0,\\n vs.msa_rz[2:-2, 2:-2, :, :]\\n * (vs.sa_rz[2:-2, 2:-2, :, :] / (vs.sa_rz[2:-2, 2:-2, :, :] + vs.sa_ss[2:-2, 2:-2, :, :]))\\n + vs.msa_ss[2:-2, 2:-2, :, :]\\n * (vs.sa_ss[2:-2, 2:-2, :, :] / (vs.sa_rz[2:-2, 2:-2, :, :] + vs.sa_ss[2:-2, 2:-2, :, :])),\\n 0,\\n ),\\n )\\n vs.msa_s = update(\\n vs.msa_s,\\n at[2:-2, 2:-2, :2, :],\\n npx.where(npx.isnan(vs.msa_s[2:-2, 2:-2, :2, :]), 0, vs.msa_s[2:-2, 2:-2, :2, :]),\\n )\\n vs.csa_s = update(\\n vs.csa_s,\\n at[2:-2, 2:-2, vs.tau, :],\\n transport.conc_to_delta(state, vs.msa_s[2:-2, 2:-2, vs.tau, :]),\\n )\\n vs.C_s = update(\\n vs.C_s,\\n at[2:-2, 2:-2, vs.tau],\\n transport.calc_conc_iso_storage(state, vs.sa_s, vs.msa_s)[2:-2, 2:-2] * vs.maskCatch[2:-2, 2:-2],\\n )\\n vs.C_iso_s = update(\\n vs.C_iso_s,\\n at[2:-2, 2:-2, vs.tau],\\n transport.conc_to_delta(state, vs.C_s[2:-2, 2:-2, vs.tau]) * vs.maskCatch[2:-2, 2:-2],\\n )\\n\\n return KernelOutput(\\n sa_s=vs.sa_s, SA_s=vs.SA_s, msa_s=vs.msa_s, csa_s=vs.csa_s, C_s=vs.C_s, C_iso_s=vs.C_iso_s, M_s=vs.M_s\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc96da78b5db5f4b7a04d60195bf937\",\n \"score\": \"0.51915836\",\n \"text\": \"def _ss(data, n):\\n # Get mean of our dataset\\n c = mean(data)\\n '''\\n foreach item in the dataset, get its difference to the mean (c) - [Hence the deviation]\\n and square the result by the number of derivations (n)\\n Add all of these final results together.\\n '''\\n ss = sum((x - c) ** n for x in data)\\n return ss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f43c158e2d5256267fb64a8c5b36741c\",\n \"score\": \"0.5184169\",\n \"text\": \"def find_clus(cluster,beta): \\n global model\\n G = nx.Graph()\\n totClus = {}\\n n = len(cluster)\\n # print n\\n for i in range(n-1):\\n w1 = cluster[i] \\n for j in range(i+1,n):\\n w2 = cluster[j]\\n\\n cr = cosine_similarity([model[w1]], [model[w2]])[0][0]\\n\\n if cr>= beta:\\n \\n try:\\n cr = cr * cooccurDist(w1,w2)\\n if cr > 0:\\n G.add_edge(w1,w2,weight=cr)\\n G.add_edge(w2,w1, weight=cr)\\n except:\\n pass\\n \\n \\n \\n \\n \\n \\n part = community.best_partition(G)\\n for k, v in part.iteritems():\\n\\n try:\\n totClus[v].append(k)\\n except:\\n totClus[v] = [k]\\n \\n return totClus\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb2e7dc7e6a14c4e7166170de87d3274\",\n \"score\": \"0.5182931\",\n \"text\": \"def LSQ_CJspeed(x,y):\\n #Calculate Sums\\n k = 0\\n X = 0.0; X2 = 0.0; X3 = 0.0; X4 = 0.0;\\n Y = 0.0; Y1 = 0.0; Y2 = 0.0;\\n a = 0.0; b = 0.0; c = 0.0; R2 = 0.0\\n n = size(x)\\n\\n while k < n:\\n X = X + x[k]\\n X2 = X2 + x[k]**2\\n X3 = X3 + x[k]**3\\n X4 = X4 + x[k]**4\\n Y = Y + y[k]\\n Y1 = Y1 + y[k]*x[k]\\n Y2 = Y2 + y[k]*x[k]**2\\n k= k + 1\\n m = float(Y)/float(n)\\n\\n den = (X3*float(n) - X2*X)\\n temp = (den*(X*X2-X3*float(n))+X2*X2*(X*X-float(n)*X2)-X4*float(n)*(X*X-X2*float(n)))\\n temp2 = (den*(Y*X2-Y2*float(n)) + (Y1*float(n)-Y*X)*(X4*float(n)-X2*X2))\\n\\n b = temp2/temp\\n a = 1.0/den*(float(n)*Y1 - Y*X - b*(X2*float(n)-X*X))\\n c = 1/float(n)*(Y - a*X2 - b*X)\\n\\n k= 0; SSE = 0.0; SST = 0.0;\\n\\n f = zeros(size(x),float)\\n \\n while k < size(x):\\n f[k] = a*x[k]**2 + b*x[k] + c\\n SSE = SSE + (y[k] - f[k])**2\\n SST = SST + (y[k] - m)**2\\n k = k + 1\\n R2 = 1 - SSE/SST\\n\\n return [a,b,c,R2,SSE,SST]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83fbcef235f5a30a8d7b86a595f10391\",\n \"score\": \"0.51827276\",\n \"text\": \"def test_s6_3_3_N_c_torsion(self):\\n\\n # test double symmetry\\n # region\\n\\n An = (\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.00189,\\n 0.00189,\\n 0.00189,\\n 0.00189,\\n )\\n\\n kf = (\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 0.95,\\n 0.95,\\n 0.95,\\n 0.95,\\n 0.95,\\n 1,\\n 1,\\n 1,\\n 1,\\n )\\n\\n lex = (80, 40, 20, 10, 5, 1, 40, 20, 10, 5, 1, 40, 20, 10, 5, 1, 10, 5, 1, 0.5)\\n\\n ley = (80, 40, 20, 10, 5, 1, 40, 20, 10, 5, 1, 40, 20, 10, 5, 1, 10, 5, 1, 0.5)\\n\\n lez = (80, 40, 20, 10, 5, 1, 40, 20, 10, 5, 1, 40, 20, 10, 5, 1, 10, 5, 1, 0.5)\\n\\n rx = (\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.0411,\\n 0.0411,\\n 0.0411,\\n 0.0411,\\n )\\n\\n ry = (\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0245,\\n 0.0245,\\n 0.0245,\\n 0.0245,\\n )\\n\\n xo = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)\\n\\n yo = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)\\n\\n J = (\\n 2.200_000_0e-5,\\n 2.200_000_0e-5,\\n 2.200_000_0e-5,\\n 2.200_000_0e-5,\\n 2.200_000_0e-5,\\n 2.200_000_0e-5,\\n 5.750_000_0e-6,\\n 5.750_000_0e-6,\\n 5.750_000_0e-6,\\n 5.750_000_0e-6,\\n 5.750_000_0e-6,\\n 1.560_000_0e-6,\\n 1.560_000_0e-6,\\n 1.560_000_0e-6,\\n 1.560_000_0e-6,\\n 1.560_000_0e-6,\\n 3.490_000_0e-8,\\n 3.490_000_0e-8,\\n 3.490_000_0e-8,\\n 3.490_000_0e-8,\\n )\\n\\n Iw = (\\n 2.800_000_0e-4,\\n 2.800_000_0e-4,\\n 2.800_000_0e-4,\\n 2.800_000_0e-4,\\n 2.800_000_0e-4,\\n 2.800_000_0e-4,\\n 4.600_000_0e-6,\\n 4.600_000_0e-6,\\n 4.600_000_0e-6,\\n 4.600_000_0e-6,\\n 4.600_000_0e-6,\\n 3.450_000_0e-6,\\n 3.450_000_0e-6,\\n 3.450_000_0e-6,\\n 3.450_000_0e-6,\\n 3.450_000_0e-6,\\n 2.300_000_0e-9,\\n 2.300_000_0e-9,\\n 2.300_000_0e-9,\\n 2.300_000_0e-9,\\n )\\n\\n fy = (\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n )\\n\\n E = (\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n )\\n\\n G = (\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n )\\n\\n Axis = (\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n [\\\"x\\\", \\\"y\\\"],\\n )\\n\\n Ncexp = (\\n 2.955_199_2e6,\\n 4.683_790_5e6,\\n 6.305_155_6e6,\\n 8.889_923_6e6,\\n 1.065_261_6e7,\\n 1.149_462_4e7,\\n 4.459_966_5e5,\\n 1.783_986_6e6,\\n 4.393_361_6e6,\\n 5.418_653_8e6,\\n 5.923_733_8e6,\\n 8.667_035_2e5,\\n 1.542_729_6e6,\\n 1.933_264_3e6,\\n 2.668_463_4e6,\\n 3.555_092_9e6,\\n 4.697_791_2e4,\\n 1.879_116_5e5,\\n 4.750_095_3e5,\\n 5.000_549_7e5,\\n )\\n\\n for i, v in enumerate(\\n zip(An, kf, lex, ley, lez, rx, ry, xo, yo, J, Iw, fy, E, G, Axis, Ncexp)\\n ):\\n Ani, kfi, lexi, leyi, lezi, rxi, ryi, xoi, yoi, Ji, Iwi, fyi, Ei, Gi, Axi, Ncexpi = (\\n v\\n )\\n\\n with self.subTest(f\\\"{i} N_c_torsion, double symmetry\\\"):\\n Nc = S6.s6_3_3_N_c_torsion(\\n A_n=Ani,\\n k_f=kfi,\\n l_ex=lexi,\\n l_ey=leyi,\\n l_ez=lezi,\\n r_x=rxi,\\n r_y=ryi,\\n x_o=xoi,\\n y_o=yoi,\\n J=Ji,\\n I_w=Iwi,\\n f_y=fyi,\\n E=Ei,\\n G=Gi,\\n sym=Symmetry(Axi),\\n )[\\\"N_cz\\\"]\\n\\n delta = Ncexpi / Test_UT_S6.delta_p\\n self.assertAlmostEqual(first=Nc, second=Ncexpi, delta=delta)\\n\\n # endregion\\n\\n # test single symmetry\\n # region\\n\\n An = (\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.011,\\n 0.011,\\n 0.011,\\n 0.011,\\n 0.013,\\n 0.013,\\n 0.013,\\n 0.013,\\n )\\n\\n kf = (\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n )\\n\\n lex = (\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n ley = (\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n lez = (\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n rx = (\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.21,\\n 0.21,\\n 0.21,\\n 0.21,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n )\\n\\n ry = (\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.036_979,\\n 0.036_979,\\n 0.036_979,\\n 0.036_979,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n )\\n\\n xo = (\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n )\\n\\n yo = (\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0,\\n 0,\\n 0,\\n 0,\\n )\\n\\n J = (\\n 8.890_000_0e-8,\\n 8.890_000_0e-8,\\n 8.890_000_0e-8,\\n 8.890_000_0e-8,\\n 8.890_000_0e-8,\\n 8.890_000_0e-8,\\n 5.150_000_0e-10,\\n 5.150_000_0e-10,\\n 5.150_000_0e-10,\\n 5.150_000_0e-10,\\n 5.150_000_0e-10,\\n 4.720_000_0e-7,\\n 4.720_000_0e-7,\\n 4.720_000_0e-7,\\n 4.720_000_0e-7,\\n 4.720_000_0e-7,\\n 8.130_000_0e-9,\\n 8.130_000_0e-9,\\n 8.130_000_0e-9,\\n 8.130_000_0e-9,\\n 8.130_000_0e-9,\\n 9.183_880_1e-7,\\n 9.183_880_1e-7,\\n 9.183_880_1e-7,\\n 9.183_880_1e-7,\\n 1.584_755_6e-6,\\n 1.584_755_6e-6,\\n 1.584_755_6e-6,\\n 1.584_755_6e-6,\\n )\\n\\n Iw = (\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 0.000_000_0e0,\\n 1.510_000_0e-7,\\n 1.510_000_0e-7,\\n 1.510_000_0e-7,\\n 1.510_000_0e-7,\\n 1.510_000_0e-7,\\n 1.060_000_0e-10,\\n 1.060_000_0e-10,\\n 1.060_000_0e-10,\\n 1.060_000_0e-10,\\n 1.060_000_0e-10,\\n 4.029_090_7e-7,\\n 4.029_090_7e-7,\\n 4.029_090_7e-7,\\n 4.029_090_7e-7,\\n 3.063_442_4e-8,\\n 3.063_442_4e-8,\\n 3.063_442_4e-8,\\n 3.063_442_4e-8,\\n )\\n\\n fy = (\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 2.800_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.200_000_0e8,\\n 3.000_000_0e8,\\n 3.000_000_0e8,\\n 3.000_000_0e8,\\n 3.000_000_0e8,\\n 3.000_000_0e8,\\n 3.000_000_0e8,\\n 3.000_000_0e8,\\n 3.000_000_0e8,\\n )\\n\\n E = (\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n 2.000_000_0e11,\\n )\\n\\n G = (\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n 8.000_000_0e10,\\n )\\n\\n Axis = (\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"y\\\"],\\n [\\\"y\\\"],\\n [\\\"y\\\"],\\n [\\\"y\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n [\\\"x\\\"],\\n )\\n\\n Ncexp = (\\n 3.387_928_1e4,\\n 1.355_171_2e5,\\n 4.443_595_3e5,\\n 5.789_588_8e5,\\n 5.789_588_8e5,\\n 5.789_588_8e5,\\n 1.788_735_6e2,\\n 7.154_942_2e2,\\n 1.750_876_7e4,\\n 3.185_976_7e4,\\n 3.700_202_0e4,\\n 5.062_446_2e5,\\n 9.374_124_2e5,\\n 1.080_888_0e6,\\n 1.570_160_3e6,\\n 1.644_385_7e6,\\n 9.897_325_2e3,\\n 3.771_960_6e4,\\n 1.598_315_5e5,\\n 1.799_224_8e5,\\n 2.032_971_0e5,\\n 2.008_178_5e5,\\n 6.233_479_9e5,\\n 2.460_658_5e6,\\n 2.709_803_1e6,\\n 2.203_686_1e6,\\n 2.606_442_2e6,\\n 2.816_155_5e6,\\n 3.051_553_1e6,\\n )\\n\\n for i, v in enumerate(\\n zip(An, kf, lex, ley, lez, rx, ry, xo, yo, J, Iw, fy, E, G, Axis, Ncexp)\\n ):\\n Ani, kfi, lexi, leyi, lezi, rxi, ryi, xoi, yoi, Ji, Iwi, fyi, Ei, Gi, Axi, Ncexpi = (\\n v\\n )\\n\\n with self.subTest(f\\\"{i} N_c_torsion, single symmetry\\\"):\\n Nc = S6.s6_3_3_N_c_torsion(\\n A_n=Ani,\\n k_f=kfi,\\n l_ex=lexi,\\n l_ey=leyi,\\n l_ez=lezi,\\n r_x=rxi,\\n r_y=ryi,\\n x_o=xoi,\\n y_o=yoi,\\n J=Ji,\\n I_w=Iwi,\\n f_y=fyi,\\n E=Ei,\\n G=Gi,\\n sym=Symmetry(Axi),\\n )[\\\"N_cz\\\"]\\n\\n delta = Ncexpi / Test_UT_S6.delta_p\\n self.assertAlmostEqual(first=Nc, second=Ncexpi, delta=delta)\\n\\n # endregion\\n\\n # test point symmetry\\n # region\\n\\n An = (\\n 0.00189,\\n 0.00189,\\n 0.00189,\\n 0.00189,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.011,\\n 0.011,\\n 0.011,\\n 0.011,\\n 0.013,\\n 0.013,\\n 0.013,\\n 0.013,\\n )\\n\\n kf = (\\n 1,\\n 1,\\n 1,\\n 1,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 0.95,\\n 0.95,\\n 0.95,\\n 0.95,\\n 0.95,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n )\\n\\n lex = (\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 80,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n ley = (\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 80,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n lez = (\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 80,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n rx = (\\n 0.0411,\\n 0.0411,\\n 0.0411,\\n 0.0411,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.21,\\n 0.21,\\n 0.21,\\n 0.21,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n )\\n\\n ry = (\\n 0.0245,\\n 0.0245,\\n 0.0245,\\n 0.0245,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.036_979,\\n 0.036_979,\\n 0.036_979,\\n 0.036_979,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n )\\n\\n xo = (\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n )\\n\\n yo = (\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0,\\n 0,\\n 0,\\n 0,\\n )\\n\\n J = (\\n 0.000_000_034_9,\\n 0.000_000_034_9,\\n 0.000_000_034_9,\\n 0.000_000_034_9,\\n 0.000_022,\\n 0.000_022,\\n 0.000_022,\\n 0.000_022,\\n 0.000_022,\\n 0.000_022,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_000_515,\\n 0.000_000_000_515,\\n 0.000_000_000_515,\\n 0.000_000_000_515,\\n 0.000_000_000_515,\\n 0.000_005_75,\\n 0.000_005_75,\\n 0.000_005_75,\\n 0.000_005_75,\\n 0.000_005_75,\\n 0.000_000_472,\\n 0.000_000_472,\\n 0.000_000_472,\\n 0.000_000_472,\\n 0.000_000_472,\\n 0.000_001_56,\\n 0.000_001_56,\\n 0.000_001_56,\\n 0.000_001_56,\\n 0.000_001_56,\\n 0.000_000_008_13,\\n 0.000_000_008_13,\\n 0.000_000_008_13,\\n 0.000_000_008_13,\\n 0.000_000_008_13,\\n 0.000_000_918_388_007_92,\\n 0.000_000_918_388_007_92,\\n 0.000_000_918_388_007_92,\\n 0.000_000_918_388_007_92,\\n 0.000_001_584_755_618_8,\\n 0.000_001_584_755_618_8,\\n 0.000_001_584_755_618_8,\\n 0.000_001_584_755_618_8,\\n )\\n\\n Iw = (\\n 0.000_000_002_3,\\n 0.000_000_002_3,\\n 0.000_000_002_3,\\n 0.000_000_002_3,\\n 0.00028,\\n 0.00028,\\n 0.00028,\\n 0.00028,\\n 0.00028,\\n 0.00028,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.000_004_6,\\n 0.000_004_6,\\n 0.000_004_6,\\n 0.000_004_6,\\n 0.000_004_6,\\n 0.000_000_151,\\n 0.000_000_151,\\n 0.000_000_151,\\n 0.000_000_151,\\n 0.000_000_151,\\n 0.000_003_45,\\n 0.000_003_45,\\n 0.000_003_45,\\n 0.000_003_45,\\n 0.000_003_45,\\n 0.000_000_000_106,\\n 0.000_000_000_106,\\n 0.000_000_000_106,\\n 0.000_000_000_106,\\n 0.000_000_000_106,\\n 0.000_000_402_909_074_37,\\n 0.000_000_402_909_074_37,\\n 0.000_000_402_909_074_37,\\n 0.000_000_402_909_074_37,\\n 3.063_442_393_88e-08,\\n 3.063_442_393_88e-08,\\n 3.063_442_393_88e-08,\\n 3.063_442_393_88e-08,\\n )\\n\\n fy = (\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n )\\n\\n E = (\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n )\\n\\n G = (\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n )\\n\\n Axis = (\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n [\\\"point\\\"],\\n )\\n\\n Ncexp = (\\n 419_106.155_746_496,\\n 423_045.512_255_854,\\n 475_009.533_425_687,\\n 500_054.974_957_511,\\n 4_119_875.923_039_97,\\n 4_683_790.478_014_97,\\n 6_305_155.642_714_61,\\n 8_889_923.629_036_2,\\n 10_652_615.705_617_5,\\n 11_494_624.348_339_3,\\n 578_958.884_095_936,\\n 578_958.884_095_936,\\n 578_958.884_095_936,\\n 578_958.884_095_936,\\n 578_958.884_095_936,\\n 578_958.884_095_936,\\n 37002.019_768_243_3,\\n 37002.019_768_243_3,\\n 37002.019_768_243_3,\\n 37002.019_768_243_3,\\n 37002.019_768_243_3,\\n 5_027_703.565_267_41,\\n 5_058_371.868_827_53,\\n 5_163_478.914_596_12,\\n 5_418_653.755_326_58,\\n 5_923_733.788_839_68,\\n 964_432.579_707_819,\\n 994_031.743_422_623,\\n 1_091_690.508_416_4,\\n 1_570_720.193_292_56,\\n 1_644_531.831_067_96,\\n 1_417_976.883_016_73,\\n 1_542_729.591_895_77,\\n 1_933_264.339_018_83,\\n 2_668_463.372_097_56,\\n 3_555_092.885_703,\\n 155_110.114_279_886,\\n 155_523.464_847_291,\\n 165_908.779_342_738,\\n 181_437.173_491_866,\\n 203_362.838_693_571,\\n 876_230.281_613_051,\\n 1_130_467.777_494_14,\\n 2_512_488.847_521_47,\\n 2_723_741.764_911_02,\\n 2_608_784.571_759_65,\\n 2_617_574.955_386_2,\\n 2_816_451.117_729_63,\\n 3_051_632.208_376_09,\\n )\\n\\n for i, v in enumerate(\\n zip(An, kf, lex, ley, lez, rx, ry, xo, yo, J, Iw, fy, E, G, Axis, Ncexp)\\n ):\\n\\n Ani, kfi, lexi, leyi, lezi, rxi, ryi, xoi, yoi, Ji, Iwi, fyi, Ei, Gi, Axi, Ncexpi = (\\n v\\n )\\n\\n with self.subTest(f\\\"{i} N_c_torsion Point Symmetry\\\"):\\n\\n Nc = S6.s6_3_3_N_c_torsion(\\n A_n=Ani,\\n k_f=kfi,\\n l_ex=lexi,\\n l_ey=leyi,\\n l_ez=lezi,\\n r_x=rxi,\\n r_y=ryi,\\n x_o=xoi,\\n y_o=yoi,\\n J=Ji,\\n I_w=Iwi,\\n f_y=fyi,\\n E=Ei,\\n G=Gi,\\n sym=Symmetry(Axi),\\n )[\\\"N_cz\\\"]\\n\\n delta = Ncexpi / Test_UT_S6.delta_p\\n self.assertAlmostEqual(first=Nc, second=Ncexpi, delta=delta)\\n\\n # endregion\\n\\n # test no symmetry\\n # region\\n\\n An = (\\n 0.00189,\\n 0.00189,\\n 0.00189,\\n 0.00189,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.0579,\\n 0.00117,\\n 0.00117,\\n 0.00117,\\n 0.00117,\\n 0.00117,\\n 0.00117,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.00279,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.000_143,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.0251,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.00703,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.016,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.000_754,\\n 0.011,\\n 0.011,\\n 0.011,\\n 0.011,\\n 0.013,\\n 0.013,\\n 0.013,\\n 0.013,\\n )\\n\\n kf = (\\n 1,\\n 1,\\n 1,\\n 1,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.837,\\n 0.824,\\n 0.824,\\n 0.824,\\n 0.824,\\n 0.824,\\n 0.824,\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 0.958,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 0.95,\\n 0.95,\\n 0.95,\\n 0.95,\\n 0.95,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n 1,\\n )\\n\\n lex = (\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 80,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n ley = (\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 80,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n lez = (\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 80,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 20,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 40,\\n 20,\\n 10,\\n 5,\\n 1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 0.1,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n 10,\\n 5,\\n 1,\\n 0.5,\\n )\\n\\n rx = (\\n 0.0411,\\n 0.0411,\\n 0.0411,\\n 0.0411,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.515,\\n 0.0425,\\n 0.0425,\\n 0.0425,\\n 0.0425,\\n 0.0425,\\n 0.0425,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.0587,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.00922,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.139,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.147,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.249,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.0301,\\n 0.21,\\n 0.21,\\n 0.21,\\n 0.21,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n 0.127_100_303_6,\\n )\\n\\n ry = (\\n 0.0245,\\n 0.0245,\\n 0.0245,\\n 0.0245,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.12,\\n 0.0164,\\n 0.0164,\\n 0.0164,\\n 0.0164,\\n 0.0164,\\n 0.0164,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.0298,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.00473,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0893,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0304,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0496,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.0126,\\n 0.036_979,\\n 0.036_979,\\n 0.036_979,\\n 0.036_979,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n 0.045_328_083_000_3,\\n )\\n\\n xo = (\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0.0567,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0.0272,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n 0.022_061_408_061_9,\\n )\\n\\n yo = (\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0.154_285_047_87,\\n 0,\\n 0,\\n 0,\\n 0,\\n )\\n\\n J = (\\n 0.000_000_034_9,\\n 0.000_000_034_9,\\n 0.000_000_034_9,\\n 0.000_000_034_9,\\n 0.000_022,\\n 0.000_022,\\n 0.000_022,\\n 0.000_022,\\n 0.000_022,\\n 0.000_022,\\n 0.000_000_014_8,\\n 0.000_000_014_8,\\n 0.000_000_014_8,\\n 0.000_000_014_8,\\n 0.000_000_014_8,\\n 0.000_000_014_8,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_088_9,\\n 0.000_000_000_515,\\n 0.000_000_000_515,\\n 0.000_000_000_515,\\n 0.000_000_000_515,\\n 0.000_000_000_515,\\n 0.000_005_75,\\n 0.000_005_75,\\n 0.000_005_75,\\n 0.000_005_75,\\n 0.000_005_75,\\n 0.000_000_472,\\n 0.000_000_472,\\n 0.000_000_472,\\n 0.000_000_472,\\n 0.000_000_472,\\n 0.000_001_56,\\n 0.000_001_56,\\n 0.000_001_56,\\n 0.000_001_56,\\n 0.000_001_56,\\n 0.000_000_008_13,\\n 0.000_000_008_13,\\n 0.000_000_008_13,\\n 0.000_000_008_13,\\n 0.000_000_008_13,\\n 0.000_000_918_388_007_92,\\n 0.000_000_918_388_007_92,\\n 0.000_000_918_388_007_92,\\n 0.000_000_918_388_007_92,\\n 0.000_001_584_755_618_8,\\n 0.000_001_584_755_618_8,\\n 0.000_001_584_755_618_8,\\n 0.000_001_584_755_618_8,\\n )\\n\\n Iw = (\\n 0.000_000_002_3,\\n 0.000_000_002_3,\\n 0.000_000_002_3,\\n 0.000_000_002_3,\\n 0.00028,\\n 0.00028,\\n 0.00028,\\n 0.00028,\\n 0.00028,\\n 0.00028,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0,\\n 0.000_004_6,\\n 0.000_004_6,\\n 0.000_004_6,\\n 0.000_004_6,\\n 0.000_004_6,\\n 0.000_000_151,\\n 0.000_000_151,\\n 0.000_000_151,\\n 0.000_000_151,\\n 0.000_000_151,\\n 0.000_003_45,\\n 0.000_003_45,\\n 0.000_003_45,\\n 0.000_003_45,\\n 0.000_003_45,\\n 0.000_000_000_106,\\n 0.000_000_000_106,\\n 0.000_000_000_106,\\n 0.000_000_000_106,\\n 0.000_000_000_106,\\n 0.000_000_402_909_074_37,\\n 0.000_000_402_909_074_37,\\n 0.000_000_402_909_074_37,\\n 0.000_000_402_909_074_37,\\n 3.063_442_393_88e-08,\\n 3.063_442_393_88e-08,\\n 3.063_442_393_88e-08,\\n 3.063_442_393_88e-08,\\n )\\n\\n fy = (\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 280_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 320_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n 300_000_000,\\n )\\n\\n E = (\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n 200_000_000_000,\\n )\\n\\n G = (\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n 80_000_000_000,\\n )\\n\\n Axis = (\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n [],\\n )\\n\\n Ncexp = (\\n 1.669_330_134e4,\\n 6.677_320_534e4,\\n 4.591_320_923e5,\\n 4.997_553_404e5,\\n 1.604_481_818e5,\\n 6.417_927_271e5,\\n 2.567_170_908e6,\\n 7.636_973_676e6,\\n 1.040_437_645e7,\\n 1.148_657_049e7,\\n 9.538_696_667e2,\\n 3.815_478_667e3,\\n 1.526_191_467e4,\\n 1.992_503_406e5,\\n 1.992_503_406e5,\\n 1.992_503_406e5,\\n 8.731_533_193e3,\\n 3.492_613_277e4,\\n 1.397_045_311e5,\\n 5.789_588_841e5,\\n 5.789_588_841e5,\\n 5.789_588_841e5,\\n 4.707_676_148e1,\\n 1.883_070_459e2,\\n 4.707_676_148e3,\\n 1.822_306_529e4,\\n 3.700_201_977e4,\\n 1.840_792_841e5,\\n 7.363_171_365e5,\\n 2.837_345_351e6,\\n 4.959_250_649e6,\\n 5.923_733_789e6,\\n 2.389_668_692e4,\\n 9.555_144_229e4,\\n 3.816_249_868e5,\\n 1.562_386_903e6,\\n 1.643_246_839e6,\\n 3.439_024_093e4,\\n 1.375_609_637e5,\\n 5.502_438_549e5,\\n 1.978_786_886e6,\\n 3.531_341_223e6,\\n 1.760_403_024e3,\\n 7.029_416_318e3,\\n 1.296_114_307e5,\\n 1.768_000_222e5,\\n 2.032_422_271e5,\\n 2.003_404_466e5,\\n 6.208_973_168e5,\\n 2.460_201_882e6,\\n 2.709_683_520e6,\\n 3.929_055_972e5,\\n 1.526_786_241e6,\\n 2.816_086_259e6,\\n 3.051_544_932e6,\\n )\\n\\n for i, v in enumerate(\\n zip(An, kf, lex, ley, lez, rx, ry, xo, yo, J, Iw, fy, E, G, Axis, Ncexp)\\n ):\\n\\n Ani, kfi, lexi, leyi, lezi, rxi, ryi, xoi, yoi, Ji, Iwi, fyi, Ei, Gi, Axi, Ncexpi = (\\n v\\n )\\n\\n with self.subTest(f\\\"{i} N_c_torsion, No symmetry\\\"):\\n Nc = S6.s6_3_3_N_c_torsion(\\n A_n=Ani,\\n k_f=kfi,\\n l_ex=lexi,\\n l_ey=leyi,\\n l_ez=lezi,\\n r_x=rxi,\\n r_y=ryi,\\n x_o=xoi,\\n y_o=yoi,\\n J=Ji,\\n I_w=Iwi,\\n f_y=fyi,\\n E=Ei,\\n G=Gi,\\n sym=Symmetry(Axi),\\n )[\\\"N_cz\\\"]\\n\\n delta = Ncexpi / Test_UT_S6.delta_p\\n self.assertAlmostEqual(first=Nc, second=Ncexpi, delta=delta)\\n\\n # endregion\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c296a4289f7f811da60f68bac77fe30\",\n \"score\": \"0.5179343\",\n \"text\": \"def _precompute(self, n):\\n base_ring = self.base_ring()\\n zero = base_ring.zero()\\n\\n # Handle the n == 0 and n == 1 cases separately\\n if n == 0 or n == 1:\\n part = sage.combinat.partition.Partition([1]*n)\\n self._to_self_cache[ part ] = { part: base_ring.one() }\\n self._from_self_cache[ part ] = { part: base_ring.one() }\\n self._transition_matrices[n] = matrix(base_ring, [[1]])\\n self._inverse_transition_matrices[n] = matrix(base_ring, [[1]])\\n return\\n\\n partitions_n = sage.combinat.partition.Partitions_n(n).list()\\n\\n # We now get separated into two cases, depending on whether we can\\n # use the power-sum basis to compute the matrix, or we have to use\\n # the Schur basis.\\n\\n from sage.rings.rational_field import RationalField\\n if (not base_ring.has_coerce_map_from(RationalField())) and self._scalar == sage.combinat.sf.sfa.zee:\\n # This is the case when (due to the base ring not being a\\n # \\\\mathbb{Q}-algebra) we cannot use the power-sum basis,\\n # but (due to zee being the standard zee function) we can\\n # use the Schur basis.\\n\\n schur = self._sym.schur()\\n\\n # Get all the basis elements of the n^th homogeneous component\\n # of the dual basis and express them in the Schur basis\\n d = {}\\n for part in partitions_n:\\n d[part] = schur(self._dual_basis(part))._monomial_coefficients\\n\\n # This contains the data for the transition matrix from the\\n # dual basis to self.\\n transition_matrix_n = matrix(base_ring, len(partitions_n), len(partitions_n))\\n\\n # This first section calculates how the basis elements of the\\n # dual basis are expressed in terms of self's basis.\\n\\n # For every partition p of size n, compute self(p) in\\n # terms of the dual basis using the scalar product.\\n i = 0\\n for s_part in partitions_n:\\n # s_part corresponds to self(dual_basis(part))\\n # s_mcs corresponds to self(dual_basis(part))._monomial_coefficients\\n s_mcs = {}\\n\\n # We need to compute the scalar product of d[s_part] and\\n # all of the d[p_part]'s\\n j = 0\\n for p_part in partitions_n:\\n # Compute the scalar product of d[s_part] and d[p_part]\\n sp = zero\\n for ds_part in d[s_part]:\\n if ds_part in d[p_part]:\\n sp += d[s_part][ds_part]*d[p_part][ds_part]\\n if sp != zero:\\n s_mcs[p_part] = sp\\n transition_matrix_n[i,j] = sp\\n\\n j += 1\\n\\n self._to_self_cache[ s_part ] = s_mcs\\n i += 1\\n\\n else:\\n # Now the other case. Note that just being in this case doesn't\\n # guarantee that we can use the power-sum basis, but we can at\\n # least try.\\n\\n # Get all the basis elements of the n^th homogeneous component\\n # of the dual basis and express them in the power-sum basis\\n d = {}\\n for part in partitions_n:\\n d[part] = self._p(self._dual_basis(part))._monomial_coefficients\\n\\n # This contains the data for the transition matrix from the\\n # dual basis to self.\\n transition_matrix_n = matrix(base_ring, len(partitions_n), len(partitions_n))\\n\\n # This first section calculates how the basis elements of the\\n # dual basis are expressed in terms of self's basis.\\n\\n # For every partition p of size n, compute self(p) in\\n # terms of the dual basis using the scalar product.\\n i = 0\\n for s_part in partitions_n:\\n # s_part corresponds to self(dual_basis(part))\\n # s_mcs corresponds to self(dual_basis(part))._monomial_coefficients\\n s_mcs = {}\\n\\n # We need to compute the scalar product of d[s_part] and\\n # all of the d[p_part]'s\\n j = 0\\n for p_part in partitions_n:\\n # Compute the scalar product of d[s_part] and d[p_part]\\n sp = zero\\n for ds_part in d[s_part]:\\n if ds_part in d[p_part]:\\n sp += d[s_part][ds_part]*d[p_part][ds_part]*self._scalar(ds_part)\\n if sp != zero:\\n s_mcs[p_part] = sp\\n transition_matrix_n[i,j] = sp\\n\\n j += 1\\n\\n self._to_self_cache[ s_part ] = s_mcs\\n i += 1\\n\\n # Save the transition matrix\\n self._transition_matrices[n] = transition_matrix_n\\n\\n # This second section calculates how the basis elements of\\n # self expand in terms of the dual basis. We do this by\\n # computing the inverse of the matrix obtained above.\\n inverse_transition = ~transition_matrix_n\\n\\n for i in range(len(partitions_n)):\\n d_mcs = {}\\n for j in range(len(partitions_n)):\\n if inverse_transition[i,j] != zero:\\n d_mcs[ partitions_n[j] ] = inverse_transition[i,j]\\n\\n self._from_self_cache[ partitions_n[i] ] = d_mcs\\n\\n self._inverse_transition_matrices[n] = inverse_transition\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f7a56ae5797506751323bf14a5645f\",\n \"score\": \"0.5175565\",\n \"text\": \"def _compute_morph_sdr(mri_from, mri_to,\\n niter_affine=(100, 100, 10),\\n niter_sdr=(5, 5, 3),\\n grid_spacing=(5., 5., 5.),\\n verbose=None):\\n import nibabel as nib\\n from dipy.align import imaffine, imwarp, metrics, transforms\\n from dipy.align.reslice import reslice\\n\\n logger.info('Computing nonlinear Symmetric Diffeomorphic Registration...')\\n\\n morph = dict()\\n\\n # use voxel size of mri_from\\n if grid_spacing is None:\\n grid_spacing = mri_from.header.get_zooms()[:3]\\n\\n # reslice mri_from\\n mri_from_res, mri_from_res_affine = reslice(\\n mri_from.get_data(),\\n mri_from.affine,\\n mri_from.header.get_zooms()[:3],\\n grid_spacing)\\n\\n mri_from = nib.Nifti1Image(mri_from_res, mri_from_res_affine)\\n\\n # reslice mri_to\\n mri_to_res, mri_to_res_affine = reslice(\\n mri_to.get_data(),\\n mri_to.affine,\\n mri_to.header.get_zooms()[:3],\\n grid_spacing)\\n\\n mri_to = nib.Nifti1Image(mri_to_res, mri_to_res_affine)\\n\\n # get mri_to to world transform\\n mri_to_grid2world = mri_to.affine\\n\\n # output mri_to as ndarray\\n mri_to = mri_to.dataobj[:, :, :]\\n\\n # normalize values\\n mri_to = mri_to.astype('float') / mri_to.max()\\n\\n # get mri_from to world transform\\n mri_from_grid2world = mri_from.affine\\n\\n # output mri_from as ndarray\\n mri_from = mri_from.dataobj[:, :, :]\\n\\n # normalize values\\n mri_from = mri_from.astype('float') / mri_from.max()\\n\\n # compute center of mass\\n c_of_mass = imaffine.transform_centers_of_mass(mri_to, mri_to_grid2world,\\n mri_from,\\n mri_from_grid2world)\\n\\n nbins = 32\\n\\n # set up Affine Registration\\n affreg = imaffine.AffineRegistration(\\n metric=imaffine.MutualInformationMetric(nbins, None),\\n level_iters=list(niter_affine),\\n sigmas=[3.0, 1.0, 0.0],\\n factors=[4, 2, 1])\\n\\n # translation\\n translation = affreg.optimize(mri_to, mri_from,\\n transforms.TranslationTransform3D(), None,\\n mri_to_grid2world, mri_from_grid2world,\\n starting_affine=c_of_mass.affine)\\n\\n # rigid body transform (translation + rotation)\\n rigid = affreg.optimize(mri_to, mri_from,\\n transforms.RigidTransform3D(), None,\\n mri_to_grid2world, mri_from_grid2world,\\n starting_affine=translation.affine)\\n\\n # affine transform (translation + rotation + scaling)\\n affine = affreg.optimize(mri_to, mri_from,\\n transforms.AffineTransform3D(), None,\\n mri_to_grid2world, mri_from_grid2world,\\n starting_affine=rigid.affine)\\n\\n # apply affine transformation\\n mri_from_affine = affine.transform(mri_from)\\n\\n # set up Symmetric Diffeomorphic Registration (metric, iterations)\\n sdr = imwarp.SymmetricDiffeomorphicRegistration(\\n metrics.CCMetric(3), list(niter_sdr))\\n\\n # compute mapping\\n mapping = sdr.optimize(mri_to, mri_from_affine)\\n\\n morph['morph_shape'] = tuple(mapping.domain_shape.astype('float'))\\n morph['morph_zooms'] = grid_spacing\\n morph['morph_affine'] = mri_to_grid2world\\n\\n morph['AffineMap'] = affine.__dict__\\n morph['DiffeomorphicMap'] = mapping.__dict__\\n\\n logger.info('done.')\\n\\n return morph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d54ddcaf0f366d620195dda6ee4efeef\",\n \"score\": \"0.51709425\",\n \"text\": \"def __init__(self, sntype, observations=None, z_range=[1.8,2.2],\\n t0_range=[0,0], c_distrib=[0, 0.1], x1_distrib=[0, 1.],\\n nsim=100, perfect=True,\\n Om=0.3, H0=70, filterset='hst', mwdust=False, mwEBV=.018):\\n from astropy import cosmology\\n import sncosmo\\n from numpy.random import normal, uniform, choice\\n import numpy as np\\n\\n self.sntype = sntype\\n self.z_range = z_range\\n self.nsim = nsim\\n self.perfect = perfect\\n\\n if observations is None :\\n observations = mkobservationsTable( filterset=filterset )\\n self.observations = observations\\n\\n # Make a list of all the unique sncosmo source models available,\\n # and assign a relative probability that any given simulated SN of this\\n # type (CC or Ia) belongs to that subclass\\n if sntype.lower() in ['cc','ii','ibc'] :\\n subClassDict = SubClassDict_SNANA[sntype.lower()]\\n subClassProbs = ccSubClassProbs[sntype.lower()]\\n #self.SourcenameSet = np.array( subClassDict.keys() )\\n self.SourcenameSet = list(subClassDict.keys()) # kfo\\n self.SubclassSet = np.array([ subClassDict[source] for source in self.SourcenameSet ])\\n self.SubclassCount = np.array([ len(np.where(self.SubclassSet==subclass)[0])\\n for subclass in self.SubclassSet ], dtype=float)\\n self.SourceprobSet = np.array([ subClassProbs[subclass]\\n for subclass in self.SubclassSet ]) / self.SubclassCount\\n self.SourceprobSet /= self.SourceprobSet.sum()\\n elif sntype.lower()=='ia' :\\n # No sub-class divisions for SNIa\\n self.SourcenameSet = np.array(['salt2-extended'])\\n self.SubclassSet = np.array( ['Ia'] )\\n self.SourceprobSet = np.array( [1] )\\n self.SubclassCount = np.array( [1] )\\n\\n # load the O'Donnell 1994 dust model\\n self.dust = sncosmo.OD94Dust()\\n\\n # Define an sncosmo SN model for each available source\\n # kfo, mwdust defaults ~ False, will just use the host extinction effect \\n if mwdust:\\n modelset = np.array([ sncosmo.Model(source=source, effects=[self.dust,self.dust],\\n effect_names=['host','mw'], effect_frames=['rest','obs'])\\n for source in self.SourcenameSet ])\\n else:\\n modelset = np.array([ sncosmo.Model(source=source, effects=[self.dust],\\n effect_names=['host'], effect_frames=['rest'])\\n for source in self.SourcenameSet ])\\n\\n # Define a cosmology\\n # self.Om = Om\\n # self.H0 = H0\\n self.cosmo = cosmology.FlatLambdaCDM(Om0=Om, H0=H0)\\n\\n # For each simulated SN, draw random Av from distributions\\n # as defined in Rodney et al 2014a :\\n # For SN Ia : P(Av) = exp(-Av/0.33)\\n # For CC SN : P(Av) = 4 * gauss(0.6) + exp(-Rv/1.7)\\n if sntype=='Ia':\\n tau,sigma,R0 = 0.33, 0, 0\\n else :\\n tau,sigma,R0 = 1.7, 0.6, 4\\n self.Av = mcsample( pAv, nsim, tau=tau, sigma=sigma, R0=R0 )\\n\\n # For each simulated SN, draw a random Rv from a normal\\n # distribution centered on 3.1 with width 0.5\\n self.Rv = normal( 3.1, 0.5, nsim )\\n self.Rv = np.where( self.Rv>0, self.Rv, 0.01 )\\n\\n # Convert Av and Rv to E(B-V) :\\n # Rv = Av/EBV ==> EBV = Av/Rv\\n self.EBV = self.Av / self.Rv\\n\\n # kfo convert, simplest assume mw Rv 3.1 each time, user provides ebv, solve extinction\\n if mwdust:\\n self.mwRv = 3.1 \\n self.mwEBV = mwEBV\\n self.mwAv = self.mwRv*self.mwEBV\\n\\n # TODO : draw the redshifts with a metropolis-hastings sampler to match\\n # a distribution defined based on the expected SN rate\\n\\n # Disabled : uniform redshift spacing\\n # zlist = np.linspace( z_range[0], z_range[1], nsim )\\n\\n # Draw a random redshift from a uniform distribution\\n self.z = uniform( low=z_range[0], high=z_range[1], size=nsim )\\n\\n lightcurvelist = []\\n peakabsmagRlist = []\\n modelparamlist = []\\n subclasslist = []\\n modelindexlist = []\\n sourcenamelist = []\\n t0list = []\\n if sntype=='Ia':\\n x0list = []\\n x1list = []\\n clist = []\\n else :\\n amplitudelist = []\\n for isim in range(self.nsim):\\n # Randomly draw an sncosmo model from the available list, according to\\n # the predefined probability list, setting the SN sub-class for this\\n # simulated SN\\n imodel = choice( np.arange(len(modelset)), replace=True, p=self.SourceprobSet )\\n model = modelset[imodel]\\n subclass = self.SubclassSet[imodel]\\n\\n z = self.z[isim]\\n EBV = self.EBV[isim]\\n Rv = self.Rv[isim]\\n # kfo I am using Rv ~ 3.1; user provides optional color excess ebv \\n if mwdust:\\n mwEBV = self.mwEBV\\n mwRv = self.mwRv\\n\\n # Set the peak absolute magnitude according to the observed\\n # luminosity functions, as defined in Table 3 of Graur:2014a;\\n # and set the host extinction according to the 'mid' dust model\\n # of Rodney:2014a.\\n if subclass == 'Ia' :\\n MR = normal( -19.37, 0.47 )\\n elif subclass == 'Ib' :\\n MR = normal( -17.90, 0.90 )\\n elif subclass == 'Ic' :\\n MR = normal( -18.30, 0.60 )\\n elif subclass == 'IIP' :\\n MR = normal( -16.56, 0.80 )\\n elif subclass == 'IIL' :\\n MR = normal( -17.66, 0.42 )\\n elif subclass == 'IIn' :\\n MR = normal( -18.25, 1.00 )\\n model.set(z=z)\\n model.set_source_peakabsmag( MR, 'bessellr', 'vega', cosmo=self.cosmo)\\n\\n modelindexlist.append( imodel )\\n subclasslist.append( subclass )\\n peakabsmagRlist.append( MR )\\n sourcenamelist.append( list(self.SourcenameSet)[imodel] )\\n if subclass =='Ia' :\\n x0 = model.get('x0')\\n # TODO : use bifurcated gaussians for more realistic x1,c dist'ns\\n # kfo added x1_range and c_range arguments (default to commented)\\n # x1 = normal(0., 1.)\\n # c = normal(0., 0.1) \\n x1 = normal(x1_distrib[0], x1_distrib[1])\\n c = normal(c_distrib[0], c_distrib[1])\\n t0 = uniform( t0_range[0], t0_range[1] )\\n # kfo \\n if mwdust:\\n modelparams = {'z':z, 't0':t0, 'x0':x0, 'x1':x1, 'c':c, 'hostebv':EBV, 'hostr_v':Rv,'mwebv':mwEBV,'mwr_v':mwRv}\\n else:\\n modelparams = {'z':z, 't0':t0, 'x0':x0, 'x1':x1, 'c':c, 'hostebv':EBV, 'hostr_v':Rv}\\n t0list.append( t0 )\\n x0list.append( x0 )\\n x1list.append( x1 )\\n clist.append( c )\\n t0list.append( t0 )\\n else :\\n amplitude = model.get('amplitude')\\n t0 = uniform( t0_range[0], t0_range[1] )\\n # kfo\\n if mwdust:\\n modelparams = {'z':z, 't0':t0, 'amplitude':amplitude, 'hostebv':EBV, 'hostr_v':Rv,'mwebv':mwEBV,'mwr_v':mwRv }\\n else:\\n modelparams = {'z':z, 't0':t0, 'amplitude':amplitude, 'hostebv':EBV, 'hostr_v':Rv }\\n amplitudelist.append( amplitude )\\n t0list.append( t0 )\\n modelparamlist.append( modelparams )\\n\\n # Generate one simulated SN:\\n snlc = sncosmo.realize_lcs(self.observations, model, [ modelparams ],\\n thresh=None)#, perfect=perfect )\\n lightcurvelist.append( snlc[0] )\\n\\n self.lightcurves = lightcurvelist\\n self.t0 = np.array( t0list )\\n self.modelindex = np.array( modelindexlist )\\n self.sourcename = np.array( sourcenamelist )\\n self.subclass = np.array( subclasslist )\\n self.modelparam = np.array( modelparamlist )\\n self.peakabsmagR = np.array( peakabsmagRlist )\\n\\n if sntype=='Ia':\\n self.x0 = np.array( x0list )\\n self.x1 = np.array( x1list )\\n self.c = np.array( clist )\\n else :\\n self.amplitude = np.array( amplitudelist )\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7a9d0d117fe01afec8f4d6e02b12c01\",\n \"score\": \"0.51641315\",\n \"text\": \"def sisdr_eval(ref_sig, out_sig, eps=1e-8):\\r\\n assert len(ref_sig) == len(out_sig)\\r\\n ref_sig = ref_sig - np.mean(ref_sig)\\r\\n out_sig = out_sig - np.mean(out_sig)\\r\\n ref_energy = np.sum(ref_sig ** 2) + eps\\r\\n proj = np.sum(ref_sig * out_sig) * ref_sig / ref_energy\\r\\n noise = out_sig - proj\\r\\n ratio = np.sum(proj ** 2) / (np.sum(noise ** 2) + eps)\\r\\n sisdr = 10 * np.log10(ratio + eps)\\r\\n \\r\\n return sisdr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef80e7e90f3cfe0098a04115e4d3fca\",\n \"score\": \"0.5162615\",\n \"text\": \"def estimateCoefs(self):\\n snodes = set(self.G.nodes()).difference(set(self.node2vars.keys()))\\n self.linear = {var: 0.0 for node in self.node2vars.keys() for var in self.node2vars[node]}\\n self.quad = {}\\n for node in self.node2vars.keys():\\n for var1 in self.node2vars[node]:\\n times1 = HistoryUtil.var2Times(var1)\\n for neighnode in set(self.G.predecessors(node)).intersection(set(self.node2vars.keys())):\\n for var2 in self.node2vars[neighnode]:\\n times2 = HistoryUtil.var2Times(var2)\\n if HistoryUtil.checkAffect(times2,times1,self.smodel,Trace.INFECTED,self.statekey,\\\"normal\\\"):\\n f1 = Dist.genPartDist(self.G[neighnode][node][self.transdist][0],self.G[neighnode][node][self.transdist][1],\\\"reverseCdf\\\",self.G[neighnode][node][Trace.SPROB]) \\n fdist = Dist.genPdf(f1)\\n fdist[0] = 1.0\\n diftime = times1[self.statekey] - times2[Trace.INFECTED]\\n assert diftime > 0 \\n first,second = self.ZERO, self.ZERO\\n if fdist.has_key(diftime-1) and fdist[diftime-1] > self.ZERO:\\n first = fdist[diftime-1]\\n if fdist.has_key(diftime) and fdist[diftime] > self.ZERO:\\n second = fdist[diftime]\\n self.quad[(var1,var2)] = (first,second)\\n if self.smodel in [\\\"sir\\\",\\\"seir\\\"]:\\n coef = boundInferUtil.estimateEndoCoefs(node,times1,self.G,\\\"i2r\\\",self.ZERO,self.maxtime)\\n if coef != 0.0:\\n self.linear[var1] += coef \\n if self.smodel == \\\"seir\\\":\\n coef = boundInferUtil.estimateEndoCoefs(node,times1,self.G,\\\"e2i\\\",self.ZERO,self.maxtime)\\n if coef != 0.0:\\n self.linear[var1] += coef \\n locallinear = boundInferUtil.estimateNoneTransCoef(snodes,self.G,self.node2vars,self.smodel,self.ZERO,self.maxtime)\\n for key in locallinear.keys():\\n self.linear[key] += locallinear[key]\\n for key in self.linear.keys():\\n assert self.linear[key] >= 0.0\\n if self.linear[key] == 0.0 or self.linear[key] == -0.0:\\n del self.linear[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"547d94c62f91cae694bc938c59d3f297\",\n \"score\": \"0.51625013\",\n \"text\": \"def run_sdgmssl_mnist(n_epochs=3):\\n n_labeled = 100 # The total number of labeled data points.\\n train_dataset, train_labeled, test_dataset, validation_dataset = mnist.load_semi_supervised(n_labeled=n_labeled, filter_std=0.0, seed=seed, train_valid_combine=True)\\n\\n n, n_x = train_dataset[0].shape\\n bs = 100\\n# init_kl_wu = 1.\\n\\n # Initialize the semi deep generative model.\\n model = SDGMSSL(n_x=n_x, n_a=100, n_z=100, n_y=10, qa_hid=[500, 500], qz_hid=[500, 500], \\n qy_hid=[500, 500], px_hid=[500, 500], pa_hid=[500, 500], \\n nonlinearity=rectify, batchnorm=True, x_dist='bernoulli')\\n\\n # Get the training functions.\\n f_train, f_validate, train_args, validate_args = model.build_model(n, n_labeled)\\n # Update the default function arguments.\\n train_args['inputs']['beta'] = .1\\n train_args['inputs']['learningrate'] = 3e-4\\n train_args['inputs']['beta1'] = 0.9\\n train_args['inputs']['beta2'] = 0.999\\n train_args['inputs']['samples'] = 5\\n validate_args['inputs']['samples'] = 5\\n\\n # Define training loop. Output training evaluations every 1 epoch\\n # and the custom evaluation method every 10 epochs.\\n train = TrainModel(model=model, output_freq=1)\\n train.add_initial_training_notes(\\\"Training the semi-supervised VAE with %i labels. bn %s. seed %i.\\\" % (\\n n_labeled, str(model.batchnorm), seed))\\n train_mode = 'SSL'\\n train.train_model(f_train, train_args, f_validate, validate_args, train_mode,\\n train_dataset[0], validation_dataset, test_dataset,\\n\\t\\t train_labeled, # labeled training data if appyling Semi-supervised training\\n batch_size=100, \\n eval_valid_batch=True, \\n eval_test_batch=False, \\n n_epochs=n_epochs,\\n anneal=[(\\\"learningrate\\\", 200, 0.75, 3e-5)],\\n# warmup=[(\\\"kl_warmup\\\", init_kl_wu, 1., 100)]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e640dbacb8cc5b78ff6cc1371970224\",\n \"score\": \"0.5161181\",\n \"text\": \"def main_SS(path_root, **params):\\n params['days'] = 2\\n params['coupled'] = False\\n\\n WNC_model = WNC_Inps(path_root, **params)\\n WNC_model.write()\\n WNC_model.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf72241b35c67543f038724b84504ed\",\n \"score\": \"0.5160406\",\n \"text\": \"def updateS_test_fista(DD0SS0, X, opts):\\n D, D0, S, S0 = DD0SS0 # where DD0SS0 is a list\\n N, K0, T = S0.shape\\n M = D0.shape[1] # dictionary atom dimension\\n M_2 = int((M-1)/2)\\n C, K, _ = D.shape\\n R = F.conv1d(S0, D0.flip(1).unsqueeze(1), groups=K0, padding=M - 1).sum(1)[:, M_2:M_2 + T] # r is shape of [N, T)\\n # '''update the current s_n,k^(c) '''\\n for c, k in [(i, j) for i in range(C) for j in range(K)]:\\n Dcopy = D.clone().flip(2).unsqueeze(2) # D shape is [C,K,1, M]\\n Crange = torch.tensor(range(C))\\n DconvS = S[:, :, 0, :].clone() # to avoid zeros for cuda decision, shape of [N, C, T]\\n for cc in range(C):\\n # the following line is doing, convolution, sum up C, and truncation for m/2: m/2+T\\n DconvS[:, cc, :] = F.conv1d(S[:, cc, :, :], Dcopy[cc, :, :, :], groups=K, padding=M - 1).sum(1)[:, M_2:M_2 + T]\\n\\n dck = D[c, k, :] # shape of [M]\\n sck = S[:, c, k, :] # shape of [N, T]\\n Tdck = (toeplitz(dck.unsqueeze(0), m=T, T=T).squeeze()).t() # shape of [T, m=T]\\n\\n dck_conv_sck = F.conv1d(sck.unsqueeze(1), dck.flip(0).reshape(1, 1, M), padding=M-1).squeeze()[:, M_2:M_2+T] # shape of [N,T]\\n # term 1, 2, 3 should be in the shape of [N, T]\\n b = X - R - (DconvS.sum(1) - dck_conv_sck) # D'*S' = (DconvS.sum(1) - dck_conv_sck\\n torch.cuda.empty_cache()\\n\\n bb = torch.cat((b, torch.zeros(N, T, device=b.device)), 1)\\n TT = torch.cat((Tdck, opts.lamb2**0.5 * torch.eye(T, device=b.device)), 0)\\n r1 = fista(bb, TT, sck, opts.lamb)\\n S[:, c, k, :] = r1\\n # print( r1.abs().sum())\\n\\n # # istead of fista using SPAMS\\n # alpha = spams.lasso(np.asfortranarray(b.t().cpu().numpy()), D=np.asfortranarray(Tdck.cpu().numpy()), lambda1=opts.lamb/2, lambda2=opts.lamb2)\\n # a = sparse.csc_matrix.todense(alpha)\\n # r2 = torch.tensor(np.asarray(a).T, device=S.device)\\n # # S[:, c, k, :] = torch.tensor(np.asarray(a).T, device=S.device)\\n # print(r2.abs().sum())\\n\\n if torch.isnan(S).sum() + torch.isinf(S).sum() >0 : print(inf_nan_happenned)\\n return S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2e8b74fd37131f5a4f63b9c35feb84\",\n \"score\": \"0.51591617\",\n \"text\": \"def testscReduce(self):\\n nsuper = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 1]], dtype=int)\\n doublebasis = [self.basis[0], np.array([0.5, 0, 0]) + self.basis[0],\\n np.array([0, 0.5, 0]) + self.basis[0], np.array([0.5, 0.5, 0]) + self.basis[0]]\\n crys = crystal.Crystal(np.dot(self.sclatt, nsuper), doublebasis)\\n self.isscMetric(crys)\\n self.assertEqual(len(crys.basis), 1) # one chemistry\\n self.assertEqual(len(crys.basis[0]), 1) # one atom in the unit cell\",\n \"title\": \"\"\n },\n {\n \"docid\": \"600381feadc55d8a7785a906fcb8b234\",\n \"score\": \"0.5157278\",\n \"text\": \"def RD2SC(rd_list, hptype='3DNA', step_size=1):\\n if hptype == '3DNA' or hptype == 'CURVES':\\n hp_list = [RD2HP(rd_list[i], rd_list[i], hptype) if i == 0 else RD2HP(\\n rd_list[i - 1], rd_list[i], hptype) for i in range(len(rd_list))]\\n elif hptype == 'MATH':\\n s = np.linspace(0, (len(rd_list) - 1) * step_size, len(rd_list))\\n rx = np.array([x.r[0] for x in rd_list])\\n ry = np.array([x.r[1] for x in rd_list])\\n rz = np.array([x.r[2] for x in rd_list])\\n tx = np.diff(rx) / np.diff(s)\\n ty = np.diff(ry) / np.diff(s)\\n tz = np.diff(rz) / np.diff(s)\\n t = np.array([tx, ty, tz]).T\\n ssr = np.array([np.dot(t[i], np.dot(np.real(la.sqrtm(np.dot(\\n rd_list[i + 1].d, rd_list[i].d.T))), rd_list[i].d).T) for i in range(len(s) - 1)])\\n d1x = np.array([x.d[0][0] for x in rd_list])\\n d1y = np.array([x.d[0][1] for x in rd_list])\\n d1z = np.array([x.d[0][2] for x in rd_list])\\n d2x = np.array([x.d[1][0] for x in rd_list])\\n d2y = np.array([x.d[1][1] for x in rd_list])\\n d2z = np.array([x.d[1][2] for x in rd_list])\\n d3x = np.array([x.d[2][0] for x in rd_list])\\n d3y = np.array([x.d[2][1] for x in rd_list])\\n d3z = np.array([x.d[2][2] for x in rd_list])\\n td1x = np.diff(d1x) / np.diff(s)\\n td1y = np.diff(d1y) / np.diff(s)\\n td1z = np.diff(d1z) / np.diff(s)\\n td2x = np.diff(d2x) / np.diff(s)\\n td2y = np.diff(d2y) / np.diff(s)\\n td2z = np.diff(d2z) / np.diff(s)\\n td3x = np.diff(d3x) / np.diff(s)\\n td3y = np.diff(d3y) / np.diff(s)\\n td3z = np.diff(d3z) / np.diff(s)\\n deltad1 = np.array([td1x, td1y, td1z]).T\\n deltad2 = np.array([td2x, td2y, td2z]).T\\n deltad3 = np.array([td3x, td3y, td3z]).T\\n Twist = [2 * np.arcsin(np.dot(np.dot(deltad1[i], np.dot(np.real(la.sqrtm(np.dot(rd_list[i + 1].d, rd_list[i].d.T))),\\n rd_list[i].d).T), np.array([0, 1, 0])) / 2) * 180 / np.pi for i in range(len(s) - 1)]\\n Roll = [2 * np.arcsin(np.dot(np.dot(deltad3[i], np.dot(np.real(la.sqrtm(np.dot(rd_list[i + 1].d, rd_list[i].d.T))),\\n rd_list[i].d).T), np.array([1, 0, 0])) / 2) * 180 / np.pi for i in range(len(s) - 1)]\\n Tilt = [2 * np.arcsin(np.dot(np.dot(deltad2[i], np.dot(np.real(la.sqrtm(np.dot(rd_list[i + 1].d, rd_list[i].d.T))),\\n rd_list[i].d).T), np.array([0, 0, 1])) / 2) * 180 / np.pi for i in range(len(s) - 1)]\\n #Twist = [np.dot(np.dot(2*np.arcsin(deltad1[i]/2),np.dot(np.real(la.sqrtm(np.dot(rd_list[i+1].d,rd_list[i].d.T))),rd_list[i].d).T),np.array([0,1,0]))*180/np.pi for i in range(len(s)-1)]\\n #Roll = [np.dot(np.dot(2*np.arcsin(deltad3[i]/2),np.dot(np.real(la.sqrtm(np.dot(rd_list[i+1].d,rd_list[i].d.T))),rd_list[i].d).T),np.array([1,0,0]))*180/np.pi for i in range(len(s)-1)]\\n #Tilt = [np.dot(np.dot(2*np.arcsin(deltad2[i]/2),np.dot(np.real(la.sqrtm(np.dot(rd_list[i+1].d,rd_list[i].d.T))),rd_list[i].d).T),np.array([0,0,1]))*180/np.pi for i in range(len(s)-1)]\\n hps = [HP(HP_intra(0.0, 0.0, 0.0, 0.0, 0.0, 0.0),\\n HP_inter(0.0, 0.0, 0.0, 0.0, 0.0, 0.0))]\\n for i in range(len(s) - 1):\\n hps.append(HP(HP_intra(0.0, 0.0, 0.0, 0.0, 0.0, 0.0), HP_inter(\\n ssr[i][0], ssr[i][1], ssr[i][2], Tilt[i], Roll[i], Twist[i])))\\n hp_list = hps\\n else:\\n print('Please provide a valid type, \\\"3DNA\\\", \\\"CURVES\\\" or \\\"MATH\\\"')\\n sys.exit(0)\\n return SC(HP=hp_list, RD=rd_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9e87268df4200f96a4f2ae9e7fe62a5\",\n \"score\": \"0.51559955\",\n \"text\": \"def S(num):\\n\\n S_sum = 0\\n \\n for i in range(2, num + 1):\\n S_sum = S_sum + R(num)\\n \\n return S_sum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2125116572313ff7601cafab10b848e\",\n \"score\": \"0.51533395\",\n \"text\": \"def experiment(graph):\\n #ken_file = open('kendall.txt', 'w')\\n #spr_file = open('spearman.txt', 'w')\\n full_soc = 3\\n niter = 20000\\n niter_install = 10\\n #I_Ratios = [0.01, 0.02, 0.05, 0.1, 0.2, 0.4, 0.8, 1]\\n #B_Rates = [0.1, 0.2, 0.4, 0.8, 1]\\n I_Ratios = [0.2]\\n B_Rates = [0.01]\\n btn = nx.betweenness_centrality(graph)\\n btn_ranking = node_values_to_ranking(btn)\\n for install_ratio in I_Ratios:\\n num_install = int(install_ratio*nx.number_of_nodes(graph))\\n for _ in range(niter_install):\\n install_nodes = random.sample(graph.nodes(), num_install)\\n install_dict = {}\\n for node in graph.nodes():\\n install_dict[node] = False\\n for node in install_nodes:\\n install_dict[node] = True\\n soc_graph = construct_soc_graph(graph, full_soc, install_dict)\\n soc_btn = soc.betweenness_centrality(graph, full_soc, install_nodes)\\n soc_ranking = node_values_to_ranking(soc_btn)\\n for birth_rate in B_Rates:\\n max_counter = particle_simulation(\\n soc_graph, graph, birth_rate,full_soc, niter)\\n sim_ranking = node_values_to_ranking(max_counter)\\n ken_strd, spr_strd = compute_stats(sim_ranking, btn_ranking)\\n ken_soc, spr_soc = compute_stats(sim_ranking, soc_ranking)\\n ken_str = \\\" \\\".join([str(install_ratio), str(birth_rate), \\n str(ken_strd.correlation), \\n str(ken_soc.correlation), \\n str(ken_strd.pvalue),\\n str(ken_soc.correlation), ' \\\\n']) \\n spr_str = \\\" \\\".join([str(install_ratio), str(birth_rate), \\n str(spr_strd.correlation), \\n str(spr_soc.correlation), \\n str(spr_strd.pvalue),\\n str(spr_soc.pvalue), ' \\\\n']) \\n #ken_file.write(ken_str)\\n #spr_file.write(spr_str)\\n #print install_ratio, birth_rate, ken_strd.correlation, \\\\\\n # ken_soc.correlation\\n #print install_ratio, birth_rate, spr_strd.correlation, \\\\\\n # spr_soc.correlation\\n print ken_strd.correlation, ken_soc.correlation\\n print spr_strd.correlation, spr_soc.correlation\\n #print ken_strd.pvalue, ken_soc.pvalue\\n #print spr_strd.pvalue, spr_soc.pvalue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fedf8ac44716e4cbc6d46de831ac144\",\n \"score\": \"0.5149663\",\n \"text\": \"def sersic(Ie, Re, R, n):\\n b = 2*n**(-1/3)\\n return Ie * np.exp(-b * ( (R/Re)**(1/n) - 1) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce12d65a7a7f45e2b0b99a61d0a06063\",\n \"score\": \"0.51477253\",\n \"text\": \"def getR1R2(gamma, xtrain, sv):\\n \\n #R1 = kernelMatern(sv, gamma[0])\\n #R1 = kernelMatern(np.log(sv), gamma[0])\\n R1 = kernelSE(np.log(sv), gamma[0]) + 1e-8 * np.identity(len(sv)) # gamma1 = gamma[0], Regularizer\\n # the product over x1, x2 and x3\\n n = len(xtrain)\\n #R2 = np.ones((n,n))\\n R2 = kernelMatern(xtrain[:,0], gamma[1])\\n # dimension of input data (-1)\\n for i in range(1,xtrain.shape[1]):\\n R2 = R2 * kernelMatern(xtrain[:,i], gamma[i+1])\\n \\n #R2 = R2 + 1e-10 * np.identity(n) #Regularizer\\n \\n return R1, R2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8790e8f9c516a1e6439d6440a842886e\",\n \"score\": \"0.5145576\",\n \"text\": \"def PIeva_1MBS_2SBS(mu, N, Bmax, P, power, Num_queue):\\n Vnew = np.ones(N+1)\\n Vold = np.zeros(N+1)\\n \\n epsilon = 0.5\\n maxits = 50\\n numits = 0\\n# while np.amax(np.fabs(np.subtract(Vnew,Vold)))/np.amax(np.fabs(Vnew))>epsilon and numits<maxits:\\n while np.amax(np.fabs(np.subtract(Vnew, Vold))) > epsilon and numits < maxits:\\n\\n Vold = copy.deepcopy(Vnew)\\n \\n # enumerate all possible request queue states \\n for ind in np.arange(0, 1 + np.ravel_multi_index(N, N+1)): \\n # given index, obtain the corresponding subscript\\n sub = np.unravel_index(ind,N+1)\\n \\n u = np.array([mu[0][sub],mu[1][sub],mu[2][sub]])\\n \\n Vnew[sub] = cost_action_1MBS_2SBS(u,Vold,sub,N,Bmax,P,power,Num_queue)\\n \\n # relative value\\n sub = np.unravel_index(0,N+1) # reference state (0,0,0)\\n Vnew = Vnew - Vnew[sub] \\n \\n numits = numits+1\\n print(\\\"iteration in PIeva:\\\", numits)\\n return Vnew\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c42e0e2bc2300cc54be2e518b96184e\",\n \"score\": \"0.5144962\",\n \"text\": \"def cal_S(self,SI,C,info):\\n\\t\\tS = ND_list(info.Nst,element=\\\"dict()\\\")\\n\\t\\tfor ist in range(info.Nst):\\n\\t\\t\\tfor it1,it2 in itertools.product( info.Nt[ist], info.Nt[ist] ):\\n\\t\\t\\t\\tS[ist][it1,it2] = ND_list(info.Nl[it1],info.Nl[it2])\\n\\n\\t\\tfor ist in range(info.Nst):\\n\\t\\t\\tfor it1,it2 in itertools.product( info.Nt[ist], info.Nt[ist] ):\\n\\t\\t\\t\\tfor il1,il2 in itertools.product( range(info.Nl[it1]), range(info.Nl[it2]) ):\\n\\t\\t\\t\\t\\t# SI_C[ia1*im1*ie1*ia2*im2,iu2]\\n\\t\\t\\t\\t\\tSI_C = torch_complex.mm( \\n\\t\\t\\t\\t\\t\\tSI[ist][it1,it2][il1][il2].view(-1,info.Ne[it2]), \\n\\t\\t\\t\\t\\t\\tC[it2][il2] )\\n\\t\\t\\t\\t\\t# SI_C[ia1*im1,ie1,ia2*im2*iu2]\\n\\t\\t\\t\\t\\tSI_C = SI_C.view( info.Na[ist][it1]*info.Nm(il1), info.Ne[it1], -1 )\\n\\t\\t\\t\\t\\t# Ct[iu1,ie1]\\n\\t\\t\\t\\t\\tCt = C[it1][il1].t()\\n\\t\\t\\t\\t\\tC_mm = functools.partial(torch_complex.mm,Ct)\\n\\t\\t\\t\\t\\t# C_SI_C[ia1*im1][iu1,ia2*im2*iu2]\\n\\t\\t\\t\\t\\tC_SI_C = list(map( C_mm, SI_C ))\\n\\t\\t\\t\\t\\t# C_SI_C[ia1*im1*iu1,ia2*im2*iu2]\\n\\t\\t\\t\\t\\tC_SI_C = torch_complex.cat( C_SI_C, dim=0 )\\n#???\\t\\t\\t\\tC_SI_C = C_SI_C.view(info.Na[ist][it1]*info.Nm(il1)*info.Nu[it1][il1],-1)\\n\\t\\t\\t\\t\\tS[ist][it1,it2][il1][il2] = C_SI_C\\n\\t\\treturn S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be08a2d6ba84969315878a9f9e28943c\",\n \"score\": \"0.5143696\",\n \"text\": \"def _do_tsne(data, nr_components=2, init='random', plex=30,\\r\\n n_iter=1000, lr=200, rs=None):\\r\\n\\r\\n tsne = t_sne.TSNE(n_components=nr_components, init=init,\\r\\n perplexity=plex, random_state=rs, n_iter=n_iter, learning_rate=lr)\\r\\n\\r\\n return tsne.fit_transform(data), tsne\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3999fb48a546cefa31b8cb6d89f4754\",\n \"score\": \"0.5143005\",\n \"text\": \"def test1_lithium_scattering_solution():\\n # Li^+ atom\\n #atomlist = [(3, (0.0, 0.0, 0.0))]\\n atomlist = [(3, np.random.rand(3))]\\n\\n # choose resolution of multicenter grids for bound orbitals\\n settings.radial_grid_factor = 20 # controls size of radial grid \\n settings.lebedev_order = 25 # controls size of angular grid\\n # 1s core orbitals for Li+^ atom\\n RDFT = BasissetFreeDFT(atomlist, None, charge=+1)\\n #bound_orbitals = RDFT.getOrbitalGuess()\\n Etot, bound_orbitals, orbital_energies = RDFT.solveKohnSham()\\n\\n # choose resolution of multicenter grids for continuum orbitals\\n settings.radial_grid_factor = 20 #120 # controls size of radial grid \\n settings.lebedev_order = 25 #41 # controls size of angular grid\\n # show number of radial and angular points in multicenter grid\\n print_grid_summary(atomlist,\\n settings.lebedev_order, settings.radial_grid_factor)\\n \\n print \\\"electron density...\\\"\\n # electron density of two electrons in the 1s core orbital\\n rho = density_func(bound_orbitals)\\n\\n print \\\"total charges...\\\"\\n qnuc, qelec, qtot = total_charge(atomlist, rho)\\n # number of electrons from integration\\n nelec = int(np.round(-qelec))\\n \\n # We have to fix the center of the coordinate system relative\\n # to which the asymptotic Coulomb waves are defined. The origin\\n # R0 should be chosen such that the effective potential at large\\n # distances appraches\\n # charge\\n # V(r) ---> V0(r) = - -------\\n # |r-R0|\\n # \\n\\n # In a neutral molecule the asymptotic behaviour of the effective\\n # Kohn-Sham potential\\n #\\n # V (r) ---> - 1/r for r -> oo\\n # eff\\n # is not the result of electrostatics (because the monopole of the\\n # total charge distribution vanishes) but of the asymptotic behaviour\\n # of the exchange functional\\n #\\n # V [rho](r) ---> -1/r for r -> oo\\n # exchange \\n\\n #\\n # The origin r0 such that\\n #\\n # V_exchange ----> -1/|r-r0|\\n #\\n # is taken as the center of electronic charge in the HOMO\\n #\\n # r0 = <HOMO|r|HOMO>\\n # \\n #\\n print \\\"\\\"\\n print \\\" Origin\\\"\\n print \\\" ======\\\"\\n \\n if nelec > 0:\\n homo = bound_orbitals[-1]\\n # density of the highest occupied molecular orbital\\n def rho_HOMO(x,y,z):\\n return homo(x,y,z)**2\\n \\n print \\\"integrals over charge density of HOMO orbital...\\\"\\n center_nuc, center_homo, center_tot = center_of_charge(atomlist, rho_HOMO, verbose=0)\\n\\n print \\\" The center of the electron density in the HOMO\\\"\\n print \\\" is chosen as the the origin.\\\"\\n center = center_homo\\n else:\\n center_nuc, dummy, dummy = center_of_charge(atomlist, None)\\n\\n print \\\" As there are no electrons, the center of the nuclear charge\\\"\\n print \\\" is chosen as the origin.\\\"\\n center = center_nuc\\n\\n print \\\" The origin of the coordinate system relative to which\\\"\\n print \\\" asymptotic solutions are defined is placed at\\\"\\n print \\\"\\\"\\n print \\\" center = %+e %+e %+e\\\" % tuple(center)\\n print \\\"\\\"\\n \\n print \\\"NOTE: Two-electron systems are treated with Hartree-Fock theory.\\\"\\n print \\\" For Li+ the effective HF potential goes as -2/r for large r!\\\"\\n print \\\" Therefore the continuum electron sees a core with charge +2.\\\"\\n xc = None\\n # THIS IS WEIRD, so the continuum electron sees Li^(2+) instead of Li^+???\\n # It looks strange, but makes perfect sense:\\n # The effective Hartree-Fock potential for Li^+\\n # Veff = -3/r + 0.5 * Vcoul(r)\\n # approaches\\n # V0 = -2/r\\n # asymptotically, since for a closed-shell 2-electron system Vcoul(r) -> 2/r.\\n # Therefore we have to match the solution to a Coulomb wave with charge +2.\\n # If we would use a different effective potential for bound (-2/r) and continuum (-1/r)\\n # states, the states would not be solutions of the same Schroedinger equation,\\n # which seems inconsistent.\\n\\n # Asymptotically the effective potential is Coulomb-like -(Q+1)/r . It's strength consists\\n # of an electrostatic part (Q=0 for a neutral molecule, Q=+1 for a cation) and an\\n # exchange part (+1, since v_xc[rho] -> -1/r for r -> oo)\\n # if there are other electrons present.\\n charge = qtot\\n if nelec > 0:\\n # add charge felt due to exchange\\n charge = qtot + 1\\n print \\\" electrostatic charge qtot = %+e\\\" % qtot\\n print \\\" asymptotic charge = %+e\\\" % charge\\n \\n print \\\"effective potential...\\\"\\n # potential energy for Li nucleus and 2 core electrons\\n potential = effective_potential_func(atomlist, rho, xc, nelec=2)\\n\\n # origin of Coulomb waves and V0\\n x0,y0,z0 = center\\n def v0(x,y,z):\\n # shift coordinates\\n x,y,z = x-x0,y-y0,z-z0\\n # distance from origin\\n r = np.sqrt(x*x+y*y+z*z)\\n return -charge/r\\n\\n def v1(x,y,z):\\n return potential(x,y,z) - v0(x,y,z)\\n \\n # The continuum orbital is specified by its energy and asymptotic\\n # angular momentum (E,l,m)\\n \\n # energy of continuum orbital\\n E = 1.0 \\n # length of wave vectors\\n k = np.sqrt(2*E)\\n# # angular quantum numbers of asymptotic solution (s-orbital)\\n# l = 0\\n# m = 0\\n # angular quantum numbers of asymptotic solution (px-orbital)\\n l = 1\\n m = +1\\n\\n print \\\" \\\"\\n print \\\" Asymptotic continuum wavefunction\\\"\\n print \\\" =================================\\\"\\n print \\\" energy E= %e Hartree ( %e eV )\\\" % (E, E*AtomicData.hartree_to_eV)\\n print \\\" wavevector k= %e a.u.\\\" % k\\n print \\\" angular moment l= %d m= %+d\\\" % (l,m)\\n print \\\" \\\"\\n \\n # asymptotically correct solution for V0 = -1/r (hydrogen)\\n Cf = regular_coulomb_func(E, charge, l, m, 0.0,\\n center=center)\\n phi0 = Cf\\n \\n # right-hand side of inhomogeneous Schroedinger equation\\n def source(x,y,z):\\n return -v1(x,y,z) * phi0(x,y,z)\\n\\n #\\n # solve (H0 + V1 - E) dphi = - V1 phi0\\n # for orbital correction dphi\\n\\n atomic_numbers, atomic_coordinates = atomlist2arrays(atomlist)\\n\\n print \\\"Schroedinger equation ...\\\"\\n dphi = multicenter_inhomogeneous_schroedinger(potential, source, E,\\n atomic_coordinates, atomic_numbers,\\n radial_grid_factor=settings.radial_grid_factor,\\n lebedev_order=settings.lebedev_order)\\n\\n # Combine asymptotically correct solution with correction \\n # phi = phi0 + dphi\\n phi = add_two_functions(atomlist, phi0, dphi, 1.0, 1.0)\\n\\n print \\\"residuals (H-E)phi ...\\\"\\n # residual for phi0 R0 = (H-E)phi0\\n residual0 = residual_func(atomlist, phi0, potential, E)\\n # residual for final solution R = (H-E)phi\\n residual = residual_func(atomlist, phi, potential, E)\\n\\n # spherical average of residual function\\n\\n # The radial grid is chosen for the atom with the largest atomic number\\n Zmax = max([Z for (Z,pos) in atomlist])\\n # The origin of the radial grid is placed at `center`\\n atom_center = (Zmax, center)\\n residual_avg = spherical_average_residual_func(atom_center, residual)\\n \\\"\\\"\\\"\\n ### DEBUG\\n # show radial wavefunction\\n wfn_rad = radial_wave_func(atomlist, phi, l, m)\\n import matplotlib.pyplot as plt\\n r = np.linspace(1.0e-3, 200.0, 4000)\\n plt.plot(r, wfn_rad(r), label=r\\\"$R_{%d,%d}(r)$\\\" % (l,m))\\n plt.show()\\n ###\\n \\\"\\\"\\\"\\n\\n print \\\"phase shifts...\\\"\\n # Which algorithm should be used for matching the continuum solution\\n # to the Coulomb wave to determine the phase shift and scaling factor?\\n phase_matching = \\\"2pts\\\" # \\\"2pts\\\" \\\"olap\\\" \\\"lstsq\\\" \\n \\n if phase_matching == \\\"olap\\\":\\n # The phase shift is determined by integrating over\\n # the radial coordinate in the interval [rmin,rmax].\\n # On the one hand rmin < rmax should be chosen large enough,\\n # so that the continuum orbital approaches its asymptotic form,\\n # on the other hand rmax should be small enough that the accuracy\\n # of the solution due to the sparse r-grid is still high enough.\\n # A compromise has to be struck depending on the size of the radial grid.\\n\\n # We integrate over several periods, but not more than 30 bohr.\\n wavelength = 2.0*np.pi / k\\n print \\\"wavelength = %e\\\" % wavelength\\n rmin = 70.0 \\n rmax = rmin + max(10*wavelength, 30.0)\\n \\n scale, delta = phaseshift_olap(atomlist, phi, E, charge, l, m, rmin, rmax,\\n center=center)\\n\\n elif phase_matching == \\\"lstsq\\\":\\n # The phase shift is determined by matching the radial wavefunction\\n # to a shifted and scaled Coulomb function at a number of radial\\n # sampling points drawn from the interval [rmin, rmax].\\n # On the one hand rmin < rmax should be chosen large enough,\\n # so that the continuum orbital approaches its asymptotic form,\\n # on the other hand rmax should be small enough that the accuracy\\n # of the solution due to the sparse r-grid is still high enough.\\n # A compromise has to be struck depending on the size of the radial grid.\\n \\n # The matching points are spread over several periods,\\n # but not more than 30 bohr.\\n wavelength = 2.0*np.pi / k\\n print \\\"wavelength = %e\\\" % wavelength\\n rmin = 70.0 \\n rmax = rmin + max(10*wavelength, 30.0)\\n Npts = 100\\n\\n # determine phase shift and scaling factor by a least square\\n # fit the the regular Coulomb function\\n scale, delta = phaseshift_lstsq(atomlist, phi, E, charge, l, m, rmin, rmax, Npts,\\n center=center)\\n\\n elif phase_matching == \\\"2pts\\\":\\n # The phase shift is determined from matching the radial wavefunction\\n # to a shifted and scaled Coulomb function at two points r1 and r2.\\n wavelength = 2.0*np.pi / k\\n print \\\"wavelength = %e\\\" % wavelength\\n\\n r1 = 50.0\\n r2 = r1 + 0.001*wavelength\\n \\n scale, delta = phaseshift_2pts(atomlist, phi, E, charge, l, m, r1, r2,\\n center=center)\\n\\n \\n \\n print \\\"scale factor (relative to Coulomb wave) = %s\\\" % scale\\n print \\\"phase shift (relative to Coulomb wave) = %e \\\" % delta\\n\\n # normalize wavefunction, so that 1/scale phi(x,y,z) approaches\\n # asymptotically a phase-shifted Coulomb wave\\n phi_norm = multicenter_operation([phi], lambda fs: fs[0]/scale, \\n atomic_coordinates, atomic_numbers,\\n radial_grid_factor=settings.radial_grid_factor,\\n lebedev_order=settings.lebedev_order)\\n\\n # The continuum orbital should be orthogonal to the bound\\n # orbitals belonging to the same Hamiltonian. I think this\\n # should come out correctly by default.\\n print \\\" \\\"\\n print \\\" Overlaps between bound orbitals and continuum orbital\\\"\\n print \\\" =====================================================\\\"\\n for ib,bound_orbital in enumerate(bound_orbitals):\\n olap_bc = overlap(atomlist, bound_orbital, phi_norm)\\n print \\\" <bound %d| continuum> = %e\\\" % (ib+1, olap_bc)\\n print \\\"\\\"\\n\\n # shifted regular coulomb function\\n Cf_shift = regular_coulomb_func(E, charge, l, m, delta,\\n center=center)\\n \\n # save radial wavefunctions and spherically averaged residual\\n # radial part of Coulomb wave without phase shift\\n phi0_rad = radial_wave_func(atomlist, phi0, l, m,\\n center=center)\\n # radial part of shifted Coulomb wave\\n Cf_shift_rad = radial_wave_func(atomlist, Cf_shift, l, m,\\n center=center)\\n # radial part of normalized scattering solution\\n phi_norm_rad = radial_wave_func(atomlist, phi_norm, l, m,\\n center=center)\\n\\n print \\\"# RADIAL_WAVEFUNCTIONS\\\"\\n print \\\"# Asymptotic wavefunction:\\\"\\n print \\\"# charge Z= %+d\\\" % charge\\n print \\\"# energy E= %e k= %e\\\" % (E, k)\\n print \\\"# angular momentum l= %d m= %+d\\\" % (l, m)\\n print \\\"# phase shift delta= %e rad\\\" % delta\\n print \\\"# \\\"\\n print \\\"# R/bohr Coulomb Coulomb radial wavefunction spherical avg. residual\\\"\\n print \\\"# shifted R_{l,m}(r) <|(H-E)phi|^2>\\\"\\n import sys\\n # write table to console\\n r = np.linspace(1.0e-3, 100, 1000)\\n data = np.vstack((r, phi0_rad(r), Cf_shift_rad(r), phi_norm_rad(r), residual_avg(r))).transpose()\\n np.savetxt(sys.stdout, data, fmt=\\\" %+e \\\")\\n print \\\"# END\\\"\\n\\n\\n \\n # plot wavefunction, potential and residual along x-axis\\n r = np.linspace(1.0e-3, 200.0, 4000)\\n\\n # save cut of wavefunction along x-axis to file\\n data = np.vstack((r, phi0(r,0*r,0*r), phi_norm(r,0*r,0*r), residual_avg(r))).transpose()\\n fh = open(\\\"/tmp/li+_wavefunction.dat\\\", \\\"w\\\")\\n print>>fh, \\\"# phase shift (relative to Coulomb wave) = %e\\\" % delta\\n print>>fh, \\\"# R / bohr Coulomb phi <(H-E)phi>\\\"\\n np.savetxt(fh, data, fmt=\\\"%+e\\\")\\n fh.close()\\n \\n import matplotlib.pyplot as plt\\n plt.ylim((-0.55, 0.25))\\n plt.xlabel(\\\"r / bohr\\\")\\n\\n # wavefunctions\\n plt.plot(r, phi0(r,0*r,0*r), label=r\\\"$\\\\phi_0$\\\")\\n plt.plot(r, dphi(r,0*r,0*r), label=r\\\"$\\\\Delta \\\\phi$\\\")\\n plt.plot(r, phi(r,0*r,0*r), label=r\\\"$\\\\phi = \\\\phi_0 + \\\\Delta \\\\phi$\\\")\\n plt.plot(r, phi_norm(r,0*r,0*r), label=r\\\"normalized $\\\\phi$\\\")\\n plt.plot(r, Cf_shift(r,0*r,0*r), ls=\\\"-.\\\", label=r\\\"shifted Coulomb\\\")\\n\\n # potentials\\n plt.plot(r, v0(r,0*r,0*r), lw=2, ls=\\\"-.\\\", label=r\\\"potential $V_0(r) = -%d/r$\\\" % int(charge))\\n plt.plot(r, v1(r,0*r,0*r), lw=2, ls=\\\"-.\\\", label=r\\\"potential $V_1(r) = V(r) - V_0(r)$\\\")\\n plt.plot(r, potential(r,0*r,0*r), lw=2, ls=\\\"-.\\\", label=r\\\"potential $V(r) = V_0(r) + V_1(r)$\\\")\\n \\n # residuals\\n plt.plot(r, residual0(r,0*r,0*r), label=r\\\"residual $(H-E)\\\\phi_0$\\\")\\n plt.plot(r, residual( r,0*r,0*r), label=r\\\"residual $(H-E)\\\\phi$\\\")\\n plt.plot(r, residual_avg(r), label=r\\\"residual $\\\\int d\\\\Omega (H-E)\\\\phi$\\\")\\n \\n plt.legend()\\n \\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0ed5c791192d06c32acf24c1b08bf2\",\n \"score\": \"0.51368755\",\n \"text\": \"def _scode_i(n):\\n if n > 1:\\n C1 = numpy.vstack((numpy.ones((1, 1)),\\n numpy.zeros((n - 1, 1))))\\n C2 = numpy.vstack((numpy.full((1, n), -1. / n),\\n _scode_i(n - 1) * numpy.sqrt(1. - 1. / (n * n))))\\n return numpy.hstack((C1, C2))\\n if n == 1:\\n return numpy.array([1, -1])\\n if n < 1:\\n raise \\\"Dimension n should be at least one\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a38cba00f3abf6ebf62a73f27398b8d\",\n \"score\": \"0.5135459\",\n \"text\": \"def Sn_vs_n(tdata,Sn_mc,Sn_data,filename,angular_radius,ending_n=180,load=True,redshift=False):\\n\\n\\tstarting_n = 1 # In the data file the S_1 is the first column\\n\\tn_range = range(0,ending_n) # index 0 corresponds to Sn_1 \\n\\n\\tall_sn = []\\n\\tall_sn_mc = []\\n\\tall_sl = [] # (length n list containing SL_1 to SL_80)\\n\\tall_std = [] # contains the standard deviations of the MC simulations\\n\\tN = len(tdata)\\n\\tjain_sigma = (0.33/N)**0.5\\n\\n\\t# Whether to calculate or load the data.\\n\\tif not load:\\n\\t\\tfor n in n_range:\\n\\t\\t\\t# print 'Now doing n = ', n+starting_n , '...' \\n\\t\\t\\tSn_mc_n = np.asarray(Sn_mc['S_'+str(n+starting_n)])\\n\\t\\t\\tav_Sn_mc_n = np.mean(Sn_mc_n)\\n\\t\\t\\tsigma = np.std(Sn_mc_n)\\n\\n\\t\\t\\tSn_data_n = Sn_data[n]\\n\\t\\t\\t# print Sn_data_n, av_Sn_mc_n\\n\\t\\t\\tSL = 1 - norm.cdf( (Sn_data_n - av_Sn_mc_n) / (sigma) )\\n\\t\\t\\tall_sn.append(Sn_data_n)\\n\\t\\t\\tall_sl.append(SL)\\n\\t\\t\\tall_sn_mc.append(av_Sn_mc_n)\\n\\t\\t\\tall_std.append(sigma)\\n\\t\\t\\n\\t\\tResults = Table()\\n\\t\\tResults['n'] = range(starting_n,ending_n+1)\\n\\t\\tResults['Sn_data'] = all_sn\\n\\t\\tResults['SL'] = all_sl\\n\\t\\tResults['Sn_mc'] = all_sn_mc\\n\\t\\tResults['angular_radius'] = angular_radius\\n\\t\\tResults['sigma_S_n'] = all_std\\n\\t\\tResults['jain_sigma'] = [jain_sigma]\\n\\telse: #(load == True)\\n\\t\\tResults = Table(fits.open('./data/Statistics_'+filename+'_results.fits')[1].data)\\n\\t\\tall_sl = Results['SL']\\n\\n\\tif redshift:\\n\\t\\t# add comma because a tuple is always returned\\n\\t\\tline, = plt.plot(Results['n'],np.log(all_sl),c='r',label='redshift')\\n\\telse:\\n\\t\\tline, = plt.plot(Results['n'],np.log(all_sl),c='b',label='no redshift')\\n\\n\\tplt.legend()\\n\\n\\t# print np.argmin(all_sl)\\n\\tif redshift:\\n\\t\\tplt.title('SL vs n for '+filename+'\\\\n\\\\n')\\n\\t\\tplt.ylabel('Log_e SL')\\n\\t\\tplt.xlim(2,180)\\n\\t\\tplt.ylim(-4,0)\\n\\t\\tplt.xlabel('n')\\n\\n\\tif angular_radius is not None and redshift:\\n\\t\\tax1 = plt.gca()\\n\\t\\tax2 = plt.twiny()\\n\\t\\tax2.set_xlabel('angular_radius (degrees)')\\n\\t\\txticks = ax1.get_xticks()\\n\\t\\tax2.set_xticks(xticks)\\n\\t\\tprint (np.asarray(xticks,dtype='int'))\\n\\t\\txticks2 = np.append(0,angular_radius)[np.asarray(xticks,dtype='int')]\\n\\t\\tax2.set_xticklabels(tick_function(xticks2))\\n\\tplt.subplots_adjust(top=0.850)\\n\\t# plt.gca().set_aspect('equal', adjustable='datalim')\\n\\n\\treturn line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad01d5290261337b5bdcdc998841af9d\",\n \"score\": \"0.51339525\",\n \"text\": \"def test2_lithium_scattering_solution():\\n # Li^+ atom\\n atomlist = [(3, np.random.rand(3))]\\n\\n # choose resolution of multicenter grids for bound orbitals\\n settings.radial_grid_factor = 20 # controls size of radial grid \\n settings.lebedev_order = 25 # controls size of angular grid\\n # 1s core orbitals for Li+^ atom\\n RDFT = BasissetFreeDFT(atomlist, None, charge=+1)\\n bound_orbitals = RDFT.getOrbitalGuess()\\n #Etot, bound_orbitals, orbital_energies = RDFT.solveKohnSham()\\n\\n # choose resolution of multicenter grids for continuum orbitals\\n settings.radial_grid_factor = 20 #120 # controls size of radial grid \\n settings.lebedev_order = 25 #41 # controls size of angular grid\\n\\n # electron density of two electrons in the 1s core orbital\\n rho = density_func(bound_orbitals)\\n # HOMO wavefunction\\n homo = bound_orbitals[-1]\\n\\n # energy of continuum orbital\\n E = 1.0 \\n# # angular quantum numbers of asymptotic solution (s-orbital)\\n# l = 0\\n# m = 0\\n # angular quantum numbers of asymptotic solution (px-orbital)\\n l = 1\\n m = +1\\n \\n delta, phi = RDFT.solveScatteringProblem(rho, homo, E, l, m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af44e323d447af109be655059dc0285e\",\n \"score\": \"0.5132864\",\n \"text\": \"def LL_snps(self, snps, Asnps=None, inter=None, identity_trick=False):\\n LL_snps = np.zeros(snps.shape[1])\\n LL_new = np.zeros(snps.shape[1])\\n LL_snps_0 = np.zeros(snps.shape[1])\\n #index_snpterm = self.gp.mean.n_terms\\n if inter is None:\\n\\n LL_snps_0[:] = self._LL_0\\n for i_snp in range(snps.shape[1]):\\n #correlated but not identical:\\n if 0:\\n self._gp.mean.clear_cache('Fstar','Astar','Xstar','Xhat',\\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\\n 'LRLdiag_Xhat_tens','Areml_grad',\\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')\\n Areml_small1 = self._gp.mean.Areml()\\n LL_new[i_snp],beta,Areml1,Areml_11 = self.LML_blockwise(snp=np.dot(self._gp.mean.Lr,snps[:,i_snp:i_snp+1]), Asnp=Asnps, identity_trick=True)\\n #worse:\\n #LL_new[i_snp],beta = self.LML_blockwise(snp=snps[:,i_snp:i_snp+1], Asnp=Asnps, identity_trick=True)\\n if 0:\\n Areml_small2 = self._gp.mean.Areml()\\n diff_small = Areml_small1-Areml_small2\\n absdiff_small=np.absolute(diff_small)\\n print((np.max(absdiff_small)))\\n self._gp.mean.addFixedEffect(F=snps[:,i_snp:i_snp+1], A=Asnps)\\n \\n if 0:\\n self._gp.mean.clear_cache('Fstar','Astar','Xstar','Xhat',\\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\\n 'LRLdiag_Xhat_tens','Areml_grad',\\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')\\n if 0:\\n Areml2_prior = self._gp.mean.Areml()\\n LL__ = self._gp.LML(identity_trick=identity_trick)\\n \\n Areml2 = self._gp.mean.Areml()\\n diff=Areml1-Areml2\\n if 0:\\n diff_prior=Areml2 - Areml2_prior\\n absdiff_prior=np.absolute(diff_prior)\\n print((np.max(absdiff_prior)))\\n absdiff=np.absolute(diff)\\n print((np.max(absdiff)))\\n print((np.max(absdiff[0:-1,0:-1])))\\n if 0:\\n diff_large_small = Areml1[0:-1,0:-1]-Areml_small2\\n absdiff_large_small=np.absolute(diff_large_small)\\n print((np.max(absdiff_large_small)))\\n diff_large_small = Areml2[0:-1,0:-1]-Areml_11\\n absdiff_large_small=np.absolute(diff_large_small)\\n print((np.max(absdiff_large_small)))\\n #import ipdb;ipdb.set_trace()\\n LL_snps[i_snp] = LL__\\n self._gp.mean.removeFixedEffect()\\n else:\\n for i_snp in range(snps.shape[1]):\\n self._gp.mean.addFixedEffect(F=snps[:,i_snp:i_snp+1], A=Asnps)\\n LL_snps_0[i_snp] = self._gp.LML(identity_trick=identity_trick)\\n self._gp.mean.addFixedEffect(F=snps[:,i_snp:i_snp+1]*inter, A=Asnps)\\n LL_snps[i_snp] = self._gp.LML(identity_trick=identity_trick)\\n self._gp.mean.removeFixedEffect()\\n self._gp.mean.removeFixedEffect()\\n return LL_snps,LL_snps_0,LL_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f99baf37f2fb2b1b6bb4e6128a128f9\",\n \"score\": \"0.5128025\",\n \"text\": \"def compute_Cstat(options):\\n\\n import numpy\\n import astropy.io.fits as fits\\n from astropy.table import Table\\n import JLA_library as JLA\\n\\n # ----------- Read in the configuration file ------------\\n\\n params=JLA.build_dictionary(options.config)\\n\\n # ----------- Read in the SN ordering ------------------------\\n SNeList = numpy.genfromtxt(options.SNlist,\\n usecols=(0, 2),\\n dtype='S30,S200',\\n names=['id', 'lc'])\\n nSNe = len(SNeList)\\n\\n for i, SN in enumerate(SNeList):\\n SNeList['id'][i] = SNeList['id'][i].replace('lc-', '').replace('.list', '').replace('_smp','')\\n\\n lcfile = JLA.get_full_path(params[options.lcfits])\\n SNe = Table.read(lcfile, format='fits')\\n\\n # ----------- Read in the data --------------------------\\n\\n print 'There are %d SNe in the sample' % (nSNe)\\n\\n indices = JLA.reindex_SNe(SNeList['id'], SNe)\\n SNe=SNe[indices]\\n\\n C_stat=numpy.zeros(9*nSNe*nSNe).reshape(3*nSNe,3*nSNe)\\n\\n for i,SN in enumerate(SNe):\\n cov=numpy.zeros(9).reshape(3,3)\\n cov[0,0]=SN['dmb']**2.\\n cov[1,1]=SN['dx1']**2.\\n cov[2,2]=SN['dcolor']**2.\\n cov[0,1]=SN['cov_m_s']\\n cov[0,2]=SN['cov_m_c']\\n cov[1,2]=SN['cov_s_c']\\n # symmetrise\\n cov=cov+cov.T-numpy.diag(cov.diagonal())\\n C_stat[i*3:i*3+3,i*3:i*3+3]=cov\\n\\n # ----------- Read in the base matrix computed using salt2_stat.cc ------------\\n\\n date = JLA.get_date()\\n fits.writeto('C_stat_lc_%s.fits' % date,C_stat,clobber=True)\\n \\n if options.base!=None:\\n C_stat+=fits.getdata(options.base)\\n\\n fits.writeto('C_stat_%s.fits' % date,C_stat,clobber=True) \\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c19e2e1a1c6819b2f3fc3d3a49a6a6f\",\n \"score\": \"0.51258594\",\n \"text\": \"def noisepsd_X2(f, model=\\\"SciRDv1\\\"):\\n\\n x = 2.0 * np.pi * lisaLT * f\\n\\n Spm, Sop = lisanoises(f, model)\\n\\n Sx = 64.0 * np.sin(x) ** 2 * np.sin(2 * x) ** 2 * Sop\\n ## TODO Check the acceleration noise term\\n Sx += 256.0 * (3 + np.cos(2 * x)) * np.cos(x) ** 2 * np.sin(x) ** 4 * Spm\\n\\n ### TODO Incule Galactic Binaries\\n # if includewd != None:\\n # Sx += WDconfusionX(f, includewd, model=model)\\n # #Sx += makewdnoise(f,includewd,'X')\\n\\n return Sx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7268cfeea78cccaa4019ece2b7fabdfe\",\n \"score\": \"0.51252484\",\n \"text\": \"def shlcar3x3(a, x,y,z, sps):\\n\\n cps=np.sqrt(1-sps**2)\\n s3ps=4*cps**2-1 # this is sin(3*ps)/sin(ps)\\n\\n hx,hy,hz= [0.]*3\\n\\n l=0\\n for m in range(2): # m=1 for 1st sum (perp symmetry), m=2 for 2nd sum (parallel symmetry)\\n for i in range(3):\\n p=a[36+i]\\n q=a[42+i]\\n cypi=np.cos(y/p)\\n cyqi=np.cos(y/q)\\n sypi=np.sin(y/p)\\n syqi=np.sin(y/q)\\n for k in range(3):\\n r=a[39+k]\\n s=a[45+k]\\n szrk=np.sin(z/r)\\n czsk=np.cos(z/s)\\n czrk=np.cos(z/r)\\n szsk=np.sin(z/s)\\n sqpr=np.sqrt(1/p**2+1/r**2)\\n sqqs=np.sqrt(1/q**2+1/s**2)\\n epr=np.exp(x*sqpr)\\n eqs=np.exp(x*sqqs)\\n for n in range(2): # n=1 for the 1st part of each coefficient, n=2 for 2nd\\n if m == 0:\\n if n == 0:\\n dx=-sqpr*epr*cypi*szrk\\n dy= epr/p*sypi*szrk\\n dz=-epr/r*cypi*czrk\\n hx=hx+a[l]*dx\\n hy=hy+a[l]*dy\\n hz=hz+a[l]*dz\\n else:\\n dx=dx*cps\\n dy=dy*cps\\n dz=dz*cps\\n hx=hx+a[l]*dx\\n hy=hy+a[l]*dy\\n hz=hz+a[l]*dz\\n else:\\n if n == 0:\\n dx=-sps*sqqs*eqs*cyqi*czsk\\n dy= sps*eqs/q*syqi*czsk\\n dz= sps*eqs/s*cyqi*szsk\\n hx=hx+a[l]*dx\\n hy=hy+a[l]*dy\\n hz=hz+a[l]*dz\\n else:\\n dx=dx*s3ps\\n dy=dy*s3ps\\n dz=dz*s3ps\\n hx=hx+a[l]*dx\\n hy=hy+a[l]*dy\\n hz=hz+a[l]*dz\\n l += 1\\n\\n return hx,hy,hz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea74b7e6da48691d603655582c25dc4\",\n \"score\": \"0.512223\",\n \"text\": \"def main():\\n cuda = torch.cuda.is_available()\\n batch_size = 32\\n M, N, O = 192, 128, 96\\n grid = torch.from_numpy(\\n np.fft.ifftshift(np.mgrid[-M//2:-M//2+M, -N//2:-N//2+N, -O//2:-O//2+O].astype('float32'), axes=(1,2,3)))\\n if cuda:\\n grid = grid.cuda()\\n sigma = 2.\\n J, L = 2, 3\\n integral_powers = [0.5, 1., 2., 3.]\\n args = {'integral_powers': integral_powers}\\n pos, energies, full_charges, valence_charges = get_qm7_positions_energies_and_charges(\\n M, N, O, J, L, sigma)\\n n_molecules = pos.size(0)\\n n_batches = np.ceil(n_molecules / batch_size).astype(int)\\n n_batches = 2\\n\\n scat = SolidHarmonicScattering(M=M, N=N, O=O, J=J, L=L, sigma_0=sigma)\\n\\n scat_0, scat_1, scat_2 = [], [], []\\n print('Computing solid harmonic scattering coefficients of molecules \\\\\\n of QM7 database on {}'.format('GPU' if cuda else 'CPU'))\\n print('L: {}, J: {}, integral powers: {}'.format(L, J, integral_powers))\\n for i in tqdm(range(n_batches)):\\n start, end = i*batch_size, min((i+1)*batch_size, n_molecules)\\n pos_batch = pos[start:end]\\n\\n full_batch = full_charges[start:end]\\n full_density_batch = generate_weighted_sum_of_gaussians(\\n grid, pos_batch, full_batch, sigma, cuda=cuda)\\n\\n full_scat_0 = compute_integrals(full_density_batch, integral_powers)\\n full_scat_1, full_scat_2 = scat(full_density_batch, True, 'integral', args)\\n\\n val_batch = valence_charges[start:end]\\n val_density_batch = generate_weighted_sum_of_gaussians(\\n grid, pos_batch, val_batch, sigma, cuda=cuda)\\n val_scat_0 = compute_integrals(val_density_batch, integral_powers)\\n val_scat_1, val_scat_2 = scat(val_density_batch, True, 'integral', args)\\n\\n core_density_batch = full_density_batch - val_density_batch\\n core_scat_0 = compute_integrals(core_density_batch, integral_powers)\\n core_scat_1, core_scat_2 = scat(core_density_batch, True, 'integral', args)\\n\\n scat_0.append(torch.stack([full_scat_0, val_scat_0, core_scat_0], dim=-1))\\n scat_1.append(torch.stack([full_scat_1, val_scat_1, core_scat_1], dim=-1))\\n scat_2.append(torch.stack([full_scat_2, val_scat_2, core_scat_2], dim=-1))\\n\\n n = batch_size * n_batches\\n\\n scat_0 = torch.cat(scat_0, dim=0)\\n scat_1 = torch.cat(scat_1, dim=0)\\n scat_2 = torch.cat(scat_2, dim=0)\\n\\n np_scat_0 = scat_0.numpy().reshape((n, -1))\\n np_scat_1 = scat_1.numpy().reshape((n, -1))\\n np_scat_2 = scat_2.numpy().reshape((n, -1))\\n\\n print('order 1 : {} coef, order 2 : {} coefs'.format(np_scat_1.shape[1], np_scat_2.shape[1]))\\n target = energies.numpy()[:n]\\n evaluate_linear_regression(np_scat_0, np_scat_1, np_scat_2, target)\\n evaluate_bilinear_regression(np_scat_0, np_scat_1, np_scat_2, target)\\n\\n basename = 'qm7_L_{}_J_{}_sigma_{}_MNO_{}_powers_{}.npy'.format(\\n L, J, sigma, (M, N, O), integral_powers)\\n np.save('scat_0_'+basename, np_scat_0)\\n np.save('scat_1_'+basename, np_scat_1)\\n np.save('scat_2_'+basename, np_scat_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e875e9fb9ccca3df704a79a26912646\",\n \"score\": \"0.512183\",\n \"text\": \"def _ng86(seq1, seq2, k, codon_table):\\n S_sites1, N_sites1 = _count_site_NG86(seq1, codon_table=codon_table, k=k)\\n S_sites2, N_sites2 = _count_site_NG86(seq2, codon_table=codon_table, k=k)\\n S_sites = (S_sites1 + S_sites2) / 2.0\\n N_sites = (N_sites1 + N_sites2) / 2.0\\n SN = [0, 0]\\n for i, j in zip(seq1, seq2):\\n SN = [\\n m + n for m, n in zip(SN, _count_diff_NG86(i, j, codon_table=codon_table))\\n ]\\n\\n ps = SN[0] / S_sites\\n pn = SN[1] / N_sites\\n if ps < 3 / 4:\\n dS = abs(-3.0 / 4 * log(1 - 4.0 / 3 * ps))\\n else:\\n dS = -1\\n if pn < 3 / 4:\\n dN = abs(-3.0 / 4 * log(1 - 4.0 / 3 * pn))\\n else:\\n dN = -1\\n return dN, dS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33807a3e705f9a711a1b76b49f007791\",\n \"score\": \"0.5120692\",\n \"text\": \"def number_test():\\n res = 1\\n size = 501\\n on = 50\\n data_set = range(10000)\\n encoder = RandomDistributedScalarEncoder(res, size, on)\\n col_shape = (1024,)\\n machine = SpatialPooler(encoder.output_shape, col_shape)\\n\\n enc_data = [encoder.encode(z) for z in random.sample(data_set, 100)]\\n rob_before = machine.noise_robustness(enc_data)\\n\\n def overlap_vs_input_dist():\\n sep = []\\n ovlp = []\\n inp_a = 765\\n sdr_a = machine.compute(encoder.encode(inp_a), learn=False)\\n orig = set(zip(*sdr_a))\\n for x in range(0, on):\\n inp_b = inp_a + x\\n sdr_b = machine.compute(encoder.encode(inp_b), learn=False)\\n\\n # Calculate the overlap in SP output.\\n near = set(zip(*sdr_b))\\n overlap = len(orig.intersection(near))\\n overlap_pct = overlap / len(orig)\\n sep.append(x)\\n ovlp.append(overlap_pct)\\n return sep, ovlp\\n\\n sep_b4, ovlp_b4 = overlap_vs_input_dist()\\n\\n # Train\\n train_time = len(data_set)\\n for i in range(train_time):\\n if True:\\n inp = random.choice(data_set)\\n inp_enc = encoder.encode(inp)\\n else:\\n # Tests w/ Random Inputs\\n inp_enc = np.random.random(encoder.output_shape) > .5\\n state = machine.compute(inp_enc)\\n\\n modulus = train_time // 10\\n if i % modulus == modulus-1:\\n machine.entropy()\\n\\n # Halftime evaluation\\n if i == train_time//2:\\n sep_mid, ovlp_mid = overlap_vs_input_dist()\\n rob_mid = machine.noise_robustness(enc_data)\\n\\n # Evaluate\\n print('duty cycle min ', np.min(machine.average_activations) *100,'%')\\n print('duty cycle mean', np.mean(machine.average_activations) *100,'%')\\n print('duty cycle std ', np.std(machine.average_activations) *100,'%')\\n print('duty cycle max ', np.max(machine.average_activations) *100,'%')\\n\\n sep, ovlp = overlap_vs_input_dist()\\n rob_after = machine.noise_robustness(enc_data)\\n\\n from matplotlib import pyplot as plt\\n plt.figure(1)\\n plt.subplot(2, 1, 1)\\n plt.plot(sep_b4, ovlp_b4, 'r', sep, ovlp, 'g', sep_mid, ovlp_mid, 'b')\\n plt.title('Overlap vs Input Distance, \\\\nRed is before, Blue is halfway, Green is after training %d cycles'%train_time)\\n\\n plt.subplot(2, 1, 2)\\n plt.plot(rob_before[0], rob_before[1], 'r', rob_after[0], rob_after[1], 'g', rob_mid[0], rob_mid[1], 'b')\\n plt.title('Overlap vs Input Noise')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"412ab40aa06d807fa38e32f01334ec8f\",\n \"score\": \"0.51185197\",\n \"text\": \"def test_Smatrix(self):\\n # Make random test object\\n atoms, ucell = make_temp_structure()\\n\\n cell = pyms.structure.fromfile(scratch_cell_name)\\n\\n # Parameters for test\\n tiling = [4, 4]\\n tiling = [1, 1]\\n # cell.unitcell[:2] = [15,15]\\n\\n gridshape = [128, 64]\\n eV = 3e5\\n app = 20\\n thicknesses = [20]\\n subslices = [0.5, 1.0]\\n df = -300\\n probe_posn = np.asarray([1.0, 1.0])\\n # probe_posn = [0.5, 0.5]\\n\\n PRISM_factor = [2, 2]\\n # PRISM_factor = [1, 1]\\n nT = 2\\n # seed = np.random.randint(0, 10, size=2)\\n\\n # Make propagator and transmission functions for multislice and\\n # S-matrix calculations\\n P, T = pyms.multislice_precursor(\\n cell,\\n gridshape,\\n eV,\\n subslices=subslices,\\n tiling=tiling,\\n nT=nT,\\n displacements=False,\\n showProgress=False,\\n band_width_limiting=[None, None],\\n )\\n device = pyms.utils.get_device()\\n\\n # S matrix calculation\\n # Calculate scattering matrix\\n rsize = np.asarray(tiling) * cell.unitcell[:2]\\n nslices = np.ceil(thicknesses[0] / cell.unitcell[2]).astype(int)\\n S = pyms.scattering_matrix(\\n rsize,\\n P,\\n T,\\n nslices,\\n eV,\\n app,\\n PRISM_factor=PRISM_factor,\\n tiling=tiling,\\n # seed=seed,\\n showProgress=False,\\n GPU_streaming=False,\\n )\\n\\n # Calculate probe and shift it to required position\\n probe = pyms.focused_probe(gridshape, rsize, eV, app, df=df, qspace=True)\\n probe = torch.from_numpy(\\n pyms.utils.fourier_shift(\\n probe,\\n np.asarray(probe_posn) / np.asarray(tiling),\\n qspacein=True,\\n qspaceout=True,\\n pixel_units=False,\\n )\\n ).to(device)\\n\\n probe_ = copy.deepcopy(probe)\\n # Calculate Smatrix result\\n S_CBED = S(\\n probe_, thicknesses[0], posn=[np.asarray(probe_posn) / np.asarray(tiling)]\\n )\\n\\n S_CBED_amp = np.fft.fftshift(pyms.utils.amplitude(S_CBED).cpu().numpy())\\n S_CBED_amp /= np.prod(S.stored_gridshape)\\n\\n # Ensure consistency of PRISM STEM routine\\n datacube = pyms.STEM_PRISM(\\n cell,\\n gridshape,\\n eV,\\n app,\\n thicknesses,\\n tiling=tiling,\\n df=df,\\n PRISM_factor=PRISM_factor,\\n FourD_STEM=True,\\n scan_posn=np.asarray(probe_posn).reshape((1, 1, 2)) / np.asarray(tiling),\\n showProgress=False,\\n S=S,\\n nT=5,\\n GPU_streaming=False,\\n device_type=torch.device(\\\"cpu\\\"),\\n )[\\\"datacube\\\"]\\n\\n # Remove every nth beam where n is the PRISM cropping factor\\n yy = np.fft.fftfreq(gridshape[0], 1.0 / gridshape[0]).astype(pyms._int)\\n xx = np.fft.fftfreq(gridshape[1], 1.0 / gridshape[1]).astype(pyms._int)\\n mask = np.logical_and(\\n np.logical_and(\\n (yy % PRISM_factor[0] == 0)[:, None],\\n (xx % PRISM_factor[1] == 0)[None, :],\\n ),\\n pyms.amplitude(probe).cpu().numpy() > 0,\\n )\\n probe[np.logical_not(mask)] = 0\\n\\n # Transform probe back to real space\\n probe = torch.fft.ifftn(probe, dim=(-2, -1))\\n\\n # Adjust normalization to account for prism cropping\\n probe *= np.prod(PRISM_factor)\\n\\n # Do multislice\\n probe = pyms.multislice(\\n probe.view([1, *gridshape]), nslices, P, T, return_numpy=False\\n )\\n\\n # Get output gridsize\\n gridout = torch.squeeze(probe).shape[:2]\\n\\n # Now crop multislice result in real space\\n grid = probe.shape[-2:]\\n stride = [x // y for x, y in zip(grid, PRISM_factor)]\\n halfstride = [x // 2 for x in stride]\\n start = [\\n int(np.round(probe_posn[i] * grid[i])) - halfstride[i] for i in range(2)\\n ]\\n windows = pyms.utils.crop_window_to_periodic_indices(\\n [start[0], stride[0], start[1], stride[1]], gridout\\n )\\n\\n new = torch.zeros(*gridout, dtype=S.dtype, device=probe.device)\\n for wind in windows:\\n new[\\n wind[0][0] : wind[0][0] + wind[0][1],\\n wind[1][0] : wind[1][0] + wind[1][1],\\n ] = probe[\\n 0,\\n wind[0][0] : wind[0][0] + wind[0][1],\\n wind[1][0] : wind[1][0] + wind[1][1],\\n ]\\n probe = new\\n probe = torch.fft.fftn(probe, dim=[-2, -1]) / np.sqrt(np.prod(gridout))\\n ms_CBED = np.fft.fftshift(np.squeeze(np.abs(probe.cpu().numpy()) ** 2))\\n\\n # fig, ax = plt.subplots(ncols=3)\\n # ax[0].imshow(ms_CBED)\\n # ax[1].imshow(datacube[0, 0])\\n # ax[2].imshow(S_CBED_amp[0])\\n # plt.show(block=True)\\n\\n # The test is passed if the result from multislice and S-matrix is\\n # within numerical error and the S matrix premixed routine is also\\n # behaving as expected\\n Smatrixtestpass = (\\n sumsqr_diff(ms_CBED, S_CBED_amp) < 1e-10\\n and sumsqr_diff(S_CBED_amp, datacube[0, 0]) < 1e-10\\n )\\n self.assertTrue(Smatrixtestpass)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992969,"cells":{"query_id":{"kind":"string","value":"ce7e9a3da80a664810b0bfee382e2446"},"query":{"kind":"string","value":"Return True if node is turned on, False if not."},"positive_passages":{"kind":"list like","value":[{"docid":"6f4ce2a1269dacaa548d5e74894ebd87","score":"0.70480686","text":"def isOn(self):\n return (int(self.get_flags) & 3) == 0","title":""}],"string":"[\n {\n \"docid\": \"6f4ce2a1269dacaa548d5e74894ebd87\",\n \"score\": \"0.70480686\",\n \"text\": \"def isOn(self):\\n return (int(self.get_flags) & 3) == 0\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"66db5174783ab412dbba26c1fdeb644b","score":"0.84309113","text":"def is_on(self) -> bool:\n return bool(self._node.status)","title":""},{"docid":"8add53e292da503c5ebe2214e4e0af4f","score":"0.7315773","text":"def is_on(self) -> bool:\n return self.device.level == \"on\"","title":""},{"docid":"749fede62b9b6c3ca0d26f3b906f65bd","score":"0.72680056","text":"def is_on(self) -> bool | None:\n return bool(\n self._tv.on and (self._tv.powerstate == \"On\" or self._tv.powerstate is None)\n )","title":""},{"docid":"1c7124198ce1b40b9ce24af7f5fa9566","score":"0.72646976","text":"def is_on(self) -> bool:\n return self._state","title":""},{"docid":"1c7124198ce1b40b9ce24af7f5fa9566","score":"0.72646976","text":"def is_on(self) -> bool:\n return self._state","title":""},{"docid":"a6d5734def8a0a32066694b4d5058cee","score":"0.7232293","text":"def is_on(self):\n return True","title":""},{"docid":"dcaef8daaf99b25d8b1185febe30e867","score":"0.72321475","text":"def is_on(self):\n return bool(self._state)","title":""},{"docid":"4034d9d775043c2b1766b1d6dd61a820","score":"0.7206458","text":"def is_on(self) -> bool | None:\n if self._node.status == ISY_VALUE_UNKNOWN:\n return None\n return bool(self._node.status)","title":""},{"docid":"a399cbd07df4cef0a997559c20e84755","score":"0.719371","text":"def nodeIsOn(self, node):\n self.__powerStatusCallbackEvent.clear()\n p = Protocol(None)\n if DaemonStatus().powerDaemonIsOnline(5):\n p.open(DaemonLocations.switchDaemon[0], \\\n DaemonLocations.switchDaemon[1])\n p.sendRequest(\"get_power_status\", node.getNodeID(), \\\n self.powerStatusHandler)\n p.readAndProcess()\n self.__powerStatusCallbackEvent.wait(POWER_STATUS_TIMEOUT)\n try:\n pnodeid = node.getPowerNodeID()\n if (pnodeid == \"None\" or pnodeid == \"none\"):\n return -1\n if self.__powerResults:\n (st,msg) = (0, self.__powerResults)\n else:\n (st,msg) = self.__hen_manager.powerSilent(node.getNodeID(), \\\n \"status\", pnodeid)\n if (st == 0 and msg == \"on\"):\n return 1\n if (st == 0 and msg == \"off\"):\n return 0\n except Exception, e:\n log.debug(\"nodeIsOn Exception: %s\" % str(e))\n return -1","title":""},{"docid":"b97d67037c039a40268438d852223c88","score":"0.71786237","text":"def is_on(self) -> bool:\n return self._is_on","title":""},{"docid":"755e2c29e0720e2ce73e297a922bb558","score":"0.7163169","text":"def is_on(self) -> bool:\n return self._level > 0","title":""},{"docid":"025c0141393375cf707c5dc49afbb0b2","score":"0.7162323","text":"def is_on(self) -> bool:\n return self._on","title":""},{"docid":"025c0141393375cf707c5dc49afbb0b2","score":"0.7162323","text":"def is_on(self) -> bool:\n return self._on","title":""},{"docid":"23aa29c16d987c9efe1b84b0b1cb277e","score":"0.71609867","text":"def is_on(self) -> bool:\n return self.__active","title":""},{"docid":"568280ef314fd1f113f517eaba106d81","score":"0.7144665","text":"def is_on(self) -> bool:\n return bool(self._computed_state)","title":""},{"docid":"abe2c288ec9e6d690d896467551ecb6f","score":"0.71433777","text":"def is_on(self):\n return bool(self._state == STATE_ON)","title":""},{"docid":"dbfa540b574f8778ed0ca31571542395","score":"0.70955837","text":"def is_on(self):\n return self._device[\"state\"][\"light\"] == 1","title":""},{"docid":"82bbd1b49338ac9e5f958eca57b82100","score":"0.7088175","text":"def is_on(self) -> bool:\n return self.device.enabled","title":""},{"docid":"9c4bde6d7b5b9d6a1ddf452c8490fa1f","score":"0.70696455","text":"def is_on(self) -> bool:","title":""},{"docid":"1f468099841bac8a2205d2ebb646bc47","score":"0.7067127","text":"def is_on(self):\n return self.on","title":""},{"docid":"8859b22b65323dca910cffe5ffedc15d","score":"0.70667416","text":"def is_on(self):\n\t\treturn self._state","title":""},{"docid":"5c286271e09f1d3cf341dc6488cc840d","score":"0.70085233","text":"def is_on(self) -> bool:\n return self._device.status.switch","title":""},{"docid":"81e3ecb440b23fb7579b56720fc9a531","score":"0.69959325","text":"def on_enabled(self, node_address):\n # pylint: disable=no-self-use\n return False","title":""},{"docid":"aaff1753d6ddf0bfe05f10af3ebc64fd","score":"0.69920224","text":"def is_on(self):\n return self.device.is_on","title":""},{"docid":"e3a26d563507773a67bb3e23fe211c7e","score":"0.6985497","text":"def is_on(self):\r\n return self._on","title":""},{"docid":"6b2271c644c577d8a203027a6da05829","score":"0.6984673","text":"def is_on(self):\n return self._device[\"current_state\"] > 0","title":""},{"docid":"a4d0b2636423a49b6aadaba47d0c9ab4","score":"0.698412","text":"def is_on(self):\n return self._device.on","title":""},{"docid":"5239e4ae67053991dd4ef6cded036891","score":"0.6979078","text":"def is_on(self) -> bool:\n return self.device.status.get(self.entity_description.key, False)","title":""},{"docid":"d050c5db8873d2b864046ba149a7b573","score":"0.6961636","text":"def is_on(self):\n return self._on","title":""},{"docid":"4f3b5913ecc1d9427c9950349c82afe9","score":"0.6959423","text":"def is_on(self) -> bool:\n return self.data[\"switches\"][self._name]","title":""},{"docid":"32f917f2903edaf2e62cf1319adb05da","score":"0.69377774","text":"def is_on(self):\n return self._is_on","title":""},{"docid":"32f917f2903edaf2e62cf1319adb05da","score":"0.69377774","text":"def is_on(self):\n return self._is_on","title":""},{"docid":"32f917f2903edaf2e62cf1319adb05da","score":"0.69377774","text":"def is_on(self):\n return self._is_on","title":""},{"docid":"32f917f2903edaf2e62cf1319adb05da","score":"0.69377774","text":"def is_on(self):\n return self._is_on","title":""},{"docid":"32f917f2903edaf2e62cf1319adb05da","score":"0.69377774","text":"def is_on(self):\n return self._is_on","title":""},{"docid":"24bbff584227726eb664d0dea1d4f261","score":"0.69155693","text":"def is_on(self):\n return True if self.coordinator.data[self._chargername][self._attribute] == \"on\" else False","title":""},{"docid":"1f0fa3fa43409f4c3dc9905dce5ba766","score":"0.6903039","text":"def is_on(self):\n return self._remote.state == STATE_ON","title":""},{"docid":"0968d88db39eba7b4b3cb9f6e1c6930e","score":"0.689228","text":"def is_on(self) -> bool:\n return bool(self.ecowitt.value)","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"dffbc0ff7f3e6d1df351e9026e06f7bf","score":"0.6877073","text":"def is_on(self):\n return self._state","title":""},{"docid":"25cee74c6b8754d0c915cbbbde9d8d18","score":"0.6800932","text":"def is_on(self) -> bool | None:\n return self._attr_is_on","title":""},{"docid":"65ca8668509609b140b76e78e75aae8a","score":"0.6800538","text":"def on_enabled(self, node_address):\n self._enable_node(node_address)\n return True","title":""},{"docid":"c009254d8084405dba51029a4240857f","score":"0.6797182","text":"def is_on(self):\n return self.tuya.state()","title":""},{"docid":"287dca81e71a54e4be7c507ea2fbf242","score":"0.6788317","text":"def is_on(self):\n return self._spaclient.get_mister() != \"Off\"","title":""},{"docid":"471ba40652bb51100f3d335223ec0d8f","score":"0.6767599","text":"def enabled(self):\n return bool(self._data.get(b'masterFXSwitch')) if self._data else False","title":""},{"docid":"fd9e363a04b4e48dec8138031f10a992","score":"0.67670983","text":"def is_on(self) -> bool:\n return self.device[\"binary_sensors\"][self.entity_description.key]","title":""},{"docid":"7fb0ea8aeafafe51a267688d8e2c13b3","score":"0.67510325","text":"def is_on(self):\n return self.is_on_status","title":""},{"docid":"7379560405090718c99a881598ce7e21","score":"0.67473906","text":"def is_on(self) -> bool:\n return self._values.get(self.value_type) == STATE_ON","title":""},{"docid":"8fdbcad8d9155c3418400334c5a20566","score":"0.67235494","text":"def light_is_on(self):\n return self._light == \"ON\"","title":""},{"docid":"8fdbcad8d9155c3418400334c5a20566","score":"0.67235494","text":"def light_is_on(self):\n return self._light == \"ON\"","title":""},{"docid":"9c1d7d8562332b41543e0bdc85b7175e","score":"0.67110986","text":"def __bool__(self):\r\n return _RMF.NodeIDs___bool__(self)","title":""},{"docid":"3f64b62eff0c81eec9a38ee13f291313","score":"0.66877735","text":"def is_on(self) -> bool:\n return self.coordinator.data.get_metric(METRIC_KEY_MODE) == MODE_ON","title":""},{"docid":"daeb9af78b18efcfacd15cf252be59bf","score":"0.6685009","text":"def is_on(self) -> bool:\n return bool(self.appliance.get_erd_value(self.erd_code))","title":""},{"docid":"41a08ab9f42eabf6e72079bf0b0f0e79","score":"0.6682509","text":"def __bool__(self):\r\n return _RMF.NodeHandles___bool__(self)","title":""},{"docid":"da6f860f2fee1c744e0da63b7953850d","score":"0.6656316","text":"def is_on(self):\n return self._spaclient.get_blower() != \"Off\"","title":""},{"docid":"438b8a8945c85a3fbbcbadab2b97b4bd","score":"0.6636541","text":"def node_is_active():\n # lexically sort node names:\n # * return True if we are the first one\n # * else return True if the other peer is unreachable\n try:\n global_init = read_json('/etc/128technology/global.init')\n except FileNotFoundError:\n return True\n\n control = global_init['init']['control']\n if control:\n # Running on a router\n fatal('This script is designed to run on a conductor. Exiting.')\n\n host_ips = get_host_ips()\n conductors = global_init['init']['conductor']\n if len(conductors) == 1:\n # Running on non-HA conductor\n return True\n\n if len(conductors) != 2:\n fatal('Something went wrong. '\n 'Number of conductors in global.init should be 2! Exiting.')\n\n ip_addresses = [c[1]['host'] for c in sorted(conductors.items())]\n host_ips = get_host_ips()\n if ip_addresses[0] in host_ips:\n debug('we are the primary HA node')\n return True\n if ip_addresses[1] in host_ips:\n # we are the secondary HA node\n try:\n ping_node(ip_addresses[0])\n debug('We are the secondary HA node and the primary is up.')\n return False\n except CalledProcessError:\n debug('the primary HA node is down - taking over')\n return True\n\n fatal('Something went wrong. '\n 'Conductor IPs do not match any local IP address. Exiting.')\n sys.exit(1)","title":""},{"docid":"5accae6af82fce10352974e85b0aaba0","score":"0.6634465","text":"def is_on(self):\n return self.device[\"status\"][\"state\"]","title":""},{"docid":"28b5ae7d760bd708bf58c36511952426","score":"0.6626046","text":"def is_on(self) -> bool:\n return self.tuyaDevice.status.get(DPCODE_SWITCH, False)","title":""},{"docid":"c96b14be2509da89c8c9fed55e964695","score":"0.657691","text":"def is_on(self):\n return self.device.value() == 100","title":""},{"docid":"fa69e07ef3bab6d8978b18bab4113ff6","score":"0.6558709","text":"def is_on(self) -> bool:\n if self.coordinator.data:\n data: dict = self.coordinator.data[Platform.SWITCH]\n for torrent in data.values():\n item = torrent.popitem()\n if not item[1]:\n return True\n return False","title":""},{"docid":"ed5c0320b7f36e0780473328cc7b08e8","score":"0.6541069","text":"def is_on(self) -> bool:\n if self.on_value is not None:\n value = self._state == self.on_value\n elif self._state is None:\n value = False\n else:\n value = bool(max(0, int(self._state)))\n\n if self.inverted:\n return not value\n\n return value","title":""},{"docid":"0b6ca4d0853a5e258bacd95d045d1bc2","score":"0.6531704","text":"def is_on(self):\n return self._spaclient.get_aux(self._aux_num) != \"Off\"","title":""},{"docid":"f4dda0cb1508dc5eb37e146b8c981adf","score":"0.652097","text":"def is_on(self):\n return self._brightness != 0","title":""},{"docid":"3c0dea4f103031fe3f0f3303d4723a81","score":"0.6519857","text":"def __nonzero__(self):\n return bool(self.morphio_root_node)","title":""},{"docid":"ffad262759c4569728f0b84a683bbcc7","score":"0.6518299","text":"def is_on(self) -> bool | None:\n if self._computed_state is None:\n # Do this first so we don't invert None on moisture or light sensors\n return None\n\n if self.device_class in (\n BinarySensorDeviceClass.LIGHT,\n BinarySensorDeviceClass.MOISTURE,\n ):\n return not self._computed_state\n\n return self._computed_state","title":""},{"docid":"5fbb6abbf5e64317f39ec0f288e88c43","score":"0.65156287","text":"def is_on(self) -> bool:\n return bool(self.entity_description.is_on_fn(self.coordinator.data))","title":""},{"docid":"549f7648a673e8865da6c51c3bbfcf11","score":"0.6509674","text":"def enabled(self) -> ConfigNodePropertyBoolean:\n return self._enabled","title":""},{"docid":"95ed58a76796bf2ce476376c4aec6678","score":"0.6501857","text":"def is_on(self) -> bool:\n if self._state is None:\n return False\n return self._state != SPEED_OFF","title":""},{"docid":"6fee3250278e70da173fcf6317b5d190","score":"0.6485342","text":"def is_on(self):\n if self._tv.on:\n effect = AmbilightEffect.from_str(self.effect)\n return effect.is_on(self._tv.powerstate)\n\n return False","title":""},{"docid":"90d3f69ae167f512fc55b70da184e30a","score":"0.6483555","text":"def is_on(self):\n return self._state != self._invert_logic","title":""},{"docid":"90d3f69ae167f512fc55b70da184e30a","score":"0.6483555","text":"def is_on(self):\n return self._state != self._invert_logic","title":""}],"string":"[\n {\n \"docid\": \"66db5174783ab412dbba26c1fdeb644b\",\n \"score\": \"0.84309113\",\n \"text\": \"def is_on(self) -> bool:\\n return bool(self._node.status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8add53e292da503c5ebe2214e4e0af4f\",\n \"score\": \"0.7315773\",\n \"text\": \"def is_on(self) -> bool:\\n return self.device.level == \\\"on\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"749fede62b9b6c3ca0d26f3b906f65bd\",\n \"score\": \"0.72680056\",\n \"text\": \"def is_on(self) -> bool | None:\\n return bool(\\n self._tv.on and (self._tv.powerstate == \\\"On\\\" or self._tv.powerstate is None)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7124198ce1b40b9ce24af7f5fa9566\",\n \"score\": \"0.72646976\",\n \"text\": \"def is_on(self) -> bool:\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7124198ce1b40b9ce24af7f5fa9566\",\n \"score\": \"0.72646976\",\n \"text\": \"def is_on(self) -> bool:\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6d5734def8a0a32066694b4d5058cee\",\n \"score\": \"0.7232293\",\n \"text\": \"def is_on(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcaef8daaf99b25d8b1185febe30e867\",\n \"score\": \"0.72321475\",\n \"text\": \"def is_on(self):\\n return bool(self._state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4034d9d775043c2b1766b1d6dd61a820\",\n \"score\": \"0.7206458\",\n \"text\": \"def is_on(self) -> bool | None:\\n if self._node.status == ISY_VALUE_UNKNOWN:\\n return None\\n return bool(self._node.status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a399cbd07df4cef0a997559c20e84755\",\n \"score\": \"0.719371\",\n \"text\": \"def nodeIsOn(self, node):\\n self.__powerStatusCallbackEvent.clear()\\n p = Protocol(None)\\n if DaemonStatus().powerDaemonIsOnline(5):\\n p.open(DaemonLocations.switchDaemon[0], \\\\\\n DaemonLocations.switchDaemon[1])\\n p.sendRequest(\\\"get_power_status\\\", node.getNodeID(), \\\\\\n self.powerStatusHandler)\\n p.readAndProcess()\\n self.__powerStatusCallbackEvent.wait(POWER_STATUS_TIMEOUT)\\n try:\\n pnodeid = node.getPowerNodeID()\\n if (pnodeid == \\\"None\\\" or pnodeid == \\\"none\\\"):\\n return -1\\n if self.__powerResults:\\n (st,msg) = (0, self.__powerResults)\\n else:\\n (st,msg) = self.__hen_manager.powerSilent(node.getNodeID(), \\\\\\n \\\"status\\\", pnodeid)\\n if (st == 0 and msg == \\\"on\\\"):\\n return 1\\n if (st == 0 and msg == \\\"off\\\"):\\n return 0\\n except Exception, e:\\n log.debug(\\\"nodeIsOn Exception: %s\\\" % str(e))\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97d67037c039a40268438d852223c88\",\n \"score\": \"0.71786237\",\n \"text\": \"def is_on(self) -> bool:\\n return self._is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"755e2c29e0720e2ce73e297a922bb558\",\n \"score\": \"0.7163169\",\n \"text\": \"def is_on(self) -> bool:\\n return self._level > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025c0141393375cf707c5dc49afbb0b2\",\n \"score\": \"0.7162323\",\n \"text\": \"def is_on(self) -> bool:\\n return self._on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025c0141393375cf707c5dc49afbb0b2\",\n \"score\": \"0.7162323\",\n \"text\": \"def is_on(self) -> bool:\\n return self._on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23aa29c16d987c9efe1b84b0b1cb277e\",\n \"score\": \"0.71609867\",\n \"text\": \"def is_on(self) -> bool:\\n return self.__active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"568280ef314fd1f113f517eaba106d81\",\n \"score\": \"0.7144665\",\n \"text\": \"def is_on(self) -> bool:\\n return bool(self._computed_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe2c288ec9e6d690d896467551ecb6f\",\n \"score\": \"0.71433777\",\n \"text\": \"def is_on(self):\\n return bool(self._state == STATE_ON)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbfa540b574f8778ed0ca31571542395\",\n \"score\": \"0.70955837\",\n \"text\": \"def is_on(self):\\n return self._device[\\\"state\\\"][\\\"light\\\"] == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82bbd1b49338ac9e5f958eca57b82100\",\n \"score\": \"0.7088175\",\n \"text\": \"def is_on(self) -> bool:\\n return self.device.enabled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c4bde6d7b5b9d6a1ddf452c8490fa1f\",\n \"score\": \"0.70696455\",\n \"text\": \"def is_on(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f468099841bac8a2205d2ebb646bc47\",\n \"score\": \"0.7067127\",\n \"text\": \"def is_on(self):\\n return self.on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8859b22b65323dca910cffe5ffedc15d\",\n \"score\": \"0.70667416\",\n \"text\": \"def is_on(self):\\n\\t\\treturn self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c286271e09f1d3cf341dc6488cc840d\",\n \"score\": \"0.70085233\",\n \"text\": \"def is_on(self) -> bool:\\n return self._device.status.switch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e3ecb440b23fb7579b56720fc9a531\",\n \"score\": \"0.69959325\",\n \"text\": \"def on_enabled(self, node_address):\\n # pylint: disable=no-self-use\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaff1753d6ddf0bfe05f10af3ebc64fd\",\n \"score\": \"0.69920224\",\n \"text\": \"def is_on(self):\\n return self.device.is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a26d563507773a67bb3e23fe211c7e\",\n \"score\": \"0.6985497\",\n \"text\": \"def is_on(self):\\r\\n return self._on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b2271c644c577d8a203027a6da05829\",\n \"score\": \"0.6984673\",\n \"text\": \"def is_on(self):\\n return self._device[\\\"current_state\\\"] > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d0b2636423a49b6aadaba47d0c9ab4\",\n \"score\": \"0.698412\",\n \"text\": \"def is_on(self):\\n return self._device.on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5239e4ae67053991dd4ef6cded036891\",\n \"score\": \"0.6979078\",\n \"text\": \"def is_on(self) -> bool:\\n return self.device.status.get(self.entity_description.key, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d050c5db8873d2b864046ba149a7b573\",\n \"score\": \"0.6961636\",\n \"text\": \"def is_on(self):\\n return self._on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3b5913ecc1d9427c9950349c82afe9\",\n \"score\": \"0.6959423\",\n \"text\": \"def is_on(self) -> bool:\\n return self.data[\\\"switches\\\"][self._name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f917f2903edaf2e62cf1319adb05da\",\n \"score\": \"0.69377774\",\n \"text\": \"def is_on(self):\\n return self._is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f917f2903edaf2e62cf1319adb05da\",\n \"score\": \"0.69377774\",\n \"text\": \"def is_on(self):\\n return self._is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f917f2903edaf2e62cf1319adb05da\",\n \"score\": \"0.69377774\",\n \"text\": \"def is_on(self):\\n return self._is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f917f2903edaf2e62cf1319adb05da\",\n \"score\": \"0.69377774\",\n \"text\": \"def is_on(self):\\n return self._is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f917f2903edaf2e62cf1319adb05da\",\n \"score\": \"0.69377774\",\n \"text\": \"def is_on(self):\\n return self._is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24bbff584227726eb664d0dea1d4f261\",\n \"score\": \"0.69155693\",\n \"text\": \"def is_on(self):\\n return True if self.coordinator.data[self._chargername][self._attribute] == \\\"on\\\" else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0fa3fa43409f4c3dc9905dce5ba766\",\n \"score\": \"0.6903039\",\n \"text\": \"def is_on(self):\\n return self._remote.state == STATE_ON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0968d88db39eba7b4b3cb9f6e1c6930e\",\n \"score\": \"0.689228\",\n \"text\": \"def is_on(self) -> bool:\\n return bool(self.ecowitt.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffbc0ff7f3e6d1df351e9026e06f7bf\",\n \"score\": \"0.6877073\",\n \"text\": \"def is_on(self):\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25cee74c6b8754d0c915cbbbde9d8d18\",\n \"score\": \"0.6800932\",\n \"text\": \"def is_on(self) -> bool | None:\\n return self._attr_is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ca8668509609b140b76e78e75aae8a\",\n \"score\": \"0.6800538\",\n \"text\": \"def on_enabled(self, node_address):\\n self._enable_node(node_address)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c009254d8084405dba51029a4240857f\",\n \"score\": \"0.6797182\",\n \"text\": \"def is_on(self):\\n return self.tuya.state()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287dca81e71a54e4be7c507ea2fbf242\",\n \"score\": \"0.6788317\",\n \"text\": \"def is_on(self):\\n return self._spaclient.get_mister() != \\\"Off\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"471ba40652bb51100f3d335223ec0d8f\",\n \"score\": \"0.6767599\",\n \"text\": \"def enabled(self):\\n return bool(self._data.get(b'masterFXSwitch')) if self._data else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd9e363a04b4e48dec8138031f10a992\",\n \"score\": \"0.67670983\",\n \"text\": \"def is_on(self) -> bool:\\n return self.device[\\\"binary_sensors\\\"][self.entity_description.key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb0ea8aeafafe51a267688d8e2c13b3\",\n \"score\": \"0.67510325\",\n \"text\": \"def is_on(self):\\n return self.is_on_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7379560405090718c99a881598ce7e21\",\n \"score\": \"0.67473906\",\n \"text\": \"def is_on(self) -> bool:\\n return self._values.get(self.value_type) == STATE_ON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdbcad8d9155c3418400334c5a20566\",\n \"score\": \"0.67235494\",\n \"text\": \"def light_is_on(self):\\n return self._light == \\\"ON\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdbcad8d9155c3418400334c5a20566\",\n \"score\": \"0.67235494\",\n \"text\": \"def light_is_on(self):\\n return self._light == \\\"ON\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c1d7d8562332b41543e0bdc85b7175e\",\n \"score\": \"0.67110986\",\n \"text\": \"def __bool__(self):\\r\\n return _RMF.NodeIDs___bool__(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f64b62eff0c81eec9a38ee13f291313\",\n \"score\": \"0.66877735\",\n \"text\": \"def is_on(self) -> bool:\\n return self.coordinator.data.get_metric(METRIC_KEY_MODE) == MODE_ON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daeb9af78b18efcfacd15cf252be59bf\",\n \"score\": \"0.6685009\",\n \"text\": \"def is_on(self) -> bool:\\n return bool(self.appliance.get_erd_value(self.erd_code))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41a08ab9f42eabf6e72079bf0b0f0e79\",\n \"score\": \"0.6682509\",\n \"text\": \"def __bool__(self):\\r\\n return _RMF.NodeHandles___bool__(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da6f860f2fee1c744e0da63b7953850d\",\n \"score\": \"0.6656316\",\n \"text\": \"def is_on(self):\\n return self._spaclient.get_blower() != \\\"Off\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"438b8a8945c85a3fbbcbadab2b97b4bd\",\n \"score\": \"0.6636541\",\n \"text\": \"def node_is_active():\\n # lexically sort node names:\\n # * return True if we are the first one\\n # * else return True if the other peer is unreachable\\n try:\\n global_init = read_json('/etc/128technology/global.init')\\n except FileNotFoundError:\\n return True\\n\\n control = global_init['init']['control']\\n if control:\\n # Running on a router\\n fatal('This script is designed to run on a conductor. Exiting.')\\n\\n host_ips = get_host_ips()\\n conductors = global_init['init']['conductor']\\n if len(conductors) == 1:\\n # Running on non-HA conductor\\n return True\\n\\n if len(conductors) != 2:\\n fatal('Something went wrong. '\\n 'Number of conductors in global.init should be 2! Exiting.')\\n\\n ip_addresses = [c[1]['host'] for c in sorted(conductors.items())]\\n host_ips = get_host_ips()\\n if ip_addresses[0] in host_ips:\\n debug('we are the primary HA node')\\n return True\\n if ip_addresses[1] in host_ips:\\n # we are the secondary HA node\\n try:\\n ping_node(ip_addresses[0])\\n debug('We are the secondary HA node and the primary is up.')\\n return False\\n except CalledProcessError:\\n debug('the primary HA node is down - taking over')\\n return True\\n\\n fatal('Something went wrong. '\\n 'Conductor IPs do not match any local IP address. Exiting.')\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5accae6af82fce10352974e85b0aaba0\",\n \"score\": \"0.6634465\",\n \"text\": \"def is_on(self):\\n return self.device[\\\"status\\\"][\\\"state\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b5ae7d760bd708bf58c36511952426\",\n \"score\": \"0.6626046\",\n \"text\": \"def is_on(self) -> bool:\\n return self.tuyaDevice.status.get(DPCODE_SWITCH, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96b14be2509da89c8c9fed55e964695\",\n \"score\": \"0.657691\",\n \"text\": \"def is_on(self):\\n return self.device.value() == 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa69e07ef3bab6d8978b18bab4113ff6\",\n \"score\": \"0.6558709\",\n \"text\": \"def is_on(self) -> bool:\\n if self.coordinator.data:\\n data: dict = self.coordinator.data[Platform.SWITCH]\\n for torrent in data.values():\\n item = torrent.popitem()\\n if not item[1]:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed5c0320b7f36e0780473328cc7b08e8\",\n \"score\": \"0.6541069\",\n \"text\": \"def is_on(self) -> bool:\\n if self.on_value is not None:\\n value = self._state == self.on_value\\n elif self._state is None:\\n value = False\\n else:\\n value = bool(max(0, int(self._state)))\\n\\n if self.inverted:\\n return not value\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b6ca4d0853a5e258bacd95d045d1bc2\",\n \"score\": \"0.6531704\",\n \"text\": \"def is_on(self):\\n return self._spaclient.get_aux(self._aux_num) != \\\"Off\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4dda0cb1508dc5eb37e146b8c981adf\",\n \"score\": \"0.652097\",\n \"text\": \"def is_on(self):\\n return self._brightness != 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c0dea4f103031fe3f0f3303d4723a81\",\n \"score\": \"0.6519857\",\n \"text\": \"def __nonzero__(self):\\n return bool(self.morphio_root_node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffad262759c4569728f0b84a683bbcc7\",\n \"score\": \"0.6518299\",\n \"text\": \"def is_on(self) -> bool | None:\\n if self._computed_state is None:\\n # Do this first so we don't invert None on moisture or light sensors\\n return None\\n\\n if self.device_class in (\\n BinarySensorDeviceClass.LIGHT,\\n BinarySensorDeviceClass.MOISTURE,\\n ):\\n return not self._computed_state\\n\\n return self._computed_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fbb6abbf5e64317f39ec0f288e88c43\",\n \"score\": \"0.65156287\",\n \"text\": \"def is_on(self) -> bool:\\n return bool(self.entity_description.is_on_fn(self.coordinator.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549f7648a673e8865da6c51c3bbfcf11\",\n \"score\": \"0.6509674\",\n \"text\": \"def enabled(self) -> ConfigNodePropertyBoolean:\\n return self._enabled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ed58a76796bf2ce476376c4aec6678\",\n \"score\": \"0.6501857\",\n \"text\": \"def is_on(self) -> bool:\\n if self._state is None:\\n return False\\n return self._state != SPEED_OFF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fee3250278e70da173fcf6317b5d190\",\n \"score\": \"0.6485342\",\n \"text\": \"def is_on(self):\\n if self._tv.on:\\n effect = AmbilightEffect.from_str(self.effect)\\n return effect.is_on(self._tv.powerstate)\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d3f69ae167f512fc55b70da184e30a\",\n \"score\": \"0.6483555\",\n \"text\": \"def is_on(self):\\n return self._state != self._invert_logic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d3f69ae167f512fc55b70da184e30a\",\n \"score\": \"0.6483555\",\n \"text\": \"def is_on(self):\\n return self._state != self._invert_logic\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992970,"cells":{"query_id":{"kind":"string","value":"273071ec9d208a722481ee675af65d9a"},"query":{"kind":"string","value":"Returns a random shuffling of the array."},"positive_passages":{"kind":"list like","value":[{"docid":"0a4886b5c0cbda5e213e526c16fb0ae5","score":"0.72436756","text":"def shuffle(self):\n ans = self.nums[:]\n for i in range(len(ans) - 1, -1, -1):\n j = random.randint(0, i)\n ans[i], ans[j] = ans[j], ans[i]\n return ans","title":""}],"string":"[\n {\n \"docid\": \"0a4886b5c0cbda5e213e526c16fb0ae5\",\n \"score\": \"0.72436756\",\n \"text\": \"def shuffle(self):\\n ans = self.nums[:]\\n for i in range(len(ans) - 1, -1, -1):\\n j = random.randint(0, i)\\n ans[i], ans[j] = ans[j], ans[i]\\n return ans\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0429db19315e7a5451dd49528814b233","score":"0.7761317","text":"def shuffle(self):\n n = len(self.arr)\n bound = [ (x, random.random()) for x in self.arr]\n bound.sort(key=lambda x:x[1])\n return [x[0] for x in bound]","title":""},{"docid":"40a3feb3f4e6598b34404fc1e7221f8b","score":"0.7651203","text":"def shuffled(self):\n random.seed(self.config.random_seed)\n new_samples = self.samples[:]\n random.shuffle(new_samples)\n return self._new(new_samples)","title":""},{"docid":"af2cbd5de9b5201b53d1a253cd20c8be","score":"0.74414164","text":"def shuffle(self) -> List[int]:\n array_copy = list(self.array)\n # # T C O N^2\n # for i in range(len(self.array)):\n # remove_idx = random.randrange(len(array_copy))\n # self.array[i] = array_copy.pop(remove_idx)\n # T C O N\n for i in range(len(self.array)):\n swap_idx = random.randrange(i, len(self.array))\n self.array[i], self.array[swap_idx] = self.array[swap_idx], self.array[i]\n\n return self.array","title":""},{"docid":"02116820cc290f17434bfb16da133370","score":"0.7400849","text":"def shuffle(x):\n random.shuffle(x)","title":""},{"docid":"4e95c8b36c8fb21d1f3d1c54fcf33fb1","score":"0.73854816","text":"def shuffle(self) -> List[int]:\n\n for i in range(len(self.array)):\n print(f'i {i}')\n swap_idx = random.randrange(i, len(self.array))\n self.array[i], self.array[swap_idx] = self.array[swap_idx], self.array[i]\n return self.array","title":""},{"docid":"ba0b1d0f9a3adb3b5aa27d904913f4c3","score":"0.7380981","text":"def shuffle():\n orig = range(1,4)\n nl = list(orig)\n for i in xrange(len(nl)):\n ni = random.randint(i, len(nl) - 1)\n nl[i], nl[ni] = nl[ni], nl[i]\n return nl","title":""},{"docid":"3bfe0dbdede996eea5b2ffd75556f7ea","score":"0.73302203","text":"def shuffle(self) -> List[int]:\n lst = copy.deepcopy(self.array)\n n = len(lst)\n\n for i in range(n):\n idx = random.randint(0, n-1)\n lst[i], lst[idx] = lst[idx], lst[i]\n\n return lst","title":""},{"docid":"f400c3005c758afe597bdc3678696ff7","score":"0.7309955","text":"def shuffle(samples):\n np.random.shuffle(samples)\n return samples","title":""},{"docid":"8a019c8bc79a5726304b383c799b4a2f","score":"0.7292532","text":"def shuffle(self):\n import random\n randomList = []\n listCopy = self.data[:]\n for i in range(len(listCopy)):\n idx = random.randint(0, len(listCopy)-1)\n listCopy[idx], listCopy[-1] = listCopy[-1], listCopy[idx]\n randomList.append(listCopy.pop())\n return randomList","title":""},{"docid":"957eac739943595fa8f5ac3eb31c6983","score":"0.72868466","text":"def fisher_yates_shuffle(arr : [object]):\n for i in range(len(arr)-1, 0, -1):\n j = randint(0, i)\n arr[i], arr[j] = arr[j], arr[i]","title":""},{"docid":"2ff5d54f5c9258d192b45b304bce5b5c","score":"0.72208476","text":"def randomize(data):\r\n permutation = np.random.permutation(data.shape[0])\r\n shuffled_data = data[permutation, :]\r\n # shuffled_y = y[permutation]\r\n return shuffled_data","title":""},{"docid":"0140ee3ee85e32cfa54b9f8fd3022e1c","score":"0.7190094","text":"def shuffle(self, list):\n if len(list) >= 2:\n i = len(list) - 1\n while i > 0:\n k = self.rndintexc(i + 1)\n tmp = list[i]\n list[i] = list[k]\n list[k] = tmp\n i -= 1\n return list","title":""},{"docid":"8096f341e1df5c2eb31660f36ae318a1","score":"0.718754","text":"def shuffle(self):\n res = self.original_nums[:]\n random.shuffle(res)\n return res","title":""},{"docid":"06b3ecfe14ae48565566df86f8d9ba4e","score":"0.7185975","text":"def shuffle(self):\n rd.shuffle(self.items)","title":""},{"docid":"e41478ca39b2adcf5fec020edcf2ea67","score":"0.71816194","text":"def shuffle(self) :\n random.shuffle(self.nums)\n return self.nums","title":""},{"docid":"b50551b1864f68150a48603b5d392127","score":"0.7152363","text":"def shuffle(self):\n\t\t# # version 1\n\t\t# arr = self.nums[:]\n\t\t# result = [None for i in range(len(self.nums))]\n\t\t# for i in range(len(self.nums)):\n\t\t# \tif arr:\n\t\t# \t\tidx = random.randint(0, len(arr) - 1)\n\t\t# \t\tresult[i] = arr[idx]\n\t\t# \t\tarr.pop(idx)\n\n\t\t# return result\n\n\n\t\t# version 2\n\t\tarr = self.nums[:]\n\t\tL = len(arr)\n\t\tfor i in range(L):\n\t\t\tj = random.randint(0, L-1)\n\t\t\tarr[i], arr[j] = arr[j], arr[i]\n\n\t\treturn arr","title":""},{"docid":"41973cf81a851e8c1fdc930a13476274","score":"0.7151598","text":"def shuffle(s):\n return rand.sample(s, len(s))","title":""},{"docid":"4da0189d48996d0e3e5a51cf3f4aa8ff","score":"0.7138123","text":"def shuffle(self) -> list:\n temp = self.original[:]\n res = []\n for i in range(len(temp)):\n res.append(temp.pop(random.randint(0, len(temp)-1)))\n return res","title":""},{"docid":"929f1c6e2b1c50b3e050d6f3e9d8b151","score":"0.7122223","text":"def shuffle(x):\n return tensorflow.compat.v1.random_shuffle(x)","title":""},{"docid":"098fa2e08ab7acef49fe218aa131cf3d","score":"0.71032465","text":"def shuffle(self):\n import random\n rng = random.Random() # Create a random generator\n rng.shuffle(self.cards) # uUse its shuffle method","title":""},{"docid":"987cfe68684d969f4bb1c1f10e213458","score":"0.70363826","text":"def _randomize(self, lst):\n return random.shuffle(lst)","title":""},{"docid":"3ae41d48169feb230ae62b4d9f3d3676","score":"0.70223194","text":"def shuffle(self):\n for i in range(self.length):\n index = random.randrange(i, self.length)\n self.nums[i], self.nums[index] = self.nums[index], self.nums[i]\n\n return self.nums","title":""},{"docid":"97a9de558d76d60584864173d433c5e3","score":"0.7018957","text":"def shuffle(*arrays, random_state=..., n_samples=...):\n ...","title":""},{"docid":"f9b693c128bc0942af1938bce4def345","score":"0.6992307","text":"def shuffle(self):\n ans = self.nums[:]\n for i in range(len(self.nums) - 1, 0, -1):\n j = random.randrange(0, i + 1)\n ans[i], ans[j] = ans[j], ans[i]\n return ans","title":""},{"docid":"697cd66bc296296d686e11b4fd2cfa7c","score":"0.69745827","text":"def shuffle(self):\n np.random.seed(0xA5EED) # For reproducible results\n order = np.arange(len(self.data))\n np.random.shuffle(order)\n self.data = self.data.iloc[order]\n return order","title":""},{"docid":"88ca329c71467e3b392aa1c1f80d3642","score":"0.6925458","text":"def test_shuffle_methods(self):\r\n arr1 = np.arange(20, 30)\r\n arr2 = np.arange(50, 60)\r\n print(arr1)\r\n print(arr2)\r\n perm = np.random.permutation(10)\r\n print(arr1[perm])\r\n print(arr2[perm])\r\n\r\n arr1 = np.arange(20, 30)\r\n arr2 = np.arange(50, 60)\r\n rng_s = np.random.get_state()\r\n np.random.shuffle(arr1)\r\n np.random.set_state(rng_s)\r\n np.random.shuffle(arr2)\r\n print(arr1)\r\n print(arr2)","title":""},{"docid":"c931d8a5734f825292f9b86d4cb72b4e","score":"0.6840419","text":"def suffle_data(data):\n indices = np.random.permutation(len(data[1]))\n shuffled = [data[0][indices], data[1][indices]]\n return shuffled","title":""},{"docid":"0e8d4d313ed7f7a3fa0a674b07ce8211","score":"0.6807566","text":"def myShuffle(this_list):\r\n random.shuffle(this_list)\r\n return(this_list)","title":""},{"docid":"4b03d6fb7217a132e76f093c8da0c427","score":"0.67915964","text":"def shuffle(self):\n import random\n index_list = [i for i in range(len(self.nums))]\n for i in range(len(self.nums)):\n j = random.randrange(0, len(index_list))\n index_list[i], index_list[j] = index_list[j], index_list[i]\n return [self.nums[i] for i in index_list]","title":""},{"docid":"17dbf12ce1d0c01dfbc32c6b810de0bf","score":"0.6776635","text":"def shuffle(self) -> List[int]:\n shuffled = random.sample(self.nums, len(self.nums))\n return shuffled","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"24251d64300c03e33b0ab6f510408517","score":"0.67698795","text":"def shuffle(self):\n random.shuffle(self.cards)","title":""},{"docid":"1498694f6ef9a23a4ba02dd93a25dcc6","score":"0.67677927","text":"def shuffle_data(data):\r\n indices = np.arange(len(data[0]), dtype=np.int32)\r\n np.random.shuffle(indices)\r\n return [x[indices] for x in data]","title":""},{"docid":"b6323c70386481718e60207987f80506","score":"0.6743605","text":"def shuffle(self):\n return random.shuffle(self._starting_cards)","title":""},{"docid":"5b7c2cbd5ea3f718933fb2edfbdb3947","score":"0.6739077","text":"def shuffle(self) -> List[int]:\n s = copy.deepcopy(self.d)\n shuffle(s)\n return s","title":""},{"docid":"6cdf095c63928edd64bc85d4241e6f3b","score":"0.6736934","text":"def shuffle(l):\n result = copy(l)\n pyshuffle(result)\n return result","title":""},{"docid":"86b65f979384246e67d9052a0d054b91","score":"0.6681268","text":"def shuffle(self):\n self.deck.shuffle()","title":""},{"docid":"1ed139bea71995044b40118eb4eb9ffa","score":"0.66747314","text":"def _get_elements_random(self):\n return shuffled(self.elements.all())","title":""},{"docid":"06476671ead77f2be394c982692d69d0","score":"0.6654657","text":"def shuffle(self) -> List[int]:\n random.shuffle(self.nums)\n return self.nums","title":""},{"docid":"6d3ccba49882b0e08a42e077edf8ab7a","score":"0.6650263","text":"def shuffle(self):\n index = torch.randperm(self.length)\n self._reindex(index)","title":""},{"docid":"1b0abbe30286aa3f7c1e09a4b7d1c23c","score":"0.6643642","text":"def shuffledP(self):\n return shuffledP(self)","title":""},{"docid":"75da86897bfb2bf8383401d3da8faa62","score":"0.66250473","text":"def shuffle(self):\n length = len(self.cur_nums)\n for idx in range(length):\n rand_idx = random.randint(idx, length-1)\n self.cur_nums[idx], self.cur_nums[rand_idx] = self.cur_nums[rand_idx], self.cur_nums[idx]\n return self.cur_nums","title":""},{"docid":"fbf44d18ae7782d99bf33dd0f13a72ec","score":"0.65894943","text":"def shuffle(self) -> List[int]:\n for i in range(len(self.nums)-1):\n j = random.randint(i, len(self.nums)-1)\n self.nums[i], self.nums[j] = self.nums[j], self.nums[i]\n self.perm[i], self.perm[j] = self.perm[j], self.perm[i]\n return self.nums","title":""},{"docid":"0a5767183aa413ad00fa28e3495797f2","score":"0.65440506","text":"def test_shuffle():\n D = make_test_data()\n before = D.tolist()\n shuffle(D)\n after = D.tolist()\n before.sort()\n after.sort()\n assert after == before, \"Shuffle mutated data.\"","title":""},{"docid":"4250cb0615fe21104469a3121fb866c6","score":"0.6535577","text":"def shuffle(self) -> List[int]:\n shuffled_nums = []\n nums = self._nums[:]\n i = len(nums)\n while i > 0:\n random_pick = random.randint(0, i-1)\n nums[random_pick], nums[-1] = nums[-1], nums[random_pick]\n shuffled_nums.append(nums.pop())\n i-=1\n # print(random_pick, shuffled_nums, nums)\n return shuffled_nums","title":""},{"docid":"b229cebf2d13fa2da6c480581509962b","score":"0.65275246","text":"def shuffle(l):\n length = len(l)\n indices = np.random.permutation(length)\n output = []\n for i in indices:\n output.append(l[i])\n return output","title":""},{"docid":"e74382da2042982beda58f3b68d9e89b","score":"0.6502599","text":"def shuffle(self) -> List[int]:\n last = len(self.nums) - 1\n for i in range(len(self.nums)):\n idx = random.randint(0, last - i)\n self.nums[idx], self.nums[last - i] = self.nums[last - i], self.nums[idx]\n self.perm[idx], self.perm[last - i] = self.perm[last - i], self.perm[idx]\n return self.nums","title":""},{"docid":"722742765fe8f1801032a332abd894fd","score":"0.64800096","text":"def _flip_values_randomly(array):\n # set up random flips\n random_vals = np.random.random((array.shape)) # ex {N, mutM, task, M}\n flips = -(random_vals <= 0.5).astype(int) \n noflips = (random_vals > 0.5).astype(int)\n random_flips = np.add(flips, noflips)\n\n # flip values accordingly\n shuffle_results = np.multiply(random_flips, array)\n shuffle_results = np.sum(shuffle_results, axis=0) # ex {mutM, task, M}\n\n return shuffle_results","title":""},{"docid":"f83778300d6397d2873c434d264a517d","score":"0.6478821","text":"def random_deck():\n # initialize cards deck\n cards = numpy.array([card for card in itertools.product(range(nfeatures),\n repeat=ndims)]).T\n n = cards.shape[1]\n # shuffle\n return cards[:, numpy.random.permutation(n)]","title":""},{"docid":"03afe1d407884df5ef7348eba5580fe3","score":"0.64781564","text":"def shuffle_cards(self):\n np.random.shuffle(self.deck)","title":""},{"docid":"33a31dd687a2ed34c9cc9f44c785064f","score":"0.6468279","text":"def shuffle(self) -> List[int]:\n return sorted(self.origin, key=lambda x: random.random())","title":""},{"docid":"dd959b6a297a13ad50b04f3a2c2a076d","score":"0.6463501","text":"def randomize_data(data, seed = 0):\r\n\tm = data.shape[0]\r\n\tnp.random.seed(seed)\r\n\tpermutation = list(np.random.permutation(m))\r\n\tdata = data[permutation,:]\r\n\r\n\treturn data","title":""},{"docid":"5516a6aa88fc72ba58d2553eadbdfb15","score":"0.6463025","text":"def randomize(data):\n\tprint \"Randomizing\"\n\tfor number in range(len(data)):\n\t\ttemp_stored_number = data[number]\n\t\tswap_index = random.randrange(len(data))\n\t\t\t\n\t\tdata[number] = data[swap_index]\n\t\tdata[swap_index] = temp_stored_number\n\t\treturn data","title":""},{"docid":"ab89e59fdc2fb1884414a165b78f16c4","score":"0.6459895","text":"def shuffle(self):\n puzzle = self\n for _ in range(1000):\n puzzle = random.choice(list(puzzle.actions()))\n return puzzle","title":""},{"docid":"37a6ce6e71c607fc3211e3c9eae23781","score":"0.6453688","text":"def shuffle(self):\n print('Shuffling the dataset...')\n random.shuffle(self.trainingSamples)","title":""},{"docid":"1741c925be95d375c6e9cccc44a34bc5","score":"0.6450331","text":"def randomize(self):\n permutation = np.random.permutation(self.y_train.shape[0])\n shuffled_x = self.x_train[permutation, :, :, :]\n shuffled_y = self.y_train[permutation]\n return shuffled_x, shuffled_y","title":""},{"docid":"be3576fd57df0949f5c4d3ff8454b233","score":"0.64395845","text":"def shuffle(self):\n self.indices = self._generate_indices()","title":""},{"docid":"25dd2f156a1ea186f863c9a72999ff48","score":"0.64374197","text":"def shuffle(strand):\n return \"\".join(random.sample(strand, len(strand)))","title":""},{"docid":"fbcfd646b210f425699560c4e53e4da9","score":"0.6427776","text":"def random_variable_order(self):\r\n x = list(range(self._n))\r\n shuffle(x)\r\n return x","title":""},{"docid":"508d3194f1e13d142b36b315506fd263","score":"0.6409891","text":"def shuffled(x, axis=None):\n if axis is None:\n axis = 0\n\n axis = np.array(axis) # Needed if axis is int to make iterable\n\n y = x[:] # copy data\n\n for aa in axis:\n y = np.swapaxes(y, 0, aa)\n np.random.shuffle(y)\n y = np.swapaxes(y, aa, 0)\n\n return y","title":""},{"docid":"738b073c6132d26bc4ab22c8afabbfc4","score":"0.64066523","text":"def _shuffle(self):\n self.deck += self.discard_pile\n self.discard_pile = []\n random.shuffle(self.deck)\n return self.deck.pop(0)","title":""},{"docid":"f4099dca173fa160700c73988e0a8932","score":"0.6382695","text":"def shuffle(self):\n indexing = [i for i in range(self.__inputs.shape[0])]\n random.shuffle(indexing)\n inputs = numpy.take(self.__inputs, indexing, axis=0)\n labels = numpy.take(self.__labels, indexing, axis=0)\n return NumpySet(inputs, labels)","title":""},{"docid":"685c89e22499e8b634111bbf7a683e73","score":"0.6381598","text":"def Shuffle(self):\n return self.get('Shuffle')","title":""},{"docid":"249e6beade441b522739787b5aba0e0f","score":"0.63815194","text":"def shuffle(self):\n new_X = np.empty(self.X_data.shape, dtype=self.X_data.dtype)\n new_Y = np.empty(self.Y_data.shape, dtype=self.Y_data.dtype)\n perm = np.random.permutation(self.X_data.shape[0])\n for old_idx, new_idx in enumerate(perm):\n new_X[new_idx,:,:,:] = self.X_data[old_idx,:,:,:]\n new_Y[new_idx] = self.Y_data[old_idx]\n self.X_data = new_X\n self.Y_data = new_Y","title":""},{"docid":"a6d7674cc7266e0a96b3dbeb3eaf1a8c","score":"0.6373855","text":"def shuffle(self):\n for i in self.dealt_cards:\n self.deck.append(i)\n self.deck = list(np.random.choice(self.deck,len(self.deck), replace=False))","title":""},{"docid":"804a5440bea0a1637636f9be9de3e730","score":"0.6365874","text":"def shuffle(self):\r\n images, labels = self.training_data\r\n images = np.asarray(images)\r\n labels = np.asarray(labels)\r\n c = np.c_[images.reshape(len(images), -1), labels.reshape(len(labels), -1)]\r\n\r\n np.random.shuffle(c)\r\n images = c[:, :images.size//len(images)].reshape(images.shape)\r\n labels = c[:, images.size//len(images)].reshape(labels.shape)\r\n self.training_data = images, labels","title":""},{"docid":"3acfdf65aff5d490c58b658db0746f97","score":"0.63475466","text":"def shuffled(iterable):\n items = list(iterable)\n random.shuffle(items)\n return items","title":""},{"docid":"d7c297104b35aecc694ba8a5578b207e","score":"0.6328437","text":"def shuffle(self):\n # Note that random.shuffle is a method on Lists,\n # not on Decks. If we want to shuffle a Deck, we\n # have to define it ourselves.\n random.shuffle(self._cards)","title":""},{"docid":"3215a3f9683c174db67f138528a71ea6","score":"0.6321513","text":"def getRandom(self):\n return self.array[random.randint(0,len(self.array)-1)]","title":""},{"docid":"2f590666320aaf50ec9d5e9fb6baae56","score":"0.63011986","text":"def reshuffle(self):\n random.shuffle(self.__drawn)\n self.__cards += self.__drawn\n self.__drawn = []","title":""},{"docid":"d18b5202c73dc371331109a42e6b57a5","score":"0.628132","text":"def shuffle(self):\n print('Shuffling the dataset...')\n random.shuffle(self.datasets['train'])","title":""},{"docid":"d18b5202c73dc371331109a42e6b57a5","score":"0.628132","text":"def shuffle(self):\n print('Shuffling the dataset...')\n random.shuffle(self.datasets['train'])","title":""},{"docid":"2e5d85e12afa896aabca7663c9bec1c5","score":"0.6268556","text":"def shuffle(self):\n idx = np.random.permutation(self.nSamples)\n self.filenames = self.filenames[idx]\n self.targets = self.targets[idx]","title":""},{"docid":"2b4911048e522d1c60c8aa0d31685992","score":"0.6267507","text":"def getRandom(self):\n return self.array[random.randint(0, len(self.array) - 1)]","title":""},{"docid":"f9f7751c430f1b2a2966579ce832c13b","score":"0.6267323","text":"def shuffle(self):\n self.train_nodes = np.random.permutation(self.train_nodes)\n self.batch_num = 0","title":""},{"docid":"b1ea4ac195db14d01fbb4ace3437814e","score":"0.62603796","text":"def shuffle_data(self):\n images = self.images.copy()\n labels = self.labels.copy()\n self.images = []\n self.labels = []\n\n # create list of permutated index and shuffle data accoding to list\n idx = np.random.permutation(len(labels))\n for i in idx:\n self.images.append(images[i])\n self.labels.append(labels[i])","title":""},{"docid":"35bce48cac384dc887238868e9168946","score":"0.6257035","text":"def random_sample(self):\n idx = np.random.permutation(range(0, len(self.strokes)))[0:1]\n return self._get_batch_from_indices(idx)","title":""},{"docid":"d8bb1707b89171ddc34be679ff51dda8","score":"0.62513936","text":"def getRandom(self):\n idx = random.randint(0,len(self.array)-1)\n return self.array[idx]","title":""},{"docid":"729c1afe3a126cac0b792d83b2ae20c1","score":"0.6244762","text":"def shuffle_cards(self):\n shuffle(self.cards)","title":""},{"docid":"2add025758daa3ab5183fb44b8acb7d0","score":"0.6243533","text":"def _shuffle_roidb_inds(self):\n # If the random flag is set,\n # then the database is shuffled according to system time\n # Useful for the validation set\n if self._random:\n st0 = np.random.get_state()\n millis = int(round(time.time() * 1000)) % 4294967295\n np.random.seed(millis)\n\n self._perm = np.random.permutation(np.arange(len(self._roidb)))\n # Restore the random state\n if self._random:\n np.random.set_state(st0)\n\n self._cur = 0","title":""},{"docid":"c3690ee834943cae762b35b8554e6b61","score":"0.6238018","text":"def shuffle(word):\n tmpList = list(word)\n random.shuffle(tmpList)\n return ''.join(tmpList)","title":""},{"docid":"932c3cf7860a7be35a4853368d86a5d8","score":"0.6236245","text":"def generate():\n global x\n global y\n random.seed(datetime)\n x = np.sort( np.random.uniform(-1, 1, size) )\n y = np.sign(x)\n for i in xrange(len(x)):\n needToFlip = random.randint(0, 4)\n if needToFlip == 1:\n y[i] = -y[i]","title":""},{"docid":"5876acca5367911187a32e6278a01072","score":"0.6234142","text":"def shuffle(matrix: np.ndarray):\n np.random.shuffle(matrix)\n np.random.shuffle(matrix.transpose())","title":""},{"docid":"a3047dd9ec2ddf14820d0722341764c0","score":"0.62299407","text":"def shuffle(lst):\n\n temp_lst = deepcopy(lst)\n m = len(temp_lst)\n while (m):\n m -= 1\n i = randint(0, m)\n temp_lst[m], temp_lst[i] = temp_lst[i], temp_lst[m]\n return temp_lst","title":""},{"docid":"6bb3a6ba36813e4db16ca88f1374edeb","score":"0.6226673","text":"def shufflem(subject):\r\n mask_start, mask_end = masks(len(subject), \"a\")\r\n selection = []\r\n for i in range(mask_start, mask_end+1):\r\n selection.append(subject[i])\r\n random.shuffle(selection)\r\n count = 0\r\n for j in range(mask_start, mask_end+1):\r\n subject[j] = selection[count]\r\n count += 1\r\n return subject","title":""},{"docid":"e472f11be619709b1e2c7489cbda8d30","score":"0.6226306","text":"def shuffle(self):\n \n deckList = self._cards\n shuffledList = []\n \n for _ in range(len(deckList)):\n choice = deckList[random.randrange(len(deckList))]\n shuffledList.append(choice)\n deckList.remove(choice)\n \n self._cards = shuffledList","title":""},{"docid":"7f2d829af065cd31501b522b69fa397a","score":"0.62127215","text":"def array_(self):\n return numpy.random.random(self.dim)","title":""},{"docid":"d067b923d2cc27ba79049569fbc9117a","score":"0.6208625","text":"def shuffle_data(self, data_size):\n self.shuffle_index = np.random.permutation(data_size)","title":""},{"docid":"d1118aa9dcea2c3d294555be170975cf","score":"0.61992896","text":"def shuffle_data(self):\n images = self.images[:]\n labels = self.labels[:]\n self.images = []\n self.labels = []\n \n #create list of permutated index and shuffle data accoding to list\n idx = np.random.permutation(len(labels))\n for i in idx:\n self.images.append(images[i])\n self.labels.append(labels[i])","title":""},{"docid":"04f5763edcf6c326366f82cc2419eccf","score":"0.61877924","text":"def fisher_yates(arr):\n n = len(arr)\n for i in range(n - 1):\n j = randrange(i, n)\n if i != j:\n arr[i], arr[j] = arr[j], arr[i]\n yield arr","title":""},{"docid":"a2639698bc3a635c4ed155dc4909a54e","score":"0.61698437","text":"def shuffle_data(inputs, targets):\n shuffle = np.arange(inputs.shape[0])\n # np.random.seed(17)\n np.random.shuffle(shuffle)\n inputs = inputs[shuffle]\n targets = targets[shuffle]\n return inputs, targets","title":""},{"docid":"57ab8ff876847be56ae0690086596603","score":"0.6166354","text":"def shuffle(self, env):\n pass","title":""},{"docid":"9551e2424628f0d4e871c3b23a2f238b","score":"0.6159529","text":"def __shuffle(self):\n for i in range(3):\n random.shuffle(self.main_deck)\n i += 1\n self._is_shuffled = True","title":""}],"string":"[\n {\n \"docid\": \"0429db19315e7a5451dd49528814b233\",\n \"score\": \"0.7761317\",\n \"text\": \"def shuffle(self):\\n n = len(self.arr)\\n bound = [ (x, random.random()) for x in self.arr]\\n bound.sort(key=lambda x:x[1])\\n return [x[0] for x in bound]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a3feb3f4e6598b34404fc1e7221f8b\",\n \"score\": \"0.7651203\",\n \"text\": \"def shuffled(self):\\n random.seed(self.config.random_seed)\\n new_samples = self.samples[:]\\n random.shuffle(new_samples)\\n return self._new(new_samples)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af2cbd5de9b5201b53d1a253cd20c8be\",\n \"score\": \"0.74414164\",\n \"text\": \"def shuffle(self) -> List[int]:\\n array_copy = list(self.array)\\n # # T C O N^2\\n # for i in range(len(self.array)):\\n # remove_idx = random.randrange(len(array_copy))\\n # self.array[i] = array_copy.pop(remove_idx)\\n # T C O N\\n for i in range(len(self.array)):\\n swap_idx = random.randrange(i, len(self.array))\\n self.array[i], self.array[swap_idx] = self.array[swap_idx], self.array[i]\\n\\n return self.array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02116820cc290f17434bfb16da133370\",\n \"score\": \"0.7400849\",\n \"text\": \"def shuffle(x):\\n random.shuffle(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e95c8b36c8fb21d1f3d1c54fcf33fb1\",\n \"score\": \"0.73854816\",\n \"text\": \"def shuffle(self) -> List[int]:\\n\\n for i in range(len(self.array)):\\n print(f'i {i}')\\n swap_idx = random.randrange(i, len(self.array))\\n self.array[i], self.array[swap_idx] = self.array[swap_idx], self.array[i]\\n return self.array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba0b1d0f9a3adb3b5aa27d904913f4c3\",\n \"score\": \"0.7380981\",\n \"text\": \"def shuffle():\\n orig = range(1,4)\\n nl = list(orig)\\n for i in xrange(len(nl)):\\n ni = random.randint(i, len(nl) - 1)\\n nl[i], nl[ni] = nl[ni], nl[i]\\n return nl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bfe0dbdede996eea5b2ffd75556f7ea\",\n \"score\": \"0.73302203\",\n \"text\": \"def shuffle(self) -> List[int]:\\n lst = copy.deepcopy(self.array)\\n n = len(lst)\\n\\n for i in range(n):\\n idx = random.randint(0, n-1)\\n lst[i], lst[idx] = lst[idx], lst[i]\\n\\n return lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f400c3005c758afe597bdc3678696ff7\",\n \"score\": \"0.7309955\",\n \"text\": \"def shuffle(samples):\\n np.random.shuffle(samples)\\n return samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a019c8bc79a5726304b383c799b4a2f\",\n \"score\": \"0.7292532\",\n \"text\": \"def shuffle(self):\\n import random\\n randomList = []\\n listCopy = self.data[:]\\n for i in range(len(listCopy)):\\n idx = random.randint(0, len(listCopy)-1)\\n listCopy[idx], listCopy[-1] = listCopy[-1], listCopy[idx]\\n randomList.append(listCopy.pop())\\n return randomList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"957eac739943595fa8f5ac3eb31c6983\",\n \"score\": \"0.72868466\",\n \"text\": \"def fisher_yates_shuffle(arr : [object]):\\n for i in range(len(arr)-1, 0, -1):\\n j = randint(0, i)\\n arr[i], arr[j] = arr[j], arr[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ff5d54f5c9258d192b45b304bce5b5c\",\n \"score\": \"0.72208476\",\n \"text\": \"def randomize(data):\\r\\n permutation = np.random.permutation(data.shape[0])\\r\\n shuffled_data = data[permutation, :]\\r\\n # shuffled_y = y[permutation]\\r\\n return shuffled_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0140ee3ee85e32cfa54b9f8fd3022e1c\",\n \"score\": \"0.7190094\",\n \"text\": \"def shuffle(self, list):\\n if len(list) >= 2:\\n i = len(list) - 1\\n while i > 0:\\n k = self.rndintexc(i + 1)\\n tmp = list[i]\\n list[i] = list[k]\\n list[k] = tmp\\n i -= 1\\n return list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8096f341e1df5c2eb31660f36ae318a1\",\n \"score\": \"0.718754\",\n \"text\": \"def shuffle(self):\\n res = self.original_nums[:]\\n random.shuffle(res)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06b3ecfe14ae48565566df86f8d9ba4e\",\n \"score\": \"0.7185975\",\n \"text\": \"def shuffle(self):\\n rd.shuffle(self.items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e41478ca39b2adcf5fec020edcf2ea67\",\n \"score\": \"0.71816194\",\n \"text\": \"def shuffle(self) :\\n random.shuffle(self.nums)\\n return self.nums\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50551b1864f68150a48603b5d392127\",\n \"score\": \"0.7152363\",\n \"text\": \"def shuffle(self):\\n\\t\\t# # version 1\\n\\t\\t# arr = self.nums[:]\\n\\t\\t# result = [None for i in range(len(self.nums))]\\n\\t\\t# for i in range(len(self.nums)):\\n\\t\\t# \\tif arr:\\n\\t\\t# \\t\\tidx = random.randint(0, len(arr) - 1)\\n\\t\\t# \\t\\tresult[i] = arr[idx]\\n\\t\\t# \\t\\tarr.pop(idx)\\n\\n\\t\\t# return result\\n\\n\\n\\t\\t# version 2\\n\\t\\tarr = self.nums[:]\\n\\t\\tL = len(arr)\\n\\t\\tfor i in range(L):\\n\\t\\t\\tj = random.randint(0, L-1)\\n\\t\\t\\tarr[i], arr[j] = arr[j], arr[i]\\n\\n\\t\\treturn arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41973cf81a851e8c1fdc930a13476274\",\n \"score\": \"0.7151598\",\n \"text\": \"def shuffle(s):\\n return rand.sample(s, len(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da0189d48996d0e3e5a51cf3f4aa8ff\",\n \"score\": \"0.7138123\",\n \"text\": \"def shuffle(self) -> list:\\n temp = self.original[:]\\n res = []\\n for i in range(len(temp)):\\n res.append(temp.pop(random.randint(0, len(temp)-1)))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929f1c6e2b1c50b3e050d6f3e9d8b151\",\n \"score\": \"0.7122223\",\n \"text\": \"def shuffle(x):\\n return tensorflow.compat.v1.random_shuffle(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"098fa2e08ab7acef49fe218aa131cf3d\",\n \"score\": \"0.71032465\",\n \"text\": \"def shuffle(self):\\n import random\\n rng = random.Random() # Create a random generator\\n rng.shuffle(self.cards) # uUse its shuffle method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987cfe68684d969f4bb1c1f10e213458\",\n \"score\": \"0.70363826\",\n \"text\": \"def _randomize(self, lst):\\n return random.shuffle(lst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae41d48169feb230ae62b4d9f3d3676\",\n \"score\": \"0.70223194\",\n \"text\": \"def shuffle(self):\\n for i in range(self.length):\\n index = random.randrange(i, self.length)\\n self.nums[i], self.nums[index] = self.nums[index], self.nums[i]\\n\\n return self.nums\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97a9de558d76d60584864173d433c5e3\",\n \"score\": \"0.7018957\",\n \"text\": \"def shuffle(*arrays, random_state=..., n_samples=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b693c128bc0942af1938bce4def345\",\n \"score\": \"0.6992307\",\n \"text\": \"def shuffle(self):\\n ans = self.nums[:]\\n for i in range(len(self.nums) - 1, 0, -1):\\n j = random.randrange(0, i + 1)\\n ans[i], ans[j] = ans[j], ans[i]\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"697cd66bc296296d686e11b4fd2cfa7c\",\n \"score\": \"0.69745827\",\n \"text\": \"def shuffle(self):\\n np.random.seed(0xA5EED) # For reproducible results\\n order = np.arange(len(self.data))\\n np.random.shuffle(order)\\n self.data = self.data.iloc[order]\\n return order\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ca329c71467e3b392aa1c1f80d3642\",\n \"score\": \"0.6925458\",\n \"text\": \"def test_shuffle_methods(self):\\r\\n arr1 = np.arange(20, 30)\\r\\n arr2 = np.arange(50, 60)\\r\\n print(arr1)\\r\\n print(arr2)\\r\\n perm = np.random.permutation(10)\\r\\n print(arr1[perm])\\r\\n print(arr2[perm])\\r\\n\\r\\n arr1 = np.arange(20, 30)\\r\\n arr2 = np.arange(50, 60)\\r\\n rng_s = np.random.get_state()\\r\\n np.random.shuffle(arr1)\\r\\n np.random.set_state(rng_s)\\r\\n np.random.shuffle(arr2)\\r\\n print(arr1)\\r\\n print(arr2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c931d8a5734f825292f9b86d4cb72b4e\",\n \"score\": \"0.6840419\",\n \"text\": \"def suffle_data(data):\\n indices = np.random.permutation(len(data[1]))\\n shuffled = [data[0][indices], data[1][indices]]\\n return shuffled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e8d4d313ed7f7a3fa0a674b07ce8211\",\n \"score\": \"0.6807566\",\n \"text\": \"def myShuffle(this_list):\\r\\n random.shuffle(this_list)\\r\\n return(this_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b03d6fb7217a132e76f093c8da0c427\",\n \"score\": \"0.67915964\",\n \"text\": \"def shuffle(self):\\n import random\\n index_list = [i for i in range(len(self.nums))]\\n for i in range(len(self.nums)):\\n j = random.randrange(0, len(index_list))\\n index_list[i], index_list[j] = index_list[j], index_list[i]\\n return [self.nums[i] for i in index_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17dbf12ce1d0c01dfbc32c6b810de0bf\",\n \"score\": \"0.6776635\",\n \"text\": \"def shuffle(self) -> List[int]:\\n shuffled = random.sample(self.nums, len(self.nums))\\n return shuffled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24251d64300c03e33b0ab6f510408517\",\n \"score\": \"0.67698795\",\n \"text\": \"def shuffle(self):\\n random.shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1498694f6ef9a23a4ba02dd93a25dcc6\",\n \"score\": \"0.67677927\",\n \"text\": \"def shuffle_data(data):\\r\\n indices = np.arange(len(data[0]), dtype=np.int32)\\r\\n np.random.shuffle(indices)\\r\\n return [x[indices] for x in data]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6323c70386481718e60207987f80506\",\n \"score\": \"0.6743605\",\n \"text\": \"def shuffle(self):\\n return random.shuffle(self._starting_cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b7c2cbd5ea3f718933fb2edfbdb3947\",\n \"score\": \"0.6739077\",\n \"text\": \"def shuffle(self) -> List[int]:\\n s = copy.deepcopy(self.d)\\n shuffle(s)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cdf095c63928edd64bc85d4241e6f3b\",\n \"score\": \"0.6736934\",\n \"text\": \"def shuffle(l):\\n result = copy(l)\\n pyshuffle(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86b65f979384246e67d9052a0d054b91\",\n \"score\": \"0.6681268\",\n \"text\": \"def shuffle(self):\\n self.deck.shuffle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ed139bea71995044b40118eb4eb9ffa\",\n \"score\": \"0.66747314\",\n \"text\": \"def _get_elements_random(self):\\n return shuffled(self.elements.all())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06476671ead77f2be394c982692d69d0\",\n \"score\": \"0.6654657\",\n \"text\": \"def shuffle(self) -> List[int]:\\n random.shuffle(self.nums)\\n return self.nums\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3ccba49882b0e08a42e077edf8ab7a\",\n \"score\": \"0.6650263\",\n \"text\": \"def shuffle(self):\\n index = torch.randperm(self.length)\\n self._reindex(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b0abbe30286aa3f7c1e09a4b7d1c23c\",\n \"score\": \"0.6643642\",\n \"text\": \"def shuffledP(self):\\n return shuffledP(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75da86897bfb2bf8383401d3da8faa62\",\n \"score\": \"0.66250473\",\n \"text\": \"def shuffle(self):\\n length = len(self.cur_nums)\\n for idx in range(length):\\n rand_idx = random.randint(idx, length-1)\\n self.cur_nums[idx], self.cur_nums[rand_idx] = self.cur_nums[rand_idx], self.cur_nums[idx]\\n return self.cur_nums\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbf44d18ae7782d99bf33dd0f13a72ec\",\n \"score\": \"0.65894943\",\n \"text\": \"def shuffle(self) -> List[int]:\\n for i in range(len(self.nums)-1):\\n j = random.randint(i, len(self.nums)-1)\\n self.nums[i], self.nums[j] = self.nums[j], self.nums[i]\\n self.perm[i], self.perm[j] = self.perm[j], self.perm[i]\\n return self.nums\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a5767183aa413ad00fa28e3495797f2\",\n \"score\": \"0.65440506\",\n \"text\": \"def test_shuffle():\\n D = make_test_data()\\n before = D.tolist()\\n shuffle(D)\\n after = D.tolist()\\n before.sort()\\n after.sort()\\n assert after == before, \\\"Shuffle mutated data.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4250cb0615fe21104469a3121fb866c6\",\n \"score\": \"0.6535577\",\n \"text\": \"def shuffle(self) -> List[int]:\\n shuffled_nums = []\\n nums = self._nums[:]\\n i = len(nums)\\n while i > 0:\\n random_pick = random.randint(0, i-1)\\n nums[random_pick], nums[-1] = nums[-1], nums[random_pick]\\n shuffled_nums.append(nums.pop())\\n i-=1\\n # print(random_pick, shuffled_nums, nums)\\n return shuffled_nums\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b229cebf2d13fa2da6c480581509962b\",\n \"score\": \"0.65275246\",\n \"text\": \"def shuffle(l):\\n length = len(l)\\n indices = np.random.permutation(length)\\n output = []\\n for i in indices:\\n output.append(l[i])\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e74382da2042982beda58f3b68d9e89b\",\n \"score\": \"0.6502599\",\n \"text\": \"def shuffle(self) -> List[int]:\\n last = len(self.nums) - 1\\n for i in range(len(self.nums)):\\n idx = random.randint(0, last - i)\\n self.nums[idx], self.nums[last - i] = self.nums[last - i], self.nums[idx]\\n self.perm[idx], self.perm[last - i] = self.perm[last - i], self.perm[idx]\\n return self.nums\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722742765fe8f1801032a332abd894fd\",\n \"score\": \"0.64800096\",\n \"text\": \"def _flip_values_randomly(array):\\n # set up random flips\\n random_vals = np.random.random((array.shape)) # ex {N, mutM, task, M}\\n flips = -(random_vals <= 0.5).astype(int) \\n noflips = (random_vals > 0.5).astype(int)\\n random_flips = np.add(flips, noflips)\\n\\n # flip values accordingly\\n shuffle_results = np.multiply(random_flips, array)\\n shuffle_results = np.sum(shuffle_results, axis=0) # ex {mutM, task, M}\\n\\n return shuffle_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f83778300d6397d2873c434d264a517d\",\n \"score\": \"0.6478821\",\n \"text\": \"def random_deck():\\n # initialize cards deck\\n cards = numpy.array([card for card in itertools.product(range(nfeatures),\\n repeat=ndims)]).T\\n n = cards.shape[1]\\n # shuffle\\n return cards[:, numpy.random.permutation(n)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03afe1d407884df5ef7348eba5580fe3\",\n \"score\": \"0.64781564\",\n \"text\": \"def shuffle_cards(self):\\n np.random.shuffle(self.deck)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33a31dd687a2ed34c9cc9f44c785064f\",\n \"score\": \"0.6468279\",\n \"text\": \"def shuffle(self) -> List[int]:\\n return sorted(self.origin, key=lambda x: random.random())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd959b6a297a13ad50b04f3a2c2a076d\",\n \"score\": \"0.6463501\",\n \"text\": \"def randomize_data(data, seed = 0):\\r\\n\\tm = data.shape[0]\\r\\n\\tnp.random.seed(seed)\\r\\n\\tpermutation = list(np.random.permutation(m))\\r\\n\\tdata = data[permutation,:]\\r\\n\\r\\n\\treturn data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5516a6aa88fc72ba58d2553eadbdfb15\",\n \"score\": \"0.6463025\",\n \"text\": \"def randomize(data):\\n\\tprint \\\"Randomizing\\\"\\n\\tfor number in range(len(data)):\\n\\t\\ttemp_stored_number = data[number]\\n\\t\\tswap_index = random.randrange(len(data))\\n\\t\\t\\t\\n\\t\\tdata[number] = data[swap_index]\\n\\t\\tdata[swap_index] = temp_stored_number\\n\\t\\treturn data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab89e59fdc2fb1884414a165b78f16c4\",\n \"score\": \"0.6459895\",\n \"text\": \"def shuffle(self):\\n puzzle = self\\n for _ in range(1000):\\n puzzle = random.choice(list(puzzle.actions()))\\n return puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37a6ce6e71c607fc3211e3c9eae23781\",\n \"score\": \"0.6453688\",\n \"text\": \"def shuffle(self):\\n print('Shuffling the dataset...')\\n random.shuffle(self.trainingSamples)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1741c925be95d375c6e9cccc44a34bc5\",\n \"score\": \"0.6450331\",\n \"text\": \"def randomize(self):\\n permutation = np.random.permutation(self.y_train.shape[0])\\n shuffled_x = self.x_train[permutation, :, :, :]\\n shuffled_y = self.y_train[permutation]\\n return shuffled_x, shuffled_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3576fd57df0949f5c4d3ff8454b233\",\n \"score\": \"0.64395845\",\n \"text\": \"def shuffle(self):\\n self.indices = self._generate_indices()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25dd2f156a1ea186f863c9a72999ff48\",\n \"score\": \"0.64374197\",\n \"text\": \"def shuffle(strand):\\n return \\\"\\\".join(random.sample(strand, len(strand)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbcfd646b210f425699560c4e53e4da9\",\n \"score\": \"0.6427776\",\n \"text\": \"def random_variable_order(self):\\r\\n x = list(range(self._n))\\r\\n shuffle(x)\\r\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508d3194f1e13d142b36b315506fd263\",\n \"score\": \"0.6409891\",\n \"text\": \"def shuffled(x, axis=None):\\n if axis is None:\\n axis = 0\\n\\n axis = np.array(axis) # Needed if axis is int to make iterable\\n\\n y = x[:] # copy data\\n\\n for aa in axis:\\n y = np.swapaxes(y, 0, aa)\\n np.random.shuffle(y)\\n y = np.swapaxes(y, aa, 0)\\n\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"738b073c6132d26bc4ab22c8afabbfc4\",\n \"score\": \"0.64066523\",\n \"text\": \"def _shuffle(self):\\n self.deck += self.discard_pile\\n self.discard_pile = []\\n random.shuffle(self.deck)\\n return self.deck.pop(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4099dca173fa160700c73988e0a8932\",\n \"score\": \"0.6382695\",\n \"text\": \"def shuffle(self):\\n indexing = [i for i in range(self.__inputs.shape[0])]\\n random.shuffle(indexing)\\n inputs = numpy.take(self.__inputs, indexing, axis=0)\\n labels = numpy.take(self.__labels, indexing, axis=0)\\n return NumpySet(inputs, labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"685c89e22499e8b634111bbf7a683e73\",\n \"score\": \"0.6381598\",\n \"text\": \"def Shuffle(self):\\n return self.get('Shuffle')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"249e6beade441b522739787b5aba0e0f\",\n \"score\": \"0.63815194\",\n \"text\": \"def shuffle(self):\\n new_X = np.empty(self.X_data.shape, dtype=self.X_data.dtype)\\n new_Y = np.empty(self.Y_data.shape, dtype=self.Y_data.dtype)\\n perm = np.random.permutation(self.X_data.shape[0])\\n for old_idx, new_idx in enumerate(perm):\\n new_X[new_idx,:,:,:] = self.X_data[old_idx,:,:,:]\\n new_Y[new_idx] = self.Y_data[old_idx]\\n self.X_data = new_X\\n self.Y_data = new_Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6d7674cc7266e0a96b3dbeb3eaf1a8c\",\n \"score\": \"0.6373855\",\n \"text\": \"def shuffle(self):\\n for i in self.dealt_cards:\\n self.deck.append(i)\\n self.deck = list(np.random.choice(self.deck,len(self.deck), replace=False))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"804a5440bea0a1637636f9be9de3e730\",\n \"score\": \"0.6365874\",\n \"text\": \"def shuffle(self):\\r\\n images, labels = self.training_data\\r\\n images = np.asarray(images)\\r\\n labels = np.asarray(labels)\\r\\n c = np.c_[images.reshape(len(images), -1), labels.reshape(len(labels), -1)]\\r\\n\\r\\n np.random.shuffle(c)\\r\\n images = c[:, :images.size//len(images)].reshape(images.shape)\\r\\n labels = c[:, images.size//len(images)].reshape(labels.shape)\\r\\n self.training_data = images, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3acfdf65aff5d490c58b658db0746f97\",\n \"score\": \"0.63475466\",\n \"text\": \"def shuffled(iterable):\\n items = list(iterable)\\n random.shuffle(items)\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7c297104b35aecc694ba8a5578b207e\",\n \"score\": \"0.6328437\",\n \"text\": \"def shuffle(self):\\n # Note that random.shuffle is a method on Lists,\\n # not on Decks. If we want to shuffle a Deck, we\\n # have to define it ourselves.\\n random.shuffle(self._cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3215a3f9683c174db67f138528a71ea6\",\n \"score\": \"0.6321513\",\n \"text\": \"def getRandom(self):\\n return self.array[random.randint(0,len(self.array)-1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f590666320aaf50ec9d5e9fb6baae56\",\n \"score\": \"0.63011986\",\n \"text\": \"def reshuffle(self):\\n random.shuffle(self.__drawn)\\n self.__cards += self.__drawn\\n self.__drawn = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d18b5202c73dc371331109a42e6b57a5\",\n \"score\": \"0.628132\",\n \"text\": \"def shuffle(self):\\n print('Shuffling the dataset...')\\n random.shuffle(self.datasets['train'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d18b5202c73dc371331109a42e6b57a5\",\n \"score\": \"0.628132\",\n \"text\": \"def shuffle(self):\\n print('Shuffling the dataset...')\\n random.shuffle(self.datasets['train'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5d85e12afa896aabca7663c9bec1c5\",\n \"score\": \"0.6268556\",\n \"text\": \"def shuffle(self):\\n idx = np.random.permutation(self.nSamples)\\n self.filenames = self.filenames[idx]\\n self.targets = self.targets[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b4911048e522d1c60c8aa0d31685992\",\n \"score\": \"0.6267507\",\n \"text\": \"def getRandom(self):\\n return self.array[random.randint(0, len(self.array) - 1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9f7751c430f1b2a2966579ce832c13b\",\n \"score\": \"0.6267323\",\n \"text\": \"def shuffle(self):\\n self.train_nodes = np.random.permutation(self.train_nodes)\\n self.batch_num = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ea4ac195db14d01fbb4ace3437814e\",\n \"score\": \"0.62603796\",\n \"text\": \"def shuffle_data(self):\\n images = self.images.copy()\\n labels = self.labels.copy()\\n self.images = []\\n self.labels = []\\n\\n # create list of permutated index and shuffle data accoding to list\\n idx = np.random.permutation(len(labels))\\n for i in idx:\\n self.images.append(images[i])\\n self.labels.append(labels[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35bce48cac384dc887238868e9168946\",\n \"score\": \"0.6257035\",\n \"text\": \"def random_sample(self):\\n idx = np.random.permutation(range(0, len(self.strokes)))[0:1]\\n return self._get_batch_from_indices(idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8bb1707b89171ddc34be679ff51dda8\",\n \"score\": \"0.62513936\",\n \"text\": \"def getRandom(self):\\n idx = random.randint(0,len(self.array)-1)\\n return self.array[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729c1afe3a126cac0b792d83b2ae20c1\",\n \"score\": \"0.6244762\",\n \"text\": \"def shuffle_cards(self):\\n shuffle(self.cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2add025758daa3ab5183fb44b8acb7d0\",\n \"score\": \"0.6243533\",\n \"text\": \"def _shuffle_roidb_inds(self):\\n # If the random flag is set,\\n # then the database is shuffled according to system time\\n # Useful for the validation set\\n if self._random:\\n st0 = np.random.get_state()\\n millis = int(round(time.time() * 1000)) % 4294967295\\n np.random.seed(millis)\\n\\n self._perm = np.random.permutation(np.arange(len(self._roidb)))\\n # Restore the random state\\n if self._random:\\n np.random.set_state(st0)\\n\\n self._cur = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3690ee834943cae762b35b8554e6b61\",\n \"score\": \"0.6238018\",\n \"text\": \"def shuffle(word):\\n tmpList = list(word)\\n random.shuffle(tmpList)\\n return ''.join(tmpList)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932c3cf7860a7be35a4853368d86a5d8\",\n \"score\": \"0.6236245\",\n \"text\": \"def generate():\\n global x\\n global y\\n random.seed(datetime)\\n x = np.sort( np.random.uniform(-1, 1, size) )\\n y = np.sign(x)\\n for i in xrange(len(x)):\\n needToFlip = random.randint(0, 4)\\n if needToFlip == 1:\\n y[i] = -y[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5876acca5367911187a32e6278a01072\",\n \"score\": \"0.6234142\",\n \"text\": \"def shuffle(matrix: np.ndarray):\\n np.random.shuffle(matrix)\\n np.random.shuffle(matrix.transpose())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3047dd9ec2ddf14820d0722341764c0\",\n \"score\": \"0.62299407\",\n \"text\": \"def shuffle(lst):\\n\\n temp_lst = deepcopy(lst)\\n m = len(temp_lst)\\n while (m):\\n m -= 1\\n i = randint(0, m)\\n temp_lst[m], temp_lst[i] = temp_lst[i], temp_lst[m]\\n return temp_lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb3a6ba36813e4db16ca88f1374edeb\",\n \"score\": \"0.6226673\",\n \"text\": \"def shufflem(subject):\\r\\n mask_start, mask_end = masks(len(subject), \\\"a\\\")\\r\\n selection = []\\r\\n for i in range(mask_start, mask_end+1):\\r\\n selection.append(subject[i])\\r\\n random.shuffle(selection)\\r\\n count = 0\\r\\n for j in range(mask_start, mask_end+1):\\r\\n subject[j] = selection[count]\\r\\n count += 1\\r\\n return subject\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e472f11be619709b1e2c7489cbda8d30\",\n \"score\": \"0.6226306\",\n \"text\": \"def shuffle(self):\\n \\n deckList = self._cards\\n shuffledList = []\\n \\n for _ in range(len(deckList)):\\n choice = deckList[random.randrange(len(deckList))]\\n shuffledList.append(choice)\\n deckList.remove(choice)\\n \\n self._cards = shuffledList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f2d829af065cd31501b522b69fa397a\",\n \"score\": \"0.62127215\",\n \"text\": \"def array_(self):\\n return numpy.random.random(self.dim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d067b923d2cc27ba79049569fbc9117a\",\n \"score\": \"0.6208625\",\n \"text\": \"def shuffle_data(self, data_size):\\n self.shuffle_index = np.random.permutation(data_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1118aa9dcea2c3d294555be170975cf\",\n \"score\": \"0.61992896\",\n \"text\": \"def shuffle_data(self):\\n images = self.images[:]\\n labels = self.labels[:]\\n self.images = []\\n self.labels = []\\n \\n #create list of permutated index and shuffle data accoding to list\\n idx = np.random.permutation(len(labels))\\n for i in idx:\\n self.images.append(images[i])\\n self.labels.append(labels[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f5763edcf6c326366f82cc2419eccf\",\n \"score\": \"0.61877924\",\n \"text\": \"def fisher_yates(arr):\\n n = len(arr)\\n for i in range(n - 1):\\n j = randrange(i, n)\\n if i != j:\\n arr[i], arr[j] = arr[j], arr[i]\\n yield arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2639698bc3a635c4ed155dc4909a54e\",\n \"score\": \"0.61698437\",\n \"text\": \"def shuffle_data(inputs, targets):\\n shuffle = np.arange(inputs.shape[0])\\n # np.random.seed(17)\\n np.random.shuffle(shuffle)\\n inputs = inputs[shuffle]\\n targets = targets[shuffle]\\n return inputs, targets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ab8ff876847be56ae0690086596603\",\n \"score\": \"0.6166354\",\n \"text\": \"def shuffle(self, env):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9551e2424628f0d4e871c3b23a2f238b\",\n \"score\": \"0.6159529\",\n \"text\": \"def __shuffle(self):\\n for i in range(3):\\n random.shuffle(self.main_deck)\\n i += 1\\n self._is_shuffled = True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992971,"cells":{"query_id":{"kind":"string","value":"83a46b9cb0746e38d0be8fbb719aebb4"},"query":{"kind":"string","value":"Convert a full matrix to a partial vector"},"positive_passages":{"kind":"list like","value":[{"docid":"2f62d8c09ee7b91a5d0d8eadde405605","score":"0.71898866","text":"def full_matrix_to_partial_matrix(self, full_matrix):\n return full_matrix[self.m_min:, self.n_min:]","title":""}],"string":"[\n {\n \"docid\": \"2f62d8c09ee7b91a5d0d8eadde405605\",\n \"score\": \"0.71898866\",\n \"text\": \"def full_matrix_to_partial_matrix(self, full_matrix):\\n return full_matrix[self.m_min:, self.n_min:]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e8dd61f6410d461c1b83e26e73167926","score":"0.85416037","text":"def full_matrix_to_partial_vector(self, full_matrix):\n part_matrix = self.full_matrix_to_partial_matrix(full_matrix)\n return self.partial_matrix_to_partial_vector(part_matrix)","title":""},{"docid":"e7ec618d329314c921519b9d975980ba","score":"0.8416663","text":"def partial_matrix_to_partial_vector(self, mat):\n return mat.flatten()","title":""},{"docid":"f37c4976bc8604c54fbe3ad3dd3e90ea","score":"0.7162625","text":"def partial_vector_to_full_matrix(self, partial_vector):\n return self.partial_matrix_to_full_matrix(self.partial_vector_to_partial_matrix(partial_vector))","title":""},{"docid":"5492b6df566b608a3ee6bc9ccefb700c","score":"0.7069103","text":"def partial_vector_to_partial_matrix(self, vec):\n return vec.reshape((self.m_max - self.m_min + 1, self.n_max - self.n_min + 1))","title":""},{"docid":"863a68eb75cda765a257f0cb4b691479","score":"0.70258355","text":"def matrix_to_vector(matrix):\n return (np.reshape(matrix, 92 * 112).astype(np.uint8) - 127.5) / 127.5","title":""},{"docid":"f36ae0a9bb5435b4ae2633baba87daac","score":"0.6973615","text":"def partial_matrix_to_full_matrix(self, partial_matrix):\n full_matrix = numpy.zeros((self.m_max + 1, self.n_max + 1))\n full_matrix[self.m_min:, self.n_min:] = partial_matrix\n return full_matrix","title":""},{"docid":"f6c3890e2097c3d2da5972a59ae9a392","score":"0.6687499","text":"def matrix_to_vector(image):\n return image.flatten()","title":""},{"docid":"04d9ebb28d13d720ae3b0e7df408d19b","score":"0.6667218","text":"def to_vector_part(self):\n return self.vector","title":""},{"docid":"09430c0bdf63a2ae2a4d18a99c067b6d","score":"0.6354226","text":"def vector_from_matrix(self, matrix):\n matrix = gs.to_ndarray(matrix, to_ndim=3)\n n_mats, m, n = matrix.shape\n return gs.reshape(matrix, (n_mats, m*n))","title":""},{"docid":"c3b44c8108712cf747f419ab6fe391ee","score":"0.6307527","text":"def so3_to_vec(matrix):\n return np.array([matrix[2][1], matrix[0][2], matrix[1][0]])","title":""},{"docid":"38de26b2eaaf51dc7c16b95d0b3d5d37","score":"0.6237089","text":"def to_vector(x):\n if not isinstance(x, np.ndarray):\n x = np.array(x)\n \n if x.ndim != 1:\n return np.reshape(x, (-1,))\n else:\n return x","title":""},{"docid":"8b78baf0bcb8e9f53ae3a32022f5772e","score":"0.6232256","text":"def as_vector(self):\n return self.h_matrix[:-1, -1]","title":""},{"docid":"bd1508ca843b1d7e1709c662c924725c","score":"0.6178901","text":"def tovector(self):\r\n return _mps2vector(self)","title":""},{"docid":"c4cbb98b9821af16340a6175f3a08c76","score":"0.61635745","text":"def to_vector(mat):\n\tout = np.zeros((mat.shape[0],mat.shape[1]))\n\tout2 = np.zeros((mat.shape[0]))\n\tfor i in range(mat.shape[0]):\n\t\tfor n, j in enumerate(mat[i]):\n\t\t\tif np.any(j == (np.amax(mat[i]))):\n\t\t\t\tout[i][n] = 1\n\t\t\t\tout2[i] = n\n\n\treturn out2","title":""},{"docid":"3f9d82393523b770db83d1e6b1d55c34","score":"0.6061278","text":"def _mat_to_vec(\n self,\n img: np.ndarray\n ) -> np.ndarray:\n\n return img.flatten()","title":""},{"docid":"1f962d1744a4bcbd48938bcc84192ddd","score":"0.60486114","text":"def to_vector(v):\n if \"todense\" in dir(v):\n v = v.todense()\n if len(v.shape) == 2:\n if v.shape[0] == 1:\n v.resize(v.size)\n if v.shape[0] == 2:\n v = numpy.reshape(v, v.shape[0] * v.shape[1])\n\n return v","title":""},{"docid":"c51d0632f9edfed6c991d5a62619cb8a","score":"0.6038777","text":"def full_matrix(self):\n return mf.b + mf.b_u[:,np.newaxis] + mf.b_i[np.newaxis:,] + mf.P.dot(mf.Q.T)","title":""},{"docid":"1845e032d48e349a08dbaf45cfbe88c8","score":"0.60255694","text":"def colvec(vec):\n return vec.reshape(-1, 1)","title":""},{"docid":"1858857fa4f1dc88618fbd1ff5ba34e2","score":"0.60173154","text":"def timeable_column_matvec():\n\n b = column_matvec(A0, x0) # noqa","title":""},{"docid":"b5468a9500f41f007297bbea3b4a8dbe","score":"0.6004301","text":"def full_matrix(self):\n return self.b + self.b_u[:,np.newaxis] + self.b_i[np.newaxis:,] + self.P.dot(self.Q.T)","title":""},{"docid":"b5468a9500f41f007297bbea3b4a8dbe","score":"0.6004301","text":"def full_matrix(self):\n return self.b + self.b_u[:,np.newaxis] + self.b_i[np.newaxis:,] + self.P.dot(self.Q.T)","title":""},{"docid":"4fecf69b22382d0a923e627496dcd281","score":"0.5999792","text":"def rowvec(vec):\n return vec.reshape(1, -1)","title":""},{"docid":"549d095909437c48b0994aaf308cf683","score":"0.59953827","text":"def left_epipole(f_matrix):\n\n u, d, vh = svd(f_matrix)\n return u[:, 2]","title":""},{"docid":"158813340ae76692ad59d7c62a53ac28","score":"0.59917504","text":"def as_vector(self):\n params = self.h_matrix - np.eye(self.n_dims + 1)\n return params[:self.n_dims, :].flatten(order='F')","title":""},{"docid":"93f71dd18633210f519858a3ea08d96f","score":"0.5990209","text":"def vec(A):\n cols = A.shape[1]\n rows = A.shape[0]\n v = []\n for i in range(cols):\n for j in range(rows):\n v.append(A[:,i][j])\n v = np.array(v)\n v.shape = (cols*rows,1)\n return v","title":""},{"docid":"1ddc28d016975aae6b524b4a9a86f36a","score":"0.5979065","text":"def ToVector(self):\n vector = np.matrix(np.zeros((dim, 1)))\n for i, index in enumerate(indices):\n vector[index] = self[i]\n return vector","title":""},{"docid":"0c931e70a1d68018b60d7ba1c67b650e","score":"0.58927494","text":"def get_unit_vector(vector):\r\n return vector / np.linalg.norm(vector)","title":""},{"docid":"3509ce8330472f0147730ebaf0ff6e55","score":"0.5892268","text":"def spm_vec(X):\n for v_it, v in enumerate(X):\n if v_it == 0:\n try:\n nr,nc = v.shape\n vX = np.reshape(v,[nr*nc,1])\n except:\n vX = np.array([v][:,np.newaxis])\n else:\n try:\n nr,nc = v.shape\n vX = np.concatenate([vX,np.reshape(v,[nr*nc,1])])\n except:\n vX = np.concatenate([vX,np.array([v])[:,np.newaxis]]) \n return np.squeeze(vX)[:,np.newaxis]","title":""},{"docid":"af93e5dd4601aecb9af79c55dd46f4e0","score":"0.5870909","text":"def vector(x):\n x = np.array(x, copy=True, dtype=float)\n x.setflags(write=False)\n if x.ndim != 1:\n n = np.max(x.shape)\n if np.prod(x.shape) != n:\n raise ValueError('Unable to convert to 1d vector of scalar values')\n x = x.reshape((n,))\n return x","title":""},{"docid":"602858000c256e948e778c661d366b36","score":"0.5844983","text":"def right_epipole(f_matrix):\n\n u, d, vh = svd(f_matrix)\n return vh[2, :]","title":""},{"docid":"3fc10598eeb1a15ebea2e116089d5e67","score":"0.58297443","text":"def vect(self):\n return self[..., 1:4]","title":""},{"docid":"e2cd207a2ca32330eb1d23f89a9a5f3a","score":"0.58286273","text":"def unit_vector(vector):\n #vec_array = np.array(vector, dtype=np.float)\n mag = np.linalg.norm(vector)\n return vector / mag","title":""},{"docid":"c8dad86eac1621edd261a042d3ba1d64","score":"0.58090496","text":"def components2matrix(self, a):\n A = torch.zeros(*a.shape, 3, device=a.device, dtype=a.dtype)\n A[..., 2, :2] = a[..., 1:]\n A[..., 0, 1] = a[..., 0]\n A[..., 1, 0] = -a[..., 0]\n return A","title":""},{"docid":"a2019f2b9ae720c29c78f53c398267b6","score":"0.57953227","text":"def matvec(self,x):\n x=x.reshape((self.W1.shape[2], self.Lp.shape[0], self.W2.shape[2], self.Rp.shape[0]))\n x=np.tensordot(self.Lp, x, axes=([0], [1]))\n x=np.tensordot(x, self.W1, axes=([0,2], [0,2]))\n x=np.tensordot(x, self.W2, axes=([3,1], [0,2]))\n x=np.tensordot(x, self.Rp, axes=([1,3], [0,1])).transpose((1,0,2,3))\n x=x.reshape((self.dim))\n if(self.dtype==float):\n return np.real(x)\n else:\n return(x)","title":""},{"docid":"860a830b86ccc520b4787b1821c1660f","score":"0.57810867","text":"def _matvec(self, matrix: np.ndarray):\n prod = self.sparse_mat.dot(matrix)\n if len(matrix.shape) == 1:\n for (x, y) in self.low_rank_tuples:\n prod += x * matrix.dot(y)\n else:\n transposed = matrix.T\n for (x, y) in self.low_rank_tuples:\n prod += x[:, np.newaxis].dot(transposed.dot(y)[:, np.newaxis].T)\n return prod","title":""},{"docid":"f702b1e7774b38099c6bee3652c52ee9","score":"0.5773628","text":"def viewvect(self, aff: torch.Tensor) -> torch.Tensor:\n assert aff.ndimension() >= 1, \"input should have ndim >= 1\"\n assert aff.shape[-1] == self.flatdim, \"input should match dim of model\"\n\n return aff[..., (self.dim * self.dim):, None]","title":""},{"docid":"bb4df542bc7cdaae335b2c960678a8b5","score":"0.5761781","text":"def timeable_numpy_matvec():\n\n b = A0.dot(x0) # noqa","title":""},{"docid":"a738730070cc00d71137805757654ba3","score":"0.57479984","text":"def arr2vec(x):\n x = jnp.asarray(x)\n return jnp.ravel(x)","title":""},{"docid":"854dd214e5d8c122b33fa8cc987ccd2a","score":"0.5719174","text":"def vector(self,parameter):\n vector = np.zeros(len(self)) #generate new vector of correct length\n\n for i in range(len(vector)):\n vector[i] = np.interp(parameter,self.parameters,self.vecs[:,i])\n\n return vector","title":""},{"docid":"143fff483b37ba41d6b502cc1a69796e","score":"0.5718751","text":"def to_col_vector(arr):\n return arr.reshape(-1, 1)","title":""},{"docid":"d92e783bf3abad9798feecce3611e61b","score":"0.57162726","text":"def timeable_basic_matvec():\n\n b = basic_matvec(A0, x0) # noqa","title":""},{"docid":"16b928bf5c7b27b1d7bebcc04e99c360","score":"0.57124776","text":"def create_vector(node):\n return np.array([node[dim] for dim in ma.dimensions])","title":""},{"docid":"77739918bbc6d3672681eec2ca430686","score":"0.5703072","text":"def array_one_norm(self):\n return _vnl_matrixPython.vnl_matrixSI_array_one_norm(self)","title":""},{"docid":"301022c30f8275485e17a3149e0bc847","score":"0.56889474","text":"def reflect_complet_a_matrix(m):\n shape = np.shape(m)\n if shape[0] != shape[1]:\n raise ValueError, 'Must be square matrix'\n length = shape[0]\n for j in range(length):\n for i in range(j+1, length):\n m[i][j] = m[j][i]\n return m","title":""},{"docid":"ea5892adac8a3b98bf284ce02019ba2b","score":"0.5687934","text":"def array_one_norm(self):\n return _vnl_matrixPython.vnl_matrixUC_array_one_norm(self)","title":""},{"docid":"891f61a1e8e7aa11427bc9fd8918e5c7","score":"0.5687541","text":"def viewmat(self, vect: torch.Tensor) -> torch.Tensor:\n assert vect.ndimension() >= 1, \"input should have ndim >= 1\"\n assert vect.shape[-1] == self.flatdim, \"input should match dim of model\"\n batch_shape = vect.shape[:-1]\n\n return vect.view(batch_shape + (self.dim, self.dim))","title":""},{"docid":"088c8db1eebf4b0cfb04397303fcdf46","score":"0.567677","text":"def unit_vector(vector):\r\n return vector / np.linalg.norm(vector)","title":""},{"docid":"fa9d5b8e7c9adaebae005587feb480f1","score":"0.56622785","text":"def unit_vector(vector):\n v_norm = np.linalg.norm(vector)\n if v_norm == 0.0:\n return np.asarray([0.0, 0.0])\n\n return vector / v_norm","title":""},{"docid":"b43040ca19da6891f391eb7b084a8f58","score":"0.56526285","text":"def list_to_vec(list_of_matrices):\n return np.concatenate([l.ravel() for l in list_of_matrices])","title":""},{"docid":"8407bac70cdc8b95ec37c5edef1788c2","score":"0.56477743","text":"def f2c(vector):\n return (vector[:, 0] + 1j * vector[:, 1]).reshape((len(vector[:, 0]), 1))","title":""},{"docid":"e336614bdcde8884c96a202f70d85556","score":"0.56466556","text":"def transformed(self, matrix):\n vector = self.copy()\n vector.transform(matrix)\n return vector","title":""},{"docid":"6ef08b4b03e6de865f755d626990a303","score":"0.5628214","text":"def to_vector(self):\n self._clean_paramvec() # will rebuild if needed\n return self._paramvec","title":""},{"docid":"6ef08b4b03e6de865f755d626990a303","score":"0.5628214","text":"def to_vector(self):\n self._clean_paramvec() # will rebuild if needed\n return self._paramvec","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"6a1ca01dddd61c047fda49056fb81ddc","score":"0.5622103","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"854d42f467b71129e27a3d6586223753","score":"0.5613258","text":"def expand_vector_broadcast(node):\n assert isinstance(node, VectorBroadcast)\n scalar_input = node.get_input(0)\n output_vsize = node.get_precision().get_vector_size()\n return VectorAssembling(*tuple(scalar_input for _ in range(output_vsize)), precision=node.get_precision())","title":""},{"docid":"aaa9913377bf870dfd3571b66ae4cf92","score":"0.5601545","text":"def tensor2vec(self, tensor):\n\n return np.array(\n [\n tensor[0, 0],\n tensor[1, 1],\n tensor[2, 2],\n tensor[0, 1],\n tensor[0, 2],\n tensor[1, 2],\n ]\n )","title":""},{"docid":"a37681d665bc6ce2b068859a3ea61117","score":"0.55954295","text":"def vectorize(self):\n v = np.zeros(21)\n v[0] = self.mass\n v[1:4] = self.com\n v[4:7] = self.dimensions\n v[7:10] = self.pose.pos\n v[10:14] = self.pose.orn\n v[14:18] = self.rotation\n v[18:21] = self.color\n\n return v","title":""},{"docid":"d476d4863b80db3d0cbc4301efc80074","score":"0.5579178","text":"def array_one_norm(self):\n return _vnl_matrixPython.vnl_matrixSC_array_one_norm(self)","title":""},{"docid":"29df828cefe3d2fa7e04018907cd96d9","score":"0.55756503","text":"def full(self):\n\n # Compute tensor unfolding along first mode\n unf = self.factors[0] @ khatri_rao(self.factors[1:]).T\n\n # Inverse unfolding along first mode\n return np.reshape(unf, self.shape)","title":""},{"docid":"26a42c86ee448de3f462e4636fe673e6","score":"0.5573735","text":"def v_mat(self):\n v = self.v_func(self.grid_points)\n v_matrix = np.diag(v)\n return v_matrix","title":""},{"docid":"52a8bff00f380c23a0f01d9ffb33ffc6","score":"0.55732036","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"52a8bff00f380c23a0f01d9ffb33ffc6","score":"0.55732036","text":"def unit_vector(vector):\n return vector / np.linalg.norm(vector)","title":""},{"docid":"b8320e5139811bf1c1dd8d7793467e05","score":"0.5572605","text":"def toDenseVector(x):\n return Vectors.dense(x)","title":""},{"docid":"44abdfa7d430a052576d7b7513ced3d1","score":"0.55714715","text":"def unit_vector(vector):\n \n return vector / np.linalg.norm(vector)","title":""},{"docid":"850e41e4eaaaeddcef43d2dda108ab72","score":"0.5566713","text":"def array_one_norm(self):\n return _vnl_matrixPython.vnl_matrixUL_array_one_norm(self)","title":""},{"docid":"4f3530e60e0044b188417c7581446e2b","score":"0.5557167","text":"def Vector(*args):\n ret = np.squeeze(np.asarray(args, dtype='float'))\n if ret.ndim == 0:\n ret = np.expand_dims(ret, axis=0)\n return ret","title":""},{"docid":"5e0d8432ee36d830330938c71e1789c7","score":"0.55499184","text":"def nullspace(matrix, tol=1e-14):\n u,s,vh = numpy.linalg.svd(matrix)\n return vh[s<tol].T","title":""},{"docid":"9432cdec1704a42fe8f250063b0a27f3","score":"0.55477893","text":"def array_one_norm(self):\n return _vnl_matrixPython.vnl_matrixUI_array_one_norm(self)","title":""},{"docid":"3812aa5fb3dbc1b21a4a347cd0e640a0","score":"0.5535775","text":"def array_one_norm(self):\n return _vnl_matrixPython.vnl_matrixSL_array_one_norm(self)","title":""},{"docid":"0a48718be7bbce26fcbf626d39885fea","score":"0.5531828","text":"def vector(self) -> np.ndarray:\n pass","title":""},{"docid":"fc351ad3ea13ffcb5f7a8b1ec2bd6540","score":"0.55307317","text":"def NormalVector(self) -> _n_2_t_0:","title":""},{"docid":"99568d6d17f8f5463d9f09970e28fa7e","score":"0.55254555","text":"def to_vector(self):\n return self.paramvec","title":""},{"docid":"37aff0a01ee5cdeb607df7fd7ced0df2","score":"0.5516306","text":"def matvec(data, v):\n A, = data\n return matrix_matvec(A, v)","title":""},{"docid":"85c5fbcf163e8c1b59381d6d75f62aad","score":"0.5515313","text":"def unit_vector(vector):\n return vector / numpy.linalg.norm(vector)","title":""},{"docid":"85c5fbcf163e8c1b59381d6d75f62aad","score":"0.5515313","text":"def unit_vector(vector):\n return vector / numpy.linalg.norm(vector)","title":""},{"docid":"538ba73d1635da7d205a9130b0056b8d","score":"0.551474","text":"def normalize_vector(U):\n # iterate through each component and convert to array :\n U_v = []\n for u in U:\n # convert to array and normailze the components of U :\n u_v = u.vector().array()\n U_v.append(u_v)\n U_v = np.array(U_v)\n\n # calculate the norm :\n norm_u = np.sqrt(sum(U_v**2))\n \n # normalize the vector :\n U_v /= norm_u\n \n # convert back to fenics :\n U_f = []\n for u_v in U_v:\n u_f = Function(Q)\n u_f.vector().set_local(u_v)\n u_f.vector().apply('insert')\n U_f.append(u_f)\n\n # return a UFL vector :\n return as_vector(U_f)","title":""},{"docid":"538ba73d1635da7d205a9130b0056b8d","score":"0.551474","text":"def normalize_vector(U):\n # iterate through each component and convert to array :\n U_v = []\n for u in U:\n # convert to array and normailze the components of U :\n u_v = u.vector().array()\n U_v.append(u_v)\n U_v = np.array(U_v)\n\n # calculate the norm :\n norm_u = np.sqrt(sum(U_v**2))\n \n # normalize the vector :\n U_v /= norm_u\n \n # convert back to fenics :\n U_f = []\n for u_v in U_v:\n u_f = Function(Q)\n u_f.vector().set_local(u_v)\n u_f.vector().apply('insert')\n U_f.append(u_f)\n\n # return a UFL vector :\n return as_vector(U_f)","title":""},{"docid":"26521cb044e232017f4d4940f8ed5fe8","score":"0.55120546","text":"def basic_matvec(A, x):\n\n # Extracting dimensions and initialising vector b\n m , n = A.shape\n b = np.zeros(m)\n\n # Computing Ax=b using definition of matrix-vector multiplication\n for i in range(m):\n for j in range(n):\n b[i] += A[i,j] * x[j]\n\n return b","title":""},{"docid":"ab0afc5904f38af4aeea2421efc37936","score":"0.55117184","text":"def to_vector(self):\n return self._paramvec","title":""},{"docid":"ab0afc5904f38af4aeea2421efc37936","score":"0.55117184","text":"def to_vector(self):\n return self._paramvec","title":""},{"docid":"e758ae9e88b4af79b7f89139f6489366","score":"0.5509216","text":"def mobius_matvec(self, m, x, c):\n raise NotImplementedError","title":""},{"docid":"9bf4ccf8bed95290195d0c6360861960","score":"0.5509049","text":"def matrix2xyz(matrix_v):\n \n data=np.reshape(matrix_v,(-1),order='C')\n return data","title":""},{"docid":"50a81867623261dd3393db7020bd5020","score":"0.5506996","text":"def inv(matrix):\n return chol_inv(check_pd(matrix)[1])","title":""}],"string":"[\n {\n \"docid\": \"e8dd61f6410d461c1b83e26e73167926\",\n \"score\": \"0.85416037\",\n \"text\": \"def full_matrix_to_partial_vector(self, full_matrix):\\n part_matrix = self.full_matrix_to_partial_matrix(full_matrix)\\n return self.partial_matrix_to_partial_vector(part_matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7ec618d329314c921519b9d975980ba\",\n \"score\": \"0.8416663\",\n \"text\": \"def partial_matrix_to_partial_vector(self, mat):\\n return mat.flatten()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37c4976bc8604c54fbe3ad3dd3e90ea\",\n \"score\": \"0.7162625\",\n \"text\": \"def partial_vector_to_full_matrix(self, partial_vector):\\n return self.partial_matrix_to_full_matrix(self.partial_vector_to_partial_matrix(partial_vector))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5492b6df566b608a3ee6bc9ccefb700c\",\n \"score\": \"0.7069103\",\n \"text\": \"def partial_vector_to_partial_matrix(self, vec):\\n return vec.reshape((self.m_max - self.m_min + 1, self.n_max - self.n_min + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863a68eb75cda765a257f0cb4b691479\",\n \"score\": \"0.70258355\",\n \"text\": \"def matrix_to_vector(matrix):\\n return (np.reshape(matrix, 92 * 112).astype(np.uint8) - 127.5) / 127.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f36ae0a9bb5435b4ae2633baba87daac\",\n \"score\": \"0.6973615\",\n \"text\": \"def partial_matrix_to_full_matrix(self, partial_matrix):\\n full_matrix = numpy.zeros((self.m_max + 1, self.n_max + 1))\\n full_matrix[self.m_min:, self.n_min:] = partial_matrix\\n return full_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c3890e2097c3d2da5972a59ae9a392\",\n \"score\": \"0.6687499\",\n \"text\": \"def matrix_to_vector(image):\\n return image.flatten()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d9ebb28d13d720ae3b0e7df408d19b\",\n \"score\": \"0.6667218\",\n \"text\": \"def to_vector_part(self):\\n return self.vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09430c0bdf63a2ae2a4d18a99c067b6d\",\n \"score\": \"0.6354226\",\n \"text\": \"def vector_from_matrix(self, matrix):\\n matrix = gs.to_ndarray(matrix, to_ndim=3)\\n n_mats, m, n = matrix.shape\\n return gs.reshape(matrix, (n_mats, m*n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3b44c8108712cf747f419ab6fe391ee\",\n \"score\": \"0.6307527\",\n \"text\": \"def so3_to_vec(matrix):\\n return np.array([matrix[2][1], matrix[0][2], matrix[1][0]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38de26b2eaaf51dc7c16b95d0b3d5d37\",\n \"score\": \"0.6237089\",\n \"text\": \"def to_vector(x):\\n if not isinstance(x, np.ndarray):\\n x = np.array(x)\\n \\n if x.ndim != 1:\\n return np.reshape(x, (-1,))\\n else:\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b78baf0bcb8e9f53ae3a32022f5772e\",\n \"score\": \"0.6232256\",\n \"text\": \"def as_vector(self):\\n return self.h_matrix[:-1, -1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1508ca843b1d7e1709c662c924725c\",\n \"score\": \"0.6178901\",\n \"text\": \"def tovector(self):\\r\\n return _mps2vector(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4cbb98b9821af16340a6175f3a08c76\",\n \"score\": \"0.61635745\",\n \"text\": \"def to_vector(mat):\\n\\tout = np.zeros((mat.shape[0],mat.shape[1]))\\n\\tout2 = np.zeros((mat.shape[0]))\\n\\tfor i in range(mat.shape[0]):\\n\\t\\tfor n, j in enumerate(mat[i]):\\n\\t\\t\\tif np.any(j == (np.amax(mat[i]))):\\n\\t\\t\\t\\tout[i][n] = 1\\n\\t\\t\\t\\tout2[i] = n\\n\\n\\treturn out2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9d82393523b770db83d1e6b1d55c34\",\n \"score\": \"0.6061278\",\n \"text\": \"def _mat_to_vec(\\n self,\\n img: np.ndarray\\n ) -> np.ndarray:\\n\\n return img.flatten()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f962d1744a4bcbd48938bcc84192ddd\",\n \"score\": \"0.60486114\",\n \"text\": \"def to_vector(v):\\n if \\\"todense\\\" in dir(v):\\n v = v.todense()\\n if len(v.shape) == 2:\\n if v.shape[0] == 1:\\n v.resize(v.size)\\n if v.shape[0] == 2:\\n v = numpy.reshape(v, v.shape[0] * v.shape[1])\\n\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c51d0632f9edfed6c991d5a62619cb8a\",\n \"score\": \"0.6038777\",\n \"text\": \"def full_matrix(self):\\n return mf.b + mf.b_u[:,np.newaxis] + mf.b_i[np.newaxis:,] + mf.P.dot(mf.Q.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1845e032d48e349a08dbaf45cfbe88c8\",\n \"score\": \"0.60255694\",\n \"text\": \"def colvec(vec):\\n return vec.reshape(-1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1858857fa4f1dc88618fbd1ff5ba34e2\",\n \"score\": \"0.60173154\",\n \"text\": \"def timeable_column_matvec():\\n\\n b = column_matvec(A0, x0) # noqa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5468a9500f41f007297bbea3b4a8dbe\",\n \"score\": \"0.6004301\",\n \"text\": \"def full_matrix(self):\\n return self.b + self.b_u[:,np.newaxis] + self.b_i[np.newaxis:,] + self.P.dot(self.Q.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5468a9500f41f007297bbea3b4a8dbe\",\n \"score\": \"0.6004301\",\n \"text\": \"def full_matrix(self):\\n return self.b + self.b_u[:,np.newaxis] + self.b_i[np.newaxis:,] + self.P.dot(self.Q.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fecf69b22382d0a923e627496dcd281\",\n \"score\": \"0.5999792\",\n \"text\": \"def rowvec(vec):\\n return vec.reshape(1, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549d095909437c48b0994aaf308cf683\",\n \"score\": \"0.59953827\",\n \"text\": \"def left_epipole(f_matrix):\\n\\n u, d, vh = svd(f_matrix)\\n return u[:, 2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"158813340ae76692ad59d7c62a53ac28\",\n \"score\": \"0.59917504\",\n \"text\": \"def as_vector(self):\\n params = self.h_matrix - np.eye(self.n_dims + 1)\\n return params[:self.n_dims, :].flatten(order='F')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f71dd18633210f519858a3ea08d96f\",\n \"score\": \"0.5990209\",\n \"text\": \"def vec(A):\\n cols = A.shape[1]\\n rows = A.shape[0]\\n v = []\\n for i in range(cols):\\n for j in range(rows):\\n v.append(A[:,i][j])\\n v = np.array(v)\\n v.shape = (cols*rows,1)\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ddc28d016975aae6b524b4a9a86f36a\",\n \"score\": \"0.5979065\",\n \"text\": \"def ToVector(self):\\n vector = np.matrix(np.zeros((dim, 1)))\\n for i, index in enumerate(indices):\\n vector[index] = self[i]\\n return vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c931e70a1d68018b60d7ba1c67b650e\",\n \"score\": \"0.58927494\",\n \"text\": \"def get_unit_vector(vector):\\r\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3509ce8330472f0147730ebaf0ff6e55\",\n \"score\": \"0.5892268\",\n \"text\": \"def spm_vec(X):\\n for v_it, v in enumerate(X):\\n if v_it == 0:\\n try:\\n nr,nc = v.shape\\n vX = np.reshape(v,[nr*nc,1])\\n except:\\n vX = np.array([v][:,np.newaxis])\\n else:\\n try:\\n nr,nc = v.shape\\n vX = np.concatenate([vX,np.reshape(v,[nr*nc,1])])\\n except:\\n vX = np.concatenate([vX,np.array([v])[:,np.newaxis]]) \\n return np.squeeze(vX)[:,np.newaxis]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af93e5dd4601aecb9af79c55dd46f4e0\",\n \"score\": \"0.5870909\",\n \"text\": \"def vector(x):\\n x = np.array(x, copy=True, dtype=float)\\n x.setflags(write=False)\\n if x.ndim != 1:\\n n = np.max(x.shape)\\n if np.prod(x.shape) != n:\\n raise ValueError('Unable to convert to 1d vector of scalar values')\\n x = x.reshape((n,))\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"602858000c256e948e778c661d366b36\",\n \"score\": \"0.5844983\",\n \"text\": \"def right_epipole(f_matrix):\\n\\n u, d, vh = svd(f_matrix)\\n return vh[2, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc10598eeb1a15ebea2e116089d5e67\",\n \"score\": \"0.58297443\",\n \"text\": \"def vect(self):\\n return self[..., 1:4]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2cd207a2ca32330eb1d23f89a9a5f3a\",\n \"score\": \"0.58286273\",\n \"text\": \"def unit_vector(vector):\\n #vec_array = np.array(vector, dtype=np.float)\\n mag = np.linalg.norm(vector)\\n return vector / mag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8dad86eac1621edd261a042d3ba1d64\",\n \"score\": \"0.58090496\",\n \"text\": \"def components2matrix(self, a):\\n A = torch.zeros(*a.shape, 3, device=a.device, dtype=a.dtype)\\n A[..., 2, :2] = a[..., 1:]\\n A[..., 0, 1] = a[..., 0]\\n A[..., 1, 0] = -a[..., 0]\\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2019f2b9ae720c29c78f53c398267b6\",\n \"score\": \"0.57953227\",\n \"text\": \"def matvec(self,x):\\n x=x.reshape((self.W1.shape[2], self.Lp.shape[0], self.W2.shape[2], self.Rp.shape[0]))\\n x=np.tensordot(self.Lp, x, axes=([0], [1]))\\n x=np.tensordot(x, self.W1, axes=([0,2], [0,2]))\\n x=np.tensordot(x, self.W2, axes=([3,1], [0,2]))\\n x=np.tensordot(x, self.Rp, axes=([1,3], [0,1])).transpose((1,0,2,3))\\n x=x.reshape((self.dim))\\n if(self.dtype==float):\\n return np.real(x)\\n else:\\n return(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"860a830b86ccc520b4787b1821c1660f\",\n \"score\": \"0.57810867\",\n \"text\": \"def _matvec(self, matrix: np.ndarray):\\n prod = self.sparse_mat.dot(matrix)\\n if len(matrix.shape) == 1:\\n for (x, y) in self.low_rank_tuples:\\n prod += x * matrix.dot(y)\\n else:\\n transposed = matrix.T\\n for (x, y) in self.low_rank_tuples:\\n prod += x[:, np.newaxis].dot(transposed.dot(y)[:, np.newaxis].T)\\n return prod\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f702b1e7774b38099c6bee3652c52ee9\",\n \"score\": \"0.5773628\",\n \"text\": \"def viewvect(self, aff: torch.Tensor) -> torch.Tensor:\\n assert aff.ndimension() >= 1, \\\"input should have ndim >= 1\\\"\\n assert aff.shape[-1] == self.flatdim, \\\"input should match dim of model\\\"\\n\\n return aff[..., (self.dim * self.dim):, None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4df542bc7cdaae335b2c960678a8b5\",\n \"score\": \"0.5761781\",\n \"text\": \"def timeable_numpy_matvec():\\n\\n b = A0.dot(x0) # noqa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a738730070cc00d71137805757654ba3\",\n \"score\": \"0.57479984\",\n \"text\": \"def arr2vec(x):\\n x = jnp.asarray(x)\\n return jnp.ravel(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854dd214e5d8c122b33fa8cc987ccd2a\",\n \"score\": \"0.5719174\",\n \"text\": \"def vector(self,parameter):\\n vector = np.zeros(len(self)) #generate new vector of correct length\\n\\n for i in range(len(vector)):\\n vector[i] = np.interp(parameter,self.parameters,self.vecs[:,i])\\n\\n return vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"143fff483b37ba41d6b502cc1a69796e\",\n \"score\": \"0.5718751\",\n \"text\": \"def to_col_vector(arr):\\n return arr.reshape(-1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d92e783bf3abad9798feecce3611e61b\",\n \"score\": \"0.57162726\",\n \"text\": \"def timeable_basic_matvec():\\n\\n b = basic_matvec(A0, x0) # noqa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16b928bf5c7b27b1d7bebcc04e99c360\",\n \"score\": \"0.57124776\",\n \"text\": \"def create_vector(node):\\n return np.array([node[dim] for dim in ma.dimensions])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77739918bbc6d3672681eec2ca430686\",\n \"score\": \"0.5703072\",\n \"text\": \"def array_one_norm(self):\\n return _vnl_matrixPython.vnl_matrixSI_array_one_norm(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301022c30f8275485e17a3149e0bc847\",\n \"score\": \"0.56889474\",\n \"text\": \"def reflect_complet_a_matrix(m):\\n shape = np.shape(m)\\n if shape[0] != shape[1]:\\n raise ValueError, 'Must be square matrix'\\n length = shape[0]\\n for j in range(length):\\n for i in range(j+1, length):\\n m[i][j] = m[j][i]\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea5892adac8a3b98bf284ce02019ba2b\",\n \"score\": \"0.5687934\",\n \"text\": \"def array_one_norm(self):\\n return _vnl_matrixPython.vnl_matrixUC_array_one_norm(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891f61a1e8e7aa11427bc9fd8918e5c7\",\n \"score\": \"0.5687541\",\n \"text\": \"def viewmat(self, vect: torch.Tensor) -> torch.Tensor:\\n assert vect.ndimension() >= 1, \\\"input should have ndim >= 1\\\"\\n assert vect.shape[-1] == self.flatdim, \\\"input should match dim of model\\\"\\n batch_shape = vect.shape[:-1]\\n\\n return vect.view(batch_shape + (self.dim, self.dim))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"088c8db1eebf4b0cfb04397303fcdf46\",\n \"score\": \"0.567677\",\n \"text\": \"def unit_vector(vector):\\r\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa9d5b8e7c9adaebae005587feb480f1\",\n \"score\": \"0.56622785\",\n \"text\": \"def unit_vector(vector):\\n v_norm = np.linalg.norm(vector)\\n if v_norm == 0.0:\\n return np.asarray([0.0, 0.0])\\n\\n return vector / v_norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43040ca19da6891f391eb7b084a8f58\",\n \"score\": \"0.56526285\",\n \"text\": \"def list_to_vec(list_of_matrices):\\n return np.concatenate([l.ravel() for l in list_of_matrices])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8407bac70cdc8b95ec37c5edef1788c2\",\n \"score\": \"0.56477743\",\n \"text\": \"def f2c(vector):\\n return (vector[:, 0] + 1j * vector[:, 1]).reshape((len(vector[:, 0]), 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e336614bdcde8884c96a202f70d85556\",\n \"score\": \"0.56466556\",\n \"text\": \"def transformed(self, matrix):\\n vector = self.copy()\\n vector.transform(matrix)\\n return vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ef08b4b03e6de865f755d626990a303\",\n \"score\": \"0.5628214\",\n \"text\": \"def to_vector(self):\\n self._clean_paramvec() # will rebuild if needed\\n return self._paramvec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ef08b4b03e6de865f755d626990a303\",\n \"score\": \"0.5628214\",\n \"text\": \"def to_vector(self):\\n self._clean_paramvec() # will rebuild if needed\\n return self._paramvec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1ca01dddd61c047fda49056fb81ddc\",\n \"score\": \"0.5622103\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854d42f467b71129e27a3d6586223753\",\n \"score\": \"0.5613258\",\n \"text\": \"def expand_vector_broadcast(node):\\n assert isinstance(node, VectorBroadcast)\\n scalar_input = node.get_input(0)\\n output_vsize = node.get_precision().get_vector_size()\\n return VectorAssembling(*tuple(scalar_input for _ in range(output_vsize)), precision=node.get_precision())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa9913377bf870dfd3571b66ae4cf92\",\n \"score\": \"0.5601545\",\n \"text\": \"def tensor2vec(self, tensor):\\n\\n return np.array(\\n [\\n tensor[0, 0],\\n tensor[1, 1],\\n tensor[2, 2],\\n tensor[0, 1],\\n tensor[0, 2],\\n tensor[1, 2],\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a37681d665bc6ce2b068859a3ea61117\",\n \"score\": \"0.55954295\",\n \"text\": \"def vectorize(self):\\n v = np.zeros(21)\\n v[0] = self.mass\\n v[1:4] = self.com\\n v[4:7] = self.dimensions\\n v[7:10] = self.pose.pos\\n v[10:14] = self.pose.orn\\n v[14:18] = self.rotation\\n v[18:21] = self.color\\n\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d476d4863b80db3d0cbc4301efc80074\",\n \"score\": \"0.5579178\",\n \"text\": \"def array_one_norm(self):\\n return _vnl_matrixPython.vnl_matrixSC_array_one_norm(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29df828cefe3d2fa7e04018907cd96d9\",\n \"score\": \"0.55756503\",\n \"text\": \"def full(self):\\n\\n # Compute tensor unfolding along first mode\\n unf = self.factors[0] @ khatri_rao(self.factors[1:]).T\\n\\n # Inverse unfolding along first mode\\n return np.reshape(unf, self.shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26a42c86ee448de3f462e4636fe673e6\",\n \"score\": \"0.5573735\",\n \"text\": \"def v_mat(self):\\n v = self.v_func(self.grid_points)\\n v_matrix = np.diag(v)\\n return v_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52a8bff00f380c23a0f01d9ffb33ffc6\",\n \"score\": \"0.55732036\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52a8bff00f380c23a0f01d9ffb33ffc6\",\n \"score\": \"0.55732036\",\n \"text\": \"def unit_vector(vector):\\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8320e5139811bf1c1dd8d7793467e05\",\n \"score\": \"0.5572605\",\n \"text\": \"def toDenseVector(x):\\n return Vectors.dense(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44abdfa7d430a052576d7b7513ced3d1\",\n \"score\": \"0.55714715\",\n \"text\": \"def unit_vector(vector):\\n \\n return vector / np.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"850e41e4eaaaeddcef43d2dda108ab72\",\n \"score\": \"0.5566713\",\n \"text\": \"def array_one_norm(self):\\n return _vnl_matrixPython.vnl_matrixUL_array_one_norm(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3530e60e0044b188417c7581446e2b\",\n \"score\": \"0.5557167\",\n \"text\": \"def Vector(*args):\\n ret = np.squeeze(np.asarray(args, dtype='float'))\\n if ret.ndim == 0:\\n ret = np.expand_dims(ret, axis=0)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0d8432ee36d830330938c71e1789c7\",\n \"score\": \"0.55499184\",\n \"text\": \"def nullspace(matrix, tol=1e-14):\\n u,s,vh = numpy.linalg.svd(matrix)\\n return vh[s<tol].T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9432cdec1704a42fe8f250063b0a27f3\",\n \"score\": \"0.55477893\",\n \"text\": \"def array_one_norm(self):\\n return _vnl_matrixPython.vnl_matrixUI_array_one_norm(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3812aa5fb3dbc1b21a4a347cd0e640a0\",\n \"score\": \"0.5535775\",\n \"text\": \"def array_one_norm(self):\\n return _vnl_matrixPython.vnl_matrixSL_array_one_norm(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a48718be7bbce26fcbf626d39885fea\",\n \"score\": \"0.5531828\",\n \"text\": \"def vector(self) -> np.ndarray:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc351ad3ea13ffcb5f7a8b1ec2bd6540\",\n \"score\": \"0.55307317\",\n \"text\": \"def NormalVector(self) -> _n_2_t_0:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99568d6d17f8f5463d9f09970e28fa7e\",\n \"score\": \"0.55254555\",\n \"text\": \"def to_vector(self):\\n return self.paramvec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37aff0a01ee5cdeb607df7fd7ced0df2\",\n \"score\": \"0.5516306\",\n \"text\": \"def matvec(data, v):\\n A, = data\\n return matrix_matvec(A, v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c5fbcf163e8c1b59381d6d75f62aad\",\n \"score\": \"0.5515313\",\n \"text\": \"def unit_vector(vector):\\n return vector / numpy.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c5fbcf163e8c1b59381d6d75f62aad\",\n \"score\": \"0.5515313\",\n \"text\": \"def unit_vector(vector):\\n return vector / numpy.linalg.norm(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538ba73d1635da7d205a9130b0056b8d\",\n \"score\": \"0.551474\",\n \"text\": \"def normalize_vector(U):\\n # iterate through each component and convert to array :\\n U_v = []\\n for u in U:\\n # convert to array and normailze the components of U :\\n u_v = u.vector().array()\\n U_v.append(u_v)\\n U_v = np.array(U_v)\\n\\n # calculate the norm :\\n norm_u = np.sqrt(sum(U_v**2))\\n \\n # normalize the vector :\\n U_v /= norm_u\\n \\n # convert back to fenics :\\n U_f = []\\n for u_v in U_v:\\n u_f = Function(Q)\\n u_f.vector().set_local(u_v)\\n u_f.vector().apply('insert')\\n U_f.append(u_f)\\n\\n # return a UFL vector :\\n return as_vector(U_f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538ba73d1635da7d205a9130b0056b8d\",\n \"score\": \"0.551474\",\n \"text\": \"def normalize_vector(U):\\n # iterate through each component and convert to array :\\n U_v = []\\n for u in U:\\n # convert to array and normailze the components of U :\\n u_v = u.vector().array()\\n U_v.append(u_v)\\n U_v = np.array(U_v)\\n\\n # calculate the norm :\\n norm_u = np.sqrt(sum(U_v**2))\\n \\n # normalize the vector :\\n U_v /= norm_u\\n \\n # convert back to fenics :\\n U_f = []\\n for u_v in U_v:\\n u_f = Function(Q)\\n u_f.vector().set_local(u_v)\\n u_f.vector().apply('insert')\\n U_f.append(u_f)\\n\\n # return a UFL vector :\\n return as_vector(U_f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26521cb044e232017f4d4940f8ed5fe8\",\n \"score\": \"0.55120546\",\n \"text\": \"def basic_matvec(A, x):\\n\\n # Extracting dimensions and initialising vector b\\n m , n = A.shape\\n b = np.zeros(m)\\n\\n # Computing Ax=b using definition of matrix-vector multiplication\\n for i in range(m):\\n for j in range(n):\\n b[i] += A[i,j] * x[j]\\n\\n return b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0afc5904f38af4aeea2421efc37936\",\n \"score\": \"0.55117184\",\n \"text\": \"def to_vector(self):\\n return self._paramvec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0afc5904f38af4aeea2421efc37936\",\n \"score\": \"0.55117184\",\n \"text\": \"def to_vector(self):\\n return self._paramvec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e758ae9e88b4af79b7f89139f6489366\",\n \"score\": \"0.5509216\",\n \"text\": \"def mobius_matvec(self, m, x, c):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf4ccf8bed95290195d0c6360861960\",\n \"score\": \"0.5509049\",\n \"text\": \"def matrix2xyz(matrix_v):\\n \\n data=np.reshape(matrix_v,(-1),order='C')\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a81867623261dd3393db7020bd5020\",\n \"score\": \"0.5506996\",\n \"text\": \"def inv(matrix):\\n return chol_inv(check_pd(matrix)[1])\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992972,"cells":{"query_id":{"kind":"string","value":"be93cbea7f8dafbfc9fc3b3b4969a88b"},"query":{"kind":"string","value":"writes a given list to a file with given path to that file"},"positive_passages":{"kind":"list like","value":[{"docid":"ae140e5d156ed02d7b7035f1aa8f9f1d","score":"0.0","text":"def save_result(self, timestamp, folder_name, file_name, result_list):\n path = (self.spider_name, timestamp, folder_name)\n write_file(result_list, path, file_name)","title":""}],"string":"[\n {\n \"docid\": \"ae140e5d156ed02d7b7035f1aa8f9f1d\",\n \"score\": \"0.0\",\n \"text\": \"def save_result(self, timestamp, folder_name, file_name, result_list):\\n path = (self.spider_name, timestamp, folder_name)\\n write_file(result_list, path, file_name)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e3ffdb2da1d96b4981a8989227493f81","score":"0.79097027","text":"def write_list_to_file(list_to_save, save_path):\n \n Path(save_path).parent.mkdir(exist_ok=True, parents=True)\n \n with open(str(save_path), 'w') as fid:\n for line in list_to_save:\n fid.write(\"%s\\n\" % line)","title":""},{"docid":"3f64745fc63cfb08318cae2959d47ce4","score":"0.7550756","text":"def save_list_to_file(list_data: list, save_path: str) -> None:\n with open(save_path, \"w\") as f:\n for line in list_data:\n f.write(line + \"\\n\")","title":""},{"docid":"d62675cd415e1e9654f5f8cc1a64aec3","score":"0.75506246","text":"def write_list_to_file(fpath: str, lst: List[Any]) -> None: \n with open(fpath, 'w') as fp:\n for elem in lst:\n print(str(elem).strip(), file=fp)","title":""},{"docid":"5ed42740c9ec7a386e77852aa813febb","score":"0.74805975","text":"def list2file(mylist,path):\n try:\n with open(path,'w') as f:\n for item in mylist:\n f.write(\"{}\\r\\n\".format(item))\n return True\n except Exception as err:\n sys.stderr.write(str(err))\n sys.stderr.write('\\n')\n return False","title":""},{"docid":"66198ee4646ddc2de79e22485ce8f6d5","score":"0.745095","text":"def list_to_file(lines, path_output=\"./\", file_output=\"defaultName.go\"):\n f = open(path_output+file_output,'w+')\n [f.write(line) for line in lines]\n f.close()","title":""},{"docid":"090fed6dab6c54e1e9f2df46bd69d7eb","score":"0.741671","text":"def write_file(str_list, file_path):\n try:\n print(\"write to file...\")\n with open(file_path, \"w\") as file:\n for word in str_list:\n file.write(word + \" \")\n except IOError as e:\n print(e)","title":""},{"docid":"93805b5668a2ba9bc3ae9821e5495cb1","score":"0.74016905","text":"def list2file(lst, filename):\n with open(filename, \"w\") as f:\n for i in lst:\n f.write(i+'\\n')","title":""},{"docid":"d56f9d580fc1a6533c58713869ebdb4d","score":"0.73118794","text":"def strListToFile(filename, str_list):\n with open(filename,'w') as f:\n for i in str_list:\n f.write(i)","title":""},{"docid":"15f3fbf4c062d0aa1388c2affef86b32","score":"0.7205356","text":"def write_list_to_file(lst):\n list_to_print = '\\n'.join(map(str, lst))\n error_file = open(\"error_files.txt\", \"w\")\n error_file.write(list_to_print)\n error_file.close()","title":""},{"docid":"aefef52ad4a447acbb668f908a492f3b","score":"0.7203913","text":"def write_to_file(file_name, list_of_bills):\r\n open_file = open(file_name, \"wb\")\r\n pickle.dump(list_of_bills, open_file)\r\n open_file.close()","title":""},{"docid":"a0bbcfe20ffaacacfc89922d007aadad","score":"0.7200537","text":"def write_list(fname,strlist):\n with open(fname,'w') as f:\n f.write('\\n'.join(strlist))\n f.write('\\n')","title":""},{"docid":"15a238b3746ceb2a1125864ef06d15c2","score":"0.71149987","text":"def write_file(filename, list):\n f = open(filename, \"w\")\n s = \" -- \"\n for line in list:\n t = s.join(str(i) for i in line)\n f.write(t + \"\\n\")\n f.close()","title":""},{"docid":"8fb0a78857ef1fd3d84f6b2394f8369e","score":"0.7114547","text":"def save_list(lst, fn=\"list.txt\"):\n with open(fn, \"w\") as f:\n for line in lst:\n f.write(f\"{line}\\n\")","title":""},{"docid":"3ddac2d1128e1963f85160b30f91c336","score":"0.71099144","text":"def write_filelist(img_list, filelist_name):\n with open(filelist_name, \"w\") as f:\n for line in img_list:\n f.write(line + \"\\n\")","title":""},{"docid":"30c2501b13a5b378bba00c77927743d6","score":"0.7033325","text":"def List2File(fileName, list):\r\n \r\n with open(fileName + '.txt', 'w') as f:\r\n for element in list:\r\n print (element, file = f)","title":""},{"docid":"3c8b318dd960aacbdcf4255f5f610e80","score":"0.70122874","text":"def pathlist2file(mylist,path,root):\n try:\n with open(path,'w') as f:\n for item in mylist:\n f.write(\"{}\\r\\n\".format(item[len(root):]))\n return True\n except Exception as err:\n sys.stderr.write(str(err))\n sys.stderr.write('\\n')\n return False","title":""},{"docid":"e022bcba702d790b7fd2ac367edccb45","score":"0.7001319","text":"def write_into_file(self,filename,superlist):\n fo = open(filename, \"w\")\n for ele in superlist:\n fo.write(ele + \"\\n\")\n fo.close()","title":""},{"docid":"579c92219f186c8b7b545cec35c1732e","score":"0.699836","text":"def writeFile(filename, geo_list):\n fname = filename + \".\" + geo_list[0].getType()\n f = open(fname, \"w\")\n n = len(geo_list)\n f.write(repr(n)+\"\\n\")\n for elem in geo_list:\n f.write(elem.printString())\n f.close()","title":""},{"docid":"644a66e92ce795187a9294b8be2555e0","score":"0.6971146","text":"def write_review_files(path, reviewlist):\n if not path.endswith(\"/\"):\n path = path + \"/\"\n try:\n for i, row in enumerate(reviewlist):\n filename = \"review\" + str(i) + \".txt\"\n with open(path + filename, \"w\") as handle:\n handle.write(row)\n except:\n print(\"Something wrong with the path or file list. Does the directory exist?\")\n print(\"Wrote %s files to %s.\" % (len(reviewlist), path))","title":""},{"docid":"644a66e92ce795187a9294b8be2555e0","score":"0.6971146","text":"def write_review_files(path, reviewlist):\n if not path.endswith(\"/\"):\n path = path + \"/\"\n try:\n for i, row in enumerate(reviewlist):\n filename = \"review\" + str(i) + \".txt\"\n with open(path + filename, \"w\") as handle:\n handle.write(row)\n except:\n print(\"Something wrong with the path or file list. Does the directory exist?\")\n print(\"Wrote %s files to %s.\" % (len(reviewlist), path))","title":""},{"docid":"469702687f20178eec7a42c949bf88cd","score":"0.69698966","text":"def write_to_file(lis_2d, file_name):\n file = open(file_name, 'a+')\n for row in range(0, len(lis_2d)):\n new_line = ''.join(lis_2d[row])\n file.write(new_line + '\\n')\n file.close()","title":""},{"docid":"5e76c8da102b9b9fe23325043f04f7a6","score":"0.69566214","text":"def write_txt_file(file_path: str, content: list):\n with open(file_path, mode=\"w\", encoding=\"utf-8\") as fw:\n for line in content:\n fw.write(str(line) + \"\\n\")","title":""},{"docid":"958db0923c22a72e30225a1e37141a3d","score":"0.69501966","text":"def saveToFile(liste, filename):\r\n \r\n with open(filename, \"w\") as f:\r\n for el in liste:\r\n f.write(str(el)+\"\\n\")","title":""},{"docid":"0a35f7cddcc8eff82f4952a047511f2a","score":"0.69397426","text":"def escreverArquivo(nomeArquivo, lista):\r\n with open(nomeArquivo, 'w') as arquivo:\r\n for linha in lista:\r\n arquivo.write(linha)","title":""},{"docid":"a0bc6622ce3e7b9aae72915b959c1331","score":"0.6939149","text":"def write_data(list_name, fileout):\n \n file_wt = open(fileout, 'w')\n for i in range(0,len(list_name)):\n filestr = list_name[i].replace(\"\\n\",\"\")\n file_rd = open(filestr, 'r')\n list_dat = file_rd.readlines()\n \n for j in range(0,len(list_dat)):\n linestr = filestr.replace(\".txt\",\"\") + '\\t' + list_dat[j]\n file_wt.writelines(linestr)","title":""},{"docid":"c934b043ea482a733c2145c691d683ac","score":"0.6917027","text":"def write_file(self, content: List[str], file_name: str) -> None:\n # Setup save path.\n file_path = os.path.join(self.results_path, file_name)\n make_dirs(os.path.dirname(file_path))\n\n with open(file_path, 'w') as filehandle:\n filehandle.writelines(\"%s\\n\" % line for line in content)","title":""},{"docid":"7ee0a20d823ce05689d7ebf37f8c9721","score":"0.69107515","text":"def write_to_file(output_filename, file_header, input_list):\n output_file = open(output_filename, 'w')\n output_file.write(file_header + '\\n')\n output_list = [';'.join(map(str, tup)) for tup in input_list]\n for output_line in output_list:\n output_file.write(output_line + '\\n')\n output_file.close()","title":""},{"docid":"0e1a54c184282c2b304a6f257d64ca69","score":"0.6808002","text":"def list_to_file(a_list, a_file, validate=True):\n with open(a_file, \"w\", 8192000) as f:\n for e in a_list:\n f.write(\"{}\\n\".format(e))\n if validate:\n assert line_counter(a_file) == len(a_list)","title":""},{"docid":"5b9fa31649f79c833574e15a1e609931","score":"0.68075925","text":"def save_list(self, list_name, path):\r\n\r\n with open(path, 'w') as out:\r\n lines = []\r\n for item in self.items.get(list_name, []):\r\n if isinstance(item, basestring):\r\n lines.append(smart_str(item))\r\n else:\r\n lines.append(json.dumps(item))\r\n out.write('\\n'.join(lines) + '\\n')","title":""},{"docid":"45f3079ecd37e18d26db56bedff7ebda","score":"0.6802555","text":"def write_Out_file(out_filename, out_list): \n \n with open(out_filename, \"wb\") as out_file:\n pickle.dump(out_list, out_file)","title":""},{"docid":"9ac73680815bc67eca566bdee0cb08e3","score":"0.6802001","text":"def write_to_file(ascii_list):\n with open('output.txt', 'w') as f:\n for line in ascii_list:\n f.write(f\"{line}\\n\")\n print('Done')","title":""},{"docid":"955bc54d4345e89a82a18b0c98d4d02b","score":"0.6742082","text":"def save_data_to_file(file_name, list):\n file = open(file_name, \"ab\") # append new data to a file\n pickle.dump(list, file)\n file.close()","title":""},{"docid":"c41ac99dbda13eb3e2f7e35e2391b000","score":"0.66481847","text":"def write(fpath, data):\n if isinstance(data, list):\n with fpath.open(mode=\"w\") as csvfile:\n csvwriter = csv.writer(\n csvfile,\n delimiter=\",\",\n quoting=csv.QUOTE_MINIMAL\n )\n csvwriter.writerows(data)\n else:\n with fpath.open(mode=\"w\") as f:\n f.write(data)","title":""},{"docid":"863258c6b444f536f3a38ec952284d66","score":"0.66439","text":"def write_files(company, list):\n dir = 'link2gophish-output'\n if not os.path.exists(dir):\n os.makedirs(dir)\n gophish = open(dir + '/' + company + '-gophish.csv', 'w')\n\n for u in list:\n gophish.write(u.toGophish())\n gophish.close()","title":""},{"docid":"fd87063061ff3b29f4491da5eff0320b","score":"0.6635326","text":"def print_list_to_file(L,f):\n linestring = \"\"\n for element in L:\n linestring += str(element) + ' '\n linestring = linestring[:-1] + '\\n'\n f.write(linestring)","title":""},{"docid":"9bc1b5e17fbc8a5958704b6ed8d67b0a","score":"0.66242594","text":"def flush_list_to_file(data, save_file_name):\n with open(save_file_name, mode='rw') as writer:\n for key in data:\n print(\"{0}\".format(key), file=writer)","title":""},{"docid":"1218182f18f030130800fa0dcb3da8b7","score":"0.6623087","text":"def make_obsfile(obslist, outpath):\n with open(outpath, 'w') as f:\n for obs in obslist:\n f.write(f\"{obs}\\n\")","title":""},{"docid":"680563df2630e4e40c1653e901e0090f","score":"0.66021025","text":"def saveList(itemToSave, listToSave):\r\n if not os.path.exists(listToSave.strip()+\".txt\"):\r\n \tf = open(listToSave.strip() + \".txt\", \"w\")\r\n \tf.close()\r\n with open(listToSave.strip() + \".txt\", \"r+\") as f: \r\n \tcontent = f.read()\r\n \tf.seek(0,0)\r\n \tf.write(itemToSave.replace('\\r\\n', '') + '\\r\\n' + content)","title":""},{"docid":"5befdafc2ee5c092bf46ac7d9f7d20dd","score":"0.65765536","text":"def save_to_file(self, data_list):\n with open(self.json_file, 'w', encoding='utf-8') as outfile:\n json.dump(data_list, outfile, indent=4, ensure_ascii=False)\n print(f\"{self.json_file} saved\")","title":""},{"docid":"daf733bcccd78554dc3530ad9d8d5d0a","score":"0.6568339","text":"def save_csv_to_path(self, csv_list, path):\n try:\n with open(path, 'w', encoding='utf-8') as f:\n for line in csv_list:\n f.write(str(line[0]) + ',' + str(line[1]) + ',' + str(line[2]))\n f.write('\\n')\n except Exception as e:\n print(str(e))","title":""},{"docid":"81c5a720cd565b61416275869a3cf842","score":"0.65642464","text":"def write_file_list(out_name, files):\n outfile = open(out_name, 'w')\n outfile.write(\"{0:s}\".format(files[0][0]))\n for fdat in files[1:]:\n outfile.write(\"\\n{0:s}\".format(fdat[0]))\n outfile.write(\"\\n\")","title":""},{"docid":"77ff1f92df06f4264cb9309be65a5565","score":"0.655157","text":"def write_puzzles_to_file(list_of_tuples: list) -> None:\n with open('../input/input_puzzles.txt', 'w') as file:\n file.flush()\n for ls in list_of_tuples:\n file.write(str(ls) + '\\n')","title":""},{"docid":"4f660e189145a427db7d286faf533e34","score":"0.654729","text":"def list_to_file(post_list, outfile):\n print(PROJ_DIR)\n with open(os.path.join(PROJ_DIR, \"data\", outfile), 'w') as f: #../data/{}\".format(outfile), 'w') as f:\n for post in post_list:\n post_string = json.dumps(post)\n f.write(post_string)\n f.write('\\n')\n print(\"Finished writing {}\".format(outfile))\n f.close()","title":""},{"docid":"4f3b9ab7356a56d22e4cde059d35277f","score":"0.6541723","text":"def write_to_file(self):\n out_file = os.path.join(OUT_DIR, \"new_%s\" %\n os.path.basename(self.fdi_file))\n with open(out_file, 'w') as f_out:\n f_out.write(''.join(self.final_list))","title":""},{"docid":"3171478769b38a9a5967d617b7b38e93","score":"0.64794755","text":"def write_to_file(self, list):\n f = open(\"myScheduler.txt\", \"w+\")\n f.write(self.__format_str.format(self.__file_headers[0],\n self.__file_headers[1],\n (self.__file_headers[2] + \"\\r\\n\")))\n f.write(\n self.__format_str.format(\"=========\", \"========\", \"==========\\r\\n\"))\n\n for i in list:\n f.write(self.__format_str.format(ScheduleLogger.__add__(self,\n list.index(i)), str(i.time), i.name + \"\\r\\n\"))\n f.write(\"\\r\\n\")\n f.close()","title":""},{"docid":"9e4b83353cd2002d717abbbe3954a893","score":"0.6478461","text":"def write_output_file(pair_list, out_name):\n with open(out_name, 'a') as file1:\n file1.write('\\t'.join(map(str, pair_list)) + '\\n')\n file1.close()","title":""},{"docid":"9c2b5b216ae29f34cdff368614d61b0c","score":"0.645445","text":"def write_users_list_in_file(filename, users):\n file = open(filename, 'w')\n for user in users:\n file.write(str(user) + \"\\n\")\n file.close()","title":""},{"docid":"d42f7f18b742ef215c605652c333b7df","score":"0.6448855","text":"def write_to_file(self, f):\n f.write(\" \".join(self.paths) + \"\\n\")\n f.write(\" \".join(self.snps) + \"\\n\")","title":""},{"docid":"e4edee33274f8e8585adfdf152c06b84","score":"0.6443718","text":"def write_json_file(file_path: str, content: list):\n with open(file_path, mode=\"w\", encoding=\"utf-8\") as f_json:\n json.dump(content, f_json, ensure_ascii=False)","title":""},{"docid":"3e4a7a48a3546ecafbe20daa05f8ac58","score":"0.6430349","text":"def writewords(filename, wordlist):\n with open(filename, 'wt') as outfile:\n outfile.writelines([w + '\\n' for w in wordlist])","title":""},{"docid":"df2f59dc8a1af46fb00ec5af3e5df1c0","score":"0.6429284","text":"def write(self, data_list):\n self.check_if_file_exist()\n\n try:\n with open(self.path, 'w', newline='') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=self.fields, extrasaction='ignore')\n for data in data_list:\n writer.writerow(data)\n except:\n csvfile.close()\n raise Exception\n \n csvfile.close()","title":""},{"docid":"d7853a66f918ee96822fde549d201023","score":"0.6423519","text":"def saveUnitsLinksToFile(linkList):\n if linkList:\n try:\n f = open(ppsvar.LINKS_FILE,'a')\n except IOError:\n print 'blad dostepu do pliku'\n else:\n f.write('\\n'.join(linkList))\n f.close()","title":""},{"docid":"574b7bf2abb143b79d4557189b275b91","score":"0.64155775","text":"def write_file(contents, file_path):\n with open(file_path, 'w') as f_handle:\n f_handle.writelines(contents)","title":""},{"docid":"ed5026d3c6200c4228697ecbc9f964db","score":"0.6392026","text":"def _write(fp, lines):\n with open(fp, 'w') as f:\n for l in lines:\n f.write(l)\n return fp","title":""},{"docid":"32f5a623c4239afa6ae9451408720b25","score":"0.6360186","text":"def pickle_to_file(file_name, list_of_titles):\n file = open(file_name, 'wb') # open the file for writing\n pickle.dump(list_of_titles, file) # \"dump\" pickled data to file\n file.close() # close file\n print('\\n' + 'Reading list saved to file.')","title":""},{"docid":"77c54cff7ea599ea06927d82438766e0","score":"0.6357948","text":"def write_list_to_json_file(a_list, file_name, sort_keys=False):\n beauty_output = Util.json_beautifier(json_data=Util.from_list_to_json(a_list), sort_keys=sort_keys)\n text_file = open(file_name, \"w\")\n text_file.write(\"%s\" % beauty_output)\n text_file.close()","title":""},{"docid":"e65e6ba7ff71d42d1e13c32885682023","score":"0.6347744","text":"def write_f(path, lines):\r\n with open(path, \"w\") as f:\r\n for line in lines:\r\n f.write(line)","title":""},{"docid":"620e3c6f7b7c61099209d2517fcd1f76","score":"0.63383687","text":"def send_to_file(in_list=growth_rate()):\n with open('growth_list.csv', 'w') as f:\n for line in in_list:\n f.write(line + \"\\n\")","title":""},{"docid":"591d8496b4c599cc093900598badd623","score":"0.6330364","text":"def write_file(path, content, binary=False):\n if isinstance(path, tuple):\n path = os.path.join(*path)\n with open(path, 'wb' if binary else 'w') as fp:\n fp.write(content)","title":""},{"docid":"c1f0997760e9d95f918505e9e5e52de3","score":"0.63287807","text":"def save_to_file(cls, list_objs):\n\n if list_objs is None:\n list_objs = []\n\n list_objs = [obj.to_dictionary() for obj in list_objs]\n list_objs = cls.to_json_string(list_objs)\n name = cls.__name__ + \".json\"\n with open(name, \"wt\") as my_file:\n my_file.write(list_objs)","title":""},{"docid":"03eaa13454c3cb53b189987a72ae735f","score":"0.63249105","text":"def write_csv(content: List[List[Any]], file_path: Path) -> None:\n with open(file_path, \"w\", newline=\"\") as file:\n csv_writer = csv.writer(file)\n for row in content:\n csv_writer.writerow(row)","title":""},{"docid":"36efb5ba8ce28a939d6a3536c0045887","score":"0.6317012","text":"def save_to_file(cls, list_objs):\n file = cls.__name__ + \".json\"\n with open(file, \"w\") as f:\n li = []\n if list_objs is not None:\n li = [item.to_dictionary() for item in list_objs]\n f.write(Base.to_json_string(li))","title":""},{"docid":"da1460d6b2419ff4d9ca38d46040ec0e","score":"0.6315371","text":"def append2file(lst, filename):\n # if the file doesn't exist, create one\n if not os.path.exists(filename):\n list2file(lst, filename)\n else: # if file exists, load the existing list\n lst_exist = file2list(filename)\n lst_new = list(set(lst_exist).union(set(lst)))\n list2file(lst_new, filename)","title":""},{"docid":"ac65debc1f3564a45be36cdfc019892a","score":"0.63033104","text":"def print_list(xs, filename, format):\n f = open(filename, \"w\")\n for x in xs:\n f.write(format % x)\n f.close()","title":""},{"docid":"45f7f3ce04071313f0196ab24a4d47a4","score":"0.62903416","text":"def save_to_file(cls, list_objs):\n\n filename = cls.__name__ + \".json\"\n mylist = \"[]\"\n\n if list_objs is None:\n with open(filename, 'w') as f:\n f.write(mylist)\n else:\n mylist = []\n with open(filename, 'w') as f:\n for objects in list_objs:\n mydic2 = cls.to_dictionary(objects)\n mylist.append(mydic2)\n mydict = cls.to_json_string(mylist)\n f.write(mydict)","title":""},{"docid":"24195410a6bc8b216320379237eea2fd","score":"0.6287782","text":"def file_save(filename, contents):\n with open(filename, 'w') as file_wr:\n for l in contents:\n file_wr.write(l + '\\n')","title":""},{"docid":"6f6db0c4ddffc830433b78eb9bf2d090","score":"0.6279754","text":"def write_file(path_file, data, first):\n #first tells if it writtes or appends\n print(data)\n path_to_2 = os.path.join(path_file, 'id_file_data.txt')\n #with open(path_to_1,'w') as file:\n #file.writelines('\\t'.join(i) + '\\n' for i in data[0])\n #file.close()\n with open(path_to_2, first) as file:\n file.writelines('\\t'.join(i) + '\\n' for i in data)\n file.close()","title":""},{"docid":"86bd5d5ae1324dd962c7d2f2fefab6cb","score":"0.62718546","text":"def writeFile(data):\n f = open(outputFile, \"w\")\n for value in data:\n f.write(\"{}\\n\".format(value))\n f.close()","title":""},{"docid":"86bd5d5ae1324dd962c7d2f2fefab6cb","score":"0.62718546","text":"def writeFile(data):\n f = open(outputFile, \"w\")\n for value in data:\n f.write(\"{}\\n\".format(value))\n f.close()","title":""},{"docid":"261ba966ca6a8c9b43c49baea8eac89d","score":"0.6269071","text":"def write_data_to_file(file_name, list_of_rows):\r\n # TODO: Add code Here - Done\r\n objFile = open(file_name, \"w\")\r\n for dicRow in list_of_rows: # Write each row of data to the file\r\n objFile.write(dicRow[\"Task\"] + \",\" + dicRow[\"Priority\"] + \"\\n\")\r\n objFile.close()\r\n return list_of_rows, \"Data Saved to File!\"","title":""},{"docid":"55dcb20d300598003be27ef20899a26f","score":"0.6268501","text":"def WriteFileList(self, file_names):\n filename = self.GetListFile()\n MakeDirIfNeeded(filename)\n with open(filename, 'w') as f:\n for name in file_names:\n f.write(name + '\\n')","title":""},{"docid":"45a294799acae636b8d53ab7060494dc","score":"0.62637466","text":"def writeList (\n \n self,\n values = None,\n path = None,\n text = None,\n fieldDelimiter = None,\n commentDelimiter = None\n ) :\n\n ok = self.writeLists(\n lists = [ values ],\n path = path,\n text = text,\n fieldDelimiter = fieldDelimiter,\n commentDelimiter = commentDelimiter\n )\n\n return ok","title":""},{"docid":"ea314aeec9493b2bad6b72cf2602135c","score":"0.6261345","text":"def write_file(contents, file_path, file_name):\n full_path = file_path + file_name\n try:\n with open(full_path, 'w+') as output_file:\n output_file.write(contents)\n for item in range(0,5):\n output_file.write('\\nAppended line {} by write_file function.'.format(item))\n\n print('A new file is created successfully in', full_path)\n\n except Exception as e:\n print('Output file cannot be written', e)\n raise","title":""},{"docid":"4c2509bf97522a3a5bfa8d6d952eefa2","score":"0.6261012","text":"def write_file(outfile, high_dict_list):\n\n fout = open(outfile, \"w\")\n\n for i in range(len(high_dict_list[\"points\"])):\n fout.write(high_dict_list[\"name\"][i] + \" \")\n fout.write(high_dict_list[\"points\"][i] + \"\\n\")","title":""},{"docid":"8ab71d68e5b1fb67a29e0c53f219a765","score":"0.625377","text":"def write_data(df, list_of_filepaths):\n pass","title":""},{"docid":"805b72b3ddc48c3c84e97038f70070f3","score":"0.6247957","text":"def List2File(L, FileName):\n\tFilout = open(FileName, \"w\")\n\tFilout.write('\\n'.join(L))\n\tFilout.close()","title":""},{"docid":"de60d26f85565dff61183444c5d276d0","score":"0.6247396","text":"def write_out(lines, path, file):\r\n\tlogging.debug('IN')\r\n\twith open(path+file, 'w') as output_file:\r\n\t\tlogging.info('Writing {} lines in {}{}'.format(len(lines), path, file))\r\n\t\tfor line in lines: # just in case\r\n\t\t\toutput_file.write(line+'\\n')","title":""},{"docid":"fe801bbfdf2fc55414dc6116585312c8","score":"0.6246481","text":"def write_file_list(data, outfile):\r\n with open(outfile, 'w', encoding=\"utf8\", newline='') as writing:\r\n if len(data) > 0:\r\n writer = csv.DictWriter(writing, fieldnames=list(data[0].keys()), dialect=\"excel\")\r\n writer.writeheader()\r\n for entry in data:\r\n writer.writerow(entry)","title":""},{"docid":"c23ad035774120abca075e76c602207f","score":"0.6246394","text":"def write_to_file(l,file_name,title,o_type='list', mode='w'):\n file_path = CURRDIR + file_name\n cprint(C_G, \"file saving mode: \", mode)\n f = codecs.open(file_path, encoding='utf-8', mode=mode)\n count = 0\n now = str(datetime.datetime.now())\n now_stamp = '##### Generated: ' + now + '\\n\\n'\n hash_title = '# ' + title\n f.write(hash_title)\n f.write('\\n')\n f.write(now_stamp)\n if o_type == 'list':\n count = write_list(l,f)\n elif o_type == 'dict':\n count = write_dict(l,f)\n end_title = \"\"\"\n##### end generated log\n##### total items = \"\"\" + str(count) + '\\n\\n'\n f.write(end_title)\n f.close()\n cprint (C_G, 'Saved ', C_P, title, C_G, ' to file ', C_LG, file_path)\n return","title":""},{"docid":"44e3c7be2971d3ac2cdc00c331a25320","score":"0.6245316","text":"def writeFiles(self, filename, data, listbool=False, overwrite=False):\n csvname = filename\n if overwrite:\n with open(csvname,'w') as out:\n csv_out = csv.writer(out)\n for row in data:\n if listbool:\n for col in row:\n csv_out.writerow(col)\n else:\n csv_out.writerow(row)\n else:\n with open(csvname,'a+') as out:\n csv_out = csv.writer(out)\n for row in data:\n if listbool:\n for col in row:\n csv_out.writerow(col)\n else:\n csv_out.writerow(row)","title":""},{"docid":"f2b15c7c70af5a93a1dbf1bc6c38e59d","score":"0.62435836","text":"def set_to_file(set_of_data, file):\r\n with open(file, 'w') as f:\r\n for l in sorted(set_of_data):\r\n try:\r\n f.write(l + '\\n')\r\n except:\r\n pass","title":""},{"docid":"204257eba148ae87bdb2171f0318a1d0","score":"0.62363946","text":"def WriteTo(linelist, outfile, SoP):\r\n file = open(str(outfile),'w')\r\n i=0\r\n if SoP == 'P':\r\n for line in linelist:\r\n file.write(linelist[i].borough+ ',')\r\n file.write(linelist[i].block+ ',') \r\n file.write(linelist[i].lot+ ',')\r\n file.write(linelist[i].numbers+ ',')\r\n file.write(linelist[i].spaced_numbers+ ',')\r\n file.write(linelist[i].letters+ ',') \r\n file.write(linelist[i].BBL+ ',')\r\n file.write(linelist[i].x+ ',')\r\n file.write(linelist[i].y+ ',')\r\n file.write(linelist[i].E_des)\r\n i+=1\r\n file.close()\r\n \r\n elif SoP == 'S':\r\n for line in linelist:\r\n file.write(linelist[i].numbers+ ',')\r\n file.write(linelist[i].spaced_numbers+ ',')\r\n file.write(linelist[i].letters)\r\n i+=1\r\n file.close()","title":""},{"docid":"f0a167d3129904270030d76c7cb93df1","score":"0.6234262","text":"def save_data_to_file(file_name, list_of_rows):\r\n file = open(file_name, \"w\")\r\n for row in list_of_rows:\r\n file.write(row.name + \",\" + str(row.price) + \"\\n\")\r\n file.close()\r\n return \"\\nData successfully saved to the file!\\n\"","title":""},{"docid":"3ead5363e7d52d18ee088bb7b58487e1","score":"0.6231364","text":"def save_data_to_file(file_name, list_of_rows):\n pass # TODO: Add code Here\n\n objFile = open(file_name, \"w\")\n for dicRow in list_of_rows: # Write each row of data to the file\n objFile.write(dicRow[\"Product\"] + \",\" + dicRow[\"Price\"] + \"\\n\")\n objFile.close()","title":""},{"docid":"b954939a1bbcd96d3a91ce2aff1f7ab1","score":"0.62311023","text":"def save_text_file(lines: List[str], file_path: str):\n with open(file_path, \"w\") as file:\n for line in lines:\n file.write(line + \"\\n\")","title":""},{"docid":"153d4cb88ae636fee614a9610d508461","score":"0.6221206","text":"def write_keys(list_of_keys):\n if os.path.isfile('article_names.txt'):\n os.remove('article_names.txt')\n with open('article_names.txt', 'w') as f:\n for article in list_of_keys:\n f.write(article + '\\n')","title":""},{"docid":"6ff27b8f8d03e4573355d70780069970","score":"0.62141734","text":"def write(self, filename, *args, **kwargs):\n self.to_hdulist().writeto(str(make_path(filename)), *args, **kwargs)","title":""},{"docid":"89528ab1d6b59b4f57db7f98bd4d4f0a","score":"0.62100166","text":"def write(self, list_files):\n try:\n self.homonym()\n report = open(self.name, \"w\")#TODO using decorator\n report.write(self.csvLabel())\n self.writerows( list_files, report)\n report.close()\n except:\n print ( sys.exc_info() )\n print (\"The file {0} is finally written\".format ( self.name) )","title":""},{"docid":"0a754941f1a7c7138f53aa6aab099cfc","score":"0.6207147","text":"def write_to_file(path, contents, close=True):\n f = open(path, \"w\")\n f.writelines(contents)\n if close:\n f.close()","title":""},{"docid":"2122c257630f23baedbcc17b0307ee4c","score":"0.6199983","text":"def save_to_file(cls, list_objs):\n outFile = cls.__name__ + \".json\"\n list_objs = [x.to_dictionary() for x in list_objs] if list_objs else []\n with open(outFile, \"w\", encoding=\"utf-8\") as f:\n f.write(cls.to_json_string(list_objs))","title":""},{"docid":"ce4817b0f0ffe1b362f11b1050998734","score":"0.61976564","text":"def save_to_file(cls, list_objs):\n filename = cls.__name__ + \".json\"\n l = []\n with open(filename, \"w\") as f:\n if list_objs is not None:\n for i in list_objs:\n l.append(i.to_dictionary())\n l = cls.to_json_string(l)\n f.write(l)","title":""},{"docid":"fd6c60fb91fd3f705cee5e65d2c4e9c4","score":"0.61877286","text":"def writeListCfg(lst, cfgname):\n cfg_out = open(cfgname, 'w')\n for e in lst:\n cfg_out.write(str(e) + '\\n')\n cfg_out.close()\n return cfgname","title":""},{"docid":"e8c631be6111b5a9cbffc229fb661ec8","score":"0.6169147","text":"def commitURLsToFile(list, csvfile):\n\twith open(csvfile, 'w') as fp:\n\t\tfor item in list:\n\t\t\tprint(item, file=fp)\n\tprint('File saved to', csvfile)","title":""},{"docid":"dd389bfa9ce9f8b503901f0764f066a4","score":"0.6168257","text":"def list2txt(list_, fname):\n with open(fname, 'w') as filehandle:\n for listitem in list_:\n filehandle.write('%s\\n' % listitem)","title":""},{"docid":"505146dc7e4b46eefb890973eb69eb49","score":"0.61646545","text":"def save_to_file(cls, list_objs):\n with open(cls.__name__ + '.json', mode='w', encoding='utf-8') as f:\n if list_objs is None:\n f.write(\"[]\")\n else:\n objs = [cls.to_dictionary(obj) for obj in list_objs]\n f.write(cls.to_json_string(objs))","title":""},{"docid":"c9ef37fc1c7b0cc6ae2e5602d766aced","score":"0.61623865","text":"def save_cmd_list(cmd_list, path):\n output = \"QGC WPL 110\"\n\n for cmd in cmd_list:\n line = \"{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n\".format(\n cmd.seq, cmd.current, cmd.frame, cmd.command,\n cmd.param1, cmd.param2, cmd.param3, cmd.param4,\n cmd.x, cmd.y, cmd.z,\n cmd.autocontinue)\n\n output += line\n\n with open(path, \"w\") as file_:\n file_.write(output)","title":""},{"docid":"9e66e597ae712c6fc27756a32abdcf2e","score":"0.6160831","text":"def save_data_to_file(file_name, list_of_rows):\n with open(file_name, \"w\") as f:\n table = \"\"\n for row in list_of_rows:\n new_row = row[\"Product\"] + \",\" + str(row[\"Price\"]) + \"\\n\"\n table += new_row\n f.write(table)\n return \"Current list saved to your file!\"","title":""},{"docid":"815680e635115d7fc7bc989943350d6a","score":"0.6157605","text":"def save_file(file_name, items_list):\n\n logging.debug(f\"save_file() is run\")\n\n with open(file_name, 'w', newline='') as csv_file:\n logging.debug(f\"- write '{file_name}'\")\n keys = ['name','quantity','unit','unit_price']\n csv_writer = csv.DictWriter(csv_file, keys)\n csv_writer.writeheader()\n\n for dictionary in items_list:\n csv_writer.writerow(dictionary)\n \n logging.debug(\"- file is saved\\n\")","title":""},{"docid":"11aac1902a7fc43a9ea81201f7f7c208","score":"0.6130153","text":"def store(arr, path):\n with open(path, \"a\") as vec_file:\n for row in arr:\n vec_file.write(row)","title":""},{"docid":"acc014aa4fff17b06583b9c12dca56ea","score":"0.6127763","text":"def path2file(pathlist):\n file = \"\"\n for item in pathlist:\n file = pjoin(file, item)\n return file","title":""},{"docid":"11d9561a83dd8ee1b9ba22efdd0fdb8c","score":"0.6126391","text":"def save_to_file(cls, list_objs):\n with open(cls.__name__ + '.json', 'w', encoding='utf-8') as f:\n if list_objs is None:\n f.write(\"[]\")\n else:\n f.write(\n cls.to_json_string([e.to_dictionary() for e in list_objs])\n )","title":""}],"string":"[\n {\n \"docid\": \"e3ffdb2da1d96b4981a8989227493f81\",\n \"score\": \"0.79097027\",\n \"text\": \"def write_list_to_file(list_to_save, save_path):\\n \\n Path(save_path).parent.mkdir(exist_ok=True, parents=True)\\n \\n with open(str(save_path), 'w') as fid:\\n for line in list_to_save:\\n fid.write(\\\"%s\\\\n\\\" % line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f64745fc63cfb08318cae2959d47ce4\",\n \"score\": \"0.7550756\",\n \"text\": \"def save_list_to_file(list_data: list, save_path: str) -> None:\\n with open(save_path, \\\"w\\\") as f:\\n for line in list_data:\\n f.write(line + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d62675cd415e1e9654f5f8cc1a64aec3\",\n \"score\": \"0.75506246\",\n \"text\": \"def write_list_to_file(fpath: str, lst: List[Any]) -> None: \\n with open(fpath, 'w') as fp:\\n for elem in lst:\\n print(str(elem).strip(), file=fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed42740c9ec7a386e77852aa813febb\",\n \"score\": \"0.74805975\",\n \"text\": \"def list2file(mylist,path):\\n try:\\n with open(path,'w') as f:\\n for item in mylist:\\n f.write(\\\"{}\\\\r\\\\n\\\".format(item))\\n return True\\n except Exception as err:\\n sys.stderr.write(str(err))\\n sys.stderr.write('\\\\n')\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66198ee4646ddc2de79e22485ce8f6d5\",\n \"score\": \"0.745095\",\n \"text\": \"def list_to_file(lines, path_output=\\\"./\\\", file_output=\\\"defaultName.go\\\"):\\n f = open(path_output+file_output,'w+')\\n [f.write(line) for line in lines]\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"090fed6dab6c54e1e9f2df46bd69d7eb\",\n \"score\": \"0.741671\",\n \"text\": \"def write_file(str_list, file_path):\\n try:\\n print(\\\"write to file...\\\")\\n with open(file_path, \\\"w\\\") as file:\\n for word in str_list:\\n file.write(word + \\\" \\\")\\n except IOError as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93805b5668a2ba9bc3ae9821e5495cb1\",\n \"score\": \"0.74016905\",\n \"text\": \"def list2file(lst, filename):\\n with open(filename, \\\"w\\\") as f:\\n for i in lst:\\n f.write(i+'\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56f9d580fc1a6533c58713869ebdb4d\",\n \"score\": \"0.73118794\",\n \"text\": \"def strListToFile(filename, str_list):\\n with open(filename,'w') as f:\\n for i in str_list:\\n f.write(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f3fbf4c062d0aa1388c2affef86b32\",\n \"score\": \"0.7205356\",\n \"text\": \"def write_list_to_file(lst):\\n list_to_print = '\\\\n'.join(map(str, lst))\\n error_file = open(\\\"error_files.txt\\\", \\\"w\\\")\\n error_file.write(list_to_print)\\n error_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aefef52ad4a447acbb668f908a492f3b\",\n \"score\": \"0.7203913\",\n \"text\": \"def write_to_file(file_name, list_of_bills):\\r\\n open_file = open(file_name, \\\"wb\\\")\\r\\n pickle.dump(list_of_bills, open_file)\\r\\n open_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bbcfe20ffaacacfc89922d007aadad\",\n \"score\": \"0.7200537\",\n \"text\": \"def write_list(fname,strlist):\\n with open(fname,'w') as f:\\n f.write('\\\\n'.join(strlist))\\n f.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a238b3746ceb2a1125864ef06d15c2\",\n \"score\": \"0.71149987\",\n \"text\": \"def write_file(filename, list):\\n f = open(filename, \\\"w\\\")\\n s = \\\" -- \\\"\\n for line in list:\\n t = s.join(str(i) for i in line)\\n f.write(t + \\\"\\\\n\\\")\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb0a78857ef1fd3d84f6b2394f8369e\",\n \"score\": \"0.7114547\",\n \"text\": \"def save_list(lst, fn=\\\"list.txt\\\"):\\n with open(fn, \\\"w\\\") as f:\\n for line in lst:\\n f.write(f\\\"{line}\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ddac2d1128e1963f85160b30f91c336\",\n \"score\": \"0.71099144\",\n \"text\": \"def write_filelist(img_list, filelist_name):\\n with open(filelist_name, \\\"w\\\") as f:\\n for line in img_list:\\n f.write(line + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30c2501b13a5b378bba00c77927743d6\",\n \"score\": \"0.7033325\",\n \"text\": \"def List2File(fileName, list):\\r\\n \\r\\n with open(fileName + '.txt', 'w') as f:\\r\\n for element in list:\\r\\n print (element, file = f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8b318dd960aacbdcf4255f5f610e80\",\n \"score\": \"0.70122874\",\n \"text\": \"def pathlist2file(mylist,path,root):\\n try:\\n with open(path,'w') as f:\\n for item in mylist:\\n f.write(\\\"{}\\\\r\\\\n\\\".format(item[len(root):]))\\n return True\\n except Exception as err:\\n sys.stderr.write(str(err))\\n sys.stderr.write('\\\\n')\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e022bcba702d790b7fd2ac367edccb45\",\n \"score\": \"0.7001319\",\n \"text\": \"def write_into_file(self,filename,superlist):\\n fo = open(filename, \\\"w\\\")\\n for ele in superlist:\\n fo.write(ele + \\\"\\\\n\\\")\\n fo.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579c92219f186c8b7b545cec35c1732e\",\n \"score\": \"0.699836\",\n \"text\": \"def writeFile(filename, geo_list):\\n fname = filename + \\\".\\\" + geo_list[0].getType()\\n f = open(fname, \\\"w\\\")\\n n = len(geo_list)\\n f.write(repr(n)+\\\"\\\\n\\\")\\n for elem in geo_list:\\n f.write(elem.printString())\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"644a66e92ce795187a9294b8be2555e0\",\n \"score\": \"0.6971146\",\n \"text\": \"def write_review_files(path, reviewlist):\\n if not path.endswith(\\\"/\\\"):\\n path = path + \\\"/\\\"\\n try:\\n for i, row in enumerate(reviewlist):\\n filename = \\\"review\\\" + str(i) + \\\".txt\\\"\\n with open(path + filename, \\\"w\\\") as handle:\\n handle.write(row)\\n except:\\n print(\\\"Something wrong with the path or file list. Does the directory exist?\\\")\\n print(\\\"Wrote %s files to %s.\\\" % (len(reviewlist), path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"644a66e92ce795187a9294b8be2555e0\",\n \"score\": \"0.6971146\",\n \"text\": \"def write_review_files(path, reviewlist):\\n if not path.endswith(\\\"/\\\"):\\n path = path + \\\"/\\\"\\n try:\\n for i, row in enumerate(reviewlist):\\n filename = \\\"review\\\" + str(i) + \\\".txt\\\"\\n with open(path + filename, \\\"w\\\") as handle:\\n handle.write(row)\\n except:\\n print(\\\"Something wrong with the path or file list. Does the directory exist?\\\")\\n print(\\\"Wrote %s files to %s.\\\" % (len(reviewlist), path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469702687f20178eec7a42c949bf88cd\",\n \"score\": \"0.69698966\",\n \"text\": \"def write_to_file(lis_2d, file_name):\\n file = open(file_name, 'a+')\\n for row in range(0, len(lis_2d)):\\n new_line = ''.join(lis_2d[row])\\n file.write(new_line + '\\\\n')\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e76c8da102b9b9fe23325043f04f7a6\",\n \"score\": \"0.69566214\",\n \"text\": \"def write_txt_file(file_path: str, content: list):\\n with open(file_path, mode=\\\"w\\\", encoding=\\\"utf-8\\\") as fw:\\n for line in content:\\n fw.write(str(line) + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958db0923c22a72e30225a1e37141a3d\",\n \"score\": \"0.69501966\",\n \"text\": \"def saveToFile(liste, filename):\\r\\n \\r\\n with open(filename, \\\"w\\\") as f:\\r\\n for el in liste:\\r\\n f.write(str(el)+\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a35f7cddcc8eff82f4952a047511f2a\",\n \"score\": \"0.69397426\",\n \"text\": \"def escreverArquivo(nomeArquivo, lista):\\r\\n with open(nomeArquivo, 'w') as arquivo:\\r\\n for linha in lista:\\r\\n arquivo.write(linha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bc6622ce3e7b9aae72915b959c1331\",\n \"score\": \"0.6939149\",\n \"text\": \"def write_data(list_name, fileout):\\n \\n file_wt = open(fileout, 'w')\\n for i in range(0,len(list_name)):\\n filestr = list_name[i].replace(\\\"\\\\n\\\",\\\"\\\")\\n file_rd = open(filestr, 'r')\\n list_dat = file_rd.readlines()\\n \\n for j in range(0,len(list_dat)):\\n linestr = filestr.replace(\\\".txt\\\",\\\"\\\") + '\\\\t' + list_dat[j]\\n file_wt.writelines(linestr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c934b043ea482a733c2145c691d683ac\",\n \"score\": \"0.6917027\",\n \"text\": \"def write_file(self, content: List[str], file_name: str) -> None:\\n # Setup save path.\\n file_path = os.path.join(self.results_path, file_name)\\n make_dirs(os.path.dirname(file_path))\\n\\n with open(file_path, 'w') as filehandle:\\n filehandle.writelines(\\\"%s\\\\n\\\" % line for line in content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee0a20d823ce05689d7ebf37f8c9721\",\n \"score\": \"0.69107515\",\n \"text\": \"def write_to_file(output_filename, file_header, input_list):\\n output_file = open(output_filename, 'w')\\n output_file.write(file_header + '\\\\n')\\n output_list = [';'.join(map(str, tup)) for tup in input_list]\\n for output_line in output_list:\\n output_file.write(output_line + '\\\\n')\\n output_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e1a54c184282c2b304a6f257d64ca69\",\n \"score\": \"0.6808002\",\n \"text\": \"def list_to_file(a_list, a_file, validate=True):\\n with open(a_file, \\\"w\\\", 8192000) as f:\\n for e in a_list:\\n f.write(\\\"{}\\\\n\\\".format(e))\\n if validate:\\n assert line_counter(a_file) == len(a_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b9fa31649f79c833574e15a1e609931\",\n \"score\": \"0.68075925\",\n \"text\": \"def save_list(self, list_name, path):\\r\\n\\r\\n with open(path, 'w') as out:\\r\\n lines = []\\r\\n for item in self.items.get(list_name, []):\\r\\n if isinstance(item, basestring):\\r\\n lines.append(smart_str(item))\\r\\n else:\\r\\n lines.append(json.dumps(item))\\r\\n out.write('\\\\n'.join(lines) + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f3079ecd37e18d26db56bedff7ebda\",\n \"score\": \"0.6802555\",\n \"text\": \"def write_Out_file(out_filename, out_list): \\n \\n with open(out_filename, \\\"wb\\\") as out_file:\\n pickle.dump(out_list, out_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac73680815bc67eca566bdee0cb08e3\",\n \"score\": \"0.6802001\",\n \"text\": \"def write_to_file(ascii_list):\\n with open('output.txt', 'w') as f:\\n for line in ascii_list:\\n f.write(f\\\"{line}\\\\n\\\")\\n print('Done')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"955bc54d4345e89a82a18b0c98d4d02b\",\n \"score\": \"0.6742082\",\n \"text\": \"def save_data_to_file(file_name, list):\\n file = open(file_name, \\\"ab\\\") # append new data to a file\\n pickle.dump(list, file)\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c41ac99dbda13eb3e2f7e35e2391b000\",\n \"score\": \"0.66481847\",\n \"text\": \"def write(fpath, data):\\n if isinstance(data, list):\\n with fpath.open(mode=\\\"w\\\") as csvfile:\\n csvwriter = csv.writer(\\n csvfile,\\n delimiter=\\\",\\\",\\n quoting=csv.QUOTE_MINIMAL\\n )\\n csvwriter.writerows(data)\\n else:\\n with fpath.open(mode=\\\"w\\\") as f:\\n f.write(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863258c6b444f536f3a38ec952284d66\",\n \"score\": \"0.66439\",\n \"text\": \"def write_files(company, list):\\n dir = 'link2gophish-output'\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n gophish = open(dir + '/' + company + '-gophish.csv', 'w')\\n\\n for u in list:\\n gophish.write(u.toGophish())\\n gophish.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd87063061ff3b29f4491da5eff0320b\",\n \"score\": \"0.6635326\",\n \"text\": \"def print_list_to_file(L,f):\\n linestring = \\\"\\\"\\n for element in L:\\n linestring += str(element) + ' '\\n linestring = linestring[:-1] + '\\\\n'\\n f.write(linestring)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc1b5e17fbc8a5958704b6ed8d67b0a\",\n \"score\": \"0.66242594\",\n \"text\": \"def flush_list_to_file(data, save_file_name):\\n with open(save_file_name, mode='rw') as writer:\\n for key in data:\\n print(\\\"{0}\\\".format(key), file=writer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1218182f18f030130800fa0dcb3da8b7\",\n \"score\": \"0.6623087\",\n \"text\": \"def make_obsfile(obslist, outpath):\\n with open(outpath, 'w') as f:\\n for obs in obslist:\\n f.write(f\\\"{obs}\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"680563df2630e4e40c1653e901e0090f\",\n \"score\": \"0.66021025\",\n \"text\": \"def saveList(itemToSave, listToSave):\\r\\n if not os.path.exists(listToSave.strip()+\\\".txt\\\"):\\r\\n \\tf = open(listToSave.strip() + \\\".txt\\\", \\\"w\\\")\\r\\n \\tf.close()\\r\\n with open(listToSave.strip() + \\\".txt\\\", \\\"r+\\\") as f: \\r\\n \\tcontent = f.read()\\r\\n \\tf.seek(0,0)\\r\\n \\tf.write(itemToSave.replace('\\\\r\\\\n', '') + '\\\\r\\\\n' + content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5befdafc2ee5c092bf46ac7d9f7d20dd\",\n \"score\": \"0.65765536\",\n \"text\": \"def save_to_file(self, data_list):\\n with open(self.json_file, 'w', encoding='utf-8') as outfile:\\n json.dump(data_list, outfile, indent=4, ensure_ascii=False)\\n print(f\\\"{self.json_file} saved\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daf733bcccd78554dc3530ad9d8d5d0a\",\n \"score\": \"0.6568339\",\n \"text\": \"def save_csv_to_path(self, csv_list, path):\\n try:\\n with open(path, 'w', encoding='utf-8') as f:\\n for line in csv_list:\\n f.write(str(line[0]) + ',' + str(line[1]) + ',' + str(line[2]))\\n f.write('\\\\n')\\n except Exception as e:\\n print(str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c5a720cd565b61416275869a3cf842\",\n \"score\": \"0.65642464\",\n \"text\": \"def write_file_list(out_name, files):\\n outfile = open(out_name, 'w')\\n outfile.write(\\\"{0:s}\\\".format(files[0][0]))\\n for fdat in files[1:]:\\n outfile.write(\\\"\\\\n{0:s}\\\".format(fdat[0]))\\n outfile.write(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77ff1f92df06f4264cb9309be65a5565\",\n \"score\": \"0.655157\",\n \"text\": \"def write_puzzles_to_file(list_of_tuples: list) -> None:\\n with open('../input/input_puzzles.txt', 'w') as file:\\n file.flush()\\n for ls in list_of_tuples:\\n file.write(str(ls) + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f660e189145a427db7d286faf533e34\",\n \"score\": \"0.654729\",\n \"text\": \"def list_to_file(post_list, outfile):\\n print(PROJ_DIR)\\n with open(os.path.join(PROJ_DIR, \\\"data\\\", outfile), 'w') as f: #../data/{}\\\".format(outfile), 'w') as f:\\n for post in post_list:\\n post_string = json.dumps(post)\\n f.write(post_string)\\n f.write('\\\\n')\\n print(\\\"Finished writing {}\\\".format(outfile))\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3b9ab7356a56d22e4cde059d35277f\",\n \"score\": \"0.6541723\",\n \"text\": \"def write_to_file(self):\\n out_file = os.path.join(OUT_DIR, \\\"new_%s\\\" %\\n os.path.basename(self.fdi_file))\\n with open(out_file, 'w') as f_out:\\n f_out.write(''.join(self.final_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3171478769b38a9a5967d617b7b38e93\",\n \"score\": \"0.64794755\",\n \"text\": \"def write_to_file(self, list):\\n f = open(\\\"myScheduler.txt\\\", \\\"w+\\\")\\n f.write(self.__format_str.format(self.__file_headers[0],\\n self.__file_headers[1],\\n (self.__file_headers[2] + \\\"\\\\r\\\\n\\\")))\\n f.write(\\n self.__format_str.format(\\\"=========\\\", \\\"========\\\", \\\"==========\\\\r\\\\n\\\"))\\n\\n for i in list:\\n f.write(self.__format_str.format(ScheduleLogger.__add__(self,\\n list.index(i)), str(i.time), i.name + \\\"\\\\r\\\\n\\\"))\\n f.write(\\\"\\\\r\\\\n\\\")\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e4b83353cd2002d717abbbe3954a893\",\n \"score\": \"0.6478461\",\n \"text\": \"def write_output_file(pair_list, out_name):\\n with open(out_name, 'a') as file1:\\n file1.write('\\\\t'.join(map(str, pair_list)) + '\\\\n')\\n file1.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2b5b216ae29f34cdff368614d61b0c\",\n \"score\": \"0.645445\",\n \"text\": \"def write_users_list_in_file(filename, users):\\n file = open(filename, 'w')\\n for user in users:\\n file.write(str(user) + \\\"\\\\n\\\")\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d42f7f18b742ef215c605652c333b7df\",\n \"score\": \"0.6448855\",\n \"text\": \"def write_to_file(self, f):\\n f.write(\\\" \\\".join(self.paths) + \\\"\\\\n\\\")\\n f.write(\\\" \\\".join(self.snps) + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4edee33274f8e8585adfdf152c06b84\",\n \"score\": \"0.6443718\",\n \"text\": \"def write_json_file(file_path: str, content: list):\\n with open(file_path, mode=\\\"w\\\", encoding=\\\"utf-8\\\") as f_json:\\n json.dump(content, f_json, ensure_ascii=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e4a7a48a3546ecafbe20daa05f8ac58\",\n \"score\": \"0.6430349\",\n \"text\": \"def writewords(filename, wordlist):\\n with open(filename, 'wt') as outfile:\\n outfile.writelines([w + '\\\\n' for w in wordlist])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2f59dc8a1af46fb00ec5af3e5df1c0\",\n \"score\": \"0.6429284\",\n \"text\": \"def write(self, data_list):\\n self.check_if_file_exist()\\n\\n try:\\n with open(self.path, 'w', newline='') as csvfile:\\n writer = csv.DictWriter(csvfile, fieldnames=self.fields, extrasaction='ignore')\\n for data in data_list:\\n writer.writerow(data)\\n except:\\n csvfile.close()\\n raise Exception\\n \\n csvfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7853a66f918ee96822fde549d201023\",\n \"score\": \"0.6423519\",\n \"text\": \"def saveUnitsLinksToFile(linkList):\\n if linkList:\\n try:\\n f = open(ppsvar.LINKS_FILE,'a')\\n except IOError:\\n print 'blad dostepu do pliku'\\n else:\\n f.write('\\\\n'.join(linkList))\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"574b7bf2abb143b79d4557189b275b91\",\n \"score\": \"0.64155775\",\n \"text\": \"def write_file(contents, file_path):\\n with open(file_path, 'w') as f_handle:\\n f_handle.writelines(contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed5026d3c6200c4228697ecbc9f964db\",\n \"score\": \"0.6392026\",\n \"text\": \"def _write(fp, lines):\\n with open(fp, 'w') as f:\\n for l in lines:\\n f.write(l)\\n return fp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f5a623c4239afa6ae9451408720b25\",\n \"score\": \"0.6360186\",\n \"text\": \"def pickle_to_file(file_name, list_of_titles):\\n file = open(file_name, 'wb') # open the file for writing\\n pickle.dump(list_of_titles, file) # \\\"dump\\\" pickled data to file\\n file.close() # close file\\n print('\\\\n' + 'Reading list saved to file.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c54cff7ea599ea06927d82438766e0\",\n \"score\": \"0.6357948\",\n \"text\": \"def write_list_to_json_file(a_list, file_name, sort_keys=False):\\n beauty_output = Util.json_beautifier(json_data=Util.from_list_to_json(a_list), sort_keys=sort_keys)\\n text_file = open(file_name, \\\"w\\\")\\n text_file.write(\\\"%s\\\" % beauty_output)\\n text_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e65e6ba7ff71d42d1e13c32885682023\",\n \"score\": \"0.6347744\",\n \"text\": \"def write_f(path, lines):\\r\\n with open(path, \\\"w\\\") as f:\\r\\n for line in lines:\\r\\n f.write(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"620e3c6f7b7c61099209d2517fcd1f76\",\n \"score\": \"0.63383687\",\n \"text\": \"def send_to_file(in_list=growth_rate()):\\n with open('growth_list.csv', 'w') as f:\\n for line in in_list:\\n f.write(line + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591d8496b4c599cc093900598badd623\",\n \"score\": \"0.6330364\",\n \"text\": \"def write_file(path, content, binary=False):\\n if isinstance(path, tuple):\\n path = os.path.join(*path)\\n with open(path, 'wb' if binary else 'w') as fp:\\n fp.write(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f0997760e9d95f918505e9e5e52de3\",\n \"score\": \"0.63287807\",\n \"text\": \"def save_to_file(cls, list_objs):\\n\\n if list_objs is None:\\n list_objs = []\\n\\n list_objs = [obj.to_dictionary() for obj in list_objs]\\n list_objs = cls.to_json_string(list_objs)\\n name = cls.__name__ + \\\".json\\\"\\n with open(name, \\\"wt\\\") as my_file:\\n my_file.write(list_objs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03eaa13454c3cb53b189987a72ae735f\",\n \"score\": \"0.63249105\",\n \"text\": \"def write_csv(content: List[List[Any]], file_path: Path) -> None:\\n with open(file_path, \\\"w\\\", newline=\\\"\\\") as file:\\n csv_writer = csv.writer(file)\\n for row in content:\\n csv_writer.writerow(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36efb5ba8ce28a939d6a3536c0045887\",\n \"score\": \"0.6317012\",\n \"text\": \"def save_to_file(cls, list_objs):\\n file = cls.__name__ + \\\".json\\\"\\n with open(file, \\\"w\\\") as f:\\n li = []\\n if list_objs is not None:\\n li = [item.to_dictionary() for item in list_objs]\\n f.write(Base.to_json_string(li))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1460d6b2419ff4d9ca38d46040ec0e\",\n \"score\": \"0.6315371\",\n \"text\": \"def append2file(lst, filename):\\n # if the file doesn't exist, create one\\n if not os.path.exists(filename):\\n list2file(lst, filename)\\n else: # if file exists, load the existing list\\n lst_exist = file2list(filename)\\n lst_new = list(set(lst_exist).union(set(lst)))\\n list2file(lst_new, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac65debc1f3564a45be36cdfc019892a\",\n \"score\": \"0.63033104\",\n \"text\": \"def print_list(xs, filename, format):\\n f = open(filename, \\\"w\\\")\\n for x in xs:\\n f.write(format % x)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f7f3ce04071313f0196ab24a4d47a4\",\n \"score\": \"0.62903416\",\n \"text\": \"def save_to_file(cls, list_objs):\\n\\n filename = cls.__name__ + \\\".json\\\"\\n mylist = \\\"[]\\\"\\n\\n if list_objs is None:\\n with open(filename, 'w') as f:\\n f.write(mylist)\\n else:\\n mylist = []\\n with open(filename, 'w') as f:\\n for objects in list_objs:\\n mydic2 = cls.to_dictionary(objects)\\n mylist.append(mydic2)\\n mydict = cls.to_json_string(mylist)\\n f.write(mydict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24195410a6bc8b216320379237eea2fd\",\n \"score\": \"0.6287782\",\n \"text\": \"def file_save(filename, contents):\\n with open(filename, 'w') as file_wr:\\n for l in contents:\\n file_wr.write(l + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f6db0c4ddffc830433b78eb9bf2d090\",\n \"score\": \"0.6279754\",\n \"text\": \"def write_file(path_file, data, first):\\n #first tells if it writtes or appends\\n print(data)\\n path_to_2 = os.path.join(path_file, 'id_file_data.txt')\\n #with open(path_to_1,'w') as file:\\n #file.writelines('\\\\t'.join(i) + '\\\\n' for i in data[0])\\n #file.close()\\n with open(path_to_2, first) as file:\\n file.writelines('\\\\t'.join(i) + '\\\\n' for i in data)\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86bd5d5ae1324dd962c7d2f2fefab6cb\",\n \"score\": \"0.62718546\",\n \"text\": \"def writeFile(data):\\n f = open(outputFile, \\\"w\\\")\\n for value in data:\\n f.write(\\\"{}\\\\n\\\".format(value))\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86bd5d5ae1324dd962c7d2f2fefab6cb\",\n \"score\": \"0.62718546\",\n \"text\": \"def writeFile(data):\\n f = open(outputFile, \\\"w\\\")\\n for value in data:\\n f.write(\\\"{}\\\\n\\\".format(value))\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261ba966ca6a8c9b43c49baea8eac89d\",\n \"score\": \"0.6269071\",\n \"text\": \"def write_data_to_file(file_name, list_of_rows):\\r\\n # TODO: Add code Here - Done\\r\\n objFile = open(file_name, \\\"w\\\")\\r\\n for dicRow in list_of_rows: # Write each row of data to the file\\r\\n objFile.write(dicRow[\\\"Task\\\"] + \\\",\\\" + dicRow[\\\"Priority\\\"] + \\\"\\\\n\\\")\\r\\n objFile.close()\\r\\n return list_of_rows, \\\"Data Saved to File!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55dcb20d300598003be27ef20899a26f\",\n \"score\": \"0.6268501\",\n \"text\": \"def WriteFileList(self, file_names):\\n filename = self.GetListFile()\\n MakeDirIfNeeded(filename)\\n with open(filename, 'w') as f:\\n for name in file_names:\\n f.write(name + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45a294799acae636b8d53ab7060494dc\",\n \"score\": \"0.62637466\",\n \"text\": \"def writeList (\\n \\n self,\\n values = None,\\n path = None,\\n text = None,\\n fieldDelimiter = None,\\n commentDelimiter = None\\n ) :\\n\\n ok = self.writeLists(\\n lists = [ values ],\\n path = path,\\n text = text,\\n fieldDelimiter = fieldDelimiter,\\n commentDelimiter = commentDelimiter\\n )\\n\\n return ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea314aeec9493b2bad6b72cf2602135c\",\n \"score\": \"0.6261345\",\n \"text\": \"def write_file(contents, file_path, file_name):\\n full_path = file_path + file_name\\n try:\\n with open(full_path, 'w+') as output_file:\\n output_file.write(contents)\\n for item in range(0,5):\\n output_file.write('\\\\nAppended line {} by write_file function.'.format(item))\\n\\n print('A new file is created successfully in', full_path)\\n\\n except Exception as e:\\n print('Output file cannot be written', e)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c2509bf97522a3a5bfa8d6d952eefa2\",\n \"score\": \"0.6261012\",\n \"text\": \"def write_file(outfile, high_dict_list):\\n\\n fout = open(outfile, \\\"w\\\")\\n\\n for i in range(len(high_dict_list[\\\"points\\\"])):\\n fout.write(high_dict_list[\\\"name\\\"][i] + \\\" \\\")\\n fout.write(high_dict_list[\\\"points\\\"][i] + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ab71d68e5b1fb67a29e0c53f219a765\",\n \"score\": \"0.625377\",\n \"text\": \"def write_data(df, list_of_filepaths):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"805b72b3ddc48c3c84e97038f70070f3\",\n \"score\": \"0.6247957\",\n \"text\": \"def List2File(L, FileName):\\n\\tFilout = open(FileName, \\\"w\\\")\\n\\tFilout.write('\\\\n'.join(L))\\n\\tFilout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de60d26f85565dff61183444c5d276d0\",\n \"score\": \"0.6247396\",\n \"text\": \"def write_out(lines, path, file):\\r\\n\\tlogging.debug('IN')\\r\\n\\twith open(path+file, 'w') as output_file:\\r\\n\\t\\tlogging.info('Writing {} lines in {}{}'.format(len(lines), path, file))\\r\\n\\t\\tfor line in lines: # just in case\\r\\n\\t\\t\\toutput_file.write(line+'\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe801bbfdf2fc55414dc6116585312c8\",\n \"score\": \"0.6246481\",\n \"text\": \"def write_file_list(data, outfile):\\r\\n with open(outfile, 'w', encoding=\\\"utf8\\\", newline='') as writing:\\r\\n if len(data) > 0:\\r\\n writer = csv.DictWriter(writing, fieldnames=list(data[0].keys()), dialect=\\\"excel\\\")\\r\\n writer.writeheader()\\r\\n for entry in data:\\r\\n writer.writerow(entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c23ad035774120abca075e76c602207f\",\n \"score\": \"0.6246394\",\n \"text\": \"def write_to_file(l,file_name,title,o_type='list', mode='w'):\\n file_path = CURRDIR + file_name\\n cprint(C_G, \\\"file saving mode: \\\", mode)\\n f = codecs.open(file_path, encoding='utf-8', mode=mode)\\n count = 0\\n now = str(datetime.datetime.now())\\n now_stamp = '##### Generated: ' + now + '\\\\n\\\\n'\\n hash_title = '# ' + title\\n f.write(hash_title)\\n f.write('\\\\n')\\n f.write(now_stamp)\\n if o_type == 'list':\\n count = write_list(l,f)\\n elif o_type == 'dict':\\n count = write_dict(l,f)\\n end_title = \\\"\\\"\\\"\\n##### end generated log\\n##### total items = \\\"\\\"\\\" + str(count) + '\\\\n\\\\n'\\n f.write(end_title)\\n f.close()\\n cprint (C_G, 'Saved ', C_P, title, C_G, ' to file ', C_LG, file_path)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44e3c7be2971d3ac2cdc00c331a25320\",\n \"score\": \"0.6245316\",\n \"text\": \"def writeFiles(self, filename, data, listbool=False, overwrite=False):\\n csvname = filename\\n if overwrite:\\n with open(csvname,'w') as out:\\n csv_out = csv.writer(out)\\n for row in data:\\n if listbool:\\n for col in row:\\n csv_out.writerow(col)\\n else:\\n csv_out.writerow(row)\\n else:\\n with open(csvname,'a+') as out:\\n csv_out = csv.writer(out)\\n for row in data:\\n if listbool:\\n for col in row:\\n csv_out.writerow(col)\\n else:\\n csv_out.writerow(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b15c7c70af5a93a1dbf1bc6c38e59d\",\n \"score\": \"0.62435836\",\n \"text\": \"def set_to_file(set_of_data, file):\\r\\n with open(file, 'w') as f:\\r\\n for l in sorted(set_of_data):\\r\\n try:\\r\\n f.write(l + '\\\\n')\\r\\n except:\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"204257eba148ae87bdb2171f0318a1d0\",\n \"score\": \"0.62363946\",\n \"text\": \"def WriteTo(linelist, outfile, SoP):\\r\\n file = open(str(outfile),'w')\\r\\n i=0\\r\\n if SoP == 'P':\\r\\n for line in linelist:\\r\\n file.write(linelist[i].borough+ ',')\\r\\n file.write(linelist[i].block+ ',') \\r\\n file.write(linelist[i].lot+ ',')\\r\\n file.write(linelist[i].numbers+ ',')\\r\\n file.write(linelist[i].spaced_numbers+ ',')\\r\\n file.write(linelist[i].letters+ ',') \\r\\n file.write(linelist[i].BBL+ ',')\\r\\n file.write(linelist[i].x+ ',')\\r\\n file.write(linelist[i].y+ ',')\\r\\n file.write(linelist[i].E_des)\\r\\n i+=1\\r\\n file.close()\\r\\n \\r\\n elif SoP == 'S':\\r\\n for line in linelist:\\r\\n file.write(linelist[i].numbers+ ',')\\r\\n file.write(linelist[i].spaced_numbers+ ',')\\r\\n file.write(linelist[i].letters)\\r\\n i+=1\\r\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a167d3129904270030d76c7cb93df1\",\n \"score\": \"0.6234262\",\n \"text\": \"def save_data_to_file(file_name, list_of_rows):\\r\\n file = open(file_name, \\\"w\\\")\\r\\n for row in list_of_rows:\\r\\n file.write(row.name + \\\",\\\" + str(row.price) + \\\"\\\\n\\\")\\r\\n file.close()\\r\\n return \\\"\\\\nData successfully saved to the file!\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ead5363e7d52d18ee088bb7b58487e1\",\n \"score\": \"0.6231364\",\n \"text\": \"def save_data_to_file(file_name, list_of_rows):\\n pass # TODO: Add code Here\\n\\n objFile = open(file_name, \\\"w\\\")\\n for dicRow in list_of_rows: # Write each row of data to the file\\n objFile.write(dicRow[\\\"Product\\\"] + \\\",\\\" + dicRow[\\\"Price\\\"] + \\\"\\\\n\\\")\\n objFile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b954939a1bbcd96d3a91ce2aff1f7ab1\",\n \"score\": \"0.62311023\",\n \"text\": \"def save_text_file(lines: List[str], file_path: str):\\n with open(file_path, \\\"w\\\") as file:\\n for line in lines:\\n file.write(line + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153d4cb88ae636fee614a9610d508461\",\n \"score\": \"0.6221206\",\n \"text\": \"def write_keys(list_of_keys):\\n if os.path.isfile('article_names.txt'):\\n os.remove('article_names.txt')\\n with open('article_names.txt', 'w') as f:\\n for article in list_of_keys:\\n f.write(article + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff27b8f8d03e4573355d70780069970\",\n \"score\": \"0.62141734\",\n \"text\": \"def write(self, filename, *args, **kwargs):\\n self.to_hdulist().writeto(str(make_path(filename)), *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89528ab1d6b59b4f57db7f98bd4d4f0a\",\n \"score\": \"0.62100166\",\n \"text\": \"def write(self, list_files):\\n try:\\n self.homonym()\\n report = open(self.name, \\\"w\\\")#TODO using decorator\\n report.write(self.csvLabel())\\n self.writerows( list_files, report)\\n report.close()\\n except:\\n print ( sys.exc_info() )\\n print (\\\"The file {0} is finally written\\\".format ( self.name) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a754941f1a7c7138f53aa6aab099cfc\",\n \"score\": \"0.6207147\",\n \"text\": \"def write_to_file(path, contents, close=True):\\n f = open(path, \\\"w\\\")\\n f.writelines(contents)\\n if close:\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2122c257630f23baedbcc17b0307ee4c\",\n \"score\": \"0.6199983\",\n \"text\": \"def save_to_file(cls, list_objs):\\n outFile = cls.__name__ + \\\".json\\\"\\n list_objs = [x.to_dictionary() for x in list_objs] if list_objs else []\\n with open(outFile, \\\"w\\\", encoding=\\\"utf-8\\\") as f:\\n f.write(cls.to_json_string(list_objs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce4817b0f0ffe1b362f11b1050998734\",\n \"score\": \"0.61976564\",\n \"text\": \"def save_to_file(cls, list_objs):\\n filename = cls.__name__ + \\\".json\\\"\\n l = []\\n with open(filename, \\\"w\\\") as f:\\n if list_objs is not None:\\n for i in list_objs:\\n l.append(i.to_dictionary())\\n l = cls.to_json_string(l)\\n f.write(l)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6c60fb91fd3f705cee5e65d2c4e9c4\",\n \"score\": \"0.61877286\",\n \"text\": \"def writeListCfg(lst, cfgname):\\n cfg_out = open(cfgname, 'w')\\n for e in lst:\\n cfg_out.write(str(e) + '\\\\n')\\n cfg_out.close()\\n return cfgname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8c631be6111b5a9cbffc229fb661ec8\",\n \"score\": \"0.6169147\",\n \"text\": \"def commitURLsToFile(list, csvfile):\\n\\twith open(csvfile, 'w') as fp:\\n\\t\\tfor item in list:\\n\\t\\t\\tprint(item, file=fp)\\n\\tprint('File saved to', csvfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd389bfa9ce9f8b503901f0764f066a4\",\n \"score\": \"0.6168257\",\n \"text\": \"def list2txt(list_, fname):\\n with open(fname, 'w') as filehandle:\\n for listitem in list_:\\n filehandle.write('%s\\\\n' % listitem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505146dc7e4b46eefb890973eb69eb49\",\n \"score\": \"0.61646545\",\n \"text\": \"def save_to_file(cls, list_objs):\\n with open(cls.__name__ + '.json', mode='w', encoding='utf-8') as f:\\n if list_objs is None:\\n f.write(\\\"[]\\\")\\n else:\\n objs = [cls.to_dictionary(obj) for obj in list_objs]\\n f.write(cls.to_json_string(objs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ef37fc1c7b0cc6ae2e5602d766aced\",\n \"score\": \"0.61623865\",\n \"text\": \"def save_cmd_list(cmd_list, path):\\n output = \\\"QGC WPL 110\\\"\\n\\n for cmd in cmd_list:\\n line = \\\"{}\\\\t{}\\\\t{}\\\\t{}\\\\t{}\\\\t{}\\\\t{}\\\\t{}\\\\t{}\\\\t{}\\\\t{}\\\\t{}\\\\n\\\".format(\\n cmd.seq, cmd.current, cmd.frame, cmd.command,\\n cmd.param1, cmd.param2, cmd.param3, cmd.param4,\\n cmd.x, cmd.y, cmd.z,\\n cmd.autocontinue)\\n\\n output += line\\n\\n with open(path, \\\"w\\\") as file_:\\n file_.write(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e66e597ae712c6fc27756a32abdcf2e\",\n \"score\": \"0.6160831\",\n \"text\": \"def save_data_to_file(file_name, list_of_rows):\\n with open(file_name, \\\"w\\\") as f:\\n table = \\\"\\\"\\n for row in list_of_rows:\\n new_row = row[\\\"Product\\\"] + \\\",\\\" + str(row[\\\"Price\\\"]) + \\\"\\\\n\\\"\\n table += new_row\\n f.write(table)\\n return \\\"Current list saved to your file!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"815680e635115d7fc7bc989943350d6a\",\n \"score\": \"0.6157605\",\n \"text\": \"def save_file(file_name, items_list):\\n\\n logging.debug(f\\\"save_file() is run\\\")\\n\\n with open(file_name, 'w', newline='') as csv_file:\\n logging.debug(f\\\"- write '{file_name}'\\\")\\n keys = ['name','quantity','unit','unit_price']\\n csv_writer = csv.DictWriter(csv_file, keys)\\n csv_writer.writeheader()\\n\\n for dictionary in items_list:\\n csv_writer.writerow(dictionary)\\n \\n logging.debug(\\\"- file is saved\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11aac1902a7fc43a9ea81201f7f7c208\",\n \"score\": \"0.6130153\",\n \"text\": \"def store(arr, path):\\n with open(path, \\\"a\\\") as vec_file:\\n for row in arr:\\n vec_file.write(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc014aa4fff17b06583b9c12dca56ea\",\n \"score\": \"0.6127763\",\n \"text\": \"def path2file(pathlist):\\n file = \\\"\\\"\\n for item in pathlist:\\n file = pjoin(file, item)\\n return file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d9561a83dd8ee1b9ba22efdd0fdb8c\",\n \"score\": \"0.6126391\",\n \"text\": \"def save_to_file(cls, list_objs):\\n with open(cls.__name__ + '.json', 'w', encoding='utf-8') as f:\\n if list_objs is None:\\n f.write(\\\"[]\\\")\\n else:\\n f.write(\\n cls.to_json_string([e.to_dictionary() for e in list_objs])\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992973,"cells":{"query_id":{"kind":"string","value":"d8f25f1d7324169a17d0281b43ae95a2"},"query":{"kind":"string","value":"Since print_relation is the most computationally heavy function, handle each query's print_relation in it's own process and save the results to a multithreading dictionary."},"positive_passages":{"kind":"list like","value":[{"docid":"c8fcfb6ae6b52aea274b5959e93c58b5","score":"0.5012408","text":"def _str_worker(self, proc: int, query: datalog_parser.Query, results: dict):\n result = str(query) + \"? \"\n if self.rdbms[query] is SINGLE_MATCH:\n result += \"Yes(1)\\n\"\n elif self.rdbms[query] is None or self.rdbms[query].empty:\n result += \"No\\n\"\n else:\n result += \"Yes({})\\n{}\\n\".format(len(self.rdbms[query]), self.print_relation(self.rdbms[query]))\n results[proc] = result","title":""}],"string":"[\n {\n \"docid\": \"c8fcfb6ae6b52aea274b5959e93c58b5\",\n \"score\": \"0.5012408\",\n \"text\": \"def _str_worker(self, proc: int, query: datalog_parser.Query, results: dict):\\n result = str(query) + \\\"? \\\"\\n if self.rdbms[query] is SINGLE_MATCH:\\n result += \\\"Yes(1)\\\\n\\\"\\n elif self.rdbms[query] is None or self.rdbms[query].empty:\\n result += \\\"No\\\\n\\\"\\n else:\\n result += \\\"Yes({})\\\\n{}\\\\n\\\".format(len(self.rdbms[query]), self.print_relation(self.rdbms[query]))\\n results[proc] = result\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"fc821650accb518306cf716fe0ad1622","score":"0.56099725","text":"def process_relationships(cur, resp, where_relationship, params_relationship, sql):\n try:\n results = cur.fetchall()\n retrived_people_count = len(results)\n\n if results and len(results) > 0 and u.response_has_attribute_and_value(resp['entities'], 'entity', 'relationship'):\n # get relationship records. Take top 1 even if there are multiple records retrived\n peopleid = 0\n columnindex = 0\n for key in results[0].keys():\n if key in 'rowid':\n peopleid = results[0][columnindex]\n break\n columnindex += 1\n\n sql_rel = 'select relatedto from relationship rs inner join relationshiptypes rst \\\n on rs.relationshiptype = rst.rowid \\\n where rs.peopleid = {}'.format(peopleid)\n\n if where_relationship and len(where_relationship) > 0:\n sql_rel += ' AND rst.name = \"'\"{}\"'\"'.format(\n params_relationship['name'])\n try:\n cur.execute(sql_rel)\n except sqlite3.Error as e:\n logger.exception(\n msg=\"Error is occured while excute {0}\".format(e))\n return\n results = cur.fetchall()\n peopleids = []\n if results and len(results) > 0:\n for result in results:\n columnindex = 0\n for key in result.keys():\n if key in 'relatedto':\n peopleids.append(result[columnindex])\n break\n columnindex += 1\n\n # convert list into comma separated if the list has int, float, then use map(str, [])\n # Else just pass []\n sql += ' where rowid in ({})'.format(\n \", \".join(map(str, peopleids)))\n try:\n cur.execute(sql)\n except sqlite3.Error as e:\n logger.exception(\n msg=\"Error is occured while excute {0}\".format(e))\n return\n results = cur.fetchall()\n\n if results and len(results) > 0:\n if retrived_people_count > 1:\n output = 'I could remember so many but i can able to tell you only one now'\n\n for res in results:\n column_index = 0\n fullname = ''\n for col in res.keys():\n if col == 'Firstname' and res[column_index]:\n fullname += '{} '.format(res[column_index])\n elif col == 'Lastname' and res[column_index]:\n fullname += '{} '.format(res[column_index])\n elif col == 'aliasname' and res[column_index]:\n output += 'people will call as {} '.format(\n res[column_index])\n elif col == 'DOB' and res[column_index]:\n output += 'Born on {} '.format(res[column_index])\n elif col == 'bornplace' and res[column_index]:\n output += 'Born at {} '.format(res[column_index])\n elif col == 'DOD' and res[column_index]:\n output += 'Died on {} '.format(res[column_index])\n elif col == 'diedplace' and res[column_index]:\n output += 'Died at {} '.format(res[column_index])\n column_index += 1\n if fullname:\n output += \"full name is {}\".format(fullname)\n if retrived_people_count > 1:\n break\n except Exception as exp: \n logger.error(\n \"An error is occured while process person's relationship information {0}\".format(exp))","title":""},{"docid":"18ee9dcfdd8499152e3c41545c83210c","score":"0.54565597","text":"def print_relation(id_num):\n root = build_person(id_num)\n # visited record all the people we have visited\n visited = {id_num}\n # more_friend record people we are going to visit\n more_friend = build_friends(root.friends)\n # result record all the relationships, using ordered dictionary so root is always at first\n result = collections.OrderedDict()\n result[root.name] = [name for name in more_friend]\n # print_one_result(root.name, result[root.name])\n # BFS loop to find all friends of friends\n while more_friend:\n to_add = []\n for friend in more_friend:\n # if we have visited this person, skip\n if friend.id_num in visited:\n continue\n else:\n visited.add(friend.id_num)\n # find all friends of this friend\n friends_of_friend = build_friends(friend.friends)\n # record into result\n result[friend.name] = [name for name in friends_of_friend]\n # print_one_result(friend.name, result[friend.name])\n # will visited all his/her friends in next loop\n to_add += friends_of_friend\n more_friend = to_add\n\n print_result(result)\n return result","title":""},{"docid":"e24a798dda31b61a3a05b0121eadd9a1","score":"0.5446227","text":"def query_process( pheader, pworkers, pworker, qry_template, tqueue ):\n\n if DEBUG:\n print(\"Init Separate Process: \",pworker)\n\n conn = get_connection(thread_id=pworker)\n\n t_start = time.time()\n for qindex in range(args.iterations):\n qry_string = qry_template % ( ids[random.randint (0,ID_COUNT-1)].get(\"personid\") )\n conn.execute(qry_string)\n if DEBUG:\n if qindex % PROGRESS_COUNT == 0:\n print(\"Queried \",qindex,\"...\")\n t_end = time.time()\n\n avg_latency = 1000*(t_end-t_start) / args.iterations\n duration = round(t_end - t_start,4)\n qry_rate = args.iterations / duration\n\n # push duration result for this thread to queue\n tqueue.put( duration )\n\n prline = args.database + \",\" + pheader + \",\" + str(len(hosts)) + \",\" + \\\n str(pworkers) + \",\" + str(round(avg_latency,5)) + \",\" + str(round(qry_rate,2))\n print(prline)\n\n # close connection at end of run\n conn.close()","title":""},{"docid":"f05f6258e51c2e4f9acb267164a2b713","score":"0.54349244","text":"def ParallelQuery(psqlInstances, theConnectionInfos, theQueries):\n start = timeit.default_timer() \n\n try:\n pool = mp.Pool(psqlInstances, maxtasksperchild=1) \n results = pool.map(NodeQuery, (i for i in zip(theConnectionInfos, theQueries)) )\n except:\n \n print(mp.get_logger())\n else:\n pass\n\n finally:\n pool.close()\n pool.join()\n stop = timeit.default_timer() \n print(\"Parallel Query Time %s\" % (stop-start) )\n return results","title":""},{"docid":"72763a0fa7b404b415198d05391a8bea","score":"0.54160416","text":"def __str__(self) -> str:\n manager = multiprocessing.Manager()\n results = manager.dict()\n result = \"\"\n jobs = []\n for i, query in enumerate(self.rdbms.keys()):\n p = multiprocessing.Process(target=self._str_worker, args=(i, query, results))\n jobs.append(p)\n p.start()\n for proc in jobs:\n proc.join()\n for i, _ in enumerate(self.rdbms.keys()):\n result += results[i]\n return result","title":""},{"docid":"cc1c7045535b641a4b49182559063460","score":"0.5198982","text":"def run(self, logger):\n query_batches = []\n count = 0\n for qobject in self.__query_obj:\n count += 1\n logger.info('Processed %d out of %d query objects' \n % (count, len(self.__query_obj)))\n query_batches += qobject.produce_query_batches()\n queries = qids.total_num_queries()\n logger.info(\"%d total queries created\" % queries) \n return query_batches","title":""},{"docid":"fca95d04ae50f4123e9ad2a15298f098","score":"0.51460373","text":"def _execute_ds_queries_multithread(self, queries):\n # TODO: If any query times out, the entire report fails. It might be\n # better if partial results could be returned.\n finished = {}\n timeout = zillion_config[\"DATASOURCE_QUERY_TIMEOUT\"]\n workers = min(\n zillion_config.get(\"DATASOURCE_QUERY_WORKERS\", len(queries)), len(queries)\n )\n\n with ThreadPoolExecutor(max_workers=workers) as executor:\n futures_map = {}\n for i, query in enumerate(queries):\n label = self._get_query_label(\"%s / %s\" % (i + 1, len(queries)))\n future = executor.submit(query.execute, timeout=timeout, label=label)\n futures_map[future] = query\n\n for future in as_completed(futures_map):\n data = future.result()\n query = futures_map[future]\n finished[future] = data\n\n return finished.values()","title":""},{"docid":"6337fee35da614235be9ed8d6de44eaa","score":"0.5076181","text":"def all_weld_query(w_cursor, w_conn):\n try:\n w_cursor.execute(\n \"\"\"SELECT %s||'+'||%s,attributes.gps_shot,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n FROM %s\n INNER JOIN %s\n ON attributes.gps_shot = weld.gps_shot;\"\"\"\n % (cols.whole_station, cols.offset_station,\n cols.grade_point, cols.depth_cover, cols.jottings,\n cols.wld_type, cols.wld_x_id, cols.upstream_jt,\n cols.downstream_jt, cols.ah_length, cols.ht, cols.wll_chng,\n cols.ditch_loc, cols.welder_initials, cols.attributes_table,\n cols.weld_table))\n # Assigning the results of the cursor to this tuple and fetching all the records.\n wld_many_result = w_cursor.fetchall()\n for record in wld_many_result:\n for a, b in zip(common_labels + weld_labels, record):\n print(a, b)\n except pg2.DatabaseError as e:\n print(e.pgerror)\n print(\"No Record(s) Found.\")","title":""},{"docid":"410b1a0874e776184a3b7bded49a35ab","score":"0.5071244","text":"def multiprocess_pages(base_URL, job_title, job_location, page_num):\n\n url = query_URL + '&page=' + str(page_num)\n html = get_html(url)\n rows = html.select('.job_result')\n threads = []\n mongo_update_lst = []\n for row in rows: \n thread = RequestInfoThread(row, job_title, job_location)\n thread.start()\n threads.append(thread)\n for thread in threads: \n thread.join()\n mongo_update_lst.append(thread.json_dct)\n\n store_in_mongo(mongo_update_lst, 'job_postings', 'ziprecruiter')","title":""},{"docid":"ee017b532025346003bf1e5c114df765","score":"0.5028501","text":"def make_relations(self):\n self.flush_temp_files()\n with open(self.relations_ordered_used, 'r', encoding='utf-8') as f:\n for ele in f:\n self.fbs = ele.strip()\n cur_path = os.path.join(r\"data_processed/relations\", ele.strip() + '.txt')\n self.process_files(self.data_freebase, cur_path, util.extract_rels,\n 'Relations extraction: ' + ele.strip())\n # recognize entities\n self.make_entities()\n # relations with indices\n print('\\nCreating relations with indices\\n')\n s0 = datetime.now()\n self.get_indices()\n dirs_to_explore = {self.relations_dir: self.relations_indices_dir, self.test_dir: self.test_indices_dir}\n for dirs, indice_dirs in dirs_to_explore.items():\n for r, d, f in os.walk(dirs):\n for file in f:\n if '.txt' in file:\n s = datetime.now()\n print(file)\n with open(os.path.join(r, file), 'r') as f2r:\n with open(os.path.join(indice_dirs, file), 'w') as f2w:\n for lines in f2r:\n line = lines.split()\n head_entity = line[1].strip().split('.')[0]\n tail_entity = line[1].strip().split('.')[2]\n output = str(self.indices['entity'][head_entity][line[0].strip()]) + '\\t' + str(\n self.indices['relation'][line[1].strip()]) + '\\t' + \\\n str(self.indices['entity'][tail_entity][line[2].strip()]) + '\\n'\n f2w.write(output)\n print('Processing completed. Time elapsed: ', datetime.now() - s)\n print('Total time elapsed: ', datetime.now() - s0)","title":""},{"docid":"fd0b233e2a824fa5f6ee391a3a171ce3","score":"0.5010407","text":"def get_pagelinks_info(\n db, function_name, replicas_port=None, user_db_port=None, user=None, password=None\n):\n\n query = (\n \"SELECT page_id, \"\n \"COUNT(pl_from) AS pls \"\n \"FROM page \"\n \"INNER JOIN pagelinks \"\n \" ON page_title=pl_title \"\n \" AND page_namespace=828 \"\n \" AND page_content_model='Scribunto' \"\n \" AND pl_namespace=828 \"\n \"GROUP BY page_id\"\n )\n\n cols = [\"page_id\", \"pls\"]\n for df in query_data_generator(\n query, function_name, cols, db, replicas_port, user_db_port, user, password\n ):\n save_data(df, db, function_name, user_db_port, user, password)","title":""},{"docid":"ae498b5a05b5e66aada260e43b73977a","score":"0.49458304","text":"def run(database, document, person):\n report = AllRelReport(database, document, person)\n report.run()","title":""},{"docid":"0ce38c6693e63d47e9b054babecb45b7","score":"0.4943456","text":"def run_once(sql, dbname, print_rows):\n with connections[dbname].cursor() as cursor, timer(dbname):\n cursor.execute(sql)\n if print_rows:\n rows = fetch_dicts(cursor)\n for row in rows:\n pprint.pprint(row)\n print(\"({} rows from {})\".format(len(rows), dbname))","title":""},{"docid":"076f20827cb8fc3d85ae9ff1bb55b303","score":"0.49356064","text":"def dump_collection(collection, config, force_ids, print_to_screen=False):\n tbl_ori, tbl_rel, tbl_fin = collection['relations'].split(\"-\")\n query = \"SELECT * FROM %s\" % (wash_table_column_name(tbl_ori))\n lst_ori = query2list(query, tbl_ori)\n tbl_ori_id = get_primary_keys(tbl_ori)[0]\n for index_ori, result_ori in enumerate(lst_ori):\n dict_rels = get_relationship(collection, tbl_ori, tbl_ori_id)\n query = \"SELECT * FROM %s WHERE %s=%s\" % (wash_table_column_name(tbl_rel), \n dict_rels[tbl_ori+\".\"+tbl_ori_id], \n result_ori[tbl_ori_id])\n if collection['tables'][tbl_ori].startswith('extend'):\n add_special_field(collection, tbl_ori, result_ori)\n lst_rel = query2list(query, tbl_rel)\n for result_rel in lst_rel:\n tbl_fin_id = get_primary_keys(tbl_fin)[0]\n tbl_rel_id = dict_rels[tbl_fin+\".\"+tbl_fin_id].split(\".\")[1].strip()\n query = \"SELECT * FROM %s WHERE %s=%s\" % (wash_table_column_name(tbl_fin), \n tbl_fin_id, result_rel[tbl_rel_id])\n lst_fin = query2list(query, tbl_fin)\n for index_fin, result_fin in enumerate(lst_fin):\n result_ori[tbl_fin+\".\"+create_section_id(index_fin, with_date=False)] = result_fin\n\n section_name = tbl_ori + \".\" + create_section_id(index_ori)\n if force_ids == False:#Remove the ids from the dict\n results = delete_ids(result_ori, collection['relations'].split(\"-\"))\n config[section_name] = results\n else:\n config[section_name] = result_ori\n\n if print_to_screen == True: \n output = StringIO.StringIO()\n config.write(output)#Write to the output string instead of the file\n print output.getvalue()\n else:\n config.write()","title":""},{"docid":"f0947b7a33b2f900b7f542252963bdf8","score":"0.4925689","text":"def parallel_execute(self, conn_str, pool_size=None, taskq=None, resq=None,\n logq=None):\n\n workers = []\n max_workers = 25\n\n if not pool_size:\n if len(self) <= max_workers:\n pool_size = len(self)\n else:\n pool_size = max_workers\n\n taskq = taskq or multiprocessing.JoinableQueue()\n resq = resq or multiprocessing.Queue()\n logq = logq or multiprocessing.Queue()\n\n conn_info = get_conn_info_dict(conn_str)\n msg_dict = combine_dicts({'msg': 'executing sql statement set in'\n ' parallel', 'len': len(self),\n 'pool_size': pool_size}, conn_info)\n logger.info(msg_dict)\n\n # Start the worker processes.\n for i in range(pool_size):\n wp = multiprocessing.Process(target=_worker_process,\n args=(conn_str, taskq, resq, logq))\n workers.append(wp)\n wp.start()\n\n # Load the tasks onto the queue.\n for task in self:\n taskq.put(task)\n\n # Start the logging thread to receive logs from the workers.\n logp = threading.Thread(target=_logger_thread, args=(logq,))\n logp.start()\n\n # Wait for all the work to be done.\n taskq.join()\n\n # Stop all the workers (I'm a computah).\n for i in range(pool_size):\n taskq.put(None)\n for wp in workers:\n wp.join()\n\n # End the logging thread.\n logq.put(None)\n logp.join()\n\n # Collect the results.\n self.clear()\n resq.put(None)\n while True:\n result = resq.get()\n if result is None:\n break\n self.add(result)\n\n return self","title":""},{"docid":"1180eb6da75bd26dac6f5f292b237ff7","score":"0.48238552","text":"def printout(queries,things,default=None, f=sys.stdout, **kwargs):\n \n results = denorm(queries,things,default=None)\n fields = set(itertools.chain(*(x.keys() for x in results)))\n \n W = csv.DictWriter(f=f,fieldnames=fields,**kwargs)\n #print \"---prod---\"\n #print list(prod)\n W.writeheader()\n for r in results:\n W.writerow(r)","title":""},{"docid":"b25295162cfbdbcb89d645b7d7d115d2","score":"0.48142192","text":"def execute_user_queries(self):\n print(self._get_guide())\n while True:\n query = next(self._get_query())\n self.execute(query)\n self.pretty_print_query_result()","title":""},{"docid":"8ba2071eca26d00754440b4cca07665b","score":"0.48141378","text":"def get_relations(self):\n self.relations = {}\n\n with open(self.data_freebase, 'r', encoding='utf-8') as f:\n for line in f:\n # print(line)\n self.relations[line.split()[1]] = self.relations.get(line.split()[1], 0) + 1\n\n sorted_relations = sorted(self.relations.items(), key=operator.itemgetter(1), reverse=True)\n\n with open(self.relations_ordered, 'w', encoding='utf-8') as f:\n for ele in sorted_relations:\n f.write(ele[0] + '\\t' + str(ele[1]) + '\\n')\n\n with open(self.relations_ordered_used, 'w', encoding='utf-8') as f:\n for ele in sorted_relations:\n if int(ele[1]) > 10000:\n f.write(ele[0].strip() + '\\n')","title":""},{"docid":"7459bed287f2ae298048e25d7596368f","score":"0.481377","text":"def printout(queries,things,default=None, f=sys.stdout, **kwargs):\n\n results = denorm(queries,things,default=None)\n fields = set(itertools.chain(*(x.keys() for x in results)))\n\n W = csv.DictWriter(f=f,fieldnames=fields,**kwargs)\n #print(\"---prod---\")\n #print(list(prod))\n W.writeheader()\n for r in results:\n W.writerow(r)","title":""},{"docid":"edd8e1c25a51f423e5188f49384cd6c0","score":"0.4804436","text":"def batch_local(self, query_type, id_list, **kwargs) -> Dict:\n count = len(id_list)\n result_dict = dict()\n\n for start in range(0, count, self.elink_batch):\n end = min(count, start + self.elink_batch)\n logging.info(\"Looking up PMIDs for GDSs {} to {}\"\n .format(start + 1, end))\n\n # elink each batch\n fetch_handle = self.http_attempts(\n query_type, **dict(id=','.join(id_list[start:end]), **kwargs)\n )\n record = Entrez.read(fetch_handle)\n fetch_handle.close()\n # for each Link'Id', dig down through dict add to list\n for el in record:\n result_dict[el['IdList'][0]] = el['LinkSetDb'][0]['Link'][0]['Id']\n return result_dict","title":""},{"docid":"9d787ab14a95d5c321fdf857c1c3f43b","score":"0.47816938","text":"def print_pool_out(output_list):\n for session_name, result_list in output_list:\n print(session_name)\n if result_list:\n for link in result_list:\n print(f'\\t{link}')\n else:\n print(f'\\tNo Results Found')","title":""},{"docid":"dcab5dab79b18bff9e5b5d444defba4f","score":"0.4771668","text":"def get_rel_info(\n rel_name: str,\n root_dir: str,\n db: MgmtDB,\n ch_count_type: constants.ChCountType,\n):\n fault_name = simulation_structure.get_fault_from_realisation(rel_name)\n params = utils.load_sim_params(\n simulation_structure.get_sim_params_yaml_path(\n f\"{root_dir}/Runs/{fault_name}/{rel_name}\"\n ),\n load_vm=True,\n )\n\n # Create relisation df\n df = pd.DataFrame(\n columns=COLUMNS, index=[rel_name], data=np.zeros(shape=(1, len(COLUMNS)))\n )\n\n # General Parameter Metadata\n for k, v in GENERAL_PARAM_LOCATIONS.items():\n if isinstance(v, list):\n value = params\n for index in v:\n value = value[index]\n else:\n value = params[v]\n df.loc[rel_name, k] = value\n\n states = db.get_core_hour_states(rel_name, ch_count_type)\n resub_counter = dict()\n # DB Metadata\n for state in states:\n # Get proc_type and job_id\n _, _, proc_type, status, job_id, _ = state\n proc_type_name = const.ProcessType(proc_type).str_value\n if proc_type in METADATA_PROC_TYPES:\n (\n _,\n _,\n queued_time,\n start_time,\n end_time,\n nodes,\n cores,\n memory,\n WCT,\n ) = db.get_job_duration_info(job_id)\n # Add runtime and cores to df\n runtime = end_time - start_time\n df.loc[rel_name, f\"{proc_type_name}_runtime\"] += runtime / 60\n df = add_db_stat(df, rel_name, f\"{proc_type_name}_cores\", cores)\n # Add to core hours\n df.loc[rel_name, f\"{proc_type_name}_core_hours\"] += cores * runtime / 3600\n df.loc[rel_name, f\"Total_core_hours\"] += cores * runtime / 3600\n # Add to resubmits dict\n if resub_counter.get(proc_type_name) is None:\n resub_counter[proc_type_name] = 0\n else:\n resub_counter[proc_type_name] += 1\n # Add resubmits to df\n for k, v in resub_counter.items():\n df.loc[rel_name, f\"{k}_resubmits\"] = v\n\n # Extra Metadata\n df.loc[rel_name, \"IM_calc_components\"] = len(params[\"ims\"][\"component\"])\n df.loc[rel_name, \"EMOD3D_nt\"] = params[\"sim_duration\"] / params[\"dt\"]\n df.loc[rel_name, \"HF_nt\"] = params[\"sim_duration\"] / params[\"hf\"][\"dt\"]\n df.loc[rel_name, \"BB_nt\"] = params[\"sim_duration\"] / params[\"bb\"][\"dt\"]\n df.loc[rel_name, \"advanced_IM_models\"] = len(params[\"advanced_IM\"][\"models\"])\n stations = shared.get_stations(params[\"stat_file\"])\n df.loc[rel_name, \"HF_n_stats\"] = len(stations)\n df.loc[rel_name, \"BB_n_stats\"] = len(stations)\n df.loc[rel_name, \"advanced_IM_stations\"] = len(stations)\n\n # From IM calc csv\n csv_ffp = f\"{root_dir}/Runs/{fault_name}/{rel_name}/IM_calc/{rel_name}.csv\"\n try:\n csv_columns = pd.read_csv(csv_ffp).columns.values\n df.loc[rel_name, \"IM_calc_pSA_count\"] = len(\n [True for column in csv_columns if \"pSA\" in column]\n )\n df.loc[rel_name, \"IM_calc_FAS_count\"] = len(\n [True for column in csv_columns if \"FAS\" in column]\n )\n except FileNotFoundError:\n print(f\"Could not find file {csv_ffp} (Will not exist if IM_calc was not run)\")\n return df","title":""},{"docid":"fbc25140a18b976c2788d32dffc88b42","score":"0.47700396","text":"def re_list(request):\n from .serializers import DocumentRelationSerializer\n cmd_str = \"\"\n with open('mark2cure/api/commands/get-relations.sql', 'r') as f:\n cmd_str = f.read()\n\n # Start the DB Connection\n c = connection.cursor()\n\n c.execute('SET @user_work_max = {rel_work_size};'.format(rel_work_size=20))\n c.execute('SET @k_max = {completions};'.format(completions=settings.ENTITY_RECOGNITION_K))\n c.execute('SET @user_id = {user_id};'.format(user_id=request.user.pk))\n c.execute('SET @rel_ann_content_type_id = 56;')\n c.execute(cmd_str)\n\n queryset = [{'id': x[0],\n 'document_id': x[1],\n 'title': x[2],\n\n 'total_document_relationships': x[3],\n 'user_document_relationships': x[4],\n\n 'community_answered': x[5],\n 'community_completed': x[6],\n 'community_progress': x[7],\n\n 'user_completed': x[8],\n 'user_progress': x[9],\n 'user_answered': x[10],\n 'user_view_completed': x[11]} for x in c.fetchall()]\n\n # Close the connection\n c.close()\n\n # Prevent documents from being shown that have since been completed\n # by the community before the requqest.user could complete everything\n for idx, item in enumerate(queryset):\n if int(item['user_document_relationships']) <= 0:\n document = get_object_or_404(Document, pk=item['id'])\n first_section = document.section_set.first()\n view = View.objects.filter(task_type='re', section=first_section, user=request.user).last()\n\n # print(' - X:', document, first_section, view)\n # (TODO) Why is there no View on these sections?\n if view:\n Point.objects.create(user=request.user,\n amount=settings.RELATION_DOC_POINTS,\n content_type=ContentType.objects.get_for_model(view),\n object_id=view.id)\n view.completed = True\n view.save()\n\n del queryset[idx]\n\n serializer = DocumentRelationSerializer(queryset, many=True)\n return Response(serializer.data)","title":""},{"docid":"5bd144f154ee8169142bae2c0d875ba0","score":"0.47680968","text":"def _do_relation_import(request, model_import_info, book, sheet, process_option_dict, status_dict):\r\n\trelationship_source_id_dict = {}\r\n\trelationship_target_id_dict = {}\r\n\tfor row in range(process_option_dict['start_row']-1,process_option_dict['end_row']):\r\n\t\tstatus_dict['num_rows_processed'] = status_dict['num_rows_processed'] + 1\r\n\t\ttry:\r\n\t\t\trow_value_list = []\r\n\t\t\tfor cell in sheet.row(row):\r\n\t\t\t\tif cell.ctype == 3:\r\n\t\t\t\t\tdate_tuple = xlrd.xldate_as_tuple(cell.value, book.datemode)\r\n\t\t\t\t\tcell_value = str(date_tuple[0]) + '-' + str(date_tuple[1]) + '-' + str(date_tuple[2])\r\n\t\t\t\telse:\r\n\t\t\t\t\tcell_value = cell.value\r\n\t\t\t\trow_value_list.append(cell_value)\r\n\t\t\trelationship_source_id_dict = model_import_info.get_relationship_source_id_dict(request, row_value_list)\r\n\t\t\trelationship_target_id_dict = model_import_info.get_relationship_target_id_dict(request, row_value_list)\r\n\t\t\tsource_object = model_import_info.source_model.objects.get(**relationship_source_id_dict)\r\n\t\t\ttarget_object = model_import_info.target_model.objects.get(**relationship_target_id_dict)\r\n\t\t\trelated_field_collection = getattr(source_object, model_import_info.mapping_field_name)\r\n\t\t\trelated_field_collection.add(target_object)\r\n\t\t\tsource_object.save()\r\n\t\t\tstatus_msg = 'spreadsheet row#' + str(row)+' successfully updated.'\r\n\t\t\tstatus_dict['num_items_updated'] = status_dict['num_items_updated'] + 1\r\n\t\t\tif process_option_dict['show_successful_updates']:\r\n\t\t\t\tstatus_dict['combined_results_messages'].append(status_msg)\r\n\t\t\tstatus_dict['update_results_messages'].append(status_msg)\r\n\t\texcept:\r\n\t\t\tstatus_dict['num_errors'] = status_dict['num_errors'] + 1\r\n\t\t\tstatus_msg = 'spreadsheet row#' + str(row)+' ERROR: ' + `sys.exc_info()[1]`\r\n\t\t\tif process_option_dict['show_errors']:\r\n\t\t\t\tstatus_dict['combined_results_messages'].append(status_msg)\r\n\t\t\tstatus_dict['error_results_messages'].append(status_msg)\r\n\t\t\tif process_option_dict['stop_on_first_error']:\r\n\t\t\t\tbreak\r\n\treturn status_dict","title":""},{"docid":"430beb09fc94e739e2450c5fecdb3e4a","score":"0.47642788","text":"def execute_parallel(self, queries):\n if isinstance(queries, dict):\n return self._execute_parallel_dict(queries)\n return self._execute_parallel_list(queries)","title":""},{"docid":"ff50b708420970fa87e72a2a55379fee","score":"0.47636887","text":"def run_helper(queries):\n total_query_to_process=len(queries)\n\n sdlog.debug(\"SDPROXMT-003\",\"%d search-API queries to process (max_thread_per_host=%d,timeout=%d)\"%(total_query_to_process,max_thread_per_host,sdconst.SEARCH_API_HTTP_TIMEOUT))\n\n while True:\n if sdconfig.proxymt_progress_stat:\n sdlog.info(\"SDPROXMT-033\",\"threads per host: %s\"%\",\".join(['%s=%s'%(host,len(searchAPIServices[host]['threadlist'])) for host in searchAPIServices.keys()]))\n\n if len(queries)>0:\n distribute_queries(queries)\n else:\n # leave the loop only if all threads completed\n if all_threads_completed():\n break\n\n # remove completed threads from list\n for host in searchAPIServices.keys():\n li=[]\n for t in searchAPIServices[host]['threadlist']:\n if t.is_alive():\n li.append(t)\n searchAPIServices[host]['threadlist']=li\n\n # log\n total_query_already_processed = total_query_to_process - len(queries)\n if total_query_to_process > 0: # display progress only when there are a lot of queries\n if len(queries) > 0: # display progress only when still query to process\n sdlog.info(\"SDPROXMT-004\",\"total_queries=%d, running_or_done_queries=%d, waiting_queries=%d\"%(total_query_to_process,total_query_already_processed,len(queries)))\n\n # if all services are busy, we sleep to limit loop speed\n # (note that all the code around the \"sleep\" call is to detect system overload)\n sleep_time=10\n warning_threshold=5 # threshold not to emit warning for every small load exceedance\n befo=time.time()\n time.sleep(sleep_time)\n afte=time.time()\n diff=afte-befo\n if diff>sleep_time+warning_threshold:\n sdlog.warning(\"SDPROXMT-005\",\"WARNING: system overload detected (sleep takes %d second to complete).\"%diff)\n\n # retrieve result from output queue\n metadata=sdtypes.Metadata()\n while not __result_queue.empty():\n success=__result_queue.get(False) # retrieve result from ONE successful search-API call\n success.connect() # TAGKLK434L3K34K\n metadata.slurp(success) # warning: success is modified here\n\n # retrieve error from output queue and insert them into a list\n errors=[]\n while not __error_queue.empty():\n query=__error_queue.get(False)\n errors.append(query)\n\n return (metadata,errors)","title":""},{"docid":"c90db233b317ca64380872dd6d6bafd1","score":"0.47551122","text":"def process_relationships(fout, root, dirpath, filename):\n if root is None:\n return\n\n # transaction_margin,dwh,CONSUMED,sold_quantity,dwh\n source_db = None\n dest_db = None\n\n logger.info(root)\n insert_tables, select_tables = root.collect_tables()\n\n if len(insert_tables) == 0:\n logger.error(\"Missing insert statements at: {0}\".format(filename))\n return\n\n insert_table, insert_db = extract_table_and_db(insert_tables[0], dest_db)\n for table in select_tables:\n select_table, select_db = extract_table_and_db(table, source_db)\n if select_table is None or len(select_table) == 0:\n continue\n elems = [insert_table.lower()]\n elems.append(insert_db.lower())\n elems.append('CONSUMED')\n elems.append(select_table.lower())\n elems.append(select_db.lower())\n line = \",\".join(elems)\n fout.write(line)\n fout.write(\"\\n\")","title":""},{"docid":"99d92fce117b0e5516fca104d57f0d41","score":"0.47441036","text":"def process_results(self, agg_results, db_object, query_file_handle, refined_queries = None):\n \n #refine queries if not already refined.\n if not refined_queries:\n self.refine_queries(agg_results)\n else:\n self.refined_queries_results = refined_queries\n #create result objects and write to ground truth database\n for (q, r) in self.refined_queries_results:\n query_result = CAT_TO_CLASSES[q[qs.QRY_ENUM]].result_class(q,r,db_object, self.full_query)\n try:\n query_result.write_query()\n except Exception as detail:\n logger.critical(\"ERROR failed to write query with qid=%d\"\n \". Got error: %s\" % (q[qs.QRY_QID], detail))\n \n #writing queries in sql format to file\n if self.full_query: \n for (q,r) in self.refined_queries_results:\n if q != None:\n self._print_query(q, query_file_handle)","title":""},{"docid":"8808fd80151e311679d2fb98356e6e42","score":"0.47110108","text":"def _ProcessResults(self):\n for (key, value) in self._results.iteritems():\n (graph_name, trace_name) = key\n (units, results) = value\n print \"RESULT %s: %s= %s %s\" % (graph_name, trace_name,\n str(results), units)","title":""},{"docid":"bee905e42f13bea64eada18099a7f126","score":"0.470631","text":"def printpool(self):\n for f,c in self.pool:\n print f,c\n print","title":""},{"docid":"59c2ca9d74e8a2022bee4e2b35828b8a","score":"0.47021446","text":"def multiprocess(data):\n pool = multiprocessing.Pool(processes=4)\n rx_dict = dict(pool.map(getrx, (ndc_list)))\n pool.close()\n pool.join()\n return rx_dict","title":""},{"docid":"feb73ffd59f5820cc852805485322153","score":"0.46973732","text":"def synapse_writer(db):\n global n_synapses\n n_synapses = 0\n print(\"[ synapse writer ] : Loading DB: %g\" %timer.elapsedTime())\n \n image_map = db.get_image_map()\n \n # TODO 48 only cycle the existing processors\n \n chip_map = [ (c['x'],c['y']) for c in image_map ]\n chip_map = list(set(chip_map)) # removing duplicates http://love-python.blogspot.co.uk/2008/09/remove-duplicate-items-from-list-using.html\n\n if PARALLEL: \n num_processes = pacman.pacman_configuration.getint('synapse_writer', 'processes') # number of processes - why do I need this here?\n print '[ synapse writer ] : Spawning %d parallel threads' % num_processes\n # parallelizing http://stackoverflow.com/questions/8521883/multiprocessing-pool-map-and-function-with-two-arguments\n # 16.6.1.5. Using a pool of workers: http://docs.python.org/library/multiprocessing.html \n # not with SQLITE apparently\n from multiprocessing import Process, Queue\n\n chip_map = [ (c['x'],c['y']) for c in image_map ]\n chip_map = list(set(chip_map)) # removing duplicates http://love-python.blogspot.co.uk/2008/09/remove-duplicate-items-from-list-using.html\n\n # http://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks-in-python/312644 \n \n num_processes = min(num_processes, len(chip_map))\n if len(chip_map)%num_processes==0:\n chunk_size = len(chip_map)/num_processes\n else:\n chunk_size = len(chip_map)/num_processes+1\n chunks = [ chip_map[i:i+chunk_size] for i in range(0, len(chip_map), chunk_size)] \n\n # Instantiates the db_writer \n global queue\n global db_global\n db_global = db\n queue = Queue(0) \n\n db_queue = db_writer()\n db_queue.start()\n db_queue.setDaemon = True\n \n processes = list()\n for p in range(num_processes):\n print \"[ synapse writer ] : [ Process %d ] : chunk: %s\" % (p, chunks[p])\n if len(chunks) > p:\n processes.append(Process(target=callback, args=(db.db_abs_path, chunks[p], p)))\n processes[p].start()\n\n for p in range(num_processes): \n processes[p].join() # wait for all processes to finish\n \n \n db_queue.running = False\n\n queue.put(None)\n db_queue.join()\n\n \n \n else: \n [ compute_sdram_file(chip_map[i][0], chip_map[i][1], db, image_map) for i in range(len(chip_map)) ] # creates all the files for the system size # TODO reorganize to use only used chips\n\n \n print \"[ synapse writer ] : Written %d synapses\" % n_synapses","title":""},{"docid":"2c9315b6e921c9e37c3ead09eaf28b61","score":"0.4691319","text":"def print_responses(response_set, sqlite_cursor):\n for result in response_set.response:\n doc = response_set.document[result.documentIndex]\n query = f\"\"\"\n SELECT title, date, hotel, review FROM reviews\n WHERE doc_id=\"{doc.id}\"\n \"\"\"\n for row in sqlite_cursor.execute(query):\n title, date, hotel, fulltext = row\n print(Style.BRIGHT + title)\n if is_title(result):\n print(f\"{Style.RESET_ALL}{head(fulltext)}\")\n else:\n print(f\"{Style.RESET_ALL}{highlight(fulltext, result.text)}\")\n print(Style.RESET_ALL + Style.DIM +\n f\"{hotel_name(hotel)} reviewed on {date}\" +\n Style.RESET_ALL)\n print()\n break","title":""},{"docid":"7ed70d3a085b5c6cc5d1be5ac887581a","score":"0.46757925","text":"def process_search(cb_conn, query, query_base=None):\n results = set()\n\n query += query_base\n log(\" Query: %s\" % query)\n\n try:\n for proc in cb_conn.select(Process).where(query):\n results.add((proc.hostname.lower(),\n proc.username.lower(), \n proc.path,\n proc.cmdline,\n proc.process_md5,\n proc.start,\n proc.webui_link))\n except KeyboardInterrupt:\n log(\"Caught CTRL-C. Returning what we have . . .\\n\")\n\n return results","title":""},{"docid":"1b97af818440662fcc9479d4ebd39245","score":"0.46736822","text":"def dump_connections(self):\n for x in self.connections:\n print(x.id, end=',')","title":""},{"docid":"b277ee4017c48cf2098ff9b2fa64033e","score":"0.46648812","text":"def run(self):\t\t\n\t\tcommunicator = MPI.world.duplicate()\n\t\tnode_rank = communicator.rank\n\t\tif node_rank == 0:\n\t\t\t(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)\n\t\t\told_schema_instance = form_schema_tables(self.input_fname)\n\t\t\tnew_schema_instance = form_schema_tables(self.jnput_fname)\n\t\t\tgene_no2go = get_gene_no2go_no_set(curs)\n\t\t\tgene_no2go_pickle = cPickle.dumps(gene_no2go, -1)\t#-1 means use the highest protocol\n\t\t\t\n\t\t\tif self.max_layer:\n\t\t\t\tcrs_sentence = 'DECLARE crs CURSOR FOR SELECT p.p_gene_id, p.gene_no, p.go_no, p.is_correct, p.is_correct_l1, \\\n\t\t\t\tp.is_correct_lca, p.avg_p_value, p.no_of_clusters, p.cluster_array, p.p_value_cut_off, p.recurrence_cut_off, \\\n\t\t\t\tp.connectivity_cut_off, p.cluster_size_cut_off, p.unknown_cut_off, p.depth_cut_off, p.mcl_id, p.lca_list, \\\n\t\t\t\tp.vertex_gradient, p.edge_gradient, p2.vertex_set, p2.edge_set, p2.d_matrix, p2.recurrence_array from %s p, %s p2 where \\\n\t\t\t\tp.mcl_id=p2.id'%(old_schema_instance.p_gene_table, old_schema_instance.pattern_table)\n\t\t\telse:\n\t\t\t\tcrs_sentence = \"DECLARE crs CURSOR FOR SELECT p.p_gene_id, p.gene_no, p.go_no, p.is_correct, p.is_correct_l1, \\\n\t\t\t\tp.is_correct_lca, p.avg_p_value, p.no_of_clusters, p.cluster_array, p.p_value_cut_off, p.recurrence_cut_off, \\\n\t\t\t\tp.connectivity_cut_off, p.cluster_size_cut_off, p.unknown_cut_off, p.depth_cut_off, p.mcl_id, p.lca_list, p.vertex_gradient,\\\n\t\t\t\tp.edge_gradient, 'vertex_set', 'edge_set', 'd_matrix', 'recurrence_array' \\\n\t\t\t\tfrom %s p\"%(old_schema_instance.p_gene_table)\n\t\t\t\t\n\t\t\t\t#some placeholders 'vertex_set', 'edge_set', 'd_matrix' for prediction_attributes()\n\t\t\t\n\t\t\tif self.acc_cut_off:\n\t\t\t\tmcl_id2accuracy = self.get_mcl_id2accuracy(curs, old_schema_instance.p_gene_table, crs_sentence, self.is_correct_type)\n\t\t\telse:\n\t\t\t\tmcl_id2accuracy = None\n\t\t\tmcl_id2accuracy_pickle = cPickle.dumps(mcl_id2accuracy, -1)\t#-1 means use the highest protocol\n\t\t\tfor node in range(1, communicator.size-1):\t#send it to the computing_node\n\t\t\t\tcommunicator.send(gene_no2go_pickle, node, 0)\n\t\t\tfor node in range(1, communicator.size-1):\t#send it to the computing_node\n\t\t\t\tcommunicator.send(mcl_id2accuracy_pickle, node, 0)\n\t\telif node_rank<=communicator.size-2:\t#exclude the last node\n\t\t\tdata, source, tag = communicator.receiveString(0, 0)\n\t\t\tgene_no2go = cPickle.loads(data)\t#take the data\n\t\t\tdata, source, tag = communicator.receiveString(0, 0)\n\t\t\tmcl_id2accuracy = cPickle.loads(data)\t#take the data\n\t\t\t#choose a functor for recurrence_array\n\t\t\tfunctor_dict = {0: None,\n\t\t\t\t1: lambda x: int(x>=self.recurrence_x),\n\t\t\t\t2: lambda x: math.pow(x, self.recurrence_x)}\n\t\t\tfunctor = functor_dict[self.recurrence_x_type]\n\t\telif node_rank==communicator.size-1:\n\t\t\t(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)\n\t\t\told_schema_instance = form_schema_tables(self.input_fname)\n\t\t\tnew_schema_instance = form_schema_tables(self.jnput_fname)\n\t\t\tself.view_from_table(curs, old_schema_instance.splat_table, new_schema_instance.splat_table)\n\t\t\tself.view_from_table(curs, old_schema_instance.mcl_table, new_schema_instance.mcl_table)\n\t\t\tself.view_from_table(curs, old_schema_instance.pattern_table, new_schema_instance.pattern_table)\n\t\t\tself.createGeneTable(curs, new_schema_instance.p_gene_table)\n\t\t\n\t\tmpi_synchronize(communicator)\n\t\t\n\t\tif node_rank == 0:\n\t\t\tself.input_node(communicator, curs, old_schema_instance, crs_sentence, self.size)\n\t\telif node_rank<=communicator.size-2:\t#exclude the last node\n\t\t\tself.computing_node(communicator, gene_no2go, self.exponent, self.score_list, \\\n\t\t\t\tself.max_layer, self.norm_exp, self.eg_d_type, mcl_id2accuracy, self.acc_cut_off, functor)\n\t\telif node_rank==communicator.size-1:\n\t\t\tparameter_list = [curs, new_schema_instance.p_gene_table]\n\t\t\tfree_computing_nodes = range(1,communicator.size-1)\n\t\t\toutput_node(communicator, free_computing_nodes, parameter_list, self.output_node_handler)\n\t\t\tif self.commit:\n\t\t\t\tcurs.execute(\"end\")","title":""},{"docid":"f0624bc66640753d9ac530ee8aecde9b","score":"0.46631014","text":"def all_bend_query(cb_cursor, cb_conn):\n try:\n cb_cursor.execute(\n \"\"\"SELECT %s||'+'||%s,attributes.gps_shot,%s,%s,%s,%s,%s,%s\n FROM %s\n INNER JOIN %s\n ON attributes.gps_shot = bend.gps_shot;\"\"\"\n % (cols.whole_station, cols.offset_station, cols.grade_point,\n cols.depth_cover, cols.jottings, cols.deg, cols.bnd_dir,\n cols.bnd_type, cols.attributes_table, cols.bend_table))\n # Assigning the results of the cursor to this tuple and fetching all the records.\n b_many_result = cb_cursor.fetchall()\n # Merging the common_labels and the bend_labels together for\n # printing with the query results.\n for record in b_many_result:\n for a, b in zip(common_labels + bnd_labels, record):\n print(a,b)\n except pg2.DatabaseError as e:\n print(e.pgerror)\n print(\"No Record(s) Found.\")","title":""},{"docid":"0911d5b24add2284762975de2c2ee67d","score":"0.46616054","text":"def fetch_steps(db_cursor, workflow_list):\n # Get steps information\n db_cursor.execute(\"\"\"\n SELECT dpu_instance.id AS 'stepId', dpu_instance.name AS 'stepTitle',\n dpu_instance.description AS 'description',\n dpu_template.name AS 'templateName',\n dpu_instance.configuration AS 'configuration',\n ppl_model.id AS 'workflowId',\n ppl_model.description AS 'description',\n ppl_model.name AS 'workflowTitle',\n ppl_model.visibility AS 'visibility',\n exec_pipeline.id AS 'activityId',\n exec_pipeline.status AS 'status'\n FROM exec_pipeline, ppl_model, dpu_template, dpu_instance, ppl_node,ppl_graph\n WHERE ppl_node.instance_id=dpu_instance.id AND\n ppl_node.graph_id = ppl_graph.id AND\n ppl_graph.pipeline_id = ppl_model.id AND\n dpu_instance.dpu_id = dpu_template.id AND\n exec_pipeline.pipeline_id = ppl_model.id\n \"\"\")\n\n result_set = db_cursor.fetchall()\n\n for row in result_set:\n workflow_graph = dict()\n workflow_graph[\"provenance\"] = dict()\n workflow_graph[\"payload\"] = dict()\n workflow_graph[\"provenance\"][\"agent\"] = dict()\n workflow_graph[\"provenance\"][\"agent\"][\"ID\"] = artifact_id\n workflow_graph[\"provenance\"][\"agent\"][\"role\"] = agent_role\n\n graph = workflow_graph[\"provenance\"]\n graph[\"context\"] = dict()\n graph[\"context\"][\"activityID\"] = str(row['activityId'])\n graph[\"context\"][\"workflowID\"] = str(row['workflowId'])\n graph[\"context\"][\"stepID\"] = str(row['stepId'])\n\n graph[\"activity\"] = dict()\n graph[\"activity\"][\"status\"] = determine_status(row['status'])\n graph[\"activity\"][\"configuration\"] = parse_config(row['configuration'])\n graph[\"activity\"][\"type\"] = \"StepExecution\"\n graph[\"activity\"][\"title\"] = str(row[\"stepTitle\"])\n\n app_logger.info('Construct provenance information for Step{0}.' .format(row['stepId']))\n\n workflow_list.append(workflow_graph)\n # return graph","title":""},{"docid":"a5e796bf53edeace6406a5fb94cc013d","score":"0.46581215","text":"def _traverse_and_execute(self, executable: GraphExecutable) -> None: # noqa mccabe: disable=MC0001\n start_time = time.time()\n if self.barf:\n with open(os.path.join(self.barf_output, f'{[n for n in executable.graph.nodes][0].name}.component'), 'wb') as cmp_file: # noqa pylint: disable=unnecessary-comprehension\n nx.write_multiline_adjlist(executable.graph, cmp_file)\n try:\n logger.debug(\n f\"Executing graph with {len(executable.graph)} relations in it...\")\n for i, relation in enumerate(\n nx.algorithms.dag.topological_sort(executable.graph)):\n relation.population_size = executable.source_adapter.scalar_query(\n executable.source_adapter.population_count_statement(relation))\n logger.info(f'Executing source query for relation {relation.dot_notation} '\n f'({i+1} of {len(executable.graph)} in graph)...')\n\n relation.sampling.prepare(relation,\n executable.source_adapter)\n relation = RuntimeSourceCompiler.compile_queries_for_relation(\n relation, executable.graph, executable.source_adapter, executable.analyze)\n\n if executable.analyze:\n if relation.is_view:\n relation.population_size = \"N/A\"\n relation.sample_size = \"N/A\"\n logger.info(\n f'Relation {relation.dot_notation} is a view, skipping.')\n else:\n result = executable.source_adapter.check_count_and_query(relation.compiled_query,\n MAX_ALLOWED_ROWS,\n relation.unsampled).iloc[0]\n relation.population_size = result.population_size\n relation.sample_size = result.sample_size\n logger.info(\n f'Analysis of relation {relation.dot_notation} completed in {duration(start_time)}.')\n else:\n executable.target_adapter.create_database_if_not_exists(\n relation.quoted(relation.database))\n executable.target_adapter.create_schema_if_not_exists(\n relation.quoted(relation.database),\n relation.quoted(relation.schema))\n if relation.is_view:\n logger.info(\n f'Retrieving DDL statement for view {relation.dot_notation} in source...')\n relation.population_size = \"N/A\"\n relation.sample_size = \"N/A\"\n try:\n relation.view_ddl = executable.source_adapter.scalar_query(\n relation.compiled_query)\n except Exception:\n raise SystemError(\n f'Failed to extract DDL statement: {relation.compiled_query}')\n logger.info(\n f'Successfully extracted DDL statement for view {relation.quoted_dot_notation}')\n else:\n logger.info(\n f'Retrieving records from source {relation.dot_notation}...')\n try:\n relation.data = executable.source_adapter.check_count_and_query(\n relation.compiled_query, MAX_ALLOWED_ROWS, relation.unsampled)\n except Exception as exc:\n raise SystemError(\n f'Failed execution of extraction sql statement: {relation.compiled_query} {exc}')\n\n relation.sample_size = len(relation.data)\n logger.info(\n f'{relation.sample_size} records retrieved for relation {relation.dot_notation}.')\n\n logger.info(\n f'Inserting relation {relation.quoted_dot_notation} into target...')\n try:\n executable.target_adapter.create_and_load_relation(\n relation)\n except Exception as exc:\n raise SystemError(\n f'Failed to load relation {relation.quoted_dot_notation} into target: {exc}')\n\n logger.info(\n f'Done replication of relation {relation.dot_notation} in {duration(start_time)}.')\n relation.target_loaded = True\n relation.source_extracted = True\n logger.info(\n f'population:{relation.population_size}, sample:{relation.sample_size}')\n if self.barf:\n with open(os.path.join(self.barf_output, f'{relation.dot_notation}.sql'), 'w') as barf_file:\n barf_file.write(relation.compiled_query)\n try:\n for relation in executable.graph.nodes:\n del relation.data\n except AttributeError:\n pass\n gc.collect()\n except Exception as exc:\n logger.error(f'failed with error of type {type(exc)}: {str(exc)}')\n raise exc","title":""},{"docid":"5a9d574b92f02d3381547f29127005af","score":"0.46548247","text":"def get_templatelinks_info(\n db, function_name, replicas_port=None, user_db_port=None, user=None, password=None\n):\n\n query = (\n \"SELECT page_id, \"\n \"COUNT(tl_from) AS transcluded_in \"\n \"FROM page \"\n \"INNER JOIN templatelinks \"\n \" ON page_title=tl_title \"\n \" AND page_namespace=828 \"\n \" AND page_content_model='Scribunto' \"\n \" AND tl_namespace=828 \"\n \"GROUP BY page_id\"\n )\n\n cols = [\"page_id\", \"transcluded_in\"]\n for df in query_data_generator(\n query,\n function_name,\n cols,\n db,\n replicas_port,\n user_db_port,\n user,\n password,\n ):\n save_data(df, db, function_name, user_db_port, user, password)","title":""},{"docid":"7faaed1642db7991a3bd0be95f317f38","score":"0.46472684","text":"def info(self):\n rd = self.round\n fp = Primers.query.get(rd.forward_primer or 0)\n rp = Primers.query.get(rd.reverse_primer or 0)\n fpi = Primers.query.get(self.fp_id )\n rpi = Primers.query.get(self.rp_id )\n return [(rd.round_name, rd.__tablename__, rd.id)] + [(p.name, p.__tablename__,p.id) if p else (None,None,None)for p in (fp,rp,fpi,rpi)]","title":""},{"docid":"34ac14168f7b821b33c30c2bea5a7246","score":"0.46457297","text":"def main():\n global conn\n\n conn = pg.connect(database=db, user=usr, password=pasw, host=host,\n port=port)\n\n for folder in range(3):\n print('Processing folder #{}'.format(folder))\n process(folder)\n\n conn.close()","title":""},{"docid":"5697f46c1caa3f30007251caaf8fb4a0","score":"0.4645182","text":"def slow_loop(scraper, id_):\n # Update the database to confirm that task has started processing\n result = Result.query.filter_by(id=id_).first()\n result.started = datetime.datetime.now()\n db.session.add(result)\n db.session.commit()\n \n # Initializing scraper and empty dataframe to store results\n scraper.count_all_pages()\n dataframe = defaultdict(list)\n\n # Store completion percentage in redis under the process id\n job = get_current_job()\n job.meta[\"progress\"] = 0\n job.save_meta()\n\n job.meta[\"total\"] = len(scraper.all_pages)\n job.save_meta()\n\n for page_number in scraper.all_pages:\n title_list, location_list, price_list, type_list, bedroom_list, bathroom_list, area_list = scraper.scrape_page(page_number = page_number)\n dataframe['title_list'].extend(title_list)\n dataframe['location_list'].extend(location_list)\n dataframe['price_list'].extend(price_list)\n dataframe['type_list'].extend(type_list)\n dataframe['bedroom_list'].extend(bedroom_list)\n dataframe['bathroom_list'].extend(bathroom_list)\n dataframe['area_list'].extend(area_list)\n \n # update completion percentage so it's available from front-end\n job.meta[\"progress\"] = page_number\n job.save_meta()\n\n dataframe = DataFrame(dataframe)\n dataframe['area_list'] = dataframe['area_list'].str.extract(\"([0-9,]+)\", expand = False)\n\n res = dataframe.to_json(orient='index')\n\n # update the database to confirm that task has completed processing\n result.completed = datetime.datetime.now()\n result.result = res\n db.session.add(result)\n db.session.commit()\n\n return res","title":""},{"docid":"f0f00d28bc08bdf8ecf038440f724bcd","score":"0.46402127","text":"def getConnections():\n ids = {}\n container = obj\n while container is not None:\n if getattr(container, 'objectValues', None) is not None:\n for ob in container.objectValues():\n if ( getattr(ob, '_isAnSQLConnection', None) and\n getattr(ob, 'id', None) ):\n ob_id = ob.id\n\n if callable(ob_id):\n ob_id = ob_id()\n\n if ob_id not in ids:\n if hasattr(ob, 'title_and_id'):\n title = ob.title_and_id()\n else:\n title = ob_id\n ids[ob_id] = title\n\n container = aq_parent(aq_inner(container))\n\n ids = [(item[1], item[0]) for item in ids.items()]\n ids.sort()\n import pdb;pdb.set_trace()\n return ids","title":""},{"docid":"aa6b6586db301bfd21146be01cd2b81d","score":"0.4639239","text":"def handle(self, *args, **kwargs):\n\n with connection.cursor() as cursor:\n cursor.execute(\n sql.SQL(\n \"\"\"\n SELECT relname as \"relation\"\n FROM pg_class LEFT JOIN pg_namespace ON (pg_class.relnamespace = pg_namespace.oid)\n WHERE nspname = 'public' AND relkind = 'r'\n \"\"\"\n )\n )\n\n # VACUUM FULL aggressively reclaims disk space by rewriting the heap\n # and removing deleted tuples. Requires an exclusive table lock, so only\n # do this on tables that are not direclty used by the application.\n # This is necessary to reduce disk usage when tables are frequently\n # truncated and reimported.\n for (relation,) in cursor.fetchall():\n self.stdout.write(\"Cleaning table '%s'\" % relation)\n _t0 = time()\n cursor.execute(\n sql.SQL(\"VACUUM FULL {}\").format(sql.Identifier(relation))\n )\n _t1 = time()\n self.stdout.write(\"Finished cleaning in %.2f seconds\" % (_t1 - _t0))\n\n with transaction.atomic():\n cursor.execute(RECURSIVE_VIEWS_QUERY)\n\n for (view,) in cursor.fetchall():\n self.stdout.write(\"Refreshing materialized view '%s'...\" % view)\n _t0 = time()\n cursor.execute(\n sql.SQL(\"REFRESH MATERIALIZED VIEW CONCURRENTLY {}\").format(\n sql.Identifier(view)\n )\n )\n _t1 = time()\n self.stdout.write(\"Refreshed view in %.2f seconds\" % (_t1 - _t0))\n self.stdout.write(\"Updating database statistics...\")\n _t0 = time()\n # VACUUM ANALYZE updates PostgreSQL's internal statistics about the database.\n # It also updates the visibility map, allowing for index-only\n # scans in many cases.\n cursor.execute(sql.SQL(\"VACUUM ANALYZE\"))\n _t1 = time()\n self.stdout.write(\n \"Updated database statistics in %.2f seconds\" % (_t1 - _t0)\n )","title":""},{"docid":"53aff3bf0cbcef6d882230fe3024dd49","score":"0.46227002","text":"def _traverse_and_execute(self, executable: GraphExecutable) -> None: # noqa: pylint: disable=too-many-statements\n start_time = time.time()\n if self.barf:\n with open(os.path.join(self.barf_output,\n f'{[n for n in executable.graph.nodes][0].dot_notation}.component'), # noqa: pylint: disable=unnecessary-comprehension\n 'wb'\n ) as cmp_file:\n nx.write_multiline_adjlist(executable.graph, cmp_file)\n try:\n logger.debug(\n f\"Executing graph with {len(executable.graph)} relations in it...\")\n for i, relation in enumerate(\n nx.algorithms.dag.topological_sort(executable.graph)):\n relation.population_size = executable.source_adapter.scalar_query(\n executable.source_adapter.population_count_statement(relation))\n logger.info(f'Executing source query for relation {relation.dot_notation} '\n f'({i+1} of {len(executable.graph)} in graph)...')\n\n relation.sampling.prepare(relation,\n executable.source_adapter)\n relation = RuntimeSourceCompiler.compile_queries_for_relation(\n relation, executable.graph, executable.source_adapter, executable.analyze)\n\n if executable.analyze:\n if relation.is_view:\n relation.population_size = \"N/A\"\n relation.sample_size = \"N/A\"\n logger.info(\n f'Relation {relation.dot_notation} is a view, skipping.')\n else:\n result = executable.source_adapter.check_count_and_query(relation.compiled_query,\n relation.sampling.max_allowed_rows,\n relation.unsampled).iloc[0]\n relation.population_size = result.population_size\n relation.sample_size = result.sample_size\n logger.info(\n f'Analysis of relation {relation.dot_notation} completed in {duration(start_time)}.')\n else:\n executable.target_adapter.create_database_if_not_exists(\n relation.database)\n executable.target_adapter.create_schema_if_not_exists(\n relation.database,\n relation.schema)\n if relation.is_view:\n logger.info(\n f'Retrieving DDL statement for view {relation.dot_notation} in source...')\n relation.population_size = \"N/A\"\n relation.sample_size = \"N/A\"\n try:\n relation.view_ddl = executable.source_adapter.scalar_query(\n relation.compiled_query)\n except Exception as exc:\n raise SystemError(\n f'Failed to extract DDL statement: {relation.compiled_query}') from exc\n logger.info('Successfully extracted DDL statement for view ' \n f'{executable.target_adapter.quoted_dot_notation(relation)}')\n else:\n logger.info(\n f'Retrieving records from source {relation.dot_notation}...')\n try:\n relation.data = executable.source_adapter.check_count_and_query(\n relation.compiled_query, relation.sampling.max_allowed_rows, relation.unsampled)\n except Exception as exc:\n raise SystemError(\n f'Failed execution of extraction sql statement: {relation.compiled_query} {exc}') \\\n from exc\n\n relation.sample_size = len(relation.data)\n logger.info(\n f'{relation.sample_size} records retrieved for relation {relation.dot_notation}.')\n\n logger.info(f'Inserting relation {executable.target_adapter.quoted_dot_notation(relation)}' \n ' into target...')\n try:\n executable.target_adapter.create_and_load_relation(\n relation)\n except Exception as exc:\n raise SystemError('Failed to load relation '\n f'{executable.target_adapter.quoted_dot_notation(relation)} ' \n f' into target: {exc}') from exc\n\n logger.info('Done replication of relation ' \n f'{executable.target_adapter.quoted_dot_notation(relation)} ' \n f' in {duration(start_time)}.')\n relation.target_loaded = True\n relation.source_extracted = True\n logger.info(\n f'population:{relation.population_size}, sample:{relation.sample_size}')\n if self.barf:\n with open(os.path.join(self.barf_output, f'{relation.dot_notation}.sql'), 'w') as barf_file: # noqa pylint: disable=unspecified-encoding\n barf_file.write(relation.compiled_query)\n\n for relation in executable.graph.nodes:\n try:\n del relation.data\n except AttributeError:\n logger.warning(\"Failed to purge data of the %s relation\", relation)\n\n gc.collect()\n except Exception as exc:\n logger.error(f'failed with error of type {type(exc)}: {str(exc)}')\n raise exc","title":""},{"docid":"8e0d9c1ec2f2845c27b70735d077374b","score":"0.46221495","text":"def main():\n\n gene_k = 45 #This kmer size is huge. However, it was needed to avoid cycles in the DB graph, which we had a hard time getting around.\n prob = 50\n read_length = 50\n depth = 15 #average read depth\n prune_threshold = 10\n pruned = 0\n\n genome_db, kmer_positions, gene = db.main(gene_k) #gets reference stuff\n\n ###CREATES MATERNAL AND PATERNAL SNV SEQUENCES\n mat = gene[:-3] + 'Z' + gene[-2:]\n pat = gene[:-50] + \"X\" + gene[-49:]\n\n reads, positions = uniform_reads(mat, pat, gene, depth, read_length) #get list of reads, positions of reads.\n\n genome_db, genome_hash = thread(reads, genome_db, kmer_positions, gene_k, positions) #thread all reads\n\n genome_db, pruned = pruning(genome_db, prune_threshold) #prune\n\n #writes to files for debugging\n with open(\"genome_hash.txt\", \"w\") as text_file:\n text_file.write(str(genome_hash))\n with open(\"genome_db.txt\", \"w\") as text_file:\n text_file.write(str(genome_db))\n\n\n return genome_db, kmer_positions, gene, pruned","title":""},{"docid":"5e5d34c4c530cfc66dfec343e63a6161","score":"0.46189845","text":"def multiprocess_pairs(self):\n # Initialize key variables\n pattoo_db_records_lists_tuple = self._arguments\n pool_size = self._pool_size\n\n # Create a pool of sub process resources\n with get_context('spawn').Pool(processes=pool_size) as pool:\n\n # Create sub processes from the pool\n per_process_key_value_pairs = pool.starmap(\n _process_kvps_exception, pattoo_db_records_lists_tuple)\n\n # Wait for all the processes to end and get results\n pool.join()\n\n # Test for exceptions\n for result in per_process_key_value_pairs:\n if isinstance(result, ExceptionWrapper):\n result.re_raise()\n\n # Update the database with key value pairs\n pair.insert_rows(per_process_key_value_pairs)","title":""},{"docid":"4e76519ac11f8cb31512a1af4657d674","score":"0.4610883","text":"def get_poi_dict(city_id):\n global white_list_data\n _poi_dict = defaultdict(dict)\n _online_official_data = {}\n _online_nonofficial_data = {}\n # get whole data process\n conn = data_process_pool.connection()\n cursor = conn.cursor(cursor=DictCursor)\n sql = '''SELECT *\nFROM {}\nWHERE city_id='{}';'''.format(data_process_table_name, city_id)\n _t = time.time()\n cursor.execute(sql)\n logger.debug('[query][sql: {}][takes: {}]'.format(sql, time.time() - _t))\n for each in cursor.fetchall():\n if int(each[\"official\"]) == 0:\n _online_nonofficial_data[each['id']] = each\n else:\n _online_official_data[each['id']] = each\n cursor.close()\n conn.close()\n\n # get whole base data, and update data process result\n conn = base_data_pool.connection()\n cursor = conn.cursor(cursor=DictCursor)\n sql = '''SELECT *\n FROM {}\n WHERE city_id='{}';'''.format(data_process_table_name, city_id)\n _t = time.time()\n cursor.execute(sql)\n logger.debug('[query][sql: {}][takes: {}]'.format(sql, time.time() - _t))\n for each in cursor.fetchall():\n each.pop('tag_id')\n if int(each[\"official\"]) == 0:\n _online_nonofficial_data[each['id']].update(each)\n else:\n _online_official_data[each['id']].update(each)\n cursor.close()\n conn.close()\n\n # get whole source data\n conn = poi_ori_pool.connection()\n cursor = conn.cursor(cursor=DictCursor)\n sql = \"select * from {} where city_id='{}'\".format(poi_type, city_id)\n _t = time.time()\n cursor.execute(sql)\n logger.debug('[query][sql: {}][takes: {}]'.format(sql, time.time() - _t))\n for line in cursor.fetchall():\n source = line['source']\n source_id = line['id']\n _poi_dict[(source, source_id)] = line\n cursor.close()\n conn.close()\n\n for k, v in white_list_data.items():\n _poi_dict[k] = v\n return _poi_dict, _online_official_data, _online_nonofficial_data","title":""},{"docid":"3d2ff44975d89f669d722e8755521fcd","score":"0.46009216","text":"def output_relation_plain(self, db):\n\t\t_str = \"\"\n\t\trelations = self.get_relations(db)\n\t\tif relations:\n\t\t\tfor relation in relations:\n\t\t\t\tif relation[1] != \"-1\": # ensures not the dummy relations used to generate buttons\n\t\t\t\t\t_range = json.loads(relation[0])[0]\n\t\t\t\t\tdomain = json.loads(relation[1])[0]\n\t\t\t\t\t_str += \"%s\\t%s\\t%s\\n\"%(_range, domain, relation[2])\n\n\t\treturn _str","title":""},{"docid":"89b6cdb2cb76304b5f66d211ad923b63","score":"0.4598858","text":"def batch_query(self, querylist):\n seen_references = set()\n for q in querylist:\n for res in self.paged_query(q):\n res['instances'] = [inst for inst in res['instances'] if inst['id'] not in seen_references]\n seen_references.update([inst['id'] for inst in res['instances']])\n yield res","title":""},{"docid":"8946a41f5cc4b9febb2181ee4c9f5cc0","score":"0.45912752","text":"def _fetch_data(self):\r\n connections = {}\r\n while True:\r\n self.log.debug('---begin for loop---')\r\n try:\r\n ls = self._get_active_resource()\r\n self.update_resource(ls)\r\n # print ls\r\n self.log.debug(\"{}\".format(str(ls)))\r\n except exception.DatabaseException:\r\n time.sleep(2)\r\n continue\r\n\r\n # remove dead threads in the conn pool\r\n main_thread = threading.currentThread()\r\n running_id = [th.getName() for th in threading.enumerate() if th is not main_thread and th.is_alive()]\r\n self.log.info('running ids: {}, total: {}'.format(str([rid for rid in running_id]), len(running_id)))\r\n\r\n dead_id = set(connections.keys()) - set(running_id)\r\n for rid in dead_id:\r\n self.log.info(\"Thread {} is dead. Remove it from the connection pool. \".format(rid))\r\n del connections[rid]\r\n self.log.info('dead ids: ' + str(dead_id))\r\n\r\n # latest resource ids pulled from the gateway\r\n existing_id_list = [str(rid) for di, href, rt, obs, rid in ls if rid is not None]\r\n\r\n # find threads with obsolete ids and remove them\r\n zombie_id = set(connections.keys()) - set(existing_id_list)\r\n for rid in zombie_id:\r\n self.log.info(\"kill zombie threads: {}\".format(rid))\r\n connections[rid].kill()\r\n del connections[rid]\r\n\r\n self.log.info('active threads: {}, total: {}'.format(str([rid for rid in connections.keys()]),\r\n len(connections.keys())))\r\n\r\n for di, href, rt, obs, rid in ls:\r\n if rid and str(rid) not in connections.keys() and str(rid) not in running_id:\r\n if len(connections) >= self.MAX_THREAD:\r\n # quit the loop cos it exceeds max threads limit\r\n self.log.info(\"Exceeded maximal thread threshold {}, queuing request \"\r\n \"for {}?di={}\".format(self.MAX_THREAD, href, di))\r\n self.log.info(connections.keys())\r\n break\r\n time.sleep(randint(0, 5))\r\n t = FetchData(self.gateway_id, (href, di, obs))\r\n connections[str(rid)] = t\r\n self.log.info('threads in the pool: ' + str(connections))\r\n # logging.debug('end of for.')