANDS'),\n additional_data=[(FeatureType.MASK, 'dataMask')],\n size=self.size,\n maxcc=self.maxcc,\n time_difference=self.time_difference,\n data_collection=DataCollection.SENTINEL2_L1C,\n max_threads=self.max_threads,\n cache_folder=cache_folder\n )\n\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\n bands = eopatch[(FeatureType.DATA, 'BANDS')]\n is_data = eopatch[(FeatureType.MASK, 'dataMask')]\n\n self.assertTrue(np.allclose(array_stats(bands), [0.0233, 0.0468, 0.0252]))\n\n width, height = self.size\n self.assertTrue(bands.shape == (4, height, width, 13))\n self.assertTrue(is_data.shape == (4, height, width, 1))\n self.assertTrue(len(eopatch.timestamp) == 4)\n self.assertTrue(bands.dtype == np.float32)\n\n self.assertTrue(os.path.exists(cache_folder))\n\n # change task's bans_dtype and run it again\n task.bands_dtype = np.uint16\n\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\n bands = eopatch[(FeatureType.DATA, 'BANDS')]\n\n self.assertTrue(np.allclose(array_stats(bands), [232.5769, 467.5385, 251.8654]))\n\n self.assertTrue(bands.dtype == np.uint16)\n\n shutil.rmtree(cache_folder)","def to_bf16(self, params: Union[Dict, FrozenDict], mask: Any = None):\n return self._cast_floating_to(params, jnp.bfloat16, mask)","def write_float32(self, f: float) -> None:\n self.buffer += struct.pack(\"<f\", f)","def ts_float32(val):\n return np.float64(val)","def test_f8_xf16_roundtrip(in_dtype, out_dtype):\n check_type_supported(out_dtype)\n\n @triton.jit\n def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\n offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n mask = offsets < n_elements\n input = tl.load(input_ptr + offsets, mask=mask)\n output = input\n tl.store(output_ptr + offsets, output, mask=mask)\n\n f8_tensor = torch.tensor(range(-128, 128), dtype=torch.int8, device='cuda')\n # f32_to_f8 doesn't handle nan, so we make sure f8_tensor doesn't contain any nan\n all_exp_ones = (f8_tensor & 0b01111100) == 128 - 2**in_dtype.fp_mantissa_width\n f8_tensor[all_exp_ones] = 0\n f8 = triton.reinterpret(f8_tensor, in_dtype)\n n_elements = f8_tensor.numel()\n xf16 = torch.empty_like(f8_tensor, dtype=out_dtype)\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n copy_kernel[grid](f8, xf16, n_elements, BLOCK_SIZE=1024)\n\n # exponent_mask = 0b01111100 for float8e5\n # exponent_mask = 0b01111000 for float8e4\n exponent_mask = 0b01111111 ^ ((1 << in_dtype.fp_mantissa_width) - 1)\n normal = torch.logical_and((f8_tensor & exponent_mask) != 0, (f8_tensor & exponent_mask) != exponent_mask)\n ref16 = convert_float_to_float32(f8_tensor, in_dtype)\n # WARN: currently only normal float8s are handled\n assert torch.all(xf16[normal] == ref16[normal])\n\n f8_output_tensor = torch.empty_like(xf16, dtype=torch.int8)\n f8_output = triton.reinterpret(f8_output_tensor, in_dtype)\n copy_kernel[grid](xf16, f8_output, n_elements, BLOCK_SIZE=1024)\n\n assert torch.all(f8_tensor == f8_output_tensor)","def get_data_type(params):\n return tf.float16 if params.use_fp16 else tf.float32","def data_convert2float32 (self, data):\r\n data = data.astype(np.float32)\r\n\r\n return data","def benchmark_fp16_xla_synth_forward_batch128(self):\n params = self._shared_params()._replace(\n batch_size=128, use_fp16=True, xla=True)\n self._run_benchmark(params)","def __float__(self):\n return float(self.encoded) / (1 << self.frac_bits)","def test_f16_to_f8_rounding(in_dtype, out_dtype):\n @triton.jit\n def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\n offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n mask = offsets < n_elements\n input = tl.load(input_ptr + offsets, mask=mask)\n output = input\n tl.store(output_ptr + offsets, output, mask=mask)\n\n i16_input = torch.tensor(range(-int(2 ** (16 - 1)), int(2 ** (16 - 1))), dtype=torch.int16, device='cuda')\n f16_input = i16_input.view(out_dtype)\n n_elements = f16_input.numel()\n f8_output_tensor = torch.empty_like(f16_input, dtype=torch.int8)\n f8_output = triton.reinterpret(f8_output_tensor, in_dtype)\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n copy_kernel[grid](f16_input, f8_output, n_elements, BLOCK_SIZE=1024)\n\n f16_output = torch.empty_like(f16_input, dtype=out_dtype)\n copy_kernel[grid](f8_output, f16_output, n_elements, BLOCK_SIZE=1024)\n\n abs_error = torch.abs(f16_input - f16_output)\n\n all_f8_vals_tensor = torch.tensor(range(2 ** 8), dtype=torch.uint8, device='cuda')\n all_f8_vals = triton.reinterpret(all_f8_vals_tensor, in_dtype)\n all_f8_vals_in_f16 = torch.empty_like(all_f8_vals_tensor, dtype=out_dtype)\n copy_kernel[grid](all_f8_vals, all_f8_vals_in_f16, n_elements=256, BLOCK_SIZE=1024)\n\n all_finite_f8_vals_in_f16 = all_f8_vals_in_f16[\n torch.isfinite(all_f8_vals_in_f16)\n ]\n\n min_error = torch.min(\n torch.abs(\n f16_input.reshape((-1, 1))\n - all_finite_f8_vals_in_f16.reshape((1, -1))\n ),\n dim=1,\n )[0]\n\n # WARN: only normalized numbers are handled\n f8_normal_min = 1 << in_dtype.fp_mantissa_width # 0b00001000 for float8e4\n f8_normal_max = 0b01111110 if in_dtype == tl.float8e4 else 0b01111011\n f16_min, f16_max, f16_max_minus_1 = convert_float_to_float32(torch.tensor([f8_normal_min, f8_normal_max, f8_normal_max - 1], dtype=torch.int8), in_dtype)\n assert torch.all(torch.isfinite(f16_min))\n assert torch.all(torch.isfinite(f16_max))\n thres_error = f16_max - f16_max_minus_1\n mismatch = torch.logical_and(\n torch.logical_or(abs_error != min_error, abs_error > thres_error), torch.logical_and(torch.isfinite(f16_input), torch.logical_and(torch.abs(f16_input) <= f16_max, torch.abs(f16_input) >= f16_min))\n )\n assert torch.all(\n torch.logical_not(mismatch)\n ), f\"f16_input[mismatch]={f16_input[mismatch]} f16_output[mismatch]={f16_output[mismatch]} abs_error[mismatch]={abs_error[mismatch]} min_error[mismatch]={min_error[mismatch]}\"","def _shared_params_fp16(self):\n return BenchmarkBase._shared_params(self)._replace(\n model='resnet50_v1.5',\n batch_size=256,\n distortions=False,\n use_fp16=True,\n optimizer='momentum',\n loss_type_to_report='base_loss',\n compute_lr_on_cpu=True,\n single_l2_loss_op=True\n )","def read(reader: BitStreamReader, _index: int) -> float:\n\n return reader.readFloat64()","def cfloat(queue=None):\n return np.float32","def to_uint16(f):\n from numpy import array, clip\n\n img = array(clip(f,0,65535)).astype('H')\n return img","def _shared_params_fp16(self):\n return BenchmarkBase._shared_params(self)._replace(\n model='resnet50',\n batch_size=256,\n distortions=False,\n use_fp16=True,\n optimizer='momentum',\n loss_type_to_report='base_loss',\n compute_lr_on_cpu=True,\n single_l2_loss_op=True\n )","def read(reader: BitStreamReader, _index: int) -> float:\n\n return reader.readFloat32()","def _convert_example(example, use_bfloat16):\n for key in list(example.keys()):\n val = example[key]\n if tf.keras.backend.is_sparse(val):\n val = tf.sparse.to_dense(val)\n if val.dtype == tf.int64:\n val = tf.cast(val, tf.int32)\n if use_bfloat16 and val.dtype == tf.float32:\n val = tf.cast(val, tf.bfloat16)\n\n example[key] = val","def readFloat(self) -> float:\n return self._unpack('!f', 4)","def read_u16(self) -> int:","def read_u16(self) -> int:\n ...","def compress(self,float32):\n\n F16_EXPONENT_BITS = 0x1F\n F16_EXPONENT_SHIFT = 10\n F16_EXPONENT_BIAS = 15\n F16_MANTISSA_BITS = 0x3ff\n F16_MANTISSA_SHIFT = (23 - F16_EXPONENT_SHIFT)\n F16_MAX_EXPONENT = (F16_EXPONENT_BITS << F16_EXPONENT_SHIFT)\n\n if type(float32) == float:\n f32 = self.unpack(float32)\n else:\n f32 = float32\n f16 = 0\n sign = (f32 >> 16) & 0x8000\n exponent = ((f32 >> 23) & 0xff) - 127\n mantissa = f32 & 0x007fffff\n \n if exponent == 128:\n f16 = sign | F16_MAX_EXPONENT\n if mantissa:\n f16 |= (mantissa & F16_MANTISSA_BITS)\n elif exponent > 15:\n f16 = sign | F16_MAX_EXPONENT\n elif exponent > -15:\n exponent += F16_EXPONENT_BIAS\n mantissa >>= F16_MANTISSA_SHIFT\n f16 = sign | exponent << F16_EXPONENT_SHIFT | mantissa\n else:\n f16 = sign\n return f16","def to_float(self) -> float:\n return (1.0 * self.as_int) / sensor_fixed_point_conversion","def uint8_to_float(im: np.array):\n if im.dtype == np.float32:\n warnings.warn(\"Image is already np.float32\")\n return im\n im = im.astype(np.float32) / 255\n return im","def uint8_to_float(im: np.array):\n if im.dtype == np.float32:\n warnings.warn(\"Image is already np.float32\")\n return im\n im = im.astype(np.float32) / 255\n return im","def convert_to_fp32(tensor):\n\n def _convert_to_fp32(tensor):\n return tensor.float()\n\n def _is_fp16_bf16_tensor(tensor):\n return hasattr(tensor, \"dtype\") and tensor.dtype in (torch.float16, torch.bfloat16)\n\n return recursively_apply(_convert_to_fp32, tensor, test_type=_is_fp16_bf16_tensor)","def convertSpeed(self, v):\n\t\tconverted = v/(self.microstep*9.375)\n\t\treturn converted","def convert_example(example, use_bfloat16=False):\n for key in list(example.keys()):\n val = example[key]\n if tf.keras.backend.is_sparse(val):\n val = tf.sparse.to_dense(val)\n if val.dtype == tf.int64:\n val = tf.cast(val, tf.int32)\n if use_bfloat16 and val.dtype == tf.float32:\n val = tf.cast(val, tf.bfloat16)\n\n example[key] = val","def float2wav(rawData, wavFile, bit=16, samplingRate = 16000):\n rawData = rawData * np.power(2.0, bit-1)\n rawData[rawData >= np.power(2.0, bit-1)] = np.power(2.0, bit-1)-1\n rawData[rawData < -1*np.power(2.0, bit-1)] = -1*np.power(2.0, bit-1)\n \n # write as signed 16bit PCM\n if bit == 16:\n rawData = np.asarray(rawData, dtype=np.int16)\n elif bit == 32:\n rawData = np.asarray(rawData, dtype=np.int32)\n else:\n print(\"Only be able to save wav in int16 and int32 type\")\n print(\"Save to int16\")\n rawData = np.asarray(rawData, dtype=np.int16)\n scipy.io.wavfile.write(wavFile, samplingRate, rawData)\n return","def write_float(self, f: float) -> None:\n self.write(STRUCT_FLOAT.pack(f))","def cos_fp16(x: tf.Tensor) -> tf.Tensor:\n if x.dtype == tf.float16:\n return tf.cos(x)\n x_16 = tf.cast(tf.mod(x, 2 * np.pi), tf.float16)\n return tf.cos(x_16)","def readRawSamples(fname):\n\n d = numpy.fromfile(fname, dtype=numpy.float32)\n #d = d.astype(numpy.float64)\n #d = (d - 128) / 128.0\n\n return d[::2] + 1j * d[1::2]","def to_float32(n):\n return np.cast[\"float32\"](n)","def benchmark_fp16_fake_1gpu_gpuparams(self):\n params = self._shared_params_fp16()._replace(\n num_gpus=1, data_dir=self.fake_data_dir, data_name='imagenet')\n self._run_benchmark(params)","def convertToFloat(boolean: bool) -> float:\n ...","def read_S16LE(self, register):\n raise NotImplementedError","def _convert_samples_to_float32(samples):\n float32_samples = samples.astype('float32')\n if samples.dtype in np.sctypes['int']:\n bits = np.iinfo(samples.dtype).bits\n float32_samples *= 1.0 / 2 ** (bits - 1)\n elif samples.dtype in np.sctypes['float']:\n pass\n else:\n raise TypeError(\"Unsupported sample type: %s.\" % samples.dtype)\n return float32_samples","def benchmark_synth_forward_batch16(self):\n params = self._shared_params()._replace(batch_size=16)\n self._run_benchmark(params)","def bit_to_short(bits: str) -> float:\n ints = int(bits, 2)\n result = ints / _max_16bit\n return result","def to_float(self): \n return (self._num / self._den)","def read_float(self):\n return self._packers[\"f\"].unpack(self.read(4))[0]","def _float_feature(value):\n\treturn tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))","def _eight_byte_real_to_float(value):\n short1, short2, long3 = struct.unpack(\">HHL\", value)\n exponent = (short1 & 0x7F00) // 256 - 64\n mantissa = (\n ((short1 & 0x00FF) * 65536 + short2) * 4294967296 + long3\n ) / 72057594037927936.0\n if short1 & 0x8000:\n return -mantissa * 16.0 ** exponent\n return mantissa * 16.0 ** exponent","def test_op_one_float(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\n offl_a = stream.bind(a)\n offl_a.one()\n offl_a.update_host()\n stream.sync()\n self.assertTrue((a == 1.0).all(),\n \"Array should be all one.\" + str(a))","def imread_float(infile):\n return img_as_float(imread(infile))","def benchmark_fp16_synth_1gpu_gpuparams(self):\n params = self._shared_params()._replace(\n num_gpus=1, use_fp16=True, variable_update='parameter_server')\n self._run_benchmark(params)","def benchmark_fp16_synth_1gpu_gpuparams(self):\n params = self._shared_params()._replace(\n num_gpus=1, use_fp16=True, variable_update='parameter_server')\n self._run_benchmark(params)","def benchmark_fp16_synth_1gpu_gpuparams(self):\n params = self._shared_params()._replace(\n num_gpus=1, use_fp16=True, variable_update='parameter_server')\n self._run_benchmark(params)","def cdouble(queue):\n if 'fp64' in queue.device.get_info(ocl.device_info.EXTENSIONS):\n return np.float64\n else:\n return np.float32","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))","def _float_feature(value):\r\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def write(writer: BitStreamWriter, value: float) -> None:\n\n writer.writeFloat32(value)","def writeFloat(self, value: float):\n self._pack('!f', value)","def convert_fp32_or_fp16(\n input_model_dir, output_model_dir, batch_size, precision_mode):\n trt.create_inference_graph(\n input_graph_def=None,\n outputs=None,\n max_batch_size=batch_size,\n input_saved_model_dir=input_model_dir,\n output_saved_model_dir=output_model_dir,\n precision_mode=precision_mode)","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _convert_samples_to_float32(samples):\n float32_samples = samples.astype('float32')\n if samples.dtype in np.sctypes['int']:\n bits = np.iinfo(samples.dtype).bits\n float32_samples *= (1. / 2 ** (bits - 1))\n elif samples.dtype in np.sctypes['float']:\n pass\n else:\n raise TypeError(\"Unsupported sample type: %s.\" % samples.dtype)\n return float32_samples","def _float_feature(value):\n return tf.train.Feature(\n float_list=tf.train.FloatList(value=value))","def benchmark_fp16_synth_1gpu_gpuparams(self):\n params = self._shared_params_fp16()._replace(num_gpus=1)\n self._run_benchmark(params)","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def read_float32(self):\n return self.read(BitTypes.FLOAT_LE_32.value)","def binfloat(filename):\n return np.memmap(filename, dtype='float32')","def _eight_byte_real_to_float(value):\n short1, short2, long3 = struct.unpack('>HHL', value)\n exponent = (short1 & 0x7f00) // 256 - 64\n mantissa = (((short1 & 0x00ff) * 65536 + short2) * 4294967296 +\n long3) / 72057594037927936.0\n if short1 & 0x8000:\n return -mantissa * 16.**exponent\n return mantissa * 16.**exponent","def normalise_image_16bit(file_name, max_possible_intensity=65535):\n print(file_name)\n normalised_image_name = file_name[:-4] + 'normed16bit.png'\n image = cv2.imread(file_name, cv2.IMREAD_ANYDEPTH)\n\n minimum_intensity = np.amin(image)\n maximum_intensity = np.amax(image)\n factor = float(max_possible_intensity) / float(maximum_intensity - minimum_intensity)\n\n\n subtracted_image = np.subtract(image, np.full(np.shape(image), minimum_intensity))\n subtracted_scaled_image = (subtracted_image * factor)\n normalised_image = subtracted_scaled_image.astype(np.uint16)\n cv2.imwrite(normalised_image_name, normalised_image)","def benchmark_fp16_fake_1gpu_gpuparams(self):\n params = self._shared_params_fp16()._replace(\n num_gpus=1,\n data_dir=self.fake_data_dir,\n data_name='imagenet',\n variable_update='parameter_server')\n self._run_benchmark(params)","def convert_stream(self, stream):\n return np.fromstring(stream, \"Float32\")","def read_S16BE(self, register):\n raise NotImplementedError","def read_U16LE(self, register):\n raise NotImplementedError","def waveReadAsFloat(wavFileIn):\n \n sr, wavdata = scipy.io.wavfile.read(wavFileIn)\n \n if wavdata.dtype is np.dtype(np.int16):\n wavdata = np.array(wavdata, dtype=np.float32) / \\\n np.power(2.0, 16-1)\n elif wavdata.dtype is np.dtype(np.int32):\n wavdata = np.array(wavdata, dtype=np.float32) / \\\n np.power(2.0, 32-1)\n elif wavdata.dtype is np.dtype(np.float32):\n pass\n else:\n print(\"Unknown waveform format %s\" % (wavFileIn))\n sys.exit(1)\n return sr, wavdata","def load_wav_16k_mono(self, filename):\n filename = utils.get_file_path('webapp/static/processed', filename)\n\n file_contents = tf.io.read_file(filename)\n wav, sample_rate = tf.audio.decode_wav(file_contents,\n desired_channels=1)\n wav = tf.squeeze(wav, axis=-1)\n sample_rate = tf.cast(sample_rate, dtype=tf.int64)\n wav = tfio.audio.resample(wav, rate_in=sample_rate, rate_out=16000)\n return wav","def ReadFloat(self, endian=\"<\"):\n return self.unpack(\"%sf\" % endian, 4)","def set_microphone_sample_rate_to_16khz():\n\n return _update_device_state_bit(_16khz_bit, 1)","def benchmark_fp16_batch128_synth_1gpu_gpuparams(self):\n params = self._shared_params_fp16()._replace(num_gpus=1, batch_size=128)\n self._run_benchmark(params)","def img_as_float64(image, force_copy=False): # reliably restored by inspect\n pass","def from_uint16(traces, scalers):\n mask = traces < 1\n min_val, max_val = scalers\n traces -= 1\n traces = traces.astype(float) * max_val / (65535 - 1)\n traces += min_val\n return traces, mask","def test_float(self):\n htype = h5t.py_create('f')\n self.assertIsInstance(htype, h5t.TypeFloatID)"],"string":"[\n \"def _update_use_bfloat16(configs, use_bfloat16):\\n configs[\\\"train_config\\\"].use_bfloat16 = use_bfloat16\",\n \"def data_type():\\n if FLAGS.use_fp16:\\n return tf.float16\\n else:\\n return tf.float32\",\n \"def data_type():\\n if FLAGS.use_fp16:\\n return tf.float16\\n else:\\n return tf.float32\",\n \"def read(reader: BitStreamReader, _index: int) -> float:\\n\\n return reader.readFloat16()\",\n \"def float_to_int_16(x):\\n return np.float16(x).view(np.int16)\",\n \"def benchmark_fp16_synth_forward_batch16(self):\\n params = self._shared_params()._replace(batch_size=16, use_fp16=True)\\n self._run_benchmark(params)\",\n \"def _float_to_16_bit_sample(value):\\n sample = int(32767.0 * value)\\n byte0 = sample & 255\\n byte1 = (sample >> 8) & 255\\n return byte0, byte1\",\n \"def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):\\n return self._cast_floating_to(params, jnp.float16, mask)\",\n \"def dtype_float(dtype: DType):\\n return promote_dtypes(dtype, np.float16)\",\n \"def test_convert_float16_to_float32(in_dtype):\\n check_type_supported(in_dtype)\\n\\n f16_input = torch.tensor(range(-int(2 ** (16 - 1)), int(2 ** (16 - 1))), dtype=torch.int16).view(in_dtype)\\n f32_output = convert_float_to_float32(f16_input)\\n\\n nan = f16_input.isnan()\\n assert torch.all(f32_output[nan].isnan())\\n inf = f16_input.isinf()\\n assert torch.all(f32_output[inf].isinf())\\n other = torch.logical_not(torch.logical_or(nan, inf))\\n assert torch.all(f16_input[other] == f32_output[other])\",\n \"def cshort(queue=None):\\n return np.float16\",\n \"def bfloat16_to_float32(tensor):\\n if tensor.dtype == tf.bfloat16:\\n return tf.cast(tensor, dtype=tf.float32)\\n else:\\n return tensor\",\n \"def benchmark_fp16_synth_forward_batch128(self):\\n params = self._shared_params()._replace(batch_size=128, use_fp16=True)\\n self._run_benchmark(params)\",\n \"def write(writer: BitStreamWriter, value: float) -> None:\\n\\n writer.writeFloat16(value)\",\n \"def benchmark_fp16_xla_synth_forward_batch16(self):\\n params = self._shared_params()._replace(\\n batch_size=16, use_fp16=True, xla=True)\\n self._run_benchmark(params)\",\n \"def convert_uint16_to_float_ifneed(self, actual_np, expect_np):\\n if actual_np.dtype == np.uint16:\\n if expect_np.dtype in [np.float32, np.float64]:\\n actual_np = convert_uint16_to_float(actual_np)\\n self.rtol = 1.0e-2\\n elif actual_np.dtype == np.float16:\\n self.rtol = 1.0e-3\\n else:\\n self.rtol = 1.0e-5\\n if (\\n expect_np.dtype == np.uint16\\n and actual_np.dtype == np.uint16\\n ):\\n nonlocal atol\\n expect_np = convert_uint16_to_float(expect_np)\\n actual_np = convert_uint16_to_float(actual_np)\\n atol = max(atol, 0.03)\\n return actual_np, expect_np\",\n \"def _shorts2float(lo_byte_pair, hi_byte_pair):\\n\\tba = bytearray(struct.pack(\\\"HH\\\", lo_byte_pair, hi_byte_pair))\\n\\t[f] = struct.unpack('f', ba)\\n\\treturn f\",\n \"def test_S2L1C_float32_uint16(self):\\n test_dir = os.path.dirname(os.path.realpath(__file__))\\n cache_folder = os.path.join(test_dir, 'cache_test')\\n\\n if os.path.exists(cache_folder):\\n shutil.rmtree(cache_folder)\\n\\n task = SentinelHubInputTask(\\n bands_feature=(FeatureType.DATA, 'BANDS'),\\n additional_data=[(FeatureType.MASK, 'dataMask')],\\n size=self.size,\\n maxcc=self.maxcc,\\n time_difference=self.time_difference,\\n data_collection=DataCollection.SENTINEL2_L1C,\\n max_threads=self.max_threads,\\n cache_folder=cache_folder\\n )\\n\\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\\n bands = eopatch[(FeatureType.DATA, 'BANDS')]\\n is_data = eopatch[(FeatureType.MASK, 'dataMask')]\\n\\n self.assertTrue(np.allclose(array_stats(bands), [0.0233, 0.0468, 0.0252]))\\n\\n width, height = self.size\\n self.assertTrue(bands.shape == (4, height, width, 13))\\n self.assertTrue(is_data.shape == (4, height, width, 1))\\n self.assertTrue(len(eopatch.timestamp) == 4)\\n self.assertTrue(bands.dtype == np.float32)\\n\\n self.assertTrue(os.path.exists(cache_folder))\\n\\n # change task's bans_dtype and run it again\\n task.bands_dtype = np.uint16\\n\\n eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval)\\n bands = eopatch[(FeatureType.DATA, 'BANDS')]\\n\\n self.assertTrue(np.allclose(array_stats(bands), [232.5769, 467.5385, 251.8654]))\\n\\n self.assertTrue(bands.dtype == np.uint16)\\n\\n shutil.rmtree(cache_folder)\",\n \"def to_bf16(self, params: Union[Dict, FrozenDict], mask: Any = None):\\n return self._cast_floating_to(params, jnp.bfloat16, mask)\",\n \"def write_float32(self, f: float) -> None:\\n self.buffer += struct.pack(\\\"<f\\\", f)\",\n \"def ts_float32(val):\\n return np.float64(val)\",\n \"def test_f8_xf16_roundtrip(in_dtype, out_dtype):\\n check_type_supported(out_dtype)\\n\\n @triton.jit\\n def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\\n offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\\n mask = offsets < n_elements\\n input = tl.load(input_ptr + offsets, mask=mask)\\n output = input\\n tl.store(output_ptr + offsets, output, mask=mask)\\n\\n f8_tensor = torch.tensor(range(-128, 128), dtype=torch.int8, device='cuda')\\n # f32_to_f8 doesn't handle nan, so we make sure f8_tensor doesn't contain any nan\\n all_exp_ones = (f8_tensor & 0b01111100) == 128 - 2**in_dtype.fp_mantissa_width\\n f8_tensor[all_exp_ones] = 0\\n f8 = triton.reinterpret(f8_tensor, in_dtype)\\n n_elements = f8_tensor.numel()\\n xf16 = torch.empty_like(f8_tensor, dtype=out_dtype)\\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\\n copy_kernel[grid](f8, xf16, n_elements, BLOCK_SIZE=1024)\\n\\n # exponent_mask = 0b01111100 for float8e5\\n # exponent_mask = 0b01111000 for float8e4\\n exponent_mask = 0b01111111 ^ ((1 << in_dtype.fp_mantissa_width) - 1)\\n normal = torch.logical_and((f8_tensor & exponent_mask) != 0, (f8_tensor & exponent_mask) != exponent_mask)\\n ref16 = convert_float_to_float32(f8_tensor, in_dtype)\\n # WARN: currently only normal float8s are handled\\n assert torch.all(xf16[normal] == ref16[normal])\\n\\n f8_output_tensor = torch.empty_like(xf16, dtype=torch.int8)\\n f8_output = triton.reinterpret(f8_output_tensor, in_dtype)\\n copy_kernel[grid](xf16, f8_output, n_elements, BLOCK_SIZE=1024)\\n\\n assert torch.all(f8_tensor == f8_output_tensor)\",\n \"def get_data_type(params):\\n return tf.float16 if params.use_fp16 else tf.float32\",\n \"def data_convert2float32 (self, data):\\r\\n data = data.astype(np.float32)\\r\\n\\r\\n return data\",\n \"def benchmark_fp16_xla_synth_forward_batch128(self):\\n params = self._shared_params()._replace(\\n batch_size=128, use_fp16=True, xla=True)\\n self._run_benchmark(params)\",\n \"def __float__(self):\\n return float(self.encoded) / (1 << self.frac_bits)\",\n \"def test_f16_to_f8_rounding(in_dtype, out_dtype):\\n @triton.jit\\n def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\\n offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\\n mask = offsets < n_elements\\n input = tl.load(input_ptr + offsets, mask=mask)\\n output = input\\n tl.store(output_ptr + offsets, output, mask=mask)\\n\\n i16_input = torch.tensor(range(-int(2 ** (16 - 1)), int(2 ** (16 - 1))), dtype=torch.int16, device='cuda')\\n f16_input = i16_input.view(out_dtype)\\n n_elements = f16_input.numel()\\n f8_output_tensor = torch.empty_like(f16_input, dtype=torch.int8)\\n f8_output = triton.reinterpret(f8_output_tensor, in_dtype)\\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\\n copy_kernel[grid](f16_input, f8_output, n_elements, BLOCK_SIZE=1024)\\n\\n f16_output = torch.empty_like(f16_input, dtype=out_dtype)\\n copy_kernel[grid](f8_output, f16_output, n_elements, BLOCK_SIZE=1024)\\n\\n abs_error = torch.abs(f16_input - f16_output)\\n\\n all_f8_vals_tensor = torch.tensor(range(2 ** 8), dtype=torch.uint8, device='cuda')\\n all_f8_vals = triton.reinterpret(all_f8_vals_tensor, in_dtype)\\n all_f8_vals_in_f16 = torch.empty_like(all_f8_vals_tensor, dtype=out_dtype)\\n copy_kernel[grid](all_f8_vals, all_f8_vals_in_f16, n_elements=256, BLOCK_SIZE=1024)\\n\\n all_finite_f8_vals_in_f16 = all_f8_vals_in_f16[\\n torch.isfinite(all_f8_vals_in_f16)\\n ]\\n\\n min_error = torch.min(\\n torch.abs(\\n f16_input.reshape((-1, 1))\\n - all_finite_f8_vals_in_f16.reshape((1, -1))\\n ),\\n dim=1,\\n )[0]\\n\\n # WARN: only normalized numbers are handled\\n f8_normal_min = 1 << in_dtype.fp_mantissa_width # 0b00001000 for float8e4\\n f8_normal_max = 0b01111110 if in_dtype == tl.float8e4 else 0b01111011\\n f16_min, f16_max, f16_max_minus_1 = convert_float_to_float32(torch.tensor([f8_normal_min, f8_normal_max, f8_normal_max - 1], dtype=torch.int8), in_dtype)\\n assert torch.all(torch.isfinite(f16_min))\\n assert torch.all(torch.isfinite(f16_max))\\n thres_error = f16_max - f16_max_minus_1\\n mismatch = torch.logical_and(\\n torch.logical_or(abs_error != min_error, abs_error > thres_error), torch.logical_and(torch.isfinite(f16_input), torch.logical_and(torch.abs(f16_input) <= f16_max, torch.abs(f16_input) >= f16_min))\\n )\\n assert torch.all(\\n torch.logical_not(mismatch)\\n ), f\\\"f16_input[mismatch]={f16_input[mismatch]} f16_output[mismatch]={f16_output[mismatch]} abs_error[mismatch]={abs_error[mismatch]} min_error[mismatch]={min_error[mismatch]}\\\"\",\n \"def _shared_params_fp16(self):\\n return BenchmarkBase._shared_params(self)._replace(\\n model='resnet50_v1.5',\\n batch_size=256,\\n distortions=False,\\n use_fp16=True,\\n optimizer='momentum',\\n loss_type_to_report='base_loss',\\n compute_lr_on_cpu=True,\\n single_l2_loss_op=True\\n )\",\n \"def read(reader: BitStreamReader, _index: int) -> float:\\n\\n return reader.readFloat64()\",\n \"def cfloat(queue=None):\\n return np.float32\",\n \"def to_uint16(f):\\n from numpy import array, clip\\n\\n img = array(clip(f,0,65535)).astype('H')\\n return img\",\n \"def _shared_params_fp16(self):\\n return BenchmarkBase._shared_params(self)._replace(\\n model='resnet50',\\n batch_size=256,\\n distortions=False,\\n use_fp16=True,\\n optimizer='momentum',\\n loss_type_to_report='base_loss',\\n compute_lr_on_cpu=True,\\n single_l2_loss_op=True\\n )\",\n \"def read(reader: BitStreamReader, _index: int) -> float:\\n\\n return reader.readFloat32()\",\n \"def _convert_example(example, use_bfloat16):\\n for key in list(example.keys()):\\n val = example[key]\\n if tf.keras.backend.is_sparse(val):\\n val = tf.sparse.to_dense(val)\\n if val.dtype == tf.int64:\\n val = tf.cast(val, tf.int32)\\n if use_bfloat16 and val.dtype == tf.float32:\\n val = tf.cast(val, tf.bfloat16)\\n\\n example[key] = val\",\n \"def readFloat(self) -> float:\\n return self._unpack('!f', 4)\",\n \"def read_u16(self) -> int:\",\n \"def read_u16(self) -> int:\\n ...\",\n \"def compress(self,float32):\\n\\n F16_EXPONENT_BITS = 0x1F\\n F16_EXPONENT_SHIFT = 10\\n F16_EXPONENT_BIAS = 15\\n F16_MANTISSA_BITS = 0x3ff\\n F16_MANTISSA_SHIFT = (23 - F16_EXPONENT_SHIFT)\\n F16_MAX_EXPONENT = (F16_EXPONENT_BITS << F16_EXPONENT_SHIFT)\\n\\n if type(float32) == float:\\n f32 = self.unpack(float32)\\n else:\\n f32 = float32\\n f16 = 0\\n sign = (f32 >> 16) & 0x8000\\n exponent = ((f32 >> 23) & 0xff) - 127\\n mantissa = f32 & 0x007fffff\\n \\n if exponent == 128:\\n f16 = sign | F16_MAX_EXPONENT\\n if mantissa:\\n f16 |= (mantissa & F16_MANTISSA_BITS)\\n elif exponent > 15:\\n f16 = sign | F16_MAX_EXPONENT\\n elif exponent > -15:\\n exponent += F16_EXPONENT_BIAS\\n mantissa >>= F16_MANTISSA_SHIFT\\n f16 = sign | exponent << F16_EXPONENT_SHIFT | mantissa\\n else:\\n f16 = sign\\n return f16\",\n \"def to_float(self) -> float:\\n return (1.0 * self.as_int) / sensor_fixed_point_conversion\",\n \"def uint8_to_float(im: np.array):\\n if im.dtype == np.float32:\\n warnings.warn(\\\"Image is already np.float32\\\")\\n return im\\n im = im.astype(np.float32) / 255\\n return im\",\n \"def uint8_to_float(im: np.array):\\n if im.dtype == np.float32:\\n warnings.warn(\\\"Image is already np.float32\\\")\\n return im\\n im = im.astype(np.float32) / 255\\n return im\",\n \"def convert_to_fp32(tensor):\\n\\n def _convert_to_fp32(tensor):\\n return tensor.float()\\n\\n def _is_fp16_bf16_tensor(tensor):\\n return hasattr(tensor, \\\"dtype\\\") and tensor.dtype in (torch.float16, torch.bfloat16)\\n\\n return recursively_apply(_convert_to_fp32, tensor, test_type=_is_fp16_bf16_tensor)\",\n \"def convertSpeed(self, v):\\n\\t\\tconverted = v/(self.microstep*9.375)\\n\\t\\treturn converted\",\n \"def convert_example(example, use_bfloat16=False):\\n for key in list(example.keys()):\\n val = example[key]\\n if tf.keras.backend.is_sparse(val):\\n val = tf.sparse.to_dense(val)\\n if val.dtype == tf.int64:\\n val = tf.cast(val, tf.int32)\\n if use_bfloat16 and val.dtype == tf.float32:\\n val = tf.cast(val, tf.bfloat16)\\n\\n example[key] = val\",\n \"def float2wav(rawData, wavFile, bit=16, samplingRate = 16000):\\n rawData = rawData * np.power(2.0, bit-1)\\n rawData[rawData >= np.power(2.0, bit-1)] = np.power(2.0, bit-1)-1\\n rawData[rawData < -1*np.power(2.0, bit-1)] = -1*np.power(2.0, bit-1)\\n \\n # write as signed 16bit PCM\\n if bit == 16:\\n rawData = np.asarray(rawData, dtype=np.int16)\\n elif bit == 32:\\n rawData = np.asarray(rawData, dtype=np.int32)\\n else:\\n print(\\\"Only be able to save wav in int16 and int32 type\\\")\\n print(\\\"Save to int16\\\")\\n rawData = np.asarray(rawData, dtype=np.int16)\\n scipy.io.wavfile.write(wavFile, samplingRate, rawData)\\n return\",\n \"def write_float(self, f: float) -> None:\\n self.write(STRUCT_FLOAT.pack(f))\",\n \"def cos_fp16(x: tf.Tensor) -> tf.Tensor:\\n if x.dtype == tf.float16:\\n return tf.cos(x)\\n x_16 = tf.cast(tf.mod(x, 2 * np.pi), tf.float16)\\n return tf.cos(x_16)\",\n \"def readRawSamples(fname):\\n\\n d = numpy.fromfile(fname, dtype=numpy.float32)\\n #d = d.astype(numpy.float64)\\n #d = (d - 128) / 128.0\\n\\n return d[::2] + 1j * d[1::2]\",\n \"def to_float32(n):\\n return np.cast[\\\"float32\\\"](n)\",\n \"def benchmark_fp16_fake_1gpu_gpuparams(self):\\n params = self._shared_params_fp16()._replace(\\n num_gpus=1, data_dir=self.fake_data_dir, data_name='imagenet')\\n self._run_benchmark(params)\",\n \"def convertToFloat(boolean: bool) -> float:\\n ...\",\n \"def read_S16LE(self, register):\\n raise NotImplementedError\",\n \"def _convert_samples_to_float32(samples):\\n float32_samples = samples.astype('float32')\\n if samples.dtype in np.sctypes['int']:\\n bits = np.iinfo(samples.dtype).bits\\n float32_samples *= 1.0 / 2 ** (bits - 1)\\n elif samples.dtype in np.sctypes['float']:\\n pass\\n else:\\n raise TypeError(\\\"Unsupported sample type: %s.\\\" % samples.dtype)\\n return float32_samples\",\n \"def benchmark_synth_forward_batch16(self):\\n params = self._shared_params()._replace(batch_size=16)\\n self._run_benchmark(params)\",\n \"def bit_to_short(bits: str) -> float:\\n ints = int(bits, 2)\\n result = ints / _max_16bit\\n return result\",\n \"def to_float(self): \\n return (self._num / self._den)\",\n \"def read_float(self):\\n return self._packers[\\\"f\\\"].unpack(self.read(4))[0]\",\n \"def _float_feature(value):\\n\\treturn tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\",\n \"def _eight_byte_real_to_float(value):\\n short1, short2, long3 = struct.unpack(\\\">HHL\\\", value)\\n exponent = (short1 & 0x7F00) // 256 - 64\\n mantissa = (\\n ((short1 & 0x00FF) * 65536 + short2) * 4294967296 + long3\\n ) / 72057594037927936.0\\n if short1 & 0x8000:\\n return -mantissa * 16.0 ** exponent\\n return mantissa * 16.0 ** exponent\",\n \"def test_op_one_float(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\\n offl_a = stream.bind(a)\\n offl_a.one()\\n offl_a.update_host()\\n stream.sync()\\n self.assertTrue((a == 1.0).all(),\\n \\\"Array should be all one.\\\" + str(a))\",\n \"def imread_float(infile):\\n return img_as_float(imread(infile))\",\n \"def benchmark_fp16_synth_1gpu_gpuparams(self):\\n params = self._shared_params()._replace(\\n num_gpus=1, use_fp16=True, variable_update='parameter_server')\\n self._run_benchmark(params)\",\n \"def benchmark_fp16_synth_1gpu_gpuparams(self):\\n params = self._shared_params()._replace(\\n num_gpus=1, use_fp16=True, variable_update='parameter_server')\\n self._run_benchmark(params)\",\n \"def benchmark_fp16_synth_1gpu_gpuparams(self):\\n params = self._shared_params()._replace(\\n num_gpus=1, use_fp16=True, variable_update='parameter_server')\\n self._run_benchmark(params)\",\n \"def cdouble(queue):\\n if 'fp64' in queue.device.get_info(ocl.device_info.EXTENSIONS):\\n return np.float64\\n else:\\n return np.float32\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\",\n \"def _float_feature(value):\\r\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def write(writer: BitStreamWriter, value: float) -> None:\\n\\n writer.writeFloat32(value)\",\n \"def writeFloat(self, value: float):\\n self._pack('!f', value)\",\n \"def convert_fp32_or_fp16(\\n input_model_dir, output_model_dir, batch_size, precision_mode):\\n trt.create_inference_graph(\\n input_graph_def=None,\\n outputs=None,\\n max_batch_size=batch_size,\\n input_saved_model_dir=input_model_dir,\\n output_saved_model_dir=output_model_dir,\\n precision_mode=precision_mode)\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _convert_samples_to_float32(samples):\\n float32_samples = samples.astype('float32')\\n if samples.dtype in np.sctypes['int']:\\n bits = np.iinfo(samples.dtype).bits\\n float32_samples *= (1. / 2 ** (bits - 1))\\n elif samples.dtype in np.sctypes['float']:\\n pass\\n else:\\n raise TypeError(\\\"Unsupported sample type: %s.\\\" % samples.dtype)\\n return float32_samples\",\n \"def _float_feature(value):\\n return tf.train.Feature(\\n float_list=tf.train.FloatList(value=value))\",\n \"def benchmark_fp16_synth_1gpu_gpuparams(self):\\n params = self._shared_params_fp16()._replace(num_gpus=1)\\n self._run_benchmark(params)\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def read_float32(self):\\n return self.read(BitTypes.FLOAT_LE_32.value)\",\n \"def binfloat(filename):\\n return np.memmap(filename, dtype='float32')\",\n \"def _eight_byte_real_to_float(value):\\n short1, short2, long3 = struct.unpack('>HHL', value)\\n exponent = (short1 & 0x7f00) // 256 - 64\\n mantissa = (((short1 & 0x00ff) * 65536 + short2) * 4294967296 +\\n long3) / 72057594037927936.0\\n if short1 & 0x8000:\\n return -mantissa * 16.**exponent\\n return mantissa * 16.**exponent\",\n \"def normalise_image_16bit(file_name, max_possible_intensity=65535):\\n print(file_name)\\n normalised_image_name = file_name[:-4] + 'normed16bit.png'\\n image = cv2.imread(file_name, cv2.IMREAD_ANYDEPTH)\\n\\n minimum_intensity = np.amin(image)\\n maximum_intensity = np.amax(image)\\n factor = float(max_possible_intensity) / float(maximum_intensity - minimum_intensity)\\n\\n\\n subtracted_image = np.subtract(image, np.full(np.shape(image), minimum_intensity))\\n subtracted_scaled_image = (subtracted_image * factor)\\n normalised_image = subtracted_scaled_image.astype(np.uint16)\\n cv2.imwrite(normalised_image_name, normalised_image)\",\n \"def benchmark_fp16_fake_1gpu_gpuparams(self):\\n params = self._shared_params_fp16()._replace(\\n num_gpus=1,\\n data_dir=self.fake_data_dir,\\n data_name='imagenet',\\n variable_update='parameter_server')\\n self._run_benchmark(params)\",\n \"def convert_stream(self, stream):\\n return np.fromstring(stream, \\\"Float32\\\")\",\n \"def read_S16BE(self, register):\\n raise NotImplementedError\",\n \"def read_U16LE(self, register):\\n raise NotImplementedError\",\n \"def waveReadAsFloat(wavFileIn):\\n \\n sr, wavdata = scipy.io.wavfile.read(wavFileIn)\\n \\n if wavdata.dtype is np.dtype(np.int16):\\n wavdata = np.array(wavdata, dtype=np.float32) / \\\\\\n np.power(2.0, 16-1)\\n elif wavdata.dtype is np.dtype(np.int32):\\n wavdata = np.array(wavdata, dtype=np.float32) / \\\\\\n np.power(2.0, 32-1)\\n elif wavdata.dtype is np.dtype(np.float32):\\n pass\\n else:\\n print(\\\"Unknown waveform format %s\\\" % (wavFileIn))\\n sys.exit(1)\\n return sr, wavdata\",\n \"def load_wav_16k_mono(self, filename):\\n filename = utils.get_file_path('webapp/static/processed', filename)\\n\\n file_contents = tf.io.read_file(filename)\\n wav, sample_rate = tf.audio.decode_wav(file_contents,\\n desired_channels=1)\\n wav = tf.squeeze(wav, axis=-1)\\n sample_rate = tf.cast(sample_rate, dtype=tf.int64)\\n wav = tfio.audio.resample(wav, rate_in=sample_rate, rate_out=16000)\\n return wav\",\n \"def ReadFloat(self, endian=\\\"<\\\"):\\n return self.unpack(\\\"%sf\\\" % endian, 4)\",\n \"def set_microphone_sample_rate_to_16khz():\\n\\n return _update_device_state_bit(_16khz_bit, 1)\",\n \"def benchmark_fp16_batch128_synth_1gpu_gpuparams(self):\\n params = self._shared_params_fp16()._replace(num_gpus=1, batch_size=128)\\n self._run_benchmark(params)\",\n \"def img_as_float64(image, force_copy=False): # reliably restored by inspect\\n pass\",\n \"def from_uint16(traces, scalers):\\n mask = traces < 1\\n min_val, max_val = scalers\\n traces -= 1\\n traces = traces.astype(float) * max_val / (65535 - 1)\\n traces += min_val\\n return traces, mask\",\n \"def test_float(self):\\n htype = h5t.py_create('f')\\n self.assertIsInstance(htype, h5t.TypeFloatID)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69121385","0.65502363","0.65130097","0.647946","0.6455792","0.6408274","0.63850254","0.6354032","0.63375044","0.63351613","0.6293508","0.62589705","0.6142071","0.6129017","0.60476065","0.6029137","0.59642065","0.59619236","0.5940161","0.5863883","0.584219","0.58120924","0.5797429","0.5779282","0.5775224","0.57643133","0.5752109","0.5737403","0.5707745","0.57077396","0.5702319","0.5677328","0.5667515","0.56469727","0.5629804","0.56263846","0.55859077","0.5520673","0.5515101","0.5480838","0.5480838","0.54706","0.54673856","0.5461771","0.5444612","0.54015976","0.53979766","0.53955925","0.53935385","0.53779876","0.5357399","0.5346563","0.5342086","0.5338994","0.5324537","0.5307026","0.530419","0.5297682","0.5284494","0.5282834","0.5278126","0.52721065","0.52680254","0.52680254","0.52680254","0.52640676","0.52626324","0.52626324","0.52563345","0.52489066","0.52421856","0.5240005","0.5235732","0.52352667","0.52352667","0.52352667","0.52352667","0.52352667","0.5231537","0.52248025","0.5221182","0.52168274","0.52168274","0.52168274","0.5215986","0.5210188","0.5207378","0.520638","0.51985204","0.51973784","0.5196472","0.51934844","0.5182379","0.5175041","0.5162615","0.515839","0.51481295","0.5141128","0.51386684","0.5134986"],"string":"[\n \"0.69121385\",\n \"0.65502363\",\n \"0.65130097\",\n \"0.647946\",\n \"0.6455792\",\n \"0.6408274\",\n \"0.63850254\",\n \"0.6354032\",\n \"0.63375044\",\n \"0.63351613\",\n \"0.6293508\",\n \"0.62589705\",\n \"0.6142071\",\n \"0.6129017\",\n \"0.60476065\",\n \"0.6029137\",\n \"0.59642065\",\n \"0.59619236\",\n \"0.5940161\",\n \"0.5863883\",\n \"0.584219\",\n \"0.58120924\",\n \"0.5797429\",\n \"0.5779282\",\n \"0.5775224\",\n \"0.57643133\",\n \"0.5752109\",\n \"0.5737403\",\n \"0.5707745\",\n \"0.57077396\",\n \"0.5702319\",\n \"0.5677328\",\n \"0.5667515\",\n \"0.56469727\",\n \"0.5629804\",\n \"0.56263846\",\n \"0.55859077\",\n \"0.5520673\",\n \"0.5515101\",\n \"0.5480838\",\n \"0.5480838\",\n \"0.54706\",\n \"0.54673856\",\n \"0.5461771\",\n \"0.5444612\",\n \"0.54015976\",\n \"0.53979766\",\n \"0.53955925\",\n \"0.53935385\",\n \"0.53779876\",\n \"0.5357399\",\n \"0.5346563\",\n \"0.5342086\",\n \"0.5338994\",\n \"0.5324537\",\n \"0.5307026\",\n \"0.530419\",\n \"0.5297682\",\n \"0.5284494\",\n \"0.5282834\",\n \"0.5278126\",\n \"0.52721065\",\n \"0.52680254\",\n \"0.52680254\",\n \"0.52680254\",\n \"0.52640676\",\n \"0.52626324\",\n \"0.52626324\",\n \"0.52563345\",\n \"0.52489066\",\n \"0.52421856\",\n \"0.5240005\",\n \"0.5235732\",\n \"0.52352667\",\n \"0.52352667\",\n \"0.52352667\",\n \"0.52352667\",\n \"0.52352667\",\n \"0.5231537\",\n \"0.52248025\",\n \"0.5221182\",\n \"0.52168274\",\n \"0.52168274\",\n \"0.52168274\",\n \"0.5215986\",\n \"0.5210188\",\n \"0.5207378\",\n \"0.520638\",\n \"0.51985204\",\n \"0.51973784\",\n \"0.5196472\",\n \"0.51934844\",\n \"0.5182379\",\n \"0.5175041\",\n \"0.5162615\",\n \"0.515839\",\n \"0.51481295\",\n \"0.5141128\",\n \"0.51386684\",\n \"0.5134986\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7207755"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19598,"cells":{"query":{"kind":"string","value":"Return if Persona object passed into args is in defaul componenti propperty"},"document":{"kind":"string","value":"def has_componente(self, persona):\n return True if persona.pk in self.pks_componenti else 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 XCAFDoc_ShapeTool_IsComponent(*args):\n return _XCAFDoc.XCAFDoc_ShapeTool_IsComponent(*args)","def IsComponent(*args):\n return _XCAFDoc.XCAFDoc_ShapeTool_IsComponent(*args)","def __contains__(self, arg):\r\n\r\n return arg in self.grfx[0]","def particleExists(*args, **kwargs)->bool:\n pass","def __contains__(self, arg):\n # All arguments should have a default value of some sort\n if arg not in self.args:\n raise AttributeError(\n \"arg {} doesn't exist on {}\".format(arg, self.args)\n )\n # If the value is the sentinel then the argument was not provided AND\n # there is no default\n if getattr(self.args, arg) is NoArgument:\n return False\n\n if isinstance(getattr(self.args, arg), list):\n raise AttributeError(\n \"tried to check for presence of arg {} on {}, which is a \"\n \"list\".format(arg, self.args)\n )\n\n return True","def is_mandatory_arg(self, arg_name): \n return arg_name in self.get_mandatory_args()","def object_is_valid_pipeline(o):\n return (o is not None and\n hasattr(o, 'fit') and\n hasattr(o, 'predict') and\n hasattr(o, 'steps'))","def object_is_valid_pipeline(o):\n return (o is not None and\n hasattr(o, 'fit') and\n hasattr(o, 'predict') and\n hasattr(o, 'steps'))","def objExists(*args, **kwargs)->bool:\n pass","def is_command_ancillary(args):\n # pylint: disable=bad-continuation\n if (\n # skip the parent check and only\n # determine if the parameter is present\n is_valid_executes(args, skip=True)\n ):\n return True\n return False","def SBO_isParticipant(*args):\n return _libsbml.SBO_isParticipant(*args)","def __contains__(self, component):\n if issubclass(component, Component):\n try:\n my_component = self.type.components[component.interface]\n except KeyError:\n return False\n else:\n return issubclass(my_component, component)\n else:\n return component in self.type.components","def isinstance_blender_object(self, b_obj):\n # lame and slow, but functional\n return b_obj in Blender.Object.Get()","def check_args(args):\n for arg in vars(args):\n if getattr(args, arg):\n return True\n return False","def isParticipant(*args):\n return _libsbml.SBO_isParticipant(*args)","def class_name_arg_required(args):\n no_class_name_flags = ['list_courses', 'version']\n return not any(\n getattr(args, flag)\n for flag in no_class_name_flags\n )","def is_call_object_of(self, *args):\n return _ida_hexrays.cexpr_t_is_call_object_of(self, *args)","def no_params(self) -> bool:\n result = True\n # Fixing issue #92\n if self.properties.parameters:\n return False\n else:\n return True\n # for parameter in self.properties.parameters:\n # if parameter == \"effect\":\n # continue\n # else:\n # result = False\n # break\n # return result","def ismemberdescriptor(object):\r\n return False","def is_parameter_present(self, obj):\n val_ref = obj.value_reference\n for p in self.parameters:\n if p.value_reference == val_ref:\n # there is already a parameter in the list with the same value_reference\n logger.error(\"There is already a parameter in the list with the same value reference: {0}\".format(val_ref))\n return True\n return False","def has_custom_param(plot):\n return Plot.has_custom_param(plot)","def is_procedure(vba_object):\n if hasattr(vba_object, 'statements'):\n return True\n else:\n return False","def has_component(self, component):\n return component.name in self.components","def has_component(self, var_name):\n if var_name in self._var_names:\n return True\n else:\n return False","def get_is_por_holder(self, obj):\n user = self.context['request'].user\n if not user.is_authenticated:\n return False\n # pylint: disable=no-member\n profile = UserProfile.objects.get(user=user)\n\n if profile == obj.club.secy:\n return True\n\n if profile in obj.club.joint_secy.all():\n return True\n\n if profile == obj.club.council.gensec:\n return True\n\n if profile in obj.club.council.joint_gensec.all():\n return True\n\n return False","def is_function(self):\n return self.args is not None","def __bool__(self):\n return True if self._name is not None else False","def arg_validation(arg, cla):\n if is_subclass(cla, arg):\n return arg\n else:\n print(str(arg)+\" is not a valid \" + cla.__module__ + \" name.\")\n sys.exit(2)","def is_P(self):\n return isinstance(self,P)","def __bool__(self):\n context, active_obj, actual_mode, mode = self.get_context()\n if not mode: return False\n \n if mode == 'OBJECT':\n return bool(context.selected_objects)\n elif mode == 'EDIT_MESH':\n mesh = active_obj.data\n if actual_mode == 'EDIT_MESH':\n return bool(mesh.total_vert_sel)\n else:\n return any(item.select for item in mesh.vertices)\n elif mode in {'EDIT_CURVE', 'EDIT_SURFACE'}:\n for spline in active_obj.data.splines:\n for item in spline.bezier_points:\n if (item.select_control_point or\n item.select_left_handle or\n item.select_right_handle):\n return True\n for item in spline.points:\n if item.select:\n return True\n elif mode == 'EDIT_METABALL':\n return bool(active_obj.data.elements.active)\n elif mode == 'EDIT_LATTICE':\n return any(item.select for item in active_obj.data.points)\n elif mode == 'EDIT_ARMATURE':\n return any(item.select_head or item.select_tail\n for item in active_obj.data.edit_bones)\n elif mode == 'POSE':\n return any(item.select for item in active_obj.data.bones)\n elif mode == 'PARTICLE':\n # Theoretically, particle keys can be selected,\n # but there seems to be no API for working with this\n pass\n else:\n pass # no selectable elements in other modes\n \n return False","def is_actor():\n return False","def SBO_isPhysicalParticipant(*args):\n return _libsbml.SBO_isPhysicalParticipant(*args)","def is_satisfied_by(self, val):","def is_plugin_data(cls, attributes):\n return attributes.get('metadata', {}).get('class') == 'plugin'","def toolHasOptions(*args, **kwargs)->bool:\n pass","def isrequired(self, typename):\n return typename in self.required_products","def is_injected(self, name):\n return name in self.__provisions","def __contains__(name):","def is_ap(self, obj):\n return hasattr(obj, 'attachment_point_info')","def bool(self, obj):\n return True","def bool(self, obj):\n return True","def exists(self, arg):\n raise NotImplementedError","def ismemberdescriptor(object):\r\n return isinstance(object, types.MemberDescriptorType)","def check_filters(self, pname, base, fealty=\"\"):\n if \"org\" in self.switches:\n return True\n if not self.args:\n return True\n if self.args.lower() == \"afk\":\n return \"(AFK)\" in pname\n if self.args.lower() == \"lrp\":\n return \"(LRP)\" in pname\n if self.args.lower() == \"staff\":\n return \"(Staff)\" in pname\n if self.args.lower() == str(fealty).lower():\n return True\n return base.lower().startswith(self.args.lower())","def can_start(cls, context):\n ob = context.active_object\n return ob is not None and ob.type == \"MESH\"","def is_required_data(self, typename):\n return typename in self.required_data_products","def __contains__(self, obj):\n return obj in self.actors","def has_parameter(self, name):\n for par in self.params:\n if par.name == name:\n return True\n return False","def objExists(self, attr):\n try:\n exists = cmds.objExists(\"%s.%s\" % (self.data['shapeNode'], attr))\n self.logger.debug(\"objExists : %s.%s = %s\" % (self.data['shapeNode'], attr, exists))\n return exists\n except Exception as e:\n self.logger.error(\"objExists : %s\" % e)\n return False","def is_pipeline(self):\n try:\n self.pipeline\n except ObjectDoesNotExist:\n return False\n return True","def __contains__(self, name):\n return (name in self._defs) or \\\n ((self._parent is not None) and (name in self._parent))","def has_sclass(self, w: Wrapper, prop: Any) -> bool:\n if not prop:\n return None\n props = self.sclasses(w)\n if isinstance(prop, str):\n ans = [prop in props]\n else:\n ans = [i in props for i in prop]\n return all(ans)","def predicate(obj):\n return inspect.isclass(obj) and issubclass(obj, MafColumnRecord)","def has(self, entity):\n\t\treturn entity.has_component(self.component_type)","def has_func(cls, obj, *args):\n methods = dir(obj)\n matched = [x for x in args if x in methods]\n return len(matched) == len(args)","def is_valide(self):\n if self.arguments:\n return True\n else:\n return False","def is_pdf(obj):\n return isinstance(obj, PDF)","def get_is_por_holder(self, obj):\n user = self.context['request'].user\n if not user.is_authenticated:\n return False\n # pylint: disable=no-member\n profile = UserProfile.objects.get(user=user)\n if obj in profile.get_club_privileges():\n return True\n return False","def _target_filter(self, obj):\r\n return type(obj).__name__ in ['Cube'] and not obj.is_grasped # List because may be extended to other objects.\r","def _check_whether_has_params(self, params) -> bool:\n\n if params:\n return True\n return False","def check_class_definition(cls):\n super().check_class_definition()\n\n if not cls.objects_arg:\n cls.definition_error('Must provide \"objects_arg\" attribute.')\n\n func_parameters = inspect.signature(arg.s()(cls.func).func).parameters\n if cls.objects_arg not in func_parameters:\n cls.definition_error(\n f'objects_arg \"{cls.objects_arg}\" not an argument to callable.'\n f' Possible parameters={func_parameters}'\n )","def attribute(self):\n\n return not bool(self.arguments)","def valid_args(args):\n return args is not None and len(args) > 0","def _is_dataclass_instance(obj):\n return hasattr(type(obj), '__dataclass_fields__')","def XCAFDoc_ShapeTool_IsReference(*args):\n return _XCAFDoc.XCAFDoc_ShapeTool_IsReference(*args)","def _check_value(self,val,obj=None):\n if not val in self.objects:\n # CEBALERT: can be called before __init__ has called\n # super's __init__, i.e. before attrib_name has been set.