\r\n\r\n self.log.debug('---sleep 10 secs---')\r\n time.sleep(self.RESOURCE_UPDATE_INTERVAL)","title":""},{"docid":"787f6e51480462cc6f305dc2d28c7a05","score":"0.45885554","text":"def cache_relations(self):\r\n self.cached_relations = defaultdict(list)\r\n for relation in CIRelation.objects.values(\r\n 'parent_id', 'child_id', 'type', 'id').filter(\r\n type=CI_RELATION_TYPES.CONTAINS.id):\r\n self.cached_relations[relation.get('parent_id')].append({\r\n 'parent': relation.get('parent_id'),\r\n 'child': relation.get('child_id'),\r\n 'type': relation.get('type'),\r\n 'id': relation.get('id'),\r\n })","title":""},{"docid":"6fc3b5082e3e1fee77ecf0635f33edcb","score":"0.458544","text":"def print_info(session, detailed=False):\n if detailed:\n for proj in session.query(Project).order_by(Project.id):\n print(\"\\nProject: %s\\nDeviceDB: %s\\nGateware: %s\\nDescription: %s\" % (proj.name, proj.devicedb.name, proj.devicedb.gateware.name, proj.description))\n table = []\n mes_cnt_query = session.query(Measurement.sequence_id, func.count('*').label('measurement_count')).group_by(Measurement.sequence_id).subquery()\n for seq, mes_cnt in session.query(Sequence, mes_cnt_query.c.measurement_count).outerjoin(mes_cnt_query, Sequence.id == mes_cnt_query.c.sequence_id).filter(Sequence.pipeline.project == proj).order_by(Sequence.id):\n table.append([seq.name, mes_cnt])\n print(tabulate(table, headers=['Sequence', 'Measurements']))\n else:\n table = []\n seq_cnt_query = session.query(Sequence.pipeline.project_id, func.count('*').label('sequence_count')).group_by(Sequence.pipeline.project_id).subquery()\n for proj, seq_cnt in session.query(Project, seq_cnt_query.c.sequence_count).outerjoin(seq_cnt_query, Project.id == seq_cnt_query.c.project_id).order_by(Project.id):\n table.append([proj.name, proj.devicedb.name, proj.devicedb.gateware.name, seq_cnt])\n print(tabulate(table, headers=['Project', 'Device Database', 'Gateware', 'Sequences']))","title":""},{"docid":"98018a77be4f0c68bdc2c7dfe4a8629c","score":"0.45842588","text":"def print_tables():\n # courses:\n print(\"courses\")\n data_cursor.execute(\"SELECT * FROM courses\")\n for row in data_cursor:\n print(row)\n # classrooms:\n print(\"classrooms\")\n data_cursor.execute(\"SELECT * FROM classrooms\")\n for row in data_cursor:\n print(row)\n # students:\n print(\"students\")\n data_cursor.execute(\"SELECT * FROM students\")\n for row in data_cursor:\n print(row)","title":""},{"docid":"2d43678f6ee82a71f18a6ed532a83035","score":"0.4582817","text":"def get_relations(self, db, relation_name=None):\n\t\tcur = db.cursor()\n\t\tsql = None\n\t\tif relation_name:\n\t\t\tsql = \"SELECT * FROM relations WHERE sessionid=? AND relation=? ORDER BY range\"\n\t\t\tcur.execute(sql, (self.sessionid, relation_name))\n\t\telse:\n\t\t\tsql = \"SELECT * FROM relations WHERE sessionid=? ORDER BY range\"\n\t\t\tcur.execute(sql, (self.sessionid,))\n\n\t\tresults = cur.fetchall()\n\n\t\tif results:\n\t\t\t#return results # includes session\n\t\t\trelations = []\n\t\t\tfor result in results:\n\t\t\t\tif result[1] and result[1] != \"-1\":\n\t\t\t\t\t# THE ORDER IS NEEDED FOR [domain, range, relation] \n\t\t\t\t\trelations.append([result[2],result[1],result[3]]) \n\n\n\t\t\treturn relations\n\n\t\treturn None","title":""},{"docid":"1e005cfed60ae43e629340d13131c8b0","score":"0.45826247","text":"def bridge_phrases_extraction(row): \n question = row[\"question\"] \n sentences = row['context'][\"sentences\"].copy()\n titles = row['context'][\"title\"] \n answer = row[\"answer\"] \n titles_phrases, question_phrases, question_phrases_text, paras_phrases, answer_phrases, answer_phrases_text = noun_phrases_extraction(question, sentences, titles, answer)\n RG = create_relevant_graph(paras_phrases, question_phrases_text) # this RG could still be disjoint\n\n RG, common_phrases, mapping, _, merged_question_phrases = find_common_mapping(RG, question_phrases, paras_phrases) # mapping matchs paras_phrases with question_phrases\n\n row['common_phrases'] = common_phrases\n \n assert len(set(merged_question_phrases.keys()) - set(question_phrases_text)) == 0\n \n # connect disjoint subgraphs\n RG, disjoint = connect_disjoint_subgraphs(RG)\n RG, disjoint = always_connect_disjoint_subgraphs(RG)\n row['disjoint'] = disjoint\n RG.remove_edges_from(nx.selfloop_edges(RG)) # remove (500 mile, 500 mile, {'src': [(9, 2)]}) \n\n # add weight to RG, to lower chance of [p4] video game -> video game -> [p8] video game \n for u,v,d in RG.edges(data=True):\n if('src' not in d): # {}\n d['weight']=5\n \n question_only_phrase = list(set(question_phrases_text).difference(common_phrases | set(merged_question_phrases.keys()))) \n row['question_only_phrase'] = question_only_phrase\n\n if(len(common_phrases) > 1): \n steiner_graph = approx.steinertree.steiner_tree(RG, common_phrases) \n path_phrases = list(steiner_graph.nodes) # to find the smallest subgraph covers all common_phrases \n \n for i, phrase in enumerate(path_phrases):\n path_phrases[i] = remove_prefix(phrase, '[P', paraid = True)\n path_phrases = list(set(path_phrases))\n if(len(path_phrases) == 0):\n print(row['id'], 'no path found') \n path_edges = nx.get_edge_attributes(steiner_graph, 'src') \n \n\n else: # 0 or 1 common phrases\n path_phrases = list(common_phrases) \n path_edges = {}\n for phrase in common_phrases:\n path_edges[(phrase, None)] = []\n for pi, para_phrase in enumerate(paras_phrases):\n for si, sentence_phrases in enumerate(para_phrase):\n if si > 0: # not the title \n sentence_phrases = [ remove_prefix(sentence_phrase[0], '[P', paraid = True) for sentence_phrase in sentence_phrases] \n if any([find_match_with_tolerance(phrase, sentence_phrase) or find_match_with_tolerance(sentence_phrase, phrase) for sentence_phrase in sentence_phrases]):\n path_edges[(phrase, None)].append((pi,si-1)) \n path_edges = list(path_edges.items())\n path_sents = get_path_sents(path_edges)\n joint_context = basic_normalize(\" \".join(list(flatten(row[\"context\"]['title']))) + ' ' + \" \".join(list(flatten(row[\"context\"]['sentences']))) )\n joint_title = basic_normalize(\" \".join(list(flatten(row[\"context\"]['title'])))) \n\n \n steiner_points = set(path_phrases) - set(question_phrases_text)\n _, steiner_points = phrase_postpreocess(steiner_points, joint_context, list(flatten(titles_phrases)))\n \n row['steiner_points'] = steiner_points \n \n supporting_facts_phrases = [] \n supporting_facts_text = []\n supporting_facts_titles_phrases = []\n sp_sent_ids = row['supporting_facts']['sent_id'] \n for spi, sp_title in enumerate(row['supporting_facts']['title']): \n pi = titles.index(sp_title)\n sent_id = sp_sent_ids[spi]\n if pi < len(sentences) and sent_id < len(sentences[pi]): # in case edge case \n supporting_facts_text.append(sentences[pi][sent_id])\n sp_sent_phrases = paras_phrases[pi][sent_id+1] # +1 since [0] is title phrase\n sp_sent_phrases = [remove_prefix(phrase[0], '[P', paraid = True) for phrase in sp_sent_phrases]\n supporting_facts_phrases.append(sp_sent_phrases) \n \n t_phrases = titles_phrases[pi]\n t_phrases = [remove_prefix(phrase, '[P', paraid = True) for phrase in t_phrases]\n supporting_facts_titles_phrases.append(t_phrases) \n \n # oracle bridge_phrases \n bridge_phrases = annotate_bridge_phrases(supporting_facts_phrases, row['supporting_facts']['title'], supporting_facts_titles_phrases) \n \n _, row['bridge_phrases'] = phrase_postpreocess(set(bridge_phrases) - set(question_phrases_text), joint_context, list(flatten(titles_phrases))) \n \n supporting_facts_phrases, _ = phrase_postpreocess(list(flatten(supporting_facts_phrases)), joint_context, [])\n supporting_facts_titles_phrases, _ = phrase_postpreocess(list(flatten(supporting_facts_titles_phrases)), joint_title, [])\n row['supporting_facts_text'] = supporting_facts_text \n \n \n return row","title":""},{"docid":"7b154fcce18c7657313c7f5cf861b4c6","score":"0.45775887","text":"def extract_text():\n count = 0\n q = queue.Queue()\n try:\n for doc in coll.find({}, {\"title\": 1, \"subtitle\": 1, \"instructions\": 1,\n \"miscellaneousText\": 1, \"ingredientsText\": 1, \"tags\": 1, \"_id\": 0}):\n s = \" \".join([doc['title'], doc['subtitle'],\n doc['instructions'], doc['miscellaneousText'],\n doc['ingredientsText']] + doc['tags']).replace(\"\\n\", \" \")\n q.put(s)\n count += 1\n if count % 500 == 0:\n print(count)\n\n\n finally:\n with open(\"storage/all_words.txt\", \"w+\", encoding=\"utf-8\") as f:\n while q.qsize():\n f.write(q.get())\n f.write(\" \")","title":""},{"docid":"7a80bc6cb8f8801213276544799d3370","score":"0.45763612","text":"def aggregates_results(self):\n # clean shell\n clear = lambda: os.system('clear')\n res = {}\n\n for pond in ['w', 'tf_idf', 'proba']:\n\n coordinates = []\n count = 0\n local_coords = []\n for q_id in xrange(1, 65):\n clear()\n print '|> processing query number '+str(q_id)+' out of 64'+' ['+pond+']'\n\n if len(self.set.results[str(q_id)]):\n measures = self.compute_measures(str(q_id), pond)\n local_coords = self.interpolate(measures)\n count += 1\n\n if len(coordinates) == 0:\n coordinates = local_coords\n else:\n for j in xrange(0, len(coordinates)):\n coordinates[j] += local_coords[j]\n # ponderation\n for j in xrange(0, 11):\n coordinates[j] = coordinates[j] / count\n\n res[pond] = coordinates\n\n return res","title":""},{"docid":"da4934350cf2f119114210a239540816","score":"0.4566866","text":"def process_behavior(self, results, bulk_submit_size=1000):\n for process in results.get(\"behavior\", {}).get(\"processes\", []):\n bulk_index = []\n\n for call in process[\"calls\"]:\n base_document = self.get_base_document()\n call_document = {\n \"pid\": process[\"pid\"],\n }\n call_document.update(call)\n call_document.update(base_document)\n bulk_index.append({\n \"_index\": self.dated_index,\n \"_type\": self.call_type,\n \"_source\": call_document\n })\n if len(bulk_index) == bulk_submit_size:\n self.do_bulk_index(bulk_index)\n bulk_index = []\n\n if len(bulk_index) > 0:\n self.do_bulk_index(bulk_index)","title":""},{"docid":"a2d1d13f9a499ea9a4324c832ea59ee1","score":"0.45665073","text":"def multiprocess_data(self):\n # Initialize key variables\n pattoo_db_records_lists_tuple = self._arguments\n pool_size = self._pool_size\n\n # Troubleshooting log\n log_message = 'Processing {} agents from cache'.format(\n len(pattoo_db_records_lists_tuple))\n log.log2debug(20009, log_message)\n\n # Create a pool of sub process resources\n with get_context('spawn').Pool(processes=pool_size) as pool:\n\n # Create sub processes from the pool\n results = pool.starmap(\n _process_data_exception, pattoo_db_records_lists_tuple)\n\n # Wait for all the processes to end and get results\n pool.join()\n\n # Test for exceptions\n for result in results:\n if isinstance(result, ExceptionWrapper):\n result.re_raise()","title":""},{"docid":"f0b1a33197910c3cc4a2308e279f932a","score":"0.4565902","text":"def print_all(cls):\n all_objects = db_sess.query(cls).all()\n for obj in all_objects:\n print obj","title":""},{"docid":"a8a3d675a3c780ac745c86dac14487c4","score":"0.45610136","text":"def update_database_results(self, session):\n while not self._processed_submission_queue.empty():\n submission_id, submission_name = \\\n self._processed_submission_queue.get_nowait()\n if 'error' in get_submission_state(session, submission_id):\n update_leaderboards(session, self._ramp_config['event_name'])\n update_all_user_leaderboards(session,\n self._ramp_config['event_name'])\n logger.info('Skip update for {} due to failure during the '\n 'processing'.format(submission_name))\n continue\n logger.info('Update the results obtained on each fold for '\n '{}'.format(submission_name))\n path_predictions = os.path.join(\n self._worker_config['predictions_dir'], submission_name\n )\n set_predictions(session, submission_id, path_predictions)\n set_time(session, submission_id, path_predictions)\n set_scores(session, submission_id, path_predictions)\n set_bagged_scores(session, submission_id, path_predictions)\n set_submission_state(session, submission_id, 'scored')\n update_leaderboards(session, self._ramp_config['event_name'])\n update_all_user_leaderboards(session,\n self._ramp_config['event_name'])","title":""},{"docid":"82ec676e2f0a7395e7186f486d164bf3","score":"0.45577753","text":"def printParallelDependencies(sentence):\n\n printLinkageDependencies(sentence, \"parallel\")","title":""},{"docid":"9c85910ab74b012245ea9eab2f7429d4","score":"0.45541316","text":"def process_query(q):\n try:\n db = psycopg2.connect(database=DB_NAME)\n c = db.cursor()\n c.execute(q)\n results = c.fetchall()\n db.close()\n return list(results)\n except Exception as e:\n print(\"Error processing query !\\n{}\".format(e))\n return None","title":""},{"docid":"d3d0d26a394e749668cf1bbf079cf621","score":"0.45479274","text":"def do_query(self):\n f_date, to_date = self.start, self.end\n result = defaultdict(list)\n client = self.get_client()\n key_fields = [field for field in self.fields if self.fields[field].group]\n for field in list(self.fields):\n if field in key_fields:\n continue\n self.fields = self.get_fields(key_fields + [field])\n query = self.get_query_ch(f_date, to_date)\n # print(\"Query: %s\", query)\n if self.allobjectids or not self.objectids:\n for row in client.execute(query % \"\"):\n if row[0] not in result:\n result[row[0]] = row\n else:\n result[row[0]] += row[1:]\n else:\n # chunked query\n ids = self.objectids\n while ids:\n chunk, ids = ids[: self.CHUNK_SIZE], ids[self.CHUNK_SIZE :]\n for row in client.execute(query % f\" AND {self.get_object_filter(chunk)}\"):\n if row[0] not in result:\n result[row[0]] = row\n else:\n result[row[0]] += row[1:]\n self.fields = self.get_fields(self.query_fields)\n for v in result.values():\n yield v","title":""},{"docid":"62e5e587d7c9238d6b59204aa995a0b5","score":"0.4545974","text":"def process_all_queries(queries_file_path):\r\n with open(queries_file_path, 'r', encoding=\"utf-8\") as query_file:\r\n for query_item in query_file:\r\n\r\n # Get the query_text with query_id\r\n query_id, query_text = query_item.strip().split(\"|\")\r\n\r\n # process query\r\n query_output = get_top_ranked_documents(process_query(query_text))\r\n\r\n # write to output file\r\n write_query_output(query_id, query_output, path.dirname(queries_file_path),\r\n \"Query_\" + query_id + \"_Output.txt\")","title":""},{"docid":"0709564f602eceeb451b09e9e631a3f4","score":"0.45445633","text":"def get_query(self, ):\n except_count = 0\n dictionary_query_tuples = {}\n for j in range(1, 21):\n temp_address = self.queries_directory_path + '/' + str(j) + '.html'\n try:\n (ext, content) = MathDocument.read_doc_file(temp_address)\n formulas = MathExtractor.parse_from_xml(content, 1, operator=(not self.read_slt), missing_tags=None,\n problem_files=None)\n for key in formulas:\n tuples = formulas[key].get_pairs(window=2, eob=True)\n dictionary_query_tuples[j] = tuples\n except:\n except_count += 1\n print(j)\n return dictionary_query_tuples","title":""},{"docid":"f72e900827c7a4a60654e4a2fbf214b6","score":"0.45441452","text":"def dump_data_console(Pro):\n for pro in Pro.processes.values():\n print(pro.name, pro.operation_counter) \n for ops in pro.operations:\n ops.print_op_console()","title":""},{"docid":"20fd2d2f7592a362027c17e558490b3d","score":"0.4536247","text":"def print_persons_db(self):\n print(\"Persons DB:\")\n for row in self.get_all():\n print(row)","title":""},{"docid":"9dbce51b820470e07411aecc9638351b","score":"0.45330602","text":"def do_relations_program(poa_article):\n do_relations = None\n for dataset in poa_article.datasets:\n if do_dataset_related_item(dataset) is True:\n do_relations = True\n break\n if do_relations is not True and poa_article.ref_list:\n for ref in poa_article.ref_list:\n if do_citation_related_item(ref) is True:\n do_relations = True\n break\n if do_relations is not True and do_preprint_related_item(poa_article):\n do_relations = True\n return do_relations","title":""},{"docid":"730852513fabd8f867ceb0fe371d2d2d","score":"0.45286277","text":"def print_cache(self):\n for key in self.storage:\n self.storage[key].print_node()","title":""},{"docid":"59fe0412c7f7e2ec93edd74fb4fb1930","score":"0.45223185","text":"def dump_lp_links(self, links):\n for link in links:\n self.lp_queue.put(link)\n #print(\"Added link to hp_queue: \" + link)\n result = {}\n # block until all links are added into the crawled_lp dictionary\n while True:\n exists_flag = False\n for link in links:\n if link not in self.crawled_lp:\n exists_flag = True\n if exists_flag is False:\n break\n # block until all items are finished processing.\n for items in self.crawled_lp.items():\n while items[1].running():\n continue\n result[items[0]] = items[1].result()\n self.crawled_lp.clear()\n return result","title":""},{"docid":"e5ce75e129024010e33b7564ab2d15d4","score":"0.45149547","text":"def prepare_results(self):\n poolsize = self.state.config.getint(\"core\", \"poolsize\", fallback=5)\n\n pool = multiprocessing.Pool(poolsize)\n\n arguments = []\n for cls_name, args in self.parse_display():\n arguments.append({\n 'cls_name': cls_name,\n 'args': args,\n 'state': self.state\n })\n raw_out = pool.map(_mp_render_helper, arguments)\n outputs = list(map(self.format_item,\n filter(bool, raw_out)))\n\n\n return outputs + [\"\"] * (18 - len(outputs))","title":""},{"docid":"1696fd7227477e23dea1349bc672bf96","score":"0.45146492","text":"def find_out_related_objects(self):\n output = {}\n tally = 0\n for n_related_object in self.premis_core_data.related_objects:\n output[tally] = {'loc': self.premis_core_data.related_objects[i],\n 'identifier': str(i)}\n return output","title":""},{"docid":"24a753b814f6b48e1d6fc95e7dc77509","score":"0.45146403","text":"def prepare_relation_dataset(self):\n logger.info(\"generation of relations having ambiguous entities\")\n # pour chaque entité associée à une forme ambigue\n # on récupère tous les vecteurs-entités associés a la même formes\n # on récupère les vecteurs de ses entités en relation\n # X, Y = [], []\n\n for en_id, ambiguities, linked, _formes in generate(\"ambiguity\"):\n linked = linked.split()\n ambiguities = ambiguities.split()\n # extracted_entities = [en_id] + ambiguities + linked\n extracted_entities = [{\"start\": 0, \"end\": 1, \"id\": en_id}]\n extracted_entities += [\n {\"start\": 0, \"end\": 1, \"id\": e_id} for e_id in ambiguities\n ]\n extracted_entities += [\n {\"start\": i + 1, \"end\": i + 2, \"id\": e_id}\n for i, e_id in enumerate(linked)\n ]\n df = pd.DataFrame(extracted_entities)\n doc_vec = np.zeros(100)\n # il faut générer ici un doc_vecteur pour cette entité\n # une moyenne des documents en lien avec l'entité ambigue ?\n # ou un doc vecteur des formes entités\n # UTILISER LES FORMES DU TRIE + WORD EMBEDDINGS ICI\n en_positive = [en_id] + linked\n yield df, en_positive, doc_vec\n\n # for en_seqs, x, y in self.prepare_doc(df, en_positive, doc_vec):\n # if y != []:\n # yield x, y\n\n # Gérer ici le cas des documents/contexte vide\n # doc_emb = ctx_emb = wemb(en), avg(wemb(en_i))\n # ent_emb = pemb(en), avg(wemb(en_i))","title":""},{"docid":"cafb5bc546ff6e2b0d336075c9db9865","score":"0.45141473","text":"def printDicts(self):\n def __prntDict(Dict,msg):\n \"\"\"utility function capable to print a dictionary\"\"\"\n for key in Dict:\n msg+=key+'= '+str(Dict[key])+'\\n'\n return msg\n msg=''\n msg=__prntDict(self.runInfoDict,msg)\n msg=__prntDict(self.stepsDict,msg)\n msg=__prntDict(self.dataDict,msg)\n msg=__prntDict(self.samplersDict,msg)\n msg=__prntDict(self.modelsDict,msg)\n msg=__prntDict(self.metricsDict,msg)\n #msg=__prntDict(self.testsDict,msg)\n msg=__prntDict(self.filesDict,msg)\n msg=__prntDict(self.dataBasesDict,msg)\n msg=__prntDict(self.outStreamsDict,msg)\n msg=__prntDict(self.entityModules,msg)\n msg=__prntDict(self.entities,msg)\n self.raiseADebug(msg)","title":""},{"docid":"142ac7f74b22e941db7494e450b77724","score":"0.45109677","text":"def batch_query(request, article_titles, print_num_queries=False):\n request[\"action\"] = \"query\"\n request[\"format\"] = \"json\"\n results = {}\n num_queries = 0\n # can only query for 50 titles at a time, and even then have to do some fanciness to actually get the info\n for i in range(0, len(article_titles), 50):\n last_continue = {\"continue\": \"\"}\n request[\"titles\"] = \"|\".join(article_titles[i:i + 50])\n while True:\n req = request.copy()\n req.update(last_continue)\n r = requests.get(\"https://en.wikipedia.org/w/api.php\", req, headers={\"User-Agent\": USER_AGENT}).json()\n num_queries += 1\n if \"error\" in r:\n raise ConnectionError(r[\"error\"])\n if \"query\" in r:\n for page_key, page_val in r[\"query\"][\"pages\"].items():\n # returns piecemeal, not all props in the same request\n if request[\"prop\"] in page_val:\n if page_val[\"title\"] in results:\n results[page_val[\"title\"]].update(page_val[request[\"prop\"]])\n else:\n results[page_val[\"title\"]] = page_val[request[\"prop\"]]\n if \"batchcomplete\" in r:\n break\n last_continue = r[\"continue\"]\n if print_num_queries:\n print(\"Number of calls to complete batch query: \", num_queries)\n return results","title":""},{"docid":"f121b0e70b1c8a1c5b3f1d4d9f9140f8","score":"0.45091134","text":"def run_query(report):\n\n print(report.explain())\n print(\"REDSTREAK RESULT:\")\n print(\"-\" * 80)\n print(f\"# : {'Title':39.39} |{'Genres':39.39}\")\n\n print(\"-\" * 80)\n for i, row in enumerate(report):\n print(f\"{i} {row}\")","title":""},{"docid":"f113af9a22049faf5b7fd2aed450f882","score":"0.45069277","text":"def parallel(self, files):\n def start_process(csv_file):\n \"\"\" Start process on given csv_file\n\n Arguments:\n csv_file {string} -- csv_file to start insert process on\n\n Returns:\n process, Queue -- process started, Queue with dict of results\n \"\"\"\n results = Queue()\n process = Process(target=self.insert_to_mongo, args=(csv_file, results))\n logger.info(f\"Starting {process} : {csv_file}\")\n process.start()\n return process, results\n\n def join_process(process):\n \"\"\" Joins processes in process argument\n\n Arguments:\n [process1, process2, process3, ...] -- list of processes to join\n\n Returns:\n {collection_name: {\"success\", \"fail\", \"total_records\", \"elapsed\"}}\n \"\"\"\n logger.info(f\"Joining process {process[0]}\")\n process[0].join()\n return process[1].get()\n\n return list(\n map(join_process, list(map(start_process, files)))\n )","title":""},{"docid":"f89542f7626572de77ef897a3c94300a","score":"0.45055506","text":"def find_links(self):\n\n fields = \"d.id\", \"u.value\", \"d.title\", \"t.value\"\n query = db.Query(\"query_term t\", *fields).unique()\n query.join(\"query_term u\", \"u.doc_id = t.doc_id\",\n \"u.node_loc = t.node_loc\")\n query.join(\"document d\", \"d.id = t.doc_id\")\n if not self.doc_id:\n query.where(\"d.active_status = 'A'\")\n query.where(\"t.path LIKE '/%s/%%/@SourceTitle'\" % self.doc_type)\n query.where(\"u.path LIKE '/%s/%%/@cdr:xref'\" % self.doc_type)\n if self.doc_id:\n query.where(query.Condition(\"d.id\", self.doc_id))\n elif self.doc_type == \"Summary\":\n if self.boards:\n sq = self.summary_board_subquery(self.boards, self.language)\n query.where(query.Condition(\"u.doc_id\", sq, \"IN\"))\n elif self.language:\n query.join(\"query_term l\", \"l.doc_id = u.doc_id\")\n query.where(\"l.path = '%s'\" % self.SUMMARY_LANGUAGE)\n query.where(\"l.value = '%s'\" % self.language)\n if self.audience:\n query.join(\"query_term a\", \"a.doc_id = u.doc_id\")\n query.where(\"a.path = '%s'\" % self.SUMMARY_AUDIENCE)\n query.where(\"a.value = '%ss'\" % self.audience)\n if self.doc_type == self.GTC:\n if self.language:\n language = self.GTC_LANGUAGES[self.language]\n query.join(\"query_term l\", \"l.doc_id = t.doc_id\",\n \"l.node_loc = t.node_loc\")\n query.where(\"l.path = '%s'\" % self.GTC_USE_WITH)\n query.where(\"l.value = '%s'\" % language)\n self.log_query(query)\n rows = query.execute(self.cursor).fetchall()\n return [self.Link(self, *row) for row in rows]","title":""},{"docid":"e763c5f5673558358bfa8f53ac0ea3e4","score":"0.4505347","text":"def promoter_profile(self):\n self.promoter = { \"de\": {},\n \"nde\": {} }\n\n #################################################\n # Each promoter to all related merged DBD\n print(\"\\tEach promoter to all related merged DBD...\")\n # Targeted promoters\n self.promoter[\"de\"][\"rd\"] = self.txp_def.sort_rd_by_regions(regionset=self.de_regions)\n self.promoter[\"nde\"][\"rd\"] = self.txp_ndef.sort_rd_by_regions(regionset=self.nde_regions)\n #################################################\n \n # Each promoter to all related DBS\n print(\"\\tEach promoter to all related DBS...\")\n # Targeted promoters\n \n self.promoter[\"de\"][\"merged_dbs\"] = {}\n self.promoter[\"de\"][\"dbs\"] = {}\n for k, rds in self.promoter[\"de\"][\"rd\"].items():\n self.promoter[\"de\"][\"dbs\"][k] = rds.get_dbs()\n self.promoter[\"de\"][\"merged_dbs\"][k] = self.promoter[\"de\"][\"dbs\"][k].merge(w_return=True) \n # Untargeted promoters\n self.promoter[\"nde\"][\"merged_dbs\"] = {}\n self.promoter[\"nde\"][\"dbs\"] = {}\n for k, rds in self.promoter[\"nde\"][\"rd\"].items():\n self.promoter[\"nde\"][\"dbs\"][k] = rds.get_dbs()\n self.promoter[\"nde\"][\"merged_dbs\"][k] = self.promoter[\"nde\"][\"dbs\"][k].merge(w_return=True)\n \n # DBS coverage\n self.promoter[\"de\"][\"dbs_coverage\"] = {}\n for promoter in self.de_regions:\n self.promoter[\"de\"][\"dbs_coverage\"][promoter.toString()] = float(self.promoter[\"de\"][\"merged_dbs\"][promoter.toString()].total_coverage()) / len(promoter)\n self.promoter[\"nde\"][\"dbs_coverage\"] = {}\n for promoter in self.nde_regions:\n self.promoter[\"nde\"][\"dbs_coverage\"][promoter.toString()] = float(self.promoter[\"nde\"][\"merged_dbs\"][promoter.toString()].total_coverage()) / len(promoter)","title":""},{"docid":"6c600e828e185a319d4a2cb87c7e3f44","score":"0.45045093","text":"def _query_worker(self):\n while True:\n query = self.query_queue.get()\n response = self.query_handler(query)\n self.connection.send(response)","title":""},{"docid":"97de81bd2982e3da606bc5c131f7f283","score":"0.45034683","text":"def thread_worker():\r\n links = find_subcategories_links()\r\n if links:\r\n ThreadPool(processes=len(links)).map(extract_asins, links)","title":""},{"docid":"afe28d2dcab6ee8a544855ab793349fb","score":"0.4498639","text":"def worker(pickledir, networks, out_q):\n\n ## unpickle the stuff I need for parallel processing, but only do it the first\n ## time worker is called for this process, otherwise we do a slow operation everytime\n\n global validComparisonsHash\n global pairGraph\n global assemblages\n global args\n global typeFrequencyUpperCI\n global typeFrequencyLowerCI\n\n vchashfile = pickledir + \"/validComparisonsHash.p\"\n pgfile = pickledir + \"/pairGraph.p\"\n assemfile = pickledir + \"/assemblages.p\"\n argsfile = pickledir + \"/args.p\"\n fucifile = pickledir + \"/typeFrequencyUpperCI.p\"\n flcifile = pickledir + \"/typeFrequencyLowerCI.p\"\n\n\n validComparisonsHash=pickle.load(open(vchashfile,'rb'))\n pairGraph=pickle.load(open(pgfile,'rb'))\n assemblages=pickle.load(open(assemfile,'rb'))\n args=pickle.load(open(argsfile,'rb'))\n typeFrequencyUpperCI=pickle.load(open(fucifile,'rb'))\n typeFrequencyLowerCI=pickle.load(open(flcifile,'rb'))\n\n outdict = []\n\n for n in networks:\n output=checkForValidAdditions(n)\n for o in output:\n outdict.append(o)\n out_q.put(outdict)","title":""},{"docid":"e29a4dc7d351a25d5a55b02a647297fc","score":"0.4496885","text":"def results_iter(self):\r\n resolve_columns = hasattr(self, 'resolve_columns')\r\n fields = None\r\n for rows in self.execute_sql(MULTI):\r\n for row in rows:\r\n if resolve_columns:\r\n if fields is None:\r\n # We only set this up here because\r\n # related_select_fields isn't populated until\r\n # execute_sql() has been called.\r\n if self.select_fields:\r\n fields = self.select_fields + self.related_select_fields\r\n else:\r\n fields = self.model._meta.fields\r\n row = self.resolve_columns(row, fields)\r\n yield row","title":""},{"docid":"35e87889f33f58b2ea4aad8c8eed96f9","score":"0.44930238","text":"def print_info(self):\n print(\" Intersection \" + str(self.__uid) + \"(\" + str(self.__intersection_type) + \")\")\n print(\" --------------------\")\n for connection in self.__connections:\n if(self.__connections[connection] == None):\n continue\n else:\n print(\" \" + str(connection))\n self.__connections[connection].print_info()\n\n print()","title":""},{"docid":"4a52be3999dc6b0464866376f4fb6509","score":"0.44859868","text":"def get_uniprot_data(gbk_dict, query_data, connection, args):\r\n logger = logging.getLogger(__name__)\r\n \r\n gbk_accessions = list(gbk_dict.keys())\r\n\r\n # retrieve the data from the Taxonomy and Kingdom tables\r\n with Session(bind=connection) as session:\r\n uniprot_query = session.query(Genbank, Uniprot).\\\r\n join(Uniprot, (Uniprot.genbank_id == Genbank.genbank_id)).\\\r\n filter(Genbank.genbank_accession.in_(gbk_accessions)).\\\r\n all()\r\n\r\n if len(uniprot_query) == 0:\r\n logger.warning(\"No UniProt records retrieved for any of the selected GenBank accessions.\")\r\n return query_data\r\n\r\n for record in tqdm(uniprot_query, desc=\"Getting UniProt data\"):\r\n gbk_acc = record[0].genbank_accession\r\n\r\n if 'uniprot_acc' in args.include:\r\n uniprot_accession = record[1].uniprot_accession\r\n\r\n try:\r\n query_data[gbk_acc]\r\n \r\n try:\r\n query_data[gbk_acc]['uniprot_accession']\r\n logger.warning(\r\n f\"Multiple UniProt records found for GBK acc {gbk_acc}\\n\"\r\n \"Retreiving only one.\"\r\n )\r\n query_data[gbk_acc]['uniprot_accession'] = uniprot_accession\r\n\r\n except KeyError:\r\n query_data[gbk_acc]['uniprot_accession'] = uniprot_accession\r\n\r\n except KeyError:\r\n query_data[gbk_acc] = {\r\n 'uniprot_accession': uniprot_accession,\r\n }\r\n\r\n if 'uniprot_name' in args.include:\r\n uniprot_name = record[1].uniprot_name\r\n\r\n try:\r\n query_data[gbk_acc]\r\n \r\n try:\r\n query_data[gbk_acc]['uniprot_name']\r\n logger.warning(\r\n f\"Multiple UniProt records found for GBK acc {gbk_acc}\\n\"\r\n \"Retreiving only one.\"\r\n )\r\n query_data[gbk_acc]['uniprot_name'] = uniprot_name\r\n \r\n except KeyError:\r\n query_data[gbk_acc]['uniprot_name'] = uniprot_name\r\n\r\n except KeyError:\r\n query_data[gbk_acc] = {\r\n 'uniprot_name': uniprot_name,\r\n }\r\n\r\n if 'uniprot_seq' in args.include:\r\n seq = record[1].sequence\r\n seq_date = record[1].seq_update_date\r\n\r\n try:\r\n query_data[gbk_acc]\r\n \r\n try:\r\n query_data[gbk_acc]['uniprot_sequence']\r\n logger.warning(\r\n f\"Multiple UniProt records found for GBK acc {gbk_acc}\\n\"\r\n \"Retreiving only one record.\"\r\n )\r\n query_data[gbk_acc]['uniprot_sequence'] = seq\r\n query_data[gbk_acc]['uniprot_sequence_date'] = seq_date\r\n except KeyError:\r\n query_data[gbk_acc]['uniprot_sequence'] = seq\r\n query_data[gbk_acc]['uniprot_sequence_date'] = seq_date\r\n\r\n except KeyError:\r\n query_data[gbk_acc] = {'sequence': seq, 'sequence_date': seq_date}\r\n\r\n return query_data","title":""},{"docid":"ff891604026486a90777e377de6882b5","score":"0.4481341","text":"def __scrape_queries(self, folders_dict):\n queries_dict = {}\n for folder_id, folders in folders_dict.items():\n query_ids = self.__get_folders_related_query_ids(folders)\n queries_dict[folder_id] = \\\n [self.__scrape_query(query_id) for query_id in query_ids]\n\n self.__log_queries_related_scraping_results(queries_dict)\n\n return queries_dict","title":""},{"docid":"b72568eda0d71e98f621df365bef6b58","score":"0.44785514","text":"def print_db_logs(self):\n LOG.info(\"************ DB Logs Start ************\")\n print_pipe(self.db_instance.db_process)\n LOG.info(\"************* DB Logs End *************\")","title":""},{"docid":"2e78db46c0e7652002c0a45546c7c20b","score":"0.446959","text":"def multiprocess(func, iterable):\n def create_pool_size(session_list):\n \"\"\"\n Create an appropriate pool size. If the length of domains exceeds the number of threads\n than the pool_size will be the max number of threads, else the pool size will be the\n number of domains entered.\n :param session_list:\n :return:\n \"\"\"\n # multiprocess crawl threads\n list_count = len(session_list)\n thread_count = multiprocessing.cpu_count() * 2\n\n # create a pool size based on the size of session_list\n if list_count > thread_count:\n pool_size = thread_count\n else:\n pool_size = list_count\n return pool_size\n\n def print_pool_out(output_list):\n \"\"\"\n Print the output of each crawl process from the output list\n :param output_list: a list that contains tuples (session_name, list_of_results)\n :return: None\n \"\"\"\n for session_name, result_list in output_list:\n print(session_name)\n if result_list:\n for link in result_list:\n print(f'\\t{link}')\n else:\n print(f'\\tNo Results Found')\n\n pool_size = create_pool_size(iterable)\n pool = multiprocessing.Pool(\n processes=pool_size\n )\n pool_outputs = pool.map(func, iterable)\n pool.close()\n pool.join()\n\n print(pool_outputs)\n print_pool_out(pool_outputs)","title":""},{"docid":"15658226aefb222de86a1c358c7ae44c","score":"0.44639853","text":"def run_models(m):\n querys = getQuerys()\n \n for no, query in querys.iteritems():\n## m.models(no, query)\n m.proximity_model(no, query)","title":""},{"docid":"5c773327719f1c3b8c4d6223a0ce4d19","score":"0.4463504","text":"def do_query(args):\n\n opts = parse_sel(args) \n for row in db.query(opts):\n request_time = ns_to_date(row['requested_time'])\n print row['exper_id'], row['runnum'], row['status'], request_time, \\\n row['irods_filepath'], row['requested_uid']","title":""},{"docid":"5c6ab7c3ca5751e2a660a7647078ccc4","score":"0.44604963","text":"def thread(variant_reads, genome_db, genome_hash, gene_k, positions):\n ###GO THROUGH ALL READS\n for k in range(len(variant_reads)):\n read_kmers = db.kmerList(gene_k - 1, variant_reads[k])\n ###GO THROUGH KMERS OF SIZE GENE_K WITHIN READ\n for i in range(len(read_kmers)-1):\n cur_kmer = read_kmers[i] #current kmer in read\n if(cur_kmer in genome_db): #if already in the graph\n #check to see if it points to the next kmer in genome_db\n next_kmer = read_kmers[i+1]\n try:\n index = genome_db[cur_kmer][0].index(next_kmer)\n except ValueError:\n index = -1\n\n if(index > -1):\n genome_db[cur_kmer][1][index] += 1\n else:\n genome_db[cur_kmer][0].append(next_kmer)\n genome_db[cur_kmer][1].append(1)\n\n\n else:\n genome_db[cur_kmer] = [[read_kmers[i+1]], [1]] #create a new node\n genome_hash[cur_kmer] = [positions[k] + i]\n\n return genome_db, genome_hash","title":""},{"docid":"64a8529e95ea4c7c0202ec34fdbc8f3c","score":"0.44586724","text":"def process_query(target=_DEFAULT_INPUT_QUERY):\n\tglobal _QUERY_SCHEMA, _DB_GAMES, _RESULT_FILENAME\n\tquery = _translate_json(target)\n\tschema = _translate_json(_QUERY_SCHEMA)\n\tvalidate(query, schema)\n\tresultfile = query.get(\"filename\",_RESULT_FILENAME)\n\tif _DB_GAMES is None:\n\t\tload_db()\n\tdf = _process_filter_query(_DB_GAMES, query)\n\tdf = _process_preference_query(df, query)\n\tconn = sqlite3.connect(resultfile)\n\tdf.to_sql(\"results\",conn,index=False)\n\tconn.close()\n\tprint(\"Finished computing results.\")","title":""},{"docid":"0674092b21aea4debd297d66c6696945","score":"0.44420987","text":"def print_results_per_node(self, node_map):\n for bucket in node_map.keys():\n print(\"----- Bucket {0} -----\".format(bucket))\n for node in node_map[bucket].keys():\n print(\"-------------Node {0}------------\".format(node))\n for vbucket in node_map[bucket][node].keys():\n print(\" for vbucket {0}\".format(vbucket))\n for key in node_map[bucket][node][vbucket].keys():\n print(\" :: for key {0} = {1}\"\n .format(key,\n node_map[bucket][node][vbucket][key]))","title":""},{"docid":"ce79ef2c022dbeca5d798c4fffc41296","score":"0.4434571","text":"def stats_printer(stats_queue):\n proc_stats = [ProcessStats(i) for i in range(FLAGS.parallel)]\n print_time = start_time = time.time()\n width = 107\n\n running = True\n while running:\n print_time += 10\n\n while time.time() < print_time:\n try:\n s = stats_queue.get(True, print_time - time.time())\n if s is None: # Signal to print and exit NOW!\n running = False\n break\n proc_stats[s.proc_id] = s\n except queue.Empty:\n pass\n\n replay_stats = ReplayStats()\n for s in proc_stats:\n replay_stats.merge(s.replay_stats)\n\n print((\" Summary %0d secs \" % (print_time - start_time)).center(width, \"=\"))\n print(replay_stats)\n print(\" Process stats \".center(width, \"-\"))\n print(\"\\n\".join(str(s) for s in proc_stats))\n print(\"=\" * width)","title":""},{"docid":"7ebf705c8f97bb611c97d5243dd1fef0","score":"0.44261095","text":"def create_tables(self) -> None:\n cursor = self.con.cursor()\n\n for rel_name, query in self.relations.items():\n if type(query) is str:\n try:\n cursor.execute(query)\n except Exception as e:\n l.error(\n f\"Error occured while executing query for {rel_name}: {e} \\n\"\n \"Proceed tracking without database logging\"\n )\n raise ConnectionError","title":""},{"docid":"7c04c33c910bb9a02c4fc77f89a88cf7","score":"0.44144392","text":"def get_lineage_ids(taxid, conn): \n\n query=\"SELECT nd.tax_id, nd.parent_id, nd.rank, na.tax_id, na.tax_name, na.name_class from nodes nd inner join names na on nd.tax_id=na.tax_id where na.name_class=='scientific name' AND na.tax_id==\" + \"'\"+taxid+\"'\"\n\n df = pd.read_sql_query(query, conn)\n #print(df)\n\n df.columns=['tax_id', 'parent_id', 'rank', 'tax_id_drop', 'tax_name', 'name_class']\n df.drop(\"tax_id_drop\",axis=1, inplace=True)\n rankorder=np.array(['no_rank','superkingdom','phylum','class','order','family','genus','species'])[::-1]\n #print(df['rank'])\n if not df['rank'].iloc[0].strip() in rankorder:\n rankorder=np.append(rankorder,df['rank'].iloc[0])\n rank_ind=np.where(df['rank'].iloc[0]==rankorder)[0][0]\n# if len(df.tax_name.iloc[0].split(\" \"))>=2:\n# lineage={rankorder[rank_ind]:\" \".join(df.tax_name.iloc[0].split(\" \")[1:])}\n# else:\n# lineage={rankorder[rank_ind]:df.tax_name.iloc[0]}\n\n lineage={rankorder[rank_ind]:df.tax_name.iloc[0]}\n ids={rankorder[rank_ind]: taxid}\n stop=False\n temp=df.copy()\n #print(lineage, ids)\n while not stop:\n parent_id=temp['parent_id'].iloc[0]\n if parent_id is None or parent_id==\"\" or parent_id=='0':\n stop=True\n break\n #print(parent_id)\n query=\"SELECT nd.tax_id, nd.parent_id, nd.rank, na.tax_id, na.tax_name, na.name_class from nodes nd inner join names na on nd.tax_id=na.tax_id where na.name_class=='scientific name' AND na.tax_id==\" + \"'\"+parent_id+\"'\"\n temp = pd.read_sql_query(query, conn)\n temp.columns=['tax_id', 'parent_id', 'rank', 'tax_id_drop', 'tax_name', 'name_class']\n temp.drop(\"tax_id_drop\",axis=1, inplace=True)\n lineage.update({temp['rank'].iloc[0]:temp['tax_name'].iloc[0]})\n\n ids.update({temp['rank'].iloc[0]: temp.tax_id.iloc[0]})\n\n \n\n return lineage, ids","title":""},{"docid":"ed5984bb4213b1ebc203b50229d04568","score":"0.43985403","text":"def query_generator(self):\n for q in self.iterqueries():\n yield self._query_to_concrete_str(q)","title":""}],"string":"[\n {\n \"docid\": \"fc821650accb518306cf716fe0ad1622\",\n \"score\": \"0.56099725\",\n \"text\": \"def process_relationships(cur, resp, where_relationship, params_relationship, sql):\\n try:\\n results = cur.fetchall()\\n retrived_people_count = len(results)\\n\\n if results and len(results) > 0 and u.response_has_attribute_and_value(resp['entities'], 'entity', 'relationship'):\\n # get relationship records. Take top 1 even if there are multiple records retrived\\n peopleid = 0\\n columnindex = 0\\n for key in results[0].keys():\\n if key in 'rowid':\\n peopleid = results[0][columnindex]\\n break\\n columnindex += 1\\n\\n sql_rel = 'select relatedto from relationship rs inner join relationshiptypes rst \\\\\\n on rs.relationshiptype = rst.rowid \\\\\\n where rs.peopleid = {}'.format(peopleid)\\n\\n if where_relationship and len(where_relationship) > 0:\\n sql_rel += ' AND rst.name = \\\"'\\\"{}\\\"'\\\"'.format(\\n params_relationship['name'])\\n try:\\n cur.execute(sql_rel)\\n except sqlite3.Error as e:\\n logger.exception(\\n msg=\\\"Error is occured while excute {0}\\\".format(e))\\n return\\n results = cur.fetchall()\\n peopleids = []\\n if results and len(results) > 0:\\n for result in results:\\n columnindex = 0\\n for key in result.keys():\\n if key in 'relatedto':\\n peopleids.append(result[columnindex])\\n break\\n columnindex += 1\\n\\n # convert list into comma separated if the list has int, float, then use map(str, [])\\n # Else just pass []\\n sql += ' where rowid in ({})'.format(\\n \\\", \\\".join(map(str, peopleids)))\\n try:\\n cur.execute(sql)\\n except sqlite3.Error as e:\\n logger.exception(\\n msg=\\\"Error is occured while excute {0}\\\".format(e))\\n return\\n results = cur.fetchall()\\n\\n if results and len(results) > 0:\\n if retrived_people_count > 1:\\n output = 'I could remember so many but i can able to tell you only one now'\\n\\n for res in results:\\n column_index = 0\\n fullname = ''\\n for col in res.keys():\\n if col == 'Firstname' and res[column_index]:\\n fullname += '{} '.format(res[column_index])\\n elif col == 'Lastname' and res[column_index]:\\n fullname += '{} '.format(res[column_index])\\n elif col == 'aliasname' and res[column_index]:\\n output += 'people will call as {} '.format(\\n res[column_index])\\n elif col == 'DOB' and res[column_index]:\\n output += 'Born on {} '.format(res[column_index])\\n elif col == 'bornplace' and res[column_index]:\\n output += 'Born at {} '.format(res[column_index])\\n elif col == 'DOD' and res[column_index]:\\n output += 'Died on {} '.format(res[column_index])\\n elif col == 'diedplace' and res[column_index]:\\n output += 'Died at {} '.format(res[column_index])\\n column_index += 1\\n if fullname:\\n output += \\\"full name is {}\\\".format(fullname)\\n if retrived_people_count > 1:\\n break\\n except Exception as exp: \\n logger.error(\\n \\\"An error is occured while process person's relationship information {0}\\\".format(exp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ee9dcfdd8499152e3c41545c83210c\",\n \"score\": \"0.54565597\",\n \"text\": \"def print_relation(id_num):\\n root = build_person(id_num)\\n # visited record all the people we have visited\\n visited = {id_num}\\n # more_friend record people we are going to visit\\n more_friend = build_friends(root.friends)\\n # result record all the relationships, using ordered dictionary so root is always at first\\n result = collections.OrderedDict()\\n result[root.name] = [name for name in more_friend]\\n # print_one_result(root.name, result[root.name])\\n # BFS loop to find all friends of friends\\n while more_friend:\\n to_add = []\\n for friend in more_friend:\\n # if we have visited this person, skip\\n if friend.id_num in visited:\\n continue\\n else:\\n visited.add(friend.id_num)\\n # find all friends of this friend\\n friends_of_friend = build_friends(friend.friends)\\n # record into result\\n result[friend.name] = [name for name in friends_of_friend]\\n # print_one_result(friend.name, result[friend.name])\\n # will visited all his/her friends in next loop\\n to_add += friends_of_friend\\n more_friend = to_add\\n\\n print_result(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e24a798dda31b61a3a05b0121eadd9a1\",\n \"score\": \"0.5446227\",\n \"text\": \"def query_process( pheader, pworkers, pworker, qry_template, tqueue ):\\n\\n if DEBUG:\\n print(\\\"Init Separate Process: \\\",pworker)\\n\\n conn = get_connection(thread_id=pworker)\\n\\n t_start = time.time()\\n for qindex in range(args.iterations):\\n qry_string = qry_template % ( ids[random.randint (0,ID_COUNT-1)].get(\\\"personid\\\") )\\n conn.execute(qry_string)\\n if DEBUG:\\n if qindex % PROGRESS_COUNT == 0:\\n print(\\\"Queried \\\",qindex,\\\"...\\\")\\n t_end = time.time()\\n\\n avg_latency = 1000*(t_end-t_start) / args.iterations\\n duration = round(t_end - t_start,4)\\n qry_rate = args.iterations / duration\\n\\n # push duration result for this thread to queue\\n tqueue.put( duration )\\n\\n prline = args.database + \\\",\\\" + pheader + \\\",\\\" + str(len(hosts)) + \\\",\\\" + \\\\\\n str(pworkers) + \\\",\\\" + str(round(avg_latency,5)) + \\\",\\\" + str(round(qry_rate,2))\\n print(prline)\\n\\n # close connection at end of run\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05f6258e51c2e4f9acb267164a2b713\",\n \"score\": \"0.54349244\",\n \"text\": \"def ParallelQuery(psqlInstances, theConnectionInfos, theQueries):\\n start = timeit.default_timer() \\n\\n try:\\n pool = mp.Pool(psqlInstances, maxtasksperchild=1) \\n results = pool.map(NodeQuery, (i for i in zip(theConnectionInfos, theQueries)) )\\n except:\\n \\n print(mp.get_logger())\\n else:\\n pass\\n\\n finally:\\n pool.close()\\n pool.join()\\n stop = timeit.default_timer() \\n print(\\\"Parallel Query Time %s\\\" % (stop-start) )\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72763a0fa7b404b415198d05391a8bea\",\n \"score\": \"0.54160416\",\n \"text\": \"def __str__(self) -> str:\\n manager = multiprocessing.Manager()\\n results = manager.dict()\\n result = \\\"\\\"\\n jobs = []\\n for i, query in enumerate(self.rdbms.keys()):\\n p = multiprocessing.Process(target=self._str_worker, args=(i, query, results))\\n jobs.append(p)\\n p.start()\\n for proc in jobs:\\n proc.join()\\n for i, _ in enumerate(self.rdbms.keys()):\\n result += results[i]\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc1c7045535b641a4b49182559063460\",\n \"score\": \"0.5198982\",\n \"text\": \"def run(self, logger):\\n query_batches = []\\n count = 0\\n for qobject in self.__query_obj:\\n count += 1\\n logger.info('Processed %d out of %d query objects' \\n % (count, len(self.__query_obj)))\\n query_batches += qobject.produce_query_batches()\\n queries = qids.total_num_queries()\\n logger.info(\\\"%d total queries created\\\" % queries) \\n return query_batches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca95d04ae50f4123e9ad2a15298f098\",\n \"score\": \"0.51460373\",\n \"text\": \"def _execute_ds_queries_multithread(self, queries):\\n # TODO: If any query times out, the entire report fails. It might be\\n # better if partial results could be returned.\\n finished = {}\\n timeout = zillion_config[\\\"DATASOURCE_QUERY_TIMEOUT\\\"]\\n workers = min(\\n zillion_config.get(\\\"DATASOURCE_QUERY_WORKERS\\\", len(queries)), len(queries)\\n )\\n\\n with ThreadPoolExecutor(max_workers=workers) as executor:\\n futures_map = {}\\n for i, query in enumerate(queries):\\n label = self._get_query_label(\\\"%s / %s\\\" % (i + 1, len(queries)))\\n future = executor.submit(query.execute, timeout=timeout, label=label)\\n futures_map[future] = query\\n\\n for future in as_completed(futures_map):\\n data = future.result()\\n query = futures_map[future]\\n finished[future] = data\\n\\n return finished.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6337fee35da614235be9ed8d6de44eaa\",\n \"score\": \"0.5076181\",\n \"text\": \"def all_weld_query(w_cursor, w_conn):\\n try:\\n w_cursor.execute(\\n \\\"\\\"\\\"SELECT %s||'+'||%s,attributes.gps_shot,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\\n FROM %s\\n INNER JOIN %s\\n ON attributes.gps_shot = weld.gps_shot;\\\"\\\"\\\"\\n % (cols.whole_station, cols.offset_station,\\n cols.grade_point, cols.depth_cover, cols.jottings,\\n cols.wld_type, cols.wld_x_id, cols.upstream_jt,\\n cols.downstream_jt, cols.ah_length, cols.ht, cols.wll_chng,\\n cols.ditch_loc, cols.welder_initials, cols.attributes_table,\\n cols.weld_table))\\n # Assigning the results of the cursor to this tuple and fetching all the records.\\n wld_many_result = w_cursor.fetchall()\\n for record in wld_many_result:\\n for a, b in zip(common_labels + weld_labels, record):\\n print(a, b)\\n except pg2.DatabaseError as e:\\n print(e.pgerror)\\n print(\\\"No Record(s) Found.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"410b1a0874e776184a3b7bded49a35ab\",\n \"score\": \"0.5071244\",\n \"text\": \"def multiprocess_pages(base_URL, job_title, job_location, page_num):\\n\\n url = query_URL + '&page=' + str(page_num)\\n html = get_html(url)\\n rows = html.select('.job_result')\\n threads = []\\n mongo_update_lst = []\\n for row in rows: \\n thread = RequestInfoThread(row, job_title, job_location)\\n thread.start()\\n threads.append(thread)\\n for thread in threads: \\n thread.join()\\n mongo_update_lst.append(thread.json_dct)\\n\\n store_in_mongo(mongo_update_lst, 'job_postings', 'ziprecruiter')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee017b532025346003bf1e5c114df765\",\n \"score\": \"0.5028501\",\n \"text\": \"def make_relations(self):\\n self.flush_temp_files()\\n with open(self.relations_ordered_used, 'r', encoding='utf-8') as f:\\n for ele in f:\\n self.fbs = ele.strip()\\n cur_path = os.path.join(r\\\"data_processed/relations\\\", ele.strip() + '.txt')\\n self.process_files(self.data_freebase, cur_path, util.extract_rels,\\n 'Relations extraction: ' + ele.strip())\\n # recognize entities\\n self.make_entities()\\n # relations with indices\\n print('\\\\nCreating relations with indices\\\\n')\\n s0 = datetime.now()\\n self.get_indices()\\n dirs_to_explore = {self.relations_dir: self.relations_indices_dir, self.test_dir: self.test_indices_dir}\\n for dirs, indice_dirs in dirs_to_explore.items():\\n for r, d, f in os.walk(dirs):\\n for file in f:\\n if '.txt' in file:\\n s = datetime.now()\\n print(file)\\n with open(os.path.join(r, file), 'r') as f2r:\\n with open(os.path.join(indice_dirs, file), 'w') as f2w:\\n for lines in f2r:\\n line = lines.split()\\n head_entity = line[1].strip().split('.')[0]\\n tail_entity = line[1].strip().split('.')[2]\\n output = str(self.indices['entity'][head_entity][line[0].strip()]) + '\\\\t' + str(\\n self.indices['relation'][line[1].strip()]) + '\\\\t' + \\\\\\n str(self.indices['entity'][tail_entity][line[2].strip()]) + '\\\\n'\\n f2w.write(output)\\n print('Processing completed. Time elapsed: ', datetime.now() - s)\\n print('Total time elapsed: ', datetime.now() - s0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd0b233e2a824fa5f6ee391a3a171ce3\",\n \"score\": \"0.5010407\",\n \"text\": \"def get_pagelinks_info(\\n db, function_name, replicas_port=None, user_db_port=None, user=None, password=None\\n):\\n\\n query = (\\n \\\"SELECT page_id, \\\"\\n \\\"COUNT(pl_from) AS pls \\\"\\n \\\"FROM page \\\"\\n \\\"INNER JOIN pagelinks \\\"\\n \\\" ON page_title=pl_title \\\"\\n \\\" AND page_namespace=828 \\\"\\n \\\" AND page_content_model='Scribunto' \\\"\\n \\\" AND pl_namespace=828 \\\"\\n \\\"GROUP BY page_id\\\"\\n )\\n\\n cols = [\\\"page_id\\\", \\\"pls\\\"]\\n for df in query_data_generator(\\n query, function_name, cols, db, replicas_port, user_db_port, user, password\\n ):\\n save_data(df, db, function_name, user_db_port, user, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae498b5a05b5e66aada260e43b73977a\",\n \"score\": \"0.49458304\",\n \"text\": \"def run(database, document, person):\\n report = AllRelReport(database, document, person)\\n report.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ce38c6693e63d47e9b054babecb45b7\",\n \"score\": \"0.4943456\",\n \"text\": \"def run_once(sql, dbname, print_rows):\\n with connections[dbname].cursor() as cursor, timer(dbname):\\n cursor.execute(sql)\\n if print_rows:\\n rows = fetch_dicts(cursor)\\n for row in rows:\\n pprint.pprint(row)\\n print(\\\"({} rows from {})\\\".format(len(rows), dbname))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"076f20827cb8fc3d85ae9ff1bb55b303\",\n \"score\": \"0.49356064\",\n \"text\": \"def dump_collection(collection, config, force_ids, print_to_screen=False):\\n tbl_ori, tbl_rel, tbl_fin = collection['relations'].split(\\\"-\\\")\\n query = \\\"SELECT * FROM %s\\\" % (wash_table_column_name(tbl_ori))\\n lst_ori = query2list(query, tbl_ori)\\n tbl_ori_id = get_primary_keys(tbl_ori)[0]\\n for index_ori, result_ori in enumerate(lst_ori):\\n dict_rels = get_relationship(collection, tbl_ori, tbl_ori_id)\\n query = \\\"SELECT * FROM %s WHERE %s=%s\\\" % (wash_table_column_name(tbl_rel), \\n dict_rels[tbl_ori+\\\".\\\"+tbl_ori_id], \\n result_ori[tbl_ori_id])\\n if collection['tables'][tbl_ori].startswith('extend'):\\n add_special_field(collection, tbl_ori, result_ori)\\n lst_rel = query2list(query, tbl_rel)\\n for result_rel in lst_rel:\\n tbl_fin_id = get_primary_keys(tbl_fin)[0]\\n tbl_rel_id = dict_rels[tbl_fin+\\\".\\\"+tbl_fin_id].split(\\\".\\\")[1].strip()\\n query = \\\"SELECT * FROM %s WHERE %s=%s\\\" % (wash_table_column_name(tbl_fin), \\n tbl_fin_id, result_rel[tbl_rel_id])\\n lst_fin = query2list(query, tbl_fin)\\n for index_fin, result_fin in enumerate(lst_fin):\\n result_ori[tbl_fin+\\\".\\\"+create_section_id(index_fin, with_date=False)] = result_fin\\n\\n section_name = tbl_ori + \\\".\\\" + create_section_id(index_ori)\\n if force_ids == False:#Remove the ids from the dict\\n results = delete_ids(result_ori, collection['relations'].split(\\\"-\\\"))\\n config[section_name] = results\\n else:\\n config[section_name] = result_ori\\n\\n if print_to_screen == True: \\n output = StringIO.StringIO()\\n config.write(output)#Write to the output string instead of the file\\n print output.getvalue()\\n else:\\n config.write()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0947b7a33b2f900b7f542252963bdf8\",\n \"score\": \"0.4925689\",\n \"text\": \"def parallel_execute(self, conn_str, pool_size=None, taskq=None, resq=None,\\n logq=None):\\n\\n workers = []\\n max_workers = 25\\n\\n if not pool_size:\\n if len(self) <= max_workers:\\n pool_size = len(self)\\n else:\\n pool_size = max_workers\\n\\n taskq = taskq or multiprocessing.JoinableQueue()\\n resq = resq or multiprocessing.Queue()\\n logq = logq or multiprocessing.Queue()\\n\\n conn_info = get_conn_info_dict(conn_str)\\n msg_dict = combine_dicts({'msg': 'executing sql statement set in'\\n ' parallel', 'len': len(self),\\n 'pool_size': pool_size}, conn_info)\\n logger.info(msg_dict)\\n\\n # Start the worker processes.\\n for i in range(pool_size):\\n wp = multiprocessing.Process(target=_worker_process,\\n args=(conn_str, taskq, resq, logq))\\n workers.append(wp)\\n wp.start()\\n\\n # Load the tasks onto the queue.\\n for task in self:\\n taskq.put(task)\\n\\n # Start the logging thread to receive logs from the workers.\\n logp = threading.Thread(target=_logger_thread, args=(logq,))\\n logp.start()\\n\\n # Wait for all the work to be done.\\n taskq.join()\\n\\n # Stop all the workers (I'm a computah).\\n for i in range(pool_size):\\n taskq.put(None)\\n for wp in workers:\\n wp.join()\\n\\n # End the logging thread.\\n logq.put(None)\\n logp.join()\\n\\n # Collect the results.\\n self.clear()\\n resq.put(None)\\n while True:\\n result = resq.get()\\n if result is None:\\n break\\n self.add(result)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1180eb6da75bd26dac6f5f292b237ff7\",\n \"score\": \"0.48238552\",\n \"text\": \"def printout(queries,things,default=None, f=sys.stdout, **kwargs):\\n \\n results = denorm(queries,things,default=None)\\n fields = set(itertools.chain(*(x.keys() for x in results)))\\n \\n W = csv.DictWriter(f=f,fieldnames=fields,**kwargs)\\n #print \\\"---prod---\\\"\\n #print list(prod)\\n W.writeheader()\\n for r in results:\\n W.writerow(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b25295162cfbdbcb89d645b7d7d115d2\",\n \"score\": \"0.48142192\",\n \"text\": \"def execute_user_queries(self):\\n print(self._get_guide())\\n while True:\\n query = next(self._get_query())\\n self.execute(query)\\n self.pretty_print_query_result()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba2071eca26d00754440b4cca07665b\",\n \"score\": \"0.48141378\",\n \"text\": \"def get_relations(self):\\n self.relations = {}\\n\\n with open(self.data_freebase, 'r', encoding='utf-8') as f:\\n for line in f:\\n # print(line)\\n self.relations[line.split()[1]] = self.relations.get(line.split()[1], 0) + 1\\n\\n sorted_relations = sorted(self.relations.items(), key=operator.itemgetter(1), reverse=True)\\n\\n with open(self.relations_ordered, 'w', encoding='utf-8') as f:\\n for ele in sorted_relations:\\n f.write(ele[0] + '\\\\t' + str(ele[1]) + '\\\\n')\\n\\n with open(self.relations_ordered_used, 'w', encoding='utf-8') as f:\\n for ele in sorted_relations:\\n if int(ele[1]) > 10000:\\n f.write(ele[0].strip() + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7459bed287f2ae298048e25d7596368f\",\n \"score\": \"0.481377\",\n \"text\": \"def printout(queries,things,default=None, f=sys.stdout, **kwargs):\\n\\n results = denorm(queries,things,default=None)\\n fields = set(itertools.chain(*(x.keys() for x in results)))\\n\\n W = csv.DictWriter(f=f,fieldnames=fields,**kwargs)\\n #print(\\\"---prod---\\\")\\n #print(list(prod))\\n W.writeheader()\\n for r in results:\\n W.writerow(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edd8e1c25a51f423e5188f49384cd6c0\",\n \"score\": \"0.4804436\",\n \"text\": \"def batch_local(self, query_type, id_list, **kwargs) -> Dict:\\n count = len(id_list)\\n result_dict = dict()\\n\\n for start in range(0, count, self.elink_batch):\\n end = min(count, start + self.elink_batch)\\n logging.info(\\\"Looking up PMIDs for GDSs {} to {}\\\"\\n .format(start + 1, end))\\n\\n # elink each batch\\n fetch_handle = self.http_attempts(\\n query_type, **dict(id=','.join(id_list[start:end]), **kwargs)\\n )\\n record = Entrez.read(fetch_handle)\\n fetch_handle.close()\\n # for each Link'Id', dig down through dict add to list\\n for el in record:\\n result_dict[el['IdList'][0]] = el['LinkSetDb'][0]['Link'][0]['Id']\\n return result_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d787ab14a95d5c321fdf857c1c3f43b\",\n \"score\": \"0.47816938\",\n \"text\": \"def print_pool_out(output_list):\\n for session_name, result_list in output_list:\\n print(session_name)\\n if result_list:\\n for link in result_list:\\n print(f'\\\\t{link}')\\n else:\\n print(f'\\\\tNo Results Found')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcab5dab79b18bff9e5b5d444defba4f\",\n \"score\": \"0.4771668\",\n \"text\": \"def get_rel_info(\\n rel_name: str,\\n root_dir: str,\\n db: MgmtDB,\\n ch_count_type: constants.ChCountType,\\n):\\n fault_name = simulation_structure.get_fault_from_realisation(rel_name)\\n params = utils.load_sim_params(\\n simulation_structure.get_sim_params_yaml_path(\\n f\\\"{root_dir}/Runs/{fault_name}/{rel_name}\\\"\\n ),\\n load_vm=True,\\n )\\n\\n # Create relisation df\\n df = pd.DataFrame(\\n columns=COLUMNS, index=[rel_name], data=np.zeros(shape=(1, len(COLUMNS)))\\n )\\n\\n # General Parameter Metadata\\n for k, v in GENERAL_PARAM_LOCATIONS.items():\\n if isinstance(v, list):\\n value = params\\n for index in v:\\n value = value[index]\\n else:\\n value = params[v]\\n df.loc[rel_name, k] = value\\n\\n states = db.get_core_hour_states(rel_name, ch_count_type)\\n resub_counter = dict()\\n # DB Metadata\\n for state in states:\\n # Get proc_type and job_id\\n _, _, proc_type, status, job_id, _ = state\\n proc_type_name = const.ProcessType(proc_type).str_value\\n if proc_type in METADATA_PROC_TYPES:\\n (\\n _,\\n _,\\n queued_time,\\n start_time,\\n end_time,\\n nodes,\\n cores,\\n memory,\\n WCT,\\n ) = db.get_job_duration_info(job_id)\\n # Add runtime and cores to df\\n runtime = end_time - start_time\\n df.loc[rel_name, f\\\"{proc_type_name}_runtime\\\"] += runtime / 60\\n df = add_db_stat(df, rel_name, f\\\"{proc_type_name}_cores\\\", cores)\\n # Add to core hours\\n df.loc[rel_name, f\\\"{proc_type_name}_core_hours\\\"] += cores * runtime / 3600\\n df.loc[rel_name, f\\\"Total_core_hours\\\"] += cores * runtime / 3600\\n # Add to resubmits dict\\n if resub_counter.get(proc_type_name) is None:\\n resub_counter[proc_type_name] = 0\\n else:\\n resub_counter[proc_type_name] += 1\\n # Add resubmits to df\\n for k, v in resub_counter.items():\\n df.loc[rel_name, f\\\"{k}_resubmits\\\"] = v\\n\\n # Extra Metadata\\n df.loc[rel_name, \\\"IM_calc_components\\\"] = len(params[\\\"ims\\\"][\\\"component\\\"])\\n df.loc[rel_name, \\\"EMOD3D_nt\\\"] = params[\\\"sim_duration\\\"] / params[\\\"dt\\\"]\\n df.loc[rel_name, \\\"HF_nt\\\"] = params[\\\"sim_duration\\\"] / params[\\\"hf\\\"][\\\"dt\\\"]\\n df.loc[rel_name, \\\"BB_nt\\\"] = params[\\\"sim_duration\\\"] / params[\\\"bb\\\"][\\\"dt\\\"]\\n df.loc[rel_name, \\\"advanced_IM_models\\\"] = len(params[\\\"advanced_IM\\\"][\\\"models\\\"])\\n stations = shared.get_stations(params[\\\"stat_file\\\"])\\n df.loc[rel_name, \\\"HF_n_stats\\\"] = len(stations)\\n df.loc[rel_name, \\\"BB_n_stats\\\"] = len(stations)\\n df.loc[rel_name, \\\"advanced_IM_stations\\\"] = len(stations)\\n\\n # From IM calc csv\\n csv_ffp = f\\\"{root_dir}/Runs/{fault_name}/{rel_name}/IM_calc/{rel_name}.csv\\\"\\n try:\\n csv_columns = pd.read_csv(csv_ffp).columns.values\\n df.loc[rel_name, \\\"IM_calc_pSA_count\\\"] = len(\\n [True for column in csv_columns if \\\"pSA\\\" in column]\\n )\\n df.loc[rel_name, \\\"IM_calc_FAS_count\\\"] = len(\\n [True for column in csv_columns if \\\"FAS\\\" in column]\\n )\\n except FileNotFoundError:\\n print(f\\\"Could not find file {csv_ffp} (Will not exist if IM_calc was not run)\\\")\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc25140a18b976c2788d32dffc88b42\",\n \"score\": \"0.47700396\",\n \"text\": \"def re_list(request):\\n from .serializers import DocumentRelationSerializer\\n cmd_str = \\\"\\\"\\n with open('mark2cure/api/commands/get-relations.sql', 'r') as f:\\n cmd_str = f.read()\\n\\n # Start the DB Connection\\n c = connection.cursor()\\n\\n c.execute('SET @user_work_max = {rel_work_size};'.format(rel_work_size=20))\\n c.execute('SET @k_max = {completions};'.format(completions=settings.ENTITY_RECOGNITION_K))\\n c.execute('SET @user_id = {user_id};'.format(user_id=request.user.pk))\\n c.execute('SET @rel_ann_content_type_id = 56;')\\n c.execute(cmd_str)\\n\\n queryset = [{'id': x[0],\\n 'document_id': x[1],\\n 'title': x[2],\\n\\n 'total_document_relationships': x[3],\\n 'user_document_relationships': x[4],\\n\\n 'community_answered': x[5],\\n 'community_completed': x[6],\\n 'community_progress': x[7],\\n\\n 'user_completed': x[8],\\n 'user_progress': x[9],\\n 'user_answered': x[10],\\n 'user_view_completed': x[11]} for x in c.fetchall()]\\n\\n # Close the connection\\n c.close()\\n\\n # Prevent documents from being shown that have since been completed\\n # by the community before the requqest.user could complete everything\\n for idx, item in enumerate(queryset):\\n if int(item['user_document_relationships']) <= 0:\\n document = get_object_or_404(Document, pk=item['id'])\\n first_section = document.section_set.first()\\n view = View.objects.filter(task_type='re', section=first_section, user=request.user).last()\\n\\n # print(' - X:', document, first_section, view)\\n # (TODO) Why is there no View on these sections?\\n if view:\\n Point.objects.create(user=request.user,\\n amount=settings.RELATION_DOC_POINTS,\\n content_type=ContentType.objects.get_for_model(view),\\n object_id=view.id)\\n view.completed = True\\n view.save()\\n\\n del queryset[idx]\\n\\n serializer = DocumentRelationSerializer(queryset, many=True)\\n return Response(serializer.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd144f154ee8169142bae2c0d875ba0\",\n \"score\": \"0.47680968\",\n \"text\": \"def _do_relation_import(request, model_import_info, book, sheet, process_option_dict, status_dict):\\r\\n\\trelationship_source_id_dict = {}\\r\\n\\trelationship_target_id_dict = {}\\r\\n\\tfor row in range(process_option_dict['start_row']-1,process_option_dict['end_row']):\\r\\n\\t\\tstatus_dict['num_rows_processed'] = status_dict['num_rows_processed'] + 1\\r\\n\\t\\ttry:\\r\\n\\t\\t\\trow_value_list = []\\r\\n\\t\\t\\tfor cell in sheet.row(row):\\r\\n\\t\\t\\t\\tif cell.ctype == 3:\\r\\n\\t\\t\\t\\t\\tdate_tuple = xlrd.xldate_as_tuple(cell.value, book.datemode)\\r\\n\\t\\t\\t\\t\\tcell_value = str(date_tuple[0]) + '-' + str(date_tuple[1]) + '-' + str(date_tuple[2])\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tcell_value = cell.value\\r\\n\\t\\t\\t\\trow_value_list.append(cell_value)\\r\\n\\t\\t\\trelationship_source_id_dict = model_import_info.get_relationship_source_id_dict(request, row_value_list)\\r\\n\\t\\t\\trelationship_target_id_dict = model_import_info.get_relationship_target_id_dict(request, row_value_list)\\r\\n\\t\\t\\tsource_object = model_import_info.source_model.objects.get(**relationship_source_id_dict)\\r\\n\\t\\t\\ttarget_object = model_import_info.target_model.objects.get(**relationship_target_id_dict)\\r\\n\\t\\t\\trelated_field_collection = getattr(source_object, model_import_info.mapping_field_name)\\r\\n\\t\\t\\trelated_field_collection.add(target_object)\\r\\n\\t\\t\\tsource_object.save()\\r\\n\\t\\t\\tstatus_msg = 'spreadsheet row#' + str(row)+' successfully updated.'\\r\\n\\t\\t\\tstatus_dict['num_items_updated'] = status_dict['num_items_updated'] + 1\\r\\n\\t\\t\\tif process_option_dict['show_successful_updates']:\\r\\n\\t\\t\\t\\tstatus_dict['combined_results_messages'].append(status_msg)\\r\\n\\t\\t\\tstatus_dict['update_results_messages'].append(status_msg)\\r\\n\\t\\texcept:\\r\\n\\t\\t\\tstatus_dict['num_errors'] = status_dict['num_errors'] + 1\\r\\n\\t\\t\\tstatus_msg = 'spreadsheet row#' + str(row)+' ERROR: ' + `sys.exc_info()[1]`\\r\\n\\t\\t\\tif process_option_dict['show_errors']:\\r\\n\\t\\t\\t\\tstatus_dict['combined_results_messages'].append(status_msg)\\r\\n\\t\\t\\tstatus_dict['error_results_messages'].append(status_msg)\\r\\n\\t\\t\\tif process_option_dict['stop_on_first_error']:\\r\\n\\t\\t\\t\\tbreak\\r\\n\\treturn status_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430beb09fc94e739e2450c5fecdb3e4a\",\n \"score\": \"0.47642788\",\n \"text\": \"def execute_parallel(self, queries):\\n if isinstance(queries, dict):\\n return self._execute_parallel_dict(queries)\\n return self._execute_parallel_list(queries)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff50b708420970fa87e72a2a55379fee\",\n \"score\": \"0.47636887\",\n \"text\": \"def run_helper(queries):\\n total_query_to_process=len(queries)\\n\\n sdlog.debug(\\\"SDPROXMT-003\\\",\\\"%d search-API queries to process (max_thread_per_host=%d,timeout=%d)\\\"%(total_query_to_process,max_thread_per_host,sdconst.SEARCH_API_HTTP_TIMEOUT))\\n\\n while True:\\n if sdconfig.proxymt_progress_stat:\\n sdlog.info(\\\"SDPROXMT-033\\\",\\\"threads per host: %s\\\"%\\\",\\\".join(['%s=%s'%(host,len(searchAPIServices[host]['threadlist'])) for host in searchAPIServices.keys()]))\\n\\n if len(queries)>0:\\n distribute_queries(queries)\\n else:\\n # leave the loop only if all threads completed\\n if all_threads_completed():\\n break\\n\\n # remove completed threads from list\\n for host in searchAPIServices.keys():\\n li=[]\\n for t in searchAPIServices[host]['threadlist']:\\n if t.is_alive():\\n li.append(t)\\n searchAPIServices[host]['threadlist']=li\\n\\n # log\\n total_query_already_processed = total_query_to_process - len(queries)\\n if total_query_to_process > 0: # display progress only when there are a lot of queries\\n if len(queries) > 0: # display progress only when still query to process\\n sdlog.info(\\\"SDPROXMT-004\\\",\\\"total_queries=%d, running_or_done_queries=%d, waiting_queries=%d\\\"%(total_query_to_process,total_query_already_processed,len(queries)))\\n\\n # if all services are busy, we sleep to limit loop speed\\n # (note that all the code around the \\\"sleep\\\" call is to detect system overload)\\n sleep_time=10\\n warning_threshold=5 # threshold not to emit warning for every small load exceedance\\n befo=time.time()\\n time.sleep(sleep_time)\\n afte=time.time()\\n diff=afte-befo\\n if diff>sleep_time+warning_threshold:\\n sdlog.warning(\\\"SDPROXMT-005\\\",\\\"WARNING: system overload detected (sleep takes %d second to complete).\\\"%diff)\\n\\n # retrieve result from output queue\\n metadata=sdtypes.Metadata()\\n while not __result_queue.empty():\\n success=__result_queue.get(False) # retrieve result from ONE successful search-API call\\n success.connect() # TAGKLK434L3K34K\\n metadata.slurp(success) # warning: success is modified here\\n\\n # retrieve error from output queue and insert them into a list\\n errors=[]\\n while not __error_queue.empty():\\n query=__error_queue.get(False)\\n errors.append(query)\\n\\n return (metadata,errors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c90db233b317ca64380872dd6d6bafd1\",\n \"score\": \"0.47551122\",\n \"text\": \"def process_relationships(fout, root, dirpath, filename):\\n if root is None:\\n return\\n\\n # transaction_margin,dwh,CONSUMED,sold_quantity,dwh\\n source_db = None\\n dest_db = None\\n\\n logger.info(root)\\n insert_tables, select_tables = root.collect_tables()\\n\\n if len(insert_tables) == 0:\\n logger.error(\\\"Missing insert statements at: {0}\\\".format(filename))\\n return\\n\\n insert_table, insert_db = extract_table_and_db(insert_tables[0], dest_db)\\n for table in select_tables:\\n select_table, select_db = extract_table_and_db(table, source_db)\\n if select_table is None or len(select_table) == 0:\\n continue\\n elems = [insert_table.lower()]\\n elems.append(insert_db.lower())\\n elems.append('CONSUMED')\\n elems.append(select_table.lower())\\n elems.append(select_db.lower())\\n line = \\\",\\\".join(elems)\\n fout.write(line)\\n fout.write(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d92fce117b0e5516fca104d57f0d41\",\n \"score\": \"0.47441036\",\n \"text\": \"def process_results(self, agg_results, db_object, query_file_handle, refined_queries = None):\\n \\n #refine queries if not already refined.\\n if not refined_queries:\\n self.refine_queries(agg_results)\\n else:\\n self.refined_queries_results = refined_queries\\n #create result objects and write to ground truth database\\n for (q, r) in self.refined_queries_results:\\n query_result = CAT_TO_CLASSES[q[qs.QRY_ENUM]].result_class(q,r,db_object, self.full_query)\\n try:\\n query_result.write_query()\\n except Exception as detail:\\n logger.critical(\\\"ERROR failed to write query with qid=%d\\\"\\n \\\". Got error: %s\\\" % (q[qs.QRY_QID], detail))\\n \\n #writing queries in sql format to file\\n if self.full_query: \\n for (q,r) in self.refined_queries_results:\\n if q != None:\\n self._print_query(q, query_file_handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8808fd80151e311679d2fb98356e6e42\",\n \"score\": \"0.47110108\",\n \"text\": \"def _ProcessResults(self):\\n for (key, value) in self._results.iteritems():\\n (graph_name, trace_name) = key\\n (units, results) = value\\n print \\\"RESULT %s: %s= %s %s\\\" % (graph_name, trace_name,\\n str(results), units)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee905e42f13bea64eada18099a7f126\",\n \"score\": \"0.470631\",\n \"text\": \"def printpool(self):\\n for f,c in self.pool:\\n print f,c\\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c2ca9d74e8a2022bee4e2b35828b8a\",\n \"score\": \"0.47021446\",\n \"text\": \"def multiprocess(data):\\n pool = multiprocessing.Pool(processes=4)\\n rx_dict = dict(pool.map(getrx, (ndc_list)))\\n pool.close()\\n pool.join()\\n return rx_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb73ffd59f5820cc852805485322153\",\n \"score\": \"0.46973732\",\n \"text\": \"def synapse_writer(db):\\n global n_synapses\\n n_synapses = 0\\n print(\\\"[ synapse writer ] : Loading DB: %g\\\" %timer.elapsedTime())\\n \\n image_map = db.get_image_map()\\n \\n # TODO 48 only cycle the existing processors\\n \\n chip_map = [ (c['x'],c['y']) for c in image_map ]\\n chip_map = list(set(chip_map)) # removing duplicates http://love-python.blogspot.co.uk/2008/09/remove-duplicate-items-from-list-using.html\\n\\n if PARALLEL: \\n num_processes = pacman.pacman_configuration.getint('synapse_writer', 'processes') # number of processes - why do I need this here?\\n print '[ synapse writer ] : Spawning %d parallel threads' % num_processes\\n # parallelizing http://stackoverflow.com/questions/8521883/multiprocessing-pool-map-and-function-with-two-arguments\\n # 16.6.1.5. Using a pool of workers: http://docs.python.org/library/multiprocessing.html \\n # not with SQLITE apparently\\n from multiprocessing import Process, Queue\\n\\n chip_map = [ (c['x'],c['y']) for c in image_map ]\\n chip_map = list(set(chip_map)) # removing duplicates http://love-python.blogspot.co.uk/2008/09/remove-duplicate-items-from-list-using.html\\n\\n # http://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks-in-python/312644 \\n \\n num_processes = min(num_processes, len(chip_map))\\n if len(chip_map)%num_processes==0:\\n chunk_size = len(chip_map)/num_processes\\n else:\\n chunk_size = len(chip_map)/num_processes+1\\n chunks = [ chip_map[i:i+chunk_size] for i in range(0, len(chip_map), chunk_size)] \\n\\n # Instantiates the db_writer \\n global queue\\n global db_global\\n db_global = db\\n queue = Queue(0) \\n\\n db_queue = db_writer()\\n db_queue.start()\\n db_queue.setDaemon = True\\n \\n processes = list()\\n for p in range(num_processes):\\n print \\\"[ synapse writer ] : [ Process %d ] : chunk: %s\\\" % (p, chunks[p])\\n if len(chunks) > p:\\n processes.append(Process(target=callback, args=(db.db_abs_path, chunks[p], p)))\\n processes[p].start()\\n\\n for p in range(num_processes): \\n processes[p].join() # wait for all processes to finish\\n \\n \\n db_queue.running = False\\n\\n queue.put(None)\\n db_queue.join()\\n\\n \\n \\n else: \\n [ compute_sdram_file(chip_map[i][0], chip_map[i][1], db, image_map) for i in range(len(chip_map)) ] # creates all the files for the system size # TODO reorganize to use only used chips\\n\\n \\n print \\\"[ synapse writer ] : Written %d synapses\\\" % n_synapses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c9315b6e921c9e37c3ead09eaf28b61\",\n \"score\": \"0.4691319\",\n \"text\": \"def print_responses(response_set, sqlite_cursor):\\n for result in response_set.response:\\n doc = response_set.document[result.documentIndex]\\n query = f\\\"\\\"\\\"\\n SELECT title, date, hotel, review FROM reviews\\n WHERE doc_id=\\\"{doc.id}\\\"\\n \\\"\\\"\\\"\\n for row in sqlite_cursor.execute(query):\\n title, date, hotel, fulltext = row\\n print(Style.BRIGHT + title)\\n if is_title(result):\\n print(f\\\"{Style.RESET_ALL}{head(fulltext)}\\\")\\n else:\\n print(f\\\"{Style.RESET_ALL}{highlight(fulltext, result.text)}\\\")\\n print(Style.RESET_ALL + Style.DIM +\\n f\\\"{hotel_name(hotel)} reviewed on {date}\\\" +\\n Style.RESET_ALL)\\n print()\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed70d3a085b5c6cc5d1be5ac887581a\",\n \"score\": \"0.46757925\",\n \"text\": \"def process_search(cb_conn, query, query_base=None):\\n results = set()\\n\\n query += query_base\\n log(\\\" Query: %s\\\" % query)\\n\\n try:\\n for proc in cb_conn.select(Process).where(query):\\n results.add((proc.hostname.lower(),\\n proc.username.lower(), \\n proc.path,\\n proc.cmdline,\\n proc.process_md5,\\n proc.start,\\n proc.webui_link))\\n except KeyboardInterrupt:\\n log(\\\"Caught CTRL-C. Returning what we have . . .\\\\n\\\")\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b97af818440662fcc9479d4ebd39245\",\n \"score\": \"0.46736822\",\n \"text\": \"def dump_connections(self):\\n for x in self.connections:\\n print(x.id, end=',')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b277ee4017c48cf2098ff9b2fa64033e\",\n \"score\": \"0.46648812\",\n \"text\": \"def run(self):\\t\\t\\n\\t\\tcommunicator = MPI.world.duplicate()\\n\\t\\tnode_rank = communicator.rank\\n\\t\\tif node_rank == 0:\\n\\t\\t\\t(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)\\n\\t\\t\\told_schema_instance = form_schema_tables(self.input_fname)\\n\\t\\t\\tnew_schema_instance = form_schema_tables(self.jnput_fname)\\n\\t\\t\\tgene_no2go = get_gene_no2go_no_set(curs)\\n\\t\\t\\tgene_no2go_pickle = cPickle.dumps(gene_no2go, -1)\\t#-1 means use the highest protocol\\n\\t\\t\\t\\n\\t\\t\\tif self.max_layer:\\n\\t\\t\\t\\tcrs_sentence = 'DECLARE crs CURSOR FOR SELECT p.p_gene_id, p.gene_no, p.go_no, p.is_correct, p.is_correct_l1, \\\\\\n\\t\\t\\t\\tp.is_correct_lca, p.avg_p_value, p.no_of_clusters, p.cluster_array, p.p_value_cut_off, p.recurrence_cut_off, \\\\\\n\\t\\t\\t\\tp.connectivity_cut_off, p.cluster_size_cut_off, p.unknown_cut_off, p.depth_cut_off, p.mcl_id, p.lca_list, \\\\\\n\\t\\t\\t\\tp.vertex_gradient, p.edge_gradient, p2.vertex_set, p2.edge_set, p2.d_matrix, p2.recurrence_array from %s p, %s p2 where \\\\\\n\\t\\t\\t\\tp.mcl_id=p2.id'%(old_schema_instance.p_gene_table, old_schema_instance.pattern_table)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcrs_sentence = \\\"DECLARE crs CURSOR FOR SELECT p.p_gene_id, p.gene_no, p.go_no, p.is_correct, p.is_correct_l1, \\\\\\n\\t\\t\\t\\tp.is_correct_lca, p.avg_p_value, p.no_of_clusters, p.cluster_array, p.p_value_cut_off, p.recurrence_cut_off, \\\\\\n\\t\\t\\t\\tp.connectivity_cut_off, p.cluster_size_cut_off, p.unknown_cut_off, p.depth_cut_off, p.mcl_id, p.lca_list, p.vertex_gradient,\\\\\\n\\t\\t\\t\\tp.edge_gradient, 'vertex_set', 'edge_set', 'd_matrix', 'recurrence_array' \\\\\\n\\t\\t\\t\\tfrom %s p\\\"%(old_schema_instance.p_gene_table)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t#some placeholders 'vertex_set', 'edge_set', 'd_matrix' for prediction_attributes()\\n\\t\\t\\t\\n\\t\\t\\tif self.acc_cut_off:\\n\\t\\t\\t\\tmcl_id2accuracy = self.get_mcl_id2accuracy(curs, old_schema_instance.p_gene_table, crs_sentence, self.is_correct_type)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tmcl_id2accuracy = None\\n\\t\\t\\tmcl_id2accuracy_pickle = cPickle.dumps(mcl_id2accuracy, -1)\\t#-1 means use the highest protocol\\n\\t\\t\\tfor node in range(1, communicator.size-1):\\t#send it to the computing_node\\n\\t\\t\\t\\tcommunicator.send(gene_no2go_pickle, node, 0)\\n\\t\\t\\tfor node in range(1, communicator.size-1):\\t#send it to the computing_node\\n\\t\\t\\t\\tcommunicator.send(mcl_id2accuracy_pickle, node, 0)\\n\\t\\telif node_rank<=communicator.size-2:\\t#exclude the last node\\n\\t\\t\\tdata, source, tag = communicator.receiveString(0, 0)\\n\\t\\t\\tgene_no2go = cPickle.loads(data)\\t#take the data\\n\\t\\t\\tdata, source, tag = communicator.receiveString(0, 0)\\n\\t\\t\\tmcl_id2accuracy = cPickle.loads(data)\\t#take the data\\n\\t\\t\\t#choose a functor for recurrence_array\\n\\t\\t\\tfunctor_dict = {0: None,\\n\\t\\t\\t\\t1: lambda x: int(x>=self.recurrence_x),\\n\\t\\t\\t\\t2: lambda x: math.pow(x, self.recurrence_x)}\\n\\t\\t\\tfunctor = functor_dict[self.recurrence_x_type]\\n\\t\\telif node_rank==communicator.size-1:\\n\\t\\t\\t(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)\\n\\t\\t\\told_schema_instance = form_schema_tables(self.input_fname)\\n\\t\\t\\tnew_schema_instance = form_schema_tables(self.jnput_fname)\\n\\t\\t\\tself.view_from_table(curs, old_schema_instance.splat_table, new_schema_instance.splat_table)\\n\\t\\t\\tself.view_from_table(curs, old_schema_instance.mcl_table, new_schema_instance.mcl_table)\\n\\t\\t\\tself.view_from_table(curs, old_schema_instance.pattern_table, new_schema_instance.pattern_table)\\n\\t\\t\\tself.createGeneTable(curs, new_schema_instance.p_gene_table)\\n\\t\\t\\n\\t\\tmpi_synchronize(communicator)\\n\\t\\t\\n\\t\\tif node_rank == 0:\\n\\t\\t\\tself.input_node(communicator, curs, old_schema_instance, crs_sentence, self.size)\\n\\t\\telif node_rank<=communicator.size-2:\\t#exclude the last node\\n\\t\\t\\tself.computing_node(communicator, gene_no2go, self.exponent, self.score_list, \\\\\\n\\t\\t\\t\\tself.max_layer, self.norm_exp, self.eg_d_type, mcl_id2accuracy, self.acc_cut_off, functor)\\n\\t\\telif node_rank==communicator.size-1:\\n\\t\\t\\tparameter_list = [curs, new_schema_instance.p_gene_table]\\n\\t\\t\\tfree_computing_nodes = range(1,communicator.size-1)\\n\\t\\t\\toutput_node(communicator, free_computing_nodes, parameter_list, self.output_node_handler)\\n\\t\\t\\tif self.commit:\\n\\t\\t\\t\\tcurs.execute(\\\"end\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0624bc66640753d9ac530ee8aecde9b\",\n \"score\": \"0.46631014\",\n \"text\": \"def all_bend_query(cb_cursor, cb_conn):\\n try:\\n cb_cursor.execute(\\n \\\"\\\"\\\"SELECT %s||'+'||%s,attributes.gps_shot,%s,%s,%s,%s,%s,%s\\n FROM %s\\n INNER JOIN %s\\n ON attributes.gps_shot = bend.gps_shot;\\\"\\\"\\\"\\n % (cols.whole_station, cols.offset_station, cols.grade_point,\\n cols.depth_cover, cols.jottings, cols.deg, cols.bnd_dir,\\n cols.bnd_type, cols.attributes_table, cols.bend_table))\\n # Assigning the results of the cursor to this tuple and fetching all the records.\\n b_many_result = cb_cursor.fetchall()\\n # Merging the common_labels and the bend_labels together for\\n # printing with the query results.\\n for record in b_many_result:\\n for a, b in zip(common_labels + bnd_labels, record):\\n print(a,b)\\n except pg2.DatabaseError as e:\\n print(e.pgerror)\\n print(\\\"No Record(s) Found.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0911d5b24add2284762975de2c2ee67d\",\n \"score\": \"0.46616054\",\n \"text\": \"def fetch_steps(db_cursor, workflow_list):\\n # Get steps information\\n db_cursor.execute(\\\"\\\"\\\"\\n SELECT dpu_instance.id AS 'stepId', dpu_instance.name AS 'stepTitle',\\n dpu_instance.description AS 'description',\\n dpu_template.name AS 'templateName',\\n dpu_instance.configuration AS 'configuration',\\n ppl_model.id AS 'workflowId',\\n ppl_model.description AS 'description',\\n ppl_model.name AS 'workflowTitle',\\n ppl_model.visibility AS 'visibility',\\n exec_pipeline.id AS 'activityId',\\n exec_pipeline.status AS 'status'\\n FROM exec_pipeline, ppl_model, dpu_template, dpu_instance, ppl_node,ppl_graph\\n WHERE ppl_node.instance_id=dpu_instance.id AND\\n ppl_node.graph_id = ppl_graph.id AND\\n ppl_graph.pipeline_id = ppl_model.id AND\\n dpu_instance.dpu_id = dpu_template.id AND\\n exec_pipeline.pipeline_id = ppl_model.id\\n \\\"\\\"\\\")\\n\\n result_set = db_cursor.fetchall()\\n\\n for row in result_set:\\n workflow_graph = dict()\\n workflow_graph[\\\"provenance\\\"] = dict()\\n workflow_graph[\\\"payload\\\"] = dict()\\n workflow_graph[\\\"provenance\\\"][\\\"agent\\\"] = dict()\\n workflow_graph[\\\"provenance\\\"][\\\"agent\\\"][\\\"ID\\\"] = artifact_id\\n workflow_graph[\\\"provenance\\\"][\\\"agent\\\"][\\\"role\\\"] = agent_role\\n\\n graph = workflow_graph[\\\"provenance\\\"]\\n graph[\\\"context\\\"] = dict()\\n graph[\\\"context\\\"][\\\"activityID\\\"] = str(row['activityId'])\\n graph[\\\"context\\\"][\\\"workflowID\\\"] = str(row['workflowId'])\\n graph[\\\"context\\\"][\\\"stepID\\\"] = str(row['stepId'])\\n\\n graph[\\\"activity\\\"] = dict()\\n graph[\\\"activity\\\"][\\\"status\\\"] = determine_status(row['status'])\\n graph[\\\"activity\\\"][\\\"configuration\\\"] = parse_config(row['configuration'])\\n graph[\\\"activity\\\"][\\\"type\\\"] = \\\"StepExecution\\\"\\n graph[\\\"activity\\\"][\\\"title\\\"] = str(row[\\\"stepTitle\\\"])\\n\\n app_logger.info('Construct provenance information for Step{0}.' .format(row['stepId']))\\n\\n workflow_list.append(workflow_graph)\\n # return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e796bf53edeace6406a5fb94cc013d\",\n \"score\": \"0.46581215\",\n \"text\": \"def _traverse_and_execute(self, executable: GraphExecutable) -> None: # noqa mccabe: disable=MC0001\\n start_time = time.time()\\n if self.barf:\\n with open(os.path.join(self.barf_output, f'{[n for n in executable.graph.nodes][0].name}.component'), 'wb') as cmp_file: # noqa pylint: disable=unnecessary-comprehension\\n nx.write_multiline_adjlist(executable.graph, cmp_file)\\n try:\\n logger.debug(\\n f\\\"Executing graph with {len(executable.graph)} relations in it...\\\")\\n for i, relation in enumerate(\\n nx.algorithms.dag.topological_sort(executable.graph)):\\n relation.population_size = executable.source_adapter.scalar_query(\\n executable.source_adapter.population_count_statement(relation))\\n logger.info(f'Executing source query for relation {relation.dot_notation} '\\n f'({i+1} of {len(executable.graph)} in graph)...')\\n\\n relation.sampling.prepare(relation,\\n executable.source_adapter)\\n relation = RuntimeSourceCompiler.compile_queries_for_relation(\\n relation, executable.graph, executable.source_adapter, executable.analyze)\\n\\n if executable.analyze:\\n if relation.is_view:\\n relation.population_size = \\\"N/A\\\"\\n relation.sample_size = \\\"N/A\\\"\\n logger.info(\\n f'Relation {relation.dot_notation} is a view, skipping.')\\n else:\\n result = executable.source_adapter.check_count_and_query(relation.compiled_query,\\n MAX_ALLOWED_ROWS,\\n relation.unsampled).iloc[0]\\n relation.population_size = result.population_size\\n relation.sample_size = result.sample_size\\n logger.info(\\n f'Analysis of relation {relation.dot_notation} completed in {duration(start_time)}.')\\n else:\\n executable.target_adapter.create_database_if_not_exists(\\n relation.quoted(relation.database))\\n executable.target_adapter.create_schema_if_not_exists(\\n relation.quoted(relation.database),\\n relation.quoted(relation.schema))\\n if relation.is_view:\\n logger.info(\\n f'Retrieving DDL statement for view {relation.dot_notation} in source...')\\n relation.population_size = \\\"N/A\\\"\\n relation.sample_size = \\\"N/A\\\"\\n try:\\n relation.view_ddl = executable.source_adapter.scalar_query(\\n relation.compiled_query)\\n except Exception:\\n raise SystemError(\\n f'Failed to extract DDL statement: {relation.compiled_query}')\\n logger.info(\\n f'Successfully extracted DDL statement for view {relation.quoted_dot_notation}')\\n else:\\n logger.info(\\n f'Retrieving records from source {relation.dot_notation}...')\\n try:\\n relation.data = executable.source_adapter.check_count_and_query(\\n relation.compiled_query, MAX_ALLOWED_ROWS, relation.unsampled)\\n except Exception as exc:\\n raise SystemError(\\n f'Failed execution of extraction sql statement: {relation.compiled_query} {exc}')\\n\\n relation.sample_size = len(relation.data)\\n logger.info(\\n f'{relation.sample_size} records retrieved for relation {relation.dot_notation}.')\\n\\n logger.info(\\n f'Inserting relation {relation.quoted_dot_notation} into target...')\\n try:\\n executable.target_adapter.create_and_load_relation(\\n relation)\\n except Exception as exc:\\n raise SystemError(\\n f'Failed to load relation {relation.quoted_dot_notation} into target: {exc}')\\n\\n logger.info(\\n f'Done replication of relation {relation.dot_notation} in {duration(start_time)}.')\\n relation.target_loaded = True\\n relation.source_extracted = True\\n logger.info(\\n f'population:{relation.population_size}, sample:{relation.sample_size}')\\n if self.barf:\\n with open(os.path.join(self.barf_output, f'{relation.dot_notation}.sql'), 'w') as barf_file:\\n barf_file.write(relation.compiled_query)\\n try:\\n for relation in executable.graph.nodes:\\n del relation.data\\n except AttributeError:\\n pass\\n gc.collect()\\n except Exception as exc:\\n logger.error(f'failed with error of type {type(exc)}: {str(exc)}')\\n raise exc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9d574b92f02d3381547f29127005af\",\n \"score\": \"0.46548247\",\n \"text\": \"def get_templatelinks_info(\\n db, function_name, replicas_port=None, user_db_port=None, user=None, password=None\\n):\\n\\n query = (\\n \\\"SELECT page_id, \\\"\\n \\\"COUNT(tl_from) AS transcluded_in \\\"\\n \\\"FROM page \\\"\\n \\\"INNER JOIN templatelinks \\\"\\n \\\" ON page_title=tl_title \\\"\\n \\\" AND page_namespace=828 \\\"\\n \\\" AND page_content_model='Scribunto' \\\"\\n \\\" AND tl_namespace=828 \\\"\\n \\\"GROUP BY page_id\\\"\\n )\\n\\n cols = [\\\"page_id\\\", \\\"transcluded_in\\\"]\\n for df in query_data_generator(\\n query,\\n function_name,\\n cols,\\n db,\\n replicas_port,\\n user_db_port,\\n user,\\n password,\\n ):\\n save_data(df, db, function_name, user_db_port, user, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7faaed1642db7991a3bd0be95f317f38\",\n \"score\": \"0.46472684\",\n \"text\": \"def info(self):\\n rd = self.round\\n fp = Primers.query.get(rd.forward_primer or 0)\\n rp = Primers.query.get(rd.reverse_primer or 0)\\n fpi = Primers.query.get(self.fp_id )\\n rpi = Primers.query.get(self.rp_id )\\n return [(rd.round_name, rd.__tablename__, rd.id)] + [(p.name, p.__tablename__,p.id) if p else (None,None,None)for p in (fp,rp,fpi,rpi)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ac14168f7b821b33c30c2bea5a7246\",\n \"score\": \"0.46457297\",\n \"text\": \"def main():\\n global conn\\n\\n conn = pg.connect(database=db, user=usr, password=pasw, host=host,\\n port=port)\\n\\n for folder in range(3):\\n print('Processing folder #{}'.format(folder))\\n process(folder)\\n\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5697f46c1caa3f30007251caaf8fb4a0\",\n \"score\": \"0.4645182\",\n \"text\": \"def slow_loop(scraper, id_):\\n # Update the database to confirm that task has started processing\\n result = Result.query.filter_by(id=id_).first()\\n result.started = datetime.datetime.now()\\n db.session.add(result)\\n db.session.commit()\\n \\n # Initializing scraper and empty dataframe to store results\\n scraper.count_all_pages()\\n dataframe = defaultdict(list)\\n\\n # Store completion percentage in redis under the process id\\n job = get_current_job()\\n job.meta[\\\"progress\\\"] = 0\\n job.save_meta()\\n\\n job.meta[\\\"total\\\"] = len(scraper.all_pages)\\n job.save_meta()\\n\\n for page_number in scraper.all_pages:\\n title_list, location_list, price_list, type_list, bedroom_list, bathroom_list, area_list = scraper.scrape_page(page_number = page_number)\\n dataframe['title_list'].extend(title_list)\\n dataframe['location_list'].extend(location_list)\\n dataframe['price_list'].extend(price_list)\\n dataframe['type_list'].extend(type_list)\\n dataframe['bedroom_list'].extend(bedroom_list)\\n dataframe['bathroom_list'].extend(bathroom_list)\\n dataframe['area_list'].extend(area_list)\\n \\n # update completion percentage so it's available from front-end\\n job.meta[\\\"progress\\\"] = page_number\\n job.save_meta()\\n\\n dataframe = DataFrame(dataframe)\\n dataframe['area_list'] = dataframe['area_list'].str.extract(\\\"([0-9,]+)\\\", expand = False)\\n\\n res = dataframe.to_json(orient='index')\\n\\n # update the database to confirm that task has completed processing\\n result.completed = datetime.datetime.now()\\n result.result = res\\n db.session.add(result)\\n db.session.commit()\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f00d28bc08bdf8ecf038440f724bcd\",\n \"score\": \"0.46402127\",\n \"text\": \"def getConnections():\\n ids = {}\\n container = obj\\n while container is not None:\\n if getattr(container, 'objectValues', None) is not None:\\n for ob in container.objectValues():\\n if ( getattr(ob, '_isAnSQLConnection', None) and\\n getattr(ob, 'id', None) ):\\n ob_id = ob.id\\n\\n if callable(ob_id):\\n ob_id = ob_id()\\n\\n if ob_id not in ids:\\n if hasattr(ob, 'title_and_id'):\\n title = ob.title_and_id()\\n else:\\n title = ob_id\\n ids[ob_id] = title\\n\\n container = aq_parent(aq_inner(container))\\n\\n ids = [(item[1], item[0]) for item in ids.items()]\\n ids.sort()\\n import pdb;pdb.set_trace()\\n return ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa6b6586db301bfd21146be01cd2b81d\",\n \"score\": \"0.4639239\",\n \"text\": \"def handle(self, *args, **kwargs):\\n\\n with connection.cursor() as cursor:\\n cursor.execute(\\n sql.SQL(\\n \\\"\\\"\\\"\\n SELECT relname as \\\"relation\\\"\\n FROM pg_class LEFT JOIN pg_namespace ON (pg_class.relnamespace = pg_namespace.oid)\\n WHERE nspname = 'public' AND relkind = 'r'\\n \\\"\\\"\\\"\\n )\\n )\\n\\n # VACUUM FULL aggressively reclaims disk space by rewriting the heap\\n # and removing deleted tuples. Requires an exclusive table lock, so only\\n # do this on tables that are not direclty used by the application.\\n # This is necessary to reduce disk usage when tables are frequently\\n # truncated and reimported.\\n for (relation,) in cursor.fetchall():\\n self.stdout.write(\\\"Cleaning table '%s'\\\" % relation)\\n _t0 = time()\\n cursor.execute(\\n sql.SQL(\\\"VACUUM FULL {}\\\").format(sql.Identifier(relation))\\n )\\n _t1 = time()\\n self.stdout.write(\\\"Finished cleaning in %.2f seconds\\\" % (_t1 - _t0))\\n\\n with transaction.atomic():\\n cursor.execute(RECURSIVE_VIEWS_QUERY)\\n\\n for (view,) in cursor.fetchall():\\n self.stdout.write(\\\"Refreshing materialized view '%s'...\\\" % view)\\n _t0 = time()\\n cursor.execute(\\n sql.SQL(\\\"REFRESH MATERIALIZED VIEW CONCURRENTLY {}\\\").format(\\n sql.Identifier(view)\\n )\\n )\\n _t1 = time()\\n self.stdout.write(\\\"Refreshed view in %.2f seconds\\\" % (_t1 - _t0))\\n self.stdout.write(\\\"Updating database statistics...\\\")\\n _t0 = time()\\n # VACUUM ANALYZE updates PostgreSQL's internal statistics about the database.\\n # It also updates the visibility map, allowing for index-only\\n # scans in many cases.\\n cursor.execute(sql.SQL(\\\"VACUUM ANALYZE\\\"))\\n _t1 = time()\\n self.stdout.write(\\n \\\"Updated database statistics in %.2f seconds\\\" % (_t1 - _t0)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53aff3bf0cbcef6d882230fe3024dd49\",\n \"score\": \"0.46227002\",\n \"text\": \"def _traverse_and_execute(self, executable: GraphExecutable) -> None: # noqa: pylint: disable=too-many-statements\\n start_time = time.time()\\n if self.barf:\\n with open(os.path.join(self.barf_output,\\n f'{[n for n in executable.graph.nodes][0].dot_notation}.component'), # noqa: pylint: disable=unnecessary-comprehension\\n 'wb'\\n ) as cmp_file:\\n nx.write_multiline_adjlist(executable.graph, cmp_file)\\n try:\\n logger.debug(\\n f\\\"Executing graph with {len(executable.graph)} relations in it...\\\")\\n for i, relation in enumerate(\\n nx.algorithms.dag.topological_sort(executable.graph)):\\n relation.population_size = executable.source_adapter.scalar_query(\\n executable.source_adapter.population_count_statement(relation))\\n logger.info(f'Executing source query for relation {relation.dot_notation} '\\n f'({i+1} of {len(executable.graph)} in graph)...')\\n\\n relation.sampling.prepare(relation,\\n executable.source_adapter)\\n relation = RuntimeSourceCompiler.compile_queries_for_relation(\\n relation, executable.graph, executable.source_adapter, executable.analyze)\\n\\n if executable.analyze:\\n if relation.is_view:\\n relation.population_size = \\\"N/A\\\"\\n relation.sample_size = \\\"N/A\\\"\\n logger.info(\\n f'Relation {relation.dot_notation} is a view, skipping.')\\n else:\\n result = executable.source_adapter.check_count_and_query(relation.compiled_query,\\n relation.sampling.max_allowed_rows,\\n relation.unsampled).iloc[0]\\n relation.population_size = result.population_size\\n relation.sample_size = result.sample_size\\n logger.info(\\n f'Analysis of relation {relation.dot_notation} completed in {duration(start_time)}.')\\n else:\\n executable.target_adapter.create_database_if_not_exists(\\n relation.database)\\n executable.target_adapter.create_schema_if_not_exists(\\n relation.database,\\n relation.schema)\\n if relation.is_view:\\n logger.info(\\n f'Retrieving DDL statement for view {relation.dot_notation} in source...')\\n relation.population_size = \\\"N/A\\\"\\n relation.sample_size = \\\"N/A\\\"\\n try:\\n relation.view_ddl = executable.source_adapter.scalar_query(\\n relation.compiled_query)\\n except Exception as exc:\\n raise SystemError(\\n f'Failed to extract DDL statement: {relation.compiled_query}') from exc\\n logger.info('Successfully extracted DDL statement for view ' \\n f'{executable.target_adapter.quoted_dot_notation(relation)}')\\n else:\\n logger.info(\\n f'Retrieving records from source {relation.dot_notation}...')\\n try:\\n relation.data = executable.source_adapter.check_count_and_query(\\n relation.compiled_query, relation.sampling.max_allowed_rows, relation.unsampled)\\n except Exception as exc:\\n raise SystemError(\\n f'Failed execution of extraction sql statement: {relation.compiled_query} {exc}') \\\\\\n from exc\\n\\n relation.sample_size = len(relation.data)\\n logger.info(\\n f'{relation.sample_size} records retrieved for relation {relation.dot_notation}.')\\n\\n logger.info(f'Inserting relation {executable.target_adapter.quoted_dot_notation(relation)}' \\n ' into target...')\\n try:\\n executable.target_adapter.create_and_load_relation(\\n relation)\\n except Exception as exc:\\n raise SystemError('Failed to load relation '\\n f'{executable.target_adapter.quoted_dot_notation(relation)} ' \\n f' into target: {exc}') from exc\\n\\n logger.info('Done replication of relation ' \\n f'{executable.target_adapter.quoted_dot_notation(relation)} ' \\n f' in {duration(start_time)}.')\\n relation.target_loaded = True\\n relation.source_extracted = True\\n logger.info(\\n f'population:{relation.population_size}, sample:{relation.sample_size}')\\n if self.barf:\\n with open(os.path.join(self.barf_output, f'{relation.dot_notation}.sql'), 'w') as barf_file: # noqa pylint: disable=unspecified-encoding\\n barf_file.write(relation.compiled_query)\\n\\n for relation in executable.graph.nodes:\\n try:\\n del relation.data\\n except AttributeError:\\n logger.warning(\\\"Failed to purge data of the %s relation\\\", relation)\\n\\n gc.collect()\\n except Exception as exc:\\n logger.error(f'failed with error of type {type(exc)}: {str(exc)}')\\n raise exc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e0d9c1ec2f2845c27b70735d077374b\",\n \"score\": \"0.46221495\",\n \"text\": \"def main():\\n\\n gene_k = 45 #This kmer size is huge. However, it was needed to avoid cycles in the DB graph, which we had a hard time getting around.\\n prob = 50\\n read_length = 50\\n depth = 15 #average read depth\\n prune_threshold = 10\\n pruned = 0\\n\\n genome_db, kmer_positions, gene = db.main(gene_k) #gets reference stuff\\n\\n ###CREATES MATERNAL AND PATERNAL SNV SEQUENCES\\n mat = gene[:-3] + 'Z' + gene[-2:]\\n pat = gene[:-50] + \\\"X\\\" + gene[-49:]\\n\\n reads, positions = uniform_reads(mat, pat, gene, depth, read_length) #get list of reads, positions of reads.\\n\\n genome_db, genome_hash = thread(reads, genome_db, kmer_positions, gene_k, positions) #thread all reads\\n\\n genome_db, pruned = pruning(genome_db, prune_threshold) #prune\\n\\n #writes to files for debugging\\n with open(\\\"genome_hash.txt\\\", \\\"w\\\") as text_file:\\n text_file.write(str(genome_hash))\\n with open(\\\"genome_db.txt\\\", \\\"w\\\") as text_file:\\n text_file.write(str(genome_db))\\n\\n\\n return genome_db, kmer_positions, gene, pruned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e5d34c4c530cfc66dfec343e63a6161\",\n \"score\": \"0.46189845\",\n \"text\": \"def multiprocess_pairs(self):\\n # Initialize key variables\\n pattoo_db_records_lists_tuple = self._arguments\\n pool_size = self._pool_size\\n\\n # Create a pool of sub process resources\\n with get_context('spawn').Pool(processes=pool_size) as pool:\\n\\n # Create sub processes from the pool\\n per_process_key_value_pairs = pool.starmap(\\n _process_kvps_exception, pattoo_db_records_lists_tuple)\\n\\n # Wait for all the processes to end and get results\\n pool.join()\\n\\n # Test for exceptions\\n for result in per_process_key_value_pairs:\\n if isinstance(result, ExceptionWrapper):\\n result.re_raise()\\n\\n # Update the database with key value pairs\\n pair.insert_rows(per_process_key_value_pairs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e76519ac11f8cb31512a1af4657d674\",\n \"score\": \"0.4610883\",\n \"text\": \"def get_poi_dict(city_id):\\n global white_list_data\\n _poi_dict = defaultdict(dict)\\n _online_official_data = {}\\n _online_nonofficial_data = {}\\n # get whole data process\\n conn = data_process_pool.connection()\\n cursor = conn.cursor(cursor=DictCursor)\\n sql = '''SELECT *\\nFROM {}\\nWHERE city_id='{}';'''.format(data_process_table_name, city_id)\\n _t = time.time()\\n cursor.execute(sql)\\n logger.debug('[query][sql: {}][takes: {}]'.format(sql, time.time() - _t))\\n for each in cursor.fetchall():\\n if int(each[\\\"official\\\"]) == 0:\\n _online_nonofficial_data[each['id']] = each\\n else:\\n _online_official_data[each['id']] = each\\n cursor.close()\\n conn.close()\\n\\n # get whole base data, and update data process result\\n conn = base_data_pool.connection()\\n cursor = conn.cursor(cursor=DictCursor)\\n sql = '''SELECT *\\n FROM {}\\n WHERE city_id='{}';'''.format(data_process_table_name, city_id)\\n _t = time.time()\\n cursor.execute(sql)\\n logger.debug('[query][sql: {}][takes: {}]'.format(sql, time.time() - _t))\\n for each in cursor.fetchall():\\n each.pop('tag_id')\\n if int(each[\\\"official\\\"]) == 0:\\n _online_nonofficial_data[each['id']].update(each)\\n else:\\n _online_official_data[each['id']].update(each)\\n cursor.close()\\n conn.close()\\n\\n # get whole source data\\n conn = poi_ori_pool.connection()\\n cursor = conn.cursor(cursor=DictCursor)\\n sql = \\\"select * from {} where city_id='{}'\\\".format(poi_type, city_id)\\n _t = time.time()\\n cursor.execute(sql)\\n logger.debug('[query][sql: {}][takes: {}]'.format(sql, time.time() - _t))\\n for line in cursor.fetchall():\\n source = line['source']\\n source_id = line['id']\\n _poi_dict[(source, source_id)] = line\\n cursor.close()\\n conn.close()\\n\\n for k, v in white_list_data.items():\\n _poi_dict[k] = v\\n return _poi_dict, _online_official_data, _online_nonofficial_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d2ff44975d89f669d722e8755521fcd\",\n \"score\": \"0.46009216\",\n \"text\": \"def output_relation_plain(self, db):\\n\\t\\t_str = \\\"\\\"\\n\\t\\trelations = self.get_relations(db)\\n\\t\\tif relations:\\n\\t\\t\\tfor relation in relations:\\n\\t\\t\\t\\tif relation[1] != \\\"-1\\\": # ensures not the dummy relations used to generate buttons\\n\\t\\t\\t\\t\\t_range = json.loads(relation[0])[0]\\n\\t\\t\\t\\t\\tdomain = json.loads(relation[1])[0]\\n\\t\\t\\t\\t\\t_str += \\\"%s\\\\t%s\\\\t%s\\\\n\\\"%(_range, domain, relation[2])\\n\\n\\t\\treturn _str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89b6cdb2cb76304b5f66d211ad923b63\",\n \"score\": \"0.4598858\",\n \"text\": \"def batch_query(self, querylist):\\n seen_references = set()\\n for q in querylist:\\n for res in self.paged_query(q):\\n res['instances'] = [inst for inst in res['instances'] if inst['id'] not in seen_references]\\n seen_references.update([inst['id'] for inst in res['instances']])\\n yield res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8946a41f5cc4b9febb2181ee4c9f5cc0\",\n \"score\": \"0.45912752\",\n \"text\": \"def _fetch_data(self):\\r\\n connections = {}\\r\\n while True:\\r\\n self.log.debug('---begin for loop---')\\r\\n try:\\r\\n ls = self._get_active_resource()\\r\\n self.update_resource(ls)\\r\\n # print ls\\r\\n self.log.debug(\\\"{}\\\".format(str(ls)))\\r\\n except exception.DatabaseException:\\r\\n time.sleep(2)\\r\\n continue\\r\\n\\r\\n # remove dead threads in the conn pool\\r\\n main_thread = threading.currentThread()\\r\\n running_id = [th.getName() for th in threading.enumerate() if th is not main_thread and th.is_alive()]\\r\\n self.log.info('running ids: {}, total: {}'.format(str([rid for rid in running_id]), len(running_id)))\\r\\n\\r\\n dead_id = set(connections.keys()) - set(running_id)\\r\\n for rid in dead_id:\\r\\n self.log.info(\\\"Thread {} is dead. Remove it from the connection pool. \\\".format(rid))\\r\\n del connections[rid]\\r\\n self.log.info('dead ids: ' + str(dead_id))\\r\\n\\r\\n # latest resource ids pulled from the gateway\\r\\n existing_id_list = [str(rid) for di, href, rt, obs, rid in ls if rid is not None]\\r\\n\\r\\n # find threads with obsolete ids and remove them\\r\\n zombie_id = set(connections.keys()) - set(existing_id_list)\\r\\n for rid in zombie_id:\\r\\n self.log.info(\\\"kill zombie threads: {}\\\".format(rid))\\r\\n connections[rid].kill()\\r\\n del connections[rid]\\r\\n\\r\\n self.log.info('active threads: {}, total: {}'.format(str([rid for rid in connections.keys()]),\\r\\n len(connections.keys())))\\r\\n\\r\\n for di, href, rt, obs, rid in ls:\\r\\n if rid and str(rid) not in connections.keys() and str(rid) not in running_id:\\r\\n if len(connections) >= self.MAX_THREAD:\\r\\n # quit the loop cos it exceeds max threads limit\\r\\n self.log.info(\\\"Exceeded maximal thread threshold {}, queuing request \\\"\\r\\n \\\"for {}?di={}\\\".format(self.MAX_THREAD, href, di))\\r\\n self.log.info(connections.keys())\\r\\n break\\r\\n time.sleep(randint(0, 5))\\r\\n t = FetchData(self.gateway_id, (href, di, obs))\\r\\n connections[str(rid)] = t\\r\\n self.log.info('threads in the pool: ' + str(connections))\\r\\n # logging.debug('end of for.')\\r\\n\\r\\n self.log.debug('---sleep 10 secs---')\\r\\n time.sleep(self.RESOURCE_UPDATE_INTERVAL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787f6e51480462cc6f305dc2d28c7a05\",\n \"score\": \"0.45885554\",\n \"text\": \"def cache_relations(self):\\r\\n self.cached_relations = defaultdict(list)\\r\\n for relation in CIRelation.objects.values(\\r\\n 'parent_id', 'child_id', 'type', 'id').filter(\\r\\n type=CI_RELATION_TYPES.CONTAINS.id):\\r\\n self.cached_relations[relation.get('parent_id')].append({\\r\\n 'parent': relation.get('parent_id'),\\r\\n 'child': relation.get('child_id'),\\r\\n 'type': relation.get('type'),\\r\\n 'id': relation.get('id'),\\r\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc3b5082e3e1fee77ecf0635f33edcb\",\n \"score\": \"0.458544\",\n \"text\": \"def print_info(session, detailed=False):\\n if detailed:\\n for proj in session.query(Project).order_by(Project.id):\\n print(\\\"\\\\nProject: %s\\\\nDeviceDB: %s\\\\nGateware: %s\\\\nDescription: %s\\\" % (proj.name, proj.devicedb.name, proj.devicedb.gateware.name, proj.description))\\n table = []\\n mes_cnt_query = session.query(Measurement.sequence_id, func.count('*').label('measurement_count')).group_by(Measurement.sequence_id).subquery()\\n for seq, mes_cnt in session.query(Sequence, mes_cnt_query.c.measurement_count).outerjoin(mes_cnt_query, Sequence.id == mes_cnt_query.c.sequence_id).filter(Sequence.pipeline.project == proj).order_by(Sequence.id):\\n table.append([seq.name, mes_cnt])\\n print(tabulate(table, headers=['Sequence', 'Measurements']))\\n else:\\n table = []\\n seq_cnt_query = session.query(Sequence.pipeline.project_id, func.count('*').label('sequence_count')).group_by(Sequence.pipeline.project_id).subquery()\\n for proj, seq_cnt in session.query(Project, seq_cnt_query.c.sequence_count).outerjoin(seq_cnt_query, Project.id == seq_cnt_query.c.project_id).order_by(Project.id):\\n table.append([proj.name, proj.devicedb.name, proj.devicedb.gateware.name, seq_cnt])\\n print(tabulate(table, headers=['Project', 'Device Database', 'Gateware', 'Sequences']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98018a77be4f0c68bdc2c7dfe4a8629c\",\n \"score\": \"0.45842588\",\n \"text\": \"def print_tables():\\n # courses:\\n print(\\\"courses\\\")\\n data_cursor.execute(\\\"SELECT * FROM courses\\\")\\n for row in data_cursor:\\n print(row)\\n # classrooms:\\n print(\\\"classrooms\\\")\\n data_cursor.execute(\\\"SELECT * FROM classrooms\\\")\\n for row in data_cursor:\\n print(row)\\n # students:\\n print(\\\"students\\\")\\n data_cursor.execute(\\\"SELECT * FROM students\\\")\\n for row in data_cursor:\\n print(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d43678f6ee82a71f18a6ed532a83035\",\n \"score\": \"0.4582817\",\n \"text\": \"def get_relations(self, db, relation_name=None):\\n\\t\\tcur = db.cursor()\\n\\t\\tsql = None\\n\\t\\tif relation_name:\\n\\t\\t\\tsql = \\\"SELECT * FROM relations WHERE sessionid=? AND relation=? ORDER BY range\\\"\\n\\t\\t\\tcur.execute(sql, (self.sessionid, relation_name))\\n\\t\\telse:\\n\\t\\t\\tsql = \\\"SELECT * FROM relations WHERE sessionid=? ORDER BY range\\\"\\n\\t\\t\\tcur.execute(sql, (self.sessionid,))\\n\\n\\t\\tresults = cur.fetchall()\\n\\n\\t\\tif results:\\n\\t\\t\\t#return results # includes session\\n\\t\\t\\trelations = []\\n\\t\\t\\tfor result in results:\\n\\t\\t\\t\\tif result[1] and result[1] != \\\"-1\\\":\\n\\t\\t\\t\\t\\t# THE ORDER IS NEEDED FOR [domain, range, relation] \\n\\t\\t\\t\\t\\trelations.append([result[2],result[1],result[3]]) \\n\\n\\n\\t\\t\\treturn relations\\n\\n\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e005cfed60ae43e629340d13131c8b0\",\n \"score\": \"0.45826247\",\n \"text\": \"def bridge_phrases_extraction(row): \\n question = row[\\\"question\\\"] \\n sentences = row['context'][\\\"sentences\\\"].copy()\\n titles = row['context'][\\\"title\\\"] \\n answer = row[\\\"answer\\\"] \\n titles_phrases, question_phrases, question_phrases_text, paras_phrases, answer_phrases, answer_phrases_text = noun_phrases_extraction(question, sentences, titles, answer)\\n RG = create_relevant_graph(paras_phrases, question_phrases_text) # this RG could still be disjoint\\n\\n RG, common_phrases, mapping, _, merged_question_phrases = find_common_mapping(RG, question_phrases, paras_phrases) # mapping matchs paras_phrases with question_phrases\\n\\n row['common_phrases'] = common_phrases\\n \\n assert len(set(merged_question_phrases.keys()) - set(question_phrases_text)) == 0\\n \\n # connect disjoint subgraphs\\n RG, disjoint = connect_disjoint_subgraphs(RG)\\n RG, disjoint = always_connect_disjoint_subgraphs(RG)\\n row['disjoint'] = disjoint\\n RG.remove_edges_from(nx.selfloop_edges(RG)) # remove (500 mile, 500 mile, {'src': [(9, 2)]}) \\n\\n # add weight to RG, to lower chance of [p4] video game -> video game -> [p8] video game \\n for u,v,d in RG.edges(data=True):\\n if('src' not in d): # {}\\n d['weight']=5\\n \\n question_only_phrase = list(set(question_phrases_text).difference(common_phrases | set(merged_question_phrases.keys()))) \\n row['question_only_phrase'] = question_only_phrase\\n\\n if(len(common_phrases) > 1): \\n steiner_graph = approx.steinertree.steiner_tree(RG, common_phrases) \\n path_phrases = list(steiner_graph.nodes) # to find the smallest subgraph covers all common_phrases \\n \\n for i, phrase in enumerate(path_phrases):\\n path_phrases[i] = remove_prefix(phrase, '[P', paraid = True)\\n path_phrases = list(set(path_phrases))\\n if(len(path_phrases) == 0):\\n print(row['id'], 'no path found') \\n path_edges = nx.get_edge_attributes(steiner_graph, 'src') \\n \\n\\n else: # 0 or 1 common phrases\\n path_phrases = list(common_phrases) \\n path_edges = {}\\n for phrase in common_phrases:\\n path_edges[(phrase, None)] = []\\n for pi, para_phrase in enumerate(paras_phrases):\\n for si, sentence_phrases in enumerate(para_phrase):\\n if si > 0: # not the title \\n sentence_phrases = [ remove_prefix(sentence_phrase[0], '[P', paraid = True) for sentence_phrase in sentence_phrases] \\n if any([find_match_with_tolerance(phrase, sentence_phrase) or find_match_with_tolerance(sentence_phrase, phrase) for sentence_phrase in sentence_phrases]):\\n path_edges[(phrase, None)].append((pi,si-1)) \\n path_edges = list(path_edges.items())\\n path_sents = get_path_sents(path_edges)\\n joint_context = basic_normalize(\\\" \\\".join(list(flatten(row[\\\"context\\\"]['title']))) + ' ' + \\\" \\\".join(list(flatten(row[\\\"context\\\"]['sentences']))) )\\n joint_title = basic_normalize(\\\" \\\".join(list(flatten(row[\\\"context\\\"]['title'])))) \\n\\n \\n steiner_points = set(path_phrases) - set(question_phrases_text)\\n _, steiner_points = phrase_postpreocess(steiner_points, joint_context, list(flatten(titles_phrases)))\\n \\n row['steiner_points'] = steiner_points \\n \\n supporting_facts_phrases = [] \\n supporting_facts_text = []\\n supporting_facts_titles_phrases = []\\n sp_sent_ids = row['supporting_facts']['sent_id'] \\n for spi, sp_title in enumerate(row['supporting_facts']['title']): \\n pi = titles.index(sp_title)\\n sent_id = sp_sent_ids[spi]\\n if pi < len(sentences) and sent_id < len(sentences[pi]): # in case edge case \\n supporting_facts_text.append(sentences[pi][sent_id])\\n sp_sent_phrases = paras_phrases[pi][sent_id+1] # +1 since [0] is title phrase\\n sp_sent_phrases = [remove_prefix(phrase[0], '[P', paraid = True) for phrase in sp_sent_phrases]\\n supporting_facts_phrases.append(sp_sent_phrases) \\n \\n t_phrases = titles_phrases[pi]\\n t_phrases = [remove_prefix(phrase, '[P', paraid = True) for phrase in t_phrases]\\n supporting_facts_titles_phrases.append(t_phrases) \\n \\n # oracle bridge_phrases \\n bridge_phrases = annotate_bridge_phrases(supporting_facts_phrases, row['supporting_facts']['title'], supporting_facts_titles_phrases) \\n \\n _, row['bridge_phrases'] = phrase_postpreocess(set(bridge_phrases) - set(question_phrases_text), joint_context, list(flatten(titles_phrases))) \\n \\n supporting_facts_phrases, _ = phrase_postpreocess(list(flatten(supporting_facts_phrases)), joint_context, [])\\n supporting_facts_titles_phrases, _ = phrase_postpreocess(list(flatten(supporting_facts_titles_phrases)), joint_title, [])\\n row['supporting_facts_text'] = supporting_facts_text \\n \\n \\n return row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b154fcce18c7657313c7f5cf861b4c6\",\n \"score\": \"0.45775887\",\n \"text\": \"def extract_text():\\n count = 0\\n q = queue.Queue()\\n try:\\n for doc in coll.find({}, {\\\"title\\\": 1, \\\"subtitle\\\": 1, \\\"instructions\\\": 1,\\n \\\"miscellaneousText\\\": 1, \\\"ingredientsText\\\": 1, \\\"tags\\\": 1, \\\"_id\\\": 0}):\\n s = \\\" \\\".join([doc['title'], doc['subtitle'],\\n doc['instructions'], doc['miscellaneousText'],\\n doc['ingredientsText']] + doc['tags']).replace(\\\"\\\\n\\\", \\\" \\\")\\n q.put(s)\\n count += 1\\n if count % 500 == 0:\\n print(count)\\n\\n\\n finally:\\n with open(\\\"storage/all_words.txt\\\", \\\"w+\\\", encoding=\\\"utf-8\\\") as f:\\n while q.qsize():\\n f.write(q.get())\\n f.write(\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a80bc6cb8f8801213276544799d3370\",\n \"score\": \"0.45763612\",\n \"text\": \"def aggregates_results(self):\\n # clean shell\\n clear = lambda: os.system('clear')\\n res = {}\\n\\n for pond in ['w', 'tf_idf', 'proba']:\\n\\n coordinates = []\\n count = 0\\n local_coords = []\\n for q_id in xrange(1, 65):\\n clear()\\n print '|> processing query number '+str(q_id)+' out of 64'+' ['+pond+']'\\n\\n if len(self.set.results[str(q_id)]):\\n measures = self.compute_measures(str(q_id), pond)\\n local_coords = self.interpolate(measures)\\n count += 1\\n\\n if len(coordinates) == 0:\\n coordinates = local_coords\\n else:\\n for j in xrange(0, len(coordinates)):\\n coordinates[j] += local_coords[j]\\n # ponderation\\n for j in xrange(0, 11):\\n coordinates[j] = coordinates[j] / count\\n\\n res[pond] = coordinates\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4934350cf2f119114210a239540816\",\n \"score\": \"0.4566866\",\n \"text\": \"def process_behavior(self, results, bulk_submit_size=1000):\\n for process in results.get(\\\"behavior\\\", {}).get(\\\"processes\\\", []):\\n bulk_index = []\\n\\n for call in process[\\\"calls\\\"]:\\n base_document = self.get_base_document()\\n call_document = {\\n \\\"pid\\\": process[\\\"pid\\\"],\\n }\\n call_document.update(call)\\n call_document.update(base_document)\\n bulk_index.append({\\n \\\"_index\\\": self.dated_index,\\n \\\"_type\\\": self.call_type,\\n \\\"_source\\\": call_document\\n })\\n if len(bulk_index) == bulk_submit_size:\\n self.do_bulk_index(bulk_index)\\n bulk_index = []\\n\\n if len(bulk_index) > 0:\\n self.do_bulk_index(bulk_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d1d13f9a499ea9a4324c832ea59ee1\",\n \"score\": \"0.45665073\",\n \"text\": \"def multiprocess_data(self):\\n # Initialize key variables\\n pattoo_db_records_lists_tuple = self._arguments\\n pool_size = self._pool_size\\n\\n # Troubleshooting log\\n log_message = 'Processing {} agents from cache'.format(\\n len(pattoo_db_records_lists_tuple))\\n log.log2debug(20009, log_message)\\n\\n # Create a pool of sub process resources\\n with get_context('spawn').Pool(processes=pool_size) as pool:\\n\\n # Create sub processes from the pool\\n results = pool.starmap(\\n _process_data_exception, pattoo_db_records_lists_tuple)\\n\\n # Wait for all the processes to end and get results\\n pool.join()\\n\\n # Test for exceptions\\n for result in results:\\n if isinstance(result, ExceptionWrapper):\\n result.re_raise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0b1a33197910c3cc4a2308e279f932a\",\n \"score\": \"0.4565902\",\n \"text\": \"def print_all(cls):\\n all_objects = db_sess.query(cls).all()\\n for obj in all_objects:\\n print obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8a3d675a3c780ac745c86dac14487c4\",\n \"score\": \"0.45610136\",\n \"text\": \"def update_database_results(self, session):\\n while not self._processed_submission_queue.empty():\\n submission_id, submission_name = \\\\\\n self._processed_submission_queue.get_nowait()\\n if 'error' in get_submission_state(session, submission_id):\\n update_leaderboards(session, self._ramp_config['event_name'])\\n update_all_user_leaderboards(session,\\n self._ramp_config['event_name'])\\n logger.info('Skip update for {} due to failure during the '\\n 'processing'.format(submission_name))\\n continue\\n logger.info('Update the results obtained on each fold for '\\n '{}'.format(submission_name))\\n path_predictions = os.path.join(\\n self._worker_config['predictions_dir'], submission_name\\n )\\n set_predictions(session, submission_id, path_predictions)\\n set_time(session, submission_id, path_predictions)\\n set_scores(session, submission_id, path_predictions)\\n set_bagged_scores(session, submission_id, path_predictions)\\n set_submission_state(session, submission_id, 'scored')\\n update_leaderboards(session, self._ramp_config['event_name'])\\n update_all_user_leaderboards(session,\\n self._ramp_config['event_name'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ec676e2f0a7395e7186f486d164bf3\",\n \"score\": \"0.45577753\",\n \"text\": \"def printParallelDependencies(sentence):\\n\\n printLinkageDependencies(sentence, \\\"parallel\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c85910ab74b012245ea9eab2f7429d4\",\n \"score\": \"0.45541316\",\n \"text\": \"def process_query(q):\\n try:\\n db = psycopg2.connect(database=DB_NAME)\\n c = db.cursor()\\n c.execute(q)\\n results = c.fetchall()\\n db.close()\\n return list(results)\\n except Exception as e:\\n print(\\\"Error processing query !\\\\n{}\\\".format(e))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d0d26a394e749668cf1bbf079cf621\",\n \"score\": \"0.45479274\",\n \"text\": \"def do_query(self):\\n f_date, to_date = self.start, self.end\\n result = defaultdict(list)\\n client = self.get_client()\\n key_fields = [field for field in self.fields if self.fields[field].group]\\n for field in list(self.fields):\\n if field in key_fields:\\n continue\\n self.fields = self.get_fields(key_fields + [field])\\n query = self.get_query_ch(f_date, to_date)\\n # print(\\\"Query: %s\\\", query)\\n if self.allobjectids or not self.objectids:\\n for row in client.execute(query % \\\"\\\"):\\n if row[0] not in result:\\n result[row[0]] = row\\n else:\\n result[row[0]] += row[1:]\\n else:\\n # chunked query\\n ids = self.objectids\\n while ids:\\n chunk, ids = ids[: self.CHUNK_SIZE], ids[self.CHUNK_SIZE :]\\n for row in client.execute(query % f\\\" AND {self.get_object_filter(chunk)}\\\"):\\n if row[0] not in result:\\n result[row[0]] = row\\n else:\\n result[row[0]] += row[1:]\\n self.fields = self.get_fields(self.query_fields)\\n for v in result.values():\\n yield v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62e5e587d7c9238d6b59204aa995a0b5\",\n \"score\": \"0.4545974\",\n \"text\": \"def process_all_queries(queries_file_path):\\r\\n with open(queries_file_path, 'r', encoding=\\\"utf-8\\\") as query_file:\\r\\n for query_item in query_file:\\r\\n\\r\\n # Get the query_text with query_id\\r\\n query_id, query_text = query_item.strip().split(\\\"|\\\")\\r\\n\\r\\n # process query\\r\\n query_output = get_top_ranked_documents(process_query(query_text))\\r\\n\\r\\n # write to output file\\r\\n write_query_output(query_id, query_output, path.dirname(queries_file_path),\\r\\n \\\"Query_\\\" + query_id + \\\"_Output.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0709564f602eceeb451b09e9e631a3f4\",\n \"score\": \"0.45445633\",\n \"text\": \"def get_query(self, ):\\n except_count = 0\\n dictionary_query_tuples = {}\\n for j in range(1, 21):\\n temp_address = self.queries_directory_path + '/' + str(j) + '.html'\\n try:\\n (ext, content) = MathDocument.read_doc_file(temp_address)\\n formulas = MathExtractor.parse_from_xml(content, 1, operator=(not self.read_slt), missing_tags=None,\\n problem_files=None)\\n for key in formulas:\\n tuples = formulas[key].get_pairs(window=2, eob=True)\\n dictionary_query_tuples[j] = tuples\\n except:\\n except_count += 1\\n print(j)\\n return dictionary_query_tuples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f72e900827c7a4a60654e4a2fbf214b6\",\n \"score\": \"0.45441452\",\n \"text\": \"def dump_data_console(Pro):\\n for pro in Pro.processes.values():\\n print(pro.name, pro.operation_counter) \\n for ops in pro.operations:\\n ops.print_op_console()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20fd2d2f7592a362027c17e558490b3d\",\n \"score\": \"0.4536247\",\n \"text\": \"def print_persons_db(self):\\n print(\\\"Persons DB:\\\")\\n for row in self.get_all():\\n print(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dbce51b820470e07411aecc9638351b\",\n \"score\": \"0.45330602\",\n \"text\": \"def do_relations_program(poa_article):\\n do_relations = None\\n for dataset in poa_article.datasets:\\n if do_dataset_related_item(dataset) is True:\\n do_relations = True\\n break\\n if do_relations is not True and poa_article.ref_list:\\n for ref in poa_article.ref_list:\\n if do_citation_related_item(ref) is True:\\n do_relations = True\\n break\\n if do_relations is not True and do_preprint_related_item(poa_article):\\n do_relations = True\\n return do_relations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730852513fabd8f867ceb0fe371d2d2d\",\n \"score\": \"0.45286277\",\n \"text\": \"def print_cache(self):\\n for key in self.storage:\\n self.storage[key].print_node()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59fe0412c7f7e2ec93edd74fb4fb1930\",\n \"score\": \"0.45223185\",\n \"text\": \"def dump_lp_links(self, links):\\n for link in links:\\n self.lp_queue.put(link)\\n #print(\\\"Added link to hp_queue: \\\" + link)\\n result = {}\\n # block until all links are added into the crawled_lp dictionary\\n while True:\\n exists_flag = False\\n for link in links:\\n if link not in self.crawled_lp:\\n exists_flag = True\\n if exists_flag is False:\\n break\\n # block until all items are finished processing.\\n for items in self.crawled_lp.items():\\n while items[1].running():\\n continue\\n result[items[0]] = items[1].result()\\n self.crawled_lp.clear()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5ce75e129024010e33b7564ab2d15d4\",\n \"score\": \"0.45149547\",\n \"text\": \"def prepare_results(self):\\n poolsize = self.state.config.getint(\\\"core\\\", \\\"poolsize\\\", fallback=5)\\n\\n pool = multiprocessing.Pool(poolsize)\\n\\n arguments = []\\n for cls_name, args in self.parse_display():\\n arguments.append({\\n 'cls_name': cls_name,\\n 'args': args,\\n 'state': self.state\\n })\\n raw_out = pool.map(_mp_render_helper, arguments)\\n outputs = list(map(self.format_item,\\n filter(bool, raw_out)))\\n\\n\\n return outputs + [\\\"\\\"] * (18 - len(outputs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1696fd7227477e23dea1349bc672bf96\",\n \"score\": \"0.45146492\",\n \"text\": \"def find_out_related_objects(self):\\n output = {}\\n tally = 0\\n for n_related_object in self.premis_core_data.related_objects:\\n output[tally] = {'loc': self.premis_core_data.related_objects[i],\\n 'identifier': str(i)}\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24a753b814f6b48e1d6fc95e7dc77509\",\n \"score\": \"0.45146403\",\n \"text\": \"def prepare_relation_dataset(self):\\n logger.info(\\\"generation of relations having ambiguous entities\\\")\\n # pour chaque entité associée à une forme ambigue\\n # on récupère tous les vecteurs-entités associés a la même formes\\n # on récupère les vecteurs de ses entités en relation\\n # X, Y = [], []\\n\\n for en_id, ambiguities, linked, _formes in generate(\\\"ambiguity\\\"):\\n linked = linked.split()\\n ambiguities = ambiguities.split()\\n # extracted_entities = [en_id] + ambiguities + linked\\n extracted_entities = [{\\\"start\\\": 0, \\\"end\\\": 1, \\\"id\\\": en_id}]\\n extracted_entities += [\\n {\\\"start\\\": 0, \\\"end\\\": 1, \\\"id\\\": e_id} for e_id in ambiguities\\n ]\\n extracted_entities += [\\n {\\\"start\\\": i + 1, \\\"end\\\": i + 2, \\\"id\\\": e_id}\\n for i, e_id in enumerate(linked)\\n ]\\n df = pd.DataFrame(extracted_entities)\\n doc_vec = np.zeros(100)\\n # il faut générer ici un doc_vecteur pour cette entité\\n # une moyenne des documents en lien avec l'entité ambigue ?\\n # ou un doc vecteur des formes entités\\n # UTILISER LES FORMES DU TRIE + WORD EMBEDDINGS ICI\\n en_positive = [en_id] + linked\\n yield df, en_positive, doc_vec\\n\\n # for en_seqs, x, y in self.prepare_doc(df, en_positive, doc_vec):\\n # if y != []:\\n # yield x, y\\n\\n # Gérer ici le cas des documents/contexte vide\\n # doc_emb = ctx_emb = wemb(en), avg(wemb(en_i))\\n # ent_emb = pemb(en), avg(wemb(en_i))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cafb5bc546ff6e2b0d336075c9db9865\",\n \"score\": \"0.45141473\",\n \"text\": \"def printDicts(self):\\n def __prntDict(Dict,msg):\\n \\\"\\\"\\\"utility function capable to print a dictionary\\\"\\\"\\\"\\n for key in Dict:\\n msg+=key+'= '+str(Dict[key])+'\\\\n'\\n return msg\\n msg=''\\n msg=__prntDict(self.runInfoDict,msg)\\n msg=__prntDict(self.stepsDict,msg)\\n msg=__prntDict(self.dataDict,msg)\\n msg=__prntDict(self.samplersDict,msg)\\n msg=__prntDict(self.modelsDict,msg)\\n msg=__prntDict(self.metricsDict,msg)\\n #msg=__prntDict(self.testsDict,msg)\\n msg=__prntDict(self.filesDict,msg)\\n msg=__prntDict(self.dataBasesDict,msg)\\n msg=__prntDict(self.outStreamsDict,msg)\\n msg=__prntDict(self.entityModules,msg)\\n msg=__prntDict(self.entities,msg)\\n self.raiseADebug(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"142ac7f74b22e941db7494e450b77724\",\n \"score\": \"0.45109677\",\n \"text\": \"def batch_query(request, article_titles, print_num_queries=False):\\n request[\\\"action\\\"] = \\\"query\\\"\\n request[\\\"format\\\"] = \\\"json\\\"\\n results = {}\\n num_queries = 0\\n # can only query for 50 titles at a time, and even then have to do some fanciness to actually get the info\\n for i in range(0, len(article_titles), 50):\\n last_continue = {\\\"continue\\\": \\\"\\\"}\\n request[\\\"titles\\\"] = \\\"|\\\".join(article_titles[i:i + 50])\\n while True:\\n req = request.copy()\\n req.update(last_continue)\\n r = requests.get(\\\"https://en.wikipedia.org/w/api.php\\\", req, headers={\\\"User-Agent\\\": USER_AGENT}).json()\\n num_queries += 1\\n if \\\"error\\\" in r:\\n raise ConnectionError(r[\\\"error\\\"])\\n if \\\"query\\\" in r:\\n for page_key, page_val in r[\\\"query\\\"][\\\"pages\\\"].items():\\n # returns piecemeal, not all props in the same request\\n if request[\\\"prop\\\"] in page_val:\\n if page_val[\\\"title\\\"] in results:\\n results[page_val[\\\"title\\\"]].update(page_val[request[\\\"prop\\\"]])\\n else:\\n results[page_val[\\\"title\\\"]] = page_val[request[\\\"prop\\\"]]\\n if \\\"batchcomplete\\\" in r:\\n break\\n last_continue = r[\\\"continue\\\"]\\n if print_num_queries:\\n print(\\\"Number of calls to complete batch query: \\\", num_queries)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f121b0e70b1c8a1c5b3f1d4d9f9140f8\",\n \"score\": \"0.45091134\",\n \"text\": \"def run_query(report):\\n\\n print(report.explain())\\n print(\\\"REDSTREAK RESULT:\\\")\\n print(\\\"-\\\" * 80)\\n print(f\\\"# : {'Title':39.39} |{'Genres':39.39}\\\")\\n\\n print(\\\"-\\\" * 80)\\n for i, row in enumerate(report):\\n print(f\\\"{i} {row}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f113af9a22049faf5b7fd2aed450f882\",\n \"score\": \"0.45069277\",\n \"text\": \"def parallel(self, files):\\n def start_process(csv_file):\\n \\\"\\\"\\\" Start process on given csv_file\\n\\n Arguments:\\n csv_file {string} -- csv_file to start insert process on\\n\\n Returns:\\n process, Queue -- process started, Queue with dict of results\\n \\\"\\\"\\\"\\n results = Queue()\\n process = Process(target=self.insert_to_mongo, args=(csv_file, results))\\n logger.info(f\\\"Starting {process} : {csv_file}\\\")\\n process.start()\\n return process, results\\n\\n def join_process(process):\\n \\\"\\\"\\\" Joins processes in process argument\\n\\n Arguments:\\n [process1, process2, process3, ...] -- list of processes to join\\n\\n Returns:\\n {collection_name: {\\\"success\\\", \\\"fail\\\", \\\"total_records\\\", \\\"elapsed\\\"}}\\n \\\"\\\"\\\"\\n logger.info(f\\\"Joining process {process[0]}\\\")\\n process[0].join()\\n return process[1].get()\\n\\n return list(\\n map(join_process, list(map(start_process, files)))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f89542f7626572de77ef897a3c94300a\",\n \"score\": \"0.45055506\",\n \"text\": \"def find_links(self):\\n\\n fields = \\\"d.id\\\", \\\"u.value\\\", \\\"d.title\\\", \\\"t.value\\\"\\n query = db.Query(\\\"query_term t\\\", *fields).unique()\\n query.join(\\\"query_term u\\\", \\\"u.doc_id = t.doc_id\\\",\\n \\\"u.node_loc = t.node_loc\\\")\\n query.join(\\\"document d\\\", \\\"d.id = t.doc_id\\\")\\n if not self.doc_id:\\n query.where(\\\"d.active_status = 'A'\\\")\\n query.where(\\\"t.path LIKE '/%s/%%/@SourceTitle'\\\" % self.doc_type)\\n query.where(\\\"u.path LIKE '/%s/%%/@cdr:xref'\\\" % self.doc_type)\\n if self.doc_id:\\n query.where(query.Condition(\\\"d.id\\\", self.doc_id))\\n elif self.doc_type == \\\"Summary\\\":\\n if self.boards:\\n sq = self.summary_board_subquery(self.boards, self.language)\\n query.where(query.Condition(\\\"u.doc_id\\\", sq, \\\"IN\\\"))\\n elif self.language:\\n query.join(\\\"query_term l\\\", \\\"l.doc_id = u.doc_id\\\")\\n query.where(\\\"l.path = '%s'\\\" % self.SUMMARY_LANGUAGE)\\n query.where(\\\"l.value = '%s'\\\" % self.language)\\n if self.audience:\\n query.join(\\\"query_term a\\\", \\\"a.doc_id = u.doc_id\\\")\\n query.where(\\\"a.path = '%s'\\\" % self.SUMMARY_AUDIENCE)\\n query.where(\\\"a.value = '%ss'\\\" % self.audience)\\n if self.doc_type == self.GTC:\\n if self.language:\\n language = self.GTC_LANGUAGES[self.language]\\n query.join(\\\"query_term l\\\", \\\"l.doc_id = t.doc_id\\\",\\n \\\"l.node_loc = t.node_loc\\\")\\n query.where(\\\"l.path = '%s'\\\" % self.GTC_USE_WITH)\\n query.where(\\\"l.value = '%s'\\\" % language)\\n self.log_query(query)\\n rows = query.execute(self.cursor).fetchall()\\n return [self.Link(self, *row) for row in rows]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e763c5f5673558358bfa8f53ac0ea3e4\",\n \"score\": \"0.4505347\",\n \"text\": \"def promoter_profile(self):\\n self.promoter = { \\\"de\\\": {},\\n \\\"nde\\\": {} }\\n\\n #################################################\\n # Each promoter to all related merged DBD\\n print(\\\"\\\\tEach promoter to all related merged DBD...\\\")\\n # Targeted promoters\\n self.promoter[\\\"de\\\"][\\\"rd\\\"] = self.txp_def.sort_rd_by_regions(regionset=self.de_regions)\\n self.promoter[\\\"nde\\\"][\\\"rd\\\"] = self.txp_ndef.sort_rd_by_regions(regionset=self.nde_regions)\\n #################################################\\n \\n # Each promoter to all related DBS\\n print(\\\"\\\\tEach promoter to all related DBS...\\\")\\n # Targeted promoters\\n \\n self.promoter[\\\"de\\\"][\\\"merged_dbs\\\"] = {}\\n self.promoter[\\\"de\\\"][\\\"dbs\\\"] = {}\\n for k, rds in self.promoter[\\\"de\\\"][\\\"rd\\\"].items():\\n self.promoter[\\\"de\\\"][\\\"dbs\\\"][k] = rds.get_dbs()\\n self.promoter[\\\"de\\\"][\\\"merged_dbs\\\"][k] = self.promoter[\\\"de\\\"][\\\"dbs\\\"][k].merge(w_return=True) \\n # Untargeted promoters\\n self.promoter[\\\"nde\\\"][\\\"merged_dbs\\\"] = {}\\n self.promoter[\\\"nde\\\"][\\\"dbs\\\"] = {}\\n for k, rds in self.promoter[\\\"nde\\\"][\\\"rd\\\"].items():\\n self.promoter[\\\"nde\\\"][\\\"dbs\\\"][k] = rds.get_dbs()\\n self.promoter[\\\"nde\\\"][\\\"merged_dbs\\\"][k] = self.promoter[\\\"nde\\\"][\\\"dbs\\\"][k].merge(w_return=True)\\n \\n # DBS coverage\\n self.promoter[\\\"de\\\"][\\\"dbs_coverage\\\"] = {}\\n for promoter in self.de_regions:\\n self.promoter[\\\"de\\\"][\\\"dbs_coverage\\\"][promoter.toString()] = float(self.promoter[\\\"de\\\"][\\\"merged_dbs\\\"][promoter.toString()].total_coverage()) / len(promoter)\\n self.promoter[\\\"nde\\\"][\\\"dbs_coverage\\\"] = {}\\n for promoter in self.nde_regions:\\n self.promoter[\\\"nde\\\"][\\\"dbs_coverage\\\"][promoter.toString()] = float(self.promoter[\\\"nde\\\"][\\\"merged_dbs\\\"][promoter.toString()].total_coverage()) / len(promoter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c600e828e185a319d4a2cb87c7e3f44\",\n \"score\": \"0.45045093\",\n \"text\": \"def _query_worker(self):\\n while True:\\n query = self.query_queue.get()\\n response = self.query_handler(query)\\n self.connection.send(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97de81bd2982e3da606bc5c131f7f283\",\n \"score\": \"0.45034683\",\n \"text\": \"def thread_worker():\\r\\n links = find_subcategories_links()\\r\\n if links:\\r\\n ThreadPool(processes=len(links)).map(extract_asins, links)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afe28d2dcab6ee8a544855ab793349fb\",\n \"score\": \"0.4498639\",\n \"text\": \"def worker(pickledir, networks, out_q):\\n\\n ## unpickle the stuff I need for parallel processing, but only do it the first\\n ## time worker is called for this process, otherwise we do a slow operation everytime\\n\\n global validComparisonsHash\\n global pairGraph\\n global assemblages\\n global args\\n global typeFrequencyUpperCI\\n global typeFrequencyLowerCI\\n\\n vchashfile = pickledir + \\\"/validComparisonsHash.p\\\"\\n pgfile = pickledir + \\\"/pairGraph.p\\\"\\n assemfile = pickledir + \\\"/assemblages.p\\\"\\n argsfile = pickledir + \\\"/args.p\\\"\\n fucifile = pickledir + \\\"/typeFrequencyUpperCI.p\\\"\\n flcifile = pickledir + \\\"/typeFrequencyLowerCI.p\\\"\\n\\n\\n validComparisonsHash=pickle.load(open(vchashfile,'rb'))\\n pairGraph=pickle.load(open(pgfile,'rb'))\\n assemblages=pickle.load(open(assemfile,'rb'))\\n args=pickle.load(open(argsfile,'rb'))\\n typeFrequencyUpperCI=pickle.load(open(fucifile,'rb'))\\n typeFrequencyLowerCI=pickle.load(open(flcifile,'rb'))\\n\\n outdict = []\\n\\n for n in networks:\\n output=checkForValidAdditions(n)\\n for o in output:\\n outdict.append(o)\\n out_q.put(outdict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e29a4dc7d351a25d5a55b02a647297fc\",\n \"score\": \"0.4496885\",\n \"text\": \"def results_iter(self):\\r\\n resolve_columns = hasattr(self, 'resolve_columns')\\r\\n fields = None\\r\\n for rows in self.execute_sql(MULTI):\\r\\n for row in rows:\\r\\n if resolve_columns:\\r\\n if fields is None:\\r\\n # We only set this up here because\\r\\n # related_select_fields isn't populated until\\r\\n # execute_sql() has been called.\\r\\n if self.select_fields:\\r\\n fields = self.select_fields + self.related_select_fields\\r\\n else:\\r\\n fields = self.model._meta.fields\\r\\n row = self.resolve_columns(row, fields)\\r\\n yield row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e87889f33f58b2ea4aad8c8eed96f9\",\n \"score\": \"0.44930238\",\n \"text\": \"def print_info(self):\\n print(\\\" Intersection \\\" + str(self.__uid) + \\\"(\\\" + str(self.__intersection_type) + \\\")\\\")\\n print(\\\" --------------------\\\")\\n for connection in self.__connections:\\n if(self.__connections[connection] == None):\\n continue\\n else:\\n print(\\\" \\\" + str(connection))\\n self.__connections[connection].print_info()\\n\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a52be3999dc6b0464866376f4fb6509\",\n \"score\": \"0.44859868\",\n \"text\": \"def get_uniprot_data(gbk_dict, query_data, connection, args):\\r\\n logger = logging.getLogger(__name__)\\r\\n \\r\\n gbk_accessions = list(gbk_dict.keys())\\r\\n\\r\\n # retrieve the data from the Taxonomy and Kingdom tables\\r\\n with Session(bind=connection) as session:\\r\\n uniprot_query = session.query(Genbank, Uniprot).\\\\\\r\\n join(Uniprot, (Uniprot.genbank_id == Genbank.genbank_id)).\\\\\\r\\n filter(Genbank.genbank_accession.in_(gbk_accessions)).\\\\\\r\\n all()\\r\\n\\r\\n if len(uniprot_query) == 0:\\r\\n logger.warning(\\\"No UniProt records retrieved for any of the selected GenBank accessions.\\\")\\r\\n return query_data\\r\\n\\r\\n for record in tqdm(uniprot_query, desc=\\\"Getting UniProt data\\\"):\\r\\n gbk_acc = record[0].genbank_accession\\r\\n\\r\\n if 'uniprot_acc' in args.include:\\r\\n uniprot_accession = record[1].uniprot_accession\\r\\n\\r\\n try:\\r\\n query_data[gbk_acc]\\r\\n \\r\\n try:\\r\\n query_data[gbk_acc]['uniprot_accession']\\r\\n logger.warning(\\r\\n f\\\"Multiple UniProt records found for GBK acc {gbk_acc}\\\\n\\\"\\r\\n \\\"Retreiving only one.\\\"\\r\\n )\\r\\n query_data[gbk_acc]['uniprot_accession'] = uniprot_accession\\r\\n\\r\\n except KeyError:\\r\\n query_data[gbk_acc]['uniprot_accession'] = uniprot_accession\\r\\n\\r\\n except KeyError:\\r\\n query_data[gbk_acc] = {\\r\\n 'uniprot_accession': uniprot_accession,\\r\\n }\\r\\n\\r\\n if 'uniprot_name' in args.include:\\r\\n uniprot_name = record[1].uniprot_name\\r\\n\\r\\n try:\\r\\n query_data[gbk_acc]\\r\\n \\r\\n try:\\r\\n query_data[gbk_acc]['uniprot_name']\\r\\n logger.warning(\\r\\n f\\\"Multiple UniProt records found for GBK acc {gbk_acc}\\\\n\\\"\\r\\n \\\"Retreiving only one.\\\"\\r\\n )\\r\\n query_data[gbk_acc]['uniprot_name'] = uniprot_name\\r\\n \\r\\n except KeyError:\\r\\n query_data[gbk_acc]['uniprot_name'] = uniprot_name\\r\\n\\r\\n except KeyError:\\r\\n query_data[gbk_acc] = {\\r\\n 'uniprot_name': uniprot_name,\\r\\n }\\r\\n\\r\\n if 'uniprot_seq' in args.include:\\r\\n seq = record[1].sequence\\r\\n seq_date = record[1].seq_update_date\\r\\n\\r\\n try:\\r\\n query_data[gbk_acc]\\r\\n \\r\\n try:\\r\\n query_data[gbk_acc]['uniprot_sequence']\\r\\n logger.warning(\\r\\n f\\\"Multiple UniProt records found for GBK acc {gbk_acc}\\\\n\\\"\\r\\n \\\"Retreiving only one record.\\\"\\r\\n )\\r\\n query_data[gbk_acc]['uniprot_sequence'] = seq\\r\\n query_data[gbk_acc]['uniprot_sequence_date'] = seq_date\\r\\n except KeyError:\\r\\n query_data[gbk_acc]['uniprot_sequence'] = seq\\r\\n query_data[gbk_acc]['uniprot_sequence_date'] = seq_date\\r\\n\\r\\n except KeyError:\\r\\n query_data[gbk_acc] = {'sequence': seq, 'sequence_date': seq_date}\\r\\n\\r\\n return query_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff891604026486a90777e377de6882b5\",\n \"score\": \"0.4481341\",\n \"text\": \"def __scrape_queries(self, folders_dict):\\n queries_dict = {}\\n for folder_id, folders in folders_dict.items():\\n query_ids = self.__get_folders_related_query_ids(folders)\\n queries_dict[folder_id] = \\\\\\n [self.__scrape_query(query_id) for query_id in query_ids]\\n\\n self.__log_queries_related_scraping_results(queries_dict)\\n\\n return queries_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b72568eda0d71e98f621df365bef6b58\",\n \"score\": \"0.44785514\",\n \"text\": \"def print_db_logs(self):\\n LOG.info(\\\"************ DB Logs Start ************\\\")\\n print_pipe(self.db_instance.db_process)\\n LOG.info(\\\"************* DB Logs End *************\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e78db46c0e7652002c0a45546c7c20b\",\n \"score\": \"0.446959\",\n \"text\": \"def multiprocess(func, iterable):\\n def create_pool_size(session_list):\\n \\\"\\\"\\\"\\n Create an appropriate pool size. If the length of domains exceeds the number of threads\\n than the pool_size will be the max number of threads, else the pool size will be the\\n number of domains entered.\\n :param session_list:\\n :return:\\n \\\"\\\"\\\"\\n # multiprocess crawl threads\\n list_count = len(session_list)\\n thread_count = multiprocessing.cpu_count() * 2\\n\\n # create a pool size based on the size of session_list\\n if list_count > thread_count:\\n pool_size = thread_count\\n else:\\n pool_size = list_count\\n return pool_size\\n\\n def print_pool_out(output_list):\\n \\\"\\\"\\\"\\n Print the output of each crawl process from the output list\\n :param output_list: a list that contains tuples (session_name, list_of_results)\\n :return: None\\n \\\"\\\"\\\"\\n for session_name, result_list in output_list:\\n print(session_name)\\n if result_list:\\n for link in result_list:\\n print(f'\\\\t{link}')\\n else:\\n print(f'\\\\tNo Results Found')\\n\\n pool_size = create_pool_size(iterable)\\n pool = multiprocessing.Pool(\\n processes=pool_size\\n )\\n pool_outputs = pool.map(func, iterable)\\n pool.close()\\n pool.join()\\n\\n print(pool_outputs)\\n print_pool_out(pool_outputs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15658226aefb222de86a1c358c7ae44c\",\n \"score\": \"0.44639853\",\n \"text\": \"def run_models(m):\\n querys = getQuerys()\\n \\n for no, query in querys.iteritems():\\n## m.models(no, query)\\n m.proximity_model(no, query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c773327719f1c3b8c4d6223a0ce4d19\",\n \"score\": \"0.4463504\",\n \"text\": \"def do_query(args):\\n\\n opts = parse_sel(args) \\n for row in db.query(opts):\\n request_time = ns_to_date(row['requested_time'])\\n print row['exper_id'], row['runnum'], row['status'], request_time, \\\\\\n row['irods_filepath'], row['requested_uid']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6ab7c3ca5751e2a660a7647078ccc4\",\n \"score\": \"0.44604963\",\n \"text\": \"def thread(variant_reads, genome_db, genome_hash, gene_k, positions):\\n ###GO THROUGH ALL READS\\n for k in range(len(variant_reads)):\\n read_kmers = db.kmerList(gene_k - 1, variant_reads[k])\\n ###GO THROUGH KMERS OF SIZE GENE_K WITHIN READ\\n for i in range(len(read_kmers)-1):\\n cur_kmer = read_kmers[i] #current kmer in read\\n if(cur_kmer in genome_db): #if already in the graph\\n #check to see if it points to the next kmer in genome_db\\n next_kmer = read_kmers[i+1]\\n try:\\n index = genome_db[cur_kmer][0].index(next_kmer)\\n except ValueError:\\n index = -1\\n\\n if(index > -1):\\n genome_db[cur_kmer][1][index] += 1\\n else:\\n genome_db[cur_kmer][0].append(next_kmer)\\n genome_db[cur_kmer][1].append(1)\\n\\n\\n else:\\n genome_db[cur_kmer] = [[read_kmers[i+1]], [1]] #create a new node\\n genome_hash[cur_kmer] = [positions[k] + i]\\n\\n return genome_db, genome_hash\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64a8529e95ea4c7c0202ec34fdbc8f3c\",\n \"score\": \"0.44586724\",\n \"text\": \"def process_query(target=_DEFAULT_INPUT_QUERY):\\n\\tglobal _QUERY_SCHEMA, _DB_GAMES, _RESULT_FILENAME\\n\\tquery = _translate_json(target)\\n\\tschema = _translate_json(_QUERY_SCHEMA)\\n\\tvalidate(query, schema)\\n\\tresultfile = query.get(\\\"filename\\\",_RESULT_FILENAME)\\n\\tif _DB_GAMES is None:\\n\\t\\tload_db()\\n\\tdf = _process_filter_query(_DB_GAMES, query)\\n\\tdf = _process_preference_query(df, query)\\n\\tconn = sqlite3.connect(resultfile)\\n\\tdf.to_sql(\\\"results\\\",conn,index=False)\\n\\tconn.close()\\n\\tprint(\\\"Finished computing results.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0674092b21aea4debd297d66c6696945\",\n \"score\": \"0.44420987\",\n \"text\": \"def print_results_per_node(self, node_map):\\n for bucket in node_map.keys():\\n print(\\\"----- Bucket {0} -----\\\".format(bucket))\\n for node in node_map[bucket].keys():\\n print(\\\"-------------Node {0}------------\\\".format(node))\\n for vbucket in node_map[bucket][node].keys():\\n print(\\\" for vbucket {0}\\\".format(vbucket))\\n for key in node_map[bucket][node][vbucket].keys():\\n print(\\\" :: for key {0} = {1}\\\"\\n .format(key,\\n node_map[bucket][node][vbucket][key]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce79ef2c022dbeca5d798c4fffc41296\",\n \"score\": \"0.4434571\",\n \"text\": \"def stats_printer(stats_queue):\\n proc_stats = [ProcessStats(i) for i in range(FLAGS.parallel)]\\n print_time = start_time = time.time()\\n width = 107\\n\\n running = True\\n while running:\\n print_time += 10\\n\\n while time.time() < print_time:\\n try:\\n s = stats_queue.get(True, print_time - time.time())\\n if s is None: # Signal to print and exit NOW!\\n running = False\\n break\\n proc_stats[s.proc_id] = s\\n except queue.Empty:\\n pass\\n\\n replay_stats = ReplayStats()\\n for s in proc_stats:\\n replay_stats.merge(s.replay_stats)\\n\\n print((\\\" Summary %0d secs \\\" % (print_time - start_time)).center(width, \\\"=\\\"))\\n print(replay_stats)\\n print(\\\" Process stats \\\".center(width, \\\"-\\\"))\\n print(\\\"\\\\n\\\".join(str(s) for s in proc_stats))\\n print(\\\"=\\\" * width)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ebf705c8f97bb611c97d5243dd1fef0\",\n \"score\": \"0.44261095\",\n \"text\": \"def create_tables(self) -> None:\\n cursor = self.con.cursor()\\n\\n for rel_name, query in self.relations.items():\\n if type(query) is str:\\n try:\\n cursor.execute(query)\\n except Exception as e:\\n l.error(\\n f\\\"Error occured while executing query for {rel_name}: {e} \\\\n\\\"\\n \\\"Proceed tracking without database logging\\\"\\n )\\n raise ConnectionError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c04c33c910bb9a02c4fc77f89a88cf7\",\n \"score\": \"0.44144392\",\n \"text\": \"def get_lineage_ids(taxid, conn): \\n\\n query=\\\"SELECT nd.tax_id, nd.parent_id, nd.rank, na.tax_id, na.tax_name, na.name_class from nodes nd inner join names na on nd.tax_id=na.tax_id where na.name_class=='scientific name' AND na.tax_id==\\\" + \\\"'\\\"+taxid+\\\"'\\\"\\n\\n df = pd.read_sql_query(query, conn)\\n #print(df)\\n\\n df.columns=['tax_id', 'parent_id', 'rank', 'tax_id_drop', 'tax_name', 'name_class']\\n df.drop(\\\"tax_id_drop\\\",axis=1, inplace=True)\\n rankorder=np.array(['no_rank','superkingdom','phylum','class','order','family','genus','species'])[::-1]\\n #print(df['rank'])\\n if not df['rank'].iloc[0].strip() in rankorder:\\n rankorder=np.append(rankorder,df['rank'].iloc[0])\\n rank_ind=np.where(df['rank'].iloc[0]==rankorder)[0][0]\\n# if len(df.tax_name.iloc[0].split(\\\" \\\"))>=2:\\n# lineage={rankorder[rank_ind]:\\\" \\\".join(df.tax_name.iloc[0].split(\\\" \\\")[1:])}\\n# else:\\n# lineage={rankorder[rank_ind]:df.tax_name.iloc[0]}\\n\\n lineage={rankorder[rank_ind]:df.tax_name.iloc[0]}\\n ids={rankorder[rank_ind]: taxid}\\n stop=False\\n temp=df.copy()\\n #print(lineage, ids)\\n while not stop:\\n parent_id=temp['parent_id'].iloc[0]\\n if parent_id is None or parent_id==\\\"\\\" or parent_id=='0':\\n stop=True\\n break\\n #print(parent_id)\\n query=\\\"SELECT nd.tax_id, nd.parent_id, nd.rank, na.tax_id, na.tax_name, na.name_class from nodes nd inner join names na on nd.tax_id=na.tax_id where na.name_class=='scientific name' AND na.tax_id==\\\" + \\\"'\\\"+parent_id+\\\"'\\\"\\n temp = pd.read_sql_query(query, conn)\\n temp.columns=['tax_id', 'parent_id', 'rank', 'tax_id_drop', 'tax_name', 'name_class']\\n temp.drop(\\\"tax_id_drop\\\",axis=1, inplace=True)\\n lineage.update({temp['rank'].iloc[0]:temp['tax_name'].iloc[0]})\\n\\n ids.update({temp['rank'].iloc[0]: temp.tax_id.iloc[0]})\\n\\n \\n\\n return lineage, ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed5984bb4213b1ebc203b50229d04568\",\n \"score\": \"0.43985403\",\n \"text\": \"def query_generator(self):\\n for q in self.iterqueries():\\n yield self._query_to_concrete_str(q)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992974,"cells":{"query_id":{"kind":"string","value":"7b21a01e9cdf70bb16ad95d29653df67"},"query":{"kind":"string","value":"Assume identity of specified user. As an administrator, you can assume the identity of any user in your organization."},"positive_passages":{"kind":"list like","value":[{"docid":"da4948de5c0338d416ac0c2d01c87764","score":"0.59164953","text":"def assume_user(self, email=None):\n if email is None:\n self._assume_user = None\n else:\n # email = email.replace('@', '%40')\n self._assume_user = six.moves.urllib.parse.quote(email)","title":""}],"string":"[\n {\n \"docid\": \"da4948de5c0338d416ac0c2d01c87764\",\n \"score\": \"0.59164953\",\n \"text\": \"def assume_user(self, email=None):\\n if email is None:\\n self._assume_user = None\\n else:\\n # email = email.replace('@', '%40')\\n self._assume_user = six.moves.urllib.parse.quote(email)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"16238f629c91702c44993b44da470106","score":"0.66630304","text":"def authorized_userid(self, identity):\n pass","title":""},{"docid":"aff2125ab5f51068eab41c9339fc9d5c","score":"0.6395221","text":"def confirm_identity():\n msg = \"The current user is {}\".format(user())\n return statement(msg)","title":""},{"docid":"342f44acccc67d0be3301e3d316c432b","score":"0.6209662","text":"async def id(self, ctx, *, user: Optional[discord.User] = commands.Author):\n await ctx.invoke(self.user_id, user = user)","title":""},{"docid":"df597bce50d07ff46cabfaa92e565144","score":"0.61188346","text":"async def authorized_userid(self, identity):\r\n async with aiosqlite.connect(self.dbname) as db:\r\n try:\r\n async with db.execute('''select roles from user where email = ? and active and roles is not null''', (identity,)) as cur:\r\n row = await cur.fetchone()\r\n if row is None:\r\n return None\r\n\r\n return identity\r\n except Exception:\r\n print(\r\n 'db_error in authorized_userid',\r\n identity,\r\n sys.exc_info())\r\n return None","title":""},{"docid":"36952ee866e69fa8ad418b1c5d7dc8d9","score":"0.60722107","text":"def spoof_user(client):\n pool = client.testing_db_pool\n conn = pool.getconn()\n s_id = ms.create_staff_member(\n conn, 'rrobot', 'password', ('Roberto', 'Robot'),\n Permission.robot\n )\n conn.commit()\n pool.putconn(conn)\n with client.session_transaction() as sess:\n sess['username'] = 'rrobot'\n return s_id","title":""},{"docid":"1d7cd43257edba875dc003309fc5a802","score":"0.60563624","text":"def user_identity_lookup(user):\n return user.id","title":""},{"docid":"f05fbdb67ab3cef849187058f9464bcc","score":"0.60366464","text":"def authorized(name, user_id):\n\n return name == user_id","title":""},{"docid":"79969eb1c7d95d9882944947410a3dd6","score":"0.6024009","text":"def _authorize_user(self):\n user = common.RoomsUser.from_request(self.rs)\n if user and (user.staff or user.captain):\n self._user = user\n return\n raise remote.ApplicationError('Must be a ROOMS user to use this API.')","title":""},{"docid":"454a4d94524c8b4d067bee8ea644a7e3","score":"0.6004629","text":"def admin_enable_user(UserPoolId=None, Username=None):\n pass","title":""},{"docid":"a9a4ea73641ec794c79947de615d5d13","score":"0.5919048","text":"def authed(userId=None):\n\n if 'userId' not in session:\n flash('Please log in to continue')\n return False\n\n if userId:\n if session['userId'] == userId:\n return True\n else:\n flash('You are not authorised to perform this operation')\n return False\n return True","title":""},{"docid":"b353dd38a238cd90aed510e4183a3e55","score":"0.58388853","text":"def assign_role_to_a_user(self):","title":""},{"docid":"ec73fc88ab32afb9b330fc7dde5bb85f","score":"0.58281446","text":"def check_userid_hook(action, **params):\n\n if not 'user_id' in session:\n c.user = users_model.AnonymousUser()\n return\n\n user = meta.Session.query(users_model.User).get(session['user_id'])\n if not user:\n # Bogus id in the session somehow. Clear it\n del session['user_id']\n session.save()\n\n c.user = users_model.AnonymousUser()\n return\n\n c.user = user","title":""},{"docid":"93a19428a9cb58c6aea6add9b9c69b71","score":"0.58010787","text":"def becomeOwner(context):\n user_id = getSecurityManager().getUser().getId()\n owners = context.users_with_local_role('Owner')\n\n for owner_id in owners:\n roles = context.get_local_roles_for_userid(owner_id)\n roles = [x for x in roles if x != 'Owner']\n if roles:\n context.manage_setLocalRoles(owner_id, roles)\n else:\n context.manage_delLocalRoles([owner_id])\n\n roles = context.get_local_roles_for_userid(user_id)\n if 'Owner' not in roles:\n roles += ('Owner',)\n context.manage_setLocalRoles(user_id, roles)","title":""},{"docid":"dba015a30a159c4053cdf99991fa2cb7","score":"0.5800908","text":"def allow_user(user):\n def processor(action, argument):\n db.session.add(\n ActionUsers.allow(action, argument=argument, user_id=user.id)\n )\n return processor","title":""},{"docid":"5d65fa0611fa24a8127e42b0abefc9ee","score":"0.57825416","text":"def get_user_id(self, user):","title":""},{"docid":"6644d86da060ec4bcee868c6eb7bf2ad","score":"0.5772611","text":"def identify_user():\n if 'user_id' not in session.keys():\n session['user_id'] = generate_user_id()","title":""},{"docid":"70079164bcc01c23cca4ad3d83f388e7","score":"0.5753188","text":"def is_protected(self, user):","title":""},{"docid":"93be37d617ec7a73864efea926e8562c","score":"0.5746929","text":"def get_current_user(user_id):\n return Employee.query.get(user_id)","title":""},{"docid":"7b5761aa80fd0cbc7878f07e4e4fba6d","score":"0.57146513","text":"def on_identity_loaded(sender, identity):\n # Set the identity user object\n identity.user = current_user\n # Add the UserNeed to the identity\n if hasattr(current_user, 'id'):\n identity.provides.add(UserNeed(current_user.id))\n # Assuming the User model has a list of roles, update the\n # identity with the roles that the user provides\n if hasattr(current_user, 'roles'):\n for role in current_user.roles:\n identity.provides.add(RoleNeed(role))","title":""},{"docid":"84f363951ca040a818d198a7ca4dd08a","score":"0.57086164","text":"def current_user():\n jwt = current_user_token_info()\n if not jwt:\n return None\n user_id = jwt['sub']\n return ndb.Key(UserModel, user_id).get()","title":""},{"docid":"93ee2bb7a0fa2f10c36a40aa8065b75f","score":"0.5662216","text":"def ident(self):\n if self.is_disabled:\n return\n\n self.create_or_update_identity()\n\n # user is already logged in\n # so simply associate identity with the user\n if all((self.session.get(i) for i in\n [SESSION_KEY, BACKEND_SESSION_KEY, HASH_SESSION_KEY])):\n self.identity.user_id = self.session.get(SESSION_KEY)\n\n # user is already associated with identity\n # so we can login the user\n elif self.identity.user_id:\n user = self.identity.user\n user.backend = SQRL_MODEL_BACKEND\n\n session_auth_hash = user.get_session_auth_hash()\n\n self.session[SESSION_KEY] = user.pk\n self.session[BACKEND_SESSION_KEY] = user.backend\n self.session[HASH_SESSION_KEY] = session_auth_hash\n\n log.info('Successfully authenticated user \"{}\" via SQRL'.format(user.username))\n\n # user was not found so lets save identity information in session\n # so that we can complete user registration\n else:\n serialized = serializers.serialize('json', [self.identity])\n self.session[SQRL_IDENTITY_SESSION_KEY] = serialized\n log.debug('Storing sqrl identity in session \"{}\" to complete registration:\\n{}'\n ''.format(self.session.session_key,\n pformat(json.loads(serialized)[0]['fields'])))\n\n self.nut.is_transaction_complete = True","title":""},{"docid":"b265285c5bdfeff080f8e24d6686e23f","score":"0.5628003","text":"def user(self):\n raise NotImplementedError","title":""},{"docid":"645c9f58fcb6588e000e9343a81f8063","score":"0.5614129","text":"def modify_user(self, data, user_id=None):\n pass","title":""},{"docid":"1a5cd056a4b0d98acbbe889a442a9bf6","score":"0.56134194","text":"async def understand(self, ctx, userstr):\n if userstr.lower() in [\"0\", \"none\"]:\n return \"0\"\n role = await ctx.find_role(userstr, create=True, interactive=True)\n return role.id if role else None","title":""},{"docid":"b38456baa74dc7fb9ef1b074cb81cae6","score":"0.56093353","text":"def authenticated_userid(self, request):\r\n identity = self._get_identity(request)\r\n\r\n if identity is None:\r\n self.debug and self._log(\r\n 'repoze.who identity is None, returning None',\r\n 'authenticated_userid',\r\n request)\r\n return None\r\n\r\n userid = identity['repoze.who.userid']\r\n\r\n if userid is None:\r\n self.debug and self._log(\r\n 'repoze.who.userid is None, returning None' % userid,\r\n 'authenticated_userid',\r\n request)\r\n return None\r\n \r\n if self._clean_principal(userid) is None:\r\n self.debug and self._log(\r\n ('use of userid %r is disallowed by any built-in Pyramid '\r\n 'security policy, returning None' % userid),\r\n 'authenticated_userid',\r\n request)\r\n return None\r\n\r\n if self.callback is None:\r\n return userid\r\n\r\n if self.callback(identity, request) is not None: # is not None!\r\n return userid","title":""},{"docid":"8e9b4a1b6dcd3cd015b046c4141374a7","score":"0.5590296","text":"def resolve_user_identity(self, info):\n return UserIdentity()","title":""},{"docid":"465b99ddcb64cb406c227cf4796d9c8c","score":"0.559009","text":"def user(name):\n return principal(name, cls=User)","title":""},{"docid":"cb50728afdc4f7990bc83dd41655d6a7","score":"0.55872625","text":"def user_id(self):\n # the identity plugin case\n try:\n return self.session.auth.get_access(self.session).user_id\n except AttributeError: # nosec(cjschaef): attempt legacy retrival, or\n # return None\n pass\n\n # there is a case that we explicitly allow (tested by our unit tests)\n # that says you should be able to set the user_id on a legacy client\n # and it should overwrite the one retrieved via authentication. If it's\n # a legacy then self.session.auth is a client and we retrieve user_id.\n try:\n return self.session.auth.user_id\n except AttributeError: # nosec(cjschaef): retrivals failed, return\n # None\n pass\n\n return None","title":""},{"docid":"59563401514e15d632cd563725ffcefb","score":"0.55736625","text":"def varified_user(request, account_requested):\n requester = str(request.user.id)\n if requester == str(account_requested):\n return True","title":""},{"docid":"e5133e8ebf7a9eedf6369f10fd96dd4b","score":"0.5566422","text":"def this_user():\n\n user = session['user_id']\n user = User.query.get(user)\n\n return user","title":""},{"docid":"a531e652696b11fc2130268cd59b3c94","score":"0.55603683","text":"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","title":""},{"docid":"a531e652696b11fc2130268cd59b3c94","score":"0.55603683","text":"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","title":""},{"docid":"a531e652696b11fc2130268cd59b3c94","score":"0.55603683","text":"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","title":""},{"docid":"a531e652696b11fc2130268cd59b3c94","score":"0.55603683","text":"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","title":""},{"docid":"fb8a915d9a32dfb5f081e87b7e2d28a9","score":"0.5551824","text":"def _get_admin_user(self):\n return self.__admin_user","title":""},{"docid":"0207395b3b8f708e0d6b8621c434d3a3","score":"0.5542092","text":"def user_id(self) -> int:","title":""},{"docid":"2512641fa8adec6fd21545f8dd88c18d","score":"0.5538953","text":"def user_id(self):\n return None","title":""},{"docid":"c1fbb489b62acfe08473fe6937f4f6de","score":"0.55260265","text":"def configure_user(self, user):\n return user","title":""},{"docid":"76955fdb991386d005fcbe7814478872","score":"0.5523805","text":"def user_assigned_identity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_assigned_identity\")","title":""},{"docid":"9df47f19843ca4dae4af7f5ade3769ce","score":"0.55152583","text":"def ixia_api_serv_user(duthost):\n #return duthost.host.options['variable_manager']._hostvars[duthost.hostname]['secret_group_vars']['ixia_api_server']['user']\n return 'admin'","title":""},{"docid":"eedccd5a5d938fb4a1064d7b552956c5","score":"0.5504705","text":"def test_approve(self):\n self.access.register(\"foo\", \"bar\")\n self.access.approve_user(\"foo\")\n self.assertFalse(\"foo\" in self.access._db[\"pending_users\"])\n self.assertTrue(\"foo\" in self.access._db[\"users\"])","title":""},{"docid":"1da4b3765189fe37b12157ab8196ac95","score":"0.5495571","text":"def test_superuser_access(self):\n req = self.factory.get('/', {}, HTTP_ORGANIZATION=self.john_org.id)\n req.user = self.superuser\n mw = SelectOrganizationMiddleware()\n mw.process_request(req)\n self.assertEquals(req.organization, self.john_org)","title":""},{"docid":"c176a6eed9727cd7ea5266a373b0bfaf","score":"0.54906553","text":"def get_identity_proofing(self, user):\n obj_id = user['_id']\n # TODO\n # This method need to be implemented\n al1_urn = 'http://www.swamid.se/policy/assurance/al1'\n al2_urn = 'http://www.swamid.se/policy/assurance/al2'\n user = self._coll.find_one({'_id': obj_id})\n if user is not None:\n nins = user.get('norEduPersonNIN')\n if nins is not None and len(nins) > 0:\n return al2_urn\n\n return al1_urn","title":""},{"docid":"bd7bbc787176e6b393918c45ef397498","score":"0.54711014","text":"def _authorize_user(self):\n pass","title":""},{"docid":"bd3c1c2c8e040af1eac85afb355653f1","score":"0.54697216","text":"def user_loader(identity):\n return User.query.filter_by(id=identity).first()","title":""},{"docid":"3a1f1fa0ecc36b550776a90dca8f0859","score":"0.5465504","text":"def get_current_user(self):\n user = users.get_current_user()\n if user:\n user.administrator = users.is_current_user_admin()\n return user","title":""},{"docid":"4374f4d972fab8eb22eceb7df9ccb95e","score":"0.54625255","text":"def _get_user(self):\n return self.user_id","title":""},{"docid":"c89ee479541cbf6b976c0022b5eb701e","score":"0.54600275","text":"def get_admin(self):\n try:\n user = User.objects.get(id=self.admin)\n return user\n except User.DoesNotExist:\n return None","title":""},{"docid":"4735bcb943aabda2027ca50a242960b3","score":"0.5456862","text":"def restricted(func):\n @wraps(func)\n def wrapped(update, context, *args, **kwargs):\n user_id = update.effective_user.id\n if user_id not in conf['restricted_ids']:\n print(user_id, \" is not in \", conf['restricted_ids'])\n print(\"WARNING: Unauthorized access denied for {}.\".format(user_id))\n update.message.reply_text('User disallowed.')\n return # quit function\n return func(update, context, *args, **kwargs)\n return wrapped","title":""},{"docid":"937c8b403381b43993d2d9a91606219b","score":"0.545164","text":"def user_authorized(model):\n def decorator(f):\n @wraps(f)\n def decorated_function(id, *args, **kwargs):\n \"\"\"Checks that logged in user_id matches user_id of instance.\"\"\"\n # Get user_id of logged in user\n user_id = session['user_id']\n # Get item by id. If it does not exist, return 404.\n item = model.query.get_or_404(id)\n # Make sure that item does have a user, or return 500 error\n assert item.user_id\n # If user does not exist, or user does not own item,\n # return 403 status\n if user_id != item.user_id:\n abort(403)\n return f(id, *args, **kwargs)\n return decorated_function\n return decorator","title":""},{"docid":"dff6cdad374c6940416473deb7b55bd2","score":"0.5451623","text":"def restricted(func):\n @wraps(func)\n def wrapped(update: Update, context: CallbackContext, *args, **kwargs):\n user_id = str(update.effective_user.id)\n log_command(update)\n if users and user_id not in users:\n print(\"Unauthorized access denied for {}.\".format(user_id))\n return\n\n return func(update, context, *args, **kwargs)\n return wrapped","title":""},{"docid":"02bcead4f600146c2e3e58cdc3ade4eb","score":"0.5445352","text":"def user_check(self, user):\n return True","title":""},{"docid":"23b74ce218c822360a6d7b8aa4bda24f","score":"0.54429954","text":"def user_assigned_identity(self) -> Optional[str]:\n return pulumi.get(self, \"user_assigned_identity\")","title":""},{"docid":"321b512404686556ec3b91fa4235f892","score":"0.54389566","text":"def identity(payload):\n user_id = payload['identity']\n return user_id","title":""},{"docid":"5a2e2a0c8ddf50b22c8e1c1515ee9a8b","score":"0.54366314","text":"def user_get(self, user):\n pass","title":""},{"docid":"63e5e61624a0bd922603edc45fc3c6de","score":"0.5431395","text":"def test_impersonation(self):\n self.run_backfill(\"test_impersonation\", \"test_impersonated_user\")","title":""},{"docid":"37ebce8d0ed29c84db77458d1bdf366b","score":"0.54283875","text":"def user(self):\n return User.objects.get(id=self.xmodule_runtime.user_id)","title":""},{"docid":"64dfe05551eaec645674e0ad1f289fa4","score":"0.5424488","text":"def set_admin(self, user):\n self.admin = user.id\n self.save()","title":""},{"docid":"95809c54decbe4a7e0677074eb06443d","score":"0.54212415","text":"def admin_get_user(UserPoolId=None, Username=None):\n pass","title":""},{"docid":"acdee96407bb00cadb11c1e549c0a863","score":"0.5415371","text":"def _authorize_staff(self):\n user = common.RoomsUser.from_request(self.rs)\n if user and user.staff:\n self._user = user\n return\n raise remote.ApplicationError('Must be staff to use this API.')","title":""},{"docid":"8dcb956dab30ccf8fd6c2abdb4e4c179","score":"0.5413098","text":"def change_user(userid):\n with error_wrapper(\"Unable to set the user ({exc})\"):\n os.setuid(userid)","title":""},{"docid":"da8cf3a8a3dcf4134c7412b6a3a95653","score":"0.5405882","text":"def test_012_user_view_other_user(self):\n\n user = self.create_user(login=False)\n user_id = user[\"data\"][\"user_id\"]\n\n self.create_user()\n result = self.client.get(\"/users/{}\".format(user_id))\n assert result.status_code == 403","title":""},{"docid":"3fa169a16b958957a53094ed3512f46f","score":"0.5405196","text":"def _get_user(self):\n return self.__user","title":""},{"docid":"3fa169a16b958957a53094ed3512f46f","score":"0.5405196","text":"def _get_user(self):\n return self.__user","title":""},{"docid":"d4ec19c13a00d83ad7a2ddbde5c52783","score":"0.539736","text":"def login_dummy_user(self):\n self.make_dummy_user()\n self.login('admin@admin', 'admin')","title":""},{"docid":"0a121614699c5c114eec0c705da0188d","score":"0.539527","text":"def owner(self):\n return self.tenant if self.owner_is_tenant else self.user","title":""},{"docid":"3fdf25ebc9b77892cf3da0e11090d833","score":"0.5392534","text":"def userLoggedIn(self, user):\n pass","title":""},{"docid":"a3a76c88d64f6612c74bda26a1c44fa0","score":"0.5391697","text":"def setUp(self):\n # Ensure that current user with developer privilege is created\n user_properties = {\n 'account': users.get_current_user(),\n 'link_id': 'current_user',\n 'name': 'Current User',\n 'is_developer': True,\n }\n user_logic.updateOrCreateFromFields(user_properties)","title":""},{"docid":"647b8d7f7bf949379984fe6fd1c23ddd","score":"0.5391532","text":"def assert_user_is_system_admin(user_db):\n return True","title":""},{"docid":"8a59cd2b34e0e80be9b5646efbbe939f","score":"0.53912693","text":"def test_admin_api_organization_request_with_lambda_user(self):\n admin = factories.UserFactory(is_staff=False, is_superuser=False)\n self.client.login(username=admin.username, password=\"password\")\n\n response = self.client.get(\"/api/v1.0/admin/organizations/\")\n\n self.assertEqual(response.status_code, 403)\n content = response.json()\n self.assertEqual(\n content[\"detail\"], \"You do not have permission to perform this action.\"\n )","title":""},{"docid":"94efb9f5c69205363cffe41d07bc2112","score":"0.5388221","text":"def authenticated_userid(self, request):\r\n debug = self.debug\r\n userid = self.unauthenticated_userid(request)\r\n if userid is None:\r\n debug and self._log(\r\n 'call to unauthenticated_userid returned None; returning None',\r\n 'authenticated_userid',\r\n request)\r\n return None\r\n if self._clean_principal(userid) is None:\r\n debug and self._log(\r\n ('use of userid %r is disallowed by any built-in Pyramid '\r\n 'security policy, returning None' % userid),\r\n 'authenticated_userid' ,\r\n request)\r\n return None\r\n \r\n if self.callback is None:\r\n debug and self._log(\r\n 'there was no groupfinder callback; returning %r' % (userid,),\r\n 'authenticated_userid',\r\n request)\r\n return userid\r\n callback_ok = self.callback(userid, request)\r\n if callback_ok is not None: # is not None!\r\n debug and self._log(\r\n 'groupfinder callback returned %r; returning %r' % (\r\n callback_ok, userid),\r\n 'authenticated_userid',\r\n request\r\n )\r\n return userid\r\n debug and self._log(\r\n 'groupfinder callback returned None; returning None',\r\n 'authenticated_userid',\r\n request\r\n )","title":""},{"docid":"2168b164d70b8035d736edf209d94e9f","score":"0.53868794","text":"def on_identity_loaded(sender, identity):\n\tidentity.user = current_user\n\n\tif hasattr(current_user, 'id'):\n\t\tidentity.provides.add(UserNeed(current_user.id))\n\n\tif hasattr(current_user, 'roles'):\n\t\tfor role in current_user.roles:\n\t\t\tidentity.provides.add(RoleNeed(role))","title":""},{"docid":"9464a17acb35b52ace6e84602366ce50","score":"0.53755635","text":"def is_owner(self, user):\n return user == self.owner","title":""},{"docid":"d095eef0764fc56a4bebfbc7ac111265","score":"0.5372622","text":"def initiating_user(self, initiating_user):\n \n self._initiating_user = initiating_user","title":""},{"docid":"a5f0624dc6304595c93b0ec0cc918cac","score":"0.5359191","text":"def assert_user_is_admin_or_operating_on_own_resource(user_db, user=None):\n return True","title":""},{"docid":"270f36ac98971a13f164c932de2748f6","score":"0.5357705","text":"def user(self):","title":""},{"docid":"270f36ac98971a13f164c932de2748f6","score":"0.5357705","text":"def user(self):","title":""},{"docid":"7539fed058da1ed12f595f8606b97ba1","score":"0.5357133","text":"def authenticated_userid(request):","title":""},{"docid":"ba0e183ec4fa1e028a8aab3b5967cccc","score":"0.53552693","text":"def user_check(self, user):\n if isinstance(user, AnonymousUser):\n return True\n return False","title":""},{"docid":"3026b49e92f47de4edbd2da2d098f8aa","score":"0.5355248","text":"def initiating_user(self):\n return self._initiating_user","title":""},{"docid":"e8f782e031b5f6bb04784866b1c08e79","score":"0.53540957","text":"def cmd_paident(self, data, client, cmd=None):\n x = self._adminPlugin.parseUserCmd(data)\n if not x:\n cmd.sayLoudOrPM(client, '%s: %s' % (client.name, client.guid))\n else:\n sclient = self._adminPlugin.findClientPrompt(x[0], client)\n if sclient:\n cmd.sayLoudOrPM(client, '%s: %s' % (sclient.name, sclient.guid))","title":""},{"docid":"898dd97a3ad86b5885dd87fce41b0036","score":"0.5344874","text":"def current_user():\n user = os.environ.get(\"SUDO_USER\", None)\n if user is None:\n user = os.environ.get(\"USER\", None)\n return bmodels.Person.lookup(user)","title":""},{"docid":"f3b0691dbc222e3ef0c329fd0b904b99","score":"0.53437823","text":"def promote_user(username):\n user = annotator.credentials.find_one({'username': username})\n if user:\n if user['admin']:\n flash(\"User {0} is already an administrator\".format(username), 'warning')\n else:\n annotator.credentials.update_one(user, {'$set': {'admin': True}})\n flash(\"User {0} promoted to administrator successfully\".format(username), 'info')\n else:\n flash(\"Cannot promote unknown user {0} to administrator\".format(username), 'warning')\n\n return redirect(url_for('manage_users'))","title":""},{"docid":"fa30c1827d19a29215f3ffc3b4b7305c","score":"0.53402245","text":"def save_user_on_elk(self):\n pass","title":""},{"docid":"bc4082cbb914f827a3af895dc06650f2","score":"0.5332963","text":"def userBlocking(self, userName:str, ormSession=None) -> Optional[UserDetailTuple]:","title":""},{"docid":"f1b986d2d4fc44f121a38b1534f8c98f","score":"0.5331239","text":"def owner(self):\n return self.get_user(396805720353275924)","title":""},{"docid":"83cd0bc1658d7e9c3d892452209f4828","score":"0.53294","text":"def user():\n # _name = str(name).capitalize()\n # user = db_operations.find_one({'name':_name})\n # resp = dumps(user)\n # return resp\n\n get_user_by_id(request.headers)","title":""},{"docid":"0480189fa7c159f3c2b082948ee6c574","score":"0.5327571","text":"def user():\n pass","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53252757","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53240997","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53240997","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53240997","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53240997","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53240997","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53240997","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53235704","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"fbdd7218ef9869eee0cc13e6421193b5","score":"0.53235704","text":"def run_as_user(self) -> Optional[int]:\n return pulumi.get(self, \"run_as_user\")","title":""},{"docid":"6f06fbf19c8ef45d751816d071f79ba0","score":"0.5321691","text":"def test_anon_user_interface():\n user = allow_anon.read_only(None)\n assert isinstance(user, allow_anon.AnonymousUser)\n assert user.name == 'anonymous'","title":""},{"docid":"7ec3fe2af225ffd1a8cebd161f3e1f08","score":"0.53181225","text":"def current_user(self):\n username = self.current_user_name()\n bokehuser = user.User.load(bokeh_app.servermodel_storage, username)\n if bokehuser is not None:\n return bokehuser\n bokehuser = user.new_user(bokeh_app.servermodel_storage, \"defaultuser\",\n str(uuid.uuid4()), apikey='nokey', docs=[])\n return bokehuser","title":""},{"docid":"3ee9fd68761f8935ba19f0ea65b7c601","score":"0.5312066","text":"async def approve_user_by_id(self, request):\n user_id = request.match_info['id']\n\n print(f'Approve user GET requested for {user_id}')\n entry = await self.store.add_user(user_id, True)\n\n return web.json_response(entry)","title":""}],"string":"[\n {\n \"docid\": \"16238f629c91702c44993b44da470106\",\n \"score\": \"0.66630304\",\n \"text\": \"def authorized_userid(self, identity):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff2125ab5f51068eab41c9339fc9d5c\",\n \"score\": \"0.6395221\",\n \"text\": \"def confirm_identity():\\n msg = \\\"The current user is {}\\\".format(user())\\n return statement(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"342f44acccc67d0be3301e3d316c432b\",\n \"score\": \"0.6209662\",\n \"text\": \"async def id(self, ctx, *, user: Optional[discord.User] = commands.Author):\\n await ctx.invoke(self.user_id, user = user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df597bce50d07ff46cabfaa92e565144\",\n \"score\": \"0.61188346\",\n \"text\": \"async def authorized_userid(self, identity):\\r\\n async with aiosqlite.connect(self.dbname) as db:\\r\\n try:\\r\\n async with db.execute('''select roles from user where email = ? and active and roles is not null''', (identity,)) as cur:\\r\\n row = await cur.fetchone()\\r\\n if row is None:\\r\\n return None\\r\\n\\r\\n return identity\\r\\n except Exception:\\r\\n print(\\r\\n 'db_error in authorized_userid',\\r\\n identity,\\r\\n sys.exc_info())\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36952ee866e69fa8ad418b1c5d7dc8d9\",\n \"score\": \"0.60722107\",\n \"text\": \"def spoof_user(client):\\n pool = client.testing_db_pool\\n conn = pool.getconn()\\n s_id = ms.create_staff_member(\\n conn, 'rrobot', 'password', ('Roberto', 'Robot'),\\n Permission.robot\\n )\\n conn.commit()\\n pool.putconn(conn)\\n with client.session_transaction() as sess:\\n sess['username'] = 'rrobot'\\n return s_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d7cd43257edba875dc003309fc5a802\",\n \"score\": \"0.60563624\",\n \"text\": \"def user_identity_lookup(user):\\n return user.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05fbdb67ab3cef849187058f9464bcc\",\n \"score\": \"0.60366464\",\n \"text\": \"def authorized(name, user_id):\\n\\n return name == user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79969eb1c7d95d9882944947410a3dd6\",\n \"score\": \"0.6024009\",\n \"text\": \"def _authorize_user(self):\\n user = common.RoomsUser.from_request(self.rs)\\n if user and (user.staff or user.captain):\\n self._user = user\\n return\\n raise remote.ApplicationError('Must be a ROOMS user to use this API.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"454a4d94524c8b4d067bee8ea644a7e3\",\n \"score\": \"0.6004629\",\n \"text\": \"def admin_enable_user(UserPoolId=None, Username=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a4ea73641ec794c79947de615d5d13\",\n \"score\": \"0.5919048\",\n \"text\": \"def authed(userId=None):\\n\\n if 'userId' not in session:\\n flash('Please log in to continue')\\n return False\\n\\n if userId:\\n if session['userId'] == userId:\\n return True\\n else:\\n flash('You are not authorised to perform this operation')\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b353dd38a238cd90aed510e4183a3e55\",\n \"score\": \"0.58388853\",\n \"text\": \"def assign_role_to_a_user(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec73fc88ab32afb9b330fc7dde5bb85f\",\n \"score\": \"0.58281446\",\n \"text\": \"def check_userid_hook(action, **params):\\n\\n if not 'user_id' in session:\\n c.user = users_model.AnonymousUser()\\n return\\n\\n user = meta.Session.query(users_model.User).get(session['user_id'])\\n if not user:\\n # Bogus id in the session somehow. Clear it\\n del session['user_id']\\n session.save()\\n\\n c.user = users_model.AnonymousUser()\\n return\\n\\n c.user = user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a19428a9cb58c6aea6add9b9c69b71\",\n \"score\": \"0.58010787\",\n \"text\": \"def becomeOwner(context):\\n user_id = getSecurityManager().getUser().getId()\\n owners = context.users_with_local_role('Owner')\\n\\n for owner_id in owners:\\n roles = context.get_local_roles_for_userid(owner_id)\\n roles = [x for x in roles if x != 'Owner']\\n if roles:\\n context.manage_setLocalRoles(owner_id, roles)\\n else:\\n context.manage_delLocalRoles([owner_id])\\n\\n roles = context.get_local_roles_for_userid(user_id)\\n if 'Owner' not in roles:\\n roles += ('Owner',)\\n context.manage_setLocalRoles(user_id, roles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dba015a30a159c4053cdf99991fa2cb7\",\n \"score\": \"0.5800908\",\n \"text\": \"def allow_user(user):\\n def processor(action, argument):\\n db.session.add(\\n ActionUsers.allow(action, argument=argument, user_id=user.id)\\n )\\n return processor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d65fa0611fa24a8127e42b0abefc9ee\",\n \"score\": \"0.57825416\",\n \"text\": \"def get_user_id(self, user):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6644d86da060ec4bcee868c6eb7bf2ad\",\n \"score\": \"0.5772611\",\n \"text\": \"def identify_user():\\n if 'user_id' not in session.keys():\\n session['user_id'] = generate_user_id()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70079164bcc01c23cca4ad3d83f388e7\",\n \"score\": \"0.5753188\",\n \"text\": \"def is_protected(self, user):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93be37d617ec7a73864efea926e8562c\",\n \"score\": \"0.5746929\",\n \"text\": \"def get_current_user(user_id):\\n return Employee.query.get(user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b5761aa80fd0cbc7878f07e4e4fba6d\",\n \"score\": \"0.57146513\",\n \"text\": \"def on_identity_loaded(sender, identity):\\n # Set the identity user object\\n identity.user = current_user\\n # Add the UserNeed to the identity\\n if hasattr(current_user, 'id'):\\n identity.provides.add(UserNeed(current_user.id))\\n # Assuming the User model has a list of roles, update the\\n # identity with the roles that the user provides\\n if hasattr(current_user, 'roles'):\\n for role in current_user.roles:\\n identity.provides.add(RoleNeed(role))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84f363951ca040a818d198a7ca4dd08a\",\n \"score\": \"0.57086164\",\n \"text\": \"def current_user():\\n jwt = current_user_token_info()\\n if not jwt:\\n return None\\n user_id = jwt['sub']\\n return ndb.Key(UserModel, user_id).get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ee2bb7a0fa2f10c36a40aa8065b75f\",\n \"score\": \"0.5662216\",\n \"text\": \"def ident(self):\\n if self.is_disabled:\\n return\\n\\n self.create_or_update_identity()\\n\\n # user is already logged in\\n # so simply associate identity with the user\\n if all((self.session.get(i) for i in\\n [SESSION_KEY, BACKEND_SESSION_KEY, HASH_SESSION_KEY])):\\n self.identity.user_id = self.session.get(SESSION_KEY)\\n\\n # user is already associated with identity\\n # so we can login the user\\n elif self.identity.user_id:\\n user = self.identity.user\\n user.backend = SQRL_MODEL_BACKEND\\n\\n session_auth_hash = user.get_session_auth_hash()\\n\\n self.session[SESSION_KEY] = user.pk\\n self.session[BACKEND_SESSION_KEY] = user.backend\\n self.session[HASH_SESSION_KEY] = session_auth_hash\\n\\n log.info('Successfully authenticated user \\\"{}\\\" via SQRL'.format(user.username))\\n\\n # user was not found so lets save identity information in session\\n # so that we can complete user registration\\n else:\\n serialized = serializers.serialize('json', [self.identity])\\n self.session[SQRL_IDENTITY_SESSION_KEY] = serialized\\n log.debug('Storing sqrl identity in session \\\"{}\\\" to complete registration:\\\\n{}'\\n ''.format(self.session.session_key,\\n pformat(json.loads(serialized)[0]['fields'])))\\n\\n self.nut.is_transaction_complete = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b265285c5bdfeff080f8e24d6686e23f\",\n \"score\": \"0.5628003\",\n \"text\": \"def user(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"645c9f58fcb6588e000e9343a81f8063\",\n \"score\": \"0.5614129\",\n \"text\": \"def modify_user(self, data, user_id=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a5cd056a4b0d98acbbe889a442a9bf6\",\n \"score\": \"0.56134194\",\n \"text\": \"async def understand(self, ctx, userstr):\\n if userstr.lower() in [\\\"0\\\", \\\"none\\\"]:\\n return \\\"0\\\"\\n role = await ctx.find_role(userstr, create=True, interactive=True)\\n return role.id if role else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38456baa74dc7fb9ef1b074cb81cae6\",\n \"score\": \"0.56093353\",\n \"text\": \"def authenticated_userid(self, request):\\r\\n identity = self._get_identity(request)\\r\\n\\r\\n if identity is None:\\r\\n self.debug and self._log(\\r\\n 'repoze.who identity is None, returning None',\\r\\n 'authenticated_userid',\\r\\n request)\\r\\n return None\\r\\n\\r\\n userid = identity['repoze.who.userid']\\r\\n\\r\\n if userid is None:\\r\\n self.debug and self._log(\\r\\n 'repoze.who.userid is None, returning None' % userid,\\r\\n 'authenticated_userid',\\r\\n request)\\r\\n return None\\r\\n \\r\\n if self._clean_principal(userid) is None:\\r\\n self.debug and self._log(\\r\\n ('use of userid %r is disallowed by any built-in Pyramid '\\r\\n 'security policy, returning None' % userid),\\r\\n 'authenticated_userid',\\r\\n request)\\r\\n return None\\r\\n\\r\\n if self.callback is None:\\r\\n return userid\\r\\n\\r\\n if self.callback(identity, request) is not None: # is not None!\\r\\n return userid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9b4a1b6dcd3cd015b046c4141374a7\",\n \"score\": \"0.5590296\",\n \"text\": \"def resolve_user_identity(self, info):\\n return UserIdentity()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"465b99ddcb64cb406c227cf4796d9c8c\",\n \"score\": \"0.559009\",\n \"text\": \"def user(name):\\n return principal(name, cls=User)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb50728afdc4f7990bc83dd41655d6a7\",\n \"score\": \"0.55872625\",\n \"text\": \"def user_id(self):\\n # the identity plugin case\\n try:\\n return self.session.auth.get_access(self.session).user_id\\n except AttributeError: # nosec(cjschaef): attempt legacy retrival, or\\n # return None\\n pass\\n\\n # there is a case that we explicitly allow (tested by our unit tests)\\n # that says you should be able to set the user_id on a legacy client\\n # and it should overwrite the one retrieved via authentication. If it's\\n # a legacy then self.session.auth is a client and we retrieve user_id.\\n try:\\n return self.session.auth.user_id\\n except AttributeError: # nosec(cjschaef): retrivals failed, return\\n # None\\n pass\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59563401514e15d632cd563725ffcefb\",\n \"score\": \"0.55736625\",\n \"text\": \"def varified_user(request, account_requested):\\n requester = str(request.user.id)\\n if requester == str(account_requested):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5133e8ebf7a9eedf6369f10fd96dd4b\",\n \"score\": \"0.5566422\",\n \"text\": \"def this_user():\\n\\n user = session['user_id']\\n user = User.query.get(user)\\n\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a531e652696b11fc2130268cd59b3c94\",\n \"score\": \"0.55603683\",\n \"text\": \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a531e652696b11fc2130268cd59b3c94\",\n \"score\": \"0.55603683\",\n \"text\": \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a531e652696b11fc2130268cd59b3c94\",\n \"score\": \"0.55603683\",\n \"text\": \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a531e652696b11fc2130268cd59b3c94\",\n \"score\": \"0.55603683\",\n \"text\": \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb8a915d9a32dfb5f081e87b7e2d28a9\",\n \"score\": \"0.5551824\",\n \"text\": \"def _get_admin_user(self):\\n return self.__admin_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0207395b3b8f708e0d6b8621c434d3a3\",\n \"score\": \"0.5542092\",\n \"text\": \"def user_id(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2512641fa8adec6fd21545f8dd88c18d\",\n \"score\": \"0.5538953\",\n \"text\": \"def user_id(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1fbb489b62acfe08473fe6937f4f6de\",\n \"score\": \"0.55260265\",\n \"text\": \"def configure_user(self, user):\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76955fdb991386d005fcbe7814478872\",\n \"score\": \"0.5523805\",\n \"text\": \"def user_assigned_identity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_assigned_identity\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9df47f19843ca4dae4af7f5ade3769ce\",\n \"score\": \"0.55152583\",\n \"text\": \"def ixia_api_serv_user(duthost):\\n #return duthost.host.options['variable_manager']._hostvars[duthost.hostname]['secret_group_vars']['ixia_api_server']['user']\\n return 'admin'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eedccd5a5d938fb4a1064d7b552956c5\",\n \"score\": \"0.5504705\",\n \"text\": \"def test_approve(self):\\n self.access.register(\\\"foo\\\", \\\"bar\\\")\\n self.access.approve_user(\\\"foo\\\")\\n self.assertFalse(\\\"foo\\\" in self.access._db[\\\"pending_users\\\"])\\n self.assertTrue(\\\"foo\\\" in self.access._db[\\\"users\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1da4b3765189fe37b12157ab8196ac95\",\n \"score\": \"0.5495571\",\n \"text\": \"def test_superuser_access(self):\\n req = self.factory.get('/', {}, HTTP_ORGANIZATION=self.john_org.id)\\n req.user = self.superuser\\n mw = SelectOrganizationMiddleware()\\n mw.process_request(req)\\n self.assertEquals(req.organization, self.john_org)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c176a6eed9727cd7ea5266a373b0bfaf\",\n \"score\": \"0.54906553\",\n \"text\": \"def get_identity_proofing(self, user):\\n obj_id = user['_id']\\n # TODO\\n # This method need to be implemented\\n al1_urn = 'http://www.swamid.se/policy/assurance/al1'\\n al2_urn = 'http://www.swamid.se/policy/assurance/al2'\\n user = self._coll.find_one({'_id': obj_id})\\n if user is not None:\\n nins = user.get('norEduPersonNIN')\\n if nins is not None and len(nins) > 0:\\n return al2_urn\\n\\n return al1_urn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd7bbc787176e6b393918c45ef397498\",\n \"score\": \"0.54711014\",\n \"text\": \"def _authorize_user(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3c1c2c8e040af1eac85afb355653f1\",\n \"score\": \"0.54697216\",\n \"text\": \"def user_loader(identity):\\n return User.query.filter_by(id=identity).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a1f1fa0ecc36b550776a90dca8f0859\",\n \"score\": \"0.5465504\",\n \"text\": \"def get_current_user(self):\\n user = users.get_current_user()\\n if user:\\n user.administrator = users.is_current_user_admin()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4374f4d972fab8eb22eceb7df9ccb95e\",\n \"score\": \"0.54625255\",\n \"text\": \"def _get_user(self):\\n return self.user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89ee479541cbf6b976c0022b5eb701e\",\n \"score\": \"0.54600275\",\n \"text\": \"def get_admin(self):\\n try:\\n user = User.objects.get(id=self.admin)\\n return user\\n except User.DoesNotExist:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4735bcb943aabda2027ca50a242960b3\",\n \"score\": \"0.5456862\",\n \"text\": \"def restricted(func):\\n @wraps(func)\\n def wrapped(update, context, *args, **kwargs):\\n user_id = update.effective_user.id\\n if user_id not in conf['restricted_ids']:\\n print(user_id, \\\" is not in \\\", conf['restricted_ids'])\\n print(\\\"WARNING: Unauthorized access denied for {}.\\\".format(user_id))\\n update.message.reply_text('User disallowed.')\\n return # quit function\\n return func(update, context, *args, **kwargs)\\n return wrapped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937c8b403381b43993d2d9a91606219b\",\n \"score\": \"0.545164\",\n \"text\": \"def user_authorized(model):\\n def decorator(f):\\n @wraps(f)\\n def decorated_function(id, *args, **kwargs):\\n \\\"\\\"\\\"Checks that logged in user_id matches user_id of instance.\\\"\\\"\\\"\\n # Get user_id of logged in user\\n user_id = session['user_id']\\n # Get item by id. If it does not exist, return 404.\\n item = model.query.get_or_404(id)\\n # Make sure that item does have a user, or return 500 error\\n assert item.user_id\\n # If user does not exist, or user does not own item,\\n # return 403 status\\n if user_id != item.user_id:\\n abort(403)\\n return f(id, *args, **kwargs)\\n return decorated_function\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dff6cdad374c6940416473deb7b55bd2\",\n \"score\": \"0.5451623\",\n \"text\": \"def restricted(func):\\n @wraps(func)\\n def wrapped(update: Update, context: CallbackContext, *args, **kwargs):\\n user_id = str(update.effective_user.id)\\n log_command(update)\\n if users and user_id not in users:\\n print(\\\"Unauthorized access denied for {}.\\\".format(user_id))\\n return\\n\\n return func(update, context, *args, **kwargs)\\n return wrapped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02bcead4f600146c2e3e58cdc3ade4eb\",\n \"score\": \"0.5445352\",\n \"text\": \"def user_check(self, user):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b74ce218c822360a6d7b8aa4bda24f\",\n \"score\": \"0.54429954\",\n \"text\": \"def user_assigned_identity(self) -> Optional[str]:\\n return pulumi.get(self, \\\"user_assigned_identity\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321b512404686556ec3b91fa4235f892\",\n \"score\": \"0.54389566\",\n \"text\": \"def identity(payload):\\n user_id = payload['identity']\\n return user_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a2e2a0c8ddf50b22c8e1c1515ee9a8b\",\n \"score\": \"0.54366314\",\n \"text\": \"def user_get(self, user):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63e5e61624a0bd922603edc45fc3c6de\",\n \"score\": \"0.5431395\",\n \"text\": \"def test_impersonation(self):\\n self.run_backfill(\\\"test_impersonation\\\", \\\"test_impersonated_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37ebce8d0ed29c84db77458d1bdf366b\",\n \"score\": \"0.54283875\",\n \"text\": \"def user(self):\\n return User.objects.get(id=self.xmodule_runtime.user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64dfe05551eaec645674e0ad1f289fa4\",\n \"score\": \"0.5424488\",\n \"text\": \"def set_admin(self, user):\\n self.admin = user.id\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95809c54decbe4a7e0677074eb06443d\",\n \"score\": \"0.54212415\",\n \"text\": \"def admin_get_user(UserPoolId=None, Username=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acdee96407bb00cadb11c1e549c0a863\",\n \"score\": \"0.5415371\",\n \"text\": \"def _authorize_staff(self):\\n user = common.RoomsUser.from_request(self.rs)\\n if user and user.staff:\\n self._user = user\\n return\\n raise remote.ApplicationError('Must be staff to use this API.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dcb956dab30ccf8fd6c2abdb4e4c179\",\n \"score\": \"0.5413098\",\n \"text\": \"def change_user(userid):\\n with error_wrapper(\\\"Unable to set the user ({exc})\\\"):\\n os.setuid(userid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da8cf3a8a3dcf4134c7412b6a3a95653\",\n \"score\": \"0.5405882\",\n \"text\": \"def test_012_user_view_other_user(self):\\n\\n user = self.create_user(login=False)\\n user_id = user[\\\"data\\\"][\\\"user_id\\\"]\\n\\n self.create_user()\\n result = self.client.get(\\\"/users/{}\\\".format(user_id))\\n assert result.status_code == 403\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa169a16b958957a53094ed3512f46f\",\n \"score\": \"0.5405196\",\n \"text\": \"def _get_user(self):\\n return self.__user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa169a16b958957a53094ed3512f46f\",\n \"score\": \"0.5405196\",\n \"text\": \"def _get_user(self):\\n return self.__user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ec19c13a00d83ad7a2ddbde5c52783\",\n \"score\": \"0.539736\",\n \"text\": \"def login_dummy_user(self):\\n self.make_dummy_user()\\n self.login('admin@admin', 'admin')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a121614699c5c114eec0c705da0188d\",\n \"score\": \"0.539527\",\n \"text\": \"def owner(self):\\n return self.tenant if self.owner_is_tenant else self.user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fdf25ebc9b77892cf3da0e11090d833\",\n \"score\": \"0.5392534\",\n \"text\": \"def userLoggedIn(self, user):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a76c88d64f6612c74bda26a1c44fa0\",\n \"score\": \"0.5391697\",\n \"text\": \"def setUp(self):\\n # Ensure that current user with developer privilege is created\\n user_properties = {\\n 'account': users.get_current_user(),\\n 'link_id': 'current_user',\\n 'name': 'Current User',\\n 'is_developer': True,\\n }\\n user_logic.updateOrCreateFromFields(user_properties)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"647b8d7f7bf949379984fe6fd1c23ddd\",\n \"score\": \"0.5391532\",\n \"text\": \"def assert_user_is_system_admin(user_db):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a59cd2b34e0e80be9b5646efbbe939f\",\n \"score\": \"0.53912693\",\n \"text\": \"def test_admin_api_organization_request_with_lambda_user(self):\\n admin = factories.UserFactory(is_staff=False, is_superuser=False)\\n self.client.login(username=admin.username, password=\\\"password\\\")\\n\\n response = self.client.get(\\\"/api/v1.0/admin/organizations/\\\")\\n\\n self.assertEqual(response.status_code, 403)\\n content = response.json()\\n self.assertEqual(\\n content[\\\"detail\\\"], \\\"You do not have permission to perform this action.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94efb9f5c69205363cffe41d07bc2112\",\n \"score\": \"0.5388221\",\n \"text\": \"def authenticated_userid(self, request):\\r\\n debug = self.debug\\r\\n userid = self.unauthenticated_userid(request)\\r\\n if userid is None:\\r\\n debug and self._log(\\r\\n 'call to unauthenticated_userid returned None; returning None',\\r\\n 'authenticated_userid',\\r\\n request)\\r\\n return None\\r\\n if self._clean_principal(userid) is None:\\r\\n debug and self._log(\\r\\n ('use of userid %r is disallowed by any built-in Pyramid '\\r\\n 'security policy, returning None' % userid),\\r\\n 'authenticated_userid' ,\\r\\n request)\\r\\n return None\\r\\n \\r\\n if self.callback is None:\\r\\n debug and self._log(\\r\\n 'there was no groupfinder callback; returning %r' % (userid,),\\r\\n 'authenticated_userid',\\r\\n request)\\r\\n return userid\\r\\n callback_ok = self.callback(userid, request)\\r\\n if callback_ok is not None: # is not None!\\r\\n debug and self._log(\\r\\n 'groupfinder callback returned %r; returning %r' % (\\r\\n callback_ok, userid),\\r\\n 'authenticated_userid',\\r\\n request\\r\\n )\\r\\n return userid\\r\\n debug and self._log(\\r\\n 'groupfinder callback returned None; returning None',\\r\\n 'authenticated_userid',\\r\\n request\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2168b164d70b8035d736edf209d94e9f\",\n \"score\": \"0.53868794\",\n \"text\": \"def on_identity_loaded(sender, identity):\\n\\tidentity.user = current_user\\n\\n\\tif hasattr(current_user, 'id'):\\n\\t\\tidentity.provides.add(UserNeed(current_user.id))\\n\\n\\tif hasattr(current_user, 'roles'):\\n\\t\\tfor role in current_user.roles:\\n\\t\\t\\tidentity.provides.add(RoleNeed(role))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9464a17acb35b52ace6e84602366ce50\",\n \"score\": \"0.53755635\",\n \"text\": \"def is_owner(self, user):\\n return user == self.owner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d095eef0764fc56a4bebfbc7ac111265\",\n \"score\": \"0.5372622\",\n \"text\": \"def initiating_user(self, initiating_user):\\n \\n self._initiating_user = initiating_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f0624dc6304595c93b0ec0cc918cac\",\n \"score\": \"0.5359191\",\n \"text\": \"def assert_user_is_admin_or_operating_on_own_resource(user_db, user=None):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270f36ac98971a13f164c932de2748f6\",\n \"score\": \"0.5357705\",\n \"text\": \"def user(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270f36ac98971a13f164c932de2748f6\",\n \"score\": \"0.5357705\",\n \"text\": \"def user(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7539fed058da1ed12f595f8606b97ba1\",\n \"score\": \"0.5357133\",\n \"text\": \"def authenticated_userid(request):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba0e183ec4fa1e028a8aab3b5967cccc\",\n \"score\": \"0.53552693\",\n \"text\": \"def user_check(self, user):\\n if isinstance(user, AnonymousUser):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3026b49e92f47de4edbd2da2d098f8aa\",\n \"score\": \"0.5355248\",\n \"text\": \"def initiating_user(self):\\n return self._initiating_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8f782e031b5f6bb04784866b1c08e79\",\n \"score\": \"0.53540957\",\n \"text\": \"def cmd_paident(self, data, client, cmd=None):\\n x = self._adminPlugin.parseUserCmd(data)\\n if not x:\\n cmd.sayLoudOrPM(client, '%s: %s' % (client.name, client.guid))\\n else:\\n sclient = self._adminPlugin.findClientPrompt(x[0], client)\\n if sclient:\\n cmd.sayLoudOrPM(client, '%s: %s' % (sclient.name, sclient.guid))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"898dd97a3ad86b5885dd87fce41b0036\",\n \"score\": \"0.5344874\",\n \"text\": \"def current_user():\\n user = os.environ.get(\\\"SUDO_USER\\\", None)\\n if user is None:\\n user = os.environ.get(\\\"USER\\\", None)\\n return bmodels.Person.lookup(user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b0691dbc222e3ef0c329fd0b904b99\",\n \"score\": \"0.53437823\",\n \"text\": \"def promote_user(username):\\n user = annotator.credentials.find_one({'username': username})\\n if user:\\n if user['admin']:\\n flash(\\\"User {0} is already an administrator\\\".format(username), 'warning')\\n else:\\n annotator.credentials.update_one(user, {'$set': {'admin': True}})\\n flash(\\\"User {0} promoted to administrator successfully\\\".format(username), 'info')\\n else:\\n flash(\\\"Cannot promote unknown user {0} to administrator\\\".format(username), 'warning')\\n\\n return redirect(url_for('manage_users'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa30c1827d19a29215f3ffc3b4b7305c\",\n \"score\": \"0.53402245\",\n \"text\": \"def save_user_on_elk(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc4082cbb914f827a3af895dc06650f2\",\n \"score\": \"0.5332963\",\n \"text\": \"def userBlocking(self, userName:str, ormSession=None) -> Optional[UserDetailTuple]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1b986d2d4fc44f121a38b1534f8c98f\",\n \"score\": \"0.5331239\",\n \"text\": \"def owner(self):\\n return self.get_user(396805720353275924)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83cd0bc1658d7e9c3d892452209f4828\",\n \"score\": \"0.53294\",\n \"text\": \"def user():\\n # _name = str(name).capitalize()\\n # user = db_operations.find_one({'name':_name})\\n # resp = dumps(user)\\n # return resp\\n\\n get_user_by_id(request.headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0480189fa7c159f3c2b082948ee6c574\",\n \"score\": \"0.5327571\",\n \"text\": \"def user():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53252757\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53240997\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53240997\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53240997\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53240997\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53240997\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53240997\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53235704\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd7218ef9869eee0cc13e6421193b5\",\n \"score\": \"0.53235704\",\n \"text\": \"def run_as_user(self) -> Optional[int]:\\n return pulumi.get(self, \\\"run_as_user\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f06fbf19c8ef45d751816d071f79ba0\",\n \"score\": \"0.5321691\",\n \"text\": \"def test_anon_user_interface():\\n user = allow_anon.read_only(None)\\n assert isinstance(user, allow_anon.AnonymousUser)\\n assert user.name == 'anonymous'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ec3fe2af225ffd1a8cebd161f3e1f08\",\n \"score\": \"0.53181225\",\n \"text\": \"def current_user(self):\\n username = self.current_user_name()\\n bokehuser = user.User.load(bokeh_app.servermodel_storage, username)\\n if bokehuser is not None:\\n return bokehuser\\n bokehuser = user.new_user(bokeh_app.servermodel_storage, \\\"defaultuser\\\",\\n str(uuid.uuid4()), apikey='nokey', docs=[])\\n return bokehuser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee9fd68761f8935ba19f0ea65b7c601\",\n \"score\": \"0.5312066\",\n \"text\": \"async def approve_user_by_id(self, request):\\n user_id = request.match_info['id']\\n\\n print(f'Approve user GET requested for {user_id}')\\n entry = await self.store.add_user(user_id, True)\\n\\n return web.json_response(entry)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992975,"cells":{"query_id":{"kind":"string","value":"0b18184a200b8545fb9728198b231d8b"},"query":{"kind":"string","value":"This method prints information in a reasonably easy to read format, and compensates for some formatting challenges in pprint."},"positive_passages":{"kind":"list like","value":[{"docid":"6a7d176560527fff04045d801eb91383","score":"0.0","text":"def print_object( self, arg_object, arg_name_for_object = None ) :\n if not arg_name_for_object == None :\n\n print( arg_name_for_object, \"=\", )\n\n print( \"\\n\".join( self._print_info_get_list_of_strings_formatted( arg_object ) ), \"\\n\\n\", )","title":""}],"string":"[\n {\n \"docid\": \"6a7d176560527fff04045d801eb91383\",\n \"score\": \"0.0\",\n \"text\": \"def print_object( self, arg_object, arg_name_for_object = None ) :\\n if not arg_name_for_object == None :\\n\\n print( arg_name_for_object, \\\"=\\\", )\\n\\n print( \\\"\\\\n\\\".join( self._print_info_get_list_of_strings_formatted( arg_object ) ), \\\"\\\\n\\\\n\\\", )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ef2c8025e8e85397262f486816a47e92","score":"0.7855242","text":"def pprint(self):\n try:\n print(self.pformat(self))\n except:\n print(self.__dict__)","title":""},{"docid":"722ea2b2105afac63e72370b535659a1","score":"0.7789151","text":"def pprint(self):\n\n print(self.pformat())","title":""},{"docid":"2fd24c5589c82a2ff174bef3cb1752db","score":"0.74743855","text":"def pprint(self, **kwargs):\n print(self.pformat(**kwargs))","title":""},{"docid":"a330a6c619548ca9c6168de3e7919d97","score":"0.7414905","text":"def display_print_info(info):","title":""},{"docid":"fdc059761c69f148febefcac5a1a6068","score":"0.7285847","text":"def print_info(self):\n print(self.__repr__())","title":""},{"docid":"30dc33251f7177a95d6c63dbb043dcb3","score":"0.72463","text":"def pretty_print(self):\n\n print(\"-----------------------------\")\n print(self.title)\n print(\" --- \" + self.description)\n print(\"\")\n print(\"Release: \" + self.release)\n print(\"MPAA rating: \" + self.mpaa_rating)\n print(\"Director: \" + self.director)\n print(\"Stars: \" + self.stars)\n print(\"\")\n print(\"poster: \" + self.poster)\n print(\"trailer: \" + self.trailer)\n print(\"IMDB rating: \" + self.imdb_rating)","title":""},{"docid":"7a15dc08ef794fd602e804a1221881d6","score":"0.7124662","text":"def print_info (self):","title":""},{"docid":"7a15dc08ef794fd602e804a1221881d6","score":"0.7124662","text":"def print_info (self):","title":""},{"docid":"e54060f3a7d0b17b88619b3f25dfad4c","score":"0.7111678","text":"def prettyPrint(self):","title":""},{"docid":"5f2a46224be240d4621bd72f45526557","score":"0.7009409","text":"def pprint(self, msg):\n if self.verbose_attr:\n print(msg)","title":""},{"docid":"ee523a317e8ea5b054c593dfe0867d5e","score":"0.69759774","text":"def pp_info(self):\n\n def print_rel(i, dictionary):\n \"\"\"Printing helper\"\"\"\n if i in dictionary:\n if isinstance(dictionary[i], frozenset):\n print(i, \": \", ', '.join(dictionary[i]))\n else:\n print(i, \": \", dictionary[i])\n\n print(\"\\n\\n====== \"+self.info[\"OriginalForm\"].upper()+\" ======\")\n\n print(\"\\n====== MORPHOLOGICAL INFO ======\")\n for i in [\"OriginalForm\", \"POS\", \"IsLemma\", \"Lemmas\"]:\n print_rel(i, self.info)\n print(\"\\n====== DERIVATIONAL INFO ======\")\n for i in [\"Stems\", \"Suffixes\", \"Prefixes\", \"OtherDerivation\",\n \"RelatedWords\"]:\n print_rel(i, self.info)\n print(\"\\n====== SEMANTIC INFO ======\")\n for i in [\"Synonyms\", \"Antonyms\", \"Meronyms\", \"Hyponyms\",\n \"Hypernyms\", \"Other\"]:\n print_rel(i, self.info)\n print(\"\\n====== EXTRA WORD CLASSES ======\")\n for i in [\"ProperNouns\", \"Noise\", \"Numbers\", \"URLs\", \"Hashtags\", \"Filenames\", \\\n \"ForeignWords\", \"Misspellings\", \"Missing\"]:\n print_rel(i, self.info)\n print(\"\\n\")","title":""},{"docid":"a00697450ef88f1b8f5b44e34e3de028","score":"0.696849","text":"def pprint(self):\n banner = \" Description{0}Path\\n{1} {2}\".format((\" \" * 70),\n (\"-\" * 80),\n (\"-\" * 25))\n print(banner)\n if self.parsed_results is None:\n print()\n else:\n for result in self.parsed_results:\n print(result['description'],\n \" \" * (79 - len(result['description'])),\n result['file'][9:])","title":""},{"docid":"ab903afcaa36cd176e9bc38739299dd7","score":"0.6911193","text":"def prnt(obj):\n pp = pprint.PrettyPrinter(indent=2)\n try:\n return(pp.pprint(obj))\n except:\n return(\"Problem with the printer!\")","title":""},{"docid":"5706eb57aef369febb50c9df94988702","score":"0.68842727","text":"def pretty_print(self):\n indent_str = ' '\n indent = 0\n for tag, value in self:\n if tag == TAG_COLLECTION_END:\n indent -= 1\n tag_str = tag[:3].decode('utf-8')\n if tag == TAG_COLLECTION_START:\n c = Collection.decode(value)\n print(\"%s%s id=%d, type=%d, start=%d, end=%d\" %\n (indent_str * indent, tag_str, c.id_, c.type_, c.start_position, c.end_position))\n indent += 1\n elif tag == TAG_SIGNATURE_END:\n print(\"%s%s %s\" % (indent_str * indent, tag_str, value))\n else:\n print(\"%s%s %d\" % (indent_str * indent, tag_str, len(value)))","title":""},{"docid":"dd34b1b59f65e4660def137c568106b0","score":"0.68501186","text":"def print_info():\n horizontal_line()\n print(\"SE755 A2: Special Topics - Bayesian Machine Learning\")\n print(\"Authors: Joshua Brundan and Kevin Hira\")\n horizontal_line()","title":""},{"docid":"7e93c68f7e46dc964343b5fde0ad82fa","score":"0.6834699","text":"def _repr_pretty_(self, p, cycle: bool):\n self.display()","title":""},{"docid":"5fff4f74c05c16898dab14517d8201ac","score":"0.68103015","text":"def do_pp(self, *args, stream=None, indent=1, width=80, depth=None, compact=False, sort_dicts=True):\n if len(args) == 0:\n pprint.pprint(stream=stream, indent=indent, width=width,\n depth=depth, compact=compact, sort_dicts=sort_dicts)\n else:\n return super().do_pp(*args)","title":""},{"docid":"b73a7825b97a6eb343a46c4a9ed401f7","score":"0.6800915","text":"def prettyPrint(self):\n x=Utils.prettyPrint(self.data)\n print x\n return","title":""},{"docid":"4ce99c477e816c2e10ff77f403d3eb02","score":"0.67927146","text":"def pprint(smartfeedparserdict):\n import pprint\n\n pp = pprint.PrettyPrinter(indent=2)\n pp.pprint(smartfeedparserdict.__feed_dict__)","title":""},{"docid":"b47e222f560cf45db970ea4c8eb4db86","score":"0.6785372","text":"def pprint_names(self, debug=None):\n pr_name, branch_name = self.generate_name()\n print\n print 'PR Name:', pr_name\n print 'Branch Name:', branch_name\n print\n if debug:\n print '\\nDEBUG\\n'\n pprint.pprint(self.as_dict())\n return","title":""},{"docid":"7b17d3332e223c981313f9a74752f4c4","score":"0.67347354","text":"def pprint(self, prefix=\"\", maxLevel=np.inf):\n print self.asString(prefix, maxLevel)","title":""},{"docid":"fd6891e8fd68126edbb3141853e69f36","score":"0.67211056","text":"def _repr_pretty_(self, p, cycle):\n if len(self) == 1:\n p.text(str(self))\n else:\n for i, x in enumerate(self):\n if i > 0:\n p.break_()\n p.text(f\"{i:3d}: {str(x)}\")","title":""},{"docid":"fd6891e8fd68126edbb3141853e69f36","score":"0.67211056","text":"def _repr_pretty_(self, p, cycle):\n if len(self) == 1:\n p.text(str(self))\n else:\n for i, x in enumerate(self):\n if i > 0:\n p.break_()\n p.text(f\"{i:3d}: {str(x)}\")","title":""},{"docid":"56bb941a844188570a9a50819910eadd","score":"0.6720532","text":"def print_info (self):\n print \"############################################\"","title":""},{"docid":"16b1b7fbfb2fae868d62f8253457381f","score":"0.67191845","text":"def pprint(v):\n if (v == playerIndex):\n if args.output_bbcode: print \"[color=#dd2423][%s][/color]\" % players[v][3],\n elif args.output_html: print '<span class=\"hilight\">[%s]</span>' % players[v][3],\n elif args.output_wikia: print '<u>[%s]</u>' % players[v][3],\n else: print '[%s]' % players[v][3],\n else: print \"%s\" % players[v][3],","title":""},{"docid":"753e9638b6d98c1274ab44a712d35664","score":"0.66926646","text":"def pprint(self):\n for y in range(10, 0, -1):\n row_length = self.length_of_row(y)\n top_row = chr(ord('a') + y - 1) + \" \" * ((19 - row_length) // 2)\n bottom_row = chr(ord('a') + y - 1) + \" \" * ((19 - row_length) // 2)\n for x in range(1, row_length + 1):\n dot = self.board[(y, x)]\n if not dot:\n dot = \".\"\n if (y >= 6 and x % 2 == 0) or (y <= 5 and x % 2 == 1):\n top_row += dot\n bottom_row += \" \"\n else:\n bottom_row += dot\n top_row += \" \"\n\n print(top_row)\n print(bottom_row)","title":""},{"docid":"476cf3499fec042d1c97db0c044ba8c2","score":"0.6679485","text":"def get_print_info():","title":""},{"docid":"b68837f1a0e35b31ff9aa74a11408c85","score":"0.6670572","text":"def __repr__(self):\n return ppretty(self, seq_length=10)","title":""},{"docid":"08fd94f0afcbb075f68108cef7271b3d","score":"0.6660994","text":"def __repr__(self):\n pretty_print_data_list = list() # Empty List to append strings\n\n # Add patient id to the pretty print list\n pretty_print_data_list.append(\n \"Patient ID: {}\".format(self.get_patient_id()))\n\n # Add DOB to the pretty print list\n pretty_print_data_list.append(\n \"Date of Birth: {}\".format(self.get_dob()))\n\n # Add name to the pretty print list\n pretty_print_data_list.append(\"Name: {},{}\".format(\n self.get_last_name(),\n self.get_first_name()))\n\n # Add address details to the pretty print list\n pretty_print_data_list.append(\"Address:\\n{},{},\\n{},{}-{}\".format(\n self.get_address_line_1(),\n self.get_address_line_2(),\n self.get_address_city(),\n self.get_address_state(),\n self.get_address_zip()))\n\n # Add contact details to the pretty print list\n pretty_print_data_list.append(\n \"Contact Details:\\nPhone {}\\nEmail {}\".format(\n self.get_phone(),\n self.get_email()))\n\n # Join the list with new line character\n pretty_print_str = \"\\n\".join(pretty_print_data_list)\n\n return pretty_print_str # Return the pretty print string","title":""},{"docid":"e4c59bd67a71c25f8ac047c67e57eaa2","score":"0.6592102","text":"def pprint_props(self, indent=0):\n for p in self.__properties__:\n print \" \"*indent + p + \":\", getattr(self, p)","title":""},{"docid":"ad2629acf2b5e10a8019a788fff26400","score":"0.6572868","text":"def print_info (self):\n\n print \"############################################\"\n print \"Title: \" + self.comicTitle\n print \"Issue number: \" + str(self.issue)\n print \"Volume: \" + str(self.volume)\n print \"Order number: \" + str(self.orderNumber)\n\n print \"### Story Info #############################\"\n print \"Story: \" + self.storyName\n if (self.storySynopsys):\n print \"Synopsys: \" + self.storySynopsys\n \n ## Iterate through the comic info\n #for key,val in self.info.iteritems():\n # print key + \": \" + val","title":""},{"docid":"f4a7f12a995fb6132ce910fb9ff6b642","score":"0.6571966","text":"def print_info(self):\n print(f\"I'm a {self.color} {self.model} built in {self.year}\")","title":""},{"docid":"637db2e79fa851b4dcf40e2fe3b228b2","score":"0.65707445","text":"def info(self):\n print(repr(self))","title":""},{"docid":"637db2e79fa851b4dcf40e2fe3b228b2","score":"0.65707445","text":"def info(self):\n print(repr(self))","title":""},{"docid":"1873108467b4a8ec491734a3ff4cdbca","score":"0.65696824","text":"def pretty_print(self) -> None:\n pt: PrettyTable = PrettyTable(field_names=[\"File Name\", \"Classes\", \"Functions\", \"Lines\", \"Characters\"])\n for item in self.files_summary:\n pt.add_row([item, self.files_summary[item]['class'], self.files_summary[item]['function'], self.files_summary[item]['line'], self.files_summary[item]['char']])\n print(pt)","title":""},{"docid":"cc8ff18ae8b73e2b9493a095f334d086","score":"0.65450895","text":"def pretty_print(self):\r\n\t\tpp = pprint.PrettyPrinter()\r\n\t\tfor section_name in self.sections:\r\n\t\t\tsection = self[section_name]\r\n\t\t\tprint(\"[%s]\" % section.name)\r\n\t\t\tpp.pprint(section.dict())","title":""},{"docid":"338ee9a599c76c43ca5e03f2d5c2e960","score":"0.65317005","text":"def pretty_print(self):\n\n print('---Individuals---')\n indi_rows = list()\n for uid, indi in self.indis.items():\n indi_rows.append(indi.pp_row())\n print(tabulate(indi_rows, headers=Individual.pp_header, tablefmt='fancy_grid', showindex='never'))\n\n print('\\n\\n')\n\n print('---Families---')\n fams_rows = list()\n for uid, fam in self.fams.items():\n id_, marr, div, husb_id, wife_id, chil_ids = fam.pp_row()\n husb_nm = ', '.join([self.indis[husb_id].name['last'], self.indis[husb_id].name['first']])\n wife_nm = ', '.join([self.indis[wife_id].name['last'], self.indis[wife_id].name['first']])\n fams_rows.append((id_, marr, div, husb_id, husb_nm, wife_id, wife_nm, chil_ids))\n\n print(tabulate(fams_rows, headers=Family.pp_header, tablefmt='fancy_grid', showindex='never'))","title":""},{"docid":"1b45cdea56af800d1e81d4d9e5f45ca6","score":"0.65126747","text":"def print(self):\n result = self.tidy()\n print(\"\\n\".join(result))","title":""},{"docid":"fdbcacf4fb8fa6498b7584d1314b95fa","score":"0.6501284","text":"def print_summary(self):\n print(get_bold(self.name))\n print(f\"{get_bold('Prep Time')}: {self.prep_time} min\")\n print(f\"{get_bold('Veggie')}: {'Yes' if self.veg else 'No'}\")\n print(f\"{get_bold('Type')}: {self.type}\")\n print(f\"{get_bold('Cuisine')}: {self.cuisine}\")\n print(f\"{get_bold('Ingredients')}: {', '.join(self.ingredients)}\")\n print(f\"{get_bold('Recipe')}: \\n\\t{self.get_pretty_recipe()}\")","title":""},{"docid":"551ecde7b01c05dc03edc5afdfe11291","score":"0.6500994","text":"def __str__(self):\n return ppretty(self, seq_length=20)","title":""},{"docid":"a2737ed13bf7ef5a9912ef428fa106cf","score":"0.64939606","text":"def print_info(self):\n print ' Number Available:',self.avail\n print ' Price For:',self.price_for\n print ' Minimum Order Quantity:',self.min_order\n print ' Order Multiple:',self.multi\n print ' Pricing:'\n\n n = self.min_order\n for p in self.prices:\n if n != p[0]:\n print \"\\t%i - %i: \\t£%s\" % (n, p[0], p[1])\n n = p[0] + 1\n else:\n print \"\\t%i +: \\t£%s\" % (n, p[1])","title":""},{"docid":"ea42d4743e2baa0170dae0e6d9b3ada2","score":"0.64634985","text":"def pprint(obj: object = \"\", end: str = \"\\n\"):\n if tvo.get_run_policy() == \"mpi\" and dist.get_rank() != 0:\n return\n\n print(obj, end=end)","title":""},{"docid":"4cae4240490fff085cab0152a76339a4","score":"0.6461935","text":"def print_info(s):\n print(Style.BRIGHT + Fore.GREEN + s +Style.RESET_ALL)","title":""},{"docid":"60b9b25f11fa3bb450bf5263b1fec68f","score":"0.64522886","text":"def debug_print(self):\n if self.debug:\n templ = \"{first} {last} is {age} years old.\"\n s = templ.format(\n first=self.first_name, last=self.last_name, age=self.age,\n )\n print s","title":""},{"docid":"ce516a66e1c1539448e69894fe6fa50d","score":"0.64512753","text":"def print_meta_info(self):\n for par, val in self.meta_info:\n print(par + ': ' + str(val))","title":""},{"docid":"04acd615d0260828b1746821ec8346ee","score":"0.64471275","text":"def print(self):\n\n\t\tif self.records is None:\n\t\t\tprint(\"No records loaded.\")\n\t\t\treturn\n\n\t\tfor ir, record in enumerate(self.records):\n\t\t\tid, name = record.get(\"patient_id\"), record.get(\"patient_name\")\n\t\t\tprint(\"[%d] Name: %s (%s)\" % (ir, name, id))\n\n\t\t\tfor ist, study in enumerate(record.get(\"studies\")):\n\t\t\t\tid, desc = study.get(\"id\"), study.get(\"desc\")\n\t\t\t\tprint(\"-\"*2 + \"[%d] Id: %s Desc: %s\" % (ist, id, desc))\n\n\t\t\t\tfor ise, series in enumerate(study.get(\"series\")):\n\t\t\t\t\tnumber, desc, imc = series.get(\"number\"), series.get(\"desc\"), series.get(\"images_count\")\n\t\t\t\t\tprint(\"-\"*4 + \"[%d] Number: %s Desc: %s Images: %d\" % (ise, number, desc, imc))","title":""},{"docid":"9a87e22f52d9031f1644e5d3af0f8899","score":"0.6446953","text":"def pretty_print(self):\n str = \"DN: \" + self.dn + \"\\n\"\n for a, v_list in self.attrs.iteritems():\n str = str + \"Name: \" + a + \"\\n\"\n for v in v_list:\n str = str + \" Value: \" + v + \"\\n\"\n str = str + \"========\"\n return str","title":""},{"docid":"b2df13f9968245b4ac1521135006ea97","score":"0.64459544","text":"def print_info (self):\n\n print \"############################################\"\n print \"First Name: \" + self.firstName\n if self.middleName:\n print \"Middle Name: \" + self.middleName\n print \"Last Name: \" + self.lastName","title":""},{"docid":"7485197e20b89d4cb3da3ab93979dd9c","score":"0.6419348","text":"def main():\n print(json.dumps(info(), sort_keys=True, indent=2))","title":""},{"docid":"84fc076cc77c29bc04fcf8c21f5d8d3c","score":"0.64127684","text":"def print_structures():\n print(\n 'training examples \\\n \\n\\tcount: {} \\\n \\n\\tdimensions: {} \\\n \\n\\tshape: {} \\\n \\n\\tdata type: {}\\n\\n'.format(\n len(train_examples),\n train_examples.ndim,\n train_examples.shape,\n train_examples.dtype\n ),\n 'testing examples \\\n \\n\\tcount: {} \\\n \\n\\tdimensions: {} \\\n \\n\\tshape: {} \\\n \\n\\tdata type: {}\\n'.format(\n len(test_labels),\n train_labels.ndim,\n test_labels.shape,\n test_labels.dtype\n )\n )","title":""},{"docid":"387a1e6ecb13d84b85451480b550733f","score":"0.6407966","text":"def pprint(self) -> None:\n deprecated('1.3', f'{type(self).__name__}.pprint', 'print')\n print(self)","title":""},{"docid":"cc49ed66897a268374b56499133c2715","score":"0.6398836","text":"def pp(o, add_metadata=True):\n dispatch = {dict:pretty_print_dict}\n if type(o) in dispatch:\n return dispatch[type(o)](o, add_metadata)\n else:\n return o.__repr__()","title":""},{"docid":"11480dc8150de8c28f3ce4f68bd74851","score":"0.6395588","text":"def main() -> None:\n print(json.dumps(info(), sort_keys=True, indent=2))","title":""},{"docid":"96df8a8211f1b4704a401786c693767a","score":"0.6375361","text":"def print_all(data):\n pp.pprint(data)","title":""},{"docid":"5ae7abfd1a56b701042fc82e226936db","score":"0.6369845","text":"def __repr__(self):\n\n d = self.get_data() # vars(self) # ???\n return PP.pformat(d, indent=4, width=100)","title":""},{"docid":"f069f4d61ff2579e2e37319d48eebcd8","score":"0.6369725","text":"def dump_self(self):\n\n attrs = []\n\n if self.number > 0:\n attrs.append(' ' + format(self.number))\n\n if self.output_tag:\n attrs.append(\", tag=\" + self.output_tag)\n\n if self.output_filename:\n attrs.append(', output=' + self.output_filename)\n\n if self.docbook_id:\n attrs.append(', id=' + self.docbook_id)\n\n if self.title:\n title = self.title\n else:\n title = '<No Title>'\n\n prefix = ' ' * self.output_depth\n\n print('%d %s%s [%s%s]' % (\n self.input_depth,\n prefix,\n title,\n self.docbook_element,\n ''.join(attrs)))","title":""},{"docid":"81570721d19270aa99924d9560591356","score":"0.63639736","text":"def short_print(self):\n puts('{}{}{}'.format(attr(1), self.name, attr(0)))\n with indent():\n puts('{}{}{}'.format(attr(2), self.url, attr(0)))","title":""},{"docid":"266748351043cd250177c4b58eff6fa7","score":"0.6361171","text":"def print_details(conn, printer, jobs):\n printer_name = printer[\"printer-uri-supported\"].rsplit(\"/\", 1)[1]\n print \"Printer Name:\\t{}\".format(printer_name)\n print \"Location:\\t{}\".format(printer[\"printer-location\"])\n print \"Model:\\t\\t{}\".format(printer[\"printer-make-and-model\"])\n\n if \"printer-state-message\" in printer and printer[\"printer-state-message\"]:\n print \"Status:\\t\\t{}\".format(printer[\"printer-state-message\"])\n if printer[\"printer-state-reasons\"][0] != \"none\":\n first_message = printer[\"printer-state-reasons\"].pop()\n print \"Messages:\\t{}\".format(pretty_string(first_message))\n for reason in printer[\"printer-state-reasons\"]:\n print \"\\t\\t{}\".format(pretty_string(reason))\n\n if jobs:\n print \"Jobs:\\t\\t\",\n print_job(conn, jobs[0])\n for job in jobs[1:]:\n print \"\\t\\t\",\n print_job(conn, job)\n print","title":""},{"docid":"90158716fa4797e61f0ce0c2214805dd","score":"0.6359507","text":"def print_dictionary(dict_obj):\n print(pformat(dict_obj))","title":""},{"docid":"e18261f7e1588bc409c09d00999dec55","score":"0.63497144","text":"def print_me(self):\n print (\"key: \" + str(self._key), \"value: \" + str(self._value), \"parent: \" + str(self._parentKey))","title":""},{"docid":"1aef80b65e11375573d3adb735d797b2","score":"0.6340874","text":"def __repr__(self):\n\n # print additional info only if there are attributes to show\n attrnames = list(self._v_attrnames)\n if attrnames:\n rep = [f'{attr} := {getattr(self, attr)!r}' for attr in attrnames]\n return f\"{self!s}:\\n [\" + ',\\n '.join(rep) + \"]\"\n else:\n return str(self)","title":""},{"docid":"da1fae3a9e0577f411cd4ecfbc357956","score":"0.6328278","text":"def pprint(self):\n results = self.results_dict()\n if not results:\n print('No match results for {}'.format(self.current_date))\n else:\n for match in results.values():\n print('{team_one:^20} {team_one_score:<2} - '\n '{team_two_score:>2} {team_two:^20}'\n ' {maps:<13}'.format(**match))\n print('\\nCS:GO match results for {}'.format(self.current_date))\n print('Powered by HLTV.org')","title":""},{"docid":"d9cbc75d9e24f84efb0efc1577bf624b","score":"0.63222045","text":"def info_print(avto_info):\n print(f\"{avto_info['rang'].title()} {avto_info['kompaniya'].upper()} \"\n f\"{avto_info['model'].upper()}, {avto_info['korobka']} korobka, \"\n f\"{avto_info['yil']}-yil, {avto_info['narx']}$\")","title":""},{"docid":"d29416841779de4db1dbaf7c92e1bee0","score":"0.63202745","text":"def print_( self, params ):\n what = params[0]\n if what == \"files\":\n self._checkParsed()\n for key in cooker.pkgdata.keys(): print key\n elif what == \"providers\":\n self._checkParsed()\n for key in cooker.status.providers.keys(): print key\n else:\n print \"Usage: print %s\" % self.print_.usage","title":""},{"docid":"dadb3c1bb180fae18a8818bde024d312","score":"0.6317465","text":"def __repr__(self):\n\t\treturn pretty_string(self.__dict__)","title":""},{"docid":"dadb3c1bb180fae18a8818bde024d312","score":"0.6317465","text":"def __repr__(self):\n\t\treturn pretty_string(self.__dict__)","title":""},{"docid":"530bd71a260d0afe780144f555a34ce7","score":"0.6299847","text":"def __str__(self):\n if len(self.linearData) > config.NUM_ITEMS_TO_PRINT:\n out = []\n # welcome to perl\n for index in range(config.NUM_ITEMS_TO_PRINT):\n out.append(\"%s: %s\" % (index, \", \".join([\"%s: %s\" % (key, self.linearData[index][key]) for key in self.linearData[index]])))\n out = \"%s\\n... truncated, showing %s/%s\" % (\"\\n\".join(out), config.NUM_ITEMS_TO_PRINT, len(self.linearData))\n\n if config.PRINT_LAST_ITEM:\n out = \"%s\\n%s\" % (out, \"%s: %s\" % (len(self.linearData), \", \".join([\"%s: %s\" % (key, self.linearData[-1][key]) for key in self.linearData[-1]])))\n\n elif len(self.linearData) == 0:\n out = \"This list is empty\"\n\n else: # just print first entry.\n out = []\n for index in range(len(self.linearData)):\n out.append(\"%s: %s\" % (index, \", \".join([\"%s: %s\" % (key, self.linearData[index][key]) for key in self.linearData[index]])))\n out = \"%s\\nShowing %s/%s\" % (\"\\n\".join(out), len(self.linearData), len(self.linearData))\n\n return out","title":""},{"docid":"f7da09fa4bf0a27ca574b4ff19062f66","score":"0.62952936","text":"def __str__(self):\n\n print(\"Name: {name}; Label: {label}; Country: {country}; \"\n \"Year: {year}; Comments: {coms}\".format(name=self.name, label=self.label, country=self.country,\n year=self.date_release, coms=self.info))","title":""},{"docid":"586a413c1e971a8a4b3c68db10e89cb1","score":"0.6291012","text":"def _print_info(self):\n print(\"\\nAverage Regret: {0}\".format(\n self.total_regret / self.iterations))\n\n for i in self.alg.var_dict:\n try:\n print(\"{0} : {1}\".format(i, self.alg.var_dict[i].__name__))\n except AttributeError:\n print(\"{0} : {1}\".format(i, self.alg.var_dict[i]))\n\n print(\"{0} Arms: {1}\".format(self.bandit.n_arms, self.arm_info))\n print(\"Horizon: {0}\".format(self.horizon))\n print (\"Total runtime: {0} seconds\".format(self.runtime))\n print (\"Average runtime per iteration: {0} seconds\".format(\n self.runtime / self.iterations))\n print(\"\\n\" + \"-\"*50 + \"\\n\")\n return None","title":""},{"docid":"582ca937bef1c4a1b9295e39d1400d12","score":"0.62904286","text":"def print_summary(self):\n if self.rc4_dict:\n print('\\n********RC4 Hosts********')\n for k, v in sorted(self.rc4_dict.items()):\n print('{} {}'.format(k, v))\n if self.sslv2_dict:\n print('\\n********SSLv2 Hosts********')\n for k, v in sorted(self.sslv2_dict.items()):\n print('{} {}'.format(k, v))\n if self.sslv3_dict:\n print('\\n********SSLv3 Hosts********')\n for k, v in sorted(self.sslv3_dict.items()):\n print('{} {}'.format(k, v))\n if self.des_dict:\n print('\\n********DES Hosts********')\n for k, v in sorted(self.des_dict.items()):\n print('{} {}'.format(k, v))\n if self.tls10_dict:\n print('\\n********TLS v1.0 Hosts********')\n for k, v in sorted(self.tls10_dict.items()):\n print('{} {}'.format(k, v))\n if self.weakbits_dict:\n print('\\n********Weak Key Size********')\n for k, v in sorted(self.weakbits_dict.items()):\n print('{} {}'.format(k, v))\n if self.heartbleed_dict:\n print('\\n********Heartbleed Hosts********')\n for k, v in sorted(self.heartbleed_dict.items()):\n print('{} {}'.format(k, v))\n if self.md5_dict:\n print('\\n********MD5 Hosts********')\n for k, v in sorted(self.md5_dict.items()):\n print('{} {}'.format(k, v))","title":""},{"docid":"f4eb18e286d5e5c8009d9c8555ac2147","score":"0.6285786","text":"def showinfo(self):\n start=self._head\n print \"List:[\",\n for i in range(self._size):\n print start._element,\n start=start._next\n print \"]\"","title":""},{"docid":"3069bac857de8d8ca33af68d44d860af","score":"0.62746614","text":"def _print_str(self):\n print('*[INFO] ' + str(self))","title":""},{"docid":"90d0404c1cd8d4a5be0a365ae883cd7c","score":"0.6270032","text":"def printInfo(self):\n\n print('Number of inputs =', self.n_inputs)\n print('Number of outputs =', self.n_outputs)\n print('Number of units =', self.n_units)\n print('Number of layers =', self.n_layers)\n print('Number of params =', self.n_params)\n print('Data type =', dtype)","title":""},{"docid":"90d0404c1cd8d4a5be0a365ae883cd7c","score":"0.6270032","text":"def printInfo(self):\n\n print('Number of inputs =', self.n_inputs)\n print('Number of outputs =', self.n_outputs)\n print('Number of units =', self.n_units)\n print('Number of layers =', self.n_layers)\n print('Number of params =', self.n_params)\n print('Data type =', dtype)","title":""},{"docid":"1e4bd360ee3401f399a4ce198beeb781","score":"0.625362","text":"def debug_print(self):\n print(dict(self.mem))","title":""},{"docid":"9312efa1b6f261327e4c8386ce3acd74","score":"0.6242685","text":"def dump(pr):\n enc = 'utf-8'\n N = 150\n\n print('-'*10)\n for f in pr:\n val = pr[f]\n if not isinstance(val,unicode):\n val = unicode(pr[f])\n val = val.encode(enc)\n if len(val.split('\\n')) > 1:\n print \"###%s###\\n%s\" %(f,val)\n else:\n print \"###%s###: %s\" %(f,val)","title":""},{"docid":"69bfcda13b48a060d9e270231f38e6c6","score":"0.62426317","text":"def _print(self, *args, **kwargs) -> None:\n if self.verbose:\n print(f\"[{type(self).__name__}-{self.identifier}]\", *args, **kwargs)","title":""},{"docid":"48199de721097b214a12d4ae134fb9ec","score":"0.6237935","text":"def print(self) -> None:\n pass","title":""},{"docid":"43bcdc4e057173f19d13723b1a73ac19","score":"0.623104","text":"def pretty_print(self) -> None:\r\n\r\n result: PrettyTable = PrettyTable()\r\n result.field_names = ['File Name', 'Classes',\r\n 'Fucntions', 'Lines', 'Character']\r\n\r\n for key, value in self.file_summary.items():\r\n result.add_row([key] + list(value.values()))\r\n\r\n print(\"\\nSummary for \", self.directory)\r\n print(result)","title":""},{"docid":"c3a14e61e7f26ea2f94442f933f64190","score":"0.62295544","text":"def show(self):\n keys = self.__dict__.keys()\n keys.sort()\n for k in keys:\n val = self.__dict__.get(k)\n if val:\n print '%s: [%s]' % (k, val)","title":""},{"docid":"78315080938caf64a24f3c70e9862011","score":"0.62247586","text":"def ShowData(self):\n part_of_message = \"Person's %s is %s. \"\n message = \"\"\n for key in self.__dict__:\n message += part_of_message % (key ,self.__dict__[key])\n\n print message.rstrip()","title":""},{"docid":"5f2f1053f1a0303c5c011ad8b70b5d2b","score":"0.6220555","text":"def pretty_print(self) -> None:\r\n pt: PrettyTable = PrettyTable(field_names=['File Name','Class','Function','Lines','Characters'])\r\n for key,value in self.files_summary.items():\r\n filename = key\r\n classTotal = value['class']\r\n funcTotal = value['func']\r\n lineTotal = value['lines']\r\n charTotal = value['char']\r\n pt.add_row([filename, classTotal, funcTotal, lineTotal, charTotal])\r\n print(pt)","title":""},{"docid":"f58a11327d27a16bb8ccdddfeb314633","score":"0.6220063","text":"def _print_dict(d):\n print(pprint.PrettyPrinter().pformat(d).replace(\"°\", \"@\"))","title":""},{"docid":"dd2572119da9ca798e443c391d727981","score":"0.6215773","text":"def pprint_tree(tree, indent=0):\n\tname, values = tree[0], tree[1:]\n\tif len(values) == 1 and isinstance(values[0], basestring):\n\t\tprint \"{:<{indent}s}{} {}\".format(\"\", name, values[0], indent=indent)\n\telse:\n\t\tprint \"{:<{indent}s}{}:\".format(\"\", name, indent=indent)\n\t\tfor value in values:\n\t\t\tpprint_tree(value, indent=indent+1)","title":""},{"docid":"cbad29209c05d77e9c2f034511f4ce99","score":"0.62134445","text":"def prnt(self, deep_indent=50):\n # e.g. print(dialect.prnt())\n return '\\n'.join(self.fmt(deep_indent=deep_indent))","title":""},{"docid":"8a3c89ec3c12a99bad85b89d75e8830d","score":"0.6211106","text":"def print_structure(self):\n for l in range(self.num_levels):\n print(self.get_level(l))","title":""},{"docid":"88895d3d43aaf4b31c6684bd51b5f2b2","score":"0.62081957","text":"def display(self):\r\n\r\n print(\"\\tname:\", self.name) \r\n print(\"\\tdescription:\", self.description) \r\n print(\"\\tvalue:\", self.value) \r\n print(\"\\ttype:\", self.type)\r\n print(\"\\tio:\", self.io) \r\n print(\"\\tdefault_value:\", self.default_value) \r\n print(\"\\tvalue_suffix:\", self.value_suffix)\r\n print(\"\\tis_advanced:\", self.is_advanced)\r\n print(\"\\t------------\")","title":""},{"docid":"bde9bba3e1a5eaf8425249a3387d7f0f","score":"0.62045693","text":"def show_struct(self):\n for chunk in self.chunks:\n print(chunk.type, end=\" \")","title":""},{"docid":"6e4dc3151165ea5246fefae98b0673d3","score":"0.6201701","text":"def dump(self):\n print(\"Page #\", \"Valid\", \"Ref.\", \"Dirty\", \"History\\t\", \"Frame\", sep=\"\\t\")\n for page in sorted(self.table):\n print(page,\n (\"Yes\" if self.table[page].valid else \"No\"),\n (\"Yes\" if self.table[page].ref else \"No\"),\n (\"Yes\" if self.table[page].dirty else \"No\"),\n str(bin(self.table[page].history))[2:],\n self.table[page].frame,\n sep=\"\\t\")","title":""},{"docid":"e10e2e9791c1405cb6bc5e491496730e","score":"0.620058","text":"def pretty_print(self):\n\n if self.dtype == np.int:\n fmt = \"%4d\"\n elif self.dtype == np.float64:\n fmt = \"%10.5g\"\n else:\n msg.fail(\"ERROR: dtype not supported\")\n\n # print j descending, so it looks like a grid (y increasing\n # with height)\n for j in reversed(range(self.g.qy)):\n for i in range(self.g.qx):\n\n if (j < self.g.jlo or j > self.g.jhi or i < self.g.ilo or i > self.g.ihi):\n gc = 1\n else:\n gc = 0\n\n if gc:\n print(\"\\033[31m\" + fmt % (self[i, j]) + \"\\033[0m\", end=\"\")\n else:\n print(fmt % (self[i, j]), end=\"\")\n\n print(\" \")\n\n leg = \"\"\"\n ^ y\n |\n +---> x\n \"\"\"\n print(leg)","title":""},{"docid":"0769c916cafc075e614c34f4ff381be5","score":"0.61995906","text":"def pprint(json_data):\n\n print(json.dumps(json_data, indent=4, separators=(' , ', ' : ')))","title":""},{"docid":"a83e80ab0eaba5dfbee07659b62c1cdc","score":"0.61941296","text":"def _repr_pretty_(self, p: Any, cycle: bool) -> None:\n # There should never be a cycle, but just in case use the default repr.\n p.text(repr(self) if cycle else str(self))","title":""},{"docid":"486d8972a226610a2668351e8596cfe2","score":"0.6193596","text":"def pprint(obj):\r\n # Get dicts' items.\r\n if hasattr(obj, \"items\"):\r\n obj = sorted(obj.items())\r\n\r\n buf = StringIO()\r\n _pprint(obj, buf)\r\n\r\n value = buf.getvalue()\r\n buf.close()\r\n\r\n return value","title":""},{"docid":"b92c0a14352b3ea94d5f0d62b3de0ed3","score":"0.6191571","text":"def dump(self):\n for pname, param in self.params.items():\n print \"* %s\" % pname\n print \" - default: %s\" % param.default\n print \" - conffile: %s\" % param.conffile\n print \" - type: %s\" % param.param_type\n print \" - allowed_values: %s\" % param.allowed_values\n print \" - allowed_range: %s\" % param.allowed_range\n print \" - cmd_line: %s\" % str(param.cmd_line)\n if param.ignore:\n print \" - IS IGNORED!\"\n else:\n print \" - current value: %s\" % str(param.value)","title":""},{"docid":"a15fad983a9fe2b689dde84dfac111a3","score":"0.61807734","text":"def print_parseable(d):\n for k in sorted(d.keys()):\n print('{}\\t{}'.format(k, d[k]))","title":""},{"docid":"b2fc4948cd60f0ed0d8538fb2d123666","score":"0.6177769","text":"def print_info(self) -> None:\n msg = [\n f'Dataset collection: {self.collection.info.name}',\n f'Version: {self.requested_version}',\n f'Description: {self.collection.info.description}',\n ]\n if self.collection.info.citation:\n msg.append('Citation:')\n msg.append(self.collection.info.citation)\n print('\\n'.join(msg))","title":""},{"docid":"7fa93332b6d2b61e6562a11f7a796a2d","score":"0.617707","text":"def pprint(self, object: Author or Publication)->None:\n if 'container_type' not in object:\n self.logger.warning(\"Not a scholarly container object\")\n return\n\n to_print = copy.deepcopy(object)\n if to_print['container_type'] == 'Publication':\n to_print['source'] = PublicationSource(to_print['source']).name\n elif to_print['container_type'] == 'Author':\n parser = AuthorParser(self.__nav)\n to_print['source'] = AuthorSource(to_print['source']).name\n if parser._sections == to_print['filled']:\n to_print['filled'] = True\n else:\n to_print['filled'] = False\n\n if 'coauthors' in to_print:\n for coauthor in to_print['coauthors']:\n coauthor['filled'] = False\n del coauthor['container_type']\n coauthor['source'] = AuthorSource(coauthor['source']).name\n\n if 'publications' in to_print:\n for publication in to_print['publications']:\n publication['source'] = PublicationSource(publication['source']).name\n del publication['container_type']\n\n del to_print['container_type']\n print(pprint.pformat(to_print).encode(\"utf-8\"))","title":""},{"docid":"5c36e62f7f061929a9f2dd542d93fa84","score":"0.6172355","text":"def print_format():\n print 'INFO: Printing the variables available for the csv as subject/session level\\\n (required as well in the csv to know where to set the information specified by the user): '\n print 'Required variables:'\n for value in REQUIRED_VARIABLES_LIST:\n print ' * %*s ' % (-30, value)\n print 'Default Subject demographic attributes:'\n for value in SUBJECT_PARAMETERS_LIST:\n print ' * %*s ' % (-30, value)\n print 'Default Session demographic attributes:'\n for value in SESSION_PARAMETERS_LIST:\n print ' * %*s ' % (-30, value)\n print'\\nINFO: You can generate your own attributes by creating them on the XNAT directly and use the script to upload the values.'\n print'WARNING:'\n print' * you need to have \"project_id,subject_label\" if you upload demographic data to a subject.'\n print' * for session attributes, you need to give the session_label as well.'\n print' * for specific attributes that you created on XNAT, please put the name of the variables in the header.'","title":""},{"docid":"7bd284cc67d149918279a7c674b5dd72","score":"0.6170657","text":"def print_info(self, fp = None):\n\n if fp == None:\n print(\"-----------------------------------------\")\n print(\"Module: {}, Version: {}\".format(self.name, self.version))\n print(\"Install Location Abs: {}\".format(self.abs_path))\n print(\"Install Location Rel: {}\".format(self.rel_path))\n print(\"Repository: {}{}\".format(self.url, self.repository))\n else:\n fp.write(\"-----------------------------------------\\n\")\n fp.write(\"Module: {}, Version: {}\\n\".format(self.name, self.version))\n fp.write(\"Install Location Abs: {}\\n\".format(self.abs_path))\n fp.write(\"Install Location Rel: {}\\n\".format(self.rel_path))\n fp.write(\"Repository: {}{}\\n\".format(self.url, self.repository))","title":""},{"docid":"6690f2328055a9917f714ca679b63f3a","score":"0.61591893","text":"def print(self) -> None:\n grn, red, ylw, gry, ppl, blu, rst = '\\033[92m', '\\033[91m', '\\033[93m', '\\033[90m', '\\033[95m', '\\033[94m', '\\033[0m'\n clr_ns, clr_param, clr_val, clr_glb = ppl, blu, ylw, red\n # print namespaces\n configured_global = self._NAMESPACE_CONFIGS.get(Config.GLOBAL_NAMESPACE, {})\n # opening brace\n sb = [f'{gry}{{{rst}\\n']\n # append strings\n for namespace in sorted(self._NAMESPACE_PARAMS):\n configured = self._NAMESPACE_CONFIGS.get(namespace, {})\n for param in sorted(self._NAMESPACE_PARAMS[namespace]):\n is_l, is_g = (param in configured), (param in configured_global)\n # space or comment out\n sb.append(' ' if (is_l or is_g) else f'{gry}# ')\n # dictionary key as the namespace.param\n val = (configured[param] if is_l else configured_global[param]) if (is_l or is_g) else None\n sb.append(f'{gry}\"{grn}{_Instanced.get_prefix(val)}{rst}{clr_ns}{namespace}{gry}.{clr_param}{param}{gry}\"{rst}: ')\n # dictionary func\n if is_l or is_g:\n if is_l:\n sb.append(f'{clr_val}{repr(val)}')\n elif is_g:\n sb.append(f'{clr_glb}{repr(val)}')\n # comma\n sb.append(f'{gry},{rst}')\n # comment if has a global func assigned to it\n if is_g:\n val = configured_global[param]\n sb.append(f' {gry}# \"{_Instanced.get_prefix(val)}{Config.GLOBAL_NAMESPACE}.{param}{gry}\": {repr(val)},{rst}')\n # new line\n sb.append('\\n')\n # closing brace\n sb.append(f'{gry}}}{rst}')\n # generate string!\n print(''.join(sb))","title":""},{"docid":"26ab1d5c365bd2ead31129d693b12e60","score":"0.61412597","text":"def _pretty_dump(self, label, data):\n # split data string items to a list of hex value\n dump = [\"%02X\" % c for c in bytearray(data)]\n # format for TCP or RTU\n if self.__mode == const.MODBUS_TCP:\n if len(dump) > 6:\n # \"[MBAP] ...\"\n dump[0] = \"[\" + dump[0]\n dump[6] = dump[6] + \"]\"\n elif self.__mode == const.MODBUS_RTU:\n if len(dump) > 4:\n # \"... [CRC]\"\n dump[-2] = \"[\" + dump[-2]\n dump[-1] = dump[-1] + \"]\"\n # print result\n print(label)\n s = \"\"\n for i in dump:\n s += i + \" \"\n print(s)","title":""}],"string":"[\n {\n \"docid\": \"ef2c8025e8e85397262f486816a47e92\",\n \"score\": \"0.7855242\",\n \"text\": \"def pprint(self):\\n try:\\n print(self.pformat(self))\\n except:\\n print(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722ea2b2105afac63e72370b535659a1\",\n \"score\": \"0.7789151\",\n \"text\": \"def pprint(self):\\n\\n print(self.pformat())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fd24c5589c82a2ff174bef3cb1752db\",\n \"score\": \"0.74743855\",\n \"text\": \"def pprint(self, **kwargs):\\n print(self.pformat(**kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a330a6c619548ca9c6168de3e7919d97\",\n \"score\": \"0.7414905\",\n \"text\": \"def display_print_info(info):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc059761c69f148febefcac5a1a6068\",\n \"score\": \"0.7285847\",\n \"text\": \"def print_info(self):\\n print(self.__repr__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30dc33251f7177a95d6c63dbb043dcb3\",\n \"score\": \"0.72463\",\n \"text\": \"def pretty_print(self):\\n\\n print(\\\"-----------------------------\\\")\\n print(self.title)\\n print(\\\" --- \\\" + self.description)\\n print(\\\"\\\")\\n print(\\\"Release: \\\" + self.release)\\n print(\\\"MPAA rating: \\\" + self.mpaa_rating)\\n print(\\\"Director: \\\" + self.director)\\n print(\\\"Stars: \\\" + self.stars)\\n print(\\\"\\\")\\n print(\\\"poster: \\\" + self.poster)\\n print(\\\"trailer: \\\" + self.trailer)\\n print(\\\"IMDB rating: \\\" + self.imdb_rating)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a15dc08ef794fd602e804a1221881d6\",\n \"score\": \"0.7124662\",\n \"text\": \"def print_info (self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a15dc08ef794fd602e804a1221881d6\",\n \"score\": \"0.7124662\",\n \"text\": \"def print_info (self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54060f3a7d0b17b88619b3f25dfad4c\",\n \"score\": \"0.7111678\",\n \"text\": \"def prettyPrint(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f2a46224be240d4621bd72f45526557\",\n \"score\": \"0.7009409\",\n \"text\": \"def pprint(self, msg):\\n if self.verbose_attr:\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee523a317e8ea5b054c593dfe0867d5e\",\n \"score\": \"0.69759774\",\n \"text\": \"def pp_info(self):\\n\\n def print_rel(i, dictionary):\\n \\\"\\\"\\\"Printing helper\\\"\\\"\\\"\\n if i in dictionary:\\n if isinstance(dictionary[i], frozenset):\\n print(i, \\\": \\\", ', '.join(dictionary[i]))\\n else:\\n print(i, \\\": \\\", dictionary[i])\\n\\n print(\\\"\\\\n\\\\n====== \\\"+self.info[\\\"OriginalForm\\\"].upper()+\\\" ======\\\")\\n\\n print(\\\"\\\\n====== MORPHOLOGICAL INFO ======\\\")\\n for i in [\\\"OriginalForm\\\", \\\"POS\\\", \\\"IsLemma\\\", \\\"Lemmas\\\"]:\\n print_rel(i, self.info)\\n print(\\\"\\\\n====== DERIVATIONAL INFO ======\\\")\\n for i in [\\\"Stems\\\", \\\"Suffixes\\\", \\\"Prefixes\\\", \\\"OtherDerivation\\\",\\n \\\"RelatedWords\\\"]:\\n print_rel(i, self.info)\\n print(\\\"\\\\n====== SEMANTIC INFO ======\\\")\\n for i in [\\\"Synonyms\\\", \\\"Antonyms\\\", \\\"Meronyms\\\", \\\"Hyponyms\\\",\\n \\\"Hypernyms\\\", \\\"Other\\\"]:\\n print_rel(i, self.info)\\n print(\\\"\\\\n====== EXTRA WORD CLASSES ======\\\")\\n for i in [\\\"ProperNouns\\\", \\\"Noise\\\", \\\"Numbers\\\", \\\"URLs\\\", \\\"Hashtags\\\", \\\"Filenames\\\", \\\\\\n \\\"ForeignWords\\\", \\\"Misspellings\\\", \\\"Missing\\\"]:\\n print_rel(i, self.info)\\n print(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00697450ef88f1b8f5b44e34e3de028\",\n \"score\": \"0.696849\",\n \"text\": \"def pprint(self):\\n banner = \\\" Description{0}Path\\\\n{1} {2}\\\".format((\\\" \\\" * 70),\\n (\\\"-\\\" * 80),\\n (\\\"-\\\" * 25))\\n print(banner)\\n if self.parsed_results is None:\\n print()\\n else:\\n for result in self.parsed_results:\\n print(result['description'],\\n \\\" \\\" * (79 - len(result['description'])),\\n result['file'][9:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab903afcaa36cd176e9bc38739299dd7\",\n \"score\": \"0.6911193\",\n \"text\": \"def prnt(obj):\\n pp = pprint.PrettyPrinter(indent=2)\\n try:\\n return(pp.pprint(obj))\\n except:\\n return(\\\"Problem with the printer!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5706eb57aef369febb50c9df94988702\",\n \"score\": \"0.68842727\",\n \"text\": \"def pretty_print(self):\\n indent_str = ' '\\n indent = 0\\n for tag, value in self:\\n if tag == TAG_COLLECTION_END:\\n indent -= 1\\n tag_str = tag[:3].decode('utf-8')\\n if tag == TAG_COLLECTION_START:\\n c = Collection.decode(value)\\n print(\\\"%s%s id=%d, type=%d, start=%d, end=%d\\\" %\\n (indent_str * indent, tag_str, c.id_, c.type_, c.start_position, c.end_position))\\n indent += 1\\n elif tag == TAG_SIGNATURE_END:\\n print(\\\"%s%s %s\\\" % (indent_str * indent, tag_str, value))\\n else:\\n print(\\\"%s%s %d\\\" % (indent_str * indent, tag_str, len(value)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd34b1b59f65e4660def137c568106b0\",\n \"score\": \"0.68501186\",\n \"text\": \"def print_info():\\n horizontal_line()\\n print(\\\"SE755 A2: Special Topics - Bayesian Machine Learning\\\")\\n print(\\\"Authors: Joshua Brundan and Kevin Hira\\\")\\n horizontal_line()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e93c68f7e46dc964343b5fde0ad82fa\",\n \"score\": \"0.6834699\",\n \"text\": \"def _repr_pretty_(self, p, cycle: bool):\\n self.display()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fff4f74c05c16898dab14517d8201ac\",\n \"score\": \"0.68103015\",\n \"text\": \"def do_pp(self, *args, stream=None, indent=1, width=80, depth=None, compact=False, sort_dicts=True):\\n if len(args) == 0:\\n pprint.pprint(stream=stream, indent=indent, width=width,\\n depth=depth, compact=compact, sort_dicts=sort_dicts)\\n else:\\n return super().do_pp(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b73a7825b97a6eb343a46c4a9ed401f7\",\n \"score\": \"0.6800915\",\n \"text\": \"def prettyPrint(self):\\n x=Utils.prettyPrint(self.data)\\n print x\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce99c477e816c2e10ff77f403d3eb02\",\n \"score\": \"0.67927146\",\n \"text\": \"def pprint(smartfeedparserdict):\\n import pprint\\n\\n pp = pprint.PrettyPrinter(indent=2)\\n pp.pprint(smartfeedparserdict.__feed_dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b47e222f560cf45db970ea4c8eb4db86\",\n \"score\": \"0.6785372\",\n \"text\": \"def pprint_names(self, debug=None):\\n pr_name, branch_name = self.generate_name()\\n print\\n print 'PR Name:', pr_name\\n print 'Branch Name:', branch_name\\n print\\n if debug:\\n print '\\\\nDEBUG\\\\n'\\n pprint.pprint(self.as_dict())\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b17d3332e223c981313f9a74752f4c4\",\n \"score\": \"0.67347354\",\n \"text\": \"def pprint(self, prefix=\\\"\\\", maxLevel=np.inf):\\n print self.asString(prefix, maxLevel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6891e8fd68126edbb3141853e69f36\",\n \"score\": \"0.67211056\",\n \"text\": \"def _repr_pretty_(self, p, cycle):\\n if len(self) == 1:\\n p.text(str(self))\\n else:\\n for i, x in enumerate(self):\\n if i > 0:\\n p.break_()\\n p.text(f\\\"{i:3d}: {str(x)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6891e8fd68126edbb3141853e69f36\",\n \"score\": \"0.67211056\",\n \"text\": \"def _repr_pretty_(self, p, cycle):\\n if len(self) == 1:\\n p.text(str(self))\\n else:\\n for i, x in enumerate(self):\\n if i > 0:\\n p.break_()\\n p.text(f\\\"{i:3d}: {str(x)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56bb941a844188570a9a50819910eadd\",\n \"score\": \"0.6720532\",\n \"text\": \"def print_info (self):\\n print \\\"############################################\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16b1b7fbfb2fae868d62f8253457381f\",\n \"score\": \"0.67191845\",\n \"text\": \"def pprint(v):\\n if (v == playerIndex):\\n if args.output_bbcode: print \\\"[color=#dd2423][%s][/color]\\\" % players[v][3],\\n elif args.output_html: print '<span class=\\\"hilight\\\">[%s]</span>' % players[v][3],\\n elif args.output_wikia: print '<u>[%s]</u>' % players[v][3],\\n else: print '[%s]' % players[v][3],\\n else: print \\\"%s\\\" % players[v][3],\",\n \"title\": \"\"\n },\n {\n \"docid\": \"753e9638b6d98c1274ab44a712d35664\",\n \"score\": \"0.66926646\",\n \"text\": \"def pprint(self):\\n for y in range(10, 0, -1):\\n row_length = self.length_of_row(y)\\n top_row = chr(ord('a') + y - 1) + \\\" \\\" * ((19 - row_length) // 2)\\n bottom_row = chr(ord('a') + y - 1) + \\\" \\\" * ((19 - row_length) // 2)\\n for x in range(1, row_length + 1):\\n dot = self.board[(y, x)]\\n if not dot:\\n dot = \\\".\\\"\\n if (y >= 6 and x % 2 == 0) or (y <= 5 and x % 2 == 1):\\n top_row += dot\\n bottom_row += \\\" \\\"\\n else:\\n bottom_row += dot\\n top_row += \\\" \\\"\\n\\n print(top_row)\\n print(bottom_row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"476cf3499fec042d1c97db0c044ba8c2\",\n \"score\": \"0.6679485\",\n \"text\": \"def get_print_info():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b68837f1a0e35b31ff9aa74a11408c85\",\n \"score\": \"0.6670572\",\n \"text\": \"def __repr__(self):\\n return ppretty(self, seq_length=10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08fd94f0afcbb075f68108cef7271b3d\",\n \"score\": \"0.6660994\",\n \"text\": \"def __repr__(self):\\n pretty_print_data_list = list() # Empty List to append strings\\n\\n # Add patient id to the pretty print list\\n pretty_print_data_list.append(\\n \\\"Patient ID: {}\\\".format(self.get_patient_id()))\\n\\n # Add DOB to the pretty print list\\n pretty_print_data_list.append(\\n \\\"Date of Birth: {}\\\".format(self.get_dob()))\\n\\n # Add name to the pretty print list\\n pretty_print_data_list.append(\\\"Name: {},{}\\\".format(\\n self.get_last_name(),\\n self.get_first_name()))\\n\\n # Add address details to the pretty print list\\n pretty_print_data_list.append(\\\"Address:\\\\n{},{},\\\\n{},{}-{}\\\".format(\\n self.get_address_line_1(),\\n self.get_address_line_2(),\\n self.get_address_city(),\\n self.get_address_state(),\\n self.get_address_zip()))\\n\\n # Add contact details to the pretty print list\\n pretty_print_data_list.append(\\n \\\"Contact Details:\\\\nPhone {}\\\\nEmail {}\\\".format(\\n self.get_phone(),\\n self.get_email()))\\n\\n # Join the list with new line character\\n pretty_print_str = \\\"\\\\n\\\".join(pretty_print_data_list)\\n\\n return pretty_print_str # Return the pretty print string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c59bd67a71c25f8ac047c67e57eaa2\",\n \"score\": \"0.6592102\",\n \"text\": \"def pprint_props(self, indent=0):\\n for p in self.__properties__:\\n print \\\" \\\"*indent + p + \\\":\\\", getattr(self, p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad2629acf2b5e10a8019a788fff26400\",\n \"score\": \"0.6572868\",\n \"text\": \"def print_info (self):\\n\\n print \\\"############################################\\\"\\n print \\\"Title: \\\" + self.comicTitle\\n print \\\"Issue number: \\\" + str(self.issue)\\n print \\\"Volume: \\\" + str(self.volume)\\n print \\\"Order number: \\\" + str(self.orderNumber)\\n\\n print \\\"### Story Info #############################\\\"\\n print \\\"Story: \\\" + self.storyName\\n if (self.storySynopsys):\\n print \\\"Synopsys: \\\" + self.storySynopsys\\n \\n ## Iterate through the comic info\\n #for key,val in self.info.iteritems():\\n # print key + \\\": \\\" + val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a7f12a995fb6132ce910fb9ff6b642\",\n \"score\": \"0.6571966\",\n \"text\": \"def print_info(self):\\n print(f\\\"I'm a {self.color} {self.model} built in {self.year}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637db2e79fa851b4dcf40e2fe3b228b2\",\n \"score\": \"0.65707445\",\n \"text\": \"def info(self):\\n print(repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637db2e79fa851b4dcf40e2fe3b228b2\",\n \"score\": \"0.65707445\",\n \"text\": \"def info(self):\\n print(repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1873108467b4a8ec491734a3ff4cdbca\",\n \"score\": \"0.65696824\",\n \"text\": \"def pretty_print(self) -> None:\\n pt: PrettyTable = PrettyTable(field_names=[\\\"File Name\\\", \\\"Classes\\\", \\\"Functions\\\", \\\"Lines\\\", \\\"Characters\\\"])\\n for item in self.files_summary:\\n pt.add_row([item, self.files_summary[item]['class'], self.files_summary[item]['function'], self.files_summary[item]['line'], self.files_summary[item]['char']])\\n print(pt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8ff18ae8b73e2b9493a095f334d086\",\n \"score\": \"0.65450895\",\n \"text\": \"def pretty_print(self):\\r\\n\\t\\tpp = pprint.PrettyPrinter()\\r\\n\\t\\tfor section_name in self.sections:\\r\\n\\t\\t\\tsection = self[section_name]\\r\\n\\t\\t\\tprint(\\\"[%s]\\\" % section.name)\\r\\n\\t\\t\\tpp.pprint(section.dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"338ee9a599c76c43ca5e03f2d5c2e960\",\n \"score\": \"0.65317005\",\n \"text\": \"def pretty_print(self):\\n\\n print('---Individuals---')\\n indi_rows = list()\\n for uid, indi in self.indis.items():\\n indi_rows.append(indi.pp_row())\\n print(tabulate(indi_rows, headers=Individual.pp_header, tablefmt='fancy_grid', showindex='never'))\\n\\n print('\\\\n\\\\n')\\n\\n print('---Families---')\\n fams_rows = list()\\n for uid, fam in self.fams.items():\\n id_, marr, div, husb_id, wife_id, chil_ids = fam.pp_row()\\n husb_nm = ', '.join([self.indis[husb_id].name['last'], self.indis[husb_id].name['first']])\\n wife_nm = ', '.join([self.indis[wife_id].name['last'], self.indis[wife_id].name['first']])\\n fams_rows.append((id_, marr, div, husb_id, husb_nm, wife_id, wife_nm, chil_ids))\\n\\n print(tabulate(fams_rows, headers=Family.pp_header, tablefmt='fancy_grid', showindex='never'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b45cdea56af800d1e81d4d9e5f45ca6\",\n \"score\": \"0.65126747\",\n \"text\": \"def print(self):\\n result = self.tidy()\\n print(\\\"\\\\n\\\".join(result))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdbcacf4fb8fa6498b7584d1314b95fa\",\n \"score\": \"0.6501284\",\n \"text\": \"def print_summary(self):\\n print(get_bold(self.name))\\n print(f\\\"{get_bold('Prep Time')}: {self.prep_time} min\\\")\\n print(f\\\"{get_bold('Veggie')}: {'Yes' if self.veg else 'No'}\\\")\\n print(f\\\"{get_bold('Type')}: {self.type}\\\")\\n print(f\\\"{get_bold('Cuisine')}: {self.cuisine}\\\")\\n print(f\\\"{get_bold('Ingredients')}: {', '.join(self.ingredients)}\\\")\\n print(f\\\"{get_bold('Recipe')}: \\\\n\\\\t{self.get_pretty_recipe()}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"551ecde7b01c05dc03edc5afdfe11291\",\n \"score\": \"0.6500994\",\n \"text\": \"def __str__(self):\\n return ppretty(self, seq_length=20)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2737ed13bf7ef5a9912ef428fa106cf\",\n \"score\": \"0.64939606\",\n \"text\": \"def print_info(self):\\n print ' Number Available:',self.avail\\n print ' Price For:',self.price_for\\n print ' Minimum Order Quantity:',self.min_order\\n print ' Order Multiple:',self.multi\\n print ' Pricing:'\\n\\n n = self.min_order\\n for p in self.prices:\\n if n != p[0]:\\n print \\\"\\\\t%i - %i: \\\\t£%s\\\" % (n, p[0], p[1])\\n n = p[0] + 1\\n else:\\n print \\\"\\\\t%i +: \\\\t£%s\\\" % (n, p[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea42d4743e2baa0170dae0e6d9b3ada2\",\n \"score\": \"0.64634985\",\n \"text\": \"def pprint(obj: object = \\\"\\\", end: str = \\\"\\\\n\\\"):\\n if tvo.get_run_policy() == \\\"mpi\\\" and dist.get_rank() != 0:\\n return\\n\\n print(obj, end=end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cae4240490fff085cab0152a76339a4\",\n \"score\": \"0.6461935\",\n \"text\": \"def print_info(s):\\n print(Style.BRIGHT + Fore.GREEN + s +Style.RESET_ALL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60b9b25f11fa3bb450bf5263b1fec68f\",\n \"score\": \"0.64522886\",\n \"text\": \"def debug_print(self):\\n if self.debug:\\n templ = \\\"{first} {last} is {age} years old.\\\"\\n s = templ.format(\\n first=self.first_name, last=self.last_name, age=self.age,\\n )\\n print s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce516a66e1c1539448e69894fe6fa50d\",\n \"score\": \"0.64512753\",\n \"text\": \"def print_meta_info(self):\\n for par, val in self.meta_info:\\n print(par + ': ' + str(val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04acd615d0260828b1746821ec8346ee\",\n \"score\": \"0.64471275\",\n \"text\": \"def print(self):\\n\\n\\t\\tif self.records is None:\\n\\t\\t\\tprint(\\\"No records loaded.\\\")\\n\\t\\t\\treturn\\n\\n\\t\\tfor ir, record in enumerate(self.records):\\n\\t\\t\\tid, name = record.get(\\\"patient_id\\\"), record.get(\\\"patient_name\\\")\\n\\t\\t\\tprint(\\\"[%d] Name: %s (%s)\\\" % (ir, name, id))\\n\\n\\t\\t\\tfor ist, study in enumerate(record.get(\\\"studies\\\")):\\n\\t\\t\\t\\tid, desc = study.get(\\\"id\\\"), study.get(\\\"desc\\\")\\n\\t\\t\\t\\tprint(\\\"-\\\"*2 + \\\"[%d] Id: %s Desc: %s\\\" % (ist, id, desc))\\n\\n\\t\\t\\t\\tfor ise, series in enumerate(study.get(\\\"series\\\")):\\n\\t\\t\\t\\t\\tnumber, desc, imc = series.get(\\\"number\\\"), series.get(\\\"desc\\\"), series.get(\\\"images_count\\\")\\n\\t\\t\\t\\t\\tprint(\\\"-\\\"*4 + \\\"[%d] Number: %s Desc: %s Images: %d\\\" % (ise, number, desc, imc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a87e22f52d9031f1644e5d3af0f8899\",\n \"score\": \"0.6446953\",\n \"text\": \"def pretty_print(self):\\n str = \\\"DN: \\\" + self.dn + \\\"\\\\n\\\"\\n for a, v_list in self.attrs.iteritems():\\n str = str + \\\"Name: \\\" + a + \\\"\\\\n\\\"\\n for v in v_list:\\n str = str + \\\" Value: \\\" + v + \\\"\\\\n\\\"\\n str = str + \\\"========\\\"\\n return str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2df13f9968245b4ac1521135006ea97\",\n \"score\": \"0.64459544\",\n \"text\": \"def print_info (self):\\n\\n print \\\"############################################\\\"\\n print \\\"First Name: \\\" + self.firstName\\n if self.middleName:\\n print \\\"Middle Name: \\\" + self.middleName\\n print \\\"Last Name: \\\" + self.lastName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7485197e20b89d4cb3da3ab93979dd9c\",\n \"score\": \"0.6419348\",\n \"text\": \"def main():\\n print(json.dumps(info(), sort_keys=True, indent=2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84fc076cc77c29bc04fcf8c21f5d8d3c\",\n \"score\": \"0.64127684\",\n \"text\": \"def print_structures():\\n print(\\n 'training examples \\\\\\n \\\\n\\\\tcount: {} \\\\\\n \\\\n\\\\tdimensions: {} \\\\\\n \\\\n\\\\tshape: {} \\\\\\n \\\\n\\\\tdata type: {}\\\\n\\\\n'.format(\\n len(train_examples),\\n train_examples.ndim,\\n train_examples.shape,\\n train_examples.dtype\\n ),\\n 'testing examples \\\\\\n \\\\n\\\\tcount: {} \\\\\\n \\\\n\\\\tdimensions: {} \\\\\\n \\\\n\\\\tshape: {} \\\\\\n \\\\n\\\\tdata type: {}\\\\n'.format(\\n len(test_labels),\\n train_labels.ndim,\\n test_labels.shape,\\n test_labels.dtype\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"387a1e6ecb13d84b85451480b550733f\",\n \"score\": \"0.6407966\",\n \"text\": \"def pprint(self) -> None:\\n deprecated('1.3', f'{type(self).__name__}.pprint', 'print')\\n print(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc49ed66897a268374b56499133c2715\",\n \"score\": \"0.6398836\",\n \"text\": \"def pp(o, add_metadata=True):\\n dispatch = {dict:pretty_print_dict}\\n if type(o) in dispatch:\\n return dispatch[type(o)](o, add_metadata)\\n else:\\n return o.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11480dc8150de8c28f3ce4f68bd74851\",\n \"score\": \"0.6395588\",\n \"text\": \"def main() -> None:\\n print(json.dumps(info(), sort_keys=True, indent=2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96df8a8211f1b4704a401786c693767a\",\n \"score\": \"0.6375361\",\n \"text\": \"def print_all(data):\\n pp.pprint(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ae7abfd1a56b701042fc82e226936db\",\n \"score\": \"0.6369845\",\n \"text\": \"def __repr__(self):\\n\\n d = self.get_data() # vars(self) # ???\\n return PP.pformat(d, indent=4, width=100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f069f4d61ff2579e2e37319d48eebcd8\",\n \"score\": \"0.6369725\",\n \"text\": \"def dump_self(self):\\n\\n attrs = []\\n\\n if self.number > 0:\\n attrs.append(' ' + format(self.number))\\n\\n if self.output_tag:\\n attrs.append(\\\", tag=\\\" + self.output_tag)\\n\\n if self.output_filename:\\n attrs.append(', output=' + self.output_filename)\\n\\n if self.docbook_id:\\n attrs.append(', id=' + self.docbook_id)\\n\\n if self.title:\\n title = self.title\\n else:\\n title = '<No Title>'\\n\\n prefix = ' ' * self.output_depth\\n\\n print('%d %s%s [%s%s]' % (\\n self.input_depth,\\n prefix,\\n title,\\n self.docbook_element,\\n ''.join(attrs)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81570721d19270aa99924d9560591356\",\n \"score\": \"0.63639736\",\n \"text\": \"def short_print(self):\\n puts('{}{}{}'.format(attr(1), self.name, attr(0)))\\n with indent():\\n puts('{}{}{}'.format(attr(2), self.url, attr(0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266748351043cd250177c4b58eff6fa7\",\n \"score\": \"0.6361171\",\n \"text\": \"def print_details(conn, printer, jobs):\\n printer_name = printer[\\\"printer-uri-supported\\\"].rsplit(\\\"/\\\", 1)[1]\\n print \\\"Printer Name:\\\\t{}\\\".format(printer_name)\\n print \\\"Location:\\\\t{}\\\".format(printer[\\\"printer-location\\\"])\\n print \\\"Model:\\\\t\\\\t{}\\\".format(printer[\\\"printer-make-and-model\\\"])\\n\\n if \\\"printer-state-message\\\" in printer and printer[\\\"printer-state-message\\\"]:\\n print \\\"Status:\\\\t\\\\t{}\\\".format(printer[\\\"printer-state-message\\\"])\\n if printer[\\\"printer-state-reasons\\\"][0] != \\\"none\\\":\\n first_message = printer[\\\"printer-state-reasons\\\"].pop()\\n print \\\"Messages:\\\\t{}\\\".format(pretty_string(first_message))\\n for reason in printer[\\\"printer-state-reasons\\\"]:\\n print \\\"\\\\t\\\\t{}\\\".format(pretty_string(reason))\\n\\n if jobs:\\n print \\\"Jobs:\\\\t\\\\t\\\",\\n print_job(conn, jobs[0])\\n for job in jobs[1:]:\\n print \\\"\\\\t\\\\t\\\",\\n print_job(conn, job)\\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90158716fa4797e61f0ce0c2214805dd\",\n \"score\": \"0.6359507\",\n \"text\": \"def print_dictionary(dict_obj):\\n print(pformat(dict_obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18261f7e1588bc409c09d00999dec55\",\n \"score\": \"0.63497144\",\n \"text\": \"def print_me(self):\\n print (\\\"key: \\\" + str(self._key), \\\"value: \\\" + str(self._value), \\\"parent: \\\" + str(self._parentKey))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aef80b65e11375573d3adb735d797b2\",\n \"score\": \"0.6340874\",\n \"text\": \"def __repr__(self):\\n\\n # print additional info only if there are attributes to show\\n attrnames = list(self._v_attrnames)\\n if attrnames:\\n rep = [f'{attr} := {getattr(self, attr)!r}' for attr in attrnames]\\n return f\\\"{self!s}:\\\\n [\\\" + ',\\\\n '.join(rep) + \\\"]\\\"\\n else:\\n return str(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1fae3a9e0577f411cd4ecfbc357956\",\n \"score\": \"0.6328278\",\n \"text\": \"def pprint(self):\\n results = self.results_dict()\\n if not results:\\n print('No match results for {}'.format(self.current_date))\\n else:\\n for match in results.values():\\n print('{team_one:^20} {team_one_score:<2} - '\\n '{team_two_score:>2} {team_two:^20}'\\n ' {maps:<13}'.format(**match))\\n print('\\\\nCS:GO match results for {}'.format(self.current_date))\\n print('Powered by HLTV.org')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9cbc75d9e24f84efb0efc1577bf624b\",\n \"score\": \"0.63222045\",\n \"text\": \"def info_print(avto_info):\\n print(f\\\"{avto_info['rang'].title()} {avto_info['kompaniya'].upper()} \\\"\\n f\\\"{avto_info['model'].upper()}, {avto_info['korobka']} korobka, \\\"\\n f\\\"{avto_info['yil']}-yil, {avto_info['narx']}$\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d29416841779de4db1dbaf7c92e1bee0\",\n \"score\": \"0.63202745\",\n \"text\": \"def print_( self, params ):\\n what = params[0]\\n if what == \\\"files\\\":\\n self._checkParsed()\\n for key in cooker.pkgdata.keys(): print key\\n elif what == \\\"providers\\\":\\n self._checkParsed()\\n for key in cooker.status.providers.keys(): print key\\n else:\\n print \\\"Usage: print %s\\\" % self.print_.usage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dadb3c1bb180fae18a8818bde024d312\",\n \"score\": \"0.6317465\",\n \"text\": \"def __repr__(self):\\n\\t\\treturn pretty_string(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dadb3c1bb180fae18a8818bde024d312\",\n \"score\": \"0.6317465\",\n \"text\": \"def __repr__(self):\\n\\t\\treturn pretty_string(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"530bd71a260d0afe780144f555a34ce7\",\n \"score\": \"0.6299847\",\n \"text\": \"def __str__(self):\\n if len(self.linearData) > config.NUM_ITEMS_TO_PRINT:\\n out = []\\n # welcome to perl\\n for index in range(config.NUM_ITEMS_TO_PRINT):\\n out.append(\\\"%s: %s\\\" % (index, \\\", \\\".join([\\\"%s: %s\\\" % (key, self.linearData[index][key]) for key in self.linearData[index]])))\\n out = \\\"%s\\\\n... truncated, showing %s/%s\\\" % (\\\"\\\\n\\\".join(out), config.NUM_ITEMS_TO_PRINT, len(self.linearData))\\n\\n if config.PRINT_LAST_ITEM:\\n out = \\\"%s\\\\n%s\\\" % (out, \\\"%s: %s\\\" % (len(self.linearData), \\\", \\\".join([\\\"%s: %s\\\" % (key, self.linearData[-1][key]) for key in self.linearData[-1]])))\\n\\n elif len(self.linearData) == 0:\\n out = \\\"This list is empty\\\"\\n\\n else: # just print first entry.\\n out = []\\n for index in range(len(self.linearData)):\\n out.append(\\\"%s: %s\\\" % (index, \\\", \\\".join([\\\"%s: %s\\\" % (key, self.linearData[index][key]) for key in self.linearData[index]])))\\n out = \\\"%s\\\\nShowing %s/%s\\\" % (\\\"\\\\n\\\".join(out), len(self.linearData), len(self.linearData))\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7da09fa4bf0a27ca574b4ff19062f66\",\n \"score\": \"0.62952936\",\n \"text\": \"def __str__(self):\\n\\n print(\\\"Name: {name}; Label: {label}; Country: {country}; \\\"\\n \\\"Year: {year}; Comments: {coms}\\\".format(name=self.name, label=self.label, country=self.country,\\n year=self.date_release, coms=self.info))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"586a413c1e971a8a4b3c68db10e89cb1\",\n \"score\": \"0.6291012\",\n \"text\": \"def _print_info(self):\\n print(\\\"\\\\nAverage Regret: {0}\\\".format(\\n self.total_regret / self.iterations))\\n\\n for i in self.alg.var_dict:\\n try:\\n print(\\\"{0} : {1}\\\".format(i, self.alg.var_dict[i].__name__))\\n except AttributeError:\\n print(\\\"{0} : {1}\\\".format(i, self.alg.var_dict[i]))\\n\\n print(\\\"{0} Arms: {1}\\\".format(self.bandit.n_arms, self.arm_info))\\n print(\\\"Horizon: {0}\\\".format(self.horizon))\\n print (\\\"Total runtime: {0} seconds\\\".format(self.runtime))\\n print (\\\"Average runtime per iteration: {0} seconds\\\".format(\\n self.runtime / self.iterations))\\n print(\\\"\\\\n\\\" + \\\"-\\\"*50 + \\\"\\\\n\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"582ca937bef1c4a1b9295e39d1400d12\",\n \"score\": \"0.62904286\",\n \"text\": \"def print_summary(self):\\n if self.rc4_dict:\\n print('\\\\n********RC4 Hosts********')\\n for k, v in sorted(self.rc4_dict.items()):\\n print('{} {}'.format(k, v))\\n if self.sslv2_dict:\\n print('\\\\n********SSLv2 Hosts********')\\n for k, v in sorted(self.sslv2_dict.items()):\\n print('{} {}'.format(k, v))\\n if self.sslv3_dict:\\n print('\\\\n********SSLv3 Hosts********')\\n for k, v in sorted(self.sslv3_dict.items()):\\n print('{} {}'.format(k, v))\\n if self.des_dict:\\n print('\\\\n********DES Hosts********')\\n for k, v in sorted(self.des_dict.items()):\\n print('{} {}'.format(k, v))\\n if self.tls10_dict:\\n print('\\\\n********TLS v1.0 Hosts********')\\n for k, v in sorted(self.tls10_dict.items()):\\n print('{} {}'.format(k, v))\\n if self.weakbits_dict:\\n print('\\\\n********Weak Key Size********')\\n for k, v in sorted(self.weakbits_dict.items()):\\n print('{} {}'.format(k, v))\\n if self.heartbleed_dict:\\n print('\\\\n********Heartbleed Hosts********')\\n for k, v in sorted(self.heartbleed_dict.items()):\\n print('{} {}'.format(k, v))\\n if self.md5_dict:\\n print('\\\\n********MD5 Hosts********')\\n for k, v in sorted(self.md5_dict.items()):\\n print('{} {}'.format(k, v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4eb18e286d5e5c8009d9c8555ac2147\",\n \"score\": \"0.6285786\",\n \"text\": \"def showinfo(self):\\n start=self._head\\n print \\\"List:[\\\",\\n for i in range(self._size):\\n print start._element,\\n start=start._next\\n print \\\"]\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3069bac857de8d8ca33af68d44d860af\",\n \"score\": \"0.62746614\",\n \"text\": \"def _print_str(self):\\n print('*[INFO] ' + str(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d0404c1cd8d4a5be0a365ae883cd7c\",\n \"score\": \"0.6270032\",\n \"text\": \"def printInfo(self):\\n\\n print('Number of inputs =', self.n_inputs)\\n print('Number of outputs =', self.n_outputs)\\n print('Number of units =', self.n_units)\\n print('Number of layers =', self.n_layers)\\n print('Number of params =', self.n_params)\\n print('Data type =', dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d0404c1cd8d4a5be0a365ae883cd7c\",\n \"score\": \"0.6270032\",\n \"text\": \"def printInfo(self):\\n\\n print('Number of inputs =', self.n_inputs)\\n print('Number of outputs =', self.n_outputs)\\n print('Number of units =', self.n_units)\\n print('Number of layers =', self.n_layers)\\n print('Number of params =', self.n_params)\\n print('Data type =', dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4bd360ee3401f399a4ce198beeb781\",\n \"score\": \"0.625362\",\n \"text\": \"def debug_print(self):\\n print(dict(self.mem))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9312efa1b6f261327e4c8386ce3acd74\",\n \"score\": \"0.6242685\",\n \"text\": \"def dump(pr):\\n enc = 'utf-8'\\n N = 150\\n\\n print('-'*10)\\n for f in pr:\\n val = pr[f]\\n if not isinstance(val,unicode):\\n val = unicode(pr[f])\\n val = val.encode(enc)\\n if len(val.split('\\\\n')) > 1:\\n print \\\"###%s###\\\\n%s\\\" %(f,val)\\n else:\\n print \\\"###%s###: %s\\\" %(f,val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69bfcda13b48a060d9e270231f38e6c6\",\n \"score\": \"0.62426317\",\n \"text\": \"def _print(self, *args, **kwargs) -> None:\\n if self.verbose:\\n print(f\\\"[{type(self).__name__}-{self.identifier}]\\\", *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48199de721097b214a12d4ae134fb9ec\",\n \"score\": \"0.6237935\",\n \"text\": \"def print(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43bcdc4e057173f19d13723b1a73ac19\",\n \"score\": \"0.623104\",\n \"text\": \"def pretty_print(self) -> None:\\r\\n\\r\\n result: PrettyTable = PrettyTable()\\r\\n result.field_names = ['File Name', 'Classes',\\r\\n 'Fucntions', 'Lines', 'Character']\\r\\n\\r\\n for key, value in self.file_summary.items():\\r\\n result.add_row([key] + list(value.values()))\\r\\n\\r\\n print(\\\"\\\\nSummary for \\\", self.directory)\\r\\n print(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a14e61e7f26ea2f94442f933f64190\",\n \"score\": \"0.62295544\",\n \"text\": \"def show(self):\\n keys = self.__dict__.keys()\\n keys.sort()\\n for k in keys:\\n val = self.__dict__.get(k)\\n if val:\\n print '%s: [%s]' % (k, val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78315080938caf64a24f3c70e9862011\",\n \"score\": \"0.62247586\",\n \"text\": \"def ShowData(self):\\n part_of_message = \\\"Person's %s is %s. \\\"\\n message = \\\"\\\"\\n for key in self.__dict__:\\n message += part_of_message % (key ,self.__dict__[key])\\n\\n print message.rstrip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f2f1053f1a0303c5c011ad8b70b5d2b\",\n \"score\": \"0.6220555\",\n \"text\": \"def pretty_print(self) -> None:\\r\\n pt: PrettyTable = PrettyTable(field_names=['File Name','Class','Function','Lines','Characters'])\\r\\n for key,value in self.files_summary.items():\\r\\n filename = key\\r\\n classTotal = value['class']\\r\\n funcTotal = value['func']\\r\\n lineTotal = value['lines']\\r\\n charTotal = value['char']\\r\\n pt.add_row([filename, classTotal, funcTotal, lineTotal, charTotal])\\r\\n print(pt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58a11327d27a16bb8ccdddfeb314633\",\n \"score\": \"0.6220063\",\n \"text\": \"def _print_dict(d):\\n print(pprint.PrettyPrinter().pformat(d).replace(\\\"°\\\", \\\"@\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd2572119da9ca798e443c391d727981\",\n \"score\": \"0.6215773\",\n \"text\": \"def pprint_tree(tree, indent=0):\\n\\tname, values = tree[0], tree[1:]\\n\\tif len(values) == 1 and isinstance(values[0], basestring):\\n\\t\\tprint \\\"{:<{indent}s}{} {}\\\".format(\\\"\\\", name, values[0], indent=indent)\\n\\telse:\\n\\t\\tprint \\\"{:<{indent}s}{}:\\\".format(\\\"\\\", name, indent=indent)\\n\\t\\tfor value in values:\\n\\t\\t\\tpprint_tree(value, indent=indent+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbad29209c05d77e9c2f034511f4ce99\",\n \"score\": \"0.62134445\",\n \"text\": \"def prnt(self, deep_indent=50):\\n # e.g. print(dialect.prnt())\\n return '\\\\n'.join(self.fmt(deep_indent=deep_indent))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a3c89ec3c12a99bad85b89d75e8830d\",\n \"score\": \"0.6211106\",\n \"text\": \"def print_structure(self):\\n for l in range(self.num_levels):\\n print(self.get_level(l))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88895d3d43aaf4b31c6684bd51b5f2b2\",\n \"score\": \"0.62081957\",\n \"text\": \"def display(self):\\r\\n\\r\\n print(\\\"\\\\tname:\\\", self.name) \\r\\n print(\\\"\\\\tdescription:\\\", self.description) \\r\\n print(\\\"\\\\tvalue:\\\", self.value) \\r\\n print(\\\"\\\\ttype:\\\", self.type)\\r\\n print(\\\"\\\\tio:\\\", self.io) \\r\\n print(\\\"\\\\tdefault_value:\\\", self.default_value) \\r\\n print(\\\"\\\\tvalue_suffix:\\\", self.value_suffix)\\r\\n print(\\\"\\\\tis_advanced:\\\", self.is_advanced)\\r\\n print(\\\"\\\\t------------\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde9bba3e1a5eaf8425249a3387d7f0f\",\n \"score\": \"0.62045693\",\n \"text\": \"def show_struct(self):\\n for chunk in self.chunks:\\n print(chunk.type, end=\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e4dc3151165ea5246fefae98b0673d3\",\n \"score\": \"0.6201701\",\n \"text\": \"def dump(self):\\n print(\\\"Page #\\\", \\\"Valid\\\", \\\"Ref.\\\", \\\"Dirty\\\", \\\"History\\\\t\\\", \\\"Frame\\\", sep=\\\"\\\\t\\\")\\n for page in sorted(self.table):\\n print(page,\\n (\\\"Yes\\\" if self.table[page].valid else \\\"No\\\"),\\n (\\\"Yes\\\" if self.table[page].ref else \\\"No\\\"),\\n (\\\"Yes\\\" if self.table[page].dirty else \\\"No\\\"),\\n str(bin(self.table[page].history))[2:],\\n self.table[page].frame,\\n sep=\\\"\\\\t\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e10e2e9791c1405cb6bc5e491496730e\",\n \"score\": \"0.620058\",\n \"text\": \"def pretty_print(self):\\n\\n if self.dtype == np.int:\\n fmt = \\\"%4d\\\"\\n elif self.dtype == np.float64:\\n fmt = \\\"%10.5g\\\"\\n else:\\n msg.fail(\\\"ERROR: dtype not supported\\\")\\n\\n # print j descending, so it looks like a grid (y increasing\\n # with height)\\n for j in reversed(range(self.g.qy)):\\n for i in range(self.g.qx):\\n\\n if (j < self.g.jlo or j > self.g.jhi or i < self.g.ilo or i > self.g.ihi):\\n gc = 1\\n else:\\n gc = 0\\n\\n if gc:\\n print(\\\"\\\\033[31m\\\" + fmt % (self[i, j]) + \\\"\\\\033[0m\\\", end=\\\"\\\")\\n else:\\n print(fmt % (self[i, j]), end=\\\"\\\")\\n\\n print(\\\" \\\")\\n\\n leg = \\\"\\\"\\\"\\n ^ y\\n |\\n +---> x\\n \\\"\\\"\\\"\\n print(leg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0769c916cafc075e614c34f4ff381be5\",\n \"score\": \"0.61995906\",\n \"text\": \"def pprint(json_data):\\n\\n print(json.dumps(json_data, indent=4, separators=(' , ', ' : ')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a83e80ab0eaba5dfbee07659b62c1cdc\",\n \"score\": \"0.61941296\",\n \"text\": \"def _repr_pretty_(self, p: Any, cycle: bool) -> None:\\n # There should never be a cycle, but just in case use the default repr.\\n p.text(repr(self) if cycle else str(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486d8972a226610a2668351e8596cfe2\",\n \"score\": \"0.6193596\",\n \"text\": \"def pprint(obj):\\r\\n # Get dicts' items.\\r\\n if hasattr(obj, \\\"items\\\"):\\r\\n obj = sorted(obj.items())\\r\\n\\r\\n buf = StringIO()\\r\\n _pprint(obj, buf)\\r\\n\\r\\n value = buf.getvalue()\\r\\n buf.close()\\r\\n\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92c0a14352b3ea94d5f0d62b3de0ed3\",\n \"score\": \"0.6191571\",\n \"text\": \"def dump(self):\\n for pname, param in self.params.items():\\n print \\\"* %s\\\" % pname\\n print \\\" - default: %s\\\" % param.default\\n print \\\" - conffile: %s\\\" % param.conffile\\n print \\\" - type: %s\\\" % param.param_type\\n print \\\" - allowed_values: %s\\\" % param.allowed_values\\n print \\\" - allowed_range: %s\\\" % param.allowed_range\\n print \\\" - cmd_line: %s\\\" % str(param.cmd_line)\\n if param.ignore:\\n print \\\" - IS IGNORED!\\\"\\n else:\\n print \\\" - current value: %s\\\" % str(param.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a15fad983a9fe2b689dde84dfac111a3\",\n \"score\": \"0.61807734\",\n \"text\": \"def print_parseable(d):\\n for k in sorted(d.keys()):\\n print('{}\\\\t{}'.format(k, d[k]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2fc4948cd60f0ed0d8538fb2d123666\",\n \"score\": \"0.6177769\",\n \"text\": \"def print_info(self) -> None:\\n msg = [\\n f'Dataset collection: {self.collection.info.name}',\\n f'Version: {self.requested_version}',\\n f'Description: {self.collection.info.description}',\\n ]\\n if self.collection.info.citation:\\n msg.append('Citation:')\\n msg.append(self.collection.info.citation)\\n print('\\\\n'.join(msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fa93332b6d2b61e6562a11f7a796a2d\",\n \"score\": \"0.617707\",\n \"text\": \"def pprint(self, object: Author or Publication)->None:\\n if 'container_type' not in object:\\n self.logger.warning(\\\"Not a scholarly container object\\\")\\n return\\n\\n to_print = copy.deepcopy(object)\\n if to_print['container_type'] == 'Publication':\\n to_print['source'] = PublicationSource(to_print['source']).name\\n elif to_print['container_type'] == 'Author':\\n parser = AuthorParser(self.__nav)\\n to_print['source'] = AuthorSource(to_print['source']).name\\n if parser._sections == to_print['filled']:\\n to_print['filled'] = True\\n else:\\n to_print['filled'] = False\\n\\n if 'coauthors' in to_print:\\n for coauthor in to_print['coauthors']:\\n coauthor['filled'] = False\\n del coauthor['container_type']\\n coauthor['source'] = AuthorSource(coauthor['source']).name\\n\\n if 'publications' in to_print:\\n for publication in to_print['publications']:\\n publication['source'] = PublicationSource(publication['source']).name\\n del publication['container_type']\\n\\n del to_print['container_type']\\n print(pprint.pformat(to_print).encode(\\\"utf-8\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c36e62f7f061929a9f2dd542d93fa84\",\n \"score\": \"0.6172355\",\n \"text\": \"def print_format():\\n print 'INFO: Printing the variables available for the csv as subject/session level\\\\\\n (required as well in the csv to know where to set the information specified by the user): '\\n print 'Required variables:'\\n for value in REQUIRED_VARIABLES_LIST:\\n print ' * %*s ' % (-30, value)\\n print 'Default Subject demographic attributes:'\\n for value in SUBJECT_PARAMETERS_LIST:\\n print ' * %*s ' % (-30, value)\\n print 'Default Session demographic attributes:'\\n for value in SESSION_PARAMETERS_LIST:\\n print ' * %*s ' % (-30, value)\\n print'\\\\nINFO: You can generate your own attributes by creating them on the XNAT directly and use the script to upload the values.'\\n print'WARNING:'\\n print' * you need to have \\\"project_id,subject_label\\\" if you upload demographic data to a subject.'\\n print' * for session attributes, you need to give the session_label as well.'\\n print' * for specific attributes that you created on XNAT, please put the name of the variables in the header.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bd284cc67d149918279a7c674b5dd72\",\n \"score\": \"0.6170657\",\n \"text\": \"def print_info(self, fp = None):\\n\\n if fp == None:\\n print(\\\"-----------------------------------------\\\")\\n print(\\\"Module: {}, Version: {}\\\".format(self.name, self.version))\\n print(\\\"Install Location Abs: {}\\\".format(self.abs_path))\\n print(\\\"Install Location Rel: {}\\\".format(self.rel_path))\\n print(\\\"Repository: {}{}\\\".format(self.url, self.repository))\\n else:\\n fp.write(\\\"-----------------------------------------\\\\n\\\")\\n fp.write(\\\"Module: {}, Version: {}\\\\n\\\".format(self.name, self.version))\\n fp.write(\\\"Install Location Abs: {}\\\\n\\\".format(self.abs_path))\\n fp.write(\\\"Install Location Rel: {}\\\\n\\\".format(self.rel_path))\\n fp.write(\\\"Repository: {}{}\\\\n\\\".format(self.url, self.repository))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6690f2328055a9917f714ca679b63f3a\",\n \"score\": \"0.61591893\",\n \"text\": \"def print(self) -> None:\\n grn, red, ylw, gry, ppl, blu, rst = '\\\\033[92m', '\\\\033[91m', '\\\\033[93m', '\\\\033[90m', '\\\\033[95m', '\\\\033[94m', '\\\\033[0m'\\n clr_ns, clr_param, clr_val, clr_glb = ppl, blu, ylw, red\\n # print namespaces\\n configured_global = self._NAMESPACE_CONFIGS.get(Config.GLOBAL_NAMESPACE, {})\\n # opening brace\\n sb = [f'{gry}{{{rst}\\\\n']\\n # append strings\\n for namespace in sorted(self._NAMESPACE_PARAMS):\\n configured = self._NAMESPACE_CONFIGS.get(namespace, {})\\n for param in sorted(self._NAMESPACE_PARAMS[namespace]):\\n is_l, is_g = (param in configured), (param in configured_global)\\n # space or comment out\\n sb.append(' ' if (is_l or is_g) else f'{gry}# ')\\n # dictionary key as the namespace.param\\n val = (configured[param] if is_l else configured_global[param]) if (is_l or is_g) else None\\n sb.append(f'{gry}\\\"{grn}{_Instanced.get_prefix(val)}{rst}{clr_ns}{namespace}{gry}.{clr_param}{param}{gry}\\\"{rst}: ')\\n # dictionary func\\n if is_l or is_g:\\n if is_l:\\n sb.append(f'{clr_val}{repr(val)}')\\n elif is_g:\\n sb.append(f'{clr_glb}{repr(val)}')\\n # comma\\n sb.append(f'{gry},{rst}')\\n # comment if has a global func assigned to it\\n if is_g:\\n val = configured_global[param]\\n sb.append(f' {gry}# \\\"{_Instanced.get_prefix(val)}{Config.GLOBAL_NAMESPACE}.{param}{gry}\\\": {repr(val)},{rst}')\\n # new line\\n sb.append('\\\\n')\\n # closing brace\\n sb.append(f'{gry}}}{rst}')\\n # generate string!\\n print(''.join(sb))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26ab1d5c365bd2ead31129d693b12e60\",\n \"score\": \"0.61412597\",\n \"text\": \"def _pretty_dump(self, label, data):\\n # split data string items to a list of hex value\\n dump = [\\\"%02X\\\" % c for c in bytearray(data)]\\n # format for TCP or RTU\\n if self.__mode == const.MODBUS_TCP:\\n if len(dump) > 6:\\n # \\\"[MBAP] ...\\\"\\n dump[0] = \\\"[\\\" + dump[0]\\n dump[6] = dump[6] + \\\"]\\\"\\n elif self.__mode == const.MODBUS_RTU:\\n if len(dump) > 4:\\n # \\\"... [CRC]\\\"\\n dump[-2] = \\\"[\\\" + dump[-2]\\n dump[-1] = dump[-1] + \\\"]\\\"\\n # print result\\n print(label)\\n s = \\\"\\\"\\n for i in dump:\\n s += i + \\\" \\\"\\n print(s)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992976,"cells":{"query_id":{"kind":"string","value":"fcaacc9cbace69879b367eed6c36fd4c"},"query":{"kind":"string","value":"Return bool whether this request is successful (200299) or not."},"positive_passages":{"kind":"list like","value":[{"docid":"fc14434367db3e27faf185f9ccd4011c","score":"0.7483755","text":"def ok(self) -> bool:\n return self._status == 0 or 200 <= self._status <= 299","title":""}],"string":"[\n {\n \"docid\": \"fc14434367db3e27faf185f9ccd4011c\",\n \"score\": \"0.7483755\",\n \"text\": \"def ok(self) -> bool:\\n return self._status == 0 or 200 <= self._status <= 299\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f9437aca13c074cb0f40b651934d58ac","score":"0.8216712","text":"def is_successful(self) -> bool:\n return self.code is not None and self.code >= 200 and self.code < 300","title":""},{"docid":"e06b19668cc2a71e82ec017ec5202a67","score":"0.81315345","text":"def success(self):\r\n i = int(self.status)\r\n return i >= 200 and i <= 299","title":""},{"docid":"2e44aea862cb0866e5bcc948ebad6591","score":"0.8063803","text":"def success(self):\r\n return self.status in [httplib.OK, httplib.CREATED, httplib.NO_CONTENT,\r\n httplib.ACCEPTED]","title":""},{"docid":"6864a76551c5dbf95a5a7341b2a205ff","score":"0.80386275","text":"def is_success(self):\n code = self.code\n return code < 400","title":""},{"docid":"d0352a0e06ed5e427a37260c90e572ed","score":"0.7793058","text":"def success(self):\r\n return self.status in [httplib.OK, httplib.CREATED]","title":""},{"docid":"491c164e95377977a103bd50e0ceb90a","score":"0.7731973","text":"def is_successful(self) -> bool:\n return self.result_state == \"SUCCESS\"","title":""},{"docid":"4b06b929d8a94d3146dad83713b4f7de","score":"0.76120174","text":"def success(self) -> bool:\n return self.status == \"completed\" and self.code == 0","title":""},{"docid":"440039885d686fdc17f21c38dfe2c600","score":"0.7600882","text":"def is_successful(self):\n return self.success","title":""},{"docid":"dec07a4439d2dace8ba3a554aef0d9d8","score":"0.7453606","text":"def is_ok(self):\n return self.response.status_code < 300","title":""},{"docid":"26b8791b7c02096c31e6d8028d9f8906","score":"0.7301751","text":"def was_successful(self):\n return len(self.result.errors) == 0","title":""},{"docid":"98ecc697f41be2717f6b4bf307790f63","score":"0.7300492","text":"def check_response_code(self):\n status = True\n if self.response.status_code != 200:\n print('Request failed, response code: {}'.format(\n self.response.status_code))\n status = False\n\n return status","title":""},{"docid":"f78f91da3516e4d5dde76385f564a01a","score":"0.72650826","text":"def successful(self):\n return self._exception is None","title":""},{"docid":"447999696fb738d472211906a17f6dd4","score":"0.72592676","text":"def _is_successful_response(self, response):\n return response.status_code < 300","title":""},{"docid":"ef4c7eb119369475e0af942042f79476","score":"0.7241699","text":"def success(self):\n return self._exitcode == 0","title":""},{"docid":"561c3d9576073d0eed3ecf3438cd07e2","score":"0.72387636","text":"def is_success(self):\n logging.warning(\"is_success not defined!\")\n return False","title":""},{"docid":"a3b683851874c95c7b4fdb48cd325e0a","score":"0.7192327","text":"def is_ok(self) -> bool:\n return self._code == 0","title":""},{"docid":"b67f086d37f8b7671c56b79519858610","score":"0.7184553","text":"def is_successful(self) -> bool:\n return len(self.errors) == 0","title":""},{"docid":"49978e98876eb97109abd0ea436fc3c9","score":"0.7149684","text":"def _parse_http_return_code(self, request):\n print('Response = ' + str(request.status_code))\n if request.status_code == 200:\n return True\n else:\n return False","title":""},{"docid":"7a1f2b5dc29ef907be1effce2436f134","score":"0.71263057","text":"def success(self):\n return not self.failed","title":""},{"docid":"9686eeaa05580c8e2f209232c534f088","score":"0.7080501","text":"def __is_code_successful(response_code):\n # type: (int) -> bool\n return 200 <= response_code < 300","title":""},{"docid":"43deda2392714587795f0d037bb55a28","score":"0.70615494","text":"def is_success(code):\n return 200 <= code <= 299","title":""},{"docid":"50a89ccadc08e244ce5de90041d40b44","score":"0.704982","text":"def successful():\n return self.ar is not None and self.ar.successful()","title":""},{"docid":"ba8fbf3230b6849fd324b1ff8fff71f8","score":"0.7047327","text":"def is_success(self):\n return self.status == EVG_SUCCESS_STATUS","title":""},{"docid":"99550d13adc3e5d1c4d433c51fe96140","score":"0.7027443","text":"def succeeded(self):\n return self['result'] != 'failure' # noqa: no-cover","title":""},{"docid":"793f75debb29379eceb1eece1141196c","score":"0.69251823","text":"def success(self):\r\n return True","title":""},{"docid":"a3e7f227050bc5bf4bea4877b4ce93d6","score":"0.6841492","text":"def status(self):\n try:\n result = requests.get(url=self.base_url + \"/status\")\n if result.status_code // 100 != 2:\n return False, \"sam status code {}, error body {}\".format(result.status_code, result.content)\n else:\n return True, \"\"\n except Exception as e:\n logging.exception(e)\n return False, str(e)","title":""},{"docid":"2cf89ae14d332a57bf791ab9ee66f912","score":"0.6841381","text":"def success(self):\r\n return len(self.errors) == 0","title":""},{"docid":"b78cd8db19508672ec141fbf1b2949c6","score":"0.68286175","text":"def success(self):\n return self._success","title":""},{"docid":"b78cd8db19508672ec141fbf1b2949c6","score":"0.68286175","text":"def success(self):\n return self._success","title":""},{"docid":"730c8d5e931f904012d0a92c906bbb78","score":"0.6783178","text":"def is_ok(self):\n return not self.error","title":""},{"docid":"1faa3bac8e46a1145d053c2f593a4763","score":"0.6765283","text":"def check(self) -> Tuple[int, bool]:\n if self.status_code == 200:\n return 200, True\n\n return self.status_code, False","title":""},{"docid":"554e8b6895bffd372a42724188ac1082","score":"0.67605007","text":"def exists(self):\n \n if str(self.status_code)[0] == '2':\n return True\n else:\n return False","title":""},{"docid":"6d4c48c93f62680ce7956ca119758be4","score":"0.6747559","text":"def status_is_ok(self):\n status = self.status()\n return status == PCAN_ERROR_OK","title":""},{"docid":"f0382e87bf1c2366ed6195309f533416","score":"0.6733285","text":"def error(self):\n return not self.success()","title":""},{"docid":"2d9ef3185a1c0440bcc53cef9041332d","score":"0.67062443","text":"def is_success(transaction_result):\n\t\tif transaction_result == False:\n\t\t\treturn False\n\n\t\tassert isinstance(transaction_result, dict)\n\t\tassert len(transaction_result['status']) > 0\n\t\tassert len(transaction_result['status']) == len(transaction_result['return'])\n\n\t\tfor operation_status in transaction_result['status']:\n\t\t\tassert isinstance(operation_status, str)\n\n\t\t\tif operation_status != 'commited':\n\t\t\t\treturn False\n\n\t\treturn True","title":""},{"docid":"118a6e3b9e891b83bd13e05d7d72be3c","score":"0.6693682","text":"def response_is_ok(self):\n meta = self.response.get(\"meta\")\n return False if meta else True","title":""},{"docid":"c31905b69d9b61623f0f63194a9205d7","score":"0.666618","text":"def success(self):\n ret = self._get_attr(\"success\")\n return ret","title":""},{"docid":"4fe13a8dac9661c1ca13f13a51460c07","score":"0.6609006","text":"def is_http_status_code_success(status_code):\n status_code_string = str(status_code)\n return status_code_string.startswith(\"2\") and len(status_code_string) == 3","title":""},{"docid":"ed4d4a11308c9bda4e5130058143e588","score":"0.65976876","text":"def check_request_status(status_code):\n\n if status_code == 200:\n return True\n else:\n return False","title":""},{"docid":"307d18b5ed5b51936888fb581040749b","score":"0.65952015","text":"def success(self) -> int:\n return self._SUCCESS","title":""},{"docid":"995c81ec0bc5673d5aef7a0052ad8434","score":"0.6581461","text":"def _check_http_response(self, response):\n success = response is not None and (200 <= response.status_code <= 299)\n if not success:\n LOGGER.error(\n '[%s] HTTP request failed: [%d] %s', self, response.status_code, response.content\n )\n return success","title":""},{"docid":"d298f1fad580aae4717f3053f60ddf4f","score":"0.6529848","text":"def execute(self, response):\r\n return response.get_status() is self._status","title":""},{"docid":"eddc416c8d24da5f282bf2240a94b8c8","score":"0.65248007","text":"def status_ok(status_code):\r\n OK_STATUS_CODES = [200, 201]\r\n return status_code in OK_STATUS_CODES","title":""},{"docid":"bd283af75099f388c8f11368ccc6e6d7","score":"0.65168315","text":"def good_response(self, status_code):\n return status_code == http_client.CREATED","title":""},{"docid":"ab2d4fa0263e4065c071d181614fd21e","score":"0.6501202","text":"def wait_for_success(self) -> bool:\n return pulumi.get(self, \"wait_for_success\")","title":""},{"docid":"1258dfd1404ac25162151e60cb478b00","score":"0.6497974","text":"def success(self):\n return Ok()","title":""},{"docid":"a7391eee5f842544918f91f74eaeb374","score":"0.6481534","text":"def ok(self):\n return self._ok","title":""},{"docid":"f7f734c8386ad6901093265d345e7eb1","score":"0.64779735","text":"def get_success_status(self):\n\t\treturn self.get(name = 'success')","title":""},{"docid":"bb4ce1a23307778c0ba7660b71ce2f16","score":"0.6469184","text":"def __bool__(self):\n\n return self.status == \"OK\"","title":""},{"docid":"ae28d109d94dcb3af0f191feba2d7f34","score":"0.6449038","text":"def _ok_status(self, status_code):\n status_code = int(status_code)\n if status_code / 200 == 1:\n return True\n elif status_code / 400 == 1:\n if status_code is 404:\n return True\n else:\n return False\n elif status_code is 500:\n return False","title":""},{"docid":"f669c97686a511d95f0d31019ef293de","score":"0.6436616","text":"def async_success(self):\r\n if self.success():\r\n # So we have a 'task' object in the body\r\n task = self.parse_body()\r\n return task.findtext('state') == 'FINISHED_SUCCESSFULLY'\r\n else:\r\n return False","title":""},{"docid":"d91a9b43340401482d6c0f4ddff6e628","score":"0.6429521","text":"def _default_is_success(status_code):\n\n return 200 <= status_code < 300","title":""},{"docid":"21c5052e74a1a90c968433e1c2ee050d","score":"0.64084077","text":"def wasSuccessful(self, ):\n\t\tpass","title":""},{"docid":"95aa1870bff4a920e48f40ec299a1326","score":"0.639101","text":"def successful_execution(self):\n return self._transition_state == self._FINISHED and self.check_result","title":""},{"docid":"784d5c08ea205d8e4f9705928ebfe4a3","score":"0.6385554","text":"def fail(self):\n return not self.success()","title":""},{"docid":"32a6dac0d3d10b9a1d5a08526a5d2a9c","score":"0.63427967","text":"def check(self):\n if self._check_preflight():\n return\n # actually, the _check_authorization tool will handle it\n self._check_authorization()\n return {\"success\": True}","title":""},{"docid":"5bd473762e38948a3206c6ea129beabd","score":"0.6330199","text":"def check_success(cls, response, msg):\r\n pass","title":""},{"docid":"6efe5a5a7e61622800d5c16c33ee41a5","score":"0.6302711","text":"def __success(self, xml):\n return xml.xpath('//SUCCESS/text()')[0].lower() == 'true'","title":""},{"docid":"75ba6b27dae419077a796f2dfb904252","score":"0.62970227","text":"def check_valid(self):\n if self.response['ErrorNo'] == \"0\":\n return True\n else:\n return False","title":""},{"docid":"35c7271ab7e5c95aef10d91cd9aa3d84","score":"0.6273162","text":"def execute(self, method, **params):\n response = self._request(method, **params)\n return response and response.get(\"status\") == \"success\"","title":""},{"docid":"cdad83105b3bee441847acc6d3ebbe62","score":"0.6268759","text":"def status(self):\r\n return self._resp.code","title":""},{"docid":"f5f6505427d589679dc6eee2b5546e82","score":"0.6255274","text":"def is_error_response(res):\n if res is None:\n return True\n if res.status_code < 200 or res.status_code > 299:\n return True\n return False","title":""},{"docid":"f5f6505427d589679dc6eee2b5546e82","score":"0.6255274","text":"def is_error_response(res):\n if res is None:\n return True\n if res.status_code < 200 or res.status_code > 299:\n return True\n return False","title":""},{"docid":"606819c4443b65dc1a1709b984a9f687","score":"0.624731","text":"def successful(self):\n\n if not self.ready():\n raise exceptions.AssertionError(\"Function is not ready\")\n res = self._q.get()\n self._q.put(res)\n\n if isinstance(res, Exception):\n return False\n return True","title":""},{"docid":"c4e5060d428839be3bb23768e8ee3ce3","score":"0.6244622","text":"def failed(self):\n if self.status == 'FAILED':\n return True\n return False","title":""},{"docid":"d4d572501dbac8bb8c150110e97471c6","score":"0.62412167","text":"def is_success(self, status_code, **options):\n\n strict_status = options.get('strict_status', True)\n if strict_status is False:\n return self.SUCCESS_CODE_MIN <= status_code <= self.SUCCESS_CODE_MAX\n\n return status_code in SuccessfulResponseCodeEnum.values()","title":""},{"docid":"6babc64e3f1575ddf4cea40d88779977","score":"0.62343943","text":"def is_success_error_code(error_code: int) -> bool:\n\n return error_code in (QuicErrorCode.NO_ERROR, H3ErrorCode.H3_NO_ERROR)","title":""},{"docid":"ed56ad041a5ffc14269ecb006d889ab3","score":"0.62213355","text":"def is_successful(self, protocol, reason):\n if reason == 0:\n return True\n elif isinstance(reason, INTEGER_TYPES):\n return False\n elif hasattr(reason, \"type\"):\n return (\n reason.type is ProcessDone and\n reason.value.exitCode == 0)\n else:\n raise NotImplementedError(\n \"Don't know how to handle is_successful(%r)\" % reason)","title":""},{"docid":"0dd9df5980032c3491b6cf7d7e7ce0c9","score":"0.6214624","text":"def exists(self):\r\n res = asyncio.get_event_loop().run_until_complete(c(self.i))\r\n return True if res[1] == 200 else False","title":""},{"docid":"d780e7a2503405889de3a858fcbf1f9a","score":"0.6191613","text":"def wasSuccessful(self):\r\n # assume failure, plugins must affirmatively declare success\r\n try:\r\n return self._success\r\n except AttributeError:\r\n event = events.ResultSuccessEvent(self, False)\r\n self.session.hooks.wasSuccessful(event)\r\n self._success = event.success\r\n return self._success","title":""},{"docid":"82194bc0430cb341e652ded742d68d9f","score":"0.6179885","text":"def check_json_result(json_result):\n return json_result['status'] == 'success'","title":""},{"docid":"8f1ecafed02fdc0473db7cc4cef194b3","score":"0.6179647","text":"def is_ok(self) -> bool:\n return self.client_id is not None and self.client_secret is not None","title":""},{"docid":"fdde3a0f9f2be610cd67bf5107c877e9","score":"0.6167421","text":"def has_error(self):\n return self.api_code > constants.BIZ_CODE_OK","title":""},{"docid":"0f1d940d194c889dd49359f352e0e70c","score":"0.6166395","text":"def checking_requested_resource(self) -> bool:\n try:\n requests.get(self.url_upload)\n return True\n except requests.exceptions.ConnectionError as ex:\n logging.error(ex)\n return False","title":""},{"docid":"402c2e79c8477af3f390e16e8caa1a8f","score":"0.6151968","text":"def query_successful(api_response):\n try:\n success_values = ['successful', 'success']\n successful = True if api_response['status'] in success_values else False\n except (KeyError, IndexError, ValueError, TypeError):\n successful = False\n return successful","title":""},{"docid":"49202e9d3560e61312e3d0364b348f1b","score":"0.61419326","text":"def wait_for_success(self) -> Optional[bool]:\n return pulumi.get(self, \"wait_for_success\")","title":""},{"docid":"49202e9d3560e61312e3d0364b348f1b","score":"0.61419326","text":"def wait_for_success(self) -> Optional[bool]:\n return pulumi.get(self, \"wait_for_success\")","title":""},{"docid":"8774280554a9ca93b0679e6a3062b677","score":"0.61094594","text":"def failure(self) -> bool:\n return not self.success","title":""},{"docid":"8774280554a9ca93b0679e6a3062b677","score":"0.61094594","text":"def failure(self) -> bool:\n return not self.success","title":""},{"docid":"ef1600a06e873185875245dcfc70f9a8","score":"0.6102435","text":"def api_check_state_ok(self) -> bool:\n raise NotImplementedError","title":""},{"docid":"c37523b1773896617f2aeec45841ab1d","score":"0.6065059","text":"def exists(self):\n status = self.api.get(self._url(), check_errors=False).status_code\n if status == 200:\n return True\n if status == 404:\n return False\n raise ConjurException(\"Request Failed: {0}\".format(status))","title":""},{"docid":"89a6912e65b29496fb295065f46d8663","score":"0.6063469","text":"def badHTTPCode(response):\n if (response.status_code != requests.codes.ok):\n return True\n return False","title":""},{"docid":"9c5a0621e32789163516e77da3f6a66f","score":"0.6061725","text":"def check_query_was_successful(response):\n\n # Checking to see that our request was successful\n if response.status_code != 200:\n print(\"Status code is not 200, exiting\")\n sys.exit()","title":""},{"docid":"5a8ec6ef22b3cacea79b7add9c7ff05e","score":"0.6040177","text":"def is_good_response(resp)-> bool:\n content_type = resp.headers['Content-Type'].lower()\n print(\"HTTP Status Code: {0}\".format(resp.status_code))\n return (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)","title":""},{"docid":"3fac2ca38cf3794d7cf7e4b4ec926d66","score":"0.60335004","text":"def expect_response(self):\n return True","title":""},{"docid":"9227304d111f25ea03b473b351c83694","score":"0.60292673","text":"def is_verified(self):\n\n # First do the normal checking of reason codes.\n if not super().is_verified():\n return False\n\n # If we pass a reason code check, look at the statusCode.\n # 0 is the 'success' code.\n return self.json['StatusCode'] == 0","title":""},{"docid":"cee3da339ff657101934cb1245bb43ef","score":"0.60277164","text":"def has_error(self):\n return self.no_code","title":""},{"docid":"e0ce67c120247ca8511bd4c4aa3a4dbb","score":"0.60274726","text":"def is_running(self):\n response = None\n url = baseUrl + \"/status\"\n try:\n response = urllib2.urlopen(url, timeout=5)\n if response.code == 200:\n return True\n else:\n return False\n except URLError:\n return False\n finally:\n if response:\n response.close()","title":""},{"docid":"889b9b7182dc6040d01461a41e915ff9","score":"0.60242265","text":"def assertOk(self, response):\r\n return self.assertStatusCode(response, 200)","title":""},{"docid":"5bd46af82231df22ae0c8536b0fdac7c","score":"0.6014986","text":"def completed_successfully(self) -> bool:\n return not any([self.is_running(), self.timed_out, self.interrupted, self.failure])","title":""},{"docid":"578cfee861b7dd6978cb11147603441f","score":"0.6011041","text":"def check_status(self):\n log = logging.getLogger(\"%s.%s\" % (self.log_name,\n inspect.stack()[0][3]))\n log.setLevel(self.log_level)\n\n if self.url:\n return True\n try:\n result = requests.get(self.ext_url)\n self.url = self.ext_url\n self.es_url = self.url\n self.es = Elasticsearch([self.url])\n return True\n except requests.exceptions.ConnectionError:\n pass\n try:\n result = requests.get(self.local_url)\n log.warning(\"Url '%s' not connecting. Using local_url '%s'\" % \\\n (self.ext_url, self.local_url))\n self.url = self.local_url\n self.es_url = self.url\n self.es = Elasticsearch([self.url])\n return True\n except requests.exceptions.ConnectionError:\n self.url = None\n log.warning(\"Unable to connect using urls: %s\" % set([self.ext_url,\n self.local_url]))\n return False","title":""},{"docid":"70d05fec438e4765f61d8cf2bd55e160","score":"0.600635","text":"def se_puede_acceder_a_url(url_):\n try:\n respuesta = urllib2.urlopen(url_)\n return respuesta.code == 200\n except (urllib2.HTTPError, urllib2.URLError) as error:\n LOGGER.warning('Error al acceder a la URL %s. Error: %s', url_, error)\n return False","title":""},{"docid":"39eaadfa7082b5bc5c7cff248a75e59f","score":"0.60057807","text":"def is_successful(self, task_id):\n return self.get_status(task_id) == \"SUCCESS\"","title":""},{"docid":"f981d42293e3b6d0274d000e268201ff","score":"0.6003406","text":"def is_redirect(self):\n return \"location\" in self.headers and self.status in range(300, 400)","title":""},{"docid":"927b3d51612b2561f135b910dbf6dd03","score":"0.60026515","text":"def is_processing(self):\r\n return self.status == 202","title":""},{"docid":"4c242cd4a8d81b7b842d29eef349adba","score":"0.59926605","text":"def task_succeeded(self):\n return self.task_exitcode == 0","title":""},{"docid":"180430761a5183adb1fc3fb89457a962","score":"0.5976377","text":"def finished(self):\n return self.result is not None","title":""},{"docid":"bb4981a6b84d67e075f34dc158782dd6","score":"0.5975629","text":"def has_valid_code(self):\n if self._status_code:\n return self._status_code in VALID_CODES\n return False","title":""},{"docid":"ede789d770577aedd8323773cd298198","score":"0.5962401","text":"def valid_credentials(self):\n add_headers = self.auth_method(\"requests\")\n r = self._http_request('GET', self.cfg.api_url, '/', headers=add_headers)\n if r.status_code in [200]:\n return True\n LOG.info('API response to token = {} {}'.format(r.status_code, r.reason))\n return False","title":""},{"docid":"99a35df6e1e6bbde3e09bd8264cbf55a","score":"0.5937359","text":"def domain_return_ok(self, domain, request):\r\n return True","title":""}],"string":"[\n {\n \"docid\": \"f9437aca13c074cb0f40b651934d58ac\",\n \"score\": \"0.8216712\",\n \"text\": \"def is_successful(self) -> bool:\\n return self.code is not None and self.code >= 200 and self.code < 300\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e06b19668cc2a71e82ec017ec5202a67\",\n \"score\": \"0.81315345\",\n \"text\": \"def success(self):\\r\\n i = int(self.status)\\r\\n return i >= 200 and i <= 299\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e44aea862cb0866e5bcc948ebad6591\",\n \"score\": \"0.8063803\",\n \"text\": \"def success(self):\\r\\n return self.status in [httplib.OK, httplib.CREATED, httplib.NO_CONTENT,\\r\\n httplib.ACCEPTED]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6864a76551c5dbf95a5a7341b2a205ff\",\n \"score\": \"0.80386275\",\n \"text\": \"def is_success(self):\\n code = self.code\\n return code < 400\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0352a0e06ed5e427a37260c90e572ed\",\n \"score\": \"0.7793058\",\n \"text\": \"def success(self):\\r\\n return self.status in [httplib.OK, httplib.CREATED]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"491c164e95377977a103bd50e0ceb90a\",\n \"score\": \"0.7731973\",\n \"text\": \"def is_successful(self) -> bool:\\n return self.result_state == \\\"SUCCESS\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b06b929d8a94d3146dad83713b4f7de\",\n \"score\": \"0.76120174\",\n \"text\": \"def success(self) -> bool:\\n return self.status == \\\"completed\\\" and self.code == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"440039885d686fdc17f21c38dfe2c600\",\n \"score\": \"0.7600882\",\n \"text\": \"def is_successful(self):\\n return self.success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dec07a4439d2dace8ba3a554aef0d9d8\",\n \"score\": \"0.7453606\",\n \"text\": \"def is_ok(self):\\n return self.response.status_code < 300\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26b8791b7c02096c31e6d8028d9f8906\",\n \"score\": \"0.7301751\",\n \"text\": \"def was_successful(self):\\n return len(self.result.errors) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98ecc697f41be2717f6b4bf307790f63\",\n \"score\": \"0.7300492\",\n \"text\": \"def check_response_code(self):\\n status = True\\n if self.response.status_code != 200:\\n print('Request failed, response code: {}'.format(\\n self.response.status_code))\\n status = False\\n\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f78f91da3516e4d5dde76385f564a01a\",\n \"score\": \"0.72650826\",\n \"text\": \"def successful(self):\\n return self._exception is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"447999696fb738d472211906a17f6dd4\",\n \"score\": \"0.72592676\",\n \"text\": \"def _is_successful_response(self, response):\\n return response.status_code < 300\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef4c7eb119369475e0af942042f79476\",\n \"score\": \"0.7241699\",\n \"text\": \"def success(self):\\n return self._exitcode == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"561c3d9576073d0eed3ecf3438cd07e2\",\n \"score\": \"0.72387636\",\n \"text\": \"def is_success(self):\\n logging.warning(\\\"is_success not defined!\\\")\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b683851874c95c7b4fdb48cd325e0a\",\n \"score\": \"0.7192327\",\n \"text\": \"def is_ok(self) -> bool:\\n return self._code == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b67f086d37f8b7671c56b79519858610\",\n \"score\": \"0.7184553\",\n \"text\": \"def is_successful(self) -> bool:\\n return len(self.errors) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49978e98876eb97109abd0ea436fc3c9\",\n \"score\": \"0.7149684\",\n \"text\": \"def _parse_http_return_code(self, request):\\n print('Response = ' + str(request.status_code))\\n if request.status_code == 200:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1f2b5dc29ef907be1effce2436f134\",\n \"score\": \"0.71263057\",\n \"text\": \"def success(self):\\n return not self.failed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9686eeaa05580c8e2f209232c534f088\",\n \"score\": \"0.7080501\",\n \"text\": \"def __is_code_successful(response_code):\\n # type: (int) -> bool\\n return 200 <= response_code < 300\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43deda2392714587795f0d037bb55a28\",\n \"score\": \"0.70615494\",\n \"text\": \"def is_success(code):\\n return 200 <= code <= 299\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a89ccadc08e244ce5de90041d40b44\",\n \"score\": \"0.704982\",\n \"text\": \"def successful():\\n return self.ar is not None and self.ar.successful()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8fbf3230b6849fd324b1ff8fff71f8\",\n \"score\": \"0.7047327\",\n \"text\": \"def is_success(self):\\n return self.status == EVG_SUCCESS_STATUS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99550d13adc3e5d1c4d433c51fe96140\",\n \"score\": \"0.7027443\",\n \"text\": \"def succeeded(self):\\n return self['result'] != 'failure' # noqa: no-cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"793f75debb29379eceb1eece1141196c\",\n \"score\": \"0.69251823\",\n \"text\": \"def success(self):\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e7f227050bc5bf4bea4877b4ce93d6\",\n \"score\": \"0.6841492\",\n \"text\": \"def status(self):\\n try:\\n result = requests.get(url=self.base_url + \\\"/status\\\")\\n if result.status_code // 100 != 2:\\n return False, \\\"sam status code {}, error body {}\\\".format(result.status_code, result.content)\\n else:\\n return True, \\\"\\\"\\n except Exception as e:\\n logging.exception(e)\\n return False, str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cf89ae14d332a57bf791ab9ee66f912\",\n \"score\": \"0.6841381\",\n \"text\": \"def success(self):\\r\\n return len(self.errors) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b78cd8db19508672ec141fbf1b2949c6\",\n \"score\": \"0.68286175\",\n \"text\": \"def success(self):\\n return self._success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b78cd8db19508672ec141fbf1b2949c6\",\n \"score\": \"0.68286175\",\n \"text\": \"def success(self):\\n return self._success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730c8d5e931f904012d0a92c906bbb78\",\n \"score\": \"0.6783178\",\n \"text\": \"def is_ok(self):\\n return not self.error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1faa3bac8e46a1145d053c2f593a4763\",\n \"score\": \"0.6765283\",\n \"text\": \"def check(self) -> Tuple[int, bool]:\\n if self.status_code == 200:\\n return 200, True\\n\\n return self.status_code, False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"554e8b6895bffd372a42724188ac1082\",\n \"score\": \"0.67605007\",\n \"text\": \"def exists(self):\\n \\n if str(self.status_code)[0] == '2':\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4c48c93f62680ce7956ca119758be4\",\n \"score\": \"0.6747559\",\n \"text\": \"def status_is_ok(self):\\n status = self.status()\\n return status == PCAN_ERROR_OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0382e87bf1c2366ed6195309f533416\",\n \"score\": \"0.6733285\",\n \"text\": \"def error(self):\\n return not self.success()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9ef3185a1c0440bcc53cef9041332d\",\n \"score\": \"0.67062443\",\n \"text\": \"def is_success(transaction_result):\\n\\t\\tif transaction_result == False:\\n\\t\\t\\treturn False\\n\\n\\t\\tassert isinstance(transaction_result, dict)\\n\\t\\tassert len(transaction_result['status']) > 0\\n\\t\\tassert len(transaction_result['status']) == len(transaction_result['return'])\\n\\n\\t\\tfor operation_status in transaction_result['status']:\\n\\t\\t\\tassert isinstance(operation_status, str)\\n\\n\\t\\t\\tif operation_status != 'commited':\\n\\t\\t\\t\\treturn False\\n\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"118a6e3b9e891b83bd13e05d7d72be3c\",\n \"score\": \"0.6693682\",\n \"text\": \"def response_is_ok(self):\\n meta = self.response.get(\\\"meta\\\")\\n return False if meta else True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31905b69d9b61623f0f63194a9205d7\",\n \"score\": \"0.666618\",\n \"text\": \"def success(self):\\n ret = self._get_attr(\\\"success\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fe13a8dac9661c1ca13f13a51460c07\",\n \"score\": \"0.6609006\",\n \"text\": \"def is_http_status_code_success(status_code):\\n status_code_string = str(status_code)\\n return status_code_string.startswith(\\\"2\\\") and len(status_code_string) == 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed4d4a11308c9bda4e5130058143e588\",\n \"score\": \"0.65976876\",\n \"text\": \"def check_request_status(status_code):\\n\\n if status_code == 200:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307d18b5ed5b51936888fb581040749b\",\n \"score\": \"0.65952015\",\n \"text\": \"def success(self) -> int:\\n return self._SUCCESS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995c81ec0bc5673d5aef7a0052ad8434\",\n \"score\": \"0.6581461\",\n \"text\": \"def _check_http_response(self, response):\\n success = response is not None and (200 <= response.status_code <= 299)\\n if not success:\\n LOGGER.error(\\n '[%s] HTTP request failed: [%d] %s', self, response.status_code, response.content\\n )\\n return success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d298f1fad580aae4717f3053f60ddf4f\",\n \"score\": \"0.6529848\",\n \"text\": \"def execute(self, response):\\r\\n return response.get_status() is self._status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eddc416c8d24da5f282bf2240a94b8c8\",\n \"score\": \"0.65248007\",\n \"text\": \"def status_ok(status_code):\\r\\n OK_STATUS_CODES = [200, 201]\\r\\n return status_code in OK_STATUS_CODES\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd283af75099f388c8f11368ccc6e6d7\",\n \"score\": \"0.65168315\",\n \"text\": \"def good_response(self, status_code):\\n return status_code == http_client.CREATED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab2d4fa0263e4065c071d181614fd21e\",\n \"score\": \"0.6501202\",\n \"text\": \"def wait_for_success(self) -> bool:\\n return pulumi.get(self, \\\"wait_for_success\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1258dfd1404ac25162151e60cb478b00\",\n \"score\": \"0.6497974\",\n \"text\": \"def success(self):\\n return Ok()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7391eee5f842544918f91f74eaeb374\",\n \"score\": \"0.6481534\",\n \"text\": \"def ok(self):\\n return self._ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f734c8386ad6901093265d345e7eb1\",\n \"score\": \"0.64779735\",\n \"text\": \"def get_success_status(self):\\n\\t\\treturn self.get(name = 'success')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4ce1a23307778c0ba7660b71ce2f16\",\n \"score\": \"0.6469184\",\n \"text\": \"def __bool__(self):\\n\\n return self.status == \\\"OK\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae28d109d94dcb3af0f191feba2d7f34\",\n \"score\": \"0.6449038\",\n \"text\": \"def _ok_status(self, status_code):\\n status_code = int(status_code)\\n if status_code / 200 == 1:\\n return True\\n elif status_code / 400 == 1:\\n if status_code is 404:\\n return True\\n else:\\n return False\\n elif status_code is 500:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f669c97686a511d95f0d31019ef293de\",\n \"score\": \"0.6436616\",\n \"text\": \"def async_success(self):\\r\\n if self.success():\\r\\n # So we have a 'task' object in the body\\r\\n task = self.parse_body()\\r\\n return task.findtext('state') == 'FINISHED_SUCCESSFULLY'\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91a9b43340401482d6c0f4ddff6e628\",\n \"score\": \"0.6429521\",\n \"text\": \"def _default_is_success(status_code):\\n\\n return 200 <= status_code < 300\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21c5052e74a1a90c968433e1c2ee050d\",\n \"score\": \"0.64084077\",\n \"text\": \"def wasSuccessful(self, ):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95aa1870bff4a920e48f40ec299a1326\",\n \"score\": \"0.639101\",\n \"text\": \"def successful_execution(self):\\n return self._transition_state == self._FINISHED and self.check_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784d5c08ea205d8e4f9705928ebfe4a3\",\n \"score\": \"0.6385554\",\n \"text\": \"def fail(self):\\n return not self.success()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32a6dac0d3d10b9a1d5a08526a5d2a9c\",\n \"score\": \"0.63427967\",\n \"text\": \"def check(self):\\n if self._check_preflight():\\n return\\n # actually, the _check_authorization tool will handle it\\n self._check_authorization()\\n return {\\\"success\\\": True}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd473762e38948a3206c6ea129beabd\",\n \"score\": \"0.6330199\",\n \"text\": \"def check_success(cls, response, msg):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6efe5a5a7e61622800d5c16c33ee41a5\",\n \"score\": \"0.6302711\",\n \"text\": \"def __success(self, xml):\\n return xml.xpath('//SUCCESS/text()')[0].lower() == 'true'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ba6b27dae419077a796f2dfb904252\",\n \"score\": \"0.62970227\",\n \"text\": \"def check_valid(self):\\n if self.response['ErrorNo'] == \\\"0\\\":\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c7271ab7e5c95aef10d91cd9aa3d84\",\n \"score\": \"0.6273162\",\n \"text\": \"def execute(self, method, **params):\\n response = self._request(method, **params)\\n return response and response.get(\\\"status\\\") == \\\"success\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdad83105b3bee441847acc6d3ebbe62\",\n \"score\": \"0.6268759\",\n \"text\": \"def status(self):\\r\\n return self._resp.code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f6505427d589679dc6eee2b5546e82\",\n \"score\": \"0.6255274\",\n \"text\": \"def is_error_response(res):\\n if res is None:\\n return True\\n if res.status_code < 200 or res.status_code > 299:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f6505427d589679dc6eee2b5546e82\",\n \"score\": \"0.6255274\",\n \"text\": \"def is_error_response(res):\\n if res is None:\\n return True\\n if res.status_code < 200 or res.status_code > 299:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606819c4443b65dc1a1709b984a9f687\",\n \"score\": \"0.624731\",\n \"text\": \"def successful(self):\\n\\n if not self.ready():\\n raise exceptions.AssertionError(\\\"Function is not ready\\\")\\n res = self._q.get()\\n self._q.put(res)\\n\\n if isinstance(res, Exception):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e5060d428839be3bb23768e8ee3ce3\",\n \"score\": \"0.6244622\",\n \"text\": \"def failed(self):\\n if self.status == 'FAILED':\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d572501dbac8bb8c150110e97471c6\",\n \"score\": \"0.62412167\",\n \"text\": \"def is_success(self, status_code, **options):\\n\\n strict_status = options.get('strict_status', True)\\n if strict_status is False:\\n return self.SUCCESS_CODE_MIN <= status_code <= self.SUCCESS_CODE_MAX\\n\\n return status_code in SuccessfulResponseCodeEnum.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc64e3f1575ddf4cea40d88779977\",\n \"score\": \"0.62343943\",\n \"text\": \"def is_success_error_code(error_code: int) -> bool:\\n\\n return error_code in (QuicErrorCode.NO_ERROR, H3ErrorCode.H3_NO_ERROR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed56ad041a5ffc14269ecb006d889ab3\",\n \"score\": \"0.62213355\",\n \"text\": \"def is_successful(self, protocol, reason):\\n if reason == 0:\\n return True\\n elif isinstance(reason, INTEGER_TYPES):\\n return False\\n elif hasattr(reason, \\\"type\\\"):\\n return (\\n reason.type is ProcessDone and\\n reason.value.exitCode == 0)\\n else:\\n raise NotImplementedError(\\n \\\"Don't know how to handle is_successful(%r)\\\" % reason)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd9df5980032c3491b6cf7d7e7ce0c9\",\n \"score\": \"0.6214624\",\n \"text\": \"def exists(self):\\r\\n res = asyncio.get_event_loop().run_until_complete(c(self.i))\\r\\n return True if res[1] == 200 else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d780e7a2503405889de3a858fcbf1f9a\",\n \"score\": \"0.6191613\",\n \"text\": \"def wasSuccessful(self):\\r\\n # assume failure, plugins must affirmatively declare success\\r\\n try:\\r\\n return self._success\\r\\n except AttributeError:\\r\\n event = events.ResultSuccessEvent(self, False)\\r\\n self.session.hooks.wasSuccessful(event)\\r\\n self._success = event.success\\r\\n return self._success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82194bc0430cb341e652ded742d68d9f\",\n \"score\": \"0.6179885\",\n \"text\": \"def check_json_result(json_result):\\n return json_result['status'] == 'success'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f1ecafed02fdc0473db7cc4cef194b3\",\n \"score\": \"0.6179647\",\n \"text\": \"def is_ok(self) -> bool:\\n return self.client_id is not None and self.client_secret is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdde3a0f9f2be610cd67bf5107c877e9\",\n \"score\": \"0.6167421\",\n \"text\": \"def has_error(self):\\n return self.api_code > constants.BIZ_CODE_OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f1d940d194c889dd49359f352e0e70c\",\n \"score\": \"0.6166395\",\n \"text\": \"def checking_requested_resource(self) -> bool:\\n try:\\n requests.get(self.url_upload)\\n return True\\n except requests.exceptions.ConnectionError as ex:\\n logging.error(ex)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"402c2e79c8477af3f390e16e8caa1a8f\",\n \"score\": \"0.6151968\",\n \"text\": \"def query_successful(api_response):\\n try:\\n success_values = ['successful', 'success']\\n successful = True if api_response['status'] in success_values else False\\n except (KeyError, IndexError, ValueError, TypeError):\\n successful = False\\n return successful\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49202e9d3560e61312e3d0364b348f1b\",\n \"score\": \"0.61419326\",\n \"text\": \"def wait_for_success(self) -> Optional[bool]:\\n return pulumi.get(self, \\\"wait_for_success\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49202e9d3560e61312e3d0364b348f1b\",\n \"score\": \"0.61419326\",\n \"text\": \"def wait_for_success(self) -> Optional[bool]:\\n return pulumi.get(self, \\\"wait_for_success\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8774280554a9ca93b0679e6a3062b677\",\n \"score\": \"0.61094594\",\n \"text\": \"def failure(self) -> bool:\\n return not self.success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8774280554a9ca93b0679e6a3062b677\",\n \"score\": \"0.61094594\",\n \"text\": \"def failure(self) -> bool:\\n return not self.success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef1600a06e873185875245dcfc70f9a8\",\n \"score\": \"0.6102435\",\n \"text\": \"def api_check_state_ok(self) -> bool:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c37523b1773896617f2aeec45841ab1d\",\n \"score\": \"0.6065059\",\n \"text\": \"def exists(self):\\n status = self.api.get(self._url(), check_errors=False).status_code\\n if status == 200:\\n return True\\n if status == 404:\\n return False\\n raise ConjurException(\\\"Request Failed: {0}\\\".format(status))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a6912e65b29496fb295065f46d8663\",\n \"score\": \"0.6063469\",\n \"text\": \"def badHTTPCode(response):\\n if (response.status_code != requests.codes.ok):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5a0621e32789163516e77da3f6a66f\",\n \"score\": \"0.6061725\",\n \"text\": \"def check_query_was_successful(response):\\n\\n # Checking to see that our request was successful\\n if response.status_code != 200:\\n print(\\\"Status code is not 200, exiting\\\")\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8ec6ef22b3cacea79b7add9c7ff05e\",\n \"score\": \"0.6040177\",\n \"text\": \"def is_good_response(resp)-> bool:\\n content_type = resp.headers['Content-Type'].lower()\\n print(\\\"HTTP Status Code: {0}\\\".format(resp.status_code))\\n return (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fac2ca38cf3794d7cf7e4b4ec926d66\",\n \"score\": \"0.60335004\",\n \"text\": \"def expect_response(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9227304d111f25ea03b473b351c83694\",\n \"score\": \"0.60292673\",\n \"text\": \"def is_verified(self):\\n\\n # First do the normal checking of reason codes.\\n if not super().is_verified():\\n return False\\n\\n # If we pass a reason code check, look at the statusCode.\\n # 0 is the 'success' code.\\n return self.json['StatusCode'] == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee3da339ff657101934cb1245bb43ef\",\n \"score\": \"0.60277164\",\n \"text\": \"def has_error(self):\\n return self.no_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0ce67c120247ca8511bd4c4aa3a4dbb\",\n \"score\": \"0.60274726\",\n \"text\": \"def is_running(self):\\n response = None\\n url = baseUrl + \\\"/status\\\"\\n try:\\n response = urllib2.urlopen(url, timeout=5)\\n if response.code == 200:\\n return True\\n else:\\n return False\\n except URLError:\\n return False\\n finally:\\n if response:\\n response.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889b9b7182dc6040d01461a41e915ff9\",\n \"score\": \"0.60242265\",\n \"text\": \"def assertOk(self, response):\\r\\n return self.assertStatusCode(response, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd46af82231df22ae0c8536b0fdac7c\",\n \"score\": \"0.6014986\",\n \"text\": \"def completed_successfully(self) -> bool:\\n return not any([self.is_running(), self.timed_out, self.interrupted, self.failure])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"578cfee861b7dd6978cb11147603441f\",\n \"score\": \"0.6011041\",\n \"text\": \"def check_status(self):\\n log = logging.getLogger(\\\"%s.%s\\\" % (self.log_name,\\n inspect.stack()[0][3]))\\n log.setLevel(self.log_level)\\n\\n if self.url:\\n return True\\n try:\\n result = requests.get(self.ext_url)\\n self.url = self.ext_url\\n self.es_url = self.url\\n self.es = Elasticsearch([self.url])\\n return True\\n except requests.exceptions.ConnectionError:\\n pass\\n try:\\n result = requests.get(self.local_url)\\n log.warning(\\\"Url '%s' not connecting. Using local_url '%s'\\\" % \\\\\\n (self.ext_url, self.local_url))\\n self.url = self.local_url\\n self.es_url = self.url\\n self.es = Elasticsearch([self.url])\\n return True\\n except requests.exceptions.ConnectionError:\\n self.url = None\\n log.warning(\\\"Unable to connect using urls: %s\\\" % set([self.ext_url,\\n self.local_url]))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d05fec438e4765f61d8cf2bd55e160\",\n \"score\": \"0.600635\",\n \"text\": \"def se_puede_acceder_a_url(url_):\\n try:\\n respuesta = urllib2.urlopen(url_)\\n return respuesta.code == 200\\n except (urllib2.HTTPError, urllib2.URLError) as error:\\n LOGGER.warning('Error al acceder a la URL %s. Error: %s', url_, error)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39eaadfa7082b5bc5c7cff248a75e59f\",\n \"score\": \"0.60057807\",\n \"text\": \"def is_successful(self, task_id):\\n return self.get_status(task_id) == \\\"SUCCESS\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f981d42293e3b6d0274d000e268201ff\",\n \"score\": \"0.6003406\",\n \"text\": \"def is_redirect(self):\\n return \\\"location\\\" in self.headers and self.status in range(300, 400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927b3d51612b2561f135b910dbf6dd03\",\n \"score\": \"0.60026515\",\n \"text\": \"def is_processing(self):\\r\\n return self.status == 202\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c242cd4a8d81b7b842d29eef349adba\",\n \"score\": \"0.59926605\",\n \"text\": \"def task_succeeded(self):\\n return self.task_exitcode == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180430761a5183adb1fc3fb89457a962\",\n \"score\": \"0.5976377\",\n \"text\": \"def finished(self):\\n return self.result is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4981a6b84d67e075f34dc158782dd6\",\n \"score\": \"0.5975629\",\n \"text\": \"def has_valid_code(self):\\n if self._status_code:\\n return self._status_code in VALID_CODES\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede789d770577aedd8323773cd298198\",\n \"score\": \"0.5962401\",\n \"text\": \"def valid_credentials(self):\\n add_headers = self.auth_method(\\\"requests\\\")\\n r = self._http_request('GET', self.cfg.api_url, '/', headers=add_headers)\\n if r.status_code in [200]:\\n return True\\n LOG.info('API response to token = {} {}'.format(r.status_code, r.reason))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a35df6e1e6bbde3e09bd8264cbf55a\",\n \"score\": \"0.5937359\",\n \"text\": \"def domain_return_ok(self, domain, request):\\r\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992977,"cells":{"query_id":{"kind":"string","value":"93e884109e1522efaa30c02a475e8f20"},"query":{"kind":"string","value":"Decode an ODB2 stream into a pandas dataframe"},"positive_passages":{"kind":"list like","value":[{"docid":"14780229a223ee27c7006785698c72d3","score":"0.0","text":"def read_odb(source, columns=None, aggregated=True, single=False):\n if single:\n assert aggregated\n return _read_odb_oneshot(source, columns)\n else:\n return _read_odb_generator(source, columns, aggregated)","title":""}],"string":"[\n {\n \"docid\": \"14780229a223ee27c7006785698c72d3\",\n \"score\": \"0.0\",\n \"text\": \"def read_odb(source, columns=None, aggregated=True, single=False):\\n if single:\\n assert aggregated\\n return _read_odb_oneshot(source, columns)\\n else:\\n return _read_odb_generator(source, columns, aggregated)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f9956bf1a9ca018b1310380d6db7118c","score":"0.628087","text":"def df_from_bytes_np_records(bytes_: bytes) -> pd.DataFrame:\n b = BytesIO(bytes_)\n rec = np.load(b)\n df = pd.DataFrame.from_records(rec, index=\"index\")\n return df","title":""},{"docid":"37cd2858b66e61ee01e91fd718ea96d2","score":"0.6058689","text":"def df_from_bytes_parquet(bytes_: bytes) -> pd.DataFrame:\n try:\n b = BytesIO(bytes_)\n table = parquet.read_table(b)\n df = table.to_pandas()\n except pyarrow.lib.ArrowIOError:\n df = df_from_bytes_msgpack(bytes_)\n return df","title":""},{"docid":"93bd50c27a4ef072bfc89bb62c34bfc0","score":"0.60527617","text":"def df_from_bytes_json(bytes_: bytes) -> pd.DataFrame:\n return pd.read_json(bytes_.decode())","title":""},{"docid":"e0f6b232245b3a6789b94a61f2ddb24c","score":"0.5961877","text":"def df_from_bytes_msgpack(bytes_: bytes) -> pd.DataFrame:\n try:\n df = pd.read_msgpack(BytesIO(bytes_))\n except UnicodeDecodeError:\n raise DataFrameLoadException(\"Not a msgpack DataFrame\")\n if not isinstance(df, pd.DataFrame):\n raise DataFrameLoadException(\"Not a msgpack DataFrame\")\n return df","title":""},{"docid":"ecf9097b2a108a7c339c1a031cc6dca3","score":"0.5924327","text":"def sql_to_pandas(database, query):\n #logger.debug('Getting data from sql using: ' + query)\n\n engine = create_mssql_connection(database)\n \n cnxn = engine.connect().connection\n\n new_query = limit_query(query,1000000)\n \n ds = pd.read_sql(new_query, cnxn)\n cnxn.invalidate()\n cnxn.close()\n engine.dispose()\n return ds","title":""},{"docid":"df64b118213875e5e5d4297d2e1be28e","score":"0.5895524","text":"def getdb(strproto, query):\n conx = create_engine(strproto)\n dataset = pd.read_sql_query(query, conx)\n return dataset","title":""},{"docid":"2f9009abaa38fb05ac448fd0d3f4e070","score":"0.5893495","text":"def as_dataframe(self, query):\n try:\n return pd.read_sql_query(query, self.connection)\n except apsw.ExecutionCompleteError:\n return None","title":""},{"docid":"1a036dace609278d4d7be58ecf05f1c4","score":"0.5892258","text":"def df_from_sql(query: str, url: str) -> pd.DataFrame:\n return pd.read_sql(query, url)","title":""},{"docid":"857c4dd811de4c0eea07d2d499f865fb","score":"0.5885995","text":"def b64_to_df(b64_string):\n\n return pd.read_msgpack(io.BytesIO(base64.b64decode(b64_string)))","title":""},{"docid":"2c603a39f35681c80ae3c55b090c6af7","score":"0.5868778","text":"def df_from_bytes(bytes_: bytes) -> pd.DataFrame:\n if pyarrow is None:\n try:\n return df_from_bytes_csv(bytes_)\n except DataFrameLoadException:\n raise DataFrameLoadException(\n \"Not a csv DataFrame. An installation of pyarrow \"\n \"may be required, e.g. via `pip install pyabc[pyarrow]`\"\n )\n return df_from_bytes_parquet(bytes_)","title":""},{"docid":"2f2dbbf42a1b90ed79e9b9a2942cf778","score":"0.5858165","text":"def to_dataframe(self, split: str, max_rows: int = 100) -> pd.DataFrame:\n\n self._validate_payload()\n\n if max_rows and max_rows < 0:\n raise ValueError('`max_rows` must not be negative. Got: %d' % max_rows)\n filepaths = gfile.glob(os.path.join(self.uri, split, '*'))\n ds = tf.data.TFRecordDataset(filepaths, compression_type='GZIP').take(\n max_rows)\n return self._load_table(ds)","title":""},{"docid":"f6155b74f28e4e001402b618814584d5","score":"0.5853318","text":"def rpy2_to_pandas(rdf, index_col=None):\n intermediate = 'rdf.txt'\n rdf = r['as.data.frame'](rdf)\n rdf.to_csvfile(path=intermediate, sep='\\t', row_names=False)\n df = pandas.read_table(intermediate, index_col=index_col)\n os.unlink(intermediate)\n return df","title":""},{"docid":"8212556acc31d921fb7b1db55fef624b","score":"0.5776487","text":"def query_response_to_dataframe(query_response):\n rows = [tuple(map(lambda field: field['v'], row['f']))\n for row in query_response['rows']]\n\n # Now create dtype list of tuples for np.array\n dtypes = []\n # save timestamp columns to convert in DF to datetime64 once complete\n tstamp_cols = []\n for field in query_response['schema']['fields']:\n dtype_tuple = (str(field['name']),\n BQ_NP_TYPE_MAPPING[field['type']])\n dtypes.append(dtype_tuple)\n if field['type'] == 'TIMESTAMP':\n tstamp_cols.append(field['name'])\n\n arr = np.array(rows, dtype=dtypes)\n df = pd.DataFrame(arr)\n\n # now go back over dataframe and convert temporary float columns\n # into datetime64's.\n for col in tstamp_cols:\n df[col] = df[col].astype('datetime64[s]')\n return df","title":""},{"docid":"862b82eea56bb799caebb8b236048d7a","score":"0.5743509","text":"def query_to_df(query: str, db_name: str = \"default\") -> pd.DataFrame:\n log.debug(f\"Getting results of query to df: {query}\")\n return pd.read_sql_query(sql=query, con=_make_engine(db_name))","title":""},{"docid":"e02a1df6d829151b26d4ece2c96dc3bd","score":"0.5741575","text":"def dataframe(self, context=None):\n import pandas.io.sql as sqlio\n import pandas as pd\n\n self.construct()\n self.context(context)\n constructed_sql = self.parser.sql\n sql = self.parser.mogrify(constructed_sql, self.parser._context)\n df = pd.read_sql(sql, self.parser.connection)\n df.columns = self.parser.column_headers\n return df","title":""},{"docid":"ef08e5a240f3611be1d0e05f292b2da2","score":"0.5725743","text":"def query_table(\n query: str,\n uri: str,\n debug: bool = False\n) -> pd.DataFrame:\n\n if debug:\n print('-' * 40)\n print(f'## QUERYING via Pandas on {uri}')\n print(query)\n\n engine = sqlalchemy.create_engine(uri)\n\n df = pd.read_sql(query, engine)\n\n engine.dispose()\n\n return df","title":""},{"docid":"9349428ae8cd1ce6e7a8b152ff280f3d","score":"0.5711633","text":"def to_pandas_dataframe(self):","title":""},{"docid":"08c371cd8c104d0bd9d691e91769665f","score":"0.5699115","text":"def cvt_db_to_DataFrames(path_to_db):\n data = load_db(path_to_db)\n return cvt_to_DataFrames(data)","title":""},{"docid":"3bac56881c8c58c6fc1bf7681fe81a11","score":"0.56983453","text":"def java_data_fd(original_df):\n # type: (IO[bytes]) -> IO[bytes]\n # Read the first bytes\n start_idx = original_df.tell()\n magic_header = [byte_to_int(x) for x in original_df.read(2)] # type: ignore\n original_df.seek(start_idx, os.SEEK_SET)\n\n if magic_header[0] == 0xAC:\n # Consider we have a raw seralized stream: use it\n original_df.seek(start_idx, os.SEEK_SET)\n return original_df\n elif magic_header[0] == 0x1F and magic_header[1] == 0x8B:\n # Open the GZip file\n return gzip.GzipFile(fileobj=original_df, mode=\"rb\") # type: ignore\n else:\n # Let the parser raise the error\n return original_df","title":""},{"docid":"7454a65dd9fad331e68df799c672e3bd","score":"0.56982136","text":"def __query_read(self, sql:str) -> pd.DataFrame:\n # load data\n con = self.__get_db()\n df = pd.read_sql(sql, con)\n self.__close_db()\n\n return df","title":""},{"docid":"63a8558a1f21ecc827c09f8878826b72","score":"0.5664379","text":"def process_data(self, stream, metadata):\n ch, metadata = self._get_column_headers_and_update_metadata(stream, metadata)\n df = self._convert_data_block_and_headers_to_df(stream, ch)\n return df, metadata","title":""},{"docid":"1ebbd9ec20a454fdee81e840aa4f7288","score":"0.56584823","text":"def load_RDF_to_dataframe(path_or_fileobject, debug = False):\n\n data_list = load_RDF_to_list(path_or_fileobject, debug)\n\n\n if debug:\n start_time = datetime.datetime.now()\n\n data = pandas.DataFrame(data_list, columns = [\"ID\", \"KEY\", \"VALUE\", \"INSTANCE_ID\"])\n\n if debug:\n _, start_time = print_duration(\"List of data loaded to DataFrame\", start_time)\n\n return data","title":""},{"docid":"369a66300af2c84a1fd7310d2e67b360","score":"0.5652436","text":"def load_data_raw_sql(self, query):\n\n # Transform the string into an actual sql query\n query = pd.read_sql_query(query, self.db.connection())\n\n return pd.DataFrame(query)","title":""},{"docid":"9cefa7239f8fe3b2eb39618ec29ec8e4","score":"0.5631363","text":"def read_query(connection, query):\n cursor = connection.cursor()\n result = None\n try:\n cursor.execute(query)\n result = cursor.fetchall()\n # Turn into df\n return result\n except Error as err:\n print(f\"Error: '{err}'\")","title":""},{"docid":"f92a8e2c5810f6010d92d5b8c209e913","score":"0.5614448","text":"def to_dataframe(self):\n raise NotImplementedError","title":""},{"docid":"2ee38edf34306a450db6bbfa44c56afa","score":"0.55852544","text":"def to_data_frame(self):\n import pandas\n return pandas.DataFrame(self.to_dict())","title":""},{"docid":"627c561d0d9c8ec74b114d305cc516b8","score":"0.55709565","text":"def to_dataframe(self, size=None):\n if size is None and self.cursor._query_result_format == \"arrow\":\n return self.cursor.fetch_pandas_all()\n else:\n return super(Snowflake, self).to_dataframe(size)","title":""},{"docid":"361e12d4eca6205a847c128cef0be53e","score":"0.5567572","text":"def as_dataframe(self):\n return pd.read_csv(self._collate_summary(), sep=\"\\t\", header=0)","title":""},{"docid":"6f98970cebba91a899fc5ec4483d8aed","score":"0.5554936","text":"def decode(byte_stream: bytes):\n\n newQuery = Query()\n newQuery.name = byte_stream[1:].decode('utf-8')\n newQuery.which_fields = int.from_bytes(\n byte_stream[:1], byteorder='big')\n return newQuery","title":""},{"docid":"a600f77d4cf39d60ec334800ccfa51d9","score":"0.55484307","text":"def load(source):\n import json\n import gopen\n with gopen.readable(source) as fp:\n dd = json.load(fp)\n index = dd['index']\n columns = dd['columns']\n columns.sort()\n columns = ['id'] + columns[1:]\n df = SequenceDataFrame(([identifier] + list(sequence)\n for identifier, sequence in dd['records']),\n index=index.sort(),\n columns=columns,\n alphabet=dd['alphabet'])\n # sort rows/columns by index and reset column labels\n return df","title":""},{"docid":"0e143b27848b09c1275e6793e5da5d8b","score":"0.55102","text":"def query2df(queryContent, filterSynapseFields=True, savedSynapseFields = ('id', 'name')):\n import pandas as pd\n removedProperties = set(SYNAPSE_PROPERTIES) - set(savedSynapseFields)\n try:\n queryContent = queryContent['results']\n except TypeError: #If 'results' not present it is a geneartor\n queryContent = list(queryContent)\n for row in queryContent:\n for key in row.keys():\n prefix, new_key = key.split('.', 1)\n item = row.pop(key)\n row[new_key] = item[0] if type(item) is list else item\n if filterSynapseFields and new_key in removedProperties:\n del row[new_key]\n return pd.DataFrame(queryContent)","title":""},{"docid":"a575adbddd98b0a9424a2458cc668c98","score":"0.5509608","text":"def get_dataframe(db, cursor):\n print(list(cursor))\n return pd.DataFrame(list(cursor))","title":""},{"docid":"acf83ab2455dcad4d48d7b09768174be","score":"0.5506226","text":"def build_record_df(dbase, record_style):\n df = []\n for record in dbase.iget_records(style=record_style):\n df.append(record.todict(full=False))\n return pd.DataFrame(df)","title":""},{"docid":"a6212031e1228b534669562d38d5376a","score":"0.5503826","text":"def get_dataframe():\r\n jsonified_df = redis_instance.hget(\r\n tasks.REDIS_HASH_NAME, tasks.REDIS_KEYS[\"DATASETUP\"]\r\n ).decode(\"utf-8\")\r\n # df = pd.DataFrame(json.loads(jsonified_df))\r\n return json.loads(jsonified_df)","title":""},{"docid":"e107bf62efff2f7119b5c0d8d7d7efe0","score":"0.5481719","text":"def to_pandas(self):\n dataframe = self.get()\n assert type(dataframe) is pandas.DataFrame or type(dataframe) is pandas.Series\n return dataframe","title":""},{"docid":"5b74a7e8a2151ed1d0a8b5d8009c2d4e","score":"0.54766506","text":"def _parse_raw(df):\r\n return df","title":""},{"docid":"4a7595f0e87c3c7e5090f6417e27b64c","score":"0.5468235","text":"def get_data(_connector, database) -> pd.DataFrame:\n query = f\"SELECT * FROM {database}.INFORMATION_SCHEMA.TABLES;\"\n return pd.read_sql(query, _connector)","title":""},{"docid":"4bad4c915e1d8e67c0c28a7e473a6a63","score":"0.5463642","text":"def from_dlpack(pycapsule_obj):\n\n res = libdlpack.from_dlpack(pycapsule_obj)\n\n if res._num_columns == 1:\n return Series(res._data[0])\n else:\n return DataFrame(data=res._data)","title":""},{"docid":"a30fa186541d205780eb2c1d4014f37b","score":"0.5460425","text":"def read_data_delta(spark: SparkSession) -> DataFrame:\n\n return None","title":""},{"docid":"f1e5702abb7da2771b87a5619aaf635c","score":"0.5450121","text":"def read_sql(self, code: str) -> pandas.DataFrame:\n if self.kind != SessionKind.SQL:\n raise ValueError(\"not a SQL session\")\n output = self._execute(code)\n output.raise_for_status()\n if output.json is None:\n raise RuntimeError(\"statement had no JSON output\")\n return dataframe_from_json_output(output.json)","title":""},{"docid":"94709adad16067866ff9a729840d305f","score":"0.544312","text":"def get_df_from_query() -> pd.DataFrame:\n username, password = get_credentials()\n print('Connecting to Redshift...')\n con = connect(host='analytics-bi.cdlqehqrgcus.us-east-1.redshift.amazonaws.com',\n dbname='hmlredshift', port=5439, user=username, password=password)\n print('Querying...')\n query: str = \"\"\"\n SELECT\n subscription_id,\n CAST(rec_liq_novo AS REAL),\n categoria_detalhe\n FROM\n sales.tb_rep_receita\n WHERE\n subscription_id IS NOT NULL\n AND CAST(rec_liq_novo AS REAL) > 0.0\n AND ult_dia_mes BETWEEN '2017-07-01' AND '2017-12-31';\n \"\"\"\n df: pd.DataFrame = pd.read_sql(query, con=con)\n print('Closing connection...')\n con.close()\n return df","title":""},{"docid":"b6be8f40bd53cd336efdc5659b0bd648","score":"0.5442066","text":"def open_gzip_read_tsv(bytes_io):\n try:\n with gzip.open(bytes_io, 'rt') as read_file:\n df = pd.read_csv(read_file, sep='\\t', names=['date', 'time', 'user_id', 'url', 'ip', 'user_agent_string'],\n low_memory=False)\n return df\n except IOError as e:\n print(\"Error reading file: {}\".format(e))","title":""},{"docid":"0c43986ce028e83bbac48f8c262e6f69","score":"0.5439498","text":"def _construct_df(self, mol2_lines, col_names, col_types):\r\n return self._atomsection_to_pandas(self._get_atomsection(mol2_lines),\r\n col_names=col_names,\r\n col_types=col_types)","title":""},{"docid":"ddadba232d1533999acfb4668424a467","score":"0.54248106","text":"def to_data_frame(self, index=None, columns=None, dtype=None):\n from py2neo.integration.pandas import cursor_to_data_frame\n return cursor_to_data_frame(self, index, columns, dtype)","title":""},{"docid":"fe2fd761278b8cb5967f004cff0e324f","score":"0.5417649","text":"def load_dataframe(pair, days_history=30, exchange='Bitfinex'):\n # Convert to Pandas dataframe with datetime format\n data = get_past(pair, days_history, exchange)\n df = pd.DataFrame(data)\n df['date'] = pd.to_datetime(df['time'], unit='s')\n df['volume'] = df['volumefrom'] # TODO mb improve\n df = df.set_index(['date'])\n\n return df","title":""},{"docid":"33df377564ec4fde81c3bc92ce951bb1","score":"0.53854334","text":"def to_pandas(self):\n dataframe = self.get()\n assert type(dataframe) is pandas.DataFrame or type(dataframe) is pandas.Series\n\n return dataframe","title":""},{"docid":"a265fc8b81dc4fdf32ba0ebf8025b0b9","score":"0.53836834","text":"def to_pandas(\n self, connection: SnowflakeConnection | None = None, **kwargs\n ) -> DataFrame:\n self._check_can_use_pandas()\n table = self.to_arrow(connection=connection)\n return table.to_pandas(**kwargs)","title":""},{"docid":"fc2391f73452b3a725288614b5b76561","score":"0.5361266","text":"def decode_from_stream(self, stream, nested):\n raise NotImplementedError","title":""},{"docid":"8f787076391ee01d7c63adefeba0c8e4","score":"0.53590393","text":"def sql_to_dataframe(sql_file, connection):\n f = open(sql_file, 'r')\n sql = f.read()\n f.close()\n\n curs = connection.cursor()\n curs.execute(sql)\n results = curs.fetchall()\n df = pd.DataFrame(results)\n df.columns = [column[0] for column in curs.description]\n curs.close()\n return df","title":""},{"docid":"e245e75fc6c8d1284286c63085466150","score":"0.5355277","text":"def _get_single_df(\n stream: IO[bytes] | IO[str], filetype: TypeEnum | None, **kwargs: Any\n ) -> pd.DataFrame | Iterable[pd.DataFrame]:\n if filetype is None:\n filetype = TypeEnum(detect_type(stream.name))\n allowed_params = get_reader_allowed_params(filetype)\n\n # Check encoding\n encoding = kwargs.get(\"encoding\")\n if \"encoding\" in allowed_params:\n if not validate_encoding(stream.name, encoding):\n encoding = detect_encoding(stream.name)\n kwargs[\"encoding\"] = encoding\n\n # Check separator for CSV files if it's not set\n if \"sep\" in allowed_params and \"sep\" not in kwargs:\n if not validate_sep(stream.name, encoding=encoding):\n kwargs[\"sep\"] = detect_sep(stream.name, encoding)\n\n try:\n df = pd_read(stream.name, filetype, kwargs)\n finally:\n stream.close()\n\n return df","title":""},{"docid":"9b3eb4c648985902f7cb4690a7054624","score":"0.5352881","text":"def get_data_from_sql(db_name, query ):\n df = pd.read_sql(query, get_connection(db_name))\n return df","title":""},{"docid":"edc0ac58cee3087827322822cd82c94b","score":"0.5340269","text":"def getDataFrame(self):\n return dataTableToDataFrame(self.getDataTable())","title":""},{"docid":"7bc3abdfbe719466b9aa506cd17e4e59","score":"0.53367525","text":"def convert_raw_data_to_df(raw_data):\n\n # read CSV file\n raw_data = root_path + '/data/' + raw_data\n source = pd.read_csv(raw_data, header=0, skipinitialspace=True)\n source_data = pd.DataFrame(source)\n\n # return source data\n return source_data","title":""},{"docid":"1a1b4e9e13862ff2ae41b0a034037677","score":"0.5329766","text":"def string_to_df(string):\n try:\n data_frame = pd.read_pickle(BytesIO(base64.b64decode(string)))\n except ValueError:\n data_frame = pickle.load(\n BytesIO(base64.b64decode(string)),\n encoding='latin1')\n except Exception:\n return None\n\n return data_frame","title":""},{"docid":"35e5e5c0689ec2c75977f468d0f7a8f5","score":"0.5322118","text":"def df_from_bytes_csv(bytes_: bytes) -> pd.DataFrame:\n try:\n s = StringIO(bytes_.decode())\n s.seek(0)\n return pd.read_csv(\n s,\n index_col=0,\n header=0,\n float_precision=\"round_trip\",\n quotechar='\"',\n )\n except UnicodeDecodeError:\n raise DataFrameLoadException(\"Not a csv DataFrame\")","title":""},{"docid":"7633e84bf1962e6c8d5f93ef51bed53f","score":"0.5311086","text":"def to_dataframe(self,data):\n return self.pandas.DataFrame(data).set_index('token')","title":""},{"docid":"7e8ab2f37b8971494365b9cc9fdd07db","score":"0.53056073","text":"def to_df(response, batch=False):\n \n if batch:\n records = []\n for tup in to_df(response).itertuples():\n if tup[3] != None:\n for mappings in tup[3].split(','):\n target = mappings.split(':', 1)\n if len(target) > 1:\n records.append((tup[1], tup[2], target[1], target[0]))\n else:\n records.append((tup[1], tup[2], target[0], target[0]))\n return pd.DataFrame(records, columns = ['original', 'source', 'mapping', 'target'])\n \n return pd.DataFrame([line.split('\\t') for line in response.text.split('\\n')])","title":""},{"docid":"4ad17e6dc23510bbf77c98ac59e8b0f6","score":"0.53007203","text":"def load_from_server(self, sql):\n try:\n connection = pyhdb.connect(\n host=config.db_address,\n port=config.db_port,\n user=config.db_user,\n password=config.db_password,\n )\n with open(sql, \"r\") as f:\n query = f.read()\n cursor = connection.cursor()\n cursor = cursor.execute(query)\n data = pd.DataFrame(cursor.fetchall())\n except Exception as e:\n print(\n \"***Error connecting to DB. Check your environment variables.