\n try:\n attrib_name = self._attrib_name\n except AttributeError:\n attrib_name = \"\"\n raise ValueError(\"%s not in Parameter %s's list of possible objects\" \\\n %(val,attrib_name))","def isPhysicalParticipant(*args):\n return _libsbml.SBO_isPhysicalParticipant(*args)","def _is_propertyable(\n names, # type: List[str]\n attrs, # type: Dict[str, Any]\n annotations, # type: Dict[str, type]\n attr, # Dict[str, Any]\n):\n # type: (...) -> bool\n return (\n attr in annotations\n and not attr.startswith(\"_\")\n and not attr.isupper()\n and \"__{}\".format(attr) not in names\n and not isinstance(getattr(attrs, attr, None), types.MethodType)\n )","def hasname(self):\n\t\treturn self.name is not None","def is_required_data(self, typename):\r\n return typename in self.required_data_products","def get_is_por_holder(self, obj):\n user = self.context['request'].user\n if not user.is_authenticated:\n return False\n # pylint: disable=no-member\n profile = UserProfile.objects.get(user=user)\n if obj in profile.get_council_privileges():\n return True\n return False","def isFunctionalCompartment(*args):\n return _libsbml.SBO_isFunctionalCompartment(*args)","def has_descriptor(self, uuid):","def _check_args(self, args):\n if len(args) == 0:\n print(\"No parameters provided.\")\n return False\n else:\n return True","def __contains__(self, feature):\n return feature == 'cvarsort' or feature in self.features","def __contains__(self, name: str) -> bool:\n ...","def isdouparname(self,parname_): # 3\n res,resargs = self.__obj.isdouparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = dparam(_param_return_value)\n return _param_return_value","def is_userAS(self, obj):\n # Some other places simply check for owner=None.\n return UserAS.objects.filter(as_ptr=obj).exists()","def is_satisfied(self, item: Any) -> bool:","def matches_property_name(fun):\n return callable(fun) and getattr(fun, annotation, None) == value","def _is_primitive_type(io: type) -> bool:\n return any(io.__module__.startswith(mldesigner_pkg) and item.__name__ == param_name for item in getmro(io))","def __bool__(self):\n return len(self.atoms) >= 1","def exist(self):","def test_component_specifications_exist(self):\r\n\t\tself.assertTrue(not (self._configuration_[\"AddWordDefinitionTask\"] is None\r\n\t\t or self._configuration_[\"ListWordDefinitionsTask\"] is None or\r\n\t\t self._configuration_[\"RemoveWordDefinitionTask\"] is None))","def is_presub(func):\n return key(func) in opt[\"presub\"]","def __contains__(self, item: Any) -> bool:\n try:\n self.__getattr__(item)\n return True\n except RuntimeError:\n return False","def __contains__(self, point):\n for component, dim in zip(point, self.dimensions):\n if component not in dim:\n return False\n return True","def XCAFDoc_ShapeTool_IsShape(*args):\n return _XCAFDoc.XCAFDoc_ShapeTool_IsShape(*args)","def _is_valid_passed_arg(self, console: io.IO, step: str,\n value: Optional[str],\n validate: Callable[[str], None]) -> bool:\n pass","def __dominates__(component, other):\n # Refine in subclasses.\n return issubclass(component, other)","def has_arg(self, arg_name):\n raise NotImplementedError(\"ICallable.has_arg\")","def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n return False","def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n return False","def isprop(v):\n return isinstance(v, property)","def _is_valid_pose(self):\n contacts = self.gc.getRobot().robot.contacts\n n_object_contacts = 0\n is_thumb_in_contact = False\n for contact in contacts:\n if contact.body1 == self.object_name:\n n_object_contacts += 1\n if contact.body2 == '_chain4_link2':\n is_thumb_in_contact = True\n elif contact.body2 == self.object_name:\n n_object_contacts += 1\n if contact.body1 == '_chain4_link2':\n is_thumb_in_contact = True\n\n is_valid = n_object_contacts >= 2\n return is_valid","def isProduct(*args):\n return _libsbml.SBO_isProduct(*args)","def is_call_arg_of(self, *args):\n return _ida_hexrays.cexpr_t_is_call_arg_of(self, *args)","def is_oppio(opp):\n return DOMAIN in opp.config.components","def is_valid_para(self, para_type, type_table):\n # The values of the table contain all known destination types\n if para_type in type_table.values():\n return True\n return True","def is_valid_function(self, paras):\n if len(paras) != 0:\n return True\n return True"],"string":"[\n \"def XCAFDoc_ShapeTool_IsComponent(*args):\\n return _XCAFDoc.XCAFDoc_ShapeTool_IsComponent(*args)\",\n \"def IsComponent(*args):\\n return _XCAFDoc.XCAFDoc_ShapeTool_IsComponent(*args)\",\n \"def __contains__(self, arg):\\r\\n\\r\\n return arg in self.grfx[0]\",\n \"def particleExists(*args, **kwargs)->bool:\\n pass\",\n \"def __contains__(self, arg):\\n # All arguments should have a default value of some sort\\n if arg not in self.args:\\n raise AttributeError(\\n \\\"arg {} doesn't exist on {}\\\".format(arg, self.args)\\n )\\n # If the value is the sentinel then the argument was not provided AND\\n # there is no default\\n if getattr(self.args, arg) is NoArgument:\\n return False\\n\\n if isinstance(getattr(self.args, arg), list):\\n raise AttributeError(\\n \\\"tried to check for presence of arg {} on {}, which is a \\\"\\n \\\"list\\\".format(arg, self.args)\\n )\\n\\n return True\",\n \"def is_mandatory_arg(self, arg_name): \\n return arg_name in self.get_mandatory_args()\",\n \"def object_is_valid_pipeline(o):\\n return (o is not None and\\n hasattr(o, 'fit') and\\n hasattr(o, 'predict') and\\n hasattr(o, 'steps'))\",\n \"def object_is_valid_pipeline(o):\\n return (o is not None and\\n hasattr(o, 'fit') and\\n hasattr(o, 'predict') and\\n hasattr(o, 'steps'))\",\n \"def objExists(*args, **kwargs)->bool:\\n pass\",\n \"def is_command_ancillary(args):\\n # pylint: disable=bad-continuation\\n if (\\n # skip the parent check and only\\n # determine if the parameter is present\\n is_valid_executes(args, skip=True)\\n ):\\n return True\\n return False\",\n \"def SBO_isParticipant(*args):\\n return _libsbml.SBO_isParticipant(*args)\",\n \"def __contains__(self, component):\\n if issubclass(component, Component):\\n try:\\n my_component = self.type.components[component.interface]\\n except KeyError:\\n return False\\n else:\\n return issubclass(my_component, component)\\n else:\\n return component in self.type.components\",\n \"def isinstance_blender_object(self, b_obj):\\n # lame and slow, but functional\\n return b_obj in Blender.Object.Get()\",\n \"def check_args(args):\\n for arg in vars(args):\\n if getattr(args, arg):\\n return True\\n return False\",\n \"def isParticipant(*args):\\n return _libsbml.SBO_isParticipant(*args)\",\n \"def class_name_arg_required(args):\\n no_class_name_flags = ['list_courses', 'version']\\n return not any(\\n getattr(args, flag)\\n for flag in no_class_name_flags\\n )\",\n \"def is_call_object_of(self, *args):\\n return _ida_hexrays.cexpr_t_is_call_object_of(self, *args)\",\n \"def no_params(self) -> bool:\\n result = True\\n # Fixing issue #92\\n if self.properties.parameters:\\n return False\\n else:\\n return True\\n # for parameter in self.properties.parameters:\\n # if parameter == \\\"effect\\\":\\n # continue\\n # else:\\n # result = False\\n # break\\n # return result\",\n \"def ismemberdescriptor(object):\\r\\n return False\",\n \"def is_parameter_present(self, obj):\\n val_ref = obj.value_reference\\n for p in self.parameters:\\n if p.value_reference == val_ref:\\n # there is already a parameter in the list with the same value_reference\\n logger.error(\\\"There is already a parameter in the list with the same value reference: {0}\\\".format(val_ref))\\n return True\\n return False\",\n \"def has_custom_param(plot):\\n return Plot.has_custom_param(plot)\",\n \"def is_procedure(vba_object):\\n if hasattr(vba_object, 'statements'):\\n return True\\n else:\\n return False\",\n \"def has_component(self, component):\\n return component.name in self.components\",\n \"def has_component(self, var_name):\\n if var_name in self._var_names:\\n return True\\n else:\\n return False\",\n \"def get_is_por_holder(self, obj):\\n user = self.context['request'].user\\n if not user.is_authenticated:\\n return False\\n # pylint: disable=no-member\\n profile = UserProfile.objects.get(user=user)\\n\\n if profile == obj.club.secy:\\n return True\\n\\n if profile in obj.club.joint_secy.all():\\n return True\\n\\n if profile == obj.club.council.gensec:\\n return True\\n\\n if profile in obj.club.council.joint_gensec.all():\\n return True\\n\\n return False\",\n \"def is_function(self):\\n return self.args is not None\",\n \"def __bool__(self):\\n return True if self._name is not None else False\",\n \"def arg_validation(arg, cla):\\n if is_subclass(cla, arg):\\n return arg\\n else:\\n print(str(arg)+\\\" is not a valid \\\" + cla.__module__ + \\\" name.\\\")\\n sys.exit(2)\",\n \"def is_P(self):\\n return isinstance(self,P)\",\n \"def __bool__(self):\\n context, active_obj, actual_mode, mode = self.get_context()\\n if not mode: return False\\n \\n if mode == 'OBJECT':\\n return bool(context.selected_objects)\\n elif mode == 'EDIT_MESH':\\n mesh = active_obj.data\\n if actual_mode == 'EDIT_MESH':\\n return bool(mesh.total_vert_sel)\\n else:\\n return any(item.select for item in mesh.vertices)\\n elif mode in {'EDIT_CURVE', 'EDIT_SURFACE'}:\\n for spline in active_obj.data.splines:\\n for item in spline.bezier_points:\\n if (item.select_control_point or\\n item.select_left_handle or\\n item.select_right_handle):\\n return True\\n for item in spline.points:\\n if item.select:\\n return True\\n elif mode == 'EDIT_METABALL':\\n return bool(active_obj.data.elements.active)\\n elif mode == 'EDIT_LATTICE':\\n return any(item.select for item in active_obj.data.points)\\n elif mode == 'EDIT_ARMATURE':\\n return any(item.select_head or item.select_tail\\n for item in active_obj.data.edit_bones)\\n elif mode == 'POSE':\\n return any(item.select for item in active_obj.data.bones)\\n elif mode == 'PARTICLE':\\n # Theoretically, particle keys can be selected,\\n # but there seems to be no API for working with this\\n pass\\n else:\\n pass # no selectable elements in other modes\\n \\n return False\",\n \"def is_actor():\\n return False\",\n \"def SBO_isPhysicalParticipant(*args):\\n return _libsbml.SBO_isPhysicalParticipant(*args)\",\n \"def is_satisfied_by(self, val):\",\n \"def is_plugin_data(cls, attributes):\\n return attributes.get('metadata', {}).get('class') == 'plugin'\",\n \"def toolHasOptions(*args, **kwargs)->bool:\\n pass\",\n \"def isrequired(self, typename):\\n return typename in self.required_products\",\n \"def is_injected(self, name):\\n return name in self.__provisions\",\n \"def __contains__(name):\",\n \"def is_ap(self, obj):\\n return hasattr(obj, 'attachment_point_info')\",\n \"def bool(self, obj):\\n return True\",\n \"def bool(self, obj):\\n return True\",\n \"def exists(self, arg):\\n raise NotImplementedError\",\n \"def ismemberdescriptor(object):\\r\\n return isinstance(object, types.MemberDescriptorType)\",\n \"def check_filters(self, pname, base, fealty=\\\"\\\"):\\n if \\\"org\\\" in self.switches:\\n return True\\n if not self.args:\\n return True\\n if self.args.lower() == \\\"afk\\\":\\n return \\\"(AFK)\\\" in pname\\n if self.args.lower() == \\\"lrp\\\":\\n return \\\"(LRP)\\\" in pname\\n if self.args.lower() == \\\"staff\\\":\\n return \\\"(Staff)\\\" in pname\\n if self.args.lower() == str(fealty).lower():\\n return True\\n return base.lower().startswith(self.args.lower())\",\n \"def can_start(cls, context):\\n ob = context.active_object\\n return ob is not None and ob.type == \\\"MESH\\\"\",\n \"def is_required_data(self, typename):\\n return typename in self.required_data_products\",\n \"def __contains__(self, obj):\\n return obj in self.actors\",\n \"def has_parameter(self, name):\\n for par in self.params:\\n if par.name == name:\\n return True\\n return False\",\n \"def objExists(self, attr):\\n try:\\n exists = cmds.objExists(\\\"%s.%s\\\" % (self.data['shapeNode'], attr))\\n self.logger.debug(\\\"objExists : %s.%s = %s\\\" % (self.data['shapeNode'], attr, exists))\\n return exists\\n except Exception as e:\\n self.logger.error(\\\"objExists : %s\\\" % e)\\n return False\",\n \"def is_pipeline(self):\\n try:\\n self.pipeline\\n except ObjectDoesNotExist:\\n return False\\n return True\",\n \"def __contains__(self, name):\\n return (name in self._defs) or \\\\\\n ((self._parent is not None) and (name in self._parent))\",\n \"def has_sclass(self, w: Wrapper, prop: Any) -> bool:\\n if not prop:\\n return None\\n props = self.sclasses(w)\\n if isinstance(prop, str):\\n ans = [prop in props]\\n else:\\n ans = [i in props for i in prop]\\n return all(ans)\",\n \"def predicate(obj):\\n return inspect.isclass(obj) and issubclass(obj, MafColumnRecord)\",\n \"def has(self, entity):\\n\\t\\treturn entity.has_component(self.component_type)\",\n \"def has_func(cls, obj, *args):\\n methods = dir(obj)\\n matched = [x for x in args if x in methods]\\n return len(matched) == len(args)\",\n \"def is_valide(self):\\n if self.arguments:\\n return True\\n else:\\n return False\",\n \"def is_pdf(obj):\\n return isinstance(obj, PDF)\",\n \"def get_is_por_holder(self, obj):\\n user = self.context['request'].user\\n if not user.is_authenticated:\\n return False\\n # pylint: disable=no-member\\n profile = UserProfile.objects.get(user=user)\\n if obj in profile.get_club_privileges():\\n return True\\n return False\",\n \"def _target_filter(self, obj):\\r\\n return type(obj).__name__ in ['Cube'] and not obj.is_grasped # List because may be extended to other objects.\\r\",\n \"def _check_whether_has_params(self, params) -> bool:\\n\\n if params:\\n return True\\n return False\",\n \"def check_class_definition(cls):\\n super().check_class_definition()\\n\\n if not cls.objects_arg:\\n cls.definition_error('Must provide \\\"objects_arg\\\" attribute.')\\n\\n func_parameters = inspect.signature(arg.s()(cls.func).func).parameters\\n if cls.objects_arg not in func_parameters:\\n cls.definition_error(\\n f'objects_arg \\\"{cls.objects_arg}\\\" not an argument to callable.'\\n f' Possible parameters={func_parameters}'\\n )\",\n \"def attribute(self):\\n\\n return not bool(self.arguments)\",\n \"def valid_args(args):\\n return args is not None and len(args) > 0\",\n \"def _is_dataclass_instance(obj):\\n return hasattr(type(obj), '__dataclass_fields__')\",\n \"def XCAFDoc_ShapeTool_IsReference(*args):\\n return _XCAFDoc.XCAFDoc_ShapeTool_IsReference(*args)\",\n \"def _check_value(self,val,obj=None):\\n if not val in self.objects:\\n # CEBALERT: can be called before __init__ has called\\n # super's __init__, i.e. before attrib_name has been set.\\n try:\\n attrib_name = self._attrib_name\\n except AttributeError:\\n attrib_name = \\\"\\\"\\n raise ValueError(\\\"%s not in Parameter %s's list of possible objects\\\" \\\\\\n %(val,attrib_name))\",\n \"def isPhysicalParticipant(*args):\\n return _libsbml.SBO_isPhysicalParticipant(*args)\",\n \"def _is_propertyable(\\n names, # type: List[str]\\n attrs, # type: Dict[str, Any]\\n annotations, # type: Dict[str, type]\\n attr, # Dict[str, Any]\\n):\\n # type: (...) -> bool\\n return (\\n attr in annotations\\n and not attr.startswith(\\\"_\\\")\\n and not attr.isupper()\\n and \\\"__{}\\\".format(attr) not in names\\n and not isinstance(getattr(attrs, attr, None), types.MethodType)\\n )\",\n \"def hasname(self):\\n\\t\\treturn self.name is not None\",\n \"def is_required_data(self, typename):\\r\\n return typename in self.required_data_products\",\n \"def get_is_por_holder(self, obj):\\n user = self.context['request'].user\\n if not user.is_authenticated:\\n return False\\n # pylint: disable=no-member\\n profile = UserProfile.objects.get(user=user)\\n if obj in profile.get_council_privileges():\\n return True\\n return False\",\n \"def isFunctionalCompartment(*args):\\n return _libsbml.SBO_isFunctionalCompartment(*args)\",\n \"def has_descriptor(self, uuid):\",\n \"def _check_args(self, args):\\n if len(args) == 0:\\n print(\\\"No parameters provided.\\\")\\n return False\\n else:\\n return True\",\n \"def __contains__(self, feature):\\n return feature == 'cvarsort' or feature in self.features\",\n \"def __contains__(self, name: str) -> bool:\\n ...\",\n \"def isdouparname(self,parname_): # 3\\n res,resargs = self.__obj.isdouparname(parname_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _param_return_value = resargs\\n _param_return_value = dparam(_param_return_value)\\n return _param_return_value\",\n \"def is_userAS(self, obj):\\n # Some other places simply check for owner=None.\\n return UserAS.objects.filter(as_ptr=obj).exists()\",\n \"def is_satisfied(self, item: Any) -> bool:\",\n \"def matches_property_name(fun):\\n return callable(fun) and getattr(fun, annotation, None) == value\",\n \"def _is_primitive_type(io: type) -> bool:\\n return any(io.__module__.startswith(mldesigner_pkg) and item.__name__ == param_name for item in getmro(io))\",\n \"def __bool__(self):\\n return len(self.atoms) >= 1\",\n \"def exist(self):\",\n \"def test_component_specifications_exist(self):\\r\\n\\t\\tself.assertTrue(not (self._configuration_[\\\"AddWordDefinitionTask\\\"] is None\\r\\n\\t\\t or self._configuration_[\\\"ListWordDefinitionsTask\\\"] is None or\\r\\n\\t\\t self._configuration_[\\\"RemoveWordDefinitionTask\\\"] is None))\",\n \"def is_presub(func):\\n return key(func) in opt[\\\"presub\\\"]\",\n \"def __contains__(self, item: Any) -> bool:\\n try:\\n self.__getattr__(item)\\n return True\\n except RuntimeError:\\n return False\",\n \"def __contains__(self, point):\\n for component, dim in zip(point, self.dimensions):\\n if component not in dim:\\n return False\\n return True\",\n \"def XCAFDoc_ShapeTool_IsShape(*args):\\n return _XCAFDoc.XCAFDoc_ShapeTool_IsShape(*args)\",\n \"def _is_valid_passed_arg(self, console: io.IO, step: str,\\n value: Optional[str],\\n validate: Callable[[str], None]) -> bool:\\n pass\",\n \"def __dominates__(component, other):\\n # Refine in subclasses.\\n return issubclass(component, other)\",\n \"def has_arg(self, arg_name):\\n raise NotImplementedError(\\\"ICallable.has_arg\\\")\",\n \"def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n return False\",\n \"def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n return False\",\n \"def isprop(v):\\n return isinstance(v, property)\",\n \"def _is_valid_pose(self):\\n contacts = self.gc.getRobot().robot.contacts\\n n_object_contacts = 0\\n is_thumb_in_contact = False\\n for contact in contacts:\\n if contact.body1 == self.object_name:\\n n_object_contacts += 1\\n if contact.body2 == '_chain4_link2':\\n is_thumb_in_contact = True\\n elif contact.body2 == self.object_name:\\n n_object_contacts += 1\\n if contact.body1 == '_chain4_link2':\\n is_thumb_in_contact = True\\n\\n is_valid = n_object_contacts >= 2\\n return is_valid\",\n \"def isProduct(*args):\\n return _libsbml.SBO_isProduct(*args)\",\n \"def is_call_arg_of(self, *args):\\n return _ida_hexrays.cexpr_t_is_call_arg_of(self, *args)\",\n \"def is_oppio(opp):\\n return DOMAIN in opp.config.components\",\n \"def is_valid_para(self, para_type, type_table):\\n # The values of the table contain all known destination types\\n if para_type in type_table.values():\\n return True\\n return True\",\n \"def is_valid_function(self, paras):\\n if len(paras) != 0:\\n return True\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6626677","0.6543731","0.6026395","0.60095984","0.59585935","0.5927557","0.5893944","0.5893944","0.58926296","0.5830509","0.5820323","0.57275677","0.5694341","0.56732976","0.5672737","0.5656124","0.5637087","0.5625537","0.5621446","0.5609848","0.5569029","0.5564577","0.5546229","0.5536116","0.5527483","0.5497136","0.5474361","0.54593337","0.5454757","0.54544353","0.5450758","0.54459316","0.5440117","0.5434363","0.54021555","0.5395286","0.5368226","0.5366388","0.53575563","0.5343469","0.5343469","0.53408563","0.5333224","0.53277165","0.5319041","0.5311865","0.530526","0.53027964","0.53021246","0.5301344","0.52995706","0.5295906","0.52797776","0.52787626","0.52725345","0.52721155","0.52717847","0.52691144","0.5267818","0.526765","0.5257356","0.5249878","0.5243343","0.52424985","0.5239169","0.5236652","0.52316695","0.5231473","0.52310616","0.52160054","0.52140915","0.52090985","0.5206646","0.5205884","0.52010286","0.52002746","0.5197175","0.51963437","0.51921135","0.51914734","0.5167865","0.5166872","0.51654506","0.51647604","0.51622033","0.51570135","0.51516664","0.5151625","0.51500964","0.51373273","0.5131499","0.5129575","0.5129575","0.5126734","0.512634","0.5120941","0.51202375","0.51157695","0.5106585","0.51036227"],"string":"[\n \"0.6626677\",\n \"0.6543731\",\n \"0.6026395\",\n \"0.60095984\",\n \"0.59585935\",\n \"0.5927557\",\n \"0.5893944\",\n \"0.5893944\",\n \"0.58926296\",\n \"0.5830509\",\n \"0.5820323\",\n \"0.57275677\",\n \"0.5694341\",\n \"0.56732976\",\n \"0.5672737\",\n \"0.5656124\",\n \"0.5637087\",\n \"0.5625537\",\n \"0.5621446\",\n \"0.5609848\",\n \"0.5569029\",\n \"0.5564577\",\n \"0.5546229\",\n \"0.5536116\",\n \"0.5527483\",\n \"0.5497136\",\n \"0.5474361\",\n \"0.54593337\",\n \"0.5454757\",\n \"0.54544353\",\n \"0.5450758\",\n \"0.54459316\",\n \"0.5440117\",\n \"0.5434363\",\n \"0.54021555\",\n \"0.5395286\",\n \"0.5368226\",\n \"0.5366388\",\n \"0.53575563\",\n \"0.5343469\",\n \"0.5343469\",\n \"0.53408563\",\n \"0.5333224\",\n \"0.53277165\",\n \"0.5319041\",\n \"0.5311865\",\n \"0.530526\",\n \"0.53027964\",\n \"0.53021246\",\n \"0.5301344\",\n \"0.52995706\",\n \"0.5295906\",\n \"0.52797776\",\n \"0.52787626\",\n \"0.52725345\",\n \"0.52721155\",\n \"0.52717847\",\n \"0.52691144\",\n \"0.5267818\",\n \"0.526765\",\n \"0.5257356\",\n \"0.5249878\",\n \"0.5243343\",\n \"0.52424985\",\n \"0.5239169\",\n \"0.5236652\",\n \"0.52316695\",\n \"0.5231473\",\n \"0.52310616\",\n \"0.52160054\",\n \"0.52140915\",\n \"0.52090985\",\n \"0.5206646\",\n \"0.5205884\",\n \"0.52010286\",\n \"0.52002746\",\n \"0.5197175\",\n \"0.51963437\",\n \"0.51921135\",\n \"0.51914734\",\n \"0.5167865\",\n \"0.5166872\",\n \"0.51654506\",\n \"0.51647604\",\n \"0.51622033\",\n \"0.51570135\",\n \"0.51516664\",\n \"0.5151625\",\n \"0.51500964\",\n \"0.51373273\",\n \"0.5131499\",\n \"0.5129575\",\n \"0.5129575\",\n \"0.5126734\",\n \"0.512634\",\n \"0.5120941\",\n \"0.51202375\",\n \"0.51157695\",\n \"0.5106585\",\n \"0.51036227\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71104115"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19599,"cells":{"query":{"kind":"string","value":"If an bundle is for native iOS, it has these properties in the Info.plist"},"document":{"kind":"string","value":"def is_info_plist_native(plist):\n return (\n 'CFBundleSupportedPlatforms' in plist and\n 'iPhoneOS' in plist['CFBundleSupportedPlatforms']\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 ios_app_info(self) -> Optional[pulumi.Input['IosAppInfoArgs']]:\n return pulumi.get(self, \"ios_app_info\")","def system_properties(self):\r\n return dict(self._get_system_properties(self.java))","def ios_app_info(self) -> 'outputs.IosAppInfoResponse':\n return pulumi.get(self, \"ios_app_info\")","def _bundle(self):\n # Default to DEFAULT_BUNDLE_NAME\n bundle_path = os.path.join(self.working_dir, DEFAULT_BUNDLE_NAME)\n return self.config['app'].get('bundle', bundle_path)","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']","def __init__(__self__, *,\n bundle_id: str,\n xcode_version: str):\n pulumi.set(__self__, \"bundle_id\", bundle_id)\n pulumi.set(__self__, \"xcode_version\", xcode_version)","def __init__(__self__, *,\n bundle_id: Optional[pulumi.Input[str]] = None,\n xcode_version: Optional[pulumi.Input[str]] = None):\n if bundle_id is not None:\n pulumi.set(__self__, \"bundle_id\", bundle_id)\n if xcode_version is not None:\n pulumi.set(__self__, \"xcode_version\", xcode_version)","def select_app():\n panel = Cocoa.NSOpenPanel.openPanel()\n panel.setCanChooseFiles_(True)\n panel.setCanChooseDirectories_(True)\n panel.setResolvesAliases_(True)\n\n if(panel.runModal() == Cocoa.NSOKButton):\n pathArray = panel.filenames()\n path = pathlib.Path(pathArray[0])\n\n plistPath = path /'Contents'/'Info.plist'\n infoFile = plistPath\n\n try:\n appSize = subprocess.check_output(['du', '-shg', str(path)]).split()[0].decode('utf-8')\n n.views['appSize'].setStringValue_(str(appSize))\n except Exception as err:\n print(err)\n\n n.views['appLocation'].setStringValue_(str(path))\n\n try:\n plist = str(infoFile)\n with open(plist, 'rb') as f:\n info = plistlib.load(f)\n\n if 'CFBundleName' in info:\n global collectedName\n collectedName = info['CFBundleName']\n n.views['appName'].setStringValue_(collectedName)\n else:\n n.views['appName'].setStringValue_('')\n\n if 'CFBundleShortVersionString' in info:\n global collectedVersion\n collectedVersion= info['CFBundleShortVersionString']\n n.views['appVersion'].setStringValue_(collectedVersion)\n else:\n n.views['appVersion'].setStringValue_('')\n\n if 'CFBundleIconFile' in info:\n global collectedIcon\n collectedIcon = pathlib.Path(plist).parent / 'Resources' / info['CFBundleIconFile']\n n.views['appIcon'].setStringValue_(str(collectedIcon))\n else:\n n.views['appIcon'].setStringValue_('')\n\n if 'CFBundleIdentifier' in info:\n global collectedIdentifier\n collectedIdentifier = info['CFBundleIdentifier']\n n.views['appIdentifier'].setStringValue_(collectedIdentifier)\n else:\n n.views['appIdentifier'].setStringValue_('')\n\n except Exception as err:\n print('An Error Occured: {0}'.format(err))","def is_apple():\n return sys.platform == \"darwin\"","def security_compliance_notification_phones(self):\n if \"securityComplianceNotificationPhones\" in self._prop_dict:\n return self._prop_dict[\"securityComplianceNotificationPhones\"]\n else:\n return None","def hide_ios(self) -> bool:\n return pulumi.get(self, \"hide_ios\")","def launch_properties(self) -> Optional[pulumi.Input['BuildpackBindingLaunchPropertiesArgs']]:\n return pulumi.get(self, \"launch_properties\")","def getProductInfo(path=\"./logs/SystemVersion/SystemVersion.plist\"):\n result = {\n \"ProductName\" : None,\n \"ProductionVersion\" : None,\n \"ProductBuildVersion\" : None\n }\n try:\n fd = open(path, 'rb')\n plist = plistlib.load(fd)\n for key in [\"ProductName\", \"ProductVersion\", \"ProductBuildVersion\", \"BuildID\", \"SystemImageID\"]:\n if key in plist.keys():\n result[key] = plist[key]\n else:\n print(\"WARNING: %s not found in %s plist\" % (key, path))\n fd.close()\n except Exception as e:\n print(\"Impossible to parse %s: %s\" % (path, str(e)))\n return result","def _get_via_app_bundle(self, path: pathlib.Path | str) -> str:\n\n path = pathlib.Path(path) / \"Contents\" / \"Info.plist\"\n\n if not path.exists():\n logger.warning(\n f\"Could not determine application version. Missing: {path}...