\\n\",\n repr(e),\n )\n raise e\n return data","title":""},{"docid":"2935f82caf9d68f4b55f732524e7eb5c","score":"0.5291846","text":"def sparse_dataframe_from_records(records):\n reformatted = []\n for (a0, a1, bt, length), count in records:\n reformatted.append((a0, a1, bt, length, count))\n df = pd.DataFrame.from_records(\n reformatted,\n columns=[\n 'atom_char_0', 'atom_char_1', 'bond_type', 'length_str', 'count'\n ])\n return df.sort_values(\n ['atom_char_0', 'atom_char_1', 'bond_type', 'length_str'])","title":""},{"docid":"73adc210918b2044128e32b926df6956","score":"0.5279534","text":"def toDataFrame(input: SerializableType):\n params = toParamDict(input, includeMeta=['unit', 'vals'])\n return pd.DataFrame(params).T.sort_index()","title":""},{"docid":"9be9dfcbddb205211d425a19b2c6886f","score":"0.5259061","text":"def to_dataframe(self):\n return pd.DataFrame.from_dict(self._data, orient='index')","title":""},{"docid":"e9e099cf95cb490297ba4dd7d0ea093f","score":"0.52468336","text":"def load_pandas():\n data = _get_data()\n return du.process_pandas(data, endog_idx=0)","title":""},{"docid":"e9e099cf95cb490297ba4dd7d0ea093f","score":"0.52468336","text":"def load_pandas():\n data = _get_data()\n return du.process_pandas(data, endog_idx=0)","title":""},{"docid":"a2788e90682847e4a7612d685c62c91f","score":"0.5246668","text":"def sql2df(self, sql_str, columns=None):\n if isinstance(sql_str, sql.Composed):\n sql_str = sql_str.as_string(self.cursor)\n if isinstance(columns, str):\n columns = [columns]\n\n df = pd.read_sql(sql=sql_str, con=self.get_engine().connect(),\n columns=columns)\n\n return df","title":""},{"docid":"55a49aaaddcc367d123f1b2ee1c1da48","score":"0.5245327","text":"def query(self, sql: str):\n return self.execute(lambda tp: tp.query(sql).as_pandas_dataframe())","title":""},{"docid":"540cec72c98cd3506e907e2beb5cc96b","score":"0.5223218","text":"def query_indb_df(auth, query,\n database=None,\n chunked=False,\n sensor_id='sensor_id'):\n\n data = query_indb(auth, query, database=database, chunked=chunked)\n return response_to_df(data, sensor_id=sensor_id)","title":""},{"docid":"d6d833621b7a5e78b00cb87e2e66dad5","score":"0.51981354","text":"def test_a_pd2table(self):\r\n self.sql_api.pd2table(self.df, \"pd2table\")","title":""},{"docid":"b96f5031a03d815d4957b174475f7620","score":"0.51917964","text":"def decode_from_stream(self, stream, nested):\n return self.decode(stream.read_all(nested))","title":""},{"docid":"fe11a482b89ad78bf7f860fd07197db2","score":"0.5183238","text":"def save_cursor_result_to_df(cursor: pyodbc.Cursor) -> pd.DataFrame:\n descriptions = cursor.description\n col_names = [desc[0] for desc in descriptions]\n df = pd.DataFrame.from_records(cursor.fetchall(), columns=col_names)\n LOG.debug('saved cursor to DataFrame')\n return df","title":""},{"docid":"ab9d64db7a5483f7e685d5055b53a18a","score":"0.51749444","text":"def as_dataframe(self):\n return self._data","title":""},{"docid":"bfe244328dba554ee23bb8701cd52046","score":"0.51724964","text":"def get_pandas_df(self, sql):\n conn = self.get_conn(self.snowflake_conn_id)\n df = pd.read_sql_query(sql, conn)\n return df","title":""},{"docid":"77351b7d5608251850e5dcf07618b5e9","score":"0.5166094","text":"def _convert_to_dataframe(content):\n\n try:\n dfs = [pd.DataFrame(c) for c in content]\n except ValueError:\n dfs = [pd.DataFrame(c, index=[i]) for i, c in enumerate(content)]\n\n return pd.concat(dfs)","title":""},{"docid":"1d98dfcb840cab0dfbf45cbc2a4fc662","score":"0.516002","text":"def hexcountsresults_to_df(db, save=False):\n\n cursor=db.hexcounts.find()\n #.limit(10)\n\n prueba=pd.DataFrame(list(cursor) )\n\n df1=pd.DataFrame(pd.concat([prueba[['_id']],json_normalize(prueba[\"nonresidents\"])],axis=1 ).set_index('_id').stack()).reset_index().rename(columns={0:'nonresidents'})\n df2=pd.DataFrame(pd.concat([prueba[['_id']],json_normalize(prueba[\"nonresidentsandnonneighbors\"])],axis=1 ).set_index('_id').stack()).reset_index().rename(columns={0:'nonresidentsandnonneighbors'})\n df3=pd.DataFrame(pd.concat([prueba[['_id']],json_normalize(prueba[\"residents\"])],axis=1 ).set_index('_id').stack()).reset_index().rename(columns={0:'residents'})\n df4=pd.DataFrame(pd.concat([prueba[['_id']],json_normalize(prueba[\"totalcounts\"])],axis=1 ).set_index('_id').stack()).reset_index().rename(columns={0:'totalcounts'})\n\n\n df1=df1.merge(df2,on=['_id','level_1'],how='outer')\n df1=df1.merge(df3,on=['_id','level_1'],how='outer')\n df1=df1.merge(df4,on=['_id','level_1'],how='outer')\n\n\n if df1['level_1'].dtype == '<M8[ns]': # if data is already timestamped then just copy\n df1['time'] =df1['level_1']\n else:\n df1['time']=pd.to_datetime(pd.to_numeric(df1['level_1'], errors='coerce') // 1000, unit='s' )\n\n df1.drop(columns='level_1')\n\n if save:\n df1.to_pickle(\"./hexcountsdf.pkl\")\n\n return df1","title":""},{"docid":"e118044b197b519ca27bcd7ff997d3bd","score":"0.5149264","text":"def extract(self, config: dict) -> DataFrame:\r\n path = config['source_path']\r\n read_options = config.get('read_options')\r\n schema = config.get('schema', dict())\r\n\r\n _read_options = read_options.copy()\r\n\r\n if schema:\r\n # StructType.fromJson method looks for 'fields' key ..\r\n schema_struct = T.StructType.fromJson(schema) \r\n schema_struct.add(T.StructField('iptmeta_corrupt_record', T.StringType(), True))\r\n _read_options['schema'] = schema_struct\r\n else:\r\n _read_options['inferSchema'] = 'true'\r\n\r\n df = self.get_spark().read.csv(path, **_read_options)\r\n #df.cache()\r\n\r\n return df","title":""},{"docid":"3960f394bbf730642d16667f93d5da06","score":"0.51449734","text":"def query_and_flatten(self, sql: str):\n return self.execute_and_flatten(lambda tp: tp.query(sql).\n as_pandas_dataframe())","title":""},{"docid":"aba785cfe29ccb6d46a5a5d190f0068d","score":"0.51396745","text":"def to_df(value: Any) -> pd.DataFrame:\n if value is None:\n # This return the empty dataframe, which atm is the same as\n # the empty file object in the CAS.\n # However this is not ensured as pyarrow changes\n return pd.DataFrame()\n\n if isinstance(value, pd.DataFrame):\n return value.copy(deep=True)\n\n if isinstance(value, (pd.Series, pd.Index)):\n if value.name is not None:\n return pd.DataFrame(value)\n\n return pd.DataFrame({\"Result\": value})\n\n if isinstance(value, (Number, str, bool, datetime.datetime, datetime.timedelta)):\n return pd.DataFrame({\"Result\": value}, index=[0])\n\n if isinstance(value, np.ndarray):\n try:\n out_df = pd.DataFrame(value)\n except ValueError:\n squeezed = np.squeeze(value)\n if squeezed.shape == ():\n # must be a scalar\n out_df = pd.DataFrame({\"Result\": squeezed}, index=[0])\n else:\n out_df = pd.DataFrame(squeezed)\n\n if out_df.columns.tolist() == [0]:\n out_df.columns = [\"Result\"]\n\n return out_df\n\n raise ValueError(\"Must return a primitive, pd.DataFrame, pd.Series or numpy array.\")","title":""},{"docid":"f7943af4068684f0b2383df5bebb9d68","score":"0.51285917","text":"def read_forex_table_from_db(table):\r\n database_path = 'sqlite:///data/data.db'\r\n engine = create_engine(database_path, echo=False)\r\n data = pd.read_sql_table(table, database_path)\r\n engine.dispose()\r\n data.set_index(keys=\"Date\", drop=True, inplace=True)\r\n return data","title":""},{"docid":"211a4efd318ba6824571f62aff334e10","score":"0.5123826","text":"def read_exchanges():\n # <COMPLETE THIS PART>\n data = from_json(EXCHANGES)['result']\n df = pd.DataFrame(data)\n df.set_index('id', inplace=True)\n cols = ['active', 'name', 'route', 'symbol']\n return df[cols]","title":""},{"docid":"1d128be9fe7a96b16630c9c1c76b4857","score":"0.51054394","text":"def to_dataframe(cls) -> pd.DataFrame:\n return NotImplemented","title":""},{"docid":"af3ce112875d30471d48c6fd9b461416","score":"0.51015055","text":"def data_import(path):\n\n\tdbf = ps.open(path)\n\td = dict([(col,np.array(dbf.by_col(col))) for col in dbf.header])\n\tdf = pd.DataFrame(d)\n\treturn df","title":""},{"docid":"eaea448fa58ea6d5e78c015d4adf5624","score":"0.5079912","text":"def decode_dataframe(data_frame: DataFrame) -> Datatable:\n columns = []\n\n for column_name in data_frame.columns:\n column = data_frame[column_name]\n values = column.tolist()\n if column.dtype in (numpy.bool_, numpy.bool8):\n schema = BooleanSchema()\n values = [bool(row) for row in values]\n elif column.dtype in (numpy.int8, numpy.int16, numpy.int32, numpy.int64):\n schema = IntegerSchema()\n values = [int(row) for row in values]\n elif column.dtype in (numpy.float16, numpy.float32, numpy.float64):\n schema = NumberSchema()\n values = [float(row) for row in values]\n elif column.dtype in (numpy.str_, numpy.unicode_,):\n schema = StringSchema()\n else:\n schema = None\n\n columns.append(\n DatatableColumn(column_name, values, schema=ArraySchema(items=schema))\n )\n\n return Datatable(columns)","title":""},{"docid":"19b498aca3d691c6684d60216670841e","score":"0.5059325","text":"def sample_data_stream(spark, df, timeout=10):\n query = df.writeStream.outputMode(\"append\") \\\n .queryName(\"tmp\").format(\"memory\") \\\n .start()\n query.awaitTermination(timeout)\n query.stop()\n return spark.sql(\"select * from tmp\").toPandas()","title":""},{"docid":"1188ed67f79411681c15f469dd84d515","score":"0.50578386","text":"def test_DataFrame(self):\n url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\"\n self.assertEquals(type(url_to_df(url)),type(pd.DataFrame([{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}])))","title":""},{"docid":"22c7e4e70969eb8e43c46324b76c9e77","score":"0.50519264","text":"def _dataframe_from_txt(reader):\n return pd.read_csv(reader, header=None, sep=\"\\n\", encoding='utf-8-sig')","title":""},{"docid":"0f7b6fb15c99b78996f8660ce6b2e9d4","score":"0.50459737","text":"def read(self, dataframe_name: str) -> pandas.DataFrame:\n code = serialise_dataframe_code(dataframe_name, self.kind)\n output = self._execute(code)\n output.raise_for_status()\n if output.text is None:\n raise RuntimeError(\"statement had no text output\")\n return deserialise_dataframe(output.text)","title":""},{"docid":"8c80fa4647dae1f2972ad6a61dc7f3a3","score":"0.5038185","text":"def parse_RequestResult(resp):\n if resp['result']['status'] != 'Complete':\n logger.info(resp['result'])\n return resp\n df = pd.read_csv(io.BytesIO(resp['result']['data']), compression='gzip')\n df.dropna(axis=1, how='all', inplace=True) # Dropping all completely empty columns\n return df","title":""},{"docid":"121ba4ca24d8496560bdb3a12b6953f2","score":"0.5037319","text":"def query(call, connection, creds=None):\n if creds is None:\n creds = read_creds()\n DB = \"DB_NAME\"\n engine = sql.create_engine(DB)\n try:\n df = pd.read_sql_query(call, engine)\n except sql.exc.ResourceClosedError:\n df = pd.DataFrame()\n return df","title":""},{"docid":"9847c1c9c04464533e83fd2a52a967a4","score":"0.50269103","text":"def process_data(self, stream, metadata):\n index = np.arange(metadata[\"firstyear\"], metadata[\"lastyear\"] + 1)\n\n # The first variable is the global values\n globe = stream.read_chunk(\"d\")\n\n assert len(globe) == len(index)\n\n regions = stream.read_chunk(\"d\")\n num_regions = int(len(regions) / len(index))\n regions = regions.reshape((-1, num_regions), order=\"F\")\n\n data = np.concatenate((globe[:, np.newaxis], regions), axis=1)\n\n df = pd.DataFrame(data, index=index)\n\n if isinstance(df.index, pd.core.indexes.numeric.Float64Index):\n df.index = df.index.to_series().round(3)\n\n df.index.name = \"time\"\n\n regions = [\n \"World\",\n \"World|Northern Hemisphere|Ocean\",\n \"World|Northern Hemisphere|Land\",\n \"World|Southern Hemisphere|Ocean\",\n \"World|Southern Hemisphere|Land\",\n ]\n\n variable = convert_magicc6_to_magicc7_variables(\n self._get_variable_from_filepath()\n )\n variable = convert_magicc7_to_openscm_variables(variable)\n column_headers = {\n \"variables\": [variable] * (num_regions + 1),\n \"regions\": regions,\n \"units\": [\"unknown\"] * len(regions),\n \"todos\": [\"SET\"] * len(regions),\n }\n df.columns = self._get_columns_multiindex_from_column_headers(column_headers)\n df = pd.DataFrame(df.T.stack(), columns=[\"value\"]).reset_index()\n df = self._convert_to_categorical_columns(df)\n\n return df, metadata","title":""},{"docid":"fa4140518c4eafc86334bfa2a1e9c18e","score":"0.50243026","text":"def get_sparql_dataframe(service, query):\n sparql = SPARQLWrapper(service)\n sparql.setQuery(query)\n sparql.setReturnFormat(JSON)\n result = sparql.query()\n\n processed_results = jsonasobj.load(result.response)\n cols = processed_results.head.vars\n\n out = []\n for row in processed_results.results.bindings:\n item = []\n for c in cols:\n item.append(row._as_dict.get(c, {}).get('value'))\n out.append(item)\n\n return pd.DataFrame(out, columns=cols)","title":""},{"docid":"6026504e2b23cc111052c8ef46baaa33","score":"0.5017274","text":"def to_h2odf(self):\n try:\n table_data = self.to_df()\n if isinstance(table_data, pd.DataFrame):\n return h2o.H2OFrame(table_data)\n h2o_df_series = pd.Series()\n for table_name in table_data.index:\n current_h2o_df = h2o.H2OFrame(table_data[table_name])\n h2o_df_series[table_name] = current_h2o_df\n return h2o_df_series\n except NameError:\n print('H2O not installed.')\n return None","title":""},{"docid":"418b6ce8f94dbeac788cdb098d137c71","score":"0.50166273","text":"def to_dataFrame(self):\n dssp = []\n start = 0\n for l in self.fl:\n if re.search('#',l):\n start=1\n continue\n\n if start:\n try:\n fields = {'chain':l[10:12].strip(),'seqResNum':int(l[:5].strip()),\\\n 'pdbResNum':int(l[5:10].strip()),'aa':l[12:14].strip(),\\\n 'acc':float(l[34:38].strip()),\\\n 'kappa':float(l[91:97].strip()),'alpha':float(l[97:103].strip()),\\\n 'phi':float(l[103:109].strip()),'psi':float(l[109:115].strip()),\\\n 'catom':(),'NHO_d':[],'NHO_e':[],'OHN_d':[],'OHN_e':[]}\n\n sse_tmp = l[14:17].strip()\n if re.match('[GHITEBS]', sse_tmp):\n fields['sse'] = sse_tmp\n else:\n fields['sse'] = 'C'\n \n # calpha x, y and z\n x = float(l[117:122].strip())\n y = float(l[124:129].strip())\n z = float(l[131:].strip())\n fields['catom'] = (x, y, z)\n\n _h1 = l[40:51].strip().split(\",\")\n _h2 = l[51:62].strip().split(\",\")\n _h3 = l[62:73].strip().split(\",\")\n _h4 = l[73:84].strip().split(\",\")\n\n fields['NHO_d'] = [int(_h1[0]),int(_h3[0])]\n fields['OHN_d'] = [int(_h2[0]),int(_h4[0])]\n fields['NHO_e'] = [float(_h1[1]),float(_h3[1])]\n fields['OHN_e'] = [float(_h2[1]),float(_h4[1])]\n dssp.append(fields)\n except:\n print (\"Warning residue line escaping\", l)\n next\n \n self.dsspDF = pd.DataFrame(dssp)\n assert len(dssp) == self.dsspDF.shape[0]","title":""},{"docid":"24c1426ce589e8b8b0c58645d44cc18c","score":"0.5012296","text":"def read_csv(filepath_or_buffer: Union[str, io.BytesIO], encoding: str = \"utf-8\") -> pd.DataFrame:\n return pd.read_csv(filepath_or_buffer, encoding=encoding)","title":""},{"docid":"18b3246729eea9815c7a7c2b6b0558a2","score":"0.5004827","text":"def read_mysql_database(sql_statement):\n df_table = pd.read_sql(sql_statement, engine)\n return df_table","title":""},{"docid":"d6e9b99c78af92e0deb59042df8e2606","score":"0.50040734","text":"def hexcountsresults_to_df_DEPRECATED(db, save=False):\n\n #para pasar de la coleccion al dataframe\n #voy a loopear la coleccion, convertir cada documento en un dataframe y despues unirlos\n cursor=db.hexcounts.find()\n listofdfis=[]\n\n for doc in cursor:\n dfi=pd.DataFrame(doc).reset_index().rename(columns={\"index\": \"time\"})\n\n if dfi['time'].dtype != '<M8[ns]': # if data is already timestamped then just copy\n dfi['time']=pd.to_datetime(pd.to_numeric(dfi['time'], errors='coerce') // 1000, unit='s' )\n\n listofdfis.append(dfi)\n\n #print(listofdfis)\n df=pd.concat(listofdfis)\n df=df.reset_index(drop=True)\n\n if save:\n df.to_pickle(\"./hexcountsdf.pkl\")\n\n return df","title":""},{"docid":"5daf4aa494779abb81363f4b844b1417","score":"0.5001567","text":"def get_df_sql(sql, connection):\n connection = source_engine.connect()\n df = pd.read_sql_query(sql, connection)\n connection.close()\n return df","title":""},{"docid":"04b597de6c0e5b9fc8197aee915fc40e","score":"0.5001092","text":"def make_df(resp) -> DataFrameType:\n\n def maker(series) -> pd.DataFrame:\n df = pd.DataFrame(series.get('values', []), columns=series['columns'])\n if 'time' not in df.columns:\n return df\n df: pd.DataFrame = df.set_index(pd.to_datetime(df['time'])).drop('time', axis=1)\n df.index = df.index.tz_localize('UTC')\n df.index.name = None\n if 'tags' in series:\n for k, v in series['tags'].items():\n df[k] = v\n if 'name' in series:\n df.name = series['name']\n return df\n\n def drop_zero_index(df):\n if isinstance(df.index, pd.DatetimeIndex):\n if all(i.value == 0 for i in df.index):\n df.reset_index(drop=True, inplace=True)\n\n # Parsing\n df_list = [((series.get('name'), tuple(series.get('tags', {}).items())), maker(series))\n for statement in resp['results'] if 'series' in statement\n for series in statement['series']]\n\n # Concatenation\n d = defaultdict(list)\n for k, df in sorted(df_list, key=lambda x: x[0]):\n d[k].append(df)\n dfs = {k: pd.concat(v, axis=0) for k, v in d.items()}\n\n # Post-processing\n for df in dfs.values():\n drop_zero_index(df)\n\n # Return\n if len(dfs) == 1:\n return dfs[list(dfs.keys())[0]]\n return dfs","title":""},{"docid":"d074d2d73a76b81c66a6b6437d90733a","score":"0.49961397","text":"def load_data(database_path):\n name = 'sqlite:///' + database_path\n engine = create_engine(name)\n \n df = pd.read_sql_table('clean_dataset', con=engine).head(10000)\n X = df['message']\n y = df.drop(['original','genre','message','offer','request'], axis=1)\n y = y.astype(int)\n return X, y","title":""},{"docid":"8e7c6d7f7e7b44c71bc274a31f12ccc4","score":"0.49943295","text":"def read_table_as_df(h5, tpath):\n x = h5.get(tpath)\n if x.dtype.names:\n result = pd.DataFrame(\n [[v[k].astype(str).strip() if v.dtype.fields[k][0].name.startswith(\n 'bytes') else v[k] for k in v.dtype.fields] for v in x],\n columns=[name for name in x.dtype.names]\n )\n result = result.astype(dtype={k: 'unicode' if x.dtype[k].name.startswith(\n 'bytes') else x.dtype[k].name for k in x.dtype.fields})\n else:\n result = pd.DataFrame(\n [v.astype(str).strip() if x.dtype.name.startswith('bytes') else v for v in x])\n result = result.astype(\n dtype='unicode' if x.dtype.name.startswith('bytes') else x.dtype.name)\n result = strip(result)\n return result","title":""},{"docid":"e0857d307750355bacf3f10b747bf737","score":"0.49931386","text":"def read_stock_table_from_db(table):\r\n database_path = 'sqlite:///data/data.db'\r\n engine = create_engine(database_path, echo=False)\r\n data = pd.read_sql_table(table, database_path)\r\n engine.dispose()\r\n data.set_index(keys=\"Date\", drop=True, inplace=True)\r\n return data","title":""},{"docid":"cdc4ffbfa24b54fd59fc368b2fd4aaae","score":"0.4992499","text":"def execfetchDf(self, cmd):\n import pandas as pd\n try:\n return pd.read_sql_query(cmd, self.connection)\n except AttributeError: # probably an older version of pandas\n assert not getattr(pd, 'read_sql_table', None)\n tmpcur = self.connection.cursor(cursor_factory=psycopg2.extras.DictCursor)\n tmpcur.execute(cmd)\n colNames = [desc[0] for desc in tmpcur.description]\n results = tmpcur.fetchall()\n return pd.DataFrame([[row[col] for col in colNames] for row in results], columns=colNames)","title":""},{"docid":"9c8dbbc7c023bb6bebe0d8b6d3ae6fcf","score":"0.49861366","text":"def getDataFrame(path:str):\n return pandas.read_csv(path, encoding = \"ISO-8859-1\")","title":""}],"string":"[\n {\n \"docid\": \"f9956bf1a9ca018b1310380d6db7118c\",\n \"score\": \"0.628087\",\n \"text\": \"def df_from_bytes_np_records(bytes_: bytes) -> pd.DataFrame:\\n b = BytesIO(bytes_)\\n rec = np.load(b)\\n df = pd.DataFrame.from_records(rec, index=\\\"index\\\")\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37cd2858b66e61ee01e91fd718ea96d2\",\n \"score\": \"0.6058689\",\n \"text\": \"def df_from_bytes_parquet(bytes_: bytes) -> pd.DataFrame:\\n try:\\n b = BytesIO(bytes_)\\n table = parquet.read_table(b)\\n df = table.to_pandas()\\n except pyarrow.lib.ArrowIOError:\\n df = df_from_bytes_msgpack(bytes_)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93bd50c27a4ef072bfc89bb62c34bfc0\",\n \"score\": \"0.60527617\",\n \"text\": \"def df_from_bytes_json(bytes_: bytes) -> pd.DataFrame:\\n return pd.read_json(bytes_.decode())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0f6b232245b3a6789b94a61f2ddb24c\",\n \"score\": \"0.5961877\",\n \"text\": \"def df_from_bytes_msgpack(bytes_: bytes) -> pd.DataFrame:\\n try:\\n df = pd.read_msgpack(BytesIO(bytes_))\\n except UnicodeDecodeError:\\n raise DataFrameLoadException(\\\"Not a msgpack DataFrame\\\")\\n if not isinstance(df, pd.DataFrame):\\n raise DataFrameLoadException(\\\"Not a msgpack DataFrame\\\")\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecf9097b2a108a7c339c1a031cc6dca3\",\n \"score\": \"0.5924327\",\n \"text\": \"def sql_to_pandas(database, query):\\n #logger.debug('Getting data from sql using: ' + query)\\n\\n engine = create_mssql_connection(database)\\n \\n cnxn = engine.connect().connection\\n\\n new_query = limit_query(query,1000000)\\n \\n ds = pd.read_sql(new_query, cnxn)\\n cnxn.invalidate()\\n cnxn.close()\\n engine.dispose()\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df64b118213875e5e5d4297d2e1be28e\",\n \"score\": \"0.5895524\",\n \"text\": \"def getdb(strproto, query):\\n conx = create_engine(strproto)\\n dataset = pd.read_sql_query(query, conx)\\n return dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9009abaa38fb05ac448fd0d3f4e070\",\n \"score\": \"0.5893495\",\n \"text\": \"def as_dataframe(self, query):\\n try:\\n return pd.read_sql_query(query, self.connection)\\n except apsw.ExecutionCompleteError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a036dace609278d4d7be58ecf05f1c4\",\n \"score\": \"0.5892258\",\n \"text\": \"def df_from_sql(query: str, url: str) -> pd.DataFrame:\\n return pd.read_sql(query, url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"857c4dd811de4c0eea07d2d499f865fb\",\n \"score\": \"0.5885995\",\n \"text\": \"def b64_to_df(b64_string):\\n\\n return pd.read_msgpack(io.BytesIO(base64.b64decode(b64_string)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c603a39f35681c80ae3c55b090c6af7\",\n \"score\": \"0.5868778\",\n \"text\": \"def df_from_bytes(bytes_: bytes) -> pd.DataFrame:\\n if pyarrow is None:\\n try:\\n return df_from_bytes_csv(bytes_)\\n except DataFrameLoadException:\\n raise DataFrameLoadException(\\n \\\"Not a csv DataFrame. An installation of pyarrow \\\"\\n \\\"may be required, e.g. via `pip install pyabc[pyarrow]`\\\"\\n )\\n return df_from_bytes_parquet(bytes_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f2dbbf42a1b90ed79e9b9a2942cf778\",\n \"score\": \"0.5858165\",\n \"text\": \"def to_dataframe(self, split: str, max_rows: int = 100) -> pd.DataFrame:\\n\\n self._validate_payload()\\n\\n if max_rows and max_rows < 0:\\n raise ValueError('`max_rows` must not be negative. Got: %d' % max_rows)\\n filepaths = gfile.glob(os.path.join(self.uri, split, '*'))\\n ds = tf.data.TFRecordDataset(filepaths, compression_type='GZIP').take(\\n max_rows)\\n return self._load_table(ds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6155b74f28e4e001402b618814584d5\",\n \"score\": \"0.5853318\",\n \"text\": \"def rpy2_to_pandas(rdf, index_col=None):\\n intermediate = 'rdf.txt'\\n rdf = r['as.data.frame'](rdf)\\n rdf.to_csvfile(path=intermediate, sep='\\\\t', row_names=False)\\n df = pandas.read_table(intermediate, index_col=index_col)\\n os.unlink(intermediate)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8212556acc31d921fb7b1db55fef624b\",\n \"score\": \"0.5776487\",\n \"text\": \"def query_response_to_dataframe(query_response):\\n rows = [tuple(map(lambda field: field['v'], row['f']))\\n for row in query_response['rows']]\\n\\n # Now create dtype list of tuples for np.array\\n dtypes = []\\n # save timestamp columns to convert in DF to datetime64 once complete\\n tstamp_cols = []\\n for field in query_response['schema']['fields']:\\n dtype_tuple = (str(field['name']),\\n BQ_NP_TYPE_MAPPING[field['type']])\\n dtypes.append(dtype_tuple)\\n if field['type'] == 'TIMESTAMP':\\n tstamp_cols.append(field['name'])\\n\\n arr = np.array(rows, dtype=dtypes)\\n df = pd.DataFrame(arr)\\n\\n # now go back over dataframe and convert temporary float columns\\n # into datetime64's.\\n for col in tstamp_cols:\\n df[col] = df[col].astype('datetime64[s]')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862b82eea56bb799caebb8b236048d7a\",\n \"score\": \"0.5743509\",\n \"text\": \"def query_to_df(query: str, db_name: str = \\\"default\\\") -> pd.DataFrame:\\n log.debug(f\\\"Getting results of query to df: {query}\\\")\\n return pd.read_sql_query(sql=query, con=_make_engine(db_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02a1df6d829151b26d4ece2c96dc3bd\",\n \"score\": \"0.5741575\",\n \"text\": \"def dataframe(self, context=None):\\n import pandas.io.sql as sqlio\\n import pandas as pd\\n\\n self.construct()\\n self.context(context)\\n constructed_sql = self.parser.sql\\n sql = self.parser.mogrify(constructed_sql, self.parser._context)\\n df = pd.read_sql(sql, self.parser.connection)\\n df.columns = self.parser.column_headers\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef08e5a240f3611be1d0e05f292b2da2\",\n \"score\": \"0.5725743\",\n \"text\": \"def query_table(\\n query: str,\\n uri: str,\\n debug: bool = False\\n) -> pd.DataFrame:\\n\\n if debug:\\n print('-' * 40)\\n print(f'## QUERYING via Pandas on {uri}')\\n print(query)\\n\\n engine = sqlalchemy.create_engine(uri)\\n\\n df = pd.read_sql(query, engine)\\n\\n engine.dispose()\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9349428ae8cd1ce6e7a8b152ff280f3d\",\n \"score\": \"0.5711633\",\n \"text\": \"def to_pandas_dataframe(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08c371cd8c104d0bd9d691e91769665f\",\n \"score\": \"0.5699115\",\n \"text\": \"def cvt_db_to_DataFrames(path_to_db):\\n data = load_db(path_to_db)\\n return cvt_to_DataFrames(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bac56881c8c58c6fc1bf7681fe81a11\",\n \"score\": \"0.56983453\",\n \"text\": \"def java_data_fd(original_df):\\n # type: (IO[bytes]) -> IO[bytes]\\n # Read the first bytes\\n start_idx = original_df.tell()\\n magic_header = [byte_to_int(x) for x in original_df.read(2)] # type: ignore\\n original_df.seek(start_idx, os.SEEK_SET)\\n\\n if magic_header[0] == 0xAC:\\n # Consider we have a raw seralized stream: use it\\n original_df.seek(start_idx, os.SEEK_SET)\\n return original_df\\n elif magic_header[0] == 0x1F and magic_header[1] == 0x8B:\\n # Open the GZip file\\n return gzip.GzipFile(fileobj=original_df, mode=\\\"rb\\\") # type: ignore\\n else:\\n # Let the parser raise the error\\n return original_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7454a65dd9fad331e68df799c672e3bd\",\n \"score\": \"0.56982136\",\n \"text\": \"def __query_read(self, sql:str) -> pd.DataFrame:\\n # load data\\n con = self.__get_db()\\n df = pd.read_sql(sql, con)\\n self.__close_db()\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a8558a1f21ecc827c09f8878826b72\",\n \"score\": \"0.5664379\",\n \"text\": \"def process_data(self, stream, metadata):\\n ch, metadata = self._get_column_headers_and_update_metadata(stream, metadata)\\n df = self._convert_data_block_and_headers_to_df(stream, ch)\\n return df, metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ebbd9ec20a454fdee81e840aa4f7288\",\n \"score\": \"0.56584823\",\n \"text\": \"def load_RDF_to_dataframe(path_or_fileobject, debug = False):\\n\\n data_list = load_RDF_to_list(path_or_fileobject, debug)\\n\\n\\n if debug:\\n start_time = datetime.datetime.now()\\n\\n data = pandas.DataFrame(data_list, columns = [\\\"ID\\\", \\\"KEY\\\", \\\"VALUE\\\", \\\"INSTANCE_ID\\\"])\\n\\n if debug:\\n _, start_time = print_duration(\\\"List of data loaded to DataFrame\\\", start_time)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369a66300af2c84a1fd7310d2e67b360\",\n \"score\": \"0.5652436\",\n \"text\": \"def load_data_raw_sql(self, query):\\n\\n # Transform the string into an actual sql query\\n query = pd.read_sql_query(query, self.db.connection())\\n\\n return pd.DataFrame(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cefa7239f8fe3b2eb39618ec29ec8e4\",\n \"score\": \"0.5631363\",\n \"text\": \"def read_query(connection, query):\\n cursor = connection.cursor()\\n result = None\\n try:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n # Turn into df\\n return result\\n except Error as err:\\n print(f\\\"Error: '{err}'\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f92a8e2c5810f6010d92d5b8c209e913\",\n \"score\": \"0.5614448\",\n \"text\": \"def to_dataframe(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee38edf34306a450db6bbfa44c56afa\",\n \"score\": \"0.55852544\",\n \"text\": \"def to_data_frame(self):\\n import pandas\\n return pandas.DataFrame(self.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"627c561d0d9c8ec74b114d305cc516b8\",\n \"score\": \"0.55709565\",\n \"text\": \"def to_dataframe(self, size=None):\\n if size is None and self.cursor._query_result_format == \\\"arrow\\\":\\n return self.cursor.fetch_pandas_all()\\n else:\\n return super(Snowflake, self).to_dataframe(size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"361e12d4eca6205a847c128cef0be53e\",\n \"score\": \"0.5567572\",\n \"text\": \"def as_dataframe(self):\\n return pd.read_csv(self._collate_summary(), sep=\\\"\\\\t\\\", header=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f98970cebba91a899fc5ec4483d8aed\",\n \"score\": \"0.5554936\",\n \"text\": \"def decode(byte_stream: bytes):\\n\\n newQuery = Query()\\n newQuery.name = byte_stream[1:].decode('utf-8')\\n newQuery.which_fields = int.from_bytes(\\n byte_stream[:1], byteorder='big')\\n return newQuery\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a600f77d4cf39d60ec334800ccfa51d9\",\n \"score\": \"0.55484307\",\n \"text\": \"def load(source):\\n import json\\n import gopen\\n with gopen.readable(source) as fp:\\n dd = json.load(fp)\\n index = dd['index']\\n columns = dd['columns']\\n columns.sort()\\n columns = ['id'] + columns[1:]\\n df = SequenceDataFrame(([identifier] + list(sequence)\\n for identifier, sequence in dd['records']),\\n index=index.sort(),\\n columns=columns,\\n alphabet=dd['alphabet'])\\n # sort rows/columns by index and reset column labels\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e143b27848b09c1275e6793e5da5d8b\",\n \"score\": \"0.55102\",\n \"text\": \"def query2df(queryContent, filterSynapseFields=True, savedSynapseFields = ('id', 'name')):\\n import pandas as pd\\n removedProperties = set(SYNAPSE_PROPERTIES) - set(savedSynapseFields)\\n try:\\n queryContent = queryContent['results']\\n except TypeError: #If 'results' not present it is a geneartor\\n queryContent = list(queryContent)\\n for row in queryContent:\\n for key in row.keys():\\n prefix, new_key = key.split('.', 1)\\n item = row.pop(key)\\n row[new_key] = item[0] if type(item) is list else item\\n if filterSynapseFields and new_key in removedProperties:\\n del row[new_key]\\n return pd.DataFrame(queryContent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a575adbddd98b0a9424a2458cc668c98\",\n \"score\": \"0.5509608\",\n \"text\": \"def get_dataframe(db, cursor):\\n print(list(cursor))\\n return pd.DataFrame(list(cursor))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf83ab2455dcad4d48d7b09768174be\",\n \"score\": \"0.5506226\",\n \"text\": \"def build_record_df(dbase, record_style):\\n df = []\\n for record in dbase.iget_records(style=record_style):\\n df.append(record.todict(full=False))\\n return pd.DataFrame(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6212031e1228b534669562d38d5376a\",\n \"score\": \"0.5503826\",\n \"text\": \"def get_dataframe():\\r\\n jsonified_df = redis_instance.hget(\\r\\n tasks.REDIS_HASH_NAME, tasks.REDIS_KEYS[\\\"DATASETUP\\\"]\\r\\n ).decode(\\\"utf-8\\\")\\r\\n # df = pd.DataFrame(json.loads(jsonified_df))\\r\\n return json.loads(jsonified_df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e107bf62efff2f7119b5c0d8d7d7efe0\",\n \"score\": \"0.5481719\",\n \"text\": \"def to_pandas(self):\\n dataframe = self.get()\\n assert type(dataframe) is pandas.DataFrame or type(dataframe) is pandas.Series\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b74a7e8a2151ed1d0a8b5d8009c2d4e\",\n \"score\": \"0.54766506\",\n \"text\": \"def _parse_raw(df):\\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a7595f0e87c3c7e5090f6417e27b64c\",\n \"score\": \"0.5468235\",\n \"text\": \"def get_data(_connector, database) -> pd.DataFrame:\\n query = f\\\"SELECT * FROM {database}.INFORMATION_SCHEMA.TABLES;\\\"\\n return pd.read_sql(query, _connector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bad4c915e1d8e67c0c28a7e473a6a63\",\n \"score\": \"0.5463642\",\n \"text\": \"def from_dlpack(pycapsule_obj):\\n\\n res = libdlpack.from_dlpack(pycapsule_obj)\\n\\n if res._num_columns == 1:\\n return Series(res._data[0])\\n else:\\n return DataFrame(data=res._data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30fa186541d205780eb2c1d4014f37b\",\n \"score\": \"0.5460425\",\n \"text\": \"def read_data_delta(spark: SparkSession) -> DataFrame:\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e5702abb7da2771b87a5619aaf635c\",\n \"score\": \"0.5450121\",\n \"text\": \"def read_sql(self, code: str) -> pandas.DataFrame:\\n if self.kind != SessionKind.SQL:\\n raise ValueError(\\\"not a SQL session\\\")\\n output = self._execute(code)\\n output.raise_for_status()\\n if output.json is None:\\n raise RuntimeError(\\\"statement had no JSON output\\\")\\n return dataframe_from_json_output(output.json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94709adad16067866ff9a729840d305f\",\n \"score\": \"0.544312\",\n \"text\": \"def get_df_from_query() -> pd.DataFrame:\\n username, password = get_credentials()\\n print('Connecting to Redshift...')\\n con = connect(host='analytics-bi.cdlqehqrgcus.us-east-1.redshift.amazonaws.com',\\n dbname='hmlredshift', port=5439, user=username, password=password)\\n print('Querying...')\\n query: str = \\\"\\\"\\\"\\n SELECT\\n subscription_id,\\n CAST(rec_liq_novo AS REAL),\\n categoria_detalhe\\n FROM\\n sales.tb_rep_receita\\n WHERE\\n subscription_id IS NOT NULL\\n AND CAST(rec_liq_novo AS REAL) > 0.0\\n AND ult_dia_mes BETWEEN '2017-07-01' AND '2017-12-31';\\n \\\"\\\"\\\"\\n df: pd.DataFrame = pd.read_sql(query, con=con)\\n print('Closing connection...')\\n con.close()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6be8f40bd53cd336efdc5659b0bd648\",\n \"score\": \"0.5442066\",\n \"text\": \"def open_gzip_read_tsv(bytes_io):\\n try:\\n with gzip.open(bytes_io, 'rt') as read_file:\\n df = pd.read_csv(read_file, sep='\\\\t', names=['date', 'time', 'user_id', 'url', 'ip', 'user_agent_string'],\\n low_memory=False)\\n return df\\n except IOError as e:\\n print(\\\"Error reading file: {}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c43986ce028e83bbac48f8c262e6f69\",\n \"score\": \"0.5439498\",\n \"text\": \"def _construct_df(self, mol2_lines, col_names, col_types):\\r\\n return self._atomsection_to_pandas(self._get_atomsection(mol2_lines),\\r\\n col_names=col_names,\\r\\n col_types=col_types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddadba232d1533999acfb4668424a467\",\n \"score\": \"0.54248106\",\n \"text\": \"def to_data_frame(self, index=None, columns=None, dtype=None):\\n from py2neo.integration.pandas import cursor_to_data_frame\\n return cursor_to_data_frame(self, index, columns, dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2fd761278b8cb5967f004cff0e324f\",\n \"score\": \"0.5417649\",\n \"text\": \"def load_dataframe(pair, days_history=30, exchange='Bitfinex'):\\n # Convert to Pandas dataframe with datetime format\\n data = get_past(pair, days_history, exchange)\\n df = pd.DataFrame(data)\\n df['date'] = pd.to_datetime(df['time'], unit='s')\\n df['volume'] = df['volumefrom'] # TODO mb improve\\n df = df.set_index(['date'])\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33df377564ec4fde81c3bc92ce951bb1\",\n \"score\": \"0.53854334\",\n \"text\": \"def to_pandas(self):\\n dataframe = self.get()\\n assert type(dataframe) is pandas.DataFrame or type(dataframe) is pandas.Series\\n\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a265fc8b81dc4fdf32ba0ebf8025b0b9\",\n \"score\": \"0.53836834\",\n \"text\": \"def to_pandas(\\n self, connection: SnowflakeConnection | None = None, **kwargs\\n ) -> DataFrame:\\n self._check_can_use_pandas()\\n table = self.to_arrow(connection=connection)\\n return table.to_pandas(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2391f73452b3a725288614b5b76561\",\n \"score\": \"0.5361266\",\n \"text\": \"def decode_from_stream(self, stream, nested):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f787076391ee01d7c63adefeba0c8e4\",\n \"score\": \"0.53590393\",\n \"text\": \"def sql_to_dataframe(sql_file, connection):\\n f = open(sql_file, 'r')\\n sql = f.read()\\n f.close()\\n\\n curs = connection.cursor()\\n curs.execute(sql)\\n results = curs.fetchall()\\n df = pd.DataFrame(results)\\n df.columns = [column[0] for column in curs.description]\\n curs.close()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e245e75fc6c8d1284286c63085466150\",\n \"score\": \"0.5355277\",\n \"text\": \"def _get_single_df(\\n stream: IO[bytes] | IO[str], filetype: TypeEnum | None, **kwargs: Any\\n ) -> pd.DataFrame | Iterable[pd.DataFrame]:\\n if filetype is None:\\n filetype = TypeEnum(detect_type(stream.name))\\n allowed_params = get_reader_allowed_params(filetype)\\n\\n # Check encoding\\n encoding = kwargs.get(\\\"encoding\\\")\\n if \\\"encoding\\\" in allowed_params:\\n if not validate_encoding(stream.name, encoding):\\n encoding = detect_encoding(stream.name)\\n kwargs[\\\"encoding\\\"] = encoding\\n\\n # Check separator for CSV files if it's not set\\n if \\\"sep\\\" in allowed_params and \\\"sep\\\" not in kwargs:\\n if not validate_sep(stream.name, encoding=encoding):\\n kwargs[\\\"sep\\\"] = detect_sep(stream.name, encoding)\\n\\n try:\\n df = pd_read(stream.name, filetype, kwargs)\\n finally:\\n stream.close()\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3eb4c648985902f7cb4690a7054624\",\n \"score\": \"0.5352881\",\n \"text\": \"def get_data_from_sql(db_name, query ):\\n df = pd.read_sql(query, get_connection(db_name))\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edc0ac58cee3087827322822cd82c94b\",\n \"score\": \"0.5340269\",\n \"text\": \"def getDataFrame(self):\\n return dataTableToDataFrame(self.getDataTable())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bc3abdfbe719466b9aa506cd17e4e59\",\n \"score\": \"0.53367525\",\n \"text\": \"def convert_raw_data_to_df(raw_data):\\n\\n # read CSV file\\n raw_data = root_path + '/data/' + raw_data\\n source = pd.read_csv(raw_data, header=0, skipinitialspace=True)\\n source_data = pd.DataFrame(source)\\n\\n # return source data\\n return source_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a1b4e9e13862ff2ae41b0a034037677\",\n \"score\": \"0.5329766\",\n \"text\": \"def string_to_df(string):\\n try:\\n data_frame = pd.read_pickle(BytesIO(base64.b64decode(string)))\\n except ValueError:\\n data_frame = pickle.load(\\n BytesIO(base64.b64decode(string)),\\n encoding='latin1')\\n except Exception:\\n return None\\n\\n return data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e5e5c0689ec2c75977f468d0f7a8f5\",\n \"score\": \"0.5322118\",\n \"text\": \"def df_from_bytes_csv(bytes_: bytes) -> pd.DataFrame:\\n try:\\n s = StringIO(bytes_.decode())\\n s.seek(0)\\n return pd.read_csv(\\n s,\\n index_col=0,\\n header=0,\\n float_precision=\\\"round_trip\\\",\\n quotechar='\\\"',\\n )\\n except UnicodeDecodeError:\\n raise DataFrameLoadException(\\\"Not a csv DataFrame\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7633e84bf1962e6c8d5f93ef51bed53f\",\n \"score\": \"0.5311086\",\n \"text\": \"def to_dataframe(self,data):\\n return self.pandas.DataFrame(data).set_index('token')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e8ab2f37b8971494365b9cc9fdd07db\",\n \"score\": \"0.53056073\",\n \"text\": \"def to_df(response, batch=False):\\n \\n if batch:\\n records = []\\n for tup in to_df(response).itertuples():\\n if tup[3] != None:\\n for mappings in tup[3].split(','):\\n target = mappings.split(':', 1)\\n if len(target) > 1:\\n records.append((tup[1], tup[2], target[1], target[0]))\\n else:\\n records.append((tup[1], tup[2], target[0], target[0]))\\n return pd.DataFrame(records, columns = ['original', 'source', 'mapping', 'target'])\\n \\n return pd.DataFrame([line.split('\\\\t') for line in response.text.split('\\\\n')])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ad17e6dc23510bbf77c98ac59e8b0f6\",\n \"score\": \"0.53007203\",\n \"text\": \"def load_from_server(self, sql):\\n try:\\n connection = pyhdb.connect(\\n host=config.db_address,\\n port=config.db_port,\\n user=config.db_user,\\n password=config.db_password,\\n )\\n with open(sql, \\\"r\\\") as f:\\n query = f.read()\\n cursor = connection.cursor()\\n cursor = cursor.execute(query)\\n data = pd.DataFrame(cursor.fetchall())\\n except Exception as e:\\n print(\\n \\\"***Error connecting to DB. Check your environment variables.\\\\n\\\",\\n repr(e),\\n )\\n raise e\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2935f82caf9d68f4b55f732524e7eb5c\",\n \"score\": \"0.5291846\",\n \"text\": \"def sparse_dataframe_from_records(records):\\n reformatted = []\\n for (a0, a1, bt, length), count in records:\\n reformatted.append((a0, a1, bt, length, count))\\n df = pd.DataFrame.from_records(\\n reformatted,\\n columns=[\\n 'atom_char_0', 'atom_char_1', 'bond_type', 'length_str', 'count'\\n ])\\n return df.sort_values(\\n ['atom_char_0', 'atom_char_1', 'bond_type', 'length_str'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73adc210918b2044128e32b926df6956\",\n \"score\": \"0.5279534\",\n \"text\": \"def toDataFrame(input: SerializableType):\\n params = toParamDict(input, includeMeta=['unit', 'vals'])\\n return pd.DataFrame(params).T.sort_index()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9be9dfcbddb205211d425a19b2c6886f\",\n \"score\": \"0.5259061\",\n \"text\": \"def to_dataframe(self):\\n return pd.DataFrame.from_dict(self._data, orient='index')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e099cf95cb490297ba4dd7d0ea093f\",\n \"score\": \"0.52468336\",\n \"text\": \"def load_pandas():\\n data = _get_data()\\n return du.process_pandas(data, endog_idx=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e099cf95cb490297ba4dd7d0ea093f\",\n \"score\": \"0.52468336\",\n \"text\": \"def load_pandas():\\n data = _get_data()\\n return du.process_pandas(data, endog_idx=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2788e90682847e4a7612d685c62c91f\",\n \"score\": \"0.5246668\",\n \"text\": \"def sql2df(self, sql_str, columns=None):\\n if isinstance(sql_str, sql.Composed):\\n sql_str = sql_str.as_string(self.cursor)\\n if isinstance(columns, str):\\n columns = [columns]\\n\\n df = pd.read_sql(sql=sql_str, con=self.get_engine().connect(),\\n columns=columns)\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a49aaaddcc367d123f1b2ee1c1da48\",\n \"score\": \"0.5245327\",\n \"text\": \"def query(self, sql: str):\\n return self.execute(lambda tp: tp.query(sql).as_pandas_dataframe())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540cec72c98cd3506e907e2beb5cc96b\",\n \"score\": \"0.5223218\",\n \"text\": \"def query_indb_df(auth, query,\\n database=None,\\n chunked=False,\\n sensor_id='sensor_id'):\\n\\n data = query_indb(auth, query, database=database, chunked=chunked)\\n return response_to_df(data, sensor_id=sensor_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6d833621b7a5e78b00cb87e2e66dad5\",\n \"score\": \"0.51981354\",\n \"text\": \"def test_a_pd2table(self):\\r\\n self.sql_api.pd2table(self.df, \\\"pd2table\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b96f5031a03d815d4957b174475f7620\",\n \"score\": \"0.51917964\",\n \"text\": \"def decode_from_stream(self, stream, nested):\\n return self.decode(stream.read_all(nested))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe11a482b89ad78bf7f860fd07197db2\",\n \"score\": \"0.5183238\",\n \"text\": \"def save_cursor_result_to_df(cursor: pyodbc.Cursor) -> pd.DataFrame:\\n descriptions = cursor.description\\n col_names = [desc[0] for desc in descriptions]\\n df = pd.DataFrame.from_records(cursor.fetchall(), columns=col_names)\\n LOG.debug('saved cursor to DataFrame')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9d64db7a5483f7e685d5055b53a18a\",\n \"score\": \"0.51749444\",\n \"text\": \"def as_dataframe(self):\\n return self._data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfe244328dba554ee23bb8701cd52046\",\n \"score\": \"0.51724964\",\n \"text\": \"def get_pandas_df(self, sql):\\n conn = self.get_conn(self.snowflake_conn_id)\\n df = pd.read_sql_query(sql, conn)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77351b7d5608251850e5dcf07618b5e9\",\n \"score\": \"0.5166094\",\n \"text\": \"def _convert_to_dataframe(content):\\n\\n try:\\n dfs = [pd.DataFrame(c) for c in content]\\n except ValueError:\\n dfs = [pd.DataFrame(c, index=[i]) for i, c in enumerate(content)]\\n\\n return pd.concat(dfs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d98dfcb840cab0dfbf45cbc2a4fc662\",\n \"score\": \"0.516002\",\n \"text\": \"def hexcountsresults_to_df(db, save=False):\\n\\n cursor=db.hexcounts.find()\\n #.limit(10)\\n\\n prueba=pd.DataFrame(list(cursor) )\\n\\n df1=pd.DataFrame(pd.concat([prueba[['_id']],json_normalize(prueba[\\\"nonresidents\\\"])],axis=1 ).set_index('_id').stack()).reset_index().rename(columns={0:'nonresidents'})\\n df2=pd.DataFrame(pd.concat([prueba[['_id']],json_normalize(prueba[\\\"nonresidentsandnonneighbors\\\"])],axis=1 ).set_index('_id').stack()).reset_index().rename(columns={0:'nonresidentsandnonneighbors'})\\n df3=pd.DataFrame(pd.concat([prueba[['_id']],json_normalize(prueba[\\\"residents\\\"])],axis=1 ).set_index('_id').stack()).reset_index().rename(columns={0:'residents'})\\n df4=pd.DataFrame(pd.concat([prueba[['_id']],json_normalize(prueba[\\\"totalcounts\\\"])],axis=1 ).set_index('_id').stack()).reset_index().rename(columns={0:'totalcounts'})\\n\\n\\n df1=df1.merge(df2,on=['_id','level_1'],how='outer')\\n df1=df1.merge(df3,on=['_id','level_1'],how='outer')\\n df1=df1.merge(df4,on=['_id','level_1'],how='outer')\\n\\n\\n if df1['level_1'].dtype == '<M8[ns]': # if data is already timestamped then just copy\\n df1['time'] =df1['level_1']\\n else:\\n df1['time']=pd.to_datetime(pd.to_numeric(df1['level_1'], errors='coerce') // 1000, unit='s' )\\n\\n df1.drop(columns='level_1')\\n\\n if save:\\n df1.to_pickle(\\\"./hexcountsdf.pkl\\\")\\n\\n return df1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e118044b197b519ca27bcd7ff997d3bd\",\n \"score\": \"0.5149264\",\n \"text\": \"def extract(self, config: dict) -> DataFrame:\\r\\n path = config['source_path']\\r\\n read_options = config.get('read_options')\\r\\n schema = config.get('schema', dict())\\r\\n\\r\\n _read_options = read_options.copy()\\r\\n\\r\\n if schema:\\r\\n # StructType.fromJson method looks for 'fields' key ..\\r\\n schema_struct = T.StructType.fromJson(schema) \\r\\n schema_struct.add(T.StructField('iptmeta_corrupt_record', T.StringType(), True))\\r\\n _read_options['schema'] = schema_struct\\r\\n else:\\r\\n _read_options['inferSchema'] = 'true'\\r\\n\\r\\n df = self.get_spark().read.csv(path, **_read_options)\\r\\n #df.cache()\\r\\n\\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3960f394bbf730642d16667f93d5da06\",\n \"score\": \"0.51449734\",\n \"text\": \"def query_and_flatten(self, sql: str):\\n return self.execute_and_flatten(lambda tp: tp.query(sql).\\n as_pandas_dataframe())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba785cfe29ccb6d46a5a5d190f0068d\",\n \"score\": \"0.51396745\",\n \"text\": \"def to_df(value: Any) -> pd.DataFrame:\\n if value is None:\\n # This return the empty dataframe, which atm is the same as\\n # the empty file object in the CAS.\\n # However this is not ensured as pyarrow changes\\n return pd.DataFrame()\\n\\n if isinstance(value, pd.DataFrame):\\n return value.copy(deep=True)\\n\\n if isinstance(value, (pd.Series, pd.Index)):\\n if value.name is not None:\\n return pd.DataFrame(value)\\n\\n return pd.DataFrame({\\\"Result\\\": value})\\n\\n if isinstance(value, (Number, str, bool, datetime.datetime, datetime.timedelta)):\\n return pd.DataFrame({\\\"Result\\\": value}, index=[0])\\n\\n if isinstance(value, np.ndarray):\\n try:\\n out_df = pd.DataFrame(value)\\n except ValueError:\\n squeezed = np.squeeze(value)\\n if squeezed.shape == ():\\n # must be a scalar\\n out_df = pd.DataFrame({\\\"Result\\\": squeezed}, index=[0])\\n else:\\n out_df = pd.DataFrame(squeezed)\\n\\n if out_df.columns.tolist() == [0]:\\n out_df.columns = [\\\"Result\\\"]\\n\\n return out_df\\n\\n raise ValueError(\\\"Must return a primitive, pd.DataFrame, pd.Series or numpy array.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7943af4068684f0b2383df5bebb9d68\",\n \"score\": \"0.51285917\",\n \"text\": \"def read_forex_table_from_db(table):\\r\\n database_path = 'sqlite:///data/data.db'\\r\\n engine = create_engine(database_path, echo=False)\\r\\n data = pd.read_sql_table(table, database_path)\\r\\n engine.dispose()\\r\\n data.set_index(keys=\\\"Date\\\", drop=True, inplace=True)\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211a4efd318ba6824571f62aff334e10\",\n \"score\": \"0.5123826\",\n \"text\": \"def read_exchanges():\\n # <COMPLETE THIS PART>\\n data = from_json(EXCHANGES)['result']\\n df = pd.DataFrame(data)\\n df.set_index('id', inplace=True)\\n cols = ['active', 'name', 'route', 'symbol']\\n return df[cols]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d128be9fe7a96b16630c9c1c76b4857\",\n \"score\": \"0.51054394\",\n \"text\": \"def to_dataframe(cls) -> pd.DataFrame:\\n return NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3ce112875d30471d48c6fd9b461416\",\n \"score\": \"0.51015055\",\n \"text\": \"def data_import(path):\\n\\n\\tdbf = ps.open(path)\\n\\td = dict([(col,np.array(dbf.by_col(col))) for col in dbf.header])\\n\\tdf = pd.DataFrame(d)\\n\\treturn df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaea448fa58ea6d5e78c015d4adf5624\",\n \"score\": \"0.5079912\",\n \"text\": \"def decode_dataframe(data_frame: DataFrame) -> Datatable:\\n columns = []\\n\\n for column_name in data_frame.columns:\\n column = data_frame[column_name]\\n values = column.tolist()\\n if column.dtype in (numpy.bool_, numpy.bool8):\\n schema = BooleanSchema()\\n values = [bool(row) for row in values]\\n elif column.dtype in (numpy.int8, numpy.int16, numpy.int32, numpy.int64):\\n schema = IntegerSchema()\\n values = [int(row) for row in values]\\n elif column.dtype in (numpy.float16, numpy.float32, numpy.float64):\\n schema = NumberSchema()\\n values = [float(row) for row in values]\\n elif column.dtype in (numpy.str_, numpy.unicode_,):\\n schema = StringSchema()\\n else:\\n schema = None\\n\\n columns.append(\\n DatatableColumn(column_name, values, schema=ArraySchema(items=schema))\\n )\\n\\n return Datatable(columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19b498aca3d691c6684d60216670841e\",\n \"score\": \"0.5059325\",\n \"text\": \"def sample_data_stream(spark, df, timeout=10):\\n query = df.writeStream.outputMode(\\\"append\\\") \\\\\\n .queryName(\\\"tmp\\\").format(\\\"memory\\\") \\\\\\n .start()\\n query.awaitTermination(timeout)\\n query.stop()\\n return spark.sql(\\\"select * from tmp\\\").toPandas()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1188ed67f79411681c15f469dd84d515\",\n \"score\": \"0.50578386\",\n \"text\": \"def test_DataFrame(self):\\n url = \\\"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\\\"\\n self.assertEquals(type(url_to_df(url)),type(pd.DataFrame([{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22c7e4e70969eb8e43c46324b76c9e77\",\n \"score\": \"0.50519264\",\n \"text\": \"def _dataframe_from_txt(reader):\\n return pd.read_csv(reader, header=None, sep=\\\"\\\\n\\\", encoding='utf-8-sig')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7b6fb15c99b78996f8660ce6b2e9d4\",\n \"score\": \"0.50459737\",\n \"text\": \"def read(self, dataframe_name: str) -> pandas.DataFrame:\\n code = serialise_dataframe_code(dataframe_name, self.kind)\\n output = self._execute(code)\\n output.raise_for_status()\\n if output.text is None:\\n raise RuntimeError(\\\"statement had no text output\\\")\\n return deserialise_dataframe(output.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c80fa4647dae1f2972ad6a61dc7f3a3\",\n \"score\": \"0.5038185\",\n \"text\": \"def parse_RequestResult(resp):\\n if resp['result']['status'] != 'Complete':\\n logger.info(resp['result'])\\n return resp\\n df = pd.read_csv(io.BytesIO(resp['result']['data']), compression='gzip')\\n df.dropna(axis=1, how='all', inplace=True) # Dropping all completely empty columns\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"121ba4ca24d8496560bdb3a12b6953f2\",\n \"score\": \"0.5037319\",\n \"text\": \"def query(call, connection, creds=None):\\n if creds is None:\\n creds = read_creds()\\n DB = \\\"DB_NAME\\\"\\n engine = sql.create_engine(DB)\\n try:\\n df = pd.read_sql_query(call, engine)\\n except sql.exc.ResourceClosedError:\\n df = pd.DataFrame()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9847c1c9c04464533e83fd2a52a967a4\",\n \"score\": \"0.50269103\",\n \"text\": \"def process_data(self, stream, metadata):\\n index = np.arange(metadata[\\\"firstyear\\\"], metadata[\\\"lastyear\\\"] + 1)\\n\\n # The first variable is the global values\\n globe = stream.read_chunk(\\\"d\\\")\\n\\n assert len(globe) == len(index)\\n\\n regions = stream.read_chunk(\\\"d\\\")\\n num_regions = int(len(regions) / len(index))\\n regions = regions.reshape((-1, num_regions), order=\\\"F\\\")\\n\\n data = np.concatenate((globe[:, np.newaxis], regions), axis=1)\\n\\n df = pd.DataFrame(data, index=index)\\n\\n if isinstance(df.index, pd.core.indexes.numeric.Float64Index):\\n df.index = df.index.to_series().round(3)\\n\\n df.index.name = \\\"time\\\"\\n\\n regions = [\\n \\\"World\\\",\\n \\\"World|Northern Hemisphere|Ocean\\\",\\n \\\"World|Northern Hemisphere|Land\\\",\\n \\\"World|Southern Hemisphere|Ocean\\\",\\n \\\"World|Southern Hemisphere|Land\\\",\\n ]\\n\\n variable = convert_magicc6_to_magicc7_variables(\\n self._get_variable_from_filepath()\\n )\\n variable = convert_magicc7_to_openscm_variables(variable)\\n column_headers = {\\n \\\"variables\\\": [variable] * (num_regions + 1),\\n \\\"regions\\\": regions,\\n \\\"units\\\": [\\\"unknown\\\"] * len(regions),\\n \\\"todos\\\": [\\\"SET\\\"] * len(regions),\\n }\\n df.columns = self._get_columns_multiindex_from_column_headers(column_headers)\\n df = pd.DataFrame(df.T.stack(), columns=[\\\"value\\\"]).reset_index()\\n df = self._convert_to_categorical_columns(df)\\n\\n return df, metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa4140518c4eafc86334bfa2a1e9c18e\",\n \"score\": \"0.50243026\",\n \"text\": \"def get_sparql_dataframe(service, query):\\n sparql = SPARQLWrapper(service)\\n sparql.setQuery(query)\\n sparql.setReturnFormat(JSON)\\n result = sparql.query()\\n\\n processed_results = jsonasobj.load(result.response)\\n cols = processed_results.head.vars\\n\\n out = []\\n for row in processed_results.results.bindings:\\n item = []\\n for c in cols:\\n item.append(row._as_dict.get(c, {}).get('value'))\\n out.append(item)\\n\\n return pd.DataFrame(out, columns=cols)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6026504e2b23cc111052c8ef46baaa33\",\n \"score\": \"0.5017274\",\n \"text\": \"def to_h2odf(self):\\n try:\\n table_data = self.to_df()\\n if isinstance(table_data, pd.DataFrame):\\n return h2o.H2OFrame(table_data)\\n h2o_df_series = pd.Series()\\n for table_name in table_data.index:\\n current_h2o_df = h2o.H2OFrame(table_data[table_name])\\n h2o_df_series[table_name] = current_h2o_df\\n return h2o_df_series\\n except NameError:\\n print('H2O not installed.')\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"418b6ce8f94dbeac788cdb098d137c71\",\n \"score\": \"0.50166273\",\n \"text\": \"def to_dataFrame(self):\\n dssp = []\\n start = 0\\n for l in self.fl:\\n if re.search('#',l):\\n start=1\\n continue\\n\\n if start:\\n try:\\n fields = {'chain':l[10:12].strip(),'seqResNum':int(l[:5].strip()),\\\\\\n 'pdbResNum':int(l[5:10].strip()),'aa':l[12:14].strip(),\\\\\\n 'acc':float(l[34:38].strip()),\\\\\\n 'kappa':float(l[91:97].strip()),'alpha':float(l[97:103].strip()),\\\\\\n 'phi':float(l[103:109].strip()),'psi':float(l[109:115].strip()),\\\\\\n 'catom':(),'NHO_d':[],'NHO_e':[],'OHN_d':[],'OHN_e':[]}\\n\\n sse_tmp = l[14:17].strip()\\n if re.match('[GHITEBS]', sse_tmp):\\n fields['sse'] = sse_tmp\\n else:\\n fields['sse'] = 'C'\\n \\n # calpha x, y and z\\n x = float(l[117:122].strip())\\n y = float(l[124:129].strip())\\n z = float(l[131:].strip())\\n fields['catom'] = (x, y, z)\\n\\n _h1 = l[40:51].strip().split(\\\",\\\")\\n _h2 = l[51:62].strip().split(\\\",\\\")\\n _h3 = l[62:73].strip().split(\\\",\\\")\\n _h4 = l[73:84].strip().split(\\\",\\\")\\n\\n fields['NHO_d'] = [int(_h1[0]),int(_h3[0])]\\n fields['OHN_d'] = [int(_h2[0]),int(_h4[0])]\\n fields['NHO_e'] = [float(_h1[1]),float(_h3[1])]\\n fields['OHN_e'] = [float(_h2[1]),float(_h4[1])]\\n dssp.append(fields)\\n except:\\n print (\\\"Warning residue line escaping\\\", l)\\n next\\n \\n self.dsspDF = pd.DataFrame(dssp)\\n assert len(dssp) == self.dsspDF.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c1426ce589e8b8b0c58645d44cc18c\",\n \"score\": \"0.5012296\",\n \"text\": \"def read_csv(filepath_or_buffer: Union[str, io.BytesIO], encoding: str = \\\"utf-8\\\") -> pd.DataFrame:\\n return pd.read_csv(filepath_or_buffer, encoding=encoding)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b3246729eea9815c7a7c2b6b0558a2\",\n \"score\": \"0.5004827\",\n \"text\": \"def read_mysql_database(sql_statement):\\n df_table = pd.read_sql(sql_statement, engine)\\n return df_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e9b99c78af92e0deb59042df8e2606\",\n \"score\": \"0.50040734\",\n \"text\": \"def hexcountsresults_to_df_DEPRECATED(db, save=False):\\n\\n #para pasar de la coleccion al dataframe\\n #voy a loopear la coleccion, convertir cada documento en un dataframe y despues unirlos\\n cursor=db.hexcounts.find()\\n listofdfis=[]\\n\\n for doc in cursor:\\n dfi=pd.DataFrame(doc).reset_index().rename(columns={\\\"index\\\": \\\"time\\\"})\\n\\n if dfi['time'].dtype != '<M8[ns]': # if data is already timestamped then just copy\\n dfi['time']=pd.to_datetime(pd.to_numeric(dfi['time'], errors='coerce') // 1000, unit='s' )\\n\\n listofdfis.append(dfi)\\n\\n #print(listofdfis)\\n df=pd.concat(listofdfis)\\n df=df.reset_index(drop=True)\\n\\n if save:\\n df.to_pickle(\\\"./hexcountsdf.pkl\\\")\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5daf4aa494779abb81363f4b844b1417\",\n \"score\": \"0.5001567\",\n \"text\": \"def get_df_sql(sql, connection):\\n connection = source_engine.connect()\\n df = pd.read_sql_query(sql, connection)\\n connection.close()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04b597de6c0e5b9fc8197aee915fc40e\",\n \"score\": \"0.5001092\",\n \"text\": \"def make_df(resp) -> DataFrameType:\\n\\n def maker(series) -> pd.DataFrame:\\n df = pd.DataFrame(series.get('values', []), columns=series['columns'])\\n if 'time' not in df.columns:\\n return df\\n df: pd.DataFrame = df.set_index(pd.to_datetime(df['time'])).drop('time', axis=1)\\n df.index = df.index.tz_localize('UTC')\\n df.index.name = None\\n if 'tags' in series:\\n for k, v in series['tags'].items():\\n df[k] = v\\n if 'name' in series:\\n df.name = series['name']\\n return df\\n\\n def drop_zero_index(df):\\n if isinstance(df.index, pd.DatetimeIndex):\\n if all(i.value == 0 for i in df.index):\\n df.reset_index(drop=True, inplace=True)\\n\\n # Parsing\\n df_list = [((series.get('name'), tuple(series.get('tags', {}).items())), maker(series))\\n for statement in resp['results'] if 'series' in statement\\n for series in statement['series']]\\n\\n # Concatenation\\n d = defaultdict(list)\\n for k, df in sorted(df_list, key=lambda x: x[0]):\\n d[k].append(df)\\n dfs = {k: pd.concat(v, axis=0) for k, v in d.items()}\\n\\n # Post-processing\\n for df in dfs.values():\\n drop_zero_index(df)\\n\\n # Return\\n if len(dfs) == 1:\\n return dfs[list(dfs.keys())[0]]\\n return dfs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d074d2d73a76b81c66a6b6437d90733a\",\n \"score\": \"0.49961397\",\n \"text\": \"def load_data(database_path):\\n name = 'sqlite:///' + database_path\\n engine = create_engine(name)\\n \\n df = pd.read_sql_table('clean_dataset', con=engine).head(10000)\\n X = df['message']\\n y = df.drop(['original','genre','message','offer','request'], axis=1)\\n y = y.astype(int)\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e7c6d7f7e7b44c71bc274a31f12ccc4\",\n \"score\": \"0.49943295\",\n \"text\": \"def read_table_as_df(h5, tpath):\\n x = h5.get(tpath)\\n if x.dtype.names:\\n result = pd.DataFrame(\\n [[v[k].astype(str).strip() if v.dtype.fields[k][0].name.startswith(\\n 'bytes') else v[k] for k in v.dtype.fields] for v in x],\\n columns=[name for name in x.dtype.names]\\n )\\n result = result.astype(dtype={k: 'unicode' if x.dtype[k].name.startswith(\\n 'bytes') else x.dtype[k].name for k in x.dtype.fields})\\n else:\\n result = pd.DataFrame(\\n [v.astype(str).strip() if x.dtype.name.startswith('bytes') else v for v in x])\\n result = result.astype(\\n dtype='unicode' if x.dtype.name.startswith('bytes') else x.dtype.name)\\n result = strip(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0857d307750355bacf3f10b747bf737\",\n \"score\": \"0.49931386\",\n \"text\": \"def read_stock_table_from_db(table):\\r\\n database_path = 'sqlite:///data/data.db'\\r\\n engine = create_engine(database_path, echo=False)\\r\\n data = pd.read_sql_table(table, database_path)\\r\\n engine.dispose()\\r\\n data.set_index(keys=\\\"Date\\\", drop=True, inplace=True)\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdc4ffbfa24b54fd59fc368b2fd4aaae\",\n \"score\": \"0.4992499\",\n \"text\": \"def execfetchDf(self, cmd):\\n import pandas as pd\\n try:\\n return pd.read_sql_query(cmd, self.connection)\\n except AttributeError: # probably an older version of pandas\\n assert not getattr(pd, 'read_sql_table', None)\\n tmpcur = self.connection.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n tmpcur.execute(cmd)\\n colNames = [desc[0] for desc in tmpcur.description]\\n results = tmpcur.fetchall()\\n return pd.DataFrame([[row[col] for col in colNames] for row in results], columns=colNames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c8dbbc7c023bb6bebe0d8b6d3ae6fcf\",\n \"score\": \"0.49861366\",\n \"text\": \"def getDataFrame(path:str):\\n return pandas.read_csv(path, encoding = \\\"ISO-8859-1\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992978,"cells":{"query_id":{"kind":"string","value":"cc7460de9c01f90a3a5d92c4c564cf62"},"query":{"kind":"string","value":"Convert ase atoms' index to castep like return (Specie, Ion) Deprecated, use ase_to_castep_index"},"positive_passages":{"kind":"list like","value":[{"docid":"80c739bcb425f97c8ec8969783ccacd1","score":"0.62893224","text":"def atoms_to_castep(atoms, index):\n atom = atoms[index]\n symbol = atom.symbol\n # Start counter\n count = 0\n for atom in atoms:\n if atom.symbol == symbol:\n count += 1\n if atom.index == index:\n break\n return symbol, count","title":""}],"string":"[\n {\n \"docid\": \"80c739bcb425f97c8ec8969783ccacd1\",\n \"score\": \"0.62893224\",\n \"text\": \"def atoms_to_castep(atoms, index):\\n atom = atoms[index]\\n symbol = atom.symbol\\n # Start counter\\n count = 0\\n for atom in atoms:\\n if atom.symbol == symbol:\\n count += 1\\n if atom.index == index:\\n break\\n return symbol, count\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b4b9a359fbc4f4838157c22f060ebd60","score":"0.7522824","text":"def ase_to_castep_index(atoms, indices):\n if isinstance(indices, int):\n indices = [indices]\n symbols = np.array(atoms.get_chemical_symbols())\n iatoms = np.arange(len(atoms))\n res = []\n # Iterate through given indices\n for i in indices:\n mask = symbols == symbols[i] # Select the same species\n\n # CASTEP start counting from 1\n counter = np.where(iatoms[mask] == i)[0][0] + 1\n res.append([symbols[i], counter])\n return res","title":""},{"docid":"1868d6ec27a0d349ec03176c5301771b","score":"0.7079155","text":"def castep_to_atoms(atoms, specie, ion):\n return [atom for atom in atoms if atom.symbol == specie][ion - 1].index","title":""},{"docid":"2e44d228e7b4b76cfa7bc94580bfee41","score":"0.512058","text":"def atom(self, ao_index):\n atom_index = ao_index // self.orbitals_per_atom\n return atom_index","title":""},{"docid":"fa599f5fb0aa6685c0d7360eae8669eb","score":"0.50818807","text":"def decode(self,eps=0.01):\r\n # take argmax\r\n codes = self.u.argmax(1)\r\n # get sorted ids\r\n params = self.showModel(min_pi=eps)\r\n # assign each observation to corresponding cluster\r\n clust_pos = []\r\n for p in params:\r\n clust_pos.append(codes==p[1])\r\n return clust_pos","title":""},{"docid":"fd11963aeb5fac1035f4d935934dc37b","score":"0.5079113","text":"def get_index():","title":""},{"docid":"2f85373571ea87d32ee1192f35b9fa74","score":"0.50703","text":"def _sense2idx(self, a_rels):\n n_senses = len(self.cls2idx)\n isense = isenses = vsense = None\n for irel in a_rels:\n isenses = irel[SENSE]\n vsense = np.zeros(n_senses)\n for isense in isenses:\n isense = SHORT2FULL.get(isense, isense)\n vsense[self.cls2idx[isense]] = 1\n irel[SENSE] = vsense / sum(vsense)","title":""},{"docid":"6be1c548bbee245c57e79385f3714af2","score":"0.506561","text":"def CreateAssemblyAddrFromIndex( self, assy_ndx ):\n result = ( -1, -1 -1 )\n if self.core is not None:\n places = np.argwhere( self.core.coreMap == assy_ndx + 1 )\n if len( places ) > 0:\n place = places[ -1 ]\n result = ( assy_ndx, int( place[ 1 ] ), int( place[ 0 ] ) )\n return result","title":""},{"docid":"4800fba4b94ed9614935d8c66ca85404","score":"0.505327","text":"def to_index(self, value):\n return 0","title":""},{"docid":"5edbf0fbe9e52ab430a03f361d7bafbb","score":"0.5052407","text":"def _standardize_mass_index_energy_map_indexing(self):\n mie_map = self._mass_index_spe_map\n std_mie_map = dict()\n for m, ie_map in mie_map.items():\n std_ie_map = dict()\n for idx, energy in ie_map.items():\n next_idx = self._standard_index(idx)\n std_ie_map[next_idx] = energy\n std_mie_map[m] = std_ie_map\n self._mass_index_spe_map = std_mie_map","title":""},{"docid":"ef824da26a4c24ba43c12720e7f67c54","score":"0.49817502","text":"def xray_refractive_index(elements, energy_kev, atom_per_volume):\n f1, f2 = atomic_scattering_factor(elements, energy_kev)\n ft = f1 + 1j * f2\n ft = ft.reshape(np.size(elements), np.size(energy_kev)).sum(axis=0) # sum over elements\n atom_per_volume = atom_per_volume * 1e30 # atom per m^3\n wavelength = energy2wave(energy_kev) * 1e-10 # m\n return 1 - (ft * fg.r0 * atom_per_volume * wavelength**2 / (2 * fg.pi))","title":""},{"docid":"df054d7ca949e3096d60c970cf1c8762","score":"0.49738985","text":"def index(value):","title":""},{"docid":"47051b08439274d4d9eac96093f3e6fd","score":"0.4967376","text":"def indices_to_atoms(self, mdl, atom_indices):\n return [mdl.atoms[x-1] for x in atom_indices]","title":""},{"docid":"64cb96c7523dd6fbddda154b8df0759f","score":"0.49537516","text":"def to_indexer(self, cutoff=None, from_cutoff=True):\n if from_cutoff:\n relative_index = self.to_relative(cutoff).to_pandas()\n if is_integer_index(relative_index):\n return relative_index - 1\n else:\n # What does indexer mean if fh is timedelta?\n msg = (\n \"The indexer for timedelta-like forecasting horizon \"\n \"is not yet implemented\"\n )\n raise NotImplementedError(msg)\n else:\n relative = self.to_relative(cutoff)\n return relative - relative.to_pandas()[0]","title":""},{"docid":"bee8929607afa11df1d8c4dcceaad430","score":"0.49421525","text":"def convert_number(self, atoms: List[int]) -> np.ndarray:\n return self.dax[atoms, :]","title":""},{"docid":"898aecc0bb02961a6924f4ff0e3f8ea5","score":"0.4937189","text":"def titles_indices(estaticos_market_filter_raw):\n \n titles = estaticos_market_filter_raw['id']\n indices = pd.Series(estaticos_market_filter_raw.index, index=estaticos_market_filter_raw['id'])\n \n return titles, indices","title":""},{"docid":"8040b94a6c89d4a51610976b4be7dc18","score":"0.49319562","text":"def __index_convert(self, index):\n try:\n # index is column index\n index = int(float(index))\n except:\n pass\n finally:\n if type(index) == str:\n row = (self.csv['name'] == index)\n elif type(index) == int:\n row = (self.csv['index'] == index)\n else:\n raise ValueError(\"Index input error !\")\n\n if sum(row) == 0:\n raise ValueError('Index {0} is not find in csv'.format(index))\n else:\n return str(self.csv[row]['name'].values[0])","title":""},{"docid":"afd50289a35b5a8a7be5161eb58748d4","score":"0.4919603","text":"def indices(self):\n return [\n b's:po___',\n b'p:so___',\n b'o:sp___',\n b'po:s___',\n b'so:p___',\n b'sp:o___',\n b'spo:c__',\n b'c:spo__',\n ]","title":""},{"docid":"22395c2759c5731cd58dca155885d1ed","score":"0.49045992","text":"def decode_index(self,index):\n\t\treturn index%self.n_h,index//self.n_h","title":""},{"docid":"faa9a6cedc43eb3f81104d2bf4e09462","score":"0.48876017","text":"def _transform_index(self, index):\r\n # in ImageGrid index is guaranteed to be a tuple.\r\n\r\n # first thing, switch the coordinates since ImageGrid is column\r\n # major and ._grid is row major.\r\n new_ind = [index[1], index[0]]\r\n\r\n # now take into account that the ImageGrid origin may be lower-left,\r\n # while the ._grid origin is upper-left.\r\n if self._origin == 'lower-left':\r\n new_ind[0] = self._height - new_ind[0] - 1\r\n\r\n return tuple(new_ind)","title":""},{"docid":"485c0320b2c2725bfd89c643fb189f84","score":"0.48617253","text":"def get(self, eid, etype=\"n\"):\n eid = str(eid)\n if eid[0] not in \"nwr\":\n eid = etype[0].lower() + eid\n return self.index[eid]","title":""},{"docid":"75e5e187238615c7138026cb0d3af470","score":"0.48196426","text":"def convert_network_output_to_analysis_model_input(self):\n self.xtfreq = self.result[:, :self.max_sines]\n self.xtmag = self.result[:, self.max_sines : 2*self.max_sines]\n self.xtphase = self.result[:, 2*self.max_sines : 3*self.max_sines]\n\n assert self.xtfreq.shape == self.xtmag.shape == self.xtphase.shape\n\n phase_range = self.settings['sine_model_settings']['phase_range']\n freq_range = self.settings['sine_model_settings']['freq_range']\n mag_range = self.settings['sine_model_settings']['mag_range']\n\n mag_normalised_range = self.settings['sine_model_settings']['mag_normalised_range']\n phase_normalised_range = self.settings['sine_model_settings']['phase_normalised_range']\n freq_normalised_range = self.settings['sine_model_settings']['freq_normalised_range']\n\n # Unnormalise\n self.xtfreq = unnormalise_range(self.xtfreq, freq_normalised_range, freq_range)\n self.xtphase = unnormalise_range(self.xtphase, phase_normalised_range, phase_range)\n self.xtmag = unnormalise_range(self.xtmag, mag_normalised_range, mag_range)\n\n return self.xtfreq, self.xtmag, self.xtphase","title":""},{"docid":"9c2d841bbd4862e7099764db54a26469","score":"0.4818752","text":"def generate_ionic_fix_cons(atoms, indices, mask=None, count_start=1):\n\n # Convert to castep style indices (element, index)\n castep_indices = ase_to_castep_index(atoms, indices)\n count = count_start\n lines = []\n if mask is None:\n mask = (1, 1, 1)\n for symbol, i in castep_indices:\n if mask[0]:\n lines.append(f\"{count:<4d} {symbol:<2} {i:<4d} 1 0 0\")\n if mask[1]:\n lines.append(f\"{count + 1:<4d} {symbol:<2} {i:<4d} 0 1 0\")\n if mask[2]:\n lines.append(f\"{count + 2:<4d} {symbol:<2} {i:<4d} 0 0 1\")\n count += sum(mask)\n return lines, count","title":""},{"docid":"f6a4a10f9f1794fb6de870fc25c8b937","score":"0.48107785","text":"def sample2index(self):\n return self.sample_to_idx","title":""},{"docid":"e8845796ff85417ce2ee3c2773b4eb62","score":"0.48075914","text":"def visit_index_expr(self, e: IndexExpr) -> Type:\r\n if e.analyzed:\r\n # It's actually a type application.\r\n return self.accept(e.analyzed)\r\n left_type = self.accept(e.base)\r\n if isinstance(left_type, TupleType):\r\n # Special case for tuples. They support indexing only by integer\r\n # literals.\r\n index = self.unwrap(e.index)\r\n ok = False\r\n if isinstance(index, IntExpr):\r\n n = index.value\r\n ok = True\r\n elif isinstance(index, UnaryExpr):\r\n if index.op == '-':\r\n operand = index.expr\r\n if isinstance(operand, IntExpr):\r\n n = len(left_type.items) - operand.value\r\n ok = True\r\n if ok:\r\n if n >= 0 and n < len(left_type.items):\r\n return left_type.items[n]\r\n else:\r\n self.chk.fail(messages.TUPLE_INDEX_OUT_OF_RANGE, e)\r\n return AnyType()\r\n else:\r\n self.chk.fail(messages.TUPLE_INDEX_MUST_BE_AN_INT_LITERAL, e)\r\n return AnyType()\r\n else:\r\n result, method_type = self.check_op('__getitem__', left_type,\r\n e.index, e)\r\n e.method_type = method_type\r\n return result","title":""},{"docid":"9ef9dab683d2a233e7757fdaffe61b60","score":"0.47921318","text":"def node_to_index(self, node):\n return node","title":""},{"docid":"28cf4c77e861e914d5029c32a00ac4fc","score":"0.47902948","text":"def iE(e):\n return int((e + 2*N)/4)","title":""},{"docid":"a24e6cd7982c1735ce04c702db36a968","score":"0.477807","text":"def index00(x: iter):\n return x[0][0]","title":""},{"docid":"6c7f5c742e4c6c6557eb9d80f3ee93df","score":"0.47689655","text":"def convert_adsorption_energy_units(AE):\n return (AE * 96.485 * 1000)","title":""},{"docid":"6aa8ec817d3985849d5fe81b370d6f8e","score":"0.47571853","text":"def type_converted(self):\n return \"indexlist\"","title":""},{"docid":"f25837cabaac3dd3a39d0f88449c5cfb","score":"0.47503555","text":"def adjustEquitites(newsLocation):\n equityWeights = analyseNews(newsLocation)\n #Find compound adjustment and transpose so equities are columns.\n return equityWeights[['compound']].T","title":""},{"docid":"e3734dd9b7bbdf8ff36f81e732c3b15b","score":"0.4749277","text":"def convert1Dto3Dindex(index,NX,NY,NZ):\n k = index / (NX*NY)\n j = (index - k*NX*NY) / NX\n i = index - k*NX*NY - j*NX\n return [i,j,k]","title":""},{"docid":"a96139e0d433dfda756a94f3b3c26601","score":"0.47192687","text":"def orig_atom(atom,big_table):\n n = 0\n bonds_per_pair = len(big_table[0])/len(big_table)\n while atom > bonds_per_pair:\n atom -= bonds_per_pair\n n += 1\n # n is the index of atom in the original atom list\n # atom is now the index of the unit cell the atom came from\n return n","title":""},{"docid":"74f3e3317362ad4de3fe2da1d732e1dc","score":"0.47184482","text":"def masking_cluster(chs, eeg):\n\n cluster = []\n for ch in chs:\n cluster.append(eeg.info['ch_names'].index(ch))\n\n return cluster","title":""},{"docid":"938b563d0c44a6c98df693bf4d5dbe54","score":"0.47155836","text":"def ean2ean_hi(ean):\r\n eanCompact = re.sub('-', '', ean)\r\n match = re.match(r'(\\d{5})(\\d{8})', eanCompact)\r\n return int(f'0x{match.group(1)}', 0)","title":""},{"docid":"34d56f2bae417c66089a9c68dc40ebb3","score":"0.4710259","text":"def J_index_to_interaction(self, index):\n if self.net_topology is None:\n return self.interactions[index]\n else:\n symbols = sorted(set(self.net_topology.values()))\n interactions = []\n for interaction, symb in self.net_topology.items():\n if symb == symbols[index]:\n interactions.append(interaction)\n return interactions","title":""},{"docid":"922d4a3f903815fcee40900faecf32ab","score":"0.4697826","text":"def idx(self):\n return isotope.ufunc_idx(self.ions)","title":""},{"docid":"c6db570916bfe056d66f51300f64ec98","score":"0.4688453","text":"def convert_patch_index(\n self, datafile_idx: int, channel: int, pidx: int\n ) -> Tuple[int, int, int]:","title":""},{"docid":"ecae7c8b884f81c6e7d20ef36b771984","score":"0.46882156","text":"def convertir_lettre_index(lettre):\n\n lettres = \"ABCD\"\n for i in range(4):\n if lettres[i] == lettre:\n return i\n\n # erreur de coordonnees (-1 n'est jamais valide)\n # la lettre n'est pas dans A,B,C,D\n return -1","title":""},{"docid":"8f4a06bc3fc0ac8f3d524992192c632c","score":"0.4684729","text":"def osa_index(n,m):\n j = (n*(n+2)+m)/2\n return(j)","title":""},{"docid":"03b87942e0406a17ad4cac73f94effd1","score":"0.4676638","text":"def Index(self) -> int:","title":""},{"docid":"03b87942e0406a17ad4cac73f94effd1","score":"0.46765798","text":"def Index(self) -> int:","title":""},{"docid":"03b87942e0406a17ad4cac73f94effd1","score":"0.46765798","text":"def Index(self) -> int:","title":""},{"docid":"03b87942e0406a17ad4cac73f94effd1","score":"0.46765798","text":"def Index(self) -> int:","title":""},{"docid":"2e0448b5371cb320b06399ae1e705af0","score":"0.46684965","text":"def inline_to_indexes(self, inline):\n n_samples_per_inline = self.nNorth * self.nDepth\n shift = ((inline - self.startInline) // self.stepInline) * \\\n n_samples_per_inline\n for i in range(n_samples_per_inline):\n yield i + shift + 1","title":""},{"docid":"4d0bf4875f3abeaedb7f7babec692c8b","score":"0.46682584","text":"def index01(x: iter):\n return x[0][1]","title":""},{"docid":"506c66fce8d8ee6dbe1ad1e8d8189cb0","score":"0.4667202","text":"def get_alpha_indices(protein: pd.AtomGroup) -> List[int]:\n return [i for i, a in enumerate(protein.iterAtoms()) if a.getName() == \"CA\"]","title":""},{"docid":"a8d74647843bb7208ba6fa1541830ce1","score":"0.46653485","text":"def model2index(self,model):\n return self.models[model]['index']","title":""},{"docid":"57744e1f74ccf5cd755c6c1559ebfc79","score":"0.46580896","text":"def prun_inverted_index(self):\n\n\t\tself.esa_importer.open_esa_db()\n\n\t\tindex = EsaIndex(self.esa_importer.connection)\n\t\tresult = []\n\t\tappend = result.append\n\n\t\tfor term, vector in index:\n\t\t\tappend( (term, vector) )\n\n\t\tself.esa_importer.save_pruned_index(result)\n\n\t\tself.esa_importer.close_esa_db()","title":""},{"docid":"62af2c193474d31c92df64349f96b21a","score":"0.4654351","text":"def index_to_state(self, index):\n\n return np.array([t[(index // p) % n]\n for p, n, t in zip(self.prod, self.n_bins, self.translations)])","title":""},{"docid":"d9b0cbae2bd9f3a448deac341a3355fb","score":"0.46413788","text":"def setup_item_index(self):\n\t\titem_ids = self.train.anime_id\n\t\titem_index = defaultdict(lambda: -1) # maps an anime_id to the index.\n\t\tcounter = 0\n\n\t\tfor item in item_ids:\n\t\t\tif item_index[item] == -1:\n\t\t\t\titem_index[item] = counter\n\t\t\t\tcounter += 1 \n\n\t\tself.item_index = item_index","title":""},{"docid":"8ac433fdd50359b7146ac9dd63390e50","score":"0.46322238","text":"def einsteinium_atom(cls):\n return cls._namespace_SIO('SIO_011113')","title":""},{"docid":"33bda2c96ef1f79cbf6a58eee936c26d","score":"0.46231613","text":"def rules2indices(self, sents):\n if type(sents[0]) == list:\n return [[self[rule] if rule == 'Reduce' or not isinstance(rule, str) else self.pad_id for rule in sent] for sent in sents]\n else:\n sent = sents\n return [self[rule] if rule == 'Reduce' or not isinstance(rule, str) else self.pad_id for rule in sent]","title":""},{"docid":"7d8077a3d8e39df4fc9ea671be45b2f8","score":"0.46156695","text":"def index_to_num(ind):\n return int((ind + 1) * 2 + 1)","title":""},{"docid":"63a581c9d0c565579b4535cbfa687496","score":"0.46141195","text":"def compute_day_index(self):\n # (nothing to do here. Just a placeholder for future projects)\n # to-be-implemented: \n # update index_eod\n # update index_delta1\n # update index_delta2\n return self","title":""},{"docid":"19c8efb0c9ff791180f70483f79a53b5","score":"0.46120003","text":"def _get_index(self,item):\n return tuple([f(item) for f in self._filters])","title":""},{"docid":"f9ccd0987097f01445106c7bdb50d580","score":"0.46119085","text":"def fasta_indexer():\n mapping = tf.constant(FULL_RESIDUE_VOCAB)\n table = contrib_lookup.index_table_from_tensor(mapping)\n\n def mapper(residues):\n return tf.ragged.map_flat_values(table.lookup, residues)\n\n return mapper","title":""},{"docid":"8001d10dd96b882fcfd8b5b014af4408","score":"0.46097916","text":"def compute_day_index_list(self):\n # (nothing to do here. Just a placeholder for future projects)\n # to-be-implemented: \n # update index_eod\n # update index_delta1\n # update index_delta2\n return self","title":""},{"docid":"c8bec8af694ad7d6659436afede98077","score":"0.46051994","text":"def idx_to_a1(row, col):\n div = col\n column_label = \"\"\n\n while div:\n (div, mod) = divmod(div, 26)\n if mod == 0:\n mod = 26\n div -= 1\n column_label = chr(mod + 64) + column_label\n\n label = f\"{column_label}{row}\"\n return label","title":""},{"docid":"5a3657f89fe83b2861778cce04adda60","score":"0.45951632","text":"def labels_to_indices(self):\r\n if isinstance(self.xcol, str):\r\n self.xcol = self.scan.cols.index(self.xcol)\r\n if isinstance(self.x2col, str):\r\n self.x2col = self.scan.cols.index(self.x2col)","title":""},{"docid":"70effce80804c11f5dd1b9d1f2da9003","score":"0.45796746","text":"def choose_smaller_subscript(itemindex):\n if len(itemindex[0]) > 1:\n itemindex = tuple(np.asarray([[itemindex[0][0]], [itemindex[1][0]]]))\n return itemindex","title":""},{"docid":"67ba00495076e8a9e94d264a9930f350","score":"0.45781916","text":"def calculate_technical_ind_index(handle: str, start: dt.date, end: dt.date) -> pd.DataFrame:\n toReturn: pd.DataFrame = si.get_data(handle).loc[start - dt.timedelta(days=365): end]\n\n with np.errstate(invalid='ignore'):\n toReturn = add_all_ta_features(\n toReturn, open=\"open\", high=\"high\", low=\"low\", close=\"adjclose\", volume=\"volume\")\n\n # add ema20\n\n from ta.trend import ema_indicator\n from ta.trend import SMAIndicator\n\n toReturn['trend_ema20'] = ema_indicator(toReturn['close'], window=20, fillna=True)\n\n toReturn['trend_ma10'] = SMAIndicator(toReturn['close'], window=5, fillna=True)\n toReturn['trend_ma5'] = SMAIndicator(toReturn['close'], window=5, fillna=True)\n\n return toReturn.loc[start: end]","title":""},{"docid":"08c7a723c02a7f342f32038ccfc00fc4","score":"0.45706066","text":"def GetIndex(self):","title":""},{"docid":"4949803ca957f4c14b88f886bb7690b6","score":"0.45649603","text":"def pybel2ase(mol):\n species = [ase.data.chemical_symbols[atm.atomicnum] for atm in mol.atoms]\n pos = np.asarray([atm.coords for atm in mol.atoms])\n pca = sklearn.decomposition.PCA(n_components=3)\n posnew = pca.fit_transform(pos)\n atoms = ase.Atoms(species, positions=posnew)\n sys_size = np.ptp(atoms.positions, axis=0)\n atoms.rotate(-90, \"z\") # cdxml are rotated\n atoms.pbc = True\n atoms.cell = sys_size + 10\n atoms.center()\n\n return atoms","title":""},{"docid":"922022d55af6673a6098fc913b1f3aba","score":"0.45450997","text":"def oplsaa_atomtypes(strucC,cov_nblist, cov_nbindx):\n \n for pid_i, ptclObj_i in strucC.ptclC:\n NNAB = calc_nnab(pid_i,cov_nbindx)\n ELCNT = calc_elcnt(pid_i,strucC,cov_nblist,cov_nbindx)\n #\n # label carbons \n #\n if ptclObj_i.tagsDict[\"number\"] == 6 :\n if int(NNAB) == 4 :\n ptclObj_i.tagsDict[\"fftype\"] = 'CT' # Alkane\n if int(NNAB) == 3 :\n ptclObj_i.tagsDict[\"fftype\"] = 'CA' # Conjugated \n if int(NNAB) == 2 :\n ptclObj_i.tagsDict[\"fftype\"] = 'C:' # Allene\n\n \n if int(NNAB) == 1 :\n ptclObj_i.tagsDict[\"fftype\"] = '' # Aromatic C\n error_line = \" WARNING!!! carbon index \",pid_i,\" bonded to single atom \"\n sys.exit(error_line)\n #\n # label oxygens\n #\n if( ptclObj_i.tagsDict[\"number\"] == 8 ):\n if int(NNAB) == 1 :\n ptclObj_i.tagsDict[\"fftype\"] = 'O' # double bonded\n if int(NNAB) == 2 :\n ptclObj_i.tagsDict[\"fftype\"] = 'OS' # ether\n\n #\n # label nitrogens \n #\n if ptclObj_i.tagsDict[\"number\"] == 7 :\n if int(NNAB) == 3 : # amide\n ptclObj_i.tagsDict[\"fftype\"] = 'N' \n\n #\n # label sulfurs\n #\n if( ptclObj_i.tagsDict[\"number\"] == 16 ):\n if int(NNAB) == 2 :\n ptclObj_i.tagsDict[\"fftype\"] = 'S' # Thioether RSR (UA)\n\n\n #\n # label hydrogens\n #\n for pid_i, ptclObj_i in strucC.ptclC:\n NNAB = calc_nnab(pid_i,cov_nbindx)\n ELCNT = calc_elcnt(pid_i,strucC,cov_nblist,cov_nbindx)\n if( ptclObj_i.tagsDict[\"number\"] == 1 ):\n if ( NNAB > 1 ):\n sys.exit(' over coordinated H')\n pid_j = cov_nblist[ cov_nbindx[pid_i] ]\n ptclObj_j = strucC.ptclC[pid_j]\n ELCNT_j = calc_elcnt(pid_j,strucC,cov_nblist,cov_nbindx)\n\n if ( ptclObj_j.tagsDict[\"fftype\"]== 'CA' ):\n ptclObj_i.tagsDict[\"fftype\"] = 'HA' #\n if ( ptclObj_j.tagsDict[\"fftype\"]== 'CT' ):\n ptclObj_i.tagsDict[\"fftype\"] = 'HC' #\n\n\n # Check for unidentified atoms\n for pid_i, ptclObj_i in strucC.ptclC:\n if ( ptclObj_i.tagsDict[\"fftype\"] == '?' ):\n print ' atom ', pid_i , ptclObj_i.tagsDict[\"number\"],' unknow '\n sys.exit(' Unknow atom ')","title":""},{"docid":"25eec9552a0bd705a6d5916b811ecfef","score":"0.45392784","text":"def pandasIndexForFlatCoordinate(self, index):\n assert 0 <= index < self.fixedFlattenedDimension\n for name, feature in self.namedFeatures:\n domain = feature.domain\n if feature.lengthDomain:\n if index == 0:\n return (name, 'length')\n else:\n index -= 1\n elem = index // domain.flattenedDimension\n if elem < feature.maxLength:\n subIndex = index % domain.flattenedDimension\n panda = domain.pandasIndexForFlatCoordinate(subIndex)\n return (name, elem) + panda\n index -= feature.maxLength * domain.flattenedDimension\n else:\n if index < domain.flattenedDimension:\n panda = domain.pandasIndexForFlatCoordinate(index)\n return (name,) + panda\n index -= domain.flattenedDimension\n raise RuntimeError('impossible index arithmetic')","title":""},{"docid":"eb3da1fdd4210c8c2f31a013cf8c3715","score":"0.45367664","text":"def index_to_node(self, index):\n return index","title":""},{"docid":"3716df431418da52bdd4124679ad489b","score":"0.45351464","text":"def index(self, iso):\n if isinstance(iso, str) or not isinstance(iso, (Iterable, np.ndarray)):\n return np.where(self.iso == iso)[0][0]\n elif isinstance(iso, np.ndarray):\n ii = np.ndarray(iso.shape, dtype=np.int64)\n for i,k in enumerate(iso.flat):\n ii[i] = np.where(self.iso == k)[0][0]\n return ii\n elif isinstance(iso, Iterable):\n ii = []\n for k in iso:\n if isinstance(k, str):\n i = np.where(self.iso == k)[0][0]\n else:\n i = self.index(k)\n ii += [i]\n if not isinstance(iso, list):\n ii = type(iso)(ii)\n return ii\n else:\n raise AttributeError('Argument type not supported.')","title":""},{"docid":"abd75d68ce8aeecc830fa8af289fa0aa","score":"0.45337844","text":"def lustre_ost_index2string(index_number):\n if index_number > 0xffff:\n return -1, \"\"\n index_string = \"OST%04x\" % index_number\n return 0, index_string","title":""},{"docid":"c34f0262ca3f0570539b03f2aa500965","score":"0.453185","text":"def get_switched_peak_indices(asig):\n values = asig\n if hasattr(asig, \"values\"):\n values = asig.values\n return get_switched_peak_array_indices(values)","title":""},{"docid":"e7a679388f0adfbe460d8918ac15ed82","score":"0.45309493","text":"def _unflatten_2d_index(self, index):\n input_shape = index.shape\n if input_shape[-1] != 1:\n raise ValueError('Expected final dimension to be 1 but it was {}'\n .format(input_shape[-1]))\n index = index.view(-1)\n invalid_idx = index == -100\n unflat_index = torch.stack((index // self._resolution, index %\n self._resolution), dim=-1)\n unflat_index[invalid_idx, :] = -100\n return unflat_index.view(input_shape[:-1] + (2,))","title":""},{"docid":"579d8abcd8b2ad787159dc390a778cc7","score":"0.45308042","text":"def coord_to_idx(self, coord, clip=False):\n coord = u.Quantity(coord, self.unit, copy=False).value\n return coord_to_idx(self.edges.value, coord, clip)","title":""},{"docid":"e63b2aede60e894418afa0a67427079a","score":"0.45288175","text":"def _parse_annual_toxics_EFs(self):\r\n toxics = pd.read_excel(self.fpath_toxicsEFs,\r\n header=3,\r\n usecols=[0,2,3],\r\n nrows=89)\r\n toxics.rename(columns={'Chemical' : 'pollutant',\r\n 'EF' : 'ef',\r\n 'EF Units' : 'units'},\r\n inplace=True)\r\n toxics['units'] = toxics['units'].str.strip().str.lower()\r\n # calculating H2S from CEMS, so drop from EF list\r\n toxics= toxics[toxics['pollutant'] != 'Hydrogen sulfide']\r\n return toxics","title":""},{"docid":"76d184351ebf7a30eb6bb79421b89bd5","score":"0.45251414","text":"def ao_index(self, atom_p, orb_p):\n p = atom_p * self.orbitals_per_atom\n p += self.orbital_types.index(orb_p)\n\n return p","title":""},{"docid":"1967c4ff6997b1d42728707148de3d6a","score":"0.45242682","text":"def index(self, point):\n return tuple(((point + self.offset) // self.mesh_size).astype(int))","title":""},{"docid":"2817d35cfd15bfe49dd34da989151e3c","score":"0.45218515","text":"def ean2ean_lo(ean):\r\n eanCompact = re.sub('-', '', ean)\r\n match = re.match(r'(\\d{5})(\\d{8})', eanCompact)\r\n return int(f'0x{match.group(2)}', 0)","title":""},{"docid":"5bb7787c1689f6594eff10ab370e1f1c","score":"0.4517905","text":"def to_index(X, idx, UKN_IDX=1):\n X_idx = []\n for x in X:\n # We map the unknown symbols to one\n x_idx = list(map(lambda x: idx.get(x, UKN_IDX), x))\n X_idx += [x_idx]\n return X_idx","title":""},{"docid":"2ef063f50a8be80a0a4a4e76ad296fc2","score":"0.45158973","text":"def index_components(self):\n return ((self.comment, \"A\"),)","title":""},{"docid":"b4912b800a4ff5c6443d5c9c265d39f5","score":"0.45096654","text":"def get_edge_index(self,name):\n return self.get_index(name)","title":""},{"docid":"c0b377c5ddd58b110718daade7cd2070","score":"0.45093864","text":"def pidlist2indexlist(painting_df, painting_list):\n index_list = [pid2index(painting_df, painting_id) for painting_id in painting_list]\n return index_list","title":""},{"docid":"40cdc7594bb448744b2be9c3eefb3ddd","score":"0.4506455","text":"def get(self, index: int | pli.Series | list[int]) -> pli.Series:","title":""},{"docid":"8936fbb67ba7ab4836f01ada9753df74","score":"0.45050225","text":"def to_dense_index(game, data):\n\tif isinstance(game, MnkGame):\n\t\treturn game.horSize * data[1] + data[0]\n\telse:\n\t\treturn game[0] * data[1] + data[0]","title":""},{"docid":"9a6d64af3b58a2827a849587c5131126","score":"0.45023936","text":"def _mapColumn(self, index):\n columnCoords = numpy.unravel_index(index, self._columnDimensions)\n columnCoords = numpy.array(columnCoords, dtype=realDType)\n ratios = columnCoords / self._columnDimensions\n inputCoords = self._inputDimensions * ratios\n inputCoords += 0.5 * self._inputDimensions / self._columnDimensions\n inputCoords = inputCoords.astype(int)\n inputIndex = numpy.ravel_multi_index(inputCoords, self._inputDimensions)\n return inputIndex","title":""},{"docid":"fd4ceaaf0495a1b2f025c3c93b65b749","score":"0.44900307","text":"def get_object_id_by_index(self, col_index):\n object_id = self.dict_col_index_2_obj_id.get(col_index)\n if type(object_id) is int:\n if CEAnalysis.OFFSET_CHEMICAL < object_id < CEAnalysis.OFFSET_UTILITY_TYPE:\n return object_id - CEAnalysis.OFFSET_CHEMICAL, SHORT_NAME_CHEMICAL\n elif CEAnalysis.OFFSET_UTILITY_TYPE < object_id < CEAnalysis.OFFSET_EMISSION:\n return object_id - CEAnalysis.OFFSET_UTILITY_TYPE, SHORT_NAME_UTILITY_TYPE\n else:\n # since the column name for emission starts with 'emis_', so we return a substring of the column name\n return object_id[5:], SHORT_NAME_EMISSION","title":""},{"docid":"00d7fed52762577b30b5b39d87be6e9c","score":"0.44854128","text":"def id_to_index(self, cell_id):\n\n # TODO: Need __getitem__\n raise NotImplementedError","title":""},{"docid":"7116f21eb4c0c9c01a21feb2ca9d3d34","score":"0.44758412","text":"def index_important_species(self) -> None:\n self.species_indices = {}\n names = [species.name for species in self.get_species_list()]\n for element in [\n \"C+\",\n \"H+\",\n \"H2\",\n \"SI+\",\n \"S+\",\n \"CL+\",\n \"CO\",\n \"HE+\",\n \"#H\",\n \"#H2\",\n \"#N\",\n \"#O\",\n \"#OH\",\n \"SURFACE\",\n \"BULK\",\n ] + elementList:\n try:\n species_index = names.index(element) + 1\n except:\n logging.info(f\"\\t{element} not in network, adding dummy index\")\n species_index = len(self.get_species_list()) + 1\n name = \"n\" + element.lower().replace(\"+\", \"x\").replace(\n \"e-\", \"elec\"\n ).replace(\"#\", \"g\")\n self.species_indices[name] = species_index","title":""},{"docid":"f03ce2224af5478681d1ea8f90beb769","score":"0.4473924","text":"def gaseste_iesire(self): \n nod = self.start.tiles\n m = len(nod) - 1\n for i in range(len(nod[0])):\n if nod[0][i] != '#':\n return (0, i), (0, nod[0].index('#', i) - 1)\n if nod[m][i] != '#':\n return (m, i), (m, nod[m].index('#', i) - 1)\n n = len(nod[0]) - 1\n for i in range(m + 1):\n if nod[i][0] != '#':\n start = i\n while nod[i][0] != '#':\n i += 1\n return (start, 0), (i - 1, 0)\n elif nod[i][n] != '#':\n start = i\n while nod[i][n] != '#':\n i += 1\n return (start, n), (i - 1, n)","title":""},{"docid":"363d0cb44fb66322d1fbf435265585d7","score":"0.44682097","text":"def get_label2index(self):\n\t\treturn self.label2index","title":""},{"docid":"7ce3d1d6efc1bf3f424acabf3a6ad069","score":"0.44664037","text":"def ra(self):\n return self.index_map[\"ra\"][:]","title":""},{"docid":"5e799dc86a7be036393d22e5f39f8209","score":"0.4463673","text":"def idx(self):\n ...","title":""},{"docid":"5e799dc86a7be036393d22e5f39f8209","score":"0.4463673","text":"def idx(self):\n ...","title":""},{"docid":"e56ddaddfb54c4e57ae52563b37952e7","score":"0.44579217","text":"def anes(self) -> List[ANE]:\n return self._anes","title":""},{"docid":"3f7752c45ed681faa170cb82aceb8a83","score":"0.4452417","text":"def _indexed_eci(self, detector):\n indexes=self._detector.detect_indexes(detector)\n sub_eci=detector.vectorized_eci()\n\n from_ix=np.array(indexes)[:,0]\n to_ix=np.array(indexes)[:,1]\n\n #Ensure that the from indexes are simply values of 0,1,2,3,4...n\n #This part is now redundant due to check at detector construction,\n #but I'll leave it just in case\n try:\n assert(not np.any(from_ix-list(range(len(from_ix)))))\n except AssertionError:\n raise ValueError(\"The provided indexes of the detector are not sequential! If you are sure you're not missing\\\n basis functions, reindex the basis functions and try again.\")\n\n sub_eci.index=to_ix\n return sub_eci","title":""},{"docid":"23fe5fb9a8f5865ec26c221d8dbf06fb","score":"0.4449667","text":"def transform_str_to_int(orig_node_list, edges):\n for i in range(len(edges)):\n ind_first_ele = [y[0] for y in orig_node_list].index(edges[i][0])\n ind_sec_ele = [y[0] for y in orig_node_list].index(edges[i][1])\n edges[i] = (ind_first_ele, ind_sec_ele)\n return edges","title":""},{"docid":"fc80d590ba1e7b1f241f55e4f245537e","score":"0.44437078","text":"def pandasIndexForFlatCoordinate(self, index):\n return NotImplementedError('domain flattening not implemented')","title":""},{"docid":"88702a752908fb44d58c85896c9e9ba8","score":"0.44415355","text":"def abstract2ids(abstract_words, vocab, article_oovs):\n ids = []\n unk_id = vocab.word2id(UNKNOWN_TOKEN)\n for w in abstract_words:\n i = vocab.word2id(w)\n if i == unk_id: # If w is an OOV word\n if w in article_oovs: # If w is an in-article OOV\n vocab_idx = vocab.size() + article_oovs.index(w) # Map to its temporary article OOV number\n ids.append(vocab_idx)\n else: # If w is an out-of-article OOV\n ids.append(unk_id) # Map to the UNK token id\n else:\n ids.append(i)\n return ids","title":""},{"docid":"173bf1089b6732dd51f680d67facf67b","score":"0.44381887","text":"def _toindex(value):\n indx = np.empty(value.shape, dtype=np.int32)\n indx = np.floor(value + 0.5, out=indx)\n return indx","title":""},{"docid":"71c49b5b01dc60f0e28b6537fb9a4d1e","score":"0.44376734","text":"def index_to_coordinate(dims):\n _ = divmod # SO WE KEEP THE IMPORT\n\n if len(dims) == 1:\n return _identity\n\n num_dims = len(dims)\n prod = [1] * num_dims\n acc = 1\n domain = range(0, num_dims)\n for i in reversed(domain):\n prod[i] = acc\n acc *= dims[i]\n\n commands = []\n coords = []\n for i in domain:\n if i == num_dims - 1:\n commands.append(\"\\tc\" + unicode(i) + \" = index\")\n else:\n commands.append(\"\\tc\" + unicode(i) + \", index = divmod(index, \" + unicode(prod[i]) + \")\")\n coords.append(\"c\" + unicode(i))\n output = None\n code = \"def output(index):\\n\" + \\\n \"\\n\".join(commands) + \"\\n\" + \\\n \"\\treturn \" + \", \".join(coords)\n exec code\n return output","title":""},{"docid":"19bafe1b548f622842999e7f6420ba18","score":"0.4436351","text":"def SweepEntityTransform(self) -> _n_2_t_6:","title":""},{"docid":"19bafe1b548f622842999e7f6420ba18","score":"0.4436351","text":"def SweepEntityTransform(self) -> _n_2_t_6:","title":""},{"docid":"6d20feea106d530df1b6c845d9d998d9","score":"0.44357267","text":"def index_to_symbol(self, index):\n\n symbol = self.alphabet[index]\n return symbol","title":""}],"string":"[\n {\n \"docid\": \"b4b9a359fbc4f4838157c22f060ebd60\",\n \"score\": \"0.7522824\",\n \"text\": \"def ase_to_castep_index(atoms, indices):\\n if isinstance(indices, int):\\n indices = [indices]\\n symbols = np.array(atoms.get_chemical_symbols())\\n iatoms = np.arange(len(atoms))\\n res = []\\n # Iterate through given indices\\n for i in indices:\\n mask = symbols == symbols[i] # Select the same species\\n\\n # CASTEP start counting from 1\\n counter = np.where(iatoms[mask] == i)[0][0] + 1\\n res.append([symbols[i], counter])\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1868d6ec27a0d349ec03176c5301771b\",\n \"score\": \"0.7079155\",\n \"text\": \"def castep_to_atoms(atoms, specie, ion):\\n return [atom for atom in atoms if atom.symbol == specie][ion - 1].index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e44d228e7b4b76cfa7bc94580bfee41\",\n \"score\": \"0.512058\",\n \"text\": \"def atom(self, ao_index):\\n atom_index = ao_index // self.orbitals_per_atom\\n return atom_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa599f5fb0aa6685c0d7360eae8669eb\",\n \"score\": \"0.50818807\",\n \"text\": \"def decode(self,eps=0.01):\\r\\n # take argmax\\r\\n codes = self.u.argmax(1)\\r\\n # get sorted ids\\r\\n params = self.showModel(min_pi=eps)\\r\\n # assign each observation to corresponding cluster\\r\\n clust_pos = []\\r\\n for p in params:\\r\\n clust_pos.append(codes==p[1])\\r\\n return clust_pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd11963aeb5fac1035f4d935934dc37b\",\n \"score\": \"0.5079113\",\n \"text\": \"def get_index():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f85373571ea87d32ee1192f35b9fa74\",\n \"score\": \"0.50703\",\n \"text\": \"def _sense2idx(self, a_rels):\\n n_senses = len(self.cls2idx)\\n isense = isenses = vsense = None\\n for irel in a_rels:\\n isenses = irel[SENSE]\\n vsense = np.zeros(n_senses)\\n for isense in isenses:\\n isense = SHORT2FULL.get(isense, isense)\\n vsense[self.cls2idx[isense]] = 1\\n irel[SENSE] = vsense / sum(vsense)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6be1c548bbee245c57e79385f3714af2\",\n \"score\": \"0.506561\",\n \"text\": \"def CreateAssemblyAddrFromIndex( self, assy_ndx ):\\n result = ( -1, -1 -1 )\\n if self.core is not None:\\n places = np.argwhere( self.core.coreMap == assy_ndx + 1 )\\n if len( places ) > 0:\\n place = places[ -1 ]\\n result = ( assy_ndx, int( place[ 1 ] ), int( place[ 0 ] ) )\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4800fba4b94ed9614935d8c66ca85404\",\n \"score\": \"0.505327\",\n \"text\": \"def to_index(self, value):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5edbf0fbe9e52ab430a03f361d7bafbb\",\n \"score\": \"0.5052407\",\n \"text\": \"def _standardize_mass_index_energy_map_indexing(self):\\n mie_map = self._mass_index_spe_map\\n std_mie_map = dict()\\n for m, ie_map in mie_map.items():\\n std_ie_map = dict()\\n for idx, energy in ie_map.items():\\n next_idx = self._standard_index(idx)\\n std_ie_map[next_idx] = energy\\n std_mie_map[m] = std_ie_map\\n self._mass_index_spe_map = std_mie_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef824da26a4c24ba43c12720e7f67c54\",\n \"score\": \"0.49817502\",\n \"text\": \"def xray_refractive_index(elements, energy_kev, atom_per_volume):\\n f1, f2 = atomic_scattering_factor(elements, energy_kev)\\n ft = f1 + 1j * f2\\n ft = ft.reshape(np.size(elements), np.size(energy_kev)).sum(axis=0) # sum over elements\\n atom_per_volume = atom_per_volume * 1e30 # atom per m^3\\n wavelength = energy2wave(energy_kev) * 1e-10 # m\\n return 1 - (ft * fg.r0 * atom_per_volume * wavelength**2 / (2 * fg.pi))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df054d7ca949e3096d60c970cf1c8762\",\n \"score\": \"0.49738985\",\n \"text\": \"def index(value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47051b08439274d4d9eac96093f3e6fd\",\n \"score\": \"0.4967376\",\n \"text\": \"def indices_to_atoms(self, mdl, atom_indices):\\n return [mdl.atoms[x-1] for x in atom_indices]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64cb96c7523dd6fbddda154b8df0759f\",\n \"score\": \"0.49537516\",\n \"text\": \"def to_indexer(self, cutoff=None, from_cutoff=True):\\n if from_cutoff:\\n relative_index = self.to_relative(cutoff).to_pandas()\\n if is_integer_index(relative_index):\\n return relative_index - 1\\n else:\\n # What does indexer mean if fh is timedelta?\\n msg = (\\n \\\"The indexer for timedelta-like forecasting horizon \\\"\\n \\\"is not yet implemented\\\"\\n )\\n raise NotImplementedError(msg)\\n else:\\n relative = self.to_relative(cutoff)\\n return relative - relative.to_pandas()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee8929607afa11df1d8c4dcceaad430\",\n \"score\": \"0.49421525\",\n \"text\": \"def convert_number(self, atoms: List[int]) -> np.ndarray:\\n return self.dax[atoms, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"898aecc0bb02961a6924f4ff0e3f8ea5\",\n \"score\": \"0.4937189\",\n \"text\": \"def titles_indices(estaticos_market_filter_raw):\\n \\n titles = estaticos_market_filter_raw['id']\\n indices = pd.Series(estaticos_market_filter_raw.index, index=estaticos_market_filter_raw['id'])\\n \\n return titles, indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8040b94a6c89d4a51610976b4be7dc18\",\n \"score\": \"0.49319562\",\n \"text\": \"def __index_convert(self, index):\\n try:\\n # index is column index\\n index = int(float(index))\\n except:\\n pass\\n finally:\\n if type(index) == str:\\n row = (self.csv['name'] == index)\\n elif type(index) == int:\\n row = (self.csv['index'] == index)\\n else:\\n raise ValueError(\\\"Index input error !\\\")\\n\\n if sum(row) == 0:\\n raise ValueError('Index {0} is not find in csv'.format(index))\\n else:\\n return str(self.csv[row]['name'].values[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd50289a35b5a8a7be5161eb58748d4\",\n \"score\": \"0.4919603\",\n \"text\": \"def indices(self):\\n return [\\n b's:po___',\\n b'p:so___',\\n b'o:sp___',\\n b'po:s___',\\n b'so:p___',\\n b'sp:o___',\\n b'spo:c__',\\n b'c:spo__',\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22395c2759c5731cd58dca155885d1ed\",\n \"score\": \"0.49045992\",\n \"text\": \"def decode_index(self,index):\\n\\t\\treturn index%self.n_h,index//self.n_h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa9a6cedc43eb3f81104d2bf4e09462\",\n \"score\": \"0.48876017\",\n \"text\": \"def _transform_index(self, index):\\r\\n # in ImageGrid index is guaranteed to be a tuple.\\r\\n\\r\\n # first thing, switch the coordinates since ImageGrid is column\\r\\n # major and ._grid is row major.\\r\\n new_ind = [index[1], index[0]]\\r\\n\\r\\n # now take into account that the ImageGrid origin may be lower-left,\\r\\n # while the ._grid origin is upper-left.\\r\\n if self._origin == 'lower-left':\\r\\n new_ind[0] = self._height - new_ind[0] - 1\\r\\n\\r\\n return tuple(new_ind)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485c0320b2c2725bfd89c643fb189f84\",\n \"score\": \"0.48617253\",\n \"text\": \"def get(self, eid, etype=\\\"n\\\"):\\n eid = str(eid)\\n if eid[0] not in \\\"nwr\\\":\\n eid = etype[0].lower() + eid\\n return self.index[eid]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75e5e187238615c7138026cb0d3af470\",\n \"score\": \"0.48196426\",\n \"text\": \"def convert_network_output_to_analysis_model_input(self):\\n self.xtfreq = self.result[:, :self.max_sines]\\n self.xtmag = self.result[:, self.max_sines : 2*self.max_sines]\\n self.xtphase = self.result[:, 2*self.max_sines : 3*self.max_sines]\\n\\n assert self.xtfreq.shape == self.xtmag.shape == self.xtphase.shape\\n\\n phase_range = self.settings['sine_model_settings']['phase_range']\\n freq_range = self.settings['sine_model_settings']['freq_range']\\n mag_range = self.settings['sine_model_settings']['mag_range']\\n\\n mag_normalised_range = self.settings['sine_model_settings']['mag_normalised_range']\\n phase_normalised_range = self.settings['sine_model_settings']['phase_normalised_range']\\n freq_normalised_range = self.settings['sine_model_settings']['freq_normalised_range']\\n\\n # Unnormalise\\n self.xtfreq = unnormalise_range(self.xtfreq, freq_normalised_range, freq_range)\\n self.xtphase = unnormalise_range(self.xtphase, phase_normalised_range, phase_range)\\n self.xtmag = unnormalise_range(self.xtmag, mag_normalised_range, mag_range)\\n\\n return self.xtfreq, self.xtmag, self.xtphase\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2d841bbd4862e7099764db54a26469\",\n \"score\": \"0.4818752\",\n \"text\": \"def generate_ionic_fix_cons(atoms, indices, mask=None, count_start=1):\\n\\n # Convert to castep style indices (element, index)\\n castep_indices = ase_to_castep_index(atoms, indices)\\n count = count_start\\n lines = []\\n if mask is None:\\n mask = (1, 1, 1)\\n for symbol, i in castep_indices:\\n if mask[0]:\\n lines.append(f\\\"{count:<4d} {symbol:<2} {i:<4d} 1 0 0\\\")\\n if mask[1]:\\n lines.append(f\\\"{count + 1:<4d} {symbol:<2} {i:<4d} 0 1 0\\\")\\n if mask[2]:\\n lines.append(f\\\"{count + 2:<4d} {symbol:<2} {i:<4d} 0 0 1\\\")\\n count += sum(mask)\\n return lines, count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6a4a10f9f1794fb6de870fc25c8b937\",\n \"score\": \"0.48107785\",\n \"text\": \"def sample2index(self):\\n return self.sample_to_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8845796ff85417ce2ee3c2773b4eb62\",\n \"score\": \"0.48075914\",\n \"text\": \"def visit_index_expr(self, e: IndexExpr) -> Type:\\r\\n if e.analyzed:\\r\\n # It's actually a type application.\\r\\n return self.accept(e.analyzed)\\r\\n left_type = self.accept(e.base)\\r\\n if isinstance(left_type, TupleType):\\r\\n # Special case for tuples. They support indexing only by integer\\r\\n # literals.\\r\\n index = self.unwrap(e.index)\\r\\n ok = False\\r\\n if isinstance(index, IntExpr):\\r\\n n = index.value\\r\\n ok = True\\r\\n elif isinstance(index, UnaryExpr):\\r\\n if index.op == '-':\\r\\n operand = index.expr\\r\\n if isinstance(operand, IntExpr):\\r\\n n = len(left_type.items) - operand.value\\r\\n ok = True\\r\\n if ok:\\r\\n if n >= 0 and n < len(left_type.items):\\r\\n return left_type.items[n]\\r\\n else:\\r\\n self.chk.fail(messages.TUPLE_INDEX_OUT_OF_RANGE, e)\\r\\n return AnyType()\\r\\n else:\\r\\n self.chk.fail(messages.TUPLE_INDEX_MUST_BE_AN_INT_LITERAL, e)\\r\\n return AnyType()\\r\\n else:\\r\\n result, method_type = self.check_op('__getitem__', left_type,\\r\\n e.index, e)\\r\\n e.method_type = method_type\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef9dab683d2a233e7757fdaffe61b60\",\n \"score\": \"0.47921318\",\n \"text\": \"def node_to_index(self, node):\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28cf4c77e861e914d5029c32a00ac4fc\",\n \"score\": \"0.47902948\",\n \"text\": \"def iE(e):\\n return int((e + 2*N)/4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a24e6cd7982c1735ce04c702db36a968\",\n \"score\": \"0.477807\",\n \"text\": \"def index00(x: iter):\\n return x[0][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c7f5c742e4c6c6557eb9d80f3ee93df\",\n \"score\": \"0.47689655\",\n \"text\": \"def convert_adsorption_energy_units(AE):\\n return (AE * 96.485 * 1000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aa8ec817d3985849d5fe81b370d6f8e\",\n \"score\": \"0.47571853\",\n \"text\": \"def type_converted(self):\\n return \\\"indexlist\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f25837cabaac3dd3a39d0f88449c5cfb\",\n \"score\": \"0.47503555\",\n \"text\": \"def adjustEquitites(newsLocation):\\n equityWeights = analyseNews(newsLocation)\\n #Find compound adjustment and transpose so equities are columns.\\n return equityWeights[['compound']].T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3734dd9b7bbdf8ff36f81e732c3b15b\",\n \"score\": \"0.4749277\",\n \"text\": \"def convert1Dto3Dindex(index,NX,NY,NZ):\\n k = index / (NX*NY)\\n j = (index - k*NX*NY) / NX\\n i = index - k*NX*NY - j*NX\\n return [i,j,k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96139e0d433dfda756a94f3b3c26601\",\n \"score\": \"0.47192687\",\n \"text\": \"def orig_atom(atom,big_table):\\n n = 0\\n bonds_per_pair = len(big_table[0])/len(big_table)\\n while atom > bonds_per_pair:\\n atom -= bonds_per_pair\\n n += 1\\n # n is the index of atom in the original atom list\\n # atom is now the index of the unit cell the atom came from\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74f3e3317362ad4de3fe2da1d732e1dc\",\n \"score\": \"0.47184482\",\n \"text\": \"def masking_cluster(chs, eeg):\\n\\n cluster = []\\n for ch in chs:\\n cluster.append(eeg.info['ch_names'].index(ch))\\n\\n return cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"938b563d0c44a6c98df693bf4d5dbe54\",\n \"score\": \"0.47155836\",\n \"text\": \"def ean2ean_hi(ean):\\r\\n eanCompact = re.sub('-', '', ean)\\r\\n match = re.match(r'(\\\\d{5})(\\\\d{8})', eanCompact)\\r\\n return int(f'0x{match.group(1)}', 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34d56f2bae417c66089a9c68dc40ebb3\",\n \"score\": \"0.4710259\",\n \"text\": \"def J_index_to_interaction(self, index):\\n if self.net_topology is None:\\n return self.interactions[index]\\n else:\\n symbols = sorted(set(self.net_topology.values()))\\n interactions = []\\n for interaction, symb in self.net_topology.items():\\n if symb == symbols[index]:\\n interactions.append(interaction)\\n return interactions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"922d4a3f903815fcee40900faecf32ab\",\n \"score\": \"0.4697826\",\n \"text\": \"def idx(self):\\n return isotope.ufunc_idx(self.ions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6db570916bfe056d66f51300f64ec98\",\n \"score\": \"0.4688453\",\n \"text\": \"def convert_patch_index(\\n self, datafile_idx: int, channel: int, pidx: int\\n ) -> Tuple[int, int, int]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecae7c8b884f81c6e7d20ef36b771984\",\n \"score\": \"0.46882156\",\n \"text\": \"def convertir_lettre_index(lettre):\\n\\n lettres = \\\"ABCD\\\"\\n for i in range(4):\\n if lettres[i] == lettre:\\n return i\\n\\n # erreur de coordonnees (-1 n'est jamais valide)\\n # la lettre n'est pas dans A,B,C,D\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f4a06bc3fc0ac8f3d524992192c632c\",\n \"score\": \"0.4684729\",\n \"text\": \"def osa_index(n,m):\\n j = (n*(n+2)+m)/2\\n return(j)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b87942e0406a17ad4cac73f94effd1\",\n \"score\": \"0.4676638\",\n \"text\": \"def Index(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b87942e0406a17ad4cac73f94effd1\",\n \"score\": \"0.46765798\",\n \"text\": \"def Index(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b87942e0406a17ad4cac73f94effd1\",\n \"score\": \"0.46765798\",\n \"text\": \"def Index(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b87942e0406a17ad4cac73f94effd1\",\n \"score\": \"0.46765798\",\n \"text\": \"def Index(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e0448b5371cb320b06399ae1e705af0\",\n \"score\": \"0.46684965\",\n \"text\": \"def inline_to_indexes(self, inline):\\n n_samples_per_inline = self.nNorth * self.nDepth\\n shift = ((inline - self.startInline) // self.stepInline) * \\\\\\n n_samples_per_inline\\n for i in range(n_samples_per_inline):\\n yield i + shift + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0bf4875f3abeaedb7f7babec692c8b\",\n \"score\": \"0.46682584\",\n \"text\": \"def index01(x: iter):\\n return x[0][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506c66fce8d8ee6dbe1ad1e8d8189cb0\",\n \"score\": \"0.4667202\",\n \"text\": \"def get_alpha_indices(protein: pd.AtomGroup) -> List[int]:\\n return [i for i, a in enumerate(protein.iterAtoms()) if a.getName() == \\\"CA\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d74647843bb7208ba6fa1541830ce1\",\n \"score\": \"0.46653485\",\n \"text\": \"def model2index(self,model):\\n return self.models[model]['index']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57744e1f74ccf5cd755c6c1559ebfc79\",\n \"score\": \"0.46580896\",\n \"text\": \"def prun_inverted_index(self):\\n\\n\\t\\tself.esa_importer.open_esa_db()\\n\\n\\t\\tindex = EsaIndex(self.esa_importer.connection)\\n\\t\\tresult = []\\n\\t\\tappend = result.append\\n\\n\\t\\tfor term, vector in index:\\n\\t\\t\\tappend( (term, vector) )\\n\\n\\t\\tself.esa_importer.save_pruned_index(result)\\n\\n\\t\\tself.esa_importer.close_esa_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62af2c193474d31c92df64349f96b21a\",\n \"score\": \"0.4654351\",\n \"text\": \"def index_to_state(self, index):\\n\\n return np.array([t[(index // p) % n]\\n for p, n, t in zip(self.prod, self.n_bins, self.translations)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b0cbae2bd9f3a448deac341a3355fb\",\n \"score\": \"0.46413788\",\n \"text\": \"def setup_item_index(self):\\n\\t\\titem_ids = self.train.anime_id\\n\\t\\titem_index = defaultdict(lambda: -1) # maps an anime_id to the index.\\n\\t\\tcounter = 0\\n\\n\\t\\tfor item in item_ids:\\n\\t\\t\\tif item_index[item] == -1:\\n\\t\\t\\t\\titem_index[item] = counter\\n\\t\\t\\t\\tcounter += 1 \\n\\n\\t\\tself.item_index = item_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac433fdd50359b7146ac9dd63390e50\",\n \"score\": \"0.46322238\",\n \"text\": \"def einsteinium_atom(cls):\\n return cls._namespace_SIO('SIO_011113')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33bda2c96ef1f79cbf6a58eee936c26d\",\n \"score\": \"0.46231613\",\n \"text\": \"def rules2indices(self, sents):\\n if type(sents[0]) == list:\\n return [[self[rule] if rule == 'Reduce' or not isinstance(rule, str) else self.pad_id for rule in sent] for sent in sents]\\n else:\\n sent = sents\\n return [self[rule] if rule == 'Reduce' or not isinstance(rule, str) else self.pad_id for rule in sent]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d8077a3d8e39df4fc9ea671be45b2f8\",\n \"score\": \"0.46156695\",\n \"text\": \"def index_to_num(ind):\\n return int((ind + 1) * 2 + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63a581c9d0c565579b4535cbfa687496\",\n \"score\": \"0.46141195\",\n \"text\": \"def compute_day_index(self):\\n # (nothing to do here. Just a placeholder for future projects)\\n # to-be-implemented: \\n # update index_eod\\n # update index_delta1\\n # update index_delta2\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c8efb0c9ff791180f70483f79a53b5\",\n \"score\": \"0.46120003\",\n \"text\": \"def _get_index(self,item):\\n return tuple([f(item) for f in self._filters])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ccd0987097f01445106c7bdb50d580\",\n \"score\": \"0.46119085\",\n \"text\": \"def fasta_indexer():\\n mapping = tf.constant(FULL_RESIDUE_VOCAB)\\n table = contrib_lookup.index_table_from_tensor(mapping)\\n\\n def mapper(residues):\\n return tf.ragged.map_flat_values(table.lookup, residues)\\n\\n return mapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8001d10dd96b882fcfd8b5b014af4408\",\n \"score\": \"0.46097916\",\n \"text\": \"def compute_day_index_list(self):\\n # (nothing to do here. Just a placeholder for future projects)\\n # to-be-implemented: \\n # update index_eod\\n # update index_delta1\\n # update index_delta2\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8bec8af694ad7d6659436afede98077\",\n \"score\": \"0.46051994\",\n \"text\": \"def idx_to_a1(row, col):\\n div = col\\n column_label = \\\"\\\"\\n\\n while div:\\n (div, mod) = divmod(div, 26)\\n if mod == 0:\\n mod = 26\\n div -= 1\\n column_label = chr(mod + 64) + column_label\\n\\n label = f\\\"{column_label}{row}\\\"\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3657f89fe83b2861778cce04adda60\",\n \"score\": \"0.45951632\",\n \"text\": \"def labels_to_indices(self):\\r\\n if isinstance(self.xcol, str):\\r\\n self.xcol = self.scan.cols.index(self.xcol)\\r\\n if isinstance(self.x2col, str):\\r\\n self.x2col = self.scan.cols.index(self.x2col)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70effce80804c11f5dd1b9d1f2da9003\",\n \"score\": \"0.45796746\",\n \"text\": \"def choose_smaller_subscript(itemindex):\\n if len(itemindex[0]) > 1:\\n itemindex = tuple(np.asarray([[itemindex[0][0]], [itemindex[1][0]]]))\\n return itemindex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67ba00495076e8a9e94d264a9930f350\",\n \"score\": \"0.45781916\",\n \"text\": \"def calculate_technical_ind_index(handle: str, start: dt.date, end: dt.date) -> pd.DataFrame:\\n toReturn: pd.DataFrame = si.get_data(handle).loc[start - dt.timedelta(days=365): end]\\n\\n with np.errstate(invalid='ignore'):\\n toReturn = add_all_ta_features(\\n toReturn, open=\\\"open\\\", high=\\\"high\\\", low=\\\"low\\\", close=\\\"adjclose\\\", volume=\\\"volume\\\")\\n\\n # add ema20\\n\\n from ta.trend import ema_indicator\\n from ta.trend import SMAIndicator\\n\\n toReturn['trend_ema20'] = ema_indicator(toReturn['close'], window=20, fillna=True)\\n\\n toReturn['trend_ma10'] = SMAIndicator(toReturn['close'], window=5, fillna=True)\\n toReturn['trend_ma5'] = SMAIndicator(toReturn['close'], window=5, fillna=True)\\n\\n return toReturn.loc[start: end]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08c7a723c02a7f342f32038ccfc00fc4\",\n \"score\": \"0.45706066\",\n \"text\": \"def GetIndex(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4949803ca957f4c14b88f886bb7690b6\",\n \"score\": \"0.45649603\",\n \"text\": \"def pybel2ase(mol):\\n species = [ase.data.chemical_symbols[atm.atomicnum] for atm in mol.atoms]\\n pos = np.asarray([atm.coords for atm in mol.atoms])\\n pca = sklearn.decomposition.PCA(n_components=3)\\n posnew = pca.fit_transform(pos)\\n atoms = ase.Atoms(species, positions=posnew)\\n sys_size = np.ptp(atoms.positions, axis=0)\\n atoms.rotate(-90, \\\"z\\\") # cdxml are rotated\\n atoms.pbc = True\\n atoms.cell = sys_size + 10\\n atoms.center()\\n\\n return atoms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"922022d55af6673a6098fc913b1f3aba\",\n \"score\": \"0.45450997\",\n \"text\": \"def oplsaa_atomtypes(strucC,cov_nblist, cov_nbindx):\\n \\n for pid_i, ptclObj_i in strucC.ptclC:\\n NNAB = calc_nnab(pid_i,cov_nbindx)\\n ELCNT = calc_elcnt(pid_i,strucC,cov_nblist,cov_nbindx)\\n #\\n # label carbons \\n #\\n if ptclObj_i.tagsDict[\\\"number\\\"] == 6 :\\n if int(NNAB) == 4 :\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'CT' # Alkane\\n if int(NNAB) == 3 :\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'CA' # Conjugated \\n if int(NNAB) == 2 :\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'C:' # Allene\\n\\n \\n if int(NNAB) == 1 :\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = '' # Aromatic C\\n error_line = \\\" WARNING!!! carbon index \\\",pid_i,\\\" bonded to single atom \\\"\\n sys.exit(error_line)\\n #\\n # label oxygens\\n #\\n if( ptclObj_i.tagsDict[\\\"number\\\"] == 8 ):\\n if int(NNAB) == 1 :\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'O' # double bonded\\n if int(NNAB) == 2 :\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'OS' # ether\\n\\n #\\n # label nitrogens \\n #\\n if ptclObj_i.tagsDict[\\\"number\\\"] == 7 :\\n if int(NNAB) == 3 : # amide\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'N' \\n\\n #\\n # label sulfurs\\n #\\n if( ptclObj_i.tagsDict[\\\"number\\\"] == 16 ):\\n if int(NNAB) == 2 :\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'S' # Thioether RSR (UA)\\n\\n\\n #\\n # label hydrogens\\n #\\n for pid_i, ptclObj_i in strucC.ptclC:\\n NNAB = calc_nnab(pid_i,cov_nbindx)\\n ELCNT = calc_elcnt(pid_i,strucC,cov_nblist,cov_nbindx)\\n if( ptclObj_i.tagsDict[\\\"number\\\"] == 1 ):\\n if ( NNAB > 1 ):\\n sys.exit(' over coordinated H')\\n pid_j = cov_nblist[ cov_nbindx[pid_i] ]\\n ptclObj_j = strucC.ptclC[pid_j]\\n ELCNT_j = calc_elcnt(pid_j,strucC,cov_nblist,cov_nbindx)\\n\\n if ( ptclObj_j.tagsDict[\\\"fftype\\\"]== 'CA' ):\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'HA' #\\n if ( ptclObj_j.tagsDict[\\\"fftype\\\"]== 'CT' ):\\n ptclObj_i.tagsDict[\\\"fftype\\\"] = 'HC' #\\n\\n\\n # Check for unidentified atoms\\n for pid_i, ptclObj_i in strucC.ptclC:\\n if ( ptclObj_i.tagsDict[\\\"fftype\\\"] == '?' ):\\n print ' atom ', pid_i , ptclObj_i.tagsDict[\\\"number\\\"],' unknow '\\n sys.exit(' Unknow atom ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25eec9552a0bd705a6d5916b811ecfef\",\n \"score\": \"0.45392784\",\n \"text\": \"def pandasIndexForFlatCoordinate(self, index):\\n assert 0 <= index < self.fixedFlattenedDimension\\n for name, feature in self.namedFeatures:\\n domain = feature.domain\\n if feature.lengthDomain:\\n if index == 0:\\n return (name, 'length')\\n else:\\n index -= 1\\n elem = index // domain.flattenedDimension\\n if elem < feature.maxLength:\\n subIndex = index % domain.flattenedDimension\\n panda = domain.pandasIndexForFlatCoordinate(subIndex)\\n return (name, elem) + panda\\n index -= feature.maxLength * domain.flattenedDimension\\n else:\\n if index < domain.flattenedDimension:\\n panda = domain.pandasIndexForFlatCoordinate(index)\\n return (name,) + panda\\n index -= domain.flattenedDimension\\n raise RuntimeError('impossible index arithmetic')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb3da1fdd4210c8c2f31a013cf8c3715\",\n \"score\": \"0.45367664\",\n \"text\": \"def index_to_node(self, index):\\n return index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3716df431418da52bdd4124679ad489b\",\n \"score\": \"0.45351464\",\n \"text\": \"def index(self, iso):\\n if isinstance(iso, str) or not isinstance(iso, (Iterable, np.ndarray)):\\n return np.where(self.iso == iso)[0][0]\\n elif isinstance(iso, np.ndarray):\\n ii = np.ndarray(iso.shape, dtype=np.int64)\\n for i,k in enumerate(iso.flat):\\n ii[i] = np.where(self.iso == k)[0][0]\\n return ii\\n elif isinstance(iso, Iterable):\\n ii = []\\n for k in iso:\\n if isinstance(k, str):\\n i = np.where(self.iso == k)[0][0]\\n else:\\n i = self.index(k)\\n ii += [i]\\n if not isinstance(iso, list):\\n ii = type(iso)(ii)\\n return ii\\n else:\\n raise AttributeError('Argument type not supported.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd75d68ce8aeecc830fa8af289fa0aa\",\n \"score\": \"0.45337844\",\n \"text\": \"def lustre_ost_index2string(index_number):\\n if index_number > 0xffff:\\n return -1, \\\"\\\"\\n index_string = \\\"OST%04x\\\" % index_number\\n return 0, index_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34f0262ca3f0570539b03f2aa500965\",\n \"score\": \"0.453185\",\n \"text\": \"def get_switched_peak_indices(asig):\\n values = asig\\n if hasattr(asig, \\\"values\\\"):\\n values = asig.values\\n return get_switched_peak_array_indices(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a679388f0adfbe460d8918ac15ed82\",\n \"score\": \"0.45309493\",\n \"text\": \"def _unflatten_2d_index(self, index):\\n input_shape = index.shape\\n if input_shape[-1] != 1:\\n raise ValueError('Expected final dimension to be 1 but it was {}'\\n .format(input_shape[-1]))\\n index = index.view(-1)\\n invalid_idx = index == -100\\n unflat_index = torch.stack((index // self._resolution, index %\\n self._resolution), dim=-1)\\n unflat_index[invalid_idx, :] = -100\\n return unflat_index.view(input_shape[:-1] + (2,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579d8abcd8b2ad787159dc390a778cc7\",\n \"score\": \"0.45308042\",\n \"text\": \"def coord_to_idx(self, coord, clip=False):\\n coord = u.Quantity(coord, self.unit, copy=False).value\\n return coord_to_idx(self.edges.value, coord, clip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63b2aede60e894418afa0a67427079a\",\n \"score\": \"0.45288175\",\n \"text\": \"def _parse_annual_toxics_EFs(self):\\r\\n toxics = pd.read_excel(self.fpath_toxicsEFs,\\r\\n header=3,\\r\\n usecols=[0,2,3],\\r\\n nrows=89)\\r\\n toxics.rename(columns={'Chemical' : 'pollutant',\\r\\n 'EF' : 'ef',\\r\\n 'EF Units' : 'units'},\\r\\n inplace=True)\\r\\n toxics['units'] = toxics['units'].str.strip().str.lower()\\r\\n # calculating H2S from CEMS, so drop from EF list\\r\\n toxics= toxics[toxics['pollutant'] != 'Hydrogen sulfide']\\r\\n return toxics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76d184351ebf7a30eb6bb79421b89bd5\",\n \"score\": \"0.45251414\",\n \"text\": \"def ao_index(self, atom_p, orb_p):\\n p = atom_p * self.orbitals_per_atom\\n p += self.orbital_types.index(orb_p)\\n\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1967c4ff6997b1d42728707148de3d6a\",\n \"score\": \"0.45242682\",\n \"text\": \"def index(self, point):\\n return tuple(((point + self.offset) // self.mesh_size).astype(int))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2817d35cfd15bfe49dd34da989151e3c\",\n \"score\": \"0.45218515\",\n \"text\": \"def ean2ean_lo(ean):\\r\\n eanCompact = re.sub('-', '', ean)\\r\\n match = re.match(r'(\\\\d{5})(\\\\d{8})', eanCompact)\\r\\n return int(f'0x{match.group(2)}', 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb7787c1689f6594eff10ab370e1f1c\",\n \"score\": \"0.4517905\",\n \"text\": \"def to_index(X, idx, UKN_IDX=1):\\n X_idx = []\\n for x in X:\\n # We map the unknown symbols to one\\n x_idx = list(map(lambda x: idx.get(x, UKN_IDX), x))\\n X_idx += [x_idx]\\n return X_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef063f50a8be80a0a4a4e76ad296fc2\",\n \"score\": \"0.45158973\",\n \"text\": \"def index_components(self):\\n return ((self.comment, \\\"A\\\"),)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4912b800a4ff5c6443d5c9c265d39f5\",\n \"score\": \"0.45096654\",\n \"text\": \"def get_edge_index(self,name):\\n return self.get_index(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0b377c5ddd58b110718daade7cd2070\",\n \"score\": \"0.45093864\",\n \"text\": \"def pidlist2indexlist(painting_df, painting_list):\\n index_list = [pid2index(painting_df, painting_id) for painting_id in painting_list]\\n return index_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40cdc7594bb448744b2be9c3eefb3ddd\",\n \"score\": \"0.4506455\",\n \"text\": \"def get(self, index: int | pli.Series | list[int]) -> pli.Series:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8936fbb67ba7ab4836f01ada9753df74\",\n \"score\": \"0.45050225\",\n \"text\": \"def to_dense_index(game, data):\\n\\tif isinstance(game, MnkGame):\\n\\t\\treturn game.horSize * data[1] + data[0]\\n\\telse:\\n\\t\\treturn game[0] * data[1] + data[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a6d64af3b58a2827a849587c5131126\",\n \"score\": \"0.45023936\",\n \"text\": \"def _mapColumn(self, index):\\n columnCoords = numpy.unravel_index(index, self._columnDimensions)\\n columnCoords = numpy.array(columnCoords, dtype=realDType)\\n ratios = columnCoords / self._columnDimensions\\n inputCoords = self._inputDimensions * ratios\\n inputCoords += 0.5 * self._inputDimensions / self._columnDimensions\\n inputCoords = inputCoords.astype(int)\\n inputIndex = numpy.ravel_multi_index(inputCoords, self._inputDimensions)\\n return inputIndex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd4ceaaf0495a1b2f025c3c93b65b749\",\n \"score\": \"0.44900307\",\n \"text\": \"def get_object_id_by_index(self, col_index):\\n object_id = self.dict_col_index_2_obj_id.get(col_index)\\n if type(object_id) is int:\\n if CEAnalysis.OFFSET_CHEMICAL < object_id < CEAnalysis.OFFSET_UTILITY_TYPE:\\n return object_id - CEAnalysis.OFFSET_CHEMICAL, SHORT_NAME_CHEMICAL\\n elif CEAnalysis.OFFSET_UTILITY_TYPE < object_id < CEAnalysis.OFFSET_EMISSION:\\n return object_id - CEAnalysis.OFFSET_UTILITY_TYPE, SHORT_NAME_UTILITY_TYPE\\n else:\\n # since the column name for emission starts with 'emis_', so we return a substring of the column name\\n return object_id[5:], SHORT_NAME_EMISSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00d7fed52762577b30b5b39d87be6e9c\",\n \"score\": \"0.44854128\",\n \"text\": \"def id_to_index(self, cell_id):\\n\\n # TODO: Need __getitem__\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7116f21eb4c0c9c01a21feb2ca9d3d34\",\n \"score\": \"0.44758412\",\n \"text\": \"def index_important_species(self) -> None:\\n self.species_indices = {}\\n names = [species.name for species in self.get_species_list()]\\n for element in [\\n \\\"C+\\\",\\n \\\"H+\\\",\\n \\\"H2\\\",\\n \\\"SI+\\\",\\n \\\"S+\\\",\\n \\\"CL+\\\",\\n \\\"CO\\\",\\n \\\"HE+\\\",\\n \\\"#H\\\",\\n \\\"#H2\\\",\\n \\\"#N\\\",\\n \\\"#O\\\",\\n \\\"#OH\\\",\\n \\\"SURFACE\\\",\\n \\\"BULK\\\",\\n ] + elementList:\\n try:\\n species_index = names.index(element) + 1\\n except:\\n logging.info(f\\\"\\\\t{element} not in network, adding dummy index\\\")\\n species_index = len(self.get_species_list()) + 1\\n name = \\\"n\\\" + element.lower().replace(\\\"+\\\", \\\"x\\\").replace(\\n \\\"e-\\\", \\\"elec\\\"\\n ).replace(\\\"#\\\", \\\"g\\\")\\n self.species_indices[name] = species_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03ce2224af5478681d1ea8f90beb769\",\n \"score\": \"0.4473924\",\n \"text\": \"def gaseste_iesire(self): \\n nod = self.start.tiles\\n m = len(nod) - 1\\n for i in range(len(nod[0])):\\n if nod[0][i] != '#':\\n return (0, i), (0, nod[0].index('#', i) - 1)\\n if nod[m][i] != '#':\\n return (m, i), (m, nod[m].index('#', i) - 1)\\n n = len(nod[0]) - 1\\n for i in range(m + 1):\\n if nod[i][0] != '#':\\n start = i\\n while nod[i][0] != '#':\\n i += 1\\n return (start, 0), (i - 1, 0)\\n elif nod[i][n] != '#':\\n start = i\\n while nod[i][n] != '#':\\n i += 1\\n return (start, n), (i - 1, n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"363d0cb44fb66322d1fbf435265585d7\",\n \"score\": \"0.44682097\",\n \"text\": \"def get_label2index(self):\\n\\t\\treturn self.label2index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ce3d1d6efc1bf3f424acabf3a6ad069\",\n \"score\": \"0.44664037\",\n \"text\": \"def ra(self):\\n return self.index_map[\\\"ra\\\"][:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e799dc86a7be036393d22e5f39f8209\",\n \"score\": \"0.4463673\",\n \"text\": \"def idx(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e799dc86a7be036393d22e5f39f8209\",\n \"score\": \"0.4463673\",\n \"text\": \"def idx(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e56ddaddfb54c4e57ae52563b37952e7\",\n \"score\": \"0.44579217\",\n \"text\": \"def anes(self) -> List[ANE]:\\n return self._anes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f7752c45ed681faa170cb82aceb8a83\",\n \"score\": \"0.4452417\",\n \"text\": \"def _indexed_eci(self, detector):\\n indexes=self._detector.detect_indexes(detector)\\n sub_eci=detector.vectorized_eci()\\n\\n from_ix=np.array(indexes)[:,0]\\n to_ix=np.array(indexes)[:,1]\\n\\n #Ensure that the from indexes are simply values of 0,1,2,3,4...n\\n #This part is now redundant due to check at detector construction,\\n #but I'll leave it just in case\\n try:\\n assert(not np.any(from_ix-list(range(len(from_ix)))))\\n except AssertionError:\\n raise ValueError(\\\"The provided indexes of the detector are not sequential! If you are sure you're not missing\\\\\\n basis functions, reindex the basis functions and try again.\\\")\\n\\n sub_eci.index=to_ix\\n return sub_eci\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23fe5fb9a8f5865ec26c221d8dbf06fb\",\n \"score\": \"0.4449667\",\n \"text\": \"def transform_str_to_int(orig_node_list, edges):\\n for i in range(len(edges)):\\n ind_first_ele = [y[0] for y in orig_node_list].index(edges[i][0])\\n ind_sec_ele = [y[0] for y in orig_node_list].index(edges[i][1])\\n edges[i] = (ind_first_ele, ind_sec_ele)\\n return edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc80d590ba1e7b1f241f55e4f245537e\",\n \"score\": \"0.44437078\",\n \"text\": \"def pandasIndexForFlatCoordinate(self, index):\\n return NotImplementedError('domain flattening not implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88702a752908fb44d58c85896c9e9ba8\",\n \"score\": \"0.44415355\",\n \"text\": \"def abstract2ids(abstract_words, vocab, article_oovs):\\n ids = []\\n unk_id = vocab.word2id(UNKNOWN_TOKEN)\\n for w in abstract_words:\\n i = vocab.word2id(w)\\n if i == unk_id: # If w is an OOV word\\n if w in article_oovs: # If w is an in-article OOV\\n vocab_idx = vocab.size() + article_oovs.index(w) # Map to its temporary article OOV number\\n ids.append(vocab_idx)\\n else: # If w is an out-of-article OOV\\n ids.append(unk_id) # Map to the UNK token id\\n else:\\n ids.append(i)\\n return ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"173bf1089b6732dd51f680d67facf67b\",\n \"score\": \"0.44381887\",\n \"text\": \"def _toindex(value):\\n indx = np.empty(value.shape, dtype=np.int32)\\n indx = np.floor(value + 0.5, out=indx)\\n return indx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71c49b5b01dc60f0e28b6537fb9a4d1e\",\n \"score\": \"0.44376734\",\n \"text\": \"def index_to_coordinate(dims):\\n _ = divmod # SO WE KEEP THE IMPORT\\n\\n if len(dims) == 1:\\n return _identity\\n\\n num_dims = len(dims)\\n prod = [1] * num_dims\\n acc = 1\\n domain = range(0, num_dims)\\n for i in reversed(domain):\\n prod[i] = acc\\n acc *= dims[i]\\n\\n commands = []\\n coords = []\\n for i in domain:\\n if i == num_dims - 1:\\n commands.append(\\\"\\\\tc\\\" + unicode(i) + \\\" = index\\\")\\n else:\\n commands.append(\\\"\\\\tc\\\" + unicode(i) + \\\", index = divmod(index, \\\" + unicode(prod[i]) + \\\")\\\")\\n coords.append(\\\"c\\\" + unicode(i))\\n output = None\\n code = \\\"def output(index):\\\\n\\\" + \\\\\\n \\\"\\\\n\\\".join(commands) + \\\"\\\\n\\\" + \\\\\\n \\\"\\\\treturn \\\" + \\\", \\\".join(coords)\\n exec code\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19bafe1b548f622842999e7f6420ba18\",\n \"score\": \"0.4436351\",\n \"text\": \"def SweepEntityTransform(self) -> _n_2_t_6:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19bafe1b548f622842999e7f6420ba18\",\n \"score\": \"0.4436351\",\n \"text\": \"def SweepEntityTransform(self) -> _n_2_t_6:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d20feea106d530df1b6c845d9d998d9\",\n \"score\": \"0.44357267\",\n \"text\": \"def index_to_symbol(self, index):\\n\\n symbol = self.alphabet[index]\\n return symbol\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992979,"cells":{"query_id":{"kind":"string","value":"c4141dbf62408002212c668a55290779"},"query":{"kind":"string","value":"Return a default filename"},"positive_passages":{"kind":"list like","value":[{"docid":"acef1e2dd1bbad11f4e30a2ed285067a","score":"0.8789937","text":"def _default_filename(default_filename, label=None):\n if not label:\n filename = default_filename.format(round(time.time()))\n else:\n filename = default_filename.format(label)\n return filename","title":""}],"string":"[\n {\n \"docid\": \"acef1e2dd1bbad11f4e30a2ed285067a\",\n \"score\": \"0.8789937\",\n \"text\": \"def _default_filename(default_filename, label=None):\\n if not label:\\n filename = default_filename.format(round(time.time()))\\n else:\\n filename = default_filename.format(label)\\n return filename\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a44d0dc7aab878667cbe8d496b938052","score":"0.86708117","text":"def default_filename(self):\n if self.path:\n return Path(self.path).name","title":""},{"docid":"7b6b1f7dc30f57f3aa9b17a824128229","score":"0.8010142","text":"def get_defaultfilename():\n return Path(__file__).parent / \"reporter.log\"","title":""},{"docid":"7b6fed368996c621437e7ad3c2bbbbc4","score":"0.79677075","text":"def _default_filename(self):\n if self.filename is None:\n if 'jobname' in self.options:\n self.filename = \"%s.%s\" \\\n % (self.options['jobname'],\n self.backends[self.backend]['extension'])","title":""},{"docid":"fe3e600fff59ffaca4f3b14347b8371c","score":"0.78667235","text":"def getFilename(prompt, defaulttype = None, defaultname = None):\n val = getString(prompt,defaultname)\n filename = getExpandedFilename(val) # Expand to process env/user\n if defaulttype != None:\n defaultext = \".\" + defaulttype\n name,ext = p.splitext(filename)\n if ext != defaultext:\n filename += defaultext\n return filename","title":""},{"docid":"bafef9f18ee07b2ab127afcf0abb067e","score":"0.75066715","text":"def default_filename(self):\n if self.magnet_name is not None and len(self.magnet_name) != 0:\n name = self.magnet_name + '_' + self.label\n else:\n name = self.label\n\n if self.npts != 0:\n if self.pos1.size == 1:\n name = name + '_pos1={0:.0f}mm'.format(self.pos1[0])\n if self.pos2.size == 1:\n name = name + '_pos2={0:.0f}mm'.format(self.pos2[0])\n if self.pos3.size == 1:\n name = name + '_pos3={0:.0f}mm'.format(self.pos3[0])\n\n self.date, self.hour = _utils.get_date_hour()\n timestamp = _utils.date_hour_to_timestamp(self.date, self.hour)\n if self.idn is not None:\n filename = '{0:s}_{1:s}_ID={2:s}.dat'.format(\n timestamp, name, str(self.idn))\n else:\n filename = '{0:s}_{1:s}.dat'.format(timestamp, name)\n return filename","title":""},{"docid":"bafef9f18ee07b2ab127afcf0abb067e","score":"0.75066715","text":"def default_filename(self):\n if self.magnet_name is not None and len(self.magnet_name) != 0:\n name = self.magnet_name + '_' + self.label\n else:\n name = self.label\n\n if self.npts != 0:\n if self.pos1.size == 1:\n name = name + '_pos1={0:.0f}mm'.format(self.pos1[0])\n if self.pos2.size == 1:\n name = name + '_pos2={0:.0f}mm'.format(self.pos2[0])\n if self.pos3.size == 1:\n name = name + '_pos3={0:.0f}mm'.format(self.pos3[0])\n\n self.date, self.hour = _utils.get_date_hour()\n timestamp = _utils.date_hour_to_timestamp(self.date, self.hour)\n if self.idn is not None:\n filename = '{0:s}_{1:s}_ID={2:s}.dat'.format(\n timestamp, name, str(self.idn))\n else:\n filename = '{0:s}_{1:s}.dat'.format(timestamp, name)\n return filename","title":""},{"docid":"cdf507aa38fb3027818504fee54d687e","score":"0.7481528","text":"def getDefaultFname(defaultFname_template, suffix=\"\"):\n \n if not os.path.isdir(os.path.dirname(defaultFname_template)):\n os.mkdir(os.path.dirname(defaultFname_template) )\n defaultFname_pattern = os.path.basename(defaultFname_template) + r'(\\d+)'\n defaultFname_idx = 1 + int(max([ 0 if re.match(defaultFname_pattern , x ) is None \n else re.match(defaultFname_pattern , x ).group(1) for x in os.listdir(os.path.dirname(defaultFname_template) ) + [\"\"] ]) )\n defaultFname = defaultFname_template +\"{:d}\".format( defaultFname_idx) + suffix \n return defaultFname","title":""},{"docid":"477cd203fc0e5bfaa81ff3e70475c7c4","score":"0.73396647","text":"def get_filename():\n return FILENAME","title":""},{"docid":"0d8ed7e68798ebb5763f1300395016ae","score":"0.7174406","text":"def default_filepath(self):\n return self.dirpath.joinpath(f\"{self.today}.json\")","title":""},{"docid":"011eae2be09eed40ffdaf90e1f0e0972","score":"0.7088123","text":"def _get_default_path(self):\n return os.path.join('data', 'WIDER2015')","title":""},{"docid":"0c675c538014e30f1e718bb1d2a62a47","score":"0.7024356","text":"def make_default_file_name(cfg, sprint_data, variant):\n start_date = dateutil.parser.parse(sprint_data[\"start date\"]).date()\n end_date = dateutil.parser.parse(sprint_data[\"end date\"]).date()\n\n return \"{0:s}_{1:d}-{2:s}_{3:s}.json\".format(\n sprint_data[\"project\"][\"name\"].lower(),\n isoweek.Week.withdate(start_date).year,\n cjm.sprint.generate_sprint_period_name(cfg, start_date, end_date).lower(), variant)","title":""},{"docid":"cb30208f4d85bbf230592c464d96ecd9","score":"0.6886078","text":"def get_save_as_filename(\n defaultfilename: str, defaultextension: str, title: str = \"Save As\"\n) -> str:\n root = tkinter.Tk() # create and get Tk topmost window\n # (don't do this too early; the command prompt loses focus)\n root.withdraw() # won't need this; this gets rid of a blank Tk window\n root.attributes(\"-topmost\", True) # makes the tk window topmost\n filename = filedialog.asksaveasfilename(\n initialfile=defaultfilename,\n defaultextension=defaultextension,\n parent=root,\n title=title,\n )\n root.attributes(\"-topmost\", False) # stop the tk window being topmost\n return filename","title":""},{"docid":"c3f0640f9cfb27a8c6b9d62271bc5a0a","score":"0.68610364","text":"def _get_short_filename(self):\n return None","title":""},{"docid":"6d2eb656988bcc89d0fa4c76c9714bd5","score":"0.68573564","text":"def _get_default_path(self):\n return os.path.join(cfg.DATA_DIR, 'CUB_200_2011')","title":""},{"docid":"58b3360602a4beb700798b5f61a2ff83","score":"0.6813847","text":"def base_filename(self):\r\n return self.filename.split('.')[0]","title":""},{"docid":"b3ccf2ac3df88f567e2fd474224c8bed","score":"0.6766652","text":"def get_default_basename(self, viewset):\n ...","title":""},{"docid":"b3ccf2ac3df88f567e2fd474224c8bed","score":"0.6766652","text":"def get_default_basename(self, viewset):\n ...","title":""},{"docid":"c56ec5e6b43f96c53d5366613fd6db93","score":"0.6747876","text":"def file_name(self) -> Optional[str]:\n return pulumi.get(self, \"file_name\")","title":""},{"docid":"8f67c200e2c57a1f34ac39321faa8f75","score":"0.67384523","text":"def getDefaultDownloadPath():\n localFrame = inspect.currentframe()\n # No file name in this one for now\n # suffix = datetime.now().strftime('%Y_%m_%d-%H-%M-%S')\n # fileName = 'SureDone_Downloads_' + suffix + '.csv' \n\n # If the platform is windows, set the download path to the current user's Downloads folder\n if sys.platform == 'win32' or sys.platform == 'win64': # Windows\n downloadPath = os.path.expandvars(r'%USERPROFILE%')\n downloadPath = os.path.join(downloadPath, 'Downloads')\n return downloadPath\n # If Linux, set the download path to the $HOME/downloads folder\n elif sys.platform == 'linux' or sys.platform == 'linux2': # Linux\n downloadPath = expanduser('~') \n return downloadPath\n # Unrecognized operating system \n else:\n LOGGER.writeLog(\"Platform couldn't be recognized. Are you sure you are running this script on Windows or Ubuntu Linux?\", localFrame.f_lineno, severity='code-breaker', data={'code':1})\n exit()\n\n LOGGER.writeLog(\"A download directory could not be specified in default locations!\\nSpecify a path to the directory using (-o --output) argument.\", localFrame.f_lineno, severity='code-breaker', data={'code':1})\n exit()","title":""},{"docid":"6fd1d1420cfd857631db273b3b1a4d42","score":"0.6715815","text":"def _get_default_path(self):\n return os.path.join(cfg.FLAGS2[\"data_dir\"], 'VOCdevkit' + self._year)","title":""},{"docid":"1dc41fb4cc6bfeee4b086e588e71a711","score":"0.671217","text":"def filename(self):\r\n return self.base_filename","title":""},{"docid":"4e7ff8b091969b2466431e9633fe1a26","score":"0.6675536","text":"def _template_file_default(self):\n return 'report.jinja'","title":""},{"docid":"2836c7e77b6d06705187da669e2c0fe9","score":"0.667197","text":"def get_default_asset_name(self):\n\n return self.config.get('default_name', default='New Asset')","title":""},{"docid":"3a5d4dfd27e4e216219e343dc9171228","score":"0.66675276","text":"def default_path(arg, base_dir, default_name):\n path = default_name if arg is None else arg\n if not op.isabs(path):\n path = op.abspath(op.join(base_dir, path))\n return path","title":""},{"docid":"e677aec7b781b50d1b5cf90838f4f087","score":"0.66591245","text":"def get_file_name(self):\n pass","title":""},{"docid":"575084f1835cf798507a1dfbec41a987","score":"0.6651537","text":"def default(self):\n return self._default_name","title":""},{"docid":"d41cab983ea82a8b444de03ad26e4fc7","score":"0.6648756","text":"def filename(self) -> Optional[str]:\n return self.filepath.name","title":""},{"docid":"6c9732f05a2e615707dda22a5cd5cc2e","score":"0.6643198","text":"def _get_default_path(self):\n return os.path.join(cfg.DATA_DIR, 'RGZdevkit' + self._year)","title":""},{"docid":"38e1abb1e2a650ce6ad579cbe6f4974b","score":"0.6636678","text":"def get_filename(name):\n path = osp.join(osp.dirname(osp.abspath(__file__)), PiConfigParser.language, name)\n if not osp.isfile(path):\n # Look for common image\n return osp.join(osp.dirname(osp.abspath(__file__)), 'com', name)\n return path","title":""},{"docid":"43c19143a5a09adcbffbea179b1d2d72","score":"0.6621322","text":"def filename(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"filename\")","title":""},{"docid":"8215999c73b3d9cb53aa7129497b4a87","score":"0.65981174","text":"def _template_file_default(self):\n return 'simple' # full","title":""},{"docid":"be2b3baf278512d933018a026d435960","score":"0.6587339","text":"def default_filename_encoder():\n return BaseCWRFileNameEncoder(4)","title":""},{"docid":"c0175f2654932c628ddab52a5d978a4c","score":"0.658498","text":"def _get_default_path(self):\n return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)","title":""},{"docid":"b4a22e14c0291d6e742620f0d4f51f0b","score":"0.6583861","text":"def get_open_filename(\n defaultfilename: str, defaultextension: str, title: str = \"Open\"\n) -> str:\n root = tkinter.Tk() # create and get Tk topmost window\n # (don't do this too early; the command prompt loses focus)\n root.withdraw() # won't need this; this gets rid of a blank Tk window\n root.attributes(\"-topmost\", True) # makes the tk window topmost\n filename = filedialog.askopenfilename(\n initialfile=defaultfilename,\n defaultextension=defaultextension,\n parent=root,\n title=title,\n )\n root.attributes(\"-topmost\", False) # stop the tk window being topmost\n return filename","title":""},{"docid":"553bf3fd7130c8cfc21daa62077776b0","score":"0.6563618","text":"def get_filename(self):\n components = self.name.split(\"/\")\n assert components and components[-1]\n return components[-1]","title":""},{"docid":"7490b19dcd2b387129a4736719377caa","score":"0.6553981","text":"def _get_default_path(self):\n return 'dataset/jaad'","title":""},{"docid":"b87cc71f4bd8c8d8b84e8c2ccb21678c","score":"0.6550989","text":"def get_default_name():\n return random.choice(config.configuration['nicknames']['default_nicknames'])","title":""},{"docid":"76a00fae26a1e52c51bfa8b654b41306","score":"0.6538393","text":"def test_attrs_default_filename(self):\n c = CallFile(self.call, self.action)\n self.assertTrue(c.filename)","title":""},{"docid":"6cda925ce769dfd74427fe4bbb0fd779","score":"0.6534086","text":"def _get_filename(self, filename):\n\n if filename is None:\n if self.LIB_PATH_ENV_VAR in os.environ:\n filename = os.path.expanduser(os.environ[self.LIB_PATH_ENV_VAR])\n else:\n filename = os.path.expanduser(self.LIB_PATH_DEFAULT)\n elif '~' in filename:\n filename = os.path.expanduser(filename)\n return filename","title":""},{"docid":"a5c28a9f89bc43778b82caf43842d5a9","score":"0.65294266","text":"def get_filename(self):\n assert self.valid_url\n return self.inferred_filename","title":""},{"docid":"106c3830691e810a09cb0504c1f16f8f","score":"0.65193486","text":"def define_filename():\n root = tk.Tk()\n root.withdraw()\n file_name = filedialog.askopenfilename()\n return file_name","title":""},{"docid":"1c7a6b9b31c80d1ebb1bfb2e44423d5c","score":"0.6505492","text":"def _get_filename(self):\n return os.path.basename(self.filepath)","title":""},{"docid":"d5734642ad3c62dc183f009877143205","score":"0.6472982","text":"def getFilename(filename):\n base,ext=os.path.splitext(filename)\n f=base+\"_\"+date.today().isoformat()\n if os.path.exists(f+ext):\n template=f+\"-%03d\"\n for n in range(1,100):\n if not os.path.exists(template%n+ext):\n return template%n+ext\n print \"no valid filename found for \",filename\n return \"temp\"+ext\n else:\n return f+ext","title":""},{"docid":"814f8b52322bacaad7e7b850fd4f91ab","score":"0.6470427","text":"def default_name(self):\n return self.__class__.__name__","title":""},{"docid":"12838fa3b58e633bf6b33e77cb5df937","score":"0.6467176","text":"def get_output_filename(fname_kwarg: Text,\n work_dir: Text,\n name: Optional[Text] = None):\n # Separate attributions because these results will be written per method.\n fname = DEFAULT_FILES[fname_kwarg]\n is_formatable = '{' in fname and '}' in fname\n if is_formatable and name is None:\n raise ValueError(\n f'{fname_kwarg} is formattable, missing name argument.')\n if is_formatable:\n return os.path.join(work_dir, fname.format(name))\n else:\n return os.path.join(work_dir, fname)","title":""},{"docid":"b39f3f01d911e0a0a298db9b2b825ff9","score":"0.6463444","text":"def getFileName(self, **kwargs):\n \n pass","title":""},{"docid":"b39f3f01d911e0a0a298db9b2b825ff9","score":"0.6463444","text":"def getFileName(self, **kwargs):\n \n pass","title":""},{"docid":"739b635426a0b3da41d286998ae889e1","score":"0.645662","text":"def _get_filename(subject, name, target_dir=None):\n\n if target_dir is None:\n target_dir = os.path.dirname(os.path.realpath(__file__))\n target_dir = os.path.join(target_dir, '../results')\n target_dir = os.path.realpath(target_dir)\n return os.path.join(target_dir, f\"s{subject}_{name}.png\")","title":""},{"docid":"18d922412ac686103354a57eebe483bd","score":"0.64538074","text":"def file_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"file_name\")","title":""},{"docid":"18d922412ac686103354a57eebe483bd","score":"0.64538074","text":"def file_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"file_name\")","title":""},{"docid":"3f03fd4b72abb598993135c57ac83caf","score":"0.6435663","text":"def get_filename(name):\n return osp.join(osp.dirname(osp.abspath(__file__)), PtbConfigParser.language, name)","title":""},{"docid":"9c3cf81b8a7116ef91a226d2705c6b70","score":"0.6417916","text":"def get_filename(self):\n return self.file_title","title":""},{"docid":"5ffa8780b52c5b9ed9805e44ccfb953b","score":"0.63990796","text":"def default_filename(self):\n if self.magnet_name is not None and len(self.magnet_name) != 0:\n name = self.magnet_name + '_' + self.label\n else:\n name = self.label\n\n if len(self.map) != 0:\n x = _np.unique(self.map[:, 0])\n y = _np.unique(self.map[:, 1])\n z = _np.unique(self.map[:, 2])\n if len(x) > 1:\n name = name + '_X={0:.0f}_{1:.0f}mm'.format(x[0], x[-1])\n if len(y) > 1:\n name = name + '_Y={0:.0f}_{1:.0f}mm'.format(y[0], y[-1])\n if len(z) > 1:\n name = name + '_Z={0:.0f}_{1:.0f}mm'.format(z[0], z[-1])\n\n mt = _re.search('phase(\\d+)', self.comments.lower().replace(\n ' ', ''))\n# mt = _re.search('phase(\\d+\\.\\d+)', self.comments.lower().replace(\n# ' ', ''))\n if mt:\n name = name + '_Y={0:.0f}mm'.format(y[0])\n\n for coil in ['main', 'trim', 'ch', 'cv', 'qs']:\n current = getattr(self, 'current_' + coil)\n if current is not None and len(current) != 0:\n if coil == 'trim':\n ac = 'tc'\n elif coil == 'main':\n ac = 'mc'\n else:\n ac = coil\n name = name + '_I' + ac + '=' + current + 'A'\n\n if self.date is None or self.hour is None:\n self.date, self.hour = _utils.get_date_hour()\n if self.idn is not None:\n filename = '{0:s}_{1:s}_ID={2:s}.dat'.format(\n self.date, name, str(self.idn))\n else:\n filename = '{0:s}_{1:s}.dat'.format(self.date, name)\n\n if mt:\n phase = mt.group(1)\n filename = filename.replace(\n '.dat', '') + '_Phase={0:s}mm.dat'.format(phase)\n\n return filename","title":""},{"docid":"95b90a49d5642f5acb5217b3513221c8","score":"0.63967866","text":"def __get_file_name(self):\n file_path = self.file_path.split(\"/\")\n return os.path.splitext(file_path[-1])[0]","title":""},{"docid":"b2cf441e24d93517e3b1da5332e387c0","score":"0.6391369","text":"def get_filename(self) :\n return self.filename","title":""},{"docid":"fb007499108027d50895427fa758d245","score":"0.6391081","text":"def get_logfilename(filename=\"\"):\n\n # If the given filename exists, return it. No need to do any other processing\n if os.path.isfile(filename):\n return(filename)\n\n # figure out some paths and defaults\n scriptdir = os.path.dirname(os.path.realpath(__file__))\n default_logpath = os.path.abspath(scriptdir + '/../logs')\n default_filename = os.path.abspath(default_logpath + '/server.log')\n\n # use default filename if no filename is given\n if filename == '':\n filename = default_filename\n\n # if filename doesn't exist, try prepending the logdir\n if not os.path.isfile(filename):\n old_filename = filename\n if '/' not in filename:\n filename = os.path.abspath(default_logpath + '/' + filename)\n\n # return filename if it exists, otherwise thrown an error and exit\n if os.path.isfile(filename):\n return(filename)\n else:\n usage(\"ERROR: Can not find logfile at {} or {}\".format(\n old_filename, filename), 1)","title":""},{"docid":"e2c86e5819fd50e2719c8cbfae12a14d","score":"0.63865626","text":"def get_file_name(self) -> str:\n return str(self.get_file())","title":""},{"docid":"b3cbcf93fddf36e74329a5c95d37f3ac","score":"0.63854957","text":"def _get_default_configuration_file(self):\n paths = []\n\n resource = os.path.join(\n Configuration.DEFAULT_PATH, Configuration.DEFAULT_FILE)\n paths.extend(self._xdg_helper.build_config_paths(resource))\n paths.append(os.path.expanduser(os.path.join('~', '.' + resource)))\n paths.append(os.path.expanduser(Configuration.DEFAULT_FILE))\n\n for path in paths:\n if os.path.exists(path):\n return path\n\n return None","title":""},{"docid":"5ade16776314ffea71a4f3afa949b1e5","score":"0.63826376","text":"def filename(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"filename\")","title":""},{"docid":"5ade16776314ffea71a4f3afa949b1e5","score":"0.63826376","text":"def filename(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"filename\")","title":""},{"docid":"5ec51f685de17eec09b02ed50d0aa3ea","score":"0.637966","text":"def get_filename() -> str:\n return os.path.basename(__file__).replace('.py', '')","title":""},{"docid":"3d63b6bbb5282b7351fa99666fdc3bf3","score":"0.6371428","text":"def get_def_file_name(self):\n lesson_type_index = self.lesson_type\n if (self.lesson_type == ''):\n lesson_type_index = 'ad'\n file_name = \"SS-{0}-{1}'{2}.devotions.SQLite3\"\\\n .format(self.lang_code, lesson_type_index, str(self.year)[2:4])\n return file_name","title":""},{"docid":"25f5287c469ed5ea974a5ff28801f01f","score":"0.63401616","text":"def get_filename(self):\n filename = os.path.split(self.file_path)[-1]\n if filename.split(\".\")[-1] in [\"markdown\", \"md\"]:\n return \".\".join(filename.split(\".\")[:-1])\n else:\n return filename","title":""},{"docid":"d47849d5204a067a3d80e2dae12679f5","score":"0.63372827","text":"def make_newspaper_filename(filename: str):\n if '_data_' in filename:\n splt = filename.split('_data_')\n return splt[0]\n elif '.ann' in filename or '.txt' in filename or '.json' in filename: return None\n else: return filename","title":""},{"docid":"54aec3eaeed9e2d74912e90e8e14ec90","score":"0.6327587","text":"def full_filename(self):\n App.ensure_prefs()\n return os.path.join(App.prefs['exordium__base_path'], self.filename)","title":""},{"docid":"d9f8033b0fad6f40610038bc7ecc7758","score":"0.63259935","text":"def get_filename(self, filename):\n return datetime.now().strftime(\"%Y-%m-%dT%H:%M:%SZ\") + \"_\" + filename","title":""},{"docid":"8b989fce2b41b8d8a62beefde353c1d6","score":"0.6316126","text":"def filename(self):\n return self.controller.exp_get('DATFILENAME')[0]","title":""},{"docid":"854818d76a9b5ffe0748f32963da1e62","score":"0.6309568","text":"def guess_filename(obj):\r\n name = getattr(obj, 'name', None)\r\n if (name and isinstance(name, str) and name[0] != '<' and\r\n name[-1] != '>'):\r\n return os.path.basename(name)","title":""},{"docid":"96c42fbf76d637d5e3da8d74661f3788","score":"0.6302564","text":"def _configureOutputFilename(self, filename):\n assert not os.path.isabs(filename), filename\n\n if not os.path.splitext(filename)[1]:\n filename += \".tss\"\n\n if hasattr(self._userModel, \"nrSamples\"):\n filename = os.path.join(str(self._userModel.currentSampleNumber()),\n filename)\n\n return filename","title":""},{"docid":"394e756173f4f4db419e7974aa6df6fa","score":"0.62977123","text":"def gen_file_name():\n return os.path.join(APP_PATH, 'export-%s.csv' % time.strftime('%Y%m%d-%H%M%S'))","title":""},{"docid":"0db7b7e118c84d5f195a5bd996254e96","score":"0.6295201","text":"def _get_save_filename(self) -> str:\n init_directory = os.path.expanduser('~')\n filename = QtWidgets.QFileDialog.getSaveFileName(self, 'Save File',\n init_directory)[0]\n return filename","title":""},{"docid":"1440898adbbfb83e093bd0b6ea14e824","score":"0.6282081","text":"def build_filename(instance, filename):\n now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n name = hashlib.md5('%s' % now).hexdigest()\n ext = os.path.splitext(filename)\n return os.path.join('%s/%s' % (instance._meta.app_label, instance._meta.module_name), '%s%s' % (name, ext[1]))","title":""},{"docid":"0bd2d36a9283eab34954fd7605d2f69e","score":"0.6279729","text":"def _template_file_default(self):\n return 'hugo_markdown.tpl'","title":""},{"docid":"7c00a6c1dc7ccb94880733dbb1062630","score":"0.6275213","text":"def _get_dump_default_path(dump):\n if hasattr(dump, '__file__') and hasattr(dump, '__name__'):\n # Default value. We check it is none travis or appveyor.\n from ..pycode import is_travis_or_appveyor\n if is_travis_or_appveyor():\n dump = None\n if dump is not None:\n # We guess the package name.\n name = dump.__name__.split('.')[-1]\n loc = os.path.dirname(dump.__file__)\n src_loc = os.path.split(loc)\n if src_loc[-1] == 'src':\n # We choose a path for the dumps in a way\n fold = os.path.join(loc, \"..\", \"..\", \"..\", \"_notebook_dumps\")\n else:\n src_loc_loc = os.path.split(src_loc[0])\n if src_loc_loc[-1] == 'src':\n # We choose a path for the dumps in a way\n fold = os.path.join(\n loc, \"..\", \"..\", \"..\", \"_notebook_dumps\")\n else:\n # This should be a parameter.\n fold = os.path.join(loc, \"..\", \"..\", \"_notebook_dumps\")\n if not os.path.exists(fold):\n os.mkdir(fold)\n dump = os.path.join(fold, f\"notebook.{name}.txt\")\n return dump\n return dump","title":""},{"docid":"d77f5f52a590c27702f3b9b021cac47b","score":"0.6264666","text":"def get_available_name(self, name):\n # uuid4() returns a randomly generated v4 UUID, per RFC 4122\n filename = str(uuid.uuid4()) + '.' + settings.PHOTO_FORMAT\n return filename","title":""},{"docid":"f122fbc1d6337ca66075e1b770f780e3","score":"0.6264528","text":"def filename(name):\n return pkg_resources.resource_filename(__name__, name)","title":""},{"docid":"091ed650cb56661d70d6a172af585102","score":"0.62619567","text":"def get_filepath(filename: str) -> str:\n filepath = 'data/' + filename\n if os.getcwd().split('/')[-1] == 'body_to_title':\n filepath = '../' + filepath\n return filepath","title":""},{"docid":"3237894f66a34c7bcbfb370f86779e3d","score":"0.62584925","text":"def FileName(self) -> str:","title":""},{"docid":"18c8eae1fa394f12b5635fa35d91dbc7","score":"0.625676","text":"def _file_extension_default(self):\n return '.py'","title":""},{"docid":"6cb9e1eabea4deccdb9eedb2f9c22286","score":"0.62567216","text":"def get_filename(self):\n return self.filename","title":""},{"docid":"6cb9e1eabea4deccdb9eedb2f9c22286","score":"0.62567216","text":"def get_filename(self):\n return self.filename","title":""},{"docid":"6cb9e1eabea4deccdb9eedb2f9c22286","score":"0.62567216","text":"def get_filename(self):\n return self.filename","title":""},{"docid":"6b0bcbe22bd944f05aa6c3ec5890d4b7","score":"0.62373203","text":"def getfilename(self):\n\n # get filename\n return tkFileDialog.asksaveasfilename(**self.file_opt)","title":""},{"docid":"95e62df17504bd228e000c0b745e515f","score":"0.6236713","text":"def generate_filename(self):\n date = datetime.datetime.now().strftime('%m-%d_%H:%M:%S')\n relname = date + '_' + str(self._name_idx) + '.pkl'\n self._name_idx = (self._name_idx + 1) % int(1e10) # Arbitrary cutoff so doesn't grow indefinitely\n\n return os.path.join(self.folder, relname)","title":""},{"docid":"9d134c72da951dad68725aaf01c05b4a","score":"0.62362605","text":"def input_filename(self):\n filename = re.match(self.FILE_REGEX, self.original).group('filename')\n if not os.path.exists(filename):\n raise Exception('Could not retrieve filename {}'.format(filename))\n return filename","title":""},{"docid":"940f1627838fd540f5a25fbcb652e354","score":"0.6215249","text":"def getFilename():\n filename = nuke.root().name()\n if filename == 'Root':\n raise CueNukeFileException('File is not saved! Please save the file before submitting.')\n return filename","title":""},{"docid":"c64a629236ee49d0ea58fe5ab54d0797","score":"0.62146187","text":"def _GetDefaultImageName(cls):\n return cls.IMAGE_NAME_PREFIX","title":""},{"docid":"1a3aaa89d482ddfbe82ce2c9155adf45","score":"0.6214413","text":"def output_file_name(self) -> typing.Optional[builtins.str]:\n result = self._values.get(\"output_file_name\")\n return result","title":""},{"docid":"1a3aaa89d482ddfbe82ce2c9155adf45","score":"0.6214413","text":"def output_file_name(self) -> typing.Optional[builtins.str]:\n result = self._values.get(\"output_file_name\")\n return result","title":""},{"docid":"1a3aaa89d482ddfbe82ce2c9155adf45","score":"0.6214413","text":"def output_file_name(self) -> typing.Optional[builtins.str]:\n result = self._values.get(\"output_file_name\")\n return result","title":""},{"docid":"1a3aaa89d482ddfbe82ce2c9155adf45","score":"0.6214413","text":"def output_file_name(self) -> typing.Optional[builtins.str]:\n result = self._values.get(\"output_file_name\")\n return result","title":""},{"docid":"070f20ea21013a5075121ce305529565","score":"0.6210778","text":"def get_filename(filename, extension):\n now = datetime.now()\n return filename + '_' + now.strftime(\"%Y-%m-%dT%H-%M-%S\") + '.' + extension","title":""},{"docid":"abb4ec5ffba5db4e91b5414f0859cc1c","score":"0.6210235","text":"def file_name():\n filename = input('What is the file name for the song you want to convert?')\n if len(filename) < 1: filename = 'song.txt' # For ease in testing\n return filename","title":""},{"docid":"1a202c01389ad94091064fa0b851769c","score":"0.6208146","text":"def generate_filename(self, filename):\n # `filename` may include a path as returned by FileField.upload_to.\n dirname, filename = os.path.split(filename)\n return os.path.normpath(os.path.join(dirname, self.get_valid_name(filename)))","title":""},{"docid":"4239b401c079dd2f64feec5b7bdbc0ca","score":"0.6196896","text":"def filename(self) -> str:\n return self.basename[: -(len(self.ext) + 1)]","title":""},{"docid":"8112dd51d5b79113e8d6dad5aebbdc6c","score":"0.619284","text":"def GetFileName( self, name ) :\n \n # modules:\n import os\n\n # target file:\n #jbfile = '%s.jb' % name.replace('.','_')\n jbfile = '%s.jb' % name\n \n # ok\n return jbfile","title":""},{"docid":"6595cb68a8809994993a8aff3727d76d","score":"0.6174571","text":"def get_default_path() -> str:\n if os.name == \"nt\":\n \"\"\" TODO TODO TODO\n #import winreg\n sub_key = r\"SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\"\n downloads_guid = '{374DE290-123F-4565-9164-39C4925E467B}'\n with winreg.OpenKey(winreg.HKEY_CURRENT_USER, sub_key) as key:\n location = winreg.QueryValueEx(key, downloads_guid)[0]\n return location\n \"\"\"\n return None\n else:\n return os.path.join(os.path.expanduser(\"~\"), \"Downloads\")","title":""},{"docid":"266064b97dacbef9743be26939cad09f","score":"0.61661017","text":"def get_save_filename (self):\n return os.path.splitext(self.pidfile)[0] + \".save\"","title":""},{"docid":"d942d0aaacdf073f870c9a6bfc5eba0d","score":"0.6161404","text":"def generateFilename():\n return \"scan_\" + datetime.now().strftime(\"%d-%m-%Y_%H:%M:%S\") + \".\" + DEST_FORMAT","title":""},{"docid":"5775f8c6378934f3605429eb710fdf8a","score":"0.6160226","text":"def get_filename(self):\n\t\treturn self.fexp.format(self.ep.current_page)","title":""}],"string":"[\n {\n \"docid\": \"a44d0dc7aab878667cbe8d496b938052\",\n \"score\": \"0.86708117\",\n \"text\": \"def default_filename(self):\\n if self.path:\\n return Path(self.path).name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b6b1f7dc30f57f3aa9b17a824128229\",\n \"score\": \"0.8010142\",\n \"text\": \"def get_defaultfilename():\\n return Path(__file__).parent / \\\"reporter.log\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b6fed368996c621437e7ad3c2bbbbc4\",\n \"score\": \"0.79677075\",\n \"text\": \"def _default_filename(self):\\n if self.filename is None:\\n if 'jobname' in self.options:\\n self.filename = \\\"%s.%s\\\" \\\\\\n % (self.options['jobname'],\\n self.backends[self.backend]['extension'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe3e600fff59ffaca4f3b14347b8371c\",\n \"score\": \"0.78667235\",\n \"text\": \"def getFilename(prompt, defaulttype = None, defaultname = None):\\n val = getString(prompt,defaultname)\\n filename = getExpandedFilename(val) # Expand to process env/user\\n if defaulttype != None:\\n defaultext = \\\".\\\" + defaulttype\\n name,ext = p.splitext(filename)\\n if ext != defaultext:\\n filename += defaultext\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bafef9f18ee07b2ab127afcf0abb067e\",\n \"score\": \"0.75066715\",\n \"text\": \"def default_filename(self):\\n if self.magnet_name is not None and len(self.magnet_name) != 0:\\n name = self.magnet_name + '_' + self.label\\n else:\\n name = self.label\\n\\n if self.npts != 0:\\n if self.pos1.size == 1:\\n name = name + '_pos1={0:.0f}mm'.format(self.pos1[0])\\n if self.pos2.size == 1:\\n name = name + '_pos2={0:.0f}mm'.format(self.pos2[0])\\n if self.pos3.size == 1:\\n name = name + '_pos3={0:.0f}mm'.format(self.pos3[0])\\n\\n self.date, self.hour = _utils.get_date_hour()\\n timestamp = _utils.date_hour_to_timestamp(self.date, self.hour)\\n if self.idn is not None:\\n filename = '{0:s}_{1:s}_ID={2:s}.dat'.format(\\n timestamp, name, str(self.idn))\\n else:\\n filename = '{0:s}_{1:s}.dat'.format(timestamp, name)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bafef9f18ee07b2ab127afcf0abb067e\",\n \"score\": \"0.75066715\",\n \"text\": \"def default_filename(self):\\n if self.magnet_name is not None and len(self.magnet_name) != 0:\\n name = self.magnet_name + '_' + self.label\\n else:\\n name = self.label\\n\\n if self.npts != 0:\\n if self.pos1.size == 1:\\n name = name + '_pos1={0:.0f}mm'.format(self.pos1[0])\\n if self.pos2.size == 1:\\n name = name + '_pos2={0:.0f}mm'.format(self.pos2[0])\\n if self.pos3.size == 1:\\n name = name + '_pos3={0:.0f}mm'.format(self.pos3[0])\\n\\n self.date, self.hour = _utils.get_date_hour()\\n timestamp = _utils.date_hour_to_timestamp(self.date, self.hour)\\n if self.idn is not None:\\n filename = '{0:s}_{1:s}_ID={2:s}.dat'.format(\\n timestamp, name, str(self.idn))\\n else:\\n filename = '{0:s}_{1:s}.dat'.format(timestamp, name)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdf507aa38fb3027818504fee54d687e\",\n \"score\": \"0.7481528\",\n \"text\": \"def getDefaultFname(defaultFname_template, suffix=\\\"\\\"):\\n \\n if not os.path.isdir(os.path.dirname(defaultFname_template)):\\n os.mkdir(os.path.dirname(defaultFname_template) )\\n defaultFname_pattern = os.path.basename(defaultFname_template) + r'(\\\\d+)'\\n defaultFname_idx = 1 + int(max([ 0 if re.match(defaultFname_pattern , x ) is None \\n else re.match(defaultFname_pattern , x ).group(1) for x in os.listdir(os.path.dirname(defaultFname_template) ) + [\\\"\\\"] ]) )\\n defaultFname = defaultFname_template +\\\"{:d}\\\".format( defaultFname_idx) + suffix \\n return defaultFname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"477cd203fc0e5bfaa81ff3e70475c7c4\",\n \"score\": \"0.73396647\",\n \"text\": \"def get_filename():\\n return FILENAME\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d8ed7e68798ebb5763f1300395016ae\",\n \"score\": \"0.7174406\",\n \"text\": \"def default_filepath(self):\\n return self.dirpath.joinpath(f\\\"{self.today}.json\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"011eae2be09eed40ffdaf90e1f0e0972\",\n \"score\": \"0.7088123\",\n \"text\": \"def _get_default_path(self):\\n return os.path.join('data', 'WIDER2015')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c675c538014e30f1e718bb1d2a62a47\",\n \"score\": \"0.7024356\",\n \"text\": \"def make_default_file_name(cfg, sprint_data, variant):\\n start_date = dateutil.parser.parse(sprint_data[\\\"start date\\\"]).date()\\n end_date = dateutil.parser.parse(sprint_data[\\\"end date\\\"]).date()\\n\\n return \\\"{0:s}_{1:d}-{2:s}_{3:s}.json\\\".format(\\n sprint_data[\\\"project\\\"][\\\"name\\\"].lower(),\\n isoweek.Week.withdate(start_date).year,\\n cjm.sprint.generate_sprint_period_name(cfg, start_date, end_date).lower(), variant)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb30208f4d85bbf230592c464d96ecd9\",\n \"score\": \"0.6886078\",\n \"text\": \"def get_save_as_filename(\\n defaultfilename: str, defaultextension: str, title: str = \\\"Save As\\\"\\n) -> str:\\n root = tkinter.Tk() # create and get Tk topmost window\\n # (don't do this too early; the command prompt loses focus)\\n root.withdraw() # won't need this; this gets rid of a blank Tk window\\n root.attributes(\\\"-topmost\\\", True) # makes the tk window topmost\\n filename = filedialog.asksaveasfilename(\\n initialfile=defaultfilename,\\n defaultextension=defaultextension,\\n parent=root,\\n title=title,\\n )\\n root.attributes(\\\"-topmost\\\", False) # stop the tk window being topmost\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3f0640f9cfb27a8c6b9d62271bc5a0a\",\n \"score\": \"0.68610364\",\n \"text\": \"def _get_short_filename(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d2eb656988bcc89d0fa4c76c9714bd5\",\n \"score\": \"0.68573564\",\n \"text\": \"def _get_default_path(self):\\n return os.path.join(cfg.DATA_DIR, 'CUB_200_2011')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58b3360602a4beb700798b5f61a2ff83\",\n \"score\": \"0.6813847\",\n \"text\": \"def base_filename(self):\\r\\n return self.filename.split('.')[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ccf2ac3df88f567e2fd474224c8bed\",\n \"score\": \"0.6766652\",\n \"text\": \"def get_default_basename(self, viewset):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ccf2ac3df88f567e2fd474224c8bed\",\n \"score\": \"0.6766652\",\n \"text\": \"def get_default_basename(self, viewset):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56ec5e6b43f96c53d5366613fd6db93\",\n \"score\": \"0.6747876\",\n \"text\": \"def file_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"file_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f67c200e2c57a1f34ac39321faa8f75\",\n \"score\": \"0.67384523\",\n \"text\": \"def getDefaultDownloadPath():\\n localFrame = inspect.currentframe()\\n # No file name in this one for now\\n # suffix = datetime.now().strftime('%Y_%m_%d-%H-%M-%S')\\n # fileName = 'SureDone_Downloads_' + suffix + '.csv' \\n\\n # If the platform is windows, set the download path to the current user's Downloads folder\\n if sys.platform == 'win32' or sys.platform == 'win64': # Windows\\n downloadPath = os.path.expandvars(r'%USERPROFILE%')\\n downloadPath = os.path.join(downloadPath, 'Downloads')\\n return downloadPath\\n # If Linux, set the download path to the $HOME/downloads folder\\n elif sys.platform == 'linux' or sys.platform == 'linux2': # Linux\\n downloadPath = expanduser('~') \\n return downloadPath\\n # Unrecognized operating system \\n else:\\n LOGGER.writeLog(\\\"Platform couldn't be recognized. Are you sure you are running this script on Windows or Ubuntu Linux?\\\", localFrame.f_lineno, severity='code-breaker', data={'code':1})\\n exit()\\n\\n LOGGER.writeLog(\\\"A download directory could not be specified in default locations!\\\\nSpecify a path to the directory using (-o --output) argument.\\\", localFrame.f_lineno, severity='code-breaker', data={'code':1})\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd1d1420cfd857631db273b3b1a4d42\",\n \"score\": \"0.6715815\",\n \"text\": \"def _get_default_path(self):\\n return os.path.join(cfg.FLAGS2[\\\"data_dir\\\"], 'VOCdevkit' + self._year)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc41fb4cc6bfeee4b086e588e71a711\",\n \"score\": \"0.671217\",\n \"text\": \"def filename(self):\\r\\n return self.base_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e7ff8b091969b2466431e9633fe1a26\",\n \"score\": \"0.6675536\",\n \"text\": \"def _template_file_default(self):\\n return 'report.jinja'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2836c7e77b6d06705187da669e2c0fe9\",\n \"score\": \"0.667197\",\n \"text\": \"def get_default_asset_name(self):\\n\\n return self.config.get('default_name', default='New Asset')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5d4dfd27e4e216219e343dc9171228\",\n \"score\": \"0.66675276\",\n \"text\": \"def default_path(arg, base_dir, default_name):\\n path = default_name if arg is None else arg\\n if not op.isabs(path):\\n path = op.abspath(op.join(base_dir, path))\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e677aec7b781b50d1b5cf90838f4f087\",\n \"score\": \"0.66591245\",\n \"text\": \"def get_file_name(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575084f1835cf798507a1dfbec41a987\",\n \"score\": \"0.6651537\",\n \"text\": \"def default(self):\\n return self._default_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d41cab983ea82a8b444de03ad26e4fc7\",\n \"score\": \"0.6648756\",\n \"text\": \"def filename(self) -> Optional[str]:\\n return self.filepath.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c9732f05a2e615707dda22a5cd5cc2e\",\n \"score\": \"0.6643198\",\n \"text\": \"def _get_default_path(self):\\n return os.path.join(cfg.DATA_DIR, 'RGZdevkit' + self._year)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38e1abb1e2a650ce6ad579cbe6f4974b\",\n \"score\": \"0.6636678\",\n \"text\": \"def get_filename(name):\\n path = osp.join(osp.dirname(osp.abspath(__file__)), PiConfigParser.language, name)\\n if not osp.isfile(path):\\n # Look for common image\\n return osp.join(osp.dirname(osp.abspath(__file__)), 'com', name)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c19143a5a09adcbffbea179b1d2d72\",\n \"score\": \"0.6621322\",\n \"text\": \"def filename(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"filename\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8215999c73b3d9cb53aa7129497b4a87\",\n \"score\": \"0.65981174\",\n \"text\": \"def _template_file_default(self):\\n return 'simple' # full\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2b3baf278512d933018a026d435960\",\n \"score\": \"0.6587339\",\n \"text\": \"def default_filename_encoder():\\n return BaseCWRFileNameEncoder(4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0175f2654932c628ddab52a5d978a4c\",\n \"score\": \"0.658498\",\n \"text\": \"def _get_default_path(self):\\n return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4a22e14c0291d6e742620f0d4f51f0b\",\n \"score\": \"0.6583861\",\n \"text\": \"def get_open_filename(\\n defaultfilename: str, defaultextension: str, title: str = \\\"Open\\\"\\n) -> str:\\n root = tkinter.Tk() # create and get Tk topmost window\\n # (don't do this too early; the command prompt loses focus)\\n root.withdraw() # won't need this; this gets rid of a blank Tk window\\n root.attributes(\\\"-topmost\\\", True) # makes the tk window topmost\\n filename = filedialog.askopenfilename(\\n initialfile=defaultfilename,\\n defaultextension=defaultextension,\\n parent=root,\\n title=title,\\n )\\n root.attributes(\\\"-topmost\\\", False) # stop the tk window being topmost\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"553bf3fd7130c8cfc21daa62077776b0\",\n \"score\": \"0.6563618\",\n \"text\": \"def get_filename(self):\\n components = self.name.split(\\\"/\\\")\\n assert components and components[-1]\\n return components[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7490b19dcd2b387129a4736719377caa\",\n \"score\": \"0.6553981\",\n \"text\": \"def _get_default_path(self):\\n return 'dataset/jaad'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b87cc71f4bd8c8d8b84e8c2ccb21678c\",\n \"score\": \"0.6550989\",\n \"text\": \"def get_default_name():\\n return random.choice(config.configuration['nicknames']['default_nicknames'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a00fae26a1e52c51bfa8b654b41306\",\n \"score\": \"0.6538393\",\n \"text\": \"def test_attrs_default_filename(self):\\n c = CallFile(self.call, self.action)\\n self.assertTrue(c.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cda925ce769dfd74427fe4bbb0fd779\",\n \"score\": \"0.6534086\",\n \"text\": \"def _get_filename(self, filename):\\n\\n if filename is None:\\n if self.LIB_PATH_ENV_VAR in os.environ:\\n filename = os.path.expanduser(os.environ[self.LIB_PATH_ENV_VAR])\\n else:\\n filename = os.path.expanduser(self.LIB_PATH_DEFAULT)\\n elif '~' in filename:\\n filename = os.path.expanduser(filename)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c28a9f89bc43778b82caf43842d5a9\",\n \"score\": \"0.65294266\",\n \"text\": \"def get_filename(self):\\n assert self.valid_url\\n return self.inferred_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106c3830691e810a09cb0504c1f16f8f\",\n \"score\": \"0.65193486\",\n \"text\": \"def define_filename():\\n root = tk.Tk()\\n root.withdraw()\\n file_name = filedialog.askopenfilename()\\n return file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7a6b9b31c80d1ebb1bfb2e44423d5c\",\n \"score\": \"0.6505492\",\n \"text\": \"def _get_filename(self):\\n return os.path.basename(self.filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5734642ad3c62dc183f009877143205\",\n \"score\": \"0.6472982\",\n \"text\": \"def getFilename(filename):\\n base,ext=os.path.splitext(filename)\\n f=base+\\\"_\\\"+date.today().isoformat()\\n if os.path.exists(f+ext):\\n template=f+\\\"-%03d\\\"\\n for n in range(1,100):\\n if not os.path.exists(template%n+ext):\\n return template%n+ext\\n print \\\"no valid filename found for \\\",filename\\n return \\\"temp\\\"+ext\\n else:\\n return f+ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"814f8b52322bacaad7e7b850fd4f91ab\",\n \"score\": \"0.6470427\",\n \"text\": \"def default_name(self):\\n return self.__class__.__name__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12838fa3b58e633bf6b33e77cb5df937\",\n \"score\": \"0.6467176\",\n \"text\": \"def get_output_filename(fname_kwarg: Text,\\n work_dir: Text,\\n name: Optional[Text] = None):\\n # Separate attributions because these results will be written per method.\\n fname = DEFAULT_FILES[fname_kwarg]\\n is_formatable = '{' in fname and '}' in fname\\n if is_formatable and name is None:\\n raise ValueError(\\n f'{fname_kwarg} is formattable, missing name argument.')\\n if is_formatable:\\n return os.path.join(work_dir, fname.format(name))\\n else:\\n return os.path.join(work_dir, fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b39f3f01d911e0a0a298db9b2b825ff9\",\n \"score\": \"0.6463444\",\n \"text\": \"def getFileName(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b39f3f01d911e0a0a298db9b2b825ff9\",\n \"score\": \"0.6463444\",\n \"text\": \"def getFileName(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739b635426a0b3da41d286998ae889e1\",\n \"score\": \"0.645662\",\n \"text\": \"def _get_filename(subject, name, target_dir=None):\\n\\n if target_dir is None:\\n target_dir = os.path.dirname(os.path.realpath(__file__))\\n target_dir = os.path.join(target_dir, '../results')\\n target_dir = os.path.realpath(target_dir)\\n return os.path.join(target_dir, f\\\"s{subject}_{name}.png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d922412ac686103354a57eebe483bd\",\n \"score\": \"0.64538074\",\n \"text\": \"def file_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"file_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d922412ac686103354a57eebe483bd\",\n \"score\": \"0.64538074\",\n \"text\": \"def file_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"file_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f03fd4b72abb598993135c57ac83caf\",\n \"score\": \"0.6435663\",\n \"text\": \"def get_filename(name):\\n return osp.join(osp.dirname(osp.abspath(__file__)), PtbConfigParser.language, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3cf81b8a7116ef91a226d2705c6b70\",\n \"score\": \"0.6417916\",\n \"text\": \"def get_filename(self):\\n return self.file_title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ffa8780b52c5b9ed9805e44ccfb953b\",\n \"score\": \"0.63990796\",\n \"text\": \"def default_filename(self):\\n if self.magnet_name is not None and len(self.magnet_name) != 0:\\n name = self.magnet_name + '_' + self.label\\n else:\\n name = self.label\\n\\n if len(self.map) != 0:\\n x = _np.unique(self.map[:, 0])\\n y = _np.unique(self.map[:, 1])\\n z = _np.unique(self.map[:, 2])\\n if len(x) > 1:\\n name = name + '_X={0:.0f}_{1:.0f}mm'.format(x[0], x[-1])\\n if len(y) > 1:\\n name = name + '_Y={0:.0f}_{1:.0f}mm'.format(y[0], y[-1])\\n if len(z) > 1:\\n name = name + '_Z={0:.0f}_{1:.0f}mm'.format(z[0], z[-1])\\n\\n mt = _re.search('phase(\\\\d+)', self.comments.lower().replace(\\n ' ', ''))\\n# mt = _re.search('phase(\\\\d+\\\\.\\\\d+)', self.comments.lower().replace(\\n# ' ', ''))\\n if mt:\\n name = name + '_Y={0:.0f}mm'.format(y[0])\\n\\n for coil in ['main', 'trim', 'ch', 'cv', 'qs']:\\n current = getattr(self, 'current_' + coil)\\n if current is not None and len(current) != 0:\\n if coil == 'trim':\\n ac = 'tc'\\n elif coil == 'main':\\n ac = 'mc'\\n else:\\n ac = coil\\n name = name + '_I' + ac + '=' + current + 'A'\\n\\n if self.date is None or self.hour is None:\\n self.date, self.hour = _utils.get_date_hour()\\n if self.idn is not None:\\n filename = '{0:s}_{1:s}_ID={2:s}.dat'.format(\\n self.date, name, str(self.idn))\\n else:\\n filename = '{0:s}_{1:s}.dat'.format(self.date, name)\\n\\n if mt:\\n phase = mt.group(1)\\n filename = filename.replace(\\n '.dat', '') + '_Phase={0:s}mm.dat'.format(phase)\\n\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95b90a49d5642f5acb5217b3513221c8\",\n \"score\": \"0.63967866\",\n \"text\": \"def __get_file_name(self):\\n file_path = self.file_path.split(\\\"/\\\")\\n return os.path.splitext(file_path[-1])[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2cf441e24d93517e3b1da5332e387c0\",\n \"score\": \"0.6391369\",\n \"text\": \"def get_filename(self) :\\n return self.filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb007499108027d50895427fa758d245\",\n \"score\": \"0.6391081\",\n \"text\": \"def get_logfilename(filename=\\\"\\\"):\\n\\n # If the given filename exists, return it. No need to do any other processing\\n if os.path.isfile(filename):\\n return(filename)\\n\\n # figure out some paths and defaults\\n scriptdir = os.path.dirname(os.path.realpath(__file__))\\n default_logpath = os.path.abspath(scriptdir + '/../logs')\\n default_filename = os.path.abspath(default_logpath + '/server.log')\\n\\n # use default filename if no filename is given\\n if filename == '':\\n filename = default_filename\\n\\n # if filename doesn't exist, try prepending the logdir\\n if not os.path.isfile(filename):\\n old_filename = filename\\n if '/' not in filename:\\n filename = os.path.abspath(default_logpath + '/' + filename)\\n\\n # return filename if it exists, otherwise thrown an error and exit\\n if os.path.isfile(filename):\\n return(filename)\\n else:\\n usage(\\\"ERROR: Can not find logfile at {} or {}\\\".format(\\n old_filename, filename), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c86e5819fd50e2719c8cbfae12a14d\",\n \"score\": \"0.63865626\",\n \"text\": \"def get_file_name(self) -> str:\\n return str(self.get_file())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3cbcf93fddf36e74329a5c95d37f3ac\",\n \"score\": \"0.63854957\",\n \"text\": \"def _get_default_configuration_file(self):\\n paths = []\\n\\n resource = os.path.join(\\n Configuration.DEFAULT_PATH, Configuration.DEFAULT_FILE)\\n paths.extend(self._xdg_helper.build_config_paths(resource))\\n paths.append(os.path.expanduser(os.path.join('~', '.' + resource)))\\n paths.append(os.path.expanduser(Configuration.DEFAULT_FILE))\\n\\n for path in paths:\\n if os.path.exists(path):\\n return path\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ade16776314ffea71a4f3afa949b1e5\",\n \"score\": \"0.63826376\",\n \"text\": \"def filename(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"filename\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ade16776314ffea71a4f3afa949b1e5\",\n \"score\": \"0.63826376\",\n \"text\": \"def filename(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"filename\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec51f685de17eec09b02ed50d0aa3ea\",\n \"score\": \"0.637966\",\n \"text\": \"def get_filename() -> str:\\n return os.path.basename(__file__).replace('.py', '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d63b6bbb5282b7351fa99666fdc3bf3\",\n \"score\": \"0.6371428\",\n \"text\": \"def get_def_file_name(self):\\n lesson_type_index = self.lesson_type\\n if (self.lesson_type == ''):\\n lesson_type_index = 'ad'\\n file_name = \\\"SS-{0}-{1}'{2}.devotions.SQLite3\\\"\\\\\\n .format(self.lang_code, lesson_type_index, str(self.year)[2:4])\\n return file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f5287c469ed5ea974a5ff28801f01f\",\n \"score\": \"0.63401616\",\n \"text\": \"def get_filename(self):\\n filename = os.path.split(self.file_path)[-1]\\n if filename.split(\\\".\\\")[-1] in [\\\"markdown\\\", \\\"md\\\"]:\\n return \\\".\\\".join(filename.split(\\\".\\\")[:-1])\\n else:\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47849d5204a067a3d80e2dae12679f5\",\n \"score\": \"0.63372827\",\n \"text\": \"def make_newspaper_filename(filename: str):\\n if '_data_' in filename:\\n splt = filename.split('_data_')\\n return splt[0]\\n elif '.ann' in filename or '.txt' in filename or '.json' in filename: return None\\n else: return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54aec3eaeed9e2d74912e90e8e14ec90\",\n \"score\": \"0.6327587\",\n \"text\": \"def full_filename(self):\\n App.ensure_prefs()\\n return os.path.join(App.prefs['exordium__base_path'], self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f8033b0fad6f40610038bc7ecc7758\",\n \"score\": \"0.63259935\",\n \"text\": \"def get_filename(self, filename):\\n return datetime.now().strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") + \\\"_\\\" + filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b989fce2b41b8d8a62beefde353c1d6\",\n \"score\": \"0.6316126\",\n \"text\": \"def filename(self):\\n return self.controller.exp_get('DATFILENAME')[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854818d76a9b5ffe0748f32963da1e62\",\n \"score\": \"0.6309568\",\n \"text\": \"def guess_filename(obj):\\r\\n name = getattr(obj, 'name', None)\\r\\n if (name and isinstance(name, str) and name[0] != '<' and\\r\\n name[-1] != '>'):\\r\\n return os.path.basename(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c42fbf76d637d5e3da8d74661f3788\",\n \"score\": \"0.6302564\",\n \"text\": \"def _configureOutputFilename(self, filename):\\n assert not os.path.isabs(filename), filename\\n\\n if not os.path.splitext(filename)[1]:\\n filename += \\\".tss\\\"\\n\\n if hasattr(self._userModel, \\\"nrSamples\\\"):\\n filename = os.path.join(str(self._userModel.currentSampleNumber()),\\n filename)\\n\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"394e756173f4f4db419e7974aa6df6fa\",\n \"score\": \"0.62977123\",\n \"text\": \"def gen_file_name():\\n return os.path.join(APP_PATH, 'export-%s.csv' % time.strftime('%Y%m%d-%H%M%S'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0db7b7e118c84d5f195a5bd996254e96\",\n \"score\": \"0.6295201\",\n \"text\": \"def _get_save_filename(self) -> str:\\n init_directory = os.path.expanduser('~')\\n filename = QtWidgets.QFileDialog.getSaveFileName(self, 'Save File',\\n init_directory)[0]\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1440898adbbfb83e093bd0b6ea14e824\",\n \"score\": \"0.6282081\",\n \"text\": \"def build_filename(instance, filename):\\n now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n name = hashlib.md5('%s' % now).hexdigest()\\n ext = os.path.splitext(filename)\\n return os.path.join('%s/%s' % (instance._meta.app_label, instance._meta.module_name), '%s%s' % (name, ext[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bd2d36a9283eab34954fd7605d2f69e\",\n \"score\": \"0.6279729\",\n \"text\": \"def _template_file_default(self):\\n return 'hugo_markdown.tpl'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c00a6c1dc7ccb94880733dbb1062630\",\n \"score\": \"0.6275213\",\n \"text\": \"def _get_dump_default_path(dump):\\n if hasattr(dump, '__file__') and hasattr(dump, '__name__'):\\n # Default value. We check it is none travis or appveyor.\\n from ..pycode import is_travis_or_appveyor\\n if is_travis_or_appveyor():\\n dump = None\\n if dump is not None:\\n # We guess the package name.\\n name = dump.__name__.split('.')[-1]\\n loc = os.path.dirname(dump.__file__)\\n src_loc = os.path.split(loc)\\n if src_loc[-1] == 'src':\\n # We choose a path for the dumps in a way\\n fold = os.path.join(loc, \\\"..\\\", \\\"..\\\", \\\"..\\\", \\\"_notebook_dumps\\\")\\n else:\\n src_loc_loc = os.path.split(src_loc[0])\\n if src_loc_loc[-1] == 'src':\\n # We choose a path for the dumps in a way\\n fold = os.path.join(\\n loc, \\\"..\\\", \\\"..\\\", \\\"..\\\", \\\"_notebook_dumps\\\")\\n else:\\n # This should be a parameter.\\n fold = os.path.join(loc, \\\"..\\\", \\\"..\\\", \\\"_notebook_dumps\\\")\\n if not os.path.exists(fold):\\n os.mkdir(fold)\\n dump = os.path.join(fold, f\\\"notebook.{name}.txt\\\")\\n return dump\\n return dump\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d77f5f52a590c27702f3b9b021cac47b\",\n \"score\": \"0.6264666\",\n \"text\": \"def get_available_name(self, name):\\n # uuid4() returns a randomly generated v4 UUID, per RFC 4122\\n filename = str(uuid.uuid4()) + '.' + settings.PHOTO_FORMAT\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f122fbc1d6337ca66075e1b770f780e3\",\n \"score\": \"0.6264528\",\n \"text\": \"def filename(name):\\n return pkg_resources.resource_filename(__name__, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"091ed650cb56661d70d6a172af585102\",\n \"score\": \"0.62619567\",\n \"text\": \"def get_filepath(filename: str) -> str:\\n filepath = 'data/' + filename\\n if os.getcwd().split('/')[-1] == 'body_to_title':\\n filepath = '../' + filepath\\n return filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3237894f66a34c7bcbfb370f86779e3d\",\n \"score\": \"0.62584925\",\n \"text\": \"def FileName(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c8eae1fa394f12b5635fa35d91dbc7\",\n \"score\": \"0.625676\",\n \"text\": \"def _file_extension_default(self):\\n return '.py'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb9e1eabea4deccdb9eedb2f9c22286\",\n \"score\": \"0.62567216\",\n \"text\": \"def get_filename(self):\\n return self.filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb9e1eabea4deccdb9eedb2f9c22286\",\n \"score\": \"0.62567216\",\n \"text\": \"def get_filename(self):\\n return self.filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb9e1eabea4deccdb9eedb2f9c22286\",\n \"score\": \"0.62567216\",\n \"text\": \"def get_filename(self):\\n return self.filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b0bcbe22bd944f05aa6c3ec5890d4b7\",\n \"score\": \"0.62373203\",\n \"text\": \"def getfilename(self):\\n\\n # get filename\\n return tkFileDialog.asksaveasfilename(**self.file_opt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e62df17504bd228e000c0b745e515f\",\n \"score\": \"0.6236713\",\n \"text\": \"def generate_filename(self):\\n date = datetime.datetime.now().strftime('%m-%d_%H:%M:%S')\\n relname = date + '_' + str(self._name_idx) + '.pkl'\\n self._name_idx = (self._name_idx + 1) % int(1e10) # Arbitrary cutoff so doesn't grow indefinitely\\n\\n return os.path.join(self.folder, relname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d134c72da951dad68725aaf01c05b4a\",\n \"score\": \"0.62362605\",\n \"text\": \"def input_filename(self):\\n filename = re.match(self.FILE_REGEX, self.original).group('filename')\\n if not os.path.exists(filename):\\n raise Exception('Could not retrieve filename {}'.format(filename))\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"940f1627838fd540f5a25fbcb652e354\",\n \"score\": \"0.6215249\",\n \"text\": \"def getFilename():\\n filename = nuke.root().name()\\n if filename == 'Root':\\n raise CueNukeFileException('File is not saved! Please save the file before submitting.')\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c64a629236ee49d0ea58fe5ab54d0797\",\n \"score\": \"0.62146187\",\n \"text\": \"def _GetDefaultImageName(cls):\\n return cls.IMAGE_NAME_PREFIX\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3aaa89d482ddfbe82ce2c9155adf45\",\n \"score\": \"0.6214413\",\n \"text\": \"def output_file_name(self) -> typing.Optional[builtins.str]:\\n result = self._values.get(\\\"output_file_name\\\")\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3aaa89d482ddfbe82ce2c9155adf45\",\n \"score\": \"0.6214413\",\n \"text\": \"def output_file_name(self) -> typing.Optional[builtins.str]:\\n result = self._values.get(\\\"output_file_name\\\")\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3aaa89d482ddfbe82ce2c9155adf45\",\n \"score\": \"0.6214413\",\n \"text\": \"def output_file_name(self) -> typing.Optional[builtins.str]:\\n result = self._values.get(\\\"output_file_name\\\")\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3aaa89d482ddfbe82ce2c9155adf45\",\n \"score\": \"0.6214413\",\n \"text\": \"def output_file_name(self) -> typing.Optional[builtins.str]:\\n result = self._values.get(\\\"output_file_name\\\")\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"070f20ea21013a5075121ce305529565\",\n \"score\": \"0.6210778\",\n \"text\": \"def get_filename(filename, extension):\\n now = datetime.now()\\n return filename + '_' + now.strftime(\\\"%Y-%m-%dT%H-%M-%S\\\") + '.' + extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb4ec5ffba5db4e91b5414f0859cc1c\",\n \"score\": \"0.6210235\",\n \"text\": \"def file_name():\\n filename = input('What is the file name for the song you want to convert?')\\n if len(filename) < 1: filename = 'song.txt' # For ease in testing\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a202c01389ad94091064fa0b851769c\",\n \"score\": \"0.6208146\",\n \"text\": \"def generate_filename(self, filename):\\n # `filename` may include a path as returned by FileField.upload_to.\\n dirname, filename = os.path.split(filename)\\n return os.path.normpath(os.path.join(dirname, self.get_valid_name(filename)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4239b401c079dd2f64feec5b7bdbc0ca\",\n \"score\": \"0.6196896\",\n \"text\": \"def filename(self) -> str:\\n return self.basename[: -(len(self.ext) + 1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8112dd51d5b79113e8d6dad5aebbdc6c\",\n \"score\": \"0.619284\",\n \"text\": \"def GetFileName( self, name ) :\\n \\n # modules:\\n import os\\n\\n # target file:\\n #jbfile = '%s.jb' % name.replace('.','_')\\n jbfile = '%s.jb' % name\\n \\n # ok\\n return jbfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6595cb68a8809994993a8aff3727d76d\",\n \"score\": \"0.6174571\",\n \"text\": \"def get_default_path() -> str:\\n if os.name == \\\"nt\\\":\\n \\\"\\\"\\\" TODO TODO TODO\\n #import winreg\\n sub_key = r\\\"SOFTWARE\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\\Explorer\\\\Shell Folders\\\"\\n downloads_guid = '{374DE290-123F-4565-9164-39C4925E467B}'\\n with winreg.OpenKey(winreg.HKEY_CURRENT_USER, sub_key) as key:\\n location = winreg.QueryValueEx(key, downloads_guid)[0]\\n return location\\n \\\"\\\"\\\"\\n return None\\n else:\\n return os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Downloads\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266064b97dacbef9743be26939cad09f\",\n \"score\": \"0.61661017\",\n \"text\": \"def get_save_filename (self):\\n return os.path.splitext(self.pidfile)[0] + \\\".save\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d942d0aaacdf073f870c9a6bfc5eba0d\",\n \"score\": \"0.6161404\",\n \"text\": \"def generateFilename():\\n return \\\"scan_\\\" + datetime.now().strftime(\\\"%d-%m-%Y_%H:%M:%S\\\") + \\\".\\\" + DEST_FORMAT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5775f8c6378934f3605429eb710fdf8a\",\n \"score\": \"0.6160226\",\n \"text\": \"def get_filename(self):\\n\\t\\treturn self.fexp.format(self.ep.current_page)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992980,"cells":{"query_id":{"kind":"string","value":"fe6e130838ca6791afb3d7327d21832b"},"query":{"kind":"string","value":"Returns if there's a next element in the iteration."},"positive_passages":{"kind":"list like","value":[{"docid":"ffc74addc44719c306d6e42bd489da75","score":"0.8082671","text":"def hasNext(self):\n if self._hasnext is None:\n try:\n self._thenext = next(self.it)\n except StopIteration:\n self._hasnext = False\n else:\n self._hasnext = True\n return self._hasnext","title":""}],"string":"[\n {\n \"docid\": \"ffc74addc44719c306d6e42bd489da75\",\n \"score\": \"0.8082671\",\n \"text\": \"def hasNext(self):\\n if self._hasnext is None:\\n try:\\n self._thenext = next(self.it)\\n except StopIteration:\\n self._hasnext = False\\n else:\\n self._hasnext = True\\n return self._hasnext\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f946b915ce9bb5d7f9bbda02971b6eb3","score":"0.8134432","text":"def has_next(self):\n return self._has_next","title":""},{"docid":"f4613058c77a4e3a13081c8f06103364","score":"0.79846334","text":"def has_next(self):\n return self.sequence.has_next()","title":""},{"docid":"bfa777f97f3002d3e7a36fbd8c4056d5","score":"0.79816526","text":"def has_next(self):\n return True","title":""},{"docid":"983a88e29e623ded6d28783b2f572f41","score":"0.7870355","text":"def has_next(self):\n return self._source.has_next()","title":""},{"docid":"23894fa3cda807dc5f6b9ed7fb92c943","score":"0.7831783","text":"def hasNext(self):\n return self.next_ is not None","title":""},{"docid":"5a735a025cd56a37d275c2fcff97d819","score":"0.78143907","text":"def probably_has_next(self):\n # Due to the fact that _MultiQuery batches incorrectly report more_results,\n # _more_results may be False even if there are more results. Likewise due\n # to post filtering, _more_results may be True even if next() would throw\n # an exception.\n if self._more_results:\n return True\n return self.has_next()","title":""},{"docid":"3ea92e39f66d4c3cd186495575548210","score":"0.7786273","text":"def has_next(self):\n ...","title":""},{"docid":"ff78bc401970f80e0d58d2fdd9891d5d","score":"0.7760544","text":"def has_next(self) -> bool:\n raise NotImplementedError()","title":""},{"docid":"3cfb4b4d7921e9030e7e030d6eb95489","score":"0.7753905","text":"def HasNext(self):\n return self._frame is not None","title":""},{"docid":"6f68a33e0d804ecc1cb5f459aee0a772","score":"0.76535106","text":"def has_next(self):\n\t\ttoken = self.__get_token()\n\t\tif token is not None:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False","title":""},{"docid":"63d5edf1c67bb3db044dc9ad52071298","score":"0.75784093","text":"def has_next(self):\n return self.page < self.num_pages","title":""},{"docid":"d27760e850b8e6c1a32c14ec9ad10744","score":"0.7573966","text":"def HasNext(self):\n return not self.videoEOF","title":""},{"docid":"04c886b7b6d4e2f5a08009de3e3216a5","score":"0.7555984","text":"def hasNext(self) -> bool:\n return self._next is not None","title":""},{"docid":"a355197db9329d9228b27231dbdfae5e","score":"0.7547951","text":"def hasNext(self):\n return self.idx != self.size - 1","title":""},{"docid":"55b16742b685e0df033c4e95812bed37","score":"0.7481893","text":"def __has_next(self):\n try:\n next_link = self.browser.find_element_by_css_selector(\".coreSpriteRightPaginationArrow\")\n return next_link\n except excp.NoSuchElementException:\n return False","title":""},{"docid":"3f508e2f5b4e0b75903d9e29302120cd","score":"0.74809784","text":"def hasNext(self):\n return self.i < len(self.nums)","title":""},{"docid":"4faa7faba18af68e6e482961dc41f476","score":"0.74660546","text":"def has_next(self):\n try:\n self.v = next(self.g)\n return True\n except:\n return False","title":""},{"docid":"d2445ea2f240f4d3ed153e859fe23535","score":"0.7461125","text":"def hasNext(self) -> bool:\n return self.index + 1 < len(self.ordered_list)\n # 和之前的 self.index = -1 联系起来 因为从0开始 所以要找到下标需要-1 \n # 为了匹配长度需要对index + 1","title":""},{"docid":"11bb71ea20fa33baf7906a4146f3bb62","score":"0.74412924","text":"def hasNext(self):\n return self.i != len(self.nums)","title":""},{"docid":"960128895e438fa5d5caf1f31203e34f","score":"0.7427118","text":"def has_next(self):\n return self._action('AVT', 'Next') is not None","title":""},{"docid":"da4f24fd1284cb760629b051c3e012f0","score":"0.74166065","text":"def has_next(self) -> bool:\n return self._mu is not None or self._source.has_next()","title":""},{"docid":"28d0707ea8d7083d7b34f8220eccf266","score":"0.73867106","text":"def has_next(self):\n return self.page < self.pages","title":""},{"docid":"28d0707ea8d7083d7b34f8220eccf266","score":"0.73867106","text":"def has_next(self):\n return self.page < self.pages","title":""},{"docid":"28d0707ea8d7083d7b34f8220eccf266","score":"0.73867106","text":"def has_next(self):\n return self.page < self.pages","title":""},{"docid":"28d0707ea8d7083d7b34f8220eccf266","score":"0.73867106","text":"def has_next(self):\n return self.page < self.pages","title":""},{"docid":"a60292bb483d0646b5934c88a5fdf162","score":"0.7372905","text":"def has_next(self):\n return self.has_next_async().get_result()","title":""},{"docid":"468148e6cc21a76fb72db7a87e8b53a1","score":"0.7344295","text":"def hasNext(self) -> bool:\n if self.point < self.size-1:\n return True\n else:\n return False","title":""},{"docid":"d86daa4bf4d840746398024f9e82fa9a","score":"0.72891116","text":"def hasNext(self) -> bool:\n return self.cur < self.len","title":""},{"docid":"b4176f8f9c9403dfe42ab45640c9d62e","score":"0.7183013","text":"def hasNext(self) -> bool:\n return self.current is not None or len(self.stack) > 0","title":""},{"docid":"7819c98fc4cce8a9c7834a7904c1bb81","score":"0.7171504","text":"def has_next(self) -> bool:\n if len(self._last_reads) == 0:\n self._last_reads = self._cursor.fetchmany(size=self._fetch_size)\n return len(self._last_reads) > 0","title":""},{"docid":"0fc00d126b92bc0863bcccd0101f1d06","score":"0.7160344","text":"def hasNext(self):\n return self.index < len(self.vals)","title":""},{"docid":"492d200a3528629059a3d290887ab3c6","score":"0.70703715","text":"def hasNext(self):\n if self.cnt == len(self.val_list):\n return False\n return True","title":""},{"docid":"40431dee84dfaa06c9e4a80e2e786739","score":"0.7031511","text":"def hasNext(self) -> bool:","title":""},{"docid":"d4aae966d6dd9218d036270028a4f83a","score":"0.7029448","text":"def hasNext(self) -> bool:\n if self.node_stack:\n return True\n else:\n return False","title":""},{"docid":"518f59ecd42d30ace8af577ec955645b","score":"0.6976629","text":"def hasNext(self):\n\t\treturn len(self.stack) > 0","title":""},{"docid":"f19f22268453a67d53765f471f723264","score":"0.6955297","text":"def has_next(self, limit):\n return len(self.collection) and len(self.collection) == limit","title":""},{"docid":"9e6e0f90839a46be30bade1cc49179d5","score":"0.69545543","text":"def has_next(self):\n if len(self.__rels) > 0:\n self.__next_variables()\n if len(self.__vars) > 0:\n return True\n return False","title":""},{"docid":"bb96cb79c1f101afe2ca81f27a5a7d71","score":"0.6936562","text":"def hasNext(self) -> bool:\n if len(self.lst): return True\n else: return False","title":""},{"docid":"6a151894e9d1a805f8f2ad9f24da16e3","score":"0.6924528","text":"def hasNext(self) -> bool:\n return self.s != []","title":""},{"docid":"68ebb9928cda882426a30614949b3ca1","score":"0.68996197","text":"def hasNext(self) -> bool:\n return self.start != self.lenth","title":""},{"docid":"b5c419493208b40d133acd4f2c9807f7","score":"0.68911886","text":"def hasNext(self) -> bool:\n return len(self.stk) > 0","title":""},{"docid":"df9ed40a5f318e14bc93237fd69b91a9","score":"0.6887697","text":"def hasNext(self) -> bool:\n return True if self.stack else False","title":""},{"docid":"99d2a68c9142100891d8748758399413","score":"0.6873881","text":"def hasNext(self) -> bool:\n return len(self.queue) > 0","title":""},{"docid":"157762fb237bbad04dbe4f78e1b76e8e","score":"0.6859342","text":"def hasNext(self) -> bool:\n ...","title":""},{"docid":"a8f75463c610ff0ed26378fa474f926b","score":"0.68507123","text":"def hasNext(self):\n return len(self.stack) > 0","title":""},{"docid":"8fcdf41cc3b673e012158d5c8c38f94c","score":"0.68320656","text":"def hasNext(self) -> bool:\n return self.q.qsize() > 0","title":""},{"docid":"300a5d39ff1dc21648f758eb5773d183","score":"0.682111","text":"def hasNextToken(self):\n return self._currentToken != None","title":""},{"docid":"8ac483b1c5eb66671ec11cf3e8586b6b","score":"0.6820075","text":"def hasNext(self) -> bool:\n return not len(self.stack) == 0","title":""},{"docid":"e1cc33ba7c42057b495383f1773394f0","score":"0.68099785","text":"def next(self):\n if not self.next_:\n raise Exception('The array has no next value.')\n return self.next\n next = self.next_\n if len(self.array[self.arrayCounter]) > self.currCounter:\n self.next_ = self.array[self.arrayCounter][self.currCounter]\n self.currCounter += 1\n else:\n if len(self.array) <= self.arrayCounter+1:\n self.next_ = None\n else:\n for i, a in enumerate(self.array[self.arrayCounter+1:]):\n if len(a) != 0:\n self.next_ = self.array[self.arrayCounter+i+1][0]\n self.currCounter = 1\n self.arrayCounter += i+1\n break\n return next","title":""},{"docid":"be14d023fa1af3cc83eecd760094dcc2","score":"0.67327607","text":"def hasNext(self) -> bool:\n return self.stack","title":""},{"docid":"103fa00b6bbec08ecd1d5c657ab616ff","score":"0.67274076","text":"def has_next_result(self):\n raise NotImplementedError()","title":""},{"docid":"9c56d38a477fb08e082a59b33990a581","score":"0.67270166","text":"def hasNext(self) -> bool:\n return len(self.stack) > 0","title":""},{"docid":"9c56d38a477fb08e082a59b33990a581","score":"0.67270166","text":"def hasNext(self) -> bool:\n return len(self.stack) > 0","title":""},{"docid":"c8c01fe6be6cdad0b633cd67c3538efa","score":"0.67225665","text":"def next(self):\n return self._next","title":""},{"docid":"fb859b41b5d84754d2b5eeb8c12c9900","score":"0.6720153","text":"def hasNext(self) -> bool:\n return len(self._my_stack) > 0","title":""},{"docid":"abb85a2308616300aea2e93b257ca176","score":"0.6640482","text":"def is_iterator(obj) -> bool:\n try:\n next(obj, None)\n return True\n except TypeError:\n return False","title":""},{"docid":"6e9fd20e6dca4d6c043dd98c86469848","score":"0.6636237","text":"def CanNavigateNext(cls):\n return cls._poscache.HasNext()","title":""},{"docid":"ed1a45ddcbb72f5f9f4db052ac98c766","score":"0.6527142","text":"def Next(self):\n try:\n self.key, self.value = next(self.enumerator)\n except StopIteration:\n return False\n\n return True","title":""},{"docid":"0a9e02a427e24333f88bf0852e7f51dc","score":"0.64465904","text":"def is_next_first(self):\n return None if (self.prev_count == 0) else (len(self._past_cache) == 0)","title":""},{"docid":"708f4f6001c51afdec08f00f2a17c66f","score":"0.64384586","text":"def next(self):\n return self._next()","title":""},{"docid":"708f4f6001c51afdec08f00f2a17c66f","score":"0.64384586","text":"def next(self):\n return self._next()","title":""},{"docid":"708f4f6001c51afdec08f00f2a17c66f","score":"0.64384586","text":"def next(self):\n return self._next()","title":""},{"docid":"708f4f6001c51afdec08f00f2a17c66f","score":"0.64384586","text":"def next(self):\n return self._next()","title":""},{"docid":"708f4f6001c51afdec08f00f2a17c66f","score":"0.64384586","text":"def next(self):\n return self._next()","title":""},{"docid":"708f4f6001c51afdec08f00f2a17c66f","score":"0.64384586","text":"def next(self):\n return self._next()","title":""},{"docid":"2a48e75be52876d1c31ecd219b02840f","score":"0.64100367","text":"def has_next_page(self):\n raise NotImplementedError","title":""},{"docid":"93b869c39deac4185c98a5691c99e103","score":"0.639849","text":"def get_next(self):\n return self.next","title":""},{"docid":"93b869c39deac4185c98a5691c99e103","score":"0.639849","text":"def get_next(self):\n return self.next","title":""},{"docid":"93b869c39deac4185c98a5691c99e103","score":"0.639849","text":"def get_next(self):\n return self.next","title":""},{"docid":"41f1469c08833f11040d0d4cdd56e5cb","score":"0.6365385","text":"def __next__(self):\n if self._hasnext:\n result = self._thenext\n else:\n result = next(self.it)\n self._hasnext = None\n return result","title":""},{"docid":"8b81095523d0429ba2eaadb21cf7a823","score":"0.63633984","text":"def next(self):\r\n if self._count == 1:\r\n return None\r\n return self._rest","title":""},{"docid":"27170efa534fa00397c60575a1ede757","score":"0.63483995","text":"def next_if(self, expr):\n if self.current.test(expr):\n return next(self)","title":""},{"docid":"62818c9a7fae1cc649bd9e913ecd5e54","score":"0.63257754","text":"def playlist_next(self): # type: () -> bool\n return self._change_playlist_index(1)","title":""},{"docid":"9fe7b1ee2322d914b2909a8393bb286d","score":"0.6312332","text":"def exhausted(self) -> bool:\n return self.__exhausted","title":""},{"docid":"168ffc8f1309f6cecff7f63d8d78ca9c","score":"0.6288565","text":"def hasNext(self):\n return self.stack","title":""},{"docid":"148c8026e3af10c3ab6ee2a9b576f64b","score":"0.6282588","text":"def next(self):\n if self.iterator is None:\n self.iterator = self.iterate()\n return self.iterator.next()","title":""},{"docid":"170ba8085b1f40d668e95b7d427a4493","score":"0.6270974","text":"def has_next_line(self):\n\t\tif self.contents != \"\":\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False","title":""},{"docid":"34a3c9d6cc440554050ef4e30279cd33","score":"0.6262461","text":"def check_stops(it):\n try:\n val = next(it)\n except StopIteration:\n return\n assert False, \"expected exhausted iterator; got {}\".format(str(val))","title":""},{"docid":"ae012ada56c227dcd9399d227aaf868e","score":"0.6241489","text":"def next(self):\n if self.cur<self.total_pages:\n return self.cur+1\n else:\n return None","title":""},{"docid":"1961b3a02e970267942150fc482512df","score":"0.6225808","text":"def next(self, next_state=None):\n return next_state or self.next_state","title":""},{"docid":"c97a10c1cb6385dd103401710532d425","score":"0.6224619","text":"def has_next_async(self):\n if self._fut is None:\n self._fut = self._iter.getq()\n flag = True\n try:\n yield self._fut\n except EOFError:\n flag = False\n raise tasklets.Return(flag)","title":""},{"docid":"8f35730a8e616ac17f322087bdfcbf50","score":"0.62224275","text":"def check_infinite(self):\n p1 = p2 = self.head\n while p1 is not None and p2 is not None:\n if p2.next is None:\n return False\n\n p1 = p1.next\n p2 = p2.next.next\n\n if p1 == p2:\n return True\n return False","title":""},{"docid":"c5e3231bdca90f5e96bf404d116c2f82","score":"0.6214791","text":"def next(self):\n return self.iter.next()","title":""},{"docid":"cbf1df28e4fb7ade2c267b9e46281eab","score":"0.6207509","text":"def has_next_int(self):\n\t\ttoken = self.__get_token()\n\n\t\t# Handle the possiblity of an empty token\n\t\tif token is None:\n\t\t\treturn False\n\n\t\t# Attempt to convert the token into an integer in order to tell if it is an integer or not\n\t\ttry:\n\t\t\tint(token['token'])\n\t\t\treturn True\n\t\texcept ValueError:\n\t\t\treturn False","title":""},{"docid":"ae03d5930cb6b5f37277a093fc47e8e9","score":"0.62036884","text":"def isExisting(self, x):\n temp = self.head\n while True:\n if temp.value == x:\n return True\n if temp.next == None:\n return False\n temp = temp.next","title":""},{"docid":"c262522c863a2144dc999c1f85f4137e","score":"0.6190526","text":"def next(self) -> int:\n if self.cur >= self.len:\n raise StopIteration\n\n self.cur += 1\n return self.in_order_list[self.cur-1]","title":""},{"docid":"b92354a5fe6625ef2be6cb88ef29d064","score":"0.617034","text":"def next(self):\n return self.__next__()","title":""},{"docid":"b92354a5fe6625ef2be6cb88ef29d064","score":"0.617034","text":"def next(self):\n return self.__next__()","title":""},{"docid":"b92354a5fe6625ef2be6cb88ef29d064","score":"0.617034","text":"def next(self):\n return self.__next__()","title":""},{"docid":"4932d057f0d2ae67efe5140732dbff4c","score":"0.6154307","text":"def has_next_page(s: bs4.BeautifulSoup):\n more_a = s.find('a', attrs={'class': 'morelink'})\n if more_a is None:\n return False\n else:\n # If there's a \"More\" link at the bottom of the page, then\n # there's a next page\n return True","title":""},{"docid":"c81c6552851bd736c1de3833033782b2","score":"0.61435056","text":"def next(self) -> None:\n raise NotImplemented","title":""},{"docid":"4bc4a4dcc98aae66e4be0ed6b1d7b76f","score":"0.61025965","text":"def _next_problem(self):\n try:\n self._current_problem = self._problems.pop()\n return True\n except IndexError:\n self._current_problem = None\n return False","title":""},{"docid":"0533f3f4225f51607f92ab90a7a9c593","score":"0.6081089","text":"def next_line(self):\n indexx_plus_one = self.page.lines.index(self) + 1\n if any(line for line in self.page.lines if self.page.lines.index(line) == indexx_plus_one + 1):\n next_line = self.page.lines[indexx_plus_one]\n if next_line.content == self.content:\n return next_line\n\n next_page = self.page.next_page()\n if next_page:\n next_line = next_page.lines[0]\n if next_line.content == self.content:\n return next_line\n # if this is the last line ever return None. Though it is foolish\n return None","title":""},{"docid":"faae6dd357cfd706ce6d884b73144f60","score":"0.60590273","text":"def __next__(self):\r\n # using the attribute that you initialized in __iter__(),\r\n # fetch the next value and return it. If there are no more\r\n # values to fetch, raise a StopIteration exception.\r\n if self._iter_index == self._size:\r\n raise StopIteration\r\n current_val = self.get_element_at(self._iter_index)\r\n self._iter_index = self._iter_index + 1\r\n return current_val","title":""},{"docid":"d61db33bacfa421a4b13b637be7ecae0","score":"0.60442346","text":"def has_cycle(self):\n if self.head is None:\n return True\n\n walker = self.head\n runner = self.head\n while walker and runner and runner.next:\n walker = walker.next\n runner = runner.next.next\n if walker == runner:\n return True\n\n return False","title":""},{"docid":"c120de414130b4cffe5b2201bc5710e2","score":"0.6041407","text":"def can_next(self):\n return self.has_next and \\\n 'next' in self._current_transport_actions","title":""},{"docid":"14e6634aa4ebc777e46e6ef836c4b43f","score":"0.60289556","text":"def has_more(self, context):\n return context['page_obj'].has_next() if context['page_obj'] else False","title":""},{"docid":"0571728f0fcfa0d8e23a1eac132b5339","score":"0.6027765","text":"def moveNext(self):\n if self._index +1 == self.size:\n self.last = False\n else:\n self.last = True\n self._index += 1\n return self.last","title":""},{"docid":"66bd55a8a696e2d030ac20923fd96398","score":"0.6018709","text":"def next_element(elem):\n while elem is not None:\n elem = elem.next_sibling\n if hasattr(elem, 'name'):\n return elem","title":""},{"docid":"bfb6e5051b1af5fcb4d62c5620913ec6","score":"0.6003611","text":"def next(self):\n found_pass = False\n value = None\n while not found_pass:\n try:\n value = self.input.next()\n except StopIteration:\n # this is kind of implied, but easier to see\n # what happens with this catch/raise\n raise\n found_pass = self.action(value)\n return value","title":""}],"string":"[\n {\n \"docid\": \"f946b915ce9bb5d7f9bbda02971b6eb3\",\n \"score\": \"0.8134432\",\n \"text\": \"def has_next(self):\\n return self._has_next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4613058c77a4e3a13081c8f06103364\",\n \"score\": \"0.79846334\",\n \"text\": \"def has_next(self):\\n return self.sequence.has_next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfa777f97f3002d3e7a36fbd8c4056d5\",\n \"score\": \"0.79816526\",\n \"text\": \"def has_next(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"983a88e29e623ded6d28783b2f572f41\",\n \"score\": \"0.7870355\",\n \"text\": \"def has_next(self):\\n return self._source.has_next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23894fa3cda807dc5f6b9ed7fb92c943\",\n \"score\": \"0.7831783\",\n \"text\": \"def hasNext(self):\\n return self.next_ is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a735a025cd56a37d275c2fcff97d819\",\n \"score\": \"0.78143907\",\n \"text\": \"def probably_has_next(self):\\n # Due to the fact that _MultiQuery batches incorrectly report more_results,\\n # _more_results may be False even if there are more results. Likewise due\\n # to post filtering, _more_results may be True even if next() would throw\\n # an exception.\\n if self._more_results:\\n return True\\n return self.has_next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ea92e39f66d4c3cd186495575548210\",\n \"score\": \"0.7786273\",\n \"text\": \"def has_next(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff78bc401970f80e0d58d2fdd9891d5d\",\n \"score\": \"0.7760544\",\n \"text\": \"def has_next(self) -> bool:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfb4b4d7921e9030e7e030d6eb95489\",\n \"score\": \"0.7753905\",\n \"text\": \"def HasNext(self):\\n return self._frame is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f68a33e0d804ecc1cb5f459aee0a772\",\n \"score\": \"0.76535106\",\n \"text\": \"def has_next(self):\\n\\t\\ttoken = self.__get_token()\\n\\t\\tif token is not None:\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63d5edf1c67bb3db044dc9ad52071298\",\n \"score\": \"0.75784093\",\n \"text\": \"def has_next(self):\\n return self.page < self.num_pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27760e850b8e6c1a32c14ec9ad10744\",\n \"score\": \"0.7573966\",\n \"text\": \"def HasNext(self):\\n return not self.videoEOF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c886b7b6d4e2f5a08009de3e3216a5\",\n \"score\": \"0.7555984\",\n \"text\": \"def hasNext(self) -> bool:\\n return self._next is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a355197db9329d9228b27231dbdfae5e\",\n \"score\": \"0.7547951\",\n \"text\": \"def hasNext(self):\\n return self.idx != self.size - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55b16742b685e0df033c4e95812bed37\",\n \"score\": \"0.7481893\",\n \"text\": \"def __has_next(self):\\n try:\\n next_link = self.browser.find_element_by_css_selector(\\\".coreSpriteRightPaginationArrow\\\")\\n return next_link\\n except excp.NoSuchElementException:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f508e2f5b4e0b75903d9e29302120cd\",\n \"score\": \"0.74809784\",\n \"text\": \"def hasNext(self):\\n return self.i < len(self.nums)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4faa7faba18af68e6e482961dc41f476\",\n \"score\": \"0.74660546\",\n \"text\": \"def has_next(self):\\n try:\\n self.v = next(self.g)\\n return True\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2445ea2f240f4d3ed153e859fe23535\",\n \"score\": \"0.7461125\",\n \"text\": \"def hasNext(self) -> bool:\\n return self.index + 1 < len(self.ordered_list)\\n # 和之前的 self.index = -1 联系起来 因为从0开始 所以要找到下标需要-1 \\n # 为了匹配长度需要对index + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11bb71ea20fa33baf7906a4146f3bb62\",\n \"score\": \"0.74412924\",\n \"text\": \"def hasNext(self):\\n return self.i != len(self.nums)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"960128895e438fa5d5caf1f31203e34f\",\n \"score\": \"0.7427118\",\n \"text\": \"def has_next(self):\\n return self._action('AVT', 'Next') is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4f24fd1284cb760629b051c3e012f0\",\n \"score\": \"0.74166065\",\n \"text\": \"def has_next(self) -> bool:\\n return self._mu is not None or self._source.has_next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d0707ea8d7083d7b34f8220eccf266\",\n \"score\": \"0.73867106\",\n \"text\": \"def has_next(self):\\n return self.page < self.pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d0707ea8d7083d7b34f8220eccf266\",\n \"score\": \"0.73867106\",\n \"text\": \"def has_next(self):\\n return self.page < self.pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d0707ea8d7083d7b34f8220eccf266\",\n \"score\": \"0.73867106\",\n \"text\": \"def has_next(self):\\n return self.page < self.pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d0707ea8d7083d7b34f8220eccf266\",\n \"score\": \"0.73867106\",\n \"text\": \"def has_next(self):\\n return self.page < self.pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60292bb483d0646b5934c88a5fdf162\",\n \"score\": \"0.7372905\",\n \"text\": \"def has_next(self):\\n return self.has_next_async().get_result()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"468148e6cc21a76fb72db7a87e8b53a1\",\n \"score\": \"0.7344295\",\n \"text\": \"def hasNext(self) -> bool:\\n if self.point < self.size-1:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d86daa4bf4d840746398024f9e82fa9a\",\n \"score\": \"0.72891116\",\n \"text\": \"def hasNext(self) -> bool:\\n return self.cur < self.len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4176f8f9c9403dfe42ab45640c9d62e\",\n \"score\": \"0.7183013\",\n \"text\": \"def hasNext(self) -> bool:\\n return self.current is not None or len(self.stack) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7819c98fc4cce8a9c7834a7904c1bb81\",\n \"score\": \"0.7171504\",\n \"text\": \"def has_next(self) -> bool:\\n if len(self._last_reads) == 0:\\n self._last_reads = self._cursor.fetchmany(size=self._fetch_size)\\n return len(self._last_reads) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fc00d126b92bc0863bcccd0101f1d06\",\n \"score\": \"0.7160344\",\n \"text\": \"def hasNext(self):\\n return self.index < len(self.vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"492d200a3528629059a3d290887ab3c6\",\n \"score\": \"0.70703715\",\n \"text\": \"def hasNext(self):\\n if self.cnt == len(self.val_list):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40431dee84dfaa06c9e4a80e2e786739\",\n \"score\": \"0.7031511\",\n \"text\": \"def hasNext(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4aae966d6dd9218d036270028a4f83a\",\n \"score\": \"0.7029448\",\n \"text\": \"def hasNext(self) -> bool:\\n if self.node_stack:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518f59ecd42d30ace8af577ec955645b\",\n \"score\": \"0.6976629\",\n \"text\": \"def hasNext(self):\\n\\t\\treturn len(self.stack) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f19f22268453a67d53765f471f723264\",\n \"score\": \"0.6955297\",\n \"text\": \"def has_next(self, limit):\\n return len(self.collection) and len(self.collection) == limit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e6e0f90839a46be30bade1cc49179d5\",\n \"score\": \"0.69545543\",\n \"text\": \"def has_next(self):\\n if len(self.__rels) > 0:\\n self.__next_variables()\\n if len(self.__vars) > 0:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb96cb79c1f101afe2ca81f27a5a7d71\",\n \"score\": \"0.6936562\",\n \"text\": \"def hasNext(self) -> bool:\\n if len(self.lst): return True\\n else: return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a151894e9d1a805f8f2ad9f24da16e3\",\n \"score\": \"0.6924528\",\n \"text\": \"def hasNext(self) -> bool:\\n return self.s != []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68ebb9928cda882426a30614949b3ca1\",\n \"score\": \"0.68996197\",\n \"text\": \"def hasNext(self) -> bool:\\n return self.start != self.lenth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5c419493208b40d133acd4f2c9807f7\",\n \"score\": \"0.68911886\",\n \"text\": \"def hasNext(self) -> bool:\\n return len(self.stk) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df9ed40a5f318e14bc93237fd69b91a9\",\n \"score\": \"0.6887697\",\n \"text\": \"def hasNext(self) -> bool:\\n return True if self.stack else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d2a68c9142100891d8748758399413\",\n \"score\": \"0.6873881\",\n \"text\": \"def hasNext(self) -> bool:\\n return len(self.queue) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157762fb237bbad04dbe4f78e1b76e8e\",\n \"score\": \"0.6859342\",\n \"text\": \"def hasNext(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f75463c610ff0ed26378fa474f926b\",\n \"score\": \"0.68507123\",\n \"text\": \"def hasNext(self):\\n return len(self.stack) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fcdf41cc3b673e012158d5c8c38f94c\",\n \"score\": \"0.68320656\",\n \"text\": \"def hasNext(self) -> bool:\\n return self.q.qsize() > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"300a5d39ff1dc21648f758eb5773d183\",\n \"score\": \"0.682111\",\n \"text\": \"def hasNextToken(self):\\n return self._currentToken != None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac483b1c5eb66671ec11cf3e8586b6b\",\n \"score\": \"0.6820075\",\n \"text\": \"def hasNext(self) -> bool:\\n return not len(self.stack) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1cc33ba7c42057b495383f1773394f0\",\n \"score\": \"0.68099785\",\n \"text\": \"def next(self):\\n if not self.next_:\\n raise Exception('The array has no next value.')\\n return self.next\\n next = self.next_\\n if len(self.array[self.arrayCounter]) > self.currCounter:\\n self.next_ = self.array[self.arrayCounter][self.currCounter]\\n self.currCounter += 1\\n else:\\n if len(self.array) <= self.arrayCounter+1:\\n self.next_ = None\\n else:\\n for i, a in enumerate(self.array[self.arrayCounter+1:]):\\n if len(a) != 0:\\n self.next_ = self.array[self.arrayCounter+i+1][0]\\n self.currCounter = 1\\n self.arrayCounter += i+1\\n break\\n return next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be14d023fa1af3cc83eecd760094dcc2\",\n \"score\": \"0.67327607\",\n \"text\": \"def hasNext(self) -> bool:\\n return self.stack\",\n \"title\": \"\"\n },\n {\n \"docid\": \"103fa00b6bbec08ecd1d5c657ab616ff\",\n \"score\": \"0.67274076\",\n \"text\": \"def has_next_result(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c56d38a477fb08e082a59b33990a581\",\n \"score\": \"0.67270166\",\n \"text\": \"def hasNext(self) -> bool:\\n return len(self.stack) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c56d38a477fb08e082a59b33990a581\",\n \"score\": \"0.67270166\",\n \"text\": \"def hasNext(self) -> bool:\\n return len(self.stack) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c01fe6be6cdad0b633cd67c3538efa\",\n \"score\": \"0.67225665\",\n \"text\": \"def next(self):\\n return self._next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb859b41b5d84754d2b5eeb8c12c9900\",\n \"score\": \"0.6720153\",\n \"text\": \"def hasNext(self) -> bool:\\n return len(self._my_stack) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb85a2308616300aea2e93b257ca176\",\n \"score\": \"0.6640482\",\n \"text\": \"def is_iterator(obj) -> bool:\\n try:\\n next(obj, None)\\n return True\\n except TypeError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e9fd20e6dca4d6c043dd98c86469848\",\n \"score\": \"0.6636237\",\n \"text\": \"def CanNavigateNext(cls):\\n return cls._poscache.HasNext()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1a45ddcbb72f5f9f4db052ac98c766\",\n \"score\": \"0.6527142\",\n \"text\": \"def Next(self):\\n try:\\n self.key, self.value = next(self.enumerator)\\n except StopIteration:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9e02a427e24333f88bf0852e7f51dc\",\n \"score\": \"0.64465904\",\n \"text\": \"def is_next_first(self):\\n return None if (self.prev_count == 0) else (len(self._past_cache) == 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708f4f6001c51afdec08f00f2a17c66f\",\n \"score\": \"0.64384586\",\n \"text\": \"def next(self):\\n return self._next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708f4f6001c51afdec08f00f2a17c66f\",\n \"score\": \"0.64384586\",\n \"text\": \"def next(self):\\n return self._next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708f4f6001c51afdec08f00f2a17c66f\",\n \"score\": \"0.64384586\",\n \"text\": \"def next(self):\\n return self._next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708f4f6001c51afdec08f00f2a17c66f\",\n \"score\": \"0.64384586\",\n \"text\": \"def next(self):\\n return self._next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708f4f6001c51afdec08f00f2a17c66f\",\n \"score\": \"0.64384586\",\n \"text\": \"def next(self):\\n return self._next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708f4f6001c51afdec08f00f2a17c66f\",\n \"score\": \"0.64384586\",\n \"text\": \"def next(self):\\n return self._next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a48e75be52876d1c31ecd219b02840f\",\n \"score\": \"0.64100367\",\n \"text\": \"def has_next_page(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b869c39deac4185c98a5691c99e103\",\n \"score\": \"0.639849\",\n \"text\": \"def get_next(self):\\n return self.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b869c39deac4185c98a5691c99e103\",\n \"score\": \"0.639849\",\n \"text\": \"def get_next(self):\\n return self.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b869c39deac4185c98a5691c99e103\",\n \"score\": \"0.639849\",\n \"text\": \"def get_next(self):\\n return self.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f1469c08833f11040d0d4cdd56e5cb\",\n \"score\": \"0.6365385\",\n \"text\": \"def __next__(self):\\n if self._hasnext:\\n result = self._thenext\\n else:\\n result = next(self.it)\\n self._hasnext = None\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b81095523d0429ba2eaadb21cf7a823\",\n \"score\": \"0.63633984\",\n \"text\": \"def next(self):\\r\\n if self._count == 1:\\r\\n return None\\r\\n return self._rest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27170efa534fa00397c60575a1ede757\",\n \"score\": \"0.63483995\",\n \"text\": \"def next_if(self, expr):\\n if self.current.test(expr):\\n return next(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62818c9a7fae1cc649bd9e913ecd5e54\",\n \"score\": \"0.63257754\",\n \"text\": \"def playlist_next(self): # type: () -> bool\\n return self._change_playlist_index(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe7b1ee2322d914b2909a8393bb286d\",\n \"score\": \"0.6312332\",\n \"text\": \"def exhausted(self) -> bool:\\n return self.__exhausted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"168ffc8f1309f6cecff7f63d8d78ca9c\",\n \"score\": \"0.6288565\",\n \"text\": \"def hasNext(self):\\n return self.stack\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148c8026e3af10c3ab6ee2a9b576f64b\",\n \"score\": \"0.6282588\",\n \"text\": \"def next(self):\\n if self.iterator is None:\\n self.iterator = self.iterate()\\n return self.iterator.next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170ba8085b1f40d668e95b7d427a4493\",\n \"score\": \"0.6270974\",\n \"text\": \"def has_next_line(self):\\n\\t\\tif self.contents != \\\"\\\":\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34a3c9d6cc440554050ef4e30279cd33\",\n \"score\": \"0.6262461\",\n \"text\": \"def check_stops(it):\\n try:\\n val = next(it)\\n except StopIteration:\\n return\\n assert False, \\\"expected exhausted iterator; got {}\\\".format(str(val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae012ada56c227dcd9399d227aaf868e\",\n \"score\": \"0.6241489\",\n \"text\": \"def next(self):\\n if self.cur<self.total_pages:\\n return self.cur+1\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1961b3a02e970267942150fc482512df\",\n \"score\": \"0.6225808\",\n \"text\": \"def next(self, next_state=None):\\n return next_state or self.next_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c97a10c1cb6385dd103401710532d425\",\n \"score\": \"0.6224619\",\n \"text\": \"def has_next_async(self):\\n if self._fut is None:\\n self._fut = self._iter.getq()\\n flag = True\\n try:\\n yield self._fut\\n except EOFError:\\n flag = False\\n raise tasklets.Return(flag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f35730a8e616ac17f322087bdfcbf50\",\n \"score\": \"0.62224275\",\n \"text\": \"def check_infinite(self):\\n p1 = p2 = self.head\\n while p1 is not None and p2 is not None:\\n if p2.next is None:\\n return False\\n\\n p1 = p1.next\\n p2 = p2.next.next\\n\\n if p1 == p2:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e3231bdca90f5e96bf404d116c2f82\",\n \"score\": \"0.6214791\",\n \"text\": \"def next(self):\\n return self.iter.next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf1df28e4fb7ade2c267b9e46281eab\",\n \"score\": \"0.6207509\",\n \"text\": \"def has_next_int(self):\\n\\t\\ttoken = self.__get_token()\\n\\n\\t\\t# Handle the possiblity of an empty token\\n\\t\\tif token is None:\\n\\t\\t\\treturn False\\n\\n\\t\\t# Attempt to convert the token into an integer in order to tell if it is an integer or not\\n\\t\\ttry:\\n\\t\\t\\tint(token['token'])\\n\\t\\t\\treturn True\\n\\t\\texcept ValueError:\\n\\t\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae03d5930cb6b5f37277a093fc47e8e9\",\n \"score\": \"0.62036884\",\n \"text\": \"def isExisting(self, x):\\n temp = self.head\\n while True:\\n if temp.value == x:\\n return True\\n if temp.next == None:\\n return False\\n temp = temp.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c262522c863a2144dc999c1f85f4137e\",\n \"score\": \"0.6190526\",\n \"text\": \"def next(self) -> int:\\n if self.cur >= self.len:\\n raise StopIteration\\n\\n self.cur += 1\\n return self.in_order_list[self.cur-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92354a5fe6625ef2be6cb88ef29d064\",\n \"score\": \"0.617034\",\n \"text\": \"def next(self):\\n return self.__next__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92354a5fe6625ef2be6cb88ef29d064\",\n \"score\": \"0.617034\",\n \"text\": \"def next(self):\\n return self.__next__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92354a5fe6625ef2be6cb88ef29d064\",\n \"score\": \"0.617034\",\n \"text\": \"def next(self):\\n return self.__next__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4932d057f0d2ae67efe5140732dbff4c\",\n \"score\": \"0.6154307\",\n \"text\": \"def has_next_page(s: bs4.BeautifulSoup):\\n more_a = s.find('a', attrs={'class': 'morelink'})\\n if more_a is None:\\n return False\\n else:\\n # If there's a \\\"More\\\" link at the bottom of the page, then\\n # there's a next page\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81c6552851bd736c1de3833033782b2\",\n \"score\": \"0.61435056\",\n \"text\": \"def next(self) -> None:\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc4a4dcc98aae66e4be0ed6b1d7b76f\",\n \"score\": \"0.61025965\",\n \"text\": \"def _next_problem(self):\\n try:\\n self._current_problem = self._problems.pop()\\n return True\\n except IndexError:\\n self._current_problem = None\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0533f3f4225f51607f92ab90a7a9c593\",\n \"score\": \"0.6081089\",\n \"text\": \"def next_line(self):\\n indexx_plus_one = self.page.lines.index(self) + 1\\n if any(line for line in self.page.lines if self.page.lines.index(line) == indexx_plus_one + 1):\\n next_line = self.page.lines[indexx_plus_one]\\n if next_line.content == self.content:\\n return next_line\\n\\n next_page = self.page.next_page()\\n if next_page:\\n next_line = next_page.lines[0]\\n if next_line.content == self.content:\\n return next_line\\n # if this is the last line ever return None. Though it is foolish\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faae6dd357cfd706ce6d884b73144f60\",\n \"score\": \"0.60590273\",\n \"text\": \"def __next__(self):\\r\\n # using the attribute that you initialized in __iter__(),\\r\\n # fetch the next value and return it. If there are no more\\r\\n # values to fetch, raise a StopIteration exception.\\r\\n if self._iter_index == self._size:\\r\\n raise StopIteration\\r\\n current_val = self.get_element_at(self._iter_index)\\r\\n self._iter_index = self._iter_index + 1\\r\\n return current_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d61db33bacfa421a4b13b637be7ecae0\",\n \"score\": \"0.60442346\",\n \"text\": \"def has_cycle(self):\\n if self.head is None:\\n return True\\n\\n walker = self.head\\n runner = self.head\\n while walker and runner and runner.next:\\n walker = walker.next\\n runner = runner.next.next\\n if walker == runner:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c120de414130b4cffe5b2201bc5710e2\",\n \"score\": \"0.6041407\",\n \"text\": \"def can_next(self):\\n return self.has_next and \\\\\\n 'next' in self._current_transport_actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e6634aa4ebc777e46e6ef836c4b43f\",\n \"score\": \"0.60289556\",\n \"text\": \"def has_more(self, context):\\n return context['page_obj'].has_next() if context['page_obj'] else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0571728f0fcfa0d8e23a1eac132b5339\",\n \"score\": \"0.6027765\",\n \"text\": \"def moveNext(self):\\n if self._index +1 == self.size:\\n self.last = False\\n else:\\n self.last = True\\n self._index += 1\\n return self.last\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66bd55a8a696e2d030ac20923fd96398\",\n \"score\": \"0.6018709\",\n \"text\": \"def next_element(elem):\\n while elem is not None:\\n elem = elem.next_sibling\\n if hasattr(elem, 'name'):\\n return elem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb6e5051b1af5fcb4d62c5620913ec6\",\n \"score\": \"0.6003611\",\n \"text\": \"def next(self):\\n found_pass = False\\n value = None\\n while not found_pass:\\n try:\\n value = self.input.next()\\n except StopIteration:\\n # this is kind of implied, but easier to see\\n # what happens with this catch/raise\\n raise\\n found_pass = self.action(value)\\n return value\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992981,"cells":{"query_id":{"kind":"string","value":"2ed1953506251b65b2535fb5de771473"},"query":{"kind":"string","value":"Print the version of acloud. The VERSION file is built into the acloud binary. The version file path is under \"public/data\"."},"positive_passages":{"kind":"list like","value":[{"docid":"6388687e7e576f68edc6db43fcc75e54","score":"0.5592797","text":"def GetVersion():\n version_file_path = os.path.join(_CONFIG_DATA_PATH, _VERSION_FILE)\n if os.path.exists(version_file_path):\n with open(version_file_path) as version_file:\n return version_file.read()\n return _UNKNOWN","title":""}],"string":"[\n {\n \"docid\": \"6388687e7e576f68edc6db43fcc75e54\",\n \"score\": \"0.5592797\",\n \"text\": \"def GetVersion():\\n version_file_path = os.path.join(_CONFIG_DATA_PATH, _VERSION_FILE)\\n if os.path.exists(version_file_path):\\n with open(version_file_path) as version_file:\\n return version_file.read()\\n return _UNKNOWN\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"579001e9d9b87d09e853ec395b288c69","score":"0.69931334","text":"def version():\n print(version)","title":""},{"docid":"71acca3ffe13eb8627a650b75a853da4","score":"0.69509643","text":"def show_version():\n print(version)","title":""},{"docid":"8b4d7589e7a3ce0442158c175870bbbb","score":"0.6874289","text":"def version(ctx):\n print(VERSION)","title":""},{"docid":"6d17e4054b1edf5c19222bf38acd8cf0","score":"0.6846713","text":"def version():\n print('v.' + app_version)","title":""},{"docid":"cf5b0cb83aeda7043a08724317771457","score":"0.66774803","text":"def version():\n version_string = \"cogctl %s (build: %s)\" % (get_build_tag(),\n get_build_version())\n click.echo(version_string)","title":""},{"docid":"832983517caa840fc49e467d53f9f61f","score":"0.66368026","text":"def print_version(self):\n self.stdout.write(\"%s\\n\" % util.get_version())","title":""},{"docid":"f109bf36498c95374619baa5e2bf14ee","score":"0.6615078","text":"def version():\n version_info = f\"OSDU Cli Version {osducli.__VERSION__}\"\n version_info += get_runtime_version()\n print(version_info)","title":""},{"docid":"0f93412781a65ca803d594bf5b61063f","score":"0.6609807","text":"def version():\n console.print(ASCIIART, style=ASCIIART_STYLE)\n console.print(f\"\\n[green]{NAME} version: {VERSION}[/]\\n\")","title":""},{"docid":"14ed5432effcd602caf9885d59c6272a","score":"0.653391","text":"def show_version():\n print(f\"linder: {__version__}\")","title":""},{"docid":"8c4a6028e0ea230480afdedfc869a9b9","score":"0.6509685","text":"def version(arg):\n\n info(\"%s %s\" % (APP_NAME, APP_VERSION))","title":""},{"docid":"3c0495e163e654e8fb34b2106da0cd26","score":"0.6503043","text":"def version():\n\twith open(os.path.join(cheesepi_dir,'VERSION')) as f:\n\t\treturn f.read().strip()","title":""},{"docid":"468ad6e01380def7bebaa61358725899","score":"0.64091104","text":"def showVer(self):\n print(\"Your version is \", self.version)","title":""},{"docid":"1de638ba2d777bf80e4c3c14fa2e66ff","score":"0.6408298","text":"def get_version():\n return utils.read_file(utils.get_data_path(), \"VERSION\").strip()","title":""},{"docid":"911372dfe31fd6efa7eb47f7e902303d","score":"0.63363576","text":"def print_app_version():\n execute(tasks.print_app_version, 'demoapp')","title":""},{"docid":"ccc40e9ddb138e51b5223e3bb66e8845","score":"0.63325423","text":"def version_command():\n print_version()","title":""},{"docid":"d288d39591e4f9031273a974966afcca","score":"0.6309663","text":"def version():\n version = pkg_resources.require(PROJECT_NAME)[0].version\n floyd_logger.info(version)","title":""},{"docid":"37fe4b9b1f350e969fbc0749b3931cc4","score":"0.63017356","text":"async def print_version(\n args: argparse.Namespace, session: aiohttp.ClientSession\n) -> None:\n logger.setLevel(logging.WARNING)\n version = await get_server_version(args.url, session)\n print(\"Driver:\", version.driver_version)\n print(\"Server:\", version.server_version)\n print(\"Home ID:\", version.home_id)","title":""},{"docid":"7e842f1f98fdc286397fe8f3c00956ce","score":"0.6299312","text":"def version() -> str:\n return f\"Xanadu Cloud Client version {__version__}\"","title":""},{"docid":"5a2a40df12d63334eab597ed5b592079","score":"0.62918115","text":"def do_version(self, line):\n dd = self.devs.default_device()\n release, machine, version = dd.remote_eval(upy_version)\n fmt = \"{:>10s} = {}\"\n oprint(fmt.format(\"board\", dd.get('name')))\n oprint(fmt.format(\"firmware\", version))\n oprint(fmt.format(\"release\", release))\n oprint(fmt.format(\"machine\", machine))\n oprint(fmt.format(\"shell49\", __version__))","title":""},{"docid":"d423edc8b1f28fe3597a8e2f6d7d7e59","score":"0.62792027","text":"def version():\n click.echo(click.style(f'{__version__}', bold=True))","title":""},{"docid":"7adca914ab22c652f6a4a0ea7b639cea","score":"0.62685543","text":"def get_version():\n\n version_file = open('../VERSION.txt')\n return version_file.read().strip()","title":""},{"docid":"90b358d8cbf3ac674a12f7cba9200214","score":"0.62406415","text":"def show_versions(file: TextIO = sys.stdout) -> None:\n deps = [\n # async_retriever\n \"async-retriever\",\n \"aiodns\",\n \"aiohttp\",\n \"aiohttp-client-cache\",\n \"aiosqlite\",\n \"brotli\",\n \"cytoolz\",\n \"ujson\",\n # pygeoogc\n \"pygeoogc\",\n \"defusedxml\",\n \"owslib\",\n \"yaml\",\n \"pyproj\",\n \"requests\",\n \"requests-cache\",\n \"shapely\",\n \"urllib3\",\n # pygeoutils\n \"pygeoutils\",\n \"dask\",\n \"geopandas\",\n \"netCDF4\",\n \"numpy\",\n \"rasterio\",\n \"xarray\",\n \"rioxarray\",\n # py3dep\n \"py3dep\",\n \"click\",\n \"scipy\",\n \"richdem\",\n # pynhd\n \"pynhd\",\n \"networkx\",\n \"pandas\",\n \"pyarrow\",\n # pygeohydro\n \"pygeohydro\",\n \"folium\",\n \"lxml\",\n \"matplotlib\",\n # pydaymet\n \"pydaymet\",\n # hydrosignatures\n \"hydrosignatures\",\n # pynldas2\n \"pynldas2\",\n \"h5netcdf\",\n # misc\n \"numba\",\n \"bottleneck\",\n \"pygeos\",\n \"tables\",\n # test\n \"pytest\",\n \"pytest-cov\",\n \"xdist\",\n ]\n pad = len(max(deps, key=len)) + 1\n\n deps_blob = {}\n for modname in sorted(deps):\n try:\n deps_blob[modname] = get_version(modname)\n except PackageNotFoundError:\n deps_blob[modname] = \"N/A\"\n except (NotImplementedError, AttributeError):\n deps_blob[modname] = \"installed\"\n\n print(\"\\nSYS INFO\", file=file)\n print(\"--------\", file=file)\n\n for k, stat in get_sys_info():\n print(f\"{k}: {stat}\", file=file)\n\n header = f\"\\n{'PACKAGE':<{pad}} VERSION\"\n print(header, file=file)\n print(\"-\" * len(header), file=file)\n for k, stat in deps_blob.items():\n print(f\"{k:<{pad}} {stat}\", file=file)\n print(\"-\" * len(header), file=file)","title":""},{"docid":"b4d96f0316b2a6c4c306a009099abbc9","score":"0.62399304","text":"def version():\n click.echo(_get_version_from_pkg() or _get_version_from_file() or 'unknown')","title":""},{"docid":"ce958dd6305f3d7e2b6ad9c4a3b15715","score":"0.6219644","text":"def version():\n\tlog(\"FonB Installation Script v0.01\")\n\tlog(\"FonB v1.0.5\")","title":""},{"docid":"265d08599c473a9fa8d974cfa6127175","score":"0.6176032","text":"def version():\n click.echo('Fetching........Please wait')\n from github import GitHub\n rc = GitHub().get_silentor_latest()\n click.echo('verison: %s' % rc['tag_name'])\n click.echo('Note:\\r\\n%s' % rc['body'])","title":""},{"docid":"ba7fd032bc5b41218badbd55edd2308a","score":"0.61707115","text":"def display_version():\n verline = \"# Terabee Multiple Counting v{} #\".format(MPC.__version__)\n print(\"#\" * len(verline))\n print(verline)\n print(\"#\" * len(verline))","title":""},{"docid":"97c3d7f60cc4947b2944fe67ef6598a1","score":"0.6150367","text":"def print_version():\n\t\tprint(\"{} {}\".format(program_name, __version__))\n\t\tprint(\"Written by {}\".format(', '.join(program_authors)))","title":""},{"docid":"8889b2f25c8523a16d48b9badec4d983","score":"0.6135514","text":"def get_version():\n f = open('version.txt')\n version = ''.join(f.readlines()).rstrip()\n f.close()\n return version","title":""},{"docid":"c731aeff0dbcbd7f226da04038c27ab8","score":"0.6118964","text":"async def cmd_version(ctx: Context):\n try:\n return await ctx.reply(f\"version {metadata.version('discord_university')}\")\n except metadata.PackageNotFoundError:\n return await ctx.reply(\"version ?.?.? (Unversioned source run) \")","title":""},{"docid":"36b2d4a522368d0bece34912bed169f2","score":"0.6115746","text":"def version(ctx):\n print(json.dumps({\n 'commit':\n os.getenv('CIRCLE_SHA1', None),\n 'version':\n os.getenv('CIRCLE_SHA1', None),\n 'source':\n 'https://github.com/%s/%s' % (\n os.getenv('CIRCLE_PROJECT_USERNAME', 'mozilla-services'),\n os.getenv('CIRCLE_PROJECT_REPONAME', 'mozphab')\n ),\n 'build':\n os.getenv('CIRCLE_BUILD_URL', None),\n }))","title":""},{"docid":"e875ed6caa5351cb8910ef9532451f20","score":"0.6100186","text":"def print_version():\n\n print(\"\"\"\n%s version %s - released %s\n \"\"\" % (__docname__, __version__, __release__))\n\n return 1","title":""},{"docid":"4606c7bedb55b4d2a43274059502bc21","score":"0.6098758","text":"def show_version(a_device):\n remote_conn = ConnectHandler(**a_device)\n print()\n print(\"#\" * 80)\n print(remote_conn.send_command(\"show version\"))\n print(\"#\" * 80)\n print()","title":""},{"docid":"3120b71431ffdbc5a5f49db9ab3df8af","score":"0.60894096","text":"def show_versions() -> None:\n # note: we import 'platform' here as a micro-optimisation for initial import\n import platform\n\n # optional dependencies\n deps = _get_dependency_info()\n\n # determine key length for alignment\n keylen = (\n max(\n len(x) for x in [*deps.keys(), \"Polars\", \"Index type\", \"Platform\", \"Python\"]\n )\n + 1\n )\n\n print(\"--------Version info---------\")\n print(f\"{'Polars:':{keylen}s} {get_polars_version()}\")\n print(f\"{'Index type:':{keylen}s} {get_index_type()}\")\n print(f\"{'Platform:':{keylen}s} {platform.platform()}\")\n print(f\"{'Python:':{keylen}s} {sys.version}\")\n\n print(\"\\n----Optional dependencies----\")\n for name, v in deps.items():\n print(f\"{name:{keylen}s} {v}\")","title":""},{"docid":"fcb127a7f12b6437389cac012bcc4542","score":"0.6081658","text":"def show_version(a_device):\n remote_conn = ConnectHandler(**a_device)\n print()\n print(\"#\" * 80)\n print(remote_conn.send_command_expect(\"show version\"))\n print(\"#\" * 80)\n print()","title":""},{"docid":"b6d8ec5e0069ff6f5d720c4c864c5dd6","score":"0.6077762","text":"def _printVersions():\n print \"ILCDirac Version:\", ILCDIRAC.version\n print \"With DIRAC version:\", DIRAC.version","title":""},{"docid":"32bf53c6ad0b6dc1f8fc2cbb6364a917","score":"0.605855","text":"def printVersionInfo():\n # pass\n pathname = sys.argv[0]\n myMtime = os.stat(pathname)[ST_MTIME]\n modDate = CONFIG['utils'].mktime(myMtime)\n logIt(\"Python Script: \" + pathname + \"\\n\")\n logIt(\"Version Date: \" + modDate + \"\\n\")","title":""},{"docid":"3b7c052e024a799c1a449c130ce1ebe6","score":"0.604286","text":"def version():\n print(shared.get_r2dt_version_header())","title":""},{"docid":"bc22681de8abce01ad7b121243b093bd","score":"0.6031499","text":"def version():\n return VERSION","title":""},{"docid":"83eaab3f9c7d1b186bd94dcfebefa19f","score":"0.6014642","text":"def printVersion():\n print(MSG_VERSION)\n sys.exit(EXIT_SUCCESS)","title":""},{"docid":"16f028f7f4a0b4334a55661574caa383","score":"0.60100853","text":"def _printVersion(name):\n\n from . import version\n\n print('{}/FSLeyes version {}'.format(name, version.__version__))","title":""},{"docid":"6fbaf96609974d0b8124823697d4f685","score":"0.60047644","text":"def version():\n return meta.version","title":""},{"docid":"bd57df086eea090dc7f0668f3ba37b2a","score":"0.60023886","text":"def _print_version(ctx, _, value):\n if not value or ctx.resilient_parsing:\n return\n\n click.echo(\"{!s}\".format(githubcap_version))\n ctx.exit()","title":""},{"docid":"84eab6d561fa2438a135f5b2086d8837","score":"0.5993619","text":"def Version(self) -> str:","title":""},{"docid":"186917808fdd4fceb8a9c3a6081a6cb6","score":"0.59918094","text":"def cmd_version(self, event):\n url = self._url(\"/rest/api/2/serverInfo\")\n response = json.loads(requests.get(url).text)\n\n info = \"%s : version %s : build %s\" % (response[\"serverTitle\"],\n response[\"version\"], response[\"buildNumber\"])\n\n return info","title":""},{"docid":"44083c9d9faa6893234d321933007ffa","score":"0.5988048","text":"def do_version(self, line):\n args = [self.config['ipvsadm'], '--version']\n ipvsadm = utils.check_output(args)\n header = [\"\", \"Linux Virtual Server\",\n \"====================\"]\n\n print '\\n'.join(header)\n print ipvsadm\n print\n\n header = [\"Director\",\n \"========\"]\n print '\\n'.join(header)\n\n if not self.config['director_bin'] :\n director = 'director binary not defined. Unable to get version!'\n else:\n args = [self.config['director_bin'], '--version']\n director = utils.check_output(args)\n\n print director\n print\n\n args = [self.config['iptables'], '--version']\n iptables = utils.check_output(args)\n header = [\"Packet Filtering\",\n \"================\"]\n\n print '\\n'.join(header)\n print iptables\n print","title":""},{"docid":"0265d5a9b2787bc2a389b4ad04d7d33f","score":"0.5964907","text":"def show_version():\n\n messagebox.showinfo('Version', 'Version 0.3.0')","title":""},{"docid":"1d0cc5fe6263398eb76ed12656fc5071","score":"0.59635186","text":"def getCvpVersion():\n return json.load( open( '/cvp/property/version.txt', 'r' ) )[ 'version' ]","title":""},{"docid":"921d804cd128ca081748576ffba494e6","score":"0.591384","text":"def version_command():\n print(f\"{prog} version {DEXY_VERSION}\")","title":""},{"docid":"d2f299c5878f355a1005c2378dd8fb26","score":"0.5905265","text":"def shortversion():\n if not getattr(sys, 'frozen', False):\n ver = bbconstants.VERSION\n else:\n verfile = glob.glob(os.path.join(os.getcwd(), \"version.txt\"))[0]\n with open(verfile) as afile:\n ver = afile.read()\n return ver","title":""},{"docid":"16ea635fe80fc2e0264f6d09e0b18bfe","score":"0.5866145","text":"def cmd_version(_):\n print(f'autograde version {autograde.__version__}')\n return 0","title":""},{"docid":"f3f0dfef0a4a9601cf39fff1c1839fc6","score":"0.5866107","text":"def show_version(a_device, q):\n output_dict = {}\n creds = a_device.credentials\n remote_conn = ConnectHandler(device_type=a_device.device_type,\n ip=a_device.ip_address,\n username=creds.username,\n password=creds.password,\n port=a_device.port, secret='')\n \n output = ('#' * 80) + \"\\n\" \n output += remote_conn.send_command_expect(\"show version\") + \"\\n\"\n output += ('#' * 80) + \"\\n\" \n\n ## this assigns the vaule of ouput (type dict) to the 'device_name' key\n output_dict[a_device.device_name] = output\n q.put(output_dict)","title":""},{"docid":"5db637e4cbc89c6e5ba0f6654d503198","score":"0.5829124","text":"def version(self):\n from insights import get_nvr\n print(get_nvr())\n sys.exit()","title":""},{"docid":"788a66e8d5e861ececcb0020e9a184ca","score":"0.5820957","text":"def version():\n return os.getenv(\"APP_VERSION\", \"No version\")","title":""},{"docid":"42422d55c74054fd57b827bf79866140","score":"0.58081627","text":"def show_version(a_device):\n remote_conn = ConnectHandler(**a_device)\n sh_ver = remote_conn.send_command_expect(\"show version\")\n\n parse_ver = parse_output(platform='cisco_ios',\n command='show version', data=sh_ver)\n print(parse_ver[0]['hostname'], parse_ver[0]['version'])","title":""},{"docid":"229707d6afeb2fe20609183294e9606f","score":"0.57915276","text":"def version():\n return '1.3.0'","title":""},{"docid":"3d7ac395e1dd87cae4095bc9c435daed","score":"0.5772179","text":"def print_versions():\n print \"Python version: %s\" % platform.python_version()\n print \"Cosmos CLI version: %s\" % pkg_resources.require(\"cosmos\")[0].version\n version_tags = [\"v%s\" % version for version in SUPPORTED_VERSIONS]\n print \"Supported Cosmos API versions: %s\" % \", \".join(version_tags)","title":""},{"docid":"72a6f52b430b1f29a4d38443239062a0","score":"0.57605934","text":"def cli():\n click.echo('{} {}'.format(__title__, __version__))","title":""},{"docid":"e4add0ca14c055f5bece954baa7e730d","score":"0.5751432","text":"def main():\n\n if len(sys.argv) < 2:\n print(f\"Use: {sys.argv[0]} CURRENT_VERSION\")\n sys.exit()\n\n ver_list = sys.argv[1].split('.')\n ver_list[-1] = str(int(ver_list[-1])+1)\n new_ver = '.'.join(ver_list)\n print(new_ver)","title":""},{"docid":"9fe69c266b11df9f16ca8b8510c2904a","score":"0.5741409","text":"def version(self) -> str:\n result: str = check_output(\n args=(self.path, \"--version\"),\n stderr=open(devnull, \"w\")\n ).decode(\"utf-8\")\n match = re.match(r\"DeaDBeeF (\\d+\\.\\d+\\.\\d+)\", result)\n return match.groups()[0] if match else \"\"","title":""},{"docid":"1025d5d200c646248b2a803cea628167","score":"0.57406676","text":"def lacp_api_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"lacp_api_version\")","title":""},{"docid":"bde3647fb8b95b8a86de1b639055595e","score":"0.57328445","text":"def show_version(dev, queue):\n output_dict = {}\n output = \"#\" * 80\n output += \"\\n\"\n conn = ConnectHandler(device_type=dev.device_type, ip=dev.ip_address,\n username=dev.credentials.username, password=dev.credentials.password,\n port=dev.port)\n output += conn.send_command_expect('show version')\n output += \"#\" * 80\n output += \"\\n\\n\"\n output_dict[dev.device_name] = output\n queue.put(output_dict)","title":""},{"docid":"3cd7c92c0d60a4ea749cf5ba73e7523c","score":"0.5720847","text":"def show(version):\n assure_state()\n if not valid_version(version):\n logger.error(\"Invalid version specifier!\")\n sys.exit(1)\n if not REGISTRY.installed(version):\n logger.error(\"Version [salmon]{}[/salmon] is not installed!\".format(version))\n sys.exit(1)\n instance = REGISTRY.get_instance(version)\n print(\"Version: [green]{}[/green]\".format(version))\n print(\"Install Location: {}\".format(os.path.join(CACHE, instance.path)))\n sdk_filename = \"renpy-{}-sdk.zip\".format(version)\n rapt_filename = \"renpy-{}-rapt.zip\".format(version)\n SDK_URL = \"https://www.renpy.org/dl/{}/{}\".format(version, sdk_filename)\n RAPT_URL = \"https://www.renpy.org/dl/{}/{}\".format(version, rapt_filename)\n print(\"SDK Url: {}\".format(SDK_URL))\n print(\"RAPT Url: {}\".format(RAPT_URL))","title":""},{"docid":"af2c020a39353485de09af06c782853c","score":"0.5697556","text":"def _figureVersion(self):\n try:\n sVer = utils.processOutputChecked(['VBoxManage', '--version'])\n\n sVer = sVer.strip();\n sVer = re.sub(r'_BETA.*r', '.', sVer);\n sVer = re.sub(r'_ALPHA.*r', '.', sVer);\n sVer = re.sub(r'_RC.*r', '.', sVer);\n sVer = sVer.replace('r', '.');\n\n self.aiVBoxVer = [int(sComp) for sComp in sVer.split('.')];\n\n reporter.log('VBox version: %s' % (self.aiVBoxVer,));\n except:\n reporter.logXcpt();\n return False;\n return True;","title":""},{"docid":"81223dde840919c8d7467d63c5beefa6","score":"0.56832784","text":"def show_version2(device):\n print()\n print(\"#\" * 80)\n net_connect = ConnectHandler(**device)\n output = net_connect.send_command_expect(\"show version\")\n net_connect.disconnect()\n print(output)\n print(\"#\" * 80)\n print()","title":""},{"docid":"85591391c95e2bb0d09766b7097d66e2","score":"0.56783324","text":"def version(self, progname, version, extracontent):\n tmp = \"%s version %s\\n%s\" % (progname, version, extracontent)\n self.printer(tmp)\n self.horizontalrule()","title":""},{"docid":"0479d7b8dbc440e66e6075c897c681f4","score":"0.56717056","text":"def version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"0479d7b8dbc440e66e6075c897c681f4","score":"0.56717056","text":"def version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"0479d7b8dbc440e66e6075c897c681f4","score":"0.56717056","text":"def version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"0479d7b8dbc440e66e6075c897c681f4","score":"0.56717056","text":"def version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"a7aec31b093463359006631c3a6cb031","score":"0.56664556","text":"def version_string(version):\n if version == -1 or version is None:\n raise RuntimeError('no string for version %s'%version)\n return \";Gamebryo KFM File Version %s\"%({\n 0x01000000 : \"1.0\",\n 0x01024b00 : \"1.2.4b\",\n 0x0200000b : \"2.0.0.0b\",\n 0x0201000b : \"2.1.0.0b\",\n 0x0202000b : \"2.2.0.0b\" } [version])","title":""},{"docid":"82137b305161c253dd0ce76d3cbc52cc","score":"0.5665965","text":"def _getVersion(self):\n ## Skip Error check since handling of errors is version specific\n idn = self._instQuery('*IDN?', checkErrors=False).split(',')\n ver = idn[3].split('.')\n # put major and minor version into floating point format so can numerically compare\n self._version = float(ver[0]+'.'+ver[1])","title":""},{"docid":"e4ad44fe43a652aa46a32b7abf4942e7","score":"0.5661323","text":"def _format_file_version(self, old_version: str) -> str:\n azul.dss.validate_version(old_version)\n d = datetime.strptime(old_version, azul.dss.version_format)\n return format_dcp2_datetime(d)","title":""},{"docid":"d05d8232e6a83c137c616317d43dbba3","score":"0.5643196","text":"def getSaLTVersion():\n _checkLive()\n return open('/mnt/salt/salt-version', 'r').read().strip()","title":""},{"docid":"68ad2dfef04bb649ea75845e223453ea","score":"0.56405735","text":"def get_version():\r\n return get_config()['version']","title":""},{"docid":"1964580059fd75f2c3e053e1aba32752","score":"0.56364334","text":"def version(self):\n s = self._name + \" \"\n modinfo = Command(['modinfo', self._name]).runOutput()\n for line in modinfo.splitlines():\n if line.find('version') == 0:\n s += line\n return s","title":""},{"docid":"39d7aa77ae5cb73d84a8dfc1c8a8bad8","score":"0.5632172","text":"def version():\n return __version__","title":""},{"docid":"9ed5b899fc7c6eeaba3e3efd52b3034d","score":"0.56171614","text":"def get_version(self):\n url = self.gi._make_url(self, None)\n url = url.rstrip('configuration') + 'version'\n return self._get(url=url)","title":""},{"docid":"8a0620b62d27081f589652d50cce4303","score":"0.5613159","text":"def show_version(emproot, userhome, options):\n if len(options.args) > 0:\n return show_error(\n \"Unexpected arguments for %s: (%s)\"%(options.command, \" \".join(options.args)), \n EMP_UNEXPECTEDARGS\n )\n status = EMP_SUCCESS\n print(EMP_VERSION)\n # with open(logfilename, \"r\") as logfile:\n # shutil.copyfileobj(logfile, sys.stdout)\n return status","title":""},{"docid":"336fe72f9d303058aa42b4c777397929","score":"0.56004447","text":"def get_version():\n return VERSION","title":""},{"docid":"3888ac67cb78dfb848129971f39da320","score":"0.55970466","text":"def version(self):\n return self._controller.query('++ver').rstrip()","title":""},{"docid":"ad014303a6b8f939a8bff605bd5f08a0","score":"0.5596041","text":"def GetVersion(self):","title":""},{"docid":"a9cd09f50e3d5f42032bbd20b525a019","score":"0.55944794","text":"def print_version(ctx, param, value):\n\n if not value or ctx.resilient_parsing:\n return\n print(\"Version\\t1.2\")\n sys.exit()","title":""},{"docid":"529f2e8bf742584943789c6d6b56a158","score":"0.55926114","text":"def ChemicsVersion():\n fid = open(CONFIG.CHEMICSROOTPATH+\"ChemicsWS/ChemicsWS/tag.txt\")\n tag = string.strip(fid.readline())\n fid.close()\n return tag","title":""},{"docid":"1bd52534499ed4885cb83253cba8a08f","score":"0.5589338","text":"def version(self) -> str:\n response = self.session.get(self.url)\n if not response.ok: raise CouchDBException.auto(response)\n return response.json()['version']","title":""},{"docid":"5308a939d9122fb5e96378bc55ecba10","score":"0.55849224","text":"def version(self):","title":""},{"docid":"5308a939d9122fb5e96378bc55ecba10","score":"0.55849224","text":"def version(self):","title":""},{"docid":"5308a939d9122fb5e96378bc55ecba10","score":"0.55849224","text":"def version(self):","title":""},{"docid":"3b3781183f954121216f2f9d1bc8b7a4","score":"0.55709267","text":"def get_version():\n return {\"version\": f\"{API_VERSION}\"}","title":""},{"docid":"73dab7406c10a2fe43f4ef51c538ecf0","score":"0.5564526","text":"def version(self):\n return str(self(\"version\")).strip()","title":""},{"docid":"c33170cde36e72ed8fdf757d93fa89c6","score":"0.5558771","text":"async def version(ctx):\n await ctx.message.channel.send(VNUMBER)\n current_timestamp = datetime.now()\n current_timestamp_string = current_timestamp.strftime(\"%d/%m/%Y %H:%M:%S\")\n await ctx.message.channel.send(current_timestamp_string)","title":""},{"docid":"2d71972846a0e5abc46fb0b3c6e6ce9f","score":"0.55564415","text":"def _version() -> str:\n with io.open(\"graphtik/__init__.py\", \"rt\", encoding=\"utf8\") as f:\n return re.search(r'__version__ = \"(.*?)\"', f.read()).group(1).strip()","title":""},{"docid":"6a3ad76e420865a001145349852a7967","score":"0.5543408","text":"def getClientVersion(verbose=False):\n try:\n cmd=\"%s version client\" % (Utils.EosClientPath)\n if verbose: Utils.Print(\"cmd: %s\" % (cmd))\n response=Utils.checkOutput(cmd.split())\n assert(response)\n assert(isinstance(response, str))\n if verbose: Utils.Print(\"response: <%s>\" % (response))\n verStr=response.strip()\n return verStr\n except subprocess.CalledProcessError as ex:\n msg=ex.output.decode(\"utf-8\")\n Utils.Print(\"ERROR: Exception during client version query. %s\" % (msg))\n raise","title":""},{"docid":"387d3eee82e524dd43349c3c9d97c816","score":"0.5540558","text":"def index(self, appid=None):\n print \"IN CVMFSAppVersion: appid=(%s)\" % appid\n if appid not in self.valid_apps:\n print \"Invalid app type %s\" % appid\n return ''\n html_ = html.HTML()\n dirs = []\n try:\n _, dirs, _ = os.walk(os.path.join(self.cvmfs_root, appid)).next()\n except StopIteration:\n logger.error(\"Couldn't access CVMFS dir: %s\",\n os.path.join(self.cvmfs_root, appid))\n\n for dir_ in natsorted(dirs, reverse=True):\n for version in VERSION_RE.findall(dir_):\n html_.option(version)\n return str(html_)","title":""},{"docid":"8f9878b1eb026f81fe6538930a0734fe","score":"0.55323595","text":"def mamage_version(self, value):\n \n term.pos(16, 3)\n term.write(value[\"system\"])\n term.pos(16, 10)\n term.write(value[\"version\"])","title":""},{"docid":"84367cb535eb7ea4837a08cfd88d7f6c","score":"0.55283386","text":"def show_version(args):\n version_string = f\"ssm-tools/{ssm_tools_version}\"\n if args.log_level <= logging.INFO:\n version_string += f\" python/{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}\"\n version_string += f\" boto3/{boto3.__version__}\"\n print(version_string)\n sys.exit(0)","title":""},{"docid":"c4dd8fce97c53d6e29204bf142fc2653","score":"0.55266124","text":"def get_version(self) -> str:\n pass","title":""},{"docid":"de2548b6996b51499208b90d96f0197d","score":"0.5521013","text":"def getversioninfo():\n controllers.updateControllerVersionInfo()\n return createResponse(controllers.getControllerVersionInfo())","title":""},{"docid":"66a2da06ab68bf4cc0d03cf1e8c11d64","score":"0.55182695","text":"def version_string(self):\n return '%s %s\\n' \\\n 'Copyright (c) 2017 Jonathan Simmonds\\n\\n' \\\n 'Loaded modules:\\n' \\\n '%s\\n' % (os.path.basename(sys.argv[0]), __version__,\n '\\n'.join([' ' + str(m) for m in SEARCH_MODULES]))","title":""},{"docid":"df4c0ff31e0c183fb2667ec58d86d448","score":"0.55063576","text":"def get_version():\n with io.open('flask_snow/__init__.py') as input_file:\n for line in input_file:\n if line.startswith('__version__'):\n return ast.parse(line).body[0].value.s","title":""},{"docid":"9ef6b2af03f7d20f95e9d7c232e1592a","score":"0.5505784","text":"def version(self) -> str:\n return pulumi.get(self, \"version\")","title":""}],"string":"[\n {\n \"docid\": \"579001e9d9b87d09e853ec395b288c69\",\n \"score\": \"0.69931334\",\n \"text\": \"def version():\\n print(version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71acca3ffe13eb8627a650b75a853da4\",\n \"score\": \"0.69509643\",\n \"text\": \"def show_version():\\n print(version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b4d7589e7a3ce0442158c175870bbbb\",\n \"score\": \"0.6874289\",\n \"text\": \"def version(ctx):\\n print(VERSION)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d17e4054b1edf5c19222bf38acd8cf0\",\n \"score\": \"0.6846713\",\n \"text\": \"def version():\\n print('v.' + app_version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf5b0cb83aeda7043a08724317771457\",\n \"score\": \"0.66774803\",\n \"text\": \"def version():\\n version_string = \\\"cogctl %s (build: %s)\\\" % (get_build_tag(),\\n get_build_version())\\n click.echo(version_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"832983517caa840fc49e467d53f9f61f\",\n \"score\": \"0.66368026\",\n \"text\": \"def print_version(self):\\n self.stdout.write(\\\"%s\\\\n\\\" % util.get_version())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f109bf36498c95374619baa5e2bf14ee\",\n \"score\": \"0.6615078\",\n \"text\": \"def version():\\n version_info = f\\\"OSDU Cli Version {osducli.__VERSION__}\\\"\\n version_info += get_runtime_version()\\n print(version_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f93412781a65ca803d594bf5b61063f\",\n \"score\": \"0.6609807\",\n \"text\": \"def version():\\n console.print(ASCIIART, style=ASCIIART_STYLE)\\n console.print(f\\\"\\\\n[green]{NAME} version: {VERSION}[/]\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14ed5432effcd602caf9885d59c6272a\",\n \"score\": \"0.653391\",\n \"text\": \"def show_version():\\n print(f\\\"linder: {__version__}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c4a6028e0ea230480afdedfc869a9b9\",\n \"score\": \"0.6509685\",\n \"text\": \"def version(arg):\\n\\n info(\\\"%s %s\\\" % (APP_NAME, APP_VERSION))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c0495e163e654e8fb34b2106da0cd26\",\n \"score\": \"0.6503043\",\n \"text\": \"def version():\\n\\twith open(os.path.join(cheesepi_dir,'VERSION')) as f:\\n\\t\\treturn f.read().strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"468ad6e01380def7bebaa61358725899\",\n \"score\": \"0.64091104\",\n \"text\": \"def showVer(self):\\n print(\\\"Your version is \\\", self.version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1de638ba2d777bf80e4c3c14fa2e66ff\",\n \"score\": \"0.6408298\",\n \"text\": \"def get_version():\\n return utils.read_file(utils.get_data_path(), \\\"VERSION\\\").strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911372dfe31fd6efa7eb47f7e902303d\",\n \"score\": \"0.63363576\",\n \"text\": \"def print_app_version():\\n execute(tasks.print_app_version, 'demoapp')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccc40e9ddb138e51b5223e3bb66e8845\",\n \"score\": \"0.63325423\",\n \"text\": \"def version_command():\\n print_version()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d288d39591e4f9031273a974966afcca\",\n \"score\": \"0.6309663\",\n \"text\": \"def version():\\n version = pkg_resources.require(PROJECT_NAME)[0].version\\n floyd_logger.info(version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37fe4b9b1f350e969fbc0749b3931cc4\",\n \"score\": \"0.63017356\",\n \"text\": \"async def print_version(\\n args: argparse.Namespace, session: aiohttp.ClientSession\\n) -> None:\\n logger.setLevel(logging.WARNING)\\n version = await get_server_version(args.url, session)\\n print(\\\"Driver:\\\", version.driver_version)\\n print(\\\"Server:\\\", version.server_version)\\n print(\\\"Home ID:\\\", version.home_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e842f1f98fdc286397fe8f3c00956ce\",\n \"score\": \"0.6299312\",\n \"text\": \"def version() -> str:\\n return f\\\"Xanadu Cloud Client version {__version__}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a2a40df12d63334eab597ed5b592079\",\n \"score\": \"0.62918115\",\n \"text\": \"def do_version(self, line):\\n dd = self.devs.default_device()\\n release, machine, version = dd.remote_eval(upy_version)\\n fmt = \\\"{:>10s} = {}\\\"\\n oprint(fmt.format(\\\"board\\\", dd.get('name')))\\n oprint(fmt.format(\\\"firmware\\\", version))\\n oprint(fmt.format(\\\"release\\\", release))\\n oprint(fmt.format(\\\"machine\\\", machine))\\n oprint(fmt.format(\\\"shell49\\\", __version__))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d423edc8b1f28fe3597a8e2f6d7d7e59\",\n \"score\": \"0.62792027\",\n \"text\": \"def version():\\n click.echo(click.style(f'{__version__}', bold=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7adca914ab22c652f6a4a0ea7b639cea\",\n \"score\": \"0.62685543\",\n \"text\": \"def get_version():\\n\\n version_file = open('../VERSION.txt')\\n return version_file.read().strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b358d8cbf3ac674a12f7cba9200214\",\n \"score\": \"0.62406415\",\n \"text\": \"def show_versions(file: TextIO = sys.stdout) -> None:\\n deps = [\\n # async_retriever\\n \\\"async-retriever\\\",\\n \\\"aiodns\\\",\\n \\\"aiohttp\\\",\\n \\\"aiohttp-client-cache\\\",\\n \\\"aiosqlite\\\",\\n \\\"brotli\\\",\\n \\\"cytoolz\\\",\\n \\\"ujson\\\",\\n # pygeoogc\\n \\\"pygeoogc\\\",\\n \\\"defusedxml\\\",\\n \\\"owslib\\\",\\n \\\"yaml\\\",\\n \\\"pyproj\\\",\\n \\\"requests\\\",\\n \\\"requests-cache\\\",\\n \\\"shapely\\\",\\n \\\"urllib3\\\",\\n # pygeoutils\\n \\\"pygeoutils\\\",\\n \\\"dask\\\",\\n \\\"geopandas\\\",\\n \\\"netCDF4\\\",\\n \\\"numpy\\\",\\n \\\"rasterio\\\",\\n \\\"xarray\\\",\\n \\\"rioxarray\\\",\\n # py3dep\\n \\\"py3dep\\\",\\n \\\"click\\\",\\n \\\"scipy\\\",\\n \\\"richdem\\\",\\n # pynhd\\n \\\"pynhd\\\",\\n \\\"networkx\\\",\\n \\\"pandas\\\",\\n \\\"pyarrow\\\",\\n # pygeohydro\\n \\\"pygeohydro\\\",\\n \\\"folium\\\",\\n \\\"lxml\\\",\\n \\\"matplotlib\\\",\\n # pydaymet\\n \\\"pydaymet\\\",\\n # hydrosignatures\\n \\\"hydrosignatures\\\",\\n # pynldas2\\n \\\"pynldas2\\\",\\n \\\"h5netcdf\\\",\\n # misc\\n \\\"numba\\\",\\n \\\"bottleneck\\\",\\n \\\"pygeos\\\",\\n \\\"tables\\\",\\n # test\\n \\\"pytest\\\",\\n \\\"pytest-cov\\\",\\n \\\"xdist\\\",\\n ]\\n pad = len(max(deps, key=len)) + 1\\n\\n deps_blob = {}\\n for modname in sorted(deps):\\n try:\\n deps_blob[modname] = get_version(modname)\\n except PackageNotFoundError:\\n deps_blob[modname] = \\\"N/A\\\"\\n except (NotImplementedError, AttributeError):\\n deps_blob[modname] = \\\"installed\\\"\\n\\n print(\\\"\\\\nSYS INFO\\\", file=file)\\n print(\\\"--------\\\", file=file)\\n\\n for k, stat in get_sys_info():\\n print(f\\\"{k}: {stat}\\\", file=file)\\n\\n header = f\\\"\\\\n{'PACKAGE':<{pad}} VERSION\\\"\\n print(header, file=file)\\n print(\\\"-\\\" * len(header), file=file)\\n for k, stat in deps_blob.items():\\n print(f\\\"{k:<{pad}} {stat}\\\", file=file)\\n print(\\\"-\\\" * len(header), file=file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4d96f0316b2a6c4c306a009099abbc9\",\n \"score\": \"0.62399304\",\n \"text\": \"def version():\\n click.echo(_get_version_from_pkg() or _get_version_from_file() or 'unknown')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce958dd6305f3d7e2b6ad9c4a3b15715\",\n \"score\": \"0.6219644\",\n \"text\": \"def version():\\n\\tlog(\\\"FonB Installation Script v0.01\\\")\\n\\tlog(\\\"FonB v1.0.5\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"265d08599c473a9fa8d974cfa6127175\",\n \"score\": \"0.6176032\",\n \"text\": \"def version():\\n click.echo('Fetching........Please wait')\\n from github import GitHub\\n rc = GitHub().get_silentor_latest()\\n click.echo('verison: %s' % rc['tag_name'])\\n click.echo('Note:\\\\r\\\\n%s' % rc['body'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba7fd032bc5b41218badbd55edd2308a\",\n \"score\": \"0.61707115\",\n \"text\": \"def display_version():\\n verline = \\\"# Terabee Multiple Counting v{} #\\\".format(MPC.__version__)\\n print(\\\"#\\\" * len(verline))\\n print(verline)\\n print(\\\"#\\\" * len(verline))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97c3d7f60cc4947b2944fe67ef6598a1\",\n \"score\": \"0.6150367\",\n \"text\": \"def print_version():\\n\\t\\tprint(\\\"{} {}\\\".format(program_name, __version__))\\n\\t\\tprint(\\\"Written by {}\\\".format(', '.join(program_authors)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8889b2f25c8523a16d48b9badec4d983\",\n \"score\": \"0.6135514\",\n \"text\": \"def get_version():\\n f = open('version.txt')\\n version = ''.join(f.readlines()).rstrip()\\n f.close()\\n return version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c731aeff0dbcbd7f226da04038c27ab8\",\n \"score\": \"0.6118964\",\n \"text\": \"async def cmd_version(ctx: Context):\\n try:\\n return await ctx.reply(f\\\"version {metadata.version('discord_university')}\\\")\\n except metadata.PackageNotFoundError:\\n return await ctx.reply(\\\"version ?.?.? (Unversioned source run) \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b2d4a522368d0bece34912bed169f2\",\n \"score\": \"0.6115746\",\n \"text\": \"def version(ctx):\\n print(json.dumps({\\n 'commit':\\n os.getenv('CIRCLE_SHA1', None),\\n 'version':\\n os.getenv('CIRCLE_SHA1', None),\\n 'source':\\n 'https://github.com/%s/%s' % (\\n os.getenv('CIRCLE_PROJECT_USERNAME', 'mozilla-services'),\\n os.getenv('CIRCLE_PROJECT_REPONAME', 'mozphab')\\n ),\\n 'build':\\n os.getenv('CIRCLE_BUILD_URL', None),\\n }))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e875ed6caa5351cb8910ef9532451f20\",\n \"score\": \"0.6100186\",\n \"text\": \"def print_version():\\n\\n print(\\\"\\\"\\\"\\n%s version %s - released %s\\n \\\"\\\"\\\" % (__docname__, __version__, __release__))\\n\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4606c7bedb55b4d2a43274059502bc21\",\n \"score\": \"0.6098758\",\n \"text\": \"def show_version(a_device):\\n remote_conn = ConnectHandler(**a_device)\\n print()\\n print(\\\"#\\\" * 80)\\n print(remote_conn.send_command(\\\"show version\\\"))\\n print(\\\"#\\\" * 80)\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3120b71431ffdbc5a5f49db9ab3df8af\",\n \"score\": \"0.60894096\",\n \"text\": \"def show_versions() -> None:\\n # note: we import 'platform' here as a micro-optimisation for initial import\\n import platform\\n\\n # optional dependencies\\n deps = _get_dependency_info()\\n\\n # determine key length for alignment\\n keylen = (\\n max(\\n len(x) for x in [*deps.keys(), \\\"Polars\\\", \\\"Index type\\\", \\\"Platform\\\", \\\"Python\\\"]\\n )\\n + 1\\n )\\n\\n print(\\\"--------Version info---------\\\")\\n print(f\\\"{'Polars:':{keylen}s} {get_polars_version()}\\\")\\n print(f\\\"{'Index type:':{keylen}s} {get_index_type()}\\\")\\n print(f\\\"{'Platform:':{keylen}s} {platform.platform()}\\\")\\n print(f\\\"{'Python:':{keylen}s} {sys.version}\\\")\\n\\n print(\\\"\\\\n----Optional dependencies----\\\")\\n for name, v in deps.items():\\n print(f\\\"{name:{keylen}s} {v}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb127a7f12b6437389cac012bcc4542\",\n \"score\": \"0.6081658\",\n \"text\": \"def show_version(a_device):\\n remote_conn = ConnectHandler(**a_device)\\n print()\\n print(\\\"#\\\" * 80)\\n print(remote_conn.send_command_expect(\\\"show version\\\"))\\n print(\\\"#\\\" * 80)\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d8ec5e0069ff6f5d720c4c864c5dd6\",\n \"score\": \"0.6077762\",\n \"text\": \"def _printVersions():\\n print \\\"ILCDirac Version:\\\", ILCDIRAC.version\\n print \\\"With DIRAC version:\\\", DIRAC.version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32bf53c6ad0b6dc1f8fc2cbb6364a917\",\n \"score\": \"0.605855\",\n \"text\": \"def printVersionInfo():\\n # pass\\n pathname = sys.argv[0]\\n myMtime = os.stat(pathname)[ST_MTIME]\\n modDate = CONFIG['utils'].mktime(myMtime)\\n logIt(\\\"Python Script: \\\" + pathname + \\\"\\\\n\\\")\\n logIt(\\\"Version Date: \\\" + modDate + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b7c052e024a799c1a449c130ce1ebe6\",\n \"score\": \"0.604286\",\n \"text\": \"def version():\\n print(shared.get_r2dt_version_header())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc22681de8abce01ad7b121243b093bd\",\n \"score\": \"0.6031499\",\n \"text\": \"def version():\\n return VERSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83eaab3f9c7d1b186bd94dcfebefa19f\",\n \"score\": \"0.6014642\",\n \"text\": \"def printVersion():\\n print(MSG_VERSION)\\n sys.exit(EXIT_SUCCESS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f028f7f4a0b4334a55661574caa383\",\n \"score\": \"0.60100853\",\n \"text\": \"def _printVersion(name):\\n\\n from . import version\\n\\n print('{}/FSLeyes version {}'.format(name, version.__version__))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fbaf96609974d0b8124823697d4f685\",\n \"score\": \"0.60047644\",\n \"text\": \"def version():\\n return meta.version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd57df086eea090dc7f0668f3ba37b2a\",\n \"score\": \"0.60023886\",\n \"text\": \"def _print_version(ctx, _, value):\\n if not value or ctx.resilient_parsing:\\n return\\n\\n click.echo(\\\"{!s}\\\".format(githubcap_version))\\n ctx.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84eab6d561fa2438a135f5b2086d8837\",\n \"score\": \"0.5993619\",\n \"text\": \"def Version(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"186917808fdd4fceb8a9c3a6081a6cb6\",\n \"score\": \"0.59918094\",\n \"text\": \"def cmd_version(self, event):\\n url = self._url(\\\"/rest/api/2/serverInfo\\\")\\n response = json.loads(requests.get(url).text)\\n\\n info = \\\"%s : version %s : build %s\\\" % (response[\\\"serverTitle\\\"],\\n response[\\\"version\\\"], response[\\\"buildNumber\\\"])\\n\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44083c9d9faa6893234d321933007ffa\",\n \"score\": \"0.5988048\",\n \"text\": \"def do_version(self, line):\\n args = [self.config['ipvsadm'], '--version']\\n ipvsadm = utils.check_output(args)\\n header = [\\\"\\\", \\\"Linux Virtual Server\\\",\\n \\\"====================\\\"]\\n\\n print '\\\\n'.join(header)\\n print ipvsadm\\n print\\n\\n header = [\\\"Director\\\",\\n \\\"========\\\"]\\n print '\\\\n'.join(header)\\n\\n if not self.config['director_bin'] :\\n director = 'director binary not defined. Unable to get version!'\\n else:\\n args = [self.config['director_bin'], '--version']\\n director = utils.check_output(args)\\n\\n print director\\n print\\n\\n args = [self.config['iptables'], '--version']\\n iptables = utils.check_output(args)\\n header = [\\\"Packet Filtering\\\",\\n \\\"================\\\"]\\n\\n print '\\\\n'.join(header)\\n print iptables\\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0265d5a9b2787bc2a389b4ad04d7d33f\",\n \"score\": \"0.5964907\",\n \"text\": \"def show_version():\\n\\n messagebox.showinfo('Version', 'Version 0.3.0')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d0cc5fe6263398eb76ed12656fc5071\",\n \"score\": \"0.59635186\",\n \"text\": \"def getCvpVersion():\\n return json.load( open( '/cvp/property/version.txt', 'r' ) )[ 'version' ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921d804cd128ca081748576ffba494e6\",\n \"score\": \"0.591384\",\n \"text\": \"def version_command():\\n print(f\\\"{prog} version {DEXY_VERSION}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2f299c5878f355a1005c2378dd8fb26\",\n \"score\": \"0.5905265\",\n \"text\": \"def shortversion():\\n if not getattr(sys, 'frozen', False):\\n ver = bbconstants.VERSION\\n else:\\n verfile = glob.glob(os.path.join(os.getcwd(), \\\"version.txt\\\"))[0]\\n with open(verfile) as afile:\\n ver = afile.read()\\n return ver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ea635fe80fc2e0264f6d09e0b18bfe\",\n \"score\": \"0.5866145\",\n \"text\": \"def cmd_version(_):\\n print(f'autograde version {autograde.__version__}')\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3f0dfef0a4a9601cf39fff1c1839fc6\",\n \"score\": \"0.5866107\",\n \"text\": \"def show_version(a_device, q):\\n output_dict = {}\\n creds = a_device.credentials\\n remote_conn = ConnectHandler(device_type=a_device.device_type,\\n ip=a_device.ip_address,\\n username=creds.username,\\n password=creds.password,\\n port=a_device.port, secret='')\\n \\n output = ('#' * 80) + \\\"\\\\n\\\" \\n output += remote_conn.send_command_expect(\\\"show version\\\") + \\\"\\\\n\\\"\\n output += ('#' * 80) + \\\"\\\\n\\\" \\n\\n ## this assigns the vaule of ouput (type dict) to the 'device_name' key\\n output_dict[a_device.device_name] = output\\n q.put(output_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5db637e4cbc89c6e5ba0f6654d503198\",\n \"score\": \"0.5829124\",\n \"text\": \"def version(self):\\n from insights import get_nvr\\n print(get_nvr())\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"788a66e8d5e861ececcb0020e9a184ca\",\n \"score\": \"0.5820957\",\n \"text\": \"def version():\\n return os.getenv(\\\"APP_VERSION\\\", \\\"No version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42422d55c74054fd57b827bf79866140\",\n \"score\": \"0.58081627\",\n \"text\": \"def show_version(a_device):\\n remote_conn = ConnectHandler(**a_device)\\n sh_ver = remote_conn.send_command_expect(\\\"show version\\\")\\n\\n parse_ver = parse_output(platform='cisco_ios',\\n command='show version', data=sh_ver)\\n print(parse_ver[0]['hostname'], parse_ver[0]['version'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"229707d6afeb2fe20609183294e9606f\",\n \"score\": \"0.57915276\",\n \"text\": \"def version():\\n return '1.3.0'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7ac395e1dd87cae4095bc9c435daed\",\n \"score\": \"0.5772179\",\n \"text\": \"def print_versions():\\n print \\\"Python version: %s\\\" % platform.python_version()\\n print \\\"Cosmos CLI version: %s\\\" % pkg_resources.require(\\\"cosmos\\\")[0].version\\n version_tags = [\\\"v%s\\\" % version for version in SUPPORTED_VERSIONS]\\n print \\\"Supported Cosmos API versions: %s\\\" % \\\", \\\".join(version_tags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a6f52b430b1f29a4d38443239062a0\",\n \"score\": \"0.57605934\",\n \"text\": \"def cli():\\n click.echo('{} {}'.format(__title__, __version__))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4add0ca14c055f5bece954baa7e730d\",\n \"score\": \"0.5751432\",\n \"text\": \"def main():\\n\\n if len(sys.argv) < 2:\\n print(f\\\"Use: {sys.argv[0]} CURRENT_VERSION\\\")\\n sys.exit()\\n\\n ver_list = sys.argv[1].split('.')\\n ver_list[-1] = str(int(ver_list[-1])+1)\\n new_ver = '.'.join(ver_list)\\n print(new_ver)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe69c266b11df9f16ca8b8510c2904a\",\n \"score\": \"0.5741409\",\n \"text\": \"def version(self) -> str:\\n result: str = check_output(\\n args=(self.path, \\\"--version\\\"),\\n stderr=open(devnull, \\\"w\\\")\\n ).decode(\\\"utf-8\\\")\\n match = re.match(r\\\"DeaDBeeF (\\\\d+\\\\.\\\\d+\\\\.\\\\d+)\\\", result)\\n return match.groups()[0] if match else \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1025d5d200c646248b2a803cea628167\",\n \"score\": \"0.57406676\",\n \"text\": \"def lacp_api_version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"lacp_api_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde3647fb8b95b8a86de1b639055595e\",\n \"score\": \"0.57328445\",\n \"text\": \"def show_version(dev, queue):\\n output_dict = {}\\n output = \\\"#\\\" * 80\\n output += \\\"\\\\n\\\"\\n conn = ConnectHandler(device_type=dev.device_type, ip=dev.ip_address,\\n username=dev.credentials.username, password=dev.credentials.password,\\n port=dev.port)\\n output += conn.send_command_expect('show version')\\n output += \\\"#\\\" * 80\\n output += \\\"\\\\n\\\\n\\\"\\n output_dict[dev.device_name] = output\\n queue.put(output_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd7c92c0d60a4ea749cf5ba73e7523c\",\n \"score\": \"0.5720847\",\n \"text\": \"def show(version):\\n assure_state()\\n if not valid_version(version):\\n logger.error(\\\"Invalid version specifier!\\\")\\n sys.exit(1)\\n if not REGISTRY.installed(version):\\n logger.error(\\\"Version [salmon]{}[/salmon] is not installed!\\\".format(version))\\n sys.exit(1)\\n instance = REGISTRY.get_instance(version)\\n print(\\\"Version: [green]{}[/green]\\\".format(version))\\n print(\\\"Install Location: {}\\\".format(os.path.join(CACHE, instance.path)))\\n sdk_filename = \\\"renpy-{}-sdk.zip\\\".format(version)\\n rapt_filename = \\\"renpy-{}-rapt.zip\\\".format(version)\\n SDK_URL = \\\"https://www.renpy.org/dl/{}/{}\\\".format(version, sdk_filename)\\n RAPT_URL = \\\"https://www.renpy.org/dl/{}/{}\\\".format(version, rapt_filename)\\n print(\\\"SDK Url: {}\\\".format(SDK_URL))\\n print(\\\"RAPT Url: {}\\\".format(RAPT_URL))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af2c020a39353485de09af06c782853c\",\n \"score\": \"0.5697556\",\n \"text\": \"def _figureVersion(self):\\n try:\\n sVer = utils.processOutputChecked(['VBoxManage', '--version'])\\n\\n sVer = sVer.strip();\\n sVer = re.sub(r'_BETA.*r', '.', sVer);\\n sVer = re.sub(r'_ALPHA.*r', '.', sVer);\\n sVer = re.sub(r'_RC.*r', '.', sVer);\\n sVer = sVer.replace('r', '.');\\n\\n self.aiVBoxVer = [int(sComp) for sComp in sVer.split('.')];\\n\\n reporter.log('VBox version: %s' % (self.aiVBoxVer,));\\n except:\\n reporter.logXcpt();\\n return False;\\n return True;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81223dde840919c8d7467d63c5beefa6\",\n \"score\": \"0.56832784\",\n \"text\": \"def show_version2(device):\\n print()\\n print(\\\"#\\\" * 80)\\n net_connect = ConnectHandler(**device)\\n output = net_connect.send_command_expect(\\\"show version\\\")\\n net_connect.disconnect()\\n print(output)\\n print(\\\"#\\\" * 80)\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85591391c95e2bb0d09766b7097d66e2\",\n \"score\": \"0.56783324\",\n \"text\": \"def version(self, progname, version, extracontent):\\n tmp = \\\"%s version %s\\\\n%s\\\" % (progname, version, extracontent)\\n self.printer(tmp)\\n self.horizontalrule()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0479d7b8dbc440e66e6075c897c681f4\",\n \"score\": \"0.56717056\",\n \"text\": \"def version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0479d7b8dbc440e66e6075c897c681f4\",\n \"score\": \"0.56717056\",\n \"text\": \"def version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0479d7b8dbc440e66e6075c897c681f4\",\n \"score\": \"0.56717056\",\n \"text\": \"def version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0479d7b8dbc440e66e6075c897c681f4\",\n \"score\": \"0.56717056\",\n \"text\": \"def version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7aec31b093463359006631c3a6cb031\",\n \"score\": \"0.56664556\",\n \"text\": \"def version_string(version):\\n if version == -1 or version is None:\\n raise RuntimeError('no string for version %s'%version)\\n return \\\";Gamebryo KFM File Version %s\\\"%({\\n 0x01000000 : \\\"1.0\\\",\\n 0x01024b00 : \\\"1.2.4b\\\",\\n 0x0200000b : \\\"2.0.0.0b\\\",\\n 0x0201000b : \\\"2.1.0.0b\\\",\\n 0x0202000b : \\\"2.2.0.0b\\\" } [version])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82137b305161c253dd0ce76d3cbc52cc\",\n \"score\": \"0.5665965\",\n \"text\": \"def _getVersion(self):\\n ## Skip Error check since handling of errors is version specific\\n idn = self._instQuery('*IDN?', checkErrors=False).split(',')\\n ver = idn[3].split('.')\\n # put major and minor version into floating point format so can numerically compare\\n self._version = float(ver[0]+'.'+ver[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ad44fe43a652aa46a32b7abf4942e7\",\n \"score\": \"0.5661323\",\n \"text\": \"def _format_file_version(self, old_version: str) -> str:\\n azul.dss.validate_version(old_version)\\n d = datetime.strptime(old_version, azul.dss.version_format)\\n return format_dcp2_datetime(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05d8232e6a83c137c616317d43dbba3\",\n \"score\": \"0.5643196\",\n \"text\": \"def getSaLTVersion():\\n _checkLive()\\n return open('/mnt/salt/salt-version', 'r').read().strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68ad2dfef04bb649ea75845e223453ea\",\n \"score\": \"0.56405735\",\n \"text\": \"def get_version():\\r\\n return get_config()['version']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1964580059fd75f2c3e053e1aba32752\",\n \"score\": \"0.56364334\",\n \"text\": \"def version(self):\\n s = self._name + \\\" \\\"\\n modinfo = Command(['modinfo', self._name]).runOutput()\\n for line in modinfo.splitlines():\\n if line.find('version') == 0:\\n s += line\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d7aa77ae5cb73d84a8dfc1c8a8bad8\",\n \"score\": \"0.5632172\",\n \"text\": \"def version():\\n return __version__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed5b899fc7c6eeaba3e3efd52b3034d\",\n \"score\": \"0.56171614\",\n \"text\": \"def get_version(self):\\n url = self.gi._make_url(self, None)\\n url = url.rstrip('configuration') + 'version'\\n return self._get(url=url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a0620b62d27081f589652d50cce4303\",\n \"score\": \"0.5613159\",\n \"text\": \"def show_version(emproot, userhome, options):\\n if len(options.args) > 0:\\n return show_error(\\n \\\"Unexpected arguments for %s: (%s)\\\"%(options.command, \\\" \\\".join(options.args)), \\n EMP_UNEXPECTEDARGS\\n )\\n status = EMP_SUCCESS\\n print(EMP_VERSION)\\n # with open(logfilename, \\\"r\\\") as logfile:\\n # shutil.copyfileobj(logfile, sys.stdout)\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"336fe72f9d303058aa42b4c777397929\",\n \"score\": \"0.56004447\",\n \"text\": \"def get_version():\\n return VERSION\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3888ac67cb78dfb848129971f39da320\",\n \"score\": \"0.55970466\",\n \"text\": \"def version(self):\\n return self._controller.query('++ver').rstrip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad014303a6b8f939a8bff605bd5f08a0\",\n \"score\": \"0.5596041\",\n \"text\": \"def GetVersion(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9cd09f50e3d5f42032bbd20b525a019\",\n \"score\": \"0.55944794\",\n \"text\": \"def print_version(ctx, param, value):\\n\\n if not value or ctx.resilient_parsing:\\n return\\n print(\\\"Version\\\\t1.2\\\")\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"529f2e8bf742584943789c6d6b56a158\",\n \"score\": \"0.55926114\",\n \"text\": \"def ChemicsVersion():\\n fid = open(CONFIG.CHEMICSROOTPATH+\\\"ChemicsWS/ChemicsWS/tag.txt\\\")\\n tag = string.strip(fid.readline())\\n fid.close()\\n return tag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd52534499ed4885cb83253cba8a08f\",\n \"score\": \"0.5589338\",\n \"text\": \"def version(self) -> str:\\n response = self.session.get(self.url)\\n if not response.ok: raise CouchDBException.auto(response)\\n return response.json()['version']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5308a939d9122fb5e96378bc55ecba10\",\n \"score\": \"0.55849224\",\n \"text\": \"def version(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5308a939d9122fb5e96378bc55ecba10\",\n \"score\": \"0.55849224\",\n \"text\": \"def version(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5308a939d9122fb5e96378bc55ecba10\",\n \"score\": \"0.55849224\",\n \"text\": \"def version(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b3781183f954121216f2f9d1bc8b7a4\",\n \"score\": \"0.55709267\",\n \"text\": \"def get_version():\\n return {\\\"version\\\": f\\\"{API_VERSION}\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73dab7406c10a2fe43f4ef51c538ecf0\",\n \"score\": \"0.5564526\",\n \"text\": \"def version(self):\\n return str(self(\\\"version\\\")).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c33170cde36e72ed8fdf757d93fa89c6\",\n \"score\": \"0.5558771\",\n \"text\": \"async def version(ctx):\\n await ctx.message.channel.send(VNUMBER)\\n current_timestamp = datetime.now()\\n current_timestamp_string = current_timestamp.strftime(\\\"%d/%m/%Y %H:%M:%S\\\")\\n await ctx.message.channel.send(current_timestamp_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d71972846a0e5abc46fb0b3c6e6ce9f\",\n \"score\": \"0.55564415\",\n \"text\": \"def _version() -> str:\\n with io.open(\\\"graphtik/__init__.py\\\", \\\"rt\\\", encoding=\\\"utf8\\\") as f:\\n return re.search(r'__version__ = \\\"(.*?)\\\"', f.read()).group(1).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3ad76e420865a001145349852a7967\",\n \"score\": \"0.5543408\",\n \"text\": \"def getClientVersion(verbose=False):\\n try:\\n cmd=\\\"%s version client\\\" % (Utils.EosClientPath)\\n if verbose: Utils.Print(\\\"cmd: %s\\\" % (cmd))\\n response=Utils.checkOutput(cmd.split())\\n assert(response)\\n assert(isinstance(response, str))\\n if verbose: Utils.Print(\\\"response: <%s>\\\" % (response))\\n verStr=response.strip()\\n return verStr\\n except subprocess.CalledProcessError as ex:\\n msg=ex.output.decode(\\\"utf-8\\\")\\n Utils.Print(\\\"ERROR: Exception during client version query. %s\\\" % (msg))\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"387d3eee82e524dd43349c3c9d97c816\",\n \"score\": \"0.5540558\",\n \"text\": \"def index(self, appid=None):\\n print \\\"IN CVMFSAppVersion: appid=(%s)\\\" % appid\\n if appid not in self.valid_apps:\\n print \\\"Invalid app type %s\\\" % appid\\n return ''\\n html_ = html.HTML()\\n dirs = []\\n try:\\n _, dirs, _ = os.walk(os.path.join(self.cvmfs_root, appid)).next()\\n except StopIteration:\\n logger.error(\\\"Couldn't access CVMFS dir: %s\\\",\\n os.path.join(self.cvmfs_root, appid))\\n\\n for dir_ in natsorted(dirs, reverse=True):\\n for version in VERSION_RE.findall(dir_):\\n html_.option(version)\\n return str(html_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f9878b1eb026f81fe6538930a0734fe\",\n \"score\": \"0.55323595\",\n \"text\": \"def mamage_version(self, value):\\n \\n term.pos(16, 3)\\n term.write(value[\\\"system\\\"])\\n term.pos(16, 10)\\n term.write(value[\\\"version\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84367cb535eb7ea4837a08cfd88d7f6c\",\n \"score\": \"0.55283386\",\n \"text\": \"def show_version(args):\\n version_string = f\\\"ssm-tools/{ssm_tools_version}\\\"\\n if args.log_level <= logging.INFO:\\n version_string += f\\\" python/{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}\\\"\\n version_string += f\\\" boto3/{boto3.__version__}\\\"\\n print(version_string)\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4dd8fce97c53d6e29204bf142fc2653\",\n \"score\": \"0.55266124\",\n \"text\": \"def get_version(self) -> str:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2548b6996b51499208b90d96f0197d\",\n \"score\": \"0.5521013\",\n \"text\": \"def getversioninfo():\\n controllers.updateControllerVersionInfo()\\n return createResponse(controllers.getControllerVersionInfo())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a2da06ab68bf4cc0d03cf1e8c11d64\",\n \"score\": \"0.55182695\",\n \"text\": \"def version_string(self):\\n return '%s %s\\\\n' \\\\\\n 'Copyright (c) 2017 Jonathan Simmonds\\\\n\\\\n' \\\\\\n 'Loaded modules:\\\\n' \\\\\\n '%s\\\\n' % (os.path.basename(sys.argv[0]), __version__,\\n '\\\\n'.join([' ' + str(m) for m in SEARCH_MODULES]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4c0ff31e0c183fb2667ec58d86d448\",\n \"score\": \"0.55063576\",\n \"text\": \"def get_version():\\n with io.open('flask_snow/__init__.py') as input_file:\\n for line in input_file:\\n if line.startswith('__version__'):\\n return ast.parse(line).body[0].value.s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef6b2af03f7d20f95e9d7c232e1592a\",\n \"score\": \"0.5505784\",\n \"text\": \"def version(self) -> str:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992982,"cells":{"query_id":{"kind":"string","value":"fc635732e2656cd76225818361e24cb0"},"query":{"kind":"string","value":"Create complement automaton (p23)."},"positive_passages":{"kind":"list like","value":[{"docid":"bfd4822fcdb6a0ba67282ba993d0da4d","score":"0.6469388","text":"def complement (self):\n S = self.S\n I = self.I\n Σ = self.Σ\n T = self.T\n F = [ s for s in S if s not in self.F ]\n\n return FA(S, I, Σ, T, F)","title":""}],"string":"[\n {\n \"docid\": \"bfd4822fcdb6a0ba67282ba993d0da4d\",\n \"score\": \"0.6469388\",\n \"text\": \"def complement (self):\\n S = self.S\\n I = self.I\\n Σ = self.Σ\\n T = self.T\\n F = [ s for s in S if s not in self.F ]\\n\\n return FA(S, I, Σ, T, F)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c0b9abd88068a80201d69b1a94761a78","score":"0.741861","text":"def complement(sequence):\n\tpass","title":""},{"docid":"82e2c1796e75d6700dbbecd3b02784d5","score":"0.7122255","text":"def complement(seq):\n seq = ''.join([_complement[s] for s in seq])\n return seq","title":""},{"docid":"702194e7315e1264a41f0ed27b32e73b","score":"0.6778584","text":"def complement(sequence):\n return \"\".join(COMPLEMENT_MAP[x] for x in sequence)","title":""},{"docid":"8ad701eddaa1f9ed5fdbdf58e094f504","score":"0.67661643","text":"def reverse_complement(seq):\n #Initialize empty string\n rev_comp = ''\n #loop through seq and add to rev_seq\n for base in reversed(seq):\n rev_comp += complement_base(base)\n return rev_comp","title":""},{"docid":"fb02349df81e13bec8b59624d8fa5781","score":"0.6578684","text":"def reversecomplement(sequence):\n \n # IUPAC complimentary nucleotides. Dictionary used for speed, includes\n # entries for UPPER and lower case.\n IUPAC_NUC = {\"A\": \"T\", \"a\": \"t\", # A -> T\n \"C\": \"G\", \"c\": \"g\", # C -> G\n \"G\": \"C\", \"g\": \"c\", # G -> T\n \"T\": \"A\", \"t\": \"a\", # T -> A\n \"R\": \"Y\", \"r\": \"y\", # A or G -> T or C\n \"Y\": \"R\", \"y\": \"r\", # C or T -> G or A\n \"S\": \"S\", \"s\": \"s\", # G or C -> G or C\n \"W\": \"W\", \"w\": \"w\", # A or T -> A or T\n \"K\": \"M\", \"k\": \"m\", # G or T -> A or C\n \"M\": \"K\", \"m\": \"k\", # A or C -> G or T\n \"B\": \"V\", \"b\": \"v\", # C or G or T -> G or C or A\n \"V\": \"B\", \"v\": \"b\", # G or C or A -> C or G or T\n \"D\": \"H\", \"d\": \"h\", # A or G or T -> T or C or A\n \"H\": \"D\", \"h\": \"d\", # T or C or A -> A or G or T\n \"N\": \"N\", \"n\": \"n\", # any base\n \"-\": \"-\"} # gap\n revcomp = []\n # compliment the sequence\n for base in sequence:\n # get the complimentary code, if one does not exist add 'x'\n revcomp.append(IUPAC_NUC.get(base, \"x\"))\n # reverse it\n revcomp.reverse()\n # return as a string rather than a list\n return ''.join(revcomp)","title":""},{"docid":"10d088993dc1c836a5876ae377c45c38","score":"0.6511946","text":"def reverse_complement(sequence):\n return complement(sequence[::-1])","title":""},{"docid":"ea68c9f5239841e6cc2f9c31031a1212","score":"0.65061605","text":"def reverse_complement(sequence): ## function to get the reverse complement of a sequence\n complement = {'A':'T','C':'G','G':'C','T':'A','N':'N'}\n return \"\".join([complement.get(nt.upper(), '') for nt in sequence[::-1]])","title":""},{"docid":"dcd2df49d2ee3f2241c65d487940fbcd","score":"0.6495557","text":"def complement(sequence):\n if not sequence:\n return sequence\n \n upseq = sequence.upper()\n # is there a better way to do this? match object seems heavy\n return re.sub(BASE_RE, lambda m: COMPLIMENTS[m.group(0)], upseq)","title":""},{"docid":"905a15925e449d55663140d48fea230c","score":"0.64877456","text":"def reverse_complement(seq, material = 'DNA'):\n\n #initialize reverse\n rev_complement = ''\n\n for base in seq[::-1]:\n rev_complement += complement(base)\n\n return rev_complement","title":""},{"docid":"b5744d9d21ac4cf73ccaa30215358580","score":"0.6468411","text":"def complement(sequence):\n return sequence.translate(DNA_Translation)","title":""},{"docid":"a00ba6e078c4d095a46f4f13250f19b1","score":"0.6456894","text":"def reverse_complement(self):\n mapping = str.maketrans('ATCG', 'TAGC')\n return self.seq.translate(mapping)[::-1]","title":""},{"docid":"ccfebec2aeec849b778e23306929bb6b","score":"0.6441931","text":"def complement(s):\n c = \"\".join(__complementTranslation[n] for n in s)\n return c","title":""},{"docid":"fa64eda570c9d1d9e445d48a272710da","score":"0.64220285","text":"def reverse_complement(seq):\n return seq.translate(complements)[::-1]","title":""},{"docid":"c8fad6910445766cc50f7cb510535f96","score":"0.63891155","text":"def reverse_complement(pattern):\n reverse_complement = ''\n for i in range(0,len(pattern)): #can we vectorize?\n current = pattern[len(pattern)-i-1]\n if(current == 'A'):\n reverse_complement += 'T'\n elif(current == 'T'):\n reverse_complement += 'A'\n elif(current == 'G'):\n reverse_complement += 'C'\n else:\n reverse_complement += 'G'\n return reverse_complement","title":""},{"docid":"9febeaf0e1fc9bde77b6b5eafae9cc8d","score":"0.6330548","text":"def reverseComplement(seq):\n seq = seq[::-1] # reverse\n seq = complement(seq)\n return seq","title":""},{"docid":"2a3123b5a33ee9dfbfdd9bcf6852fb9a","score":"0.6305401","text":"def reverse_complement(table, seq):\n return seq.translate(str.maketrans(\"ATGC\",\"TACG\"))[::-1]","title":""},{"docid":"4d4e8ac3701c1c6573bb6cb89a0b6b2c","score":"0.62578833","text":"def reverse_complement(sequence):\n return reverse(complement(sequence))","title":""},{"docid":"110ed809fd091fb80bbc62ad27c907fc","score":"0.61952794","text":"def reverse_complement(sequence):\n forward = 'ACGTNacgtn'\n reverse = 'TGCANtgcan'\n try: # Type checking\n assert isinstance(sequence, (str, list, tuple))\n if isinstance(sequence, (list, tuple)):\n for base in sequence:\n assert isinstance(base, str)\n assert len(base) is 1\n for base in sequence:\n assert base in forward or base in Lookup.IUPAC_CODES\n except AssertionError:\n raise TypeError(\"'sequence' must be of type 'str' or be a list or tuple of single-character 'str' objects within '%s' or 'RYSWKM'\" % forward)\n else:\n rc_table = str.maketrans(forward, reverse)\n return ''.join(tuple(base.translate(rc_table) for base in sequence))","title":""},{"docid":"cc51b71a7fbeb240b9afcb644d43c300","score":"0.6157103","text":"def get_reverse_complement_unit_tests():\n print 'input: ATGCCCGCTTT, expected output: AAAGCGGGCAT, actual output:',\n print get_reverse_complement(\"ATGCCCGCTTT\")\n print 'input: CCGCGTTCA, expected output: TGAACGCGG, actual output:',\n print get_reverse_complement(\"CCGCGTTCA\")","title":""},{"docid":"cd6ba77410cc375975ab48a87030891a","score":"0.61516976","text":"def get_complement(string):\n string=string.replace('G','1')\n string=string.replace('A','2')\n string=string.replace('T','3')\n string=string.replace('C','4')\n string=string.replace('1','C')\n string=string.replace('2','T')\n string=string.replace('3','A')\n string=string.replace('4','G')\n string=string.replace('X','Z')#for 'blank' sticky ends and cinnamate\n return string[::-1]","title":""},{"docid":"e8b624302c349d29482d9b53345055db","score":"0.6151313","text":"def reverse_complement(s):\n\n ret = ''\n complement = {\"A\": \"T\", \"T\": \"A\", \"C\": \"G\", \"G\": \"C\", \"N\": \"N\", \"a\": \"t\", \"t\": \"a\", \"c\": \"g\", \"g\": \"c\", \"n\": \"n\"}\n for base in s[::-1]:\n ret += complement[base]\n return ret","title":""},{"docid":"262b77b39bbb17ac9752fca6e85898d6","score":"0.6141288","text":"def complement(self):\n return self._complement","title":""},{"docid":"d2345e36f171eb85e05f46d969292db6","score":"0.6059809","text":"def _complement(self):\n if not all(elem.is_number for elem in self.elements):\n raise ValueError(\"%s: Complement not defined for symbolic inputs\"\n %self)\n sorted_elements = sorted(list(self.elements))\n\n intervals = [] # Build up a list of intervals between the elements\n intervals += [Interval(S.NegativeInfinity,sorted_elements[0],True,True)]\n for a,b in zip(sorted_elements[0:-1], sorted_elements[1:]):\n intervals.append(Interval(a,b, True, True)) # open intervals\n intervals.append(Interval(sorted_elements[-1], S.Infinity, True, True))\n return Union(*intervals)","title":""},{"docid":"dd3899d349decc47989612b5186123fd","score":"0.60411805","text":"def reverse_complement(self, **kwargs) -> \"SeqLike\":\n if self._type == \"AA\":\n raise ValueError(\"Proteins do not have complements!\")\n\n if hasattr(self, \"annotations\"):\n annotations = self.annotations.copy()\n else:\n annotations = dict()\n\n if \"reversed\" in annotations:\n annotations[\"reversed\"] = not annotations[\"reversed\"]\n else:\n annotations[\"reversed\"] = True\n\n _nt_record = self._nt_record.reverse_complement(\n id=True, name=True, description=True, annotations=annotations, dbxrefs=True\n )\n\n s = SeqLike(\n _nt_record,\n seq_type=self._type,\n alphabet=self.alphabet,\n codon_map=self.codon_map,\n )\n return s","title":""},{"docid":"b6d6873dae6b89da19ee63a866cea622","score":"0.6022227","text":"def get_reverse_complement(dna):\n # TODO:\n complement = ''\n for i in dna:\n complement = get_complement(i) + complement\n return complement","title":""},{"docid":"ff65a35b9657f80674db26cacd8eeda3","score":"0.60204303","text":"def complement(value):\n return 0xFFFF - value + 1","title":""},{"docid":"3912cf9fb949611e73dfb3c92ff21361","score":"0.6019483","text":"def reverseComplement(s):\n complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'}\n t = ''\n for base in s:\n t = complement[base] + t\n return t","title":""},{"docid":"369d54f5effbfa4fb02528154293ea3a","score":"0.5992025","text":"def reverse_complement(s):\n s = s.upper()\n assert is_DNA(s), \"Your sequence must be DNA!\"\n\n r = reverse(s)\n rc = complement(r)\n\n return rc","title":""},{"docid":"2143d5d5561679815e6838f2dd8988ef","score":"0.59663814","text":"def get_reverse_complement(dna):\n\n answer = \"\"\n for x in range(0, len(dna)):\n answer = answer + get_complement(dna[len(dna) - x - 1])\n return answer","title":""},{"docid":"163c6763d5aa8d83c57b533021ee23d3","score":"0.5960762","text":"def reverse_complement2(seq):\n\n #reversing the sequence\n seq[::-1]\n #could have made everything lower() to aviod the aliases and better allow to give out answers in RNA\n\n #exchanging the vocbulary\n seq1 = seq.replace('Uu', 'F')\n seq2 = seq1.replace('Tt', 'I')\n seq3 = seq2.replace('Aa', 'V')\n seq4 = seq3.replace('Gg', 'E')\n seq5 = seq4.replace('Cc', 'S')\n\n #echanging back complementary vocabulary\n seq6 = seq5.replace('F', 'A')\n seq7 = seq6.replace('I', 'A')\n seq8 = seq7.replace('V', 'T')\n seq9 = seq8.replace('E', 'C')\n seq10 = seq9.replace('S', 'G')\n\n return seq10","title":""},{"docid":"9cd7db49a611f9023c0d92435cdabc80","score":"0.5924333","text":"def reverse_complement(self, number):\n number = ~number\n result = 0x00\n\n for i in range(self.length):\n result = (result << 2) | (number & 0x03)\n number >>= 2\n\n return result","title":""},{"docid":"163ecda6ddc7583aea2e73477b36a30d","score":"0.5920495","text":"def get_complement(base):\n\n # iterates through a two dimensional list, returns the complement if the given base is equal to the first entry in the list\n return ''.join([pair[1] for pair in [[\"A\", \"T\"], [\"T\", \"A\"], [\"G\", \"C\"], [\"C\", \"G\"]] if (base == pair[0])])","title":""},{"docid":"49db111a62e5463f2ae4141701933419","score":"0.5919934","text":"def reverse_complement(dna):\n\n # iterates backwards through the list and gets the complement of each letter in list\n return ''.join([get_complement(dna[x]) for x in range(len(dna)-1, -1, -1)])","title":""},{"docid":"16fbed5ff2c04a3c1f455c570e3d1495","score":"0.59115165","text":"def get_reverse_complement(dna):\n s = ''\n for c in dna:\n s = get_complement(c) + s\n return s","title":""},{"docid":"c59f99300e113562e8265b48645d160c","score":"0.58904934","text":"def reverse_complement(dna_sequence):\n complement = string.maketrans(\"ATCG\", \"TAGC\")\n rev_comp = dna_sequence.translate(complement)[::-1]\n return rev_comp","title":""},{"docid":"be9dfd4c5e118612d86b7e1468cc120b","score":"0.5884089","text":"def get_reverse_complement(dna):\n\n reverse=dna[::-1]\n revdna=''\n for x in reverse:\n \trevnuc=get_complement(x)\n \trevdna+=revnuc\n return revdna","title":""},{"docid":"f9542209b165b92de373650259570e58","score":"0.58805555","text":"def complement(dna):\n dna_complement = [None]*len(dna)\n i = 0\n for nb in dna:\n if nb in COMPLEMENTARY_NUCLEOTIDE_MAP:\n dna_complement[i] = COMPLEMENTARY_NUCLEOTIDE_MAP[nb]\n i += 1\n else:\n raise ValueError(\"Invalid nucleotide base \\\"{}\\\" in DNA string \\\"{}\\\"\".format(nb, dna))\n return \"\".join(dna_complement)","title":""},{"docid":"671a47fc398d147b2bc61b805de816d1","score":"0.588038","text":"def reverse_complement(dna):\n\tfrom string import maketrans, translate\n\tcomp = translate(dna, maketrans('CGAT', 'GCTA'))\n\treturn comp[::-1]","title":""},{"docid":"67e6177b22b2d0bce663fe302ebb6080","score":"0.5878832","text":"def get_reverse_complement(dna):\n \n dna=dna.upper()\n for i in range(len(dna)):\n if dna[i] != 'A' and dna[i] != 'T' and dna[i] != 'C' and dna[i] != 'G':\n print 'Must input valid amino acids'\n return\n dna = dna[::-1]\n result = ''\n for i in range(len(dna)):\n if dna[i] == 'A':\n complement = 'T'\n elif dna[i] == 'T':\n complement = 'A'\n elif dna[i] == 'G':\n complement = 'C'\n elif dna[i] == 'C':\n complement = 'G'\n result += complement\n return result","title":""},{"docid":"7c7660b7e402e7cfb1088a829af30279","score":"0.58681613","text":"def self_and_rev_complement(in_dna):\n all_possible = [in_dna]\n # Get reverse complement\n dna = Seq(in_dna, generic_dna)\n rev_complement = str(dna.reverse_complement())\n all_possible.append(rev_complement)\n return(all_possible)","title":""},{"docid":"f3eb07d463f04ff09f2b79c1c17cc676","score":"0.5854894","text":"def get_reverse_complement(dna):\n i = 0\n j = len(dna)\n data = \"\"\n temp = \"\"\n\n while i < j:\n temp += get_complement(dna[i])\n i += 1\n i = 0\n while j > 0:\n i += 1\n data += temp[j-1]\n j -= 1\n return data","title":""},{"docid":"2140d082c329d2ca8264be940633cea2","score":"0.5839853","text":"def get_reverse_complement(dna):\n reverse = ''\n complements = ''\n for i in range(len(dna)):\n reverse = reverse + dna[(len(dna)-1)-i]\n for i in range(len(dna)):\n if reverse[i] == 'A':\n complements = complements + 'T'\n elif reverse[i] == 'T':\n complements = complements + 'A'\n elif reverse[i] == 'C':\n complements = complements + 'G'\n elif reverse[i] == 'G':\n complements = complements + 'C'\n \n return complements","title":""},{"docid":"18eb6edb24912d488eee5ee2b864602d","score":"0.5838365","text":"def get_reverse_complement(dna):\n n = len(dna)\n i = 0\n reverse_dna = []\n\n for i in range(n):\n reverse_dna.append (get_complement(dna[n - 1 - i]))\n reverse_complement = ''.join(reverse_dna)\n return reverse_complement","title":""},{"docid":"b31624562b26afcf75beac523b6d65a8","score":"0.5829853","text":"def get_reverse_complement(dna):\n\n rev_dna = ''\n for i in dna:\n rev_dna = get_complement(i) + rev_dna\n return rev_dna\n pass","title":""},{"docid":"52fef343f468e6846f3d775734e008b2","score":"0.58165497","text":"def reverse_complement(kmer):\n complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A'}\n return ''.join([complement[base] for base in kmer[::-1]])","title":""},{"docid":"5887ae46c3f89ffd1caa8863e8cd2db3","score":"0.5801767","text":"def get_complement(dna):\n \n # YOUR IMPLEMENTATION HERE\n #import re\n rvs_dna = dna[::-1]\n rvs_cmpl = list(dna)\n\n #rep = {'A':'T','T':'A','G':'C','C':'G'}\n for i in range(len(dna)):\n if rvs_dna[i] == 'A':\n rvs_cmpl[i] = 'T'\n elif rvs_dna[i] == 'T':\n rvs_cmpl[i] = 'A'\n elif rvs_dna[i] == 'G':\n rvs_cmpl[i] = 'C'\n else:\n rvs_cmpl[i] = 'G'\n outcmp = ''.join(rvs_cmpl)\n return outcmp\n\n #rep_dict = dict(rep)\n #repfun = lambda match: rep_dict[match.group(0)]\n #pattern = re.compile(\"|\".join([re.escape(i) for i, j in rep]), re.M)\n #rvs_cmpl = lambda rvs_dna: pattern.sub(repfun, rvs_dna)\n \n \n \n \n #for i,j in dic.iteritems():\n #rvs_cmpl = rvs_dna.replace(i,j)","title":""},{"docid":"8f215780d7d086e0d03681851b37d9bf","score":"0.5800605","text":"def get_complement(s):\n\n s = s.upper()\n\n new_string = \"\"\n\n for i in s:\n if(i == 'A'):\n new_string = new_string + 'T'\n elif(i == 'T'):\n new_string = new_string + 'A'\n elif(i == 'G'):\n new_string = new_string + 'C'\n elif(i == 'C'):\n new_string = new_string + 'G'\n\n return new_string","title":""},{"docid":"a60c616551b220e9c3cc44fe15ea137f","score":"0.5787215","text":"def get_reverse_complement(dna):\n sequence = \"\"\n for letter in dna: \n complement = get_complement(letter)\n sequence = sequence + complement \n reverse_sequence = sequence[::-1]\n return reverse_sequence","title":""},{"docid":"67fb026895f271c80d86091dc78787b9","score":"0.57857114","text":"def get_complement(nucleotide):\n\n if nucleotide is 'A':\n return 'T'\n if nucleotide is 'T':\n return 'A'\n if nucleotide is 'C':\n return 'G'\n if nucleotide is 'G':\n return 'C'","title":""},{"docid":"64fb1197cf69c62766d38c2cd4afd873","score":"0.57832575","text":"def complement_base(base):\n\n if base in 'Aa':\n return 'T'\n elif base in 'Tt':\n return 'A'\n elif base in 'Gg':\n return 'C'\n else:\n return 'G'","title":""},{"docid":"2a2e2e3f2522edeb067001e4ab8302ee","score":"0.57759184","text":"def reverseComplement(seq, alphabet='ACGT'):\n compl= dict(zip('ACGTNRYWSMKBHDV', 'TGCANYRWSKMVDHB'))\n return ''.join([compl[base]\n for base in seq.upper().replace('U', 'T')])[::-1]","title":""},{"docid":"0339d53d5e500fb603e7b4affdede283","score":"0.5762521","text":"def complement(self):\n return Type([~f for f in self.primary])","title":""},{"docid":"b83ab631d35539d5d9ba252d411f85a0","score":"0.5753111","text":"def get_reverse_complement(s):\n\n s = s.upper()\n\n string_list = []\n\n for i in s:\n if(i == 'A'):\n string_list.append('T')\n elif(i == 'T'):\n string_list.append('A')\n elif(i == 'G'):\n string_list.append('C')\n elif(i == 'C'):\n string_list.append('G')\n\n reversed_string_list = string_list[::-1]\n reversed_string = \"\".join(reversed_string_list)\n\n return reversed_string","title":""},{"docid":"dbbe1975abe7d0887ff92d60ed0233ae","score":"0.57054514","text":"def get_complement(nucleotide):\n if nucleotide == 'A':\n return 'T'\n elif nucleotide == 'T':\n return 'A'\n elif nucleotide == 'C':\n return 'G'\n elif nucleotide == 'G':\n return 'C'","title":""},{"docid":"29a4d85e11d005986fbbbd9ee0e7c483","score":"0.5703421","text":"def get_complement(nucleotide):\n\n if nucleotide == 'A':\n return 'T'\n if nucleotide == 'C':\n return 'G'\n if nucleotide == 'T':\n return 'A'\n if nucleotide == 'G':\n return 'C'","title":""},{"docid":"293ba73d74ac359d4c7402503287b028","score":"0.56934303","text":"def get_complement(nucleotide):\n if nucleotide == \"A\":\n return(\"T\")\n elif nucleotide == \"C\":\n return(\"G\")\n elif nucleotide == \"T\":\n return(\"A\")\n elif nucleotide == \"G\":\n return(\"C\")","title":""},{"docid":"933e9b5d99c8f0052f03ddd107e2820f","score":"0.5692761","text":"def complement(base, material = 'DNA'):\n # we will use this to complement each base individually\n if base in 'Aa':\n if material == 'DNA':\n return 'T'\n elif material == 'RNA':\n return 'U'\n elif base in 'UuTt':\n return 'A'\n elif base in 'Gg':\n return 'C'\n elif base in 'Cc':\n return 'G'","title":""},{"docid":"f2220b590143ebb3cd3d98a1e465cd5e","score":"0.56878346","text":"def test_reverse_complement(self):\n background = {\"A\": 0.3, \"C\": 0.2, \"G\": 0.2, \"T\": 0.3}\n pseudocounts = 0.5\n m = motifs.create([Seq(\"ATATA\")])\n m.background = background\n m.pseudocounts = pseudocounts\n received_forward = format(m, \"transfac\")\n expected_forward = \"\"\"\\\nP0 A C G T\n01 1 0 0 0 A\n02 0 0 0 1 T\n03 1 0 0 0 A\n04 0 0 0 1 T\n05 1 0 0 0 A\nXX\n//\n\"\"\"\n self.assertEqual(received_forward, expected_forward)\n expected_forward_pwm = \"\"\"\\\n 0 1 2 3 4\nA: 0.50 0.17 0.50 0.17 0.50\nC: 0.17 0.17 0.17 0.17 0.17\nG: 0.17 0.17 0.17 0.17 0.17\nT: 0.17 0.50 0.17 0.50 0.17\n\"\"\"\n self.assertEqual(str(m.pwm), expected_forward_pwm)\n m = m.reverse_complement()\n received_reverse = format(m, \"transfac\")\n expected_reverse = \"\"\"\\\nP0 A C G T\n01 0 0 0 1 T\n02 1 0 0 0 A\n03 0 0 0 1 T\n04 1 0 0 0 A\n05 0 0 0 1 T\nXX\n//\n\"\"\"\n self.assertEqual(received_reverse, expected_reverse)\n expected_reverse_pwm = \"\"\"\\\n 0 1 2 3 4\nA: 0.17 0.50 0.17 0.50 0.17\nC: 0.17 0.17 0.17 0.17 0.17\nG: 0.17 0.17 0.17 0.17 0.17\nT: 0.50 0.17 0.50 0.17 0.50\n\"\"\"\n self.assertEqual(str(m.pwm), expected_reverse_pwm)\n # Same thing, but now start with a motif calculated from a count matrix\n m = motifs.create([Seq(\"ATATA\")])\n counts = m.counts\n m = motifs.Motif(counts=counts)\n m.background = background\n m.pseudocounts = pseudocounts\n received_forward = format(m, \"transfac\")\n self.assertEqual(received_forward, expected_forward)\n self.assertEqual(str(m.pwm), expected_forward_pwm)\n m = m.reverse_complement()\n received_reverse = format(m, \"transfac\")\n self.assertEqual(received_reverse, expected_reverse)\n self.assertEqual(str(m.pwm), expected_reverse_pwm)","title":""},{"docid":"f9116f072d52c4d5e607bbc96a06d294","score":"0.56758195","text":"def get_reverse_complement(dna):\n reverse_complement = \"\"\n\n for character in dna[::-1]:\n if character == \"A\":\n reverse_complement += \"T\"\n elif character == \"T\":\n reverse_complement += \"A\"\n elif character == \"C\":\n reverse_complement += \"G\"\n elif character == \"G\":\n reverse_complement += \"C\"\n\n return reverse_complement","title":""},{"docid":"fdd0a6c7df7d61a6924f9db45a0cb247","score":"0.56448746","text":"def get_reverse_complement(dna):\n \n # YOUR IMPLEMENTATION BELOW\n rev_comp = \"\"; #rev_comp is the returned string that is updated each loop\n for x in range(0,len(dna)):\n if dna[x] == 'A': rev_comp +='T'\n elif dna[x] == 'T': rev_comp += 'A'\n elif dna[x] == 'G': rev_comp += 'C'\n elif dna[x] == 'C': rev_comp += 'G'\n rev_comp = rev_comp[::-1] #reverses the string\n return rev_comp","title":""},{"docid":"6d741c8ffdae441a80c5ef6fb35085d5","score":"0.5642061","text":"def get_reverse_complement(dna):\n comp = ''\n for letter in dna:\n if get_complement(letter) == 'Error, Invalid Input':\n return('Error, Invalid Input')\n else:\n comp = get_complement(letter) + comp\n return comp","title":""},{"docid":"8c5e8d04a25d38b67e0be21c6b5c95ea","score":"0.5631098","text":"def get_complement_unit_tests():\n \n # YOUR IMPLEMENTATION HERE\n rvscheck1 = \"CAAATTCGT\"\n out_hyp1 = \"ACGAATTTG\"\n out_act1 = get_complement(rvscheck1)\n print \"input: \" + rvscheck1 +\", \" +\"expected output: \"\n print out_hyp1 + \", actual output: \"\n print out_act1\n \n rvscheck2 = \"CTTGTTCCTTAT\"\n out_hyp2 = \"ATAAGGAACAAG\"\n out_act2 = get_complement(rvscheck2)\n print \"input: \" + rvscheck2 +\", \" +\"expected output: \"\n print out_hyp2 + \", actual output: \"\n print out_act2","title":""},{"docid":"dc165d6059a8c80837e7a3be995a9cdb","score":"0.5608422","text":"def get_complement(nucleotide):\n\n if nucleotide == 'A':\n return 'T'\n elif nucleotide == 'T':\n return 'A'\n elif nucleotide == 'G':\n return 'C'\n elif nucleotide == 'C':\n return 'G'\n else:\n return 'Error, Invalid Input'","title":""},{"docid":"52ca695781e1f240d7e4216ae835f2a6","score":"0.56059504","text":"def get_complement(nucleotide):\n if nucleotide == 'A':\n return 'T'\n elif nucleotide == 'C':\n return 'G'\n elif nucleotide == 'G':\n return 'C'\n elif nucleotide == 'T':\n return 'A'\n else:\n return False","title":""},{"docid":"037a8c910c5e0c7e2ba9a46e76322bef","score":"0.55957747","text":"def get_complement(dna):\n\n complement = ''\n for char_index in range(len(dna)):\n if dna[char_index] == 'T':\n complement = complement + 'A'\n elif dna[char_index] == 'A':\n complement = complement + 'T'\n elif dna[char_index] == 'G':\n complement = complement + 'C'\n else:\n dna[char_index] == 'C'\n complement = complement + 'G'\n \n return complement","title":""},{"docid":"d327533db5b4b7c476cad41c2784580a","score":"0.5586668","text":"def get_complement(self, total_intv):\n # type: (Tuple[int, int]) -> IntervalSet\n return IntervalSet(intv_list=self.complement_iter(total_intv))","title":""},{"docid":"9d4b73722b1f7c3f8c39159ce285cc4c","score":"0.55858916","text":"def reverse_complement(self) -> str:\n\n return self.get_reverse_complement(self.sequence)","title":""},{"docid":"4900827cff3536a838c18dff950b3b06","score":"0.55817723","text":"def get_complement(nucleotide):\n if nucleotide == 'A':\n return 'T'\n elif nucleotide == 'C':\n return 'G'\n elif nucleotide == 'T':\n return 'A'\n else: #if nucleotide = G\n return 'C'","title":""},{"docid":"943cb2032dee953ab7c482017b39d79a","score":"0.55724967","text":"def get_reverse_complement_unit_tests():\n \n # YOUR IMPLEMENTATION BELOW\n input1 = \"ATGCCCTTTGC\"\n input2 = \"CCGCGTTCA\"\n expected1 = 'GCAAAGGGCAT' \n expected2 = 'TGAACGCGG'\n \n actual1 = get_reverse_complement(input1)\n actual2 = get_reverse_complement(input2) \n\n print \"GET_REVERSE input: \" + input1 + \", expected output: \" + expected1 + \", actual output: \" + actual1\n print \"GET_ REVERSE input: \" + input2 + \", expected output: \" + expected2 + \", actual output: \" + actual2","title":""},{"docid":"1834b43733d1b3135689aa0dee891c6e","score":"0.5565416","text":"def reverse_comp(dna):\n\treturn dna[::-1].translate(complement_table)","title":""},{"docid":"cda28b68f72bc0bdb8144181d45a8b5a","score":"0.55471283","text":"def get_reverse_complement(dna):\n reverse_seq = [];\n seqlength = len(dna);\n for i in range(seqlength):\n comp = get_complement(dna[i]);\n reverse_seq = [comp] + reverse_seq;\n delimiter = '';\n reverse_string = delimiter.join(reverse_seq);\n return reverse_string","title":""},{"docid":"34100ae68bb3bf9f4067f5b8343ef6c2","score":"0.5543102","text":"def antiparallel(sequence):\n if not sequence:\n return sequence\n \n return complement(sequence[::-1])","title":""},{"docid":"95c2ba8010d4ed0d1043a698e496ca47","score":"0.55184215","text":"def get_reverse_complement(dna):\n complement = ''\n r_complement = ''\n\n # compute the compliment dna string\n for i in range(len(dna)):\n complement = complement + get_complement(dna[i])\n\n return complement[::-1]","title":""},{"docid":"a5b58e72968f99af743b30eaa16de458","score":"0.5504031","text":"def _complement(self, simplex):\n return Simplex(set(self.vertices()).difference(simplex))","title":""},{"docid":"601dbb274b8038a3e9bbe88e351e9b42","score":"0.5478565","text":"def get_complement(nucleotide):\n\n # TODO: implement this\n if nucleotide == 'A':\n return 'T'\n elif nucleotide == 'C':\n return 'G'\n elif nucleotide == 'G':\n return 'C'\n elif nucleotide == 'T':\n return 'A'\n else:\n return 'Unknown'","title":""},{"docid":"edcc07dd250391fd811e1ab0d789ae68","score":"0.54752547","text":"def get_complement(nucleotide):\n\n pairs = {'A': 'T', 'C': 'G', 'T': 'A', 'G': 'C'}\n return pairs[nucleotide]\n pass","title":""},{"docid":"6f5315388f5ed06a30180099db2ae0d3","score":"0.5419198","text":"def get_complement(nucleotide):\n\n nucDict={'A':'T','G':'C','T':'A','C':'G'}\n return(nucDict[nucleotide])","title":""},{"docid":"e27afb8d73386e02ac60a6db26eb05c5","score":"0.54068613","text":"def complement(self) -> 'Variable':\n return type(self)(values=self.value.map(self.twos_comp),\n name=VAR_RESULT_NAME)","title":""},{"docid":"c101df8eeb1564f6f34a003f59a7a74e","score":"0.5381457","text":"def Reverse_Complement(x):\n\trc_list = [dna_codes[c] if c in dna_codes else 'N' if ord(c) < 91 else 'n' for c in reversed(x)]\n\treturn ''.join(rc_list)","title":""},{"docid":"c86aae5bc0deae8342f00809b111da32","score":"0.53718275","text":"def reverse_complement(args):\n p = OptionParser(reverse_complement.__doc__)\n opts, args = p.parse_args(args)\n\n if len(args) < 2:\n sys.exit(not p.print_help())\n\n complement_fasta, reverse_fasta = args\n\n with open(complement_fasta) as fa_in:\n with open(reverse_fasta, 'w') as fa_out:\n fa = SeqIO.parse(fa_in,\"fasta\")\n for record in fa:\n record.seq = Seq.reverse_complement(record.seq)\n SeqIO.write(record, fa_out, \"fasta\")","title":""},{"docid":"5b12484e36ebee89d80fa343192456eb","score":"0.5364162","text":"def complement(self):\n assert not self.is_frozen(), \"cannot modify frozen set\"\n icu.uset_complement(self._uset)","title":""},{"docid":"12169755e301652a11563f608bd4e7d3","score":"0.53563035","text":"def get_reverse_complement_unit_tests():\n \n tests = ['aaaaaa', 'gcgcgcgcgcgc', 'ttta','aaaacccc']\n expected = ['TTTTTT','GCGCGCGCGCGC','TAAA','GGGGTTTT']\n for i in range(len(tests)):\n result = get_reverse_complement(tests[i])\n print 'Input: ', tests[i], '\\n','Expected output: ', expected[i], 'Actual output: ', result,'\\n'","title":""},{"docid":"2a3e3569bc279590fa03b8ee1338faaf","score":"0.53544736","text":"def get_complement(nucleotide):\n #TODO use the list index trick\n #complements = ['T','G','A','C']\n #return complements[['A','C','T','G'].index(nucleotide)]\n if nucleotide == 'A':\n return 'T'\n elif nucleotide == 'T':\n return 'A'\n elif nucleotide == 'G':\n return 'C'\n elif nucleotide == 'C':\n return 'G'","title":""},{"docid":"89f106fde1de274cf708cfe4a02af36d","score":"0.5337708","text":"def reverse_comlement(seq):\n #doc string not working?\n return ''.join([DNA_ReverseComplement[nuc] for nuc in seq])[::-1]","title":""},{"docid":"69222bb3038376dcaf0f83403e8b54cb","score":"0.5332515","text":"def get_complementary_sequence(dna):\n\n complement = ''\n for char_index in range(len(dna)):\n if dna[char_index] == 'T':\n complement = complement + 'A'\n if dna[char_index] == 'A':\n complement = complement + 'T'\n if dna[char_index] == 'G':\n complement = complement + 'C'\n if dna[char_index] == 'C':\n complement = complement + 'G'\n \n return complement","title":""},{"docid":"8ef3a6677801c222c1b9fb45097eb8d3","score":"0.5320282","text":"def get_reverse_complement(sequence):\n return get_strand_complement(sequence)[::-1]","title":""},{"docid":"f82a86dbb8e17fabf0124be9eb37525c","score":"0.52877825","text":"def RevComp(seq):\n return seq[::-1].translate(str.maketrans('ATCG','TAGC'))","title":""},{"docid":"31b56a4a240e58bb004488471cea1860","score":"0.5285655","text":"def negate(clause):\n\n\t# negate each atom\n\tfor i, a in enumerate(clause):\n\t\tclause[i] = [negateAtom(a)]\n\n\t# return atoms, add each to kb\n\treturn clause","title":""},{"docid":"a1e6554f10c5ab406fd80464e7117829","score":"0.52774394","text":"def reverse_complement(dna):\n dna_reverse_complement = [None] * len(dna)\n i = len(dna) - 1\n for nb in dna:\n if nb in COMPLEMENTARY_NUCLEOTIDE_MAP:\n dna_reverse_complement[i] = COMPLEMENTARY_NUCLEOTIDE_MAP[nb]\n i -= 1\n else:\n raise ValueError(\"Invalid nucleotide base \\\"{}\\\" in DNA string \\\"{}\\\"\".format(nb, dna))\n return \"\".join(dna_reverse_complement)","title":""},{"docid":"12d4e127aa564b58f7dce7d3ae623573","score":"0.5269842","text":"def complemento_base(base):\n #desarrollo de la funcion\n complento = \"\"\n if (base == \"A\"):\n complento = complento + \"T\"\n elif (base == \"T\"):\n complento = complento + \"A\"\n elif (base == \"C\"):\n complento = complento + \"G\"\n else:\n complento = complento + \"C\"\n return complento","title":""},{"docid":"c1ca19ce15277b2cae16d90bf9e3a177","score":"0.5263336","text":"def reverse_complement_SeqRecord(record):\n return SeqRecord(seq = record.seq.reverse_complement(), \\\n id = record.id, description = \"reverse complement\" )","title":""},{"docid":"1600e7dbd4c88400ba53b9cceeb26499","score":"0.52608025","text":"def get_strand_complement(self):\n change = str.maketrans('ACGT', 'TGCA')\n return self.sequence.translate(change)","title":""},{"docid":"a333c691ce3832c82ca71521512047b8","score":"0.52557725","text":"def reverse_complement(sequence, strand_type='RNA'):\n if strand_type == 'RNA':\n sequence = convert_to_RNA(sequence)\n tempseq = sequence.replace('A', 'x')\n tempseq = tempseq.replace('U', 'A')\n tempseq = tempseq.replace('x', 'U')\n tempseq = tempseq.replace('G', 'x')\n tempseq = tempseq.replace('C', 'G')\n tempseq = tempseq.replace('x', 'C')\n sequence = tempseq[::-1]\n if strand_type == 'DNA':\n sequence = convert_to_DNA(sequence)\n tempseq = sequence.replace('A', 'x')\n tempseq = tempseq.replace('T', 'A')\n tempseq = tempseq.replace('x', 'T')\n tempseq = tempseq.replace('G', 'x')\n tempseq = tempseq.replace('C', 'G')\n tempseq = tempseq.replace('x', 'C')\n sequence = tempseq[::-1]\n return sequence","title":""},{"docid":"cecc78002b21c75867cb75ce748defd2","score":"0.525354","text":"def test():\n\n dnaseq = 'CCGGAAGAGcttacttag'\n dnarevcomp = dna_revcomp(dnaseq) \n if dnarevcomp != 'ctaagtaagCTCTTCCGG':\n raise ValueError, \"Oops...revcomp gave wrong answer\"\n\n print \"Reverse complement of %s is %s.\" % (\n dnaseq, dnarevcomp)","title":""},{"docid":"943ca9261fa2a88da8f34c39e5802e73","score":"0.5200643","text":"def remove_clausal_complement_verb_from_verbs_list(verb,verbs):\r\n \r\n for tok in list(verb.lefts) + list(verb.rights):\r\n if tok.dep_ == \"xcomp\":\r\n verbs.remove(tok)","title":""},{"docid":"eedbf807be7b8489cbc99c32c90e806d","score":"0.5187543","text":"def get_strand_complement(sequence):\n change = str.maketrans('ACGT', 'TGCA')\n return sequence.translate(change)","title":""},{"docid":"4ae06bcf4ff8f05c7b4ec6564dfeb688","score":"0.51800025","text":"def complement_nb(name: str, norm: typing.Union[bool, Apply] = None, **kwargs):\n\n def _name(msg):\n return f\"{name}.complement_nb_{msg}\"\n\n hp_space = _nb_hp_space(_name, **kwargs)\n hp_space[\"norm\"] = hp.choice(_name(\"norm\"), [True, False]) if norm is None else norm\n\n return scope.sklearn_ComplementNB(**hp_space)","title":""},{"docid":"f2f216cbe6a7bb2576b477bc03e50bed","score":"0.5167726","text":"def revcomp_str(seq):\n\n\tcomp = str.maketrans('ACGTRYMKWSBDHV', 'TGCAYRKMWSVHDB')\n\tanti = seq.translate(comp)[::-1]\n\treturn anti","title":""},{"docid":"942a4b3b565c9cefd785273bc36dda6f","score":"0.5144414","text":"def get_complement(nucleotide):\n nucl = ['A', 'C', 'G', 'T'];\n comps = ['T', 'G', 'C', 'A'];\n idx = nucl.index(nucleotide);\n return comps[idx];","title":""},{"docid":"46e67b710f3c9efc77cca8f30e6b335c","score":"0.5122441","text":"def conjugado_complejos(numero1):\r\n\r\n a = numero1[0]\r\n b = numero1[1]\r\n\r\n c = [a, -b]\r\n \r\n return c","title":""}],"string":"[\n {\n \"docid\": \"c0b9abd88068a80201d69b1a94761a78\",\n \"score\": \"0.741861\",\n \"text\": \"def complement(sequence):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e2c1796e75d6700dbbecd3b02784d5\",\n \"score\": \"0.7122255\",\n \"text\": \"def complement(seq):\\n seq = ''.join([_complement[s] for s in seq])\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702194e7315e1264a41f0ed27b32e73b\",\n \"score\": \"0.6778584\",\n \"text\": \"def complement(sequence):\\n return \\\"\\\".join(COMPLEMENT_MAP[x] for x in sequence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad701eddaa1f9ed5fdbdf58e094f504\",\n \"score\": \"0.67661643\",\n \"text\": \"def reverse_complement(seq):\\n #Initialize empty string\\n rev_comp = ''\\n #loop through seq and add to rev_seq\\n for base in reversed(seq):\\n rev_comp += complement_base(base)\\n return rev_comp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb02349df81e13bec8b59624d8fa5781\",\n \"score\": \"0.6578684\",\n \"text\": \"def reversecomplement(sequence):\\n \\n # IUPAC complimentary nucleotides. Dictionary used for speed, includes\\n # entries for UPPER and lower case.\\n IUPAC_NUC = {\\\"A\\\": \\\"T\\\", \\\"a\\\": \\\"t\\\", # A -> T\\n \\\"C\\\": \\\"G\\\", \\\"c\\\": \\\"g\\\", # C -> G\\n \\\"G\\\": \\\"C\\\", \\\"g\\\": \\\"c\\\", # G -> T\\n \\\"T\\\": \\\"A\\\", \\\"t\\\": \\\"a\\\", # T -> A\\n \\\"R\\\": \\\"Y\\\", \\\"r\\\": \\\"y\\\", # A or G -> T or C\\n \\\"Y\\\": \\\"R\\\", \\\"y\\\": \\\"r\\\", # C or T -> G or A\\n \\\"S\\\": \\\"S\\\", \\\"s\\\": \\\"s\\\", # G or C -> G or C\\n \\\"W\\\": \\\"W\\\", \\\"w\\\": \\\"w\\\", # A or T -> A or T\\n \\\"K\\\": \\\"M\\\", \\\"k\\\": \\\"m\\\", # G or T -> A or C\\n \\\"M\\\": \\\"K\\\", \\\"m\\\": \\\"k\\\", # A or C -> G or T\\n \\\"B\\\": \\\"V\\\", \\\"b\\\": \\\"v\\\", # C or G or T -> G or C or A\\n \\\"V\\\": \\\"B\\\", \\\"v\\\": \\\"b\\\", # G or C or A -> C or G or T\\n \\\"D\\\": \\\"H\\\", \\\"d\\\": \\\"h\\\", # A or G or T -> T or C or A\\n \\\"H\\\": \\\"D\\\", \\\"h\\\": \\\"d\\\", # T or C or A -> A or G or T\\n \\\"N\\\": \\\"N\\\", \\\"n\\\": \\\"n\\\", # any base\\n \\\"-\\\": \\\"-\\\"} # gap\\n revcomp = []\\n # compliment the sequence\\n for base in sequence:\\n # get the complimentary code, if one does not exist add 'x'\\n revcomp.append(IUPAC_NUC.get(base, \\\"x\\\"))\\n # reverse it\\n revcomp.reverse()\\n # return as a string rather than a list\\n return ''.join(revcomp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d088993dc1c836a5876ae377c45c38\",\n \"score\": \"0.6511946\",\n \"text\": \"def reverse_complement(sequence):\\n return complement(sequence[::-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea68c9f5239841e6cc2f9c31031a1212\",\n \"score\": \"0.65061605\",\n \"text\": \"def reverse_complement(sequence): ## function to get the reverse complement of a sequence\\n complement = {'A':'T','C':'G','G':'C','T':'A','N':'N'}\\n return \\\"\\\".join([complement.get(nt.upper(), '') for nt in sequence[::-1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcd2df49d2ee3f2241c65d487940fbcd\",\n \"score\": \"0.6495557\",\n \"text\": \"def complement(sequence):\\n if not sequence:\\n return sequence\\n \\n upseq = sequence.upper()\\n # is there a better way to do this? match object seems heavy\\n return re.sub(BASE_RE, lambda m: COMPLIMENTS[m.group(0)], upseq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"905a15925e449d55663140d48fea230c\",\n \"score\": \"0.64877456\",\n \"text\": \"def reverse_complement(seq, material = 'DNA'):\\n\\n #initialize reverse\\n rev_complement = ''\\n\\n for base in seq[::-1]:\\n rev_complement += complement(base)\\n\\n return rev_complement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5744d9d21ac4cf73ccaa30215358580\",\n \"score\": \"0.6468411\",\n \"text\": \"def complement(sequence):\\n return sequence.translate(DNA_Translation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00ba6e078c4d095a46f4f13250f19b1\",\n \"score\": \"0.6456894\",\n \"text\": \"def reverse_complement(self):\\n mapping = str.maketrans('ATCG', 'TAGC')\\n return self.seq.translate(mapping)[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccfebec2aeec849b778e23306929bb6b\",\n \"score\": \"0.6441931\",\n \"text\": \"def complement(s):\\n c = \\\"\\\".join(__complementTranslation[n] for n in s)\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa64eda570c9d1d9e445d48a272710da\",\n \"score\": \"0.64220285\",\n \"text\": \"def reverse_complement(seq):\\n return seq.translate(complements)[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8fad6910445766cc50f7cb510535f96\",\n \"score\": \"0.63891155\",\n \"text\": \"def reverse_complement(pattern):\\n reverse_complement = ''\\n for i in range(0,len(pattern)): #can we vectorize?\\n current = pattern[len(pattern)-i-1]\\n if(current == 'A'):\\n reverse_complement += 'T'\\n elif(current == 'T'):\\n reverse_complement += 'A'\\n elif(current == 'G'):\\n reverse_complement += 'C'\\n else:\\n reverse_complement += 'G'\\n return reverse_complement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9febeaf0e1fc9bde77b6b5eafae9cc8d\",\n \"score\": \"0.6330548\",\n \"text\": \"def reverseComplement(seq):\\n seq = seq[::-1] # reverse\\n seq = complement(seq)\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a3123b5a33ee9dfbfdd9bcf6852fb9a\",\n \"score\": \"0.6305401\",\n \"text\": \"def reverse_complement(table, seq):\\n return seq.translate(str.maketrans(\\\"ATGC\\\",\\\"TACG\\\"))[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d4e8ac3701c1c6573bb6cb89a0b6b2c\",\n \"score\": \"0.62578833\",\n \"text\": \"def reverse_complement(sequence):\\n return reverse(complement(sequence))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110ed809fd091fb80bbc62ad27c907fc\",\n \"score\": \"0.61952794\",\n \"text\": \"def reverse_complement(sequence):\\n forward = 'ACGTNacgtn'\\n reverse = 'TGCANtgcan'\\n try: # Type checking\\n assert isinstance(sequence, (str, list, tuple))\\n if isinstance(sequence, (list, tuple)):\\n for base in sequence:\\n assert isinstance(base, str)\\n assert len(base) is 1\\n for base in sequence:\\n assert base in forward or base in Lookup.IUPAC_CODES\\n except AssertionError:\\n raise TypeError(\\\"'sequence' must be of type 'str' or be a list or tuple of single-character 'str' objects within '%s' or 'RYSWKM'\\\" % forward)\\n else:\\n rc_table = str.maketrans(forward, reverse)\\n return ''.join(tuple(base.translate(rc_table) for base in sequence))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc51b71a7fbeb240b9afcb644d43c300\",\n \"score\": \"0.6157103\",\n \"text\": \"def get_reverse_complement_unit_tests():\\n print 'input: ATGCCCGCTTT, expected output: AAAGCGGGCAT, actual output:',\\n print get_reverse_complement(\\\"ATGCCCGCTTT\\\")\\n print 'input: CCGCGTTCA, expected output: TGAACGCGG, actual output:',\\n print get_reverse_complement(\\\"CCGCGTTCA\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd6ba77410cc375975ab48a87030891a\",\n \"score\": \"0.61516976\",\n \"text\": \"def get_complement(string):\\n string=string.replace('G','1')\\n string=string.replace('A','2')\\n string=string.replace('T','3')\\n string=string.replace('C','4')\\n string=string.replace('1','C')\\n string=string.replace('2','T')\\n string=string.replace('3','A')\\n string=string.replace('4','G')\\n string=string.replace('X','Z')#for 'blank' sticky ends and cinnamate\\n return string[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b624302c349d29482d9b53345055db\",\n \"score\": \"0.6151313\",\n \"text\": \"def reverse_complement(s):\\n\\n ret = ''\\n complement = {\\\"A\\\": \\\"T\\\", \\\"T\\\": \\\"A\\\", \\\"C\\\": \\\"G\\\", \\\"G\\\": \\\"C\\\", \\\"N\\\": \\\"N\\\", \\\"a\\\": \\\"t\\\", \\\"t\\\": \\\"a\\\", \\\"c\\\": \\\"g\\\", \\\"g\\\": \\\"c\\\", \\\"n\\\": \\\"n\\\"}\\n for base in s[::-1]:\\n ret += complement[base]\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262b77b39bbb17ac9752fca6e85898d6\",\n \"score\": \"0.6141288\",\n \"text\": \"def complement(self):\\n return self._complement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2345e36f171eb85e05f46d969292db6\",\n \"score\": \"0.6059809\",\n \"text\": \"def _complement(self):\\n if not all(elem.is_number for elem in self.elements):\\n raise ValueError(\\\"%s: Complement not defined for symbolic inputs\\\"\\n %self)\\n sorted_elements = sorted(list(self.elements))\\n\\n intervals = [] # Build up a list of intervals between the elements\\n intervals += [Interval(S.NegativeInfinity,sorted_elements[0],True,True)]\\n for a,b in zip(sorted_elements[0:-1], sorted_elements[1:]):\\n intervals.append(Interval(a,b, True, True)) # open intervals\\n intervals.append(Interval(sorted_elements[-1], S.Infinity, True, True))\\n return Union(*intervals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd3899d349decc47989612b5186123fd\",\n \"score\": \"0.60411805\",\n \"text\": \"def reverse_complement(self, **kwargs) -> \\\"SeqLike\\\":\\n if self._type == \\\"AA\\\":\\n raise ValueError(\\\"Proteins do not have complements!\\\")\\n\\n if hasattr(self, \\\"annotations\\\"):\\n annotations = self.annotations.copy()\\n else:\\n annotations = dict()\\n\\n if \\\"reversed\\\" in annotations:\\n annotations[\\\"reversed\\\"] = not annotations[\\\"reversed\\\"]\\n else:\\n annotations[\\\"reversed\\\"] = True\\n\\n _nt_record = self._nt_record.reverse_complement(\\n id=True, name=True, description=True, annotations=annotations, dbxrefs=True\\n )\\n\\n s = SeqLike(\\n _nt_record,\\n seq_type=self._type,\\n alphabet=self.alphabet,\\n codon_map=self.codon_map,\\n )\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d6873dae6b89da19ee63a866cea622\",\n \"score\": \"0.6022227\",\n \"text\": \"def get_reverse_complement(dna):\\n # TODO:\\n complement = ''\\n for i in dna:\\n complement = get_complement(i) + complement\\n return complement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff65a35b9657f80674db26cacd8eeda3\",\n \"score\": \"0.60204303\",\n \"text\": \"def complement(value):\\n return 0xFFFF - value + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3912cf9fb949611e73dfb3c92ff21361\",\n \"score\": \"0.6019483\",\n \"text\": \"def reverseComplement(s):\\n complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'}\\n t = ''\\n for base in s:\\n t = complement[base] + t\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369d54f5effbfa4fb02528154293ea3a\",\n \"score\": \"0.5992025\",\n \"text\": \"def reverse_complement(s):\\n s = s.upper()\\n assert is_DNA(s), \\\"Your sequence must be DNA!