\"\n )\n return \"?\"\n\n with open(path, \"rb\") as f:\n data = plistlib.load(f)\n\n bundle_short_version: str = data.get(\"CFBundleShortVersionString\", \"?\")\n bundle_version: str = data.get(\"CFBundleVersion\", None)\n\n if bundle_version is None:\n return f\"{bundle_short_version}\"\n\n return f\"{bundle_short_version}-{bundle_version}\"","def python_software_properties(self):\n self.install_package(\"python-software-properties\")","def _has_native_dhcp_metadata(self):\n pass","def custom_properties(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\n return pulumi.get(self, \"custom_properties\")","def custom_properties(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\n return pulumi.get(self, \"custom_properties\")","def _get_app_info(self):\n info_plist = None\n\n for data in self.filelist:\n if re.match(self.info_plist_regex, data.filename):\n info_plist = data\n\n if not info_plist:\n self._raise_ipa_error()\n\n info_plist = self.read(info_plist)\n self.app_info = readPlistFromString(info_plist)\n\n return self.app_info","def getProperties():","def is_osx():\n return sys.platform == \"darwin\"","def test_plist_items(tmp_path: Path):\n create_package(tmp_path, source=PLIST_ITEMS_TEST[4])\n output = check_output(\n [sys.executable, \"setup.py\", \"bdist_mac\"],\n text=True,\n cwd=os.fspath(tmp_path),\n )\n print(output)\n # Test that the additional keys were correctly added to the plist.\n sys.path.insert(0, os.fspath(tmp_path))\n data = import_module(\"plist_data\")\n path = f\"{data.BUILD_DIR}/{data.BUNDLE_NAME}.app/Contents/Info.plist\"\n contents = plistlib.loads(tmp_path.joinpath(path).read_bytes())\n assert contents[data.TEST_KEY] == data.TEST_VALUE","def platform_info(self):\n return self.msg.platform_info","def is_osx():\r\n return sys.platform == \"darwin\"","def config_bundle(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"config_bundle\")","def web_hook_properties(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"web_hook_properties\")","def is_jvm_app(self):\r\n return False","def install_properties(self):\n\n return self._install_properties","def get_properties():","def test_test_property():\n\n contents = (\"[Info]\\n\"\n \"sdk = 23\")\n\n testutils.deploy_config_raw(contents)\n\n assert prop.test_prop('info', 'sdk') == 1\n\n testutils.undeploy()\n\n return 0","def readProperties(self, QSettings, QString): # real signature unknown; restored from __doc__\r\n return False","def readProperties(self, QSettings, QString): # real signature unknown; restored from __doc__\r\n return False","def missing_in_gyp(self):\n return self._missing_gyp_flags","def _detect_platform(request):\n output = {\"platform\": \"unknown\",\n \"mobile\": False,\n }\n if \"Android\" in request.META['HTTP_USER_AGENT']:\n output['mobile'] = True\n output['platform'] = \"android\"\n elif \"iPad\" in request.META['HTTP_USER_AGENT']:\n output['mobile'] = True\n output['platform'] = \"apple\"\n elif \"iPhone\" in request.META['HTTP_USER_AGENT']:\n output['mobile'] = True\n output['platform'] = \"apple\"\n elif \"iPod\" in request.META['HTTP_USER_AGENT']:\n output['mobile'] = True\n output['platform'] = \"apple\"\n return output","def get_os_info(hass: HomeAssistant) -> dict[str, Any] | None:\n return hass.data.get(DATA_OS_INFO)","def has_platform(cls, plist, platforms):\n if platforms is None:\n raise Exception(\"no platforms?\")\n\n return (\n 'CFBundleSupportedPlatforms' in plist and\n any(map(lambda p: p in plist['CFBundleSupportedPlatforms'], platforms))\n )","def get_properties_code(self, obj):\n return []","def additional_info(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"additional_info\")","def readProperties(self):\r\n print('not yet implemented')","def as_dict(self, short=False):\n\n res = super(PidfileApp, self).as_dict(short=short)\n res['pidfilename'] = self.pidfilename\n res['simulate'] = self.simulate\n\n return res","def _create_properties(self): # pylint: disable=no-self-use\n properties = {}\n properties[\"product\"] = \"eventhub.python\"\n properties[\"version\"] = __version__\n properties[\"framework\"] = \"Python {}.{}.{}\".format(*sys.version_info[0:3])\n properties[\"platform\"] = sys.platform\n return properties","def pref(pref_name):\n default_prefs = {\n 'ServerURL': 'http://sal',\n }\n pref_value = CFPreferencesCopyAppValue(pref_name, BUNDLE_ID)\n if pref_value == None:\n pref_value = default_prefs.get(pref_name)\n # we're using a default value. We'll write it out to\n # /Library/Preferences/<BUNDLE_ID>.plist for admin\n # discoverability\n set_pref(pref_name, pref_value)\n if isinstance(pref_value, NSDate):\n # convert NSDate/CFDates to strings\n pref_value = str(pref_value)\n return pref_value","def has_configuration_set():\r\n return getattr(settings, \"MICROSITE_CONFIGURATION\", False)","def test_get_property():\n\n sdk = '23'\n contents = (\"[Info]\\n\"\n \"sdk = %s\" % sdk)\n\n testutils.deploy_config_raw(contents)\n\n assert prop.get_prop('info', 'sdk') == sdk\n\n testutils.undeploy()\n\n return 0","def hide_ios(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"hide_ios\")","def hide_ios(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"hide_ios\")","def is_macosx():\n (sysname, nodename, release, version, machine) = os.uname()\n return sysname == 'Darwin'","def test_plugin_macosx_pretend_old_macos(mocker, macos_version):\n\n # When patching something which has a side effect on the module-level code\n # of a plugin, make sure to reload it.\n mocker.patch(\"platform.mac_ver\",\n return_value=(macos_version, ('', '', ''), ''))\n reload_plugin(\"NotifyMacOSX\")\n\n obj = apprise.Apprise.instantiate('macosx://', suppress_exceptions=False)\n assert obj is None","def targetProperties(self):\n text=\"\"\n if self.rlibs.strip() :\n text=\"SET_TARGET_PROPERTIES( %sEngine PROPERTIES INSTALL_RPATH %s)\\n\" % (self.name, self.rlibs)\n return text","def on_mac():\n if \"Darwin\" in platform.system():\n return True\n else:\n return False","def fact():\n\n result = \"None\"\n\n objc.loadBundle(\n \"IOBluetooth\",\n globals(),\n bundle_path=objc.pathForFramework(\n u\"/System/Library/Frameworks/IOBluetooth.framework\"\n ),\n )\n btprefs = IOBluetoothPreferences.alloc().init()\n result = bool(btprefs.fileTransferServicesEnabled())\n\n return {factoid: result}","def is_mac():\n return sys.platform==\"darwin\"","def test_app_properties(s):\n simple_app = s['simple-app']\n derived_app = s['derived-app']\n\n assert simple_app.url == (s.url + '/simple-app')\n assert simple_app.title == \"Simple App\"\n assert derived_app.url == (s.url + '/derived-app')\n assert derived_app.title == \"Derived App\"","def process_app_info(self):\n pass","def ca_bundle(self) -> Optional[str]:\n return pulumi.get(self, \"ca_bundle\")","def ca_bundle(self) -> Optional[str]:\n return pulumi.get(self, \"ca_bundle\")","def properties(self):\n return None","def properties(self):\n return None","def pref(pref_name):\n default_prefs = {\n 'ServerURL': 'http://munkiwebadmin',\n 'authKey': '',\n }\n pref_value = CFPreferencesCopyAppValue(pref_name, BUNDLE_ID)\n if pref_value is None:\n pref_value = default_prefs.get(pref_name)\n # we're using a default value. We'll write it out to\n # /Library/Preferences/<BUNDLE_ID>.plist for admin\n # discoverability\n set_pref(pref_name, pref_value)\n if isinstance(pref_value, NSDate):\n # convert NSDate/CFDates to strings\n pref_value = str(pref_value)\n return pref_value","def _get_meta_properties_notes(cls, suite_meta):\n if (\n suite_meta is not None\n and \"notes\" in suite_meta\n and \"format\" in suite_meta[\"notes\"]\n and suite_meta[\"notes\"][\"format\"]\n == LegacyDiagnosticRendererType.META_PROPERTIES\n ):\n return suite_meta[\"notes\"][\"content\"]\n else:\n return None","def xcode_version(self) -> str:\n return pulumi.get(self, \"xcode_version\")","def persist_macos(self) -> None:","def bundle_id(self) -> str:\n return pulumi.get(self, \"bundle_id\")","def bundle_id(self) -> str:\n return pulumi.get(self, \"bundle_id\")","def __setDetails(self):\n self.MainWindow.setWindowTitle(\"{0} {1}\".format(\n const.APP_NAME, const.VERSION))\n return True","def feature_static_signature(self):\n # Check availability of digital signature\n self.features[\"signed\"] = \\\n bool(self.report.get(\"static\", {}).get(\"signature\", []))\n\n # ExifTool output\n et_tokens = [\"Comments\", \"ProductName\", \"LegalCopyright\", \\\n \"InternalName\", \"CompanyName\"]\n for token in et_tokens:\n self.features[token] = None\n for attr in self.report.get(\"static\", {}).get(\"pe_versioninfo\", []):\n attr_name = attr.get(\"name\")\n if attr_name in et_tokens:\n self.features[attr_name] = attr.get(\"value\")","def hide_ios(self) -> pulumi.Output[Optional[bool]]:\n return pulumi.get(self, \"hide_ios\")","def test_set_new_section_property():\n\n value = '1'\n testutils.deploy_config_raw(\"\")\n\n prop.set_prop('info', 'sdk', value)\n assert prop.get_prop('info', 'sdk') == value\n\n testutils.undeploy()\n\n return 0","def dict_support_required(self):\n\t\treturn self.typemanager.has_dicts","def metadata_json(self):\n return {\n \"flavor_classes\": \"*,!onmetal\",\n \"image_type\": \"base\",\n \"os_type\": \"windows\",\n \"vm_mode\": \"\",\n \"auto_disk_config\": \"disabled\",\n \"org.openstack__1__os_distro\": \"com.microsoft.server\"\n }","def ui_additional_info(self) -> Optional[str]:\n return None","def has_usfm_bundle(self, formats):\n for format in formats:\n if 'text/usfm' in format['format'] and 'type=bundle' in format['format']:\n return True\n return False","def __init__(self, install_single_profile=False, signing_mdm_profile_enabled=False, mdm_signing_certificate=None, restrict_reenrollment=False, flush_location_information=False, flush_location_history_information=False, flush_policy_history=False, flush_extension_attributes=False, flush_mdm_commands_on_reenroll='DELETE_EVERYTHING_EXCEPT_ACKNOWLEDGED', mac_os_enterprise_enrollment_enabled=False, management_username='', management_password='null', management_password_set=None, password_type='STATIC', random_password_length=8, create_management_account=True, hide_management_account=False, allow_ssh_only_management_account=False, ensure_ssh_running=True, launch_self_service=False, sign_quick_add=False, developer_certificate_identity=None, developer_certificate_identity_details=None, mdm_signing_certificate_details=None, ios_enterprise_enrollment_enabled=True, ios_personal_enrollment_enabled=False, personal_device_enrollment_type='PERSONALDEVICEPROFILES', local_vars_configuration=None): # noqa: E501 # noqa: E501\n if local_vars_configuration is None:\n local_vars_configuration = Configuration()\n self.local_vars_configuration = local_vars_configuration\n\n self._install_single_profile = None\n self._signing_mdm_profile_enabled = None\n self._mdm_signing_certificate = None\n self._restrict_reenrollment = None\n self._flush_location_information = None\n self._flush_location_history_information = None\n self._flush_policy_history = None\n self._flush_extension_attributes = None\n self._flush_mdm_commands_on_reenroll = None\n self._mac_os_enterprise_enrollment_enabled = None\n self._management_username = None\n self._management_password = None\n self._management_password_set = None\n self._password_type = None\n self._random_password_length = None\n self._create_management_account = None\n self._hide_management_account = None\n self._allow_ssh_only_management_account = None\n self._ensure_ssh_running = None\n self._launch_self_service = None\n self._sign_quick_add = None\n self._developer_certificate_identity = None\n self._developer_certificate_identity_details = None\n self._mdm_signing_certificate_details = None\n self._ios_enterprise_enrollment_enabled = None\n self._ios_personal_enrollment_enabled = None\n self._personal_device_enrollment_type = None\n self.discriminator = None\n\n if install_single_profile is not None:\n self.install_single_profile = install_single_profile\n if signing_mdm_profile_enabled is not None:\n self.signing_mdm_profile_enabled = signing_mdm_profile_enabled\n if mdm_signing_certificate is not None:\n self.mdm_signing_certificate = mdm_signing_certificate\n if restrict_reenrollment is not None:\n self.restrict_reenrollment = restrict_reenrollment\n if flush_location_information is not None:\n self.flush_location_information = flush_location_information\n if flush_location_history_information is not None:\n self.flush_location_history_information = flush_location_history_information\n if flush_policy_history is not None:\n self.flush_policy_history = flush_policy_history\n if flush_extension_attributes is not None:\n self.flush_extension_attributes = flush_extension_attributes\n if flush_mdm_commands_on_reenroll is not None:\n self.flush_mdm_commands_on_reenroll = flush_mdm_commands_on_reenroll\n if mac_os_enterprise_enrollment_enabled is not None:\n self.mac_os_enterprise_enrollment_enabled = mac_os_enterprise_enrollment_enabled\n if management_username is not None:\n self.management_username = management_username\n if management_password is not None:\n self.management_password = management_password\n if management_password_set is not None:\n self.management_password_set = management_password_set\n if password_type is not None:\n self.password_type = password_type\n if random_password_length is not None:\n self.random_password_length = random_password_length\n if create_management_account is not None:\n self.create_management_account = create_management_account\n if hide_management_account is not None:\n self.hide_management_account = hide_management_account\n if allow_ssh_only_management_account is not None:\n self.allow_ssh_only_management_account = allow_ssh_only_management_account\n if ensure_ssh_running is not None:\n self.ensure_ssh_running = ensure_ssh_running\n if launch_self_service is not None:\n self.launch_self_service = launch_self_service\n if sign_quick_add is not None:\n self.sign_quick_add = sign_quick_add\n if developer_certificate_identity is not None:\n self.developer_certificate_identity = developer_certificate_identity\n if developer_certificate_identity_details is not None:\n self.developer_certificate_identity_details = developer_certificate_identity_details\n if mdm_signing_certificate_details is not None:\n self.mdm_signing_certificate_details = mdm_signing_certificate_details\n if ios_enterprise_enrollment_enabled is not None:\n self.ios_enterprise_enrollment_enabled = ios_enterprise_enrollment_enabled\n if ios_personal_enrollment_enabled is not None:\n self.ios_personal_enrollment_enabled = ios_personal_enrollment_enabled\n if personal_device_enrollment_type is not None:\n self.personal_device_enrollment_type = personal_device_enrollment_type","def fiddle_with_flags():\n flags['c++'] += '-arch x86_64 -bundle'\n flags['c'] += '-arch x86_64'","def applicationDidFinishLaunching_(self, aNotification):","def _parse_mobileprovision_file(mobileprovision_file):\n plist_xml = subprocess.check_output([\n \"security\",\n \"cms\",\n \"-D\",\n \"-i\",\n mobileprovision_file,\n ])\n return plist_from_bytes(plist_xml)","def test_get_property_no_option():\n\n contents = (\"[Info]\\n\"\n \"vmtype = arm64\")\n\n testutils.deploy_config_raw(contents)\n\n with pytest.raises(prop.PropertyError):\n prop.get_prop('info', 'sdk')\n\n testutils.undeploy()\n\n return 0","def supported_provisioning_types(self):\n return self.properties.get(\"supportedProvisioningTypes\", StringCollection())","def test_get_software_bundle(self):\n pass","def ios_test(self) -> Optional[pulumi.Input['IosTestArgs']]:\n return pulumi.get(self, \"ios_test\")","def is_bundle(conf, path):\n \n parts = path.split('/')\n \n if len(parts) < 3:\n return False\n \n asset_type = parts[1]\n bundle_name = parts[2]\n \n return asset_type in conf and bundle_name in conf[asset_type]","def properties(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"properties\")","def properties(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"properties\")","def set_default_bundle(bundle):\n data = {\n \"default_bundle\": bundle\n }\n response = houston.put(\"/zipline/config\", data=data)\n houston.raise_for_status_with_json(response)\n return response.json()","def properties(self) -> Optional[str]:\n return pulumi.get(self, \"properties\")","def config_bundle(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"config_bundle\")","def properties(self) -> Optional[pulumi.Input['SmsChannelPropertiesArgs']]:\n return pulumi.get(self, \"properties\")","def has_custom_install_prefix(self):\n return self.converter.install_prefix not in KNOWN_INSTALL_PREFIXES","def dehydrate_extra_info(self, bundle):\n extra_info = bundle.data[\"extra_info\"]\n return json.loads(extra_info)","def metadata_json(self):\n return {\n \"flavor_classes\": \"*,!onmetal\",\n \"image_type\": \"base\",\n \"os_type\": \"linux\",\n \"org.openstack__1__os_distro\": \"org.freebsd\",\n \"vm_mode\": \"hvm\",\n \"auto_disk_config\": \"disabled\"\n }","def _get_launch_attributes(self):\n launch_attributes = self._cfg.launch_attributes\n attributes = gen_attributes(\n launch_attributes) if launch_attributes else []\n system_attributes = get_launch_sys_attrs()\n system_attributes[\"agent\"] = f\"{self.agent_name}|{self.agent_version}\"\n return attributes + dict_to_payload(system_attributes)","def retrieve_properties(self):\n raise DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)","def valid_config_keys():\n click.echo(', '.join(get_class_properties(OffshoreInputsConfig)))","def ca_bundle(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"ca_bundle\")","def app_settings_json(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"app_settings_json\")","def app_settings_json(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"app_settings_json\")","def pref(prefname):\n return CFPreferencesCopyAppValue(prefname, BUNDLE_ID)","def is_osx() -> bool:\n return sys.platform == \"darwin\"","def get_plugin_config():\n return json.loads(os.getenv(\"SKU_CUSTOM_RECIPE_PLUGIN_CONFIG\"))","def json_properties(self):\n properties = Jsonifiable.json_properties(self)\n properties.remove('image_blob_key')\n return properties"],"string":"[\n \"def ios_app_info(self) -> Optional[pulumi.Input['IosAppInfoArgs']]:\\n return pulumi.get(self, \\\"ios_app_info\\\")\",\n \"def system_properties(self):\\r\\n return dict(self._get_system_properties(self.java))\",\n \"def ios_app_info(self) -> 'outputs.IosAppInfoResponse':\\n return pulumi.get(self, \\\"ios_app_info\\\")\",\n \"def _bundle(self):\\n # Default to DEFAULT_BUNDLE_NAME\\n bundle_path = os.path.join(self.working_dir, DEFAULT_BUNDLE_NAME)\\n return self.config['app'].get('bundle', bundle_path)\",\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 \"def __init__(__self__, *,\\n bundle_id: str,\\n xcode_version: str):\\n pulumi.set(__self__, \\\"bundle_id\\\", bundle_id)\\n pulumi.set(__self__, \\\"xcode_version\\\", xcode_version)\",\n \"def __init__(__self__, *,\\n bundle_id: Optional[pulumi.Input[str]] = None,\\n xcode_version: Optional[pulumi.Input[str]] = None):\\n if bundle_id is not None:\\n pulumi.set(__self__, \\\"bundle_id\\\", bundle_id)\\n if xcode_version is not None:\\n pulumi.set(__self__, \\\"xcode_version\\\", xcode_version)\",\n \"def select_app():\\n panel = Cocoa.NSOpenPanel.openPanel()\\n panel.setCanChooseFiles_(True)\\n panel.setCanChooseDirectories_(True)\\n panel.setResolvesAliases_(True)\\n\\n if(panel.runModal() == Cocoa.NSOKButton):\\n pathArray = panel.filenames()\\n path = pathlib.Path(pathArray[0])\\n\\n plistPath = path /'Contents'/'Info.plist'\\n infoFile = plistPath\\n\\n try:\\n appSize = subprocess.check_output(['du', '-shg', str(path)]).split()[0].decode('utf-8')\\n n.views['appSize'].setStringValue_(str(appSize))\\n except Exception as err:\\n print(err)\\n\\n n.views['appLocation'].setStringValue_(str(path))\\n\\n try:\\n plist = str(infoFile)\\n with open(plist, 'rb') as f:\\n info = plistlib.load(f)\\n\\n if 'CFBundleName' in info:\\n global collectedName\\n collectedName = info['CFBundleName']\\n n.views['appName'].setStringValue_(collectedName)\\n else:\\n n.views['appName'].setStringValue_('')\\n\\n if 'CFBundleShortVersionString' in info:\\n global collectedVersion\\n collectedVersion= info['CFBundleShortVersionString']\\n n.views['appVersion'].setStringValue_(collectedVersion)\\n else:\\n n.views['appVersion'].setStringValue_('')\\n\\n if 'CFBundleIconFile' in info:\\n global collectedIcon\\n collectedIcon = pathlib.Path(plist).parent / 'Resources' / info['CFBundleIconFile']\\n n.views['appIcon'].setStringValue_(str(collectedIcon))\\n else:\\n n.views['appIcon'].setStringValue_('')\\n\\n if 'CFBundleIdentifier' in info:\\n global collectedIdentifier\\n collectedIdentifier = info['CFBundleIdentifier']\\n n.views['appIdentifier'].setStringValue_(collectedIdentifier)\\n else:\\n n.views['appIdentifier'].setStringValue_('')\\n\\n except Exception as err:\\n print('An Error Occured: {0}'.format(err))\",\n \"def is_apple():\\n return sys.platform == \\\"darwin\\\"\",\n \"def security_compliance_notification_phones(self):\\n if \\\"securityComplianceNotificationPhones\\\" in self._prop_dict:\\n return self._prop_dict[\\\"securityComplianceNotificationPhones\\\"]\\n else:\\n return None\",\n \"def hide_ios(self) -> bool:\\n return pulumi.get(self, \\\"hide_ios\\\")\",\n \"def launch_properties(self) -> Optional[pulumi.Input['BuildpackBindingLaunchPropertiesArgs']]:\\n return pulumi.get(self, \\\"launch_properties\\\")\",\n \"def getProductInfo(path=\\\"./logs/SystemVersion/SystemVersion.plist\\\"):\\n result = {\\n \\\"ProductName\\\" : None,\\n \\\"ProductionVersion\\\" : None,\\n \\\"ProductBuildVersion\\\" : None\\n }\\n try:\\n fd = open(path, 'rb')\\n plist = plistlib.load(fd)\\n for key in [\\\"ProductName\\\", \\\"ProductVersion\\\", \\\"ProductBuildVersion\\\", \\\"BuildID\\\", \\\"SystemImageID\\\"]:\\n if key in plist.keys():\\n result[key] = plist[key]\\n else:\\n print(\\\"WARNING: %s not found in %s plist\\\" % (key, path))\\n fd.close()\\n except Exception as e:\\n print(\\\"Impossible to parse %s: %s\\\" % (path, str(e)))\\n return result\",\n \"def _get_via_app_bundle(self, path: pathlib.Path | str) -> str:\\n\\n path = pathlib.Path(path) / \\\"Contents\\\" / \\\"Info.plist\\\"\\n\\n if not path.exists():\\n logger.warning(\\n f\\\"Could not determine application version. Missing: {path}...\\\"\\n )\\n return \\\"?\\\"\\n\\n with open(path, \\\"rb\\\") as f:\\n data = plistlib.load(f)\\n\\n bundle_short_version: str = data.get(\\\"CFBundleShortVersionString\\\", \\\"?\\\")\\n bundle_version: str = data.get(\\\"CFBundleVersion\\\", None)\\n\\n if bundle_version is None:\\n return f\\\"{bundle_short_version}\\\"\\n\\n return f\\\"{bundle_short_version}-{bundle_version}\\\"\",\n \"def python_software_properties(self):\\n self.install_package(\\\"python-software-properties\\\")\",\n \"def _has_native_dhcp_metadata(self):\\n pass\",\n \"def custom_properties(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"custom_properties\\\")\",\n \"def custom_properties(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"custom_properties\\\")\",\n \"def _get_app_info(self):\\n info_plist = None\\n\\n for data in self.filelist:\\n if re.match(self.info_plist_regex, data.filename):\\n info_plist = data\\n\\n if not info_plist:\\n self._raise_ipa_error()\\n\\n info_plist = self.read(info_plist)\\n self.app_info = readPlistFromString(info_plist)\\n\\n return self.app_info\",\n \"def getProperties():\",\n \"def is_osx():\\n return sys.platform == \\\"darwin\\\"\",\n \"def test_plist_items(tmp_path: Path):\\n create_package(tmp_path, source=PLIST_ITEMS_TEST[4])\\n output = check_output(\\n [sys.executable, \\\"setup.py\\\", \\\"bdist_mac\\\"],\\n text=True,\\n cwd=os.fspath(tmp_path),\\n )\\n print(output)\\n # Test that the additional keys were correctly added to the plist.\\n sys.path.insert(0, os.fspath(tmp_path))\\n data = import_module(\\\"plist_data\\\")\\n path = f\\\"{data.BUILD_DIR}/{data.BUNDLE_NAME}.app/Contents/Info.plist\\\"\\n contents = plistlib.loads(tmp_path.joinpath(path).read_bytes())\\n assert contents[data.TEST_KEY] == data.TEST_VALUE\",\n \"def platform_info(self):\\n return self.msg.platform_info\",\n \"def is_osx():\\r\\n return sys.platform == \\\"darwin\\\"\",\n \"def config_bundle(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"config_bundle\\\")\",\n \"def web_hook_properties(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"web_hook_properties\\\")\",\n \"def is_jvm_app(self):\\r\\n return False\",\n \"def install_properties(self):\\n\\n return self._install_properties\",\n \"def get_properties():\",\n \"def test_test_property():\\n\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = 23\\\")\\n\\n testutils.deploy_config_raw(contents)\\n\\n assert prop.test_prop('info', 'sdk') == 1\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def readProperties(self, QSettings, QString): # real signature unknown; restored from __doc__\\r\\n return False\",\n \"def readProperties(self, QSettings, QString): # real signature unknown; restored from __doc__\\r\\n return False\",\n \"def missing_in_gyp(self):\\n return self._missing_gyp_flags\",\n \"def _detect_platform(request):\\n output = {\\\"platform\\\": \\\"unknown\\\",\\n \\\"mobile\\\": False,\\n }\\n if \\\"Android\\\" in request.META['HTTP_USER_AGENT']:\\n output['mobile'] = True\\n output['platform'] = \\\"android\\\"\\n elif \\\"iPad\\\" in request.META['HTTP_USER_AGENT']:\\n output['mobile'] = True\\n output['platform'] = \\\"apple\\\"\\n elif \\\"iPhone\\\" in request.META['HTTP_USER_AGENT']:\\n output['mobile'] = True\\n output['platform'] = \\\"apple\\\"\\n elif \\\"iPod\\\" in request.META['HTTP_USER_AGENT']:\\n output['mobile'] = True\\n output['platform'] = \\\"apple\\\"\\n return output\",\n \"def get_os_info(hass: HomeAssistant) -> dict[str, Any] | None:\\n return hass.data.get(DATA_OS_INFO)\",\n \"def has_platform(cls, plist, platforms):\\n if platforms is None:\\n raise Exception(\\\"no platforms?