\\\"\\n\\n r = reverse(s)\\n rc = complement(r)\\n\\n return rc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2143d5d5561679815e6838f2dd8988ef\",\n \"score\": \"0.59663814\",\n \"text\": \"def get_reverse_complement(dna):\\n\\n answer = \\\"\\\"\\n for x in range(0, len(dna)):\\n answer = answer + get_complement(dna[len(dna) - x - 1])\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"163c6763d5aa8d83c57b533021ee23d3\",\n \"score\": \"0.5960762\",\n \"text\": \"def reverse_complement2(seq):\\n\\n #reversing the sequence\\n seq[::-1]\\n #could have made everything lower() to aviod the aliases and better allow to give out answers in RNA\\n\\n #exchanging the vocbulary\\n seq1 = seq.replace('Uu', 'F')\\n seq2 = seq1.replace('Tt', 'I')\\n seq3 = seq2.replace('Aa', 'V')\\n seq4 = seq3.replace('Gg', 'E')\\n seq5 = seq4.replace('Cc', 'S')\\n\\n #echanging back complementary vocabulary\\n seq6 = seq5.replace('F', 'A')\\n seq7 = seq6.replace('I', 'A')\\n seq8 = seq7.replace('V', 'T')\\n seq9 = seq8.replace('E', 'C')\\n seq10 = seq9.replace('S', 'G')\\n\\n return seq10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd7db49a611f9023c0d92435cdabc80\",\n \"score\": \"0.5924333\",\n \"text\": \"def reverse_complement(self, number):\\n number = ~number\\n result = 0x00\\n\\n for i in range(self.length):\\n result = (result << 2) | (number & 0x03)\\n number >>= 2\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"163ecda6ddc7583aea2e73477b36a30d\",\n \"score\": \"0.5920495\",\n \"text\": \"def get_complement(base):\\n\\n # iterates through a two dimensional list, returns the complement if the given base is equal to the first entry in the list\\n return ''.join([pair[1] for pair in [[\\\"A\\\", \\\"T\\\"], [\\\"T\\\", \\\"A\\\"], [\\\"G\\\", \\\"C\\\"], [\\\"C\\\", \\\"G\\\"]] if (base == pair[0])])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49db111a62e5463f2ae4141701933419\",\n \"score\": \"0.5919934\",\n \"text\": \"def reverse_complement(dna):\\n\\n # iterates backwards through the list and gets the complement of each letter in list\\n return ''.join([get_complement(dna[x]) for x in range(len(dna)-1, -1, -1)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16fbed5ff2c04a3c1f455c570e3d1495\",\n \"score\": \"0.59115165\",\n \"text\": \"def get_reverse_complement(dna):\\n s = ''\\n for c in dna:\\n s = get_complement(c) + s\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59f99300e113562e8265b48645d160c\",\n \"score\": \"0.58904934\",\n \"text\": \"def reverse_complement(dna_sequence):\\n complement = string.maketrans(\\\"ATCG\\\", \\\"TAGC\\\")\\n rev_comp = dna_sequence.translate(complement)[::-1]\\n return rev_comp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9dfd4c5e118612d86b7e1468cc120b\",\n \"score\": \"0.5884089\",\n \"text\": \"def get_reverse_complement(dna):\\n\\n reverse=dna[::-1]\\n revdna=''\\n for x in reverse:\\n \\trevnuc=get_complement(x)\\n \\trevdna+=revnuc\\n return revdna\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9542209b165b92de373650259570e58\",\n \"score\": \"0.58805555\",\n \"text\": \"def complement(dna):\\n dna_complement = [None]*len(dna)\\n i = 0\\n for nb in dna:\\n if nb in COMPLEMENTARY_NUCLEOTIDE_MAP:\\n dna_complement[i] = COMPLEMENTARY_NUCLEOTIDE_MAP[nb]\\n i += 1\\n else:\\n raise ValueError(\\\"Invalid nucleotide base \\\\\\\"{}\\\\\\\" in DNA string \\\\\\\"{}\\\\\\\"\\\".format(nb, dna))\\n return \\\"\\\".join(dna_complement)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671a47fc398d147b2bc61b805de816d1\",\n \"score\": \"0.588038\",\n \"text\": \"def reverse_complement(dna):\\n\\tfrom string import maketrans, translate\\n\\tcomp = translate(dna, maketrans('CGAT', 'GCTA'))\\n\\treturn comp[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e6177b22b2d0bce663fe302ebb6080\",\n \"score\": \"0.5878832\",\n \"text\": \"def get_reverse_complement(dna):\\n \\n dna=dna.upper()\\n for i in range(len(dna)):\\n if dna[i] != 'A' and dna[i] != 'T' and dna[i] != 'C' and dna[i] != 'G':\\n print 'Must input valid amino acids'\\n return\\n dna = dna[::-1]\\n result = ''\\n for i in range(len(dna)):\\n if dna[i] == 'A':\\n complement = 'T'\\n elif dna[i] == 'T':\\n complement = 'A'\\n elif dna[i] == 'G':\\n complement = 'C'\\n elif dna[i] == 'C':\\n complement = 'G'\\n result += complement\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7660b7e402e7cfb1088a829af30279\",\n \"score\": \"0.58681613\",\n \"text\": \"def self_and_rev_complement(in_dna):\\n all_possible = [in_dna]\\n # Get reverse complement\\n dna = Seq(in_dna, generic_dna)\\n rev_complement = str(dna.reverse_complement())\\n all_possible.append(rev_complement)\\n return(all_possible)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3eb07d463f04ff09f2b79c1c17cc676\",\n \"score\": \"0.5854894\",\n \"text\": \"def get_reverse_complement(dna):\\n i = 0\\n j = len(dna)\\n data = \\\"\\\"\\n temp = \\\"\\\"\\n\\n while i < j:\\n temp += get_complement(dna[i])\\n i += 1\\n i = 0\\n while j > 0:\\n i += 1\\n data += temp[j-1]\\n j -= 1\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2140d082c329d2ca8264be940633cea2\",\n \"score\": \"0.5839853\",\n \"text\": \"def get_reverse_complement(dna):\\n reverse = ''\\n complements = ''\\n for i in range(len(dna)):\\n reverse = reverse + dna[(len(dna)-1)-i]\\n for i in range(len(dna)):\\n if reverse[i] == 'A':\\n complements = complements + 'T'\\n elif reverse[i] == 'T':\\n complements = complements + 'A'\\n elif reverse[i] == 'C':\\n complements = complements + 'G'\\n elif reverse[i] == 'G':\\n complements = complements + 'C'\\n \\n return complements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18eb6edb24912d488eee5ee2b864602d\",\n \"score\": \"0.5838365\",\n \"text\": \"def get_reverse_complement(dna):\\n n = len(dna)\\n i = 0\\n reverse_dna = []\\n\\n for i in range(n):\\n reverse_dna.append (get_complement(dna[n - 1 - i]))\\n reverse_complement = ''.join(reverse_dna)\\n return reverse_complement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b31624562b26afcf75beac523b6d65a8\",\n \"score\": \"0.5829853\",\n \"text\": \"def get_reverse_complement(dna):\\n\\n rev_dna = ''\\n for i in dna:\\n rev_dna = get_complement(i) + rev_dna\\n return rev_dna\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52fef343f468e6846f3d775734e008b2\",\n \"score\": \"0.58165497\",\n \"text\": \"def reverse_complement(kmer):\\n complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A'}\\n return ''.join([complement[base] for base in kmer[::-1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5887ae46c3f89ffd1caa8863e8cd2db3\",\n \"score\": \"0.5801767\",\n \"text\": \"def get_complement(dna):\\n \\n # YOUR IMPLEMENTATION HERE\\n #import re\\n rvs_dna = dna[::-1]\\n rvs_cmpl = list(dna)\\n\\n #rep = {'A':'T','T':'A','G':'C','C':'G'}\\n for i in range(len(dna)):\\n if rvs_dna[i] == 'A':\\n rvs_cmpl[i] = 'T'\\n elif rvs_dna[i] == 'T':\\n rvs_cmpl[i] = 'A'\\n elif rvs_dna[i] == 'G':\\n rvs_cmpl[i] = 'C'\\n else:\\n rvs_cmpl[i] = 'G'\\n outcmp = ''.join(rvs_cmpl)\\n return outcmp\\n\\n #rep_dict = dict(rep)\\n #repfun = lambda match: rep_dict[match.group(0)]\\n #pattern = re.compile(\\\"|\\\".join([re.escape(i) for i, j in rep]), re.M)\\n #rvs_cmpl = lambda rvs_dna: pattern.sub(repfun, rvs_dna)\\n \\n \\n \\n \\n #for i,j in dic.iteritems():\\n #rvs_cmpl = rvs_dna.replace(i,j)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f215780d7d086e0d03681851b37d9bf\",\n \"score\": \"0.5800605\",\n \"text\": \"def get_complement(s):\\n\\n s = s.upper()\\n\\n new_string = \\\"\\\"\\n\\n for i in s:\\n if(i == 'A'):\\n new_string = new_string + 'T'\\n elif(i == 'T'):\\n new_string = new_string + 'A'\\n elif(i == 'G'):\\n new_string = new_string + 'C'\\n elif(i == 'C'):\\n new_string = new_string + 'G'\\n\\n return new_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60c616551b220e9c3cc44fe15ea137f\",\n \"score\": \"0.5787215\",\n \"text\": \"def get_reverse_complement(dna):\\n sequence = \\\"\\\"\\n for letter in dna: \\n complement = get_complement(letter)\\n sequence = sequence + complement \\n reverse_sequence = sequence[::-1]\\n return reverse_sequence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67fb026895f271c80d86091dc78787b9\",\n \"score\": \"0.57857114\",\n \"text\": \"def get_complement(nucleotide):\\n\\n if nucleotide is 'A':\\n return 'T'\\n if nucleotide is 'T':\\n return 'A'\\n if nucleotide is 'C':\\n return 'G'\\n if nucleotide is 'G':\\n return 'C'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64fb1197cf69c62766d38c2cd4afd873\",\n \"score\": \"0.57832575\",\n \"text\": \"def complement_base(base):\\n\\n if base in 'Aa':\\n return 'T'\\n elif base in 'Tt':\\n return 'A'\\n elif base in 'Gg':\\n return 'C'\\n else:\\n return 'G'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a2e2e3f2522edeb067001e4ab8302ee\",\n \"score\": \"0.57759184\",\n \"text\": \"def reverseComplement(seq, alphabet='ACGT'):\\n compl= dict(zip('ACGTNRYWSMKBHDV', 'TGCANYRWSKMVDHB'))\\n return ''.join([compl[base]\\n for base in seq.upper().replace('U', 'T')])[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0339d53d5e500fb603e7b4affdede283\",\n \"score\": \"0.5762521\",\n \"text\": \"def complement(self):\\n return Type([~f for f in self.primary])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b83ab631d35539d5d9ba252d411f85a0\",\n \"score\": \"0.5753111\",\n \"text\": \"def get_reverse_complement(s):\\n\\n s = s.upper()\\n\\n string_list = []\\n\\n for i in s:\\n if(i == 'A'):\\n string_list.append('T')\\n elif(i == 'T'):\\n string_list.append('A')\\n elif(i == 'G'):\\n string_list.append('C')\\n elif(i == 'C'):\\n string_list.append('G')\\n\\n reversed_string_list = string_list[::-1]\\n reversed_string = \\\"\\\".join(reversed_string_list)\\n\\n return reversed_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbe1975abe7d0887ff92d60ed0233ae\",\n \"score\": \"0.57054514\",\n \"text\": \"def get_complement(nucleotide):\\n if nucleotide == 'A':\\n return 'T'\\n elif nucleotide == 'T':\\n return 'A'\\n elif nucleotide == 'C':\\n return 'G'\\n elif nucleotide == 'G':\\n return 'C'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29a4d85e11d005986fbbbd9ee0e7c483\",\n \"score\": \"0.5703421\",\n \"text\": \"def get_complement(nucleotide):\\n\\n if nucleotide == 'A':\\n return 'T'\\n if nucleotide == 'C':\\n return 'G'\\n if nucleotide == 'T':\\n return 'A'\\n if nucleotide == 'G':\\n return 'C'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293ba73d74ac359d4c7402503287b028\",\n \"score\": \"0.56934303\",\n \"text\": \"def get_complement(nucleotide):\\n if nucleotide == \\\"A\\\":\\n return(\\\"T\\\")\\n elif nucleotide == \\\"C\\\":\\n return(\\\"G\\\")\\n elif nucleotide == \\\"T\\\":\\n return(\\\"A\\\")\\n elif nucleotide == \\\"G\\\":\\n return(\\\"C\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933e9b5d99c8f0052f03ddd107e2820f\",\n \"score\": \"0.5692761\",\n \"text\": \"def complement(base, material = 'DNA'):\\n # we will use this to complement each base individually\\n if base in 'Aa':\\n if material == 'DNA':\\n return 'T'\\n elif material == 'RNA':\\n return 'U'\\n elif base in 'UuTt':\\n return 'A'\\n elif base in 'Gg':\\n return 'C'\\n elif base in 'Cc':\\n return 'G'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2220b590143ebb3cd3d98a1e465cd5e\",\n \"score\": \"0.56878346\",\n \"text\": \"def test_reverse_complement(self):\\n background = {\\\"A\\\": 0.3, \\\"C\\\": 0.2, \\\"G\\\": 0.2, \\\"T\\\": 0.3}\\n pseudocounts = 0.5\\n m = motifs.create([Seq(\\\"ATATA\\\")])\\n m.background = background\\n m.pseudocounts = pseudocounts\\n received_forward = format(m, \\\"transfac\\\")\\n expected_forward = \\\"\\\"\\\"\\\\\\nP0 A C G T\\n01 1 0 0 0 A\\n02 0 0 0 1 T\\n03 1 0 0 0 A\\n04 0 0 0 1 T\\n05 1 0 0 0 A\\nXX\\n//\\n\\\"\\\"\\\"\\n self.assertEqual(received_forward, expected_forward)\\n expected_forward_pwm = \\\"\\\"\\\"\\\\\\n 0 1 2 3 4\\nA: 0.50 0.17 0.50 0.17 0.50\\nC: 0.17 0.17 0.17 0.17 0.17\\nG: 0.17 0.17 0.17 0.17 0.17\\nT: 0.17 0.50 0.17 0.50 0.17\\n\\\"\\\"\\\"\\n self.assertEqual(str(m.pwm), expected_forward_pwm)\\n m = m.reverse_complement()\\n received_reverse = format(m, \\\"transfac\\\")\\n expected_reverse = \\\"\\\"\\\"\\\\\\nP0 A C G T\\n01 0 0 0 1 T\\n02 1 0 0 0 A\\n03 0 0 0 1 T\\n04 1 0 0 0 A\\n05 0 0 0 1 T\\nXX\\n//\\n\\\"\\\"\\\"\\n self.assertEqual(received_reverse, expected_reverse)\\n expected_reverse_pwm = \\\"\\\"\\\"\\\\\\n 0 1 2 3 4\\nA: 0.17 0.50 0.17 0.50 0.17\\nC: 0.17 0.17 0.17 0.17 0.17\\nG: 0.17 0.17 0.17 0.17 0.17\\nT: 0.50 0.17 0.50 0.17 0.50\\n\\\"\\\"\\\"\\n self.assertEqual(str(m.pwm), expected_reverse_pwm)\\n # Same thing, but now start with a motif calculated from a count matrix\\n m = motifs.create([Seq(\\\"ATATA\\\")])\\n counts = m.counts\\n m = motifs.Motif(counts=counts)\\n m.background = background\\n m.pseudocounts = pseudocounts\\n received_forward = format(m, \\\"transfac\\\")\\n self.assertEqual(received_forward, expected_forward)\\n self.assertEqual(str(m.pwm), expected_forward_pwm)\\n m = m.reverse_complement()\\n received_reverse = format(m, \\\"transfac\\\")\\n self.assertEqual(received_reverse, expected_reverse)\\n self.assertEqual(str(m.pwm), expected_reverse_pwm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9116f072d52c4d5e607bbc96a06d294\",\n \"score\": \"0.56758195\",\n \"text\": \"def get_reverse_complement(dna):\\n reverse_complement = \\\"\\\"\\n\\n for character in dna[::-1]:\\n if character == \\\"A\\\":\\n reverse_complement += \\\"T\\\"\\n elif character == \\\"T\\\":\\n reverse_complement += \\\"A\\\"\\n elif character == \\\"C\\\":\\n reverse_complement += \\\"G\\\"\\n elif character == \\\"G\\\":\\n reverse_complement += \\\"C\\\"\\n\\n return reverse_complement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd0a6c7df7d61a6924f9db45a0cb247\",\n \"score\": \"0.56448746\",\n \"text\": \"def get_reverse_complement(dna):\\n \\n # YOUR IMPLEMENTATION BELOW\\n rev_comp = \\\"\\\"; #rev_comp is the returned string that is updated each loop\\n for x in range(0,len(dna)):\\n if dna[x] == 'A': rev_comp +='T'\\n elif dna[x] == 'T': rev_comp += 'A'\\n elif dna[x] == 'G': rev_comp += 'C'\\n elif dna[x] == 'C': rev_comp += 'G'\\n rev_comp = rev_comp[::-1] #reverses the string\\n return rev_comp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d741c8ffdae441a80c5ef6fb35085d5\",\n \"score\": \"0.5642061\",\n \"text\": \"def get_reverse_complement(dna):\\n comp = ''\\n for letter in dna:\\n if get_complement(letter) == 'Error, Invalid Input':\\n return('Error, Invalid Input')\\n else:\\n comp = get_complement(letter) + comp\\n return comp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c5e8d04a25d38b67e0be21c6b5c95ea\",\n \"score\": \"0.5631098\",\n \"text\": \"def get_complement_unit_tests():\\n \\n # YOUR IMPLEMENTATION HERE\\n rvscheck1 = \\\"CAAATTCGT\\\"\\n out_hyp1 = \\\"ACGAATTTG\\\"\\n out_act1 = get_complement(rvscheck1)\\n print \\\"input: \\\" + rvscheck1 +\\\", \\\" +\\\"expected output: \\\"\\n print out_hyp1 + \\\", actual output: \\\"\\n print out_act1\\n \\n rvscheck2 = \\\"CTTGTTCCTTAT\\\"\\n out_hyp2 = \\\"ATAAGGAACAAG\\\"\\n out_act2 = get_complement(rvscheck2)\\n print \\\"input: \\\" + rvscheck2 +\\\", \\\" +\\\"expected output: \\\"\\n print out_hyp2 + \\\", actual output: \\\"\\n print out_act2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc165d6059a8c80837e7a3be995a9cdb\",\n \"score\": \"0.5608422\",\n \"text\": \"def get_complement(nucleotide):\\n\\n if nucleotide == 'A':\\n return 'T'\\n elif nucleotide == 'T':\\n return 'A'\\n elif nucleotide == 'G':\\n return 'C'\\n elif nucleotide == 'C':\\n return 'G'\\n else:\\n return 'Error, Invalid Input'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52ca695781e1f240d7e4216ae835f2a6\",\n \"score\": \"0.56059504\",\n \"text\": \"def get_complement(nucleotide):\\n if nucleotide == 'A':\\n return 'T'\\n elif nucleotide == 'C':\\n return 'G'\\n elif nucleotide == 'G':\\n return 'C'\\n elif nucleotide == 'T':\\n return 'A'\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"037a8c910c5e0c7e2ba9a46e76322bef\",\n \"score\": \"0.55957747\",\n \"text\": \"def get_complement(dna):\\n\\n complement = ''\\n for char_index in range(len(dna)):\\n if dna[char_index] == 'T':\\n complement = complement + 'A'\\n elif dna[char_index] == 'A':\\n complement = complement + 'T'\\n elif dna[char_index] == 'G':\\n complement = complement + 'C'\\n else:\\n dna[char_index] == 'C'\\n complement = complement + 'G'\\n \\n return complement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d327533db5b4b7c476cad41c2784580a\",\n \"score\": \"0.5586668\",\n \"text\": \"def get_complement(self, total_intv):\\n # type: (Tuple[int, int]) -> IntervalSet\\n return IntervalSet(intv_list=self.complement_iter(total_intv))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d4b73722b1f7c3f8c39159ce285cc4c\",\n \"score\": \"0.55858916\",\n \"text\": \"def reverse_complement(self) -> str:\\n\\n return self.get_reverse_complement(self.sequence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4900827cff3536a838c18dff950b3b06\",\n \"score\": \"0.55817723\",\n \"text\": \"def get_complement(nucleotide):\\n if nucleotide == 'A':\\n return 'T'\\n elif nucleotide == 'C':\\n return 'G'\\n elif nucleotide == 'T':\\n return 'A'\\n else: #if nucleotide = G\\n return 'C'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943cb2032dee953ab7c482017b39d79a\",\n \"score\": \"0.55724967\",\n \"text\": \"def get_reverse_complement_unit_tests():\\n \\n # YOUR IMPLEMENTATION BELOW\\n input1 = \\\"ATGCCCTTTGC\\\"\\n input2 = \\\"CCGCGTTCA\\\"\\n expected1 = 'GCAAAGGGCAT' \\n expected2 = 'TGAACGCGG'\\n \\n actual1 = get_reverse_complement(input1)\\n actual2 = get_reverse_complement(input2) \\n\\n print \\\"GET_REVERSE input: \\\" + input1 + \\\", expected output: \\\" + expected1 + \\\", actual output: \\\" + actual1\\n print \\\"GET_ REVERSE input: \\\" + input2 + \\\", expected output: \\\" + expected2 + \\\", actual output: \\\" + actual2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1834b43733d1b3135689aa0dee891c6e\",\n \"score\": \"0.5565416\",\n \"text\": \"def reverse_comp(dna):\\n\\treturn dna[::-1].translate(complement_table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cda28b68f72bc0bdb8144181d45a8b5a\",\n \"score\": \"0.55471283\",\n \"text\": \"def get_reverse_complement(dna):\\n reverse_seq = [];\\n seqlength = len(dna);\\n for i in range(seqlength):\\n comp = get_complement(dna[i]);\\n reverse_seq = [comp] + reverse_seq;\\n delimiter = '';\\n reverse_string = delimiter.join(reverse_seq);\\n return reverse_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34100ae68bb3bf9f4067f5b8343ef6c2\",\n \"score\": \"0.5543102\",\n \"text\": \"def antiparallel(sequence):\\n if not sequence:\\n return sequence\\n \\n return complement(sequence[::-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95c2ba8010d4ed0d1043a698e496ca47\",\n \"score\": \"0.55184215\",\n \"text\": \"def get_reverse_complement(dna):\\n complement = ''\\n r_complement = ''\\n\\n # compute the compliment dna string\\n for i in range(len(dna)):\\n complement = complement + get_complement(dna[i])\\n\\n return complement[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b58e72968f99af743b30eaa16de458\",\n \"score\": \"0.5504031\",\n \"text\": \"def _complement(self, simplex):\\n return Simplex(set(self.vertices()).difference(simplex))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"601dbb274b8038a3e9bbe88e351e9b42\",\n \"score\": \"0.5478565\",\n \"text\": \"def get_complement(nucleotide):\\n\\n # TODO: implement this\\n if nucleotide == 'A':\\n return 'T'\\n elif nucleotide == 'C':\\n return 'G'\\n elif nucleotide == 'G':\\n return 'C'\\n elif nucleotide == 'T':\\n return 'A'\\n else:\\n return 'Unknown'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edcc07dd250391fd811e1ab0d789ae68\",\n \"score\": \"0.54752547\",\n \"text\": \"def get_complement(nucleotide):\\n\\n pairs = {'A': 'T', 'C': 'G', 'T': 'A', 'G': 'C'}\\n return pairs[nucleotide]\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f5315388f5ed06a30180099db2ae0d3\",\n \"score\": \"0.5419198\",\n \"text\": \"def get_complement(nucleotide):\\n\\n nucDict={'A':'T','G':'C','T':'A','C':'G'}\\n return(nucDict[nucleotide])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27afb8d73386e02ac60a6db26eb05c5\",\n \"score\": \"0.54068613\",\n \"text\": \"def complement(self) -> 'Variable':\\n return type(self)(values=self.value.map(self.twos_comp),\\n name=VAR_RESULT_NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c101df8eeb1564f6f34a003f59a7a74e\",\n \"score\": \"0.5381457\",\n \"text\": \"def Reverse_Complement(x):\\n\\trc_list = [dna_codes[c] if c in dna_codes else 'N' if ord(c) < 91 else 'n' for c in reversed(x)]\\n\\treturn ''.join(rc_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86aae5bc0deae8342f00809b111da32\",\n \"score\": \"0.53718275\",\n \"text\": \"def reverse_complement(args):\\n p = OptionParser(reverse_complement.__doc__)\\n opts, args = p.parse_args(args)\\n\\n if len(args) < 2:\\n sys.exit(not p.print_help())\\n\\n complement_fasta, reverse_fasta = args\\n\\n with open(complement_fasta) as fa_in:\\n with open(reverse_fasta, 'w') as fa_out:\\n fa = SeqIO.parse(fa_in,\\\"fasta\\\")\\n for record in fa:\\n record.seq = Seq.reverse_complement(record.seq)\\n SeqIO.write(record, fa_out, \\\"fasta\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b12484e36ebee89d80fa343192456eb\",\n \"score\": \"0.5364162\",\n \"text\": \"def complement(self):\\n assert not self.is_frozen(), \\\"cannot modify frozen set\\\"\\n icu.uset_complement(self._uset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12169755e301652a11563f608bd4e7d3\",\n \"score\": \"0.53563035\",\n \"text\": \"def get_reverse_complement_unit_tests():\\n \\n tests = ['aaaaaa', 'gcgcgcgcgcgc', 'ttta','aaaacccc']\\n expected = ['TTTTTT','GCGCGCGCGCGC','TAAA','GGGGTTTT']\\n for i in range(len(tests)):\\n result = get_reverse_complement(tests[i])\\n print 'Input: ', tests[i], '\\\\n','Expected output: ', expected[i], 'Actual output: ', result,'\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a3e3569bc279590fa03b8ee1338faaf\",\n \"score\": \"0.53544736\",\n \"text\": \"def get_complement(nucleotide):\\n #TODO use the list index trick\\n #complements = ['T','G','A','C']\\n #return complements[['A','C','T','G'].index(nucleotide)]\\n if nucleotide == 'A':\\n return 'T'\\n elif nucleotide == 'T':\\n return 'A'\\n elif nucleotide == 'G':\\n return 'C'\\n elif nucleotide == 'C':\\n return 'G'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89f106fde1de274cf708cfe4a02af36d\",\n \"score\": \"0.5337708\",\n \"text\": \"def reverse_comlement(seq):\\n #doc string not working?\\n return ''.join([DNA_ReverseComplement[nuc] for nuc in seq])[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69222bb3038376dcaf0f83403e8b54cb\",\n \"score\": \"0.5332515\",\n \"text\": \"def get_complementary_sequence(dna):\\n\\n complement = ''\\n for char_index in range(len(dna)):\\n if dna[char_index] == 'T':\\n complement = complement + 'A'\\n if dna[char_index] == 'A':\\n complement = complement + 'T'\\n if dna[char_index] == 'G':\\n complement = complement + 'C'\\n if dna[char_index] == 'C':\\n complement = complement + 'G'\\n \\n return complement\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef3a6677801c222c1b9fb45097eb8d3\",\n \"score\": \"0.5320282\",\n \"text\": \"def get_reverse_complement(sequence):\\n return get_strand_complement(sequence)[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f82a86dbb8e17fabf0124be9eb37525c\",\n \"score\": \"0.52877825\",\n \"text\": \"def RevComp(seq):\\n return seq[::-1].translate(str.maketrans('ATCG','TAGC'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b56a4a240e58bb004488471cea1860\",\n \"score\": \"0.5285655\",\n \"text\": \"def negate(clause):\\n\\n\\t# negate each atom\\n\\tfor i, a in enumerate(clause):\\n\\t\\tclause[i] = [negateAtom(a)]\\n\\n\\t# return atoms, add each to kb\\n\\treturn clause\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e6554f10c5ab406fd80464e7117829\",\n \"score\": \"0.52774394\",\n \"text\": \"def reverse_complement(dna):\\n dna_reverse_complement = [None] * len(dna)\\n i = len(dna) - 1\\n for nb in dna:\\n if nb in COMPLEMENTARY_NUCLEOTIDE_MAP:\\n dna_reverse_complement[i] = COMPLEMENTARY_NUCLEOTIDE_MAP[nb]\\n i -= 1\\n else:\\n raise ValueError(\\\"Invalid nucleotide base \\\\\\\"{}\\\\\\\" in DNA string \\\\\\\"{}\\\\\\\"\\\".format(nb, dna))\\n return \\\"\\\".join(dna_reverse_complement)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12d4e127aa564b58f7dce7d3ae623573\",\n \"score\": \"0.5269842\",\n \"text\": \"def complemento_base(base):\\n #desarrollo de la funcion\\n complento = \\\"\\\"\\n if (base == \\\"A\\\"):\\n complento = complento + \\\"T\\\"\\n elif (base == \\\"T\\\"):\\n complento = complento + \\\"A\\\"\\n elif (base == \\\"C\\\"):\\n complento = complento + \\\"G\\\"\\n else:\\n complento = complento + \\\"C\\\"\\n return complento\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1ca19ce15277b2cae16d90bf9e3a177\",\n \"score\": \"0.5263336\",\n \"text\": \"def reverse_complement_SeqRecord(record):\\n return SeqRecord(seq = record.seq.reverse_complement(), \\\\\\n id = record.id, description = \\\"reverse complement\\\" )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1600e7dbd4c88400ba53b9cceeb26499\",\n \"score\": \"0.52608025\",\n \"text\": \"def get_strand_complement(self):\\n change = str.maketrans('ACGT', 'TGCA')\\n return self.sequence.translate(change)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a333c691ce3832c82ca71521512047b8\",\n \"score\": \"0.52557725\",\n \"text\": \"def reverse_complement(sequence, strand_type='RNA'):\\n if strand_type == 'RNA':\\n sequence = convert_to_RNA(sequence)\\n tempseq = sequence.replace('A', 'x')\\n tempseq = tempseq.replace('U', 'A')\\n tempseq = tempseq.replace('x', 'U')\\n tempseq = tempseq.replace('G', 'x')\\n tempseq = tempseq.replace('C', 'G')\\n tempseq = tempseq.replace('x', 'C')\\n sequence = tempseq[::-1]\\n if strand_type == 'DNA':\\n sequence = convert_to_DNA(sequence)\\n tempseq = sequence.replace('A', 'x')\\n tempseq = tempseq.replace('T', 'A')\\n tempseq = tempseq.replace('x', 'T')\\n tempseq = tempseq.replace('G', 'x')\\n tempseq = tempseq.replace('C', 'G')\\n tempseq = tempseq.replace('x', 'C')\\n sequence = tempseq[::-1]\\n return sequence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cecc78002b21c75867cb75ce748defd2\",\n \"score\": \"0.525354\",\n \"text\": \"def test():\\n\\n dnaseq = 'CCGGAAGAGcttacttag'\\n dnarevcomp = dna_revcomp(dnaseq) \\n if dnarevcomp != 'ctaagtaagCTCTTCCGG':\\n raise ValueError, \\\"Oops...revcomp gave wrong answer\\\"\\n\\n print \\\"Reverse complement of %s is %s.\\\" % (\\n dnaseq, dnarevcomp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943ca9261fa2a88da8f34c39e5802e73\",\n \"score\": \"0.5200643\",\n \"text\": \"def remove_clausal_complement_verb_from_verbs_list(verb,verbs):\\r\\n \\r\\n for tok in list(verb.lefts) + list(verb.rights):\\r\\n if tok.dep_ == \\\"xcomp\\\":\\r\\n verbs.remove(tok)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eedbf807be7b8489cbc99c32c90e806d\",\n \"score\": \"0.5187543\",\n \"text\": \"def get_strand_complement(sequence):\\n change = str.maketrans('ACGT', 'TGCA')\\n return sequence.translate(change)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae06bcf4ff8f05c7b4ec6564dfeb688\",\n \"score\": \"0.51800025\",\n \"text\": \"def complement_nb(name: str, norm: typing.Union[bool, Apply] = None, **kwargs):\\n\\n def _name(msg):\\n return f\\\"{name}.complement_nb_{msg}\\\"\\n\\n hp_space = _nb_hp_space(_name, **kwargs)\\n hp_space[\\\"norm\\\"] = hp.choice(_name(\\\"norm\\\"), [True, False]) if norm is None else norm\\n\\n return scope.sklearn_ComplementNB(**hp_space)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2f216cbe6a7bb2576b477bc03e50bed\",\n \"score\": \"0.5167726\",\n \"text\": \"def revcomp_str(seq):\\n\\n\\tcomp = str.maketrans('ACGTRYMKWSBDHV', 'TGCAYRKMWSVHDB')\\n\\tanti = seq.translate(comp)[::-1]\\n\\treturn anti\",\n \"title\": \"\"\n },\n {\n \"docid\": \"942a4b3b565c9cefd785273bc36dda6f\",\n \"score\": \"0.5144414\",\n \"text\": \"def get_complement(nucleotide):\\n nucl = ['A', 'C', 'G', 'T'];\\n comps = ['T', 'G', 'C', 'A'];\\n idx = nucl.index(nucleotide);\\n return comps[idx];\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e67b710f3c9efc77cca8f30e6b335c\",\n \"score\": \"0.5122441\",\n \"text\": \"def conjugado_complejos(numero1):\\r\\n\\r\\n a = numero1[0]\\r\\n b = numero1[1]\\r\\n\\r\\n c = [a, -b]\\r\\n \\r\\n return c\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992983,"cells":{"query_id":{"kind":"string","value":"dbf20236f2fd6d0052a70ddb05755e6e"},"query":{"kind":"string","value":"Draws text on a given surface, but does not flip() the pygame display"},"positive_passages":{"kind":"list like","value":[{"docid":"106742215e118bae2e9e5ae015c375ef","score":"0.7643315","text":"def draw_text(surf, text, size, x, y):\n font = pygame.font.Font(font_name, size)\n text_surface = font.render(text, True, WHITE)\n text_rect = text_surface.get_rect()\n text_rect.midtop = (x, y)\n surf.blit(text_surface, text_rect)","title":""}],"string":"[\n {\n \"docid\": \"106742215e118bae2e9e5ae015c375ef\",\n \"score\": \"0.7643315\",\n \"text\": \"def draw_text(surf, text, size, x, y):\\n font = pygame.font.Font(font_name, size)\\n text_surface = font.render(text, True, WHITE)\\n text_rect = text_surface.get_rect()\\n text_rect.midtop = (x, y)\\n surf.blit(text_surface, text_rect)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"202ecba47fcea0d6df43104a67c2964a","score":"0.7655349","text":"def draw_text(surf, text, size, x, y):\n font = pygame.font.Font(FONT_NAME, size)\n text_surface = font.render(text, True, WHITE)\n text_rect = text_surface.get_rect()\n text_rect.midtop = (x, y)\n surf.blit(text_surface, text_rect)","title":""},{"docid":"7bc9956b2b618d48d0bfbc0245fe2a6f","score":"0.75433457","text":"def draw_text(self, display, text, size, x, y, mode):\n font = pygame.font.Font(self.font_name, size)\n text_surface = font.render(text, True, (0,0,0))\n text_rect = text_surface.get_rect()\n if mode == \"left\":\n text_rect.topleft = (x,y)\n elif mode == \"center\":\n text_rect.center = (x, y)\n display.blit(text_surface, text_rect)","title":""},{"docid":"d5f87c699804b8b92f2ccc3b502bf365","score":"0.74932194","text":"def message_display(horiz, vert, text_size, text):\r\n print_text = pygame.font.Font(\"fixedsys.ttf\", text_size)\r\n text_surface = print_text.render(text, True, colors[\"black\"])\r\n text_rect = text_surface.get_rect()\r\n text_rect.center = (horiz, vert)\r\n gameDisplay.blit(text_surface, text_rect)","title":""},{"docid":"2f70754fcb422135ac48e78746e2795c","score":"0.745215","text":"def draw(self):\n self.surface = self.font.render(self.text, False, self.color)\n self.center()\n self.screen.blit(self.surface, (self.x, self.y))","title":""},{"docid":"8cbceaad3fb23bbca341083ecbdfe741","score":"0.7374574","text":"def draw_text(self, text):\n surface = self.font.render(text, True, (0, 0, 0)) \n self._screen.blit(surface, (25, 25))","title":""},{"docid":"5d90a7c7dc2f249f434600d6f989dc38","score":"0.73461396","text":"def draw_text(self, display: Surface):\n text_surf, text_rect = self.render_text()\n center_x = (self.rect.left + (self.rect.width / 2))\n center_y = (self.rect.top + (self.rect.height / 2))\n text_rect.center = (center_x, center_y)\n display.blit(text_surf, text_rect)","title":""},{"docid":"ef30ae9060d01b963abdb4a1eb07999a","score":"0.7320974","text":"def draw(self, screen: pygame.surface.Surface):\n\n for line in self.rendered_text:\n if line != None:\n screen.blit(line[0], line[1])","title":""},{"docid":"d32265bdf48122abc4371780c9544f14","score":"0.73008966","text":"def print_surface(self, msg, surface, topleft, font_obj):\r\n # Surface containing game over text\r\n # Do not anti-alias, render in white\r\n msg_surface = font_obj.render(msg, True, white)\r\n # Create rect object to specify where to place text\r\n msg_rect = msg_surface.get_rect()\r\n msg_rect.topleft = topleft\r\n surface.blit(msg_surface, msg_rect)","title":""},{"docid":"b4cfe086597ba1e49e7ec20bbf2cc575","score":"0.7234013","text":"def draw_text(self, text):\n old_pos = text.get_old_pos()\n pos = text.get_pos()\n\n rects = []\n rects.append(old_pos)\n\n filler = pygame.Surface((self.screen_width, self.screen_height))\n filler.fill(self.black)\n\n self.background.blit(filler, old_pos, old_pos)\n\n rects.append(pos)\n\n self.background.blit(text.get_text(), pos)\n self.screen.blit(self.background, self.screen_pos)\n\n pygame.display.update(rects)","title":""},{"docid":"6519dcef6597c377d668d46fd8a82c76","score":"0.72224784","text":"def show_text(window,text,color):\r\n font = pygame.freetype.SysFont(\"Courier\", 30)\r\n text_surface, _ = font.render(text, color)\r\n text_rect = text_surface.get_rect(center = (WIDTH//2,HEIGHT//2-50))\r\n window.blit(text_surface, text_rect)","title":""},{"docid":"4497dc7a76ee13f7431bcbc1b51038d6","score":"0.7202334","text":"def display_message(text):\n largeText = pygame.font.Font('freesansbold.ttf', 115) # define font, size\n TextSurf, TextRect = text_objects(text, largeText) # get image, wireframe\n TextRect.center = ((g.gameWidth / 2), (g.gameHeight / 2)) # change wirefrawme position\n gameDisplay.blit(TextSurf, TextRect) # show image on wireframe\n pygame.display.update() # notify pygame there was an update\n time.sleep(2) # wait for 2 seconds","title":""},{"docid":"f6fc2dbf61a05705a3a75b9441d64fb7","score":"0.71537554","text":"def draw(self, surface): \n \n if self.state != None:\n self.state.draw(surface);\n else:\n surface.fill((255,255,255))\n # Draw title\n title_text = get_asset(\"TitleFont\").render(\"The Chase\", True,\n (0, 0, 0)) \n title_position = \\\n (((surface.get_width() - title_text.get_width()) / 2), 100)\n surface.blit(title_text, title_position) \n item_font = get_asset(\"MenuFont\"); \n version_text = item_font.render(get_asset(\"Version\")[:-1], False,\n (0, 0, 0))\n version_position = (100, 400)\n surface.blit(version_text, version_position) \n\n for item_number, item in enumerate(get_asset(\"AboutText\")):\n item_text = item_font.render(item[:-1],False, (0,0,0));\n item_position = \\\n (((surface.get_width() - item_text.get_width()) / 2), \n 460 + (item_text.get_height() + 10) * item_number)\n surface.blit(item_text, item_position);","title":""},{"docid":"a3524f29c46991e51bd1753c950196f0","score":"0.7147775","text":"def draw(self, surface: Union[pygame.Surface, pygame.SurfaceType], padding: int = 5) -> None:\n if self._visible:\n # draw background ----------------------------------------------\n if self._bg_col is not None:\n pygame.draw.rect(surface, self._bg_col,\n pygame.Rect(self._rect.x,\n self._rect.y,\n self._rect.width,\n self._rect.height))\n\n # Draw custom function\n if self._draw_func is not None:\n self._draw_func(surface,\n self._rect.x, self._rect.y,\n self._rect.width, self._rect.height,\n self.text)\n\n # draw text ---------------------------------------------------\n width, height = self._text_surface.get_size()\n # bottom left align\n if self._txt_align == 0:\n surface.blit(self._text_surface, (self._rect.x + self._rect.width / 15,\n max(self._rect.y,\n self._rect.y + self._rect.height - height)),\n pygame.Rect(0, max(0, height - self._rect.height),\n self._rect.width, self._rect.height))\n # center align\n elif self._txt_align == 1:\n surface.blit(self._text_surface,\n (max(self._rect.x, (self._rect.width - width) // 2 + self._rect.x),\n max(self._rect.y,\n self._rect.y + (self._rect.height - height) // 2)),\n pygame.Rect(max(0, (width - self._rect.width) // 2),\n max(0, (height - self._rect.height) // 2),\n self._rect.width, self._rect.height))\n # bottom right align\n elif self._txt_align == 2:\n surface.blit(self._text_surface,\n (max(self._rect.x, self._rect.x + self._rect.width - width)\n - self._rect.width / 15,\n max(self._rect.y, self._rect.y + self._rect.height - height)),\n pygame.Rect(max(0, width - self._rect.width),\n max(0, height - self._rect.height),\n self._rect.width, self._rect.height))\n # top left align\n elif self._txt_align == 3:\n surface.blit(self._text_surface, (self._rect.x + padding,\n self._rect.y + padding))","title":""},{"docid":"e6cd94c10beb85c75db79b467624cd6f","score":"0.71426034","text":"def display_text(window, font, txt, x, y, color=\"white\"):\n tr = font.render(str(txt), 1, pygame.Color(color))\n window.blit(tr, (x, y))","title":""},{"docid":"4c9383bc2eccda3687e17a2b69fa916f","score":"0.7077754","text":"def draw_text(screen, text, position, color=(0,0,0),\n font_type='mono', font_size = 150,\n background_color=(0,125,0)):\n screen.fill(background_color,position)\n font = pygame.font.SysFont(font_type, font_size)\n surface = font.render(text, True, color)\n surface = pygame.transform.scale(surface,(position[2],position[3]))\n screen.blit(surface, position)\n pygame.display.flip()","title":""},{"docid":"8120388a4dad99914c68fc77130567a4","score":"0.7015071","text":"def DrawText(text, size, color, pos, surface, align_flag=0, outline=0,\n outline_color=0, width=-1, effect=0, effectPer=1.0, rectDim=None,\n font_filename=None):\n # Initialize the offset\n offset = [0, 0]\n\n # Initialize counter\n count = 0\n \n if font_filename == None:\n font_filename = DEFAULT_FONT\n \n # If there is no font system, return\n if not pygame.font:\n raise Exception('Pygame Fonts not found')\n \n # If there is no string, return\n if text == '':\n return offset\n \n font = GetFont(font_filename, size)\n \n # Determine the rect size. May have been passed an argument, otherwise use the surface\n if rectDim is None:\n rectDim = (0, 0, surface.get_width(), surface.get_height())\n \n # Split the text into lines\n if text.find(\"\\n\", 0 ) != -1:\n lines = text.split(\"\\n\")\n else:\n lines = text.split(\"\\\\n\")\n \n # There is an effect\n if effect != 0:\n # If effect is vertical stretch\n if effect == 1:\n # If the percentage is over half\n if effectPer > 0.5:\n # Invert effect percentage, so it goes down again\n effectPer = 0.5 - (effectPer - 0.5)\n # Find scale\n scale = 1.0 + (0.17 * effectPer)\n \n # Loop through the lines\n for source_line in lines:\n # Check to see if this line is too long\n wrap_list = []\t# Wrapped sentence list\n \n # If the width of this line is too big for our drawing width\n if font.size(source_line)[0] > rectDim[2]:\n # Split the line by spaces, and add a word each time checking if it is now too long\n words = source_line.split(' ')\n last_sentence = test_sentence = last_word = ''\n # Loop until we are out of words\n while len(words) > 0:\n last_sentence = test_sentence\t\t# Save the last sentence\n last_word = words[0]\t\t\t\t# Get the first word left in the list\n words.remove (last_word)\t\t\t# Remove the word we just took from the list\n if test_sentence != '':\n test_sentence += \" \"\t\t\t# Add space gap, if this isnt a fresh new sentence\n test_sentence += \"%s\" % last_word\t# Add the word to the test sentence\n # If the test sentence is now too long\n if font.size(test_sentence)[0] > rectDim[2]:\n words.insert(0, last_word)\t\t# Put the last word into the word list again, cause it's over the limit\n wrap_list.append(last_sentence)# Add the last sentence that was under the width to the wrapped sentence list\n last_sentence = test_sentence = last_word = ''\n # If the test sentence isnt blank, add it to the wrap list\n if test_sentence != '':\n wrap_list.append(test_sentence)\n # Else, it fits so just add the source line to our wrapped sentence list and move on\n else:\n wrap_list.append(source_line)\n \n # We now have a list of wrapped sentences. Cycle through these to print them out\n for line in wrap_list:\n # If this isn't a blank line - No point in drawing blank lines\n if line != '':\n # If the text alignment should be centered\n if align_flag == 1:\n font_size = font.size(line)\n # If the width is not specified, use the Surface width\n if width == -1:\n offset[0] = (rectDim[2] / 2) - (font_size[0] / 2)\n # Else, use the specified width\n else:\n offset[0] = (width / 2) - (font_size[0] / 2)\n \n # If the text should be drawn with a black outline\n if outline == 1 or outline == 2 or outline == 3:\n # Set data_flag_data to black value if not provided\n if outline_color == 0:\n outline_color = (0,0,0,255)\n \n # Render the font\n text_font = font.render(line, 1, outline_color)\n \n \n # If effect is vertical stretch\n if effect == 1:\n text_font = pygame.transform.scale(text_font, (text_font.get_width(), float(text_font.get_height()) * scale))\n \n \n # Blit the font - offset and line number adjusted\n if outline == 1 or outline == 2:\n surface.blit(text_font, (pos[0] + offset[0] + 1, pos[1] + offset[1] + 1 + (size * count * 1.3)))\n if outline == 2:\n surface.blit(text_font, (pos[0] + offset[0] + 2, pos[1] + offset[1] + 2 + (size * count * 1.3)))\n # Thick outline, wastefully rendering 4x...\n if outline == 3:\n surface.blit(text_font, (pos[0] + offset[0] - 2, pos[1] + offset[1] + (size * count * 1.3)))\n surface.blit(text_font, (pos[0] + offset[0] + 2, pos[1] + offset[1] + (size * count * 1.3)))\n surface.blit(text_font, (pos[0] + offset[0], pos[1] + offset[1] - 1 + (size * count * 1.3)))\n surface.blit(text_font, (pos[0] + offset[0], pos[1] + offset[1] + 1 + (size * count * 1.3)))\n \n # Render the font\n text_font = font.render(line, 1, color)\n \n # If effect is vertical stretch\n if effect == 1:\n # If we havent already done this above in the flags\n if outline == 0:\n oldHeight = text_font.get_height()\n newHeight = text_font.get_height()\n offset[1] -= (newHeight-oldHeight) / 2\n \n # Blit the font - offset and line number adjusted - Also add the rectangle size we wanted to use for this writing (since we may want to draw in a portion of the surface, and treat it as a whole surface)\n surface.blit(text_font, (rectDim[0] + pos[0] + offset[0], rectDim[1] + pos[1] + offset[1] + (size * count * 1.2)))\n \n # Increment the counter\n count += 1\n \n # Return the offset and count information\n return (offset, count)","title":""},{"docid":"97e0c4da6489e42ac127b008d8a83ae9","score":"0.70117736","text":"def draw_text(display_surface: T_SURFACE, text: str, coords: T_COORDINATE,\n text_color: T_COLOR, back_color: T_COLOR = None, font: T_FONT = None, mode: str = \"corner\"):\n if font is None:\n font = ASSETS.F_STANDARD\n\n text_surf, text_rect = helper_text_objects(text, text_color, back_color, font)\n\n if mode == \"center\":\n text_rect.center = coords\n else:\n text_rect.topleft = coords\n\n display_surface.blit(text_surf, text_rect)","title":""},{"docid":"10136422d1ca061034fd28327345c91e","score":"0.7000952","text":"def _drawText(self) -> None:\n if len(self.text) < 1:\n return\n\n x, y, w, h = self.dims\n small_text = pygame.font.Font(\"freesansbold.ttf\", 20)\n text_surf = small_text.render(self.text, True, kBLACK_COLOR)\n text_rect = text_surf.get_rect()\n text_rect.center = ((x + (w / 2)), (y + (h / 2)))\n self.gameDisp.blit(text_surf, text_rect)","title":""},{"docid":"eb9845a97f7d639736ec37947636d635","score":"0.70002973","text":"def draw(self, surface):\r\n pass","title":""},{"docid":"b6c97a9aa6a842e6e9adcc7fc312618b","score":"0.69809306","text":"def _draw_text(self, surface, **kwargs):\n text = self.font_40.render(\"Select\", True, (255, 255, 255))\n text_rect = text.get_rect()\n screen_rectangle = self.game_loop.surface.get_rect()\n text_rect.midtop = screen_rectangle.midtop\n text_rect.centerx = screen_rectangle.centerx\n text_rect.y += 60\n surface.blit(text, text_rect)\n\n # Track rectange information for drawn info\n rectangle_data = {}\n\n new_game_text = self.font_20.render(\"New Game\", True, (255, 255, 255))\n new_game_rect = new_game_text.get_rect()\n new_game_rect.midtop = screen_rectangle.midtop\n new_game_rect.centerx = screen_rectangle.centerx\n new_game_rect.y = text_rect.y + 60\n rectangle_data[0] = new_game_rect\n surface.blit(new_game_text, new_game_rect)\n\n for idx, fn in enumerate(self.save_files, start=1):\n display = fn[:-5].replace(\"_\", \" \").title()\n slot_text = self.font_20.render(display, True, (255, 255, 255))\n slot_rect = slot_text.get_rect()\n slot_rect.midtop = screen_rectangle.midtop\n slot_rect.centerx = screen_rectangle.centerx\n y_val = 40 * idx\n slot_rect.y = new_game_rect.y + y_val\n rectangle_data[idx] = slot_rect\n surface.blit(slot_text, slot_rect)\n\n display = \"Exit\"\n exit_text = self.font_20.render(display, True, (255, 255, 255))\n exit_rect = exit_text.get_rect()\n exit_rect.midtop = screen_rectangle.midtop\n exit_rect.centerx = screen_rectangle.centerx\n y_val = 40 * (len(self.save_files) + 1)\n exit_rect.y = new_game_rect.y + y_val\n rectangle_data[len(self.save_files) + 1] = exit_rect\n surface.blit(exit_text, exit_rect)\n\n # Given the current index value for the selections, draw an arrow.\n select = self.font_20.render(\">> \", True, (255, 255, 255))\n select_rect = new_game_text.get_rect()\n select_rect.midleft = rectangle_data[self.select_index].midleft\n select_rect.x = select_rect.x - 50\n select_rect.y = rectangle_data[self.select_index].y\n surface.blit(select, select_rect)","title":""},{"docid":"f8babfd0eec13f78953e044314be7e93","score":"0.69743544","text":"def draw(self, surface):\n pass","title":""},{"docid":"089c8d6c9247f3cfb64fd7f4064a4558","score":"0.6934426","text":"def draw(self, surface):\n pass;","title":""},{"docid":"b422d8e8ad96e4044085cae231bd0abc","score":"0.6928526","text":"def draw(self, win):\n font = pygame.font.SysFont('consolas', 20)\n text = font.render(self.text, True, self.color, (0, 0, 0))\n win.blit(text, (self.x, self.y))","title":""},{"docid":"71d5fe2b55df3ab3f44331a435f2dfc2","score":"0.69261247","text":"def _debug_outtext(self, screen):\r\n textsurface = self._debug_text.render(\r\n self._debug_text_message,\r\n False,\r\n DEBUG_COLOR)\r\n screen.blit(textsurface, (20, 20))","title":""},{"docid":"e15ce90a700fc5633b6d01240be31d3f","score":"0.6920018","text":"def render(self, screen, text):\n self.surface.blit(self.subsurf, (TEXTOFFSETX, TEXTOFFSETY))\n self.subsurf.blit(self.background, (0, 0))\n pygame.draw.rect(self.surface, self.linecolor, self.surface.get_rect(), 1)\n\n self.index = 0\n\n # als het tuple is met comma's gescheiden voor een nieuwe regel\n if type(text) == tuple:\n for line in text:\n # Beschouw de door komma gescheiden regels toch als individuele string_text.\n self.string_text(line)\n\n # als het een list is met paren van tuples\n elif type(text) == list:\n self.tuple_pair_text(text)\n\n # als het alleen maar text achter elkaar is\n elif type(text) == str:\n self.string_text(text)\n\n screen.blit(self.surface, self.rect.topleft)","title":""},{"docid":"69d9827134eb6708374715fc0b9f4330","score":"0.69138813","text":"def draw(self, surface):\n\n\t\tsurface.blit(self.surface, (0, 0), (-self._shift[0], -self._shift[1], self._resx, self._resy))","title":""},{"docid":"6762b21920949238b78d6183c70223ef","score":"0.69088197","text":"def create_text_surface(text, size=16):\r\n return pygame.font.SysFont(\"comic sans\", size).render(text, 1, (0, 0, 0));","title":""},{"docid":"aba15af49e9f07c65797c5d64c2b3b01","score":"0.68811274","text":"def draw_text(self, text, x=50, y=150, centered=False, \n color=(0,0,0), fontsize=24):\n self.font = pygame.font.SysFont('mono', fontsize, bold=True)\n fw, fh = self.font.size(text) # calculate size of rect\n surface = self.font.render(text, True, color)\n if centered:\n self.screen.blit(surface, (x - fw/2,y-fh/2))\n else:\n self.screen.blit(surface, (x,y)) # x,y is topleft corner","title":""},{"docid":"dbe3c67d2acfa233c431a847c6ae2852","score":"0.6877316","text":"def _draw_text(self, text, x_position, y_position):\n\n text_surface = self.font.render(text, True, self.white)\n text_rect = text_surface.get_rect()\n text_rect.center = (x_position, y_position)\n self.display.blit(text_surface, text_rect)","title":""},{"docid":"88fbc5b49153b17f97c21fe771e241cb","score":"0.6870249","text":"def draw_text(self, text, **kwargs):\n surf = self.font.render(text, 1, self.config[\"font\"][\"color\"],\n self.config[\"font\"][\"bgd_color\"])\n rect = surf.get_rect(**kwargs)\n self.game.display.blit(surf, rect)\n self.game.changed_areas.append(rect)","title":""},{"docid":"ec5093359e8cbb12dc7111daa3902f53","score":"0.6853393","text":"def showText(self, text, position, color, fontName=\"Times\", fontSize=12, bold=False, italic=False,\n anchorCenter=False, shadowWidth=0, secondaryColor=(0, 0, 0), outlineWidth=0, shiftPosition=False):\n\n if shiftPosition:\n if self.usePygame:\n if outlineWidth != 0:\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\n screenText = font.render(text, 1, secondaryColor)\n if anchorCenter:\n textW = screenText.get_width()\n textH = screenText.get_height()\n else:\n textW = 0\n textH = 0\n\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\n x = outlineWidth * math.sin(angle)\n y = outlineWidth * math.cos(angle)\n self.display.blit(screenText, (int(self.getScreenX(position[0] - textW / 2 + x)),\n int(self.getScreenY(position[1] - textH / 2 + y))))\n elif shadowWidth != 0:\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\n screenText = font.render(text, 1, secondaryColor)\n if anchorCenter:\n textW = screenText.get_width()\n textH = screenText.get_height()\n else:\n textW = 0\n textH = 0\n for shift in range(shadowWidth):\n self.display.blit(screenText, (int(self.getScreenX(position[0] - textW / 2 + shift)),\n int(self.getScreenY(position[1] - textH / 2))))\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\n screenText = font.render(text, 1, color)\n if anchorCenter:\n textW = screenText.get_width()\n textH = screenText.get_height()\n else:\n textW = 0\n textH = 0\n self.display.blit(screenText, (\n int(self.getScreenX(position[0] - textW / 2)), int(self.getScreenY(position[1] - textH / 2))))\n else:\n tk_rgb = \"#%02x%02x%02x\" % color\n fontString = fontName + \" \" + str(fontSize)\n if bold:\n fontString += \" bold\"\n if italic:\n fontString += \" italic\"\n if anchorCenter:\n if outlineWidth != 0:\n secondary_tk_rgb = \"#%02x%02x%02x\" % secondaryColor\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\n x = outlineWidth * math.sin(angle)\n y = outlineWidth * math.cos(angle)\n self.canvas.create_text(int(self.getScreenX(position[0] + x)),\n int(self.getScreenY(position[1] + y)), fill=secondary_tk_rgb,\n font=fontString, text=text)\n elif shadowWidth != 0:\n secondary_tk_rgb = \"#%02x%02x%02x\" % secondaryColor\n for shift in range(shadowWidth):\n self.canvas.create_text(int(self.getScreenX(position[0] + shift)),\n int(self.getScreenY(position[1])), fill=secondary_tk_rgb,\n font=fontString, text=text)\n self.canvas.create_text(int(self.getScreenX(position[0])), int(self.getScreenY(position[1])),\n fill=tk_rgb, font=fontString, text=text)\n else:\n if outlineWidth != 0:\n secondary_tk_rgb = \"#%02x%02x%02x\" % secondaryColor\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\n x = outlineWidth * math.sin(angle)\n y = outlineWidth * math.cos(angle)\n self.canvas.create_text(int(self.getScreenX(position[0] + x)),\n int(self.getScreenY(position[1] + y)), fill=secondary_tk_rgb,\n font=fontString, text=text, anchor=NW)\n elif shadowWidth != 0:\n secondary_tk_rgb = \"#%02x%02x%02x\" % secondaryColor\n for shift in range(shadowWidth):\n self.canvas.create_text(int(self.getScreenX(position[0] + shift)),\n int(self.getScreenY(position[1])), fill=secondary_tk_rgb,\n font=fontString, text=text, anchor=NW)\n self.canvas.create_text(int(self.getScreenX(position[0])), int(self.getScreenY(position[1])),\n fill=tk_rgb, font=fontString, text=text, anchor=NW)\n else:\n if self.usePygame:\n if outlineWidth != 0:\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\n screenText = font.render(text, 1, secondaryColor)\n if anchorCenter:\n textW = screenText.get_width()\n textH = screenText.get_height()\n else:\n textW = 0\n textH = 0\n\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\n x = outlineWidth * math.sin(angle)\n y = outlineWidth * math.cos(angle)\n self.display.blit(screenText,\n (int(position[0] - textW / 2) + x, int(position[1] - textH / 2) + y))\n elif shadowWidth != 0:\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\n screenText = font.render(text, 1, secondaryColor)\n if anchorCenter:\n textW = screenText.get_width()\n textH = screenText.get_height()\n else:\n textW = 0\n textH = 0\n for shift in range(shadowWidth):\n self.display.blit(screenText,\n (int(position[0] - textW / 2) + shift, int(position[1] - textH / 2)))\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\n screenText = font.render(text, 1, color)\n if anchorCenter:\n textW = screenText.get_width()\n textH = screenText.get_height()\n else:\n textW = 0\n textH = 0\n self.display.blit(screenText, (int(position[0] - textW / 2), int(position[1] - textH / 2)))\n else:\n tk_rgb = \"#%02x%02x%02x\" % color\n fontString = fontName + \" \" + str(fontSize)\n if bold:\n fontString += \" bold\"\n if italic:\n fontString += \" italic\"\n if anchorCenter:\n if outlineWidth != 0:\n secondary_tk_rgb = \"#%02x%02x%02x\" % secondaryColor\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\n x = outlineWidth * math.sin(angle)\n y = outlineWidth * math.cos(angle)\n self.canvas.create_text(position[0] + x, position[1] + y, fill=secondary_tk_rgb,\n font=fontString, text=text)\n elif shadowWidth != 0:\n secondary_tk_rgb = \"#%02x%02x%02x\" % secondaryColor\n for shift in range(shadowWidth):\n self.canvas.create_text(position[0] + shift, position[1], fill=secondary_tk_rgb,\n font=fontString, text=text)\n self.canvas.create_text(position[0], position[1], fill=tk_rgb, font=fontString, text=text)\n else:\n if outlineWidth != 0:\n secondary_tk_rgb = \"#%02x%02x%02x\" % secondaryColor\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\n x = outlineWidth * math.sin(angle)\n y = outlineWidth * math.cos(angle)\n self.canvas.create_text(position[0] + x, position[1] + y, fill=secondary_tk_rgb,\n font=fontString, text=text, anchor=NW)\n elif shadowWidth != 0:\n secondary_tk_rgb = \"#%02x%02x%02x\" % secondaryColor\n for shift in range(shadowWidth):\n self.canvas.create_text(position[0] + shift, position[1], fill=secondary_tk_rgb,\n font=fontString, text=text, anchor=NW)\n self.canvas.create_text(position[0], position[1], fill=tk_rgb, font=fontString, text=text,\n anchor=NW)","title":""},{"docid":"72d5b91aaf53db24008ea971dfc6c714","score":"0.68530595","text":"def draw(self, surface):\n\n\t\tsurface.blit(self.surface, self._pos)","title":""},{"docid":"dce7b7260a3c294ad138dc01e504ebc9","score":"0.68499744","text":"def draw(self, surface):\n if self.message_str:\n # message_rect = pygame.Rect(0,0,0,0)\n x = self.rect.centerx\n bottom = self.rect.top - 10\n # message_rect.bottom = self.rect.top - 10\n return draw_message(x, bottom, self.message_str, surface)","title":""},{"docid":"92336245a9f236548813e5c9c6c1c926","score":"0.6843732","text":"def draw_text(self, text, size, color, x, y, align='center', fontname=\"Monospace_default.ttf\"):\n font = pygame.font.Font(path.join(self.font_folder, fontname), size)\n text_surface = font.render(text, True, color)\n text_rect = text_surface.get_rect()\n if align == \"nw\":\n text_rect.topleft = (x, y)\n if align == \"ne\":\n text_rect.topright = (x, y)\n if align == \"sw\":\n text_rect.bottomleft = (x, y)\n if align == \"se\":\n text_rect.bottomright = (x, y)\n if align == \"n\":\n text_rect.midtop = (x, y)\n if align == \"s\":\n text_rect.midbottom = (x, y)\n if align == \"e\":\n text_rect.midright = (x, y)\n if align == \"w\":\n text_rect.midleft = (x, y)\n if align == \"center\":\n text_rect.center = (x, y)\n self.screen.blit(text_surface, text_rect)","title":""},{"docid":"b896d8ba58dedf1a28cddae9b4be71c6","score":"0.6811034","text":"def draw(self, surface):\n surface.blit(pygame.transform.scale(self.title_img, (600, 540)), (0, 0))","title":""},{"docid":"b8490c3a7ea103550cbd86fa643388d1","score":"0.6777048","text":"def _draw_text(self, text, font_size, x_coordinate, y_coordinate, color=(255, 255, 255)):\n\n font = pygame.font.Font(FONT_PATH, font_size)\n text_surf = font.render(text, True, color)\n text_rect = text_surf.get_rect()\n text_rect.center = (x_coordinate, y_coordinate)\n self._display.blit(text_surf, text_rect)","title":""},{"docid":"871fafecf48059389cec3a291f128bc0","score":"0.6725989","text":"def paint(self, screen):\n if self._background:\n screen.blit(self._background, (0, 0))\n else:\n screen.fill(self._background_color)\n if self._overlay:\n screen.blit(self._overlay, self._overlay.get_rect(center=self._rect.center))\n for text_surface, pos in self._texts:\n screen.blit(text_surface, pos)\n self._need_update = False","title":""},{"docid":"e7602c3f4acbfe0d8992cb302c791eab","score":"0.6723012","text":"def draw_text(surf, text, font, pos, color):\n\tlabel = font.render(text, 1, color)\n\tposi = label.get_rect(centerx=pos[0], centery=pos[1])\n\tsurf.blit(label, posi)","title":""},{"docid":"607ff7f5de096c8dd87a6b235bc3dfe9","score":"0.67189693","text":"def draw_text(SCREEN, text, x, y):\r\n font = pygame.font.SysFont('Consolas', 30, False, False)\r\n text_to_draw = font.render(text, True, Color(255,255,255))\r\n SCREEN.blit(text_to_draw, (x, y))","title":""},{"docid":"f17a9d6724050f3b9fae94a61a5408ff","score":"0.6700834","text":"def DrawText(self):\n\n titleText = \"Game of Life\"\n controlsText = ['Sterowanie:',\n 'R - resetowanie planszy', \n 'D - losowy uklad', \n 'S/Spacja - zatrzymanie/wznowienie',\n 'B - aktywacja/dezaktywacja scian',\n 'Up/Down - zmiana tempa, Right - przejscie o 1 generacje',\n 'Q - wyjscie']\n configText = ['Obecne ustawienia:',\n 'Tempo: ', \n 'Obecna generacja:',\n 'Sciany:']\n \n font = pygame.font.SysFont('Harrington', 70, True)\n text = font.render(titleText, 1, (255, 0, 0))\n self.screen.blit(text, (350, 0))\n\n font = pygame.font.SysFont('DejaVu Sans Mono', 20)\n offset_y = 20\n\n for line in controlsText:\n text = font.render(line, True, (120, 155, 220))\n self.screen.blit(text, (20, offset_y))\n offset_y += 20\n\n offset_y = 20\n for line in configText:\n text = font.render(line, True, (120, 155, 220))\n self.screen.blit(text, (1000, offset_y))\n offset_y += 20","title":""},{"docid":"38b4d16046cdc14350098baf5d7e4db3","score":"0.6684227","text":"def TextDisplay(file,y_pos,width,size,p,xx=\" \",xr=\" \",yy=\" \",yr=\" \"):\n string = filter(None,[strin.replace(\"\\n\",'') for strin in open(file,'r').readlines()])\n string = [strin.decode('utf-8') for strin in string]\n string = [strin.replace(xx,xr).replace(yy,yr) for strin in string]\n wrappedstring=[]\n for stri in string:\n new=textwrap.wrap(stri,width)\n for st in new:\n wrappedstring.append(st)\n wrappedstring.append('')\n shift=0\n for strin in wrappedstring: \n font = pygame.font.Font(None, size)\n text = font.render(strin,1, (10, 10, 10))\n textpos = text.get_rect()\n textpos.centerx= (p.rect[0]/2)\n textpos.centery = (y_pos+shift)\n p.screen.blit(text, textpos)\n shift+=size","title":""},{"docid":"bed2a813bec599d4f4b03e56455d8142","score":"0.66830486","text":"def showTieText(self):\n self.screen.blit(self.tie_text, (200, 900))\n pygame.display.update()","title":""},{"docid":"ddbad452dd25e1e9b59119c37443a783","score":"0.6679969","text":"def update_surface(self):\n if (self.__m_font):\n self.__m_font.set_bold(self.highlight)\n self.image = self.__m_font.render(self.text\n , True\n , self.colour)\n if (self.rect):\n # Used the current rects center point\n self.rect = self.image.get_rect(center=self.rect.center)\n else:\n self.rect = self.image.get_rect()","title":""},{"docid":"b97df67d20e5d80bbb241fdcabe98e60","score":"0.666756","text":"def draw_text(self, surface, text, pos, color=TEXT_COLOR, align_center=False):\n text_surface = self.font.render(text, True, color)\n pos = list(pos)\n if align_center:\n # offset pos if was passed center\n pos[0] -= text_surface.get_width() // 2\n pos[1] -= text_surface.get_height() // 2\n surface.blit(text_surface, pos)","title":""},{"docid":"c51734959bb42ee35ab31c1d201ec2af","score":"0.6636659","text":"def after_draw(self, surface: pygame.Surface) -> None:","title":""},{"docid":"60044312d6f34aa5000b70c11a6743b8","score":"0.66356754","text":"def draw(self, window):\n self.image.blit(self.text, self.text_rect)\n window.blit(self.image, self.rect)","title":""},{"docid":"0a079efea1305b1a287e9b85a642e5d0","score":"0.6620129","text":"def render(self, display):\n\n surf = pygame.transform.rotate(self.surf, -math.degrees(self.angle))\n size = surf.get_size()\n\n display.blit(surf, [self.pos[i] - (size[i] / 2) for i in range(2)])","title":""},{"docid":"0b21786898a6cc2d40fc77f98bcefbc7","score":"0.66106874","text":"def draw(self, surface: pygame.Surface) -> None:\n raise NotImplementedError","title":""},{"docid":"22360b68f4ded552e71a356d26ccd2a5","score":"0.6589932","text":"def on_draw(self):\n\n arcade.start_render()\n arcade.draw_text(self.text, 100, 100, arcade.color.BLACK, 32)","title":""},{"docid":"901cba4144309571f1201f73cf020fde","score":"0.6586272","text":"def flip(self):\r\n pygame.display.flip()","title":""},{"docid":"24ef64bba15dc1ee4f35469008caaae1","score":"0.6578322","text":"def draw(self, score):\n self.surface = self.font.render(str(score), False, self.color)\n self.center()\n self.screen.blit(self.surface, (self.x, self.y))","title":""},{"docid":"1fb8542485a1d94a141743b563fc57e1","score":"0.65721506","text":"def _render_surfaces(self):\r\n\r\n rect = self.base[\"rect\"]\r\n totals = [size_dict.get(-1, (0, 0)) for size_dict in self.surface_sizes]\r\n total_height = sum([val[1] for val in totals])\r\n text_left = self.base[\"frame\"] * 2 # left margin for text\r\n\r\n surface = pygame.Surface(rect.size)\r\n if self.base[\"background_colour\"] is None:\r\n surface.fill(BLACK)\r\n surface.set_colorkey(BLACK)\r\n else:\r\n surface.fill(self.base[\"background_colour\"])\r\n\r\n self._do_frame(surface)\r\n\r\n if self.base[\"v_justification\"] > 0:\r\n text_y = (rect.height - (self.base[\"v_justification\"] - 1) * 40 - total_height) / 2\r\n else:\r\n text_y = 0\r\n\r\n number = 0\r\n for dictionary in self.surface_dicts:\r\n size_dict = self.surface_sizes[number]\r\n text_width, text_height, text_start = 0, 0, 0\r\n\r\n for key in dictionary:\r\n if key == 0:\r\n text_width, text_height = size_dict[-1]\r\n if self.base[\"justification\"] == 1:\r\n text_start = (rect.width - text_width) / 2\r\n elif self.base[\"justification\"] == 2:\r\n text_start = rect.width - text_width - text_left\r\n else:\r\n text_start = text_left\r\n # Blit sections aligned by base\r\n surface.blit(dictionary[key], (text_start, text_y + text_height - size_dict[key][1]))\r\n text_start += size_dict[key][0]\r\n text_y += text_height\r\n number += 1\r\n return surface","title":""},{"docid":"a17d8ae807a82a5630129fe0308f0824","score":"0.6562091","text":"def set_text(self, text=()):\n self.surfaces = []\n for i, string in enumerate(text):\n surface = self.font.render(string, True, self.color)\n x = -surface.get_width() / 2 if self.centralised else 0\n y = i * self.letter_h\n self.surfaces.append([surface, x, y])\n\n self.w = max(line.get_size()[0] for line, _, _ in self.surfaces) if text else 0\n self.h = self.letter_h * len(text)","title":""},{"docid":"9d39eb933d0e6009aa1943bd7d81ec47","score":"0.6551663","text":"def draw(self):\n pygame.draw.rect(self.screen, self.current_color,\n (self.x, self.y, self.width, self.height))\n small_text = pygame.font.SysFont(\"comicsansms\", 30)\n text_surface, text_rectangle = self.create_text_objects(self.name,\n small_text)\n text_rectangle.center = ((self.x + (self.width / 2)),\n (self.y + (self.height / 2)))\n self.screen.blit(text_surface, text_rectangle)","title":""},{"docid":"5ac116c550278775021bd0c67054833f","score":"0.6544178","text":"def message(window, text, col, size=20):\n info_text = pygame.font.Font(None, size)\n text_surf, text_rect = text_objects(text, info_text, col)\n text_rect.center = ((constants.WINDOW_SIDE/2), (constants.WINDOW_SIDE/2))\n window.blit(text_surf, text_rect)\n pygame.display.flip()","title":""},{"docid":"60da591b5521b775e28a901d446f5df2","score":"0.6542048","text":"def addText(self):\n displayWindow.blit(self.font.render(self.text, True, (0, 0, 0)),\n (self.pos_x + self.width / 2 - 10, self.pos_y))\n pygame.display.update()","title":""},{"docid":"aefd87745e0e6f9abb63d0b87b4c9861","score":"0.65417427","text":"def redraw_window(surface, font, size, colors, UI, texts):\n\n surface.fill((0, 0, 0)) # black color\n for element, text in zip(UI, texts):\n pygame.draw.rect(surface, colors[1], element)\n gobox = text.get_rect(center=(element.centerx, element.centery))\n surface.blit(text, gobox)\n\n pygame.display.update()","title":""},{"docid":"3ffd3a2d362afc626750604cdc81ec8a","score":"0.6521876","text":"def board(self):\n self.screen.blit(self.board, (0, 0))\n self.screen.blit(self.quiz_text, (800, 0))\n self.screen.blit(self.directions_text, (50, 75))\n pygame.display.flip()","title":""},{"docid":"61cea29540fccb9641d4f9f6b417f715","score":"0.6512937","text":"def UpdateText(self):\n\n font = pygame.font.SysFont('DejaVu Sans Mono', 25)\n offset_y = 40\n \n # draw empty rectangle over old text\n rect = (1200, offset_y, 1250, offset_y + 20)\n pygame.draw.rect(self.screen, self.BACKGROUND_COLOR, rect)\n\n # Tempo\n text = font.render(str(self.speed), True, (120, 155, 220))\n self.screen.blit(text, (1200, offset_y))\n offset_y += 20\n\n # Generation counter\n\n rect = (1200, offset_y, 1250, offset_y + 20)\n pygame.draw.rect(self.screen, self.BACKGROUND_COLOR, rect)\n\n text = font.render(str(self.generationCounter), True, (120, 155, 220))\n self.screen.blit(text, (1200, offset_y))\n offset_y += 20\n\n # BorderMode\n\n if self.bordersUp == True:\n borderText = \"Aktywne\"\n else:\n borderText = \"Nieaktywne\"\n\n rect = (1200, offset_y, 1250, offset_y + 20)\n pygame.draw.rect(self.screen, self.BACKGROUND_COLOR, rect)\n\n text = font.render(borderText, True, (120, 155, 220))\n self.screen.blit(text, (1200, offset_y))","title":""},{"docid":"f5b2df5955d79e7b3c69c2ffb3fc898e","score":"0.65028906","text":"def render(self, surface):\n pass","title":""},{"docid":"0f40ad4ea0af40f44739f218ea5fec68","score":"0.6500956","text":"def draw_text(screen, text, X, Y):\n font = pygame.font.Font(\"fonts/dpcomic/dpcomic.ttf\", text_size)\n text_img = font.render(text, True, text_colour)\n text_rect = text_img.get_rect()\n text_rect.center = (X, Y)\n screen.blit(text_img, text_rect)\n return [text_img, text_rect]","title":""},{"docid":"95b2465c6fa038c14248047953d98ad5","score":"0.648434","text":"def draw_text(self, text, color = (0, 255, 150), dw = 0, dh = 0):\n fw, fh = self.font.size(text) # fw: font width, fh: font height\n surface = self.font.render(text, True, color)\n # // makes integer division in python3\n self.screen.blit(surface, ((self.width - fw - dw) // 2, (self.height - dh) // 2))","title":""},{"docid":"95b2465c6fa038c14248047953d98ad5","score":"0.648434","text":"def draw_text(self, text, color = (0, 255, 150), dw = 0, dh = 0):\n fw, fh = self.font.size(text) # fw: font width, fh: font height\n surface = self.font.render(text, True, color)\n # // makes integer division in python3\n self.screen.blit(surface, ((self.width - fw - dw) // 2, (self.height - dh) // 2))","title":""},{"docid":"088362a0277febe9bc4a655bc0ad3732","score":"0.64792496","text":"def display_score(surface):\n font = pygame.font.Font('freesansbold.ttf', 25)\n text = font.render(str(score), True, (255, 255, 255))\n textRect = text.get_rect()\n textRect.center = (WIDTH * 0.9, HEIGHT * 0.95)\n surface.blit(text, textRect)","title":""},{"docid":"5f04ee3f3f26e2a0dbd555f1f913f1e4","score":"0.64735323","text":"def draw(self):\r\n pygame.draw.line(screen, (0, 0, 0), (self.side_buffer // 2, self.y), (WIDTH - self.side_buffer // 2, self.y), 2);\r\n for i in drange(self.start, self.end + 1, STEP):\r\n x = self.__apply(i * self.scale);\r\n if not i % 1:\r\n pygame.draw.line(screen, (100, 100, 255), (x, self.y - self.hash_height), (x, self.y + self.hash_height), 3);\r\n text = create_text_surface(str(i));\r\n screen.blit(text, (x - text.get_width() / 2, self.y + self.number_position * 2 * self.hash_height + self.number_position * 8));","title":""},{"docid":"51ef8074450f68ab5dee5917a4c9ff26","score":"0.64438915","text":"def effect(my_canvas, text_img):\n # Derive working variables and working surfaces\n (pic_wid, pic_ht) = text_img.get_size()\n cent_x = 250\n\n tmp_screen = pg.Surface((4, pic_ht))\n tmp_screen2 = pg.Surface((4, pic_ht))\n\n # go through the text message image, one slice at a time\n for x in range(0, pic_wid, 4):\n tmp_screen.fill((255, 255, 255))\n tmp_screen.blit(text_img, (0, 0), (x, 0, 4, pic_ht))\n tmp_screen.set_colorkey((255, 255, 255))\n\n trig_x = x + config.scrollback\n my_xy = (trig_x, config.Y_OFFSET + (pic_ht / 2))\n\n if (trig_x > (cent_x - MAG_RAD)) & (trig_x < (cent_x + MAG_RAD)):\n\n mag_x = abs(cent_x - trig_x)\n mag_factor = int(math.sin(math.acos(mag_x / MAG_RAD)) * MAG_RAD)\n\n tmp_screen2 = pg.transform.scale(tmp_screen, (4, mag_factor * 4))\n (tmp_wid, tmp_ht) = tmp_screen2.get_size()\n if tmp_ht < (pic_ht / 2):\n tmp_screen2 = tmp_screen\n\n my_xy = (trig_x, config.Y_OFFSET - (tmp_ht / 2) + 50)\n my_canvas.blit(tmp_screen2, my_xy)\n else:\n my_canvas.blit(tmp_screen, my_xy)\n\n return my_canvas, text_img","title":""},{"docid":"b918ae060d4d8c491db65284121dbeb5","score":"0.6432668","text":"def render(self) -> NoReturn:\n self.surface.fill(self.background_colour)\n\n for text_object in self.text_objects:\n if not text_object.hidden:\n text_object.render(self.surface)\n\n pygame.display.update()","title":""},{"docid":"8aacff62773689d02735acc8d88847f3","score":"0.6425863","text":"def draw_message_text(self, text, color):\n pygame.draw.rect(self.screen, Conf.SCREEN_COLOR, Conf.MESSAGE_RECT, 0) # clean\n\n font = pygame.font.SysFont(pygame.font.get_default_font(), 20)\n text_img = font.render(text, 1, color)\n \n self.screen.blit(text_img, Conf.MESSAGE_RECT)","title":""},{"docid":"25878ae13df442b8718b099f5196594a","score":"0.63702816","text":"def draw(self, surface):\n if self.dirty:\n self.dirty = False\n for i in range(3):\n for j in range(3):\n corner = [i, j]\n originpos = [0, 0]\n originsize = [0, 0]\n size = [0, 0]\n destpos = [0, 0]\n for k in range(2):\n if corner[k] == 0:\n originpos[k] = 0\n size[k] = self.frameSize\n originsize[k] = self.frameSize\n destpos[k] = 0\n elif corner[k] == 1:\n originpos[k] = self.frameSize\n size[k] = self.size[k] - self.frameSize * 2\n originsize[k] = self.borderWidth\n destpos[k] = self.frameSize\n else:\n originpos[k] = self.frameSize + self.borderWidth\n size[k] = self.frameSize\n originsize[k] = self.frameSize\n destpos[k] = self.size[k] - self.frameSize\n # print >> stderr, \"destpos: \", destpos, self.size, size\n surface.blit(pygame.transform.scale(self.image.subsurface((originpos[0], originpos[1],\n originsize[0], originsize[1])),\n size),\n destpos)","title":""},{"docid":"55001682f21adddf8415997c8778f3fb","score":"0.63672936","text":"def draw(self, surface):\n surface.blit(self.bimage, self.brect)","title":""},{"docid":"aab155ddf920c53a3f81c05242583819","score":"0.635951","text":"def text_box(text):\n\n pygame.draw.rect(window, black, (50, 350, 750, 140))\n pygame.draw.rect(window, white, (55, 355, 740, 130))\n pygame.draw.rect(window, black, (60, 360, 730, 120))\n\n smallText = pygame.font.SysFont(\"comicsansms\",20)\n TextSurf, TextRect = text_objects(text, smallText, white)\n TextRect.center = (415, 415)\n window.blit(TextSurf, TextRect)","title":""},{"docid":"192a2b689ed69edb08e82f9c51404522","score":"0.63206553","text":"def draw(self):\n self.beams.draw(self.screen)\n pg.display.flip()","title":""},{"docid":"cfba5ef6c66e8c9f706680d758e8a668","score":"0.6300541","text":"def play(self, screen, new_position,inverse=False,hidden=False):\n self.erase(screen)\n self.position=new_position\n screen.blit(self.display(inverse=inverse,hidden=hidden),new_position) #rendre transparent","title":""},{"docid":"7fb851b12d315b1abbd1356d079d2d41","score":"0.62951726","text":"def render_text(self, text, z_pos, y_pos, x_pos,\n font=GLUT.GLUT_BITMAP_HELVETICA_18):\n GL.glDisable(GL.GL_LIGHTING)\n GL.glColor3f(*self.white)\n GL.glRasterPos3f(x_pos, y_pos, z_pos)\n for character in text:\n if character == '\\n':\n y_pos = y_pos - 20\n GL.glRasterPos3f(x_pos, y_pos, z_pos)\n else:\n GLUT.glutBitmapCharacter(font, ord(character))\n\n GL.glEnable(GL.GL_LIGHTING)","title":""},{"docid":"378ed7786229c6f76b8b0750b5282c10","score":"0.62896353","text":"def draw(self,surface):\n if self.image is not \"\":\n surface.blit(self.image, (self.x,self.y))","title":""},{"docid":"0f067076a217174245c7b58c454d0198","score":"0.6285832","text":"def update_text(screen, messages):\n textSize = 20\n font = pygame.font.Font(None, 20)\n\n #use a rectangle to cover up old text\n pygame.draw.rect(screen,black,[310,0,200,620],0)\n\n #loop through each of the strings in the list to print them on the side panel\n Y_increment=0\n for message in messages:\n textY=textSize+Y_increment\n text=font.render(message, True, white, black)\n textRect = text.get_rect()\n textRect.centerx = (NUM_COLS + 1) * WIDTH + 30\n textRect.centery = textY\n screen.blit(text, textRect)\n Y_increment+=textSize","title":""},{"docid":"10efb7248f301043482222f733d67062","score":"0.62805206","text":"def draw(self):\n\n self.Game.screen.blit(self.background_image, self.background_rect)\n self.Game.screen.blit(self.enter_text, self.enter_rect)\n self.Game.screen.blit(self.ending_text, self.ending_text_rect)","title":""},{"docid":"f01d543304c25fe744b9c343e6f9153e","score":"0.6278696","text":"def ezViewText(self, text, location, fgcolor = Color.WHITE, bgcolor = Color.BLACK):\r\n if(len(text)<0):\r\n return None\r\n alpha = 255\r\n tsurface = self._mFont.render(text, True, self._csvRGB2pgColor(fgcolor, alpha), self._csvRGB2pgColor(bgcolor, alpha))\r\n self._mSurface.blit(tsurface, location)\r\n return None","title":""},{"docid":"846f94ab66f174924852f50e1702619a","score":"0.62671095","text":"def drawTextOnPosition2D(x, y, text):\n glRasterPos3f(x, y, 0.0)\n drawTextOnDisplay(text)","title":""},{"docid":"fc2781fd7964eaef745ffd2a1f3f9a92","score":"0.62662536","text":"def draw(self, surface, font_name, size, text, rect_or_pos_to_draw_to, color, align_horiz='left', align_vert='top', antialias=False):\n pair = (font_name, size)\n assert pair in self._font_dict, 'Font: %s Size: %d is not available in cFontManager.' % pair\n font_surf = self._font_dict[(font_name, size)].render(text, antialias, color)\n if isinstance(rect_or_pos_to_draw_to, tuple):\n surface.blit(font_surf, rect_or_pos_to_draw_to)\n elif isinstance(rect_or_pos_to_draw_to, pygame.Rect):\n font_rect = font_surf.get_rect()\n # align horiz\n if align_horiz == 'center':\n font_rect.centerx = rect_or_pos_to_draw_to.centerx\n elif align_horiz == 'right':\n font_rect.right = rect_or_pos_to_draw_to.right\n else:\n font_rect.x = rect_or_pos_to_draw_to.x # left\n # align vert\n if align_vert == 'center':\n font_rect.centery = rect_or_pos_to_draw_to.centery\n elif align_vert == 'bottom':\n font_rect.bottom = rect_or_pos_to_draw_to.bottom\n else:\n font_rect.y = rect_or_pos_to_draw_to.y # top\n \n surface.blit(font_surf, font_rect)","title":""},{"docid":"8940f32300c8800baacf00d2fd450209","score":"0.62630963","text":"def draw_debug():\n global SURFACE_MAIN\n\n draw_text(SURFACE_MAIN, f\"FPS: {int(CLOCK.get_fps())}\", (0, 0), COLOR_WHITE, COLOR_BLACK)","title":""},{"docid":"c5dca6d5df7f7753382c1a474fa615fe","score":"0.6246966","text":"def display_start(surface):\n surface.fill((0, 0, 100))\n draw_stars(surface)\n font1 = pygame.font.Font('freesansbold.ttf', 80)\n font2 = pygame.font.Font('freesansbold.ttf', 30)\n font3 = pygame.font.Font('freesansbold.ttf', 20)\n\n title = font1.render(\"Galaga\", True, (255, 255, 255))\n author = font2.render(\"Taylor Barmak\", True, (255, 255, 255))\n howTo1 = font3.render(\"Use the arrow keys to move\", True, (255, 255, 255))\n howTo2 = font3.render(\"and the space bar to shoot.\", True, (255, 255, 255))\n instructions = font3.render(\"Press [space] to play.\", True, (255, 255, 255))\n\n textRectTitle = title.get_rect()\n textRectAuthor = author.get_rect()\n textRectHowTo1 = howTo1.get_rect()\n textRectHowTo2 = howTo2.get_rect()\n textRectInstructions = instructions.get_rect()\n\n textRectTitle.center = (WIDTH * 0.5, HEIGHT * 0.5)\n textRectAuthor.center = (WIDTH * 0.5, HEIGHT * 0.6)\n textRectHowTo1.center = (WIDTH * 0.5, HEIGHT * 0.7)\n textRectHowTo2.center = (WIDTH * 0.5, HEIGHT * 0.75)\n textRectInstructions.center = (WIDTH*0.5, HEIGHT * 0.9)\n\n\n surface.blit(title, textRectTitle)\n surface.blit(author, textRectAuthor)\n surface.blit(howTo1, textRectHowTo1)\n surface.blit(howTo2, textRectHowTo2)\n surface.blit(instructions, textRectInstructions)","title":""},{"docid":"a3421fe325dadda9bace4480e22fe3bc","score":"0.6236516","text":"def showCWinText(self):\n self.screen.blit(self.lost_text, (550, 900))\n pygame.display.update()\n boo = pygame.mixer.Sound('fx/boo.wav')\n boo.play()","title":""},{"docid":"4d9d424ce3b4e0dcc2ed749e2400a0cc","score":"0.6231531","text":"def draw(self, surface):\n if self.visible:\n surface.blit(self.image, self.rect)","title":""},{"docid":"487d2b087e872c1dddc1fdd09b32f89f","score":"0.6225743","text":"def draw_text(screen, font, text, location_x, location_y, justify):\n line = font.render(text, True, Color.BLUE_RGB)\n if justify == \"LEFT\":\n screen.blit(line, (location_x, location_y))\n elif justify == \"RIGHT\":\n screen.blit(line, (location_x - line.get_width(), location_y))\n elif justify == \"CENTER\":\n screen.blit(line, (location_x - (line.get_width() / 2.0), location_y))\n else:\n # Default justify to the LEFT\n screen.blit(line, (location_x, location_y))","title":""},{"docid":"c5c3829e066fe2f773083fbd7be57976","score":"0.62204725","text":"def display_box(screen, message):\r\n fontobject = pygame.font.Font(\"./font/Inconsolata.otf\", 14)\r\n pygame.draw.rect(screen, (0, 0, 0),\r\n ((screen.get_width() / 2) - 150,\r\n (screen.get_height() / 2) - 10,\r\n 250, 20), 0)\r\n pygame.draw.rect(screen, (255, 255, 255),\r\n ((screen.get_width() / 2) - 152,\r\n (screen.get_height() / 2) - 12,\r\n 254, 24), 1)\r\n\r\n if len(message) != 0:\r\n screen.blit(fontobject.render(message, 1, (230, 200, 70)),\r\n ((screen.get_width() / 2) - 150,\r\n (screen.get_height() / 2) - 10))\r\n pygame.display.flip()","title":""},{"docid":"ef25caf57afb55e3a61c91336c17f919","score":"0.6214439","text":"def draw(self, surface):\n surface.blit(self.image, self.rect)","title":""},{"docid":"ef25caf57afb55e3a61c91336c17f919","score":"0.6214439","text":"def draw(self, surface):\n surface.blit(self.image, self.rect)","title":""},{"docid":"ef25caf57afb55e3a61c91336c17f919","score":"0.6214439","text":"def draw(self, surface):\n surface.blit(self.image, self.rect)","title":""},{"docid":"3b6c59b2d0fe0b02547adf8e959b36ae","score":"0.6214225","text":"def draw(self, surface: Union[pygame.Surface, pygame.SurfaceType]) -> None:\n if self._visible:\n for label in self.labels:\n label.draw(surface)","title":""},{"docid":"5d5298ecec7eca87acf381c03861a836","score":"0.6210216","text":"def draw_as_selected(self):\n inverted_color = invert_color(self.color)\n self.surface = self.font.render(self.text, False, inverted_color)\n self.center()\n self.screen.blit(self.surface, (self.x, self.y))","title":""},{"docid":"427e06e22b7b0396f3fe92c60250eb99","score":"0.62070024","text":"def draw(self):\n\n if self.display_type == \"icon\":\n new_surf = self.icon_surface.copy()\n else:\n new_surf = self.surface.copy()\n\n self.draw_flash()\n\n # Camera smoothing\n if self.smoothing < 255:\n self.screen.set_alpha(self.smoothing)\n\n # Apply lighten effect\n self.lighten_surface.set_alpha(min(self.lighten_amt, 255))\n new_surf.blit(self.lighten_surface.convert(), (0, 0))\n\n new_surf.set_alpha(self.alpha)\n\n xoff, yoff = 0, 0\n if abs(self.cur_scale - 1.0) > 0.01 and self.display_type == \"full\":\n new_surf = pygame.transform.scale(new_surf,\n (int(CARD_WIDTH*self.cur_scale),\n int(CARD_HEIGHT*self.cur_scale)))\n xoff = int(CARD_WIDTH*(self.cur_scale - 1)/2)\n yoff = int(CARD_HEIGHT*(self.cur_scale - 1)/2)\n \n self.screen.blit(new_surf,\n (self.render_pos[0] - xoff,\n self.render_pos[1] - yoff))","title":""},{"docid":"edaf5bf97c6ec34e6d650db7f731f9ee","score":"0.6199231","text":"def prep_text(text, fontsize, fontcolour):\n font = pygame.font.SysFont(None, fontsize)\n msg = font.render(text, False, fontcolour)\n return msg","title":""},{"docid":"e0007ca65c76fb26dd1eec4e34db8d9c","score":"0.6192074","text":"def draw_text_middle(surface, text, size, color, position):\n font = pygame.font.SysFont(\"comicsans\", size, bold=True)\n label = font.render(text, 1, color)\n\n surface.blit(label, position)","title":""},{"docid":"c9264a7b7a7b9337290d82190a604c68","score":"0.61898667","text":"def gameover() -> pygame.Surface:\r\n gameover = pygame.Surface((500, 500))\r\n gameover.set_alpha(120)\r\n font = pygame.font.Font(\"clear-sans.bold.ttf\", 56)\r\n text = font.render(\"Game over\", True, [255, 0, 0])\r\n w, h = text.get_rect().center\r\n gameover.blit(text, ((250 - w, 250 - h - 5)))\r\n return gameover","title":""},{"docid":"06d4e1ac372d1d29d999fab8261e27a7","score":"0.6179655","text":"def render_font(font, charmap):\n renders = [font.render(c, True, constants.TEXT_COLOUR) for _, c in charmap]\n surf = Surface((max([r.get_rect().w for r in renders]),\n (_TEXT_HEIGHT + _VERTICAL_SPACING) * len(renders)))\n surf.fill(_BACKGROUND_COLOUR)\n\n y = _VERTICAL_PADDING\n for r in renders:\n surf.blit(r, (0, y))\n y += _TEXT_HEIGHT + _VERTICAL_SPACING\n\n return surf","title":""},{"docid":"ea21ed5687bdd98dddcb0689624c7be4","score":"0.61513746","text":"def draw(self):\n self.screen.blit(self.surface, (self.x, self.y))","title":""},{"docid":"a4ce8cfcbe9f5555efbc82e26b596b35","score":"0.6148734","text":"def drawScreen():\n # render to screen backbuffer\n renderScreen(bgColor)\n # flip to foreground\n pygame.display.flip()","title":""},{"docid":"051c55195737e4820ffc12109a662483","score":"0.6135749","text":"def paint_text(word: str, x: int, y: int, color=(0, 0, 0)):\n myfont = pygame.font.SysFont('Comic Sans MS', 30)\n textsurface = myfont.render(word, False, color)\n text_pos = (x * width + width // 2 - textsurface.get_width() // 2, y * height + height // 2 -\n textsurface.get_height() // 2)\n self.win.blit(textsurface, text_pos)","title":""},{"docid":"3a168222c9afa905243eed0174667b3d","score":"0.6126816","text":"def display_text(self, message, position, color=(255, 0, 0)):\r\n\r\n text = self.regular_font.render(message, False, color)\r\n self.screen.blit(text, position)","title":""}],"string":"[\n {\n \"docid\": \"202ecba47fcea0d6df43104a67c2964a\",\n \"score\": \"0.7655349\",\n \"text\": \"def draw_text(surf, text, size, x, y):\\n font = pygame.font.Font(FONT_NAME, size)\\n text_surface = font.render(text, True, WHITE)\\n text_rect = text_surface.get_rect()\\n text_rect.midtop = (x, y)\\n surf.blit(text_surface, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bc9956b2b618d48d0bfbc0245fe2a6f\",\n \"score\": \"0.75433457\",\n \"text\": \"def draw_text(self, display, text, size, x, y, mode):\\n font = pygame.font.Font(self.font_name, size)\\n text_surface = font.render(text, True, (0,0,0))\\n text_rect = text_surface.get_rect()\\n if mode == \\\"left\\\":\\n text_rect.topleft = (x,y)\\n elif mode == \\\"center\\\":\\n text_rect.center = (x, y)\\n display.blit(text_surface, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f87c699804b8b92f2ccc3b502bf365\",\n \"score\": \"0.74932194\",\n \"text\": \"def message_display(horiz, vert, text_size, text):\\r\\n print_text = pygame.font.Font(\\\"fixedsys.ttf\\\", text_size)\\r\\n text_surface = print_text.render(text, True, colors[\\\"black\\\"])\\r\\n text_rect = text_surface.get_rect()\\r\\n text_rect.center = (horiz, vert)\\r\\n gameDisplay.blit(text_surface, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f70754fcb422135ac48e78746e2795c\",\n \"score\": \"0.745215\",\n \"text\": \"def draw(self):\\n self.surface = self.font.render(self.text, False, self.color)\\n self.center()\\n self.screen.blit(self.surface, (self.x, self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cbceaad3fb23bbca341083ecbdfe741\",\n \"score\": \"0.7374574\",\n \"text\": \"def draw_text(self, text):\\n surface = self.font.render(text, True, (0, 0, 0)) \\n self._screen.blit(surface, (25, 25))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d90a7c7dc2f249f434600d6f989dc38\",\n \"score\": \"0.73461396\",\n \"text\": \"def draw_text(self, display: Surface):\\n text_surf, text_rect = self.render_text()\\n center_x = (self.rect.left + (self.rect.width / 2))\\n center_y = (self.rect.top + (self.rect.height / 2))\\n text_rect.center = (center_x, center_y)\\n display.blit(text_surf, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef30ae9060d01b963abdb4a1eb07999a\",\n \"score\": \"0.7320974\",\n \"text\": \"def draw(self, screen: pygame.surface.Surface):\\n\\n for line in self.rendered_text:\\n if line != None:\\n screen.blit(line[0], line[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32265bdf48122abc4371780c9544f14\",\n \"score\": \"0.73008966\",\n \"text\": \"def print_surface(self, msg, surface, topleft, font_obj):\\r\\n # Surface containing game over text\\r\\n # Do not anti-alias, render in white\\r\\n msg_surface = font_obj.render(msg, True, white)\\r\\n # Create rect object to specify where to place text\\r\\n msg_rect = msg_surface.get_rect()\\r\\n msg_rect.topleft = topleft\\r\\n surface.blit(msg_surface, msg_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4cfe086597ba1e49e7ec20bbf2cc575\",\n \"score\": \"0.7234013\",\n \"text\": \"def draw_text(self, text):\\n old_pos = text.get_old_pos()\\n pos = text.get_pos()\\n\\n rects = []\\n rects.append(old_pos)\\n\\n filler = pygame.Surface((self.screen_width, self.screen_height))\\n filler.fill(self.black)\\n\\n self.background.blit(filler, old_pos, old_pos)\\n\\n rects.append(pos)\\n\\n self.background.blit(text.get_text(), pos)\\n self.screen.blit(self.background, self.screen_pos)\\n\\n pygame.display.update(rects)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6519dcef6597c377d668d46fd8a82c76\",\n \"score\": \"0.72224784\",\n \"text\": \"def show_text(window,text,color):\\r\\n font = pygame.freetype.SysFont(\\\"Courier\\\", 30)\\r\\n text_surface, _ = font.render(text, color)\\r\\n text_rect = text_surface.get_rect(center = (WIDTH//2,HEIGHT//2-50))\\r\\n window.blit(text_surface, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4497dc7a76ee13f7431bcbc1b51038d6\",\n \"score\": \"0.7202334\",\n \"text\": \"def display_message(text):\\n largeText = pygame.font.Font('freesansbold.ttf', 115) # define font, size\\n TextSurf, TextRect = text_objects(text, largeText) # get image, wireframe\\n TextRect.center = ((g.gameWidth / 2), (g.gameHeight / 2)) # change wirefrawme position\\n gameDisplay.blit(TextSurf, TextRect) # show image on wireframe\\n pygame.display.update() # notify pygame there was an update\\n time.sleep(2) # wait for 2 seconds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6fc2dbf61a05705a3a75b9441d64fb7\",\n \"score\": \"0.71537554\",\n \"text\": \"def draw(self, surface): \\n \\n if self.state != None:\\n self.state.draw(surface);\\n else:\\n surface.fill((255,255,255))\\n # Draw title\\n title_text = get_asset(\\\"TitleFont\\\").render(\\\"The Chase\\\", True,\\n (0, 0, 0)) \\n title_position = \\\\\\n (((surface.get_width() - title_text.get_width()) / 2), 100)\\n surface.blit(title_text, title_position) \\n item_font = get_asset(\\\"MenuFont\\\"); \\n version_text = item_font.render(get_asset(\\\"Version\\\")[:-1], False,\\n (0, 0, 0))\\n version_position = (100, 400)\\n surface.blit(version_text, version_position) \\n\\n for item_number, item in enumerate(get_asset(\\\"AboutText\\\")):\\n item_text = item_font.render(item[:-1],False, (0,0,0));\\n item_position = \\\\\\n (((surface.get_width() - item_text.get_width()) / 2), \\n 460 + (item_text.get_height() + 10) * item_number)\\n surface.blit(item_text, item_position);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3524f29c46991e51bd1753c950196f0\",\n \"score\": \"0.7147775\",\n \"text\": \"def draw(self, surface: Union[pygame.Surface, pygame.SurfaceType], padding: int = 5) -> None:\\n if self._visible:\\n # draw background ----------------------------------------------\\n if self._bg_col is not None:\\n pygame.draw.rect(surface, self._bg_col,\\n pygame.Rect(self._rect.x,\\n self._rect.y,\\n self._rect.width,\\n self._rect.height))\\n\\n # Draw custom function\\n if self._draw_func is not None:\\n self._draw_func(surface,\\n self._rect.x, self._rect.y,\\n self._rect.width, self._rect.height,\\n self.text)\\n\\n # draw text ---------------------------------------------------\\n width, height = self._text_surface.get_size()\\n # bottom left align\\n if self._txt_align == 0:\\n surface.blit(self._text_surface, (self._rect.x + self._rect.width / 15,\\n max(self._rect.y,\\n self._rect.y + self._rect.height - height)),\\n pygame.Rect(0, max(0, height - self._rect.height),\\n self._rect.width, self._rect.height))\\n # center align\\n elif self._txt_align == 1:\\n surface.blit(self._text_surface,\\n (max(self._rect.x, (self._rect.width - width) // 2 + self._rect.x),\\n max(self._rect.y,\\n self._rect.y + (self._rect.height - height) // 2)),\\n pygame.Rect(max(0, (width - self._rect.width) // 2),\\n max(0, (height - self._rect.height) // 2),\\n self._rect.width, self._rect.height))\\n # bottom right align\\n elif self._txt_align == 2:\\n surface.blit(self._text_surface,\\n (max(self._rect.x, self._rect.x + self._rect.width - width)\\n - self._rect.width / 15,\\n max(self._rect.y, self._rect.y + self._rect.height - height)),\\n pygame.Rect(max(0, width - self._rect.width),\\n max(0, height - self._rect.height),\\n self._rect.width, self._rect.height))\\n # top left align\\n elif self._txt_align == 3:\\n surface.blit(self._text_surface, (self._rect.x + padding,\\n self._rect.y + padding))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6cd94c10beb85c75db79b467624cd6f\",\n \"score\": \"0.71426034\",\n \"text\": \"def display_text(window, font, txt, x, y, color=\\\"white\\\"):\\n tr = font.render(str(txt), 1, pygame.Color(color))\\n window.blit(tr, (x, y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c9383bc2eccda3687e17a2b69fa916f\",\n \"score\": \"0.7077754\",\n \"text\": \"def draw_text(screen, text, position, color=(0,0,0),\\n font_type='mono', font_size = 150,\\n background_color=(0,125,0)):\\n screen.fill(background_color,position)\\n font = pygame.font.SysFont(font_type, font_size)\\n surface = font.render(text, True, color)\\n surface = pygame.transform.scale(surface,(position[2],position[3]))\\n screen.blit(surface, position)\\n pygame.display.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8120388a4dad99914c68fc77130567a4\",\n \"score\": \"0.7015071\",\n \"text\": \"def DrawText(text, size, color, pos, surface, align_flag=0, outline=0,\\n outline_color=0, width=-1, effect=0, effectPer=1.0, rectDim=None,\\n font_filename=None):\\n # Initialize the offset\\n offset = [0, 0]\\n\\n # Initialize counter\\n count = 0\\n \\n if font_filename == None:\\n font_filename = DEFAULT_FONT\\n \\n # If there is no font system, return\\n if not pygame.font:\\n raise Exception('Pygame Fonts not found')\\n \\n # If there is no string, return\\n if text == '':\\n return offset\\n \\n font = GetFont(font_filename, size)\\n \\n # Determine the rect size. May have been passed an argument, otherwise use the surface\\n if rectDim is None:\\n rectDim = (0, 0, surface.get_width(), surface.get_height())\\n \\n # Split the text into lines\\n if text.find(\\\"\\\\n\\\", 0 ) != -1:\\n lines = text.split(\\\"\\\\n\\\")\\n else:\\n lines = text.split(\\\"\\\\\\\\n\\\")\\n \\n # There is an effect\\n if effect != 0:\\n # If effect is vertical stretch\\n if effect == 1:\\n # If the percentage is over half\\n if effectPer > 0.5:\\n # Invert effect percentage, so it goes down again\\n effectPer = 0.5 - (effectPer - 0.5)\\n # Find scale\\n scale = 1.0 + (0.17 * effectPer)\\n \\n # Loop through the lines\\n for source_line in lines:\\n # Check to see if this line is too long\\n wrap_list = []\\t# Wrapped sentence list\\n \\n # If the width of this line is too big for our drawing width\\n if font.size(source_line)[0] > rectDim[2]:\\n # Split the line by spaces, and add a word each time checking if it is now too long\\n words = source_line.split(' ')\\n last_sentence = test_sentence = last_word = ''\\n # Loop until we are out of words\\n while len(words) > 0:\\n last_sentence = test_sentence\\t\\t# Save the last sentence\\n last_word = words[0]\\t\\t\\t\\t# Get the first word left in the list\\n words.remove (last_word)\\t\\t\\t# Remove the word we just took from the list\\n if test_sentence != '':\\n test_sentence += \\\" \\\"\\t\\t\\t# Add space gap, if this isnt a fresh new sentence\\n test_sentence += \\\"%s\\\" % last_word\\t# Add the word to the test sentence\\n # If the test sentence is now too long\\n if font.size(test_sentence)[0] > rectDim[2]:\\n words.insert(0, last_word)\\t\\t# Put the last word into the word list again, cause it's over the limit\\n wrap_list.append(last_sentence)# Add the last sentence that was under the width to the wrapped sentence list\\n last_sentence = test_sentence = last_word = ''\\n # If the test sentence isnt blank, add it to the wrap list\\n if test_sentence != '':\\n wrap_list.append(test_sentence)\\n # Else, it fits so just add the source line to our wrapped sentence list and move on\\n else:\\n wrap_list.append(source_line)\\n \\n # We now have a list of wrapped sentences. Cycle through these to print them out\\n for line in wrap_list:\\n # If this isn't a blank line - No point in drawing blank lines\\n if line != '':\\n # If the text alignment should be centered\\n if align_flag == 1:\\n font_size = font.size(line)\\n # If the width is not specified, use the Surface width\\n if width == -1:\\n offset[0] = (rectDim[2] / 2) - (font_size[0] / 2)\\n # Else, use the specified width\\n else:\\n offset[0] = (width / 2) - (font_size[0] / 2)\\n \\n # If the text should be drawn with a black outline\\n if outline == 1 or outline == 2 or outline == 3:\\n # Set data_flag_data to black value if not provided\\n if outline_color == 0:\\n outline_color = (0,0,0,255)\\n \\n # Render the font\\n text_font = font.render(line, 1, outline_color)\\n \\n \\n # If effect is vertical stretch\\n if effect == 1:\\n text_font = pygame.transform.scale(text_font, (text_font.get_width(), float(text_font.get_height()) * scale))\\n \\n \\n # Blit the font - offset and line number adjusted\\n if outline == 1 or outline == 2:\\n surface.blit(text_font, (pos[0] + offset[0] + 1, pos[1] + offset[1] + 1 + (size * count * 1.3)))\\n if outline == 2:\\n surface.blit(text_font, (pos[0] + offset[0] + 2, pos[1] + offset[1] + 2 + (size * count * 1.3)))\\n # Thick outline, wastefully rendering 4x...\\n if outline == 3:\\n surface.blit(text_font, (pos[0] + offset[0] - 2, pos[1] + offset[1] + (size * count * 1.3)))\\n surface.blit(text_font, (pos[0] + offset[0] + 2, pos[1] + offset[1] + (size * count * 1.3)))\\n surface.blit(text_font, (pos[0] + offset[0], pos[1] + offset[1] - 1 + (size * count * 1.3)))\\n surface.blit(text_font, (pos[0] + offset[0], pos[1] + offset[1] + 1 + (size * count * 1.3)))\\n \\n # Render the font\\n text_font = font.render(line, 1, color)\\n \\n # If effect is vertical stretch\\n if effect == 1:\\n # If we havent already done this above in the flags\\n if outline == 0:\\n oldHeight = text_font.get_height()\\n newHeight = text_font.get_height()\\n offset[1] -= (newHeight-oldHeight) / 2\\n \\n # Blit the font - offset and line number adjusted - Also add the rectangle size we wanted to use for this writing (since we may want to draw in a portion of the surface, and treat it as a whole surface)\\n surface.blit(text_font, (rectDim[0] + pos[0] + offset[0], rectDim[1] + pos[1] + offset[1] + (size * count * 1.2)))\\n \\n # Increment the counter\\n count += 1\\n \\n # Return the offset and count information\\n return (offset, count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e0c4da6489e42ac127b008d8a83ae9\",\n \"score\": \"0.70117736\",\n \"text\": \"def draw_text(display_surface: T_SURFACE, text: str, coords: T_COORDINATE,\\n text_color: T_COLOR, back_color: T_COLOR = None, font: T_FONT = None, mode: str = \\\"corner\\\"):\\n if font is None:\\n font = ASSETS.F_STANDARD\\n\\n text_surf, text_rect = helper_text_objects(text, text_color, back_color, font)\\n\\n if mode == \\\"center\\\":\\n text_rect.center = coords\\n else:\\n text_rect.topleft = coords\\n\\n display_surface.blit(text_surf, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10136422d1ca061034fd28327345c91e\",\n \"score\": \"0.7000952\",\n \"text\": \"def _drawText(self) -> None:\\n if len(self.text) < 1:\\n return\\n\\n x, y, w, h = self.dims\\n small_text = pygame.font.Font(\\\"freesansbold.ttf\\\", 20)\\n text_surf = small_text.render(self.text, True, kBLACK_COLOR)\\n text_rect = text_surf.get_rect()\\n text_rect.center = ((x + (w / 2)), (y + (h / 2)))\\n self.gameDisp.blit(text_surf, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb9845a97f7d639736ec37947636d635\",\n \"score\": \"0.70002973\",\n \"text\": \"def draw(self, surface):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c97a9aa6a842e6e9adcc7fc312618b\",\n \"score\": \"0.69809306\",\n \"text\": \"def _draw_text(self, surface, **kwargs):\\n text = self.font_40.render(\\\"Select\\\", True, (255, 255, 255))\\n text_rect = text.get_rect()\\n screen_rectangle = self.game_loop.surface.get_rect()\\n text_rect.midtop = screen_rectangle.midtop\\n text_rect.centerx = screen_rectangle.centerx\\n text_rect.y += 60\\n surface.blit(text, text_rect)\\n\\n # Track rectange information for drawn info\\n rectangle_data = {}\\n\\n new_game_text = self.font_20.render(\\\"New Game\\\", True, (255, 255, 255))\\n new_game_rect = new_game_text.get_rect()\\n new_game_rect.midtop = screen_rectangle.midtop\\n new_game_rect.centerx = screen_rectangle.centerx\\n new_game_rect.y = text_rect.y + 60\\n rectangle_data[0] = new_game_rect\\n surface.blit(new_game_text, new_game_rect)\\n\\n for idx, fn in enumerate(self.save_files, start=1):\\n display = fn[:-5].replace(\\\"_\\\", \\\" \\\").title()\\n slot_text = self.font_20.render(display, True, (255, 255, 255))\\n slot_rect = slot_text.get_rect()\\n slot_rect.midtop = screen_rectangle.midtop\\n slot_rect.centerx = screen_rectangle.centerx\\n y_val = 40 * idx\\n slot_rect.y = new_game_rect.y + y_val\\n rectangle_data[idx] = slot_rect\\n surface.blit(slot_text, slot_rect)\\n\\n display = \\\"Exit\\\"\\n exit_text = self.font_20.render(display, True, (255, 255, 255))\\n exit_rect = exit_text.get_rect()\\n exit_rect.midtop = screen_rectangle.midtop\\n exit_rect.centerx = screen_rectangle.centerx\\n y_val = 40 * (len(self.save_files) + 1)\\n exit_rect.y = new_game_rect.y + y_val\\n rectangle_data[len(self.save_files) + 1] = exit_rect\\n surface.blit(exit_text, exit_rect)\\n\\n # Given the current index value for the selections, draw an arrow.\\n select = self.font_20.render(\\\">> \\\", True, (255, 255, 255))\\n select_rect = new_game_text.get_rect()\\n select_rect.midleft = rectangle_data[self.select_index].midleft\\n select_rect.x = select_rect.x - 50\\n select_rect.y = rectangle_data[self.select_index].y\\n surface.blit(select, select_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8babfd0eec13f78953e044314be7e93\",\n \"score\": \"0.69743544\",\n \"text\": \"def draw(self, surface):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"089c8d6c9247f3cfb64fd7f4064a4558\",\n \"score\": \"0.6934426\",\n \"text\": \"def draw(self, surface):\\n pass;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b422d8e8ad96e4044085cae231bd0abc\",\n \"score\": \"0.6928526\",\n \"text\": \"def draw(self, win):\\n font = pygame.font.SysFont('consolas', 20)\\n text = font.render(self.text, True, self.color, (0, 0, 0))\\n win.blit(text, (self.x, self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d5fe2b55df3ab3f44331a435f2dfc2\",\n \"score\": \"0.69261247\",\n \"text\": \"def _debug_outtext(self, screen):\\r\\n textsurface = self._debug_text.render(\\r\\n self._debug_text_message,\\r\\n False,\\r\\n DEBUG_COLOR)\\r\\n screen.blit(textsurface, (20, 20))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e15ce90a700fc5633b6d01240be31d3f\",\n \"score\": \"0.6920018\",\n \"text\": \"def render(self, screen, text):\\n self.surface.blit(self.subsurf, (TEXTOFFSETX, TEXTOFFSETY))\\n self.subsurf.blit(self.background, (0, 0))\\n pygame.draw.rect(self.surface, self.linecolor, self.surface.get_rect(), 1)\\n\\n self.index = 0\\n\\n # als het tuple is met comma's gescheiden voor een nieuwe regel\\n if type(text) == tuple:\\n for line in text:\\n # Beschouw de door komma gescheiden regels toch als individuele string_text.\\n self.string_text(line)\\n\\n # als het een list is met paren van tuples\\n elif type(text) == list:\\n self.tuple_pair_text(text)\\n\\n # als het alleen maar text achter elkaar is\\n elif type(text) == str:\\n self.string_text(text)\\n\\n screen.blit(self.surface, self.rect.topleft)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69d9827134eb6708374715fc0b9f4330\",\n \"score\": \"0.69138813\",\n \"text\": \"def draw(self, surface):\\n\\n\\t\\tsurface.blit(self.surface, (0, 0), (-self._shift[0], -self._shift[1], self._resx, self._resy))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6762b21920949238b78d6183c70223ef\",\n \"score\": \"0.69088197\",\n \"text\": \"def create_text_surface(text, size=16):\\r\\n return pygame.font.SysFont(\\\"comic sans\\\", size).render(text, 1, (0, 0, 0));\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba15af49e9f07c65797c5d64c2b3b01\",\n \"score\": \"0.68811274\",\n \"text\": \"def draw_text(self, text, x=50, y=150, centered=False, \\n color=(0,0,0), fontsize=24):\\n self.font = pygame.font.SysFont('mono', fontsize, bold=True)\\n fw, fh = self.font.size(text) # calculate size of rect\\n surface = self.font.render(text, True, color)\\n if centered:\\n self.screen.blit(surface, (x - fw/2,y-fh/2))\\n else:\\n self.screen.blit(surface, (x,y)) # x,y is topleft corner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbe3c67d2acfa233c431a847c6ae2852\",\n \"score\": \"0.6877316\",\n \"text\": \"def _draw_text(self, text, x_position, y_position):\\n\\n text_surface = self.font.render(text, True, self.white)\\n text_rect = text_surface.get_rect()\\n text_rect.center = (x_position, y_position)\\n self.display.blit(text_surface, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88fbc5b49153b17f97c21fe771e241cb\",\n \"score\": \"0.6870249\",\n \"text\": \"def draw_text(self, text, **kwargs):\\n surf = self.font.render(text, 1, self.config[\\\"font\\\"][\\\"color\\\"],\\n self.config[\\\"font\\\"][\\\"bgd_color\\\"])\\n rect = surf.get_rect(**kwargs)\\n self.game.display.blit(surf, rect)\\n self.game.changed_areas.append(rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5093359e8cbb12dc7111daa3902f53\",\n \"score\": \"0.6853393\",\n \"text\": \"def showText(self, text, position, color, fontName=\\\"Times\\\", fontSize=12, bold=False, italic=False,\\n anchorCenter=False, shadowWidth=0, secondaryColor=(0, 0, 0), outlineWidth=0, shiftPosition=False):\\n\\n if shiftPosition:\\n if self.usePygame:\\n if outlineWidth != 0:\\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\\n screenText = font.render(text, 1, secondaryColor)\\n if anchorCenter:\\n textW = screenText.get_width()\\n textH = screenText.get_height()\\n else:\\n textW = 0\\n textH = 0\\n\\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\\n x = outlineWidth * math.sin(angle)\\n y = outlineWidth * math.cos(angle)\\n self.display.blit(screenText, (int(self.getScreenX(position[0] - textW / 2 + x)),\\n int(self.getScreenY(position[1] - textH / 2 + y))))\\n elif shadowWidth != 0:\\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\\n screenText = font.render(text, 1, secondaryColor)\\n if anchorCenter:\\n textW = screenText.get_width()\\n textH = screenText.get_height()\\n else:\\n textW = 0\\n textH = 0\\n for shift in range(shadowWidth):\\n self.display.blit(screenText, (int(self.getScreenX(position[0] - textW / 2 + shift)),\\n int(self.getScreenY(position[1] - textH / 2))))\\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\\n screenText = font.render(text, 1, color)\\n if anchorCenter:\\n textW = screenText.get_width()\\n textH = screenText.get_height()\\n else:\\n textW = 0\\n textH = 0\\n self.display.blit(screenText, (\\n int(self.getScreenX(position[0] - textW / 2)), int(self.getScreenY(position[1] - textH / 2))))\\n else:\\n tk_rgb = \\\"#%02x%02x%02x\\\" % color\\n fontString = fontName + \\\" \\\" + str(fontSize)\\n if bold:\\n fontString += \\\" bold\\\"\\n if italic:\\n fontString += \\\" italic\\\"\\n if anchorCenter:\\n if outlineWidth != 0:\\n secondary_tk_rgb = \\\"#%02x%02x%02x\\\" % secondaryColor\\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\\n x = outlineWidth * math.sin(angle)\\n y = outlineWidth * math.cos(angle)\\n self.canvas.create_text(int(self.getScreenX(position[0] + x)),\\n int(self.getScreenY(position[1] + y)), fill=secondary_tk_rgb,\\n font=fontString, text=text)\\n elif shadowWidth != 0:\\n secondary_tk_rgb = \\\"#%02x%02x%02x\\\" % secondaryColor\\n for shift in range(shadowWidth):\\n self.canvas.create_text(int(self.getScreenX(position[0] + shift)),\\n int(self.getScreenY(position[1])), fill=secondary_tk_rgb,\\n font=fontString, text=text)\\n self.canvas.create_text(int(self.getScreenX(position[0])), int(self.getScreenY(position[1])),\\n fill=tk_rgb, font=fontString, text=text)\\n else:\\n if outlineWidth != 0:\\n secondary_tk_rgb = \\\"#%02x%02x%02x\\\" % secondaryColor\\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\\n x = outlineWidth * math.sin(angle)\\n y = outlineWidth * math.cos(angle)\\n self.canvas.create_text(int(self.getScreenX(position[0] + x)),\\n int(self.getScreenY(position[1] + y)), fill=secondary_tk_rgb,\\n font=fontString, text=text, anchor=NW)\\n elif shadowWidth != 0:\\n secondary_tk_rgb = \\\"#%02x%02x%02x\\\" % secondaryColor\\n for shift in range(shadowWidth):\\n self.canvas.create_text(int(self.getScreenX(position[0] + shift)),\\n int(self.getScreenY(position[1])), fill=secondary_tk_rgb,\\n font=fontString, text=text, anchor=NW)\\n self.canvas.create_text(int(self.getScreenX(position[0])), int(self.getScreenY(position[1])),\\n fill=tk_rgb, font=fontString, text=text, anchor=NW)\\n else:\\n if self.usePygame:\\n if outlineWidth != 0:\\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\\n screenText = font.render(text, 1, secondaryColor)\\n if anchorCenter:\\n textW = screenText.get_width()\\n textH = screenText.get_height()\\n else:\\n textW = 0\\n textH = 0\\n\\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\\n x = outlineWidth * math.sin(angle)\\n y = outlineWidth * math.cos(angle)\\n self.display.blit(screenText,\\n (int(position[0] - textW / 2) + x, int(position[1] - textH / 2) + y))\\n elif shadowWidth != 0:\\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\\n screenText = font.render(text, 1, secondaryColor)\\n if anchorCenter:\\n textW = screenText.get_width()\\n textH = screenText.get_height()\\n else:\\n textW = 0\\n textH = 0\\n for shift in range(shadowWidth):\\n self.display.blit(screenText,\\n (int(position[0] - textW / 2) + shift, int(position[1] - textH / 2)))\\n font = pygame.font.SysFont(fontName, fontSize, bold, italic)\\n screenText = font.render(text, 1, color)\\n if anchorCenter:\\n textW = screenText.get_width()\\n textH = screenText.get_height()\\n else:\\n textW = 0\\n textH = 0\\n self.display.blit(screenText, (int(position[0] - textW / 2), int(position[1] - textH / 2)))\\n else:\\n tk_rgb = \\\"#%02x%02x%02x\\\" % color\\n fontString = fontName + \\\" \\\" + str(fontSize)\\n if bold:\\n fontString += \\\" bold\\\"\\n if italic:\\n fontString += \\\" italic\\\"\\n if anchorCenter:\\n if outlineWidth != 0:\\n secondary_tk_rgb = \\\"#%02x%02x%02x\\\" % secondaryColor\\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\\n x = outlineWidth * math.sin(angle)\\n y = outlineWidth * math.cos(angle)\\n self.canvas.create_text(position[0] + x, position[1] + y, fill=secondary_tk_rgb,\\n font=fontString, text=text)\\n elif shadowWidth != 0:\\n secondary_tk_rgb = \\\"#%02x%02x%02x\\\" % secondaryColor\\n for shift in range(shadowWidth):\\n self.canvas.create_text(position[0] + shift, position[1], fill=secondary_tk_rgb,\\n font=fontString, text=text)\\n self.canvas.create_text(position[0], position[1], fill=tk_rgb, font=fontString, text=text)\\n else:\\n if outlineWidth != 0:\\n secondary_tk_rgb = \\\"#%02x%02x%02x\\\" % secondaryColor\\n for angle in range(0, 361, int(8 / math.sqrt(outlineWidth)) + 1):\\n x = outlineWidth * math.sin(angle)\\n y = outlineWidth * math.cos(angle)\\n self.canvas.create_text(position[0] + x, position[1] + y, fill=secondary_tk_rgb,\\n font=fontString, text=text, anchor=NW)\\n elif shadowWidth != 0:\\n secondary_tk_rgb = \\\"#%02x%02x%02x\\\" % secondaryColor\\n for shift in range(shadowWidth):\\n self.canvas.create_text(position[0] + shift, position[1], fill=secondary_tk_rgb,\\n font=fontString, text=text, anchor=NW)\\n self.canvas.create_text(position[0], position[1], fill=tk_rgb, font=fontString, text=text,\\n anchor=NW)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d5b91aaf53db24008ea971dfc6c714\",\n \"score\": \"0.68530595\",\n \"text\": \"def draw(self, surface):\\n\\n\\t\\tsurface.blit(self.surface, self._pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce7b7260a3c294ad138dc01e504ebc9\",\n \"score\": \"0.68499744\",\n \"text\": \"def draw(self, surface):\\n if self.message_str:\\n # message_rect = pygame.Rect(0,0,0,0)\\n x = self.rect.centerx\\n bottom = self.rect.top - 10\\n # message_rect.bottom = self.rect.top - 10\\n return draw_message(x, bottom, self.message_str, surface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92336245a9f236548813e5c9c6c1c926\",\n \"score\": \"0.6843732\",\n \"text\": \"def draw_text(self, text, size, color, x, y, align='center', fontname=\\\"Monospace_default.ttf\\\"):\\n font = pygame.font.Font(path.join(self.font_folder, fontname), size)\\n text_surface = font.render(text, True, color)\\n text_rect = text_surface.get_rect()\\n if align == \\\"nw\\\":\\n text_rect.topleft = (x, y)\\n if align == \\\"ne\\\":\\n text_rect.topright = (x, y)\\n if align == \\\"sw\\\":\\n text_rect.bottomleft = (x, y)\\n if align == \\\"se\\\":\\n text_rect.bottomright = (x, y)\\n if align == \\\"n\\\":\\n text_rect.midtop = (x, y)\\n if align == \\\"s\\\":\\n text_rect.midbottom = (x, y)\\n if align == \\\"e\\\":\\n text_rect.midright = (x, y)\\n if align == \\\"w\\\":\\n text_rect.midleft = (x, y)\\n if align == \\\"center\\\":\\n text_rect.center = (x, y)\\n self.screen.blit(text_surface, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b896d8ba58dedf1a28cddae9b4be71c6\",\n \"score\": \"0.6811034\",\n \"text\": \"def draw(self, surface):\\n surface.blit(pygame.transform.scale(self.title_img, (600, 540)), (0, 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8490c3a7ea103550cbd86fa643388d1\",\n \"score\": \"0.6777048\",\n \"text\": \"def _draw_text(self, text, font_size, x_coordinate, y_coordinate, color=(255, 255, 255)):\\n\\n font = pygame.font.Font(FONT_PATH, font_size)\\n text_surf = font.render(text, True, color)\\n text_rect = text_surf.get_rect()\\n text_rect.center = (x_coordinate, y_coordinate)\\n self._display.blit(text_surf, text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"871fafecf48059389cec3a291f128bc0\",\n \"score\": \"0.6725989\",\n \"text\": \"def paint(self, screen):\\n if self._background:\\n screen.blit(self._background, (0, 0))\\n else:\\n screen.fill(self._background_color)\\n if self._overlay:\\n screen.blit(self._overlay, self._overlay.get_rect(center=self._rect.center))\\n for text_surface, pos in self._texts:\\n screen.blit(text_surface, pos)\\n self._need_update = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7602c3f4acbfe0d8992cb302c791eab\",\n \"score\": \"0.6723012\",\n \"text\": \"def draw_text(surf, text, font, pos, color):\\n\\tlabel = font.render(text, 1, color)\\n\\tposi = label.get_rect(centerx=pos[0], centery=pos[1])\\n\\tsurf.blit(label, posi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607ff7f5de096c8dd87a6b235bc3dfe9\",\n \"score\": \"0.67189693\",\n \"text\": \"def draw_text(SCREEN, text, x, y):\\r\\n font = pygame.font.SysFont('Consolas', 30, False, False)\\r\\n text_to_draw = font.render(text, True, Color(255,255,255))\\r\\n SCREEN.blit(text_to_draw, (x, y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17a9d6724050f3b9fae94a61a5408ff\",\n \"score\": \"0.6700834\",\n \"text\": \"def DrawText(self):\\n\\n titleText = \\\"Game of Life\\\"\\n controlsText = ['Sterowanie:',\\n 'R - resetowanie planszy', \\n 'D - losowy uklad', \\n 'S/Spacja - zatrzymanie/wznowienie',\\n 'B - aktywacja/dezaktywacja scian',\\n 'Up/Down - zmiana tempa, Right - przejscie o 1 generacje',\\n 'Q - wyjscie']\\n configText = ['Obecne ustawienia:',\\n 'Tempo: ', \\n 'Obecna generacja:',\\n 'Sciany:']\\n \\n font = pygame.font.SysFont('Harrington', 70, True)\\n text = font.render(titleText, 1, (255, 0, 0))\\n self.screen.blit(text, (350, 0))\\n\\n font = pygame.font.SysFont('DejaVu Sans Mono', 20)\\n offset_y = 20\\n\\n for line in controlsText:\\n text = font.render(line, True, (120, 155, 220))\\n self.screen.blit(text, (20, offset_y))\\n offset_y += 20\\n\\n offset_y = 20\\n for line in configText:\\n text = font.render(line, True, (120, 155, 220))\\n self.screen.blit(text, (1000, offset_y))\\n offset_y += 20\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b4d16046cdc14350098baf5d7e4db3\",\n \"score\": \"0.6684227\",\n \"text\": \"def TextDisplay(file,y_pos,width,size,p,xx=\\\" \\\",xr=\\\" \\\",yy=\\\" \\\",yr=\\\" \\\"):\\n string = filter(None,[strin.replace(\\\"\\\\n\\\",'') for strin in open(file,'r').readlines()])\\n string = [strin.decode('utf-8') for strin in string]\\n string = [strin.replace(xx,xr).replace(yy,yr) for strin in string]\\n wrappedstring=[]\\n for stri in string:\\n new=textwrap.wrap(stri,width)\\n for st in new:\\n wrappedstring.append(st)\\n wrappedstring.append('')\\n shift=0\\n for strin in wrappedstring: \\n font = pygame.font.Font(None, size)\\n text = font.render(strin,1, (10, 10, 10))\\n textpos = text.get_rect()\\n textpos.centerx= (p.rect[0]/2)\\n textpos.centery = (y_pos+shift)\\n p.screen.blit(text, textpos)\\n shift+=size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed2a813bec599d4f4b03e56455d8142\",\n \"score\": \"0.66830486\",\n \"text\": \"def showTieText(self):\\n self.screen.blit(self.tie_text, (200, 900))\\n pygame.display.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddbad452dd25e1e9b59119c37443a783\",\n \"score\": \"0.6679969\",\n \"text\": \"def update_surface(self):\\n if (self.__m_font):\\n self.__m_font.set_bold(self.highlight)\\n self.image = self.__m_font.render(self.text\\n , True\\n , self.colour)\\n if (self.rect):\\n # Used the current rects center point\\n self.rect = self.image.get_rect(center=self.rect.center)\\n else:\\n self.rect = self.image.get_rect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97df67d20e5d80bbb241fdcabe98e60\",\n \"score\": \"0.666756\",\n \"text\": \"def draw_text(self, surface, text, pos, color=TEXT_COLOR, align_center=False):\\n text_surface = self.font.render(text, True, color)\\n pos = list(pos)\\n if align_center:\\n # offset pos if was passed center\\n pos[0] -= text_surface.get_width() // 2\\n pos[1] -= text_surface.get_height() // 2\\n surface.blit(text_surface, pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c51734959bb42ee35ab31c1d201ec2af\",\n \"score\": \"0.6636659\",\n \"text\": \"def after_draw(self, surface: pygame.Surface) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60044312d6f34aa5000b70c11a6743b8\",\n \"score\": \"0.66356754\",\n \"text\": \"def draw(self, window):\\n self.image.blit(self.text, self.text_rect)\\n window.blit(self.image, self.rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a079efea1305b1a287e9b85a642e5d0\",\n \"score\": \"0.6620129\",\n \"text\": \"def render(self, display):\\n\\n surf = pygame.transform.rotate(self.surf, -math.degrees(self.angle))\\n size = surf.get_size()\\n\\n display.blit(surf, [self.pos[i] - (size[i] / 2) for i in range(2)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b21786898a6cc2d40fc77f98bcefbc7\",\n \"score\": \"0.66106874\",\n \"text\": \"def draw(self, surface: pygame.Surface) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22360b68f4ded552e71a356d26ccd2a5\",\n \"score\": \"0.6589932\",\n \"text\": \"def on_draw(self):\\n\\n arcade.start_render()\\n arcade.draw_text(self.text, 100, 100, arcade.color.BLACK, 32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901cba4144309571f1201f73cf020fde\",\n \"score\": \"0.6586272\",\n \"text\": \"def flip(self):\\r\\n pygame.display.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ef64bba15dc1ee4f35469008caaae1\",\n \"score\": \"0.6578322\",\n \"text\": \"def draw(self, score):\\n self.surface = self.font.render(str(score), False, self.color)\\n self.center()\\n self.screen.blit(self.surface, (self.x, self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb8542485a1d94a141743b563fc57e1\",\n \"score\": \"0.65721506\",\n \"text\": \"def _render_surfaces(self):\\r\\n\\r\\n rect = self.base[\\\"rect\\\"]\\r\\n totals = [size_dict.get(-1, (0, 0)) for size_dict in self.surface_sizes]\\r\\n total_height = sum([val[1] for val in totals])\\r\\n text_left = self.base[\\\"frame\\\"] * 2 # left margin for text\\r\\n\\r\\n surface = pygame.Surface(rect.size)\\r\\n if self.base[\\\"background_colour\\\"] is None:\\r\\n surface.fill(BLACK)\\r\\n surface.set_colorkey(BLACK)\\r\\n else:\\r\\n surface.fill(self.base[\\\"background_colour\\\"])\\r\\n\\r\\n self._do_frame(surface)\\r\\n\\r\\n if self.base[\\\"v_justification\\\"] > 0:\\r\\n text_y = (rect.height - (self.base[\\\"v_justification\\\"] - 1) * 40 - total_height) / 2\\r\\n else:\\r\\n text_y = 0\\r\\n\\r\\n number = 0\\r\\n for dictionary in self.surface_dicts:\\r\\n size_dict = self.surface_sizes[number]\\r\\n text_width, text_height, text_start = 0, 0, 0\\r\\n\\r\\n for key in dictionary:\\r\\n if key == 0:\\r\\n text_width, text_height = size_dict[-1]\\r\\n if self.base[\\\"justification\\\"] == 1:\\r\\n text_start = (rect.width - text_width) / 2\\r\\n elif self.base[\\\"justification\\\"] == 2:\\r\\n text_start = rect.width - text_width - text_left\\r\\n else:\\r\\n text_start = text_left\\r\\n # Blit sections aligned by base\\r\\n surface.blit(dictionary[key], (text_start, text_y + text_height - size_dict[key][1]))\\r\\n text_start += size_dict[key][0]\\r\\n text_y += text_height\\r\\n number += 1\\r\\n return surface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a17d8ae807a82a5630129fe0308f0824\",\n \"score\": \"0.6562091\",\n \"text\": \"def set_text(self, text=()):\\n self.surfaces = []\\n for i, string in enumerate(text):\\n surface = self.font.render(string, True, self.color)\\n x = -surface.get_width() / 2 if self.centralised else 0\\n y = i * self.letter_h\\n self.surfaces.append([surface, x, y])\\n\\n self.w = max(line.get_size()[0] for line, _, _ in self.surfaces) if text else 0\\n self.h = self.letter_h * len(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d39eb933d0e6009aa1943bd7d81ec47\",\n \"score\": \"0.6551663\",\n \"text\": \"def draw(self):\\n pygame.draw.rect(self.screen, self.current_color,\\n (self.x, self.y, self.width, self.height))\\n small_text = pygame.font.SysFont(\\\"comicsansms\\\", 30)\\n text_surface, text_rectangle = self.create_text_objects(self.name,\\n small_text)\\n text_rectangle.center = ((self.x + (self.width / 2)),\\n (self.y + (self.height / 2)))\\n self.screen.blit(text_surface, text_rectangle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ac116c550278775021bd0c67054833f\",\n \"score\": \"0.6544178\",\n \"text\": \"def message(window, text, col, size=20):\\n info_text = pygame.font.Font(None, size)\\n text_surf, text_rect = text_objects(text, info_text, col)\\n text_rect.center = ((constants.WINDOW_SIDE/2), (constants.WINDOW_SIDE/2))\\n window.blit(text_surf, text_rect)\\n pygame.display.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60da591b5521b775e28a901d446f5df2\",\n \"score\": \"0.6542048\",\n \"text\": \"def addText(self):\\n displayWindow.blit(self.font.render(self.text, True, (0, 0, 0)),\\n (self.pos_x + self.width / 2 - 10, self.pos_y))\\n pygame.display.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aefd87745e0e6f9abb63d0b87b4c9861\",\n \"score\": \"0.65417427\",\n \"text\": \"def redraw_window(surface, font, size, colors, UI, texts):\\n\\n surface.fill((0, 0, 0)) # black color\\n for element, text in zip(UI, texts):\\n pygame.draw.rect(surface, colors[1], element)\\n gobox = text.get_rect(center=(element.centerx, element.centery))\\n surface.blit(text, gobox)\\n\\n pygame.display.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ffd3a2d362afc626750604cdc81ec8a\",\n \"score\": \"0.6521876\",\n \"text\": \"def board(self):\\n self.screen.blit(self.board, (0, 0))\\n self.screen.blit(self.quiz_text, (800, 0))\\n self.screen.blit(self.directions_text, (50, 75))\\n pygame.display.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cea29540fccb9641d4f9f6b417f715\",\n \"score\": \"0.6512937\",\n \"text\": \"def UpdateText(self):\\n\\n font = pygame.font.SysFont('DejaVu Sans Mono', 25)\\n offset_y = 40\\n \\n # draw empty rectangle over old text\\n rect = (1200, offset_y, 1250, offset_y + 20)\\n pygame.draw.rect(self.screen, self.BACKGROUND_COLOR, rect)\\n\\n # Tempo\\n text = font.render(str(self.speed), True, (120, 155, 220))\\n self.screen.blit(text, (1200, offset_y))\\n offset_y += 20\\n\\n # Generation counter\\n\\n rect = (1200, offset_y, 1250, offset_y + 20)\\n pygame.draw.rect(self.screen, self.BACKGROUND_COLOR, rect)\\n\\n text = font.render(str(self.generationCounter), True, (120, 155, 220))\\n self.screen.blit(text, (1200, offset_y))\\n offset_y += 20\\n\\n # BorderMode\\n\\n if self.bordersUp == True:\\n borderText = \\\"Aktywne\\\"\\n else:\\n borderText = \\\"Nieaktywne\\\"\\n\\n rect = (1200, offset_y, 1250, offset_y + 20)\\n pygame.draw.rect(self.screen, self.BACKGROUND_COLOR, rect)\\n\\n text = font.render(borderText, True, (120, 155, 220))\\n self.screen.blit(text, (1200, offset_y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b2df5955d79e7b3c69c2ffb3fc898e\",\n \"score\": \"0.65028906\",\n \"text\": \"def render(self, surface):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f40ad4ea0af40f44739f218ea5fec68\",\n \"score\": \"0.6500956\",\n \"text\": \"def draw_text(screen, text, X, Y):\\n font = pygame.font.Font(\\\"fonts/dpcomic/dpcomic.ttf\\\", text_size)\\n text_img = font.render(text, True, text_colour)\\n text_rect = text_img.get_rect()\\n text_rect.center = (X, Y)\\n screen.blit(text_img, text_rect)\\n return [text_img, text_rect]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95b2465c6fa038c14248047953d98ad5\",\n \"score\": \"0.648434\",\n \"text\": \"def draw_text(self, text, color = (0, 255, 150), dw = 0, dh = 0):\\n fw, fh = self.font.size(text) # fw: font width, fh: font height\\n surface = self.font.render(text, True, color)\\n # // makes integer division in python3\\n self.screen.blit(surface, ((self.width - fw - dw) // 2, (self.height - dh) // 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95b2465c6fa038c14248047953d98ad5\",\n \"score\": \"0.648434\",\n \"text\": \"def draw_text(self, text, color = (0, 255, 150), dw = 0, dh = 0):\\n fw, fh = self.font.size(text) # fw: font width, fh: font height\\n surface = self.font.render(text, True, color)\\n # // makes integer division in python3\\n self.screen.blit(surface, ((self.width - fw - dw) // 2, (self.height - dh) // 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"088362a0277febe9bc4a655bc0ad3732\",\n \"score\": \"0.64792496\",\n \"text\": \"def display_score(surface):\\n font = pygame.font.Font('freesansbold.ttf', 25)\\n text = font.render(str(score), True, (255, 255, 255))\\n textRect = text.get_rect()\\n textRect.center = (WIDTH * 0.9, HEIGHT * 0.95)\\n surface.blit(text, textRect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f04ee3f3f26e2a0dbd555f1f913f1e4\",\n \"score\": \"0.64735323\",\n \"text\": \"def draw(self):\\r\\n pygame.draw.line(screen, (0, 0, 0), (self.side_buffer // 2, self.y), (WIDTH - self.side_buffer // 2, self.y), 2);\\r\\n for i in drange(self.start, self.end + 1, STEP):\\r\\n x = self.__apply(i * self.scale);\\r\\n if not i % 1:\\r\\n pygame.draw.line(screen, (100, 100, 255), (x, self.y - self.hash_height), (x, self.y + self.hash_height), 3);\\r\\n text = create_text_surface(str(i));\\r\\n screen.blit(text, (x - text.get_width() / 2, self.y + self.number_position * 2 * self.hash_height + self.number_position * 8));\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51ef8074450f68ab5dee5917a4c9ff26\",\n \"score\": \"0.64438915\",\n \"text\": \"def effect(my_canvas, text_img):\\n # Derive working variables and working surfaces\\n (pic_wid, pic_ht) = text_img.get_size()\\n cent_x = 250\\n\\n tmp_screen = pg.Surface((4, pic_ht))\\n tmp_screen2 = pg.Surface((4, pic_ht))\\n\\n # go through the text message image, one slice at a time\\n for x in range(0, pic_wid, 4):\\n tmp_screen.fill((255, 255, 255))\\n tmp_screen.blit(text_img, (0, 0), (x, 0, 4, pic_ht))\\n tmp_screen.set_colorkey((255, 255, 255))\\n\\n trig_x = x + config.scrollback\\n my_xy = (trig_x, config.Y_OFFSET + (pic_ht / 2))\\n\\n if (trig_x > (cent_x - MAG_RAD)) & (trig_x < (cent_x + MAG_RAD)):\\n\\n mag_x = abs(cent_x - trig_x)\\n mag_factor = int(math.sin(math.acos(mag_x / MAG_RAD)) * MAG_RAD)\\n\\n tmp_screen2 = pg.transform.scale(tmp_screen, (4, mag_factor * 4))\\n (tmp_wid, tmp_ht) = tmp_screen2.get_size()\\n if tmp_ht < (pic_ht / 2):\\n tmp_screen2 = tmp_screen\\n\\n my_xy = (trig_x, config.Y_OFFSET - (tmp_ht / 2) + 50)\\n my_canvas.blit(tmp_screen2, my_xy)\\n else:\\n my_canvas.blit(tmp_screen, my_xy)\\n\\n return my_canvas, text_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b918ae060d4d8c491db65284121dbeb5\",\n \"score\": \"0.6432668\",\n \"text\": \"def render(self) -> NoReturn:\\n self.surface.fill(self.background_colour)\\n\\n for text_object in self.text_objects:\\n if not text_object.hidden:\\n text_object.render(self.surface)\\n\\n pygame.display.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aacff62773689d02735acc8d88847f3\",\n \"score\": \"0.6425863\",\n \"text\": \"def draw_message_text(self, text, color):\\n pygame.draw.rect(self.screen, Conf.SCREEN_COLOR, Conf.MESSAGE_RECT, 0) # clean\\n\\n font = pygame.font.SysFont(pygame.font.get_default_font(), 20)\\n text_img = font.render(text, 1, color)\\n \\n self.screen.blit(text_img, Conf.MESSAGE_RECT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25878ae13df442b8718b099f5196594a\",\n \"score\": \"0.63702816\",\n \"text\": \"def draw(self, surface):\\n if self.dirty:\\n self.dirty = False\\n for i in range(3):\\n for j in range(3):\\n corner = [i, j]\\n originpos = [0, 0]\\n originsize = [0, 0]\\n size = [0, 0]\\n destpos = [0, 0]\\n for k in range(2):\\n if corner[k] == 0:\\n originpos[k] = 0\\n size[k] = self.frameSize\\n originsize[k] = self.frameSize\\n destpos[k] = 0\\n elif corner[k] == 1:\\n originpos[k] = self.frameSize\\n size[k] = self.size[k] - self.frameSize * 2\\n originsize[k] = self.borderWidth\\n destpos[k] = self.frameSize\\n else:\\n originpos[k] = self.frameSize + self.borderWidth\\n size[k] = self.frameSize\\n originsize[k] = self.frameSize\\n destpos[k] = self.size[k] - self.frameSize\\n # print >> stderr, \\\"destpos: \\\", destpos, self.size, size\\n surface.blit(pygame.transform.scale(self.image.subsurface((originpos[0], originpos[1],\\n originsize[0], originsize[1])),\\n size),\\n destpos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55001682f21adddf8415997c8778f3fb\",\n \"score\": \"0.63672936\",\n \"text\": \"def draw(self, surface):\\n surface.blit(self.bimage, self.brect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab155ddf920c53a3f81c05242583819\",\n \"score\": \"0.635951\",\n \"text\": \"def text_box(text):\\n\\n pygame.draw.rect(window, black, (50, 350, 750, 140))\\n pygame.draw.rect(window, white, (55, 355, 740, 130))\\n pygame.draw.rect(window, black, (60, 360, 730, 120))\\n\\n smallText = pygame.font.SysFont(\\\"comicsansms\\\",20)\\n TextSurf, TextRect = text_objects(text, smallText, white)\\n TextRect.center = (415, 415)\\n window.blit(TextSurf, TextRect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192a2b689ed69edb08e82f9c51404522\",\n \"score\": \"0.63206553\",\n \"text\": \"def draw(self):\\n self.beams.draw(self.screen)\\n pg.display.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfba5ef6c66e8c9f706680d758e8a668\",\n \"score\": \"0.6300541\",\n \"text\": \"def play(self, screen, new_position,inverse=False,hidden=False):\\n self.erase(screen)\\n self.position=new_position\\n screen.blit(self.display(inverse=inverse,hidden=hidden),new_position) #rendre transparent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb851b12d315b1abbd1356d079d2d41\",\n \"score\": \"0.62951726\",\n \"text\": \"def render_text(self, text, z_pos, y_pos, x_pos,\\n font=GLUT.GLUT_BITMAP_HELVETICA_18):\\n GL.glDisable(GL.GL_LIGHTING)\\n GL.glColor3f(*self.white)\\n GL.glRasterPos3f(x_pos, y_pos, z_pos)\\n for character in text:\\n if character == '\\\\n':\\n y_pos = y_pos - 20\\n GL.glRasterPos3f(x_pos, y_pos, z_pos)\\n else:\\n GLUT.glutBitmapCharacter(font, ord(character))\\n\\n GL.glEnable(GL.GL_LIGHTING)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"378ed7786229c6f76b8b0750b5282c10\",\n \"score\": \"0.62896353\",\n \"text\": \"def draw(self,surface):\\n if self.image is not \\\"\\\":\\n surface.blit(self.image, (self.x,self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f067076a217174245c7b58c454d0198\",\n \"score\": \"0.6285832\",\n \"text\": \"def update_text(screen, messages):\\n textSize = 20\\n font = pygame.font.Font(None, 20)\\n\\n #use a rectangle to cover up old text\\n pygame.draw.rect(screen,black,[310,0,200,620],0)\\n\\n #loop through each of the strings in the list to print them on the side panel\\n Y_increment=0\\n for message in messages:\\n textY=textSize+Y_increment\\n text=font.render(message, True, white, black)\\n textRect = text.get_rect()\\n textRect.centerx = (NUM_COLS + 1) * WIDTH + 30\\n textRect.centery = textY\\n screen.blit(text, textRect)\\n Y_increment+=textSize\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10efb7248f301043482222f733d67062\",\n \"score\": \"0.62805206\",\n \"text\": \"def draw(self):\\n\\n self.Game.screen.blit(self.background_image, self.background_rect)\\n self.Game.screen.blit(self.enter_text, self.enter_rect)\\n self.Game.screen.blit(self.ending_text, self.ending_text_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f01d543304c25fe744b9c343e6f9153e\",\n \"score\": \"0.6278696\",\n \"text\": \"def ezViewText(self, text, location, fgcolor = Color.WHITE, bgcolor = Color.BLACK):\\r\\n if(len(text)<0):\\r\\n return None\\r\\n alpha = 255\\r\\n tsurface = self._mFont.render(text, True, self._csvRGB2pgColor(fgcolor, alpha), self._csvRGB2pgColor(bgcolor, alpha))\\r\\n self._mSurface.blit(tsurface, location)\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"846f94ab66f174924852f50e1702619a\",\n \"score\": \"0.62671095\",\n \"text\": \"def drawTextOnPosition2D(x, y, text):\\n glRasterPos3f(x, y, 0.0)\\n drawTextOnDisplay(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2781fd7964eaef745ffd2a1f3f9a92\",\n \"score\": \"0.62662536\",\n \"text\": \"def draw(self, surface, font_name, size, text, rect_or_pos_to_draw_to, color, align_horiz='left', align_vert='top', antialias=False):\\n pair = (font_name, size)\\n assert pair in self._font_dict, 'Font: %s Size: %d is not available in cFontManager.' % pair\\n font_surf = self._font_dict[(font_name, size)].render(text, antialias, color)\\n if isinstance(rect_or_pos_to_draw_to, tuple):\\n surface.blit(font_surf, rect_or_pos_to_draw_to)\\n elif isinstance(rect_or_pos_to_draw_to, pygame.Rect):\\n font_rect = font_surf.get_rect()\\n # align horiz\\n if align_horiz == 'center':\\n font_rect.centerx = rect_or_pos_to_draw_to.centerx\\n elif align_horiz == 'right':\\n font_rect.right = rect_or_pos_to_draw_to.right\\n else:\\n font_rect.x = rect_or_pos_to_draw_to.x # left\\n # align vert\\n if align_vert == 'center':\\n font_rect.centery = rect_or_pos_to_draw_to.centery\\n elif align_vert == 'bottom':\\n font_rect.bottom = rect_or_pos_to_draw_to.bottom\\n else:\\n font_rect.y = rect_or_pos_to_draw_to.y # top\\n \\n surface.blit(font_surf, font_rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8940f32300c8800baacf00d2fd450209\",\n \"score\": \"0.62630963\",\n \"text\": \"def draw_debug():\\n global SURFACE_MAIN\\n\\n draw_text(SURFACE_MAIN, f\\\"FPS: {int(CLOCK.get_fps())}\\\", (0, 0), COLOR_WHITE, COLOR_BLACK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5dca6d5df7f7753382c1a474fa615fe\",\n \"score\": \"0.6246966\",\n \"text\": \"def display_start(surface):\\n surface.fill((0, 0, 100))\\n draw_stars(surface)\\n font1 = pygame.font.Font('freesansbold.ttf', 80)\\n font2 = pygame.font.Font('freesansbold.ttf', 30)\\n font3 = pygame.font.Font('freesansbold.ttf', 20)\\n\\n title = font1.render(\\\"Galaga\\\", True, (255, 255, 255))\\n author = font2.render(\\\"Taylor Barmak\\\", True, (255, 255, 255))\\n howTo1 = font3.render(\\\"Use the arrow keys to move\\\", True, (255, 255, 255))\\n howTo2 = font3.render(\\\"and the space bar to shoot.\\\", True, (255, 255, 255))\\n instructions = font3.render(\\\"Press [space] to play.\\\", True, (255, 255, 255))\\n\\n textRectTitle = title.get_rect()\\n textRectAuthor = author.get_rect()\\n textRectHowTo1 = howTo1.get_rect()\\n textRectHowTo2 = howTo2.get_rect()\\n textRectInstructions = instructions.get_rect()\\n\\n textRectTitle.center = (WIDTH * 0.5, HEIGHT * 0.5)\\n textRectAuthor.center = (WIDTH * 0.5, HEIGHT * 0.6)\\n textRectHowTo1.center = (WIDTH * 0.5, HEIGHT * 0.7)\\n textRectHowTo2.center = (WIDTH * 0.5, HEIGHT * 0.75)\\n textRectInstructions.center = (WIDTH*0.5, HEIGHT * 0.9)\\n\\n\\n surface.blit(title, textRectTitle)\\n surface.blit(author, textRectAuthor)\\n surface.blit(howTo1, textRectHowTo1)\\n surface.blit(howTo2, textRectHowTo2)\\n surface.blit(instructions, textRectInstructions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3421fe325dadda9bace4480e22fe3bc\",\n \"score\": \"0.6236516\",\n \"text\": \"def showCWinText(self):\\n self.screen.blit(self.lost_text, (550, 900))\\n pygame.display.update()\\n boo = pygame.mixer.Sound('fx/boo.wav')\\n boo.play()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d9d424ce3b4e0dcc2ed749e2400a0cc\",\n \"score\": \"0.6231531\",\n \"text\": \"def draw(self, surface):\\n if self.visible:\\n surface.blit(self.image, self.rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487d2b087e872c1dddc1fdd09b32f89f\",\n \"score\": \"0.6225743\",\n \"text\": \"def draw_text(screen, font, text, location_x, location_y, justify):\\n line = font.render(text, True, Color.BLUE_RGB)\\n if justify == \\\"LEFT\\\":\\n screen.blit(line, (location_x, location_y))\\n elif justify == \\\"RIGHT\\\":\\n screen.blit(line, (location_x - line.get_width(), location_y))\\n elif justify == \\\"CENTER\\\":\\n screen.blit(line, (location_x - (line.get_width() / 2.0), location_y))\\n else:\\n # Default justify to the LEFT\\n screen.blit(line, (location_x, location_y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c3829e066fe2f773083fbd7be57976\",\n \"score\": \"0.62204725\",\n \"text\": \"def display_box(screen, message):\\r\\n fontobject = pygame.font.Font(\\\"./font/Inconsolata.otf\\\", 14)\\r\\n pygame.draw.rect(screen, (0, 0, 0),\\r\\n ((screen.get_width() / 2) - 150,\\r\\n (screen.get_height() / 2) - 10,\\r\\n 250, 20), 0)\\r\\n pygame.draw.rect(screen, (255, 255, 255),\\r\\n ((screen.get_width() / 2) - 152,\\r\\n (screen.get_height() / 2) - 12,\\r\\n 254, 24), 1)\\r\\n\\r\\n if len(message) != 0:\\r\\n screen.blit(fontobject.render(message, 1, (230, 200, 70)),\\r\\n ((screen.get_width() / 2) - 150,\\r\\n (screen.get_height() / 2) - 10))\\r\\n pygame.display.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef25caf57afb55e3a61c91336c17f919\",\n \"score\": \"0.6214439\",\n \"text\": \"def draw(self, surface):\\n surface.blit(self.image, self.rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef25caf57afb55e3a61c91336c17f919\",\n \"score\": \"0.6214439\",\n \"text\": \"def draw(self, surface):\\n surface.blit(self.image, self.rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef25caf57afb55e3a61c91336c17f919\",\n \"score\": \"0.6214439\",\n \"text\": \"def draw(self, surface):\\n surface.blit(self.image, self.rect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b6c59b2d0fe0b02547adf8e959b36ae\",\n \"score\": \"0.6214225\",\n \"text\": \"def draw(self, surface: Union[pygame.Surface, pygame.SurfaceType]) -> None:\\n if self._visible:\\n for label in self.labels:\\n label.draw(surface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d5298ecec7eca87acf381c03861a836\",\n \"score\": \"0.6210216\",\n \"text\": \"def draw_as_selected(self):\\n inverted_color = invert_color(self.color)\\n self.surface = self.font.render(self.text, False, inverted_color)\\n self.center()\\n self.screen.blit(self.surface, (self.x, self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"427e06e22b7b0396f3fe92c60250eb99\",\n \"score\": \"0.62070024\",\n \"text\": \"def draw(self):\\n\\n if self.display_type == \\\"icon\\\":\\n new_surf = self.icon_surface.copy()\\n else:\\n new_surf = self.surface.copy()\\n\\n self.draw_flash()\\n\\n # Camera smoothing\\n if self.smoothing < 255:\\n self.screen.set_alpha(self.smoothing)\\n\\n # Apply lighten effect\\n self.lighten_surface.set_alpha(min(self.lighten_amt, 255))\\n new_surf.blit(self.lighten_surface.convert(), (0, 0))\\n\\n new_surf.set_alpha(self.alpha)\\n\\n xoff, yoff = 0, 0\\n if abs(self.cur_scale - 1.0) > 0.01 and self.display_type == \\\"full\\\":\\n new_surf = pygame.transform.scale(new_surf,\\n (int(CARD_WIDTH*self.cur_scale),\\n int(CARD_HEIGHT*self.cur_scale)))\\n xoff = int(CARD_WIDTH*(self.cur_scale - 1)/2)\\n yoff = int(CARD_HEIGHT*(self.cur_scale - 1)/2)\\n \\n self.screen.blit(new_surf,\\n (self.render_pos[0] - xoff,\\n self.render_pos[1] - yoff))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edaf5bf97c6ec34e6d650db7f731f9ee\",\n \"score\": \"0.6199231\",\n \"text\": \"def prep_text(text, fontsize, fontcolour):\\n font = pygame.font.SysFont(None, fontsize)\\n msg = font.render(text, False, fontcolour)\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0007ca65c76fb26dd1eec4e34db8d9c\",\n \"score\": \"0.6192074\",\n \"text\": \"def draw_text_middle(surface, text, size, color, position):\\n font = pygame.font.SysFont(\\\"comicsans\\\", size, bold=True)\\n label = font.render(text, 1, color)\\n\\n surface.blit(label, position)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9264a7b7a7b9337290d82190a604c68\",\n \"score\": \"0.61898667\",\n \"text\": \"def gameover() -> pygame.Surface:\\r\\n gameover = pygame.Surface((500, 500))\\r\\n gameover.set_alpha(120)\\r\\n font = pygame.font.Font(\\\"clear-sans.bold.ttf\\\", 56)\\r\\n text = font.render(\\\"Game over\\\", True, [255, 0, 0])\\r\\n w, h = text.get_rect().center\\r\\n gameover.blit(text, ((250 - w, 250 - h - 5)))\\r\\n return gameover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06d4e1ac372d1d29d999fab8261e27a7\",\n \"score\": \"0.6179655\",\n \"text\": \"def render_font(font, charmap):\\n renders = [font.render(c, True, constants.TEXT_COLOUR) for _, c in charmap]\\n surf = Surface((max([r.get_rect().w for r in renders]),\\n (_TEXT_HEIGHT + _VERTICAL_SPACING) * len(renders)))\\n surf.fill(_BACKGROUND_COLOUR)\\n\\n y = _VERTICAL_PADDING\\n for r in renders:\\n surf.blit(r, (0, y))\\n y += _TEXT_HEIGHT + _VERTICAL_SPACING\\n\\n return surf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea21ed5687bdd98dddcb0689624c7be4\",\n \"score\": \"0.61513746\",\n \"text\": \"def draw(self):\\n self.screen.blit(self.surface, (self.x, self.y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ce8cfcbe9f5555efbc82e26b596b35\",\n \"score\": \"0.6148734\",\n \"text\": \"def drawScreen():\\n # render to screen backbuffer\\n renderScreen(bgColor)\\n # flip to foreground\\n pygame.display.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"051c55195737e4820ffc12109a662483\",\n \"score\": \"0.6135749\",\n \"text\": \"def paint_text(word: str, x: int, y: int, color=(0, 0, 0)):\\n myfont = pygame.font.SysFont('Comic Sans MS', 30)\\n textsurface = myfont.render(word, False, color)\\n text_pos = (x * width + width // 2 - textsurface.get_width() // 2, y * height + height // 2 -\\n textsurface.get_height() // 2)\\n self.win.blit(textsurface, text_pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a168222c9afa905243eed0174667b3d\",\n \"score\": \"0.6126816\",\n \"text\": \"def display_text(self, message, position, color=(255, 0, 0)):\\r\\n\\r\\n text = self.regular_font.render(message, False, color)\\r\\n self.screen.blit(text, position)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992984,"cells":{"query_id":{"kind":"string","value":"35212d110695ed541874c14e3ca8778d"},"query":{"kind":"string","value":"Periodically we send the actual status for every relais The main topic is extended by \"stat/\"+ status (0 = off 1 = on)"},"positive_passages":{"kind":"list like","value":[{"docid":"a165e531a04e8e5ca54363d4a12eff97","score":"0.79904836","text":"def send_status(relist) :\n global CONF\n # log (\"send_status\")\n for i in range(len(relist)):\n rstat = relist[i].get()\n if rstat :\n mqtt_sta = 1\n else:\n mqtt_sta = 0\n client.publish (CONF['MQTOPIC']+\"stat/\"+str(i+1),payload=mqtt_sta, qos=0, retain=False)\n log (\"status sent \",str(i+1)+\" = \"+str(mqtt_sta))\n return","title":""}],"string":"[\n {\n \"docid\": \"a165e531a04e8e5ca54363d4a12eff97\",\n \"score\": \"0.79904836\",\n \"text\": \"def send_status(relist) :\\n global CONF\\n # log (\\\"send_status\\\")\\n for i in range(len(relist)):\\n rstat = relist[i].get()\\n if rstat :\\n mqtt_sta = 1\\n else:\\n mqtt_sta = 0\\n client.publish (CONF['MQTOPIC']+\\\"stat/\\\"+str(i+1),payload=mqtt_sta, qos=0, retain=False)\\n log (\\\"status sent \\\",str(i+1)+\\\" = \\\"+str(mqtt_sta))\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"78b8259175309804b0111ec3462d84ba","score":"0.7426226","text":"def send_stat (relais_nrß) :\n global CONF\n for i in range(0,len(relist)) :\n if relist[i].get() : \n mqtt_sta = 1\n else:\n mqtt_sta = 0\n rel = str(i+1)\n client.publish (CONF['MQTOPIC']+\"stat/\"+rel,payload=mqtt_sta, qos=0, retain=False)","title":""},{"docid":"73dc6e10a03ab10eac4e4ff8bfb15bed","score":"0.7406092","text":"def notifyStatus(self, status):\r\n if status == \"TM_CONNECTED\":\r\n self.tmConnectedNotify()\r\n elif status == \"TM_RECORDING\":\r\n self.tmRecordingNotify()\r\n elif status == \"PACKETDATA_SET\":\r\n self.packetDataSetNotify()\r\n elif status == \"UPDATE_REPLAY\":\r\n self.updateReplayNotify()\r\n elif status == \"ENABLED_CYCLIC\":\r\n self.enabledCyclicNotify()\r\n elif status == \"DISABLED_CYCLIC\":\r\n self.disabledCyclicNotify()\r\n elif status == \"OBC_ENABLED_ACK1\":\r\n self.obcEnabledAck1Notify()\r\n elif status == \"OBC_ENABLED_NAK1\":\r\n self.obcEnabledNak1Notify()\r\n elif status == \"OBC_DISABLED_ACK1\":\r\n self.obcDisabledAck1Notify()\r\n elif status == \"OBC_ENABLED_ACK2\":\r\n self.obcEnabledAck2Notify()\r\n elif status == \"OBC_ENABLED_NAK2\":\r\n self.obcEnabledNak2Notify()\r\n elif status == \"OBC_DISABLED_ACK2\":\r\n self.obcDisabledAck2Notify()\r\n elif status == \"OBC_ENABLED_ACK3\":\r\n self.obcEnabledAck3Notify()\r\n elif status == \"OBC_ENABLED_NAK3\":\r\n self.obcEnabledNak3Notify()\r\n elif status == \"OBC_DISABLED_ACK3\":\r\n self.obcDisabledAck3Notify()\r\n elif status == \"OBC_ENABLED_ACK4\":\r\n self.obcEnabledAck4Notify()\r\n elif status == \"OBC_ENABLED_NAK4\":\r\n self.obcEnabledNak4Notify()\r\n elif status == \"OBC_DISABLED_ACK4\":\r\n self.obcDisabledAck4Notify()\r\n elif status == \"FRAME_REC_STARTED\":\r\n self.frameRecStarted()\r\n elif status == \"FRAME_REC_STOPPED\":\r\n self.frameRecStopped()","title":""},{"docid":"468b1a0edb1bee142f0973b2e1d2b967","score":"0.6909292","text":"def send_status(self):\n if self.pstat[0] > 0:\n # send statistics via bulletin\n time_on = datetime.datetime.now() - self.start_datetime\n p_tot = self.pstat[0] + self.pstat[1] + self.pstat[2]\n n_calls = len(self.pstat[3])\n status_txt = f\"IGate up {time_on.days} days \" \\\n f\"{round(time_on.seconds/3600,1)} h \" \\\n f\"{p_tot} rcvd, {self.pstat[0]} gtd, \" \\\n f\"{n_calls} unique calls\"\n else:\n status_txt = self.STATUS_TXT\n status = f\"{self.user.my_call}-{self.user.ssid}>{self.VERS},\" \\\n f\"TCPIP*:>{status_txt}\\r\\n\"\n threading.Timer(self.HOURLY, self.send_status).start()\n self.send_aprs(status)","title":""},{"docid":"a18774f4aec9f17a6dcb0a920a59c0d5","score":"0.68421644","text":"def status():","title":""},{"docid":"b2a11ddf6b9ab75fad40692d3e372788","score":"0.6783032","text":"def request_status():\n global request\n for drone in drones:\n m = {\n 'code': REPLY_STATUS,\n 'id': dev_id\n }\n send_message(drone, json.dumps(m))\n request = False","title":""},{"docid":"518649f4c0150db0ca497bc4ef76a574","score":"0.6711619","text":"def print_status():\n\n scr.addstr(curses.LINES-1,1,\"@%s - [%i] Unread messages - Last updated %s - %s\"%(username,msgs,now.strftime(\"%Y-%m-%d %H%M\"),status))","title":""},{"docid":"3370b6d86f96370a292b34bdc1e8eb62","score":"0.6701341","text":"def on_status(self, status):\n msg = status.text.encode('utf-8')\n #print(msg)\n try:\n self.producer.send_messages('twitterstream', msg)\n except Exception as e:\n print(e)\n return False\n return True","title":""},{"docid":"827b0427c58c3ef17e1e87fce059c2c0","score":"0.66875154","text":"def _on_status(self, status):\n self.logger.debug(\"_on_status started\")\n self._wolk._on_status(status)\n self.logger.debug(\"_on_status ended\")","title":""},{"docid":"a5cf7fcd5f260a5ab79c876891bc3b19","score":"0.6659487","text":"def statusReceived(self, status):","title":""},{"docid":"6d7977367e55d4f4c707ccd5db0c6d8f","score":"0.65679973","text":"def update_status(self, status):\n for cat, s in status.items():\n if cat == 'sensors':\n continue\n\n window = self.windows[cat]\n linenum = 1\n for key, val in s.items():\n if key == 'alerts':\n # print out monitor alerts\n self.write_line(window, linenum, 'Alerts:')\n linenum += 1\n for k, v in val.items():\n self.write_key_value(window, linenum, ' '+k, v)\n linenum += 1\n else:\n self.write_key_value(window, linenum, key, val)\n linenum += 1\n\n sen_line = 1\n for sen in status['sensors']:\n val = \"%s%s\" % (sen['value'], sen['units'])\n self.write_key_value(self.windows['sensors'], sen_line, sen['name'], val)\n sen_line += 1","title":""},{"docid":"2fa4d11b372eba0d3312f439c8708faf","score":"0.6542748","text":"def talk_status(self, sender, status):\n pass","title":""},{"docid":"abe624e6454a7479d9acf13eb056329e","score":"0.65381527","text":"def on_status(self, status):\r\n return","title":""},{"docid":"abe624e6454a7479d9acf13eb056329e","score":"0.65381527","text":"def on_status(self, status):\r\n return","title":""},{"docid":"2ca89015f17f220ad1b916379b82798f","score":"0.65372473","text":"def queue_status():","title":""},{"docid":"0c0350467d63c1dc466b13dca84dd57b","score":"0.64841574","text":"def status (self):","title":""},{"docid":"0a82a0bb27078c334f5973a54d148405","score":"0.64776254","text":"def oneSecondHook():\n global StatusUpdateCount\n \n mcastRpc(1, HOP_COUNT, 'powerOn173', True)\n \n # Have the node periodically send an update of its status, set for 10 seconds\n StatusUpdateCount -= 1\n if StatusUpdateCount <= 0:\n reportStateAllLEDs()\n StatusUpdateCount = 10","title":""},{"docid":"c0465e28fde9ed0a1605d65b820ada04","score":"0.6463386","text":"def onStatusNoticiesRecv(self):\n pass","title":""},{"docid":"1320bf142fe7a0cc52a88e38356a2876","score":"0.6439513","text":"def getStatus(self):\n self.status.update()","title":""},{"docid":"ff3573c73886d0f255e8fc88040ec069","score":"0.6435214","text":"async def status_changer(self):\n playing = []\n streaming = []\n listening = []\n watching = []\n statuses = playing + streaming + listening + watching\n\n while True:\n if not self.is_ready():\n continue\n elif self.is_closed():\n return\n else:\n await self.change_presence(activity=random.choice(statuses))\n await asyncio.sleep(self.config['Bot']['StatusTimer'])","title":""},{"docid":"39cac5dd69692cf269cf326bf2a4e5f5","score":"0.640287","text":"def send_refresh(self):\n self.mqtt_discovery_broadcast()\n if self.num_recent_feeds(24) <= 2:\n self.turn_on()\n self.send_mqtt_update()","title":""},{"docid":"67da542fc31dca989239ed2de4680b0c","score":"0.6402793","text":"def _stat(self):\n self._send_message_to_controller(self._get_stats())","title":""},{"docid":"276b6705ca838ae38d96b37ff0aa771e","score":"0.63653386","text":"def refreshStatusInfo(self):\n reqSubHeader = {'procDiv': 1,\n 'cmdNo': (0x72, 0x00), #Command No. 0x72\n 'inst': (1, 0), #Fixed to 1\n 'attr': 0, #1: Data 1, 2: Data 2\n 'service': 0x01, #Get_Attribute_Single: 0x0E, Get_Attribute_All: 0x01\n 'padding': (0, 0) }\n\n reqData = []\n\n ansPacket = self.sendCmd(reqSubHeader, reqData)\n\n if( ansPacket == None or ansPacket.status != 0):\n return False\n\n #parse answer data\n byte1=ansPacket.data[0]\n byte2=ansPacket.data[4]\n\n def testBit(int_type, offset):\n mask = 1 << offset\n if (int_type & mask) == mask:\n return True\n else:\n return False\n\n self.status['Step'] = testBit(byte1,0)\n self.status['Cycle'] = testBit(byte1,1)\n self.status['Auto'] = testBit(byte1,2)\n self.status['Running'] = testBit(byte1,3)\n self.status['InGuard'] = testBit(byte1,4)\n self.status['Teach'] = testBit(byte1,5)\n self.status['Play'] = testBit(byte1,6)\n self.status['Remote'] = testBit(byte1,7)\n\n self.status['Hold_PP'] = testBit(byte2,1)\n self.status['Hold_Ext'] = testBit(byte2,2)\n self.status['Hold_Cmd'] = testBit(byte2,3)\n self.status['Alarm'] = testBit(byte2,4)\n self.status['Error'] = testBit(byte2,5)\n self.status['ServoOn'] = testBit(byte2,6)\n\n return True","title":""},{"docid":"3b2b746371b034603233cdd9066b54dd","score":"0.6362009","text":"def sendStatus(self, status):\r\n if self.debug:\r\n log.msg(\"sendStatus\", status)\r\n if not self.running:\r\n log.msg(\"would sendStatus but not .running\")\r\n return\r\n self.builder.sendUpdate(status)","title":""},{"docid":"d9abbbef3d7f92f0dac615cea5063224","score":"0.63299716","text":"def send_offline_status(self): \n\n # pi('send_offline_status')\n\n # removing the subscribed but now unsubscribed and disconected mqtt mosqitto client \n self._participants = self.delMqttMosquittoClient()\n\n # publishing status message to private/clientid/status topic\n statusmsg = {\n 'msg_type': 'status',\n 'status': 'offline',\n 'msg': {\n 'name': self._name,\n 'clientid': self._clientid\n }\n }\n\n self.publish(topic='private/' + self._clientid + '/status', msg=statusmsg, qos=2, retain=True)\n\n\n # pr('send_offline_status')","title":""},{"docid":"b38e9cd19e696e329d6380cb401cc775","score":"0.6248638","text":"def status_online(self):\n pass","title":""},{"docid":"0254ed55d5977e24a8678bc8909802f4","score":"0.6246707","text":"async def status(self, ctx):\n if ctx.message.channel.name != \"minecraft\":\n await self.bot.say(\"Please move to #minecraft for this command.\")\n else: \n server = MinecraftServer.lookup(self.bot.mcip)\n try:\n status = server.status()\n except IOError as e:\n await self.bot.say(\"It's dead Jim.\")\n # try:\n # query = server.query()\n # except Sock as e:\n # await bot.say(\"Server too slow for query!\")\n em = discord.Embed(title='CSSS FTB Server Status', description=\n \"\"\"The server has {0} players and replied in {1} ms.\\n\"\"\".format(status.players.online, status.latency), colour=0x3D85C6 )\n # + \"\\n{} are currently online.\".format(\", \".join(query.players.names)), colour=0x3D85C6)\n await self.bot.send_message(ctx.message.channel, embed=em)","title":""},{"docid":"c20ac656f1fd96d70aff5b0cc10a1043","score":"0.62240726","text":"def status_message(self):\n slave_id, timestamp, msg, *args = self.status_queue.recv_pyobj()\n if msg == 'STATUS':\n self.update_status(args[0])\n else:\n self.slave_list.message(slave_id, timestamp, msg, *args)","title":""},{"docid":"a371587547d55e3f98305572f4d12d22","score":"0.6216164","text":"def _checkStatus(self):\n if self.last_messages == self.receivedMsgs:\n print(\"Time is out\") \n self.channel.stop_consuming()\n self.print_dictionary() \n else:\n self.last_messages = self.receivedMsgs\n self.timer_id = self.conn.call_later(self.timeout, self._checkStatus)","title":""},{"docid":"9f392e4d43599039e2f9ae291219a9b1","score":"0.62157136","text":"async def test_other_status(self):\n self.status_command = StatusRequestCommand(self._address, status_type=0)\n self.status_command.subscribe(self.set_status)\n status_type = 1\n status_1_command = StatusRequestCommand(self._address, status_type=status_type)\n status_1_command.subscribe(self.set_status_1)\n cmd1 = 0x19\n cmd2 = 0x01\n db_version = 0x66\n status = 0x77\n topics = [\n TopicItem(\n self.ack_topic,\n {\"cmd1\": cmd1, \"cmd2\": cmd2, \"user_data\": None},\n 0.1,\n ),\n TopicItem(\n self.direct_ack_topic,\n {\n \"cmd1\": db_version,\n \"cmd2\": status,\n \"target\": \"112233\",\n \"user_data\": None,\n \"hops_left\": 3,\n },\n 0.1,\n ),\n ]\n self.set_status(-1, -1)\n self.set_status_1(-2, -2)\n send_topics(topics)\n assert await status_1_command.async_send()\n\n assert self._db_version_1 == db_version\n assert self._status_1 == status\n\n # Confirm status 1 did not respond.\n assert self._status == -1\n assert self._db_version == -1","title":""},{"docid":"d7610ba27c3443c6e58152da3c8ff03b","score":"0.6202714","text":"async def status(self, ctx):\n dipgame = scraper.get_dipgame_checked(self.game_id)\n status_message = app.announce_overall_game_state(dipgame)\n await ctx.message.channel.send(status_message)","title":""},{"docid":"87b41c647d89a02edeacb8db57683dc0","score":"0.6187948","text":"async def mc_status(message):\n r = await http.get(MINECRAFT_STATUS_URL, headers=HEADERS)\n data = r.json()['report']\n status = []\n for name, v in data.items():\n text = v['title']\n if text == \"Online\":\n text = \":ok:\"\n else:\n text = \":warning: \" + text\n status.append(\"{}: {}\".format(name.title(), text))\n return Response(\", \".join(status))","title":""},{"docid":"ed2a950600d357b3a981b09cb3e3c8c2","score":"0.61839575","text":"def update(self):\n # Data fetching is taken care of through the Main sensor\n\n if self.syncthru.is_online():\n self._attributes = self.syncthru.output_tray_status().get(self._number, {})\n self._state = self._attributes.get(\"status\")\n if self._state == \"\":\n self._state = \"Ready\"","title":""},{"docid":"1cd79bc0cc25623b3f231a4d9f5725ce","score":"0.6171635","text":"def update(self):\n # Data fetching is taken care of through the Main sensor\n\n if self.syncthru.is_online():\n self._attributes = self.syncthru.toner_status().get(self._color, {})\n self._state = self._attributes.get(\"remaining\")","title":""},{"docid":"584721b9d01e2d355c0dd7bd4d729c76","score":"0.61621267","text":"def status_callback(self, data):\n print \"Arm status callback\", data.data\n if data.data == \"busy\" or data.data == \"error\":\n self.status = 0\n elif data.data == \"free\":\n self.status = 1","title":""},{"docid":"6b0880c00282c910c84038088e5bf828","score":"0.6137847","text":"def online(client, device):\n d_id = ObjectId(dict(device).get(\"id\"))\n mongo.db.devices.update_one({\"_id\": d_id},{\n \"$set\": {\n \"status\": Constant.Status.STATUS_WAIT,\n \"version\": dict(device).get(\"version\")\n }\n })\n client.publish(dict(device).get(\"id\") + \"/status/d\", Constant.Status.STATUS_WAIT)\n if \"TOPICS_NEED\" in os.environ:\n topics_need = json.loads(os.environ.get(\"TOPICS_NEED\"))\n for item in topics_need:\n client.subscribe(dict(device).get(\"id\") + \"/\" + item + \"/u\")\n client.subscribe(dict(device).get(\"id\") + \"/\" + item + \"/d\")\n print(\"Auto subscribing\", dict(device).get(\"id\") + \"/\" + item + \"/(u|d)\")\n print(dict(device).get(\"id\") + \" Online\")","title":""},{"docid":"85858e9d2708a15b94d53f9629543bd4","score":"0.6130322","text":"def send_status_message(self, status):\n broadcasters = []\n active_users = []\n\n # get list active connections\n for user in self.channels.lget(self.user_id):\n if self.client.subscribers.get(user):\n broadcasters.extend(self.client.subscribers.get(user).keys())\n active_users.append(user)\n if broadcasters:\n self.broadcast(broadcasters, json.dumps({'type': status, 'users': [self.user_id]}))\n self.send(json.dumps({'type': status, 'users': active_users}))","title":""},{"docid":"d809a8c99cd33fcef0b7b5a31432b267","score":"0.6129792","text":"def status(program):\n for message in program.master.status():\n print message","title":""},{"docid":"30a9339449a5f98b94fd7a30f980cca5","score":"0.61022997","text":"def SetStatus(self, status):\n\n if status == 'OFFLINE':\n if not self.offline:\n self.StatusChanged('OFFLINE')\n self.offline = True\n else:\n# XXX flush queue?\n# self._flush_queue()\n if self.offline:\n self.StatusChanged('ONLINE')\n self.offline = False","title":""},{"docid":"59cd33011a252a8832a814fc751eaa2e","score":"0.6095115","text":"def get_status(self):","title":""},{"docid":"1d5466eea43dadabf85fba33c527c93e","score":"0.6090604","text":"def update_status(self, state):\n\n if state == \"connected Lightstreamer session\":\n connected_color = QtGui.QColor(\"#23A627\")\n status_icon = funcMisc.create_status_icons(connected_color)\n self.lbl_status.setPixmap(status_icon)\n\n elif state == \"disconnected from Lightstreamer\":\n disconnected_color = QtGui.QColor(\"#F51616\")\n status_icon = funcMisc.create_status_icons(disconnected_color)\n self.lbl_status.setPixmap(status_icon)","title":""},{"docid":"5e9d3e097d52fa37fe37f2c0cd3b7c51","score":"0.6087035","text":"def getStatus():","title":""},{"docid":"0c658d0ad0f6263420f9122f161e98c2","score":"0.60826665","text":"async def timed_status(self,channel):\n print(\"Entering timed_status\")\n shutdown = False\n while not shutdown:\n try:\n counter = 0\n await self.bot.wait_until_ready()\n while not self.bot.is_closed:\n await asyncio.sleep(60)\n result = await self.check()\n if result is False:\n counter += 1\n if counter == 5:\n await self.bot.send_message(channel, \"Agnarr's current population/status is {} as of {}\"\n .format(self.state, self.time.strftime(\"%H:%M\")))\n elif result is True:\n counter = 0\n await self.bot.change_presence(game=discord.Game(\n name='Agnarr: {} @ {}'.format(self.state, self.time.strftime(\"%H:%M\"))))\n print(\"Bot has closed, zomg\")\n return\n except concurrent.futures.CancelledError:\n print (\"EQServerStatus Task Cancelled\")\n shutdown = True\n except (websockets.exceptions.ConnectionClosed,TypeError,aiohttp.errors.ClientOSError, ConnectionError) as e:\n pass\n except Exception as e:\n exc = '{}: {}'.format(type(e).__name__, e)\n print('Error in timed status:\\n{}'.format( exc))\n traceback.print_exc()\n print(\"Leaving timed_status\")","title":""},{"docid":"0221bcc255252a4bee1ed2ccaa4fb2fc","score":"0.6081622","text":"def publish_status(self, status):\n self.publisher.publish(status = status)","title":""},{"docid":"7b343b91c0c595b5b48dc5401ce3f8fe","score":"0.6072817","text":"def set_status(self, status, summary=\"\"):\n self.__stop = time.time()\n self.__status = status\n self.__status_summary = summary","title":""},{"docid":"8617541fc2c7bc8d9b52840106e21c86","score":"0.6066825","text":"def on_status(self, status):\n print(status.text)","title":""},{"docid":"f3ae9a77b5b6dd3a8b77c72a7f54f793","score":"0.6066149","text":"def update_status(self, msg, current=None, total=None):\n raise NotImplementedError","title":""},{"docid":"7317ecd8a6aced20a179f7fc500ee5a1","score":"0.6065251","text":"def change_status(self, status):\n\n if not self.hasUser(): return \n \n self.output = self.send(\"#self.activate();\") if status else self.send(\"#self.stop();\")\n print(\" \" + self.output)","title":""},{"docid":"2ba66bef76d87f3b85b3b57a86b5d7af","score":"0.6057035","text":"def publish(self, thing_id, state):\n print \"Status update: \", thing_id\n self.aio.send(thing_id + '-batt', int(state['batt']))\n sleep(1) # Pause for a second so we don't hit the API throttle limits\n self.aio.send(thing_id + '-state', int(state['button_state']))\n sleep(1) # Pause for a second so we don't hit the API throttle limits\n self.aio.send(thing_id + '-count', state['button_count'])","title":""},{"docid":"3edc875b5f15d3a2669cc695b212daac","score":"0.6032335","text":"def set_status(self, message):\n msg = \"record_status_message '{}'\".format(message)\n self.tracker.sendCommand(msg)","title":""},{"docid":"771b70d5a2f1c7ef3b557f7cf4f96735","score":"0.60233724","text":"async def onlinestatus(self, ctx):\n\t\tdevice = {\n\t\t\t(True, True, True): 0,\n\t\t\t(False, True, True): 1,\n\t\t\t(True, False, True): 2,\n\t\t\t(True, True, False): 3,\n\t\t\t(False, False, True): 4,\n\t\t\t(True, False, False): 5,\n\t\t\t(False, True, False): 6,\n\t\t\t(False, False, False): 7\n\t\t}\n\t\tstore = [0, 0, 0, 0, 0, 0, 0, 0]\n\t\tfor m in ctx.guild.members:\n\t\t\tvalue = (\n\t\t\t\tm.desktop_status == discord.Status.offline,\n\t\t\t\tm.web_status == discord.Status.offline,\n\t\t\t\tm.mobile_status == discord.Status.offline\n\t\t\t)\n\t\t\tstore[device[value]] += 1\n\t\tmsg = (\n\t\t\tf'offline all: {str(store[0])}'\n\t\t\tf'\\ndesktop only: {str(store[1])}'\n\t\t\tf'\\nweb only: {str(store[2])}'\n\t\t\tf'\\nmobile only: {str(store[3])}'\n\t\t\tf'\\ndesktop web: {str(store[4])}'\n\t\t\tf'\\nweb mobile: {str(store[5])}'\n\t\t\tf'\\ndesktop mobile: {str(store[6])}'\n\t\t\tf'\\nonline all: {str(store[7])}'\n\t\t)\n\t\tawait ctx.send(f'```py\\n{msg}```')","title":""},{"docid":"2d48b2527c8f49be379519e1da08242b","score":"0.6016253","text":"def on_status(self, status):\n tweet = status._json\n if tweet['in_reply_to_status_id']:\n # リプライである場合、オリジナルツイートを取得する\n try:\n origin_tweet = self.twitter_api.get_status(\n tweet['in_reply_to_status_id'])._json\n except tweepy.RateLimitError:\n logging.warning('[on_status] Rate limit exceeded.')\n logging.warning('[on_status] time.sleep(15 * 60)')\n time.sleep(15 * 60)\n return\n except tweepy.TweepError as ex:\n if ex.api_code == 179:\n # 鍵アカウントへのリプライの場合\n return\n else:\n logging.warning('[on_status] TweepyError raised. msg: %s'\n % ex.reason)\n # プログラムの中止を避けるため\n return\n\n # 改行文字の置換\n reply_text = tweet['text'].replace('\\n', '。')\n origin_text = origin_tweet['text'].replace('\\n', '。')\n\n # screen_id の除去\n match = re.match(self.REPLY_PATTERN, origin_text)\n origin_text = match.group('text')\n match = re.match(self.REPLY_PATTERN, reply_text)\n reply_text = match.group('text')\n\n # URL の除去\n rex = re.compile(self.URL_PATTERN)\n origin_text = rex.sub('', origin_text)\n reply_text = rex.sub('', reply_text)\n\n # 空白のみからなる文でないなら出力に追加する\n if not (re.match(r'\\s*$', origin_text) or\n re.match(r'\\s*$', reply_text)):\n self.buf += 'A: %s\\nB: %s\\n' % (origin_text, reply_text)\n self.tweet_counter += 1\n if self.tweet_counter >= self.BUF_MAX:\n self.output_file.write(self.buf)\n self.tweet_counter = 0\n self.buf = ''\n self.output_file.flush()","title":""},{"docid":"8bf50be915208ea67b3342f8242617b4","score":"0.6009318","text":"def _send_heartbeat(self) -> None:\n ...","title":""},{"docid":"2b8610589a749b4ae21167180e6f9b57","score":"0.60083944","text":"def process_status(ctx):\n ctx.obj[\"output\"](status_processes(ctx.obj[\"cfg\"]))","title":""},{"docid":"40dab733bb52dfa989a97b1c512229dd","score":"0.6001468","text":"def playstatus_update(self, updater, playstatus: pyatv.interface.Playing) -> None:\n clients = self.app[\"clients\"].get(self.identifier, [])\n for client in clients:\n asyncio.ensure_future(client.send_str(str(playstatus)))","title":""},{"docid":"b02092dbff2273c9fc63b749400cc9d1","score":"0.598739","text":"def SetStatus(self, msg):\n self.statusMsg.append(msg)","title":""},{"docid":"e1b103148aefdfac77f2a5656866a789","score":"0.5981189","text":"def _xmpp_changed_status(self, presence):\n if presence['from'] != self.xmpp.boundjid and self.verbose:\n \n jid = str(presence['from']).split(\"/\")[0]\n name = self.roster_manager.jid2summoner(jid)\n print \"<RiotDEBUG> CHAGE: \"+ name + \"(\" + \\\n str(presence['from']) + \")\"+\" has been changed its status\"\n \n if presence['type'] == 'unavailable': #let got_offline handle it\n return\n\n if presence['from'] != self.xmpp.boundjid:\n jid = str(presence['from']).split(\"/\")[0]\n resource = str(presence['from']).split(\"/\")[1]\n #need to handle off line status == None\n status_dic = xmltodict.parse(presence['status'])\n self.roster_manager.updateStatus(jid, resource, \n status_dic['body'], online=True)\n\n self._trigger_event(\"change_status\", data=presence)","title":""},{"docid":"62e42e2e1d913c150c690255039ae7a8","score":"0.59781015","text":"def _StatusUpdateThreadMain(self):\n while self._status_update_active:\n self._UpdateStatus()\n\n time.sleep(self._STATUS_UPDATE_INTERVAL)","title":""},{"docid":"606596da23cb7c981d3a67d3d4effbca","score":"0.5966187","text":"def status():\n\n command_response = send_command('heartbeat')\n\n if command_response['status'] == 'ok':\n print(\"The background client is active\")\n\n else:\n print(\"The background client is not running\")","title":""},{"docid":"ef961fa98059d2e54e94ff60deb77e56","score":"0.59600794","text":"def _update_status(self, status, test_number):\n if self.on_status is not None:\n self.on_status(status, test_number)","title":""},{"docid":"b85cd8130a36f58a52cc28101aea1fd9","score":"0.5959098","text":"def display_status(self):\n time = float2str(self.scheduler.time, '10.2f')\n tx = float2str(self.tx_total, '10g')\n rx = float2str(self.rx_total, '10g')\n dup = float2str(self.dup_total, '10g')\n uniq_total = float2str(self.uniq_total, '10g')\n delivered_total = float2str(self.delivered_total, '10g')\n uniq_delivered_total = float2str(self.uniq_delivered_total, '10g')\n print(\n f'define status_l text Time:{time},____TX:{tx},____RX:{rx},____DUP:{dup},____Delivered:{uniq_delivered_total}__/__{uniq_total},____Arrived:{delivered_total} 14 white 0.5 0.05'\n )","title":""},{"docid":"3ce0eab461f1a128243932b7f0992d5f","score":"0.59505075","text":"def collect_status(self):\r\n self._cmd(self.STATUS_CMD)","title":""},{"docid":"82223d9e18e72b3b164bdcffebe05ec6","score":"0.5945344","text":"async def test_status_command(self):\n self.status_command = StatusRequestCommand(self._address, status_type=0)\n self.status_command.subscribe(self.set_status)\n cmd1 = 0x19\n cmd2 = 0x00\n db_version = 0x22\n status = 0x33\n topics = [\n TopicItem(\n self.ack_topic,\n {\"cmd1\": cmd1, \"cmd2\": cmd2, \"user_data\": None},\n 0.5,\n ),\n TopicItem(\n self.direct_ack_topic,\n {\n \"cmd1\": db_version,\n \"cmd2\": status,\n \"target\": \"112233\",\n \"user_data\": None,\n \"hops_left\": 3,\n },\n 0.5,\n ),\n ]\n send_topics(topics)\n assert await self.status_command.async_send()\n await asyncio.sleep(1)\n assert self._db_version == db_version\n assert self._status == status","title":""},{"docid":"5e655d9cdbd35af3cd64198d15aaf400","score":"0.5939916","text":"def report_status(self):\n import time\n\n while 1:\n time.sleep(2)\n try:\n status = self.get_status()\n except TaskCompleted:\n break\n except Exception as error:\n self.report('running', '(1) Error getting status (%s)' % error)\n #return\n else:\n # self.report('running', 'Time: %s days' % status)\n self.report('running', '{message}'.format(message=status))\n\n self.report('success', 'completed')","title":""},{"docid":"6c8da7c212adf314e76d386514eb76ef","score":"0.5939347","text":"async def status(self, ctx, arg):\n if await self.is_key_valid(ctx, arg):\n reminder = await self.get_reminder(arg)\n interval_unit = get_time_unit(reminder.runInterval)\n await ctx.send(wrap_code_block(\n \"Name: {0}\\r\\n\"\n \"Current Text: {1}\\r\\n\"\n \"Current Link: {2}\\r\\n\"\n \"Running Interval: {3} {4}(s)\\r\\n\"\n \"Running: {5}\\r\\n\"\n \"Last Ran: {6}\".format(arg,\n reminder.notification_text if reminder.notification_text is not None else \"N/A\",\n reminder.notification_link if reminder.notification_link is not None else \"N/A\",\n round(reminder.runInterval / interval_lookup[interval_unit], 2),\n interval_unit,\n reminder.started,\n reminder.lastRunTime if reminder.lastRunTime is not None else \"N/A\")))","title":""},{"docid":"25f857a453b8a76e2805b9a851bb29b9","score":"0.5929943","text":"async def test_status_request_hub(self):\n self.status_command = StatusRequestCommand(self._address, status_type=0)\n self.status_command.subscribe(self.set_status)\n cmd1 = 0x19\n cmd2 = 0x00\n db_version = 0x44\n status = 0x55\n topics = [\n TopicItem(\n self.ack_topic,\n {\"cmd1\": cmd1, \"cmd2\": cmd2, \"user_data\": None},\n 0.1,\n ),\n TopicItem(\n self.direct_ack_topic,\n {\n \"cmd1\": db_version,\n \"cmd2\": status,\n \"target\": \"aabbcc\",\n \"user_data\": None,\n \"hops_left\": 3,\n },\n 0.2,\n ),\n ]\n send_topics(topics)\n await asyncio.sleep(0.5)\n assert self._db_version == db_version\n assert self._status == status","title":""},{"docid":"c74fc2bbf00db495e085d6ea9e152b24","score":"0.5925259","text":"def switch_status(self):\n if str(self.stop['state']) == 'disabled':\n self.start_tac_client()\n self.start_coord_server()\n self.stop.configure(state=\"normal\")\n self.start.configure(state=\"disabled\")\n self.status_value.configure(text=\"Running\")\n r.get(\"http://127.0.0.1:5000/set_username/\" + self.user.get())\n LOG.info('Status values updated correctly...')\n else:\n self.stop_process()\n LOG.info('Process stopped...updating status values...')\n self.stop.configure(state=\"disabled\")\n self.start.configure(state=\"normal\")\n self.status_value.configure(text=\"Stopped\")\n LOG.info('Status values updated correctly...')","title":""},{"docid":"8160bdf04756126235d8a2294cb78847","score":"0.59208834","text":"async def status(self):\n\n # Count stored statements and reactions\n session = self.bot.Session()\n statements = session.query(Statement).count()\n reactions = session.query(Reaction).count()\n session.close()\n\n # Set as status\n await self.bot.change_presence(\n activity=discord.Activity(\n type=discord.ActivityType.listening,\n name=f'{statements} 💬 {reactions} 👍'\n )\n )","title":""},{"docid":"72a82a3110462c6399c13d2af342b8ad","score":"0.59141296","text":"def notify(self):\n #self.update_grid()\n t = self.get_time_str() + ' (update every ' + str(int(self.update_sec)) + 's)'\n self.statusbar.SetStatusText(t, self.SB_RIGHT)","title":""},{"docid":"fe99e66cb2f40f05bd1d1f13b16cd481","score":"0.5904745","text":"def set_status(self, showId, status):\r\n\t\tprint \"change_status\", showId, status\r\n\t\tp = xmpp.protocol.Presence()\r\n\t\tp.setShow(STATUS_TEXT[showId])\r\n\t\tif status:\r\n\t\t\tp.setStatus(status)\r\n\t\tif showId == STATUS.available:\r\n\t\t\tp.setPriority(5)\r\n\t\tself.jabber.send(p)\r\n\t\tself.emit(SIGNAL(\"debug\"), unicode(p)+\"\\n\\n\")","title":""},{"docid":"bde2d4c2f2d7a9a7199959bd023eb005","score":"0.58999074","text":"def on_private_status(self, client, userdata, msg):\n\n # pi('on_private_status')\n\n # LOGGER.info('[MosquittoClient] Received message with mid : %s from topic : %s with qos : %s and retain = %s ' % (str(msg.mid), msg.topic, str(msg.qos), str(msg.retain)))\n \n json_decoded_body = json.loads(msg.payload)\n\n if json_decoded_body['status'] == 'offline':\n\n # print '[MosquittoClient] received status === offline'\n\n if json_decoded_body['msg']['clientid'] == self._clientid:\n\n # print 'received offline status on self._clientid, hence stopping mqtt client'\n\n # since status message has been sent, its safe to close the mqtt mosquitto connection now\n self.stop() \n\n # return from this function so as to avoid send the status message to the correspongin \n # websocket since its already closed.\n \n\n # pr('on_private_status')\n\n return\n \n last_seen = time.localtime()\n json_decoded_body['last_seen'] = {\n 'date': last_seen.tm_mday,\n 'month': last_seen.tm_mon,\n 'year': last_seen.tm_year,\n 'hour': last_seen.tm_hour,\n 'min': last_seen.tm_min\n } \n \n # LOGGER.info('[MosquittoClient] sending the message to corresponsding websoket: %s ' % self.websocket)\n\n # print '[MosquittoClient] msg to be sent : ', json_decoded_body\n\n self.sendMsgToWebsocket(json_decoded_body)\n\n\n # pr('on_private_status')","title":""},{"docid":"f4c05de85af48280a1b2c4aaa15ab327","score":"0.58973175","text":"def on_status(self, status_info):\n if config.DEBUG_MODE:\n for list_item in status_info:\n if type(list_item) is rtmp.pyamf.ASObject:\n for k in list_item:\n self.console_write(COLOR['white'], k + ': ' + str(list_item[k]))\n else:\n self.console_write(COLOR['white'], str(list_item))","title":""},{"docid":"caad3fbb0113f3656bcb5bba4e5cbf2c","score":"0.58957666","text":"def _update_status(self, event):\n self.status = event.status","title":""},{"docid":"caad3fbb0113f3656bcb5bba4e5cbf2c","score":"0.58957666","text":"def _update_status(self, event):\n self.status = event.status","title":""},{"docid":"1fceeb8591c7edbcef3fa77904163c51","score":"0.58836824","text":"def status(self, verbose=True):\n\n response = requests.get(self.base_url + 'rest/rec/onchange?last=' \n + str(0), headers=self.header)\n status_info = json.loads(response.text)\n self.last_change = status_info['lastUpdateTag']\n self.state = status_info['moduleState']\n \n current_status = (f\"\\n\\tBnK state : {self.state}\\n\"\n f\"\\tcommands sent : {self.last_change}\\n\"\n f\"\\tBnK clock : {response.headers['Date']}\"\n )\n if verbose:\n print(current_status)\n else:\n return current_status","title":""},{"docid":"8e09c2848037dc8070da646c475e4618","score":"0.58783233","text":"def run(self, status):\n pass","title":""},{"docid":"796657d4dc1837adc5da4d7eb3881486","score":"0.58764285","text":"def status():\n\n # Get db\n runs = db[\"runs\"]\n\n # Display the status page from the other sites\n ajaxRequest = validation.convertRequestToPythonBool(\"ajaxRequest\", request.args)\n\n # Where the statuses will be collected\n statuses = collections.OrderedDict()\n\n # Determine if a run is ongoing\n # To do so, we need the most recent run\n mostRecentRun = runs[runs.keys()[-1]]\n runOngoing = mostRecentRun.isRunOngoing()\n if runOngoing:\n runOngoingNumber = \"- \" + mostRecentRun.prettyName\n else:\n runOngoingNumber = \"\"\n # Add to status\n statuses[\"Ongoing run?\"] = \"{0} {1}\".format(runOngoing, runOngoingNumber)\n\n if \"config\" in db and \"receiverLogLastModified\" in db[\"config\"]:\n receiverLogLastModified = db[\"config\"][\"receiverLogLastModified\"]\n lastModified = time.time() - receiverLogLastModified\n # Display in minutes\n lastModified = int(lastModified//60)\n lastModifiedMessage = \"{0} minutes ago\".format(lastModified)\n else:\n lastModified = -1\n lastModifiedMessage = \"Error! Could not retrieve receiver log information!\"\n # Add to status\n statuses[\"Last requested data\"] = lastModifiedMessage\n\n # Determine server statuses\n # TODO: Consider reducing the max number of retries\n sites = serverParameters[\"statusRequestSites\"]\n for site, url in iteritems(sites):\n serverError = {}\n statusResult = \"\"\n try:\n serverRequest = requests.post(url + \"/status\", timeout = 0.5)\n if serverRequest.status_code != 200:\n serverError.setdefault(\"Request error\", []).append(\"Request to \\\"{0}\\\" at \\\"{1}\\\" returned error response {2}!\".format(site, url, serverRequest.status_code))\n else:\n statusResult = \"Site is up!\"\n except requests.exceptions.Timeout as e:\n serverError.setdefault(\"Timeout error\", []).append(\"Request to \\\"{0}\\\" at \\\"{1}\\\" timed out with error {2}!\".format(site, url, e))\n except requests.exceptions.ConnectionError as e:\n serverError.setdefault(\"Connection error\", []).append(\"Request to \\\"{0}\\\" at \\\"{1}\\\" had a connection error with message {2}!\".format(site, url, e))\n except requests.exceptions.RequestException as e:\n serverError.setdefault(\"General Requests error\", []).append(\"Request to \\\"{0}\\\" at \\\"{1}\\\" had a general requests error with message {2}!\".format(site, url, e))\n\n # Return error if one occurred\n if serverError != {}:\n statusResult = serverError\n\n # Add to status\n statuses[site] = statusResult\n\n if ajaxRequest == False:\n return render_template(\"status.html\", statuses = statuses)\n else:\n drawerContent = \"\"\n mainContent = render_template(\"statusMainContent.html\", statuses = statuses)\n\n return jsonify(drawerContent = drawerContent, mainContent = mainContent)","title":""},{"docid":"2e69266064849b86f7e03da45f5c88b0","score":"0.58747745","text":"async def ping(self):\n while True:\n self.expect_update(WAIT_TIME)\n await self.mqtt.pub(f\"{self.name}/send_state\", \"\")\n await asyncio.sleep(600 if self.state['reachable'] else 30)","title":""},{"docid":"c71e286a23c0f25ea51875164ce8a887","score":"0.58738345","text":"def status(self, status):\n self._status = status","title":""},{"docid":"c71e286a23c0f25ea51875164ce8a887","score":"0.58738345","text":"def status(self, status):\n self._status = status","title":""},{"docid":"d373424d4b6aba3d174bffcb1fbae508","score":"0.5866176","text":"def on_status(self, status):\n\n # Collect relevant tweet data for each tweet\n self.tweet.append(status.id)\n self.tweet.append(status.text)\n self.tweet.append(str(status.created_at))\n self.tweet.append(str(status.user.location))\n self.tweet.append(str(status.coordinates))\n self.tweet.append(status.user.followers_count)\n self.tweet.append(status.user.friends_count)\n self.collected_tweet_list.append(self.tweet)\n self.tweet = []\n self.tweet_count += 1\n # Specify number of tweets to collect (takes some time depending on keyword popularity) \n if self.tweet_count > 1000:\n return False","title":""},{"docid":"3f8e0150f51e2f90d0c3d610b0fc57c0","score":"0.58563584","text":"def on_status(self, status):\n # handling display options\n self.counter -= 1\n\n if self.counter >= 0:\n print(status.author.screen_name, 'said')\n print(status.text)\n print('at', status.created_at)\n return True\n else:\n return False","title":""},{"docid":"4c93beedb7f7340cace1c2eef04a0193","score":"0.58543605","text":"def statusupdate( ):\n t = time.strftime(\"%Y-%m-%d %H:%M:%S \")\n window.statusBar().showMessage( t )\n #print \"statusupdate\", t","title":""},{"docid":"f6a29b2e01455af013f12750966b8440","score":"0.5847774","text":"def _report_status(state):\n msgs = []\n for idx, source in enumerate([\"<oplog>\", \"<profiler>\"]):\n tailer_state = state.tailer_states[idx]\n msg = \"\\n\\t{}: received {} entries, {} of them were written, \"\\\n \"last received entry ts: {}, last get-none ts: {}\" .format(\n source,\n tailer_state.entries_received,\n tailer_state.entries_written,\n str(tailer_state.last_received_ts),\n str(tailer_state.last_get_none_ts))\n msgs.append(msg)\n\n utils.LOG.info(\"\".join(msgs))","title":""},{"docid":"6072451c20febdd7f0d8241dd0ce4903","score":"0.58383334","text":"def heating_status():\n with channel() as socket:\n socket.send_json({\"command\": \"heating_status\"})\n response = socket.recv_json()\n return make_response(response[\"content\"], response[\"status\"])","title":""},{"docid":"bf91bea0aa65f725decec09f45bd1213","score":"0.5835701","text":"def _updateStatus (self):\r\n if self._growth > 60 :\r\n self._status = 'Old'\r\n elif self._growth > 40 :\r\n self._status = 'Mature'\r\n elif self._growth > 20 :\r\n self._status = 'Young'\r\n elif self._growth > 0 :\r\n self._status = 'Seeding'\r\n else :\r\n self._status = 'Seed'","title":""},{"docid":"e830e7646106e95ab17e69c86a5bb491","score":"0.58349633","text":"def status(self, val):\n self._set_single(\"status\", val, coerce=dataobj.to_unicode(), allowed_values=[\"queued\", \"failed\", \"sent\"])","title":""},{"docid":"d83e37f9634d87f04f0e50c08e507396","score":"0.58308125","text":"def tweet_current_status() -> str:\n msg = compose_tweet()\n if current_app.config['SEND_TWEETS']:\n tweepy_api.update_status(msg)\n return msg","title":""},{"docid":"808bb65cd43baefec834b706625664aa","score":"0.58303446","text":"async def status_verbose(self, ctx):\n dipgame = scraper.get_dipgame_checked(self.game_id)\n status_message = app.announce_overall_game_state(dipgame, verbose=True)\n await ctx.message.channel.send(status_message)","title":""},{"docid":"b64c226f0014d0786de639ad497e4d13","score":"0.58111775","text":"def writing_status_callback(self, data):\n print \"writing status callback\", data.data\n if data.data == \"writing\":\n print \"Busy writing\"\n self.writing_status = 0\n elif data.data == \"done\":\n print \"Free\"\n self.writing_status = 1","title":""},{"docid":"c0b177362de685f70a9cf318334e125b","score":"0.5811004","text":"def update(self):\n # Data fetching is taken care of through the Main sensor\n\n if self.syncthru.is_online():\n self._attributes = self.syncthru.drum_status().get(self._color, {})\n self._state = self._attributes.get(\"remaining\")","title":""},{"docid":"9367c4be3163bad16b4abd409aed3e60","score":"0.5804909","text":"def adv_received(topic, payload, qos):\n onoff = payload[7:8]\n if onoff == '1':\n self._state = False\n if onoff == '9':\n self._state = True\n # 01 = remote off\n # f9 = local on\n # e1 = local off\n # 19 = remote on\n self.hass.async_add_job(self.async_update_ha_state())","title":""},{"docid":"dc10b9b594f375273e29681083e77140","score":"0.58020127","text":"def status(self, new_status):\n self._status = new_status","title":""},{"docid":"8cf999cb8a9ae869c95fc5d66513f828","score":"0.5801866","text":"def update_status(self) -> None:\n self._pipeline_response = self.request_status(self._operation.get_polling_url())\n _raise_if_bad_http_status_and_method(self._pipeline_response.http_response)\n self._status = self._operation.get_status(self._pipeline_response)","title":""},{"docid":"f4f36c1e247e39504428626e572bce00","score":"0.57877666","text":"def trigger(self):\n\n # User defined function. Only user will call it.\n # Internal methods should not use this.\n # Use _trigger() instead.\n\n while len(self.status) != 0:\n # while there are incomplete operations\n time.sleep(0.001)\n\n self.status.append(2)","title":""},{"docid":"1f4db563e779a843732eb128bb43ad6f","score":"0.57828367","text":"async def status_handler():\n msg = await zmq_to_manager.send_message(method=\"status\")\n return msg","title":""},{"docid":"3a61e5aa28c304fb4a3128aa01377932","score":"0.57821923","text":"def read_status_updates(self):\n try:\n while True:\n message = self._in_queue.get_nowait()\n session_id = message.get('session')\n del message['session']\n self.set_session_status(session_id, message)\n except multiprocessing.queues.Empty:\n pass","title":""},{"docid":"da8377854293bb2baeeb21a503f52e7e","score":"0.5782013","text":"def sendStatusKeys(self, cmd):\n\n cmd.inform('cameraName=%s; readNoise=%0.2f' % (self.name, self.readNoise))","title":""},{"docid":"ae3a64672e30357ac28d803ab432b8d0","score":"0.5781363","text":"def __update_lcd__(self):\n\n # If we start shutting down or rebooting\n # then __lcd__ is set to None to avoid\n # any more updates\n if self.__lcd__ is None:\n return\n\n self.__lcd__.clear()\n\n # Moves to the next status message based on the\n # interval in given to RecurringEvent (Normally 5 seconds)\n try:\n if self.__lcd_status_id__ == 0:\n self.__lcd__.write_text(self.__get_fona_status__())\n if self.__lcd_status_id__ == 1:\n self.__lcd__.write_text(self.__get_heater_status__())\n if self.__lcd_status_id__ == 2:\n if self.__sensors__.current_gas_sensor_reading is not None:\n self.__lcd__.write_text(self.__get_gas_sensor_status__())\n else:\n self.__lcd_status_id__ += 1\n if self.__lcd_status_id__ == 3:\n if self.__sensors__.current_light_sensor_reading is not None \\\n and self.__sensors__.current_light_sensor_reading.enabled:\n self.__lcd__.write_text(self.__get_light_status__())\n else:\n self.__lcd_status_id__ += 1\n if self.__lcd_status_id__ == 4:\n if self.__sensors__.current_temperature_sensor_reading is not None:\n self.__lcd__.write_text(self.__get_temp_probe_status__())\n else:\n self.__lcd_status_id__ += 1\n if self.__lcd_status_id__ >= 5:\n self.__lcd_status_id__ = -1\n self.__lcd__.write_text(\"UPTIME:\\n\" + self.__get_uptime_status__())\n except:\n self.__lcd__.write(0, 0, \"ERROR: LCD_ID=\" +\n str(self.__lcd_status_id__))\n self.__lcd_status_id__ = -1\n\n self.__lcd_status_id__ = self.__lcd_status_id__ + 1","title":""},{"docid":"23800337c61e7a43b578aa16c60c93f7","score":"0.5780289","text":"def status(\n self,\n pubnub: Union[pubnub.pubnub.PubNub, pubnub.pubnub_asyncio.PubNubAsyncio],\n status: pubnub.models.consumer.common.PNStatus,\n ) -> None:\n _LOGGER.info(\n f\"pubnub status update {status.category}, error: {status.error}, error_data: {status.error_data}\"\n )","title":""},{"docid":"c537929beac37629e7e01042f8cc4458","score":"0.57728565","text":"def status(ctx):\n ctx.obj[\"output\"](\n {\n \"processes\": status_processes(ctx.obj[\"cfg\"]),\n \"neighbors\": status_neighbors(ctx.obj[\"cfg\"]),\n }\n )","title":""}],"string":"[\n {\n \"docid\": \"78b8259175309804b0111ec3462d84ba\",\n \"score\": \"0.7426226\",\n \"text\": \"def send_stat (relais_nrß) :\\n global CONF\\n for i in range(0,len(relist)) :\\n if relist[i].get() : \\n mqtt_sta = 1\\n else:\\n mqtt_sta = 0\\n rel = str(i+1)\\n client.publish (CONF['MQTOPIC']+\\\"stat/\\\"+rel,payload=mqtt_sta, qos=0, retain=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73dc6e10a03ab10eac4e4ff8bfb15bed\",\n \"score\": \"0.7406092\",\n \"text\": \"def notifyStatus(self, status):\\r\\n if status == \\\"TM_CONNECTED\\\":\\r\\n self.tmConnectedNotify()\\r\\n elif status == \\\"TM_RECORDING\\\":\\r\\n self.tmRecordingNotify()\\r\\n elif status == \\\"PACKETDATA_SET\\\":\\r\\n self.packetDataSetNotify()\\r\\n elif status == \\\"UPDATE_REPLAY\\\":\\r\\n self.updateReplayNotify()\\r\\n elif status == \\\"ENABLED_CYCLIC\\\":\\r\\n self.enabledCyclicNotify()\\r\\n elif status == \\\"DISABLED_CYCLIC\\\":\\r\\n self.disabledCyclicNotify()\\r\\n elif status == \\\"OBC_ENABLED_ACK1\\\":\\r\\n self.obcEnabledAck1Notify()\\r\\n elif status == \\\"OBC_ENABLED_NAK1\\\":\\r\\n self.obcEnabledNak1Notify()\\r\\n elif status == \\\"OBC_DISABLED_ACK1\\\":\\r\\n self.obcDisabledAck1Notify()\\r\\n elif status == \\\"OBC_ENABLED_ACK2\\\":\\r\\n self.obcEnabledAck2Notify()\\r\\n elif status == \\\"OBC_ENABLED_NAK2\\\":\\r\\n self.obcEnabledNak2Notify()\\r\\n elif status == \\\"OBC_DISABLED_ACK2\\\":\\r\\n self.obcDisabledAck2Notify()\\r\\n elif status == \\\"OBC_ENABLED_ACK3\\\":\\r\\n self.obcEnabledAck3Notify()\\r\\n elif status == \\\"OBC_ENABLED_NAK3\\\":\\r\\n self.obcEnabledNak3Notify()\\r\\n elif status == \\\"OBC_DISABLED_ACK3\\\":\\r\\n self.obcDisabledAck3Notify()\\r\\n elif status == \\\"OBC_ENABLED_ACK4\\\":\\r\\n self.obcEnabledAck4Notify()\\r\\n elif status == \\\"OBC_ENABLED_NAK4\\\":\\r\\n self.obcEnabledNak4Notify()\\r\\n elif status == \\\"OBC_DISABLED_ACK4\\\":\\r\\n self.obcDisabledAck4Notify()\\r\\n elif status == \\\"FRAME_REC_STARTED\\\":\\r\\n self.frameRecStarted()\\r\\n elif status == \\\"FRAME_REC_STOPPED\\\":\\r\\n self.frameRecStopped()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"468b1a0edb1bee142f0973b2e1d2b967\",\n \"score\": \"0.6909292\",\n \"text\": \"def send_status(self):\\n if self.pstat[0] > 0:\\n # send statistics via bulletin\\n time_on = datetime.datetime.now() - self.start_datetime\\n p_tot = self.pstat[0] + self.pstat[1] + self.pstat[2]\\n n_calls = len(self.pstat[3])\\n status_txt = f\\\"IGate up {time_on.days} days \\\" \\\\\\n f\\\"{round(time_on.seconds/3600,1)} h \\\" \\\\\\n f\\\"{p_tot} rcvd, {self.pstat[0]} gtd, \\\" \\\\\\n f\\\"{n_calls} unique calls\\\"\\n else:\\n status_txt = self.STATUS_TXT\\n status = f\\\"{self.user.my_call}-{self.user.ssid}>{self.VERS},\\\" \\\\\\n f\\\"TCPIP*:>{status_txt}\\\\r\\\\n\\\"\\n threading.Timer(self.HOURLY, self.send_status).start()\\n self.send_aprs(status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18774f4aec9f17a6dcb0a920a59c0d5\",\n \"score\": \"0.68421644\",\n \"text\": \"def status():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a11ddf6b9ab75fad40692d3e372788\",\n \"score\": \"0.6783032\",\n \"text\": \"def request_status():\\n global request\\n for drone in drones:\\n m = {\\n 'code': REPLY_STATUS,\\n 'id': dev_id\\n }\\n send_message(drone, json.dumps(m))\\n request = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518649f4c0150db0ca497bc4ef76a574\",\n \"score\": \"0.6711619\",\n \"text\": \"def print_status():\\n\\n scr.addstr(curses.LINES-1,1,\\\"@%s - [%i] Unread messages - Last updated %s - %s\\\"%(username,msgs,now.strftime(\\\"%Y-%m-%d %H%M\\\"),status))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3370b6d86f96370a292b34bdc1e8eb62\",\n \"score\": \"0.6701341\",\n \"text\": \"def on_status(self, status):\\n msg = status.text.encode('utf-8')\\n #print(msg)\\n try:\\n self.producer.send_messages('twitterstream', msg)\\n except Exception as e:\\n print(e)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827b0427c58c3ef17e1e87fce059c2c0\",\n \"score\": \"0.66875154\",\n \"text\": \"def _on_status(self, status):\\n self.logger.debug(\\\"_on_status started\\\")\\n self._wolk._on_status(status)\\n self.logger.debug(\\\"_on_status ended\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5cf7fcd5f260a5ab79c876891bc3b19\",\n \"score\": \"0.6659487\",\n \"text\": \"def statusReceived(self, status):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d7977367e55d4f4c707ccd5db0c6d8f\",\n \"score\": \"0.65679973\",\n \"text\": \"def update_status(self, status):\\n for cat, s in status.items():\\n if cat == 'sensors':\\n continue\\n\\n window = self.windows[cat]\\n linenum = 1\\n for key, val in s.items():\\n if key == 'alerts':\\n # print out monitor alerts\\n self.write_line(window, linenum, 'Alerts:')\\n linenum += 1\\n for k, v in val.items():\\n self.write_key_value(window, linenum, ' '+k, v)\\n linenum += 1\\n else:\\n self.write_key_value(window, linenum, key, val)\\n linenum += 1\\n\\n sen_line = 1\\n for sen in status['sensors']:\\n val = \\\"%s%s\\\" % (sen['value'], sen['units'])\\n self.write_key_value(self.windows['sensors'], sen_line, sen['name'], val)\\n sen_line += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa4d11b372eba0d3312f439c8708faf\",\n \"score\": \"0.6542748\",\n \"text\": \"def talk_status(self, sender, status):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe624e6454a7479d9acf13eb056329e\",\n \"score\": \"0.65381527\",\n \"text\": \"def on_status(self, status):\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe624e6454a7479d9acf13eb056329e\",\n \"score\": \"0.65381527\",\n \"text\": \"def on_status(self, status):\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ca89015f17f220ad1b916379b82798f\",\n \"score\": \"0.65372473\",\n \"text\": \"def queue_status():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c0350467d63c1dc466b13dca84dd57b\",\n \"score\": \"0.64841574\",\n \"text\": \"def status (self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a82a0bb27078c334f5973a54d148405\",\n \"score\": \"0.64776254\",\n \"text\": \"def oneSecondHook():\\n global StatusUpdateCount\\n \\n mcastRpc(1, HOP_COUNT, 'powerOn173', True)\\n \\n # Have the node periodically send an update of its status, set for 10 seconds\\n StatusUpdateCount -= 1\\n if StatusUpdateCount <= 0:\\n reportStateAllLEDs()\\n StatusUpdateCount = 10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0465e28fde9ed0a1605d65b820ada04\",\n \"score\": \"0.6463386\",\n \"text\": \"def onStatusNoticiesRecv(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1320bf142fe7a0cc52a88e38356a2876\",\n \"score\": \"0.6439513\",\n \"text\": \"def getStatus(self):\\n self.status.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3573c73886d0f255e8fc88040ec069\",\n \"score\": \"0.6435214\",\n \"text\": \"async def status_changer(self):\\n playing = []\\n streaming = []\\n listening = []\\n watching = []\\n statuses = playing + streaming + listening + watching\\n\\n while True:\\n if not self.is_ready():\\n continue\\n elif self.is_closed():\\n return\\n else:\\n await self.change_presence(activity=random.choice(statuses))\\n await asyncio.sleep(self.config['Bot']['StatusTimer'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cac5dd69692cf269cf326bf2a4e5f5\",\n \"score\": \"0.640287\",\n \"text\": \"def send_refresh(self):\\n self.mqtt_discovery_broadcast()\\n if self.num_recent_feeds(24) <= 2:\\n self.turn_on()\\n self.send_mqtt_update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67da542fc31dca989239ed2de4680b0c\",\n \"score\": \"0.6402793\",\n \"text\": \"def _stat(self):\\n self._send_message_to_controller(self._get_stats())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"276b6705ca838ae38d96b37ff0aa771e\",\n \"score\": \"0.63653386\",\n \"text\": \"def refreshStatusInfo(self):\\n reqSubHeader = {'procDiv': 1,\\n 'cmdNo': (0x72, 0x00), #Command No. 0x72\\n 'inst': (1, 0), #Fixed to 1\\n 'attr': 0, #1: Data 1, 2: Data 2\\n 'service': 0x01, #Get_Attribute_Single: 0x0E, Get_Attribute_All: 0x01\\n 'padding': (0, 0) }\\n\\n reqData = []\\n\\n ansPacket = self.sendCmd(reqSubHeader, reqData)\\n\\n if( ansPacket == None or ansPacket.status != 0):\\n return False\\n\\n #parse answer data\\n byte1=ansPacket.data[0]\\n byte2=ansPacket.data[4]\\n\\n def testBit(int_type, offset):\\n mask = 1 << offset\\n if (int_type & mask) == mask:\\n return True\\n else:\\n return False\\n\\n self.status['Step'] = testBit(byte1,0)\\n self.status['Cycle'] = testBit(byte1,1)\\n self.status['Auto'] = testBit(byte1,2)\\n self.status['Running'] = testBit(byte1,3)\\n self.status['InGuard'] = testBit(byte1,4)\\n self.status['Teach'] = testBit(byte1,5)\\n self.status['Play'] = testBit(byte1,6)\\n self.status['Remote'] = testBit(byte1,7)\\n\\n self.status['Hold_PP'] = testBit(byte2,1)\\n self.status['Hold_Ext'] = testBit(byte2,2)\\n self.status['Hold_Cmd'] = testBit(byte2,3)\\n self.status['Alarm'] = testBit(byte2,4)\\n self.status['Error'] = testBit(byte2,5)\\n self.status['ServoOn'] = testBit(byte2,6)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b2b746371b034603233cdd9066b54dd\",\n \"score\": \"0.6362009\",\n \"text\": \"def sendStatus(self, status):\\r\\n if self.debug:\\r\\n log.msg(\\\"sendStatus\\\", status)\\r\\n if not self.running:\\r\\n log.msg(\\\"would sendStatus but not .running\\\")\\r\\n return\\r\\n self.builder.sendUpdate(status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9abbbef3d7f92f0dac615cea5063224\",\n \"score\": \"0.63299716\",\n \"text\": \"def send_offline_status(self): \\n\\n # pi('send_offline_status')\\n\\n # removing the subscribed but now unsubscribed and disconected mqtt mosqitto client \\n self._participants = self.delMqttMosquittoClient()\\n\\n # publishing status message to private/clientid/status topic\\n statusmsg = {\\n 'msg_type': 'status',\\n 'status': 'offline',\\n 'msg': {\\n 'name': self._name,\\n 'clientid': self._clientid\\n }\\n }\\n\\n self.publish(topic='private/' + self._clientid + '/status', msg=statusmsg, qos=2, retain=True)\\n\\n\\n # pr('send_offline_status')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38e9cd19e696e329d6380cb401cc775\",\n \"score\": \"0.6248638\",\n \"text\": \"def status_online(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0254ed55d5977e24a8678bc8909802f4\",\n \"score\": \"0.6246707\",\n \"text\": \"async def status(self, ctx):\\n if ctx.message.channel.name != \\\"minecraft\\\":\\n await self.bot.say(\\\"Please move to #minecraft for this command.\\\")\\n else: \\n server = MinecraftServer.lookup(self.bot.mcip)\\n try:\\n status = server.status()\\n except IOError as e:\\n await self.bot.say(\\\"It's dead Jim.\\\")\\n # try:\\n # query = server.query()\\n # except Sock as e:\\n # await bot.say(\\\"Server too slow for query!\\\")\\n em = discord.Embed(title='CSSS FTB Server Status', description=\\n \\\"\\\"\\\"The server has {0} players and replied in {1} ms.\\\\n\\\"\\\"\\\".format(status.players.online, status.latency), colour=0x3D85C6 )\\n # + \\\"\\\\n{} are currently online.\\\".format(\\\", \\\".join(query.players.names)), colour=0x3D85C6)\\n await self.bot.send_message(ctx.message.channel, embed=em)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c20ac656f1fd96d70aff5b0cc10a1043\",\n \"score\": \"0.62240726\",\n \"text\": \"def status_message(self):\\n slave_id, timestamp, msg, *args = self.status_queue.recv_pyobj()\\n if msg == 'STATUS':\\n self.update_status(args[0])\\n else:\\n self.slave_list.message(slave_id, timestamp, msg, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a371587547d55e3f98305572f4d12d22\",\n \"score\": \"0.6216164\",\n \"text\": \"def _checkStatus(self):\\n if self.last_messages == self.receivedMsgs:\\n print(\\\"Time is out\\\") \\n self.channel.stop_consuming()\\n self.print_dictionary() \\n else:\\n self.last_messages = self.receivedMsgs\\n self.timer_id = self.conn.call_later(self.timeout, self._checkStatus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f392e4d43599039e2f9ae291219a9b1\",\n \"score\": \"0.62157136\",\n \"text\": \"async def test_other_status(self):\\n self.status_command = StatusRequestCommand(self._address, status_type=0)\\n self.status_command.subscribe(self.set_status)\\n status_type = 1\\n status_1_command = StatusRequestCommand(self._address, status_type=status_type)\\n status_1_command.subscribe(self.set_status_1)\\n cmd1 = 0x19\\n cmd2 = 0x01\\n db_version = 0x66\\n status = 0x77\\n topics = [\\n TopicItem(\\n self.ack_topic,\\n {\\\"cmd1\\\": cmd1, \\\"cmd2\\\": cmd2, \\\"user_data\\\": None},\\n 0.1,\\n ),\\n TopicItem(\\n self.direct_ack_topic,\\n {\\n \\\"cmd1\\\": db_version,\\n \\\"cmd2\\\": status,\\n \\\"target\\\": \\\"112233\\\",\\n \\\"user_data\\\": None,\\n \\\"hops_left\\\": 3,\\n },\\n 0.1,\\n ),\\n ]\\n self.set_status(-1, -1)\\n self.set_status_1(-2, -2)\\n send_topics(topics)\\n assert await status_1_command.async_send()\\n\\n assert self._db_version_1 == db_version\\n assert self._status_1 == status\\n\\n # Confirm status 1 did not respond.\\n assert self._status == -1\\n assert self._db_version == -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7610ba27c3443c6e58152da3c8ff03b\",\n \"score\": \"0.6202714\",\n \"text\": \"async def status(self, ctx):\\n dipgame = scraper.get_dipgame_checked(self.game_id)\\n status_message = app.announce_overall_game_state(dipgame)\\n await ctx.message.channel.send(status_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b41c647d89a02edeacb8db57683dc0\",\n \"score\": \"0.6187948\",\n \"text\": \"async def mc_status(message):\\n r = await http.get(MINECRAFT_STATUS_URL, headers=HEADERS)\\n data = r.json()['report']\\n status = []\\n for name, v in data.items():\\n text = v['title']\\n if text == \\\"Online\\\":\\n text = \\\":ok:\\\"\\n else:\\n text = \\\":warning: \\\" + text\\n status.append(\\\"{}: {}\\\".format(name.title(), text))\\n return Response(\\\", \\\".join(status))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed2a950600d357b3a981b09cb3e3c8c2\",\n \"score\": \"0.61839575\",\n \"text\": \"def update(self):\\n # Data fetching is taken care of through the Main sensor\\n\\n if self.syncthru.is_online():\\n self._attributes = self.syncthru.output_tray_status().get(self._number, {})\\n self._state = self._attributes.get(\\\"status\\\")\\n if self._state == \\\"\\\":\\n self._state = \\\"Ready\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cd79bc0cc25623b3f231a4d9f5725ce\",\n \"score\": \"0.6171635\",\n \"text\": \"def update(self):\\n # Data fetching is taken care of through the Main sensor\\n\\n if self.syncthru.is_online():\\n self._attributes = self.syncthru.toner_status().get(self._color, {})\\n self._state = self._attributes.get(\\\"remaining\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"584721b9d01e2d355c0dd7bd4d729c76\",\n \"score\": \"0.61621267\",\n \"text\": \"def status_callback(self, data):\\n print \\\"Arm status callback\\\", data.data\\n if data.data == \\\"busy\\\" or data.data == \\\"error\\\":\\n self.status = 0\\n elif data.data == \\\"free\\\":\\n self.status = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b0880c00282c910c84038088e5bf828\",\n \"score\": \"0.6137847\",\n \"text\": \"def online(client, device):\\n d_id = ObjectId(dict(device).get(\\\"id\\\"))\\n mongo.db.devices.update_one({\\\"_id\\\": d_id},{\\n \\\"$set\\\": {\\n \\\"status\\\": Constant.Status.STATUS_WAIT,\\n \\\"version\\\": dict(device).get(\\\"version\\\")\\n }\\n })\\n client.publish(dict(device).get(\\\"id\\\") + \\\"/status/d\\\", Constant.Status.STATUS_WAIT)\\n if \\\"TOPICS_NEED\\\" in os.environ:\\n topics_need = json.loads(os.environ.get(\\\"TOPICS_NEED\\\"))\\n for item in topics_need:\\n client.subscribe(dict(device).get(\\\"id\\\") + \\\"/\\\" + item + \\\"/u\\\")\\n client.subscribe(dict(device).get(\\\"id\\\") + \\\"/\\\" + item + \\\"/d\\\")\\n print(\\\"Auto subscribing\\\", dict(device).get(\\\"id\\\") + \\\"/\\\" + item + \\\"/(u|d)\\\")\\n print(dict(device).get(\\\"id\\\") + \\\" Online\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85858e9d2708a15b94d53f9629543bd4\",\n \"score\": \"0.6130322\",\n \"text\": \"def send_status_message(self, status):\\n broadcasters = []\\n active_users = []\\n\\n # get list active connections\\n for user in self.channels.lget(self.user_id):\\n if self.client.subscribers.get(user):\\n broadcasters.extend(self.client.subscribers.get(user).keys())\\n active_users.append(user)\\n if broadcasters:\\n self.broadcast(broadcasters, json.dumps({'type': status, 'users': [self.user_id]}))\\n self.send(json.dumps({'type': status, 'users': active_users}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d809a8c99cd33fcef0b7b5a31432b267\",\n \"score\": \"0.6129792\",\n \"text\": \"def status(program):\\n for message in program.master.status():\\n print message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a9339449a5f98b94fd7a30f980cca5\",\n \"score\": \"0.61022997\",\n \"text\": \"def SetStatus(self, status):\\n\\n if status == 'OFFLINE':\\n if not self.offline:\\n self.StatusChanged('OFFLINE')\\n self.offline = True\\n else:\\n# XXX flush queue?\\n# self._flush_queue()\\n if self.offline:\\n self.StatusChanged('ONLINE')\\n self.offline = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cd33011a252a8832a814fc751eaa2e\",\n \"score\": \"0.6095115\",\n \"text\": \"def get_status(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d5466eea43dadabf85fba33c527c93e\",\n \"score\": \"0.6090604\",\n \"text\": \"def update_status(self, state):\\n\\n if state == \\\"connected Lightstreamer session\\\":\\n connected_color = QtGui.QColor(\\\"#23A627\\\")\\n status_icon = funcMisc.create_status_icons(connected_color)\\n self.lbl_status.setPixmap(status_icon)\\n\\n elif state == \\\"disconnected from Lightstreamer\\\":\\n disconnected_color = QtGui.QColor(\\\"#F51616\\\")\\n status_icon = funcMisc.create_status_icons(disconnected_color)\\n self.lbl_status.setPixmap(status_icon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e9d3e097d52fa37fe37f2c0cd3b7c51\",\n \"score\": \"0.6087035\",\n \"text\": \"def getStatus():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c658d0ad0f6263420f9122f161e98c2\",\n \"score\": \"0.60826665\",\n \"text\": \"async def timed_status(self,channel):\\n print(\\\"Entering timed_status\\\")\\n shutdown = False\\n while not shutdown:\\n try:\\n counter = 0\\n await self.bot.wait_until_ready()\\n while not self.bot.is_closed:\\n await asyncio.sleep(60)\\n result = await self.check()\\n if result is False:\\n counter += 1\\n if counter == 5:\\n await self.bot.send_message(channel, \\\"Agnarr's current population/status is {} as of {}\\\"\\n .format(self.state, self.time.strftime(\\\"%H:%M\\\")))\\n elif result is True:\\n counter = 0\\n await self.bot.change_presence(game=discord.Game(\\n name='Agnarr: {} @ {}'.format(self.state, self.time.strftime(\\\"%H:%M\\\"))))\\n print(\\\"Bot has closed, zomg\\\")\\n return\\n except concurrent.futures.CancelledError:\\n print (\\\"EQServerStatus Task Cancelled\\\")\\n shutdown = True\\n except (websockets.exceptions.ConnectionClosed,TypeError,aiohttp.errors.ClientOSError, ConnectionError) as e:\\n pass\\n except Exception as e:\\n exc = '{}: {}'.format(type(e).__name__, e)\\n print('Error in timed status:\\\\n{}'.format( exc))\\n traceback.print_exc()\\n print(\\\"Leaving timed_status\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0221bcc255252a4bee1ed2ccaa4fb2fc\",\n \"score\": \"0.6081622\",\n \"text\": \"def publish_status(self, status):\\n self.publisher.publish(status = status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b343b91c0c595b5b48dc5401ce3f8fe\",\n \"score\": \"0.6072817\",\n \"text\": \"def set_status(self, status, summary=\\\"\\\"):\\n self.__stop = time.time()\\n self.__status = status\\n self.__status_summary = summary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8617541fc2c7bc8d9b52840106e21c86\",\n \"score\": \"0.6066825\",\n \"text\": \"def on_status(self, status):\\n print(status.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ae9a77b5b6dd3a8b77c72a7f54f793\",\n \"score\": \"0.6066149\",\n \"text\": \"def update_status(self, msg, current=None, total=None):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7317ecd8a6aced20a179f7fc500ee5a1\",\n \"score\": \"0.6065251\",\n \"text\": \"def change_status(self, status):\\n\\n if not self.hasUser(): return \\n \\n self.output = self.send(\\\"#self.activate();\\\") if status else self.send(\\\"#self.stop();\\\")\\n print(\\\" \\\" + self.output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba66bef76d87f3b85b3b57a86b5d7af\",\n \"score\": \"0.6057035\",\n \"text\": \"def publish(self, thing_id, state):\\n print \\\"Status update: \\\", thing_id\\n self.aio.send(thing_id + '-batt', int(state['batt']))\\n sleep(1) # Pause for a second so we don't hit the API throttle limits\\n self.aio.send(thing_id + '-state', int(state['button_state']))\\n sleep(1) # Pause for a second so we don't hit the API throttle limits\\n self.aio.send(thing_id + '-count', state['button_count'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3edc875b5f15d3a2669cc695b212daac\",\n \"score\": \"0.6032335\",\n \"text\": \"def set_status(self, message):\\n msg = \\\"record_status_message '{}'\\\".format(message)\\n self.tracker.sendCommand(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"771b70d5a2f1c7ef3b557f7cf4f96735\",\n \"score\": \"0.60233724\",\n \"text\": \"async def onlinestatus(self, ctx):\\n\\t\\tdevice = {\\n\\t\\t\\t(True, True, True): 0,\\n\\t\\t\\t(False, True, True): 1,\\n\\t\\t\\t(True, False, True): 2,\\n\\t\\t\\t(True, True, False): 3,\\n\\t\\t\\t(False, False, True): 4,\\n\\t\\t\\t(True, False, False): 5,\\n\\t\\t\\t(False, True, False): 6,\\n\\t\\t\\t(False, False, False): 7\\n\\t\\t}\\n\\t\\tstore = [0, 0, 0, 0, 0, 0, 0, 0]\\n\\t\\tfor m in ctx.guild.members:\\n\\t\\t\\tvalue = (\\n\\t\\t\\t\\tm.desktop_status == discord.Status.offline,\\n\\t\\t\\t\\tm.web_status == discord.Status.offline,\\n\\t\\t\\t\\tm.mobile_status == discord.Status.offline\\n\\t\\t\\t)\\n\\t\\t\\tstore[device[value]] += 1\\n\\t\\tmsg = (\\n\\t\\t\\tf'offline all: {str(store[0])}'\\n\\t\\t\\tf'\\\\ndesktop only: {str(store[1])}'\\n\\t\\t\\tf'\\\\nweb only: {str(store[2])}'\\n\\t\\t\\tf'\\\\nmobile only: {str(store[3])}'\\n\\t\\t\\tf'\\\\ndesktop web: {str(store[4])}'\\n\\t\\t\\tf'\\\\nweb mobile: {str(store[5])}'\\n\\t\\t\\tf'\\\\ndesktop mobile: {str(store[6])}'\\n\\t\\t\\tf'\\\\nonline all: {str(store[7])}'\\n\\t\\t)\\n\\t\\tawait ctx.send(f'```py\\\\n{msg}```')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d48b2527c8f49be379519e1da08242b\",\n \"score\": \"0.6016253\",\n \"text\": \"def on_status(self, status):\\n tweet = status._json\\n if tweet['in_reply_to_status_id']:\\n # リプライである場合、オリジナルツイートを取得する\\n try:\\n origin_tweet = self.twitter_api.get_status(\\n tweet['in_reply_to_status_id'])._json\\n except tweepy.RateLimitError:\\n logging.warning('[on_status] Rate limit exceeded.')\\n logging.warning('[on_status] time.sleep(15 * 60)')\\n time.sleep(15 * 60)\\n return\\n except tweepy.TweepError as ex:\\n if ex.api_code == 179:\\n # 鍵アカウントへのリプライの場合\\n return\\n else:\\n logging.warning('[on_status] TweepyError raised. msg: %s'\\n % ex.reason)\\n # プログラムの中止を避けるため\\n return\\n\\n # 改行文字の置換\\n reply_text = tweet['text'].replace('\\\\n', '。')\\n origin_text = origin_tweet['text'].replace('\\\\n', '。')\\n\\n # screen_id の除去\\n match = re.match(self.REPLY_PATTERN, origin_text)\\n origin_text = match.group('text')\\n match = re.match(self.REPLY_PATTERN, reply_text)\\n reply_text = match.group('text')\\n\\n # URL の除去\\n rex = re.compile(self.URL_PATTERN)\\n origin_text = rex.sub('', origin_text)\\n reply_text = rex.sub('', reply_text)\\n\\n # 空白のみからなる文でないなら出力に追加する\\n if not (re.match(r'\\\\s*$', origin_text) or\\n re.match(r'\\\\s*$', reply_text)):\\n self.buf += 'A: %s\\\\nB: %s\\\\n' % (origin_text, reply_text)\\n self.tweet_counter += 1\\n if self.tweet_counter >= self.BUF_MAX:\\n self.output_file.write(self.buf)\\n self.tweet_counter = 0\\n self.buf = ''\\n self.output_file.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bf50be915208ea67b3342f8242617b4\",\n \"score\": \"0.6009318\",\n \"text\": \"def _send_heartbeat(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b8610589a749b4ae21167180e6f9b57\",\n \"score\": \"0.60083944\",\n \"text\": \"def process_status(ctx):\\n ctx.obj[\\\"output\\\"](status_processes(ctx.obj[\\\"cfg\\\"]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40dab733bb52dfa989a97b1c512229dd\",\n \"score\": \"0.6001468\",\n \"text\": \"def playstatus_update(self, updater, playstatus: pyatv.interface.Playing) -> None:\\n clients = self.app[\\\"clients\\\"].get(self.identifier, [])\\n for client in clients:\\n asyncio.ensure_future(client.send_str(str(playstatus)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b02092dbff2273c9fc63b749400cc9d1\",\n \"score\": \"0.598739\",\n \"text\": \"def SetStatus(self, msg):\\n self.statusMsg.append(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1b103148aefdfac77f2a5656866a789\",\n \"score\": \"0.5981189\",\n \"text\": \"def _xmpp_changed_status(self, presence):\\n if presence['from'] != self.xmpp.boundjid and self.verbose:\\n \\n jid = str(presence['from']).split(\\\"/\\\")[0]\\n name = self.roster_manager.jid2summoner(jid)\\n print \\\"<RiotDEBUG> CHAGE: \\\"+ name + \\\"(\\\" + \\\\\\n str(presence['from']) + \\\")\\\"+\\\" has been changed its status\\\"\\n \\n if presence['type'] == 'unavailable': #let got_offline handle it\\n return\\n\\n if presence['from'] != self.xmpp.boundjid:\\n jid = str(presence['from']).split(\\\"/\\\")[0]\\n resource = str(presence['from']).split(\\\"/\\\")[1]\\n #need to handle off line status == None\\n status_dic = xmltodict.parse(presence['status'])\\n self.roster_manager.updateStatus(jid, resource, \\n status_dic['body'], online=True)\\n\\n self._trigger_event(\\\"change_status\\\", data=presence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62e42e2e1d913c150c690255039ae7a8\",\n \"score\": \"0.59781015\",\n \"text\": \"def _StatusUpdateThreadMain(self):\\n while self._status_update_active:\\n self._UpdateStatus()\\n\\n time.sleep(self._STATUS_UPDATE_INTERVAL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606596da23cb7c981d3a67d3d4effbca\",\n \"score\": \"0.5966187\",\n \"text\": \"def status():\\n\\n command_response = send_command('heartbeat')\\n\\n if command_response['status'] == 'ok':\\n print(\\\"The background client is active\\\")\\n\\n else:\\n print(\\\"The background client is not running\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef961fa98059d2e54e94ff60deb77e56\",\n \"score\": \"0.59600794\",\n \"text\": \"def _update_status(self, status, test_number):\\n if self.on_status is not None:\\n self.on_status(status, test_number)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b85cd8130a36f58a52cc28101aea1fd9\",\n \"score\": \"0.5959098\",\n \"text\": \"def display_status(self):\\n time = float2str(self.scheduler.time, '10.2f')\\n tx = float2str(self.tx_total, '10g')\\n rx = float2str(self.rx_total, '10g')\\n dup = float2str(self.dup_total, '10g')\\n uniq_total = float2str(self.uniq_total, '10g')\\n delivered_total = float2str(self.delivered_total, '10g')\\n uniq_delivered_total = float2str(self.uniq_delivered_total, '10g')\\n print(\\n f'define status_l text Time:{time},____TX:{tx},____RX:{rx},____DUP:{dup},____Delivered:{uniq_delivered_total}__/__{uniq_total},____Arrived:{delivered_total} 14 white 0.5 0.05'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce0eab461f1a128243932b7f0992d5f\",\n \"score\": \"0.59505075\",\n \"text\": \"def collect_status(self):\\r\\n self._cmd(self.STATUS_CMD)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82223d9e18e72b3b164bdcffebe05ec6\",\n \"score\": \"0.5945344\",\n \"text\": \"async def test_status_command(self):\\n self.status_command = StatusRequestCommand(self._address, status_type=0)\\n self.status_command.subscribe(self.set_status)\\n cmd1 = 0x19\\n cmd2 = 0x00\\n db_version = 0x22\\n status = 0x33\\n topics = [\\n TopicItem(\\n self.ack_topic,\\n {\\\"cmd1\\\": cmd1, \\\"cmd2\\\": cmd2, \\\"user_data\\\": None},\\n 0.5,\\n ),\\n TopicItem(\\n self.direct_ack_topic,\\n {\\n \\\"cmd1\\\": db_version,\\n \\\"cmd2\\\": status,\\n \\\"target\\\": \\\"112233\\\",\\n \\\"user_data\\\": None,\\n \\\"hops_left\\\": 3,\\n },\\n 0.5,\\n ),\\n ]\\n send_topics(topics)\\n assert await self.status_command.async_send()\\n await asyncio.sleep(1)\\n assert self._db_version == db_version\\n assert self._status == status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e655d9cdbd35af3cd64198d15aaf400\",\n \"score\": \"0.5939916\",\n \"text\": \"def report_status(self):\\n import time\\n\\n while 1:\\n time.sleep(2)\\n try:\\n status = self.get_status()\\n except TaskCompleted:\\n break\\n except Exception as error:\\n self.report('running', '(1) Error getting status (%s)' % error)\\n #return\\n else:\\n # self.report('running', 'Time: %s days' % status)\\n self.report('running', '{message}'.format(message=status))\\n\\n self.report('success', 'completed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8da7c212adf314e76d386514eb76ef\",\n \"score\": \"0.5939347\",\n \"text\": \"async def status(self, ctx, arg):\\n if await self.is_key_valid(ctx, arg):\\n reminder = await self.get_reminder(arg)\\n interval_unit = get_time_unit(reminder.runInterval)\\n await ctx.send(wrap_code_block(\\n \\\"Name: {0}\\\\r\\\\n\\\"\\n \\\"Current Text: {1}\\\\r\\\\n\\\"\\n \\\"Current Link: {2}\\\\r\\\\n\\\"\\n \\\"Running Interval: {3} {4}(s)\\\\r\\\\n\\\"\\n \\\"Running: {5}\\\\r\\\\n\\\"\\n \\\"Last Ran: {6}\\\".format(arg,\\n reminder.notification_text if reminder.notification_text is not None else \\\"N/A\\\",\\n reminder.notification_link if reminder.notification_link is not None else \\\"N/A\\\",\\n round(reminder.runInterval / interval_lookup[interval_unit], 2),\\n interval_unit,\\n reminder.started,\\n reminder.lastRunTime if reminder.lastRunTime is not None else \\\"N/A\\\")))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f857a453b8a76e2805b9a851bb29b9\",\n \"score\": \"0.5929943\",\n \"text\": \"async def test_status_request_hub(self):\\n self.status_command = StatusRequestCommand(self._address, status_type=0)\\n self.status_command.subscribe(self.set_status)\\n cmd1 = 0x19\\n cmd2 = 0x00\\n db_version = 0x44\\n status = 0x55\\n topics = [\\n TopicItem(\\n self.ack_topic,\\n {\\\"cmd1\\\": cmd1, \\\"cmd2\\\": cmd2, \\\"user_data\\\": None},\\n 0.1,\\n ),\\n TopicItem(\\n self.direct_ack_topic,\\n {\\n \\\"cmd1\\\": db_version,\\n \\\"cmd2\\\": status,\\n \\\"target\\\": \\\"aabbcc\\\",\\n \\\"user_data\\\": None,\\n \\\"hops_left\\\": 3,\\n },\\n 0.2,\\n ),\\n ]\\n send_topics(topics)\\n await asyncio.sleep(0.5)\\n assert self._db_version == db_version\\n assert self._status == status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c74fc2bbf00db495e085d6ea9e152b24\",\n \"score\": \"0.5925259\",\n \"text\": \"def switch_status(self):\\n if str(self.stop['state']) == 'disabled':\\n self.start_tac_client()\\n self.start_coord_server()\\n self.stop.configure(state=\\\"normal\\\")\\n self.start.configure(state=\\\"disabled\\\")\\n self.status_value.configure(text=\\\"Running\\\")\\n r.get(\\\"http://127.0.0.1:5000/set_username/\\\" + self.user.get())\\n LOG.info('Status values updated correctly...')\\n else:\\n self.stop_process()\\n LOG.info('Process stopped...updating status values...')\\n self.stop.configure(state=\\\"disabled\\\")\\n self.start.configure(state=\\\"normal\\\")\\n self.status_value.configure(text=\\\"Stopped\\\")\\n LOG.info('Status values updated correctly...')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8160bdf04756126235d8a2294cb78847\",\n \"score\": \"0.59208834\",\n \"text\": \"async def status(self):\\n\\n # Count stored statements and reactions\\n session = self.bot.Session()\\n statements = session.query(Statement).count()\\n reactions = session.query(Reaction).count()\\n session.close()\\n\\n # Set as status\\n await self.bot.change_presence(\\n activity=discord.Activity(\\n type=discord.ActivityType.listening,\\n name=f'{statements} 💬 {reactions} 👍'\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a82a3110462c6399c13d2af342b8ad\",\n \"score\": \"0.59141296\",\n \"text\": \"def notify(self):\\n #self.update_grid()\\n t = self.get_time_str() + ' (update every ' + str(int(self.update_sec)) + 's)'\\n self.statusbar.SetStatusText(t, self.SB_RIGHT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe99e66cb2f40f05bd1d1f13b16cd481\",\n \"score\": \"0.5904745\",\n \"text\": \"def set_status(self, showId, status):\\r\\n\\t\\tprint \\\"change_status\\\", showId, status\\r\\n\\t\\tp = xmpp.protocol.Presence()\\r\\n\\t\\tp.setShow(STATUS_TEXT[showId])\\r\\n\\t\\tif status:\\r\\n\\t\\t\\tp.setStatus(status)\\r\\n\\t\\tif showId == STATUS.available:\\r\\n\\t\\t\\tp.setPriority(5)\\r\\n\\t\\tself.jabber.send(p)\\r\\n\\t\\tself.emit(SIGNAL(\\\"debug\\\"), unicode(p)+\\\"\\\\n\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde2d4c2f2d7a9a7199959bd023eb005\",\n \"score\": \"0.58999074\",\n \"text\": \"def on_private_status(self, client, userdata, msg):\\n\\n # pi('on_private_status')\\n\\n # LOGGER.info('[MosquittoClient] Received message with mid : %s from topic : %s with qos : %s and retain = %s ' % (str(msg.mid), msg.topic, str(msg.qos), str(msg.retain)))\\n \\n json_decoded_body = json.loads(msg.payload)\\n\\n if json_decoded_body['status'] == 'offline':\\n\\n # print '[MosquittoClient] received status === offline'\\n\\n if json_decoded_body['msg']['clientid'] == self._clientid:\\n\\n # print 'received offline status on self._clientid, hence stopping mqtt client'\\n\\n # since status message has been sent, its safe to close the mqtt mosquitto connection now\\n self.stop() \\n\\n # return from this function so as to avoid send the status message to the correspongin \\n # websocket since its already closed.\\n \\n\\n # pr('on_private_status')\\n\\n return\\n \\n last_seen = time.localtime()\\n json_decoded_body['last_seen'] = {\\n 'date': last_seen.tm_mday,\\n 'month': last_seen.tm_mon,\\n 'year': last_seen.tm_year,\\n 'hour': last_seen.tm_hour,\\n 'min': last_seen.tm_min\\n } \\n \\n # LOGGER.info('[MosquittoClient] sending the message to corresponsding websoket: %s ' % self.websocket)\\n\\n # print '[MosquittoClient] msg to be sent : ', json_decoded_body\\n\\n self.sendMsgToWebsocket(json_decoded_body)\\n\\n\\n # pr('on_private_status')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c05de85af48280a1b2c4aaa15ab327\",\n \"score\": \"0.58973175\",\n \"text\": \"def on_status(self, status_info):\\n if config.DEBUG_MODE:\\n for list_item in status_info:\\n if type(list_item) is rtmp.pyamf.ASObject:\\n for k in list_item:\\n self.console_write(COLOR['white'], k + ': ' + str(list_item[k]))\\n else:\\n self.console_write(COLOR['white'], str(list_item))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caad3fbb0113f3656bcb5bba4e5cbf2c\",\n \"score\": \"0.58957666\",\n \"text\": \"def _update_status(self, event):\\n self.status = event.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caad3fbb0113f3656bcb5bba4e5cbf2c\",\n \"score\": \"0.58957666\",\n \"text\": \"def _update_status(self, event):\\n self.status = event.status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fceeb8591c7edbcef3fa77904163c51\",\n \"score\": \"0.58836824\",\n \"text\": \"def status(self, verbose=True):\\n\\n response = requests.get(self.base_url + 'rest/rec/onchange?last=' \\n + str(0), headers=self.header)\\n status_info = json.loads(response.text)\\n self.last_change = status_info['lastUpdateTag']\\n self.state = status_info['moduleState']\\n \\n current_status = (f\\\"\\\\n\\\\tBnK state : {self.state}\\\\n\\\"\\n f\\\"\\\\tcommands sent : {self.last_change}\\\\n\\\"\\n f\\\"\\\\tBnK clock : {response.headers['Date']}\\\"\\n )\\n if verbose:\\n print(current_status)\\n else:\\n return current_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e09c2848037dc8070da646c475e4618\",\n \"score\": \"0.58783233\",\n \"text\": \"def run(self, status):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"796657d4dc1837adc5da4d7eb3881486\",\n \"score\": \"0.58764285\",\n \"text\": \"def status():\\n\\n # Get db\\n runs = db[\\\"runs\\\"]\\n\\n # Display the status page from the other sites\\n ajaxRequest = validation.convertRequestToPythonBool(\\\"ajaxRequest\\\", request.args)\\n\\n # Where the statuses will be collected\\n statuses = collections.OrderedDict()\\n\\n # Determine if a run is ongoing\\n # To do so, we need the most recent run\\n mostRecentRun = runs[runs.keys()[-1]]\\n runOngoing = mostRecentRun.isRunOngoing()\\n if runOngoing:\\n runOngoingNumber = \\\"- \\\" + mostRecentRun.prettyName\\n else:\\n runOngoingNumber = \\\"\\\"\\n # Add to status\\n statuses[\\\"Ongoing run?\\\"] = \\\"{0} {1}\\\".format(runOngoing, runOngoingNumber)\\n\\n if \\\"config\\\" in db and \\\"receiverLogLastModified\\\" in db[\\\"config\\\"]:\\n receiverLogLastModified = db[\\\"config\\\"][\\\"receiverLogLastModified\\\"]\\n lastModified = time.time() - receiverLogLastModified\\n # Display in minutes\\n lastModified = int(lastModified//60)\\n lastModifiedMessage = \\\"{0} minutes ago\\\".format(lastModified)\\n else:\\n lastModified = -1\\n lastModifiedMessage = \\\"Error! Could not retrieve receiver log information!\\\"\\n # Add to status\\n statuses[\\\"Last requested data\\\"] = lastModifiedMessage\\n\\n # Determine server statuses\\n # TODO: Consider reducing the max number of retries\\n sites = serverParameters[\\\"statusRequestSites\\\"]\\n for site, url in iteritems(sites):\\n serverError = {}\\n statusResult = \\\"\\\"\\n try:\\n serverRequest = requests.post(url + \\\"/status\\\", timeout = 0.5)\\n if serverRequest.status_code != 200:\\n serverError.setdefault(\\\"Request error\\\", []).append(\\\"Request to \\\\\\\"{0}\\\\\\\" at \\\\\\\"{1}\\\\\\\" returned error response {2}!\\\".format(site, url, serverRequest.status_code))\\n else:\\n statusResult = \\\"Site is up!\\\"\\n except requests.exceptions.Timeout as e:\\n serverError.setdefault(\\\"Timeout error\\\", []).append(\\\"Request to \\\\\\\"{0}\\\\\\\" at \\\\\\\"{1}\\\\\\\" timed out with error {2}!\\\".format(site, url, e))\\n except requests.exceptions.ConnectionError as e:\\n serverError.setdefault(\\\"Connection error\\\", []).append(\\\"Request to \\\\\\\"{0}\\\\\\\" at \\\\\\\"{1}\\\\\\\" had a connection error with message {2}!\\\".format(site, url, e))\\n except requests.exceptions.RequestException as e:\\n serverError.setdefault(\\\"General Requests error\\\", []).append(\\\"Request to \\\\\\\"{0}\\\\\\\" at \\\\\\\"{1}\\\\\\\" had a general requests error with message {2}!\\\".format(site, url, e))\\n\\n # Return error if one occurred\\n if serverError != {}:\\n statusResult = serverError\\n\\n # Add to status\\n statuses[site] = statusResult\\n\\n if ajaxRequest == False:\\n return render_template(\\\"status.html\\\", statuses = statuses)\\n else:\\n drawerContent = \\\"\\\"\\n mainContent = render_template(\\\"statusMainContent.html\\\", statuses = statuses)\\n\\n return jsonify(drawerContent = drawerContent, mainContent = mainContent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e69266064849b86f7e03da45f5c88b0\",\n \"score\": \"0.58747745\",\n \"text\": \"async def ping(self):\\n while True:\\n self.expect_update(WAIT_TIME)\\n await self.mqtt.pub(f\\\"{self.name}/send_state\\\", \\\"\\\")\\n await asyncio.sleep(600 if self.state['reachable'] else 30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c71e286a23c0f25ea51875164ce8a887\",\n \"score\": \"0.58738345\",\n \"text\": \"def status(self, status):\\n self._status = status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c71e286a23c0f25ea51875164ce8a887\",\n \"score\": \"0.58738345\",\n \"text\": \"def status(self, status):\\n self._status = status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d373424d4b6aba3d174bffcb1fbae508\",\n \"score\": \"0.5866176\",\n \"text\": \"def on_status(self, status):\\n\\n # Collect relevant tweet data for each tweet\\n self.tweet.append(status.id)\\n self.tweet.append(status.text)\\n self.tweet.append(str(status.created_at))\\n self.tweet.append(str(status.user.location))\\n self.tweet.append(str(status.coordinates))\\n self.tweet.append(status.user.followers_count)\\n self.tweet.append(status.user.friends_count)\\n self.collected_tweet_list.append(self.tweet)\\n self.tweet = []\\n self.tweet_count += 1\\n # Specify number of tweets to collect (takes some time depending on keyword popularity) \\n if self.tweet_count > 1000:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8e0150f51e2f90d0c3d610b0fc57c0\",\n \"score\": \"0.58563584\",\n \"text\": \"def on_status(self, status):\\n # handling display options\\n self.counter -= 1\\n\\n if self.counter >= 0:\\n print(status.author.screen_name, 'said')\\n print(status.text)\\n print('at', status.created_at)\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c93beedb7f7340cace1c2eef04a0193\",\n \"score\": \"0.58543605\",\n \"text\": \"def statusupdate( ):\\n t = time.strftime(\\\"%Y-%m-%d %H:%M:%S \\\")\\n window.statusBar().showMessage( t )\\n #print \\\"statusupdate\\\", t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6a29b2e01455af013f12750966b8440\",\n \"score\": \"0.5847774\",\n \"text\": \"def _report_status(state):\\n msgs = []\\n for idx, source in enumerate([\\\"<oplog>\\\", \\\"<profiler>\\\"]):\\n tailer_state = state.tailer_states[idx]\\n msg = \\\"\\\\n\\\\t{}: received {} entries, {} of them were written, \\\"\\\\\\n \\\"last received entry ts: {}, last get-none ts: {}\\\" .format(\\n source,\\n tailer_state.entries_received,\\n tailer_state.entries_written,\\n str(tailer_state.last_received_ts),\\n str(tailer_state.last_get_none_ts))\\n msgs.append(msg)\\n\\n utils.LOG.info(\\\"\\\".join(msgs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6072451c20febdd7f0d8241dd0ce4903\",\n \"score\": \"0.58383334\",\n \"text\": \"def heating_status():\\n with channel() as socket:\\n socket.send_json({\\\"command\\\": \\\"heating_status\\\"})\\n response = socket.recv_json()\\n return make_response(response[\\\"content\\\"], response[\\\"status\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf91bea0aa65f725decec09f45bd1213\",\n \"score\": \"0.5835701\",\n \"text\": \"def _updateStatus (self):\\r\\n if self._growth > 60 :\\r\\n self._status = 'Old'\\r\\n elif self._growth > 40 :\\r\\n self._status = 'Mature'\\r\\n elif self._growth > 20 :\\r\\n self._status = 'Young'\\r\\n elif self._growth > 0 :\\r\\n self._status = 'Seeding'\\r\\n else :\\r\\n self._status = 'Seed'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e830e7646106e95ab17e69c86a5bb491\",\n \"score\": \"0.58349633\",\n \"text\": \"def status(self, val):\\n self._set_single(\\\"status\\\", val, coerce=dataobj.to_unicode(), allowed_values=[\\\"queued\\\", \\\"failed\\\", \\\"sent\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83e37f9634d87f04f0e50c08e507396\",\n \"score\": \"0.58308125\",\n \"text\": \"def tweet_current_status() -> str:\\n msg = compose_tweet()\\n if current_app.config['SEND_TWEETS']:\\n tweepy_api.update_status(msg)\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808bb65cd43baefec834b706625664aa\",\n \"score\": \"0.58303446\",\n \"text\": \"async def status_verbose(self, ctx):\\n dipgame = scraper.get_dipgame_checked(self.game_id)\\n status_message = app.announce_overall_game_state(dipgame, verbose=True)\\n await ctx.message.channel.send(status_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b64c226f0014d0786de639ad497e4d13\",\n \"score\": \"0.58111775\",\n \"text\": \"def writing_status_callback(self, data):\\n print \\\"writing status callback\\\", data.data\\n if data.data == \\\"writing\\\":\\n print \\\"Busy writing\\\"\\n self.writing_status = 0\\n elif data.data == \\\"done\\\":\\n print \\\"Free\\\"\\n self.writing_status = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0b177362de685f70a9cf318334e125b\",\n \"score\": \"0.5811004\",\n \"text\": \"def update(self):\\n # Data fetching is taken care of through the Main sensor\\n\\n if self.syncthru.is_online():\\n self._attributes = self.syncthru.drum_status().get(self._color, {})\\n self._state = self._attributes.get(\\\"remaining\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9367c4be3163bad16b4abd409aed3e60\",\n \"score\": \"0.5804909\",\n \"text\": \"def adv_received(topic, payload, qos):\\n onoff = payload[7:8]\\n if onoff == '1':\\n self._state = False\\n if onoff == '9':\\n self._state = True\\n # 01 = remote off\\n # f9 = local on\\n # e1 = local off\\n # 19 = remote on\\n self.hass.async_add_job(self.async_update_ha_state())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc10b9b594f375273e29681083e77140\",\n \"score\": \"0.58020127\",\n \"text\": \"def status(self, new_status):\\n self._status = new_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf999cb8a9ae869c95fc5d66513f828\",\n \"score\": \"0.5801866\",\n \"text\": \"def update_status(self) -> None:\\n self._pipeline_response = self.request_status(self._operation.get_polling_url())\\n _raise_if_bad_http_status_and_method(self._pipeline_response.http_response)\\n self._status = self._operation.get_status(self._pipeline_response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f36c1e247e39504428626e572bce00\",\n \"score\": \"0.57877666\",\n \"text\": \"def trigger(self):\\n\\n # User defined function. Only user will call it.\\n # Internal methods should not use this.\\n # Use _trigger() instead.\\n\\n while len(self.status) != 0:\\n # while there are incomplete operations\\n time.sleep(0.001)\\n\\n self.status.append(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f4db563e779a843732eb128bb43ad6f\",\n \"score\": \"0.57828367\",\n \"text\": \"async def status_handler():\\n msg = await zmq_to_manager.send_message(method=\\\"status\\\")\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a61e5aa28c304fb4a3128aa01377932\",\n \"score\": \"0.57821923\",\n \"text\": \"def read_status_updates(self):\\n try:\\n while True:\\n message = self._in_queue.get_nowait()\\n session_id = message.get('session')\\n del message['session']\\n self.set_session_status(session_id, message)\\n except multiprocessing.queues.Empty:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da8377854293bb2baeeb21a503f52e7e\",\n \"score\": \"0.5782013\",\n \"text\": \"def sendStatusKeys(self, cmd):\\n\\n cmd.inform('cameraName=%s; readNoise=%0.2f' % (self.name, self.readNoise))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae3a64672e30357ac28d803ab432b8d0\",\n \"score\": \"0.5781363\",\n \"text\": \"def __update_lcd__(self):\\n\\n # If we start shutting down or rebooting\\n # then __lcd__ is set to None to avoid\\n # any more updates\\n if self.__lcd__ is None:\\n return\\n\\n self.__lcd__.clear()\\n\\n # Moves to the next status message based on the\\n # interval in given to RecurringEvent (Normally 5 seconds)\\n try:\\n if self.__lcd_status_id__ == 0:\\n self.__lcd__.write_text(self.__get_fona_status__())\\n if self.__lcd_status_id__ == 1:\\n self.__lcd__.write_text(self.__get_heater_status__())\\n if self.__lcd_status_id__ == 2:\\n if self.__sensors__.current_gas_sensor_reading is not None:\\n self.__lcd__.write_text(self.__get_gas_sensor_status__())\\n else:\\n self.__lcd_status_id__ += 1\\n if self.__lcd_status_id__ == 3:\\n if self.__sensors__.current_light_sensor_reading is not None \\\\\\n and self.__sensors__.current_light_sensor_reading.enabled:\\n self.__lcd__.write_text(self.__get_light_status__())\\n else:\\n self.__lcd_status_id__ += 1\\n if self.__lcd_status_id__ == 4:\\n if self.__sensors__.current_temperature_sensor_reading is not None:\\n self.__lcd__.write_text(self.__get_temp_probe_status__())\\n else:\\n self.__lcd_status_id__ += 1\\n if self.__lcd_status_id__ >= 5:\\n self.__lcd_status_id__ = -1\\n self.__lcd__.write_text(\\\"UPTIME:\\\\n\\\" + self.__get_uptime_status__())\\n except:\\n self.__lcd__.write(0, 0, \\\"ERROR: LCD_ID=\\\" +\\n str(self.__lcd_status_id__))\\n self.__lcd_status_id__ = -1\\n\\n self.__lcd_status_id__ = self.__lcd_status_id__ + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23800337c61e7a43b578aa16c60c93f7\",\n \"score\": \"0.5780289\",\n \"text\": \"def status(\\n self,\\n pubnub: Union[pubnub.pubnub.PubNub, pubnub.pubnub_asyncio.PubNubAsyncio],\\n status: pubnub.models.consumer.common.PNStatus,\\n ) -> None:\\n _LOGGER.info(\\n f\\\"pubnub status update {status.category}, error: {status.error}, error_data: {status.error_data}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c537929beac37629e7e01042f8cc4458\",\n \"score\": \"0.57728565\",\n \"text\": \"def status(ctx):\\n ctx.obj[\\\"output\\\"](\\n {\\n \\\"processes\\\": status_processes(ctx.obj[\\\"cfg\\\"]),\\n \\\"neighbors\\\": status_neighbors(ctx.obj[\\\"cfg\\\"]),\\n }\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992985,"cells":{"query_id":{"kind":"string","value":"71044b0a01e6275f12aa76e42795f48c"},"query":{"kind":"string","value":"This method takes the smiles string and runs through the validation check of a smiles string."},"positive_passages":{"kind":"list like","value":[{"docid":"ee20d6c3ed5ea6ae56b705c74b42cbbb","score":"0.6944015","text":"def validate_smiles(self, smiles):\n\n # Use MolVS to validate the smiles to make sure enumeration and r group connections are correct\n # at least in the 1D Format.\n from molvs import validate_smiles as vs\n\n try:\n vs(smiles)\n return True\n except RaiseMoleculeError as RME:\n print (\"Not a Valid Smiles, Please check the formatting: %s\" % self.original_smiles)\n print (\"MolVs Stacktrace %s\" % RME)\n\n return False","title":""}],"string":"[\n {\n \"docid\": \"ee20d6c3ed5ea6ae56b705c74b42cbbb\",\n \"score\": \"0.6944015\",\n \"text\": \"def validate_smiles(self, smiles):\\n\\n # Use MolVS to validate the smiles to make sure enumeration and r group connections are correct\\n # at least in the 1D Format.\\n from molvs import validate_smiles as vs\\n\\n try:\\n vs(smiles)\\n return True\\n except RaiseMoleculeError as RME:\\n print (\\\"Not a Valid Smiles, Please check the formatting: %s\\\" % self.original_smiles)\\n print (\\\"MolVs Stacktrace %s\\\" % RME)\\n\\n return False\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0092f72c071ae329d215d8a58d019ba5","score":"0.71211714","text":"def IsValidSMILES(text):\n is_valid = False\n results = smiles.parseString(text)\n if results:\n is_valid = True\n return(is_valid)\n return is_valid","title":""},{"docid":"274fb728b4e2a2ea699b579529e35803","score":"0.6430498","text":"def testRuleSix(self):\n smis = [\"C(1)CCC1\", \"C(CCC1)1\"]\n for smi in smis:\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\n ParseSmiles(smi, False, 32)","title":""},{"docid":"f0443d6d4452ce1227881b52bc1e7d97","score":"0.63043696","text":"def testRuleFive(self):\n smis = [\"C1.C1\"]\n for smi in smis:\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\n ParseSmiles(smi, False, 16)","title":""},{"docid":"c98f35c423535d15cce23b4f4aec669f","score":"0.6265145","text":"def testRuleFour(self):\n smis = [\"C(C.C)C\"]\n for smi in smis:\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\n ParseSmiles(smi, False, 8)","title":""},{"docid":"285c7836af964afbd9b6b1606609d5d6","score":"0.6210074","text":"def testRuleOne(self):\n smi = \"C..C\"\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\n ParseSmiles(smi, False, 1)","title":""},{"docid":"c360ed31452d76c7d5420426d72a2ea7","score":"0.61493397","text":"def testRuleSeven(self):\n # Aromatic bond symbols are not needed in a SMILES string\n # - an aromatic bond connecting two lowercase atoms is implicit\n # - the meaning of an aromatic bond attached to an uppercase atom\n # is a matter of intense debate\n smi = \"c1:c:c:c:c:c1\"\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\n ParseSmiles(smi, False, 64)","title":""},{"docid":"c68900f369baab44c00f6883889bc627","score":"0.59261775","text":"def test_should_complete():\n # GitHub Issue #14\n assert standardize_smiles('CCCCCCCCCCCCCCCCCC(=O)CC(=C)C(=O)O[Ti](=O)(OC(C)C)C(C)C') == 'C=C(CC(=O)[CH-]CCCCCCCCCCCCCCCC)C(=O)[O-].CC(C)[O-].CCC.[O-2].[Ti+5]'\n assert standardize_smiles('OP(=O)(O)[O-].OP(=O)([O-])[O-].[O-]S(=O)(=O)[O-].[Na+].[Na+].[Na+].[Mg+2].[Cl-].[Cl-].[K+].[K+]') == 'O=P([O-])(O)O.O=P([O-])([O-])O.O=S(=O)([O-])[O-].[Cl-].[Cl-].[K+].[K+].[Mg+2].[Na+].[Na+].[Na+]'","title":""},{"docid":"88ba0162ed03a2dc09a4f68d72f180b0","score":"0.58013153","text":"def check_smiles(seqs, voc):\n valids = []\n smiles = []\n for j, seq in enumerate(seqs.cpu()):\n smile = voc.decode(seq)\n valids.append(1 if Chem.MolFromSmiles(smile) else 0)\n smiles.append(smile)\n valids = np.array(valids, dtype=np.byte)\n return smiles, valids","title":""},{"docid":"3e6efe55fe656e4d5907560ac84ae639","score":"0.5784007","text":"def isValid(self, s):\r\n if s == \"\":\r\n return True\r\n length = len(s);\r\n if length % 2 == 1:\r\n return False\r\n lis = []\r\n lookUp = {\"(\":\")\", \"[\":\"]\", \"{\":\"}\"}\r\n for i in s:\r\n if i in lookUp:\r\n lis.append(i)\r\n elif len(lis) == 0 or lookUp[lis.pop()] != i:\r\n return False\r\n return len(lis) == 0","title":""},{"docid":"79a099e8e1e08f1fe48e620a2e12967d","score":"0.5770343","text":"def _check_contributions_string(string):\n assert string in ('', 'S', 'P', 'SP', 'PS')","title":""},{"docid":"35868cadc1a121aa4fafa38fb10f9d95","score":"0.573639","text":"def check_valid_input(s):\n\n if s == \"rock\":\n return True\n if s == \"lizard\":\n return True\n if s == \"gun\":\n return True\n if s == \"zombie\":\n return True\n if s == \"spock\":\n return True\n if s == \"scissors\":\n return True\n if s == \"paper\":\n return True\n else:\n return False","title":""},{"docid":"9dbaa59abf392c92de3e1831cf3eeda3","score":"0.5588584","text":"def check_ascii(self, smiles):\n try:\n smiles.encode('ascii')\n return True\n except UnicodeEncodeError:\n return False","title":""},{"docid":"234d5034e2192d0289414ee6c276fc5d","score":"0.5574101","text":"def is_valid(smi):\n mol = Chem.MolFromSmiles(smi)\n if mol is None:\n return False\n else:\n return Chem.MolToSmiles(mol, isomericSmiles=False)","title":""},{"docid":"d74fcc30822c71de118a897eca9b71c6","score":"0.5493523","text":"def testRuleThree(self):\n smis = [\"C(O(C))\", \"C(O).C\"]\n for smi in smis:\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\n ParseSmiles(smi, False, 4)\n\n smis = [\"C(O)\"] # these are fine as partial but not otherwise\n for smi in smis:\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\n ParseSmiles(smi, True)\n ParseSmiles(smi, False, 4)","title":""},{"docid":"3f397f1098f791caeccd051e81c328ab","score":"0.5461919","text":"def input_is_correct_e5(strings):\n #regular expressions for the lines\n reg1 = re.compile('(states)\\=\\{(\\w+[,])*(\\w+)*\\}\\n')\n reg2 = re.compile('(alpha)\\=\\{(\\w+[,])*(\\w+)*\\}\\n')\n reg3 = re.compile('(init.st)\\=\\{(\\w+[,])*(\\w+)*\\}\\n')\n reg4 = re.compile('(fin.st)\\=\\{(\\w+[,])*(\\w+)*\\}\\n')\n reg5 = re.compile('(trans)\\=\\{(\\w*[>]\\w*[>]\\w*[,])*(\\w*[>]\\w*[>]\\w*)*\\\\}')\n\n\n #check if they match\n c1=reg1.fullmatch(strings[0])\n c2=reg2.fullmatch(strings[1])\n c3=reg3.fullmatch(strings[2])\n c4=reg4.fullmatch(strings[3])\n c5=reg5.fullmatch(strings[4])\n\n if (c1 and c2 and c3 and c4 and c5):\n return True\n\n return False","title":""},{"docid":"ebf4811a9f123fc52e5b2d21f50ad2b0","score":"0.54266065","text":"def is_valid(self, string):\n for token in string:\n if not token in self.unit_productions:\n return False\n return True","title":""},{"docid":"a0fd2535f4e5ff769d1ad123e0921c24","score":"0.54258144","text":"def validate_string(m, min, max):\n # Begins a validation loop\n # This loop will continue until the user enters\n # an appropriate answer\n while True:\n # Defines all of the user's input as\n # an uppercase string\n user_input = input(m).upper()\n # Replaces all spaces in the user's input\n # with an enclosed space\n user_input = user_input.replace(\" \", \"\")\n # The customer has entered a value\n # Below the minimum value\n if len(user_input) < min:\n # Error message\n # The loop continues\n print(\"Your answer is too short. \\n\"\n \"Please ensure you have entered an appropriate response.\")\n # The customer has entered a value\n # Above the maximum value\n elif len(user_input) > max:\n # Error message\n # The loop continues\n print(\"Your answer is too long. \\n\"\n \"Please ensure you have entered an appropriate response.\")\n # The customer has met all of these requirements\n # Their input is returns and the loop ends\n else:\n return user_input","title":""},{"docid":"9015303cca6ec23c6ea5798ce63f8a68","score":"0.54199356","text":"def test_input_validate_str(self):\n self.assertRaises(pyhsm.exception.YHSM_WrongInputType, pyhsm.util.input_validate_str, \\\n 0, 'foo', exact_len = 5)\n\n self.assertRaises(pyhsm.exception.YHSM_InputTooLong, pyhsm.util.input_validate_str, \\\n '1234', 'foo', max_len = 3)\n self.assertEquals('1234', pyhsm.util.input_validate_str('1234', 'foo', max_len = 4))\n self.assertEquals('1234', pyhsm.util.input_validate_str('1234', 'foo', max_len = 14))\n\n self.assertRaises(pyhsm.exception.YHSM_WrongInputSize, pyhsm.util.input_validate_str, \\\n '1234', 'foo', exact_len = 5)\n self.assertEquals('1234', pyhsm.util.input_validate_str('1234', 'foo', exact_len = 4))","title":""},{"docid":"be3dccb55eb69d141c7b101a83bb2b0b","score":"0.54074156","text":"def sanityString(self, word, regex, expected):\r\n print(\"=\" * 30)\r\n print(\"Start Test Sanity 2 : String Input\")\r\n print(\"=\" * 30)\r\n se = Searcher()\r\n se.validateUserInput(word)\r\n se.IS_M = True\r\n seValue = se.handleString(word, regex, returnValue=True)\r\n\r\n assert (seValue == expected), \"Test Sanity Failed\"\r\n print(\"returnValue:{}\\nexpected:{}\".format(seValue, expected))","title":""},{"docid":"6ad1880351455415ecc04315d7823dc6","score":"0.5404408","text":"def squares(s):\n \"*** YOUR CODE HERE ***\"","title":""},{"docid":"20a73c7d6283999125bc658cc3337d1a","score":"0.5357808","text":"def string_validator(self, the_str):\n\n the_str = str(the_str).replace(\"-\", '').replace(\"(\", '').replace(\")\", '').replace(\" \", '')\n\n self.phn = self.validator(the_str)","title":""},{"docid":"4268d3223306c88b64893e4aa3ec5dfe","score":"0.53511256","text":"def test_uncharge2():\n assert uncharge_smiles('n(C)1cc[n+]2cccc([O-])c12') == 'Cn1cc[n+]2cccc([O-])c12'","title":""},{"docid":"3d9aa131e443658fecca8ed1706741fe","score":"0.53423464","text":"def tokenize_smiles(smiles: str) -> str:\n\n tokens = [token for token in SMILES_REGEX.findall(smiles)]\n return \" \".join(tokens)","title":""},{"docid":"a63fc1ab863489b62cac958fd0a26ddf","score":"0.53204864","text":"def isvalid(self, s: str) -> bool:\n if len(s) == 1:\n return False\n par_stack = []\n for par in s:\n if par in ['(', '{', '[']:\n par_stack.append(par)\n else:\n if len(par_stack) == 0:\n return False\n prev = par_stack.pop()\n if not self.check_stack(prev=prev, par=par):\n return False\n if len(par_stack) != 0:\n return False\n return True","title":""},{"docid":"2498350272c15804055d6c2c7255cdc6","score":"0.5316063","text":"def check_for_strings(data, checklist):\n return type_checks(is_valid_string, \"field cannot be a non string.\", data, checklist)","title":""},{"docid":"81e907ec386b4719b13f2e859def28b0","score":"0.53120446","text":"def test_uncharge15():\n assert uncharge_smiles('[NH+](C)(C)CC([O-])C[O-]') == 'CN(C)CC(O)CO'","title":""},{"docid":"7ff311256881a8a3d3119722cc41f95c","score":"0.5272296","text":"def test_uncharge5():\n assert uncharge_smiles('[Na+].O=C([O-])c1ccccc1') == 'O=C([O-])c1ccccc1.[Na+]'","title":""},{"docid":"a013ab6b26bc52d0cfc9ba2f5a4afbe8","score":"0.5246803","text":"def test_forced_charge1():\n assert standardize_smiles('[Na].O=C(O)c1ccccc1') == 'O=C([O-])c1ccccc1.[Na+]'","title":""},{"docid":"5bbc4ed68bd619da435275a031ee60be","score":"0.52101415","text":"def test_uncharge13():\n assert uncharge_smiles('C[N+](C)(C)CC([O-])C[O-]') == 'C[N+](C)(C)CC([O-])CO'","title":""},{"docid":"7e4e9689ad9d292e3ae8b5e958192baa","score":"0.5206992","text":"def check(self, input_string):\n return self._check_input(self.start_state, input_string, [self.start_stack], [])","title":""},{"docid":"64b6f66e1964ec0293a40389893896a3","score":"0.5194161","text":"def validate_moves(moves: str):\n for move in moves:\n if move not in ['^', 'v', '>', '<']:\n raise ValueError('Invalid move passed. Must be ^, v, <, or >')","title":""},{"docid":"b8477ffd800124db2e29769165dfc813","score":"0.5181313","text":"def test_uncharge6():\n assert uncharge_smiles('O=C([O-])c1ccccc1') == 'O=C(O)c1ccccc1'","title":""},{"docid":"f6be5079a0905b1dc8988bb955fa36a6","score":"0.5174386","text":"def test_uncharge11():\n assert uncharge_smiles('[N+](=O)([O-])[O-].[CH2]') == 'O=[N+]([O-])O.[CH2]'","title":""},{"docid":"e21f1ad8db7314ad9b7aa378032de7fb","score":"0.516819","text":"def validPuzzleStr(state):\n try:\n length = len(state)\n size = sqrt(length)\n expectedVals = range(length)\n for tile in state:\n expectedVals.remove(int(tile))\n return True\n except ValueError:\n return False","title":""},{"docid":"5912783bbb428975d7ed4c9151348bf0","score":"0.5167037","text":"def test_valid_list(self):\r\n\r\n self.assertTrue(calculator.is_valid(\"1,2,3,4,5\"),True)\r\n self.assertTrue(calculator.is_valid(\"1,2,3,me,4,5\") == False)","title":""},{"docid":"65c4b1c5b8094a568b11dbc267355973","score":"0.5166201","text":"def test_get_smiles_from_name(self):\n\n\t\tprint(\">>> Running calculator get_smiles_from_name unit test..\")\n\n\t\texpected_json = self.get_example_result_json(\"get_smiles_from_name\") # gets expected response json for mock requests.post\n\t\texpected_response = {'smiles': self.test_smiles, 'format': \"smiles\"} # expected response from test function\n\n\t\t# Testing function with a mock of web_call function:\n\t\twith patch('qed.cts_app.cts_calcs.calculator.Calculator.web_call') as service_mock:\n\n\t\t\t# Sets web_call mock to return expected json when test function calls it:\n\t\t\tservice_mock.return_value = expected_json\n\n\t\t\t# Calls test function, which will use the mock web_call for unit testing:\n\t\t\tresponse = self.calc_obj.get_smiles_from_name(self.test_chemical)\n\n\t\ttry:\n\t\t\t# Compares function response with expected json:\n\t\t\tself.assertDictEqual(response, expected_response)\n\t\tfinally:\n\t\t\ttab = [[response], [expected_response]]\n\t\t\tprint(\"\\n\")\n\t\t\tprint(inspect.currentframe().f_code.co_name)\n\t\t\tprint(tabulate(tab, headers='keys', tablefmt='rst'))","title":""},{"docid":"4c0d47ea2c00e0eb860e5b1c9b5ff305","score":"0.5151674","text":"def parse_checks(string):\r\n return __checkparser.parseString(string, parseAll=True)","title":""},{"docid":"ead630eb5fc240b1bc543d8308ae1d58","score":"0.51309705","text":"def test_uncharge16():\n assert uncharge_smiles('CNCC([O-])C[O-]') == 'CNCC(O)CO'","title":""},{"docid":"f258ab90e4d8b75dbb49859b4c74fdce","score":"0.51305634","text":"def test_validation(self):\n\n validate = IS_FLOAT_AMOUNT()\n\n samples = ((\"123 456 789 012,00\", 123456789012.0),\n (\"0,00\", 0.0),\n (\"1 305,00\", 1305.0),\n (12.345, 12.345),\n )\n\n assertEqual = self.assertEqual\n for inputstr, expected in samples:\n value, error = validate(inputstr)\n assertEqual(value, expected)\n assertEqual(error, None)","title":""},{"docid":"1c704f692b11656dc0d704fc285fdcbb","score":"0.5129067","text":"def input_process(s, lst):\n\ts = s.split()\n\t# Check there are 4 letters in one row\n\tif len(s) != SIZE:\n\t\treturn False\n\telse:\n\t\t# Check every element is only one alphabet\n\t\tfor i in range(len(s)):\n\t\t\tif len(s[i]) != 1 or not s[i].isalpha():\n\t\t\t\treturn False\n\t\ts = list(map(lambda e: e.lower(), s)) # Case insensitive\n\t\tlst.append(s)\n\t\treturn True","title":""},{"docid":"ef16fcd55b956646428a3a217fa3913f","score":"0.51239216","text":"def test_invalid_name_validator(self):\n \"\"\"I think these names will not work\"\"\"\n invalid_names = [\"Anthony Pietrzak\", \" 5676AlissaTyreen\", \"TroyBaker#$% \"]\n for values in invalid_names:\n self.assertFalse(self.my_phone.name_validator(values))","title":""},{"docid":"df0c84d431c1386327fdfe93f685ade4","score":"0.5105053","text":"def validate_shot_codes_callback(self):\n sm = ShotManager()\n invalid_shots = sm.validate_shot_codes()\n if invalid_shots:\n QtWidgets.QMessageBox.critical(\n self.parent_widget,\n \"Invalid shot names!!!\",\n \"There are invalid shot codes:<br>%s\"\n % \"<br>\".join([shot_clip.shot_code for shot_clip in invalid_shots]),\n )\n return False\n else:\n QtWidgets.QMessageBox.information(\n self.parent_widget, \"All shots valid 👍\", \"All shots valid 👍\"\n )\n return True","title":""},{"docid":"0b2501915e4cbdce9dc9742c493b2782","score":"0.508446","text":"def sicovam_valid(record):\n sicovam = record['sicovam']\n assert shared.is_int(sicovam) and shared.is_length(6, sicovam)","title":""},{"docid":"a0a847412e2e011c6511dd6524f807ef","score":"0.50842154","text":"def test_type_validator_failure(self):\n \"\"\"I think these will work\"\"\"\n invalid_type = [\"hsadkjh\", \" office$%\", \"cells \",\"1235467\",\"fash46\"]\n for values in invalid_type:\n self.assertFalse(self.my_phone.type_validator(values))","title":""},{"docid":"459160f4c504e164bc0269f0b9acc9fc","score":"0.5056056","text":"def isValidMatchCode():","title":""},{"docid":"a1e8fd92c292886e0228801b00a7b1e6","score":"0.5041497","text":"def _validateSMIRKS(smirks, node=None):\n qmol = oechem.OEQMol()\n if not oechem.OEParseSmarts(qmol, smirks):\n if (node is not None) and ('sourceline' in node.attrib):\n raise Exception(\"Line %s: Error parsing SMIRKS '%s'\" % (node.attrib['sourceline'], node.attrib['smirks']))\n else:\n raise Exception(\"Error parsing SMIRKS '%s'\" % (node.attrib['smirks']))\n\n return smirks","title":""},{"docid":"085d9023b3b3759d8e6bd4035680551c","score":"0.5038179","text":"def test_validation(self):\n\n validate = IS_INT_AMOUNT()\n\n samples = ((\"123,456,789,012\", 123456789012),\n (\"0\", 0),\n (\"993667\", 993667),\n )\n\n assertEqual = self.assertEqual\n for inputstr, expected in samples:\n value, error = validate(inputstr)\n assertEqual(value, expected)\n assertEqual(error, None)","title":""},{"docid":"9d303635b281ca20a47e30cd0f7a5bf1","score":"0.50365496","text":"def _validate(self):\n for str_ in self.strs:\n key = list(str_.keys())[0]\n if not isinstance(str_[key], str):\n raise ValueError(f'{key} must be str')\n\n for allowed_type in self.ALLOWED_TYPES:\n try:\n allowed_type(str_.get(key))\n except Exception:\n pass\n else:\n str_['type'] = allowed_type\n break","title":""},{"docid":"e26007fc045ea300f2c24efc92e3de3f","score":"0.5029785","text":"def isValidString(self, symstr):\n for sym in symstr:\n if not(self.isValidSymbol(sym)):\n return False\n return True","title":""},{"docid":"ba28876f7c76109bce08ac98dc2a9a41","score":"0.50251484","text":"def test_convertToSMILES(self):\n\n\t\tprint(\">>> Running calculator convertToSmiles unit test..\")\n\n\t\texpected_json = self.get_example_result_json(\"convert_to_smiles\") # gets expected response json\n\n\t\t# Testing function with a mock of web_call function:\n\t\twith patch('qed.cts_app.cts_calcs.calculator.Calculator.web_call') as service_mock:\n\n\t\t\t# Sets web_call mock to return expected json when test function calls it:\n\t\t\tservice_mock.return_value = expected_json\n\n\t\t\t# Calls test function, which will use the mock web_call for unit testing:\n\t\t\tresponse = self.calc_obj.convertToSMILES({'structure': self.test_chemical})\n\n\t\ttry:\n\t\t\t# Compares function response with expected json:\n\t\t\tself.assertDictEqual(response, expected_json)\n\t\tfinally:\n\t\t\ttab = [[response], [expected_json]]\n\t\t\tprint(\"\\n\")\n\t\t\tprint(inspect.currentframe().f_code.co_name)\n\t\t\tprint(tabulate(tab, headers='keys', tablefmt='rst'))","title":""},{"docid":"d190a097d0514d36c832e042ae9ef624","score":"0.5014409","text":"def check_string(self, chk, li):\n\t\tif not isinstance(chk, str):\n\t\t\traise TypeError(\"arg1 must be a str\")\n\t\tif not isinstance(li, list):\n\t\t\traise TypeError(\"arg2 must be a list of str\")\n\t\tfor i in li:\n\t\t\tif not isinstance(i, str):\n\t\t\t\traise TypeError(\"arg2 must be a list of str\")\n\n\t\tfor i in range(len(li)):\n\t\t\tli[i] = li[i].lower()\n\t\tchk = chk.lower()\n\t\tchk = chk.replace(\" \",\"\")\n\t\t\n\t\tfor item in li:\n\t\t\tif item in chk:\n\t\t\t\treturn True\n\n\t\tsl = list(chk)\n\n\t\tmapIndex, iterable = -1, -1\n\t\tfor _ in range(len(self.maps) + 1):\n\t\t\tfor stringIndex in range(len(sl)):\n\t\t\t\tif stringIndex > iterable:\n\t\t\t\t\tfor mi in range(len(self.maps)):\n\t\t\t\t\t\tif sl[stringIndex] in self.maps[mi]:\n\t\t\t\t\t\t\tplaceholder = sl[stringIndex]\n\t\t\t\t\t\t\tsl[stringIndex] = self.maps[mi].get(sl[stringIndex])\n\t\t\t\t\t\t\tfor item in li:\n\t\t\t\t\t\t\t\tif item in \"\".join(sl):\n\t\t\t\t\t\t\t\t\treturn True\n\t\t\t\t\t\t\tsl[stringIndex] = placeholder\n\t\t\t\t\tif mapIndex != -1 and sl[stringIndex] in self.maps[mapIndex]:\n\t\t\t\t\t\tsl[stringIndex] = self.maps[mapIndex].get(sl[stringIndex])\n\t\t\t\t\titerable += 1\n\t\t\tif mapIndex != -1 and sl[stringIndex] in self.maps[mapIndex]:\n\t\t\t\tsl[stringIndex] = self.maps[mapIndex].get(sl[stringIndex])\n\t\t\titerable = -1\n\t\t\tmapIndex += 1\n\n\t\treturn False","title":""},{"docid":"79709baf55e80d93d4ac3167b359a872","score":"0.4963088","text":"def test_cell_size_to_meters(self):\r\n self.assertEqual(3.5, AnalysisHelpers.cell_size_to_meters(\"3.5 Meters\"))\r\n self.assertEqual(3500, AnalysisHelpers.cell_size_to_meters(\"3,5 Kilometers\"))\r\n self.assertAlmostEqual(0.9144, AnalysisHelpers.cell_size_to_meters(\"3 Feet\"), 1)\r\n self.assertAlmostEqual(2.7432, AnalysisHelpers.cell_size_to_meters(\"3 Yards\"), 1)\r\n self.assertAlmostEqual(4828.03, AnalysisHelpers.cell_size_to_meters(\"3 Miles\"), 1)\r\n bad_units = \"3 BadUnits\"\r\n with self.assertRaises(ValueError) as ex:\r\n AnalysisHelpers.cell_size_to_meters(bad_units)\r\n self.assertEqual(\"Invalid cell size units: BadUnits\", str(ex.exception))","title":""},{"docid":"46b0aef21c4eecda82718ee6266107c8","score":"0.49533468","text":"def test14(self):\n self.verify(\"$*5*[ aStr ]\", \"blarg\")","title":""},{"docid":"b47304454cbd9f22237956f7eaeb0e7f","score":"0.49511278","text":"def test_uncharge3():\n assert uncharge_smiles('C[N+](C)(C)CCO') == 'C[N+](C)(C)CCO'","title":""},{"docid":"bf745b6703be89ded18b599dc7d7df47","score":"0.4943416","text":"def isSno(n,check):\n for i in genArr(str(n)):\n if sum(map(int,i.split(' '))) == check:\n return True\n return False","title":""},{"docid":"8add9f089102ae324396388bdab18197","score":"0.492978","text":"def test_name_validator(self):\n \"\"\"I think these name will work\"\"\"\n valid_names = [\"AnthonyPietrzak\", \" AlissaTyreen\", \"TroyBaker \"]\n for values in valid_names:\n self.assertTrue(self.my_phone.name_validator(values))","title":""},{"docid":"977deb3c5e70cea0417f54d8fb400264","score":"0.49262866","text":"def testStrInvalidStr2(self):\n with self.assertRaises(ValueError):\n sc()(str(\"1aaaf42\"), actionWarning=aWarning, actionError=aErr)","title":""},{"docid":"20b21d536083dd8ceff43f44ef48b7c3","score":"0.49235252","text":"def test17(self):\n self.verify(\"$*5*aStr\", \"blarg\")","title":""},{"docid":"b549f13816045876c0d6ac0349c24f41","score":"0.49182823","text":"def _OnValidate_iti(self, d, i, P, s, S, v, V, W):\t\t\n\t\tif S in '0123456789.':\n\t\t\ttry:\n\t\t\t\tfloat(P)\n\t\t\t\treturn (float(P)>=iti_min) and (float(P)<=iti_max)\n\t\t\texcept ValueError:\n\t\t\t\treturn False\n\t\telse:\n\t\t\treturn False","title":""},{"docid":"3ed12264932db56d4f1702ce51f05441","score":"0.49076775","text":"def test_sanitize_line(self):\n for line in ('1 BEC 1', '1 BE CMG1', '1 BEMG 1'):\n self.assertEqual(core.sanitize_line(line), '1 BECMG 1')\n for line in ('1 TEMP0 1', '1 TEMP 1', '1 TEMO1', '1 T EMPO1'):\n self.assertEqual(core.sanitize_line(line), '1 TEMPO 1')\n self.assertEqual(core.sanitize_line('1 2 3 4 5'), '1 2 3 4 5')","title":""},{"docid":"b11aec5a28a2790b82e1d52c95047771","score":"0.48961592","text":"def test_validate_ci_string_clean_true(input):\n assert ciuy.validate_ci(input)","title":""},{"docid":"5add41b8e808d35ae9b2df8dde26ed2a","score":"0.48907986","text":"def _check_surrounded_by_space(self, tokens, i):\n self._check_space(tokens, i, (_MUST, _MUST))","title":""},{"docid":"569645e46acbc15349dc14e3dd58ce2d","score":"0.4890228","text":"def test_kg_times_meters_per_second_squared_in_slugs():\n up = unit_parser()\n with pytest.raises(ValueError):\n up.multiply(\"2 kilograms\", \"5 meters_per_second_squared\", \"slug\")","title":""},{"docid":"313ed8908d6093ae954123599c88f39f","score":"0.48895824","text":"def _compute_canonical_smiles(smiles: str) -> str:\n\n mol = Chem.MolFromSmiles(smiles)\n return Chem.MolToSmiles(mol)","title":""},{"docid":"7db6b93acd5853e62a89084aaae72f5f","score":"0.48846138","text":"def test5(self):\n self.verify(\"${aStr}\", \"blarg\")","title":""},{"docid":"6943946bbf001cb069faa7e09286e0a4","score":"0.48844984","text":"def test_xyz_to_smiles(self):\n xyz1 = \"\"\"S -0.06618943 -0.12360663 -0.07631983\nO -0.79539707 0.86755487 1.02675668\nO -0.68919931 0.25421823 -1.34830853\nN 0.01546439 -1.54297548 0.44580391\nC 1.59721519 0.47861334 0.00711000\nH 1.94428095 0.40772394 1.03719428\nH 2.20318015 -0.14715186 -0.64755729\nH 1.59252246 1.51178950 -0.33908352\nH -0.87856890 -2.02453514 0.38494433\nH -1.34135876 1.49608206 0.53295071\"\"\"\n\n xyz2 = \"\"\"O 2.64631000 -0.59546000 0.29327900\nO 2.64275300 2.05718500 -0.72942300\nC 1.71639100 1.97990400 0.33793200\nC -3.48200000 1.50082200 0.03091100\nC -3.85550400 -1.05695100 -0.03598300\nC 3.23017500 -1.88003900 0.34527100\nC -2.91846400 0.11144600 0.02829400\nC 0.76935400 0.80820200 0.23396500\nC -1.51123800 -0.09830700 0.09199100\nC 1.28495500 -0.50051800 0.22531700\nC -0.59550400 0.98573400 0.16444900\nC -0.94480400 -1.39242500 0.08331900\nC 0.42608700 -1.59172200 0.14650400\nH 2.24536500 1.93452800 1.29979800\nH 1.14735500 2.91082400 0.31665700\nH -3.24115200 2.03800800 0.95768700\nH -3.08546100 2.10616100 -0.79369800\nH -4.56858900 1.48636200 -0.06630800\nH -4.89652000 -0.73067200 -0.04282300\nH -3.69325500 -1.65970000 -0.93924100\nH -3.72742500 -1.73294900 0.81894100\nH 3.02442400 -2.44854700 -0.56812500\nH 4.30341500 -1.72127600 0.43646000\nH 2.87318600 -2.44236600 1.21464900\nH -0.97434200 2.00182800 0.16800300\nH -1.58581300 -2.26344700 0.02264400\nH 0.81122400 -2.60336100 0.13267800\nH 3.16280800 1.25020800 -0.70346900\"\"\"\n\n xyz3 = \"\"\"N 2.24690600 -0.00006500 0.11597700\nC -1.05654800 1.29155000 -0.02642500\nC -1.05661400 -1.29150400 -0.02650600\nC -0.30514100 0.00000200 0.00533200\nC 1.08358900 -0.00003400 0.06558000\nH -0.39168300 2.15448600 -0.00132500\nH -1.67242600 1.35091400 -0.93175000\nH -1.74185400 1.35367700 0.82742800\nH -0.39187100 -2.15447800 0.00045500\nH -1.74341400 -1.35278100 0.82619100\nH -1.67091600 -1.35164600 -0.93286400\"\"\"\n\n xyz4 = \"\"\"C -0.86594600 0.19886100 2.37159000\nC 0.48486900 -0.16232000 1.75422500\nC 1.58322700 0.83707500 2.14923200\nC 0.88213600 -1.51753600 2.17861400\nN 1.17852900 -2.57013900 2.53313600\nN 0.51051200 -0.21074800 0.26080100\nN -0.51042000 0.21074000 -0.26079600\nC -0.48479200 0.16232300 -1.75422300\nC 0.86590400 -0.19926100 -2.37161200\nC -1.58344900 -0.83674100 -2.14921800\nC -0.88166600 1.51765700 -2.17859800\nN -1.17777100 2.57034900 -2.53309500\nH -1.16019200 1.20098300 2.05838400\nH -1.64220300 -0.50052400 2.05954500\nH -0.78054100 0.17214100 3.45935000\nH 1.70120000 0.85267300 3.23368300\nH 2.53492600 0.56708700 1.69019900\nH 1.29214500 1.83331400 1.80886700\nH 1.15987300 -1.20145600 -2.05838100\nH 0.78046800 -0.17257000 -3.45937100\nH 1.64236100 0.49992400 -2.05962300\nH -2.53504500 -0.56650600 -1.69011500\nH -1.70149200 -0.85224500 -3.23366300\nH -1.29263300 -1.83308300 -1.80892900\"\"\"\n\n xyz5 = \"\"\"O 0.90973400 -0.03064000 -0.09605500\nO 0.31656600 -0.00477100 -1.21127600\nO 2.17315400 -0.03069900 -0.09349100\"\"\"\n\n xyz6 = \"\"\"S 0.38431300 0.05370100 0.00000000\nN -1.13260000 0.07859900 0.00000000\nH 0.85151800 -1.28998600 0.00000000\"\"\"\n\n xyz7 = \"\"\"N 0.00000000 0.00000000 0.44654700\nN 0.00000000 0.00000000 -0.77510900\nH 0.86709400 0.00000000 1.02859700\nH -0.86709400 0.00000000 1.02859700\"\"\"\n\n xyz8 = \"\"\"N 0.00000000 0.00000000 0.65631400\nC 0.00000000 0.00000000 -0.50136500\nH 0.00000000 0.00000000 -1.57173600\"\"\"\n\n# xyz9 = \"\"\"S -0.00866000 -0.60254900 0.00000000\n# N -0.96878800 0.63275900 0.00000000\n# N 1.01229100 0.58298500 0.00000000\"\"\"\n#\n# xyz10 = \"\"\"O -0.79494500 -0.93969200 0.00000000\n# O -0.32753500 1.24003800 0.00000000\n# O 1.28811400 -0.24729000 0.00000000\n# N 0.14143500 0.11571500 0.00000000\n# H -1.65602000 -0.48026800 0.00000000\"\"\"\n#\n# xyz11 = \"\"\"O 1.64973000 -0.57433600 0.02610800\n# O 0.49836300 1.28744800 -0.18806200\n# N -0.57621600 -0.65116600 0.24595200\n# N -1.78357200 -0.10211200 -0.14953800\n# N 0.61460400 0.08152700 -0.00952700\n# H -0.42001200 -1.61494900 -0.03311600\n# H -1.72480300 0.33507600 -1.06884500\n# H -2.07362100 0.59363400 0.53038600\"\"\"\n\n xyz12 = \"\"\"O 1.10621000 0.00000000 -0.13455300\nO -1.10621000 0.00000000 -0.13455300\nN 0.00000000 0.00000000 0.33490500\"\"\"\n\n# xyz13 = \"\"\"O -0.37723000 -1.27051900 0.00000000\n# N -0.12115000 -0.04252600 0.00000000\n# N -0.95339100 0.91468300 0.00000000\n# C 1.31648000 0.33217600 0.00000000\n# H 1.76422500 -0.11051900 -0.89038300\n# H 1.76422500 -0.11051900 0.89038300\n# H 1.40045900 1.41618100 0.00000000\n# H -1.88127600 0.47189500 0.00000000\"\"\"\n\n xyz14 = \"\"\"S -0.12942800 0.11104800 0.22427200\nO 0.98591500 -1.00752300 -0.31179100\nO -1.43956200 -0.44459900 -0.15048900\nO 0.32982400 1.44755400 -0.21682700\nH 1.85512700 -0.56879900 -0.36563700\"\"\"\n\n xyz15 = \"\"\"N 1.11543700 0.11100500 0.00000000\nN -0.11982300 -0.03150800 0.00000000\nN -1.25716400 0.01530300 0.00000000\nH 1.57747800 -0.80026300 0.00000000\"\"\"\n\n xyz16 = \"\"\"O 1.21678000 -0.01490600 0.00000000\nN 0.04560300 0.35628400 0.00000000\nC -1.08941100 -0.23907800 0.00000000\nH -1.97763400 0.37807800 0.00000000\nH -1.14592100 -1.32640500 0.00000000\"\"\"\n\n xyz17 = \"\"\"S 0.00000000 0.00000000 0.18275300\nO -0.94981300 -0.83167500 -0.84628900\nO 0.94981300 0.83167500 -0.84628900\nO 0.80426500 -0.99804200 0.85548500\nO -0.80426500 0.99804200 0.85548500\nH -1.67833300 -0.25442300 -1.13658700\nH 1.67833300 0.25442300 -1.13658700\"\"\"\n\n xyz18 = \"\"\"S 0.00000000 0.00000000 0.12264300\nO 1.45413200 0.00000000 0.12264300\nO -0.72706600 1.25931500 0.12264300\nO -0.72706600 -1.25931500 0.12264300\"\"\"\n\n xyz19 = \"\"\"N 1.16672400 0.35870400 -0.00000400\nN -1.16670800 0.35879500 -0.00000400\nC -0.73775600 -0.89086600 -0.00000100\nC 0.73767000 -0.89093000 -0.00000100\nC 0.00005200 1.08477000 -0.00000500\nH -1.40657400 -1.74401100 0.00000000\nH 1.40645000 -1.74411900 0.00000000\nH 0.00009400 2.16788100 -0.00000700\"\"\"\n\n xyz20 = \"\"\"C 3.09980400 -0.16068000 0.00000600\nC 1.73521600 0.45534600 -0.00002200\nC 0.55924400 -0.24765400 -0.00000300\nC -0.73300200 0.32890400 -0.00001600\nC -1.93406200 -0.42115800 0.00001300\nC -3.19432700 0.11090700 0.00000900\nH 3.67991400 0.15199400 -0.87914100\nH 3.67984100 0.15191400 0.87923000\nH 3.04908000 -1.25419800 -0.00004300\nH 1.68713300 1.54476700 -0.00005100\nH -0.81003200 1.41627100 -0.00004600\nH -1.83479400 -1.50747300 0.00004100\nH 0.61489300 -1.33808300 0.00002500\nH -3.35410300 1.18597200 -0.00001700\nH -4.07566100 -0.52115800 0.00003300\"\"\"\n\n mol1 = converter.molecules_from_xyz(converter.str_to_xyz(xyz1))[1]\n mol2 = converter.molecules_from_xyz(converter.str_to_xyz(xyz2))[1]\n mol3 = converter.molecules_from_xyz(converter.str_to_xyz(xyz3))[1]\n mol4 = converter.molecules_from_xyz(converter.str_to_xyz(xyz4))[1]\n mol5 = converter.molecules_from_xyz(converter.str_to_xyz(xyz5))[1]\n mol6 = converter.molecules_from_xyz(converter.str_to_xyz(xyz6), multiplicity=1)[1]\n mol7 = converter.molecules_from_xyz(converter.str_to_xyz(xyz7), multiplicity=1)[1]\n mol8 = converter.molecules_from_xyz(converter.str_to_xyz(xyz8))[1]\n # mol9 = converter.molecules_from_xyz(converter.str_to_xyz(xyz9), multiplicity=1)[1]\n # mol10 = converter.molecules_from_xyz(converter.str_to_xyz(xyz10))[1]\n # mol11 = converter.molecules_from_xyz(converter.str_to_xyz(xyz11))[1]\n mol12 = converter.molecules_from_xyz(converter.str_to_xyz(xyz12))[1]\n # mol13 = converter.molecules_from_xyz(converter.str_to_xyz(xyz13))[1]\n mol14 = converter.molecules_from_xyz(converter.str_to_xyz(xyz14))[1]\n mol15 = converter.molecules_from_xyz(converter.str_to_xyz(xyz15))[1]\n mol16 = converter.molecules_from_xyz(converter.str_to_xyz(xyz16))[1]\n mol17 = converter.molecules_from_xyz(converter.str_to_xyz(xyz17))[1]\n mol18 = converter.molecules_from_xyz(converter.str_to_xyz(xyz18))[1]\n mol19 = converter.molecules_from_xyz(converter.str_to_xyz(xyz19))[1]\n mol20 = converter.molecules_from_xyz(converter.str_to_xyz(xyz20))[1]\n\n self.assertEqual(mol1.to_smiles(), '[NH-][S+](=O)(O)C')\n self.assertIn(mol2.to_smiles(), ['COC1=C(CO)C=C([C](C)C)C=C1', 'COC1C=CC(=CC=1CO)[C](C)C'])\n self.assertEqual(mol3.to_smiles(), '[N]=C=C(C)C')\n self.assertEqual(mol4.to_smiles(), 'N#CC(N=NC(C#N)(C)C)(C)C')\n self.assertEqual(mol5.to_smiles(), '[O-][O+]=O')\n self.assertEqual(mol6.to_smiles(), 'N#S')\n self.assertEqual(mol7.to_smiles(), '[N-]=[NH2+]')\n self.assertEqual(mol8.to_smiles(), 'C#N')\n # self.assertEqual(mol9.to_smiles(), '[N-]=[S+]#N') # gives [N]S#N, multiplicity 3\n # self.assertEqual(mol10.to_smiles(), '[N+](=O)(O)[O-]') # gives None\n # self.assertEqual(mol11.to_smiles(), 'N(N)[N+](=O)[O-]') # gives None\n self.assertEqual(mol12.to_smiles(), '[O]N=O')\n # self.assertEqual(mol13.to_smiles(), 'C[N+]([NH-])=O') # gives None\n self.assertEqual(mol14.to_smiles(), 'OS(=O)[O]')\n self.assertEqual(mol15.to_smiles(), '[N-]=[N+]=N')\n self.assertEqual(mol16.to_smiles(), '[O]N=C')\n self.assertEqual(mol17.to_smiles(), 'OS(=O)(=O)O')\n self.assertEqual(mol18.to_smiles(), 'O=S(=O)=O')\n self.assertEqual(mol19.to_adjacency_list(), \"\"\"multiplicity 2\n1 N u1 p1 c0 {4,S} {5,S}\n2 N u0 p1 c0 {3,S} {5,D}\n3 C u0 p0 c0 {2,S} {4,D} {6,S}\n4 C u0 p0 c0 {1,S} {3,D} {7,S}\n5 C u0 p0 c0 {1,S} {2,D} {8,S}\n6 H u0 p0 c0 {3,S}\n7 H u0 p0 c0 {4,S}\n8 H u0 p0 c0 {5,S}\n\"\"\") # cannot read SMILES 'c1ncc[n]1' (but can generate them)\n self.assertEqual(mol20.to_smiles(), 'C=C[CH]C=CC')","title":""},{"docid":"fec09a3cf60bb8f7abf05b366fed6d9c","score":"0.4883265","text":"def validateStringInput(input_key,input_data, read=False):\n log = clog.error_log\n func = None\n if '*' in input_data or '%' in input_data:\n func = validationFunctionWildcard.get(input_key)\n if func is None:\n func = searchstr\n elif input_key == 'migration_input' :\n if input_data.find('#') != -1 : func = block\n else : func = dataset\n else:\n if not read:\n func = validationFunction.get(input_key)\n if func is None:\n func = namestr\n else:\n if input_key == 'dataset':\n func = reading_dataset_check\n elif input_key == 'block_name':\n func = reading_block_check\n elif input_key == 'logical_file_name':\n func = reading_lfn_check\n else:\n func = namestr\n try:\n func(input_data)\n except AssertionError as ae:\n serverLog = str(ae) + \" key-value pair (%s, %s) cannot pass input checking\" %(input_key, input_data)\n #print serverLog\n dbsExceptionHandler(\"dbsException-invalid-input2\", message=\"Invalid Input Data %s...: Not Match Required Format\" %input_data[:10], \\\n logger=log.error, serverError=serverLog)\n return input_data","title":""},{"docid":"f364d26d22b039b9bdc5611e525a421e","score":"0.48775777","text":"def validate(self, newtext):\n print('validate={}'.format(newtext))\n self.calc_gross_net()\n return True","title":""},{"docid":"1eff032ed05d886cded6966a22d006e5","score":"0.48729396","text":"def test_args_valid_types_str(self):\n Base.reset()\n r1 = Square(10, 10, 10)\n s = \"string\"\n msg = \" must be an integer\"\n err = TypeError\n try:\n r1.update(21, s)\n except err as e:\n self.assertEqual((str(e)), \"width\" + msg)\n try:\n r1.update(21, 32, s)\n except err as e:\n self.assertEqual((str(e)), \"x\" + msg)\n try:\n r1.update(43, 43, 32, s)\n except err as e:\n self.assertEqual((str(e)), \"y\" + msg)","title":""},{"docid":"ff3933dea6fc90d8170e4f3fabf526d4","score":"0.4869997","text":"def is_valid(self, string):\n\t\tself.possible_states = self.e_closure([self.start_state])\n\t\tfor char in string:\n\t\t\tif char not in self.alphabet:\n\t\t\t\treturn False\n\t\t\tself.move(char)\n\t\treturn any(self.possible_states)","title":""},{"docid":"6d648843e3e148647803c24ec291fd5c","score":"0.48519418","text":"def filter_invalid_smiles(data: MoleculeDataset) -> MoleculeDataset:\n datapoint_list = []\n for idx, datapoint in enumerate(data):\n if datapoint.smiles == \"\":\n print(f\"invalid smiles {idx}: {datapoint.smiles}\")\n continue\n mol = Chem.MolFromSmiles(datapoint.smiles)\n if mol.GetNumHeavyAtoms() == 0:\n print(f\"invalid heavy {idx}\")\n continue\n datapoint_list.append(datapoint)\n return MoleculeDataset(datapoint_list)","title":""},{"docid":"26cd455fee6195a8a7ff49515ac1d478","score":"0.48450083","text":"def validate_all(self, object, name, value):\n try:\n value = strx(value)\n if (self.minlen <= len(value) <= self.maxlen) and (\n self.match(value) is not None\n ):\n return value\n except:\n pass\n\n self.error(object, name, value)","title":""},{"docid":"7ff6b6eb0f6d28c626df63d5ac201b94","score":"0.4838273","text":"def valid_sentence(self, sentence ):\n\tif not sentence: return False\n\tif sentence[0:6] == \"$GPGGA\":\n\t dbg(\"Found a GP G G A sentence\")\n\t try:\n\t\tfields = sentence.split(\",\")\n\t\tif fields[6] == '1': \n\t\t self.haveFix = 'valid'\n\t\t self.lat, self.lon = (float( fields[2] ), float( fields[4] ))\n\t\t return 'GPGAA'\n\t except:\n\t\tdbg(\"Problem splitting up GPGAA record\")\n\t return False\n\tif sentence[0:6] == \"$GPRMC\":\n\t dbg(\"Found a GP R M C sentence\")\n\t try:\n\t\t(GPRMC,ut,av,lat,ns,lon,ew,speed,course,utc,mag,magns,check)=sentence.split(\",\")\n\t\tdbg('split the GPRMC fields sucessful',True)\n\t\tself.lat, self.lon = (float(lat), float(lon) )\n\t\tif av == 'A': \n\t\t self.haveFix = 'valid'\n\t\t if speed and course:\n\t\t\tself.speed, self.course = (float(speed)*1.150779,float(course))\n\t\t return 'GPRMC'\n\t\telse:\n\t\t self.haveFix = 'invalid'\n\t except:\n\t\tdbg(\"Problem splitting up GPRMC record\")\n\t return False","title":""},{"docid":"f0518c7e38b3f0c388b3fd2d11cd751b","score":"0.48350266","text":"def checkSquishingFunctionsArg(self, arg):\n # list of function associated to each layer\n nb_layer = len(self.neural_network)\n if arg == \"Sigmoid\":\n self.squishing_funcs = [(Sigmoid, InvSigmoid, DerSigmoid)] \\\n * (nb_layer-1)\n self.squishing_funcs_str = \"Sigmoid\"\n elif arg == \"ReEU\":\n self.squishing_funcs = [(ReEU, InvReEU, DerReEU)] * (nb_layer-1)\n self.squishing_funcs_str = \"ReEU\"\n elif arg == \"ReLU\":\n self.squishing_funcs = [(ReLU, InvReLU, DerReLU)] * (nb_layer-2)\n # BEWARE : end with a function that squishes the number in [0, 1]\n self.squishing_funcs.append((ReEU, InvReEU, DerReEU))\n self.squishing_funcs_str = \"ReLU\"\n else:\n print(\"The given squishing function\", arg, \"doesn't correspond to\"\n \" any possible function :\", POSSIBLE_SQUISHING_FUNC)\n sys.exit(1)","title":""},{"docid":"861f5ebcc8d901f1e7105a3f8063046b","score":"0.48337695","text":"def test_uncharge4():\n assert uncharge_smiles('C[NH+](C)CCO') == 'CN(C)CCO'","title":""},{"docid":"bd2d2344a9a65dffe16d015ed9f629d2","score":"0.48174107","text":"def test_uncharge7():\n assert uncharge_smiles('[NH3+]C(Cc1cnc[nH]1)C(=O)[O-]') == 'NC(Cc1cnc[nH]1)C(=O)O'","title":""},{"docid":"5f9a1c3bc58a40e61b41ed39bf471325","score":"0.4816726","text":"def _validate(self):\n # TODO: create phrase validation\n # 1. removes all substitutions\n # 2. checks pronouncable symbol sets\n pass","title":""},{"docid":"152f451b18fcb2624a65b65809fe1864","score":"0.48117536","text":"def test_uncharge14():\n assert uncharge_smiles('[O-]C(=O)C[n+]1ccn2cccc([O-])c12') == 'O=C([O-])C[n+]1ccn2cccc(O)c21'","title":""},{"docid":"9d54453f45cb196680870c1d7ee335ae","score":"0.4811299","text":"def test_invalid_str_error(self):\n values = ['not_true_or_false', 'A', '1', '0',\n 'false_is_not_true', 'true_is_not_false']\n for value in values:\n with self.assertRaises(RuntimeError):\n str_to_bool(value)","title":""},{"docid":"c79d67155c78c6e724b8fc91a956df05","score":"0.48063695","text":"def test_forced_charge2():\n # GitHub Issue #15\n assert standardize_smiles('[Na].[Na]') == '[Na+].[Na+]'\n # TODO: Arguably should become selenite ion... O=[Se]([O-])[O-]. Need an AcidBasePair?\n assert standardize_smiles('[Na].[Na].O[Se](O)=O') == 'O=[Se](O)O.[Na+].[Na+]'","title":""},{"docid":"6b504236e8f885f8215178028d72b302","score":"0.48045254","text":"def minion_game(string):\r\n s = string.lower()\r\n vow = 'aeiou'\r\n stuart_score = 0\r\n kevin_score = 0\r\n\r\n for index, character in enumerate(s):\r\n if character in vow:\r\n kevin_score += len(string) - index\r\n else:\r\n stuart_score += len(string) - index\r\n\r\n if stuart_score > kevin_score:\r\n print(f'Stuart {stuart_score}')\r\n elif stuart_score < kevin_score:\r\n print(f'Kevin {kevin_score}')\r\n else:\r\n print('Draw')","title":""},{"docid":"a6a184fda38b3617aabbb80109a839f3","score":"0.480397","text":"def test_phone_number_failure(self):\n \"\"\"I think these will not work\"\"\"\n invalid_phones = [\"5862462296#$\", \" 576@#475657\", \"5679830 \",\" \",\" 6985\",\"049587%^&\"]\n for values in invalid_phones:\n self.assertFalse(self.my_phone.phone_validator(values))","title":""},{"docid":"6ba8d721dcb3396c5e1dda34cbc1b090","score":"0.47929287","text":"def test_code_checking(self):\r\n # Try valid codes.\r\n for code in TelephoneNumber.valid_codes: \r\n self.assertTrue(TelephoneNumber(code + ' 123456').has_valid_code())\r\n \r\n self.assertTrue(TelephoneNumber('0274123456').has_valid_code())\r\n\r\n # Try invalid codes.\r\n self.assertFalse(TelephoneNumber('05 123 4567').has_valid_code())\r\n self.assertFalse(TelephoneNumber('0274 123 456').has_valid_code())","title":""},{"docid":"0405a3401fc1453a17633e195d7488ed","score":"0.47909024","text":"def validate_value(value):\n if isinstance(value, basestring):\n string_validators = [is_valid_string]\n for validator in string_validators:\n value = validator(value)\n return value","title":""},{"docid":"09508297750fce258bb8efa87be353a2","score":"0.4790869","text":"def test11(self):\n\n for badSyntax in (\"#if $*5*emptyString\\n$aStr\\n#end if\\n\",\n \"#if ${emptyString}\\n$aStr\\n#end if\\n\",\n \"#if $(emptyString)\\n$aStr\\n#end if\\n\",\n \"#if $[emptyString]\\n$aStr\\n#end if\\n\",\n \"#if $!emptyString\\n$aStr\\n#end if\\n\",\n ):\n try:\n self.verify(badSyntax, \"\")\n except ParseError:\n pass\n else:\n self.fail('This should barf')","title":""},{"docid":"1c01451c50b79cf9eb3cc52ceab61dff","score":"0.47899103","text":"def _validate_memory_string(self, memory_string):\n if re.match(r\"^[0-9]+(E|Ei|P|Pi|T|Ti|G|Gi|M|Mi|K|Ki){0,1}$\",\n memory_string) is None:\n raise ValueError(\"Invalid memory string. Should be an integer, \" +\n \"or integer followed by one of \" +\n '\"E|Ei|P|Pi|T|Ti|G|Gi|M|Mi|K|Ki\"')","title":""},{"docid":"4ef01f364e7f2ed26411dad468ac6a30","score":"0.4789367","text":"def validate_shot_code(self):\n import re\n\n regex = re.compile(r\"([\\w]+)_(\\d{3})_(\\d{3}[A-Z]{0,1})_(\\d{4})\")\n\n shot_code = self.shot_code\n match = regex.match(shot_code)\n if not match:\n raise ValueError(\"Shot code format is not valid: %s\" % shot_code)","title":""},{"docid":"c4477b49ed8cfbb7865bc89f0a6c484a","score":"0.4784115","text":"def check_validation(text: str):\n holder = text.split()\n indexes = holder[0].split('-')\n fx = int(indexes[0])\n sx = int(indexes[1])\n ch = holder[1][0]\n pw = holder[2]\n\n if (pw[fx-1]==ch) ^ (pw[sx-1]==ch):\n return True\n return False","title":""},{"docid":"a9759a19d3a8c0d71b28ee7ebc8c6e4c","score":"0.47808066","text":"def test_type_validator_success(self):\n \"\"\"I think these will work\"\"\"\n valid_type = [\"office\", \" cell\", \"home \"]\n for values in valid_type:\n self.assertTrue(self.my_phone.type_validator(values))","title":""},{"docid":"a4130522894687d745e06b85a39e2e9a","score":"0.4780536","text":"def is_valid_move(board, word, candidate, tilebag):\r\n pass","title":""},{"docid":"ca82e7543e2f74363a2d95f792030693","score":"0.47728872","text":"def test_validating_films_on_a_per_character_basis():\n pass","title":""},{"docid":"41c324bb87f492244416c91af1b05ced","score":"0.47666553","text":"def smiles_from_seq(seq):\r\n gs, bs, terminal, capping = split_seq_components(seq)\r\n # modifies the Cterminal\r\n if terminal:\r\n molecule = rdmolfiles.MolFromSmiles(T_SMILES[terminal[0]])\r\n else:\r\n molecule = ''\r\n\r\n # creates the dendrimer structure\r\n for gen in gs:\r\n for aa in gen:\r\n if aa == '-':\r\n metbond = True\r\n continue\r\n if molecule == '':\r\n molecule = rdmolfiles.MolFromSmiles(AA_SMILES[aa])\r\n else:\r\n molecule = connect_mol(molecule, rdmolfiles.MolFromSmiles(AA_SMILES[aa]))\r\n\r\n if bs:\r\n if bs[0] == '-':\r\n metbond = True\r\n bs.pop(0)\r\n if molecule == '':\r\n molecule = rdmolfiles.MolFromSmiles(B_SMILES[bs[0]])\r\n else:\r\n molecule = connect_mol(molecule, rdmolfiles.MolFromSmiles(B_SMILES[bs[0]]))\r\n bs.pop(0)\r\n\r\n # adds capping to the N-terminal (the called clip function is different, cause the listed smiles\r\n # for the capping are already without OH, it is not necessary removing any atom after foming the new bond)\r\n if capping and molecule != '':\r\n molecule = attach_capping(molecule, rdmolfiles.MolFromSmiles(C_SMILES[capping[0]]))\r\n # clean the smile from all the tags\r\n for atom in molecule.GetAtoms():\r\n atom.SetAtomMapNum(0)\r\n\r\n molecule_smile = rdmolfiles.MolToSmiles(molecule, isomericSmiles=True).replace('[N]', 'N').replace('[C]', 'C')\r\n return molecule_smile","title":""},{"docid":"d3e7b997d8281f01c1f28f6eb77bd445","score":"0.47647664","text":"def test_validate_ci_string_dirty_true(input):\n assert ciuy.validate_ci(input)","title":""},{"docid":"35f776be0d20e9dc292a5abb0d881a0d","score":"0.47628546","text":"def test13(self):\n self.verify(\"$*5*{ aStr }\", \"blarg\")","title":""},{"docid":"a513b57114a9fa2143aa94036c861795","score":"0.47498873","text":"def evalUnits(unitStr):\n pass","title":""},{"docid":"762eec545c7ce262d0e9c7ec0cb62f8f","score":"0.47475383","text":"def process_message(text):\n regex = r'^([0-9]+)[*](tank|dps|heal|bench)$'\n message = ''\n for line in text.splitlines():\n matches = re.search(regex, line, re.IGNORECASE)\n if matches:\n if len(matches.groups()) != 2:\n message += 'malformed input\\n'\n else:\n amount = int(matches.group(1))\n role = matches.group(2)\n message += f'{role}: \\n' * amount\n roster.extend([(role, '') for i in range(amount)])\n else:\n message += line+'\\n'\n return message","title":""},{"docid":"a87329c24dc245e51aed98aaa7469052","score":"0.47442102","text":"def __init__(self, string):\n size = math.sqrt(len(string))\n if not size.is_integer():\n raise RuntimeError(f'The specified board has length {len(string)} and does not seem to be square')\n self.data = [0 if x == '.' else int(x) for x in string]\n self.size_ = int(size)","title":""},{"docid":"11febd95c86611436db289ba63a52d1f","score":"0.47435883","text":"def test_string_rule():\n\ts1 = '\"Hello world! My name is matthew\"' # normal doule quote\n\ts2 = \"'Hello world! My name is matthew'\" # normal single quote\n\ts3 = '\"Hello world! \" Hello Dog!\"' # bad quote in middle\n\ts4 = '\"Hello world! \\\" Hello DOg!\"' # okay quote in middle\n\ts5 = '\"Hello World! Holl Dog!\\\\\"' # bad escape\n\ts6 = 'r\"hello world\"' # okay prefix\n\ts7 = 'q\"Hello world\"' # bad prefix\n\t\n\truleS = StringRule('(r|u|R|U|b|B|(br)|(Br)|(BR)|(rb)|(rB)|(Rb)|(RB))?'+\\\n\t\t\t\t '[\\'\"].*',PyTokenType.string)\n\tprint(ruleS.could_match(s1),True)\n\tprint(ruleS.could_match(s2),True)\n\tprint(ruleS.could_match(s3),False)\n\tprint(ruleS.could_match(s4),True)\n\tprint(ruleS.could_match(s5),False)\n\tprint(ruleS.could_match(s6),True)\n\tprint(ruleS.could_match(s7),False)","title":""},{"docid":"fa40d5688ff41567944a986c3d52b425","score":"0.47433335","text":"def test_bespoke_smirks(mol, smirks_type, n_expected, expected_lambda, central_bond):\n gen = SMIRKSGenerator(target_smirks=[smirks_type])\n\n if isinstance(mol, str):\n mol = Molecule.from_smiles(mol)\n\n generated_parameters = gen.generate_smirks_from_molecule(\n molecule=mol, central_bond=central_bond\n )\n assert len(generated_parameters) == n_expected\n\n all_matches = [\n match\n for parameter in generated_parameters\n for match in condense_matches(\n mol.chemical_environment_matches(parameter.smirks)\n )\n ]\n\n assert len({*all_matches}) == len(all_matches)\n assert compare_matches({*all_matches}, expected_lambda(mol))","title":""},{"docid":"ecceb9e8c2638a66c1265e6185d67a09","score":"0.47411263","text":"def test_for_str_type_error(self):\n self.assertEqual(primesieve('random str'), 'TypeError')","title":""}],"string":"[\n {\n \"docid\": \"0092f72c071ae329d215d8a58d019ba5\",\n \"score\": \"0.71211714\",\n \"text\": \"def IsValidSMILES(text):\\n is_valid = False\\n results = smiles.parseString(text)\\n if results:\\n is_valid = True\\n return(is_valid)\\n return is_valid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274fb728b4e2a2ea699b579529e35803\",\n \"score\": \"0.6430498\",\n \"text\": \"def testRuleSix(self):\\n smis = [\\\"C(1)CCC1\\\", \\\"C(CCC1)1\\\"]\\n for smi in smis:\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\\n ParseSmiles(smi, False, 32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0443d6d4452ce1227881b52bc1e7d97\",\n \"score\": \"0.63043696\",\n \"text\": \"def testRuleFive(self):\\n smis = [\\\"C1.C1\\\"]\\n for smi in smis:\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\\n ParseSmiles(smi, False, 16)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98f35c423535d15cce23b4f4aec669f\",\n \"score\": \"0.6265145\",\n \"text\": \"def testRuleFour(self):\\n smis = [\\\"C(C.C)C\\\"]\\n for smi in smis:\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\\n ParseSmiles(smi, False, 8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"285c7836af964afbd9b6b1606609d5d6\",\n \"score\": \"0.6210074\",\n \"text\": \"def testRuleOne(self):\\n smi = \\\"C..C\\\"\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\\n ParseSmiles(smi, False, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c360ed31452d76c7d5420426d72a2ea7\",\n \"score\": \"0.61493397\",\n \"text\": \"def testRuleSeven(self):\\n # Aromatic bond symbols are not needed in a SMILES string\\n # - an aromatic bond connecting two lowercase atoms is implicit\\n # - the meaning of an aromatic bond attached to an uppercase atom\\n # is a matter of intense debate\\n smi = \\\"c1:c:c:c:c:c1\\\"\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\\n ParseSmiles(smi, False, 64)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c68900f369baab44c00f6883889bc627\",\n \"score\": \"0.59261775\",\n \"text\": \"def test_should_complete():\\n # GitHub Issue #14\\n assert standardize_smiles('CCCCCCCCCCCCCCCCCC(=O)CC(=C)C(=O)O[Ti](=O)(OC(C)C)C(C)C') == 'C=C(CC(=O)[CH-]CCCCCCCCCCCCCCCC)C(=O)[O-].CC(C)[O-].CCC.[O-2].[Ti+5]'\\n assert standardize_smiles('OP(=O)(O)[O-].OP(=O)([O-])[O-].[O-]S(=O)(=O)[O-].[Na+].[Na+].[Na+].[Mg+2].[Cl-].[Cl-].[K+].[K+]') == 'O=P([O-])(O)O.O=P([O-])([O-])O.O=S(=O)([O-])[O-].[Cl-].[Cl-].[K+].[K+].[Mg+2].[Na+].[Na+].[Na+]'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ba0162ed03a2dc09a4f68d72f180b0\",\n \"score\": \"0.58013153\",\n \"text\": \"def check_smiles(seqs, voc):\\n valids = []\\n smiles = []\\n for j, seq in enumerate(seqs.cpu()):\\n smile = voc.decode(seq)\\n valids.append(1 if Chem.MolFromSmiles(smile) else 0)\\n smiles.append(smile)\\n valids = np.array(valids, dtype=np.byte)\\n return smiles, valids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6efe55fe656e4d5907560ac84ae639\",\n \"score\": \"0.5784007\",\n \"text\": \"def isValid(self, s):\\r\\n if s == \\\"\\\":\\r\\n return True\\r\\n length = len(s);\\r\\n if length % 2 == 1:\\r\\n return False\\r\\n lis = []\\r\\n lookUp = {\\\"(\\\":\\\")\\\", \\\"[\\\":\\\"]\\\", \\\"{\\\":\\\"}\\\"}\\r\\n for i in s:\\r\\n if i in lookUp:\\r\\n lis.append(i)\\r\\n elif len(lis) == 0 or lookUp[lis.pop()] != i:\\r\\n return False\\r\\n return len(lis) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a099e8e1e08f1fe48e620a2e12967d\",\n \"score\": \"0.5770343\",\n \"text\": \"def _check_contributions_string(string):\\n assert string in ('', 'S', 'P', 'SP', 'PS')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35868cadc1a121aa4fafa38fb10f9d95\",\n \"score\": \"0.573639\",\n \"text\": \"def check_valid_input(s):\\n\\n if s == \\\"rock\\\":\\n return True\\n if s == \\\"lizard\\\":\\n return True\\n if s == \\\"gun\\\":\\n return True\\n if s == \\\"zombie\\\":\\n return True\\n if s == \\\"spock\\\":\\n return True\\n if s == \\\"scissors\\\":\\n return True\\n if s == \\\"paper\\\":\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dbaa59abf392c92de3e1831cf3eeda3\",\n \"score\": \"0.5588584\",\n \"text\": \"def check_ascii(self, smiles):\\n try:\\n smiles.encode('ascii')\\n return True\\n except UnicodeEncodeError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"234d5034e2192d0289414ee6c276fc5d\",\n \"score\": \"0.5574101\",\n \"text\": \"def is_valid(smi):\\n mol = Chem.MolFromSmiles(smi)\\n if mol is None:\\n return False\\n else:\\n return Chem.MolToSmiles(mol, isomericSmiles=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74fcc30822c71de118a897eca9b71c6\",\n \"score\": \"0.5493523\",\n \"text\": \"def testRuleThree(self):\\n smis = [\\\"C(O(C))\\\", \\\"C(O).C\\\"]\\n for smi in smis:\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, True)\\n ParseSmiles(smi, False, 4)\\n\\n smis = [\\\"C(O)\\\"] # these are fine as partial but not otherwise\\n for smi in smis:\\n self.assertRaises(SMILESSyntaxError, ParseSmiles, smi, False)\\n ParseSmiles(smi, True)\\n ParseSmiles(smi, False, 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f397f1098f791caeccd051e81c328ab\",\n \"score\": \"0.5461919\",\n \"text\": \"def input_is_correct_e5(strings):\\n #regular expressions for the lines\\n reg1 = re.compile('(states)\\\\=\\\\{(\\\\w+[,])*(\\\\w+)*\\\\}\\\\n')\\n reg2 = re.compile('(alpha)\\\\=\\\\{(\\\\w+[,])*(\\\\w+)*\\\\}\\\\n')\\n reg3 = re.compile('(init.st)\\\\=\\\\{(\\\\w+[,])*(\\\\w+)*\\\\}\\\\n')\\n reg4 = re.compile('(fin.st)\\\\=\\\\{(\\\\w+[,])*(\\\\w+)*\\\\}\\\\n')\\n reg5 = re.compile('(trans)\\\\=\\\\{(\\\\w*[>]\\\\w*[>]\\\\w*[,])*(\\\\w*[>]\\\\w*[>]\\\\w*)*\\\\\\\\}')\\n\\n\\n #check if they match\\n c1=reg1.fullmatch(strings[0])\\n c2=reg2.fullmatch(strings[1])\\n c3=reg3.fullmatch(strings[2])\\n c4=reg4.fullmatch(strings[3])\\n c5=reg5.fullmatch(strings[4])\\n\\n if (c1 and c2 and c3 and c4 and c5):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebf4811a9f123fc52e5b2d21f50ad2b0\",\n \"score\": \"0.54266065\",\n \"text\": \"def is_valid(self, string):\\n for token in string:\\n if not token in self.unit_productions:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0fd2535f4e5ff769d1ad123e0921c24\",\n \"score\": \"0.54258144\",\n \"text\": \"def validate_string(m, min, max):\\n # Begins a validation loop\\n # This loop will continue until the user enters\\n # an appropriate answer\\n while True:\\n # Defines all of the user's input as\\n # an uppercase string\\n user_input = input(m).upper()\\n # Replaces all spaces in the user's input\\n # with an enclosed space\\n user_input = user_input.replace(\\\" \\\", \\\"\\\")\\n # The customer has entered a value\\n # Below the minimum value\\n if len(user_input) < min:\\n # Error message\\n # The loop continues\\n print(\\\"Your answer is too short. \\\\n\\\"\\n \\\"Please ensure you have entered an appropriate response.\\\")\\n # The customer has entered a value\\n # Above the maximum value\\n elif len(user_input) > max:\\n # Error message\\n # The loop continues\\n print(\\\"Your answer is too long. \\\\n\\\"\\n \\\"Please ensure you have entered an appropriate response.\\\")\\n # The customer has met all of these requirements\\n # Their input is returns and the loop ends\\n else:\\n return user_input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9015303cca6ec23c6ea5798ce63f8a68\",\n \"score\": \"0.54199356\",\n \"text\": \"def test_input_validate_str(self):\\n self.assertRaises(pyhsm.exception.YHSM_WrongInputType, pyhsm.util.input_validate_str, \\\\\\n 0, 'foo', exact_len = 5)\\n\\n self.assertRaises(pyhsm.exception.YHSM_InputTooLong, pyhsm.util.input_validate_str, \\\\\\n '1234', 'foo', max_len = 3)\\n self.assertEquals('1234', pyhsm.util.input_validate_str('1234', 'foo', max_len = 4))\\n self.assertEquals('1234', pyhsm.util.input_validate_str('1234', 'foo', max_len = 14))\\n\\n self.assertRaises(pyhsm.exception.YHSM_WrongInputSize, pyhsm.util.input_validate_str, \\\\\\n '1234', 'foo', exact_len = 5)\\n self.assertEquals('1234', pyhsm.util.input_validate_str('1234', 'foo', exact_len = 4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3dccb55eb69d141c7b101a83bb2b0b\",\n \"score\": \"0.54074156\",\n \"text\": \"def sanityString(self, word, regex, expected):\\r\\n print(\\\"=\\\" * 30)\\r\\n print(\\\"Start Test Sanity 2 : String Input\\\")\\r\\n print(\\\"=\\\" * 30)\\r\\n se = Searcher()\\r\\n se.validateUserInput(word)\\r\\n se.IS_M = True\\r\\n seValue = se.handleString(word, regex, returnValue=True)\\r\\n\\r\\n assert (seValue == expected), \\\"Test Sanity Failed\\\"\\r\\n print(\\\"returnValue:{}\\\\nexpected:{}\\\".format(seValue, expected))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ad1880351455415ecc04315d7823dc6\",\n \"score\": \"0.5404408\",\n \"text\": \"def squares(s):\\n \\\"*** YOUR CODE HERE ***\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20a73c7d6283999125bc658cc3337d1a\",\n \"score\": \"0.5357808\",\n \"text\": \"def string_validator(self, the_str):\\n\\n the_str = str(the_str).replace(\\\"-\\\", '').replace(\\\"(\\\", '').replace(\\\")\\\", '').replace(\\\" \\\", '')\\n\\n self.phn = self.validator(the_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4268d3223306c88b64893e4aa3ec5dfe\",\n \"score\": \"0.53511256\",\n \"text\": \"def test_uncharge2():\\n assert uncharge_smiles('n(C)1cc[n+]2cccc([O-])c12') == 'Cn1cc[n+]2cccc([O-])c12'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d9aa131e443658fecca8ed1706741fe\",\n \"score\": \"0.53423464\",\n \"text\": \"def tokenize_smiles(smiles: str) -> str:\\n\\n tokens = [token for token in SMILES_REGEX.findall(smiles)]\\n return \\\" \\\".join(tokens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63fc1ab863489b62cac958fd0a26ddf\",\n \"score\": \"0.53204864\",\n \"text\": \"def isvalid(self, s: str) -> bool:\\n if len(s) == 1:\\n return False\\n par_stack = []\\n for par in s:\\n if par in ['(', '{', '[']:\\n par_stack.append(par)\\n else:\\n if len(par_stack) == 0:\\n return False\\n prev = par_stack.pop()\\n if not self.check_stack(prev=prev, par=par):\\n return False\\n if len(par_stack) != 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2498350272c15804055d6c2c7255cdc6\",\n \"score\": \"0.5316063\",\n \"text\": \"def check_for_strings(data, checklist):\\n return type_checks(is_valid_string, \\\"field cannot be a non string.\\\", data, checklist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e907ec386b4719b13f2e859def28b0\",\n \"score\": \"0.53120446\",\n \"text\": \"def test_uncharge15():\\n assert uncharge_smiles('[NH+](C)(C)CC([O-])C[O-]') == 'CN(C)CC(O)CO'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ff311256881a8a3d3119722cc41f95c\",\n \"score\": \"0.5272296\",\n \"text\": \"def test_uncharge5():\\n assert uncharge_smiles('[Na+].O=C([O-])c1ccccc1') == 'O=C([O-])c1ccccc1.[Na+]'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a013ab6b26bc52d0cfc9ba2f5a4afbe8\",\n \"score\": \"0.5246803\",\n \"text\": \"def test_forced_charge1():\\n assert standardize_smiles('[Na].O=C(O)c1ccccc1') == 'O=C([O-])c1ccccc1.[Na+]'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bbc4ed68bd619da435275a031ee60be\",\n \"score\": \"0.52101415\",\n \"text\": \"def test_uncharge13():\\n assert uncharge_smiles('C[N+](C)(C)CC([O-])C[O-]') == 'C[N+](C)(C)CC([O-])CO'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4e9689ad9d292e3ae8b5e958192baa\",\n \"score\": \"0.5206992\",\n \"text\": \"def check(self, input_string):\\n return self._check_input(self.start_state, input_string, [self.start_stack], [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b6f66e1964ec0293a40389893896a3\",\n \"score\": \"0.5194161\",\n \"text\": \"def validate_moves(moves: str):\\n for move in moves:\\n if move not in ['^', 'v', '>', '<']:\\n raise ValueError('Invalid move passed. Must be ^, v, <, or >')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8477ffd800124db2e29769165dfc813\",\n \"score\": \"0.5181313\",\n \"text\": \"def test_uncharge6():\\n assert uncharge_smiles('O=C([O-])c1ccccc1') == 'O=C(O)c1ccccc1'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6be5079a0905b1dc8988bb955fa36a6\",\n \"score\": \"0.5174386\",\n \"text\": \"def test_uncharge11():\\n assert uncharge_smiles('[N+](=O)([O-])[O-].[CH2]') == 'O=[N+]([O-])O.[CH2]'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e21f1ad8db7314ad9b7aa378032de7fb\",\n \"score\": \"0.516819\",\n \"text\": \"def validPuzzleStr(state):\\n try:\\n length = len(state)\\n size = sqrt(length)\\n expectedVals = range(length)\\n for tile in state:\\n expectedVals.remove(int(tile))\\n return True\\n except ValueError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5912783bbb428975d7ed4c9151348bf0\",\n \"score\": \"0.5167037\",\n \"text\": \"def test_valid_list(self):\\r\\n\\r\\n self.assertTrue(calculator.is_valid(\\\"1,2,3,4,5\\\"),True)\\r\\n self.assertTrue(calculator.is_valid(\\\"1,2,3,me,4,5\\\") == False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c4b1c5b8094a568b11dbc267355973\",\n \"score\": \"0.5166201\",\n \"text\": \"def test_get_smiles_from_name(self):\\n\\n\\t\\tprint(\\\">>> Running calculator get_smiles_from_name unit test..\\\")\\n\\n\\t\\texpected_json = self.get_example_result_json(\\\"get_smiles_from_name\\\") # gets expected response json for mock requests.post\\n\\t\\texpected_response = {'smiles': self.test_smiles, 'format': \\\"smiles\\\"} # expected response from test function\\n\\n\\t\\t# Testing function with a mock of web_call function:\\n\\t\\twith patch('qed.cts_app.cts_calcs.calculator.Calculator.web_call') as service_mock:\\n\\n\\t\\t\\t# Sets web_call mock to return expected json when test function calls it:\\n\\t\\t\\tservice_mock.return_value = expected_json\\n\\n\\t\\t\\t# Calls test function, which will use the mock web_call for unit testing:\\n\\t\\t\\tresponse = self.calc_obj.get_smiles_from_name(self.test_chemical)\\n\\n\\t\\ttry:\\n\\t\\t\\t# Compares function response with expected json:\\n\\t\\t\\tself.assertDictEqual(response, expected_response)\\n\\t\\tfinally:\\n\\t\\t\\ttab = [[response], [expected_response]]\\n\\t\\t\\tprint(\\\"\\\\n\\\")\\n\\t\\t\\tprint(inspect.currentframe().f_code.co_name)\\n\\t\\t\\tprint(tabulate(tab, headers='keys', tablefmt='rst'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c0d47ea2c00e0eb860e5b1c9b5ff305\",\n \"score\": \"0.5151674\",\n \"text\": \"def parse_checks(string):\\r\\n return __checkparser.parseString(string, parseAll=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ead630eb5fc240b1bc543d8308ae1d58\",\n \"score\": \"0.51309705\",\n \"text\": \"def test_uncharge16():\\n assert uncharge_smiles('CNCC([O-])C[O-]') == 'CNCC(O)CO'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f258ab90e4d8b75dbb49859b4c74fdce\",\n \"score\": \"0.51305634\",\n \"text\": \"def test_validation(self):\\n\\n validate = IS_FLOAT_AMOUNT()\\n\\n samples = ((\\\"123 456 789 012,00\\\", 123456789012.0),\\n (\\\"0,00\\\", 0.0),\\n (\\\"1 305,00\\\", 1305.0),\\n (12.345, 12.345),\\n )\\n\\n assertEqual = self.assertEqual\\n for inputstr, expected in samples:\\n value, error = validate(inputstr)\\n assertEqual(value, expected)\\n assertEqual(error, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c704f692b11656dc0d704fc285fdcbb\",\n \"score\": \"0.5129067\",\n \"text\": \"def input_process(s, lst):\\n\\ts = s.split()\\n\\t# Check there are 4 letters in one row\\n\\tif len(s) != SIZE:\\n\\t\\treturn False\\n\\telse:\\n\\t\\t# Check every element is only one alphabet\\n\\t\\tfor i in range(len(s)):\\n\\t\\t\\tif len(s[i]) != 1 or not s[i].isalpha():\\n\\t\\t\\t\\treturn False\\n\\t\\ts = list(map(lambda e: e.lower(), s)) # Case insensitive\\n\\t\\tlst.append(s)\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef16fcd55b956646428a3a217fa3913f\",\n \"score\": \"0.51239216\",\n \"text\": \"def test_invalid_name_validator(self):\\n \\\"\\\"\\\"I think these names will not work\\\"\\\"\\\"\\n invalid_names = [\\\"Anthony Pietrzak\\\", \\\" 5676AlissaTyreen\\\", \\\"TroyBaker#$% \\\"]\\n for values in invalid_names:\\n self.assertFalse(self.my_phone.name_validator(values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df0c84d431c1386327fdfe93f685ade4\",\n \"score\": \"0.5105053\",\n \"text\": \"def validate_shot_codes_callback(self):\\n sm = ShotManager()\\n invalid_shots = sm.validate_shot_codes()\\n if invalid_shots:\\n QtWidgets.QMessageBox.critical(\\n self.parent_widget,\\n \\\"Invalid shot names!!!\\\",\\n \\\"There are invalid shot codes:<br>%s\\\"\\n % \\\"<br>\\\".join([shot_clip.shot_code for shot_clip in invalid_shots]),\\n )\\n return False\\n else:\\n QtWidgets.QMessageBox.information(\\n self.parent_widget, \\\"All shots valid 👍\\\", \\\"All shots valid 👍\\\"\\n )\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b2501915e4cbdce9dc9742c493b2782\",\n \"score\": \"0.508446\",\n \"text\": \"def sicovam_valid(record):\\n sicovam = record['sicovam']\\n assert shared.is_int(sicovam) and shared.is_length(6, sicovam)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a847412e2e011c6511dd6524f807ef\",\n \"score\": \"0.50842154\",\n \"text\": \"def test_type_validator_failure(self):\\n \\\"\\\"\\\"I think these will work\\\"\\\"\\\"\\n invalid_type = [\\\"hsadkjh\\\", \\\" office$%\\\", \\\"cells \\\",\\\"1235467\\\",\\\"fash46\\\"]\\n for values in invalid_type:\\n self.assertFalse(self.my_phone.type_validator(values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"459160f4c504e164bc0269f0b9acc9fc\",\n \"score\": \"0.5056056\",\n \"text\": \"def isValidMatchCode():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e8fd92c292886e0228801b00a7b1e6\",\n \"score\": \"0.5041497\",\n \"text\": \"def _validateSMIRKS(smirks, node=None):\\n qmol = oechem.OEQMol()\\n if not oechem.OEParseSmarts(qmol, smirks):\\n if (node is not None) and ('sourceline' in node.attrib):\\n raise Exception(\\\"Line %s: Error parsing SMIRKS '%s'\\\" % (node.attrib['sourceline'], node.attrib['smirks']))\\n else:\\n raise Exception(\\\"Error parsing SMIRKS '%s'\\\" % (node.attrib['smirks']))\\n\\n return smirks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"085d9023b3b3759d8e6bd4035680551c\",\n \"score\": \"0.5038179\",\n \"text\": \"def test_validation(self):\\n\\n validate = IS_INT_AMOUNT()\\n\\n samples = ((\\\"123,456,789,012\\\", 123456789012),\\n (\\\"0\\\", 0),\\n (\\\"993667\\\", 993667),\\n )\\n\\n assertEqual = self.assertEqual\\n for inputstr, expected in samples:\\n value, error = validate(inputstr)\\n assertEqual(value, expected)\\n assertEqual(error, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d303635b281ca20a47e30cd0f7a5bf1\",\n \"score\": \"0.50365496\",\n \"text\": \"def _validate(self):\\n for str_ in self.strs:\\n key = list(str_.keys())[0]\\n if not isinstance(str_[key], str):\\n raise ValueError(f'{key} must be str')\\n\\n for allowed_type in self.ALLOWED_TYPES:\\n try:\\n allowed_type(str_.get(key))\\n except Exception:\\n pass\\n else:\\n str_['type'] = allowed_type\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e26007fc045ea300f2c24efc92e3de3f\",\n \"score\": \"0.5029785\",\n \"text\": \"def isValidString(self, symstr):\\n for sym in symstr:\\n if not(self.isValidSymbol(sym)):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba28876f7c76109bce08ac98dc2a9a41\",\n \"score\": \"0.50251484\",\n \"text\": \"def test_convertToSMILES(self):\\n\\n\\t\\tprint(\\\">>> Running calculator convertToSmiles unit test..\\\")\\n\\n\\t\\texpected_json = self.get_example_result_json(\\\"convert_to_smiles\\\") # gets expected response json\\n\\n\\t\\t# Testing function with a mock of web_call function:\\n\\t\\twith patch('qed.cts_app.cts_calcs.calculator.Calculator.web_call') as service_mock:\\n\\n\\t\\t\\t# Sets web_call mock to return expected json when test function calls it:\\n\\t\\t\\tservice_mock.return_value = expected_json\\n\\n\\t\\t\\t# Calls test function, which will use the mock web_call for unit testing:\\n\\t\\t\\tresponse = self.calc_obj.convertToSMILES({'structure': self.test_chemical})\\n\\n\\t\\ttry:\\n\\t\\t\\t# Compares function response with expected json:\\n\\t\\t\\tself.assertDictEqual(response, expected_json)\\n\\t\\tfinally:\\n\\t\\t\\ttab = [[response], [expected_json]]\\n\\t\\t\\tprint(\\\"\\\\n\\\")\\n\\t\\t\\tprint(inspect.currentframe().f_code.co_name)\\n\\t\\t\\tprint(tabulate(tab, headers='keys', tablefmt='rst'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d190a097d0514d36c832e042ae9ef624\",\n \"score\": \"0.5014409\",\n \"text\": \"def check_string(self, chk, li):\\n\\t\\tif not isinstance(chk, str):\\n\\t\\t\\traise TypeError(\\\"arg1 must be a str\\\")\\n\\t\\tif not isinstance(li, list):\\n\\t\\t\\traise TypeError(\\\"arg2 must be a list of str\\\")\\n\\t\\tfor i in li:\\n\\t\\t\\tif not isinstance(i, str):\\n\\t\\t\\t\\traise TypeError(\\\"arg2 must be a list of str\\\")\\n\\n\\t\\tfor i in range(len(li)):\\n\\t\\t\\tli[i] = li[i].lower()\\n\\t\\tchk = chk.lower()\\n\\t\\tchk = chk.replace(\\\" \\\",\\\"\\\")\\n\\t\\t\\n\\t\\tfor item in li:\\n\\t\\t\\tif item in chk:\\n\\t\\t\\t\\treturn True\\n\\n\\t\\tsl = list(chk)\\n\\n\\t\\tmapIndex, iterable = -1, -1\\n\\t\\tfor _ in range(len(self.maps) + 1):\\n\\t\\t\\tfor stringIndex in range(len(sl)):\\n\\t\\t\\t\\tif stringIndex > iterable:\\n\\t\\t\\t\\t\\tfor mi in range(len(self.maps)):\\n\\t\\t\\t\\t\\t\\tif sl[stringIndex] in self.maps[mi]:\\n\\t\\t\\t\\t\\t\\t\\tplaceholder = sl[stringIndex]\\n\\t\\t\\t\\t\\t\\t\\tsl[stringIndex] = self.maps[mi].get(sl[stringIndex])\\n\\t\\t\\t\\t\\t\\t\\tfor item in li:\\n\\t\\t\\t\\t\\t\\t\\t\\tif item in \\\"\\\".join(sl):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\treturn True\\n\\t\\t\\t\\t\\t\\t\\tsl[stringIndex] = placeholder\\n\\t\\t\\t\\t\\tif mapIndex != -1 and sl[stringIndex] in self.maps[mapIndex]:\\n\\t\\t\\t\\t\\t\\tsl[stringIndex] = self.maps[mapIndex].get(sl[stringIndex])\\n\\t\\t\\t\\t\\titerable += 1\\n\\t\\t\\tif mapIndex != -1 and sl[stringIndex] in self.maps[mapIndex]:\\n\\t\\t\\t\\tsl[stringIndex] = self.maps[mapIndex].get(sl[stringIndex])\\n\\t\\t\\titerable = -1\\n\\t\\t\\tmapIndex += 1\\n\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79709baf55e80d93d4ac3167b359a872\",\n \"score\": \"0.4963088\",\n \"text\": \"def test_cell_size_to_meters(self):\\r\\n self.assertEqual(3.5, AnalysisHelpers.cell_size_to_meters(\\\"3.5 Meters\\\"))\\r\\n self.assertEqual(3500, AnalysisHelpers.cell_size_to_meters(\\\"3,5 Kilometers\\\"))\\r\\n self.assertAlmostEqual(0.9144, AnalysisHelpers.cell_size_to_meters(\\\"3 Feet\\\"), 1)\\r\\n self.assertAlmostEqual(2.7432, AnalysisHelpers.cell_size_to_meters(\\\"3 Yards\\\"), 1)\\r\\n self.assertAlmostEqual(4828.03, AnalysisHelpers.cell_size_to_meters(\\\"3 Miles\\\"), 1)\\r\\n bad_units = \\\"3 BadUnits\\\"\\r\\n with self.assertRaises(ValueError) as ex:\\r\\n AnalysisHelpers.cell_size_to_meters(bad_units)\\r\\n self.assertEqual(\\\"Invalid cell size units: BadUnits\\\", str(ex.exception))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b0aef21c4eecda82718ee6266107c8\",\n \"score\": \"0.49533468\",\n \"text\": \"def test14(self):\\n self.verify(\\\"$*5*[ aStr ]\\\", \\\"blarg\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b47304454cbd9f22237956f7eaeb0e7f\",\n \"score\": \"0.49511278\",\n \"text\": \"def test_uncharge3():\\n assert uncharge_smiles('C[N+](C)(C)CCO') == 'C[N+](C)(C)CCO'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf745b6703be89ded18b599dc7d7df47\",\n \"score\": \"0.4943416\",\n \"text\": \"def isSno(n,check):\\n for i in genArr(str(n)):\\n if sum(map(int,i.split(' '))) == check:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8add9f089102ae324396388bdab18197\",\n \"score\": \"0.492978\",\n \"text\": \"def test_name_validator(self):\\n \\\"\\\"\\\"I think these name will work\\\"\\\"\\\"\\n valid_names = [\\\"AnthonyPietrzak\\\", \\\" AlissaTyreen\\\", \\\"TroyBaker \\\"]\\n for values in valid_names:\\n self.assertTrue(self.my_phone.name_validator(values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977deb3c5e70cea0417f54d8fb400264\",\n \"score\": \"0.49262866\",\n \"text\": \"def testStrInvalidStr2(self):\\n with self.assertRaises(ValueError):\\n sc()(str(\\\"1aaaf42\\\"), actionWarning=aWarning, actionError=aErr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b21d536083dd8ceff43f44ef48b7c3\",\n \"score\": \"0.49235252\",\n \"text\": \"def test17(self):\\n self.verify(\\\"$*5*aStr\\\", \\\"blarg\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b549f13816045876c0d6ac0349c24f41\",\n \"score\": \"0.49182823\",\n \"text\": \"def _OnValidate_iti(self, d, i, P, s, S, v, V, W):\\t\\t\\n\\t\\tif S in '0123456789.':\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tfloat(P)\\n\\t\\t\\t\\treturn (float(P)>=iti_min) and (float(P)<=iti_max)\\n\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ed12264932db56d4f1702ce51f05441\",\n \"score\": \"0.49076775\",\n \"text\": \"def test_sanitize_line(self):\\n for line in ('1 BEC 1', '1 BE CMG1', '1 BEMG 1'):\\n self.assertEqual(core.sanitize_line(line), '1 BECMG 1')\\n for line in ('1 TEMP0 1', '1 TEMP 1', '1 TEMO1', '1 T EMPO1'):\\n self.assertEqual(core.sanitize_line(line), '1 TEMPO 1')\\n self.assertEqual(core.sanitize_line('1 2 3 4 5'), '1 2 3 4 5')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b11aec5a28a2790b82e1d52c95047771\",\n \"score\": \"0.48961592\",\n \"text\": \"def test_validate_ci_string_clean_true(input):\\n assert ciuy.validate_ci(input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5add41b8e808d35ae9b2df8dde26ed2a\",\n \"score\": \"0.48907986\",\n \"text\": \"def _check_surrounded_by_space(self, tokens, i):\\n self._check_space(tokens, i, (_MUST, _MUST))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569645e46acbc15349dc14e3dd58ce2d\",\n \"score\": \"0.4890228\",\n \"text\": \"def test_kg_times_meters_per_second_squared_in_slugs():\\n up = unit_parser()\\n with pytest.raises(ValueError):\\n up.multiply(\\\"2 kilograms\\\", \\\"5 meters_per_second_squared\\\", \\\"slug\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"313ed8908d6093ae954123599c88f39f\",\n \"score\": \"0.48895824\",\n \"text\": \"def _compute_canonical_smiles(smiles: str) -> str:\\n\\n mol = Chem.MolFromSmiles(smiles)\\n return Chem.MolToSmiles(mol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db6b93acd5853e62a89084aaae72f5f\",\n \"score\": \"0.48846138\",\n \"text\": \"def test5(self):\\n self.verify(\\\"${aStr}\\\", \\\"blarg\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6943946bbf001cb069faa7e09286e0a4\",\n \"score\": \"0.48844984\",\n \"text\": \"def test_xyz_to_smiles(self):\\n xyz1 = \\\"\\\"\\\"S -0.06618943 -0.12360663 -0.07631983\\nO -0.79539707 0.86755487 1.02675668\\nO -0.68919931 0.25421823 -1.34830853\\nN 0.01546439 -1.54297548 0.44580391\\nC 1.59721519 0.47861334 0.00711000\\nH 1.94428095 0.40772394 1.03719428\\nH 2.20318015 -0.14715186 -0.64755729\\nH 1.59252246 1.51178950 -0.33908352\\nH -0.87856890 -2.02453514 0.38494433\\nH -1.34135876 1.49608206 0.53295071\\\"\\\"\\\"\\n\\n xyz2 = \\\"\\\"\\\"O 2.64631000 -0.59546000 0.29327900\\nO 2.64275300 2.05718500 -0.72942300\\nC 1.71639100 1.97990400 0.33793200\\nC -3.48200000 1.50082200 0.03091100\\nC -3.85550400 -1.05695100 -0.03598300\\nC 3.23017500 -1.88003900 0.34527100\\nC -2.91846400 0.11144600 0.02829400\\nC 0.76935400 0.80820200 0.23396500\\nC -1.51123800 -0.09830700 0.09199100\\nC 1.28495500 -0.50051800 0.22531700\\nC -0.59550400 0.98573400 0.16444900\\nC -0.94480400 -1.39242500 0.08331900\\nC 0.42608700 -1.59172200 0.14650400\\nH 2.24536500 1.93452800 1.29979800\\nH 1.14735500 2.91082400 0.31665700\\nH -3.24115200 2.03800800 0.95768700\\nH -3.08546100 2.10616100 -0.79369800\\nH -4.56858900 1.48636200 -0.06630800\\nH -4.89652000 -0.73067200 -0.04282300\\nH -3.69325500 -1.65970000 -0.93924100\\nH -3.72742500 -1.73294900 0.81894100\\nH 3.02442400 -2.44854700 -0.56812500\\nH 4.30341500 -1.72127600 0.43646000\\nH 2.87318600 -2.44236600 1.21464900\\nH -0.97434200 2.00182800 0.16800300\\nH -1.58581300 -2.26344700 0.02264400\\nH 0.81122400 -2.60336100 0.13267800\\nH 3.16280800 1.25020800 -0.70346900\\\"\\\"\\\"\\n\\n xyz3 = \\\"\\\"\\\"N 2.24690600 -0.00006500 0.11597700\\nC -1.05654800 1.29155000 -0.02642500\\nC -1.05661400 -1.29150400 -0.02650600\\nC -0.30514100 0.00000200 0.00533200\\nC 1.08358900 -0.00003400 0.06558000\\nH -0.39168300 2.15448600 -0.00132500\\nH -1.67242600 1.35091400 -0.93175000\\nH -1.74185400 1.35367700 0.82742800\\nH -0.39187100 -2.15447800 0.00045500\\nH -1.74341400 -1.35278100 0.82619100\\nH -1.67091600 -1.35164600 -0.93286400\\\"\\\"\\\"\\n\\n xyz4 = \\\"\\\"\\\"C -0.86594600 0.19886100 2.37159000\\nC 0.48486900 -0.16232000 1.75422500\\nC 1.58322700 0.83707500 2.14923200\\nC 0.88213600 -1.51753600 2.17861400\\nN 1.17852900 -2.57013900 2.53313600\\nN 0.51051200 -0.21074800 0.26080100\\nN -0.51042000 0.21074000 -0.26079600\\nC -0.48479200 0.16232300 -1.75422300\\nC 0.86590400 -0.19926100 -2.37161200\\nC -1.58344900 -0.83674100 -2.14921800\\nC -0.88166600 1.51765700 -2.17859800\\nN -1.17777100 2.57034900 -2.53309500\\nH -1.16019200 1.20098300 2.05838400\\nH -1.64220300 -0.50052400 2.05954500\\nH -0.78054100 0.17214100 3.45935000\\nH 1.70120000 0.85267300 3.23368300\\nH 2.53492600 0.56708700 1.69019900\\nH 1.29214500 1.83331400 1.80886700\\nH 1.15987300 -1.20145600 -2.05838100\\nH 0.78046800 -0.17257000 -3.45937100\\nH 1.64236100 0.49992400 -2.05962300\\nH -2.53504500 -0.56650600 -1.69011500\\nH -1.70149200 -0.85224500 -3.23366300\\nH -1.29263300 -1.83308300 -1.80892900\\\"\\\"\\\"\\n\\n xyz5 = \\\"\\\"\\\"O 0.90973400 -0.03064000 -0.09605500\\nO 0.31656600 -0.00477100 -1.21127600\\nO 2.17315400 -0.03069900 -0.09349100\\\"\\\"\\\"\\n\\n xyz6 = \\\"\\\"\\\"S 0.38431300 0.05370100 0.00000000\\nN -1.13260000 0.07859900 0.00000000\\nH 0.85151800 -1.28998600 0.00000000\\\"\\\"\\\"\\n\\n xyz7 = \\\"\\\"\\\"N 0.00000000 0.00000000 0.44654700\\nN 0.00000000 0.00000000 -0.77510900\\nH 0.86709400 0.00000000 1.02859700\\nH -0.86709400 0.00000000 1.02859700\\\"\\\"\\\"\\n\\n xyz8 = \\\"\\\"\\\"N 0.00000000 0.00000000 0.65631400\\nC 0.00000000 0.00000000 -0.50136500\\nH 0.00000000 0.00000000 -1.57173600\\\"\\\"\\\"\\n\\n# xyz9 = \\\"\\\"\\\"S -0.00866000 -0.60254900 0.00000000\\n# N -0.96878800 0.63275900 0.00000000\\n# N 1.01229100 0.58298500 0.00000000\\\"\\\"\\\"\\n#\\n# xyz10 = \\\"\\\"\\\"O -0.79494500 -0.93969200 0.00000000\\n# O -0.32753500 1.24003800 0.00000000\\n# O 1.28811400 -0.24729000 0.00000000\\n# N 0.14143500 0.11571500 0.00000000\\n# H -1.65602000 -0.48026800 0.00000000\\\"\\\"\\\"\\n#\\n# xyz11 = \\\"\\\"\\\"O 1.64973000 -0.57433600 0.02610800\\n# O 0.49836300 1.28744800 -0.18806200\\n# N -0.57621600 -0.65116600 0.24595200\\n# N -1.78357200 -0.10211200 -0.14953800\\n# N 0.61460400 0.08152700 -0.00952700\\n# H -0.42001200 -1.61494900 -0.03311600\\n# H -1.72480300 0.33507600 -1.06884500\\n# H -2.07362100 0.59363400 0.53038600\\\"\\\"\\\"\\n\\n xyz12 = \\\"\\\"\\\"O 1.10621000 0.00000000 -0.13455300\\nO -1.10621000 0.00000000 -0.13455300\\nN 0.00000000 0.00000000 0.33490500\\\"\\\"\\\"\\n\\n# xyz13 = \\\"\\\"\\\"O -0.37723000 -1.27051900 0.00000000\\n# N -0.12115000 -0.04252600 0.00000000\\n# N -0.95339100 0.91468300 0.00000000\\n# C 1.31648000 0.33217600 0.00000000\\n# H 1.76422500 -0.11051900 -0.89038300\\n# H 1.76422500 -0.11051900 0.89038300\\n# H 1.40045900 1.41618100 0.00000000\\n# H -1.88127600 0.47189500 0.00000000\\\"\\\"\\\"\\n\\n xyz14 = \\\"\\\"\\\"S -0.12942800 0.11104800 0.22427200\\nO 0.98591500 -1.00752300 -0.31179100\\nO -1.43956200 -0.44459900 -0.15048900\\nO 0.32982400 1.44755400 -0.21682700\\nH 1.85512700 -0.56879900 -0.36563700\\\"\\\"\\\"\\n\\n xyz15 = \\\"\\\"\\\"N 1.11543700 0.11100500 0.00000000\\nN -0.11982300 -0.03150800 0.00000000\\nN -1.25716400 0.01530300 0.00000000\\nH 1.57747800 -0.80026300 0.00000000\\\"\\\"\\\"\\n\\n xyz16 = \\\"\\\"\\\"O 1.21678000 -0.01490600 0.00000000\\nN 0.04560300 0.35628400 0.00000000\\nC -1.08941100 -0.23907800 0.00000000\\nH -1.97763400 0.37807800 0.00000000\\nH -1.14592100 -1.32640500 0.00000000\\\"\\\"\\\"\\n\\n xyz17 = \\\"\\\"\\\"S 0.00000000 0.00000000 0.18275300\\nO -0.94981300 -0.83167500 -0.84628900\\nO 0.94981300 0.83167500 -0.84628900\\nO 0.80426500 -0.99804200 0.85548500\\nO -0.80426500 0.99804200 0.85548500\\nH -1.67833300 -0.25442300 -1.13658700\\nH 1.67833300 0.25442300 -1.13658700\\\"\\\"\\\"\\n\\n xyz18 = \\\"\\\"\\\"S 0.00000000 0.00000000 0.12264300\\nO 1.45413200 0.00000000 0.12264300\\nO -0.72706600 1.25931500 0.12264300\\nO -0.72706600 -1.25931500 0.12264300\\\"\\\"\\\"\\n\\n xyz19 = \\\"\\\"\\\"N 1.16672400 0.35870400 -0.00000400\\nN -1.16670800 0.35879500 -0.00000400\\nC -0.73775600 -0.89086600 -0.00000100\\nC 0.73767000 -0.89093000 -0.00000100\\nC 0.00005200 1.08477000 -0.00000500\\nH -1.40657400 -1.74401100 0.00000000\\nH 1.40645000 -1.74411900 0.00000000\\nH 0.00009400 2.16788100 -0.00000700\\\"\\\"\\\"\\n\\n xyz20 = \\\"\\\"\\\"C 3.09980400 -0.16068000 0.00000600\\nC 1.73521600 0.45534600 -0.00002200\\nC 0.55924400 -0.24765400 -0.00000300\\nC -0.73300200 0.32890400 -0.00001600\\nC -1.93406200 -0.42115800 0.00001300\\nC -3.19432700 0.11090700 0.00000900\\nH 3.67991400 0.15199400 -0.87914100\\nH 3.67984100 0.15191400 0.87923000\\nH 3.04908000 -1.25419800 -0.00004300\\nH 1.68713300 1.54476700 -0.00005100\\nH -0.81003200 1.41627100 -0.00004600\\nH -1.83479400 -1.50747300 0.00004100\\nH 0.61489300 -1.33808300 0.00002500\\nH -3.35410300 1.18597200 -0.00001700\\nH -4.07566100 -0.52115800 0.00003300\\\"\\\"\\\"\\n\\n mol1 = converter.molecules_from_xyz(converter.str_to_xyz(xyz1))[1]\\n mol2 = converter.molecules_from_xyz(converter.str_to_xyz(xyz2))[1]\\n mol3 = converter.molecules_from_xyz(converter.str_to_xyz(xyz3))[1]\\n mol4 = converter.molecules_from_xyz(converter.str_to_xyz(xyz4))[1]\\n mol5 = converter.molecules_from_xyz(converter.str_to_xyz(xyz5))[1]\\n mol6 = converter.molecules_from_xyz(converter.str_to_xyz(xyz6), multiplicity=1)[1]\\n mol7 = converter.molecules_from_xyz(converter.str_to_xyz(xyz7), multiplicity=1)[1]\\n mol8 = converter.molecules_from_xyz(converter.str_to_xyz(xyz8))[1]\\n # mol9 = converter.molecules_from_xyz(converter.str_to_xyz(xyz9), multiplicity=1)[1]\\n # mol10 = converter.molecules_from_xyz(converter.str_to_xyz(xyz10))[1]\\n # mol11 = converter.molecules_from_xyz(converter.str_to_xyz(xyz11))[1]\\n mol12 = converter.molecules_from_xyz(converter.str_to_xyz(xyz12))[1]\\n # mol13 = converter.molecules_from_xyz(converter.str_to_xyz(xyz13))[1]\\n mol14 = converter.molecules_from_xyz(converter.str_to_xyz(xyz14))[1]\\n mol15 = converter.molecules_from_xyz(converter.str_to_xyz(xyz15))[1]\\n mol16 = converter.molecules_from_xyz(converter.str_to_xyz(xyz16))[1]\\n mol17 = converter.molecules_from_xyz(converter.str_to_xyz(xyz17))[1]\\n mol18 = converter.molecules_from_xyz(converter.str_to_xyz(xyz18))[1]\\n mol19 = converter.molecules_from_xyz(converter.str_to_xyz(xyz19))[1]\\n mol20 = converter.molecules_from_xyz(converter.str_to_xyz(xyz20))[1]\\n\\n self.assertEqual(mol1.to_smiles(), '[NH-][S+](=O)(O)C')\\n self.assertIn(mol2.to_smiles(), ['COC1=C(CO)C=C([C](C)C)C=C1', 'COC1C=CC(=CC=1CO)[C](C)C'])\\n self.assertEqual(mol3.to_smiles(), '[N]=C=C(C)C')\\n self.assertEqual(mol4.to_smiles(), 'N#CC(N=NC(C#N)(C)C)(C)C')\\n self.assertEqual(mol5.to_smiles(), '[O-][O+]=O')\\n self.assertEqual(mol6.to_smiles(), 'N#S')\\n self.assertEqual(mol7.to_smiles(), '[N-]=[NH2+]')\\n self.assertEqual(mol8.to_smiles(), 'C#N')\\n # self.assertEqual(mol9.to_smiles(), '[N-]=[S+]#N') # gives [N]S#N, multiplicity 3\\n # self.assertEqual(mol10.to_smiles(), '[N+](=O)(O)[O-]') # gives None\\n # self.assertEqual(mol11.to_smiles(), 'N(N)[N+](=O)[O-]') # gives None\\n self.assertEqual(mol12.to_smiles(), '[O]N=O')\\n # self.assertEqual(mol13.to_smiles(), 'C[N+]([NH-])=O') # gives None\\n self.assertEqual(mol14.to_smiles(), 'OS(=O)[O]')\\n self.assertEqual(mol15.to_smiles(), '[N-]=[N+]=N')\\n self.assertEqual(mol16.to_smiles(), '[O]N=C')\\n self.assertEqual(mol17.to_smiles(), 'OS(=O)(=O)O')\\n self.assertEqual(mol18.to_smiles(), 'O=S(=O)=O')\\n self.assertEqual(mol19.to_adjacency_list(), \\\"\\\"\\\"multiplicity 2\\n1 N u1 p1 c0 {4,S} {5,S}\\n2 N u0 p1 c0 {3,S} {5,D}\\n3 C u0 p0 c0 {2,S} {4,D} {6,S}\\n4 C u0 p0 c0 {1,S} {3,D} {7,S}\\n5 C u0 p0 c0 {1,S} {2,D} {8,S}\\n6 H u0 p0 c0 {3,S}\\n7 H u0 p0 c0 {4,S}\\n8 H u0 p0 c0 {5,S}\\n\\\"\\\"\\\") # cannot read SMILES 'c1ncc[n]1' (but can generate them)\\n self.assertEqual(mol20.to_smiles(), 'C=C[CH]C=CC')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec09a3cf60bb8f7abf05b366fed6d9c\",\n \"score\": \"0.4883265\",\n \"text\": \"def validateStringInput(input_key,input_data, read=False):\\n log = clog.error_log\\n func = None\\n if '*' in input_data or '%' in input_data:\\n func = validationFunctionWildcard.get(input_key)\\n if func is None:\\n func = searchstr\\n elif input_key == 'migration_input' :\\n if input_data.find('#') != -1 : func = block\\n else : func = dataset\\n else:\\n if not read:\\n func = validationFunction.get(input_key)\\n if func is None:\\n func = namestr\\n else:\\n if input_key == 'dataset':\\n func = reading_dataset_check\\n elif input_key == 'block_name':\\n func = reading_block_check\\n elif input_key == 'logical_file_name':\\n func = reading_lfn_check\\n else:\\n func = namestr\\n try:\\n func(input_data)\\n except AssertionError as ae:\\n serverLog = str(ae) + \\\" key-value pair (%s, %s) cannot pass input checking\\\" %(input_key, input_data)\\n #print serverLog\\n dbsExceptionHandler(\\\"dbsException-invalid-input2\\\", message=\\\"Invalid Input Data %s...: Not Match Required Format\\\" %input_data[:10], \\\\\\n logger=log.error, serverError=serverLog)\\n return input_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f364d26d22b039b9bdc5611e525a421e\",\n \"score\": \"0.48775777\",\n \"text\": \"def validate(self, newtext):\\n print('validate={}'.format(newtext))\\n self.calc_gross_net()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eff032ed05d886cded6966a22d006e5\",\n \"score\": \"0.48729396\",\n \"text\": \"def test_args_valid_types_str(self):\\n Base.reset()\\n r1 = Square(10, 10, 10)\\n s = \\\"string\\\"\\n msg = \\\" must be an integer\\\"\\n err = TypeError\\n try:\\n r1.update(21, s)\\n except err as e:\\n self.assertEqual((str(e)), \\\"width\\\" + msg)\\n try:\\n r1.update(21, 32, s)\\n except err as e:\\n self.assertEqual((str(e)), \\\"x\\\" + msg)\\n try:\\n r1.update(43, 43, 32, s)\\n except err as e:\\n self.assertEqual((str(e)), \\\"y\\\" + msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3933dea6fc90d8170e4f3fabf526d4\",\n \"score\": \"0.4869997\",\n \"text\": \"def is_valid(self, string):\\n\\t\\tself.possible_states = self.e_closure([self.start_state])\\n\\t\\tfor char in string:\\n\\t\\t\\tif char not in self.alphabet:\\n\\t\\t\\t\\treturn False\\n\\t\\t\\tself.move(char)\\n\\t\\treturn any(self.possible_states)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d648843e3e148647803c24ec291fd5c\",\n \"score\": \"0.48519418\",\n \"text\": \"def filter_invalid_smiles(data: MoleculeDataset) -> MoleculeDataset:\\n datapoint_list = []\\n for idx, datapoint in enumerate(data):\\n if datapoint.smiles == \\\"\\\":\\n print(f\\\"invalid smiles {idx}: {datapoint.smiles}\\\")\\n continue\\n mol = Chem.MolFromSmiles(datapoint.smiles)\\n if mol.GetNumHeavyAtoms() == 0:\\n print(f\\\"invalid heavy {idx}\\\")\\n continue\\n datapoint_list.append(datapoint)\\n return MoleculeDataset(datapoint_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26cd455fee6195a8a7ff49515ac1d478\",\n \"score\": \"0.48450083\",\n \"text\": \"def validate_all(self, object, name, value):\\n try:\\n value = strx(value)\\n if (self.minlen <= len(value) <= self.maxlen) and (\\n self.match(value) is not None\\n ):\\n return value\\n except:\\n pass\\n\\n self.error(object, name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ff6b6eb0f6d28c626df63d5ac201b94\",\n \"score\": \"0.4838273\",\n \"text\": \"def valid_sentence(self, sentence ):\\n\\tif not sentence: return False\\n\\tif sentence[0:6] == \\\"$GPGGA\\\":\\n\\t dbg(\\\"Found a GP G G A sentence\\\")\\n\\t try:\\n\\t\\tfields = sentence.split(\\\",\\\")\\n\\t\\tif fields[6] == '1': \\n\\t\\t self.haveFix = 'valid'\\n\\t\\t self.lat, self.lon = (float( fields[2] ), float( fields[4] ))\\n\\t\\t return 'GPGAA'\\n\\t except:\\n\\t\\tdbg(\\\"Problem splitting up GPGAA record\\\")\\n\\t return False\\n\\tif sentence[0:6] == \\\"$GPRMC\\\":\\n\\t dbg(\\\"Found a GP R M C sentence\\\")\\n\\t try:\\n\\t\\t(GPRMC,ut,av,lat,ns,lon,ew,speed,course,utc,mag,magns,check)=sentence.split(\\\",\\\")\\n\\t\\tdbg('split the GPRMC fields sucessful',True)\\n\\t\\tself.lat, self.lon = (float(lat), float(lon) )\\n\\t\\tif av == 'A': \\n\\t\\t self.haveFix = 'valid'\\n\\t\\t if speed and course:\\n\\t\\t\\tself.speed, self.course = (float(speed)*1.150779,float(course))\\n\\t\\t return 'GPRMC'\\n\\t\\telse:\\n\\t\\t self.haveFix = 'invalid'\\n\\t except:\\n\\t\\tdbg(\\\"Problem splitting up GPRMC record\\\")\\n\\t return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0518c7e38b3f0c388b3fd2d11cd751b\",\n \"score\": \"0.48350266\",\n \"text\": \"def checkSquishingFunctionsArg(self, arg):\\n # list of function associated to each layer\\n nb_layer = len(self.neural_network)\\n if arg == \\\"Sigmoid\\\":\\n self.squishing_funcs = [(Sigmoid, InvSigmoid, DerSigmoid)] \\\\\\n * (nb_layer-1)\\n self.squishing_funcs_str = \\\"Sigmoid\\\"\\n elif arg == \\\"ReEU\\\":\\n self.squishing_funcs = [(ReEU, InvReEU, DerReEU)] * (nb_layer-1)\\n self.squishing_funcs_str = \\\"ReEU\\\"\\n elif arg == \\\"ReLU\\\":\\n self.squishing_funcs = [(ReLU, InvReLU, DerReLU)] * (nb_layer-2)\\n # BEWARE : end with a function that squishes the number in [0, 1]\\n self.squishing_funcs.append((ReEU, InvReEU, DerReEU))\\n self.squishing_funcs_str = \\\"ReLU\\\"\\n else:\\n print(\\\"The given squishing function\\\", arg, \\\"doesn't correspond to\\\"\\n \\\" any possible function :\\\", POSSIBLE_SQUISHING_FUNC)\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861f5ebcc8d901f1e7105a3f8063046b\",\n \"score\": \"0.48337695\",\n \"text\": \"def test_uncharge4():\\n assert uncharge_smiles('C[NH+](C)CCO') == 'CN(C)CCO'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd2d2344a9a65dffe16d015ed9f629d2\",\n \"score\": \"0.48174107\",\n \"text\": \"def test_uncharge7():\\n assert uncharge_smiles('[NH3+]C(Cc1cnc[nH]1)C(=O)[O-]') == 'NC(Cc1cnc[nH]1)C(=O)O'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f9a1c3bc58a40e61b41ed39bf471325\",\n \"score\": \"0.4816726\",\n \"text\": \"def _validate(self):\\n # TODO: create phrase validation\\n # 1. removes all substitutions\\n # 2. checks pronouncable symbol sets\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152f451b18fcb2624a65b65809fe1864\",\n \"score\": \"0.48117536\",\n \"text\": \"def test_uncharge14():\\n assert uncharge_smiles('[O-]C(=O)C[n+]1ccn2cccc([O-])c12') == 'O=C([O-])C[n+]1ccn2cccc(O)c21'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d54453f45cb196680870c1d7ee335ae\",\n \"score\": \"0.4811299\",\n \"text\": \"def test_invalid_str_error(self):\\n values = ['not_true_or_false', 'A', '1', '0',\\n 'false_is_not_true', 'true_is_not_false']\\n for value in values:\\n with self.assertRaises(RuntimeError):\\n str_to_bool(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c79d67155c78c6e724b8fc91a956df05\",\n \"score\": \"0.48063695\",\n \"text\": \"def test_forced_charge2():\\n # GitHub Issue #15\\n assert standardize_smiles('[Na].[Na]') == '[Na+].[Na+]'\\n # TODO: Arguably should become selenite ion... O=[Se]([O-])[O-]. Need an AcidBasePair?\\n assert standardize_smiles('[Na].[Na].O[Se](O)=O') == 'O=[Se](O)O.[Na+].[Na+]'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b504236e8f885f8215178028d72b302\",\n \"score\": \"0.48045254\",\n \"text\": \"def minion_game(string):\\r\\n s = string.lower()\\r\\n vow = 'aeiou'\\r\\n stuart_score = 0\\r\\n kevin_score = 0\\r\\n\\r\\n for index, character in enumerate(s):\\r\\n if character in vow:\\r\\n kevin_score += len(string) - index\\r\\n else:\\r\\n stuart_score += len(string) - index\\r\\n\\r\\n if stuart_score > kevin_score:\\r\\n print(f'Stuart {stuart_score}')\\r\\n elif stuart_score < kevin_score:\\r\\n print(f'Kevin {kevin_score}')\\r\\n else:\\r\\n print('Draw')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6a184fda38b3617aabbb80109a839f3\",\n \"score\": \"0.480397\",\n \"text\": \"def test_phone_number_failure(self):\\n \\\"\\\"\\\"I think these will not work\\\"\\\"\\\"\\n invalid_phones = [\\\"5862462296#$\\\", \\\" 576@#475657\\\", \\\"5679830 \\\",\\\" \\\",\\\" 6985\\\",\\\"049587%^&\\\"]\\n for values in invalid_phones:\\n self.assertFalse(self.my_phone.phone_validator(values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ba8d721dcb3396c5e1dda34cbc1b090\",\n \"score\": \"0.47929287\",\n \"text\": \"def test_code_checking(self):\\r\\n # Try valid codes.\\r\\n for code in TelephoneNumber.valid_codes: \\r\\n self.assertTrue(TelephoneNumber(code + ' 123456').has_valid_code())\\r\\n \\r\\n self.assertTrue(TelephoneNumber('0274123456').has_valid_code())\\r\\n\\r\\n # Try invalid codes.\\r\\n self.assertFalse(TelephoneNumber('05 123 4567').has_valid_code())\\r\\n self.assertFalse(TelephoneNumber('0274 123 456').has_valid_code())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0405a3401fc1453a17633e195d7488ed\",\n \"score\": \"0.47909024\",\n \"text\": \"def validate_value(value):\\n if isinstance(value, basestring):\\n string_validators = [is_valid_string]\\n for validator in string_validators:\\n value = validator(value)\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09508297750fce258bb8efa87be353a2\",\n \"score\": \"0.4790869\",\n \"text\": \"def test11(self):\\n\\n for badSyntax in (\\\"#if $*5*emptyString\\\\n$aStr\\\\n#end if\\\\n\\\",\\n \\\"#if ${emptyString}\\\\n$aStr\\\\n#end if\\\\n\\\",\\n \\\"#if $(emptyString)\\\\n$aStr\\\\n#end if\\\\n\\\",\\n \\\"#if $[emptyString]\\\\n$aStr\\\\n#end if\\\\n\\\",\\n \\\"#if $!emptyString\\\\n$aStr\\\\n#end if\\\\n\\\",\\n ):\\n try:\\n self.verify(badSyntax, \\\"\\\")\\n except ParseError:\\n pass\\n else:\\n self.fail('This should barf')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c01451c50b79cf9eb3cc52ceab61dff\",\n \"score\": \"0.47899103\",\n \"text\": \"def _validate_memory_string(self, memory_string):\\n if re.match(r\\\"^[0-9]+(E|Ei|P|Pi|T|Ti|G|Gi|M|Mi|K|Ki){0,1}$\\\",\\n memory_string) is None:\\n raise ValueError(\\\"Invalid memory string. Should be an integer, \\\" +\\n \\\"or integer followed by one of \\\" +\\n '\\\"E|Ei|P|Pi|T|Ti|G|Gi|M|Mi|K|Ki\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef01f364e7f2ed26411dad468ac6a30\",\n \"score\": \"0.4789367\",\n \"text\": \"def validate_shot_code(self):\\n import re\\n\\n regex = re.compile(r\\\"([\\\\w]+)_(\\\\d{3})_(\\\\d{3}[A-Z]{0,1})_(\\\\d{4})\\\")\\n\\n shot_code = self.shot_code\\n match = regex.match(shot_code)\\n if not match:\\n raise ValueError(\\\"Shot code format is not valid: %s\\\" % shot_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4477b49ed8cfbb7865bc89f0a6c484a\",\n \"score\": \"0.4784115\",\n \"text\": \"def check_validation(text: str):\\n holder = text.split()\\n indexes = holder[0].split('-')\\n fx = int(indexes[0])\\n sx = int(indexes[1])\\n ch = holder[1][0]\\n pw = holder[2]\\n\\n if (pw[fx-1]==ch) ^ (pw[sx-1]==ch):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9759a19d3a8c0d71b28ee7ebc8c6e4c\",\n \"score\": \"0.47808066\",\n \"text\": \"def test_type_validator_success(self):\\n \\\"\\\"\\\"I think these will work\\\"\\\"\\\"\\n valid_type = [\\\"office\\\", \\\" cell\\\", \\\"home \\\"]\\n for values in valid_type:\\n self.assertTrue(self.my_phone.type_validator(values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4130522894687d745e06b85a39e2e9a\",\n \"score\": \"0.4780536\",\n \"text\": \"def is_valid_move(board, word, candidate, tilebag):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca82e7543e2f74363a2d95f792030693\",\n \"score\": \"0.47728872\",\n \"text\": \"def test_validating_films_on_a_per_character_basis():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c324bb87f492244416c91af1b05ced\",\n \"score\": \"0.47666553\",\n \"text\": \"def smiles_from_seq(seq):\\r\\n gs, bs, terminal, capping = split_seq_components(seq)\\r\\n # modifies the Cterminal\\r\\n if terminal:\\r\\n molecule = rdmolfiles.MolFromSmiles(T_SMILES[terminal[0]])\\r\\n else:\\r\\n molecule = ''\\r\\n\\r\\n # creates the dendrimer structure\\r\\n for gen in gs:\\r\\n for aa in gen:\\r\\n if aa == '-':\\r\\n metbond = True\\r\\n continue\\r\\n if molecule == '':\\r\\n molecule = rdmolfiles.MolFromSmiles(AA_SMILES[aa])\\r\\n else:\\r\\n molecule = connect_mol(molecule, rdmolfiles.MolFromSmiles(AA_SMILES[aa]))\\r\\n\\r\\n if bs:\\r\\n if bs[0] == '-':\\r\\n metbond = True\\r\\n bs.pop(0)\\r\\n if molecule == '':\\r\\n molecule = rdmolfiles.MolFromSmiles(B_SMILES[bs[0]])\\r\\n else:\\r\\n molecule = connect_mol(molecule, rdmolfiles.MolFromSmiles(B_SMILES[bs[0]]))\\r\\n bs.pop(0)\\r\\n\\r\\n # adds capping to the N-terminal (the called clip function is different, cause the listed smiles\\r\\n # for the capping are already without OH, it is not necessary removing any atom after foming the new bond)\\r\\n if capping and molecule != '':\\r\\n molecule = attach_capping(molecule, rdmolfiles.MolFromSmiles(C_SMILES[capping[0]]))\\r\\n # clean the smile from all the tags\\r\\n for atom in molecule.GetAtoms():\\r\\n atom.SetAtomMapNum(0)\\r\\n\\r\\n molecule_smile = rdmolfiles.MolToSmiles(molecule, isomericSmiles=True).replace('[N]', 'N').replace('[C]', 'C')\\r\\n return molecule_smile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3e7b997d8281f01c1f28f6eb77bd445\",\n \"score\": \"0.47647664\",\n \"text\": \"def test_validate_ci_string_dirty_true(input):\\n assert ciuy.validate_ci(input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f776be0d20e9dc292a5abb0d881a0d\",\n \"score\": \"0.47628546\",\n \"text\": \"def test13(self):\\n self.verify(\\\"$*5*{ aStr }\\\", \\\"blarg\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a513b57114a9fa2143aa94036c861795\",\n \"score\": \"0.47498873\",\n \"text\": \"def evalUnits(unitStr):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"762eec545c7ce262d0e9c7ec0cb62f8f\",\n \"score\": \"0.47475383\",\n \"text\": \"def process_message(text):\\n regex = r'^([0-9]+)[*](tank|dps|heal|bench)$'\\n message = ''\\n for line in text.splitlines():\\n matches = re.search(regex, line, re.IGNORECASE)\\n if matches:\\n if len(matches.groups()) != 2:\\n message += 'malformed input\\\\n'\\n else:\\n amount = int(matches.group(1))\\n role = matches.group(2)\\n message += f'{role}: \\\\n' * amount\\n roster.extend([(role, '') for i in range(amount)])\\n else:\\n message += line+'\\\\n'\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a87329c24dc245e51aed98aaa7469052\",\n \"score\": \"0.47442102\",\n \"text\": \"def __init__(self, string):\\n size = math.sqrt(len(string))\\n if not size.is_integer():\\n raise RuntimeError(f'The specified board has length {len(string)} and does not seem to be square')\\n self.data = [0 if x == '.' else int(x) for x in string]\\n self.size_ = int(size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11febd95c86611436db289ba63a52d1f\",\n \"score\": \"0.47435883\",\n \"text\": \"def test_string_rule():\\n\\ts1 = '\\\"Hello world! My name is matthew\\\"' # normal doule quote\\n\\ts2 = \\\"'Hello world! My name is matthew'\\\" # normal single quote\\n\\ts3 = '\\\"Hello world! \\\" Hello Dog!\\\"' # bad quote in middle\\n\\ts4 = '\\\"Hello world! \\\\\\\" Hello DOg!\\\"' # okay quote in middle\\n\\ts5 = '\\\"Hello World! Holl Dog!\\\\\\\\\\\"' # bad escape\\n\\ts6 = 'r\\\"hello world\\\"' # okay prefix\\n\\ts7 = 'q\\\"Hello world\\\"' # bad prefix\\n\\t\\n\\truleS = StringRule('(r|u|R|U|b|B|(br)|(Br)|(BR)|(rb)|(rB)|(Rb)|(RB))?'+\\\\\\n\\t\\t\\t\\t '[\\\\'\\\"].*',PyTokenType.string)\\n\\tprint(ruleS.could_match(s1),True)\\n\\tprint(ruleS.could_match(s2),True)\\n\\tprint(ruleS.could_match(s3),False)\\n\\tprint(ruleS.could_match(s4),True)\\n\\tprint(ruleS.could_match(s5),False)\\n\\tprint(ruleS.could_match(s6),True)\\n\\tprint(ruleS.could_match(s7),False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa40d5688ff41567944a986c3d52b425\",\n \"score\": \"0.47433335\",\n \"text\": \"def test_bespoke_smirks(mol, smirks_type, n_expected, expected_lambda, central_bond):\\n gen = SMIRKSGenerator(target_smirks=[smirks_type])\\n\\n if isinstance(mol, str):\\n mol = Molecule.from_smiles(mol)\\n\\n generated_parameters = gen.generate_smirks_from_molecule(\\n molecule=mol, central_bond=central_bond\\n )\\n assert len(generated_parameters) == n_expected\\n\\n all_matches = [\\n match\\n for parameter in generated_parameters\\n for match in condense_matches(\\n mol.chemical_environment_matches(parameter.smirks)\\n )\\n ]\\n\\n assert len({*all_matches}) == len(all_matches)\\n assert compare_matches({*all_matches}, expected_lambda(mol))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecceb9e8c2638a66c1265e6185d67a09\",\n \"score\": \"0.47411263\",\n \"text\": \"def test_for_str_type_error(self):\\n self.assertEqual(primesieve('random str'), 'TypeError')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992986,"cells":{"query_id":{"kind":"string","value":"6469ce076bc38cc2b48241c249df8846"},"query":{"kind":"string","value":"itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass_cast(itkLightObject obj) > itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass"},"positive_passages":{"kind":"list like","value":[{"docid":"6a94c199fbfb62acbe989871883c5582","score":"0.87611955","text":"def itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass_cast(*args):\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass_cast(*args)","title":""}],"string":"[\n {\n \"docid\": \"6a94c199fbfb62acbe989871883c5582\",\n \"score\": \"0.87611955\",\n \"text\": \"def itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass_cast(*args):\\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"39ecbda0f71ab287d0ba2da17f76c271","score":"0.86661303","text":"def itkSigmoidImageFilterIF3IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIF3IF3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIF3IF3_Superclass_cast(obj)","title":""},{"docid":"5dde03410acb2ab13d6480b182e15d69","score":"0.8608069","text":"def itkSquaredDifferenceImageFilterIUL3IUL3IUL3_Superclass_cast(*args):\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIUL3IUL3IUL3_Superclass_cast(*args)","title":""},{"docid":"a78d37d371c2bbcbf79c7690dda70dd3","score":"0.8601987","text":"def itkSquaredDifferenceImageFilterID3ID3ID3_Superclass_cast(*args):\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterID3ID3ID3_Superclass_cast(*args)","title":""},{"docid":"097b9a897a80f76a125ca8338344a45b","score":"0.8560069","text":"def itkSigmoidImageFilterISS3ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterISS3ISS3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterISS3ISS3_Superclass_cast(obj)","title":""},{"docid":"136382e2a80c005ad21e1c6e5f447ce6","score":"0.85251564","text":"def itkSigmoidImageFilterIUC3IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIUC3IUC3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUC3IUC3_Superclass_cast(obj)","title":""},{"docid":"58d768f181e822345111198301ee958d","score":"0.85137403","text":"def itkSigmoidImageFilterIUS3IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIUS3IUS3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS3IUS3_Superclass_cast(obj)","title":""},{"docid":"44686471db432bffa5ffa5abebe9c4ca","score":"0.8508351","text":"def itkVectorMagnitudeImageFilterICVF23IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF23IF3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IF3_Superclass_cast(obj)","title":""},{"docid":"b1de94b447211074b2e3142d472d0b7c","score":"0.8502601","text":"def itkVectorMagnitudeImageFilterICVF23ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF23ISS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23ISS3_Superclass_cast(obj)","title":""},{"docid":"f1f738b6186cfce5e96f63ec6f15b029","score":"0.84599555","text":"def itkVectorMagnitudeImageFilterICVF33ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF33ISS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33ISS3_Superclass_cast(obj)","title":""},{"docid":"e3ce4a3e1481420b998ddeca30099e29","score":"0.84575987","text":"def itkVectorMagnitudeImageFilterICVF23IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF23IUS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IUS3_Superclass_cast(obj)","title":""},{"docid":"3275a393482be7301dd7322a90b1dd7b","score":"0.8457163","text":"def itkSquaredDifferenceImageFilterIUC3IUC3IUC3_Superclass_cast(*args):\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIUC3IUC3IUC3_Superclass_cast(*args)","title":""},{"docid":"366db96eace3f1c72f5f0225e81651d4","score":"0.841865","text":"def itkVectorMagnitudeImageFilterICVF43ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF43ISS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43ISS3_Superclass_cast(obj)","title":""},{"docid":"cd8f89227a203d930c366c4956e67912","score":"0.8418057","text":"def itkVectorMagnitudeImageFilterICVF33IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF33IF3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IF3_Superclass_cast(obj)","title":""},{"docid":"6fd5c6a8b8f3db76c2cde8f2b4478ddf","score":"0.83943975","text":"def itkVectorMagnitudeImageFilterICVF23IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF23IUC3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IUC3_Superclass_cast(obj)","title":""},{"docid":"33912adcfb5cfe028cc1eda3c3c73204","score":"0.83859414","text":"def itkVectorMagnitudeImageFilterICVF43IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF43IF3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IF3_Superclass_cast(obj)","title":""},{"docid":"eda84b8039dd389040e84ac229ee299c","score":"0.8382607","text":"def itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass_cast(obj)","title":""},{"docid":"068a69c44e2694cd3628e2f4f039f65d","score":"0.8380838","text":"def itkVectorMagnitudeImageFilterICVF33IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF33IUS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IUS3_Superclass_cast(obj)","title":""},{"docid":"3009cc38266f607e0ec637c1283d60d7","score":"0.8377044","text":"def itkVectorMagnitudeImageFilterICVF43IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF43IUS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IUS3_Superclass_cast(obj)","title":""},{"docid":"d4be0cd61540f607bd765282d3c2ae27","score":"0.83715445","text":"def itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass_cast(obj)","title":""},{"docid":"a14081f49932280bf8abb019436d4ad3","score":"0.83521384","text":"def itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass_cast(obj)","title":""},{"docid":"cf1283dc8ee49f10578ef45eca3c531f","score":"0.8349974","text":"def itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass_cast(obj)","title":""},{"docid":"6a863d6f8eb49bf72ed9766febf08418","score":"0.83484143","text":"def itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass_cast(obj)","title":""},{"docid":"d27bfb944de051c060677470235c3052","score":"0.83388984","text":"def itkVectorMagnitudeImageFilterICVF43IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF43IUC3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IUC3_Superclass_cast(obj)","title":""},{"docid":"a506d0661bc94cca3c62acc632ed23cf","score":"0.83383346","text":"def itkVectorMagnitudeImageFilterICVF33IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF33IUC3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IUC3_Superclass_cast(obj)","title":""},{"docid":"3a555b8ead408b316adab3dc720fc48a","score":"0.8330667","text":"def cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIF3IF3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIF3IF3_Superclass_cast(obj)","title":""},{"docid":"4d711e075fab53aa2bed57a37082a302","score":"0.8329492","text":"def itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass_cast(obj)","title":""},{"docid":"08f655eee5c93df240f2aca7aa3c21c5","score":"0.83148664","text":"def itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass_cast(obj)","title":""},{"docid":"83c788ff779359a50bdf9c999a318c02","score":"0.83132774","text":"def itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass_cast(obj)","title":""},{"docid":"8f5ef32a1c32501f459329058f2290d3","score":"0.8308878","text":"def itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass_cast(obj)","title":""},{"docid":"7aeaeced2be5fc682f877d29096247ef","score":"0.82958156","text":"def itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass_cast(obj)","title":""},{"docid":"275da1eba7b44644c96bc6c5db53927b","score":"0.8269206","text":"def itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIF3IF3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(obj)","title":""},{"docid":"4d0cb24a3e9613503605e2e9dc7c8626","score":"0.8257514","text":"def itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass_cast(obj)","title":""},{"docid":"1dad2b862a7cebab8e0fad8169c2f27f","score":"0.8253241","text":"def itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass_cast(obj)","title":""},{"docid":"8dccbdc57f401d2e287b765a1fab0223","score":"0.8251636","text":"def cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterISS3ISS3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterISS3ISS3_Superclass_cast(obj)","title":""},{"docid":"01dcd82cd3cb8b89e7e8ad2135e3f060","score":"0.82498026","text":"def itkIntensityWindowingImageFilterIF3ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIF3ISS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ISS3_Superclass_cast(obj)","title":""},{"docid":"d13f1382b1d878a4451e7d8f37d797f7","score":"0.82468706","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass_cast(obj)","title":""},{"docid":"3c61bf1f9af7cd98865986ee9944acfd","score":"0.82458246","text":"def cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIUC3IUC3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUC3IUC3_Superclass_cast(obj)","title":""},{"docid":"647aa8a4389498174c245fd0dfc9166b","score":"0.82455754","text":"def itkIntensityWindowingImageFilterISS3IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterISS3IF3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IF3_Superclass_cast(obj)","title":""},{"docid":"8ebc701b16cabfceb65fb8f77d3a89d8","score":"0.8245034","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF33ISS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33ISS3_Superclass_cast(obj)","title":""},{"docid":"91502f56d7b76be4c022398b1fa7cb6b","score":"0.8237269","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF23ISS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23ISS3_Superclass_cast(obj)","title":""},{"docid":"62c063747eec26ff55b8b69aeac07953","score":"0.8229421","text":"def itkSigmoidImageFilterIF2IF2_Superclass_cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIF2IF2_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIF2IF2_Superclass_cast(obj)","title":""},{"docid":"534182e8a9eb95d37e8e013b43ce9d3d","score":"0.8226537","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIF3IF3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(obj)","title":""},{"docid":"2052a8762146a4ce78bfe95f3447fb2e","score":"0.82245517","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterISS3IF3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IF3_Superclass_cast(obj)","title":""},{"docid":"c8e59bc934994fa35ce03f27e3650553","score":"0.8224325","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass_cast(obj)","title":""},{"docid":"384f19636518a051a776455e6e8f9d74","score":"0.82242167","text":"def itkSquaredDifferenceImageFilterIUS3IUS3IUS3_Superclass_cast(*args):\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIUS3IUS3IUS3_Superclass_cast(*args)","title":""},{"docid":"9b9c752aff33a433db22370280739ffc","score":"0.82234114","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass_cast(obj)","title":""},{"docid":"238b714d9cc96b786d5149c4c1487df4","score":"0.82214767","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF23IF3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IF3_Superclass_cast(obj)","title":""},{"docid":"152a5591ec0fd1709f3ca924298be998","score":"0.82202965","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass_cast(obj)","title":""},{"docid":"aa1d9b4b4f5abb6227605c5211479cea","score":"0.8219701","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass_cast(obj)","title":""},{"docid":"68d9e3261f2217d802ad66accc6b8b55","score":"0.8215747","text":"def itkSigmoidImageFilterIUS2IUS2_Superclass_cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIUS2IUS2_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS2IUS2_Superclass_cast(obj)","title":""},{"docid":"020d12bf63f524b253648cc84cabc779","score":"0.82150745","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass_cast(obj)","title":""},{"docid":"fa2d02d41b5b7ad902b82d9139a87bf3","score":"0.8214298","text":"def itkIntensityWindowingImageFilterISS3ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterISS3ISS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3ISS3_Superclass_cast(obj)","title":""},{"docid":"67e940df5911c595f5e228738f057451","score":"0.82126564","text":"def itkIntensityWindowingImageFilterISS3IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterISS3IUS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IUS3_Superclass_cast(obj)","title":""},{"docid":"79682c583e6cf8093e1df0029cede62b","score":"0.82106787","text":"def itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUS3IF3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(obj)","title":""},{"docid":"70da1c5a9375cb69f16267f7b7b938e4","score":"0.8210286","text":"def itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass_cast(obj)","title":""},{"docid":"4c0c8b839f24275f394ebdb6c4d61b37","score":"0.8209101","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF33IF3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IF3_Superclass_cast(obj)","title":""},{"docid":"cf26a52f48f2e51b442f69a71ab7e587","score":"0.8207528","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass_cast(obj)","title":""},{"docid":"45dfab7ec05148afc41f3b3bff2eed60","score":"0.820622","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF33IUC3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IUC3_Superclass_cast(obj)","title":""},{"docid":"f89ee200073628423fb776df715ee7c3","score":"0.8205108","text":"def itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIF3IUS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(obj)","title":""},{"docid":"642488bcd649951525f85d3c49096874","score":"0.8204365","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF43ISS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43ISS3_Superclass_cast(obj)","title":""},{"docid":"b4c1843637870066fbf6fba2814489f5","score":"0.82042193","text":"def itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass_cast(obj)","title":""},{"docid":"725b7560be4e23b8819ecdb71104f082","score":"0.82030606","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF23IUC3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IUC3_Superclass_cast(obj)","title":""},{"docid":"5ac9be374c48f431be9b5313a0512839","score":"0.8201773","text":"def itkIntensityWindowingImageFilterIUS3ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUS3ISS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3ISS3_Superclass_cast(obj)","title":""},{"docid":"59653702ab524399c02584a215e93e9c","score":"0.82015413","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass_cast(obj)","title":""},{"docid":"434ecc0d776b8366904e9ae125ec0a30","score":"0.82002217","text":"def itkSigmoidImageFilterIUC2IUC2_Superclass_cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIUC2IUC2_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUC2IUC2_Superclass_cast(obj)","title":""},{"docid":"f1c3cae2a9eff21c80593cc286188e87","score":"0.81998277","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass_cast(obj)","title":""},{"docid":"05b663dbb1b0b911dd2fc3940702d307","score":"0.81994444","text":"def itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass_cast(obj)","title":""},{"docid":"bc00a862b1f64bef623be69e1ed52ef7","score":"0.81994206","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIF3ISS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ISS3_Superclass_cast(obj)","title":""},{"docid":"0092c0cdb75e1c8f88b0fa9ef3501b9e","score":"0.81992066","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass_cast(obj)","title":""},{"docid":"04bd9eff6033423df1f1633dffd732ab","score":"0.8193769","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass_cast(obj)","title":""},{"docid":"457c642c649f24a51533560a7abf5d29","score":"0.8193076","text":"def itkIntensityWindowingImageFilterIUC3ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUC3ISS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3ISS3_Superclass_cast(obj)","title":""},{"docid":"b4aa31737964041f6c4ce6bc8ebc54be","score":"0.8189135","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF23IUS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IUS3_Superclass_cast(obj)","title":""},{"docid":"d964ef9198bbc8461d47082564d7dc80","score":"0.81869185","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUC3IF3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(obj)","title":""},{"docid":"a7168bcb8d7a25fae2de761a8b9ec83d","score":"0.8185297","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass_cast(obj)","title":""},{"docid":"a7a972710a711295f76e14ec1e18e522","score":"0.81773484","text":"def itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass_cast(obj)","title":""},{"docid":"ccc296bc20fb371b6860b8bdfd04f6e2","score":"0.8177334","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF33IUS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IUS3_Superclass_cast(obj)","title":""},{"docid":"e0128c6e153ddfa2330ecaf389b6d859","score":"0.81757396","text":"def cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIUS3IUS3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS3IUS3_Superclass_cast(obj)","title":""},{"docid":"92a9e50ef3992c61f58e9183cb10f786","score":"0.8175381","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF43IUC3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IUC3_Superclass_cast(obj)","title":""},{"docid":"33d2afda7aef75e2fc3911533fdf0248","score":"0.8174667","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUS3IF3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(obj)","title":""},{"docid":"47c27012f0be06372331858f3bb76422","score":"0.8173239","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUC3ISS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3ISS3_Superclass_cast(obj)","title":""},{"docid":"a9c4095708cc0a726f7e2f68b3ae90c4","score":"0.81691325","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF43IUS3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IUS3_Superclass_cast(obj)","title":""},{"docid":"9b7f65390ed47fa85aea115e9c2f52af","score":"0.8169121","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass_cast(obj)","title":""},{"docid":"46ef4504a2d146749caf29ecedfdfb62","score":"0.81672275","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass_cast(obj)","title":""},{"docid":"0bdde8161661af6b5b4d9be01dfe4aa2","score":"0.8166979","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIF3IUC3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(obj)","title":""},{"docid":"096c3b797f68104a92a58642c6a12755","score":"0.8166587","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass_cast(obj)","title":""},{"docid":"d78da5e26d4cc2928b0f5a0ca5e15254","score":"0.81627876","text":"def cast(obj: 'itkLightObject') -> \"itkVectorMagnitudeImageFilterICVF43IF3_Superclass *\":\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IF3_Superclass_cast(obj)","title":""},{"docid":"898410e433cb5b181575419e12fe7076","score":"0.8160143","text":"def itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass_cast(obj)","title":""},{"docid":"a203802caaccb0209b04cd03d8073c35","score":"0.8155998","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass_cast(obj)","title":""},{"docid":"797c7e654ff93b88a93b687bd189b817","score":"0.8155178","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass_cast(obj)","title":""},{"docid":"70f335df21a213d1799ef70c39792fce","score":"0.8154827","text":"def itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUC3IF3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(obj)","title":""},{"docid":"5fb91f82e940e4f0cd2b5cca7d55413f","score":"0.8154032","text":"def cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass_cast(obj)","title":""},{"docid":"1a081ba0f57fe949a10c6a0859d8b08b","score":"0.8147064","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIF3IUS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(obj)","title":""},{"docid":"2a7204e1fd812144e975cc88a404ab5c","score":"0.8143384","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterISS3IUC3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IUC3_Superclass_cast(obj)","title":""},{"docid":"b165cc24adeb3429418c39d9dd04cb27","score":"0.8142079","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterISS3IUS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IUS3_Superclass_cast(obj)","title":""},{"docid":"ed155a51419723909fffc03a1ea954c3","score":"0.81399554","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUS3ISS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3ISS3_Superclass_cast(obj)","title":""},{"docid":"c31d7d3b3ac62562552182f450daa175","score":"0.8137303","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterISS3ISS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3ISS3_Superclass_cast(obj)","title":""},{"docid":"8e6480314d41a0c329c762eeb73d31f1","score":"0.8135175","text":"def itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIF3IUC3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(obj)","title":""},{"docid":"1df7d64e3298e9e8546c42fcfe9c648e","score":"0.8124424","text":"def cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterIUC3IUS3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUS3_Superclass_cast(obj)","title":""},{"docid":"d32d343c9d38e62eafecdc8318cac6e0","score":"0.8122408","text":"def itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass *\":\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass_cast(obj)","title":""},{"docid":"6160b7fbbcd7015eaf62d86db3c6a157","score":"0.81176585","text":"def itkIntensityWindowingImageFilterISS3IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkIntensityWindowingImageFilterISS3IUC3_Superclass *\":\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IUC3_Superclass_cast(obj)","title":""}],"string":"[\n {\n \"docid\": \"39ecbda0f71ab287d0ba2da17f76c271\",\n \"score\": \"0.86661303\",\n \"text\": \"def itkSigmoidImageFilterIF3IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIF3IF3_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIF3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dde03410acb2ab13d6480b182e15d69\",\n \"score\": \"0.8608069\",\n \"text\": \"def itkSquaredDifferenceImageFilterIUL3IUL3IUL3_Superclass_cast(*args):\\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIUL3IUL3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a78d37d371c2bbcbf79c7690dda70dd3\",\n \"score\": \"0.8601987\",\n \"text\": \"def itkSquaredDifferenceImageFilterID3ID3ID3_Superclass_cast(*args):\\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterID3ID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097b9a897a80f76a125ca8338344a45b\",\n \"score\": \"0.8560069\",\n \"text\": \"def itkSigmoidImageFilterISS3ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterISS3ISS3_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterISS3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"136382e2a80c005ad21e1c6e5f447ce6\",\n \"score\": \"0.85251564\",\n \"text\": \"def itkSigmoidImageFilterIUC3IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIUC3IUC3_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUC3IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d768f181e822345111198301ee958d\",\n \"score\": \"0.85137403\",\n \"text\": \"def itkSigmoidImageFilterIUS3IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIUS3IUS3_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44686471db432bffa5ffa5abebe9c4ca\",\n \"score\": \"0.8508351\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF23IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF23IF3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1de94b447211074b2e3142d472d0b7c\",\n \"score\": \"0.8502601\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF23ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF23ISS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f738b6186cfce5e96f63ec6f15b029\",\n \"score\": \"0.84599555\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF33ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF33ISS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ce4a3e1481420b998ddeca30099e29\",\n \"score\": \"0.84575987\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF23IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF23IUS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3275a393482be7301dd7322a90b1dd7b\",\n \"score\": \"0.8457163\",\n \"text\": \"def itkSquaredDifferenceImageFilterIUC3IUC3IUC3_Superclass_cast(*args):\\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIUC3IUC3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"366db96eace3f1c72f5f0225e81651d4\",\n \"score\": \"0.841865\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF43ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF43ISS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd8f89227a203d930c366c4956e67912\",\n \"score\": \"0.8418057\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF33IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF33IF3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd5c6a8b8f3db76c2cde8f2b4478ddf\",\n \"score\": \"0.83943975\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF23IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF23IUC3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33912adcfb5cfe028cc1eda3c3c73204\",\n \"score\": \"0.83859414\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF43IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF43IF3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda84b8039dd389040e84ac229ee299c\",\n \"score\": \"0.8382607\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"068a69c44e2694cd3628e2f4f039f65d\",\n \"score\": \"0.8380838\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF33IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF33IUS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3009cc38266f607e0ec637c1283d60d7\",\n \"score\": \"0.8377044\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF43IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF43IUS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4be0cd61540f607bd765282d3c2ae27\",\n \"score\": \"0.83715445\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14081f49932280bf8abb019436d4ad3\",\n \"score\": \"0.83521384\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf1283dc8ee49f10578ef45eca3c531f\",\n \"score\": \"0.8349974\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a863d6f8eb49bf72ed9766febf08418\",\n \"score\": \"0.83484143\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27bfb944de051c060677470235c3052\",\n \"score\": \"0.83388984\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF43IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF43IUC3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a506d0661bc94cca3c62acc632ed23cf\",\n \"score\": \"0.83383346\",\n \"text\": \"def itkVectorMagnitudeImageFilterICVF33IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF33IUC3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a555b8ead408b316adab3dc720fc48a\",\n \"score\": \"0.8330667\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIF3IF3_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIF3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d711e075fab53aa2bed57a37082a302\",\n \"score\": \"0.8329492\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f655eee5c93df240f2aca7aa3c21c5\",\n \"score\": \"0.83148664\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c788ff779359a50bdf9c999a318c02\",\n \"score\": \"0.83132774\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5ef32a1c32501f459329058f2290d3\",\n \"score\": \"0.8308878\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aeaeced2be5fc682f877d29096247ef\",\n \"score\": \"0.82958156\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"275da1eba7b44644c96bc6c5db53927b\",\n \"score\": \"0.8269206\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIF3IF3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0cb24a3e9613503605e2e9dc7c8626\",\n \"score\": \"0.8257514\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dad2b862a7cebab8e0fad8169c2f27f\",\n \"score\": \"0.8253241\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dccbdc57f401d2e287b765a1fab0223\",\n \"score\": \"0.8251636\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterISS3ISS3_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterISS3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01dcd82cd3cb8b89e7e8ad2135e3f060\",\n \"score\": \"0.82498026\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIF3ISS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d13f1382b1d878a4451e7d8f37d797f7\",\n \"score\": \"0.82468706\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c61bf1f9af7cd98865986ee9944acfd\",\n \"score\": \"0.82458246\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIUC3IUC3_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUC3IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"647aa8a4389498174c245fd0dfc9166b\",\n \"score\": \"0.82455754\",\n \"text\": \"def itkIntensityWindowingImageFilterISS3IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterISS3IF3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ebc701b16cabfceb65fb8f77d3a89d8\",\n \"score\": \"0.8245034\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF33ISS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91502f56d7b76be4c022398b1fa7cb6b\",\n \"score\": \"0.8237269\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF23ISS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62c063747eec26ff55b8b69aeac07953\",\n \"score\": \"0.8229421\",\n \"text\": \"def itkSigmoidImageFilterIF2IF2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIF2IF2_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIF2IF2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"534182e8a9eb95d37e8e013b43ce9d3d\",\n \"score\": \"0.8226537\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIF3IF3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2052a8762146a4ce78bfe95f3447fb2e\",\n \"score\": \"0.82245517\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterISS3IF3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e59bc934994fa35ce03f27e3650553\",\n \"score\": \"0.8224325\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"384f19636518a051a776455e6e8f9d74\",\n \"score\": \"0.82242167\",\n \"text\": \"def itkSquaredDifferenceImageFilterIUS3IUS3IUS3_Superclass_cast(*args):\\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIUS3IUS3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b9c752aff33a433db22370280739ffc\",\n \"score\": \"0.82234114\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"238b714d9cc96b786d5149c4c1487df4\",\n \"score\": \"0.82214767\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF23IF3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152a5591ec0fd1709f3ca924298be998\",\n \"score\": \"0.82202965\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1d9b4b4f5abb6227605c5211479cea\",\n \"score\": \"0.8219701\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d9e3261f2217d802ad66accc6b8b55\",\n \"score\": \"0.8215747\",\n \"text\": \"def itkSigmoidImageFilterIUS2IUS2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIUS2IUS2_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS2IUS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"020d12bf63f524b253648cc84cabc779\",\n \"score\": \"0.82150745\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa2d02d41b5b7ad902b82d9139a87bf3\",\n \"score\": \"0.8214298\",\n \"text\": \"def itkIntensityWindowingImageFilterISS3ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterISS3ISS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e940df5911c595f5e228738f057451\",\n \"score\": \"0.82126564\",\n \"text\": \"def itkIntensityWindowingImageFilterISS3IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterISS3IUS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79682c583e6cf8093e1df0029cede62b\",\n \"score\": \"0.82106787\",\n \"text\": \"def itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUS3IF3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70da1c5a9375cb69f16267f7b7b938e4\",\n \"score\": \"0.8210286\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c0c8b839f24275f394ebdb6c4d61b37\",\n \"score\": \"0.8209101\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF33IF3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf26a52f48f2e51b442f69a71ab7e587\",\n \"score\": \"0.8207528\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45dfab7ec05148afc41f3b3bff2eed60\",\n \"score\": \"0.820622\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF33IUC3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f89ee200073628423fb776df715ee7c3\",\n \"score\": \"0.8205108\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIF3IUS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"642488bcd649951525f85d3c49096874\",\n \"score\": \"0.8204365\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF43ISS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4c1843637870066fbf6fba2814489f5\",\n \"score\": \"0.82042193\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725b7560be4e23b8819ecdb71104f082\",\n \"score\": \"0.82030606\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF23IUC3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ac9be374c48f431be9b5313a0512839\",\n \"score\": \"0.8201773\",\n \"text\": \"def itkIntensityWindowingImageFilterIUS3ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUS3ISS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59653702ab524399c02584a215e93e9c\",\n \"score\": \"0.82015413\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"434ecc0d776b8366904e9ae125ec0a30\",\n \"score\": \"0.82002217\",\n \"text\": \"def itkSigmoidImageFilterIUC2IUC2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIUC2IUC2_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUC2IUC2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c3cae2a9eff21c80593cc286188e87\",\n \"score\": \"0.81998277\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05b663dbb1b0b911dd2fc3940702d307\",\n \"score\": \"0.81994444\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc00a862b1f64bef623be69e1ed52ef7\",\n \"score\": \"0.81994206\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIF3ISS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0092c0cdb75e1c8f88b0fa9ef3501b9e\",\n \"score\": \"0.81992066\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04bd9eff6033423df1f1633dffd732ab\",\n \"score\": \"0.8193769\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457c642c649f24a51533560a7abf5d29\",\n \"score\": \"0.8193076\",\n \"text\": \"def itkIntensityWindowingImageFilterIUC3ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUC3ISS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4aa31737964041f6c4ce6bc8ebc54be\",\n \"score\": \"0.8189135\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF23IUS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF23IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d964ef9198bbc8461d47082564d7dc80\",\n \"score\": \"0.81869185\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUC3IF3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7168bcb8d7a25fae2de761a8b9ec83d\",\n \"score\": \"0.8185297\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a972710a711295f76e14ec1e18e522\",\n \"score\": \"0.81773484\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccc296bc20fb371b6860b8bdfd04f6e2\",\n \"score\": \"0.8177334\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF33IUS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF33IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0128c6e153ddfa2330ecaf389b6d859\",\n \"score\": \"0.81757396\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkSigmoidImageFilterIUS3IUS3_Superclass *\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a9e50ef3992c61f58e9183cb10f786\",\n \"score\": \"0.8175381\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF43IUC3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d2afda7aef75e2fc3911533fdf0248\",\n \"score\": \"0.8174667\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUS3IF3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47c27012f0be06372331858f3bb76422\",\n \"score\": \"0.8173239\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUC3ISS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c4095708cc0a726f7e2f68b3ae90c4\",\n \"score\": \"0.81691325\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF43IUS3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7f65390ed47fa85aea115e9c2f52af\",\n \"score\": \"0.8169121\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF43IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46ef4504a2d146749caf29ecedfdfb62\",\n \"score\": \"0.81672275\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bdde8161661af6b5b4d9be01dfe4aa2\",\n \"score\": \"0.8166979\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIF3IUC3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"096c3b797f68104a92a58642c6a12755\",\n \"score\": \"0.8166587\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF23IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d78da5e26d4cc2928b0f5a0ca5e15254\",\n \"score\": \"0.81627876\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorMagnitudeImageFilterICVF43IF3_Superclass *\\\":\\n return _itkVectorMagnitudeImageFilterPython.itkVectorMagnitudeImageFilterICVF43IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"898410e433cb5b181575419e12fe7076\",\n \"score\": \"0.8160143\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a203802caaccb0209b04cd03d8073c35\",\n \"score\": \"0.8155998\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF23IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797c7e654ff93b88a93b687bd189b817\",\n \"score\": \"0.8155178\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF43IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70f335df21a213d1799ef70c39792fce\",\n \"score\": \"0.8154827\",\n \"text\": \"def itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUC3IF3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb91f82e940e4f0cd2b5cca7d55413f\",\n \"score\": \"0.8154032\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a081ba0f57fe949a10c6a0859d8b08b\",\n \"score\": \"0.8147064\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIF3IUS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7204e1fd812144e975cc88a404ab5c\",\n \"score\": \"0.8143384\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterISS3IUC3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b165cc24adeb3429418c39d9dd04cb27\",\n \"score\": \"0.8142079\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterISS3IUS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed155a51419723909fffc03a1ea954c3\",\n \"score\": \"0.81399554\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUS3ISS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31d7d3b3ac62562552182f450daa175\",\n \"score\": \"0.8137303\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterISS3ISS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e6480314d41a0c329c762eeb73d31f1\",\n \"score\": \"0.8135175\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIF3IUC3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df7d64e3298e9e8546c42fcfe9c648e\",\n \"score\": \"0.8124424\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterIUC3IUS3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32d343c9d38e62eafecdc8318cac6e0\",\n \"score\": \"0.8122408\",\n \"text\": \"def itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass *\\\":\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterICVF33IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6160b7fbbcd7015eaf62d86db3c6a157\",\n \"score\": \"0.81176585\",\n \"text\": \"def itkIntensityWindowingImageFilterISS3IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkIntensityWindowingImageFilterISS3IUC3_Superclass *\\\":\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterISS3IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992987,"cells":{"query_id":{"kind":"string","value":"2bdc32a8c8461f86ea251bbd2a44e058"},"query":{"kind":"string","value":"Reverts a transaction if before the configured withdrawal lock."},"positive_passages":{"kind":"list like","value":[{"docid":"5b00103636c430cd2d9212960266c0eb","score":"0.60975236","text":"def _revert_on_withdrawal_lock(self, _user: Address, _id: int) -> None:\n\n deposit_time = self._withdraw_lock[_id][_user]\n if deposit_time + WITHDRAW_LOCK_TIMEOUT > self.now():\n revert(f\"{TAG}: Assets must remain in the pool for 24 hours, please try again later.\")","title":""}],"string":"[\n {\n \"docid\": \"5b00103636c430cd2d9212960266c0eb\",\n \"score\": \"0.60975236\",\n \"text\": \"def _revert_on_withdrawal_lock(self, _user: Address, _id: int) -> None:\\n\\n deposit_time = self._withdraw_lock[_id][_user]\\n if deposit_time + WITHDRAW_LOCK_TIMEOUT > self.now():\\n revert(f\\\"{TAG}: Assets must remain in the pool for 24 hours, please try again later.\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2e127a7d6d971ce6e17af3f2112331b1","score":"0.6691026","text":"def rollback(self) -> None:\n self._check_thread()\n if self.state not in (_STATE_ACTIVE, _STATE_PARTIAL_COMMIT):\n raise TransactionError(\"Transaction is not active.\")\n try:\n if self.state != _STATE_PARTIAL_COMMIT:\n request = transaction_rollback_codec.encode_request(self.id, self.thread_id)\n invocation = Invocation(request, connection=self.connection)\n invocation_service = self._context.invocation_service\n invocation_service.invoke(invocation)\n invocation.future.result()\n self.state = _STATE_ROLLED_BACK\n finally:\n self._locals.transaction_exists = False","title":""},{"docid":"4136b5b5e1521deb210dd63ae0478351","score":"0.6650817","text":"def rollback_transaction( self ):\n self.session.rollback()","title":""},{"docid":"7758a2444b350729dad4c9eb19c6b750","score":"0.6456801","text":"def rollback(self):\n self.local.tx.rollback()\n del self.local.tx\n self._release_internal()","title":""},{"docid":"d0208acdce14a3c7b8facca1166cffa9","score":"0.6456093","text":"def rollback(self):\n if self._autocommit:\n warnings.warn(AUTOCOMMIT_MODE_WARNING, UserWarning, stacklevel=2)\n elif self._transaction:\n self._transaction.rollback()\n self._release_session()","title":""},{"docid":"02b8c4f4d454b389bb1cf042cba8fd7d","score":"0.63717693","text":"def rollback(self):\n try:\n self.autocommit = True\n if self.con:\n self.con.rollback()\n except:\n pass","title":""},{"docid":"ffb3c9e9999a63bfe88b8d72b2dd6a93","score":"0.6293933","text":"def step_see_transaction_rolled_back(context):\n wrappers.expect_pager(context, \"ROLLBACK\\r\\n\", timeout=2)","title":""},{"docid":"5da72a9b30c58b6a794562bae60841d2","score":"0.62907517","text":"def resetTransaction(self) :\n self._Connection.rollback()","title":""},{"docid":"c2dabd328baa36a75edd4a03cb818026","score":"0.6281952","text":"def rollback(self):\n self._check_update_state('started', 'rolledback')\n self._mutations = []","title":""},{"docid":"8aba4d887eebf8202441eb60d69f2b4b","score":"0.6272492","text":"def rollback ( self ):\n pass","title":""},{"docid":"9e6806c9dd1fc2f00e461621838058c7","score":"0.62411606","text":"def rollback():\n\tpass","title":""},{"docid":"2cfd178d19dd88745ce6666bfe318b21","score":"0.623781","text":"def rollback(self, **kwargs):\n\n self.state = State.ROLLBACKING\n\n result = self.driver.process(transaction=self, **kwargs)\n\n self.state = State.COMMITTING if self.autocommit else State.PENDING\n\n return result","title":""},{"docid":"1cd941c48b6c95bd078bacfe49edaad8","score":"0.62114483","text":"def do_rollback(self, connection):\n\n connection.rollback()","title":""},{"docid":"21f0605381a9c3990af2efcd24563be0","score":"0.6204951","text":"def _rollback(self):\n\n session = self.registry.get()\n if session is not None:\n session.rollback()","title":""},{"docid":"e4bcd29e62d2c600ebf19084fdb2243a","score":"0.6173155","text":"def rollback(self):\n self.connection.rollback()","title":""},{"docid":"b82d35aa13eb80c21cdc6d9d0f69b231","score":"0.6168288","text":"def rollback(self):\n self.dbconn.rollback()","title":""},{"docid":"056eabaf87c2c53b5a487b50f640c40e","score":"0.61432356","text":"def rollback(self):\n return self.adaptor.rollback()","title":""},{"docid":"0ffb93b0db58c8a8a7cfce7ceff4e1a5","score":"0.6125452","text":"def rollback_session(self):\n self.__session.rollback()","title":""},{"docid":"fc9b6805c35e96f9baf6ffcf5d2ad731","score":"0.60994065","text":"def rollback(self):\n self._crsr.rollback()","title":""},{"docid":"5d377ed15eb9b6cfd8266930ce95a35c","score":"0.60916424","text":"def rollback(self):\n self.db_conn.rollback()","title":""},{"docid":"50b3346ea3ee430eba76c000673fc6f3","score":"0.60875446","text":"def rollback(self):\n self.success = False\n self.close()","title":""},{"docid":"93ade94a81dc90878bb42760833ff79f","score":"0.60712856","text":"def ensure_transaction(self):\n if not self._latest_transaction:\n self.new_transaction()","title":""},{"docid":"c2d589f31b05c0a1a4217a626f5150e2","score":"0.6067326","text":"def resumeTransactionHelper(self):\n try:\n transaction = self.mint.completedTransactions.pop(0) # FIFO\n except IndexError:\n return\n\n while True:\n transaction_id = transaction['transaction_id']\n del transaction['transaction_id']\n \n self.updateTransaction(transaction_id, **transaction)\n\n try:\n transaction = self.mint.completedTransactions.pop(0) # FIFO\n except IndexError:\n return","title":""},{"docid":"029df82fa54ce31981a325d43c29fc8b","score":"0.60606766","text":"def refuse_transaction(self, tx):\n block_with_tx = self._find_tx(tx)\n if block_with_tx is None:\n pass\n elif block_with_tx != self[0]:\n raise NotRemovableTransactionError\n else:\n if self[0].is_signed():\n raise NotRemovableTransactionError\n self[0]._remove_tx(tx)\n return Transaction(\n VERSION,\n Transaction.REFUSAL,\n signer=tx.target_user,\n amount=tx.amount,\n tx_date=datetime.date.today().isoformat(),\n target_user=tx.signer,\n detail=tx.signature,\n )","title":""},{"docid":"ac29a9a72c4ab03cc6cf3f4a0939b6ae","score":"0.6016893","text":"def rollback(self):\n return self.conn.rollback()","title":""},{"docid":"a4627d6f54108ac1d93175be9be18359","score":"0.6006479","text":"def rollback():\n global _db_connection\n _db_connection.rollback()","title":""},{"docid":"4af2ef8f42d41fbd7c82c9bcef0160f4","score":"0.59700215","text":"def rollback(self) -> NoReturn:\n raise NotImplementedError(\"BigQueryConnection does not have transactions\")","title":""},{"docid":"8861e12525bb52718ba71df381f28a1e","score":"0.5967468","text":"def rollback(self):\n self._db_connection.rollback()","title":""},{"docid":"407d3704c697029244b0185d3cf64e6e","score":"0.5948704","text":"def rollback(self, start_transaction=True) -> None:\n self._rpsl_upsert_buffer = []\n self._rpsl_pk_source_seen = set()\n self.status_tracker.reset()\n self.changed_objects_tracker.reset()\n self._transaction.rollback()\n if start_transaction:\n self._start_transaction()","title":""},{"docid":"41bd4cf0b481aa347fabf227699ccdd3","score":"0.5889816","text":"def test_revert_error(error_contract, not_owner):\n with reverts(error_contract.Unauthorized):\n error_contract.withdraw(sender=not_owner)","title":""},{"docid":"a0aa4816a56fb122b013432e2833f24a","score":"0.58798784","text":"def do_rollback(self, args):\n self.__ensure_connected()\n if self.__flag_is_set('autocommit'):\n log.warning('Autocommit is enabled. \"rollback\" ignored')\n else:\n assert self.__db != None\n self.__db.rollback()","title":""},{"docid":"aea98b9522cd6f6067fadc5e677b69a5","score":"0.58307755","text":"def abortTransaction(txnService):","title":""},{"docid":"c1cc3cf3992180ba158fb06c5ff940bf","score":"0.57920086","text":"def revert(self):\n pass","title":""},{"docid":"2fd1a633ba19678e1e76f0d3b084cc45","score":"0.5791256","text":"def __rollback_adhoc_txn(self, request, v1_transaction):\n try:\n v1_rollback_req = googledatastore.RollbackRequest()\n v1_rollback_req.project_id = request.project_id\n v1_rollback_req.transaction = v1_transaction\n self._Dynamic_Rollback(v1_rollback_req,\n googledatastore.RollbackResponse())\n except apiproxy_errors.ApplicationError, e:\n pass","title":""},{"docid":"063cba6a3c6dc0deac56c75c52511328","score":"0.5769397","text":"async def rollback(self) -> None:\n await greenlet_spawn(self.sync_session.rollback)","title":""},{"docid":"4b577a234a3fd135b99a11f21babf40d","score":"0.5766262","text":"def revert():","title":""},{"docid":"d545a4d6c8c6661fb4906299c9e9df15","score":"0.57517993","text":"def resumeTransaction(self, transaction_id):\n #FIXME: Only resumes minting/exchanges right now\n self.resumeTransactionHelper()\n return self.getTransaction(transaction_id)","title":""},{"docid":"4cb7132e3caa24e285b87cfb2089aac7","score":"0.5740604","text":"def rollback(self):\n if not self.in_transaction:\n raise NotInTransaction\n for collection,store in self.stores.items():\n store.rollback()\n indexes = self.indexes[collection]\n indexes_to_rebuild = []\n for key,index in indexes.items():\n try:\n index.rollback()\n except NotInTransaction:\n #this index is \"dirty\" and needs to be rebuilt (probably it has been created within a transaction)\n indexes_to_rebuild.append(key)\n if indexes_to_rebuild:\n self.rebuild_indexes(collection,indexes_to_rebuild)\n self.in_transaction = False","title":""},{"docid":"bf9478439d40ce5c0a25bf1ac5798752","score":"0.57385117","text":"def Revert():\n raise Exception(0x00)\n # return False","title":""},{"docid":"a22b464b3f2c2725037a8262d25eaa4f","score":"0.5735402","text":"def after_rollback(self, session):","title":""},{"docid":"5bff1f3f15ba87b8de17a466ddadd819","score":"0.5733681","text":"def roll_back(cls, key):\r\n\r\n logging.info('Rolling back the transfer with key %s.', key)\r\n\r\n transfer = key.get()\r\n assert transfer.state == 'pending'\r\n\r\n _id = '%s_rollback' % transfer.key.id()\r\n _parent = transfer.key.parent()\r\n _tags = [t for t in transfer.tags if t.prop != 'display']\r\n _tags.append(Tag.from_str('display:false'))\r\n dest_transfer = Tx.get_by_id(_id, parent=_parent)\r\n if not dest_transfer:\r\n dest_transfer = Tx(key=ndb.Key(Tx, _id, parent=_parent))\r\n dest_transfer.amount = -transfer.amount\r\n dest_transfer.target = transfer.key.parent()\r\n dest_transfer.state = 'canceled'\r\n dest_transfer.other = transfer.key\r\n\r\n dest_transfer.asset = transfer.asset\r\n dest_transfer.tags = _tags\r\n\r\n account = transfer.key.parent().get()\r\n\r\n value = account.balance(transfer.asset) - transfer.amount\r\n account.balance_update(transfer.asset, value)\r\n\r\n transfer.state = 'canceled'\r\n transfer.other = dest_transfer.key\r\n\r\n ndb.put_multi([account, dest_transfer, transfer])\r\n return dest_transfer","title":""},{"docid":"156e59072f81cbfecb5e2593b679d65e","score":"0.57209146","text":"def withdraw(self, amount: float):\n if amount > 0:\n self.balance -= amount\n timestamp = dt.today()\n self.history['Withdrawals'].append(\n {\n 'Date': timestamp,\n 'Amount': -amount,\n 'Balance': self.balance\n }\n )\n self.doc_manager.transaction_notice(trans_type='withdraw', amount=amount, timestamp=timestamp)\n if self.balance < self.minimum_bal:\n self.charge_overdraft_fee()\n else:\n raise ValueError('Withdrawal amount must be positive')","title":""},{"docid":"f8aacdb5ce86e0c36fa78fae8f41c216","score":"0.5709667","text":"async def rollback(self) -> None:\n await greenlet_spawn(self._proxied.rollback)","title":""},{"docid":"4cc1327ab2639c70e46834b3b1bf7ac2","score":"0.5704441","text":"def unapply_transaction(a_id, e_id, amt):\n if a_id is not None:\n newaccountbalance = get_account_balance(a_id) + amt\n update_account_balance(a_id, newaccountbalance)\n if e_id is not None:\n newenvelopebalance = get_envelope_balance(e_id) + amt\n update_envelope_balance(e_id, newenvelopebalance)","title":""},{"docid":"4d78579c49dc73c1196ca0bc7d2c5b9f","score":"0.56839937","text":"def rebalance(context, data):\r\n pass","title":""},{"docid":"74c9474b6c2a3d219119fcb5c14eddcf","score":"0.5675721","text":"def setTxnState(outsideTxn):","title":""},{"docid":"3a46cf60f5a39eb3faa88b2efa0281c4","score":"0.5624804","text":"def rollback(self, x=None):\n if self.halt:\n return\n self.halt = True\n if self.status != 0:\n self.update(\"The process cannot be interrupted at this stage\")\n return\n\n self.update(\"Rolling back any changes...\")\n if self.dbh:\n self.dbh.rollback()\n try:\n os.remove(self.tarfile)\n except:\n self.update(f\"Could not remove {self.archive_root}/log/{self.tarfile}\", True)","title":""},{"docid":"dd3fe880197b084331de5007716b4aa2","score":"0.56194234","text":"def start_rollback(self) -> bool:\n self.event.state = ROLLBACK\n return self._run_around('Commanding {} to roll back')","title":""},{"docid":"72ab021c81b6716230207c9ff62f196d","score":"0.5615406","text":"async def rollback_to_block(self, height: int) -> None:\n\n async with self.db_wrapper.writer_maybe_transaction() as conn:\n await (await conn.execute(\"DELETE FROM coin_record WHERE confirmed_height>?\", (height,))).close()\n await (\n await conn.execute(\n \"UPDATE coin_record SET spent_height = 0, spent = 0 WHERE spent_height>?\",\n (height,),\n )\n ).close()\n self.total_count_cache.cache.clear()","title":""},{"docid":"79ad030084e79ef3c5be1bc3d2e1bd43","score":"0.56015414","text":"def rollback(self, blockhash):\n txs = self.db.txes.find({\"blockhash\": blockhash})\n transactions = []\n if txs:\n for i in txs:\n transactions.append(i)\n self.rollback_addresses(transactions)\n self.db.txes.delete_many({\"blockhash\": blockhash})\n self.db.blocks.delete_many({\"hash\": blockhash})\n self.db.votes.delete_many({\"block_hash\": blockhash})","title":""},{"docid":"238c5528b82792bf6629c3e2fd807301","score":"0.55953515","text":"def test_revert_error_inputs(error_contract, not_owner):\n with reverts(error_contract.Unauthorized, addr=not_owner.address, counter=123):\n error_contract.withdraw(sender=not_owner)","title":""},{"docid":"7427ba374814f4a3be142b18dd85ad05","score":"0.5594636","text":"def on_return(self, connection) -> None:\n connection.rollback()","title":""},{"docid":"2c79f0173b68552a5ee876023e0929e1","score":"0.55746764","text":"def test_revert(owner, reverts_contract_instance):\n with reverts(\"zero\"):\n reverts_contract_instance.revertStrings(0, sender=owner)","title":""},{"docid":"9ae3a6bf887542d2a32c3b56c708c8ed","score":"0.553997","text":"def withdraw(self, amount):\r\n self.balance -= amount","title":""},{"docid":"9ae3a6bf887542d2a32c3b56c708c8ed","score":"0.553997","text":"def withdraw(self, amount):\r\n self.balance -= amount","title":""},{"docid":"af5757794b8953a6f081b9c36205067f","score":"0.5524872","text":"def mark_app_valid_cancel_rollback(\n cls,\n ) -> Any:\n ...","title":""},{"docid":"bab043779d6ef1213edff42f18e94040","score":"0.5499845","text":"def test_locking(self):\n self.cur1.execute(\"create table test(i)\")\n self.cur1.execute(\"insert into test(i) values (5)\")\n with self.assertRaises(sqlite.OperationalError):\n self.cur2.execute(\"insert into test(i) values (5)\")\n # NO self.con2.rollback() HERE!!!\n self.con1.commit()","title":""},{"docid":"2cf94871aba749d634a8b1ff71bd4af9","score":"0.54977715","text":"def test_postcondition_transaction(self):\n self.command.run(username='rich_user', stock_symbol='ABAB',\n amount='23.45')\n set_transaction = self.session.query(Trigger).filter_by(\n username='rich_user',\n stock_symbol='ABAB', \n state=Trigger.State.INACTIVE,\n operation='BUY').first()\n self.assertNotEqual(set_transaction, None)\n self.assertEqual(set_transaction.amount, Money(23, 45))","title":""},{"docid":"4cac0eeef9b86ee8152ae72868686808","score":"0.5491284","text":"def test_withdraw_transaction(self):\n user = User.objects.get(phone_number=user_1_login['phone_number'])\n account = Account.objects.get(id=self.account.data['id'])\n transaction = Transaction()\n transaction.created_by = user\n transaction.account = account\n transaction.type = \"withdraw\"\n transaction.amount = 5000\n transaction.prev_balance = 10000\n transaction.calculate_new_balance()\n transaction.save()\n self.assertEqual(transaction.new_balance, 5000)","title":""},{"docid":"a7b668d1e8721ec521558f891c030a93","score":"0.54802364","text":"def act(self) -> None:\n transactions = cast(Transactions, self.context.transactions)\n transactions.update_confirmed_transactions()\n transactions.cleanup_pending_transactions()","title":""},{"docid":"5d25a64f440768b544df85afc6a28c75","score":"0.54692274","text":"def decline(self):\n self.is_redeemed = False\n self.is_active = False\n self.save()","title":""},{"docid":"3726441c44f4abe6484b338fe66f2750","score":"0.54662144","text":"def transaction_checkout(self):\n if not self.autocommit:\n if (\n not self._transaction\n or self._transaction.committed\n or self._transaction.rolled_back\n ):\n self._transaction = self._session_checkout().transaction()\n self._transaction.begin()\n\n return self._transaction","title":""},{"docid":"7fcefdc7c51e6f7bab023cd775939cd9","score":"0.5444686","text":"def Revert():\n raise Exception(0x00)","title":""},{"docid":"f5ebc87a3a9d25290027632893d0bf59","score":"0.5437343","text":"def undo(self, context, event):\n return False","title":""},{"docid":"686d14e22d96a9bf5eebb903c267d52d","score":"0.54356784","text":"def test_postcondition_transaction(self):\n self.command.run(username='rich_user', stock_symbol='ABAB',\n quantity='2')\n set_transaction = self.session.query(Trigger).filter_by(\n username='rich_user',\n stock_symbol='ABAB', \n state=Trigger.State.INACTIVE,\n quantity=2,\n operation='SELL').first()\n self.assertNotEqual(set_transaction, None)","title":""},{"docid":"9baf91835f719cd56c361d4d3038bb31","score":"0.54167587","text":"def untransact(database=None):\n\n def _f(f):\n def g(*args, **kw):\n starttime = datetime.datetime.utcnow()\n\n dbase = database\n if dbase is None:\n raise Exception('dbase is None')\n \n _transact_begin_log_helper('@untransact', f, dbase)\n if (dbase is not None) and hasattr(dbase, 'store') and (dbase.store is not None):\n # hasattr + store check is to fix #621, this is also an API issue with rdf.py\n t = dbase.begin_untransaction()\n else:\n t = None\n\n global toplevel_transaction\n\n # Log transact locktime here if active\n if t is not None:\n # XXX: There is no sensible date value to show here,\n # because we are not actually logging anything for\n # current untransaction: we log the locktime of the\n # toplevel transaction which is committed here. Using\n # current time as both timestamps for now.\n _transact_end_log_helper('@transact (in untransact)', toplevel_transaction, dbase, True, datetime.datetime.utcnow(), datetime.datetime.utcnow(), t.get_txn_locked_time(), TRANSACT_LOCK_TIME_WARNING_LIMIT, untransact_point=f)\n\n try:\n ret = f(*args, **kw)\n except:\n t_locked_time = None\n if t is not None:\n t_locked_time = t.get_locked_time()\n t.commit()\n endtime = datetime.datetime.utcnow()\n _transact_end_log_helper('@untransact', f, dbase, False, starttime, endtime, t_locked_time, UNTRANSACT_LOCK_TIME_WARNING_LIMIT)\n raise\n\n t_locked_time = None\n if t is not None:\n t_locked_time = t.get_locked_time()\n t.commit()\n endtime = datetime.datetime.utcnow()\n _transact_end_log_helper('@untransact', f, dbase, True, starttime, endtime, t_locked_time, UNTRANSACT_LOCK_TIME_WARNING_LIMIT)\n \n return ret\n\n mergeFunctionMetadata(f, g)\n return g\n return _f","title":""},{"docid":"6d7ebf48b5fe2ba2e04f8d6fabf04ec8","score":"0.54075825","text":"def revert(self, x: bool):","title":""},{"docid":"7d22517edd3852f2cdf44b9aecc920ff","score":"0.5406235","text":"def revert(self, record, new_password):\n self.rotate(record, new_password, revert=True)","title":""},{"docid":"1960790c4e6bcd26fd254c245fb4c970","score":"0.54058766","text":"def withdraw(self, amount: float):\n timestamp = dt.today()\n if len(self.history['Withdrawals']) < self.withdrawal_lim:\n if 0 < amount <= self.balance:\n self.balance -= amount\n self.history['Withdrawals'].append(\n {\n 'Date': timestamp,\n 'Amount': -amount,\n 'Balance': self.balance\n }\n )\n self.doc_manager.transaction_notice(trans_type='withdraw', amount=amount, timestamp=timestamp)\n if len(self.history['Withdrawals']) == self.withdrawal_lim:\n self.doc_manager.withdraw_lim_notice(timestamp, past_limit=False)\n if self.balance < self.minimum_bal:\n self.charge_overdraft_fee()\n elif amount > 0:\n self.doc_manager.insufficient_balance_notice(timestamp, amount)\n else:\n raise ValueError('Withdrawal amount must be positive')\n else:\n self.doc_manager.withdraw_lim_notice(timestamp, past_limit=True)","title":""},{"docid":"090238f37c7a355a9bc0187828c6ecbd","score":"0.54015446","text":"def withdraw(self, amount):\n self.initial_balance -= amount\n if self.initial_balance < 0:\n self.initial_balance -= 5\n self.fee_count += 1","title":""},{"docid":"db62db91d3d3d95d47df5942179d251d","score":"0.53914046","text":"def undo(self):\n pass","title":""},{"docid":"db62db91d3d3d95d47df5942179d251d","score":"0.53914046","text":"def undo(self):\n pass","title":""},{"docid":"db62db91d3d3d95d47df5942179d251d","score":"0.53914046","text":"def undo(self):\n pass","title":""},{"docid":"624941732c7ede972950817498a1e9fa","score":"0.53727204","text":"def transaction(self):\n pass","title":""},{"docid":"6864303bf5378b1cd878c6582bbdd26d","score":"0.53647685","text":"def rollback(self, err=None, swallow=False):\n if not self.no_commit:\n try:\n self.connection.dbc.rollback()\n if err is not None:\n logging.info(\n \"rollback invoked for transaction following error %s\",\n str(err))\n except self.api.NotSupportedError:\n if err is not None:\n if self.query_count:\n logging.error(\n \"Data Integrity Error on TABLE %s: rollback \"\n \"invoked on a connection that does not \"\n \"support transactions after error %s\",\n self.table_name,\n str(err))\n else:\n logging.info(\n \"Query failed following error %s\", str(err))\n pass\n if err is not None and not swallow:\n logging.debug(\n ' '.join(\n traceback.format_exception(*sys.exc_info(), limit=6)))\n if isinstance(err, self.api.Error):\n raise SQLError(str(err))\n else:\n raise err","title":""},{"docid":"bb2988be3d68619e8f9e97c4ce751874","score":"0.53640985","text":"def rollback(self):\n if self.changed:\n rollback_cmd = '<load><config><from>{0}</from></config></load>'.format(self.backup_file)\n self.device.op(cmd=rollback_cmd)\n time.sleep(5)\n\n if self.ssh_connection is False:\n self._open_ssh()\n try:\n self.ssh_device.commit()\n self.loaded = False\n self.changed = False\n self.merge_config = False\n except: # noqa\n ReplaceConfigException(\"Error while loading backup config\")","title":""},{"docid":"3a5cae5efbc908b30d1d5b441354454e","score":"0.53530693","text":"def test_rollback():","title":""},{"docid":"d68a6cad2a59b303050393306eaf6229","score":"0.53448886","text":"def withdraw(self, amount: float):\r\n if amount <= self.__balance:\r\n self.__balance -= amount\r\n print(\"Withdraw Accepted!!\")\r\n else:\r\n print(\"Fund Unavailable!!\")","title":""},{"docid":"428e8d974e8de4669d0d833960a5e009","score":"0.5343247","text":"def withdraw(self, amount):\r\n if amount > self.balance:\r\n raise RuntimeError('Amount greater than available balance.')\r\n self.balance -= amount\r\n return self.balance","title":""},{"docid":"cca28e2023bbe7ddedd7cf8be68d55bb","score":"0.5341239","text":"def unlock(self):\r\n self.locked = False\r\n self.lock_id = None\r\n\r\n self.save()","title":""},{"docid":"2892e562d20a7ebcbf68e3bdce32e196","score":"0.5339073","text":"def withdraw(self, amount):\r\n self.setBalance(self.getBalance() - amount)","title":""},{"docid":"58d2f48f384f0bf055382e309b19b8cb","score":"0.5329359","text":"def test_revert_abi(error_contract, not_owner):\n with reverts(error_contract.Unauthorized.abi):\n error_contract.withdraw(sender=not_owner)","title":""},{"docid":"b662e66a411fefb469506ba1b3d09cf7","score":"0.53228027","text":"def pop_locked_tx(self, transaction_id: TransactionId) -> Transaction:\n assert transaction_id in self.locked_txs\n transaction = self.locked_txs.pop(transaction_id)\n self.locked_txs_as_buyer.pop(transaction_id, None)\n self.locked_txs_as_seller.pop(transaction_id, None)\n return transaction","title":""},{"docid":"d91d2c172703a08f8572c3acbded2667","score":"0.52968985","text":"def withdraw(self, amount):\n self.cash -= amount\n return amount","title":""},{"docid":"b728bbfc35b6e1ce307b19911e99edea","score":"0.5291398","text":"def _rollback(self, pk):\n\n # use `set()`, before the loop, to avoid using self set that may be updated during the process\n cache = self._get_rollback_cache(pk)\n indexed_values = set(cache.get('indexed_values'))\n deindexed_values = set(cache.get('deindexed_values'))\n\n for args in indexed_values:\n self.remove(pk, *args)\n for args in deindexed_values:\n self.add(pk, *args, check_uniqueness=False)","title":""},{"docid":"810096229f3b0dba25604dbf2bcb2803","score":"0.52866334","text":"def withdraw(self, amount):\n if amount > self.balance:\n raise RuntimeError('Amount greater than available balance.')\n self.balance -= amount\n return self.balance","title":""},{"docid":"d46939a71acf827c57589b0ce4fef952","score":"0.52706575","text":"async def rollback(self, height: int, wallet_id_arg: int) -> None:\n\n async with self.db_wrapper.writer_maybe_transaction() as conn:\n cursor = await conn.execute(\n \"DELETE FROM pool_state_transitions WHERE height>? AND wallet_id=?\", (height, wallet_id_arg)\n )\n await cursor.close()","title":""},{"docid":"97920a9bfefb778f949dd2efa7da61ff","score":"0.5257855","text":"def RevertToPrevState( self ):\n self.ChangeState( self.PreviousState )","title":""},{"docid":"f3741e2f7f34705ac7516ba2dfc2a8c1","score":"0.5254019","text":"def decline(self):\n self.is_active=False\n self.save()","title":""},{"docid":"d99ec010c0624a0b574b9e107f149918","score":"0.5242379","text":"def norollback(*pats, **opts):\n\traise hg_util.Abort(\"codereview extension enabled; use undo instead of rollback\")","title":""},{"docid":"1ad626017b0f9bf1ab251801908f247d","score":"0.52417153","text":"def withdraw(self, withdrawAmount): \n if withdrawAmount > self.balance: \n raise RuntimeError('Amount greater than available balance.')\n else:\n self.balance -= withdrawAmount \n return self.balance","title":""},{"docid":"abf7bb52a5f4ad77dea23f5b7038f77e","score":"0.5238773","text":"def transBegin(self):\n self.autocommit = False","title":""},{"docid":"cc6dc85094d69ef1f3745c1e7e414346","score":"0.52346563","text":"def commit_transaction(self):\n pass","title":""},{"docid":"3f3531ec8ef01abb757fe7e15c4941fd","score":"0.5232039","text":"def cancel_rr_transaction(self):\n self._rr_txn = None","title":""},{"docid":"3831d8d650420d609ab643854cf4ea3c","score":"0.522349","text":"def _action_finally(self):\n self.model_update_suspended = False","title":""},{"docid":"6d8f6bc98a21fc65d07e5eb26088bac8","score":"0.5218564","text":"def revert_call(context: 'IconScoreContext') -> None:\n if context.type != IconScoreContextType.INVOKE:\n return\n\n context.tx_batch.revert_call()\n context.event_logs.clear()","title":""},{"docid":"1d7d6f0c868569bf5d69ac880528c880","score":"0.52167886","text":"def test_revert_fails(owner, reverts_contract_instance):\n with pytest.raises(AssertionError):\n with reverts(\"one\"):\n reverts_contract_instance.revertStrings(0, sender=owner)","title":""},{"docid":"6681a205e3d3dfb9bc6746199a420224","score":"0.5216677","text":"def recommit(self):\n\t\tpass","title":""},{"docid":"1f1dc1bb1515c557f3459065f413309b","score":"0.52152956","text":"def withdrawal(self, amount=0, date=None):\n self.cash[date:] -= amount\n return self","title":""},{"docid":"8c0283939cf9ff41c25d3ca464e3f081","score":"0.52143127","text":"def __unlock_trade(self):\n if self.trd_env == TrdEnv.REAL:\n ret, data = self.trade_ctx.unlock_trade(password_md5=self.password_md5)\n if ret == RET_OK:\n self.default_logger.info(\"Account Unlock Success.\")\n else:\n raise Exception(\"Account Unlock Unsuccessful: {}\".format(data))","title":""},{"docid":"854d51e11e90bca5af2ddec95167ac68","score":"0.52122086","text":"def end_transaction(self):\n self.locked = False\n for market in self.markets:\n market.end_transaction()","title":""}],"string":"[\n {\n \"docid\": \"2e127a7d6d971ce6e17af3f2112331b1\",\n \"score\": \"0.6691026\",\n \"text\": \"def rollback(self) -> None:\\n self._check_thread()\\n if self.state not in (_STATE_ACTIVE, _STATE_PARTIAL_COMMIT):\\n raise TransactionError(\\\"Transaction is not active.\\\")\\n try:\\n if self.state != _STATE_PARTIAL_COMMIT:\\n request = transaction_rollback_codec.encode_request(self.id, self.thread_id)\\n invocation = Invocation(request, connection=self.connection)\\n invocation_service = self._context.invocation_service\\n invocation_service.invoke(invocation)\\n invocation.future.result()\\n self.state = _STATE_ROLLED_BACK\\n finally:\\n self._locals.transaction_exists = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4136b5b5e1521deb210dd63ae0478351\",\n \"score\": \"0.6650817\",\n \"text\": \"def rollback_transaction( self ):\\n self.session.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7758a2444b350729dad4c9eb19c6b750\",\n \"score\": \"0.6456801\",\n \"text\": \"def rollback(self):\\n self.local.tx.rollback()\\n del self.local.tx\\n self._release_internal()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0208acdce14a3c7b8facca1166cffa9\",\n \"score\": \"0.6456093\",\n \"text\": \"def rollback(self):\\n if self._autocommit:\\n warnings.warn(AUTOCOMMIT_MODE_WARNING, UserWarning, stacklevel=2)\\n elif self._transaction:\\n self._transaction.rollback()\\n self._release_session()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02b8c4f4d454b389bb1cf042cba8fd7d\",\n \"score\": \"0.63717693\",\n \"text\": \"def rollback(self):\\n try:\\n self.autocommit = True\\n if self.con:\\n self.con.rollback()\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb3c9e9999a63bfe88b8d72b2dd6a93\",\n \"score\": \"0.6293933\",\n \"text\": \"def step_see_transaction_rolled_back(context):\\n wrappers.expect_pager(context, \\\"ROLLBACK\\\\r\\\\n\\\", timeout=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da72a9b30c58b6a794562bae60841d2\",\n \"score\": \"0.62907517\",\n \"text\": \"def resetTransaction(self) :\\n self._Connection.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2dabd328baa36a75edd4a03cb818026\",\n \"score\": \"0.6281952\",\n \"text\": \"def rollback(self):\\n self._check_update_state('started', 'rolledback')\\n self._mutations = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aba4d887eebf8202441eb60d69f2b4b\",\n \"score\": \"0.6272492\",\n \"text\": \"def rollback ( self ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e6806c9dd1fc2f00e461621838058c7\",\n \"score\": \"0.62411606\",\n \"text\": \"def rollback():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cfd178d19dd88745ce6666bfe318b21\",\n \"score\": \"0.623781\",\n \"text\": \"def rollback(self, **kwargs):\\n\\n self.state = State.ROLLBACKING\\n\\n result = self.driver.process(transaction=self, **kwargs)\\n\\n self.state = State.COMMITTING if self.autocommit else State.PENDING\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cd941c48b6c95bd078bacfe49edaad8\",\n \"score\": \"0.62114483\",\n \"text\": \"def do_rollback(self, connection):\\n\\n connection.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f0605381a9c3990af2efcd24563be0\",\n \"score\": \"0.6204951\",\n \"text\": \"def _rollback(self):\\n\\n session = self.registry.get()\\n if session is not None:\\n session.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bcd29e62d2c600ebf19084fdb2243a\",\n \"score\": \"0.6173155\",\n \"text\": \"def rollback(self):\\n self.connection.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b82d35aa13eb80c21cdc6d9d0f69b231\",\n \"score\": \"0.6168288\",\n \"text\": \"def rollback(self):\\n self.dbconn.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"056eabaf87c2c53b5a487b50f640c40e\",\n \"score\": \"0.61432356\",\n \"text\": \"def rollback(self):\\n return self.adaptor.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ffb93b0db58c8a8a7cfce7ceff4e1a5\",\n \"score\": \"0.6125452\",\n \"text\": \"def rollback_session(self):\\n self.__session.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc9b6805c35e96f9baf6ffcf5d2ad731\",\n \"score\": \"0.60994065\",\n \"text\": \"def rollback(self):\\n self._crsr.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d377ed15eb9b6cfd8266930ce95a35c\",\n \"score\": \"0.60916424\",\n \"text\": \"def rollback(self):\\n self.db_conn.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b3346ea3ee430eba76c000673fc6f3\",\n \"score\": \"0.60875446\",\n \"text\": \"def rollback(self):\\n self.success = False\\n self.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ade94a81dc90878bb42760833ff79f\",\n \"score\": \"0.60712856\",\n \"text\": \"def ensure_transaction(self):\\n if not self._latest_transaction:\\n self.new_transaction()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d589f31b05c0a1a4217a626f5150e2\",\n \"score\": \"0.6067326\",\n \"text\": \"def resumeTransactionHelper(self):\\n try:\\n transaction = self.mint.completedTransactions.pop(0) # FIFO\\n except IndexError:\\n return\\n\\n while True:\\n transaction_id = transaction['transaction_id']\\n del transaction['transaction_id']\\n \\n self.updateTransaction(transaction_id, **transaction)\\n\\n try:\\n transaction = self.mint.completedTransactions.pop(0) # FIFO\\n except IndexError:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"029df82fa54ce31981a325d43c29fc8b\",\n \"score\": \"0.60606766\",\n \"text\": \"def refuse_transaction(self, tx):\\n block_with_tx = self._find_tx(tx)\\n if block_with_tx is None:\\n pass\\n elif block_with_tx != self[0]:\\n raise NotRemovableTransactionError\\n else:\\n if self[0].is_signed():\\n raise NotRemovableTransactionError\\n self[0]._remove_tx(tx)\\n return Transaction(\\n VERSION,\\n Transaction.REFUSAL,\\n signer=tx.target_user,\\n amount=tx.amount,\\n tx_date=datetime.date.today().isoformat(),\\n target_user=tx.signer,\\n detail=tx.signature,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac29a9a72c4ab03cc6cf3f4a0939b6ae\",\n \"score\": \"0.6016893\",\n \"text\": \"def rollback(self):\\n return self.conn.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4627d6f54108ac1d93175be9be18359\",\n \"score\": \"0.6006479\",\n \"text\": \"def rollback():\\n global _db_connection\\n _db_connection.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4af2ef8f42d41fbd7c82c9bcef0160f4\",\n \"score\": \"0.59700215\",\n \"text\": \"def rollback(self) -> NoReturn:\\n raise NotImplementedError(\\\"BigQueryConnection does not have transactions\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8861e12525bb52718ba71df381f28a1e\",\n \"score\": \"0.5967468\",\n \"text\": \"def rollback(self):\\n self._db_connection.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"407d3704c697029244b0185d3cf64e6e\",\n \"score\": \"0.5948704\",\n \"text\": \"def rollback(self, start_transaction=True) -> None:\\n self._rpsl_upsert_buffer = []\\n self._rpsl_pk_source_seen = set()\\n self.status_tracker.reset()\\n self.changed_objects_tracker.reset()\\n self._transaction.rollback()\\n if start_transaction:\\n self._start_transaction()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41bd4cf0b481aa347fabf227699ccdd3\",\n \"score\": \"0.5889816\",\n \"text\": \"def test_revert_error(error_contract, not_owner):\\n with reverts(error_contract.Unauthorized):\\n error_contract.withdraw(sender=not_owner)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0aa4816a56fb122b013432e2833f24a\",\n \"score\": \"0.58798784\",\n \"text\": \"def do_rollback(self, args):\\n self.__ensure_connected()\\n if self.__flag_is_set('autocommit'):\\n log.warning('Autocommit is enabled. \\\"rollback\\\" ignored')\\n else:\\n assert self.__db != None\\n self.__db.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aea98b9522cd6f6067fadc5e677b69a5\",\n \"score\": \"0.58307755\",\n \"text\": \"def abortTransaction(txnService):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1cc3cf3992180ba158fb06c5ff940bf\",\n \"score\": \"0.57920086\",\n \"text\": \"def revert(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fd1a633ba19678e1e76f0d3b084cc45\",\n \"score\": \"0.5791256\",\n \"text\": \"def __rollback_adhoc_txn(self, request, v1_transaction):\\n try:\\n v1_rollback_req = googledatastore.RollbackRequest()\\n v1_rollback_req.project_id = request.project_id\\n v1_rollback_req.transaction = v1_transaction\\n self._Dynamic_Rollback(v1_rollback_req,\\n googledatastore.RollbackResponse())\\n except apiproxy_errors.ApplicationError, e:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"063cba6a3c6dc0deac56c75c52511328\",\n \"score\": \"0.5769397\",\n \"text\": \"async def rollback(self) -> None:\\n await greenlet_spawn(self.sync_session.rollback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b577a234a3fd135b99a11f21babf40d\",\n \"score\": \"0.5766262\",\n \"text\": \"def revert():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d545a4d6c8c6661fb4906299c9e9df15\",\n \"score\": \"0.57517993\",\n \"text\": \"def resumeTransaction(self, transaction_id):\\n #FIXME: Only resumes minting/exchanges right now\\n self.resumeTransactionHelper()\\n return self.getTransaction(transaction_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb7132e3caa24e285b87cfb2089aac7\",\n \"score\": \"0.5740604\",\n \"text\": \"def rollback(self):\\n if not self.in_transaction:\\n raise NotInTransaction\\n for collection,store in self.stores.items():\\n store.rollback()\\n indexes = self.indexes[collection]\\n indexes_to_rebuild = []\\n for key,index in indexes.items():\\n try:\\n index.rollback()\\n except NotInTransaction:\\n #this index is \\\"dirty\\\" and needs to be rebuilt (probably it has been created within a transaction)\\n indexes_to_rebuild.append(key)\\n if indexes_to_rebuild:\\n self.rebuild_indexes(collection,indexes_to_rebuild)\\n self.in_transaction = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf9478439d40ce5c0a25bf1ac5798752\",\n \"score\": \"0.57385117\",\n \"text\": \"def Revert():\\n raise Exception(0x00)\\n # return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22b464b3f2c2725037a8262d25eaa4f\",\n \"score\": \"0.5735402\",\n \"text\": \"def after_rollback(self, session):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bff1f3f15ba87b8de17a466ddadd819\",\n \"score\": \"0.5733681\",\n \"text\": \"def roll_back(cls, key):\\r\\n\\r\\n logging.info('Rolling back the transfer with key %s.', key)\\r\\n\\r\\n transfer = key.get()\\r\\n assert transfer.state == 'pending'\\r\\n\\r\\n _id = '%s_rollback' % transfer.key.id()\\r\\n _parent = transfer.key.parent()\\r\\n _tags = [t for t in transfer.tags if t.prop != 'display']\\r\\n _tags.append(Tag.from_str('display:false'))\\r\\n dest_transfer = Tx.get_by_id(_id, parent=_parent)\\r\\n if not dest_transfer:\\r\\n dest_transfer = Tx(key=ndb.Key(Tx, _id, parent=_parent))\\r\\n dest_transfer.amount = -transfer.amount\\r\\n dest_transfer.target = transfer.key.parent()\\r\\n dest_transfer.state = 'canceled'\\r\\n dest_transfer.other = transfer.key\\r\\n\\r\\n dest_transfer.asset = transfer.asset\\r\\n dest_transfer.tags = _tags\\r\\n\\r\\n account = transfer.key.parent().get()\\r\\n\\r\\n value = account.balance(transfer.asset) - transfer.amount\\r\\n account.balance_update(transfer.asset, value)\\r\\n\\r\\n transfer.state = 'canceled'\\r\\n transfer.other = dest_transfer.key\\r\\n\\r\\n ndb.put_multi([account, dest_transfer, transfer])\\r\\n return dest_transfer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"156e59072f81cbfecb5e2593b679d65e\",\n \"score\": \"0.57209146\",\n \"text\": \"def withdraw(self, amount: float):\\n if amount > 0:\\n self.balance -= amount\\n timestamp = dt.today()\\n self.history['Withdrawals'].append(\\n {\\n 'Date': timestamp,\\n 'Amount': -amount,\\n 'Balance': self.balance\\n }\\n )\\n self.doc_manager.transaction_notice(trans_type='withdraw', amount=amount, timestamp=timestamp)\\n if self.balance < self.minimum_bal:\\n self.charge_overdraft_fee()\\n else:\\n raise ValueError('Withdrawal amount must be positive')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8aacdb5ce86e0c36fa78fae8f41c216\",\n \"score\": \"0.5709667\",\n \"text\": \"async def rollback(self) -> None:\\n await greenlet_spawn(self._proxied.rollback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc1327ab2639c70e46834b3b1bf7ac2\",\n \"score\": \"0.5704441\",\n \"text\": \"def unapply_transaction(a_id, e_id, amt):\\n if a_id is not None:\\n newaccountbalance = get_account_balance(a_id) + amt\\n update_account_balance(a_id, newaccountbalance)\\n if e_id is not None:\\n newenvelopebalance = get_envelope_balance(e_id) + amt\\n update_envelope_balance(e_id, newenvelopebalance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d78579c49dc73c1196ca0bc7d2c5b9f\",\n \"score\": \"0.56839937\",\n \"text\": \"def rebalance(context, data):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c9474b6c2a3d219119fcb5c14eddcf\",\n \"score\": \"0.5675721\",\n \"text\": \"def setTxnState(outsideTxn):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a46cf60f5a39eb3faa88b2efa0281c4\",\n \"score\": \"0.5624804\",\n \"text\": \"def rollback(self, x=None):\\n if self.halt:\\n return\\n self.halt = True\\n if self.status != 0:\\n self.update(\\\"The process cannot be interrupted at this stage\\\")\\n return\\n\\n self.update(\\\"Rolling back any changes...\\\")\\n if self.dbh:\\n self.dbh.rollback()\\n try:\\n os.remove(self.tarfile)\\n except:\\n self.update(f\\\"Could not remove {self.archive_root}/log/{self.tarfile}\\\", True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd3fe880197b084331de5007716b4aa2\",\n \"score\": \"0.56194234\",\n \"text\": \"def start_rollback(self) -> bool:\\n self.event.state = ROLLBACK\\n return self._run_around('Commanding {} to roll back')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ab021c81b6716230207c9ff62f196d\",\n \"score\": \"0.5615406\",\n \"text\": \"async def rollback_to_block(self, height: int) -> None:\\n\\n async with self.db_wrapper.writer_maybe_transaction() as conn:\\n await (await conn.execute(\\\"DELETE FROM coin_record WHERE confirmed_height>?\\\", (height,))).close()\\n await (\\n await conn.execute(\\n \\\"UPDATE coin_record SET spent_height = 0, spent = 0 WHERE spent_height>?\\\",\\n (height,),\\n )\\n ).close()\\n self.total_count_cache.cache.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ad030084e79ef3c5be1bc3d2e1bd43\",\n \"score\": \"0.56015414\",\n \"text\": \"def rollback(self, blockhash):\\n txs = self.db.txes.find({\\\"blockhash\\\": blockhash})\\n transactions = []\\n if txs:\\n for i in txs:\\n transactions.append(i)\\n self.rollback_addresses(transactions)\\n self.db.txes.delete_many({\\\"blockhash\\\": blockhash})\\n self.db.blocks.delete_many({\\\"hash\\\": blockhash})\\n self.db.votes.delete_many({\\\"block_hash\\\": blockhash})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"238c5528b82792bf6629c3e2fd807301\",\n \"score\": \"0.55953515\",\n \"text\": \"def test_revert_error_inputs(error_contract, not_owner):\\n with reverts(error_contract.Unauthorized, addr=not_owner.address, counter=123):\\n error_contract.withdraw(sender=not_owner)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7427ba374814f4a3be142b18dd85ad05\",\n \"score\": \"0.5594636\",\n \"text\": \"def on_return(self, connection) -> None:\\n connection.rollback()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c79f0173b68552a5ee876023e0929e1\",\n \"score\": \"0.55746764\",\n \"text\": \"def test_revert(owner, reverts_contract_instance):\\n with reverts(\\\"zero\\\"):\\n reverts_contract_instance.revertStrings(0, sender=owner)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae3a6bf887542d2a32c3b56c708c8ed\",\n \"score\": \"0.553997\",\n \"text\": \"def withdraw(self, amount):\\r\\n self.balance -= amount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae3a6bf887542d2a32c3b56c708c8ed\",\n \"score\": \"0.553997\",\n \"text\": \"def withdraw(self, amount):\\r\\n self.balance -= amount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af5757794b8953a6f081b9c36205067f\",\n \"score\": \"0.5524872\",\n \"text\": \"def mark_app_valid_cancel_rollback(\\n cls,\\n ) -> Any:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bab043779d6ef1213edff42f18e94040\",\n \"score\": \"0.5499845\",\n \"text\": \"def test_locking(self):\\n self.cur1.execute(\\\"create table test(i)\\\")\\n self.cur1.execute(\\\"insert into test(i) values (5)\\\")\\n with self.assertRaises(sqlite.OperationalError):\\n self.cur2.execute(\\\"insert into test(i) values (5)\\\")\\n # NO self.con2.rollback() HERE!!!\\n self.con1.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cf94871aba749d634a8b1ff71bd4af9\",\n \"score\": \"0.54977715\",\n \"text\": \"def test_postcondition_transaction(self):\\n self.command.run(username='rich_user', stock_symbol='ABAB',\\n amount='23.45')\\n set_transaction = self.session.query(Trigger).filter_by(\\n username='rich_user',\\n stock_symbol='ABAB', \\n state=Trigger.State.INACTIVE,\\n operation='BUY').first()\\n self.assertNotEqual(set_transaction, None)\\n self.assertEqual(set_transaction.amount, Money(23, 45))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cac0eeef9b86ee8152ae72868686808\",\n \"score\": \"0.5491284\",\n \"text\": \"def test_withdraw_transaction(self):\\n user = User.objects.get(phone_number=user_1_login['phone_number'])\\n account = Account.objects.get(id=self.account.data['id'])\\n transaction = Transaction()\\n transaction.created_by = user\\n transaction.account = account\\n transaction.type = \\\"withdraw\\\"\\n transaction.amount = 5000\\n transaction.prev_balance = 10000\\n transaction.calculate_new_balance()\\n transaction.save()\\n self.assertEqual(transaction.new_balance, 5000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7b668d1e8721ec521558f891c030a93\",\n \"score\": \"0.54802364\",\n \"text\": \"def act(self) -> None:\\n transactions = cast(Transactions, self.context.transactions)\\n transactions.update_confirmed_transactions()\\n transactions.cleanup_pending_transactions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d25a64f440768b544df85afc6a28c75\",\n \"score\": \"0.54692274\",\n \"text\": \"def decline(self):\\n self.is_redeemed = False\\n self.is_active = False\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3726441c44f4abe6484b338fe66f2750\",\n \"score\": \"0.54662144\",\n \"text\": \"def transaction_checkout(self):\\n if not self.autocommit:\\n if (\\n not self._transaction\\n or self._transaction.committed\\n or self._transaction.rolled_back\\n ):\\n self._transaction = self._session_checkout().transaction()\\n self._transaction.begin()\\n\\n return self._transaction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fcefdc7c51e6f7bab023cd775939cd9\",\n \"score\": \"0.5444686\",\n \"text\": \"def Revert():\\n raise Exception(0x00)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5ebc87a3a9d25290027632893d0bf59\",\n \"score\": \"0.5437343\",\n \"text\": \"def undo(self, context, event):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"686d14e22d96a9bf5eebb903c267d52d\",\n \"score\": \"0.54356784\",\n \"text\": \"def test_postcondition_transaction(self):\\n self.command.run(username='rich_user', stock_symbol='ABAB',\\n quantity='2')\\n set_transaction = self.session.query(Trigger).filter_by(\\n username='rich_user',\\n stock_symbol='ABAB', \\n state=Trigger.State.INACTIVE,\\n quantity=2,\\n operation='SELL').first()\\n self.assertNotEqual(set_transaction, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9baf91835f719cd56c361d4d3038bb31\",\n \"score\": \"0.54167587\",\n \"text\": \"def untransact(database=None):\\n\\n def _f(f):\\n def g(*args, **kw):\\n starttime = datetime.datetime.utcnow()\\n\\n dbase = database\\n if dbase is None:\\n raise Exception('dbase is None')\\n \\n _transact_begin_log_helper('@untransact', f, dbase)\\n if (dbase is not None) and hasattr(dbase, 'store') and (dbase.store is not None):\\n # hasattr + store check is to fix #621, this is also an API issue with rdf.py\\n t = dbase.begin_untransaction()\\n else:\\n t = None\\n\\n global toplevel_transaction\\n\\n # Log transact locktime here if active\\n if t is not None:\\n # XXX: There is no sensible date value to show here,\\n # because we are not actually logging anything for\\n # current untransaction: we log the locktime of the\\n # toplevel transaction which is committed here. Using\\n # current time as both timestamps for now.\\n _transact_end_log_helper('@transact (in untransact)', toplevel_transaction, dbase, True, datetime.datetime.utcnow(), datetime.datetime.utcnow(), t.get_txn_locked_time(), TRANSACT_LOCK_TIME_WARNING_LIMIT, untransact_point=f)\\n\\n try:\\n ret = f(*args, **kw)\\n except:\\n t_locked_time = None\\n if t is not None:\\n t_locked_time = t.get_locked_time()\\n t.commit()\\n endtime = datetime.datetime.utcnow()\\n _transact_end_log_helper('@untransact', f, dbase, False, starttime, endtime, t_locked_time, UNTRANSACT_LOCK_TIME_WARNING_LIMIT)\\n raise\\n\\n t_locked_time = None\\n if t is not None:\\n t_locked_time = t.get_locked_time()\\n t.commit()\\n endtime = datetime.datetime.utcnow()\\n _transact_end_log_helper('@untransact', f, dbase, True, starttime, endtime, t_locked_time, UNTRANSACT_LOCK_TIME_WARNING_LIMIT)\\n \\n return ret\\n\\n mergeFunctionMetadata(f, g)\\n return g\\n return _f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d7ebf48b5fe2ba2e04f8d6fabf04ec8\",\n \"score\": \"0.54075825\",\n \"text\": \"def revert(self, x: bool):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d22517edd3852f2cdf44b9aecc920ff\",\n \"score\": \"0.5406235\",\n \"text\": \"def revert(self, record, new_password):\\n self.rotate(record, new_password, revert=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1960790c4e6bcd26fd254c245fb4c970\",\n \"score\": \"0.54058766\",\n \"text\": \"def withdraw(self, amount: float):\\n timestamp = dt.today()\\n if len(self.history['Withdrawals']) < self.withdrawal_lim:\\n if 0 < amount <= self.balance:\\n self.balance -= amount\\n self.history['Withdrawals'].append(\\n {\\n 'Date': timestamp,\\n 'Amount': -amount,\\n 'Balance': self.balance\\n }\\n )\\n self.doc_manager.transaction_notice(trans_type='withdraw', amount=amount, timestamp=timestamp)\\n if len(self.history['Withdrawals']) == self.withdrawal_lim:\\n self.doc_manager.withdraw_lim_notice(timestamp, past_limit=False)\\n if self.balance < self.minimum_bal:\\n self.charge_overdraft_fee()\\n elif amount > 0:\\n self.doc_manager.insufficient_balance_notice(timestamp, amount)\\n else:\\n raise ValueError('Withdrawal amount must be positive')\\n else:\\n self.doc_manager.withdraw_lim_notice(timestamp, past_limit=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"090238f37c7a355a9bc0187828c6ecbd\",\n \"score\": \"0.54015446\",\n \"text\": \"def withdraw(self, amount):\\n self.initial_balance -= amount\\n if self.initial_balance < 0:\\n self.initial_balance -= 5\\n self.fee_count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db62db91d3d3d95d47df5942179d251d\",\n \"score\": \"0.53914046\",\n \"text\": \"def undo(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db62db91d3d3d95d47df5942179d251d\",\n \"score\": \"0.53914046\",\n \"text\": \"def undo(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db62db91d3d3d95d47df5942179d251d\",\n \"score\": \"0.53914046\",\n \"text\": \"def undo(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"624941732c7ede972950817498a1e9fa\",\n \"score\": \"0.53727204\",\n \"text\": \"def transaction(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6864303bf5378b1cd878c6582bbdd26d\",\n \"score\": \"0.53647685\",\n \"text\": \"def rollback(self, err=None, swallow=False):\\n if not self.no_commit:\\n try:\\n self.connection.dbc.rollback()\\n if err is not None:\\n logging.info(\\n \\\"rollback invoked for transaction following error %s\\\",\\n str(err))\\n except self.api.NotSupportedError:\\n if err is not None:\\n if self.query_count:\\n logging.error(\\n \\\"Data Integrity Error on TABLE %s: rollback \\\"\\n \\\"invoked on a connection that does not \\\"\\n \\\"support transactions after error %s\\\",\\n self.table_name,\\n str(err))\\n else:\\n logging.info(\\n \\\"Query failed following error %s\\\", str(err))\\n pass\\n if err is not None and not swallow:\\n logging.debug(\\n ' '.join(\\n traceback.format_exception(*sys.exc_info(), limit=6)))\\n if isinstance(err, self.api.Error):\\n raise SQLError(str(err))\\n else:\\n raise err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb2988be3d68619e8f9e97c4ce751874\",\n \"score\": \"0.53640985\",\n \"text\": \"def rollback(self):\\n if self.changed:\\n rollback_cmd = '<load><config><from>{0}</from></config></load>'.format(self.backup_file)\\n self.device.op(cmd=rollback_cmd)\\n time.sleep(5)\\n\\n if self.ssh_connection is False:\\n self._open_ssh()\\n try:\\n self.ssh_device.commit()\\n self.loaded = False\\n self.changed = False\\n self.merge_config = False\\n except: # noqa\\n ReplaceConfigException(\\\"Error while loading backup config\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5cae5efbc908b30d1d5b441354454e\",\n \"score\": \"0.53530693\",\n \"text\": \"def test_rollback():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d68a6cad2a59b303050393306eaf6229\",\n \"score\": \"0.53448886\",\n \"text\": \"def withdraw(self, amount: float):\\r\\n if amount <= self.__balance:\\r\\n self.__balance -= amount\\r\\n print(\\\"Withdraw Accepted!!\\\")\\r\\n else:\\r\\n print(\\\"Fund Unavailable!!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428e8d974e8de4669d0d833960a5e009\",\n \"score\": \"0.5343247\",\n \"text\": \"def withdraw(self, amount):\\r\\n if amount > self.balance:\\r\\n raise RuntimeError('Amount greater than available balance.')\\r\\n self.balance -= amount\\r\\n return self.balance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca28e2023bbe7ddedd7cf8be68d55bb\",\n \"score\": \"0.5341239\",\n \"text\": \"def unlock(self):\\r\\n self.locked = False\\r\\n self.lock_id = None\\r\\n\\r\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2892e562d20a7ebcbf68e3bdce32e196\",\n \"score\": \"0.5339073\",\n \"text\": \"def withdraw(self, amount):\\r\\n self.setBalance(self.getBalance() - amount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d2f48f384f0bf055382e309b19b8cb\",\n \"score\": \"0.5329359\",\n \"text\": \"def test_revert_abi(error_contract, not_owner):\\n with reverts(error_contract.Unauthorized.abi):\\n error_contract.withdraw(sender=not_owner)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b662e66a411fefb469506ba1b3d09cf7\",\n \"score\": \"0.53228027\",\n \"text\": \"def pop_locked_tx(self, transaction_id: TransactionId) -> Transaction:\\n assert transaction_id in self.locked_txs\\n transaction = self.locked_txs.pop(transaction_id)\\n self.locked_txs_as_buyer.pop(transaction_id, None)\\n self.locked_txs_as_seller.pop(transaction_id, None)\\n return transaction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91d2c172703a08f8572c3acbded2667\",\n \"score\": \"0.52968985\",\n \"text\": \"def withdraw(self, amount):\\n self.cash -= amount\\n return amount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b728bbfc35b6e1ce307b19911e99edea\",\n \"score\": \"0.5291398\",\n \"text\": \"def _rollback(self, pk):\\n\\n # use `set()`, before the loop, to avoid using self set that may be updated during the process\\n cache = self._get_rollback_cache(pk)\\n indexed_values = set(cache.get('indexed_values'))\\n deindexed_values = set(cache.get('deindexed_values'))\\n\\n for args in indexed_values:\\n self.remove(pk, *args)\\n for args in deindexed_values:\\n self.add(pk, *args, check_uniqueness=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"810096229f3b0dba25604dbf2bcb2803\",\n \"score\": \"0.52866334\",\n \"text\": \"def withdraw(self, amount):\\n if amount > self.balance:\\n raise RuntimeError('Amount greater than available balance.')\\n self.balance -= amount\\n return self.balance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46939a71acf827c57589b0ce4fef952\",\n \"score\": \"0.52706575\",\n \"text\": \"async def rollback(self, height: int, wallet_id_arg: int) -> None:\\n\\n async with self.db_wrapper.writer_maybe_transaction() as conn:\\n cursor = await conn.execute(\\n \\\"DELETE FROM pool_state_transitions WHERE height>? AND wallet_id=?\\\", (height, wallet_id_arg)\\n )\\n await cursor.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97920a9bfefb778f949dd2efa7da61ff\",\n \"score\": \"0.5257855\",\n \"text\": \"def RevertToPrevState( self ):\\n self.ChangeState( self.PreviousState )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3741e2f7f34705ac7516ba2dfc2a8c1\",\n \"score\": \"0.5254019\",\n \"text\": \"def decline(self):\\n self.is_active=False\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d99ec010c0624a0b574b9e107f149918\",\n \"score\": \"0.5242379\",\n \"text\": \"def norollback(*pats, **opts):\\n\\traise hg_util.Abort(\\\"codereview extension enabled; use undo instead of rollback\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ad626017b0f9bf1ab251801908f247d\",\n \"score\": \"0.52417153\",\n \"text\": \"def withdraw(self, withdrawAmount): \\n if withdrawAmount > self.balance: \\n raise RuntimeError('Amount greater than available balance.')\\n else:\\n self.balance -= withdrawAmount \\n return self.balance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abf7bb52a5f4ad77dea23f5b7038f77e\",\n \"score\": \"0.5238773\",\n \"text\": \"def transBegin(self):\\n self.autocommit = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6dc85094d69ef1f3745c1e7e414346\",\n \"score\": \"0.52346563\",\n \"text\": \"def commit_transaction(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f3531ec8ef01abb757fe7e15c4941fd\",\n \"score\": \"0.5232039\",\n \"text\": \"def cancel_rr_transaction(self):\\n self._rr_txn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3831d8d650420d609ab643854cf4ea3c\",\n \"score\": \"0.522349\",\n \"text\": \"def _action_finally(self):\\n self.model_update_suspended = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8f6bc98a21fc65d07e5eb26088bac8\",\n \"score\": \"0.5218564\",\n \"text\": \"def revert_call(context: 'IconScoreContext') -> None:\\n if context.type != IconScoreContextType.INVOKE:\\n return\\n\\n context.tx_batch.revert_call()\\n context.event_logs.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d7d6f0c868569bf5d69ac880528c880\",\n \"score\": \"0.52167886\",\n \"text\": \"def test_revert_fails(owner, reverts_contract_instance):\\n with pytest.raises(AssertionError):\\n with reverts(\\\"one\\\"):\\n reverts_contract_instance.revertStrings(0, sender=owner)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6681a205e3d3dfb9bc6746199a420224\",\n \"score\": \"0.5216677\",\n \"text\": \"def recommit(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1dc1bb1515c557f3459065f413309b\",\n \"score\": \"0.52152956\",\n \"text\": \"def withdrawal(self, amount=0, date=None):\\n self.cash[date:] -= amount\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c0283939cf9ff41c25d3ca464e3f081\",\n \"score\": \"0.52143127\",\n \"text\": \"def __unlock_trade(self):\\n if self.trd_env == TrdEnv.REAL:\\n ret, data = self.trade_ctx.unlock_trade(password_md5=self.password_md5)\\n if ret == RET_OK:\\n self.default_logger.info(\\\"Account Unlock Success.\\\")\\n else:\\n raise Exception(\\\"Account Unlock Unsuccessful: {}\\\".format(data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854d51e11e90bca5af2ddec95167ac68\",\n \"score\": \"0.52122086\",\n \"text\": \"def end_transaction(self):\\n self.locked = False\\n for market in self.markets:\\n market.end_transaction()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992988,"cells":{"query_id":{"kind":"string","value":"9092e79339bd6a32b19c144508d510a5"},"query":{"kind":"string","value":"Transform image perspective using OpenCV functions"},"positive_passages":{"kind":"list like","value":[{"docid":"b437a995dca3f0361343df4ad097ff0e","score":"0.7015952","text":"def warp(self, image):\n\t\treturn cv2.warpPerspective(image, self.transformation_matrix, (image.shape[0], image.shape[1]), flags=cv2.INTER_CUBIC)","title":""}],"string":"[\n {\n \"docid\": \"b437a995dca3f0361343df4ad097ff0e\",\n \"score\": \"0.7015952\",\n \"text\": \"def warp(self, image):\\n\\t\\treturn cv2.warpPerspective(image, self.transformation_matrix, (image.shape[0], image.shape[1]), flags=cv2.INTER_CUBIC)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e4d720145f38baea418efe75d75cc9c6","score":"0.7743494","text":"def perspect_transform(img, src, dst):\n M = cv2.getPerspectiveTransform(src, dst)\n warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))# keep same size as input image\n \n return warped","title":""},{"docid":"8ed84371c6e4b9b0a616b87197831285","score":"0.77332985","text":"def perspective_transform(self):\n bc_im = self.barrel_correct()\n self.perspective_coeffs = self.run.perspective_coefficients[self.cam]\n return processing.perspective_transform(bc_im, self.perspective_coeffs)","title":""},{"docid":"89a683b5949e64e99a707a19c63d3c5f","score":"0.7535765","text":"def perspective_transform(src_points, dst_points):\n mtx_perp = cv2.getPerspectiveTransform(src_points, dst_points)\n mtx_perp_inv = cv2.getPerspectiveTransform(dst_points, src_points)\n return mtx_perp, mtx_perp_inv","title":""},{"docid":"e984ad46c79faa367fe323de4107c42d","score":"0.7529859","text":"def perspect_transform(img, src, dst):\n M = cv2.getPerspectiveTransform(src, dst)\n # Keep same size as input image\n warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))\n return warped","title":""},{"docid":"51a927c8a65a526b5a9c11340148cda6","score":"0.7499476","text":"def perspective_tf(src, dst):\n M = cv2.getPerspectiveTransform(src, dst)\n Minv = cv2.getPerspectiveTransform(dst, src)\n\n return M, Minv","title":""},{"docid":"0b029d32a9f17f4674fab0a20c53c6de","score":"0.7456419","text":"def compute_perspective_transform(mtx, dist, img_path='../test_images/straight_lines1.jpg'):\n # Finding perspective transformation matrix to get bird's eye view on images.\n src_image = load_image(img_path)\n undistorted = undistort_image(src_image, mtx, dist)\n return find_perspective_transform(undistorted, save_transform=True)","title":""},{"docid":"6415178aabe8acac3bc29f9d89da512a","score":"0.73797","text":"def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC):\n\n coeffs = _get_perspective_coeffs(startpoints, endpoints)\n return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation, fillcolor=(255, 255, 255))","title":""},{"docid":"0b9e56e2bcc27756f8c993b9cd774b68","score":"0.7188372","text":"def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC):\n if not _is_pil_image(img):\n raise TypeError('img should be PIL Image. Got {}'.format(type(img)))\n\n coeffs = _get_perspective_coeffs(startpoints, endpoints)\n return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation)","title":""},{"docid":"89f11bcc6757d97bf9756bdc9f246a08","score":"0.7187068","text":"def find_perspective(self, save = False, path=\"\"):\n \n #Get corresponding points\n pnts1, pnts2 = self._image_points()\n if len(pnts1)!=4 or len(pnts2)!=4:\n raise ValueError(\"Must have exactly four corresponding image points\")\n #Get transform\n perspective_transform = cv2.getPerspectiveTransform(pnts1, pnts2)\n if save:\n #Save data to text file\n np.savetxt(path+\"perspective_transform.txt\", \n perspective_transform.reshape(1,9), \n delimiter=',', fmt=\"%f\") \n \n return perspective_transform","title":""},{"docid":"1ecfe5b6e5f81f4ba6b97dfb36597a60","score":"0.7159043","text":"def four_point_transform(image, pts):\n # obtain a consistent order of the points and unpack them\n # individually\n rect = order_points(pts)\n (tl, tr, br, bl) = rect\n\n # compute the width of the new image, which will be the\n # maximum distance between bottom-right and bottom-left\n # x-coordiates or the top-right and top-left x-coordinates\n widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\n widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\n maxWidth = max(int(widthA), int(widthB))\n\n # compute the height of the new image, which will be the\n # maximum distance between the top-right and bottom-right\n # y-coordinates or the top-left and bottom-left y-coordinates\n heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\n heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\n maxHeight = max(int(heightA), int(heightB))\n\n # now that we have the dimensions of the new image, construct\n # the set of destination points to obtain a \"birds eye view\",\n # (i.e. top-down view) of the image, again specifying points\n # in the top-left, top-right, bottom-right, and bottom-left\n # order\n dst = np.array([\n [0, 0],\n [maxWidth - 1, 0],\n [maxWidth - 1, maxHeight - 1],\n [0, maxHeight - 1]], dtype=\"float32\")\n\n # compute the perspective transform matrix and then apply it\n mat = cv2.getPerspectiveTransform(rect, dst)\n warped = cv2.warpPerspective(image, mat, (maxWidth, maxHeight))\n\n # If the image is horizontally long, rotate it by 90\n if maxWidth > maxHeight:\n center = (maxHeight / 2, maxHeight / 2)\n mat_rot = cv2.getRotationMatrix2D(center, 270, 1.0)\n warped = cv2.warpAffine(warped, mat_rot, (maxHeight, maxWidth))\n\n # return the warped image\n return warped","title":""},{"docid":"94b40f13a3d100a5b765329e6b433983","score":"0.7141698","text":"def warp_image(img, mtx_perp, flags=cv2.INTER_LINEAR):\n img_size = (img.shape[1], img.shape[0])\n return cv2.warpPerspective(img, mtx_perp, img_size, flags=cv2.INTER_LINEAR)","title":""},{"docid":"254531bce92c9cc7dddb238d8901910d","score":"0.7080208","text":"def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC, fill=None):\n\n opts = _parse_fill(fill, img)\n\n coeffs = _get_perspective_coeffs(startpoints, endpoints)\n return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation, **opts)","title":""},{"docid":"f1aa5afeddd91e71fde0257483e2908b","score":"0.6956818","text":"def transform(x, y, M):\n xy_t = np.squeeze(\n cv2.perspectiveTransform(\n np.dstack(\n [\n x,\n y\n ]\n ),\n np.asarray(M)\n )\n )\n return xy_t[:, 0], xy_t[:, 1]","title":""},{"docid":"1dbe17fa188f33afc935b49cb6af2484","score":"0.69471586","text":"def warp(self, image):\n return cv2.warpPerspective(image, self._transformationMatrix, self._imageSize)","title":""},{"docid":"bca9bbbcb8c14a4fa9ca716bba6b1e3d","score":"0.6917844","text":"def four_point_transform(image, pts):\n rect = order_points(pts)\n (tl, tr, br, bl) = rect\n widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\n widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\n maxWidth = max(int(widthA), int(widthB))\n heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\n heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\n maxHeight = max(int(heightA), int(heightB))\n dst = np.array([\n [0, 0],\n [maxWidth - 1, 0],\n [maxWidth - 1, maxHeight - 1],\n [0, maxHeight - 1]], dtype=\"float32\")\n M = cv2.getPerspectiveTransform(rect, dst)\n warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n return [warped, M]","title":""},{"docid":"6b8ce4e0dfb348632359173d0af3d802","score":"0.6833071","text":"def perspective_2_top(img):\n\n warped = cv2.warpPerspective(\n img,\n PERSPECTIVE_2_TOP,\n (TOP_WIDTH, TOP_HEIGHT))\n\n warped[TOP_CENTER_Y:, :] = 0\n\n return warped","title":""},{"docid":"21729961e276007e1eaf40a7f9695188","score":"0.6827648","text":"def getTransformationMatrix(input_points, output_img_res = (2000, 1300)):\n\n mat = cv2.getPerspectiveTransform(input_points, output_points)\n\n return mat","title":""},{"docid":"7303e20bf6f200677223b0e370e4e2c4","score":"0.68097365","text":"def warp_p(binary_img):\n \n src = np.float32([[580, 450],\n [180, 720],\n [1120, 720],\n [700, 450]])\n \n dst = np.float32([[350, 0],\n [350, 720],\n [900, 720],\n [900, 0]])\n \n M = cv2.getPerspectiveTransform(src, dst)\n \n Minv = cv2.getPerspectiveTransform(dst, src)\n\n binary_warped = cv2.warpPerspective(binary_img, M, (binary_img.shape[1], binary_img.shape[0]), flags=cv2.INTER_LINEAR)\n \n return binary_warped, M, Minv","title":""},{"docid":"239831ef6722ba63d1ae742704ea259c","score":"0.6792931","text":"def addPerspectivePoints(img, topLeft, topRight, bottomLeft, bottomRight):\n cv2.circle(img, topLeft, radius=5, color=(255,255,255))\n cv2.circle(img, topRight, radius=5, color=(255,255,255))\n cv2.circle(img, bottomLeft, radius=5, color=(255,255,255))\n cv2.circle(img, bottomRight, radius=5, color=(255,255,255))\n cv2.line(img=img, pt1=topLeft, pt2=topRight, color=(255,255,255), thickness=2)\n cv2.line(img=img, pt1=topLeft, pt2=bottomLeft, color=(255,255,255), thickness=2)\n cv2.line(img=img, pt1=bottomRight, pt2=bottomLeft, color=(255,255,255), thickness=2)\n cv2.line(img=img, pt1=bottomRight, pt2=topRight, color=(255,255,255), thickness=2)","title":""},{"docid":"0152ff3c90418d880ea2c6196e9b1636","score":"0.67800635","text":"def warper(img):\n M, src, dst = get_warp_matrix(img)\n warped = cv.warpPerspective(img, M, (img.shape[1], img.shape[0]), flags=cv.INTER_LINEAR)\n return warped, src, dst","title":""},{"docid":"a78e821a1f64a81a4d838dfaed67c961","score":"0.67549574","text":"def unwarp(self, image):\t\n\t\treturn cv2.warpPerspective(image, self.inverse_transformation_matrix, (image.shape[0], image.shape[1]), flags=cv2.INTER_CUBIC)","title":""},{"docid":"b07361018e8ba9da0c22527422fcf464","score":"0.67405623","text":"def __init__(self, src, dst):\n self.src = src\n self.dst = dst\n self.M = cv2.getPerspectiveTransform(src, dst)\n self.M_inv = cv2.getPerspectiveTransform(dst, src)","title":""},{"docid":"9b172335cbd4819dd5a11a583a322c88","score":"0.66762197","text":"def warpCanvas(image, homography, min_xy, max_xy):\n # canvas_size properly encodes the size parameter for cv2.warpPerspective,\n # which requires a tuple of ints to specify size, or else it may throw\n # a warning/error, or fail silently\n canvas_size = tuple(np.round(max_xy - min_xy).astype(np.int))\n # WRITE YOUR CODE HERE\n #print ('canvas_size =', canvas_size)\n translation_mat = np.float32(np.identity(3))\n translation_mat[0,2] = np.float32(-min_xy[0])\n translation_mat[1,2] = np.float32(-min_xy[1])\n #print translation_mat\n new_mat = np.dot(translation_mat,homography)\n \n new_image = cv2.warpPerspective(image, new_mat, (canvas_size))\n #new_image = cv2.warpPerspective(image, new_mat, (canvas_size))\n #print new_mat\n #cv2.imshow('image_new.png', new_image)\n #cv2.waitKey(0)\n #print new_image\n return new_image","title":""},{"docid":"1296a53c77c631a770e9b29491ff9aae","score":"0.66579324","text":"def birds_eye_view(corner_points,width,height,image):\n # Create an array out of the 4 corner points\n corner_points = np.float32(corner_points)\n # Create an array with the parameters (the dimensions) required to build the matrix\n img_params = np.float32([[0,0],[width,0],[0,height],[width,height]])\n # Compute and return the transformation matrix\n matrix = cv.getPerspectiveTransform(corner_points,img_params)\n img_transformed = cv.warpPerspective(image,matrix,(width,height))\n \n return matrix,img_transformed","title":""},{"docid":"0fe8f12d862a4707a48302d77871a703","score":"0.6622924","text":"def compute_homography(image, scene_description, x0, x1, y0, y1):\n # create the correct transformation and apply it \n # first, use the scene description to get image space to world space transformation\n # origin is set to the bottom-left corner of the foreground object\n cam_to_im = scene_description.camera_matrix\n world_to_cam, T = scene_description.get_world_to_camera_transformation(origin=(x0, y0))\n cam_to_world = numpy.linalg.inv(world_to_cam)\n world_to_im = cam_to_im.dot(world_to_cam)\n im_to_world = cam_to_world.dot(numpy.linalg.inv(cam_to_im))\n \n ## usually in world space, x points left, y toward cam, z points up (z=0 is ground plane) rel to image space \n # but don't assume anything about how the world-space coords are aligned, just figure it out based on\n # biggest diff between top and bottom, left and right of image about the principal point\n \n world_top = im_to_world.dot(numpy.array((image.width / 2.0, 0, 1)))\n world_bottom = im_to_world.dot(numpy.array((image.width / 2.0, image.height - 1, 1)))\n up_index = abs(world_top - world_bottom)[:3].argmax()\n up_flipped = False\n if (world_top - world_bottom)[up_index] < 0:\n up_flipped = True\n \n world_left = im_to_world.dot(numpy.array((0, image.height / 2.0, 1)))\n world_right = im_to_world.dot(numpy.array((image.width - 1, image.height / 2.0, 1)))\n right_index = abs(world_right - world_left)[:3].argmax()\n right_flipped = False\n if (world_right - world_left)[right_index] < 0:\n right_flipped = True\n cam_index = 0 if ((up_index == 1 and right_index == 2) or (up_index == 2 and right_index == 1)) else \\\n (1 if ((up_index == 0 and right_index == 2) or (up_index == 2 and right_index == 0)) else 2)\n \n # get height of object in world space\n object_top = im_to_world.dot(numpy.array([x0,y1,1]))\n height = abs(object_top[up_index] - T[up_index])\n\n # TODO: would be nice to parametrize the desired light position in the absence of automatic light estimation\n # for now, just pick an arbitrary position\n # set the light object 1 height above the camera, partway between the object and the camera\n cam_to_obj = T\n w = .2 # how far along the line from camera to object plane [0,1]\n h = 1.6 # how many units in foreground hight space the light is above the camera\n # make the light a bit to the side to show off the shadow a bit more\n light_pos = numpy.zeros((4,), dtype='float64')\n if right_flipped:\n light_pos[right_index] = cam_to_obj[right_index] * w + height/4\n else:\n light_pos[right_index] = cam_to_obj[right_index] * w - height/4\n light_pos[cam_index] = cam_to_obj[cam_index] * w\n light_pos[up_index] = h*height if not up_flipped else - h*height \n light_pos[3] = 1\n\n # create a matrix to project the planar polygon onto the ground plane (y=foreground_pos[1])\n # from the light \n # e.g. see http://math.stackexchange.com/questions/320527/projecting-a-point-on-a-plane-through-a-matrix\n plane = numpy.zeros((4,))\n plane[up_index] = 1\n plane[3] = -T[up_index] # assumes bottom left point of foreground plane touches ground\n lambd = numpy.dot(plane, light_pos)\n l = light_pos\n f = plane\n world_to_ground = numpy.array([[lambd - f[0]*l[0], -f[1]*l[0], -f[2]*l[0], -f[3]*l[0]],\n [-f[0]*l[1], lambd - f[1]*l[1], -f[2]*l[1], -f[3]*l[1]],\n [-f[0]*l[2], -f[1]*l[2], lambd - f[2]*l[2], -f[3]*l[2]],\n [-f[0]*l[3], -f[1]*l[3], -f[2]*l[3], lambd - f[3]*l[3]]])\n \n # finally, just project the 4 corners of the object in world space onto the ground plane and then\n # convert those 4 points back into image space\n # this gives us the correspondences needed to get the perspective transformation in image space\n im_pts = numpy.array([(x0,y0),(x0,y1),(x1,y0),(x1,y1)],dtype='float64')\n im_pts_hom = cv2.convertPointsToHomogeneous(im_pts).squeeze()\n im_pts_ground = []\n \n for im_pt in im_pts_hom:\n pt_world = im_to_world.dot(im_pt)\n pt_world = numpy.array([pt_world[0], pt_world[1], pt_world[2], 1])\n ground_world = world_to_ground.dot(pt_world)\n ground_im = world_to_im.dot(ground_world[:3])\n im_pts_ground.append(ground_im)\n\n # perspective divide\n im_pts_ground = cv2.convertPointsFromHomogeneous(numpy.array(im_pts_ground)).squeeze()\n return cv2.getPerspectiveTransform(im_pts.astype('float32'), im_pts_ground.astype('float32'))","title":""},{"docid":"93dd5a0defc78ea6d8534294e25cc1ed","score":"0.6585992","text":"def warped_img(img, M):\n img_size = (img.shape[1], img.shape[0])\n warped = cv2.warpPerspective(img, M, img_size)\n return warped","title":""},{"docid":"fdbb5851ce96fe63ecb8e1c2672f5037","score":"0.65187114","text":"def main():\n # initializing points\n img = cv2.imread(\"easy.png\")\n \"\"\"\n pts NxNx3\n pts[x,y,0] = z\n pts[x,y,1] = color\n pts[x,y,2] = (x, y) of projected points\n \"\"\"\n pts1 = np.ones((img.shape[1], img.shape[0], 3), dtype=object)\n #pts1[:,:,1] = (255,255,255)\n for x in range(16,310):\n for y in range(13, 239):\n pts1[x,y,0] = [2, 5]\n pts1[x,y,1] = (53,95,255)\n\n for x in range(309,484):\n for y in range(161, 239):\n pts1[x,y,0] = [3]\n pts1[x,y,1] = (255,80,0)\n\n cam_original_position = [0.0, -383.0127018922193, 100.0, -336.60254037844396] #[0.0, -816.0254037844386, 100.0, -586.602540378444]\n cam_original_orientation = np.matrix([[0.5,0.,-0.8660254],[0.,1.,0.],[0.8660254,0.,0.5]])\n cam_position = get_cam_position(angle=math.radians(-12), no_frames=10, cam_original_position=cam_original_position)\n cam_orientation = get_cam_orientation(angle=math.radians(12), no_frames=10, cam_original_orientation = cam_original_orientation)\n #all_pts = project_points(pts1, cam_position, cam_orientation, 0, 3)\n #draw_image(all_pts)\n project_and_draw(pts1, cam_position, cam_orientation, 0, 10)","title":""},{"docid":"efad03cc73ea0cd024fadaaa637081b8","score":"0.6492519","text":"def warp_image(img, pts):\n # pts = np.array(eval(str(pts)), dtype=\"float32\")\n tl, tr, br, bl = pts[0], pts[1], pts[2], pts[3]\n\n # tl, tr, br, bl = self.pts[0], self.pts[1], self.pts[2], self.pts[3]\n\n # compute the width of the new image, which will be the\n # maximum distance between bottom-right and bottom-left\n # x-coordiates or the top-right and top-left x-coordinates\n widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\n widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\n maxWidth = max(int(widthA), int(widthB))\n\n # compute the height of the new image, which will be the\n # maximum distance between the top-right and bottom-right\n # y-coordinates or the top-left and bottom-left y-coordinates\n heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\n heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\n maxHeight = max(int(heightA), int(heightB))\n\n # now that we have the dimensions of the new image, construct\n # the set of destination points to obtain a \"birds eye view\",\n # (i.e. top-down view) of the image, again specifying points\n # in the top-left, top-right, bottom-right, and bottom-left\n # order\n dst = np.array([\n [0, 0],\n [maxWidth - 1, 0],\n [maxWidth - 1, maxHeight - 1],\n [0, maxHeight - 1]], dtype=\"float32\")\n\n # compute the perspective transform matrix and then apply it\n M = cv2.getPerspectiveTransform(pts, dst)\n\n # return the warped image\n return cv2.warpPerspective(img, M, (maxWidth, maxHeight))","title":""},{"docid":"0433ad93556a8534793ffa742695d9ac","score":"0.63928014","text":"def debug():\n data_choice = 1\n img_path = None\n corners_warped = None\n corners_unwarped = None\n\n \"\"\"read the image\"\"\"\n if data_choice == 1:\n img_path = \"./data/0000000000.png\"\n elif data_choice == 2:\n img_path = \"./challenge_video_cache/challenge_video_Moment2.jpg\"\n img = cv2.imread(img_path)\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n \"\"\"adjust the corners of lane on target image\"\"\"\n if data_choice == 1:\n corners_warped = [(512, 325), (760, 325), (886, 470), (274, 470)] # four corners on lane\n width = 108\n length = 400 # longer side of rectangle\n corners_unwarped = perspective.rectangle(width, length) # width and height of rectangle (108, 400)\n offset = np.array([img_gray.shape[0] / 2, img_gray.shape[1] - length * (17 / 12)]).astype(\n int) # where the rectangle to be on dest image\n corners_unwarped, _ = perspective.shiftToASpot(corners_unwarped, offset)\n elif data_choice == 2:\n corners_warped = [(598, 495), (758, 495), (876, 592), (462, 592)]\n width = 108\n length = 400 # longer side of rectangle\n corners_unwarped = perspective.rectangle(width, length)\n offset = np.array([img_gray.shape[0] / 2, img_gray.shape[1] - length * (4 / 3)]).astype(\n int) # where the rectangle to be on dest image\n corners_unwarped, _ = perspective.shiftToASpot(corners_unwarped, offset)\n\n \"\"\"eyeball the corner of lane for images input\"\"\"\n corners_warped = eyeballLaneCorners(img_gray, corners_warped)\n \"\"\"calculate the homograph\"\"\"\n homo = homoToResCenter(corners_warped, corners_unwarped)\n img_unwarped = cv2.warpPerspective(img, homo, (img_gray.shape[0], img_gray.shape[1]))\n\n # img_gray = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR)\n cv2.imshow('Image unwarped', img_unwarped)\n cv2.waitKey(0)\n cv2.destroyAllWindows()","title":""},{"docid":"2b9f94e2bb6315655399682f924a6d26","score":"0.63859767","text":"def Perspective(image, pts1):\n # Change the color of the image from RGB to gray\n imgGray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n \n # Blurring\n imgBlur = cv2.GaussianBlur(imgGray, (5,5), 0)\n # Binary Threshold\n imgThresh = cv2.inRange(imgBlur, 135, 255, cv2.THRESH_BINARY) # 149, 255\n \n cv2.imshow(\"Binary Threshold\", imgThresh)\n cv2.waitKey(1)\n\n # Canny Edge Detection\n imgEdge = cv2.Canny(imgGray, 58, 129) # 270, 300\n\n cv2.imshow(\"canny edge\", imgEdge)\n cv2.waitKey(1)\n\n # Combine the two images\n imgFinal = cv2.add(imgThresh, imgEdge)\n \n cv2.line(image,pt1=tuple(pts1[0]),pt2=tuple(pts1[1]),color=(255,0,0),thickness=2)\n cv2.line(image,pt1=tuple(pts1[1]),pt2=tuple(pts1[3]),color=(255,0,0),thickness=2)\n cv2.line(image,pt1=tuple(pts1[3]),pt2=tuple(pts1[2]),color=(255,0,0),thickness=2)\n cv2.line(image,pt1=tuple(pts1[2]),pt2=tuple(pts1[0]),color=(255,0,0),thickness=2)\n\n # cv2.line(image,pt1=tuple(pts2[0]),pt2=tuple(pts2[1]),color=(255,0,0),thickness=2)\n # cv2.line(image,pt1=tuple(pts2[1]),pt2=tuple(pts2[3]),color=(255,0,0),thickness=2)\n # cv2.line(image,pt1=tuple(pts2[3]),pt2=tuple(pts2[2]),color=(255,0,0),thickness=2)\n # cv2.line(image,pt1=tuple(pts2[2]),pt2=tuple(pts2[0]),color=(255,0,0),thickness=2)\n\n Matrix = cv2.getPerspectiveTransform(pts1.astype(np.float32), pts2.astype(np.float32))\n imgPers = cv2.warpPerspective(imgFinal, Matrix, (WIDTH, HEIGHT))\n\n imgFinal = cv2.cvtColor(imgPers, cv2.COLOR_GRAY2BGR)\n imgFinalDuplicate = imgFinal.copy()\n cv2.imshow(\"Test\", imgPers)\n cv2.waitKey(1)\n return imgFinal, imgFinalDuplicate","title":""},{"docid":"d24056ba6b6e10ca46f1b62e89b3314c","score":"0.63586324","text":"def transform_road(binary_image):\n # Image size\n xsize = binary_image.shape[1]\n ysize = binary_image.shape[0]\n\n # Select source points\n #src = np.float32([[554, 465], [735, 465], [90, ysize], [xsize, ysize]])\n src = np.float32([[565, 460], [725, 460], [90, ysize], [xsize, ysize]])\n\n # Select destination points\n dest = np.float32([[0,0],[xsize,0],[0, ysize],[xsize,ysize]])\n\n # Perform transformation\n M = cv2.getPerspectiveTransform(src, dest)\n Minv = cv2.getPerspectiveTransform(dest, src)\n warped = cv2.warpPerspective(binary_image, M, (xsize, ysize), flags=cv2.INTER_LINEAR)\n\n return warped, Minv","title":""},{"docid":"059d891865b676bac6911476f5abf045","score":"0.6353194","text":"def unWarp(self, image):\n return cv2.warpPerspective(image, self._inverseTransformationMatrix, self._imageSize)","title":""},{"docid":"488cbf521ae1e8275b8af8d3ce62cef5","score":"0.6344563","text":"def crop_and_warp(img, crop_rect):\n\n\n top_left, top_right, bottom_right, bottom_left = crop_rect[0], crop_rect[1], crop_rect[2], crop_rect[3]\n\n src = np.array([top_left, top_right, bottom_right, bottom_left], dtype='float32')\n\n \n side = max([\n distance.euclidean(bottom_right, top_right),\n distance.euclidean(top_left, bottom_left),\n distance.euclidean(bottom_right, bottom_left),\n distance.euclidean(top_left, top_right)\n ])\n\n height= distance.euclidean(bottom_right, top_right)\n width= distance.euclidean(bottom_right, bottom_left)\n\n dst = np.array([[0, 0], [side - 1, 0], [side - 1, side - 1], [0, side - 1]], dtype='float32')\n\n m = cv2.cv2.getPerspectiveTransform(src, dst)\n\n warp = cv2.cv2.warpPerspective(img, m, (int(side), int(side)))\n plt.imshow(warp, cmap='gray')\n plt.title('Warped Image')\n plt.show()\n return warp,side,height, width","title":""},{"docid":"8d67750b43b534908858f3f6c7d73345","score":"0.6315676","text":"def perspective_transform_coeffs(pa, pb):\n import numpy\n matrix = []\n for p1, p2 in zip(pa, pb):\n matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]])\n matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]])\n\n A = numpy.matrix(matrix, dtype=numpy.float)\n B = numpy.array(pb).reshape(8)\n\n res = numpy.dot(numpy.linalg.inv(A.T * A) * A.T, B)\n return numpy.array(res).reshape(8)","title":""},{"docid":"b0fbf9b9502be6911f58d6b333678292","score":"0.63064027","text":"def compute_transforms(self):\n\n point_names = [\n \"model\",\n \"box\"\n ]\n\n point_arrays = {}\n point_arrays['box'] = self.default_box\n point_arrays['model'] = self.model\n\n transforms = {}\n for a in point_names:\n for b in point_names:\n if a == b:\n continue\n if not (a in point_arrays):\n continue\n if not (b in point_arrays):\n continue\n transform_name = a + '2' + b\n transform = cv2.getPerspectiveTransform(\n point_arrays[a],\n point_arrays[b]\n )\n transforms[transform_name] = transform\n\n return transforms","title":""},{"docid":"7c01356c7fd6d4d92709c64a213865bc","score":"0.628881","text":"def compute_homography_and_warp(image, vp1, vp2, clip=True, clip_factor=3):\n # Find Projective Transform\n vanishing_line = np.cross(vp1, vp2)\n H = np.eye(3)\n H[2] = vanishing_line / vanishing_line[2]\n H = H / H[2, 2]\n\n # Find directions corresponding to vanishing points\n v_post1 = np.dot(H, vp1)\n v_post2 = np.dot(H, vp2)\n v_post1 = v_post1 / np.sqrt(v_post1[0]**2 + v_post1[1]**2)\n v_post2 = v_post2 / np.sqrt(v_post2[0]**2 + v_post2[1]**2)\n\n directions = np.array([[v_post1[0], -v_post1[0], v_post2[0], -v_post2[0]],\n [v_post1[1], -v_post1[1], v_post2[1], -v_post2[1]]])\n\n thetas = np.arctan2(directions[0], directions[1])\n\n # Find direction closest to horizontal axis\n h_ind = np.argmin(np.abs(thetas))\n\n # Find positve angle among the rest for the vertical axis\n if h_ind // 2 == 0:\n v_ind = 2 + np.argmax([thetas[2], thetas[3]])\n else:\n v_ind = np.argmax([thetas[2], thetas[3]])\n\n A1 = np.array([[directions[0, v_ind], directions[0, h_ind], 0],\n [directions[1, v_ind], directions[1, h_ind], 0],\n [0, 0, 1]])\n # Might be a reflection. If so, remove reflection.\n if np.linalg.det(A1) < 0:\n A1[:, 0] = -A1[:, 0]\n\n A = np.linalg.inv(A1)\n\n # Translate so that whole of the image is covered\n inter_matrix = np.dot(A, H)\n\n cords = np.dot(inter_matrix, [[0, 0, image.shape[1], image.shape[1]],\n [0, image.shape[0], 0, image.shape[0]],\n [1, 1, 1, 1]])\n cords = cords[:2] / cords[2]\n\n tx = min(0, cords[0].min())\n ty = min(0, cords[1].min())\n\n max_x = cords[0].max() - tx\n max_y = cords[1].max() - ty\n\n if clip:\n # These might be too large. Clip them.\n max_offset = max(image.shape) * clip_factor / 2\n tx = max(tx, -max_offset)\n ty = max(ty, -max_offset)\n\n max_x = min(max_x, -tx + max_offset)\n max_y = min(max_y, -ty + max_offset)\n\n max_x = int(max_x)\n max_y = int(max_y)\n\n T = np.array([[1, 0, -tx],\n [0, 1, -ty],\n [0, 0, 1]])\n\n final_homography = np.dot(T, inter_matrix)\n\n warped_img = transform.warp(image, np.linalg.inv(final_homography),\n output_shape=(max_y, max_x))\n return warped_img","title":""},{"docid":"8ba64b73cef6b20b352762d3691d873a","score":"0.6276881","text":"def run(image, res=(1000, 550)):\n\n # Number of views\n N = len(image)\n\n # Output points to which the input points are to be mapped\n output_points = [(0, 0), (0, res[1]), (res[0], res[1]), (res[0], 0)]\n\n # Images after line extraction in binary form\n bin_image = []\n # List of Corner points for each input images (view)\n input_points = []\n\n print(\"\\nInitializing camera parameters : \")\n print(\"\\n\\tFinding Corners For : \")\n for i in range(N):\n print(\"\\tview \", i)\n bin_image.append(extractFieldLines(image[i]))\n input_points.append(extractCorners(getPerfectContours(bin_image[-1].copy())))\n\n # List of Transformation matrix for each input images (view)\n proj_mat = []\n\n # List of output corner points for each image (view)\n op = [output_points for i in range(4)]\n\n # Find the projection matrix for each image (i.e. each camera view)\n for i in range(N):\n proj_mat.append(cv2.getPerspectiveTransform(np.float32(input_points[i]), np.float32(op[i])))\n\n # Take the first view as reference view\n # prefixed by 'trans' if the image is \"transformed\" image\n transRef = cv2.warpPerspective(bin_image[0], proj_mat[0], res)\n\n\n # Find the orientation of every other image, align them using cost function\n # and find the Projection (transformation) matrix\n print(\"\\n\\tFinding Projection Matrix For : \")\n for i in range(1, N):\n print(\"\\tView \", i)\n # Find the values of cost function for different rotations of the output image\n cost_value = []\n\n for j in range(4): # since there are only four rotations for rectangle\n transImg = cv2.warpPerspective(bin_image[i], proj_mat[i], res) # Finds the transformed view for given image\n\n difference = transImg - transRef # Finds the difference between two images\n\n cost_value.append(cost(difference)) # Calculates the cost using the difference between two images\n\n op[i].append(op[i].pop(0)) # Rotate the plane-to-project-on by 90 degrees\n proj_mat[i] = cv2.getPerspectiveTransform(np.float32(input_points[i]), np.float32(op[i])) # Recalculates the projection matrix\n\n # Now, find the rotation at which the cost is minimum and then find the projection matrix\n for j in range(np.argmin(cost_value)):\n op[i].append(op[i].pop(0))\n\n proj_mat[i] = cv2.getPerspectiveTransform(np.float32(input_points[i]), np.float32(op[i]))\n\n return proj_mat","title":""},{"docid":"3a735e290f1ecab87c65b70fde6b5b90","score":"0.6259704","text":"def perspective(fov, aspect, near, far):\n sy = 1.0 / tan(radians(fov) / 2.0)\n sx = sy / aspect\n zz = (far + near) / (near - far)\n zw = 2 * far * near / (near - far)\n matrix = [[sx, 0, 0, 0], [0, sy, 0, 0], [0, 0, zz, zw], [0, 0, -1, 0]] # noqa: E201 # noqa: E201 # noqa: E201\n return Transformation.from_matrix(matrix)","title":""},{"docid":"4f2a074358aa63a757d41dedfcc74506","score":"0.625155","text":"def process_image(test_img,temp_img):\n masked_image=image_masking(test_img)\n key_pt_temp, des_temp = compute_kp_des(temp_img)\n key_pt_test, des_test = compute_kp_des(masked_image)\n points_test,points_temp=find_good_matches(key_pt_temp,key_pt_test,des_test,des_temp)\n homo, _ = cv2.findHomography(points_test, points_temp, cv2.RANSAC)\n height, width, _ = temp_img.shape\n image_aligned = cv2.warpPerspective(test_img, homo, (width, height))\n return image_aligned, homo","title":""},{"docid":"5a6a0f68c11d6ba0879bc73a3c9d461c","score":"0.6207081","text":"def adjust_to_see(img):\n\n (h, w) = img.shape[:2]\n (cX, cY) = (w // 2, h // 2)\n\n M = cv2.getRotationMatrix2D((cX, cY), -90, 1.0)\n cos = np.abs(M[0, 0])\n sin = np.abs(M[0, 1])\n\n nW = int((h * sin) + (w * cos))\n nH = int((h * cos) + (w * sin))\n\n M[0, 2] += (nW / 2) - cX\n M[1, 2] += (nH / 2) - cY\n\n img = cv2.warpAffine(img, M, (nW + 1, nH + 1))\n img = cv2.warpAffine(img.transpose(), M, (nW, nH))\n\n return img","title":""},{"docid":"99f3cb5b7e9b676a849e29a86924f908","score":"0.6182457","text":"def contour_transform(contours_list, perspective_matrix):\n result=[]\n for cnt in contours_list:\n cnt = cv2.perspectiveTransform(cnt.reshape(1, len(cnt), 2).astype('float32'), perspective_matrix)\n result.append(cnt.reshape(len(cnt[0]), 1, 2).astype('int'))\n return result","title":""},{"docid":"77edeaa730a850549e23e6fade1a9108","score":"0.6162987","text":"def get_perspective_transform(screen_resolution, prop_file, flipped):\n\n camera_matrix, dist_coeffs, camResolution =load_camera_props(prop_file)\n shutter = None\n awbgains = ()\n\n rect = None\n while True :\n #pygame.display.toggle_fullscreen()\n displayBGColor()\n # Delay execution a quarter of a second to make sure the image is displayed \n # Don't use time.sleep() here, we want the IO loop to run. Sleep doesn't do that\n cv2.waitKey(150)\n # Grab a photo of the frame\n with picamera.PiCamera() as camera:\n camera.resolution = camResolution\n camera.framerate = 12\n # Wait for the automatic gain control to settle\n time.sleep(1)\n # Now fix the values\n camera.shutter_speed = camera.exposure_speed\n shutter = camera.shutter_speed\n camera.exposure_mode = 'off'\n g = camera.awb_gains\n camera.awb_mode = 'off'\n camera.awb_gains = g\n awbgains = g\n #new.text = str(camera.awb_gains)\n camera.capture(\"persp-calibration-ref.jpg\")\n\n camera.close()\n # back to resizable windowed app\n #pygame.display.toggle_fullscreen()\n #\n print(\"[INFO] Writing ref image\")\n #cv2.imwrite(\"persp-calibration-ref.jpg\", frame)\n frame = cv2.imread(\"persp-calibration-ref.jpg\")\n # Undistort the camera image\n #cv2.waitKey(250)\n frame = undistort_image(frame, camera_matrix, dist_coeffs)\n # We're going to work with a smaller image, so we need to save the scale\n ratio = frame.shape[0] / 900.0\n orig = frame.copy()\n # Resize our image smaller, this will make things a lot faster\n frame = imutils.resize(frame, height=900)\n #cv2.waitKey(50)\n rect = get_region_corners(frame, flipped)\n if not rect is None :\n if len(rect) < 4 :\n # contour not valid, so we try again\n continue\n else :\n break\n else :\n # no contour found -> abort mission\n quit()\n break\n\n rect *= ratio # We shrank the image, so now we have to scale our points up\n dst, max_width, max_height = get_destination_array(rect)\n\n # process correction on ref image and show result\n m = cv2.getPerspectiveTransform(rect, dst)\n wrap = cv2.warpPerspective(orig, m, (max_width, max_height))\n wraped = convertToSurfaceImg(wrap)\n displaySurfaceImage(wraped)\n\n # update XML file with warping datas\n # cam setup\n camSetup=ET.Element('cameraSetup')\n shut=ET.SubElement(camSetup,'shutter-speed')\n shut.text = str(shutter)\n gains=ET.SubElement(camSetup,'awb-gains')\n gains.text = '\\n'.join(map(str, [' '.join(str(frac(f)).split(\"/\")) for f in awbgains]))\n root.append(camSetup)\n # distortion parameters\n dstParam=ET.Element('distortionParam')\n ref=ET.SubElement(dstParam,'refPoints')\n ref.text = '\\n'.join(map(str, [\" \".join(item) for item in rect.astype(str)]))\n dest=ET.SubElement(dstParam,'destPoints')\n dest.text = '\\n'.join(map(str, [\" \".join(item) for item in dst.astype(str)]))\n root.append(dstParam)\n # save xml\n wrTmp(root, datasPath)\n print(\"xml updated\")\n #\n try :\n user_check = str(input(\"Copy new datas to main directory ? [y/n] \\n\"))\n except ValueError:\n print(\"Sorry, I didn't understand that.\")\n if user_check == \"n\":\n print(\"Ok ! Good bye !\")\n elif user_check != \"y\" :\n print(\"Sorry, answer '\",user_check,\"' is not valid...\")\n else :\n print(\"OK, let's copy the file,then...\")\n shutil.copy2(datasPath, \"../cameraDatas.xml\")\n #\n print(\"press esc to quit\")\n return m, max_width, max_height","title":""},{"docid":"645af425b81f76726d96871559604c59","score":"0.6156459","text":"def crop_and_warp(img, crop_rect):\r\n\r\n # Rectangle described by top left, top right, bottom right and bottom left points\r\n top_left, top_right, bottom_right, bottom_left = crop_rect[0], crop_rect[1], crop_rect[2], crop_rect[3]\r\n\r\n # Explicitly set the data type to float32 or `getPerspectiveTransform` will throw an error\r\n src = np.array([top_left, top_right, bottom_right, bottom_left], dtype='float32')\r\n #print(src)\r\n # Get the longest side in the rectangle\r\n \r\n side = max([\r\n distance_between(bottom_right, top_right),\r\n distance_between(top_left, bottom_left),\r\n distance_between(bottom_right, bottom_left),\r\n distance_between(top_left, top_right)\r\n ])\r\n\r\n # Describe a square with side of the calculated length, this is the new perspective we want to warp to\r\n dst = np.array([[0, 0], [side - 1, 0], [side - 1, side - 1], [0, side - 1]], dtype='float32')\r\n\r\n # Gets the transformation matrix for skewing the image to fit a square by comparing the 4 before and after points\r\n m = cv2.getPerspectiveTransform(src, dst)\r\n\r\n # Performs the transformation on the original image\r\n warp = cv2.warpPerspective(img, m, (int(side), int(side)))\r\n plt.imshow(warp, cmap='gray')\r\n plt.title('warp_image')\r\n plt.show()\r\n return warp","title":""},{"docid":"2358c4883fd1c2ec528acaaee950520f","score":"0.6148024","text":"def flattener(image, pts, w, h):\n temp_rect = np.zeros((4, 2), dtype=\"float32\")\n\n s = np.sum(pts, axis=2)\n\n tl = pts[np.argmin(s)]\n br = pts[np.argmax(s)]\n\n diff = np.diff(pts, axis=-1)\n tr = pts[np.argmin(diff)]\n bl = pts[np.argmax(diff)]\n\n # Need to create an array listing points in order of\n # [top left, top right, bottom right, bottom left]\n # before doing the perspective transform\n\n if w <= 0.8 * h: # If card is vertically oriented\n temp_rect[0] = tl\n temp_rect[1] = tr\n temp_rect[2] = br\n temp_rect[3] = bl\n\n if w >= 1.2 * h: # If card is horizontally oriented\n temp_rect[0] = bl\n temp_rect[1] = tl\n temp_rect[2] = tr\n temp_rect[3] = br\n\n # If the card is 'diamond' oriented, a different algorithm\n # has to be used to identify which point is top left, top right\n # bottom left, and bottom right.\n\n if w > 0.8 * h and w < 1.2 * h: # If card is diamond oriented\n # If furthest left point is higher than furthest right point,\n # card is tilted to the left.\n if pts[1][0][1] <= pts[3][0][1]:\n # If card is titled to the left, approxPolyDP returns points\n # in this order: top right, top left, bottom left, bottom right\n temp_rect[0] = pts[1][0] # Top left\n temp_rect[1] = pts[0][0] # Top right\n temp_rect[2] = pts[3][0] # Bottom right\n temp_rect[3] = pts[2][0] # Bottom left\n\n # If furthest left point is lower than furthest right point,\n # card is tilted to the right\n if pts[1][0][1] > pts[3][0][1]:\n # If card is titled to the right, approxPolyDP returns points\n # in this order: top left, bottom left, bottom right, top right\n temp_rect[0] = pts[0][0] # Top left\n temp_rect[1] = pts[3][0] # Top right\n temp_rect[2] = pts[2][0] # Bottom right\n temp_rect[3] = pts[1][0] # Bottom left\n\n maxWidth = 200\n maxHeight = 300\n\n # Create destination array, calculate perspective transform matrix,\n # and warp card image\n dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], np.float32)\n M = cv2.getPerspectiveTransform(temp_rect, dst)\n warp = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n #this just transforms and flattens the card\n \n #warp = cv2.cvtColor(warp, cv2.COLOR_BGR2GRAY)\n return warp","title":""},{"docid":"800ffd40059ebfacd94c46ef0268006d","score":"0.6146747","text":"def flattener(image, pts, w, h):\n temp_rect = np.zeros((4, 2), dtype=\"float32\")\n\n s = np.sum(pts, axis=2)\n\n tl = pts[np.argmin(s)]\n br = pts[np.argmax(s)]\n\n diff = np.diff(pts, axis=-1)\n tr = pts[np.argmin(diff)]\n bl = pts[np.argmax(diff)]\n\n # Need to create an array listing points in order of\n # [top left, top right, bottom right, bottom left]\n # before doing the perspective transform\n\n if w <= 0.8 * h: # If card is vertically oriented\n temp_rect[0] = tl\n temp_rect[1] = tr\n temp_rect[2] = br\n temp_rect[3] = bl\n\n if w >= 1.2 * h: # If card is horizontally oriented\n temp_rect[0] = bl\n temp_rect[1] = tl\n temp_rect[2] = tr\n temp_rect[3] = br\n\n # If the card is 'diamond' oriented, a different algorithm\n # has to be used to identify which point is top left, top right\n # bottom left, and bottom right.\n\n if 0.8 * h < w < 1.2 * h: # If card is diamond oriented\n # If furthest left point is higher than furthest right point,\n # card is tilted to the left.\n if pts[1][0][1] <= pts[3][0][1]:\n # If card is titled to the left, approxPolyDP returns points\n # in this order: top right, top left, bottom left, bottom right\n temp_rect[0] = pts[1][0] # Top left\n temp_rect[1] = pts[0][0] # Top right\n temp_rect[2] = pts[3][0] # Bottom right\n temp_rect[3] = pts[2][0] # Bottom left\n\n # If furthest left point is lower than furthest right point,\n # card is tilted to the right\n if pts[1][0][1] > pts[3][0][1]:\n # If card is titled to the right, approxPolyDP returns points\n # in this order: top left, bottom left, bottom right, top right\n temp_rect[0] = pts[0][0] # Top left\n temp_rect[1] = pts[3][0] # Top right\n temp_rect[2] = pts[2][0] # Bottom right\n temp_rect[3] = pts[1][0] # Bottom left\n\n max_width = 200\n max_height = 300\n\n # Create destination array, calculate perspective transform matrix,\n # and warp card image\n dst = np.array([[0, 0], [max_width - 1, 0], [max_width - 1, max_height - 1], [0, max_height - 1]], np.float32)\n m = cv2.getPerspectiveTransform(temp_rect, dst)\n warp = cv2.warpPerspective(image, m, (max_width, max_height))\n warp = cv2.cvtColor(warp, cv2.COLOR_BGR2GRAY)\n\n return warp","title":""},{"docid":"ff6145b14bdc9de544eac6770360de3b","score":"0.6146022","text":"def warp_image_from_match(im1, im2, points1, points2):\n \n val, trans = cv2.findHomography(points2, points1, cv2.RANSAC)\n return cv2.warpPerspective(im2, val, (im1.shape[1], im1.shape[0]))","title":""},{"docid":"46578c314bb6b33bd414528f539f8ab8","score":"0.6132989","text":"def flattener(image, pts, w, h):\n\ttemp_rect = np.zeros((4,2), dtype = \"float32\")\n\t\n\ts = np.sum(pts, axis = 2)\n\t\n\ttl = pts[np.argmin(s)]\n\tbr = pts[np.argmax(s)]\n\t\n\tdiff = np.diff(pts, axis = -1)\n\ttr = pts[np.argmin(diff)]\n\tbl = pts[np.argmax(diff)]\n\t\n\t# Need to create an array listing points in order of\n\t# [top left, top right, bottom right, bottom left]\n\t# before doing the perspective transform\n\t\n\tif w <= 0.8*h: # If card is vertically oriented\n\t\ttemp_rect[0] = tl\n\t\ttemp_rect[1] = tr\n\t\ttemp_rect[2] = br\n\t\ttemp_rect[3] = bl\n\t\n\tif w >= 1.2*h: # If card is horizontally oriented\n\t\ttemp_rect[0] = bl\n\t\ttemp_rect[1] = tl\n\t\ttemp_rect[2] = tr\n\t\ttemp_rect[3] = br\n\t\n\t# If the card is 'diamond' oriented, a different algorithm\n\t# has to be used to identify which point is top left, top right\n\t# bottom left, and bottom right.\n\t\n\tif w > 0.8*h and w < 1.2*h: #If card is diamond oriented\n\t\t# If furthest left point is higher than furthest right point,\n\t\t# card is tilted to the left.\n\t\tif pts[1][0][1] <= pts[3][0][1]:\n\t\t\t# If card is titled to the left, approxPolyDP returns points\n\t\t\t# in this order: top right, top left, bottom left, bottom right\n\t\t\ttemp_rect[0] = pts[1][0] # Top left\n\t\t\ttemp_rect[1] = pts[0][0] # Top right\n\t\t\ttemp_rect[2] = pts[3][0] # Bottom right\n\t\t\ttemp_rect[3] = pts[2][0] # Bottom left\n\t\t\t\n\t\t# If furthest left point is lower than furthest right point,\n\t\t# card is tilted to the right\n\t\tif pts[1][0][1] > pts[3][0][1]:\n\t\t\t# If card is titled to the right, approxPolyDP returns points\n\t\t\t# in this order: top left, bottom left, bottom right, top right\n\t\t\ttemp_rect[0] = pts[0][0] # Top left\n\t\t\ttemp_rect[1] = pts[3][0] # Top right\n\t\t\ttemp_rect[2] = pts[2][0] # Bottom right\n\t\t\ttemp_rect[3] = pts[1][0] # Bottom left\n\t\t\t\n\t\t\t\n\tmaxWidth = 200\n\tmaxHeight = 300\n\t\n\t# Create destination array, calculate perspective transform matrix,\n\t# and warp card image\n\tdst = np.array([[0,0],[maxWidth-1,0],[maxWidth-1,maxHeight-1],[0, maxHeight-1]], np.float32)\n\tM = cv2.getPerspectiveTransform(temp_rect,dst)\n\twarp = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n#\twarp = cv2.cvtColor(warp,cv2.COLOR_BGR2GRAY)\n\n\t\n\t\n\t\n\treturn warp","title":""},{"docid":"ba04b3efc26867354de83dd87be34a77","score":"0.6123402","text":"def correctPerspective(vidpath: str, outputpath: str) -> list[tuple[int, int]]:\n from get_img_coords import get_perspective_points\n\n # y = rf\".\\bin\\ffmpeg.exe -i .\\vid\\src-10.mp4 -vf perspective={x},scale=960:720,setdar=4/3 43.mp4\"\n points = get_perspective_points(vidpath)\n\n # ffplay perspective is broken on normal pixel video files, not sure why\n # probably some pixel ratio thing idk\n # cmd = f\"ffplay -i {vidpath} -vf perspective={ppoints},scale=960:720,setdar=4/3\"\n\n # workaround is to render a short preview and ask for confirmation on that\n\n # make preview video\n cmd = gen_perspective_ffmpeg_cmd(\n vidpath,\n outputpath,\n points,\n preview_only=True,\n text_overlay=\"preview\",\n )\n print(cmd)\n # cmd = f\"ffmpeg -i {vidpath} -vf perspective={ppoints},scale=960:720,setdar=4u3 out.mp4\"\n\n run(split(cmd))\n run([\"mpv\", outputpath])\n return points","title":""},{"docid":"65d41bcb0340ae150786a8a619e9a99b","score":"0.6087891","text":"def _transform_projective(x, y, H):\n z_ = H[2][0] * x + H[2][1] * y + H[2][2]\n x_ = (H[0][0] * x + H[0][1] * y + H[0][2]) / z_\n y_ = (H[1][0] * x + H[1][1] * y + H[1][2]) / z_\n return (x_, y_)","title":""},{"docid":"717a92d008422554716eb92525ad70a2","score":"0.6074759","text":"def setup_transformation_pipeline(offset=10):\n pts = calibrate_camera()\n images = glob.glob(\"test_images/*\")\n # Pick the image with a straight lane to calibrate the camera\n img = cv2.imread(images[0])\n # run the same pipeline\n dst = undistort_image(img, pts)\n masked_img = lane_masking(dst)\n _img = region_of_interest(masked_img)\n # instead of polynomial fit\n # use linear regression to determine the src for perspective transformation\n left,right = fit_lanes(_img)\n src = retrieve_src_points(left, right, masked_img.shape[0])\n dst = np.float32([src[0], (src[0][0], offset), src[2], (src[2][0], offset)])\n return TransformationPipeline(pts, src, dst)","title":""},{"docid":"cb7015566e333f043a3e3db8c141b44e","score":"0.60576177","text":"def warp_images(image1, image2, H):\n\n rows1, cols1 = image1.shape[:2]\n rows2, cols2 = image2.shape[:2]\n\n # Coordinates of the image to transform\n temp_points1 = float32([[0, 0], [0, rows1], [cols1, rows1], [cols1, 0]]).reshape(-1, 1, 2)\n\n # Coordinates of a reference image\n points2 = float32([[0, 0], [0, rows2], [cols2, rows2], [cols2, 0]]).reshape(-1, 1, 2)\n points1 = perspectiveTransform(temp_points1, H)\n points = concatenate((points2, points1), axis=0)\n\n [x_min, y_min] = int32(points.min(axis=0).ravel() - 0.5)\n [x_max, y_max] = int32(points.max(axis=0).ravel() + 0.5)\n\n translation_dist = [-x_min, -y_min]\n H_translation = array([[1, 0, translation_dist[0]], [0, 1, translation_dist[1]], [0, 0, 1]])\n\n output_img = warpPerspective(image1, H_translation.dot(H), (x_max - x_min, y_max - y_min))\n output_img[translation_dist[1]:rows2 + translation_dist[1],\n translation_dist[0]:cols2 + translation_dist[0]] = image2\n\n return output_img","title":""},{"docid":"79ccd124e2fd30ef665ee968166f84d9","score":"0.60440665","text":"def plate_transform(image, box):\r\n\r\n\t# obtain the bounding rectangle's vertices and order them\r\n\trect = order_points(box)\r\n\t(tl, tr, br, bl) = rect\r\n\r\n\t# Computes the width of the plate\r\n\tlower_width = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\r\n\tupper_width = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\r\n\twidth = max(int(lower_width), int(upper_width))\r\n\r\n\t# Computes the height of the plate\r\n\tright_height = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\r\n\tleft_height = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\r\n\theight = max(int(right_height), int(left_height))\r\n\r\n\t# Construct the set of destination points to obtain a \"birds eye view\" of the plate\r\n\tdst = np.array([\r\n\t\t[0, 0],\r\n\t\t[width - 1, 0],\r\n\t\t[width - 1, height - 1],\r\n\t\t[0, height - 1]], dtype=\"float32\")\r\n\r\n\t# compute the perspective transform matrix\r\n\ttransform_matrix = cv2.getPerspectiveTransform(rect, dst)\r\n\twarped = cv2.warpPerspective(image, transform_matrix, (width, height))\r\n\r\n\t# return the warped image\r\n\treturn warped","title":""},{"docid":"40fcc2418e758685dd7aec9c859b0600","score":"0.60433745","text":"def projective_inverse_warp(img, depth, pose, intrinsics, rot_mat=None):\n batch, height, width, _ = img.get_shape().as_list()\n # Convert pose vector to matrix\n pose, rot_mat, translation = pose_vec2mat(pose, rot_mat)\n # Construct pixel grid coordinates\n pixel_coords = meshgrid(batch, height, width)\n # Convert pixel coordinates to the camera frame\n cam_coords = pixel2cam(depth, pixel_coords, intrinsics)\n # Construct a 4x4 intrinsic matrix (TODO: can it be 3x4?)\n filler = tf.constant([0.0, 0.0, 0.0, 1.0], shape=[1, 1, 4])\n filler = tf.tile(filler, [batch, 1, 1])\n intrinsics = tf.concat([intrinsics, tf.zeros([batch, 3, 1])], axis=2)\n intrinsics = tf.concat([intrinsics, filler], axis=1)\n # Get a 4x4 transformation matrix from 'target' camera frame to 'source'\n # pixel frame.\n proj_tgt_cam_to_src_pixel = tf.matmul(intrinsics, pose)\n src_pixel_coords = cam2pixel(cam_coords, proj_tgt_cam_to_src_pixel)\n output_img = bilinear_sampler(img, src_pixel_coords)\n return output_img, rot_mat","title":""},{"docid":"cc8819af67babf47092d342270930fff","score":"0.6036917","text":"def stitching(\r\n images: Tuple[Image, Image],\r\n ratio: float = 0.55,\r\n reproj_thresh: float = 4.0\r\n) -> Image:\r\n # unpack the images, then detect keypoints and extract\r\n # local invariant descriptors from them\r\n img1 = images[0].data\r\n img0 = images[1].data\r\n\r\n # To calculate the features\r\n (kps0, features0) = detect_and_describe(img0)\r\n (kps1, features1) = detect_and_describe(img1)\r\n\r\n # match features between the two images\r\n M = match_keypoints(kps0, kps1, features0, features1, ratio, reproj_thresh)\r\n\r\n # if the match is None, then there aren't enough matched keypoints to create a panorama\r\n if M is None:\r\n return None\r\n\r\n # otherwise, apply a perspective warp to stitch the images together\r\n (matches, H, status) = M\r\n\r\n # distinguish the direction and to apply corresponding stitching\r\n direction = distinguish_direction(img0, kps0, kps1, matches, status)\r\n if direction == UP:\r\n result = cv2.warpPerspective(img0, H, (img0.shape[1], img1.shape[0] + img0.shape[0]))\r\n result[0:img1.shape[0], 0:img1.shape[1]] = img1\r\n elif direction == DOWN:\r\n # For Down direction it is required to transform it into Up direction\r\n M = match_keypoints(kps1, kps0, features1, features0, ratio, reproj_thresh)\r\n H = M[1]\r\n result = cv2.warpPerspective(img1, H, (img1.shape[1], img1.shape[0] + img0.shape[0]))\r\n result[0:img1.shape[0], 0:img0.shape[1]] = img0\r\n elif direction == LEFT:\r\n result = cv2.warpPerspective(img0, H, (img0.shape[1] + img1.shape[1], img0.shape[0]))\r\n result[0:img1.shape[0], 0:img1.shape[1]] = img1\r\n else:\r\n # For Right direction it is required to transform it into Left direction\r\n M = match_keypoints(kps1, kps0, features1, features0, ratio, reproj_thresh)\r\n H = M[1]\r\n result = cv2.warpPerspective(img1, H,\r\n (img0.shape[1] + img1.shape[1], img1.shape[0]))\r\n result[0 : img0.shape[0], 0:img0.shape[1]] = img0\r\n return Image(result)","title":""},{"docid":"ef6c282874263504ef525f7c6c9716c5","score":"0.6009642","text":"def simpleDistortionBack(xx, yy, xs, ys, x1, y1):\n\n # subtract average to align\n\n nn = len(xs)\n pts1 = np.zeros((1, nn, 2))\n pts2 = np.zeros((1, nn, 2))\n xav1 = xs.mean()\n yav1 = ys.mean()\n xav2 = xx.mean()\n yav2 = yy.mean()\n\n for i in range(len(xs)):\n # pts1[0,i,0]=xs[i]-xav1\n # pts1[0,i,1]=ys[i]-yav1\n # pts2[0,i,0]=xx[i]-xav2\n # pts2[0,i,1]=yy[i]-yav2\n pts1[0, i, 0] = xs[i]\n pts1[0, i, 1] = ys[i]\n pts2[0, i, 0] = xx[i]\n pts2[0, i, 1] = yy[i]\n\n # cv2 needs float32\n\n pts1 = np.float32(pts1)\n pts2 = np.float32(pts2)\n\n # get the affine transformation - translation, rotation, scaling\n # uses opencv library\n\n transformation = cv2.estimateRigidTransform(pts1, pts2, False)\n transformFull = transformation.copy()\n\n # calculate the scaling from the result\n sx = np.sqrt(transformation[0, 0]**2+transformation[0, 1]**2)\n sy = np.sqrt(transformation[1, 0]**2+transformation[1, 1]**2)\n\n print(\"sx=\", sx, \" sy=\", sy)\n\n # remove the scaling from the transformation matrix\n transformation[0, 0] /= sx\n transformation[0, 1] /= sx\n transformation[1, 0] /= sy\n transformation[1, 1] /= sy\n\n # calculate the transformed points\n points = cv2.transform(pts1, transformation)\n\n # difference between expected and actual point positions\n diffx = (pts2[0, :, 0]-points[0, :, 0])\n diffy = (pts2[0, :, 1]-points[0, :, 1])\n\n # in mm and in % of field\n\n c = np.sqrt(diffx*diffx+diffy*diffy)\n c1 = np.sqrt(diffx*diffx+diffy*diffy)/336.*100\n\n pts1[:, :, 0] = ma.masked_values(pts1[:, :, 0], 0)\n pts1[:, :, 1] = ma.masked_values(pts1[:, :, 1], 0)\n pts2[:, :, 0] = ma.masked_values(pts2[:, :, 0], 0)\n pts2[:, :, 1] = ma.masked_values(pts2[:, :, 1], 0)\n pts1 = ma.masked_invalid(pts1)\n pts2 = ma.masked_invalid(pts2)\n\n return c, c1, pts1, pts2, diffx, diffy, transformFull","title":""},{"docid":"1980566551fb58755d999bc835ef11c8","score":"0.59884036","text":"def project(self, points, camera_params):\n # points_proj = self.rotate(points, camera_params[:, :3])\n # points_proj -= camera_params[:, 3:6]\n # points_proj = -points_proj[:, :2] / points_proj[:, 2, np.newaxis]\n #\n # f = self.mtx[0,0]\n # print(\"f\", f)\n # k1 = 0#self.distortion[0][0]\n # k2 = 0#self.distortion[0][1]\n # n = np.sum(points_proj ** 2, axis=1)\n # r = 1 + k1 * n + k2 * n ** 2\n # points_proj *= (r * f)[:, np.newaxis]\n\n points_proj = np.zeros((points.shape[0],2))\n for i in range(points.shape[0]):\n curr_proj_point = cv.projectPoints(points[i,:].reshape(1,3), camera_params[i, :3].reshape(3,1), camera_params[i, 3:].reshape(3,1), self.mtx, None)\n points_proj[i, :] = curr_proj_point[0]\n\n return points_proj","title":""},{"docid":"3625c419b803ac60c8291ce477ed9d0a","score":"0.59597665","text":"def flip_image(image):\n\treturn cv2.flip(image, 1)","title":""},{"docid":"1d7bc8c1b1594944eb608d2b0574368f","score":"0.5940707","text":"def process_image(img, camera, left, right):\n # undistort image\n undist_img = camera.undistort(img)\n\n # get perspective View\n wraped = c.birds_eye(undist_img, display=False)\n\n # apply color threshold on image\n warped_binary = apply_thresholds(wraped, display=False)\n\n nonzerox, nonzeroy = np.nonzero(np.transpose(warped_binary))\n\n # Get lanes\n if left.detected is True:\n leftx, lefty, left.detected = left.quick_search(nonzerox, nonzeroy)\n if right.detected is True:\n rightx, righty, right.detected = right.quick_search(nonzerox, nonzeroy)\n if left.detected is False:\n leftx, lefty, left.detected = left.blind_search(\n nonzerox, nonzeroy, warped_binary)\n if right.detected is False:\n rightx, righty, right.detected = right.blind_search(\n nonzerox, nonzeroy, warped_binary)\n\n left.y = np.array(lefty).astype(np.float32)\n left.x = np.array(leftx).astype(np.float32)\n right.y = np.array(righty).astype(np.float32)\n right.x = np.array(rightx).astype(np.float32)\n\n # Based on searched x and y coordinates, polyfit with second order.\n left_fit, left_fitx, left_fity = left.get_fit()\n right_fit, right_fitx, right_fity = right.get_fit()\n\n # Visualize Results\n image_shape = img.shape\n ploty = np.linspace(0, image_shape[0] - 1, image_shape[0])\n offset, mean_curv = get_car_pos(\n left_fit, right_fit, ploty, (image_shape[0], image_shape[1]))\n\n result = draw_lane(undist_img, warped_binary, left_fit,\n right_fit, camera, display=False)\n\n font = cv2.FONT_HERSHEY_SIMPLEX\n text1 = 'Radius of Curvature: %d(m)'\n text2 = 'Offset from center: %.2f(m)'\n text3 = 'Radius of Curvature: Inf (m)'\n\n if mean_curv < 3000:\n cv2.putText(result, text1 % (int(mean_curv)),\n (60, 100), font, 1.0, (255, 255, 255), thickness=2)\n else:\n cv2.putText(result, text3,\n (60, 100), font, 1.0, (255, 255, 255), thickness=2)\n cv2.putText(result, text2 % (-offset),\n (60, 130), font, 1.0, (255, 255, 255), thickness=2)\n\n return result","title":""},{"docid":"e103bd458884c576493782ada695e422","score":"0.59372807","text":"def display_homographied_plan(h, source):\n # TODO\n\n cap=cv2.VideoCapture(source)\n # Reading our first frame\n ret1,frame1= cap.read()\n cv2.imshow('window',frame1)\n\n frame2 = cv2.perspectiveTransform(frame1, h)\n cv2.imshow('homography',frame2)\n\n\n while True:\n if cv2.waitKey(20) == ord('q'):\n # pour 'quitter'\n break\n\n cap.release()\n cv2.destroyAllWindows()\n return()","title":""},{"docid":"496986648e535e585a283c763e4b36d4","score":"0.59346884","text":"def translate(img, x, y):\n transMat = np.float32([[1,0,x], [0,1,y]])\n dimemsion = (img.shape[1], img.shape[0])\n return cv.warpAffine(img, transMat, dimemsion)","title":""},{"docid":"1b6b8857d87e1375c4b6f2bff122c88f","score":"0.59341764","text":"def perspective_coefficients(self, oldplane, newplane):\n # first find the transform coefficients, thanks to http://stackoverflow.com/questions/14177744/how-does-perspective-transformation-work-in-pil\n pb,pa = oldplane,newplane\n grid = []\n for p1,p2 in zip(pa, pb):\n grid.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]])\n grid.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]])\n \n # then do some matrix magic\n A = mt.Matrix(grid)\n B = mt.Vec([xory for xy in pb for xory in xy])\n AT = A.tr()\n ATA = AT.mmul(A)\n gridinv = ATA.inverse()\n invAT = gridinv.mmul(AT)\n res = invAT.mmul(B)\n a,b,c,d,e,f,g,h = res.flatten()\n \n # finito\n return a,b,c,d,e,f,g,h","title":""},{"docid":"966c7a44c3cedadd09bd9c97c773fb5c","score":"0.5932009","text":"def affine_transform(image: np.array, pts1: np.float32([[], [], []]), pts2: np.float32([[], [], []])) -> np.array:\n rows, cols = image.shape[:2]\n M = cv.getAffineTransform(pts1, pts2)\n transformed = cv.warpAffine(image, M, (cols, rows))\n return transformed","title":""},{"docid":"986e25b3d95c2ccf5a57c5cff573bfcb","score":"0.59113497","text":"def warpImgByCorners(image, corners, zoomed_out=False):\n\n nw, ne, sw, se = [np.array(corners[qd]) for qd in QD_KEYS]\n\n # calculate maximum width and height for destination points\n width1 = norm(se - sw)\n width2 = norm(ne - nw)\n maxWidth = max(int(width1), int(width2))\n\n height1 = norm(ne - se)\n height2 = norm(nw - sw)\n maxHeight = max(int(height1), int(height2))\n\n if zoomed_out:\n factor = float(MAX_OBJ_HEIGHT) / CAMERA_HEIGHT / 2\n min_dst_x = factor * maxWidth\n max_dst_x = (1 + factor) * maxWidth\n min_dst_y = factor * maxHeight\n max_dst_y = (1 + factor) * maxHeight\n dst_size = (int((1 + 2 * factor) * maxWidth), int((1 + 2 * factor) * maxHeight))\n else:\n min_dst_x, max_dst_x = 0, maxWidth - 1\n min_dst_y, max_dst_y = 0, maxHeight - 1\n dst_size = (maxWidth, maxHeight)\n\n # source points for matrix calculation\n src = np.array((nw, ne, se, sw), dtype=\"float32\")\n\n # destination points in the same order\n dst = np.array(\n [\n [min_dst_x, min_dst_y], # nw\n [max_dst_x, min_dst_y], # ne\n [max_dst_x, max_dst_y], # sw\n [min_dst_x, max_dst_y], # se\n ],\n dtype=\"float32\",\n )\n\n # get the perspective transform matrix\n transMatrix = cv2.getPerspectiveTransform(src, dst)\n\n # compute warped image\n warpedImg = cv2.warpPerspective(image, transMatrix, dst_size)\n return warpedImg","title":""},{"docid":"3c14f8874bb11b5bc9e9a62737c5739b","score":"0.58761185","text":"def __init__(self, imageSize):\n self._imageSize = imageSize\n self._sourcePoints = self._createSource()\n self._destinationPoints = self._createDestination()\n self._transformationMatrix = cv2.getPerspectiveTransform(self._sourcePoints, self._destinationPoints)\n self._inverseTransformationMatrix = cv2.getPerspectiveTransform(self._destinationPoints, self._sourcePoints)","title":""},{"docid":"e09a1394336580ce5ef571ceca52b98f","score":"0.5808014","text":"def warp_perspective(\n inp: Tensor,\n mat: Tensor,\n out_shape: Union[Tuple[int, int], int, Tensor],\n mat_idx: Optional[Union[Iterable[int], Tensor]] = None,\n border_mode: str = \"replicate\",\n border_val: float = 0.0,\n format: str = \"NCHW\",\n interp_mode: str = \"linear\",\n) -> Tensor:\n if inp.dtype == np.float32:\n mat = mat.astype(\"float32\")\n if inp.dtype == np.float16:\n inp = inp.astype(\"float32\")\n op = builtin.WarpPerspective(\n imode=interp_mode, bmode=border_mode, format=format, border_val=border_val\n )\n out_shape = astensor1d(out_shape, inp, dtype=\"int32\", device=inp.device)\n if mat_idx is not None:\n mat_idx = astensor1d(mat_idx, inp, dtype=\"int32\", device=inp.device)\n (result,) = apply(op, inp, mat, mat_idx, out_shape)\n return result\n (result,) = apply(op, inp, mat, out_shape)\n return result","title":""},{"docid":"5ed86d5138bbd420348220fd5df78d7f","score":"0.5801215","text":"def translate(mat, x, y):\n trans_mat = np.float32([[1, 0, x],\n [0, 1, y]])\n return cv2.warpAffine(mat, trans_mat, (mat.shape[1], mat.shape[0]))","title":""},{"docid":"2c2aef8f8a5d997283210e00941a8342","score":"0.57737416","text":"def __call__(self, image):\n image = self.prepare_image(image)\n return self.get_transforms(image)","title":""},{"docid":"fb8ece7473284dff2bfbe98a56216062","score":"0.5773236","text":"def inverse_warp(img, depth, pose_mat, intrinsics, padding_mode='border'):\n depth = depth[:,0,:,:]\n check_sizes(img, 'img', 'B3HW')\n check_sizes(depth, 'depth', 'BHW')\n check_sizes(intrinsics, 'intrinsics', '133')\n batch_size, _, img_height, img_width = img.shape\n\n pixel_coords = set_id_grid(depth)\n intrinsics = intrinsics.broadcast_to((img.shape[0], intrinsics.shape[1], intrinsics.shape[2]))\n\n cam_coords = pixel2cam(depth, nd.linalg.inverse(intrinsics), pixel_coords) # [B,3,H,W]\n # Get projection matrix for the camera frame to source pixel frame\n proj_cam_to_src_pixel = nd.linalg.gemm2(intrinsics, pose_mat[:,:3,:]) # [B, 3, 4]\n\n\n src_pixel_coords, mask = cam2pixel(cam_coords, proj_cam_to_src_pixel[:,:,:3], proj_cam_to_src_pixel[:,:,-1:], padding_mode) # [B,H,W,2]\n src_pixel_coords = nd.transpose(src_pixel_coords, (0, 3, 1, 2))\n src_pixel_coords = nd.clip(src_pixel_coords, a_min=-1.0, a_max=1.0)\n\n # projected_img = F.grid_sample(img, src_pixel_coords, padding_mode=padding_mode)\n projected_img = nd.BilinearSampler(data=img, grid=src_pixel_coords)\n return projected_img, src_pixel_coords, mask","title":""},{"docid":"8f173f7024592f4133e5086b3a40bb9c","score":"0.57649505","text":"def project_to_image_plane(self, point_in_world):\n\n fx = self.config['camera_info']['focal_length_x']\n fy = self.config['camera_info']['focal_length_y']\n image_width = self.config['camera_info']['image_width']\n image_height = self.config['camera_info']['image_height']\n\n # get transform between pose of camera and world frame\n trans = None\n try:\n now = rospy.Time.now()\n self.listener.waitForTransform(\"/base_link\",\n \"/world\", now, rospy.Duration(1.0))\n (trans, rot) = self.listener.lookupTransform(\"/base_link\",\n \"/world\", now)\n\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\n rospy.logerr(\"Failed to find camera to map transform\")\n\n #TODO Use tranform and rotation to calculate 2D position of light in image\n # KB 10Oct2017\n # commenting line below, and using dummy values, so that tf_classifier.py can be tested \n # x, y = self.project_to_image_plane(light.pose.pose.position)\n x = 0\n y = 0\n\n return (x, y)","title":""},{"docid":"c93e36676cf4a741c04c31edded141dd","score":"0.57443106","text":"def perspective_coefficients(self):\n run_data = self.run_data\n\n lower_right_1 = (run_data['l0x']), (run_data['l0y'])\n upper_right_1 = (run_data['lsx']), (run_data['lsy'])\n lower_left_1 = (run_data['j10x']), (run_data['j10y'])\n upper_left_1 = (run_data['j1sx']), (run_data['j1sy'])\n\n lower_right_2 = (run_data['j20x']), (run_data['j20y'])\n upper_right_2 = (run_data['j2sx']), (run_data['j2sy'])\n lower_left_2 = (run_data['r0x']), (run_data['r0y'])\n upper_left_2 = (run_data['rsx']), (run_data['rsy'])\n # need some standard vertical lines in both cameras.\n # cam1: use lock gate and tank join\n # cam2: tank join and ruler at 2.5m\n # (checked to be vertical, extrapolate to surface)\n # so for each camera, 4 locations (8 numbers) need\n # to be recorded.\n\n style = self.style\n\n # units here are pixels, i.e in the coordinate system of\n # an image.\n x1 = (lower_right_1, upper_right_1, lower_left_1, upper_left_1)\n X1 = self.perspective_reference(lower_right_1, style, 'cam1')\n\n x2 = (lower_right_2, upper_right_2, lower_left_2, upper_left_2)\n X2 = self.perspective_reference(lower_right_2, style, 'cam2')\n\n cam1_coeff = processing.perspective_coefficients(x1, X1).tolist()\n if lower_right_2[0] == 0:\n cam2_coeff = 0\n else:\n cam2_coeff = processing.perspective_coefficients(x2, X2).tolist()\n\n return {'cam1': cam1_coeff, 'cam2': cam2_coeff}","title":""},{"docid":"e38332db46fa997c53930521dd141130","score":"0.5742328","text":"def perspective_projection(points, rotation, translation,\n focal_length, camera_center):\n batch_size = points.shape[0]\n K = torch.zeros([batch_size, 3, 3], device=points.device)\n K[:,0,0] = focal_length\n K[:,1,1] = focal_length\n K[:,2,2] = 1.\n K[:,:-1, -1] = camera_center\n\n # Transform points\n points = torch.einsum('bij,bkj->bki', rotation, points)\n points = points + translation.unsqueeze(1)\n\n # Apply perspective distortion\n projected_points = points / points[:,:,-1].unsqueeze(-1)\n\n # Apply camera intrinsics\n projected_points = torch.einsum('bij,bkj->bki', K, projected_points)\n\n return projected_points[:, :, :-1]","title":""},{"docid":"25d91600c3f32a9a000dc5861e5d0097","score":"0.57386327","text":"def translation(img, vec):\n tx = vec[0]\n ty = vec[1]\n M = np.float32([[1, 0, tx], [0, 1, ty]])\n return cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))","title":""},{"docid":"1a9ec68e4043260f2df653db7140b7a7","score":"0.57374907","text":"def flattener(self, image, pts, w, h):\n temp_rect = np.zeros((4, 2), dtype=\"float32\")\n\n s = np.sum(pts, axis=2)\n\n tl = pts[np.argmin(s)]\n br = pts[np.argmax(s)]\n\n diff = np.diff(pts, axis=-1)\n tr = pts[np.argmin(diff)]\n bl = pts[np.argmax(diff)]\n\n # Need to create an array listing points in order of\n # [top left, top right, bottom right, bottom left]\n # before doing the perspective transform\n\n if w <= 0.8 * h: # If card is vertically oriented\n temp_rect[0] = tl\n temp_rect[1] = tr\n temp_rect[2] = br\n temp_rect[3] = bl\n\n if w >= 1.2 * h: # If card is horizontally oriented\n temp_rect[0] = bl\n temp_rect[1] = tl\n temp_rect[2] = tr\n temp_rect[3] = br\n\n # If the card is 'diamond' oriented, a different algorithm\n # has to be used to identify which point is top left, top right\n # bottom left, and bottom right.\n\n if 0.8 * h < w < 1.2 * h: # If card is diamond oriented\n # If furthest left point is higher than furthest right point,\n # card is tilted to the left.\n if pts[1][0][1] <= pts[3][0][1]:\n # If card is titled to the left, approxPolyDP returns points\n # in this order: top right, top left, bottom left, bottom right\n temp_rect[0] = pts[1][0] # Top left\n temp_rect[1] = pts[0][0] # Top right\n temp_rect[2] = pts[3][0] # Bottom right\n temp_rect[3] = pts[2][0] # Bottom left\n\n # If furthest left point is lower than furthest right point,\n # card is tilted to the right\n if pts[1][0][1] > pts[3][0][1]:\n # If card is titled to the right, approxPolyDP returns points\n # in this order: top left, bottom left, bottom right, top right\n temp_rect[0] = pts[0][0] # Top left\n temp_rect[1] = pts[3][0] # Top right\n temp_rect[2] = pts[2][0] # Bottom right\n temp_rect[3] = pts[1][0] # Bottom left\n\n maxWidth = 633\n maxHeight = 1008\n\n # Create destination array, calculate perspective transform matrix,\n # and warp card image\n dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], np.float32)\n M = cv2.getPerspectiveTransform(temp_rect, dst)\n warp = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n warp = warp[30:990, 30:620]\n warp = cv2.resize(warp, (633, 1008))\n\n return warp","title":""},{"docid":"9470cb2204fd577bc3e879fd9927a34f","score":"0.5719011","text":"def perspective(fov, aspect, zNear, zFar):\n fovR = math.radians(45.0)\n f = 1.0/math.tan(fovR/2.0)\n return numpy.array([f/float(aspect), 0.0, 0.0, 0.0, \n 0.0, f, 0.0, 0.0, \n 0.0, 0.0, (zFar+zNear)/float(zNear-zFar), -1.0, \n 0.0, 0.0, 2.0*zFar*zNear/float(zNear-zFar), 0.0], \n numpy.float32)","title":""},{"docid":"c224dca347f9a953886dc0cc555234c0","score":"0.5714747","text":"def preprocess(img, imgref):\n im = mesh_align(img, imgref)\n im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n # im = cv2.resize(im, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)\n im = align(im)\n return im","title":""},{"docid":"f9cb7ff2577259d590400e583f040158","score":"0.57090056","text":"def view_transform(fr, to, up):\n forward = normalize(to - fr)\n upn = normalize(up)\n left = cross(forward, upn)\n true_up = cross(left, forward)\n orientation = matrix(left[0], left[1], left[2], 0,\n true_up[0], true_up[1], true_up[2], 0,\n -forward[0], -forward[1], -forward[2], 0,\n 0,0,0,1)\n return orientation * translation(-fr[0], -fr[1], -fr[2])","title":""},{"docid":"a3ed344147e6dca63a335b9521fd9d27","score":"0.5700831","text":"def visualize():\n # Plot the Perspective Transformation\n image = mpimg.imread('./test_images/straight_lines2.jpg')\n plt.figure(figsize=(10, 8))\n plt.subplot(121)\n plt.imshow(image)\n plt.xlim(0, 1280)\n plt.ylim(720, 0)\n draw_lines(src)\n plt.title('Original Image', fontsize=12)\n plt.subplot(122)\n plt.imshow(warp(image))\n plt.xlim(0, 1280)\n plt.ylim(720, 0)\n draw_lines(dst)\n plt.title('Warped image with dest. points', fontsize=12)\n plt.show()\n\n # Plot Perspective_Binary\n # plt.figure(figsize=(8,2))\n # plt.subplot(121)\n # plt.imshow(binary, cmap='gray')\n # draw_lines(src)\n # plt.title('Threshold Image', fontsize=12)\n # plt.subplot(122)\n # plt.imshow(warped, cmap='gray')\n # draw_lines(dst)\n # plt.title('Warped image with dest. points drawn', fontsize=12)\n\n # Plot historgram\n # plt.figure(figsize=(9,4))\n # histogram = np.sum(warped[int(warped.shape[0]/2):,:], axis=0)\n # plt.plot(histogram)\n # plt.xlabel('Pixel Position')\n # plt.ylabel('Counts')","title":""},{"docid":"3aa00f75ef8317973a4ddf95c5954cd0","score":"0.5695022","text":"def _homography(self, image):\n\t\t# get matching points\n\t\tkp1, des1 = sift.detectAndCompute(image, None)\n\t\tkp2, des2 = sift.detectAndCompute(self.template, None)\n\t\tbf = cv2.BFMatcher()\n\t\tmatches = bf.knnMatch(des1, des2, k=2)\n\t\tgood = []\n\t\t# prune the matching\n\t\tfor m,n in matches:\n\t\t\tif m.distance < 0.5 * n.distance:\n\t\t\t good.append([m])\n\t\t# if no good matching, return \n\t\tif len(good) < 4:\n\t\t\treturn\n\t\t# extract location info from matching point\n\t\tkp1_index = [m[0].queryIdx for m in good]\n\t\tkp2_index =\t[m[0].trainIdx for m in good]\n\t\tkp1 = np.array([k.pt for k in np.array(kp1)[kp1_index]])\n\t\tkp2 = np.array([k.pt for k in np.array(kp2)[kp2_index]])\n\t\t# calculate homography using RANSAC\n\t\tH, _ = cv2.findHomography(kp2, kp1, cv2.RANSAC, 0.1)\n\t\tif H is None:\n\t\t\treturn\n\t\t# get location of the self.template in the image\n\t\theight, width = self.template.shape\n\t\ttop_left = H.dot(np.array([[0, 0, 1]]).reshape(3, 1)).flatten()\n\t\tbottom_left = H.dot(np.array([[0, height, 1]]).reshape(3, 1)).flatten()\n\t\tbottom_right = H.dot(np.array([[width, height, 1]]).reshape(3, 1)).flatten()\n\t\ttop_right = H.dot(np.array([[width, 0, 1]]).reshape(3, 1)).flatten()\n\t\t# rescale the points\n\t\ttop_left = top_left[:2] / top_left[-1]\n\t\tbottom_left = bottom_left[:2] / bottom_left[-1]\n\t\tbottom_right = bottom_right[:2] / bottom_right[-1]\n\t\ttop_right = top_right[:2] / top_right[-1]\n\t\t# int type\n\t\ttop_left = top_left.astype(int)\n\t\tbottom_left = bottom_left.astype(int)\n\t\tbottom_right = bottom_right.astype(int) \n\t\ttop_right = top_right.astype(int)\n\n\t\treturn top_left, bottom_left, bottom_right, top_right","title":""},{"docid":"07ab5d5938f5da7e55e6a3965ea5252b","score":"0.5690912","text":"def rotate(image, angle):\n # Get the image size\n # No that's not an error - NumPy stores image matricies backwards\n image_size = (image.shape[1], image.shape[0])\n image_center = tuple(np.array(image_size) / 2)\n\n # Convert the OpenCV 3x2 rotation matrix to 3x3\n rot_mat = np.vstack(\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\n )\n\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\n\n # Shorthand for below calcs\n image_w2 = image_size[0] * 0.5\n image_h2 = image_size[1] * 0.5\n\n # Obtain the rotated coordinates of the image corners\n rotated_coords = [\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]\n ]\n\n # Find the size of the new image\n x_coords = [pt[0] for pt in rotated_coords]\n x_pos = [x for x in x_coords if x > 0]\n x_neg = [x for x in x_coords if x < 0]\n\n y_coords = [pt[1] for pt in rotated_coords]\n y_pos = [y for y in y_coords if y > 0]\n y_neg = [y for y in y_coords if y < 0]\n\n right_bound = max(x_pos)\n left_bound = min(x_neg)\n top_bound = max(y_pos)\n bot_bound = min(y_neg)\n\n new_w = int(abs(right_bound - left_bound))\n new_h = int(abs(top_bound - bot_bound))\n\n # We require a translation matrix to keep the image centred\n trans_mat = np.matrix([\n [1, 0, int(new_w * 0.5 - image_w2)],\n [0, 1, int(new_h * 0.5 - image_h2)],\n [0, 0, 1]\n ])\n\n # Compute the tranform for the combined rotation and translation\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\n\n # Apply the transform\n result = cv2.warpAffine(\n image,\n affine_mat,\n (new_w, new_h),\n flags=cv2.INTER_LINEAR\n )\n\n return result","title":""},{"docid":"0c8cb37186955619075ec67770911b08","score":"0.56868243","text":"def warp(image, U, V, interpolation, border_mode):\n \n M, N = image.shape\n X, Y = np.meshgrid(xrange(N), xrange(M))\n x = X * 1.0 + U * 1.0\n y = Y * 1.0 + V * 1.0\n x_32 = x.astype('float32')\n y_32 = y.astype('float32')\n C = cv2.remap(image, x_32, y_32, interpolation, borderMode=border_mode)\n return C","title":""},{"docid":"99af80ed6ed8d571155cd97e9b419c6d","score":"0.56614906","text":"def project_to_image_plane(self, point_in_world):\n fx = self.config['camera_info']['focal_length_x']\n fy = self.config['camera_info']['focal_length_y']\n image_width = self.config['camera_info']['image_width']\n image_height = self.config['camera_info']['image_height']\n\n try:\n now = rospy.Time.now()\n self.listener.waitForTransform(\"/base_link\",\n \"/world\", now, rospy.Duration(1.0))\n (transT, rotT) = self.listener.lookupTransform(\"/base_link\",\n \"/world\", now)\n\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\n rospy.logerr(\"Failed to find camera to map transform\")\n return None, None\n pt = PointStamped()\n pt.header.stamp = point_in_world.header.stamp\n pt.header.frame_id = \"world\"\n pt.point.x = point_in_world.pose.pose.position.x\n pt.point.y = point_in_world.pose.pose.position.y\n pt.point.z = point_in_world.pose.pose.position.z\n #rospy.loginfo(\"Point: \" + str(pt))\n target_pt = self.listener.transformPoint(\"base_link\", pt)\n cx = image_width/2\n cy = image_height/2\n \n ##########################################################################################\n # DELETE THIS MAYBE - MANUAL TWEAKS TO GET THE PROJECTION TO COME OUT CORRECTLY IN SIMULATOR\n # just override the simulator parameters. probably need a more reliable way to determine if \n # using simulator and not real car\n # See discussion in forum\n if fx < 10:\n fx = 2344\n fy = 2552 #303.8\n target_pt.point.y += 0.5\n target_pt.point.z -= 1.2\n cy = cy * 2 \n ##########################################################################################\n\n \n #rospy.loginfo(\"Target Point: \" + str(target_pt))\n x = -target_pt.point.y * fx / target_pt.point.x; \n y = -target_pt.point.z * fy / target_pt.point.x; \n #rospy.loginfo(\"x: %.3f, y:%.3f\"%(x,y))\n x = int(x + cx)\n y = int(y + cy) \n\n return (x, y)","title":""},{"docid":"763196bb46fbf32fd9d2f0f82efa8f7d","score":"0.5652015","text":"def crop_and_warp(img, crop_rect):\n\n # Rectangle described by top left, top right, bottom right and bottom left points\n top_left, top_right, bottom_right, bottom_left = crop_rect[0], crop_rect[1], crop_rect[2], crop_rect[3]\n\n # Explicitly set the data type to float32 or `getPerspectiveTransform` will throw an error\n src = np.array([top_left, top_right, bottom_right, bottom_left], dtype='float32')\n\n # Get the longest side in the rectangle\n side = max([\n distance_between(bottom_right, top_right),\n distance_between(top_left, bottom_left),\n distance_between(bottom_right, bottom_left),\n distance_between(top_left, top_right)\n ])\n\n # Describe a square with side of the calculated length, this is the new perspective we want to warp to\n dst = np.array([[0, 0], [side - 1, 0], [side - 1, side - 1], [0, side - 1]], dtype='float32')\n\n # Gets the transformation matrix for skewing the image to fit a square by comparing the 4 before and after points\n m = cv2.getPerspectiveTransform(src, dst)\n\n # Performs the transformation on the original image\n return cv2.warpPerspective(img, m, (int(side), int(side)))","title":""},{"docid":"763196bb46fbf32fd9d2f0f82efa8f7d","score":"0.5652015","text":"def crop_and_warp(img, crop_rect):\n\n # Rectangle described by top left, top right, bottom right and bottom left points\n top_left, top_right, bottom_right, bottom_left = crop_rect[0], crop_rect[1], crop_rect[2], crop_rect[3]\n\n # Explicitly set the data type to float32 or `getPerspectiveTransform` will throw an error\n src = np.array([top_left, top_right, bottom_right, bottom_left], dtype='float32')\n\n # Get the longest side in the rectangle\n side = max([\n distance_between(bottom_right, top_right),\n distance_between(top_left, bottom_left),\n distance_between(bottom_right, bottom_left),\n distance_between(top_left, top_right)\n ])\n\n # Describe a square with side of the calculated length, this is the new perspective we want to warp to\n dst = np.array([[0, 0], [side - 1, 0], [side - 1, side - 1], [0, side - 1]], dtype='float32')\n\n # Gets the transformation matrix for skewing the image to fit a square by comparing the 4 before and after points\n m = cv2.getPerspectiveTransform(src, dst)\n\n # Performs the transformation on the original image\n return cv2.warpPerspective(img, m, (int(side), int(side)))","title":""},{"docid":"8d67a96f4a0767f2d1ea46f4ac12b5ea","score":"0.5626017","text":"def deslant(img):\n h, w = img.shape[:2]\n m = cv2.moments(img)\n if abs(m['mu02']) < 1e-2:\n return img.copy()\n skew = m['mu11'] / m['mu02']\n M = np.float32([[1, skew, -0.5 * h * skew], [0, 1, 0]])\n img = cv2.warpAffine(img, M, (h, h),\n flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)\n return img","title":""},{"docid":"47b6c4b406a83fedd0c923f27d134e99","score":"0.56103444","text":"def calculate_pose_from_essential_matrix(essential_matrix, img0_points, img1_points, intrinsic_camera_matrix):\n u, s, v = np.linalg.svd(essential_matrix)\n # http://igt.ip.uca.fr/~ab/Classes/DIKU-3DCV2/Handouts/Lecture16.pdf\n w = np.asarray([[0, -1, 0],\n [1, 0, 0],\n [0, 0, 1]])\n possible_rotations = [u @ w @ v, u @ w.T @ v]\n rotation_multiplier = 1 if np.linalg.det(possible_rotations[0]) > 0 else -1\n possible_rotations = [rotation_multiplier * rotation for rotation in possible_rotations]\n\n # translation X points right, Y points up, Z points into the image\n possible_translations = [u[:, -1], -1*u[:, -1]]\n\n winning_num_points = -1\n winning_rotation = None\n winning_translation = None\n winning_triangulated_points = None\n for rotation in possible_rotations:\n for translation in possible_translations:\n # The camera matrix describes how the WORLD is transformed relative to the CAMERA\n # To see the motion of the camera, rotation/translation need to be inverted\n img0_camera_matrix = intrinsic_camera_matrix @ util.rotation_translation_to_pose(np.identity(3), np.zeros(3))[:3]\n img1_camera_matrix = intrinsic_camera_matrix @ util.rotation_translation_to_pose(rotation, translation)[:3]\n triangulated_points = triangulation.triangulate_points_from_pose(img0_camera_matrix,\n img1_camera_matrix,\n img0_points,\n img1_points)\n camera_2_vector = rotation @ np.asarray([0, 0, 1])\n\n points_in_front_of_camera_1 = triangulated_points[:, 2] > 0\n points_in_front_of_camera_2 = np.dot((triangulated_points - translation), camera_2_vector) > 0\n num_points_in_front_of_camera = np.count_nonzero(np.multiply(points_in_front_of_camera_1, points_in_front_of_camera_2))\n\n if num_points_in_front_of_camera > winning_num_points:\n winning_rotation = rotation\n winning_translation = translation\n winning_num_points = num_points_in_front_of_camera\n winning_triangulated_points = triangulated_points\n logger.debug(\"essential matrix - winning num points: %d/%d\" % (winning_num_points, len(img0_points)))\n\n return util.rotation_translation_to_pose(winning_rotation, winning_translation), winning_triangulated_points","title":""},{"docid":"c41f65625e6b5fd19115c8c6128f2187","score":"0.5606679","text":"def panorama(imgs):\r\n h_max = max([x.shape[0] for x in imgs])\r\n w_max = sum([x.shape[1] for x in imgs])\r\n\r\n # create the final stitched canvas\r\n dst = np.zeros((h_max, w_max, imgs[0].shape[2]), dtype=np.uint8)\r\n dst[:imgs[0].shape[0], :imgs[0].shape[1]] = imgs[0]\r\n last_best_H = np.eye(3)\r\n out = None\r\n\r\n # for all images to be stitched:\r\n for idx in range(len(imgs) - 1):\r\n im1 = imgs[idx]\r\n im2 = imgs[idx + 1]\r\n\r\n # TODO: 1.feature detection & matching\r\n\r\n # TODO: 2. apply RANSAC to choose best H\r\n\r\n # TODO: 3. chain the homographies\r\n\r\n # TODO: 4. apply warping\r\n\r\n return out","title":""},{"docid":"3f1d673a8ca367cb9faac38dc96a4c81","score":"0.5604625","text":"def simpleDistortion(xx, yy, xs, ys):\n\n # subtract average to align\n\n nn = len(xs)\n pts1 = np.zeros((1, nn, 2))\n pts2 = np.zeros((1, nn, 2))\n xav1 = xs.mean()\n yav1 = ys.mean()\n xav2 = xx.mean()\n yav2 = yy.mean()\n\n for i in range(len(xs)):\n pts1[0, i, 0] = xs[i]-xav1\n pts1[0, i, 1] = ys[i]-yav1\n pts2[0, i, 0] = xx[i]-xav2\n pts2[0, i, 1] = yy[i]-yav2\n\n # cv2 needs float32\n\n pts1 = np.float32(pts1)\n pts2 = np.float32(pts2)\n\n # get the affine transformation - translation, rotation, scaling\n # uses opencv library\n\n transformation = cv2.estimateRigidTransform(pts1, pts2, False)\n print(transformation)\n\n # calculate the scaling from the result\n sx = np.sqrt(transformation[0, 0]**2+transformation[0, 1]**2)\n sy = np.sqrt(transformation[1, 0]**2+transformation[1, 1]**2)\n\n print(\"sx=\", sx, \" sy=\", sy)\n\n # remove the scaling from the transformation matrix\n transformation[0, 0] /= sx\n transformation[0, 1] /= sx\n transformation[1, 0] /= sy\n transformation[1, 1] /= sy\n\n print(transformation)\n\n # calculate the transformed points\n points = cv2.transform(pts1, transformation)\n\n # difference between expected and actual point positions\n diffx = (pts2[0, :, 0]-points[0, :, 0])\n diffy = (pts2[0, :, 1]-points[0, :, 1])\n\n # in mm and in % of field\n\n c = np.sqrt(diffx*diffx+diffy*diffy)\n c1 = np.sqrt(diffx*diffx+diffy*diffy)/336.*100\n\n return c, c1, pts1, pts2, diffx, diffy,","title":""},{"docid":"03fd375afc643bab8fc88257d7048b93","score":"0.5601717","text":"def warp_image(self, image):\r\n\r\n # We're just wrapping OpenCV library with appropriate options here\r\n\r\n # May as well make warped image have same dimensions as original\r\n (self.y_size, self.x_size) = np.shape(image)\r\n\r\n img_size = (self.x_size, self.y_size)\r\n return cv2.warpPerspective(image, self.warp_to_birdseye_matrix, img_size, flags=cv2.INTER_LINEAR)","title":""},{"docid":"c338f633c936588c692998ecec78f125","score":"0.56016487","text":"def project(self, win_width, win_height, fov, viewer_distance):\n factor = fov / (viewer_distance + self.z)\n x = self.x * factor + win_width / 2\n y = -self.y * factor + win_height / 2\n return Point3D(x, y, 1)","title":""},{"docid":"a52c5699aceeb2f2fe7452d0eeeeb33a","score":"0.5593834","text":"def project_to_image(pts_3d, P):\n n = pts_3d.shape[0]\n pts_3d_extend = np.hstack((pts_3d, np.ones((n, 1))))\n # print(('pts_3d_extend shape: ', pts_3d_extend.shape))\n pts_2d = np.dot(pts_3d_extend, np.transpose(P)) # nx3\n pts_2d[:, 0] /= pts_2d[:, 2]\n pts_2d[:, 1] /= pts_2d[:, 2]\n return pts_2d[:, 0:2]","title":""},{"docid":"679e59a0a957428c240752bd134268a6","score":"0.55919784","text":"def project_to_image_plane(self, point_in_world):\n\n fx = self.config['camera_info']['focal_length_x']\n fy = self.config['camera_info']['focal_length_y']\n image_width = self.config['camera_info']['image_width']\n image_height = self.config['camera_info']['image_height']\n\n # get transform between pose of camera and world frame\n trans = None\n try:\n now = rospy.Time.now()\n self.listener.waitForTransform(\"/base_link\",\n \"/world\", now, rospy.Duration(1.0))\n (trans, rot) = self.listener.lookupTransform(\"/base_link\",\n \"/world\", now)\n\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\n rospy.logerr(\"Failed to find camera to map transform\")\n\n #TODO Use tranform and rotation to calculate 2D position of light in image\n\n x = 0\n y = 0\n\n return (x, y)","title":""},{"docid":"8742a82fb44c2a1c3d22c15054753c31","score":"0.55826426","text":"def project_to_image_plane(self, point_in_world):\n\n fx = self.config['camera_info']['focal_length_x']\n fy = self.config['camera_info']['focal_length_y']\n image_width = self.config['camera_info']['image_width']\n image_height = self.config['camera_info']['image_height']\n\n\n # get transform between pose of camera and world frame\n trans = None\n try:\n now = rospy.Time.now()\n self.listener.waitForTransform(\"/base_link\",\n \"/world\", now, rospy.Duration(1.0))\n (trans, rot) = self.listener.lookupTransform(\"/base_link\",\n \"/world\", now)\n\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\n rospy.logerr(\"Failed to find camera to map transform\")\n\n\n camera_point=PointStamped()\n camera_point.header.frame_id = \"/world\"\n camera_point.header.stamp =rospy.Time(0)\n camera_point.point.x = point_in_world.x\n camera_point.point.y = point_in_world.y\n camera_point.point.z = point_in_world.z\n p = self.listener.transformPoint(\"/base_link\",camera_point)\n\n #correct shift of traffic light to left / right because of car heading\n roll, pitch, yaw = self.get_orientations_from_quaternion(self.pose.pose.orientation)\n\n y_offset = p.point.x*math.sin(yaw)\n x = -(p.point.y + y_offset)/ p.point.x * fx + image_width*0.5\n\n y = 62 + image_height - (p.point.z / p.point.x * fy + image_height*0.5)\n\n return (int(x), int(y))","title":""},{"docid":"e17b791c6f42f909aafb83e76bc1f075","score":"0.55526483","text":"def stitch_image_pair(img_a, img_b, stitch_direc):\n img_a_gray = cv2.cvtColor(img_a, cv2.COLOR_BGR2GRAY)\n img_b_gray = cv2.cvtColor(img_b, cv2.COLOR_BGR2GRAY)\n matches_a, matches_b = get_matches(img_a_gray, img_b_gray, num_keypoints=1000, threshold=0.8)\n h_mat = compute_homography_ransac(matches_a, matches_b)\n if stitch_direc == 0:\n canvas = cv2.warpPerspective(img_b, h_mat, (img_a.shape[1], img_a.shape[0] + img_b.shape[0]))\n canvas[0:img_a.shape[0], :, :] = img_a[:, :, :]\n x_start, y_start, x_end, y_end = get_crop_points(h_mat, img_a, img_b, 0)\n else:\n canvas = cv2.warpPerspective(img_b, h_mat, (img_a.shape[1] + img_b.shape[1], img_a.shape[0]))\n canvas[:, 0:img_a.shape[1], :] = img_a[:, :, :]\n x_start, y_start, x_end, y_end = get_crop_points(h_mat, img_a, img_b, 1)\n \n stitched_img = canvas[y_start:y_end,x_start:x_end,:]\n return stitched_img","title":""},{"docid":"076089da63155af35fcec49a688b0d53","score":"0.5547256","text":"def _rotate_image(image, angle):\n\n # Get the image size\n # No that's not an error - NumPy stores image matricies backwards\n image_size = (image.shape[1], image.shape[0])\n image_center = tuple(np.array(image_size) / 2)\n\n # Convert the OpenCV 3x2 rotation matrix to 3x3\n rot_mat = np.vstack(\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\n )\n\n rot_mat_notranslate = np.array(rot_mat[0:2, 0:2])\n\n # Shorthand for below calcs\n image_w2 = image_size[0] * 0.5\n image_h2 = image_size[1] * 0.5\n\n # Obtain the rotated coordinates of the image corners\n rotated_coords = [\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate)[0],\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate)[0],\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate)[0],\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate)[0]\n ]\n\n # Find the size of the new image\n x_coords = [pt[0] for pt in rotated_coords]\n x_pos = [x for x in x_coords if x > 0]\n x_neg = [x for x in x_coords if x < 0]\n\n y_coords = [pt[1] for pt in rotated_coords]\n y_pos = [y for y in y_coords if y > 0]\n y_neg = [y for y in y_coords if y < 0]\n\n right_bound = max(x_pos)\n left_bound = min(x_neg)\n top_bound = max(y_pos)\n bot_bound = min(y_neg)\n\n new_w = int(abs(right_bound - left_bound))\n new_h = int(abs(top_bound - bot_bound))\n\n # We require a translation matrix to keep the image centred\n trans_mat = np.array([\n [1, 0, int(new_w * 0.5 - image_w2)],\n [0, 1, int(new_h * 0.5 - image_h2)],\n [0, 0, 1]\n ])\n\n # Compute the tranform for the combined rotation and translation\n affine_mat = (np.array(trans_mat) * np.array(rot_mat))[0:2, :]\n\n # Apply the transform\n result = cv2.warpAffine(\n image,\n affine_mat,\n (new_w, new_h),\n flags=cv2.INTER_LINEAR\n )\n\n return result","title":""},{"docid":"693c4e705c49adf681e35c6f91df9a40","score":"0.55401814","text":"def solveCamera(cameraMatrix, distortion, imagePoints, objectPoints):\n _, rotVector, transVector = cv2.solvePnP(objectPoints, imagePoints, cameraMatrix, distortion)\n\n rotMatrix = cv2.Rodrigues(rotVector)[0]\n position = -1 * np.matrix(rotMatrix).T * np.matrix(transVector)\n rotation = euler.mat2euler(np.matrix(rotMatrix).T)\n\n return position, rotation","title":""},{"docid":"6ff63c7a28b55954c030a85c191c99cb","score":"0.55352044","text":"def undistortImage(image, o_points, i_points):\n # Convert to gray scale\n #gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n\n # Calculate Camera matrix and distance coefficients \n ret, cam_matrix, distance_coeff, rot_vec, trans_vec = cv2.calibrateCamera(o_points, i_points, gray.shape, None, None)\n if ret:\n # return undistorted image\n return cv2.undistort(image, cam_matrix, distance_coeff)\n else:\n # raise error if camera calibration fails\n raise ValueError(\"Can not undistort the given image!\")","title":""},{"docid":"5faa506b4962aba621baa2abfaa73f2e","score":"0.55290407","text":"def project(self, win_width, win_height, fov, viewer_distance):\r\n factor = fov / (viewer_distance + self.z)\r\n x = self.x * factor + win_width / 2\r\n y = -self.y * factor + win_height / 2\r\n return Point3D(x, y, self.z)","title":""},{"docid":"d1599d58482ff0df13fd2b654de06e34","score":"0.5526499","text":"def simpleDistortion(xx, yy, xs, ys, removeScale):\n\n # subtract average to align\n\n nn = len(xs)\n pts1 = np.zeros((1, nn, 2))\n pts2 = np.zeros((1, nn, 2))\n xav1 = xs.mean()\n yav1 = ys.mean()\n xav2 = xx.mean()\n yav2 = yy.mean()\n\n for i in range(len(xs)):\n # pts1[0,i,0]=xs[i]-xav1\n # pts1[0,i,1]=ys[i]-yav1\n # pts2[0,i,0]=xx[i]-xav2\n # pts2[0,i,1]=yy[i]-yav2\n pts1[0, i, 0] = xs[i]\n pts1[0, i, 1] = ys[i]\n pts2[0, i, 0] = xx[i]\n pts2[0, i, 1] = yy[i]\n\n # cv2 needs float32\n\n pts1 = np.float32(pts1)\n pts2 = np.float32(pts2)\n\n # get the affine transformation - translation, rotation, scaling\n # uses opencv library\n transformation = cv2.estimateRigidTransform(pts1, pts2, False)\n\n visplot.checkMatched(pts1[0, :, 0], pts1[0, :, 1], pts2[0, :, 0], pts2[0, :, 1], \"test\", 1)\n\n transformFull = transformation.copy()\n\n # calculate the scaling from the result\n sx = np.sqrt(transformation[0, 0]**2+transformation[0, 1]**2)\n sy = np.sqrt(transformation[1, 0]**2+transformation[1, 1]**2)\n\n print(\"sx=\", sx, \" sy=\", sy)\n\n if(removeScale == 1):\n # remove the scaling from the transformation matrix\n transformation[0, 0] /= sx\n transformation[0, 1] /= sx\n transformation[1, 0] /= sy\n transformation[1, 1] /= sy\n\n # calculate the transformed points\n points = cv2.transform(pts1, transformation)\n visplot.checkMatched(points[0, :, 0], points[0, :, 1], pts2[0, :, 0], pts2[0, :, 1], \"test\", 1)\n\n # difference between expected and actual point positions\n diffx = (pts2[0, :, 0]-points[0, :, 0])\n diffy = (pts2[0, :, 1]-points[0, :, 1])\n\n # in mm and in % of field\n\n c = np.sqrt(diffx*diffx+diffy*diffy)\n c1 = np.sqrt(diffx*diffx+diffy*diffy)/336.*100\n\n pts1[:, :, 0] = ma.masked_values(pts1[:, :, 0], 0)\n pts1[:, :, 1] = ma.masked_values(pts1[:, :, 1], 0)\n pts2[:, :, 0] = ma.masked_values(pts2[:, :, 0], 0)\n pts2[:, :, 1] = ma.masked_values(pts2[:, :, 1], 0)\n pts1 = ma.masked_invalid(pts1)\n pts2 = ma.masked_invalid(pts2)\n\n return c, c1, pts1, pts2, diffx, diffy, transformFull","title":""},{"docid":"439b982a3286ca60fd2a871e4637fc60","score":"0.55210596","text":"def project_points(wph_cam):\n return (wph_cam/wph_cam[:,2])[:,0:3]","title":""},{"docid":"3d17d144dd6fae6d112fca99f363cd09","score":"0.5520979","text":"def _angles_to_projective_transforms(angle, image_w, image_h):\n with tf.name_scope(\"rotate_parent\"):\n angle_or_angles = tf.convert_to_tensor(angle,\n name=\"angles\",\n dtype=tf.dtypes.float32)\n angles = angle_or_angles[None]\n x_offset = ((image_w - 1) - (tf.math.cos(angles) *\n (image_w - 1) - tf.math.sin(angles) *\n (image_h - 1))) / 2.0\n y_offset = ((image_h - 1) - (tf.math.sin(angles) *\n (image_w - 1) + tf.math.cos(angles) *\n (image_h - 1))) / 2.0\n num_angles = tf.shape(angles)[0]\n return tf.concat([\n tf.math.cos(angles)[:, None], -tf.math.sin(angles)[:, None],\n x_offset[:, None],\n tf.math.sin(angles)[:, None],\n tf.math.cos(angles)[:, None], y_offset[:, None],\n tf.zeros((1, 2))\n ],\n axis=1)","title":""}],"string":"[\n {\n \"docid\": \"e4d720145f38baea418efe75d75cc9c6\",\n \"score\": \"0.7743494\",\n \"text\": \"def perspect_transform(img, src, dst):\\n M = cv2.getPerspectiveTransform(src, dst)\\n warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))# keep same size as input image\\n \\n return warped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ed84371c6e4b9b0a616b87197831285\",\n \"score\": \"0.77332985\",\n \"text\": \"def perspective_transform(self):\\n bc_im = self.barrel_correct()\\n self.perspective_coeffs = self.run.perspective_coefficients[self.cam]\\n return processing.perspective_transform(bc_im, self.perspective_coeffs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a683b5949e64e99a707a19c63d3c5f\",\n \"score\": \"0.7535765\",\n \"text\": \"def perspective_transform(src_points, dst_points):\\n mtx_perp = cv2.getPerspectiveTransform(src_points, dst_points)\\n mtx_perp_inv = cv2.getPerspectiveTransform(dst_points, src_points)\\n return mtx_perp, mtx_perp_inv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e984ad46c79faa367fe323de4107c42d\",\n \"score\": \"0.7529859\",\n \"text\": \"def perspect_transform(img, src, dst):\\n M = cv2.getPerspectiveTransform(src, dst)\\n # Keep same size as input image\\n warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))\\n return warped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a927c8a65a526b5a9c11340148cda6\",\n \"score\": \"0.7499476\",\n \"text\": \"def perspective_tf(src, dst):\\n M = cv2.getPerspectiveTransform(src, dst)\\n Minv = cv2.getPerspectiveTransform(dst, src)\\n\\n return M, Minv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b029d32a9f17f4674fab0a20c53c6de\",\n \"score\": \"0.7456419\",\n \"text\": \"def compute_perspective_transform(mtx, dist, img_path='../test_images/straight_lines1.jpg'):\\n # Finding perspective transformation matrix to get bird's eye view on images.\\n src_image = load_image(img_path)\\n undistorted = undistort_image(src_image, mtx, dist)\\n return find_perspective_transform(undistorted, save_transform=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6415178aabe8acac3bc29f9d89da512a\",\n \"score\": \"0.73797\",\n \"text\": \"def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC):\\n\\n coeffs = _get_perspective_coeffs(startpoints, endpoints)\\n return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation, fillcolor=(255, 255, 255))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b9e56e2bcc27756f8c993b9cd774b68\",\n \"score\": \"0.7188372\",\n \"text\": \"def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC):\\n if not _is_pil_image(img):\\n raise TypeError('img should be PIL Image. Got {}'.format(type(img)))\\n\\n coeffs = _get_perspective_coeffs(startpoints, endpoints)\\n return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89f11bcc6757d97bf9756bdc9f246a08\",\n \"score\": \"0.7187068\",\n \"text\": \"def find_perspective(self, save = False, path=\\\"\\\"):\\n \\n #Get corresponding points\\n pnts1, pnts2 = self._image_points()\\n if len(pnts1)!=4 or len(pnts2)!=4:\\n raise ValueError(\\\"Must have exactly four corresponding image points\\\")\\n #Get transform\\n perspective_transform = cv2.getPerspectiveTransform(pnts1, pnts2)\\n if save:\\n #Save data to text file\\n np.savetxt(path+\\\"perspective_transform.txt\\\", \\n perspective_transform.reshape(1,9), \\n delimiter=',', fmt=\\\"%f\\\") \\n \\n return perspective_transform\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ecfe5b6e5f81f4ba6b97dfb36597a60\",\n \"score\": \"0.7159043\",\n \"text\": \"def four_point_transform(image, pts):\\n # obtain a consistent order of the points and unpack them\\n # individually\\n rect = order_points(pts)\\n (tl, tr, br, bl) = rect\\n\\n # compute the width of the new image, which will be the\\n # maximum distance between bottom-right and bottom-left\\n # x-coordiates or the top-right and top-left x-coordinates\\n widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\\n widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\\n maxWidth = max(int(widthA), int(widthB))\\n\\n # compute the height of the new image, which will be the\\n # maximum distance between the top-right and bottom-right\\n # y-coordinates or the top-left and bottom-left y-coordinates\\n heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\\n heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\\n maxHeight = max(int(heightA), int(heightB))\\n\\n # now that we have the dimensions of the new image, construct\\n # the set of destination points to obtain a \\\"birds eye view\\\",\\n # (i.e. top-down view) of the image, again specifying points\\n # in the top-left, top-right, bottom-right, and bottom-left\\n # order\\n dst = np.array([\\n [0, 0],\\n [maxWidth - 1, 0],\\n [maxWidth - 1, maxHeight - 1],\\n [0, maxHeight - 1]], dtype=\\\"float32\\\")\\n\\n # compute the perspective transform matrix and then apply it\\n mat = cv2.getPerspectiveTransform(rect, dst)\\n warped = cv2.warpPerspective(image, mat, (maxWidth, maxHeight))\\n\\n # If the image is horizontally long, rotate it by 90\\n if maxWidth > maxHeight:\\n center = (maxHeight / 2, maxHeight / 2)\\n mat_rot = cv2.getRotationMatrix2D(center, 270, 1.0)\\n warped = cv2.warpAffine(warped, mat_rot, (maxHeight, maxWidth))\\n\\n # return the warped image\\n return warped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b40f13a3d100a5b765329e6b433983\",\n \"score\": \"0.7141698\",\n \"text\": \"def warp_image(img, mtx_perp, flags=cv2.INTER_LINEAR):\\n img_size = (img.shape[1], img.shape[0])\\n return cv2.warpPerspective(img, mtx_perp, img_size, flags=cv2.INTER_LINEAR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"254531bce92c9cc7dddb238d8901910d\",\n \"score\": \"0.7080208\",\n \"text\": \"def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC, fill=None):\\n\\n opts = _parse_fill(fill, img)\\n\\n coeffs = _get_perspective_coeffs(startpoints, endpoints)\\n return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation, **opts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1aa5afeddd91e71fde0257483e2908b\",\n \"score\": \"0.6956818\",\n \"text\": \"def transform(x, y, M):\\n xy_t = np.squeeze(\\n cv2.perspectiveTransform(\\n np.dstack(\\n [\\n x,\\n y\\n ]\\n ),\\n np.asarray(M)\\n )\\n )\\n return xy_t[:, 0], xy_t[:, 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dbe17fa188f33afc935b49cb6af2484\",\n \"score\": \"0.69471586\",\n \"text\": \"def warp(self, image):\\n return cv2.warpPerspective(image, self._transformationMatrix, self._imageSize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bca9bbbcb8c14a4fa9ca716bba6b1e3d\",\n \"score\": \"0.6917844\",\n \"text\": \"def four_point_transform(image, pts):\\n rect = order_points(pts)\\n (tl, tr, br, bl) = rect\\n widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\\n widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\\n maxWidth = max(int(widthA), int(widthB))\\n heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\\n heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\\n maxHeight = max(int(heightA), int(heightB))\\n dst = np.array([\\n [0, 0],\\n [maxWidth - 1, 0],\\n [maxWidth - 1, maxHeight - 1],\\n [0, maxHeight - 1]], dtype=\\\"float32\\\")\\n M = cv2.getPerspectiveTransform(rect, dst)\\n warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\\n return [warped, M]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b8ce4e0dfb348632359173d0af3d802\",\n \"score\": \"0.6833071\",\n \"text\": \"def perspective_2_top(img):\\n\\n warped = cv2.warpPerspective(\\n img,\\n PERSPECTIVE_2_TOP,\\n (TOP_WIDTH, TOP_HEIGHT))\\n\\n warped[TOP_CENTER_Y:, :] = 0\\n\\n return warped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21729961e276007e1eaf40a7f9695188\",\n \"score\": \"0.6827648\",\n \"text\": \"def getTransformationMatrix(input_points, output_img_res = (2000, 1300)):\\n\\n mat = cv2.getPerspectiveTransform(input_points, output_points)\\n\\n return mat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7303e20bf6f200677223b0e370e4e2c4\",\n \"score\": \"0.68097365\",\n \"text\": \"def warp_p(binary_img):\\n \\n src = np.float32([[580, 450],\\n [180, 720],\\n [1120, 720],\\n [700, 450]])\\n \\n dst = np.float32([[350, 0],\\n [350, 720],\\n [900, 720],\\n [900, 0]])\\n \\n M = cv2.getPerspectiveTransform(src, dst)\\n \\n Minv = cv2.getPerspectiveTransform(dst, src)\\n\\n binary_warped = cv2.warpPerspective(binary_img, M, (binary_img.shape[1], binary_img.shape[0]), flags=cv2.INTER_LINEAR)\\n \\n return binary_warped, M, Minv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"239831ef6722ba63d1ae742704ea259c\",\n \"score\": \"0.6792931\",\n \"text\": \"def addPerspectivePoints(img, topLeft, topRight, bottomLeft, bottomRight):\\n cv2.circle(img, topLeft, radius=5, color=(255,255,255))\\n cv2.circle(img, topRight, radius=5, color=(255,255,255))\\n cv2.circle(img, bottomLeft, radius=5, color=(255,255,255))\\n cv2.circle(img, bottomRight, radius=5, color=(255,255,255))\\n cv2.line(img=img, pt1=topLeft, pt2=topRight, color=(255,255,255), thickness=2)\\n cv2.line(img=img, pt1=topLeft, pt2=bottomLeft, color=(255,255,255), thickness=2)\\n cv2.line(img=img, pt1=bottomRight, pt2=bottomLeft, color=(255,255,255), thickness=2)\\n cv2.line(img=img, pt1=bottomRight, pt2=topRight, color=(255,255,255), thickness=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0152ff3c90418d880ea2c6196e9b1636\",\n \"score\": \"0.67800635\",\n \"text\": \"def warper(img):\\n M, src, dst = get_warp_matrix(img)\\n warped = cv.warpPerspective(img, M, (img.shape[1], img.shape[0]), flags=cv.INTER_LINEAR)\\n return warped, src, dst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a78e821a1f64a81a4d838dfaed67c961\",\n \"score\": \"0.67549574\",\n \"text\": \"def unwarp(self, image):\\t\\n\\t\\treturn cv2.warpPerspective(image, self.inverse_transformation_matrix, (image.shape[0], image.shape[1]), flags=cv2.INTER_CUBIC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b07361018e8ba9da0c22527422fcf464\",\n \"score\": \"0.67405623\",\n \"text\": \"def __init__(self, src, dst):\\n self.src = src\\n self.dst = dst\\n self.M = cv2.getPerspectiveTransform(src, dst)\\n self.M_inv = cv2.getPerspectiveTransform(dst, src)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b172335cbd4819dd5a11a583a322c88\",\n \"score\": \"0.66762197\",\n \"text\": \"def warpCanvas(image, homography, min_xy, max_xy):\\n # canvas_size properly encodes the size parameter for cv2.warpPerspective,\\n # which requires a tuple of ints to specify size, or else it may throw\\n # a warning/error, or fail silently\\n canvas_size = tuple(np.round(max_xy - min_xy).astype(np.int))\\n # WRITE YOUR CODE HERE\\n #print ('canvas_size =', canvas_size)\\n translation_mat = np.float32(np.identity(3))\\n translation_mat[0,2] = np.float32(-min_xy[0])\\n translation_mat[1,2] = np.float32(-min_xy[1])\\n #print translation_mat\\n new_mat = np.dot(translation_mat,homography)\\n \\n new_image = cv2.warpPerspective(image, new_mat, (canvas_size))\\n #new_image = cv2.warpPerspective(image, new_mat, (canvas_size))\\n #print new_mat\\n #cv2.imshow('image_new.png', new_image)\\n #cv2.waitKey(0)\\n #print new_image\\n return new_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1296a53c77c631a770e9b29491ff9aae\",\n \"score\": \"0.66579324\",\n \"text\": \"def birds_eye_view(corner_points,width,height,image):\\n # Create an array out of the 4 corner points\\n corner_points = np.float32(corner_points)\\n # Create an array with the parameters (the dimensions) required to build the matrix\\n img_params = np.float32([[0,0],[width,0],[0,height],[width,height]])\\n # Compute and return the transformation matrix\\n matrix = cv.getPerspectiveTransform(corner_points,img_params)\\n img_transformed = cv.warpPerspective(image,matrix,(width,height))\\n \\n return matrix,img_transformed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fe8f12d862a4707a48302d77871a703\",\n \"score\": \"0.6622924\",\n \"text\": \"def compute_homography(image, scene_description, x0, x1, y0, y1):\\n # create the correct transformation and apply it \\n # first, use the scene description to get image space to world space transformation\\n # origin is set to the bottom-left corner of the foreground object\\n cam_to_im = scene_description.camera_matrix\\n world_to_cam, T = scene_description.get_world_to_camera_transformation(origin=(x0, y0))\\n cam_to_world = numpy.linalg.inv(world_to_cam)\\n world_to_im = cam_to_im.dot(world_to_cam)\\n im_to_world = cam_to_world.dot(numpy.linalg.inv(cam_to_im))\\n \\n ## usually in world space, x points left, y toward cam, z points up (z=0 is ground plane) rel to image space \\n # but don't assume anything about how the world-space coords are aligned, just figure it out based on\\n # biggest diff between top and bottom, left and right of image about the principal point\\n \\n world_top = im_to_world.dot(numpy.array((image.width / 2.0, 0, 1)))\\n world_bottom = im_to_world.dot(numpy.array((image.width / 2.0, image.height - 1, 1)))\\n up_index = abs(world_top - world_bottom)[:3].argmax()\\n up_flipped = False\\n if (world_top - world_bottom)[up_index] < 0:\\n up_flipped = True\\n \\n world_left = im_to_world.dot(numpy.array((0, image.height / 2.0, 1)))\\n world_right = im_to_world.dot(numpy.array((image.width - 1, image.height / 2.0, 1)))\\n right_index = abs(world_right - world_left)[:3].argmax()\\n right_flipped = False\\n if (world_right - world_left)[right_index] < 0:\\n right_flipped = True\\n cam_index = 0 if ((up_index == 1 and right_index == 2) or (up_index == 2 and right_index == 1)) else \\\\\\n (1 if ((up_index == 0 and right_index == 2) or (up_index == 2 and right_index == 0)) else 2)\\n \\n # get height of object in world space\\n object_top = im_to_world.dot(numpy.array([x0,y1,1]))\\n height = abs(object_top[up_index] - T[up_index])\\n\\n # TODO: would be nice to parametrize the desired light position in the absence of automatic light estimation\\n # for now, just pick an arbitrary position\\n # set the light object 1 height above the camera, partway between the object and the camera\\n cam_to_obj = T\\n w = .2 # how far along the line from camera to object plane [0,1]\\n h = 1.6 # how many units in foreground hight space the light is above the camera\\n # make the light a bit to the side to show off the shadow a bit more\\n light_pos = numpy.zeros((4,), dtype='float64')\\n if right_flipped:\\n light_pos[right_index] = cam_to_obj[right_index] * w + height/4\\n else:\\n light_pos[right_index] = cam_to_obj[right_index] * w - height/4\\n light_pos[cam_index] = cam_to_obj[cam_index] * w\\n light_pos[up_index] = h*height if not up_flipped else - h*height \\n light_pos[3] = 1\\n\\n # create a matrix to project the planar polygon onto the ground plane (y=foreground_pos[1])\\n # from the light \\n # e.g. see http://math.stackexchange.com/questions/320527/projecting-a-point-on-a-plane-through-a-matrix\\n plane = numpy.zeros((4,))\\n plane[up_index] = 1\\n plane[3] = -T[up_index] # assumes bottom left point of foreground plane touches ground\\n lambd = numpy.dot(plane, light_pos)\\n l = light_pos\\n f = plane\\n world_to_ground = numpy.array([[lambd - f[0]*l[0], -f[1]*l[0], -f[2]*l[0], -f[3]*l[0]],\\n [-f[0]*l[1], lambd - f[1]*l[1], -f[2]*l[1], -f[3]*l[1]],\\n [-f[0]*l[2], -f[1]*l[2], lambd - f[2]*l[2], -f[3]*l[2]],\\n [-f[0]*l[3], -f[1]*l[3], -f[2]*l[3], lambd - f[3]*l[3]]])\\n \\n # finally, just project the 4 corners of the object in world space onto the ground plane and then\\n # convert those 4 points back into image space\\n # this gives us the correspondences needed to get the perspective transformation in image space\\n im_pts = numpy.array([(x0,y0),(x0,y1),(x1,y0),(x1,y1)],dtype='float64')\\n im_pts_hom = cv2.convertPointsToHomogeneous(im_pts).squeeze()\\n im_pts_ground = []\\n \\n for im_pt in im_pts_hom:\\n pt_world = im_to_world.dot(im_pt)\\n pt_world = numpy.array([pt_world[0], pt_world[1], pt_world[2], 1])\\n ground_world = world_to_ground.dot(pt_world)\\n ground_im = world_to_im.dot(ground_world[:3])\\n im_pts_ground.append(ground_im)\\n\\n # perspective divide\\n im_pts_ground = cv2.convertPointsFromHomogeneous(numpy.array(im_pts_ground)).squeeze()\\n return cv2.getPerspectiveTransform(im_pts.astype('float32'), im_pts_ground.astype('float32'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93dd5a0defc78ea6d8534294e25cc1ed\",\n \"score\": \"0.6585992\",\n \"text\": \"def warped_img(img, M):\\n img_size = (img.shape[1], img.shape[0])\\n warped = cv2.warpPerspective(img, M, img_size)\\n return warped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdbb5851ce96fe63ecb8e1c2672f5037\",\n \"score\": \"0.65187114\",\n \"text\": \"def main():\\n # initializing points\\n img = cv2.imread(\\\"easy.png\\\")\\n \\\"\\\"\\\"\\n pts NxNx3\\n pts[x,y,0] = z\\n pts[x,y,1] = color\\n pts[x,y,2] = (x, y) of projected points\\n \\\"\\\"\\\"\\n pts1 = np.ones((img.shape[1], img.shape[0], 3), dtype=object)\\n #pts1[:,:,1] = (255,255,255)\\n for x in range(16,310):\\n for y in range(13, 239):\\n pts1[x,y,0] = [2, 5]\\n pts1[x,y,1] = (53,95,255)\\n\\n for x in range(309,484):\\n for y in range(161, 239):\\n pts1[x,y,0] = [3]\\n pts1[x,y,1] = (255,80,0)\\n\\n cam_original_position = [0.0, -383.0127018922193, 100.0, -336.60254037844396] #[0.0, -816.0254037844386, 100.0, -586.602540378444]\\n cam_original_orientation = np.matrix([[0.5,0.,-0.8660254],[0.,1.,0.],[0.8660254,0.,0.5]])\\n cam_position = get_cam_position(angle=math.radians(-12), no_frames=10, cam_original_position=cam_original_position)\\n cam_orientation = get_cam_orientation(angle=math.radians(12), no_frames=10, cam_original_orientation = cam_original_orientation)\\n #all_pts = project_points(pts1, cam_position, cam_orientation, 0, 3)\\n #draw_image(all_pts)\\n project_and_draw(pts1, cam_position, cam_orientation, 0, 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efad03cc73ea0cd024fadaaa637081b8\",\n \"score\": \"0.6492519\",\n \"text\": \"def warp_image(img, pts):\\n # pts = np.array(eval(str(pts)), dtype=\\\"float32\\\")\\n tl, tr, br, bl = pts[0], pts[1], pts[2], pts[3]\\n\\n # tl, tr, br, bl = self.pts[0], self.pts[1], self.pts[2], self.pts[3]\\n\\n # compute the width of the new image, which will be the\\n # maximum distance between bottom-right and bottom-left\\n # x-coordiates or the top-right and top-left x-coordinates\\n widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\\n widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\\n maxWidth = max(int(widthA), int(widthB))\\n\\n # compute the height of the new image, which will be the\\n # maximum distance between the top-right and bottom-right\\n # y-coordinates or the top-left and bottom-left y-coordinates\\n heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\\n heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\\n maxHeight = max(int(heightA), int(heightB))\\n\\n # now that we have the dimensions of the new image, construct\\n # the set of destination points to obtain a \\\"birds eye view\\\",\\n # (i.e. top-down view) of the image, again specifying points\\n # in the top-left, top-right, bottom-right, and bottom-left\\n # order\\n dst = np.array([\\n [0, 0],\\n [maxWidth - 1, 0],\\n [maxWidth - 1, maxHeight - 1],\\n [0, maxHeight - 1]], dtype=\\\"float32\\\")\\n\\n # compute the perspective transform matrix and then apply it\\n M = cv2.getPerspectiveTransform(pts, dst)\\n\\n # return the warped image\\n return cv2.warpPerspective(img, M, (maxWidth, maxHeight))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0433ad93556a8534793ffa742695d9ac\",\n \"score\": \"0.63928014\",\n \"text\": \"def debug():\\n data_choice = 1\\n img_path = None\\n corners_warped = None\\n corners_unwarped = None\\n\\n \\\"\\\"\\\"read the image\\\"\\\"\\\"\\n if data_choice == 1:\\n img_path = \\\"./data/0000000000.png\\\"\\n elif data_choice == 2:\\n img_path = \\\"./challenge_video_cache/challenge_video_Moment2.jpg\\\"\\n img = cv2.imread(img_path)\\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n\\n \\\"\\\"\\\"adjust the corners of lane on target image\\\"\\\"\\\"\\n if data_choice == 1:\\n corners_warped = [(512, 325), (760, 325), (886, 470), (274, 470)] # four corners on lane\\n width = 108\\n length = 400 # longer side of rectangle\\n corners_unwarped = perspective.rectangle(width, length) # width and height of rectangle (108, 400)\\n offset = np.array([img_gray.shape[0] / 2, img_gray.shape[1] - length * (17 / 12)]).astype(\\n int) # where the rectangle to be on dest image\\n corners_unwarped, _ = perspective.shiftToASpot(corners_unwarped, offset)\\n elif data_choice == 2:\\n corners_warped = [(598, 495), (758, 495), (876, 592), (462, 592)]\\n width = 108\\n length = 400 # longer side of rectangle\\n corners_unwarped = perspective.rectangle(width, length)\\n offset = np.array([img_gray.shape[0] / 2, img_gray.shape[1] - length * (4 / 3)]).astype(\\n int) # where the rectangle to be on dest image\\n corners_unwarped, _ = perspective.shiftToASpot(corners_unwarped, offset)\\n\\n \\\"\\\"\\\"eyeball the corner of lane for images input\\\"\\\"\\\"\\n corners_warped = eyeballLaneCorners(img_gray, corners_warped)\\n \\\"\\\"\\\"calculate the homograph\\\"\\\"\\\"\\n homo = homoToResCenter(corners_warped, corners_unwarped)\\n img_unwarped = cv2.warpPerspective(img, homo, (img_gray.shape[0], img_gray.shape[1]))\\n\\n # img_gray = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR)\\n cv2.imshow('Image unwarped', img_unwarped)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9f94e2bb6315655399682f924a6d26\",\n \"score\": \"0.63859767\",\n \"text\": \"def Perspective(image, pts1):\\n # Change the color of the image from RGB to gray\\n imgGray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\\n \\n # Blurring\\n imgBlur = cv2.GaussianBlur(imgGray, (5,5), 0)\\n # Binary Threshold\\n imgThresh = cv2.inRange(imgBlur, 135, 255, cv2.THRESH_BINARY) # 149, 255\\n \\n cv2.imshow(\\\"Binary Threshold\\\", imgThresh)\\n cv2.waitKey(1)\\n\\n # Canny Edge Detection\\n imgEdge = cv2.Canny(imgGray, 58, 129) # 270, 300\\n\\n cv2.imshow(\\\"canny edge\\\", imgEdge)\\n cv2.waitKey(1)\\n\\n # Combine the two images\\n imgFinal = cv2.add(imgThresh, imgEdge)\\n \\n cv2.line(image,pt1=tuple(pts1[0]),pt2=tuple(pts1[1]),color=(255,0,0),thickness=2)\\n cv2.line(image,pt1=tuple(pts1[1]),pt2=tuple(pts1[3]),color=(255,0,0),thickness=2)\\n cv2.line(image,pt1=tuple(pts1[3]),pt2=tuple(pts1[2]),color=(255,0,0),thickness=2)\\n cv2.line(image,pt1=tuple(pts1[2]),pt2=tuple(pts1[0]),color=(255,0,0),thickness=2)\\n\\n # cv2.line(image,pt1=tuple(pts2[0]),pt2=tuple(pts2[1]),color=(255,0,0),thickness=2)\\n # cv2.line(image,pt1=tuple(pts2[1]),pt2=tuple(pts2[3]),color=(255,0,0),thickness=2)\\n # cv2.line(image,pt1=tuple(pts2[3]),pt2=tuple(pts2[2]),color=(255,0,0),thickness=2)\\n # cv2.line(image,pt1=tuple(pts2[2]),pt2=tuple(pts2[0]),color=(255,0,0),thickness=2)\\n\\n Matrix = cv2.getPerspectiveTransform(pts1.astype(np.float32), pts2.astype(np.float32))\\n imgPers = cv2.warpPerspective(imgFinal, Matrix, (WIDTH, HEIGHT))\\n\\n imgFinal = cv2.cvtColor(imgPers, cv2.COLOR_GRAY2BGR)\\n imgFinalDuplicate = imgFinal.copy()\\n cv2.imshow(\\\"Test\\\", imgPers)\\n cv2.waitKey(1)\\n return imgFinal, imgFinalDuplicate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d24056ba6b6e10ca46f1b62e89b3314c\",\n \"score\": \"0.63586324\",\n \"text\": \"def transform_road(binary_image):\\n # Image size\\n xsize = binary_image.shape[1]\\n ysize = binary_image.shape[0]\\n\\n # Select source points\\n #src = np.float32([[554, 465], [735, 465], [90, ysize], [xsize, ysize]])\\n src = np.float32([[565, 460], [725, 460], [90, ysize], [xsize, ysize]])\\n\\n # Select destination points\\n dest = np.float32([[0,0],[xsize,0],[0, ysize],[xsize,ysize]])\\n\\n # Perform transformation\\n M = cv2.getPerspectiveTransform(src, dest)\\n Minv = cv2.getPerspectiveTransform(dest, src)\\n warped = cv2.warpPerspective(binary_image, M, (xsize, ysize), flags=cv2.INTER_LINEAR)\\n\\n return warped, Minv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"059d891865b676bac6911476f5abf045\",\n \"score\": \"0.6353194\",\n \"text\": \"def unWarp(self, image):\\n return cv2.warpPerspective(image, self._inverseTransformationMatrix, self._imageSize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"488cbf521ae1e8275b8af8d3ce62cef5\",\n \"score\": \"0.6344563\",\n \"text\": \"def crop_and_warp(img, crop_rect):\\n\\n\\n top_left, top_right, bottom_right, bottom_left = crop_rect[0], crop_rect[1], crop_rect[2], crop_rect[3]\\n\\n src = np.array([top_left, top_right, bottom_right, bottom_left], dtype='float32')\\n\\n \\n side = max([\\n distance.euclidean(bottom_right, top_right),\\n distance.euclidean(top_left, bottom_left),\\n distance.euclidean(bottom_right, bottom_left),\\n distance.euclidean(top_left, top_right)\\n ])\\n\\n height= distance.euclidean(bottom_right, top_right)\\n width= distance.euclidean(bottom_right, bottom_left)\\n\\n dst = np.array([[0, 0], [side - 1, 0], [side - 1, side - 1], [0, side - 1]], dtype='float32')\\n\\n m = cv2.cv2.getPerspectiveTransform(src, dst)\\n\\n warp = cv2.cv2.warpPerspective(img, m, (int(side), int(side)))\\n plt.imshow(warp, cmap='gray')\\n plt.title('Warped Image')\\n plt.show()\\n return warp,side,height, width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d67750b43b534908858f3f6c7d73345\",\n \"score\": \"0.6315676\",\n \"text\": \"def perspective_transform_coeffs(pa, pb):\\n import numpy\\n matrix = []\\n for p1, p2 in zip(pa, pb):\\n matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]])\\n matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]])\\n\\n A = numpy.matrix(matrix, dtype=numpy.float)\\n B = numpy.array(pb).reshape(8)\\n\\n res = numpy.dot(numpy.linalg.inv(A.T * A) * A.T, B)\\n return numpy.array(res).reshape(8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0fbf9b9502be6911f58d6b333678292\",\n \"score\": \"0.63064027\",\n \"text\": \"def compute_transforms(self):\\n\\n point_names = [\\n \\\"model\\\",\\n \\\"box\\\"\\n ]\\n\\n point_arrays = {}\\n point_arrays['box'] = self.default_box\\n point_arrays['model'] = self.model\\n\\n transforms = {}\\n for a in point_names:\\n for b in point_names:\\n if a == b:\\n continue\\n if not (a in point_arrays):\\n continue\\n if not (b in point_arrays):\\n continue\\n transform_name = a + '2' + b\\n transform = cv2.getPerspectiveTransform(\\n point_arrays[a],\\n point_arrays[b]\\n )\\n transforms[transform_name] = transform\\n\\n return transforms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c01356c7fd6d4d92709c64a213865bc\",\n \"score\": \"0.628881\",\n \"text\": \"def compute_homography_and_warp(image, vp1, vp2, clip=True, clip_factor=3):\\n # Find Projective Transform\\n vanishing_line = np.cross(vp1, vp2)\\n H = np.eye(3)\\n H[2] = vanishing_line / vanishing_line[2]\\n H = H / H[2, 2]\\n\\n # Find directions corresponding to vanishing points\\n v_post1 = np.dot(H, vp1)\\n v_post2 = np.dot(H, vp2)\\n v_post1 = v_post1 / np.sqrt(v_post1[0]**2 + v_post1[1]**2)\\n v_post2 = v_post2 / np.sqrt(v_post2[0]**2 + v_post2[1]**2)\\n\\n directions = np.array([[v_post1[0], -v_post1[0], v_post2[0], -v_post2[0]],\\n [v_post1[1], -v_post1[1], v_post2[1], -v_post2[1]]])\\n\\n thetas = np.arctan2(directions[0], directions[1])\\n\\n # Find direction closest to horizontal axis\\n h_ind = np.argmin(np.abs(thetas))\\n\\n # Find positve angle among the rest for the vertical axis\\n if h_ind // 2 == 0:\\n v_ind = 2 + np.argmax([thetas[2], thetas[3]])\\n else:\\n v_ind = np.argmax([thetas[2], thetas[3]])\\n\\n A1 = np.array([[directions[0, v_ind], directions[0, h_ind], 0],\\n [directions[1, v_ind], directions[1, h_ind], 0],\\n [0, 0, 1]])\\n # Might be a reflection. If so, remove reflection.\\n if np.linalg.det(A1) < 0:\\n A1[:, 0] = -A1[:, 0]\\n\\n A = np.linalg.inv(A1)\\n\\n # Translate so that whole of the image is covered\\n inter_matrix = np.dot(A, H)\\n\\n cords = np.dot(inter_matrix, [[0, 0, image.shape[1], image.shape[1]],\\n [0, image.shape[0], 0, image.shape[0]],\\n [1, 1, 1, 1]])\\n cords = cords[:2] / cords[2]\\n\\n tx = min(0, cords[0].min())\\n ty = min(0, cords[1].min())\\n\\n max_x = cords[0].max() - tx\\n max_y = cords[1].max() - ty\\n\\n if clip:\\n # These might be too large. Clip them.\\n max_offset = max(image.shape) * clip_factor / 2\\n tx = max(tx, -max_offset)\\n ty = max(ty, -max_offset)\\n\\n max_x = min(max_x, -tx + max_offset)\\n max_y = min(max_y, -ty + max_offset)\\n\\n max_x = int(max_x)\\n max_y = int(max_y)\\n\\n T = np.array([[1, 0, -tx],\\n [0, 1, -ty],\\n [0, 0, 1]])\\n\\n final_homography = np.dot(T, inter_matrix)\\n\\n warped_img = transform.warp(image, np.linalg.inv(final_homography),\\n output_shape=(max_y, max_x))\\n return warped_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba64b73cef6b20b352762d3691d873a\",\n \"score\": \"0.6276881\",\n \"text\": \"def run(image, res=(1000, 550)):\\n\\n # Number of views\\n N = len(image)\\n\\n # Output points to which the input points are to be mapped\\n output_points = [(0, 0), (0, res[1]), (res[0], res[1]), (res[0], 0)]\\n\\n # Images after line extraction in binary form\\n bin_image = []\\n # List of Corner points for each input images (view)\\n input_points = []\\n\\n print(\\\"\\\\nInitializing camera parameters : \\\")\\n print(\\\"\\\\n\\\\tFinding Corners For : \\\")\\n for i in range(N):\\n print(\\\"\\\\tview \\\", i)\\n bin_image.append(extractFieldLines(image[i]))\\n input_points.append(extractCorners(getPerfectContours(bin_image[-1].copy())))\\n\\n # List of Transformation matrix for each input images (view)\\n proj_mat = []\\n\\n # List of output corner points for each image (view)\\n op = [output_points for i in range(4)]\\n\\n # Find the projection matrix for each image (i.e. each camera view)\\n for i in range(N):\\n proj_mat.append(cv2.getPerspectiveTransform(np.float32(input_points[i]), np.float32(op[i])))\\n\\n # Take the first view as reference view\\n # prefixed by 'trans' if the image is \\\"transformed\\\" image\\n transRef = cv2.warpPerspective(bin_image[0], proj_mat[0], res)\\n\\n\\n # Find the orientation of every other image, align them using cost function\\n # and find the Projection (transformation) matrix\\n print(\\\"\\\\n\\\\tFinding Projection Matrix For : \\\")\\n for i in range(1, N):\\n print(\\\"\\\\tView \\\", i)\\n # Find the values of cost function for different rotations of the output image\\n cost_value = []\\n\\n for j in range(4): # since there are only four rotations for rectangle\\n transImg = cv2.warpPerspective(bin_image[i], proj_mat[i], res) # Finds the transformed view for given image\\n\\n difference = transImg - transRef # Finds the difference between two images\\n\\n cost_value.append(cost(difference)) # Calculates the cost using the difference between two images\\n\\n op[i].append(op[i].pop(0)) # Rotate the plane-to-project-on by 90 degrees\\n proj_mat[i] = cv2.getPerspectiveTransform(np.float32(input_points[i]), np.float32(op[i])) # Recalculates the projection matrix\\n\\n # Now, find the rotation at which the cost is minimum and then find the projection matrix\\n for j in range(np.argmin(cost_value)):\\n op[i].append(op[i].pop(0))\\n\\n proj_mat[i] = cv2.getPerspectiveTransform(np.float32(input_points[i]), np.float32(op[i]))\\n\\n return proj_mat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a735e290f1ecab87c65b70fde6b5b90\",\n \"score\": \"0.6259704\",\n \"text\": \"def perspective(fov, aspect, near, far):\\n sy = 1.0 / tan(radians(fov) / 2.0)\\n sx = sy / aspect\\n zz = (far + near) / (near - far)\\n zw = 2 * far * near / (near - far)\\n matrix = [[sx, 0, 0, 0], [0, sy, 0, 0], [0, 0, zz, zw], [0, 0, -1, 0]] # noqa: E201 # noqa: E201 # noqa: E201\\n return Transformation.from_matrix(matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2a074358aa63a757d41dedfcc74506\",\n \"score\": \"0.625155\",\n \"text\": \"def process_image(test_img,temp_img):\\n masked_image=image_masking(test_img)\\n key_pt_temp, des_temp = compute_kp_des(temp_img)\\n key_pt_test, des_test = compute_kp_des(masked_image)\\n points_test,points_temp=find_good_matches(key_pt_temp,key_pt_test,des_test,des_temp)\\n homo, _ = cv2.findHomography(points_test, points_temp, cv2.RANSAC)\\n height, width, _ = temp_img.shape\\n image_aligned = cv2.warpPerspective(test_img, homo, (width, height))\\n return image_aligned, homo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a6a0f68c11d6ba0879bc73a3c9d461c\",\n \"score\": \"0.6207081\",\n \"text\": \"def adjust_to_see(img):\\n\\n (h, w) = img.shape[:2]\\n (cX, cY) = (w // 2, h // 2)\\n\\n M = cv2.getRotationMatrix2D((cX, cY), -90, 1.0)\\n cos = np.abs(M[0, 0])\\n sin = np.abs(M[0, 1])\\n\\n nW = int((h * sin) + (w * cos))\\n nH = int((h * cos) + (w * sin))\\n\\n M[0, 2] += (nW / 2) - cX\\n M[1, 2] += (nH / 2) - cY\\n\\n img = cv2.warpAffine(img, M, (nW + 1, nH + 1))\\n img = cv2.warpAffine(img.transpose(), M, (nW, nH))\\n\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f3cb5b7e9b676a849e29a86924f908\",\n \"score\": \"0.6182457\",\n \"text\": \"def contour_transform(contours_list, perspective_matrix):\\n result=[]\\n for cnt in contours_list:\\n cnt = cv2.perspectiveTransform(cnt.reshape(1, len(cnt), 2).astype('float32'), perspective_matrix)\\n result.append(cnt.reshape(len(cnt[0]), 1, 2).astype('int'))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77edeaa730a850549e23e6fade1a9108\",\n \"score\": \"0.6162987\",\n \"text\": \"def get_perspective_transform(screen_resolution, prop_file, flipped):\\n\\n camera_matrix, dist_coeffs, camResolution =load_camera_props(prop_file)\\n shutter = None\\n awbgains = ()\\n\\n rect = None\\n while True :\\n #pygame.display.toggle_fullscreen()\\n displayBGColor()\\n # Delay execution a quarter of a second to make sure the image is displayed \\n # Don't use time.sleep() here, we want the IO loop to run. Sleep doesn't do that\\n cv2.waitKey(150)\\n # Grab a photo of the frame\\n with picamera.PiCamera() as camera:\\n camera.resolution = camResolution\\n camera.framerate = 12\\n # Wait for the automatic gain control to settle\\n time.sleep(1)\\n # Now fix the values\\n camera.shutter_speed = camera.exposure_speed\\n shutter = camera.shutter_speed\\n camera.exposure_mode = 'off'\\n g = camera.awb_gains\\n camera.awb_mode = 'off'\\n camera.awb_gains = g\\n awbgains = g\\n #new.text = str(camera.awb_gains)\\n camera.capture(\\\"persp-calibration-ref.jpg\\\")\\n\\n camera.close()\\n # back to resizable windowed app\\n #pygame.display.toggle_fullscreen()\\n #\\n print(\\\"[INFO] Writing ref image\\\")\\n #cv2.imwrite(\\\"persp-calibration-ref.jpg\\\", frame)\\n frame = cv2.imread(\\\"persp-calibration-ref.jpg\\\")\\n # Undistort the camera image\\n #cv2.waitKey(250)\\n frame = undistort_image(frame, camera_matrix, dist_coeffs)\\n # We're going to work with a smaller image, so we need to save the scale\\n ratio = frame.shape[0] / 900.0\\n orig = frame.copy()\\n # Resize our image smaller, this will make things a lot faster\\n frame = imutils.resize(frame, height=900)\\n #cv2.waitKey(50)\\n rect = get_region_corners(frame, flipped)\\n if not rect is None :\\n if len(rect) < 4 :\\n # contour not valid, so we try again\\n continue\\n else :\\n break\\n else :\\n # no contour found -> abort mission\\n quit()\\n break\\n\\n rect *= ratio # We shrank the image, so now we have to scale our points up\\n dst, max_width, max_height = get_destination_array(rect)\\n\\n # process correction on ref image and show result\\n m = cv2.getPerspectiveTransform(rect, dst)\\n wrap = cv2.warpPerspective(orig, m, (max_width, max_height))\\n wraped = convertToSurfaceImg(wrap)\\n displaySurfaceImage(wraped)\\n\\n # update XML file with warping datas\\n # cam setup\\n camSetup=ET.Element('cameraSetup')\\n shut=ET.SubElement(camSetup,'shutter-speed')\\n shut.text = str(shutter)\\n gains=ET.SubElement(camSetup,'awb-gains')\\n gains.text = '\\\\n'.join(map(str, [' '.join(str(frac(f)).split(\\\"/\\\")) for f in awbgains]))\\n root.append(camSetup)\\n # distortion parameters\\n dstParam=ET.Element('distortionParam')\\n ref=ET.SubElement(dstParam,'refPoints')\\n ref.text = '\\\\n'.join(map(str, [\\\" \\\".join(item) for item in rect.astype(str)]))\\n dest=ET.SubElement(dstParam,'destPoints')\\n dest.text = '\\\\n'.join(map(str, [\\\" \\\".join(item) for item in dst.astype(str)]))\\n root.append(dstParam)\\n # save xml\\n wrTmp(root, datasPath)\\n print(\\\"xml updated\\\")\\n #\\n try :\\n user_check = str(input(\\\"Copy new datas to main directory ? [y/n] \\\\n\\\"))\\n except ValueError:\\n print(\\\"Sorry, I didn't understand that.\\\")\\n if user_check == \\\"n\\\":\\n print(\\\"Ok ! Good bye !\\\")\\n elif user_check != \\\"y\\\" :\\n print(\\\"Sorry, answer '\\\",user_check,\\\"' is not valid...\\\")\\n else :\\n print(\\\"OK, let's copy the file,then...\\\")\\n shutil.copy2(datasPath, \\\"../cameraDatas.xml\\\")\\n #\\n print(\\\"press esc to quit\\\")\\n return m, max_width, max_height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"645af425b81f76726d96871559604c59\",\n \"score\": \"0.6156459\",\n \"text\": \"def crop_and_warp(img, crop_rect):\\r\\n\\r\\n # Rectangle described by top left, top right, bottom right and bottom left points\\r\\n top_left, top_right, bottom_right, bottom_left = crop_rect[0], crop_rect[1], crop_rect[2], crop_rect[3]\\r\\n\\r\\n # Explicitly set the data type to float32 or `getPerspectiveTransform` will throw an error\\r\\n src = np.array([top_left, top_right, bottom_right, bottom_left], dtype='float32')\\r\\n #print(src)\\r\\n # Get the longest side in the rectangle\\r\\n \\r\\n side = max([\\r\\n distance_between(bottom_right, top_right),\\r\\n distance_between(top_left, bottom_left),\\r\\n distance_between(bottom_right, bottom_left),\\r\\n distance_between(top_left, top_right)\\r\\n ])\\r\\n\\r\\n # Describe a square with side of the calculated length, this is the new perspective we want to warp to\\r\\n dst = np.array([[0, 0], [side - 1, 0], [side - 1, side - 1], [0, side - 1]], dtype='float32')\\r\\n\\r\\n # Gets the transformation matrix for skewing the image to fit a square by comparing the 4 before and after points\\r\\n m = cv2.getPerspectiveTransform(src, dst)\\r\\n\\r\\n # Performs the transformation on the original image\\r\\n warp = cv2.warpPerspective(img, m, (int(side), int(side)))\\r\\n plt.imshow(warp, cmap='gray')\\r\\n plt.title('warp_image')\\r\\n plt.show()\\r\\n return warp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2358c4883fd1c2ec528acaaee950520f\",\n \"score\": \"0.6148024\",\n \"text\": \"def flattener(image, pts, w, h):\\n temp_rect = np.zeros((4, 2), dtype=\\\"float32\\\")\\n\\n s = np.sum(pts, axis=2)\\n\\n tl = pts[np.argmin(s)]\\n br = pts[np.argmax(s)]\\n\\n diff = np.diff(pts, axis=-1)\\n tr = pts[np.argmin(diff)]\\n bl = pts[np.argmax(diff)]\\n\\n # Need to create an array listing points in order of\\n # [top left, top right, bottom right, bottom left]\\n # before doing the perspective transform\\n\\n if w <= 0.8 * h: # If card is vertically oriented\\n temp_rect[0] = tl\\n temp_rect[1] = tr\\n temp_rect[2] = br\\n temp_rect[3] = bl\\n\\n if w >= 1.2 * h: # If card is horizontally oriented\\n temp_rect[0] = bl\\n temp_rect[1] = tl\\n temp_rect[2] = tr\\n temp_rect[3] = br\\n\\n # If the card is 'diamond' oriented, a different algorithm\\n # has to be used to identify which point is top left, top right\\n # bottom left, and bottom right.\\n\\n if w > 0.8 * h and w < 1.2 * h: # If card is diamond oriented\\n # If furthest left point is higher than furthest right point,\\n # card is tilted to the left.\\n if pts[1][0][1] <= pts[3][0][1]:\\n # If card is titled to the left, approxPolyDP returns points\\n # in this order: top right, top left, bottom left, bottom right\\n temp_rect[0] = pts[1][0] # Top left\\n temp_rect[1] = pts[0][0] # Top right\\n temp_rect[2] = pts[3][0] # Bottom right\\n temp_rect[3] = pts[2][0] # Bottom left\\n\\n # If furthest left point is lower than furthest right point,\\n # card is tilted to the right\\n if pts[1][0][1] > pts[3][0][1]:\\n # If card is titled to the right, approxPolyDP returns points\\n # in this order: top left, bottom left, bottom right, top right\\n temp_rect[0] = pts[0][0] # Top left\\n temp_rect[1] = pts[3][0] # Top right\\n temp_rect[2] = pts[2][0] # Bottom right\\n temp_rect[3] = pts[1][0] # Bottom left\\n\\n maxWidth = 200\\n maxHeight = 300\\n\\n # Create destination array, calculate perspective transform matrix,\\n # and warp card image\\n dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], np.float32)\\n M = cv2.getPerspectiveTransform(temp_rect, dst)\\n warp = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\\n #this just transforms and flattens the card\\n \\n #warp = cv2.cvtColor(warp, cv2.COLOR_BGR2GRAY)\\n return warp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800ffd40059ebfacd94c46ef0268006d\",\n \"score\": \"0.6146747\",\n \"text\": \"def flattener(image, pts, w, h):\\n temp_rect = np.zeros((4, 2), dtype=\\\"float32\\\")\\n\\n s = np.sum(pts, axis=2)\\n\\n tl = pts[np.argmin(s)]\\n br = pts[np.argmax(s)]\\n\\n diff = np.diff(pts, axis=-1)\\n tr = pts[np.argmin(diff)]\\n bl = pts[np.argmax(diff)]\\n\\n # Need to create an array listing points in order of\\n # [top left, top right, bottom right, bottom left]\\n # before doing the perspective transform\\n\\n if w <= 0.8 * h: # If card is vertically oriented\\n temp_rect[0] = tl\\n temp_rect[1] = tr\\n temp_rect[2] = br\\n temp_rect[3] = bl\\n\\n if w >= 1.2 * h: # If card is horizontally oriented\\n temp_rect[0] = bl\\n temp_rect[1] = tl\\n temp_rect[2] = tr\\n temp_rect[3] = br\\n\\n # If the card is 'diamond' oriented, a different algorithm\\n # has to be used to identify which point is top left, top right\\n # bottom left, and bottom right.\\n\\n if 0.8 * h < w < 1.2 * h: # If card is diamond oriented\\n # If furthest left point is higher than furthest right point,\\n # card is tilted to the left.\\n if pts[1][0][1] <= pts[3][0][1]:\\n # If card is titled to the left, approxPolyDP returns points\\n # in this order: top right, top left, bottom left, bottom right\\n temp_rect[0] = pts[1][0] # Top left\\n temp_rect[1] = pts[0][0] # Top right\\n temp_rect[2] = pts[3][0] # Bottom right\\n temp_rect[3] = pts[2][0] # Bottom left\\n\\n # If furthest left point is lower than furthest right point,\\n # card is tilted to the right\\n if pts[1][0][1] > pts[3][0][1]:\\n # If card is titled to the right, approxPolyDP returns points\\n # in this order: top left, bottom left, bottom right, top right\\n temp_rect[0] = pts[0][0] # Top left\\n temp_rect[1] = pts[3][0] # Top right\\n temp_rect[2] = pts[2][0] # Bottom right\\n temp_rect[3] = pts[1][0] # Bottom left\\n\\n max_width = 200\\n max_height = 300\\n\\n # Create destination array, calculate perspective transform matrix,\\n # and warp card image\\n dst = np.array([[0, 0], [max_width - 1, 0], [max_width - 1, max_height - 1], [0, max_height - 1]], np.float32)\\n m = cv2.getPerspectiveTransform(temp_rect, dst)\\n warp = cv2.warpPerspective(image, m, (max_width, max_height))\\n warp = cv2.cvtColor(warp, cv2.COLOR_BGR2GRAY)\\n\\n return warp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6145b14bdc9de544eac6770360de3b\",\n \"score\": \"0.6146022\",\n \"text\": \"def warp_image_from_match(im1, im2, points1, points2):\\n \\n val, trans = cv2.findHomography(points2, points1, cv2.RANSAC)\\n return cv2.warpPerspective(im2, val, (im1.shape[1], im1.shape[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46578c314bb6b33bd414528f539f8ab8\",\n \"score\": \"0.6132989\",\n \"text\": \"def flattener(image, pts, w, h):\\n\\ttemp_rect = np.zeros((4,2), dtype = \\\"float32\\\")\\n\\t\\n\\ts = np.sum(pts, axis = 2)\\n\\t\\n\\ttl = pts[np.argmin(s)]\\n\\tbr = pts[np.argmax(s)]\\n\\t\\n\\tdiff = np.diff(pts, axis = -1)\\n\\ttr = pts[np.argmin(diff)]\\n\\tbl = pts[np.argmax(diff)]\\n\\t\\n\\t# Need to create an array listing points in order of\\n\\t# [top left, top right, bottom right, bottom left]\\n\\t# before doing the perspective transform\\n\\t\\n\\tif w <= 0.8*h: # If card is vertically oriented\\n\\t\\ttemp_rect[0] = tl\\n\\t\\ttemp_rect[1] = tr\\n\\t\\ttemp_rect[2] = br\\n\\t\\ttemp_rect[3] = bl\\n\\t\\n\\tif w >= 1.2*h: # If card is horizontally oriented\\n\\t\\ttemp_rect[0] = bl\\n\\t\\ttemp_rect[1] = tl\\n\\t\\ttemp_rect[2] = tr\\n\\t\\ttemp_rect[3] = br\\n\\t\\n\\t# If the card is 'diamond' oriented, a different algorithm\\n\\t# has to be used to identify which point is top left, top right\\n\\t# bottom left, and bottom right.\\n\\t\\n\\tif w > 0.8*h and w < 1.2*h: #If card is diamond oriented\\n\\t\\t# If furthest left point is higher than furthest right point,\\n\\t\\t# card is tilted to the left.\\n\\t\\tif pts[1][0][1] <= pts[3][0][1]:\\n\\t\\t\\t# If card is titled to the left, approxPolyDP returns points\\n\\t\\t\\t# in this order: top right, top left, bottom left, bottom right\\n\\t\\t\\ttemp_rect[0] = pts[1][0] # Top left\\n\\t\\t\\ttemp_rect[1] = pts[0][0] # Top right\\n\\t\\t\\ttemp_rect[2] = pts[3][0] # Bottom right\\n\\t\\t\\ttemp_rect[3] = pts[2][0] # Bottom left\\n\\t\\t\\t\\n\\t\\t# If furthest left point is lower than furthest right point,\\n\\t\\t# card is tilted to the right\\n\\t\\tif pts[1][0][1] > pts[3][0][1]:\\n\\t\\t\\t# If card is titled to the right, approxPolyDP returns points\\n\\t\\t\\t# in this order: top left, bottom left, bottom right, top right\\n\\t\\t\\ttemp_rect[0] = pts[0][0] # Top left\\n\\t\\t\\ttemp_rect[1] = pts[3][0] # Top right\\n\\t\\t\\ttemp_rect[2] = pts[2][0] # Bottom right\\n\\t\\t\\ttemp_rect[3] = pts[1][0] # Bottom left\\n\\t\\t\\t\\n\\t\\t\\t\\n\\tmaxWidth = 200\\n\\tmaxHeight = 300\\n\\t\\n\\t# Create destination array, calculate perspective transform matrix,\\n\\t# and warp card image\\n\\tdst = np.array([[0,0],[maxWidth-1,0],[maxWidth-1,maxHeight-1],[0, maxHeight-1]], np.float32)\\n\\tM = cv2.getPerspectiveTransform(temp_rect,dst)\\n\\twarp = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\\n#\\twarp = cv2.cvtColor(warp,cv2.COLOR_BGR2GRAY)\\n\\n\\t\\n\\t\\n\\t\\n\\treturn warp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba04b3efc26867354de83dd87be34a77\",\n \"score\": \"0.6123402\",\n \"text\": \"def correctPerspective(vidpath: str, outputpath: str) -> list[tuple[int, int]]:\\n from get_img_coords import get_perspective_points\\n\\n # y = rf\\\".\\\\bin\\\\ffmpeg.exe -i .\\\\vid\\\\src-10.mp4 -vf perspective={x},scale=960:720,setdar=4/3 43.mp4\\\"\\n points = get_perspective_points(vidpath)\\n\\n # ffplay perspective is broken on normal pixel video files, not sure why\\n # probably some pixel ratio thing idk\\n # cmd = f\\\"ffplay -i {vidpath} -vf perspective={ppoints},scale=960:720,setdar=4/3\\\"\\n\\n # workaround is to render a short preview and ask for confirmation on that\\n\\n # make preview video\\n cmd = gen_perspective_ffmpeg_cmd(\\n vidpath,\\n outputpath,\\n points,\\n preview_only=True,\\n text_overlay=\\\"preview\\\",\\n )\\n print(cmd)\\n # cmd = f\\\"ffmpeg -i {vidpath} -vf perspective={ppoints},scale=960:720,setdar=4u3 out.mp4\\\"\\n\\n run(split(cmd))\\n run([\\\"mpv\\\", outputpath])\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d41bcb0340ae150786a8a619e9a99b\",\n \"score\": \"0.6087891\",\n \"text\": \"def _transform_projective(x, y, H):\\n z_ = H[2][0] * x + H[2][1] * y + H[2][2]\\n x_ = (H[0][0] * x + H[0][1] * y + H[0][2]) / z_\\n y_ = (H[1][0] * x + H[1][1] * y + H[1][2]) / z_\\n return (x_, y_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717a92d008422554716eb92525ad70a2\",\n \"score\": \"0.6074759\",\n \"text\": \"def setup_transformation_pipeline(offset=10):\\n pts = calibrate_camera()\\n images = glob.glob(\\\"test_images/*\\\")\\n # Pick the image with a straight lane to calibrate the camera\\n img = cv2.imread(images[0])\\n # run the same pipeline\\n dst = undistort_image(img, pts)\\n masked_img = lane_masking(dst)\\n _img = region_of_interest(masked_img)\\n # instead of polynomial fit\\n # use linear regression to determine the src for perspective transformation\\n left,right = fit_lanes(_img)\\n src = retrieve_src_points(left, right, masked_img.shape[0])\\n dst = np.float32([src[0], (src[0][0], offset), src[2], (src[2][0], offset)])\\n return TransformationPipeline(pts, src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb7015566e333f043a3e3db8c141b44e\",\n \"score\": \"0.60576177\",\n \"text\": \"def warp_images(image1, image2, H):\\n\\n rows1, cols1 = image1.shape[:2]\\n rows2, cols2 = image2.shape[:2]\\n\\n # Coordinates of the image to transform\\n temp_points1 = float32([[0, 0], [0, rows1], [cols1, rows1], [cols1, 0]]).reshape(-1, 1, 2)\\n\\n # Coordinates of a reference image\\n points2 = float32([[0, 0], [0, rows2], [cols2, rows2], [cols2, 0]]).reshape(-1, 1, 2)\\n points1 = perspectiveTransform(temp_points1, H)\\n points = concatenate((points2, points1), axis=0)\\n\\n [x_min, y_min] = int32(points.min(axis=0).ravel() - 0.5)\\n [x_max, y_max] = int32(points.max(axis=0).ravel() + 0.5)\\n\\n translation_dist = [-x_min, -y_min]\\n H_translation = array([[1, 0, translation_dist[0]], [0, 1, translation_dist[1]], [0, 0, 1]])\\n\\n output_img = warpPerspective(image1, H_translation.dot(H), (x_max - x_min, y_max - y_min))\\n output_img[translation_dist[1]:rows2 + translation_dist[1],\\n translation_dist[0]:cols2 + translation_dist[0]] = image2\\n\\n return output_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ccd124e2fd30ef665ee968166f84d9\",\n \"score\": \"0.60440665\",\n \"text\": \"def plate_transform(image, box):\\r\\n\\r\\n\\t# obtain the bounding rectangle's vertices and order them\\r\\n\\trect = order_points(box)\\r\\n\\t(tl, tr, br, bl) = rect\\r\\n\\r\\n\\t# Computes the width of the plate\\r\\n\\tlower_width = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\\r\\n\\tupper_width = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\\r\\n\\twidth = max(int(lower_width), int(upper_width))\\r\\n\\r\\n\\t# Computes the height of the plate\\r\\n\\tright_height = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\\r\\n\\tleft_height = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\\r\\n\\theight = max(int(right_height), int(left_height))\\r\\n\\r\\n\\t# Construct the set of destination points to obtain a \\\"birds eye view\\\" of the plate\\r\\n\\tdst = np.array([\\r\\n\\t\\t[0, 0],\\r\\n\\t\\t[width - 1, 0],\\r\\n\\t\\t[width - 1, height - 1],\\r\\n\\t\\t[0, height - 1]], dtype=\\\"float32\\\")\\r\\n\\r\\n\\t# compute the perspective transform matrix\\r\\n\\ttransform_matrix = cv2.getPerspectiveTransform(rect, dst)\\r\\n\\twarped = cv2.warpPerspective(image, transform_matrix, (width, height))\\r\\n\\r\\n\\t# return the warped image\\r\\n\\treturn warped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40fcc2418e758685dd7aec9c859b0600\",\n \"score\": \"0.60433745\",\n \"text\": \"def projective_inverse_warp(img, depth, pose, intrinsics, rot_mat=None):\\n batch, height, width, _ = img.get_shape().as_list()\\n # Convert pose vector to matrix\\n pose, rot_mat, translation = pose_vec2mat(pose, rot_mat)\\n # Construct pixel grid coordinates\\n pixel_coords = meshgrid(batch, height, width)\\n # Convert pixel coordinates to the camera frame\\n cam_coords = pixel2cam(depth, pixel_coords, intrinsics)\\n # Construct a 4x4 intrinsic matrix (TODO: can it be 3x4?)\\n filler = tf.constant([0.0, 0.0, 0.0, 1.0], shape=[1, 1, 4])\\n filler = tf.tile(filler, [batch, 1, 1])\\n intrinsics = tf.concat([intrinsics, tf.zeros([batch, 3, 1])], axis=2)\\n intrinsics = tf.concat([intrinsics, filler], axis=1)\\n # Get a 4x4 transformation matrix from 'target' camera frame to 'source'\\n # pixel frame.\\n proj_tgt_cam_to_src_pixel = tf.matmul(intrinsics, pose)\\n src_pixel_coords = cam2pixel(cam_coords, proj_tgt_cam_to_src_pixel)\\n output_img = bilinear_sampler(img, src_pixel_coords)\\n return output_img, rot_mat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8819af67babf47092d342270930fff\",\n \"score\": \"0.6036917\",\n \"text\": \"def stitching(\\r\\n images: Tuple[Image, Image],\\r\\n ratio: float = 0.55,\\r\\n reproj_thresh: float = 4.0\\r\\n) -> Image:\\r\\n # unpack the images, then detect keypoints and extract\\r\\n # local invariant descriptors from them\\r\\n img1 = images[0].data\\r\\n img0 = images[1].data\\r\\n\\r\\n # To calculate the features\\r\\n (kps0, features0) = detect_and_describe(img0)\\r\\n (kps1, features1) = detect_and_describe(img1)\\r\\n\\r\\n # match features between the two images\\r\\n M = match_keypoints(kps0, kps1, features0, features1, ratio, reproj_thresh)\\r\\n\\r\\n # if the match is None, then there aren't enough matched keypoints to create a panorama\\r\\n if M is None:\\r\\n return None\\r\\n\\r\\n # otherwise, apply a perspective warp to stitch the images together\\r\\n (matches, H, status) = M\\r\\n\\r\\n # distinguish the direction and to apply corresponding stitching\\r\\n direction = distinguish_direction(img0, kps0, kps1, matches, status)\\r\\n if direction == UP:\\r\\n result = cv2.warpPerspective(img0, H, (img0.shape[1], img1.shape[0] + img0.shape[0]))\\r\\n result[0:img1.shape[0], 0:img1.shape[1]] = img1\\r\\n elif direction == DOWN:\\r\\n # For Down direction it is required to transform it into Up direction\\r\\n M = match_keypoints(kps1, kps0, features1, features0, ratio, reproj_thresh)\\r\\n H = M[1]\\r\\n result = cv2.warpPerspective(img1, H, (img1.shape[1], img1.shape[0] + img0.shape[0]))\\r\\n result[0:img1.shape[0], 0:img0.shape[1]] = img0\\r\\n elif direction == LEFT:\\r\\n result = cv2.warpPerspective(img0, H, (img0.shape[1] + img1.shape[1], img0.shape[0]))\\r\\n result[0:img1.shape[0], 0:img1.shape[1]] = img1\\r\\n else:\\r\\n # For Right direction it is required to transform it into Left direction\\r\\n M = match_keypoints(kps1, kps0, features1, features0, ratio, reproj_thresh)\\r\\n H = M[1]\\r\\n result = cv2.warpPerspective(img1, H,\\r\\n (img0.shape[1] + img1.shape[1], img1.shape[0]))\\r\\n result[0 : img0.shape[0], 0:img0.shape[1]] = img0\\r\\n return Image(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef6c282874263504ef525f7c6c9716c5\",\n \"score\": \"0.6009642\",\n \"text\": \"def simpleDistortionBack(xx, yy, xs, ys, x1, y1):\\n\\n # subtract average to align\\n\\n nn = len(xs)\\n pts1 = np.zeros((1, nn, 2))\\n pts2 = np.zeros((1, nn, 2))\\n xav1 = xs.mean()\\n yav1 = ys.mean()\\n xav2 = xx.mean()\\n yav2 = yy.mean()\\n\\n for i in range(len(xs)):\\n # pts1[0,i,0]=xs[i]-xav1\\n # pts1[0,i,1]=ys[i]-yav1\\n # pts2[0,i,0]=xx[i]-xav2\\n # pts2[0,i,1]=yy[i]-yav2\\n pts1[0, i, 0] = xs[i]\\n pts1[0, i, 1] = ys[i]\\n pts2[0, i, 0] = xx[i]\\n pts2[0, i, 1] = yy[i]\\n\\n # cv2 needs float32\\n\\n pts1 = np.float32(pts1)\\n pts2 = np.float32(pts2)\\n\\n # get the affine transformation - translation, rotation, scaling\\n # uses opencv library\\n\\n transformation = cv2.estimateRigidTransform(pts1, pts2, False)\\n transformFull = transformation.copy()\\n\\n # calculate the scaling from the result\\n sx = np.sqrt(transformation[0, 0]**2+transformation[0, 1]**2)\\n sy = np.sqrt(transformation[1, 0]**2+transformation[1, 1]**2)\\n\\n print(\\\"sx=\\\", sx, \\\" sy=\\\", sy)\\n\\n # remove the scaling from the transformation matrix\\n transformation[0, 0] /= sx\\n transformation[0, 1] /= sx\\n transformation[1, 0] /= sy\\n transformation[1, 1] /= sy\\n\\n # calculate the transformed points\\n points = cv2.transform(pts1, transformation)\\n\\n # difference between expected and actual point positions\\n diffx = (pts2[0, :, 0]-points[0, :, 0])\\n diffy = (pts2[0, :, 1]-points[0, :, 1])\\n\\n # in mm and in % of field\\n\\n c = np.sqrt(diffx*diffx+diffy*diffy)\\n c1 = np.sqrt(diffx*diffx+diffy*diffy)/336.*100\\n\\n pts1[:, :, 0] = ma.masked_values(pts1[:, :, 0], 0)\\n pts1[:, :, 1] = ma.masked_values(pts1[:, :, 1], 0)\\n pts2[:, :, 0] = ma.masked_values(pts2[:, :, 0], 0)\\n pts2[:, :, 1] = ma.masked_values(pts2[:, :, 1], 0)\\n pts1 = ma.masked_invalid(pts1)\\n pts2 = ma.masked_invalid(pts2)\\n\\n return c, c1, pts1, pts2, diffx, diffy, transformFull\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1980566551fb58755d999bc835ef11c8\",\n \"score\": \"0.59884036\",\n \"text\": \"def project(self, points, camera_params):\\n # points_proj = self.rotate(points, camera_params[:, :3])\\n # points_proj -= camera_params[:, 3:6]\\n # points_proj = -points_proj[:, :2] / points_proj[:, 2, np.newaxis]\\n #\\n # f = self.mtx[0,0]\\n # print(\\\"f\\\", f)\\n # k1 = 0#self.distortion[0][0]\\n # k2 = 0#self.distortion[0][1]\\n # n = np.sum(points_proj ** 2, axis=1)\\n # r = 1 + k1 * n + k2 * n ** 2\\n # points_proj *= (r * f)[:, np.newaxis]\\n\\n points_proj = np.zeros((points.shape[0],2))\\n for i in range(points.shape[0]):\\n curr_proj_point = cv.projectPoints(points[i,:].reshape(1,3), camera_params[i, :3].reshape(3,1), camera_params[i, 3:].reshape(3,1), self.mtx, None)\\n points_proj[i, :] = curr_proj_point[0]\\n\\n return points_proj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3625c419b803ac60c8291ce477ed9d0a\",\n \"score\": \"0.59597665\",\n \"text\": \"def flip_image(image):\\n\\treturn cv2.flip(image, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d7bc8c1b1594944eb608d2b0574368f\",\n \"score\": \"0.5940707\",\n \"text\": \"def process_image(img, camera, left, right):\\n # undistort image\\n undist_img = camera.undistort(img)\\n\\n # get perspective View\\n wraped = c.birds_eye(undist_img, display=False)\\n\\n # apply color threshold on image\\n warped_binary = apply_thresholds(wraped, display=False)\\n\\n nonzerox, nonzeroy = np.nonzero(np.transpose(warped_binary))\\n\\n # Get lanes\\n if left.detected is True:\\n leftx, lefty, left.detected = left.quick_search(nonzerox, nonzeroy)\\n if right.detected is True:\\n rightx, righty, right.detected = right.quick_search(nonzerox, nonzeroy)\\n if left.detected is False:\\n leftx, lefty, left.detected = left.blind_search(\\n nonzerox, nonzeroy, warped_binary)\\n if right.detected is False:\\n rightx, righty, right.detected = right.blind_search(\\n nonzerox, nonzeroy, warped_binary)\\n\\n left.y = np.array(lefty).astype(np.float32)\\n left.x = np.array(leftx).astype(np.float32)\\n right.y = np.array(righty).astype(np.float32)\\n right.x = np.array(rightx).astype(np.float32)\\n\\n # Based on searched x and y coordinates, polyfit with second order.\\n left_fit, left_fitx, left_fity = left.get_fit()\\n right_fit, right_fitx, right_fity = right.get_fit()\\n\\n # Visualize Results\\n image_shape = img.shape\\n ploty = np.linspace(0, image_shape[0] - 1, image_shape[0])\\n offset, mean_curv = get_car_pos(\\n left_fit, right_fit, ploty, (image_shape[0], image_shape[1]))\\n\\n result = draw_lane(undist_img, warped_binary, left_fit,\\n right_fit, camera, display=False)\\n\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n text1 = 'Radius of Curvature: %d(m)'\\n text2 = 'Offset from center: %.2f(m)'\\n text3 = 'Radius of Curvature: Inf (m)'\\n\\n if mean_curv < 3000:\\n cv2.putText(result, text1 % (int(mean_curv)),\\n (60, 100), font, 1.0, (255, 255, 255), thickness=2)\\n else:\\n cv2.putText(result, text3,\\n (60, 100), font, 1.0, (255, 255, 255), thickness=2)\\n cv2.putText(result, text2 % (-offset),\\n (60, 130), font, 1.0, (255, 255, 255), thickness=2)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e103bd458884c576493782ada695e422\",\n \"score\": \"0.59372807\",\n \"text\": \"def display_homographied_plan(h, source):\\n # TODO\\n\\n cap=cv2.VideoCapture(source)\\n # Reading our first frame\\n ret1,frame1= cap.read()\\n cv2.imshow('window',frame1)\\n\\n frame2 = cv2.perspectiveTransform(frame1, h)\\n cv2.imshow('homography',frame2)\\n\\n\\n while True:\\n if cv2.waitKey(20) == ord('q'):\\n # pour 'quitter'\\n break\\n\\n cap.release()\\n cv2.destroyAllWindows()\\n return()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"496986648e535e585a283c763e4b36d4\",\n \"score\": \"0.59346884\",\n \"text\": \"def translate(img, x, y):\\n transMat = np.float32([[1,0,x], [0,1,y]])\\n dimemsion = (img.shape[1], img.shape[0])\\n return cv.warpAffine(img, transMat, dimemsion)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b6b8857d87e1375c4b6f2bff122c88f\",\n \"score\": \"0.59341764\",\n \"text\": \"def perspective_coefficients(self, oldplane, newplane):\\n # first find the transform coefficients, thanks to http://stackoverflow.com/questions/14177744/how-does-perspective-transformation-work-in-pil\\n pb,pa = oldplane,newplane\\n grid = []\\n for p1,p2 in zip(pa, pb):\\n grid.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]])\\n grid.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]])\\n \\n # then do some matrix magic\\n A = mt.Matrix(grid)\\n B = mt.Vec([xory for xy in pb for xory in xy])\\n AT = A.tr()\\n ATA = AT.mmul(A)\\n gridinv = ATA.inverse()\\n invAT = gridinv.mmul(AT)\\n res = invAT.mmul(B)\\n a,b,c,d,e,f,g,h = res.flatten()\\n \\n # finito\\n return a,b,c,d,e,f,g,h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966c7a44c3cedadd09bd9c97c773fb5c\",\n \"score\": \"0.5932009\",\n \"text\": \"def affine_transform(image: np.array, pts1: np.float32([[], [], []]), pts2: np.float32([[], [], []])) -> np.array:\\n rows, cols = image.shape[:2]\\n M = cv.getAffineTransform(pts1, pts2)\\n transformed = cv.warpAffine(image, M, (cols, rows))\\n return transformed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"986e25b3d95c2ccf5a57c5cff573bfcb\",\n \"score\": \"0.59113497\",\n \"text\": \"def warpImgByCorners(image, corners, zoomed_out=False):\\n\\n nw, ne, sw, se = [np.array(corners[qd]) for qd in QD_KEYS]\\n\\n # calculate maximum width and height for destination points\\n width1 = norm(se - sw)\\n width2 = norm(ne - nw)\\n maxWidth = max(int(width1), int(width2))\\n\\n height1 = norm(ne - se)\\n height2 = norm(nw - sw)\\n maxHeight = max(int(height1), int(height2))\\n\\n if zoomed_out:\\n factor = float(MAX_OBJ_HEIGHT) / CAMERA_HEIGHT / 2\\n min_dst_x = factor * maxWidth\\n max_dst_x = (1 + factor) * maxWidth\\n min_dst_y = factor * maxHeight\\n max_dst_y = (1 + factor) * maxHeight\\n dst_size = (int((1 + 2 * factor) * maxWidth), int((1 + 2 * factor) * maxHeight))\\n else:\\n min_dst_x, max_dst_x = 0, maxWidth - 1\\n min_dst_y, max_dst_y = 0, maxHeight - 1\\n dst_size = (maxWidth, maxHeight)\\n\\n # source points for matrix calculation\\n src = np.array((nw, ne, se, sw), dtype=\\\"float32\\\")\\n\\n # destination points in the same order\\n dst = np.array(\\n [\\n [min_dst_x, min_dst_y], # nw\\n [max_dst_x, min_dst_y], # ne\\n [max_dst_x, max_dst_y], # sw\\n [min_dst_x, max_dst_y], # se\\n ],\\n dtype=\\\"float32\\\",\\n )\\n\\n # get the perspective transform matrix\\n transMatrix = cv2.getPerspectiveTransform(src, dst)\\n\\n # compute warped image\\n warpedImg = cv2.warpPerspective(image, transMatrix, dst_size)\\n return warpedImg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c14f8874bb11b5bc9e9a62737c5739b\",\n \"score\": \"0.58761185\",\n \"text\": \"def __init__(self, imageSize):\\n self._imageSize = imageSize\\n self._sourcePoints = self._createSource()\\n self._destinationPoints = self._createDestination()\\n self._transformationMatrix = cv2.getPerspectiveTransform(self._sourcePoints, self._destinationPoints)\\n self._inverseTransformationMatrix = cv2.getPerspectiveTransform(self._destinationPoints, self._sourcePoints)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e09a1394336580ce5ef571ceca52b98f\",\n \"score\": \"0.5808014\",\n \"text\": \"def warp_perspective(\\n inp: Tensor,\\n mat: Tensor,\\n out_shape: Union[Tuple[int, int], int, Tensor],\\n mat_idx: Optional[Union[Iterable[int], Tensor]] = None,\\n border_mode: str = \\\"replicate\\\",\\n border_val: float = 0.0,\\n format: str = \\\"NCHW\\\",\\n interp_mode: str = \\\"linear\\\",\\n) -> Tensor:\\n if inp.dtype == np.float32:\\n mat = mat.astype(\\\"float32\\\")\\n if inp.dtype == np.float16:\\n inp = inp.astype(\\\"float32\\\")\\n op = builtin.WarpPerspective(\\n imode=interp_mode, bmode=border_mode, format=format, border_val=border_val\\n )\\n out_shape = astensor1d(out_shape, inp, dtype=\\\"int32\\\", device=inp.device)\\n if mat_idx is not None:\\n mat_idx = astensor1d(mat_idx, inp, dtype=\\\"int32\\\", device=inp.device)\\n (result,) = apply(op, inp, mat, mat_idx, out_shape)\\n return result\\n (result,) = apply(op, inp, mat, out_shape)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed86d5138bbd420348220fd5df78d7f\",\n \"score\": \"0.5801215\",\n \"text\": \"def translate(mat, x, y):\\n trans_mat = np.float32([[1, 0, x],\\n [0, 1, y]])\\n return cv2.warpAffine(mat, trans_mat, (mat.shape[1], mat.shape[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c2aef8f8a5d997283210e00941a8342\",\n \"score\": \"0.57737416\",\n \"text\": \"def __call__(self, image):\\n image = self.prepare_image(image)\\n return self.get_transforms(image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb8ece7473284dff2bfbe98a56216062\",\n \"score\": \"0.5773236\",\n \"text\": \"def inverse_warp(img, depth, pose_mat, intrinsics, padding_mode='border'):\\n depth = depth[:,0,:,:]\\n check_sizes(img, 'img', 'B3HW')\\n check_sizes(depth, 'depth', 'BHW')\\n check_sizes(intrinsics, 'intrinsics', '133')\\n batch_size, _, img_height, img_width = img.shape\\n\\n pixel_coords = set_id_grid(depth)\\n intrinsics = intrinsics.broadcast_to((img.shape[0], intrinsics.shape[1], intrinsics.shape[2]))\\n\\n cam_coords = pixel2cam(depth, nd.linalg.inverse(intrinsics), pixel_coords) # [B,3,H,W]\\n # Get projection matrix for the camera frame to source pixel frame\\n proj_cam_to_src_pixel = nd.linalg.gemm2(intrinsics, pose_mat[:,:3,:]) # [B, 3, 4]\\n\\n\\n src_pixel_coords, mask = cam2pixel(cam_coords, proj_cam_to_src_pixel[:,:,:3], proj_cam_to_src_pixel[:,:,-1:], padding_mode) # [B,H,W,2]\\n src_pixel_coords = nd.transpose(src_pixel_coords, (0, 3, 1, 2))\\n src_pixel_coords = nd.clip(src_pixel_coords, a_min=-1.0, a_max=1.0)\\n\\n # projected_img = F.grid_sample(img, src_pixel_coords, padding_mode=padding_mode)\\n projected_img = nd.BilinearSampler(data=img, grid=src_pixel_coords)\\n return projected_img, src_pixel_coords, mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f173f7024592f4133e5086b3a40bb9c\",\n \"score\": \"0.57649505\",\n \"text\": \"def project_to_image_plane(self, point_in_world):\\n\\n fx = self.config['camera_info']['focal_length_x']\\n fy = self.config['camera_info']['focal_length_y']\\n image_width = self.config['camera_info']['image_width']\\n image_height = self.config['camera_info']['image_height']\\n\\n # get transform between pose of camera and world frame\\n trans = None\\n try:\\n now = rospy.Time.now()\\n self.listener.waitForTransform(\\\"/base_link\\\",\\n \\\"/world\\\", now, rospy.Duration(1.0))\\n (trans, rot) = self.listener.lookupTransform(\\\"/base_link\\\",\\n \\\"/world\\\", now)\\n\\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\\n rospy.logerr(\\\"Failed to find camera to map transform\\\")\\n\\n #TODO Use tranform and rotation to calculate 2D position of light in image\\n # KB 10Oct2017\\n # commenting line below, and using dummy values, so that tf_classifier.py can be tested \\n # x, y = self.project_to_image_plane(light.pose.pose.position)\\n x = 0\\n y = 0\\n\\n return (x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93e36676cf4a741c04c31edded141dd\",\n \"score\": \"0.57443106\",\n \"text\": \"def perspective_coefficients(self):\\n run_data = self.run_data\\n\\n lower_right_1 = (run_data['l0x']), (run_data['l0y'])\\n upper_right_1 = (run_data['lsx']), (run_data['lsy'])\\n lower_left_1 = (run_data['j10x']), (run_data['j10y'])\\n upper_left_1 = (run_data['j1sx']), (run_data['j1sy'])\\n\\n lower_right_2 = (run_data['j20x']), (run_data['j20y'])\\n upper_right_2 = (run_data['j2sx']), (run_data['j2sy'])\\n lower_left_2 = (run_data['r0x']), (run_data['r0y'])\\n upper_left_2 = (run_data['rsx']), (run_data['rsy'])\\n # need some standard vertical lines in both cameras.\\n # cam1: use lock gate and tank join\\n # cam2: tank join and ruler at 2.5m\\n # (checked to be vertical, extrapolate to surface)\\n # so for each camera, 4 locations (8 numbers) need\\n # to be recorded.\\n\\n style = self.style\\n\\n # units here are pixels, i.e in the coordinate system of\\n # an image.\\n x1 = (lower_right_1, upper_right_1, lower_left_1, upper_left_1)\\n X1 = self.perspective_reference(lower_right_1, style, 'cam1')\\n\\n x2 = (lower_right_2, upper_right_2, lower_left_2, upper_left_2)\\n X2 = self.perspective_reference(lower_right_2, style, 'cam2')\\n\\n cam1_coeff = processing.perspective_coefficients(x1, X1).tolist()\\n if lower_right_2[0] == 0:\\n cam2_coeff = 0\\n else:\\n cam2_coeff = processing.perspective_coefficients(x2, X2).tolist()\\n\\n return {'cam1': cam1_coeff, 'cam2': cam2_coeff}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e38332db46fa997c53930521dd141130\",\n \"score\": \"0.5742328\",\n \"text\": \"def perspective_projection(points, rotation, translation,\\n focal_length, camera_center):\\n batch_size = points.shape[0]\\n K = torch.zeros([batch_size, 3, 3], device=points.device)\\n K[:,0,0] = focal_length\\n K[:,1,1] = focal_length\\n K[:,2,2] = 1.\\n K[:,:-1, -1] = camera_center\\n\\n # Transform points\\n points = torch.einsum('bij,bkj->bki', rotation, points)\\n points = points + translation.unsqueeze(1)\\n\\n # Apply perspective distortion\\n projected_points = points / points[:,:,-1].unsqueeze(-1)\\n\\n # Apply camera intrinsics\\n projected_points = torch.einsum('bij,bkj->bki', K, projected_points)\\n\\n return projected_points[:, :, :-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25d91600c3f32a9a000dc5861e5d0097\",\n \"score\": \"0.57386327\",\n \"text\": \"def translation(img, vec):\\n tx = vec[0]\\n ty = vec[1]\\n M = np.float32([[1, 0, tx], [0, 1, ty]])\\n return cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a9ec68e4043260f2df653db7140b7a7\",\n \"score\": \"0.57374907\",\n \"text\": \"def flattener(self, image, pts, w, h):\\n temp_rect = np.zeros((4, 2), dtype=\\\"float32\\\")\\n\\n s = np.sum(pts, axis=2)\\n\\n tl = pts[np.argmin(s)]\\n br = pts[np.argmax(s)]\\n\\n diff = np.diff(pts, axis=-1)\\n tr = pts[np.argmin(diff)]\\n bl = pts[np.argmax(diff)]\\n\\n # Need to create an array listing points in order of\\n # [top left, top right, bottom right, bottom left]\\n # before doing the perspective transform\\n\\n if w <= 0.8 * h: # If card is vertically oriented\\n temp_rect[0] = tl\\n temp_rect[1] = tr\\n temp_rect[2] = br\\n temp_rect[3] = bl\\n\\n if w >= 1.2 * h: # If card is horizontally oriented\\n temp_rect[0] = bl\\n temp_rect[1] = tl\\n temp_rect[2] = tr\\n temp_rect[3] = br\\n\\n # If the card is 'diamond' oriented, a different algorithm\\n # has to be used to identify which point is top left, top right\\n # bottom left, and bottom right.\\n\\n if 0.8 * h < w < 1.2 * h: # If card is diamond oriented\\n # If furthest left point is higher than furthest right point,\\n # card is tilted to the left.\\n if pts[1][0][1] <= pts[3][0][1]:\\n # If card is titled to the left, approxPolyDP returns points\\n # in this order: top right, top left, bottom left, bottom right\\n temp_rect[0] = pts[1][0] # Top left\\n temp_rect[1] = pts[0][0] # Top right\\n temp_rect[2] = pts[3][0] # Bottom right\\n temp_rect[3] = pts[2][0] # Bottom left\\n\\n # If furthest left point is lower than furthest right point,\\n # card is tilted to the right\\n if pts[1][0][1] > pts[3][0][1]:\\n # If card is titled to the right, approxPolyDP returns points\\n # in this order: top left, bottom left, bottom right, top right\\n temp_rect[0] = pts[0][0] # Top left\\n temp_rect[1] = pts[3][0] # Top right\\n temp_rect[2] = pts[2][0] # Bottom right\\n temp_rect[3] = pts[1][0] # Bottom left\\n\\n maxWidth = 633\\n maxHeight = 1008\\n\\n # Create destination array, calculate perspective transform matrix,\\n # and warp card image\\n dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], np.float32)\\n M = cv2.getPerspectiveTransform(temp_rect, dst)\\n warp = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\\n warp = warp[30:990, 30:620]\\n warp = cv2.resize(warp, (633, 1008))\\n\\n return warp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9470cb2204fd577bc3e879fd9927a34f\",\n \"score\": \"0.5719011\",\n \"text\": \"def perspective(fov, aspect, zNear, zFar):\\n fovR = math.radians(45.0)\\n f = 1.0/math.tan(fovR/2.0)\\n return numpy.array([f/float(aspect), 0.0, 0.0, 0.0, \\n 0.0, f, 0.0, 0.0, \\n 0.0, 0.0, (zFar+zNear)/float(zNear-zFar), -1.0, \\n 0.0, 0.0, 2.0*zFar*zNear/float(zNear-zFar), 0.0], \\n numpy.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c224dca347f9a953886dc0cc555234c0\",\n \"score\": \"0.5714747\",\n \"text\": \"def preprocess(img, imgref):\\n im = mesh_align(img, imgref)\\n im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\\n # im = cv2.resize(im, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)\\n im = align(im)\\n return im\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9cb7ff2577259d590400e583f040158\",\n \"score\": \"0.57090056\",\n \"text\": \"def view_transform(fr, to, up):\\n forward = normalize(to - fr)\\n upn = normalize(up)\\n left = cross(forward, upn)\\n true_up = cross(left, forward)\\n orientation = matrix(left[0], left[1], left[2], 0,\\n true_up[0], true_up[1], true_up[2], 0,\\n -forward[0], -forward[1], -forward[2], 0,\\n 0,0,0,1)\\n return orientation * translation(-fr[0], -fr[1], -fr[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3ed344147e6dca63a335b9521fd9d27\",\n \"score\": \"0.5700831\",\n \"text\": \"def visualize():\\n # Plot the Perspective Transformation\\n image = mpimg.imread('./test_images/straight_lines2.jpg')\\n plt.figure(figsize=(10, 8))\\n plt.subplot(121)\\n plt.imshow(image)\\n plt.xlim(0, 1280)\\n plt.ylim(720, 0)\\n draw_lines(src)\\n plt.title('Original Image', fontsize=12)\\n plt.subplot(122)\\n plt.imshow(warp(image))\\n plt.xlim(0, 1280)\\n plt.ylim(720, 0)\\n draw_lines(dst)\\n plt.title('Warped image with dest. points', fontsize=12)\\n plt.show()\\n\\n # Plot Perspective_Binary\\n # plt.figure(figsize=(8,2))\\n # plt.subplot(121)\\n # plt.imshow(binary, cmap='gray')\\n # draw_lines(src)\\n # plt.title('Threshold Image', fontsize=12)\\n # plt.subplot(122)\\n # plt.imshow(warped, cmap='gray')\\n # draw_lines(dst)\\n # plt.title('Warped image with dest. points drawn', fontsize=12)\\n\\n # Plot historgram\\n # plt.figure(figsize=(9,4))\\n # histogram = np.sum(warped[int(warped.shape[0]/2):,:], axis=0)\\n # plt.plot(histogram)\\n # plt.xlabel('Pixel Position')\\n # plt.ylabel('Counts')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa00f75ef8317973a4ddf95c5954cd0\",\n \"score\": \"0.5695022\",\n \"text\": \"def _homography(self, image):\\n\\t\\t# get matching points\\n\\t\\tkp1, des1 = sift.detectAndCompute(image, None)\\n\\t\\tkp2, des2 = sift.detectAndCompute(self.template, None)\\n\\t\\tbf = cv2.BFMatcher()\\n\\t\\tmatches = bf.knnMatch(des1, des2, k=2)\\n\\t\\tgood = []\\n\\t\\t# prune the matching\\n\\t\\tfor m,n in matches:\\n\\t\\t\\tif m.distance < 0.5 * n.distance:\\n\\t\\t\\t good.append([m])\\n\\t\\t# if no good matching, return \\n\\t\\tif len(good) < 4:\\n\\t\\t\\treturn\\n\\t\\t# extract location info from matching point\\n\\t\\tkp1_index = [m[0].queryIdx for m in good]\\n\\t\\tkp2_index =\\t[m[0].trainIdx for m in good]\\n\\t\\tkp1 = np.array([k.pt for k in np.array(kp1)[kp1_index]])\\n\\t\\tkp2 = np.array([k.pt for k in np.array(kp2)[kp2_index]])\\n\\t\\t# calculate homography using RANSAC\\n\\t\\tH, _ = cv2.findHomography(kp2, kp1, cv2.RANSAC, 0.1)\\n\\t\\tif H is None:\\n\\t\\t\\treturn\\n\\t\\t# get location of the self.template in the image\\n\\t\\theight, width = self.template.shape\\n\\t\\ttop_left = H.dot(np.array([[0, 0, 1]]).reshape(3, 1)).flatten()\\n\\t\\tbottom_left = H.dot(np.array([[0, height, 1]]).reshape(3, 1)).flatten()\\n\\t\\tbottom_right = H.dot(np.array([[width, height, 1]]).reshape(3, 1)).flatten()\\n\\t\\ttop_right = H.dot(np.array([[width, 0, 1]]).reshape(3, 1)).flatten()\\n\\t\\t# rescale the points\\n\\t\\ttop_left = top_left[:2] / top_left[-1]\\n\\t\\tbottom_left = bottom_left[:2] / bottom_left[-1]\\n\\t\\tbottom_right = bottom_right[:2] / bottom_right[-1]\\n\\t\\ttop_right = top_right[:2] / top_right[-1]\\n\\t\\t# int type\\n\\t\\ttop_left = top_left.astype(int)\\n\\t\\tbottom_left = bottom_left.astype(int)\\n\\t\\tbottom_right = bottom_right.astype(int) \\n\\t\\ttop_right = top_right.astype(int)\\n\\n\\t\\treturn top_left, bottom_left, bottom_right, top_right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ab5d5938f5da7e55e6a3965ea5252b\",\n \"score\": \"0.5690912\",\n \"text\": \"def rotate(image, angle):\\n # Get the image size\\n # No that's not an error - NumPy stores image matricies backwards\\n image_size = (image.shape[1], image.shape[0])\\n image_center = tuple(np.array(image_size) / 2)\\n\\n # Convert the OpenCV 3x2 rotation matrix to 3x3\\n rot_mat = np.vstack(\\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\\n )\\n\\n rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])\\n\\n # Shorthand for below calcs\\n image_w2 = image_size[0] * 0.5\\n image_h2 = image_size[1] * 0.5\\n\\n # Obtain the rotated coordinates of the image corners\\n rotated_coords = [\\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],\\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]\\n ]\\n\\n # Find the size of the new image\\n x_coords = [pt[0] for pt in rotated_coords]\\n x_pos = [x for x in x_coords if x > 0]\\n x_neg = [x for x in x_coords if x < 0]\\n\\n y_coords = [pt[1] for pt in rotated_coords]\\n y_pos = [y for y in y_coords if y > 0]\\n y_neg = [y for y in y_coords if y < 0]\\n\\n right_bound = max(x_pos)\\n left_bound = min(x_neg)\\n top_bound = max(y_pos)\\n bot_bound = min(y_neg)\\n\\n new_w = int(abs(right_bound - left_bound))\\n new_h = int(abs(top_bound - bot_bound))\\n\\n # We require a translation matrix to keep the image centred\\n trans_mat = np.matrix([\\n [1, 0, int(new_w * 0.5 - image_w2)],\\n [0, 1, int(new_h * 0.5 - image_h2)],\\n [0, 0, 1]\\n ])\\n\\n # Compute the tranform for the combined rotation and translation\\n affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]\\n\\n # Apply the transform\\n result = cv2.warpAffine(\\n image,\\n affine_mat,\\n (new_w, new_h),\\n flags=cv2.INTER_LINEAR\\n )\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c8cb37186955619075ec67770911b08\",\n \"score\": \"0.56868243\",\n \"text\": \"def warp(image, U, V, interpolation, border_mode):\\n \\n M, N = image.shape\\n X, Y = np.meshgrid(xrange(N), xrange(M))\\n x = X * 1.0 + U * 1.0\\n y = Y * 1.0 + V * 1.0\\n x_32 = x.astype('float32')\\n y_32 = y.astype('float32')\\n C = cv2.remap(image, x_32, y_32, interpolation, borderMode=border_mode)\\n return C\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99af80ed6ed8d571155cd97e9b419c6d\",\n \"score\": \"0.56614906\",\n \"text\": \"def project_to_image_plane(self, point_in_world):\\n fx = self.config['camera_info']['focal_length_x']\\n fy = self.config['camera_info']['focal_length_y']\\n image_width = self.config['camera_info']['image_width']\\n image_height = self.config['camera_info']['image_height']\\n\\n try:\\n now = rospy.Time.now()\\n self.listener.waitForTransform(\\\"/base_link\\\",\\n \\\"/world\\\", now, rospy.Duration(1.0))\\n (transT, rotT) = self.listener.lookupTransform(\\\"/base_link\\\",\\n \\\"/world\\\", now)\\n\\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\\n rospy.logerr(\\\"Failed to find camera to map transform\\\")\\n return None, None\\n pt = PointStamped()\\n pt.header.stamp = point_in_world.header.stamp\\n pt.header.frame_id = \\\"world\\\"\\n pt.point.x = point_in_world.pose.pose.position.x\\n pt.point.y = point_in_world.pose.pose.position.y\\n pt.point.z = point_in_world.pose.pose.position.z\\n #rospy.loginfo(\\\"Point: \\\" + str(pt))\\n target_pt = self.listener.transformPoint(\\\"base_link\\\", pt)\\n cx = image_width/2\\n cy = image_height/2\\n \\n ##########################################################################################\\n # DELETE THIS MAYBE - MANUAL TWEAKS TO GET THE PROJECTION TO COME OUT CORRECTLY IN SIMULATOR\\n # just override the simulator parameters. probably need a more reliable way to determine if \\n # using simulator and not real car\\n # See discussion in forum\\n if fx < 10:\\n fx = 2344\\n fy = 2552 #303.8\\n target_pt.point.y += 0.5\\n target_pt.point.z -= 1.2\\n cy = cy * 2 \\n ##########################################################################################\\n\\n \\n #rospy.loginfo(\\\"Target Point: \\\" + str(target_pt))\\n x = -target_pt.point.y * fx / target_pt.point.x; \\n y = -target_pt.point.z * fy / target_pt.point.x; \\n #rospy.loginfo(\\\"x: %.3f, y:%.3f\\\"%(x,y))\\n x = int(x + cx)\\n y = int(y + cy) \\n\\n return (x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763196bb46fbf32fd9d2f0f82efa8f7d\",\n \"score\": \"0.5652015\",\n \"text\": \"def crop_and_warp(img, crop_rect):\\n\\n # Rectangle described by top left, top right, bottom right and bottom left points\\n top_left, top_right, bottom_right, bottom_left = crop_rect[0], crop_rect[1], crop_rect[2], crop_rect[3]\\n\\n # Explicitly set the data type to float32 or `getPerspectiveTransform` will throw an error\\n src = np.array([top_left, top_right, bottom_right, bottom_left], dtype='float32')\\n\\n # Get the longest side in the rectangle\\n side = max([\\n distance_between(bottom_right, top_right),\\n distance_between(top_left, bottom_left),\\n distance_between(bottom_right, bottom_left),\\n distance_between(top_left, top_right)\\n ])\\n\\n # Describe a square with side of the calculated length, this is the new perspective we want to warp to\\n dst = np.array([[0, 0], [side - 1, 0], [side - 1, side - 1], [0, side - 1]], dtype='float32')\\n\\n # Gets the transformation matrix for skewing the image to fit a square by comparing the 4 before and after points\\n m = cv2.getPerspectiveTransform(src, dst)\\n\\n # Performs the transformation on the original image\\n return cv2.warpPerspective(img, m, (int(side), int(side)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763196bb46fbf32fd9d2f0f82efa8f7d\",\n \"score\": \"0.5652015\",\n \"text\": \"def crop_and_warp(img, crop_rect):\\n\\n # Rectangle described by top left, top right, bottom right and bottom left points\\n top_left, top_right, bottom_right, bottom_left = crop_rect[0], crop_rect[1], crop_rect[2], crop_rect[3]\\n\\n # Explicitly set the data type to float32 or `getPerspectiveTransform` will throw an error\\n src = np.array([top_left, top_right, bottom_right, bottom_left], dtype='float32')\\n\\n # Get the longest side in the rectangle\\n side = max([\\n distance_between(bottom_right, top_right),\\n distance_between(top_left, bottom_left),\\n distance_between(bottom_right, bottom_left),\\n distance_between(top_left, top_right)\\n ])\\n\\n # Describe a square with side of the calculated length, this is the new perspective we want to warp to\\n dst = np.array([[0, 0], [side - 1, 0], [side - 1, side - 1], [0, side - 1]], dtype='float32')\\n\\n # Gets the transformation matrix for skewing the image to fit a square by comparing the 4 before and after points\\n m = cv2.getPerspectiveTransform(src, dst)\\n\\n # Performs the transformation on the original image\\n return cv2.warpPerspective(img, m, (int(side), int(side)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d67a96f4a0767f2d1ea46f4ac12b5ea\",\n \"score\": \"0.5626017\",\n \"text\": \"def deslant(img):\\n h, w = img.shape[:2]\\n m = cv2.moments(img)\\n if abs(m['mu02']) < 1e-2:\\n return img.copy()\\n skew = m['mu11'] / m['mu02']\\n M = np.float32([[1, skew, -0.5 * h * skew], [0, 1, 0]])\\n img = cv2.warpAffine(img, M, (h, h),\\n flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47b6c4b406a83fedd0c923f27d134e99\",\n \"score\": \"0.56103444\",\n \"text\": \"def calculate_pose_from_essential_matrix(essential_matrix, img0_points, img1_points, intrinsic_camera_matrix):\\n u, s, v = np.linalg.svd(essential_matrix)\\n # http://igt.ip.uca.fr/~ab/Classes/DIKU-3DCV2/Handouts/Lecture16.pdf\\n w = np.asarray([[0, -1, 0],\\n [1, 0, 0],\\n [0, 0, 1]])\\n possible_rotations = [u @ w @ v, u @ w.T @ v]\\n rotation_multiplier = 1 if np.linalg.det(possible_rotations[0]) > 0 else -1\\n possible_rotations = [rotation_multiplier * rotation for rotation in possible_rotations]\\n\\n # translation X points right, Y points up, Z points into the image\\n possible_translations = [u[:, -1], -1*u[:, -1]]\\n\\n winning_num_points = -1\\n winning_rotation = None\\n winning_translation = None\\n winning_triangulated_points = None\\n for rotation in possible_rotations:\\n for translation in possible_translations:\\n # The camera matrix describes how the WORLD is transformed relative to the CAMERA\\n # To see the motion of the camera, rotation/translation need to be inverted\\n img0_camera_matrix = intrinsic_camera_matrix @ util.rotation_translation_to_pose(np.identity(3), np.zeros(3))[:3]\\n img1_camera_matrix = intrinsic_camera_matrix @ util.rotation_translation_to_pose(rotation, translation)[:3]\\n triangulated_points = triangulation.triangulate_points_from_pose(img0_camera_matrix,\\n img1_camera_matrix,\\n img0_points,\\n img1_points)\\n camera_2_vector = rotation @ np.asarray([0, 0, 1])\\n\\n points_in_front_of_camera_1 = triangulated_points[:, 2] > 0\\n points_in_front_of_camera_2 = np.dot((triangulated_points - translation), camera_2_vector) > 0\\n num_points_in_front_of_camera = np.count_nonzero(np.multiply(points_in_front_of_camera_1, points_in_front_of_camera_2))\\n\\n if num_points_in_front_of_camera > winning_num_points:\\n winning_rotation = rotation\\n winning_translation = translation\\n winning_num_points = num_points_in_front_of_camera\\n winning_triangulated_points = triangulated_points\\n logger.debug(\\\"essential matrix - winning num points: %d/%d\\\" % (winning_num_points, len(img0_points)))\\n\\n return util.rotation_translation_to_pose(winning_rotation, winning_translation), winning_triangulated_points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c41f65625e6b5fd19115c8c6128f2187\",\n \"score\": \"0.5606679\",\n \"text\": \"def panorama(imgs):\\r\\n h_max = max([x.shape[0] for x in imgs])\\r\\n w_max = sum([x.shape[1] for x in imgs])\\r\\n\\r\\n # create the final stitched canvas\\r\\n dst = np.zeros((h_max, w_max, imgs[0].shape[2]), dtype=np.uint8)\\r\\n dst[:imgs[0].shape[0], :imgs[0].shape[1]] = imgs[0]\\r\\n last_best_H = np.eye(3)\\r\\n out = None\\r\\n\\r\\n # for all images to be stitched:\\r\\n for idx in range(len(imgs) - 1):\\r\\n im1 = imgs[idx]\\r\\n im2 = imgs[idx + 1]\\r\\n\\r\\n # TODO: 1.feature detection & matching\\r\\n\\r\\n # TODO: 2. apply RANSAC to choose best H\\r\\n\\r\\n # TODO: 3. chain the homographies\\r\\n\\r\\n # TODO: 4. apply warping\\r\\n\\r\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1d673a8ca367cb9faac38dc96a4c81\",\n \"score\": \"0.5604625\",\n \"text\": \"def simpleDistortion(xx, yy, xs, ys):\\n\\n # subtract average to align\\n\\n nn = len(xs)\\n pts1 = np.zeros((1, nn, 2))\\n pts2 = np.zeros((1, nn, 2))\\n xav1 = xs.mean()\\n yav1 = ys.mean()\\n xav2 = xx.mean()\\n yav2 = yy.mean()\\n\\n for i in range(len(xs)):\\n pts1[0, i, 0] = xs[i]-xav1\\n pts1[0, i, 1] = ys[i]-yav1\\n pts2[0, i, 0] = xx[i]-xav2\\n pts2[0, i, 1] = yy[i]-yav2\\n\\n # cv2 needs float32\\n\\n pts1 = np.float32(pts1)\\n pts2 = np.float32(pts2)\\n\\n # get the affine transformation - translation, rotation, scaling\\n # uses opencv library\\n\\n transformation = cv2.estimateRigidTransform(pts1, pts2, False)\\n print(transformation)\\n\\n # calculate the scaling from the result\\n sx = np.sqrt(transformation[0, 0]**2+transformation[0, 1]**2)\\n sy = np.sqrt(transformation[1, 0]**2+transformation[1, 1]**2)\\n\\n print(\\\"sx=\\\", sx, \\\" sy=\\\", sy)\\n\\n # remove the scaling from the transformation matrix\\n transformation[0, 0] /= sx\\n transformation[0, 1] /= sx\\n transformation[1, 0] /= sy\\n transformation[1, 1] /= sy\\n\\n print(transformation)\\n\\n # calculate the transformed points\\n points = cv2.transform(pts1, transformation)\\n\\n # difference between expected and actual point positions\\n diffx = (pts2[0, :, 0]-points[0, :, 0])\\n diffy = (pts2[0, :, 1]-points[0, :, 1])\\n\\n # in mm and in % of field\\n\\n c = np.sqrt(diffx*diffx+diffy*diffy)\\n c1 = np.sqrt(diffx*diffx+diffy*diffy)/336.*100\\n\\n return c, c1, pts1, pts2, diffx, diffy,\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03fd375afc643bab8fc88257d7048b93\",\n \"score\": \"0.5601717\",\n \"text\": \"def warp_image(self, image):\\r\\n\\r\\n # We're just wrapping OpenCV library with appropriate options here\\r\\n\\r\\n # May as well make warped image have same dimensions as original\\r\\n (self.y_size, self.x_size) = np.shape(image)\\r\\n\\r\\n img_size = (self.x_size, self.y_size)\\r\\n return cv2.warpPerspective(image, self.warp_to_birdseye_matrix, img_size, flags=cv2.INTER_LINEAR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c338f633c936588c692998ecec78f125\",\n \"score\": \"0.56016487\",\n \"text\": \"def project(self, win_width, win_height, fov, viewer_distance):\\n factor = fov / (viewer_distance + self.z)\\n x = self.x * factor + win_width / 2\\n y = -self.y * factor + win_height / 2\\n return Point3D(x, y, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a52c5699aceeb2f2fe7452d0eeeeb33a\",\n \"score\": \"0.5593834\",\n \"text\": \"def project_to_image(pts_3d, P):\\n n = pts_3d.shape[0]\\n pts_3d_extend = np.hstack((pts_3d, np.ones((n, 1))))\\n # print(('pts_3d_extend shape: ', pts_3d_extend.shape))\\n pts_2d = np.dot(pts_3d_extend, np.transpose(P)) # nx3\\n pts_2d[:, 0] /= pts_2d[:, 2]\\n pts_2d[:, 1] /= pts_2d[:, 2]\\n return pts_2d[:, 0:2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679e59a0a957428c240752bd134268a6\",\n \"score\": \"0.55919784\",\n \"text\": \"def project_to_image_plane(self, point_in_world):\\n\\n fx = self.config['camera_info']['focal_length_x']\\n fy = self.config['camera_info']['focal_length_y']\\n image_width = self.config['camera_info']['image_width']\\n image_height = self.config['camera_info']['image_height']\\n\\n # get transform between pose of camera and world frame\\n trans = None\\n try:\\n now = rospy.Time.now()\\n self.listener.waitForTransform(\\\"/base_link\\\",\\n \\\"/world\\\", now, rospy.Duration(1.0))\\n (trans, rot) = self.listener.lookupTransform(\\\"/base_link\\\",\\n \\\"/world\\\", now)\\n\\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\\n rospy.logerr(\\\"Failed to find camera to map transform\\\")\\n\\n #TODO Use tranform and rotation to calculate 2D position of light in image\\n\\n x = 0\\n y = 0\\n\\n return (x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8742a82fb44c2a1c3d22c15054753c31\",\n \"score\": \"0.55826426\",\n \"text\": \"def project_to_image_plane(self, point_in_world):\\n\\n fx = self.config['camera_info']['focal_length_x']\\n fy = self.config['camera_info']['focal_length_y']\\n image_width = self.config['camera_info']['image_width']\\n image_height = self.config['camera_info']['image_height']\\n\\n\\n # get transform between pose of camera and world frame\\n trans = None\\n try:\\n now = rospy.Time.now()\\n self.listener.waitForTransform(\\\"/base_link\\\",\\n \\\"/world\\\", now, rospy.Duration(1.0))\\n (trans, rot) = self.listener.lookupTransform(\\\"/base_link\\\",\\n \\\"/world\\\", now)\\n\\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\\n rospy.logerr(\\\"Failed to find camera to map transform\\\")\\n\\n\\n camera_point=PointStamped()\\n camera_point.header.frame_id = \\\"/world\\\"\\n camera_point.header.stamp =rospy.Time(0)\\n camera_point.point.x = point_in_world.x\\n camera_point.point.y = point_in_world.y\\n camera_point.point.z = point_in_world.z\\n p = self.listener.transformPoint(\\\"/base_link\\\",camera_point)\\n\\n #correct shift of traffic light to left / right because of car heading\\n roll, pitch, yaw = self.get_orientations_from_quaternion(self.pose.pose.orientation)\\n\\n y_offset = p.point.x*math.sin(yaw)\\n x = -(p.point.y + y_offset)/ p.point.x * fx + image_width*0.5\\n\\n y = 62 + image_height - (p.point.z / p.point.x * fy + image_height*0.5)\\n\\n return (int(x), int(y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17b791c6f42f909aafb83e76bc1f075\",\n \"score\": \"0.55526483\",\n \"text\": \"def stitch_image_pair(img_a, img_b, stitch_direc):\\n img_a_gray = cv2.cvtColor(img_a, cv2.COLOR_BGR2GRAY)\\n img_b_gray = cv2.cvtColor(img_b, cv2.COLOR_BGR2GRAY)\\n matches_a, matches_b = get_matches(img_a_gray, img_b_gray, num_keypoints=1000, threshold=0.8)\\n h_mat = compute_homography_ransac(matches_a, matches_b)\\n if stitch_direc == 0:\\n canvas = cv2.warpPerspective(img_b, h_mat, (img_a.shape[1], img_a.shape[0] + img_b.shape[0]))\\n canvas[0:img_a.shape[0], :, :] = img_a[:, :, :]\\n x_start, y_start, x_end, y_end = get_crop_points(h_mat, img_a, img_b, 0)\\n else:\\n canvas = cv2.warpPerspective(img_b, h_mat, (img_a.shape[1] + img_b.shape[1], img_a.shape[0]))\\n canvas[:, 0:img_a.shape[1], :] = img_a[:, :, :]\\n x_start, y_start, x_end, y_end = get_crop_points(h_mat, img_a, img_b, 1)\\n \\n stitched_img = canvas[y_start:y_end,x_start:x_end,:]\\n return stitched_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"076089da63155af35fcec49a688b0d53\",\n \"score\": \"0.5547256\",\n \"text\": \"def _rotate_image(image, angle):\\n\\n # Get the image size\\n # No that's not an error - NumPy stores image matricies backwards\\n image_size = (image.shape[1], image.shape[0])\\n image_center = tuple(np.array(image_size) / 2)\\n\\n # Convert the OpenCV 3x2 rotation matrix to 3x3\\n rot_mat = np.vstack(\\n [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]\\n )\\n\\n rot_mat_notranslate = np.array(rot_mat[0:2, 0:2])\\n\\n # Shorthand for below calcs\\n image_w2 = image_size[0] * 0.5\\n image_h2 = image_size[1] * 0.5\\n\\n # Obtain the rotated coordinates of the image corners\\n rotated_coords = [\\n (np.array([-image_w2, image_h2]) * rot_mat_notranslate)[0],\\n (np.array([ image_w2, image_h2]) * rot_mat_notranslate)[0],\\n (np.array([-image_w2, -image_h2]) * rot_mat_notranslate)[0],\\n (np.array([ image_w2, -image_h2]) * rot_mat_notranslate)[0]\\n ]\\n\\n # Find the size of the new image\\n x_coords = [pt[0] for pt in rotated_coords]\\n x_pos = [x for x in x_coords if x > 0]\\n x_neg = [x for x in x_coords if x < 0]\\n\\n y_coords = [pt[1] for pt in rotated_coords]\\n y_pos = [y for y in y_coords if y > 0]\\n y_neg = [y for y in y_coords if y < 0]\\n\\n right_bound = max(x_pos)\\n left_bound = min(x_neg)\\n top_bound = max(y_pos)\\n bot_bound = min(y_neg)\\n\\n new_w = int(abs(right_bound - left_bound))\\n new_h = int(abs(top_bound - bot_bound))\\n\\n # We require a translation matrix to keep the image centred\\n trans_mat = np.array([\\n [1, 0, int(new_w * 0.5 - image_w2)],\\n [0, 1, int(new_h * 0.5 - image_h2)],\\n [0, 0, 1]\\n ])\\n\\n # Compute the tranform for the combined rotation and translation\\n affine_mat = (np.array(trans_mat) * np.array(rot_mat))[0:2, :]\\n\\n # Apply the transform\\n result = cv2.warpAffine(\\n image,\\n affine_mat,\\n (new_w, new_h),\\n flags=cv2.INTER_LINEAR\\n )\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"693c4e705c49adf681e35c6f91df9a40\",\n \"score\": \"0.55401814\",\n \"text\": \"def solveCamera(cameraMatrix, distortion, imagePoints, objectPoints):\\n _, rotVector, transVector = cv2.solvePnP(objectPoints, imagePoints, cameraMatrix, distortion)\\n\\n rotMatrix = cv2.Rodrigues(rotVector)[0]\\n position = -1 * np.matrix(rotMatrix).T * np.matrix(transVector)\\n rotation = euler.mat2euler(np.matrix(rotMatrix).T)\\n\\n return position, rotation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff63c7a28b55954c030a85c191c99cb\",\n \"score\": \"0.55352044\",\n \"text\": \"def undistortImage(image, o_points, i_points):\\n # Convert to gray scale\\n #gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\\n gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\\n\\n # Calculate Camera matrix and distance coefficients \\n ret, cam_matrix, distance_coeff, rot_vec, trans_vec = cv2.calibrateCamera(o_points, i_points, gray.shape, None, None)\\n if ret:\\n # return undistorted image\\n return cv2.undistort(image, cam_matrix, distance_coeff)\\n else:\\n # raise error if camera calibration fails\\n raise ValueError(\\\"Can not undistort the given image!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5faa506b4962aba621baa2abfaa73f2e\",\n \"score\": \"0.55290407\",\n \"text\": \"def project(self, win_width, win_height, fov, viewer_distance):\\r\\n factor = fov / (viewer_distance + self.z)\\r\\n x = self.x * factor + win_width / 2\\r\\n y = -self.y * factor + win_height / 2\\r\\n return Point3D(x, y, self.z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1599d58482ff0df13fd2b654de06e34\",\n \"score\": \"0.5526499\",\n \"text\": \"def simpleDistortion(xx, yy, xs, ys, removeScale):\\n\\n # subtract average to align\\n\\n nn = len(xs)\\n pts1 = np.zeros((1, nn, 2))\\n pts2 = np.zeros((1, nn, 2))\\n xav1 = xs.mean()\\n yav1 = ys.mean()\\n xav2 = xx.mean()\\n yav2 = yy.mean()\\n\\n for i in range(len(xs)):\\n # pts1[0,i,0]=xs[i]-xav1\\n # pts1[0,i,1]=ys[i]-yav1\\n # pts2[0,i,0]=xx[i]-xav2\\n # pts2[0,i,1]=yy[i]-yav2\\n pts1[0, i, 0] = xs[i]\\n pts1[0, i, 1] = ys[i]\\n pts2[0, i, 0] = xx[i]\\n pts2[0, i, 1] = yy[i]\\n\\n # cv2 needs float32\\n\\n pts1 = np.float32(pts1)\\n pts2 = np.float32(pts2)\\n\\n # get the affine transformation - translation, rotation, scaling\\n # uses opencv library\\n transformation = cv2.estimateRigidTransform(pts1, pts2, False)\\n\\n visplot.checkMatched(pts1[0, :, 0], pts1[0, :, 1], pts2[0, :, 0], pts2[0, :, 1], \\\"test\\\", 1)\\n\\n transformFull = transformation.copy()\\n\\n # calculate the scaling from the result\\n sx = np.sqrt(transformation[0, 0]**2+transformation[0, 1]**2)\\n sy = np.sqrt(transformation[1, 0]**2+transformation[1, 1]**2)\\n\\n print(\\\"sx=\\\", sx, \\\" sy=\\\", sy)\\n\\n if(removeScale == 1):\\n # remove the scaling from the transformation matrix\\n transformation[0, 0] /= sx\\n transformation[0, 1] /= sx\\n transformation[1, 0] /= sy\\n transformation[1, 1] /= sy\\n\\n # calculate the transformed points\\n points = cv2.transform(pts1, transformation)\\n visplot.checkMatched(points[0, :, 0], points[0, :, 1], pts2[0, :, 0], pts2[0, :, 1], \\\"test\\\", 1)\\n\\n # difference between expected and actual point positions\\n diffx = (pts2[0, :, 0]-points[0, :, 0])\\n diffy = (pts2[0, :, 1]-points[0, :, 1])\\n\\n # in mm and in % of field\\n\\n c = np.sqrt(diffx*diffx+diffy*diffy)\\n c1 = np.sqrt(diffx*diffx+diffy*diffy)/336.*100\\n\\n pts1[:, :, 0] = ma.masked_values(pts1[:, :, 0], 0)\\n pts1[:, :, 1] = ma.masked_values(pts1[:, :, 1], 0)\\n pts2[:, :, 0] = ma.masked_values(pts2[:, :, 0], 0)\\n pts2[:, :, 1] = ma.masked_values(pts2[:, :, 1], 0)\\n pts1 = ma.masked_invalid(pts1)\\n pts2 = ma.masked_invalid(pts2)\\n\\n return c, c1, pts1, pts2, diffx, diffy, transformFull\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439b982a3286ca60fd2a871e4637fc60\",\n \"score\": \"0.55210596\",\n \"text\": \"def project_points(wph_cam):\\n return (wph_cam/wph_cam[:,2])[:,0:3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d17d144dd6fae6d112fca99f363cd09\",\n \"score\": \"0.5520979\",\n \"text\": \"def _angles_to_projective_transforms(angle, image_w, image_h):\\n with tf.name_scope(\\\"rotate_parent\\\"):\\n angle_or_angles = tf.convert_to_tensor(angle,\\n name=\\\"angles\\\",\\n dtype=tf.dtypes.float32)\\n angles = angle_or_angles[None]\\n x_offset = ((image_w - 1) - (tf.math.cos(angles) *\\n (image_w - 1) - tf.math.sin(angles) *\\n (image_h - 1))) / 2.0\\n y_offset = ((image_h - 1) - (tf.math.sin(angles) *\\n (image_w - 1) + tf.math.cos(angles) *\\n (image_h - 1))) / 2.0\\n num_angles = tf.shape(angles)[0]\\n return tf.concat([\\n tf.math.cos(angles)[:, None], -tf.math.sin(angles)[:, None],\\n x_offset[:, None],\\n tf.math.sin(angles)[:, None],\\n tf.math.cos(angles)[:, None], y_offset[:, None],\\n tf.zeros((1, 2))\\n ],\\n axis=1)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992989,"cells":{"query_id":{"kind":"string","value":"3b4d553fc9f2f47851b8d99eea361a49"},"query":{"kind":"string","value":"get size info by downloading small part of it"},"positive_passages":{"kind":"list like","value":[{"docid":"db338b1bf733ca268854f043d9d43366","score":"0.66978264","text":"def quickQueryImgSize(src):\n try:\n with request.urlopen(src) as file:\n p = ImageFile.Parser()\n while True:\n data = file.read(1024)\n if not data:\n break\n p.feed(data)\n if p.image:\n return list(p.image.size)\n except BaseException as e:\n print_color('fetch error: ' + src, Color.RED.value)\n print_color(e, Color.RED.value)\n return None","title":""}],"string":"[\n {\n \"docid\": \"db338b1bf733ca268854f043d9d43366\",\n \"score\": \"0.66978264\",\n \"text\": \"def quickQueryImgSize(src):\\n try:\\n with request.urlopen(src) as file:\\n p = ImageFile.Parser()\\n while True:\\n data = file.read(1024)\\n if not data:\\n break\\n p.feed(data)\\n if p.image:\\n return list(p.image.size)\\n except BaseException as e:\\n print_color('fetch error: ' + src, Color.RED.value)\\n print_color(e, Color.RED.value)\\n return None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9721d38a16ecec0c460f437a152a81b4","score":"0.7361434","text":"def get_info(self):\n try:\n resp = urllib.request.urlopen(self.url)\n self.size = int(resp.headers['content-length'])\n except urllib.error.HTTPError:\n self.size = -1;","title":""},{"docid":"e2cd69fb561343dcccce19cfb6498f46","score":"0.7127644","text":"def get_filesize(url, human_unit=True):\n unit = \"bytes\"\n if url[:4] == \"http\":\n f = requests.head(url).headers\n try:\n s = int(f[\"Content-length\"])\n except:\n return None\n else:\n s = os.path.getsize(url)\n if human_unit is True:\n s, unit = format_bytes(s)\n return s, unit","title":""},{"docid":"a5d37390b70c55064be333483977b3cd","score":"0.71073705","text":"async def filesize(url: str) -> int:\n return int((await head(url))[\"content-length\"])","title":""},{"docid":"4e09cee53cd82b42ed57cecc71a5fa41","score":"0.7092909","text":"def get_size():","title":""},{"docid":"13bd915d03ffeefb31bf8beea1ad8cad","score":"0.7045596","text":"def download_size(self, tile, product):\n url = self.url(tile, product)\n return size_from_url(self.session, url)","title":""},{"docid":"5089a0106cb5b78654e6947f548d8109","score":"0.702554","text":"def get_file_size(self, url):\n # Perform a HEAD request to get only the head of the request\n\n header_name = 'Content-Length'\n size = int(self.__get_header__(url, header_name))\n return size","title":""},{"docid":"9be4ae0d5c49aba8b29499156b31b928","score":"0.7002131","text":"def get_size(self):\n return os.path.getsize(self._url)","title":""},{"docid":"8ff67367101c26bab4a885a4b839f7d9","score":"0.6982189","text":"def test_extract_size_node(self):\n self.assertEqual(\"306.13kb\", Crawler._extract_size_node('39185'))","title":""},{"docid":"0975aa491f5985dbb69340caf1b3e75e","score":"0.69718766","text":"def get_remote_file_size(url):\n headers = requests.head(url).headers\n return int(headers['content-length'])","title":""},{"docid":"93860f3c51179ba08712780411a45432","score":"0.69359195","text":"def check_size(url):\n r = requests.get(url, stream=True)\n return int(r.headers['Content-Length'])","title":""},{"docid":"8bb85dc20d4c90c9800f8ab28a7966d0","score":"0.6841103","text":"def getsizes(uri, loc):\n logger = getLogger()\n # logger.info(uri)\n try:\n file = urllib.request.urlopen(uri)\n size = file.headers.get(\"content-length\")\n if size: \n size = int(size)\n p = ImageFile.Parser()\n while True:\n data = file.read(1024)\n if not data:\n break\n p.feed(data)\n if p.image:\n return size, p.image.size, uri\n file.close()\n except:\n pass\n return None","title":""},{"docid":"9926215d3f063b5ea31444ca89803ad2","score":"0.6773187","text":"def get_size(ytid, url, preloading=False):\r\n\r\n # try cached value\r\n stream = [x for x in g.streams[ytid]['meta'] if x['url'] == url][0]\r\n size = stream['size']\r\n prefix = \"preload: \" if preloading else \"\"\r\n\r\n if not size == -1:\r\n dbg(\"%s%susing cached size: %s%s\", c.g, prefix, size, c.w)\r\n\r\n else:\r\n writestatus(\"Getting content length\", mute=preloading)\r\n stream['size'] = get_content_length(url, preloading=preloading)\r\n dbg(\"%s%s - content-length: %s%s\", c.y, prefix, stream['size'], c.w)\r\n\r\n return stream['size']","title":""},{"docid":"70506f05247906d0d5c2e584fe6dfd01","score":"0.6773145","text":"def get_remote_file_size(self, url):\n headers = requests.head(url).headers\n return int(headers['content-length'])","title":""},{"docid":"70506f05247906d0d5c2e584fe6dfd01","score":"0.6773145","text":"def get_remote_file_size(self, url):\n headers = requests.head(url).headers\n return int(headers['content-length'])","title":""},{"docid":"6c623b1c5468b5270abeb75f4cb0f4f4","score":"0.67408395","text":"def get_size(self, path):\n pass","title":""},{"docid":"b6218bba3482d73caeace7f6627e6f80","score":"0.66974837","text":"def get_size(self):","title":""},{"docid":"bed591e44fd904e2690e26ee32ca3160","score":"0.6696596","text":"def DoGetBestSize(self):","title":""},{"docid":"c3a6bd1d7116a03ac8890fc43f5ef1b0","score":"0.66948307","text":"def getsize(self, _path):\n try:\n _file = self.items[str(_path.encode('utf-8')).replace('/', '').replace('..', '')]\n print(\"getsize - \" + _path + \" - \" + str(_file[2]))\n return _file[2]\n\n except Exception as e:\n self.do_on_status(str(e))\n return None","title":""},{"docid":"8f446e5fa8dbc4fadf25d52f8fef1612","score":"0.6694659","text":"def downloaded_size(self):\n return sum(url_info.size for url_info in self._recorded_url_infos.values())","title":""},{"docid":"28de6b442f3c2075d50346f724f4346d","score":"0.666462","text":"def get_remote_server_file_size(url, user='', password=''):\n url_in_list = url.split(\"://\", 1)\n url_in_list.insert(1, \"://\" + user + ':' + password + '@')\n new_url = ''.join(url_in_list)\n\n r = subprocess.call(\"curl -sI \" + new_url +\n \" | grep -i content-length | awk \\'{print $2}\\' | tr -d '\\r\\n'\", shell=True)\n\n return int(r)","title":""},{"docid":"b8ac84458345a1d9a394c15fafc10085","score":"0.66499954","text":"def get_filesize(resp):\n fs = resp.headers.get('content-length', None)\n return int(fs) if fs else fs","title":""},{"docid":"d488ae648a563331bd843ed591d4c029","score":"0.6605132","text":"def update(self):\n f = urllib.urlopen(self.url)\n remote_size = f.info()['Content-Length']\n try:\n local_size = open(self.size_filename()).read()\n except:\n local_size = None\n if local_size != remote_size or \\\n not os.path.isdir(self.pathname):\n self.manager.say_downloading()\n zip_name = os.path.join(self.tmp_path, self.name + \".zip\")\n urllib.urlretrieve(self.url, zip_name)\n self.manager.say_extracting()\n self._unzip(zip_name)\n open(self.size_filename(), \"w\").write(remote_size)","title":""},{"docid":"0c7697c06238ade24c7bc68ee519ee3b","score":"0.6579573","text":"def get_url_file_size(self):\n if self.type == 'http':\n if self.auth:\n self.__auth_http()\n urllib2_obj = urllib.request.urlopen(\n self.url, timeout=self.timeout)\n size = urllib2_obj.headers.get('content-length')\n return int(size)","title":""},{"docid":"7217308b4b889180ecff152f54c9b0b2","score":"0.65282875","text":"def get_download_file_size(download_url, **kwargs):\n # res_length = requests.get(download_url, stream=True)\n with closing(download_file_requester.get_url(url=download_url, stream=True)) as res:\n total_size = int(res.headers['Content-Length'])\n return total_size","title":""},{"docid":"14550bf57bba42adfc18a4805d95ed1a","score":"0.64998436","text":"def get_webpage_size(url):\n try :\n site = urlopen(url)\n meta_data = site.info()\n if 'Content-Length' in meta_data.keys():\n return meta_data['Content-Length']\n else:\n return len(site.read())\n except URLError as e:\n print(e.reason)\n except HTTPError as e:\n print(e.code)","title":""},{"docid":"179af4c31b97737c48ab84fe5e49c8cf","score":"0.64843947","text":"def fetch_url(self, url):\n url_data = {\n \"url\": url,\n \"content\": None,\n \"size\": 0\n }\n corp_file_name = self.corpus.get_file_name(url)\n content = b''\n for data in open(corp_file_name, mode = 'rb'):\n content += data\n if corp_file_name != None:\n url_data[\"content\"] = content \n url_data[\"size\"] = os.path.getsize(corp_file_name)\n print(url_data[\"size\"]) \n return url_data","title":""},{"docid":"8ddd3c94c0bbe5140730add5ab106cbb","score":"0.64777935","text":"def size(self):\n return self._response.get(\"size\")","title":""},{"docid":"d72f0fe622310885c441e0889135b496","score":"0.64754725","text":"def size(url):\r\n resp = requests.get(url)\r\n if resp:\r\n return len(resp.text)\r\n return 0","title":""},{"docid":"d8f280d2328a675ac4861817859eee4d","score":"0.64550316","text":"def DoGetBestClientSize(self):","title":""},{"docid":"e49810847d808d693cb24141ed1962b5","score":"0.64464366","text":"def parse_and_get_size(self, stream):\n raise NotImplementedError(\"Must implement parse_and_get_size!\")","title":""},{"docid":"b0a3b1fc7ce2ed5d427de839d2a26eb3","score":"0.64436203","text":"def get_local_file_size(self):\n size = os.stat(self.download_path).st_size\n return size","title":""},{"docid":"dee52a198129e64785983124ebb2362a","score":"0.64416856","text":"def fetch_info(url):\n # The first 512 bytes are sufficient for extracting the metadata:\n page = requests.get(url, headers = { \"Range\" : \"bytes=0-512\" })\n\n if page.status_code not in [ 200, 206 ]: # 206 = partial content\n raise requests.exceptions.HTTPError(\"http code \" + str(page.status_code) + \".\")\n\n with BytesIO(page.content) as bs:\n mp3 = mp4.File(bs)\n \n return types.Obj(\n duration = mp3.duration,\n bitrate = mp3.bitrate\n )","title":""},{"docid":"7b63c60f887c12f9791f887709599f88","score":"0.64073336","text":"def size_of_content(url):\n try:\n r = requests.head(url, timeout=2)\n except requests.exceptions.Timeout as texc:\n print('timed out on', url, texc)\n return 0\n except Exception as ex:\n raise ex\n\n value = 0\n if not r.ok:\n print('E url {} does not exist'.format(url))\n return value\n\n value = int(r.headers.get('content-length'))\n return value","title":""},{"docid":"0e6bfaa7a4dffa5c2ec4399dd549bdb5","score":"0.6400419","text":"def size(self):\n resp = self.procRequest(['size'])\n if(self.procResponse(resp)):\n return resp[1]\n else:\n return 'shouldNeverHitThis'","title":""},{"docid":"dca8302040f53418a052b284387c0c2e","score":"0.6388033","text":"def get_size(inf: IO) -> str:\n return subprocess.getoutput(\"size \" + inf + \" | awk \\'{print $1}\\' | tail -1\")","title":""},{"docid":"fcfe87f23e3002530792720f5912bbe1","score":"0.635936","text":"def size(self):\n if self._size == None:\n if self.opener == None:\n self.opener = urllib2.urlopen(self.remote_url)\n self._size = self.opener.headers['content-length']\n self._size = int(self._size)\n return self._size","title":""},{"docid":"f82ea537839601b6b8e3ac060cd0f92b","score":"0.6348115","text":"def get_size_and_version(src):\n src_url = urlparse(src)\n path = unquote(src_url.path)\n\n if not path or path.endswith('/'):\n raise QuiltException(\"Invalid path: %r; cannot be a directory\" % path)\n\n version = None\n if src_url.scheme == 'file':\n src_path = pathlib.Path(parse_file_url(src_url))\n if not src_path.is_file():\n raise QuiltException(\"Not a file: %r\" % str(src_path))\n size = src_path.stat().st_size\n elif src_url.scheme == 's3':\n bucket, key, version_id = parse_s3_url(src_url)\n params = dict(\n Bucket=bucket,\n Key=key\n )\n if version_id:\n params.update(dict(VersionId=version_id))\n s3_client = create_s3_client()\n resp = s3_client.head_object(**params)\n size = resp['ContentLength']\n if resp.get('VersionId', 'null') != 'null': # Yes, 'null'\n version = resp['VersionId']\n else:\n raise NotImplementedError\n return size, version","title":""},{"docid":"38414144e3e48ecc603504656d951356","score":"0.6341663","text":"def size(self, uri):\n filename = urllib.parse.urlparse(uri).path\n return self.size(filename)","title":""},{"docid":"b06248ac30253d06c78949cc1966f52d","score":"0.6298286","text":"def _size(self):\n\t\treturn self.ftp.size(self._last_version_file_name)","title":""},{"docid":"5ecfabfc939fdf143ce6d7da4e6ae7e9","score":"0.62860864","text":"def get_size(self) -> int:\n ...","title":""},{"docid":"32d38bfab4979a4b92f645df1472b4c9","score":"0.6281899","text":"def test_getblob_correct_length(self):\n # setup -----------------------\n partname = '/docProps/thumbnail.jpeg'\n fs = FileSystem(zip_pkg_path)\n # exercise --------------------\n blob = fs.getblob(partname)\n # verify ----------------------\n self.assertLength(blob, 8147)","title":""},{"docid":"0d8b20c6e270c05634bda0686d1a7c4d","score":"0.6267599","text":"def GetBestSize(self):","title":""},{"docid":"0d8b20c6e270c05634bda0686d1a7c4d","score":"0.62674415","text":"def GetBestSize(self):","title":""},{"docid":"723b041ceecf5e0854ae9fbcdc851718","score":"0.6257077","text":"def getSize() -> Tuple[int, int]:\n request = requests.get(\"https://pixels.pythondiscord.com/get_size\")\n logging.debug(request.json())\n request.raise_for_status()\n return (request.json()[\"width\"], request.json()[\"height\"])","title":""},{"docid":"c482ad353a6817fbb2ea988fb469da9a","score":"0.62492704","text":"def test_get_file_size(self):\n # @REVIEWED\n fullPath = '{}/{}'.format(self.exportTestDataPath,\n self.compressedTestFilename)\n fSize = self.fileUtil.fileSize(fullPath)\n self.logger.log('size: {}'.format(fSize))\n self.assertEqual(fSize, 12279, 'File size is correct.')","title":""},{"docid":"c6dbf97a6a801a9576e3855d10a9a963","score":"0.6247892","text":"def size(self, filename):\n # The SIZE command is defined in RFC-3659\n resp = self.sendcmd('SIZE ' + filename)\n if resp[:3] == '213':\n s = resp[3:].strip()\n return int(s)","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.6245051","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.6245051","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.6244672","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.6244672","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.62441766","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.62441766","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.62441766","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.62441766","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.62441766","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.62441766","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.62441766","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.62441766","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.6244174","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.6244174","text":"def GetSize(self):","title":""},{"docid":"0b810b12511bf5750dac1ccd7f9846b6","score":"0.6244174","text":"def GetSize(self):","title":""},{"docid":"c2a149791854e599a54e7f514c128244","score":"0.6240939","text":"def get_data(url, filename):\n open_url = urllib.request.urlopen(url)\n total = int(open_url.getheader(\"Content-Length\"))\n \n return download_data(url, filename, total)","title":""},{"docid":"a57706650b940ddd778a330d92bbbf67","score":"0.6231567","text":"def size(self):\n return int(self._info['numBytes']) if 'numBytes' in self._info else -1","title":""},{"docid":"7f5b2475efd1e3c7114964d67511cbf9","score":"0.62057686","text":"def getSize(self) -> int:\n ...","title":""},{"docid":"7f5b2475efd1e3c7114964d67511cbf9","score":"0.62057686","text":"def getSize(self) -> int:\n ...","title":""},{"docid":"7f5b2475efd1e3c7114964d67511cbf9","score":"0.62057686","text":"def getSize(self) -> int:\n ...","title":""},{"docid":"485b4501fc8924e22d82d2669b2f1bf1","score":"0.6190935","text":"def get_size(self, path):\n lpp = urlparse(path)\n return os.path.getsize(lpp.path)","title":""},{"docid":"a00d633348f2fb756c553aa217a97c49","score":"0.61838484","text":"def filesize(self):\n return util.buf_to_npy(self._ptr.contents.filesize, (200,))","title":""},{"docid":"8fc93837de02e5b6aa718cfa917ce9d9","score":"0.61831725","text":"def getImageSize():","title":""},{"docid":"66b3f9b0a21bdf1343ececaab6911e31","score":"0.6179003","text":"def getsize(self, path):\n info = self.getinfo(path)\n size = info.get('size', None)\n if 'size' is None:\n raise OperationFailedError(\"get size of resource\", path)\n return size","title":""},{"docid":"66c75330abf24a865f3afd018eff9a98","score":"0.61775625","text":"def size(self):\n if self.exists():\n return self.__response['ContentLength']\n else:\n return -1","title":""},{"docid":"117eb4b415da46ad60471932327b9223","score":"0.6158704","text":"def get_file_size(url):\n handler = _pywrap_s3_io.S3Init()\n return handler.get_file_size(url)","title":""},{"docid":"8cf2384ebf07ada91404042f7579ed13","score":"0.6148235","text":"def hit(url):\n\treq = urllib.request.Request(url=url,method = 'HEAD')\n\tf = urllib.request.urlopen(req)\n\treturn f.info()[\"Content-Length\"]","title":""},{"docid":"9a477eb33cb4a33b1fc818aa131340ce","score":"0.61446804","text":"def size_and_date(href):\n for attempt in range(10):\n s, host, path, p, q, f = urlparse(href)\n \n conn = HTTPConnection(host, 80)\n conn.request('HEAD', path)\n resp = conn.getresponse()\n \n if resp.status in range(300, 399):\n href = urljoin(href, resp.getheader('location'))\n continue\n \n if resp.status != 200:\n raise IOError('not found')\n \n content_length = resp.getheader('content-length')\n last_modified = resp.getheader('last-modified')\n \n return content_length, last_modified","title":""},{"docid":"178a57bffc71902351b21cc0b664ba42","score":"0.6144287","text":"def size(self):\n\t\treturn self.partSize","title":""},{"docid":"f723f9c838997b46434c2a0f47b49ab1","score":"0.6139227","text":"def __getTableContentsSize(self,table):\n\n req = \"SELECT SUM(FileSize) FROM %s\" % table\n result = self._query(req)\n if not result['OK']:\n return result\n if result['Value'][0][0]:\n size = int(result['Value'][0][0])\n else:\n size = 0\n return S_OK(size)","title":""},{"docid":"5b96b4c0bcad7f982c75d137da0ab0d5","score":"0.613474","text":"def get_artifact(self, url, file_handle, cb_progress=None, timed=False):\n ret = dict()\n self.log.debug(\"url:\"+str(url))\n\n # Content-Length in the server replay is used by this function\n # to calculate if the server sent all the expected content\n # Content-Length is compared with sum of all chunks coming from iter_content\n # Problem is when server sent the data encoded as gzip\n # In this case the Content-Length is the size af the compressed content.\n # But iter_content provide uncompressed data so the two size will not match.\n # The trick is to sent a head only request convincing the server that\n # we are not able to use gzip content. So Content-Length in server replay is\n # the uncompressed size.\n # This first request is not used to download the content, but just to get the size\n my_headers = {\"Accept-Encoding\": \"deflate\"}\n response = requests.head(url, headers=my_headers)\n self.log.debug(\"GET status_code:\"+str(response.status_code))\n self.log.debug(\"HEAD Headers:\"+str(response.headers))\n if response.status_code != 200:\n ret['result'] = False\n return ret\n\n file_size = int(response.headers[\"Content-Length\"])\n self.log.info(\"File size:\" + str(file_size))\n response.close()\n\n # Now create a second request, that will use default header that eventually support gzip\n t_start = time.time()\n response = requests.get(url, stream=True)\n if timed:\n ret['time_get'] = time.time() - t_start\n response.raise_for_status()\n self.log.debug(\"GET Headers:\"+str(response.headers))\n\n done = 0\n self.log.debug(\"len:0 \"+str(done) +\"/\"+str(file_size) + \" progress:\" + str(done/float(file_size)))\n t_start = time.time()\n for chunk in response.iter_content(chunk_size=8192*4):\n if chunk: # filter out keep-alive new chunks\n l = len(chunk)\n done += l \n self.log.debug(\"len:\" + str(l) + \" \"+str(done) +\"/\"+str(file_size) + \" progress:\" + str(done/float(file_size)))\n file_handle.write(chunk)\n file_handle.flush()\n if cb_progress:\n cb_progress(done/float(file_size))\n else:\n self.log.debug(\"Empty chunk\")\n if timed:\n ret['time_download'] = time.time() - t_start\n if done == file_size:\n ret['result'] = True\n else:\n ret['result'] = False\n return ret","title":""},{"docid":"4cc874d0a6bec671c9f4ee7411641821","score":"0.61320555","text":"def CacheBestSize(self, size):","title":""},{"docid":"0064094c5c837712132fe86d0c772b28","score":"0.61181164","text":"def getSize(self):\n\t\treturn self.size","title":""},{"docid":"dd0712be5845f25dfae7016b0e4b75da","score":"0.611784","text":"def downloadChunks(url,rep,nom_fic):\n try:\n req = urllib.request.urlopen(url)\n #if downloaded file is html\n if (req.info().get('Content-Type')=='text/html'):\n print(\"error : file is in html and not an expected binary file\")\n lines=req.read()\n if lines.find('Download Not Found')>0 :\n raise TypeError\n else:\n with open(\"error_output.html\",\"w\") as f:\n f.write(lines)\n print(\"result saved in ./error_output.html\")\n sys.exit(-1)\n #if file too small\n total_size = int(req.info().get('Content-Length').strip())\n if (total_size<50000):\n print(\"Error: The file is too small to be a Landsat Image\")\n print(url)\n sys.exit(-1)\n print(nom_fic,total_size)\n total_size_fmt = sizeof_fmt(total_size)\n\n #download\n downloaded = 0\n CHUNK = 1024 * 1024 *8\n print(rep+'/'+nom_fic)\n with open(rep+'/'+nom_fic, 'wb') as fp:\n start = time.clock()\n print('Downloading {0} ({1}):'.format(nom_fic, total_size_fmt))\n while True:\n chunk = req.read(CHUNK)\n downloaded += len(chunk)\n done = int(50 * downloaded / total_size)\n sys.stdout.write('\\r[{1}{2}]{0:3.0f}% {3}ps'\n .format(math.floor((float(downloaded)\n / total_size) * 100),\n '=' * done,\n ' ' * (50 - done),\n sizeof_fmt((downloaded // (time.clock() - start)) / 8)))\n sys.stdout.flush()\n if not chunk: break\n fp.write(chunk)\n except urllib.error.HTTPError as e:\n if e.code == 500:\n pass # File doesn't exist\n else:\n print( \"HTTP Error:\", e.code , url)\n return False\n except urllib.error.URLError as e:\n print( \"URL Error:\",e.reason , url)\n return False\n\n return rep,nom_fic","title":""},{"docid":"47e08062bf7694fa0d8fe497bfcfad4c","score":"0.61128","text":"def size_info(self):\n pass","title":""},{"docid":"cb304631e9f99995eb40fd26957780ea","score":"0.60970116","text":"def _get_object_size(ctx, lfs_spec):\n depot_path = lfs_spec.depot_path(ctx)\n cmd = ['fstat', '-m1', '-Ol', '-T', 'fileSize', depot_path]\n return int(p4gf_util.first_value_for_key(ctx.p4run(cmd), 'fileSize'))","title":""},{"docid":"e0f1e994e133f5dbc6b209a1f2d9191f","score":"0.6096715","text":"def getSize(self):\r\n\t\treturn self.size","title":""},{"docid":"cc15dd0d7ac17f39c961c1c9a91f5158","score":"0.6088864","text":"def GetFileSize(self, path):\n raise NotImplementedError","title":""},{"docid":"fc70012497c31e9513712c6540dc07d7","score":"0.60878223","text":"def get_size(size):\n if not size:\n return None\n elif 'g' in size.lower():\n # bring back to bytes\n size = int(size.lower().split('g')[0]) * 1024 * 1024 * 1024\n elif 'm' in size.lower():\n size = int(size.lower().split('m')[0]) * 1024 * 1024\n elif 'k' in size.lower():\n size = int(size.lower().split('k')[0]) * 1024\n else:\n size = int(size)\n return size","title":""},{"docid":"1077d38eb17a4b3ed12016e884374d93","score":"0.6086476","text":"def get_size(filename):\n fileinfo = os.stat(filename)\n return fileinfo","title":""},{"docid":"1077d38eb17a4b3ed12016e884374d93","score":"0.6086476","text":"def get_size(filename):\n fileinfo = os.stat(filename)\n return fileinfo","title":""},{"docid":"1077d38eb17a4b3ed12016e884374d93","score":"0.6086476","text":"def get_size(filename):\n fileinfo = os.stat(filename)\n return fileinfo","title":""},{"docid":"f9778a1e0a6fd607691db197cc29ffb8","score":"0.6084967","text":"def download(self):\n\n if not self.filesize:\n # get filesize from a HEAD request to the URL\n if not self.parse_url:\n response = requests.head(self.url, headers=self.x_headers, auth=self.auth)\n if self.debug:\n print\n print \"HTTP Headers:\"\n print \"=============\"\n for key in response.headers:\n print key + \" = \" + response.headers[key]\n print \"-------------\"\n #print response.request.headers\n\n # should I check the response status code?\n try:\n content_length = int(response.headers['content-length'])\n except KeyError as e:\n self.x_headers.update({'Range': 'bytes=0-0'})\n resp = requests.get(self.url, headers=self.x_headers, auth=self.auth)\n cr = resp.headers['content-range'].split('/')[-1]\n content_length = int(cr)\n del self.x_headers['Range'] \n else: \n # get filesize from the query string of the user-provided url\n # works only if 'fsize' is present (e.g. yandex disk) \n parsed_qs = parse_qs(urlparse(self.url).query)\n fsize = int(parsed_qs['fsize'][0])\n content_length = int(fsize)\n else:\n content_length = int(filesize)\n if self.debug:\n print \"Content length: %s bytes\" % content_length\n\n bytes_per_thread = content_length / self.num_threads\n if self.debug:\n print \"Number of bytes per thread: %s\" % bytes_per_thread\n\n # create the download threads\n try:\n with Timer() as timer:\n threads = []\n for i in range(self.num_threads):\n thread_file = self.out_file + \"_part_\" + str(i)\n if self.debug:\n print \"Thread %d output file: %s\" % (i, thread_file)\n\n begin_range = i * bytes_per_thread\n end_range = (i + 1) * bytes_per_thread - 1\n remainder = (content_length - 1) - end_range\n if self.debug:\n print \"Thread %d begin range at %d\" % (i, begin_range)\n print \"Thread %d end range at %d\" % (i, end_range)\n print \"Thread %d remainder: %d bytes\" % (i, remainder)\n if remainder < bytes_per_thread:\n end_range += remainder\n if self.debug:\n print \"Thread %d end range updated at %d\" % (i, end_range)\n\n t = DownloaderThread(self.url, begin_range, end_range, thread_file, self.x_headers, self.verify, self.auth)\n threads.append(t)\n t.start()\n\n # wait for the threads\n for t in threads:\n t.join()\n with open(self.out_file, 'ab') as final_out_file:\n with open(t.out_file, 'rb') as thread_out_file:\n # read data from thread_out_file and write to final_out_file\n bytes = 'empty'\n while (bytes):\n bytes = thread_out_file.read(512*1024)\n #bytes = thread_out_file.read() \n final_out_file.write(bytes)\n \n os.remove(t.out_file) # delete the thread's output file\n\n finally:\n print \"%s %d %d %f\" % (self.url, self.num_threads, content_length, timer.interval)\n print \"avg download speed: %s/s\" % naturalsize(content_length/timer.interval, binary=True, format='%.2f')","title":""},{"docid":"f1c85854d8f04ae9c0404d944183ffde","score":"0.6076422","text":"def _size(self):\n return self._system.getsize(header=self._head())","title":""},{"docid":"00c1c9c6d456b29cd94bbd7426479e2f","score":"0.6069701","text":"def file_size():\n list_of_files = os.listdir(OUT_DIR)\n response = 0\n for file in list_of_files:\n file_info = os.stat(os.path.join(OUT_DIR, file))\n response += file_info.st_size\n print_line(70, \"*\")\n print(\"Used SPACE --> \" + convert_bytes(response))\n print_line(70, \"*\")","title":""},{"docid":"3e62af18854abbfd7af025ec1da3aab0","score":"0.6068732","text":"def get_size(self, path):\n proc = self.tools.pmemdetect('-z', path)\n if int(proc.stdout) != 2**64 - 1:\n return int(proc.stdout)\n futils.fail('Could not get size of the file, '\n 'it is inaccessible or does not exist')","title":""},{"docid":"f8dd941babab982d3c53350832d9bee0","score":"0.6065854","text":"def get_size(self) -> Tuple[int, int]:\n # self.sync_ratelimit(\"get_size\")\n status, data = self._request(\"/get_size\")\n return data[\"width\"], data[\"height\"]","title":""},{"docid":"b3c20a0f4445856e7f3a07584944adc4","score":"0.60547876","text":"def size(self):\n return self.data[\"size\"]","title":""},{"docid":"f4acbcfc09622296893ad8814846aa35","score":"0.60515106","text":"def size(self, path):\n # type: (str) -> int\n raise NotImplementedError","title":""},{"docid":"1aec107eefe3b41b815f9febd444e254","score":"0.60494757","text":"def get_size(self) -> LiveImage.Size:","title":""},{"docid":"c4d04a489e137027ff9195ae5ca32d1d","score":"0.6046222","text":"def size(self, args):\n if self.check_connection() and self.check_logged_in():\n if not args or type(args) is list:\n self.usage('size file.txt')\n return False\n self.connection.send_request('SIZE ' + args)\n # Put response in to a more readable format\n response = self.connection.get_response(True)\n print 'Size:' + response['message'].strip('\\r\\n') + ' bytes'","title":""},{"docid":"f333b987448c245f350ea74ceb3e8d46","score":"0.6033329","text":"def EstimateTotalSize(self):","title":""},{"docid":"9c42dca196940fdee134ab08c0dc76e7","score":"0.6032902","text":"def get_file(prefix: str, filepath: str, url: str, *, chunk_size: int = 1000, show_bar: bool = True,\n secs_between_range: float = 0.25, bar_size: int = 50, dl_char: str = '#', empty_char: str = '-') -> None:\n def __format(n: float) -> str:\n return '{:.2f}'.format(n)\n\n def __in_mbs(n: int) -> str:\n return __format((n / 1000) / 1000)\n\n # Start get\n r = requests.get(url, stream=True)\n total_length = int(r.headers.get('content-length'))\n\n with open(filepath, 'wb') as f: # Adapted from script found on stack overflow\n amt_in_range = 0\n last_time_stamp = time.time()\n\n vel = 0\n estimated_minutes = 0\n already_downloaded = 0\n\n for data in r.iter_content(chunk_size=chunk_size):\n f.write(data)\n\n if show_bar:\n already_downloaded += len(data)\n\n prcnt_done = int(bar_size * already_downloaded / total_length)\n msg = f'\\r{prefix} : {filepath} - [{dl_char * prcnt_done}{empty_char * (bar_size - 1 - prcnt_done)}] ' \\\n f'[ {__in_mbs(already_downloaded)} mbs / {__in_mbs(total_length)} mbs] ' \\\n f'[ {__format(vel)} kbs | ETA: {__format(estimated_minutes)} minutes ]'\n sys.stdout.write(msg)\n sys.stdout.flush()\n\n # Download information\n if (time.time() - last_time_stamp) < secs_between_range:\n amt_in_range += 1\n else:\n vel = (amt_in_range * (1 / secs_between_range))\n vel = 0.001 if int(vel) == 0 else vel # Do this to avoid ZeroDivisionError when connection drops\n last_time_stamp = time.time()\n amt_in_range = 0\n estimated_minutes = ((total_length/chunk_size)/vel) / 60","title":""},{"docid":"46019953fa3363ad53a30354ad4afe14","score":"0.6032654","text":"def test_s3_file_get_size():\n # When I have an S3File\n f = S3File(\"foo\", 42, datetime.datetime.now())\n\n # And I get the size\n size = f.get_size()\n\n # Then it returns the size\n size.should.equal(42)","title":""}],"string":"[\n {\n \"docid\": \"9721d38a16ecec0c460f437a152a81b4\",\n \"score\": \"0.7361434\",\n \"text\": \"def get_info(self):\\n try:\\n resp = urllib.request.urlopen(self.url)\\n self.size = int(resp.headers['content-length'])\\n except urllib.error.HTTPError:\\n self.size = -1;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2cd69fb561343dcccce19cfb6498f46\",\n \"score\": \"0.7127644\",\n \"text\": \"def get_filesize(url, human_unit=True):\\n unit = \\\"bytes\\\"\\n if url[:4] == \\\"http\\\":\\n f = requests.head(url).headers\\n try:\\n s = int(f[\\\"Content-length\\\"])\\n except:\\n return None\\n else:\\n s = os.path.getsize(url)\\n if human_unit is True:\\n s, unit = format_bytes(s)\\n return s, unit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d37390b70c55064be333483977b3cd\",\n \"score\": \"0.71073705\",\n \"text\": \"async def filesize(url: str) -> int:\\n return int((await head(url))[\\\"content-length\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e09cee53cd82b42ed57cecc71a5fa41\",\n \"score\": \"0.7092909\",\n \"text\": \"def get_size():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13bd915d03ffeefb31bf8beea1ad8cad\",\n \"score\": \"0.7045596\",\n \"text\": \"def download_size(self, tile, product):\\n url = self.url(tile, product)\\n return size_from_url(self.session, url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5089a0106cb5b78654e6947f548d8109\",\n \"score\": \"0.702554\",\n \"text\": \"def get_file_size(self, url):\\n # Perform a HEAD request to get only the head of the request\\n\\n header_name = 'Content-Length'\\n size = int(self.__get_header__(url, header_name))\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9be4ae0d5c49aba8b29499156b31b928\",\n \"score\": \"0.7002131\",\n \"text\": \"def get_size(self):\\n return os.path.getsize(self._url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ff67367101c26bab4a885a4b839f7d9\",\n \"score\": \"0.6982189\",\n \"text\": \"def test_extract_size_node(self):\\n self.assertEqual(\\\"306.13kb\\\", Crawler._extract_size_node('39185'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0975aa491f5985dbb69340caf1b3e75e\",\n \"score\": \"0.69718766\",\n \"text\": \"def get_remote_file_size(url):\\n headers = requests.head(url).headers\\n return int(headers['content-length'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93860f3c51179ba08712780411a45432\",\n \"score\": \"0.69359195\",\n \"text\": \"def check_size(url):\\n r = requests.get(url, stream=True)\\n return int(r.headers['Content-Length'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb85dc20d4c90c9800f8ab28a7966d0\",\n \"score\": \"0.6841103\",\n \"text\": \"def getsizes(uri, loc):\\n logger = getLogger()\\n # logger.info(uri)\\n try:\\n file = urllib.request.urlopen(uri)\\n size = file.headers.get(\\\"content-length\\\")\\n if size: \\n size = int(size)\\n p = ImageFile.Parser()\\n while True:\\n data = file.read(1024)\\n if not data:\\n break\\n p.feed(data)\\n if p.image:\\n return size, p.image.size, uri\\n file.close()\\n except:\\n pass\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9926215d3f063b5ea31444ca89803ad2\",\n \"score\": \"0.6773187\",\n \"text\": \"def get_size(ytid, url, preloading=False):\\r\\n\\r\\n # try cached value\\r\\n stream = [x for x in g.streams[ytid]['meta'] if x['url'] == url][0]\\r\\n size = stream['size']\\r\\n prefix = \\\"preload: \\\" if preloading else \\\"\\\"\\r\\n\\r\\n if not size == -1:\\r\\n dbg(\\\"%s%susing cached size: %s%s\\\", c.g, prefix, size, c.w)\\r\\n\\r\\n else:\\r\\n writestatus(\\\"Getting content length\\\", mute=preloading)\\r\\n stream['size'] = get_content_length(url, preloading=preloading)\\r\\n dbg(\\\"%s%s - content-length: %s%s\\\", c.y, prefix, stream['size'], c.w)\\r\\n\\r\\n return stream['size']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70506f05247906d0d5c2e584fe6dfd01\",\n \"score\": \"0.6773145\",\n \"text\": \"def get_remote_file_size(self, url):\\n headers = requests.head(url).headers\\n return int(headers['content-length'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70506f05247906d0d5c2e584fe6dfd01\",\n \"score\": \"0.6773145\",\n \"text\": \"def get_remote_file_size(self, url):\\n headers = requests.head(url).headers\\n return int(headers['content-length'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c623b1c5468b5270abeb75f4cb0f4f4\",\n \"score\": \"0.67408395\",\n \"text\": \"def get_size(self, path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6218bba3482d73caeace7f6627e6f80\",\n \"score\": \"0.66974837\",\n \"text\": \"def get_size(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed591e44fd904e2690e26ee32ca3160\",\n \"score\": \"0.6696596\",\n \"text\": \"def DoGetBestSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a6bd1d7116a03ac8890fc43f5ef1b0\",\n \"score\": \"0.66948307\",\n \"text\": \"def getsize(self, _path):\\n try:\\n _file = self.items[str(_path.encode('utf-8')).replace('/', '').replace('..', '')]\\n print(\\\"getsize - \\\" + _path + \\\" - \\\" + str(_file[2]))\\n return _file[2]\\n\\n except Exception as e:\\n self.do_on_status(str(e))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f446e5fa8dbc4fadf25d52f8fef1612\",\n \"score\": \"0.6694659\",\n \"text\": \"def downloaded_size(self):\\n return sum(url_info.size for url_info in self._recorded_url_infos.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28de6b442f3c2075d50346f724f4346d\",\n \"score\": \"0.666462\",\n \"text\": \"def get_remote_server_file_size(url, user='', password=''):\\n url_in_list = url.split(\\\"://\\\", 1)\\n url_in_list.insert(1, \\\"://\\\" + user + ':' + password + '@')\\n new_url = ''.join(url_in_list)\\n\\n r = subprocess.call(\\\"curl -sI \\\" + new_url +\\n \\\" | grep -i content-length | awk \\\\'{print $2}\\\\' | tr -d '\\\\r\\\\n'\\\", shell=True)\\n\\n return int(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ac84458345a1d9a394c15fafc10085\",\n \"score\": \"0.66499954\",\n \"text\": \"def get_filesize(resp):\\n fs = resp.headers.get('content-length', None)\\n return int(fs) if fs else fs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d488ae648a563331bd843ed591d4c029\",\n \"score\": \"0.6605132\",\n \"text\": \"def update(self):\\n f = urllib.urlopen(self.url)\\n remote_size = f.info()['Content-Length']\\n try:\\n local_size = open(self.size_filename()).read()\\n except:\\n local_size = None\\n if local_size != remote_size or \\\\\\n not os.path.isdir(self.pathname):\\n self.manager.say_downloading()\\n zip_name = os.path.join(self.tmp_path, self.name + \\\".zip\\\")\\n urllib.urlretrieve(self.url, zip_name)\\n self.manager.say_extracting()\\n self._unzip(zip_name)\\n open(self.size_filename(), \\\"w\\\").write(remote_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c7697c06238ade24c7bc68ee519ee3b\",\n \"score\": \"0.6579573\",\n \"text\": \"def get_url_file_size(self):\\n if self.type == 'http':\\n if self.auth:\\n self.__auth_http()\\n urllib2_obj = urllib.request.urlopen(\\n self.url, timeout=self.timeout)\\n size = urllib2_obj.headers.get('content-length')\\n return int(size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7217308b4b889180ecff152f54c9b0b2\",\n \"score\": \"0.65282875\",\n \"text\": \"def get_download_file_size(download_url, **kwargs):\\n # res_length = requests.get(download_url, stream=True)\\n with closing(download_file_requester.get_url(url=download_url, stream=True)) as res:\\n total_size = int(res.headers['Content-Length'])\\n return total_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14550bf57bba42adfc18a4805d95ed1a\",\n \"score\": \"0.64998436\",\n \"text\": \"def get_webpage_size(url):\\n try :\\n site = urlopen(url)\\n meta_data = site.info()\\n if 'Content-Length' in meta_data.keys():\\n return meta_data['Content-Length']\\n else:\\n return len(site.read())\\n except URLError as e:\\n print(e.reason)\\n except HTTPError as e:\\n print(e.code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179af4c31b97737c48ab84fe5e49c8cf\",\n \"score\": \"0.64843947\",\n \"text\": \"def fetch_url(self, url):\\n url_data = {\\n \\\"url\\\": url,\\n \\\"content\\\": None,\\n \\\"size\\\": 0\\n }\\n corp_file_name = self.corpus.get_file_name(url)\\n content = b''\\n for data in open(corp_file_name, mode = 'rb'):\\n content += data\\n if corp_file_name != None:\\n url_data[\\\"content\\\"] = content \\n url_data[\\\"size\\\"] = os.path.getsize(corp_file_name)\\n print(url_data[\\\"size\\\"]) \\n return url_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddd3c94c0bbe5140730add5ab106cbb\",\n \"score\": \"0.64777935\",\n \"text\": \"def size(self):\\n return self._response.get(\\\"size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72f0fe622310885c441e0889135b496\",\n \"score\": \"0.64754725\",\n \"text\": \"def size(url):\\r\\n resp = requests.get(url)\\r\\n if resp:\\r\\n return len(resp.text)\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8f280d2328a675ac4861817859eee4d\",\n \"score\": \"0.64550316\",\n \"text\": \"def DoGetBestClientSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e49810847d808d693cb24141ed1962b5\",\n \"score\": \"0.64464366\",\n \"text\": \"def parse_and_get_size(self, stream):\\n raise NotImplementedError(\\\"Must implement parse_and_get_size!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a3b1fc7ce2ed5d427de839d2a26eb3\",\n \"score\": \"0.64436203\",\n \"text\": \"def get_local_file_size(self):\\n size = os.stat(self.download_path).st_size\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dee52a198129e64785983124ebb2362a\",\n \"score\": \"0.64416856\",\n \"text\": \"def fetch_info(url):\\n # The first 512 bytes are sufficient for extracting the metadata:\\n page = requests.get(url, headers = { \\\"Range\\\" : \\\"bytes=0-512\\\" })\\n\\n if page.status_code not in [ 200, 206 ]: # 206 = partial content\\n raise requests.exceptions.HTTPError(\\\"http code \\\" + str(page.status_code) + \\\".\\\")\\n\\n with BytesIO(page.content) as bs:\\n mp3 = mp4.File(bs)\\n \\n return types.Obj(\\n duration = mp3.duration,\\n bitrate = mp3.bitrate\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b63c60f887c12f9791f887709599f88\",\n \"score\": \"0.64073336\",\n \"text\": \"def size_of_content(url):\\n try:\\n r = requests.head(url, timeout=2)\\n except requests.exceptions.Timeout as texc:\\n print('timed out on', url, texc)\\n return 0\\n except Exception as ex:\\n raise ex\\n\\n value = 0\\n if not r.ok:\\n print('E url {} does not exist'.format(url))\\n return value\\n\\n value = int(r.headers.get('content-length'))\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6bfaa7a4dffa5c2ec4399dd549bdb5\",\n \"score\": \"0.6400419\",\n \"text\": \"def size(self):\\n resp = self.procRequest(['size'])\\n if(self.procResponse(resp)):\\n return resp[1]\\n else:\\n return 'shouldNeverHitThis'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca8302040f53418a052b284387c0c2e\",\n \"score\": \"0.6388033\",\n \"text\": \"def get_size(inf: IO) -> str:\\n return subprocess.getoutput(\\\"size \\\" + inf + \\\" | awk \\\\'{print $1}\\\\' | tail -1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcfe87f23e3002530792720f5912bbe1\",\n \"score\": \"0.635936\",\n \"text\": \"def size(self):\\n if self._size == None:\\n if self.opener == None:\\n self.opener = urllib2.urlopen(self.remote_url)\\n self._size = self.opener.headers['content-length']\\n self._size = int(self._size)\\n return self._size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f82ea537839601b6b8e3ac060cd0f92b\",\n \"score\": \"0.6348115\",\n \"text\": \"def get_size_and_version(src):\\n src_url = urlparse(src)\\n path = unquote(src_url.path)\\n\\n if not path or path.endswith('/'):\\n raise QuiltException(\\\"Invalid path: %r; cannot be a directory\\\" % path)\\n\\n version = None\\n if src_url.scheme == 'file':\\n src_path = pathlib.Path(parse_file_url(src_url))\\n if not src_path.is_file():\\n raise QuiltException(\\\"Not a file: %r\\\" % str(src_path))\\n size = src_path.stat().st_size\\n elif src_url.scheme == 's3':\\n bucket, key, version_id = parse_s3_url(src_url)\\n params = dict(\\n Bucket=bucket,\\n Key=key\\n )\\n if version_id:\\n params.update(dict(VersionId=version_id))\\n s3_client = create_s3_client()\\n resp = s3_client.head_object(**params)\\n size = resp['ContentLength']\\n if resp.get('VersionId', 'null') != 'null': # Yes, 'null'\\n version = resp['VersionId']\\n else:\\n raise NotImplementedError\\n return size, version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38414144e3e48ecc603504656d951356\",\n \"score\": \"0.6341663\",\n \"text\": \"def size(self, uri):\\n filename = urllib.parse.urlparse(uri).path\\n return self.size(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06248ac30253d06c78949cc1966f52d\",\n \"score\": \"0.6298286\",\n \"text\": \"def _size(self):\\n\\t\\treturn self.ftp.size(self._last_version_file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ecfabfc939fdf143ce6d7da4e6ae7e9\",\n \"score\": \"0.62860864\",\n \"text\": \"def get_size(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32d38bfab4979a4b92f645df1472b4c9\",\n \"score\": \"0.6281899\",\n \"text\": \"def test_getblob_correct_length(self):\\n # setup -----------------------\\n partname = '/docProps/thumbnail.jpeg'\\n fs = FileSystem(zip_pkg_path)\\n # exercise --------------------\\n blob = fs.getblob(partname)\\n # verify ----------------------\\n self.assertLength(blob, 8147)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d8b20c6e270c05634bda0686d1a7c4d\",\n \"score\": \"0.6267599\",\n \"text\": \"def GetBestSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d8b20c6e270c05634bda0686d1a7c4d\",\n \"score\": \"0.62674415\",\n \"text\": \"def GetBestSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723b041ceecf5e0854ae9fbcdc851718\",\n \"score\": \"0.6257077\",\n \"text\": \"def getSize() -> Tuple[int, int]:\\n request = requests.get(\\\"https://pixels.pythondiscord.com/get_size\\\")\\n logging.debug(request.json())\\n request.raise_for_status()\\n return (request.json()[\\\"width\\\"], request.json()[\\\"height\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c482ad353a6817fbb2ea988fb469da9a\",\n \"score\": \"0.62492704\",\n \"text\": \"def test_get_file_size(self):\\n # @REVIEWED\\n fullPath = '{}/{}'.format(self.exportTestDataPath,\\n self.compressedTestFilename)\\n fSize = self.fileUtil.fileSize(fullPath)\\n self.logger.log('size: {}'.format(fSize))\\n self.assertEqual(fSize, 12279, 'File size is correct.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6dbf97a6a801a9576e3855d10a9a963\",\n \"score\": \"0.6247892\",\n \"text\": \"def size(self, filename):\\n # The SIZE command is defined in RFC-3659\\n resp = self.sendcmd('SIZE ' + filename)\\n if resp[:3] == '213':\\n s = resp[3:].strip()\\n return int(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.6245051\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.6245051\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.6244672\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.6244672\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.62441766\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.62441766\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.62441766\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.62441766\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.62441766\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.62441766\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.62441766\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.62441766\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.6244174\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.6244174\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b810b12511bf5750dac1ccd7f9846b6\",\n \"score\": \"0.6244174\",\n \"text\": \"def GetSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a149791854e599a54e7f514c128244\",\n \"score\": \"0.6240939\",\n \"text\": \"def get_data(url, filename):\\n open_url = urllib.request.urlopen(url)\\n total = int(open_url.getheader(\\\"Content-Length\\\"))\\n \\n return download_data(url, filename, total)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a57706650b940ddd778a330d92bbbf67\",\n \"score\": \"0.6231567\",\n \"text\": \"def size(self):\\n return int(self._info['numBytes']) if 'numBytes' in self._info else -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5b2475efd1e3c7114964d67511cbf9\",\n \"score\": \"0.62057686\",\n \"text\": \"def getSize(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5b2475efd1e3c7114964d67511cbf9\",\n \"score\": \"0.62057686\",\n \"text\": \"def getSize(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5b2475efd1e3c7114964d67511cbf9\",\n \"score\": \"0.62057686\",\n \"text\": \"def getSize(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485b4501fc8924e22d82d2669b2f1bf1\",\n \"score\": \"0.6190935\",\n \"text\": \"def get_size(self, path):\\n lpp = urlparse(path)\\n return os.path.getsize(lpp.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a00d633348f2fb756c553aa217a97c49\",\n \"score\": \"0.61838484\",\n \"text\": \"def filesize(self):\\n return util.buf_to_npy(self._ptr.contents.filesize, (200,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fc93837de02e5b6aa718cfa917ce9d9\",\n \"score\": \"0.61831725\",\n \"text\": \"def getImageSize():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b3f9b0a21bdf1343ececaab6911e31\",\n \"score\": \"0.6179003\",\n \"text\": \"def getsize(self, path):\\n info = self.getinfo(path)\\n size = info.get('size', None)\\n if 'size' is None:\\n raise OperationFailedError(\\\"get size of resource\\\", path)\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c75330abf24a865f3afd018eff9a98\",\n \"score\": \"0.61775625\",\n \"text\": \"def size(self):\\n if self.exists():\\n return self.__response['ContentLength']\\n else:\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117eb4b415da46ad60471932327b9223\",\n \"score\": \"0.6158704\",\n \"text\": \"def get_file_size(url):\\n handler = _pywrap_s3_io.S3Init()\\n return handler.get_file_size(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf2384ebf07ada91404042f7579ed13\",\n \"score\": \"0.6148235\",\n \"text\": \"def hit(url):\\n\\treq = urllib.request.Request(url=url,method = 'HEAD')\\n\\tf = urllib.request.urlopen(req)\\n\\treturn f.info()[\\\"Content-Length\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a477eb33cb4a33b1fc818aa131340ce\",\n \"score\": \"0.61446804\",\n \"text\": \"def size_and_date(href):\\n for attempt in range(10):\\n s, host, path, p, q, f = urlparse(href)\\n \\n conn = HTTPConnection(host, 80)\\n conn.request('HEAD', path)\\n resp = conn.getresponse()\\n \\n if resp.status in range(300, 399):\\n href = urljoin(href, resp.getheader('location'))\\n continue\\n \\n if resp.status != 200:\\n raise IOError('not found')\\n \\n content_length = resp.getheader('content-length')\\n last_modified = resp.getheader('last-modified')\\n \\n return content_length, last_modified\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178a57bffc71902351b21cc0b664ba42\",\n \"score\": \"0.6144287\",\n \"text\": \"def size(self):\\n\\t\\treturn self.partSize\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f723f9c838997b46434c2a0f47b49ab1\",\n \"score\": \"0.6139227\",\n \"text\": \"def __getTableContentsSize(self,table):\\n\\n req = \\\"SELECT SUM(FileSize) FROM %s\\\" % table\\n result = self._query(req)\\n if not result['OK']:\\n return result\\n if result['Value'][0][0]:\\n size = int(result['Value'][0][0])\\n else:\\n size = 0\\n return S_OK(size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b96b4c0bcad7f982c75d137da0ab0d5\",\n \"score\": \"0.613474\",\n \"text\": \"def get_artifact(self, url, file_handle, cb_progress=None, timed=False):\\n ret = dict()\\n self.log.debug(\\\"url:\\\"+str(url))\\n\\n # Content-Length in the server replay is used by this function\\n # to calculate if the server sent all the expected content\\n # Content-Length is compared with sum of all chunks coming from iter_content\\n # Problem is when server sent the data encoded as gzip\\n # In this case the Content-Length is the size af the compressed content.\\n # But iter_content provide uncompressed data so the two size will not match.\\n # The trick is to sent a head only request convincing the server that\\n # we are not able to use gzip content. So Content-Length in server replay is\\n # the uncompressed size.\\n # This first request is not used to download the content, but just to get the size\\n my_headers = {\\\"Accept-Encoding\\\": \\\"deflate\\\"}\\n response = requests.head(url, headers=my_headers)\\n self.log.debug(\\\"GET status_code:\\\"+str(response.status_code))\\n self.log.debug(\\\"HEAD Headers:\\\"+str(response.headers))\\n if response.status_code != 200:\\n ret['result'] = False\\n return ret\\n\\n file_size = int(response.headers[\\\"Content-Length\\\"])\\n self.log.info(\\\"File size:\\\" + str(file_size))\\n response.close()\\n\\n # Now create a second request, that will use default header that eventually support gzip\\n t_start = time.time()\\n response = requests.get(url, stream=True)\\n if timed:\\n ret['time_get'] = time.time() - t_start\\n response.raise_for_status()\\n self.log.debug(\\\"GET Headers:\\\"+str(response.headers))\\n\\n done = 0\\n self.log.debug(\\\"len:0 \\\"+str(done) +\\\"/\\\"+str(file_size) + \\\" progress:\\\" + str(done/float(file_size)))\\n t_start = time.time()\\n for chunk in response.iter_content(chunk_size=8192*4):\\n if chunk: # filter out keep-alive new chunks\\n l = len(chunk)\\n done += l \\n self.log.debug(\\\"len:\\\" + str(l) + \\\" \\\"+str(done) +\\\"/\\\"+str(file_size) + \\\" progress:\\\" + str(done/float(file_size)))\\n file_handle.write(chunk)\\n file_handle.flush()\\n if cb_progress:\\n cb_progress(done/float(file_size))\\n else:\\n self.log.debug(\\\"Empty chunk\\\")\\n if timed:\\n ret['time_download'] = time.time() - t_start\\n if done == file_size:\\n ret['result'] = True\\n else:\\n ret['result'] = False\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc874d0a6bec671c9f4ee7411641821\",\n \"score\": \"0.61320555\",\n \"text\": \"def CacheBestSize(self, size):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0064094c5c837712132fe86d0c772b28\",\n \"score\": \"0.61181164\",\n \"text\": \"def getSize(self):\\n\\t\\treturn self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0712be5845f25dfae7016b0e4b75da\",\n \"score\": \"0.611784\",\n \"text\": \"def downloadChunks(url,rep,nom_fic):\\n try:\\n req = urllib.request.urlopen(url)\\n #if downloaded file is html\\n if (req.info().get('Content-Type')=='text/html'):\\n print(\\\"error : file is in html and not an expected binary file\\\")\\n lines=req.read()\\n if lines.find('Download Not Found')>0 :\\n raise TypeError\\n else:\\n with open(\\\"error_output.html\\\",\\\"w\\\") as f:\\n f.write(lines)\\n print(\\\"result saved in ./error_output.html\\\")\\n sys.exit(-1)\\n #if file too small\\n total_size = int(req.info().get('Content-Length').strip())\\n if (total_size<50000):\\n print(\\\"Error: The file is too small to be a Landsat Image\\\")\\n print(url)\\n sys.exit(-1)\\n print(nom_fic,total_size)\\n total_size_fmt = sizeof_fmt(total_size)\\n\\n #download\\n downloaded = 0\\n CHUNK = 1024 * 1024 *8\\n print(rep+'/'+nom_fic)\\n with open(rep+'/'+nom_fic, 'wb') as fp:\\n start = time.clock()\\n print('Downloading {0} ({1}):'.format(nom_fic, total_size_fmt))\\n while True:\\n chunk = req.read(CHUNK)\\n downloaded += len(chunk)\\n done = int(50 * downloaded / total_size)\\n sys.stdout.write('\\\\r[{1}{2}]{0:3.0f}% {3}ps'\\n .format(math.floor((float(downloaded)\\n / total_size) * 100),\\n '=' * done,\\n ' ' * (50 - done),\\n sizeof_fmt((downloaded // (time.clock() - start)) / 8)))\\n sys.stdout.flush()\\n if not chunk: break\\n fp.write(chunk)\\n except urllib.error.HTTPError as e:\\n if e.code == 500:\\n pass # File doesn't exist\\n else:\\n print( \\\"HTTP Error:\\\", e.code , url)\\n return False\\n except urllib.error.URLError as e:\\n print( \\\"URL Error:\\\",e.reason , url)\\n return False\\n\\n return rep,nom_fic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47e08062bf7694fa0d8fe497bfcfad4c\",\n \"score\": \"0.61128\",\n \"text\": \"def size_info(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb304631e9f99995eb40fd26957780ea\",\n \"score\": \"0.60970116\",\n \"text\": \"def _get_object_size(ctx, lfs_spec):\\n depot_path = lfs_spec.depot_path(ctx)\\n cmd = ['fstat', '-m1', '-Ol', '-T', 'fileSize', depot_path]\\n return int(p4gf_util.first_value_for_key(ctx.p4run(cmd), 'fileSize'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0f1e994e133f5dbc6b209a1f2d9191f\",\n \"score\": \"0.6096715\",\n \"text\": \"def getSize(self):\\r\\n\\t\\treturn self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc15dd0d7ac17f39c961c1c9a91f5158\",\n \"score\": \"0.6088864\",\n \"text\": \"def GetFileSize(self, path):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc70012497c31e9513712c6540dc07d7\",\n \"score\": \"0.60878223\",\n \"text\": \"def get_size(size):\\n if not size:\\n return None\\n elif 'g' in size.lower():\\n # bring back to bytes\\n size = int(size.lower().split('g')[0]) * 1024 * 1024 * 1024\\n elif 'm' in size.lower():\\n size = int(size.lower().split('m')[0]) * 1024 * 1024\\n elif 'k' in size.lower():\\n size = int(size.lower().split('k')[0]) * 1024\\n else:\\n size = int(size)\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1077d38eb17a4b3ed12016e884374d93\",\n \"score\": \"0.6086476\",\n \"text\": \"def get_size(filename):\\n fileinfo = os.stat(filename)\\n return fileinfo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1077d38eb17a4b3ed12016e884374d93\",\n \"score\": \"0.6086476\",\n \"text\": \"def get_size(filename):\\n fileinfo = os.stat(filename)\\n return fileinfo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1077d38eb17a4b3ed12016e884374d93\",\n \"score\": \"0.6086476\",\n \"text\": \"def get_size(filename):\\n fileinfo = os.stat(filename)\\n return fileinfo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9778a1e0a6fd607691db197cc29ffb8\",\n \"score\": \"0.6084967\",\n \"text\": \"def download(self):\\n\\n if not self.filesize:\\n # get filesize from a HEAD request to the URL\\n if not self.parse_url:\\n response = requests.head(self.url, headers=self.x_headers, auth=self.auth)\\n if self.debug:\\n print\\n print \\\"HTTP Headers:\\\"\\n print \\\"=============\\\"\\n for key in response.headers:\\n print key + \\\" = \\\" + response.headers[key]\\n print \\\"-------------\\\"\\n #print response.request.headers\\n\\n # should I check the response status code?