\\\")\\n\\n return (\\n 'CFBundleSupportedPlatforms' in plist and\\n any(map(lambda p: p in plist['CFBundleSupportedPlatforms'], platforms))\\n )\",\n \"def get_properties_code(self, obj):\\n return []\",\n \"def additional_info(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"additional_info\\\")\",\n \"def readProperties(self):\\r\\n print('not yet implemented')\",\n \"def as_dict(self, short=False):\\n\\n res = super(PidfileApp, self).as_dict(short=short)\\n res['pidfilename'] = self.pidfilename\\n res['simulate'] = self.simulate\\n\\n return res\",\n \"def _create_properties(self): # pylint: disable=no-self-use\\n properties = {}\\n properties[\\\"product\\\"] = \\\"eventhub.python\\\"\\n properties[\\\"version\\\"] = __version__\\n properties[\\\"framework\\\"] = \\\"Python {}.{}.{}\\\".format(*sys.version_info[0:3])\\n properties[\\\"platform\\\"] = sys.platform\\n return properties\",\n \"def pref(pref_name):\\n default_prefs = {\\n 'ServerURL': 'http://sal',\\n }\\n pref_value = CFPreferencesCopyAppValue(pref_name, BUNDLE_ID)\\n if pref_value == None:\\n pref_value = default_prefs.get(pref_name)\\n # we're using a default value. We'll write it out to\\n # /Library/Preferences/<BUNDLE_ID>.plist for admin\\n # discoverability\\n set_pref(pref_name, pref_value)\\n if isinstance(pref_value, NSDate):\\n # convert NSDate/CFDates to strings\\n pref_value = str(pref_value)\\n return pref_value\",\n \"def has_configuration_set():\\r\\n return getattr(settings, \\\"MICROSITE_CONFIGURATION\\\", False)\",\n \"def test_get_property():\\n\\n sdk = '23'\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = %s\\\" % sdk)\\n\\n testutils.deploy_config_raw(contents)\\n\\n assert prop.get_prop('info', 'sdk') == sdk\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def hide_ios(self) -> Optional[pulumi.Input[bool]]:\\n return pulumi.get(self, \\\"hide_ios\\\")\",\n \"def hide_ios(self) -> Optional[pulumi.Input[bool]]:\\n return pulumi.get(self, \\\"hide_ios\\\")\",\n \"def is_macosx():\\n (sysname, nodename, release, version, machine) = os.uname()\\n return sysname == 'Darwin'\",\n \"def test_plugin_macosx_pretend_old_macos(mocker, macos_version):\\n\\n # When patching something which has a side effect on the module-level code\\n # of a plugin, make sure to reload it.\\n mocker.patch(\\\"platform.mac_ver\\\",\\n return_value=(macos_version, ('', '', ''), ''))\\n reload_plugin(\\\"NotifyMacOSX\\\")\\n\\n obj = apprise.Apprise.instantiate('macosx://', suppress_exceptions=False)\\n assert obj is None\",\n \"def targetProperties(self):\\n text=\\\"\\\"\\n if self.rlibs.strip() :\\n text=\\\"SET_TARGET_PROPERTIES( %sEngine PROPERTIES INSTALL_RPATH %s)\\\\n\\\" % (self.name, self.rlibs)\\n return text\",\n \"def on_mac():\\n if \\\"Darwin\\\" in platform.system():\\n return True\\n else:\\n return False\",\n \"def fact():\\n\\n result = \\\"None\\\"\\n\\n objc.loadBundle(\\n \\\"IOBluetooth\\\",\\n globals(),\\n bundle_path=objc.pathForFramework(\\n u\\\"/System/Library/Frameworks/IOBluetooth.framework\\\"\\n ),\\n )\\n btprefs = IOBluetoothPreferences.alloc().init()\\n result = bool(btprefs.fileTransferServicesEnabled())\\n\\n return {factoid: result}\",\n \"def is_mac():\\n return sys.platform==\\\"darwin\\\"\",\n \"def test_app_properties(s):\\n simple_app = s['simple-app']\\n derived_app = s['derived-app']\\n\\n assert simple_app.url == (s.url + '/simple-app')\\n assert simple_app.title == \\\"Simple App\\\"\\n assert derived_app.url == (s.url + '/derived-app')\\n assert derived_app.title == \\\"Derived App\\\"\",\n \"def process_app_info(self):\\n pass\",\n \"def ca_bundle(self) -> Optional[str]:\\n return pulumi.get(self, \\\"ca_bundle\\\")\",\n \"def ca_bundle(self) -> Optional[str]:\\n return pulumi.get(self, \\\"ca_bundle\\\")\",\n \"def properties(self):\\n return None\",\n \"def properties(self):\\n return None\",\n \"def pref(pref_name):\\n default_prefs = {\\n 'ServerURL': 'http://munkiwebadmin',\\n 'authKey': '',\\n }\\n pref_value = CFPreferencesCopyAppValue(pref_name, BUNDLE_ID)\\n if pref_value is None:\\n pref_value = default_prefs.get(pref_name)\\n # we're using a default value. We'll write it out to\\n # /Library/Preferences/<BUNDLE_ID>.plist for admin\\n # discoverability\\n set_pref(pref_name, pref_value)\\n if isinstance(pref_value, NSDate):\\n # convert NSDate/CFDates to strings\\n pref_value = str(pref_value)\\n return pref_value\",\n \"def _get_meta_properties_notes(cls, suite_meta):\\n if (\\n suite_meta is not None\\n and \\\"notes\\\" in suite_meta\\n and \\\"format\\\" in suite_meta[\\\"notes\\\"]\\n and suite_meta[\\\"notes\\\"][\\\"format\\\"]\\n == LegacyDiagnosticRendererType.META_PROPERTIES\\n ):\\n return suite_meta[\\\"notes\\\"][\\\"content\\\"]\\n else:\\n return None\",\n \"def xcode_version(self) -> str:\\n return pulumi.get(self, \\\"xcode_version\\\")\",\n \"def persist_macos(self) -> None:\",\n \"def bundle_id(self) -> str:\\n return pulumi.get(self, \\\"bundle_id\\\")\",\n \"def bundle_id(self) -> str:\\n return pulumi.get(self, \\\"bundle_id\\\")\",\n \"def __setDetails(self):\\n self.MainWindow.setWindowTitle(\\\"{0} {1}\\\".format(\\n const.APP_NAME, const.VERSION))\\n return True\",\n \"def feature_static_signature(self):\\n # Check availability of digital signature\\n self.features[\\\"signed\\\"] = \\\\\\n bool(self.report.get(\\\"static\\\", {}).get(\\\"signature\\\", []))\\n\\n # ExifTool output\\n et_tokens = [\\\"Comments\\\", \\\"ProductName\\\", \\\"LegalCopyright\\\", \\\\\\n \\\"InternalName\\\", \\\"CompanyName\\\"]\\n for token in et_tokens:\\n self.features[token] = None\\n for attr in self.report.get(\\\"static\\\", {}).get(\\\"pe_versioninfo\\\", []):\\n attr_name = attr.get(\\\"name\\\")\\n if attr_name in et_tokens:\\n self.features[attr_name] = attr.get(\\\"value\\\")\",\n \"def hide_ios(self) -> pulumi.Output[Optional[bool]]:\\n return pulumi.get(self, \\\"hide_ios\\\")\",\n \"def test_set_new_section_property():\\n\\n value = '1'\\n testutils.deploy_config_raw(\\\"\\\")\\n\\n prop.set_prop('info', 'sdk', value)\\n assert prop.get_prop('info', 'sdk') == value\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def dict_support_required(self):\\n\\t\\treturn self.typemanager.has_dicts\",\n \"def metadata_json(self):\\n return {\\n \\\"flavor_classes\\\": \\\"*,!onmetal\\\",\\n \\\"image_type\\\": \\\"base\\\",\\n \\\"os_type\\\": \\\"windows\\\",\\n \\\"vm_mode\\\": \\\"\\\",\\n \\\"auto_disk_config\\\": \\\"disabled\\\",\\n \\\"org.openstack__1__os_distro\\\": \\\"com.microsoft.server\\\"\\n }\",\n \"def ui_additional_info(self) -> Optional[str]:\\n return None\",\n \"def has_usfm_bundle(self, formats):\\n for format in formats:\\n if 'text/usfm' in format['format'] and 'type=bundle' in format['format']:\\n return True\\n return False\",\n \"def __init__(self, install_single_profile=False, signing_mdm_profile_enabled=False, mdm_signing_certificate=None, restrict_reenrollment=False, flush_location_information=False, flush_location_history_information=False, flush_policy_history=False, flush_extension_attributes=False, flush_mdm_commands_on_reenroll='DELETE_EVERYTHING_EXCEPT_ACKNOWLEDGED', mac_os_enterprise_enrollment_enabled=False, management_username='', management_password='null', management_password_set=None, password_type='STATIC', random_password_length=8, create_management_account=True, hide_management_account=False, allow_ssh_only_management_account=False, ensure_ssh_running=True, launch_self_service=False, sign_quick_add=False, developer_certificate_identity=None, developer_certificate_identity_details=None, mdm_signing_certificate_details=None, ios_enterprise_enrollment_enabled=True, ios_personal_enrollment_enabled=False, personal_device_enrollment_type='PERSONALDEVICEPROFILES', local_vars_configuration=None): # noqa: E501 # noqa: E501\\n if local_vars_configuration is None:\\n local_vars_configuration = Configuration()\\n self.local_vars_configuration = local_vars_configuration\\n\\n self._install_single_profile = None\\n self._signing_mdm_profile_enabled = None\\n self._mdm_signing_certificate = None\\n self._restrict_reenrollment = None\\n self._flush_location_information = None\\n self._flush_location_history_information = None\\n self._flush_policy_history = None\\n self._flush_extension_attributes = None\\n self._flush_mdm_commands_on_reenroll = None\\n self._mac_os_enterprise_enrollment_enabled = None\\n self._management_username = None\\n self._management_password = None\\n self._management_password_set = None\\n self._password_type = None\\n self._random_password_length = None\\n self._create_management_account = None\\n self._hide_management_account = None\\n self._allow_ssh_only_management_account = None\\n self._ensure_ssh_running = None\\n self._launch_self_service = None\\n self._sign_quick_add = None\\n self._developer_certificate_identity = None\\n self._developer_certificate_identity_details = None\\n self._mdm_signing_certificate_details = None\\n self._ios_enterprise_enrollment_enabled = None\\n self._ios_personal_enrollment_enabled = None\\n self._personal_device_enrollment_type = None\\n self.discriminator = None\\n\\n if install_single_profile is not None:\\n self.install_single_profile = install_single_profile\\n if signing_mdm_profile_enabled is not None:\\n self.signing_mdm_profile_enabled = signing_mdm_profile_enabled\\n if mdm_signing_certificate is not None:\\n self.mdm_signing_certificate = mdm_signing_certificate\\n if restrict_reenrollment is not None:\\n self.restrict_reenrollment = restrict_reenrollment\\n if flush_location_information is not None:\\n self.flush_location_information = flush_location_information\\n if flush_location_history_information is not None:\\n self.flush_location_history_information = flush_location_history_information\\n if flush_policy_history is not None:\\n self.flush_policy_history = flush_policy_history\\n if flush_extension_attributes is not None:\\n self.flush_extension_attributes = flush_extension_attributes\\n if flush_mdm_commands_on_reenroll is not None:\\n self.flush_mdm_commands_on_reenroll = flush_mdm_commands_on_reenroll\\n if mac_os_enterprise_enrollment_enabled is not None:\\n self.mac_os_enterprise_enrollment_enabled = mac_os_enterprise_enrollment_enabled\\n if management_username is not None:\\n self.management_username = management_username\\n if management_password is not None:\\n self.management_password = management_password\\n if management_password_set is not None:\\n self.management_password_set = management_password_set\\n if password_type is not None:\\n self.password_type = password_type\\n if random_password_length is not None:\\n self.random_password_length = random_password_length\\n if create_management_account is not None:\\n self.create_management_account = create_management_account\\n if hide_management_account is not None:\\n self.hide_management_account = hide_management_account\\n if allow_ssh_only_management_account is not None:\\n self.allow_ssh_only_management_account = allow_ssh_only_management_account\\n if ensure_ssh_running is not None:\\n self.ensure_ssh_running = ensure_ssh_running\\n if launch_self_service is not None:\\n self.launch_self_service = launch_self_service\\n if sign_quick_add is not None:\\n self.sign_quick_add = sign_quick_add\\n if developer_certificate_identity is not None:\\n self.developer_certificate_identity = developer_certificate_identity\\n if developer_certificate_identity_details is not None:\\n self.developer_certificate_identity_details = developer_certificate_identity_details\\n if mdm_signing_certificate_details is not None:\\n self.mdm_signing_certificate_details = mdm_signing_certificate_details\\n if ios_enterprise_enrollment_enabled is not None:\\n self.ios_enterprise_enrollment_enabled = ios_enterprise_enrollment_enabled\\n if ios_personal_enrollment_enabled is not None:\\n self.ios_personal_enrollment_enabled = ios_personal_enrollment_enabled\\n if personal_device_enrollment_type is not None:\\n self.personal_device_enrollment_type = personal_device_enrollment_type\",\n \"def fiddle_with_flags():\\n flags['c++'] += '-arch x86_64 -bundle'\\n flags['c'] += '-arch x86_64'\",\n \"def applicationDidFinishLaunching_(self, aNotification):\",\n \"def _parse_mobileprovision_file(mobileprovision_file):\\n plist_xml = subprocess.check_output([\\n \\\"security\\\",\\n \\\"cms\\\",\\n \\\"-D\\\",\\n \\\"-i\\\",\\n mobileprovision_file,\\n ])\\n return plist_from_bytes(plist_xml)\",\n \"def test_get_property_no_option():\\n\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"vmtype = arm64\\\")\\n\\n testutils.deploy_config_raw(contents)\\n\\n with pytest.raises(prop.PropertyError):\\n prop.get_prop('info', 'sdk')\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def supported_provisioning_types(self):\\n return self.properties.get(\\\"supportedProvisioningTypes\\\", StringCollection())\",\n \"def test_get_software_bundle(self):\\n pass\",\n \"def ios_test(self) -> Optional[pulumi.Input['IosTestArgs']]:\\n return pulumi.get(self, \\\"ios_test\\\")\",\n \"def is_bundle(conf, path):\\n \\n parts = path.split('/')\\n \\n if len(parts) < 3:\\n return False\\n \\n asset_type = parts[1]\\n bundle_name = parts[2]\\n \\n return asset_type in conf and bundle_name in conf[asset_type]\",\n \"def properties(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def properties(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def set_default_bundle(bundle):\\n data = {\\n \\\"default_bundle\\\": bundle\\n }\\n response = houston.put(\\\"/zipline/config\\\", data=data)\\n houston.raise_for_status_with_json(response)\\n return response.json()\",\n \"def properties(self) -> Optional[str]:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def config_bundle(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"config_bundle\\\")\",\n \"def properties(self) -> Optional[pulumi.Input['SmsChannelPropertiesArgs']]:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def has_custom_install_prefix(self):\\n return self.converter.install_prefix not in KNOWN_INSTALL_PREFIXES\",\n \"def dehydrate_extra_info(self, bundle):\\n extra_info = bundle.data[\\\"extra_info\\\"]\\n return json.loads(extra_info)\",\n \"def metadata_json(self):\\n return {\\n \\\"flavor_classes\\\": \\\"*,!onmetal\\\",\\n \\\"image_type\\\": \\\"base\\\",\\n \\\"os_type\\\": \\\"linux\\\",\\n \\\"org.openstack__1__os_distro\\\": \\\"org.freebsd\\\",\\n \\\"vm_mode\\\": \\\"hvm\\\",\\n \\\"auto_disk_config\\\": \\\"disabled\\\"\\n }\",\n \"def _get_launch_attributes(self):\\n launch_attributes = self._cfg.launch_attributes\\n attributes = gen_attributes(\\n launch_attributes) if launch_attributes else []\\n system_attributes = get_launch_sys_attrs()\\n system_attributes[\\\"agent\\\"] = f\\\"{self.agent_name}|{self.agent_version}\\\"\\n return attributes + dict_to_payload(system_attributes)\",\n \"def retrieve_properties(self):\\n raise DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)\",\n \"def valid_config_keys():\\n click.echo(', '.join(get_class_properties(OffshoreInputsConfig)))\",\n \"def ca_bundle(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"ca_bundle\\\")\",\n \"def app_settings_json(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"app_settings_json\\\")\",\n \"def app_settings_json(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"app_settings_json\\\")\",\n \"def pref(prefname):\\n return CFPreferencesCopyAppValue(prefname, BUNDLE_ID)\",\n \"def is_osx() -> bool:\\n return sys.platform == \\\"darwin\\\"\",\n \"def get_plugin_config():\\n return json.loads(os.getenv(\\\"SKU_CUSTOM_RECIPE_PLUGIN_CONFIG\\\"))\",\n \"def json_properties(self):\\n properties = Jsonifiable.json_properties(self)\\n properties.remove('image_blob_key')\\n return properties\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.550685","0.5398409","0.5094902","0.5077413","0.5070626","0.505988","0.5054438","0.49296305","0.49282697","0.48895228","0.48720244","0.48373243","0.48084444","0.48046267","0.4804201","0.47875956","0.47442275","0.47442275","0.47250745","0.4656228","0.46466342","0.46392918","0.46200755","0.46166146","0.46164423","0.45948228","0.45732585","0.45700577","0.45539594","0.45462084","0.45460573","0.45460573","0.4530852","0.45242944","0.45202413","0.450558","0.45026827","0.4481326","0.44784784","0.4474545","0.44629738","0.44569525","0.44551376","0.44447914","0.44360527","0.44360527","0.44263983","0.44217756","0.44074264","0.4405151","0.44048378","0.44046858","0.43963972","0.43815118","0.43773964","0.43773964","0.43751702","0.43751702","0.4374459","0.43670005","0.43633428","0.4360838","0.43577468","0.43577468","0.43458596","0.43447882","0.43443063","0.43372288","0.43356612","0.4326112","0.43231744","0.43185824","0.4318218","0.43165034","0.4312027","0.4303837","0.43008614","0.42960742","0.4294963","0.42947155","0.42934504","0.42854995","0.42854995","0.4284806","0.42788237","0.4278084","0.42774358","0.42751935","0.42743447","0.42713177","0.42627567","0.4258497","0.42557374","0.42529583","0.42512652","0.42512652","0.42451245","0.42418605","0.42418522","0.42386973"],"string":"[\n \"0.550685\",\n \"0.5398409\",\n \"0.5094902\",\n \"0.5077413\",\n \"0.5070626\",\n \"0.505988\",\n \"0.5054438\",\n \"0.49296305\",\n \"0.49282697\",\n \"0.48895228\",\n \"0.48720244\",\n \"0.48373243\",\n \"0.48084444\",\n \"0.48046267\",\n \"0.4804201\",\n \"0.47875956\",\n \"0.47442275\",\n \"0.47442275\",\n \"0.47250745\",\n \"0.4656228\",\n \"0.46466342\",\n \"0.46392918\",\n \"0.46200755\",\n \"0.46166146\",\n \"0.46164423\",\n \"0.45948228\",\n \"0.45732585\",\n \"0.45700577\",\n \"0.45539594\",\n \"0.45462084\",\n \"0.45460573\",\n \"0.45460573\",\n \"0.4530852\",\n \"0.45242944\",\n \"0.45202413\",\n \"0.450558\",\n \"0.45026827\",\n \"0.4481326\",\n \"0.44784784\",\n \"0.4474545\",\n \"0.44629738\",\n \"0.44569525\",\n \"0.44551376\",\n \"0.44447914\",\n \"0.44360527\",\n \"0.44360527\",\n \"0.44263983\",\n \"0.44217756\",\n \"0.44074264\",\n \"0.4405151\",\n \"0.44048378\",\n \"0.44046858\",\n \"0.43963972\",\n \"0.43815118\",\n \"0.43773964\",\n \"0.43773964\",\n \"0.43751702\",\n \"0.43751702\",\n \"0.4374459\",\n \"0.43670005\",\n \"0.43633428\",\n \"0.4360838\",\n \"0.43577468\",\n \"0.43577468\",\n \"0.43458596\",\n \"0.43447882\",\n \"0.43443063\",\n \"0.43372288\",\n \"0.43356612\",\n \"0.4326112\",\n \"0.43231744\",\n \"0.43185824\",\n \"0.4318218\",\n \"0.43165034\",\n \"0.4312027\",\n \"0.4303837\",\n \"0.43008614\",\n \"0.42960742\",\n \"0.4294963\",\n \"0.42947155\",\n \"0.42934504\",\n \"0.42854995\",\n \"0.42854995\",\n \"0.4284806\",\n \"0.42788237\",\n \"0.4278084\",\n \"0.42774358\",\n \"0.42751935\",\n \"0.42743447\",\n \"0.42713177\",\n \"0.42627567\",\n \"0.4258497\",\n \"0.42557374\",\n \"0.42529583\",\n \"0.42512652\",\n \"0.42512652\",\n \"0.42451245\",\n \"0.42418605\",\n \"0.42418522\",\n \"0.42386973\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6288776"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":195,"numItemsPerPage":100,"numTotalItems":95772,"offset":19500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjMwMTc1NSwianRpIjoiOTVmZmFiNDEtNmRiMC00ZjA3LWJkNDItYmUxOGI2MjIzYjI3Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYzMDUzNTUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.DCiCmBxnxFsiLAmknew0mJwh4XRZMdw9afa-06pApxp2am2YGHxlhnTLxUxqGRe3lsPdV487uHLnnksItJJKDQ","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 |
|---|---|---|---|---|---|---|
Return a scalar type which is common to the input arrays. The return type will always be an inexact (i.e. floating point) scalar type, even if all the arrays are integer arrays. If one of the inputs is an integer array, the minimum precision type that is returned is a 64bit floating point dtype. All input arrays except... | def common_type(*arrays):
arrays = [numpoly.aspolynomial(array) for array in arrays]
arrays = [array[array.keys[0]] for array in arrays]
return numpy.common_type(*arrays) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def common_type(*arrays):\n dtypes = {array.dtype.name for array in arrays}\n has_complex = not _COMPLEX_DTYPES.isdisjoint(dtypes)\n has_double = not _DOUBLE_DTYPES.isdisjoint(dtypes)\n return _DTYPE_MAP[has_complex, has_double]",
"def scalar(name=None, dtype=None):\r\n if dtype is None:\r\n ... | [
"0.68365544",
"0.63595915",
"0.6185176",
"0.59608775",
"0.5895391",
"0.58816814",
"0.5827463",
"0.5799943",
"0.5797248",
"0.5737268",
"0.5683238",
"0.5681322",
"0.5662812",
"0.56623214",
"0.5653717",
"0.56307465",
"0.55718017",
"0.5552709",
"0.5547326",
"0.5509954",
"0.550467... | 0.62060404 | 2 |
Initialize the bzip2 package. | def __init__(self, system):
super(Bzip2106, self).__init__("bzip2-1.0.6", system, "bzip2-1.0.6.tar.gz") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, *args, **kwargs):\n\t\tself.verbose = kwargs.pop('verbose', self.verbose)\n\t\t#super(ZipArchive, self).__init__(*args, **kwargs)\n\t\tzipfile.ZipFile.__init__(self, *args, **kwargs)",
"def __init__(self):\n _snap.TStrHashF_DJB_swiginit(self, _snap.new_TStrHashF_DJB())",
"def hasBzip2... | [
"0.61423457",
"0.5892953",
"0.5870998",
"0.58182406",
"0.5793603",
"0.5732819",
"0.5602661",
"0.5576144",
"0.55652636",
"0.55539757",
"0.5521145",
"0.5483834",
"0.5482151",
"0.54801524",
"0.5467347",
"0.54053587",
"0.54005855",
"0.53714246",
"0.53280556",
"0.5319646",
"0.5318... | 0.7995384 | 0 |
returns self's full name | def getName(self):
return self.name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_full_name(self):\n return self.name #self is base and it hits name filed",
"def get_full_name(self):\n return self.name",
"def get_full_name(self):\n return self.name",
"def get_full_name(self):\n return self.name",
"def get_full_name(self):\n return self.name",
... | [
"0.90718335",
"0.88658005",
"0.88658005",
"0.88658005",
"0.88658005",
"0.88658005",
"0.8824497",
"0.87989116",
"0.8789187",
"0.87752736",
"0.87752736",
"0.86491096",
"0.8622617",
"0.8599389",
"0.8584429",
"0.8488957",
"0.84057194",
"0.84046555",
"0.83086085",
"0.8293897",
"0.... | 0.0 | -1 |
sets birthday to datetime.date format? | def setBirthday(self, birthdate):
self.birthday = birthdate | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setBirthday(self,month,day,year):\n self.birthday = datetime.date(year,month,day)",
"def setBirthday(self,month,day,year):\n self.birthday = datetime.date(year,month,day)",
"def setBirthday(self, month, day, year):\n self.birthday = datetime.date(year, month, day)",
"def set_birthday... | [
"0.8183557",
"0.8183557",
"0.8017441",
"0.80071366",
"0.79808784",
"0.7911469",
"0.78016704",
"0.7736576",
"0.77311146",
"0.7486346",
"0.71630347",
"0.6962517",
"0.69354504",
"0.6931364",
"0.6888113",
"0.6837638",
"0.677436",
"0.6762404",
"0.675598",
"0.6698934",
"0.6669101",... | 0.7907243 | 6 |
returns true if self's name is lexicographically less than other's name, false otherwise | def __lt__(self, other):
if self.lastName == other.lastName:
return self.name < other.name
return self.lastName < other.lastName | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __lt__(self, other) -> bool:\n return type(self) == type(other) and \\\n self.name < other.name",
"def __lt__(self, other):\n return self.name.lower() < other.name.lower()",
"def __lt__(self, other):\n return self.name.lower() < other.name.lower()",
"def __lt__(self, ot... | [
"0.83991265",
"0.8365852",
"0.8365852",
"0.8289976",
"0.81914055",
"0.8110103",
"0.7988708",
"0.7988708",
"0.7988708",
"0.7988708",
"0.7988708",
"0.79862463",
"0.79520786",
"0.78966457",
"0.78007",
"0.7710017",
"0.7662206",
"0.75784177",
"0.754334",
"0.7513246",
"0.7303679",
... | 0.7950968 | 13 |
Helper function to check for blacklisted tokens | def check_blacklisted_token(token):
token = models.TokenBlackList.query.filter_by(token=token).first()
if token:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_if_token_in_blacklist(decrypted_token):\n return (\n decrypted_token[\"jti\"] in BLACKLIST\n ) # if True, go to revoked_token_callback",
"def check_if_token_in_blacklist(decrypted_token):\n jti = decrypted_token['jti']\n return model.revoked_token.RevokedToken.is_blackli... | [
"0.76352197",
"0.76228154",
"0.7478889",
"0.7431049",
"0.7411897",
"0.7238838",
"0.7137883",
"0.69916326",
"0.69205165",
"0.68788165",
"0.66148496",
"0.65662944",
"0.64836353",
"0.6441574",
"0.635617",
"0.6342027",
"0.63384205",
"0.62987286",
"0.6216427",
"0.6215166",
"0.6212... | 0.7947959 | 0 |
Determine the anticipated host switch name for the logical switch respresented by and store it in caller's . If an existing name is present, use it. | def _preprocess_resolve_switch_name(obj, kwargs):
# Determine the expected host_switch_name from the associated
# TransportZone. This must be done via API regardless of requested
# execution_type.
if kwargs.get('host_switch_name') is None:
# XXX(jschmidt): read() should be able to default to pro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_switch(self,host):\n switch_list = self.__graph_dict[host]\n switch_num = switch_list[0]\n return switch_num",
"def get_initiator_host_name(self, connector):\n name = connector.get('initiator',\n connector.get('wwnns', [''])[0])[::-1]\n if se... | [
"0.59917486",
"0.5900308",
"0.58105445",
"0.5809452",
"0.57702166",
"0.57147825",
"0.562613",
"0.558732",
"0.55398726",
"0.55224764",
"0.5492891",
"0.5492891",
"0.5477548",
"0.5439147",
"0.53883976",
"0.5365079",
"0.5351319",
"0.534847",
"0.53416914",
"0.5320049",
"0.53086567... | 0.7307711 | 0 |
Fit LDA from a scipy CSR matrix (X). | def fit_lda(X, vocab):
print('fitting lda...')