\\n try:\\n content_length = int(response.headers['content-length'])\\n except KeyError as e:\\n self.x_headers.update({'Range': 'bytes=0-0'})\\n resp = requests.get(self.url, headers=self.x_headers, auth=self.auth)\\n cr = resp.headers['content-range'].split('/')[-1]\\n content_length = int(cr)\\n del self.x_headers['Range'] \\n else: \\n # get filesize from the query string of the user-provided url\\n # works only if 'fsize' is present (e.g. yandex disk) \\n parsed_qs = parse_qs(urlparse(self.url).query)\\n fsize = int(parsed_qs['fsize'][0])\\n content_length = int(fsize)\\n else:\\n content_length = int(filesize)\\n if self.debug:\\n print \\\"Content length: %s bytes\\\" % content_length\\n\\n bytes_per_thread = content_length / self.num_threads\\n if self.debug:\\n print \\\"Number of bytes per thread: %s\\\" % bytes_per_thread\\n\\n # create the download threads\\n try:\\n with Timer() as timer:\\n threads = []\\n for i in range(self.num_threads):\\n thread_file = self.out_file + \\\"_part_\\\" + str(i)\\n if self.debug:\\n print \\\"Thread %d output file: %s\\\" % (i, thread_file)\\n\\n begin_range = i * bytes_per_thread\\n end_range = (i + 1) * bytes_per_thread - 1\\n remainder = (content_length - 1) - end_range\\n if self.debug:\\n print \\\"Thread %d begin range at %d\\\" % (i, begin_range)\\n print \\\"Thread %d end range at %d\\\" % (i, end_range)\\n print \\\"Thread %d remainder: %d bytes\\\" % (i, remainder)\\n if remainder < bytes_per_thread:\\n end_range += remainder\\n if self.debug:\\n print \\\"Thread %d end range updated at %d\\\" % (i, end_range)\\n\\n t = DownloaderThread(self.url, begin_range, end_range, thread_file, self.x_headers, self.verify, self.auth)\\n threads.append(t)\\n t.start()\\n\\n # wait for the threads\\n for t in threads:\\n t.join()\\n with open(self.out_file, 'ab') as final_out_file:\\n with open(t.out_file, 'rb') as thread_out_file:\\n # read data from thread_out_file and write to final_out_file\\n bytes = 'empty'\\n while (bytes):\\n bytes = thread_out_file.read(512*1024)\\n #bytes = thread_out_file.read() \\n final_out_file.write(bytes)\\n \\n os.remove(t.out_file) # delete the thread's output file\\n\\n finally:\\n print \\\"%s %d %d %f\\\" % (self.url, self.num_threads, content_length, timer.interval)\\n print \\\"avg download speed: %s/s\\\" % naturalsize(content_length/timer.interval, binary=True, format='%.2f')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c85854d8f04ae9c0404d944183ffde\",\n \"score\": \"0.6076422\",\n \"text\": \"def _size(self):\\n return self._system.getsize(header=self._head())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c1c9c6d456b29cd94bbd7426479e2f\",\n \"score\": \"0.6069701\",\n \"text\": \"def file_size():\\n list_of_files = os.listdir(OUT_DIR)\\n response = 0\\n for file in list_of_files:\\n file_info = os.stat(os.path.join(OUT_DIR, file))\\n response += file_info.st_size\\n print_line(70, \\\"*\\\")\\n print(\\\"Used SPACE --> \\\" + convert_bytes(response))\\n print_line(70, \\\"*\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e62af18854abbfd7af025ec1da3aab0\",\n \"score\": \"0.6068732\",\n \"text\": \"def get_size(self, path):\\n proc = self.tools.pmemdetect('-z', path)\\n if int(proc.stdout) != 2**64 - 1:\\n return int(proc.stdout)\\n futils.fail('Could not get size of the file, '\\n 'it is inaccessible or does not exist')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8dd941babab982d3c53350832d9bee0\",\n \"score\": \"0.6065854\",\n \"text\": \"def get_size(self) -> Tuple[int, int]:\\n # self.sync_ratelimit(\\\"get_size\\\")\\n status, data = self._request(\\\"/get_size\\\")\\n return data[\\\"width\\\"], data[\\\"height\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c20a0f4445856e7f3a07584944adc4\",\n \"score\": \"0.60547876\",\n \"text\": \"def size(self):\\n return self.data[\\\"size\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4acbcfc09622296893ad8814846aa35\",\n \"score\": \"0.60515106\",\n \"text\": \"def size(self, path):\\n # type: (str) -> int\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aec107eefe3b41b815f9febd444e254\",\n \"score\": \"0.60494757\",\n \"text\": \"def get_size(self) -> LiveImage.Size:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d04a489e137027ff9195ae5ca32d1d\",\n \"score\": \"0.6046222\",\n \"text\": \"def size(self, args):\\n if self.check_connection() and self.check_logged_in():\\n if not args or type(args) is list:\\n self.usage('size file.txt')\\n return False\\n self.connection.send_request('SIZE ' + args)\\n # Put response in to a more readable format\\n response = self.connection.get_response(True)\\n print 'Size:' + response['message'].strip('\\\\r\\\\n') + ' bytes'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f333b987448c245f350ea74ceb3e8d46\",\n \"score\": \"0.6033329\",\n \"text\": \"def EstimateTotalSize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c42dca196940fdee134ab08c0dc76e7\",\n \"score\": \"0.6032902\",\n \"text\": \"def get_file(prefix: str, filepath: str, url: str, *, chunk_size: int = 1000, show_bar: bool = True,\\n secs_between_range: float = 0.25, bar_size: int = 50, dl_char: str = '#', empty_char: str = '-') -> None:\\n def __format(n: float) -> str:\\n return '{:.2f}'.format(n)\\n\\n def __in_mbs(n: int) -> str:\\n return __format((n / 1000) / 1000)\\n\\n # Start get\\n r = requests.get(url, stream=True)\\n total_length = int(r.headers.get('content-length'))\\n\\n with open(filepath, 'wb') as f: # Adapted from script found on stack overflow\\n amt_in_range = 0\\n last_time_stamp = time.time()\\n\\n vel = 0\\n estimated_minutes = 0\\n already_downloaded = 0\\n\\n for data in r.iter_content(chunk_size=chunk_size):\\n f.write(data)\\n\\n if show_bar:\\n already_downloaded += len(data)\\n\\n prcnt_done = int(bar_size * already_downloaded / total_length)\\n msg = f'\\\\r{prefix} : {filepath} - [{dl_char * prcnt_done}{empty_char * (bar_size - 1 - prcnt_done)}] ' \\\\\\n f'[ {__in_mbs(already_downloaded)} mbs / {__in_mbs(total_length)} mbs] ' \\\\\\n f'[ {__format(vel)} kbs | ETA: {__format(estimated_minutes)} minutes ]'\\n sys.stdout.write(msg)\\n sys.stdout.flush()\\n\\n # Download information\\n if (time.time() - last_time_stamp) < secs_between_range:\\n amt_in_range += 1\\n else:\\n vel = (amt_in_range * (1 / secs_between_range))\\n vel = 0.001 if int(vel) == 0 else vel # Do this to avoid ZeroDivisionError when connection drops\\n last_time_stamp = time.time()\\n amt_in_range = 0\\n estimated_minutes = ((total_length/chunk_size)/vel) / 60\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46019953fa3363ad53a30354ad4afe14\",\n \"score\": \"0.6032654\",\n \"text\": \"def test_s3_file_get_size():\\n # When I have an S3File\\n f = S3File(\\\"foo\\\", 42, datetime.datetime.now())\\n\\n # And I get the size\\n size = f.get_size()\\n\\n # Then it returns the size\\n size.should.equal(42)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992990,"cells":{"query_id":{"kind":"string","value":"68400876ee6dc25f8b277ea69c814ab5"},"query":{"kind":"string","value":"Count number of files in each subdirectory in dataDir."},"positive_passages":{"kind":"list like","value":[{"docid":"cfe70ada7d43ad163104289f72cee968","score":"0.6957413","text":"def count_samples(dataDir):\n assert(isdir(dataDir))\n filenames = [f for f in listdir(dataDir)]\n class_count = {}\n for f in filenames:\n path = dataDir+\"/\"+f\n if isdir(path):\n class_count[f] = len(listdir(path))\n\n for k in class_count:\n print \"class:%s\\tcount:%d\"%(k,class_count[k])","title":""}],"string":"[\n {\n \"docid\": \"cfe70ada7d43ad163104289f72cee968\",\n \"score\": \"0.6957413\",\n \"text\": \"def count_samples(dataDir):\\n assert(isdir(dataDir))\\n filenames = [f for f in listdir(dataDir)]\\n class_count = {}\\n for f in filenames:\\n path = dataDir+\\\"/\\\"+f\\n if isdir(path):\\n class_count[f] = len(listdir(path))\\n\\n for k in class_count:\\n print \\\"class:%s\\\\tcount:%d\\\"%(k,class_count[k])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"dd9caba0bd91e567e3c8d24a7508861a","score":"0.7474413","text":"def files_in_directory(path: str):\r\n count = 0\r\n for _, _, files in os.walk(path):\r\n count += len(files)\r\n \r\n return count","title":""},{"docid":"849e66dd3a5a459db82f2fd30d516294","score":"0.7418929","text":"def getFileCount():\n count = 0\n\n for dirpath, _, filenames in os.walk(DIR):\n for f in filenames:\n count += 1\n\n return count","title":""},{"docid":"d635b69f4f731977d6c9a90731d6cfb5","score":"0.7398125","text":"def _count_data(path):\n matcher = re.compile(r'[0-9]+\\.json')\n match = lambda name: bool(matcher.match(name))\n names = os.listdir(path)\n n_data = len(list(filter(match, names)))\n return n_data","title":""},{"docid":"a5e91aaacf30a590b49e804c04220e2e","score":"0.7217494","text":"def count_files_dir(directory):\n count = 0\n for _ in os.listdir(directory):\n count += 1\n return count","title":""},{"docid":"ebdf02c48ff0621a60bd95bf6eb97b13","score":"0.7185711","text":"def count_files(path_to_folder):\n count = 0\n for item in path_to_folder.iterdir():\n count += 1\n return count","title":""},{"docid":"8e5cc733613ff63ad134d5727da5d899","score":"0.7174023","text":"def count_files(directory):\n if not os.path.exists(directory):\n return 0\n cnt = 0\n for r, dirs, files in os.walk(directory):\n for dr in dirs:\n cnt += len(glob.glob(os.path.join(r, dr + \"/*\")))\n return cnt","title":""},{"docid":"d06ddedfa54344fe627af9e0d5485d31","score":"0.6984029","text":"def get_directory_file_count(directory_path):\n file_count = 0\n for (root, _, files) in walk(directory_path):\n for filename in files:\n file_path = os.path.join(root, filename)\n if not os.path.isfile(file_path):\n continue\n file_count += 1\n\n return file_count","title":""},{"docid":"661cf714431d8d7193e33f682cb0cc1c","score":"0.69409174","text":"def test_get_file_data_file_count_on_root(self):\n\n result, _ = FileMetaDataModularInput.get_file_data(\"../src/bin\")\n self.assertGreaterEqual(result['file_count'], 1)\n self.assertGreaterEqual(result['directory_count'], 1)","title":""},{"docid":"09b4967bf81798acd88c97f6eb3e3513","score":"0.6924665","text":"def get_nb_files(directory):\r\n if not os.path.exists(directory):\r\n return 0\r\n cnt = 0\r\n for r, dirs, files in os.walk(directory):\r\n for dr in dirs:\r\n cnt += len(glob.glob(os.path.join(r, dr + \"/*\")))\r\n return cnt","title":""},{"docid":"8c09e08af30f751d452d3fae0369e372","score":"0.68857867","text":"def amount_of_files(source_dir):\n count = 0\n for root_dir, subdirectories, files in os.walk(source_dir):\n # Loop trough every file in current directory\n for file in files:\n print(\".\", end=\"\")\n count += 1\n return count","title":""},{"docid":"6d26fecb3a623971a9337c2dd0f1aa74","score":"0.68786466","text":"def count_files():\n if os.path.exists(purge_dir):\n for root, dirs, files in os.walk(purge_dir):\n if not root.endswith(purge_dir_name):\n print \"%s : %d\" % (root, len(files))\n else:\n print 'No such Directory : %s' % (purge_dir)","title":""},{"docid":"34b73fd69e92f0bb8f282b94a7ff2dc1","score":"0.68721706","text":"def total_file_count():\n\n logger.info(f'admin_message', msg=f'Counting files from all libraries')\n\n file_count = 0\n library_size = 0\n for row in session.query(InputDirectories):\n path = row.folder_path\n\n try:\n os.chdir(path)\n except Exception as e:\n logger.error(f'admin_message', msg=f'Directory does not exist!', exception=e)\n continue\n\n for root, directory, files in os.walk(path):\n\n for f in files:\n path = os.path.join(root, f)\n file_count += 1\n\n if not os.path.islink(path):\n library_size += os.path.getsize(path)\n\n logger.info(f'user_message', msg=f'Total quantity of files: {file_count}')\n logger.info(f'user_message', msg=f'Total size from all libraries: {library_size} bytes')\n\n return file_count, library_size","title":""},{"docid":"45f51febd43845525c19a39c36093dd0","score":"0.6861628","text":"def count_h5_files(basedir):\n cnt = 0\n try:\n for root, dirs, files in os.walk(basedir):\n files = glob.glob(os.path.join(root,'*.h5'))\n cnt += len(files)\n return cnt\n except (IOError,OSError),e:\n print 'ERROR:',e,'in count_h5_files, return 0'\n return 0","title":""},{"docid":"65611788eff912d2e35436060881d062","score":"0.68571067","text":"def count_files(dirroot):\n # for root, dirs, files in os.walk(dirroot):\n # print(root, sum(getsize(join(root, name)) for name in files), \"Bytes in:\", len(files), \"files\")\n filescount=0\n for files in os.walk(dirroot,followlinks=False):\n c = int(len(files[2]))\n filescount=filescount + c\n return(filescount)","title":""},{"docid":"3c153d923f420287e8c44c7fcccaf051","score":"0.6842541","text":"def get_nb_files(directory):\n if not os.path.exists(directory):\n return 0\n cnt = 0\n for r, dirs, files in os.walk(directory):\n for dr in dirs:\n cnt += len(glob.glob(os.path.join(r, dr + \"/*\")))\n return cnt","title":""},{"docid":"ea95867b1c64e87998110a0a1f30dcef","score":"0.6815522","text":"def get_nb_files(directory):\n if not os.path.exists(directory):\n return 0\n cnt = 0\n for r, dirs, files in os.walk(directory):\n for dr in dirs:\n cnt += len(glob.glob(os.path.join(r, dr + \"/*\")))\n return cnt","title":""},{"docid":"20d5f2f7f86367a4e2c571bbec679fd4","score":"0.6767043","text":"def dir_count(self, feed_dir):\n dir_count = 0\n if os.path.isfile('%s/av_dir_size' % feed_dir):\n # using BASH directly to count the number of directories is the fastest way when they may be\n # in the 10's of thousands. messy but quick\n os.system('nice -n 19 ls -l %s/video | grep -v .*\\.jpg | wc -l > /tmp/kmotion_bash' % feed_dir)\n f = open('/tmp/kmotion_bash', 'r')\n dir_count = int(f.readline())\n f.close()\n return dir_count","title":""},{"docid":"61bd5f723edb186719fb1efd38431888","score":"0.66790867","text":"def count_num_jpg_files(self, data_path):\n num_files = 0\n for root, dirs, files in os.walk(data_path):\n for file in files:\n if self.JPG_EXTENSION in file:\n num_files += 1\n return num_files","title":""},{"docid":"709d5f94ce054cf5a33a9643384e85b9","score":"0.6670533","text":"def get_number_of_files(dir) -> int:\n count = 0\n for _, _, files in os.walk(dir):\n count += len([filename for filename in files if filename[0] != \".\"])\n return count","title":""},{"docid":"6022a700abdfddc0300637f36347e35a","score":"0.6657571","text":"def getFolderCount(self) -> int:\n ...","title":""},{"docid":"a507fe90d6dcc79cc1f6c36b5a14a119","score":"0.6628464","text":"def test_get_files_data_file_count(self):\n\n results, _ = FileMetaDataModularInput.get_files_data(\"../src\")\n\n self.assertGreaterEqual(len(results), 5)\n\n for result in results:\n if result['path'].endswith('file_info_app'):\n self.assertGreaterEqual(result['file_count'], 1)\n\n if result['path'].endswith('bin'):\n self.assertGreaterEqual(result['file_count'], 3)","title":""},{"docid":"b743b0c533d89f0bcbf4d35428a4a05f","score":"0.66123843","text":"def count_tweets()->int:\n directories = [item for item in os.listdir(path_to_tweets)]\n for i in range(0,len(directories)):\n directories[i] = path_to_tweets+directories[i]\n total_count = sum(len(os.listdir(directory)) for directory in directories)\n return total_count","title":""},{"docid":"54f53caf77bdbe7731581521aacdd0c7","score":"0.65962297","text":"def TotalDeep(starting_dir):\n total = no_of_files = 0\n for this_dir, dir_names, file_names in os.walk(starting_dir):\n #print (\"file_names: \", file_names)\n for this_file in file_names:\n #print (\"this_dir:\", this_dir, \"this_file: \", this_file)\n path_name = os.path.join(this_dir, this_file)\n #print (\"path_name: \", path_name)\n try:\n total += totaler.TotalFile(path_name)\n no_of_files += 1\n # print path_name, \"so far: %d files, adding to %g\" % (no_of_files, total)\n except IOError as msg:\n print (path_name, msg)\n return (no_of_files, total)","title":""},{"docid":"d6cba8c95e032f2754bc9cc69fa0c13a","score":"0.65846497","text":"def count_files(self):\r\n file_count = 0\r\n\r\n for i in os.listdir(self.input_directory.get()):\r\n if i.upper().endswith(self.input_file.get()):\r\n file_count += 1\r\n\r\n return file_count","title":""},{"docid":"f30257cc39da4c88344983aa749da8b8","score":"0.65625215","text":"def count_dirs_and_files(directory='.'):\r\n counts = [(len(dirs), len(files)) for dir, dirs, files in os.walk(directory)]\r\n return tuple(sum(file_count) for file_count in zip(*counts))","title":""},{"docid":"28e5524ed5909555d499bf7cd4bcdc51","score":"0.65421474","text":"def count_files_with_keywords(self, root_dir, keyword):\n res = {}\n if not os.path.isdir(root_dir):\n print \"ERROR: %s is not a valid directory!\".format(root_dir)\n return res\n pattern = re.compile(keyword)\n for root, dirs, files in os.walk(root_dir):\n count = 0\n for f in files:\n if self.check_keyword(os.path.join(root, f), pattern):\n count += 1\n res[root] = count\n self.plot_graph(res, keyword)\n return res","title":""},{"docid":"1e7c20cac42ea55b1ff802cf70dee508","score":"0.651239","text":"def test_all_counts_splitdir(self):\n runner = CliRunner()\n with TemporaryDirectory(dir=\".\") as dirname:\n outpath = Path(dirname)\n r = runner.invoke(\n all_count_main, [\"-cdata/directions/*.txt\", f\"-o{outpath}\", \"-s\"]\n )\n\n # should produce directory containing two files\n self.assertEqual(r.exit_code, 0, r.output)\n dirlist = {str(p.name) for p in outpath.glob(\"*\")}\n self.assertEqual(\n dirlist, set([\"combined_counts.txt\", \"combined_counts.log\"])\n )\n counts = load_table(f\"{outpath / 'combined_counts.txt'}\", sep=\"\\t\")\n self.assertIn(\"strand\", counts.header)\n\n with TemporaryDirectory(dir=\".\") as dirname:\n outpath = Path(dirname)\n splitdir = outpath / \"splitdir\"\n r = runner.invoke(\n all_count_main,\n [\n \"-cdata/directions/*.txt\",\n f\"-o{outpath}\",\n \"-s\",\n f\"--split_dir={splitdir}\",\n ],\n )\n # should produce directory containing 6 txt files\n self.assertEqual(r.exit_code, 0, r.output)\n dirlist = {str(p.name) for p in splitdir.glob(f\"*\")}\n self.assertEqual(len(dirlist), 6)\n for p in splitdir.glob(f\"*\"):\n counts = load_table(str(p), sep=\"\\t\")\n self.assertIn(\"strand\", counts.header)\n # num_pos = 4, so there are 4**4 possible seqs, x 2 strands\n # x 2 samples (M and R)\n self.assertEqual(counts.shape[0], 4**4 * 2 * 2)","title":""},{"docid":"be9b6c42da74b87f9c5ad519e0afcd63","score":"0.64891845","text":"def counter_directory_contains_files(model_path,must_contain):\n model_ouput_file_counter = len(glob.glob1(model_path,must_contain))\n return model_ouput_file_counter","title":""},{"docid":"4ba05210b936a4649956658bc57c05f9","score":"0.6467683","text":"def __walk_directory__(path: str, counts: List[int]):\n for (root, dirs, files) in os.walk(path, topdown=True):\n for file in files:\n if file.endswith(\".txt\"):\n file_path = os.path.join(root, file)\n try:\n c = __count_file__(file_path)\n counts.append(c)\n except:\n print(\"Could not open \" + file_path)\n elif file.endswith(\".zip\"):\n file_path = os.path.join(root, file)\n __walk_zip__(file_path, counts)\n elif file.endswith(\".tgz\"):\n file_path = os.path.join(root, file)\n __walk_tgz__(file_path, counts)","title":""},{"docid":"f7fb40a07cf3b0bcefa961cc56e6b9cf","score":"0.64392084","text":"def get_size(data_path, kind='train'):\n assert kind in ['train', 'test'], \\\n \"Either use 'train' or 'test', not {}\".format(kind)\n\n path = os.path.join(data_path, kind)\n\n count = 0\n for folder in os.listdir(path):\n folder = os.path.join(path, folder)\n if os.path.isdir(folder) and not folder.startswith('.'):\n count += len(os.listdir(folder))\n\n return count","title":""},{"docid":"c60dfeaeff759a7ecac749b7e27a96b8","score":"0.6402636","text":"def getFileCount(startPath):\n filesCount = 0\n for root, dirs, files in os.walk(startPath):\n for f in files:\n if f.endswith('.aif') and not f.startswith(\".\"):\n filesCount += 1\n return filesCount","title":""},{"docid":"23caf2de78d3bb166bdcf097bfb7a510","score":"0.63994515","text":"def file_count(self, feed_dir):\n # FIXME: if there are 10's of thousands of files a more efficient way to count would be to read\n # in 'journal_snap' file, decode it and calculate the number of files from there. thats a fair bit\n # of code for an unlikely event.\n file_count = 0\n if os.path.isfile('%s/av_file_size' % feed_dir):\n # using BASH directly to count the number of files is the fastest way when they may be\n # in the 10's of thousands. messy but quick\n os.system('nice -n 19 ls -l %s/video | grep .*\\.jpg | wc -l > /tmp/kmotion_bash' % feed_dir)\n f = open('/tmp/kmotion_bash', 'r')\n file_count = int(f.readline())\n f.close()\n return file_count","title":""},{"docid":"8a6f7a639cd58b1a3381b553a271ea47","score":"0.63742435","text":"def fcount(path):\n count = 0\n for f in os.listdir(path):\n if os.path.isfile(os.path.join(path, f)):\n count += 1\n return count","title":""},{"docid":"6d4f4cdda01885cf5be77f4f1c5e55be","score":"0.63486856","text":"def total_vids(self):\n num_vids = 0\n for vids in self._dir_tree.values():\n num_vids += len(vids)\n return num_vids","title":""},{"docid":"bc045a06ed45428f7b0d3609b4082552","score":"0.6339923","text":"def __files_count(path):\n files_count = 0\n print '\\nGathering Information Required Before Processing...'\n for _, _, files in os.walk(path):\n for file_ in files:\n if not file_.startswith('.'):\n files_count += 1\n\n if files_count < 1:\n print 'NO FILES WERE FOUND TO PROCESS.'\n\n return files_count","title":""},{"docid":"ade7710a5d5f9e44306c56e626ea2e3f","score":"0.6334988","text":"def _get_image_count(self):\n\t\tcounter = 0\n\t\tfor number in range(5):\n\t\t\tnumber += 1 # to get it in range 1-5 not 0-4\n\t\t\ttry: # try to find folder\n\t\t\t\tos.chdir('./' + str(number))\n\t\t\texcept:\n\t\t\t\tcontinue\n\t\t\tcontents = os.listdir(self._directory)\n\t\t\tif contents == []: # if folder is empty\n\t\t\t\tos.chdir('..')\n\t\t\t\tcontinue\n\t\t\tcounter += len(contents)\n\t\t\tos.chdir('..')\n\t\treturn counter","title":""},{"docid":"e27f814c63ebb756b61aea1573042b08","score":"0.63349456","text":"def count(self):\n self.__get_file_list()\n\n for fl in self.file_str_list:\n a_file = FileStat(fl)\n a_file.count()\n self.files.append(a_file)","title":""},{"docid":"57f6deb3ca6ed1b7612888e23516f8ef","score":"0.6323689","text":"def len(self, dataset):\r\n # os.walk returns list of tuples containing list (directiory, [folders], [files])\r\n if self._len is None:\r\n self._len = {\r\n dataset: sum([len(files) for r, d, files in os.walk(self.base_dir / dataset)]) for dataset in\r\n ['train']}\r\n self._len['train'] = int(self._len['train'] * (1 - self.validation_fraction))\r\n self._len['val'] = int(self._len['train'] * self.validation_fraction)\r\n return self._len[dataset]","title":""},{"docid":"50d14aade4a0b9ddcbeb66b7b6e05ad7","score":"0.6304768","text":"def count_dirs_and_files(directory='.'):\n dirs, files = 0, 0\n\n for _, dirs_list, files_list in os.walk(directory):\n dirs += len(dirs_list)\n files += len(files_list)\n\n return dirs, files\n\n # functional variant:\n # return reduce(lambda a, b: (a[0] + b[0], a[1] + b[1]), (\n # (len(dirs), len(files)) for _, dirs, files in os.walk(directory)\n # ))","title":""},{"docid":"5861307bd94ee9c72fb2e3882d08cea4","score":"0.6295061","text":"def _dir_size(self, dir_entry):\n size = 0\n for entry in dir_entry.contents.values():\n if entry.isfile():\n size += len(entry.data)\n else:\n size += self._dir_size(entry)\n return size","title":""},{"docid":"26e0bb7206e3613ba1bfa10953c73d6b","score":"0.62928027","text":"def fcount(path):\n count = 0\n for f in os.listdir(path):\n #print(f)\n if os.path.isfile(os.path.join(path, f)):\n if (f[-4:] == '.jpg'):\n count += 1\n else:\n count += fcount(os.path.join(path, f))\n\n return count","title":""},{"docid":"59ff75180603a5a551e30ba4a2e17987","score":"0.61776936","text":"def files_count(self):\n return len(self.entry_list)","title":""},{"docid":"5ecd74db29db48b5c6e47158817f185d","score":"0.6160982","text":"def _recursive_list(base_dir, current, parent):\n ffile = base_dir + os.sep + current\n if os.path.isdir(ffile):\n listdir = os.listdir(ffile)\n elif os.path.isfile(ffile):\n size = os.lstat(ffile).st_size\n child = Node(current, size, parent)\n parent.child.append(child)\n return size\n else:\n # ignoring unknow type\n return 0\n dir_child = Node(current, 0, parent)\n dir_child.is_dir = True\n for d in listdir:\n dir_child.size += _recursive_list(ffile, d, dir_child)\n parent.child.append(dir_child)\n dir_child.child.sort(key = lambda x: x.size, reverse=True)\n return dir_child.size","title":""},{"docid":"b8e3ecf3b4b158a8764466769bfa9298","score":"0.61586666","text":"def file_count(self) -> int:","title":""},{"docid":"191cf2fc29838384475487836109298f","score":"0.6152833","text":"def pathCount(*args, **kwargs):\n \n pass","title":""},{"docid":"c9fb2894dfc347da8320268572d82994","score":"0.6110833","text":"def count(self, path):\r\n try:\r\n (dir_count, file_count, content_size, ppath) = \\\r\n self.get_bite().count(list_path(path)).next().split()\r\n except StopIteration:\r\n dir_count = file_count = content_size = 0\r\n return {'content_size': content_size, 'dir_count': dir_count,\r\n 'file_count': file_count}","title":""},{"docid":"941c71b1d25155f6d9628baeac4c12d7","score":"0.60768527","text":"def _get_image_count(self):\n\t\treturn len(os.listdir(self._directory))","title":""},{"docid":"c1f30049d256088dee147165025317dd","score":"0.6050042","text":"def file_counts(self):\n return self.aiml_files.count()","title":""},{"docid":"f518c300453117d4462118275ff18879","score":"0.6044442","text":"def _total_number_and_size_of_files(files_tree):\n total_files = 0\n total_size = 0\n if 'url' in files_tree:\n total_files += 1\n total_size += files_tree.get('size_bytes', 0)\n for child in files_tree.get('children', []):\n child_files, child_size = _total_number_and_size_of_files(child)\n total_files += child_files\n total_size += child_size\n return total_files, total_size","title":""},{"docid":"af816d588c554e1b1917d4504f1e1c50","score":"0.6034482","text":"def count_file(file_, L):\n\n count = []\n for (dirpath, _, filenames) in os.walk(file_, True, None, L):\n for name in filenames:\n path = dirpath + '/' + name\n try:\n type_ = magic.from_file(path)\n except IOError:\n printError(\"ERROR COUNTING FILE - PROGRESS BAR MAY BE WRONG!\")\n return count\n except UnicodeDecodeError:\n printError(path + \" will be ignored! ( UnicodeDecodeError )\")\n continue\n\n if \"MPEG\" in type_:\n count.append(path)\n\n if \"image\" in type_:\n count.append(path)\n\n if \"video\" in type_:\n count.append(path)\n\n\n\n return count","title":""},{"docid":"16583e5d0120fb32b24a555e445b2d0d","score":"0.6023566","text":"def directory_size(path):\n from os.path import join, getsize\n result = 0\n for root, dirs, files in os.walk(path):\n for name in files:\n try:\n sz = getsize(join(root,name))\n result = result + sz\n except OSError, exc:\n pass\n return result","title":""},{"docid":"3a4ee065d283e2ff43b6b1a0999f17fd","score":"0.6018723","text":"def directory_size(directory_path: str) -> int:\n\n size_in_bytes = 0\n\n for file in os.scandir(directory_path):\n if file.is_file():\n size_in_bytes += os.path.getsize(file.path)\n elif file.is_dir():\n size_in_bytes += directory_size(file.path)\n\n return size_in_bytes","title":""},{"docid":"d95374db740f030f65c02fce9588dfa5","score":"0.5981652","text":"def get_directory_size(directory_path):\n directory_size = 0\n for (root, _, files) in walk(directory_path):\n for filename in files:\n file_path = os.path.join(root, filename)\n directory_size += os.path.getsize(file_path)\n\n return directory_size","title":""},{"docid":"c0197f38e4244deb99df8f9c3be8fe5a","score":"0.5974189","text":"def getFileCount(self) -> int:\n ...","title":""},{"docid":"a22756498bae6d2ce523c57c00fb8212","score":"0.59656006","text":"def __len__(self):\r\n dirname = os.path.join(self.root_dir, self.semantic_dir)\r\n return len([name for name in os.listdir(dirname) if os.path.isfile(os.path.join(dirname, name))])","title":""},{"docid":"4f02c1d7cebc22e52ac9189143efb93e","score":"0.59543926","text":"def aggregate_files(data_files, output_path):\n c = Counter()\n for data_file in data_files:\n for line in data_file:\n length, count = line.strip().split()\n c[int(length)] += int(count)\n with open(output_path, \"w\") as outfile:\n for mrl, count in sorted(c.items()):\n outfile.write(f\"{mrl} {count}\\n\")","title":""},{"docid":"b73cf870722aa2f7604a1c55c3b5f095","score":"0.5916396","text":"def file_count(self) -> int:\n return len(self.__api.iter_files(allow_ancillary=False))","title":""},{"docid":"74b8bf73745d4e374efaa7633c4586fd","score":"0.58839047","text":"def process_directory():\n\n _tasks = []\n\n # Walk through each subdirectory of working directory\n for root, dirs, files in os.walk(os.getcwd()):\n if not _check_capture_dir(files):\n continue\n else:\n # Add meta file to list\n _file = _get_capture_meta(files)\n assert _file is not None\n _tasks.append(os.path.join(root, _file))\n\n print(\"Found {} completed data sets\".format(len(_tasks)))\n\n _results = 0\n if _tasks:\n with Pool(processes=4) as pool:\n for result in tqdm(pool.imap_unordered(fix_meta, _tasks), total=len(_tasks)):\n if result:\n _results += 1\n\n return _results","title":""},{"docid":"a1f93aff3ff5ddaedb4e6b08956346d9","score":"0.5877267","text":"def get_dir_size(dir_path):\n dir_size = 0\n for root, _, files in os.walk(dir_path):\n dir_size += sum([getsize(join(root, name)) for name in files])\n return dir_size","title":""},{"docid":"15b346d3508213f054f1bf58b016faa6","score":"0.5841933","text":"def perform(self):\n\n filepaths = []\n\n for dirpath, _, filenames in os.walk(self.root_directory):\n if len(self.filters) == 0:\n # There are no filters so look at each file.\n for filename in filenames:\n filepaths.append(os.path.join(dirpath, filename))\n else:\n # Apply the filters.\n for filter in self.filters:\n filepaths.extend(glob.glob(os.path.join(dirpath, filter)))\n\n # Count the files in each file.\n line_count = 0\n\n for filepath in filepaths:\n try:\n with open(filepath) as fh:\n line_count += len(fh.readlines())\n except UnicodeDecodeError:\n # Assume it is a binary file and ignore it.\n pass\n except EnvironmentError as err:\n Application.warning(\"Line counter\",\n \"There was an error reading the file <tt>{0}</tt>.\"\n .format(filepath),\n detail=str(err))\n\n # Tell the user.\n Application.information(\"Line counter\",\n \"There were {0} lines in {1} files.\".format(line_count,\n len(filepaths)))","title":""},{"docid":"7d1165ff6ef540f63f95748c0564a6bd","score":"0.58406067","text":"def count(self):\n \n return len(self._files)","title":""},{"docid":"10fe3bb8ef825e96d56ad1c1fa562ffe","score":"0.5840402","text":"def dir_size(\n working_dir\n) -> int:\n total_size = 0\n for dirpath, dirnames, filenames in os.walk(working_dir):\n for filename in filenames:\n fp = os.path.join(dirpath, filename)\n # skip if it is symbolic link\n if not os.path.islink(fp):\n total_size += os.path.getsize(fp)\n return total_size","title":""},{"docid":"3a19a8868d7299b01ebc14565d0ab1fc","score":"0.5826264","text":"def count_records(folder: str, tp: str) -> int:\n ret = 0\n for _ in tf.data.TFRecordDataset(\n tf.data.Dataset.list_files(os.path.join(folder, f\"{tp}*tfrecord\"))\n ):\n ret += 1\n\n return ret","title":""},{"docid":"33e77bd1532e14bcd7618cdc39367562","score":"0.58261156","text":"def get_num_partitions(data_dir):\n files = glob.glob(\"%s/indexes/indexes_p*_r000.txt\" % data_dir)\n return len(files)","title":""},{"docid":"c87f8b842ee475d562f5f32e3f34c9f6","score":"0.581629","text":"def get_dir_size(path: str):\n total = 0\n # Recursively scan directory to find entries\n with os.scandir(path) as it:\n for entry in it:\n if entry.is_file():\n total += entry.stat().st_size\n elif entry.is_dir():\n total += get_dir_size(entry.path)\n return total","title":""},{"docid":"f164138596a4912cae1891bb20a4d600","score":"0.5800591","text":"def ancillary_file_count(self) -> int:","title":""},{"docid":"bdd0edad085238c06b541870620e90a0","score":"0.58002454","text":"def count_file(wildcards):\n return rules.count_variants_local.output.counts \\\n if wildcards.run_location == 'local' \\\n else GS.remote(rules.count_singletons_GCP.output.maps,keep_local=True)","title":""},{"docid":"445d312806ed06ea67cf1b7887cb7c35","score":"0.5792365","text":"def count(path):\n\treturn len(read(path))","title":""},{"docid":"2db44c923aa9663c60dc614700b8ea66","score":"0.5780863","text":"def file_count(self):\n return len(self.file_names)","title":""},{"docid":"2db44c923aa9663c60dc614700b8ea66","score":"0.5780863","text":"def file_count(self):\n return len(self.file_names)","title":""},{"docid":"e8e8ba834c88c51e5b90f755b560b55d","score":"0.5773868","text":"def count_files(self):\n return 0 if self._files is None else len(self._files)","title":""},{"docid":"249102d7442562176bce16ff0f068de8","score":"0.57670724","text":"def __len__(self):\n file_num = len(self.data_paths)\n return int(file_num / 2)","title":""},{"docid":"030e40f64d74b9edc08b3b6c9486d1af","score":"0.57648844","text":"def count_burst_lines(direc, sfx='.dat'):\n fls = os.listdir(direc)\n n = 0\n for flnm in fls:\n if flnm.endswith(sfx):\n with open(direc + flnm, 'r') as fl:\n c = count_lines(fl)\n print('%d lines in %s' % (c, flnm))\n n += c\n return n","title":""},{"docid":"6eaa8b672f6ad3ecf8d434261afc055f","score":"0.57646954","text":"def _entity_files_count(entity_files_info):\n c = 0\n for e in entity_files_info:\n for f in e:\n c = c + 1\n return c","title":""},{"docid":"72303c170a385c9b71403b8f6dc4de0a","score":"0.5764253","text":"def scan_db_folder(self):\n self.size_in_bytes = 0\n self.size = 0\n self.pid_to_uid.clear()\n all_file_name = listdir(self.db_path)\n all_uid = (folder for folder in all_file_name if isdir(join(self.db_path, folder)))\n for uid in all_uid:\n all_pid = set([])\n uid_dir = join(self.db_path, uid)\n all_image_name = listdir(uid_dir)\n self.size += len(all_image_name)\n for image_name in all_image_name:\n pid = image_name.split(\"_\")[0]\n all_pid.add(pid)\n self.size_in_bytes += getsize(join(uid_dir, image_name))\n self.pid_to_uid[pid] = uid\n self._validate_history()\n LOGGER.debug(\"Updated DataBase\")\n self.print_db_status()","title":""},{"docid":"aac3d8a1d5e8489314161bea485f8df8","score":"0.57574487","text":"def get_size(start_path=\".\"):\n total_size = 0\n for dirpath, _, filenames in walk(start_path):\n for filename in filenames:\n filename_path = join(dirpath, filename)\n total_size += getsize(filename_path)\n return total_size","title":""},{"docid":"d651b872d025c4ddd6a74c7fdf5a6a87","score":"0.57527596","text":"def nfiles(self):\n return int(len(self._filepaths) * len(self._filepaths[0]))","title":""},{"docid":"ca08996a81835a25d5606e45db348c0d","score":"0.57232994","text":"def nfiles(self):\n return len(self._filepaths)","title":""},{"docid":"5fd4361abb2decec6a0b6a9d8a2ceaa7","score":"0.5718657","text":"def count(self) -> int:\n return self._count(self.path)","title":""},{"docid":"717f8a51105720c207c7dc87bd08caac","score":"0.5715079","text":"def get_char_count_forall_files(file_list):\n total_cnt = 0\n for filename in file_list:\n f = open(filename, 'r')\n # txt = f.read()\n # print(txt)\n cnt = len(f.read())\n total_cnt += cnt\n # print(\n # if DEBUG: print(\"Length: \", cnt) \n f.close()\n\n return total_cnt","title":""},{"docid":"f9a2f0221916a9620384f77d0546e929","score":"0.5703137","text":"def do_child_count(self, params):\n for child, level in self._zk.tree(params.path, params.depth, full_path=True):\n self.show_output(\"%s: %d\", child, self._zk.child_count(child))","title":""},{"docid":"bae960b81f1cdf1c51cbea34f6f43440","score":"0.56926835","text":"def file_count(self):\n return len(self.files)","title":""},{"docid":"fb4634e702fa0d41ecf8f477511e112d","score":"0.56859165","text":"def part_1(file: str):\n parser = FileParser(dir_path, file)\n commands = parser.read()\n file_system = FileSystem()\n file_system.fill_nodes(commands)\n return file_system.calculate_dirs_size()","title":""},{"docid":"23c80bcc933d787a3f36bc87df89bdaa","score":"0.5665058","text":"def depth(self):\n return len(self.path.split(os.sep))","title":""},{"docid":"30e6ea14e2c46b18ec70d8982540fa31","score":"0.56505656","text":"def _fetch_num_images(cls, test_path):\n return len(os.listdir(test_path))","title":""},{"docid":"5e8a3980b4a86481bab5d162e1e79596","score":"0.5621846","text":"def _calculate_total_file_count(self):\r\n mod = self.query_result.record_count % self.chunk_size\r\n if mod > 0:\r\n total_file_count = int(self.query_result.record_count / self.chunk_size) + 1\r\n else:\r\n total_file_count = int(self.query_result.record_count / self.chunk_size)\r\n return total_file_count","title":""},{"docid":"7efecda54883ef556277d09ae2ed92a3","score":"0.56080985","text":"def count(self, value):\n count = 0\n for file in self.__file_list__():\n if self.__read__(file) == value:\n count += 1\n return count","title":""},{"docid":"5d45cf94d1e77b704d6df8405ac7a05c","score":"0.55873036","text":"def getFileNumberName(path) -> int:\n _root, _dirs, files = next(os.walk(path))\n return len(files)","title":""},{"docid":"e7025fe49ff1d1fa6dc758a82da689ad","score":"0.55861795","text":"def getNumberFiles(self):\n return len(self._fileNames)","title":""},{"docid":"b1ae9966f6900a149cd4425ed33f5693","score":"0.55850625","text":"def dir_size(folder):\n if isfile(folder): return getsize(folder)\n total_size = 0\n for item in listdir(folder):\n item_path = join(folder, item)\n if isfile(item_path):\n total_size += getsize(item_path)\n elif isdir(item_path):\n total_size += dir_size(item_path)\n return total_size","title":""},{"docid":"149c63a31f0c7cb3babda16610a04749","score":"0.5575279","text":"def compute_df(data_pack):\n\n file_name_list = data_pack[1]\n dir_malwords = data_pack[2]\n dfs = Counter()\n\n for sample in sorted(file_name_list):\n\n # Initialize per-file variables\n freqs_file = os.path.join(dir_malwords, sample)\n in_file = None\n proc = None\n\n # If it is a zipped file use fast gzip cat\n if os.path.splitext(freqs_file)[1] == '.gz':\n proc = subprocess.Popen(['gzip', '-cdfq', freqs_file], stdout=subprocess.PIPE, bufsize=4096)\n lines = proc.stdout\n else:\n in_file = open(freqs_file, 'rb')\n lines = in_file\n\n for line in lines:\n line = line.strip().split()\n\n word = line[0].decode('utf-8')\n dfs[word] += 1\n\n # Cleanup\n if os.path.splitext(freqs_file)[1] == '.gz':\n proc.terminate()\n else:\n in_file.close()\n\n return dfs","title":""},{"docid":"d440eeff71a671fe8a873270f4f321d4","score":"0.55670017","text":"def get_filesizes_sum(directory):\n size_total = 0\n treeroot = directory\n\n largest = (None,0)\n \n size_total += os.path.getsize(treeroot)\n\n for dir,subdirs,files in os.walk(treeroot):\n for f in files: \n filepath = os.path.abspath(os.path.join(dir,f))\n if not os.path.islink(filepath):\n filesize = os.path.getsize(filepath)\n if filesize > largest[1]: largest = (filepath,filesize)\n print(str(filesize) + \"\\t\" + filepath) \n size_total += filesize\n #size_total += os.path.getsize(filepath)\n\n for d in subdirs: \n subdirpath = os.path.abspath(os.path.join(dir,d))\n if not os.path.islink(subdirpath):\n subdirsize = os.path.getsize(subdirpath)\n #if subdirsize > largest: largest = (subdirpath,subdirsize)\n #print(str(subdirsize) + \"\\t\" + subdirpath) \n size_total += subdirsize\n #size_total += os.path.getsize(subdirpath)\n print('Largest:',largest)\n return size_total","title":""},{"docid":"61cc27678ee0c6a5bccd130fb4d3b7fc","score":"0.55663365","text":"def iterate_test_data(directory_path, data_format):\n for root, dirs, files in sorted(os.walk(directory_path)):\n for file in files:\n full_path = root + cs.SLASH + file\n yield full_path","title":""},{"docid":"a5a5d6d9085188ad6d4f3f742dc28194","score":"0.55546755","text":"def test_file_count():\n cmd = BuildTest(\n buildspecs=[\n os.path.join(here, \"file_count.yml\"),\n os.path.join(here, \"file_count_pattern.yml\"),\n os.path.join(here, \"file_count_filetype.yml\"),\n os.path.join(here, \"file_count_file_traverse_limit.yml\"),\n ],\n buildtest_system=system,\n configuration=config,\n )\n cmd.build()","title":""},{"docid":"413e76366e9d5dde68d61fc482ed459f","score":"0.5551342","text":"def enumerateDir(self):\n dir_collection = []\n for dirpath, dirnames, filenames in os.walk(self.path):\n for dir in dirnames:\n dir_collection.append(dir)\n return dir_collection","title":""},{"docid":"e3efb794c856458a31e2bbedb757d8ba","score":"0.55506223","text":"def get_directory_size(dir_path):\n total_size = 0\n for path, dirs, files in os.walk(dir_path):\n for f in files:\n fp = os.path.join(path, f)\n total_size = total_size + os.path.getsize(fp)\n\n return total_size / (1024 * 1024)","title":""},{"docid":"86fddbf545cb6c8610d0af06069eaa4c","score":"0.5534423","text":"def disk_usage(path):\r\n\r\n total = os.path.getsize(path)\r\n if os.path.isdir(path):\r\n for file_name in os.listdir(path):\r\n child_path = os.path.join(path, file_name)\r\n total += disk_usage(child_path)\r\n\r\n return total","title":""},{"docid":"caa27997505e8a4f0559fa71432432f0","score":"0.5527007","text":"def countProjectLines(directory, extensions, forbiddenFolders):\n \n #print(\"\\nCurrent directory -> {}\".format(directory))\n totalLines = 0\n\n directoryEntries = os.listdir(directory)\n files = list()\n directories = list()\n\n for entry in directoryEntries:\n if os.path.isfile(os.path.join(directory, entry)):\n files.append(os.path.join(directory, entry))\n elif os.path.isdir(os.path.join(directory, entry)):\n if not entry in forbiddenFolders:\n directories.append(os.path.join(directory, entry))\n\n for currentFile in files:\n for extension in extensions:\n if currentFile.endswith(extension):\n with open(currentFile, 'r') as f:\n contents = f.read().splitlines()\n totalLines += len(contents)\n break\n\n #print('End of directory!')\n #print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\n \n for currentDirectory in directories:\n totalLines += countProjectLines(currentDirectory, extensions, forbiddenFolders)\n\n return totalLines","title":""},{"docid":"609b5122a60d949aea60e71bcb903a3c","score":"0.5521235","text":"def test_get_files_depth_limit(self):\n\n results, _ = FileMetaDataModularInput.get_files_data(\"test_dir\", must_be_later_than=10,\n depth_limit=2)\n self.assertGreaterEqual(len(results), 7)\n\n # By default. assume the root directory was not found\n root_directory_included = False\n\n for result in results:\n\n if result['path'].endswith('test_dir'):\n root_directory_included = True\n\n self.assertEqual(result['file_count'], 2)\n self.assertEqual(result['file_count_recursive'], 5)\n self.assertEqual(result['directory_count_recursive'], 4)\n\n if result['path'].endswith('dir_1'):\n\n self.assertGreaterEqual(result['file_count'], 1)\n self.assertNotIn('file_count_recursive', result)\n self.assertNotIn('directory_count_recursive', result)\n\n if not root_directory_included:\n self.fail(\"Root directory was not included in the results\")","title":""},{"docid":"25a3cd590ea5bb99890ff01a75724070","score":"0.5519503","text":"def test_get_files_data_missing_root_directory(self):\n\n results, _ = FileMetaDataModularInput.get_files_data(\"../src/default\")\n\n self.assertGreaterEqual(len(results), 5)\n\n # By default. assume the root directory was not found\n root_directory_included = False\n\n for result in results:\n\n if result['path'].endswith('default'):\n root_directory_included = True\n\n self.assertEqual(result['file_count'], 4)\n self.assertGreaterEqual(result['file_count_recursive'], 3)\n self.assertEqual(result['directory_count_recursive'], 3)\n\n if not root_directory_included:\n self.fail(\"Root directory was not included in the results\")","title":""}],"string":"[\n {\n \"docid\": \"dd9caba0bd91e567e3c8d24a7508861a\",\n \"score\": \"0.7474413\",\n \"text\": \"def files_in_directory(path: str):\\r\\n count = 0\\r\\n for _, _, files in os.walk(path):\\r\\n count += len(files)\\r\\n \\r\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"849e66dd3a5a459db82f2fd30d516294\",\n \"score\": \"0.7418929\",\n \"text\": \"def getFileCount():\\n count = 0\\n\\n for dirpath, _, filenames in os.walk(DIR):\\n for f in filenames:\\n count += 1\\n\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d635b69f4f731977d6c9a90731d6cfb5\",\n \"score\": \"0.7398125\",\n \"text\": \"def _count_data(path):\\n matcher = re.compile(r'[0-9]+\\\\.json')\\n match = lambda name: bool(matcher.match(name))\\n names = os.listdir(path)\\n n_data = len(list(filter(match, names)))\\n return n_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e91aaacf30a590b49e804c04220e2e\",\n \"score\": \"0.7217494\",\n \"text\": \"def count_files_dir(directory):\\n count = 0\\n for _ in os.listdir(directory):\\n count += 1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebdf02c48ff0621a60bd95bf6eb97b13\",\n \"score\": \"0.7185711\",\n \"text\": \"def count_files(path_to_folder):\\n count = 0\\n for item in path_to_folder.iterdir():\\n count += 1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5cc733613ff63ad134d5727da5d899\",\n \"score\": \"0.7174023\",\n \"text\": \"def count_files(directory):\\n if not os.path.exists(directory):\\n return 0\\n cnt = 0\\n for r, dirs, files in os.walk(directory):\\n for dr in dirs:\\n cnt += len(glob.glob(os.path.join(r, dr + \\\"/*\\\")))\\n return cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06ddedfa54344fe627af9e0d5485d31\",\n \"score\": \"0.6984029\",\n \"text\": \"def get_directory_file_count(directory_path):\\n file_count = 0\\n for (root, _, files) in walk(directory_path):\\n for filename in files:\\n file_path = os.path.join(root, filename)\\n if not os.path.isfile(file_path):\\n continue\\n file_count += 1\\n\\n return file_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"661cf714431d8d7193e33f682cb0cc1c\",\n \"score\": \"0.69409174\",\n \"text\": \"def test_get_file_data_file_count_on_root(self):\\n\\n result, _ = FileMetaDataModularInput.get_file_data(\\\"../src/bin\\\")\\n self.assertGreaterEqual(result['file_count'], 1)\\n self.assertGreaterEqual(result['directory_count'], 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b4967bf81798acd88c97f6eb3e3513\",\n \"score\": \"0.6924665\",\n \"text\": \"def get_nb_files(directory):\\r\\n if not os.path.exists(directory):\\r\\n return 0\\r\\n cnt = 0\\r\\n for r, dirs, files in os.walk(directory):\\r\\n for dr in dirs:\\r\\n cnt += len(glob.glob(os.path.join(r, dr + \\\"/*\\\")))\\r\\n return cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c09e08af30f751d452d3fae0369e372\",\n \"score\": \"0.68857867\",\n \"text\": \"def amount_of_files(source_dir):\\n count = 0\\n for root_dir, subdirectories, files in os.walk(source_dir):\\n # Loop trough every file in current directory\\n for file in files:\\n print(\\\".\\\", end=\\\"\\\")\\n count += 1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d26fecb3a623971a9337c2dd0f1aa74\",\n \"score\": \"0.68786466\",\n \"text\": \"def count_files():\\n if os.path.exists(purge_dir):\\n for root, dirs, files in os.walk(purge_dir):\\n if not root.endswith(purge_dir_name):\\n print \\\"%s : %d\\\" % (root, len(files))\\n else:\\n print 'No such Directory : %s' % (purge_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34b73fd69e92f0bb8f282b94a7ff2dc1\",\n \"score\": \"0.68721706\",\n \"text\": \"def total_file_count():\\n\\n logger.info(f'admin_message', msg=f'Counting files from all libraries')\\n\\n file_count = 0\\n library_size = 0\\n for row in session.query(InputDirectories):\\n path = row.folder_path\\n\\n try:\\n os.chdir(path)\\n except Exception as e:\\n logger.error(f'admin_message', msg=f'Directory does not exist!', exception=e)\\n continue\\n\\n for root, directory, files in os.walk(path):\\n\\n for f in files:\\n path = os.path.join(root, f)\\n file_count += 1\\n\\n if not os.path.islink(path):\\n library_size += os.path.getsize(path)\\n\\n logger.info(f'user_message', msg=f'Total quantity of files: {file_count}')\\n logger.info(f'user_message', msg=f'Total size from all libraries: {library_size} bytes')\\n\\n return file_count, library_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f51febd43845525c19a39c36093dd0\",\n \"score\": \"0.6861628\",\n \"text\": \"def count_h5_files(basedir):\\n cnt = 0\\n try:\\n for root, dirs, files in os.walk(basedir):\\n files = glob.glob(os.path.join(root,'*.h5'))\\n cnt += len(files)\\n return cnt\\n except (IOError,OSError),e:\\n print 'ERROR:',e,'in count_h5_files, return 0'\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65611788eff912d2e35436060881d062\",\n \"score\": \"0.68571067\",\n \"text\": \"def count_files(dirroot):\\n # for root, dirs, files in os.walk(dirroot):\\n # print(root, sum(getsize(join(root, name)) for name in files), \\\"Bytes in:\\\", len(files), \\\"files\\\")\\n filescount=0\\n for files in os.walk(dirroot,followlinks=False):\\n c = int(len(files[2]))\\n filescount=filescount + c\\n return(filescount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c153d923f420287e8c44c7fcccaf051\",\n \"score\": \"0.6842541\",\n \"text\": \"def get_nb_files(directory):\\n if not os.path.exists(directory):\\n return 0\\n cnt = 0\\n for r, dirs, files in os.walk(directory):\\n for dr in dirs:\\n cnt += len(glob.glob(os.path.join(r, dr + \\\"/*\\\")))\\n return cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea95867b1c64e87998110a0a1f30dcef\",\n \"score\": \"0.6815522\",\n \"text\": \"def get_nb_files(directory):\\n if not os.path.exists(directory):\\n return 0\\n cnt = 0\\n for r, dirs, files in os.walk(directory):\\n for dr in dirs:\\n cnt += len(glob.glob(os.path.join(r, dr + \\\"/*\\\")))\\n return cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d5f2f7f86367a4e2c571bbec679fd4\",\n \"score\": \"0.6767043\",\n \"text\": \"def dir_count(self, feed_dir):\\n dir_count = 0\\n if os.path.isfile('%s/av_dir_size' % feed_dir):\\n # using BASH directly to count the number of directories is the fastest way when they may be\\n # in the 10's of thousands. messy but quick\\n os.system('nice -n 19 ls -l %s/video | grep -v .*\\\\.jpg | wc -l > /tmp/kmotion_bash' % feed_dir)\\n f = open('/tmp/kmotion_bash', 'r')\\n dir_count = int(f.readline())\\n f.close()\\n return dir_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61bd5f723edb186719fb1efd38431888\",\n \"score\": \"0.66790867\",\n \"text\": \"def count_num_jpg_files(self, data_path):\\n num_files = 0\\n for root, dirs, files in os.walk(data_path):\\n for file in files:\\n if self.JPG_EXTENSION in file:\\n num_files += 1\\n return num_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"709d5f94ce054cf5a33a9643384e85b9\",\n \"score\": \"0.6670533\",\n \"text\": \"def get_number_of_files(dir) -> int:\\n count = 0\\n for _, _, files in os.walk(dir):\\n count += len([filename for filename in files if filename[0] != \\\".\\\"])\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6022a700abdfddc0300637f36347e35a\",\n \"score\": \"0.6657571\",\n \"text\": \"def getFolderCount(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a507fe90d6dcc79cc1f6c36b5a14a119\",\n \"score\": \"0.6628464\",\n \"text\": \"def test_get_files_data_file_count(self):\\n\\n results, _ = FileMetaDataModularInput.get_files_data(\\\"../src\\\")\\n\\n self.assertGreaterEqual(len(results), 5)\\n\\n for result in results:\\n if result['path'].endswith('file_info_app'):\\n self.assertGreaterEqual(result['file_count'], 1)\\n\\n if result['path'].endswith('bin'):\\n self.assertGreaterEqual(result['file_count'], 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b743b0c533d89f0bcbf4d35428a4a05f\",\n \"score\": \"0.66123843\",\n \"text\": \"def count_tweets()->int:\\n directories = [item for item in os.listdir(path_to_tweets)]\\n for i in range(0,len(directories)):\\n directories[i] = path_to_tweets+directories[i]\\n total_count = sum(len(os.listdir(directory)) for directory in directories)\\n return total_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f53caf77bdbe7731581521aacdd0c7\",\n \"score\": \"0.65962297\",\n \"text\": \"def TotalDeep(starting_dir):\\n total = no_of_files = 0\\n for this_dir, dir_names, file_names in os.walk(starting_dir):\\n #print (\\\"file_names: \\\", file_names)\\n for this_file in file_names:\\n #print (\\\"this_dir:\\\", this_dir, \\\"this_file: \\\", this_file)\\n path_name = os.path.join(this_dir, this_file)\\n #print (\\\"path_name: \\\", path_name)\\n try:\\n total += totaler.TotalFile(path_name)\\n no_of_files += 1\\n # print path_name, \\\"so far: %d files, adding to %g\\\" % (no_of_files, total)\\n except IOError as msg:\\n print (path_name, msg)\\n return (no_of_files, total)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6cba8c95e032f2754bc9cc69fa0c13a\",\n \"score\": \"0.65846497\",\n \"text\": \"def count_files(self):\\r\\n file_count = 0\\r\\n\\r\\n for i in os.listdir(self.input_directory.get()):\\r\\n if i.upper().endswith(self.input_file.get()):\\r\\n file_count += 1\\r\\n\\r\\n return file_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30257cc39da4c88344983aa749da8b8\",\n \"score\": \"0.65625215\",\n \"text\": \"def count_dirs_and_files(directory='.'):\\r\\n counts = [(len(dirs), len(files)) for dir, dirs, files in os.walk(directory)]\\r\\n return tuple(sum(file_count) for file_count in zip(*counts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e5524ed5909555d499bf7cd4bcdc51\",\n \"score\": \"0.65421474\",\n \"text\": \"def count_files_with_keywords(self, root_dir, keyword):\\n res = {}\\n if not os.path.isdir(root_dir):\\n print \\\"ERROR: %s is not a valid directory!\\\".format(root_dir)\\n return res\\n pattern = re.compile(keyword)\\n for root, dirs, files in os.walk(root_dir):\\n count = 0\\n for f in files:\\n if self.check_keyword(os.path.join(root, f), pattern):\\n count += 1\\n res[root] = count\\n self.plot_graph(res, keyword)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7c20cac42ea55b1ff802cf70dee508\",\n \"score\": \"0.651239\",\n \"text\": \"def test_all_counts_splitdir(self):\\n runner = CliRunner()\\n with TemporaryDirectory(dir=\\\".\\\") as dirname:\\n outpath = Path(dirname)\\n r = runner.invoke(\\n all_count_main, [\\\"-cdata/directions/*.txt\\\", f\\\"-o{outpath}\\\", \\\"-s\\\"]\\n )\\n\\n # should produce directory containing two files\\n self.assertEqual(r.exit_code, 0, r.output)\\n dirlist = {str(p.name) for p in outpath.glob(\\\"*\\\")}\\n self.assertEqual(\\n dirlist, set([\\\"combined_counts.txt\\\", \\\"combined_counts.log\\\"])\\n )\\n counts = load_table(f\\\"{outpath / 'combined_counts.txt'}\\\", sep=\\\"\\\\t\\\")\\n self.assertIn(\\\"strand\\\", counts.header)\\n\\n with TemporaryDirectory(dir=\\\".\\\") as dirname:\\n outpath = Path(dirname)\\n splitdir = outpath / \\\"splitdir\\\"\\n r = runner.invoke(\\n all_count_main,\\n [\\n \\\"-cdata/directions/*.txt\\\",\\n f\\\"-o{outpath}\\\",\\n \\\"-s\\\",\\n f\\\"--split_dir={splitdir}\\\",\\n ],\\n )\\n # should produce directory containing 6 txt files\\n self.assertEqual(r.exit_code, 0, r.output)\\n dirlist = {str(p.name) for p in splitdir.glob(f\\\"*\\\")}\\n self.assertEqual(len(dirlist), 6)\\n for p in splitdir.glob(f\\\"*\\\"):\\n counts = load_table(str(p), sep=\\\"\\\\t\\\")\\n self.assertIn(\\\"strand\\\", counts.header)\\n # num_pos = 4, so there are 4**4 possible seqs, x 2 strands\\n # x 2 samples (M and R)\\n self.assertEqual(counts.shape[0], 4**4 * 2 * 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9b6c42da74b87f9c5ad519e0afcd63\",\n \"score\": \"0.64891845\",\n \"text\": \"def counter_directory_contains_files(model_path,must_contain):\\n model_ouput_file_counter = len(glob.glob1(model_path,must_contain))\\n return model_ouput_file_counter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba05210b936a4649956658bc57c05f9\",\n \"score\": \"0.6467683\",\n \"text\": \"def __walk_directory__(path: str, counts: List[int]):\\n for (root, dirs, files) in os.walk(path, topdown=True):\\n for file in files:\\n if file.endswith(\\\".txt\\\"):\\n file_path = os.path.join(root, file)\\n try:\\n c = __count_file__(file_path)\\n counts.append(c)\\n except:\\n print(\\\"Could not open \\\" + file_path)\\n elif file.endswith(\\\".zip\\\"):\\n file_path = os.path.join(root, file)\\n __walk_zip__(file_path, counts)\\n elif file.endswith(\\\".tgz\\\"):\\n file_path = os.path.join(root, file)\\n __walk_tgz__(file_path, counts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7fb40a07cf3b0bcefa961cc56e6b9cf\",\n \"score\": \"0.64392084\",\n \"text\": \"def get_size(data_path, kind='train'):\\n assert kind in ['train', 'test'], \\\\\\n \\\"Either use 'train' or 'test', not {}\\\".format(kind)\\n\\n path = os.path.join(data_path, kind)\\n\\n count = 0\\n for folder in os.listdir(path):\\n folder = os.path.join(path, folder)\\n if os.path.isdir(folder) and not folder.startswith('.'):\\n count += len(os.listdir(folder))\\n\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c60dfeaeff759a7ecac749b7e27a96b8\",\n \"score\": \"0.6402636\",\n \"text\": \"def getFileCount(startPath):\\n filesCount = 0\\n for root, dirs, files in os.walk(startPath):\\n for f in files:\\n if f.endswith('.aif') and not f.startswith(\\\".\\\"):\\n filesCount += 1\\n return filesCount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23caf2de78d3bb166bdcf097bfb7a510\",\n \"score\": \"0.63994515\",\n \"text\": \"def file_count(self, feed_dir):\\n # FIXME: if there are 10's of thousands of files a more efficient way to count would be to read\\n # in 'journal_snap' file, decode it and calculate the number of files from there. thats a fair bit\\n # of code for an unlikely event.\\n file_count = 0\\n if os.path.isfile('%s/av_file_size' % feed_dir):\\n # using BASH directly to count the number of files is the fastest way when they may be\\n # in the 10's of thousands. messy but quick\\n os.system('nice -n 19 ls -l %s/video | grep .*\\\\.jpg | wc -l > /tmp/kmotion_bash' % feed_dir)\\n f = open('/tmp/kmotion_bash', 'r')\\n file_count = int(f.readline())\\n f.close()\\n return file_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6f7a639cd58b1a3381b553a271ea47\",\n \"score\": \"0.63742435\",\n \"text\": \"def fcount(path):\\n count = 0\\n for f in os.listdir(path):\\n if os.path.isfile(os.path.join(path, f)):\\n count += 1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4f4cdda01885cf5be77f4f1c5e55be\",\n \"score\": \"0.63486856\",\n \"text\": \"def total_vids(self):\\n num_vids = 0\\n for vids in self._dir_tree.values():\\n num_vids += len(vids)\\n return num_vids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc045a06ed45428f7b0d3609b4082552\",\n \"score\": \"0.6339923\",\n \"text\": \"def __files_count(path):\\n files_count = 0\\n print '\\\\nGathering Information Required Before Processing...'\\n for _, _, files in os.walk(path):\\n for file_ in files:\\n if not file_.startswith('.'):\\n files_count += 1\\n\\n if files_count < 1:\\n print 'NO FILES WERE FOUND TO PROCESS.'\\n\\n return files_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade7710a5d5f9e44306c56e626ea2e3f\",\n \"score\": \"0.6334988\",\n \"text\": \"def _get_image_count(self):\\n\\t\\tcounter = 0\\n\\t\\tfor number in range(5):\\n\\t\\t\\tnumber += 1 # to get it in range 1-5 not 0-4\\n\\t\\t\\ttry: # try to find folder\\n\\t\\t\\t\\tos.chdir('./' + str(number))\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tcontents = os.listdir(self._directory)\\n\\t\\t\\tif contents == []: # if folder is empty\\n\\t\\t\\t\\tos.chdir('..')\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tcounter += len(contents)\\n\\t\\t\\tos.chdir('..')\\n\\t\\treturn counter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27f814c63ebb756b61aea1573042b08\",\n \"score\": \"0.63349456\",\n \"text\": \"def count(self):\\n self.__get_file_list()\\n\\n for fl in self.file_str_list:\\n a_file = FileStat(fl)\\n a_file.count()\\n self.files.append(a_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f6deb3ca6ed1b7612888e23516f8ef\",\n \"score\": \"0.6323689\",\n \"text\": \"def len(self, dataset):\\r\\n # os.walk returns list of tuples containing list (directiory, [folders], [files])\\r\\n if self._len is None:\\r\\n self._len = {\\r\\n dataset: sum([len(files) for r, d, files in os.walk(self.base_dir / dataset)]) for dataset in\\r\\n ['train']}\\r\\n self._len['train'] = int(self._len['train'] * (1 - self.validation_fraction))\\r\\n self._len['val'] = int(self._len['train'] * self.validation_fraction)\\r\\n return self._len[dataset]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50d14aade4a0b9ddcbeb66b7b6e05ad7\",\n \"score\": \"0.6304768\",\n \"text\": \"def count_dirs_and_files(directory='.'):\\n dirs, files = 0, 0\\n\\n for _, dirs_list, files_list in os.walk(directory):\\n dirs += len(dirs_list)\\n files += len(files_list)\\n\\n return dirs, files\\n\\n # functional variant:\\n # return reduce(lambda a, b: (a[0] + b[0], a[1] + b[1]), (\\n # (len(dirs), len(files)) for _, dirs, files in os.walk(directory)\\n # ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5861307bd94ee9c72fb2e3882d08cea4\",\n \"score\": \"0.6295061\",\n \"text\": \"def _dir_size(self, dir_entry):\\n size = 0\\n for entry in dir_entry.contents.values():\\n if entry.isfile():\\n size += len(entry.data)\\n else:\\n size += self._dir_size(entry)\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26e0bb7206e3613ba1bfa10953c73d6b\",\n \"score\": \"0.62928027\",\n \"text\": \"def fcount(path):\\n count = 0\\n for f in os.listdir(path):\\n #print(f)\\n if os.path.isfile(os.path.join(path, f)):\\n if (f[-4:] == '.jpg'):\\n count += 1\\n else:\\n count += fcount(os.path.join(path, f))\\n\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ff75180603a5a551e30ba4a2e17987\",\n \"score\": \"0.61776936\",\n \"text\": \"def files_count(self):\\n return len(self.entry_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ecd74db29db48b5c6e47158817f185d\",\n \"score\": \"0.6160982\",\n \"text\": \"def _recursive_list(base_dir, current, parent):\\n ffile = base_dir + os.sep + current\\n if os.path.isdir(ffile):\\n listdir = os.listdir(ffile)\\n elif os.path.isfile(ffile):\\n size = os.lstat(ffile).st_size\\n child = Node(current, size, parent)\\n parent.child.append(child)\\n return size\\n else:\\n # ignoring unknow type\\n return 0\\n dir_child = Node(current, 0, parent)\\n dir_child.is_dir = True\\n for d in listdir:\\n dir_child.size += _recursive_list(ffile, d, dir_child)\\n parent.child.append(dir_child)\\n dir_child.child.sort(key = lambda x: x.size, reverse=True)\\n return dir_child.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e3ecf3b4b158a8764466769bfa9298\",\n \"score\": \"0.61586666\",\n \"text\": \"def file_count(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"191cf2fc29838384475487836109298f\",\n \"score\": \"0.6152833\",\n \"text\": \"def pathCount(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9fb2894dfc347da8320268572d82994\",\n \"score\": \"0.6110833\",\n \"text\": \"def count(self, path):\\r\\n try:\\r\\n (dir_count, file_count, content_size, ppath) = \\\\\\r\\n self.get_bite().count(list_path(path)).next().split()\\r\\n except StopIteration:\\r\\n dir_count = file_count = content_size = 0\\r\\n return {'content_size': content_size, 'dir_count': dir_count,\\r\\n 'file_count': file_count}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"941c71b1d25155f6d9628baeac4c12d7\",\n \"score\": \"0.60768527\",\n \"text\": \"def _get_image_count(self):\\n\\t\\treturn len(os.listdir(self._directory))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f30049d256088dee147165025317dd\",\n \"score\": \"0.6050042\",\n \"text\": \"def file_counts(self):\\n return self.aiml_files.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f518c300453117d4462118275ff18879\",\n \"score\": \"0.6044442\",\n \"text\": \"def _total_number_and_size_of_files(files_tree):\\n total_files = 0\\n total_size = 0\\n if 'url' in files_tree:\\n total_files += 1\\n total_size += files_tree.get('size_bytes', 0)\\n for child in files_tree.get('children', []):\\n child_files, child_size = _total_number_and_size_of_files(child)\\n total_files += child_files\\n total_size += child_size\\n return total_files, total_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af816d588c554e1b1917d4504f1e1c50\",\n \"score\": \"0.6034482\",\n \"text\": \"def count_file(file_, L):\\n\\n count = []\\n for (dirpath, _, filenames) in os.walk(file_, True, None, L):\\n for name in filenames:\\n path = dirpath + '/' + name\\n try:\\n type_ = magic.from_file(path)\\n except IOError:\\n printError(\\\"ERROR COUNTING FILE - PROGRESS BAR MAY BE WRONG!\\\")\\n return count\\n except UnicodeDecodeError:\\n printError(path + \\\" will be ignored! ( UnicodeDecodeError )\\\")\\n continue\\n\\n if \\\"MPEG\\\" in type_:\\n count.append(path)\\n\\n if \\\"image\\\" in type_:\\n count.append(path)\\n\\n if \\\"video\\\" in type_:\\n count.append(path)\\n\\n\\n\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16583e5d0120fb32b24a555e445b2d0d\",\n \"score\": \"0.6023566\",\n \"text\": \"def directory_size(path):\\n from os.path import join, getsize\\n result = 0\\n for root, dirs, files in os.walk(path):\\n for name in files:\\n try:\\n sz = getsize(join(root,name))\\n result = result + sz\\n except OSError, exc:\\n pass\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a4ee065d283e2ff43b6b1a0999f17fd\",\n \"score\": \"0.6018723\",\n \"text\": \"def directory_size(directory_path: str) -> int:\\n\\n size_in_bytes = 0\\n\\n for file in os.scandir(directory_path):\\n if file.is_file():\\n size_in_bytes += os.path.getsize(file.path)\\n elif file.is_dir():\\n size_in_bytes += directory_size(file.path)\\n\\n return size_in_bytes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95374db740f030f65c02fce9588dfa5\",\n \"score\": \"0.5981652\",\n \"text\": \"def get_directory_size(directory_path):\\n directory_size = 0\\n for (root, _, files) in walk(directory_path):\\n for filename in files:\\n file_path = os.path.join(root, filename)\\n directory_size += os.path.getsize(file_path)\\n\\n return directory_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0197f38e4244deb99df8f9c3be8fe5a\",\n \"score\": \"0.5974189\",\n \"text\": \"def getFileCount(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22756498bae6d2ce523c57c00fb8212\",\n \"score\": \"0.59656006\",\n \"text\": \"def __len__(self):\\r\\n dirname = os.path.join(self.root_dir, self.semantic_dir)\\r\\n return len([name for name in os.listdir(dirname) if os.path.isfile(os.path.join(dirname, name))])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f02c1d7cebc22e52ac9189143efb93e\",\n \"score\": \"0.59543926\",\n \"text\": \"def aggregate_files(data_files, output_path):\\n c = Counter()\\n for data_file in data_files:\\n for line in data_file:\\n length, count = line.strip().split()\\n c[int(length)] += int(count)\\n with open(output_path, \\\"w\\\") as outfile:\\n for mrl, count in sorted(c.items()):\\n outfile.write(f\\\"{mrl} {count}\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b73cf870722aa2f7604a1c55c3b5f095\",\n \"score\": \"0.5916396\",\n \"text\": \"def file_count(self) -> int:\\n return len(self.__api.iter_files(allow_ancillary=False))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b8bf73745d4e374efaa7633c4586fd\",\n \"score\": \"0.58839047\",\n \"text\": \"def process_directory():\\n\\n _tasks = []\\n\\n # Walk through each subdirectory of working directory\\n for root, dirs, files in os.walk(os.getcwd()):\\n if not _check_capture_dir(files):\\n continue\\n else:\\n # Add meta file to list\\n _file = _get_capture_meta(files)\\n assert _file is not None\\n _tasks.append(os.path.join(root, _file))\\n\\n print(\\\"Found {} completed data sets\\\".format(len(_tasks)))\\n\\n _results = 0\\n if _tasks:\\n with Pool(processes=4) as pool:\\n for result in tqdm(pool.imap_unordered(fix_meta, _tasks), total=len(_tasks)):\\n if result:\\n _results += 1\\n\\n return _results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1f93aff3ff5ddaedb4e6b08956346d9\",\n \"score\": \"0.5877267\",\n \"text\": \"def get_dir_size(dir_path):\\n dir_size = 0\\n for root, _, files in os.walk(dir_path):\\n dir_size += sum([getsize(join(root, name)) for name in files])\\n return dir_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b346d3508213f054f1bf58b016faa6\",\n \"score\": \"0.5841933\",\n \"text\": \"def perform(self):\\n\\n filepaths = []\\n\\n for dirpath, _, filenames in os.walk(self.root_directory):\\n if len(self.filters) == 0:\\n # There are no filters so look at each file.\\n for filename in filenames:\\n filepaths.append(os.path.join(dirpath, filename))\\n else:\\n # Apply the filters.\\n for filter in self.filters:\\n filepaths.extend(glob.glob(os.path.join(dirpath, filter)))\\n\\n # Count the files in each file.\\n line_count = 0\\n\\n for filepath in filepaths:\\n try:\\n with open(filepath) as fh:\\n line_count += len(fh.readlines())\\n except UnicodeDecodeError:\\n # Assume it is a binary file and ignore it.\\n pass\\n except EnvironmentError as err:\\n Application.warning(\\\"Line counter\\\",\\n \\\"There was an error reading the file <tt>{0}</tt>.\\\"\\n .format(filepath),\\n detail=str(err))\\n\\n # Tell the user.\\n Application.information(\\\"Line counter\\\",\\n \\\"There were {0} lines in {1} files.\\\".format(line_count,\\n len(filepaths)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d1165ff6ef540f63f95748c0564a6bd\",\n \"score\": \"0.58406067\",\n \"text\": \"def count(self):\\n \\n return len(self._files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10fe3bb8ef825e96d56ad1c1fa562ffe\",\n \"score\": \"0.5840402\",\n \"text\": \"def dir_size(\\n working_dir\\n) -> int:\\n total_size = 0\\n for dirpath, dirnames, filenames in os.walk(working_dir):\\n for filename in filenames:\\n fp = os.path.join(dirpath, filename)\\n # skip if it is symbolic link\\n if not os.path.islink(fp):\\n total_size += os.path.getsize(fp)\\n return total_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a19a8868d7299b01ebc14565d0ab1fc\",\n \"score\": \"0.5826264\",\n \"text\": \"def count_records(folder: str, tp: str) -> int:\\n ret = 0\\n for _ in tf.data.TFRecordDataset(\\n tf.data.Dataset.list_files(os.path.join(folder, f\\\"{tp}*tfrecord\\\"))\\n ):\\n ret += 1\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e77bd1532e14bcd7618cdc39367562\",\n \"score\": \"0.58261156\",\n \"text\": \"def get_num_partitions(data_dir):\\n files = glob.glob(\\\"%s/indexes/indexes_p*_r000.txt\\\" % data_dir)\\n return len(files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c87f8b842ee475d562f5f32e3f34c9f6\",\n \"score\": \"0.581629\",\n \"text\": \"def get_dir_size(path: str):\\n total = 0\\n # Recursively scan directory to find entries\\n with os.scandir(path) as it:\\n for entry in it:\\n if entry.is_file():\\n total += entry.stat().st_size\\n elif entry.is_dir():\\n total += get_dir_size(entry.path)\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f164138596a4912cae1891bb20a4d600\",\n \"score\": \"0.5800591\",\n \"text\": \"def ancillary_file_count(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd0edad085238c06b541870620e90a0\",\n \"score\": \"0.58002454\",\n \"text\": \"def count_file(wildcards):\\n return rules.count_variants_local.output.counts \\\\\\n if wildcards.run_location == 'local' \\\\\\n else GS.remote(rules.count_singletons_GCP.output.maps,keep_local=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445d312806ed06ea67cf1b7887cb7c35\",\n \"score\": \"0.5792365\",\n \"text\": \"def count(path):\\n\\treturn len(read(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db44c923aa9663c60dc614700b8ea66\",\n \"score\": \"0.5780863\",\n \"text\": \"def file_count(self):\\n return len(self.file_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db44c923aa9663c60dc614700b8ea66\",\n \"score\": \"0.5780863\",\n \"text\": \"def file_count(self):\\n return len(self.file_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e8ba834c88c51e5b90f755b560b55d\",\n \"score\": \"0.5773868\",\n \"text\": \"def count_files(self):\\n return 0 if self._files is None else len(self._files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"249102d7442562176bce16ff0f068de8\",\n \"score\": \"0.57670724\",\n \"text\": \"def __len__(self):\\n file_num = len(self.data_paths)\\n return int(file_num / 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"030e40f64d74b9edc08b3b6c9486d1af\",\n \"score\": \"0.57648844\",\n \"text\": \"def count_burst_lines(direc, sfx='.dat'):\\n fls = os.listdir(direc)\\n n = 0\\n for flnm in fls:\\n if flnm.endswith(sfx):\\n with open(direc + flnm, 'r') as fl:\\n c = count_lines(fl)\\n print('%d lines in %s' % (c, flnm))\\n n += c\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eaa8b672f6ad3ecf8d434261afc055f\",\n \"score\": \"0.57646954\",\n \"text\": \"def _entity_files_count(entity_files_info):\\n c = 0\\n for e in entity_files_info:\\n for f in e:\\n c = c + 1\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72303c170a385c9b71403b8f6dc4de0a\",\n \"score\": \"0.5764253\",\n \"text\": \"def scan_db_folder(self):\\n self.size_in_bytes = 0\\n self.size = 0\\n self.pid_to_uid.clear()\\n all_file_name = listdir(self.db_path)\\n all_uid = (folder for folder in all_file_name if isdir(join(self.db_path, folder)))\\n for uid in all_uid:\\n all_pid = set([])\\n uid_dir = join(self.db_path, uid)\\n all_image_name = listdir(uid_dir)\\n self.size += len(all_image_name)\\n for image_name in all_image_name:\\n pid = image_name.split(\\\"_\\\")[0]\\n all_pid.add(pid)\\n self.size_in_bytes += getsize(join(uid_dir, image_name))\\n self.pid_to_uid[pid] = uid\\n self._validate_history()\\n LOGGER.debug(\\\"Updated DataBase\\\")\\n self.print_db_status()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac3d8a1d5e8489314161bea485f8df8\",\n \"score\": \"0.57574487\",\n \"text\": \"def get_size(start_path=\\\".\\\"):\\n total_size = 0\\n for dirpath, _, filenames in walk(start_path):\\n for filename in filenames:\\n filename_path = join(dirpath, filename)\\n total_size += getsize(filename_path)\\n return total_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d651b872d025c4ddd6a74c7fdf5a6a87\",\n \"score\": \"0.57527596\",\n \"text\": \"def nfiles(self):\\n return int(len(self._filepaths) * len(self._filepaths[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca08996a81835a25d5606e45db348c0d\",\n \"score\": \"0.57232994\",\n \"text\": \"def nfiles(self):\\n return len(self._filepaths)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd4361abb2decec6a0b6a9d8a2ceaa7\",\n \"score\": \"0.5718657\",\n \"text\": \"def count(self) -> int:\\n return self._count(self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717f8a51105720c207c7dc87bd08caac\",\n \"score\": \"0.5715079\",\n \"text\": \"def get_char_count_forall_files(file_list):\\n total_cnt = 0\\n for filename in file_list:\\n f = open(filename, 'r')\\n # txt = f.read()\\n # print(txt)\\n cnt = len(f.read())\\n total_cnt += cnt\\n # print(\\n # if DEBUG: print(\\\"Length: \\\", cnt) \\n f.close()\\n\\n return total_cnt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9a2f0221916a9620384f77d0546e929\",\n \"score\": \"0.5703137\",\n \"text\": \"def do_child_count(self, params):\\n for child, level in self._zk.tree(params.path, params.depth, full_path=True):\\n self.show_output(\\\"%s: %d\\\", child, self._zk.child_count(child))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae960b81f1cdf1c51cbea34f6f43440\",\n \"score\": \"0.56926835\",\n \"text\": \"def file_count(self):\\n return len(self.files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb4634e702fa0d41ecf8f477511e112d\",\n \"score\": \"0.56859165\",\n \"text\": \"def part_1(file: str):\\n parser = FileParser(dir_path, file)\\n commands = parser.read()\\n file_system = FileSystem()\\n file_system.fill_nodes(commands)\\n return file_system.calculate_dirs_size()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23c80bcc933d787a3f36bc87df89bdaa\",\n \"score\": \"0.5665058\",\n \"text\": \"def depth(self):\\n return len(self.path.split(os.sep))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30e6ea14e2c46b18ec70d8982540fa31\",\n \"score\": \"0.56505656\",\n \"text\": \"def _fetch_num_images(cls, test_path):\\n return len(os.listdir(test_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8a3980b4a86481bab5d162e1e79596\",\n \"score\": \"0.5621846\",\n \"text\": \"def _calculate_total_file_count(self):\\r\\n mod = self.query_result.record_count % self.chunk_size\\r\\n if mod > 0:\\r\\n total_file_count = int(self.query_result.record_count / self.chunk_size) + 1\\r\\n else:\\r\\n total_file_count = int(self.query_result.record_count / self.chunk_size)\\r\\n return total_file_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7efecda54883ef556277d09ae2ed92a3\",\n \"score\": \"0.56080985\",\n \"text\": \"def count(self, value):\\n count = 0\\n for file in self.__file_list__():\\n if self.__read__(file) == value:\\n count += 1\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d45cf94d1e77b704d6df8405ac7a05c\",\n \"score\": \"0.55873036\",\n \"text\": \"def getFileNumberName(path) -> int:\\n _root, _dirs, files = next(os.walk(path))\\n return len(files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7025fe49ff1d1fa6dc758a82da689ad\",\n \"score\": \"0.55861795\",\n \"text\": \"def getNumberFiles(self):\\n return len(self._fileNames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ae9966f6900a149cd4425ed33f5693\",\n \"score\": \"0.55850625\",\n \"text\": \"def dir_size(folder):\\n if isfile(folder): return getsize(folder)\\n total_size = 0\\n for item in listdir(folder):\\n item_path = join(folder, item)\\n if isfile(item_path):\\n total_size += getsize(item_path)\\n elif isdir(item_path):\\n total_size += dir_size(item_path)\\n return total_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149c63a31f0c7cb3babda16610a04749\",\n \"score\": \"0.5575279\",\n \"text\": \"def compute_df(data_pack):\\n\\n file_name_list = data_pack[1]\\n dir_malwords = data_pack[2]\\n dfs = Counter()\\n\\n for sample in sorted(file_name_list):\\n\\n # Initialize per-file variables\\n freqs_file = os.path.join(dir_malwords, sample)\\n in_file = None\\n proc = None\\n\\n # If it is a zipped file use fast gzip cat\\n if os.path.splitext(freqs_file)[1] == '.gz':\\n proc = subprocess.Popen(['gzip', '-cdfq', freqs_file], stdout=subprocess.PIPE, bufsize=4096)\\n lines = proc.stdout\\n else:\\n in_file = open(freqs_file, 'rb')\\n lines = in_file\\n\\n for line in lines:\\n line = line.strip().split()\\n\\n word = line[0].decode('utf-8')\\n dfs[word] += 1\\n\\n # Cleanup\\n if os.path.splitext(freqs_file)[1] == '.gz':\\n proc.terminate()\\n else:\\n in_file.close()\\n\\n return dfs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d440eeff71a671fe8a873270f4f321d4\",\n \"score\": \"0.55670017\",\n \"text\": \"def get_filesizes_sum(directory):\\n size_total = 0\\n treeroot = directory\\n\\n largest = (None,0)\\n \\n size_total += os.path.getsize(treeroot)\\n\\n for dir,subdirs,files in os.walk(treeroot):\\n for f in files: \\n filepath = os.path.abspath(os.path.join(dir,f))\\n if not os.path.islink(filepath):\\n filesize = os.path.getsize(filepath)\\n if filesize > largest[1]: largest = (filepath,filesize)\\n print(str(filesize) + \\\"\\\\t\\\" + filepath) \\n size_total += filesize\\n #size_total += os.path.getsize(filepath)\\n\\n for d in subdirs: \\n subdirpath = os.path.abspath(os.path.join(dir,d))\\n if not os.path.islink(subdirpath):\\n subdirsize = os.path.getsize(subdirpath)\\n #if subdirsize > largest: largest = (subdirpath,subdirsize)\\n #print(str(subdirsize) + \\\"\\\\t\\\" + subdirpath) \\n size_total += subdirsize\\n #size_total += os.path.getsize(subdirpath)\\n print('Largest:',largest)\\n return size_total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cc27678ee0c6a5bccd130fb4d3b7fc\",\n \"score\": \"0.55663365\",\n \"text\": \"def iterate_test_data(directory_path, data_format):\\n for root, dirs, files in sorted(os.walk(directory_path)):\\n for file in files:\\n full_path = root + cs.SLASH + file\\n yield full_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a5d6d9085188ad6d4f3f742dc28194\",\n \"score\": \"0.55546755\",\n \"text\": \"def test_file_count():\\n cmd = BuildTest(\\n buildspecs=[\\n os.path.join(here, \\\"file_count.yml\\\"),\\n os.path.join(here, \\\"file_count_pattern.yml\\\"),\\n os.path.join(here, \\\"file_count_filetype.yml\\\"),\\n os.path.join(here, \\\"file_count_file_traverse_limit.yml\\\"),\\n ],\\n buildtest_system=system,\\n configuration=config,\\n )\\n cmd.build()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413e76366e9d5dde68d61fc482ed459f\",\n \"score\": \"0.5551342\",\n \"text\": \"def enumerateDir(self):\\n dir_collection = []\\n for dirpath, dirnames, filenames in os.walk(self.path):\\n for dir in dirnames:\\n dir_collection.append(dir)\\n return dir_collection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3efb794c856458a31e2bbedb757d8ba\",\n \"score\": \"0.55506223\",\n \"text\": \"def get_directory_size(dir_path):\\n total_size = 0\\n for path, dirs, files in os.walk(dir_path):\\n for f in files:\\n fp = os.path.join(path, f)\\n total_size = total_size + os.path.getsize(fp)\\n\\n return total_size / (1024 * 1024)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86fddbf545cb6c8610d0af06069eaa4c\",\n \"score\": \"0.5534423\",\n \"text\": \"def disk_usage(path):\\r\\n\\r\\n total = os.path.getsize(path)\\r\\n if os.path.isdir(path):\\r\\n for file_name in os.listdir(path):\\r\\n child_path = os.path.join(path, file_name)\\r\\n total += disk_usage(child_path)\\r\\n\\r\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa27997505e8a4f0559fa71432432f0\",\n \"score\": \"0.5527007\",\n \"text\": \"def countProjectLines(directory, extensions, forbiddenFolders):\\n \\n #print(\\\"\\\\nCurrent directory -> {}\\\".format(directory))\\n totalLines = 0\\n\\n directoryEntries = os.listdir(directory)\\n files = list()\\n directories = list()\\n\\n for entry in directoryEntries:\\n if os.path.isfile(os.path.join(directory, entry)):\\n files.append(os.path.join(directory, entry))\\n elif os.path.isdir(os.path.join(directory, entry)):\\n if not entry in forbiddenFolders:\\n directories.append(os.path.join(directory, entry))\\n\\n for currentFile in files:\\n for extension in extensions:\\n if currentFile.endswith(extension):\\n with open(currentFile, 'r') as f:\\n contents = f.read().splitlines()\\n totalLines += len(contents)\\n break\\n\\n #print('End of directory!')\\n #print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\\n \\n for currentDirectory in directories:\\n totalLines += countProjectLines(currentDirectory, extensions, forbiddenFolders)\\n\\n return totalLines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"609b5122a60d949aea60e71bcb903a3c\",\n \"score\": \"0.5521235\",\n \"text\": \"def test_get_files_depth_limit(self):\\n\\n results, _ = FileMetaDataModularInput.get_files_data(\\\"test_dir\\\", must_be_later_than=10,\\n depth_limit=2)\\n self.assertGreaterEqual(len(results), 7)\\n\\n # By default. assume the root directory was not found\\n root_directory_included = False\\n\\n for result in results:\\n\\n if result['path'].endswith('test_dir'):\\n root_directory_included = True\\n\\n self.assertEqual(result['file_count'], 2)\\n self.assertEqual(result['file_count_recursive'], 5)\\n self.assertEqual(result['directory_count_recursive'], 4)\\n\\n if result['path'].endswith('dir_1'):\\n\\n self.assertGreaterEqual(result['file_count'], 1)\\n self.assertNotIn('file_count_recursive', result)\\n self.assertNotIn('directory_count_recursive', result)\\n\\n if not root_directory_included:\\n self.fail(\\\"Root directory was not included in the results\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a3cd590ea5bb99890ff01a75724070\",\n \"score\": \"0.5519503\",\n \"text\": \"def test_get_files_data_missing_root_directory(self):\\n\\n results, _ = FileMetaDataModularInput.get_files_data(\\\"../src/default\\\")\\n\\n self.assertGreaterEqual(len(results), 5)\\n\\n # By default. assume the root directory was not found\\n root_directory_included = False\\n\\n for result in results:\\n\\n if result['path'].endswith('default'):\\n root_directory_included = True\\n\\n self.assertEqual(result['file_count'], 4)\\n self.assertGreaterEqual(result['file_count_recursive'], 3)\\n self.assertEqual(result['directory_count_recursive'], 3)\\n\\n if not root_directory_included:\\n self.fail(\\\"Root directory was not included in the results\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992991,"cells":{"query_id":{"kind":"string","value":"066ae097295fc95829622edcb8e2468b"},"query":{"kind":"string","value":"Return list of stations w/ status"},"positive_passages":{"kind":"list like","value":[{"docid":"d89236c484845cb571bb6eafdec2d80f","score":"0.7941429","text":"def get_status(self, stations:list = None):\n url = 'https://gbfs.urbansharing.com/trondheimbysykkel.no/station_status.json'\n\n data = self._FetchData(url)\n\n if stations:\n results = []\n for station in data['data']['stations']:\n if station.get('station_id') in stations:\n results.append(station)\n return results\n\n else:\n return data['data']['stations']","title":""}],"string":"[\n {\n \"docid\": \"d89236c484845cb571bb6eafdec2d80f\",\n \"score\": \"0.7941429\",\n \"text\": \"def get_status(self, stations:list = None):\\n url = 'https://gbfs.urbansharing.com/trondheimbysykkel.no/station_status.json'\\n\\n data = self._FetchData(url)\\n\\n if stations:\\n results = []\\n for station in data['data']['stations']:\\n if station.get('station_id') in stations:\\n results.append(station)\\n return results\\n\\n else:\\n return data['data']['stations']\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7c63df56a0bff2f9e6b7994576f818c1","score":"0.74598414","text":"def get_stations(self):\n return self.call_api('stn', 'stns')","title":""},{"docid":"3bac02a9813b4730b91ad0f486ac6fdf","score":"0.7244118","text":"def _get_stations(self):\n r = self.session.get(self.URL)\n html_stations = Soup(r.content).find(id='station-list').find('ul')\n\n ids = []\n names = []\n get_urls = []\n stream_urls = []\n\n for station in html_stations.findAll('li'):\n id = station.a['href'].replace('#', '').replace('error-package', '')\n name = station.h2.string\n get_url = 'http://tvonline.vectra.pl/player/params/?os=ios&station=' + id\n stream_url = self._get_stream_url(get_url)\n\n ids.append(id)\n names.append(name)\n get_urls.append(get_url)\n stream_urls.append(stream_url)\n\n return json.dumps(\n [{'id': id, 'name': name, 'get_url': get_url, 'stream_url': stream_url}\n for id, name, get_url, stream_url in zip(ids, names, get_urls, stream_urls)], indent=4, sort_keys=True\n )","title":""},{"docid":"117c0e754e0be6d29cf6408dd0af9bd6","score":"0.71337295","text":"def station_list():\n stations = [\n {'data': data,'status': 'ONLINE' if data.IsAlive() else 'OFFLINE'}\n for _, data in cache().iteritems()]\n return flask.render_template('station_list.html',\n stations=stations,\n host=socket.gethostname())","title":""},{"docid":"524d709078c9c58697a8f83d3b4351f9","score":"0.70809597","text":"def stations():\r\n # Query of stations\r\n results = session.query(stat.station).all()\r\n\r\n # Convert list of tuples into normal list\r\n all_names = list(np.ravel(results))\r\n\r\n return jsonify(all_names)","title":""},{"docid":"360c59525d4d895e38161fc03b2b4eeb","score":"0.6975589","text":"def get_stations(self):\n conn = sqlite3.connect(self.args.zip2ws_db)\n c = conn.cursor()\n c.execute(\"select rowid, id, name, lat, lon, type from stations\")\n stations = []\n for r in c:\n stations.append(r)\n conn.close()\n return stations","title":""},{"docid":"09b1d5f590063b06ffe430a1ab62a4d3","score":"0.69704664","text":"def get_stations(self):\n url = 'https://gbfs.urbansharing.com/trondheimbysykkel.no/station_information.json'\n \n data = self._FetchData(url)\n return data['data']['stations']","title":""},{"docid":"63be3d797aacd49b19bc6358dd182312","score":"0.6958073","text":"def stations():\n # Query all stations\n results = session.query(Measurement.station).all()\n all_stations = []\n for station in results:\n stations_dict = {}\n stations_dict[\"station\"] = station\n all_stations.append(stations_dict)\n\n return jsonify(all_stations)","title":""},{"docid":"fa939f3cb8c0c3ab824137152fc51f3c","score":"0.69410914","text":"def get(self):\n return get_all_stations()","title":""},{"docid":"53b44c1d8176404c41367ab534453fac","score":"0.6917256","text":"def stations():\n\n\tresults = session.query(Measurement.station).distinct().all()\n\n\treturn jsonify(results)","title":""},{"docid":"413651b387f552584bb157de048bab92","score":"0.69172275","text":"def get_all_stations():\n\n return Station.objects","title":""},{"docid":"8b0b13652d176f1e39a9a4b91be624c2","score":"0.6900441","text":"def stations():\n # Query all stations names along with its code\n sel = [measurement.station, station.name]\n results = session.query(*sel)\\\n .group_by(measurement.station).order_by(func.count(measurement.station).desc())\\\n .filter(measurement.station == station.station)\n\n # Create a dictionary from the row data and append to a list of all_stations\n all_stations = []\n for stations in results:\n stations_dict = {}\n stations_dict[\"code\"] = stations.station\n stations_dict[\"name\"] = stations.name\n all_stations.append(stations_dict)\n\n return jsonify(all_stations)","title":""},{"docid":"e7d6bc66230300083ec606dc46a6b8af","score":"0.6898497","text":"def stations():\n # Query all stations\n query_stations = session.query(Measurement.station).group_by(Measurement.station).all()\n\n # Convert list of tuples into normal list\n #stations_list = list(np.ravel(query_stations))\n\n return jsonify(query_stations)","title":""},{"docid":"4ebe3bc45356a695c51b3378e1abd383","score":"0.6858366","text":"def stations():\n # Query all stations (distinct)\n results = session.query(Station.station).distinct().all()\n\n # Convert list of tuples into normal list\n all_stations = list(np.ravel(results))\n\n return jsonify(all_stations)","title":""},{"docid":"0415f9e86ee14400b75f2f7acb874352","score":"0.67902035","text":"def get_stationlist(self):\n indexes = self.model()._data[:, 0]\n stationlist = WeatherSationList()\n for index in indexes:\n stationlist.append(self.stationlist[int(index)])\n return stationlist","title":""},{"docid":"cf7d06e1ab50dd1974326695bda67225","score":"0.67843646","text":"def stations():\n #Create session link from Python to DB\n session = Session(engine)\n #Create query for all stations\n results_stations = session.query(Sta.station).all()\n # close session\n session.close() \n # use numpy unravel to convert to a list\n stations = list(np.ravel(results_stations))\n return jsonify(stations=stations)","title":""},{"docid":"6c2330ede208b891314822b73a4cafd2","score":"0.6778367","text":"def stations():\n stations_route = session.query(station.name, station.station).all()\n return jsonify(stations_route)","title":""},{"docid":"06a8c16800012fad248310f2e1e252ac","score":"0.67733496","text":"def stations():\n\t# Query all Stations\n \n\tresults = session.query(Stations.station + '-'+ Stations.name).\\\n\torder_by(Stations.station.asc()).all()\n \n # Convert list of tuples into normal list using np.ravel\n\trec_list = list(np.ravel(results))\n\t\n\tprint(rec_list)\n\t\n\treturn jsonify(rec_list)","title":""},{"docid":"d957840f115ed706267caad2b7a1d41a","score":"0.67599905","text":"def stations():\n # Query all stations\n results = session.query(Station.station).all()\n session.close()\n stations_list = list(np.ravel(results))\n return jsonify(stations_list)","title":""},{"docid":"aa9aa0e2379e1694e4a71dc0f69edddc","score":"0.675964","text":"def stations():\n # Query all stations\n results = session.query(Station.name).all()\n\n # Convert list of tuples into normal list\n station_list = list(np.ravel(results))\n\n return jsonify(station_list)","title":""},{"docid":"ca5500451d1bad9c8a7a4d70bc60e2c5","score":"0.6752356","text":"def get_active_stations():\n\n station_ids = get_station_ids()\n active_shower = active_shower_station(station_ids)\n active_weather = active_weather_station(station_ids)\n\n return active_shower, active_weather","title":""},{"docid":"72be559a8f74804ef3706440ef597b92","score":"0.6712846","text":"def search_operational_stations():\n\n if os.path.isfile('active_station_IDs.txt'):\n read_list_from_file()\n else:\n print ''\n print 'You do not have a list with operational stations in your current working directory.'\n\n update = query_yes_no('Do you want to update/create the list with operational stations?')\n print ''\n\n if update == True:\n active_shower, active_weather = get_active_stations()\n with open('active_station_IDs.txt', 'w') as file:\n file.write('active_shower = [')\n file.write(', '.join(str(id) for id in active_shower))\n file.write(']\\nactive_weather = [')\n file.write(', '.join(str(id) for id in active_weather))\n file.write(']')\n read_list_from_file()","title":""},{"docid":"e17062faea83c8a68d042d0b6796dc4c","score":"0.67104703","text":"def stations():\n # Query all stations\n station_names = session.query(Station.station).all()\n\n # Convert list of tuples into normal list\n #stations_list = list(np.ravel(station_names)\n\n return jsonify(station_names)","title":""},{"docid":"7d5b60ea3df8bb3204c8970981a8f2d9","score":"0.67008585","text":"def get_stations(self):\n stations = self.iw.get_stations()\n result = []\n minstrel = self.minstrel.get_frame_error_rates()\n for station in stations:\n result.append({\n 'addr': station['addr'], \n 'tx_rate': WifiHelpers.get_numbers_from_str(station['tx bitrate'])[0],\n 'rx_rate': WifiHelpers.get_numbers_from_str(station['rx bitrate'])[0],\n 'signal': WifiHelpers.get_numbers_from_str(station['signal'])[0],\n 'signal_avg': WifiHelpers.get_numbers_from_str(station['signal avg'])[0],\n 'fer': minstrel[station['addr']]['current'],\n 'fer_avg' : minstrel[station['addr']]['avg']})\n return result","title":""},{"docid":"23179b92cca2a4309ae90e72ddeced04","score":"0.6700289","text":"def stations():\n # Query all stations\n results = session.query(Station.station).all()\n\n # Convert list of tuples into normal list\n all_names = list(np.ravel(results))\n\n return jsonify(all_names)","title":""},{"docid":"522abcf2d35ab48a1ae677b09219039b","score":"0.66876996","text":"def stations():\n # Query all station data\n station_results = session.query(Station).all()\n all_stations = []\n for entry in station_results:\n station_dict = {}\n station_dict[\"station\"] = entry.station\n station_dict[\"name\"] = entry.name\n station_dict[\"latitude\"] = entry.latitude\n station_dict[\"longitude\"] = entry.longitude\n station_dict[\"elevation\"] = entry.elevation\n all_stations.append(station_dict)\n return jsonify(all_stations)","title":""},{"docid":"d1fbb0788e36106c671b29dfb4d16b65","score":"0.665675","text":"def stations():\n stations_results = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).all()\n\n stations = []\n for result in results:\n station_dict = {}\n station_dict['station'] = result.station\n station_dict['name'] = result.name\n station_dict['latitude'] = result.latitude\n station_dict['longitude'] = result.longitude\n station_dict['elevation'] = result.elevation\n stations.append(station_dict)\n\n return jsonify(stations)","title":""},{"docid":"2e66422f83d5712cda4f25f0a6cdd8e7","score":"0.66406256","text":"def stations():\n results = list(np.ravel(session.query(Station.station).all()))\n\n return jsonify(results)","title":""},{"docid":"24f2be690846ef8e46a73fa2f15a53f4","score":"0.6626754","text":"def stations():\n station_results = session.query(Station.name).all()\n \n session.close()\n \n return jsonify(station_results)","title":""},{"docid":"12185b5afd762f59342872e587135c09","score":"0.6606777","text":"def stations():\n # Query all passengers\n station = session.query(Station.station).all()\n\n # Convert list of tuples into normal list\n station_names = list(np.ravel(station))\n\n return jsonify(station_names)","title":""},{"docid":"e872a25fc9b31ad5be0506ea4e9f9269","score":"0.65941554","text":"def stations():\n\n results = session.query(Station.name).all()\n \n return jsonify(results)","title":""},{"docid":"1e714ec34fa224496eb8d5c631354c23","score":"0.65858024","text":"def stations():\n results = session.query(Station_tbl.station).all()\n\n # Unravel results into a 1D array and convert to a list\n stations = list(np.ravel(results))\n return jsonify(stations=stations)","title":""},{"docid":"32533ecf3c32c6438113f586be2c7fa3","score":"0.65793765","text":"def _get_stations_local() -> List[WeatherStation]:\n logger.info('Using pre-generated json to retrieve station list')\n with open(weather_stations_file_path, encoding=\"utf-8\") as weather_stations_file:\n json_data = json.load(weather_stations_file)\n results = []\n for station in json_data['weather_stations']:\n results.append(WeatherStation(**station))\n return results","title":""},{"docid":"a9dda6de5d4b9e62e5c731a57a31a6b2","score":"0.65770566","text":"def stations():\n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n # Query for stations.\n stations = session.query(Station.station, Station.name,\n Station.latitude, Station.longitude, Station.elevation).all()\n\n session.close()\n\n # Convert the query results to a dictionary.\n all_stations = []\n for station, name, latitude, longitude, elevation in stations:\n station_dict = {}\n station_dict[\"station\"] = station\n station_dict[\"name\"] = name\n station_dict[\"latitude\"] = latitude\n station_dict[\"longitude\"] = longitude\n station_dict[\"elevation\"] = elevation\n all_stations.append(station_dict)\n\n # Return the JSON representation of dictionary.\n return jsonify(all_stations)","title":""},{"docid":"1e75338cba66ed0e22ed66fcfa14718e","score":"0.6572708","text":"def stations():\n # Query all station\n station_sum= session.query(Station.station).all()\n\n # Convert list of tuples into normal list\n all_names2 = list(np.ravel(station_sum))\n\n return jsonify(all_names2)","title":""},{"docid":"990d8002296b6a5d4731e7fc772753ea","score":"0.6550659","text":"def getStations():\r\n\r\n # Query all Stations\r\n\r\n results = session.query(Measurement.station).distinct().all()\r\n\r\n\r\n\r\n # Convert list of tuples into normal list\r\n\r\n all_names = list(np.ravel(results))\r\n\r\n\r\n\r\n return jsonify(all_names)","title":""},{"docid":"3ff9db663b8c2a393412d4695b373bea","score":"0.65325254","text":"def get_stations(sock):\n stations = []\n\n arp_table = get_arp_table()\n\n # query hostapd for all attached stations\n sock.sendto(\"STA-FIRST\", hostapd_ctrl_sock_name)\n p = sock.recv(1024)\n while p:\n lines = p.splitlines()\n mac = lines[0]\n ips = get_mac_ips(mac, arp_table)\n for ip in ips:\n stations.append((mac, ip))\n sock.sendto(\"STA-NEXT %s\" % mac, hostapd_ctrl_sock_name)\n p = sock.recv(1024)\n\n logger.debug(\"get_stations: %s\", str(arp_table))\n\n return stations","title":""},{"docid":"45b418d58ae251687f25b76ab3a12344","score":"0.65283376","text":"def stations():\n # Query all passengers\n results = session.query(Station).all()\n\n # Create a dictionary from the row data and append to a list of all_passengers\n all_stations = []\n for station in results:\n station_dict = {}\n station_dict[\"station\"] = station.station\n \n all_stations.append(station_dict)\n\n return jsonify(all_stations)","title":""},{"docid":"47a61bae26a3056ccbc4a71e772af908","score":"0.65192854","text":"def get_user_stations(self):\n path = \"/user/stations\"\n return self.request_json(\"GET\", path)","title":""},{"docid":"a8ae36ed17f44458c0171708a9b97dac","score":"0.64833647","text":"def stations():\n session = Session(engine)\n results = session.query(Station.station, Station.name).all()\n\n # Create a dictionary \n all_results = []\n for station, station_name in results:\n dict_entry = {}\n dict_entry[station] = station_name\n all_results.append(dict_entry)\n\n return jsonify(all_results)","title":""},{"docid":"107fb5b0190e3dd9df3564723784b62e","score":"0.6453889","text":"def stations():\n\n # Query all stations\n station_results = session.query(Station.station, Station.latitude, Station.longitude,\n Station.name, Station.id, Station.elevation).all()\n\n # Create a dictionary from the row data and append to a list of all_stations\n all_stations = []\n for station, latitude, longitude, name, id, elevation in station_results:\n station_dict = {}\n station_dict[\"station\"] = station\n station_dict[\"latitude\"] = latitude\n station_dict[\"longitude\"] = longitude\n station_dict[\"name\"] = name\n station_dict[\"id\"] = id\n station_dict[\"elevation\"] = elevation\n all_stations.append(station_dict)\n\n return jsonify(all_stations)","title":""},{"docid":"0d184a99f63d43d6ab81aa00527afb52","score":"0.64419955","text":"def list_sessions(rundate):\n return config.where[TECH].stations.list","title":""},{"docid":"3fa44c423c7302eaad8059f0e408bfef","score":"0.64366823","text":"def stations():\n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n # Query all stations\n station_results = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).all()\n\n session.close()\n\n # Create a dictionary from the row data and append to a list of all_stations\n all_stations = []\n for station, name, latitude, longitude, elevation in station_results:\n station_dict = {}\n station_dict[\"station\"] = station\n station_dict[\"name\"] = name\n station_dict[\"latitude\"] = latitude\n station_dict[\"longitude\"] = longitude\n station_dict[\"elevation\"] = elevation\n all_stations.append(station_dict)\n\n return jsonify(all_stations)","title":""},{"docid":"dc93f6fe8afa57b3e9576c2316f0f02c","score":"0.6431296","text":"def stations():\r\n # Query data table station and list variables (station number and name)\r\n results = session.query(Station.station, Station.name).all()\r\n session.commit()\r\n \r\n # Create a dictionary from the row data and append to a list, places\r\n places=[]\r\n for place in results:\r\n place_dict = {}\r\n place_dict[\"station_ID\"] = place[0]\r\n place_dict[\"station_name\"] = place[1]\r\n places.append(place_dict)\r\n time.sleep(2)\r\n return jsonify(places)","title":""},{"docid":"fedccbeb2c665a4044d104c2b9275847","score":"0.6424203","text":"def stations():\n\n results = session.query(Measurement.station).all()\n station_list = list(np.ravel(results))\n # station_dict = []\n # for station, stations in results:\n # results_dict = {}\n # results_dict[\"date\"] = date\n # results_dict[\"prcp\"] = prcp\n # station_dict.append(station_dict)\n return jsonify(station_list)","title":""},{"docid":"cba675a9c2bda2fd7e2ebdcf16b82058","score":"0.6396752","text":"def get_stations(url: str, city: str) -> List[Station]:\n resp = requests.get(url)\n stations = resp.json()\n\n station_list = [Station.from_repr(raw_data, city=city, dt=datetime.now()) for raw_data in stations]\n\n return station_list","title":""},{"docid":"cba675a9c2bda2fd7e2ebdcf16b82058","score":"0.6396752","text":"def get_stations(url: str, city: str) -> List[Station]:\n resp = requests.get(url)\n stations = resp.json()\n\n station_list = [Station.from_repr(raw_data, city=city, dt=datetime.now()) for raw_data in stations]\n\n return station_list","title":""},{"docid":"ac8a72e544493d2306a4d8a0c8ea2e01","score":"0.63945","text":"def stations():\n \n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n # Query all the stations\n results = session.query(Station).all()\n \n session.close()\n\n # Create a dictionary from the row data and append to a list of all_stations_list.\n all_stations_list = []\n for stations in results:\n stations_dict = {}\n stations_dict[\"Station\"] = stations.station\n stations_dict[\"Station Name\"] = stations.name\n stations_dict[\"Latitude\"] = stations.latitude\n stations_dict[\"Longitude\"] = stations.longitude\n stations_dict[\"Elevation\"] = stations.elevation\n all_stations_list.append(stations_dict)\n \n return jsonify(all_stations_list)","title":""},{"docid":"6952ac7622a036bca7cf939a00290649","score":"0.6375675","text":"def stations():\r\n all_stations = session.query(Station.station).all()\r\n all_stations_list = list(np.ravel(all_stations))\r\n return jsonify(all_stations_list)","title":""},{"docid":"ead29767037ce3fb3fd12d65cd0766f3","score":"0.63741326","text":"def station():\r\n\r\n # Pull all stations and station names from Station\r\n results = session.query(Station.name).all()\r\n \r\n # Create a list to track our results to eventually turn into a JSON\r\n station_list = []\r\n \r\n # Loop through the query results and add the station name to the list\r\n for x in results:\r\n station_list.append(x.name)\r\n\r\n return jsonify(station_list)","title":""},{"docid":"768cf2daf565edc8fd5c45a4f936d12c","score":"0.6372585","text":"def active_shower_station(station_ids):\n\n url_template = \"http://data.hisparc.nl/api/station/%s/data/\" + yesterday_url()\n active_ids = [id for id in station_ids if get_json(url_template % id)]\n\n return active_ids","title":""},{"docid":"ecefbb5584acc36ce68134f4ea855ae4","score":"0.63409036","text":"def list_servers_status(self):\n if self.get_servers():\n x = PrettyTable()\n x.field_names = [\"Name\", \"UUID\", \"Current State\"]\n x.align[\"Name\"] = \"l\"\n x.align[\"Current State\"] = \"l\"\n for server in self.servers:\n if server.state == \"running\":\n x.add_row([server.name, server.uuid, prGreen(server.state)])\n elif server.state == \"stopped\":\n x.add_row([server.name, server.uuid, prRed(server.state)])\n else:\n x.add_row([server.name, server.uuid, server.state])\n print(x)\n return True","title":""},{"docid":"80faf3698d39bf3b40eff7a7bbb6f4c9","score":"0.63397574","text":"def stations():\n\n session = Session(bind=engine)\n\n # Query all stations\n stations = session.query(Station).all()\n\n # close the session to end the communication with the database\n session.close()\n\n # store query results in dictionary\n all_stations = []\n for station in stations:\n station_dict = {}\n station_dict[\"id\"] = station.id\n station_dict[\"station\"] = station.station\n station_dict[\"name\"] = station.name\n station_dict[\"latitude\"] = station.latitude\n station_dict[\"longitude\"] = station.longitude\n station_dict[\"elevation\"] = station.elevation\n all_stations.append(station_dict)\n\n return jsonify(all_stations)","title":""},{"docid":"50c0e79a9c026e5f6eb8eac7441dce92","score":"0.6313038","text":"def get_status_list(url): # -> list[Unknown]:\n ...","title":""},{"docid":"50fbfb53fdfb06235da2ab14c09c5c02","score":"0.62859654","text":"def get_statuses ():\n res = []\n for val, name in TaskInstance.STATUSES:\n if val != 0:\n res.append ((name, name))\n return res","title":""},{"docid":"5b82f462f33c01f23aa9d53d30d26ac5","score":"0.62786394","text":"async def get_stations_by_codes(station_codes: List[int]) -> List[WeatherStation]:\n if use_wfwx():\n logger.info('Fetching stations from WFWX')\n return await wfwx_api.get_stations_by_codes(station_codes)\n return _get_stations_by_codes_local(station_codes)","title":""},{"docid":"2291945257917d64599ef57113db0e5f","score":"0.6230371","text":"def get_stations(self):\n # Check that we have run `create_df`\n df = getattr(self, \"df\", None)\n if df is None:\n return []\n return list(sorted(set(map(lambda s: s.split(\"_\")[0], df.columns))))","title":""},{"docid":"465f94ca339ef17762834afe61646718","score":"0.61809564","text":"def prepare_stations_status_response(chat_id, stations):\n messages = [EMOJIS['bicycle'] + ' - ' + EMOJIS['no_bicycle']]\n for station in stations:\n if 'error' in station:\n messages.append(station['error'])\n else:\n if station['status'] == 1:\n bikes = '{} - {}'.format(pad_number(station['bikes']), pad_number(station['slots']))\n else:\n bikes = tr('disabled_station', chat_id=chat_id)\n messages.append('{} [{}] {}'.format(bikes, station['id'], compact_address(station['streetName'])))\n return '\\n'.join(messages)","title":""},{"docid":"471b39de2b30e76f10e78f30bdce6fcc","score":"0.6176198","text":"def loadStations(self):\n\n station_list = []\n\n for dir_name in os.listdir(self.dir_path):\n\n # Check if the dir name matches the station name pattern\n if os.path.isdir(os.path.join(self.dir_path, dir_name)):\n if re.match(\"^[A-Z]{2}[A-Z0-9]{4}$\", dir_name):\n print(\"Using station:\", dir_name)\n station_list.append(dir_name)\n else:\n print(\"Skipping directory:\", dir_name)\n\n\n return station_list","title":""},{"docid":"b60bf5722e03033a65a111d7a4fb7fa0","score":"0.6170425","text":"def get_stations(key, year):\n ready = False\n while not ready:\n resp = am.getJson(token, path + key + \"/stations/\")\n if \"Ready\" in resp:\n ready = resp[\"Ready\"]\n else:\n if \"ErrorMessage\" in resp:\n print(resp[\"ErrorMessage\"])\n break\n else:\n if \"Message\" in resp:\n raise Exception(resp[\"Message\"])\n else:\n print(resp)\n raise Exception(\"Something was wrong!\")\n\n for i in range(resp[\"Pages\"]):\n statResponse = am.getJson(token, path + key + \"/stations/\" + str(i))\n stations = statResponse[\"Items\"]\n for stat in stations:\n stid = stat[\"Id\"]\n if stid in station_years:\n if not station_years[stid][len(station_years[stid]) - 1] == year:\n station_years[stid].append(year)\n else:\n station_years[stid] = [year]\n station_projects[stid] = []\n proj_id = stat[\"Project\"][\"Id\"]\n\n if len(station_years[stid]) == 1:\n station_projects[stid].append(proj_id)\n\n if not proj_id in project_name_count.keys():\n project_name_count[proj_id] = {\"Count\": 0}\n am.deleteJson(token, path + key)","title":""},{"docid":"41efaef203f9915a4218dbe3951784e8","score":"0.61364734","text":"def fetch_bart_stations():\n url = 'http://api.bart.gov/api/stn.aspx?cmd=stns&key=MW9S-E7SL-26DU-VV8V&json=y'\n r = requests.get(url)\n stations = r.json()[\"root\"][\"stations\"][\"station\"]\n return stations","title":""},{"docid":"2b14ba2ec9921456835a53d9c9787afc","score":"0.61325943","text":"def statuses(self) -> List[Dict[str, Any]]:\n self.wait_until_done()\n return self._fetch_remote_ndjson(self.status_file_url)","title":""},{"docid":"e7cc460f192269cfbd8a0f659202a1c6","score":"0.6117029","text":"def get_status(self) -> dict:\n station = self._get_closest_radio_station(self.current_freq)\n return {\n \"station_name\": station.name,\n \"station_vol\": 1 if abs(self.current_freq - station.frequency) <= 0.3 else 0,\n \"stations\": {station.name: station.frequency for station in self.stations}\n }","title":""},{"docid":"098568315abf57b0b7efce288f25dd04","score":"0.6111742","text":"def build_station_list(use_cache=True, test=False):\n # Fetch station data - if testing use the fixed test data\n if test:\n data = datafetcher.fetch_test_station_data()\n else:\n data = datafetcher.fetch_station_data(use_cache)\n\n # Build list of MonitoringStation objects\n stations = []\n for e in data[\"items\"]:\n # Extract town string (not always available)\n town = None\n if 'town' in e:\n town = e['town']\n\n # Extract river name (not always available)\n river = None\n if 'riverName' in e:\n river = e['riverName']\n\n # Attempt to extract typical range (low, high)\n try:\n typical_range = (float(e['stageScale']['typicalRangeLow']),\n float(e['stageScale']['typicalRangeHigh']))\n except KeyError:\n typical_range = None\n\n try:\n # Create mesure station object if all required data is\n # available, and add to list\n s = MonitoringStation(\n station_id=e['@id'],\n measure_id=e['measures'][-1]['@id'],\n label=e['label'],\n coord=(float(e['lat']), float(e['long'])),\n typical_range=typical_range,\n river=river,\n town=town)\n stations.append(s)\n except KeyError:\n # Not all required data on the station was available, so\n # skip over\n pass\n\n return stations","title":""},{"docid":"6b805253a64fcb3b9757ecac682c268e","score":"0.6101596","text":"async def stations(self, orderby: str = 'name', reverse: bool = False, offset=0, limit = 100000) -> List[dict]:\n if not self.__intialized:\n raise NotInitialized(\"Please initialize the RadioBrowser.\")\n\n reverse = str(reverse).lower()\n\n\n params = {\n 'order': orderby,\n 'reverse': reverse,\n 'offset': offset,\n 'limit': limit\n }\n\n\n return await self.http.request(\"stations/\", params=params)","title":""},{"docid":"eefd6846ebe7f4bc4cc6a9693cbd5d04","score":"0.60952306","text":"def getStationsForHighway(hw, dir):\n\tstations = []\n\thwy_q = Highway.query(Highway.highwayname == hw, Highway.shortdirection == dir)\n\tfor hwy in hwy_q.fetch():\n\t\tstations = [station.get() for station in hwy.stations]\n\treturn stations","title":""},{"docid":"8d8b80bccc6e76209ff3e78f3f3fd98d","score":"0.60813785","text":"def extract_active_curw_obs_rainfall_stations():\n # Connect to the database\n pool = get_Pool(host=CURW_OBS_HOST, port=CURW_OBS_PORT, user=CURW_OBS_USERNAME, password=CURW_OBS_PASSWORD,\n db=CURW_OBS_DATABASE)\n\n obs_stations = [['hash_id', 'station_id', 'station_name', 'latitude', 'longitude']]\n\n connection = pool.connection()\n\n try:\n\n with connection.cursor() as cursor1:\n cursor1.callproc(procname='getActiveRainfallObsStations')\n results = cursor1.fetchall()\n\n for result in results:\n # if (result.get('station_id') != 100083):\n obs_stations.append([result.get('hash_id'), result.get('station_id'), result.get('station_name'),\n result.get('latitude'), result.get('longitude')])\n\n # Write to csv file\n create_csv('grids/obs_stations/rainfall/curw_active_rainfall_obs_stations.csv', obs_stations)\n\n except Exception as ex:\n traceback.print_exc()\n finally:\n connection.close()\n destroy_Pool(pool)","title":""},{"docid":"d1af121baf99a0bf0267d7100533691a","score":"0.6069473","text":"def __get_stations(ids=None, progress=True):\n\n # invert the progress parameter, tqdm interpretation is\n # DEFAULT disable = False -> progressbar is visible\n progress = not progress\n \n # Use directory listing from stilt-web data. Ignore stations that\n # may be in the queue but are not finished yet.\n allStations = [s for s in os.listdir(CPC.STILTPATH) if os.path.exists(CPC.STILTPATH + s)]\n \n # if ids are provided, select only valid ids from allstations\n if ids:\n # make sure they are all upper case\n ids = [i.upper() for i in ids]\n #select only valid id from allstations\n allStations = list(set(ids).intersection(allStations))\n\n \n # add information on station name (and new STILT station id)\n # from stations.csv file used in stiltweb.\n # this is available from the backend through a url\n df = pd.read_csv(CPC.STILTINFO)\n\n # add ICOS flag to the station\n icos_stations_df = cpstation.getIdList(project='ICOS', theme='AS') \n\n # dictionary to return\n stations = {}\n\n # load precomputed geoinfo\n geo = geoinfo.get()\n\n # fill dictionary with ICOS station id, latitude, longitude and altitude\n # implement progress True/False\n for ist in tqdm(sorted(allStations), disable=progress):\n \n stations[ist] = {}\n # get filename of link (original stiltweb directory structure) and\n # extract location information\n\n loc_ident = os.readlink(CPC.STILTPATH+ist)\n clon = loc_ident[-13:-6]\n lon = float(clon[:-1])\n if clon[-1:] == 'W':\n lon = -lon\n clat = loc_ident[-20:-14]\n lat = float(clat[:-1])\n if clat[-1:] == 'S':\n lat = -lat\n alt = int(loc_ident[-5:])\n\n stations[ist]['lat']=lat\n stations[ist]['lon']=lon\n stations[ist]['alt']=alt\n stations[ist]['locIdent']=os.path.split(loc_ident)[-1]\n\n # set the name and id\n stations[ist]['id'] = ist\n \n # check if station id is in the manual curated list at\n # CPC.STILTINFO\n id = [a.lower() for a in df['STILT id'].values] \n \n if ist.lower() in id:\n stationName = str(df.loc[df['STILT id'] ==\n ist]['STILT name'].item())\n # get the index for this station, to extract corresponding \n # ICOS sampling height from CPC.STILTINFO\n idx = df.index[df['STILT id'] == ist].tolist()\n \n else:\n stationName = ''\n \n stations[ist]['name'] = __stationName(stations[ist]['id'],\n stationName,\n stations[ist]['alt'])\n\n # set a flag and metadata if it is an ICOS station\n \n # convert the ICOS id to strings\n df['ICOS id'] = df['ICOS id'].astype(str)\n stn = df.iloc[idx]['ICOS id'].tolist()[0]\n \n if 'nan' not in stn:\n stations[ist]['icos'] = cpstation.get(stn, icos_stations_df).info()\n \n # add corresponding ICOS Sampling Height\n sh = df.iloc[idx]['ICOS height'].tolist()\n sh = float(sh[0])\n stations[ist]['icos']['SamplingHeight'] = sh \n \n else:\n stations[ist]['icos'] = False\n\n # set years and month of available data\n years = [y for y in os.listdir(CPC.STILTPATH + '/' + ist) if\n os.path.exists(CPC.STILTPATH + '/' + ist + '/' + y)]\n \n # for atmo access zip files may exist for download. we need to exclude them\n years = [y for y in years if not y.endswith('.zip')]\n \n stations[ist]['years'] = years\n for yy in sorted(stations[ist]['years']):\n stations[ist][yy] = {}\n months = [m for m in os.listdir(CPC.STILTPATH + '/' + ist + '/' + yy) if\n os.path.exists(CPC.STILTPATH + '/' + ist + '/' + yy + '/' + m)]\n # remove cache txt entry\n sub = 'cache'\n months = sorted([m for m in months if not sub.lower() in m.lower()])\n stations[ist][yy]['months'] = months\n stations[ist][yy]['nmonths'] = len(stations[ist][yy]['months'])\n \n # add geoinfo\n if ist in geo.keys():\n #get the precomputed country info\n stations[ist]['geoinfo'] = geo[ist]['geoinfo']\n elif stations[ist]['icos']:\n # get country from ICOS coordinates\n stations[ist]['geoinfo'] = __country([stations[ist]['icos']['lat'],stations[ist]['icos']['lon']])\n else:\n # get country from STILT coordiantes\n stations[ist]['geoinfo'] = __country([stations[ist]['lat'],stations[ist]['lon']])\n \n\n return stations","title":""},{"docid":"d5a8ae44cf37168dd59ba5b2bbbcbc18","score":"0.60223424","text":"def fixture_stations():\n return {\n \"status\": \"success\",\n \"data\": [\n {\n \"location\": {\"type\": \"Point\", \"coordinates\": [116.466258, 39.954352]},\n \"station\": \"US Embassy in Beijing\",\n },\n {\n \"location\": {\n \"type\": \"Point\",\n \"coordinates\": [116.2148532181, 40.0078007235],\n },\n \"station\": \"Botanical Garden\",\n },\n ],\n }","title":""},{"docid":"7fea1b1f9db18f445980b1e4a6de75ca","score":"0.5993486","text":"def send_stations_status(chat_id, stations):\n stations_status = Manager().dict()\n processes = []\n for station_index, station_id in enumerate(stations):\n # Start a process per station and add it to processes list\n process = Process(target=get_station_multiproc, args=(station_id, chat_id, stations_status, station_index))\n process.start()\n processes.append(process)\n # Wait until all processes have stopped\n for process in processes:\n process.join()\n if stations_status:\n # Get stations status sorted by index\n stations_status = [stations_status[key] for key in sorted(stations_status.keys())]\n get_bot().send_message(chat_id=chat_id, text=prepare_stations_status_response(chat_id, stations_status))","title":""},{"docid":"ffd626bbf0768ae2bab1dd5a1d9d542b","score":"0.59933895","text":"def active_weather_station(station_ids):\n\n url_template = \"http://data.hisparc.nl/api/station/%s/weather/\" + yesterday_url()\n active_ids = [id for id in station_ids if get_json(url_template % id)]\n\n return active_ids","title":""},{"docid":"1d3a7dd25642acb5a5f9902f0eb0f3d7","score":"0.598828","text":"def get_station_names(self):\n return self.worker().wxdatasets.station_names","title":""},{"docid":"cbf0da53241f31b261c5fb49eade409c","score":"0.5982597","text":"def get_stations():\n station_names=[]\n with open(STATIONFILES_LOC) as infp:\n for line in infp:\n if line.strip():\n station_name = line.split()[-1]\n station_names.append(station_name)\n\n # print num_lines\n return station_names","title":""},{"docid":"92f3d8135c713b85611cf3d4fc807b21","score":"0.5952542","text":"def getStations():\n ls = []\n with open('ghcnd-stations.txt', 'r') as f:\n for line in f.readlines():\n ls += [[i for i in line.strip().split() if i != ''][:3]]\n return ls","title":""},{"docid":"702152e28e1960802711dd084dd8d350","score":"0.59523463","text":"def get_stations_list(urlroot, strdate):\n all_stations_list = []\n all_stations_html = get_html_string(urlroot+strdate+\"/\")\n all_stations_soup = BeautifulSoup.BeautifulSoup(all_stations_html)\n\n for tag in all_stations_soup.findAll('a', href=True):\n #length is 5: eg. \"CVSL/\", we remove the \"/\" to get station name\n if tag['href'].__len__() == 5:\n tag['href'] = tag['href'].replace(\"/\",\"\")\n tag['href'] = tag['href'][1:].encode('ascii','ignore')\n all_stations_list.append(tag['href'])\n \n return all_stations_list","title":""},{"docid":"3be5927a0eb5b0a5578fd7b8a6c752cc","score":"0.5942769","text":"def test_display_stations(self):\n\n with self.client as client:\n response = client.get(\"/api/red/stations\")\n data = response.json\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(data, {\"stations\": [{\"station_name\": \"69th\"}]})","title":""},{"docid":"9cea18de1a627bbc58604b1cee50b237","score":"0.5917357","text":"def get_train_stations(self, filename=None):\n if filename is None:\n url = \"https://rata.digitraffic.fi/api/v1/metadata/stations\"\n\n with urllib.request.urlopen(url) as u:\n data = json.loads(u.read().decode(\"utf-8\"))\n else:\n with open(filename) as f:\n data = json.load(f)\n\n stations = dict()\n\n for s in data:\n latlon = {'lat': s['latitude'], 'lon': s['longitude']}\n stations[s['stationShortCode'].encode('utf-8').decode()] = latlon\n\n return stations","title":""},{"docid":"874bbaae45d0d9384b0306509ade8e50","score":"0.5913973","text":"def get_status(self) -> list:\n result = []\n for machine_id in self.busy_machines:\n result.append((machine_id, self.get_remaining_time(machine_id), ))\n return result","title":""},{"docid":"336652dfee39ad67ca903b4afa47e883","score":"0.5910264","text":"def get_stations_from_filelist(airports):\n station = []\n for airport in airports:\n station.append(airport[\"stations\"])\n return station","title":""},{"docid":"d1d89e6b87a2a5d9f2e544dbed888f82","score":"0.590772","text":"def stations(): \n session = Session(engine)\n \n #query stations data\n stations = session.query(Station)\n \n #loop to create dictionary\n station_dictionary = []\n for station in stations:\n stations = {}\n stations[\"Station\"] = station.station\n stations[\"Name\"] = station.name\n station_dictionary.append(station_dict)\n \n return jsonify(station_dictionary)","title":""},{"docid":"0d4e6e4aced0b3f174d851894b5b1ab5","score":"0.5879276","text":"def _get_stations_by_codes_local(station_codes: List[int]) -> List[WeatherStation]:\n logger.info('Using pre-generated json to retrieve station by code')\n with open(weather_stations_file_path, encoding=\"utf-8\") as file_pointer:\n stations = json.load(file_pointer)\n results = []\n for station in stations['weather_stations']:\n if int(station['code']) in station_codes:\n results.append(WeatherStation(**station))\n return results","title":""},{"docid":"6e3a966d61240e91a440b5b0fc595f99","score":"0.5875365","text":"def station_stats(self):\n\n self._start_clock()\n\n self.dataFrame['StartEnd Stations'] = self.dataFrame['Start Station'] \\\n + ' -> ' \\\n + self.dataFrame['End Station']\n\n most_common_start_station = self.dataFrame['Start Station'].mode()[0]\n most_common_end_station = self.dataFrame['End Station'].mode()[0]\n most_common_start_end_stations = self.dataFrame['StartEnd Stations'].mode()[0]\n\n self._stop_clock('station')\n\n return StationStats(most_common_start_station,\n most_common_end_station,\n most_common_start_end_stations)","title":""},{"docid":"022b9406279c53b6b8fb7d3027d8c57d","score":"0.5868583","text":"def station_names(self):\n if self.metadata is None or self.metadata.empty:\n return []\n else:\n return self.metadata['Station Name'].tolist()","title":""},{"docid":"10b6928e2d3b00bd4825407cf9a8104c","score":"0.58667845","text":"def download_stations(self):\n station_path = Path(self.station_path)\n station_path.mkdir(exist_ok=True, parents=True)\n client = Client(\"IRIS\")\n\n station_params = dict(\n starttime=self.starttime,\n endtime=self.endtime,\n latitude=sum(self.latitudes) / 2.,\n longitude=sum(self.longitudes) / 2.,\n maxradius=self.station_search_degrees, # radius in degrees\n channel='H*', # only include channels that start with H\n level='response', # indicates we want instrument responses\n )\n\n inv = client.get_stations(**station_params)\n inv.write(str(station_path / 'inventory.xml'), 'stationxml')","title":""},{"docid":"decf35b17d3c66e097e1d22e8d3f2993","score":"0.5847852","text":"def load_stations(self):\n if not self.addon.exists(self.path_stations):\n self._save_stations(self._get_stations())\n\n return self.addon.read_file(self.path_stations)","title":""},{"docid":"ea1dcb45d75372b27c5c2229bd820059","score":"0.5844812","text":"def status(self) -> List[Tuple[str, str]]:\n return [(n.id, n.status) for n in self._db.filter()]","title":""},{"docid":"24992a07e115e140149df0437847e611","score":"0.5833598","text":"def get_all_device_status(self) -> List[Dict[str, Any]]:\r\n statuses: List[Dict[str, Any]] = []\r\n\r\n for device in self.devices:\r\n statuses.append(\r\n {\r\n \"id\": device.get_id(),\r\n \"status\": device.get_status_string()\r\n }\r\n )\r\n \r\n return statuses","title":""},{"docid":"4cb0491b24769c40c34c536679449a82","score":"0.5828314","text":"def stations():\n\tquery = \"https://www.aviationweather.gov/docs/metar/stations.txt\"\n\tr = requests.get(query)\n\tif r.status_code != 200:\n\t\traise Exception(f\"request error (code={r.status_code})\")\n\tresult = {}\n\tlinenum = 0\n\tsection = None\n\tfor line in r.text.split(\"\\n\"):\n\t\tlinenum += 1\n\t\tif not line:\n\t\t\tsection = None\n\t\telif line[0] != '!':\n\t\t\tif not section:\n\t\t\t\tsection = line\n\t\t\telif line[3] != ' ':\n\t\t\t\ttry:\n\t\t\t\t\tregion = line[0:2].strip()\n\t\t\t\t\tif not region:\n\t\t\t\t\t\tregion = None\n\t\t\t\t\tstation = line[3:19].strip()\n\t\t\t\t\ticao = line[20:24].strip()\n\t\t\t\t\tif not icao:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tiata = line[26:29].strip()\n\t\t\t\t\ttry:\n\t\t\t\t\t\tsynop = int(line[32:37])\n\t\t\t\t\texcept:\n\t\t\t\t\t\tsynop = None\n\t\t\t\t\tlat = round(float(line[39:41])+float(line[42:44])/60,2)\n\t\t\t\t\tif line[44] == 'S': lat = -lat\n\t\t\t\t\telif line[44] != 'N': raise Exception(f\"latitude invalid\")\n\t\t\t\t\tlon = round(float(line[47:50]) + float(line[51:53])/60,2)\n\t\t\t\t\tif line[53] == 'W': lon = -lon\n\t\t\t\t\telif line[53] != 'E': raise Exception(f\"longitude invalid\")\n\t\t\t\t\telev = float(line[55:59])\n\t\t\t\t\tcountry = line[80:83].strip()\n\t\t\t\texcept Exception as err:\n\t\t\t\t\tprint(f\"stations.txt({linenum}): '{line}': {err}\")\n\t\t\t\t\traise\n\t\t\t\tif country:\n\t\t\t\t\tresult[icao] = dict(\n\t\t\t\t\t\tcountry = country,\n\t\t\t\t\t\tregion = region,\n\t\t\t\t\t\tstation = station,\n\t\t\t\t\t\tlatitude = lat,\n\t\t\t\t\t\tlongitude = lon,\n\t\t\t\t\t\t# linenum = linenum,\n\t\t\t\t\t\televation = elev,\n\t\t\t\t\t)\n\n\treturn result","title":""},{"docid":"14ad076925f61c5836ee3ae4ec91b90e","score":"0.5822643","text":"def get_station_locations(settings: dict, st: \"obspy.Stream\" = None):\n if settings[\"do_synth\"] and settings[\"use_synth_stations\"]:\n return get_synth_stations(settings, wiggle=0)\n else:\n if settings[\"synth_stations_mode\"] == \"real_locations_worldwide\":\n return get_real_station_locations_worldwide()\n return get_station_locs_from_staxml(st, settings=settings)","title":""},{"docid":"1d296fd116ce39c768525a022b57b9be","score":"0.5822628","text":"def get_stations(name):\n\n return Station.objects(name__icontains=name)","title":""},{"docid":"2234ebf3c6fca0d4812b39dd4b52efd3","score":"0.5817798","text":"def ap_clients(interface='ap0'):\n try:\n p = run([\"iw\", \"dev\", interface, \"station\", \"dump\"], encoding=\"UTF-8\", check=True, capture_output=True)\n stations = list(Counter([line for line in p.stdout if \"Station\" in line]).elements())\n LOGGER.debug(stations)\n return stations\n except CalledProcessError as e:\n LOGGER.warning(e)\n return []","title":""},{"docid":"d5f7ba01f5e3d2fcab91c0585b5996f6","score":"0.5811706","text":"def get_station_list(counties, bbox, data_types, start_time, end_time, output_file):\n # Translate bbox if it is used\n if bbox is not None:\n bbox = translate_bbox(bbox)\n\n station_getter = getStations.StationIDGetter(\n counties,\n bbox=bbox,\n dataTypes=data_types,\n startTime=revert_datetime_to_long_string(start_time),\n endTime=revert_datetime_to_long_string(end_time),\n outputFile=output_file,\n noprint=1)\n\n return station_getter.stList","title":""},{"docid":"469f40e7347d0f8354aaa7370a3c650d","score":"0.58079964","text":"def get_statuses():\n db = get_db()\n cur = db.execute(\"\"\"\n SELECT *\n FROM project_status\n \"\"\")\n \n return cur.fetchall()","title":""},{"docid":"b516fe19ea4959f5a0e1319c715f2f98","score":"0.58079314","text":"def _get_station_status(station: str, date: Optional[str] = None) -> Optional[StationStatus]:\n defect_coll = get_defect_collection()\n exif_coll = get_exif_collection()\n\n if date is None:\n dates = exif_coll.find({\"station\": station}, {\"_id\": 0, \"date\": 1}).distinct(\"date\")\n dates.sort()\n\n if len(dates) == 0:\n return\n\n date = dates[-1]\n\n n_to_confirm = defect_coll.find({\"station\": station, \"date\": date, \"category\": 0}).count()\n n_confirmed = defect_coll.find({\"station\": station, \"date\": date, \"category\": 1}).count()\n n_healthy = defect_coll.find({\"station\": station, \"date\": date, \"category\": 2}).count()\n n_in_fix = defect_coll.find({\"station\": station, \"date\": date, \"category\": 3}).count()\n\n n_panel_groups = _get_n_panel_group(station)\n\n if n_panel_groups == 0:\n overall_status = \"unknown\"\n else:\n problem_ratio = (n_to_confirm+n_confirmed+n_in_fix) / n_panel_groups\n if problem_ratio > 0.5:\n overall_status = \"red\"\n elif problem_ratio > 0.05:\n overall_status = \"yellow\"\n else:\n overall_status = \"green\"\n\n return StationStatus(date=date,\n healthy=n_healthy,\n toconfirm=n_to_confirm,\n infix=n_in_fix,\n confirmed=n_confirmed,\n overallStatus=overall_status)","title":""},{"docid":"44d673cdcf4146405fee8bb80a9b521c","score":"0.5807605","text":"def getstationlijst(self):\n url = self.settings['settings']['api']['url']['stationlijst']\n data = self.sendcall(url)\n return data","title":""},{"docid":"9654d1727ff25647c018f15f7ca337ec","score":"0.5801834","text":"def services_of_status(self, status='ok'):\n return sorted([svc for svc in self if self[svc]['Status'].lower() == status.lower()])","title":""},{"docid":"d8c7d4fad68a06bfb7710c3bfa507328","score":"0.5783262","text":"async def all_zone_status(self, unit: int) -> list[ZoneStatus]:\n if unit < 1 or unit > 3:\n return []\n # Reading 7 lines, since response starts with EOL and each zone's status is followed by EOL\n response = await self._protocol.send(\n _format_all_zones_status_request(unit), num_eols_to_read=7\n )\n return ZoneStatus.from_strings(response.split(sep=EOL.decode('ascii')))","title":""},{"docid":"a84b44a5c0e4b3f193b99ead5897649e","score":"0.5779344","text":"def statuses(self):\n return self._statuses","title":""},{"docid":"a84b44a5c0e4b3f193b99ead5897649e","score":"0.5779344","text":"def statuses(self):\n return self._statuses","title":""},{"docid":"21bd93f79bf60715150af0431835cfcf","score":"0.5762371","text":"def return_sensors_online(self):\n return [\"{} - {}\".format(k, v)\n for k, v in self.__sensors_online_status__.items()]","title":""},{"docid":"4d796c16bc6e4ba6c3789aae19e91cf3","score":"0.5758235","text":"def parse_all_stations(all_stations_json):\n stations = json.loads(all_stations_json)['Stations']\n station_list = {}\n for i in stations.keys():\n station = stations[\"{0}\".format(i)]\n nm = station['NAME'].upper()\n if 'ABBR' not in station:\n print('Could not find ABBR key for ' + station['NAME'])\n else:\n try:\n abbr = station['ABBR'].upper()\n station_list[nm] = abbr\n except:\n pass\n print len(station_list)\n return station_list","title":""}],"string":"[\n {\n \"docid\": \"7c63df56a0bff2f9e6b7994576f818c1\",\n \"score\": \"0.74598414\",\n \"text\": \"def get_stations(self):\\n return self.call_api('stn', 'stns')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bac02a9813b4730b91ad0f486ac6fdf\",\n \"score\": \"0.7244118\",\n \"text\": \"def _get_stations(self):\\n r = self.session.get(self.URL)\\n html_stations = Soup(r.content).find(id='station-list').find('ul')\\n\\n ids = []\\n names = []\\n get_urls = []\\n stream_urls = []\\n\\n for station in html_stations.findAll('li'):\\n id = station.a['href'].replace('#', '').replace('error-package', '')\\n name = station.h2.string\\n get_url = 'http://tvonline.vectra.pl/player/params/?os=ios&station=' + id\\n stream_url = self._get_stream_url(get_url)\\n\\n ids.append(id)\\n names.append(name)\\n get_urls.append(get_url)\\n stream_urls.append(stream_url)\\n\\n return json.dumps(\\n [{'id': id, 'name': name, 'get_url': get_url, 'stream_url': stream_url}\\n for id, name, get_url, stream_url in zip(ids, names, get_urls, stream_urls)], indent=4, sort_keys=True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117c0e754e0be6d29cf6408dd0af9bd6\",\n \"score\": \"0.71337295\",\n \"text\": \"def station_list():\\n stations = [\\n {'data': data,'status': 'ONLINE' if data.IsAlive() else 'OFFLINE'}\\n for _, data in cache().iteritems()]\\n return flask.render_template('station_list.html',\\n stations=stations,\\n host=socket.gethostname())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524d709078c9c58697a8f83d3b4351f9\",\n \"score\": \"0.70809597\",\n \"text\": \"def stations():\\r\\n # Query of stations\\r\\n results = session.query(stat.station).all()\\r\\n\\r\\n # Convert list of tuples into normal list\\r\\n all_names = list(np.ravel(results))\\r\\n\\r\\n return jsonify(all_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"360c59525d4d895e38161fc03b2b4eeb\",\n \"score\": \"0.6975589\",\n \"text\": \"def get_stations(self):\\n conn = sqlite3.connect(self.args.zip2ws_db)\\n c = conn.cursor()\\n c.execute(\\\"select rowid, id, name, lat, lon, type from stations\\\")\\n stations = []\\n for r in c:\\n stations.append(r)\\n conn.close()\\n return stations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b1d5f590063b06ffe430a1ab62a4d3\",\n \"score\": \"0.69704664\",\n \"text\": \"def get_stations(self):\\n url = 'https://gbfs.urbansharing.com/trondheimbysykkel.no/station_information.json'\\n \\n data = self._FetchData(url)\\n return data['data']['stations']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63be3d797aacd49b19bc6358dd182312\",\n \"score\": \"0.6958073\",\n \"text\": \"def stations():\\n # Query all stations\\n results = session.query(Measurement.station).all()\\n all_stations = []\\n for station in results:\\n stations_dict = {}\\n stations_dict[\\\"station\\\"] = station\\n all_stations.append(stations_dict)\\n\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa939f3cb8c0c3ab824137152fc51f3c\",\n \"score\": \"0.69410914\",\n \"text\": \"def get(self):\\n return get_all_stations()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53b44c1d8176404c41367ab534453fac\",\n \"score\": \"0.6917256\",\n \"text\": \"def stations():\\n\\n\\tresults = session.query(Measurement.station).distinct().all()\\n\\n\\treturn jsonify(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413651b387f552584bb157de048bab92\",\n \"score\": \"0.69172275\",\n \"text\": \"def get_all_stations():\\n\\n return Station.objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b0b13652d176f1e39a9a4b91be624c2\",\n \"score\": \"0.6900441\",\n \"text\": \"def stations():\\n # Query all stations names along with its code\\n sel = [measurement.station, station.name]\\n results = session.query(*sel)\\\\\\n .group_by(measurement.station).order_by(func.count(measurement.station).desc())\\\\\\n .filter(measurement.station == station.station)\\n\\n # Create a dictionary from the row data and append to a list of all_stations\\n all_stations = []\\n for stations in results:\\n stations_dict = {}\\n stations_dict[\\\"code\\\"] = stations.station\\n stations_dict[\\\"name\\\"] = stations.name\\n all_stations.append(stations_dict)\\n\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7d6bc66230300083ec606dc46a6b8af\",\n \"score\": \"0.6898497\",\n \"text\": \"def stations():\\n # Query all stations\\n query_stations = session.query(Measurement.station).group_by(Measurement.station).all()\\n\\n # Convert list of tuples into normal list\\n #stations_list = list(np.ravel(query_stations))\\n\\n return jsonify(query_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ebe3bc45356a695c51b3378e1abd383\",\n \"score\": \"0.6858366\",\n \"text\": \"def stations():\\n # Query all stations (distinct)\\n results = session.query(Station.station).distinct().all()\\n\\n # Convert list of tuples into normal list\\n all_stations = list(np.ravel(results))\\n\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0415f9e86ee14400b75f2f7acb874352\",\n \"score\": \"0.67902035\",\n \"text\": \"def get_stationlist(self):\\n indexes = self.model()._data[:, 0]\\n stationlist = WeatherSationList()\\n for index in indexes:\\n stationlist.append(self.stationlist[int(index)])\\n return stationlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf7d06e1ab50dd1974326695bda67225\",\n \"score\": \"0.67843646\",\n \"text\": \"def stations():\\n #Create session link from Python to DB\\n session = Session(engine)\\n #Create query for all stations\\n results_stations = session.query(Sta.station).all()\\n # close session\\n session.close() \\n # use numpy unravel to convert to a list\\n stations = list(np.ravel(results_stations))\\n return jsonify(stations=stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2330ede208b891314822b73a4cafd2\",\n \"score\": \"0.6778367\",\n \"text\": \"def stations():\\n stations_route = session.query(station.name, station.station).all()\\n return jsonify(stations_route)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a8c16800012fad248310f2e1e252ac\",\n \"score\": \"0.67733496\",\n \"text\": \"def stations():\\n\\t# Query all Stations\\n \\n\\tresults = session.query(Stations.station + '-'+ Stations.name).\\\\\\n\\torder_by(Stations.station.asc()).all()\\n \\n # Convert list of tuples into normal list using np.ravel\\n\\trec_list = list(np.ravel(results))\\n\\t\\n\\tprint(rec_list)\\n\\t\\n\\treturn jsonify(rec_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d957840f115ed706267caad2b7a1d41a\",\n \"score\": \"0.67599905\",\n \"text\": \"def stations():\\n # Query all stations\\n results = session.query(Station.station).all()\\n session.close()\\n stations_list = list(np.ravel(results))\\n return jsonify(stations_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa9aa0e2379e1694e4a71dc0f69edddc\",\n \"score\": \"0.675964\",\n \"text\": \"def stations():\\n # Query all stations\\n results = session.query(Station.name).all()\\n\\n # Convert list of tuples into normal list\\n station_list = list(np.ravel(results))\\n\\n return jsonify(station_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca5500451d1bad9c8a7a4d70bc60e2c5\",\n \"score\": \"0.6752356\",\n \"text\": \"def get_active_stations():\\n\\n station_ids = get_station_ids()\\n active_shower = active_shower_station(station_ids)\\n active_weather = active_weather_station(station_ids)\\n\\n return active_shower, active_weather\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72be559a8f74804ef3706440ef597b92\",\n \"score\": \"0.6712846\",\n \"text\": \"def search_operational_stations():\\n\\n if os.path.isfile('active_station_IDs.txt'):\\n read_list_from_file()\\n else:\\n print ''\\n print 'You do not have a list with operational stations in your current working directory.'\\n\\n update = query_yes_no('Do you want to update/create the list with operational stations?')\\n print ''\\n\\n if update == True:\\n active_shower, active_weather = get_active_stations()\\n with open('active_station_IDs.txt', 'w') as file:\\n file.write('active_shower = [')\\n file.write(', '.join(str(id) for id in active_shower))\\n file.write(']\\\\nactive_weather = [')\\n file.write(', '.join(str(id) for id in active_weather))\\n file.write(']')\\n read_list_from_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17062faea83c8a68d042d0b6796dc4c\",\n \"score\": \"0.67104703\",\n \"text\": \"def stations():\\n # Query all stations\\n station_names = session.query(Station.station).all()\\n\\n # Convert list of tuples into normal list\\n #stations_list = list(np.ravel(station_names)\\n\\n return jsonify(station_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5b60ea3df8bb3204c8970981a8f2d9\",\n \"score\": \"0.67008585\",\n \"text\": \"def get_stations(self):\\n stations = self.iw.get_stations()\\n result = []\\n minstrel = self.minstrel.get_frame_error_rates()\\n for station in stations:\\n result.append({\\n 'addr': station['addr'], \\n 'tx_rate': WifiHelpers.get_numbers_from_str(station['tx bitrate'])[0],\\n 'rx_rate': WifiHelpers.get_numbers_from_str(station['rx bitrate'])[0],\\n 'signal': WifiHelpers.get_numbers_from_str(station['signal'])[0],\\n 'signal_avg': WifiHelpers.get_numbers_from_str(station['signal avg'])[0],\\n 'fer': minstrel[station['addr']]['current'],\\n 'fer_avg' : minstrel[station['addr']]['avg']})\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23179b92cca2a4309ae90e72ddeced04\",\n \"score\": \"0.6700289\",\n \"text\": \"def stations():\\n # Query all stations\\n results = session.query(Station.station).all()\\n\\n # Convert list of tuples into normal list\\n all_names = list(np.ravel(results))\\n\\n return jsonify(all_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"522abcf2d35ab48a1ae677b09219039b\",\n \"score\": \"0.66876996\",\n \"text\": \"def stations():\\n # Query all station data\\n station_results = session.query(Station).all()\\n all_stations = []\\n for entry in station_results:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = entry.station\\n station_dict[\\\"name\\\"] = entry.name\\n station_dict[\\\"latitude\\\"] = entry.latitude\\n station_dict[\\\"longitude\\\"] = entry.longitude\\n station_dict[\\\"elevation\\\"] = entry.elevation\\n all_stations.append(station_dict)\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1fbb0788e36106c671b29dfb4d16b65\",\n \"score\": \"0.665675\",\n \"text\": \"def stations():\\n stations_results = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).all()\\n\\n stations = []\\n for result in results:\\n station_dict = {}\\n station_dict['station'] = result.station\\n station_dict['name'] = result.name\\n station_dict['latitude'] = result.latitude\\n station_dict['longitude'] = result.longitude\\n station_dict['elevation'] = result.elevation\\n stations.append(station_dict)\\n\\n return jsonify(stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e66422f83d5712cda4f25f0a6cdd8e7\",\n \"score\": \"0.66406256\",\n \"text\": \"def stations():\\n results = list(np.ravel(session.query(Station.station).all()))\\n\\n return jsonify(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f2be690846ef8e46a73fa2f15a53f4\",\n \"score\": \"0.6626754\",\n \"text\": \"def stations():\\n station_results = session.query(Station.name).all()\\n \\n session.close()\\n \\n return jsonify(station_results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12185b5afd762f59342872e587135c09\",\n \"score\": \"0.6606777\",\n \"text\": \"def stations():\\n # Query all passengers\\n station = session.query(Station.station).all()\\n\\n # Convert list of tuples into normal list\\n station_names = list(np.ravel(station))\\n\\n return jsonify(station_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e872a25fc9b31ad5be0506ea4e9f9269\",\n \"score\": \"0.65941554\",\n \"text\": \"def stations():\\n\\n results = session.query(Station.name).all()\\n \\n return jsonify(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e714ec34fa224496eb8d5c631354c23\",\n \"score\": \"0.65858024\",\n \"text\": \"def stations():\\n results = session.query(Station_tbl.station).all()\\n\\n # Unravel results into a 1D array and convert to a list\\n stations = list(np.ravel(results))\\n return jsonify(stations=stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32533ecf3c32c6438113f586be2c7fa3\",\n \"score\": \"0.65793765\",\n \"text\": \"def _get_stations_local() -> List[WeatherStation]:\\n logger.info('Using pre-generated json to retrieve station list')\\n with open(weather_stations_file_path, encoding=\\\"utf-8\\\") as weather_stations_file:\\n json_data = json.load(weather_stations_file)\\n results = []\\n for station in json_data['weather_stations']:\\n results.append(WeatherStation(**station))\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9dda6de5d4b9e62e5c731a57a31a6b2\",\n \"score\": \"0.65770566\",\n \"text\": \"def stations():\\n # Create our session (link) from Python to the DB\\n session = Session(engine)\\n\\n # Query for stations.\\n stations = session.query(Station.station, Station.name,\\n Station.latitude, Station.longitude, Station.elevation).all()\\n\\n session.close()\\n\\n # Convert the query results to a dictionary.\\n all_stations = []\\n for station, name, latitude, longitude, elevation in stations:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = station\\n station_dict[\\\"name\\\"] = name\\n station_dict[\\\"latitude\\\"] = latitude\\n station_dict[\\\"longitude\\\"] = longitude\\n station_dict[\\\"elevation\\\"] = elevation\\n all_stations.append(station_dict)\\n\\n # Return the JSON representation of dictionary.\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e75338cba66ed0e22ed66fcfa14718e\",\n \"score\": \"0.6572708\",\n \"text\": \"def stations():\\n # Query all station\\n station_sum= session.query(Station.station).all()\\n\\n # Convert list of tuples into normal list\\n all_names2 = list(np.ravel(station_sum))\\n\\n return jsonify(all_names2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"990d8002296b6a5d4731e7fc772753ea\",\n \"score\": \"0.6550659\",\n \"text\": \"def getStations():\\r\\n\\r\\n # Query all Stations\\r\\n\\r\\n results = session.query(Measurement.station).distinct().all()\\r\\n\\r\\n\\r\\n\\r\\n # Convert list of tuples into normal list\\r\\n\\r\\n all_names = list(np.ravel(results))\\r\\n\\r\\n\\r\\n\\r\\n return jsonify(all_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ff9db663b8c2a393412d4695b373bea\",\n \"score\": \"0.65325254\",\n \"text\": \"def get_stations(sock):\\n stations = []\\n\\n arp_table = get_arp_table()\\n\\n # query hostapd for all attached stations\\n sock.sendto(\\\"STA-FIRST\\\", hostapd_ctrl_sock_name)\\n p = sock.recv(1024)\\n while p:\\n lines = p.splitlines()\\n mac = lines[0]\\n ips = get_mac_ips(mac, arp_table)\\n for ip in ips:\\n stations.append((mac, ip))\\n sock.sendto(\\\"STA-NEXT %s\\\" % mac, hostapd_ctrl_sock_name)\\n p = sock.recv(1024)\\n\\n logger.debug(\\\"get_stations: %s\\\", str(arp_table))\\n\\n return stations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b418d58ae251687f25b76ab3a12344\",\n \"score\": \"0.65283376\",\n \"text\": \"def stations():\\n # Query all passengers\\n results = session.query(Station).all()\\n\\n # Create a dictionary from the row data and append to a list of all_passengers\\n all_stations = []\\n for station in results:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = station.station\\n \\n all_stations.append(station_dict)\\n\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47a61bae26a3056ccbc4a71e772af908\",\n \"score\": \"0.65192854\",\n \"text\": \"def get_user_stations(self):\\n path = \\\"/user/stations\\\"\\n return self.request_json(\\\"GET\\\", path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8ae36ed17f44458c0171708a9b97dac\",\n \"score\": \"0.64833647\",\n \"text\": \"def stations():\\n session = Session(engine)\\n results = session.query(Station.station, Station.name).all()\\n\\n # Create a dictionary \\n all_results = []\\n for station, station_name in results:\\n dict_entry = {}\\n dict_entry[station] = station_name\\n all_results.append(dict_entry)\\n\\n return jsonify(all_results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"107fb5b0190e3dd9df3564723784b62e\",\n \"score\": \"0.6453889\",\n \"text\": \"def stations():\\n\\n # Query all stations\\n station_results = session.query(Station.station, Station.latitude, Station.longitude,\\n Station.name, Station.id, Station.elevation).all()\\n\\n # Create a dictionary from the row data and append to a list of all_stations\\n all_stations = []\\n for station, latitude, longitude, name, id, elevation in station_results:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = station\\n station_dict[\\\"latitude\\\"] = latitude\\n station_dict[\\\"longitude\\\"] = longitude\\n station_dict[\\\"name\\\"] = name\\n station_dict[\\\"id\\\"] = id\\n station_dict[\\\"elevation\\\"] = elevation\\n all_stations.append(station_dict)\\n\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d184a99f63d43d6ab81aa00527afb52\",\n \"score\": \"0.64419955\",\n \"text\": \"def list_sessions(rundate):\\n return config.where[TECH].stations.list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa44c423c7302eaad8059f0e408bfef\",\n \"score\": \"0.64366823\",\n \"text\": \"def stations():\\n # Create our session (link) from Python to the DB\\n session = Session(engine)\\n\\n # Query all stations\\n station_results = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).all()\\n\\n session.close()\\n\\n # Create a dictionary from the row data and append to a list of all_stations\\n all_stations = []\\n for station, name, latitude, longitude, elevation in station_results:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = station\\n station_dict[\\\"name\\\"] = name\\n station_dict[\\\"latitude\\\"] = latitude\\n station_dict[\\\"longitude\\\"] = longitude\\n station_dict[\\\"elevation\\\"] = elevation\\n all_stations.append(station_dict)\\n\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc93f6fe8afa57b3e9576c2316f0f02c\",\n \"score\": \"0.6431296\",\n \"text\": \"def stations():\\r\\n # Query data table station and list variables (station number and name)\\r\\n results = session.query(Station.station, Station.name).all()\\r\\n session.commit()\\r\\n \\r\\n # Create a dictionary from the row data and append to a list, places\\r\\n places=[]\\r\\n for place in results:\\r\\n place_dict = {}\\r\\n place_dict[\\\"station_ID\\\"] = place[0]\\r\\n place_dict[\\\"station_name\\\"] = place[1]\\r\\n places.append(place_dict)\\r\\n time.sleep(2)\\r\\n return jsonify(places)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fedccbeb2c665a4044d104c2b9275847\",\n \"score\": \"0.6424203\",\n \"text\": \"def stations():\\n\\n results = session.query(Measurement.station).all()\\n station_list = list(np.ravel(results))\\n # station_dict = []\\n # for station, stations in results:\\n # results_dict = {}\\n # results_dict[\\\"date\\\"] = date\\n # results_dict[\\\"prcp\\\"] = prcp\\n # station_dict.append(station_dict)\\n return jsonify(station_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba675a9c2bda2fd7e2ebdcf16b82058\",\n \"score\": \"0.6396752\",\n \"text\": \"def get_stations(url: str, city: str) -> List[Station]:\\n resp = requests.get(url)\\n stations = resp.json()\\n\\n station_list = [Station.from_repr(raw_data, city=city, dt=datetime.now()) for raw_data in stations]\\n\\n return station_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba675a9c2bda2fd7e2ebdcf16b82058\",\n \"score\": \"0.6396752\",\n \"text\": \"def get_stations(url: str, city: str) -> List[Station]:\\n resp = requests.get(url)\\n stations = resp.json()\\n\\n station_list = [Station.from_repr(raw_data, city=city, dt=datetime.now()) for raw_data in stations]\\n\\n return station_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8a72e544493d2306a4d8a0c8ea2e01\",\n \"score\": \"0.63945\",\n \"text\": \"def stations():\\n \\n # Create our session (link) from Python to the DB\\n session = Session(engine)\\n\\n # Query all the stations\\n results = session.query(Station).all()\\n \\n session.close()\\n\\n # Create a dictionary from the row data and append to a list of all_stations_list.\\n all_stations_list = []\\n for stations in results:\\n stations_dict = {}\\n stations_dict[\\\"Station\\\"] = stations.station\\n stations_dict[\\\"Station Name\\\"] = stations.name\\n stations_dict[\\\"Latitude\\\"] = stations.latitude\\n stations_dict[\\\"Longitude\\\"] = stations.longitude\\n stations_dict[\\\"Elevation\\\"] = stations.elevation\\n all_stations_list.append(stations_dict)\\n \\n return jsonify(all_stations_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6952ac7622a036bca7cf939a00290649\",\n \"score\": \"0.6375675\",\n \"text\": \"def stations():\\r\\n all_stations = session.query(Station.station).all()\\r\\n all_stations_list = list(np.ravel(all_stations))\\r\\n return jsonify(all_stations_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ead29767037ce3fb3fd12d65cd0766f3\",\n \"score\": \"0.63741326\",\n \"text\": \"def station():\\r\\n\\r\\n # Pull all stations and station names from Station\\r\\n results = session.query(Station.name).all()\\r\\n \\r\\n # Create a list to track our results to eventually turn into a JSON\\r\\n station_list = []\\r\\n \\r\\n # Loop through the query results and add the station name to the list\\r\\n for x in results:\\r\\n station_list.append(x.name)\\r\\n\\r\\n return jsonify(station_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"768cf2daf565edc8fd5c45a4f936d12c\",\n \"score\": \"0.6372585\",\n \"text\": \"def active_shower_station(station_ids):\\n\\n url_template = \\\"http://data.hisparc.nl/api/station/%s/data/\\\" + yesterday_url()\\n active_ids = [id for id in station_ids if get_json(url_template % id)]\\n\\n return active_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecefbb5584acc36ce68134f4ea855ae4\",\n \"score\": \"0.63409036\",\n \"text\": \"def list_servers_status(self):\\n if self.get_servers():\\n x = PrettyTable()\\n x.field_names = [\\\"Name\\\", \\\"UUID\\\", \\\"Current State\\\"]\\n x.align[\\\"Name\\\"] = \\\"l\\\"\\n x.align[\\\"Current State\\\"] = \\\"l\\\"\\n for server in self.servers:\\n if server.state == \\\"running\\\":\\n x.add_row([server.name, server.uuid, prGreen(server.state)])\\n elif server.state == \\\"stopped\\\":\\n x.add_row([server.name, server.uuid, prRed(server.state)])\\n else:\\n x.add_row([server.name, server.uuid, server.state])\\n print(x)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80faf3698d39bf3b40eff7a7bbb6f4c9\",\n \"score\": \"0.63397574\",\n \"text\": \"def stations():\\n\\n session = Session(bind=engine)\\n\\n # Query all stations\\n stations = session.query(Station).all()\\n\\n # close the session to end the communication with the database\\n session.close()\\n\\n # store query results in dictionary\\n all_stations = []\\n for station in stations:\\n station_dict = {}\\n station_dict[\\\"id\\\"] = station.id\\n station_dict[\\\"station\\\"] = station.station\\n station_dict[\\\"name\\\"] = station.name\\n station_dict[\\\"latitude\\\"] = station.latitude\\n station_dict[\\\"longitude\\\"] = station.longitude\\n station_dict[\\\"elevation\\\"] = station.elevation\\n all_stations.append(station_dict)\\n\\n return jsonify(all_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c0e79a9c026e5f6eb8eac7441dce92\",\n \"score\": \"0.6313038\",\n \"text\": \"def get_status_list(url): # -> list[Unknown]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50fbfb53fdfb06235da2ab14c09c5c02\",\n \"score\": \"0.62859654\",\n \"text\": \"def get_statuses ():\\n res = []\\n for val, name in TaskInstance.STATUSES:\\n if val != 0:\\n res.append ((name, name))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b82f462f33c01f23aa9d53d30d26ac5\",\n \"score\": \"0.62786394\",\n \"text\": \"async def get_stations_by_codes(station_codes: List[int]) -> List[WeatherStation]:\\n if use_wfwx():\\n logger.info('Fetching stations from WFWX')\\n return await wfwx_api.get_stations_by_codes(station_codes)\\n return _get_stations_by_codes_local(station_codes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2291945257917d64599ef57113db0e5f\",\n \"score\": \"0.6230371\",\n \"text\": \"def get_stations(self):\\n # Check that we have run `create_df`\\n df = getattr(self, \\\"df\\\", None)\\n if df is None:\\n return []\\n return list(sorted(set(map(lambda s: s.split(\\\"_\\\")[0], df.columns))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"465f94ca339ef17762834afe61646718\",\n \"score\": \"0.61809564\",\n \"text\": \"def prepare_stations_status_response(chat_id, stations):\\n messages = [EMOJIS['bicycle'] + ' - ' + EMOJIS['no_bicycle']]\\n for station in stations:\\n if 'error' in station:\\n messages.append(station['error'])\\n else:\\n if station['status'] == 1:\\n bikes = '{} - {}'.format(pad_number(station['bikes']), pad_number(station['slots']))\\n else:\\n bikes = tr('disabled_station', chat_id=chat_id)\\n messages.append('{} [{}] {}'.format(bikes, station['id'], compact_address(station['streetName'])))\\n return '\\\\n'.join(messages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"471b39de2b30e76f10e78f30bdce6fcc\",\n \"score\": \"0.6176198\",\n \"text\": \"def loadStations(self):\\n\\n station_list = []\\n\\n for dir_name in os.listdir(self.dir_path):\\n\\n # Check if the dir name matches the station name pattern\\n if os.path.isdir(os.path.join(self.dir_path, dir_name)):\\n if re.match(\\\"^[A-Z]{2}[A-Z0-9]{4}$\\\", dir_name):\\n print(\\\"Using station:\\\", dir_name)\\n station_list.append(dir_name)\\n else:\\n print(\\\"Skipping directory:\\\", dir_name)\\n\\n\\n return station_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b60bf5722e03033a65a111d7a4fb7fa0\",\n \"score\": \"0.6170425\",\n \"text\": \"def get_stations(key, year):\\n ready = False\\n while not ready:\\n resp = am.getJson(token, path + key + \\\"/stations/\\\")\\n if \\\"Ready\\\" in resp:\\n ready = resp[\\\"Ready\\\"]\\n else:\\n if \\\"ErrorMessage\\\" in resp:\\n print(resp[\\\"ErrorMessage\\\"])\\n break\\n else:\\n if \\\"Message\\\" in resp:\\n raise Exception(resp[\\\"Message\\\"])\\n else:\\n print(resp)\\n raise Exception(\\\"Something was wrong!\\\")\\n\\n for i in range(resp[\\\"Pages\\\"]):\\n statResponse = am.getJson(token, path + key + \\\"/stations/\\\" + str(i))\\n stations = statResponse[\\\"Items\\\"]\\n for stat in stations:\\n stid = stat[\\\"Id\\\"]\\n if stid in station_years:\\n if not station_years[stid][len(station_years[stid]) - 1] == year:\\n station_years[stid].append(year)\\n else:\\n station_years[stid] = [year]\\n station_projects[stid] = []\\n proj_id = stat[\\\"Project\\\"][\\\"Id\\\"]\\n\\n if len(station_years[stid]) == 1:\\n station_projects[stid].append(proj_id)\\n\\n if not proj_id in project_name_count.keys():\\n project_name_count[proj_id] = {\\\"Count\\\": 0}\\n am.deleteJson(token, path + key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41efaef203f9915a4218dbe3951784e8\",\n \"score\": \"0.61364734\",\n \"text\": \"def fetch_bart_stations():\\n url = 'http://api.bart.gov/api/stn.aspx?cmd=stns&key=MW9S-E7SL-26DU-VV8V&json=y'\\n r = requests.get(url)\\n stations = r.json()[\\\"root\\\"][\\\"stations\\\"][\\\"station\\\"]\\n return stations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b14ba2ec9921456835a53d9c9787afc\",\n \"score\": \"0.61325943\",\n \"text\": \"def statuses(self) -> List[Dict[str, Any]]:\\n self.wait_until_done()\\n return self._fetch_remote_ndjson(self.status_file_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7cc460f192269cfbd8a0f659202a1c6\",\n \"score\": \"0.6117029\",\n \"text\": \"def get_status(self) -> dict:\\n station = self._get_closest_radio_station(self.current_freq)\\n return {\\n \\\"station_name\\\": station.name,\\n \\\"station_vol\\\": 1 if abs(self.current_freq - station.frequency) <= 0.3 else 0,\\n \\\"stations\\\": {station.name: station.frequency for station in self.stations}\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"098568315abf57b0b7efce288f25dd04\",\n \"score\": \"0.6111742\",\n \"text\": \"def build_station_list(use_cache=True, test=False):\\n # Fetch station data - if testing use the fixed test data\\n if test:\\n data = datafetcher.fetch_test_station_data()\\n else:\\n data = datafetcher.fetch_station_data(use_cache)\\n\\n # Build list of MonitoringStation objects\\n stations = []\\n for e in data[\\\"items\\\"]:\\n # Extract town string (not always available)\\n town = None\\n if 'town' in e:\\n town = e['town']\\n\\n # Extract river name (not always available)\\n river = None\\n if 'riverName' in e:\\n river = e['riverName']\\n\\n # Attempt to extract typical range (low, high)\\n try:\\n typical_range = (float(e['stageScale']['typicalRangeLow']),\\n float(e['stageScale']['typicalRangeHigh']))\\n except KeyError:\\n typical_range = None\\n\\n try:\\n # Create mesure station object if all required data is\\n # available, and add to list\\n s = MonitoringStation(\\n station_id=e['@id'],\\n measure_id=e['measures'][-1]['@id'],\\n label=e['label'],\\n coord=(float(e['lat']), float(e['long'])),\\n typical_range=typical_range,\\n river=river,\\n town=town)\\n stations.append(s)\\n except KeyError:\\n # Not all required data on the station was available, so\\n # skip over\\n pass\\n\\n return stations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b805253a64fcb3b9757ecac682c268e\",\n \"score\": \"0.6101596\",\n \"text\": \"async def stations(self, orderby: str = 'name', reverse: bool = False, offset=0, limit = 100000) -> List[dict]:\\n if not self.__intialized:\\n raise NotInitialized(\\\"Please initialize the RadioBrowser.\\\")\\n\\n reverse = str(reverse).lower()\\n\\n\\n params = {\\n 'order': orderby,\\n 'reverse': reverse,\\n 'offset': offset,\\n 'limit': limit\\n }\\n\\n\\n return await self.http.request(\\\"stations/\\\", params=params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eefd6846ebe7f4bc4cc6a9693cbd5d04\",\n \"score\": \"0.60952306\",\n \"text\": \"def getStationsForHighway(hw, dir):\\n\\tstations = []\\n\\thwy_q = Highway.query(Highway.highwayname == hw, Highway.shortdirection == dir)\\n\\tfor hwy in hwy_q.fetch():\\n\\t\\tstations = [station.get() for station in hwy.stations]\\n\\treturn stations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d8b80bccc6e76209ff3e78f3f3fd98d\",\n \"score\": \"0.60813785\",\n \"text\": \"def extract_active_curw_obs_rainfall_stations():\\n # Connect to the database\\n pool = get_Pool(host=CURW_OBS_HOST, port=CURW_OBS_PORT, user=CURW_OBS_USERNAME, password=CURW_OBS_PASSWORD,\\n db=CURW_OBS_DATABASE)\\n\\n obs_stations = [['hash_id', 'station_id', 'station_name', 'latitude', 'longitude']]\\n\\n connection = pool.connection()\\n\\n try:\\n\\n with connection.cursor() as cursor1:\\n cursor1.callproc(procname='getActiveRainfallObsStations')\\n results = cursor1.fetchall()\\n\\n for result in results:\\n # if (result.get('station_id') != 100083):\\n obs_stations.append([result.get('hash_id'), result.get('station_id'), result.get('station_name'),\\n result.get('latitude'), result.get('longitude')])\\n\\n # Write to csv file\\n create_csv('grids/obs_stations/rainfall/curw_active_rainfall_obs_stations.csv', obs_stations)\\n\\n except Exception as ex:\\n traceback.print_exc()\\n finally:\\n connection.close()\\n destroy_Pool(pool)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1af121baf99a0bf0267d7100533691a\",\n \"score\": \"0.6069473\",\n \"text\": \"def __get_stations(ids=None, progress=True):\\n\\n # invert the progress parameter, tqdm interpretation is\\n # DEFAULT disable = False -> progressbar is visible\\n progress = not progress\\n \\n # Use directory listing from stilt-web data. Ignore stations that\\n # may be in the queue but are not finished yet.\\n allStations = [s for s in os.listdir(CPC.STILTPATH) if os.path.exists(CPC.STILTPATH + s)]\\n \\n # if ids are provided, select only valid ids from allstations\\n if ids:\\n # make sure they are all upper case\\n ids = [i.upper() for i in ids]\\n #select only valid id from allstations\\n allStations = list(set(ids).intersection(allStations))\\n\\n \\n # add information on station name (and new STILT station id)\\n # from stations.csv file used in stiltweb.\\n # this is available from the backend through a url\\n df = pd.read_csv(CPC.STILTINFO)\\n\\n # add ICOS flag to the station\\n icos_stations_df = cpstation.getIdList(project='ICOS', theme='AS') \\n\\n # dictionary to return\\n stations = {}\\n\\n # load precomputed geoinfo\\n geo = geoinfo.get()\\n\\n # fill dictionary with ICOS station id, latitude, longitude and altitude\\n # implement progress True/False\\n for ist in tqdm(sorted(allStations), disable=progress):\\n \\n stations[ist] = {}\\n # get filename of link (original stiltweb directory structure) and\\n # extract location information\\n\\n loc_ident = os.readlink(CPC.STILTPATH+ist)\\n clon = loc_ident[-13:-6]\\n lon = float(clon[:-1])\\n if clon[-1:] == 'W':\\n lon = -lon\\n clat = loc_ident[-20:-14]\\n lat = float(clat[:-1])\\n if clat[-1:] == 'S':\\n lat = -lat\\n alt = int(loc_ident[-5:])\\n\\n stations[ist]['lat']=lat\\n stations[ist]['lon']=lon\\n stations[ist]['alt']=alt\\n stations[ist]['locIdent']=os.path.split(loc_ident)[-1]\\n\\n # set the name and id\\n stations[ist]['id'] = ist\\n \\n # check if station id is in the manual curated list at\\n # CPC.STILTINFO\\n id = [a.lower() for a in df['STILT id'].values] \\n \\n if ist.lower() in id:\\n stationName = str(df.loc[df['STILT id'] ==\\n ist]['STILT name'].item())\\n # get the index for this station, to extract corresponding \\n # ICOS sampling height from CPC.STILTINFO\\n idx = df.index[df['STILT id'] == ist].tolist()\\n \\n else:\\n stationName = ''\\n \\n stations[ist]['name'] = __stationName(stations[ist]['id'],\\n stationName,\\n stations[ist]['alt'])\\n\\n # set a flag and metadata if it is an ICOS station\\n \\n # convert the ICOS id to strings\\n df['ICOS id'] = df['ICOS id'].astype(str)\\n stn = df.iloc[idx]['ICOS id'].tolist()[0]\\n \\n if 'nan' not in stn:\\n stations[ist]['icos'] = cpstation.get(stn, icos_stations_df).info()\\n \\n # add corresponding ICOS Sampling Height\\n sh = df.iloc[idx]['ICOS height'].tolist()\\n sh = float(sh[0])\\n stations[ist]['icos']['SamplingHeight'] = sh \\n \\n else:\\n stations[ist]['icos'] = False\\n\\n # set years and month of available data\\n years = [y for y in os.listdir(CPC.STILTPATH + '/' + ist) if\\n os.path.exists(CPC.STILTPATH + '/' + ist + '/' + y)]\\n \\n # for atmo access zip files may exist for download. we need to exclude them\\n years = [y for y in years if not y.endswith('.zip')]\\n \\n stations[ist]['years'] = years\\n for yy in sorted(stations[ist]['years']):\\n stations[ist][yy] = {}\\n months = [m for m in os.listdir(CPC.STILTPATH + '/' + ist + '/' + yy) if\\n os.path.exists(CPC.STILTPATH + '/' + ist + '/' + yy + '/' + m)]\\n # remove cache txt entry\\n sub = 'cache'\\n months = sorted([m for m in months if not sub.lower() in m.lower()])\\n stations[ist][yy]['months'] = months\\n stations[ist][yy]['nmonths'] = len(stations[ist][yy]['months'])\\n \\n # add geoinfo\\n if ist in geo.keys():\\n #get the precomputed country info\\n stations[ist]['geoinfo'] = geo[ist]['geoinfo']\\n elif stations[ist]['icos']:\\n # get country from ICOS coordinates\\n stations[ist]['geoinfo'] = __country([stations[ist]['icos']['lat'],stations[ist]['icos']['lon']])\\n else:\\n # get country from STILT coordiantes\\n stations[ist]['geoinfo'] = __country([stations[ist]['lat'],stations[ist]['lon']])\\n \\n\\n return stations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5a8ae44cf37168dd59ba5b2bbbcbc18\",\n \"score\": \"0.60223424\",\n \"text\": \"def fixture_stations():\\n return {\\n \\\"status\\\": \\\"success\\\",\\n \\\"data\\\": [\\n {\\n \\\"location\\\": {\\\"type\\\": \\\"Point\\\", \\\"coordinates\\\": [116.466258, 39.954352]},\\n \\\"station\\\": \\\"US Embassy in Beijing\\\",\\n },\\n {\\n \\\"location\\\": {\\n \\\"type\\\": \\\"Point\\\",\\n \\\"coordinates\\\": [116.2148532181, 40.0078007235],\\n },\\n \\\"station\\\": \\\"Botanical Garden\\\",\\n },\\n ],\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fea1b1f9db18f445980b1e4a6de75ca\",\n \"score\": \"0.5993486\",\n \"text\": \"def send_stations_status(chat_id, stations):\\n stations_status = Manager().dict()\\n processes = []\\n for station_index, station_id in enumerate(stations):\\n # Start a process per station and add it to processes list\\n process = Process(target=get_station_multiproc, args=(station_id, chat_id, stations_status, station_index))\\n process.start()\\n processes.append(process)\\n # Wait until all processes have stopped\\n for process in processes:\\n process.join()\\n if stations_status:\\n # Get stations status sorted by index\\n stations_status = [stations_status[key] for key in sorted(stations_status.keys())]\\n get_bot().send_message(chat_id=chat_id, text=prepare_stations_status_response(chat_id, stations_status))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd626bbf0768ae2bab1dd5a1d9d542b\",\n \"score\": \"0.59933895\",\n \"text\": \"def active_weather_station(station_ids):\\n\\n url_template = \\\"http://data.hisparc.nl/api/station/%s/weather/\\\" + yesterday_url()\\n active_ids = [id for id in station_ids if get_json(url_template % id)]\\n\\n return active_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3a7dd25642acb5a5f9902f0eb0f3d7\",\n \"score\": \"0.598828\",\n \"text\": \"def get_station_names(self):\\n return self.worker().wxdatasets.station_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf0da53241f31b261c5fb49eade409c\",\n \"score\": \"0.5982597\",\n \"text\": \"def get_stations():\\n station_names=[]\\n with open(STATIONFILES_LOC) as infp:\\n for line in infp:\\n if line.strip():\\n station_name = line.split()[-1]\\n station_names.append(station_name)\\n\\n # print num_lines\\n return station_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f3d8135c713b85611cf3d4fc807b21\",\n \"score\": \"0.5952542\",\n \"text\": \"def getStations():\\n ls = []\\n with open('ghcnd-stations.txt', 'r') as f:\\n for line in f.readlines():\\n ls += [[i for i in line.strip().split() if i != ''][:3]]\\n return ls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702152e28e1960802711dd084dd8d350\",\n \"score\": \"0.59523463\",\n \"text\": \"def get_stations_list(urlroot, strdate):\\n all_stations_list = []\\n all_stations_html = get_html_string(urlroot+strdate+\\\"/\\\")\\n all_stations_soup = BeautifulSoup.BeautifulSoup(all_stations_html)\\n\\n for tag in all_stations_soup.findAll('a', href=True):\\n #length is 5: eg. \\\"CVSL/\\\", we remove the \\\"/\\\" to get station name\\n if tag['href'].__len__() == 5:\\n tag['href'] = tag['href'].replace(\\\"/\\\",\\\"\\\")\\n tag['href'] = tag['href'][1:].encode('ascii','ignore')\\n all_stations_list.append(tag['href'])\\n \\n return all_stations_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be5927a0eb5b0a5578fd7b8a6c752cc\",\n \"score\": \"0.5942769\",\n \"text\": \"def test_display_stations(self):\\n\\n with self.client as client:\\n response = client.get(\\\"/api/red/stations\\\")\\n data = response.json\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(data, {\\\"stations\\\": [{\\\"station_name\\\": \\\"69th\\\"}]})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cea18de1a627bbc58604b1cee50b237\",\n \"score\": \"0.5917357\",\n \"text\": \"def get_train_stations(self, filename=None):\\n if filename is None:\\n url = \\\"https://rata.digitraffic.fi/api/v1/metadata/stations\\\"\\n\\n with urllib.request.urlopen(url) as u:\\n data = json.loads(u.read().decode(\\\"utf-8\\\"))\\n else:\\n with open(filename) as f:\\n data = json.load(f)\\n\\n stations = dict()\\n\\n for s in data:\\n latlon = {'lat': s['latitude'], 'lon': s['longitude']}\\n stations[s['stationShortCode'].encode('utf-8').decode()] = latlon\\n\\n return stations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874bbaae45d0d9384b0306509ade8e50\",\n \"score\": \"0.5913973\",\n \"text\": \"def get_status(self) -> list:\\n result = []\\n for machine_id in self.busy_machines:\\n result.append((machine_id, self.get_remaining_time(machine_id), ))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"336652dfee39ad67ca903b4afa47e883\",\n \"score\": \"0.5910264\",\n \"text\": \"def get_stations_from_filelist(airports):\\n station = []\\n for airport in airports:\\n station.append(airport[\\\"stations\\\"])\\n return station\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1d89e6b87a2a5d9f2e544dbed888f82\",\n \"score\": \"0.590772\",\n \"text\": \"def stations(): \\n session = Session(engine)\\n \\n #query stations data\\n stations = session.query(Station)\\n \\n #loop to create dictionary\\n station_dictionary = []\\n for station in stations:\\n stations = {}\\n stations[\\\"Station\\\"] = station.station\\n stations[\\\"Name\\\"] = station.name\\n station_dictionary.append(station_dict)\\n \\n return jsonify(station_dictionary)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4e6e4aced0b3f174d851894b5b1ab5\",\n \"score\": \"0.5879276\",\n \"text\": \"def _get_stations_by_codes_local(station_codes: List[int]) -> List[WeatherStation]:\\n logger.info('Using pre-generated json to retrieve station by code')\\n with open(weather_stations_file_path, encoding=\\\"utf-8\\\") as file_pointer:\\n stations = json.load(file_pointer)\\n results = []\\n for station in stations['weather_stations']:\\n if int(station['code']) in station_codes:\\n results.append(WeatherStation(**station))\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3a966d61240e91a440b5b0fc595f99\",\n \"score\": \"0.5875365\",\n \"text\": \"def station_stats(self):\\n\\n self._start_clock()\\n\\n self.dataFrame['StartEnd Stations'] = self.dataFrame['Start Station'] \\\\\\n + ' -> ' \\\\\\n + self.dataFrame['End Station']\\n\\n most_common_start_station = self.dataFrame['Start Station'].mode()[0]\\n most_common_end_station = self.dataFrame['End Station'].mode()[0]\\n most_common_start_end_stations = self.dataFrame['StartEnd Stations'].mode()[0]\\n\\n self._stop_clock('station')\\n\\n return StationStats(most_common_start_station,\\n most_common_end_station,\\n most_common_start_end_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"022b9406279c53b6b8fb7d3027d8c57d\",\n \"score\": \"0.5868583\",\n \"text\": \"def station_names(self):\\n if self.metadata is None or self.metadata.empty:\\n return []\\n else:\\n return self.metadata['Station Name'].tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b6928e2d3b00bd4825407cf9a8104c\",\n \"score\": \"0.58667845\",\n \"text\": \"def download_stations(self):\\n station_path = Path(self.station_path)\\n station_path.mkdir(exist_ok=True, parents=True)\\n client = Client(\\\"IRIS\\\")\\n\\n station_params = dict(\\n starttime=self.starttime,\\n endtime=self.endtime,\\n latitude=sum(self.latitudes) / 2.,\\n longitude=sum(self.longitudes) / 2.,\\n maxradius=self.station_search_degrees, # radius in degrees\\n channel='H*', # only include channels that start with H\\n level='response', # indicates we want instrument responses\\n )\\n\\n inv = client.get_stations(**station_params)\\n inv.write(str(station_path / 'inventory.xml'), 'stationxml')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"decf35b17d3c66e097e1d22e8d3f2993\",\n \"score\": \"0.5847852\",\n \"text\": \"def load_stations(self):\\n if not self.addon.exists(self.path_stations):\\n self._save_stations(self._get_stations())\\n\\n return self.addon.read_file(self.path_stations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea1dcb45d75372b27c5c2229bd820059\",\n \"score\": \"0.5844812\",\n \"text\": \"def status(self) -> List[Tuple[str, str]]:\\n return [(n.id, n.status) for n in self._db.filter()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24992a07e115e140149df0437847e611\",\n \"score\": \"0.5833598\",\n \"text\": \"def get_all_device_status(self) -> List[Dict[str, Any]]:\\r\\n statuses: List[Dict[str, Any]] = []\\r\\n\\r\\n for device in self.devices:\\r\\n statuses.append(\\r\\n {\\r\\n \\\"id\\\": device.get_id(),\\r\\n \\\"status\\\": device.get_status_string()\\r\\n }\\r\\n )\\r\\n \\r\\n return statuses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb0491b24769c40c34c536679449a82\",\n \"score\": \"0.5828314\",\n \"text\": \"def stations():\\n\\tquery = \\\"https://www.aviationweather.gov/docs/metar/stations.txt\\\"\\n\\tr = requests.get(query)\\n\\tif r.status_code != 200:\\n\\t\\traise Exception(f\\\"request error (code={r.status_code})\\\")\\n\\tresult = {}\\n\\tlinenum = 0\\n\\tsection = None\\n\\tfor line in r.text.split(\\\"\\\\n\\\"):\\n\\t\\tlinenum += 1\\n\\t\\tif not line:\\n\\t\\t\\tsection = None\\n\\t\\telif line[0] != '!':\\n\\t\\t\\tif not section:\\n\\t\\t\\t\\tsection = line\\n\\t\\t\\telif line[3] != ' ':\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tregion = line[0:2].strip()\\n\\t\\t\\t\\t\\tif not region:\\n\\t\\t\\t\\t\\t\\tregion = None\\n\\t\\t\\t\\t\\tstation = line[3:19].strip()\\n\\t\\t\\t\\t\\ticao = line[20:24].strip()\\n\\t\\t\\t\\t\\tif not icao:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tiata = line[26:29].strip()\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tsynop = int(line[32:37])\\n\\t\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t\\tsynop = None\\n\\t\\t\\t\\t\\tlat = round(float(line[39:41])+float(line[42:44])/60,2)\\n\\t\\t\\t\\t\\tif line[44] == 'S': lat = -lat\\n\\t\\t\\t\\t\\telif line[44] != 'N': raise Exception(f\\\"latitude invalid\\\")\\n\\t\\t\\t\\t\\tlon = round(float(line[47:50]) + float(line[51:53])/60,2)\\n\\t\\t\\t\\t\\tif line[53] == 'W': lon = -lon\\n\\t\\t\\t\\t\\telif line[53] != 'E': raise Exception(f\\\"longitude invalid\\\")\\n\\t\\t\\t\\t\\telev = float(line[55:59])\\n\\t\\t\\t\\t\\tcountry = line[80:83].strip()\\n\\t\\t\\t\\texcept Exception as err:\\n\\t\\t\\t\\t\\tprint(f\\\"stations.txt({linenum}): '{line}': {err}\\\")\\n\\t\\t\\t\\t\\traise\\n\\t\\t\\t\\tif country:\\n\\t\\t\\t\\t\\tresult[icao] = dict(\\n\\t\\t\\t\\t\\t\\tcountry = country,\\n\\t\\t\\t\\t\\t\\tregion = region,\\n\\t\\t\\t\\t\\t\\tstation = station,\\n\\t\\t\\t\\t\\t\\tlatitude = lat,\\n\\t\\t\\t\\t\\t\\tlongitude = lon,\\n\\t\\t\\t\\t\\t\\t# linenum = linenum,\\n\\t\\t\\t\\t\\t\\televation = elev,\\n\\t\\t\\t\\t\\t)\\n\\n\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14ad076925f61c5836ee3ae4ec91b90e\",\n \"score\": \"0.5822643\",\n \"text\": \"def get_station_locations(settings: dict, st: \\\"obspy.Stream\\\" = None):\\n if settings[\\\"do_synth\\\"] and settings[\\\"use_synth_stations\\\"]:\\n return get_synth_stations(settings, wiggle=0)\\n else:\\n if settings[\\\"synth_stations_mode\\\"] == \\\"real_locations_worldwide\\\":\\n return get_real_station_locations_worldwide()\\n return get_station_locs_from_staxml(st, settings=settings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d296fd116ce39c768525a022b57b9be\",\n \"score\": \"0.5822628\",\n \"text\": \"def get_stations(name):\\n\\n return Station.objects(name__icontains=name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2234ebf3c6fca0d4812b39dd4b52efd3\",\n \"score\": \"0.5817798\",\n \"text\": \"def ap_clients(interface='ap0'):\\n try:\\n p = run([\\\"iw\\\", \\\"dev\\\", interface, \\\"station\\\", \\\"dump\\\"], encoding=\\\"UTF-8\\\", check=True, capture_output=True)\\n stations = list(Counter([line for line in p.stdout if \\\"Station\\\" in line]).elements())\\n LOGGER.debug(stations)\\n return stations\\n except CalledProcessError as e:\\n LOGGER.warning(e)\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f7ba01f5e3d2fcab91c0585b5996f6\",\n \"score\": \"0.5811706\",\n \"text\": \"def get_station_list(counties, bbox, data_types, start_time, end_time, output_file):\\n # Translate bbox if it is used\\n if bbox is not None:\\n bbox = translate_bbox(bbox)\\n\\n station_getter = getStations.StationIDGetter(\\n counties,\\n bbox=bbox,\\n dataTypes=data_types,\\n startTime=revert_datetime_to_long_string(start_time),\\n endTime=revert_datetime_to_long_string(end_time),\\n outputFile=output_file,\\n noprint=1)\\n\\n return station_getter.stList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469f40e7347d0f8354aaa7370a3c650d\",\n \"score\": \"0.58079964\",\n \"text\": \"def get_statuses():\\n db = get_db()\\n cur = db.execute(\\\"\\\"\\\"\\n SELECT *\\n FROM project_status\\n \\\"\\\"\\\")\\n \\n return cur.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b516fe19ea4959f5a0e1319c715f2f98\",\n \"score\": \"0.58079314\",\n \"text\": \"def _get_station_status(station: str, date: Optional[str] = None) -> Optional[StationStatus]:\\n defect_coll = get_defect_collection()\\n exif_coll = get_exif_collection()\\n\\n if date is None:\\n dates = exif_coll.find({\\\"station\\\": station}, {\\\"_id\\\": 0, \\\"date\\\": 1}).distinct(\\\"date\\\")\\n dates.sort()\\n\\n if len(dates) == 0:\\n return\\n\\n date = dates[-1]\\n\\n n_to_confirm = defect_coll.find({\\\"station\\\": station, \\\"date\\\": date, \\\"category\\\": 0}).count()\\n n_confirmed = defect_coll.find({\\\"station\\\": station, \\\"date\\\": date, \\\"category\\\": 1}).count()\\n n_healthy = defect_coll.find({\\\"station\\\": station, \\\"date\\\": date, \\\"category\\\": 2}).count()\\n n_in_fix = defect_coll.find({\\\"station\\\": station, \\\"date\\\": date, \\\"category\\\": 3}).count()\\n\\n n_panel_groups = _get_n_panel_group(station)\\n\\n if n_panel_groups == 0:\\n overall_status = \\\"unknown\\\"\\n else:\\n problem_ratio = (n_to_confirm+n_confirmed+n_in_fix) / n_panel_groups\\n if problem_ratio > 0.5:\\n overall_status = \\\"red\\\"\\n elif problem_ratio > 0.05:\\n overall_status = \\\"yellow\\\"\\n else:\\n overall_status = \\\"green\\\"\\n\\n return StationStatus(date=date,\\n healthy=n_healthy,\\n toconfirm=n_to_confirm,\\n infix=n_in_fix,\\n confirmed=n_confirmed,\\n overallStatus=overall_status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d673cdcf4146405fee8bb80a9b521c\",\n \"score\": \"0.5807605\",\n \"text\": \"def getstationlijst(self):\\n url = self.settings['settings']['api']['url']['stationlijst']\\n data = self.sendcall(url)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9654d1727ff25647c018f15f7ca337ec\",\n \"score\": \"0.5801834\",\n \"text\": \"def services_of_status(self, status='ok'):\\n return sorted([svc for svc in self if self[svc]['Status'].lower() == status.lower()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c7d4fad68a06bfb7710c3bfa507328\",\n \"score\": \"0.5783262\",\n \"text\": \"async def all_zone_status(self, unit: int) -> list[ZoneStatus]:\\n if unit < 1 or unit > 3:\\n return []\\n # Reading 7 lines, since response starts with EOL and each zone's status is followed by EOL\\n response = await self._protocol.send(\\n _format_all_zones_status_request(unit), num_eols_to_read=7\\n )\\n return ZoneStatus.from_strings(response.split(sep=EOL.decode('ascii')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a84b44a5c0e4b3f193b99ead5897649e\",\n \"score\": \"0.5779344\",\n \"text\": \"def statuses(self):\\n return self._statuses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a84b44a5c0e4b3f193b99ead5897649e\",\n \"score\": \"0.5779344\",\n \"text\": \"def statuses(self):\\n return self._statuses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21bd93f79bf60715150af0431835cfcf\",\n \"score\": \"0.5762371\",\n \"text\": \"def return_sensors_online(self):\\n return [\\\"{} - {}\\\".format(k, v)\\n for k, v in self.__sensors_online_status__.items()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d796c16bc6e4ba6c3789aae19e91cf3\",\n \"score\": \"0.5758235\",\n \"text\": \"def parse_all_stations(all_stations_json):\\n stations = json.loads(all_stations_json)['Stations']\\n station_list = {}\\n for i in stations.keys():\\n station = stations[\\\"{0}\\\".format(i)]\\n nm = station['NAME'].upper()\\n if 'ABBR' not in station:\\n print('Could not find ABBR key for ' + station['NAME'])\\n else:\\n try:\\n abbr = station['ABBR'].upper()\\n station_list[nm] = abbr\\n except:\\n pass\\n print len(station_list)\\n return station_list\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992992,"cells":{"query_id":{"kind":"string","value":"b514582c9166d3f669a30d662f644aaf"},"query":{"kind":"string","value":"Should correctly create a keyspace in Cassandra"},"positive_passages":{"kind":"list like","value":[{"docid":"cf2a0bb45f7445fac787994e431d5633","score":"0.7807259","text":"def test_create_keyspace(self):\n self.render_config_template(\n cassandra_ports=[9042],\n cassandra_send_request=True,\n cassandra_send_response=True,\n cassandra_send_request_header=True,\n cassandra_send_response_header=True,\n )\n self.run_packetbeat(pcap=\"cassandra/v4/cassandra_create_keyspace.pcap\", debug_selectors=[\"*\"])\n objs = self.read_output()\n o = objs[0]\n\n assert o[\"type\"] == \"cassandra\"\n assert o[\"port\"] == 9042\n\n assert o[\n \"cassandra.request.query\"] == \"CREATE KEYSPACE mykeyspace WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 1 };\"\n assert o[\"cassandra.request.headers.version\"] == \"4\"\n assert o[\"cassandra.request.headers.op\"] == \"QUERY\"\n assert o[\"cassandra.request.headers.length\"] == 124\n assert o[\"cassandra.request.headers.flags\"] == \"Default\"\n assert o[\"cassandra.request.headers.stream\"] == 20\n\n assert o[\"cassandra.response.result.type\"] == \"schemaChanged\"\n assert o[\"cassandra.response.result.schema_change.change\"] == \"CREATED\"\n assert o[\"cassandra.response.result.schema_change.keyspace\"] == \"mykeyspace\"\n assert o[\"cassandra.response.result.schema_change.target\"] == \"KEYSPACE\"\n\n assert o[\"cassandra.response.headers.version\"] == \"4\"\n assert o[\"cassandra.response.headers.length\"] == 35\n assert o[\"cassandra.response.headers.op\"] == \"RESULT\"\n assert o[\"cassandra.response.headers.flags\"] == \"Default\"\n assert o[\"cassandra.response.headers.stream\"] == 20","title":""}],"string":"[\n {\n \"docid\": \"cf2a0bb45f7445fac787994e431d5633\",\n \"score\": \"0.7807259\",\n \"text\": \"def test_create_keyspace(self):\\n self.render_config_template(\\n cassandra_ports=[9042],\\n cassandra_send_request=True,\\n cassandra_send_response=True,\\n cassandra_send_request_header=True,\\n cassandra_send_response_header=True,\\n )\\n self.run_packetbeat(pcap=\\\"cassandra/v4/cassandra_create_keyspace.pcap\\\", debug_selectors=[\\\"*\\\"])\\n objs = self.read_output()\\n o = objs[0]\\n\\n assert o[\\\"type\\\"] == \\\"cassandra\\\"\\n assert o[\\\"port\\\"] == 9042\\n\\n assert o[\\n \\\"cassandra.request.query\\\"] == \\\"CREATE KEYSPACE mykeyspace WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 1 };\\\"\\n assert o[\\\"cassandra.request.headers.version\\\"] == \\\"4\\\"\\n assert o[\\\"cassandra.request.headers.op\\\"] == \\\"QUERY\\\"\\n assert o[\\\"cassandra.request.headers.length\\\"] == 124\\n assert o[\\\"cassandra.request.headers.flags\\\"] == \\\"Default\\\"\\n assert o[\\\"cassandra.request.headers.stream\\\"] == 20\\n\\n assert o[\\\"cassandra.response.result.type\\\"] == \\\"schemaChanged\\\"\\n assert o[\\\"cassandra.response.result.schema_change.change\\\"] == \\\"CREATED\\\"\\n assert o[\\\"cassandra.response.result.schema_change.keyspace\\\"] == \\\"mykeyspace\\\"\\n assert o[\\\"cassandra.response.result.schema_change.target\\\"] == \\\"KEYSPACE\\\"\\n\\n assert o[\\\"cassandra.response.headers.version\\\"] == \\\"4\\\"\\n assert o[\\\"cassandra.response.headers.length\\\"] == 35\\n assert o[\\\"cassandra.response.headers.op\\\"] == \\\"RESULT\\\"\\n assert o[\\\"cassandra.response.headers.flags\\\"] == \\\"Default\\\"\\n assert o[\\\"cassandra.response.headers.stream\\\"] == 20\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"81e2224946d7a5e6fd6b7c40ba2e2782","score":"0.807247","text":"def createKeyspace(self):\n sys = SystemManager(str(self.host) + \":\" + str(self.port),framed_transport=True, timeout=30)\n try:\n print \"Trying to create new keyspace\"\n sys.create_keyspace(self.keyspace, SIMPLE_STRATEGY, {'replication_factor': '1'})\n except :\n print \"Keyspace already exist.\"\n sys.close()","title":""},{"docid":"8eb28ced38d16e7340cfa7a7ff4ef08e","score":"0.7039071","text":"def createKeyspace(self, key_name):\n try:\n key_status = self.isKeyspacePresent(key_name)\n if not key_status:\n session = self.createSession()\n key_space = session.execute(f'CREATE KEYSPACE {key_name};')\n self.closeSession(session)\n return key_space\n else:\n Logger('test.log').logger('INFO', f'keyspace {key_name} is already available')\n except Exception as e:\n print(e)\n Logger('test.log').logger('ERROR', f'Failed creating keyspace\\n + {str(e)}')","title":""},{"docid":"ee8602ae6daafcd0ac545e052f2fdca2","score":"0.6978615","text":"def _create_tables(self):\n\n log.info(\"CREATING KEYSPACE AND TABLE %s %s\",self._ksp, self._table)\n\n query_keyspace = \"CREATE KEYSPACE IF NOT EXISTS %s WITH replication = %s\" % (self._ksp, config.replication)\n config.executelocked(query_keyspace)\n\n query_simple = 'CREATE TABLE IF NOT EXISTS ' + self._ksp + '.' + self._table + \\\n '( storage_id uuid PRIMARY KEY, '\n for key, entry in self._persistent_props.items():\n query_simple += str(key) + ' '\n if entry['type'] != 'dict' and entry['type'] in valid_types:\n if entry['type'] == 'list' or entry['type'] == 'tuple':\n query_simple += entry['type'] + '<' + entry['columns'] + '>, '\n else:\n query_simple += entry['type'] + ', '\n else:\n query_simple += 'uuid, '\n try:\n config.executelocked(query_simple[:-2] + ' )')\n except Exception as ir:\n log.error(\"Unable to execute %s\", query_simple)\n raise ir","title":""},{"docid":"afaac853d8f527d9a7c33f784e50c878","score":"0.6889439","text":"def create_database():\n \n # connect to Cassandra\n cluster = Cluster(['127.0.0.1']) #If you have a locally installed Apache Cassandra instance\n session = cluster.connect()\n \n # create sparkify database\n session.execute(drop_keyspace)\n session.execute(create_keyspace)\n \n # set session to sparkify keyspace\n session.set_keyspace('sparkify')\n \n return cluster, session","title":""},{"docid":"f60b0854cff5c44d516b99cb75fcbea6","score":"0.6721522","text":"def aws_create_cassandra(nodes=1, region=None, storage_gbs=8, flavor='t2.micro'):\n cluster_uuid = uuid.uuid4().hex\n secho('Cluster UUID is ' + cluster_uuid, fg='green', blink=True)\n\n if region:\n if region not in IMAGES.keys():\n secho('Invalid region: ' + region, err=True, fg='red')\n ec2 = boto3.resource('ec2', region_name=region)\n else:\n ec2 = boto3.resource('ec2')\n\n key_name = 'cassandra_keypair_' + cluster_uuid\n secho('Creating keypair ' + key_name + ' ...')\n # On the first actual connection to AWS, we can check if our credentials\n # are invalid. No need to check afterwards.\n try:\n keypair = ec2.create_key_pair(KeyName=key_name)\n except botocore.exceptions.ClientError:\n secho('Invalid credentials', err=True, fg='red')\n return\n secho('Keypair ' + key_name + ' created.', fg='green')\n secho('Private key:')\n secho(keypair.key_material)\n\n secgroup_name = 'cassandra_sg_' + cluster_uuid\n secho('Creating security group ' + secgroup_name + \\\n ' With open ports 80,22,443,6666-9999 open ...')\n description = 'Security group for Cassandra cluster'\n ip_portrange_tuples = [\n ('0.0.0.0/0', 80, 80),\n ('0.0.0.0/0', 22, 22),\n ('0.0.0.0/0', 443, 443),\n ('0.0.0.0/0', 6666, 9999),\n ]\n mysg = ec2.create_security_group(GroupName=secgroup_name,\n Description=description)\n for ip, start_port, end_port in ip_portrange_tuples:\n mysg.authorize_ingress(IpProtocol=\"tcp\", CidrIp=ip,\n FromPort=start_port, ToPort=end_port)\n secho('Security group ' + secgroup_name + ' created.', fg='green')\n\n # Spawn VMs in AWS, and wait till all of them are running, and then return.\n\n # First create seed node, wait for it to be in 'running' state, then create\n # non seed nodes, then wait for all of them to be in running, and then\n # return all node's public IP addresses.\n\n count = nodes\n all_instance_ids = []\n\n secho('Creating ' + str(nodes) + ' instance(s) ...')\n\n # TODO(rushiagr): retry 5 times when an exception occurs which we know we\n # can retry 'e.g. connection timeout. Also identify if the credentials are\n # wrong, in that case don't retry and return error to user.\n try:\n # Will default to 8 gb root volume\n instances = ec2.create_instances(#DryRun=True,\n ImageId=IMAGES[region], UserData=userdata.seed_userdata,\n MinCount=1, MaxCount=1, InstanceType=flavor,\n KeyName=key_name, SecurityGroupIds=[mysg.id])\n except Exception as e:\n secho('exception thrown: ' + str(e), err=True, fg='red')\n return\n\n instances = list(instances)\n ids = [i.id for i in instances]\n all_instance_ids.append(ids[0])\n\n seed_private_ip_address = None\n\n while True:\n instances = ec2.instances.filter(InstanceIds=ids)\n instances = [i for i in instances]\n seed_private_ip_address = instances[0].private_ip_address\n if seed_private_ip_address is not None:\n break\n time.sleep(5)\n\n\n non_seed_userdata = userdata.non_seed_userdata.replace(\n '__SEEDS__', seed_private_ip_address)\n\n count -= 1\n\n if count != 0:\n # TODO(rushiagr): retry 5 times when an exception occurs which we know\n # we can retry 'e.g. connection timeout. Also identify if the\n # credentials are wrong, in that case don't retry and return error to\n # user.\n try:\n # Will default to 8 gb root volume\n instances = ec2.create_instances(#DryRun=True,\n ImageId=IMAGES[region], UserData=non_seed_userdata,\n MinCount=count, MaxCount=count,\n InstanceType=flavor, KeyName=key_name,\n SecurityGroupIds=[mysg.id])\n except Exception as e:\n secho('exception thrown: ' + e, err=True, fg='red')\n return\n\n instances = list(instances)\n ids = [i.id for i in instances]\n all_instance_ids.extend(ids)\n\n # Waiting for instances to all have state = 'running'\n # TODO(rushiagr): retry if instances go in error state. Even better - use\n # autoscaling group\n while True:\n instances = ec2.instances.filter(InstanceIds=all_instance_ids)\n states = [i.state['Name'] for i in instances]\n non_running_states = [state for state in states if state != 'running']\n if len(non_running_states) == 0:\n break\n time.sleep(5)\n\n secho('All instances now in running state.', fg='green')\n secho('Waiting for all instances to join the cassandra cluster. '\n 'This might take a while.')\n\n ips = [i.public_ip_address for i in instances]\n\n # Ensure cassandra cluster is created by checking 'nodetool status' on each\n # server. Wait till all nodes' 'nodetool status' shows that all nodes have\n # joined the cluster.\n\n pvt_key = paramiko.RSAKey.from_private_key(\n io.StringIO(keypair.key_material))\n\n connections = {}\n successful_creations = True\n while True:\n for ip in ips:\n try:\n ssh = paramiko.SSHClient()\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n ssh.connect(ip, username='ubuntu', pkey=pvt_key)\n connections[ip] = ssh\n except paramiko.ssh_exception.NoValidConnectionsError as e:\n\n # TODO(rushiagr): handle\n # paramiko.ssh_exception.AuthenticationException when\n # authentication exception occurs\n\n # print('exception thrown, retrying')\n # print(e)\n successful_creations = False\n time.sleep(5)\n break\n\n if successful_creations:\n break\n successful_creations = True\n time.sleep(5)\n\n while True:\n all_fine = True\n for ip, conn in connections.items():\n stdin, stdout, err = ssh.exec_command(\"nodetool status\")\n stdin.close()\n stdout = stdout.read()\n err = err.read()\n # print('stdout is')\n # print(stdout)\n # print('err is', err)\n # print('nodetool status output for', ip, 'is:\\n', stdout)\n\n stdin, stdout, err = ssh.exec_command(\n \"nodetool status | grep ^UN | grep -v '127.0.0.1' | wc | awk '{print $1}' \")\n stdin.close()\n stdout = stdout.read()\n err = err.read()\n # print('stdout is')\n # print(stdout)\n # print('err is', err)\n # print('nodetool num of nodes output for', ip, 'is:\\n', stdout)\n\n if not int(stdout.strip()) == len(ips):\n all_fine = False\n\n if all_fine:\n [ssh.close() for ssh in connections.values()]\n secho('Cluster creation complete!', fg='green', blink=True)\n return\n\n time.sleep(5)","title":""},{"docid":"0840641a522e6757cde471ebba6d80c0","score":"0.6614517","text":"def connect_db():\n\n cluster = Cluster()\n\n session = cluster.connect()\n\n session.execute(\n \"\"\"CREATE KEYSPACE IF NOT EXISTS p2_udacity \n WITH REPLICATION = {'class': 'SimpleStrategy', \n 'replication_factor': 1}\"\"\"\n )\n session.set_keyspace('p2_udacity')\n \n return session, cluster","title":""},{"docid":"44445d7ac77309ff90dbac5e65c5ecf6","score":"0.64532655","text":"def test():\n cluster = Cluster([os.environ.get('CASSANDRA_PORT_9042_TCP_ADDR', 'localhost')],\n port=int(os.environ.get('CASSANDRA_PORT_9042_TCP_PORT', 9042))\n )\n try:\n session = cluster.connect()\n except Exception as error:\n message = \"%s: %s\" % (error.__class__.__name__, str(error))\n return jsonify(message=message, hostname=os.uname()[1],\n current_time=str(datetime.now())), 500\n session.row_factory = ordered_dict_factory\n rows = session.execute('SELECT * FROM system.schema_keyspaces LIMIT 10')\n return jsonify(data=rows, hostname=os.uname()[1],\n current_time=str(datetime.now()))","title":""},{"docid":"43b0368ce9f93838943ab28959332c72","score":"0.6390614","text":"def _create_keyspace(connections):\n # Get connection IDs\n max_o = len(set([c.pre_obj for c in connections]))\n max_i = max([i for i in _get_outgoing_ids(connections).values()])\n max_d = max([c.width for c in connections])\n\n # Get the number of bits necessary for these\n (bits_o, bits_i, bits_d) = [int(math.ceil(math.log(v + 1, 2)))\n for v in [max_o, max_i, max_d]]\n bits_c = 7 # Allow for 128 splits. TODO Revise this\n\n # Ensure that these will fit within a 32-bit key\n padding = 32 - (1 + bits_o + bits_i + bits_d + bits_c)\n assert padding >= 0\n bits_o += padding\n\n # Create the keyspace\n return utils.keyspaces.create_keyspace(\n 'NengoDefault',\n [('x', 1), ('o', bits_o), ('c', bits_c), ('i', bits_i), ('d', bits_d)],\n 'xoci', 'xoi'\n )(x=0)","title":""},{"docid":"0f7adda40fd649475c3a022efddc5441","score":"0.62722695","text":"def cassandra_connect(keyspace: str = CASSANDRA_KEYSPACE\n ) -> Tuple[Cluster, Session]:\n\n # Make a connection to a Cassandra instance on the local machine\n cluster = Cluster(['127.0.0.1'])\n\n # Create session to establish connection and begin executing queries\n session = cluster.connect()\n\n # Create keyspace\n session.execute(f\"CREATE KEYSPACE IF NOT EXISTS {keyspace:s}\\n\"\n \"WITH REPLICATION = { 'class' : 'SimpleStrategy', \"\n \"'replication_factor' : 1 }\")\n\n # Set the default keyspace for all queries made through this Session\n session.set_keyspace(keyspace)\n\n return cluster, session","title":""},{"docid":"0ac7c97bd52bf05e49974ecf0004498f","score":"0.6232603","text":"def test_create_table(self):\n self.render_config_template(\n cassandra_ports=[9042],\n cassandra_send_request=True,\n cassandra_send_response=True,\n cassandra_send_request_header=True,\n cassandra_send_response_header=True,\n )\n self.run_packetbeat(pcap=\"cassandra/v4/cassandra_create_table.pcap\", debug_selectors=[\"*\"])\n objs = self.read_output()\n o = objs[0]\n assert o[\"type\"] == \"cassandra\"\n assert o[\"port\"] == 9042\n\n assert o[\n \"cassandra.request.query\"] == \"CREATE TABLE users (\\n user_id int PRIMARY KEY,\\n fname text,\\n lname text\\n);\"\n\n assert o[\"cassandra.request.headers.version\"] == \"4\"\n assert o[\"cassandra.request.headers.op\"] == \"QUERY\"\n assert o[\"cassandra.request.headers.length\"] == 98\n assert o[\"cassandra.request.headers.flags\"] == \"Default\"\n assert o[\"cassandra.request.headers.stream\"] == 49\n\n assert o[\"cassandra.response.result.type\"] == \"schemaChanged\"\n assert o[\"cassandra.response.headers.version\"] == \"4\"\n assert o[\"cassandra.response.headers.length\"] == 39\n assert o[\"cassandra.response.headers.op\"] == \"RESULT\"\n assert o[\"cassandra.response.headers.flags\"] == \"Default\"\n assert o[\"cassandra.response.headers.stream\"] == 49","title":""},{"docid":"adfa615694bd4314c29bc044cb28335f","score":"0.62283456","text":"def create_new_db(name, db='sql',\n keyspace=None, **kargs):\n from kosh import connect\n kargs[\"keyspace\"] = keyspace\n kargs[\"db\"] = db\n # Let's remove the now unused arguments\n for key in [\"token\", \"cluster\"]:\n if key in kargs:\n warnings.warn(\n \"Keyword '{}' is no longer valid, will be ignored\".format(key))\n kargs.pop(key)\n store = connect(name, **kargs)\n store.delete_all_contents(force=\"SKIP PROMPT\")\n return store","title":""},{"docid":"68e0c85622340cc1f044686b2ea73a63","score":"0.62096894","text":"def _connect_to_cassandra(keyspace):\n\n if 'BACKEND_STORAGE_IP' in os.environ:\n host = os.environ['BACKEND_STORAGE_IP']\n else:\n host = 'localhost'\n\n cluster = Cluster([host])\n session = cluster.connect(keyspace)\n \n return session","title":""},{"docid":"2f04d5a90c43cfdb02ffca40062dee6b","score":"0.6103821","text":"def test_create_cluster(self):\n pass","title":""},{"docid":"ff083eee76669443c6d70786b98eaffb","score":"0.5921743","text":"def test_type_keyspace_permission_isolation(self):\n self.fixture_dtest_setup.ignore_log_patterns = [\n # I think this happens when permissions change and a node becomes temporarily unavailable\n # and it's probably ok to ignore on this test, as I can see the schema changes propogating\n # almost immediately after\n r'Can\\'t send migration request: node.*is down',\n ]\n\n cluster = self.cluster\n config = {'authenticator': 'org.apache.cassandra.auth.PasswordAuthenticator',\n 'authorizer': 'org.apache.cassandra.auth.CassandraAuthorizer',\n 'permissions_validity_in_ms': 0}\n cluster.set_configuration_options(values=config)\n cluster.populate(3).start()\n node1, node2, node3 = cluster.nodelist()\n\n # do setup that requires a super user\n superuser_session = self.patient_cql_connection(node1, user='cassandra', password='cassandra')\n\n # role setup is async and does not log when complete; the only way to know its done (on a single instance) is to get a successful return.\n def allowed_error(error):\n return isinstance(error, InvalidRequest) and \"Cannot process role related query as the role manager isn't yet setup\" in str(error)\n def r(fn):\n retry(fn, allowed_error=allowed_error, sleep_seconds=10)\n\n # role setup process is local to each instance, so can't rely on the first query passing to say this is complete; node1 may be complete but not node2 and node3\n # for this reason, need to be aggressive about retries\n r(lambda: superuser_session.execute(\"create user ks1_user with password 'cassandra' nosuperuser;\"))\n r(lambda: superuser_session.execute(\"create user ks2_user with password 'cassandra' nosuperuser;\"))\n r(lambda: create_ks(superuser_session, 'ks1', 2))\n r(lambda: create_ks(superuser_session, 'ks2', 2))\n r(lambda: superuser_session.execute(\"grant all permissions on keyspace ks1 to ks1_user;\"))\n r(lambda: superuser_session.execute(\"grant all permissions on keyspace ks2 to ks2_user;\"))\n\n user1_session = self.patient_cql_connection(node1, user='ks1_user', password='cassandra')\n user2_session = self.patient_cql_connection(node1, user='ks2_user', password='cassandra')\n\n # first make sure the users can't create types in each other's ks\n self.assertUnauthorized(user1_session, \"CREATE TYPE ks2.simple_type (user_number int, user_text text );\", 'User ks1_user has no CREATE permission on <keyspace ks2> or any of its parents')\n\n self.assertUnauthorized(user2_session, \"CREATE TYPE ks1.simple_type (user_number int, user_text text );\", 'User ks2_user has no CREATE permission on <keyspace ks1> or any of its parents')\n\n # now, actually create the types in the correct keyspaces\n user1_session.execute(\"CREATE TYPE ks1.simple_type (user_number int, user_text text );\")\n user2_session.execute(\"CREATE TYPE ks2.simple_type (user_number int, user_text text );\")\n\n # each user now has a type belonging to their granted keyspace\n # let's make sure they can't drop each other's types (for which they have no permissions)\n\n self.assertUnauthorized(user1_session, \"DROP TYPE ks2.simple_type;\", 'User ks1_user has no DROP permission on <keyspace ks2> or any of its parents')\n\n self.assertUnauthorized(user2_session, \"DROP TYPE ks1.simple_type;\", 'User ks2_user has no DROP permission on <keyspace ks1> or any of its parents')\n\n # let's make sure they can't rename each other's types (for which they have no permissions)\n self.assertUnauthorized(user1_session, \"ALTER TYPE ks2.simple_type RENAME user_number TO user_num;\", 'User ks1_user has no ALTER permission on <keyspace ks2> or any of its parents')\n\n self.assertUnauthorized(user2_session, \"ALTER TYPE ks1.simple_type RENAME user_number TO user_num;\", 'User ks2_user has no ALTER permission on <keyspace ks1> or any of its parents')\n\n # rename the types using the correct user w/permissions to do so\n user1_session.execute(\"ALTER TYPE ks1.simple_type RENAME user_number TO user_num;\")\n user2_session.execute(\"ALTER TYPE ks2.simple_type RENAME user_number TO user_num;\")\n\n # finally, drop the types using the correct user w/permissions to do so, consistency all avoids using a sleep\n user1_session.execute(SimpleStatement(\"DROP TYPE ks1.simple_type;\", consistency_level=ConsistencyLevel.ALL))\n user2_session.execute(SimpleStatement(\"DROP TYPE ks2.simple_type;\", consistency_level=ConsistencyLevel.ALL))\n\n time.sleep(5)\n\n # verify user type metadata is gone from the system schema\n self.assertNoTypes(superuser_session)","title":""},{"docid":"b2489e8c8a806d8e70cc4811b583d29d","score":"0.5912993","text":"def connect_to_cassandra(self):\n try:\n # todo add logic to use the params\n # this requires creating a dictionary only populated with the not null-like values.\n # The Cluster object does not handle null values very well\n self.cluster = Cluster()\n self.session = self.cluster.connect()\n except Exception as ex:\n self.module.fail_json(msg=str(ex))","title":""},{"docid":"58e886a55d292726e3dc7ec44f0e8983","score":"0.58965236","text":"def test_create_index(self):\n self.render_config_template(\n cassandra_ports=[9042],\n cassandra_send_request=True,\n cassandra_send_response=True,\n cassandra_send_request_header=True,\n cassandra_send_response_header=True,\n )\n self.run_packetbeat(pcap=\"cassandra/v4/cassandra_create_index.pcap\", debug_selectors=[\"*\"])\n objs = self.read_output()\n o = objs[0]\n assert o[\"type\"] == \"cassandra\"\n assert o[\"port\"] == 9042\n\n assert o[\"cassandra.request.query\"] == \"CREATE INDEX ON users (lname);\"\n assert o[\"cassandra.request.headers.version\"] == \"4\"\n assert o[\"cassandra.request.headers.op\"] == \"QUERY\"\n assert o[\"cassandra.request.headers.length\"] == 51\n assert o[\"cassandra.request.headers.flags\"] == \"Default\"\n assert o[\"cassandra.request.headers.stream\"] == 92\n\n assert o[\"cassandra.response.result.type\"] == \"schemaChanged\"\n\n assert o[\"cassandra.response.headers.version\"] == \"4\"\n assert o[\"cassandra.response.headers.length\"] == 39\n assert o[\"cassandra.response.headers.op\"] == \"RESULT\"\n assert o[\"cassandra.response.headers.flags\"] == \"Default\"\n assert o[\"cassandra.response.headers.stream\"] == 92","title":""},{"docid":"a62733c6a3c27bfa5b420fb7b5115a14","score":"0.56926924","text":"def _createCassandraData(self, config, curr_time=None, create_table=True, amount=None, start=0):\n session = self.cassandra['session']\n keyspace = self.cassandra['keyspace']\n if not curr_time:\n curr_time = datetime.utcnow()\n if not amount:\n amount = self.data_amt\n params = config['cassandra']\n\n # create a table\n if create_table:\n stmt = ''' CREATE TABLE {keyspace}.{table} (\n {id_col} UUID,\n {version_col} bigint,\n text varchar,\n source varchar,\n {date_col} timestamp,\n PRIMARY KEY ({primary_key})\n );'''\n\n # check if it will use date as a filter\n if config.get('filter_date', None):\n primary_key = '%s, %s' % (config['id_col'], config['version_col'])\n else:\n primary_key = config['id_col']\n stmt = stmt.format(keyspace=keyspace,\n table=params['table'],\n version_col=config['version_col'],\n id_col=config['id_col'],\n date_col=config['date_col'],\n primary_key=primary_key)\n session.execute(stmt)\n if config.get('filter_date', None):\n session.execute('CREATE INDEX ON %s.%s (%s)' % (keyspace, params['table'], config['version_col']))\n\n # Prepare the statements\n stmt = \"INSERT INTO {keyspace}.{table} (\" \\\n \"{id_col}, \" \\\n \"{version_col}, \" \\\n \"text, \" \\\n \"source, \" \\\n \"{date_col}) \" \\\n \"VALUES (?, ?, ?, ?, ?)\" \\\n \"USING TIMESTAMP ? \"\n stmt = stmt.format(keyspace=keyspace,\n table=params['table'],\n version_col=config['version_col'],\n id_col=config['id_col'],\n date_col=config['date_col'])\n data_statement = session.prepare(stmt)\n\n # add the prepared statements to a batch\n count = 0\n batch = BatchStatement()\n for i in range(start, start + amount):\n batch.add(data_statement,\n [self.idList[i],\n unix_time_millis(curr_time),\n id_generator(10), 'CASSANDRA',\n unix_time_millis(curr_time),\n unix_time_millis(curr_time)])\n count += 1\n\n # every x records, commit. The parameter 65000 was giving timeout\n if (count % 5000) == 0:\n # execute the batch\n session.execute(batch)\n # hack to get around the 65k limit of python driver\n batch._statements_and_parameters = []\n count = 0\n\n if count > 0:\n session.execute(batch)\n batch._statements_and_parameters = []","title":""},{"docid":"057f73af9407f9dfb677ddce7a4dfb88","score":"0.56742734","text":"def create_cluster(hosts):\n if not state.SEED_IP:\n state.SEED_IP = ec2.Instance(hosts[0]).private_ip_address\n for host in hosts:\n util.run_cassandra(host)","title":""},{"docid":"28537a140dcf5c3c9454788112b66402","score":"0.5635075","text":"def isKeyspacePresent(self, key_name):\n try:\n session = self.createSession()\n if key_name in [i[0] for i in session.execute('SELECT * FROM system_schema.keyspaces;')]:\n self.closeSession(session)\n return True\n else:\n self.closeSession(session)\n return False\n except Exception as e:\n Logger('test.log').logger('ERROR', f'unable to check whether the keyspace is available or not \\n {str(e)}')","title":""},{"docid":"4824c0bdda054dce3b4c5f843e1d9f09","score":"0.56019694","text":"def cli_cosmosdb_managed_cassandra_cluster_create(client,\n resource_group_name,\n cluster_name,\n location,\n delegated_management_subnet_id,\n tags=None,\n identity=None,\n cluster_name_override=None,\n initial_cassandra_admin_password=None,\n client_certificates=None,\n external_gossip_certificates=None,\n external_seed_nodes=None,\n restore_from_backup_id=None,\n cassandra_version=None,\n authentication_method=None,\n hours_between_backups=None,\n repair_enabled=None):\n\n if initial_cassandra_admin_password is None and external_gossip_certificates is None:\n raise CLIError('At least one out of the Initial Cassandra Admin Password or External Gossip Certificates is required.')\n\n if initial_cassandra_admin_password is not None and external_gossip_certificates is not None:\n raise CLIError('Only one out of the Initial Cassandra Admin Password or External Gossip Certificates has to be specified.')\n\n cluster_properties = ClusterResourceProperties(\n delegated_management_subnet_id=delegated_management_subnet_id,\n cluster_name_override=cluster_name_override,\n initial_cassandra_admin_password=initial_cassandra_admin_password,\n client_certificates=client_certificates,\n external_gossip_certificates=external_gossip_certificates,\n external_seed_nodes=external_seed_nodes,\n restore_from_backup_id=restore_from_backup_id,\n cassandra_version=cassandra_version,\n authentication_method=authentication_method,\n hours_between_backups=hours_between_backups,\n repair_enabled=repair_enabled)\n\n cluster_resource_create_update_parameters = ClusterResource(\n location=location,\n tags=tags,\n identity=identity,\n properties=cluster_properties)\n\n return client.begin_create_update(resource_group_name, cluster_name, cluster_resource_create_update_parameters)","title":""},{"docid":"8d7cd06422f45860d1012bfc55cf6b65","score":"0.556428","text":"def _create_cql_table(key_space: str,\r\n table_name: str,\r\n primary_key: List,\r\n columns: List[str],\r\n column_types: List[str]\r\n ) -> str:\r\n assert len(columns) > 0, \"Columns can not be empty in a Table. \"\r\n\r\n assert len(columns) == len(column_types), \"Length of columns and type of columns length missmatch.\"\r\n\r\n # If we have same amount of columns and their types\r\n cql_create_table = \"CREATE TABLE IF NOT EXISTS \" + key_space + \".\" + table_name\r\n cql_create_table += \" (\"\r\n for col, typ in zip(columns, column_types):\r\n if col in primary_key:\r\n cql_create_table += col + \" \" + typ + \", \"\r\n else:\r\n cql_create_table += col + \" \" + typ + \" , \"\r\n cql_create_table = cql_create_table[:-2]\r\n cql_create_table += \", PRIMARY KEY (\" + \", \".join(primary_key) + \") \"\r\n cql_create_table += \");\"\r\n return cql_create_table","title":""},{"docid":"f0da33fb6782e8e6c98179486b865845","score":"0.5556857","text":"def cli_cosmosdb_managed_cassandra_cluster_create(client,\n resource_group_name,\n cluster_name,\n location,\n delegated_management_subnet_id,\n tags=None,\n identity=None,\n cluster_name_override=None,\n initial_cassandra_admin_password=None,\n client_certificates=None,\n external_gossip_certificates=None,\n external_seed_nodes=None,\n restore_from_backup_id=None,\n cassandra_version=None,\n authentication_method=None,\n hours_between_backups=None,\n repair_enabled=None):\n\n cluster_properties = ClusterResourceProperties(\n delegated_management_subnet_id=delegated_management_subnet_id,\n cluster_name_override=cluster_name_override,\n initial_cassandra_admin_password=initial_cassandra_admin_password,\n client_certificates=client_certificates,\n external_gossip_certificates=external_gossip_certificates,\n external_seed_nodes=external_seed_nodes,\n restore_from_backup_id=restore_from_backup_id,\n cassandra_version=cassandra_version,\n authentication_method=authentication_method,\n hours_between_backups=hours_between_backups,\n repair_enabled=repair_enabled)\n\n cluster_resource_create_update_parameters = ClusterResource(\n location=location,\n tags=tags,\n identity=identity,\n properties=cluster_properties)\n\n return client.create_update(resource_group_name, cluster_name, cluster_resource_create_update_parameters)","title":""},{"docid":"e42e49c25f0e1d7b3ab7d3c93c293fc6","score":"0.551919","text":"def create_tables():\n\ttry:\n\t\tCrossMkt.delete()\n\texcept:\n\t\tpass\n\ttry:\n\t\tdb.create_tables([CrossMkt])\n\t\t#db.connect()\n\texcept Exception as e:\n\t\tprint(e)","title":""},{"docid":"c0c72ccd5ac20bcf02bf051ea20af279","score":"0.551911","text":"def _create_index_cql(key_space: str,\r\n index_name: str,\r\n table_name: str,\r\n index_on: str\r\n ) -> str:\r\n cql_index = \"CREATE INDEX \" + index_name + \" ON \"\r\n cql_index += key_space + \".\" + table_name + \" (\"\r\n cql_index += index_on + \");\"\r\n return cql_index","title":""},{"docid":"cfa600b2a7e985968588d54e22b51893","score":"0.54925203","text":"def _create_delete_table_cql(key_space: str, table_name: str) -> str:\r\n cql_delete = \"DROP TABLE \" + key_space + \".\" + table_name + \";\"\r\n return cql_delete","title":""},{"docid":"6283c7e1919b84a29050a58882d880a5","score":"0.54781145","text":"def prepare_benchmark(workload=\"workloads/workload_read\", name=None, description=None, add_args=list()):\n if not (state.YCSB_INSTANCES and state.CLUSTER_INSTANCES):\n raise Exception(\"Cluster (and YCSB host) not yet initialized properly!\")\n\n state.RUN_NAME = name if name else datetime.now().strftime('%Y%m%d%H%m%S')\n state.RUN_DESCRIPTION = description\n state.WORKLOAD = workload\n\n log.info(\"Creating keyspace YCSB and table..\")\n # Create keyspace and table\n with util.Fragile(util.connect(state.CLUSTER_INSTANCES[0])) as seed_instance:\n sudo = seed_instance[\"sudo\"]\n ret = sudo[\"docker\", \"exec\", \"cassy\", \"cqlsh\", \"-e\", \"\"\"DESCRIBE KEYSPACES\"\"\"]()\n if \"ycsb\" in ret:\n log.info(\"Keyspace seems to exists already!!\")\n raise util.Fragile.Break\n ret = sudo[\"docker\", \"exec\", \"cassy\", \"cqlsh\", \"-e\",\n \"\"\"create keyspace ycsb WITH REPLICATION = {'class' : 'SimpleStrategy', 'replication_factor': 3 };\n USE ycsb;\n create table usertable (\n y_id varchar primary key,\n field0 varchar,\n field1 varchar,\n field2 varchar,\n field3 varchar,\n field4 varchar,\n field5 varchar,\n field6 varchar,\n field7 varchar,\n field8 varchar,\n field9 varchar);\"\"\"]()\n log.info(\"Result: \" + ret)\n\n # Load data with ycsb\n log.info(\"Loading YCSB data for workload '{0}'\".format(workload))\n with util.connect(state.YCSB_INSTANCES[0]) as ycsb_instance:\n with ycsb_instance.cwd(\"/home/ubuntu/ycsb\"):\n ycsb = ycsb_instance[\"bin/ycsb\"]\n ret = ycsb(\"load\", \"cassandra2-cql\", \"-threads\", \"100\", \"-P\", \"workloads/workload_base\", \"-P\", workload, \"-p\", \"hosts=\" + \",\".join(util.cluster_ips()), *add_args)\n log.info(\"Result: \" + ret)","title":""},{"docid":"1e53760a8fd734afbf8798d2aac33fd4","score":"0.5447686","text":"def keyspace_exists(self, name):\n return name in self.cluster.metadata.keyspaces","title":""},{"docid":"3eca2d2c307b0f4d8f2ebc74b14a3880","score":"0.5444273","text":"def create_distributed_table(self, on_cluster, over, name=None, node=None):\n if node is None:\n node = self.context.node\n if name is None:\n name = getuid()\n\n try:\n node.query(\n f\"CREATE TABLE {name} ON CLUSTER {on_cluster} AS {over} \"\n f\"ENGINE = Distributed({on_cluster}, default, {over}, rand())\"\n )\n yield name\n finally:\n with Finally(f\"I drop table {name} on cluster {on_cluster} on {node.name}\"):\n node.query(f\"DROP TABLE IF EXISTS {name} ON CLUSTER {on_cluster} SYNC\")","title":""},{"docid":"54a9561cc1f0a229c79f3fca6f68257b","score":"0.5430047","text":"def ensure_table(self):\n cursor = self.conn.cursor()\n columns = (\"userid INTEGER NOT NULL, \"\n \"guildid INTEGER NOT NULL, \"\n \"roleid INTEGER NOT NULL, \"\n \"starttime INTEGER NOT NULL, \"\n \"duration INTEGER NOT NULL\")\n\n cursor.execute(\"CREATE TABLE IF NOT EXISTS sessions ({})\".format(columns))\n self.conn.commit()","title":""},{"docid":"7912496011c250328899965dcd6a387b","score":"0.538153","text":"def initialise_db():\n # executing the queries with cache\n try:\n consumer_table_schema = conn.create_schema(\n hash_key_name='c_id',\n hash_key_proto_value=str,\n )\n table = conn.create_table(\n name='consumer_table',\n schema=consumer_table_schema,\n read_units=1,\n write_units=1\n )\n return table\n except Exception as e:\n print str(e)","title":""},{"docid":"15f32bb4d5749155bbf6653dc142d941","score":"0.53760165","text":"def db_setup(app_name=\"App\", username=\"admin\", password=\"cisco\", sharding=False, force=False):\n logger.debug(\"setting up new database (force:%r)\", force)\n \n if not force and db_exists():\n logger.debug(\"db already exists\")\n return True\n\n db = get_db()\n sh = db.client.admin\n if sharding:\n logger.debug(\"enabling sharding for db %s\", db.name)\n sh.command(\"enableSharding\", db.name)\n\n # get all objects registred to rest API, drop db and create with \n # proper keys\n for classname in registered_classes:\n c = registered_classes[classname]\n # drop existing collection\n logger.debug(\"dropping collection %s\" % c._classname)\n db[c._classname].drop()\n # create indexes for searching and unique keys ordered based on key order\n # indexes are unique only if expose_id is disabled\n indexes = []\n unique = not c._access[\"expose_id\"]\n if c._access[\"db_index_unique\"] is not None and type(c._access[\"db_index_unique\"]) is bool:\n unique = c._access[\"db_index_unique\"]\n if c._access[\"db_index\"] is None:\n # auto created index based on ordered keys\n for a in c._dn_attributes:\n indexes.append((a,ASCENDING))\n indexes = [(a, ASCENDING) for a in c._dn_attributes]\n elif type(c._access[\"db_index\"]) is list:\n indexes = [(a, ASCENDING) for a in c._access[\"db_index\"]]\n else:\n raise Exception(\"invalid db_index for %s: %s\" % (c._classname, c._access[\"db_index\"]))\n if len(indexes)>0:\n logger.debug(\"creating indexes for %s: %s\",c._classname,indexes)\n db[c._classname].create_index(indexes, unique=unique)\n\n # support second search-only (non-unique) index for objects\n if type(c._access[\"db_index2\"]) is list and len(c._access[\"db_index2\"])>0:\n index2 = [(a, ASCENDING) for a in c._access[\"db_index2\"]]\n logger.debug(\"creating secondary index for %s: %s\", c._classname, index2)\n db[c._classname].create_index(index2, unique=False)\n\n\n if sharding and c._access[\"db_shard_enable\"]:\n shard_indexes = {}\n if c._access[\"db_shard_index\"] is not None:\n for a in c._access[\"db_shard_index\"]: shard_indexes[a] = 1\n else:\n for (a,d) in indexes: shard_indexes[a] = 1\n logger.debug(\"creating shard index for %s: %s\", c._classname, shard_indexes)\n sh.command(\"shardCollection\", \"%s.%s\" % (db.name, c._classname), key=shard_indexes)\n\n # if uni is enabled then required before any other object is created\n uni = Universe.load()\n uni.save()\n \n # insert settings with user provided values \n lpass = \"%s\"%uuid.uuid4()\n s = Settings(app_name=app_name, lpass = lpass)\n try: s.save()\n except BadRequest as e:\n logger.error(\"failed to create settings: %s. Using all defaults\"%e)\n s = Settings(lpass=lpass)\n s.save()\n \n # create admin user with provided password and local user\n u = User(username=\"local\", password=lpass, role=Role.FULL_ADMIN)\n if not u.save(): \n logger.error(\"failed to create local user\")\n return False\n if username == \"local\":\n logger.error(\"username 'local' is reserved, use a different default username\")\n return False\n u = User(username=username, password=password, role=Role.FULL_ADMIN)\n if not u.save():\n logger.error(\"failed to create username: %s\" % args.username)\n return False\n\n #successful setup\n return True","title":""},{"docid":"12e4a932c0c0b0ffaa624910b820a027","score":"0.5375257","text":"def create_schema(connection):\n\n connection.execute(\"CREATE EXTENSION IF NOT EXISTS hstore\")\n\n biz.DomainBase.metadata.create_all(connection)","title":""},{"docid":"14f0193cf0272e9bba250a06af3e06fd","score":"0.53567296","text":"def __init__(self, databaseid=str, region=str, username=str, password=str, keyspace=str):\n self.username = username\n self.password = password\n self.databaseid = databaseid\n self.region = region\n self.keyspace = keyspace\n\n self.url = \"https://{uuid}-{region}.apps.astra.datastax.com/api/rest/v1\".format(uuid=databaseid,\n region=region)\n\n payload = {'username': self.username,\n 'password': self.password}\n x = post(self.url + \"/auth\", data=to_json(payload))\n\n self.auth_token = x.json()[\"authToken\"]","title":""},{"docid":"359c318801196f30088e50c006b9011f","score":"0.5345077","text":"def test_create_global_keys_index(self):\n hash_key = DynamoKey('id', data_type=STRING)\n index_field = DynamoKey('name')\n index = GlobalIndex.keys('name-index', index_field)\n table = Table('foobar', hash_key, global_indexes=[index])\n self.dynamo.create_table('foobar', hash_key, global_indexes=[index])\n desc = self.dynamo.describe_table('foobar')\n self.assertEqual(desc, table)","title":""},{"docid":"f93ae8770e525ce36c9fd63a32532c16","score":"0.532987","text":"def create_db_schema(context, connection):\n admin_name = context[ctx.ADMIN_NAME]\n user_name = context[ctx.USER_NAME]\n schema_name = context[ctx.SCHEMA_NAME]\n log.info(f\"Creating the schema space {schema_name}\")\n query = \"CREATE SCHEMA IF NOT EXISTS %s AUTHORIZATION %s;\"\n params = [AsIs(schema_name), AsIs(admin_name)]\n try:\n with connection.cursor() as cursor:\n cursor.execute(query, params)\n cursor.execute(\"GRANT USAGE ON SCHEMA %s TO %s;\",\n [AsIs(schema_name), AsIs(user_name)])\n except Error:\n log.exception(\"Failed to create the db schema.\")\n return False\n return True","title":""},{"docid":"907e1d88cbd4e48846c133ba2cd3f9cd","score":"0.53143376","text":"def create_cluster(self, cluster_name, db_engine, db_name, parameter_group):\n print(\"Checking for an existing DB cluster.\")\n cluster = self.aurora_wrapper.get_db_cluster(cluster_name)\n if cluster is None:\n admin_username = q.ask(\n \"Enter an administrator user name for the database: \", q.non_empty)\n admin_password = q.ask(\n \"Enter a password for the administrator (at least 8 characters): \", q.non_empty)\n engine_versions = self.aurora_wrapper.get_engine_versions(\n db_engine, parameter_group['DBParameterGroupFamily'])\n engine_choices = [ver['EngineVersion'] for ver in engine_versions]\n print(\"The available engines for your parameter group are:\")\n engine_index = q.choose(\"Which engine do you want to use? \", engine_choices)\n print(f\"Creating DB cluster {cluster_name} and database {db_name}.\\n\"\n f\"The DB cluster is configured to use\\n\"\n f\"your custom parameter group {parameter_group['DBClusterParameterGroupName']}\\n\"\n f\"and selected engine {engine_choices[engine_index]}.\\n\"\n f\"This typically takes several minutes.\")\n cluster = self.aurora_wrapper.create_db_cluster(\n cluster_name, parameter_group['DBClusterParameterGroupName'], db_name,\n db_engine, engine_choices[engine_index], admin_username, admin_password)\n while cluster.get('Status') != 'available':\n wait(30)\n cluster = self.aurora_wrapper.get_db_cluster(cluster_name)\n print(\"Cluster created and available.\\n\")\n print(\"Cluster data:\")\n pp(cluster)\n print('-'*88)\n return cluster","title":""},{"docid":"d1d55ccda266622787f998e7d26a31cc","score":"0.53118396","text":"def create_test_db(self, *args, **kw):\n self.destroy_test_db()\n self.connection.use_test_datastore = True\n self.connection.flush()","title":""},{"docid":"874b0ba71240e2d2b9f1e44740c3a4db","score":"0.53076994","text":"def create_database():\r\n model.create_table()","title":""},{"docid":"fcb5086663d93c325c3eebca6727d23c","score":"0.5279027","text":"def create_table(self, on_cluster, name=None, node=None):\n if node is None:\n node = self.context.node\n if name is None:\n name = getuid()\n\n try:\n node.query(\n f\"CREATE TABLE {name} ON CLUSTER {on_cluster} (d Date, a String, b UInt8, x String, y Int8) \"\n f\"ENGINE = ReplicatedMergeTree('/clickhouse/tables/{{shard}}/{name}', '{{replica}}') \"\n \"PARTITION BY y ORDER BY (d, b)\"\n )\n yield name\n finally:\n with Finally(f\"I drop table {name} on cluster {on_cluster} on {node.name}\"):\n node.query(f\"DROP TABLE IF EXISTS {name} ON CLUSTER {on_cluster} SYNC\")","title":""},{"docid":"9ad840499a60c9733f8872f9ff5dd819","score":"0.5274907","text":"def create_db():\n db.engine.execute('CREATE EXTENSION IF NOT EXISTS citext')\n db.engine.execute('CREATE EXTENSION IF NOT EXISTS pgcrypto')\n db.create_all()","title":""},{"docid":"e5169980a14b3c9d3cc677fdca6f595e","score":"0.5258835","text":"def createTable(self, name, comp_type=None, super_table=False):\n sys = SystemManager(str(self.host) + \":\" + str(self.port),framed_transport=True, timeout=30)\n try :\n print \"Trying to create Table: \" + str(name)\n sys.create_column_family(self.keyspace, str(name), comparator_type=comp_type)\n except :\n print \"Drop and create new Table: \" + str(name)\n sys.drop_column_family(self.keyspace, str(name))\n sys.create_column_family(self.keyspace, str(name),comparator_type=comp_type)\n \n sys.close()","title":""},{"docid":"594f4a9ac57668a69088ffae876ba5ec","score":"0.52489424","text":"async def m002_initial(db):\n await db.execute(\n \"\"\"\n CREATE TABLE satsdice.satsdice_withdraw (\n id TEXT PRIMARY KEY,\n satsdice_pay TEXT,\n value INTEGER DEFAULT 1,\n unique_hash TEXT UNIQUE,\n k1 TEXT,\n open_time INTEGER,\n used INTEGER DEFAULT 0\n );\n \"\"\"\n )","title":""},{"docid":"26247b1f8699f334be88b63faaedd6fd","score":"0.52460986","text":"def cluster(ctx, config):\n if ctx.config.get('use_existing_cluster', False) is True:\n log.info(\"'use_existing_cluster' is true; skipping cluster creation\")\n yield\n\n testdir = teuthology.get_testdir(ctx)\n cluster_name = config['cluster']\n data_dir = '{tdir}/{cluster}.data'.format(tdir=testdir, cluster=cluster_name)\n log.info('Creating ceph cluster %s...', cluster_name)\n log.info('config %s', config)\n log.info('ctx.config %s', ctx.config)\n run.wait(\n ctx.cluster.run(\n args=[\n 'install', '-d', '-m0755', '--',\n data_dir,\n ],\n wait=False,\n )\n )\n\n run.wait(\n ctx.cluster.run(\n args=[\n 'sudo',\n 'install', '-d', '-m0777', '--', '/var/run/ceph',\n ],\n wait=False,\n )\n )\n\n devs_to_clean = {}\n remote_to_roles_to_devs = {}\n remote_to_roles_to_journals = {}\n osds = ctx.cluster.only(teuthology.is_type('osd', cluster_name))\n for remote, roles_for_host in osds.remotes.items():\n devs = teuthology.get_scratch_devices(remote)\n roles_to_devs = {}\n roles_to_journals = {}\n if config.get('fs'):\n log.info('fs option selected, checking for scratch devs')\n log.info('found devs: %s' % (str(devs),))\n devs_id_map = teuthology.get_wwn_id_map(remote, devs)\n iddevs = list(devs_id_map.values())\n roles_to_devs = assign_devs(\n teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name), iddevs\n )\n if len(roles_to_devs) < len(iddevs):\n iddevs = iddevs[len(roles_to_devs):]\n devs_to_clean[remote] = []\n\n if config.get('block_journal'):\n log.info('block journal enabled')\n roles_to_journals = assign_devs(\n teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name), iddevs\n )\n log.info('journal map: %s', roles_to_journals)\n\n if config.get('tmpfs_journal'):\n log.info('tmpfs journal enabled')\n roles_to_journals = {}\n remote.run(args=['sudo', 'mount', '-t', 'tmpfs', 'tmpfs', '/mnt'])\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name):\n tmpfs = '/mnt/' + role\n roles_to_journals[role] = tmpfs\n remote.run(args=['truncate', '-s', '1500M', tmpfs])\n log.info('journal map: %s', roles_to_journals)\n\n log.info('dev map: %s' % (str(roles_to_devs),))\n remote_to_roles_to_devs[remote] = roles_to_devs\n remote_to_roles_to_journals[remote] = roles_to_journals\n\n log.info('Generating config...')\n remotes_and_roles = ctx.cluster.remotes.items()\n roles = [role_list for (remote, role_list) in remotes_and_roles]\n ips = [remote.ip_address for (remote, role_list) in remotes_and_roles]\n mons = get_mons(\n roles, ips, cluster_name,\n mon_bind_msgr2=config.get('mon_bind_msgr2'),\n mon_bind_addrvec=config.get('mon_bind_addrvec'),\n )\n conf = skeleton_config(\n ctx, roles=roles, ips=ips, mons=mons, cluster=cluster_name,\n )\n for remote, roles_to_journals in remote_to_roles_to_journals.items():\n for role, journal in roles_to_journals.items():\n name = teuthology.ceph_role(role)\n if name not in conf:\n conf[name] = {}\n conf[name]['osd journal'] = journal\n for section, keys in config['conf'].items():\n for key, value in keys.items():\n log.info(\"[%s] %s = %s\" % (section, key, value))\n if section not in conf:\n conf[section] = {}\n conf[section][key] = value\n\n if config.get('tmpfs_journal'):\n conf['journal dio'] = False\n\n if not hasattr(ctx, 'ceph'):\n ctx.ceph = {}\n ctx.ceph[cluster_name] = argparse.Namespace()\n ctx.ceph[cluster_name].conf = conf\n ctx.ceph[cluster_name].mons = mons\n\n default_keyring = '/etc/ceph/{cluster}.keyring'.format(cluster=cluster_name)\n keyring_path = config.get('keyring_path', default_keyring)\n\n coverage_dir = '{tdir}/archive/coverage'.format(tdir=testdir)\n\n firstmon = teuthology.get_first_mon(ctx, config, cluster_name)\n\n log.info('Setting up %s...' % firstmon)\n ctx.cluster.only(firstmon).run(\n args=[\n 'sudo',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-authtool',\n '--create-keyring',\n keyring_path,\n ],\n )\n ctx.cluster.only(firstmon).run(\n args=[\n 'sudo',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-authtool',\n '--gen-key',\n '--name=mon.',\n keyring_path,\n ],\n )\n ctx.cluster.only(firstmon).run(\n args=[\n 'sudo',\n 'chmod',\n '0644',\n keyring_path,\n ],\n )\n (mon0_remote,) = ctx.cluster.only(firstmon).remotes.keys()\n monmap_path = '{tdir}/{cluster}.monmap'.format(tdir=testdir,\n cluster=cluster_name)\n fsid = create_simple_monmap(\n ctx,\n remote=mon0_remote,\n conf=conf,\n mons=mons,\n path=monmap_path,\n mon_bind_addrvec=config.get('mon_bind_addrvec'),\n )\n if not 'global' in conf:\n conf['global'] = {}\n conf['global']['fsid'] = fsid\n\n default_conf_path = '/etc/ceph/{cluster}.conf'.format(cluster=cluster_name)\n conf_path = config.get('conf_path', default_conf_path)\n log.info('Writing %s for FSID %s...' % (conf_path, fsid))\n write_conf(ctx, conf_path, cluster_name)\n\n log.info('Creating admin key on %s...' % firstmon)\n ctx.cluster.only(firstmon).run(\n args=[\n 'sudo',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-authtool',\n '--gen-key',\n '--name=client.admin',\n '--cap', 'mon', 'allow *',\n '--cap', 'osd', 'allow *',\n '--cap', 'mds', 'allow *',\n '--cap', 'mgr', 'allow *',\n keyring_path,\n ],\n )\n\n log.info('Copying monmap to all nodes...')\n keyring = teuthology.get_file(\n remote=mon0_remote,\n path=keyring_path,\n )\n monmap = teuthology.get_file(\n remote=mon0_remote,\n path=monmap_path,\n )\n\n for rem in ctx.cluster.remotes.keys():\n # copy mon key and initial monmap\n log.info('Sending monmap to node {remote}'.format(remote=rem))\n teuthology.sudo_write_file(\n remote=rem,\n path=keyring_path,\n data=keyring,\n perms='0644'\n )\n teuthology.write_file(\n remote=rem,\n path=monmap_path,\n data=monmap,\n )\n\n log.info('Setting up mon nodes...')\n mons = ctx.cluster.only(teuthology.is_type('mon', cluster_name))\n\n if not config.get('skip_mgr_daemons', False):\n log.info('Setting up mgr nodes...')\n mgrs = ctx.cluster.only(teuthology.is_type('mgr', cluster_name))\n for remote, roles_for_host in mgrs.remotes.items():\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'mgr',\n cluster_name):\n _, _, id_ = teuthology.split_role(role)\n mgr_dir = DATA_PATH.format(\n type_='mgr', cluster=cluster_name, id_=id_)\n remote.run(\n args=[\n 'sudo',\n 'mkdir',\n '-p',\n mgr_dir,\n run.Raw('&&'),\n 'sudo',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-authtool',\n '--create-keyring',\n '--gen-key',\n '--name=mgr.{id}'.format(id=id_),\n mgr_dir + '/keyring',\n ],\n )\n\n log.info('Setting up mds nodes...')\n mdss = ctx.cluster.only(teuthology.is_type('mds', cluster_name))\n for remote, roles_for_host in mdss.remotes.items():\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'mds',\n cluster_name):\n _, _, id_ = teuthology.split_role(role)\n mds_dir = DATA_PATH.format(\n type_='mds', cluster=cluster_name, id_=id_)\n remote.run(\n args=[\n 'sudo',\n 'mkdir',\n '-p',\n mds_dir,\n run.Raw('&&'),\n 'sudo',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-authtool',\n '--create-keyring',\n '--gen-key',\n '--name=mds.{id}'.format(id=id_),\n mds_dir + '/keyring',\n ],\n )\n remote.run(args=[\n 'sudo', 'chown', '-R', 'ceph:ceph', mds_dir\n ])\n\n cclient.create_keyring(ctx, cluster_name)\n log.info('Running mkfs on osd nodes...')\n\n if not hasattr(ctx, 'disk_config'):\n ctx.disk_config = argparse.Namespace()\n if not hasattr(ctx.disk_config, 'remote_to_roles_to_dev'):\n ctx.disk_config.remote_to_roles_to_dev = {}\n if not hasattr(ctx.disk_config, 'remote_to_roles_to_journals'):\n ctx.disk_config.remote_to_roles_to_journals = {}\n if not hasattr(ctx.disk_config, 'remote_to_roles_to_dev_mount_options'):\n ctx.disk_config.remote_to_roles_to_dev_mount_options = {}\n if not hasattr(ctx.disk_config, 'remote_to_roles_to_dev_fstype'):\n ctx.disk_config.remote_to_roles_to_dev_fstype = {}\n\n teuthology.deep_merge(ctx.disk_config.remote_to_roles_to_dev, remote_to_roles_to_devs)\n teuthology.deep_merge(ctx.disk_config.remote_to_roles_to_journals, remote_to_roles_to_journals)\n\n log.info(\"ctx.disk_config.remote_to_roles_to_dev: {r}\".format(r=str(ctx.disk_config.remote_to_roles_to_dev)))\n for remote, roles_for_host in osds.remotes.items():\n roles_to_devs = remote_to_roles_to_devs[remote]\n roles_to_journals = remote_to_roles_to_journals[remote]\n\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name):\n _, _, id_ = teuthology.split_role(role)\n mnt_point = DATA_PATH.format(\n type_='osd', cluster=cluster_name, id_=id_)\n remote.run(\n args=[\n 'sudo',\n 'mkdir',\n '-p',\n mnt_point,\n ])\n log.info(str(roles_to_devs))\n log.info(str(roles_to_journals))\n log.info(role)\n if roles_to_devs.get(role):\n dev = roles_to_devs[role]\n fs = config.get('fs')\n package = None\n mkfs_options = config.get('mkfs_options')\n mount_options = config.get('mount_options')\n if fs == 'btrfs':\n # package = 'btrfs-tools'\n if mount_options is None:\n mount_options = ['noatime', 'user_subvol_rm_allowed']\n if mkfs_options is None:\n mkfs_options = ['-m', 'single',\n '-l', '32768',\n '-n', '32768']\n if fs == 'xfs':\n # package = 'xfsprogs'\n if mount_options is None:\n mount_options = ['noatime']\n if mkfs_options is None:\n mkfs_options = ['-f', '-i', 'size=2048']\n if fs == 'ext4' or fs == 'ext3':\n if mount_options is None:\n mount_options = ['noatime', 'user_xattr']\n\n if mount_options is None:\n mount_options = []\n if mkfs_options is None:\n mkfs_options = []\n mkfs = ['mkfs.%s' % fs] + mkfs_options\n log.info('%s on %s on %s' % (mkfs, dev, remote))\n if package is not None:\n remote.sh('sudo apt-get install -y %s' % package)\n\n try:\n remote.run(args=['yes', run.Raw('|')] + ['sudo'] + mkfs + [dev])\n except run.CommandFailedError:\n # Newer btfs-tools doesn't prompt for overwrite, use -f\n if '-f' not in mount_options:\n mkfs_options.append('-f')\n mkfs = ['mkfs.%s' % fs] + mkfs_options\n log.info('%s on %s on %s' % (mkfs, dev, remote))\n remote.run(args=['yes', run.Raw('|')] + ['sudo'] + mkfs + [dev])\n\n log.info('mount %s on %s -o %s' % (dev, remote,\n ','.join(mount_options)))\n remote.run(\n args=[\n 'sudo',\n 'mount',\n '-t', fs,\n '-o', ','.join(mount_options),\n dev,\n mnt_point,\n ]\n )\n remote.run(\n args=[\n 'sudo', '/sbin/restorecon', mnt_point,\n ],\n check_status=False,\n )\n if not remote in ctx.disk_config.remote_to_roles_to_dev_mount_options:\n ctx.disk_config.remote_to_roles_to_dev_mount_options[remote] = {}\n ctx.disk_config.remote_to_roles_to_dev_mount_options[remote][role] = mount_options\n if not remote in ctx.disk_config.remote_to_roles_to_dev_fstype:\n ctx.disk_config.remote_to_roles_to_dev_fstype[remote] = {}\n ctx.disk_config.remote_to_roles_to_dev_fstype[remote][role] = fs\n devs_to_clean[remote].append(mnt_point)\n\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name):\n _, _, id_ = teuthology.split_role(role)\n try:\n remote.run(\n args=[\n 'sudo',\n 'MALLOC_CHECK_=3',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-osd',\n '--no-mon-config',\n '--cluster',\n cluster_name,\n '--mkfs',\n '--mkkey',\n '-i', id_,\n '--monmap', monmap_path,\n ],\n )\n except run.CommandFailedError:\n # try without --no-mon-config.. this may be an upgrade test\n remote.run(\n args=[\n 'sudo',\n 'MALLOC_CHECK_=3',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-osd',\n '--cluster',\n cluster_name,\n '--mkfs',\n '--mkkey',\n '-i', id_,\n '--monmap', monmap_path,\n ],\n )\n mnt_point = DATA_PATH.format(\n type_='osd', cluster=cluster_name, id_=id_)\n try:\n remote.run(args=[\n 'sudo', 'chown', '-R', 'ceph:ceph', mnt_point\n ])\n except run.CommandFailedError as e:\n # hammer does not have ceph user, so ignore this error\n log.info('ignoring error when chown ceph:ceph,'\n 'probably installing hammer: %s', e)\n\n log.info('Reading keys from all nodes...')\n keys_fp = BytesIO()\n keys = []\n for remote, roles_for_host in ctx.cluster.remotes.items():\n for type_ in ['mgr', 'mds', 'osd']:\n if type_ == 'mgr' and config.get('skip_mgr_daemons', False):\n continue\n for role in teuthology.cluster_roles_of_type(roles_for_host, type_, cluster_name):\n _, _, id_ = teuthology.split_role(role)\n data = teuthology.get_file(\n remote=remote,\n path=os.path.join(\n DATA_PATH.format(\n type_=type_, id_=id_, cluster=cluster_name),\n 'keyring',\n ),\n sudo=True,\n )\n keys.append((type_, id_, data))\n keys_fp.write(data)\n for remote, roles_for_host in ctx.cluster.remotes.items():\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'client', cluster_name):\n _, _, id_ = teuthology.split_role(role)\n data = teuthology.get_file(\n remote=remote,\n path='/etc/ceph/{cluster}.client.{id}.keyring'.format(id=id_, cluster=cluster_name)\n )\n keys.append(('client', id_, data))\n keys_fp.write(data)\n\n log.info('Adding keys to all mons...')\n writes = mons.run(\n args=[\n 'sudo', 'tee', '-a',\n keyring_path,\n ],\n stdin=run.PIPE,\n wait=False,\n stdout=BytesIO(),\n )\n keys_fp.seek(0)\n teuthology.feed_many_stdins_and_close(keys_fp, writes)\n run.wait(writes)\n for type_, id_, data in keys:\n run.wait(\n mons.run(\n args=[\n 'sudo',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-authtool',\n keyring_path,\n '--name={type}.{id}'.format(\n type=type_,\n id=id_,\n ),\n ] + list(generate_caps(type_)),\n wait=False,\n ),\n )\n\n log.info('Running mkfs on mon nodes...')\n for remote, roles_for_host in mons.remotes.items():\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'mon', cluster_name):\n _, _, id_ = teuthology.split_role(role)\n mnt_point = DATA_PATH.format(\n type_='mon', id_=id_, cluster=cluster_name)\n remote.run(\n args=[\n 'sudo',\n 'mkdir',\n '-p',\n mnt_point,\n ],\n )\n remote.run(\n args=[\n 'sudo',\n 'adjust-ulimits',\n 'ceph-coverage',\n coverage_dir,\n 'ceph-mon',\n '--cluster', cluster_name,\n '--mkfs',\n '-i', id_,\n '--monmap', monmap_path,\n '--keyring', keyring_path,\n ],\n )\n try:\n remote.run(args=[\n 'sudo', 'chown', '-R', 'ceph:ceph', mnt_point\n ])\n except run.CommandFailedError as e:\n # hammer does not have ceph user, so ignore this error\n log.info('ignoring error when chown ceph:ceph,'\n 'probably installing hammer: %s', e)\n\n run.wait(\n mons.run(\n args=[\n 'rm',\n '--',\n monmap_path,\n ],\n wait=False,\n ),\n )\n\n try:\n yield\n except Exception:\n # we need to know this below\n ctx.summary['success'] = False\n raise\n finally:\n (mon0_remote,) = ctx.cluster.only(firstmon).remotes.keys()\n\n log.info('Checking cluster log for badness...')\n\n def first_in_ceph_log(pattern, excludes):\n \"\"\"\n Find the first occurrence of the pattern specified in the Ceph log,\n Returns None if none found.\n\n :param pattern: Pattern scanned for.\n :param excludes: Patterns to ignore.\n :return: First line of text (or None if not found)\n \"\"\"\n args = [\n 'sudo',\n 'egrep', pattern,\n '/var/log/ceph/{cluster}.log'.format(cluster=cluster_name),\n ]\n for exclude in excludes:\n args.extend([run.Raw('|'), 'egrep', '-v', exclude])\n args.extend([\n run.Raw('|'), 'head', '-n', '1',\n ])\n stdout = mon0_remote.sh(args)\n return stdout or None\n\n if first_in_ceph_log('\\[ERR\\]|\\[WRN\\]|\\[SEC\\]',\n config['log_whitelist']) is not None:\n log.warning('Found errors (ERR|WRN|SEC) in cluster log')\n ctx.summary['success'] = False\n # use the most severe problem as the failure reason\n if 'failure_reason' not in ctx.summary:\n for pattern in ['\\[SEC\\]', '\\[ERR\\]', '\\[WRN\\]']:\n match = first_in_ceph_log(pattern, config['log_whitelist'])\n if match is not None:\n ctx.summary['failure_reason'] = \\\n '\"{match}\" in cluster log'.format(\n match=match.rstrip('\\n'),\n )\n break\n\n for remote, dirs in devs_to_clean.items():\n for dir_ in dirs:\n log.info('Unmounting %s on %s' % (dir_, remote))\n try:\n remote.run(\n args=[\n 'sync',\n run.Raw('&&'),\n 'sudo',\n 'umount',\n '-f',\n dir_\n ]\n )\n except Exception as e:\n remote.run(args=[\n 'sudo',\n run.Raw('PATH=/usr/sbin:$PATH'),\n 'lsof',\n run.Raw(';'),\n 'ps', 'auxf',\n ])\n raise e\n\n if config.get('tmpfs_journal'):\n log.info('tmpfs journal enabled - unmounting tmpfs at /mnt')\n for remote, roles_for_host in osds.remotes.items():\n remote.run(\n args=['sudo', 'umount', '-f', '/mnt'],\n check_status=False,\n )\n\n if ctx.archive is not None and \\\n not (ctx.config.get('archive-on-error') and ctx.summary['success']):\n\n # archive mon data, too\n log.info('Archiving mon data...')\n path = os.path.join(ctx.archive, 'data')\n try:\n os.makedirs(path)\n except OSError as e:\n if e.errno == errno.EEXIST:\n pass\n else:\n raise\n for remote, roles in mons.remotes.items():\n for role in roles:\n is_mon = teuthology.is_type('mon', cluster_name)\n if is_mon(role):\n _, _, id_ = teuthology.split_role(role)\n mon_dir = DATA_PATH.format(\n type_='mon', id_=id_, cluster=cluster_name)\n teuthology.pull_directory_tarball(\n remote,\n mon_dir,\n path + '/' + role + '.tgz')\n\n log.info('Cleaning ceph cluster...')\n run.wait(\n ctx.cluster.run(\n args=[\n 'sudo',\n 'rm',\n '-rf',\n '--',\n conf_path,\n keyring_path,\n data_dir,\n monmap_path,\n run.Raw('{tdir}/../*.pid'.format(tdir=testdir)),\n ],\n wait=False,\n ),\n )","title":""},{"docid":"e399c36c2df5b195d84c34f11785f2ee","score":"0.522368","text":"def start_db_cluster(DBClusterIdentifier=None):\n pass","title":""},{"docid":"9dd7b00416dd713d054c1f607664f007","score":"0.5218847","text":"def main():\n try:\n cluster, session = create_database()\n except Exception as e:\n print(e)\n raise e\n \n try:\n drop_tables(session)\n create_tables(session)\n except Exception as e:\n print(e)\n finally:\n session.shutdown()\n cluster.shutdown()","title":""},{"docid":"23a6aea2dec464459d6e59edb589f5ab","score":"0.51945525","text":"def test_create_index(self):\n hash_key = DynamoKey('id', data_type=STRING)\n self.dynamo.create_table('foobar', hash_key=hash_key)\n index_field = DynamoKey('name')\n index = GlobalIndex.all('name-index', index_field, hash_key)\n self.dynamo.update_table('foobar', index_updates=[\n IndexUpdate.create(index)])\n table = self.dynamo.describe_table('foobar')\n self.assertEqual(len(table.global_indexes), 1)","title":""},{"docid":"57fa5c26763489da9e94a81f04a9b083","score":"0.5181301","text":"def index_create(self, keyspace, table, index):\n\n path = '/keyspaces/%(keyspace)s/tables/%(table)s/index/%(field)s' % {\n 'keyspace': keyspace,\n 'table': table,\n 'field': index\n }\n response = self.rest.request(method='post', path=path)\n return response","title":""},{"docid":"61c690a9cbf3d85f12bb6c106f872ef5","score":"0.517907","text":"def ensure_table(self, cls):\n id_len = len(uuid())\n index_names = cls.index_names() or []\n cols = [\n 'id char(%d) primary key' % (id_len,),\n 'value jsonb'\n ] + [\n name + ' text' for name in index_names\n ]\n\n table_name = cls.get_table_name()\n\n with self._conn() as conn:\n with conn.cursor() as cur:\n cur.execute('create table if not exists %s (%s);' % (\n table_name,\n ','.join(cols)\n ))\n for name in index_names:\n cur.execute('create index if not exists %s on %s(%s);' % (\n table_name + '_' + name + '_idx',\n table_name,\n name\n ))\n # End of conn with - transction should commit here if not exception","title":""},{"docid":"2cd2232e5fc1ba2dc6baa44c7a991ba3","score":"0.5178922","text":"def create_without_privilege(self, node=None):\n user_name = f\"user_{getuid()}\"\n\n table0_name = f\"table0_{getuid()}\"\n table1_name = f\"table1_{getuid()}\"\n\n exitcode, message = errors.not_enough_privileges(name=f\"{user_name}\")\n\n cluster = self.context.cluster_name\n\n if node is None:\n node = self.context.node\n\n with Given(\"I have a user\"):\n user(name=user_name)\n\n with And(\"I have a table on a cluster\"):\n table(name=table0_name, cluster=cluster)\n\n with When(\"I grant the user NONE privilege\"):\n node.query(f\"GRANT NONE TO {user_name}\")\n\n with And(\"I grant the user USAGE privilege\"):\n node.query(f\"GRANT USAGE ON *.* TO {user_name}\")\n\n with Then(\"I attempt to create the distributed table without privilege\"):\n node.query(f\"CREATE TABLE {table1_name} (a UInt64) ENGINE = Distributed(sharded_cluster, default, {table0_name}, rand())\", settings = [(\"user\", f\"{user_name}\")],\n exitcode=exitcode, message=message)","title":""},{"docid":"c0e7847dd7c09d38cc5dfdfab94ba8ca","score":"0.5173465","text":"def static_cf_test(self):\n cluster = self.cluster\n\n cluster.populate(1).start()\n node1 = cluster.nodelist()[0]\n time.sleep(0.2)\n\n cursor = self.cql_connection(node1, version=cql_version).cursor()\n self.create_ks(cursor, 'ks', 1)\n\n # Create\n cursor.execute(\"\"\"\n CREATE TABLE users (\n userid uuid PRIMARY KEY,\n firstname text,\n lastname text,\n age int\n );\n \"\"\")\n\n # Inserts\n cursor.execute(\"INSERT INTO users (userid, firstname, lastname, age) VALUES (550e8400-e29b-41d4-a716-446655440000, Frodo, Baggins, 32)\")\n cursor.execute(\"UPDATE users SET firstname = Samwise, lastname = Gamgee, age = 33 WHERE userid = f47ac10b-58cc-4372-a567-0e02b2c3d479\")\n\n # Queries\n cursor.execute(\"SELECT firstname, lastname FROM users WHERE userid = 550e8400-e29b-41d4-a716-446655440000\");\n res = cursor.fetchall()\n assert res == [[ 'Frodo', 'Baggins' ]], res\n\n cursor.execute(\"SELECT * FROM users WHERE userid = 550e8400-e29b-41d4-a716-446655440000\");\n res = cursor.fetchall()\n assert res == [[ UUID('550e8400-e29b-41d4-a716-446655440000'), 32, 'Frodo', 'Baggins' ]], res\n\n cursor.execute(\"SELECT * FROM users\");\n res = cursor.fetchall()\n assert res == [\n [ UUID('f47ac10b-58cc-4372-a567-0e02b2c3d479'), 33, 'Samwise', 'Gamgee' ],\n [ UUID('550e8400-e29b-41d4-a716-446655440000'), 32, 'Frodo', 'Baggins' ],\n ], res\n\n # Test batch inserts\n cursor.execute(\"\"\"\n BEGIN BATCH\n INSERT INTO users (userid, age) VALUES (550e8400-e29b-41d4-a716-446655440000, 36)\n UPDATE users SET age = 37 WHERE userid = f47ac10b-58cc-4372-a567-0e02b2c3d479\n DELETE firstname, lastname FROM users WHERE userid = 550e8400-e29b-41d4-a716-446655440000\n DELETE firstname, lastname FROM users WHERE userid = f47ac10b-58cc-4372-a567-0e02b2c3d479\n APPLY BATCH\n \"\"\")\n\n cursor.execute(\"SELECT * FROM users\")\n res = cursor.fetchall()\n assert res == [\n [ UUID('f47ac10b-58cc-4372-a567-0e02b2c3d479'), 37, None, None ],\n [ UUID('550e8400-e29b-41d4-a716-446655440000'), 36, None, None ],\n ], res","title":""},{"docid":"ce092c38c5480afb7905af3261cf538f","score":"0.5170467","text":"def tenant_table_initialize():\n global _tenant_table\n\n _tenant_table = TenantTable()\n _tenant_table.persist = False\n\n tenants = database.database_tenant_get_list()\n for tenant in tenants:\n _tenant_table[tenant.uuid] = tenant\n _tenant_table.persist = True","title":""},{"docid":"6dd7cb494480ec268098107073a69423","score":"0.51701653","text":"def test_type_as_clustering_col(self):\n # make sure we can define a table with a user type as a clustering column\n # and do a basic insert/query of data in that table.\n cluster = self.cluster\n cluster.populate(3).start()\n node1, node2, node3 = cluster.nodelist()\n session = self.patient_cql_connection(node1, consistency_level=ConsistencyLevel.LOCAL_QUORUM)\n create_ks(session, 'user_type_pkeys', 2)\n\n stmt = \"\"\"\n CREATE TYPE t_letterpair (\n first text,\n second text\n )\n \"\"\"\n session.execute(stmt)\n\n stmt = \"\"\"\n CREATE TABLE letters (\n id int,\n letterpair frozen<t_letterpair>,\n PRIMARY KEY (id, letterpair)\n )\n \"\"\"\n session.execute(stmt)\n # address CASSANDRA-11498\n session.cluster.control_connection.wait_for_schema_agreement()\n\n # create a bit of data and expect a natural order based on clustering user types\n\n ids = list(range(1, 10))\n\n for _id in ids:\n session.execute(\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'a', second:'z'}})\".format(_id))\n session.execute(\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'z', second:'a'}})\".format(_id))\n session.execute(\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'c', second:'f'}})\".format(_id))\n session.execute(\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'c', second:'a'}})\".format(_id))\n session.execute(\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'c', second:'z'}})\".format(_id))\n session.execute(\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'d', second:'e'}})\".format(_id))\n\n for _id in ids:\n res = list(session.execute(\"SELECT letterpair FROM letters where id = {}\".format(_id)))\n\n assert listify(res) == [[['a', 'z']], [['c', 'a']], [['c', 'f']], [['c', 'z']], [['d', 'e']], [['z', 'a']]]","title":""},{"docid":"6b287aefc4864cd099a2d526d6d0679b","score":"0.51599354","text":"def keyspace_size():\n return 2 ** (8 * key_size_bytes)","title":""},{"docid":"81e610a25bbd00c09f72427cdfeb2245","score":"0.51494366","text":"def make_table(self):\n hash_key = DynamoKey('id')\n self.dynamo.create_table('foobar', hash_key=hash_key)","title":""},{"docid":"81e610a25bbd00c09f72427cdfeb2245","score":"0.51494366","text":"def make_table(self):\n hash_key = DynamoKey('id')\n self.dynamo.create_table('foobar', hash_key=hash_key)","title":""},{"docid":"81e610a25bbd00c09f72427cdfeb2245","score":"0.51494366","text":"def make_table(self):\n hash_key = DynamoKey('id')\n self.dynamo.create_table('foobar', hash_key=hash_key)","title":""},{"docid":"81e610a25bbd00c09f72427cdfeb2245","score":"0.51494366","text":"def make_table(self):\n hash_key = DynamoKey('id')\n self.dynamo.create_table('foobar', hash_key=hash_key)","title":""},{"docid":"81e610a25bbd00c09f72427cdfeb2245","score":"0.51494366","text":"def make_table(self):\n hash_key = DynamoKey('id')\n self.dynamo.create_table('foobar', hash_key=hash_key)","title":""},{"docid":"81e610a25bbd00c09f72427cdfeb2245","score":"0.51494366","text":"def make_table(self):\n hash_key = DynamoKey('id')\n self.dynamo.create_table('foobar', hash_key=hash_key)","title":""},{"docid":"6143c1b797538fd80c6db898146ebd27","score":"0.5143367","text":"def _setup_couchdb(servername, database):\n \n server = couchdb.client.Server(servername)\n if database in server:\n return server[database]\n else:\n return server.create(database)","title":""},{"docid":"efb684bfa2ef42ccb46d90ad20ff8407","score":"0.51308817","text":"def create_create():\n create = upkey('create').setResultsName('action')\n hash_key = Group(upkey('hash') +\n upkey('key'))\n range_key = Group(upkey('range') +\n upkey('key'))\n\n index = Group(Optional(upkey('all') | upkey('keys') | upkey('include')) +\n upkey('index')).setResultsName('index_type')\n include_vars = Group(Suppress('[') + delimitedList(primitive) +\n Suppress(']')).setResultsName('include')\n local_index = Group(index + Suppress('(') + primitive +\n Optional(Suppress(',') + include_vars) + Suppress(')'))\n index_type = (hash_key | range_key | local_index)\\\n .setName('index specification').setResultsName('index')\n\n attr_declaration = Group(var.setResultsName('name') + type_ +\n Optional(index_type))\\\n .setName('attr').setResultsName('attr')\n attrs_declaration = (Suppress('(') +\n Group(delimitedList(attr_declaration))\n .setName('attrs').setResultsName('attrs')\n + Optional(Suppress(',') + throughput) + Suppress(')'))\n\n global_dec = Suppress(upkey('global')) + index\n range_key_etc = (Suppress(',') + Group(throughput) |\n Optional(Suppress(',') + var) +\n Optional(Suppress(',') + include_vars) +\n Optional(Group(Suppress(',') + throughput)))\n global_spec = (Suppress('(') + primitive +\n Suppress(',') + var +\n range_key_etc +\n Suppress(')'))\n global_index = Group(global_dec + global_spec).setName('global index')\n global_indexes = Group(OneOrMore(global_index))\\\n .setResultsName('global_indexes')\n\n return (create + table_key + Optional(if_not_exists) + table +\n attrs_declaration + Optional(global_indexes))","title":""},{"docid":"8468e061a84971ff630b4761ebd37f4b","score":"0.51222605","text":"def create_tables() -> None:\n db.create_all()","title":""},{"docid":"b684d670161cecd5b3248efe93111fa4","score":"0.51189005","text":"def test_create_local_keys_index(self):\n hash_key = DynamoKey('id', data_type=STRING)\n range_key = DynamoKey('num', data_type=NUMBER)\n index_field = DynamoKey('name')\n index = LocalIndex.keys('name-index', index_field)\n table = Table('foobar', hash_key, range_key, [index])\n self.dynamo.create_table(\n 'foobar', hash_key, range_key, indexes=[index])\n desc = self.dynamo.describe_table('foobar')\n self.assertEqual(desc, table)","title":""},{"docid":"ae7d75b4c485bae6bad6de7820d9b815","score":"0.51141983","text":"def __init__(self,\n cassandra_ports_info=None,\n cassandra_security_info=None,\n cassandra_version=None,\n commit_log_backup_location=None,\n config_directory=None,\n data_centers=None,\n dse_config_directory=None,\n is_dse_authenticator=None,\n is_dse_tiered_storage=None,\n is_jmx_auth_enable=None,\n kerberos_principal=None,\n primary_host=None,\n seeds=None,\n solr_nodes=None,\n solr_port=None,\n ):\n\n # Initialize members of the class\n self.cassandra_ports_info = cassandra_ports_info\n self.cassandra_security_info = cassandra_security_info\n self.cassandra_version = cassandra_version\n self.commit_log_backup_location = commit_log_backup_location\n self.config_directory = config_directory\n self.data_centers = data_centers\n self.dse_config_directory = dse_config_directory\n self.is_dse_authenticator = is_dse_authenticator\n self.is_dse_tiered_storage = is_dse_tiered_storage\n self.is_jmx_auth_enable = is_jmx_auth_enable\n self.kerberos_principal = kerberos_principal\n self.primary_host = primary_host\n self.seeds = seeds\n self.solr_nodes = solr_nodes\n self.solr_port = solr_port","title":""},{"docid":"6ff320f63d406de4a7e1397a54f003b3","score":"0.5096175","text":"def cluster_create(version, name, opts=None):\n\n # To create a cluster we need to provide an interface to exactly two programs.\n # pg_createcluster and initdb.\n\n # As arguments for both are finite and do not overlap, we'll split them here into\n # two arrays. Specialcasing where appropriate.\n\n cmd = [\"pg_createcluster\",\n version,\n name]\n initdbOpts = []\n sr_conninfo = None\n if opts is not None:\n for key, value in opts.items():\n if value is None:\n # Argparse will provide arguments. We don't need to present those to the\n # programs, so we'll skip\n continue\n if key == 'data-checksums':\n initdbOpts += ['--data-checksums',]\n elif key == 'primary_conninfo':\n sr_conninfo = value\n else:\n cmd += ['--%s=%s' % (key, value), ]\n\n (returncode, stdout, stderr) = _run_command( cmd + ['--',] + initdbOpts )\n\n # IFF the cluster was successfully created and an SR_Standby was required,\n # the data-dir of said instance can be purged.\n # This could cause data loss if pg_createcluster returns OK against existing clusters.\n if sr_conninfo != None and returncode == 0:\n # If SR_create fails, an error will be reported. For now there is no automatic cleanup.\n # This tends to be a good idea as it allows the user to investigate the error.\n (rc,err) = SR_create( version, name, sr_conninfo)\n returncode=rc\n stderr+=err\n\n return (returncode, stdout, stderr)","title":""},{"docid":"363aee139c6c703ec11c8dd16a841db0","score":"0.5094259","text":"async def upgrade(conn: asyncpg.Connection) -> None:\n await conn.execute(\n \"create table api_keys (\"\n \"id uuid primary key default uuid_generate_v4(),\"\n \"scope jsonb not null,\"\n \"user_id uuid not null,\"\n \"expiry timestamp default null);\"\n )","title":""},{"docid":"cc23c92515d2edad0c0acf3b7fc6e73d","score":"0.50939864","text":"def create_schema(cur, conn):\n try:\n print(\"execute query: \" + schema_create_query)\n cur.execute(schema_create_query)\n conn.commit() \n except Exception as e:\n print(e)","title":""},{"docid":"4543b099d685b046688be99db6cf7914","score":"0.50850797","text":"def create_indexes():\n db['schemas'].ensure_index('uuid', unique=True)\n db['datapoints'].ensure_index('uuid')","title":""},{"docid":"ff7d8ea4b29ab0e66c6a501639afc98b","score":"0.5076603","text":"def test_create_global_index(self):\n hash_key = DynamoKey('id', data_type=STRING)\n index_field = DynamoKey('name')\n index = GlobalIndex.all('name-index', index_field)\n table = Table('foobar', hash_key, global_indexes=[index])\n self.dynamo.create_table('foobar', hash_key, global_indexes=[index])\n desc = self.dynamo.describe_table('foobar')\n self.assertEqual(desc, table)","title":""},{"docid":"7b051214ebc2ffa741104a50bb5d2c9d","score":"0.50565326","text":"def setup(app):\n app.add_directive('cassandra', CassandraTable)","title":""},{"docid":"f48006a8554673600fa7117b0bd186d0","score":"0.5043568","text":"def host_table_initialize():\n global _host_table\n\n _host_table = HostTable()\n _host_table.persist = False\n\n hosts = database.database_host_get_list()\n for host in hosts:\n _host_table[host.name] = host\n _host_table.persist = True","title":""},{"docid":"28e2be0e0134d79c654e7306d179cce5","score":"0.5040673","text":"def test_create_product_table(self):\n _db = Database.instance(\":memory:\")\n try:\n create_users_table()\n except Exception as e:\n print(e)","title":""},{"docid":"4ef70f90bf24c4ab86302d2d8d958b4c","score":"0.5034538","text":"def create():\n db.create_all()","title":""},{"docid":"4ef70f90bf24c4ab86302d2d8d958b4c","score":"0.5034538","text":"def create():\n db.create_all()","title":""},{"docid":"bbd1d2705ffced132749b93952de0e8a","score":"0.5031487","text":"def test_insert_data(self):\n self.render_config_template(\n cassandra_ports=[9042],\n cassandra_send_request=True,\n cassandra_send_response=True,\n cassandra_send_request_header=True,\n cassandra_send_response_header=True,\n )\n self.run_packetbeat(pcap=\"cassandra/v4/cassandra_insert.pcap\", debug_selectors=[\"*\"])\n objs = self.read_output()\n o = objs[0]\n print(o)\n assert o[\"type\"] == \"cassandra\"\n assert o[\"port\"] == 9042\n\n assert o[\n \"cassandra.request.query\"] == \"INSERT INTO users (user_id, fname, lname)\\n VALUES (1745, 'john', 'smith');\"\n assert o[\"cassandra.request.headers.version\"] == \"4\"\n assert o[\"cassandra.request.headers.op\"] == \"QUERY\"\n assert o[\"cassandra.request.headers.length\"] == 97\n assert o[\"cassandra.request.headers.flags\"] == \"Default\"\n assert o[\"cassandra.request.headers.stream\"] == 252\n\n assert o[\"cassandra.response.result.type\"] == \"void\"\n assert o[\"cassandra.response.headers.version\"] == \"4\"\n assert o[\"cassandra.response.headers.length\"] == 4\n assert o[\"cassandra.response.headers.op\"] == \"RESULT\"\n assert o[\"cassandra.response.headers.flags\"] == \"Default\"\n assert o[\"cassandra.response.headers.stream\"] == 252","title":""},{"docid":"97b2fd95b346adafcf447e610e06b46e","score":"0.5026299","text":"def sparse_cf_test(self):\n cluster = self.cluster\n\n cluster.populate(1).start()\n node1 = cluster.nodelist()[0]\n time.sleep(0.2)\n\n cursor = self.cql_connection(node1, version=cql_version).cursor()\n self.create_ks(cursor, 'ks', 1)\n\n cursor.execute(\"\"\"\n CREATE TABLE timeline (\n userid uuid,\n posted_month int,\n posted_day int,\n body text,\n posted_by text,\n PRIMARY KEY (userid, posted_month, posted_day)\n );\n \"\"\")\n\n # Inserts\n cursor.execute(\"INSERT INTO timeline (userid, posted_month, posted_day, body, posted_by) VALUES (550e8400-e29b-41d4-a716-446655440000, 1, 12, 'Something else', 'Frodo Baggins')\")\n cursor.execute(\"INSERT INTO timeline (userid, posted_month, posted_day, body, posted_by) VALUES (550e8400-e29b-41d4-a716-446655440000, 1, 24, 'Something something', 'Frodo Baggins')\")\n cursor.execute(\"UPDATE timeline SET body = 'Yo Froddo', posted_by = 'Samwise Gamgee' WHERE userid = f47ac10b-58cc-4372-a567-0e02b2c3d479 AND posted_month = 1 AND posted_day = 3\")\n cursor.execute(\"UPDATE timeline SET body = 'Yet one more message' WHERE userid = 550e8400-e29b-41d4-a716-446655440000 AND posted_month = 1 and posted_day = 30\")\n\n # Queries\n cursor.execute(\"SELECT body, posted_by FROM timeline WHERE userid = 550e8400-e29b-41d4-a716-446655440000 AND posted_month = 1 AND posted_day = 24\")\n res = cursor.fetchall()\n assert res == [[ 'Something something', 'Frodo Baggins' ]], res\n\n cursor.execute(\"SELECT posted_day, body, posted_by FROM timeline WHERE userid = 550e8400-e29b-41d4-a716-446655440000 AND posted_month = 1 AND posted_day > 12\")\n res = cursor.fetchall()\n assert res == [\n [ 24, 'Something something', 'Frodo Baggins' ],\n [ 30, 'Yet one more message', None ]\n ], res\n\n cursor.execute(\"SELECT posted_day, body, posted_by FROM timeline WHERE userid = 550e8400-e29b-41d4-a716-446655440000 AND posted_month = 1\")\n res = cursor.fetchall()\n assert res == [\n [ 12, 'Something else', 'Frodo Baggins' ],\n [ 24, 'Something something', 'Frodo Baggins' ],\n [ 30, 'Yet one more message', None ]\n ], res","title":""},{"docid":"a241e608d229a013e607f1029f373424","score":"0.5024984","text":"def cli_cosmosdb_managed_cassandra_datacenter_create(client,\n resource_group_name,\n cluster_name,\n data_center_name,\n data_center_location,\n delegated_subnet_id,\n node_count,\n base64_encoded_cassandra_yaml_fragment=None):\n\n data_center_properties = DataCenterResourceProperties(\n data_center_location=data_center_location,\n delegated_subnet_id=delegated_subnet_id,\n node_count=node_count,\n base64_encoded_cassandra_yaml_fragment=base64_encoded_cassandra_yaml_fragment\n )\n\n data_center_resource = DataCenterResource(\n properties=data_center_properties\n )\n\n return client.begin_create_update(resource_group_name, cluster_name, data_center_name, data_center_resource)","title":""},{"docid":"84001693c605e7624c5c2b8e16733fec","score":"0.50243473","text":"async def startup_event():\n async with engine.begin() as conn:\n await conn.run_sync(db.Base.metadata.create_all)","title":""},{"docid":"fb401cbbdeb485aeaee262aae2d5e6a8","score":"0.5017989","text":"def insert_without_privilege(self, node=None):\n user_name = f\"user_{getuid()}\"\n\n table0_name = f\"table0_{getuid()}\"\n table1_name = f\"table1_{getuid()}\"\n\n exitcode, message = errors.not_enough_privileges(name=f\"{user_name}\")\n\n cluster = self.context.cluster_name\n\n if node is None:\n node = self.context.node\n\n try:\n with Given(\"I have a user\"):\n user(name=user_name)\n\n with And(\"I have a table on a cluster\"):\n table(name=table0_name, cluster=cluster)\n\n with And(\"I have a distributed table\"):\n node.query(f\"CREATE TABLE {table1_name} (a UInt64) ENGINE = Distributed({cluster}, default, {table0_name}, rand())\")\n\n with When(\"I grant the user NONE privilege\"):\n node.query(f\"GRANT NONE TO {user_name}\")\n\n with And(\"I grant the user USAGE privilege\"):\n node.query(f\"GRANT USAGE ON *.* TO {user_name}\")\n\n with Then(\"I attempt to insert into the distributed table as the user\"):\n node.query(f\"INSERT INTO {table1_name} VALUES (8888)\", settings = [(\"user\", f\"{user_name}\")],\n exitcode=exitcode, message=message)\n finally:\n with Finally(\"I drop the distributed table\"):\n node.query(f\"DROP TABLE IF EXISTS {table1_name}\")","title":""},{"docid":"329283330257528fa67b85919e116bfe","score":"0.5010762","text":"def initialize(\n self,\n cassandra_session,\n ):\n self.column_family = ColumnFamily(\n cassandra_session,\n COLUMN_FAMILY,\n )","title":""},{"docid":"2ffdfa3f96f4efc4521e1eb747c57956","score":"0.5006049","text":"def testFromElasticToCassandra(self):\n # initial setup for this test\n config = self.loadConfigFile('testConfig.json')\n sync = PyCassElastic(config)\n\n # create last run file for 5 minutes ago and setup sync\n self._createLastRunFile()\n sync.setup()\n\n # create new documents on elasticsearch and read the data\n amount = 10\n two_min_ago = datetime.utcnow() - timedelta(minutes=2)\n self._createElasticSearchData(config['syncs'][1], amount=amount, curr_time=two_min_ago)\n rows = sync.get_elasticsearch_latest(config['syncs'][1])\n self.assertIsNotNone(rows, 'The query should have returned %s rows' % amount)\n\n # create some previous data and write on cassandra\n ten_min_ago = datetime.utcnow() - timedelta(minutes=10)\n self._createCassandraData(config['syncs'][1], amount=5, curr_time=ten_min_ago)\n ok, errors = sync.insert_cassandra(config['syncs'][1], rows)\n self.assertEqual(ok, amount)\n\n # check if data are the same\n ret = self.checkSync(sync, config['syncs'][1])\n self.assertEquals(ret, -1,\n 'The DBs data should be the same. Error on id %s' % ret)","title":""},{"docid":"bea178bd99ed1e227974411e1d821ce3","score":"0.4994968","text":"def insert(self, key, columns, keyspace_name=None, cf_name=None,\n batch=None):\n pass","title":""},{"docid":"1cebbfc9749a6fb09d9b7b2940263eec","score":"0.49896723","text":"def create_db_cluster(AvailabilityZones=None, BackupRetentionPeriod=None, CharacterSetName=None, DatabaseName=None, DBClusterIdentifier=None, DBClusterParameterGroupName=None, VpcSecurityGroupIds=None, DBSubnetGroupName=None, Engine=None, EngineVersion=None, Port=None, MasterUsername=None, MasterUserPassword=None, OptionGroupName=None, PreferredBackupWindow=None, PreferredMaintenanceWindow=None, ReplicationSourceIdentifier=None, Tags=None, StorageEncrypted=None, KmsKeyId=None, PreSignedUrl=None, EnableIAMDatabaseAuthentication=None, EnableCloudwatchLogsExports=None, DeletionProtection=None, SourceRegion=None):\n pass","title":""},{"docid":"e5d7e8d159193a0ae38bfda54ba225f3","score":"0.49876595","text":"def create_db():\n db.create_all()","title":""},{"docid":"e5d7e8d159193a0ae38bfda54ba225f3","score":"0.49876595","text":"def create_db():\n db.create_all()","title":""},{"docid":"e5d7e8d159193a0ae38bfda54ba225f3","score":"0.49876595","text":"def create_db():\n db.create_all()","title":""},{"docid":"e5d7e8d159193a0ae38bfda54ba225f3","score":"0.49876595","text":"def create_db():\n db.create_all()","title":""},{"docid":"2f9a63de656497c94318b9e5b81c82c0","score":"0.49854523","text":"def test_create_key_pair(self):\n dummy = DummyKmipClient()\n dummy.create_key_pair('algoritm', 'length')","title":""},{"docid":"88167eee95a2c6d18854ac2730c94760","score":"0.49854025","text":"def test_clear_keyspace(self, coll1, coll2, coll3, coll4, coll5, coll6):\n coll1.clear_keyspace()\n coll2.clear_keyspace()\n coll3.clear_keyspace()\n coll4.clear_keyspace()\n coll5.clear_keyspace()\n coll6.clear_keyspace()\n assert coll1.size == 0\n assert coll2.size == 0\n assert coll3.size == 0\n assert coll4.size == 0\n assert coll5.size == 0\n assert coll6.size == 0\n rh.REDIS.delete('_REDIS_HELPER_COLLECTION')\n assert rh.REDIS.dbsize() == 0\n rh.stop_docker()","title":""},{"docid":"0c3b5b333f27f4f8faac4e24eba654a2","score":"0.49847418","text":"def table(self, name, cluster=None, node=None):\n if node is None:\n node = self.context.node\n try:\n if cluster:\n with Given(f\"I create table {name}\"):\n node.query(f\"DROP TABLE IF EXISTS {name}\")\n node.query(f\"CREATE TABLE {name} ON CLUSTER {cluster} (a UInt64) ENGINE = Memory\")\n else:\n with Given(f\"I create table {name}\"):\n node.query(f\"DROP TABLE IF EXISTS {name}\")\n node.query(f\"CREATE TABLE {name} (a UInt64) ENGINE = Memory\")\n yield\n finally:\n if cluster:\n with Finally(f\"I delete table {name}\"):\n node.query(f\"DROP TABLE IF EXISTS {name} ON CLUSTER {cluster}\")\n else:\n with Finally(f\"I delete role {name}\"):\n node.query(f\"DROP ROLE IF EXISTS {name}\")","title":""},{"docid":"750e5918a837db619346a2153cc23e5b","score":"0.49844953","text":"def init():\n create_schema()","title":""},{"docid":"9e81f2b25d757e2eeef9189024ea968c","score":"0.49824572","text":"def create_db():\n db.create_all()\n db.session.commit()","title":""},{"docid":"e72763c8175f2eb3510a65888d280204","score":"0.4976816","text":"def init_db(engine, session):\n tid = get_who()\n if tid not in BACKENDS.keys():\n BACKENDS[tid] = (engine, session)\n\n if len(BACKENDS) == 1:\n with CREATE_LOCK:\n try:\n lg.info(\"Creating schema if needed\")\n Base.metadata.create_all(bind=engine)\n except:\n pass","title":""},{"docid":"6458d95a767ca34e371201bc851d1879","score":"0.49739918","text":"def testFromCassandraToElastic(self):\n config = self.loadConfigFile('testConfig.json')\n sync = PyCassElastic(config)\n sync_params = config['syncs'][0]\n self._createLastRunFile()\n\n # create the data on cassandra two minutes ago\n two_minutes_ago = datetime.utcnow() - timedelta(minutes=2)\n ten_minutes_ago = datetime.utcnow() - timedelta(minutes=10)\n self._createCassandraData(sync_params, two_minutes_ago, amount=self.data_amt / 2)\n self._createCassandraData(\n sync_params,\n amount=self.data_amt / 2,\n curr_time=ten_minutes_ago,\n start=self.data_amt / 2,\n create_table=False\n )\n\n # run the setup\n sync.setup()\n\n # assert the amount found\n rows = sync.get_cassandra_latest(sync_params)\n\n # check if it will use date as a filter\n ok, errors = sync.insert_elasticsearch(sync_params, rows)\n if sync_params.get('filter_date', None):\n self.assertEqual(ok, self.data_amt / 2)\n else:\n self.assertEqual(ok, self.data_amt)\n\n # check if data is the same\n ret = self.checkSync(sync, sync_params)\n self.assertEquals(ret, -1,\n 'The DBs data should be the same. Error on id %s' % ret)","title":""},{"docid":"af26c77aa5cb1893b57dce55486bde8c","score":"0.49728554","text":"def start_cluster_creation(redshift, roleArn):\n global DWH_CLUSTER_TYPE, DWH_NODE_TYPE, DWH_NUM_NODES, \\\n DWH_DB, DWH_CLUSTER_IDENTIFIER, DWH_DB_USER, DWH_DB_PASSWORD\n print(\"2. Starting redshift cluster creation\")\n try:\n response = redshift.create_cluster(\n # HW\n ClusterType=DWH_CLUSTER_TYPE,\n NodeType=DWH_NODE_TYPE,\n NumberOfNodes=int(DWH_NUM_NODES),\n\n # Identifiers & Credentials\n DBName=DWH_DB,\n ClusterIdentifier=DWH_CLUSTER_IDENTIFIER,\n MasterUsername=DWH_DB_USER,\n MasterUserPassword=DWH_DB_PASSWORD,\n\n # Roles (for s3 access)\n IamRoles=[roleArn]\n )\n print(\"Redshift cluster creation http response status code: \")\n print(response['ResponseMetadata']['HTTPStatusCode'])\n return response['ResponseMetadata']['HTTPStatusCode'] == 200\n except Exception as e:\n print(e)\n return False","title":""},{"docid":"38196f422456875faa808fa770731d47","score":"0.49706832","text":"def init_db():\n db = engine.connect()\n Base.metadata.drop_all(engine, checkfirst=True)\n # tables = list(reversed(Base.metadata.sorted_tables))\n # tables = Base.metadata.sorted_tables\n # for table in tables:\n # table.drop(engine, checkfirst=True)\n Base.metadata.create_all(engine)\n\n # add admin user\n with session_scope() as db_session:\n db_session.add(UserClass(\\\n username=\"admin\", password=generate_password_hash('mayuxin1999'), permissions=3))","title":""},{"docid":"54c2ebb19f81ccb7ec9280f0370cf228","score":"0.49680874","text":"async def _create_database(self) -> DatabaseType:","title":""},{"docid":"ede3c9ddce57916cad29d1a0bf916149","score":"0.49632606","text":"def init_db() -> None:\n\n node.initial_setup(\n signing_key=node.signing_key,\n first_superuser_name=\"Jane Doe\",\n first_superuser_email=settings.FIRST_SUPERUSER,\n first_superuser_password=settings.FIRST_SUPERUSER_PASSWORD,\n first_superuser_budget=5.55,\n domain_name=settings.DOMAIN_NAME,\n )","title":""},{"docid":"8a548be38146b12462bb00fae9747e5f","score":"0.49594185","text":"def create_db():\n db.create_all()\n print('Database created')","title":""},{"docid":"ffb96679196e0f76c1c918d1a8523ca0","score":"0.49550986","text":"def cli_cosmosdb_managed_cassandra_datacenter_create(client,\n resource_group_name,\n cluster_name,\n data_center_name,\n data_center_location,\n delegated_subnet_id,\n node_count,\n base64_encoded_cassandra_yaml_fragment=None):\n\n data_center_properties = DataCenterResourceProperties(\n data_center_location=data_center_location,\n delegated_subnet_id=delegated_subnet_id,\n node_count=node_count,\n base64_encoded_cassandra_yaml_fragment=base64_encoded_cassandra_yaml_fragment\n )\n\n return client.create_update(resource_group_name, cluster_name, data_center_name, data_center_properties)","title":""}],"string":"[\n {\n \"docid\": \"81e2224946d7a5e6fd6b7c40ba2e2782\",\n \"score\": \"0.807247\",\n \"text\": \"def createKeyspace(self):\\n sys = SystemManager(str(self.host) + \\\":\\\" + str(self.port),framed_transport=True, timeout=30)\\n try:\\n print \\\"Trying to create new keyspace\\\"\\n sys.create_keyspace(self.keyspace, SIMPLE_STRATEGY, {'replication_factor': '1'})\\n except :\\n print \\\"Keyspace already exist.\\\"\\n sys.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eb28ced38d16e7340cfa7a7ff4ef08e\",\n \"score\": \"0.7039071\",\n \"text\": \"def createKeyspace(self, key_name):\\n try:\\n key_status = self.isKeyspacePresent(key_name)\\n if not key_status:\\n session = self.createSession()\\n key_space = session.execute(f'CREATE KEYSPACE {key_name};')\\n self.closeSession(session)\\n return key_space\\n else:\\n Logger('test.log').logger('INFO', f'keyspace {key_name} is already available')\\n except Exception as e:\\n print(e)\\n Logger('test.log').logger('ERROR', f'Failed creating keyspace\\\\n + {str(e)}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee8602ae6daafcd0ac545e052f2fdca2\",\n \"score\": \"0.6978615\",\n \"text\": \"def _create_tables(self):\\n\\n log.info(\\\"CREATING KEYSPACE AND TABLE %s %s\\\",self._ksp, self._table)\\n\\n query_keyspace = \\\"CREATE KEYSPACE IF NOT EXISTS %s WITH replication = %s\\\" % (self._ksp, config.replication)\\n config.executelocked(query_keyspace)\\n\\n query_simple = 'CREATE TABLE IF NOT EXISTS ' + self._ksp + '.' + self._table + \\\\\\n '( storage_id uuid PRIMARY KEY, '\\n for key, entry in self._persistent_props.items():\\n query_simple += str(key) + ' '\\n if entry['type'] != 'dict' and entry['type'] in valid_types:\\n if entry['type'] == 'list' or entry['type'] == 'tuple':\\n query_simple += entry['type'] + '<' + entry['columns'] + '>, '\\n else:\\n query_simple += entry['type'] + ', '\\n else:\\n query_simple += 'uuid, '\\n try:\\n config.executelocked(query_simple[:-2] + ' )')\\n except Exception as ir:\\n log.error(\\\"Unable to execute %s\\\", query_simple)\\n raise ir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afaac853d8f527d9a7c33f784e50c878\",\n \"score\": \"0.6889439\",\n \"text\": \"def create_database():\\n \\n # connect to Cassandra\\n cluster = Cluster(['127.0.0.1']) #If you have a locally installed Apache Cassandra instance\\n session = cluster.connect()\\n \\n # create sparkify database\\n session.execute(drop_keyspace)\\n session.execute(create_keyspace)\\n \\n # set session to sparkify keyspace\\n session.set_keyspace('sparkify')\\n \\n return cluster, session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60b0854cff5c44d516b99cb75fcbea6\",\n \"score\": \"0.6721522\",\n \"text\": \"def aws_create_cassandra(nodes=1, region=None, storage_gbs=8, flavor='t2.micro'):\\n cluster_uuid = uuid.uuid4().hex\\n secho('Cluster UUID is ' + cluster_uuid, fg='green', blink=True)\\n\\n if region:\\n if region not in IMAGES.keys():\\n secho('Invalid region: ' + region, err=True, fg='red')\\n ec2 = boto3.resource('ec2', region_name=region)\\n else:\\n ec2 = boto3.resource('ec2')\\n\\n key_name = 'cassandra_keypair_' + cluster_uuid\\n secho('Creating keypair ' + key_name + ' ...')\\n # On the first actual connection to AWS, we can check if our credentials\\n # are invalid. No need to check afterwards.\\n try:\\n keypair = ec2.create_key_pair(KeyName=key_name)\\n except botocore.exceptions.ClientError:\\n secho('Invalid credentials', err=True, fg='red')\\n return\\n secho('Keypair ' + key_name + ' created.', fg='green')\\n secho('Private key:')\\n secho(keypair.key_material)\\n\\n secgroup_name = 'cassandra_sg_' + cluster_uuid\\n secho('Creating security group ' + secgroup_name + \\\\\\n ' With open ports 80,22,443,6666-9999 open ...')\\n description = 'Security group for Cassandra cluster'\\n ip_portrange_tuples = [\\n ('0.0.0.0/0', 80, 80),\\n ('0.0.0.0/0', 22, 22),\\n ('0.0.0.0/0', 443, 443),\\n ('0.0.0.0/0', 6666, 9999),\\n ]\\n mysg = ec2.create_security_group(GroupName=secgroup_name,\\n Description=description)\\n for ip, start_port, end_port in ip_portrange_tuples:\\n mysg.authorize_ingress(IpProtocol=\\\"tcp\\\", CidrIp=ip,\\n FromPort=start_port, ToPort=end_port)\\n secho('Security group ' + secgroup_name + ' created.', fg='green')\\n\\n # Spawn VMs in AWS, and wait till all of them are running, and then return.\\n\\n # First create seed node, wait for it to be in 'running' state, then create\\n # non seed nodes, then wait for all of them to be in running, and then\\n # return all node's public IP addresses.\\n\\n count = nodes\\n all_instance_ids = []\\n\\n secho('Creating ' + str(nodes) + ' instance(s) ...')\\n\\n # TODO(rushiagr): retry 5 times when an exception occurs which we know we\\n # can retry 'e.g. connection timeout. Also identify if the credentials are\\n # wrong, in that case don't retry and return error to user.\\n try:\\n # Will default to 8 gb root volume\\n instances = ec2.create_instances(#DryRun=True,\\n ImageId=IMAGES[region], UserData=userdata.seed_userdata,\\n MinCount=1, MaxCount=1, InstanceType=flavor,\\n KeyName=key_name, SecurityGroupIds=[mysg.id])\\n except Exception as e:\\n secho('exception thrown: ' + str(e), err=True, fg='red')\\n return\\n\\n instances = list(instances)\\n ids = [i.id for i in instances]\\n all_instance_ids.append(ids[0])\\n\\n seed_private_ip_address = None\\n\\n while True:\\n instances = ec2.instances.filter(InstanceIds=ids)\\n instances = [i for i in instances]\\n seed_private_ip_address = instances[0].private_ip_address\\n if seed_private_ip_address is not None:\\n break\\n time.sleep(5)\\n\\n\\n non_seed_userdata = userdata.non_seed_userdata.replace(\\n '__SEEDS__', seed_private_ip_address)\\n\\n count -= 1\\n\\n if count != 0:\\n # TODO(rushiagr): retry 5 times when an exception occurs which we know\\n # we can retry 'e.g. connection timeout. Also identify if the\\n # credentials are wrong, in that case don't retry and return error to\\n # user.\\n try:\\n # Will default to 8 gb root volume\\n instances = ec2.create_instances(#DryRun=True,\\n ImageId=IMAGES[region], UserData=non_seed_userdata,\\n MinCount=count, MaxCount=count,\\n InstanceType=flavor, KeyName=key_name,\\n SecurityGroupIds=[mysg.id])\\n except Exception as e:\\n secho('exception thrown: ' + e, err=True, fg='red')\\n return\\n\\n instances = list(instances)\\n ids = [i.id for i in instances]\\n all_instance_ids.extend(ids)\\n\\n # Waiting for instances to all have state = 'running'\\n # TODO(rushiagr): retry if instances go in error state. Even better - use\\n # autoscaling group\\n while True:\\n instances = ec2.instances.filter(InstanceIds=all_instance_ids)\\n states = [i.state['Name'] for i in instances]\\n non_running_states = [state for state in states if state != 'running']\\n if len(non_running_states) == 0:\\n break\\n time.sleep(5)\\n\\n secho('All instances now in running state.', fg='green')\\n secho('Waiting for all instances to join the cassandra cluster. '\\n 'This might take a while.')\\n\\n ips = [i.public_ip_address for i in instances]\\n\\n # Ensure cassandra cluster is created by checking 'nodetool status' on each\\n # server. Wait till all nodes' 'nodetool status' shows that all nodes have\\n # joined the cluster.\\n\\n pvt_key = paramiko.RSAKey.from_private_key(\\n io.StringIO(keypair.key_material))\\n\\n connections = {}\\n successful_creations = True\\n while True:\\n for ip in ips:\\n try:\\n ssh = paramiko.SSHClient()\\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n ssh.connect(ip, username='ubuntu', pkey=pvt_key)\\n connections[ip] = ssh\\n except paramiko.ssh_exception.NoValidConnectionsError as e:\\n\\n # TODO(rushiagr): handle\\n # paramiko.ssh_exception.AuthenticationException when\\n # authentication exception occurs\\n\\n # print('exception thrown, retrying')\\n # print(e)\\n successful_creations = False\\n time.sleep(5)\\n break\\n\\n if successful_creations:\\n break\\n successful_creations = True\\n time.sleep(5)\\n\\n while True:\\n all_fine = True\\n for ip, conn in connections.items():\\n stdin, stdout, err = ssh.exec_command(\\\"nodetool status\\\")\\n stdin.close()\\n stdout = stdout.read()\\n err = err.read()\\n # print('stdout is')\\n # print(stdout)\\n # print('err is', err)\\n # print('nodetool status output for', ip, 'is:\\\\n', stdout)\\n\\n stdin, stdout, err = ssh.exec_command(\\n \\\"nodetool status | grep ^UN | grep -v '127.0.0.1' | wc | awk '{print $1}' \\\")\\n stdin.close()\\n stdout = stdout.read()\\n err = err.read()\\n # print('stdout is')\\n # print(stdout)\\n # print('err is', err)\\n # print('nodetool num of nodes output for', ip, 'is:\\\\n', stdout)\\n\\n if not int(stdout.strip()) == len(ips):\\n all_fine = False\\n\\n if all_fine:\\n [ssh.close() for ssh in connections.values()]\\n secho('Cluster creation complete!', fg='green', blink=True)\\n return\\n\\n time.sleep(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0840641a522e6757cde471ebba6d80c0\",\n \"score\": \"0.6614517\",\n \"text\": \"def connect_db():\\n\\n cluster = Cluster()\\n\\n session = cluster.connect()\\n\\n session.execute(\\n \\\"\\\"\\\"CREATE KEYSPACE IF NOT EXISTS p2_udacity \\n WITH REPLICATION = {'class': 'SimpleStrategy', \\n 'replication_factor': 1}\\\"\\\"\\\"\\n )\\n session.set_keyspace('p2_udacity')\\n \\n return session, cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44445d7ac77309ff90dbac5e65c5ecf6\",\n \"score\": \"0.64532655\",\n \"text\": \"def test():\\n cluster = Cluster([os.environ.get('CASSANDRA_PORT_9042_TCP_ADDR', 'localhost')],\\n port=int(os.environ.get('CASSANDRA_PORT_9042_TCP_PORT', 9042))\\n )\\n try:\\n session = cluster.connect()\\n except Exception as error:\\n message = \\\"%s: %s\\\" % (error.__class__.__name__, str(error))\\n return jsonify(message=message, hostname=os.uname()[1],\\n current_time=str(datetime.now())), 500\\n session.row_factory = ordered_dict_factory\\n rows = session.execute('SELECT * FROM system.schema_keyspaces LIMIT 10')\\n return jsonify(data=rows, hostname=os.uname()[1],\\n current_time=str(datetime.now()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b0368ce9f93838943ab28959332c72\",\n \"score\": \"0.6390614\",\n \"text\": \"def _create_keyspace(connections):\\n # Get connection IDs\\n max_o = len(set([c.pre_obj for c in connections]))\\n max_i = max([i for i in _get_outgoing_ids(connections).values()])\\n max_d = max([c.width for c in connections])\\n\\n # Get the number of bits necessary for these\\n (bits_o, bits_i, bits_d) = [int(math.ceil(math.log(v + 1, 2)))\\n for v in [max_o, max_i, max_d]]\\n bits_c = 7 # Allow for 128 splits. TODO Revise this\\n\\n # Ensure that these will fit within a 32-bit key\\n padding = 32 - (1 + bits_o + bits_i + bits_d + bits_c)\\n assert padding >= 0\\n bits_o += padding\\n\\n # Create the keyspace\\n return utils.keyspaces.create_keyspace(\\n 'NengoDefault',\\n [('x', 1), ('o', bits_o), ('c', bits_c), ('i', bits_i), ('d', bits_d)],\\n 'xoci', 'xoi'\\n )(x=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7adda40fd649475c3a022efddc5441\",\n \"score\": \"0.62722695\",\n \"text\": \"def cassandra_connect(keyspace: str = CASSANDRA_KEYSPACE\\n ) -> Tuple[Cluster, Session]:\\n\\n # Make a connection to a Cassandra instance on the local machine\\n cluster = Cluster(['127.0.0.1'])\\n\\n # Create session to establish connection and begin executing queries\\n session = cluster.connect()\\n\\n # Create keyspace\\n session.execute(f\\\"CREATE KEYSPACE IF NOT EXISTS {keyspace:s}\\\\n\\\"\\n \\\"WITH REPLICATION = { 'class' : 'SimpleStrategy', \\\"\\n \\\"'replication_factor' : 1 }\\\")\\n\\n # Set the default keyspace for all queries made through this Session\\n session.set_keyspace(keyspace)\\n\\n return cluster, session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac7c97bd52bf05e49974ecf0004498f\",\n \"score\": \"0.6232603\",\n \"text\": \"def test_create_table(self):\\n self.render_config_template(\\n cassandra_ports=[9042],\\n cassandra_send_request=True,\\n cassandra_send_response=True,\\n cassandra_send_request_header=True,\\n cassandra_send_response_header=True,\\n )\\n self.run_packetbeat(pcap=\\\"cassandra/v4/cassandra_create_table.pcap\\\", debug_selectors=[\\\"*\\\"])\\n objs = self.read_output()\\n o = objs[0]\\n assert o[\\\"type\\\"] == \\\"cassandra\\\"\\n assert o[\\\"port\\\"] == 9042\\n\\n assert o[\\n \\\"cassandra.request.query\\\"] == \\\"CREATE TABLE users (\\\\n user_id int PRIMARY KEY,\\\\n fname text,\\\\n lname text\\\\n);\\\"\\n\\n assert o[\\\"cassandra.request.headers.version\\\"] == \\\"4\\\"\\n assert o[\\\"cassandra.request.headers.op\\\"] == \\\"QUERY\\\"\\n assert o[\\\"cassandra.request.headers.length\\\"] == 98\\n assert o[\\\"cassandra.request.headers.flags\\\"] == \\\"Default\\\"\\n assert o[\\\"cassandra.request.headers.stream\\\"] == 49\\n\\n assert o[\\\"cassandra.response.result.type\\\"] == \\\"schemaChanged\\\"\\n assert o[\\\"cassandra.response.headers.version\\\"] == \\\"4\\\"\\n assert o[\\\"cassandra.response.headers.length\\\"] == 39\\n assert o[\\\"cassandra.response.headers.op\\\"] == \\\"RESULT\\\"\\n assert o[\\\"cassandra.response.headers.flags\\\"] == \\\"Default\\\"\\n assert o[\\\"cassandra.response.headers.stream\\\"] == 49\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adfa615694bd4314c29bc044cb28335f\",\n \"score\": \"0.62283456\",\n \"text\": \"def create_new_db(name, db='sql',\\n keyspace=None, **kargs):\\n from kosh import connect\\n kargs[\\\"keyspace\\\"] = keyspace\\n kargs[\\\"db\\\"] = db\\n # Let's remove the now unused arguments\\n for key in [\\\"token\\\", \\\"cluster\\\"]:\\n if key in kargs:\\n warnings.warn(\\n \\\"Keyword '{}' is no longer valid, will be ignored\\\".format(key))\\n kargs.pop(key)\\n store = connect(name, **kargs)\\n store.delete_all_contents(force=\\\"SKIP PROMPT\\\")\\n return store\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68e0c85622340cc1f044686b2ea73a63\",\n \"score\": \"0.62096894\",\n \"text\": \"def _connect_to_cassandra(keyspace):\\n\\n if 'BACKEND_STORAGE_IP' in os.environ:\\n host = os.environ['BACKEND_STORAGE_IP']\\n else:\\n host = 'localhost'\\n\\n cluster = Cluster([host])\\n session = cluster.connect(keyspace)\\n \\n return session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f04d5a90c43cfdb02ffca40062dee6b\",\n \"score\": \"0.6103821\",\n \"text\": \"def test_create_cluster(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff083eee76669443c6d70786b98eaffb\",\n \"score\": \"0.5921743\",\n \"text\": \"def test_type_keyspace_permission_isolation(self):\\n self.fixture_dtest_setup.ignore_log_patterns = [\\n # I think this happens when permissions change and a node becomes temporarily unavailable\\n # and it's probably ok to ignore on this test, as I can see the schema changes propogating\\n # almost immediately after\\n r'Can\\\\'t send migration request: node.*is down',\\n ]\\n\\n cluster = self.cluster\\n config = {'authenticator': 'org.apache.cassandra.auth.PasswordAuthenticator',\\n 'authorizer': 'org.apache.cassandra.auth.CassandraAuthorizer',\\n 'permissions_validity_in_ms': 0}\\n cluster.set_configuration_options(values=config)\\n cluster.populate(3).start()\\n node1, node2, node3 = cluster.nodelist()\\n\\n # do setup that requires a super user\\n superuser_session = self.patient_cql_connection(node1, user='cassandra', password='cassandra')\\n\\n # role setup is async and does not log when complete; the only way to know its done (on a single instance) is to get a successful return.\\n def allowed_error(error):\\n return isinstance(error, InvalidRequest) and \\\"Cannot process role related query as the role manager isn't yet setup\\\" in str(error)\\n def r(fn):\\n retry(fn, allowed_error=allowed_error, sleep_seconds=10)\\n\\n # role setup process is local to each instance, so can't rely on the first query passing to say this is complete; node1 may be complete but not node2 and node3\\n # for this reason, need to be aggressive about retries\\n r(lambda: superuser_session.execute(\\\"create user ks1_user with password 'cassandra' nosuperuser;\\\"))\\n r(lambda: superuser_session.execute(\\\"create user ks2_user with password 'cassandra' nosuperuser;\\\"))\\n r(lambda: create_ks(superuser_session, 'ks1', 2))\\n r(lambda: create_ks(superuser_session, 'ks2', 2))\\n r(lambda: superuser_session.execute(\\\"grant all permissions on keyspace ks1 to ks1_user;\\\"))\\n r(lambda: superuser_session.execute(\\\"grant all permissions on keyspace ks2 to ks2_user;\\\"))\\n\\n user1_session = self.patient_cql_connection(node1, user='ks1_user', password='cassandra')\\n user2_session = self.patient_cql_connection(node1, user='ks2_user', password='cassandra')\\n\\n # first make sure the users can't create types in each other's ks\\n self.assertUnauthorized(user1_session, \\\"CREATE TYPE ks2.simple_type (user_number int, user_text text );\\\", 'User ks1_user has no CREATE permission on <keyspace ks2> or any of its parents')\\n\\n self.assertUnauthorized(user2_session, \\\"CREATE TYPE ks1.simple_type (user_number int, user_text text );\\\", 'User ks2_user has no CREATE permission on <keyspace ks1> or any of its parents')\\n\\n # now, actually create the types in the correct keyspaces\\n user1_session.execute(\\\"CREATE TYPE ks1.simple_type (user_number int, user_text text );\\\")\\n user2_session.execute(\\\"CREATE TYPE ks2.simple_type (user_number int, user_text text );\\\")\\n\\n # each user now has a type belonging to their granted keyspace\\n # let's make sure they can't drop each other's types (for which they have no permissions)\\n\\n self.assertUnauthorized(user1_session, \\\"DROP TYPE ks2.simple_type;\\\", 'User ks1_user has no DROP permission on <keyspace ks2> or any of its parents')\\n\\n self.assertUnauthorized(user2_session, \\\"DROP TYPE ks1.simple_type;\\\", 'User ks2_user has no DROP permission on <keyspace ks1> or any of its parents')\\n\\n # let's make sure they can't rename each other's types (for which they have no permissions)\\n self.assertUnauthorized(user1_session, \\\"ALTER TYPE ks2.simple_type RENAME user_number TO user_num;\\\", 'User ks1_user has no ALTER permission on <keyspace ks2> or any of its parents')\\n\\n self.assertUnauthorized(user2_session, \\\"ALTER TYPE ks1.simple_type RENAME user_number TO user_num;\\\", 'User ks2_user has no ALTER permission on <keyspace ks1> or any of its parents')\\n\\n # rename the types using the correct user w/permissions to do so\\n user1_session.execute(\\\"ALTER TYPE ks1.simple_type RENAME user_number TO user_num;\\\")\\n user2_session.execute(\\\"ALTER TYPE ks2.simple_type RENAME user_number TO user_num;\\\")\\n\\n # finally, drop the types using the correct user w/permissions to do so, consistency all avoids using a sleep\\n user1_session.execute(SimpleStatement(\\\"DROP TYPE ks1.simple_type;\\\", consistency_level=ConsistencyLevel.ALL))\\n user2_session.execute(SimpleStatement(\\\"DROP TYPE ks2.simple_type;\\\", consistency_level=ConsistencyLevel.ALL))\\n\\n time.sleep(5)\\n\\n # verify user type metadata is gone from the system schema\\n self.assertNoTypes(superuser_session)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2489e8c8a806d8e70cc4811b583d29d\",\n \"score\": \"0.5912993\",\n \"text\": \"def connect_to_cassandra(self):\\n try:\\n # todo add logic to use the params\\n # this requires creating a dictionary only populated with the not null-like values.\\n # The Cluster object does not handle null values very well\\n self.cluster = Cluster()\\n self.session = self.cluster.connect()\\n except Exception as ex:\\n self.module.fail_json(msg=str(ex))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58e886a55d292726e3dc7ec44f0e8983\",\n \"score\": \"0.58965236\",\n \"text\": \"def test_create_index(self):\\n self.render_config_template(\\n cassandra_ports=[9042],\\n cassandra_send_request=True,\\n cassandra_send_response=True,\\n cassandra_send_request_header=True,\\n cassandra_send_response_header=True,\\n )\\n self.run_packetbeat(pcap=\\\"cassandra/v4/cassandra_create_index.pcap\\\", debug_selectors=[\\\"*\\\"])\\n objs = self.read_output()\\n o = objs[0]\\n assert o[\\\"type\\\"] == \\\"cassandra\\\"\\n assert o[\\\"port\\\"] == 9042\\n\\n assert o[\\\"cassandra.request.query\\\"] == \\\"CREATE INDEX ON users (lname);\\\"\\n assert o[\\\"cassandra.request.headers.version\\\"] == \\\"4\\\"\\n assert o[\\\"cassandra.request.headers.op\\\"] == \\\"QUERY\\\"\\n assert o[\\\"cassandra.request.headers.length\\\"] == 51\\n assert o[\\\"cassandra.request.headers.flags\\\"] == \\\"Default\\\"\\n assert o[\\\"cassandra.request.headers.stream\\\"] == 92\\n\\n assert o[\\\"cassandra.response.result.type\\\"] == \\\"schemaChanged\\\"\\n\\n assert o[\\\"cassandra.response.headers.version\\\"] == \\\"4\\\"\\n assert o[\\\"cassandra.response.headers.length\\\"] == 39\\n assert o[\\\"cassandra.response.headers.op\\\"] == \\\"RESULT\\\"\\n assert o[\\\"cassandra.response.headers.flags\\\"] == \\\"Default\\\"\\n assert o[\\\"cassandra.response.headers.stream\\\"] == 92\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a62733c6a3c27bfa5b420fb7b5115a14\",\n \"score\": \"0.56926924\",\n \"text\": \"def _createCassandraData(self, config, curr_time=None, create_table=True, amount=None, start=0):\\n session = self.cassandra['session']\\n keyspace = self.cassandra['keyspace']\\n if not curr_time:\\n curr_time = datetime.utcnow()\\n if not amount:\\n amount = self.data_amt\\n params = config['cassandra']\\n\\n # create a table\\n if create_table:\\n stmt = ''' CREATE TABLE {keyspace}.{table} (\\n {id_col} UUID,\\n {version_col} bigint,\\n text varchar,\\n source varchar,\\n {date_col} timestamp,\\n PRIMARY KEY ({primary_key})\\n );'''\\n\\n # check if it will use date as a filter\\n if config.get('filter_date', None):\\n primary_key = '%s, %s' % (config['id_col'], config['version_col'])\\n else:\\n primary_key = config['id_col']\\n stmt = stmt.format(keyspace=keyspace,\\n table=params['table'],\\n version_col=config['version_col'],\\n id_col=config['id_col'],\\n date_col=config['date_col'],\\n primary_key=primary_key)\\n session.execute(stmt)\\n if config.get('filter_date', None):\\n session.execute('CREATE INDEX ON %s.%s (%s)' % (keyspace, params['table'], config['version_col']))\\n\\n # Prepare the statements\\n stmt = \\\"INSERT INTO {keyspace}.{table} (\\\" \\\\\\n \\\"{id_col}, \\\" \\\\\\n \\\"{version_col}, \\\" \\\\\\n \\\"text, \\\" \\\\\\n \\\"source, \\\" \\\\\\n \\\"{date_col}) \\\" \\\\\\n \\\"VALUES (?, ?, ?, ?, ?)\\\" \\\\\\n \\\"USING TIMESTAMP ? \\\"\\n stmt = stmt.format(keyspace=keyspace,\\n table=params['table'],\\n version_col=config['version_col'],\\n id_col=config['id_col'],\\n date_col=config['date_col'])\\n data_statement = session.prepare(stmt)\\n\\n # add the prepared statements to a batch\\n count = 0\\n batch = BatchStatement()\\n for i in range(start, start + amount):\\n batch.add(data_statement,\\n [self.idList[i],\\n unix_time_millis(curr_time),\\n id_generator(10), 'CASSANDRA',\\n unix_time_millis(curr_time),\\n unix_time_millis(curr_time)])\\n count += 1\\n\\n # every x records, commit. The parameter 65000 was giving timeout\\n if (count % 5000) == 0:\\n # execute the batch\\n session.execute(batch)\\n # hack to get around the 65k limit of python driver\\n batch._statements_and_parameters = []\\n count = 0\\n\\n if count > 0:\\n session.execute(batch)\\n batch._statements_and_parameters = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057f73af9407f9dfb677ddce7a4dfb88\",\n \"score\": \"0.56742734\",\n \"text\": \"def create_cluster(hosts):\\n if not state.SEED_IP:\\n state.SEED_IP = ec2.Instance(hosts[0]).private_ip_address\\n for host in hosts:\\n util.run_cassandra(host)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28537a140dcf5c3c9454788112b66402\",\n \"score\": \"0.5635075\",\n \"text\": \"def isKeyspacePresent(self, key_name):\\n try:\\n session = self.createSession()\\n if key_name in [i[0] for i in session.execute('SELECT * FROM system_schema.keyspaces;')]:\\n self.closeSession(session)\\n return True\\n else:\\n self.closeSession(session)\\n return False\\n except Exception as e:\\n Logger('test.log').logger('ERROR', f'unable to check whether the keyspace is available or not \\\\n {str(e)}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4824c0bdda054dce3b4c5f843e1d9f09\",\n \"score\": \"0.56019694\",\n \"text\": \"def cli_cosmosdb_managed_cassandra_cluster_create(client,\\n resource_group_name,\\n cluster_name,\\n location,\\n delegated_management_subnet_id,\\n tags=None,\\n identity=None,\\n cluster_name_override=None,\\n initial_cassandra_admin_password=None,\\n client_certificates=None,\\n external_gossip_certificates=None,\\n external_seed_nodes=None,\\n restore_from_backup_id=None,\\n cassandra_version=None,\\n authentication_method=None,\\n hours_between_backups=None,\\n repair_enabled=None):\\n\\n if initial_cassandra_admin_password is None and external_gossip_certificates is None:\\n raise CLIError('At least one out of the Initial Cassandra Admin Password or External Gossip Certificates is required.')\\n\\n if initial_cassandra_admin_password is not None and external_gossip_certificates is not None:\\n raise CLIError('Only one out of the Initial Cassandra Admin Password or External Gossip Certificates has to be specified.')\\n\\n cluster_properties = ClusterResourceProperties(\\n delegated_management_subnet_id=delegated_management_subnet_id,\\n cluster_name_override=cluster_name_override,\\n initial_cassandra_admin_password=initial_cassandra_admin_password,\\n client_certificates=client_certificates,\\n external_gossip_certificates=external_gossip_certificates,\\n external_seed_nodes=external_seed_nodes,\\n restore_from_backup_id=restore_from_backup_id,\\n cassandra_version=cassandra_version,\\n authentication_method=authentication_method,\\n hours_between_backups=hours_between_backups,\\n repair_enabled=repair_enabled)\\n\\n cluster_resource_create_update_parameters = ClusterResource(\\n location=location,\\n tags=tags,\\n identity=identity,\\n properties=cluster_properties)\\n\\n return client.begin_create_update(resource_group_name, cluster_name, cluster_resource_create_update_parameters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7cd06422f45860d1012bfc55cf6b65\",\n \"score\": \"0.556428\",\n \"text\": \"def _create_cql_table(key_space: str,\\r\\n table_name: str,\\r\\n primary_key: List,\\r\\n columns: List[str],\\r\\n column_types: List[str]\\r\\n ) -> str:\\r\\n assert len(columns) > 0, \\\"Columns can not be empty in a Table. \\\"\\r\\n\\r\\n assert len(columns) == len(column_types), \\\"Length of columns and type of columns length missmatch.\\\"\\r\\n\\r\\n # If we have same amount of columns and their types\\r\\n cql_create_table = \\\"CREATE TABLE IF NOT EXISTS \\\" + key_space + \\\".\\\" + table_name\\r\\n cql_create_table += \\\" (\\\"\\r\\n for col, typ in zip(columns, column_types):\\r\\n if col in primary_key:\\r\\n cql_create_table += col + \\\" \\\" + typ + \\\", \\\"\\r\\n else:\\r\\n cql_create_table += col + \\\" \\\" + typ + \\\" , \\\"\\r\\n cql_create_table = cql_create_table[:-2]\\r\\n cql_create_table += \\\", PRIMARY KEY (\\\" + \\\", \\\".join(primary_key) + \\\") \\\"\\r\\n cql_create_table += \\\");\\\"\\r\\n return cql_create_table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0da33fb6782e8e6c98179486b865845\",\n \"score\": \"0.5556857\",\n \"text\": \"def cli_cosmosdb_managed_cassandra_cluster_create(client,\\n resource_group_name,\\n cluster_name,\\n location,\\n delegated_management_subnet_id,\\n tags=None,\\n identity=None,\\n cluster_name_override=None,\\n initial_cassandra_admin_password=None,\\n client_certificates=None,\\n external_gossip_certificates=None,\\n external_seed_nodes=None,\\n restore_from_backup_id=None,\\n cassandra_version=None,\\n authentication_method=None,\\n hours_between_backups=None,\\n repair_enabled=None):\\n\\n cluster_properties = ClusterResourceProperties(\\n delegated_management_subnet_id=delegated_management_subnet_id,\\n cluster_name_override=cluster_name_override,\\n initial_cassandra_admin_password=initial_cassandra_admin_password,\\n client_certificates=client_certificates,\\n external_gossip_certificates=external_gossip_certificates,\\n external_seed_nodes=external_seed_nodes,\\n restore_from_backup_id=restore_from_backup_id,\\n cassandra_version=cassandra_version,\\n authentication_method=authentication_method,\\n hours_between_backups=hours_between_backups,\\n repair_enabled=repair_enabled)\\n\\n cluster_resource_create_update_parameters = ClusterResource(\\n location=location,\\n tags=tags,\\n identity=identity,\\n properties=cluster_properties)\\n\\n return client.create_update(resource_group_name, cluster_name, cluster_resource_create_update_parameters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e42e49c25f0e1d7b3ab7d3c93c293fc6\",\n \"score\": \"0.551919\",\n \"text\": \"def create_tables():\\n\\ttry:\\n\\t\\tCrossMkt.delete()\\n\\texcept:\\n\\t\\tpass\\n\\ttry:\\n\\t\\tdb.create_tables([CrossMkt])\\n\\t\\t#db.connect()\\n\\texcept Exception as e:\\n\\t\\tprint(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c72ccd5ac20bcf02bf051ea20af279\",\n \"score\": \"0.551911\",\n \"text\": \"def _create_index_cql(key_space: str,\\r\\n index_name: str,\\r\\n table_name: str,\\r\\n index_on: str\\r\\n ) -> str:\\r\\n cql_index = \\\"CREATE INDEX \\\" + index_name + \\\" ON \\\"\\r\\n cql_index += key_space + \\\".\\\" + table_name + \\\" (\\\"\\r\\n cql_index += index_on + \\\");\\\"\\r\\n return cql_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfa600b2a7e985968588d54e22b51893\",\n \"score\": \"0.54925203\",\n \"text\": \"def _create_delete_table_cql(key_space: str, table_name: str) -> str:\\r\\n cql_delete = \\\"DROP TABLE \\\" + key_space + \\\".\\\" + table_name + \\\";\\\"\\r\\n return cql_delete\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6283c7e1919b84a29050a58882d880a5\",\n \"score\": \"0.54781145\",\n \"text\": \"def prepare_benchmark(workload=\\\"workloads/workload_read\\\", name=None, description=None, add_args=list()):\\n if not (state.YCSB_INSTANCES and state.CLUSTER_INSTANCES):\\n raise Exception(\\\"Cluster (and YCSB host) not yet initialized properly!\\\")\\n\\n state.RUN_NAME = name if name else datetime.now().strftime('%Y%m%d%H%m%S')\\n state.RUN_DESCRIPTION = description\\n state.WORKLOAD = workload\\n\\n log.info(\\\"Creating keyspace YCSB and table..\\\")\\n # Create keyspace and table\\n with util.Fragile(util.connect(state.CLUSTER_INSTANCES[0])) as seed_instance:\\n sudo = seed_instance[\\\"sudo\\\"]\\n ret = sudo[\\\"docker\\\", \\\"exec\\\", \\\"cassy\\\", \\\"cqlsh\\\", \\\"-e\\\", \\\"\\\"\\\"DESCRIBE KEYSPACES\\\"\\\"\\\"]()\\n if \\\"ycsb\\\" in ret:\\n log.info(\\\"Keyspace seems to exists already!!\\\")\\n raise util.Fragile.Break\\n ret = sudo[\\\"docker\\\", \\\"exec\\\", \\\"cassy\\\", \\\"cqlsh\\\", \\\"-e\\\",\\n \\\"\\\"\\\"create keyspace ycsb WITH REPLICATION = {'class' : 'SimpleStrategy', 'replication_factor': 3 };\\n USE ycsb;\\n create table usertable (\\n y_id varchar primary key,\\n field0 varchar,\\n field1 varchar,\\n field2 varchar,\\n field3 varchar,\\n field4 varchar,\\n field5 varchar,\\n field6 varchar,\\n field7 varchar,\\n field8 varchar,\\n field9 varchar);\\\"\\\"\\\"]()\\n log.info(\\\"Result: \\\" + ret)\\n\\n # Load data with ycsb\\n log.info(\\\"Loading YCSB data for workload '{0}'\\\".format(workload))\\n with util.connect(state.YCSB_INSTANCES[0]) as ycsb_instance:\\n with ycsb_instance.cwd(\\\"/home/ubuntu/ycsb\\\"):\\n ycsb = ycsb_instance[\\\"bin/ycsb\\\"]\\n ret = ycsb(\\\"load\\\", \\\"cassandra2-cql\\\", \\\"-threads\\\", \\\"100\\\", \\\"-P\\\", \\\"workloads/workload_base\\\", \\\"-P\\\", workload, \\\"-p\\\", \\\"hosts=\\\" + \\\",\\\".join(util.cluster_ips()), *add_args)\\n log.info(\\\"Result: \\\" + ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e53760a8fd734afbf8798d2aac33fd4\",\n \"score\": \"0.5447686\",\n \"text\": \"def keyspace_exists(self, name):\\n return name in self.cluster.metadata.keyspaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eca2d2c307b0f4d8f2ebc74b14a3880\",\n \"score\": \"0.5444273\",\n \"text\": \"def create_distributed_table(self, on_cluster, over, name=None, node=None):\\n if node is None:\\n node = self.context.node\\n if name is None:\\n name = getuid()\\n\\n try:\\n node.query(\\n f\\\"CREATE TABLE {name} ON CLUSTER {on_cluster} AS {over} \\\"\\n f\\\"ENGINE = Distributed({on_cluster}, default, {over}, rand())\\\"\\n )\\n yield name\\n finally:\\n with Finally(f\\\"I drop table {name} on cluster {on_cluster} on {node.name}\\\"):\\n node.query(f\\\"DROP TABLE IF EXISTS {name} ON CLUSTER {on_cluster} SYNC\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a9561cc1f0a229c79f3fca6f68257b\",\n \"score\": \"0.5430047\",\n \"text\": \"def ensure_table(self):\\n cursor = self.conn.cursor()\\n columns = (\\\"userid INTEGER NOT NULL, \\\"\\n \\\"guildid INTEGER NOT NULL, \\\"\\n \\\"roleid INTEGER NOT NULL, \\\"\\n \\\"starttime INTEGER NOT NULL, \\\"\\n \\\"duration INTEGER NOT NULL\\\")\\n\\n cursor.execute(\\\"CREATE TABLE IF NOT EXISTS sessions ({})\\\".format(columns))\\n self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7912496011c250328899965dcd6a387b\",\n \"score\": \"0.538153\",\n \"text\": \"def initialise_db():\\n # executing the queries with cache\\n try:\\n consumer_table_schema = conn.create_schema(\\n hash_key_name='c_id',\\n hash_key_proto_value=str,\\n )\\n table = conn.create_table(\\n name='consumer_table',\\n schema=consumer_table_schema,\\n read_units=1,\\n write_units=1\\n )\\n return table\\n except Exception as e:\\n print str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f32bb4d5749155bbf6653dc142d941\",\n \"score\": \"0.53760165\",\n \"text\": \"def db_setup(app_name=\\\"App\\\", username=\\\"admin\\\", password=\\\"cisco\\\", sharding=False, force=False):\\n logger.debug(\\\"setting up new database (force:%r)\\\", force)\\n \\n if not force and db_exists():\\n logger.debug(\\\"db already exists\\\")\\n return True\\n\\n db = get_db()\\n sh = db.client.admin\\n if sharding:\\n logger.debug(\\\"enabling sharding for db %s\\\", db.name)\\n sh.command(\\\"enableSharding\\\", db.name)\\n\\n # get all objects registred to rest API, drop db and create with \\n # proper keys\\n for classname in registered_classes:\\n c = registered_classes[classname]\\n # drop existing collection\\n logger.debug(\\\"dropping collection %s\\\" % c._classname)\\n db[c._classname].drop()\\n # create indexes for searching and unique keys ordered based on key order\\n # indexes are unique only if expose_id is disabled\\n indexes = []\\n unique = not c._access[\\\"expose_id\\\"]\\n if c._access[\\\"db_index_unique\\\"] is not None and type(c._access[\\\"db_index_unique\\\"]) is bool:\\n unique = c._access[\\\"db_index_unique\\\"]\\n if c._access[\\\"db_index\\\"] is None:\\n # auto created index based on ordered keys\\n for a in c._dn_attributes:\\n indexes.append((a,ASCENDING))\\n indexes = [(a, ASCENDING) for a in c._dn_attributes]\\n elif type(c._access[\\\"db_index\\\"]) is list:\\n indexes = [(a, ASCENDING) for a in c._access[\\\"db_index\\\"]]\\n else:\\n raise Exception(\\\"invalid db_index for %s: %s\\\" % (c._classname, c._access[\\\"db_index\\\"]))\\n if len(indexes)>0:\\n logger.debug(\\\"creating indexes for %s: %s\\\",c._classname,indexes)\\n db[c._classname].create_index(indexes, unique=unique)\\n\\n # support second search-only (non-unique) index for objects\\n if type(c._access[\\\"db_index2\\\"]) is list and len(c._access[\\\"db_index2\\\"])>0:\\n index2 = [(a, ASCENDING) for a in c._access[\\\"db_index2\\\"]]\\n logger.debug(\\\"creating secondary index for %s: %s\\\", c._classname, index2)\\n db[c._classname].create_index(index2, unique=False)\\n\\n\\n if sharding and c._access[\\\"db_shard_enable\\\"]:\\n shard_indexes = {}\\n if c._access[\\\"db_shard_index\\\"] is not None:\\n for a in c._access[\\\"db_shard_index\\\"]: shard_indexes[a] = 1\\n else:\\n for (a,d) in indexes: shard_indexes[a] = 1\\n logger.debug(\\\"creating shard index for %s: %s\\\", c._classname, shard_indexes)\\n sh.command(\\\"shardCollection\\\", \\\"%s.%s\\\" % (db.name, c._classname), key=shard_indexes)\\n\\n # if uni is enabled then required before any other object is created\\n uni = Universe.load()\\n uni.save()\\n \\n # insert settings with user provided values \\n lpass = \\\"%s\\\"%uuid.uuid4()\\n s = Settings(app_name=app_name, lpass = lpass)\\n try: s.save()\\n except BadRequest as e:\\n logger.error(\\\"failed to create settings: %s. Using all defaults\\\"%e)\\n s = Settings(lpass=lpass)\\n s.save()\\n \\n # create admin user with provided password and local user\\n u = User(username=\\\"local\\\", password=lpass, role=Role.FULL_ADMIN)\\n if not u.save(): \\n logger.error(\\\"failed to create local user\\\")\\n return False\\n if username == \\\"local\\\":\\n logger.error(\\\"username 'local' is reserved, use a different default username\\\")\\n return False\\n u = User(username=username, password=password, role=Role.FULL_ADMIN)\\n if not u.save():\\n logger.error(\\\"failed to create username: %s\\\" % args.username)\\n return False\\n\\n #successful setup\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e4a932c0c0b0ffaa624910b820a027\",\n \"score\": \"0.5375257\",\n \"text\": \"def create_schema(connection):\\n\\n connection.execute(\\\"CREATE EXTENSION IF NOT EXISTS hstore\\\")\\n\\n biz.DomainBase.metadata.create_all(connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14f0193cf0272e9bba250a06af3e06fd\",\n \"score\": \"0.53567296\",\n \"text\": \"def __init__(self, databaseid=str, region=str, username=str, password=str, keyspace=str):\\n self.username = username\\n self.password = password\\n self.databaseid = databaseid\\n self.region = region\\n self.keyspace = keyspace\\n\\n self.url = \\\"https://{uuid}-{region}.apps.astra.datastax.com/api/rest/v1\\\".format(uuid=databaseid,\\n region=region)\\n\\n payload = {'username': self.username,\\n 'password': self.password}\\n x = post(self.url + \\\"/auth\\\", data=to_json(payload))\\n\\n self.auth_token = x.json()[\\\"authToken\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"359c318801196f30088e50c006b9011f\",\n \"score\": \"0.5345077\",\n \"text\": \"def test_create_global_keys_index(self):\\n hash_key = DynamoKey('id', data_type=STRING)\\n index_field = DynamoKey('name')\\n index = GlobalIndex.keys('name-index', index_field)\\n table = Table('foobar', hash_key, global_indexes=[index])\\n self.dynamo.create_table('foobar', hash_key, global_indexes=[index])\\n desc = self.dynamo.describe_table('foobar')\\n self.assertEqual(desc, table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f93ae8770e525ce36c9fd63a32532c16\",\n \"score\": \"0.532987\",\n \"text\": \"def create_db_schema(context, connection):\\n admin_name = context[ctx.ADMIN_NAME]\\n user_name = context[ctx.USER_NAME]\\n schema_name = context[ctx.SCHEMA_NAME]\\n log.info(f\\\"Creating the schema space {schema_name}\\\")\\n query = \\\"CREATE SCHEMA IF NOT EXISTS %s AUTHORIZATION %s;\\\"\\n params = [AsIs(schema_name), AsIs(admin_name)]\\n try:\\n with connection.cursor() as cursor:\\n cursor.execute(query, params)\\n cursor.execute(\\\"GRANT USAGE ON SCHEMA %s TO %s;\\\",\\n [AsIs(schema_name), AsIs(user_name)])\\n except Error:\\n log.exception(\\\"Failed to create the db schema.\\\")\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"907e1d88cbd4e48846c133ba2cd3f9cd\",\n \"score\": \"0.53143376\",\n \"text\": \"def create_cluster(self, cluster_name, db_engine, db_name, parameter_group):\\n print(\\\"Checking for an existing DB cluster.\\\")\\n cluster = self.aurora_wrapper.get_db_cluster(cluster_name)\\n if cluster is None:\\n admin_username = q.ask(\\n \\\"Enter an administrator user name for the database: \\\", q.non_empty)\\n admin_password = q.ask(\\n \\\"Enter a password for the administrator (at least 8 characters): \\\", q.non_empty)\\n engine_versions = self.aurora_wrapper.get_engine_versions(\\n db_engine, parameter_group['DBParameterGroupFamily'])\\n engine_choices = [ver['EngineVersion'] for ver in engine_versions]\\n print(\\\"The available engines for your parameter group are:\\\")\\n engine_index = q.choose(\\\"Which engine do you want to use? \\\", engine_choices)\\n print(f\\\"Creating DB cluster {cluster_name} and database {db_name}.\\\\n\\\"\\n f\\\"The DB cluster is configured to use\\\\n\\\"\\n f\\\"your custom parameter group {parameter_group['DBClusterParameterGroupName']}\\\\n\\\"\\n f\\\"and selected engine {engine_choices[engine_index]}.\\\\n\\\"\\n f\\\"This typically takes several minutes.\\\")\\n cluster = self.aurora_wrapper.create_db_cluster(\\n cluster_name, parameter_group['DBClusterParameterGroupName'], db_name,\\n db_engine, engine_choices[engine_index], admin_username, admin_password)\\n while cluster.get('Status') != 'available':\\n wait(30)\\n cluster = self.aurora_wrapper.get_db_cluster(cluster_name)\\n print(\\\"Cluster created and available.\\\\n\\\")\\n print(\\\"Cluster data:\\\")\\n pp(cluster)\\n print('-'*88)\\n return cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1d55ccda266622787f998e7d26a31cc\",\n \"score\": \"0.53118396\",\n \"text\": \"def create_test_db(self, *args, **kw):\\n self.destroy_test_db()\\n self.connection.use_test_datastore = True\\n self.connection.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874b0ba71240e2d2b9f1e44740c3a4db\",\n \"score\": \"0.53076994\",\n \"text\": \"def create_database():\\r\\n model.create_table()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb5086663d93c325c3eebca6727d23c\",\n \"score\": \"0.5279027\",\n \"text\": \"def create_table(self, on_cluster, name=None, node=None):\\n if node is None:\\n node = self.context.node\\n if name is None:\\n name = getuid()\\n\\n try:\\n node.query(\\n f\\\"CREATE TABLE {name} ON CLUSTER {on_cluster} (d Date, a String, b UInt8, x String, y Int8) \\\"\\n f\\\"ENGINE = ReplicatedMergeTree('/clickhouse/tables/{{shard}}/{name}', '{{replica}}') \\\"\\n \\\"PARTITION BY y ORDER BY (d, b)\\\"\\n )\\n yield name\\n finally:\\n with Finally(f\\\"I drop table {name} on cluster {on_cluster} on {node.name}\\\"):\\n node.query(f\\\"DROP TABLE IF EXISTS {name} ON CLUSTER {on_cluster} SYNC\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad840499a60c9733f8872f9ff5dd819\",\n \"score\": \"0.5274907\",\n \"text\": \"def create_db():\\n db.engine.execute('CREATE EXTENSION IF NOT EXISTS citext')\\n db.engine.execute('CREATE EXTENSION IF NOT EXISTS pgcrypto')\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5169980a14b3c9d3cc677fdca6f595e\",\n \"score\": \"0.5258835\",\n \"text\": \"def createTable(self, name, comp_type=None, super_table=False):\\n sys = SystemManager(str(self.host) + \\\":\\\" + str(self.port),framed_transport=True, timeout=30)\\n try :\\n print \\\"Trying to create Table: \\\" + str(name)\\n sys.create_column_family(self.keyspace, str(name), comparator_type=comp_type)\\n except :\\n print \\\"Drop and create new Table: \\\" + str(name)\\n sys.drop_column_family(self.keyspace, str(name))\\n sys.create_column_family(self.keyspace, str(name),comparator_type=comp_type)\\n \\n sys.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"594f4a9ac57668a69088ffae876ba5ec\",\n \"score\": \"0.52489424\",\n \"text\": \"async def m002_initial(db):\\n await db.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE satsdice.satsdice_withdraw (\\n id TEXT PRIMARY KEY,\\n satsdice_pay TEXT,\\n value INTEGER DEFAULT 1,\\n unique_hash TEXT UNIQUE,\\n k1 TEXT,\\n open_time INTEGER,\\n used INTEGER DEFAULT 0\\n );\\n \\\"\\\"\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26247b1f8699f334be88b63faaedd6fd\",\n \"score\": \"0.52460986\",\n \"text\": \"def cluster(ctx, config):\\n if ctx.config.get('use_existing_cluster', False) is True:\\n log.info(\\\"'use_existing_cluster' is true; skipping cluster creation\\\")\\n yield\\n\\n testdir = teuthology.get_testdir(ctx)\\n cluster_name = config['cluster']\\n data_dir = '{tdir}/{cluster}.data'.format(tdir=testdir, cluster=cluster_name)\\n log.info('Creating ceph cluster %s...', cluster_name)\\n log.info('config %s', config)\\n log.info('ctx.config %s', ctx.config)\\n run.wait(\\n ctx.cluster.run(\\n args=[\\n 'install', '-d', '-m0755', '--',\\n data_dir,\\n ],\\n wait=False,\\n )\\n )\\n\\n run.wait(\\n ctx.cluster.run(\\n args=[\\n 'sudo',\\n 'install', '-d', '-m0777', '--', '/var/run/ceph',\\n ],\\n wait=False,\\n )\\n )\\n\\n devs_to_clean = {}\\n remote_to_roles_to_devs = {}\\n remote_to_roles_to_journals = {}\\n osds = ctx.cluster.only(teuthology.is_type('osd', cluster_name))\\n for remote, roles_for_host in osds.remotes.items():\\n devs = teuthology.get_scratch_devices(remote)\\n roles_to_devs = {}\\n roles_to_journals = {}\\n if config.get('fs'):\\n log.info('fs option selected, checking for scratch devs')\\n log.info('found devs: %s' % (str(devs),))\\n devs_id_map = teuthology.get_wwn_id_map(remote, devs)\\n iddevs = list(devs_id_map.values())\\n roles_to_devs = assign_devs(\\n teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name), iddevs\\n )\\n if len(roles_to_devs) < len(iddevs):\\n iddevs = iddevs[len(roles_to_devs):]\\n devs_to_clean[remote] = []\\n\\n if config.get('block_journal'):\\n log.info('block journal enabled')\\n roles_to_journals = assign_devs(\\n teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name), iddevs\\n )\\n log.info('journal map: %s', roles_to_journals)\\n\\n if config.get('tmpfs_journal'):\\n log.info('tmpfs journal enabled')\\n roles_to_journals = {}\\n remote.run(args=['sudo', 'mount', '-t', 'tmpfs', 'tmpfs', '/mnt'])\\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name):\\n tmpfs = '/mnt/' + role\\n roles_to_journals[role] = tmpfs\\n remote.run(args=['truncate', '-s', '1500M', tmpfs])\\n log.info('journal map: %s', roles_to_journals)\\n\\n log.info('dev map: %s' % (str(roles_to_devs),))\\n remote_to_roles_to_devs[remote] = roles_to_devs\\n remote_to_roles_to_journals[remote] = roles_to_journals\\n\\n log.info('Generating config...')\\n remotes_and_roles = ctx.cluster.remotes.items()\\n roles = [role_list for (remote, role_list) in remotes_and_roles]\\n ips = [remote.ip_address for (remote, role_list) in remotes_and_roles]\\n mons = get_mons(\\n roles, ips, cluster_name,\\n mon_bind_msgr2=config.get('mon_bind_msgr2'),\\n mon_bind_addrvec=config.get('mon_bind_addrvec'),\\n )\\n conf = skeleton_config(\\n ctx, roles=roles, ips=ips, mons=mons, cluster=cluster_name,\\n )\\n for remote, roles_to_journals in remote_to_roles_to_journals.items():\\n for role, journal in roles_to_journals.items():\\n name = teuthology.ceph_role(role)\\n if name not in conf:\\n conf[name] = {}\\n conf[name]['osd journal'] = journal\\n for section, keys in config['conf'].items():\\n for key, value in keys.items():\\n log.info(\\\"[%s] %s = %s\\\" % (section, key, value))\\n if section not in conf:\\n conf[section] = {}\\n conf[section][key] = value\\n\\n if config.get('tmpfs_journal'):\\n conf['journal dio'] = False\\n\\n if not hasattr(ctx, 'ceph'):\\n ctx.ceph = {}\\n ctx.ceph[cluster_name] = argparse.Namespace()\\n ctx.ceph[cluster_name].conf = conf\\n ctx.ceph[cluster_name].mons = mons\\n\\n default_keyring = '/etc/ceph/{cluster}.keyring'.format(cluster=cluster_name)\\n keyring_path = config.get('keyring_path', default_keyring)\\n\\n coverage_dir = '{tdir}/archive/coverage'.format(tdir=testdir)\\n\\n firstmon = teuthology.get_first_mon(ctx, config, cluster_name)\\n\\n log.info('Setting up %s...' % firstmon)\\n ctx.cluster.only(firstmon).run(\\n args=[\\n 'sudo',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-authtool',\\n '--create-keyring',\\n keyring_path,\\n ],\\n )\\n ctx.cluster.only(firstmon).run(\\n args=[\\n 'sudo',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-authtool',\\n '--gen-key',\\n '--name=mon.',\\n keyring_path,\\n ],\\n )\\n ctx.cluster.only(firstmon).run(\\n args=[\\n 'sudo',\\n 'chmod',\\n '0644',\\n keyring_path,\\n ],\\n )\\n (mon0_remote,) = ctx.cluster.only(firstmon).remotes.keys()\\n monmap_path = '{tdir}/{cluster}.monmap'.format(tdir=testdir,\\n cluster=cluster_name)\\n fsid = create_simple_monmap(\\n ctx,\\n remote=mon0_remote,\\n conf=conf,\\n mons=mons,\\n path=monmap_path,\\n mon_bind_addrvec=config.get('mon_bind_addrvec'),\\n )\\n if not 'global' in conf:\\n conf['global'] = {}\\n conf['global']['fsid'] = fsid\\n\\n default_conf_path = '/etc/ceph/{cluster}.conf'.format(cluster=cluster_name)\\n conf_path = config.get('conf_path', default_conf_path)\\n log.info('Writing %s for FSID %s...' % (conf_path, fsid))\\n write_conf(ctx, conf_path, cluster_name)\\n\\n log.info('Creating admin key on %s...' % firstmon)\\n ctx.cluster.only(firstmon).run(\\n args=[\\n 'sudo',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-authtool',\\n '--gen-key',\\n '--name=client.admin',\\n '--cap', 'mon', 'allow *',\\n '--cap', 'osd', 'allow *',\\n '--cap', 'mds', 'allow *',\\n '--cap', 'mgr', 'allow *',\\n keyring_path,\\n ],\\n )\\n\\n log.info('Copying monmap to all nodes...')\\n keyring = teuthology.get_file(\\n remote=mon0_remote,\\n path=keyring_path,\\n )\\n monmap = teuthology.get_file(\\n remote=mon0_remote,\\n path=monmap_path,\\n )\\n\\n for rem in ctx.cluster.remotes.keys():\\n # copy mon key and initial monmap\\n log.info('Sending monmap to node {remote}'.format(remote=rem))\\n teuthology.sudo_write_file(\\n remote=rem,\\n path=keyring_path,\\n data=keyring,\\n perms='0644'\\n )\\n teuthology.write_file(\\n remote=rem,\\n path=monmap_path,\\n data=monmap,\\n )\\n\\n log.info('Setting up mon nodes...')\\n mons = ctx.cluster.only(teuthology.is_type('mon', cluster_name))\\n\\n if not config.get('skip_mgr_daemons', False):\\n log.info('Setting up mgr nodes...')\\n mgrs = ctx.cluster.only(teuthology.is_type('mgr', cluster_name))\\n for remote, roles_for_host in mgrs.remotes.items():\\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'mgr',\\n cluster_name):\\n _, _, id_ = teuthology.split_role(role)\\n mgr_dir = DATA_PATH.format(\\n type_='mgr', cluster=cluster_name, id_=id_)\\n remote.run(\\n args=[\\n 'sudo',\\n 'mkdir',\\n '-p',\\n mgr_dir,\\n run.Raw('&&'),\\n 'sudo',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-authtool',\\n '--create-keyring',\\n '--gen-key',\\n '--name=mgr.{id}'.format(id=id_),\\n mgr_dir + '/keyring',\\n ],\\n )\\n\\n log.info('Setting up mds nodes...')\\n mdss = ctx.cluster.only(teuthology.is_type('mds', cluster_name))\\n for remote, roles_for_host in mdss.remotes.items():\\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'mds',\\n cluster_name):\\n _, _, id_ = teuthology.split_role(role)\\n mds_dir = DATA_PATH.format(\\n type_='mds', cluster=cluster_name, id_=id_)\\n remote.run(\\n args=[\\n 'sudo',\\n 'mkdir',\\n '-p',\\n mds_dir,\\n run.Raw('&&'),\\n 'sudo',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-authtool',\\n '--create-keyring',\\n '--gen-key',\\n '--name=mds.{id}'.format(id=id_),\\n mds_dir + '/keyring',\\n ],\\n )\\n remote.run(args=[\\n 'sudo', 'chown', '-R', 'ceph:ceph', mds_dir\\n ])\\n\\n cclient.create_keyring(ctx, cluster_name)\\n log.info('Running mkfs on osd nodes...')\\n\\n if not hasattr(ctx, 'disk_config'):\\n ctx.disk_config = argparse.Namespace()\\n if not hasattr(ctx.disk_config, 'remote_to_roles_to_dev'):\\n ctx.disk_config.remote_to_roles_to_dev = {}\\n if not hasattr(ctx.disk_config, 'remote_to_roles_to_journals'):\\n ctx.disk_config.remote_to_roles_to_journals = {}\\n if not hasattr(ctx.disk_config, 'remote_to_roles_to_dev_mount_options'):\\n ctx.disk_config.remote_to_roles_to_dev_mount_options = {}\\n if not hasattr(ctx.disk_config, 'remote_to_roles_to_dev_fstype'):\\n ctx.disk_config.remote_to_roles_to_dev_fstype = {}\\n\\n teuthology.deep_merge(ctx.disk_config.remote_to_roles_to_dev, remote_to_roles_to_devs)\\n teuthology.deep_merge(ctx.disk_config.remote_to_roles_to_journals, remote_to_roles_to_journals)\\n\\n log.info(\\\"ctx.disk_config.remote_to_roles_to_dev: {r}\\\".format(r=str(ctx.disk_config.remote_to_roles_to_dev)))\\n for remote, roles_for_host in osds.remotes.items():\\n roles_to_devs = remote_to_roles_to_devs[remote]\\n roles_to_journals = remote_to_roles_to_journals[remote]\\n\\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name):\\n _, _, id_ = teuthology.split_role(role)\\n mnt_point = DATA_PATH.format(\\n type_='osd', cluster=cluster_name, id_=id_)\\n remote.run(\\n args=[\\n 'sudo',\\n 'mkdir',\\n '-p',\\n mnt_point,\\n ])\\n log.info(str(roles_to_devs))\\n log.info(str(roles_to_journals))\\n log.info(role)\\n if roles_to_devs.get(role):\\n dev = roles_to_devs[role]\\n fs = config.get('fs')\\n package = None\\n mkfs_options = config.get('mkfs_options')\\n mount_options = config.get('mount_options')\\n if fs == 'btrfs':\\n # package = 'btrfs-tools'\\n if mount_options is None:\\n mount_options = ['noatime', 'user_subvol_rm_allowed']\\n if mkfs_options is None:\\n mkfs_options = ['-m', 'single',\\n '-l', '32768',\\n '-n', '32768']\\n if fs == 'xfs':\\n # package = 'xfsprogs'\\n if mount_options is None:\\n mount_options = ['noatime']\\n if mkfs_options is None:\\n mkfs_options = ['-f', '-i', 'size=2048']\\n if fs == 'ext4' or fs == 'ext3':\\n if mount_options is None:\\n mount_options = ['noatime', 'user_xattr']\\n\\n if mount_options is None:\\n mount_options = []\\n if mkfs_options is None:\\n mkfs_options = []\\n mkfs = ['mkfs.%s' % fs] + mkfs_options\\n log.info('%s on %s on %s' % (mkfs, dev, remote))\\n if package is not None:\\n remote.sh('sudo apt-get install -y %s' % package)\\n\\n try:\\n remote.run(args=['yes', run.Raw('|')] + ['sudo'] + mkfs + [dev])\\n except run.CommandFailedError:\\n # Newer btfs-tools doesn't prompt for overwrite, use -f\\n if '-f' not in mount_options:\\n mkfs_options.append('-f')\\n mkfs = ['mkfs.%s' % fs] + mkfs_options\\n log.info('%s on %s on %s' % (mkfs, dev, remote))\\n remote.run(args=['yes', run.Raw('|')] + ['sudo'] + mkfs + [dev])\\n\\n log.info('mount %s on %s -o %s' % (dev, remote,\\n ','.join(mount_options)))\\n remote.run(\\n args=[\\n 'sudo',\\n 'mount',\\n '-t', fs,\\n '-o', ','.join(mount_options),\\n dev,\\n mnt_point,\\n ]\\n )\\n remote.run(\\n args=[\\n 'sudo', '/sbin/restorecon', mnt_point,\\n ],\\n check_status=False,\\n )\\n if not remote in ctx.disk_config.remote_to_roles_to_dev_mount_options:\\n ctx.disk_config.remote_to_roles_to_dev_mount_options[remote] = {}\\n ctx.disk_config.remote_to_roles_to_dev_mount_options[remote][role] = mount_options\\n if not remote in ctx.disk_config.remote_to_roles_to_dev_fstype:\\n ctx.disk_config.remote_to_roles_to_dev_fstype[remote] = {}\\n ctx.disk_config.remote_to_roles_to_dev_fstype[remote][role] = fs\\n devs_to_clean[remote].append(mnt_point)\\n\\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'osd', cluster_name):\\n _, _, id_ = teuthology.split_role(role)\\n try:\\n remote.run(\\n args=[\\n 'sudo',\\n 'MALLOC_CHECK_=3',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-osd',\\n '--no-mon-config',\\n '--cluster',\\n cluster_name,\\n '--mkfs',\\n '--mkkey',\\n '-i', id_,\\n '--monmap', monmap_path,\\n ],\\n )\\n except run.CommandFailedError:\\n # try without --no-mon-config.. this may be an upgrade test\\n remote.run(\\n args=[\\n 'sudo',\\n 'MALLOC_CHECK_=3',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-osd',\\n '--cluster',\\n cluster_name,\\n '--mkfs',\\n '--mkkey',\\n '-i', id_,\\n '--monmap', monmap_path,\\n ],\\n )\\n mnt_point = DATA_PATH.format(\\n type_='osd', cluster=cluster_name, id_=id_)\\n try:\\n remote.run(args=[\\n 'sudo', 'chown', '-R', 'ceph:ceph', mnt_point\\n ])\\n except run.CommandFailedError as e:\\n # hammer does not have ceph user, so ignore this error\\n log.info('ignoring error when chown ceph:ceph,'\\n 'probably installing hammer: %s', e)\\n\\n log.info('Reading keys from all nodes...')\\n keys_fp = BytesIO()\\n keys = []\\n for remote, roles_for_host in ctx.cluster.remotes.items():\\n for type_ in ['mgr', 'mds', 'osd']:\\n if type_ == 'mgr' and config.get('skip_mgr_daemons', False):\\n continue\\n for role in teuthology.cluster_roles_of_type(roles_for_host, type_, cluster_name):\\n _, _, id_ = teuthology.split_role(role)\\n data = teuthology.get_file(\\n remote=remote,\\n path=os.path.join(\\n DATA_PATH.format(\\n type_=type_, id_=id_, cluster=cluster_name),\\n 'keyring',\\n ),\\n sudo=True,\\n )\\n keys.append((type_, id_, data))\\n keys_fp.write(data)\\n for remote, roles_for_host in ctx.cluster.remotes.items():\\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'client', cluster_name):\\n _, _, id_ = teuthology.split_role(role)\\n data = teuthology.get_file(\\n remote=remote,\\n path='/etc/ceph/{cluster}.client.{id}.keyring'.format(id=id_, cluster=cluster_name)\\n )\\n keys.append(('client', id_, data))\\n keys_fp.write(data)\\n\\n log.info('Adding keys to all mons...')\\n writes = mons.run(\\n args=[\\n 'sudo', 'tee', '-a',\\n keyring_path,\\n ],\\n stdin=run.PIPE,\\n wait=False,\\n stdout=BytesIO(),\\n )\\n keys_fp.seek(0)\\n teuthology.feed_many_stdins_and_close(keys_fp, writes)\\n run.wait(writes)\\n for type_, id_, data in keys:\\n run.wait(\\n mons.run(\\n args=[\\n 'sudo',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-authtool',\\n keyring_path,\\n '--name={type}.{id}'.format(\\n type=type_,\\n id=id_,\\n ),\\n ] + list(generate_caps(type_)),\\n wait=False,\\n ),\\n )\\n\\n log.info('Running mkfs on mon nodes...')\\n for remote, roles_for_host in mons.remotes.items():\\n for role in teuthology.cluster_roles_of_type(roles_for_host, 'mon', cluster_name):\\n _, _, id_ = teuthology.split_role(role)\\n mnt_point = DATA_PATH.format(\\n type_='mon', id_=id_, cluster=cluster_name)\\n remote.run(\\n args=[\\n 'sudo',\\n 'mkdir',\\n '-p',\\n mnt_point,\\n ],\\n )\\n remote.run(\\n args=[\\n 'sudo',\\n 'adjust-ulimits',\\n 'ceph-coverage',\\n coverage_dir,\\n 'ceph-mon',\\n '--cluster', cluster_name,\\n '--mkfs',\\n '-i', id_,\\n '--monmap', monmap_path,\\n '--keyring', keyring_path,\\n ],\\n )\\n try:\\n remote.run(args=[\\n 'sudo', 'chown', '-R', 'ceph:ceph', mnt_point\\n ])\\n except run.CommandFailedError as e:\\n # hammer does not have ceph user, so ignore this error\\n log.info('ignoring error when chown ceph:ceph,'\\n 'probably installing hammer: %s', e)\\n\\n run.wait(\\n mons.run(\\n args=[\\n 'rm',\\n '--',\\n monmap_path,\\n ],\\n wait=False,\\n ),\\n )\\n\\n try:\\n yield\\n except Exception:\\n # we need to know this below\\n ctx.summary['success'] = False\\n raise\\n finally:\\n (mon0_remote,) = ctx.cluster.only(firstmon).remotes.keys()\\n\\n log.info('Checking cluster log for badness...')\\n\\n def first_in_ceph_log(pattern, excludes):\\n \\\"\\\"\\\"\\n Find the first occurrence of the pattern specified in the Ceph log,\\n Returns None if none found.\\n\\n :param pattern: Pattern scanned for.\\n :param excludes: Patterns to ignore.\\n :return: First line of text (or None if not found)\\n \\\"\\\"\\\"\\n args = [\\n 'sudo',\\n 'egrep', pattern,\\n '/var/log/ceph/{cluster}.log'.format(cluster=cluster_name),\\n ]\\n for exclude in excludes:\\n args.extend([run.Raw('|'), 'egrep', '-v', exclude])\\n args.extend([\\n run.Raw('|'), 'head', '-n', '1',\\n ])\\n stdout = mon0_remote.sh(args)\\n return stdout or None\\n\\n if first_in_ceph_log('\\\\[ERR\\\\]|\\\\[WRN\\\\]|\\\\[SEC\\\\]',\\n config['log_whitelist']) is not None:\\n log.warning('Found errors (ERR|WRN|SEC) in cluster log')\\n ctx.summary['success'] = False\\n # use the most severe problem as the failure reason\\n if 'failure_reason' not in ctx.summary:\\n for pattern in ['\\\\[SEC\\\\]', '\\\\[ERR\\\\]', '\\\\[WRN\\\\]']:\\n match = first_in_ceph_log(pattern, config['log_whitelist'])\\n if match is not None:\\n ctx.summary['failure_reason'] = \\\\\\n '\\\"{match}\\\" in cluster log'.format(\\n match=match.rstrip('\\\\n'),\\n )\\n break\\n\\n for remote, dirs in devs_to_clean.items():\\n for dir_ in dirs:\\n log.info('Unmounting %s on %s' % (dir_, remote))\\n try:\\n remote.run(\\n args=[\\n 'sync',\\n run.Raw('&&'),\\n 'sudo',\\n 'umount',\\n '-f',\\n dir_\\n ]\\n )\\n except Exception as e:\\n remote.run(args=[\\n 'sudo',\\n run.Raw('PATH=/usr/sbin:$PATH'),\\n 'lsof',\\n run.Raw(';'),\\n 'ps', 'auxf',\\n ])\\n raise e\\n\\n if config.get('tmpfs_journal'):\\n log.info('tmpfs journal enabled - unmounting tmpfs at /mnt')\\n for remote, roles_for_host in osds.remotes.items():\\n remote.run(\\n args=['sudo', 'umount', '-f', '/mnt'],\\n check_status=False,\\n )\\n\\n if ctx.archive is not None and \\\\\\n not (ctx.config.get('archive-on-error') and ctx.summary['success']):\\n\\n # archive mon data, too\\n log.info('Archiving mon data...')\\n path = os.path.join(ctx.archive, 'data')\\n try:\\n os.makedirs(path)\\n except OSError as e:\\n if e.errno == errno.EEXIST:\\n pass\\n else:\\n raise\\n for remote, roles in mons.remotes.items():\\n for role in roles:\\n is_mon = teuthology.is_type('mon', cluster_name)\\n if is_mon(role):\\n _, _, id_ = teuthology.split_role(role)\\n mon_dir = DATA_PATH.format(\\n type_='mon', id_=id_, cluster=cluster_name)\\n teuthology.pull_directory_tarball(\\n remote,\\n mon_dir,\\n path + '/' + role + '.tgz')\\n\\n log.info('Cleaning ceph cluster...')\\n run.wait(\\n ctx.cluster.run(\\n args=[\\n 'sudo',\\n 'rm',\\n '-rf',\\n '--',\\n conf_path,\\n keyring_path,\\n data_dir,\\n monmap_path,\\n run.Raw('{tdir}/../*.pid'.format(tdir=testdir)),\\n ],\\n wait=False,\\n ),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e399c36c2df5b195d84c34f11785f2ee\",\n \"score\": \"0.522368\",\n \"text\": \"def start_db_cluster(DBClusterIdentifier=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dd7b00416dd713d054c1f607664f007\",\n \"score\": \"0.5218847\",\n \"text\": \"def main():\\n try:\\n cluster, session = create_database()\\n except Exception as e:\\n print(e)\\n raise e\\n \\n try:\\n drop_tables(session)\\n create_tables(session)\\n except Exception as e:\\n print(e)\\n finally:\\n session.shutdown()\\n cluster.shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a6aea2dec464459d6e59edb589f5ab\",\n \"score\": \"0.51945525\",\n \"text\": \"def test_create_index(self):\\n hash_key = DynamoKey('id', data_type=STRING)\\n self.dynamo.create_table('foobar', hash_key=hash_key)\\n index_field = DynamoKey('name')\\n index = GlobalIndex.all('name-index', index_field, hash_key)\\n self.dynamo.update_table('foobar', index_updates=[\\n IndexUpdate.create(index)])\\n table = self.dynamo.describe_table('foobar')\\n self.assertEqual(len(table.global_indexes), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57fa5c26763489da9e94a81f04a9b083\",\n \"score\": \"0.5181301\",\n \"text\": \"def index_create(self, keyspace, table, index):\\n\\n path = '/keyspaces/%(keyspace)s/tables/%(table)s/index/%(field)s' % {\\n 'keyspace': keyspace,\\n 'table': table,\\n 'field': index\\n }\\n response = self.rest.request(method='post', path=path)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61c690a9cbf3d85f12bb6c106f872ef5\",\n \"score\": \"0.517907\",\n \"text\": \"def ensure_table(self, cls):\\n id_len = len(uuid())\\n index_names = cls.index_names() or []\\n cols = [\\n 'id char(%d) primary key' % (id_len,),\\n 'value jsonb'\\n ] + [\\n name + ' text' for name in index_names\\n ]\\n\\n table_name = cls.get_table_name()\\n\\n with self._conn() as conn:\\n with conn.cursor() as cur:\\n cur.execute('create table if not exists %s (%s);' % (\\n table_name,\\n ','.join(cols)\\n ))\\n for name in index_names:\\n cur.execute('create index if not exists %s on %s(%s);' % (\\n table_name + '_' + name + '_idx',\\n table_name,\\n name\\n ))\\n # End of conn with - transction should commit here if not exception\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cd2232e5fc1ba2dc6baa44c7a991ba3\",\n \"score\": \"0.5178922\",\n \"text\": \"def create_without_privilege(self, node=None):\\n user_name = f\\\"user_{getuid()}\\\"\\n\\n table0_name = f\\\"table0_{getuid()}\\\"\\n table1_name = f\\\"table1_{getuid()}\\\"\\n\\n exitcode, message = errors.not_enough_privileges(name=f\\\"{user_name}\\\")\\n\\n cluster = self.context.cluster_name\\n\\n if node is None:\\n node = self.context.node\\n\\n with Given(\\\"I have a user\\\"):\\n user(name=user_name)\\n\\n with And(\\\"I have a table on a cluster\\\"):\\n table(name=table0_name, cluster=cluster)\\n\\n with When(\\\"I grant the user NONE privilege\\\"):\\n node.query(f\\\"GRANT NONE TO {user_name}\\\")\\n\\n with And(\\\"I grant the user USAGE privilege\\\"):\\n node.query(f\\\"GRANT USAGE ON *.* TO {user_name}\\\")\\n\\n with Then(\\\"I attempt to create the distributed table without privilege\\\"):\\n node.query(f\\\"CREATE TABLE {table1_name} (a UInt64) ENGINE = Distributed(sharded_cluster, default, {table0_name}, rand())\\\", settings = [(\\\"user\\\", f\\\"{user_name}\\\")],\\n exitcode=exitcode, message=message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0e7847dd7c09d38cc5dfdfab94ba8ca\",\n \"score\": \"0.5173465\",\n \"text\": \"def static_cf_test(self):\\n cluster = self.cluster\\n\\n cluster.populate(1).start()\\n node1 = cluster.nodelist()[0]\\n time.sleep(0.2)\\n\\n cursor = self.cql_connection(node1, version=cql_version).cursor()\\n self.create_ks(cursor, 'ks', 1)\\n\\n # Create\\n cursor.execute(\\\"\\\"\\\"\\n CREATE TABLE users (\\n userid uuid PRIMARY KEY,\\n firstname text,\\n lastname text,\\n age int\\n );\\n \\\"\\\"\\\")\\n\\n # Inserts\\n cursor.execute(\\\"INSERT INTO users (userid, firstname, lastname, age) VALUES (550e8400-e29b-41d4-a716-446655440000, Frodo, Baggins, 32)\\\")\\n cursor.execute(\\\"UPDATE users SET firstname = Samwise, lastname = Gamgee, age = 33 WHERE userid = f47ac10b-58cc-4372-a567-0e02b2c3d479\\\")\\n\\n # Queries\\n cursor.execute(\\\"SELECT firstname, lastname FROM users WHERE userid = 550e8400-e29b-41d4-a716-446655440000\\\");\\n res = cursor.fetchall()\\n assert res == [[ 'Frodo', 'Baggins' ]], res\\n\\n cursor.execute(\\\"SELECT * FROM users WHERE userid = 550e8400-e29b-41d4-a716-446655440000\\\");\\n res = cursor.fetchall()\\n assert res == [[ UUID('550e8400-e29b-41d4-a716-446655440000'), 32, 'Frodo', 'Baggins' ]], res\\n\\n cursor.execute(\\\"SELECT * FROM users\\\");\\n res = cursor.fetchall()\\n assert res == [\\n [ UUID('f47ac10b-58cc-4372-a567-0e02b2c3d479'), 33, 'Samwise', 'Gamgee' ],\\n [ UUID('550e8400-e29b-41d4-a716-446655440000'), 32, 'Frodo', 'Baggins' ],\\n ], res\\n\\n # Test batch inserts\\n cursor.execute(\\\"\\\"\\\"\\n BEGIN BATCH\\n INSERT INTO users (userid, age) VALUES (550e8400-e29b-41d4-a716-446655440000, 36)\\n UPDATE users SET age = 37 WHERE userid = f47ac10b-58cc-4372-a567-0e02b2c3d479\\n DELETE firstname, lastname FROM users WHERE userid = 550e8400-e29b-41d4-a716-446655440000\\n DELETE firstname, lastname FROM users WHERE userid = f47ac10b-58cc-4372-a567-0e02b2c3d479\\n APPLY BATCH\\n \\\"\\\"\\\")\\n\\n cursor.execute(\\\"SELECT * FROM users\\\")\\n res = cursor.fetchall()\\n assert res == [\\n [ UUID('f47ac10b-58cc-4372-a567-0e02b2c3d479'), 37, None, None ],\\n [ UUID('550e8400-e29b-41d4-a716-446655440000'), 36, None, None ],\\n ], res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce092c38c5480afb7905af3261cf538f\",\n \"score\": \"0.5170467\",\n \"text\": \"def tenant_table_initialize():\\n global _tenant_table\\n\\n _tenant_table = TenantTable()\\n _tenant_table.persist = False\\n\\n tenants = database.database_tenant_get_list()\\n for tenant in tenants:\\n _tenant_table[tenant.uuid] = tenant\\n _tenant_table.persist = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dd7cb494480ec268098107073a69423\",\n \"score\": \"0.51701653\",\n \"text\": \"def test_type_as_clustering_col(self):\\n # make sure we can define a table with a user type as a clustering column\\n # and do a basic insert/query of data in that table.\\n cluster = self.cluster\\n cluster.populate(3).start()\\n node1, node2, node3 = cluster.nodelist()\\n session = self.patient_cql_connection(node1, consistency_level=ConsistencyLevel.LOCAL_QUORUM)\\n create_ks(session, 'user_type_pkeys', 2)\\n\\n stmt = \\\"\\\"\\\"\\n CREATE TYPE t_letterpair (\\n first text,\\n second text\\n )\\n \\\"\\\"\\\"\\n session.execute(stmt)\\n\\n stmt = \\\"\\\"\\\"\\n CREATE TABLE letters (\\n id int,\\n letterpair frozen<t_letterpair>,\\n PRIMARY KEY (id, letterpair)\\n )\\n \\\"\\\"\\\"\\n session.execute(stmt)\\n # address CASSANDRA-11498\\n session.cluster.control_connection.wait_for_schema_agreement()\\n\\n # create a bit of data and expect a natural order based on clustering user types\\n\\n ids = list(range(1, 10))\\n\\n for _id in ids:\\n session.execute(\\\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'a', second:'z'}})\\\".format(_id))\\n session.execute(\\\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'z', second:'a'}})\\\".format(_id))\\n session.execute(\\\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'c', second:'f'}})\\\".format(_id))\\n session.execute(\\\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'c', second:'a'}})\\\".format(_id))\\n session.execute(\\\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'c', second:'z'}})\\\".format(_id))\\n session.execute(\\\"INSERT INTO letters (id, letterpair) VALUES ({}, {{first:'d', second:'e'}})\\\".format(_id))\\n\\n for _id in ids:\\n res = list(session.execute(\\\"SELECT letterpair FROM letters where id = {}\\\".format(_id)))\\n\\n assert listify(res) == [[['a', 'z']], [['c', 'a']], [['c', 'f']], [['c', 'z']], [['d', 'e']], [['z', 'a']]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b287aefc4864cd099a2d526d6d0679b\",\n \"score\": \"0.51599354\",\n \"text\": \"def keyspace_size():\\n return 2 ** (8 * key_size_bytes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e610a25bbd00c09f72427cdfeb2245\",\n \"score\": \"0.51494366\",\n \"text\": \"def make_table(self):\\n hash_key = DynamoKey('id')\\n self.dynamo.create_table('foobar', hash_key=hash_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e610a25bbd00c09f72427cdfeb2245\",\n \"score\": \"0.51494366\",\n \"text\": \"def make_table(self):\\n hash_key = DynamoKey('id')\\n self.dynamo.create_table('foobar', hash_key=hash_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e610a25bbd00c09f72427cdfeb2245\",\n \"score\": \"0.51494366\",\n \"text\": \"def make_table(self):\\n hash_key = DynamoKey('id')\\n self.dynamo.create_table('foobar', hash_key=hash_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e610a25bbd00c09f72427cdfeb2245\",\n \"score\": \"0.51494366\",\n \"text\": \"def make_table(self):\\n hash_key = DynamoKey('id')\\n self.dynamo.create_table('foobar', hash_key=hash_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e610a25bbd00c09f72427cdfeb2245\",\n \"score\": \"0.51494366\",\n \"text\": \"def make_table(self):\\n hash_key = DynamoKey('id')\\n self.dynamo.create_table('foobar', hash_key=hash_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e610a25bbd00c09f72427cdfeb2245\",\n \"score\": \"0.51494366\",\n \"text\": \"def make_table(self):\\n hash_key = DynamoKey('id')\\n self.dynamo.create_table('foobar', hash_key=hash_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6143c1b797538fd80c6db898146ebd27\",\n \"score\": \"0.5143367\",\n \"text\": \"def _setup_couchdb(servername, database):\\n \\n server = couchdb.client.Server(servername)\\n if database in server:\\n return server[database]\\n else:\\n return server.create(database)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb684bfa2ef42ccb46d90ad20ff8407\",\n \"score\": \"0.51308817\",\n \"text\": \"def create_create():\\n create = upkey('create').setResultsName('action')\\n hash_key = Group(upkey('hash') +\\n upkey('key'))\\n range_key = Group(upkey('range') +\\n upkey('key'))\\n\\n index = Group(Optional(upkey('all') | upkey('keys') | upkey('include')) +\\n upkey('index')).setResultsName('index_type')\\n include_vars = Group(Suppress('[') + delimitedList(primitive) +\\n Suppress(']')).setResultsName('include')\\n local_index = Group(index + Suppress('(') + primitive +\\n Optional(Suppress(',') + include_vars) + Suppress(')'))\\n index_type = (hash_key | range_key | local_index)\\\\\\n .setName('index specification').setResultsName('index')\\n\\n attr_declaration = Group(var.setResultsName('name') + type_ +\\n Optional(index_type))\\\\\\n .setName('attr').setResultsName('attr')\\n attrs_declaration = (Suppress('(') +\\n Group(delimitedList(attr_declaration))\\n .setName('attrs').setResultsName('attrs')\\n + Optional(Suppress(',') + throughput) + Suppress(')'))\\n\\n global_dec = Suppress(upkey('global')) + index\\n range_key_etc = (Suppress(',') + Group(throughput) |\\n Optional(Suppress(',') + var) +\\n Optional(Suppress(',') + include_vars) +\\n Optional(Group(Suppress(',') + throughput)))\\n global_spec = (Suppress('(') + primitive +\\n Suppress(',') + var +\\n range_key_etc +\\n Suppress(')'))\\n global_index = Group(global_dec + global_spec).setName('global index')\\n global_indexes = Group(OneOrMore(global_index))\\\\\\n .setResultsName('global_indexes')\\n\\n return (create + table_key + Optional(if_not_exists) + table +\\n attrs_declaration + Optional(global_indexes))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8468e061a84971ff630b4761ebd37f4b\",\n \"score\": \"0.51222605\",\n \"text\": \"def create_tables() -> None:\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b684d670161cecd5b3248efe93111fa4\",\n \"score\": \"0.51189005\",\n \"text\": \"def test_create_local_keys_index(self):\\n hash_key = DynamoKey('id', data_type=STRING)\\n range_key = DynamoKey('num', data_type=NUMBER)\\n index_field = DynamoKey('name')\\n index = LocalIndex.keys('name-index', index_field)\\n table = Table('foobar', hash_key, range_key, [index])\\n self.dynamo.create_table(\\n 'foobar', hash_key, range_key, indexes=[index])\\n desc = self.dynamo.describe_table('foobar')\\n self.assertEqual(desc, table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7d75b4c485bae6bad6de7820d9b815\",\n \"score\": \"0.51141983\",\n \"text\": \"def __init__(self,\\n cassandra_ports_info=None,\\n cassandra_security_info=None,\\n cassandra_version=None,\\n commit_log_backup_location=None,\\n config_directory=None,\\n data_centers=None,\\n dse_config_directory=None,\\n is_dse_authenticator=None,\\n is_dse_tiered_storage=None,\\n is_jmx_auth_enable=None,\\n kerberos_principal=None,\\n primary_host=None,\\n seeds=None,\\n solr_nodes=None,\\n solr_port=None,\\n ):\\n\\n # Initialize members of the class\\n self.cassandra_ports_info = cassandra_ports_info\\n self.cassandra_security_info = cassandra_security_info\\n self.cassandra_version = cassandra_version\\n self.commit_log_backup_location = commit_log_backup_location\\n self.config_directory = config_directory\\n self.data_centers = data_centers\\n self.dse_config_directory = dse_config_directory\\n self.is_dse_authenticator = is_dse_authenticator\\n self.is_dse_tiered_storage = is_dse_tiered_storage\\n self.is_jmx_auth_enable = is_jmx_auth_enable\\n self.kerberos_principal = kerberos_principal\\n self.primary_host = primary_host\\n self.seeds = seeds\\n self.solr_nodes = solr_nodes\\n self.solr_port = solr_port\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff320f63d406de4a7e1397a54f003b3\",\n \"score\": \"0.5096175\",\n \"text\": \"def cluster_create(version, name, opts=None):\\n\\n # To create a cluster we need to provide an interface to exactly two programs.\\n # pg_createcluster and initdb.\\n\\n # As arguments for both are finite and do not overlap, we'll split them here into\\n # two arrays. Specialcasing where appropriate.\\n\\n cmd = [\\\"pg_createcluster\\\",\\n version,\\n name]\\n initdbOpts = []\\n sr_conninfo = None\\n if opts is not None:\\n for key, value in opts.items():\\n if value is None:\\n # Argparse will provide arguments. We don't need to present those to the\\n # programs, so we'll skip\\n continue\\n if key == 'data-checksums':\\n initdbOpts += ['--data-checksums',]\\n elif key == 'primary_conninfo':\\n sr_conninfo = value\\n else:\\n cmd += ['--%s=%s' % (key, value), ]\\n\\n (returncode, stdout, stderr) = _run_command( cmd + ['--',] + initdbOpts )\\n\\n # IFF the cluster was successfully created and an SR_Standby was required,\\n # the data-dir of said instance can be purged.\\n # This could cause data loss if pg_createcluster returns OK against existing clusters.\\n if sr_conninfo != None and returncode == 0:\\n # If SR_create fails, an error will be reported. For now there is no automatic cleanup.\\n # This tends to be a good idea as it allows the user to investigate the error.\\n (rc,err) = SR_create( version, name, sr_conninfo)\\n returncode=rc\\n stderr+=err\\n\\n return (returncode, stdout, stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"363aee139c6c703ec11c8dd16a841db0\",\n \"score\": \"0.5094259\",\n \"text\": \"async def upgrade(conn: asyncpg.Connection) -> None:\\n await conn.execute(\\n \\\"create table api_keys (\\\"\\n \\\"id uuid primary key default uuid_generate_v4(),\\\"\\n \\\"scope jsonb not null,\\\"\\n \\\"user_id uuid not null,\\\"\\n \\\"expiry timestamp default null);\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc23c92515d2edad0c0acf3b7fc6e73d\",\n \"score\": \"0.50939864\",\n \"text\": \"def create_schema(cur, conn):\\n try:\\n print(\\\"execute query: \\\" + schema_create_query)\\n cur.execute(schema_create_query)\\n conn.commit() \\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4543b099d685b046688be99db6cf7914\",\n \"score\": \"0.50850797\",\n \"text\": \"def create_indexes():\\n db['schemas'].ensure_index('uuid', unique=True)\\n db['datapoints'].ensure_index('uuid')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7d8ea4b29ab0e66c6a501639afc98b\",\n \"score\": \"0.5076603\",\n \"text\": \"def test_create_global_index(self):\\n hash_key = DynamoKey('id', data_type=STRING)\\n index_field = DynamoKey('name')\\n index = GlobalIndex.all('name-index', index_field)\\n table = Table('foobar', hash_key, global_indexes=[index])\\n self.dynamo.create_table('foobar', hash_key, global_indexes=[index])\\n desc = self.dynamo.describe_table('foobar')\\n self.assertEqual(desc, table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b051214ebc2ffa741104a50bb5d2c9d\",\n \"score\": \"0.50565326\",\n \"text\": \"def setup(app):\\n app.add_directive('cassandra', CassandraTable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48006a8554673600fa7117b0bd186d0\",\n \"score\": \"0.5043568\",\n \"text\": \"def host_table_initialize():\\n global _host_table\\n\\n _host_table = HostTable()\\n _host_table.persist = False\\n\\n hosts = database.database_host_get_list()\\n for host in hosts:\\n _host_table[host.name] = host\\n _host_table.persist = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e2be0e0134d79c654e7306d179cce5\",\n \"score\": \"0.5040673\",\n \"text\": \"def test_create_product_table(self):\\n _db = Database.instance(\\\":memory:\\\")\\n try:\\n create_users_table()\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef70f90bf24c4ab86302d2d8d958b4c\",\n \"score\": \"0.5034538\",\n \"text\": \"def create():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef70f90bf24c4ab86302d2d8d958b4c\",\n \"score\": \"0.5034538\",\n \"text\": \"def create():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbd1d2705ffced132749b93952de0e8a\",\n \"score\": \"0.5031487\",\n \"text\": \"def test_insert_data(self):\\n self.render_config_template(\\n cassandra_ports=[9042],\\n cassandra_send_request=True,\\n cassandra_send_response=True,\\n cassandra_send_request_header=True,\\n cassandra_send_response_header=True,\\n )\\n self.run_packetbeat(pcap=\\\"cassandra/v4/cassandra_insert.pcap\\\", debug_selectors=[\\\"*\\\"])\\n objs = self.read_output()\\n o = objs[0]\\n print(o)\\n assert o[\\\"type\\\"] == \\\"cassandra\\\"\\n assert o[\\\"port\\\"] == 9042\\n\\n assert o[\\n \\\"cassandra.request.query\\\"] == \\\"INSERT INTO users (user_id, fname, lname)\\\\n VALUES (1745, 'john', 'smith');\\\"\\n assert o[\\\"cassandra.request.headers.version\\\"] == \\\"4\\\"\\n assert o[\\\"cassandra.request.headers.op\\\"] == \\\"QUERY\\\"\\n assert o[\\\"cassandra.request.headers.length\\\"] == 97\\n assert o[\\\"cassandra.request.headers.flags\\\"] == \\\"Default\\\"\\n assert o[\\\"cassandra.request.headers.stream\\\"] == 252\\n\\n assert o[\\\"cassandra.response.result.type\\\"] == \\\"void\\\"\\n assert o[\\\"cassandra.response.headers.version\\\"] == \\\"4\\\"\\n assert o[\\\"cassandra.response.headers.length\\\"] == 4\\n assert o[\\\"cassandra.response.headers.op\\\"] == \\\"RESULT\\\"\\n assert o[\\\"cassandra.response.headers.flags\\\"] == \\\"Default\\\"\\n assert o[\\\"cassandra.response.headers.stream\\\"] == 252\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b2fd95b346adafcf447e610e06b46e\",\n \"score\": \"0.5026299\",\n \"text\": \"def sparse_cf_test(self):\\n cluster = self.cluster\\n\\n cluster.populate(1).start()\\n node1 = cluster.nodelist()[0]\\n time.sleep(0.2)\\n\\n cursor = self.cql_connection(node1, version=cql_version).cursor()\\n self.create_ks(cursor, 'ks', 1)\\n\\n cursor.execute(\\\"\\\"\\\"\\n CREATE TABLE timeline (\\n userid uuid,\\n posted_month int,\\n posted_day int,\\n body text,\\n posted_by text,\\n PRIMARY KEY (userid, posted_month, posted_day)\\n );\\n \\\"\\\"\\\")\\n\\n # Inserts\\n cursor.execute(\\\"INSERT INTO timeline (userid, posted_month, posted_day, body, posted_by) VALUES (550e8400-e29b-41d4-a716-446655440000, 1, 12, 'Something else', 'Frodo Baggins')\\\")\\n cursor.execute(\\\"INSERT INTO timeline (userid, posted_month, posted_day, body, posted_by) VALUES (550e8400-e29b-41d4-a716-446655440000, 1, 24, 'Something something', 'Frodo Baggins')\\\")\\n cursor.execute(\\\"UPDATE timeline SET body = 'Yo Froddo', posted_by = 'Samwise Gamgee' WHERE userid = f47ac10b-58cc-4372-a567-0e02b2c3d479 AND posted_month = 1 AND posted_day = 3\\\")\\n cursor.execute(\\\"UPDATE timeline SET body = 'Yet one more message' WHERE userid = 550e8400-e29b-41d4-a716-446655440000 AND posted_month = 1 and posted_day = 30\\\")\\n\\n # Queries\\n cursor.execute(\\\"SELECT body, posted_by FROM timeline WHERE userid = 550e8400-e29b-41d4-a716-446655440000 AND posted_month = 1 AND posted_day = 24\\\")\\n res = cursor.fetchall()\\n assert res == [[ 'Something something', 'Frodo Baggins' ]], res\\n\\n cursor.execute(\\\"SELECT posted_day, body, posted_by FROM timeline WHERE userid = 550e8400-e29b-41d4-a716-446655440000 AND posted_month = 1 AND posted_day > 12\\\")\\n res = cursor.fetchall()\\n assert res == [\\n [ 24, 'Something something', 'Frodo Baggins' ],\\n [ 30, 'Yet one more message', None ]\\n ], res\\n\\n cursor.execute(\\\"SELECT posted_day, body, posted_by FROM timeline WHERE userid = 550e8400-e29b-41d4-a716-446655440000 AND posted_month = 1\\\")\\n res = cursor.fetchall()\\n assert res == [\\n [ 12, 'Something else', 'Frodo Baggins' ],\\n [ 24, 'Something something', 'Frodo Baggins' ],\\n [ 30, 'Yet one more message', None ]\\n ], res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a241e608d229a013e607f1029f373424\",\n \"score\": \"0.5024984\",\n \"text\": \"def cli_cosmosdb_managed_cassandra_datacenter_create(client,\\n resource_group_name,\\n cluster_name,\\n data_center_name,\\n data_center_location,\\n delegated_subnet_id,\\n node_count,\\n base64_encoded_cassandra_yaml_fragment=None):\\n\\n data_center_properties = DataCenterResourceProperties(\\n data_center_location=data_center_location,\\n delegated_subnet_id=delegated_subnet_id,\\n node_count=node_count,\\n base64_encoded_cassandra_yaml_fragment=base64_encoded_cassandra_yaml_fragment\\n )\\n\\n data_center_resource = DataCenterResource(\\n properties=data_center_properties\\n )\\n\\n return client.begin_create_update(resource_group_name, cluster_name, data_center_name, data_center_resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84001693c605e7624c5c2b8e16733fec\",\n \"score\": \"0.50243473\",\n \"text\": \"async def startup_event():\\n async with engine.begin() as conn:\\n await conn.run_sync(db.Base.metadata.create_all)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb401cbbdeb485aeaee262aae2d5e6a8\",\n \"score\": \"0.5017989\",\n \"text\": \"def insert_without_privilege(self, node=None):\\n user_name = f\\\"user_{getuid()}\\\"\\n\\n table0_name = f\\\"table0_{getuid()}\\\"\\n table1_name = f\\\"table1_{getuid()}\\\"\\n\\n exitcode, message = errors.not_enough_privileges(name=f\\\"{user_name}\\\")\\n\\n cluster = self.context.cluster_name\\n\\n if node is None:\\n node = self.context.node\\n\\n try:\\n with Given(\\\"I have a user\\\"):\\n user(name=user_name)\\n\\n with And(\\\"I have a table on a cluster\\\"):\\n table(name=table0_name, cluster=cluster)\\n\\n with And(\\\"I have a distributed table\\\"):\\n node.query(f\\\"CREATE TABLE {table1_name} (a UInt64) ENGINE = Distributed({cluster}, default, {table0_name}, rand())\\\")\\n\\n with When(\\\"I grant the user NONE privilege\\\"):\\n node.query(f\\\"GRANT NONE TO {user_name}\\\")\\n\\n with And(\\\"I grant the user USAGE privilege\\\"):\\n node.query(f\\\"GRANT USAGE ON *.* TO {user_name}\\\")\\n\\n with Then(\\\"I attempt to insert into the distributed table as the user\\\"):\\n node.query(f\\\"INSERT INTO {table1_name} VALUES (8888)\\\", settings = [(\\\"user\\\", f\\\"{user_name}\\\")],\\n exitcode=exitcode, message=message)\\n finally:\\n with Finally(\\\"I drop the distributed table\\\"):\\n node.query(f\\\"DROP TABLE IF EXISTS {table1_name}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329283330257528fa67b85919e116bfe\",\n \"score\": \"0.5010762\",\n \"text\": \"def initialize(\\n self,\\n cassandra_session,\\n ):\\n self.column_family = ColumnFamily(\\n cassandra_session,\\n COLUMN_FAMILY,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ffdfa3f96f4efc4521e1eb747c57956\",\n \"score\": \"0.5006049\",\n \"text\": \"def testFromElasticToCassandra(self):\\n # initial setup for this test\\n config = self.loadConfigFile('testConfig.json')\\n sync = PyCassElastic(config)\\n\\n # create last run file for 5 minutes ago and setup sync\\n self._createLastRunFile()\\n sync.setup()\\n\\n # create new documents on elasticsearch and read the data\\n amount = 10\\n two_min_ago = datetime.utcnow() - timedelta(minutes=2)\\n self._createElasticSearchData(config['syncs'][1], amount=amount, curr_time=two_min_ago)\\n rows = sync.get_elasticsearch_latest(config['syncs'][1])\\n self.assertIsNotNone(rows, 'The query should have returned %s rows' % amount)\\n\\n # create some previous data and write on cassandra\\n ten_min_ago = datetime.utcnow() - timedelta(minutes=10)\\n self._createCassandraData(config['syncs'][1], amount=5, curr_time=ten_min_ago)\\n ok, errors = sync.insert_cassandra(config['syncs'][1], rows)\\n self.assertEqual(ok, amount)\\n\\n # check if data are the same\\n ret = self.checkSync(sync, config['syncs'][1])\\n self.assertEquals(ret, -1,\\n 'The DBs data should be the same. Error on id %s' % ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bea178bd99ed1e227974411e1d821ce3\",\n \"score\": \"0.4994968\",\n \"text\": \"def insert(self, key, columns, keyspace_name=None, cf_name=None,\\n batch=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cebbfc9749a6fb09d9b7b2940263eec\",\n \"score\": \"0.49896723\",\n \"text\": \"def create_db_cluster(AvailabilityZones=None, BackupRetentionPeriod=None, CharacterSetName=None, DatabaseName=None, DBClusterIdentifier=None, DBClusterParameterGroupName=None, VpcSecurityGroupIds=None, DBSubnetGroupName=None, Engine=None, EngineVersion=None, Port=None, MasterUsername=None, MasterUserPassword=None, OptionGroupName=None, PreferredBackupWindow=None, PreferredMaintenanceWindow=None, ReplicationSourceIdentifier=None, Tags=None, StorageEncrypted=None, KmsKeyId=None, PreSignedUrl=None, EnableIAMDatabaseAuthentication=None, EnableCloudwatchLogsExports=None, DeletionProtection=None, SourceRegion=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d7e8d159193a0ae38bfda54ba225f3\",\n \"score\": \"0.49876595\",\n \"text\": \"def create_db():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d7e8d159193a0ae38bfda54ba225f3\",\n \"score\": \"0.49876595\",\n \"text\": \"def create_db():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d7e8d159193a0ae38bfda54ba225f3\",\n \"score\": \"0.49876595\",\n \"text\": \"def create_db():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d7e8d159193a0ae38bfda54ba225f3\",\n \"score\": \"0.49876595\",\n \"text\": \"def create_db():\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9a63de656497c94318b9e5b81c82c0\",\n \"score\": \"0.49854523\",\n \"text\": \"def test_create_key_pair(self):\\n dummy = DummyKmipClient()\\n dummy.create_key_pair('algoritm', 'length')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88167eee95a2c6d18854ac2730c94760\",\n \"score\": \"0.49854025\",\n \"text\": \"def test_clear_keyspace(self, coll1, coll2, coll3, coll4, coll5, coll6):\\n coll1.clear_keyspace()\\n coll2.clear_keyspace()\\n coll3.clear_keyspace()\\n coll4.clear_keyspace()\\n coll5.clear_keyspace()\\n coll6.clear_keyspace()\\n assert coll1.size == 0\\n assert coll2.size == 0\\n assert coll3.size == 0\\n assert coll4.size == 0\\n assert coll5.size == 0\\n assert coll6.size == 0\\n rh.REDIS.delete('_REDIS_HELPER_COLLECTION')\\n assert rh.REDIS.dbsize() == 0\\n rh.stop_docker()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c3b5b333f27f4f8faac4e24eba654a2\",\n \"score\": \"0.49847418\",\n \"text\": \"def table(self, name, cluster=None, node=None):\\n if node is None:\\n node = self.context.node\\n try:\\n if cluster:\\n with Given(f\\\"I create table {name}\\\"):\\n node.query(f\\\"DROP TABLE IF EXISTS {name}\\\")\\n node.query(f\\\"CREATE TABLE {name} ON CLUSTER {cluster} (a UInt64) ENGINE = Memory\\\")\\n else:\\n with Given(f\\\"I create table {name}\\\"):\\n node.query(f\\\"DROP TABLE IF EXISTS {name}\\\")\\n node.query(f\\\"CREATE TABLE {name} (a UInt64) ENGINE = Memory\\\")\\n yield\\n finally:\\n if cluster:\\n with Finally(f\\\"I delete table {name}\\\"):\\n node.query(f\\\"DROP TABLE IF EXISTS {name} ON CLUSTER {cluster}\\\")\\n else:\\n with Finally(f\\\"I delete role {name}\\\"):\\n node.query(f\\\"DROP ROLE IF EXISTS {name}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"750e5918a837db619346a2153cc23e5b\",\n \"score\": \"0.49844953\",\n \"text\": \"def init():\\n create_schema()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e81f2b25d757e2eeef9189024ea968c\",\n \"score\": \"0.49824572\",\n \"text\": \"def create_db():\\n db.create_all()\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e72763c8175f2eb3510a65888d280204\",\n \"score\": \"0.4976816\",\n \"text\": \"def init_db(engine, session):\\n tid = get_who()\\n if tid not in BACKENDS.keys():\\n BACKENDS[tid] = (engine, session)\\n\\n if len(BACKENDS) == 1:\\n with CREATE_LOCK:\\n try:\\n lg.info(\\\"Creating schema if needed\\\")\\n Base.metadata.create_all(bind=engine)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6458d95a767ca34e371201bc851d1879\",\n \"score\": \"0.49739918\",\n \"text\": \"def testFromCassandraToElastic(self):\\n config = self.loadConfigFile('testConfig.json')\\n sync = PyCassElastic(config)\\n sync_params = config['syncs'][0]\\n self._createLastRunFile()\\n\\n # create the data on cassandra two minutes ago\\n two_minutes_ago = datetime.utcnow() - timedelta(minutes=2)\\n ten_minutes_ago = datetime.utcnow() - timedelta(minutes=10)\\n self._createCassandraData(sync_params, two_minutes_ago, amount=self.data_amt / 2)\\n self._createCassandraData(\\n sync_params,\\n amount=self.data_amt / 2,\\n curr_time=ten_minutes_ago,\\n start=self.data_amt / 2,\\n create_table=False\\n )\\n\\n # run the setup\\n sync.setup()\\n\\n # assert the amount found\\n rows = sync.get_cassandra_latest(sync_params)\\n\\n # check if it will use date as a filter\\n ok, errors = sync.insert_elasticsearch(sync_params, rows)\\n if sync_params.get('filter_date', None):\\n self.assertEqual(ok, self.data_amt / 2)\\n else:\\n self.assertEqual(ok, self.data_amt)\\n\\n # check if data is the same\\n ret = self.checkSync(sync, sync_params)\\n self.assertEquals(ret, -1,\\n 'The DBs data should be the same. Error on id %s' % ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af26c77aa5cb1893b57dce55486bde8c\",\n \"score\": \"0.49728554\",\n \"text\": \"def start_cluster_creation(redshift, roleArn):\\n global DWH_CLUSTER_TYPE, DWH_NODE_TYPE, DWH_NUM_NODES, \\\\\\n DWH_DB, DWH_CLUSTER_IDENTIFIER, DWH_DB_USER, DWH_DB_PASSWORD\\n print(\\\"2. Starting redshift cluster creation\\\")\\n try:\\n response = redshift.create_cluster(\\n # HW\\n ClusterType=DWH_CLUSTER_TYPE,\\n NodeType=DWH_NODE_TYPE,\\n NumberOfNodes=int(DWH_NUM_NODES),\\n\\n # Identifiers & Credentials\\n DBName=DWH_DB,\\n ClusterIdentifier=DWH_CLUSTER_IDENTIFIER,\\n MasterUsername=DWH_DB_USER,\\n MasterUserPassword=DWH_DB_PASSWORD,\\n\\n # Roles (for s3 access)\\n IamRoles=[roleArn]\\n )\\n print(\\\"Redshift cluster creation http response status code: \\\")\\n print(response['ResponseMetadata']['HTTPStatusCode'])\\n return response['ResponseMetadata']['HTTPStatusCode'] == 200\\n except Exception as e:\\n print(e)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38196f422456875faa808fa770731d47\",\n \"score\": \"0.49706832\",\n \"text\": \"def init_db():\\n db = engine.connect()\\n Base.metadata.drop_all(engine, checkfirst=True)\\n # tables = list(reversed(Base.metadata.sorted_tables))\\n # tables = Base.metadata.sorted_tables\\n # for table in tables:\\n # table.drop(engine, checkfirst=True)\\n Base.metadata.create_all(engine)\\n\\n # add admin user\\n with session_scope() as db_session:\\n db_session.add(UserClass(\\\\\\n username=\\\"admin\\\", password=generate_password_hash('mayuxin1999'), permissions=3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54c2ebb19f81ccb7ec9280f0370cf228\",\n \"score\": \"0.49680874\",\n \"text\": \"async def _create_database(self) -> DatabaseType:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede3c9ddce57916cad29d1a0bf916149\",\n \"score\": \"0.49632606\",\n \"text\": \"def init_db() -> None:\\n\\n node.initial_setup(\\n signing_key=node.signing_key,\\n first_superuser_name=\\\"Jane Doe\\\",\\n first_superuser_email=settings.FIRST_SUPERUSER,\\n first_superuser_password=settings.FIRST_SUPERUSER_PASSWORD,\\n first_superuser_budget=5.55,\\n domain_name=settings.DOMAIN_NAME,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a548be38146b12462bb00fae9747e5f\",\n \"score\": \"0.49594185\",\n \"text\": \"def create_db():\\n db.create_all()\\n print('Database created')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb96679196e0f76c1c918d1a8523ca0\",\n \"score\": \"0.49550986\",\n \"text\": \"def cli_cosmosdb_managed_cassandra_datacenter_create(client,\\n resource_group_name,\\n cluster_name,\\n data_center_name,\\n data_center_location,\\n delegated_subnet_id,\\n node_count,\\n base64_encoded_cassandra_yaml_fragment=None):\\n\\n data_center_properties = DataCenterResourceProperties(\\n data_center_location=data_center_location,\\n delegated_subnet_id=delegated_subnet_id,\\n node_count=node_count,\\n base64_encoded_cassandra_yaml_fragment=base64_encoded_cassandra_yaml_fragment\\n )\\n\\n return client.create_update(resource_group_name, cluster_name, data_center_name, data_center_properties)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992993,"cells":{"query_id":{"kind":"string","value":"959883e1ab0bcfe8ca8ed927c82070ed"},"query":{"kind":"string","value":"Visualize faces obtained from MTCNN"},"positive_passages":{"kind":"list like","value":[{"docid":"70dbb8d91398ceb44e87f31fb5698591","score":"0.78203064","text":"def visualize_faces_mtcnn(img_path, detector):\n img = cv2.imread(img_path)\n faces = detector(img)\n for x in faces:\n x = np.asarray(x)\n x = np.transpose(x, (1, 2, 0))\n x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB)\n x = cv2.normalize(\n x, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F\n ).astype(np.uint8)\n plt.imshow(x)\n plt.show()","title":""}],"string":"[\n {\n \"docid\": \"70dbb8d91398ceb44e87f31fb5698591\",\n \"score\": \"0.78203064\",\n \"text\": \"def visualize_faces_mtcnn(img_path, detector):\\n img = cv2.imread(img_path)\\n faces = detector(img)\\n for x in faces:\\n x = np.asarray(x)\\n x = np.transpose(x, (1, 2, 0))\\n x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB)\\n x = cv2.normalize(\\n x, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F\\n ).astype(np.uint8)\\n plt.imshow(x)\\n plt.show()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ee20f8b403c10b30526608e2bb664356","score":"0.6805244","text":"def faces():","title":""},{"docid":"c4d006a66ea0674f515a62e5dacf24da","score":"0.6586629","text":"def recognize_faces_and_draw_knn(self, minDistance=0.5):\r\n index = 0\r\n new_image = self.image.copy() \r\n # loop all faces\r\n name = None\r\n for left, right, top, bottom in self.boxes:\r\n cv2.rectangle(new_image,(left,top),(right,bottom),(0,255,0),2)\r\n if self.faces_aligned == [] or self.faces_aligned is None:\r\n face = self.faces_detected[index]\r\n else:\r\n face = self.faces_aligned[index]\r\n # generate encoding for the face\r\n encoding = self.generate_encoding(face)\r\n # match a face with all faces in database\r\n # for encoding in encodings:\r\n matches = face_recognition.compare_faces(self.data[\"encodings\"], encoding, minDistance)\r\n name = \"Unknown\"\r\n if True in matches:\r\n matchedIdxs = [i for (i, b) in enumerate(matches) if b]\r\n counts = {}\r\n for i in matchedIdxs:\r\n name = self.data[\"names\"][i]\r\n counts[name] = counts.get(name, 0) + 1\r\n name = max(counts, key=counts.get)\r\n y = top - 15 if top - 15 > 15 else top + 15\r\n cv2.putText(new_image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 4)\r\n index = index + 1\r\n \r\n return new_image, name","title":""},{"docid":"df916e0d3c2488c55321c8cbf736f4d8","score":"0.6540483","text":"def show_detection(image, faces):\n \n # faces contains a list of mmod_rectangle objects\n # The mmod_rectangle object has two member variables, a dlib.rectangle object, and a confidence score\n # Therefore, we iterate over the detected mmod_rectangle objects accessing dlib.rect to draw the rectangle\n for face in faces:\n cv2.rectangle(image, (face.rect.left(), face.rect.top()), (face.rect.right(), face.rect.bottom()), (255, 0, 0), 10)\n return image","title":""},{"docid":"daec589761c06925d99233620fb49a3b","score":"0.64737916","text":"def show_detection(image, faces):\n\n for face in faces:\n top, right, bottom, left = face\n cv2.rectangle(image, (left, top), (right, bottom), (255, 0, 0), 3)\n return image","title":""},{"docid":"794475f7583c50af51313016891d9fb7","score":"0.64067626","text":"def recognize_faces_and_draw_knn_2(self, minDistance=0.5):\r\n index = 0\r\n new_image = self.image.copy() \r\n # loop all faces\r\n top_names = []\r\n name = None\r\n for left, right, top, bottom in self.boxes:\r\n # draw rectangle around the face in frame\r\n cv2.rectangle(new_image,(left,top),(right,bottom),(0,255,0),2)\r\n if self.faces_aligned == [] or self.faces_aligned is None:\r\n face = self.faces_detected[index]\r\n else:\r\n face = self.faces_aligned[index]\r\n # generate encoding for the face\r\n encoding = self.generate_encoding(face)\r\n # get distance value \r\n dict_index = -1\r\n # match_index = [] \r\n match_value = []\r\n match_name = []\r\n for db_encoding in self.data['encodings']:\r\n dict_index = dict_index + 1\r\n # compare face\r\n dist = np.linalg.norm(db_encoding - encoding)\r\n if dist < minDistance:\r\n # match_index.append(dict_index)\r\n match_value.append(dist)\r\n match_name.append(self.data['names'][dict_index])\r\n if match_value != []:\r\n # sort by 3-smallest distance value (ascending)\r\n sorted_index = np.argsort(match_value)\r\n name = match_name[sorted_index[0]]\r\n top_names.append(name)\r\n # write on the displayed window, for each value for this face\r\n yy = 0\r\n for k in sorted_index:\r\n yy = yy + 1\r\n y = top + (yy * 25)\r\n xname = \"{}: {:.2f}%\".format(match_name[k], (1-match_value[k]) * 100) \r\n cv2.putText(new_image, xname, (right+20, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\r\n if yy == 3: \r\n break\r\n else:\r\n y = top - 15 if top - 15 > 15 else top + 15\r\n cv2.putText(new_image, 'Unknown', (right+20, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\r\n \r\n index = index + 1\r\n \r\n return new_image, top_names","title":""},{"docid":"60ae08bee0932677fa9abbfbe7739057","score":"0.6376859","text":"def visualize_face(image, feature):\n\t_image = [image]\n\t_feature = [feature]\n\tfor x_row, f_row in zip(_image, _feature):\n\t\tkp, mask = generate_kp(f_row)\n\tleft_top = [-1, -1]\n\tright_bottom = [-1, -1]\n\tfor r in range(len(mask)):\n\t\tfor c in range(len(mask[r])):\n\t\t\tif mask[r][c] == 1 and left_top == [-1, -1]:\n\t\t\t\tleft_top = [r, c]\n\t\t\telif mask[r][c] == 0 and left_top != [-1, -1]:\n\t\t\t\tright_bottom = [-1, c]\n\t\t\t\tbreak\n\t\tif right_bottom != [-1, -1]:\n\t\t\tbreak\n\tfor r in range(len(mask)):\n\t\tif mask[r][left_top[1]] == 0 and r > left_top[0]:\n\t\t\tright_bottom[0] = r\n\t\t\tbreak\n\timage_found = cv2.rectangle(denormalize_image(image), (left_top[1], left_top[0]),\n\t\t\t\t (right_bottom[1], right_bottom[0]), (0, 0, 100), 1)\n\tplt.imshow(image_found)\n\tplt.scatter(x=feature[::2], y=feature[1::2], c='r', s=60)\n\tplt.show()","title":""},{"docid":"f2cd11799e0427dddfa5fd22e6afde7b","score":"0.63102883","text":"def _show_faces(image, faces):\n # last = False\n\n for i in range(0, len(faces)):\n (x, y, w, h) = faces[i]\n last = i == len(faces) - 1\n\n if last:\n # last = False\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)\n else:\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 255), 2)\n\n cv2.namedWindow(\"Faces found\", cv2.WINDOW_NORMAL)\n\n w, h, d = image.shape\n n_w = int(w / 3)\n n_h = int(h / 3)\n\n cv2.resizeWindow('Faces found', n_h, n_w)\n\n cv2.imshow(\"Faces found\", image)\n cv2.waitKey(0)","title":""},{"docid":"3d485246a8036e7bb0e1f9e9864a7f9b","score":"0.62778085","text":"def viewfaces():\n with open(con.SAVE_IMAGE_NAMES, \"r\") as f:\n facelist = f.readlines()\n facelist = [i.strip() for i in facelist if len(i.strip()) > 0]\n if len(facelist) == 0:\n print(\"No faces were selected. Run this program again and type S.\")\n return False\n if len(facelist) > con.MAX_PORTRAITS:\n s = \"Over 50 images have been selected.\\n\"\n s += \"If you would like to view all fifty then adjust def do_viewfaces(self, arg)\\n\"\n s += \"Otherwise, narrow your selection.\"\n print(s)\n return False\n for filename in facelist:\n filepath = os.path.join(con.IMAGES_DIRECTORY, filename)\n print(filepath)\n img = mpimg.imread(filepath)\n imgplot = plt.imshow(img)\n plt.show()","title":""},{"docid":"e4b41bb90a17d40f6fd20e936cfd3cc1","score":"0.61617106","text":"def show_faces(img, boxes, landmarks):\n for bb, ll in zip(boxes, landmarks):\n x1, y1, x2, y2 = int(bb[0]), int(bb[1]), int(bb[2]), int(bb[3])\n cv2.rectangle(img, (x1, y1), (x2, y2), BBOX_COLOR, 2)\n send_face(img[y1:y2, x1:x2, : ])\n for j in range(5):\n cv2.circle(img, (int(ll[j]), int(ll[j+5])), 2, BBOX_COLOR, 2)\n return img","title":""},{"docid":"893ad7e5edad30128ae96c40c4660ba3","score":"0.6119563","text":"def run():\r\n # read face image data from ORL database\r\n V = read()\r\n # preprocess ORL faces data matrix\r\n V = preprocess(V)\r\n # run factorization\r\n W, _ = factorize(V)\r\n # plot parts-based representation\r\n plot(W)","title":""},{"docid":"893ad7e5edad30128ae96c40c4660ba3","score":"0.6119563","text":"def run():\r\n # read face image data from ORL database\r\n V = read()\r\n # preprocess ORL faces data matrix\r\n V = preprocess(V)\r\n # run factorization\r\n W, _ = factorize(V)\r\n # plot parts-based representation\r\n plot(W)","title":""},{"docid":"f39d318ac7dd26dce6b5626403669e6b","score":"0.6114271","text":"def visualize(scores, faces):\n pc_min, pc_max = np.min(scores, 0), np.max(scores, 0)\n pc_scaled = (scores - pc_min) / (pc_max - pc_min) \n fig, ax = plt.subplots()\n for i in range(len(faces)):\n imagebox = offsetbox.OffsetImage(faces[i, :].reshape(64,64).T, cmap=plt.cm.gray, zoom=0.5)\n box = offsetbox.AnnotationBbox(imagebox, pc_scaled[i, 0:2])\n ax.add_artist(box)\n plt.show()","title":""},{"docid":"c0d928a6ba60ea9b0aeca42a669292c2","score":"0.6110307","text":"def visualize(args):\n import matplotlib\n matplotlib.use('Agg')\n import matplotlib.pyplot as plt\n\n batch_size = 500\n\n # Load parameters\n nn.load_parameters(os.path.join(args.model_save_path,\n 'params_%06d.h5' % args.max_iter))\n\n # Create embedded network\n image = nn.Variable([batch_size, 1, 28, 28])\n feature = mnist_lenet_feature(image, test=True)\n\n # Process all images\n features = []\n labels = []\n # Prepare MNIST data iterator\n\n rng = np.random.RandomState(313)\n data = data_iterator_mnist(batch_size, train=False, rng=rng, shuffle=True)\n for i in range(10000 // batch_size):\n image_data, label_data = data.next()\n image.d = image_data / 255.\n feature.forward(clear_buffer=True)\n features.append(feature.d.copy())\n labels.append(label_data.copy())\n features = np.vstack(features)\n labels = np.vstack(labels)\n\n # Visualize\n f = plt.figure(figsize=(16, 9))\n for i in range(10):\n c = plt.cm.Set1(i / 10.)\n plt.plot(features[labels.flat == i, 0].flatten(), features[\n labels.flat == i, 1].flatten(), '.', c=c)\n plt.legend(list(map(str, range(10))))\n plt.grid()\n plt.savefig(os.path.join(args.monitor_path, \"embed.png\"))","title":""},{"docid":"2df38043c91fcc4e0b2fd71d87d021b7","score":"0.60846716","text":"def detect_in_images(geofence):\n # Setup result directory.\n result_dir = os.path.join('results', geofence, 'faces')\n if not os.path.isdir(result_dir):\n os.makedirs(result_dir)\n\n # Setup dataset.\n filenames = sorted(glob.glob(\n os.path.join('results', geofence, 'images', '*.jpg')\n ))[:100]\n dset = [np.asarray(PIL.Image.open(i)) for i in filenames] \n\n # Detect faces using MTCNN.\n cur_face = 0\n for x in dset:\n img = PIL.Image.fromarray(x)\n bboxes, landmarks = utils.detector.detect_faces(img)\n img_drawn = utils.visualization_utils.show_bboxes(\n img, bboxes, landmarks\n )\n import matplotlib.pyplot as plt\n plt.imshow(img_drawn)\n plt.show()\n\n # Align faces.\n image = np.array(img.convert('RGB'))[:, :, ::-1] # CHW => HWC, RGB => BGR\n fa = utils.facealigner.FaceAligner(\n desiredLeftEye=(0.36, 0.5), desiredFaceWidth=178,\n desiredFaceHeight=218\n )\n for i in landmarks:\n faceAligned = fa.align(image, i)\n while True:\n cv2.imwrite(\n os.path.join(result_dir, '%06d' % cur_face + '.png'),\n faceAligned\n )\n cur_face +=1 \n break","title":""},{"docid":"bfdd0b9ee45b01abd7d92b0c74f184b7","score":"0.607651","text":"def get_faces(directory,\n nets,\n frame):\n pnet,rnet,onet = nets\n # parameters of the mtcnn \n minsize = 75 # minimum size of face for mtcnn to detect \n threshold = [0.6, 0.7, 0.7] # three steps's threshold for pnet,rnet and onet\n factor = 0.709 # scale factor\n scale_rate = 1\n \n \n frame = cv2.resize(frame, (0, 0), fx=scale_rate, fy=scale_rate)\n r_g_b_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n faces, points = detect_face.detect_face(r_g_b_frame, minsize, pnet, rnet, onet, threshold,factor) \n \n return faces","title":""},{"docid":"06e5de764318652c468f644d795c1a9e","score":"0.6054111","text":"def view(dataset):\r\n fig = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0),\r\n figure=dataset.class_name[3:])\r\n surf = mlab.pipeline.surface(dataset, opacity=0.1)\r\n mlab.pipeline.surface(mlab.pipeline.extract_edges(surf),\r\n color=(0, 0, 0), )","title":""},{"docid":"06e5de764318652c468f644d795c1a9e","score":"0.6054111","text":"def view(dataset):\r\n fig = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0),\r\n figure=dataset.class_name[3:])\r\n surf = mlab.pipeline.surface(dataset, opacity=0.1)\r\n mlab.pipeline.surface(mlab.pipeline.extract_edges(surf),\r\n color=(0, 0, 0), )","title":""},{"docid":"38b6421dd0cf8a319a651b59f2f1250e","score":"0.6052199","text":"def visualize(self,word, model):\n variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])\n figures = []\n for parm_idx in range(len(model.means_[0])):\n xmin = int(min(model.means_[:,parm_idx]) - max(variance[:,parm_idx]))\n xmax = int(max(model.means_[:,parm_idx]) + max(variance[:,parm_idx]))\n fig, axs = plt.subplots(model.n_components, sharex=True, sharey=False)\n colours = cm.rainbow(np.linspace(0, 1, model.n_components))\n for i, (ax, colour) in enumerate(zip(axs, colours)):\n x = np.linspace(xmin, xmax, 100)\n mu = model.means_[i,parm_idx]\n sigma = math.sqrt(np.diag(model.covars_[i])[parm_idx])\n ax.plot(x, mlab.normpdf(x, mu, sigma), c=colour)\n ax.set_title(\"{} feature {} hidden state #{}\".format(word, parm_idx, i))\n\n ax.grid(True)\n figures.append(plt)\n for p in figures:\n p.show()","title":""},{"docid":"c93c11ead4c78e9e76c94676a95fe904","score":"0.6048939","text":"def Faces(self):\n ...","title":""},{"docid":"32ec8e7f6d795d5070fe45f01b8cd8b3","score":"0.6045157","text":"def _draw_face_frames( faces, img ):\n for (x, y, w, h) in faces:\n cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 0), 2)\n # %%","title":""},{"docid":"2a007fc84933d3b45162810ca0e83412","score":"0.59697187","text":"def show_all_detected_faces(list_of_images):\r\n i = 0\r\n for img in list_of_images:\r\n show_img(img, 'face nb {}'.format(i))\r\n i += 1","title":""},{"docid":"11ca65974dfe3961c480d26fcb34d5fe","score":"0.594537","text":"def show(final_feed, image_size):\n logger = logging.getLogger('display')\n logger.setLevel(logging.DEBUG)\n logger.debug('using opencv for displaying')\n names_write = []\n\n for frame, name_list, aligned_list in final_feed:\n start = int(time.time() * 1000.0)\n\n if name_list and list(set(name_list) - set(names_write)):\n new_faces = list(set(name_list) - set(names_write))\n print('new faces found: ' + str(new_faces))\n\n names_write = names_write + new_faces\n with open('faces/faces_found.txt', 'w') as f:\n for item in names_write:\n f.write(\"%s\\n\" % item)\n for name in names_write:\n if not os.path.exists('faces/cap/%s' % name):\n os.makedirs('faces/cap/%s' % name)\n faces = emb_helper.get_face_in_frame(frame, aligned_list, image_size)\n for i in range(len(faces)):\n imageio.imwrite('faces/cap/%s/%s.jpg' % (name_list[i], str(uuid.uuid4())), faces[i])\n logger.debug('%s ms\\t\\t saving faces took' % (int(time.time() * 1000.0) - start))\n\n frame = redraw_frame(frame, name_list, aligned_list)\n cv2.imshow(\"stream\", cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))\n k = cv2.waitKey(1) & 0xFF\n logger.debug('%s ms\\t\\t displaying took' % (int(time.time() * 1000.0) - start))\n\n logger.info('destroying display frame')\n\n cv2.destroyAllWindows()","title":""},{"docid":"a0252ea4bff935c12ede33bd887dd516","score":"0.5928481","text":"def face_net(data_directory, required_size=(100, 100)):\n # Dataset path\n path = os.path.join('./Datasets', data_directory)\n dataset_dir = os.path.join(path, 'img')\n # Create directory for extracted faces images\n viola_dir = os.path.join(path + '_faceNet', 'img')\n Path(viola_dir).mkdir(parents=True, exist_ok=True)\n # List of all the images available\n files = sorted(os.listdir(dataset_dir), key=lambda x: int(x.split(\".\")[0]))\n cnt = 0\n result = []\n # Extract face for each image in the directory\n for file in files:\n # Create the image path\n image_path = os.path.join(dataset_dir, file)\n # Load image from file\n image = cv2.imread(image_path)\n # Create the detector\n detector = MTCNN()\n # Detect faces in the given image\n results = detector.detect_faces(image)\n # If no faces are found notify it and insert the file in a related list\n if len(results) == 0:\n cnt += 1\n print(\"In {0}, {1} face found!! -------------- counter: {2}\".format(file, len(results), cnt))\n result.append(file)\n # Otherwise extract the region of interest from the image\n else:\n x1, y1, width, height = results[0]['box']\n x1, y1 = abs(x1), abs(y1)\n x2, y2 = x1 + width, y1 + height\n # Extract the face\n extracted_image = image[y1:y2, x1:x2]\n # Resize image extracted to the required size\n resized = cv2.resize(extracted_image, required_size)\n # Save image extracted and reshaped\n cv2.imwrite(os.path.join(viola_dir, file), resized)\n print(\"Saving {0}.\".format(file))\n # Return list of files where faces are not detected\n return result","title":""},{"docid":"df5dabe18e0be8ea3e10fc49fbd80d0c","score":"0.59198755","text":"def visualize_net(net):\n # rearrange faces\n new_net = net[33:36] + net[30:33] + net[27:30]\n new_net += net[15:18] + net[12:15] + net[9:12]\n new_net += net[24:27] + net[21:24] + net[18:21]\n new_net += net[6:9] + net[3:6] + net[0:3]\n new_net += net[42:45] + net[39:42] + net[36:39]\n new_net += net[51:54] + net[48:51] + net[45:48]\n\n return new_net","title":""},{"docid":"5e3f988b2bd74a2d196c6b24e6342762","score":"0.59095603","text":"def show(final_feed, image_size):\n logger = logging.getLogger('display')\n logger.setLevel(logging.DEBUG)\n logger.debug('using opencv for displaying')\n names_write = []\n\n for frame, name_list, aligned_list in final_feed:\n start = int(time.time() * 1000.0)\n\n if name_list and list(set(name_list) - set(names_write)):\n new_faces = list(set(name_list) - set(names_write))\n print('new faces found: ' + str(new_faces))\n\n names_write = names_write + new_faces\n with open('faces/faces_found.txt', 'w') as f:\n for item in names_write:\n f.write(\"%s\\n\" % item)\n for name in names_write:\n if not os.path.exists('faces/cap/%s' % name):\n os.makedirs('faces/cap/%s' % name)\n faces = emb_helper.get_face_in_frame(frame, aligned_list, image_size)\n for i in range(len(faces)):\n imageio.imwrite('faces/cap/%s/%s.jpg' % (name_list[i], str(uuid.uuid4())), faces[i])\n logger.debug('%s ms\\t\\t displaying took' % (int(time.time() * 1000.0) - start))\n\n logger.info('exiting')","title":""},{"docid":"2dd00f98da786236a3fddd17fbe7dee4","score":"0.59058094","text":"def detect_faces(self):\r\n newboxes = []\r\n faces_detected = []\r\n if self.fd_method == 'AI':\r\n # detect Face using Deep Learning: architecture MobileNet, method SSD\r\n (boxes, scores, __, __) = self.run() \r\n boxes = np.squeeze(boxes)\r\n scores = np.squeeze(scores)\r\n max_boxes_to_draw = 20\r\n for i in range(min(max_boxes_to_draw, boxes.shape[0])):\r\n # check teh score\r\n if scores[i] > self.fd_ai_min_score:\r\n box = tuple(boxes[i].tolist())\r\n ymin, xmin, ymax, xmax = box\r\n im_height, im_width, __ = self.image.shape\r\n # convert tensorflow normalized coordinate to absolute coordinate\r\n (left, right, top, bottom) = (int(xmin*im_width), int(xmax*im_width), int(ymin*im_height), int(ymax*im_height))\r\n face = self.image[top:bottom, left:right]\r\n newboxes.append((left, right, top, bottom))\r\n faces_detected.append(face) \r\n self.boxes = newboxes\r\n self.faces_detected = faces_detected\r\n elif self.fd_method == 'HOG':\r\n # detect Face using HOG\r\n # self.rgb_image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)\r\n boxes = face_recognition.face_locations(self.rgb_image, model='hog')\r\n for box in boxes:\r\n top, right, bottom, left = box\r\n face = self.image[top:bottom, left:right]\r\n newboxes.append((left, right, top, bottom))\r\n faces_detected.append(face)\r\n self.boxes = newboxes\r\n self.faces_detected = faces_detected\r\n elif self.fd_method == 'HAAR':\r\n gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)\r\n faces = self.face_cascade.detectMultiScale(\r\n gray,\r\n scaleFactor=1.05,\r\n minNeighbors=9,\r\n minSize=(30,30),\r\n flags = cv2.CASCADE_SCALE_IMAGE\r\n )\r\n for (x, y, w, h) in faces:\r\n newboxes.append((x, y+w, y, y+h))\r\n face = self.image[y:y+h, x:x+w]\r\n faces_detected.append(face)\r\n self.boxes = newboxes\r\n self.faces_detected = faces_detected","title":""},{"docid":"220f582933d0fc57da3c4eddad085cb4","score":"0.58716697","text":"def visualizeModels(self):","title":""},{"docid":"7ba3603c888d83a95ee3aef96025fd7d","score":"0.5863185","text":"def extract_faces_from_test_image():\n image = cv2.imread(\"images/group3.jpg\")\n\n faces = face_recognition.face_locations(image)\n\n index = 0\n for face in faces:\n top, right, bottom, left = face\n\n label = predict_cnn(image[top:bottom, left:right])[0]\n cv2.imwrite(\"test_images/%s-%s.jpg\" % (label, index), image[top:bottom, left:right])\n\n index += 1","title":""},{"docid":"36173f0ff1c77aec0c080e4d57643242","score":"0.5850442","text":"def classify_face(im):\r\n img = cv2.imread(im, 1)\r\n\r\n #img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)\r\n #img = img[:,:,::-1]\r\n\r\n face_locations = face_recognition.face_locations(img)\r\n unknown_face_encodings = face_recognition.face_encodings(img, face_locations)\r\n print(unknown_face_encodings[0].tolist())\r\n face_names = []\r\n for face_encoding in unknown_face_encodings:\r\n # See if the face is a match for the known face(s)\r\n matches = face_recognition.compare_faces(faces_encoded, face_encoding)\r\n name = \"Unknown\"\r\n\r\n # use the known face with the smallest distance to the new face\r\n face_distances = face_recognition.face_distance(faces_encoded, face_encoding)\r\n best_match_index = np.argmin(face_distances)\r\n if matches[best_match_index]:\r\n name = known_face_names[best_match_index]\r\n\r\n face_names.append(name)\r\n\r\n for (top, right, bottom, left), name in zip(face_locations, face_names):\r\n # Draw a box around the face\r\n cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)\r\n\r\n # Draw a label with a name below the face\r\n cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)\r\n font = cv2.FONT_HERSHEY_DUPLEX\r\n cv2.putText(img, name, (left -20, bottom + 15),fontFace=font, fontScale=1.0, color=(255, 255, 255), thickness=2)\r\n print(face_names)\r\n cv2.imshow('Image', img)\r\n cv2.waitKey(0)\r\n return 0","title":""},{"docid":"42d1415953b95564c7d1ca238e808a73","score":"0.58485097","text":"def faces_example(path):\r\n\r\n with open(path, 'rb') as f:\r\n X = pickle.load(f)\r\n\r\n num_images, num_pixels = np.shape(X)\r\n d = int(num_pixels**0.5)\r\n print(\"The number of images in the data set is \" + str(num_images))\r\n print(\"The image size is \" + str(d) + \" by \" + str(d))\r\n\r\n # plot some examples of faces:\r\n plt.gray()\r\n for i in range(4):\r\n plt.subplot(2, 2, i+1)\r\n plt.imshow(np.reshape(X[i, :], (d, d)))\r\n plt.show()","title":""},{"docid":"69906b7ae64fc520bb6ff89059622930","score":"0.58278435","text":"def detect_faces(self, img_raw):\n\n img, scale = self.preprocessor(img_raw)\n # tic = time.time()\n with torch.no_grad():\n loc, conf, landmarks = self.model(img) # forward pass\n # print('net forward time: {:.4f}'.format(time.time() - tic))\n\n priors = prior_box(self.cfg, image_size=img.shape[2:]).to(self.device)\n boxes = decode(loc.data.squeeze(0), priors, self.cfg['variance'])\n boxes = boxes * scale\n scores = conf.squeeze(0)[:, 1]\n landmarks = decode_landmark(landmarks.squeeze(0), priors, self.cfg['variance'])\n scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],\n img.shape[3], img.shape[2], img.shape[3], img.shape[2],\n img.shape[3], img.shape[2]]).to(self.device)\n landmarks = landmarks * scale1\n\n # ignore low scores\n index = torch.where(scores > self.thresh)[0]\n boxes = boxes[index]\n landmarks = landmarks[index]\n scores = scores[index]\n\n # keep top-K before NMS\n order = scores.argsort(dim=0, descending=True)[:self.top_k]\n boxes = boxes[order]\n landmarks = landmarks[order]\n scores = scores[order]\n\n # Do NMS\n keep = nms(boxes, scores, self.nms_thresh)\n boxes = torch.abs(boxes[keep, :])\n scores = scores[:, None][keep, :]\n landmarks = landmarks[keep, :].reshape(-1, 5, 2)\n\n # # keep top-K faster NMS\n landmarks = landmarks[:self.keep_top_k, :]\n scores = scores[:self.keep_top_k, :]\n boxes = boxes[:self.keep_top_k, :]\n\n return boxes, scores, landmarks","title":""},{"docid":"82935971c20230a9ba0a6887b826fb0c","score":"0.5818002","text":"def visualize(word, model):\r\n variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])\r\n figures = []\r\n for parm_idx in range(len(model.means_[0])):\r\n xmin = int(min(model.means_[:,parm_idx]) - max(variance[:,parm_idx]))\r\n xmax = int(max(model.means_[:,parm_idx]) + max(variance[:,parm_idx]))\r\n fig, axs = plt.subplots(model.n_components, sharex=True, sharey=False)\r\n colours = cm.rainbow(np.linspace(0, 1, model.n_components))\r\n for i, (ax, colour) in enumerate(zip(axs, colours)):\r\n x = np.linspace(xmin, xmax, 100)\r\n mu = model.means_[i,parm_idx]\r\n sigma = math.sqrt(np.diag(model.covars_[i])[parm_idx])\r\n ax.plot(x, mlab.normpdf(x, mu, sigma), c=colour)\r\n ax.set_title(\"{} feature {} hidden state #{}\".format(word, parm_idx, i))\r\n\r\n ax.grid(True)\r\n figures.append(plt)\r\n for p in figures:\r\n p.show()","title":""},{"docid":"5cf5d72397595eab915d7695ae6f5b48","score":"0.5811071","text":"def recognize_faces_and_draw_svm(self, minProba=0.5):\r\n index = 0\r\n alpha = 0.6\r\n new_image = self.image.copy()\r\n text1 = None\r\n text2 = None\r\n text3 = None\r\n top_names = []\r\n # loop all faces\r\n for left, right, top, bottom in self.boxes:\r\n cv2.rectangle(new_image,(left,top),(right,bottom),(0,255,0),2)\r\n if self.faces_aligned == [] or self.faces_aligned is None:\r\n face = self.faces_detected[index]\r\n else:\r\n face = self.faces_aligned[index]\r\n # generate encoding for current face\r\n encoding = self.generate_encoding(face)\r\n encoding = encoding.reshape(1, -1)\r\n # match a face with all faces in database\r\n # perform classification to recognize the face\r\n preds = self.svm_model.predict_proba(encoding)[0]\r\n # get the biggest probability\r\n j = np.argmax(preds)\r\n proba = preds[j]\r\n # overlay = new_image.copy()\r\n left_rect = right + 10\r\n top_rect = top\r\n # if the biggest probability is bigger than allowed, then sort 3 biggest probabilities\r\n if proba > minProba: \r\n # get 3 biggest probability SVM\r\n big3_index = heapq.nlargest(3, range(len(preds)), preds.take)\r\n name = self.svm_label.classes_[big3_index[0]]\r\n prob = preds[big3_index[0]]\r\n text1 = \"{}: {:.2f}%\".format(name, prob * 100) \r\n top_names.append(name) \r\n name = self.svm_label.classes_[big3_index[1]]\r\n prob = preds[big3_index[1]] \r\n text2 = \"{}: {:.2f}%\".format(name, prob * 100) \r\n top_names.append(name) \r\n name = self.svm_label.classes_[big3_index[2]]\r\n prob = preds[big3_index[2]]\r\n text3 = \"{}: {:.2f}%\".format(name, prob * 100) \r\n top_names.append(name) \r\n right_rect = left_rect + 150\r\n bottom_rect = top + 75\r\n # cv2.rectangle(overlay, (left_rect, top_rect), (right_rect, bottom_rect), (0, 0, 0), -1) \r\n # new_image = cv2.addWeighted(overlay, alpha, new_image, 1 - alpha, 0)\r\n # *** 2MPX\r\n cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\r\n cv2.putText(new_image, text2, (left_rect+10, top_rect+40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\r\n cv2.putText(new_image, text3, (left_rect+10, top_rect+60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\r\n # *** 8MPX\r\n # cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\r\n # cv2.putText(new_image, text2, (left_rect+10, top_rect+40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\r\n # cv2.putText(new_image, text3, (left_rect+10, top_rect+60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\r\n # *** 12MPX\r\n # cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 255, 255), 4)\r\n # cv2.putText(new_image, text2, (left_rect+10, top_rect+100), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 255, 255), 4)\r\n # cv2.putText(new_image, text3, (left_rect+10, top_rect+180), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 255, 255), 4)\r\n # print(text1)\r\n # print(text2)\r\n # print(text3)\r\n else:\r\n # name = 'Unknown'\r\n text1 = 'Unknown'\r\n # print(text1)\r\n right_rect = left_rect + 100\r\n bottom_rect = top + 35\r\n # cv2.rectangle(overlay, (left_rect, top_rect), (right_rect, bottom_rect), (0, 0, 0), -1) \r\n # new_image = cv2.addWeighted(overlay, alpha, new_image, 1 - alpha, 0)\r\n # *** 2MPX\r\n cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\r\n # cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\r\n top_names.append('Unknown')\r\n \r\n # text_title = \"threshold {:.2f}%\".format(minProba * 100)\r\n # cv2.putText(new_image, text_title, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 1)\r\n index = index + 1\r\n \r\n return new_image, top_names","title":""},{"docid":"bb64c3c22ce52e413954ed4654d0162e","score":"0.5804104","text":"def visualize(self, face):\n _image = deepcopy(face.container_image)\n xmin, ymin, xmax, ymax = face.bbox\n start_pt = (xmin, ymin)\n end_pt = (xmax, ymax)\n color = (255, 0, 0)\n thickness = 1\n cv2.rectangle(_image, start_pt, end_pt, color, thickness)\n thickness = 1\n color = (255, 255, 255)\n origin = start_pt\n font = cv2.FONT_HERSHEY_SIMPLEX\n font_scale = 1\n display_name = self.data_acquisition.trn_wh.get_name(face.uid)\n if display_name is None:\n display_name = str(face.uid)\n cv2.putText(_image, display_name, origin, font, font_scale, color, thickness, cv2.LINE_AA)\n return _image","title":""},{"docid":"bc2557011859c874e8bbf0e163fe68e9","score":"0.58006907","text":"def _visualize():\n viz_words = len(model.wv.vectors)\n word_vector = model.wv.vectors\n tsne = sklearn.manifold.TSNE(n_components=2)\n embed_tsne = tsne.fit_transform(word_vector)\n fig, ax = plt.subplots(figsize=(6, 6))\n for i in range(viz_words):\n plt.scatter(*embed_tsne[i, :], s=2, alpha=0.6, color='b')\n plt.annotate(model.wv.index2word[i], (embed_tsne[i, 0], embed_tsne[i, 1]), alpha=0.6, fontsize=7)\n plt.savefig('tsne_zones_{}.png'.format(self.store_name))","title":""},{"docid":"570ae98d73e42debe105a21212a71271","score":"0.579005","text":"def visualize(word, model):\n variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])\n figures = []\n for parm_idx in range(len(model.means_[0])):\n xmin = int(min(model.means_[:,parm_idx]) - max(variance[:,parm_idx]))\n xmax = int(max(model.means_[:,parm_idx]) + max(variance[:,parm_idx]))\n fig, axs = plt.subplots(model.n_components, sharex=True, sharey=False)\n colours = cm.rainbow(np.linspace(0, 1, model.n_components))\n for i, (ax, colour) in enumerate(zip(axs, colours)):\n x = np.linspace(xmin, xmax, 100)\n mu = model.means_[i,parm_idx]\n sigma = math.sqrt(np.diag(model.covars_[i])[parm_idx])\n ax.plot(x, mlab.normpdf(x, mu, sigma), c=colour)\n ax.set_title(\"{} feature {} hidden state #{}\".format(word, parm_idx, i))\n\n ax.grid(True)\n figures.append(plt)\n for p in figures:\n p.show()","title":""},{"docid":"847ef891175fc5fe72aba9070bf83423","score":"0.5762653","text":"def get_face_from_img(img, mtcnn):\n sample = Image.fromarray(img, 'RGB')\n \n face = mtcnn.detect(sample)\n face = face.permute(1, 2, 0)\n return face.int().numpy()","title":""},{"docid":"e9f69877bf1da9daaf5883b487d3066f","score":"0.5758128","text":"def show_faces(faces, img):\n roi, blur_ = None, None\n for (x, y, w, h) in faces:\n # Calculate center point and radius for circular masks.\n cx = w // 2\n cy = h // 2\n r = h // 2 # int(((w ** 2 + h ** 2) ** 0.5)/2) # c² = a² + b², for a bigger circle, the roi itself needs to\n # be bigger which results in inconsistencies.\n\n # Draw a rectangle/circle on the image.\n # img = cv.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\n img = cv.circle(img, (x+cx, y+cy), r, (101, 201, 255), 2)\n\n # Create a ROI.\n roi = roi_image(x, y, w, h, img)\n\n # Blur the ROI.\n blur = blur_face_avg(roi)\n\n # Create masks (normal and inverted).\n mask = np.full((roi.shape[0], roi.shape[1], 1), 0, dtype=np.uint8) # Init black mask.\n cv.circle(mask, (cx, cy), r, (255, 255, 255), -1) # Fill in a white circle in the middle to create the mask.\n mask_inverted = 255 - mask\n\n # Apply the masks.\n fg = cv.bitwise_and(blur, blur, mask=mask) # Foreground: blurred circle face.\n bg = cv.bitwise_and(roi, roi, mask=mask_inverted) # Background: part outside the detected face.\n\n # Add the foreground & background together and insert it into the frame.\n img[y:y+h, x:x+w] = cv.add(fg, bg)\n\n show_image(img)\n cv.destroyAllWindows()","title":""},{"docid":"da1820fd012b609f066567d8511acbd4","score":"0.57465416","text":"def face_recognition():\n face_cascade = cv2.CascadeClassifier(cv2_resource_path+\"haarcascade_frontalface_default.xml\")\n img = cv2.imread(\"IMG_4703.JPG\")\n\n img_gray = cv2.cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)\n\n faces = face_cascade.detectMultiScale(img_gray, 1.1,4)\n\n for (x,y,w,h) in faces:\n cv2.rectangle(img, (x,y),(x+w,y+h), (0,255,0), 2)\n\n\n cv2.imshow(\"Result\", img)\n cv2.waitKey(0)","title":""},{"docid":"fd3412e1badcde85db657841bc0e1a55","score":"0.57351214","text":"def faces_embeddings(path='./faces.pickle'):\r\n with open(path, 'rb') as f:\r\n faces = pickle.load(f)\r\n\r\n title = 'faces_PCA'\r\n faces_pca = PCA(n_components=2).fit_transform(faces)\r\n plot_with_images(faces_pca, faces, title)\r\n plt.savefig(f'./figures/{title}.png')\r\n plt.show()\r\n\r\n title = 'faces_MDS'\r\n faces_mds, _ = MDS(calc_squared_euclidean_distance_matrix(faces), d=2)\r\n plot_with_images(faces_mds, faces, title)\r\n plt.savefig(f'./figures/{title}.png')\r\n plt.show()\r\n\r\n faces_lle_by_param = dict()\r\n for k in [3, 5, 7, 11, 13, 17, 19, 23]:\r\n title = f'faces_LLE_{k}_nearest_neighbors'\r\n faces_lle_by_param[k] = LLE(faces, 2, k)\r\n plot_with_images(faces_lle_by_param[k], faces, title)\r\n plt.savefig(f'./figures/{title}.png')\r\n plt.show()\r\n\r\n faces_dm_by_params = dict()\r\n for sigma in [1, 2, 3, 4, 5, 6]:\r\n for t in [3, 5, 7, 11, 17]:\r\n title = f'faces_DiffusionMap_sigma_{sigma}_t_{t}'\r\n faces_dm_by_params[(sigma, t)] = DiffusionMap(faces, 2, sigma, t)\r\n plot_with_images(faces_dm_by_params[(sigma, t)], faces, title)\r\n plt.savefig(f'./figures/{title}.png')\r\n plt.show()\r\n\r\n title = 'faces_tSNE'\r\n faces_tsne = TSNE(n_components=2).fit_transform(faces)\r\n plot_with_images(faces_tsne, faces, title)\r\n plt.savefig(f'./figures/{title}.png')\r\n plt.show()","title":""},{"docid":"48458caa8fa8e0e1435dff9f61e5be97","score":"0.57238865","text":"def show_face_markings(face_locations,small_frame,msg):\n for face_location in face_locations:\n # Scale back up face locations since the frame we detected in was scaled to 1/4 size\n top, right, bottom, left = face_location\n\n # Draw a box around the face\n cv2.rectangle(small_frame, (left, top), (right, bottom), (0, 0, 255), 2)\n\n # Draw a label with a name below the face\n cv2.rectangle(small_frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)\n\n ## This puts the clssification in the box of the face\n cv2.putText(small_frame, msg, (left + 6, bottom - 6), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 1)","title":""},{"docid":"7fa76d18be24350e2644a458d4289542","score":"0.57207197","text":"def visualize_training(self):\n pass","title":""},{"docid":"9b207dfe38210ea3c066b261693e771b","score":"0.5720429","text":"def show_detections(self, image):\n boxes = self.face_detector.predict(image=image)\n if len(boxes) == 0:\n boxes = self.body_detector.predict(image=image)\n confidences = [conf for conf, _ in boxes]\n boxes = [box for _, box in boxes]\n distances = [self.camera_calculator.rectangleToRealWorldXY(rectangle=box, h=image.shape[0],\n w=image.shape[1]) for box in boxes]\n\n show(image=image, boxes=boxes, names=None, distances=distances, confidences=confidences)","title":""},{"docid":"00362929c98df99a25c0733bd23eb4a8","score":"0.57108104","text":"def main():\n landmarks = read_pts('../bin/data/image_0010.pts')\n image_width = 1280 # Make sure to adjust these when using your own images!\n image_height = 1024\n\n model = eos.morphablemodel.load_model(\"../share/sfm_shape_3448.bin\")\n blendshapes = eos.morphablemodel.load_blendshapes(\"../share/expression_blendshapes_3448.bin\")\n # Create a MorphableModel with expressions from the loaded neutral model and blendshapes:\n morphablemodel_with_expressions = eos.morphablemodel.MorphableModel(model.get_shape_model(), blendshapes,\n color_model=eos.morphablemodel.PcaModel(),\n vertex_definitions=None,\n texture_coordinates=model.get_texture_coordinates())\n landmark_mapper = eos.core.LandmarkMapper('../share/ibug_to_sfm.txt')\n edge_topology = eos.morphablemodel.load_edge_topology('../share/sfm_3448_edge_topology.json')\n contour_landmarks = eos.fitting.ContourLandmarks.load('../share/ibug_to_sfm.txt')\n model_contour = eos.fitting.ModelContour.load('../share/sfm_model_contours.json')\n\n (mesh, pose, shape_coeffs, blendshape_coeffs) = eos.fitting.fit_shape_and_pose(morphablemodel_with_expressions,\n landmarks, landmark_mapper, image_width, image_height, edge_topology, contour_landmarks, model_contour)\n\n # Now you can use your favourite plotting/rendering library to display the fitted mesh, using the rendering\n # parameters in the 'pose' variable.\n\n # Or for example extract the texture map, like this:\n # import cv2\n # image = cv2.imread('../bin/data/image_0010.png')\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA, 4) # extract_texture(...) expects a 4-channel image\n # texturemap = eos.render.extract_texture(mesh, pose, image)","title":""},{"docid":"1fd81bfaf8db94a84b095e041059237b","score":"0.57089657","text":"def visualize(net, img_path, conv_layer_name):\n image = read_image_mxnet(img_path)\n image = preprocess(image)\n image = image.expand_dims(axis=0)\n \n pred_str = get_class_name(run_inference(net, image))\n \n orig_img = read_image_cv(img_path)\n vizs = gradcam.visualize(net, image, orig_img, conv_layer_name)\n return (pred_str, (orig_img, *vizs))","title":""},{"docid":"98c3312646851e8805731d3689f69d23","score":"0.5703212","text":"def face_detect():\n face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n cap = cv2.VideoCapture(0)\n while True:\n ret, fram = cap.read()\n faces = face_cascade.detectMultiScale(fram, 1.1, 7)\n for x, y, w, h in faces:\n cv2.rectangle(fram, (x - 5, y - 25), (x + w, y + h), (0, 255, 0), 2)\n cv2.imshow('fram', fram)","title":""},{"docid":"9f3433d7fa223ddb3e52363e792a35cb","score":"0.570318","text":"def visualize():\n args = get_args()\n print(args)\n dir_img = args.dir_in\n dir_out = args.dir_out\n\n # set up model of detectron2\n predictor, cfg = setup_model(args)\n print(f'DIR_img: {dir_img}')\n img_files = os.listdir(dir_img)\n img_files = [file for file in img_files if file.endswith('.jpeg') or file.endswith('.png')]\n for img_file in img_files:\n img_file = os.path.join(dir_img, img_file)\n im = cv2.imread(img_file)\n outputs = predictor(im)\n v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)\n out = v.draw_instance_predictions(outputs['instances'].to('cpu'))\n path = os.path.join(dir_out, os.path.basename(img_file))\n cv2.imwrite(path, out.get_image()[:, :, ::-1]) # imreadの結果がBGRの順なので::-1でRGBに変換しているっぽい","title":""},{"docid":"e03d81362ecc4f817c85326cf3d272b0","score":"0.57031417","text":"def detect_faces(img, min_face_size=20.0, thresholds=[0.6, 0.7, 0.8]):\n\n # LOAD MODELS\n pnet = PNet()\n rnet = RNet()\n onet = ONet()\n onet.eval()\n\n # BUILD AN IMAGE PYRAMID\n width, height = img.size\n min_length = min(height, width)\n\n min_detection_size = 12\n factor = 0.707 # sqrt(0.5)\n\n # scales for scaling the image\n scales = []\n\n # scales the image so that\n # minimum size that we can detect equals to\n # minimum face size that we want to detect\n m = min_detection_size/min_face_size\n min_length *= m\n\n factor_count = 0\n while min_length > min_detection_size:\n scales.append(m*factor**factor_count)\n min_length *= factor\n factor_count += 1\n\n # STAGE 1\n\n # it will be returned\n bounding_boxes = []\n\n # run P-Net on different scales\n for s in scales:\n boxes = run_first_stage(img, pnet, s, thresholds[0])\n bounding_boxes.append(boxes)\n\n bounding_boxes = [i for i in bounding_boxes if i is not None]\n bounding_boxes = np.vstack(bounding_boxes)\n\n pick = nms(bounding_boxes[:, 0:5], 0.7, 'union')\n bounding_boxes = bounding_boxes[pick]\n bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])\n bounding_boxes = convert_to_square(bounding_boxes)\n bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])\n\n # STAGE 2\n\n img_boxes = get_image_boxes(bounding_boxes, img, size=24)\n img_boxes = Variable(torch.FloatTensor(img_boxes))\n output = rnet(img_boxes)\n offsets = output[0].data.numpy()\n probs = output[1].data.numpy()\n\n passed = np.where(probs[:, 1] > thresholds[1])\n bounding_boxes = bounding_boxes[passed]\n\n bounding_boxes[:, 4] = probs[passed, 1].reshape((-1,))\n offsets = offsets[passed]\n\n pick = nms(bounding_boxes, 0.7, 'union')\n bounding_boxes = bounding_boxes[pick]\n bounding_boxes = calibrate_box(bounding_boxes, offsets[pick])\n bounding_boxes = convert_to_square(bounding_boxes)\n bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])\n\n # STAGE 3\n\n img_boxes = get_image_boxes(bounding_boxes, img, size=48)\n img_boxes = Variable(torch.FloatTensor(img_boxes))\n output = onet(img_boxes)\n landmarks = output[0].data.numpy()\n offsets = output[1].data.numpy()\n probs = output[2].data.numpy()\n\n passed = np.where(probs[:, 1] > thresholds[2])\n bounding_boxes = bounding_boxes[passed]\n\n bounding_boxes[:, 4] = probs[passed, 1].reshape((-1,))\n offsets = offsets[passed]\n landmarks = landmarks[passed]\n\n # compute landmark points\n bbw = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0\n bbh = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0\n landmarks[:, 0:5] = np.expand_dims(bounding_boxes[:, 0], 1) +\\\n np.expand_dims(bbw, 1)*landmarks[:, 0:5]\n landmarks[:, 5:10] = np.expand_dims(bounding_boxes[:, 1], 1) +\\\n np.expand_dims(bbh, 1)*landmarks[:, 5:10]\n\n bounding_boxes = calibrate_box(bounding_boxes, offsets)\n pick = nms(bounding_boxes, 0.7, 'min')\n bounding_boxes = bounding_boxes[pick]\n landmarks = landmarks[pick]\n\n return bounding_boxes, landmarks","title":""},{"docid":"97b1a96a9e4c23c3db2f08f6a31192b6","score":"0.5678757","text":"def test_visualizer1() -> None:\n H = 640\n W = 480\n img_rgb = np.ones((H, W, 3), dtype=np.uint8)\n label_map = np.zeros((H, W), dtype=np.uint8)\n label_map[: H // 2, : W // 2] = 0\n label_map[: H // 2, W // 2 :] = 1\n label_map[H // 2 :, : W // 2] = 2\n label_map[H // 2 :, W // 2 :] = 3\n\n id_to_class_name_map = {0: \"sky\", 1: \"road\", 2: \"person\", 3: \"horse\"}\n\n vis_obj = Visualizer(img_rgb, None)\n output_img = vis_obj.overlay_instances(label_map, id_to_class_name_map)\n plt.imshow(output_img)\n # plt.show()\n plt.close(\"all\")","title":""},{"docid":"7da2bfe943e5584b3f053ab7b4f8d124","score":"0.5662283","text":"def nmeshface(self):\n return self._ptr.contents.nmeshface","title":""},{"docid":"b8c6c4179b9212ac61e2d46b491a884e","score":"0.5661713","text":"def facesDetect(self):\n\t\t\n\t\tself.faces = []\n\t\tclassifier_face = self.classifiers['face']\n\t\t\n\t\tfor rotation in [0, 30, -30]:\n\t\t\tclassifier_settings = classifier_face['settings'].copy()\n\t\t\trot_image = self.image.rotate(rotation, resample=self.resample)\n\t\t\t\n\t\t\tif self.image.width > self.image.height:\n\t\t\t\tclassifier_settings['minSize'] = (\n\t\t\t\t\tint(self.image.width*.05),\n\t\t\t\t\tint(self.image.width*.05)\n\t\t\t\t)\n\t\t\telse:\n\t\t\t\tclassifier_settings['minSize'] = (\n\t\t\t\t\tint(self.image.height*.05),\n\t\t\t\t\tint(self.image.height*.05)\n\t\t\t\t)\n\t\t\t\n\t\t\tfound_faces = classifier_face['classifier'].detectMultiScale(\n\t\t\t\tself.imageToCV(rot_image),\n\t\t\t\t**classifier_settings\n\t\t\t);\n\t\t\t\n\t\t\tfor face_box in found_faces:\n\t\t\t\tface_points = Object2D.boxPoints(face_box)\n\t\t\t\tface_polygon = Object2D.boxPolygon(face_box)\n\t\t\t\tface_center = Object2D.boxCenter(face_box)\n\t\t\t\t\n\t\t\t\t# Rotate these points for drawing on the original image.\n\t\t\t\tface_polygon = Object2D.rotate(\n\t\t\t\t\tface_polygon, rotation, self.center\n\t\t\t\t)\n\t\t\t\t\n\t\t\t\tface_center = Object2D.rotate(\n\t\t\t\t\tface_center, rotation, self.center\n\t\t\t\t)\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tif not self.hasNearbyFaceCenters(face_center):\n\t\t\t\t\tface = {\n\t\t\t\t\t\t'rotation': rotation,\n\t\t\t\t\t\t'crop': face_points,\n\t\t\t\t\t\t'box': face_box,\n\t\t\t\t\t\t'draw': {\n\t\t\t\t\t\t\t'center': face_center,\n\t\t\t\t\t\t\t'polygon': face_polygon\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t\tself.faces.append(face)\n\t\t\t\n\t\t\trot_image.close()\n\t\t\n\t\treturn self.faces","title":""},{"docid":"05772ab454e74cfca92db24f53242e7e","score":"0.56489074","text":"def plot_feature_maps(model, img):\n # lets first create a list of outputs from successive layers of the model\n successive_outputs = [layer.output for layer in model.layers[1:]]\n\n # now lets create another model with the input of the model passed in,\n # and outputs of each layer of the passed in model\n visualization_model = Model(inputs=model.input, outputs=successive_outputs)\n\n # now run the image through the network, obtaining intermediate feature maps\n successive_feature_maps = visualization_model.predict(img)\n\n # layer names to have them as part of our plot for readability purposes\n layer_names = [layer.name for layer in model.layers]\n\n # loop through the layers\n for layer_name, feature_map in zip(layer_names, successive_feature_maps):\n # plot feature maps only for conv/pool layers\n if len(feature_map.shape) == 4:\n n_channels = feature_map.shape[-1]\n size = feature_map.shape[1]\n\n # We will tile our images in this matrix\n display_grid = np.zeros((size, size * n_channels))\n\n # looping through each filter of a layer\n for i in range(n_channels):\n # postprocessing the image to be visually palatable\n img = feature_map[0, :, :, i]\n img -= img.mean()\n img /= img.std()\n img *= 64\n img += 128\n img = np.clip(img, 0, 255).astype('uint8')\n # tile each filter into a horizontal grid\n display_grid[:, i * size: (i + 1) * size] = img\n\n # displaying the feature map grid\n scale = 20. / n_channels\n plt.figure(figsize=(scale * n_channels, scale))\n plt.title(layer_name)\n plt.grid(False)\n plt.imshow(display_grid, aspect='auto', cmap='viridis')","title":""},{"docid":"305330adbfd4e1f1b6d9bb4f961dfae9","score":"0.5640138","text":"def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):\n factor_count=0\n total_boxes=np.empty((0,9))\n points=np.empty(0)\n h=img.shape[0]\n w=img.shape[1]\n minl=np.amin([h, w])\n m=12.0/minsize\n minl=minl*m\n # create scale pyramid\n scales=[]\n while minl>=12:\n scales += [m*np.power(factor, factor_count)]\n minl = minl*factor\n factor_count += 1\n\n # first stage\n for scale in scales:\n hs=int(np.ceil(h*scale))\n ws=int(np.ceil(w*scale))\n im_data = imresample(img, (hs, ws))\n im_data = (im_data-127.5)*0.0078125\n img_x = np.expand_dims(im_data, 0)\n img_y = np.transpose(img_x, (0,2,1,3))\n out = pnet.predict_on_batch(img_y)\n out0 = np.transpose(out[0], (0,2,1,3))\n out1 = np.transpose(out[1], (0,2,1,3))\n\n boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0])\n\n # inter-scale nms\n pick = nms(boxes.copy(), 0.5, 'Union')\n if boxes.size>0 and pick.size>0:\n boxes = boxes[pick,:]\n total_boxes = np.append(total_boxes, boxes, axis=0)\n\n numbox = total_boxes.shape[0]\n if numbox>0:\n pick = nms(total_boxes.copy(), 0.7, 'Union')\n total_boxes = total_boxes[pick,:]\n regw = total_boxes[:,2]-total_boxes[:,0]\n regh = total_boxes[:,3]-total_boxes[:,1]\n qq1 = total_boxes[:,0]+total_boxes[:,5]*regw\n qq2 = total_boxes[:,1]+total_boxes[:,6]*regh\n qq3 = total_boxes[:,2]+total_boxes[:,7]*regw\n qq4 = total_boxes[:,3]+total_boxes[:,8]*regh\n total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]]))\n total_boxes = rerec(total_boxes.copy())\n total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32)\n dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)\n\n numbox = total_boxes.shape[0]\n if numbox>0:\n # second stage\n tempimg = np.zeros((24,24,3,numbox))\n for k in range(0,numbox):\n tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))\n tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]\n if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:\n tempimg[:,:,:,k] = imresample(tmp, (24, 24))\n else:\n return np.empty()\n tempimg = (tempimg-127.5)*0.0078125\n tempimg1 = np.transpose(tempimg, (3,1,0,2))\n out = rnet.predict_on_batch(tempimg1)\n out0 = np.transpose(out[0])\n out1 = np.transpose(out[1])\n score = out1[1,:]\n ipass = np.where(score>threshold[1])\n total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])\n mv = out0[:,ipass[0]]\n if total_boxes.shape[0]>0:\n pick = nms(total_boxes, 0.7, 'Union')\n total_boxes = total_boxes[pick,:]\n total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))\n total_boxes = rerec(total_boxes.copy())\n\n numbox = total_boxes.shape[0]\n if numbox>0:\n # third stage\n total_boxes = np.fix(total_boxes).astype(np.int32)\n dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)\n tempimg = np.zeros((48,48,3,numbox))\n for k in range(0,numbox):\n tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))\n tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]\n if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:\n tempimg[:,:,:,k] = imresample(tmp, (48, 48))\n else:\n return np.empty()\n tempimg = (tempimg-127.5)*0.0078125\n tempimg1 = np.transpose(tempimg, (3,1,0,2))\n out = onet.predict_on_batch(tempimg1)\n out0 = np.transpose(out[0])\n out1 = np.transpose(out[1])\n out2 = np.transpose(out[2])\n score = out2[1,:]\n points = out1\n ipass = np.where(score>threshold[2])\n points = points[:,ipass[0]]\n total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])\n mv = out0[:,ipass[0]]\n\n w = total_boxes[:,2]-total_boxes[:,0]+1\n h = total_boxes[:,3]-total_boxes[:,1]+1\n points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1\n points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1\n if total_boxes.shape[0]>0:\n total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))\n pick = nms(total_boxes.copy(), 0.7, 'Min')\n total_boxes = total_boxes[pick,:]\n points = points[:,pick]\n\n return total_boxes, points","title":""},{"docid":"afeaaca219fa9d486fcfd8ff69398f18","score":"0.5639469","text":"def detectAndDraw(test_img, rf, classifier,labels):\n img = test_img.copy()\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n rects = detect_face(img)\n for rect in rects:\n (x, y, w, h) = rect\n face = gray[y:y+h, x:x+w]\n test=[]\n for i in face:\n for xi in i:\n test.append(i)\n test=np.array(test)\n face=np.array(face)\n draw_rectangle(img, rect)\n label = rf.reconnaitre_un_visage(classifier, test.shape)\n print()\n draw_text(img,labels[label], rect[0], rect[1]-5)\n\n return img","title":""},{"docid":"f439777e277353130f556333b58ca398","score":"0.5637123","text":"def train_and_show_results_shapes():\n show_results_shapes(train_autoregressive_flow_pixelcnn)","title":""},{"docid":"da20761b843dde82416e8c2f9275ad4b","score":"0.561563","text":"def show_tensor(sample_tensor, epoch):\n\n tensor_view = sample_tensor.permute(1, 2, 0)\n\n print(tensor_view.shape)\n\n plt.imshow(np.asarray(tensor_view))\n\n plt.show()","title":""},{"docid":"6cf7ca93c6a253140aadd2b2470ca8ce","score":"0.55915767","text":"def demo(net, im_file, RCNN):\n\n image_name = im_file.split('\\\\')[-1]\n tmp = []\n\n # Load the demo image\n im = cv2.imread(im_file)\n\n # Detect all object classes and regress object bounds\n timer = Timer()\n timer.tic()\n scores, boxes = im_detect(net, im)\n timer.toc()\n print('Detection took {:.3f}s for {:d} object proposals'.format(\n timer.total_time(), boxes.shape[0]))\n\n # Visualize detections for each class\n CONF_THRESH = 0.3\n NMS_THRESH = 0.3\n for cls_ind, cls in enumerate(CLASSES[1:]):\n cls_ind += 1 # because we skipped background\n cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]\n cls_scores = scores[:, cls_ind]\n dets = np.hstack((cls_boxes,\n cls_scores[:, np.newaxis])).astype(np.float32)\n keep = nms(\n torch.from_numpy(cls_boxes), torch.from_numpy(cls_scores),\n NMS_THRESH)\n dets = dets[keep.numpy(), :]\n\n for det_inst in dets:\n if det_inst[4] > CONF_THRESH:\n inst_tmp = [image_name]\n if cls_ind == 1:\n inst_tmp.append('Human')\n else:\n inst_tmp.append('Object')\n inst_tmp.append(det_inst[:4])\n inst_tmp.append(np.nan)\n inst_tmp.append(cls_ind)\n inst_tmp.append(det_inst[4])\n tmp.append(inst_tmp)\n\n RCNN[image_name] = tmp","title":""},{"docid":"7e0a60a4ccba103b4407b8f8022c5c99","score":"0.5590113","text":"def visualize_results_grid(self, n_points=4000, ix=15, iy=15, vmax=1000):\n # Visualize manifold: \n if n_points is None or n_points is 0:\n n_points = self.n_training \n Ix = ix*1.0 / self.nx * self.nx_resample\n Iy = iy*1.0 / self.ny * self.ny_resample\n calibration_data = self.get_data_grid(ix,iy) \n filter = LikelihoodFilter(self.forward_model_2D)\n calibration_data_filtered, rejected = filter.filter(calibration_data, Ix,Iy, \\\n points=n_points) \n model_estimator = ModelDepthEmbedding(nz=self.nz, n_neighbours=self.n_neighbours)\n model_estimator.set_calibration_data(calibration_data_filtered)\n model_estimator.set_depth_prior(self.prior)\n model_estimator.estimate_forward_model() \n model_estimator.visualize_manifold(nd=3)\n\n # Visualize histograms of the locations of interaction: \n print \"Visualizing histograms of reconstructed locations of interaction .. \"\n if self.coordinates_centroid is not None: \n self.histogram_centroid.show(vmax=vmax)\n if self.coordinates_2D_MAP_filtered is not None: \n self.histogram_2D_MAP.show(vmax=vmax)\n if self.coordinates_3D_MAP_DE_filtered is not None: \n self.histogram_3D_MAP_DE.show(axis=2, index=None, vmax=vmax)\n if self.coordinates_3D_MAP_MLEEM_filtered is not None: \n self.histogram_3D_MAP_MLEEM.show(axis=2, index=None, vmax=vmax)\n \n # Visualize histograms of the energy: \n print \"Visualizing reconstructed energy spectra .. \"\n fig = pl.figure()\n if self.spectrum_centroid is not None: \n self.spectrum_centroid.show(fig=fig.number)\n if self.spectrum_2D_MAP is not None: \n self.spectrum_2D_MAP.show(fig=fig.number)\n if self.spectrum_3D_MAP_DE is not None: \n self.spectrum_3D_MAP_DE.show(fig=fig.number)\n if self.spectrum_3D_MAP_MLEEM is not None: \n self.spectrum_3D_MAP_MLEEM.show(fig=fig.number)\n\n # Visualizing the depth distribution \n print \"Visualizing the depth distribution of the reconstructed events ..\"\n pl.figure()\n pl.plot(self.prior)\n pl.hold(1)\n \n h = self.histogram_3D_MAP_DE.histogram.sum(0).sum(0)\n h = (1.0*h) / h.sum()\n pl.plot(h)\n\n h = self.histogram_3D_MAP_MLEEM.histogram.sum(0).sum(0)\n h = (1.0*h) / h.sum()\n pl.plot(h)","title":""},{"docid":"544dac5ff834c1d26c5c2bf354a44dbd","score":"0.5584179","text":"def _visualize_3d(self, token_map, show_detected=False, voxels=False):\n if not voxels:\n X, Y, Z = np.mgrid[:token_map.shape[0], :token_map.shape[1], :token_map.shape[2]]\n point_list = np.array([[x, y, z] for x, y, z in zip(np.ravel(X), np.ravel(Y), np.ravel(Z))]) # 'flat' (2d) index array\n colors = np.array(['black', 'midnightblue', 'lightsteelblue', 'limegreen',\n 'red', 'gold', 'darkgreen']) # black color to shift index + 1\n alphas = np.array([0, 0.05, 0.15, 0.6, 0.6, 1, 1])\n token_map_flat = np.ravel(token_map)\n color_list = colors[token_map_flat]\n alpha_list = alphas[token_map_flat]\n high_pass_filter_threshold = 1.5 if show_detected else 2.5\n mask = token_map_flat > high_pass_filter_threshold\n\n r = 25 # point radius\n points = vtkplotter.shapes.Points(point_list[mask], r=r, c=color_list[mask], alpha=alpha_list[mask])\n vtkplotter.show(points, interactive=True, newPlotter=True, axes={'xyGrid':True, 'yzGrid': True, 'zxGrid':True})\n else:\n factor = 2\n new_map = np.zeros((token_map.shape[0] * factor, token_map.shape[1] * factor, token_map.shape[2] * factor))\n for i in range(factor):\n for j in range(factor):\n for k in range(factor):\n new_map[i::factor, j::factor, k::factor] = token_map\n token_map = new_map\n\n color_list = np.array([[0.2, 1., 0.2, 0.8], [1., 0.2, 0.2, 0.6], [1., 1., 0, 1.], [0., 0.7, 0., 1.]])\n if show_detected: color_list = np.concatenate((np.array([[0.9, 0.95, 1., 0.2]]), color_list))\n color_map = ListedColormap(color_list)\n vol = vtkplotter.Volume(token_map) # , c=colors, alpha=alphas)\n lego = vol.legosurface(vmin=2 if show_detected else 3, vmax=6, cmap=color_map)\n\n vtkplotter.show(lego, interactive=True, newPlotter=True)","title":""},{"docid":"19ba868a35a10d1a84609b26355f3ba4","score":"0.55827665","text":"def faceDetection_TinyFaces(weight_file_path, videoFile, sampling_interval, detectionFrames, prob_thresh=0.5, nms_thresh=0.1, lw=3, display=False, newScale = 360):\r\n\r\n RESIZED_IMAGE_HEIGHT = newScale\r\n\r\n print('Resizing images to height ', RESIZED_IMAGE_HEIGHT);\r\n\r\n vid = cv2.VideoCapture(videoFile);\r\n frame_width = int(vid.get(3))\r\n frame_height = int(vid.get(4))\r\n total_frames = int(vid.get(7))\r\n FPS = vid.get(cv2.CAP_PROP_FPS)\r\n\r\n sampling_rate = int(FPS * sampling_interval)\r\n # TODO : may set adaptive sampling rate based on shot length\r\n\r\n print(videoFile, \" : total_frames:\", total_frames, \", FPS:\", FPS, ' SR:', sampling_rate)\r\n\r\n\r\n detections = []; # face detection bounding boxes\r\n indices = []; # frame number for detection\r\n\r\n\r\n # placeholder of input images. Currently batch size of one is supported.\r\n x = tf.placeholder(tf.float32, [1, None, None, 3]) # n, h, w, c\r\n\r\n # Create the tiny face model which weights are loaded from a pretrained model.\r\n model = tiny_face_model.Model(weight_file_path)\r\n score_final = model.tiny_face(x)\r\n\r\n\r\n # Load an average image and clusters(reference boxes of templates).\r\n with open(weight_file_path, \"rb\") as f:\r\n _, mat_params_dict = pickle.load(f)\r\n\r\n average_image = model.get_data_by_key(\"average_image\")\r\n clusters = model.get_data_by_key(\"clusters\")\r\n clusters_h = clusters[:, 3] - clusters[:, 1] + 1\r\n clusters_w = clusters[:, 2] - clusters[:, 0] + 1\r\n normal_idx = np.where(clusters[:, 4] == 1)\r\n\r\n # main\r\n with tf.Session() as sess:\r\n sess.run(tf.global_variables_initializer())\r\n\r\n for frameInd in range(0, total_frames):\r\n\r\n if (frameInd%(int(total_frames/10) ) == 0):\r\n print( str(int(frameInd/int(total_frames/10)) )+'0 %' )\r\n\r\n ret, frame = vid.read()\r\n\r\n if (ret == False):\r\n continue\r\n\r\n # subsample frames\r\n if (frameInd % sampling_rate != 0 and (frameInd-2) not in detectionFrames):\r\n continue;\r\n\r\n \r\n raw_img = frame\r\n org_raw_img = frame[:]\r\n\r\n myScale = (float(RESIZED_IMAGE_HEIGHT)/raw_img.shape[0]);\r\n\r\n # print ('org:', raw_img.shape)\r\n raw_img = cv2.resize(raw_img, ( int(raw_img.shape[1]*myScale), RESIZED_IMAGE_HEIGHT),interpolation=cv2.INTER_CUBIC) \r\n # print ('res:',raw_img.shape)\r\n\r\n raw_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB)\r\n raw_img_f = raw_img.astype(np.float32)\r\n\r\n def _calc_scales():\r\n raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]\r\n min_scale = min(np.floor(np.log2(np.max(clusters_w[normal_idx] / raw_w))),\r\n np.floor(np.log2(np.max(clusters_h[normal_idx] / raw_h))))\r\n max_scale = min(1.0, -np.log2(max(raw_h, raw_w) / MAX_INPUT_DIM_TINYFACES))\r\n scales_down = pl.frange(min_scale, 0, 1.)\r\n scales_up = pl.frange(0.5, max_scale, 0.5)\r\n scales_pow = np.hstack((scales_down, scales_up))\r\n scales = np.power(2.0, scales_pow)\r\n return scales\r\n\r\n scales = _calc_scales()\r\n start = time.time()\r\n\r\n # initialize output\r\n bboxes = np.empty(shape=(0, 5))\r\n\r\n # process input at different scales\r\n for s in scales:\r\n # print(\"Processing {} at scale {:.4f}\".format(str(frameInd), s))\r\n img = cv2.resize(raw_img_f, (0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR)\r\n img = img - average_image\r\n img = img[np.newaxis, :]\r\n\r\n # we don't run every template on every scale ids of templates to ignore\r\n tids = list(range(4, 12)) + ([] if s <= 1.0 else list(range(18, 25)))\r\n ignoredTids = list(set(range(0, clusters.shape[0])) - set(tids))\r\n\r\n # run through the net\r\n score_final_tf = sess.run(score_final, feed_dict={x: img})\r\n\r\n # collect scores\r\n score_cls_tf, score_reg_tf = score_final_tf[:, :, :, :25], score_final_tf[:, :, :, 25:125]\r\n prob_cls_tf = expit(score_cls_tf)\r\n prob_cls_tf[0, :, :, ignoredTids] = 0.0\r\n\r\n def _calc_bounding_boxes():\r\n # threshold for detection\r\n _, fy, fx, fc = np.where(prob_cls_tf > prob_thresh)\r\n\r\n # interpret heatmap into bounding boxes\r\n cy = fy * 8 - 1\r\n cx = fx * 8 - 1\r\n ch = clusters[fc, 3] - clusters[fc, 1] + 1\r\n cw = clusters[fc, 2] - clusters[fc, 0] + 1\r\n\r\n # extract bounding box refinement\r\n Nt = clusters.shape[0]\r\n tx = score_reg_tf[0, :, :, 0:Nt]\r\n ty = score_reg_tf[0, :, :, Nt:2*Nt]\r\n tw = score_reg_tf[0, :, :, 2*Nt:3*Nt]\r\n th = score_reg_tf[0, :, :, 3*Nt:4*Nt]\r\n\r\n # refine bounding boxes\r\n dcx = cw * tx[fy, fx, fc]\r\n dcy = ch * ty[fy, fx, fc]\r\n rcx = cx + dcx\r\n rcy = cy + dcy\r\n rcw = cw * np.exp(tw[fy, fx, fc])\r\n rch = ch * np.exp(th[fy, fx, fc])\r\n\r\n scores = score_cls_tf[0, fy, fx, fc]\r\n tmp_bboxes = np.vstack((rcx - rcw / 2, rcy - rch / 2, rcx + rcw / 2, rcy + rch / 2))\r\n tmp_bboxes = np.vstack((tmp_bboxes / s, scores))\r\n tmp_bboxes = tmp_bboxes.transpose()\r\n return tmp_bboxes\r\n\r\n tmp_bboxes = _calc_bounding_boxes()\r\n bboxes = np.vstack((bboxes, tmp_bboxes)) # <class 'tuple'>: (5265, 5)\r\n\r\n\r\n print(\"Took {:.2f} secs for Frame {}\".format(time.time() - start, str(frameInd)))\r\n\r\n # non maximum suppression\r\n # refind_idx = util.nms(bboxes, nms_thresh)\r\n refind_idx = tf.image.non_max_suppression(tf.convert_to_tensor(bboxes[:, :4], dtype=tf.float32),\r\n tf.convert_to_tensor(bboxes[:, 4], dtype=tf.float32),\r\n max_output_size=bboxes.shape[0], iou_threshold=nms_thresh)\r\n refind_idx = sess.run(refind_idx)\r\n refined_bboxes = bboxes[refind_idx]\r\n\r\n finalBboxes = []\r\n\r\n for ind_bb in range(len(refined_bboxes)):\r\n refined_bboxes[ind_bb][0]/=myScale\r\n refined_bboxes[ind_bb][1]/=myScale\r\n refined_bboxes[ind_bb][2]/=myScale\r\n refined_bboxes[ind_bb][3]/=myScale\r\n nbbox = list( refined_bboxes[ind_bb][0:4].astype(int) )\r\n finalBboxes.append( nbbox )\r\n\r\n # print(refined_bboxes)\r\n\r\n # overlay_bounding_boxes(org_raw_img, refined_bboxes, lw)\r\n\r\n # if display:\r\n # plt.axis('off')\r\n\r\n # plt.imshow(org_raw_img)\r\n # plt.show()\r\n\r\n # showImg(org_raw_img, 50)\r\n\r\n # save image with bounding boxes\r\n # raw_img = cv2.cvtColor(raw_img, cv2.COLOR_RGB2BGR)\r\n # cv2.imwrite(os.path.join(output_dir, str(ind)), raw_img)\r\n\r\n # faces = getFaceBoundingBoxesMTCNN(frame)\r\n detections.append(finalBboxes)\r\n indices.append(frameInd)\r\n\r\n\r\n\r\n return detections, indices;","title":""},{"docid":"dce0c4d09e00d12e751d3c76ce9395ea","score":"0.5582703","text":"def show_object_detection_image(network: gcv.model_zoo.HybridBlock, image: mx.nd.NDArray):\n image, chw_image = gcv.data.transforms.presets.yolo.transform_test(image, short=512)\n class_indices, probabilities, bounding_boxes = detect_objects(network, image)\n gcv.utils.viz.plot_bbox(chw_image,\n bounding_boxes,\n probabilities,\n class_indices,\n class_names=network.classes)\n plt.show()","title":""},{"docid":"f6e26068057a1e7cd02b7ece63678deb","score":"0.55750555","text":"def display_faces(display, shape, event = None):\n display.EraseAll()\n aocutils.display.topology.faces(display, shape, transparency=0.8)\n display.FitAll()\n display.View_Iso()","title":""},{"docid":"3ad27eedadc508fff58bddcb582db8ec","score":"0.5563226","text":"def demo(net, image_name):\n\n # Load the demo image\n #im_file = os.path.join(cfg.DATA_DIR, 'demo2', image_name)\n im_file = os.path.join(data_dir, 'Images', image_name)\n im = cv2.imread(im_file)\n\n # Detect all object classes and regress object bounds\n timer = Timer()\n timer.tic()\n scores, boxes = im_detect(net, im)\n timer.toc()\n print ('Detection took {:.3f}s for '\n '{:d} object proposals').format(timer.total_time, boxes.shape[0])\n\n\n\n im = im[:, :, (2, 1, 0)]\n fig, ax = plt.subplots(figsize=(12, 12))\n# ax.imshow(np.flipud(im), aspect='equal', origin='lower')\n ax.imshow(im, aspect='equal')\n\n # Visualize detections for each class\n CONF_THRESH = 0.8\n NMS_THRESH = 0.3\n for cls_ind, cls in enumerate(CLASSES[1:]):\n cls_ind += 1 # because we skipped background\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\n cls_scores = scores[:, cls_ind]\n dets = np.hstack((cls_boxes,\n cls_scores[:, np.newaxis])).astype(np.float32)\n# keep = nms(dets, NMS_THRESH, force_cpu=True)\n keep = nms(dets, NMS_THRESH)\n dets = dets[keep, :]\n vis_detections(im, cls, dets, ax, thresh=CONF_THRESH)","title":""},{"docid":"e4bf38baf7a24917d5afe26299318f02","score":"0.55618197","text":"def show_batch(sample_batched):\n buffer = tuple()\n for name, mod in sample_batched.items():\n if name not in ('coord', 'K'):\n '''\n min_v = mod.min()\n mod -= min_v\n max_v = mod.max()\n mod /= max_v\n '''\n buffer += (mod,)\n\n images_batch = torch.cat(buffer, 0)\n grid = torchvis.utils.make_grid(images_batch, nrow=4)\n\n plt.imshow(grid.numpy().transpose((1, 2, 0)))","title":""},{"docid":"bbe2c71a5aed4f17291098f19d05d230","score":"0.5557402","text":"def detect_faces(path):\n vision_client = vision.Client()\n\n with io.open(path, 'rb') as image_file:\n content = image_file.read()\n\n image = vision_client.image(content=content)\n\n faces = image.detect_faces()\n\n print('Faces:')\n for face in faces:\n print('anger: {}'.format(face.emotions.anger))\n print('joy: {}'.format(face.emotions.joy))\n print('surprise: {}'.format(face.emotions.surprise))","title":""},{"docid":"12827cb5d99cab25a5f31ba7b45cc5bc","score":"0.5555502","text":"def show_recognitions(self, image, verbose = False, return_faces = False):\n if verbose:\n start_time = time()\n boxes = self.face_detector.predict(image=image)\n if verbose:\n face_detection_time = time() - start_time\n confidences, names, distances = [], [], []\n if len(boxes):\n faces = self.face_detector.crop_boxes_content(image=image, boxes=boxes)\n confidences.extend([confidence for confidence, _ in boxes])\n boxes = [box for _, box in boxes]\n\n if verbose:\n embedding_start_time = time()\n embeddings = [self.embedding.predict(face) for face in faces]\n \n if verbose:\n embedding_time = time() - embedding_start_time\n if verbose:\n recognition_start_time = time()\n names.extend(self.classifier.predict(x=embeddings))\n if verbose:\n recognition_time = time() - recognition_start_time\n distances.extend([self.camera_calculator.rectangleToRealWorldXY(rectangle=box, h=image.shape[0],\n w=image.shape[1]) for box in boxes])\n if self.show_bodies:\n if verbose:\n bodies_start_time = time()\n body_boxes = self.body_detector.predict(image=cvtColor(image, code=COLOR_BGR2RGB))\n if verbose:\n body_time = time() - bodies_start_time\n if len(body_boxes):\n #bodies = self.body_detector.get_content(image=image, boxes=body_boxes)\n confidences.extend([confidence for confidence, _ in body_boxes])\n body_boxes = [box for _, box in body_boxes]\n names.extend(['body' for box in body_boxes])\n distances.extend([self.camera_calculator.rectangleToRealWorldXY(rectangle=box, h=image.shape[0],\n w=image.shape[1]) for box in boxes])\n boxes = boxes + body_boxes\n\n if verbose:\n showing_start_time = time()\n show(image=image, boxes=boxes, names=names, distances=distances, confidences=confidences)\n\n if verbose and len(boxes):\n showing_time = time() - showing_start_time\n total_time = time()-start_time\n if not self.show_bodies or len(boxes) > len(body_boxes):\n txt = 'Total prediction time: {total_time} s\\n' \\\n '\\t Face Detection: {face_time} s ({face_p})%\\n' \\\n '\\t Face Embedding: {embedding_time} s ({embedd_p})%\\n' \\\n '\\t Face Recognition: {recognition_time} s ({recognition_p})%\\n'\\\n .format(total_time = round(total_time, ndigits=2),\n face_time=round(face_detection_time, ndigits=2), face_p=round((face_detection_time/total_time)*100, ndigits=2),\n embedding_time=round(embedding_time, ndigits=2), embedd_p=round((embedding_time/total_time)*100, ndigits=2),\n recognition_time=round(recognition_time, ndigits=2), recognition_p=round((recognition_time/total_time)*100, ndigits=2))\n if self.show_bodies:\n txt += '\\t Body Detection: {detection_time} s ({detection_p})%\\n'\\\n .format(detection_time=round(body_time, ndigits=2), detection_p=round((body_time/total_time)*100, ndigits=2))\n txt += '\\t Plotting time: {plot_time} s ({plotting_p})%'\\\n .format(plot_time=round(showing_time, ndigits=2), plotting_p=round((showing_time/total_time)*100, ndigits=2))\n print(txt)\n if return_faces:\n if len(faces):\n names = names[:len(faces)]\n return {name : face for face, name in zip(faces, names)}\n else:\n return {}","title":""},{"docid":"56b3603dbb4de59466fde7771145e73d","score":"0.5555334","text":"def draw_faces(image, faces=None, plot=True):\n if faces is None:\n faces = get_faces(image)\n \n # To avoid overwriting\n image_with_faces = np.copy(image)\n \n # Get the bounding box for each detected face\n for (x,y,w,h) in faces:\n # Add a red bounding box to the detections image\n cv2.rectangle(image_with_faces, (x,y), (x+w,y+h), (255,0,0), 3)\n \n \n if plot is True:\n plot_image(image_with_faces)\n else:\n return image_with_faces","title":""},{"docid":"beb7f4ea54b9742021af4f5f33efab15","score":"0.55523175","text":"def classify_face(im):\n faces = get_encoded_faces()\n faces_encoded = list(faces.values())\n known_face_names = list(faces.keys())\n\n img = cv2.imread(im, 1)\n # img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)\n # img = img[:,:,::-1]\n\n face_locations = face_recognition.face_locations(img)\n unknown_face_encodings = face_recognition.face_encodings(img, face_locations)\n\n face_names = []\n for face_encoding in unknown_face_encodings:\n # See if the face is a match for the known face(s)\n matches = face_recognition.compare_faces(faces_encoded, face_encoding)\n name = \"Unknown\"\n\n # use the known face with the smallest distance to the new face\n face_distances = face_recognition.face_distance(faces_encoded, face_encoding)\n best_match_index = np.argmin(face_distances)\n if matches[best_match_index]:\n name = known_face_names[best_match_index]\n\n face_names.append(name)\n for (top, right, bottom, left), name in zip(face_locations, face_names):\n # Draw a box around the face\n cv2.rectangle(img, (left - 20, top - 20), (right + 20, bottom + 20), (255, 0, 0), 2)\n\n # Draw a label with a name below the face\n cv2.rectangle(img, (left - 20, bottom - 15), (right + 20, bottom + 20), (255, 0, 0), cv2.FILLED)\n font = cv2.FONT_HERSHEY_DUPLEX\n cv2.putText(img, name, (left - 20, bottom + 15), font, 1.0, (255, 255, 255), 2)\n\n return name","title":""},{"docid":"15e34e886c7326501f7f20501c6c171c","score":"0.5551539","text":"def visualize(self):\r\n fig = plt.figure(1)\r\n plt.clf()\r\n plt.cla()\r\n\r\n X_new = decomposition.pca.PCA(n_components=2).fit_transform(self.X)\r\n model = GMM(n_components=self.targetcluster, covariance_type='full')\r\n labels = model.fit_predict(X_new)\r\n totz = np.concatenate((X_new, np.expand_dims(labels, axis=1), np.expand_dims(self.y, axis=1),), axis=1)\r\n\r\n # for each cluster\r\n colors = ['red', 'green', 'blue', 'yellow', 'orange', 'purple']\r\n fig = plt.figure()\r\n\r\n for clust in range(0, self.targetcluster):\r\n totz_clust = totz[totz[:,-2] == clust]\r\n print(\"Cluster Size\")\r\n print(totz_clust.shape)\r\n\r\n benign = totz_clust[totz_clust[:,-1] == 1]\r\n malignant = totz_clust[totz_clust[:,-1] == 0]\r\n\r\n plt.scatter(benign[:, 0], benign[:, 1], color=colors[clust], marker=\".\")\r\n plt.scatter(malignant[:, 0], malignant[:, 1], color=colors[clust], marker=\"x\")\r\n\r\n plt.xlabel(\"1st Component\")\r\n plt.ylabel(\"2nd Component\")\r\n plt.show()","title":""},{"docid":"75bd5aabcbfb8348f6156ac6fef1da75","score":"0.5534348","text":"def visualize_mesh_og(cam, vert, renderer, start_pt, scale, proc_img_shape,\n im_path=None, img=None, deg=0, mesh_color='blue',\n max_img_size=300, pad=50, crop_cam=None, bbox=None):\n if img is None:\n img = imread(im_path)\n # Pre-process image to [-1, 1] bc it expects this.\n img = ((img / 255.) - 0.5) * 2\n\n if bbox is not None:\n assert(crop_cam is not None)\n img = img[bbox[0]:bbox[1], bbox[2]:bbox[3]]\n # For these, the cameras are already adjusted.\n scale = 1.\n start_pt = np.array([0, 0])\n if np.max(img.shape[:2]) > max_img_size:\n # if the image is too big it wont fit in gpu and nmr poops out.\n scale_orig = max_img_size / float(np.max(img.shape[:2]))\n img, _ = resize_img(img, scale_orig)\n undo_scale = (1. / np.array(scale)) * scale_orig\n else:\n undo_scale = 1. / np.array(scale)\n # NMR needs images to be square..\n img, pad_vals = make_square(img)\n img_size = np.max(img.shape[:2])\n renderer.renderer.image_size = img_size\n\n if bbox is not None:\n return renderer.rotated(\n verts=vert,\n deg=deg,\n cam=crop_cam,\n color_name=mesh_color,\n )\n else:\n # This is camera in crop image coord.\n cam_crop = np.hstack([proc_img_shape[0] * cam[0] * 0.5,\n cam[1:] + (2./cam[0]) * 0.5])\n\n # This is camera in orig image coord\n cam_orig = np.hstack([\n cam_crop[0] * undo_scale,\n cam_crop[1:] + (start_pt - proc_img_shape[0]) / cam_crop[0]\n ])\n\n # This is the camera in normalized orig_image coord\n new_cam = np.hstack([\n cam_orig[0] * (2. / img_size),\n cam_orig[1:] - (1 / ((2./img_size) * cam_orig[0]))\n ])\n new_cam = new_cam.astype(np.float32)\n\n return renderer.rotated(\n verts=vert,\n deg=deg,\n cam=new_cam,\n color_name=mesh_color,\n )","title":""},{"docid":"f23df4d67cc8ae6361e34774867e8f8c","score":"0.55242705","text":"def faces(self):\r\n return self._faces","title":""},{"docid":"650ff0a64783f869ee6c5ea9616f61bf","score":"0.5521417","text":"def show_inference(input_images, output_dict):\n out_images = []\n for num, image in enumerate(input_images):\n output_dict_tmp = output_dict[num]\n print(image.shape)\n print(output_dict_tmp)\n vis_util.visualize_boxes_and_labels_on_image_array(\n image,\n output_dict_tmp['detection_boxes'],\n output_dict_tmp['detection_classes'],\n output_dict_tmp['detection_scores'],\n category_index,\n instance_masks=output_dict_tmp.get('detection_masks_reframed', None),\n use_normalized_coordinates=True,\n line_thickness=2,\n min_score_thresh=.3)\n\n out_images.append(image)\n return(out_images)\n #visualization_utils.save_image_array_as_png(image, output_file)","title":""},{"docid":"bde0bede7ab7861bab6a5a066e8b1052","score":"0.55159616","text":"def visualize_layer(layer, vgg, vgg_deconv, top_k=9):\n\n # Num channels/features in the layer (batch_size, num_channels, height, weidth)\n num_feat = vgg.feature_maps[layer].shape[1]\n\n # Create clone of feature map for easy changes\n feat_map = vgg.feature_maps[layer].clone()\n\n # Now we want the feature map with maximu activation, which is simply done by\n # finding the maximum value in the tensor\n activations = []\n for i in range(num_feat):\n # Sequentially traverse all the feature maps and append the maximum value\n # in the feature map to the activations list.\n map = feat_map[0, i, :, :]\n activation = torch.max(map)\n activations.append(activation.item())\n\n # Get the indices for the top_k activations\n activations = np.array(activations)\n max_activation_idxs = activations.argsort()[-1:-top_k-1:-1]\n\n # Store the visualizations in a list\n images = []\n\n for i in max_activation_idxs:\n current_map = feat_map[:, i, :, :]\n current_max_activation = torch.max(current_map)\n\n tmp_feat_map = vgg.featrue_maps[layer].clone()\n\n # We zero out all the other activation maps/channels except the one we want\n # to get the visulizationsf for\n if i == 0:\n tmp_feat_map[:, 1:, :, :] = 0\n else:\n tmp_feat_map[:, :i, :, :] = 0\n if i != vgg.feature_maps[layer].shape[1] - 1:\n tmp_feat_map[:, i+1:, :, :] = 0\n \n # For the current selected activation map, zero out all the non-max values\n # and store it the tmp_feat_map array\n current_map = torch.where(current_map == current_max_activation,\n current_map,\n torch.zeros(current_map.shape))\n\n tmp_feat_map[0, i, :, :] = current_map\n\n # Run the deconv model\n deconv_output = vgg_deconv(tmp_feat_map, layer, vgg.pool_locs)\n\n # Get the image\n img = deconv_output.data.numpy()[0].transpose(1, 2, 0)\n img = (img - img.min()) / (img.max() - img.min()) * 255\n img = img.astype(np.uint8)\n\n images.append(img, int(current_max_activation))\n \n return images","title":""},{"docid":"28a4d3625afeb536e6b4bb1fc5d561c0","score":"0.55114794","text":"def demo(sess, net, im, fig=\"preview\", classes=CLASSES):\n\n # # Load the demo image\n # im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)\n # #im_file = os.path.join('/home/corgi/Lab/label/pos_frame/ACCV/training/000001/',image_name)\n # im = cv2.imread(im_file)\n\n # Detect all object classes and regress object bounds\n timer = Timer()\n timer.tic()\n scores, boxes = im_detect(sess, net, im)\n timer.toc()\n print(('Detection took {:.3f}s for '\n '{:d} object proposals').format(timer.total_time, boxes.shape[0]))\n\n # Visualize detections for each class\n im = im[:, :, (2, 1, 0)]\n # fig, ax = plt.subplots(figsize=(12, 12))\n # ax.imshow(im, aspect='equal')\n # cv2.imshow(fig, im)\n\n CONF_THRESH = 0.7\n NMS_THRESH = 0.3\n # for cls_ind, cls in enumerate(CLASSES[1:]):\n for cls_ind, cls in enumerate(classes[1:]):\n cls_ind += 1 # because we skipped background\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\n cls_scores = scores[:, cls_ind]\n dets = np.hstack((cls_boxes,\n cls_scores[:, np.newaxis])).astype(np.float32)\n keep = nms(dets, NMS_THRESH)\n dets = dets[keep, :]\n vis_detections(im, cls, dets, thresh=CONF_THRESH, fig=fig)","title":""},{"docid":"deb4cefd49d62d7c7bb16a388dfb6670","score":"0.55112904","text":"def instances_to_lvis(instances, img_id, max_dets=100):\n if len(instances) == 0:\n return []\n \n object_classes = instances.object_classes\n person_classes = instances.person_classes\n object_scores = instances.object_scores\n person_scores = instances.person_scores\n object_boxes = instances.object_boxes.tensor\n person_boxes = instances.person_boxes.tensor\n \n classes = torch.cat([object_classes, person_classes], dim=0)\n scores = torch.cat([object_scores * person_scores, person_scores], dim=0)\n boxes = torch.cat([object_boxes, person_boxes], dim=0)\n \n # Before NMS, remove duplicate boxes\n unique_dets, keep = set(), []\n for i in range(len(boxes)):\n det = tuple(boxes[i].tolist() + [float(classes[i])])\n if det not in unique_dets:\n unique_dets.add(det)\n keep.append(i)\n classes, scores, boxes = classes[keep], scores[keep], boxes[keep]\n\n # NMS\n keep = batched_nms(boxes, scores, classes, 0.5)[:max_dets]\n classes = classes[keep].tolist()\n scores = scores[keep].tolist()\n boxes = box_ops.box_xyxy_to_xywh(boxes[keep]).tolist()\n \n results = [{\"image_id\": img_id,\n \"category_id\": classes[i],\n \"bbox\": boxes[i],\n \"score\": scores[i]} for i in range(len(boxes))]\n return results","title":""},{"docid":"a1ae0ec07b11facc1cb0bb34ea67fdbf","score":"0.5510552","text":"def find_face():\n img = IO.read_image(\"photo1.jpg\", r\".\\data\")\n ma = img.max()\n mi = img.min()\n\n avg_r = np.empty(img.shape[0])\n for i in range(img.shape[0]):\n avg_r[i] = img[i].mean()\n\n avg_c = np.empty(img.shape[1])\n for i in range(img.shape[1]):\n avg_c[i] = img[:, i].mean()\n\n IO.plot_functions([(None, avg_r), (None, avg_c)],\n [\"avg rows\", \"avg cols\"])\n xs = []\n ys = []\n has_prev = False\n step = 5\n for i in range(step, len(avg_r) - step, step // 2):\n if abs(avg_r[i] - avg_r[i + step]) > ma / 30:\n xs.append(i + step // 2)\n\n step = 5\n for i in range(step, len(avg_c) - step, step // 2):\n if abs(avg_c[i] - avg_c[i + step]) > ma / 30:\n ys.append(i + step // 2)\n\n print(len(xs), len(ys))\n img_l = img\n for x in range(len(xs) - 1):\n for y in range(len(ys) - 1):\n print(xs[x], ys[y])\n img_l = m.draw_line(img_l, ys[y], xs[x], ys[y], xs[x + 1])\n img_l = m.draw_line(img_l, ys[y], xs[x], ys[y + 1], xs[x])\n img_l = m.draw_line(img_l, ys[y], xs[x + 1], ys[y + 1], xs[x + 1])\n img_l = m.draw_line(img_l, ys[y + 1], xs[x], ys[y + 1], xs[x + 1])\n\n plt.imshow(img_l)\n plt.show()","title":""},{"docid":"ad211d763fc841c8efb347b2b424553d","score":"0.5509361","text":"def chinese_whispers(source_dir, threshold=0.55, iterations=20, mosaic=True, plot=False):\n\n output_dir=os.path.join(source_dir, \"Face Network/\")\n network_dir=os.path.join(output_dir, \"Data/\")\n face_db=pd.read_hdf(network_dir+\"FaceDatabase.h5\")\n cluster_dir=os.path.join(output_dir, \"Clusters/\")\n face_dir=os.path.join(output_dir, \"Faces/\")\n\n encodings= list(face_db['encoding'])\n image_paths=list(face_db['face_name'])\n\n\n from random import shuffle\n import networkx as nx\n # Create graph\n nodes = []\n edges = []\n\n if len(encodings) <= 1:\n print (\"No enough encodings to cluster!\")\n return []\n\n for idx, face_encoding_to_check in enumerate(encodings):\n # Adding node of facial encoding\n node_id = idx+1\n\n # Initialize 'cluster' to unique value (cluster of itself)\n node = (node_id, {'cluster': image_paths[idx], 'path': image_paths[idx]})\n nodes.append(node)\n\n # Facial encodings to compare\n if (idx+1) >= len(encodings):\n # Node is last element, don't create edge\n break\n\n compare_encodings = encodings[idx+1:]\n distances = face_recognition.face_distance(compare_encodings, face_encoding_to_check)\n encoding_edges = []\n for i, distance in enumerate(distances):\n if distance < threshold:\n # Add edge if facial match\n edge_id = idx+i+2\n encoding_edges.append((node_id, edge_id, {'weight': distance}))\n\n edges = edges + encoding_edges\n\n G = nx.Graph()\n G.add_nodes_from(nodes)\n G.add_edges_from(edges)\n\n # Iterate\n for _ in range(0, iterations):\n cluster_nodes = G.nodes()\n #shuffle(cluster_nodes)\n for node in cluster_nodes:\n neighbors = G[node]\n clusters = {}\n\n for ne in neighbors:\n if isinstance(ne, int):\n if G.nodes[ne]['cluster'] in clusters:\n clusters[G.nodes[ne]['cluster']] += G[node][ne]['weight']\n else:\n clusters[G.nodes[ne]['cluster']] = G[node][ne]['weight']\n\n # find the class with the highest edge weight sum\n edge_weight_sum = 0\n max_cluster = 0\n #use the max sum of neighbor weights class as current node's class\n for cluster in clusters:\n if clusters[cluster] > edge_weight_sum:\n edge_weight_sum = clusters[cluster]\n max_cluster = cluster\n\n # set the class of target node to the winning local class\n G.nodes[node]['cluster'] = max_cluster\n\n\n if plot:\n print(G.nodes)\n import matplotlib.pyplot as plt\n nx.draw(G, node_size=2, edge_color='grey')\n plt.show()\n\n clusters = {}\n\n # Prepare cluster output\n for (_, data) in G.nodes.items():\n cluster = data['cluster']\n path = data['path']\n\n #print(cluster, path)\n if cluster:\n if cluster not in clusters:\n clusters[cluster] = []\n clusters[cluster].append(path)\n\n # Sort cluster output\n sorted_clusters = sorted(clusters.values(), key=len, reverse=True)\n\n\n length=[]\n\n cluster_master=pd.DataFrame()\n count=0\n for cluster in sorted_clusters:\n count+=1\n cluster_df=pd.DataFrame({'cluster':count,'face_name':cluster})\n cluster_master=cluster_master.append(cluster_df)\n\n if 'cluster' in face_db:\n face_db=face_db.drop(columns=['cluster'])\n\n if 'count' in face_db:\n face_db=face_db.drop(columns=['count'])\n\n\n face_db=face_db.merge(cluster_master, on='face_name',how='left')\n face_db['cluster']=face_db['cluster'].fillna(-1).astype(int)\n counts=face_db.groupby('cluster')['face_name'].count().reset_index().rename(columns={'face_name':'count'})\n face_db=face_db.merge(counts, how='left',on='cluster')\n print(face_db)\n\n face_db.to_hdf(network_dir+'FaceDatabase.h5', 'index', 'w',complevel=9)\n\n if mosaic:\n # build a mosaic of face tiles for each cluster\n overwrite(cluster_dir)\n clusters=face_db['cluster'].unique().tolist()\n clusters = [ elem for elem in clusters if elem > 0]\n cpus=joblib.cpu_count()-1\n rows=Parallel(n_jobs=cpus)(delayed(build_mosaic)(cluster,face_db,face_dir,cluster_dir) for cluster in clusters) \n\n return face_db","title":""},{"docid":"d91b1dd49da4b7bf17788d246b0cbfb4","score":"0.5506963","text":"def detect_faces(img):\n faces = detector.detect_faces(img)\n return faces","title":""},{"docid":"09e09c6d90e446d78237d3d18fcfeb6f","score":"0.55028784","text":"def visualize_faces_haarcascade(img_path, detector):\n # Load color (BGR) image\n img = cv2.imread(img_path)\n # Convert BGR image to grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # Find faces in image\n faces = detector.detectMultiScale(gray)\n # Get bounding box for each detected face\n for (x, y, w, h) in faces:\n # add bounding box to color image\n cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\n # Convert BGR image to RGB for plotting\n cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n # Display the image, along with bounding box\n plt.imshow(cv_rgb)\n plt.show()","title":""},{"docid":"1818c22c4769eb9443c15fcf6c4ff752","score":"0.5500044","text":"def detect_faces( cv_image ):\n return face_cascade.detectMultiScale(cv_image, 1.3, 5)","title":""},{"docid":"e78f844f1847b6b06337cf7052544a73","score":"0.5499313","text":"def getFaces(image, conf = 0.5):\n \n\n \n (h, w) = image.shape[:2]\n blob = cv.dnn.blobFromImage(cv.resize(image, (300, 300)), 1.0, (300, 300), (103.93, 116.77, 123.68))\n net.setInput(blob)\n detections = net.forward()\n\n face_arr = []\n for i in range(0, detections.shape[2]):\n confidence = detections[0,0,i,2]\n\n if confidence > conf:\n\n box = detections[0,0,i,3:7] * np.array([w,h,w,h])\n (startX, startY, endX, endY) = box.astype(\"int\")\n \n t = image[max(0, int(startY*0.9)):min(int(endY*1.1),image.shape[0]), max(0, int(startX*0.9)):min(int(endX*1.1), image.shape[1])]\n if t.shape[0] == 0 or t.shape[1] == 0:\n pass\n else:\n face_arr.append(t)\n\n \n\n return face_arr","title":""},{"docid":"0fae11d5761833d4eae0fd659ba31d1d","score":"0.5498946","text":"def drawDetectionBoundingBox(frame,result,faces,agender_model):\n args = parse_args()\n margin = args.margin\n # Drawing box for MTCNN detection (face)\n for j,face in enumerate(faces):\n face = list(map(int, face)) # convert floats to int because cv2.rectangle allows only int type\n x1_face,y1_face,x2_face,y2_face = face[0],face[1],face[2],face[3]\n \n # adding margin to the bounding box is crucial to correctly estimating age and gender margin = 40\n cropped_face = frame[face[1]-margin:face[3]+margin, face[0]-margin:face[2]+margin] \n gender,age = get_age_gender(agender_model,cropped_face)\n \n text = (gender+','+str(int(age)))\n cv2.rectangle(frame,(x1_face,y1_face),(x2_face,y2_face),(0,255,0),3)\n cv2.putText(frame,text,(face[0],face[1]),cv2.FONT_HERSHEY_PLAIN,3,(0,0,0),3)\n \n \n # Drawing box for YOLO detection (person) \n for box in result:\n x1_person,y1_person,x2_person,y2_person = (box['topleft']['x'],box['topleft']['y'],box['bottomright']['x'],box['bottomright']['y'])\n confidence = box['confidence']\n if confidence >0.1: # for YOLO\n cv2.rectangle(frame,(x1_person,y1_person),(x2_person,y2_person),(255,0,0),5)\n\n return frame","title":""},{"docid":"85aa5cb468d4d67dc081393cc2aca797","score":"0.5491066","text":"def detect_faces(self, img):\n gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n faces = self.detector.detectMultiScale(gray, 1.1, 4)\n return faces","title":""},{"docid":"5093a0940d5ecc55c0db65d9732c85b4","score":"0.5484688","text":"def show_detections(self):\n cv2.imshow(TITLE, self.detect_img)","title":""},{"docid":"f9f28ad8d5f499b6984cb1e41ea1a846","score":"0.54766023","text":"def draw_faces(image, faces=None, plot=True):\n if faces is None:\n faces = get_faces(image)\n \n # To avoid overwriting\n image_with_faces = np.copy(image)\n \n # Get the bounding box for each detected face\n for (x,y,w,h) in faces:\n # Add a red bounding box to the detections image\n cv2.rectangle(image_with_faces, (x,y), (x+w,y+h), (255,0,0), 3)\n \n if plot is True:\n plot_image(image_with_faces)\n else:\n return image_with_faces","title":""},{"docid":"9d9ab32ab115b335c36f5362ec970cf2","score":"0.54690915","text":"def demo(sess, net, im):\n\n # Detect all object classes and regress object bounds\n timer = Timer()\n timer.tic()\n scores, boxes = im_detect(sess, net, im)\n timer.toc()\n # print ('Detection took {:.3f}s for '\n # '{:d} object proposals').format(timer.total_time, boxes.shape[0])\n\n # Visualize detections for each class\n for cls_ind, cls in enumerate(CLASSES[1:]):\n cls_ind += 1 # because we skipped background\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\n cls_scores = scores[:, cls_ind]\n dets = np.hstack((cls_boxes,\n cls_scores[:, np.newaxis])).astype(np.float32)\n keep = nms(dets, NMS_THRESH)\n dets = dets[keep, :]\n vis_detections(im, cls, dets, thresh=CONF_THRESH)","title":""},{"docid":"d51b75f30c566d8707f1bdeecc413b54","score":"0.54664963","text":"def plot_manifold(model, filename, nrow=10):\n x = torch.linspace(norm.ppf(0.01), norm.ppf(0.99), nrow)\n xv, yv = torch.meshgrid(x, x)\n z = torch.stack([xv, yv], 0)\n z = z.view(2, -1).t().to(model.device)\n\n samples, means, _ = model.sample(1, z)\n\n save_sample_plot(means, filename, nrow=nrow)","title":""},{"docid":"7c7cd7579e12b8e2715f1568c89171b6","score":"0.54584986","text":"def faces(self):\n return self.getConnectivity()","title":""},{"docid":"b3fcab6ba2913fbbf9fef087d4f7533b","score":"0.5457706","text":"def visualize(self):\r\n fig = plt.figure(1)\r\n plt.clf()\r\n plt.cla()\r\n\r\n X_new = decomposition.pca.PCA(n_components=2).fit_transform(self.X)\r\n model = GMM(n_components=self.targetcluster, covariance_type='full')\r\n labels = model.fit_predict(X_new)\r\n totz = np.concatenate((X_new, np.expand_dims(labels, axis=1), np.expand_dims(self.y, axis=1),), axis=1)\r\n\r\n # for each cluster\r\n colors = ['red', 'green', 'blue', 'yellow', 'orange', 'purple']\r\n fig = plt.figure()\r\n\r\n for clust in range(0, self.targetcluster):\r\n totz_clust = totz[totz[:,-2] == clust]\r\n print(\"Cluster Size\")\r\n print(totz_clust.shape)\r\n\r\n benign = totz_clust[totz_clust[:,-1] == 1]\r\n malignant = totz_clust[totz_clust[:,-1] == 0]\r\n\r\n plt.scatter(benign[:, 0], benign[:, 1], color=colors[clust], marker=\".\")\r\n plt.scatter(malignant[:, 0], malignant[:, 1], color=colors[clust], marker=\"x\")\r\n\r\n centroids = model.cluster_centers_\r\n ax.scatter(centroids[:, 0], centroids[:, 1], centroids[:, 2],\r\n marker='x', s=169, linewidths=3, color=\"black\",\r\n zorder=10)\r\n #plt.title(\"Breast Cancer Clustering\")\r\n plt.xlabel(\"1st Component\")\r\n plt.ylabel(\"2nd Component\")\r\n plt.show()","title":""},{"docid":"909c6a22136bf5b1f667dea972ec7930","score":"0.54549277","text":"def demo(sess, net, image_name, image_name2):\r\n \r\n #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\r\n ######################### Modified ############################\r\n \r\n # Load the demo image\r\n #im_file = os.path.join(cfg.FLAGS2[\"data_dir\"], 'demo', image_name)\r\n #im = cv2.imread(im_file)\r\n \r\n im = image_name ############### Newly Added ###############\r\n im_inf = image_name2\r\n proto_im = image_name.copy()\r\n proto_imf = image_name2.copy()\r\n \r\n # Detect all object classes and regress object bounds\r\n timer = Timer()\r\n timer.tic()\r\n scores, boxes = im_detect(sess, net, im)\r\n timer.toc()\r\n\r\n ############### Modified & Newly Added #######################\r\n # print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))\r\n #cv2.putText(im, 'Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]), \\\r\n # (5,20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))\r\n \r\n # Visualize detections for each class\r\n CONF_THRESH = 0.1\r\n NMS_THRESH = 0.1\r\n #for cls_ind, cls in enumerate(CLASSES[1:]):\r\n # cls_ind += 1 # because we skipped background\r\n # cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]\r\n # cls_scores = scores[:, cls_ind]\r\n # dets = np.hstack((cls_boxes,\r\n # cls_scores[:, np.newaxis])).astype(np.float32)\r\n # keep = nms(dets, NMS_THRESH)\r\n # dets = dets[keep, :]\r\n #print(\"aaa\")\r\n dets = collect(scores, boxes)\r\n \r\n vis_detections(im, im_inf, dets, thresh=CONF_THRESH)","title":""},{"docid":"1c68a729467b1451f4de52a523bc1bf8","score":"0.5453496","text":"def visualize(self):\n \n self.model.visualize()","title":""},{"docid":"ba873c931de52911ebe129450007b2d2","score":"0.54496825","text":"def livePlotEmotions(emotion_faces: list):\n for face in emotion_faces:\n # TODO-Kaan: Update a plt with emotion values\n p.updatePlot(face)\n print(face)","title":""},{"docid":"2ce176f8276f4a8f6f05f8315b5c222c","score":"0.54491794","text":"def detection_faces(imagem_url: str) -> int:\n\n carregaAlgoritmo = cv2.CascadeClassifier(\"src\\controllers\\detects\\haarcascade\\haarcascade_frontalface_default.xml\")\n #carregaAlgoritmo = cv2.CascadeClassifier(\"detects/haarcascade/haarcascade_frontalface_default.xml\")\n imagem = url_to_image_array(imagem_url)\n\n #imagem = cv2.imread('caminho')\n imagem = cv2.resize(imagem, (0,0), fx=0.7, fy=0.7) \n imagemCinza = cv2.cvtColor(imagem, cv2.COLOR_BGR2GRAY)\n\n #DETECÇÃO DAS FACES\n faces = carregaAlgoritmo.detectMultiScale(\n imagemCinza, \n scaleFactor=1.1, \n minNeighbors=3, #abordagem de vizihança, (^) = + perder os verdadeiros positivos, (v) = + falsos positivos\n minSize=(20,20) #tamanho da detecção de uma face\n )\n\n #Pequena regra de negócio\n count = 0\n try:\n for faces_x in faces:\n print(faces[count])\n count += 1\n print(\"Quat. de faces: \", count)\n return (count)\n\n except Exception as erro:\n print(\"Erro: \", EOFError)\n return (count)","title":""},{"docid":"8ce26102fa2cb8529ea5510df647c72b","score":"0.5448516","text":"def forward_det(self):\n anno_img = self._anno[self._cur]\n\n boxes_img = self._boxes[self._cur]\n pred_cls_img = self._pred_cls[self._cur]\n pred_confs_img = self._pred_confs[self._cur]\n if (boxes_img is None or boxes_img.shape[0] < 2):\n self._cur += 1\n if (self._cur >= len(self._anno)):\n self._cur = 0\n return None\n im_path = osp.join(self.img_dir, anno_img['img_path'].split('/')[-1])\n im = cv2.imread(im_path)\n\n # 图片和路径名字对不上\n if (im is None):\n self._cur += 1\n if (self._cur >= len(self._anno)):\n self._cur = 0\n return None\n ih = im.shape[0]\n iw = im.shape[1]\n PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])\n # image_blob, im_scale = prep_im_for_blob(im, PIXEL_MEANS)\n image_blob, im_scale = prep_im_stand(im)\n blob = np.zeros((1,) + image_blob.shape, dtype=np.float32)\n blob[0] = image_blob\n # Reshape net's input blobs\n boxes = np.zeros((boxes_img.shape[0], 5))\n boxes[:, 1:5] = boxes_img * im_scale\n classes = pred_cls_img.reshape(len(pred_cls_img))\n\n # 添加词向量\n label_embeded = np.zeros((len(classes), 300))\n for index, item in enumerate(classes):\n label_embeded[index] = self._word_embed[item]\n # 添加词向量\n ix1 = []\n ix2 = []\n n_rel_inst = len(pred_cls_img) * (len(pred_cls_img) - 1)\n rel_boxes = np.zeros((n_rel_inst, 5))\n # SpatialFea = np.zeros((n_rel_inst, 2, 32, 32))\n SpatialFea = np.zeros((n_rel_inst, 8))\n rel_so_prior = np.zeros((n_rel_inst, self._num_relations))\n i_rel_inst = 0\n # 两两相互变为主宾语\n for s_idx in range(len(pred_cls_img)):\n for o_idx in range(len(pred_cls_img)):\n if (s_idx == o_idx):\n continue\n ix1.append(s_idx)\n ix2.append(o_idx)\n sBBox = boxes_img[s_idx]\n oBBox = boxes_img[o_idx]\n rBBox = self._getUnionBBox(sBBox, oBBox, ih, iw)\n # soMask = [self._getDualMask(ih, iw, sBBox), \\\n # self._getDualMask(ih, iw, oBBox)]\n rel_boxes[i_rel_inst, 1:5] = np.array(rBBox) * im_scale\n # SpatialFea[i_rel_inst] = soMask\n SpatialFea[i_rel_inst] = self._getRelativeLoc(sBBox, oBBox)\n rel_so_prior[i_rel_inst] = self._so_prior[classes[s_idx], classes[o_idx]]\n i_rel_inst += 1\n # image_blob = image_blob.astype(np.float32, copy=False)\n boxes = boxes.astype(np.float32, copy=False)\n classes = classes.astype(np.float32, copy=False)\n ix1 = np.array(ix1)\n ix2 = np.array(ix2)\n self._cur += 1\n if (self._cur >= len(self._anno)):\n self._cur = 0\n unknown = self._unknown[\"unknown\"]\n return blob, boxes, rel_boxes, SpatialFea, classes, unknown, \\\n ix1, ix2, label_embeded, boxes_img, pred_confs_img, rel_so_prior","title":""},{"docid":"b834d19b0f10e1fc98e5fd232320bb14","score":"0.54455495","text":"def use_deepface(self):\n Input_1 = Input(shape=(152, 152, 3), name='face_Input')\n Conv_1 = Conv2D(32, (11, 11), activation='relu', name='face_Conv_1')(Input_1)\n MaxPool_1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='face_MaxPool_1')(Conv_1)\n Conv_2 = Conv2D(16, (9, 9), activation='relu', name='face_Conv_2')(MaxPool_1)\n LC_1 = LocallyConnected2D(16, (9, 9), activation='relu', name='face_LC_1')(Conv_2)\n LC_2 = LocallyConnected2D(16, (7, 7), strides=(2, 2), activation='relu', name='face_LC_2')(LC_1)\n LC_3 = LocallyConnected2D(16, (5, 5), activation='relu', name='face_LC_3')(LC_2) # output 21, 21, 16\n Flatten_1 = Flatten(name='face_Flatten')(LC_3) # output 7056\n FC_1 = Dense(4096, activation='relu')(Flatten_1)\n Drop_1 = Dropout(0.5)(FC_1)\n FC_2 = Dense(8631, activation='softmax')(Drop_1)\n face = Model(inputs=Input_1, outputs=FC_2)\n return face","title":""},{"docid":"cb54b7cad574a5f79aa37d11e9b600a1","score":"0.5444366","text":"def run(self):\n cap = cv2.VideoCapture(0)\n cap.set(cv2.CAP_PROP_FRAME_WIDTH, 960)\n cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 540)\n photo = 0\n breakout = 0\n while True:\n ret, frame = cap.read()\n \n try:\n # detect face box, probability and landmarks\n boxes, probs, landmarks = self.mtcnn.detect(frame, landmarks=True)\n \n # draw on frame\n photo, breakout, boxes, probs = self._draw(frame, boxes, probs, landmarks, photo)\n\n except:\n pass\n\n # Show the frame\n cv2.imshow('Detector', frame)\n cv2.moveWindow('Detector',0,0)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n \n if breakout == 1:\n break\n\n \n cv2.destroyAllWindows()\n cap.release()\n cv2.waitKey(0)\n return boxes","title":""},{"docid":"f8d070c5e8db171052700124b9f9dc46","score":"0.5442117","text":"def imshow_det_bboxes_poses(\n img,\n bboxes,\n labels,\n class_names=None,\n score_thr=0,\n bbox_color=\"green\",\n text_color=\"green\",\n thickness=1,\n font_scale=0.5,\n show=True,\n win_name=\"\",\n wait_time=0,\n out_file=None,\n poses=None,\n corners_3d=None,\n dataste_name=None,\n renderer=None,\n K=None,\n vis_tool=\"matplotlib\",\n):\n # logger.info('poses: {}'.format(poses))\n assert bboxes.ndim == 2\n assert labels.ndim == 1\n assert bboxes.shape[0] == labels.shape[0]\n assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5\n img = imread(img)\n\n if score_thr > 0:\n assert bboxes.shape[1] == 5\n scores = bboxes[:, -1]\n inds = scores > score_thr\n bboxes = bboxes[inds, :]\n labels = labels[inds]\n\n bbox_color = color_val(bbox_color)\n text_color = color_val(text_color)\n\n for bbox, label in zip(bboxes, labels):\n # pose\n if poses is not None:\n if poses[label]:\n pose = poses[label][0] # TODO: handle multiple poses\n bgr, depth = renderer.render(label, pose[:, :3], pose[:, 3], r_type=\"mat\")\n # img = img - bgr\n pose_mask = np.zeros(depth.shape)\n pose_mask[depth != 0] = 1\n edges_3 = mask_utils.get_edge(pose_mask, bw=3)\n edges_3[:, :, [0, 1]] = 0 # red\n img[edges_3 != 0] = 255\n cls_name = class_names[label]\n corners_2d, _ = misc_6d.points_to_2D(corners_3d[cls_name], pose[:, :3], pose[:, 3], K)\n img = misc_6d.draw_projected_box3d(img, corners_2d, thickness=thickness)\n\n bbox_int = bbox.astype(np.int32)\n left_top = (bbox_int[0], bbox_int[1])\n right_bottom = (bbox_int[2], bbox_int[3])\n cv2.rectangle(img, left_top, right_bottom, bbox_color, thickness=thickness)\n label_text = class_names[label] if class_names is not None else \"cls {}\".format(label)\n if len(bbox) > 4:\n label_text += \"|{:.02f}\".format(bbox[-1])\n cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2), cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)\n\n if show:\n if vis_tool == \"matplotlib\":\n fig = plt.figure(frameon=False, figsize=(8, 6), dpi=100)\n tmp = fig.add_subplot(1, 1, 1)\n tmp.set_title(\"{}\".format(win_name))\n plt.axis(\"off\")\n plt.imshow(img[:, :, [2, 1, 0]])\n plt.show()\n else: # use 'mmcv'\n imshow(img, win_name, wait_time)\n if out_file is not None:\n imwrite(img, out_file)\n return img","title":""},{"docid":"a43b5bae862ac4a77f98f057d537d20c","score":"0.5441236","text":"def visualize_recognized_objects(\n image, boxes, classes, scores, category_index, threshold):\n # Size, in inches, of the output images. For visualization\n IMAGE_SIZE = (12, 8)\n\n # w, h = image.size\n # my_dpi = 96 # todo: find this programmatically\n\n image_np = load_image_into_numpy_array(image)\n vis_util.visualize_boxes_and_labels_on_image_array(\n image_np,\n boxes,\n classes,\n scores,\n category_index,\n use_normalized_coordinates=True,\n line_thickness=5,\n min_score_thresh=threshold)\n plt.figure(figsize=IMAGE_SIZE)\n # plt.figure(figsize=(w / my_dpi, h / my_dpi), dpi=my_dpi)\n plt.imshow(image_np)\n plt.show()\n return image_np","title":""},{"docid":"72aff96a3f45f799c962873c7a1547ac","score":"0.543901","text":"def show_intermediate_output(model, layer_name, image):\n if depth == 1:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n image = cv2.resize(image, (width, height))\n\n img_ndarray = np.asarray(image, dtype='float64') / 255\n test_data = np.ndarray.flatten(img_ndarray)\n test_data = test_data.astype('float32')\n\n if K.image_data_format() == 'channels_first':\n test_data = test_data.reshape(1, depth, height, width)\n else:\n test_data = test_data.reshape(1, height, width, depth)\n\n output = get_intermediate_output(model, layer_name, test_data)\n n = output.shape[-1] # number of feature\n size = output.shape[1] # feature map side length\n display_grid = np.zeros((size * 1, n * size)) # grid\n\n for i in range(n):\n channel_image = output[:, :, i]\n display_grid[0:size, i * size:(i + 1) * size] = channel_image\n\n plt.figure()\n plt.title(layer_name)\n plt.grid(False)\n plt.imshow(display_grid, cmap='viridis')\n plt.savefig('visualization/' + layer_name + '_output.jpg') # save output diagram\n plt.show() # must show after imshow\n\n return display_grid","title":""},{"docid":"0bc3ed77234feb630f33cf7116bc76d7","score":"0.5433919","text":"def render(img, obj, projection, model, color=False):\n vertices = obj.vertices\n scale_matrix = np.eye(3) * 20\n h = 200\n w = 200\n\n for face in obj.faces:\n face_vertices = face[0]\n points = np.array([vertices[vertex - 1] for vertex in face_vertices])\n points = np.dot(points, scale_matrix)\n # render in the middle\n points = np.array([[p[0] + w / 2, p[1] + h / 2, p[2]] for p in points])\n dst = cv2.perspectiveTransform(points.reshape(-1, 1, 3), projection)\n imgpts = np.int32(dst)\n cv2.fillConvexPoly(img, imgpts, (137, 27, 211))\n \n return img","title":""}],"string":"[\n {\n \"docid\": \"ee20f8b403c10b30526608e2bb664356\",\n \"score\": \"0.6805244\",\n \"text\": \"def faces():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d006a66ea0674f515a62e5dacf24da\",\n \"score\": \"0.6586629\",\n \"text\": \"def recognize_faces_and_draw_knn(self, minDistance=0.5):\\r\\n index = 0\\r\\n new_image = self.image.copy() \\r\\n # loop all faces\\r\\n name = None\\r\\n for left, right, top, bottom in self.boxes:\\r\\n cv2.rectangle(new_image,(left,top),(right,bottom),(0,255,0),2)\\r\\n if self.faces_aligned == [] or self.faces_aligned is None:\\r\\n face = self.faces_detected[index]\\r\\n else:\\r\\n face = self.faces_aligned[index]\\r\\n # generate encoding for the face\\r\\n encoding = self.generate_encoding(face)\\r\\n # match a face with all faces in database\\r\\n # for encoding in encodings:\\r\\n matches = face_recognition.compare_faces(self.data[\\\"encodings\\\"], encoding, minDistance)\\r\\n name = \\\"Unknown\\\"\\r\\n if True in matches:\\r\\n matchedIdxs = [i for (i, b) in enumerate(matches) if b]\\r\\n counts = {}\\r\\n for i in matchedIdxs:\\r\\n name = self.data[\\\"names\\\"][i]\\r\\n counts[name] = counts.get(name, 0) + 1\\r\\n name = max(counts, key=counts.get)\\r\\n y = top - 15 if top - 15 > 15 else top + 15\\r\\n cv2.putText(new_image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 4)\\r\\n index = index + 1\\r\\n \\r\\n return new_image, name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df916e0d3c2488c55321c8cbf736f4d8\",\n \"score\": \"0.6540483\",\n \"text\": \"def show_detection(image, faces):\\n \\n # faces contains a list of mmod_rectangle objects\\n # The mmod_rectangle object has two member variables, a dlib.rectangle object, and a confidence score\\n # Therefore, we iterate over the detected mmod_rectangle objects accessing dlib.rect to draw the rectangle\\n for face in faces:\\n cv2.rectangle(image, (face.rect.left(), face.rect.top()), (face.rect.right(), face.rect.bottom()), (255, 0, 0), 10)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daec589761c06925d99233620fb49a3b\",\n \"score\": \"0.64737916\",\n \"text\": \"def show_detection(image, faces):\\n\\n for face in faces:\\n top, right, bottom, left = face\\n cv2.rectangle(image, (left, top), (right, bottom), (255, 0, 0), 3)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"794475f7583c50af51313016891d9fb7\",\n \"score\": \"0.64067626\",\n \"text\": \"def recognize_faces_and_draw_knn_2(self, minDistance=0.5):\\r\\n index = 0\\r\\n new_image = self.image.copy() \\r\\n # loop all faces\\r\\n top_names = []\\r\\n name = None\\r\\n for left, right, top, bottom in self.boxes:\\r\\n # draw rectangle around the face in frame\\r\\n cv2.rectangle(new_image,(left,top),(right,bottom),(0,255,0),2)\\r\\n if self.faces_aligned == [] or self.faces_aligned is None:\\r\\n face = self.faces_detected[index]\\r\\n else:\\r\\n face = self.faces_aligned[index]\\r\\n # generate encoding for the face\\r\\n encoding = self.generate_encoding(face)\\r\\n # get distance value \\r\\n dict_index = -1\\r\\n # match_index = [] \\r\\n match_value = []\\r\\n match_name = []\\r\\n for db_encoding in self.data['encodings']:\\r\\n dict_index = dict_index + 1\\r\\n # compare face\\r\\n dist = np.linalg.norm(db_encoding - encoding)\\r\\n if dist < minDistance:\\r\\n # match_index.append(dict_index)\\r\\n match_value.append(dist)\\r\\n match_name.append(self.data['names'][dict_index])\\r\\n if match_value != []:\\r\\n # sort by 3-smallest distance value (ascending)\\r\\n sorted_index = np.argsort(match_value)\\r\\n name = match_name[sorted_index[0]]\\r\\n top_names.append(name)\\r\\n # write on the displayed window, for each value for this face\\r\\n yy = 0\\r\\n for k in sorted_index:\\r\\n yy = yy + 1\\r\\n y = top + (yy * 25)\\r\\n xname = \\\"{}: {:.2f}%\\\".format(match_name[k], (1-match_value[k]) * 100) \\r\\n cv2.putText(new_image, xname, (right+20, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\\r\\n if yy == 3: \\r\\n break\\r\\n else:\\r\\n y = top - 15 if top - 15 > 15 else top + 15\\r\\n cv2.putText(new_image, 'Unknown', (right+20, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\\r\\n \\r\\n index = index + 1\\r\\n \\r\\n return new_image, top_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ae08bee0932677fa9abbfbe7739057\",\n \"score\": \"0.6376859\",\n \"text\": \"def visualize_face(image, feature):\\n\\t_image = [image]\\n\\t_feature = [feature]\\n\\tfor x_row, f_row in zip(_image, _feature):\\n\\t\\tkp, mask = generate_kp(f_row)\\n\\tleft_top = [-1, -1]\\n\\tright_bottom = [-1, -1]\\n\\tfor r in range(len(mask)):\\n\\t\\tfor c in range(len(mask[r])):\\n\\t\\t\\tif mask[r][c] == 1 and left_top == [-1, -1]:\\n\\t\\t\\t\\tleft_top = [r, c]\\n\\t\\t\\telif mask[r][c] == 0 and left_top != [-1, -1]:\\n\\t\\t\\t\\tright_bottom = [-1, c]\\n\\t\\t\\t\\tbreak\\n\\t\\tif right_bottom != [-1, -1]:\\n\\t\\t\\tbreak\\n\\tfor r in range(len(mask)):\\n\\t\\tif mask[r][left_top[1]] == 0 and r > left_top[0]:\\n\\t\\t\\tright_bottom[0] = r\\n\\t\\t\\tbreak\\n\\timage_found = cv2.rectangle(denormalize_image(image), (left_top[1], left_top[0]),\\n\\t\\t\\t\\t (right_bottom[1], right_bottom[0]), (0, 0, 100), 1)\\n\\tplt.imshow(image_found)\\n\\tplt.scatter(x=feature[::2], y=feature[1::2], c='r', s=60)\\n\\tplt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2cd11799e0427dddfa5fd22e6afde7b\",\n \"score\": \"0.63102883\",\n \"text\": \"def _show_faces(image, faces):\\n # last = False\\n\\n for i in range(0, len(faces)):\\n (x, y, w, h) = faces[i]\\n last = i == len(faces) - 1\\n\\n if last:\\n # last = False\\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)\\n else:\\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 255), 2)\\n\\n cv2.namedWindow(\\\"Faces found\\\", cv2.WINDOW_NORMAL)\\n\\n w, h, d = image.shape\\n n_w = int(w / 3)\\n n_h = int(h / 3)\\n\\n cv2.resizeWindow('Faces found', n_h, n_w)\\n\\n cv2.imshow(\\\"Faces found\\\", image)\\n cv2.waitKey(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d485246a8036e7bb0e1f9e9864a7f9b\",\n \"score\": \"0.62778085\",\n \"text\": \"def viewfaces():\\n with open(con.SAVE_IMAGE_NAMES, \\\"r\\\") as f:\\n facelist = f.readlines()\\n facelist = [i.strip() for i in facelist if len(i.strip()) > 0]\\n if len(facelist) == 0:\\n print(\\\"No faces were selected. Run this program again and type S.\\\")\\n return False\\n if len(facelist) > con.MAX_PORTRAITS:\\n s = \\\"Over 50 images have been selected.\\\\n\\\"\\n s += \\\"If you would like to view all fifty then adjust def do_viewfaces(self, arg)\\\\n\\\"\\n s += \\\"Otherwise, narrow your selection.\\\"\\n print(s)\\n return False\\n for filename in facelist:\\n filepath = os.path.join(con.IMAGES_DIRECTORY, filename)\\n print(filepath)\\n img = mpimg.imread(filepath)\\n imgplot = plt.imshow(img)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4b41bb90a17d40f6fd20e936cfd3cc1\",\n \"score\": \"0.61617106\",\n \"text\": \"def show_faces(img, boxes, landmarks):\\n for bb, ll in zip(boxes, landmarks):\\n x1, y1, x2, y2 = int(bb[0]), int(bb[1]), int(bb[2]), int(bb[3])\\n cv2.rectangle(img, (x1, y1), (x2, y2), BBOX_COLOR, 2)\\n send_face(img[y1:y2, x1:x2, : ])\\n for j in range(5):\\n cv2.circle(img, (int(ll[j]), int(ll[j+5])), 2, BBOX_COLOR, 2)\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"893ad7e5edad30128ae96c40c4660ba3\",\n \"score\": \"0.6119563\",\n \"text\": \"def run():\\r\\n # read face image data from ORL database\\r\\n V = read()\\r\\n # preprocess ORL faces data matrix\\r\\n V = preprocess(V)\\r\\n # run factorization\\r\\n W, _ = factorize(V)\\r\\n # plot parts-based representation\\r\\n plot(W)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"893ad7e5edad30128ae96c40c4660ba3\",\n \"score\": \"0.6119563\",\n \"text\": \"def run():\\r\\n # read face image data from ORL database\\r\\n V = read()\\r\\n # preprocess ORL faces data matrix\\r\\n V = preprocess(V)\\r\\n # run factorization\\r\\n W, _ = factorize(V)\\r\\n # plot parts-based representation\\r\\n plot(W)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f39d318ac7dd26dce6b5626403669e6b\",\n \"score\": \"0.6114271\",\n \"text\": \"def visualize(scores, faces):\\n pc_min, pc_max = np.min(scores, 0), np.max(scores, 0)\\n pc_scaled = (scores - pc_min) / (pc_max - pc_min) \\n fig, ax = plt.subplots()\\n for i in range(len(faces)):\\n imagebox = offsetbox.OffsetImage(faces[i, :].reshape(64,64).T, cmap=plt.cm.gray, zoom=0.5)\\n box = offsetbox.AnnotationBbox(imagebox, pc_scaled[i, 0:2])\\n ax.add_artist(box)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d928a6ba60ea9b0aeca42a669292c2\",\n \"score\": \"0.6110307\",\n \"text\": \"def visualize(args):\\n import matplotlib\\n matplotlib.use('Agg')\\n import matplotlib.pyplot as plt\\n\\n batch_size = 500\\n\\n # Load parameters\\n nn.load_parameters(os.path.join(args.model_save_path,\\n 'params_%06d.h5' % args.max_iter))\\n\\n # Create embedded network\\n image = nn.Variable([batch_size, 1, 28, 28])\\n feature = mnist_lenet_feature(image, test=True)\\n\\n # Process all images\\n features = []\\n labels = []\\n # Prepare MNIST data iterator\\n\\n rng = np.random.RandomState(313)\\n data = data_iterator_mnist(batch_size, train=False, rng=rng, shuffle=True)\\n for i in range(10000 // batch_size):\\n image_data, label_data = data.next()\\n image.d = image_data / 255.\\n feature.forward(clear_buffer=True)\\n features.append(feature.d.copy())\\n labels.append(label_data.copy())\\n features = np.vstack(features)\\n labels = np.vstack(labels)\\n\\n # Visualize\\n f = plt.figure(figsize=(16, 9))\\n for i in range(10):\\n c = plt.cm.Set1(i / 10.)\\n plt.plot(features[labels.flat == i, 0].flatten(), features[\\n labels.flat == i, 1].flatten(), '.', c=c)\\n plt.legend(list(map(str, range(10))))\\n plt.grid()\\n plt.savefig(os.path.join(args.monitor_path, \\\"embed.png\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2df38043c91fcc4e0b2fd71d87d021b7\",\n \"score\": \"0.60846716\",\n \"text\": \"def detect_in_images(geofence):\\n # Setup result directory.\\n result_dir = os.path.join('results', geofence, 'faces')\\n if not os.path.isdir(result_dir):\\n os.makedirs(result_dir)\\n\\n # Setup dataset.\\n filenames = sorted(glob.glob(\\n os.path.join('results', geofence, 'images', '*.jpg')\\n ))[:100]\\n dset = [np.asarray(PIL.Image.open(i)) for i in filenames] \\n\\n # Detect faces using MTCNN.\\n cur_face = 0\\n for x in dset:\\n img = PIL.Image.fromarray(x)\\n bboxes, landmarks = utils.detector.detect_faces(img)\\n img_drawn = utils.visualization_utils.show_bboxes(\\n img, bboxes, landmarks\\n )\\n import matplotlib.pyplot as plt\\n plt.imshow(img_drawn)\\n plt.show()\\n\\n # Align faces.\\n image = np.array(img.convert('RGB'))[:, :, ::-1] # CHW => HWC, RGB => BGR\\n fa = utils.facealigner.FaceAligner(\\n desiredLeftEye=(0.36, 0.5), desiredFaceWidth=178,\\n desiredFaceHeight=218\\n )\\n for i in landmarks:\\n faceAligned = fa.align(image, i)\\n while True:\\n cv2.imwrite(\\n os.path.join(result_dir, '%06d' % cur_face + '.png'),\\n faceAligned\\n )\\n cur_face +=1 \\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfdd0b9ee45b01abd7d92b0c74f184b7\",\n \"score\": \"0.607651\",\n \"text\": \"def get_faces(directory,\\n nets,\\n frame):\\n pnet,rnet,onet = nets\\n # parameters of the mtcnn \\n minsize = 75 # minimum size of face for mtcnn to detect \\n threshold = [0.6, 0.7, 0.7] # three steps's threshold for pnet,rnet and onet\\n factor = 0.709 # scale factor\\n scale_rate = 1\\n \\n \\n frame = cv2.resize(frame, (0, 0), fx=scale_rate, fy=scale_rate)\\n r_g_b_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\\n faces, points = detect_face.detect_face(r_g_b_frame, minsize, pnet, rnet, onet, threshold,factor) \\n \\n return faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e5de764318652c468f644d795c1a9e\",\n \"score\": \"0.6054111\",\n \"text\": \"def view(dataset):\\r\\n fig = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0),\\r\\n figure=dataset.class_name[3:])\\r\\n surf = mlab.pipeline.surface(dataset, opacity=0.1)\\r\\n mlab.pipeline.surface(mlab.pipeline.extract_edges(surf),\\r\\n color=(0, 0, 0), )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e5de764318652c468f644d795c1a9e\",\n \"score\": \"0.6054111\",\n \"text\": \"def view(dataset):\\r\\n fig = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0),\\r\\n figure=dataset.class_name[3:])\\r\\n surf = mlab.pipeline.surface(dataset, opacity=0.1)\\r\\n mlab.pipeline.surface(mlab.pipeline.extract_edges(surf),\\r\\n color=(0, 0, 0), )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b6421dd0cf8a319a651b59f2f1250e\",\n \"score\": \"0.6052199\",\n \"text\": \"def visualize(self,word, model):\\n variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])\\n figures = []\\n for parm_idx in range(len(model.means_[0])):\\n xmin = int(min(model.means_[:,parm_idx]) - max(variance[:,parm_idx]))\\n xmax = int(max(model.means_[:,parm_idx]) + max(variance[:,parm_idx]))\\n fig, axs = plt.subplots(model.n_components, sharex=True, sharey=False)\\n colours = cm.rainbow(np.linspace(0, 1, model.n_components))\\n for i, (ax, colour) in enumerate(zip(axs, colours)):\\n x = np.linspace(xmin, xmax, 100)\\n mu = model.means_[i,parm_idx]\\n sigma = math.sqrt(np.diag(model.covars_[i])[parm_idx])\\n ax.plot(x, mlab.normpdf(x, mu, sigma), c=colour)\\n ax.set_title(\\\"{} feature {} hidden state #{}\\\".format(word, parm_idx, i))\\n\\n ax.grid(True)\\n figures.append(plt)\\n for p in figures:\\n p.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93c11ead4c78e9e76c94676a95fe904\",\n \"score\": \"0.6048939\",\n \"text\": \"def Faces(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ec8e7f6d795d5070fe45f01b8cd8b3\",\n \"score\": \"0.6045157\",\n \"text\": \"def _draw_face_frames( faces, img ):\\n for (x, y, w, h) in faces:\\n cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 0), 2)\\n # %%\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a007fc84933d3b45162810ca0e83412\",\n \"score\": \"0.59697187\",\n \"text\": \"def show_all_detected_faces(list_of_images):\\r\\n i = 0\\r\\n for img in list_of_images:\\r\\n show_img(img, 'face nb {}'.format(i))\\r\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ca65974dfe3961c480d26fcb34d5fe\",\n \"score\": \"0.594537\",\n \"text\": \"def show(final_feed, image_size):\\n logger = logging.getLogger('display')\\n logger.setLevel(logging.DEBUG)\\n logger.debug('using opencv for displaying')\\n names_write = []\\n\\n for frame, name_list, aligned_list in final_feed:\\n start = int(time.time() * 1000.0)\\n\\n if name_list and list(set(name_list) - set(names_write)):\\n new_faces = list(set(name_list) - set(names_write))\\n print('new faces found: ' + str(new_faces))\\n\\n names_write = names_write + new_faces\\n with open('faces/faces_found.txt', 'w') as f:\\n for item in names_write:\\n f.write(\\\"%s\\\\n\\\" % item)\\n for name in names_write:\\n if not os.path.exists('faces/cap/%s' % name):\\n os.makedirs('faces/cap/%s' % name)\\n faces = emb_helper.get_face_in_frame(frame, aligned_list, image_size)\\n for i in range(len(faces)):\\n imageio.imwrite('faces/cap/%s/%s.jpg' % (name_list[i], str(uuid.uuid4())), faces[i])\\n logger.debug('%s ms\\\\t\\\\t saving faces took' % (int(time.time() * 1000.0) - start))\\n\\n frame = redraw_frame(frame, name_list, aligned_list)\\n cv2.imshow(\\\"stream\\\", cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))\\n k = cv2.waitKey(1) & 0xFF\\n logger.debug('%s ms\\\\t\\\\t displaying took' % (int(time.time() * 1000.0) - start))\\n\\n logger.info('destroying display frame')\\n\\n cv2.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0252ea4bff935c12ede33bd887dd516\",\n \"score\": \"0.5928481\",\n \"text\": \"def face_net(data_directory, required_size=(100, 100)):\\n # Dataset path\\n path = os.path.join('./Datasets', data_directory)\\n dataset_dir = os.path.join(path, 'img')\\n # Create directory for extracted faces images\\n viola_dir = os.path.join(path + '_faceNet', 'img')\\n Path(viola_dir).mkdir(parents=True, exist_ok=True)\\n # List of all the images available\\n files = sorted(os.listdir(dataset_dir), key=lambda x: int(x.split(\\\".\\\")[0]))\\n cnt = 0\\n result = []\\n # Extract face for each image in the directory\\n for file in files:\\n # Create the image path\\n image_path = os.path.join(dataset_dir, file)\\n # Load image from file\\n image = cv2.imread(image_path)\\n # Create the detector\\n detector = MTCNN()\\n # Detect faces in the given image\\n results = detector.detect_faces(image)\\n # If no faces are found notify it and insert the file in a related list\\n if len(results) == 0:\\n cnt += 1\\n print(\\\"In {0}, {1} face found!! -------------- counter: {2}\\\".format(file, len(results), cnt))\\n result.append(file)\\n # Otherwise extract the region of interest from the image\\n else:\\n x1, y1, width, height = results[0]['box']\\n x1, y1 = abs(x1), abs(y1)\\n x2, y2 = x1 + width, y1 + height\\n # Extract the face\\n extracted_image = image[y1:y2, x1:x2]\\n # Resize image extracted to the required size\\n resized = cv2.resize(extracted_image, required_size)\\n # Save image extracted and reshaped\\n cv2.imwrite(os.path.join(viola_dir, file), resized)\\n print(\\\"Saving {0}.\\\".format(file))\\n # Return list of files where faces are not detected\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df5dabe18e0be8ea3e10fc49fbd80d0c\",\n \"score\": \"0.59198755\",\n \"text\": \"def visualize_net(net):\\n # rearrange faces\\n new_net = net[33:36] + net[30:33] + net[27:30]\\n new_net += net[15:18] + net[12:15] + net[9:12]\\n new_net += net[24:27] + net[21:24] + net[18:21]\\n new_net += net[6:9] + net[3:6] + net[0:3]\\n new_net += net[42:45] + net[39:42] + net[36:39]\\n new_net += net[51:54] + net[48:51] + net[45:48]\\n\\n return new_net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3f988b2bd74a2d196c6b24e6342762\",\n \"score\": \"0.59095603\",\n \"text\": \"def show(final_feed, image_size):\\n logger = logging.getLogger('display')\\n logger.setLevel(logging.DEBUG)\\n logger.debug('using opencv for displaying')\\n names_write = []\\n\\n for frame, name_list, aligned_list in final_feed:\\n start = int(time.time() * 1000.0)\\n\\n if name_list and list(set(name_list) - set(names_write)):\\n new_faces = list(set(name_list) - set(names_write))\\n print('new faces found: ' + str(new_faces))\\n\\n names_write = names_write + new_faces\\n with open('faces/faces_found.txt', 'w') as f:\\n for item in names_write:\\n f.write(\\\"%s\\\\n\\\" % item)\\n for name in names_write:\\n if not os.path.exists('faces/cap/%s' % name):\\n os.makedirs('faces/cap/%s' % name)\\n faces = emb_helper.get_face_in_frame(frame, aligned_list, image_size)\\n for i in range(len(faces)):\\n imageio.imwrite('faces/cap/%s/%s.jpg' % (name_list[i], str(uuid.uuid4())), faces[i])\\n logger.debug('%s ms\\\\t\\\\t displaying took' % (int(time.time() * 1000.0) - start))\\n\\n logger.info('exiting')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd00f98da786236a3fddd17fbe7dee4\",\n \"score\": \"0.59058094\",\n \"text\": \"def detect_faces(self):\\r\\n newboxes = []\\r\\n faces_detected = []\\r\\n if self.fd_method == 'AI':\\r\\n # detect Face using Deep Learning: architecture MobileNet, method SSD\\r\\n (boxes, scores, __, __) = self.run() \\r\\n boxes = np.squeeze(boxes)\\r\\n scores = np.squeeze(scores)\\r\\n max_boxes_to_draw = 20\\r\\n for i in range(min(max_boxes_to_draw, boxes.shape[0])):\\r\\n # check teh score\\r\\n if scores[i] > self.fd_ai_min_score:\\r\\n box = tuple(boxes[i].tolist())\\r\\n ymin, xmin, ymax, xmax = box\\r\\n im_height, im_width, __ = self.image.shape\\r\\n # convert tensorflow normalized coordinate to absolute coordinate\\r\\n (left, right, top, bottom) = (int(xmin*im_width), int(xmax*im_width), int(ymin*im_height), int(ymax*im_height))\\r\\n face = self.image[top:bottom, left:right]\\r\\n newboxes.append((left, right, top, bottom))\\r\\n faces_detected.append(face) \\r\\n self.boxes = newboxes\\r\\n self.faces_detected = faces_detected\\r\\n elif self.fd_method == 'HOG':\\r\\n # detect Face using HOG\\r\\n # self.rgb_image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)\\r\\n boxes = face_recognition.face_locations(self.rgb_image, model='hog')\\r\\n for box in boxes:\\r\\n top, right, bottom, left = box\\r\\n face = self.image[top:bottom, left:right]\\r\\n newboxes.append((left, right, top, bottom))\\r\\n faces_detected.append(face)\\r\\n self.boxes = newboxes\\r\\n self.faces_detected = faces_detected\\r\\n elif self.fd_method == 'HAAR':\\r\\n gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)\\r\\n faces = self.face_cascade.detectMultiScale(\\r\\n gray,\\r\\n scaleFactor=1.05,\\r\\n minNeighbors=9,\\r\\n minSize=(30,30),\\r\\n flags = cv2.CASCADE_SCALE_IMAGE\\r\\n )\\r\\n for (x, y, w, h) in faces:\\r\\n newboxes.append((x, y+w, y, y+h))\\r\\n face = self.image[y:y+h, x:x+w]\\r\\n faces_detected.append(face)\\r\\n self.boxes = newboxes\\r\\n self.faces_detected = faces_detected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"220f582933d0fc57da3c4eddad085cb4\",\n \"score\": \"0.58716697\",\n \"text\": \"def visualizeModels(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba3603c888d83a95ee3aef96025fd7d\",\n \"score\": \"0.5863185\",\n \"text\": \"def extract_faces_from_test_image():\\n image = cv2.imread(\\\"images/group3.jpg\\\")\\n\\n faces = face_recognition.face_locations(image)\\n\\n index = 0\\n for face in faces:\\n top, right, bottom, left = face\\n\\n label = predict_cnn(image[top:bottom, left:right])[0]\\n cv2.imwrite(\\\"test_images/%s-%s.jpg\\\" % (label, index), image[top:bottom, left:right])\\n\\n index += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36173f0ff1c77aec0c080e4d57643242\",\n \"score\": \"0.5850442\",\n \"text\": \"def classify_face(im):\\r\\n img = cv2.imread(im, 1)\\r\\n\\r\\n #img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)\\r\\n #img = img[:,:,::-1]\\r\\n\\r\\n face_locations = face_recognition.face_locations(img)\\r\\n unknown_face_encodings = face_recognition.face_encodings(img, face_locations)\\r\\n print(unknown_face_encodings[0].tolist())\\r\\n face_names = []\\r\\n for face_encoding in unknown_face_encodings:\\r\\n # See if the face is a match for the known face(s)\\r\\n matches = face_recognition.compare_faces(faces_encoded, face_encoding)\\r\\n name = \\\"Unknown\\\"\\r\\n\\r\\n # use the known face with the smallest distance to the new face\\r\\n face_distances = face_recognition.face_distance(faces_encoded, face_encoding)\\r\\n best_match_index = np.argmin(face_distances)\\r\\n if matches[best_match_index]:\\r\\n name = known_face_names[best_match_index]\\r\\n\\r\\n face_names.append(name)\\r\\n\\r\\n for (top, right, bottom, left), name in zip(face_locations, face_names):\\r\\n # Draw a box around the face\\r\\n cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)\\r\\n\\r\\n # Draw a label with a name below the face\\r\\n cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)\\r\\n font = cv2.FONT_HERSHEY_DUPLEX\\r\\n cv2.putText(img, name, (left -20, bottom + 15),fontFace=font, fontScale=1.0, color=(255, 255, 255), thickness=2)\\r\\n print(face_names)\\r\\n cv2.imshow('Image', img)\\r\\n cv2.waitKey(0)\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42d1415953b95564c7d1ca238e808a73\",\n \"score\": \"0.58485097\",\n \"text\": \"def faces_example(path):\\r\\n\\r\\n with open(path, 'rb') as f:\\r\\n X = pickle.load(f)\\r\\n\\r\\n num_images, num_pixels = np.shape(X)\\r\\n d = int(num_pixels**0.5)\\r\\n print(\\\"The number of images in the data set is \\\" + str(num_images))\\r\\n print(\\\"The image size is \\\" + str(d) + \\\" by \\\" + str(d))\\r\\n\\r\\n # plot some examples of faces:\\r\\n plt.gray()\\r\\n for i in range(4):\\r\\n plt.subplot(2, 2, i+1)\\r\\n plt.imshow(np.reshape(X[i, :], (d, d)))\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69906b7ae64fc520bb6ff89059622930\",\n \"score\": \"0.58278435\",\n \"text\": \"def detect_faces(self, img_raw):\\n\\n img, scale = self.preprocessor(img_raw)\\n # tic = time.time()\\n with torch.no_grad():\\n loc, conf, landmarks = self.model(img) # forward pass\\n # print('net forward time: {:.4f}'.format(time.time() - tic))\\n\\n priors = prior_box(self.cfg, image_size=img.shape[2:]).to(self.device)\\n boxes = decode(loc.data.squeeze(0), priors, self.cfg['variance'])\\n boxes = boxes * scale\\n scores = conf.squeeze(0)[:, 1]\\n landmarks = decode_landmark(landmarks.squeeze(0), priors, self.cfg['variance'])\\n scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],\\n img.shape[3], img.shape[2], img.shape[3], img.shape[2],\\n img.shape[3], img.shape[2]]).to(self.device)\\n landmarks = landmarks * scale1\\n\\n # ignore low scores\\n index = torch.where(scores > self.thresh)[0]\\n boxes = boxes[index]\\n landmarks = landmarks[index]\\n scores = scores[index]\\n\\n # keep top-K before NMS\\n order = scores.argsort(dim=0, descending=True)[:self.top_k]\\n boxes = boxes[order]\\n landmarks = landmarks[order]\\n scores = scores[order]\\n\\n # Do NMS\\n keep = nms(boxes, scores, self.nms_thresh)\\n boxes = torch.abs(boxes[keep, :])\\n scores = scores[:, None][keep, :]\\n landmarks = landmarks[keep, :].reshape(-1, 5, 2)\\n\\n # # keep top-K faster NMS\\n landmarks = landmarks[:self.keep_top_k, :]\\n scores = scores[:self.keep_top_k, :]\\n boxes = boxes[:self.keep_top_k, :]\\n\\n return boxes, scores, landmarks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82935971c20230a9ba0a6887b826fb0c\",\n \"score\": \"0.5818002\",\n \"text\": \"def visualize(word, model):\\r\\n variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])\\r\\n figures = []\\r\\n for parm_idx in range(len(model.means_[0])):\\r\\n xmin = int(min(model.means_[:,parm_idx]) - max(variance[:,parm_idx]))\\r\\n xmax = int(max(model.means_[:,parm_idx]) + max(variance[:,parm_idx]))\\r\\n fig, axs = plt.subplots(model.n_components, sharex=True, sharey=False)\\r\\n colours = cm.rainbow(np.linspace(0, 1, model.n_components))\\r\\n for i, (ax, colour) in enumerate(zip(axs, colours)):\\r\\n x = np.linspace(xmin, xmax, 100)\\r\\n mu = model.means_[i,parm_idx]\\r\\n sigma = math.sqrt(np.diag(model.covars_[i])[parm_idx])\\r\\n ax.plot(x, mlab.normpdf(x, mu, sigma), c=colour)\\r\\n ax.set_title(\\\"{} feature {} hidden state #{}\\\".format(word, parm_idx, i))\\r\\n\\r\\n ax.grid(True)\\r\\n figures.append(plt)\\r\\n for p in figures:\\r\\n p.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cf5d72397595eab915d7695ae6f5b48\",\n \"score\": \"0.5811071\",\n \"text\": \"def recognize_faces_and_draw_svm(self, minProba=0.5):\\r\\n index = 0\\r\\n alpha = 0.6\\r\\n new_image = self.image.copy()\\r\\n text1 = None\\r\\n text2 = None\\r\\n text3 = None\\r\\n top_names = []\\r\\n # loop all faces\\r\\n for left, right, top, bottom in self.boxes:\\r\\n cv2.rectangle(new_image,(left,top),(right,bottom),(0,255,0),2)\\r\\n if self.faces_aligned == [] or self.faces_aligned is None:\\r\\n face = self.faces_detected[index]\\r\\n else:\\r\\n face = self.faces_aligned[index]\\r\\n # generate encoding for current face\\r\\n encoding = self.generate_encoding(face)\\r\\n encoding = encoding.reshape(1, -1)\\r\\n # match a face with all faces in database\\r\\n # perform classification to recognize the face\\r\\n preds = self.svm_model.predict_proba(encoding)[0]\\r\\n # get the biggest probability\\r\\n j = np.argmax(preds)\\r\\n proba = preds[j]\\r\\n # overlay = new_image.copy()\\r\\n left_rect = right + 10\\r\\n top_rect = top\\r\\n # if the biggest probability is bigger than allowed, then sort 3 biggest probabilities\\r\\n if proba > minProba: \\r\\n # get 3 biggest probability SVM\\r\\n big3_index = heapq.nlargest(3, range(len(preds)), preds.take)\\r\\n name = self.svm_label.classes_[big3_index[0]]\\r\\n prob = preds[big3_index[0]]\\r\\n text1 = \\\"{}: {:.2f}%\\\".format(name, prob * 100) \\r\\n top_names.append(name) \\r\\n name = self.svm_label.classes_[big3_index[1]]\\r\\n prob = preds[big3_index[1]] \\r\\n text2 = \\\"{}: {:.2f}%\\\".format(name, prob * 100) \\r\\n top_names.append(name) \\r\\n name = self.svm_label.classes_[big3_index[2]]\\r\\n prob = preds[big3_index[2]]\\r\\n text3 = \\\"{}: {:.2f}%\\\".format(name, prob * 100) \\r\\n top_names.append(name) \\r\\n right_rect = left_rect + 150\\r\\n bottom_rect = top + 75\\r\\n # cv2.rectangle(overlay, (left_rect, top_rect), (right_rect, bottom_rect), (0, 0, 0), -1) \\r\\n # new_image = cv2.addWeighted(overlay, alpha, new_image, 1 - alpha, 0)\\r\\n # *** 2MPX\\r\\n cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\\r\\n cv2.putText(new_image, text2, (left_rect+10, top_rect+40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\\r\\n cv2.putText(new_image, text3, (left_rect+10, top_rect+60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\\r\\n # *** 8MPX\\r\\n # cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\\r\\n # cv2.putText(new_image, text2, (left_rect+10, top_rect+40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\\r\\n # cv2.putText(new_image, text3, (left_rect+10, top_rect+60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\\r\\n # *** 12MPX\\r\\n # cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 255, 255), 4)\\r\\n # cv2.putText(new_image, text2, (left_rect+10, top_rect+100), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 255, 255), 4)\\r\\n # cv2.putText(new_image, text3, (left_rect+10, top_rect+180), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 255, 255), 4)\\r\\n # print(text1)\\r\\n # print(text2)\\r\\n # print(text3)\\r\\n else:\\r\\n # name = 'Unknown'\\r\\n text1 = 'Unknown'\\r\\n # print(text1)\\r\\n right_rect = left_rect + 100\\r\\n bottom_rect = top + 35\\r\\n # cv2.rectangle(overlay, (left_rect, top_rect), (right_rect, bottom_rect), (0, 0, 0), -1) \\r\\n # new_image = cv2.addWeighted(overlay, alpha, new_image, 1 - alpha, 0)\\r\\n # *** 2MPX\\r\\n cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\\r\\n # cv2.putText(new_image, text1, (left_rect+10, top_rect+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\\r\\n top_names.append('Unknown')\\r\\n \\r\\n # text_title = \\\"threshold {:.2f}%\\\".format(minProba * 100)\\r\\n # cv2.putText(new_image, text_title, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 1)\\r\\n index = index + 1\\r\\n \\r\\n return new_image, top_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb64c3c22ce52e413954ed4654d0162e\",\n \"score\": \"0.5804104\",\n \"text\": \"def visualize(self, face):\\n _image = deepcopy(face.container_image)\\n xmin, ymin, xmax, ymax = face.bbox\\n start_pt = (xmin, ymin)\\n end_pt = (xmax, ymax)\\n color = (255, 0, 0)\\n thickness = 1\\n cv2.rectangle(_image, start_pt, end_pt, color, thickness)\\n thickness = 1\\n color = (255, 255, 255)\\n origin = start_pt\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n font_scale = 1\\n display_name = self.data_acquisition.trn_wh.get_name(face.uid)\\n if display_name is None:\\n display_name = str(face.uid)\\n cv2.putText(_image, display_name, origin, font, font_scale, color, thickness, cv2.LINE_AA)\\n return _image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc2557011859c874e8bbf0e163fe68e9\",\n \"score\": \"0.58006907\",\n \"text\": \"def _visualize():\\n viz_words = len(model.wv.vectors)\\n word_vector = model.wv.vectors\\n tsne = sklearn.manifold.TSNE(n_components=2)\\n embed_tsne = tsne.fit_transform(word_vector)\\n fig, ax = plt.subplots(figsize=(6, 6))\\n for i in range(viz_words):\\n plt.scatter(*embed_tsne[i, :], s=2, alpha=0.6, color='b')\\n plt.annotate(model.wv.index2word[i], (embed_tsne[i, 0], embed_tsne[i, 1]), alpha=0.6, fontsize=7)\\n plt.savefig('tsne_zones_{}.png'.format(self.store_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"570ae98d73e42debe105a21212a71271\",\n \"score\": \"0.579005\",\n \"text\": \"def visualize(word, model):\\n variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])\\n figures = []\\n for parm_idx in range(len(model.means_[0])):\\n xmin = int(min(model.means_[:,parm_idx]) - max(variance[:,parm_idx]))\\n xmax = int(max(model.means_[:,parm_idx]) + max(variance[:,parm_idx]))\\n fig, axs = plt.subplots(model.n_components, sharex=True, sharey=False)\\n colours = cm.rainbow(np.linspace(0, 1, model.n_components))\\n for i, (ax, colour) in enumerate(zip(axs, colours)):\\n x = np.linspace(xmin, xmax, 100)\\n mu = model.means_[i,parm_idx]\\n sigma = math.sqrt(np.diag(model.covars_[i])[parm_idx])\\n ax.plot(x, mlab.normpdf(x, mu, sigma), c=colour)\\n ax.set_title(\\\"{} feature {} hidden state #{}\\\".format(word, parm_idx, i))\\n\\n ax.grid(True)\\n figures.append(plt)\\n for p in figures:\\n p.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847ef891175fc5fe72aba9070bf83423\",\n \"score\": \"0.5762653\",\n \"text\": \"def get_face_from_img(img, mtcnn):\\n sample = Image.fromarray(img, 'RGB')\\n \\n face = mtcnn.detect(sample)\\n face = face.permute(1, 2, 0)\\n return face.int().numpy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f69877bf1da9daaf5883b487d3066f\",\n \"score\": \"0.5758128\",\n \"text\": \"def show_faces(faces, img):\\n roi, blur_ = None, None\\n for (x, y, w, h) in faces:\\n # Calculate center point and radius for circular masks.\\n cx = w // 2\\n cy = h // 2\\n r = h // 2 # int(((w ** 2 + h ** 2) ** 0.5)/2) # c² = a² + b², for a bigger circle, the roi itself needs to\\n # be bigger which results in inconsistencies.\\n\\n # Draw a rectangle/circle on the image.\\n # img = cv.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\\n img = cv.circle(img, (x+cx, y+cy), r, (101, 201, 255), 2)\\n\\n # Create a ROI.\\n roi = roi_image(x, y, w, h, img)\\n\\n # Blur the ROI.\\n blur = blur_face_avg(roi)\\n\\n # Create masks (normal and inverted).\\n mask = np.full((roi.shape[0], roi.shape[1], 1), 0, dtype=np.uint8) # Init black mask.\\n cv.circle(mask, (cx, cy), r, (255, 255, 255), -1) # Fill in a white circle in the middle to create the mask.\\n mask_inverted = 255 - mask\\n\\n # Apply the masks.\\n fg = cv.bitwise_and(blur, blur, mask=mask) # Foreground: blurred circle face.\\n bg = cv.bitwise_and(roi, roi, mask=mask_inverted) # Background: part outside the detected face.\\n\\n # Add the foreground & background together and insert it into the frame.\\n img[y:y+h, x:x+w] = cv.add(fg, bg)\\n\\n show_image(img)\\n cv.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1820fd012b609f066567d8511acbd4\",\n \"score\": \"0.57465416\",\n \"text\": \"def face_recognition():\\n face_cascade = cv2.CascadeClassifier(cv2_resource_path+\\\"haarcascade_frontalface_default.xml\\\")\\n img = cv2.imread(\\\"IMG_4703.JPG\\\")\\n\\n img_gray = cv2.cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)\\n\\n faces = face_cascade.detectMultiScale(img_gray, 1.1,4)\\n\\n for (x,y,w,h) in faces:\\n cv2.rectangle(img, (x,y),(x+w,y+h), (0,255,0), 2)\\n\\n\\n cv2.imshow(\\\"Result\\\", img)\\n cv2.waitKey(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd3412e1badcde85db657841bc0e1a55\",\n \"score\": \"0.57351214\",\n \"text\": \"def faces_embeddings(path='./faces.pickle'):\\r\\n with open(path, 'rb') as f:\\r\\n faces = pickle.load(f)\\r\\n\\r\\n title = 'faces_PCA'\\r\\n faces_pca = PCA(n_components=2).fit_transform(faces)\\r\\n plot_with_images(faces_pca, faces, title)\\r\\n plt.savefig(f'./figures/{title}.png')\\r\\n plt.show()\\r\\n\\r\\n title = 'faces_MDS'\\r\\n faces_mds, _ = MDS(calc_squared_euclidean_distance_matrix(faces), d=2)\\r\\n plot_with_images(faces_mds, faces, title)\\r\\n plt.savefig(f'./figures/{title}.png')\\r\\n plt.show()\\r\\n\\r\\n faces_lle_by_param = dict()\\r\\n for k in [3, 5, 7, 11, 13, 17, 19, 23]:\\r\\n title = f'faces_LLE_{k}_nearest_neighbors'\\r\\n faces_lle_by_param[k] = LLE(faces, 2, k)\\r\\n plot_with_images(faces_lle_by_param[k], faces, title)\\r\\n plt.savefig(f'./figures/{title}.png')\\r\\n plt.show()\\r\\n\\r\\n faces_dm_by_params = dict()\\r\\n for sigma in [1, 2, 3, 4, 5, 6]:\\r\\n for t in [3, 5, 7, 11, 17]:\\r\\n title = f'faces_DiffusionMap_sigma_{sigma}_t_{t}'\\r\\n faces_dm_by_params[(sigma, t)] = DiffusionMap(faces, 2, sigma, t)\\r\\n plot_with_images(faces_dm_by_params[(sigma, t)], faces, title)\\r\\n plt.savefig(f'./figures/{title}.png')\\r\\n plt.show()\\r\\n\\r\\n title = 'faces_tSNE'\\r\\n faces_tsne = TSNE(n_components=2).fit_transform(faces)\\r\\n plot_with_images(faces_tsne, faces, title)\\r\\n plt.savefig(f'./figures/{title}.png')\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48458caa8fa8e0e1435dff9f61e5be97\",\n \"score\": \"0.57238865\",\n \"text\": \"def show_face_markings(face_locations,small_frame,msg):\\n for face_location in face_locations:\\n # Scale back up face locations since the frame we detected in was scaled to 1/4 size\\n top, right, bottom, left = face_location\\n\\n # Draw a box around the face\\n cv2.rectangle(small_frame, (left, top), (right, bottom), (0, 0, 255), 2)\\n\\n # Draw a label with a name below the face\\n cv2.rectangle(small_frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)\\n\\n ## This puts the clssification in the box of the face\\n cv2.putText(small_frame, msg, (left + 6, bottom - 6), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fa76d18be24350e2644a458d4289542\",\n \"score\": \"0.57207197\",\n \"text\": \"def visualize_training(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b207dfe38210ea3c066b261693e771b\",\n \"score\": \"0.5720429\",\n \"text\": \"def show_detections(self, image):\\n boxes = self.face_detector.predict(image=image)\\n if len(boxes) == 0:\\n boxes = self.body_detector.predict(image=image)\\n confidences = [conf for conf, _ in boxes]\\n boxes = [box for _, box in boxes]\\n distances = [self.camera_calculator.rectangleToRealWorldXY(rectangle=box, h=image.shape[0],\\n w=image.shape[1]) for box in boxes]\\n\\n show(image=image, boxes=boxes, names=None, distances=distances, confidences=confidences)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00362929c98df99a25c0733bd23eb4a8\",\n \"score\": \"0.57108104\",\n \"text\": \"def main():\\n landmarks = read_pts('../bin/data/image_0010.pts')\\n image_width = 1280 # Make sure to adjust these when using your own images!\\n image_height = 1024\\n\\n model = eos.morphablemodel.load_model(\\\"../share/sfm_shape_3448.bin\\\")\\n blendshapes = eos.morphablemodel.load_blendshapes(\\\"../share/expression_blendshapes_3448.bin\\\")\\n # Create a MorphableModel with expressions from the loaded neutral model and blendshapes:\\n morphablemodel_with_expressions = eos.morphablemodel.MorphableModel(model.get_shape_model(), blendshapes,\\n color_model=eos.morphablemodel.PcaModel(),\\n vertex_definitions=None,\\n texture_coordinates=model.get_texture_coordinates())\\n landmark_mapper = eos.core.LandmarkMapper('../share/ibug_to_sfm.txt')\\n edge_topology = eos.morphablemodel.load_edge_topology('../share/sfm_3448_edge_topology.json')\\n contour_landmarks = eos.fitting.ContourLandmarks.load('../share/ibug_to_sfm.txt')\\n model_contour = eos.fitting.ModelContour.load('../share/sfm_model_contours.json')\\n\\n (mesh, pose, shape_coeffs, blendshape_coeffs) = eos.fitting.fit_shape_and_pose(morphablemodel_with_expressions,\\n landmarks, landmark_mapper, image_width, image_height, edge_topology, contour_landmarks, model_contour)\\n\\n # Now you can use your favourite plotting/rendering library to display the fitted mesh, using the rendering\\n # parameters in the 'pose' variable.\\n\\n # Or for example extract the texture map, like this:\\n # import cv2\\n # image = cv2.imread('../bin/data/image_0010.png')\\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA, 4) # extract_texture(...) expects a 4-channel image\\n # texturemap = eos.render.extract_texture(mesh, pose, image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fd81bfaf8db94a84b095e041059237b\",\n \"score\": \"0.57089657\",\n \"text\": \"def visualize(net, img_path, conv_layer_name):\\n image = read_image_mxnet(img_path)\\n image = preprocess(image)\\n image = image.expand_dims(axis=0)\\n \\n pred_str = get_class_name(run_inference(net, image))\\n \\n orig_img = read_image_cv(img_path)\\n vizs = gradcam.visualize(net, image, orig_img, conv_layer_name)\\n return (pred_str, (orig_img, *vizs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98c3312646851e8805731d3689f69d23\",\n \"score\": \"0.5703212\",\n \"text\": \"def face_detect():\\n face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\\n cap = cv2.VideoCapture(0)\\n while True:\\n ret, fram = cap.read()\\n faces = face_cascade.detectMultiScale(fram, 1.1, 7)\\n for x, y, w, h in faces:\\n cv2.rectangle(fram, (x - 5, y - 25), (x + w, y + h), (0, 255, 0), 2)\\n cv2.imshow('fram', fram)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f3433d7fa223ddb3e52363e792a35cb\",\n \"score\": \"0.570318\",\n \"text\": \"def visualize():\\n args = get_args()\\n print(args)\\n dir_img = args.dir_in\\n dir_out = args.dir_out\\n\\n # set up model of detectron2\\n predictor, cfg = setup_model(args)\\n print(f'DIR_img: {dir_img}')\\n img_files = os.listdir(dir_img)\\n img_files = [file for file in img_files if file.endswith('.jpeg') or file.endswith('.png')]\\n for img_file in img_files:\\n img_file = os.path.join(dir_img, img_file)\\n im = cv2.imread(img_file)\\n outputs = predictor(im)\\n v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)\\n out = v.draw_instance_predictions(outputs['instances'].to('cpu'))\\n path = os.path.join(dir_out, os.path.basename(img_file))\\n cv2.imwrite(path, out.get_image()[:, :, ::-1]) # imreadの結果がBGRの順なので::-1でRGBに変換しているっぽい\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e03d81362ecc4f817c85326cf3d272b0\",\n \"score\": \"0.57031417\",\n \"text\": \"def detect_faces(img, min_face_size=20.0, thresholds=[0.6, 0.7, 0.8]):\\n\\n # LOAD MODELS\\n pnet = PNet()\\n rnet = RNet()\\n onet = ONet()\\n onet.eval()\\n\\n # BUILD AN IMAGE PYRAMID\\n width, height = img.size\\n min_length = min(height, width)\\n\\n min_detection_size = 12\\n factor = 0.707 # sqrt(0.5)\\n\\n # scales for scaling the image\\n scales = []\\n\\n # scales the image so that\\n # minimum size that we can detect equals to\\n # minimum face size that we want to detect\\n m = min_detection_size/min_face_size\\n min_length *= m\\n\\n factor_count = 0\\n while min_length > min_detection_size:\\n scales.append(m*factor**factor_count)\\n min_length *= factor\\n factor_count += 1\\n\\n # STAGE 1\\n\\n # it will be returned\\n bounding_boxes = []\\n\\n # run P-Net on different scales\\n for s in scales:\\n boxes = run_first_stage(img, pnet, s, thresholds[0])\\n bounding_boxes.append(boxes)\\n\\n bounding_boxes = [i for i in bounding_boxes if i is not None]\\n bounding_boxes = np.vstack(bounding_boxes)\\n\\n pick = nms(bounding_boxes[:, 0:5], 0.7, 'union')\\n bounding_boxes = bounding_boxes[pick]\\n bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])\\n bounding_boxes = convert_to_square(bounding_boxes)\\n bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])\\n\\n # STAGE 2\\n\\n img_boxes = get_image_boxes(bounding_boxes, img, size=24)\\n img_boxes = Variable(torch.FloatTensor(img_boxes))\\n output = rnet(img_boxes)\\n offsets = output[0].data.numpy()\\n probs = output[1].data.numpy()\\n\\n passed = np.where(probs[:, 1] > thresholds[1])\\n bounding_boxes = bounding_boxes[passed]\\n\\n bounding_boxes[:, 4] = probs[passed, 1].reshape((-1,))\\n offsets = offsets[passed]\\n\\n pick = nms(bounding_boxes, 0.7, 'union')\\n bounding_boxes = bounding_boxes[pick]\\n bounding_boxes = calibrate_box(bounding_boxes, offsets[pick])\\n bounding_boxes = convert_to_square(bounding_boxes)\\n bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])\\n\\n # STAGE 3\\n\\n img_boxes = get_image_boxes(bounding_boxes, img, size=48)\\n img_boxes = Variable(torch.FloatTensor(img_boxes))\\n output = onet(img_boxes)\\n landmarks = output[0].data.numpy()\\n offsets = output[1].data.numpy()\\n probs = output[2].data.numpy()\\n\\n passed = np.where(probs[:, 1] > thresholds[2])\\n bounding_boxes = bounding_boxes[passed]\\n\\n bounding_boxes[:, 4] = probs[passed, 1].reshape((-1,))\\n offsets = offsets[passed]\\n landmarks = landmarks[passed]\\n\\n # compute landmark points\\n bbw = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0\\n bbh = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0\\n landmarks[:, 0:5] = np.expand_dims(bounding_boxes[:, 0], 1) +\\\\\\n np.expand_dims(bbw, 1)*landmarks[:, 0:5]\\n landmarks[:, 5:10] = np.expand_dims(bounding_boxes[:, 1], 1) +\\\\\\n np.expand_dims(bbh, 1)*landmarks[:, 5:10]\\n\\n bounding_boxes = calibrate_box(bounding_boxes, offsets)\\n pick = nms(bounding_boxes, 0.7, 'min')\\n bounding_boxes = bounding_boxes[pick]\\n landmarks = landmarks[pick]\\n\\n return bounding_boxes, landmarks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b1a96a9e4c23c3db2f08f6a31192b6\",\n \"score\": \"0.5678757\",\n \"text\": \"def test_visualizer1() -> None:\\n H = 640\\n W = 480\\n img_rgb = np.ones((H, W, 3), dtype=np.uint8)\\n label_map = np.zeros((H, W), dtype=np.uint8)\\n label_map[: H // 2, : W // 2] = 0\\n label_map[: H // 2, W // 2 :] = 1\\n label_map[H // 2 :, : W // 2] = 2\\n label_map[H // 2 :, W // 2 :] = 3\\n\\n id_to_class_name_map = {0: \\\"sky\\\", 1: \\\"road\\\", 2: \\\"person\\\", 3: \\\"horse\\\"}\\n\\n vis_obj = Visualizer(img_rgb, None)\\n output_img = vis_obj.overlay_instances(label_map, id_to_class_name_map)\\n plt.imshow(output_img)\\n # plt.show()\\n plt.close(\\\"all\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da2bfe943e5584b3f053ab7b4f8d124\",\n \"score\": \"0.5662283\",\n \"text\": \"def nmeshface(self):\\n return self._ptr.contents.nmeshface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c6c4179b9212ac61e2d46b491a884e\",\n \"score\": \"0.5661713\",\n \"text\": \"def facesDetect(self):\\n\\t\\t\\n\\t\\tself.faces = []\\n\\t\\tclassifier_face = self.classifiers['face']\\n\\t\\t\\n\\t\\tfor rotation in [0, 30, -30]:\\n\\t\\t\\tclassifier_settings = classifier_face['settings'].copy()\\n\\t\\t\\trot_image = self.image.rotate(rotation, resample=self.resample)\\n\\t\\t\\t\\n\\t\\t\\tif self.image.width > self.image.height:\\n\\t\\t\\t\\tclassifier_settings['minSize'] = (\\n\\t\\t\\t\\t\\tint(self.image.width*.05),\\n\\t\\t\\t\\t\\tint(self.image.width*.05)\\n\\t\\t\\t\\t)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tclassifier_settings['minSize'] = (\\n\\t\\t\\t\\t\\tint(self.image.height*.05),\\n\\t\\t\\t\\t\\tint(self.image.height*.05)\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\n\\t\\t\\tfound_faces = classifier_face['classifier'].detectMultiScale(\\n\\t\\t\\t\\tself.imageToCV(rot_image),\\n\\t\\t\\t\\t**classifier_settings\\n\\t\\t\\t);\\n\\t\\t\\t\\n\\t\\t\\tfor face_box in found_faces:\\n\\t\\t\\t\\tface_points = Object2D.boxPoints(face_box)\\n\\t\\t\\t\\tface_polygon = Object2D.boxPolygon(face_box)\\n\\t\\t\\t\\tface_center = Object2D.boxCenter(face_box)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Rotate these points for drawing on the original image.\\n\\t\\t\\t\\tface_polygon = Object2D.rotate(\\n\\t\\t\\t\\t\\tface_polygon, rotation, self.center\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tface_center = Object2D.rotate(\\n\\t\\t\\t\\t\\tface_center, rotation, self.center\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif not self.hasNearbyFaceCenters(face_center):\\n\\t\\t\\t\\t\\tface = {\\n\\t\\t\\t\\t\\t\\t'rotation': rotation,\\n\\t\\t\\t\\t\\t\\t'crop': face_points,\\n\\t\\t\\t\\t\\t\\t'box': face_box,\\n\\t\\t\\t\\t\\t\\t'draw': {\\n\\t\\t\\t\\t\\t\\t\\t'center': face_center,\\n\\t\\t\\t\\t\\t\\t\\t'polygon': face_polygon\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tself.faces.append(face)\\n\\t\\t\\t\\n\\t\\t\\trot_image.close()\\n\\t\\t\\n\\t\\treturn self.faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05772ab454e74cfca92db24f53242e7e\",\n \"score\": \"0.56489074\",\n \"text\": \"def plot_feature_maps(model, img):\\n # lets first create a list of outputs from successive layers of the model\\n successive_outputs = [layer.output for layer in model.layers[1:]]\\n\\n # now lets create another model with the input of the model passed in,\\n # and outputs of each layer of the passed in model\\n visualization_model = Model(inputs=model.input, outputs=successive_outputs)\\n\\n # now run the image through the network, obtaining intermediate feature maps\\n successive_feature_maps = visualization_model.predict(img)\\n\\n # layer names to have them as part of our plot for readability purposes\\n layer_names = [layer.name for layer in model.layers]\\n\\n # loop through the layers\\n for layer_name, feature_map in zip(layer_names, successive_feature_maps):\\n # plot feature maps only for conv/pool layers\\n if len(feature_map.shape) == 4:\\n n_channels = feature_map.shape[-1]\\n size = feature_map.shape[1]\\n\\n # We will tile our images in this matrix\\n display_grid = np.zeros((size, size * n_channels))\\n\\n # looping through each filter of a layer\\n for i in range(n_channels):\\n # postprocessing the image to be visually palatable\\n img = feature_map[0, :, :, i]\\n img -= img.mean()\\n img /= img.std()\\n img *= 64\\n img += 128\\n img = np.clip(img, 0, 255).astype('uint8')\\n # tile each filter into a horizontal grid\\n display_grid[:, i * size: (i + 1) * size] = img\\n\\n # displaying the feature map grid\\n scale = 20. / n_channels\\n plt.figure(figsize=(scale * n_channels, scale))\\n plt.title(layer_name)\\n plt.grid(False)\\n plt.imshow(display_grid, aspect='auto', cmap='viridis')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305330adbfd4e1f1b6d9bb4f961dfae9\",\n \"score\": \"0.5640138\",\n \"text\": \"def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):\\n factor_count=0\\n total_boxes=np.empty((0,9))\\n points=np.empty(0)\\n h=img.shape[0]\\n w=img.shape[1]\\n minl=np.amin([h, w])\\n m=12.0/minsize\\n minl=minl*m\\n # create scale pyramid\\n scales=[]\\n while minl>=12:\\n scales += [m*np.power(factor, factor_count)]\\n minl = minl*factor\\n factor_count += 1\\n\\n # first stage\\n for scale in scales:\\n hs=int(np.ceil(h*scale))\\n ws=int(np.ceil(w*scale))\\n im_data = imresample(img, (hs, ws))\\n im_data = (im_data-127.5)*0.0078125\\n img_x = np.expand_dims(im_data, 0)\\n img_y = np.transpose(img_x, (0,2,1,3))\\n out = pnet.predict_on_batch(img_y)\\n out0 = np.transpose(out[0], (0,2,1,3))\\n out1 = np.transpose(out[1], (0,2,1,3))\\n\\n boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0])\\n\\n # inter-scale nms\\n pick = nms(boxes.copy(), 0.5, 'Union')\\n if boxes.size>0 and pick.size>0:\\n boxes = boxes[pick,:]\\n total_boxes = np.append(total_boxes, boxes, axis=0)\\n\\n numbox = total_boxes.shape[0]\\n if numbox>0:\\n pick = nms(total_boxes.copy(), 0.7, 'Union')\\n total_boxes = total_boxes[pick,:]\\n regw = total_boxes[:,2]-total_boxes[:,0]\\n regh = total_boxes[:,3]-total_boxes[:,1]\\n qq1 = total_boxes[:,0]+total_boxes[:,5]*regw\\n qq2 = total_boxes[:,1]+total_boxes[:,6]*regh\\n qq3 = total_boxes[:,2]+total_boxes[:,7]*regw\\n qq4 = total_boxes[:,3]+total_boxes[:,8]*regh\\n total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]]))\\n total_boxes = rerec(total_boxes.copy())\\n total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32)\\n dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)\\n\\n numbox = total_boxes.shape[0]\\n if numbox>0:\\n # second stage\\n tempimg = np.zeros((24,24,3,numbox))\\n for k in range(0,numbox):\\n tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))\\n tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]\\n if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:\\n tempimg[:,:,:,k] = imresample(tmp, (24, 24))\\n else:\\n return np.empty()\\n tempimg = (tempimg-127.5)*0.0078125\\n tempimg1 = np.transpose(tempimg, (3,1,0,2))\\n out = rnet.predict_on_batch(tempimg1)\\n out0 = np.transpose(out[0])\\n out1 = np.transpose(out[1])\\n score = out1[1,:]\\n ipass = np.where(score>threshold[1])\\n total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])\\n mv = out0[:,ipass[0]]\\n if total_boxes.shape[0]>0:\\n pick = nms(total_boxes, 0.7, 'Union')\\n total_boxes = total_boxes[pick,:]\\n total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))\\n total_boxes = rerec(total_boxes.copy())\\n\\n numbox = total_boxes.shape[0]\\n if numbox>0:\\n # third stage\\n total_boxes = np.fix(total_boxes).astype(np.int32)\\n dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)\\n tempimg = np.zeros((48,48,3,numbox))\\n for k in range(0,numbox):\\n tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))\\n tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]\\n if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:\\n tempimg[:,:,:,k] = imresample(tmp, (48, 48))\\n else:\\n return np.empty()\\n tempimg = (tempimg-127.5)*0.0078125\\n tempimg1 = np.transpose(tempimg, (3,1,0,2))\\n out = onet.predict_on_batch(tempimg1)\\n out0 = np.transpose(out[0])\\n out1 = np.transpose(out[1])\\n out2 = np.transpose(out[2])\\n score = out2[1,:]\\n points = out1\\n ipass = np.where(score>threshold[2])\\n points = points[:,ipass[0]]\\n total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])\\n mv = out0[:,ipass[0]]\\n\\n w = total_boxes[:,2]-total_boxes[:,0]+1\\n h = total_boxes[:,3]-total_boxes[:,1]+1\\n points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1\\n points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1\\n if total_boxes.shape[0]>0:\\n total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))\\n pick = nms(total_boxes.copy(), 0.7, 'Min')\\n total_boxes = total_boxes[pick,:]\\n points = points[:,pick]\\n\\n return total_boxes, points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afeaaca219fa9d486fcfd8ff69398f18\",\n \"score\": \"0.5639469\",\n \"text\": \"def detectAndDraw(test_img, rf, classifier,labels):\\n img = test_img.copy()\\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n rects = detect_face(img)\\n for rect in rects:\\n (x, y, w, h) = rect\\n face = gray[y:y+h, x:x+w]\\n test=[]\\n for i in face:\\n for xi in i:\\n test.append(i)\\n test=np.array(test)\\n face=np.array(face)\\n draw_rectangle(img, rect)\\n label = rf.reconnaitre_un_visage(classifier, test.shape)\\n print()\\n draw_text(img,labels[label], rect[0], rect[1]-5)\\n\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f439777e277353130f556333b58ca398\",\n \"score\": \"0.5637123\",\n \"text\": \"def train_and_show_results_shapes():\\n show_results_shapes(train_autoregressive_flow_pixelcnn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da20761b843dde82416e8c2f9275ad4b\",\n \"score\": \"0.561563\",\n \"text\": \"def show_tensor(sample_tensor, epoch):\\n\\n tensor_view = sample_tensor.permute(1, 2, 0)\\n\\n print(tensor_view.shape)\\n\\n plt.imshow(np.asarray(tensor_view))\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cf7ca93c6a253140aadd2b2470ca8ce\",\n \"score\": \"0.55915767\",\n \"text\": \"def demo(net, im_file, RCNN):\\n\\n image_name = im_file.split('\\\\\\\\')[-1]\\n tmp = []\\n\\n # Load the demo image\\n im = cv2.imread(im_file)\\n\\n # Detect all object classes and regress object bounds\\n timer = Timer()\\n timer.tic()\\n scores, boxes = im_detect(net, im)\\n timer.toc()\\n print('Detection took {:.3f}s for {:d} object proposals'.format(\\n timer.total_time(), boxes.shape[0]))\\n\\n # Visualize detections for each class\\n CONF_THRESH = 0.3\\n NMS_THRESH = 0.3\\n for cls_ind, cls in enumerate(CLASSES[1:]):\\n cls_ind += 1 # because we skipped background\\n cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]\\n cls_scores = scores[:, cls_ind]\\n dets = np.hstack((cls_boxes,\\n cls_scores[:, np.newaxis])).astype(np.float32)\\n keep = nms(\\n torch.from_numpy(cls_boxes), torch.from_numpy(cls_scores),\\n NMS_THRESH)\\n dets = dets[keep.numpy(), :]\\n\\n for det_inst in dets:\\n if det_inst[4] > CONF_THRESH:\\n inst_tmp = [image_name]\\n if cls_ind == 1:\\n inst_tmp.append('Human')\\n else:\\n inst_tmp.append('Object')\\n inst_tmp.append(det_inst[:4])\\n inst_tmp.append(np.nan)\\n inst_tmp.append(cls_ind)\\n inst_tmp.append(det_inst[4])\\n tmp.append(inst_tmp)\\n\\n RCNN[image_name] = tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e0a60a4ccba103b4407b8f8022c5c99\",\n \"score\": \"0.5590113\",\n \"text\": \"def visualize_results_grid(self, n_points=4000, ix=15, iy=15, vmax=1000):\\n # Visualize manifold: \\n if n_points is None or n_points is 0:\\n n_points = self.n_training \\n Ix = ix*1.0 / self.nx * self.nx_resample\\n Iy = iy*1.0 / self.ny * self.ny_resample\\n calibration_data = self.get_data_grid(ix,iy) \\n filter = LikelihoodFilter(self.forward_model_2D)\\n calibration_data_filtered, rejected = filter.filter(calibration_data, Ix,Iy, \\\\\\n points=n_points) \\n model_estimator = ModelDepthEmbedding(nz=self.nz, n_neighbours=self.n_neighbours)\\n model_estimator.set_calibration_data(calibration_data_filtered)\\n model_estimator.set_depth_prior(self.prior)\\n model_estimator.estimate_forward_model() \\n model_estimator.visualize_manifold(nd=3)\\n\\n # Visualize histograms of the locations of interaction: \\n print \\\"Visualizing histograms of reconstructed locations of interaction .. \\\"\\n if self.coordinates_centroid is not None: \\n self.histogram_centroid.show(vmax=vmax)\\n if self.coordinates_2D_MAP_filtered is not None: \\n self.histogram_2D_MAP.show(vmax=vmax)\\n if self.coordinates_3D_MAP_DE_filtered is not None: \\n self.histogram_3D_MAP_DE.show(axis=2, index=None, vmax=vmax)\\n if self.coordinates_3D_MAP_MLEEM_filtered is not None: \\n self.histogram_3D_MAP_MLEEM.show(axis=2, index=None, vmax=vmax)\\n \\n # Visualize histograms of the energy: \\n print \\\"Visualizing reconstructed energy spectra .. \\\"\\n fig = pl.figure()\\n if self.spectrum_centroid is not None: \\n self.spectrum_centroid.show(fig=fig.number)\\n if self.spectrum_2D_MAP is not None: \\n self.spectrum_2D_MAP.show(fig=fig.number)\\n if self.spectrum_3D_MAP_DE is not None: \\n self.spectrum_3D_MAP_DE.show(fig=fig.number)\\n if self.spectrum_3D_MAP_MLEEM is not None: \\n self.spectrum_3D_MAP_MLEEM.show(fig=fig.number)\\n\\n # Visualizing the depth distribution \\n print \\\"Visualizing the depth distribution of the reconstructed events ..\\\"\\n pl.figure()\\n pl.plot(self.prior)\\n pl.hold(1)\\n \\n h = self.histogram_3D_MAP_DE.histogram.sum(0).sum(0)\\n h = (1.0*h) / h.sum()\\n pl.plot(h)\\n\\n h = self.histogram_3D_MAP_MLEEM.histogram.sum(0).sum(0)\\n h = (1.0*h) / h.sum()\\n pl.plot(h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"544dac5ff834c1d26c5c2bf354a44dbd\",\n \"score\": \"0.5584179\",\n \"text\": \"def _visualize_3d(self, token_map, show_detected=False, voxels=False):\\n if not voxels:\\n X, Y, Z = np.mgrid[:token_map.shape[0], :token_map.shape[1], :token_map.shape[2]]\\n point_list = np.array([[x, y, z] for x, y, z in zip(np.ravel(X), np.ravel(Y), np.ravel(Z))]) # 'flat' (2d) index array\\n colors = np.array(['black', 'midnightblue', 'lightsteelblue', 'limegreen',\\n 'red', 'gold', 'darkgreen']) # black color to shift index + 1\\n alphas = np.array([0, 0.05, 0.15, 0.6, 0.6, 1, 1])\\n token_map_flat = np.ravel(token_map)\\n color_list = colors[token_map_flat]\\n alpha_list = alphas[token_map_flat]\\n high_pass_filter_threshold = 1.5 if show_detected else 2.5\\n mask = token_map_flat > high_pass_filter_threshold\\n\\n r = 25 # point radius\\n points = vtkplotter.shapes.Points(point_list[mask], r=r, c=color_list[mask], alpha=alpha_list[mask])\\n vtkplotter.show(points, interactive=True, newPlotter=True, axes={'xyGrid':True, 'yzGrid': True, 'zxGrid':True})\\n else:\\n factor = 2\\n new_map = np.zeros((token_map.shape[0] * factor, token_map.shape[1] * factor, token_map.shape[2] * factor))\\n for i in range(factor):\\n for j in range(factor):\\n for k in range(factor):\\n new_map[i::factor, j::factor, k::factor] = token_map\\n token_map = new_map\\n\\n color_list = np.array([[0.2, 1., 0.2, 0.8], [1., 0.2, 0.2, 0.6], [1., 1., 0, 1.], [0., 0.7, 0., 1.]])\\n if show_detected: color_list = np.concatenate((np.array([[0.9, 0.95, 1., 0.2]]), color_list))\\n color_map = ListedColormap(color_list)\\n vol = vtkplotter.Volume(token_map) # , c=colors, alpha=alphas)\\n lego = vol.legosurface(vmin=2 if show_detected else 3, vmax=6, cmap=color_map)\\n\\n vtkplotter.show(lego, interactive=True, newPlotter=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19ba868a35a10d1a84609b26355f3ba4\",\n \"score\": \"0.55827665\",\n \"text\": \"def faceDetection_TinyFaces(weight_file_path, videoFile, sampling_interval, detectionFrames, prob_thresh=0.5, nms_thresh=0.1, lw=3, display=False, newScale = 360):\\r\\n\\r\\n RESIZED_IMAGE_HEIGHT = newScale\\r\\n\\r\\n print('Resizing images to height ', RESIZED_IMAGE_HEIGHT);\\r\\n\\r\\n vid = cv2.VideoCapture(videoFile);\\r\\n frame_width = int(vid.get(3))\\r\\n frame_height = int(vid.get(4))\\r\\n total_frames = int(vid.get(7))\\r\\n FPS = vid.get(cv2.CAP_PROP_FPS)\\r\\n\\r\\n sampling_rate = int(FPS * sampling_interval)\\r\\n # TODO : may set adaptive sampling rate based on shot length\\r\\n\\r\\n print(videoFile, \\\" : total_frames:\\\", total_frames, \\\", FPS:\\\", FPS, ' SR:', sampling_rate)\\r\\n\\r\\n\\r\\n detections = []; # face detection bounding boxes\\r\\n indices = []; # frame number for detection\\r\\n\\r\\n\\r\\n # placeholder of input images. Currently batch size of one is supported.\\r\\n x = tf.placeholder(tf.float32, [1, None, None, 3]) # n, h, w, c\\r\\n\\r\\n # Create the tiny face model which weights are loaded from a pretrained model.\\r\\n model = tiny_face_model.Model(weight_file_path)\\r\\n score_final = model.tiny_face(x)\\r\\n\\r\\n\\r\\n # Load an average image and clusters(reference boxes of templates).\\r\\n with open(weight_file_path, \\\"rb\\\") as f:\\r\\n _, mat_params_dict = pickle.load(f)\\r\\n\\r\\n average_image = model.get_data_by_key(\\\"average_image\\\")\\r\\n clusters = model.get_data_by_key(\\\"clusters\\\")\\r\\n clusters_h = clusters[:, 3] - clusters[:, 1] + 1\\r\\n clusters_w = clusters[:, 2] - clusters[:, 0] + 1\\r\\n normal_idx = np.where(clusters[:, 4] == 1)\\r\\n\\r\\n # main\\r\\n with tf.Session() as sess:\\r\\n sess.run(tf.global_variables_initializer())\\r\\n\\r\\n for frameInd in range(0, total_frames):\\r\\n\\r\\n if (frameInd%(int(total_frames/10) ) == 0):\\r\\n print( str(int(frameInd/int(total_frames/10)) )+'0 %' )\\r\\n\\r\\n ret, frame = vid.read()\\r\\n\\r\\n if (ret == False):\\r\\n continue\\r\\n\\r\\n # subsample frames\\r\\n if (frameInd % sampling_rate != 0 and (frameInd-2) not in detectionFrames):\\r\\n continue;\\r\\n\\r\\n \\r\\n raw_img = frame\\r\\n org_raw_img = frame[:]\\r\\n\\r\\n myScale = (float(RESIZED_IMAGE_HEIGHT)/raw_img.shape[0]);\\r\\n\\r\\n # print ('org:', raw_img.shape)\\r\\n raw_img = cv2.resize(raw_img, ( int(raw_img.shape[1]*myScale), RESIZED_IMAGE_HEIGHT),interpolation=cv2.INTER_CUBIC) \\r\\n # print ('res:',raw_img.shape)\\r\\n\\r\\n raw_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB)\\r\\n raw_img_f = raw_img.astype(np.float32)\\r\\n\\r\\n def _calc_scales():\\r\\n raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]\\r\\n min_scale = min(np.floor(np.log2(np.max(clusters_w[normal_idx] / raw_w))),\\r\\n np.floor(np.log2(np.max(clusters_h[normal_idx] / raw_h))))\\r\\n max_scale = min(1.0, -np.log2(max(raw_h, raw_w) / MAX_INPUT_DIM_TINYFACES))\\r\\n scales_down = pl.frange(min_scale, 0, 1.)\\r\\n scales_up = pl.frange(0.5, max_scale, 0.5)\\r\\n scales_pow = np.hstack((scales_down, scales_up))\\r\\n scales = np.power(2.0, scales_pow)\\r\\n return scales\\r\\n\\r\\n scales = _calc_scales()\\r\\n start = time.time()\\r\\n\\r\\n # initialize output\\r\\n bboxes = np.empty(shape=(0, 5))\\r\\n\\r\\n # process input at different scales\\r\\n for s in scales:\\r\\n # print(\\\"Processing {} at scale {:.4f}\\\".format(str(frameInd), s))\\r\\n img = cv2.resize(raw_img_f, (0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR)\\r\\n img = img - average_image\\r\\n img = img[np.newaxis, :]\\r\\n\\r\\n # we don't run every template on every scale ids of templates to ignore\\r\\n tids = list(range(4, 12)) + ([] if s <= 1.0 else list(range(18, 25)))\\r\\n ignoredTids = list(set(range(0, clusters.shape[0])) - set(tids))\\r\\n\\r\\n # run through the net\\r\\n score_final_tf = sess.run(score_final, feed_dict={x: img})\\r\\n\\r\\n # collect scores\\r\\n score_cls_tf, score_reg_tf = score_final_tf[:, :, :, :25], score_final_tf[:, :, :, 25:125]\\r\\n prob_cls_tf = expit(score_cls_tf)\\r\\n prob_cls_tf[0, :, :, ignoredTids] = 0.0\\r\\n\\r\\n def _calc_bounding_boxes():\\r\\n # threshold for detection\\r\\n _, fy, fx, fc = np.where(prob_cls_tf > prob_thresh)\\r\\n\\r\\n # interpret heatmap into bounding boxes\\r\\n cy = fy * 8 - 1\\r\\n cx = fx * 8 - 1\\r\\n ch = clusters[fc, 3] - clusters[fc, 1] + 1\\r\\n cw = clusters[fc, 2] - clusters[fc, 0] + 1\\r\\n\\r\\n # extract bounding box refinement\\r\\n Nt = clusters.shape[0]\\r\\n tx = score_reg_tf[0, :, :, 0:Nt]\\r\\n ty = score_reg_tf[0, :, :, Nt:2*Nt]\\r\\n tw = score_reg_tf[0, :, :, 2*Nt:3*Nt]\\r\\n th = score_reg_tf[0, :, :, 3*Nt:4*Nt]\\r\\n\\r\\n # refine bounding boxes\\r\\n dcx = cw * tx[fy, fx, fc]\\r\\n dcy = ch * ty[fy, fx, fc]\\r\\n rcx = cx + dcx\\r\\n rcy = cy + dcy\\r\\n rcw = cw * np.exp(tw[fy, fx, fc])\\r\\n rch = ch * np.exp(th[fy, fx, fc])\\r\\n\\r\\n scores = score_cls_tf[0, fy, fx, fc]\\r\\n tmp_bboxes = np.vstack((rcx - rcw / 2, rcy - rch / 2, rcx + rcw / 2, rcy + rch / 2))\\r\\n tmp_bboxes = np.vstack((tmp_bboxes / s, scores))\\r\\n tmp_bboxes = tmp_bboxes.transpose()\\r\\n return tmp_bboxes\\r\\n\\r\\n tmp_bboxes = _calc_bounding_boxes()\\r\\n bboxes = np.vstack((bboxes, tmp_bboxes)) # <class 'tuple'>: (5265, 5)\\r\\n\\r\\n\\r\\n print(\\\"Took {:.2f} secs for Frame {}\\\".format(time.time() - start, str(frameInd)))\\r\\n\\r\\n # non maximum suppression\\r\\n # refind_idx = util.nms(bboxes, nms_thresh)\\r\\n refind_idx = tf.image.non_max_suppression(tf.convert_to_tensor(bboxes[:, :4], dtype=tf.float32),\\r\\n tf.convert_to_tensor(bboxes[:, 4], dtype=tf.float32),\\r\\n max_output_size=bboxes.shape[0], iou_threshold=nms_thresh)\\r\\n refind_idx = sess.run(refind_idx)\\r\\n refined_bboxes = bboxes[refind_idx]\\r\\n\\r\\n finalBboxes = []\\r\\n\\r\\n for ind_bb in range(len(refined_bboxes)):\\r\\n refined_bboxes[ind_bb][0]/=myScale\\r\\n refined_bboxes[ind_bb][1]/=myScale\\r\\n refined_bboxes[ind_bb][2]/=myScale\\r\\n refined_bboxes[ind_bb][3]/=myScale\\r\\n nbbox = list( refined_bboxes[ind_bb][0:4].astype(int) )\\r\\n finalBboxes.append( nbbox )\\r\\n\\r\\n # print(refined_bboxes)\\r\\n\\r\\n # overlay_bounding_boxes(org_raw_img, refined_bboxes, lw)\\r\\n\\r\\n # if display:\\r\\n # plt.axis('off')\\r\\n\\r\\n # plt.imshow(org_raw_img)\\r\\n # plt.show()\\r\\n\\r\\n # showImg(org_raw_img, 50)\\r\\n\\r\\n # save image with bounding boxes\\r\\n # raw_img = cv2.cvtColor(raw_img, cv2.COLOR_RGB2BGR)\\r\\n # cv2.imwrite(os.path.join(output_dir, str(ind)), raw_img)\\r\\n\\r\\n # faces = getFaceBoundingBoxesMTCNN(frame)\\r\\n detections.append(finalBboxes)\\r\\n indices.append(frameInd)\\r\\n\\r\\n\\r\\n\\r\\n return detections, indices;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce0c4d09e00d12e751d3c76ce9395ea\",\n \"score\": \"0.5582703\",\n \"text\": \"def show_object_detection_image(network: gcv.model_zoo.HybridBlock, image: mx.nd.NDArray):\\n image, chw_image = gcv.data.transforms.presets.yolo.transform_test(image, short=512)\\n class_indices, probabilities, bounding_boxes = detect_objects(network, image)\\n gcv.utils.viz.plot_bbox(chw_image,\\n bounding_boxes,\\n probabilities,\\n class_indices,\\n class_names=network.classes)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6e26068057a1e7cd02b7ece63678deb\",\n \"score\": \"0.55750555\",\n \"text\": \"def display_faces(display, shape, event = None):\\n display.EraseAll()\\n aocutils.display.topology.faces(display, shape, transparency=0.8)\\n display.FitAll()\\n display.View_Iso()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ad27eedadc508fff58bddcb582db8ec\",\n \"score\": \"0.5563226\",\n \"text\": \"def demo(net, image_name):\\n\\n # Load the demo image\\n #im_file = os.path.join(cfg.DATA_DIR, 'demo2', image_name)\\n im_file = os.path.join(data_dir, 'Images', image_name)\\n im = cv2.imread(im_file)\\n\\n # Detect all object classes and regress object bounds\\n timer = Timer()\\n timer.tic()\\n scores, boxes = im_detect(net, im)\\n timer.toc()\\n print ('Detection took {:.3f}s for '\\n '{:d} object proposals').format(timer.total_time, boxes.shape[0])\\n\\n\\n\\n im = im[:, :, (2, 1, 0)]\\n fig, ax = plt.subplots(figsize=(12, 12))\\n# ax.imshow(np.flipud(im), aspect='equal', origin='lower')\\n ax.imshow(im, aspect='equal')\\n\\n # Visualize detections for each class\\n CONF_THRESH = 0.8\\n NMS_THRESH = 0.3\\n for cls_ind, cls in enumerate(CLASSES[1:]):\\n cls_ind += 1 # because we skipped background\\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\\n cls_scores = scores[:, cls_ind]\\n dets = np.hstack((cls_boxes,\\n cls_scores[:, np.newaxis])).astype(np.float32)\\n# keep = nms(dets, NMS_THRESH, force_cpu=True)\\n keep = nms(dets, NMS_THRESH)\\n dets = dets[keep, :]\\n vis_detections(im, cls, dets, ax, thresh=CONF_THRESH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bf38baf7a24917d5afe26299318f02\",\n \"score\": \"0.55618197\",\n \"text\": \"def show_batch(sample_batched):\\n buffer = tuple()\\n for name, mod in sample_batched.items():\\n if name not in ('coord', 'K'):\\n '''\\n min_v = mod.min()\\n mod -= min_v\\n max_v = mod.max()\\n mod /= max_v\\n '''\\n buffer += (mod,)\\n\\n images_batch = torch.cat(buffer, 0)\\n grid = torchvis.utils.make_grid(images_batch, nrow=4)\\n\\n plt.imshow(grid.numpy().transpose((1, 2, 0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe2c71a5aed4f17291098f19d05d230\",\n \"score\": \"0.5557402\",\n \"text\": \"def detect_faces(path):\\n vision_client = vision.Client()\\n\\n with io.open(path, 'rb') as image_file:\\n content = image_file.read()\\n\\n image = vision_client.image(content=content)\\n\\n faces = image.detect_faces()\\n\\n print('Faces:')\\n for face in faces:\\n print('anger: {}'.format(face.emotions.anger))\\n print('joy: {}'.format(face.emotions.joy))\\n print('surprise: {}'.format(face.emotions.surprise))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12827cb5d99cab25a5f31ba7b45cc5bc\",\n \"score\": \"0.5555502\",\n \"text\": \"def show_recognitions(self, image, verbose = False, return_faces = False):\\n if verbose:\\n start_time = time()\\n boxes = self.face_detector.predict(image=image)\\n if verbose:\\n face_detection_time = time() - start_time\\n confidences, names, distances = [], [], []\\n if len(boxes):\\n faces = self.face_detector.crop_boxes_content(image=image, boxes=boxes)\\n confidences.extend([confidence for confidence, _ in boxes])\\n boxes = [box for _, box in boxes]\\n\\n if verbose:\\n embedding_start_time = time()\\n embeddings = [self.embedding.predict(face) for face in faces]\\n \\n if verbose:\\n embedding_time = time() - embedding_start_time\\n if verbose:\\n recognition_start_time = time()\\n names.extend(self.classifier.predict(x=embeddings))\\n if verbose:\\n recognition_time = time() - recognition_start_time\\n distances.extend([self.camera_calculator.rectangleToRealWorldXY(rectangle=box, h=image.shape[0],\\n w=image.shape[1]) for box in boxes])\\n if self.show_bodies:\\n if verbose:\\n bodies_start_time = time()\\n body_boxes = self.body_detector.predict(image=cvtColor(image, code=COLOR_BGR2RGB))\\n if verbose:\\n body_time = time() - bodies_start_time\\n if len(body_boxes):\\n #bodies = self.body_detector.get_content(image=image, boxes=body_boxes)\\n confidences.extend([confidence for confidence, _ in body_boxes])\\n body_boxes = [box for _, box in body_boxes]\\n names.extend(['body' for box in body_boxes])\\n distances.extend([self.camera_calculator.rectangleToRealWorldXY(rectangle=box, h=image.shape[0],\\n w=image.shape[1]) for box in boxes])\\n boxes = boxes + body_boxes\\n\\n if verbose:\\n showing_start_time = time()\\n show(image=image, boxes=boxes, names=names, distances=distances, confidences=confidences)\\n\\n if verbose and len(boxes):\\n showing_time = time() - showing_start_time\\n total_time = time()-start_time\\n if not self.show_bodies or len(boxes) > len(body_boxes):\\n txt = 'Total prediction time: {total_time} s\\\\n' \\\\\\n '\\\\t Face Detection: {face_time} s ({face_p})%\\\\n' \\\\\\n '\\\\t Face Embedding: {embedding_time} s ({embedd_p})%\\\\n' \\\\\\n '\\\\t Face Recognition: {recognition_time} s ({recognition_p})%\\\\n'\\\\\\n .format(total_time = round(total_time, ndigits=2),\\n face_time=round(face_detection_time, ndigits=2), face_p=round((face_detection_time/total_time)*100, ndigits=2),\\n embedding_time=round(embedding_time, ndigits=2), embedd_p=round((embedding_time/total_time)*100, ndigits=2),\\n recognition_time=round(recognition_time, ndigits=2), recognition_p=round((recognition_time/total_time)*100, ndigits=2))\\n if self.show_bodies:\\n txt += '\\\\t Body Detection: {detection_time} s ({detection_p})%\\\\n'\\\\\\n .format(detection_time=round(body_time, ndigits=2), detection_p=round((body_time/total_time)*100, ndigits=2))\\n txt += '\\\\t Plotting time: {plot_time} s ({plotting_p})%'\\\\\\n .format(plot_time=round(showing_time, ndigits=2), plotting_p=round((showing_time/total_time)*100, ndigits=2))\\n print(txt)\\n if return_faces:\\n if len(faces):\\n names = names[:len(faces)]\\n return {name : face for face, name in zip(faces, names)}\\n else:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56b3603dbb4de59466fde7771145e73d\",\n \"score\": \"0.5555334\",\n \"text\": \"def draw_faces(image, faces=None, plot=True):\\n if faces is None:\\n faces = get_faces(image)\\n \\n # To avoid overwriting\\n image_with_faces = np.copy(image)\\n \\n # Get the bounding box for each detected face\\n for (x,y,w,h) in faces:\\n # Add a red bounding box to the detections image\\n cv2.rectangle(image_with_faces, (x,y), (x+w,y+h), (255,0,0), 3)\\n \\n \\n if plot is True:\\n plot_image(image_with_faces)\\n else:\\n return image_with_faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beb7f4ea54b9742021af4f5f33efab15\",\n \"score\": \"0.55523175\",\n \"text\": \"def classify_face(im):\\n faces = get_encoded_faces()\\n faces_encoded = list(faces.values())\\n known_face_names = list(faces.keys())\\n\\n img = cv2.imread(im, 1)\\n # img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)\\n # img = img[:,:,::-1]\\n\\n face_locations = face_recognition.face_locations(img)\\n unknown_face_encodings = face_recognition.face_encodings(img, face_locations)\\n\\n face_names = []\\n for face_encoding in unknown_face_encodings:\\n # See if the face is a match for the known face(s)\\n matches = face_recognition.compare_faces(faces_encoded, face_encoding)\\n name = \\\"Unknown\\\"\\n\\n # use the known face with the smallest distance to the new face\\n face_distances = face_recognition.face_distance(faces_encoded, face_encoding)\\n best_match_index = np.argmin(face_distances)\\n if matches[best_match_index]:\\n name = known_face_names[best_match_index]\\n\\n face_names.append(name)\\n for (top, right, bottom, left), name in zip(face_locations, face_names):\\n # Draw a box around the face\\n cv2.rectangle(img, (left - 20, top - 20), (right + 20, bottom + 20), (255, 0, 0), 2)\\n\\n # Draw a label with a name below the face\\n cv2.rectangle(img, (left - 20, bottom - 15), (right + 20, bottom + 20), (255, 0, 0), cv2.FILLED)\\n font = cv2.FONT_HERSHEY_DUPLEX\\n cv2.putText(img, name, (left - 20, bottom + 15), font, 1.0, (255, 255, 255), 2)\\n\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15e34e886c7326501f7f20501c6c171c\",\n \"score\": \"0.5551539\",\n \"text\": \"def visualize(self):\\r\\n fig = plt.figure(1)\\r\\n plt.clf()\\r\\n plt.cla()\\r\\n\\r\\n X_new = decomposition.pca.PCA(n_components=2).fit_transform(self.X)\\r\\n model = GMM(n_components=self.targetcluster, covariance_type='full')\\r\\n labels = model.fit_predict(X_new)\\r\\n totz = np.concatenate((X_new, np.expand_dims(labels, axis=1), np.expand_dims(self.y, axis=1),), axis=1)\\r\\n\\r\\n # for each cluster\\r\\n colors = ['red', 'green', 'blue', 'yellow', 'orange', 'purple']\\r\\n fig = plt.figure()\\r\\n\\r\\n for clust in range(0, self.targetcluster):\\r\\n totz_clust = totz[totz[:,-2] == clust]\\r\\n print(\\\"Cluster Size\\\")\\r\\n print(totz_clust.shape)\\r\\n\\r\\n benign = totz_clust[totz_clust[:,-1] == 1]\\r\\n malignant = totz_clust[totz_clust[:,-1] == 0]\\r\\n\\r\\n plt.scatter(benign[:, 0], benign[:, 1], color=colors[clust], marker=\\\".\\\")\\r\\n plt.scatter(malignant[:, 0], malignant[:, 1], color=colors[clust], marker=\\\"x\\\")\\r\\n\\r\\n plt.xlabel(\\\"1st Component\\\")\\r\\n plt.ylabel(\\\"2nd Component\\\")\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75bd5aabcbfb8348f6156ac6fef1da75\",\n \"score\": \"0.5534348\",\n \"text\": \"def visualize_mesh_og(cam, vert, renderer, start_pt, scale, proc_img_shape,\\n im_path=None, img=None, deg=0, mesh_color='blue',\\n max_img_size=300, pad=50, crop_cam=None, bbox=None):\\n if img is None:\\n img = imread(im_path)\\n # Pre-process image to [-1, 1] bc it expects this.\\n img = ((img / 255.) - 0.5) * 2\\n\\n if bbox is not None:\\n assert(crop_cam is not None)\\n img = img[bbox[0]:bbox[1], bbox[2]:bbox[3]]\\n # For these, the cameras are already adjusted.\\n scale = 1.\\n start_pt = np.array([0, 0])\\n if np.max(img.shape[:2]) > max_img_size:\\n # if the image is too big it wont fit in gpu and nmr poops out.\\n scale_orig = max_img_size / float(np.max(img.shape[:2]))\\n img, _ = resize_img(img, scale_orig)\\n undo_scale = (1. / np.array(scale)) * scale_orig\\n else:\\n undo_scale = 1. / np.array(scale)\\n # NMR needs images to be square..\\n img, pad_vals = make_square(img)\\n img_size = np.max(img.shape[:2])\\n renderer.renderer.image_size = img_size\\n\\n if bbox is not None:\\n return renderer.rotated(\\n verts=vert,\\n deg=deg,\\n cam=crop_cam,\\n color_name=mesh_color,\\n )\\n else:\\n # This is camera in crop image coord.\\n cam_crop = np.hstack([proc_img_shape[0] * cam[0] * 0.5,\\n cam[1:] + (2./cam[0]) * 0.5])\\n\\n # This is camera in orig image coord\\n cam_orig = np.hstack([\\n cam_crop[0] * undo_scale,\\n cam_crop[1:] + (start_pt - proc_img_shape[0]) / cam_crop[0]\\n ])\\n\\n # This is the camera in normalized orig_image coord\\n new_cam = np.hstack([\\n cam_orig[0] * (2. / img_size),\\n cam_orig[1:] - (1 / ((2./img_size) * cam_orig[0]))\\n ])\\n new_cam = new_cam.astype(np.float32)\\n\\n return renderer.rotated(\\n verts=vert,\\n deg=deg,\\n cam=new_cam,\\n color_name=mesh_color,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23df4d67cc8ae6361e34774867e8f8c\",\n \"score\": \"0.55242705\",\n \"text\": \"def faces(self):\\r\\n return self._faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"650ff0a64783f869ee6c5ea9616f61bf\",\n \"score\": \"0.5521417\",\n \"text\": \"def show_inference(input_images, output_dict):\\n out_images = []\\n for num, image in enumerate(input_images):\\n output_dict_tmp = output_dict[num]\\n print(image.shape)\\n print(output_dict_tmp)\\n vis_util.visualize_boxes_and_labels_on_image_array(\\n image,\\n output_dict_tmp['detection_boxes'],\\n output_dict_tmp['detection_classes'],\\n output_dict_tmp['detection_scores'],\\n category_index,\\n instance_masks=output_dict_tmp.get('detection_masks_reframed', None),\\n use_normalized_coordinates=True,\\n line_thickness=2,\\n min_score_thresh=.3)\\n\\n out_images.append(image)\\n return(out_images)\\n #visualization_utils.save_image_array_as_png(image, output_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde0bede7ab7861bab6a5a066e8b1052\",\n \"score\": \"0.55159616\",\n \"text\": \"def visualize_layer(layer, vgg, vgg_deconv, top_k=9):\\n\\n # Num channels/features in the layer (batch_size, num_channels, height, weidth)\\n num_feat = vgg.feature_maps[layer].shape[1]\\n\\n # Create clone of feature map for easy changes\\n feat_map = vgg.feature_maps[layer].clone()\\n\\n # Now we want the feature map with maximu activation, which is simply done by\\n # finding the maximum value in the tensor\\n activations = []\\n for i in range(num_feat):\\n # Sequentially traverse all the feature maps and append the maximum value\\n # in the feature map to the activations list.\\n map = feat_map[0, i, :, :]\\n activation = torch.max(map)\\n activations.append(activation.item())\\n\\n # Get the indices for the top_k activations\\n activations = np.array(activations)\\n max_activation_idxs = activations.argsort()[-1:-top_k-1:-1]\\n\\n # Store the visualizations in a list\\n images = []\\n\\n for i in max_activation_idxs:\\n current_map = feat_map[:, i, :, :]\\n current_max_activation = torch.max(current_map)\\n\\n tmp_feat_map = vgg.featrue_maps[layer].clone()\\n\\n # We zero out all the other activation maps/channels except the one we want\\n # to get the visulizationsf for\\n if i == 0:\\n tmp_feat_map[:, 1:, :, :] = 0\\n else:\\n tmp_feat_map[:, :i, :, :] = 0\\n if i != vgg.feature_maps[layer].shape[1] - 1:\\n tmp_feat_map[:, i+1:, :, :] = 0\\n \\n # For the current selected activation map, zero out all the non-max values\\n # and store it the tmp_feat_map array\\n current_map = torch.where(current_map == current_max_activation,\\n current_map,\\n torch.zeros(current_map.shape))\\n\\n tmp_feat_map[0, i, :, :] = current_map\\n\\n # Run the deconv model\\n deconv_output = vgg_deconv(tmp_feat_map, layer, vgg.pool_locs)\\n\\n # Get the image\\n img = deconv_output.data.numpy()[0].transpose(1, 2, 0)\\n img = (img - img.min()) / (img.max() - img.min()) * 255\\n img = img.astype(np.uint8)\\n\\n images.append(img, int(current_max_activation))\\n \\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a4d3625afeb536e6b4bb1fc5d561c0\",\n \"score\": \"0.55114794\",\n \"text\": \"def demo(sess, net, im, fig=\\\"preview\\\", classes=CLASSES):\\n\\n # # Load the demo image\\n # im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)\\n # #im_file = os.path.join('/home/corgi/Lab/label/pos_frame/ACCV/training/000001/',image_name)\\n # im = cv2.imread(im_file)\\n\\n # Detect all object classes and regress object bounds\\n timer = Timer()\\n timer.tic()\\n scores, boxes = im_detect(sess, net, im)\\n timer.toc()\\n print(('Detection took {:.3f}s for '\\n '{:d} object proposals').format(timer.total_time, boxes.shape[0]))\\n\\n # Visualize detections for each class\\n im = im[:, :, (2, 1, 0)]\\n # fig, ax = plt.subplots(figsize=(12, 12))\\n # ax.imshow(im, aspect='equal')\\n # cv2.imshow(fig, im)\\n\\n CONF_THRESH = 0.7\\n NMS_THRESH = 0.3\\n # for cls_ind, cls in enumerate(CLASSES[1:]):\\n for cls_ind, cls in enumerate(classes[1:]):\\n cls_ind += 1 # because we skipped background\\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\\n cls_scores = scores[:, cls_ind]\\n dets = np.hstack((cls_boxes,\\n cls_scores[:, np.newaxis])).astype(np.float32)\\n keep = nms(dets, NMS_THRESH)\\n dets = dets[keep, :]\\n vis_detections(im, cls, dets, thresh=CONF_THRESH, fig=fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb4cefd49d62d7c7bb16a388dfb6670\",\n \"score\": \"0.55112904\",\n \"text\": \"def instances_to_lvis(instances, img_id, max_dets=100):\\n if len(instances) == 0:\\n return []\\n \\n object_classes = instances.object_classes\\n person_classes = instances.person_classes\\n object_scores = instances.object_scores\\n person_scores = instances.person_scores\\n object_boxes = instances.object_boxes.tensor\\n person_boxes = instances.person_boxes.tensor\\n \\n classes = torch.cat([object_classes, person_classes], dim=0)\\n scores = torch.cat([object_scores * person_scores, person_scores], dim=0)\\n boxes = torch.cat([object_boxes, person_boxes], dim=0)\\n \\n # Before NMS, remove duplicate boxes\\n unique_dets, keep = set(), []\\n for i in range(len(boxes)):\\n det = tuple(boxes[i].tolist() + [float(classes[i])])\\n if det not in unique_dets:\\n unique_dets.add(det)\\n keep.append(i)\\n classes, scores, boxes = classes[keep], scores[keep], boxes[keep]\\n\\n # NMS\\n keep = batched_nms(boxes, scores, classes, 0.5)[:max_dets]\\n classes = classes[keep].tolist()\\n scores = scores[keep].tolist()\\n boxes = box_ops.box_xyxy_to_xywh(boxes[keep]).tolist()\\n \\n results = [{\\\"image_id\\\": img_id,\\n \\\"category_id\\\": classes[i],\\n \\\"bbox\\\": boxes[i],\\n \\\"score\\\": scores[i]} for i in range(len(boxes))]\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ae0ec07b11facc1cb0bb34ea67fdbf\",\n \"score\": \"0.5510552\",\n \"text\": \"def find_face():\\n img = IO.read_image(\\\"photo1.jpg\\\", r\\\".\\\\data\\\")\\n ma = img.max()\\n mi = img.min()\\n\\n avg_r = np.empty(img.shape[0])\\n for i in range(img.shape[0]):\\n avg_r[i] = img[i].mean()\\n\\n avg_c = np.empty(img.shape[1])\\n for i in range(img.shape[1]):\\n avg_c[i] = img[:, i].mean()\\n\\n IO.plot_functions([(None, avg_r), (None, avg_c)],\\n [\\\"avg rows\\\", \\\"avg cols\\\"])\\n xs = []\\n ys = []\\n has_prev = False\\n step = 5\\n for i in range(step, len(avg_r) - step, step // 2):\\n if abs(avg_r[i] - avg_r[i + step]) > ma / 30:\\n xs.append(i + step // 2)\\n\\n step = 5\\n for i in range(step, len(avg_c) - step, step // 2):\\n if abs(avg_c[i] - avg_c[i + step]) > ma / 30:\\n ys.append(i + step // 2)\\n\\n print(len(xs), len(ys))\\n img_l = img\\n for x in range(len(xs) - 1):\\n for y in range(len(ys) - 1):\\n print(xs[x], ys[y])\\n img_l = m.draw_line(img_l, ys[y], xs[x], ys[y], xs[x + 1])\\n img_l = m.draw_line(img_l, ys[y], xs[x], ys[y + 1], xs[x])\\n img_l = m.draw_line(img_l, ys[y], xs[x + 1], ys[y + 1], xs[x + 1])\\n img_l = m.draw_line(img_l, ys[y + 1], xs[x], ys[y + 1], xs[x + 1])\\n\\n plt.imshow(img_l)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad211d763fc841c8efb347b2b424553d\",\n \"score\": \"0.5509361\",\n \"text\": \"def chinese_whispers(source_dir, threshold=0.55, iterations=20, mosaic=True, plot=False):\\n\\n output_dir=os.path.join(source_dir, \\\"Face Network/\\\")\\n network_dir=os.path.join(output_dir, \\\"Data/\\\")\\n face_db=pd.read_hdf(network_dir+\\\"FaceDatabase.h5\\\")\\n cluster_dir=os.path.join(output_dir, \\\"Clusters/\\\")\\n face_dir=os.path.join(output_dir, \\\"Faces/\\\")\\n\\n encodings= list(face_db['encoding'])\\n image_paths=list(face_db['face_name'])\\n\\n\\n from random import shuffle\\n import networkx as nx\\n # Create graph\\n nodes = []\\n edges = []\\n\\n if len(encodings) <= 1:\\n print (\\\"No enough encodings to cluster!\\\")\\n return []\\n\\n for idx, face_encoding_to_check in enumerate(encodings):\\n # Adding node of facial encoding\\n node_id = idx+1\\n\\n # Initialize 'cluster' to unique value (cluster of itself)\\n node = (node_id, {'cluster': image_paths[idx], 'path': image_paths[idx]})\\n nodes.append(node)\\n\\n # Facial encodings to compare\\n if (idx+1) >= len(encodings):\\n # Node is last element, don't create edge\\n break\\n\\n compare_encodings = encodings[idx+1:]\\n distances = face_recognition.face_distance(compare_encodings, face_encoding_to_check)\\n encoding_edges = []\\n for i, distance in enumerate(distances):\\n if distance < threshold:\\n # Add edge if facial match\\n edge_id = idx+i+2\\n encoding_edges.append((node_id, edge_id, {'weight': distance}))\\n\\n edges = edges + encoding_edges\\n\\n G = nx.Graph()\\n G.add_nodes_from(nodes)\\n G.add_edges_from(edges)\\n\\n # Iterate\\n for _ in range(0, iterations):\\n cluster_nodes = G.nodes()\\n #shuffle(cluster_nodes)\\n for node in cluster_nodes:\\n neighbors = G[node]\\n clusters = {}\\n\\n for ne in neighbors:\\n if isinstance(ne, int):\\n if G.nodes[ne]['cluster'] in clusters:\\n clusters[G.nodes[ne]['cluster']] += G[node][ne]['weight']\\n else:\\n clusters[G.nodes[ne]['cluster']] = G[node][ne]['weight']\\n\\n # find the class with the highest edge weight sum\\n edge_weight_sum = 0\\n max_cluster = 0\\n #use the max sum of neighbor weights class as current node's class\\n for cluster in clusters:\\n if clusters[cluster] > edge_weight_sum:\\n edge_weight_sum = clusters[cluster]\\n max_cluster = cluster\\n\\n # set the class of target node to the winning local class\\n G.nodes[node]['cluster'] = max_cluster\\n\\n\\n if plot:\\n print(G.nodes)\\n import matplotlib.pyplot as plt\\n nx.draw(G, node_size=2, edge_color='grey')\\n plt.show()\\n\\n clusters = {}\\n\\n # Prepare cluster output\\n for (_, data) in G.nodes.items():\\n cluster = data['cluster']\\n path = data['path']\\n\\n #print(cluster, path)\\n if cluster:\\n if cluster not in clusters:\\n clusters[cluster] = []\\n clusters[cluster].append(path)\\n\\n # Sort cluster output\\n sorted_clusters = sorted(clusters.values(), key=len, reverse=True)\\n\\n\\n length=[]\\n\\n cluster_master=pd.DataFrame()\\n count=0\\n for cluster in sorted_clusters:\\n count+=1\\n cluster_df=pd.DataFrame({'cluster':count,'face_name':cluster})\\n cluster_master=cluster_master.append(cluster_df)\\n\\n if 'cluster' in face_db:\\n face_db=face_db.drop(columns=['cluster'])\\n\\n if 'count' in face_db:\\n face_db=face_db.drop(columns=['count'])\\n\\n\\n face_db=face_db.merge(cluster_master, on='face_name',how='left')\\n face_db['cluster']=face_db['cluster'].fillna(-1).astype(int)\\n counts=face_db.groupby('cluster')['face_name'].count().reset_index().rename(columns={'face_name':'count'})\\n face_db=face_db.merge(counts, how='left',on='cluster')\\n print(face_db)\\n\\n face_db.to_hdf(network_dir+'FaceDatabase.h5', 'index', 'w',complevel=9)\\n\\n if mosaic:\\n # build a mosaic of face tiles for each cluster\\n overwrite(cluster_dir)\\n clusters=face_db['cluster'].unique().tolist()\\n clusters = [ elem for elem in clusters if elem > 0]\\n cpus=joblib.cpu_count()-1\\n rows=Parallel(n_jobs=cpus)(delayed(build_mosaic)(cluster,face_db,face_dir,cluster_dir) for cluster in clusters) \\n\\n return face_db\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91b1dd49da4b7bf17788d246b0cbfb4\",\n \"score\": \"0.5506963\",\n \"text\": \"def detect_faces(img):\\n faces = detector.detect_faces(img)\\n return faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09e09c6d90e446d78237d3d18fcfeb6f\",\n \"score\": \"0.55028784\",\n \"text\": \"def visualize_faces_haarcascade(img_path, detector):\\n # Load color (BGR) image\\n img = cv2.imread(img_path)\\n # Convert BGR image to grayscale\\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n # Find faces in image\\n faces = detector.detectMultiScale(gray)\\n # Get bounding box for each detected face\\n for (x, y, w, h) in faces:\\n # add bounding box to color image\\n cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\\n # Convert BGR image to RGB for plotting\\n cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n # Display the image, along with bounding box\\n plt.imshow(cv_rgb)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1818c22c4769eb9443c15fcf6c4ff752\",\n \"score\": \"0.5500044\",\n \"text\": \"def detect_faces( cv_image ):\\n return face_cascade.detectMultiScale(cv_image, 1.3, 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e78f844f1847b6b06337cf7052544a73\",\n \"score\": \"0.5499313\",\n \"text\": \"def getFaces(image, conf = 0.5):\\n \\n\\n \\n (h, w) = image.shape[:2]\\n blob = cv.dnn.blobFromImage(cv.resize(image, (300, 300)), 1.0, (300, 300), (103.93, 116.77, 123.68))\\n net.setInput(blob)\\n detections = net.forward()\\n\\n face_arr = []\\n for i in range(0, detections.shape[2]):\\n confidence = detections[0,0,i,2]\\n\\n if confidence > conf:\\n\\n box = detections[0,0,i,3:7] * np.array([w,h,w,h])\\n (startX, startY, endX, endY) = box.astype(\\\"int\\\")\\n \\n t = image[max(0, int(startY*0.9)):min(int(endY*1.1),image.shape[0]), max(0, int(startX*0.9)):min(int(endX*1.1), image.shape[1])]\\n if t.shape[0] == 0 or t.shape[1] == 0:\\n pass\\n else:\\n face_arr.append(t)\\n\\n \\n\\n return face_arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fae11d5761833d4eae0fd659ba31d1d\",\n \"score\": \"0.5498946\",\n \"text\": \"def drawDetectionBoundingBox(frame,result,faces,agender_model):\\n args = parse_args()\\n margin = args.margin\\n # Drawing box for MTCNN detection (face)\\n for j,face in enumerate(faces):\\n face = list(map(int, face)) # convert floats to int because cv2.rectangle allows only int type\\n x1_face,y1_face,x2_face,y2_face = face[0],face[1],face[2],face[3]\\n \\n # adding margin to the bounding box is crucial to correctly estimating age and gender margin = 40\\n cropped_face = frame[face[1]-margin:face[3]+margin, face[0]-margin:face[2]+margin] \\n gender,age = get_age_gender(agender_model,cropped_face)\\n \\n text = (gender+','+str(int(age)))\\n cv2.rectangle(frame,(x1_face,y1_face),(x2_face,y2_face),(0,255,0),3)\\n cv2.putText(frame,text,(face[0],face[1]),cv2.FONT_HERSHEY_PLAIN,3,(0,0,0),3)\\n \\n \\n # Drawing box for YOLO detection (person) \\n for box in result:\\n x1_person,y1_person,x2_person,y2_person = (box['topleft']['x'],box['topleft']['y'],box['bottomright']['x'],box['bottomright']['y'])\\n confidence = box['confidence']\\n if confidence >0.1: # for YOLO\\n cv2.rectangle(frame,(x1_person,y1_person),(x2_person,y2_person),(255,0,0),5)\\n\\n return frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85aa5cb468d4d67dc081393cc2aca797\",\n \"score\": \"0.5491066\",\n \"text\": \"def detect_faces(self, img):\\n gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\\n faces = self.detector.detectMultiScale(gray, 1.1, 4)\\n return faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5093a0940d5ecc55c0db65d9732c85b4\",\n \"score\": \"0.5484688\",\n \"text\": \"def show_detections(self):\\n cv2.imshow(TITLE, self.detect_img)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9f28ad8d5f499b6984cb1e41ea1a846\",\n \"score\": \"0.54766023\",\n \"text\": \"def draw_faces(image, faces=None, plot=True):\\n if faces is None:\\n faces = get_faces(image)\\n \\n # To avoid overwriting\\n image_with_faces = np.copy(image)\\n \\n # Get the bounding box for each detected face\\n for (x,y,w,h) in faces:\\n # Add a red bounding box to the detections image\\n cv2.rectangle(image_with_faces, (x,y), (x+w,y+h), (255,0,0), 3)\\n \\n if plot is True:\\n plot_image(image_with_faces)\\n else:\\n return image_with_faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d9ab32ab115b335c36f5362ec970cf2\",\n \"score\": \"0.54690915\",\n \"text\": \"def demo(sess, net, im):\\n\\n # Detect all object classes and regress object bounds\\n timer = Timer()\\n timer.tic()\\n scores, boxes = im_detect(sess, net, im)\\n timer.toc()\\n # print ('Detection took {:.3f}s for '\\n # '{:d} object proposals').format(timer.total_time, boxes.shape[0])\\n\\n # Visualize detections for each class\\n for cls_ind, cls in enumerate(CLASSES[1:]):\\n cls_ind += 1 # because we skipped background\\n cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\\n cls_scores = scores[:, cls_ind]\\n dets = np.hstack((cls_boxes,\\n cls_scores[:, np.newaxis])).astype(np.float32)\\n keep = nms(dets, NMS_THRESH)\\n dets = dets[keep, :]\\n vis_detections(im, cls, dets, thresh=CONF_THRESH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d51b75f30c566d8707f1bdeecc413b54\",\n \"score\": \"0.54664963\",\n \"text\": \"def plot_manifold(model, filename, nrow=10):\\n x = torch.linspace(norm.ppf(0.01), norm.ppf(0.99), nrow)\\n xv, yv = torch.meshgrid(x, x)\\n z = torch.stack([xv, yv], 0)\\n z = z.view(2, -1).t().to(model.device)\\n\\n samples, means, _ = model.sample(1, z)\\n\\n save_sample_plot(means, filename, nrow=nrow)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7cd7579e12b8e2715f1568c89171b6\",\n \"score\": \"0.54584986\",\n \"text\": \"def faces(self):\\n return self.getConnectivity()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3fcab6ba2913fbbf9fef087d4f7533b\",\n \"score\": \"0.5457706\",\n \"text\": \"def visualize(self):\\r\\n fig = plt.figure(1)\\r\\n plt.clf()\\r\\n plt.cla()\\r\\n\\r\\n X_new = decomposition.pca.PCA(n_components=2).fit_transform(self.X)\\r\\n model = GMM(n_components=self.targetcluster, covariance_type='full')\\r\\n labels = model.fit_predict(X_new)\\r\\n totz = np.concatenate((X_new, np.expand_dims(labels, axis=1), np.expand_dims(self.y, axis=1),), axis=1)\\r\\n\\r\\n # for each cluster\\r\\n colors = ['red', 'green', 'blue', 'yellow', 'orange', 'purple']\\r\\n fig = plt.figure()\\r\\n\\r\\n for clust in range(0, self.targetcluster):\\r\\n totz_clust = totz[totz[:,-2] == clust]\\r\\n print(\\\"Cluster Size\\\")\\r\\n print(totz_clust.shape)\\r\\n\\r\\n benign = totz_clust[totz_clust[:,-1] == 1]\\r\\n malignant = totz_clust[totz_clust[:,-1] == 0]\\r\\n\\r\\n plt.scatter(benign[:, 0], benign[:, 1], color=colors[clust], marker=\\\".\\\")\\r\\n plt.scatter(malignant[:, 0], malignant[:, 1], color=colors[clust], marker=\\\"x\\\")\\r\\n\\r\\n centroids = model.cluster_centers_\\r\\n ax.scatter(centroids[:, 0], centroids[:, 1], centroids[:, 2],\\r\\n marker='x', s=169, linewidths=3, color=\\\"black\\\",\\r\\n zorder=10)\\r\\n #plt.title(\\\"Breast Cancer Clustering\\\")\\r\\n plt.xlabel(\\\"1st Component\\\")\\r\\n plt.ylabel(\\\"2nd Component\\\")\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"909c6a22136bf5b1f667dea972ec7930\",\n \"score\": \"0.54549277\",\n \"text\": \"def demo(sess, net, image_name, image_name2):\\r\\n \\r\\n #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\\r\\n ######################### Modified ############################\\r\\n \\r\\n # Load the demo image\\r\\n #im_file = os.path.join(cfg.FLAGS2[\\\"data_dir\\\"], 'demo', image_name)\\r\\n #im = cv2.imread(im_file)\\r\\n \\r\\n im = image_name ############### Newly Added ###############\\r\\n im_inf = image_name2\\r\\n proto_im = image_name.copy()\\r\\n proto_imf = image_name2.copy()\\r\\n \\r\\n # Detect all object classes and regress object bounds\\r\\n timer = Timer()\\r\\n timer.tic()\\r\\n scores, boxes = im_detect(sess, net, im)\\r\\n timer.toc()\\r\\n\\r\\n ############### Modified & Newly Added #######################\\r\\n # print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))\\r\\n #cv2.putText(im, 'Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]), \\\\\\r\\n # (5,20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))\\r\\n \\r\\n # Visualize detections for each class\\r\\n CONF_THRESH = 0.1\\r\\n NMS_THRESH = 0.1\\r\\n #for cls_ind, cls in enumerate(CLASSES[1:]):\\r\\n # cls_ind += 1 # because we skipped background\\r\\n # cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]\\r\\n # cls_scores = scores[:, cls_ind]\\r\\n # dets = np.hstack((cls_boxes,\\r\\n # cls_scores[:, np.newaxis])).astype(np.float32)\\r\\n # keep = nms(dets, NMS_THRESH)\\r\\n # dets = dets[keep, :]\\r\\n #print(\\\"aaa\\\")\\r\\n dets = collect(scores, boxes)\\r\\n \\r\\n vis_detections(im, im_inf, dets, thresh=CONF_THRESH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c68a729467b1451f4de52a523bc1bf8\",\n \"score\": \"0.5453496\",\n \"text\": \"def visualize(self):\\n \\n self.model.visualize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba873c931de52911ebe129450007b2d2\",\n \"score\": \"0.54496825\",\n \"text\": \"def livePlotEmotions(emotion_faces: list):\\n for face in emotion_faces:\\n # TODO-Kaan: Update a plt with emotion values\\n p.updatePlot(face)\\n print(face)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ce176f8276f4a8f6f05f8315b5c222c\",\n \"score\": \"0.54491794\",\n \"text\": \"def detection_faces(imagem_url: str) -> int:\\n\\n carregaAlgoritmo = cv2.CascadeClassifier(\\\"src\\\\controllers\\\\detects\\\\haarcascade\\\\haarcascade_frontalface_default.xml\\\")\\n #carregaAlgoritmo = cv2.CascadeClassifier(\\\"detects/haarcascade/haarcascade_frontalface_default.xml\\\")\\n imagem = url_to_image_array(imagem_url)\\n\\n #imagem = cv2.imread('caminho')\\n imagem = cv2.resize(imagem, (0,0), fx=0.7, fy=0.7) \\n imagemCinza = cv2.cvtColor(imagem, cv2.COLOR_BGR2GRAY)\\n\\n #DETECÇÃO DAS FACES\\n faces = carregaAlgoritmo.detectMultiScale(\\n imagemCinza, \\n scaleFactor=1.1, \\n minNeighbors=3, #abordagem de vizihança, (^) = + perder os verdadeiros positivos, (v) = + falsos positivos\\n minSize=(20,20) #tamanho da detecção de uma face\\n )\\n\\n #Pequena regra de negócio\\n count = 0\\n try:\\n for faces_x in faces:\\n print(faces[count])\\n count += 1\\n print(\\\"Quat. de faces: \\\", count)\\n return (count)\\n\\n except Exception as erro:\\n print(\\\"Erro: \\\", EOFError)\\n return (count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ce26102fa2cb8529ea5510df647c72b\",\n \"score\": \"0.5448516\",\n \"text\": \"def forward_det(self):\\n anno_img = self._anno[self._cur]\\n\\n boxes_img = self._boxes[self._cur]\\n pred_cls_img = self._pred_cls[self._cur]\\n pred_confs_img = self._pred_confs[self._cur]\\n if (boxes_img is None or boxes_img.shape[0] < 2):\\n self._cur += 1\\n if (self._cur >= len(self._anno)):\\n self._cur = 0\\n return None\\n im_path = osp.join(self.img_dir, anno_img['img_path'].split('/')[-1])\\n im = cv2.imread(im_path)\\n\\n # 图片和路径名字对不上\\n if (im is None):\\n self._cur += 1\\n if (self._cur >= len(self._anno)):\\n self._cur = 0\\n return None\\n ih = im.shape[0]\\n iw = im.shape[1]\\n PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])\\n # image_blob, im_scale = prep_im_for_blob(im, PIXEL_MEANS)\\n image_blob, im_scale = prep_im_stand(im)\\n blob = np.zeros((1,) + image_blob.shape, dtype=np.float32)\\n blob[0] = image_blob\\n # Reshape net's input blobs\\n boxes = np.zeros((boxes_img.shape[0], 5))\\n boxes[:, 1:5] = boxes_img * im_scale\\n classes = pred_cls_img.reshape(len(pred_cls_img))\\n\\n # 添加词向量\\n label_embeded = np.zeros((len(classes), 300))\\n for index, item in enumerate(classes):\\n label_embeded[index] = self._word_embed[item]\\n # 添加词向量\\n ix1 = []\\n ix2 = []\\n n_rel_inst = len(pred_cls_img) * (len(pred_cls_img) - 1)\\n rel_boxes = np.zeros((n_rel_inst, 5))\\n # SpatialFea = np.zeros((n_rel_inst, 2, 32, 32))\\n SpatialFea = np.zeros((n_rel_inst, 8))\\n rel_so_prior = np.zeros((n_rel_inst, self._num_relations))\\n i_rel_inst = 0\\n # 两两相互变为主宾语\\n for s_idx in range(len(pred_cls_img)):\\n for o_idx in range(len(pred_cls_img)):\\n if (s_idx == o_idx):\\n continue\\n ix1.append(s_idx)\\n ix2.append(o_idx)\\n sBBox = boxes_img[s_idx]\\n oBBox = boxes_img[o_idx]\\n rBBox = self._getUnionBBox(sBBox, oBBox, ih, iw)\\n # soMask = [self._getDualMask(ih, iw, sBBox), \\\\\\n # self._getDualMask(ih, iw, oBBox)]\\n rel_boxes[i_rel_inst, 1:5] = np.array(rBBox) * im_scale\\n # SpatialFea[i_rel_inst] = soMask\\n SpatialFea[i_rel_inst] = self._getRelativeLoc(sBBox, oBBox)\\n rel_so_prior[i_rel_inst] = self._so_prior[classes[s_idx], classes[o_idx]]\\n i_rel_inst += 1\\n # image_blob = image_blob.astype(np.float32, copy=False)\\n boxes = boxes.astype(np.float32, copy=False)\\n classes = classes.astype(np.float32, copy=False)\\n ix1 = np.array(ix1)\\n ix2 = np.array(ix2)\\n self._cur += 1\\n if (self._cur >= len(self._anno)):\\n self._cur = 0\\n unknown = self._unknown[\\\"unknown\\\"]\\n return blob, boxes, rel_boxes, SpatialFea, classes, unknown, \\\\\\n ix1, ix2, label_embeded, boxes_img, pred_confs_img, rel_so_prior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b834d19b0f10e1fc98e5fd232320bb14\",\n \"score\": \"0.54455495\",\n \"text\": \"def use_deepface(self):\\n Input_1 = Input(shape=(152, 152, 3), name='face_Input')\\n Conv_1 = Conv2D(32, (11, 11), activation='relu', name='face_Conv_1')(Input_1)\\n MaxPool_1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='face_MaxPool_1')(Conv_1)\\n Conv_2 = Conv2D(16, (9, 9), activation='relu', name='face_Conv_2')(MaxPool_1)\\n LC_1 = LocallyConnected2D(16, (9, 9), activation='relu', name='face_LC_1')(Conv_2)\\n LC_2 = LocallyConnected2D(16, (7, 7), strides=(2, 2), activation='relu', name='face_LC_2')(LC_1)\\n LC_3 = LocallyConnected2D(16, (5, 5), activation='relu', name='face_LC_3')(LC_2) # output 21, 21, 16\\n Flatten_1 = Flatten(name='face_Flatten')(LC_3) # output 7056\\n FC_1 = Dense(4096, activation='relu')(Flatten_1)\\n Drop_1 = Dropout(0.5)(FC_1)\\n FC_2 = Dense(8631, activation='softmax')(Drop_1)\\n face = Model(inputs=Input_1, outputs=FC_2)\\n return face\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb54b7cad574a5f79aa37d11e9b600a1\",\n \"score\": \"0.5444366\",\n \"text\": \"def run(self):\\n cap = cv2.VideoCapture(0)\\n cap.set(cv2.CAP_PROP_FRAME_WIDTH, 960)\\n cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 540)\\n photo = 0\\n breakout = 0\\n while True:\\n ret, frame = cap.read()\\n \\n try:\\n # detect face box, probability and landmarks\\n boxes, probs, landmarks = self.mtcnn.detect(frame, landmarks=True)\\n \\n # draw on frame\\n photo, breakout, boxes, probs = self._draw(frame, boxes, probs, landmarks, photo)\\n\\n except:\\n pass\\n\\n # Show the frame\\n cv2.imshow('Detector', frame)\\n cv2.moveWindow('Detector',0,0)\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n break\\n \\n if breakout == 1:\\n break\\n\\n \\n cv2.destroyAllWindows()\\n cap.release()\\n cv2.waitKey(0)\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8d070c5e8db171052700124b9f9dc46\",\n \"score\": \"0.5442117\",\n \"text\": \"def imshow_det_bboxes_poses(\\n img,\\n bboxes,\\n labels,\\n class_names=None,\\n score_thr=0,\\n bbox_color=\\\"green\\\",\\n text_color=\\\"green\\\",\\n thickness=1,\\n font_scale=0.5,\\n show=True,\\n win_name=\\\"\\\",\\n wait_time=0,\\n out_file=None,\\n poses=None,\\n corners_3d=None,\\n dataste_name=None,\\n renderer=None,\\n K=None,\\n vis_tool=\\\"matplotlib\\\",\\n):\\n # logger.info('poses: {}'.format(poses))\\n assert bboxes.ndim == 2\\n assert labels.ndim == 1\\n assert bboxes.shape[0] == labels.shape[0]\\n assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5\\n img = imread(img)\\n\\n if score_thr > 0:\\n assert bboxes.shape[1] == 5\\n scores = bboxes[:, -1]\\n inds = scores > score_thr\\n bboxes = bboxes[inds, :]\\n labels = labels[inds]\\n\\n bbox_color = color_val(bbox_color)\\n text_color = color_val(text_color)\\n\\n for bbox, label in zip(bboxes, labels):\\n # pose\\n if poses is not None:\\n if poses[label]:\\n pose = poses[label][0] # TODO: handle multiple poses\\n bgr, depth = renderer.render(label, pose[:, :3], pose[:, 3], r_type=\\\"mat\\\")\\n # img = img - bgr\\n pose_mask = np.zeros(depth.shape)\\n pose_mask[depth != 0] = 1\\n edges_3 = mask_utils.get_edge(pose_mask, bw=3)\\n edges_3[:, :, [0, 1]] = 0 # red\\n img[edges_3 != 0] = 255\\n cls_name = class_names[label]\\n corners_2d, _ = misc_6d.points_to_2D(corners_3d[cls_name], pose[:, :3], pose[:, 3], K)\\n img = misc_6d.draw_projected_box3d(img, corners_2d, thickness=thickness)\\n\\n bbox_int = bbox.astype(np.int32)\\n left_top = (bbox_int[0], bbox_int[1])\\n right_bottom = (bbox_int[2], bbox_int[3])\\n cv2.rectangle(img, left_top, right_bottom, bbox_color, thickness=thickness)\\n label_text = class_names[label] if class_names is not None else \\\"cls {}\\\".format(label)\\n if len(bbox) > 4:\\n label_text += \\\"|{:.02f}\\\".format(bbox[-1])\\n cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2), cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)\\n\\n if show:\\n if vis_tool == \\\"matplotlib\\\":\\n fig = plt.figure(frameon=False, figsize=(8, 6), dpi=100)\\n tmp = fig.add_subplot(1, 1, 1)\\n tmp.set_title(\\\"{}\\\".format(win_name))\\n plt.axis(\\\"off\\\")\\n plt.imshow(img[:, :, [2, 1, 0]])\\n plt.show()\\n else: # use 'mmcv'\\n imshow(img, win_name, wait_time)\\n if out_file is not None:\\n imwrite(img, out_file)\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43b5bae862ac4a77f98f057d537d20c\",\n \"score\": \"0.5441236\",\n \"text\": \"def visualize_recognized_objects(\\n image, boxes, classes, scores, category_index, threshold):\\n # Size, in inches, of the output images. For visualization\\n IMAGE_SIZE = (12, 8)\\n\\n # w, h = image.size\\n # my_dpi = 96 # todo: find this programmatically\\n\\n image_np = load_image_into_numpy_array(image)\\n vis_util.visualize_boxes_and_labels_on_image_array(\\n image_np,\\n boxes,\\n classes,\\n scores,\\n category_index,\\n use_normalized_coordinates=True,\\n line_thickness=5,\\n min_score_thresh=threshold)\\n plt.figure(figsize=IMAGE_SIZE)\\n # plt.figure(figsize=(w / my_dpi, h / my_dpi), dpi=my_dpi)\\n plt.imshow(image_np)\\n plt.show()\\n return image_np\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72aff96a3f45f799c962873c7a1547ac\",\n \"score\": \"0.543901\",\n \"text\": \"def show_intermediate_output(model, layer_name, image):\\n if depth == 1:\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\\n\\n image = cv2.resize(image, (width, height))\\n\\n img_ndarray = np.asarray(image, dtype='float64') / 255\\n test_data = np.ndarray.flatten(img_ndarray)\\n test_data = test_data.astype('float32')\\n\\n if K.image_data_format() == 'channels_first':\\n test_data = test_data.reshape(1, depth, height, width)\\n else:\\n test_data = test_data.reshape(1, height, width, depth)\\n\\n output = get_intermediate_output(model, layer_name, test_data)\\n n = output.shape[-1] # number of feature\\n size = output.shape[1] # feature map side length\\n display_grid = np.zeros((size * 1, n * size)) # grid\\n\\n for i in range(n):\\n channel_image = output[:, :, i]\\n display_grid[0:size, i * size:(i + 1) * size] = channel_image\\n\\n plt.figure()\\n plt.title(layer_name)\\n plt.grid(False)\\n plt.imshow(display_grid, cmap='viridis')\\n plt.savefig('visualization/' + layer_name + '_output.jpg') # save output diagram\\n plt.show() # must show after imshow\\n\\n return display_grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bc3ed77234feb630f33cf7116bc76d7\",\n \"score\": \"0.5433919\",\n \"text\": \"def render(img, obj, projection, model, color=False):\\n vertices = obj.vertices\\n scale_matrix = np.eye(3) * 20\\n h = 200\\n w = 200\\n\\n for face in obj.faces:\\n face_vertices = face[0]\\n points = np.array([vertices[vertex - 1] for vertex in face_vertices])\\n points = np.dot(points, scale_matrix)\\n # render in the middle\\n points = np.array([[p[0] + w / 2, p[1] + h / 2, p[2]] for p in points])\\n dst = cv2.perspectiveTransform(points.reshape(-1, 1, 3), projection)\\n imgpts = np.int32(dst)\\n cv2.fillConvexPoly(img, imgpts, (137, 27, 211))\\n \\n return img\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992994,"cells":{"query_id":{"kind":"string","value":"bcb2027c3c56212fac8d91ec35e638df"},"query":{"kind":"string","value":"Version number of the Redis engine used by the cluster."},"positive_passages":{"kind":"list like","value":[{"docid":"c5cc51d64dd355f2fa49949efc1f44e7","score":"0.79144454","text":"def engine_version(self) -> str:\n return pulumi.get(self, \"engine_version\")","title":""}],"string":"[\n {\n \"docid\": \"c5cc51d64dd355f2fa49949efc1f44e7\",\n \"score\": \"0.79144454\",\n \"text\": \"def engine_version(self) -> str:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4ff972cf54a6ad220b13fd20e15c481d","score":"0.7563207","text":"def engine_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"4ff972cf54a6ad220b13fd20e15c481d","score":"0.7563207","text":"def engine_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"4ff972cf54a6ad220b13fd20e15c481d","score":"0.7563207","text":"def engine_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"0b969fabd01c1526cc8b27251fad357a","score":"0.7518153","text":"def engine_version(self) -> Optional[str]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"408c7c9f0d58e8f9c76f17c2db2b711a","score":"0.7327727","text":"def engine_version(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"f5420a0690d62ac8b136859fb8a8c4ec","score":"0.7065706","text":"def engine_version(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"f5420a0690d62ac8b136859fb8a8c4ec","score":"0.7065706","text":"def engine_version(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"f5420a0690d62ac8b136859fb8a8c4ec","score":"0.7065706","text":"def engine_version(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"f5420a0690d62ac8b136859fb8a8c4ec","score":"0.7065706","text":"def engine_version(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"f5420a0690d62ac8b136859fb8a8c4ec","score":"0.7065706","text":"def engine_version(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"engine_version\")","title":""},{"docid":"9f6903ef84543c2610f44b2c985e2731","score":"0.69642663","text":"def version(self):\n return self.get_tag(PCLUSTER_VERSION_TAG)","title":""},{"docid":"3aa8deaa1018041ff441ec459a6455d1","score":"0.66802484","text":"def engine_version_actual(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"engine_version_actual\")","title":""},{"docid":"9a98fe5067a372a3b58a200d70588ce7","score":"0.6680131","text":"def engine_patch_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"engine_patch_version\")","title":""},{"docid":"659ea41286aae452326472f13fcee241","score":"0.6679079","text":"def version(self):\n response = self._request_call('/version')\n return response.version_etcdserver","title":""},{"docid":"ef1d6011fc558cc2bf0d3ec7dfe7cee3","score":"0.66346836","text":"def db_version(engine):\n return IMPL.db_version(engine)","title":""},{"docid":"57e4bcc2d5494b1e6cb287d29d7ac882","score":"0.66147625","text":"def version(self) -> int:\r\n\r\n return self.__version","title":""},{"docid":"57e4bcc2d5494b1e6cb287d29d7ac882","score":"0.66147625","text":"def version(self) -> int:\r\n\r\n return self.__version","title":""},{"docid":"df2ea5d53a4efc9edd5f3b32acd444ca","score":"0.660556","text":"def version(self) -> int:\n return self._version","title":""},{"docid":"fb5e0b33fb9699f647ee4e5b1c4f43a3","score":"0.6556487","text":"def minimum_engine_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"minimum_engine_version\")","title":""},{"docid":"3197b9f1dff80e8cbc061789c1098061","score":"0.6555064","text":"def get_version(self):\n return 0","title":""},{"docid":"9ef6b2af03f7d20f95e9d7c232e1592a","score":"0.6521547","text":"def version(self) -> str:\n return pulumi.get(self, \"version\")","title":""},{"docid":"9ef6b2af03f7d20f95e9d7c232e1592a","score":"0.6521547","text":"def version(self) -> str:\n return pulumi.get(self, \"version\")","title":""},{"docid":"9ef6b2af03f7d20f95e9d7c232e1592a","score":"0.6521547","text":"def version(self) -> str:\n return pulumi.get(self, \"version\")","title":""},{"docid":"9ef6b2af03f7d20f95e9d7c232e1592a","score":"0.6521547","text":"def version(self) -> str:\n return pulumi.get(self, \"version\")","title":""},{"docid":"b9f033e95ca7ad72fb44b7cdc2557159","score":"0.65120244","text":"def client_version(self) -> str:\n return pulumi.get(self, \"client_version\")","title":""},{"docid":"94e47111a5e848fe1568295f876c434a","score":"0.65105444","text":"def version(self):\n return self._get(\"version\")","title":""},{"docid":"b4359009d5122a8a1f72fe6da81e3713","score":"0.64658505","text":"def version(self):\n return self.__version","title":""},{"docid":"b4359009d5122a8a1f72fe6da81e3713","score":"0.64658505","text":"def version(self):\n return self.__version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"12ba601c3bb1dec04ee87652beaaadcf","score":"0.6455038","text":"def version(self):\n return self._version","title":""},{"docid":"9b97e57762bd259c20ffbe277c199a1b","score":"0.6448465","text":"def version(self):\n return self.get_current_version()","title":""},{"docid":"f58092955e4e05d01a30bc56b0fd36db","score":"0.6448401","text":"def version(self):\n\n return self._version","title":""},{"docid":"cf56b0b51506a028b4916bf30870a415","score":"0.64292794","text":"def version(self):\n if not self._version:\n self._version = self._get_version()\n\n return self._version","title":""},{"docid":"1e5b3947e1e29bb9a9192cc88045e85c","score":"0.6409707","text":"def get_kubernetes_version(self) -> str:\n return self.agentpool_context.get_kubernetes_version()","title":""},{"docid":"f7f597537a4df38cc479bc6b2123452a","score":"0.64086556","text":"def kubernetes_version(self) -> str:\n return pulumi.get(self, \"kubernetes_version\")","title":""},{"docid":"0d0a2be8f91dd7f94e5b1b7bfcc20608","score":"0.640633","text":"def version_cluster(self):\n response = self._request_call('/version')\n return response.version_etcdcluster","title":""},{"docid":"ef20f97aa9511421d57e86c050657591","score":"0.64003676","text":"def version(self):\r\n return self.version_guid","title":""},{"docid":"29122c5be6cd4a2268d25a5ff8a49dda","score":"0.6385581","text":"def version_string(self):\n return self.server_version","title":""},{"docid":"a16a8d01699a3edb677d3fe08d8d595b","score":"0.63841337","text":"def get_version(self):\n return self.cur_config['version']['name']","title":""},{"docid":"a0bda0dd6dac1ed42bedeadfc50e1b02","score":"0.6377421","text":"def get_version(self):\n return self._version","title":""},{"docid":"a0bda0dd6dac1ed42bedeadfc50e1b02","score":"0.6377421","text":"def get_version(self):\n return self._version","title":""},{"docid":"4fc225242be93e2506289775fc1b278f","score":"0.63699394","text":"def version(self):\n return 1","title":""},{"docid":"74ac1f61329038a6576656d4b8d5d15b","score":"0.6369492","text":"def version(self):\n return \"%d.%d\" % (self._vmajor, self._vminor)","title":""},{"docid":"52b4470fb5eabafda9fda53f9bdd6a2c","score":"0.63677835","text":"def version(self) -> str:\n return self._version","title":""},{"docid":"52b4470fb5eabafda9fda53f9bdd6a2c","score":"0.63677835","text":"def version(self) -> str:\n return self._version","title":""},{"docid":"52b4470fb5eabafda9fda53f9bdd6a2c","score":"0.63677835","text":"def version(self) -> str:\n return self._version","title":""},{"docid":"52b4470fb5eabafda9fda53f9bdd6a2c","score":"0.63677835","text":"def version(self) -> str:\n return self._version","title":""},{"docid":"52b4470fb5eabafda9fda53f9bdd6a2c","score":"0.63677835","text":"def version(self) -> str:\n return self._version","title":""},{"docid":"0d333004266a43206958c310bacf65d6","score":"0.63571227","text":"def get_version(self):\n\n r = self._create_operation_request(self, method=\"GET\")\n root_info = send_session_request(self._session, r).json()\n return root_info[\"currentVersion\"]","title":""},{"docid":"f4395f830a50d653b3b31db54e54aab3","score":"0.63566047","text":"def engine_version_actual(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"engine_version_actual\")","title":""},{"docid":"8e7a98c8368a1d750503c00d57e36e5c","score":"0.6349936","text":"def getVersion(self):\n return self.get('Version', type=\"numeric\")","title":""},{"docid":"ab3dcd4ffd6955f110d851269be364d5","score":"0.6340881","text":"def version(self, **kwargs):\n return version.version(self._host, self._session, **kwargs)","title":""},{"docid":"ed55179885dbeb3ebf3c813734ff891d","score":"0.6340599","text":"def model_version(self) -> str:\n return pulumi.get(self, \"model_version\")","title":""},{"docid":"72d3f5e147058fdfe0fc59b984b6335c","score":"0.6335615","text":"def version(self) -> str:\n\n return self._version","title":""},{"docid":"abd8f0f8a5267cf6fdc24b715142325a","score":"0.63223803","text":"def get_version(self):\n return version.__version__","title":""},{"docid":"abd8f0f8a5267cf6fdc24b715142325a","score":"0.63223803","text":"def get_version(self):\n return version.__version__","title":""},{"docid":"85c71335c829d4345817e241ebec55b1","score":"0.6320069","text":"def getversion(self):\n return self.__version","title":""},{"docid":"426044d44580ed6c9d5239c0e7163d96","score":"0.63188636","text":"def get_version(self):\r\n\r\n return self.versions[0].number","title":""},{"docid":"3f898ff516659b118b4ab99a09a6e8d9","score":"0.6312753","text":"def get_version(self):\n return self.version","title":""},{"docid":"016a075817161b35a15982078d3a1f2b","score":"0.63111115","text":"def version(self):\n return self._client.getVersion()","title":""},{"docid":"1efdb8899e99fbd23f5899e4abb04282","score":"0.63041973","text":"def database_installed_version(self) -> str:\n return pulumi.get(self, \"database_installed_version\")","title":""},{"docid":"7eb9d53b80c8a533ded248cb005282a5","score":"0.6296039","text":"def version_number() -> int:\n return 0","title":""},{"docid":"cb93b846065eb97422abbc098b1ae116","score":"0.6272881","text":"def database_version(self) -> str:\n return pulumi.get(self, \"database_version\")","title":""},{"docid":"c2a6ade9f531348104c6df729389dcbc","score":"0.6267963","text":"def engine_patch_version(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"engine_patch_version\")","title":""},{"docid":"a94660a8d0054ffe9dd072cf9ff90d7f","score":"0.62613696","text":"def get(self):\n return self._version","title":""},{"docid":"fc40eb6984d67ddb0719b762c8f7ab7f","score":"0.62430245","text":"def get_version(self):\r\n return self._arm.get_version()","title":""},{"docid":"5b54ba8ed53472823ac5d5ba21755fd4","score":"0.6241751","text":"def runtime_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"runtime_version\")","title":""},{"docid":"55d2db489c1a9acd83976063d2abf5e3","score":"0.6226649","text":"def Version(self):\n return self._version","title":""},{"docid":"00c85ba98aa5aaf9fc5a4a6f9e6a6d29","score":"0.6222242","text":"def version():\n return uname().version","title":""},{"docid":"00c85ba98aa5aaf9fc5a4a6f9e6a6d29","score":"0.6222242","text":"def version():\n return uname().version","title":""},{"docid":"10a74437dc1b98796159d2132eb09db3","score":"0.6210377","text":"def get_redis_server_version(self) -> Tuple[int, int, int]:\n if self.redis_server_version is None:\n self.redis_server_version = get_version(self.connection)\n\n return self.redis_server_version","title":""},{"docid":"7870ada35b1a5fd08f88138c7cf3020d","score":"0.6197556","text":"def version(self) -> str:\n return '0.1'","title":""},{"docid":"d068d089069ae050e1365f2424cfbae5","score":"0.6193551","text":"def get_version(self):\n pass","title":""},{"docid":"90af97551d5b28d038248e6d96da1aef","score":"0.6188034","text":"async def version(self) -> str:\n response = await self._request(\"status\")\n return response[\"version\"]","title":""},{"docid":"67d2f1b59da81ca409c1cc0d8955b31f","score":"0.61849797","text":"def version(self):\n pass","title":""},{"docid":"67d2f1b59da81ca409c1cc0d8955b31f","score":"0.61849797","text":"def version(self):\n pass","title":""},{"docid":"67d2f1b59da81ca409c1cc0d8955b31f","score":"0.61849797","text":"def version(self):\n pass","title":""},{"docid":"93eb69229c5931148c5827d6e46572ec","score":"0.6184044","text":"async def version(self):\n self.do(\"version\")\n return (await self.read(7)).strip()","title":""},{"docid":"b98be5764d3fbf174a321ef0033be5f1","score":"0.6179486","text":"def build_version(self):\n return self.nodes[0].get('infos').get('system_info').get('system_version')","title":""},{"docid":"78b673b506a03ac04e461c56de559e7b","score":"0.6169051","text":"def query_version(self):\n return self.connection.cursor().execute('SELECT version()').fetchone()[0]","title":""},{"docid":"18d7ec59ad0c7b7aad819df02120248b","score":"0.6155375","text":"def version(self) -> Optional[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"18d7ec59ad0c7b7aad819df02120248b","score":"0.6155375","text":"def version(self) -> Optional[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"18d7ec59ad0c7b7aad819df02120248b","score":"0.6155375","text":"def version(self) -> Optional[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"18d7ec59ad0c7b7aad819df02120248b","score":"0.6155375","text":"def version(self) -> Optional[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"18d7ec59ad0c7b7aad819df02120248b","score":"0.6155375","text":"def version(self) -> Optional[str]:\n return pulumi.get(self, \"version\")","title":""},{"docid":"ccb5b28af5c4d6098d8fadc24ec6b316","score":"0.6150088","text":"def version(self):\n\n return self.manifest[\"version\"]","title":""},{"docid":"3f8b90bbb1db1e873e7cd8b7cc417272","score":"0.6149514","text":"def config_version(self) -> str:\n return pulumi.get(self, \"config_version\")","title":""},{"docid":"3f8b90bbb1db1e873e7cd8b7cc417272","score":"0.6149514","text":"def config_version(self) -> str:\n return pulumi.get(self, \"config_version\")","title":""},{"docid":"3f8b90bbb1db1e873e7cd8b7cc417272","score":"0.6149514","text":"def config_version(self) -> str:\n return pulumi.get(self, \"config_version\")","title":""},{"docid":"3f8b90bbb1db1e873e7cd8b7cc417272","score":"0.6149514","text":"def config_version(self) -> str:\n return pulumi.get(self, \"config_version\")","title":""}],"string":"[\n {\n \"docid\": \"4ff972cf54a6ad220b13fd20e15c481d\",\n \"score\": \"0.7563207\",\n \"text\": \"def engine_version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff972cf54a6ad220b13fd20e15c481d\",\n \"score\": \"0.7563207\",\n \"text\": \"def engine_version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff972cf54a6ad220b13fd20e15c481d\",\n \"score\": \"0.7563207\",\n \"text\": \"def engine_version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b969fabd01c1526cc8b27251fad357a\",\n \"score\": \"0.7518153\",\n \"text\": \"def engine_version(self) -> Optional[str]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"408c7c9f0d58e8f9c76f17c2db2b711a\",\n \"score\": \"0.7327727\",\n \"text\": \"def engine_version(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5420a0690d62ac8b136859fb8a8c4ec\",\n \"score\": \"0.7065706\",\n \"text\": \"def engine_version(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5420a0690d62ac8b136859fb8a8c4ec\",\n \"score\": \"0.7065706\",\n \"text\": \"def engine_version(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5420a0690d62ac8b136859fb8a8c4ec\",\n \"score\": \"0.7065706\",\n \"text\": \"def engine_version(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5420a0690d62ac8b136859fb8a8c4ec\",\n \"score\": \"0.7065706\",\n \"text\": \"def engine_version(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5420a0690d62ac8b136859fb8a8c4ec\",\n \"score\": \"0.7065706\",\n \"text\": \"def engine_version(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f6903ef84543c2610f44b2c985e2731\",\n \"score\": \"0.69642663\",\n \"text\": \"def version(self):\\n return self.get_tag(PCLUSTER_VERSION_TAG)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa8deaa1018041ff441ec459a6455d1\",\n \"score\": \"0.66802484\",\n \"text\": \"def engine_version_actual(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"engine_version_actual\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a98fe5067a372a3b58a200d70588ce7\",\n \"score\": \"0.6680131\",\n \"text\": \"def engine_patch_version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"engine_patch_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"659ea41286aae452326472f13fcee241\",\n \"score\": \"0.6679079\",\n \"text\": \"def version(self):\\n response = self._request_call('/version')\\n return response.version_etcdserver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef1d6011fc558cc2bf0d3ec7dfe7cee3\",\n \"score\": \"0.66346836\",\n \"text\": \"def db_version(engine):\\n return IMPL.db_version(engine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57e4bcc2d5494b1e6cb287d29d7ac882\",\n \"score\": \"0.66147625\",\n \"text\": \"def version(self) -> int:\\r\\n\\r\\n return self.__version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57e4bcc2d5494b1e6cb287d29d7ac882\",\n \"score\": \"0.66147625\",\n \"text\": \"def version(self) -> int:\\r\\n\\r\\n return self.__version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2ea5d53a4efc9edd5f3b32acd444ca\",\n \"score\": \"0.660556\",\n \"text\": \"def version(self) -> int:\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb5e0b33fb9699f647ee4e5b1c4f43a3\",\n \"score\": \"0.6556487\",\n \"text\": \"def minimum_engine_version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"minimum_engine_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3197b9f1dff80e8cbc061789c1098061\",\n \"score\": \"0.6555064\",\n \"text\": \"def get_version(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef6b2af03f7d20f95e9d7c232e1592a\",\n \"score\": \"0.6521547\",\n \"text\": \"def version(self) -> str:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef6b2af03f7d20f95e9d7c232e1592a\",\n \"score\": \"0.6521547\",\n \"text\": \"def version(self) -> str:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef6b2af03f7d20f95e9d7c232e1592a\",\n \"score\": \"0.6521547\",\n \"text\": \"def version(self) -> str:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef6b2af03f7d20f95e9d7c232e1592a\",\n \"score\": \"0.6521547\",\n \"text\": \"def version(self) -> str:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f033e95ca7ad72fb44b7cdc2557159\",\n \"score\": \"0.65120244\",\n \"text\": \"def client_version(self) -> str:\\n return pulumi.get(self, \\\"client_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94e47111a5e848fe1568295f876c434a\",\n \"score\": \"0.65105444\",\n \"text\": \"def version(self):\\n return self._get(\\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4359009d5122a8a1f72fe6da81e3713\",\n \"score\": \"0.64658505\",\n \"text\": \"def version(self):\\n return self.__version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4359009d5122a8a1f72fe6da81e3713\",\n \"score\": \"0.64658505\",\n \"text\": \"def version(self):\\n return self.__version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ba601c3bb1dec04ee87652beaaadcf\",\n \"score\": \"0.6455038\",\n \"text\": \"def version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b97e57762bd259c20ffbe277c199a1b\",\n \"score\": \"0.6448465\",\n \"text\": \"def version(self):\\n return self.get_current_version()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58092955e4e05d01a30bc56b0fd36db\",\n \"score\": \"0.6448401\",\n \"text\": \"def version(self):\\n\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf56b0b51506a028b4916bf30870a415\",\n \"score\": \"0.64292794\",\n \"text\": \"def version(self):\\n if not self._version:\\n self._version = self._get_version()\\n\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e5b3947e1e29bb9a9192cc88045e85c\",\n \"score\": \"0.6409707\",\n \"text\": \"def get_kubernetes_version(self) -> str:\\n return self.agentpool_context.get_kubernetes_version()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f597537a4df38cc479bc6b2123452a\",\n \"score\": \"0.64086556\",\n \"text\": \"def kubernetes_version(self) -> str:\\n return pulumi.get(self, \\\"kubernetes_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d0a2be8f91dd7f94e5b1b7bfcc20608\",\n \"score\": \"0.640633\",\n \"text\": \"def version_cluster(self):\\n response = self._request_call('/version')\\n return response.version_etcdcluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef20f97aa9511421d57e86c050657591\",\n \"score\": \"0.64003676\",\n \"text\": \"def version(self):\\r\\n return self.version_guid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29122c5be6cd4a2268d25a5ff8a49dda\",\n \"score\": \"0.6385581\",\n \"text\": \"def version_string(self):\\n return self.server_version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a16a8d01699a3edb677d3fe08d8d595b\",\n \"score\": \"0.63841337\",\n \"text\": \"def get_version(self):\\n return self.cur_config['version']['name']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bda0dd6dac1ed42bedeadfc50e1b02\",\n \"score\": \"0.6377421\",\n \"text\": \"def get_version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bda0dd6dac1ed42bedeadfc50e1b02\",\n \"score\": \"0.6377421\",\n \"text\": \"def get_version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc225242be93e2506289775fc1b278f\",\n \"score\": \"0.63699394\",\n \"text\": \"def version(self):\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74ac1f61329038a6576656d4b8d5d15b\",\n \"score\": \"0.6369492\",\n \"text\": \"def version(self):\\n return \\\"%d.%d\\\" % (self._vmajor, self._vminor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b4470fb5eabafda9fda53f9bdd6a2c\",\n \"score\": \"0.63677835\",\n \"text\": \"def version(self) -> str:\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b4470fb5eabafda9fda53f9bdd6a2c\",\n \"score\": \"0.63677835\",\n \"text\": \"def version(self) -> str:\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b4470fb5eabafda9fda53f9bdd6a2c\",\n \"score\": \"0.63677835\",\n \"text\": \"def version(self) -> str:\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b4470fb5eabafda9fda53f9bdd6a2c\",\n \"score\": \"0.63677835\",\n \"text\": \"def version(self) -> str:\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b4470fb5eabafda9fda53f9bdd6a2c\",\n \"score\": \"0.63677835\",\n \"text\": \"def version(self) -> str:\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d333004266a43206958c310bacf65d6\",\n \"score\": \"0.63571227\",\n \"text\": \"def get_version(self):\\n\\n r = self._create_operation_request(self, method=\\\"GET\\\")\\n root_info = send_session_request(self._session, r).json()\\n return root_info[\\\"currentVersion\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4395f830a50d653b3b31db54e54aab3\",\n \"score\": \"0.63566047\",\n \"text\": \"def engine_version_actual(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"engine_version_actual\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e7a98c8368a1d750503c00d57e36e5c\",\n \"score\": \"0.6349936\",\n \"text\": \"def getVersion(self):\\n return self.get('Version', type=\\\"numeric\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab3dcd4ffd6955f110d851269be364d5\",\n \"score\": \"0.6340881\",\n \"text\": \"def version(self, **kwargs):\\n return version.version(self._host, self._session, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed55179885dbeb3ebf3c813734ff891d\",\n \"score\": \"0.6340599\",\n \"text\": \"def model_version(self) -> str:\\n return pulumi.get(self, \\\"model_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d3f5e147058fdfe0fc59b984b6335c\",\n \"score\": \"0.6335615\",\n \"text\": \"def version(self) -> str:\\n\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd8f0f8a5267cf6fdc24b715142325a\",\n \"score\": \"0.63223803\",\n \"text\": \"def get_version(self):\\n return version.__version__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd8f0f8a5267cf6fdc24b715142325a\",\n \"score\": \"0.63223803\",\n \"text\": \"def get_version(self):\\n return version.__version__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c71335c829d4345817e241ebec55b1\",\n \"score\": \"0.6320069\",\n \"text\": \"def getversion(self):\\n return self.__version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"426044d44580ed6c9d5239c0e7163d96\",\n \"score\": \"0.63188636\",\n \"text\": \"def get_version(self):\\r\\n\\r\\n return self.versions[0].number\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f898ff516659b118b4ab99a09a6e8d9\",\n \"score\": \"0.6312753\",\n \"text\": \"def get_version(self):\\n return self.version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"016a075817161b35a15982078d3a1f2b\",\n \"score\": \"0.63111115\",\n \"text\": \"def version(self):\\n return self._client.getVersion()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1efdb8899e99fbd23f5899e4abb04282\",\n \"score\": \"0.63041973\",\n \"text\": \"def database_installed_version(self) -> str:\\n return pulumi.get(self, \\\"database_installed_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eb9d53b80c8a533ded248cb005282a5\",\n \"score\": \"0.6296039\",\n \"text\": \"def version_number() -> int:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb93b846065eb97422abbc098b1ae116\",\n \"score\": \"0.6272881\",\n \"text\": \"def database_version(self) -> str:\\n return pulumi.get(self, \\\"database_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a6ade9f531348104c6df729389dcbc\",\n \"score\": \"0.6267963\",\n \"text\": \"def engine_patch_version(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"engine_patch_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94660a8d0054ffe9dd072cf9ff90d7f\",\n \"score\": \"0.62613696\",\n \"text\": \"def get(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc40eb6984d67ddb0719b762c8f7ab7f\",\n \"score\": \"0.62430245\",\n \"text\": \"def get_version(self):\\r\\n return self._arm.get_version()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b54ba8ed53472823ac5d5ba21755fd4\",\n \"score\": \"0.6241751\",\n \"text\": \"def runtime_version(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"runtime_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55d2db489c1a9acd83976063d2abf5e3\",\n \"score\": \"0.6226649\",\n \"text\": \"def Version(self):\\n return self._version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c85ba98aa5aaf9fc5a4a6f9e6a6d29\",\n \"score\": \"0.6222242\",\n \"text\": \"def version():\\n return uname().version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c85ba98aa5aaf9fc5a4a6f9e6a6d29\",\n \"score\": \"0.6222242\",\n \"text\": \"def version():\\n return uname().version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a74437dc1b98796159d2132eb09db3\",\n \"score\": \"0.6210377\",\n \"text\": \"def get_redis_server_version(self) -> Tuple[int, int, int]:\\n if self.redis_server_version is None:\\n self.redis_server_version = get_version(self.connection)\\n\\n return self.redis_server_version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7870ada35b1a5fd08f88138c7cf3020d\",\n \"score\": \"0.6197556\",\n \"text\": \"def version(self) -> str:\\n return '0.1'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d068d089069ae050e1365f2424cfbae5\",\n \"score\": \"0.6193551\",\n \"text\": \"def get_version(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90af97551d5b28d038248e6d96da1aef\",\n \"score\": \"0.6188034\",\n \"text\": \"async def version(self) -> str:\\n response = await self._request(\\\"status\\\")\\n return response[\\\"version\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d2f1b59da81ca409c1cc0d8955b31f\",\n \"score\": \"0.61849797\",\n \"text\": \"def version(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d2f1b59da81ca409c1cc0d8955b31f\",\n \"score\": \"0.61849797\",\n \"text\": \"def version(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d2f1b59da81ca409c1cc0d8955b31f\",\n \"score\": \"0.61849797\",\n \"text\": \"def version(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93eb69229c5931148c5827d6e46572ec\",\n \"score\": \"0.6184044\",\n \"text\": \"async def version(self):\\n self.do(\\\"version\\\")\\n return (await self.read(7)).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b98be5764d3fbf174a321ef0033be5f1\",\n \"score\": \"0.6179486\",\n \"text\": \"def build_version(self):\\n return self.nodes[0].get('infos').get('system_info').get('system_version')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78b673b506a03ac04e461c56de559e7b\",\n \"score\": \"0.6169051\",\n \"text\": \"def query_version(self):\\n return self.connection.cursor().execute('SELECT version()').fetchone()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d7ec59ad0c7b7aad819df02120248b\",\n \"score\": \"0.6155375\",\n \"text\": \"def version(self) -> Optional[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d7ec59ad0c7b7aad819df02120248b\",\n \"score\": \"0.6155375\",\n \"text\": \"def version(self) -> Optional[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d7ec59ad0c7b7aad819df02120248b\",\n \"score\": \"0.6155375\",\n \"text\": \"def version(self) -> Optional[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d7ec59ad0c7b7aad819df02120248b\",\n \"score\": \"0.6155375\",\n \"text\": \"def version(self) -> Optional[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d7ec59ad0c7b7aad819df02120248b\",\n \"score\": \"0.6155375\",\n \"text\": \"def version(self) -> Optional[str]:\\n return pulumi.get(self, \\\"version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb5b28af5c4d6098d8fadc24ec6b316\",\n \"score\": \"0.6150088\",\n \"text\": \"def version(self):\\n\\n return self.manifest[\\\"version\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8b90bbb1db1e873e7cd8b7cc417272\",\n \"score\": \"0.6149514\",\n \"text\": \"def config_version(self) -> str:\\n return pulumi.get(self, \\\"config_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8b90bbb1db1e873e7cd8b7cc417272\",\n \"score\": \"0.6149514\",\n \"text\": \"def config_version(self) -> str:\\n return pulumi.get(self, \\\"config_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8b90bbb1db1e873e7cd8b7cc417272\",\n \"score\": \"0.6149514\",\n \"text\": \"def config_version(self) -> str:\\n return pulumi.get(self, \\\"config_version\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8b90bbb1db1e873e7cd8b7cc417272\",\n \"score\": \"0.6149514\",\n \"text\": \"def config_version(self) -> str:\\n return pulumi.get(self, \\\"config_version\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992995,"cells":{"query_id":{"kind":"string","value":"da458fc2e0df01b67bf643f74d2a4719"},"query":{"kind":"string","value":"A parsing with checking that it is not ambiguous"},"positive_passages":{"kind":"list like","value":[{"docid":"39987259454b6e802d48cd0d8fc15f52","score":"0.0","text":"def __parse(self, w, var):\r\n if (w, var) in self.__cache:\r\n return self.__cache[w, var]\r\n else:\r\n n = len(w)\r\n if n == 1: return int((var, w) in self.__prods)\r\n counter = 0\r\n for p in self.__prods:\r\n if p[0] == var and p[1] == w[0]:\r\n if n > 2 and len(p) == 4:\r\n for i in xrange(2, n):\r\n cl = self.__parse(w[1:i], p[2])\r\n cr = self.__parse(w[i:], p[3])\r\n counter += cl*cr\r\n elif len(p) == 3:\r\n counter += self.__parse(w[1:], p[2])\r\n self.__cache[w, var] = counter\r\n return counter","title":""}],"string":"[\n {\n \"docid\": \"39987259454b6e802d48cd0d8fc15f52\",\n \"score\": \"0.0\",\n \"text\": \"def __parse(self, w, var):\\r\\n if (w, var) in self.__cache:\\r\\n return self.__cache[w, var]\\r\\n else:\\r\\n n = len(w)\\r\\n if n == 1: return int((var, w) in self.__prods)\\r\\n counter = 0\\r\\n for p in self.__prods:\\r\\n if p[0] == var and p[1] == w[0]:\\r\\n if n > 2 and len(p) == 4:\\r\\n for i in xrange(2, n):\\r\\n cl = self.__parse(w[1:i], p[2])\\r\\n cr = self.__parse(w[i:], p[3])\\r\\n counter += cl*cr\\r\\n elif len(p) == 3:\\r\\n counter += self.__parse(w[1:], p[2])\\r\\n self.__cache[w, var] = counter\\r\\n return counter\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"362b63e565f979e5740efa2a27e8e3c1","score":"0.639785","text":"def ambiguousParse(self, word):\r\n self.__cache.clear()\r\n return self.__parse(word, 0) != 1","title":""},{"docid":"e2d70c25ed7ca7086ea10af06cdd9443","score":"0.6308442","text":"def parse():","title":""},{"docid":"da0b395f567db447844268c55a7d8519","score":"0.6135952","text":"def test_incorrect_parsing(self):\n self.assertRaises(ValueError, self.address_parser.parse, 'Python programming language')\n self.assertRaises(AssertionError, self.address_parser.parse, 1)\n self.assertRaises(AssertionError, self.address_parser.parse, True)\n self.assertRaises(ValueError, self.address_parser.parse, '1')\n self.assertRaises(AssertionError, self.address_parser.parse, ['15, M. A. Jinnah Road'])","title":""},{"docid":"45e39cb3d9d5cb0e85d3307c038e2b3e","score":"0.61219186","text":"def test_ambiguous_sentence(self):\n self.assertEqual(list(parse_sentence(\"John called Sue from Denver\")),\n [[\"John\", [[\"called\", \"Sue\"], [\"from\", \"Denver\"]]],\n [\"John\", [\"called\", [\"Sue\", [\"from\", \"Denver\"]]]],\n [\"John\", [\"called\", \"Sue\"]]])","title":""},{"docid":"9d5ed359163cf9baddd1bf3b2795ace6","score":"0.6072876","text":"def test_unambiguous_sentence(self):\n self.assertEqual(list(parse_sentence(\"John called Sue\")),\n [[\"John\", [\"called\", \"Sue\"]]])","title":""},{"docid":"d16d2406478f2be10b56474df4deab75","score":"0.6051201","text":"def test_02_missing_everything(self):\n s = \"< \\t\\v\\r\\n > [ \\t\\v\\r\\n ]\"\n threw = False\n try:\n aa, dd = SS.parse(s)\n except ValueError:\n threw = True\n assert threw, \"No variables, did not throw: %s\" % s","title":""},{"docid":"bd913e801db0158d1e4ff145e4d4b1ea","score":"0.6032236","text":"def test_correct_parsing(self):\n result = self.address_parser.parse('15, M. A. Jinnah Road')\n self.assertTrue(result[0], '15')\n self.assertTrue(result[1], 'M. A. Jinnah Road')\n result2 = self.address_parser.parse('Nezábudková 3084/25')\n self.assertTrue(result2[0], '3084/25')\n self.assertTrue(result2[1], 'Nezábudková')\n result3 = self.address_parser.parse('Calle 39 No 1540')\n self.assertTrue(result3[0], 'No 1540')\n self.assertTrue(result3[1], 'Calle 39')","title":""},{"docid":"0c7ca9035b11b13b74c6a0b48e76aabb","score":"0.59340703","text":"def parse_validate(self, value):","title":""},{"docid":"2f249a2d4067333f11334f2e4d8136b0","score":"0.58296275","text":"def parse(parser):\n pass","title":""},{"docid":"cfc04bda9de626e1692477dce626f8dd","score":"0.5710122","text":"def parse(text, thing, devour=_default_devour):\n r, b, l = packrat_parse(text, thing, devour=devour)\n if isinstance(l, ParseError):\n raise l\n return r, l","title":""},{"docid":"ede3ad007dbe1df73a529361b3f99c1b","score":"0.569357","text":"def test_check_parse(self):\n exp = (\"1\",[\"k__a\",\"p__b\",\"c__c\",\"o__d\",\"f__e\",\"g__f\",\"s__g\"])\n obs = check_parse(good_string)\n self.assertEqual(obs, exp)\n\n self.assertRaises(ParseError, check_parse, bad_string)","title":""},{"docid":"83d7f80aa6760625689e341f2c18212c","score":"0.56627333","text":"def test_04_basic_factors(self):\n self.maxDiff = None\n\n p = MentatFilterParser()\n p.build()\n\n self.assertEqual(repr(p.parse(\"127.0.0.1\")), \"IPV4('127.0.0.1')\")\n self.assertEqual(repr(p.parse(\"::1\")), \"IPV6('::1')\")\n self.assertEqual(repr(p.parse(\"1\")), \"INTEGER(1)\")\n self.assertEqual(repr(p.parse(\"1.1\")), \"FLOAT(1.1)\")\n self.assertEqual(repr(p.parse(\"Test\")), \"VARIABLE('Test')\")\n self.assertEqual(repr(p.parse('\"constant1\"')), \"CONSTANT('constant1')\")\n\n self.assertEqual(repr(p.parse(\"(127.0.0.1)\")), \"IPV4('127.0.0.1')\")\n self.assertEqual(repr(p.parse(\"(::1)\")), \"IPV6('::1')\")\n self.assertEqual(repr(p.parse(\"(1)\")), \"INTEGER(1)\")\n self.assertEqual(repr(p.parse(\"(1.1)\")), \"FLOAT(1.1)\")\n self.assertEqual(repr(p.parse(\"(Test)\")), \"VARIABLE('Test')\")\n self.assertEqual(repr(p.parse('(\"constant1\")')), \"CONSTANT('constant1')\")\n\n self.assertEqual(repr(p.parse(\"((127.0.0.1))\")), \"IPV4('127.0.0.1')\")\n self.assertEqual(repr(p.parse(\"((::1))\")), \"IPV6('::1')\")\n self.assertEqual(repr(p.parse(\"((1))\")), \"INTEGER(1)\")\n self.assertEqual(repr(p.parse(\"((1.1))\")), \"FLOAT(1.1)\")\n self.assertEqual(repr(p.parse(\"((Test))\")), \"VARIABLE('Test')\")\n self.assertEqual(repr(p.parse('((\"constant1\"))')), \"CONSTANT('constant1')\")\n\n self.assertEqual(repr(p.parse(\"[127.0.0.1]\")), \"LIST(IPV4('127.0.0.1'))\")\n self.assertEqual(repr(p.parse(\"[::1]\")), \"LIST(IPV6('::1'))\")\n self.assertEqual(repr(p.parse(\"[1]\")), \"LIST(INTEGER(1))\")\n self.assertEqual(repr(p.parse(\"[1.1]\")), \"LIST(FLOAT(1.1))\")\n self.assertEqual(repr(p.parse(\"[Test]\")), \"LIST(VARIABLE('Test'))\")\n self.assertEqual(repr(p.parse('[\"constant1\"]')), \"LIST(CONSTANT('constant1'))\")\n\n self.assertEqual(repr(p.parse(\"[127.0.0.1 , 127.0.0.2]\")), \"LIST(IPV4('127.0.0.1'), IPV4('127.0.0.2'))\")\n self.assertEqual(repr(p.parse(\"[::1 , ::2]\")), \"LIST(IPV6('::1'), IPV6('::2'))\")\n self.assertEqual(repr(p.parse(\"[1,2, 3,4 , 5]\")), \"LIST(INTEGER(1), INTEGER(2), INTEGER(3), INTEGER(4), INTEGER(5))\")\n self.assertEqual(repr(p.parse(\"[1.1,2.2, 3.3,4.4 , 5.5]\")), \"LIST(FLOAT(1.1), FLOAT(2.2), FLOAT(3.3), FLOAT(4.4), FLOAT(5.5))\")\n self.assertEqual(repr(p.parse(\"[Var1,Var2, Var3,Var4 , Var5 ]\")), \"LIST(VARIABLE('Var1'), VARIABLE('Var2'), VARIABLE('Var3'), VARIABLE('Var4'), VARIABLE('Var5'))\")\n self.assertEqual(repr(p.parse('[\"c1\",\"c2\", \"c3\",\"c4\" , \"c5\" ]')), \"LIST(CONSTANT('c1'), CONSTANT('c2'), CONSTANT('c3'), CONSTANT('c4'), CONSTANT('c5'))\")","title":""},{"docid":"f6ce9c5473e47d52aa81465f52a4e01d","score":"0.5649365","text":"def test_integer():\n assert parse(Integer, \"integer\") == \"INTEGER\"\n assert parse(Integer, \"integer*4\") == \"INTEGER*4\"\n assert parse(Integer, \"integer*4 a\") == \"INTEGER*4 a\"\n assert parse(Integer, \"integer*4, a\") == \"INTEGER*4 a\"\n assert parse(Integer, \"integer*4 a ,b\") == \"INTEGER*4 a, b\"\n assert parse(Integer, \"integer*4 :: a ,b\") == \"INTEGER*4 a, b\"\n assert parse(Integer, \"integer*4 a(1,2)\") == \"INTEGER*4 a(1,2)\"\n assert parse(Integer, \"integer*4 a(2*(1+2)-1)\") == \"INTEGER*4 a(2*(1+2)-1)\"\n assert parse(Integer, \"integer*4 :: a(1,2),b\") == \"INTEGER*4 a(1,2), b\"\n assert parse(Integer, \"integer*4 external :: a\") == \"INTEGER*4, external :: a\"\n assert parse(Integer, \"integer*4, external :: a\") == \"INTEGER*4, external :: a\"\n assert (\n parse(Integer, \"integer*4 external , intent(in) :: a\")\n == \"INTEGER*4, external, intent(in) :: a\"\n )\n assert parse(Integer, \"integer(kind=4)\") == \"INTEGER(KIND=4)\"\n assert parse(Integer, \"integer ( kind = 4)\") == \"INTEGER(KIND=4)\"\n assert parse(Integer, \"integer(kind=2+2)\") == \"INTEGER(KIND=2+2)\"\n assert parse(Integer, \"integer(kind=f(4,5))\") == \"INTEGER(KIND=f(4,5))\"\n assert parse(Integer, \"integer a = 5\") == \"INTEGER :: a = 5\"","title":""},{"docid":"860046c74a5a7ee3da2e736c3ca9392b","score":"0.5600621","text":"def _parse_way(way):\n pass # TODO: what to do with ways","title":""},{"docid":"65f908e244a2ac9c06b2b7d2258c1101","score":"0.5542157","text":"def _validate_format(format, alternative):\r\n\r\n def _parse(string):\r\n result = []\r\n for match in PYTHON_FORMAT.finditer(string):\r\n name, format, typechar = match.groups()\r\n if typechar == '%' and name is None:\r\n continue\r\n result.append((name, str(typechar)))\r\n return result\r\n\r\n def _compatible(a, b):\r\n if a == b:\r\n return True\r\n for set in _string_format_compatibilities:\r\n if a in set and b in set:\r\n return True\r\n return False\r\n\r\n def _check_positional(results):\r\n positional = None\r\n for name, char in results:\r\n if positional is None:\r\n positional = name is None\r\n else:\r\n if (name is None) != positional:\r\n raise TranslationError('format string mixes positional '\r\n 'and named placeholders')\r\n return bool(positional)\r\n\r\n a, b = map(_parse, (format, alternative))\r\n\r\n # now check if both strings are positional or named\r\n a_positional, b_positional = map(_check_positional, (a, b))\r\n if a_positional and not b_positional and not b:\r\n raise TranslationError('placeholders are incompatible')\r\n elif a_positional != b_positional:\r\n raise TranslationError('the format strings are of different kinds')\r\n\r\n # if we are operating on positional strings both must have the\r\n # same number of format chars and those must be compatible\r\n if a_positional:\r\n if len(a) != len(b):\r\n raise TranslationError('positional format placeholders are '\r\n 'unbalanced')\r\n for idx, ((_, first), (_, second)) in enumerate(izip(a, b)):\r\n if not _compatible(first, second):\r\n raise TranslationError('incompatible format for placeholder '\r\n '%d: %r and %r are not compatible' %\r\n (idx + 1, first, second))\r\n\r\n # otherwise the second string must not have names the first one\r\n # doesn't have and the types of those included must be compatible\r\n else:\r\n type_map = dict(a)\r\n for name, typechar in b:\r\n if name not in type_map:\r\n raise TranslationError('unknown named placeholder %r' % name)\r\n elif not _compatible(typechar, type_map[name]):\r\n raise TranslationError('incompatible format for '\r\n 'placeholder %r: '\r\n '%r and %r are not compatible' %\r\n (name, typechar, type_map[name]))","title":""},{"docid":"ac03146133cf6cb40b40d42a0436918d","score":"0.55047554","text":"def _parse_case(token: Dict[str, Any]) -> Tuple[Union[str, Dict, None], str, bool]:\n if token.get(\"TEXT\"):\n return token.get(\"TEXT\"), \"TEXT\", False\n else:\n return token.get(\"LOWER\"), \"LOWER\", True","title":""},{"docid":"b7534db69230fa1b3372b584ef8c4235","score":"0.5489957","text":"def test_issue_23():\n\n grammar = \"\"\"\n List: members+=Value;\n Value: ('{' List '}') | Identifier;\n Identifier: val=ID;\n \"\"\"\n mm = metamodel_from_str(grammar)\n\n assert mm['List']._tx_type is RULE_COMMON\n assert mm['Value']._tx_type is RULE_ABSTRACT\n assert mm['Value']._tx_inh_by == [mm['List'], mm['Identifier']]\n\n grammar = \"\"\"\n List: '{' members+=Value '}';\n Value: ID;\n \"\"\"\n mm = metamodel_from_str(grammar)\n\n assert mm['List']._tx_type is RULE_COMMON\n assert mm['Value']._tx_type is RULE_MATCH\n\n grammar = \"\"\"\n ListSyntax: '{' List '}';\n List: members+=Value;\n Value: ListSyntax | Identifier;\n Identifier: val=ID;\n \"\"\"\n mm = metamodel_from_str(grammar)\n\n assert mm['ListSyntax']._tx_type is RULE_ABSTRACT\n assert mm['ListSyntax']._tx_inh_by == [mm['List']]\n assert mm['List']._tx_type is RULE_COMMON\n assert mm['Value']._tx_type is RULE_ABSTRACT\n assert mm['Value']._tx_inh_by == [mm['ListSyntax'], mm['Identifier']]","title":""},{"docid":"b9deee1ba432274a9a1c8c967c8732dc","score":"0.5476688","text":"def _Parse(self):","title":""},{"docid":"b9deee1ba432274a9a1c8c967c8732dc","score":"0.5476688","text":"def _Parse(self):","title":""},{"docid":"d2cbb94862cecb25bda510a1b5fe7f72","score":"0.5475379","text":"def test_invalid_program_wrong_format(self):\n input = \"\"\"\n ** A simple program **\n Var: x;\n Function: x = y\n Body:\n print(ans);\n EndBody.\n \"\"\"\n expect = \"Error on line 4 col 20: =\"\n self.assertTrue(TestParser.checkParser(input, expect, 295))","title":""},{"docid":"0d635af7dce654652f2421624e9640cd","score":"0.5471709","text":"def test_bad_parse_option():\n\n ff = fasta(SIMPLE_TEXT, 's', 1)","title":""},{"docid":"6519022e6eeb06a66a7e750ec5134c10","score":"0.54633343","text":"def _parseHeader(self, header):\n formatting = \"fuzzy %s\"\n (self.type,) = sscanf(header, formatting)","title":""},{"docid":"fd88a3d6882eeb9f0956c72d7c693cb2","score":"0.5451123","text":"def test_parse_raise_unparseable(self, unparseable):\n with self.assertRaises(funcparser.ParsingError):\n self.parser.parse(unparseable, raise_errors=True)","title":""},{"docid":"e26eadd06e09470b11c28e4f2e7c6f1a","score":"0.54463977","text":"def test_byte():\n assert parse(Byte, \"byte\") == \"BYTE\"\n assert parse(Byte, \"byte a\") == \"BYTE a\"\n assert parse(Byte, \"byte, a\") == \"BYTE a\"\n assert parse(Byte, \"byte a ,b\") == \"BYTE a, b\"\n assert parse(Byte, \"byte :: a ,b\") == \"BYTE a, b\"\n assert parse(Byte, \"byte a(1,2)\") == \"BYTE a(1,2)\"\n assert parse(Byte, \"byte :: a(1,2),b\") == \"BYTE a(1,2), b\"\n assert parse(Byte, \"byte external :: a\") == \"BYTE, external :: a\"\n assert parse(Byte, \"byte, external :: a\") == \"BYTE, external :: a\"\n assert (\n parse(Byte, \"byte external , intent(in) :: a\")\n == \"BYTE, external, intent(in) :: a\"\n )","title":""},{"docid":"84b7f07608dddecdd9eabd0fb384b586","score":"0.5417863","text":"def test_type_syn_parser(self):\n\n valid_examples = [\n StringIO('type Age = Int'),\n StringIO('type Name = [Char]'),\n StringIO('type Coordinate = (Int, Int)'),\n StringIO('type Person = (Name, Age)')\n ]\n\n incorrect_examples = [\n # New type does not start with a capital\n StringIO('type number = Int'),\n # Declared type is not a type\n StringIO('type Integer = char'),\n # Invalid new type name\n StringIO('type * = Bool'),\n # Cannot define new type is empty list.\n StringIO('type List = []'),\n ]\n\n i = 0\n for t in valid_examples:\n with self.subTest(i=i):\n tks = list(tokenize(t))\n res = TypeSyn.parse_strict(tks, t)\n self.assertEqual(type(res), AST.TYPESYN)\n\n i += 1\n\n i = 0\n for t in incorrect_examples:\n with self.subTest(i=i):\n tks = list(tokenize(t))\n self.assertRaises(ParseError, TypeSyn.parse_strict, tks, t)\n\n i += 1","title":""},{"docid":"7bb6098726fd98798c8a9389ef0fdf5b","score":"0.5396685","text":"def test_broken_meta() -> None:\n with pytest.raises(ParseError):\n parse(\":\")\n\n with pytest.raises(ParseError):\n parse(\":param herp derp\")\n\n with pytest.raises(ParseError):\n parse(\":param: invalid\")\n\n with pytest.raises(ParseError):\n parse(\":param with too many args: desc\")\n\n # these should not raise any errors\n parse(\":sthstrange: desc\")","title":""},{"docid":"a8be88ef2c9cbab0bef4c0e37ca07cc7","score":"0.53964823","text":"def test_parse_alias_value(data_spec: type, json_: str, value: Any) -> None:\n result = parse_string(data_spec, json_)\n assert isinstance(result, data_spec)\n assert result == value","title":""},{"docid":"10f1aac70883672795d3d4e75e2630d3","score":"0.53806776","text":"def try_parse_type(cls, s, onto=None):\n\n ts = get_type_system()\n result = ts.type_parser(s)\n return result","title":""},{"docid":"12a5f44de1a696d19b2b0e3b60e821b2","score":"0.53634226","text":"def is_parsed(obj):\r\n\treturn obj and not isinstance(obj, types.StringTypes)","title":""},{"docid":"9d2287b74f185e6a2a7fd13b4a010067","score":"0.53609675","text":"def test_00_missing_attrs(self):\n s = \"junk < \\n\\v\\r\\t > [i=0:43,*,5 , j_ = 1 : 50 , 10,1] more junk\"\n threw = False\n try:\n aa, dd = SS.parse(s)\n except ValueError:\n threw = True\n assert threw, \"No attributes, did not throw: %s\" % s\n\n s = re.sub(r\"<\\s*>\", \"<x:int64>\", s)\n threw = ''\n try:\n aa, dd = SS.parse(s)\n except ValueError as e:\n threw = str(e)\n assert not threw, \"Got attributes, threw: %s (%s)\" % (s, threw)\n assert len(aa) == 1, \"Expected one attribute, got %d\" % len(aa)\n assert len(dd) == 2, \"Expected two dimensions, got %d\" % len(dd)\n reparse(aa, dd)","title":""},{"docid":"d6ae8f6b4c8446d74553f58a5382ea7d","score":"0.53252804","text":"def tryStrParse(s):\n return utils.tryParse(s) if type(s).__name__ in ['str','unicode'] else s","title":""},{"docid":"79a63118513e49374e494c0d4ecba2db","score":"0.5321322","text":"def _check_parser(self):\n pass","title":""},{"docid":"edcc4fa33282ed6d2c96dd3a3e0ea141","score":"0.5307266","text":"def test_parse_bad_unit2(self):\n with self.assertRaises(ValueError):\n bitmath.parse_string(\"1.23 QB\")","title":""},{"docid":"6ac88a9df747095c052f9173758c9700","score":"0.5298685","text":"def _parse(self, option_format_spec: str) -> Tuple[str, str]:\n\n startswith_match = self._start + '*' + self._end + '*'\n if fnmatch.fnmatchcase(option_format_spec, startswith_match):\n # look for self._end starting char after self._start\n option_spec_end = option_format_spec.find(self._end, 1)\n option_spec = option_format_spec[:option_spec_end + 1]\n format_spec = option_format_spec[option_spec_end + 1:]\n return option_spec, format_spec\n return '', option_format_spec","title":""},{"docid":"3cef9a7c1d657933ab0bb1bdca972e52","score":"0.5294205","text":"def process_type(error):\n words = error.split(\":\")\n if words[2].strip() == \"parser error\":\n if len(words) > 4:\n return words[3].strip()\n return \"Other\"","title":""},{"docid":"5ce4563565377ad9bcac13624efc5661","score":"0.52814364","text":"def test_parsing_wrong_type(self):\n with raises(ValueError):\n parse_config('{\"count\": \"hello\"}')","title":""},{"docid":"6049a13bef54495b87290a49bee09a7f","score":"0.5279789","text":"def is_string(string):\n \n try: \n a,b = parse(string, fuzzy_with_tokens=True)\n #print(a)\n #print(b)\n values = ''.join(b)\n #\n return values\n\n except :\n return string","title":""},{"docid":"0d948c00a67537af5b309b43bdb5ee58","score":"0.5276653","text":"def _parse_identifier(self, ast, results):\n # type: (ASTNode, Tuple[Any, ...]) -> str\n return ast.match","title":""},{"docid":"1fe7a4bb07640271aeda616e5b0043d1","score":"0.52753687","text":"def test_determineFormat_unknown(self):\n print 'Running %s ...' % getName()\n\n # returns unknown because it contains spaces\n imp1 = \"\"\">P01013 GENE X PROTEIN (OVALBUMIN-RELATED)\"\"\"\n self.assertEqual('unknown', util.determineFormat(imp1))\n\n # returns unknown because it contains spaces\n imp2 = \"\"\"atgatgatga tgatgatgta cctgcagacc ccgtttccct ggtgccagtg gcagaggagt 60\"\"\"\n self.assertEqual('unknown', util.determineFormat(imp2))\n\n # returns unknown because it contains spaces\n imp3 = \"\"\"uugaccaagc uggggacccc ggucccuugg gaccaguggc agaggaguc 49\"\"\"\n self.assertEqual('unknown', util.determineFormat(imp3))\n\n # returns unknown because it contains spaces and end of line char\n imp4 = \"\"\"atgatgatga tgatgatgta cctgcagacc ccgtttccct ggtgccagtg gcagaggagt 60\n\nc 61\n\"\"\"\n self.assertEqual('unknown', util.determineFormat(imp4))\n\n # returns unknown because it is less than 3 chars\n imp5 = \"\"\"act\"\"\"\n self.assertEqual('unknown', util.determineFormat(imp5))","title":""},{"docid":"f0933e68a18c68a882b9fcd52d912bcd","score":"0.5269905","text":"def test_common_var_decl_mistake_216(self):\n testcase = r\"\"\"Function: a Body:Var:var = 0x0;IfTrueThenEndIf.Var:a=0;Var:var=\"**meant 2 b string**\";EndBody.\"\"\"\n expect = r\"\"\"Error on line 1 col 28: x0\"\"\"\n self.assertTrue(TestParser.checkParser(testcase, expect, 216))","title":""},{"docid":"d35f4d2a8b1fb20ecc1428f2aa0ca849","score":"0.5268413","text":"def test_parsing_hard_string():\n test_str = \\\n '100 x 5, 7, 70, 80,90 x 6, 110 x 6,5, 4, 120 x 3 (2), 2, 100 x 5/4/3'\n try:\n sets = Set.parse_sets(test_str)\n except SyntaxError:\n pytest.fail(\"Told you it was hard\")\n assert len(sets) == 12","title":""},{"docid":"6a732bbda6536e8d8afbbf1ca728a555","score":"0.5264484","text":"def parse(cls, s):\n requirement = cls(s)\n if not requirement.marker: # Short circuit to favour the common case.\n return requirement, None\n match = EXTRA_RE.match(s)\n if not match: # No final \"extra\" expression, yay.\n return requirement, None\n extra = match.group('extra')\n if not extra:\n return requirement, None\n s = match.group('requirement').rstrip()\n if s.endswith('and'):\n s = s[:-3].rstrip()\n if s.endswith(';'):\n s = s[:-1].rstrip()\n return cls(s), extra","title":""},{"docid":"9da46c039326999d43e4e48cf3408d18","score":"0.52572507","text":"def test_non_string_input(self):\n options = {\n 'schema': {\n 'name': 'string',\n 'result': 'string'\n }\n }\n\n record_data = {'name': 'test', 'result': 'test'}\n\n # get parsed data\n parser = JSONParser(options)\n assert_equal(parser.parse(record_data), True)\n\n expected_result = [\n record_data\n ]\n assert_equal(parser.parsed_records, expected_result)","title":""},{"docid":"ac1c641a40aaf4dc2b16db8a5739e0be","score":"0.52542686","text":"def test_real():\n\n assert parse(Real, \"real\") == \"REAL\"\n assert parse(Real, \"real*4\") == \"REAL*4\"\n assert parse(Real, \"real*4 a\") == \"REAL*4 a\"\n assert parse(Real, \"real*4, a\") == \"REAL*4 a\"\n assert parse(Real, \"real*4 a ,b\") == \"REAL*4 a, b\"\n assert parse(Real, \"real*4 :: a ,b\") == \"REAL*4 a, b\"\n assert parse(Real, \"real*4 a(1,2)\") == \"REAL*4 a(1,2)\"\n assert parse(Real, \"real*4 :: a(1,2),b\") == \"REAL*4 a(1,2), b\"\n assert parse(Real, \"real*4 external :: a\") == \"REAL*4, external :: a\"\n assert parse(Real, \"real*4, external :: a\") == \"REAL*4, external :: a\"\n assert (\n parse(Real, \"real*4 external , intent(in) :: a\")\n == \"REAL*4, external, intent(in) :: a\"\n )\n assert parse(Real, \"real(kind=4)\") == \"REAL(KIND=4)\"\n assert parse(Real, \"real ( kind = 4)\") == \"REAL(KIND=4)\"\n assert parse(Real, \"real(kind=2+2)\") == \"REAL(KIND=2+2)\"\n assert parse(Real, \"real(kind=f(4,5))\") == \"REAL(KIND=f(4,5))\"","title":""},{"docid":"e32d5e868a399df980107faed4b4fcbb","score":"0.52514064","text":"def test_parse_anneal():\n assert isinstance(test_s, str), 'Incorrect input type: string required'\n assert isinstance(test_s_output, list), \\\n 'Error: incorrect output type (list expected)'\n assert test_s_output, 'Error: no parameters detected (parsing error)'\n try:\n level0 = test_s_output[0]\n level1 = test_s_output[0].get('anneal')[0]\n level2a = test_s_output[0].get('anneal')[0].get('temps')[0]\n level2b = test_s_output[0].get('anneal')[0].get('times')[0]\n assert isinstance(level0, dict), \\\n 'Error: output must be list of dictionaries'\n assert 'anneal' in level0, \\\n 'Error: anneal parameter either not found, or parsed incorrectly'\n assert 'temps' and 'times' in level1, \\\n 'Error: Anneal parameters not detected'\n assert 'tempvalue' and 'tempunits' in level2a, \\\n 'Error: Temperature values and units incorrectly parsed'\n assert 'timevalue' and 'timeunits' in level2b, \\\n 'Error: Time values and units incorrectly parsed'\n assert isinstance(test_s_output[0], dict), \\\n 'Error: output must be list of dictionaries'\n except IndexError:\n raise Exception('Error: output is not a list, or is an empty list')\n except TypeError:\n raise Exception('Error: output must be a list of dictionaries')","title":""},{"docid":"bde85a43471504b307a1344bd67b826d","score":"0.5250075","text":"def _parse(self, arg):\n clean_arg = arg.strip()\n if self._allow_empty_string or clean_arg:\n return clean_arg\n raise ValueError('the value {!r} is not a valid string'.format(arg))","title":""},{"docid":"a507572ab37032538afeb467e74ff8e7","score":"0.52475005","text":"def _parse_type_command(line) -> typing.Tuple[str, typing.Optional[str]]:\n m = re.search(\"^(.*): not found$\", line)\n if m:\n return m.group(1), None\n\n m = re.search(\"^(.*?) is (/.*)$\", line)\n if m:\n return m.group(1), m.group(2)\n\n raise UnsupportedTypeOutputException(line)","title":""},{"docid":"80bea0131fb2d8edb14ce2f7d8d094eb","score":"0.5244718","text":"def _parse_literal(self, ast, results):\n # type: (ASTNode, Tuple[Any, ...]) -> str\n return ast.match","title":""},{"docid":"35d65ff42f5125f2f44a8f0a8efc0f61","score":"0.52444136","text":"def _checkAmbiguity(self, found, find_val, key):\n f = find_val.replace(' ', '')\n f2 = found.replace(' ', '') + '='\n if f.startswith(f2):\n return key\n else:\n alternatives = self.ambiguous[found]\n for i, a in enumerate(alternatives):\n if find_val.startswith(a[0]):\n return self.ambiguous[found][i][1]\n return key","title":""},{"docid":"84a558bf538464c42296d7620cdb7a1e","score":"0.524344","text":"def get_parse(lowercase: str):\n if _is_topset(lowercase):\n return _parse_topset\n if _is_warmup(lowercase):\n return _parse_warmup\n return _parse_simple","title":""},{"docid":"e55a2151b7242ffb200148f8656ffbf6","score":"0.5240539","text":"def parse(tokens: List[str]):\n if not tokens:\n raise ParserError(\"Expected value\")\n t = tokens[0]\n if t == \"[\":\n return _list(tokens[1:])\n if t == \"{\":\n return _object(tokens[1:])\n return t, tokens[1:]","title":""},{"docid":"3b12dfaf464eecadb41964589fa930c0","score":"0.52357906","text":"def test_basic_parsing(self):\n cn, results = io.parse_result_line('Littleton, 10, C, 11, L')\n self.assertEqual(cn, 'Littleton')\n self.assertEqual(len(results), 2)\n self.assertEqual(results[0], (10, 'C'))\n self.assertEqual(results[1], (11, 'L'))","title":""},{"docid":"2fba5795e8f4cc58e6ed91251a8bfd08","score":"0.5232905","text":"def parse(self, text):\n return self.default_rule.parse(text)","title":""},{"docid":"afddc6aa2ec0367ed82beb742d60bc75","score":"0.5222223","text":"def Parse(self, source):","title":""},{"docid":"faaf541c69da2de5c5890f295cf74cee","score":"0.5218183","text":"def parse_object(word_list) : \n skip(word_list, ['stop','error'])\n if peek(word_list) == 'noun' :\n return match(word_list, 'noun')\n elif peek(word_list) == 'direction' :\n return match(word_list, 'direction')\n else :\n raise ParseError(\"Error parsing: Expected a noun or direction.\")","title":""},{"docid":"532e90e8199cc9003997ae7023530adc","score":"0.52171725","text":"def validate_input(self, raw):\n return isinstance(raw, BaseParser)","title":""},{"docid":"4cad29778c4798ba29fee52b7c8594f0","score":"0.5214489","text":"def test_spaces(self):\n parser = ParserGABC(root='syllable')\n self.assertRaises(NoMatch, lambda: parser.parse(' a(f)'))","title":""},{"docid":"9445488933f6897aa0bfadfe231804ff","score":"0.5206893","text":"def _parse(self): # pylint: disable=unused-argument, no-self-use\n return","title":""},{"docid":"41c8eb30342d56be75eaa0189cd9cbe9","score":"0.5206762","text":"def _parse_builtin(self, ast, results):\n # type: (ASTNode, Tuple[Any, ...]) -> str\n return ast.match","title":""},{"docid":"a1c7f14fbc84c0155404805478f08fc8","score":"0.5205738","text":"def parse_ignored(lines):\n # variables\n if '=' in lines[0] or lines[0].strip().startswith('_'):\n return lines[0]\n\n return ParsingError('')","title":""},{"docid":"383d84160204413a5c7060e94e5c3e10","score":"0.5199853","text":"def test_parse_bad_parser_type(self):\n with self.assertRaises(ValueError):\n ggtk.GoParser.parse(self.graph_obo_filename, 'fake')","title":""},{"docid":"3c9dd0a6a1b828b6b51acf3e9243f892","score":"0.5185549","text":"def DoParsing(self):","title":""},{"docid":"9d9fdc35cb958604ca898f32a9ed18aa","score":"0.5180691","text":"def parse_al(msg):\n return parse_a(msg)","title":""},{"docid":"817d87284bf2b04aed2f3d538571a704","score":"0.5176819","text":"def must_parse(self, name):\r\n return self.parse \\\r\n and not self._matches_filter(name, self._parse_regexp)","title":""},{"docid":"b05b932cec099a010a5feefdef972d48","score":"0.5175094","text":"def test_word_parse():\n keys, stripped = madlib.parse(\"This is an {adjective} and also {adjective} {noun} of a test.\")\n assert keys == ['adjective', 'adjective', 'noun']\n assert stripped == 'This is an {} and also {} {} of a test.'","title":""},{"docid":"4dc80e7cd5704f462a90bc819e6d730f","score":"0.51698625","text":"def test_check_gap(self):\n id_, parsed = check_parse(good_string)\n self.assertTrue(check_gap(parsed))\n\n id_, parsed = check_parse(good_trailing)\n self.assertTrue(check_gap, parsed)\n\n id_, parsed = check_parse(gap)\n self.assertFalse(check_gap(parsed))","title":""},{"docid":"a441d7e9597ba5d70d64967afa26cd70","score":"0.51696956","text":"def test(parser, exfile):\n grammatical = []\n ungrammatical = []\n with open(exfile, 'r') as infile:\n for line in infile:\n if line[0] == '+':\n grammatical.append(line[1:].strip())\n if line[0] == '-':\n ungrammatical.append(line[1:].strip())\n print(\"\\nCorrectly recognized:\")\n for s in grammatical:\n if len([t for t in parser.parse(s.split())]) > 0:\n print(s)\n print(\"\\nShould have been recognized, but wasn't:\")\n for s in grammatical:\n if not len([t for t in parser.parse(s.split())]) > 0:\n print(s)\n print(\"\\nCorrectly not recognized:\")\n for s in ungrammatical:\n if not len([t for t in parser.parse(s.split())]) > 0:\n print(s)\n print(\"\\nShould not have been recognized, but was:\")\n for s in ungrammatical:\n if len([t for t in parser.parse(s.split())]) > 0:\n print(s)","title":""},{"docid":"25ee44eb36574d3a619afeb5f3629963","score":"0.5167718","text":"def test_invalid_parameter_wrong_order(self):\n input = \"\"\"\n ** A simple program **\n Function: foo\n Body:\n b = b - a;\n Return b;\n EndBody.\n Parameter: a, b\n \"\"\"\n expect = \"Error on line 8 col 8: Parameter\"\n self.assertTrue(TestParser.checkParser(input, expect, 292))","title":""},{"docid":"a3b0b3b4c35f9d9ef0878f6704be7be9","score":"0.5157489","text":"def __parse_value(self):\n char = self.scanner.peek(1)\n\n if char == u'{':\n return self.__parse_object()\n elif char == u'[':\n return self.__parse_array()\n elif char == u'n':\n return self.__parse_null()\n elif char == u't':\n return self.__parse_bool()\n elif char == u'f':\n return self.__parse_bool()\n elif char == u'\"':\n return self.__parse_string()\n else:\n return self.__parse_number()","title":""},{"docid":"c16a0f1304f4dc0875e0b873b12bcce8","score":"0.5155001","text":"def parse(self, s, context):\n try:\n context = super(Optional, self).parse(s, context=context.copy())\n except NoMatchError:\n context.update(\n _match='',\n _unparsed=s,\n )\n return context","title":""},{"docid":"d366dfa681d0f51499ba3b2cbea61688","score":"0.5146688","text":"def test_parseSimpleField(self):\n pass","title":""},{"docid":"7431b3000d49b4761a708f09ee5d9e9b","score":"0.5144285","text":"def parse_completely(\n text,\n thing,\n devour=_default_devour,\n devour_leftovers=True\n):\n r, b, l = packrat_parse(text, thing, devour=devour)\n if isinstance(l, ParseError):\n raise l\n if devour_leftovers:\n l = devour(l)\n if l:\n raise ParseError(\n \"Parse incomplete. Remainder: '{}'\".format(\n error_context(l)\n )\n )\n return r","title":""},{"docid":"a3b25f2137d666906bdeee3645d0ee5b","score":"0.51308614","text":"def test_only_strings_allowed(fredo):\n with pytest.raises(TypeError):\n fredo.parse_query(12)","title":""},{"docid":"450e8ee636906d913148d3bfb29de583","score":"0.51298606","text":"def _parse_val(self, val):\n\n if isinstance(val, (int, float)):\n return float(val)\n\n if isinstance(val, str):\n return val\n\n if len(val) in (3, 4):\n return tuple(val)\n\n raise TypeError(\"Unknown value type to pass to rpr\", val)","title":""},{"docid":"17670d964e1609702e0bd94107d73c28","score":"0.51269794","text":"def testParseInvalidDate2(self):\n try:\n iso8601.parseDate(\"23\")\n except iso8601.ParseError:\n pass\n else:\n self.assertEquals(1, 2)","title":""},{"docid":"4651a8b5400f3540eece38144d60c5e0","score":"0.51221293","text":"def _parse_value(value):\n\n # Check for boolean or None\n if str(value).capitalize().startswith(('False', 'True', 'None')):\n return eval(str(value).capitalize(), {}, {})\n\n # Detect strings, floats and ints\n try:\n # If this fails, this is a string\n result = literal_eval(value)\n except Exception:\n result = value\n\n return result","title":""},{"docid":"cde38ff82f458ca6ee11f97a4cb24413","score":"0.5118722","text":"def _parse_type(pattern_dict: Dict[str, Any]) -> Tuple[str, str]:\n fuzzy_text = pattern_dict.get(\"FUZZY\")\n regex_text = pattern_dict.get(\"FREGEX\")\n if isinstance(fuzzy_text, str):\n return fuzzy_text, \"FUZZY\"\n elif isinstance(regex_text, str):\n return regex_text, \"FREGEX\"\n else:\n return \"\", \"\"","title":""},{"docid":"5f96517fc6eb4c4511c424f3892b0800","score":"0.51119345","text":"def parse(self):\n\t\tpass","title":""},{"docid":"e34a2d3a7eacbdac46b22cd9aa4cd638","score":"0.5111739","text":"def parse_token(token):\n\n if token == '\\\\and':\n do_something()\n\n elif token == '\\\\or':\n do_something_else() #asjadkjad\n\n#jakiś tam tekscik.txt\n\n elif token == '\\\\xor':\n '''\n Note that we still need to provide support for the deprecated\n token \\xor. Hopefully we can drop support in libfoo 2.0.\n '''\n do_a_different_thing()\n\n else:\n raise ValueError","title":""},{"docid":"d4a1a127cdbda20dd510c39892c17e38","score":"0.51069677","text":"def test_fusion(self):\n q = ('the.rolling.stones.iregex.not:asc=sympathy,for,the,devil;'\n '!(guns.n.roses=paradise,city&queen:asc)')\n\n item = self.parse(q)\n\n # I'm lazy and don't want to walk the tree, so lets just test the repr.\n assert (six.text_type(repr(item)) ==\n \"(the.rolling.stones.iregex.not:asc :iexact 'sympathy' \"\n \"| 'for' | 'the' | 'devil') OR NOT (guns.n.roses :iexact \"\n \"'paradise' | 'city') AND (queen :iexact '')\")","title":""},{"docid":"63477b3fb6df9b55932fa5932ea7080f","score":"0.5106424","text":"def test_parse_string_non_string_input(self):\n with self.assertRaises(ValueError):\n bitmath.parse_string(12345)","title":""},{"docid":"ad0cb506140f630d2e6328f1436a5150","score":"0.5101755","text":"def parse(self, s):\n return s","title":""},{"docid":"d76d8338b2d17ab5cba3108a388de5b2","score":"0.5099613","text":"def try_parse_int_or_float(s):\n try:\n return int(s)\n except ValueError:\n try:\n return float(s)\n except ValueError:\n return s","title":""},{"docid":"cc6c4ef8805f43630cec968486218533","score":"0.5098364","text":"def parse(self, arg):\n clean_arg = arg.strip().lower()\n if clean_arg and set(clean_arg).issubset(_VERSION_CHARS):\n if clean_arg not in _VERSION_BLACKLIST:\n if not clean_arg.startswith(_VERSION_CANNOT_START_WITH):\n return clean_arg\n raise ValueError(_VERSION_REQUIREMENTS.format(arg))","title":""},{"docid":"0e64af1cf75207eca96b41d59d78f798","score":"0.5081887","text":"def inflect_by_pymorphy2(source_parsed_word: pymorphy2.analyzer.Parse, required_grammemes: Set[str]) -> \\\r\n Tuple[str, pymorphy2.analyzer.Parse]:\r\n res = source_parsed_word.inflect(required_grammemes)\r\n if res is None:\r\n inflected = source_parsed_word\r\n inflected_word = source_parsed_word.word\r\n warnings.warn(\"Word `{0}` cannot be inflected.\".format(inflected_word))\r\n else:\r\n inflected = res\r\n inflected_word = res.word\r\n return inflected_word, inflected","title":""},{"docid":"937ec9062ee2425d032e3dd1dee7182e","score":"0.50817484","text":"def test_parse_strings(self):\r\n parsed = parse_fragments('0-1000', '1500-2000')\r\n self.assertEqual(parsed, [(0, 1000), (1500, 2000)])","title":""},{"docid":"fdc1a91057df75836f1490d49ca25f36","score":"0.5075134","text":"def parse(self):\n\n self._eat_white()\n c = self.txt[0]\n if c == '(':\n self.txt = self.txt[1:]\n l = []\n while self.txt[0] != ')':\n l.append(self.parse())\n # needed in case there is whitespace before the )\n self._eat_white()\n self.txt = self.txt[1:]\n return token.tuple(l)\n\n elif c == \"'\":\n self.txt = self.txt[1:]\n return token.quoted(self.parse())\n\n if self.txt[0] in self._map:\n return getattr(self, SchemeParser._map[self.txt[0]])()\n\n value = self._parse_value()\n\n return token.label(value)","title":""},{"docid":"790172d1a32a36990f8f4435d4f23e3a","score":"0.5066718","text":"def _postprocess(self, valuestr):\r\n\r\n intpattern = re.compile(r'^\\-?\\d+$')\r\n floatpattern = re.compile(r'^\\-?\\d+\\.\\d+(E[+-]?\\d\\d+)?$')\r\n timepattern = re.compile(r'^\\d{2}:\\d{2}:\\d{2}(\\.\\d{6})?')\r\n\r\n try:\r\n if valuestr.startswith('\"') and valuestr.endswith('\"'):\r\n # it's a string\r\n return valuestr[1:-1]\r\n\r\n elif re.match(intpattern, valuestr):\r\n # it's an integer\r\n return int(valuestr)\r\n\r\n elif re.match(floatpattern, valuestr):\r\n # floating point number\r\n return float(valuestr)\r\n\r\n elif re.match(timepattern, valuestr):\r\n time = valuestr[0:-2]\r\n return datetime.datetime.strptime(time, '%H:%M:%S.%f').time()\r\n\r\n else:\r\n # now let's try the datetime objects\r\n return iso8601.parse(valuestr)\r\n\r\n except (OverflowError, ValueError) as error:\r\n print(error)\r\n pass\r\n\r\n # If we get here, we still haven't returned anything.\r\n self.logger.info('The value %s couldn\\'t be parsed as int, float, or datetime. Returning it as string.' % valuestr)\r\n\r\n return valuestr","title":""},{"docid":"414f4d1dfa7c17ccc32a535caa4d82b1","score":"0.50648725","text":"def test_parse(self, string, expected):\n # t0 = time.time()\n # from evennia import set_trace;set_trace()\n ret = self.parser.parse(string, raise_errors=True)\n # t1 = time.time()\n # print(f\"time: {(t1-t0)*1000} ms\")\n self.assertEqual(expected, ret)","title":""},{"docid":"66f3e238dcec6be9bd5760a5f826705c","score":"0.50637823","text":"def parse_mechanism(str, d):\n\n a = RE_DUAL_CIDR.split(str)\n if len(a) == 3:\n str, cidr6 = a[0], int(a[1])\n else:\n cidr6 = None\n a = RE_CIDR.split(str)\n if len(a) == 3:\n str, cidr = a[0], int(a[1])\n else:\n cidr = None\n\n a = str.split(':', 1)\n if len(a) < 2:\n str = str.lower()\n if str == 'exists': d = None\n return str, d, cidr, cidr6\n return a[0].lower(), a[1], cidr, cidr6","title":""},{"docid":"32ea650d8803048d127793967797fe20","score":"0.5062988","text":"def test_parse_basic_value(data_spec: type, json_: str, value: Any) -> None:\n result = parse_string(data_spec, json_)\n assert isinstance(result, data_spec)\n assert result == value","title":""},{"docid":"6d015e9d8ef01d61a6855af01071eb32","score":"0.5059198","text":"def test_parse_bad_unit(self):\n with self.assertRaises(ValueError):\n bitmath.parse_string(\"1.23 GIB\")","title":""},{"docid":"a05558e638701135677501abb1e13e8c","score":"0.50469685","text":"def test_terminals_with_different_names():\n\n # In this grammar we have 'd' terminal in S production and B terminal with\n # the same 'd' recognizer.\n grammar = \"\"\"\n S: 'a' A 'd' | 'b' A B;\n A: 'c' A | 'c';\n terminals\n B: 'd';\n \"\"\"\n\n with pytest.raises(GrammarError) as e:\n Grammar.from_string(grammar)\n\n assert 'B' in str(e.value)\n assert 'd' in str(e.value)\n assert 'match the same string' in str(e.value)","title":""},{"docid":"aa1dee1397a3fed366094c304c67368a","score":"0.50452876","text":"def test_complex():\n\n assert parse(Complex, \"complex\") == \"COMPLEX\"\n assert parse(Complex, \"complex\") == \"COMPLEX\"\n assert parse(Complex, \"complex*8 a\") == \"COMPLEX*8 a\"\n assert parse(Complex, \"complex*16, a\") == \"COMPLEX*16 a\"\n assert parse(Complex, \"complex a ,b\") == \"COMPLEX a, b\"\n assert parse(Complex, \"complex :: a ,b\") == \"COMPLEX a, b\"\n assert parse(Complex, \"complex a(1,2)\") == \"COMPLEX a(1,2)\"\n assert parse(Complex, \"complex :: a(1,2),b\") == \"COMPLEX a(1,2), b\"\n assert parse(Complex, \"complex external :: a\") == \"COMPLEX, external :: a\"\n assert parse(Complex, \"complex, external :: a\") == \"COMPLEX, external :: a\"\n assert (\n parse(Complex, \"complex external , intent(in) :: a\")\n == \"COMPLEX, external, intent(in) :: a\"\n )\n assert parse(Complex, \"complex(kind=8)\") == \"COMPLEX(KIND=8)\"\n assert parse(Complex, \"complex ( kind = 16)\") == \"COMPLEX(KIND=16)\"\n assert parse(Complex, \"complex(kind=4+4)\") == \"COMPLEX(KIND=4+4)\"\n assert parse(Complex, \"complex(kind=f(4,5))\") == \"COMPLEX(KIND=f(4,5))\"","title":""},{"docid":"01aff1f6dc1c5378a29133cb8b621eca","score":"0.50406414","text":"def test_invalid_parameter_initialized(self):\n input = \"\"\"\n Function: main\n Parameter: x, y\n Body:\n x = x + y;\n Return x;\n EndBody.\n\n Function: foo\n Parameter: a = 1\n Body:\n b = b - a;\n Return b;\n EndBody.\n \"\"\"\n expect = \"Error on line 10 col 21: =\"\n self.assertTrue(TestParser.checkParser(input, expect, 291))","title":""},{"docid":"59af761d62f1e90e6331a4153629bd00","score":"0.50402683","text":"def test_05_advanced(self):\n self.maxDiff = None\n\n p = MentatFilterParser()\n p.build()\n\n self.assertEqual(repr(p.parse('Category in [\"Abusive.Spam\" , \"Attempt.Exploit\"]')), \"COMPBINOP(VARIABLE('Category') OP_IN LIST(CONSTANT('Abusive.Spam'), CONSTANT('Attempt.Exploit')))\")\n self.assertEqual(repr(p.parse('Category is [\"Abusive.Spam\" , \"Attempt.Exploit\"]')), \"COMPBINOP(VARIABLE('Category') OP_IS LIST(CONSTANT('Abusive.Spam'), CONSTANT('Attempt.Exploit')))\")\n self.assertEqual(repr(p.parse('Node.Name in [\"cz.cesnet.labrea\"]')), \"COMPBINOP(VARIABLE('Node.Name') OP_IN LIST(CONSTANT('cz.cesnet.labrea')))\")\n self.assertEqual(repr(p.parse('Source.IP4 in [127.0.0.1 , 127.0.0.2]')), \"COMPBINOP(VARIABLE('Source.IP4') OP_IN LIST(IPV4('127.0.0.1'), IPV4('127.0.0.2')))\")\n self.assertEqual(repr(p.parse('(Source.IP4 eq 127.0.0.1) or (Node[#].Name is \"cz.cesnet.labrea\")')), \"LOGBINOP(COMPBINOP(VARIABLE('Source.IP4') OP_EQ IPV4('127.0.0.1')) OP_OR COMPBINOP(VARIABLE('Node[#].Name') OP_IS CONSTANT('cz.cesnet.labrea')))\")","title":""},{"docid":"3932431b29b675aa5c633d98ad1243e8","score":"0.50309515","text":"def test_invalid_return_statement_wrong_format(self):\n input = \"\"\"\n ** This \n is \n a multi-line\n comment **\n Function: main\n Parameter: x, y\n Body:\n Return x = 1;\n EndBody.\n \"\"\"\n expect = \"Error on line 9 col 25: =\"\n self.assertTrue(TestParser.checkParser(input, expect, 287))","title":""},{"docid":"c36d75781c80dd35c576c927b1229bd1","score":"0.5022875","text":"def Parse(self, dependencies):\n string = super(ConceptType, self).Parse(dependencies)\n value = self.Convert(string)\n self.Validate(value)\n return self.Normalize(value)","title":""},{"docid":"51b7c09d0c047d4f812947d00eaca162","score":"0.5017734","text":"def parse_vmt(self):\n\t\tpass","title":""},{"docid":"5525c4816b4a93104980c278af9dd23e","score":"0.50124633","text":"def _parse(self, text, scope=None):\n pass","title":""},{"docid":"c81423d8fe313f9b4ae82ed96f813718","score":"0.5010458","text":"def parse_next_val(units):\n ut, uv = units[0]\n if ut == \"word\":\n if uv == \"True\":\n return True, units[1:]\n elif uv == \"False\":\n return False, units[1:]\n elif uv == \"None\":\n return None, units[1:]\n else:\n return VariableLookup(uv), units[1:]\n elif ut == \"group\":\n subunits = []\n level = 0\n for i in range(1, len(units)):\n sut, suv = units[i]\n if sut == \"group\":\n if suv == \"open\":\n level += 1\n subunits.append((sut, suv))\n elif suv == \"close\":\n level -= 1\n if level < 0:\n break\n else:\n subunits.append((sut, suv))\n else:\n raise RuntimeError(\"Invalid lex unit 'group' value '{}'.\".format(suv))\n else:\n subunits.append((sut, suv))\n if level >= 0:\n raise ParseError(\"Unmatched '('.\")\n return parse_units(subunits), units[i+1:]\n elif ut == \"index\":\n raise ParseError(\"Unexpected index (expected value).\")\n elif ut == \"call\":\n ms = MACRO_START.match(uv)\n if not ms:\n raise ParseError(\"Invalid macro token.\")\n macro_name = ms.group(1)\n macro_content = uv[ms.end():]\n macro_args = split_macro_args(macro_content)\n return FunctionCall(macro_name, macro_args), units[1:]\n elif ut == \"op\":\n raise ParseError(\"Unexpected operator (expected value).\")\n elif ut in (\"int\", \"float\", \"string\"):\n return uv, units[1:]","title":""}],"string":"[\n {\n \"docid\": \"362b63e565f979e5740efa2a27e8e3c1\",\n \"score\": \"0.639785\",\n \"text\": \"def ambiguousParse(self, word):\\r\\n self.__cache.clear()\\r\\n return self.__parse(word, 0) != 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2d70c25ed7ca7086ea10af06cdd9443\",\n \"score\": \"0.6308442\",\n \"text\": \"def parse():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da0b395f567db447844268c55a7d8519\",\n \"score\": \"0.6135952\",\n \"text\": \"def test_incorrect_parsing(self):\\n self.assertRaises(ValueError, self.address_parser.parse, 'Python programming language')\\n self.assertRaises(AssertionError, self.address_parser.parse, 1)\\n self.assertRaises(AssertionError, self.address_parser.parse, True)\\n self.assertRaises(ValueError, self.address_parser.parse, '1')\\n self.assertRaises(AssertionError, self.address_parser.parse, ['15, M. A. Jinnah Road'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e39cb3d9d5cb0e85d3307c038e2b3e\",\n \"score\": \"0.61219186\",\n \"text\": \"def test_ambiguous_sentence(self):\\n self.assertEqual(list(parse_sentence(\\\"John called Sue from Denver\\\")),\\n [[\\\"John\\\", [[\\\"called\\\", \\\"Sue\\\"], [\\\"from\\\", \\\"Denver\\\"]]],\\n [\\\"John\\\", [\\\"called\\\", [\\\"Sue\\\", [\\\"from\\\", \\\"Denver\\\"]]]],\\n [\\\"John\\\", [\\\"called\\\", \\\"Sue\\\"]]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d5ed359163cf9baddd1bf3b2795ace6\",\n \"score\": \"0.6072876\",\n \"text\": \"def test_unambiguous_sentence(self):\\n self.assertEqual(list(parse_sentence(\\\"John called Sue\\\")),\\n [[\\\"John\\\", [\\\"called\\\", \\\"Sue\\\"]]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d16d2406478f2be10b56474df4deab75\",\n \"score\": \"0.6051201\",\n \"text\": \"def test_02_missing_everything(self):\\n s = \\\"< \\\\t\\\\v\\\\r\\\\n > [ \\\\t\\\\v\\\\r\\\\n ]\\\"\\n threw = False\\n try:\\n aa, dd = SS.parse(s)\\n except ValueError:\\n threw = True\\n assert threw, \\\"No variables, did not throw: %s\\\" % s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd913e801db0158d1e4ff145e4d4b1ea\",\n \"score\": \"0.6032236\",\n \"text\": \"def test_correct_parsing(self):\\n result = self.address_parser.parse('15, M. A. Jinnah Road')\\n self.assertTrue(result[0], '15')\\n self.assertTrue(result[1], 'M. A. Jinnah Road')\\n result2 = self.address_parser.parse('Nezábudková 3084/25')\\n self.assertTrue(result2[0], '3084/25')\\n self.assertTrue(result2[1], 'Nezábudková')\\n result3 = self.address_parser.parse('Calle 39 No 1540')\\n self.assertTrue(result3[0], 'No 1540')\\n self.assertTrue(result3[1], 'Calle 39')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c7ca9035b11b13b74c6a0b48e76aabb\",\n \"score\": \"0.59340703\",\n \"text\": \"def parse_validate(self, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f249a2d4067333f11334f2e4d8136b0\",\n \"score\": \"0.58296275\",\n \"text\": \"def parse(parser):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc04bda9de626e1692477dce626f8dd\",\n \"score\": \"0.5710122\",\n \"text\": \"def parse(text, thing, devour=_default_devour):\\n r, b, l = packrat_parse(text, thing, devour=devour)\\n if isinstance(l, ParseError):\\n raise l\\n return r, l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede3ad007dbe1df73a529361b3f99c1b\",\n \"score\": \"0.569357\",\n \"text\": \"def test_check_parse(self):\\n exp = (\\\"1\\\",[\\\"k__a\\\",\\\"p__b\\\",\\\"c__c\\\",\\\"o__d\\\",\\\"f__e\\\",\\\"g__f\\\",\\\"s__g\\\"])\\n obs = check_parse(good_string)\\n self.assertEqual(obs, exp)\\n\\n self.assertRaises(ParseError, check_parse, bad_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d7f80aa6760625689e341f2c18212c\",\n \"score\": \"0.56627333\",\n \"text\": \"def test_04_basic_factors(self):\\n self.maxDiff = None\\n\\n p = MentatFilterParser()\\n p.build()\\n\\n self.assertEqual(repr(p.parse(\\\"127.0.0.1\\\")), \\\"IPV4('127.0.0.1')\\\")\\n self.assertEqual(repr(p.parse(\\\"::1\\\")), \\\"IPV6('::1')\\\")\\n self.assertEqual(repr(p.parse(\\\"1\\\")), \\\"INTEGER(1)\\\")\\n self.assertEqual(repr(p.parse(\\\"1.1\\\")), \\\"FLOAT(1.1)\\\")\\n self.assertEqual(repr(p.parse(\\\"Test\\\")), \\\"VARIABLE('Test')\\\")\\n self.assertEqual(repr(p.parse('\\\"constant1\\\"')), \\\"CONSTANT('constant1')\\\")\\n\\n self.assertEqual(repr(p.parse(\\\"(127.0.0.1)\\\")), \\\"IPV4('127.0.0.1')\\\")\\n self.assertEqual(repr(p.parse(\\\"(::1)\\\")), \\\"IPV6('::1')\\\")\\n self.assertEqual(repr(p.parse(\\\"(1)\\\")), \\\"INTEGER(1)\\\")\\n self.assertEqual(repr(p.parse(\\\"(1.1)\\\")), \\\"FLOAT(1.1)\\\")\\n self.assertEqual(repr(p.parse(\\\"(Test)\\\")), \\\"VARIABLE('Test')\\\")\\n self.assertEqual(repr(p.parse('(\\\"constant1\\\")')), \\\"CONSTANT('constant1')\\\")\\n\\n self.assertEqual(repr(p.parse(\\\"((127.0.0.1))\\\")), \\\"IPV4('127.0.0.1')\\\")\\n self.assertEqual(repr(p.parse(\\\"((::1))\\\")), \\\"IPV6('::1')\\\")\\n self.assertEqual(repr(p.parse(\\\"((1))\\\")), \\\"INTEGER(1)\\\")\\n self.assertEqual(repr(p.parse(\\\"((1.1))\\\")), \\\"FLOAT(1.1)\\\")\\n self.assertEqual(repr(p.parse(\\\"((Test))\\\")), \\\"VARIABLE('Test')\\\")\\n self.assertEqual(repr(p.parse('((\\\"constant1\\\"))')), \\\"CONSTANT('constant1')\\\")\\n\\n self.assertEqual(repr(p.parse(\\\"[127.0.0.1]\\\")), \\\"LIST(IPV4('127.0.0.1'))\\\")\\n self.assertEqual(repr(p.parse(\\\"[::1]\\\")), \\\"LIST(IPV6('::1'))\\\")\\n self.assertEqual(repr(p.parse(\\\"[1]\\\")), \\\"LIST(INTEGER(1))\\\")\\n self.assertEqual(repr(p.parse(\\\"[1.1]\\\")), \\\"LIST(FLOAT(1.1))\\\")\\n self.assertEqual(repr(p.parse(\\\"[Test]\\\")), \\\"LIST(VARIABLE('Test'))\\\")\\n self.assertEqual(repr(p.parse('[\\\"constant1\\\"]')), \\\"LIST(CONSTANT('constant1'))\\\")\\n\\n self.assertEqual(repr(p.parse(\\\"[127.0.0.1 , 127.0.0.2]\\\")), \\\"LIST(IPV4('127.0.0.1'), IPV4('127.0.0.2'))\\\")\\n self.assertEqual(repr(p.parse(\\\"[::1 , ::2]\\\")), \\\"LIST(IPV6('::1'), IPV6('::2'))\\\")\\n self.assertEqual(repr(p.parse(\\\"[1,2, 3,4 , 5]\\\")), \\\"LIST(INTEGER(1), INTEGER(2), INTEGER(3), INTEGER(4), INTEGER(5))\\\")\\n self.assertEqual(repr(p.parse(\\\"[1.1,2.2, 3.3,4.4 , 5.5]\\\")), \\\"LIST(FLOAT(1.1), FLOAT(2.2), FLOAT(3.3), FLOAT(4.4), FLOAT(5.5))\\\")\\n self.assertEqual(repr(p.parse(\\\"[Var1,Var2, Var3,Var4 , Var5 ]\\\")), \\\"LIST(VARIABLE('Var1'), VARIABLE('Var2'), VARIABLE('Var3'), VARIABLE('Var4'), VARIABLE('Var5'))\\\")\\n self.assertEqual(repr(p.parse('[\\\"c1\\\",\\\"c2\\\", \\\"c3\\\",\\\"c4\\\" , \\\"c5\\\" ]')), \\\"LIST(CONSTANT('c1'), CONSTANT('c2'), CONSTANT('c3'), CONSTANT('c4'), CONSTANT('c5'))\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ce9c5473e47d52aa81465f52a4e01d\",\n \"score\": \"0.5649365\",\n \"text\": \"def test_integer():\\n assert parse(Integer, \\\"integer\\\") == \\\"INTEGER\\\"\\n assert parse(Integer, \\\"integer*4\\\") == \\\"INTEGER*4\\\"\\n assert parse(Integer, \\\"integer*4 a\\\") == \\\"INTEGER*4 a\\\"\\n assert parse(Integer, \\\"integer*4, a\\\") == \\\"INTEGER*4 a\\\"\\n assert parse(Integer, \\\"integer*4 a ,b\\\") == \\\"INTEGER*4 a, b\\\"\\n assert parse(Integer, \\\"integer*4 :: a ,b\\\") == \\\"INTEGER*4 a, b\\\"\\n assert parse(Integer, \\\"integer*4 a(1,2)\\\") == \\\"INTEGER*4 a(1,2)\\\"\\n assert parse(Integer, \\\"integer*4 a(2*(1+2)-1)\\\") == \\\"INTEGER*4 a(2*(1+2)-1)\\\"\\n assert parse(Integer, \\\"integer*4 :: a(1,2),b\\\") == \\\"INTEGER*4 a(1,2), b\\\"\\n assert parse(Integer, \\\"integer*4 external :: a\\\") == \\\"INTEGER*4, external :: a\\\"\\n assert parse(Integer, \\\"integer*4, external :: a\\\") == \\\"INTEGER*4, external :: a\\\"\\n assert (\\n parse(Integer, \\\"integer*4 external , intent(in) :: a\\\")\\n == \\\"INTEGER*4, external, intent(in) :: a\\\"\\n )\\n assert parse(Integer, \\\"integer(kind=4)\\\") == \\\"INTEGER(KIND=4)\\\"\\n assert parse(Integer, \\\"integer ( kind = 4)\\\") == \\\"INTEGER(KIND=4)\\\"\\n assert parse(Integer, \\\"integer(kind=2+2)\\\") == \\\"INTEGER(KIND=2+2)\\\"\\n assert parse(Integer, \\\"integer(kind=f(4,5))\\\") == \\\"INTEGER(KIND=f(4,5))\\\"\\n assert parse(Integer, \\\"integer a = 5\\\") == \\\"INTEGER :: a = 5\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"860046c74a5a7ee3da2e736c3ca9392b\",\n \"score\": \"0.5600621\",\n \"text\": \"def _parse_way(way):\\n pass # TODO: what to do with ways\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f908e244a2ac9c06b2b7d2258c1101\",\n \"score\": \"0.5542157\",\n \"text\": \"def _validate_format(format, alternative):\\r\\n\\r\\n def _parse(string):\\r\\n result = []\\r\\n for match in PYTHON_FORMAT.finditer(string):\\r\\n name, format, typechar = match.groups()\\r\\n if typechar == '%' and name is None:\\r\\n continue\\r\\n result.append((name, str(typechar)))\\r\\n return result\\r\\n\\r\\n def _compatible(a, b):\\r\\n if a == b:\\r\\n return True\\r\\n for set in _string_format_compatibilities:\\r\\n if a in set and b in set:\\r\\n return True\\r\\n return False\\r\\n\\r\\n def _check_positional(results):\\r\\n positional = None\\r\\n for name, char in results:\\r\\n if positional is None:\\r\\n positional = name is None\\r\\n else:\\r\\n if (name is None) != positional:\\r\\n raise TranslationError('format string mixes positional '\\r\\n 'and named placeholders')\\r\\n return bool(positional)\\r\\n\\r\\n a, b = map(_parse, (format, alternative))\\r\\n\\r\\n # now check if both strings are positional or named\\r\\n a_positional, b_positional = map(_check_positional, (a, b))\\r\\n if a_positional and not b_positional and not b:\\r\\n raise TranslationError('placeholders are incompatible')\\r\\n elif a_positional != b_positional:\\r\\n raise TranslationError('the format strings are of different kinds')\\r\\n\\r\\n # if we are operating on positional strings both must have the\\r\\n # same number of format chars and those must be compatible\\r\\n if a_positional:\\r\\n if len(a) != len(b):\\r\\n raise TranslationError('positional format placeholders are '\\r\\n 'unbalanced')\\r\\n for idx, ((_, first), (_, second)) in enumerate(izip(a, b)):\\r\\n if not _compatible(first, second):\\r\\n raise TranslationError('incompatible format for placeholder '\\r\\n '%d: %r and %r are not compatible' %\\r\\n (idx + 1, first, second))\\r\\n\\r\\n # otherwise the second string must not have names the first one\\r\\n # doesn't have and the types of those included must be compatible\\r\\n else:\\r\\n type_map = dict(a)\\r\\n for name, typechar in b:\\r\\n if name not in type_map:\\r\\n raise TranslationError('unknown named placeholder %r' % name)\\r\\n elif not _compatible(typechar, type_map[name]):\\r\\n raise TranslationError('incompatible format for '\\r\\n 'placeholder %r: '\\r\\n '%r and %r are not compatible' %\\r\\n (name, typechar, type_map[name]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac03146133cf6cb40b40d42a0436918d\",\n \"score\": \"0.55047554\",\n \"text\": \"def _parse_case(token: Dict[str, Any]) -> Tuple[Union[str, Dict, None], str, bool]:\\n if token.get(\\\"TEXT\\\"):\\n return token.get(\\\"TEXT\\\"), \\\"TEXT\\\", False\\n else:\\n return token.get(\\\"LOWER\\\"), \\\"LOWER\\\", True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7534db69230fa1b3372b584ef8c4235\",\n \"score\": \"0.5489957\",\n \"text\": \"def test_issue_23():\\n\\n grammar = \\\"\\\"\\\"\\n List: members+=Value;\\n Value: ('{' List '}') | Identifier;\\n Identifier: val=ID;\\n \\\"\\\"\\\"\\n mm = metamodel_from_str(grammar)\\n\\n assert mm['List']._tx_type is RULE_COMMON\\n assert mm['Value']._tx_type is RULE_ABSTRACT\\n assert mm['Value']._tx_inh_by == [mm['List'], mm['Identifier']]\\n\\n grammar = \\\"\\\"\\\"\\n List: '{' members+=Value '}';\\n Value: ID;\\n \\\"\\\"\\\"\\n mm = metamodel_from_str(grammar)\\n\\n assert mm['List']._tx_type is RULE_COMMON\\n assert mm['Value']._tx_type is RULE_MATCH\\n\\n grammar = \\\"\\\"\\\"\\n ListSyntax: '{' List '}';\\n List: members+=Value;\\n Value: ListSyntax | Identifier;\\n Identifier: val=ID;\\n \\\"\\\"\\\"\\n mm = metamodel_from_str(grammar)\\n\\n assert mm['ListSyntax']._tx_type is RULE_ABSTRACT\\n assert mm['ListSyntax']._tx_inh_by == [mm['List']]\\n assert mm['List']._tx_type is RULE_COMMON\\n assert mm['Value']._tx_type is RULE_ABSTRACT\\n assert mm['Value']._tx_inh_by == [mm['ListSyntax'], mm['Identifier']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9deee1ba432274a9a1c8c967c8732dc\",\n \"score\": \"0.5476688\",\n \"text\": \"def _Parse(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9deee1ba432274a9a1c8c967c8732dc\",\n \"score\": \"0.5476688\",\n \"text\": \"def _Parse(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2cbb94862cecb25bda510a1b5fe7f72\",\n \"score\": \"0.5475379\",\n \"text\": \"def test_invalid_program_wrong_format(self):\\n input = \\\"\\\"\\\"\\n ** A simple program **\\n Var: x;\\n Function: x = y\\n Body:\\n print(ans);\\n EndBody.\\n \\\"\\\"\\\"\\n expect = \\\"Error on line 4 col 20: =\\\"\\n self.assertTrue(TestParser.checkParser(input, expect, 295))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d635af7dce654652f2421624e9640cd\",\n \"score\": \"0.5471709\",\n \"text\": \"def test_bad_parse_option():\\n\\n ff = fasta(SIMPLE_TEXT, 's', 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6519022e6eeb06a66a7e750ec5134c10\",\n \"score\": \"0.54633343\",\n \"text\": \"def _parseHeader(self, header):\\n formatting = \\\"fuzzy %s\\\"\\n (self.type,) = sscanf(header, formatting)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd88a3d6882eeb9f0956c72d7c693cb2\",\n \"score\": \"0.5451123\",\n \"text\": \"def test_parse_raise_unparseable(self, unparseable):\\n with self.assertRaises(funcparser.ParsingError):\\n self.parser.parse(unparseable, raise_errors=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e26eadd06e09470b11c28e4f2e7c6f1a\",\n \"score\": \"0.54463977\",\n \"text\": \"def test_byte():\\n assert parse(Byte, \\\"byte\\\") == \\\"BYTE\\\"\\n assert parse(Byte, \\\"byte a\\\") == \\\"BYTE a\\\"\\n assert parse(Byte, \\\"byte, a\\\") == \\\"BYTE a\\\"\\n assert parse(Byte, \\\"byte a ,b\\\") == \\\"BYTE a, b\\\"\\n assert parse(Byte, \\\"byte :: a ,b\\\") == \\\"BYTE a, b\\\"\\n assert parse(Byte, \\\"byte a(1,2)\\\") == \\\"BYTE a(1,2)\\\"\\n assert parse(Byte, \\\"byte :: a(1,2),b\\\") == \\\"BYTE a(1,2), b\\\"\\n assert parse(Byte, \\\"byte external :: a\\\") == \\\"BYTE, external :: a\\\"\\n assert parse(Byte, \\\"byte, external :: a\\\") == \\\"BYTE, external :: a\\\"\\n assert (\\n parse(Byte, \\\"byte external , intent(in) :: a\\\")\\n == \\\"BYTE, external, intent(in) :: a\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84b7f07608dddecdd9eabd0fb384b586\",\n \"score\": \"0.5417863\",\n \"text\": \"def test_type_syn_parser(self):\\n\\n valid_examples = [\\n StringIO('type Age = Int'),\\n StringIO('type Name = [Char]'),\\n StringIO('type Coordinate = (Int, Int)'),\\n StringIO('type Person = (Name, Age)')\\n ]\\n\\n incorrect_examples = [\\n # New type does not start with a capital\\n StringIO('type number = Int'),\\n # Declared type is not a type\\n StringIO('type Integer = char'),\\n # Invalid new type name\\n StringIO('type * = Bool'),\\n # Cannot define new type is empty list.\\n StringIO('type List = []'),\\n ]\\n\\n i = 0\\n for t in valid_examples:\\n with self.subTest(i=i):\\n tks = list(tokenize(t))\\n res = TypeSyn.parse_strict(tks, t)\\n self.assertEqual(type(res), AST.TYPESYN)\\n\\n i += 1\\n\\n i = 0\\n for t in incorrect_examples:\\n with self.subTest(i=i):\\n tks = list(tokenize(t))\\n self.assertRaises(ParseError, TypeSyn.parse_strict, tks, t)\\n\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb6098726fd98798c8a9389ef0fdf5b\",\n \"score\": \"0.5396685\",\n \"text\": \"def test_broken_meta() -> None:\\n with pytest.raises(ParseError):\\n parse(\\\":\\\")\\n\\n with pytest.raises(ParseError):\\n parse(\\\":param herp derp\\\")\\n\\n with pytest.raises(ParseError):\\n parse(\\\":param: invalid\\\")\\n\\n with pytest.raises(ParseError):\\n parse(\\\":param with too many args: desc\\\")\\n\\n # these should not raise any errors\\n parse(\\\":sthstrange: desc\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8be88ef2c9cbab0bef4c0e37ca07cc7\",\n \"score\": \"0.53964823\",\n \"text\": \"def test_parse_alias_value(data_spec: type, json_: str, value: Any) -> None:\\n result = parse_string(data_spec, json_)\\n assert isinstance(result, data_spec)\\n assert result == value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10f1aac70883672795d3d4e75e2630d3\",\n \"score\": \"0.53806776\",\n \"text\": \"def try_parse_type(cls, s, onto=None):\\n\\n ts = get_type_system()\\n result = ts.type_parser(s)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12a5f44de1a696d19b2b0e3b60e821b2\",\n \"score\": \"0.53634226\",\n \"text\": \"def is_parsed(obj):\\r\\n\\treturn obj and not isinstance(obj, types.StringTypes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d2287b74f185e6a2a7fd13b4a010067\",\n \"score\": \"0.53609675\",\n \"text\": \"def test_00_missing_attrs(self):\\n s = \\\"junk < \\\\n\\\\v\\\\r\\\\t > [i=0:43,*,5 , j_ = 1 : 50 , 10,1] more junk\\\"\\n threw = False\\n try:\\n aa, dd = SS.parse(s)\\n except ValueError:\\n threw = True\\n assert threw, \\\"No attributes, did not throw: %s\\\" % s\\n\\n s = re.sub(r\\\"<\\\\s*>\\\", \\\"<x:int64>\\\", s)\\n threw = ''\\n try:\\n aa, dd = SS.parse(s)\\n except ValueError as e:\\n threw = str(e)\\n assert not threw, \\\"Got attributes, threw: %s (%s)\\\" % (s, threw)\\n assert len(aa) == 1, \\\"Expected one attribute, got %d\\\" % len(aa)\\n assert len(dd) == 2, \\\"Expected two dimensions, got %d\\\" % len(dd)\\n reparse(aa, dd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6ae8f6b4c8446d74553f58a5382ea7d\",\n \"score\": \"0.53252804\",\n \"text\": \"def tryStrParse(s):\\n return utils.tryParse(s) if type(s).__name__ in ['str','unicode'] else s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a63118513e49374e494c0d4ecba2db\",\n \"score\": \"0.5321322\",\n \"text\": \"def _check_parser(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edcc4fa33282ed6d2c96dd3a3e0ea141\",\n \"score\": \"0.5307266\",\n \"text\": \"def test_parse_bad_unit2(self):\\n with self.assertRaises(ValueError):\\n bitmath.parse_string(\\\"1.23 QB\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ac88a9df747095c052f9173758c9700\",\n \"score\": \"0.5298685\",\n \"text\": \"def _parse(self, option_format_spec: str) -> Tuple[str, str]:\\n\\n startswith_match = self._start + '*' + self._end + '*'\\n if fnmatch.fnmatchcase(option_format_spec, startswith_match):\\n # look for self._end starting char after self._start\\n option_spec_end = option_format_spec.find(self._end, 1)\\n option_spec = option_format_spec[:option_spec_end + 1]\\n format_spec = option_format_spec[option_spec_end + 1:]\\n return option_spec, format_spec\\n return '', option_format_spec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cef9a7c1d657933ab0bb1bdca972e52\",\n \"score\": \"0.5294205\",\n \"text\": \"def process_type(error):\\n words = error.split(\\\":\\\")\\n if words[2].strip() == \\\"parser error\\\":\\n if len(words) > 4:\\n return words[3].strip()\\n return \\\"Other\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce4563565377ad9bcac13624efc5661\",\n \"score\": \"0.52814364\",\n \"text\": \"def test_parsing_wrong_type(self):\\n with raises(ValueError):\\n parse_config('{\\\"count\\\": \\\"hello\\\"}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6049a13bef54495b87290a49bee09a7f\",\n \"score\": \"0.5279789\",\n \"text\": \"def is_string(string):\\n \\n try: \\n a,b = parse(string, fuzzy_with_tokens=True)\\n #print(a)\\n #print(b)\\n values = ''.join(b)\\n #\\n return values\\n\\n except :\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d948c00a67537af5b309b43bdb5ee58\",\n \"score\": \"0.5276653\",\n \"text\": \"def _parse_identifier(self, ast, results):\\n # type: (ASTNode, Tuple[Any, ...]) -> str\\n return ast.match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe7a4bb07640271aeda616e5b0043d1\",\n \"score\": \"0.52753687\",\n \"text\": \"def test_determineFormat_unknown(self):\\n print 'Running %s ...' % getName()\\n\\n # returns unknown because it contains spaces\\n imp1 = \\\"\\\"\\\">P01013 GENE X PROTEIN (OVALBUMIN-RELATED)\\\"\\\"\\\"\\n self.assertEqual('unknown', util.determineFormat(imp1))\\n\\n # returns unknown because it contains spaces\\n imp2 = \\\"\\\"\\\"atgatgatga tgatgatgta cctgcagacc ccgtttccct ggtgccagtg gcagaggagt 60\\\"\\\"\\\"\\n self.assertEqual('unknown', util.determineFormat(imp2))\\n\\n # returns unknown because it contains spaces\\n imp3 = \\\"\\\"\\\"uugaccaagc uggggacccc ggucccuugg gaccaguggc agaggaguc 49\\\"\\\"\\\"\\n self.assertEqual('unknown', util.determineFormat(imp3))\\n\\n # returns unknown because it contains spaces and end of line char\\n imp4 = \\\"\\\"\\\"atgatgatga tgatgatgta cctgcagacc ccgtttccct ggtgccagtg gcagaggagt 60\\n\\nc 61\\n\\\"\\\"\\\"\\n self.assertEqual('unknown', util.determineFormat(imp4))\\n\\n # returns unknown because it is less than 3 chars\\n imp5 = \\\"\\\"\\\"act\\\"\\\"\\\"\\n self.assertEqual('unknown', util.determineFormat(imp5))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0933e68a18c68a882b9fcd52d912bcd\",\n \"score\": \"0.5269905\",\n \"text\": \"def test_common_var_decl_mistake_216(self):\\n testcase = r\\\"\\\"\\\"Function: a Body:Var:var = 0x0;IfTrueThenEndIf.Var:a=0;Var:var=\\\"**meant 2 b string**\\\";EndBody.\\\"\\\"\\\"\\n expect = r\\\"\\\"\\\"Error on line 1 col 28: x0\\\"\\\"\\\"\\n self.assertTrue(TestParser.checkParser(testcase, expect, 216))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35f4d2a8b1fb20ecc1428f2aa0ca849\",\n \"score\": \"0.5268413\",\n \"text\": \"def test_parsing_hard_string():\\n test_str = \\\\\\n '100 x 5, 7, 70, 80,90 x 6, 110 x 6,5, 4, 120 x 3 (2), 2, 100 x 5/4/3'\\n try:\\n sets = Set.parse_sets(test_str)\\n except SyntaxError:\\n pytest.fail(\\\"Told you it was hard\\\")\\n assert len(sets) == 12\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a732bbda6536e8d8afbbf1ca728a555\",\n \"score\": \"0.5264484\",\n \"text\": \"def parse(cls, s):\\n requirement = cls(s)\\n if not requirement.marker: # Short circuit to favour the common case.\\n return requirement, None\\n match = EXTRA_RE.match(s)\\n if not match: # No final \\\"extra\\\" expression, yay.\\n return requirement, None\\n extra = match.group('extra')\\n if not extra:\\n return requirement, None\\n s = match.group('requirement').rstrip()\\n if s.endswith('and'):\\n s = s[:-3].rstrip()\\n if s.endswith(';'):\\n s = s[:-1].rstrip()\\n return cls(s), extra\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9da46c039326999d43e4e48cf3408d18\",\n \"score\": \"0.52572507\",\n \"text\": \"def test_non_string_input(self):\\n options = {\\n 'schema': {\\n 'name': 'string',\\n 'result': 'string'\\n }\\n }\\n\\n record_data = {'name': 'test', 'result': 'test'}\\n\\n # get parsed data\\n parser = JSONParser(options)\\n assert_equal(parser.parse(record_data), True)\\n\\n expected_result = [\\n record_data\\n ]\\n assert_equal(parser.parsed_records, expected_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac1c641a40aaf4dc2b16db8a5739e0be\",\n \"score\": \"0.52542686\",\n \"text\": \"def test_real():\\n\\n assert parse(Real, \\\"real\\\") == \\\"REAL\\\"\\n assert parse(Real, \\\"real*4\\\") == \\\"REAL*4\\\"\\n assert parse(Real, \\\"real*4 a\\\") == \\\"REAL*4 a\\\"\\n assert parse(Real, \\\"real*4, a\\\") == \\\"REAL*4 a\\\"\\n assert parse(Real, \\\"real*4 a ,b\\\") == \\\"REAL*4 a, b\\\"\\n assert parse(Real, \\\"real*4 :: a ,b\\\") == \\\"REAL*4 a, b\\\"\\n assert parse(Real, \\\"real*4 a(1,2)\\\") == \\\"REAL*4 a(1,2)\\\"\\n assert parse(Real, \\\"real*4 :: a(1,2),b\\\") == \\\"REAL*4 a(1,2), b\\\"\\n assert parse(Real, \\\"real*4 external :: a\\\") == \\\"REAL*4, external :: a\\\"\\n assert parse(Real, \\\"real*4, external :: a\\\") == \\\"REAL*4, external :: a\\\"\\n assert (\\n parse(Real, \\\"real*4 external , intent(in) :: a\\\")\\n == \\\"REAL*4, external, intent(in) :: a\\\"\\n )\\n assert parse(Real, \\\"real(kind=4)\\\") == \\\"REAL(KIND=4)\\\"\\n assert parse(Real, \\\"real ( kind = 4)\\\") == \\\"REAL(KIND=4)\\\"\\n assert parse(Real, \\\"real(kind=2+2)\\\") == \\\"REAL(KIND=2+2)\\\"\\n assert parse(Real, \\\"real(kind=f(4,5))\\\") == \\\"REAL(KIND=f(4,5))\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32d5e868a399df980107faed4b4fcbb\",\n \"score\": \"0.52514064\",\n \"text\": \"def test_parse_anneal():\\n assert isinstance(test_s, str), 'Incorrect input type: string required'\\n assert isinstance(test_s_output, list), \\\\\\n 'Error: incorrect output type (list expected)'\\n assert test_s_output, 'Error: no parameters detected (parsing error)'\\n try:\\n level0 = test_s_output[0]\\n level1 = test_s_output[0].get('anneal')[0]\\n level2a = test_s_output[0].get('anneal')[0].get('temps')[0]\\n level2b = test_s_output[0].get('anneal')[0].get('times')[0]\\n assert isinstance(level0, dict), \\\\\\n 'Error: output must be list of dictionaries'\\n assert 'anneal' in level0, \\\\\\n 'Error: anneal parameter either not found, or parsed incorrectly'\\n assert 'temps' and 'times' in level1, \\\\\\n 'Error: Anneal parameters not detected'\\n assert 'tempvalue' and 'tempunits' in level2a, \\\\\\n 'Error: Temperature values and units incorrectly parsed'\\n assert 'timevalue' and 'timeunits' in level2b, \\\\\\n 'Error: Time values and units incorrectly parsed'\\n assert isinstance(test_s_output[0], dict), \\\\\\n 'Error: output must be list of dictionaries'\\n except IndexError:\\n raise Exception('Error: output is not a list, or is an empty list')\\n except TypeError:\\n raise Exception('Error: output must be a list of dictionaries')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde85a43471504b307a1344bd67b826d\",\n \"score\": \"0.5250075\",\n \"text\": \"def _parse(self, arg):\\n clean_arg = arg.strip()\\n if self._allow_empty_string or clean_arg:\\n return clean_arg\\n raise ValueError('the value {!r} is not a valid string'.format(arg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a507572ab37032538afeb467e74ff8e7\",\n \"score\": \"0.52475005\",\n \"text\": \"def _parse_type_command(line) -> typing.Tuple[str, typing.Optional[str]]:\\n m = re.search(\\\"^(.*): not found$\\\", line)\\n if m:\\n return m.group(1), None\\n\\n m = re.search(\\\"^(.*?) is (/.