return LdaModel(matutils.Sparse2Corpus(X, documents_columns=False), num_topics=100, passes=1, iterations=500,
chunksize=1000, update_every=1, id2word=dict([(i, s) for i, s in enumerate(vocab)])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self, X):\n\n X_sparse = X.copy().astype(np.float64)\n self.X_sparse = X_sparse\n self._fit()\n return self",
"def fit(self, X, y=None):\n #X = check_array(X, accept_sparse='csr')\n return self",
"def fit(self, X: sp.csr_matrix, n_samples: int):\n X = ch... | [
"0.64050597",
"0.6399704",
"0.6362117",
"0.62613547",
"0.6251182",
"0.6121596",
"0.610392",
"0.6086461",
"0.59369344",
"0.5904508",
"0.5868396",
"0.5860403",
"0.585907",
"0.58524126",
"0.5779344",
"0.5778336",
"0.575929",
"0.57262206",
"0.5716203",
"0.5701557",
"0.569485",
... | 0.75815755 | 0 |
Print the top words for each topic. | def print_topics(lda):
topics = lda.show_topics(num_topics=100, num_words=10, formatted=False)
for ti, topic in enumerate(topics):
print('topic %d: %s' % (ti, ' '.join('%s/%.2f' % (t[1], t[0]) for t in topic))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_top_words(components, feature_names, n_top_words: int = 10):\n for topic_idx, topic in enumerate(components):\n message = \"Topic #%d: \" % topic_idx\n message += \" \".join(\n [feature_names[i] for i in topic.argsort()[: -n_top_words - 1 : -1]]\n )\n print(messa... | [
"0.80106956",
"0.7843566",
"0.7800577",
"0.7643584",
"0.76045525",
"0.7579483",
"0.7540006",
"0.73396826",
"0.73279583",
"0.7300888",
"0.703744",
"0.70279413",
"0.6986095",
"0.69638085",
"0.69356525",
"0.6929748",
"0.69172573",
"0.691049",
"0.6908292",
"0.6858126",
"0.6834454... | 0.692924 | 16 |
Transform user's document vector into list of tuples | def user_doc_bow(user):
user_doc = []
user_array = np.zeros(100)
nonzero_idx = [item for item in user.nonzero()[1]]
for i in nonzero_idx:
user_doc.append((i, user[0, i]))
topics = lda[user_doc]
topic_dict = {topic[0]: topic[1] for topic in topics}
np.put(user_array, topic_dict.keys()... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ex_list(data):\n return tuple(data)",
"def read_tuples_list(corpus_path):\n idx = 0\n data = []\n with open(corpus_path, encoding='utf-8') as fr:\n lines = fr.readlines()\n sent_, label_h_, pos_, ner_ = [], [], [], []\n\n for line in lines:\n idx += 1\n if line.find(\"D... | [
"0.5863828",
"0.5648858",
"0.56228894",
"0.55880904",
"0.55583906",
"0.55135846",
"0.5450894",
"0.54424894",
"0.54149765",
"0.53839064",
"0.5359936",
"0.5349167",
"0.53247696",
"0.52956575",
"0.5271087",
"0.52633893",
"0.52600634",
"0.5258497",
"0.5246498",
"0.52325004",
"0.5... | 0.51671076 | 25 |
Used at initialization to update all scan groups with their database values | def load_all_groups(self):
for _, group in self.scopes.items():
group.update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update(self):\n # clear group before rebuild\n self.clear()\n\n # build configuration groups\n self._config_names = []\n for i in range(self._n_configs):\n config_name = f\"config{i+1:02}\"\n self._config_names.append(config_name)\n self._bui... | [
"0.62571394",
"0.6019692",
"0.5947787",
"0.59254926",
"0.5868216",
"0.5769238",
"0.55904764",
"0.5585611",
"0.5578358",
"0.5528191",
"0.54845923",
"0.54744333",
"0.54352814",
"0.5428182",
"0.54216605",
"0.54042125",
"0.5394926",
"0.538905",
"0.5349979",
"0.5349979",
"0.534997... | 0.61861694 | 1 |
Set command line arguments | def _add_argument(self, args=''):
sys.argv += args.split(' ') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_args():\n # Initialise argparse object\n parser = argparse.ArgumentParser(description='Set some arguments for our script')\n # Add some arguments, elements are: short form name, long form name, type of input expected\n # default value if you don't set an argument, help string (shown if you run ... | [
"0.72329915",
"0.67563385",
"0.6751851",
"0.6713876",
"0.669729",
"0.6577188",
"0.65664786",
"0.65365773",
"0.6524342",
"0.6502149",
"0.6502149",
"0.6502149",
"0.6502149",
"0.6502149",
"0.6488107",
"0.6472675",
"0.64655954",
"0.64572567",
"0.6438185",
"0.64359045",
"0.6433804... | 0.6419336 | 23 |
Detect Dunia 2 engine (Far Cry >= 3) | def _is_dunia2(self):
dir_list = os.listdir(os.environ['PWD'])
data_list = list(filter(lambda item: 'data_win' in item, dir_list))
# Check .../data_win*/worlds/multicommon dir
for data_dir in data_list:
if os.path.exists(os.path.join(os.environ['PWD'], data_dir, 'worlds/mul... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def detect():\n pass",
"def test_denoiser_supported_version(self):\r\n\r\n pass_test = True\r\n try:\r\n check_flowgram_ali_exe()\r\n except (ApplicationNotFoundError, ApplicationError):\r\n pass_test = False\r\n\r\n self.assertTrue(pass_test,\r\n ... | [
"0.62615335",
"0.5705501",
"0.53740865",
"0.5303324",
"0.5251459",
"0.5226396",
"0.5159832",
"0.5095531",
"0.5082969",
"0.50595397",
"0.5016958",
"0.49949306",
"0.4987852",
"0.497308",
"0.49681222",
"0.49398556",
"0.4930393",
"0.49208614",
"0.49155763",
"0.49135765",
"0.49113... | 0.52787036 | 4 |
Detect RAGE engine (GTA IV/V) | def _is_rage(self):
# dir_list = os.listdir(os.environ['PWD'])
# # Check .../*/pc/data/cdimages dir
# for data_dir in dir_list:
# if os.path.exists(os.path.join(os.environ['PWD'], data_dir, 'pc/data/cdimages')):
# return True
if os.path.exists(os.path.join(os.env... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recognize():\n return 0",
"def check_gev_enabled(system):\r\n\r\n # Retrieve the System TL NodeMap and EnumerateGEVInterfaces node\r\n system_node_map = system.GetTLNodeMap()\r\n node_gev_enumeration = PySpin.CBooleanPtr(system_node_map.GetNode('EnumerateGEVInterfaces'))\r\n\r\n # Ensure t... | [
"0.5817683",
"0.5808298",
"0.55364543",
"0.5331647",
"0.50801075",
"0.5075239",
"0.5073693",
"0.50504744",
"0.5004589",
"0.4935257",
"0.49329326",
"0.49239454",
"0.49068636",
"0.49019936",
"0.4886496",
"0.4872999",
"0.4865923",
"0.48630938",
"0.48175597",
"0.4810263",
"0.4788... | 0.0 | -1 |
Detect Unreal Engine 3 | def _is_ue3(self):
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_unreal():\n\n try:\n import unreal\n except ImportError:\n return False\n\n return True",
"def check_engine_light(self):\n return self._check_engine_light",
"def detect():\n pass",
"def get_engine_type(self):",
"def test_installed(self):\n check_output('un... | [
"0.62756",
"0.6017487",
"0.5868715",
"0.5776014",
"0.5408652",
"0.53976065",
"0.5366726",
"0.52779186",
"0.52252907",
"0.52045435",
"0.51455265",
"0.5134668",
"0.5123488",
"0.5076015",
"0.5076015",
"0.5076015",
"0.5076015",
"0.5076015",
"0.5074704",
"0.5064799",
"0.5050235",
... | 0.53666824 | 7 |
Detect Unreal Engine 4 | def _is_ue4(self):
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_unreal():\n\n try:\n import unreal\n except ImportError:\n return False\n\n return True",
"def check_engine_light(self):\n return self._check_engine_light",
"def detect():\n pass",
"def get_engine_type(self):",
"def is_vrpd():\n cpu_type = platform.machine... | [
"0.6195017",
"0.5961009",
"0.5948301",
"0.5744054",
"0.54943156",
"0.5477433",
"0.5392364",
"0.5354996",
"0.5268304",
"0.52287185",
"0.5200947",
"0.5200522",
"0.5179119",
"0.51672095",
"0.51497275",
"0.5115229",
"0.51007545",
"0.507543",
"0.5072135",
"0.5057048",
"0.50519335"... | 0.55713475 | 4 |
Show some information about engine | def info(self):
if self.engine_name == 'RAGE':
self._add_argument('-help')
self._log('info', 'command line arguments')
else:
self._log('info', 'not supported', True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def engine():\r\n document.add_heading('Engine details', 1)\r\n\r\n engine_metrics = ['customProperties','listenerPorts','autosaveInterval', 'tableFilesDirectory', 'genericUndoBufferMaxSize', 'documentTimeout','documentDirectory',\r\n 'allowDataLineage', 'qrsHttpNotificationPort', 'stan... | [
"0.6732129",
"0.6522818",
"0.64047635",
"0.64047635",
"0.63712054",
"0.63676304",
"0.63366336",
"0.63179624",
"0.63136",
"0.63030654",
"0.62724555",
"0.6268214",
"0.62365055",
"0.6205008",
"0.61519957",
"0.61519957",
"0.6136013",
"0.6124394",
"0.6102039",
"0.6079782",
"0.6079... | 0.64630115 | 2 |
Generate header for oauth2 | def oauth_headers(oauth):
import base64
encoded_credentials = base64.b64encode(('{0}:{1}'.format(oauth.client_id, oauth.client_secret)).encode('utf-8'))
headers = {
'Authorization': 'Basic {0}'.format(encoded_credentials.decode('utf-8')),
'Content-Type': 'application/x-www-form-urlen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_oauth_headers(access_token: str) -> dict:\n return {'Authorization': 'Bearer ' + access_token}",
"def __header_base64(self):\n header_base64 = base64.b64encode(f'{self.client_id}:{self.client_secret}'.encode('ascii'))\n header_base64 = str(header_base64).split(\"'\")[1]\n ret... | [
"0.7510096",
"0.71629244",
"0.71587896",
"0.7056566",
"0.7027807",
"0.69251823",
"0.6914705",
"0.6914705",
"0.68121415",
"0.67767835",
"0.6775404",
"0.67447174",
"0.67390746",
"0.67297125",
"0.6694022",
"0.66927457",
"0.66704285",
"0.6578851",
"0.65618974",
"0.64950544",
"0.6... | 0.73699313 | 1 |
Checks to see if the token is still valid | def token_valid_check(start_time):
#calculate the time elapsed since token was last refreshed
elapsed_time = time.time() - start_time
#take action if token is expired
if elapsed_time > 3540:
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_unused_token_is_valid(self):\n assert self.token.is_valid()",
"async def validate_token(self, token):",
"def verify_token(self, token):\n return False",
"def test_live_thread_token_is_valid(self):\n assert self.token.is_valid()",
"def validate_token():\n global vault_token\... | [
"0.847552",
"0.82011956",
"0.81599474",
"0.80262107",
"0.7823794",
"0.775227",
"0.7649238",
"0.75251055",
"0.7454413",
"0.7403214",
"0.7285629",
"0.7264365",
"0.7250214",
"0.72220445",
"0.71471286",
"0.71447253",
"0.714289",
"0.714289",
"0.7124884",
"0.70873654",
"0.70527405"... | 0.7541496 | 7 |
Creates an access token from the supplied oauth2.0 object | def create_access_token(oauth):
#create parameters for API authorization
redirect_uri = 'oob'
params = {'client_secret': oauth.client_secret,
'redirect_uri': redirect_uri,
'response_type': 'code'}
#store the access code
url = oauth.get_authorize_url(**params)
#open a web browser to get access token ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_oauth2_access_token(self):\n if not isinstance(self.session, DropboxSession):\n raise ValueError(\"This call requires a DropboxClient that is configured with an \"\n \"OAuth 1 access token.\")\n url, params, headers = self.request(\"/oauth2/token_from... | [
"0.77427965",
"0.7283522",
"0.6906905",
"0.6883373",
"0.68754137",
"0.6776269",
"0.6742007",
"0.6687795",
"0.66251546",
"0.6614909",
"0.6581949",
"0.65400434",
"0.6519731",
"0.6517932",
"0.65080005",
"0.64990944",
"0.6489406",
"0.6487857",
"0.648599",
"0.64817834",
"0.6473712... | 0.7380083 | 1 |
API query to return all available players, ssorted by number of fantasy points\n | def available_players_query():
#start the calculation timer
calc_start = time.time()
#initialize everything
last_first_names = []
full_names = []
player_key = []
player_pos = []
start = 1
done = False
#this is where the data is actually created
#loop thru to get all of the players available
while(not done... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def playerStandings():\n\n getPlayers = \"SELECT id, name, wins, matches FROM playerstats ORDER BY wins DESC\"\n players = executeQuery({'dbname': 'tournament', 'query' : getPlayers, 'type' : 'find'})\n return players",
"def player_stats_query(week, player_list, session=s): \n #initialize lists\n ... | [
"0.688901",
"0.668698",
"0.6631293",
"0.6608959",
"0.6578679",
"0.656035",
"0.65281093",
"0.6521085",
"0.6487832",
"0.6485906",
"0.64535034",
"0.64514905",
"0.6449632",
"0.63346374",
"0.63248485",
"0.63226306",
"0.6192959",
"0.61134183",
"0.60795534",
"0.6069883",
"0.60646695... | 0.74588764 | 0 |
Finds the players of a given team\n | def team_players_query():
#start the calculation timer
calc_start = time.time()
#initialize everything
last_first_names = []
full_names = []
player_key = []
player_pos = []
#build the query URL
query_url = base_query_url + 'team/' + leagueID + teamID + '/roster'
#get the j... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_players_for_team(self, team, src='roster', season=None):\n # creating class wide variable to hold current team\n if type(team) is str:\n team = Team.find(team)\n\n print(\"+ Searching %s players for %s\" % (src, team))\n\n if src == 'roster':\n players = s... | [
"0.75389177",
"0.723137",
"0.7144066",
"0.6916057",
"0.6833467",
"0.6824319",
"0.67805666",
"0.6601074",
"0.6552959",
"0.6516057",
"0.64962745",
"0.64800054",
"0.6478671",
"0.6455705",
"0.6425193",
"0.64149076",
"0.64145464",
"0.63946116",
"0.63882154",
"0.6386933",
"0.637103... | 0.6251081 | 25 |
Returns the player stats for the given week\n Takes the player list as an argument so the function can be used for available players and rostered players\n Only works for offensive players (QB, WR, RB, TE) right now | def player_stats_query(week, player_list, session=s):
#initialize lists
pos_list = []
team_list = []
#cycle thru each player that is currently available
for player in avail_player_key:
#build the API url for the unique player key
url_player = base_query_url+'league/'+leagueI... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_player_stats_from_game(team, year, week):",
"def get_players_game_stats_for_season_for_week(self, season, week, season_type=\"REG\"):\n try:\n season = int(season)\n week = int(week)\n if season_type not in [\"REG\", \"PRE\", \"POST\"]:\n raise Value... | [
"0.85106057",
"0.6774097",
"0.67719406",
"0.6636765",
"0.6384417",
"0.6382811",
"0.6193241",
"0.616072",
"0.6121495",
"0.60404086",
"0.60138744",
"0.59989476",
"0.59980756",
"0.5972492",
"0.5948775",
"0.58840317",
"0.58802027",
"0.5877149",
"0.5872693",
"0.58488315",
"0.58389... | 0.8211842 | 1 |
Build and display svg view for current tab. | def refresh_svg_canvas(self):
if self.ui.tabWidget.currentIndex() == 0:
self.ui.svg_canvas.build_schematic()
self.ui.svg_canvas.viewport().update()
elif self.ui.tabWidget.currentIndex() in (1,2):
self.ui.svg_canvas.build_pcb()
self.ui.svg_canvas.viewport().update()
else:
raise ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw(self, stats=[]):\n clear_output(wait=True)\n svg_html = self.to_html(stats)\n display(svg_html)",
"def _repr_svg_(self):\n pass",
"def _repr_svg_(self):\n if not IPythonConsole.ipython_useSVG:\n return None\n mol = self.owner.mol\n keku = IPy... | [
"0.62321585",
"0.6213278",
"0.6118039",
"0.6118039",
"0.6038661",
"0.58348596",
"0.58015627",
"0.5744214",
"0.5506525",
"0.54956293",
"0.5444143",
"0.54360074",
"0.54353726",
"0.54283196",
"0.5415451",
"0.5410552",
"0.53604287",
"0.5355877",
"0.5347241",
"0.52882683",
"0.5273... | 0.68582183 | 0 |
Reload all valus from model to show in UI elements. | def on_model_reset(self):
cmp = self.mdl.cmp
ui = self.ui
cmp.pcb_layers["silkscreen"].attribs["stroke-width"] = "0.5"
# Set values on schematic tab
ui.txt_label.setText(cmp.part_name)
ui.spnbox_pincount.setValue(len(cmp.connectors))
ui.spn_add_width.setValue(cmp.s_add_width)
ui.spn_ad... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_view(self):\n for row in self.view.obj_list:\n for obj in row:\n obj._update(self.model)",
"def reload(self):\n for name, param in self.components.items():\n param_path = os.path.join(self.model_path, \"%s.mat\" % name)\n param_values = sci... | [
"0.6759079",
"0.6681325",
"0.66621333",
"0.66519105",
"0.6564058",
"0.65607035",
"0.65437347",
"0.6483663",
"0.6483663",
"0.64394903",
"0.6437808",
"0.6432464",
"0.6432464",
"0.6315515",
"0.62948334",
"0.6291024",
"0.62580323",
"0.62535304",
"0.6237264",
"0.6191947",
"0.61895... | 0.55130196 | 81 |
Change mount. Only smd and tht supported by Friting. | def on_mount_changed(self, btn):
sender = self.sender()
if sender == self.ui.radio_smd:
self.mdl.cmp.mount = ComponentBase.MOUNT_SMD
elif sender == self.ui.radio_tht:
self.mdl.cmp.mount = ComponentBase.MOUNT_THT
self.refresh_svg_canvas() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mount(self, mount):\n assert mount in (ComponentBase.MOUNTS)\n if mount==self.MOUNT_HYB:\n raise Exception(\"Unsupported mount.\")\n self._mount = mount\n self._check_mount()",
"def mount(self, dev, path, mount_options):\n # if directory is not a mount point, mount it\n if not ... | [
"0.7536546",
"0.67045027",
"0.6439409",
"0.64367807",
"0.63337016",
"0.6122933",
"0.6095733",
"0.59617585",
"0.5953434",
"0.5928632",
"0.5925651",
"0.5891891",
"0.5875534",
"0.58752507",
"0.5847054",
"0.5811964",
"0.58091146",
"0.58027476",
"0.5790931",
"0.57767016",
"0.57611... | 0.63388246 | 4 |
Add or remove component connectors. | def on_pincount_change(self, val):
if val<0:
return
rows = self.mdl.rowCount()
if rows<val: # Add rows
for i in range(rows, val):
self.mdl.insertRow(i)
elif rows>val: # Remove rows
for i in range(rows, val, -1):
self.mdl.removeRow(i-1)
self.refresh_svg_ca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_connector(self):\n \n no = len(self.connectors)\n state = {}\n state[\"s_pin\"] = no\n state[\"p_pin\"] = no\n state[\"s_label\"] = \"C%d\" % no\n \n if len(self.connectors)>0:\n state = self.connectors[-1].get_state()\n state[... | [
"0.63449883",
"0.6032346",
"0.57559407",
"0.53216934",
"0.52627605",
"0.5195182",
"0.51838595",
"0.5173958",
"0.5141519",
"0.5141155",
"0.51190317",
"0.51025146",
"0.50760615",
"0.50538504",
"0.502889",
"0.5020217",
"0.5013656",
"0.50024843",
"0.49962837",
"0.49697974",
"0.49... | 0.0 | -1 |
Change name on component. | def on_name_change(self, txt):
self.mdl.cmp.part_name = txt
self.refresh_svg_canvas() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_name(self, newname=\"\"):\n self.name = newname",
"def new_name(self,new_name):\n self.name = new_name",
"def update_name(self, new_name):\r\n self.__name = new_name",
"def update_name(self, new_name):\r\n self.__name = new_name",
"def updateName(self,name):\n sel... | [
"0.7760173",
"0.7759742",
"0.7747133",
"0.7747133",
"0.7532761",
"0.7531982",
"0.75248784",
"0.74675447",
"0.74527305",
"0.7436896",
"0.7427096",
"0.7427096",
"0.7427096",
"0.7427096",
"0.7427096",
"0.74243397",
"0.7422197",
"0.7418796",
"0.7418796",
"0.7418796",
"0.7418796",... | 0.0 | -1 |
Adds extra vertical spacing between schematic pins. | def on_pcb_v_spacing_changed(self, txt):
if self.ui.txt_spacing_v.hasAcceptableInput():
self.mdl.cmp.p_spacing_v = float(txt.replace(",", "."))
self.refresh_svg_canvas()
else:
self.ui.txt_spacing_v.setText("%0.3f" % self.mdl.cmp.p_spacing_v) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def additional_vertical_padding(self):\n return 0",
"def custom_spacing(width, height):\n\t\treturn c.lift(imgui.dummy, width, height)",
"def vertical_spacing(self):\r\n return self.padding[0] + self.padding[2] + self.margin[0] + self.margin[2]",
"def SetSpacerPixels(self, s):\r\n\r\n se... | [
"0.66273147",
"0.65561676",
"0.6366731",
"0.59444165",
"0.58789814",
"0.5694909",
"0.5682465",
"0.5503588",
"0.5499777",
"0.54681957",
"0.5457708",
"0.5428778",
"0.54203933",
"0.5412702",
"0.53952616",
"0.53752977",
"0.5370524",
"0.53443474",
"0.5329409",
"0.53142804",
"0.531... | 0.4634174 | 82 |
Adds extra horisontal spacing between schematic pins. | def on_pcb_h_spacing_changed(self, txt):
if self.ui.txt_spacing_h.hasAcceptableInput():
self.mdl.cmp.p_spacing_h = float(txt.replace(",", "."))
self.refresh_svg_canvas()
else:
self.ui.txt_spacing_h.setText("%0.3f" % self.mdl.cmp.p_spacing_h) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def custom_spacing(width, height):\n\t\treturn c.lift(imgui.dummy, width, height)",
"def SetSpacing(self, p_int, ):\n ...",
"def style_spacer_xs(self) -> str:\n style_spacer_xs = \"\"\".spacer--xs\n {margin-bottom: 15px;}\\n\"\"\"\n self.html_doc = self.html_doc + style_spacer_xs\n ... | [
"0.6626817",
"0.61980224",
"0.5979083",
"0.5896608",
"0.5769577",
"0.56942433",
"0.5647765",
"0.5548821",
"0.5529602",
"0.55290693",
"0.5499842",
"0.54984343",
"0.54756695",
"0.54718214",
"0.54642105",
"0.5373114",
"0.53434235",
"0.53388673",
"0.5329868",
"0.5329478",
"0.5300... | 0.51542515 | 29 |
Adds extra height to schematic body | def on_body_height_add(self, val):
val = max(0, int(val))
self.mdl.cmp.s_add_height = val
self.refresh_svg_canvas() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def footprint_height():",
"def body_resize(self):",
"def setHeight(*args):",
"def setHeight(*args):",
"def setHeight(*args):",
"def setHeight(*args):",
"def setHeight(*args):",
"def setHeight(*args):",
"def setHeight(*args):",
"def setHeight(*args):",
"def setHeight(*args):",
"def setHeight(*... | [
"0.6295264",
"0.61815864",
"0.60334027",
"0.60334027",
"0.60334027",
"0.60334027",
"0.60334027",
"0.60334027",
"0.60334027",
"0.60334027",
"0.60334027",
"0.60334027",
"0.60334027",
"0.5969041",
"0.58549297",
"0.57929575",
"0.578066",
"0.5753543",
"0.5753543",
"0.5753543",
"0.... | 0.7480933 | 0 |
Adds extra width to schematic body | def on_body_width_add(self, val):
val = max(0, int(val))
self.mdl.cmp.s_add_width = val
self.refresh_svg_canvas() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extra_width(self) -> int:\n width = 0\n if self.box and self.show_edge:\n width += 2\n if self.box:\n width += len(self.columns) - 1\n return width",
"def body_resize(self):",
"def width(self):\n\t\tpass",
"def width(self) -> int:",
"def width(self) ->... | [
"0.6539015",
"0.64924866",
"0.60851705",
"0.6076208",
"0.6076208",
"0.6055567",
"0.6055567",
"0.6055567",
"0.6055567",
"0.6055567",
"0.6055567",
"0.6055567",
"0.6055567",
"0.6055567",
"0.6055567",
"0.6055567",
"0.5926316",
"0.58366567",
"0.5834761",
"0.58082455",
"0.5742201",... | 0.7276638 | 0 |
Display the online documentation in a help window. | def on_help(self):
d = QtGui.QDialog(parent=self)
dialog = Ui_Dialog()
dialog.setupUi(d)
dialog.webView.setUrl(QUrl("http://code.google.com/p/svg-component-creator/wiki/UserDocumentation"))
d.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def openOnlineHelp(self):\r\n url = QUrl(\"http://freeseer.readthedocs.org\")\r\n QDesktopServices.openUrl(url)",
"def documentation():\n return render_template('help.html')",
"def help(self):\n if not self.fv.gpmon.has_plugin('WBrowser'):\n self._help_docstring()\n ... | [
"0.8012712",
"0.7895543",
"0.76795626",
"0.74728966",
"0.7437503",
"0.7407703",
"0.7359206",
"0.7359206",
"0.7358967",
"0.7340628",
"0.7313576",
"0.7298534",
"0.72881734",
"0.72871894",
"0.7275768",
"0.7271275",
"0.7243446",
"0.723041",
"0.7159798",
"0.7159798",
"0.71316636",... | 0.0 | -1 |
Show a file dialogue and save componet to a file. | def on_save(self):
filename = QtGui.QFileDialog.getSaveFileName(self, "Save file", "", "*.scc")
if filename == "":
return
print("Save file ", filename)
f = open(filename, mode="wb")
state = self.mdl.cmp.get_state()
pickle.dump(state, f, pickle.HIGHEST_PROTOCOL)
f.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_fileDialog(self):\n\n if self.check_data():\n options = QFileDialog.Options()\n options |= QFileDialog.DontUseNativeDialog\n fileName, _ = QFileDialog.getSaveFileName(self, \"Сохранить как\", os.path.expanduser(\"~\"), \"Все файлы (*);;XML Файлы (*.xml);;JSON Файлы ... | [
"0.7724752",
"0.7255473",
"0.7255473",
"0.7255473",
"0.7255473",
"0.72310436",
"0.7226843",
"0.7139409",
"0.70886636",
"0.69758683",
"0.6960584",
"0.69558215",
"0.6949092",
"0.6913201",
"0.6889274",
"0.6876284",
"0.68659836",
"0.6844143",
"0.681732",
"0.6783393",
"0.67787975"... | 0.6094384 | 92 |
Show a file dialoge and load saved componet. | def on_load(self):
filename = QtGui.QFileDialog.getOpenFileName(self, "Open file", "", "*.scc")
print("Load file ", filename)
if filename == "":
return
f = open(filename, mode="rb")
state = pickle.load(f)
f.close()
cmp = ComponentRect()
cmp.set_state(state)
self.mdl.set_c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load():\n messagebox.showinfo(\"Information\", \"Veuillez entrer le nom du fichier dans la console.\")\n file_name = input(\"Nom du fichier : \")\n ferme_fenetre()\n Hitori(file_name)",
"def fileCmd(self):\n filename = askopenfilename() \n self.cnvImgOrig.displayIm... | [
"0.65958446",
"0.6539871",
"0.6426099",
"0.64201653",
"0.6310796",
"0.6266483",
"0.62280816",
"0.62094724",
"0.6201977",
"0.61690474",
"0.6166653",
"0.6125027",
"0.6091769",
"0.60856396",
"0.6052059",
"0.60477245",
"0.6044098",
"0.6012188",
"0.60100627",
"0.600295",
"0.599834... | 0.6267885 | 5 |
Run figure's event loop while listening to interactive events. The events listed in event_names are passed to handler. This function is used to implement `.Figure.waitforbuttonpress`, `.Figure.ginput`, and `.Axes.clabel`. | def blocking_input_loop(figure, event_names, timeout, handler):
if figure.canvas.manager:
figure.show() # Ensure that the figure is shown if we are managing it.
# Connect the events to the on_event function call.
cids = [figure.canvas.mpl_connect(name, handler) for name in event_names]
try:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fig1_press(event):\n if event.key == 'n':\n if results.type is not None:\n print(\"Moving to next neuron\")\n callback.next_move = 'next'\n plt.close(fig1)\n else:\n print('Ensure type is set')\n\n if event.key == '... | [
"0.58663946",
"0.5447554",
"0.54228073",
"0.53521365",
"0.53415567",
"0.5315508",
"0.5248115",
"0.52316606",
"0.5220068",
"0.5174886",
"0.5119426",
"0.51046586",
"0.5099623",
"0.5095802",
"0.5055943",
"0.503854",
"0.5026388",
"0.4981488",
"0.49244776",
"0.4902189",
"0.4895454... | 0.7370716 | 0 |
Calculates the perimeter given the bottom length, top length, 1st side length, and 2nd side length. | def perimeter(self):
return self.sidelength1 + self.sidelength2 + self.baselength1 + self.baselength2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perimeter(self):\n return (\n self.side_1_length +\n self.side_2_length +\n self.side_3_length +\n self.side_4_length\n )",
"def calculateperimeter(self):\r\n return (self.width * 2) + (self.height * 2)",
"def perimeter(self):\n\t\treturn 2 *... | [
"0.73933774",
"0.7245891",
"0.720648",
"0.7175091",
"0.7159154",
"0.7143771",
"0.7052815",
"0.6963846",
"0.6854498",
"0.6804715",
"0.6780466",
"0.6780466",
"0.6722681",
"0.6501043",
"0.6399388",
"0.63739026",
"0.63721514",
"0.6371612",
"0.6364265",
"0.63316923",
"0.6298338",
... | 0.76503986 | 0 |
Calculates the area given the bottom length, top length, and height. | def area(self):
return (self.baselength1 + self.baselength2)*self.height/2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rectangle_area(base, height):\n return (base * height)",
"def rectArea(base, height):\n return base * height",
"def area(base, height):\n\n return base * height",
"def areaRect(length, width):\n return length * width",
"def area_rect(w, h):\n return w * h",
"def area_of(self, left_top,... | [
"0.7216957",
"0.71193826",
"0.7107749",
"0.7080084",
"0.7019711",
"0.70039344",
"0.6993964",
"0.6874019",
"0.6871876",
"0.6871876",
"0.6824662",
"0.6784132",
"0.6711688",
"0.66958904",
"0.66664284",
"0.66632086",
"0.664994",
"0.6608759",
"0.66025525",
"0.65858614",
"0.6525023... | 0.7118867 | 2 |
player names and bid / trick / score headers | def init_column_headers(self):
col_headers = {1: "bid", 2: "tricks", 3: "score"}
for player in range(1, 5):
ent_player_name = tk.Entry(self.master, font='courier 10 bold', fg='blue',
borderwidth=2, relief="groove")
ent_player_name.grid(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_player_data():\n names = [\"Gunther O'Brian\",\n 'Workman Gloom',\n 'Esme Ramsey',\n 'Cornelius Games',\n 'Kline Greenlemon',\n 'Hotbox Sato',\n 'Famous Owens',\n 'Jenkins Good']\n nums = [77, 31, 37, 6, 14, 53, 7, ... | [
"0.6582695",
"0.6475915",
"0.6052055",
"0.60238695",
"0.59894055",
"0.5905075",
"0.5861272",
"0.5828725",
"0.5781333",
"0.576649",
"0.57659",
"0.5753501",
"0.5747915",
"0.57395995",
"0.57395816",
"0.573417",
"0.5696014",
"0.56830585",
"0.5673088",
"0.5660423",
"0.5632163",
... | 0.6398447 | 2 |
labels with round numbers | def init_round_numbers(self):
for round_num in range(1, 13):
lbl_round_num = tk.Label(self.master, text=str(round_num), font='courier 10 bold',
fg='green', pady=2)
lbl_round_num.grid(row=round_num+1, column=0)
row = 14
for trump ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def autolabel(X_pos,values,height_lift):\r\n\theight= np.round(np.nan_to_num(values),2);y_pos = height_lift*height\r\n\tfor i in range(len(height)):\r\n\t\tax.text(X_pos[i],y_pos[i],'%4.2f' % height[i], ha='center', va='bottom',size=4)",
"def getLabels(self):\n return self.numToLabel",
"def label(self, marg... | [
"0.6358072",
"0.635775",
"0.6293751",
"0.621447",
"0.6207149",
"0.62016577",
"0.6130753",
"0.61188644",
"0.61017317",
"0.60645777",
"0.6025209",
"0.60067993",
"0.59946424",
"0.5989236",
"0.59892356",
"0.597898",
"0.597898",
"0.597898",
"0.59719455",
"0.5968639",
"0.59655595",... | 0.65299755 | 0 |
entry widgets for bids and tricks, labels for scores | def init_round_results(self):
for player in range(0, 4):
for round_num in range(0, 17):
spin_bid = tk.Spinbox(self.master, from_=-1, to=min(round_num+1, 13), width=10)
spin_bid.grid(row=round_num+2, column=player*3+1, padx=2)
self.spin_bids[player... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_widgets(self):\n Label(self, text=\"Choose your favorite movie type\").grid(row=0, column=0, sticky=W)\n\n # instructions\n Label(self, text=\"Select all that apply:\").grid(row=1, column=0, sticky=W)\n\n # comedy check button\n self.comedy = BooleanVar()\n Chec... | [
"0.62600446",
"0.619926",
"0.6122178",
"0.61055934",
"0.5994453",
"0.5788945",
"0.57255733",
"0.5698006",
"0.5689652",
"0.5653045",
"0.55936646",
"0.55931014",
"0.553044",
"0.55082005",
"0.54880196",
"0.5463582",
"0.5454248",
"0.5442131",
"0.5441836",
"0.5441345",
"0.54390967... | 0.5555503 | 12 |
label for total score for each player | def init_totals(self):
for player in range(0, 4):
lbl_total = tk.Label(self.master, text="0", font='courier 10 bold', fg='red',
width=10, borderwidth=2, relief="groove", anchor="e")
lbl_total.grid(row=19, column=player*3+3, sticky=tk.W+tk.E)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def augmenter_score():\n\n global label_score\n global score\n\n score += 1\n label_score.config(text= \"score : \" + str(score))",
"def disp_score():",
"def update_scores(self):\r\n totals = [0, 0, 0, 0]\r\n for player in range(0, 4):\r\n for round_num in range(0, 17):\r\n... | [
"0.69694656",
"0.6935289",
"0.67277586",
"0.6526006",
"0.65202165",
"0.6453057",
"0.64485866",
"0.6444573",
"0.64286613",
"0.6387012",
"0.6325333",
"0.625666",
"0.6240114",
"0.6235395",
"0.6225145",
"0.6225145",
"0.6217871",
"0.6213417",
"0.6205035",
"0.61613077",
"0.6159299"... | 0.65591896 | 3 |
command button that calculates scores | def init_button_calc(self):
btn_calc = tk.Button(self.master, text='calculate', font='courier 10 bold',
fg='purple', command=self.update_scores)
btn_calc.grid(row=20, column=1, columnspan=3, sticky=tk.W+tk.E, pady=5) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def disp_score():",
"def update_score():\n pass",
"def enter_game_scores():\n pass",
"def score(self):",
"def update_score(self, engine, *args):\n #pdb.set_trace()\n self.score_label.text = \"Gold: {}/{}\".format(str(engine.score),\n ... | [
"0.695034",
"0.676045",
"0.66949934",
"0.668477",
"0.6502419",
"0.649244",
"0.64213395",
"0.63838565",
"0.62789667",
"0.62286115",
"0.6212426",
"0.62090117",
"0.61582416",
"0.61512876",
"0.6119021",
"0.61164653",
"0.61131734",
"0.6094686",
"0.60794663",
"0.6069196",
"0.605037... | 0.6769738 | 1 |
calculate and display scores for each valid bid x trick pair | def update_scores(self):
totals = [0, 0, 0, 0]
for player in range(0, 4):
for round_num in range(0, 17):
try:
bid = int(self.spin_bids[player][round_num].get())
tricks = int(self.spin_tricks[player][round_num].get())
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def disp_score():",
"def resultat_match(self, binomes):\n for binome in binomes:\n while True:\n score_un = self.vue.entree_resultats(binome[0])\n score_deux = self.vue.entree_resultats(binome[1])\n if score_un + score_deux != 1:\n ... | [
"0.6550589",
"0.64147127",
"0.6227076",
"0.60567015",
"0.6029427",
"0.6025697",
"0.5990587",
"0.5938982",
"0.5932216",
"0.59277326",
"0.592605",
"0.59216154",
"0.5920696",
"0.5903962",
"0.58853215",
"0.5872225",
"0.5831863",
"0.58293426",
"0.5814984",
"0.58139294",
"0.581041"... | 0.6524363 | 1 |
Get the current time, with an offset from setting the time. | def get_current_time():
cur_time = datetime.datetime.now() + offset_time
return [cur_time.year, cur_time.month, cur_time.day, cur_time.hour, cur_time.min, cur_time.second] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_time(self):\n return datetime.datetime.now(self.time_zone)",
"def get_time(offset=0, with_second=True):\n today = datetime.datetime.now() - datetime.timedelta(seconds=offset)\n hour = str(today.hour)\n minute = str(today.minute)\n second = str(today.second)\n if with_second:\n re... | [
"0.7146425",
"0.714417",
"0.7019261",
"0.7012288",
"0.6905133",
"0.6807562",
"0.67945784",
"0.672395",
"0.665871",
"0.6616153",
"0.6525835",
"0.6525835",
"0.6448199",
"0.64471346",
"0.6427538",
"0.64143443",
"0.6411822",
"0.6398357",
"0.6368899",
"0.6362611",
"0.6362475",
"... | 0.6917492 | 4 |
Accept an uploaded file. This method is not named do_POST because it is only enabled if the upload flag (u) is used. | def action_POST(self):
# Use the content-length header, though being user-defined input it's not really trustworthy.
try:
l = int(self.headers.get('content-length', 0))
if l < 0:
# Parsed properly, but some joker put in a negative number.
raise Va... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def post(self):\n if validate(request.form):\n handle_upload(request.files['qqfile'], request.form)\n return make_response(200, {\"success\": True})\n else:\n return make_response(400, {\"error\": \"Invalid request\"})",
"def upload_validated(request):\n if 'file' ... | [
"0.65835524",
"0.6582667",
"0.6471456",
"0.6334828",
"0.6298631",
"0.62456393",
"0.6204695",
"0.61989796",
"0.61686116",
"0.61154824",
"0.61117285",
"0.6110108",
"0.60809696",
"0.6070663",
"0.60542065",
"0.60350883",
"0.60237575",
"0.59959",
"0.59802985",
"0.5974787",
"0.5948... | 0.61218333 | 9 |
Common code for GET and HEAD commands. This sends the response code and MIME headers. Return value is either a file object (which has to be copied to the outputfile by the caller unless the command was HEAD, and must be closed by the caller under all circumstances), or None, in which case the caller has nothing further... | def do_GET(self):
path = self.file_path
if os.path.exists(path):