*)$\\\", line)\\n if m:\\n return m.group(1), m.group(2)\\n\\n raise UnsupportedTypeOutputException(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80bea0131fb2d8edb14ce2f7d8d094eb\",\n \"score\": \"0.5244718\",\n \"text\": \"def _parse_literal(self, ast, results):\\n # type: (ASTNode, Tuple[Any, ...]) -> str\\n return ast.match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d65ff42f5125f2f44a8f0a8efc0f61\",\n \"score\": \"0.52444136\",\n \"text\": \"def _checkAmbiguity(self, found, find_val, key):\\n f = find_val.replace(' ', '')\\n f2 = found.replace(' ', '') + '='\\n if f.startswith(f2):\\n return key\\n else:\\n alternatives = self.ambiguous[found]\\n for i, a in enumerate(alternatives):\\n if find_val.startswith(a[0]):\\n return self.ambiguous[found][i][1]\\n return key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84a558bf538464c42296d7620cdb7a1e\",\n \"score\": \"0.524344\",\n \"text\": \"def get_parse(lowercase: str):\\n if _is_topset(lowercase):\\n return _parse_topset\\n if _is_warmup(lowercase):\\n return _parse_warmup\\n return _parse_simple\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55a2151b7242ffb200148f8656ffbf6\",\n \"score\": \"0.5240539\",\n \"text\": \"def parse(tokens: List[str]):\\n if not tokens:\\n raise ParserError(\\\"Expected value\\\")\\n t = tokens[0]\\n if t == \\\"[\\\":\\n return _list(tokens[1:])\\n if t == \\\"{\\\":\\n return _object(tokens[1:])\\n return t, tokens[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b12dfaf464eecadb41964589fa930c0\",\n \"score\": \"0.52357906\",\n \"text\": \"def test_basic_parsing(self):\\n cn, results = io.parse_result_line('Littleton, 10, C, 11, L')\\n self.assertEqual(cn, 'Littleton')\\n self.assertEqual(len(results), 2)\\n self.assertEqual(results[0], (10, 'C'))\\n self.assertEqual(results[1], (11, 'L'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fba5795e8f4cc58e6ed91251a8bfd08\",\n \"score\": \"0.5232905\",\n \"text\": \"def parse(self, text):\\n return self.default_rule.parse(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afddc6aa2ec0367ed82beb742d60bc75\",\n \"score\": \"0.5222223\",\n \"text\": \"def Parse(self, source):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faaf541c69da2de5c5890f295cf74cee\",\n \"score\": \"0.5218183\",\n \"text\": \"def parse_object(word_list) : \\n skip(word_list, ['stop','error'])\\n if peek(word_list) == 'noun' :\\n return match(word_list, 'noun')\\n elif peek(word_list) == 'direction' :\\n return match(word_list, 'direction')\\n else :\\n raise ParseError(\\\"Error parsing: Expected a noun or direction.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"532e90e8199cc9003997ae7023530adc\",\n \"score\": \"0.52171725\",\n \"text\": \"def validate_input(self, raw):\\n return isinstance(raw, BaseParser)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cad29778c4798ba29fee52b7c8594f0\",\n \"score\": \"0.5214489\",\n \"text\": \"def test_spaces(self):\\n parser = ParserGABC(root='syllable')\\n self.assertRaises(NoMatch, lambda: parser.parse(' a(f)'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9445488933f6897aa0bfadfe231804ff\",\n \"score\": \"0.5206893\",\n \"text\": \"def _parse(self): # pylint: disable=unused-argument, no-self-use\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c8eb30342d56be75eaa0189cd9cbe9\",\n \"score\": \"0.5206762\",\n \"text\": \"def _parse_builtin(self, ast, results):\\n # type: (ASTNode, Tuple[Any, ...]) -> str\\n return ast.match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1c7f14fbc84c0155404805478f08fc8\",\n \"score\": \"0.5205738\",\n \"text\": \"def parse_ignored(lines):\\n # variables\\n if '=' in lines[0] or lines[0].strip().startswith('_'):\\n return lines[0]\\n\\n return ParsingError('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"383d84160204413a5c7060e94e5c3e10\",\n \"score\": \"0.5199853\",\n \"text\": \"def test_parse_bad_parser_type(self):\\n with self.assertRaises(ValueError):\\n ggtk.GoParser.parse(self.graph_obo_filename, 'fake')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c9dd0a6a1b828b6b51acf3e9243f892\",\n \"score\": \"0.5185549\",\n \"text\": \"def DoParsing(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d9fdc35cb958604ca898f32a9ed18aa\",\n \"score\": \"0.5180691\",\n \"text\": \"def parse_al(msg):\\n return parse_a(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"817d87284bf2b04aed2f3d538571a704\",\n \"score\": \"0.5176819\",\n \"text\": \"def must_parse(self, name):\\r\\n return self.parse \\\\\\r\\n and not self._matches_filter(name, self._parse_regexp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b05b932cec099a010a5feefdef972d48\",\n \"score\": \"0.5175094\",\n \"text\": \"def test_word_parse():\\n keys, stripped = madlib.parse(\\\"This is an {adjective} and also {adjective} {noun} of a test.\\\")\\n assert keys == ['adjective', 'adjective', 'noun']\\n assert stripped == 'This is an {} and also {} {} of a test.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dc80e7cd5704f462a90bc819e6d730f\",\n \"score\": \"0.51698625\",\n \"text\": \"def test_check_gap(self):\\n id_, parsed = check_parse(good_string)\\n self.assertTrue(check_gap(parsed))\\n\\n id_, parsed = check_parse(good_trailing)\\n self.assertTrue(check_gap, parsed)\\n\\n id_, parsed = check_parse(gap)\\n self.assertFalse(check_gap(parsed))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a441d7e9597ba5d70d64967afa26cd70\",\n \"score\": \"0.51696956\",\n \"text\": \"def test(parser, exfile):\\n grammatical = []\\n ungrammatical = []\\n with open(exfile, 'r') as infile:\\n for line in infile:\\n if line[0] == '+':\\n grammatical.append(line[1:].strip())\\n if line[0] == '-':\\n ungrammatical.append(line[1:].strip())\\n print(\\\"\\\\nCorrectly recognized:\\\")\\n for s in grammatical:\\n if len([t for t in parser.parse(s.split())]) > 0:\\n print(s)\\n print(\\\"\\\\nShould have been recognized, but wasn't:\\\")\\n for s in grammatical:\\n if not len([t for t in parser.parse(s.split())]) > 0:\\n print(s)\\n print(\\\"\\\\nCorrectly not recognized:\\\")\\n for s in ungrammatical:\\n if not len([t for t in parser.parse(s.split())]) > 0:\\n print(s)\\n print(\\\"\\\\nShould not have been recognized, but was:\\\")\\n for s in ungrammatical:\\n if len([t for t in parser.parse(s.split())]) > 0:\\n print(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25ee44eb36574d3a619afeb5f3629963\",\n \"score\": \"0.5167718\",\n \"text\": \"def test_invalid_parameter_wrong_order(self):\\n input = \\\"\\\"\\\"\\n ** A simple program **\\n Function: foo\\n Body:\\n b = b - a;\\n Return b;\\n EndBody.\\n Parameter: a, b\\n \\\"\\\"\\\"\\n expect = \\\"Error on line 8 col 8: Parameter\\\"\\n self.assertTrue(TestParser.checkParser(input, expect, 292))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b0b3b4c35f9d9ef0878f6704be7be9\",\n \"score\": \"0.5157489\",\n \"text\": \"def __parse_value(self):\\n char = self.scanner.peek(1)\\n\\n if char == u'{':\\n return self.__parse_object()\\n elif char == u'[':\\n return self.__parse_array()\\n elif char == u'n':\\n return self.__parse_null()\\n elif char == u't':\\n return self.__parse_bool()\\n elif char == u'f':\\n return self.__parse_bool()\\n elif char == u'\\\"':\\n return self.__parse_string()\\n else:\\n return self.__parse_number()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c16a0f1304f4dc0875e0b873b12bcce8\",\n \"score\": \"0.5155001\",\n \"text\": \"def parse(self, s, context):\\n try:\\n context = super(Optional, self).parse(s, context=context.copy())\\n except NoMatchError:\\n context.update(\\n _match='',\\n _unparsed=s,\\n )\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d366dfa681d0f51499ba3b2cbea61688\",\n \"score\": \"0.5146688\",\n \"text\": \"def test_parseSimpleField(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7431b3000d49b4761a708f09ee5d9e9b\",\n \"score\": \"0.5144285\",\n \"text\": \"def parse_completely(\\n text,\\n thing,\\n devour=_default_devour,\\n devour_leftovers=True\\n):\\n r, b, l = packrat_parse(text, thing, devour=devour)\\n if isinstance(l, ParseError):\\n raise l\\n if devour_leftovers:\\n l = devour(l)\\n if l:\\n raise ParseError(\\n \\\"Parse incomplete. Remainder: '{}'\\\".format(\\n error_context(l)\\n )\\n )\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b25f2137d666906bdeee3645d0ee5b\",\n \"score\": \"0.51308614\",\n \"text\": \"def test_only_strings_allowed(fredo):\\n with pytest.raises(TypeError):\\n fredo.parse_query(12)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"450e8ee636906d913148d3bfb29de583\",\n \"score\": \"0.51298606\",\n \"text\": \"def _parse_val(self, val):\\n\\n if isinstance(val, (int, float)):\\n return float(val)\\n\\n if isinstance(val, str):\\n return val\\n\\n if len(val) in (3, 4):\\n return tuple(val)\\n\\n raise TypeError(\\\"Unknown value type to pass to rpr\\\", val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17670d964e1609702e0bd94107d73c28\",\n \"score\": \"0.51269794\",\n \"text\": \"def testParseInvalidDate2(self):\\n try:\\n iso8601.parseDate(\\\"23\\\")\\n except iso8601.ParseError:\\n pass\\n else:\\n self.assertEquals(1, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4651a8b5400f3540eece38144d60c5e0\",\n \"score\": \"0.51221293\",\n \"text\": \"def _parse_value(value):\\n\\n # Check for boolean or None\\n if str(value).capitalize().startswith(('False', 'True', 'None')):\\n return eval(str(value).capitalize(), {}, {})\\n\\n # Detect strings, floats and ints\\n try:\\n # If this fails, this is a string\\n result = literal_eval(value)\\n except Exception:\\n result = value\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde38ff82f458ca6ee11f97a4cb24413\",\n \"score\": \"0.5118722\",\n \"text\": \"def _parse_type(pattern_dict: Dict[str, Any]) -> Tuple[str, str]:\\n fuzzy_text = pattern_dict.get(\\\"FUZZY\\\")\\n regex_text = pattern_dict.get(\\\"FREGEX\\\")\\n if isinstance(fuzzy_text, str):\\n return fuzzy_text, \\\"FUZZY\\\"\\n elif isinstance(regex_text, str):\\n return regex_text, \\\"FREGEX\\\"\\n else:\\n return \\\"\\\", \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f96517fc6eb4c4511c424f3892b0800\",\n \"score\": \"0.51119345\",\n \"text\": \"def parse(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e34a2d3a7eacbdac46b22cd9aa4cd638\",\n \"score\": \"0.5111739\",\n \"text\": \"def parse_token(token):\\n\\n if token == '\\\\\\\\and':\\n do_something()\\n\\n elif token == '\\\\\\\\or':\\n do_something_else() #asjadkjad\\n\\n#jakiś tam tekscik.txt\\n\\n elif token == '\\\\\\\\xor':\\n '''\\n Note that we still need to provide support for the deprecated\\n token \\\\xor. Hopefully we can drop support in libfoo 2.0.\\n '''\\n do_a_different_thing()\\n\\n else:\\n raise ValueError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a1a127cdbda20dd510c39892c17e38\",\n \"score\": \"0.51069677\",\n \"text\": \"def test_fusion(self):\\n q = ('the.rolling.stones.iregex.not:asc=sympathy,for,the,devil;'\\n '!(guns.n.roses=paradise,city&queen:asc)')\\n\\n item = self.parse(q)\\n\\n # I'm lazy and don't want to walk the tree, so lets just test the repr.\\n assert (six.text_type(repr(item)) ==\\n \\\"(the.rolling.stones.iregex.not:asc :iexact 'sympathy' \\\"\\n \\\"| 'for' | 'the' | 'devil') OR NOT (guns.n.roses :iexact \\\"\\n \\\"'paradise' | 'city') AND (queen :iexact '')\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63477b3fb6df9b55932fa5932ea7080f\",\n \"score\": \"0.5106424\",\n \"text\": \"def test_parse_string_non_string_input(self):\\n with self.assertRaises(ValueError):\\n bitmath.parse_string(12345)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad0cb506140f630d2e6328f1436a5150\",\n \"score\": \"0.5101755\",\n \"text\": \"def parse(self, s):\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76d8338b2d17ab5cba3108a388de5b2\",\n \"score\": \"0.5099613\",\n \"text\": \"def try_parse_int_or_float(s):\\n try:\\n return int(s)\\n except ValueError:\\n try:\\n return float(s)\\n except ValueError:\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6c4ef8805f43630cec968486218533\",\n \"score\": \"0.5098364\",\n \"text\": \"def parse(self, arg):\\n clean_arg = arg.strip().lower()\\n if clean_arg and set(clean_arg).issubset(_VERSION_CHARS):\\n if clean_arg not in _VERSION_BLACKLIST:\\n if not clean_arg.startswith(_VERSION_CANNOT_START_WITH):\\n return clean_arg\\n raise ValueError(_VERSION_REQUIREMENTS.format(arg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e64af1cf75207eca96b41d59d78f798\",\n \"score\": \"0.5081887\",\n \"text\": \"def inflect_by_pymorphy2(source_parsed_word: pymorphy2.analyzer.Parse, required_grammemes: Set[str]) -> \\\\\\r\\n Tuple[str, pymorphy2.analyzer.Parse]:\\r\\n res = source_parsed_word.inflect(required_grammemes)\\r\\n if res is None:\\r\\n inflected = source_parsed_word\\r\\n inflected_word = source_parsed_word.word\\r\\n warnings.warn(\\\"Word `{0}` cannot be inflected.\\\".format(inflected_word))\\r\\n else:\\r\\n inflected = res\\r\\n inflected_word = res.word\\r\\n return inflected_word, inflected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937ec9062ee2425d032e3dd1dee7182e\",\n \"score\": \"0.50817484\",\n \"text\": \"def test_parse_strings(self):\\r\\n parsed = parse_fragments('0-1000', '1500-2000')\\r\\n self.assertEqual(parsed, [(0, 1000), (1500, 2000)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc1a91057df75836f1490d49ca25f36\",\n \"score\": \"0.5075134\",\n \"text\": \"def parse(self):\\n\\n self._eat_white()\\n c = self.txt[0]\\n if c == '(':\\n self.txt = self.txt[1:]\\n l = []\\n while self.txt[0] != ')':\\n l.append(self.parse())\\n # needed in case there is whitespace before the )\\n self._eat_white()\\n self.txt = self.txt[1:]\\n return token.tuple(l)\\n\\n elif c == \\\"'\\\":\\n self.txt = self.txt[1:]\\n return token.quoted(self.parse())\\n\\n if self.txt[0] in self._map:\\n return getattr(self, SchemeParser._map[self.txt[0]])()\\n\\n value = self._parse_value()\\n\\n return token.label(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"790172d1a32a36990f8f4435d4f23e3a\",\n \"score\": \"0.5066718\",\n \"text\": \"def _postprocess(self, valuestr):\\r\\n\\r\\n intpattern = re.compile(r'^\\\\-?\\\\d+$')\\r\\n floatpattern = re.compile(r'^\\\\-?\\\\d+\\\\.\\\\d+(E[+-]?\\\\d\\\\d+)?$')\\r\\n timepattern = re.compile(r'^\\\\d{2}:\\\\d{2}:\\\\d{2}(\\\\.\\\\d{6})?')\\r\\n\\r\\n try:\\r\\n if valuestr.startswith('\\\"') and valuestr.endswith('\\\"'):\\r\\n # it's a string\\r\\n return valuestr[1:-1]\\r\\n\\r\\n elif re.match(intpattern, valuestr):\\r\\n # it's an integer\\r\\n return int(valuestr)\\r\\n\\r\\n elif re.match(floatpattern, valuestr):\\r\\n # floating point number\\r\\n return float(valuestr)\\r\\n\\r\\n elif re.match(timepattern, valuestr):\\r\\n time = valuestr[0:-2]\\r\\n return datetime.datetime.strptime(time, '%H:%M:%S.%f').time()\\r\\n\\r\\n else:\\r\\n # now let's try the datetime objects\\r\\n return iso8601.parse(valuestr)\\r\\n\\r\\n except (OverflowError, ValueError) as error:\\r\\n print(error)\\r\\n pass\\r\\n\\r\\n # If we get here, we still haven't returned anything.\\r\\n self.logger.info('The value %s couldn\\\\'t be parsed as int, float, or datetime. Returning it as string.' % valuestr)\\r\\n\\r\\n return valuestr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"414f4d1dfa7c17ccc32a535caa4d82b1\",\n \"score\": \"0.50648725\",\n \"text\": \"def test_parse(self, string, expected):\\n # t0 = time.time()\\n # from evennia import set_trace;set_trace()\\n ret = self.parser.parse(string, raise_errors=True)\\n # t1 = time.time()\\n # print(f\\\"time: {(t1-t0)*1000} ms\\\")\\n self.assertEqual(expected, ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f3e238dcec6be9bd5760a5f826705c\",\n \"score\": \"0.50637823\",\n \"text\": \"def parse_mechanism(str, d):\\n\\n a = RE_DUAL_CIDR.split(str)\\n if len(a) == 3:\\n str, cidr6 = a[0], int(a[1])\\n else:\\n cidr6 = None\\n a = RE_CIDR.split(str)\\n if len(a) == 3:\\n str, cidr = a[0], int(a[1])\\n else:\\n cidr = None\\n\\n a = str.split(':', 1)\\n if len(a) < 2:\\n str = str.lower()\\n if str == 'exists': d = None\\n return str, d, cidr, cidr6\\n return a[0].lower(), a[1], cidr, cidr6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ea650d8803048d127793967797fe20\",\n \"score\": \"0.5062988\",\n \"text\": \"def test_parse_basic_value(data_spec: type, json_: str, value: Any) -> None:\\n result = parse_string(data_spec, json_)\\n assert isinstance(result, data_spec)\\n assert result == value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d015e9d8ef01d61a6855af01071eb32\",\n \"score\": \"0.5059198\",\n \"text\": \"def test_parse_bad_unit(self):\\n with self.assertRaises(ValueError):\\n bitmath.parse_string(\\\"1.23 GIB\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05558e638701135677501abb1e13e8c\",\n \"score\": \"0.50469685\",\n \"text\": \"def test_terminals_with_different_names():\\n\\n # In this grammar we have 'd' terminal in S production and B terminal with\\n # the same 'd' recognizer.\\n grammar = \\\"\\\"\\\"\\n S: 'a' A 'd' | 'b' A B;\\n A: 'c' A | 'c';\\n terminals\\n B: 'd';\\n \\\"\\\"\\\"\\n\\n with pytest.raises(GrammarError) as e:\\n Grammar.from_string(grammar)\\n\\n assert 'B' in str(e.value)\\n assert 'd' in str(e.value)\\n assert 'match the same string' in str(e.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1dee1397a3fed366094c304c67368a\",\n \"score\": \"0.50452876\",\n \"text\": \"def test_complex():\\n\\n assert parse(Complex, \\\"complex\\\") == \\\"COMPLEX\\\"\\n assert parse(Complex, \\\"complex\\\") == \\\"COMPLEX\\\"\\n assert parse(Complex, \\\"complex*8 a\\\") == \\\"COMPLEX*8 a\\\"\\n assert parse(Complex, \\\"complex*16, a\\\") == \\\"COMPLEX*16 a\\\"\\n assert parse(Complex, \\\"complex a ,b\\\") == \\\"COMPLEX a, b\\\"\\n assert parse(Complex, \\\"complex :: a ,b\\\") == \\\"COMPLEX a, b\\\"\\n assert parse(Complex, \\\"complex a(1,2)\\\") == \\\"COMPLEX a(1,2)\\\"\\n assert parse(Complex, \\\"complex :: a(1,2),b\\\") == \\\"COMPLEX a(1,2), b\\\"\\n assert parse(Complex, \\\"complex external :: a\\\") == \\\"COMPLEX, external :: a\\\"\\n assert parse(Complex, \\\"complex, external :: a\\\") == \\\"COMPLEX, external :: a\\\"\\n assert (\\n parse(Complex, \\\"complex external , intent(in) :: a\\\")\\n == \\\"COMPLEX, external, intent(in) :: a\\\"\\n )\\n assert parse(Complex, \\\"complex(kind=8)\\\") == \\\"COMPLEX(KIND=8)\\\"\\n assert parse(Complex, \\\"complex ( kind = 16)\\\") == \\\"COMPLEX(KIND=16)\\\"\\n assert parse(Complex, \\\"complex(kind=4+4)\\\") == \\\"COMPLEX(KIND=4+4)\\\"\\n assert parse(Complex, \\\"complex(kind=f(4,5))\\\") == \\\"COMPLEX(KIND=f(4,5))\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01aff1f6dc1c5378a29133cb8b621eca\",\n \"score\": \"0.50406414\",\n \"text\": \"def test_invalid_parameter_initialized(self):\\n input = \\\"\\\"\\\"\\n Function: main\\n Parameter: x, y\\n Body:\\n x = x + y;\\n Return x;\\n EndBody.\\n\\n Function: foo\\n Parameter: a = 1\\n Body:\\n b = b - a;\\n Return b;\\n EndBody.\\n \\\"\\\"\\\"\\n expect = \\\"Error on line 10 col 21: =\\\"\\n self.assertTrue(TestParser.checkParser(input, expect, 291))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59af761d62f1e90e6331a4153629bd00\",\n \"score\": \"0.50402683\",\n \"text\": \"def test_05_advanced(self):\\n self.maxDiff = None\\n\\n p = MentatFilterParser()\\n p.build()\\n\\n self.assertEqual(repr(p.parse('Category in [\\\"Abusive.Spam\\\" , \\\"Attempt.Exploit\\\"]')), \\\"COMPBINOP(VARIABLE('Category') OP_IN LIST(CONSTANT('Abusive.Spam'), CONSTANT('Attempt.Exploit')))\\\")\\n self.assertEqual(repr(p.parse('Category is [\\\"Abusive.Spam\\\" , \\\"Attempt.Exploit\\\"]')), \\\"COMPBINOP(VARIABLE('Category') OP_IS LIST(CONSTANT('Abusive.Spam'), CONSTANT('Attempt.Exploit')))\\\")\\n self.assertEqual(repr(p.parse('Node.Name in [\\\"cz.cesnet.labrea\\\"]')), \\\"COMPBINOP(VARIABLE('Node.Name') OP_IN LIST(CONSTANT('cz.cesnet.labrea')))\\\")\\n self.assertEqual(repr(p.parse('Source.IP4 in [127.0.0.1 , 127.0.0.2]')), \\\"COMPBINOP(VARIABLE('Source.IP4') OP_IN LIST(IPV4('127.0.0.1'), IPV4('127.0.0.2')))\\\")\\n self.assertEqual(repr(p.parse('(Source.IP4 eq 127.0.0.1) or (Node[#].Name is \\\"cz.cesnet.labrea\\\")')), \\\"LOGBINOP(COMPBINOP(VARIABLE('Source.IP4') OP_EQ IPV4('127.0.0.1')) OP_OR COMPBINOP(VARIABLE('Node[#].Name') OP_IS CONSTANT('cz.cesnet.labrea')))\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3932431b29b675aa5c633d98ad1243e8\",\n \"score\": \"0.50309515\",\n \"text\": \"def test_invalid_return_statement_wrong_format(self):\\n input = \\\"\\\"\\\"\\n ** This \\n is \\n a multi-line\\n comment **\\n Function: main\\n Parameter: x, y\\n Body:\\n Return x = 1;\\n EndBody.\\n \\\"\\\"\\\"\\n expect = \\\"Error on line 9 col 25: =\\\"\\n self.assertTrue(TestParser.checkParser(input, expect, 287))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c36d75781c80dd35c576c927b1229bd1\",\n \"score\": \"0.5022875\",\n \"text\": \"def Parse(self, dependencies):\\n string = super(ConceptType, self).Parse(dependencies)\\n value = self.Convert(string)\\n self.Validate(value)\\n return self.Normalize(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51b7c09d0c047d4f812947d00eaca162\",\n \"score\": \"0.5017734\",\n \"text\": \"def parse_vmt(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5525c4816b4a93104980c278af9dd23e\",\n \"score\": \"0.50124633\",\n \"text\": \"def _parse(self, text, scope=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81423d8fe313f9b4ae82ed96f813718\",\n \"score\": \"0.5010458\",\n \"text\": \"def parse_next_val(units):\\n ut, uv = units[0]\\n if ut == \\\"word\\\":\\n if uv == \\\"True\\\":\\n return True, units[1:]\\n elif uv == \\\"False\\\":\\n return False, units[1:]\\n elif uv == \\\"None\\\":\\n return None, units[1:]\\n else:\\n return VariableLookup(uv), units[1:]\\n elif ut == \\\"group\\\":\\n subunits = []\\n level = 0\\n for i in range(1, len(units)):\\n sut, suv = units[i]\\n if sut == \\\"group\\\":\\n if suv == \\\"open\\\":\\n level += 1\\n subunits.append((sut, suv))\\n elif suv == \\\"close\\\":\\n level -= 1\\n if level < 0:\\n break\\n else:\\n subunits.append((sut, suv))\\n else:\\n raise RuntimeError(\\\"Invalid lex unit 'group' value '{}'.\\\".format(suv))\\n else:\\n subunits.append((sut, suv))\\n if level >= 0:\\n raise ParseError(\\\"Unmatched '('.\\\")\\n return parse_units(subunits), units[i+1:]\\n elif ut == \\\"index\\\":\\n raise ParseError(\\\"Unexpected index (expected value).\\\")\\n elif ut == \\\"call\\\":\\n ms = MACRO_START.match(uv)\\n if not ms:\\n raise ParseError(\\\"Invalid macro token.\\\")\\n macro_name = ms.group(1)\\n macro_content = uv[ms.end():]\\n macro_args = split_macro_args(macro_content)\\n return FunctionCall(macro_name, macro_args), units[1:]\\n elif ut == \\\"op\\\":\\n raise ParseError(\\\"Unexpected operator (expected value).\\\")\\n elif ut in (\\\"int\\\", \\\"float\\\", \\\"string\\\"):\\n return uv, units[1:]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992996,"cells":{"query_id":{"kind":"string","value":"4ceb5896dbde5e90af77304ea6e3fd6a"},"query":{"kind":"string","value":"Convert a value to a JSON serializable value"},"positive_passages":{"kind":"list like","value":[{"docid":"d3882393097a90b9be7d3ba9047cd478","score":"0.0","text":"def serialize(value, list_obj_type, **kwargs):\n serialized_list = []\n for el in value:\n if issubclass(list_obj_type, Type):\n serialized_list.append(list_obj_type.serialize(el, **kwargs))\n else:\n # the element of the list is not valid\n raise TypeError('Element of list property if wrong type. '\n 'Element of type \"{}\" should be type \"{}\"'\n .format(list_obj_type, type(el)))\n return serialized_list","title":""}],"string":"[\n {\n \"docid\": \"d3882393097a90b9be7d3ba9047cd478\",\n \"score\": \"0.0\",\n \"text\": \"def serialize(value, list_obj_type, **kwargs):\\n serialized_list = []\\n for el in value:\\n if issubclass(list_obj_type, Type):\\n serialized_list.append(list_obj_type.serialize(el, **kwargs))\\n else:\\n # the element of the list is not valid\\n raise TypeError('Element of list property if wrong type. '\\n 'Element of type \\\"{}\\\" should be type \\\"{}\\\"'\\n .format(list_obj_type, type(el)))\\n return serialized_list\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ba48399899584d9edb4caa6c73a7016b","score":"0.83150655","text":"def to_json(self, value):\r\n return value","title":""},{"docid":"91b489dfea37aa0a9c81190c459096d5","score":"0.8236348","text":"def _serialize_value(value):\n return value","title":""},{"docid":"5c97430dc04e12da5097729a35626306","score":"0.82256335","text":"def toJson(value):\r\n\treturn json.dumps(value)","title":""},{"docid":"dea43ebca3518ed50ec8672e7add7076","score":"0.8196552","text":"def to_json(self, value):\n return value","title":""},{"docid":"4b811cb92c1a00c956b93eb055ed5f45","score":"0.81222177","text":"def _dump_json_value(self, value):\n return json.dumps(value)","title":""},{"docid":"6727e539d1099eb12042ae09b085bab7","score":"0.8119554","text":"def to_json(value):\n\n return json.dumps(value)","title":""},{"docid":"770fe1a90661e730207a9f27ceb81213","score":"0.8053105","text":"def serialize_json(self, python_value):\n return json.dumps(python_value)","title":""},{"docid":"65996a559da5bcb659a459c2adf960f1","score":"0.8024837","text":"def _serialize(self, value):\n return simplejson.dumps(value, ensure_ascii=False)","title":""},{"docid":"322a5a69a07f7a29253b2fe0b81e7c08","score":"0.7838724","text":"def to_json(self, value: typing.Any) -> typing.Any:\n raise NotImplementedError","title":""},{"docid":"8761b61fcce6eacd55260b5d95b94371","score":"0.77923846","text":"def serialize(value):\n return value","title":""},{"docid":"6ec16e0604c75525cc2801cb00d70691","score":"0.77811104","text":"def to_python(self, value):\n if value in (\"\", None):\n return None\n\n try:\n if isinstance(value, (str, unicode)):\n return json.loads(value, object_hook=decode_object if self.pickle else None)\n except ValueError:\n pass\n\n return value","title":""},{"docid":"985952912ec731b0e3374b0be9e6e42f","score":"0.7697401","text":"def serialize_value(value):\n return bytearray(json.dumps(value).encode(\"utf-8\"))","title":""},{"docid":"5dfe294ff0c55791104c6742c0bda734","score":"0.7642251","text":"def to_python(self, value):\n if value == \"\":\n return None\n\n try:\n if isinstance(value, six.string_types):\n return json.loads(value, object_hook=json_decode_object)\n except ValueError:\n pass\n\n return value","title":""},{"docid":"9e72a6754cb5a0dd03aa040a124c0a3d","score":"0.7641237","text":"def python_value(self, value):\r\n\t\treturn value if value is None else json.loads(value)","title":""},{"docid":"ab9c70cdcb1e4f7cd8c16b5dd7b95f65","score":"0.7575193","text":"def serialize(value):","title":""},{"docid":"f66ef69da73947d089a395bec99089ce","score":"0.75572896","text":"def to_python(self, value):\n #django.core.serializers.json.DjangoJSONEncoder\n\n if value in self.empty_values:\n return None\n\n try:\n return json.loads(value)\n except Exception as e:\n raise ValidationError(repr(e), code='invalid')","title":""},{"docid":"5fea59b9ee4ac2aa9b6470c2391507aa","score":"0.7551401","text":"def jsonify(self, value):\n if value is not None:\n return str(value)","title":""},{"docid":"7c8a26a57f4729c521dc3c98d85e20da","score":"0.75203425","text":"def json_encode(self, value):\r\n return simplejson.dumps(value, separators=(\",\", \":\"))","title":""},{"docid":"8de42d65b59e673c2c7f55f2fd7fa304","score":"0.7519901","text":"def json_value(value):\n try:\n out = json.loads(value)\n except json.decoder.JSONDecodeError:\n out = value\n return out","title":""},{"docid":"4e2d5cd1e6c33d1c6105894ba26136b9","score":"0.7509357","text":"def to_python(self, value):\r\n return value","title":""},{"docid":"87f81c42a57b715a5a7847c9118c33ce","score":"0.74956405","text":"def to_python(self, value):\n return value","title":""},{"docid":"87f81c42a57b715a5a7847c9118c33ce","score":"0.74956405","text":"def to_python(self, value):\n return value","title":""},{"docid":"12b112ee1d403527227c2724710854c2","score":"0.7459379","text":"def to_python(self, value):\n if isinstance(value, list) or value is None:\n return value\n return json.loads(value)","title":""},{"docid":"a9c3c4333a1e4fc9e606f06b65b20718","score":"0.72748893","text":"def to_json(self, value, fields=None, wrap=True):\n #obj = self.get_json_obj(value, fields, wrap)\n return json.dumps(value, cls=self.json_encoder)","title":""},{"docid":"94334937389d926be623b76b97b75257","score":"0.72371733","text":"def db_value(self, value):\r\n\t\treturn value if value is None else json.dumps(value)","title":""},{"docid":"22757b234152531a9891e0de9bbfeba1","score":"0.72301066","text":"def get_prep_value(self, value):\n if value == \"\" or value is None:\n return None\n return json.dumps(value, default=encode_object if self.pickle else None,\n ensure_ascii=False, separators=(',',':'))","title":""},{"docid":"7bae3c61de9dec5fe5d460d5de09add4","score":"0.7226884","text":"def json_encode(self, value):\r\n return simplejson.dumps(value, separators=(\",\", \":\"), sort_keys=True)","title":""},{"docid":"40e6fa4765b4b36c5eba1cdef18749d8","score":"0.7221748","text":"def to_python(self, value):\n if isinstance(value, basestring):\n return utils.to_python(value)\n return value","title":""},{"docid":"9d3741fef4626eb7aebec822cea43cc1","score":"0.7218533","text":"def make_value_from_datastore(self, value):\n if value is not None:\n return json_decode(value)","title":""},{"docid":"610e42c6ecb1774a40f6b530eb6b01f3","score":"0.7205728","text":"def serialize(value, user):\n return value.serialize(user)","title":""},{"docid":"efeaf49266c2fc8695d55879906fe12c","score":"0.7191969","text":"def as_json(value: Union[List, Dict]) -> str:\n return json.dumps(value)","title":""},{"docid":"18ec67125a7f14e88b9c17fdf3570998","score":"0.71517915","text":"def from_json(self, value):\r\n return value","title":""},{"docid":"923a17aa1c5ce688f83cd40dfc278f07","score":"0.7093603","text":"def to_value(self, value):\n return value","title":""},{"docid":"f99367e27bf903a8e649f45db6754164","score":"0.70728505","text":"def use_to_json_obj(value):\n if hasattr(value, 'to_json_obj'):\n if hasattr(value, 'from_json_obj'):\n return {\n 'from_json_obj': '%s/%s' % (value.__module__, value.__class__.__name__),\n 'obj': value.to_json_obj()\n }\n else:\n return value.to_json_obj()\n else:\n return value","title":""},{"docid":"7773b17aed70fa5bc80c1557cf83de8c","score":"0.705589","text":"def to_python(self, value):\n if value == \"\":\n return None\n\n try:\n if isinstance(value, six.string_types):\n return deserialize_from_string(value)\n except ValueError:\n pass\n\n return value","title":""},{"docid":"fb83822225ed829b55cef35ef9fc84f0","score":"0.7029836","text":"def json(value, pretty=True):\n if _Duration is None:\n _late_import()\n return _json_encoder(value, pretty=pretty)","title":""},{"docid":"e1f6e89ce89ee36957f65b695a94f96d","score":"0.7025157","text":"def serialize(self, python_value):\n return str(python_value)","title":""},{"docid":"a1c615995238e05446df8421c2865641","score":"0.6986837","text":"def from_json(self, value):\n return value","title":""},{"docid":"d0d411f501f08a3fb6fb4406816e4f9f","score":"0.6984771","text":"def to_json(self, value):\r\n return \"X\" * value","title":""},{"docid":"7ba8c98fb2d31be6c66e7f34f577505e","score":"0.69746","text":"def handle_json(value: Union[dict, list]) -> str:\n return json.dumps(value)","title":""},{"docid":"f50748b7427373c09628f72a46257c37","score":"0.694783","text":"def native_value(value):\n if isinstance(value, str):\n try:\n value = json.loads(value)\n except ValueError:\n return value\n return value","title":""},{"docid":"578ff118bcda00ec4eff1e49560ad7ad","score":"0.6857314","text":"def to_python(self, value):\n try:\n json_field = json.loads(value)\n except Exception as e:\n raise forms.ValidationError(\n \"Invalid json in exts field({}): {}\".format(value, e),\n code=\"invalid\",\n )\n else:\n return json_field","title":""},{"docid":"f9e14f9b1ebdba26c1e5badd809ae70c","score":"0.6845089","text":"def json_encode(value):\n return json.dumps(value, cls=ComplexEncoder).replace(\"</\", \"<\\\\/\")","title":""},{"docid":"598005ad2740daf2e2c793a64baee570","score":"0.6831455","text":"def python_value(self, value):\n try:\n value = zlib.decompress(value)\n except:\n pass\n\n return value if value is None else json.loads(value)","title":""},{"docid":"5d6e73f7683dc01ebba9649a08784111","score":"0.682787","text":"def ConvertData(self, value):\n try:\n data = json.loads(value)\n except ValueError:\n raise db.BadValueError(\"Not Valid JSON\")\n if not util.GetVariations(data) or not util.GetMetrics(data):\n raise db.BadValueError(\"Invalid Data\")\n return json.dumps(data, separators=(\",\", \":\"))","title":""},{"docid":"cde4d1f9746bae1db814c5a2fd85efe0","score":"0.682112","text":"def value(self):\n return json.loads(self._value)","title":""},{"docid":"7ed31cc21a40291789e466bff4696ff9","score":"0.6796097","text":"def get_json_value(value):\n try:\n return json.loads(value)\n except ValueError:\n return value","title":""},{"docid":"ff8740b6685ecd7b9a42832d9aab7cdd","score":"0.67891735","text":"def _deflate(self, value):\r\n return json.dumps(value, cls=Encoder)","title":""},{"docid":"ff8740b6685ecd7b9a42832d9aab7cdd","score":"0.67891735","text":"def _deflate(self, value):\r\n return json.dumps(value, cls=Encoder)","title":""},{"docid":"a2037700865da65be8430804b2cab486","score":"0.67509264","text":"def value(self):\n return cson2json(self._val)","title":""},{"docid":"71ae43954a9487d82591251ae2209f03","score":"0.67232835","text":"def to_serializable(val):\n\n if isinstance(val, datetime):\n return val.isoformat()\n elif hasattr(val, '__dict__'):\n return val.__dict__\n\n return val","title":""},{"docid":"7a8256fdc58bed76d5025d0137b8a418","score":"0.6718694","text":"def as_dict(self, value):\n if value is not None:\n return value.to_dict()","title":""},{"docid":"90b1e853a1fb706d2aa3898df25a3afd","score":"0.6706012","text":"def serialize(self, value):\r\n if isinstance(value, float) and self.as_type is unicode:\r\n value = u'{0:.{1}f}'.format(value, self.float_places)\r\n value = self.as_type(value)\r\n elif self.as_type is unicode:\r\n if isinstance(value, bool):\r\n # Store bools as 1/0 instead of True/False.\r\n value = unicode(int(bool(value)))\r\n elif isinstance(value, str):\r\n value = value.decode('utf8', 'ignore')\r\n else:\r\n value = unicode(value)\r\n else:\r\n value = self.as_type(value)\r\n\r\n if self.suffix:\r\n value += self.suffix\r\n\r\n return value","title":""},{"docid":"0c685637b696803209ada88430893b03","score":"0.66902214","text":"def send_value(stream, value):\n json.dump(encode(value), stream)","title":""},{"docid":"c7ab2cb460c62065ba8465f4fa58eebc","score":"0.6689844","text":"def _tojson(obj):\n return json.loads(json.dumps(obj, cls=_CustomEncoder))","title":""},{"docid":"eefb92d1c52a194d9141b88ca9a587e0","score":"0.668975","text":"def json_encoder(obj):\n if isinstance(obj, datetime):\n return str(obj)\n raise TypeError(\"Type %s not serializable\" % type(obj))","title":""},{"docid":"56e3fead7e331bcc9b9248530242ad63","score":"0.6668841","text":"def _inflate(self, value):\r\n if value is None:\r\n return {}\r\n if isinstance(value, unicode) or isinstance(value, str):\r\n return json.loads(value, object_hook=decode)\r\n return value","title":""},{"docid":"56e3fead7e331bcc9b9248530242ad63","score":"0.6668841","text":"def _inflate(self, value):\r\n if value is None:\r\n return {}\r\n if isinstance(value, unicode) or isinstance(value, str):\r\n return json.loads(value, object_hook=decode)\r\n return value","title":""},{"docid":"995b7b9ac17992254a4a9fb3f38ac3a9","score":"0.6662083","text":"def to_python(self, value, backend=None):\r\n return value","title":""},{"docid":"769230880bacacc4df187f01b1b9e321","score":"0.66403747","text":"def force_js(value, typename=None, encoder=None):\n if typename:\n typename = typename.lower()\n if typename in JS_CONVERT_TYPES:\n value = JS_CONVERT_TYPES[typename](value)\n return json.dumps(value, cls=(encoder or DjangoJSONEncoder))","title":""},{"docid":"d4cd48dcf2afe9a69e720e3527e1d3ed","score":"0.66193247","text":"def to_python(self, value):\n if value in (\"\", None):\n return None\n\n try:\n if isinstance(value, basestring):\n return safe_decode(value)\n except ValueError:\n pass\n\n return value","title":""},{"docid":"4fa1276511ea0f743d234757d2dd406b","score":"0.6615998","text":"def dumps(value):","title":""},{"docid":"96d75212292ebd0e82412067b22703e3","score":"0.661543","text":"def db_value(self, value):\n if value is not None:\n bts = to_bytes(json.dumps(value))\n value = buffer(zlib.compress(bts))\n\n return value","title":""},{"docid":"85e5afd2e9bfa6e8431527ae18f89ed6","score":"0.6608288","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n\n if isinstance(obj, timedelta):\n return str(obj)\n\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"d613b49b9a72ef777f5ad4b15ab0be8c","score":"0.6594507","text":"def _coerce_to_supported_type(cls, value):\n if value is None:\n return \"\" # empty string rather than none\n elif isinstance(value, dict):\n if '*' in value:\n return value['*']\n elif '__default__' in value:\n return value['__default__']\n else:\n return json.dumps(value, separators=(',', ':')) # return serialized JSON if no default\n elif isinstance(value, bool):\n return value\n elif isinstance(value, float) or isinstance(value, int):\n return Decimal(value)\n else:\n return value","title":""},{"docid":"75fc9a5991dad09a737facf736d4521c","score":"0.6583968","text":"def _load_json_value(self, value):\n return json.loads(value, use_decimal=True)","title":""},{"docid":"d08bd54f0ca0cbdc64908ed7e6d757df","score":"0.65745795","text":"def _deserialize_object(value):\n return value","title":""},{"docid":"d08bd54f0ca0cbdc64908ed7e6d757df","score":"0.65745795","text":"def _deserialize_object(value):\n return value","title":""},{"docid":"d08bd54f0ca0cbdc64908ed7e6d757df","score":"0.65745795","text":"def _deserialize_object(value):\n return value","title":""},{"docid":"a03c133288a7396866c5afafda22885e","score":"0.65634674","text":"def json_serial(obj):\r\n if isinstance(obj, (datetime, date)):\r\n return obj.isoformat()\r\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"4e92ee8ecee2098159d78b4e04a26086","score":"0.65615964","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"2f93477a7975f94cc91a4dc78a9012c7","score":"0.65503097","text":"def _json_serializer(obj):\r\n if isinstance(obj, (datetime.datetime, datetime.date)):\r\n return obj.isoformat()\r\n elif isinstance(obj, decimal.Decimal):\r\n return float(obj)\r\n else:\r\n raise TypeError(f\"Type {type(obj)} is not JSON serializable\")","title":""},{"docid":"b9ada004c0cd3b28103f9cf15d3e52c3","score":"0.65496385","text":"def json_decode(value):\n return escape.json_decode(value)","title":""},{"docid":"81f4362b5d9aba479e7185332c619d1e","score":"0.6536601","text":"def to_json(self, value):\r\n if isinstance(value, datetime.datetime):\r\n return value.strftime(self.DATETIME_FORMAT)\r\n if value is None:\r\n return None\r\n raise TypeError(\"Value stored must be a datetime object, not {}\".format(type(value)))","title":""},{"docid":"6cedfaf34aee76814e1d4ec2a0e7c0f0","score":"0.6527956","text":"def __deserialize_object(self, value):\n return value","title":""},{"docid":"4371a51c6157f791e3770a55d9f91ab1","score":"0.6513136","text":"def reprjs(value):\n\n # None\n if value is None:\n return 'null'\n\n # Strings\n if isinstance(value, basestring):\n value = value.replace('\"', '\\\\\"')\n value = '\"' + value + '\"'\n return value\n\n # Boolean\n if type(value) is bool:\n if value:\n return 'true'\n else:\n return 'false'\n\n # Integers/Longs\n if type(value) is int or type(value) is long:\n return value\n\n # Floats/Doubles\n if type(value) is float:\n return value\n\n raise ValueError('Could not convert value %r (type %r) to javascript.' %\n (value, type(value)))","title":""},{"docid":"eb3717a999d19ab4e126c8d322b509d6","score":"0.64851147","text":"def json_str(item: Any) -> str:\n return json.dumps(item, default=to_serializable)","title":""},{"docid":"7a5c6fb6da3671252eff95985998aa61","score":"0.6478753","text":"def json_serial(obj):\n\n\tif isinstance(obj, (datetime.datetime, datetime.date)):\n\t\treturn obj.isoformat()\n\telif isinstance(obj, set):\n\t\treturn list(obj)\n\n\traise TypeError(\"Type %s is not JSON serializable\".format(type(obj)))","title":""},{"docid":"b8660de59c665b44748ed398a0804a0c","score":"0.64750713","text":"def get_prep_value(self, value):\n if value == \"\" or value is None:\n return None\n\n value = json.dumps(value, default=json_encode_object,\n ensure_ascii=False, separators=(',', ':'))\n\n return super(JSONField, self).get_prep_value(value)","title":""},{"docid":"7d3071d3c9de1f134b5460cc1f98e56b","score":"0.64685345","text":"def convert_to_serializable(value):\n if type(value) is torch.device:\n value = str(value)\n elif type(value) is torch.Tensor:\n value = value.numpy().tolist()\n\n return value","title":""},{"docid":"3b6c0df6ba4e75e437103fa657fc85bd","score":"0.64508605","text":"def json_serial(obj):\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"105bbd4932507019d829d8d5fbfb8fbc","score":"0.6446971","text":"def convert_data_dict_to_str(cls, v):\n return json.dumps(v)","title":""},{"docid":"acc0704df216da7d3ff7e0ea25d2413b","score":"0.6436912","text":"def serialize(value):\n if isinstance(value, datetime):\n return value.isoformat()\n elif isinstance(value, list):\n return [serialize(item) for item in value]\n elif isnamedtuple(value):\n return serialize_dict(value._asdict())\n elif isinstance(value, dict):\n return serialize_dict(value)\n else:\n return value","title":""},{"docid":"d2528821674a5f84f43d879c04b02d1d","score":"0.64317304","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date, time)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","title":""},{"docid":"b1fcc4866af83600fbd0ff17f9cbb767","score":"0.6428057","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","title":""},{"docid":"b1fcc4866af83600fbd0ff17f9cbb767","score":"0.6428057","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","title":""},{"docid":"4f9874081f1d1c9d9154afb41eb4c6fd","score":"0.641689","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"4f9874081f1d1c9d9154afb41eb4c6fd","score":"0.641689","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"4f9874081f1d1c9d9154afb41eb4c6fd","score":"0.641689","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"4f9874081f1d1c9d9154afb41eb4c6fd","score":"0.641689","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"4f9874081f1d1c9d9154afb41eb4c6fd","score":"0.641689","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"b8e597e5d7b89bef7a82ba1e98861e80","score":"0.6407711","text":"def json_serial(obj):\n\n if isinstance(obj, datetime.date):\n return obj.isoformat()\n\n raise TypeError(\"Type {} not serializable\".format(type(obj)))","title":""},{"docid":"93fc99dfbad4862ade86bc4119aa706b","score":"0.6404137","text":"def json_serial(obj):\n\n if isinstance(obj, (datetime, date,date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","title":""},{"docid":"3b6ff2b7c28061ed32a1dc7bb6eb49ff","score":"0.6356085","text":"def __deserialize(value):\n if value is None:\n raise ValueError(\"Trying to decode an empty object\")\n value = value.decode('utf-8')\n return json.loads(value)","title":""},{"docid":"913a9f5815cbc189a28a97c70137c575","score":"0.63485944","text":"def default(self, o):\n result = self.encoder.encode(o, *self.encode_args, **self.encode_kwargs)\n\n if result is None:\n raise TypeError(\"%r is not JSON serializable\" % (o,))\n\n return result","title":""},{"docid":"3cd8d75b140a6f0312c783d8389ac740","score":"0.63474566","text":"def _serialize_field(self, field, value):\n return field._serialize(value)","title":""},{"docid":"96cca9eb6ae0cfb60f512d2985e98815","score":"0.63406575","text":"def json_serial(obj):\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError(\"Type not serializable\")","title":""},{"docid":"406cd032d38f4f827cf812ab084c2f67","score":"0.6336634","text":"def json_serial(obj):\r\n\r\n if isinstance(obj, datetime):\r\n serial = obj.isoformat()\r\n return serial\r\n raise TypeError (\"Type not serializable\")","title":""},{"docid":"5c8d40150cb53fa233afac65c7fcb6c6","score":"0.63324237","text":"def json_serializer(input_key=None, input_value=None, **kwargs):\n ############################ Custom Code Goes Below This Line #################################\n import json\n import phantom.rules as phantom\n \n outputs = {}\n outputs['json'] = json.dumps({input_key: input_value})\n \n # Return a JSON-serializable object\n assert json.dumps(outputs) # Will raise an exception if the :outputs: object is not JSON-serializable\n return outputs","title":""},{"docid":"db00883333584dea103535e594c67063","score":"0.6330245","text":"def json_serial(obj):\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError (\"Type not serializable\")","title":""},{"docid":"6896062fee5254e177ca72998925c596","score":"0.6325975","text":"def default(self, value):\n iso = _iso_datetime(value)\n if iso:\n return iso\n if isinstance(value, set):\n return list(value)\n if hasattr(value, 'to_json'):\n return value.to_json()\n return super(JsonEncoder, self).default(value)","title":""}],"string":"[\n {\n \"docid\": \"ba48399899584d9edb4caa6c73a7016b\",\n \"score\": \"0.83150655\",\n \"text\": \"def to_json(self, value):\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91b489dfea37aa0a9c81190c459096d5\",\n \"score\": \"0.8236348\",\n \"text\": \"def _serialize_value(value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c97430dc04e12da5097729a35626306\",\n \"score\": \"0.82256335\",\n \"text\": \"def toJson(value):\\r\\n\\treturn json.dumps(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea43ebca3518ed50ec8672e7add7076\",\n \"score\": \"0.8196552\",\n \"text\": \"def to_json(self, value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b811cb92c1a00c956b93eb055ed5f45\",\n \"score\": \"0.81222177\",\n \"text\": \"def _dump_json_value(self, value):\\n return json.dumps(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6727e539d1099eb12042ae09b085bab7\",\n \"score\": \"0.8119554\",\n \"text\": \"def to_json(value):\\n\\n return json.dumps(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"770fe1a90661e730207a9f27ceb81213\",\n \"score\": \"0.8053105\",\n \"text\": \"def serialize_json(self, python_value):\\n return json.dumps(python_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65996a559da5bcb659a459c2adf960f1\",\n \"score\": \"0.8024837\",\n \"text\": \"def _serialize(self, value):\\n return simplejson.dumps(value, ensure_ascii=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"322a5a69a07f7a29253b2fe0b81e7c08\",\n \"score\": \"0.7838724\",\n \"text\": \"def to_json(self, value: typing.Any) -> typing.Any:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8761b61fcce6eacd55260b5d95b94371\",\n \"score\": \"0.77923846\",\n \"text\": \"def serialize(value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ec16e0604c75525cc2801cb00d70691\",\n \"score\": \"0.77811104\",\n \"text\": \"def to_python(self, value):\\n if value in (\\\"\\\", None):\\n return None\\n\\n try:\\n if isinstance(value, (str, unicode)):\\n return json.loads(value, object_hook=decode_object if self.pickle else None)\\n except ValueError:\\n pass\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985952912ec731b0e3374b0be9e6e42f\",\n \"score\": \"0.7697401\",\n \"text\": \"def serialize_value(value):\\n return bytearray(json.dumps(value).encode(\\\"utf-8\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dfe294ff0c55791104c6742c0bda734\",\n \"score\": \"0.7642251\",\n \"text\": \"def to_python(self, value):\\n if value == \\\"\\\":\\n return None\\n\\n try:\\n if isinstance(value, six.string_types):\\n return json.loads(value, object_hook=json_decode_object)\\n except ValueError:\\n pass\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e72a6754cb5a0dd03aa040a124c0a3d\",\n \"score\": \"0.7641237\",\n \"text\": \"def python_value(self, value):\\r\\n\\t\\treturn value if value is None else json.loads(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9c70cdcb1e4f7cd8c16b5dd7b95f65\",\n \"score\": \"0.7575193\",\n \"text\": \"def serialize(value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f66ef69da73947d089a395bec99089ce\",\n \"score\": \"0.75572896\",\n \"text\": \"def to_python(self, value):\\n #django.core.serializers.json.DjangoJSONEncoder\\n\\n if value in self.empty_values:\\n return None\\n\\n try:\\n return json.loads(value)\\n except Exception as e:\\n raise ValidationError(repr(e), code='invalid')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fea59b9ee4ac2aa9b6470c2391507aa\",\n \"score\": \"0.7551401\",\n \"text\": \"def jsonify(self, value):\\n if value is not None:\\n return str(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c8a26a57f4729c521dc3c98d85e20da\",\n \"score\": \"0.75203425\",\n \"text\": \"def json_encode(self, value):\\r\\n return simplejson.dumps(value, separators=(\\\",\\\", \\\":\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8de42d65b59e673c2c7f55f2fd7fa304\",\n \"score\": \"0.7519901\",\n \"text\": \"def json_value(value):\\n try:\\n out = json.loads(value)\\n except json.decoder.JSONDecodeError:\\n out = value\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e2d5cd1e6c33d1c6105894ba26136b9\",\n \"score\": \"0.7509357\",\n \"text\": \"def to_python(self, value):\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f81c42a57b715a5a7847c9118c33ce\",\n \"score\": \"0.74956405\",\n \"text\": \"def to_python(self, value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f81c42a57b715a5a7847c9118c33ce\",\n \"score\": \"0.74956405\",\n \"text\": \"def to_python(self, value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b112ee1d403527227c2724710854c2\",\n \"score\": \"0.7459379\",\n \"text\": \"def to_python(self, value):\\n if isinstance(value, list) or value is None:\\n return value\\n return json.loads(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c3c4333a1e4fc9e606f06b65b20718\",\n \"score\": \"0.72748893\",\n \"text\": \"def to_json(self, value, fields=None, wrap=True):\\n #obj = self.get_json_obj(value, fields, wrap)\\n return json.dumps(value, cls=self.json_encoder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94334937389d926be623b76b97b75257\",\n \"score\": \"0.72371733\",\n \"text\": \"def db_value(self, value):\\r\\n\\t\\treturn value if value is None else json.dumps(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22757b234152531a9891e0de9bbfeba1\",\n \"score\": \"0.72301066\",\n \"text\": \"def get_prep_value(self, value):\\n if value == \\\"\\\" or value is None:\\n return None\\n return json.dumps(value, default=encode_object if self.pickle else None,\\n ensure_ascii=False, separators=(',',':'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bae3c61de9dec5fe5d460d5de09add4\",\n \"score\": \"0.7226884\",\n \"text\": \"def json_encode(self, value):\\r\\n return simplejson.dumps(value, separators=(\\\",\\\", \\\":\\\"), sort_keys=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e6fa4765b4b36c5eba1cdef18749d8\",\n \"score\": \"0.7221748\",\n \"text\": \"def to_python(self, value):\\n if isinstance(value, basestring):\\n return utils.to_python(value)\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d3741fef4626eb7aebec822cea43cc1\",\n \"score\": \"0.7218533\",\n \"text\": \"def make_value_from_datastore(self, value):\\n if value is not None:\\n return json_decode(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"610e42c6ecb1774a40f6b530eb6b01f3\",\n \"score\": \"0.7205728\",\n \"text\": \"def serialize(value, user):\\n return value.serialize(user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efeaf49266c2fc8695d55879906fe12c\",\n \"score\": \"0.7191969\",\n \"text\": \"def as_json(value: Union[List, Dict]) -> str:\\n return json.dumps(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ec67125a7f14e88b9c17fdf3570998\",\n \"score\": \"0.71517915\",\n \"text\": \"def from_json(self, value):\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923a17aa1c5ce688f83cd40dfc278f07\",\n \"score\": \"0.7093603\",\n \"text\": \"def to_value(self, value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f99367e27bf903a8e649f45db6754164\",\n \"score\": \"0.70728505\",\n \"text\": \"def use_to_json_obj(value):\\n if hasattr(value, 'to_json_obj'):\\n if hasattr(value, 'from_json_obj'):\\n return {\\n 'from_json_obj': '%s/%s' % (value.__module__, value.__class__.__name__),\\n 'obj': value.to_json_obj()\\n }\\n else:\\n return value.to_json_obj()\\n else:\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7773b17aed70fa5bc80c1557cf83de8c\",\n \"score\": \"0.705589\",\n \"text\": \"def to_python(self, value):\\n if value == \\\"\\\":\\n return None\\n\\n try:\\n if isinstance(value, six.string_types):\\n return deserialize_from_string(value)\\n except ValueError:\\n pass\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb83822225ed829b55cef35ef9fc84f0\",\n \"score\": \"0.7029836\",\n \"text\": \"def json(value, pretty=True):\\n if _Duration is None:\\n _late_import()\\n return _json_encoder(value, pretty=pretty)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f6e89ce89ee36957f65b695a94f96d\",\n \"score\": \"0.7025157\",\n \"text\": \"def serialize(self, python_value):\\n return str(python_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1c615995238e05446df8421c2865641\",\n \"score\": \"0.6986837\",\n \"text\": \"def from_json(self, value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d411f501f08a3fb6fb4406816e4f9f\",\n \"score\": \"0.6984771\",\n \"text\": \"def to_json(self, value):\\r\\n return \\\"X\\\" * value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba8c98fb2d31be6c66e7f34f577505e\",\n \"score\": \"0.69746\",\n \"text\": \"def handle_json(value: Union[dict, list]) -> str:\\n return json.dumps(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f50748b7427373c09628f72a46257c37\",\n \"score\": \"0.694783\",\n \"text\": \"def native_value(value):\\n if isinstance(value, str):\\n try:\\n value = json.loads(value)\\n except ValueError:\\n return value\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"578ff118bcda00ec4eff1e49560ad7ad\",\n \"score\": \"0.6857314\",\n \"text\": \"def to_python(self, value):\\n try:\\n json_field = json.loads(value)\\n except Exception as e:\\n raise forms.ValidationError(\\n \\\"Invalid json in exts field({}): {}\\\".format(value, e),\\n code=\\\"invalid\\\",\\n )\\n else:\\n return json_field\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9e14f9b1ebdba26c1e5badd809ae70c\",\n \"score\": \"0.6845089\",\n \"text\": \"def json_encode(value):\\n return json.dumps(value, cls=ComplexEncoder).replace(\\\"</\\\", \\\"<\\\\\\\\/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598005ad2740daf2e2c793a64baee570\",\n \"score\": \"0.6831455\",\n \"text\": \"def python_value(self, value):\\n try:\\n value = zlib.decompress(value)\\n except:\\n pass\\n\\n return value if value is None else json.loads(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d6e73f7683dc01ebba9649a08784111\",\n \"score\": \"0.682787\",\n \"text\": \"def ConvertData(self, value):\\n try:\\n data = json.loads(value)\\n except ValueError:\\n raise db.BadValueError(\\\"Not Valid JSON\\\")\\n if not util.GetVariations(data) or not util.GetMetrics(data):\\n raise db.BadValueError(\\\"Invalid Data\\\")\\n return json.dumps(data, separators=(\\\",\\\", \\\":\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde4d1f9746bae1db814c5a2fd85efe0\",\n \"score\": \"0.682112\",\n \"text\": \"def value(self):\\n return json.loads(self._value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed31cc21a40291789e466bff4696ff9\",\n \"score\": \"0.6796097\",\n \"text\": \"def get_json_value(value):\\n try:\\n return json.loads(value)\\n except ValueError:\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8740b6685ecd7b9a42832d9aab7cdd\",\n \"score\": \"0.67891735\",\n \"text\": \"def _deflate(self, value):\\r\\n return json.dumps(value, cls=Encoder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8740b6685ecd7b9a42832d9aab7cdd\",\n \"score\": \"0.67891735\",\n \"text\": \"def _deflate(self, value):\\r\\n return json.dumps(value, cls=Encoder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2037700865da65be8430804b2cab486\",\n \"score\": \"0.67509264\",\n \"text\": \"def value(self):\\n return cson2json(self._val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ae43954a9487d82591251ae2209f03\",\n \"score\": \"0.67232835\",\n \"text\": \"def to_serializable(val):\\n\\n if isinstance(val, datetime):\\n return val.isoformat()\\n elif hasattr(val, '__dict__'):\\n return val.__dict__\\n\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a8256fdc58bed76d5025d0137b8a418\",\n \"score\": \"0.6718694\",\n \"text\": \"def as_dict(self, value):\\n if value is not None:\\n return value.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b1e853a1fb706d2aa3898df25a3afd\",\n \"score\": \"0.6706012\",\n \"text\": \"def serialize(self, value):\\r\\n if isinstance(value, float) and self.as_type is unicode:\\r\\n value = u'{0:.{1}f}'.format(value, self.float_places)\\r\\n value = self.as_type(value)\\r\\n elif self.as_type is unicode:\\r\\n if isinstance(value, bool):\\r\\n # Store bools as 1/0 instead of True/False.\\r\\n value = unicode(int(bool(value)))\\r\\n elif isinstance(value, str):\\r\\n value = value.decode('utf8', 'ignore')\\r\\n else:\\r\\n value = unicode(value)\\r\\n else:\\r\\n value = self.as_type(value)\\r\\n\\r\\n if self.suffix:\\r\\n value += self.suffix\\r\\n\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c685637b696803209ada88430893b03\",\n \"score\": \"0.66902214\",\n \"text\": \"def send_value(stream, value):\\n json.dump(encode(value), stream)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ab2cb460c62065ba8465f4fa58eebc\",\n \"score\": \"0.6689844\",\n \"text\": \"def _tojson(obj):\\n return json.loads(json.dumps(obj, cls=_CustomEncoder))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eefb92d1c52a194d9141b88ca9a587e0\",\n \"score\": \"0.668975\",\n \"text\": \"def json_encoder(obj):\\n if isinstance(obj, datetime):\\n return str(obj)\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e3fead7e331bcc9b9248530242ad63\",\n \"score\": \"0.6668841\",\n \"text\": \"def _inflate(self, value):\\r\\n if value is None:\\r\\n return {}\\r\\n if isinstance(value, unicode) or isinstance(value, str):\\r\\n return json.loads(value, object_hook=decode)\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e3fead7e331bcc9b9248530242ad63\",\n \"score\": \"0.6668841\",\n \"text\": \"def _inflate(self, value):\\r\\n if value is None:\\r\\n return {}\\r\\n if isinstance(value, unicode) or isinstance(value, str):\\r\\n return json.loads(value, object_hook=decode)\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995b7b9ac17992254a4a9fb3f38ac3a9\",\n \"score\": \"0.6662083\",\n \"text\": \"def to_python(self, value, backend=None):\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"769230880bacacc4df187f01b1b9e321\",\n \"score\": \"0.66403747\",\n \"text\": \"def force_js(value, typename=None, encoder=None):\\n if typename:\\n typename = typename.lower()\\n if typename in JS_CONVERT_TYPES:\\n value = JS_CONVERT_TYPES[typename](value)\\n return json.dumps(value, cls=(encoder or DjangoJSONEncoder))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4cd48dcf2afe9a69e720e3527e1d3ed\",\n \"score\": \"0.66193247\",\n \"text\": \"def to_python(self, value):\\n if value in (\\\"\\\", None):\\n return None\\n\\n try:\\n if isinstance(value, basestring):\\n return safe_decode(value)\\n except ValueError:\\n pass\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa1276511ea0f743d234757d2dd406b\",\n \"score\": \"0.6615998\",\n \"text\": \"def dumps(value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d75212292ebd0e82412067b22703e3\",\n \"score\": \"0.661543\",\n \"text\": \"def db_value(self, value):\\n if value is not None:\\n bts = to_bytes(json.dumps(value))\\n value = buffer(zlib.compress(bts))\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85e5afd2e9bfa6e8431527ae18f89ed6\",\n \"score\": \"0.6608288\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n\\n if isinstance(obj, timedelta):\\n return str(obj)\\n\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d613b49b9a72ef777f5ad4b15ab0be8c\",\n \"score\": \"0.6594507\",\n \"text\": \"def _coerce_to_supported_type(cls, value):\\n if value is None:\\n return \\\"\\\" # empty string rather than none\\n elif isinstance(value, dict):\\n if '*' in value:\\n return value['*']\\n elif '__default__' in value:\\n return value['__default__']\\n else:\\n return json.dumps(value, separators=(',', ':')) # return serialized JSON if no default\\n elif isinstance(value, bool):\\n return value\\n elif isinstance(value, float) or isinstance(value, int):\\n return Decimal(value)\\n else:\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75fc9a5991dad09a737facf736d4521c\",\n \"score\": \"0.6583968\",\n \"text\": \"def _load_json_value(self, value):\\n return json.loads(value, use_decimal=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08bd54f0ca0cbdc64908ed7e6d757df\",\n \"score\": \"0.65745795\",\n \"text\": \"def _deserialize_object(value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08bd54f0ca0cbdc64908ed7e6d757df\",\n \"score\": \"0.65745795\",\n \"text\": \"def _deserialize_object(value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08bd54f0ca0cbdc64908ed7e6d757df\",\n \"score\": \"0.65745795\",\n \"text\": \"def _deserialize_object(value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a03c133288a7396866c5afafda22885e\",\n \"score\": \"0.65634674\",\n \"text\": \"def json_serial(obj):\\r\\n if isinstance(obj, (datetime, date)):\\r\\n return obj.isoformat()\\r\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e92ee8ecee2098159d78b4e04a26086\",\n \"score\": \"0.65615964\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f93477a7975f94cc91a4dc78a9012c7\",\n \"score\": \"0.65503097\",\n \"text\": \"def _json_serializer(obj):\\r\\n if isinstance(obj, (datetime.datetime, datetime.date)):\\r\\n return obj.isoformat()\\r\\n elif isinstance(obj, decimal.Decimal):\\r\\n return float(obj)\\r\\n else:\\r\\n raise TypeError(f\\\"Type {type(obj)} is not JSON serializable\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ada004c0cd3b28103f9cf15d3e52c3\",\n \"score\": \"0.65496385\",\n \"text\": \"def json_decode(value):\\n return escape.json_decode(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f4362b5d9aba479e7185332c619d1e\",\n \"score\": \"0.6536601\",\n \"text\": \"def to_json(self, value):\\r\\n if isinstance(value, datetime.datetime):\\r\\n return value.strftime(self.DATETIME_FORMAT)\\r\\n if value is None:\\r\\n return None\\r\\n raise TypeError(\\\"Value stored must be a datetime object, not {}\\\".format(type(value)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cedfaf34aee76814e1d4ec2a0e7c0f0\",\n \"score\": \"0.6527956\",\n \"text\": \"def __deserialize_object(self, value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4371a51c6157f791e3770a55d9f91ab1\",\n \"score\": \"0.6513136\",\n \"text\": \"def reprjs(value):\\n\\n # None\\n if value is None:\\n return 'null'\\n\\n # Strings\\n if isinstance(value, basestring):\\n value = value.replace('\\\"', '\\\\\\\\\\\"')\\n value = '\\\"' + value + '\\\"'\\n return value\\n\\n # Boolean\\n if type(value) is bool:\\n if value:\\n return 'true'\\n else:\\n return 'false'\\n\\n # Integers/Longs\\n if type(value) is int or type(value) is long:\\n return value\\n\\n # Floats/Doubles\\n if type(value) is float:\\n return value\\n\\n raise ValueError('Could not convert value %r (type %r) to javascript.' %\\n (value, type(value)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb3717a999d19ab4e126c8d322b509d6\",\n \"score\": \"0.64851147\",\n \"text\": \"def json_str(item: Any) -> str:\\n return json.dumps(item, default=to_serializable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a5c6fb6da3671252eff95985998aa61\",\n \"score\": \"0.6478753\",\n \"text\": \"def json_serial(obj):\\n\\n\\tif isinstance(obj, (datetime.datetime, datetime.date)):\\n\\t\\treturn obj.isoformat()\\n\\telif isinstance(obj, set):\\n\\t\\treturn list(obj)\\n\\n\\traise TypeError(\\\"Type %s is not JSON serializable\\\".format(type(obj)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8660de59c665b44748ed398a0804a0c\",\n \"score\": \"0.64750713\",\n \"text\": \"def get_prep_value(self, value):\\n if value == \\\"\\\" or value is None:\\n return None\\n\\n value = json.dumps(value, default=json_encode_object,\\n ensure_ascii=False, separators=(',', ':'))\\n\\n return super(JSONField, self).get_prep_value(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d3071d3c9de1f134b5460cc1f98e56b\",\n \"score\": \"0.64685345\",\n \"text\": \"def convert_to_serializable(value):\\n if type(value) is torch.device:\\n value = str(value)\\n elif type(value) is torch.Tensor:\\n value = value.numpy().tolist()\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b6c0df6ba4e75e437103fa657fc85bd\",\n \"score\": \"0.64508605\",\n \"text\": \"def json_serial(obj):\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105bbd4932507019d829d8d5fbfb8fbc\",\n \"score\": \"0.6446971\",\n \"text\": \"def convert_data_dict_to_str(cls, v):\\n return json.dumps(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc0704df216da7d3ff7e0ea25d2413b\",\n \"score\": \"0.6436912\",\n \"text\": \"def serialize(value):\\n if isinstance(value, datetime):\\n return value.isoformat()\\n elif isinstance(value, list):\\n return [serialize(item) for item in value]\\n elif isnamedtuple(value):\\n return serialize_dict(value._asdict())\\n elif isinstance(value, dict):\\n return serialize_dict(value)\\n else:\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2528821674a5f84f43d879c04b02d1d\",\n \"score\": \"0.64317304\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date, time)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1fcc4866af83600fbd0ff17f9cbb767\",\n \"score\": \"0.6428057\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1fcc4866af83600fbd0ff17f9cbb767\",\n \"score\": \"0.6428057\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9874081f1d1c9d9154afb41eb4c6fd\",\n \"score\": \"0.641689\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9874081f1d1c9d9154afb41eb4c6fd\",\n \"score\": \"0.641689\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9874081f1d1c9d9154afb41eb4c6fd\",\n \"score\": \"0.641689\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9874081f1d1c9d9154afb41eb4c6fd\",\n \"score\": \"0.641689\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9874081f1d1c9d9154afb41eb4c6fd\",\n \"score\": \"0.641689\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e597e5d7b89bef7a82ba1e98861e80\",\n \"score\": \"0.6407711\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, datetime.date):\\n return obj.isoformat()\\n\\n raise TypeError(\\\"Type {} not serializable\\\".format(type(obj)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93fc99dfbad4862ade86bc4119aa706b\",\n \"score\": \"0.6404137\",\n \"text\": \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date,date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b6ff2b7c28061ed32a1dc7bb6eb49ff\",\n \"score\": \"0.6356085\",\n \"text\": \"def __deserialize(value):\\n if value is None:\\n raise ValueError(\\\"Trying to decode an empty object\\\")\\n value = value.decode('utf-8')\\n return json.loads(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913a9f5815cbc189a28a97c70137c575\",\n \"score\": \"0.63485944\",\n \"text\": \"def default(self, o):\\n result = self.encoder.encode(o, *self.encode_args, **self.encode_kwargs)\\n\\n if result is None:\\n raise TypeError(\\\"%r is not JSON serializable\\\" % (o,))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd8d75b140a6f0312c783d8389ac740\",\n \"score\": \"0.63474566\",\n \"text\": \"def _serialize_field(self, field, value):\\n return field._serialize(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96cca9eb6ae0cfb60f512d2985e98815\",\n \"score\": \"0.63406575\",\n \"text\": \"def json_serial(obj):\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError(\\\"Type not serializable\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"406cd032d38f4f827cf812ab084c2f67\",\n \"score\": \"0.6336634\",\n \"text\": \"def json_serial(obj):\\r\\n\\r\\n if isinstance(obj, datetime):\\r\\n serial = obj.isoformat()\\r\\n return serial\\r\\n raise TypeError (\\\"Type not serializable\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8d40150cb53fa233afac65c7fcb6c6\",\n \"score\": \"0.63324237\",\n \"text\": \"def json_serializer(input_key=None, input_value=None, **kwargs):\\n ############################ Custom Code Goes Below This Line #################################\\n import json\\n import phantom.rules as phantom\\n \\n outputs = {}\\n outputs['json'] = json.dumps({input_key: input_value})\\n \\n # Return a JSON-serializable object\\n assert json.dumps(outputs) # Will raise an exception if the :outputs: object is not JSON-serializable\\n return outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db00883333584dea103535e594c67063\",\n \"score\": \"0.6330245\",\n \"text\": \"def json_serial(obj):\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError (\\\"Type not serializable\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6896062fee5254e177ca72998925c596\",\n \"score\": \"0.6325975\",\n \"text\": \"def default(self, value):\\n iso = _iso_datetime(value)\\n if iso:\\n return iso\\n if isinstance(value, set):\\n return list(value)\\n if hasattr(value, 'to_json'):\\n return value.to_json()\\n return super(JsonEncoder, self).default(value)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992997,"cells":{"query_id":{"kind":"string","value":"6f623d32fc14b8124f9a3193bc79b872"},"query":{"kind":"string","value":"Test call subroutine with parameter that is defined multiple times."},"positive_passages":{"kind":"list like","value":[{"docid":"daf56366e11edbc3dd865b676a3b0b2a","score":"0.62572026","text":"def test_call_with_common_parameter(self):\n amp = circuit.Parameter(\"amp\")\n\n with pulse.build() as subroutine:\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\n pulse.play(pulse.Gaussian(320, amp, 80), pulse.DriveChannel(0))\n\n with pulse.build() as main_prog:\n pulse.call(subroutine, amp=0.1)\n\n assigned_sched = target_qobj_transform(main_prog)\n\n play_0 = assigned_sched.instructions[0][1]\n play_1 = assigned_sched.instructions[1][1]\n\n self.assertEqual(play_0.pulse.amp, 0.1)\n self.assertEqual(play_1.pulse.amp, 0.1)","title":""}],"string":"[\n {\n \"docid\": \"daf56366e11edbc3dd865b676a3b0b2a\",\n \"score\": \"0.62572026\",\n \"text\": \"def test_call_with_common_parameter(self):\\n amp = circuit.Parameter(\\\"amp\\\")\\n\\n with pulse.build() as subroutine:\\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\\n pulse.play(pulse.Gaussian(320, amp, 80), pulse.DriveChannel(0))\\n\\n with pulse.build() as main_prog:\\n pulse.call(subroutine, amp=0.1)\\n\\n assigned_sched = target_qobj_transform(main_prog)\\n\\n play_0 = assigned_sched.instructions[0][1]\\n play_1 = assigned_sched.instructions[1][1]\\n\\n self.assertEqual(play_0.pulse.amp, 0.1)\\n self.assertEqual(play_1.pulse.amp, 0.1)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1ea99a172ec554e436fc623247ceef88","score":"0.67987573","text":"def test_runs_once_runs_only_once():\n func = fake_function(expect_call=True).times_called(1)\n task = decorators.runs_once(func)\n for i in range(2):\n task()","title":""},{"docid":"f9c9623f2b90025f13b27375ab224ec3","score":"0.6798225","text":"def __call__(self, dummy1, dummy2=False):\n self.call += 1\n return self.repeat","title":""},{"docid":"17a63b74832f1561f1bbc74743447198","score":"0.65965104","text":"def test_call():","title":""},{"docid":"abadf7f88ca42e59779a1de5d4133c20","score":"0.6457599","text":"def call_again():","title":""},{"docid":"cc404d654bd8ebc737c9892626f16774","score":"0.64390415","text":"def test_call(self):\n self.report('Testing the __call__ function with single input of the modifier.')\n num_modifications = 20\n num_steps_probs = [0.5, 0.25, 0.125, 0.075, 0.05]\n save_dir = os.path.join(self.save_dir, 'modifier_call_single')\n if os.path.exists(save_dir):\n rmtree(save_dir)\n test_nns = self.cnns + self.mlps\n for idx, old_nn in enumerate(test_nns):\n save_prefix = str(idx)\n modifier, constraint_checker = self._get_modifier_and_cc(old_nn)\n get_modifications = lambda arg_nn: modifier(arg_nn, num_modifications,\n num_steps_probs)\n run_orig_vs_modifications(save_dir, save_prefix, old_nn,\n get_modifications, constraint_checker, self.report)","title":""},{"docid":"5e47629b25452448e297c3985af92f8a","score":"0.64381725","text":"def test_called_with_and_partial_args(self):\n obj = MathClass()\n self.agency.spy_on(obj.do_math_mixed)\n\n obj.do_math_mixed(1, 2)\n obj.do_math_mixed(3, 4)\n\n call = obj.do_math_mixed.calls[0]\n self.assertTrue(call.called_with(1))\n self.assertFalse(call.called_with(1, 2, 3))\n self.assertFalse(call.called_with(3))","title":""},{"docid":"f8c3f1f4d56d3c61f14d4825aab009b8","score":"0.64164364","text":"def test_partial_func_locked_param_check():\n with pytest.raises(TypeError, match=r\".*got multiple values for argument*\"):\n state = 'DL'\n session7.func_num_plt(state,1000)","title":""},{"docid":"2ded51c76742a901ec63e1251e289a79","score":"0.63033974","text":"def test_call_with_parameter_name_collision(self):\n amp1 = circuit.Parameter(\"amp\")\n amp2 = circuit.Parameter(\"amp\")\n sigma = circuit.Parameter(\"sigma\")\n\n with pulse.build() as subroutine:\n pulse.play(pulse.Gaussian(160, amp1, sigma), pulse.DriveChannel(0))\n pulse.play(pulse.Gaussian(160, amp2, sigma), pulse.DriveChannel(0))\n\n with pulse.build() as main_prog:\n pulse.call(subroutine, value_dict={amp1: 0.1, amp2: 0.2}, sigma=40)\n\n assigned_sched = target_qobj_transform(main_prog)\n\n play_0 = assigned_sched.instructions[0][1]\n play_1 = assigned_sched.instructions[1][1]\n\n self.assertEqual(play_0.pulse.amp, 0.1)\n self.assertEqual(play_0.pulse.sigma, 40)\n self.assertEqual(play_1.pulse.amp, 0.2)\n self.assertEqual(play_1.pulse.sigma, 40)","title":""},{"docid":"8f0ce22d6e381aba5a4461de2e5cdb31","score":"0.6295111","text":"def test_runs_once_returns_same_value_each_run():\n return_value = \"foo\"\n task = decorators.runs_once(fake_function().returns(return_value))\n for i in range(2):\n eq_(task(), return_value)","title":""},{"docid":"a3866e88b5d56dcef83a5f2c6b2ced60","score":"0.62262106","text":"def testCallArg( self ):\n\t\tdef function( text, start, end, *arguments ):\n\t\t\tassert arguments == (1,2,3), \"\"\"Passed arguments were not what we passed in\"\"\"\n\t\t\treturn end\n\t\n\t\tself.doBasicTest(\n\t\t\t(\n\t\t\t\t( \"ab\", CallArg, (function,1,2,3), 0 ),\n\t\t\t),\n\t\t\t\"cdffgg\",\n\t\t\t( 1,[\n\t\t\t\t(\"ab\",0,6,None),\n\t\t\t],6),\n\t\t)","title":""},{"docid":"500a8c3d0e137b8bd7298ee4d8b504d7","score":"0.6221946","text":"def test_call_with(self, activity, fn):\n task_input = {\"a\": 42, \"b\": \"bla\", \"c\": {\"foo\": [1, 2], \"bar\": None}}\n res = activity.call_with(task_input)\n assert res is fn.return_value\n fn.assert_called_once_with(task_input)","title":""},{"docid":"d853f068d02364aa6bbc48af1e8aefd3","score":"0.6207401","text":"def test_partial_sub_addition_keyarg():\n startcounter = 20\n counter = startcounter\n\n class mocksocket:\n def recv_pyobj(self, flags=\"notused\"):\n nonlocal counter\n counter = counter + 1\n return counter\n\n a = mocksocket()\n\n def atest(myparam, myparamtwo=0):\n return myparam * myparamtwo\n\n myfunc = partial_sub(partial_sub(atest, a), a, keyargname=\"myparamtwo\")\n # the myfunc has to be called multiple times to simulate multiple receive operations\n # for the encapsulated sockets\n testval = myfunc()\n assert testval == (startcounter + 1) * (startcounter + 2)\n testval = myfunc()\n assert testval == (startcounter + 3) * (startcounter + 4)\n testval = myfunc()\n assert testval == (startcounter + 5) * (startcounter + 6)","title":""},{"docid":"46fa342c3578fd510bbaacd30fda6955","score":"0.6186846","text":"def testMultipleTimesUsingIsAParameter(self):\n mock_obj = self.mox.CreateMockAnything()\n mock_obj.Open()\n mock_obj.Method(mox.IsA(str)).MultipleTimes(\"IsA\").AndReturn(9)\n mock_obj.Close()\n self.mox.ReplayAll()\n\n mock_obj.Open()\n actual_one = mock_obj.Method(\"1\")\n second_one = mock_obj.Method(\"2\") # This tests MultipleTimes.\n mock_obj.Close()\n\n self.mox.VerifyAll()\n\n self.assertEqual(9, actual_one)\n # Repeated calls should return same number.\n self.assertEqual(9, second_one)","title":""},{"docid":"84cd3924d7d966ffeb45388dd01eb1e6","score":"0.61749107","text":"def test_runTwice(self):\n self.nbr.run()","title":""},{"docid":"381e20a90f013c06d79e8e05d39e963d","score":"0.6156437","text":"def testCall2( self ):\n\t\tclass X:\n\t\t\tdef __call__( self, text, start, end ):\n\t\t\t\treturn end\n\t\n\t\tself.doBasicTest(\n\t\t\t(\n\t\t\t\t( \"ab\", Call, X(), 0 ),\n\t\t\t),\n\t\t\t\"cdffgg\",\n\t\t\t( 1,[\n\t\t\t\t(\"ab\",0,6,None),\n\t\t\t],6),\n\t\t)","title":""},{"docid":"22e99abcffdc81d946b33690af9f747b","score":"0.6151879","text":"def called_times(self, method, count):\n self.assertEqual(method.call_count, count)","title":""},{"docid":"38d95b5fadb8f743edc69b7915a2b528","score":"0.61454177","text":"def test_incorrect_arg_num_call_function(self, rr):\n\n def dummy_func(r, q):\n Sgate(r) | q[0]\n BSgate() | (q[0], q[1])\n\n op = utils.operation(ns=1)\n op(dummy_func)(1, 2)\n\n with pytest.raises(ValueError, match=\"Mismatch in the number of arguments\"):\n op._call_function(rr)","title":""},{"docid":"bb7e524ea1cc5b8e0472696fb18cb5f9","score":"0.611829","text":"def test_called_with_and_partial_kwargs(self):\n obj = MathClass()\n self.agency.spy_on(obj.do_math_mixed)\n\n obj.do_math_mixed(a=1, b=2)\n obj.do_math_mixed(a=3, b=4)\n\n call = obj.do_math_mixed.calls[0]\n self.assertTrue(call.called_with(1))\n self.assertTrue(call.called_with(b=2))\n self.assertTrue(call.called_with(a=1))\n self.assertFalse(call.called_with(a=4))\n self.assertFalse(call.called_with(a=1, b=2, c=3))\n self.assertFalse(call.called_with(a=3, b=2))","title":""},{"docid":"ad13187e41ab098814c3f81fab9a8da1","score":"0.6096294","text":"def testCall( self ):\n\t\tdef function( text, start, end ):\n\t\t\treturn end\n\t\n\t\tself.doBasicTest(\n\t\t\t(\n\t\t\t\t( \"ab\", Call, function, 0 ),\n\t\t\t),\n\t\t\t\"cdffgg\",\n\t\t\t( 1,[\n\t\t\t\t(\"ab\",0,6,None),\n\t\t\t],6),\n\t\t)","title":""},{"docid":"89de23f4d14afc8eb47df1a1411c15a5","score":"0.60773927","text":"def test_call_with_third_arg(self):\n _rademacher_sampler([0, 1, 2], 4, \"ignored dummy\")","title":""},{"docid":"c16658ed5cc7278de3b7e64a461221fb","score":"0.6056042","text":"def test_repeat(self):\n \n @testing.repeat(5)\n def inc_counter(cnt):\n cnt[0] += 1\n \n counter = [0] # use mutable container to retain data\n inc_counter(counter)\n self.assertEqual(counter, [5])","title":""},{"docid":"c4c9d1fe60b5137128fcec72cd467694","score":"0.604468","text":"def test_with_extra_call(self):\n obj = MathClass()\n\n self.agency.spy_on(\n obj.do_math,\n op=SpyOpMatchInOrder([\n {\n 'kwargs': {\n 'a': 4,\n 'b': 7,\n },\n },\n ]))\n\n self.assertEqual(obj.do_math(a=4, b=7), 11)\n\n expected_message = re.escape(\n \"do_math was called 2 time(s), but only 1 call(s) were expected. \"\n \"Latest call: <SpyCall(args=(), kwargs={'a': 4, 'b': 9}, \"\n \"returned=None, raised=None)>\"\n )\n\n with self.assertRaisesRegex(UnexpectedCallError, expected_message):\n obj.do_math(a=4, b=9)","title":""},{"docid":"9827236245f7427d6646bd6fb785d786","score":"0.60427254","text":"def test_w_more_api_call_params(self):\n assert_that(\n calling(\n self.processor.process\n ).with_args(\n [2, 8], {}\n ),\n raises(ValueError)\n )","title":""},{"docid":"85b545f7be41d26cba999499e1d9b0d4","score":"0.6025817","text":"def test_assign_to_same_parameter_in_call_and_caller(self):\n with pulse.build(name=\"call_xp_xp\") as call_xp_xp:\n pulse.call(self.xp_)\n pulse.play(Gaussian(160, self.amp_xp, self.sigma), self.d0_)\n self.cals.add_schedule(call_xp_xp, num_qubits=1)\n\n my_amp = Parameter(\"amp\")\n with self.assertRaises(CalibrationError):\n self.cals.get_schedule(\n \"call_xp_xp\",\n (2,),\n assign_params={\n (\"amp\", (2,), \"xp\"): self.amp_xp,\n (\"amp\", (2,), \"call_xp_xp\"): my_amp,\n },\n )","title":""},{"docid":"4f4c7c49e4ad3576cb24a43bb53e786d","score":"0.59843713","text":"def testMultipleTimes(self):\n mock_obj = self.mox.CreateMockAnything()\n mock_obj.Method(1).MultipleTimes().AndReturn(9)\n mock_obj.Method(2).AndReturn(10)\n mock_obj.Method(3).MultipleTimes().AndReturn(42)\n self.mox.ReplayAll()\n\n actual_one = mock_obj.Method(1)\n second_one = mock_obj.Method(1) # This tests MultipleTimes.\n actual_two = mock_obj.Method(2)\n actual_three = mock_obj.Method(3)\n mock_obj.Method(3)\n mock_obj.Method(3)\n\n self.mox.VerifyAll()\n\n self.assertEqual(9, actual_one)\n # Repeated calls should return same number.\n self.assertEqual(9, second_one)\n self.assertEqual(10, actual_two)\n self.assertEqual(42, actual_three)","title":""},{"docid":"ade51b4dff78b22d91c4b96970dc46c1","score":"0.597086","text":"def test_call_args(self, mock_anyname_remove_func):\n self.assertTrue(os.remove(self.tmp1))\n self.assertTrue(os.remove(self.tmp2))\n self.assertTrue(os.remove(self.tmp3))\n\n # Function was last called with argument 10\n args, kwargs = mock_anyname_remove_func.call_args\n self.assertEqual(args, (self.tmp3,))\n self.assertEqual(kwargs, {})\n\n # All function calls were called with the following arguments\n args, kwargs = mock_anyname_remove_func.call_args_list[0]\n self.assertEqual(args, (self.tmp1,))\n self.assertEqual(kwargs, {})\n\n args, kwargs = mock_anyname_remove_func.call_args_list[1]\n self.assertEqual(args, (self.tmp2,))\n self.assertEqual(kwargs, {})\n\n args, kwargs = mock_anyname_remove_func.call_args_list[2]\n self.assertEqual(args, (self.tmp3,))\n self.assertEqual(kwargs, {})","title":""},{"docid":"1b71ac979acab72b16b0dda3cb9ef78a","score":"0.59675413","text":"def test_call_with_args(self):\n args = [1,2,3]\n kwargs = dict(a=1, b=2, c=3)\n response = self.func(*args, **kwargs)\n self.assertEqual(1, response)","title":""},{"docid":"5821d34b2df50e3f586ae206b5b0fb53","score":"0.5952542","text":"def test_parse_subroutine_call(self):\n pass","title":""},{"docid":"8488d9d4415411c1c0f378491e8a5687","score":"0.594676","text":"def test_called_with_and_keyword_args(self):\n obj = MathClass()\n self.agency.spy_on(obj.do_math_mixed)\n\n obj.do_math_mixed(a=1, b=2)\n obj.do_math_mixed(a=3, b=4)\n\n call = obj.do_math_mixed.calls[0]\n self.assertTrue(call.called_with(1, b=2))\n self.assertTrue(call.called_with(a=1, b=2))\n self.assertFalse(call.called_with(1, 2))\n self.assertFalse(call.called_with(3, b=4))","title":""},{"docid":"681969bf1b361895d32d943afeb4353b","score":"0.5929336","text":"def test_too_many_arguments(run):\n _, code = run(\"this\", \"is\", \"too\", \"many\", \"arguments\")\n\n assert code != 0","title":""},{"docid":"15d3e17c1664475e5071aa3cf3b85a22","score":"0.59211963","text":"def testMutlipleTimesUsingFunc(self):\n\n self.counter = 0\n\n def MyFunc(actual_str):\n \"\"\"Increment the counter if actual_str == 'foo'.\"\"\"\n if actual_str == 'foo':\n self.counter += 1\n return True\n\n mock_obj = self.mox.CreateMockAnything()\n mock_obj.Open()\n mock_obj.Method(mox.Func(MyFunc)).MultipleTimes()\n mock_obj.Close()\n self.mox.ReplayAll()\n\n mock_obj.Open()\n mock_obj.Method('foo')\n mock_obj.Method('foo')\n mock_obj.Method('not-foo')\n mock_obj.Close()\n\n self.mox.VerifyAll()\n\n self.assertEqual(2, self.counter)","title":""},{"docid":"6bd8292e6114e2f2d8de9cc30d7f434f","score":"0.5894836","text":"def test_call_tracking(self):\r\n i = self.invoker\r\n\r\n i.call('foo')\r\n\r\n self.assertFalse(i.isCallActive(0))","title":""},{"docid":"a639bdded13533795424613c48d131d6","score":"0.588739","text":"def test_call_subroutine_with_parametrized_duration(self):\n dur = circuit.Parameter(\"dur\")\n\n with pulse.build() as subroutine:\n pulse.play(pulse.Constant(dur, 0.1), pulse.DriveChannel(0))\n pulse.play(pulse.Constant(dur, 0.2), pulse.DriveChannel(0))\n\n with pulse.build() as main:\n pulse.call(subroutine)\n\n self.assertEqual(len(main.blocks), 1)","title":""},{"docid":"30d1a2d157d4fd8e810e864d24444fc8","score":"0.5886133","text":"def test_one(func):\n return func(1) == 1","title":""},{"docid":"0ebf52b6b936c3be439bb37a43ccceee","score":"0.58712846","text":"def call():","title":""},{"docid":"1c1659bfeaf95674ea285b587880116a","score":"0.585549","text":"def test(a,b,c):","title":""},{"docid":"85a3d224403ebc21a5128931ee4c7012","score":"0.5850725","text":"def test_call_with_different_count(self, get_credentials, post):\n buildapi.make_retrigger_request(\"repo\", \"1234567\", count=10, dry_run=False)\n post.assert_called_once_with(\n '%s/%s/request' % (buildapi.HOST_ROOT, \"repo\"),\n headers={'Accept': 'application/json'},\n data={'count': 10, 'priority': 0, 'request_id': '1234567'},\n auth=get_credentials())","title":""},{"docid":"ae1a105d4806c32f3bb9d2b730cf070e","score":"0.5845256","text":"def test_single_arg(a):\n return a","title":""},{"docid":"5fb5a2d714420d24fb9eb7b443ac67f5","score":"0.5826749","text":"def test_ten_times(self):\n\t\tcounter = 0\n\t\tprint(\"starting run_once test\")\n\t\tstate_tracker.set_state(5)\n\t\tcounter = mock_main(INITIAL_COUNTER, 10)\n\t\tprint(\"run_once test complete\")\n\t\tself.assertEqual(counter, 10);","title":""},{"docid":"74b3392305632ca304a304f68d59b768","score":"0.58201456","text":"def test_set_bespoke_duplication_count(self):\n drog = Drogulus(PRIVATE_KEY, PUBLIC_KEY, self.event_loop,\n self.connector)\n result = []\n for i in range(20):\n result.append(asyncio.Future())\n drog._node.replicate = MagicMock(return_value=result)\n pending_result = drog.set('foo', 'bar', duplicate=5)\n self.assertIsInstance(pending_result, list)\n self.assertEqual(1, drog._node.replicate.call_count)\n called_with = drog._node.replicate.call_args_list[0][0]\n self.assertEqual(called_with[0], 5)\n self.assertEqual(called_with[1], construct_key(PUBLIC_KEY, 'foo'))\n self.assertEqual(called_with[2], 'bar')\n self.assertIsInstance(called_with[3], float)\n self.assertEqual(called_with[4], 0.0)\n self.assertEqual(called_with[5], self.version)\n self.assertEqual(called_with[6], PUBLIC_KEY)\n self.assertEqual(called_with[7], 'foo')\n self.assertIsInstance(called_with[8], str)","title":""},{"docid":"e76ddd3f7e88f048e2748cadfd4288f5","score":"0.58047247","text":"def test_single_task_single_argument(add_mock):\n\n def fn(arg):\n pass\n\n # decorate & call\n single_task(60)(fn)(1)\n\n assert add_mock.call_count == 1\n assert add_mock.call_args[0] == ('lock_fn_arg_1', 'true', 60)","title":""},{"docid":"d007dbc8dc6615c5dd59e0545422d4d6","score":"0.5792286","text":"def count_calls(arg):\n call_counter[arg] += 1","title":""},{"docid":"81926a173332e7abf8ec54fbf35a3e7d","score":"0.57888985","text":"def test_multicall(self):\n class Multicall(object):\n def __init__(self):\n self.counter = 0\n self.result = 0\n\n @data(values=[2, 4, 6, 8, 10], single=True)\n def test4(self, values):\n self.counter += 1\n self.result += values[0]\n\n instance = Multicall()\n instance.test4()\n\n assert_that(instance.counter, equal_to(5))\n assert_that(instance.result, equal_to(30))","title":""},{"docid":"6138e5bb0cdc75abd78e07dbede54083","score":"0.5785142","text":"def count_calls(kwarg=0):\n call_counter[kwarg] += 1","title":""},{"docid":"d3d6df16dde3a2f3b53b0bd2bea6a856","score":"0.5780119","text":"def test_func(x1, x2, x3):","title":""},{"docid":"008d9ac2a011fe3ea47e9eece5fd465e","score":"0.5763909","text":"def test_call_with_not_existing_parameter(self):\n amp = circuit.Parameter(\"amp1\")\n\n with pulse.build() as subroutine:\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\n\n with self.assertRaises(exceptions.PulseError):\n with pulse.build():\n pulse.call(subroutine, amp=0.1)","title":""},{"docid":"a1da01e68969478dd8678bd40bbcb80b","score":"0.5758674","text":"def hasConsecutiveCalls(self, args, mock_obj):\n if len(args) < 1:\n return True\n for call_index in range(len(mock_obj.call_args_list)):\n arg_index = 0\n while mock_obj.call_args_list[call_index] == (args[arg_index],):\n call_index += 1\n arg_index += 1\n if arg_index == len(args):\n return True\n return False","title":""},{"docid":"ff5fc94e5422215465a56c22d8dc89f9","score":"0.57434964","text":"def test_initiate_already_active(self):\r\n h = self.handler\r\n\r\n callId = h.initiateCall()\r\n\r\n self.assertRaises(exc.CallFailed, h.initiateCall, callId=callId)","title":""},{"docid":"2b284a92733c0acff674d9494d156714","score":"0.57303333","text":"def _generate_single_call(self):\n pass","title":""},{"docid":"21aa56a1e4f98f2815d43065030cc784","score":"0.57278705","text":"def test_independent_parameter(self, num_directions, mocker):\n spy = mocker.spy(qml.gradients.spsa_gradient, \"generate_multishifted_tapes\")\n\n with qml.queuing.AnnotatedQueue() as q:\n qml.RX(0.543, wires=[0])\n qml.RY(-0.654, wires=[1])\n qml.expval(qml.PauliZ(0))\n\n tape = qml.tape.QuantumScript.from_queue(q)\n dev = qml.device(\"default.qubit\", wires=2)\n tapes, fn = spsa_grad(tape, num_directions=num_directions)\n res = fn(dev.batch_execute(tapes))\n assert res.shape == (1, 2)\n\n # 2 tapes per direction because the default strategy for SPSA is \"center\"\n assert len(spy.call_args_list) == num_directions\n\n # Never shift the independent parameter\n for i in range(num_directions):\n assert spy.call_args_list[i][0][0:2] == (tape, [0])\n assert spy.call_args_list[i][0][2].shape == (2, 1)","title":""},{"docid":"a3f5e4fcee793664b72ae62c9e8849de","score":"0.5725268","text":"def test_sub_repeat() -> None:\n assert sub([4, 4, 4, 4, 4, 4], 1, 3) == [4, 4]","title":""},{"docid":"2acd84f09f3dc8baffc62c87ca01977b","score":"0.5721575","text":"def simple_call(a):\n result = dll.test_simple_call(a)\n return result","title":""},{"docid":"10b1a46a0b0baffa82bc9b89c4a5df0c","score":"0.57209504","text":"def __test_call():","title":""},{"docid":"485c21f8b9f3ff328c44d1e6387531b6","score":"0.5698183","text":"def test_call_failure(self):","title":""},{"docid":"56c91473ed3d7a5a48e0022cb23839b4","score":"0.56949353","text":"def test_single_task_with_args(add_mock):\n\n def fn(*args):\n pass\n\n # decorate & call\n single_task(60)(fn)('1', 2, None, False)\n\n assert add_mock.call_count == 1\n assert add_mock.call_args[0] == ('lock_fn_1_2_None_False', 'true', 60)","title":""},{"docid":"689883f56c8665ded28097959b04f5ec","score":"0.5678","text":"def test_call_with_parameters(self):\n amp = circuit.Parameter(\"amp\")\n\n with pulse.build() as subroutine:\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\n\n with pulse.build() as main_prog:\n pulse.call(subroutine, amp=0.1)\n pulse.call(subroutine, amp=0.3)\n\n self.assertEqual(main_prog.is_parameterized(), False)\n\n assigned_sched = target_qobj_transform(main_prog)\n\n play_0 = assigned_sched.instructions[0][1]\n play_1 = assigned_sched.instructions[1][1]\n\n self.assertEqual(play_0.pulse.amp, 0.1)\n self.assertEqual(play_1.pulse.amp, 0.3)","title":""},{"docid":"018a9a240750b08093cdc76e39eae6e0","score":"0.5657119","text":"def change_random_call(\n self, test_case: tc.TestCase, statement: stmt.VariableCreatingStatement\n ) -> bool:\n objects = test_case.get_all_objects(statement.get_position())\n type_ = statement.ret_val.type\n # We need a consistent signature, otherwise nothing will match up\n signature_memo: dict[InferredSignature, dict[str, ProperType]] = {}\n calls = self._get_possible_calls(type_, objects, signature_memo)\n acc_object = statement.accessible_object()\n if acc_object in calls:\n calls.remove(acc_object)\n\n if len(calls) == 0:\n return False\n\n call = randomness.choice(calls)\n try:\n self.change_call(test_case, statement, call, signature_memo)\n return True\n except ConstructionFailedException:\n self._logger.debug(\"Failed to change call for statement.\", exc_info=True)\n return False","title":""},{"docid":"f3259628c4ebf6b42fd0318555877a99","score":"0.5653373","text":"def testCheckParams(self):\n def cb(e=None):\n self.passed = self.passed and e is None\n def check(param1, param2):\n if param1 and param2:\n self.passed = True\n return True\n \n checkFunc = FunctionCall(check, {'param1' : 'non-empty string', 'param2' : 'asdfasdf'})\n self.server.watch_function(checkFunc, cb)\n \n asyncore.poll()\n \n self.assert_(self.passed)","title":""},{"docid":"51671f0dd346bdef4f53153ca9c12e60","score":"0.56507623","text":"def test_run_1_call(self):\n \n pc = SippClient(command=\"-m 1\")\n sipp_client_proc = SippClient.launch(pc)\n time.sleep(12)\n # at this point,\n try:\n outs, errs = sipp_client_proc.communicate(input = \"q\", timeout = 10)\n except TimeoutExpired:\n sipp_client_proc.kill()\n outs.errs = sipp_client_proc.communicate()\n \n self.assertTrue(how_many_success(pc.script, pc.pid) == 1)\n \n cleanup_screen_log(pc.script, pc.pid)","title":""},{"docid":"c46a990a49eff71f30d23d6e3ce4b4d7","score":"0.56469494","text":"def test_assign_to_parameter_in_call_and_to_value_in_caller(self):\n with pulse.build(name=\"call_xp_xp\") as call_xp_xp:\n pulse.call(self.xp_)\n pulse.play(Gaussian(160, self.amp_xp, self.sigma), self.d0_)\n self.cals.add_schedule(call_xp_xp, num_qubits=1)\n\n my_amp = Parameter(\"amp\")\n sched = self.cals.get_schedule(\n \"call_xp_xp\",\n (2,),\n assign_params={\n (\"amp\", (2,), \"xp\"): my_amp,\n (\"amp\", (2,), \"call_xp_xp\"): 0.2,\n },\n )\n sched = block_to_schedule(sched)\n\n with pulse.build(name=\"xp\") as expected:\n pulse.play(Gaussian(160, my_amp, 40), DriveChannel(2))\n pulse.play(Gaussian(160, 0.2, 40), DriveChannel(2))\n expected = block_to_schedule(expected)\n\n self.assertEqual(sched, expected)","title":""},{"docid":"eabe54304f77bc779e4d12f092a8ded6","score":"0.5637324","text":"def repeatfunc(func, times=..., *args):\n ...","title":""},{"docid":"6657e8d96ac0874355f2be74c1ba67c2","score":"0.56354165","text":"def test_run_once(self):\n\t\tcounter = 0\n\t\tprint(\"starting run_once test\")\n\t\tstate_tracker.set_state(3)\n\t\tcounter = mock_main(INITIAL_COUNTER, 1)\n\t\tprint(\"run_once test complete\")\n\t\tself.assertEqual(counter, 1);","title":""},{"docid":"ebc0cf9ed74770704456dbe1d0015609","score":"0.5628558","text":"def test_single_task_with_list_as_argument(add_mock):\n\n def fn(lst):\n pass\n\n # decorate & call\n single_task(60)(fn)([1, 2])\n\n assert add_mock.call_count == 1\n assert add_mock.call_args[0] == ('lock_fn_lst_[1,2]', 'true', 60)","title":""},{"docid":"e444a7cadbe661870c4d636755756f88","score":"0.5622122","text":"def testCheckParam(self):\n def cb(e=None):\n self.passed = self.passed and e is None\n def check(param1):\n if param1:\n self.passed = True\n return True\n \n checkFunc = FunctionCall(check, {'param1' : 'non-empty string'})\n self.server.watch_function(checkFunc, cb)\n \n asyncore.poll()\n \n self.assert_(self.passed)","title":""},{"docid":"f84b3f2614e18ff322d4f5421ff93fbc","score":"0.5613881","text":"def test_direct_call():\n assert add(3, 4) == 7","title":""},{"docid":"d22c4c3baedca70d0642d1552d53f17e","score":"0.56058097","text":"def test_suite():\r\n test(is_multiple(12, 3))\r\n test(is_multiple(12, 4))\r\n test(not is_multiple(12, 5))\r\n test(is_multiple(12, 6))\r\n test(not is_multiple(12, 7))","title":""},{"docid":"77698f29e4e2e32b0f0509b647721de6","score":"0.5602916","text":"def test_no_arg_call_function(self, rr):\n\n def dummy_func():\n Sgate(r) | q[0]\n BSgate() | (q[0], q[1])\n\n op = utils.operation(ns=1)\n op(dummy_func)()\n\n with pytest.raises(ValueError, match=\"must receive the qumode register\"):\n op._call_function(rr)","title":""},{"docid":"15d63546ce6719a96222de668b5c0af3","score":"0.5601269","text":"def test_assign_new_call_when_call_is_not_undefined(self):\n self.emp.recieve_call(self.call_instance)\n self.assertRaises(NameError, lambda: self.emp.assign_new_call(self.call_instance))\n return True","title":""},{"docid":"8b664be6827ab415ba332a4bdd1152f3","score":"0.56010383","text":"def test_assign_to_parameter_in_call(self):\n with pulse.build(name=\"call_xp\") as call_xp:\n pulse.call(self.xp_)\n self.cals.add_schedule(call_xp, num_qubits=1)\n\n my_amp = Parameter(\"my_amp\")\n sched = self.cals.get_schedule(\"call_xp\", (2,), assign_params={(\"amp\", (2,), \"xp\"): my_amp})\n sched = block_to_schedule(sched)\n\n with pulse.build(name=\"xp\") as expected:\n pulse.play(Gaussian(160, my_amp, 40), DriveChannel(2))\n expected = block_to_schedule(expected)\n\n self.assertEqual(sched, expected)","title":""},{"docid":"e4a42af4786661774674754028765c83","score":"0.55976","text":"def test_repeat():\n processor = quick_processor()\n for element in processor.repeat(100, returns_42):\n assert_equals(element, 42)","title":""},{"docid":"fa9fc7dc0a49f0dfc6edce11d44d9161","score":"0.55933946","text":"def test_call(self):\n with mock.patch.object(SingletonMeta, \"__call__\", side_effect=InstantiationError) as p:\n with self.assertRaises(InstantiationError):\n self.test_class(1, 2)\n p.assert_called_with(1, 2)","title":""},{"docid":"901db44da3e4f186d21612a0931254e7","score":"0.559236","text":"def test_five_times(self):\n\t\tcounter = 0\n\t\tprint(\"starting run_once test\")\n\t\tstate_tracker.set_state(4)\n\t\tcounter = mock_main(INITIAL_COUNTER, 5)\n\t\tprint(\"run_once test complete\")\n\t\tself.assertEqual(counter, 5);","title":""},{"docid":"376a46d2c8ed79c3c67831e8e47c1673","score":"0.5588254","text":"def assert_call(mock, call_idx, *args, **kwargs):\n actual = mock.mock_calls[call_idx] if mock.mock_calls else None\n msg = f\"Call {call_idx} expected {(args, kwargs)}, was {actual}\"\n assert actual == (args, kwargs), msg","title":""},{"docid":"07303b58e17df240fc573c9b66b06b2d","score":"0.5584188","text":"def call(self, x):\r\n pass","title":""},{"docid":"d806a77438afeeae701e20549cfde89c","score":"0.5581544","text":"def test_args_and_kwargs_passed(self):\n\n def flakey(a, b, c=None):\n self.assertEqual(a, 1)\n self.assertEqual(b, 2)\n self.assertEqual(c, 3)\n\n retry(flakey, 1, 2, c=3)\n retry_on_error()(flakey)(1, 2, c=3)","title":""},{"docid":"4571b390b70bd3e6595bfae95a4475fa","score":"0.5581194","text":"def test_call_success_parameters():\n utils_mock = {\n \"json.find_json\": MagicMock(return_value={\"return\": \"result\"}),\n }\n salt_mock = {\n \"cmd.run_all\": MagicMock(return_value={\"retcode\": 0, \"stdout\": \"\"}),\n }\n with patch.dict(tu.__utils__, utils_mock), patch.dict(tu.__salt__, salt_mock):\n assert tu.call(\"module.function\", key=\"value\") == \"result\"\n\n salt_mock[\"cmd.run_all\"].assert_called_with(\n [\n \"transactional-update\",\n \"--non-interactive\",\n \"--drop-if-no-change\",\n \"--no-selfupdate\",\n \"--continue\",\n \"--quiet\",\n \"run\",\n \"salt-call\",\n \"--out\",\n \"json\",\n \"-l\",\n \"quiet\",\n \"--no-return-event\",\n \"--\",\n \"module.function\",\n \"key=value\",\n ]\n )","title":""},{"docid":"3748ccbe6c1a9dd123eb01a5776aa31b","score":"0.5579915","text":"def test_initiate(self):\r\n h = self.handler\r\n c = ('foo', ['bar', 'baz'], {})\r\n\r\n self.assertEqual(h.getNextCallId(), 1)\r\n self.assertEqual(h.getCallContext(1), None)\r\n\r\n self.assertEqual(h.initiateCall(*c), 1)\r\n self.assertEqual(h.getCallContext(1), c)\r\n\r\n self.assertEqual(h.getNextCallId(), 2)\r\n self.assertEqual(h.getCallContext(2), None)","title":""},{"docid":"e46a353bd22c06a0a2108587810cdc8f","score":"0.5572544","text":"def test_call(self):\n response = self.func()\n self.assertEqual(1, response)","title":""},{"docid":"3f757f2ea11100d259437b86b322f01b","score":"0.5560395","text":"def test_set_parameter_2(self):\n pass","title":""},{"docid":"38078e08fe64a1800197938b919fee29","score":"0.55522126","text":"def test_routine(self, sample):\n pass","title":""},{"docid":"ca9f85eb206d3a3b9849de8ec9fbc9ef","score":"0.5551688","text":"def mock_func_call(*args, **kwargs):\n return []","title":""},{"docid":"cc4418e2111eaec2b435d53eac13aea6","score":"0.55471027","text":"def test_multiple_calls(self):\n\n @task_decorator\n def do_run():\n return 4\n\n with self.dag:\n do_run()\n assert [\"do_run\"] == self.dag.task_ids\n do_run_1 = do_run()\n do_run_2 = do_run()\n assert [\"do_run\", \"do_run__1\", \"do_run__2\"] == self.dag.task_ids\n\n assert do_run_1.operator.task_id == \"do_run__1\"\n assert do_run_2.operator.task_id == \"do_run__2\"","title":""},{"docid":"e20808ae57c69bbccf7c1d16cc543d0d","score":"0.5541567","text":"def test_multiple_requests_verify_ssl_once(self, post_mock):\n post_mock.return_value.status_code = 200\n\n self._api_client.add_note('incident_id', 'this is the note')\n self._api_client.add_note('incident_id', 'this is another note')\n self._api_client.add_note('incident_id', 'this is a third note')\n\n class Anything(object):\n def __eq__(self, _):\n return True\n\n class VerifyIsCalledWith(object):\n def __init__(self, expected_verify_value):\n self._expected_verify_value = expected_verify_value\n\n def __eq__(self, other):\n return self._expected_verify_value == other\n\n post_mock.assert_has_calls(\n [\n call(\n Anything(),\n headers=Anything(), json=Anything(), timeout=Anything(),\n verify=VerifyIsCalledWith(True)\n ),\n call(\n Anything(),\n headers=Anything(), json=Anything(), timeout=Anything(),\n verify=VerifyIsCalledWith(False)\n ),\n call(\n Anything(),\n headers=Anything(), json=Anything(), timeout=Anything(),\n verify=VerifyIsCalledWith(False)\n ),\n ],\n # So the problem with assert_has_calls() is that it requires you to declare all calls\n # including chained calls. This doesn't work because we do a bunch of random stuff\n # inbetween with the return value (such as .json() calls) and it's not really feasible\n # to declare ALL of the calls.\n #\n # By setting any_order=True, we ensure all of the above calls are made at least once.\n # We lose out on the ability to detect that we called verify=True FIRST (before the\n # two verify=False calls)... but, oh well?\n any_order=True\n )","title":""},{"docid":"5ca899733085d7bf8a3c5aba04740578","score":"0.55377996","text":"def repeat(self, *args, **params): # reliably restored by inspect\n pass","title":""},{"docid":"895353c1052effb41e3bba1d575bc392","score":"0.55340666","text":"def test_too_few_arguments(run):\n _, code = run()\n\n assert code != 0","title":""},{"docid":"acb2e9801fc01162415bffcee906e9a3","score":"0.55328834","text":"def test_call_partly_with_parameters(self):\n amp = circuit.Parameter(\"amp\")\n\n with pulse.build() as subroutine:\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\n\n with pulse.build() as main_prog:\n pulse.call(subroutine, amp=0.1)\n pulse.call(subroutine)\n\n self.assertEqual(main_prog.is_parameterized(), True)\n\n main_prog.assign_parameters({amp: 0.5})\n self.assertEqual(main_prog.is_parameterized(), False)\n\n assigned_sched = target_qobj_transform(main_prog)\n\n play_0 = assigned_sched.instructions[0][1]\n play_1 = assigned_sched.instructions[1][1]\n\n self.assertEqual(play_0.pulse.amp, 0.1)\n self.assertEqual(play_1.pulse.amp, 0.5)","title":""},{"docid":"d714b85f5728f7961367b9463b0f31d1","score":"0.55314493","text":"def once():\n return OnceInvocationMatcher()","title":""},{"docid":"714eb7388e1087a8ae32a98b0a8177de","score":"0.5526994","text":"def test_set_parameter_1(self):\n pass","title":""},{"docid":"ed7d51bdb953fe7410032330eca221c3","score":"0.55262697","text":"def test_single_task_with_args_and_kwargs(add_mock):\n\n def fn(*args, **kwargs):\n pass\n\n # decorate & call\n single_task(60)(fn)(1, 2, a='b', b='c')\n\n assert add_mock.call_count == 1\n assert add_mock.call_args[0] == ('lock_fn_1_2_a_b_b_c', 'true', 60)","title":""},{"docid":"aa77ab74a38b5def4c35005296749254","score":"0.55220574","text":"def at_least_once():\n return AtLeastOnceInvocationMatcher()","title":""},{"docid":"91e5ce2b85623c51ce9f8dc6a9c1a585","score":"0.55205107","text":"def test_two_args(a,b):\n return a,b","title":""},{"docid":"fbe70da632bdb992d1cdebaac3c93718","score":"0.5518048","text":"def test_hundred_times(self):\n\t\tcounter = 0\n\t\tprint(\"starting run_once test\")\n\t\tstate_tracker.set_state(5)\n\t\tcounter = mock_main(INITIAL_COUNTER, 100)\n\t\tprint(\"run_once test complete\")\n\t\tself.assertEqual(counter, 100);","title":""},{"docid":"0f5bbcefe22a809abeb536f4961116d1","score":"0.5503572","text":"def _exec_one_test_with_repeat(self, test_name, test_method, repeat_count,\n max_consecutive_error):\n\n consecutive_error_count = 0\n\n # If max_consecutive_error is not set by user, it is considered the same as\n # the repeat_count.\n if max_consecutive_error == 0:\n max_consecutive_error = repeat_count\n\n for i in range(repeat_count):\n new_test_name = f'{test_name}_{i}'\n record = self.exec_one_test(new_test_name, test_method)\n if record.result in [\n records.TestResultEnums.TEST_RESULT_FAIL,\n records.TestResultEnums.TEST_RESULT_ERROR,\n ]:\n consecutive_error_count += 1\n else:\n consecutive_error_count = 0\n if consecutive_error_count == max_consecutive_error:\n logging.error(\n 'Repeated test case \"%s\" has consecutively failed %d iterations, '\n 'aborting the remaining %d iterations.', test_name,\n consecutive_error_count, repeat_count - 1 - i)\n return","title":""},{"docid":"a149f8bb76ad9347029c72fdfd830cc6","score":"0.55023247","text":"def test_kwargs_are_passed(self):\n with patch(\"sys.exit\") as mock_exit:\n cli = MicroCLITestCase.T(\n \"script_name f3 --awesome-option 1\".split()).run()\n mock_exit.assert_called_with(1)\n with patch(\"sys.exit\") as mock_exit:\n cli = MicroCLITestCase.T(\n \"script_name f3 --awesome-option 1234567\".split()).run()\n mock_exit.assert_called_with(7)","title":""},{"docid":"edeacbbffbbe77c368fef6a71b990987","score":"0.55000293","text":"def assert_any_call(self, *args, **kwargs):\n at_least_on_passed = False\n for kall in self.call_args_list:\n try:\n self._assert_with_constraints(args, kwargs, kall)\n #assertions passed, return because there's a match\n return\n except AssertionError:\n #keep trying\n pass\n\n expected_string = self._format_mock_call_signature(args, kwargs)\n raise AssertionError(\n '%s call not found' % expected_string\n )","title":""},{"docid":"70e2d028c9ff0df1b03cab365b78d269","score":"0.5497843","text":"def test_single_task_no_arguments(add_mock):\n\n def fn():\n pass\n\n # decorate & call\n single_task(60)(fn)()\n\n assert add_mock.call_count == 1\n assert add_mock.call_args[0] == ('lock_fn', 'true', 60)","title":""},{"docid":"3b6b5f26c52fd5f05bc6e6e1f410cbc0","score":"0.54946685","text":"def test_invalidate_args():\n f = Mock(__name__='f', return_value=None)\n decorated = cache()(f)\n decorated(1)\n decorated.invalidate(1)\n decorated(1)\n assert f.call_count == 2","title":""},{"docid":"e536ea416442b631a8312ee55a68d5b1","score":"0.54920566","text":"def test_w_more_path_params(self):\n assert_that(\n calling(\n self.processor.process\n ).with_args(\n [2], {}\n ),\n raises(ValueError)\n )","title":""},{"docid":"0eb6cc6b2d2cd3c133b0733065fc102d","score":"0.54843545","text":"def test_called(self):\n result = []\n registry = FunctionRegistry()\n registry.register(lambda: result.append(1))\n registry.register(lambda x: result.append(x), 2)\n registry.register(lambda y: result.append(y), y=3)\n registry.run()\n self.assertEqual(result, [3, 2, 1])","title":""},{"docid":"7c552083039e4740235aff4f39e9424b","score":"0.54820985","text":"def test_ignored_args(self, _, cache_decorator):\n call_count = [0]\n\n @cache_decorator\n def foo(arg1, _arg2, *args, kwarg1, _kwarg2=None, **kwargs):\n call_count[0] += 1\n\n foo(1, 2, 3, kwarg1=4, _kwarg2=5, kwarg3=6, _kwarg4=7)\n self.assertEqual([1], call_count)\n\n # Call foo again, but change the values for _arg2, _kwarg2, and _kwarg4.\n # The call count shouldn't change, because these args will not be part\n # of the hash.\n foo(1, None, 3, kwarg1=4, _kwarg2=None, kwarg3=6, _kwarg4=None)\n self.assertEqual([1], call_count)\n\n # Changing the value of any other argument will increase the call\n # count. We test each argument type:\n\n # arg1 (POSITIONAL_OR_KEYWORD)\n foo(None, 2, 3, kwarg1=4, _kwarg2=5, kwarg3=6, _kwarg4=7)\n self.assertEqual([2], call_count)\n\n # *arg (VAR_POSITIONAL)\n foo(1, 2, None, kwarg1=4, _kwarg2=5, kwarg3=6, _kwarg4=7)\n self.assertEqual([3], call_count)\n\n # kwarg1 (KEYWORD_ONLY)\n foo(1, 2, 3, kwarg1=None, _kwarg2=5, kwarg3=6, _kwarg4=7)\n self.assertEqual([4], call_count)\n\n # **kwarg (VAR_KEYWORD)\n foo(1, 2, 3, kwarg1=4, _kwarg2=5, kwarg3=None, _kwarg4=7)\n self.assertEqual([5], call_count)","title":""}],"string":"[\n {\n \"docid\": \"1ea99a172ec554e436fc623247ceef88\",\n \"score\": \"0.67987573\",\n \"text\": \"def test_runs_once_runs_only_once():\\n func = fake_function(expect_call=True).times_called(1)\\n task = decorators.runs_once(func)\\n for i in range(2):\\n task()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c9623f2b90025f13b27375ab224ec3\",\n \"score\": \"0.6798225\",\n \"text\": \"def __call__(self, dummy1, dummy2=False):\\n self.call += 1\\n return self.repeat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a63b74832f1561f1bbc74743447198\",\n \"score\": \"0.65965104\",\n \"text\": \"def test_call():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abadf7f88ca42e59779a1de5d4133c20\",\n \"score\": \"0.6457599\",\n \"text\": \"def call_again():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc404d654bd8ebc737c9892626f16774\",\n \"score\": \"0.64390415\",\n \"text\": \"def test_call(self):\\n self.report('Testing the __call__ function with single input of the modifier.')\\n num_modifications = 20\\n num_steps_probs = [0.5, 0.25, 0.125, 0.075, 0.05]\\n save_dir = os.path.join(self.save_dir, 'modifier_call_single')\\n if os.path.exists(save_dir):\\n rmtree(save_dir)\\n test_nns = self.cnns + self.mlps\\n for idx, old_nn in enumerate(test_nns):\\n save_prefix = str(idx)\\n modifier, constraint_checker = self._get_modifier_and_cc(old_nn)\\n get_modifications = lambda arg_nn: modifier(arg_nn, num_modifications,\\n num_steps_probs)\\n run_orig_vs_modifications(save_dir, save_prefix, old_nn,\\n get_modifications, constraint_checker, self.report)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e47629b25452448e297c3985af92f8a\",\n \"score\": \"0.64381725\",\n \"text\": \"def test_called_with_and_partial_args(self):\\n obj = MathClass()\\n self.agency.spy_on(obj.do_math_mixed)\\n\\n obj.do_math_mixed(1, 2)\\n obj.do_math_mixed(3, 4)\\n\\n call = obj.do_math_mixed.calls[0]\\n self.assertTrue(call.called_with(1))\\n self.assertFalse(call.called_with(1, 2, 3))\\n self.assertFalse(call.called_with(3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8c3f1f4d56d3c61f14d4825aab009b8\",\n \"score\": \"0.64164364\",\n \"text\": \"def test_partial_func_locked_param_check():\\n with pytest.raises(TypeError, match=r\\\".*got multiple values for argument*\\\"):\\n state = 'DL'\\n session7.func_num_plt(state,1000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ded51c76742a901ec63e1251e289a79\",\n \"score\": \"0.63033974\",\n \"text\": \"def test_call_with_parameter_name_collision(self):\\n amp1 = circuit.Parameter(\\\"amp\\\")\\n amp2 = circuit.Parameter(\\\"amp\\\")\\n sigma = circuit.Parameter(\\\"sigma\\\")\\n\\n with pulse.build() as subroutine:\\n pulse.play(pulse.Gaussian(160, amp1, sigma), pulse.DriveChannel(0))\\n pulse.play(pulse.Gaussian(160, amp2, sigma), pulse.DriveChannel(0))\\n\\n with pulse.build() as main_prog:\\n pulse.call(subroutine, value_dict={amp1: 0.1, amp2: 0.2}, sigma=40)\\n\\n assigned_sched = target_qobj_transform(main_prog)\\n\\n play_0 = assigned_sched.instructions[0][1]\\n play_1 = assigned_sched.instructions[1][1]\\n\\n self.assertEqual(play_0.pulse.amp, 0.1)\\n self.assertEqual(play_0.pulse.sigma, 40)\\n self.assertEqual(play_1.pulse.amp, 0.2)\\n self.assertEqual(play_1.pulse.sigma, 40)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f0ce22d6e381aba5a4461de2e5cdb31\",\n \"score\": \"0.6295111\",\n \"text\": \"def test_runs_once_returns_same_value_each_run():\\n return_value = \\\"foo\\\"\\n task = decorators.runs_once(fake_function().returns(return_value))\\n for i in range(2):\\n eq_(task(), return_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3866e88b5d56dcef83a5f2c6b2ced60\",\n \"score\": \"0.62262106\",\n \"text\": \"def testCallArg( self ):\\n\\t\\tdef function( text, start, end, *arguments ):\\n\\t\\t\\tassert arguments == (1,2,3), \\\"\\\"\\\"Passed arguments were not what we passed in\\\"\\\"\\\"\\n\\t\\t\\treturn end\\n\\t\\n\\t\\tself.doBasicTest(\\n\\t\\t\\t(\\n\\t\\t\\t\\t( \\\"ab\\\", CallArg, (function,1,2,3), 0 ),\\n\\t\\t\\t),\\n\\t\\t\\t\\\"cdffgg\\\",\\n\\t\\t\\t( 1,[\\n\\t\\t\\t\\t(\\\"ab\\\",0,6,None),\\n\\t\\t\\t],6),\\n\\t\\t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"500a8c3d0e137b8bd7298ee4d8b504d7\",\n \"score\": \"0.6221946\",\n \"text\": \"def test_call_with(self, activity, fn):\\n task_input = {\\\"a\\\": 42, \\\"b\\\": \\\"bla\\\", \\\"c\\\": {\\\"foo\\\": [1, 2], \\\"bar\\\": None}}\\n res = activity.call_with(task_input)\\n assert res is fn.return_value\\n fn.assert_called_once_with(task_input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d853f068d02364aa6bbc48af1e8aefd3\",\n \"score\": \"0.6207401\",\n \"text\": \"def test_partial_sub_addition_keyarg():\\n startcounter = 20\\n counter = startcounter\\n\\n class mocksocket:\\n def recv_pyobj(self, flags=\\\"notused\\\"):\\n nonlocal counter\\n counter = counter + 1\\n return counter\\n\\n a = mocksocket()\\n\\n def atest(myparam, myparamtwo=0):\\n return myparam * myparamtwo\\n\\n myfunc = partial_sub(partial_sub(atest, a), a, keyargname=\\\"myparamtwo\\\")\\n # the myfunc has to be called multiple times to simulate multiple receive operations\\n # for the encapsulated sockets\\n testval = myfunc()\\n assert testval == (startcounter + 1) * (startcounter + 2)\\n testval = myfunc()\\n assert testval == (startcounter + 3) * (startcounter + 4)\\n testval = myfunc()\\n assert testval == (startcounter + 5) * (startcounter + 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fa342c3578fd510bbaacd30fda6955\",\n \"score\": \"0.6186846\",\n \"text\": \"def testMultipleTimesUsingIsAParameter(self):\\n mock_obj = self.mox.CreateMockAnything()\\n mock_obj.Open()\\n mock_obj.Method(mox.IsA(str)).MultipleTimes(\\\"IsA\\\").AndReturn(9)\\n mock_obj.Close()\\n self.mox.ReplayAll()\\n\\n mock_obj.Open()\\n actual_one = mock_obj.Method(\\\"1\\\")\\n second_one = mock_obj.Method(\\\"2\\\") # This tests MultipleTimes.\\n mock_obj.Close()\\n\\n self.mox.VerifyAll()\\n\\n self.assertEqual(9, actual_one)\\n # Repeated calls should return same number.\\n self.assertEqual(9, second_one)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84cd3924d7d966ffeb45388dd01eb1e6\",\n \"score\": \"0.61749107\",\n \"text\": \"def test_runTwice(self):\\n self.nbr.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381e20a90f013c06d79e8e05d39e963d\",\n \"score\": \"0.6156437\",\n \"text\": \"def testCall2( self ):\\n\\t\\tclass X:\\n\\t\\t\\tdef __call__( self, text, start, end ):\\n\\t\\t\\t\\treturn end\\n\\t\\n\\t\\tself.doBasicTest(\\n\\t\\t\\t(\\n\\t\\t\\t\\t( \\\"ab\\\", Call, X(), 0 ),\\n\\t\\t\\t),\\n\\t\\t\\t\\\"cdffgg\\\",\\n\\t\\t\\t( 1,[\\n\\t\\t\\t\\t(\\\"ab\\\",0,6,None),\\n\\t\\t\\t],6),\\n\\t\\t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22e99abcffdc81d946b33690af9f747b\",\n \"score\": \"0.6151879\",\n \"text\": \"def called_times(self, method, count):\\n self.assertEqual(method.call_count, count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38d95b5fadb8f743edc69b7915a2b528\",\n \"score\": \"0.61454177\",\n \"text\": \"def test_incorrect_arg_num_call_function(self, rr):\\n\\n def dummy_func(r, q):\\n Sgate(r) | q[0]\\n BSgate() | (q[0], q[1])\\n\\n op = utils.operation(ns=1)\\n op(dummy_func)(1, 2)\\n\\n with pytest.raises(ValueError, match=\\\"Mismatch in the number of arguments\\\"):\\n op._call_function(rr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb7e524ea1cc5b8e0472696fb18cb5f9\",\n \"score\": \"0.611829\",\n \"text\": \"def test_called_with_and_partial_kwargs(self):\\n obj = MathClass()\\n self.agency.spy_on(obj.do_math_mixed)\\n\\n obj.do_math_mixed(a=1, b=2)\\n obj.do_math_mixed(a=3, b=4)\\n\\n call = obj.do_math_mixed.calls[0]\\n self.assertTrue(call.called_with(1))\\n self.assertTrue(call.called_with(b=2))\\n self.assertTrue(call.called_with(a=1))\\n self.assertFalse(call.called_with(a=4))\\n self.assertFalse(call.called_with(a=1, b=2, c=3))\\n self.assertFalse(call.called_with(a=3, b=2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad13187e41ab098814c3f81fab9a8da1\",\n \"score\": \"0.6096294\",\n \"text\": \"def testCall( self ):\\n\\t\\tdef function( text, start, end ):\\n\\t\\t\\treturn end\\n\\t\\n\\t\\tself.doBasicTest(\\n\\t\\t\\t(\\n\\t\\t\\t\\t( \\\"ab\\\", Call, function, 0 ),\\n\\t\\t\\t),\\n\\t\\t\\t\\\"cdffgg\\\",\\n\\t\\t\\t( 1,[\\n\\t\\t\\t\\t(\\\"ab\\\",0,6,None),\\n\\t\\t\\t],6),\\n\\t\\t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89de23f4d14afc8eb47df1a1411c15a5\",\n \"score\": \"0.60773927\",\n \"text\": \"def test_call_with_third_arg(self):\\n _rademacher_sampler([0, 1, 2], 4, \\\"ignored dummy\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c16658ed5cc7278de3b7e64a461221fb\",\n \"score\": \"0.6056042\",\n \"text\": \"def test_repeat(self):\\n \\n @testing.repeat(5)\\n def inc_counter(cnt):\\n cnt[0] += 1\\n \\n counter = [0] # use mutable container to retain data\\n inc_counter(counter)\\n self.assertEqual(counter, [5])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4c9d1fe60b5137128fcec72cd467694\",\n \"score\": \"0.604468\",\n \"text\": \"def test_with_extra_call(self):\\n obj = MathClass()\\n\\n self.agency.spy_on(\\n obj.do_math,\\n op=SpyOpMatchInOrder([\\n {\\n 'kwargs': {\\n 'a': 4,\\n 'b': 7,\\n },\\n },\\n ]))\\n\\n self.assertEqual(obj.do_math(a=4, b=7), 11)\\n\\n expected_message = re.escape(\\n \\\"do_math was called 2 time(s), but only 1 call(s) were expected. \\\"\\n \\\"Latest call: <SpyCall(args=(), kwargs={'a': 4, 'b': 9}, \\\"\\n \\\"returned=None, raised=None)>\\\"\\n )\\n\\n with self.assertRaisesRegex(UnexpectedCallError, expected_message):\\n obj.do_math(a=4, b=9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9827236245f7427d6646bd6fb785d786\",\n \"score\": \"0.60427254\",\n \"text\": \"def test_w_more_api_call_params(self):\\n assert_that(\\n calling(\\n self.processor.process\\n ).with_args(\\n [2, 8], {}\\n ),\\n raises(ValueError)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85b545f7be41d26cba999499e1d9b0d4\",\n \"score\": \"0.6025817\",\n \"text\": \"def test_assign_to_same_parameter_in_call_and_caller(self):\\n with pulse.build(name=\\\"call_xp_xp\\\") as call_xp_xp:\\n pulse.call(self.xp_)\\n pulse.play(Gaussian(160, self.amp_xp, self.sigma), self.d0_)\\n self.cals.add_schedule(call_xp_xp, num_qubits=1)\\n\\n my_amp = Parameter(\\\"amp\\\")\\n with self.assertRaises(CalibrationError):\\n self.cals.get_schedule(\\n \\\"call_xp_xp\\\",\\n (2,),\\n assign_params={\\n (\\\"amp\\\", (2,), \\\"xp\\\"): self.amp_xp,\\n (\\\"amp\\\", (2,), \\\"call_xp_xp\\\"): my_amp,\\n },\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f4c7c49e4ad3576cb24a43bb53e786d\",\n \"score\": \"0.59843713\",\n \"text\": \"def testMultipleTimes(self):\\n mock_obj = self.mox.CreateMockAnything()\\n mock_obj.Method(1).MultipleTimes().AndReturn(9)\\n mock_obj.Method(2).AndReturn(10)\\n mock_obj.Method(3).MultipleTimes().AndReturn(42)\\n self.mox.ReplayAll()\\n\\n actual_one = mock_obj.Method(1)\\n second_one = mock_obj.Method(1) # This tests MultipleTimes.\\n actual_two = mock_obj.Method(2)\\n actual_three = mock_obj.Method(3)\\n mock_obj.Method(3)\\n mock_obj.Method(3)\\n\\n self.mox.VerifyAll()\\n\\n self.assertEqual(9, actual_one)\\n # Repeated calls should return same number.\\n self.assertEqual(9, second_one)\\n self.assertEqual(10, actual_two)\\n self.assertEqual(42, actual_three)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade51b4dff78b22d91c4b96970dc46c1\",\n \"score\": \"0.597086\",\n \"text\": \"def test_call_args(self, mock_anyname_remove_func):\\n self.assertTrue(os.remove(self.tmp1))\\n self.assertTrue(os.remove(self.tmp2))\\n self.assertTrue(os.remove(self.tmp3))\\n\\n # Function was last called with argument 10\\n args, kwargs = mock_anyname_remove_func.call_args\\n self.assertEqual(args, (self.tmp3,))\\n self.assertEqual(kwargs, {})\\n\\n # All function calls were called with the following arguments\\n args, kwargs = mock_anyname_remove_func.call_args_list[0]\\n self.assertEqual(args, (self.tmp1,))\\n self.assertEqual(kwargs, {})\\n\\n args, kwargs = mock_anyname_remove_func.call_args_list[1]\\n self.assertEqual(args, (self.tmp2,))\\n self.assertEqual(kwargs, {})\\n\\n args, kwargs = mock_anyname_remove_func.call_args_list[2]\\n self.assertEqual(args, (self.tmp3,))\\n self.assertEqual(kwargs, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b71ac979acab72b16b0dda3cb9ef78a\",\n \"score\": \"0.59675413\",\n \"text\": \"def test_call_with_args(self):\\n args = [1,2,3]\\n kwargs = dict(a=1, b=2, c=3)\\n response = self.func(*args, **kwargs)\\n self.assertEqual(1, response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5821d34b2df50e3f586ae206b5b0fb53\",\n \"score\": \"0.5952542\",\n \"text\": \"def test_parse_subroutine_call(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8488d9d4415411c1c0f378491e8a5687\",\n \"score\": \"0.594676\",\n \"text\": \"def test_called_with_and_keyword_args(self):\\n obj = MathClass()\\n self.agency.spy_on(obj.do_math_mixed)\\n\\n obj.do_math_mixed(a=1, b=2)\\n obj.do_math_mixed(a=3, b=4)\\n\\n call = obj.do_math_mixed.calls[0]\\n self.assertTrue(call.called_with(1, b=2))\\n self.assertTrue(call.called_with(a=1, b=2))\\n self.assertFalse(call.called_with(1, 2))\\n self.assertFalse(call.called_with(3, b=4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"681969bf1b361895d32d943afeb4353b\",\n \"score\": \"0.5929336\",\n \"text\": \"def test_too_many_arguments(run):\\n _, code = run(\\\"this\\\", \\\"is\\\", \\\"too\\\", \\\"many\\\", \\\"arguments\\\")\\n\\n assert code != 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d3e17c1664475e5071aa3cf3b85a22\",\n \"score\": \"0.59211963\",\n \"text\": \"def testMutlipleTimesUsingFunc(self):\\n\\n self.counter = 0\\n\\n def MyFunc(actual_str):\\n \\\"\\\"\\\"Increment the counter if actual_str == 'foo'.\\\"\\\"\\\"\\n if actual_str == 'foo':\\n self.counter += 1\\n return True\\n\\n mock_obj = self.mox.CreateMockAnything()\\n mock_obj.Open()\\n mock_obj.Method(mox.Func(MyFunc)).MultipleTimes()\\n mock_obj.Close()\\n self.mox.ReplayAll()\\n\\n mock_obj.Open()\\n mock_obj.Method('foo')\\n mock_obj.Method('foo')\\n mock_obj.Method('not-foo')\\n mock_obj.Close()\\n\\n self.mox.VerifyAll()\\n\\n self.assertEqual(2, self.counter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bd8292e6114e2f2d8de9cc30d7f434f\",\n \"score\": \"0.5894836\",\n \"text\": \"def test_call_tracking(self):\\r\\n i = self.invoker\\r\\n\\r\\n i.call('foo')\\r\\n\\r\\n self.assertFalse(i.isCallActive(0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a639bdded13533795424613c48d131d6\",\n \"score\": \"0.588739\",\n \"text\": \"def test_call_subroutine_with_parametrized_duration(self):\\n dur = circuit.Parameter(\\\"dur\\\")\\n\\n with pulse.build() as subroutine:\\n pulse.play(pulse.Constant(dur, 0.1), pulse.DriveChannel(0))\\n pulse.play(pulse.Constant(dur, 0.2), pulse.DriveChannel(0))\\n\\n with pulse.build() as main:\\n pulse.call(subroutine)\\n\\n self.assertEqual(len(main.blocks), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d1a2d157d4fd8e810e864d24444fc8\",\n \"score\": \"0.5886133\",\n \"text\": \"def test_one(func):\\n return func(1) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ebf52b6b936c3be439bb37a43ccceee\",\n \"score\": \"0.58712846\",\n \"text\": \"def call():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c1659bfeaf95674ea285b587880116a\",\n \"score\": \"0.585549\",\n \"text\": \"def test(a,b,c):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85a3d224403ebc21a5128931ee4c7012\",\n \"score\": \"0.5850725\",\n \"text\": \"def test_call_with_different_count(self, get_credentials, post):\\n buildapi.make_retrigger_request(\\\"repo\\\", \\\"1234567\\\", count=10, dry_run=False)\\n post.assert_called_once_with(\\n '%s/%s/request' % (buildapi.HOST_ROOT, \\\"repo\\\"),\\n headers={'Accept': 'application/json'},\\n data={'count': 10, 'priority': 0, 'request_id': '1234567'},\\n auth=get_credentials())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1a105d4806c32f3bb9d2b730cf070e\",\n \"score\": \"0.5845256\",\n \"text\": \"def test_single_arg(a):\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb5a2d714420d24fb9eb7b443ac67f5\",\n \"score\": \"0.5826749\",\n \"text\": \"def test_ten_times(self):\\n\\t\\tcounter = 0\\n\\t\\tprint(\\\"starting run_once test\\\")\\n\\t\\tstate_tracker.set_state(5)\\n\\t\\tcounter = mock_main(INITIAL_COUNTER, 10)\\n\\t\\tprint(\\\"run_once test complete\\\")\\n\\t\\tself.assertEqual(counter, 10);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b3392305632ca304a304f68d59b768\",\n \"score\": \"0.58201456\",\n \"text\": \"def test_set_bespoke_duplication_count(self):\\n drog = Drogulus(PRIVATE_KEY, PUBLIC_KEY, self.event_loop,\\n self.connector)\\n result = []\\n for i in range(20):\\n result.append(asyncio.Future())\\n drog._node.replicate = MagicMock(return_value=result)\\n pending_result = drog.set('foo', 'bar', duplicate=5)\\n self.assertIsInstance(pending_result, list)\\n self.assertEqual(1, drog._node.replicate.call_count)\\n called_with = drog._node.replicate.call_args_list[0][0]\\n self.assertEqual(called_with[0], 5)\\n self.assertEqual(called_with[1], construct_key(PUBLIC_KEY, 'foo'))\\n self.assertEqual(called_with[2], 'bar')\\n self.assertIsInstance(called_with[3], float)\\n self.assertEqual(called_with[4], 0.0)\\n self.assertEqual(called_with[5], self.version)\\n self.assertEqual(called_with[6], PUBLIC_KEY)\\n self.assertEqual(called_with[7], 'foo')\\n self.assertIsInstance(called_with[8], str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e76ddd3f7e88f048e2748cadfd4288f5\",\n \"score\": \"0.58047247\",\n \"text\": \"def test_single_task_single_argument(add_mock):\\n\\n def fn(arg):\\n pass\\n\\n # decorate & call\\n single_task(60)(fn)(1)\\n\\n assert add_mock.call_count == 1\\n assert add_mock.call_args[0] == ('lock_fn_arg_1', 'true', 60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d007dbc8dc6615c5dd59e0545422d4d6\",\n \"score\": \"0.5792286\",\n \"text\": \"def count_calls(arg):\\n call_counter[arg] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81926a173332e7abf8ec54fbf35a3e7d\",\n \"score\": \"0.57888985\",\n \"text\": \"def test_multicall(self):\\n class Multicall(object):\\n def __init__(self):\\n self.counter = 0\\n self.result = 0\\n\\n @data(values=[2, 4, 6, 8, 10], single=True)\\n def test4(self, values):\\n self.counter += 1\\n self.result += values[0]\\n\\n instance = Multicall()\\n instance.test4()\\n\\n assert_that(instance.counter, equal_to(5))\\n assert_that(instance.result, equal_to(30))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6138e5bb0cdc75abd78e07dbede54083\",\n \"score\": \"0.5785142\",\n \"text\": \"def count_calls(kwarg=0):\\n call_counter[kwarg] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d6df16dde3a2f3b53b0bd2bea6a856\",\n \"score\": \"0.5780119\",\n \"text\": \"def test_func(x1, x2, x3):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"008d9ac2a011fe3ea47e9eece5fd465e\",\n \"score\": \"0.5763909\",\n \"text\": \"def test_call_with_not_existing_parameter(self):\\n amp = circuit.Parameter(\\\"amp1\\\")\\n\\n with pulse.build() as subroutine:\\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\\n\\n with self.assertRaises(exceptions.PulseError):\\n with pulse.build():\\n pulse.call(subroutine, amp=0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1da01e68969478dd8678bd40bbcb80b\",\n \"score\": \"0.5758674\",\n \"text\": \"def hasConsecutiveCalls(self, args, mock_obj):\\n if len(args) < 1:\\n return True\\n for call_index in range(len(mock_obj.call_args_list)):\\n arg_index = 0\\n while mock_obj.call_args_list[call_index] == (args[arg_index],):\\n call_index += 1\\n arg_index += 1\\n if arg_index == len(args):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff5fc94e5422215465a56c22d8dc89f9\",\n \"score\": \"0.57434964\",\n \"text\": \"def test_initiate_already_active(self):\\r\\n h = self.handler\\r\\n\\r\\n callId = h.initiateCall()\\r\\n\\r\\n self.assertRaises(exc.CallFailed, h.initiateCall, callId=callId)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b284a92733c0acff674d9494d156714\",\n \"score\": \"0.57303333\",\n \"text\": \"def _generate_single_call(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21aa56a1e4f98f2815d43065030cc784\",\n \"score\": \"0.57278705\",\n \"text\": \"def test_independent_parameter(self, num_directions, mocker):\\n spy = mocker.spy(qml.gradients.spsa_gradient, \\\"generate_multishifted_tapes\\\")\\n\\n with qml.queuing.AnnotatedQueue() as q:\\n qml.RX(0.543, wires=[0])\\n qml.RY(-0.654, wires=[1])\\n qml.expval(qml.PauliZ(0))\\n\\n tape = qml.tape.QuantumScript.from_queue(q)\\n dev = qml.device(\\\"default.qubit\\\", wires=2)\\n tapes, fn = spsa_grad(tape, num_directions=num_directions)\\n res = fn(dev.batch_execute(tapes))\\n assert res.shape == (1, 2)\\n\\n # 2 tapes per direction because the default strategy for SPSA is \\\"center\\\"\\n assert len(spy.call_args_list) == num_directions\\n\\n # Never shift the independent parameter\\n for i in range(num_directions):\\n assert spy.call_args_list[i][0][0:2] == (tape, [0])\\n assert spy.call_args_list[i][0][2].shape == (2, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f5e4fcee793664b72ae62c9e8849de\",\n \"score\": \"0.5725268\",\n \"text\": \"def test_sub_repeat() -> None:\\n assert sub([4, 4, 4, 4, 4, 4], 1, 3) == [4, 4]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2acd84f09f3dc8baffc62c87ca01977b\",\n \"score\": \"0.5721575\",\n \"text\": \"def simple_call(a):\\n result = dll.test_simple_call(a)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b1a46a0b0baffa82bc9b89c4a5df0c\",\n \"score\": \"0.57209504\",\n \"text\": \"def __test_call():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485c21f8b9f3ff328c44d1e6387531b6\",\n \"score\": \"0.5698183\",\n \"text\": \"def test_call_failure(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56c91473ed3d7a5a48e0022cb23839b4\",\n \"score\": \"0.56949353\",\n \"text\": \"def test_single_task_with_args(add_mock):\\n\\n def fn(*args):\\n pass\\n\\n # decorate & call\\n single_task(60)(fn)('1', 2, None, False)\\n\\n assert add_mock.call_count == 1\\n assert add_mock.call_args[0] == ('lock_fn_1_2_None_False', 'true', 60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689883f56c8665ded28097959b04f5ec\",\n \"score\": \"0.5678\",\n \"text\": \"def test_call_with_parameters(self):\\n amp = circuit.Parameter(\\\"amp\\\")\\n\\n with pulse.build() as subroutine:\\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\\n\\n with pulse.build() as main_prog:\\n pulse.call(subroutine, amp=0.1)\\n pulse.call(subroutine, amp=0.3)\\n\\n self.assertEqual(main_prog.is_parameterized(), False)\\n\\n assigned_sched = target_qobj_transform(main_prog)\\n\\n play_0 = assigned_sched.instructions[0][1]\\n play_1 = assigned_sched.instructions[1][1]\\n\\n self.assertEqual(play_0.pulse.amp, 0.1)\\n self.assertEqual(play_1.pulse.amp, 0.3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"018a9a240750b08093cdc76e39eae6e0\",\n \"score\": \"0.5657119\",\n \"text\": \"def change_random_call(\\n self, test_case: tc.TestCase, statement: stmt.VariableCreatingStatement\\n ) -> bool:\\n objects = test_case.get_all_objects(statement.get_position())\\n type_ = statement.ret_val.type\\n # We need a consistent signature, otherwise nothing will match up\\n signature_memo: dict[InferredSignature, dict[str, ProperType]] = {}\\n calls = self._get_possible_calls(type_, objects, signature_memo)\\n acc_object = statement.accessible_object()\\n if acc_object in calls:\\n calls.remove(acc_object)\\n\\n if len(calls) == 0:\\n return False\\n\\n call = randomness.choice(calls)\\n try:\\n self.change_call(test_case, statement, call, signature_memo)\\n return True\\n except ConstructionFailedException:\\n self._logger.debug(\\\"Failed to change call for statement.\\\", exc_info=True)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3259628c4ebf6b42fd0318555877a99\",\n \"score\": \"0.5653373\",\n \"text\": \"def testCheckParams(self):\\n def cb(e=None):\\n self.passed = self.passed and e is None\\n def check(param1, param2):\\n if param1 and param2:\\n self.passed = True\\n return True\\n \\n checkFunc = FunctionCall(check, {'param1' : 'non-empty string', 'param2' : 'asdfasdf'})\\n self.server.watch_function(checkFunc, cb)\\n \\n asyncore.poll()\\n \\n self.assert_(self.passed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51671f0dd346bdef4f53153ca9c12e60\",\n \"score\": \"0.56507623\",\n \"text\": \"def test_run_1_call(self):\\n \\n pc = SippClient(command=\\\"-m 1\\\")\\n sipp_client_proc = SippClient.launch(pc)\\n time.sleep(12)\\n # at this point,\\n try:\\n outs, errs = sipp_client_proc.communicate(input = \\\"q\\\", timeout = 10)\\n except TimeoutExpired:\\n sipp_client_proc.kill()\\n outs.errs = sipp_client_proc.communicate()\\n \\n self.assertTrue(how_many_success(pc.script, pc.pid) == 1)\\n \\n cleanup_screen_log(pc.script, pc.pid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c46a990a49eff71f30d23d6e3ce4b4d7\",\n \"score\": \"0.56469494\",\n \"text\": \"def test_assign_to_parameter_in_call_and_to_value_in_caller(self):\\n with pulse.build(name=\\\"call_xp_xp\\\") as call_xp_xp:\\n pulse.call(self.xp_)\\n pulse.play(Gaussian(160, self.amp_xp, self.sigma), self.d0_)\\n self.cals.add_schedule(call_xp_xp, num_qubits=1)\\n\\n my_amp = Parameter(\\\"amp\\\")\\n sched = self.cals.get_schedule(\\n \\\"call_xp_xp\\\",\\n (2,),\\n assign_params={\\n (\\\"amp\\\", (2,), \\\"xp\\\"): my_amp,\\n (\\\"amp\\\", (2,), \\\"call_xp_xp\\\"): 0.2,\\n },\\n )\\n sched = block_to_schedule(sched)\\n\\n with pulse.build(name=\\\"xp\\\") as expected:\\n pulse.play(Gaussian(160, my_amp, 40), DriveChannel(2))\\n pulse.play(Gaussian(160, 0.2, 40), DriveChannel(2))\\n expected = block_to_schedule(expected)\\n\\n self.assertEqual(sched, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabe54304f77bc779e4d12f092a8ded6\",\n \"score\": \"0.5637324\",\n \"text\": \"def repeatfunc(func, times=..., *args):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6657e8d96ac0874355f2be74c1ba67c2\",\n \"score\": \"0.56354165\",\n \"text\": \"def test_run_once(self):\\n\\t\\tcounter = 0\\n\\t\\tprint(\\\"starting run_once test\\\")\\n\\t\\tstate_tracker.set_state(3)\\n\\t\\tcounter = mock_main(INITIAL_COUNTER, 1)\\n\\t\\tprint(\\\"run_once test complete\\\")\\n\\t\\tself.assertEqual(counter, 1);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc0cf9ed74770704456dbe1d0015609\",\n \"score\": \"0.5628558\",\n \"text\": \"def test_single_task_with_list_as_argument(add_mock):\\n\\n def fn(lst):\\n pass\\n\\n # decorate & call\\n single_task(60)(fn)([1, 2])\\n\\n assert add_mock.call_count == 1\\n assert add_mock.call_args[0] == ('lock_fn_lst_[1,2]', 'true', 60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e444a7cadbe661870c4d636755756f88\",\n \"score\": \"0.5622122\",\n \"text\": \"def testCheckParam(self):\\n def cb(e=None):\\n self.passed = self.passed and e is None\\n def check(param1):\\n if param1:\\n self.passed = True\\n return True\\n \\n checkFunc = FunctionCall(check, {'param1' : 'non-empty string'})\\n self.server.watch_function(checkFunc, cb)\\n \\n asyncore.poll()\\n \\n self.assert_(self.passed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f84b3f2614e18ff322d4f5421ff93fbc\",\n \"score\": \"0.5613881\",\n \"text\": \"def test_direct_call():\\n assert add(3, 4) == 7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22c4c3baedca70d0642d1552d53f17e\",\n \"score\": \"0.56058097\",\n \"text\": \"def test_suite():\\r\\n test(is_multiple(12, 3))\\r\\n test(is_multiple(12, 4))\\r\\n test(not is_multiple(12, 5))\\r\\n test(is_multiple(12, 6))\\r\\n test(not is_multiple(12, 7))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77698f29e4e2e32b0f0509b647721de6\",\n \"score\": \"0.5602916\",\n \"text\": \"def test_no_arg_call_function(self, rr):\\n\\n def dummy_func():\\n Sgate(r) | q[0]\\n BSgate() | (q[0], q[1])\\n\\n op = utils.operation(ns=1)\\n op(dummy_func)()\\n\\n with pytest.raises(ValueError, match=\\\"must receive the qumode register\\\"):\\n op._call_function(rr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d63546ce6719a96222de668b5c0af3\",\n \"score\": \"0.5601269\",\n \"text\": \"def test_assign_new_call_when_call_is_not_undefined(self):\\n self.emp.recieve_call(self.call_instance)\\n self.assertRaises(NameError, lambda: self.emp.assign_new_call(self.call_instance))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b664be6827ab415ba332a4bdd1152f3\",\n \"score\": \"0.56010383\",\n \"text\": \"def test_assign_to_parameter_in_call(self):\\n with pulse.build(name=\\\"call_xp\\\") as call_xp:\\n pulse.call(self.xp_)\\n self.cals.add_schedule(call_xp, num_qubits=1)\\n\\n my_amp = Parameter(\\\"my_amp\\\")\\n sched = self.cals.get_schedule(\\\"call_xp\\\", (2,), assign_params={(\\\"amp\\\", (2,), \\\"xp\\\"): my_amp})\\n sched = block_to_schedule(sched)\\n\\n with pulse.build(name=\\\"xp\\\") as expected:\\n pulse.play(Gaussian(160, my_amp, 40), DriveChannel(2))\\n expected = block_to_schedule(expected)\\n\\n self.assertEqual(sched, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a42af4786661774674754028765c83\",\n \"score\": \"0.55976\",\n \"text\": \"def test_repeat():\\n processor = quick_processor()\\n for element in processor.repeat(100, returns_42):\\n assert_equals(element, 42)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa9fc7dc0a49f0dfc6edce11d44d9161\",\n \"score\": \"0.55933946\",\n \"text\": \"def test_call(self):\\n with mock.patch.object(SingletonMeta, \\\"__call__\\\", side_effect=InstantiationError) as p:\\n with self.assertRaises(InstantiationError):\\n self.test_class(1, 2)\\n p.assert_called_with(1, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901db44da3e4f186d21612a0931254e7\",\n \"score\": \"0.559236\",\n \"text\": \"def test_five_times(self):\\n\\t\\tcounter = 0\\n\\t\\tprint(\\\"starting run_once test\\\")\\n\\t\\tstate_tracker.set_state(4)\\n\\t\\tcounter = mock_main(INITIAL_COUNTER, 5)\\n\\t\\tprint(\\\"run_once test complete\\\")\\n\\t\\tself.assertEqual(counter, 5);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"376a46d2c8ed79c3c67831e8e47c1673\",\n \"score\": \"0.5588254\",\n \"text\": \"def assert_call(mock, call_idx, *args, **kwargs):\\n actual = mock.mock_calls[call_idx] if mock.mock_calls else None\\n msg = f\\\"Call {call_idx} expected {(args, kwargs)}, was {actual}\\\"\\n assert actual == (args, kwargs), msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07303b58e17df240fc573c9b66b06b2d\",\n \"score\": \"0.5584188\",\n \"text\": \"def call(self, x):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d806a77438afeeae701e20549cfde89c\",\n \"score\": \"0.5581544\",\n \"text\": \"def test_args_and_kwargs_passed(self):\\n\\n def flakey(a, b, c=None):\\n self.assertEqual(a, 1)\\n self.assertEqual(b, 2)\\n self.assertEqual(c, 3)\\n\\n retry(flakey, 1, 2, c=3)\\n retry_on_error()(flakey)(1, 2, c=3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4571b390b70bd3e6595bfae95a4475fa\",\n \"score\": \"0.5581194\",\n \"text\": \"def test_call_success_parameters():\\n utils_mock = {\\n \\\"json.find_json\\\": MagicMock(return_value={\\\"return\\\": \\\"result\\\"}),\\n }\\n salt_mock = {\\n \\\"cmd.run_all\\\": MagicMock(return_value={\\\"retcode\\\": 0, \\\"stdout\\\": \\\"\\\"}),\\n }\\n with patch.dict(tu.__utils__, utils_mock), patch.dict(tu.__salt__, salt_mock):\\n assert tu.call(\\\"module.function\\\", key=\\\"value\\\") == \\\"result\\\"\\n\\n salt_mock[\\\"cmd.run_all\\\"].assert_called_with(\\n [\\n \\\"transactional-update\\\",\\n \\\"--non-interactive\\\",\\n \\\"--drop-if-no-change\\\",\\n \\\"--no-selfupdate\\\",\\n \\\"--continue\\\",\\n \\\"--quiet\\\",\\n \\\"run\\\",\\n \\\"salt-call\\\",\\n \\\"--out\\\",\\n \\\"json\\\",\\n \\\"-l\\\",\\n \\\"quiet\\\",\\n \\\"--no-return-event\\\",\\n \\\"--\\\",\\n \\\"module.function\\\",\\n \\\"key=value\\\",\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3748ccbe6c1a9dd123eb01a5776aa31b\",\n \"score\": \"0.5579915\",\n \"text\": \"def test_initiate(self):\\r\\n h = self.handler\\r\\n c = ('foo', ['bar', 'baz'], {})\\r\\n\\r\\n self.assertEqual(h.getNextCallId(), 1)\\r\\n self.assertEqual(h.getCallContext(1), None)\\r\\n\\r\\n self.assertEqual(h.initiateCall(*c), 1)\\r\\n self.assertEqual(h.getCallContext(1), c)\\r\\n\\r\\n self.assertEqual(h.getNextCallId(), 2)\\r\\n self.assertEqual(h.getCallContext(2), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46a353bd22c06a0a2108587810cdc8f\",\n \"score\": \"0.5572544\",\n \"text\": \"def test_call(self):\\n response = self.func()\\n self.assertEqual(1, response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f757f2ea11100d259437b86b322f01b\",\n \"score\": \"0.5560395\",\n \"text\": \"def test_set_parameter_2(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38078e08fe64a1800197938b919fee29\",\n \"score\": \"0.55522126\",\n \"text\": \"def test_routine(self, sample):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca9f85eb206d3a3b9849de8ec9fbc9ef\",\n \"score\": \"0.5551688\",\n \"text\": \"def mock_func_call(*args, **kwargs):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc4418e2111eaec2b435d53eac13aea6\",\n \"score\": \"0.55471027\",\n \"text\": \"def test_multiple_calls(self):\\n\\n @task_decorator\\n def do_run():\\n return 4\\n\\n with self.dag:\\n do_run()\\n assert [\\\"do_run\\\"] == self.dag.task_ids\\n do_run_1 = do_run()\\n do_run_2 = do_run()\\n assert [\\\"do_run\\\", \\\"do_run__1\\\", \\\"do_run__2\\\"] == self.dag.task_ids\\n\\n assert do_run_1.operator.task_id == \\\"do_run__1\\\"\\n assert do_run_2.operator.task_id == \\\"do_run__2\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e20808ae57c69bbccf7c1d16cc543d0d\",\n \"score\": \"0.5541567\",\n \"text\": \"def test_multiple_requests_verify_ssl_once(self, post_mock):\\n post_mock.return_value.status_code = 200\\n\\n self._api_client.add_note('incident_id', 'this is the note')\\n self._api_client.add_note('incident_id', 'this is another note')\\n self._api_client.add_note('incident_id', 'this is a third note')\\n\\n class Anything(object):\\n def __eq__(self, _):\\n return True\\n\\n class VerifyIsCalledWith(object):\\n def __init__(self, expected_verify_value):\\n self._expected_verify_value = expected_verify_value\\n\\n def __eq__(self, other):\\n return self._expected_verify_value == other\\n\\n post_mock.assert_has_calls(\\n [\\n call(\\n Anything(),\\n headers=Anything(), json=Anything(), timeout=Anything(),\\n verify=VerifyIsCalledWith(True)\\n ),\\n call(\\n Anything(),\\n headers=Anything(), json=Anything(), timeout=Anything(),\\n verify=VerifyIsCalledWith(False)\\n ),\\n call(\\n Anything(),\\n headers=Anything(), json=Anything(), timeout=Anything(),\\n verify=VerifyIsCalledWith(False)\\n ),\\n ],\\n # So the problem with assert_has_calls() is that it requires you to declare all calls\\n # including chained calls. This doesn't work because we do a bunch of random stuff\\n # inbetween with the return value (such as .json() calls) and it's not really feasible\\n # to declare ALL of the calls.\\n #\\n # By setting any_order=True, we ensure all of the above calls are made at least once.\\n # We lose out on the ability to detect that we called verify=True FIRST (before the\\n # two verify=False calls)... but, oh well?\\n any_order=True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca899733085d7bf8a3c5aba04740578\",\n \"score\": \"0.55377996\",\n \"text\": \"def repeat(self, *args, **params): # reliably restored by inspect\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"895353c1052effb41e3bba1d575bc392\",\n \"score\": \"0.55340666\",\n \"text\": \"def test_too_few_arguments(run):\\n _, code = run()\\n\\n assert code != 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb2e9801fc01162415bffcee906e9a3\",\n \"score\": \"0.55328834\",\n \"text\": \"def test_call_partly_with_parameters(self):\\n amp = circuit.Parameter(\\\"amp\\\")\\n\\n with pulse.build() as subroutine:\\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\\n\\n with pulse.build() as main_prog:\\n pulse.call(subroutine, amp=0.1)\\n pulse.call(subroutine)\\n\\n self.assertEqual(main_prog.is_parameterized(), True)\\n\\n main_prog.assign_parameters({amp: 0.5})\\n self.assertEqual(main_prog.is_parameterized(), False)\\n\\n assigned_sched = target_qobj_transform(main_prog)\\n\\n play_0 = assigned_sched.instructions[0][1]\\n play_1 = assigned_sched.instructions[1][1]\\n\\n self.assertEqual(play_0.pulse.amp, 0.1)\\n self.assertEqual(play_1.pulse.amp, 0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d714b85f5728f7961367b9463b0f31d1\",\n \"score\": \"0.55314493\",\n \"text\": \"def once():\\n return OnceInvocationMatcher()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"714eb7388e1087a8ae32a98b0a8177de\",\n \"score\": \"0.5526994\",\n \"text\": \"def test_set_parameter_1(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed7d51bdb953fe7410032330eca221c3\",\n \"score\": \"0.55262697\",\n \"text\": \"def test_single_task_with_args_and_kwargs(add_mock):\\n\\n def fn(*args, **kwargs):\\n pass\\n\\n # decorate & call\\n single_task(60)(fn)(1, 2, a='b', b='c')\\n\\n assert add_mock.call_count == 1\\n assert add_mock.call_args[0] == ('lock_fn_1_2_a_b_b_c', 'true', 60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa77ab74a38b5def4c35005296749254\",\n \"score\": \"0.55220574\",\n \"text\": \"def at_least_once():\\n return AtLeastOnceInvocationMatcher()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e5ce2b85623c51ce9f8dc6a9c1a585\",\n \"score\": \"0.55205107\",\n \"text\": \"def test_two_args(a,b):\\n return a,b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbe70da632bdb992d1cdebaac3c93718\",\n \"score\": \"0.5518048\",\n \"text\": \"def test_hundred_times(self):\\n\\t\\tcounter = 0\\n\\t\\tprint(\\\"starting run_once test\\\")\\n\\t\\tstate_tracker.set_state(5)\\n\\t\\tcounter = mock_main(INITIAL_COUNTER, 100)\\n\\t\\tprint(\\\"run_once test complete\\\")\\n\\t\\tself.assertEqual(counter, 100);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f5bbcefe22a809abeb536f4961116d1\",\n \"score\": \"0.5503572\",\n \"text\": \"def _exec_one_test_with_repeat(self, test_name, test_method, repeat_count,\\n max_consecutive_error):\\n\\n consecutive_error_count = 0\\n\\n # If max_consecutive_error is not set by user, it is considered the same as\\n # the repeat_count.\\n if max_consecutive_error == 0:\\n max_consecutive_error = repeat_count\\n\\n for i in range(repeat_count):\\n new_test_name = f'{test_name}_{i}'\\n record = self.exec_one_test(new_test_name, test_method)\\n if record.result in [\\n records.TestResultEnums.TEST_RESULT_FAIL,\\n records.TestResultEnums.TEST_RESULT_ERROR,\\n ]:\\n consecutive_error_count += 1\\n else:\\n consecutive_error_count = 0\\n if consecutive_error_count == max_consecutive_error:\\n logging.error(\\n 'Repeated test case \\\"%s\\\" has consecutively failed %d iterations, '\\n 'aborting the remaining %d iterations.', test_name,\\n consecutive_error_count, repeat_count - 1 - i)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a149f8bb76ad9347029c72fdfd830cc6\",\n \"score\": \"0.55023247\",\n \"text\": \"def test_kwargs_are_passed(self):\\n with patch(\\\"sys.exit\\\") as mock_exit:\\n cli = MicroCLITestCase.T(\\n \\\"script_name f3 --awesome-option 1\\\".split()).run()\\n mock_exit.assert_called_with(1)\\n with patch(\\\"sys.exit\\\") as mock_exit:\\n cli = MicroCLITestCase.T(\\n \\\"script_name f3 --awesome-option 1234567\\\".split()).run()\\n mock_exit.assert_called_with(7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edeacbbffbbe77c368fef6a71b990987\",\n \"score\": \"0.55000293\",\n \"text\": \"def assert_any_call(self, *args, **kwargs):\\n at_least_on_passed = False\\n for kall in self.call_args_list:\\n try:\\n self._assert_with_constraints(args, kwargs, kall)\\n #assertions passed, return because there's a match\\n return\\n except AssertionError:\\n #keep trying\\n pass\\n\\n expected_string = self._format_mock_call_signature(args, kwargs)\\n raise AssertionError(\\n '%s call not found' % expected_string\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e2d028c9ff0df1b03cab365b78d269\",\n \"score\": \"0.5497843\",\n \"text\": \"def test_single_task_no_arguments(add_mock):\\n\\n def fn():\\n pass\\n\\n # decorate & call\\n single_task(60)(fn)()\\n\\n assert add_mock.call_count == 1\\n assert add_mock.call_args[0] == ('lock_fn', 'true', 60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b6b5f26c52fd5f05bc6e6e1f410cbc0\",\n \"score\": \"0.54946685\",\n \"text\": \"def test_invalidate_args():\\n f = Mock(__name__='f', return_value=None)\\n decorated = cache()(f)\\n decorated(1)\\n decorated.invalidate(1)\\n decorated(1)\\n assert f.call_count == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e536ea416442b631a8312ee55a68d5b1\",\n \"score\": \"0.54920566\",\n \"text\": \"def test_w_more_path_params(self):\\n assert_that(\\n calling(\\n self.processor.process\\n ).with_args(\\n [2], {}\\n ),\\n raises(ValueError)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eb6cc6b2d2cd3c133b0733065fc102d\",\n \"score\": \"0.54843545\",\n \"text\": \"def test_called(self):\\n result = []\\n registry = FunctionRegistry()\\n registry.register(lambda: result.append(1))\\n registry.register(lambda x: result.append(x), 2)\\n registry.register(lambda y: result.append(y), y=3)\\n registry.run()\\n self.assertEqual(result, [3, 2, 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c552083039e4740235aff4f39e9424b\",\n \"score\": \"0.54820985\",\n \"text\": \"def test_ignored_args(self, _, cache_decorator):\\n call_count = [0]\\n\\n @cache_decorator\\n def foo(arg1, _arg2, *args, kwarg1, _kwarg2=None, **kwargs):\\n call_count[0] += 1\\n\\n foo(1, 2, 3, kwarg1=4, _kwarg2=5, kwarg3=6, _kwarg4=7)\\n self.assertEqual([1], call_count)\\n\\n # Call foo again, but change the values for _arg2, _kwarg2, and _kwarg4.\\n # The call count shouldn't change, because these args will not be part\\n # of the hash.\\n foo(1, None, 3, kwarg1=4, _kwarg2=None, kwarg3=6, _kwarg4=None)\\n self.assertEqual([1], call_count)\\n\\n # Changing the value of any other argument will increase the call\\n # count. We test each argument type:\\n\\n # arg1 (POSITIONAL_OR_KEYWORD)\\n foo(None, 2, 3, kwarg1=4, _kwarg2=5, kwarg3=6, _kwarg4=7)\\n self.assertEqual([2], call_count)\\n\\n # *arg (VAR_POSITIONAL)\\n foo(1, 2, None, kwarg1=4, _kwarg2=5, kwarg3=6, _kwarg4=7)\\n self.assertEqual([3], call_count)\\n\\n # kwarg1 (KEYWORD_ONLY)\\n foo(1, 2, 3, kwarg1=None, _kwarg2=5, kwarg3=6, _kwarg4=7)\\n self.assertEqual([4], call_count)\\n\\n # **kwarg (VAR_KEYWORD)\\n foo(1, 2, 3, kwarg1=4, _kwarg2=5, kwarg3=None, _kwarg4=7)\\n self.assertEqual([5], call_count)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992998,"cells":{"query_id":{"kind":"string","value":"befa954a806ef81c803dd20c5190657d"},"query":{"kind":"string","value":"Base method which allows us to understand different goals."},"positive_passages":{"kind":"list like","value":[{"docid":"d421c7307c7661f2c21f9e054a633cce","score":"0.0","text":"def achieve_goals(state, goals):\n tasks = []\n for goal in goals:\n if goal.kwargs['predicate'] == 'agent-at':\n action = 'move-to'\n tasks.append((action, goal.args[0]))\n elif goal.kwargs['predicate'] == 'killed':\n targetID = goal.args[0]\n targetLoc = state.map.get_object(targetID).location\n tasks.append(('move-adjacent', targetLoc))\n tasks.append(('arm', 0))\n tasks.append(('arm', 0))\n tasks.append(('bomb', 2))\n else:\n action = goal.kwargs['predicate']\n tasks.append((action, goal.args[0]))\n\n return tasks","title":""}],"string":"[\n {\n \"docid\": \"d421c7307c7661f2c21f9e054a633cce\",\n \"score\": \"0.0\",\n \"text\": \"def achieve_goals(state, goals):\\n tasks = []\\n for goal in goals:\\n if goal.kwargs['predicate'] == 'agent-at':\\n action = 'move-to'\\n tasks.append((action, goal.args[0]))\\n elif goal.kwargs['predicate'] == 'killed':\\n targetID = goal.args[0]\\n targetLoc = state.map.get_object(targetID).location\\n tasks.append(('move-adjacent', targetLoc))\\n tasks.append(('arm', 0))\\n tasks.append(('arm', 0))\\n tasks.append(('bomb', 2))\\n else:\\n action = goal.kwargs['predicate']\\n tasks.append((action, goal.args[0]))\\n\\n return tasks\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"97e7c5eafec6ce8952affd0d1c8c4fd2","score":"0.69283324","text":"def funtion(self):\n pass","title":""},{"docid":"f240aea361128e7757b08542987ba29f","score":"0.6904391","text":"def mystere():","title":""},{"docid":"9674a53f41c819786d78160c50d1decc","score":"0.67180216","text":"def __call__(self):\n raise NotImplementedError()","title":""},{"docid":"9674a53f41c819786d78160c50d1decc","score":"0.67180216","text":"def __call__(self):\n raise NotImplementedError()","title":""},{"docid":"b12f708cae8038c16913c99cde90c4d2","score":"0.66701686","text":"def base(self):\n raise NotImplementedError","title":""},{"docid":"d388939ba349a300a7a20adb599c127d","score":"0.66510236","text":"def __call__(self):\n pass","title":""},{"docid":"d388939ba349a300a7a20adb599c127d","score":"0.66510236","text":"def __call__(self):\n pass","title":""},{"docid":"1a923128dfd8d2d94181905d2ba647ea","score":"0.65826267","text":"def ponder(self):\n pass","title":""},{"docid":"eb4d4d65635251254c703b5fff12a038","score":"0.6578684","text":"def __call__(self):\n raise NotImplementedError(\"Abstract base class\")","title":""},{"docid":"789cffa538f67fdba784053a3d030669","score":"0.6563696","text":"def perform(self):","title":""},{"docid":"a488b61f055d5cf48c4cbc7e999a10fc","score":"0.65017575","text":"def test(self):\n raise NotImplementedError(\"Abstract method\")","title":""},{"docid":"58f8c5d28e864ef3beaf37966923c29c","score":"0.64750177","text":"def __call__(self):\n raise NotImplementedError(\"must be implemented in descendants\")","title":""},{"docid":"f639c6afdff89a2ead32226a60d61e51","score":"0.6443737","text":"def decide(self):\n pass","title":""},{"docid":"9dff28c281898b08fae6c8bc69e263ee","score":"0.6436997","text":"def passed(self):\n raise NotImplementedError","title":""},{"docid":"59c7761d6d02c0520c6d4fab35e8d60b","score":"0.64321","text":"def __call__(self) -> None:\n raise NotImplementedError()","title":""},{"docid":"689aa53a38fbce4d7a7bf0464a8fac12","score":"0.6378634","text":"def what_do_you_do(self):","title":""},{"docid":"9431f55afcd37a2a6831ab6c50584bd9","score":"0.6377683","text":"def work(self):","title":""},{"docid":"9431f55afcd37a2a6831ab6c50584bd9","score":"0.6377683","text":"def work(self):","title":""},{"docid":"117ee743ff169c45e61d1ccada7734ce","score":"0.6316477","text":"def explain(self):\r\n raise NotImplementedError(\"Abstract methods requires overriding\")","title":""},{"docid":"58d9501cca603a05f87eb5e5e07cbfb6","score":"0.63130003","text":"def _check(self):\n raise NotImplementedError(\"Must be overridden in subclass\")","title":""},{"docid":"79c2f6adedbf31147b877e0b64bef4b7","score":"0.6249455","text":"def __call__(self, *args, **kwargs):\n raise NotImplementedError()","title":""},{"docid":"79c2f6adedbf31147b877e0b64bef4b7","score":"0.6249455","text":"def __call__(self, *args, **kwargs):\n raise NotImplementedError()","title":""},{"docid":"79c2f6adedbf31147b877e0b64bef4b7","score":"0.6249455","text":"def __call__(self, *args, **kwargs):\n raise NotImplementedError()","title":""},{"docid":"79c2f6adedbf31147b877e0b64bef4b7","score":"0.6249455","text":"def __call__(self, *args, **kwargs):\n raise NotImplementedError()","title":""},{"docid":"da6ab0fa4f4928bdcca00d6866e74bdc","score":"0.6229666","text":"def function(self):\r\n raise NotImplementedError","title":""},{"docid":"e3381a328c134d1c22ec7ca8e390db1d","score":"0.62227833","text":"def build_your_own(self, *args, **kwargs):\n return NotImplementedError","title":""},{"docid":"a45d20bf02c1e643467e2d3871758f0c","score":"0.62220144","text":"def logic(self, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"d69a9efb278875e9313da22c7828e57a","score":"0.6206746","text":"def __call__():\n pass","title":""},{"docid":"82bd304ea361bfaba02210865d24d1fd","score":"0.61942524","text":"def work(self):\r\n print \"OVERRIDE ME\"","title":""},{"docid":"d7cfb4c1af72c8a994c61686b4be676b","score":"0.6189657","text":"def main(self):\r\n\r\n raise NotImplementedError()","title":""},{"docid":"105009e2a2daf4a9a951961f8b6e5b71","score":"0.61852306","text":"def __call__(self, context):\n\t\traise NotImplementedError","title":""},{"docid":"b931cb4adffb6ce8764de896d8ea98f8","score":"0.61768967","text":"def method(self):\n pass","title":""},{"docid":"b931cb4adffb6ce8764de896d8ea98f8","score":"0.61768967","text":"def method(self):\n pass","title":""},{"docid":"b931cb4adffb6ce8764de896d8ea98f8","score":"0.61768967","text":"def method(self):\n pass","title":""},{"docid":"f121933450b3d8b39c9ec540317e7021","score":"0.61750984","text":"def _process(self):\n raise NotImplementedError","title":""},{"docid":"d511416ea236cead7eb1e7339104795e","score":"0.61638886","text":"def _specific_checks(self):\n raise NotImplementedError()","title":""},{"docid":"e625da2a01cb198004694d58a2f0cdbd","score":"0.6131592","text":"def explanation(self):\n assert False, 'abstract method called'","title":""},{"docid":"5285d7301b34db8db3e7e903c3728081","score":"0.6126429","text":"def __call__(self): # pylint: disable=signature-differs","title":""},{"docid":"5a5053213b3014e269541020887a4600","score":"0.61236763","text":"def guide(self):","title":""},{"docid":"d47bc0d62b2bb282a79065ed2106d9a9","score":"0.611109","text":"def operations(self):","title":""},{"docid":"a38656990b967f3e6636a2ea530f183e","score":"0.6090667","text":"def check(self):\n raise NotImplementedError()","title":""},{"docid":"98cc442e2cf244c77a17ad430f9f9d39","score":"0.6088873","text":"def sexy():\n raise NotImplementedError","title":""},{"docid":"c388c67ed8acca253ca3827f42989c7c","score":"0.60837585","text":"def goal(self) -> Goal:","title":""},{"docid":"c25434ce1bc16d88b125af7e6b1515b5","score":"0.6083067","text":"def abstract():","title":""},{"docid":"0b80a68a775eb1a309fdad9337c29a1b","score":"0.60681933","text":"def __call__():","title":""},{"docid":"0b80a68a775eb1a309fdad9337c29a1b","score":"0.60681933","text":"def __call__():","title":""},{"docid":"0b80a68a775eb1a309fdad9337c29a1b","score":"0.60681933","text":"def __call__():","title":""},{"docid":"9b563cfa225e84f9374283878b24d4b5","score":"0.60651064","text":"def _dispatch(self):","title":""},{"docid":"6b10ce085401ac63407a41029218a3c6","score":"0.60464245","text":"def original(self):","title":""},{"docid":"6b10ce085401ac63407a41029218a3c6","score":"0.60464245","text":"def original(self):","title":""},{"docid":"6b10ce085401ac63407a41029218a3c6","score":"0.60464245","text":"def original(self):","title":""},{"docid":"4e2fe00e4fdc95d77ba10cfc790874a6","score":"0.60385513","text":"def add_base_arguments(self):","title":""},{"docid":"9edc7f017f0822ac3255a6594ee518c8","score":"0.6032779","text":"def out(self):\r\n raise RuntimeError(\"You must override this method in your derived\" \\\r\n \" class\")","title":""},{"docid":"e7d33de125638670a643d16206b9c88e","score":"0.60205364","text":"def sample_method(self):\n pass","title":""},{"docid":"fd34f5755209bbb9f64bb125a6f898e2","score":"0.60201615","text":"def boe(self):\n pass","title":""},{"docid":"82787595f88605b4238b80b1613689ce","score":"0.601678","text":"def proof_of_work(self):\n ...","title":""},{"docid":"0bdddd9ec887ee69214ad7afe71c37e6","score":"0.60161006","text":"def public_method(self):\n pass","title":""},{"docid":"a37ca25e7d6b2897ec447fa1fba3426e","score":"0.5998337","text":"def perform(self):\r\n pass","title":""},{"docid":"a5b105041adc0377458c1ae2603611fa","score":"0.5986857","text":"def _assemble(self):\r\n raise NotImplementedError # pragma: nocover\r","title":""},{"docid":"f7774ddaa12117815f3bc1a52d063491","score":"0.598116","text":"def __init__(self):\n raise NotImplementedError","title":""},{"docid":"f7774ddaa12117815f3bc1a52d063491","score":"0.598116","text":"def __init__(self):\n raise NotImplementedError","title":""},{"docid":"f7774ddaa12117815f3bc1a52d063491","score":"0.598116","text":"def __init__(self):\n raise NotImplementedError","title":""},{"docid":"33471086cda57474893dcb36afaa7437","score":"0.5971278","text":"def _run(self):\n raise NotImplementedError","title":""},{"docid":"17eddc8a72638b2f062ca409a5ff99ff","score":"0.59663826","text":"def check(self):\r\n raise NotImplementedError()","title":""},{"docid":"2c334343931867769d05e01bf06a5024","score":"0.5965865","text":"def __call__(self):","title":""},{"docid":"0413cc7b27bc3ff096af6950ed4014b9","score":"0.59657526","text":"def sanity_checks(self):\n raise NotImplementedError","title":""},{"docid":"60714d4ef6df44e6071ccc1288b0a31f","score":"0.5960815","text":"def __call__(self, obj):\n raise NotImplementedError","title":""},{"docid":"1ca423b34e016ef1764bd197fc2b1a97","score":"0.5956529","text":"def Sample(self):","title":""},{"docid":"c5f929e5757d785c245990e120fb5043","score":"0.5951981","text":"def check(self):","title":""},{"docid":"c5f929e5757d785c245990e120fb5043","score":"0.5951981","text":"def check(self):","title":""},{"docid":"deb4303522b4e7b7c519d7f66007e89d","score":"0.5951262","text":"def act(self):","title":""},{"docid":"7376c6070bd45a9f7cbb12ebcedc5b63","score":"0.59480935","text":"def func(self):\n pass","title":""},{"docid":"f13e6e77e89d717449528bff38fbd195","score":"0.5943412","text":"def dummy_method2(self):","title":""},{"docid":"265b03dfe4bcf5c0e2daa0503a255161","score":"0.59050536","text":"def stand(self):","title":""},{"docid":"7ebc69ba6d6087e947e235a8ac5b8d5f","score":"0.5889242","text":"def method2(self):\n pass","title":""},{"docid":"f9c82fcce3672cf836716181212cb178","score":"0.58867246","text":"def _test(self):","title":""},{"docid":"f9c82fcce3672cf836716181212cb178","score":"0.58867246","text":"def _test(self):","title":""},{"docid":"f9c82fcce3672cf836716181212cb178","score":"0.58867246","text":"def _test(self):","title":""},{"docid":"9bc7f0a6395c77375a0db941066c4cde","score":"0.5880117","text":"def donothing(self):\n pass","title":""},{"docid":"2b566e72f4a8cdd291cd200ce5d1b5db","score":"0.5875836","text":"def __private_method(self):\n pass","title":""},{"docid":"f06194ca6445b8ded111f3a267e6df3c","score":"0.5868783","text":"def analyse(self):\r\n raise NotImplementedError","title":""},{"docid":"e41ae5522b4a5b45b71da706010c5d77","score":"0.5865488","text":"def customize(self) -> None:","title":""},{"docid":"c2fad252664688669d84d05b55933b8d","score":"0.58604884","text":"def __call__(self, *args, **kwargs):\n pass","title":""},{"docid":"c2fad252664688669d84d05b55933b8d","score":"0.58604884","text":"def __call__(self, *args, **kwargs):\n pass","title":""},{"docid":"f136cf4e5115edcc3f85e6b839759e4f","score":"0.5860304","text":"def __init__(self):\n # TODO: This isn't done yet!\n raise NotImplementedError","title":""},{"docid":"9413080d43e63a571a45db9ffe8ffa92","score":"0.5853162","text":"def _run(self):\n raise NotImplementedError","title":""},{"docid":"52115c9205aea34cbd8fb0169e571a60","score":"0.58436555","text":"def handle(self):\n raise NotImplementedError()","title":""},{"docid":"52115c9205aea34cbd8fb0169e571a60","score":"0.58436555","text":"def handle(self):\n raise NotImplementedError()","title":""},{"docid":"819ce69933746e9f256af7e64e7a0e75","score":"0.58320206","text":"def main(cls):\n raise NotImplementedError","title":""},{"docid":"8fac457cbee51ce133348883468d5d56","score":"0.58273816","text":"def __call__(self, x):\n raise NotImplementedError()","title":""},{"docid":"88b9306434da07f9c85ebc598920484c","score":"0.5824649","text":"def _base_values(self):\n raise NotImplementedError( # pragma: no cover\n f\"{type(self).__name__} must implement `._base_values`\"\n )","title":""},{"docid":"3f68e1f42f0e93fc9bacb0cafd922c42","score":"0.5822871","text":"def ipsum(self):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.58176124","text":"def __init__(__self__):\n pass","title":""}],"string":"[\n {\n \"docid\": \"97e7c5eafec6ce8952affd0d1c8c4fd2\",\n \"score\": \"0.69283324\",\n \"text\": \"def funtion(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f240aea361128e7757b08542987ba29f\",\n \"score\": \"0.6904391\",\n \"text\": \"def mystere():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9674a53f41c819786d78160c50d1decc\",\n \"score\": \"0.67180216\",\n \"text\": \"def __call__(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9674a53f41c819786d78160c50d1decc\",\n \"score\": \"0.67180216\",\n \"text\": \"def __call__(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b12f708cae8038c16913c99cde90c4d2\",\n \"score\": \"0.66701686\",\n \"text\": \"def base(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d388939ba349a300a7a20adb599c127d\",\n \"score\": \"0.66510236\",\n \"text\": \"def __call__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d388939ba349a300a7a20adb599c127d\",\n \"score\": \"0.66510236\",\n \"text\": \"def __call__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a923128dfd8d2d94181905d2ba647ea\",\n \"score\": \"0.65826267\",\n \"text\": \"def ponder(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb4d4d65635251254c703b5fff12a038\",\n \"score\": \"0.6578684\",\n \"text\": \"def __call__(self):\\n raise NotImplementedError(\\\"Abstract base class\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789cffa538f67fdba784053a3d030669\",\n \"score\": \"0.6563696\",\n \"text\": \"def perform(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a488b61f055d5cf48c4cbc7e999a10fc\",\n \"score\": \"0.65017575\",\n \"text\": \"def test(self):\\n raise NotImplementedError(\\\"Abstract method\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f8c5d28e864ef3beaf37966923c29c\",\n \"score\": \"0.64750177\",\n \"text\": \"def __call__(self):\\n raise NotImplementedError(\\\"must be implemented in descendants\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f639c6afdff89a2ead32226a60d61e51\",\n \"score\": \"0.6443737\",\n \"text\": \"def decide(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dff28c281898b08fae6c8bc69e263ee\",\n \"score\": \"0.6436997\",\n \"text\": \"def passed(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c7761d6d02c0520c6d4fab35e8d60b\",\n \"score\": \"0.64321\",\n \"text\": \"def __call__(self) -> None:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689aa53a38fbce4d7a7bf0464a8fac12\",\n \"score\": \"0.6378634\",\n \"text\": \"def what_do_you_do(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9431f55afcd37a2a6831ab6c50584bd9\",\n \"score\": \"0.6377683\",\n \"text\": \"def work(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9431f55afcd37a2a6831ab6c50584bd9\",\n \"score\": \"0.6377683\",\n \"text\": \"def work(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117ee743ff169c45e61d1ccada7734ce\",\n \"score\": \"0.6316477\",\n \"text\": \"def explain(self):\\r\\n raise NotImplementedError(\\\"Abstract methods requires overriding\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d9501cca603a05f87eb5e5e07cbfb6\",\n \"score\": \"0.63130003\",\n \"text\": \"def _check(self):\\n raise NotImplementedError(\\\"Must be overridden in subclass\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c2f6adedbf31147b877e0b64bef4b7\",\n \"score\": \"0.6249455\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c2f6adedbf31147b877e0b64bef4b7\",\n \"score\": \"0.6249455\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c2f6adedbf31147b877e0b64bef4b7\",\n \"score\": \"0.6249455\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c2f6adedbf31147b877e0b64bef4b7\",\n \"score\": \"0.6249455\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da6ab0fa4f4928bdcca00d6866e74bdc\",\n \"score\": \"0.6229666\",\n \"text\": \"def function(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3381a328c134d1c22ec7ca8e390db1d\",\n \"score\": \"0.62227833\",\n \"text\": \"def build_your_own(self, *args, **kwargs):\\n return NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a45d20bf02c1e643467e2d3871758f0c\",\n \"score\": \"0.62220144\",\n \"text\": \"def logic(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69a9efb278875e9313da22c7828e57a\",\n \"score\": \"0.6206746\",\n \"text\": \"def __call__():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82bd304ea361bfaba02210865d24d1fd\",\n \"score\": \"0.61942524\",\n \"text\": \"def work(self):\\r\\n print \\\"OVERRIDE ME\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7cfb4c1af72c8a994c61686b4be676b\",\n \"score\": \"0.6189657\",\n \"text\": \"def main(self):\\r\\n\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105009e2a2daf4a9a951961f8b6e5b71\",\n \"score\": \"0.61852306\",\n \"text\": \"def __call__(self, context):\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b931cb4adffb6ce8764de896d8ea98f8\",\n \"score\": \"0.61768967\",\n \"text\": \"def method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b931cb4adffb6ce8764de896d8ea98f8\",\n \"score\": \"0.61768967\",\n \"text\": \"def method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b931cb4adffb6ce8764de896d8ea98f8\",\n \"score\": \"0.61768967\",\n \"text\": \"def method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f121933450b3d8b39c9ec540317e7021\",\n \"score\": \"0.61750984\",\n \"text\": \"def _process(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d511416ea236cead7eb1e7339104795e\",\n \"score\": \"0.61638886\",\n \"text\": \"def _specific_checks(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e625da2a01cb198004694d58a2f0cdbd\",\n \"score\": \"0.6131592\",\n \"text\": \"def explanation(self):\\n assert False, 'abstract method called'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5285d7301b34db8db3e7e903c3728081\",\n \"score\": \"0.6126429\",\n \"text\": \"def __call__(self): # pylint: disable=signature-differs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a5053213b3014e269541020887a4600\",\n \"score\": \"0.61236763\",\n \"text\": \"def guide(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47bc0d62b2bb282a79065ed2106d9a9\",\n \"score\": \"0.611109\",\n \"text\": \"def operations(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a38656990b967f3e6636a2ea530f183e\",\n \"score\": \"0.6090667\",\n \"text\": \"def check(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98cc442e2cf244c77a17ad430f9f9d39\",\n \"score\": \"0.6088873\",\n \"text\": \"def sexy():\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c388c67ed8acca253ca3827f42989c7c\",\n \"score\": \"0.60837585\",\n \"text\": \"def goal(self) -> Goal:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25434ce1bc16d88b125af7e6b1515b5\",\n \"score\": \"0.6083067\",\n \"text\": \"def abstract():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b80a68a775eb1a309fdad9337c29a1b\",\n \"score\": \"0.60681933\",\n \"text\": \"def __call__():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b80a68a775eb1a309fdad9337c29a1b\",\n \"score\": \"0.60681933\",\n \"text\": \"def __call__():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b80a68a775eb1a309fdad9337c29a1b\",\n \"score\": \"0.60681933\",\n \"text\": \"def __call__():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b563cfa225e84f9374283878b24d4b5\",\n \"score\": \"0.60651064\",\n \"text\": \"def _dispatch(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b10ce085401ac63407a41029218a3c6\",\n \"score\": \"0.60464245\",\n \"text\": \"def original(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b10ce085401ac63407a41029218a3c6\",\n \"score\": \"0.60464245\",\n \"text\": \"def original(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b10ce085401ac63407a41029218a3c6\",\n \"score\": \"0.60464245\",\n \"text\": \"def original(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e2fe00e4fdc95d77ba10cfc790874a6\",\n \"score\": \"0.60385513\",\n \"text\": \"def add_base_arguments(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9edc7f017f0822ac3255a6594ee518c8\",\n \"score\": \"0.6032779\",\n \"text\": \"def out(self):\\r\\n raise RuntimeError(\\\"You must override this method in your derived\\\" \\\\\\r\\n \\\" class\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7d33de125638670a643d16206b9c88e\",\n \"score\": \"0.60205364\",\n \"text\": \"def sample_method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd34f5755209bbb9f64bb125a6f898e2\",\n \"score\": \"0.60201615\",\n \"text\": \"def boe(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82787595f88605b4238b80b1613689ce\",\n \"score\": \"0.601678\",\n \"text\": \"def proof_of_work(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bdddd9ec887ee69214ad7afe71c37e6\",\n \"score\": \"0.60161006\",\n \"text\": \"def public_method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a37ca25e7d6b2897ec447fa1fba3426e\",\n \"score\": \"0.5998337\",\n \"text\": \"def perform(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b105041adc0377458c1ae2603611fa\",\n \"score\": \"0.5986857\",\n \"text\": \"def _assemble(self):\\r\\n raise NotImplementedError # pragma: nocover\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7774ddaa12117815f3bc1a52d063491\",\n \"score\": \"0.598116\",\n \"text\": \"def __init__(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7774ddaa12117815f3bc1a52d063491\",\n \"score\": \"0.598116\",\n \"text\": \"def __init__(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7774ddaa12117815f3bc1a52d063491\",\n \"score\": \"0.598116\",\n \"text\": \"def __init__(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33471086cda57474893dcb36afaa7437\",\n \"score\": \"0.5971278\",\n \"text\": \"def _run(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17eddc8a72638b2f062ca409a5ff99ff\",\n \"score\": \"0.59663826\",\n \"text\": \"def check(self):\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c334343931867769d05e01bf06a5024\",\n \"score\": \"0.5965865\",\n \"text\": \"def __call__(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0413cc7b27bc3ff096af6950ed4014b9\",\n \"score\": \"0.59657526\",\n \"text\": \"def sanity_checks(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60714d4ef6df44e6071ccc1288b0a31f\",\n \"score\": \"0.5960815\",\n \"text\": \"def __call__(self, obj):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca423b34e016ef1764bd197fc2b1a97\",\n \"score\": \"0.5956529\",\n \"text\": \"def Sample(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f929e5757d785c245990e120fb5043\",\n \"score\": \"0.5951981\",\n \"text\": \"def check(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f929e5757d785c245990e120fb5043\",\n \"score\": \"0.5951981\",\n \"text\": \"def check(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb4303522b4e7b7c519d7f66007e89d\",\n \"score\": \"0.5951262\",\n \"text\": \"def act(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7376c6070bd45a9f7cbb12ebcedc5b63\",\n \"score\": \"0.59480935\",\n \"text\": \"def func(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f13e6e77e89d717449528bff38fbd195\",\n \"score\": \"0.5943412\",\n \"text\": \"def dummy_method2(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"265b03dfe4bcf5c0e2daa0503a255161\",\n \"score\": \"0.59050536\",\n \"text\": \"def stand(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ebc69ba6d6087e947e235a8ac5b8d5f\",\n \"score\": \"0.5889242\",\n \"text\": \"def method2(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c82fcce3672cf836716181212cb178\",\n \"score\": \"0.58867246\",\n \"text\": \"def _test(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c82fcce3672cf836716181212cb178\",\n \"score\": \"0.58867246\",\n \"text\": \"def _test(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c82fcce3672cf836716181212cb178\",\n \"score\": \"0.58867246\",\n \"text\": \"def _test(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc7f0a6395c77375a0db941066c4cde\",\n \"score\": \"0.5880117\",\n \"text\": \"def donothing(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b566e72f4a8cdd291cd200ce5d1b5db\",\n \"score\": \"0.5875836\",\n \"text\": \"def __private_method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06194ca6445b8ded111f3a267e6df3c\",\n \"score\": \"0.5868783\",\n \"text\": \"def analyse(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e41ae5522b4a5b45b71da706010c5d77\",\n \"score\": \"0.5865488\",\n \"text\": \"def customize(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2fad252664688669d84d05b55933b8d\",\n \"score\": \"0.58604884\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2fad252664688669d84d05b55933b8d\",\n \"score\": \"0.58604884\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f136cf4e5115edcc3f85e6b839759e4f\",\n \"score\": \"0.5860304\",\n \"text\": \"def __init__(self):\\n # TODO: This isn't done yet!\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9413080d43e63a571a45db9ffe8ffa92\",\n \"score\": \"0.5853162\",\n \"text\": \"def _run(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52115c9205aea34cbd8fb0169e571a60\",\n \"score\": \"0.58436555\",\n \"text\": \"def handle(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52115c9205aea34cbd8fb0169e571a60\",\n \"score\": \"0.58436555\",\n \"text\": \"def handle(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819ce69933746e9f256af7e64e7a0e75\",\n \"score\": \"0.58320206\",\n \"text\": \"def main(cls):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fac457cbee51ce133348883468d5d56\",\n \"score\": \"0.58273816\",\n \"text\": \"def __call__(self, x):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88b9306434da07f9c85ebc598920484c\",\n \"score\": \"0.5824649\",\n \"text\": \"def _base_values(self):\\n raise NotImplementedError( # pragma: no cover\\n f\\\"{type(self).__name__} must implement `._base_values`\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f68e1f42f0e93fc9bacb0cafd922c42\",\n \"score\": \"0.5822871\",\n \"text\": \"def ipsum(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.58176124\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":992999,"cells":{"query_id":{"kind":"string","value":"5a3b414277fd2a74b4ef90902ea40c36"},"query":{"kind":"string","value":"Return a list of students in the grade book"},"positive_passages":{"kind":"list like","value":[{"docid":"36ee0f2a014fcf975e133a1919f6bf1b","score":"0.63622546","text":"def allStudents(self):\n if not self.isSorted:\n self.students.sort()\n self.isSorted = True\n # return self.students[:]\n for s in self.students:\n yield s # generator","title":""}],"string":"[\n {\n \"docid\": \"36ee0f2a014fcf975e133a1919f6bf1b\",\n \"score\": \"0.63622546\",\n \"text\": \"def allStudents(self):\\n if not self.isSorted:\\n self.students.sort()\\n self.isSorted = True\\n # return self.students[:]\\n for s in self.students:\\n yield s # generator\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"70ac378fbf6537d2233f442e322c2655","score":"0.73335534","text":"def students(self):\n return Student.objects.filter(groups__in=list(self.groups.all()))","title":""},{"docid":"d8a1991469a65fc1caff6cdc6841e351","score":"0.7234299","text":"def students(self):\n return self.filter(groups__name=\"Student\")","title":""},{"docid":"fbdd83784c1ed0d7bf1e16bab1ca8358","score":"0.69352454","text":"def get_student_list():\n\n return list(sql(\"\"\"select * from users where account_type=\"student\";\"\"\"))","title":""},{"docid":"d3ce235f20a8851504aeaf006b910335","score":"0.69125384","text":"def students(self):\n return Student.objects.filter(courses_taken=self)","title":""},{"docid":"9f0d76709f1f6d32dfa5daae628dc2c9","score":"0.6821924","text":"def test_get_students(self) -> None:\n\n lecture = Course('CSC148')\n student1 = Student(1, 'Roy')\n student2 = Student(2, 'Martin')\n student3 = Student(999, 'Aaron')\n student4 = Student(520, 'Zara')\n students = [student1, student2, student3, student4]\n lecture.enroll_students(students)\n\n assert list(lecture.get_students()) == [student1, student2, student4, student3]","title":""},{"docid":"2aee5866fd6d1eb5591a520377c660d9","score":"0.68109393","text":"def students(self):\n return friprosveta.models.Student.objects.filter(\n enrolled_subjects__subject__in=self.subjects,\n enrolled_subjects__groupset=self.groupset,\n ).distinct()","title":""},{"docid":"31077801023f86d5d4251587dfd351fb","score":"0.6793995","text":"def all_students(self):\n\n with sqlite3.connect(self.db_path) as conn:\n db_cursor = conn.cursor()\n\n db_cursor.execute(\"\"\"\n select s.Id,\n s.First_Name,\n s.Last_Name,\n s.Slack_Handle,\n s.CohortId,\n c.Name\n from Student s\n join Cohort c on s.CohortId = c.Id\n order by s.CohortId\n \"\"\")\n\n all_students = db_cursor.fetchall()\n\n for student in all_students:\n print(f'{student[1]} {student[2]} is in {student[5]}')","title":""},{"docid":"3be604bb11d9389c89b7285602619dd9","score":"0.67150456","text":"def get_student_list(self):\n for rec in self:\n for line in rec.student_list:\n line.unlink()\n # TODO apply filter not to get student assigned previously\n students = self.env['eagleedu.student'].search([\n ('class_id', '=', rec.admitted_class.id),('assigned', '=', False)])\n if not students:\n raise ValidationError(_('No Students Available.. !'))\n values = []\n for stud in students:\n stud_line = {\n 'class_id': rec.class_id.id,\n 'student_id': stud.id,\n 'connect_id': rec.id,\n 'roll_no': stud.application_id.roll_no\n }\n stud.assigned=True\n values.append(stud_line)\n for line in values:\n rec.student_line = self.env['eagleedu.student.list'].create(line)","title":""},{"docid":"fcca80e725dbb7cc9399482e2aa21ed9","score":"0.66220725","text":"def get_students(self):\n return u', '.join([c.student.username for c in self.candidates.all()])","title":""},{"docid":"87e9d7db2725c815c1cbbe8fee745c8b","score":"0.66165775","text":"def getStudents(self):\n return User.all().filter('userType =', 'STUDENT')","title":""},{"docid":"592b62dc69b44111abece739289bd422","score":"0.6601335","text":"def get_student():\n\n github = request.args.get('github', 'jhacks')\n page = hackbright.get_grades_by_github(github)\n\n return page","title":""},{"docid":"d3268297556a52ee1145af94f58fffb0","score":"0.659833","text":"def get_students():\n\n students = db.query(Student). \\\n join(Student.info). \\\n filter(or_(Info.enrolstatus == 'C', Info.enrolstatus == 'P')). \\\n all()\n\n return jsonify({\n '_client_id': client_id,\n '_count': len(students),\n 'students': [student.json() for student in students]\n })","title":""},{"docid":"1fb9f23844155d9b0e7ab7da82bc5603","score":"0.6573903","text":"def get_students(self):\n return [int(student) for student in self.students.split(\"|\")]","title":""},{"docid":"8a05ef12cd70acd926334e30dab06f71","score":"0.6562396","text":"def get_studentset_students(ssid):\n # if studentset_name:\n # go('/py/teacher/studentset/list/')\n # soup=BeautifulSoup(show())\n # go(soup.find('a',text='formal-class').parent['href'])\n # else:\n cmd.go('/py/teacher/studentset/%s/' % ssid)\n\n return [int(d['value'])\n for d in soup().findAll('input', {'name': \"stud_group[]\"})]","title":""},{"docid":"83e7d491e4747704b5ac354d48c308da","score":"0.6545939","text":"def get_student():\n\n github = request.args.get('github')\n\n first, last, github = hackbright.get_student_by_github(github)\n\n row = hackbright.get_grade_by_github(github)\n print(row)\n html = render_template(\"student_info.html\",\n first=first,\n last=last,\n github=github,\n row=row)\n\n return html","title":""},{"docid":"63e059b46fc979dde6d096acba59605d","score":"0.65329576","text":"def get_grades_name(courses):\n result = []\n for c in courses:\n if not c.grade_name in result:\n result.append(c.grade_name)\n \n return result","title":""},{"docid":"ce3583f293940d9fcf18ac781673038e","score":"0.65206885","text":"def studentsEnrolledAtDiscipline(self, disciSearch):\n discipline = self._disciControl.find(disciSearch)\n listOfEnrolledStudents = []\n if discipline is False:\n raise ValueError(\"Error: The discipline was not found!\")\n listOfGrades = self._gradeControl.findAllGradesForDiscipline(discipline.id)\n for grade in listOfGrades:\n student = self._studControl.find(grade.studID)\n if student not in listOfEnrolledStudents:\n listOfEnrolledStudents.append(student)\n return listOfEnrolledStudents","title":""},{"docid":"9d97a6c3a049f73f91e2bee331c9e4f3","score":"0.6500632","text":"def all_students(self):\n\n with sqlite3.connect(self.db_path) as conn:\n conn.row_factory = lambda cursor, row: Student(\n row[1], row[2], row[3], row[5]\n )\n\n db_cursor = conn.cursor()\n\n db_cursor.execute(\"\"\"\n select s.Id,\n s.FirstName,\n s.LastName,\n s.SlackHandle,\n s.CohortId,\n c.Name\n from Student s\n join Cohort c on s.CohortId = c.Id\n order by s.CohortId\n \"\"\")\n\n all_students = db_cursor.fetchall()\n\n for student in all_students:\n print(\n f'{student.first_name} {student.last_name} is in {student.cohort}')","title":""},{"docid":"6676bda38bfc7138a9df216fb696b36e","score":"0.6488482","text":"def get_student():\n\n github = request.args.get('github')\n\n first, last, github = hackbright.get_student_by_github(github)\n\n rows = hackbright.get_grades_by_github(github)\n\n html = render_template(\"student_info.html\",\n first=first,\n last=last,\n github=github, \n rows=rows)\n \n return html","title":""},{"docid":"44771fb3477c5f9c4cdac763edf303cc","score":"0.64817405","text":"def Schools(self,grade:str):\n return self.__Schools[grade]","title":""},{"docid":"6fa6fd1de7827b8986fa192e699b64bc","score":"0.64458746","text":"def get_students_list(self):\n stu_list = []\n for i in range(self.stu_sum):\n stu_list.append([raw_input(), raw_input()])\n return stu_list","title":""},{"docid":"8c543ede5c3b578e945c599a928a1c4e","score":"0.6427282","text":"def all_students(self):\n\n with sqlite3.connect(self.db_path) as conn:\n conn.row_factory = lambda cursor, row: Student(\n row[1], row[2], row[3], row[5]\n )\n db_cursor = conn.cursor()\n\n db_cursor.execute(\"\"\"\n select s.Id,\n s.FirstName,\n s.LastName,\n s.SlackHandle,\n s.CohortId,\n c.Name\n from Student s\n join Cohort c on s.CohortId = c.Id\n order by s.CohortId\n \"\"\")\n\n all_students = db_cursor.fetchall()\n\n for student in all_students:\n print(student)","title":""},{"docid":"7ea0a4cb08cf49f0425c7da75dc00f21","score":"0.6422413","text":"def get_all_students(self):\n curr = self.db.cursor()\n curr.execute(\"\"\"SELECT student_num, name, email, course, date_created FROM students ;\"\"\")\n data = curr.fetchall()\n resp = []\n\n for i, items in enumerate(data):\n student_num, name, email, course, date_created = items\n students = dict(\n student_num=int(student_num),\n name=name,\n email=email,\n course=int(course),\n created_on=str(date_created)\n\n )\n resp.append(students)\n return resp","title":""},{"docid":"b3a1b6905ee7f2c2f6bea8143131cf38","score":"0.6416842","text":"def get_student():\n\n github = request.args.get('github')\n\n first, last, github = hackbright.get_student_by_github(github)\n\n project_list = hackbright.get_grades_by_github(github)\n\n html = render_template(\"student_info.html\", first=first, last=last, github=github, project_list=project_list)\n\n return html","title":""},{"docid":"f7e85ae7236c8cfb88100093b01f9951","score":"0.63915837","text":"def get_student():\n\n github = request.args.get('github')\n#get list of all tuples from database by github\n grades_by_project = hackbright.get_grades_by_github(github)\n\n#unpack tuple from database\n first, last, github = hackbright.get_student_by_github(github)\n\n# def get_grades_by_github(github):\n# \"\"\"Get a list of all grades for a student by their github username\"\"\"\n\n\n html = render_template(\"student_info.html\", first=first, \n last=last, \n github=github,\n grades_by_project=grades_by_project)\n\n return html","title":""},{"docid":"ef87341aa76e552b52c049e62c3c0956","score":"0.6362363","text":"def get_student():\n\n github = request.args.get('github','jhacks')\n first, last, github = hackbright.get_student_by_github(github)\n grades_by_title = hackbright.get_grades_by_github(github)\n\n html = render_template(\"student_info.html\",\n first=first,\n last=last,\n github=github, grades_title= grades_by_title)\n return html","title":""},{"docid":"2887ea5194a40025f30ca7df5297e53f","score":"0.6304307","text":"def get_student():\n\n github = request.args.get('github')\n\n first, last, github = hackbright.get_student_by_github(github)\n\n projects = hackbright.get_grades_by_github(github)\n \n return render_template(\"student_info.html\",\n first = first,\n last = last,\n github = github,\n projects = projects)","title":""},{"docid":"3530df5ec8dd0a7d55033794fe730be5","score":"0.62560844","text":"def get_student():\n\n github = request.args.get('github')\n\n first, last, github = hackbright.get_student_by_github(github)\n title_grade = hackbright.get_grades_by_github(github)\n\n return render_template(\"student_info.html\", first=first, last=last,\n github=github, title_grade=title_grade)","title":""},{"docid":"9ff8579eb9c167ae50bd31adcd37f331","score":"0.6244609","text":"def pretty_table_student(self)-> Iterator[Tuple[str]]:\n\n stud_list:Iterator[Tuple[str]] = []\n\n pt = PrettyTable(field_names = ['CWID','Name','Courses'])\n print(f\"Summary of Students\")\n for stud in self.stud_data.values():\n pt.add_row(stud.pretty_table_stud())\n stud_list.append(stud.pretty_table_stud())\n print(pt)\n return stud_list","title":""},{"docid":"aa2d0fda522184f8bb241420ebffe4ca","score":"0.6241686","text":"def print_student(students):\n for student in students:\n print(student.get_full_name() + \" \"+ str(student))#","title":""},{"docid":"ef62735afac946edfd3d5379cfaa2b75","score":"0.62406766","text":"def find_all(self):\n result = []\n cursor = self._cnx.cursor()\n cursor.execute(\"SELECT * FROM student\")\n tuples = cursor.fetchall()\n\n for (id, creation_date, name, google_id, matriculation_number, course_abbreviation,role_id) in tuples:\n student = Student()\n student.set_id(id)\n student.set_creation_date(creation_date)\n student.set_name(name)\n student.set_google_id(google_id)\n student.set_matriculation_number(matriculation_number)\n student.set_course_abbreviation(course_abbreviation)\n student.set_role_id(role_id)\n result.append(student)\n\n self._cnx.commit()\n cursor.close()\n\n return result","title":""},{"docid":"787b020de5f03ff2fc2ba2b648af6789","score":"0.62028843","text":"def get_student(self, email):\n return (DataLayer.mongoDB.get_student_by_email(email))","title":""},{"docid":"bed2ecffdc9aaafd357a41920ccc8ba9","score":"0.61970913","text":"def getGrades(self, student):\n try: # return copy of student's grades\n # index into dict using IdNum, then return a copy [:]\n return self.grades[student.getIdNum()][:]\n except KeyError:\n raise ValueError(\"Student not in grade book\")","title":""},{"docid":"d4aba014d7a9264fc799f206373859e0","score":"0.6183985","text":"def students_course_grade(request):\n json_header = request.headers\n\n auth, username = authorize_teacher(json_header)\n\n if not auth:\n return HttpResponse(\"failed teacher auth...\")\n\n id = request.GET.get('id')\n\n try:\n course = Course.objects.get(id=id)\n except:\n return HttpResponse(\"failed query.. specify the correct course ID...\")\n\n students = [\n stu.student for stu in StudentCourse.objects.filter(course=course)\n if stu.student is not None]\n\n student_grades = [{\"student\": {\"username\": stu.user.username,\n \"firstname\": stu.user.firstName,\n \"lastname\": stu.user.lastName},\n \"grades\": views.construct_student_grades(stu.user.username, True, int(id))}\n for stu in students]\n\n return HttpResponse(json.dumps(student_grades))","title":""},{"docid":"60048e5bbb6f9d6c10ca860373de32c9","score":"0.6178128","text":"def get_students_by_course_id(course_id, auth_token): \r\n\r\n # the Canvas endpoint to hit\r\n endpoint = CANVAS_BASE_ENDPOINT + str(course_id) + \"/users\"\r\n\r\n # necessary headers for the request, including the OAuth authentication \r\n # token generated from Canvas\r\n headers = {\r\n \"Authorization\": \"Bearer \" + auth_token\r\n }\r\n\r\n # boolean to keep track of whether or not there are more users left to find\r\n pages_left = True\r\n\r\n # keeps track of the current page number of users\r\n page_num = 1\r\n\r\n students = []\r\n\r\n while (pages_left):\r\n # iterate while there are still students left to find\r\n\r\n # create request payload with desired parameters\r\n payload = {\r\n \"page\": page_num, # get all results from the given page\r\n \"enrollment_type\": \"student\" # only find students\r\n }\r\n\r\n # send the GET request to the endpoint\r\n response = get_from_endpoint(endpoint, headers, params=payload)\r\n\r\n if (type(response) != list):\r\n # TODO: Add better error-handling here\r\n return \r\n \r\n # add all student details to the existing list\r\n students.extend(response)\r\n\r\n # if the response was empty, there are no more students left, so this \r\n # evaluates to False. If it was not empty, this evaluates to True.\r\n # Once there are no more students left, this ends the iteration of the\r\n # while loop.\r\n pages_left = bool(response)\r\n\r\n # increment the page counter\r\n page_num += 1\r\n\r\n return students","title":""},{"docid":"51de4d95d2b0ca27742c91dedf986398","score":"0.61543936","text":"def get_students(request):\r\n # get subject.\r\n subject_id = request.POST.get('subject')\r\n subject = get_object_or_404(Subject, id=subject_id)\r\n # get session year id.\r\n session_year_id = request.POST.get('session_year') \r\n # get list of students data.\r\n students_list_data = Student.objects.get_students_list_data(subject, session_year_id)\r\n return JsonResponse(json.dumps(students_list_data), content_type='application/json', safe=False)","title":""},{"docid":"335a6b42f775ca29fdca0d8f1b178a96","score":"0.6137669","text":"def test_get_students_for_school(self):\n pass","title":""},{"docid":"b6b09d0787b61834457c2a7d67ced5be","score":"0.611547","text":"def print_student_list(students):\n\n for student in students:\n print(\"{}, {}, {}\".format(student.idex, student.name, student.surname))","title":""},{"docid":"5393065a50efe470e3e93fe3dc07623e","score":"0.6107028","text":"def get_student_courses(self):\n r = self.session.get(Session.RPC_URL, params=Session.RPC_PARAMS['student_menu'])\n while True:\n if 'course_summary.php' in r.text:\n break\n r = self.session.get(Session.RPC_URL, params=Session.RPC_PARAMS['student_menu'])\n\n page = bs4.BeautifulSoup(r.text, 'lxml')\n student_courses = []\n for course in page.select('#newmenu3')[0].find_all('a', href=True):\n course_id = re.sub('[^0-9]', '', course['href'])\n student_courses.append(Course(self.session, course_id))\n return student_courses","title":""},{"docid":"59deeb32d21845da506d9d47913946fc","score":"0.60997444","text":"def get_student_enrollments(self):\n self._get(\"{0}/enrollment\".format(ENROLLMENT_API_BASE_URL))","title":""},{"docid":"603c737a9bfa92dbbabef29b88ac3d2d","score":"0.6093185","text":"def get_random_students(cls, num_students: int) -> List[Student]:\n assert num_students < len(cls.students)\n result = []\n while len(result) < num_students:\n s = cls.get_random_student()\n while s in result:\n s = cls.get_random_student()\n result.append(s)\n return result","title":""},{"docid":"7f31e021f1ac4ce53dcc86e7ea83bf7b","score":"0.6086024","text":"def _getStudentEntries(self, program_entity, student_entity, \n params, id, user):\n\n items = []\n\n timeline_entity = program_entity.timeline\n\n if timeline_helper.isActivePeriod(timeline_entity, 'student_signup'):\n items += [('/student_proposal/list_orgs/%s' % (\n student_entity.key().id_or_name()),\n \"Submit your Student Proposal\", 'any_access')]\n\n if timeline_helper.isAfterEvent(timeline_entity, 'student_signup_start'):\n items += [(redirects.getListSelfRedirect(student_entity,\n {'url_name':'student_proposal'}),\n \"List my Student Proposals\", 'any_access')]\n\n items += [(redirects.getEditRedirect(student_entity, \n {'url_name': 'student'}),\n \"Edit my Student Profile\", 'any_access')]\n\n if timeline_helper.isAfterEvent(timeline_entity,\n 'accepted_students_announced_deadline'):\n # add a link to show all projects\n items += [(redirects.getListProjectsRedirect(program_entity,\n {'url_name':'student'}),\n \"List my Student Projects\", 'any_access')]\n\n return items","title":""},{"docid":"de5543bc62cb9c83e6e456dbd596f076","score":"0.60771435","text":"def test_empty_student_list(self):\n grade_results = list(CourseGradeFactory().iter([], self.course))\n assert grade_results == []","title":""},{"docid":"c039de6cc72e79ea34816eaf0fb9dd98","score":"0.607418","text":"def get_all_students(db: Session = Depends(get_db)) -> Generator:\n\n if result := retrieve_all_students(db):\n return result\n\n raise HTTPException(\n status_code=status.HTTP_404_NOT_FOUND,\n detail=\"Não existem estudantes cadastrados.\",\n ) # pragma: nocover","title":""},{"docid":"e7c42b8d8eca2d4bca61f0cf808c249a","score":"0.60697883","text":"def get_all_students(undistributed=False):\n users = User.objects.filter(\n Q(usermeta__EnrolledBEP=True) & Q(groups=None) & Q(usermeta__TimeSlot=get_timeslot())).distinct()\n if get_timephase_number() < 6 or undistributed:\n return users\n else:\n # only students with a distributions\n return users.filter(Q(distributions__TimeSlot=get_timeslot()))","title":""},{"docid":"6567539c940672ed7a2b8641c17ddc61","score":"0.6042441","text":"def scan_student_info(self):\n\n path = \"students.txt\"\n for i in file_reader(path, 3, ';', True, 0):\n self.student_dict[i[0]] = [i[1], i[2], 0, 0, [], [], []]\n\n path = \"grades.txt\"\n for i in file_reader(path, 4, '|', True):\n self.student_dict[i[0]][2] += self.grade_dict[i[2]]\n self.student_dict[i[0]][3] += 1\n self.student_dict[i[0]][4].append(i[1])\n\n # populate course_dict\n try:\n self.course_dict[i[1]][0].append(i[0])\n\n except Exception as e:\n self.course_dict[i[1]] = [[], '', '', '']\n self.course_dict[i[1]][0].append(i[0])\n self.course_dict[i[1]][1] = i[3]\n for i in self.student_dict.values():\n i[5] = diff(self.major_dict[i[1]][0], i[4])\n if common(self.major_dict[i[1]][1], i[4]):\n i[6] = self.major_dict[i[1]][1]\n print('Student Scanned')","title":""},{"docid":"432c29893499fac0ad2c71bfffc117e6","score":"0.60337174","text":"def list_section_students(self, course_id, section_id):\n\n # XXX sections/{}?include=students crashes Canvas, so avoid for now\n section = self._get_json('courses/{}/sections/{}?include=students'\n .format(course_id, section_id))\n\n for student_json in section['students']:\n yield self._to_entity(student_json, CanvasUser)","title":""},{"docid":"f10f3af6db342f5263a5abc0b4bee666","score":"0.602322","text":"def grades(self) -> StudentGrades:\n return self._grades","title":""},{"docid":"7a36d9a5bb6775e6e7481eb8715a81ca","score":"0.5992955","text":"def create_grades(students):\n ret = []\n for s in students:\n exams = Exam.objects.filter(subject__students=s)\n for e in random_partition(2, exams)[0]:\n # Get a couple of random exams that are available.\n ret.append(Grade.objects.create(student=s, grade=random.randrange(10), exam=e))\n\n return ret","title":""},{"docid":"180c1e1ab0b238aa2752781291ca485f","score":"0.59350705","text":"def get_student():\n\n github = request.args.get('github')\n\n first, last, github = hackbright.get_student_by_github(github)\n\n html = render_template(\"student_info.html\",\n first=first,\n last=last,\n github=github)\n\n return html","title":""},{"docid":"6ae0a6b4758a79ec7fba571ce57a66b4","score":"0.5911785","text":"def exercises_with_students(self):\n\n with sqlite3.connect(self.db_path) as conn:\n\n db_cursor = conn.cursor()\n\n db_cursor.execute(\"\"\"\n select\n e.Id ExerciseId,\n e.Name,\n s.Id StudentId,\n s.FirstName,\n s.LastName\n from Exercise e\n join StudentExercise se on se.ExerciseId = e.Id\n join Student s on s.Id = se.StudentId\n \"\"\")\n\n all_exercises_with_students = db_cursor.fetchall()\n\n # for exercise_student in all_exercises_with_students:\n # print(f'{exercise_student[1]}: {exercise_student[3]} {exercise_student[4]}')\n\n # Takes our list of tuples and converts it to a dictionary with the exercise name as the key and a list of students as the value.\n exercises = dict()\n\n for exercise_student in all_exercises_with_students:\n exercise_id = exercise_student[0]\n exercise_name = exercise_student[1]\n student_id = exercise_student[2]\n student_name = f'{exercise_student[3]} {exercise_student[4]}'\n\n if exercise_name not in exercises:\n # exercises[exercise_name] is adding a new key/value pair to the exercises dictionary, where exercise_name is the variable containing the key value which is string\n\n # [student_name] is creating a list with one item, that item is the string contained in the variable student_name\n exercises[exercise_name] = [student_name]\n else:\n exercises[exercise_name].append(student_name)\n\n # print(exercises)\n for exercise_name, students in exercises.items():\n print(exercise_name)\n for student in students:\n print(f'\\t* {student}')","title":""},{"docid":"b38ed9e6b26f20f9353f1fb0c145e2c5","score":"0.58853024","text":"def list_books():\n books = database.get_all_books()\n\n \n print_book_list(books)","title":""},{"docid":"da5bf4b87a15b26c169c07ed552d21ad","score":"0.58700025","text":"def get_grades_information(self):\n self.subjects_taken = {}\n for student_key in self.student_keys:\n student = SchoolDB.models.Student.get(student_key)\n student_results_list = student.get_grading_period_results(\n self.grading_period)\n student_subject_result_dict = {}\n for result in student_results_list:\n class_session = result.class_session\n key = class_session.key()\n if not self.known_class_sessions.has_key(key):\n self.known_class_sessions[key] = \\\n class_session.subject\n self.subjects_taken[class_session.subject] = True\n subject = self.known_class_sessions[key]\n student_subject_result_dict[subject] = result.assigned_grade\n self.student_results.append(student_subject_result_dict)","title":""},{"docid":"b55d0e7b30402450cf7b3ec598a2b724","score":"0.5868739","text":"def student(self) -> None:\r\n t: Student = Student()\r\n student_file: dict = t.stu()\r\n student_course: dict = t.stu_grade()\r\n course_id: dict = {} # set all the dicts\r\n for key, value in student_course.items():\r\n if str(value) not in course_id:\r\n course_id[key] = value\r\n else:\r\n course_id[key].append(value) # get the dicts together\r\n for keys, value in student_file.items():\r\n for key, values in course_id.items():\r\n if keys == key:\r\n student_file[key].append(values) # make the dict to print table\r\n pt: PrettyTable = PrettyTable(field_names=['CWID', \"Name\", \"Completed Courses\"])\r\n for key, value in student_file.items():\r\n pt.add_row([key, value[0], value[-1]])\r\n print(\"Student Summary\")\r\n print(pt) # print prettytable\r","title":""},{"docid":"30f4b1959fc4621780ff6d8dcea0b9e9","score":"0.58569914","text":"def get_enrolled_students(self, timetable):\n return friprosveta.models.Student.objects.filter(\n enrolled_subjects__subject=self,\n enrolled_subjects__groupset=timetable.groupset,\n ).distinct()","title":""},{"docid":"78180f6ec319cee38ad5306125f4c024","score":"0.5846312","text":"def test_student_grade(self) -> None:\r\n cwid: List[str] = list()\r\n name: List[str] = list()\r\n course: List[str] = list()\r\n grade: List[str] = list()\r\n inst: List[str] = list()\r\n result: PrettyTable = self.repo.grades_pt(self.dbpath)\r\n for r in result:\r\n r.border = False\r\n r.header = False\r\n name.append(r.get_string(fields=[\"Name\"]).strip())\r\n cwid.append(r.get_string(fields=[\"CWID\"]).strip())\r\n course.append(r.get_string(fields=[\"Course\"]).strip())\r\n grade.append(r.get_string(fields=[\"Grade\"]).strip())\r\n inst.append(r.get_string(fields=[\"Instructor\"]).strip())\r\n test_name: List[str] = ['Bezos, J', 'Bezos, J', 'Gates, B',\r\n 'Gates, B', 'Gates, B', 'Jobs, S', 'Jobs, S', 'Musk, E', 'Musk, E']\r\n test_cwid: List[str] = ['10115', '10115', '11714',\r\n '11714', '11714', '10103', '10103', '10183', '10183']\r\n test_course: List[str] = ['SSW 810', 'CS 546', 'SSW 810',\r\n 'CS 546', 'CS 570', 'SSW 810', 'CS 501', 'SSW 555', 'SSW 810']\r\n test_grade: List[str] = ['A', 'F', 'B-',\r\n 'A', 'A-', 'A-', 'B', 'A', 'A']\r\n test_inst: List[str] = ['Rowland, J', 'Hawking, S', 'Rowland, J',\r\n 'Cohen, R', 'Hawking, S', 'Rowland, J', 'Hawking, S', 'Rowland, J', 'Rowland, J']\r\n self.assertEqual(name, test_name)\r\n self.assertEqual(cwid, test_cwid)\r\n self.assertEqual(course, test_course)\r\n self.assertEqual(grade, test_grade)\r\n self.assertEqual(inst, test_inst)","title":""},{"docid":"30af4d52fa77d2ceea8be159834db7e8","score":"0.58249694","text":"def get_available_class_student(user_id):\n available_class_student = []\n for class_obj in Class.objects.all():\n if int(user_id) in get_all_class_member(convert_member(class_obj.student)):\n available_class_student.append(class_obj)\n return available_class_student","title":""},{"docid":"87b7d8939fb3f73b45bf8c02893f0959","score":"0.5822902","text":"def students_in_course(request):\n json_header = request.headers\n\n auth, username = authorize_teacher(json_header)\n\n if not auth:\n return HttpResponse(\"failed teacher auth...\")\n\n id = request.GET.get('id')\n try:\n id = int(id)\n except:\n return HttpResponse(\"failed query... specify the course ID...\")\n\n # todo: check staff has access to course here\n\n course = Course.objects.get(id=id)\n\n students = [stu_course.student for stu_course in\n StudentCourse.objects.filter(course=course) \n if stu_course.student is not None]\n\n json_students = [{\"username\": student.user.username,\n \"firstname\": student.user.firstName,\n \"lastname\": student.user.lastName}\n for student in students]\n\n return HttpResponse(json.dumps(json_students))","title":""},{"docid":"8d8f874f2bdb91c7ac36675e1fae0f60","score":"0.5821699","text":"def get_students_from_request(cls, request):\n # Get the group of the student, if one is set in the request's META\n student_group = request.META.get(\"STUDENT_GROUP\", None)\n students = None\n \n if student_group:\n students = student_group.members.all()\n else:\n # No group was found, so use just the current user\n students = UserProfile.objects.filter(id=request.user.get_profile().id)\n \n return students","title":""},{"docid":"bbfa5f6e299dc9099d322a2062a82b29","score":"0.5819284","text":"def studentlist(request):\n if request.method == 'GET':\n students = Student.objects.all()\n serializer = StudentSerializer(students, many=True)\n return Response(serializer.data)","title":""},{"docid":"fb7c726e69a304e5200ada3a20a57a98","score":"0.58170843","text":"def get_grades(self):\n # Implemented from template for osid.repository.Asset.get_asset_contents_template\n return GradeList(self._my_map['grades'], runtime=self._runtime)","title":""},{"docid":"c61db91b85c34be83dadfea9786257a9","score":"0.5796599","text":"def get_student():\n\n github = request.args.get('github')\n\n first, last, github = hackbright.get_student_by_github(github)\n performance = hackbright.get_grades_by_github(github)\n\n html = render_template(\"student_info.html\",\n first=first,\n last=last,\n github=github,\n performance=performance)\n return html\n # return \"{acct} is the GitHub account for {first} {last}\".format(\n # acct=github, first=first, last=last)","title":""},{"docid":"1681d52424b8d12ee5845ece70164142","score":"0.57825017","text":"def extract_students(students, data_pos):\n updated_students = []\n for student in students:\n if 'FitNZ' in student[data_pos]:\n updated_students.append(student)\n return updated_students","title":""},{"docid":"dadb2aa72f817f75bde888a6b8f28582","score":"0.5770502","text":"def get_attendance_students(request):\r\n # get attendance record.\r\n attendance_id = request.POST.get('attendance_id')\r\n attendance = get_object_or_404(Attendance, id=attendance_id)\r\n # get list of attendance students for attendance record.\r\n attendance_students_list_data = AttendanceReport.objects.get_attendance_students_list_data(attendance)\r\n return JsonResponse(json.dumps(attendance_students_list_data), content_type='application/json', safe=False)","title":""},{"docid":"7256f31c12b64699823da3309e856cca","score":"0.5769932","text":"def enrolled_students_for_study(self, timetable, study):\n return self.get_enrolled_students(timetable).filter(\n enrolled_subjects__study=study\n )","title":""},{"docid":"4ff271bb19fde9154b5b743de140fb79","score":"0.5730652","text":"def get_grading_standards(self, **kwargs):\n return PaginatedList(\n GradingStandard,\n self._requester,\n \"GET\",\n \"courses/%s/grading_standards\" % (self.id),\n _kwargs=combine_kwargs(**kwargs),\n )","title":""},{"docid":"5885b3060061aa0acbaec85fae25af39","score":"0.57196695","text":"def _compute_student(self):\n student_obj = self.env[\"student.student\"]\n for rec in self:\n rec.student_ids = student_obj.search(\n [\n (\"standard_id\", \"=\", rec.id),\n (\"school_id\", \"=\", rec.school_id.id),\n (\"division_id\", \"=\", rec.division_id.id),\n (\"medium_id\", \"=\", rec.medium_id.id),\n (\"state\", \"=\", \"done\"),\n ]\n )","title":""},{"docid":"7b18c6fad33a7657e27bfcf7d5719b0a","score":"0.57143354","text":"def get_the_students(self, stu_list, second_lowest):\n slstu = []\n for stu in stu_list:\n if float(stu[1]) == second_lowest:\n slstu.append(stu[0])\n slstu.sort()\n return slstu","title":""},{"docid":"35400820e6e06af8010a877de4f1cc01","score":"0.56799585","text":"def get(self):\n cursor_key = self.request.GET.get('cursor')\n students, cursor, _ = models.Student.get_students(cursor_key)\n return self.render_json({\n 'students': [s.data for s in students],\n 'cursor': cursor.urlsafe() if cursor else ''\n })","title":""},{"docid":"822c366a3d6a3d780782dfb41a15876c","score":"0.5662683","text":"def get_students():\n if request.method == \"GET\":\n students = Student.query.all()\n if not students:\n return custom_error(\"No students found!\", 404)\n # The query returns Student objects, which first must be JSON serialized\n students_serialize = []\n for student in students:\n students_serialize.append({\"first_name:\": student.first_name, \"last_name:\": student.last_name,\n \"age:\": student.age, \"email:\": student.email})\n\n return {\"Students:\": students_serialize}, 200\n else:\n try:\n new_student = Student(first_name=request.form[\"first_name\"], last_name=request.form[\"last_name\"],\n age=request.form[\"age\"], email=request.form.get(\"email\", None))\n except werkzeug.exceptions.BadRequestKeyError:\n return custom_error(\"Missing argument!\", 400)\n else:\n db.session.add(new_student)\n db.session.commit()\n return {\"ID:\": new_student.id_}, 201","title":""},{"docid":"8e42e9b48718cc3e5f37e718a9c03185","score":"0.5647124","text":"def get_subjects():","title":""},{"docid":"757f83aad0f8aaac7c8f032bec564e22","score":"0.5642957","text":"def _get_all_student_info(user, dict):\n\n student = Student.objects.get(user = user)\n\n dict['first_name'] = student.first_name\n dict['last_name'] = student.last_name\n dict['full_name'] = student.first_name + ' ' + student.last_name\n dict['email'] = student.email\n\n thesis = Thesis.objects.get(student = student)\n \n dict['title'] = thesis.title\n dict['thesis_id'] = thesis.id\n dict['abstract'] = thesis.abstract\n dict['guides'] = []\n dict['keywords'] = []\n \n thesisGuides = ThesisGuide.objects.filter(thesis = thesis)\n thesisKeywords = ThesisKeyword.objects.filter(thesis = thesis)\n\n for thesisGuide in thesisGuides:\n guide_info = {}\n\n if thesisGuide.type == 'G':\n guide_info['type'] = 'Guide'\n else:\n guide_info['type'] = 'Co-guide'\n\n guide = thesisGuide.guide\n guide_info['username'] = guide.user.username\n guide_info['full_name'] = guide.first_name + ' ' + guide.last_name\n dict['guides'].append(guide_info)\n\n for thesisKeyword in thesisKeywords:\n dict['keywords'].append(thesisKeyword.keyword.keyword)\n\n return dict","title":""},{"docid":"6f5880453713ef97d953422aee89a589","score":"0.56279755","text":"def ins_grade(self) -> dict:\r\n for id, course, grade, ins in file_reader('grades.txt', 4, sep='\\t', header=False):\r\n y: dict = {course: [ins]}\r\n if ins not in self.grade_dic.keys():\r\n self.grade_dic.update(y)\r\n else:\r\n self.grade_dic[course].append([ins])\r\n return self.grade_dic","title":""},{"docid":"8bad17e6363aa9eeff1fceeb630aee70","score":"0.5620691","text":"def get_semester_list():\n token_info = Auth.get_payload(request)\n\n semesters = Semester.query.filter(Semester.name != \"CURRENT\").all()\n\n return MyResponse(data=[{\n \"uuid\": str(uuid.UUID(bytes=semester.uuid)),\n \"name\": semester.name,\n \"start_time\": semester.start_time,\n \"end_time\": semester.end_time\n } for semester in semesters]).build()","title":""},{"docid":"aa708472de873994a484f7cb302563d3","score":"0.56194293","text":"def process_all_students():\n all_students = Student.objects.all()\n output = {}\n lastnames = []\n grade_letters = defaultdict(int)\n for student in all_students:\n result = process_student(student)\n result['grade_letter'] = convert_percentage_to_letter(result['final_grade'])\n grade_letters[result['grade_letter']] += 1\n output[result['lastname']] = '%40s: %30s: %10.2f: %10.2f: %10.2f: %10.2f: %10.2f: %5s' % (result['name'],\n result['number'],\n result['cat_summary']['Assignments'],\n result['cat_summary']['Midterm: written'],\n result['cat_summary']['Take-home midterm and project'],\n result['cat_summary']['Final exam'],\n result['final_grade'],\n result['grade_letter'])\n\n #output[result['lastname']] = '%0.2f' % (result['final_grade'])\n lastnames.append(result['lastname'])\n print(output[result['lastname']])\n\n# for student in sorted(lastnames):\n# print(output[student])\n print('\\n')\n print(grade_letters)","title":""},{"docid":"ec70f1ddea321244335b41122cf7833b","score":"0.56095994","text":"def build_gender_list(self, gender):\n query = SchoolDB.models.Student.all(keys_only=True)\n SchoolDB.models.active_student_filter(query)\n query.filter('section = ', self.section)\n query.filter('organization = ', self.section.organization)\n query.filter('gender = ', gender)\n query.order('last_name')\n query.order('first_name')\n student_keys = query.fetch(1000)\n students = db.get(student_keys)\n for student in students:\n record = [student.last_name, student.first_name, \n student.middle_name]\n self.students[gender].append(record) \n self.count[gender] = len(self.students[gender])","title":""},{"docid":"e89d00a8b680bf5a2b36e41614de8666","score":"0.5587289","text":"def read_input():\n grade_list = []\n n_student = 5\n for _ in range(0, n_student):\n grade_list.append(int(input('Enter a number: ')))\n return grade_list","title":""},{"docid":"7009993a579b01e5386609a82faff111","score":"0.5585751","text":"def stu_grade(self) -> dict:\r\n for id, course, grade, ins in file_reader('grades.txt', 4, sep='\\t', header=False):\r\n y: dict = {id: [course]}\r\n if id not in self.gra_dic.keys():\r\n self.gra_dic.update(y)\r\n else:\r\n self.gra_dic[id].append([course])\r\n return self.gra_dic","title":""},{"docid":"b2396a101a9e9295929042f7f38853b9","score":"0.558574","text":"def get_queryset(self):\n user = self.request.user\n return StudentInfo.objects.filter(id=user.id)","title":""},{"docid":"e5c6c69e73d623d1d3cab2b897626fc1","score":"0.5572155","text":"def get_book_list():\n header, records = bd.get_book_all()\n return books_to_dict(header, records)","title":""},{"docid":"2688a059d89140d00c1ee50e342c96a4","score":"0.5569877","text":"def getStudent(student_list, student_name):\n theStudent = None\n for student in student_list:\n if student.get_name() == student_name:\n theStudent = student\n break\n return theStudent","title":""},{"docid":"8e8e92f050240d38457c4988ab2abec0","score":"0.556209","text":"def students_at_risk(request):\n json_header = request.headers\n\n auth, username = authorize_teacher(json_header)\n\n if not auth:\n return HttpResponse(\"failed teacher auth...\")\n\n id = request.GET.get('id')\n try:\n id = int(id)\n except:\n return HttpResponse(\"failed query... specify the course ID...\")\n\n # todo: check staff has access to course here\n\n course = Course.objects.get(id=id)\n\n students_in_course = [stu_course.student for stu_course in\n StudentCourse.objects.filter(course=course)]\n\n students_at_risk = []\n\n for student in students_in_course:\n\n grades = [grade for grade\n in StudentAssessment.objects.filter(student=student)\n if grade.assessment is not None\n and grade.assessment.course == course]\n\n total_weight = sum([float(grade.assessment.weight)\n for grade in grades])\n total_earnt = sum([(float(grade.value) / 100)\n * float(grade.assessment.weight)\n for grade in grades])\n\n if total_weight > 0:\n current_grade = 100 * (total_earnt / total_weight)\n else:\n current_grade = 100\n\n if current_grade < 50:\n students_at_risk.append({\"student\":\n {\"username\": student.user.username,\n \"firstname\": student.user.firstName,\n \"lastname\": student.user.lastName},\n \"grade\": current_grade})\n\n students_at_risk = sorted(students_at_risk, key=lambda i: i['grade'])\n\n return HttpResponse(json.dumps(students_at_risk))","title":""},{"docid":"fae83d6585259f5d9e637fab1ac2cf55","score":"0.55603045","text":"def getBookList(self):\r\n return self.bookList","title":""},{"docid":"484fafede3cc3ef359e32d2533f15846","score":"0.5554736","text":"def test_correct_student(self) -> None:\n cwd: str = \"G:/Stevens/Courses/Sem4/Assignment/SSW810_Python/StudentRepository_SSW810/input/\"\n lis = []\n repo: University = University(cwd)\n for key in repo._students:\n lis.append(repo._students[key].get_student())\n result: list = [['10103', 'Baldwin, C', ['CS 501', 'SSW 564', 'SSW 567', 'SSW 687']],\n ['10115', 'Wyatt, X', [\n 'CS 545', 'SSW 564', 'SSW 567', 'SSW 687']],\n ['10172', 'Forbes, I', ['SSW 555', 'SSW 567']],\n ['10175', 'Erickson, D', ['SSW 564', 'SSW 567', 'SSW 687']],\n ['10183', 'Chapman, O', ['SSW 689']],\n ['11399', 'Cordova, I', ['SSW 540']],\n ['11461', 'Wright, U', ['SYS 611', 'SYS 750', 'SYS 800']],\n ['11658', 'Kelly, P', ['SSW 540']],\n ['11714', 'Morton, A', ['SYS 611', 'SYS 645']],\n ['11788', 'Fuller, E', ['SSW 540']]]\n self.assertEqual(lis, result)","title":""},{"docid":"d12f4850d554e42e4f466738b77a24fc","score":"0.55483896","text":"def get_student():\n\n #Hey dictionary, here is a key (github) give me its value. If you \n #don't have this key, give me jhacks\n github = request.args.get('github','jhacks')\n first, last, github = hackbright.get_student_by_github(github)\n html = render_template(\"student_info.html\",\n first=first,\n last=last,\n github=github)\n return html","title":""},{"docid":"087a6d305d0b70d5a99a341869b8f081","score":"0.5541814","text":"def list_enrollments(self):\n try:\n self.api.get_student_enrollments()\n except NotAuthorizedException:\n self.auto_auth()","title":""},{"docid":"087a6d305d0b70d5a99a341869b8f081","score":"0.5541814","text":"def list_enrollments(self):\n try:\n self.api.get_student_enrollments()\n except NotAuthorizedException:\n self.auto_auth()","title":""},{"docid":"00f9dffd59e804c26a5bb25163f0c4f6","score":"0.55352515","text":"def test_enroll_students(self) -> None:\n\n lecture = Course('CSC148')\n student1 = Student(1, 'Roy')\n student2 = Student(2, 'Martin')\n students = [student1, student2]\n lecture.enroll_students(students)\n\n assert student1 in lecture.students and student2 in lecture.students","title":""},{"docid":"36a73a28ee401771d1378cd86020ade3","score":"0.552836","text":"def student_visible(self):\n group_ids = set()\n for group in Group.objects.all():\n if group.properties.student_visible:\n group_ids.add(group.id)\n\n return Group.objects.filter(id__in=group_ids)","title":""},{"docid":"f895c205ba7ace1eea417d8444aa2a85","score":"0.55240184","text":"def test_get_all_grades(self):\n query_string = [('page', 56),\n ('pageSize', 56)]\n headers = { \n 'Accept': 'application/json',\n }\n response = self.client.open(\n '/{version}/trustcode/{trust_code}/referenceData/grade'.format(version='version_example', trust_code='trust_code_example'),\n method='GET',\n headers=headers,\n query_string=query_string)\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","title":""},{"docid":"d8368278ffbf57806c4858794fd7aee2","score":"0.5523299","text":"def ReadGradeList(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","title":""},{"docid":"8cf3609439de36a2800263f1fa86c212","score":"0.5519184","text":"def grades_m(self, data):\n\t\tmidterm = [] # Empty list\n\t\tfor i in range(len(data)): # go through the data list\n\t\t\tmidterm.append(str(data[i][2])) #add every midterm grade into the midterm list\n\t\treturn midterm","title":""},{"docid":"8ee6b67cc28df69eca60def5867a25d0","score":"0.5516856","text":"def check_student(self, student_id, subject_code=None):\n class_list = []\n for c in self.class_list:\n for s in c.get_students():\n if subject_code:\n if c.get_subject_code() == subject_code \\\n and s == student_id:\n class_list.append(c.get_code())\n else:\n if s == student_id:\n class_list.append(c.get_code())\n if class_list:\n return class_list\n else:\n return None","title":""},{"docid":"fdab23bdc789b965b100032a461bfd9b","score":"0.5496505","text":"def table_student_grade(self,db_path)-> None:\r\n print(\"Student Grade Summary\")\r\n try:\r\n db: sqlite3.Connection = sqlite3.connect(db_path)\r\n except sqlite3.OperationalError as e:\r\n print(e)\r\n else:\r\n pt: PrettyTable = PrettyTable(field_names=[\"Name\", \"CWID\", \"Course\", \"Grade\", \"Instructor\"])\r\n try:\r\n for row in db.execute(self._query):\r\n pt.add_row(row) \r\n db.close()\r\n print(pt)\r\n except sqlite3.OperationalError as e:\r\n print(e)","title":""},{"docid":"04f87c2794be25bf79e45774ef18ffe4","score":"0.5477863","text":"def test_search_by_sid_snippet(self, client, fake_auth, asc_inactive_students):\n def _search_students_as_user(uid_, sid_snippet_):\n fake_auth.login(uid_)\n api_json = _api_search(client, sid_snippet_, students=True)\n return api_json['students'], api_json['totalStudentCount']\n\n sid_snippet = '89012'\n # Admin user and ASC advisor get same results\n for uid in ['2040', '1081940']:\n students, total_student_count = _search_students_as_user(uid, sid_snippet)\n assert len(students) == total_student_count == 2\n assert _get_common_sids(asc_inactive_students, students)","title":""},{"docid":"3df6a4949db82ace0efd039143f4c06c","score":"0.54717046","text":"def get(self, id):\n\n adm = ProjektAdministration()\n students = adm.get_students_by_projekt_id(id)\n return students","title":""},{"docid":"f24ab44dabc89ce49b58dc85ed7140b9","score":"0.5470689","text":"def generate_random_students(fname:list,lname:list, number_of_students: int) -> list:\n lst = []\n for _ in range(0,number_of_students):\n lst.append(Person(random_string(fname),random_string(lname)))\n return lst","title":""},{"docid":"0d2b24993d30ff2409ee65d2a277e0a6","score":"0.5453239","text":"def get_professors(self):\n professors = []\n # There are 190 pages of professors, 881 is SJSU's ID\n for n in range(1, 191):\n page_url = 'http://www.ratemyprofessors.com/filter/professor/?&page=' + str(n) \\\n + f'&filter=teacherlastname_sort_s+asc&query=*%3A*&queryoption=TEACHER&queryBy=schoolId&sid=' + \\\n str(881)\n prof_info = self.get_info(page_url)\n\n '''\n print(prof_info[0])\n print(prof_info[0]['tFname'])\n print(prof_info[0]['tMiddlename'])\n print(prof_info[0]['tLname'])\n print(prof_info[0]['overall_rating'])\n print(prof_info[0]['tid'])\n print(prof_info[0]['tDept'])\n '''\n\n for prof in prof_info:\n if prof['tMiddlename'] is not \"\":\n full_name = \" \".join((prof['tFname'], prof['tMiddlename'], prof['tLname']))\n else:\n full_name = \" \".join((prof['tFname'], prof['tLname']))\n rating = prof['overall_rating']\n teacher_id = prof['tid']\n dept = prof['tDept']\n professors.append(course_structures.Professor(full_name, rating, teacher_id, dept))\n\n return professors","title":""},{"docid":"149577657f594393edbf927a800dbca6","score":"0.54449004","text":"def grades_f(self, data):\n\t\tfinal = []#empty list\n\t\tfor i in range(len(data)):# go through the data list\n\t\t\tfinal.append(str(data[i][3]))#add every final grade into the midterm list\n\t\treturn final","title":""},{"docid":"84ebb98658b58daa973ede17e619c3c6","score":"0.5438037","text":"def get_grades_information(self):\n known_class_sessions = {}\n for student_key in self.student_keys:\n student = SchoolDB.models.Student.get(student_key)\n student_records_by_subject = \\\n student.get_class_records_by_subject(self.subjects_dict.values(),\n known_class_sessions)\n student_grades_by_subject = {}\n for subject in self.subjects_dict.values():\n student_class_record_key = student_records_by_subject.get(\n subject, None)\n if student_class_record_key:\n student_class_record = SchoolDB.models.StudentsClass.get(\n student_class_record_key)\n student_grades_by_subject[subject] = \\\n student_class_record.get_grading_period_grade(\n self.grading_period)\n else:\n student_grades_by_subject[subject] = None\n self.student_record_grades_dict[student.key()] = \\\n student_grades_by_subject","title":""}],"string":"[\n {\n \"docid\": \"70ac378fbf6537d2233f442e322c2655\",\n \"score\": \"0.73335534\",\n \"text\": \"def students(self):\\n return Student.objects.filter(groups__in=list(self.groups.all()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8a1991469a65fc1caff6cdc6841e351\",\n \"score\": \"0.7234299\",\n \"text\": \"def students(self):\\n return self.filter(groups__name=\\\"Student\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbdd83784c1ed0d7bf1e16bab1ca8358\",\n \"score\": \"0.69352454\",\n \"text\": \"def get_student_list():\\n\\n return list(sql(\\\"\\\"\\\"select * from users where account_type=\\\"student\\\";\\\"\\\"\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ce235f20a8851504aeaf006b910335\",\n \"score\": \"0.69125384\",\n \"text\": \"def students(self):\\n return Student.objects.filter(courses_taken=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f0d76709f1f6d32dfa5daae628dc2c9\",\n \"score\": \"0.6821924\",\n \"text\": \"def test_get_students(self) -> None:\\n\\n lecture = Course('CSC148')\\n student1 = Student(1, 'Roy')\\n student2 = Student(2, 'Martin')\\n student3 = Student(999, 'Aaron')\\n student4 = Student(520, 'Zara')\\n students = [student1, student2, student3, student4]\\n lecture.enroll_students(students)\\n\\n assert list(lecture.get_students()) == [student1, student2, student4, student3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aee5866fd6d1eb5591a520377c660d9\",\n \"score\": \"0.68109393\",\n \"text\": \"def students(self):\\n return friprosveta.models.Student.objects.filter(\\n enrolled_subjects__subject__in=self.subjects,\\n enrolled_subjects__groupset=self.groupset,\\n ).distinct()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31077801023f86d5d4251587dfd351fb\",\n \"score\": \"0.6793995\",\n \"text\": \"def all_students(self):\\n\\n with sqlite3.connect(self.db_path) as conn:\\n db_cursor = conn.cursor()\\n\\n db_cursor.execute(\\\"\\\"\\\"\\n select s.Id,\\n s.First_Name,\\n s.Last_Name,\\n s.Slack_Handle,\\n s.CohortId,\\n c.Name\\n from Student s\\n join Cohort c on s.CohortId = c.Id\\n order by s.CohortId\\n \\\"\\\"\\\")\\n\\n all_students = db_cursor.fetchall()\\n\\n for student in all_students:\\n print(f'{student[1]} {student[2]} is in {student[5]}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be604bb11d9389c89b7285602619dd9\",\n \"score\": \"0.67150456\",\n \"text\": \"def get_student_list(self):\\n for rec in self:\\n for line in rec.student_list:\\n line.unlink()\\n # TODO apply filter not to get student assigned previously\\n students = self.env['eagleedu.student'].search([\\n ('class_id', '=', rec.admitted_class.id),('assigned', '=', False)])\\n if not students:\\n raise ValidationError(_('No Students Available.. !'))\\n values = []\\n for stud in students:\\n stud_line = {\\n 'class_id': rec.class_id.id,\\n 'student_id': stud.id,\\n 'connect_id': rec.id,\\n 'roll_no': stud.application_id.roll_no\\n }\\n stud.assigned=True\\n values.append(stud_line)\\n for line in values:\\n rec.student_line = self.env['eagleedu.student.list'].create(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcca80e725dbb7cc9399482e2aa21ed9\",\n \"score\": \"0.66220725\",\n \"text\": \"def get_students(self):\\n return u', '.join([c.student.username for c in self.candidates.all()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e9d7db2725c815c1cbbe8fee745c8b\",\n \"score\": \"0.66165775\",\n \"text\": \"def getStudents(self):\\n return User.all().filter('userType =', 'STUDENT')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592b62dc69b44111abece739289bd422\",\n \"score\": \"0.6601335\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github', 'jhacks')\\n page = hackbright.get_grades_by_github(github)\\n\\n return page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3268297556a52ee1145af94f58fffb0\",\n \"score\": \"0.659833\",\n \"text\": \"def get_students():\\n\\n students = db.query(Student). \\\\\\n join(Student.info). \\\\\\n filter(or_(Info.enrolstatus == 'C', Info.enrolstatus == 'P')). \\\\\\n all()\\n\\n return jsonify({\\n '_client_id': client_id,\\n '_count': len(students),\\n 'students': [student.json() for student in students]\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb9f23844155d9b0e7ab7da82bc5603\",\n \"score\": \"0.6573903\",\n \"text\": \"def get_students(self):\\n return [int(student) for student in self.students.split(\\\"|\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a05ef12cd70acd926334e30dab06f71\",\n \"score\": \"0.6562396\",\n \"text\": \"def get_studentset_students(ssid):\\n # if studentset_name:\\n # go('/py/teacher/studentset/list/')\\n # soup=BeautifulSoup(show())\\n # go(soup.find('a',text='formal-class').parent['href'])\\n # else:\\n cmd.go('/py/teacher/studentset/%s/' % ssid)\\n\\n return [int(d['value'])\\n for d in soup().findAll('input', {'name': \\\"stud_group[]\\\"})]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83e7d491e4747704b5ac354d48c308da\",\n \"score\": \"0.6545939\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n\\n first, last, github = hackbright.get_student_by_github(github)\\n\\n row = hackbright.get_grade_by_github(github)\\n print(row)\\n html = render_template(\\\"student_info.html\\\",\\n first=first,\\n last=last,\\n github=github,\\n row=row)\\n\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63e059b46fc979dde6d096acba59605d\",\n \"score\": \"0.65329576\",\n \"text\": \"def get_grades_name(courses):\\n result = []\\n for c in courses:\\n if not c.grade_name in result:\\n result.append(c.grade_name)\\n \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce3583f293940d9fcf18ac781673038e\",\n \"score\": \"0.65206885\",\n \"text\": \"def studentsEnrolledAtDiscipline(self, disciSearch):\\n discipline = self._disciControl.find(disciSearch)\\n listOfEnrolledStudents = []\\n if discipline is False:\\n raise ValueError(\\\"Error: The discipline was not found!\\\")\\n listOfGrades = self._gradeControl.findAllGradesForDiscipline(discipline.id)\\n for grade in listOfGrades:\\n student = self._studControl.find(grade.studID)\\n if student not in listOfEnrolledStudents:\\n listOfEnrolledStudents.append(student)\\n return listOfEnrolledStudents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d97a6c3a049f73f91e2bee331c9e4f3\",\n \"score\": \"0.6500632\",\n \"text\": \"def all_students(self):\\n\\n with sqlite3.connect(self.db_path) as conn:\\n conn.row_factory = lambda cursor, row: Student(\\n row[1], row[2], row[3], row[5]\\n )\\n\\n db_cursor = conn.cursor()\\n\\n db_cursor.execute(\\\"\\\"\\\"\\n select s.Id,\\n s.FirstName,\\n s.LastName,\\n s.SlackHandle,\\n s.CohortId,\\n c.Name\\n from Student s\\n join Cohort c on s.CohortId = c.Id\\n order by s.CohortId\\n \\\"\\\"\\\")\\n\\n all_students = db_cursor.fetchall()\\n\\n for student in all_students:\\n print(\\n f'{student.first_name} {student.last_name} is in {student.cohort}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6676bda38bfc7138a9df216fb696b36e\",\n \"score\": \"0.6488482\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n\\n first, last, github = hackbright.get_student_by_github(github)\\n\\n rows = hackbright.get_grades_by_github(github)\\n\\n html = render_template(\\\"student_info.html\\\",\\n first=first,\\n last=last,\\n github=github, \\n rows=rows)\\n \\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44771fb3477c5f9c4cdac763edf303cc\",\n \"score\": \"0.64817405\",\n \"text\": \"def Schools(self,grade:str):\\n return self.__Schools[grade]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa6fd1de7827b8986fa192e699b64bc\",\n \"score\": \"0.64458746\",\n \"text\": \"def get_students_list(self):\\n stu_list = []\\n for i in range(self.stu_sum):\\n stu_list.append([raw_input(), raw_input()])\\n return stu_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c543ede5c3b578e945c599a928a1c4e\",\n \"score\": \"0.6427282\",\n \"text\": \"def all_students(self):\\n\\n with sqlite3.connect(self.db_path) as conn:\\n conn.row_factory = lambda cursor, row: Student(\\n row[1], row[2], row[3], row[5]\\n )\\n db_cursor = conn.cursor()\\n\\n db_cursor.execute(\\\"\\\"\\\"\\n select s.Id,\\n s.FirstName,\\n s.LastName,\\n s.SlackHandle,\\n s.CohortId,\\n c.Name\\n from Student s\\n join Cohort c on s.CohortId = c.Id\\n order by s.CohortId\\n \\\"\\\"\\\")\\n\\n all_students = db_cursor.fetchall()\\n\\n for student in all_students:\\n print(student)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea0a4cb08cf49f0425c7da75dc00f21\",\n \"score\": \"0.6422413\",\n \"text\": \"def get_all_students(self):\\n curr = self.db.cursor()\\n curr.execute(\\\"\\\"\\\"SELECT student_num, name, email, course, date_created FROM students ;\\\"\\\"\\\")\\n data = curr.fetchall()\\n resp = []\\n\\n for i, items in enumerate(data):\\n student_num, name, email, course, date_created = items\\n students = dict(\\n student_num=int(student_num),\\n name=name,\\n email=email,\\n course=int(course),\\n created_on=str(date_created)\\n\\n )\\n resp.append(students)\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a1b6905ee7f2c2f6bea8143131cf38\",\n \"score\": \"0.6416842\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n\\n first, last, github = hackbright.get_student_by_github(github)\\n\\n project_list = hackbright.get_grades_by_github(github)\\n\\n html = render_template(\\\"student_info.html\\\", first=first, last=last, github=github, project_list=project_list)\\n\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7e85ae7236c8cfb88100093b01f9951\",\n \"score\": \"0.63915837\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n#get list of all tuples from database by github\\n grades_by_project = hackbright.get_grades_by_github(github)\\n\\n#unpack tuple from database\\n first, last, github = hackbright.get_student_by_github(github)\\n\\n# def get_grades_by_github(github):\\n# \\\"\\\"\\\"Get a list of all grades for a student by their github username\\\"\\\"\\\"\\n\\n\\n html = render_template(\\\"student_info.html\\\", first=first, \\n last=last, \\n github=github,\\n grades_by_project=grades_by_project)\\n\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef87341aa76e552b52c049e62c3c0956\",\n \"score\": \"0.6362363\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github','jhacks')\\n first, last, github = hackbright.get_student_by_github(github)\\n grades_by_title = hackbright.get_grades_by_github(github)\\n\\n html = render_template(\\\"student_info.html\\\",\\n first=first,\\n last=last,\\n github=github, grades_title= grades_by_title)\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2887ea5194a40025f30ca7df5297e53f\",\n \"score\": \"0.6304307\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n\\n first, last, github = hackbright.get_student_by_github(github)\\n\\n projects = hackbright.get_grades_by_github(github)\\n \\n return render_template(\\\"student_info.html\\\",\\n first = first,\\n last = last,\\n github = github,\\n projects = projects)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3530df5ec8dd0a7d55033794fe730be5\",\n \"score\": \"0.62560844\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n\\n first, last, github = hackbright.get_student_by_github(github)\\n title_grade = hackbright.get_grades_by_github(github)\\n\\n return render_template(\\\"student_info.html\\\", first=first, last=last,\\n github=github, title_grade=title_grade)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ff8579eb9c167ae50bd31adcd37f331\",\n \"score\": \"0.6244609\",\n \"text\": \"def pretty_table_student(self)-> Iterator[Tuple[str]]:\\n\\n stud_list:Iterator[Tuple[str]] = []\\n\\n pt = PrettyTable(field_names = ['CWID','Name','Courses'])\\n print(f\\\"Summary of Students\\\")\\n for stud in self.stud_data.values():\\n pt.add_row(stud.pretty_table_stud())\\n stud_list.append(stud.pretty_table_stud())\\n print(pt)\\n return stud_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa2d0fda522184f8bb241420ebffe4ca\",\n \"score\": \"0.6241686\",\n \"text\": \"def print_student(students):\\n for student in students:\\n print(student.get_full_name() + \\\" \\\"+ str(student))#\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef62735afac946edfd3d5379cfaa2b75\",\n \"score\": \"0.62406766\",\n \"text\": \"def find_all(self):\\n result = []\\n cursor = self._cnx.cursor()\\n cursor.execute(\\\"SELECT * FROM student\\\")\\n tuples = cursor.fetchall()\\n\\n for (id, creation_date, name, google_id, matriculation_number, course_abbreviation,role_id) in tuples:\\n student = Student()\\n student.set_id(id)\\n student.set_creation_date(creation_date)\\n student.set_name(name)\\n student.set_google_id(google_id)\\n student.set_matriculation_number(matriculation_number)\\n student.set_course_abbreviation(course_abbreviation)\\n student.set_role_id(role_id)\\n result.append(student)\\n\\n self._cnx.commit()\\n cursor.close()\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787b020de5f03ff2fc2ba2b648af6789\",\n \"score\": \"0.62028843\",\n \"text\": \"def get_student(self, email):\\n return (DataLayer.mongoDB.get_student_by_email(email))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed2ecffdc9aaafd357a41920ccc8ba9\",\n \"score\": \"0.61970913\",\n \"text\": \"def getGrades(self, student):\\n try: # return copy of student's grades\\n # index into dict using IdNum, then return a copy [:]\\n return self.grades[student.getIdNum()][:]\\n except KeyError:\\n raise ValueError(\\\"Student not in grade book\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4aba014d7a9264fc799f206373859e0\",\n \"score\": \"0.6183985\",\n \"text\": \"def students_course_grade(request):\\n json_header = request.headers\\n\\n auth, username = authorize_teacher(json_header)\\n\\n if not auth:\\n return HttpResponse(\\\"failed teacher auth...\\\")\\n\\n id = request.GET.get('id')\\n\\n try:\\n course = Course.objects.get(id=id)\\n except:\\n return HttpResponse(\\\"failed query.. specify the correct course ID...\\\")\\n\\n students = [\\n stu.student for stu in StudentCourse.objects.filter(course=course)\\n if stu.student is not None]\\n\\n student_grades = [{\\\"student\\\": {\\\"username\\\": stu.user.username,\\n \\\"firstname\\\": stu.user.firstName,\\n \\\"lastname\\\": stu.user.lastName},\\n \\\"grades\\\": views.construct_student_grades(stu.user.username, True, int(id))}\\n for stu in students]\\n\\n return HttpResponse(json.dumps(student_grades))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60048e5bbb6f9d6c10ca860373de32c9\",\n \"score\": \"0.6178128\",\n \"text\": \"def get_students_by_course_id(course_id, auth_token): \\r\\n\\r\\n # the Canvas endpoint to hit\\r\\n endpoint = CANVAS_BASE_ENDPOINT + str(course_id) + \\\"/users\\\"\\r\\n\\r\\n # necessary headers for the request, including the OAuth authentication \\r\\n # token generated from Canvas\\r\\n headers = {\\r\\n \\\"Authorization\\\": \\\"Bearer \\\" + auth_token\\r\\n }\\r\\n\\r\\n # boolean to keep track of whether or not there are more users left to find\\r\\n pages_left = True\\r\\n\\r\\n # keeps track of the current page number of users\\r\\n page_num = 1\\r\\n\\r\\n students = []\\r\\n\\r\\n while (pages_left):\\r\\n # iterate while there are still students left to find\\r\\n\\r\\n # create request payload with desired parameters\\r\\n payload = {\\r\\n \\\"page\\\": page_num, # get all results from the given page\\r\\n \\\"enrollment_type\\\": \\\"student\\\" # only find students\\r\\n }\\r\\n\\r\\n # send the GET request to the endpoint\\r\\n response = get_from_endpoint(endpoint, headers, params=payload)\\r\\n\\r\\n if (type(response) != list):\\r\\n # TODO: Add better error-handling here\\r\\n return \\r\\n \\r\\n # add all student details to the existing list\\r\\n students.extend(response)\\r\\n\\r\\n # if the response was empty, there are no more students left, so this \\r\\n # evaluates to False. If it was not empty, this evaluates to True.\\r\\n # Once there are no more students left, this ends the iteration of the\\r\\n # while loop.\\r\\n pages_left = bool(response)\\r\\n\\r\\n # increment the page counter\\r\\n page_num += 1\\r\\n\\r\\n return students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51de4d95d2b0ca27742c91dedf986398\",\n \"score\": \"0.61543936\",\n \"text\": \"def get_students(request):\\r\\n # get subject.\\r\\n subject_id = request.POST.get('subject')\\r\\n subject = get_object_or_404(Subject, id=subject_id)\\r\\n # get session year id.\\r\\n session_year_id = request.POST.get('session_year') \\r\\n # get list of students data.\\r\\n students_list_data = Student.objects.get_students_list_data(subject, session_year_id)\\r\\n return JsonResponse(json.dumps(students_list_data), content_type='application/json', safe=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335a6b42f775ca29fdca0d8f1b178a96\",\n \"score\": \"0.6137669\",\n \"text\": \"def test_get_students_for_school(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6b09d0787b61834457c2a7d67ced5be\",\n \"score\": \"0.611547\",\n \"text\": \"def print_student_list(students):\\n\\n for student in students:\\n print(\\\"{}, {}, {}\\\".format(student.idex, student.name, student.surname))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5393065a50efe470e3e93fe3dc07623e\",\n \"score\": \"0.6107028\",\n \"text\": \"def get_student_courses(self):\\n r = self.session.get(Session.RPC_URL, params=Session.RPC_PARAMS['student_menu'])\\n while True:\\n if 'course_summary.php' in r.text:\\n break\\n r = self.session.get(Session.RPC_URL, params=Session.RPC_PARAMS['student_menu'])\\n\\n page = bs4.BeautifulSoup(r.text, 'lxml')\\n student_courses = []\\n for course in page.select('#newmenu3')[0].find_all('a', href=True):\\n course_id = re.sub('[^0-9]', '', course['href'])\\n student_courses.append(Course(self.session, course_id))\\n return student_courses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59deeb32d21845da506d9d47913946fc\",\n \"score\": \"0.60997444\",\n \"text\": \"def get_student_enrollments(self):\\n self._get(\\\"{0}/enrollment\\\".format(ENROLLMENT_API_BASE_URL))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"603c737a9bfa92dbbabef29b88ac3d2d\",\n \"score\": \"0.6093185\",\n \"text\": \"def get_random_students(cls, num_students: int) -> List[Student]:\\n assert num_students < len(cls.students)\\n result = []\\n while len(result) < num_students:\\n s = cls.get_random_student()\\n while s in result:\\n s = cls.get_random_student()\\n result.append(s)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f31e021f1ac4ce53dcc86e7ea83bf7b\",\n \"score\": \"0.6086024\",\n \"text\": \"def _getStudentEntries(self, program_entity, student_entity, \\n params, id, user):\\n\\n items = []\\n\\n timeline_entity = program_entity.timeline\\n\\n if timeline_helper.isActivePeriod(timeline_entity, 'student_signup'):\\n items += [('/student_proposal/list_orgs/%s' % (\\n student_entity.key().id_or_name()),\\n \\\"Submit your Student Proposal\\\", 'any_access')]\\n\\n if timeline_helper.isAfterEvent(timeline_entity, 'student_signup_start'):\\n items += [(redirects.getListSelfRedirect(student_entity,\\n {'url_name':'student_proposal'}),\\n \\\"List my Student Proposals\\\", 'any_access')]\\n\\n items += [(redirects.getEditRedirect(student_entity, \\n {'url_name': 'student'}),\\n \\\"Edit my Student Profile\\\", 'any_access')]\\n\\n if timeline_helper.isAfterEvent(timeline_entity,\\n 'accepted_students_announced_deadline'):\\n # add a link to show all projects\\n items += [(redirects.getListProjectsRedirect(program_entity,\\n {'url_name':'student'}),\\n \\\"List my Student Projects\\\", 'any_access')]\\n\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5543bc62cb9c83e6e456dbd596f076\",\n \"score\": \"0.60771435\",\n \"text\": \"def test_empty_student_list(self):\\n grade_results = list(CourseGradeFactory().iter([], self.course))\\n assert grade_results == []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c039de6cc72e79ea34816eaf0fb9dd98\",\n \"score\": \"0.607418\",\n \"text\": \"def get_all_students(db: Session = Depends(get_db)) -> Generator:\\n\\n if result := retrieve_all_students(db):\\n return result\\n\\n raise HTTPException(\\n status_code=status.HTTP_404_NOT_FOUND,\\n detail=\\\"Não existem estudantes cadastrados.\\\",\\n ) # pragma: nocover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7c42b8d8eca2d4bca61f0cf808c249a\",\n \"score\": \"0.60697883\",\n \"text\": \"def get_all_students(undistributed=False):\\n users = User.objects.filter(\\n Q(usermeta__EnrolledBEP=True) & Q(groups=None) & Q(usermeta__TimeSlot=get_timeslot())).distinct()\\n if get_timephase_number() < 6 or undistributed:\\n return users\\n else:\\n # only students with a distributions\\n return users.filter(Q(distributions__TimeSlot=get_timeslot()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6567539c940672ed7a2b8641c17ddc61\",\n \"score\": \"0.6042441\",\n \"text\": \"def scan_student_info(self):\\n\\n path = \\\"students.txt\\\"\\n for i in file_reader(path, 3, ';', True, 0):\\n self.student_dict[i[0]] = [i[1], i[2], 0, 0, [], [], []]\\n\\n path = \\\"grades.txt\\\"\\n for i in file_reader(path, 4, '|', True):\\n self.student_dict[i[0]][2] += self.grade_dict[i[2]]\\n self.student_dict[i[0]][3] += 1\\n self.student_dict[i[0]][4].append(i[1])\\n\\n # populate course_dict\\n try:\\n self.course_dict[i[1]][0].append(i[0])\\n\\n except Exception as e:\\n self.course_dict[i[1]] = [[], '', '', '']\\n self.course_dict[i[1]][0].append(i[0])\\n self.course_dict[i[1]][1] = i[3]\\n for i in self.student_dict.values():\\n i[5] = diff(self.major_dict[i[1]][0], i[4])\\n if common(self.major_dict[i[1]][1], i[4]):\\n i[6] = self.major_dict[i[1]][1]\\n print('Student Scanned')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"432c29893499fac0ad2c71bfffc117e6\",\n \"score\": \"0.60337174\",\n \"text\": \"def list_section_students(self, course_id, section_id):\\n\\n # XXX sections/{}?include=students crashes Canvas, so avoid for now\\n section = self._get_json('courses/{}/sections/{}?include=students'\\n .format(course_id, section_id))\\n\\n for student_json in section['students']:\\n yield self._to_entity(student_json, CanvasUser)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f10f3af6db342f5263a5abc0b4bee666\",\n \"score\": \"0.602322\",\n \"text\": \"def grades(self) -> StudentGrades:\\n return self._grades\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a36d9a5bb6775e6e7481eb8715a81ca\",\n \"score\": \"0.5992955\",\n \"text\": \"def create_grades(students):\\n ret = []\\n for s in students:\\n exams = Exam.objects.filter(subject__students=s)\\n for e in random_partition(2, exams)[0]:\\n # Get a couple of random exams that are available.\\n ret.append(Grade.objects.create(student=s, grade=random.randrange(10), exam=e))\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180c1e1ab0b238aa2752781291ca485f\",\n \"score\": \"0.59350705\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n\\n first, last, github = hackbright.get_student_by_github(github)\\n\\n html = render_template(\\\"student_info.html\\\",\\n first=first,\\n last=last,\\n github=github)\\n\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae0a6b4758a79ec7fba571ce57a66b4\",\n \"score\": \"0.5911785\",\n \"text\": \"def exercises_with_students(self):\\n\\n with sqlite3.connect(self.db_path) as conn:\\n\\n db_cursor = conn.cursor()\\n\\n db_cursor.execute(\\\"\\\"\\\"\\n select\\n e.Id ExerciseId,\\n e.Name,\\n s.Id StudentId,\\n s.FirstName,\\n s.LastName\\n from Exercise e\\n join StudentExercise se on se.ExerciseId = e.Id\\n join Student s on s.Id = se.StudentId\\n \\\"\\\"\\\")\\n\\n all_exercises_with_students = db_cursor.fetchall()\\n\\n # for exercise_student in all_exercises_with_students:\\n # print(f'{exercise_student[1]}: {exercise_student[3]} {exercise_student[4]}')\\n\\n # Takes our list of tuples and converts it to a dictionary with the exercise name as the key and a list of students as the value.\\n exercises = dict()\\n\\n for exercise_student in all_exercises_with_students:\\n exercise_id = exercise_student[0]\\n exercise_name = exercise_student[1]\\n student_id = exercise_student[2]\\n student_name = f'{exercise_student[3]} {exercise_student[4]}'\\n\\n if exercise_name not in exercises:\\n # exercises[exercise_name] is adding a new key/value pair to the exercises dictionary, where exercise_name is the variable containing the key value which is string\\n\\n # [student_name] is creating a list with one item, that item is the string contained in the variable student_name\\n exercises[exercise_name] = [student_name]\\n else:\\n exercises[exercise_name].append(student_name)\\n\\n # print(exercises)\\n for exercise_name, students in exercises.items():\\n print(exercise_name)\\n for student in students:\\n print(f'\\\\t* {student}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38ed9e6b26f20f9353f1fb0c145e2c5\",\n \"score\": \"0.58853024\",\n \"text\": \"def list_books():\\n books = database.get_all_books()\\n\\n \\n print_book_list(books)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da5bf4b87a15b26c169c07ed552d21ad\",\n \"score\": \"0.58700025\",\n \"text\": \"def get_grades_information(self):\\n self.subjects_taken = {}\\n for student_key in self.student_keys:\\n student = SchoolDB.models.Student.get(student_key)\\n student_results_list = student.get_grading_period_results(\\n self.grading_period)\\n student_subject_result_dict = {}\\n for result in student_results_list:\\n class_session = result.class_session\\n key = class_session.key()\\n if not self.known_class_sessions.has_key(key):\\n self.known_class_sessions[key] = \\\\\\n class_session.subject\\n self.subjects_taken[class_session.subject] = True\\n subject = self.known_class_sessions[key]\\n student_subject_result_dict[subject] = result.assigned_grade\\n self.student_results.append(student_subject_result_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b55d0e7b30402450cf7b3ec598a2b724\",\n \"score\": \"0.5868739\",\n \"text\": \"def student(self) -> None:\\r\\n t: Student = Student()\\r\\n student_file: dict = t.stu()\\r\\n student_course: dict = t.stu_grade()\\r\\n course_id: dict = {} # set all the dicts\\r\\n for key, value in student_course.items():\\r\\n if str(value) not in course_id:\\r\\n course_id[key] = value\\r\\n else:\\r\\n course_id[key].append(value) # get the dicts together\\r\\n for keys, value in student_file.items():\\r\\n for key, values in course_id.items():\\r\\n if keys == key:\\r\\n student_file[key].append(values) # make the dict to print table\\r\\n pt: PrettyTable = PrettyTable(field_names=['CWID', \\\"Name\\\", \\\"Completed Courses\\\"])\\r\\n for key, value in student_file.items():\\r\\n pt.add_row([key, value[0], value[-1]])\\r\\n print(\\\"Student Summary\\\")\\r\\n print(pt) # print prettytable\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f4b1959fc4621780ff6d8dcea0b9e9\",\n \"score\": \"0.58569914\",\n \"text\": \"def get_enrolled_students(self, timetable):\\n return friprosveta.models.Student.objects.filter(\\n enrolled_subjects__subject=self,\\n enrolled_subjects__groupset=timetable.groupset,\\n ).distinct()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78180f6ec319cee38ad5306125f4c024\",\n \"score\": \"0.5846312\",\n \"text\": \"def test_student_grade(self) -> None:\\r\\n cwid: List[str] = list()\\r\\n name: List[str] = list()\\r\\n course: List[str] = list()\\r\\n grade: List[str] = list()\\r\\n inst: List[str] = list()\\r\\n result: PrettyTable = self.repo.grades_pt(self.dbpath)\\r\\n for r in result:\\r\\n r.border = False\\r\\n r.header = False\\r\\n name.append(r.get_string(fields=[\\\"Name\\\"]).strip())\\r\\n cwid.append(r.get_string(fields=[\\\"CWID\\\"]).strip())\\r\\n course.append(r.get_string(fields=[\\\"Course\\\"]).strip())\\r\\n grade.append(r.get_string(fields=[\\\"Grade\\\"]).strip())\\r\\n inst.append(r.get_string(fields=[\\\"Instructor\\\"]).strip())\\r\\n test_name: List[str] = ['Bezos, J', 'Bezos, J', 'Gates, B',\\r\\n 'Gates, B', 'Gates, B', 'Jobs, S', 'Jobs, S', 'Musk, E', 'Musk, E']\\r\\n test_cwid: List[str] = ['10115', '10115', '11714',\\r\\n '11714', '11714', '10103', '10103', '10183', '10183']\\r\\n test_course: List[str] = ['SSW 810', 'CS 546', 'SSW 810',\\r\\n 'CS 546', 'CS 570', 'SSW 810', 'CS 501', 'SSW 555', 'SSW 810']\\r\\n test_grade: List[str] = ['A', 'F', 'B-',\\r\\n 'A', 'A-', 'A-', 'B', 'A', 'A']\\r\\n test_inst: List[str] = ['Rowland, J', 'Hawking, S', 'Rowland, J',\\r\\n 'Cohen, R', 'Hawking, S', 'Rowland, J', 'Hawking, S', 'Rowland, J', 'Rowland, J']\\r\\n self.assertEqual(name, test_name)\\r\\n self.assertEqual(cwid, test_cwid)\\r\\n self.assertEqual(course, test_course)\\r\\n self.assertEqual(grade, test_grade)\\r\\n self.assertEqual(inst, test_inst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30af4d52fa77d2ceea8be159834db7e8\",\n \"score\": \"0.58249694\",\n \"text\": \"def get_available_class_student(user_id):\\n available_class_student = []\\n for class_obj in Class.objects.all():\\n if int(user_id) in get_all_class_member(convert_member(class_obj.student)):\\n available_class_student.append(class_obj)\\n return available_class_student\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b7d8939fb3f73b45bf8c02893f0959\",\n \"score\": \"0.5822902\",\n \"text\": \"def students_in_course(request):\\n json_header = request.headers\\n\\n auth, username = authorize_teacher(json_header)\\n\\n if not auth:\\n return HttpResponse(\\\"failed teacher auth...\\\")\\n\\n id = request.GET.get('id')\\n try:\\n id = int(id)\\n except:\\n return HttpResponse(\\\"failed query... specify the course ID...\\\")\\n\\n # todo: check staff has access to course here\\n\\n course = Course.objects.get(id=id)\\n\\n students = [stu_course.student for stu_course in\\n StudentCourse.objects.filter(course=course) \\n if stu_course.student is not None]\\n\\n json_students = [{\\\"username\\\": student.user.username,\\n \\\"firstname\\\": student.user.firstName,\\n \\\"lastname\\\": student.user.lastName}\\n for student in students]\\n\\n return HttpResponse(json.dumps(json_students))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d8f874f2bdb91c7ac36675e1fae0f60\",\n \"score\": \"0.5821699\",\n \"text\": \"def get_students_from_request(cls, request):\\n # Get the group of the student, if one is set in the request's META\\n student_group = request.META.get(\\\"STUDENT_GROUP\\\", None)\\n students = None\\n \\n if student_group:\\n students = student_group.members.all()\\n else:\\n # No group was found, so use just the current user\\n students = UserProfile.objects.filter(id=request.user.get_profile().id)\\n \\n return students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbfa5f6e299dc9099d322a2062a82b29\",\n \"score\": \"0.5819284\",\n \"text\": \"def studentlist(request):\\n if request.method == 'GET':\\n students = Student.objects.all()\\n serializer = StudentSerializer(students, many=True)\\n return Response(serializer.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb7c726e69a304e5200ada3a20a57a98\",\n \"score\": \"0.58170843\",\n \"text\": \"def get_grades(self):\\n # Implemented from template for osid.repository.Asset.get_asset_contents_template\\n return GradeList(self._my_map['grades'], runtime=self._runtime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61db91b85c34be83dadfea9786257a9\",\n \"score\": \"0.5796599\",\n \"text\": \"def get_student():\\n\\n github = request.args.get('github')\\n\\n first, last, github = hackbright.get_student_by_github(github)\\n performance = hackbright.get_grades_by_github(github)\\n\\n html = render_template(\\\"student_info.html\\\",\\n first=first,\\n last=last,\\n github=github,\\n performance=performance)\\n return html\\n # return \\\"{acct} is the GitHub account for {first} {last}\\\".format(\\n # acct=github, first=first, last=last)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1681d52424b8d12ee5845ece70164142\",\n \"score\": \"0.57825017\",\n \"text\": \"def extract_students(students, data_pos):\\n updated_students = []\\n for student in students:\\n if 'FitNZ' in student[data_pos]:\\n updated_students.append(student)\\n return updated_students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dadb2aa72f817f75bde888a6b8f28582\",\n \"score\": \"0.5770502\",\n \"text\": \"def get_attendance_students(request):\\r\\n # get attendance record.\\r\\n attendance_id = request.POST.get('attendance_id')\\r\\n attendance = get_object_or_404(Attendance, id=attendance_id)\\r\\n # get list of attendance students for attendance record.\\r\\n attendance_students_list_data = AttendanceReport.objects.get_attendance_students_list_data(attendance)\\r\\n return JsonResponse(json.dumps(attendance_students_list_data), content_type='application/json', safe=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7256f31c12b64699823da3309e856cca\",\n \"score\": \"0.5769932\",\n \"text\": \"def enrolled_students_for_study(self, timetable, study):\\n return self.get_enrolled_students(timetable).filter(\\n enrolled_subjects__study=study\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff271bb19fde9154b5b743de140fb79\",\n \"score\": \"0.5730652\",\n \"text\": \"def get_grading_standards(self, **kwargs):\\n return PaginatedList(\\n GradingStandard,\\n self._requester,\\n \\\"GET\\\",\\n \\\"courses/%s/grading_standards\\\" % (self.id),\\n _kwargs=combine_kwargs(**kwargs),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5885b3060061aa0acbaec85fae25af39\",\n \"score\": \"0.57196695\",\n \"text\": \"def _compute_student(self):\\n student_obj = self.env[\\\"student.student\\\"]\\n for rec in self:\\n rec.student_ids = student_obj.search(\\n [\\n (\\\"standard_id\\\", \\\"=\\\", rec.id),\\n (\\\"school_id\\\", \\\"=\\\", rec.school_id.id),\\n (\\\"division_id\\\", \\\"=\\\", rec.division_id.id),\\n (\\\"medium_id\\\", \\\"=\\\", rec.medium_id.id),\\n (\\\"state\\\", \\\"=\\\", \\\"done\\\"),\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b18c6fad33a7657e27bfcf7d5719b0a\",\n \"score\": \"0.57143354\",\n \"text\": \"def get_the_students(self, stu_list, second_lowest):\\n slstu = []\\n for stu in stu_list:\\n if float(stu[1]) == second_lowest:\\n slstu.append(stu[0])\\n slstu.sort()\\n return slstu\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35400820e6e06af8010a877de4f1cc01\",\n \"score\": \"0.56799585\",\n \"text\": \"def get(self):\\n cursor_key = self.request.GET.get('cursor')\\n students, cursor, _ = models.Student.get_students(cursor_key)\\n return self.render_json({\\n 'students': [s.data for s in students],\\n 'cursor': cursor.urlsafe() if cursor else ''\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"822c366a3d6a3d780782dfb41a15876c\",\n \"score\": \"0.5662683\",\n \"text\": \"def get_students():\\n if request.method == \\\"GET\\\":\\n students = Student.query.all()\\n if not students:\\n return custom_error(\\\"No students found!\\\", 404)\\n # The query returns Student objects, which first must be JSON serialized\\n students_serialize = []\\n for student in students:\\n students_serialize.append({\\\"first_name:\\\": student.first_name, \\\"last_name:\\\": student.last_name,\\n \\\"age:\\\": student.age, \\\"email:\\\": student.email})\\n\\n return {\\\"Students:\\\": students_serialize}, 200\\n else:\\n try:\\n new_student = Student(first_name=request.form[\\\"first_name\\\"], last_name=request.form[\\\"last_name\\\"],\\n age=request.form[\\\"age\\\"], email=request.form.get(\\\"email\\\", None))\\n except werkzeug.exceptions.BadRequestKeyError:\\n return custom_error(\\\"Missing argument!\\\", 400)\\n else:\\n db.session.add(new_student)\\n db.session.commit()\\n return {\\\"ID:\\\": new_student.id_}, 201\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e42e9b48718cc3e5f37e718a9c03185\",\n \"score\": \"0.5647124\",\n \"text\": \"def get_subjects():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"757f83aad0f8aaac7c8f032bec564e22\",\n \"score\": \"0.5642957\",\n \"text\": \"def _get_all_student_info(user, dict):\\n\\n student = Student.objects.get(user = user)\\n\\n dict['first_name'] = student.first_name\\n dict['last_name'] = student.last_name\\n dict['full_name'] = student.first_name + ' ' + student.last_name\\n dict['email'] = student.email\\n\\n thesis = Thesis.objects.get(student = student)\\n \\n dict['title'] = thesis.title\\n dict['thesis_id'] = thesis.id\\n dict['abstract'] = thesis.abstract\\n dict['guides'] = []\\n dict['keywords'] = []\\n \\n thesisGuides = ThesisGuide.objects.filter(thesis = thesis)\\n thesisKeywords = ThesisKeyword.objects.filter(thesis = thesis)\\n\\n for thesisGuide in thesisGuides:\\n guide_info = {}\\n\\n if thesisGuide.type == 'G':\\n guide_info['type'] = 'Guide'\\n else:\\n guide_info['type'] = 'Co-guide'\\n\\n guide = thesisGuide.guide\\n guide_info['username'] = guide.user.username\\n guide_info['full_name'] = guide.first_name + ' ' + guide.last_name\\n dict['guides'].append(guide_info)\\n\\n for thesisKeyword in thesisKeywords:\\n dict['keywords'].append(thesisKeyword.keyword.keyword)\\n\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f5880453713ef97d953422aee89a589\",\n \"score\": \"0.56279755\",\n \"text\": \"def ins_grade(self) -> dict:\\r\\n for id, course, grade, ins in file_reader('grades.txt', 4, sep='\\\\t', header=False):\\r\\n y: dict = {course: [ins]}\\r\\n if ins not in self.grade_dic.keys():\\r\\n self.grade_dic.update(y)\\r\\n else:\\r\\n self.grade_dic[course].append([ins])\\r\\n return self.grade_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bad17e6363aa9eeff1fceeb630aee70\",\n \"score\": \"0.5620691\",\n \"text\": \"def get_semester_list():\\n token_info = Auth.get_payload(request)\\n\\n semesters = Semester.query.filter(Semester.name != \\\"CURRENT\\\").all()\\n\\n return MyResponse(data=[{\\n \\\"uuid\\\": str(uuid.UUID(bytes=semester.uuid)),\\n \\\"name\\\": semester.name,\\n \\\"start_time\\\": semester.start_time,\\n \\\"end_time\\\": semester.end_time\\n } for semester in semesters]).build()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa708472de873994a484f7cb302563d3\",\n \"score\": \"0.56194293\",\n \"text\": \"def process_all_students():\\n all_students = Student.objects.all()\\n output = {}\\n lastnames = []\\n grade_letters = defaultdict(int)\\n for student in all_students:\\n result = process_student(student)\\n result['grade_letter'] = convert_percentage_to_letter(result['final_grade'])\\n grade_letters[result['grade_letter']] += 1\\n output[result['lastname']] = '%40s: %30s: %10.2f: %10.2f: %10.2f: %10.2f: %10.2f: %5s' % (result['name'],\\n result['number'],\\n result['cat_summary']['Assignments'],\\n result['cat_summary']['Midterm: written'],\\n result['cat_summary']['Take-home midterm and project'],\\n result['cat_summary']['Final exam'],\\n result['final_grade'],\\n result['grade_letter'])\\n\\n #output[result['lastname']] = '%0.2f' % (result['final_grade'])\\n lastnames.append(result['lastname'])\\n print(output[result['lastname']])\\n\\n# for student in sorted(lastnames):\\n# print(output[student])\\n print('\\\\n')\\n print(grade_letters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec70f1ddea321244335b41122cf7833b\",\n \"score\": \"0.56095994\",\n \"text\": \"def build_gender_list(self, gender):\\n query = SchoolDB.models.Student.all(keys_only=True)\\n SchoolDB.models.active_student_filter(query)\\n query.filter('section = ', self.section)\\n query.filter('organization = ', self.section.organization)\\n query.filter('gender = ', gender)\\n query.order('last_name')\\n query.order('first_name')\\n student_keys = query.fetch(1000)\\n students = db.get(student_keys)\\n for student in students:\\n record = [student.last_name, student.first_name, \\n student.middle_name]\\n self.students[gender].append(record) \\n self.count[gender] = len(self.students[gender])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e89d00a8b680bf5a2b36e41614de8666\",\n \"score\": \"0.5587289\",\n \"text\": \"def read_input():\\n grade_list = []\\n n_student = 5\\n for _ in range(0, n_student):\\n grade_list.append(int(input('Enter a number: ')))\\n return grade_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7009993a579b01e5386609a82faff111\",\n \"score\": \"0.5585751\",\n \"text\": \"def stu_grade(self) -> dict:\\r\\n for id, course, grade, ins in file_reader('grades.txt', 4, sep='\\\\t', header=False):\\r\\n y: dict = {id: [course]}\\r\\n if id not in self.gra_dic.keys():\\r\\n self.gra_dic.update(y)\\r\\n else:\\r\\n self.gra_dic[id].append([course])\\r\\n return self.gra_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2396a101a9e9295929042f7f38853b9\",\n \"score\": \"0.558574\",\n \"text\": \"def get_queryset(self):\\n user = self.request.user\\n return StudentInfo.objects.filter(id=user.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c6c69e73d623d1d3cab2b897626fc1\",\n \"score\": \"0.5572155\",\n \"text\": \"def get_book_list():\\n header, records = bd.get_book_all()\\n return books_to_dict(header, records)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2688a059d89140d00c1ee50e342c96a4\",\n \"score\": \"0.5569877\",\n \"text\": \"def getStudent(student_list, student_name):\\n theStudent = None\\n for student in student_list:\\n if student.get_name() == student_name:\\n theStudent = student\\n break\\n return theStudent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e8e92f050240d38457c4988ab2abec0\",\n \"score\": \"0.556209\",\n \"text\": \"def students_at_risk(request):\\n json_header = request.headers\\n\\n auth, username = authorize_teacher(json_header)\\n\\n if not auth:\\n return HttpResponse(\\\"failed teacher auth...\\\")\\n\\n id = request.GET.get('id')\\n try:\\n id = int(id)\\n except:\\n return HttpResponse(\\\"failed query... specify the course ID...\\\")\\n\\n # todo: check staff has access to course here\\n\\n course = Course.objects.get(id=id)\\n\\n students_in_course = [stu_course.student for stu_course in\\n StudentCourse.objects.filter(course=course)]\\n\\n students_at_risk = []\\n\\n for student in students_in_course:\\n\\n grades = [grade for grade\\n in StudentAssessment.objects.filter(student=student)\\n if grade.assessment is not None\\n and grade.assessment.course == course]\\n\\n total_weight = sum([float(grade.assessment.weight)\\n for grade in grades])\\n total_earnt = sum([(float(grade.value) / 100)\\n * float(grade.assessment.weight)\\n for grade in grades])\\n\\n if total_weight > 0:\\n current_grade = 100 * (total_earnt / total_weight)\\n else:\\n current_grade = 100\\n\\n if current_grade < 50:\\n students_at_risk.append({\\\"student\\\":\\n {\\\"username\\\": student.user.username,\\n \\\"firstname\\\": student.user.firstName,\\n \\\"lastname\\\": student.user.lastName},\\n \\\"grade\\\": current_grade})\\n\\n students_at_risk = sorted(students_at_risk, key=lambda i: i['grade'])\\n\\n return HttpResponse(json.dumps(students_at_risk))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae83d6585259f5d9e637fab1ac2cf55\",\n \"score\": \"0.55603045\",\n \"text\": \"def getBookList(self):\\r\\n return self.bookList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"484fafede3cc3ef359e32d2533f15846\",\n \"score\": \"0.5554736\",\n \"text\": \"def test_correct_student(self) -> None:\\n cwd: str = \\\"G:/Stevens/Courses/Sem4/Assignment/SSW810_Python/StudentRepository_SSW810/input/\\\"\\n lis = []\\n repo: University = University(cwd)\\n for key in repo._students:\\n lis.append(repo._students[key].get_student())\\n result: list = [['10103', 'Baldwin, C', ['CS 501', 'SSW 564', 'SSW 567', 'SSW 687']],\\n ['10115', 'Wyatt, X', [\\n 'CS 545', 'SSW 564', 'SSW 567', 'SSW 687']],\\n ['10172', 'Forbes, I', ['SSW 555', 'SSW 567']],\\n ['10175', 'Erickson, D', ['SSW 564', 'SSW 567', 'SSW 687']],\\n ['10183', 'Chapman, O', ['SSW 689']],\\n ['11399', 'Cordova, I', ['SSW 540']],\\n ['11461', 'Wright, U', ['SYS 611', 'SYS 750', 'SYS 800']],\\n ['11658', 'Kelly, P', ['SSW 540']],\\n ['11714', 'Morton, A', ['SYS 611', 'SYS 645']],\\n ['11788', 'Fuller, E', ['SSW 540']]]\\n self.assertEqual(lis, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12f4850d554e42e4f466738b77a24fc\",\n \"score\": \"0.55483896\",\n \"text\": \"def get_student():\\n\\n #Hey dictionary, here is a key (github) give me its value. If you \\n #don't have this key, give me jhacks\\n github = request.args.get('github','jhacks')\\n first, last, github = hackbright.get_student_by_github(github)\\n html = render_template(\\\"student_info.html\\\",\\n first=first,\\n last=last,\\n github=github)\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087a6d305d0b70d5a99a341869b8f081\",\n \"score\": \"0.5541814\",\n \"text\": \"def list_enrollments(self):\\n try:\\n self.api.get_student_enrollments()\\n except NotAuthorizedException:\\n self.auto_auth()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087a6d305d0b70d5a99a341869b8f081\",\n \"score\": \"0.5541814\",\n \"text\": \"def list_enrollments(self):\\n try:\\n self.api.get_student_enrollments()\\n except NotAuthorizedException:\\n self.auto_auth()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00f9dffd59e804c26a5bb25163f0c4f6\",\n \"score\": \"0.55352515\",\n \"text\": \"def test_enroll_students(self) -> None:\\n\\n lecture = Course('CSC148')\\n student1 = Student(1, 'Roy')\\n student2 = Student(2, 'Martin')\\n students = [student1, student2]\\n lecture.enroll_students(students)\\n\\n assert student1 in lecture.students and student2 in lecture.students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a73a28ee401771d1378cd86020ade3\",\n \"score\": \"0.552836\",\n \"text\": \"def student_visible(self):\\n group_ids = set()\\n for group in Group.objects.all():\\n if group.properties.student_visible:\\n group_ids.add(group.id)\\n\\n return Group.objects.filter(id__in=group_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f895c205ba7ace1eea417d8444aa2a85\",\n \"score\": \"0.55240184\",\n \"text\": \"def test_get_all_grades(self):\\n query_string = [('page', 56),\\n ('pageSize', 56)]\\n headers = { \\n 'Accept': 'application/json',\\n }\\n response = self.client.open(\\n '/{version}/trustcode/{trust_code}/referenceData/grade'.format(version='version_example', trust_code='trust_code_example'),\\n method='GET',\\n headers=headers,\\n query_string=query_string)\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8368278ffbf57806c4858794fd7aee2\",\n \"score\": \"0.5523299\",\n \"text\": \"def ReadGradeList(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf3609439de36a2800263f1fa86c212\",\n \"score\": \"0.5519184\",\n \"text\": \"def grades_m(self, data):\\n\\t\\tmidterm = [] # Empty list\\n\\t\\tfor i in range(len(data)): # go through the data list\\n\\t\\t\\tmidterm.append(str(data[i][2])) #add every midterm grade into the midterm list\\n\\t\\treturn midterm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ee6b67cc28df69eca60def5867a25d0\",\n \"score\": \"0.5516856\",\n \"text\": \"def check_student(self, student_id, subject_code=None):\\n class_list = []\\n for c in self.class_list:\\n for s in c.get_students():\\n if subject_code:\\n if c.get_subject_code() == subject_code \\\\\\n and s == student_id:\\n class_list.append(c.get_code())\\n else:\\n if s == student_id:\\n class_list.append(c.get_code())\\n if class_list:\\n return class_list\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdab23bdc789b965b100032a461bfd9b\",\n \"score\": \"0.5496505\",\n \"text\": \"def table_student_grade(self,db_path)-> None:\\r\\n print(\\\"Student Grade Summary\\\")\\r\\n try:\\r\\n db: sqlite3.Connection = sqlite3.connect(db_path)\\r\\n except sqlite3.OperationalError as e:\\r\\n print(e)\\r\\n else:\\r\\n pt: PrettyTable = PrettyTable(field_names=[\\\"Name\\\", \\\"CWID\\\", \\\"Course\\\", \\\"Grade\\\", \\\"Instructor\\\"])\\r\\n try:\\r\\n for row in db.execute(self._query):\\r\\n pt.add_row(row) \\r\\n db.close()\\r\\n print(pt)\\r\\n except sqlite3.OperationalError as e:\\r\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f87c2794be25bf79e45774ef18ffe4\",\n \"score\": \"0.5477863\",\n \"text\": \"def test_search_by_sid_snippet(self, client, fake_auth, asc_inactive_students):\\n def _search_students_as_user(uid_, sid_snippet_):\\n fake_auth.login(uid_)\\n api_json = _api_search(client, sid_snippet_, students=True)\\n return api_json['students'], api_json['totalStudentCount']\\n\\n sid_snippet = '89012'\\n # Admin user and ASC advisor get same results\\n for uid in ['2040', '1081940']:\\n students, total_student_count = _search_students_as_user(uid, sid_snippet)\\n assert len(students) == total_student_count == 2\\n assert _get_common_sids(asc_inactive_students, students)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3df6a4949db82ace0efd039143f4c06c\",\n \"score\": \"0.54717046\",\n \"text\": \"def get(self, id):\\n\\n adm = ProjektAdministration()\\n students = adm.get_students_by_projekt_id(id)\\n return students\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24ab44dabc89ce49b58dc85ed7140b9\",\n \"score\": \"0.5470689\",\n \"text\": \"def generate_random_students(fname:list,lname:list, number_of_students: int) -> list:\\n lst = []\\n for _ in range(0,number_of_students):\\n lst.append(Person(random_string(fname),random_string(lname)))\\n return lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2b24993d30ff2409ee65d2a277e0a6\",\n \"score\": \"0.5453239\",\n \"text\": \"def get_professors(self):\\n professors = []\\n # There are 190 pages of professors, 881 is SJSU's ID\\n for n in range(1, 191):\\n page_url = 'http://www.ratemyprofessors.com/filter/professor/?&page=' + str(n) \\\\\\n + f'&filter=teacherlastname_sort_s+asc&query=*%3A*&queryoption=TEACHER&queryBy=schoolId&sid=' + \\\\\\n str(881)\\n prof_info = self.get_info(page_url)\\n\\n '''\\n print(prof_info[0])\\n print(prof_info[0]['tFname'])\\n print(prof_info[0]['tMiddlename'])\\n print(prof_info[0]['tLname'])\\n print(prof_info[0]['overall_rating'])\\n print(prof_info[0]['tid'])\\n print(prof_info[0]['tDept'])\\n '''\\n\\n for prof in prof_info:\\n if prof['tMiddlename'] is not \\\"\\\":\\n full_name = \\\" \\\".join((prof['tFname'], prof['tMiddlename'], prof['tLname']))\\n else:\\n full_name = \\\" \\\".join((prof['tFname'], prof['tLname']))\\n rating = prof['overall_rating']\\n teacher_id = prof['tid']\\n dept = prof['tDept']\\n professors.append(course_structures.Professor(full_name, rating, teacher_id, dept))\\n\\n return professors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149577657f594393edbf927a800dbca6\",\n \"score\": \"0.54449004\",\n \"text\": \"def grades_f(self, data):\\n\\t\\tfinal = []#empty list\\n\\t\\tfor i in range(len(data)):# go through the data list\\n\\t\\t\\tfinal.append(str(data[i][3]))#add every final grade into the midterm list\\n\\t\\treturn final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ebb98658b58daa973ede17e619c3c6\",\n \"score\": \"0.5438037\",\n \"text\": \"def get_grades_information(self):\\n known_class_sessions = {}\\n for student_key in self.student_keys:\\n student = SchoolDB.models.Student.get(student_key)\\n student_records_by_subject = \\\\\\n student.get_class_records_by_subject(self.subjects_dict.values(),\\n known_class_sessions)\\n student_grades_by_subject = {}\\n for subject in self.subjects_dict.values():\\n student_class_record_key = student_records_by_subject.get(\\n subject, None)\\n if student_class_record_key:\\n student_class_record = SchoolDB.models.StudentsClass.get(\\n student_class_record_key)\\n student_grades_by_subject[subject] = \\\\\\n student_class_record.get_grading_period_grade(\\n self.grading_period)\\n else:\\n student_grades_by_subject[subject] = None\\n self.student_record_grades_dict[student.key()] = \\\\\\n student_grades_by_subject\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":9929,"numItemsPerPage":100,"numTotalItems":994482,"offset":992900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NDkyOTk1MCwianRpIjoiNDE2ZDI3NmUtMTdjMi00YjllLWFhZDUtZThkYzZiNmM2MTFlIiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc4NDkzMzU1MCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.rTq6GiN91Jy-LwKqDpV7m9-Jg6sisC2-R56V-riVbbil0SflRwojUZQYHbN_xNfVjofzCV5_K9Dsh5XL9z2PBw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
c831db51a49f82c1aad70fcfb2c7a168 | Configure the action space. | [
{
"docid": "765e854f6819b2dd146069c878c01d67",
"score": "0.7577407",
"text": "def _initialize_action_space(self) -> None:",
"title": ""
}
] | [
{
"docid": "d4e74311ef892427cf268c56f6777f0c",
"score": "0.7442753",
"text": "def _set_action_space(self):\n assert isinstance(self.env.action_space, gym.spaces.Discrete), self.env.action_space\n self.action_space = self.env.action_space",
"title": ""
},
{
"docid": "d4e74311ef8... |
cfeb8d8af6510704a896ae6353ef0d5c | Regression test for bug 8106. Same sort of problem as the previous test, but this time there are more extra tables to pull in as part of the select_related() and some of them could potentially clash (so need to be kept separate). | [
{
"docid": "ee5f4f90b9acdd3898401caf24d2174a",
"score": "0.7024326",
"text": "def test_regression_8106(self):\r\n\r\n us = TUser.objects.create(name=\"std\")\r\n usp = Person.objects.create(user=us)\r\n uo = TUser.objects.create(name=\"org\")\r\n uop = Person.objects.create(u... | [
{
"docid": "f64bd0357ba1341d44bef91a90f66996",
"score": "0.70327324",
"text": "def test_regression_8106(self):\r\n\r\n us = TUser.objects.create(name=\"std\")\r\n usp = Person.objects.create(user=us)\r\n uo = TUser.objects.create(name=\"org\")\r\n uop = Person.objects.create(... |
36c63ee68e5763e9bb4f9e2b93d6a70b | Getter method for rip, mapped from YANG variable /l3vpn_ntw/vpn_services/vpn_service/vpn_nodes/vpn_node/vpn_network_accesses/vpn_network_access/routing_protocols/routing_protocol/rip (container) | [
{
"docid": "8871e6f3e7446d88fb57804a1b4aa586",
"score": "0.56625473",
"text": "def _get_rip(self):\n return self.__rip",
"title": ""
}
] | [
{
"docid": "952732d3cc85b07d414af726f561636b",
"score": "0.7309867",
"text": "def _set_rip(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_rip_ietf_l3vpn_ntw__l3vpn_ntw_vpn_services_vpn_service_vpn_nodes_vpn_node_vpn_network_access... |
78789e4c729ea48916680b63fd1b8695 | Momentum update of the key encoder | [
{
"docid": "40a33b2614b114bc49ebb4814681b3b2",
"score": "0.74493855",
"text": "def _momentum_update_key_encoder(self):\n m = self._get_m()\n for param_q, param_k in zip(self.model.parameters(), self.lagging_model.parameters()):\n param_k.data = param_k.data * m + param_q.data * ... | [
{
"docid": "48e07aaa6996bd22c5d2743d6144d105",
"score": "0.79691774",
"text": "def _momentum_update_key_encoder(self):\n for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):\n param_k.data = param_k.data * self.m + param_q.data * (1. - self.m)",
"t... |
3b8428bcc5c56e3148d620de310023e6 | Within an Ensembl release path, get the subpath for protein sequences data given an ncbi taxonomy id, e.g. the pattern on Ensembl is like 'fasta/species.name' | [
{
"docid": "d9daa28e0c5b1b527137e31b5331ab99",
"score": "0.6448442",
"text": "def __get_subpath_fasta_for_species(self, taxonomy_id):\n # The subpath is fasta/species.name\n self._get_logger().debug(\"__get_subpath_fasta_for_species for taxonomy id '{}'\".format(taxonomy_id))\n retu... | [
{
"docid": "c62de25da7c9765ca1ce4561c24fe193",
"score": "0.68920547",
"text": "def __get_subpath_protein_sequence_for_species(self, taxonomy_id):\n # The subpath is fasta/species.name/pep\n self._get_logger().debug(\"__get_subpath_protein_sequence_for_species for taxonomy id '{}'\".format(... |
a3c7062c6a4d1e2a43caa2ab02bffb88 | Test invalid authentication token | [
{
"docid": "00a093e6f48062723399fd0d7b110f70",
"score": "0.8041319",
"text": "def test_invalid_auth_token(self):\n with self.app.app_context():\n self.user.save()\n self.assertEqual(\"Invalid token. Please log in again.\", self.user.decode_auth_token(\"riignrgnrg\"))",
"... | [
{
"docid": "3bcb0ca1f106825f5c1daad370857d5c",
"score": "0.83785814",
"text": "def test_invalid_token(self):\n response = self.client.get('/api/user/', content_type='application/json',\n HTTP_AUTHORIZATION='Bearer nhbvjhsjkhfkjsfn,sjk')\n\n self.assertIn('... |
9223b6c8e27f2e13221a17a177a3666b | _parse_samples should properly parse a packet containing two samples of only digital io data for one dio line | [
{
"docid": "00429153e7af412af8180a3930c33953",
"score": "0.7083207",
"text": "def test_parse_multiple_dio_subset(self):\n # Two samples, ADC disabled\n # DIO 0 enabled\n header = b'\\x02\\x00\\x01'\n\n # First 7 bits ignored, DIO8 low, DIO 0-7 alternating\n sample = b'... | [
{
"docid": "19631da13a7aeebcda171a379807c227",
"score": "0.7278747",
"text": "def test_parse_single_dio_subset_again(self):\n # One sample, ADC disabled\n # DIO 0 enabled\n header = b'\\x01\\x00\\x01'\n\n # First 7 bits ignored, DIO8 low, DIO 0-7 alternating\n sample =... |
dbbc1c9ccba84af63176f11074f88fbd | Function to initialize prototypes in the model. | [
{
"docid": "c50d145aefe9f51bc544711bb3ba6846",
"score": "0.73591924",
"text": "def initialize_prototypes(self, initialize_by=\"class-mean\", pass_w=[], pass_w_labels=[]):\n self.initialize_by = initialize_by\n\n # Initialize the prototypes\n if (initialize_by == \"initialized\"):\n ... | [
{
"docid": "e18db8209b7abdfb6437d14e16dd5d6a",
"score": "0.6100342",
"text": "def __init__(self):\n self.iris_model = self._load_model()\n self.query_schema = IrisQuerySchema()",
"title": ""
},
{
"docid": "ab6f6f1ca407b9696970ba7f464620ff",
"score": "0.60907435",
"text"... |
e71eee33e0341ee4212c5f4391092175 | Test case for get_driver_safety_score | [
{
"docid": "8bc33e0e05ad9ae19c00748599897426",
"score": "0.75541055",
"text": "def test_get_driver_safety_score(self):\n query_string = [('access_token', 'access_token_example'),\n ('start_ms', 56),\n ('end_ms', 56)]\n headers = { \n ... | [
{
"docid": "c825af99a6b243890e1896076c32cb77",
"score": "0.6813096",
"text": "def test_company_earnings_quality_score(self):\n pass",
"title": ""
},
{
"docid": "c46e8283f137f6921182bbecf21c72ef",
"score": "0.68129355",
"text": "def test_get_score(self):\n score_dict = s... |
b98bc37159e4ab5ff692f3583e33c50c | Implement data processing similar to ToTensor. | [
{
"docid": "14841e5dbfac84b151c5c444ed6b5ced",
"score": "0.0",
"text": "def __call__(self, results):\n for i, modal in enumerate(results['modality']):\n video = results['video'][i].transpose(3, 0, 1, 2)\n results['video'][i] = torch.tensor(\n np.ascontiguousar... | [
{
"docid": "99d1e460acc07b9ca12d1daf114b4c21",
"score": "0.7140292",
"text": "def evaluate(self, data, batch_size=512):",
"title": ""
},
{
"docid": "1b750ba546005573394218900676dc48",
"score": "0.7139154",
"text": "def compute(self, data):",
"title": ""
},
{
"docid": "2a1... |
5a10b5cedb3047b8a48e09dad7ec1566 | LampStateParameters a model defined in OpenAPI | [
{
"docid": "e80e87a8180fc8a01528641e0e8ad613",
"score": "0.0",
"text": "def __init__(self, brightness=None, master_brightness=None, net_brightness=None, dim_curve_active=None, dim_curve=None, power_on=None, vibrancy=None, cct=None, x_chromaticity=None, y_chromaticity=None, local_vars_configuration=None)... | [
{
"docid": "61c666097d6089a5d2bc5f6e76f276e0",
"score": "0.601345",
"text": "def sampleModelParameters(self):\n pass",
"title": ""
},
{
"docid": "1693beeaf7ed0cad3aba2ae3bf09e3bd",
"score": "0.5515178",
"text": "def __init__(self, state: str=None, temperature: float=None):\n ... |
d725300c97bd2fc2e76f746a0950aa8b | Helper function for parallel pairwise distance calculations. | [
{
"docid": "ff1d4d64c6b4030851944bfe13604ccc",
"score": "0.0",
"text": "def _all_dist_to_end(args):\n idx = args[0]\n spiketrains = args[1]\n start = args[2]\n end = args[3]\n N = args[4]\n num_spiketrains = len(spiketrains)\n distances = []\n for jdx in range(idx+1, num_spiketra... | [
{
"docid": "1465d0103db13253dec0ab80ec0b42f2",
"score": "0.70513475",
"text": "def _pairwise_dist(self,s1,s2):\n\n return 0.0",
"title": ""
},
{
"docid": "11b0e810e8e0f2bb77487a4d5c5043d9",
"score": "0.6761033",
"text": "def _pairwise_dist(self,seq1,seq2):\n \n retur... |
dcaeaaf6692df901bfea43c6702c9c21 | SetFont(font) Sets the current font for the DC. | [
{
"docid": "0407b810ea7cee0079baedb1e8a85652",
"score": "0.780529",
"text": "def SetFont(self, font):",
"title": ""
}
] | [
{
"docid": "63cde49d565aed7a1bedde4d14357473",
"score": "0.77239305",
"text": "def setFont(self, font):\n self.textFont = font",
"title": ""
},
{
"docid": "952c56c2ce4261a89ef4ec6c1c310243",
"score": "0.7673749",
"text": "def set_font(self, font):\n self._font = font",
... |
be25159ceafaee2cda1f609648e81cb0 | Setter method for state, mapped from YANG variable /qos/interfaces/interface/output/classifers/classifier/state (container) | [
{
"docid": "9821c217eea856154e39377f61555bee",
"score": "0.83308876",
"text": "def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_output_classifers_classifier_st... | [
{
"docid": "73d7d8d1caf11d86aac69cd5916293eb",
"score": "0.83311594",
"text": "def _set_state(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_state_openconfig_qos_interfaces__qos_interfaces_interface_input_classifers_classifier_sta... |
bee74ad7e3b04dc6434706e761651b3e | Get a list of user's messages | [
{
"docid": "cd71a6c26f5d90d4ec854602899f7a0a",
"score": "0.6941459",
"text": "def list_messages(self,\n from_=None,\n to=None,\n from_date_time=None,\n to_date_time=None,\n direction=None,\n ... | [
{
"docid": "5f692e0eb2bdda918ac8d19d75108253",
"score": "0.8211176",
"text": "def list(self):\n try:\n response = self.service.users().messages().list(userId=self.user_id,\n labelIds=self.label_ids).execute()\n messages =... |
67201eebadfb05d07ab563fab01f9f62 | Store a progress operation | [
{
"docid": "eb720a8dbca1df2a8809fcf0151a809b",
"score": "0.71220106",
"text": "def store(self, progressObj, session = None):\n \n # Does an exception exist?\n if progressObj.completed and progressObj.resultCode:\n \n try:\n if session: session.un... | [
{
"docid": "a61af1fb4ecfbd5fce38ea82fbb7c55b",
"score": "0.6987925",
"text": "def save_progress(self):\n\n with self._write_lock:\n self.set_modified_at()\n self.doc_ref.collection('progress').document('progress').set({\n 'progress': self._progress.to_dict()\n... |
d784b4fd958871a98e9adbb6ae61c231 | !Make a list of detector configs a list of detector configs (lsst.afw.cameraGeom.DetectorConfig) | [
{
"docid": "fa5d2417908e4700de4eef5869faefc8",
"score": "0.8084532",
"text": "def _makeDetectorConfigList(self):\n # There is only a single detector assumed perfectly centered and aligned.\n detector0Config = cameraGeom.DetectorConfig()\n detector0Config.name = '0'\n detector... | [
{
"docid": "3e6fa7eb4574ef4fd745365289c49bc9",
"score": "0.70075893",
"text": "def makeDetectorConfigs(self, detFile):\n detectors = []\n self.detectorNameList = []\n self.detectorIdList = []\n with open(detFile) as fh:\n names = fh.readline().rstrip().lstrip(\"#\"... |
ac2d14e311f61d409115b70f90050623 | Testing json_patch with remove op and missing path | [
{
"docid": "901c071aa7cfeebf21f257d30e123803",
"score": "0.8216336",
"text": "def test_remove_with_missing_path(self):\n doc = {\n 'a': 1,\n }\n patch = [{\n 'op': 'remove',\n 'value': 1,\n }]\n\n message = 'Missing key \"path\" for pat... | [
{
"docid": "96d60e083c8addd0cfe19651052d9160",
"score": "0.8357207",
"text": "def test_remove_with_complex_path(self):\n doc = {\n 'a': [{\n 'b': 1,\n 'c': 2,\n }],\n }\n\n self.assertEqual(\n json_patch(\n ... |
dc1e25b68f60aafd2604d012b3dff8a6 | Tests the mongo map reduce utility functions | [
{
"docid": "8727c9e54e115d814f5b803acc679fe9",
"score": "0.75288475",
"text": "def test_mapreduce(Corn):\n Corn.create({'id': 1, 'name': u'foo', 'lastname': u'bar'}).save()\n Corn.create({'id': 2, 'name': u'baz', 'lastname': u'bar'}).save()\n Corn.create({'id': 3, 'name': u'foo', 'lastname': u'... | [
{
"docid": "36a9b3b59336d000345d11ca9b2e0c5e",
"score": "0.62238985",
"text": "def map_reduce(self, map_function, reduce_function):\n \n mapped = []\n for i in self.docs:\n mapped.append(map_function(i))\n \n return reduce_function(mapped)\n pass"... |
8ab33b06afd8890ccd81cf2ad9cc9f6c | A simple function to be wrapped by assemblywrapper | [
{
"docid": "62b6d1fb03cd36869c5a7c8f4d7b83c4",
"score": "0.0",
"text": "def passthrough(output):\n return output",
"title": ""
}
] | [
{
"docid": "20dac9c083de07ca26d97b3f06158ce3",
"score": "0.6395671",
"text": "def __call__(self, assembly1, assembly2):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "d3675434fd87de2d7c1ab340cef5bedc",
"score": "0.62855",
"text": "def test_assemblywrapper(self):\n ... |
c8b213203726dc688a55a8c919f2f7a5 | Switches the TPS reserved state on or off. ON OFF | [
{
"docid": "497d79af61a8f7a47a8f0da4b8f0ccae",
"score": "0.6989425",
"text": "def set_digitaltv_settings_tpsreservedstate_dvbt(self, state_type):\n self.specan.write('DVBT:TPSR:STAT {}'.format(state_type))\n logging.info('DVBT:TPSR:STAT {}'.format(state_type))\n self.specan.query('*... | [
{
"docid": "7ae76e1e96e833cce200624edf4a14a4",
"score": "0.66995955",
"text": "def ptc_on(self):\n\t #PTC control is option 1\n\t options = int(self.get_set_options())\n\t if (options | 1) != options:\n\t #make sure humidity and altitude are off\n\t options = options & ~18\n\t ... |
9ec0ed4c0dd9573e5669d6b0d7f4650a | Returns the string name of a PLinApi.TLINDirection value | [
{
"docid": "16030d19993e049c5714b8d7cc0df44d",
"score": "0.59649193",
"text": "def getFrameDirectionAsString(direction):\n # check given parameter\n if (isinstance(direction, PLinApi.TLINDirection)):\n value = direction.value\n else:\n value = int(direction)\n ... | [
{
"docid": "a27ff22c72ab89793d95b6c7b849889e",
"score": "0.61148554",
"text": "def redirect_type(self) -> str:\n return pulumi.get(self, \"redirect_type\")",
"title": ""
},
{
"docid": "3b946869b51763078d90ec142827df38",
"score": "0.6032446",
"text": "def _get_lsp_forwarding_ou... |
e5d6e4c07785259bc2bcdbe8ddcd8871 | Concatenate input arrays along the given dimension. | [
{
"docid": "10bc504b57c6fa0b220448c649b2f155",
"score": "0.62408817",
"text": "def concat(x: Sequence[VariableLikeType], dim: str) -> VariableLikeType:\n if x and isinstance(x[0], data_group.DataGroup):\n return data_group._apply_to_items(concat, x, dim)\n return _call_cpp_func(_cpp.concat,... | [
{
"docid": "9f63ddcd24ff19b22581d795cf1df2e2",
"score": "0.7381869",
"text": "def concatenate(self, arrays, axis=0):\n assert_schema(arrays, bounded=True, same_attributes=True)\n slice_at_end = arrays[0].ndim == 1 and axis == 1\n\n # pad with extra dimensions as needed\n arra... |
0a4df1e96132e4ef6cfa39d54eb82148 | Delete a collection of KubeVirt objects. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. | [
{
"docid": "a81f92514cdcc5554fe86beb26eabcdf",
"score": "0.6330248",
"text": "def delete_collection_namespaced_kube_virt(self, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.delete_collection_namespaced_kube_virt_with_http_info(**... | [
{
"docid": "969296681eb4ea99295c1d555f3e6f1a",
"score": "0.6406125",
"text": "def delete_collection(self, **kwargs):\n self._delete_collection(**kwargs)",
"title": ""
},
{
"docid": "8bc70ae6b3a96dab6cf1affd9ecd284f",
"score": "0.62253577",
"text": "def delete_collections(self,... |
a45f7d7f50bf09cb67605be19af215b1 | Trim an existing alignment based on provided primers or specific, predefined positions. Primers take precedence over the positions, i.e. if both are provided, positions will be ignored. This action uses mafft_add action to add provided primers (if any) to the existing alignment. Subsequently, start (5'most) and end (3'... | [
{
"docid": "acf7f3d307dd3455ead2c5cb930c7336",
"score": "0.6895433",
"text": "def trim_alignment(ctx,\n aligned_sequences,\n primer_fwd=None,\n primer_rev=None,\n position_start=None,\n position_end=None):\n exp... | [
{
"docid": "8c0324079a826d0fc81572d695bd8efb",
"score": "0.691214",
"text": "def _trim_alignment(expand_alignment_action,\n aligned_sequences,\n primer_fwd=None,\n primer_rev=None,\n position_start=None,\n pos... |
803bd0e96151b10bfa97985222cfae50 | Points p1 and p2 are represented as tuples of coordinates. | [
{
"docid": "7ddd65669b93b656d354ff1e1f766e1b",
"score": "0.0",
"text": "def distance (self, p1, p2):\n d = math.sqrt((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2)\n return d",
"title": ""
}
] | [
{
"docid": "5e89027a88823160eca0405c15af5445",
"score": "0.71197873",
"text": "def get_xy(kp_pairs):\n p1 = np.int32([kpp[0].pt for kpp in kp_pairs])\n p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) \n \n return p1, p2",
"title": ""
},
{
"docid": "c61341462112b79830485adde2cb9787... |
edfd537c4ada446be0d0f7837bd86b93 | Computes encodings for all quantization sim nodes in the model. | [
{
"docid": "0319c54a3a1ede111f9d2fd03b73e320",
"score": "0.5776553",
"text": "def compute_encodings(self, forward_pass_callback, forward_pass_callback_args):\n ops_with_invalid_encodings = []\n self._compute_and_set_parameter_encodings(ops_with_invalid_encodings)\n\n self._set_op_mo... | [
{
"docid": "7eea248f3d7a7cceacb35937949c5a3f",
"score": "0.70447296",
"text": "def compute_encoding(self):\n # No action required for AdaroundTensorQuantizer.\n # Function is in place if QcQuantizeWrapper calls while in Training mode",
"title": ""
},
{
"docid": "8605efbedc218f9... |
5ce77cea7310b8c8a91647bd367da23f | Perform the actual request. Retrieve a connection from the connection pool, pass all the information to it's perform_request method and return the data. If an exception was raised, mark the connection as failed and retry (up to `max_retries` times). If the operation was succesful and the connection used was previously ... | [
{
"docid": "b3052713d98bfbffe60d54588f8e4930",
"score": "0.6911943",
"text": "def perform_request(self, method, url, params=None, body=None):\n if body is not None:\n body = self.serializer.dumps(body)\n\n # some clients or environments don't support sending GET with body\n ... | [
{
"docid": "6c4f6e5f333684f5b412b9552778b598",
"score": "0.6186178",
"text": "def _http_request(self, auth_host, auth_port, method, path, **kwargs):\n conn = self._get_http_connection(auth_host, auth_port)\n RETRIES = 3\n retry = 0\n \n while True:\n ... |
4d2c66d907975b8b8e52d9b191ba1e85 | It seems that dvbsnoop can output data buffered from the previous tune. This makes sure we discard that data, 2000 is an arbitrary number. | [
{
"docid": "5effc2d29762eeaab1907368d17f3f7b",
"score": "0.5411468",
"text": "def flush(self):\n log.info('Flushing stale EIT data...')\n self.dvbsnoop_flush = subprocess.Popen(\n ['dvbsnoop', '-adapter', self.adapter, '-n', '2000', '-nph', '0x12'],\n stdout=subproces... | [
{
"docid": "99172bd7fb42bb2a074232f56e729cda",
"score": "0.58245504",
"text": "def stop_tuning(self):\n it = len(self._history)\n n_drop = int(self.tune_drop_fraction * it)\n self._history = self._history[n_drop:]\n return super().stop_tuning()",
"title": ""
},
{
... |
3a42a38c234c69881b4482a5b0a88c37 | GIVEN logged in user and app instance WHEN trying to logout users withot token in header THEN check 401 status code, and error message | [
{
"docid": "f8d5c146c36fdc7aa6928e936b1a460f",
"score": "0.6664895",
"text": "def test_logout_user_without_token(client, access_token):\n rv_logout = client.post('/logout/access')\n resp = rv_logout.get_json()\n assert rv_logout.status == '401 UNAUTHORIZED'\n assert resp['msg'] == 'Missing A... | [
{
"docid": "95ca2369f8145cc85732450654253da9",
"score": "0.7324254",
"text": "def logout():\n user = User()\n user_token = request.headers['Authorization']\n token = user_token.split(\" \")[1]\n if user.blacklist_token(token):\n return jsonify({\"message\": \"log out successful\"}), 2... |
5aeda7b5c58df5b216aa7b52f60ccaad | gets the Parental identity for a given cM position | [
{
"docid": "5c780c7987e00ae2af23423ca1ff4f92",
"score": "0.6218072",
"text": "def getParentAtMapLoc(self, mapLoc):\r\n if mapLoc < 0 or mapLoc > self.cM:\r\n raise ValueError, \"Map location must be withing the range of the chromosome.\"\r\n loc = cM/float(self.cM)\r\n return self.... | [
{
"docid": "aa1881bf6d5644ead8c92e2011483b2e",
"score": "0.633048",
"text": "def parent_id(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"parent_id\")",
"title": ""
},
{
"docid": "90231c82fe678ad1b3219d1e9fbd4f48",
"score": "0.6276556",
"text": "def parent(self, pos)... |
864fcfbf80205ca0a4666afcf46c96d1 | Initializes the user model with a password and email | [
{
"docid": "8da9f6f7fc89957347c96bfd37c08cf6",
"score": "0.6965422",
"text": "def __init__(self, email, password):\n\t\tself.email = email\n\t\tself.password = Bcrypt().generate_password_hash(password).decode()",
"title": ""
}
] | [
{
"docid": "a44f4f5b965b8c663a0ca5aa368b4f30",
"score": "0.7764653",
"text": "def __init__(self, user_email, user_password):\n if Helpers.is_valid_format_email(user_email):\n self.email = user_email\n else:\n raise ValueError('Email %s is not valid!' % user_email)\n ... |
6c51eca38bc72fb777aa80d495ccf298 | Returns an ipopt solver object with the desired options for convergence evaluation (and initialization) Returns Pyomo solver | [
{
"docid": "f92dde69679496d5463197b5cdff8256",
"score": "0.80685276",
"text": "def get_solver(self):\n opt = pe.SolverFactory('ipopt')\n opt.options = {'tol': 1e-6,\n 'mu_init': 1e-8,\n 'bound_push': 1e-8}\n return opt",
"title": ""
... | [
{
"docid": "b3fd79586ea5c1fe93ed6cfa9bbfeaea",
"score": "0.69074684",
"text": "def makeSolver(self):\n pass",
"title": ""
},
{
"docid": "b64907117cf10108065aeaaa8613f5b4",
"score": "0.69001853",
"text": "def solve(self):\n solver = SolverFactory('ipopt',executable='C:/U... |
51f5c0e033e7ae3a05a02beaacd60851 | Read in shelx file and returns a list without line endings. | [
{
"docid": "bdcc0c65ff2284891c0e01ad3e57ce7c",
"score": "0.5820845",
"text": "def read_nested_file_to_list(resfile: str) -> list:\n reslist = []\n try:\n with open(os.path.abspath(resfile), 'r') as f:\n reslist = f.read().splitlines(keepends=False)\n except... | [
{
"docid": "a727a62d4a43dedd1a22d323914477db",
"score": "0.65961695",
"text": "def read_list(fn):\n f = open(fn, \n \"r\")\n s = f.read()\n f.close()\n # If the last character in the file is a newline, delete it\n if s[-1] == \"\\n\":\n s = s[:-1]\n l = s.split(\"\\n... |
419579f351fd3412f655411a01d8124e | Log the compute_node status | [
{
"docid": "f1f8936b6566393a42f0be9252be774b",
"score": "0.0",
"text": "def _stoppedComputePartition(self, compute_node_id, compute_partition_id):\n instance = self._getSoftwareInstanceForComputePartition(\n compute_node_id,\n compute_partition_id)\n instance.setAccessStatus(\n ... | [
{
"docid": "a88817d4255fef209804cce42961c1cb",
"score": "0.63056797",
"text": "def log_state(self):\n rospy.loginfo(\"STATE: %s [%s]\" %(self.__class__.__name__, 15 - self.ros_node.get_time()))",
"title": ""
},
{
"docid": "1ad00fbb269b26dd340553b9a7b5822b",
"score": "0.62696135"... |
bfa34172375d579d902d0b8dda80bed3 | Check running state of API and start if they is close. | [
{
"docid": "1dc3fa2c6f303c4b65cd9c2cdbc70a4d",
"score": "0.61420894",
"text": "async def _homeassistant_watchdog_api():\n nonlocal retry_scan\n\n # if Home-Assistant is active\n if not await homeassistant.is_initialize() or \\\n not homeassistant.watchdog:\n ... | [
{
"docid": "b9608e5b512d5183f60b7586792a1fab",
"score": "0.7087699",
"text": "def check_api(self):\n\t\tcount = 0\n\t\twhile count < self.attempts:\n\n\t\t\tif self.check_api_process():\n\t\t\t\t# this mean that there is process for the API\n\t\t\t\tif self.check_tmux_session():\n\t\t\t\t\t# this mean t... |
eb1d6c7f3de8a924c2bd1cc63b1fd610 | This is an autogenerated method for the PySwitchLib. | [
{
"docid": "fa858fe2e6eeeb4d52f5e1f3096bdb53",
"score": "0.0",
"text": "def interface_vlan_ip_igmp_snooping_mrouter_interface_create(self, vlan=None, interface=None, api_timeout=''):\n\n operation_type = 'create'\n compositions_list = [('pybind.nos.v6_0_2f.interface_vlan.interface.vlan', u'vlan'),... | [
{
"docid": "233257f4560383deaa164012446439be",
"score": "0.6714755",
"text": "def __init__(self, switchNode):\n functions = [\"switch\"]\n ThreecomSwitch.__init__(self, switchNode, 2, 4094, 10000, 100000)",
"title": ""
},
{
"docid": "59d24211ac353392b58e2e5543bb9c5d",
"scor... |
dfe7127c52507a8fee38d3011c84b57e | Use nonspline based parameteriser and get all raw samples directly. We cannot spline the result from ParametrizerConstAccel as that would violate the constraints. Must evaluate on the result directly. Command rate is in Hz. | [
{
"docid": "d0d66882644188f6b2dd9acdedef6f8b",
"score": "0.0",
"text": "def run_topp_const_accel(\n waypts, qlim, vlim, alim, cmd_rate=1000, verify_lims=True\n):\n topp = DraculaToppra(waypts, qlim, vlim, alim)\n traj = topp.compute_const_accel()\n if verify_lims:\n logger.info(\"Veri... | [
{
"docid": "62284337a0271810127536e138a24b30",
"score": "0.54773104",
"text": "def read_raw(self):\n\t\tdata_array = np.empty(self.num_channels)\n\t\tfor chan in range(self.num_channels):\n\t\t\t#Read in data\n\t\t\trc, data_array[chan] = comedi.comedi_data_read(self.dev, self.sub_device, chan, self.ran... |
4988da0d9120b9102e31e273dbbf3116 | Sets the user_id of this User. | [
{
"docid": "fad0418aef327c1450c5988c894dccd9",
"score": "0.8165536",
"text": "def user_id(self, user_id):\n if self._configuration.client_side_validation and user_id is None:\n raise ValueError(\"Invalid value for `user_id`, must not be `None`\") # noqa: E501\n\n self._user_id ... | [
{
"docid": "4705ee352cab08b964b1848251a5676f",
"score": "0.867646",
"text": "def user_id(self, user_id: int):\n\n self._user_id = user_id",
"title": ""
},
{
"docid": "00f3a9fa8c1a23ab5d5d6b5016b3122b",
"score": "0.86753106",
"text": "def user_id(self, user_id):\n \n ... |
a7f22ae0d4b408539a6fa956ae250e09 | r""" return list of time steps values (in years) that are available. | [
{
"docid": "616fb70b0f0264fde6afb11481ed69b7",
"score": "0.69536537",
"text": "def getTimeSteps(self, a=None):\n dbi = self.getInterface(\"database\")\n timeInYears = dbi.getHistory(self.r, [\"time\"])[\"time\"]\n\n # remove the time step info. Clients don't want it\n timeInY... | [
{
"docid": "177124659fe3ba504d40dd2cf7d2055c",
"score": "0.71331424",
"text": "def get_integration_times(start_year: int, end_year: int, time_step: int):\n n_iter = int(round((end_year - start_year) / time_step)) + 1\n return numpy.linspace(start_year, end_year, n_iter).tolist()",
"title": ""
... |
05a91dec207230e1d50ca5b6f2cf76a7 | Add a new task with the description issued by the user. | [
{
"docid": "77ec2cf0327045856117ad2bc3240673",
"score": "0.85855055",
"text": "def addUserTask(self):\n desc = input(\"Task Description: \")\n self.addTask(desc)",
"title": ""
}
] | [
{
"docid": "2afd7ea64b2b38a75d0e4dfaa622051a",
"score": "0.80948436",
"text": "def addTask(self, desc):\n t = Task(desc)\n self.tasks[t.id] = t",
"title": ""
},
{
"docid": "c22e7c9d11723b5a09e4930a5b336d39",
"score": "0.7860794",
"text": "def add_task(self, name, descri... |
d8702baf45c460dd80c94708f6e1ec25 | Builds the 3D UNet Keras model.f | [
{
"docid": "18adebf71b4da8552351a203c366aab2",
"score": "0.0",
"text": "def baseline_resnet(inputs, pool_size=(2, 2, 2), n_labels=1,\n deconvolution=False,\n depth=4, n_base_filters=16,\n batch_normalization=True,\n activation_n... | [
{
"docid": "09bea2ae110a5244c62260c18a7857da",
"score": "0.71187824",
"text": "def get_model(self):\n\n\t# number of convolutional filters to use at each layer\n\tnb_filters = [16, 32, 48]\n\n\t# level of pooling to perform at each layer (POOL x POOL)\n\tnb_pool = [2, 2, 2]\n\n\t# level of convolution t... |
f266f9e386c78e90c8ebab8bccadb45b | Test 'each' and 'finally_assert' methods. | [
{
"docid": "3382762011812112dfc8826b3a41104f",
"score": "0.59889525",
"text": "def test_each_and_finally_assert_methods():\n\n # define a custom validator class\n class MyValidator(CSVValidator):\n\n def __init__(self, threshold):\n field_names = ('foo', 'bar')\n super... | [
{
"docid": "ea69c0af716754b33c989484d2855f81",
"score": "0.6643278",
"text": "def testIteratorFinalIteration(self):\n pass",
"title": ""
},
{
"docid": "ed9d55332d4dcb8b18037b48d109ff9d",
"score": "0.66406405",
"text": "def test_simple_call(self):\n i = iter([1,2,3,4,5])... |
b6401ff0eddff033effeebcc854ec141 | Test ``_safe_indexing`` with 2D container. | [
{
"docid": "d895910693b9bd6fb13539cd232c5ac5",
"score": "0.7092296",
"text": "def test_safe_indexing_2d_mask(array_type, indices_type, axis,\n expected_subset):\n columns_name = ['col_0', 'col_1', 'col_2']\n array = _convert_container(\n [[1, 2, 3], [4, 5, 6], ... | [
{
"docid": "9ba24c4063db8ef692e410cf63e7ff63",
"score": "0.74597317",
"text": "def test_safe_indexing_2d_container_axis_0(array_type, indices_type):\n indices = [1, 2]\n if indices_type == 'slice' and isinstance(indices[1], int):\n indices[1] += 1\n array = _convert_container([[1, 2, 3],... |
3f79f68440ec3b233ea37e027b33a709 | Ensure that `heisenberg_expand` raises exception if it receives an array with order > 2 | [
{
"docid": "a5082562ca28f5c2ab0d61b248c34c82",
"score": "0.6564538",
"text": "def test_wrong_input_shape(self):\n\n class DummyOp(qml.operation.CVOperation):\n num_wires = 1\n\n op = DummyOp(wires=1)\n\n with pytest.raises(ValueError, match=\"Only order-1 and order-2 arra... | [
{
"docid": "ebc0ad5231c4b5bc37c4ae35b96dbe3c",
"score": "0.707614",
"text": "def test_verification(self):\n with self.assertRaises(TypeError):\n order = 2\n _ = bch_expand(1, numpy.ones((2, 2)), order=order)\n\n with self.assertRaises(ValueError):\n order =... |
1554b0798e48624d8eb166b499760a5e | Read from stdin if input is from a pipe or file redirection, otherwise, run shell command to get input. | [
{
"docid": "d2785ff498b5fc583213cf9b95f1a836",
"score": "0.6727332",
"text": "def load_initial_input():\n mode = os.fstat(sys.stdin.fileno()).st_mode\n if stat.S_ISFIFO(mode) or stat.S_ISREG(mode):\n lines = sys.stdin.read().splitlines()\n sys.stdin = sys.stdout # Necessary to work ... | [
{
"docid": "40c99a06a502e843580fc6d2d217e0be",
"score": "0.6751126",
"text": "def run_shell_command(command, stdin=None, **kwargs):\n with run_interactive_shell_command(command, **kwargs) as p:\n ret = p.communicate(stdin)\n return ret",
"title": ""
},
{
"docid": "c1150fc304cecd... |
e3017e1c75676ee083067d2801e231dc | verify that an expected string is returned from the function | [
{
"docid": "63db23eb2ef9f040f2789a937e9258cf",
"score": "0.0",
"text": "def test_generate_sql_update(config_path, config_name):\n\n # expected output string\n expectedReturnTuple = (\"UPDATE [Sandbox1].[dbo].[unittest] SET [type] = ?, [color] = ? WHERE [id] = ? AND [number] = ?\", ['Ostrich','Mu... | [
{
"docid": "b0e2bedbb812482fd736a96b4f606e40",
"score": "0.7913029",
"text": "def func() -> str:\n return expected_result",
"title": ""
},
{
"docid": "7baa55521f33412db3f577921b288456",
"score": "0.6918",
"text": "def check_string(result, func, cargs, offset=-1, str_result=Fal... |
2c14cd0cc418d4a239d2f7f6ce2aa0bd | Render this histogram with the matplotlib pcolormesh method. This method is generally be preferred over the imshow method since it allows nonequidistant bins (which is permitted by the Histogram2D class. | [
{
"docid": "075573eea247d59853d1e8d59b708849",
"score": "0.6304512",
"text": "def pcolor(self,ax=None,**kwargs):\n\n style = copy.deepcopy(self._style)\n style.update(kwargs)\n\n if ax is None: ax = plt.gca()\n \n if style['logz']: norm = LogNorm()\n else: norm ... | [
{
"docid": "20863dd164afca9323d373161b7d3e72",
"score": "0.669269",
"text": "def pcolor_mesh_hist2( h, *args, **kwargs ):\n mask, colz, colorbar = pop_existing( kwargs, 'mask', 'colz', 'colorbar' )\n\n # get bin edges first\n x1 = r2numpy.get_bin_edges_axis(h.GetXaxis())\n y1 = r2numpy.get_b... |
c9e536b8ad286f198ddc695ea42f90a8 | get calendar list from calendar_user_list | [
{
"docid": "1fb567e6a44c41fcece4203272566026",
"score": "0.8805419",
"text": "def get_calendar_list(calendar_user_list):\r\n list_calendar = []\r\n list_calendar.append((0, '---'))\r\n for l in Calendar.objects.values_list('id', 'name').filter(user_id__in=calendar_user_list).order_by('id'):\r\n... | [
{
"docid": "447b8e786b41b6582809e0532f55c189",
"score": "0.8304395",
"text": "def get_calendar_user_list(calendar_user_list):\r\n list_calendar_user = []\r\n list_calendar_user.append((0, '---'))\r\n for l in CalendarUser.objects.values_list('id', 'username').filter(id__in=calendar_user_list).o... |
f03d62d507e5df04a000ca4c30fcd00d | Create a new DocTest containing the given examples. The DocTest's globals are initialized with a copy of `globs`. | [
{
"docid": "6103d2b95137e4e221a756d97704099f",
"score": "0.7097044",
"text": "def __init__(self, examples, globs, name, filename, lineno, docstring):\r\n assert not isinstance(examples, basestring), \\\r\n \"DocTest no longer accepts str; use DocTestParser instead\"\r\n self.... | [
{
"docid": "adc7c71aafc1898d3cdf4f74abfc036f",
"score": "0.6857998",
"text": "def test_doc_examples(self):\n start = time.time()\n files = get_all_example_rst_file_paths()\n filtered_files = []\n filtered_files = filter_exemptions(files)\n filtered_files.extend([os.pat... |
36854e24aa4bb25a68bd22035347ef14 | find out if a type is str or not | [
{
"docid": "fa186b9d0d9407ec0590e911543e8ec0",
"score": "0.8048978",
"text": "def is_string(atype):\n if atype == str:\n return True\n elif PY2:\n if atype == unicode:\n return True\n return False",
"title": ""
}
] | [
{
"docid": "fd7f5d2004effcaaa437230155096056",
"score": "0.8011629",
"text": "def is_str(_object):\n return type(_object) is str",
"title": ""
},
{
"docid": "8571c7ebe787b395b4fcb8f7a0e18fa5",
"score": "0.79255277",
"text": "def is_string_type(self, datatype):\n if datatype... |
7bb06759155c9d49451329c693ae5290 | Renders the logout function for all logout requests. | [
{
"docid": "4397a361dffe4b12cd66a14f0eee73c9",
"score": "0.70709914",
"text": "def logout_form(request):\n if request.method == 'GET':\n print(\"LOGGING OUT!\")\n logout(request)\n else:\n print(\"ALREADY LOGGED OUT!\")\n assert isinstance(request, HttpRequest)\n return ... | [
{
"docid": "65e57d6a654c93a9940bdad11f4cc14b",
"score": "0.75880826",
"text": "def logout():\n logout_user()\n return render_template('index.html', user=current_user)",
"title": ""
},
{
"docid": "69599ea2dc83497362eda534f37a7760",
"score": "0.7549292",
"text": "def logout():\n ... |
68f11fd5997991adec5679670515fc21 | This method is used to upload new plugins to the daemon. It is used when connecting to the daemon remotely and installing a new plugin on the client side. 'plugin_data' is a xmlrpc.Binary object of the file data, ie, plugin_file.read() | [
{
"docid": "90e46fc23b5fa05d0fad3374b461acd0",
"score": "0.6431145",
"text": "def upload_plugin(self, filename, filedump):\n\n try:\n filedump = base64.decodestring(filedump)\n except Exception, e:\n log.error(\"There was an error decoding the filedump string!\")\n ... | [
{
"docid": "379bec4d54c4b7728916e164925f4011",
"score": "0.6462988",
"text": "def send_plugin_message(self, plugin, data):\n\n\t\tfor client in self.registered_clients:\n\t\t\ttry: client(plugin, data)\n\t\t\texcept: self.logger.exception(\"Exception while sending plugin data to client\")",
"title":... |
3ee985dd92b74742ad9ad7d773e06393 | Sets the city of this NewUser. | [
{
"docid": "b212de62c6a6f96897a7c4f5a066d4fe",
"score": "0.7665644",
"text": "def city(self, city):\n\n self._city = city",
"title": ""
}
] | [
{
"docid": "2563e67e7f11f2ccedaa0262ea4a1899",
"score": "0.8143902",
"text": "def set_city(self, city):\n self.city = city",
"title": ""
},
{
"docid": "35ce93b60f1328fdaad1342017569606",
"score": "0.7753587",
"text": "def city(self, city: str):\n\n self._city = city",
... |
173eed50f5dfc3c0d56ee2d9fc880cd5 | Write text to the textfile | [
{
"docid": "9c7f45b5a2bdaa4e60d2917c8739554f",
"score": "0.6635086",
"text": "def append_to_file(dataset_file_path, text):\n with open(dataset_file_path, \"a\") as f:\n f.write(text)",
"title": ""
}
] | [
{
"docid": "5c40bc87ec74cffa56fe0034a8ff6213",
"score": "0.8612657",
"text": "def write_to_file(self, file_path, text):\n with open(file_path, mode='w+') as f:\n f.write(text)",
"title": ""
},
{
"docid": "98fde169b8ef127ef74ce992be656fba",
"score": "0.85078734",
"te... |
ff54f794ef029e1eb2fcd3d009586a45 | Handle all the possible mouse presses for the scene and pass the event forward if its not handled by the scene. | [
{
"docid": "ff6149dec516e3b8cc5268bf95824fc0",
"score": "0.0",
"text": "def mousePressEvent(self, event):\n if (event.button() == QtCore.Qt.LeftButton and\n not self.surface.contains(event.scenePos()) and\n not event.modifiers() & QtCore.Qt.ControlModifier and\n ... | [
{
"docid": "e1fac54ff56d6e8146b990fd8a0e6907",
"score": "0.73311126",
"text": "def handle_mouse_pressed(self, event):\n pass",
"title": ""
},
{
"docid": "f73df2a0c11892990e3d2a2f17918551",
"score": "0.7060593",
"text": "def on_event(self):\n scene = self.scene\n\n ... |
268722e9e7c4bb3df53ab83068936be5 | return the loss, gradient, and hessian. | [
{
"docid": "40594a7b08ab376e4daa13d3868760a7",
"score": "0.0",
"text": "def logistic_regression_SGD(y, tx, initial_w, batch_size, max_iters, gamma, adapt_gamma = False, pr = False):\n w = initial_w\n gamma_0 = gamma\n for n_iter in range(max_iters):\n for new_y, new_tx in batch_iter(y, t... | [
{
"docid": "7e9b5ca854216fbb7ff74d24b0d9532d",
"score": "0.67403907",
"text": "def get_LGH(y, tx, w):\n loss = calculate_loss(y, tx, w)\n gradient = calculate_gradient(y, tx, w)\n hessian = calculate_hessian(y, tx, w)\n return loss, gradient, hessian",
"title": ""
},
{
"docid": "... |
a5b92392ac4c2980db52e184ecbc42c2 | Takes a user's userID as an argument Returns a dictionary containing every user in that user's extended network with the shortest friendship path between them. The key is the friend's ID and the value is the path. | [
{
"docid": "b82c316d79e0916e47a364b5a737cfd1",
"score": "0.73624843",
"text": "def getAllSocialPaths(self, userID):\n all_paths = {} # Note that this is a dictionary, not a set\n \n for friend in self.friendships[userID]:\n path = self.bfs(userID, friend)\n\n ... | [
{
"docid": "bedbb9a6a9c2acf0eb6e295a05e8f389",
"score": "0.828359",
"text": "def getAllSocialPaths(self, userID):\n visited = {} # Note that this is a dictionary, not a set\n # !!!! IMPLEMENT ME\n \n ### NEW SOLUTION ###\n # store shortest path as UserID:UserID in fir... |
eaa444ef7df3c303bb48b8228bf10f39 | Multiplies this interval with another interval | [
{
"docid": "916b07c218a2ecb28f47f3ad951c74f7",
"score": "0.6791965",
"text": "def __mul__(self, other):\n other = as_interval(other)\n combinations = [self.left * other.left, self.left * other.right, self.right * other.left, self.right * other.right]\n return Interval(min(combinatio... | [
{
"docid": "2d5f4d15f3d8e84c11e0bbeca902ebb4",
"score": "0.6828198",
"text": "def __mul__(self, other):\n return Timestamp(self.secs * other)",
"title": ""
},
{
"docid": "544477220b23e5d399b11ed7915bb1f2",
"score": "0.6693795",
"text": "def __mul__(self, val):\n return ... |
ac09ff228d4f9b88977222a6a3d0de2d | The function samples data. | [
{
"docid": "aa3c25bb0fe24212ca49a0220006671c",
"score": "0.0",
"text": "def sampleData(period, inputfile, outputfile):\n\n with open(inputfile, 'r') as fread, open(outputfile, 'w') as fwrite:\n for i, line in enumerate(fread):\n if (i % period) == 0:\n fwrite.write(li... | [
{
"docid": "8894dacc6eeae25b0a91f7ccc7638ef2",
"score": "0.74959934",
"text": "def sample(self, x):",
"title": ""
},
{
"docid": "d921cfb2d5f3e737576e279dd162a5e5",
"score": "0.7410754",
"text": "def samples(self):\n raise NotImplementedError",
"title": ""
},
{
"doc... |
f90d35138ca48512d3d358c2ec43d914 | Quit program Consumes user input and closes the program | [
{
"docid": "85f42d710fba3e26190dffedf063e17c",
"score": "0.0",
"text": "def __close(event):\n\tsys.exit()",
"title": ""
}
] | [
{
"docid": "26b52d0300420fea5c9af688063ed615",
"score": "0.81623447",
"text": "def exit_program():\n quit()",
"title": ""
},
{
"docid": "26b52d0300420fea5c9af688063ed615",
"score": "0.81623447",
"text": "def exit_program():\n quit()",
"title": ""
},
{
"docid... |
287f1b8fca3643e248bffdf16e733c64 | Helper function to set the left, right, forward, and back cameras up on the vehicle as I've see fit. | [
{
"docid": "ea20145ba45b20bd09b911f36b4d99f9",
"score": "0.650022",
"text": "def _setup_my_cameras(self):\n left_cam_orientation = airsim.to_quaternion(pitch=-0.17, yaw=-1.04, roll=0.0)\n forward_cam_orientation = airsim.to_quaternion(pitch=-0.17, yaw=0.0, roll=0.0)\n right_cam_orientation = ai... | [
{
"docid": "ee37b9e28df59de0a26c4dd2870471b8",
"score": "0.6848906",
"text": "def fp_cam_set(controller, camera, cam_control, cam_plane, front_face): \n obj = controller.owner\n \n if obj['cam_set'] == 'Off':\n obj = controller.owner\n #how fast the camera gets to it's new positio... |
004312f2145b5d91dc3a4036c01a64fe | count the number of missing values per columns | [
{
"docid": "682cd9a4c94c2a72bb8a484a1f96d77b",
"score": "0.64475155",
"text": "def nacolcount(self):\n print(\"this function is deprecated please use nacount\")\n Serie = self.isnull().sum()\n df = DataFrame(Serie,columns = ['Nanumber'])\n df['Napercentage'] = df['Nanumber']/(self.nrow())\... | [
{
"docid": "4a92a7e246cbd08cfa1ace95dc0e940a",
"score": "0.8330409",
"text": "def number_of_missing_values(df: pd.DataFrame) -> None:\n return df.isnull().sum()",
"title": ""
},
{
"docid": "8e40a31b458d5e19d1117df09cda5569",
"score": "0.8180646",
"text": "def countMissingValues(da... |
f5c8c1eba789766e6be7e4918f8496ea | GetBf(TMemIn self, void const LBf, TSize const & LBfL) > int | [
{
"docid": "bac5d8369fc327d0c30afca8d9f0b1ab",
"score": "0.78249145",
"text": "def GetBf(self, *args):\n return _snap.TMemIn_GetBf(self, *args)",
"title": ""
}
] | [
{
"docid": "ed0cb5b7a0a4495e257b1f248f6d67ae",
"score": "0.69384676",
"text": "def GetBf(self):\n return _snap.TMem_GetBf(self)",
"title": ""
},
{
"docid": "d182b1728f757ec526d6f35315ccb2ee",
"score": "0.6666825",
"text": "def GetNextLnBf(self, *args):\n return _snap.TM... |
861e3b9958c01f8b4970164586f0c3d9 | Validate a module and execute the callback on error on the main thread pipeline a pipeline to be validated module_num the module number of the module to be validated test_mode whether pipeline is in test mode callback a callback with the signature, "fn(setting, message, pipeline_data)" where setting is the setting that... | [
{
"docid": "1f7076cb16ba22ed8423b8b94d4352c5",
"score": "0.82378304",
"text": "def validate_module(pipeline, module_num, test_mode, callback):\n pipeline.test_mode = test_mode\n modules = [m for m in pipeline.modules() if m.module_num == module_num]\n if len(modules) != 1:\n return\n ... | [
{
"docid": "a318fd4e40e2ad1f6770687309fada0d",
"score": "0.6367838",
"text": "def __init__(self, pipeline, module, callback):\n self.pipeline = cache_pipeline(pipeline)\n self.module_num = module.module_num\n self.test_mode = pipeline.test_mode\n self.callback = callback\n ... |
4d7d020018e75254dd98416bdd16efe7 | Setup the socket for incoming clients | [
{
"docid": "33c0a8390fb0881de0da27cbf2a203f9",
"score": "0.6232878",
"text": "def open_socket(self):\n try:\n self.server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR,1)\n self.server.bind((sel... | [
{
"docid": "a443115ed21b3b3ab63fcd644a873db9",
"score": "0.7407922",
"text": "def socket_init(self):\n self.socket = socket.socket(\n socket.AF_INET,\n socket.SOCK_STREAM,\n socket.IPPROTO_TCP)",
"title": ""
},
{
"docid": "d2a68c5c73b63ba56d51f75c67a82... |
34674e617acdd412666da9e644f26789 | Representation and format used | [
{
"docid": "8a1596edfcadcf7affa321ae74ac7544",
"score": "0.0",
"text": "def __str__(self):\n return \"[{}] ({}) {}\".format(self.__class__.__name__, self.id,\n self.__dict__)",
"title": ""
}
] | [
{
"docid": "de663bb7d6dfdc822d7ee90c89610f96",
"score": "0.7453937",
"text": "def __repr__(self) -> str:\n return self.fmt",
"title": ""
},
{
"docid": "065fbd9619952cc726a8853928814929",
"score": "0.72634256",
"text": "def get_info_format(self):",
"title": ""
},
{
... |
dd926a4ad890750ef312e6fa344911e6 | Configure deployment targets. Abstracted so this can be overriden for rendering before deployment. | [
{
"docid": "d0621e67eea74b210e039781919e3d32",
"score": "0.67634934",
"text": "def configure_targets(deployment_target):\n global S3_BUCKET\n global LOG_LEVEL\n global DEPLOYMENT_TARGET\n\n if deployment_target == 'production':\n S3_BUCKET = PRODUCTION_S3_BUCKET\n LOG_LEVEL = l... | [
{
"docid": "165fd21d3eef1b705b6308a219cde4ac",
"score": "0.7250672",
"text": "def configure_targets(deployment_target):\n global S3_BUCKETS\n global SERVERS\n global DEBUG\n\n if deployment_target == 'production':\n S3_BUCKETS = PRODUCTION_S3_BUCKETS\n SERVERS = PRODUCTION_SERV... |
1acd35f54c4b016506cd00744f343c70 | Implements a hybrid of the NESS and SMC2 algorithm, as recommended in the NESS article. That is, we use the SMC2 algorithm for the first part of the series and then switch to NESS when it becomes too computationally demanding to use the SMC2. | [
{
"docid": "d139b60b51f3ceca04d3e3519d4120c3",
"score": "0.0",
"text": "def __init__(self, filter_, particles, switch=500, smc2_kw=None, ness_kw=None):\n\n super().__init__(filter_, particles, switch, first_kw=smc2_kw, second_kw=ness_kw)",
"title": ""
}
] | [
{
"docid": "253ea4322dcf5608375e8521609fe30e",
"score": "0.6247595",
"text": "def eSC(params, (n1,n2), pts):\n\tS, N1, N2, M12, M21, Ta, Ts = params\n\t\n\txx = dadi.Numerics.default_grid(pts)\n\t\n\tN1_func = lambda t: S * (N1/S)**(t/(Ta+Ts))\n\tN2_func = lambda t: (1-S) * (N2/(1-S))**(t/(Ta+Ts))\n\n\t... |
ce7e9a3da80a664810b0bfee382e2446 | Return True if node is turned on, False if not. | [
{
"docid": "6f4ce2a1269dacaa548d5e74894ebd87",
"score": "0.70480686",
"text": "def isOn(self):\n return (int(self.get_flags) & 3) == 0",
"title": ""
}
] | [
{
"docid": "66db5174783ab412dbba26c1fdeb644b",
"score": "0.84309113",
"text": "def is_on(self) -> bool:\n return bool(self._node.status)",
"title": ""
},
{
"docid": "8add53e292da503c5ebe2214e4e0af4f",
"score": "0.7315773",
"text": "def is_on(self) -> bool:\n return self... |
273071ec9d208a722481ee675af65d9a | Returns a random shuffling of the array. | [
{
"docid": "0a4886b5c0cbda5e213e526c16fb0ae5",
"score": "0.72436756",
"text": "def shuffle(self):\n ans = self.nums[:]\n for i in range(len(ans) - 1, -1, -1):\n j = random.randint(0, i)\n ans[i], ans[j] = ans[j], ans[i]\n return ans",
"title": ""
}
] | [
{
"docid": "0429db19315e7a5451dd49528814b233",
"score": "0.7761317",
"text": "def shuffle(self):\n n = len(self.arr)\n bound = [ (x, random.random()) for x in self.arr]\n bound.sort(key=lambda x:x[1])\n return [x[0] for x in bound]",
"title": ""
},
{
"docid": "40a... |
83a46b9cb0746e38d0be8fbb719aebb4 | Convert a full matrix to a partial vector | [
{
"docid": "2f62d8c09ee7b91a5d0d8eadde405605",
"score": "0.71898866",
"text": "def full_matrix_to_partial_matrix(self, full_matrix):\n return full_matrix[self.m_min:, self.n_min:]",
"title": ""
}
] | [
{
"docid": "e8dd61f6410d461c1b83e26e73167926",
"score": "0.85416037",
"text": "def full_matrix_to_partial_vector(self, full_matrix):\n part_matrix = self.full_matrix_to_partial_matrix(full_matrix)\n return self.partial_matrix_to_partial_vector(part_matrix)",
"title": ""
},
{
"d... |
be93cbea7f8dafbfc9fc3b3b4969a88b | writes a given list to a file with given path to that file | [
{
"docid": "ae140e5d156ed02d7b7035f1aa8f9f1d",
"score": "0.0",
"text": "def save_result(self, timestamp, folder_name, file_name, result_list):\n path = (self.spider_name, timestamp, folder_name)\n write_file(result_list, path, file_name)",
"title": ""
}
] | [
{
"docid": "e3ffdb2da1d96b4981a8989227493f81",
"score": "0.79097027",
"text": "def write_list_to_file(list_to_save, save_path):\n \n Path(save_path).parent.mkdir(exist_ok=True, parents=True)\n \n with open(str(save_path), 'w') as fid:\n for line in list_to_save:\n fid.write... |
d8f25f1d7324169a17d0281b43ae95a2 | Since print_relation is the most computationally heavy function, handle each query's print_relation in it's own process and save the results to a multithreading dictionary. | [
{
"docid": "c8fcfb6ae6b52aea274b5959e93c58b5",
"score": "0.5012408",
"text": "def _str_worker(self, proc: int, query: datalog_parser.Query, results: dict):\n result = str(query) + \"? \"\n if self.rdbms[query] is SINGLE_MATCH:\n result += \"Yes(1)\\n\"\n elif self.rdbms[q... | [
{
"docid": "fc821650accb518306cf716fe0ad1622",
"score": "0.56099725",
"text": "def process_relationships(cur, resp, where_relationship, params_relationship, sql):\n try:\n results = cur.fetchall()\n retrived_people_count = len(results)\n\n if results and len(results) > 0 and u.re... |
7b21a01e9cdf70bb16ad95d29653df67 | Assume identity of specified user. As an administrator, you can assume the identity of any user in your organization. | [
{
"docid": "da4948de5c0338d416ac0c2d01c87764",
"score": "0.59164953",
"text": "def assume_user(self, email=None):\n if email is None:\n self._assume_user = None\n else:\n # email = email.replace('@', '%40')\n self._assume_user = six.moves.urllib.parse.quote... | [
{
"docid": "16238f629c91702c44993b44da470106",
"score": "0.66630304",
"text": "def authorized_userid(self, identity):\n pass",
"title": ""
},
{
"docid": "aff2125ab5f51068eab41c9339fc9d5c",
"score": "0.6395221",
"text": "def confirm_identity():\n msg = \"The current user is ... |
0b18184a200b8545fb9728198b231d8b | This method prints information in a reasonably easy to read format, and compensates for some formatting challenges in pprint. | [
{
"docid": "6a7d176560527fff04045d801eb91383",
"score": "0.0",
"text": "def print_object( self, arg_object, arg_name_for_object = None ) :\n if not arg_name_for_object == None :\n\n print( arg_name_for_object, \"=\", )\n\n print( \"\\n\".join( self._print_info_get_list_of_string... | [
{
"docid": "ef2c8025e8e85397262f486816a47e92",
"score": "0.7855242",
"text": "def pprint(self):\n try:\n print(self.pformat(self))\n except:\n print(self.__dict__)",
"title": ""
},
{
"docid": "722ea2b2105afac63e72370b535659a1",
"score": "0.7789151",
... |
fcaacc9cbace69879b367eed6c36fd4c | Return bool whether this request is successful (200299) or not. | [
{
"docid": "fc14434367db3e27faf185f9ccd4011c",
"score": "0.7483755",
"text": "def ok(self) -> bool:\n return self._status == 0 or 200 <= self._status <= 299",
"title": ""
}
] | [
{
"docid": "f9437aca13c074cb0f40b651934d58ac",
"score": "0.8216712",
"text": "def is_successful(self) -> bool:\n return self.code is not None and self.code >= 200 and self.code < 300",
"title": ""
},
{
"docid": "e06b19668cc2a71e82ec017ec5202a67",
"score": "0.81315345",
"text":... |
93e884109e1522efaa30c02a475e8f20 | Decode an ODB2 stream into a pandas dataframe | [
{
"docid": "14780229a223ee27c7006785698c72d3",
"score": "0.0",
"text": "def read_odb(source, columns=None, aggregated=True, single=False):\n if single:\n assert aggregated\n return _read_odb_oneshot(source, columns)\n else:\n return _read_odb_generator(source, columns, aggrega... | [
{
"docid": "f9956bf1a9ca018b1310380d6db7118c",
"score": "0.628087",
"text": "def df_from_bytes_np_records(bytes_: bytes) -> pd.DataFrame:\n b = BytesIO(bytes_)\n rec = np.load(b)\n df = pd.DataFrame.from_records(rec, index=\"index\")\n return df",
"title": ""
},
{
"docid": "37cd2... |
cc7460de9c01f90a3a5d92c4c564cf62 | Convert ase atoms' index to castep like return (Specie, Ion) Deprecated, use ase_to_castep_index | [
{
"docid": "80c739bcb425f97c8ec8969783ccacd1",
"score": "0.62893224",
"text": "def atoms_to_castep(atoms, index):\n atom = atoms[index]\n symbol = atom.symbol\n # Start counter\n count = 0\n for atom in atoms:\n if atom.symbol == symbol:\n count += 1\n if atom.ind... | [
{
"docid": "b4b9a359fbc4f4838157c22f060ebd60",
"score": "0.7522824",
"text": "def ase_to_castep_index(atoms, indices):\n if isinstance(indices, int):\n indices = [indices]\n symbols = np.array(atoms.get_chemical_symbols())\n iatoms = np.arange(len(atoms))\n res = []\n # Iterate thr... |
c4141dbf62408002212c668a55290779 | Return a default filename | [
{
"docid": "acef1e2dd1bbad11f4e30a2ed285067a",
"score": "0.8789937",
"text": "def _default_filename(default_filename, label=None):\n if not label:\n filename = default_filename.format(round(time.time()))\n else:\n filename = default_filename.format(label)\n return filename",
"... | [
{
"docid": "a44d0dc7aab878667cbe8d496b938052",
"score": "0.86708117",
"text": "def default_filename(self):\n if self.path:\n return Path(self.path).name",
"title": ""
},
{
"docid": "7b6b1f7dc30f57f3aa9b17a824128229",
"score": "0.8010142",
"text": "def get_defaultfil... |
fe6e130838ca6791afb3d7327d21832b | Returns if there's a next element in the iteration. | [
{
"docid": "ffc74addc44719c306d6e42bd489da75",
"score": "0.8082671",
"text": "def hasNext(self):\n if self._hasnext is None:\n try:\n self._thenext = next(self.it)\n except StopIteration:\n self._hasnext = False\n else:\n ... | [
{
"docid": "f946b915ce9bb5d7f9bbda02971b6eb3",
"score": "0.8134432",
"text": "def has_next(self):\n return self._has_next",
"title": ""
},
{
"docid": "f4613058c77a4e3a13081c8f06103364",
"score": "0.79846334",
"text": "def has_next(self):\n return self.sequence.has_next(... |
2ed1953506251b65b2535fb5de771473 | Print the version of acloud. The VERSION file is built into the acloud binary. The version file path is under "public/data". | [
{
"docid": "6388687e7e576f68edc6db43fcc75e54",
"score": "0.5592797",
"text": "def GetVersion():\n version_file_path = os.path.join(_CONFIG_DATA_PATH, _VERSION_FILE)\n if os.path.exists(version_file_path):\n with open(version_file_path) as version_file:\n return version_file.read(... | [
{
"docid": "579001e9d9b87d09e853ec395b288c69",
"score": "0.69931334",
"text": "def version():\n print(version)",
"title": ""
},
{
"docid": "71acca3ffe13eb8627a650b75a853da4",
"score": "0.69509643",
"text": "def show_version():\n print(version)",
"title": ""
},
{
... |
fc635732e2656cd76225818361e24cb0 | Create complement automaton (p23). | [
{
"docid": "bfd4822fcdb6a0ba67282ba993d0da4d",
"score": "0.6469388",
"text": "def complement (self):\n S = self.S\n I = self.I\n Σ = self.Σ\n T = self.T\n F = [ s for s in S if s not in self.F ]\n\n return FA(S, I, Σ, T, F)",
"title": ""
}
] | [
{
"docid": "c0b9abd88068a80201d69b1a94761a78",
"score": "0.741861",
"text": "def complement(sequence):\n\tpass",
"title": ""
},
{
"docid": "82e2c1796e75d6700dbbecd3b02784d5",
"score": "0.7122255",
"text": "def complement(seq):\n seq = ''.join([_complement[s] for s in seq])\n return... |
dbf20236f2fd6d0052a70ddb05755e6e | Draws text on a given surface, but does not flip() the pygame display | [
{
"docid": "106742215e118bae2e9e5ae015c375ef",
"score": "0.7643315",
"text": "def draw_text(surf, text, size, x, y):\n font = pygame.font.Font(font_name, size)\n text_surface = font.render(text, True, WHITE)\n text_rect = text_surface.get_rect()\n text_rect.midtop = (x, y)\n surf.blit(tex... | [
{
"docid": "202ecba47fcea0d6df43104a67c2964a",
"score": "0.7655349",
"text": "def draw_text(surf, text, size, x, y):\n font = pygame.font.Font(FONT_NAME, size)\n text_surface = font.render(text, True, WHITE)\n text_rect = text_surface.get_rect()\n text_rect.midtop = (x, y)\n surf.blit(tex... |
35212d110695ed541874c14e3ca8778d | Periodically we send the actual status for every relais The main topic is extended by "stat/"+ status (0 = off 1 = on) | [
{
"docid": "a165e531a04e8e5ca54363d4a12eff97",
"score": "0.79904836",
"text": "def send_status(relist) :\n global CONF\n # log (\"send_status\")\n for i in range(len(relist)):\n rstat = relist[i].get()\n if rstat :\n mqtt_sta = 1\n else:\n mqtt_sta = 0... | [
{
"docid": "78b8259175309804b0111ec3462d84ba",
"score": "0.7426226",
"text": "def send_stat (relais_nrß) :\n global CONF\n for i in range(0,len(relist)) :\n if relist[i].get() : \n mqtt_sta = 1\n else:\n mqtt_sta = 0\n rel = str(i+1)\n client.publi... |
71044b0a01e6275f12aa76e42795f48c | This method takes the smiles string and runs through the validation check of a smiles string. | [
{
"docid": "ee20d6c3ed5ea6ae56b705c74b42cbbb",
"score": "0.6944015",
"text": "def validate_smiles(self, smiles):\n\n # Use MolVS to validate the smiles to make sure enumeration and r group connections are correct\n # at least in the 1D Format.\n from molvs import validate_smiles as ... | [
{
"docid": "0092f72c071ae329d215d8a58d019ba5",
"score": "0.71211714",
"text": "def IsValidSMILES(text):\n is_valid = False\n results = smiles.parseString(text)\n if results:\n is_valid = True\n return(is_valid)\n return is_valid",
"title": ""
},
{
"docid": "274fb728... |
6469ce076bc38cc2b48241c249df8846 | itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass_cast(itkLightObject obj) > itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass | [
{
"docid": "6a94c199fbfb62acbe989871883c5582",
"score": "0.87611955",
"text": "def itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass_cast(*args):\n return _itkSquaredDifferenceImageFilterPython.itkSquaredDifferenceImageFilterIF3IF3IF3_Superclass_cast(*args)",
"title": ""
}
] | [
{
"docid": "39ecbda0f71ab287d0ba2da17f76c271",
"score": "0.86661303",
"text": "def itkSigmoidImageFilterIF3IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkSigmoidImageFilterIF3IF3_Superclass *\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIF3IF3_Superclass_cast(obj)",
"title": ... |
2bdc32a8c8461f86ea251bbd2a44e058 | Reverts a transaction if before the configured withdrawal lock. | [
{
"docid": "5b00103636c430cd2d9212960266c0eb",
"score": "0.60975236",
"text": "def _revert_on_withdrawal_lock(self, _user: Address, _id: int) -> None:\n\n deposit_time = self._withdraw_lock[_id][_user]\n if deposit_time + WITHDRAW_LOCK_TIMEOUT > self.now():\n revert(f\"{TAG}: As... | [
{
"docid": "2e127a7d6d971ce6e17af3f2112331b1",
"score": "0.6691026",
"text": "def rollback(self) -> None:\n self._check_thread()\n if self.state not in (_STATE_ACTIVE, _STATE_PARTIAL_COMMIT):\n raise TransactionError(\"Transaction is not active.\")\n try:\n if ... |
9092e79339bd6a32b19c144508d510a5 | Transform image perspective using OpenCV functions | [
{
"docid": "b437a995dca3f0361343df4ad097ff0e",
"score": "0.7015952",
"text": "def warp(self, image):\n\t\treturn cv2.warpPerspective(image, self.transformation_matrix, (image.shape[0], image.shape[1]), flags=cv2.INTER_CUBIC)",
"title": ""
}
] | [
{
"docid": "e4d720145f38baea418efe75d75cc9c6",
"score": "0.7743494",
"text": "def perspect_transform(img, src, dst):\n M = cv2.getPerspectiveTransform(src, dst)\n warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))# keep same size as input image\n \n return warped",
"title... |
3b4d553fc9f2f47851b8d99eea361a49 | get size info by downloading small part of it | [
{
"docid": "db338b1bf733ca268854f043d9d43366",
"score": "0.66978264",
"text": "def quickQueryImgSize(src):\n try:\n with request.urlopen(src) as file:\n p = ImageFile.Parser()\n while True:\n data = file.read(1024)\n if not data:\n ... | [
{
"docid": "9721d38a16ecec0c460f437a152a81b4",
"score": "0.7361434",
"text": "def get_info(self):\n try:\n resp = urllib.request.urlopen(self.url)\n self.size = int(resp.headers['content-length'])\n except urllib.error.HTTPError:\n self.size = -1;",
"ti... |
68400876ee6dc25f8b277ea69c814ab5 | Count number of files in each subdirectory in dataDir. | [
{
"docid": "cfe70ada7d43ad163104289f72cee968",
"score": "0.6957413",
"text": "def count_samples(dataDir):\n assert(isdir(dataDir))\n filenames = [f for f in listdir(dataDir)]\n class_count = {}\n for f in filenames:\n path = dataDir+\"/\"+f\n if isdir(path):\n class_count[f] = len(listdir... | [
{
"docid": "dd9caba0bd91e567e3c8d24a7508861a",
"score": "0.7474413",
"text": "def files_in_directory(path: str):\r\n count = 0\r\n for _, _, files in os.walk(path):\r\n count += len(files)\r\n \r\n return count",
"title": ""
},
{
"docid": "849e66dd3a5a459db82f2fd30d516294"... |
066ae097295fc95829622edcb8e2468b | Return list of stations w/ status | [
{
"docid": "d89236c484845cb571bb6eafdec2d80f",
"score": "0.7941429",
"text": "def get_status(self, stations:list = None):\n url = 'https://gbfs.urbansharing.com/trondheimbysykkel.no/station_status.json'\n\n data = self._FetchData(url)\n\n if stations:\n results = []\n ... | [
{
"docid": "7c63df56a0bff2f9e6b7994576f818c1",
"score": "0.74598414",
"text": "def get_stations(self):\n return self.call_api('stn', 'stns')",
"title": ""
},
{
"docid": "3bac02a9813b4730b91ad0f486ac6fdf",
"score": "0.7244118",
"text": "def _get_stations(self):\n r = sel... |
b514582c9166d3f669a30d662f644aaf | Should correctly create a keyspace in Cassandra | [
{
"docid": "cf2a0bb45f7445fac787994e431d5633",
"score": "0.7807259",
"text": "def test_create_keyspace(self):\n self.render_config_template(\n cassandra_ports=[9042],\n cassandra_send_request=True,\n cassandra_send_response=True,\n cassandra_send_reques... | [
{
"docid": "81e2224946d7a5e6fd6b7c40ba2e2782",
"score": "0.807247",
"text": "def createKeyspace(self):\n sys = SystemManager(str(self.host) + \":\" + str(self.port),framed_transport=True, timeout=30)\n try:\n print \"Trying to create new keyspace\"\n sys.create_keyspa... |
959883e1ab0bcfe8ca8ed927c82070ed | Visualize faces obtained from MTCNN | [
{
"docid": "70dbb8d91398ceb44e87f31fb5698591",
"score": "0.78203064",
"text": "def visualize_faces_mtcnn(img_path, detector):\n img = cv2.imread(img_path)\n faces = detector(img)\n for x in faces:\n x = np.asarray(x)\n x = np.transpose(x, (1, 2, 0))\n x = cv2.cvtColor(x, cv... | [
{
"docid": "ee20f8b403c10b30526608e2bb664356",
"score": "0.6805244",
"text": "def faces():",
"title": ""
},
{
"docid": "c4d006a66ea0674f515a62e5dacf24da",
"score": "0.6586629",
"text": "def recognize_faces_and_draw_knn(self, minDistance=0.5):\r\n index = 0\r\n new_image... |
bcb2027c3c56212fac8d91ec35e638df | Version number of the Redis engine used by the cluster. | [
{
"docid": "c5cc51d64dd355f2fa49949efc1f44e7",
"score": "0.79144454",
"text": "def engine_version(self) -> str:\n return pulumi.get(self, \"engine_version\")",
"title": ""
}
] | [
{
"docid": "4ff972cf54a6ad220b13fd20e15c481d",
"score": "0.7563207",
"text": "def engine_version(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"engine_version\")",
"title": ""
},
{
"docid": "4ff972cf54a6ad220b13fd20e15c481d",
"score": "0.7563207",
"text": "def engine... |
da458fc2e0df01b67bf643f74d2a4719 | A parsing with checking that it is not ambiguous | [
{
"docid": "39987259454b6e802d48cd0d8fc15f52",
"score": "0.0",
"text": "def __parse(self, w, var):\r\n if (w, var) in self.__cache:\r\n return self.__cache[w, var]\r\n else:\r\n n = len(w)\r\n if n == 1: return int((var, w) in self.__prods)\r\n counter = 0\r\n for p in sel... | [
{
"docid": "362b63e565f979e5740efa2a27e8e3c1",
"score": "0.639785",
"text": "def ambiguousParse(self, word):\r\n self.__cache.clear()\r\n return self.__parse(word, 0) != 1",
"title": ""
},
{
"docid": "e2d70c25ed7ca7086ea10af06cdd9443",
"score": "0.6308442",
"text": "def parse()... |
4ceb5896dbde5e90af77304ea6e3fd6a | Convert a value to a JSON serializable value | [
{
"docid": "d3882393097a90b9be7d3ba9047cd478",
"score": "0.0",
"text": "def serialize(value, list_obj_type, **kwargs):\n serialized_list = []\n for el in value:\n if issubclass(list_obj_type, Type):\n serialized_list.append(list_obj_type.serialize(el, **kwargs))\n... | [
{
"docid": "ba48399899584d9edb4caa6c73a7016b",
"score": "0.83150655",
"text": "def to_json(self, value):\r\n return value",
"title": ""
},
{
"docid": "91b489dfea37aa0a9c81190c459096d5",
"score": "0.8236348",
"text": "def _serialize_value(value):\n return value",
"ti... |
6f623d32fc14b8124f9a3193bc79b872 | Test call subroutine with parameter that is defined multiple times. | [
{
"docid": "daf56366e11edbc3dd865b676a3b0b2a",
"score": "0.62572026",
"text": "def test_call_with_common_parameter(self):\n amp = circuit.Parameter(\"amp\")\n\n with pulse.build() as subroutine:\n pulse.play(pulse.Gaussian(160, amp, 40), pulse.DriveChannel(0))\n pulse... | [
{
"docid": "1ea99a172ec554e436fc623247ceef88",
"score": "0.67987573",
"text": "def test_runs_once_runs_only_once():\n func = fake_function(expect_call=True).times_called(1)\n task = decorators.runs_once(func)\n for i in range(2):\n task()",
"title": ""
},
{
"docid": "f9c9623f... |
befa954a806ef81c803dd20c5190657d | Base method which allows us to understand different goals. | [
{
"docid": "d421c7307c7661f2c21f9e054a633cce",
"score": "0.0",
"text": "def achieve_goals(state, goals):\n tasks = []\n for goal in goals:\n if goal.kwargs['predicate'] == 'agent-at':\n action = 'move-to'\n tasks.append((action, goal.args[0]))\n elif goal.kwargs... | [
{
"docid": "97e7c5eafec6ce8952affd0d1c8c4fd2",
"score": "0.69283324",
"text": "def funtion(self):\n pass",
"title": ""
},
{
"docid": "f240aea361128e7757b08542987ba29f",
"score": "0.6904391",
"text": "def mystere():",
"title": ""
},
{
"docid": "9674a53f41c819786d781... |
5a3b414277fd2a74b4ef90902ea40c36 | Return a list of students in the grade book | [
{
"docid": "36ee0f2a014fcf975e133a1919f6bf1b",
"score": "0.63622546",
"text": "def allStudents(self):\n if not self.isSorted:\n self.students.sort()\n self.isSorted = True\n # return self.students[:]\n for s in self.students:\n yield s # generato... | [
{
"docid": "70ac378fbf6537d2233f442e322c2655",
"score": "0.73335534",
"text": "def students(self):\n return Student.objects.filter(groups__in=list(self.groups.all()))",
"title": ""
},
{
"docid": "d8a1991469a65fc1caff6cdc6841e351",
"score": "0.7234299",
"text": "def students(se... |