# Symbolic link judgement.
# Paths with denied symbolic links will pretend to be 404 errors.
if args[TITLE_LOCAL_LINKS] and not ("%s/" % os.path.realpath(path)).startswith(os.getcwd() + "/"):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_GET(self):\n f = self.send_head()\n if f:\n try:\n self.copyfile(f, self.wfile)\n finally:\n f.close()",
"def do_GET(self):\n f = self.send_head()\n if f:\n try:\n \n self.copyfile(f, s... | [
"0.73561096",
"0.7330471",
"0.724983",
"0.71072185",
"0.7020951",
"0.7008843",
"0.69666696",
"0.6840107",
"0.6787616",
"0.6743435",
"0.6736264",
"0.66644365",
"0.66616446",
"0.661352",
"0.6601374",
"0.65817034",
"0.6558699",
"0.6517581",
"0.64791644",
"0.6423894",
"0.6351152"... | 0.6008821 | 39 |
Helper to produce a directory listing (absent index.html). Return value is either a file object, or None (indicating an error). In either case, the headers are sent, making the interface the same as for send_head(). | def list_directory(self, path):
table_content = self.render_file_table(path)
if not table_content:
# Intentionally keeping the reason for this vague.
# Could be due to a not-found file or a permissions problem.
self.send_error(404, "Unable to list directory")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_head(self):\n print 'url_path', self.path\n file_path = self.url_path_to_file_path(self.path)\n print 'file_path', file_path\n f = None\n if os.path.isdir(file_path):\n if not self.path.endswith('/'):\n # redirect browser - doing basically what ... | [
"0.75678825",
"0.7337772",
"0.7279455",
"0.725182",
"0.7109132",
"0.7086577",
"0.7033438",
"0.69411206",
"0.6808663",
"0.67981875",
"0.67918867",
"0.6644201",
"0.66291624",
"0.65504634",
"0.6504529",
"0.6458196",
"0.63638103",
"0.62457836",
"0.61806464",
"0.61667067",
"0.6126... | 0.6708497 | 11 |
Connect to address and return the socket object. Convenience function. Connect to address (a 2tuple ``(host, port)``) and return the socket object. Passing the optional timeout parameter will set the timeout on the socket instance before attempting to connect. If no timeout is supplied, the | def create_connection(address, timeout=_GLOBAL_DEFAULT_TIMEOUT):
msg = "getaddrinfo returns an empty list"
host, port = address
for res in getaddrinfo(host, port, 0, SOCK_STREAM):
af, socktype, proto, canonname, sa = res
sock = None
try:
sock = socket(af, socktype, proto... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect(spec, timeout=None, nagle_off=True, cache=0,\n _cache=_connect_cache, _lock=_connect_cache_lock):\n # pylint: disable = W0102, R0912, R0915\n\n sock = None\n try:\n adi = None\n if cache > 0:\n _lock.acquire()\n try:\n if spec in _c... | [
"0.744305",
"0.7351968",
"0.71428746",
"0.7093566",
"0.69392675",
"0.68707865",
"0.6829429",
"0.6805412",
"0.67925763",
"0.6737057",
"0.6718961",
"0.66749173",
"0.6562161",
"0.65577984",
"0.65336025",
"0.64614576",
"0.6453029",
"0.64274627",
"0.6397214",
"0.6356111",
"0.63293... | 0.73768765 | 1 |
Returns the target functional and the gradients | def target_and_grad(x, ref_params, data_modelers, SIM, params):
target_functional = 0
grad = np.zeros(len(x))
save_name = params.geometry.optimized_detector_name
if not all(params.geometry.fix.panel_rotations) and not all(params.geometry.fix.panel_rotations):
geometry_refiner.update_detector(x,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getGradients(self, image, target_class=None):\n\n image = image.requires_grad_()\n out, features = self._getFeatures(image)\n\n if target_class is None:\n target_class = out.data.max(1, keepdim=True)[1]\n\n agg = 0\n for i in range(image.size(0)):\n agg... | [
"0.7091655",
"0.69808656",
"0.69808656",
"0.6909302",
"0.68390083",
"0.6812944",
"0.66931266",
"0.6678249",
"0.6654693",
"0.6641082",
"0.66244924",
"0.66052693",
"0.66036254",
"0.65789163",
"0.6561227",
"0.65580636",
"0.65580636",
"0.6550413",
"0.6538096",
"0.6532786",
"0.649... | 0.64274645 | 24 |
Writes refl and exper files for each experiment modeled during the ensemble refiner | def write_output_files(Xopt, LMP, Modelers, SIM, params):
opt_det = geometry_refiner.get_optimized_detector(Xopt, LMP, SIM)
# Store the hessian of negative log likelihood for error estimation
# must determine total number of refined Fhkls and then create a vector of 0's of that length
num_fhkl_param = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _OpenOutputFiles(self):\n self.gfile = open(self.geomout, \"w\")\n self.efile = open(self.energyout, \"w\")\n self.PrintEnergyHeader()",
"def write_data_model(doc_filename='data/documents.txt'):\n\n numiters = num_iters(doc_filename) + 1\n print 'number of iterations:', numiters - 1\n\n pic... | [
"0.5975395",
"0.59592813",
"0.58979046",
"0.58952695",
"0.5826259",
"0.58093685",
"0.58026266",
"0.5795915",
"0.57447165",
"0.57213265",
"0.57084817",
"0.56667113",
"0.56539685",
"0.562931",
"0.56153154",
"0.5604128",
"0.5590978",
"0.5588866",
"0.55703115",
"0.55635935",
"0.5... | 0.62107396 | 0 |
Given a URL, try to return its associated region, bucket, and key names based on this object's endpoint info as well as all S3 endpoints given in the configuration. | def resolve_url_to_location(self, url):
parsed_url = six.moves.urllib.parse.urlparse(url)
if not parsed_url.scheme:
parsed_url = six.moves.urllib.parse.urlparse('http://' + url)
parsed_own_url = six.moves.urllib.parse.urlparse(self.endpoint)
bucket, key = self.__match_path(pa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_config_for_bucket(self, base_url, extra_configurations=None):\n\n warnings.warn(\n \"Use backend_config.bucket_config.BucketList.get_config_for_uri\",\n DeprecationWarning,\n )\n configs = S3BucketConfig.from_list(self.get(\"sdk.aws.s3.credentials\", []))\n ... | [
"0.6745669",
"0.6435143",
"0.62151855",
"0.5942863",
"0.59099114",
"0.59099114",
"0.5813266",
"0.5788725",
"0.5757353",
"0.5635177",
"0.56279933",
"0.5561384",
"0.55386037",
"0.55036676",
"0.5499892",
"0.54674625",
"0.54305124",
"0.54026806",
"0.5393976",
"0.53860795",
"0.536... | 0.78750426 | 0 |
Get a presigned URL for this request that expires after a given number of seconds. | def get_presigned_url2(self, timeout):
# requestbuilder 0.3
self.preprocess()
if self.__should_use_sigv4():
# UNSIGNED-PAYLOAD is a magical string used for S3 V4 query auth.
auth = requestbuilder.auth.aws.QueryHmacV4Auth.from_other(
self.auth, timeout=time... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def presigned_url(self, url, expiration=3600, force_download=False):\n force_download = \"?force_download=1\" if force_download else \"\"\n public_url = Path(self.config.get(\"public_url\", \"\"))\n resource_url = public_url / url\n return resource_url.as_posix() + force_download",
"d... | [
"0.7014345",
"0.7014051",
"0.6956774",
"0.67384046",
"0.6545521",
"0.6445912",
"0.63070875",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
"0.614691",
... | 0.6424251 | 6 |
construct the P_kd (kappaDelta) matrix such that kappa = P_kd Delta equivalent to equation 31 & 32 in Simon 2009, using Delta = delta/a as in Hu and Keeton 2003 | def construct_P_kd(N1,N2,z_kappa,z_Delta,
cosmo=None,**kwargs):
if cosmo==None:
cosmo = Cosmology(**kwargs)
Nj = len(z_kappa)
Nk = len(z_Delta)
if max(z_Delta) > max(z_kappa):
print "-------"
print "WARNING: construct_P_kd: singular matrix [ min(z_kappa) < min... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_kappa(D):\n\n return -0.5*D*np.log(2*np.pi) + 0.5*np.log(D*np.pi) - 1",
"def em_epsilon_cdp(epsilon, delta, k):\n if delta <= 0:\n return epsilon / k\n else:\n log_delta = np.log(1 / delta)\n return max(\n epsilon / k,\n np.sqrt((8 * log_delta + 8 * epsilon) / k) -\n n... | [
"0.6006358",
"0.5726812",
"0.5607486",
"0.55964506",
"0.5561811",
"0.55442095",
"0.5524946",
"0.5505259",
"0.5502432",
"0.54426163",
"0.537911",
"0.5328839",
"0.5327884",
"0.53229845",
"0.53121215",
"0.53063035",
"0.530413",
"0.52961487",
"0.52916247",
"0.52867043",
"0.528461... | 0.67988276 | 0 |
Show that basic numpy operations with Column behave sensibly | def test_numpy_ops(self):
arr = np.array([1, 2, 3])
c = Column('a', arr)
eq = c == arr
assert np.all(eq)
assert len(eq) == 3
assert type(eq) == Column
assert eq.dtype.str == '|b1'
eq = arr == c
assert np.all(eq)
lt = c - 1 < arr
a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getattr__(self, col):\n return self._obj[col].to_numpy()",
"def _modify_columns(self, cols, X, y=None):",
"def _create_metric_column(\n data: pd.DataFrame,\n column_a: str,\n column_b: str,\n numpy_method: str,\n conjunction: str,\n) -> pd.DataFrame:\n column_operation = getattr(... | [
"0.6132716",
"0.5897211",
"0.57506585",
"0.57329494",
"0.5698936",
"0.5586861",
"0.55237365",
"0.548468",
"0.54814553",
"0.5479346",
"0.5474814",
"0.54733694",
"0.5452359",
"0.54214543",
"0.54059917",
"0.54054594",
"0.53947806",
"0.53844583",
"0.53784984",
"0.5374963",
"0.537... | 0.70421773 | 0 |
Show that the formatted output from str() works | def test_format(self):
MAX_LINES_val = pprint.MAX_LINES()
pprint.MAX_LINES.set(7)
c1 = Column(name='a', data=np.arange(2000), dtype=float,
format='%6.2f')
assert str(c1) == (' a \n-------\n 0.00\n'
' 1.00\n ...\n1998.00\n1999.00')... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_str_(self):\n str(self.standardcode)\n repr(self.standardcode)",
"def test_stringify(self) -> None:\n assert repr(Ok(1)) == str(Ok(1)) == \"Ok(1)\"\n assert repr(Err(1)) == str(Err(1)) == \"Err(1)\"",
"def pretty_str(self) -> str:\n ...",
"def format(self) -> str:"... | [
"0.71492827",
"0.7069855",
"0.6896951",
"0.6828211",
"0.67778987",
"0.6773498",
"0.67273897",
"0.6725174",
"0.6685986",
"0.66774666",
"0.6668654",
"0.66682696",
"0.66526073",
"0.6590294",
"0.658641",
"0.65541434",
"0.6531819",
"0.65306425",
"0.6523459",
"0.6522288",
"0.649797... | 0.0 | -1 |
Testing generators count Pallas's cats | def asd(message):
if message.params[0] != 'test':
for i in range(int(message.params[0])):
yield str(i) + ' ' + morph.parse(
'манул')[0].make_agree_with_number(i).word
time.sleep(0.5)
else:
try:
# print(1)
# server = message.cls.vk_a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_count(self):",
"def Count():\n return CheckForError(lib.Generators_Get_Count())",
"def count():",
"def test_num_circs_shots(self):\n backend = FakeValencia()\n generator = Generator(backend)\n max_experiments = 5\n max_shots = 10\n backend._configuration.max_e... | [
"0.62914926",
"0.62529856",
"0.6237697",
"0.6077749",
"0.60630804",
"0.6033289",
"0.5990379",
"0.596132",
"0.5943474",
"0.5941403",
"0.5940434",
"0.59065664",
"0.58812034",
"0.58721775",
"0.58513904",
"0.58485705",
"0.58479345",
"0.58215755",
"0.5821307",
"0.5818511",
"0.5811... | 0.0 | -1 |
Returns a tuple for zeropadding for 2D convolution with downsampling. Arguments | def correct_pad(backend, inputs, kernel_size):
img_dim = 2 if backend.image_data_format() == 'channels_first' else 1
input_size = backend.int_shape(inputs)[img_dim:(img_dim + 2)]
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size)
if input_size[0] is None:
adjust ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conv2d_backward(d_top, x, w, b, pad, stride):\n raise NotImplementedError\n #######################################################################\n # #\n # #\n ... | [
"0.57575804",
"0.5677426",
"0.561482",
"0.5541626",
"0.54889554",
"0.54487675",
"0.5432683",
"0.54277915",
"0.53716093",
"0.53380644",
"0.53306156",
"0.53285414",
"0.52913517",
"0.52740836",
"0.5271762",
"0.52553904",
"0.5249014",
"0.5216504",
"0.52129334",
"0.5189569",
"0.51... | 0.0 | -1 |
create a database connection to a SQLite database | def create_connection(db_file):
try:
conn = sqlite3.connect(db_file)
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS contacts
(id INTEGER PRIMARY KEY, name text NOT NULL, address text NOT NULL, email text NOT NULL, phone text NOT NULL)''')
e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect_db(self) -> sqlite3.Connection:",
"def create_connection():\n dir_path = os.path.dirname(os.path.abspath(__file__))\n db_file = dir_path + '/py_sqlite.db'\n try:\n DbUtil.connection = sqlite3.connect(db_file)\n except Error as e:\n print(e)\n f... | [
"0.8269675",
"0.8154287",
"0.8153167",
"0.80855244",
"0.8006094",
"0.79054296",
"0.7893724",
"0.78821623",
"0.7875023",
"0.7869494",
"0.7866777",
"0.7866777",
"0.7866777",
"0.7866019",
"0.78544617",
"0.7850683",
"0.78433925",
"0.7838268",
"0.78342485",
"0.78307635",
"0.782310... | 0.0 | -1 |
Returns the number of samples in this signal block | def get_num_samples(self):
return self._num_samples | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _number_of_samples(self):\n return len(self._raw_data.samples)",
"def sample_count(self):\n if self._sample_count:\n return self._sample_count\n else:\n return self._wave.getnframes()",
"def getNrSamples(self): \r\n return self.numSamples",
"def get_nu... | [
"0.83632773",
"0.8249422",
"0.8128629",
"0.81282437",
"0.7986192",
"0.77264965",
"0.7699206",
"0.76810426",
"0.76648474",
"0.762112",
"0.76069885",
"0.7594044",
"0.7592407",
"0.75706035",
"0.7565927",
"0.7559769",
"0.7524813",
"0.7473026",
"0.74660444",
"0.7433234",
"0.743323... | 0.8102221 | 4 |
Method to create embedddings for documents by encoding their image. | def encode(
self,
docs: Optional[DocumentArray] = None,
parameters: dict = {},
*args,
**kwargs,
) -> None:
if not docs:
return
batch_generator = docs.batch(
traversal_paths=parameters.get('traversal_paths', self.traversal_paths),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_image_embeddings(self):\n inception_output = image_embedding.inception_v3(\n self.images,\n trainable=self.train_inception,\n is_training=self.is_training())\n\n # Map inception output onto embedding space.\n with tf.variable_scope(\"image_embedding\") as scope:\n image... | [
"0.5972184",
"0.5909413",
"0.5838329",
"0.5812766",
"0.5676898",
"0.566756",
"0.56353486",
"0.5634795",
"0.5614541",
"0.560931",
"0.55710936",
"0.5562259",
"0.5523897",
"0.5518542",
"0.5506819",
"0.5501866",
"0.54833275",
"0.54608405",
"0.5443685",
"0.5399601",
"0.5336045",
... | 0.6467108 | 0 |
Test that the digits are classified correctly by a classifier. | def __test_digits(self, X, y, clf):
self.assertEqual(len(X), len(y))
correct = 0
for i in xrange(len(y)):
expected = y[i]
prediction = clf.classify([X[i]])[0]
if expected == prediction:
correct += 1
self.assertGreaterEqual(correct, sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_classify(self):\n classifiers, estimates =\\\n ada_boost.train_dataset(self.larger_matrix,\n self.larger_class_labels,\n 9)\n data_to_classify = [1, 0.5]\n classifications = ada_boost.classify(data_to_cla... | [
"0.6504808",
"0.6338742",
"0.63143575",
"0.62959284",
"0.62814325",
"0.62447387",
"0.61741614",
"0.61499375",
"0.61478704",
"0.61478704",
"0.6142268",
"0.61027217",
"0.6074408",
"0.6066596",
"0.60418713",
"0.60381013",
"0.603409",
"0.6030845",
"0.6029761",
"0.6024929",
"0.601... | 0.7913877 | 0 |
Load an image as grayscale. | def imgfile_to_grayscale(filename):
img = cv2.imread(filename)
return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_to_gray_scale(img):\r\n #reading image\r\n im = Image.open(\"filename\")\r\n\r\n if im.mode != \"L\":\r\n im = im.convert(\"L\")\r\n\r\n return img",
"def grayscale(filename):\r\n image = SimpleImage(filename)\r\n for pixel in image:\r\n luminosity = compute_luminosity... | [
"0.7603708",
"0.7325186",
"0.7322381",
"0.7228985",
"0.7035624",
"0.7020066",
"0.69925356",
"0.69703823",
"0.69242543",
"0.6916675",
"0.69085276",
"0.6878698",
"0.6871946",
"0.6870448",
"0.68381256",
"0.68381256",
"0.67890686",
"0.67610466",
"0.6748764",
"0.67421776",
"0.6711... | 0.680726 | 16 |
Load training data from digits.png | def load_digits(cls):
gray = cls.imgfile_to_grayscale(cls.DIGITS_FILE)
# Now we split the image to 5000 cells, each 20x20 size
cells = [np.hsplit(row, 100) for row in np.vsplit(gray, 50)]
# Make it into a Numpy array. It size will be (50,100,20,20)
x = np.array(cells)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_digits():\n \n images, target = [], []\n for image_file in digit_image_filenames:\n image = cv2.imread(image_file)\n if image is None:\n raise RuntimeError(\"Failed to read the image file '{}'\".format(\n image_file))\n image = cv2.cvtColor(image, cv... | [
"0.708967",
"0.6994615",
"0.6965878",
"0.6935963",
"0.6672894",
"0.66682076",
"0.65903187",
"0.6515253",
"0.64869475",
"0.64020413",
"0.6389433",
"0.6385904",
"0.63520086",
"0.63297844",
"0.632445",
"0.632055",
"0.63086367",
"0.62679493",
"0.6263167",
"0.6256531",
"0.6240129"... | 0.7322466 | 0 |
Using the public method mount to test _get_drive_mount_point_name | def test_get_drive_mount_point_name_unique_id_None(self):
try:
tmpdir = mkdtemp()
root = os.path.join(tmpdir, 'mnt/gluster-object')
drive = 'test'
_init_mock_variables(tmpdir)
gfs._allow_mount_per_server = True
self.assertTrue(gfs.mount... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_googledrive_mounting_point():\n return None",
"def test_get_drive_mount_point_name_unique_id_exists(self):\n try:\n tmpdir = mkdtemp()\n root = os.path.join(tmpdir, 'mnt/gluster-object')\n drive = 'test'\n\n _init_mock_variables(tmpdir)\n ... | [
"0.71002924",
"0.6954491",
"0.6827536",
"0.6759451",
"0.6704294",
"0.66067576",
"0.64347553",
"0.6420853",
"0.63291794",
"0.63125104",
"0.6307517",
"0.62765527",
"0.62722826",
"0.6255401",
"0.61517626",
"0.61497533",
"0.6133829",
"0.6132875",
"0.6093856",
"0.6090868",
"0.6059... | 0.75672483 | 0 |
Using the public method mount to test _get_drive_mount_point_name and the _unique_id is already defined | def test_get_drive_mount_point_name_unique_id_exists(self):
try:
tmpdir = mkdtemp()
root = os.path.join(tmpdir, 'mnt/gluster-object')
drive = 'test'
_init_mock_variables(tmpdir)
gfs._allow_mount_per_server = True
gfs._unique_id = 0
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_drive_mount_point_name_unique_id_None(self):\n try:\n tmpdir = mkdtemp()\n root = os.path.join(tmpdir, 'mnt/gluster-object')\n drive = 'test'\n\n _init_mock_variables(tmpdir)\n gfs._allow_mount_per_server = True\n self.assertT... | [
"0.8202178",
"0.71788365",
"0.69768405",
"0.67600346",
"0.659675",
"0.6513326",
"0.6325042",
"0.62535024",
"0.62465084",
"0.62291175",
"0.61986035",
"0.61748713",
"0.6147795",
"0.61267954",
"0.6116906",
"0.6063496",
"0.6041668",
"0.6027162",
"0.6020935",
"0.6009902",
"0.60019... | 0.7969075 | 1 |
map key names in settings file to key names in HotKeys | def _translate_keyname(inp):
convert = {'Equal': '=', 'Escape': 'Esc', 'Delete': 'Del', 'Return': 'Enter',
'Page_up': 'PgUp', 'Page_down': 'PgDn'}
if inp in convert:
out = convert[inp]
else:
out = inp
return out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setupKeyMappings(self) :\n\t\t# first create keyMap object with default values\n\t\tself.keyMap = { \"left\":0, \"right\":0, \\\n\t\t\t\t\"forward\":0, \"backward\":0, \"dash\":0, \\\n\t\t\t\t\"slide-left\":0, \"slide-right\":0, \\\n \t\t\t\t\"cam-up\":0, \"cam-down\":0, \\\n\t\t\t\t\"cam-left\":0, \"cam-right... | [
"0.6528743",
"0.6146608",
"0.6123254",
"0.6027307",
"0.5847011",
"0.5746393",
"0.5736031",
"0.57267994",
"0.5722244",
"0.57204807",
"0.5720454",
"0.56882066",
"0.56624234",
"0.5647908",
"0.56392825",
"0.5626465",
"0.5624188",
"0.5606162",
"0.55517554",
"0.5546671",
"0.5544279... | 0.0 | -1 |
lees de keyboard definities uit het/de settings file(s) van het tool zelf en geef ze terug voor schrijven naar het csv bestand | def buildcsv(settnames, page, showinfo=True):
shortcuts = collections.OrderedDict()
fdesc = ("File containing keymappings", "File containing command descriptions")
## pdb.set_trace()
for ix, name in enumerate(settnames):
try:
initial = page.settings[name]
except KeyError:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dataSave():\n # NR5G = gui_reader()\n try: #Python3\n f = open(__file__ + \".csv\",'wt', encoding='utf-8')\n except:\n f = open(__file__ + \".csv\",'wb')\n f.write('%s,'%(entryCol.entry0.get()))\n f.write('%s,'%(entryCol.entry1.get()))\n f.write('%s,'%(entryCol.entry2.get()))\n ... | [
"0.58790845",
"0.57085",
"0.56216234",
"0.5558641",
"0.5554129",
"0.5521753",
"0.54928106",
"0.54494387",
"0.54430705",
"0.54299235",
"0.5418328",
"0.54119194",
"0.5343505",
"0.5333444",
"0.532513",
"0.53054166",
"0.53034294",
"0.5302163",
"0.5286992",
"0.52804625",
"0.528005... | 0.63631856 | 0 |
specifics for extra panel | def add_extra_attributes(win):
## print(win.__dict__)
win.keylist += ['Num' + x for x in string.digits] + ['>', '<']
win.contextslist = win.otherstuff['contexts']
win.contextactionsdict = win.otherstuff['actionscontext']
win.actionslist = win.otherstuff['actions']
win.descriptions = win.otherstu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hdri_adding_panel(self, context):\r\n \r\n AM = context.window_manager.asset_m\r\n layout = self.layout\r\n \r\n box = layout.box()\r\n row = box.row()\r\n row.prop(AM, \"existing_thumb\", text = \"Use existing Thumbnails\")\r\n \r\n row = box.row()\r\n row.label(\"Thumbnail exte... | [
"0.6022063",
"0.60132813",
"0.59916925",
"0.5965154",
"0.5920031",
"0.5918263",
"0.5831781",
"0.5825664",
"0.5774073",
"0.575796",
"0.5717596",
"0.56632066",
"0.56539726",
"0.564417",
"0.5628545",
"0.56106246",
"0.56101924",
"0.5604618",
"0.5584738",
"0.55847",
"0.5523856",
... | 0.0 | -1 |
Returns the graph complement of G. | def complement(G):
R = G.__class__()
R.add_nodes_from(G)
R.add_edges_from(((n, n2)
for n, nbrs in G.adjacency()
for n2 in G if n2 not in nbrs
if n != n2))
return R | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complement(G):\n\n nset = set(G.nodes())\n n_nodes = G.order()\n n_edges = n_nodes * (n_nodes - 1) - G.size() + 1\n \n cmp_edges = ((u, v) for u in G.nodes()\n\t\t for v in nset - set(G.successors(u)))\n deg = make_deg(n_nodes, cmp_edges)\n cmp_edges = ((u, v) for u in G.nodes()\n\t\t ... | [
"0.8520391",
"0.64977443",
"0.64359856",
"0.6389732",
"0.6337305",
"0.62457705",
"0.6208965",
"0.60704297",
"0.60221344",
"0.59848946",
"0.5968795",
"0.5959875",
"0.59059066",
"0.589863",
"0.5895703",
"0.5880331",
"0.5879972",
"0.58771133",
"0.58479995",
"0.58314204",
"0.5827... | 0.8689336 | 0 |
Returns the reverse directed graph of G. | def reverse(G, copy=True):
if not G.is_directed():
raise nx.NetworkXError("Cannot reverse an undirected graph.")
else:
return G.reverse(copy=copy) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_reversed_graph(directed_graph):\n\n reversed = directed_graph.__class__()\n for i in directed_graph.get_vertices().keys():\n reversed.add_vertex(i)\n\n for i in directed_graph.get_vertices().keys():\n vertex = directed_graph.get_vertex(i)\n for j in vertex.get_heads():\n ... | [
"0.7581556",
"0.75628114",
"0.7346692",
"0.70150614",
"0.6863429",
"0.65913934",
"0.64809585",
"0.64787996",
"0.643853",
"0.63429785",
"0.6326808",
"0.62946874",
"0.62814265",
"0.627383",
"0.62476474",
"0.61388195",
"0.6093543",
"0.60812247",
"0.6077702",
"0.60706794",
"0.605... | 0.83771706 | 0 |
Reads an INIfile containing domain type definitions and fills them into a TypeDefsobject. | def readDomainTypes(self, domainTypeFilePath):
result = TypeDefs()
inifile = IniFile(domainTypeFilePath)
for section in inifile.getSections():
if section.endswith("(n)"):
td = TypeDef(section[:-3], withLength = True)
else:
td = TypeDef(sect... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_domain(self, domainfile):\n\n with open(domainfile) as dfile:\n dfile_array = self._get_file_as_array(dfile)\n #Deal with front/end define, problem, :domain\n if dfile_array[0:4] != ['(', 'define', '(', 'domain']:\n print('PARSING ERROR: Expected (define (domain ... at start of domain ... | [
"0.5692447",
"0.56770426",
"0.55325735",
"0.5285232",
"0.50543946",
"0.4953483",
"0.4840075",
"0.48231658",
"0.48218992",
"0.4821366",
"0.47643015",
"0.47455364",
"0.4673499",
"0.4642409",
"0.46207514",
"0.46002218",
"0.4586546",
"0.4583959",
"0.45795232",
"0.4578879",
"0.455... | 0.7333319 | 0 |
write code back to directory, not the spec code | def writeCode(self,dir=""):
for codefile in self.codestructures:
codefile.writeCode(self.codeformatter,dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writeToFile(self, basedir, write_code=0):",
"def writeSpec(self,dir=\"\"):\n for codestruct in self.codestructures:\n codestruct.writeSpec(dir)",
"def write(self):\n # # Sometimes file is not written properly. So delete and rewrite it\n # os.system('rm {}'.format(snip_dir + ... | [
"0.7297589",
"0.6704298",
"0.6461738",
"0.6427127",
"0.6356845",
"0.62959635",
"0.6293319",
"0.62514675",
"0.6150123",
"0.6071547",
"0.60249424",
"0.59400237",
"0.58946234",
"0.58486485",
"0.57936925",
"0.5743563",
"0.57151085",
"0.56687397",
"0.56618214",
"0.5659408",
"0.565... | 0.6584302 | 2 |
write spec back to directory, (if dir not specified is default spec dir) | def writeSpec(self,dir=""):
for codestruct in self.codestructures:
codestruct.writeSpec(dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_change_dir_to_file(self):\n dir0, dir1 = self.make_temp_dirs(2)\n self.write_dir(dir0, \"foo\")\n self.sync_all()\n self.assertDirPresent(dir0, \"foo\")\n self.assertDirPresent(dir1, \"foo\")\n\n self.delete_dir(dir0, \"foo\")\n self.write_file(dir0, \"foo\... | [
"0.59088475",
"0.5767855",
"0.56413054",
"0.54185474",
"0.53972125",
"0.5375559",
"0.5354217",
"0.5278695",
"0.5238211",
"0.52255595",
"0.5221744",
"0.5195567",
"0.5194306",
"0.5183714",
"0.51730597",
"0.5154644",
"0.51394916",
"0.51373714",
"0.5113076",
"0.5090471",
"0.50892... | 0.658977 | 0 |
read spec code and populate codestructures | def processSpecs(self):
specSubDirName="_spec"
codestructure = CodeStructure()
for dir in self._dirs:
if q.system.fs.exists(q.system.fs.joinPaths(dir,specSubDirName)):
files=q.system.fs.listPyScriptsInDir(q.system.fs.joinPaths(dir,specSubDirName))
for ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processSourceCode(self):\n specSubDirName=\"\"\n codestructure = CodeStructure() \n for dir in self._dirs:\n if q.system.fs.exists(q.system.fs.joinPaths(dir,specSubDirName)): \n files=q.system.fs.listPyScriptsInDir(q.system.fs.joinPaths(dir,specSubD... | [
"0.6069532",
"0.602706",
"0.5843338",
"0.5768507",
"0.5756641",
"0.571061",
"0.5692741",
"0.5651732",
"0.55933094",
"0.5569754",
"0.5564679",
"0.5557777",
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"0.5437613",
"0.5425164",
"0.535459",
"0.53148675",
"0.529252",
"0.5287907",
"0... | 0.65098464 | 0 |
read source code and populate codestructures | def processSourceCode(self):
specSubDirName=""
codestructure = CodeStructure()
for dir in self._dirs:
if q.system.fs.exists(q.system.fs.joinPaths(dir,specSubDirName)):
files=q.system.fs.listPyScriptsInDir(q.system.fs.joinPaths(dir,specSubDirName))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(source_code):\n tokens = tokenize(source_code)\n return read(tokens)",
"def parse_main(self):\n self.decompiler = self.createDecompiler(self.compilerEnv)\n self.nf = IRFactory(self)\n self.currentScriptOrFn = self.nf.createScript()\n sourceStartOffset = self.decompiler... | [
"0.71463597",
"0.65457433",
"0.6436156",
"0.63833",
"0.617009",
"0.6169049",
"0.61645854",
"0.6013546",
"0.5984505",
"0.5978225",
"0.597422",
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"0.5847921",
"0.58116406",
"0.57947946",
"0.5789077",
"0.5787705",
"0.5781246",
... | 0.6231014 | 4 |
todo connect to db, insert data, handle errors | def add_to_db(name, country, catches):
try:
with sqlite3.connect(db_name) as db:
cur = db.cursor()
create_table()
cur.execute('insert into chainsaw values (?,?,?)', (name, country, catches))
except sqlite3.Error as er:
print('Changes being rolled back because ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_data(self):\n # Make a connexion with a mock database\n self.generate_data_collection()",
"def insert_db():\n populate_tables()",
"def insert_data():\n\tBase.metadata.drop_all(engine)\n\tBase.metadata.create_all(engine)\n\tu1 = insert_user(\"233@B.com\", \"/static/image/avatar.JPG\"... | [
"0.7211377",
"0.693141",
"0.6857348",
"0.67883915",
"0.67747855",
"0.66680896",
"0.65844685",
"0.6545915",
"0.6525444",
"0.64385474",
"0.6436744",
"0.6404376",
"0.63957894",
"0.63482755",
"0.6333015",
"0.6330649",
"0.63280785",
"0.6319391",
"0.6316546",
"0.6313329",
"0.630663... | 0.0 | -1 |
If the pipe is too short, the HagenPoiseuille equation may result in unphysically high flow rates; the flow | def bern_max_metric(pipe_diameter, delta_p):
rho = 1000 # density of water kg/m^3
flow_rate_max = ((math.pi * (pipe_diameter**2)) / 4) * math.sqrt((2 * delta_p) / rho)
return flow_rate_max | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Piping(T_in, p_in, m_dot, d_inner, l_pipe, f, epsilon_pipe, T_shield, N):\r\n\r\n ## Estimation of the influence of the arcs\r\n # Calculation according to VDI Heatatlas 2013\r\n # Assumption isoenthalpic flow\r\n state_Arc = FlowRestriction(T_in, p_in, m_dot, d_inner, f)\r\n p_Arc = state_Arc.g... | [
"0.6446182",
"0.60636187",
"0.6003516",
"0.59648126",
"0.5854791",
"0.5832429",
"0.5788334",
"0.54462",
"0.54144984",
"0.5404328",
"0.5350867",
"0.5328617",
"0.52919537",
"0.5287042",
"0.52654266",
"0.52486366",
"0.5230265",
"0.5214893",
"0.5213517",
"0.5200162",
"0.5170976",... | 0.5857332 | 4 |
If the flow in the pipe is laminar, you can use the Poiseuille Equation to calculate the flow rate mu = 0.001 @ 25 degrees C Q = (pi (D4) delta_p) / (128 mu pipe_length) | def pois_metric(pipe_diameter, delta_p, pipe_length):
mu = 0.001 # water @ 25 degrees C
pois = mu * 10
flow_rate_lam = (math.pi * (pipe_diameter ** 4) * delta_p) / (128 * pois * pipe_length)
return flow_rate_lam | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bern_metric(pipe_diameter, delta_p, pipe_length):\n fr_c = 0.003 # assuming Reynolds number is 10**5 and pipe material is smooth copper\n fr_reyn = 0.046 / (reynolds_num(pipe_diameter, delta_p, pipe_length) ** 0.2) # Taitel and Dukler approximation\n rho = 1000 # density of water @ 4 deg celsius (k... | [
"0.64306885",
"0.6375345",
"0.6155883",
"0.6086178",
"0.5807599",
"0.57985467",
"0.5757301",
"0.5742598",
"0.57329386",
"0.57231694",
"0.5673357",
"0.56649464",
"0.56552863",
"0.55984855",
"0.5589797",
"0.55843973",
"0.55824184",
"0.5581834",
"0.55515593",
"0.5546592",
"0.554... | 0.7763178 | 0 |
For turbulent flow, we can use Bernoulli's Equation with a friction term. Assuming the pipe is horizontal, (delta_p / rho) + (velocity^2 / 2) = F where F accounts for friction heating and is given in terms of an empirical friction factor, fr. | def bern_metric(pipe_diameter, delta_p, pipe_length):
fr_c = 0.003 # assuming Reynolds number is 10**5 and pipe material is smooth copper
fr_reyn = 0.046 / (reynolds_num(pipe_diameter, delta_p, pipe_length) ** 0.2) # Taitel and Dukler approximation
rho = 1000 # density of water @ 4 deg celsius (kg/m**3)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eval_dryfriction():\n # Environment\n env = WAMBallInCupSim(num_dof=7, max_steps=1500)\n\n # Policy (random init)\n policy_hparam = dict(num_feat_per_dim=12, bounds=(np.array([0.0]), np.array([1.0])))\n policy = DualRBFLinearPolicy(env.spec, policy_hparam, dim_mask=2)\n\n # Do the rolllouts\n... | [
"0.58735925",
"0.58659214",
"0.58183104",
"0.5818074",
"0.5753325",
"0.5712705",
"0.5690621",
"0.5683825",
"0.56798863",
"0.5655784",
"0.56407523",
"0.5617837",
"0.56171685",
"0.5589888",
"0.5564685",
"0.5555104",
"0.555289",
"0.5549329",
"0.5536939",
"0.55097634",
"0.5494157... | 0.58481055 | 2 |
Serve a file from the upload directory. | def download(path):
return send_from_directory(UPLOAD_DIRECTORY, path, as_attachment=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def uploaded_file(filename):\n return send_from_directory('/static/images/uploads/', filename)",
"def uploaded_file(filename):\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)",
"def uploaded_file(filename):\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)",
"de... | [
"0.72789335",
"0.7054051",
"0.7054051",
"0.7054051",
"0.7007223",
"0.69452924",
"0.6715913",
"0.6583728",
"0.65780145",
"0.65292954",
"0.63780475",
"0.6367171",
"0.62917274",
"0.6272483",
"0.62396723",
"0.62340975",
"0.6162114",
"0.6161394",
"0.613007",
"0.61287713",
"0.60720... | 0.561204 | 64 |
Create a Plotly Dash 'A' element that downloads a file from the app. | def file_download_link(filename):
location = f"/{UPLOAD_DIRECTORY}/{filename}"
return html.A(filename, href=location) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def file_download_link(filename):\n location = \"/download/{}\".format(urlquote(filename))\n return html.A(filename, href=location)",
"def download_link(request, job_id, filename):\n template_values = remote_view_util.fill_job_values(request, job_id)\n template_values = remote_view_util.fill_template... | [
"0.6015341",
"0.56226736",
"0.55951804",
"0.552248",
"0.5511452",
"0.5382601",
"0.53783137",
"0.53105325",
"0.5288451",
"0.52870387",
"0.5222768",
"0.5213722",
"0.5212254",
"0.5193048",
"0.5184498",
"0.5182813",
"0.51763964",
"0.51701194",
"0.5161087",
"0.5158966",
"0.515461"... | 0.61712605 | 0 |
Ground a node and look it up in the db | def query (node, grounding, db):
return db.funcVal(grounding.groundNode(node)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def node_lookup(self, path, for_update=False):\n\n q = \"select node from nodes where path = ?\"\n self.execute(q, (path,))\n r = self.fetchone()\n if r is not None:\n return r[0]\n return None",
"def get_node(self, key: str) -> Node:",
"def get_node(cur, id):\n ... | [
"0.71134365",
"0.6668859",
"0.65132576",
"0.6439901",
"0.635995",
"0.635122",
"0.62648284",
"0.62508345",
"0.6188875",
"0.6179738",
"0.6151343",
"0.6136216",
"0.61359334",
"0.61337614",
"0.61033213",
"0.609807",
"0.6092274",
"0.6070642",
"0.6068153",
"0.6040271",
"0.6040146",... | 0.63679963 | 4 |
Return a grounded node, with the value for its functor according to db | def fillNode(node, grounding, db):
gn = copy.deepcopy(node)
gn.val = query(gn, grounding, db)
return gn | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query (node, grounding, db):\n return db.funcVal(grounding.groundNode(node))",
"def _expr2bddnode(expr):\n\t# print(\"_expr2bddnode\")\n\tif expr.is_zero():\n\t\treturn BDDNODEZERO\n\telif expr.is_one():\n\t\treturn BDDNODEONE\n\telse:\n\t\ttop = expr.top\n\n\t\t# Register this variable\n\t\t_ = bddvar(to... | [
"0.6327783",
"0.5721543",
"0.552557",
"0.54853195",
"0.53288656",
"0.5328032",
"0.52322",
"0.52084863",
"0.5185801",
"0.5182241",
"0.51591873",
"0.5155981",
"0.51427555",
"0.51391935",
"0.5104471",
"0.50723195",
"0.5068302",
"0.50631833",
"0.50392216",
"0.50341773",
"0.502435... | 0.5392836 | 4 |
Locate the value for a grounded node and its parents in a rule set, return 1 if not found. For functors with binary ranges, when all parents match but child's value does not, return 1prob for other value. | def ruleMatch (ruleSet, node, parents):
def getProb (node):
for rule in ruleSet:
#print rule
if (rule.child.eq(node) and
len(rule.parentList)==len(parents) and
all([n[0].eq(n[1]) for n in zip(rule.parentList,parents)])):
#print "winning... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getValue(self):\n r = 1 if self.left.getValue() <= self.right.getValue() else 0\n return r",
"def find(self, node):\n if not node:\n return 0\n\n left = self.find(node.left)\n right = self.find(node.right)\n cur = 1 # node.val\n path = 1\n i... | [
"0.5753097",
"0.5747222",
"0.57390654",
"0.57036936",
"0.56338394",
"0.55915225",
"0.5584634",
"0.5555824",
"0.5537557",
"0.5537557",
"0.5511906",
"0.54953927",
"0.5484709",
"0.5481439",
"0.5480224",
"0.5455876",
"0.5380829",
"0.53786016",
"0.53774273",
"0.537409",
"0.5355800... | 0.62124074 | 0 |
Return default uniform distribution for the range of a functor | def default(functor):
return 1.0/functorRangeSize(functor) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _uniform(val_range):\r\n return np.random.uniform(val_range[0], val_range[1])",
"def initializeDistribution(self):\n self.minVal = min(math.exp(self.upperBound),math.exp(self.lowerBound))\n self.maxVal = max(math.exp(self.upperBound),math.exp(self.lowerBound))",
"def glorot_uniform(seed=None):\n ... | [
"0.67329484",
"0.63917905",
"0.634777",
"0.63349366",
"0.61998284",
"0.61475074",
"0.61413395",
"0.6124729",
"0.6119773",
"0.61055756",
"0.6086929",
"0.6079064",
"0.60665655",
"0.6043183",
"0.6039257",
"0.5985008",
"0.5984233",
"0.59805816",
"0.59690744",
"0.59467566",
"0.594... | 0.7167602 | 0 |
Look up the range for a functor | def functorRange(functor):
for (name, range) in functorRangeList:
if functor == name:
return range
else:
raise Exception ("Functor " + functor + " not present in range list") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _in_range_op(spec):",
"def _range_func(self, withscores, score_cast_func, decode_value_func=lambda x: x):\n if withscores:\n return lambda score_member: (decode_value_func(score_member[1]), score_cast_func(self._encode(score_member[0]))) # noqa\n else:\n return lambda sco... | [
"0.68109703",
"0.63385564",
"0.6327256",
"0.6227952",
"0.62047696",
"0.61920094",
"0.61834943",
"0.6165325",
"0.6165325",
"0.61133087",
"0.61056334",
"0.6076185",
"0.60739946",
"0.60496026",
"0.6020257",
"0.60012704",
"0.6000408",
"0.5993944",
"0.5981103",
"0.5969238",
"0.591... | 0.839487 | 0 |
Return cardinality of range for a functor | def functorRangeSize(functor):
return len(functorRange(functor)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cardinality(self):\n estimate = self._alpha * math.pow(self._m, 2) / sum(math.pow(2, -x) for x in self._registers)\n\n if estimate <= 2.5 * self._m:\n # get number of registers equal to zero\n empty_registers = self._registers.count(0)\n if empty_registers != 0:\n... | [
"0.6364935",
"0.6203069",
"0.6184483",
"0.6088533",
"0.5999436",
"0.5949017",
"0.5914987",
"0.5850817",
"0.5838362",
"0.58077663",
"0.57850456",
"0.56294644",
"0.55813044",
"0.5576867",
"0.5546719",
"0.55258554",
"0.55099475",
"0.5464677",
"0.5460476",
"0.54486597",
"0.543011... | 0.799879 | 0 |
For functors with a binary range, return the other element | def functorOtherValue(functor, val):
range = functorRange(functor)
assert len(range) == 2
if val == range[0]:
return range[1]
else:
return range[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def functorRange(functor):\n for (name, range) in functorRangeList:\n if functor == name:\n return range\n else:\n raise Exception (\"Functor \" + functor + \" not present in range list\")",
"def ranges(self, predicate):\n\n x = np.zeros(len(self)).astype(np.bool)\n f... | [
"0.6828715",
"0.6507789",
"0.62094086",
"0.6026386",
"0.5969396",
"0.5849859",
"0.5836691",
"0.58243334",
"0.57149607",
"0.5674531",
"0.5671843",
"0.56514573",
"0.56158537",
"0.56145406",
"0.55590016",
"0.55359095",
"0.5534223",
"0.5493425",
"0.54904515",
"0.5454143",
"0.5448... | 0.75341356 | 0 |
Return the atoms, derived from the first entry in the joint probability table | def atomList(joints):
assert len(joints) > 0
first = joints[0]
functorList = first[1][:-2] # Second element of row, last two elements of that are joint prob and log prob
atomList = []
for (node,_) in functorList:
atomList.append(node.functor+"("+",".join(node.varList)+")")
return atomLis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def jointProbabilities(constants, db, ruleList, bn):\n vars = bn.variableList()\n combs = generateCombos(vars, constants)\n joints = []\n for grounding in combs:\n joints.append((grounding, bn.jointProbs(grounding, db, ruleList)))\n return (vars, atomList(joints), joints)",
"def enumerate_j... | [
"0.594754",
"0.5753266",
"0.57007027",
"0.5424559",
"0.539321",
"0.5265403",
"0.52582085",
"0.52560824",
"0.5244419",
"0.5229816",
"0.5205455",
"0.51626754",
"0.51596713",
"0.5148809",
"0.51186013",
"0.51104856",
"0.51007",
"0.50988847",
"0.50907356",
"0.5085001",
"0.50847787... | 0.63074166 | 0 |
Compute the joint probabilities for all combinations of values | def jointProbabilities(constants, db, ruleList, bn):
vars = bn.variableList()
combs = generateCombos(vars, constants)
joints = []
for grounding in combs:
joints.append((grounding, bn.jointProbs(grounding, db, ruleList)))
return (vars, atomList(joints), joints) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def joint_proba(self, X):\n return self.weights * self._bernoulli(X)",
"def joint_prob(network, assignment):\n prob = 1\n for a_key in assignment:\n conditions = []\n current = network[a_key]\n for parent in current['Parents']:\n conditions.append(True) if assignment[... | [
"0.6887764",
"0.6807377",
"0.6577042",
"0.6448017",
"0.64271957",
"0.6388704",
"0.621264",
"0.61288685",
"0.6084318",
"0.60133076",
"0.59769166",
"0.59629256",
"0.5954798",
"0.5925714",
"0.5911252",
"0.5900835",
"0.5882425",
"0.5880176",
"0.5865823",
"0.5850093",
"0.580452",
... | 0.6838733 | 1 |
Generate all possible groundings (assignments of constants to variables) | def generateCombos(vars,constants):
# SUPER NOT GENERALIZED---TOO LATE AT NIGHT FOR ME TO DO RECURSIVE ALGORITHMS
assert len(vars) == 2 and len(constants) == 2
combs = []
for c1 in constants:
for c2 in constants:
combs.append(Grounding([(vars[0], c1), (vars[1], c2)]))
return comb... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ground_operator(self, op_name):\n op = self.domain.operators[op_name]\n self._set_operator_groundspace( op_name, op.variable_list.items() )\n for ground in self._instantiate( op_name ):\n # print('grounded', ground)\n st = dict(ground)\n gop = Operator(op_n... | [
"0.586086",
"0.5699041",
"0.5360215",
"0.53582025",
"0.53362375",
"0.53046995",
"0.5301871",
"0.52834463",
"0.521038",
"0.51668525",
"0.5156945",
"0.51415384",
"0.5131329",
"0.51303786",
"0.51272744",
"0.51083404",
"0.51081413",
"0.5107868",
"0.50947726",
"0.5074924",
"0.5066... | 0.62475884 | 0 |
` Given a joint probability table, format it for LaTeX. This function will have to be tailored for every paper. This function simply generates the {tabular} part of the table. The prologue and epilogue, including the caption and label, must be specified in the including file. | def formatJointTableForLaTeX(joints):
(varList, atoms, probs) = joints
cols = len(varList) + len (probs[0][1])
with open("table1.tex","w") as out:
out.write ("\\begin{tabular}{|" + "|".join(["c"]*(cols-2))+"||c|c|}\n")
out.write ("\\hline\n")
# Table header
out.write (" & ".j... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_latex_table(true_hmm, sampled_hmm_list, conf=0.95, dt=1, time_unit='ms', obs_name='force', obs_units='pN', outfile=None):\n\n # confidence interval\n for sampled_hmm in sampled_hmm_list:\n sampled_hmm.set_confidence(conf)\n # dt\n dt = float(dt)\n # nstates\n nstates = sampled... | [
"0.6535216",
"0.6499372",
"0.6494908",
"0.6360739",
"0.6058271",
"0.605461",
"0.60403585",
"0.6008605",
"0.59048015",
"0.5904452",
"0.59012544",
"0.5884655",
"0.58371955",
"0.5809221",
"0.5790608",
"0.57705444",
"0.576766",
"0.57066625",
"0.5678048",
"0.56749755",
"0.5656739"... | 0.8194145 | 0 |
Take replay file from request Extract basic information about replay Return information to client or tell client data is invalid | async def accept_replay(request, game):
game = game.lower()
replay_file = request.files.get('replay')
if replay_file:
if game == STARCRAFT:
load_map = request.args.get("load_map", False)
result = await SC2Replay.analyze_replay(replay_file, load_map)
if result:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def parse_replay(request, game):\n\n game = game.lower()\n replay_file = request.files.get(\"replay\")\n if replay_file:\n if game == STARCRAFT:\n basic, result = await SC2Replay.process_replay(replay_file, request.args.get(\"load_map\", False))\n if result:\n ... | [
"0.66388625",
"0.6589973",
"0.561222",
"0.55880296",
"0.55790913",
"0.54335433",
"0.53088397",
"0.5289022",
"0.52817225",
"0.52215827",
"0.51933885",
"0.51402473",
"0.5130604",
"0.5128931",
"0.5114549",
"0.5101588",
"0.50950605",
"0.50752",
"0.50751144",
"0.50603235",
"0.5057... | 0.6063962 | 2 |
Take replay file that was uploaded to ObjectStore and process data and store it to database and link to account | async def parse_replay(request, game):
game = game.lower()
replay_file = request.files.get("replay")
if replay_file:
if game == STARCRAFT:
basic, result = await SC2Replay.process_replay(replay_file, request.args.get("load_map", False))
if result:
# Lets creat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_to_restore(file_name, data):\n urlfetch.fetch(url=config.RESTORE_URL + '?name=' + file_name + '&source=db&packet',\n payload=urllib.urlencode({\"data\": services.event.entity_to_string(data)}),\n method=urlfetch.POST)",
"def transfer(file_obj):",
"def post_origin... | [
"0.59654164",
"0.5942923",
"0.58948725",
"0.58847404",
"0.5826352",
"0.57525676",
"0.5647753",
"0.56387746",
"0.5543896",
"0.55360585",
"0.5521623",
"0.5506403",
"0.5493576",
"0.5484211",
"0.54712117",
"0.54331356",
"0.5429796",
"0.53877634",
"0.5359096",
"0.5353566",
"0.5341... | 0.62600714 | 0 |
_execute_ map the request name provided to an ID | def execute(self, requestName, conn = None, trans = False):
self.sql = "SELECT request_id from reqmgr_request WHERE "
self.sql += "request_name=:request_name"
binds = {"request_name": requestName}
reqID = self.dbi.processData(self.sql, binds, conn = conn, transaction = trans)
res... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _execute(self, _):\r\n pass",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):",
"def getId(*args):"... | [
"0.55304587",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5463426",
"0.5370444",
"0.5351898",
"0.53309184",
"0.5321808",
"0.5240275",
"0.5239346",
"0.52391875",
"0.52137643",... | 0.5936447 | 0 |
Update the matrix with the coordinates of the operation. | def update_matrix(self, ope, mat):
ope_coord = []
for coord in self.coord_name:
if np.isnan(ope[coord]):
return
ope_coord.append(int(ope[coord]))
mat[tuple(ope_coord)] += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(mat) -> np.ndarray:\n return mat",
"def update(self, row, col):\n self.update_m(row, col)\n self.update_ix(row, col)\n self.update_iy(row, col)",
"def _UpdateCoords(self, new_coords):\n for i in range(self.mol.n_atoms):\n for j in range(const.NUMDIM):\n self.mo... | [
"0.64133954",
"0.6272482",
"0.6020012",
"0.60073805",
"0.59557956",
"0.5955312",
"0.5928968",
"0.5921737",
"0.590957",
"0.586239",
"0.58351856",
"0.58159703",
"0.579463",
"0.5779063",
"0.57384187",
"0.57379836",
"0.56909096",
"0.56815416",
"0.56763625",
"0.5650514",
"0.562645... | 0.6219569 | 2 |
Use float16 for faster IO during training. | def save_float16_npy(data, path):
np.save(path, data.astype(np.float16)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_use_bfloat16(configs, use_bfloat16):\n configs[\"train_config\"].use_bfloat16 = use_bfloat16",
"def data_type():\n if FLAGS.use_fp16:\n return tf.float16\n else:\n return tf.float32",
"def data_type():\n if FLAGS.use_fp16:\n return tf.float16\n else:\n return tf.float32... | [
"0.69121385",
"0.65502363",
"0.65130097",
"0.647946",
"0.6455792",
"0.6408274",
"0.63850254",
"0.6354032",
"0.63375044",
"0.63351613",
"0.6293508",
"0.62589705",
"0.6142071",
"0.6129017",
"0.60476065",
"0.6029137",
"0.59642065",
"0.59619236",
"0.5940161",
"0.5863883",
"0.5842... | 0.7207755 | 0 |
Return if Persona object passed into args is in defaul componenti propperty | def has_componente(self, persona):
return True if persona.pk in self.pks_componenti else False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def XCAFDoc_ShapeTool_IsComponent(*args):\n return _XCAFDoc.XCAFDoc_ShapeTool_IsComponent(*args)",
"def IsComponent(*args):\n return _XCAFDoc.XCAFDoc_ShapeTool_IsComponent(*args)",
"def __contains__(self, arg):\r\n\r\n return arg in self.grfx[0]",
"def particleExists(*args, **kwargs)->bool:\n p... | [
"0.6626677",
"0.6543731",
"0.6026395",
"0.60095984",
"0.59585935",
"0.5927557",
"0.5893944",
"0.5893944",
"0.58926296",
"0.5830509",
"0.5820323",
"0.57275677",
"0.5694341",
"0.56732976",
"0.5672737",
"0.5656124",
"0.5637087",
"0.5625537",
"0.5621446",
"0.5609848",
"0.5569029"... | 0.71104115 | 0 |
If an bundle is for native iOS, it has these properties in the Info.plist | def is_info_plist_native(plist):
return (
'CFBundleSupportedPlatforms' in plist and
'iPhoneOS' in plist['CFBundleSupportedPlatforms']
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ios_app_info(self) -> Optional[pulumi.Input['IosAppInfoArgs']]:\n return pulumi.get(self, \"ios_app_info\")",
"def system_properties(self):\r\n return dict(self._get_system_properties(self.java))",
"def ios_app_info(self) -> 'outputs.IosAppInfoResponse':\n return pulumi.get(self, \"ios_app... | [
"0.550685",
"0.5398409",
"0.5094902",
"0.5077413",
"0.5070626",
"0.505988",
"0.5054438",
"0.49296305",
"0.49282697",
"0.48895228",
"0.48720244",
"0.48373243",
"0.48084444",
"0.48046267",
"0.4804201",
"0.47875956",
"0.47442275",
"0.47442275",
"0.47250745",
"0.4656228",
"0.4646... | 0.6288776 | 0 |
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